From c8466e606c7a4da2c4e2e1d904e3501383b6266a Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 2 May 2014 12:06:59 -0400
Subject: [PATCH 001/248] closed anchor tag

---
 not_so_obvious_python_stuff.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/not_so_obvious_python_stuff.ipynb b/not_so_obvious_python_stuff.ipynb
index ca70215..932b43f 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f2f197ff240bf1759d4cf4537d7ed82331d608b53fc97fe2cac43003f8429ea7"
+  "signature": "sha256:80251f5a867d952a8908417662c4e9a63acad3a148fcbcd67d9a19345eb1828f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -2992,7 +2992,7 @@
      "source": [
       "<br>\n",
       "<br>\n",
-      "<a name='generator_rhs'>"
+      "<a name='generator_rhs'></a>"
      ]
     },
     {

From 81610f64f6f659de7fd5aaf688e3286d77e6665f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 2 May 2014 14:29:16 -0400
Subject: [PATCH 002/248] metaclasses section

---
 ...t_so_obvious_python_stuff-checkpoint.ipynb | 330 ++++++++++++++++--
 not_so_obvious_python_stuff.ipynb             | 121 ++++++-
 2 files changed, 424 insertions(+), 27 deletions(-)

diff --git a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
index 3ea736a..848d37c 100644
--- a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
+++ b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9a07a78204a51f0faab65e52657f0446cd604ed470627f9c6af1ba74c047fe23"
+  "signature": "sha256:92f953fd8004f1dd1e9969ce61096f5a0b43190de3f3c7c8f85642b667754fae"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -81,7 +81,9 @@
       "- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n",
       "- [Abortive statements in `finally` blocks](#finally_blocks)\n",
       "- [Assigning types to variables as values](#variable_types)\n",
-      "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)"
+      "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n",
+      "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)\n",
+      "- [Metaclasses - What creates a new instance of a class?](#new_instance)"
      ]
     },
     {
@@ -275,13 +277,13 @@
      "collapsed": false,
      "input": [
       "a_list = []\n",
-      "print('ID:',id(a_list))\n",
+      "print(a_list, '\\nID (initial):',id(a_list), '\\n')\n",
       "\n",
       "a_list.append(1)\n",
-      "print('ID (append):',id(a_list))\n",
+      "print(a_list, '\\nID (append):',id(a_list), '\\n')\n",
       "\n",
-      "a_list.append(2)\n",
-      "print('ID (extend):',id(a_list))"
+      "a_list.extend([2])\n",
+      "print(a_list, '\\nID (extend):',id(a_list))"
      ],
      "language": "python",
      "metadata": {},
@@ -290,13 +292,18 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "ID: 4366495544\n",
-        "ID (append): 4366495544\n",
-        "ID (extend): 4366495544\n"
+        "[] \n",
+        "ID (initial): 140704077653128 \n",
+        "\n",
+        "[1] \n",
+        "ID (append): 140704077653128 \n",
+        "\n",
+        "[1, 2] \n",
+        "ID (extend): 140704077653128\n"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -2980,6 +2987,109 @@
      ],
      "prompt_number": 4
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<a name='generator_rhs'></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Only the first clause of generators is evaluated immediately"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The main reason why we love to use generators in certain cases (i.e., when we are dealing with large numbers of computations) is that it only computes the next value when it is needed, which is also known as \"lazy\" evaluation.\n",
+      "However, the first clause of an generator is already checked upon it's creation, as the following example demonstrates:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "gen_fails = (i for i in 1/0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ZeroDivisionError",
+       "evalue": "division by zero",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-18-29312e1ece8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Certainly, this is a nice feature, since it notifies us about syntax erros immediately. However, this is (unfortunately) not the case if we have multiple cases in our generator."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "gen_succeeds = (i for i in range(5) for j in 1/0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('But obviously fails when we iterate ...')\n",
+      "for i in gen_succeeds:\n",
+      "    print(i)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ZeroDivisionError",
+       "evalue": "division by zero",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-20-8a83a1022971>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m<ipython-input-19-c54c53f2218a>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "But obviously fails when we iterate ...\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -2996,6 +3106,13 @@
       "##Keyword argument unpacking syntax - `*args` and `**kwargs`"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3034,7 +3151,7 @@
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -3062,12 +3179,12 @@
        "stream": "stdout",
        "text": [
         "type of kwargs: <class 'dict'>\n",
-        "kwargs contents:  {'d': 4, 'c': 3, 'b': 2, 'a': 1}\n",
+        "kwargs contents:  {'b': 2, 'a': 1, 'c': 3, 'd': 4}\n",
         "value of argument a: 1\n"
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -3097,7 +3214,176 @@
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"new_instance\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Metaclasses - What creates a new instance of a class?"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Usually, it is the `__init__` method when we think of instanciating a new object from a class. However, it is the static method `__new__` (it is not a class method!) that creates and returns a new instance before `__init__()` is called. \n",
+      "\n",
+      "\n",
+      "`__new__` is a so-called static method that returns \n",
+      "`return super(<currentclass>, cls).__new__(subcls, *args, **kwargs)`"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(help(object.__new__))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Help on built-in function __new__:\n",
+        "\n",
+        "__new__(...)\n",
+        "    T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
+        "\n",
+        "None\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(dir(object))\n",
+      "print(help(object.__new__))\n",
+      "print(help(object.__init__))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__']\n",
+        "Help on built-in function __new__:\n",
+        "\n",
+        "__new__(...)\n",
+        "    T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
+        "\n",
+        "None\n",
+        "Help on wrapper_descriptor:\n",
+        "\n",
+        "__init__(...)\n",
+        "    x.__init__(...) initializes x; see help(type(x)) for signature\n",
+        "\n",
+        "None\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class a_class(object):\n",
+      "    def __new__(clss, *args, **kwargs):\n",
+      "        print('excecuted __new__')\n",
+      "        return None\n",
+      "    def __init__(self, an_arg):\n",
+      "        print('excecuted __init__')\n",
+      "        self.an_arg = an_arg\n",
+      "        \n",
+      "a_object = a_class(1)\n",
+      "print('Type of a_object:', type(a_object))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "excecuted __new__\n",
+        "Type of a_object: <class 'NoneType'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class a_class(object):\n",
+      "    def __new__(clss, *args, **kwargs):\n",
+      "        print('excecuted __new__')\n",
+      "        inst = super(a_class, clss).__new__(clss, *args, **kwargs)\n",
+      "        return inst\n",
+      "    def __init__(self):\n",
+      "        print('excecuted __init__')\n",
+      "        \n",
+      "a_object = a_class()\n",
+      "print('Type of a_object:', type(a_object))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "excecuted __new__\n",
+        "excecuted __init__\n",
+        "Type of a_object: <class '__main__.a_class'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class a_class(object):\n",
+      "    def __init__(self, an_arg):\n",
+      "        print('excecuted __init__')\n",
+      "        self.an_arg = an_arg\n",
+      "        \n",
+      "a_object = a_class(1)\n",
+      "print('Type of a_object:', type(a_object))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "excecuted __init__\n",
+        "Type of a_object <class '__main__.a_class'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -3128,23 +3414,17 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "#### 05/02/2014\n",
+      "- new section: Metaclasses - What creates a new instance of a class?  \n",
+      "\n",
       "#### 05/01/2014\n",
       "- new section: keyword argument unpacking syntax\n",
       "\n",
       "#### 04/27/2014\n",
-      "- minor fixes of typos\n",
-      "- single- vs. double-underscore clarification in the private class section."
+      "- minor fixes of typos  \n",
+      "- new section: \"Only the first clause of generators is evaluated immediately\""
      ]
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
     {
      "cell_type": "code",
      "collapsed": false,
diff --git a/not_so_obvious_python_stuff.ipynb b/not_so_obvious_python_stuff.ipynb
index 932b43f..ced0fb1 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:80251f5a867d952a8908417662c4e9a63acad3a148fcbcd67d9a19345eb1828f"
+  "signature": "sha256:8d4ed5310005c7fea9f7cfda8b7624b8b3cb390a566950a3bc179c516f89b410"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -82,7 +82,8 @@
       "- [Abortive statements in `finally` blocks](#finally_blocks)\n",
       "- [Assigning types to variables as values](#variable_types)\n",
       "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n",
-      "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)"
+      "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)\n",
+      "- [Metaclasses - What creates a new instance of a class?](#new_instance)"
      ]
     },
     {
@@ -3002,6 +3003,13 @@
       "# Only the first clause of generators is evaluated immediately"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3098,6 +3106,13 @@
       "##Keyword argument unpacking syntax - `*args` and `**kwargs`"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3201,6 +3216,105 @@
      ],
      "prompt_number": 3
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"new_instance\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Metaclasses - What creates a new instance of a class?"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Usually, it is the `__init__` method when we think of instanciating a new object from a class. However, it is the static method `__new__` (it is not a class method!) that creates and returns a new instance before `__init__()` is called. \n",
+      "More specifically, this is what is returned:  \n",
+      "`return super(<currentclass>, cls).__new__(subcls, *args, **kwargs)`  \n",
+      "\n",
+      "For more information about the `__new__` method, please see the [documentation](https://www.python.org/download/releases/2.2/descrintro/#__new__).\n",
+      "\n",
+      "As a little experiment, let us screw with `__new__` so that it returns `None` and see if `__init__` will be executed:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class a_class(object):\n",
+      "    def __new__(clss, *args, **kwargs):\n",
+      "        print('excecuted __new__')\n",
+      "        return None\n",
+      "    def __init__(self, an_arg):\n",
+      "        print('excecuted __init__')\n",
+      "        self.an_arg = an_arg\n",
+      "        \n",
+      "a_object = a_class(1)\n",
+      "print('Type of a_object:', type(a_object))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "excecuted __new__\n",
+        "Type of a_object: <class 'NoneType'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 53
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As we can see in the code above, `__init__` requires the returned instance from `__new__` in order to called. So, here we just created a `NoneType` object.  \n",
+      "Let us override the `__new__`, now and let us confirm that `__init__` is called now to instantiate the new object\":"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class a_class(object):\n",
+      "    def __new__(cls, *args, **kwargs):\n",
+      "        print('excecuted __new__')\n",
+      "        inst = super(a_class, cls).__new__(cls)\n",
+      "        return inst\n",
+      "    def __init__(self, an_arg):\n",
+      "        print('excecuted __init__')\n",
+      "        self.an_arg = an_arg\n",
+      "        \n",
+      "a_object = a_class(1)\n",
+      "print('Type of a_object:', type(a_object))\n",
+      "print('a_object.an_arg: ', a_object.an_arg)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "excecuted __new__\n",
+        "excecuted __init__\n",
+        "Type of a_object: <class '__main__.a_class'>\n",
+        "a_object.an_arg:  1\n"
+       ]
+      }
+     ],
+     "prompt_number": 54
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3230,6 +3344,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "#### 05/02/2014\n",
+      "- new section: Metaclasses - What creates a new instance of a class?  \n",
+      "\n",
       "#### 05/01/2014\n",
       "- new section: keyword argument unpacking syntax\n",
       "\n",

From afacae6cc4638ac900cbec09ffda10ca0c601e17 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 2 May 2014 14:52:17 -0400
Subject: [PATCH 003/248] 2 new sections

---
 ...t_so_obvious_python_stuff-checkpoint.ipynb | 130 ++++--------------
 not_so_obvious_python_stuff.ipynb             |  20 +--
 2 files changed, 32 insertions(+), 118 deletions(-)

diff --git a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
index 848d37c..9c32267 100644
--- a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
+++ b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:92f953fd8004f1dd1e9969ce61096f5a0b43190de3f3c7c8f85642b667754fae"
+  "signature": "sha256:70671a8e3f32d21b27867ecdc6d80f3b5c10f00339a2fae3860c4db8930dc7c6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "Sebastian Raschka  \n",
-      "last updated: 04/27/2014 ([Changelog](#changelog))\n",
+      "last updated: 05/02/2014 ([Changelog](#changelog))\n",
       "\n",
       "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)\n",
       "\n",
@@ -3151,7 +3151,7 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 55
     },
     {
      "cell_type": "markdown",
@@ -3179,12 +3179,12 @@
        "stream": "stdout",
        "text": [
         "type of kwargs: <class 'dict'>\n",
-        "kwargs contents:  {'b': 2, 'a': 1, 'c': 3, 'd': 4}\n",
+        "kwargs contents:  {'d': 4, 'a': 1, 'c': 3, 'b': 2}\n",
         "value of argument a: 1\n"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 56
     },
     {
      "cell_type": "markdown",
@@ -3214,7 +3214,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 57
     },
     {
      "cell_type": "markdown",
@@ -3237,69 +3237,14 @@
      "metadata": {},
      "source": [
       "Usually, it is the `__init__` method when we think of instanciating a new object from a class. However, it is the static method `__new__` (it is not a class method!) that creates and returns a new instance before `__init__()` is called. \n",
+      "More specifically, this is what is returned:  \n",
+      "`return super(<currentclass>, cls).__new__(subcls, *args, **kwargs)`  \n",
       "\n",
+      "For more information about the `__new__` method, please see the [documentation](https://www.python.org/download/releases/2.2/descrintro/#__new__).\n",
       "\n",
-      "`__new__` is a so-called static method that returns \n",
-      "`return super(<currentclass>, cls).__new__(subcls, *args, **kwargs)`"
+      "As a little experiment, let us screw with `__new__` so that it returns `None` and see if `__init__` will be executed:"
      ]
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(help(object.__new__))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Help on built-in function __new__:\n",
-        "\n",
-        "__new__(...)\n",
-        "    T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
-        "\n",
-        "None\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(dir(object))\n",
-      "print(help(object.__new__))\n",
-      "print(help(object.__init__))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__le__', '__lt__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__']\n",
-        "Help on built-in function __new__:\n",
-        "\n",
-        "__new__(...)\n",
-        "    T.__new__(S, ...) -> a new object with type S, a subtype of T\n",
-        "\n",
-        "None\n",
-        "Help on wrapper_descriptor:\n",
-        "\n",
-        "__init__(...)\n",
-        "    x.__init__(...) initializes x; see help(type(x)) for signature\n",
-        "\n",
-        "None\n"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -3327,49 +3272,32 @@
        ]
       }
      ],
-     "prompt_number": 33
+     "prompt_number": 53
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class a_class(object):\n",
-      "    def __new__(clss, *args, **kwargs):\n",
-      "        print('excecuted __new__')\n",
-      "        inst = super(a_class, clss).__new__(clss, *args, **kwargs)\n",
-      "        return inst\n",
-      "    def __init__(self):\n",
-      "        print('excecuted __init__')\n",
-      "        \n",
-      "a_object = a_class()\n",
-      "print('Type of a_object:', type(a_object))"
-     ],
-     "language": "python",
+     "cell_type": "markdown",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "excecuted __new__\n",
-        "excecuted __init__\n",
-        "Type of a_object: <class '__main__.a_class'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 40
+     "source": [
+      "As we can see in the code above, `__init__` requires the returned instance from `__new__` in order to called. So, here we just created a `NoneType` object.  \n",
+      "Let us override the `__new__`, now and let us confirm that `__init__` is called now to instantiate the new object\":"
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "class a_class(object):\n",
+      "    def __new__(cls, *args, **kwargs):\n",
+      "        print('excecuted __new__')\n",
+      "        inst = super(a_class, cls).__new__(cls)\n",
+      "        return inst\n",
       "    def __init__(self, an_arg):\n",
       "        print('excecuted __init__')\n",
       "        self.an_arg = an_arg\n",
       "        \n",
       "a_object = a_class(1)\n",
-      "print('Type of a_object:', type(a_object))"
+      "print('Type of a_object:', type(a_object))\n",
+      "print('a_object.an_arg: ', a_object.an_arg)"
      ],
      "language": "python",
      "metadata": {},
@@ -3378,12 +3306,14 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
+        "excecuted __new__\n",
         "excecuted __init__\n",
-        "Type of a_object <class '__main__.a_class'>\n"
+        "Type of a_object: <class '__main__.a_class'>\n",
+        "a_object.an_arg:  1\n"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 54
     },
     {
      "cell_type": "markdown",
@@ -3424,14 +3354,6 @@
       "- minor fixes of typos  \n",
       "- new section: \"Only the first clause of generators is evaluated immediately\""
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/not_so_obvious_python_stuff.ipynb b/not_so_obvious_python_stuff.ipynb
index ced0fb1..9c32267 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8d4ed5310005c7fea9f7cfda8b7624b8b3cb390a566950a3bc179c516f89b410"
+  "signature": "sha256:70671a8e3f32d21b27867ecdc6d80f3b5c10f00339a2fae3860c4db8930dc7c6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "Sebastian Raschka  \n",
-      "last updated: 04/27/2014 ([Changelog](#changelog))\n",
+      "last updated: 05/02/2014 ([Changelog](#changelog))\n",
       "\n",
       "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)\n",
       "\n",
@@ -3151,7 +3151,7 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 55
     },
     {
      "cell_type": "markdown",
@@ -3179,12 +3179,12 @@
        "stream": "stdout",
        "text": [
         "type of kwargs: <class 'dict'>\n",
-        "kwargs contents:  {'b': 2, 'a': 1, 'c': 3, 'd': 4}\n",
+        "kwargs contents:  {'d': 4, 'a': 1, 'c': 3, 'b': 2}\n",
         "value of argument a: 1\n"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 56
     },
     {
      "cell_type": "markdown",
@@ -3214,7 +3214,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 57
     },
     {
      "cell_type": "markdown",
@@ -3354,14 +3354,6 @@
       "- minor fixes of typos  \n",
       "- new section: \"Only the first clause of generators is evaluated immediately\""
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
    "metadata": {}

From 3c42a45c609f401574b640a24b62c25e09b7a3f1 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 2 May 2014 16:10:00 -0400
Subject: [PATCH 004/248] output fix for for-loop leak

---
 ...cope_resolution_legb_rule-checkpoint.ipynb | 1084 +++++++++++++++++
 not_so_obvious_python_stuff.ipynb             |   48 +-
 tutorials/scope_resolution_legb_rule.ipynb    |    5 +-
 3 files changed, 1134 insertions(+), 3 deletions(-)
 create mode 100644 .ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb

diff --git a/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb b/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb
new file mode 100644
index 0000000..f1924c9
--- /dev/null
+++ b/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb
@@ -0,0 +1,1084 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:1e3a847a38ee33d41e745f74fe6b8a143b9aeb47d0a1e3029ea52899d97687cb"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
+      "last updated: 04/28/2014\n",
+      "\n",
+      "- [Link to the containing GitHub Repository](https://github.com/rasbt/python_reference)\n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb)\n",
+      "\n",
+      "Note: The code in this IPython notebook was executed in Python 3.4.0"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I am really looking forward to your comments and suggestions to improve and extend this tutorial! Just send me a quick note   \n",
+      "via Twitter: [@rasbt](https://twitter.com/rasbt)  \n",
+      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#A beginner's guide to Python's namespaces, scope resolution, and the LEGB rule"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download this IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"sections\"><a/>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Sections\n",
+      "- [Introduction to namespaces and scopes](#introduction)  \n",
+      "- [1. LG - Local and Global scopes](#section_1)  \n",
+      "- [2. LEG - Local, Enclosed, and Global scope](#section_2)  \n",
+      "- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3)  \n",
+      "- [Self-assessment exercise](#assessment)\n",
+      "- [Conclusion](#conclusion)  \n",
+      "- [Solutions](#solutions)\n",
+      "- [Warning: For-loop variables \"leaking\" into the global namespace](#for_loop)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Objectives\n",
+      "- Namespaces and scopes - where does Python look for variable names?\n",
+      "- Can we define/reuse variable names for multiple objects at the same time?\n",
+      "- In which order does Python search different namespaces for variable names?"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"introduction\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Introduction to namespaces and scopes"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Namespaces"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object.  \n",
+      "Such a \"name-to-object\" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = \"Hello string\"`, we created a reference to the `\"Hello string\"` object, and henceforth we can access via its variable name `a_string`.\n",
+      "\n",
+      "We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., \n",
+      "\n",
+      "<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}</pre>  \n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example:\n",
+      "\n",
+      "<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}\n",
+      "b_namespace = {'name_a':object_3, 'name_b':object_4, ...}</pre>\n",
+      "\n",
+      "For example, everytime we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called \"scope\"), which we will discuss in more detail in the next section."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Scope"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of \"scope\". The \"scope\" in Python defines the \"hierarchy level\" in which we search namespaces for certain \"name-to-object\" mappings.  \n",
+      "For example, let us consider the following code:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = 1\n",
+      "\n",
+      "def foo():\n",
+      "    i = 5\n",
+      "    print(i, 'in foo()')\n",
+      "\n",
+      "print(i, 'global')\n",
+      "\n",
+      "foo()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1 global\n",
+        "5 in foo()\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here, we just defined the variable name `i` twice, once on the `foo` function."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- `foo_namespace = {'i':object_3, ...}`  \n",
+      "- `global_namespace = {'i':object_1, 'name_b':object_2, ...}`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "So, how does Python now which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Tip:\n",
+      "If we want to print out the dictionary mapping of the global and local variables, we can use the\n",
+      "the functions `global()` and `local()"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#print(globals()) # prints global namespace\n",
+      "#print(locals()) # prints local namespace\n",
+      "\n",
+      "glob = 1\n",
+      "\n",
+      "def foo():\n",
+      "    loc = 5\n",
+      "    print('loc in foo():', 'loc' in locals())\n",
+      "\n",
+      "foo()\n",
+      "print('loc in global:', 'loc' in globals())    \n",
+      "print('glob in global:', 'foo' in globals())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "loc in foo(): True\n",
+        "loc in global: False\n",
+        "glob in global: True\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Scope resolution for variable names via the LEGB rule."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The \"scope\" defines on which hierarchy level Python searches for a particular \"variable name\" for its associated object. Now, the next question is: \"In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?\"  \n",
+      "To answer is: It uses the LEGB-rule, which stands for\n",
+      "\n",
+      "**Local -> Enclosed -> Global -> Built-in**, \n",
+      "\n",
+      "where the arrows should denote the direction of the namespace-hierarchy search order.  \n",
+      "\n",
+      "- *Local* can be inside a function or class method, for example.  \n",
+      "- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function.  \n",
+      "- *Global* refers to the uppermost level of the executing script itself, and  \n",
+      "- *Built-in* are special names that Python reserves for itself.  \n",
+      "\n",
+      "So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the global namespaces (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).\n",
+      "\n",
+      "**Note**:  \n",
+      "Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy\n",
+      "import math\n",
+      "import scipy\n",
+      "\n",
+      "print(math.pi, 'from the math module')\n",
+      "print(numpy.pi, 'from the numpy package')\n",
+      "print(scipy.pi, 'from the scipy package')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "3.141592653589793 from the math module\n",
+        "3.141592653589793 from the numpy package\n",
+        "3.141592653589793 from the scipy package\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "(This is also why we have to be careful if we import modules via \"`from a_module import *`\", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"section_1\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## 1. LG - Local and Global scopes"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Example 1.1**  \n",
+      "As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes.  \n",
+      "What does the following code print?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a_var = 'global variable'\n",
+      "\n",
+      "def a_func():\n",
+      "    print(a_var, '[ a_var inside a_func() ]')\n",
+      "\n",
+      "a_func()\n",
+      "print(a_var, '[ a_var outside a_func() ]')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**a)**\n",
+      "<pre>raises an error</pre>\n",
+      "\n",
+      "**b)** \n",
+      "<pre>\n",
+      "global value [ a_var outside a_func() ]</pre>\n",
+      "\n",
+      "**c)** \n",
+      "<pre>global value [ a_var in a_func() ]  \n",
+      "global value [ a_var outside a_func() ]</pre>\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[go to solution](#solutions)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Here is why:\n",
+      "\n",
+      "We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously.\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Example 1.2**  \n",
+      "Now, let us define the variable `a_var` in the global and the local scope.  \n",
+      "Can you guess what the following code will produce?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a_var = 'global value'\n",
+      "\n",
+      "def a_func():\n",
+      "    a_var = 'local value'\n",
+      "    print(a_var, '[ a_var inside a_func() ]')\n",
+      "\n",
+      "a_func()\n",
+      "print(a_var, '[ a_var outside a_func() ]')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**a)**\n",
+      "<pre>raises an error</pre>\n",
+      "\n",
+      "**b)** \n",
+      "<pre>local value [ a_var in a_func() ]\n",
+      "global value [ a_var outside a_func() ]</pre>\n",
+      "\n",
+      "**c)** \n",
+      "<pre>global value [ a_var in a_func() ]  \n",
+      "global value [ a_var outside a_func() ]</pre>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[go to solution](#solutions)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Here is why:\n",
+      "\n",
+      "When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a_var = 'global value'\n",
+      "\n",
+      "def a_func():\n",
+      "    global a_var\n",
+      "    a_var = 'local value'\n",
+      "    print(a_var, '[ a_var inside a_func() ]')\n",
+      "\n",
+      "print(a_var, '[ a_var outside a_func() ]')\n",
+      "a_func()\n",
+      "print(a_var, '[ a_var outside a_func() ]')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "global value [ a_var outside a_func() ]\n",
+        "local value [ a_var inside a_func() ]\n",
+        "local value [ a_var outside a_func() ]\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first):"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a_var = 1\n",
+      "\n",
+      "def a_func():\n",
+      "    a_var = a_var + 1\n",
+      "    print(a_var, '[ a_var inside a_func() ]')\n",
+      "\n",
+      "print(a_var, '[ a_var outside a_func() ]')\n",
+      "a_func()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "UnboundLocalError",
+       "evalue": "local variable 'a_var' referenced before assignment",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-4-a6cdd0ee9a55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var outside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m<ipython-input-4-a6cdd0ee9a55>\u001b[0m in \u001b[0;36ma_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0ma_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_var\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var inside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'a_var' referenced before assignment"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1 [ a_var outside a_func() ]\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"section_2\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## 2. LEG - Local, Enclosed, and Global scope\n",
+      "\n",
+      "\n",
+      "\n",
+      "Now, let us introduce the concept of the enclosed (E) scope. Following the order \"Local -> Enclosed -> Global\", can you guess what the following code will print?"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Example 2.1**"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a_var = 'global value'\n",
+      "\n",
+      "def outer():\n",
+      "    a_var = 'enclosed value'\n",
+      "    \n",
+      "    def inner():\n",
+      "        a_var = 'local value'\n",
+      "        print(a_var)\n",
+      "    \n",
+      "    inner()\n",
+      "\n",
+      "outer()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**a)**\n",
+      "<pre>global value</pre>\n",
+      "\n",
+      "**b)** \n",
+      "<pre>enclosed value</pre>\n",
+      "\n",
+      "**c)** \n",
+      "<pre>local value</pre>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[go to solution](#solutions)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Here is why:\n",
+      "\n",
+      "Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitely access a variable from the outer (enclosed) scope in order to modify its value.  \n",
+      "Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a_var = 'global value'\n",
+      "\n",
+      "def outer():\n",
+      "       a_var = 'local value'\n",
+      "       print('outer before:', a_var)\n",
+      "       def inner():\n",
+      "           nonlocal a_var\n",
+      "           a_var = 'inner value'\n",
+      "           print('in inner():', a_var)\n",
+      "       inner()\n",
+      "       print(\"outer after:\", a_var)\n",
+      "outer()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "outer before: local value\n",
+        "in inner(): inner value\n",
+        "outer after: inner value\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"section_3\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## 3. LEGB - Local, Enclosed, Global, Built-in\n",
+      "\n",
+      "To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our \"own\" length-funcion, which happens to bear the same name as the in-built `len()` function. What outcome do you excpect if we'd execute the following code?"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Example 3**"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a_var = 'global variable'\n",
+      "\n",
+      "def len(in_var):\n",
+      "    print('called my len() function')\n",
+      "    l = 0\n",
+      "    for i in in_var:\n",
+      "        l += 1\n",
+      "    return l\n",
+      "\n",
+      "def a_func(in_var):\n",
+      "    len_in_var = len(in_var)\n",
+      "    print('Input variable is of length', len_in_var)\n",
+      "\n",
+      "a_func('Hello, World!')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**a)**\n",
+      "<pre>raises an error (conflict with in-built `len()` function)</pre>\n",
+      "\n",
+      "**b)** \n",
+      "<pre>called my len() function\n",
+      "Input variable is of length 13</pre>\n",
+      "\n",
+      "**c)** \n",
+      "<pre>Input variable is of length 13</pre>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[go to solution](#solutions)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Here is why:\n",
+      "\n",
+      "Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration pruposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope first before it attempts"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name =\"assessment\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Self-assessment exercise"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a = 'global'\n",
+      "\n",
+      "def outer():\n",
+      "    \n",
+      "    def len(in_var):\n",
+      "        print('called my len() function: ', end=\"\")\n",
+      "        l = 0\n",
+      "        for i in in_var:\n",
+      "            l += 1\n",
+      "        return l\n",
+      "    \n",
+      "    a = 'local'\n",
+      "    \n",
+      "    def inner():\n",
+      "        global len\n",
+      "        nonlocal a\n",
+      "        a += ' variable'\n",
+      "    inner()\n",
+      "    print('a is', a)\n",
+      "    print(len(a))\n",
+      "\n",
+      "\n",
+      "outer()\n",
+      "\n",
+      "print(len(a))\n",
+      "print('a is', a)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 59
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"conclusion\"\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[go to solution](#solutions)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Conclusion"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### A rule of thumb"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug.  \n",
+      "If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value.  \n",
+      "For example:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a_var = 2\n",
+      "\n",
+      "def a_func(some_var):\n",
+      "    return 2**3\n",
+      "\n",
+      "a_var = a_func(a_var)\n",
+      "print(a_var)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "8\n"
+       ]
+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name = \"solutions\">\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Solutions\n",
+      "\n",
+      "In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Example 1.1:', chr(int('01100011',2)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Example 1.2:', chr(int('01100001',2)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Example 2.1:', chr(int('01100011',2)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Example 3.1:', chr(int('01100010',2)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Execute to run the self-assessment solution\n",
+      "\n",
+      "sol = \"000010100110111101110101011101000110010101110010001010\"\\\n",
+      "\"0000101001001110100000101000001010011000010010000001101001011100110\"\\\n",
+      "\"0100000011011000110111101100011011000010110110000100000011101100110\"\\\n",
+      "\"0001011100100110100101100001011000100110110001100101000010100110001\"\\\n",
+      "\"1011000010110110001101100011001010110010000100000011011010111100100\"\\\n",
+      "\"1000000110110001100101011011100010100000101001001000000110011001110\"\\\n",
+      "\"1010110111001100011011101000110100101101111011011100011101000100000\"\\\n",
+      "\"0011000100110100000010100000101001100111011011000110111101100010011\"\\\n",
+      "\"0000101101100001110100000101000001010001101100000101001100001001000\"\\\n",
+      "\"0001101001011100110010000001100111011011000110111101100010011000010\"\\\n",
+      "\"1101100\"\n",
+      "\n",
+      "sol_str =''.join(chr(int(sol[i:i+8], 2)) for i in range(0, len(sol), 8))\n",
+      "for line in sol_str.split('\\n'):\n",
+      "    print(line)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 58
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<a name=\"for_loop\"></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Warning: For-loop variables \"leaking\" into the global namespace"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for a in range(5):\n",
+      "    if a == 4:\n",
+      "        print(a, '-> a in for-loop')\n",
+      "print(a, '-> a in global')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "4 -> a in for-loop\n",
+        "4 -> a in global\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**This also applies if we explicitely defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "b = 1\n",
+      "for b in range(5):\n",
+      "    if b == 4:\n",
+      "        print(b, '-> b in for-loop')\n",
+      "print(b, '-> b in global')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "4 -> b in for-loop\n",
+        "4 -> b in global\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = 1\n",
+      "print([i for i in range(5)])\n",
+      "print(i, '-> i in global')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[0, 1, 2, 3, 4]\n",
+        "1 -> i in global\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Why did I mention \"Python 3.x\"? Well, as it happens, the same code executed in Python 2.x would print:\n",
+      "\n",
+      "<pre>\n",
+      "4 -> i in global\n",
+      "<pre>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
+      "\n",
+      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/not_so_obvious_python_stuff.ipynb b/not_so_obvious_python_stuff.ipynb
index 9c32267..a17f6dc 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:70671a8e3f32d21b27867ecdc6d80f3b5c10f00339a2fae3860c4db8930dc7c6"
+  "signature": "sha256:08b042706cb433ce1f2446c6b6ac00be197268042e605da41164e7d02562766d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -2577,6 +2577,52 @@
      "metadata": {},
      "outputs": []
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### In Python 3.x for-loop variables don't leak into the global namespace anymore"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
+      "\n",
+      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = 1\n",
+      "print([i for i in range(5)])\n",
+      "print(i, '-> i in global')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[0, 1, 2, 3, 4]\n",
+        "1 -> i in global\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In Python 2.x this would print  \n",
+      "`print(4, '-> i in global')`"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index ce414a8..f1924c9 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": "scope_resolution_legb_rule"
+  "name": "",
+  "signature": "sha256:1e3a847a38ee33d41e745f74fe6b8a143b9aeb47d0a1e3029ea52899d97687cb"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -1063,7 +1064,7 @@
       "Why did I mention \"Python 3.x\"? Well, as it happens, the same code executed in Python 2.x would print:\n",
       "\n",
       "<pre>\n",
-      "print(4, '-> i in global')\n",
+      "4 -> i in global\n",
       "<pre>"
      ]
     },

From cb5853e6daa293ef3c69ed4432869e7dd4010680 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 2 May 2014 16:14:09 -0400
Subject: [PATCH 005/248] output fix for for-loop leak

---
 ...t_so_obvious_python_stuff-checkpoint.ipynb | 57 ++++++++++++++++++-
 ...cope_resolution_legb_rule-checkpoint.ipynb | 10 +++-
 not_so_obvious_python_stuff.ipynb             | 13 ++++-
 tutorials/scope_resolution_legb_rule.ipynb    | 10 +++-
 4 files changed, 85 insertions(+), 5 deletions(-)

diff --git a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
index 9c32267..be8ba25 100644
--- a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
+++ b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:70671a8e3f32d21b27867ecdc6d80f3b5c10f00339a2fae3860c4db8930dc7c6"
+  "signature": "sha256:3b7a6d43400c23d25d965b726e6fba3db0b9b0d2d0bbad73c96b8857f8eaa7ee"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -2577,6 +2577,52 @@
      "metadata": {},
      "outputs": []
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### In Python 3.x for-loop variables don't leak into the global namespace anymore"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
+      "\n",
+      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = 1\n",
+      "print([i for i in range(5)])\n",
+      "print(i, '-> i in global')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[0, 1, 2, 3, 4]\n",
+        "1 -> i in global\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In Python 2.x this would print  \n",
+      "`4 -> i in global`"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3345,6 +3391,7 @@
      "metadata": {},
      "source": [
       "#### 05/02/2014\n",
+      "- new section in Python 3.x and Python 2.x key differences: for-loop leak<\n",
       "- new section: Metaclasses - What creates a new instance of a class?  \n",
       "\n",
       "#### 05/01/2014\n",
@@ -3354,6 +3401,14 @@
       "- minor fixes of typos  \n",
       "- new section: \"Only the first clause of generators is evaluated immediately\""
      ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb b/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb
index f1924c9..70cbf9d 100644
--- a/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb
+++ b/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:1e3a847a38ee33d41e745f74fe6b8a143b9aeb47d0a1e3029ea52899d97687cb"
+  "signature": "sha256:eb9ececefb4c35b16d0496c7f613e4a64bb35b2e9f79b7e049c5b434bd2e6654"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -1076,6 +1076,14 @@
       "\n",
       "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
      ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/not_so_obvious_python_stuff.ipynb b/not_so_obvious_python_stuff.ipynb
index a17f6dc..be8ba25 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:08b042706cb433ce1f2446c6b6ac00be197268042e605da41164e7d02562766d"
+  "signature": "sha256:3b7a6d43400c23d25d965b726e6fba3db0b9b0d2d0bbad73c96b8857f8eaa7ee"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -2620,7 +2620,7 @@
      "metadata": {},
      "source": [
       "In Python 2.x this would print  \n",
-      "`print(4, '-> i in global')`"
+      "`4 -> i in global`"
      ]
     },
     {
@@ -3391,6 +3391,7 @@
      "metadata": {},
      "source": [
       "#### 05/02/2014\n",
+      "- new section in Python 3.x and Python 2.x key differences: for-loop leak<\n",
       "- new section: Metaclasses - What creates a new instance of a class?  \n",
       "\n",
       "#### 05/01/2014\n",
@@ -3400,6 +3401,14 @@
       "- minor fixes of typos  \n",
       "- new section: \"Only the first clause of generators is evaluated immediately\""
      ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index f1924c9..70cbf9d 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:1e3a847a38ee33d41e745f74fe6b8a143b9aeb47d0a1e3029ea52899d97687cb"
+  "signature": "sha256:eb9ececefb4c35b16d0496c7f613e4a64bb35b2e9f79b7e049c5b434bd2e6654"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -1076,6 +1076,14 @@
       "\n",
       "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
      ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}

From 34175c785a150611fd08e646da5f6f9a875341a7 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 3 May 2014 14:51:53 -0400
Subject: [PATCH 006/248] 2 new sections

---
 ...t_so_obvious_python_stuff-checkpoint.ipynb | 560 +++++++++++++++++-
 not_so_obvious_python_stuff.ipynb             | 560 +++++++++++++++++-
 2 files changed, 1112 insertions(+), 8 deletions(-)

diff --git a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
index be8ba25..1e0ef3c 100644
--- a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
+++ b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3b7a6d43400c23d25d965b726e6fba3db0b9b0d2d0bbad73c96b8857f8eaa7ee"
+  "signature": "sha256:0e1c6e74b301e23ea4146d660afb3f07765686c6c7fa4752f3a4495da7949787"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "Sebastian Raschka  \n",
-      "last updated: 05/02/2014 ([Changelog](#changelog))\n",
+      "last updated: 05/03/2014 ([Changelog](#changelog))\n",
       "\n",
       "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)\n",
       "\n",
@@ -83,7 +83,9 @@
       "- [Assigning types to variables as values](#variable_types)\n",
       "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n",
       "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)\n",
-      "- [Metaclasses - What creates a new instance of a class?](#new_instance)"
+      "- [Metaclasses - What creates a new instance of a class?](#new_instance)\n",
+      "- [Else-clauses: \"conditional else\" and \"completion else\"](#else_clauses)\n",
+      "- [Interning of compile-time constants vs. run-time expressions](#string_interning)"
      ]
     },
     {
@@ -3278,6 +3280,13 @@
       "## Metaclasses - What creates a new instance of a class?"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3361,6 +3370,545 @@
      ],
      "prompt_number": 54
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(5):\n",
+      "    if i == 1:\n",
+      "        print('in for')\n",
+      "else:\n",
+      "    print('in else')\n",
+      "print('after for-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "in for\n",
+        "in else\n",
+        "after for-loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(5):\n",
+      "    if i == 1:\n",
+      "        break\n",
+      "else:\n",
+      "    print('in else')\n",
+      "print('after for-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "after for-loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"else_clauses\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Else-clauses: \"conditional else\" and \"completion else\""
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I would claim that the conditional \"else\" is every programmer's daily bread and butter. However, there is a second flavor of \"else\"-clauses in Python, which I will call \"completion else\" (for reason that will become clear later).  \n",
+      "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with.  \n",
+      "### Conditional else:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# conditional else\n",
+      "\n",
+      "a_list = [1,2]\n",
+      "if a_list[0] == 1:\n",
+      "    print('Hello, World!')\n",
+      "else:\n",
+      "    print('Bye, World!')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Hello, World!\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# conditional else\n",
+      "\n",
+      "a_list = [1,2]\n",
+      "if a_list[0] == 2:\n",
+      "    print('Hello, World!')\n",
+      "else:\n",
+      "    print('Bye, World!')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Bye, World!\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Why am I showing those simple examples? I think they are good to highlight some of the key points: It is **either** the code under the `if` clause that is executed, **or** the code under the `else` block, but not both.  \n",
+      "If the condition of the `if` clause evaluates to `True`, the `if`-block is exectured, and if it evaluated to `False`, it is the `else` block.  \n",
+      "\n",
+      "### Completion else\n",
+      "**In contrast** to the **either...or*** situation that we know from the conditional `else`, the completion `else` is executed if a code block finished.  \n",
+      "To show you an example, let us use `else` for error-handling:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Completion else (try-except)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "try:\n",
+      "    print('first element:', a_list[0])\n",
+      "except IndexError:\n",
+      "    print('raised IndexError')\n",
+      "else:\n",
+      "    print('no error in try-block')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "first element: 1\n",
+        "no error in try-block\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "try:\n",
+      "    print('third element:', a_list[2])\n",
+      "except IndexError:\n",
+      "    print('raised IndexError')\n",
+      "else:\n",
+      "    print('no error in try-block')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "raised IndexError\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "In the code above, we can see that the code under the **`else`-clause is only executed if the `try-block` was executed without encountering an error, i.e., if the `try`-block is \"complete\".**  \n",
+      "The same rule applies to the \"completion\" `else` in while- and for-loops, which you can confirm in the following samples below."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Completion else (while-loop)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = 0\n",
+      "while i < 2:\n",
+      "    print(i)\n",
+      "    i += 1\n",
+      "else:\n",
+      "    print('in else')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n",
+        "1\n",
+        "in else\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = 0\n",
+      "while i < 2:\n",
+      "    print(i)\n",
+      "    i += 1\n",
+      "    break\n",
+      "else:\n",
+      "    print('completed while-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Completion else (for-loop)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(2):\n",
+      "    print(i)\n",
+      "else:\n",
+      "    print('completed for-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n",
+        "1\n",
+        "completed for-loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(2):\n",
+      "    print(i)\n",
+      "    break\n",
+      "else:\n",
+      "    print('completed for-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"string_interning\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Interning of compile-time constants vs. run-time expressions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This might not be particularly useful, but it is nonetheless interesting: Python's interpreter is interning compile-time constants but not run-time expressions (note that this is implementation-specific).\n",
+      "\n",
+      "(Original source: [Stackoverflow](http://stackoverflow.com/questions/15541404/python-string-interning))"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let us have a look at the simple example below. Here we are creating 3 variables and assign the value \"Hello\" to them in different ways before we test them for identity."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "hello1 = 'Hello'\n",
+      "\n",
+      "hello2 = 'Hell' + 'o'\n",
+      "\n",
+      "hello3 = 'Hell'\n",
+      "hello3 = hello3 + 'o'\n",
+      "\n",
+      "print('hello1 is hello2:', hello1 is hello2)\n",
+      "print('hello1 is hello3:', hello1 is hello3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "hello1 is hello2: True\n",
+        "hello1 is hello3: False\n"
+       ]
+      }
+     ],
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, how does it come that the first expression evaluates to true, but the second does not?  To answer this question, we need to take a closer look at the underlying byte codes:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import dis\n",
+      "def hello1_func():\n",
+      "    s = 'Hello'\n",
+      "    return s\n",
+      "dis.dis(hello1_func)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "  3           0 LOAD_CONST               1 ('Hello')\n",
+        "              3 STORE_FAST               0 (s)\n",
+        "\n",
+        "  4           6 LOAD_FAST                0 (s)\n",
+        "              9 RETURN_VALUE\n"
+       ]
+      }
+     ],
+     "prompt_number": 38
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def hello2_func():\n",
+      "    s = 'Hell' + 'o'\n",
+      "    return s\n",
+      "dis.dis(hello2_func)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "  2           0 LOAD_CONST               3 ('Hello')\n",
+        "              3 STORE_FAST               0 (s)\n",
+        "\n",
+        "  3           6 LOAD_FAST                0 (s)\n",
+        "              9 RETURN_VALUE\n"
+       ]
+      }
+     ],
+     "prompt_number": 39
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def hello3_func():\n",
+      "    s = 'Hell'\n",
+      "    s = s + 'o'\n",
+      "    return s\n",
+      "dis.dis(hello3_func)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "  2           0 LOAD_CONST               1 ('Hell')\n",
+        "              3 STORE_FAST               0 (s)\n",
+        "\n",
+        "  3           6 LOAD_FAST                0 (s)\n",
+        "              9 LOAD_CONST               2 ('o')\n",
+        "             12 BINARY_ADD\n",
+        "             13 STORE_FAST               0 (s)\n",
+        "\n",
+        "  4          16 LOAD_FAST                0 (s)\n",
+        "             19 RETURN_VALUE\n"
+       ]
+      }
+     ],
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "It looks like that `'Hello'` and `'Hell'` + `'o'` are both evaluated and stored as `'Hello'` at compile-time, whereas the third version   \n",
+      "`s = 'Hell'`  \n",
+      "`s = s + 'o'` seems to be not interned.  Let us quickly confirm the behavior with the following code:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(hello1_func() is hello2_func())\n",
+      "print(hello1_func() is hello3_func())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "True\n",
+        "False\n"
+       ]
+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Finally, to show that this hypothesis is the answer to this rather unexpected observation, let us `intern` the value manually:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "\n",
+      "print(hello1_func() is sys.intern(hello3_func()))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "True\n"
+       ]
+      }
+     ],
+     "prompt_number": 45
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3390,8 +3938,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "#### 05/03/2014\n",
+      "- new section: else clauses: conditional vs. completion\n",
+      "- new section: Interning of compile-time constants vs. run-time expressions\n",
+      "\n",
       "#### 05/02/2014\n",
-      "- new section in Python 3.x and Python 2.x key differences: for-loop leak<\n",
+      "- new section in Python 3.x and Python 2.x key differences: for-loop leak\n",
       "- new section: Metaclasses - What creates a new instance of a class?  \n",
       "\n",
       "#### 05/01/2014\n",
diff --git a/not_so_obvious_python_stuff.ipynb b/not_so_obvious_python_stuff.ipynb
index be8ba25..1e0ef3c 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3b7a6d43400c23d25d965b726e6fba3db0b9b0d2d0bbad73c96b8857f8eaa7ee"
+  "signature": "sha256:0e1c6e74b301e23ea4146d660afb3f07765686c6c7fa4752f3a4495da7949787"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "Sebastian Raschka  \n",
-      "last updated: 05/02/2014 ([Changelog](#changelog))\n",
+      "last updated: 05/03/2014 ([Changelog](#changelog))\n",
       "\n",
       "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)\n",
       "\n",
@@ -83,7 +83,9 @@
       "- [Assigning types to variables as values](#variable_types)\n",
       "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n",
       "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)\n",
-      "- [Metaclasses - What creates a new instance of a class?](#new_instance)"
+      "- [Metaclasses - What creates a new instance of a class?](#new_instance)\n",
+      "- [Else-clauses: \"conditional else\" and \"completion else\"](#else_clauses)\n",
+      "- [Interning of compile-time constants vs. run-time expressions](#string_interning)"
      ]
     },
     {
@@ -3278,6 +3280,13 @@
       "## Metaclasses - What creates a new instance of a class?"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3361,6 +3370,545 @@
      ],
      "prompt_number": 54
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(5):\n",
+      "    if i == 1:\n",
+      "        print('in for')\n",
+      "else:\n",
+      "    print('in else')\n",
+      "print('after for-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "in for\n",
+        "in else\n",
+        "after for-loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(5):\n",
+      "    if i == 1:\n",
+      "        break\n",
+      "else:\n",
+      "    print('in else')\n",
+      "print('after for-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "after for-loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"else_clauses\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Else-clauses: \"conditional else\" and \"completion else\""
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I would claim that the conditional \"else\" is every programmer's daily bread and butter. However, there is a second flavor of \"else\"-clauses in Python, which I will call \"completion else\" (for reason that will become clear later).  \n",
+      "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with.  \n",
+      "### Conditional else:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# conditional else\n",
+      "\n",
+      "a_list = [1,2]\n",
+      "if a_list[0] == 1:\n",
+      "    print('Hello, World!')\n",
+      "else:\n",
+      "    print('Bye, World!')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Hello, World!\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# conditional else\n",
+      "\n",
+      "a_list = [1,2]\n",
+      "if a_list[0] == 2:\n",
+      "    print('Hello, World!')\n",
+      "else:\n",
+      "    print('Bye, World!')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Bye, World!\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Why am I showing those simple examples? I think they are good to highlight some of the key points: It is **either** the code under the `if` clause that is executed, **or** the code under the `else` block, but not both.  \n",
+      "If the condition of the `if` clause evaluates to `True`, the `if`-block is exectured, and if it evaluated to `False`, it is the `else` block.  \n",
+      "\n",
+      "### Completion else\n",
+      "**In contrast** to the **either...or*** situation that we know from the conditional `else`, the completion `else` is executed if a code block finished.  \n",
+      "To show you an example, let us use `else` for error-handling:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Completion else (try-except)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "try:\n",
+      "    print('first element:', a_list[0])\n",
+      "except IndexError:\n",
+      "    print('raised IndexError')\n",
+      "else:\n",
+      "    print('no error in try-block')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "first element: 1\n",
+        "no error in try-block\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "try:\n",
+      "    print('third element:', a_list[2])\n",
+      "except IndexError:\n",
+      "    print('raised IndexError')\n",
+      "else:\n",
+      "    print('no error in try-block')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "raised IndexError\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "In the code above, we can see that the code under the **`else`-clause is only executed if the `try-block` was executed without encountering an error, i.e., if the `try`-block is \"complete\".**  \n",
+      "The same rule applies to the \"completion\" `else` in while- and for-loops, which you can confirm in the following samples below."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Completion else (while-loop)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = 0\n",
+      "while i < 2:\n",
+      "    print(i)\n",
+      "    i += 1\n",
+      "else:\n",
+      "    print('in else')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n",
+        "1\n",
+        "in else\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "i = 0\n",
+      "while i < 2:\n",
+      "    print(i)\n",
+      "    i += 1\n",
+      "    break\n",
+      "else:\n",
+      "    print('completed while-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Completion else (for-loop)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(2):\n",
+      "    print(i)\n",
+      "else:\n",
+      "    print('completed for-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n",
+        "1\n",
+        "completed for-loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for i in range(2):\n",
+      "    print(i)\n",
+      "    break\n",
+      "else:\n",
+      "    print('completed for-loop')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"string_interning\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Interning of compile-time constants vs. run-time expressions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This might not be particularly useful, but it is nonetheless interesting: Python's interpreter is interning compile-time constants but not run-time expressions (note that this is implementation-specific).\n",
+      "\n",
+      "(Original source: [Stackoverflow](http://stackoverflow.com/questions/15541404/python-string-interning))"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let us have a look at the simple example below. Here we are creating 3 variables and assign the value \"Hello\" to them in different ways before we test them for identity."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "hello1 = 'Hello'\n",
+      "\n",
+      "hello2 = 'Hell' + 'o'\n",
+      "\n",
+      "hello3 = 'Hell'\n",
+      "hello3 = hello3 + 'o'\n",
+      "\n",
+      "print('hello1 is hello2:', hello1 is hello2)\n",
+      "print('hello1 is hello3:', hello1 is hello3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "hello1 is hello2: True\n",
+        "hello1 is hello3: False\n"
+       ]
+      }
+     ],
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, how does it come that the first expression evaluates to true, but the second does not?  To answer this question, we need to take a closer look at the underlying byte codes:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import dis\n",
+      "def hello1_func():\n",
+      "    s = 'Hello'\n",
+      "    return s\n",
+      "dis.dis(hello1_func)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "  3           0 LOAD_CONST               1 ('Hello')\n",
+        "              3 STORE_FAST               0 (s)\n",
+        "\n",
+        "  4           6 LOAD_FAST                0 (s)\n",
+        "              9 RETURN_VALUE\n"
+       ]
+      }
+     ],
+     "prompt_number": 38
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def hello2_func():\n",
+      "    s = 'Hell' + 'o'\n",
+      "    return s\n",
+      "dis.dis(hello2_func)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "  2           0 LOAD_CONST               3 ('Hello')\n",
+        "              3 STORE_FAST               0 (s)\n",
+        "\n",
+        "  3           6 LOAD_FAST                0 (s)\n",
+        "              9 RETURN_VALUE\n"
+       ]
+      }
+     ],
+     "prompt_number": 39
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def hello3_func():\n",
+      "    s = 'Hell'\n",
+      "    s = s + 'o'\n",
+      "    return s\n",
+      "dis.dis(hello3_func)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "  2           0 LOAD_CONST               1 ('Hell')\n",
+        "              3 STORE_FAST               0 (s)\n",
+        "\n",
+        "  3           6 LOAD_FAST                0 (s)\n",
+        "              9 LOAD_CONST               2 ('o')\n",
+        "             12 BINARY_ADD\n",
+        "             13 STORE_FAST               0 (s)\n",
+        "\n",
+        "  4          16 LOAD_FAST                0 (s)\n",
+        "             19 RETURN_VALUE\n"
+       ]
+      }
+     ],
+     "prompt_number": 40
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "It looks like that `'Hello'` and `'Hell'` + `'o'` are both evaluated and stored as `'Hello'` at compile-time, whereas the third version   \n",
+      "`s = 'Hell'`  \n",
+      "`s = s + 'o'` seems to be not interned.  Let us quickly confirm the behavior with the following code:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(hello1_func() is hello2_func())\n",
+      "print(hello1_func() is hello3_func())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "True\n",
+        "False\n"
+       ]
+      }
+     ],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Finally, to show that this hypothesis is the answer to this rather unexpected observation, let us `intern` the value manually:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "\n",
+      "print(hello1_func() is sys.intern(hello3_func()))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "True\n"
+       ]
+      }
+     ],
+     "prompt_number": 45
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3390,8 +3938,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "#### 05/03/2014\n",
+      "- new section: else clauses: conditional vs. completion\n",
+      "- new section: Interning of compile-time constants vs. run-time expressions\n",
+      "\n",
       "#### 05/02/2014\n",
-      "- new section in Python 3.x and Python 2.x key differences: for-loop leak<\n",
+      "- new section in Python 3.x and Python 2.x key differences: for-loop leak\n",
       "- new section: Metaclasses - What creates a new instance of a class?  \n",
       "\n",
       "#### 05/01/2014\n",

From d294ad30783e42d63d699cb82826e3e5a7ed54ac Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 4 May 2014 17:12:18 -0400
Subject: [PATCH 007/248] images for least squares fit

---
 Images/least_squares_perpendicular.png | Bin 0 -> 17989 bytes
 Images/least_squares_vertical.png      | Bin 0 -> 13512 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Images/least_squares_perpendicular.png
 create mode 100644 Images/least_squares_vertical.png

diff --git a/Images/least_squares_perpendicular.png b/Images/least_squares_perpendicular.png
new file mode 100644
index 0000000000000000000000000000000000000000..a0d425f79ef5403d7df7c07d0d3b4cf9f74aff9e
GIT binary patch
literal 17989
zcmeIaRZyKlw=D`}5iGd7ySqCC4ess|+}+(FxCM8DyGxMZ?!kgP1lRkKf1kZ~)qT5l
zA5SW&g0<Sa=j<`Zn4Jg(ISF`J99S?gFnB3RF(oiC@CV=@2Q&om$>~?`8Q_-)OHok;
zDN#`(1xGtGOKVdwFe>yP3H&l2gpoowqNb@}u*fW66ClOu&UT{3WgC7$rG<-!!+v#Z
z&5DSR`{PLb_~FF50m%mKhB2H2BgH5$gb2EKg9rpcvX#{-ssxD(<+{J@W-e&<(LO!0
zDs>HP1budxVQ6`8f#WaRRtO?#xRI=)l_y=IW>GxBf^SeyvmUwcqTJ)L&ta~E18a>V
z2QvErL71<ponA$mKa*r&Ms=C#HcD;jQK*!+G#$Ha_Fqzmo?Ju}PHs%R*PX2T<pE_F
ztm9?SCM?R66T+lD16N0nCOwTuq!e=01Yx*;p31k7-Eg#e^*63$8JCe+4n6uDwPg*`
z^AhG*v~LE`qMt!1M#W7M4-gUMU3MoJkrYvirtB%DDK~9p*d#LQbD9YuJKX6*m){)_
z8|qvcgQrug)H*>Vi9#EO>Z93UZg)Z#WffntKlDfSoYq%iMLH3Vi>$sZwl%R5*8PFh
z$2)kdsa#;&<o?Jbk#gf>s+n!&nd5n@@CZ)kX1SRs(~$^lO17nnhO@>OSsr6M8wNuY
zJ0nvDcN=?P)4;&^+<AZ>ZA_gFiQH|hZJl`B`APnj-~oPq|Co`4=wA_MD}EA<FA7AW
zc8;b*><nxSOe6xZL_|b<jwWV2N@Ab?t2*!-KZ%92vpo+Zqnn!>gBvS@oufG;GdDLk
zBNGcF3ky9^g5Jr)*4faV-qwlqKaKpKcEn7bj2$iQoh|KbiQd~aG_rGX<|iR}@94k(
z{AZk|?w0@4ldaQ#O$(SH<NG&^%nVG7|J^oFmGAvi9tBHxQ)>+|OB+*LC!h}jb|!Yd
zf93yQ-~3OH|Es3v|I}n<X8PYX|JOJFRg;hLeFFb$LjPH<f1d)&B>>CE_}@z}0E_$G
zKn@H{2uw;$Sj8RuG#koYSrvCEb2_0gDg}OIUGx?S-1OHv(UlKL<YQiVDVHKLnlQ2$
znlR)*07RWh&?jLNYTRW}4i;ElsZ0vXE<c<Woy3MOv(a9OeuRX4{CClJ&vK9JiFY0Q
z{IC`CID>>JMBpey0l5^ppN^&-?8$^6fWI6Nb2ra;@nA6VLKmpHRfw<Ld(cw;AW8qW
z?>Rtea1>}?phU0W5FRlMP)cF(FYCX0z4HET0{8oAq-5`<jy!!oN&>Zr?tv0eZ+K3@
z|613boCF#Mg8>PGA3bj77{|r~4TDOLAF%(8WRp@zm+;wX4Efu8DHndh5AU^*o`DiU
zezDvl@7?EC?VG<32NakC=HhcP$t)rHK5SsnpZ<Rw^u6?ANl{TzNePso;C}|WBh;hb
z=DX5hndAFdh6k<i`M=fzSjnRsMI&+jE^iMuuf4JKg9D4|=E;x$)(iwI)<b#hS$UA?
z=-bl3IAv19^gjLjaR0l-n_F>y6@D@~S=pVLbqUrXv-cW3J`n5ri>sJ7t}wZmtqpE|
zEE?b5C+jWNGkWZKnZM4!oO&ye!OdYs8K0f;mB(SMd@*dXtb*p7JuNH!yz_I5Y7T*3
zMJx?-LM2d5#dt9Kk|bolLOn;o2M4#_eDb?^{SWPC2E7jV2o&;i_3E<$J(2RVV|jkF
zwR&wQKisF%9~85Vubp+TJh_&$6Nq?K@SaP~CmM4;Kc1&jp>)$Go_+o%C#2&pV7u8O
zR_loSxN)-?3Ao)3R*<qTz8Q^HDk$o91wzr+!@_{D!5iI%W=Z#<Y-rG6pW7{GDbAZF
z7bJybd+t{$tU`%he-2+9v7_<)cv0Lt@7A21H4YUdRIG0j@uF4M8P&F|Sk9_7OKuYx
z2-j-toTpeN1}8+msH?h5NV2{~08ggb^P8ly!=*jj2{WNNt`3^IQv9J6C1EFGmR#P)
zNqk9qUi_oD@_M<RExY|=K$W29^+BF!*jd|g6~&hf_f!<^L}>8y^KoetgI=0Yk+M1G
z1FYpVzxXC>jvW31*%s3ROtZC=Uv7E+q}{>4OgwiIB)-m-PB+_a6o1KVvY6gJny+|J
z-FA9F&aFz_$Wq+q1=ZE}qs8VJ(y}DSrn{9h-Pl1G55;D)n#aaB+pIQ85c;UxCr9KG
zBT3@MI-H+Xet2?u`AF5cQu*S=y6|Eo;KU<~PoP(u-)6L1+H{(PvpA^scK~j1qq*m)
z&wHz*T;=d0GC6;xNj;T;^n0?kPn++SJo4RKT&4O(i(A}bPgf<W#I~NVYO)4e8_~;?
zU0;P-)Yo+gJv{90O6Idi(x*Xq)pQmMntb7cwXSakMtgRdvSEnna(IaKTEyn=<0}U%
zUEIXi-)kK@caM?}z~2HsrDt-Q;;-4gKH0Trt$goHW@Z+Va_DPul33{dQ2b}8a!_`O
z+`2g8NsGF#f57zQ@S3n&J(=N;bwSPZU-`WSdJAx@WairPnIiU6!st?`xE9FKaokW-
zK?x399L4$+u8r>#OEnXYFIv~4AF>I#cb4|7UU)`#WtM;a8d=g%U9QVO$R_!^h>Tfp
zW>!4;hMb-Nk86Iqk?LZQb2z+}wy&p<N=MGHmo>qG#ll&|nayJ<;39&ITvnyR-Jx|;
zx`0TfLgQs<rKa|{hIeDqm?57n`*dryBeA|Xa{px41YuVHFt%|!R+{pIx#U*MVyfoZ
zd^7}ZnfliVNyJh6dFX?<`&UNN=gihqx1~Vq*#uJ+^->Tx8=`9)ssF$?f&EpcMC@H=
zDlh#NTYeRoW^Z5F68V7o-M)vcE|fX9-D}mdx~VQ_6u|Oak<7+3*i#wx8B}R<EFDV+
z^vUIj;@j4puO$cTavHPSihXZP_ljf6>)eE0v{+eklk!ON0{`gY7cnR=1Q_hy{;-@a
zCQ;UH{b~~Qc`pi+b)j<5k9m?0cRs&J0qZo^LEMDwI^<O%Q%}KyZHc7`y+-SDc%>?c
zg+ajO3Q7TQ>ghg{Ge^)|qp|PeuhDPO4~;Jbj#Hb<hlgd#6sD)E#<MG`75Uh+zfj|6
zTa2|&!ZGu-ZRq!(dtUmTuPsz6o|Ff>t(4aqkHk1EEV{gxYph{lhdN(V+D^JZhR}C4
zFq-$Iiy@;IRcU?EEkn7+Ow!$}#@*KQ`s-TZ_L@`cC@$9NiO@#CI58JTp~=cRhfXWp
zY*tcea{Ak9?a^ze^KPK)zK#jQN*Z<$fm1BCZN0^~{cz{9!=-cHNGhF+*lLk*1cjXB
z>zvYD^n_fOC66<b4>-=cePFIy!88Y?bkXkiz%`yrW%vyAH|PO8l#6`fC-<mQ>15by
zwGSK)bkLhBA8=|JyYgw2K)b|2hnwKq;uYv4bKeJL22{;-y`DnRM-8-xpuh-mJ0|je
zP7}RG`C5lJSs{91KXoJV-qg!SYd2FxrTYKYM@9}v1+9||*TUGZ2F)(0%XuTq%-q!A
z8CISWcFZr9qaW(~q|d4+hlj@4_}Hw3zc~jH%-$T3pUJ>lKGjM<N!hNo2<x8rh@EwR
zR9akJHcZZF^iqF5a3eV@GoKU={~o<7GwXMMws*`(DVI}*CO-G;*+=Fm39;X50p;~X
z5weZKbfj=DPC#W4(?R2#HiIsU?MC_GocL^Gd%2iYZT_z=U;f>oOJtX$K?Fa7YLM6B
z-e{^)iEm-f8|iDb3nIVu<j-ye2F?xd@vF(*H1klal_t!q+F<$22al0L#K>qL`Qe7-
z$+4D)BN;`+hM3oLA}Y8i(hgkPSb}7ht>3|xIZ@<NFFT}}O19H86u(YG_*C?+?%lpg
z#04H9H=nTBti70z1UtBE4tBGvDSTeGw4_d4=d^v&)o-$1KUy`-Y&AXdyGMxB<Gkoz
z(eue0IvuD9?&jX3?+<)P_>#e0nMAK%Ei(DJ(NAHuAhq0t`m!gxz2`0ux4JXf?9WgK
z?d#`n5374~lf~=aFGWA)&HPK03Z&AmnalN07f~a5ZGT4H7GHd;Hx*dJ+}Pj#+(pu0
z*X0bui0iG|*RFi87m^C1V0gjZSq$zjR^akj!>Sa?hvNSj5F+b+ES=4P9)}|E2omjq
zAcQb6$EtdB1MBh~_~ao?6=A1-xZzz?r9}T4(;SQrrq@YL_+`%T?Tc6p&X#J?uHRaR
zhZB@vYjALIId=uNp@J+3JfJ6&Jw}^jsQacV_n;TePkQByzW_=g@B<5Wg*tT7mR2m|
zgu=2r^U|W*t%SnmWx;3TxQXI|x0j$stXCuB@Arf!9}%=Z(q3QCs2WLhkx+T6sHW23
zzuod$ARWP^fby8>+UWNu^YCl)hm^TmB?Paix=zgTA7!YlWzxpC8m)%-UW0QF5}-oz
zPv;;RKb4~7F;&7VTqDP14t8C9%RRV6tJ)SHl>d2!aTX+Em>PkXIM;k1tF-g`GB964
zimEX!ufK2ZC|Z^FqRLToF=%XTynA~ztV-Tbc)9@Dn{?XsEHK#J;&$+|Zklh2+x38{
z#w876fBi;q9F7&|&juc;+BMZj`OLJEFmwI(lv>wDryt%<vSDwqqZ13R=j6bAz-yNb
zNiUQ*Z8s<yz_Jl=vu%6)ent|=lV)-RJtgk0Y0}{b_c5SqN+5)2(yb8iLyE^4jkL5Z
zndeC8r{{O+sNMPP{ft0mzaQ~>f^uT>xZ1f`wTfL`e(pYj0qHGD%vGE_78M-Mz<>Q+
zW7LWifoc|WVx{g9_X*=z?CFz`zLnQC2mT|R)xO_&gjUS6-)^|?^bO%=rB98Nzj(!b
z?hvN8mnN0+0g-C!71Mas*A3~fD{d-32l}u6TuAPh>$@bEQ+>=PM_brenY%gMr-O;^
zzW+>;G)8Z@)`Wge{AFmuXxE7G(@i-p6A~;!6?LmBqk=$}h~Yr_>C$p5#=_!oaLCGK
zf^^aG?;+;d`STR3#S0RcYF#Ke`~7`IdTr_`jCn@A@I!mw4EflV+ZS{Wo-m{GZ{!7p
zeoDGk9q~B==TN3bigfJ3c*Mxd+X5#CSiX8BJ1w$QDotH<`&o}PYIL5%%3CwMOY<!b
zf3kVq5U`kXT~AlCd0hG&hI@mc+VOB$O}`LbaAXaqyg9~VSkTNJUEr`A?v8bN^mpx=
z^0<=87DZNW2vL#BEp>+)H>*AS;8EaQD#_`ro5hUpuCjg2Bdesky;=tB_T%XXo9uaJ
z*v)|trUAYKG5v@j-pIF+Uk?Mm;e?;Pw-O$HI;?)TpWqc5Mnh4&Yi6d?Bzrz$G34>Y
zdZ0SIDyvf0f8M`JfJ$$wR;cdW(m_5quG~mCuMB67DBaZ=;2wIuqps|#%{+RB=4Q$i
z<M<j_FVi$BYNVKI>Npy0y+rk;jIB~5l7DBoczob*>FLau(&X%ffaG?Y`rq^A7>%1O
z@i+T&c^#u-V*R0MKK<pp<?ZLO)~@H>k4J0srpf;13uVgYXKSs2SjFA)e>!R}Xi<BG
zi7wg*qVCod${jh(YGIp>J+1qV{8YQ`yGE}T@Diike?=Q<6tvPs%c@48wDK!q&=O!E
z2b?3_LhQ+4CiIj~?Rjn;K~?-dDLhti3e^0-0iXIvLT7Hb)KwCeVxU#4(;hjlt^Q->
z%G&<q=~ruV4+_e5*}TTzJ-we#@-@maZqW~8*rUmQ2FWZryyjozot9&kDtGGq<1Jgg
zu@5`;iY%DX{DnJm+Xyz?^9j8mD4ymJH-;8G#a=!lyPQXqYRg-9)i-9bPNyZTQQ+tr
zn?v9dx6%_$g)3pk-0;fYmL&@Gyo!mvgd)2fuRKOZ_9-8}VWlwiUZ4u3$1;Y@q^6*>
z$pdSkm;Og{OUbCc7S}f6wq$v+y4QTL#Vd8RZ&+nFSQJ?>BwOXVf-8H-0k?7h?a1Dt
zyA)dD^8vd9+%}t@On5^PQinql&E@Rc49AN_?G7=S9u};wKx(H(mA(Yr_KmUaZx3=p
zI{~WkN?pHvc5&y1at#Mlk$jAasK#Rh%H>u#td=ZR-M!7^2yavCGvyT4%v&P{?O{?>
zl<i}!!AJeN+q2$}A7A^A`SSHXSbvA>(!2N1gUl>2adfo(vc;OxTqPmC)B-@t@um3L
z#Uu14e#&bXc^_?k)ANxZrV%?5ja`JHbp6{Qwf)}{!zI!BX+aRx$MwjE0+?Y|?u-WU
zeUbBKunHBZCEi;82^+3x4po84El$P!P?<e7i?mK^3B!tms}0FjZ~c0RoW<d>y>~v=
z!uvE4(`l=onx%nXEL+_BBbD4;;uPV=+hlvn_V%n8w~%$vNwphL&g1)?=1kGI+G9sc
zwq;ExIQd3n$ZElLb->JtE@;;owq@^T?H%JhP9kcC`mAj;B;xwmIh;oKaG&l5W3VEA
zs5cllv-ci$U8pg(%n$NCx-S*Ptv>R&&fO2mWsM2x*G_g~%|Gkincn(I3H8)|S{kvx
z5LaCjoqNU1<lmDKO1Fd2$E?xmh}Xuvb!jlz5r$y=NVUB-f=NXvx6zs$woga^GYD!2
zt5MleTmR#|lxt`@66KGZT^^b0zlx|MsT&#4uZ(@X&?ox5p!_t-xO&uP@B91EFC&Um
zd5yMUGm$~<+G5;R)M>dgGyV3aaW@jaYn0HPoS1MGmm#Mu2L=(3>TYRYPcn^#e#``=
zGVm5VEN}3o_=?{0xI(Ia$y|cW`x)Dn^b_3Pt~U85r1jY+*bv`eef}~c;2o0h2AVOv
z>s~jj6ytZ&gMwJ{^%T;&J$vcHn!}K&oDMPpj#U3^7;4S-7~c#NB-Wd=E*7Pm0<t2?
z>Aa2JmKOa4Hn$yLKA0@O6r+Hf)5!tf<H#T6j_J#<4r?o&y5O%*&55nA8(o;$t%oU#
z@uJ)>9fY$2Z*;u#x9~86u2I+v6Sw*UZz7?5)|j{7_PXW!ZU5K{EW8ePiPPEE<XUQr
z85N;aaWT;LL1HNTe^6rzZ>NiwFdGR&MZ#I}NcNw1Gf6Don=PT^bQ+Vm6u&qM@qjHB
z*<Uay>%LqW4Nu8x{y_VDP%X&3^q>;l=4{zvu!&^K>qUM_Xk9DTXbIKKnVN%YIVyMP
zNRH&L8`sU|o$QLC(7j1n+1lXq2ouMK&nvutggNH)D%v+lopoGm82*JR&$|l-m`7G-
z;GTFXSe8tr#mw9~gzJSp8eD}P7bJIcI)_)v?g{qg1K2)`$IjAo%Z~*%yn5%yfL^j@
z%)AylIcvjnaQ(JZSea*N{pd~Z^6&`cwfS0^vZ=$(EE)zgdE@c3ti$-A81Pa)^CFn&
z$LHq|ybQ-z^cX817j{CyQ1_P!d3YI&?cj{Ftv4RQ5l#u+Du^!RHXU*UuBL38?1}a8
zCO3ULh)=Ysyv;DQO%Fervp6wG$k%B8o}Wu<WcMf!7ZG-{lcL6&NMoImy&2aVY5th_
z{_`7V7Q~)3a3}=oOe^N(i?J3eQBq{?|B8<JM<<VN#t<xbgb?v!IIpa~7<jcl3|4I0
zDE$>oRTj*K<^4RSEIMGG8XU=h%H<t3e>UE%7pC2;09LGuw;=8fx$2>w@FBX%%58~x
z)=V~NV;!HH9wR&k=?GfJUsOqi6jGXKN?I%^wD`kQ;)a(*$8or*6c+_7l=AMW=tC=e
z)QdLW<-oV+J=|(&-+ZcQha$N*G_USjc|kZRwLBj0+e_d1$s8|pB5q+E`$DR?m#<zf
z)=Yt{f$-t;orm|+I}XNR@D+}i9nk!3IaB<6NLk7hv6D;J74}sS6(`r{-2=A7#j<!G
z`(Mm#@~n;i%yd2Lc<;b(%-9<-SZ%Bie~t->gP$1bRg@6i*S5Ij^|t?b)I5$&oBBsP
zq)FZ{PygppZ~Q=gs6@C(B>eYl%^XsMZwfODKwzWk<+_Cy81y$z;j<Eq#u2hVv<&<S
zr=&C!>>uU;UYedi6tJ4eVbk+&sq#4wtpID_K!mzZ$As2XhrL|M%~6Nvdh6WzNMs_i
zQpx!3J8-<wTbPWy5i;OpSn(pM<4J_>nHqAQCk_hpL=7N971y1w3fb8q_3^hq$GBc>
z4_Ort7gAs3%jCQy`<@}zP*C@gj_pt6x@FOeNH+F8gZDqj2*Gh>c)gSjZIuM@8%9H0
ziMj_4eZqD4^I-UZGH8a=p_sR9*TICR2EFzj9MXhWut=e25$QBsyXR7|L$W3^tN^-+
zwzlM}fCoe*x<oiDXQc!mo`YdnL$P*TWxwCn1<gciIkI|>bzlM6HLnQKPr+Nz3GV%%
z{hxvTd?>^K<pNP%H{Su_CK6NuF|LdT`I17vm!dW$^bQeg-#atbwwlvQ&+8z&PtfW9
z_Mb1%0*BB9xDA&v?jwx+2+rfMvMf4Zf-ub!?kJ0V`Cu*yHhrMsGDRv_a|ErNo;%xh
z)?}(9NnH&B;uiVjW+=LOKjsL5nxc9P1Jg7>7d1ERd-SXO<tCPRJUzTvxHMd(7`)pd
ztGZGzZ}*WwjCKD0-3n!+(hqAyo4ZeDAq1yUMuwIYJ5i|6sumje%G+bh_Bj$dLaSwq
z1!0%X@Tb^%ojlK5KWVKT#DBJmt%*^IafyjGE(g*z9dB+r#W*ofeTP=s^EQAzX4m<H
zIbZbwqoW3*fcm~xC{O73s@n1mZmF%;W;m}n4*HgYp@0vHE@{kghCSO)PDCi~T6nN8
zF*n(wF3+&6aD5y?h%0uZsF*!0NX;+H4c^i$*T}IEEAWi=sKK1g8v1`mgUdCTy-qM4
z;FeU_&Q^{49WNoCiEK1h8YH9~u)T0bWuLK0DKMYmY`_<3)I_j8XjsPhodNJwnT~so
zC&LjsHO?q~waHAsGmxE&O<)XXGc+Y{+M4+j(l!J#p-fV+Arzcw2WB34w&^e_6d^9x
zue3P)fjoi^v3^>$kLF>L61PW9q%uV#Tpelf73%qJKQo;UNN|ZceKoV4C^jSMIViE#
zy>dp)h`}mvi<K$IO62?PgH6Fdh8wuXF5t?Rj&C=wUoV2^_Bb5#o@IXgpg;unu_=Su
zr}^>G4Nn2;lI-4UWG=4mbt&Qgfo`9486_!&WcP~@O`Cunb?1{x<#FdKi#BP10g3<V
zBkrVN6r@0)GURNFh!b5v^I{l<TWkiXP#z3JJlJDcqZ36xqL7iEd@K_n$=AK_lRzRF
z{#AB<Sy{sxyM4^mg{{N(7jri(S!|KA5U(V;4Eo^U%{IhcTjm3V+^EonRT5+Ayl&zT
zm?V!Oq%nR^CU#^*G7wEgKAxU~8Oe1?-$gbkkcK;#S6MFJ0QUVGh5ujFV&#%ZUx5my
zu7Pg^hS1QM!!AD1o4ZI-L|D)Vj{?C3lr0cESRYZ#jR93*K7GGMs>cR`3lc-Gonb;X
zVp67EHxrUv*uPB=Wmbq-QfM3G{6f$NBODz0ttAVMkrQC}TN4ZgVB&l5A)sv|=R4^k
zbQv(w*QiI5$N-H&#!-t34MsA~zi9(Z?8X86RMoRji9`aZ6)Sen3lr}z1CelZM2dqC
z5lb=>J_<3G!~%#E)RPFNe^CP%yYBNf>&;Xv)bQCXrVeLIUgj!3wFk_vyHfY1Y?x4a
zl}5?FJu19`?4KDsuV;3CB|;)ZL`1Z^p3>-db;S1vr}e}nklGgr_03J<ZO257GSLe|
z=q$?ED^Y!WgU;gjave#c-hTb|?b~1sF6D%wmaNd~9q~^TPTN(Q2@|bHK;I}5lt`Ki
z1G%2BXJ==hPW2M{wRD9q<!M;FM#==+i<bCK%X@6dA!pbZ@_qfUadQV4Rh>CHq#rsd
zn#_RP?g<Cujkeae42n^ZD9hr$OE3r`89kkM>)I7+^iUvqfrmm-1V41t9v{6HPxWqt
zF0Mq&2teXVy$31sZJ;QWsmP=<{7^25?aT}h4|m(}aqIC1?>e1_`cYC?=>2lDxMZnO
zYZOwiI?|56&G6ag+_wdD1wj68+GbW=q(Xvt8?~mRb;Mx1(Ea?brnqbtxvO31n74xh
ztU4SsVIilh&D8)Ku3n{6JG$29a-2-7e&o!v0T0e_D>aFrIygYt@s@Y)X*jA!*5lHN
zX;??yilRW19D~al+D50>!Bu5AverVN{$s?HX^C_I6Ipm#^kh7fGme0NrR2)kvif*_
z$a`C3;nhdrG(5&D3e2UmS+Xvhh&cCj1zvIG2F}zt`t`WFI~JdJPsndNUmSs3?~VN^
zplRf%*oSkbOCHS)Hc8!c+$w9;CQuS{ZAPqaw6-6oeivDkFjmZGb8cNT9!y%<SQ0)y
z+qr|5eSI2P(#qraW9ch89%~{hwAv|=re&kiAcTRk_(L1Bm=a2Hwb|}G{!f&C|ERj;
z<VXgm-FHSD^UQT}A>DJ`@7DBK&<3N5vt7mj4Ns?Tcj_wbp>lzr!ES}|iXJTHA9lCl
zr++^$5wWwgQ)@hwLaTnZ6-KyTZyp!tX;{Bb0xY;)f(fs%c#k6~_2D6<;kQ_P9$AbJ
zyv_$xW9h7n`dvEJdYvUXzB~c~>m^2n0^ThifA`AD$_~hRcsvKAvEN?K{d`tPaT&<~
zF-`=+<R+Iq{&x41PJa@YPcaR%;^&}XPLs#GjZ&G^Y3G9j-rB9_AW@k8$sC}mdpO1p
ze}9Lr<0yKs5HK4qz24!^f${!L<~d9i^EE&B5$M-Vq3k5H9B1CxCfnWm0ln$+>AV|L
zOuR6-)oGu^5l_xeaT-peFbg<;S0@ThLMRI8j07%znbGjwCX^eW3h^M%wHFsN-TFoH
z1pkh)Cex^Tp41M>ednVN4|3_OrWY5{mqq&yc(*O=x{zG*+|%EjW7ZsWx+K-epXbV~
z+Hr69#Y4JrT}M;sRm)WZxC!L4pkdsO=BPdLavLB3>i~yDCEJt~(ydcb8C45ytf-&0
z&WvYy6i#|Lv08-n<D{!PrqOM4Uam70eAr3setUiDg(6Hs21U|P2<b8w_p-rop-kPL
z5vC-YYBbGV1~mA^Rc`duw;MXZ-euZ#A%DaqAn5u!PeZp~3x$i0PQJrR%nO{Bx=a?>
z6LpBAI#befn}k|V@g{-a)%cigth(K=Ew~GSC}G>w;C8;g-s$zn1Ve{otDSnTT(iD-
z$wPnGTtp9W6}#;ENgQAxXP-xHRu&dDIz*w3kOP){MO2s8t0uU@EQWPQTl7mOq(4r-
zzLDD(S1RN|bl7H%GD$1|l}-75(AEh-xy+;)Z0wafDcOb>5`KC|A8Zp=d=WJn^w?}S
zGVBt=FEtx16zPuq9uFzg9B|mbCfT85#A5=h<}r?01pf)#omuPVF?zlNVz#rS^D{^L
zqATZ#2&P*<Kx`{or%Kd3;q$r@A^3Ltz94Na&mw5bw}H6IX0mamDexh-l4&H^EG#r$
z>*rM!qz;D<i6TyImjrIx4c%bl7AiGo^Tnf@Y*u5jm?4;Lg1z0R3OWG(MffwIFAq~B
z^<Sccug(atvm2j&)pyy<bS2f^S%dfD#d~Y8oaJ#jYOq=5xDFlWUaZ!?`_gQ!>Ih7l
zoRb_)G6U3Cp^8}Tg*c(ASzlL3ohMUf`y1ZtG09=l77dHbo=7CQ(P|OFO?|7xad(8l
zikbBu8qlscvK+;_NDQL*<GoppUl_ZNdR9-h=u!y%o*lLxqTCfVW6~`Ih>5ojrVEZ2
zept<yCpUW;dwrwm;Y$7XodA}S9pas3cf9$!ZLYtmMp5>QB4x;$V=|r9{Q3TpQZ9>1
zF&`A$@s$n70lZi=25j*rkK+%XT^?{{@+W0GcV5&&!IB5#CJ#kp2Pv2;T-%ph{%&jJ
zpX$K9(IB&U7uQec91XgNkR0TF02cC*91fXc9rM(yVjPe<jfmr(L^tUK{a(H4PCMsW
zRuia!Ed=zw0R${y5no5p*A%drV6u2Pus$gl%Ld3b3-&HA8l^Joak;=E?_154ZmDEp
z=K<Qk0GfHWF~g^T;n0|Qbeiow2R9=`v|>`XqvE0>pNn3o7K7UXOnp4|uV`b}r6KLo
zO6z`%j{B}|S_bJz#DJ(zf;w*KndskS=LcwegErk>^@?#TINXjqR@I#V4apA5JDDkh
z>$}ZO2NceC6-0xgK2e_l+<@E-m}9$4<gTO;5z=x)>rqv^?jQlu$USAiuD7i0CpG~-
z8zdtFORw&5eY-DG6B^A6f<`r%hwt~=E1sgrFXWm5f}WYzXYK;OQ!@2xy#X3uz@#A(
z0P&Xe^`cNJQ96Z=+JppIv0lyn$4Y2XRHxT*WeZ&Kt~dZi^!|!l7KspD>HQ?2veCFt
z^aBNo451KAhZ<%R@_Vwm>WBCuH_1vyp*HdF;Bo>2B&InFejRY)L|u=#FGP?PsFG_|
zj4|h0j^jkwcl34*`$KzYKfZDR;Nj)zuKB!oPnSJ^g8!&|{#ph@YK@=mT=Al;88~)c
zQDmzy+z<fuOA9DY^XrJDVTT_Dt_Xeax0dV8x9ox{z<(dAEcyxEHdf3CKI~3tXcWf-
z{ldY-&x@vl^+g_f-m&L!eQ)%ucB94<^!2{j?8W7<M#`|6Df|UEU_H;nqBS}WK+7em
z^A&X&kpB29=NV;mtRW8Q{0W*a6CMTvAZ8UQ0N%O9CA{pySit!JNknlQY_563_i2Ln
zJRJA>WJX=%ls=Uax7ik%bHEuI$jw0PmD-Q*{0xRX)zJBv7K=B*bDgS=<Fl7;zBiGb
znVlVzDf$r}hipCQrt&3x@QbQj>g=DLR14n4`eF^4|KpzoE<kKHtF(Wj4lu|5MpD^z
zTz+o+I^Xql)(+qYcx;x!3rz9lHPPCJZu|9YZSzhR5C_Q@z_rd~VM>$ZLPUl`+r;S=
z2SLdW#bT%&0O=0fO6P)a&&s7sfKBv=Aqrj%lhPzh57U|SxgJ}mv)Ci<&UD3-nd)Y@
z=`GAkY>^8K{WYKzx2}A><@Y-q2K)-e+P&w7&)pB*cGtBQhiw|Y?iJj#ycU-Q?_1f0
z9NqzZW_+dhUH{s~t&xMIAolrd`_0%&{i^d(Auqmx&&!(Aq|I6ja!4kJ&EuT1e5-N=
zlPSr$RS}J%Qn$UZA&#N0=KF!YxmI*9Lz;3vs+>Ap(^=^cecj8nTfw~Hbv+RpW6?+K
z2Zo#DyQfSU?Pb$EaYYFNY$I&5&}n#`GI!3RQP<1lr53dqC2XNKQoqC9q2O~&&2JjM
zzU4)F?WP_c#+`c}<oQQ%(^?~6xTDSEt(uoWV9OY(m{Tah7eQ>gZ8U5+m&{r%aub=H
z)waLed>*ftJH0q<{akcs8Czi+wH7O?4-XP5gPvYyh<Ot*0AFG`4po%E2wud%<9QKZ
z%3%{iEVM}a9o!Z?c)Hl@?hIILOae$U<oV+4I>|sxd0$8qw39hj2w1{a!{wJipSc!u
zQB!)93&8*Yegl@V@ZfdUI`r1bduO3hOXqW2dGBy8F;{x{oWw0G3wWV$%2d(6wBQBP
z_cx3Bq&U0P-LG23ve@0Xr>^E|1(%Oglzp^n(SBBytH~NnSkP0UsGsVGnL#OF4bO9B
z%Cu^g;m}Zk&rRcUFf!QvRp6HCWf7;R`E;8h_kFkkix86VJuN9yId*shWRN=4bzCHq
ziuC>D7iEt3>%;yFy|D0OefD!??4_DQSm5<oT>x1wg310#Cdfpl$EX(C@Dm#j{H;N4
zzTeHf`gz+iLqwcZ*|bIb&D3PJG0$H&_Xk*%)Rb+HD6rF;b&_AMA|X8hfE9eb{{z_c
z`D!yGF|lxL|HQ&^VkkmHsh9lPX!WR~@5l)__=-KB{~!&qG!Z%ZE--UO^(s7)(Hy|9
z&&mrXQ$rWYl^m{(1A6(xGj}>dfDvXAfQy79YP}Co{B?aFuMaIPEgc*j>^g7MdR}(n
zSW#E1exoZYK8nA#2-Pw<)>Sn_PwAc0f3v3Kg6SEeU}2dNXyR)09O!?E8>GDU^Ff>I
ztc0qJj+k~nG16%Ix#pJ(u#cK?A${$l{>PqfRJF2HC|pi^V^evewCYvXOjsv>BuXET
z2FHBcs9I?~pX?icdj*%xhYo!5#YQ2tJ9>;yGef8o0&Vg?-2aJFFH_o$V(e<M--3{n
zT&{phcWPr{(0j+ld-{M1j24avH<|#zc|7wNmZz+qIKfv7j?66~ZkrXhmNB@OU9J)d
zs2HcVCi7~hBBgHkJSHKEz+_;FdwJ^d)O(=~hS(`62K7pu4`;#+l=H<R0JSSDEG!DQ
zj=RL|G%x2))Sj!P0tLlE-*zz6r6o}<Swn?SsW>PaxQm53^6=yWV2D=iC%^L}XMTe7
z$YXKlPx*@wx&Ep$+gc$~!A_UPh?e3avRYx6Hvd(lPGg}U_u>ICt4T~`I1qh6fkd3^
z*&23pIGt`AK+L`AHCQC@iguo6b<uNW&8iMWivQlAFn-Y*9nU=blw)cl4ct1#sQZYu
z)%xA9eQ=C()%t|ZyS>)%=Z=2YM?(VkQJfG61^u2kNAx-^NN(b-%O3IEC01T{@bwy-
zeusM(nzFGVEK=dv`aQ(-#Kc5gPFwG*o$S#%La!6%UeW<Vl>1`Dj-E)2PUEye7>$lG
zAQDrXTA%5D8>~6m4=$Sx>gVRaNsME0vu1pJP#;-Z3d}d~&_vp7)5DlW10zN*RIfZB
z1}Ew1X}||l?3mjvSFHd7o7bKXh~d>!*!-RiP>}>EmhE?+DE6Ig&R-2RHA8o0IzgG^
zlCi)Z?i7oWK-g?_{nq>jAVe;6X+bDs%cUB_R0drlL0izfdcI|fz_Rb`vRs#N*8~t5
zB-%N8?p)Twf$unui~1W9H8b<HCPHfZ#6V$6P|cOs?MZ+NK9BQKo$2Vy-NrdOOwbs&
z?qW$dJ1MF0fMa!!A*m860N5L`6C(L)CV0b@0MAGlgU?d*KpQv;WbJ9j!$)~8?Yf<<
zMPzH7u);lr$S!5ivikitkdme`QC2FgVo(>zmtyT7F<r)PHWHB(rWx6!2d<lS5^U?!
z1^_ZDu7uR+w+I14Htn+{{S~`l!eaQL71*Me#~W0*h?=A?8EnY=?&LnJ+`s9~zf@lz
z%zn1dA1Plnuhcr6)mw5Gf}TR8Bw8|^Jp?lK3{MsgodJi9cHL!Cq+e?^P(LXEOj1)4
z+Hd3o6vAA&>geK3vD_OQp!?*rn$i4;x^H6?1X$B`4IOc~Fg(!PrKsCUpB<>B>JIm-
zrYorZ3lJmM3?i;}b3SysRnEB94(a+UTz_r!oXQmrLnOQn${#9R$(kKE>z^TVkZYzt
zRaRzQs7l%0PZrsi1H7&qhc^j{0efcs?Fq}-62%Cdw^H>YDMAsE#A=hxhSzx~a4q{h
zoi@*xtD;`{WB?gi=<{{ShNnj-b1FHmJ2`{>6>0$Rjx#(ovQkU&^E6?61u)?`;Y8gz
zdEIhnIg9BHoF58r36#AG5T2q^$WrNd@rjH@4fNn|pwp_M?cWpQn@k+Gbs?HkQkqx3
z4ImyYg>1Y775K&uUL~sJwyqa?^GTDJR@P0@-ZR<;zBwL*-q*+DYNOOl|6vTEE^_17
zu*Fi#dym?A6If_@*CRM!1G{o@vTQy#S(MRe!pc1PBO)$}{O<5z<>7hUP1XNAR;N@X
z4XIN|<Rlk1FTi`~3FKyhL&<=50Kjlvn=GgsKSa9dbavAFsL&y<o0MFq)lu9!_564H
z9l#GcNNfW62(?~xkk{N3hhJDZY?AJ88#5~bXj^@_uB==8Ept~>_7Qa8ME%13Ek1ZD
z?~CWHAD5_#$+Os7xXb0XIMp=}B8YSYfx?hrw&z0B?qlN^(*TgnXsFib77PC2qmbET
z#y624fPruN4*=mvfP#Z)=qF)3VrofoY(7f=9~}D6OORLMqt>i7pifq6x`)5V-3@XG
ze`0akW^K;t*O~mfzIGOLK61dchZUjnzRcOKsO~y)unPEbKPmX8QmP<~6(y3FCg9^~
zAZ~OXLMDV8d*NZ$M-QQBvqFuEa_!ri4D<Ngf^A!0!&)wzCxlejHUcYGB**7=xk|VF
zXZ9aln6>8tZbvE7LJ)@t$TH9Lb1Ys&au>jtodBCRYC8f!S3|<5vsxc_IHj*C(NR5M
zub8w{_E93g>-kECDjQSZTd`vPqx;T-3*u~yC~~P}_0KtHiv`4Po;XOwX(WYVMBREg
zLLmL}4tdGIR&BN_h=gIi#^5_9l*h%UFjkAh40>2zu$jYeS_Yq($Ve2&!Yu!obxpqi
zIFCO{)~V@AiCI@W@1Z3p%h+Tx7)jA)act$wlPqQIzE&v_;&<OiOPy1GAb`?wx?i>H
zCYCl8{wh;Mx2}qi|8sC~TR=NZR`dCY4+GD_VxcmfF1RF~;XPpa**E45Z9#BS7#=&#
zn3W^XeMbNgU)X!GKrG!eYj(cgNs~O%={Z9KS&eHwXoNe<fPu!puE!E{FKF~L{Ox&P
z5JKNNeN#ZsdmP0vEgm2IQD?+<q>zLOLG^Hdf|}*<&jl<x91SYNr!viYifbY{Goue+
z1auYZ)uB~l_9KBD<c(KL(IGJedkB>WrsW5ag?_IOCBOGk?l7kONevP#1K+P1O!wOQ
zo5#XDd9k@4xOZ2_XEOCbgaR;sm@5}Coa^qb8iDf$;eIb)?keiZ01nk0WYQgdWxZ6X
z>JA~DEjL?K;V|U-ms`8T-OaaVPTbw3-3qpf8puvfY&=GsP3P_MIb-W%0r+78Bw&xg
za4ObFa!+S;k&C7owKge`*C4~c0q9(>+m}C@BV$4rUF%0LW7VUr^so^bQYGN;gIvsB
zm>EoEql=Br|1@okr!sKgg`F;lM`840F!FhBg<x)))dT1plp#@knkl?*8y;aN;3r4b
z4*~|u`0gMAzd|>{?2j@vuGHM?%Xo2p0l-!q5ayq)HlIghvx4woU|?EC74FetY}CD3
z!)F*_y-#FoRb&%|E`Vc&*u{Nw9G>Lhkb!{D%K7%S^(wB@nCcOTV0w%Z38?Lb_#C#y
z2q1R7Sr3fXjvh0G3D6(HL6W&b?~zz=RERFp)a?m=u@bi3h9SiHz1k10UavYofJwSL
zf7^~?)R{vz1#U|FS2H5diTCxzgr3@;fkP7Qh!`6igVgtB-aIr9uIC8<mX?*J)9LBL
z`K!=}pVs&$^5<k1K$mTqp<|GOqfjA%SN-emO}xi|Nj9AY@~#~L=aR4LFUI$+w4$d-
zg2o1ZuMq0w#{swyj5HvJ{(=EW%+55n$`b`c-~iaM+OVSN@NlG|5;`i7j&Hu@s(^7n
zkB(1?G@aJe5uH2)Sv(`q#b5EC5LGZl8GJgF>^S32ll*P~vTR=W9ChKob8PH<4AuM*
zN*?u_c0vi*aY#}jFP~ob5XZy9LNctco1~1vcYTlnOA#RhgR~k-C4L#LrcTq?tjU++
z9Wz9@#Na4M(Du5FxdtTa5I~eJ4w4XS9M~1(A|OwqRW6QhM&n<ZyFJ4-<G@W}KwdEq
z1XAU-K^E0_7{79Dpyb#Ada#d!?lTr4AtDqmA-w8;vwDV;ii3lXrI;Kt5P4Gm$B$E4
zcM#Dyw09gfD`@muXq=z3`Mqu<D8Ew)?LuJ8{}-Lt)!=`lkp;Y7AcSaG-2XrYA~Y^=
zS?}-w0xFb2->tq767k6U{jcXqaTI2!%h~q>>eAU!v@f89_r?9M?VufoBm7U?4P1+&
z_8!6cXrHy4tTo@LJg-ZSXE!3fD%I&QJ>O@NdjZUZk*}Y~a%(t&G+ZqdSD*Piz(EOb
zd^9mQa@_q7RabntE?PGIQS76uG(@iiFclzBL6itS);sUK)3ffM$Y6&99|h3do}p++
zRq^ptylVBNMrZjHeeL@rkN3qw{2d2ji#iZO41iJ$q|Fnn%k6<8`$o4bFXJzi@@Oy5
zwBja(L<Fes28+7cwr=dqQq|&eyZ}(<9F{XXr_CFogP5O{%2lYTsXT{+3}9L=M;srN
zq@`hvDqv7pgdxZ_v4NOyvgKC`MwM6RdDqiIm2MO>(NxDTlW&^_KsLJIYmNl&-4T4;
zYg!uCnujC6{J@MzB}fV}fGqkGMrA#|$mzgqRO|8otQ7`jGU&Fs|In@G0L2k?g#En3
z<oG&gu|JUw9d$nMyMzT33=96c3wo2S22_cYrP@9&WB|d0!UMr_d}V%Esd9-NI<4o<
z$x7qstF9Op^!v2VfGFE7CwWZT=%vg3a{KY-xW8a8+ijiO^(2i63@;FUW=1xP&tpcD
z>HUgG69r}@>;HN)Asa|$4s3f1<di@f0zS_Y{{AitM~g2{lvb;w>Avh9@TQ|h)c+O;
zg?`mVFIxk^8ZRzTmtfG=BPQVXA7`2@0Iba6WP&zIU-rk|-7O-<E><#ndU!cH+xb+z
zJm7TzJiyOvboqR3Y?Fv1U^N?~Qmah6{#htSMuZ4pm8-N8zCt#f7=53cc_>0}`?ED}
zz<myzLeu6*czEy!`uUicn5d{Eb^I)DAR}T2&aI5iXIMH!H2#I|($Jgpo`4T<I{GW<
z+uPfP^h8CZ;2!Ugp5Sj%SQ50-$9C@$xokbJ(?(cynjOV&alq3NkrV)~j};Se!~kjM
z#NWW#+KG6^xR{)wyWclj2m%4{arpj{cZ=@;BzPJKkps2CYX5iwFJOX$L5Gntzko&x
z#fa`tZ(2C)9<Fx7bLOjbd4GP4M|lqX6}Zb6E0%|V_QE>J*KYw1**`QxhO`0-^wt$L
zpU5H>4K*<}T{eG6R@V#Yjyy5rJ03O+kxtjE1g)#a2|OPBofMoz?>wPMfJhWFfC1H4
z4MhS}dwF1fbI>48x7A`*K**WgdCN9>H8~z3KC`#`OucBbg9FUDe4PdzN(A@G1d>K5
zNK%Lg6v$10FxM8859KC?5Rc%K`mG1)t^4f-QH$Vn=H;IubF7%Qb)Rv{SlqkQqI@^0
z=yYJ}4=B)Py60E1a4nTPaO5?RHQ-MGVBDrxZF4?UEco2ZK<MI!3#4QA_dNulM|UQI
z!3?JgF#-94gFN!lqq6%(;A0*SyQvi_Wmn7QIa?q)*}q&Y<<N+Ww@1c2j$Fr6`rv5M
zy8m76#KeeIjgJ#i;GXD~V}Ki6o{-BPY?=yKCu&Jx9ZCFpXFs2?R9mj<VG~yCGs#m8
z0@ojKAmxeD?#xcn?3JOcubF-uh$D)}0TwWOX(^@C#S9SjrlTpuQ{KSN<Zbl#_y0YZ
zuJN{f<kUw`XF|UFyQ>)eehMUe(uFR7hev5&B;taplebhWB3M5p6Y#k+ktpZiX*GWB
zyd1<u<iA!}XZZV%fFTCh{h@Ad3I&#Wec**uI<Qo@vHeLJ^eIVklqKWecjF05^=r>p
zY79la_qp6JHv16;vzqh6;v<J8ZJNJpdflbT<-jJ5oumTWQw$Q3T%=W@$^5&0X{-r#
z`{dWO@I!kM2st1Ap5M>(+!2~(3HUs2LTGUAPXA-gEE?TVF{2W|V4&Zt8jk_V>#7uF
z_nPWeT8)5;V%7p_4+ug`Q~f-QQ;ZfOWz%2VZ-*a^OCC<r0~bs^07t1nfuz;;n(}vh
zOEjEkvsp8&{&^yscl!4SL_Ysv)3fSrUFp)p8P&Y$1I*W*D{9t%ueQhore<!0axGVd
zkuj^^euYg$AvC-HQ4+fMe0_a72P`DG&5axW%$&w$Izvu3X0<tkQOCX;Lk4)~7R(h8
zKYP=JKS9tb3v~-{^LLMHq<ZlAKYFXpPFEU5{w(?H)|-8a!k`b%5}|1L?HMdChK1o|
zsV^B{DF;C;MzrJ1zo+&D@RiQdzP;HtO4YLWi~4>sPk96$I|+cN?_)Sm=FsKHkB%8Y
zVrhOhd)==C<nMp4^q?T}N{;~|2uMh;bA3D1-5&sa_8GvG*=OT18T9Pu_?>~Rw0D1P
zyMt9O`xI3~Ff`s?U}p92et;I&Mbu#Opl#q0-E!r^547ldxm^wV2+!kvf3eo;WH@-~
zF1=P(N~0t;``SD91($xYs#W>B>APpi5(+=^EK-9D!}v3Cy60rhe-Apid6Y_|03>1C
zp;&womy`BwJi0M1kyv?D;L%Y(8f-rb!>LbXdQ75FJQB2`ZrpNEK^uv5fqp}-5UV9i
zI+8NMmK9RWRgT}Q$J@&toXYR05Y-5A2OS~+$N(vnj2xrIK+l2Sgbt28P1&+Ac>&Iv
zS-<Vzb&iB@IS>-AO24~m+5*Ym9e^}vi#MK9cpnf(Cpexvejd};3j@c3h!m-<jQ};`
zg2ixaEA%<#wq_~Z93Ru0RE5PhE`!Asb{jE@@PYdWjUGw)%?BU_(xim!snyC=0)mW)
z5rX)~u|q_~$BC$Jn>w9`q1^`ER!7LD3?RV7=XMmMn);#5xdsAYv;#4vKMWwO8=BP{
zE>D6d1-yF!$s8fcV^3K?x9as!Jm2X^`~F=32oeBSZGgx@Pk&kvC-7}gr(7mA)_R)7
zc)a|ifz47{vh3ac3K7k@be2m(RC(8NGwQvi{v{c^1Q@!p;bGu*Sqg?hC962T#Gbi1
zG#?)WGNx%lCQ(fR2op~MUZyjHCdAV+g5E~Z+*&+Wo){l*kSbQB)Vs0Q7Xl|5hA{uN
z5gd(iqiWvmH~#X;I`BFGAo-|W+Pp{W-`|cxqd@=gDs#--EO@%@<n*`#G(7-DxGw6t
z?W)PvJruM0ZQyZ>aBd2=`65cGNy2^x-gyc^<k|KEya|m6&fM1}B<4|r+DjMf4}{~+
z0CS2QlYym1H##wVp@?an0yu{SkcjT$(BPo$dIz>*IN(fK0G0$0%14}eYV(_mOAHg$
zim#Nd0VkA$0xga^F%X40nnX=%Fc3*_7iQpsh}#)YJtTJ3ZkH*S!|?b(_tPe&;5jKB
zaCvSZ5t+q57(MSP5Kp(KTrkOLX$QQO;FinI*TWAFPN`uMp+lI5(*<GoJ{U+IOVj{#
zcZLp*tdeNh0<Nwe9b<{Ob})chVaT?;h$I6r391oCk2%_gy64^5X>!UkG*Dp;2B;t}
zB_*sO{k#g$UOKQ#Wy+MTlUYH}(1m>Vn?ep0I^yy<0=oHPFb)*6fX(Ge5n0@s{ZOM_
zxaun$92oclB<}+Z6Trd135drTGUyy&tt}ZaRqAzW5%mC9fDjcbw1_TZ2@X{k0CYHQ
zR#fnsPNpRY0jyzDBiA%-;Y^V6ejAMe;2YW#qPO@Mkezh_AmID*c)HOoNKI`yU#@T|
zY`s{O-5-YDOx$ESJ4l?^{qD3V0t{wv>;(c6a0HS703Ey@HiMYp;q!P{0$^Nff|E36
zV|xpstbR6N7{e&>FY9y+KKve6Ccq205bB@e{_r?#Jx6-OFbN=q_so*+*GDU7XSP_l
z2D=Rbv5ymmKp#6YLW6ArtgKFeBn(X+r^?r4=wPhURbcv`SIA!=9AlgwMj5*es?j;S
zj8<}Y9r%EYK`oCv1dsjw{sZb)-9Abu(*U9kJqrs8bC`S<m#}!wx$M6KN`)F915e4#
z{qX$o*NFBRNVY%}MEwi_y&K0kBnRL=3UKTL&lWk~=gT4B#V_^TBS0H(NgwPjX+*z2
z$d&Ao6FRzm1Oz#7HJr6qSO*6*3^#A!YKlq(Odfc(tUv^zq)#eZFFK7$t@MtaKif;w
zJVJ~!zyT&5@1O0zW6cGC*&xd<KxygwG@c_!h-__N79Rf|yuMpd0JNm!;-8LVz)I~=
z%7M82B9Cs0Nh9rF?*JxLs@;<P&esau!CbheEa{tePKPr^e8GF9?+=rS0w9p%BdhG@
zN)wi?KZieHvzl!pSpx;&*gj!U{9D1{Apc-%E+FOSbgh+D6n}?V+gt2?<lm9w<E!qh
z!79c7%>xMi%9iY}(32qFXYCK-1F|2^nR(RyT_DRqT%S6wqNwoR=S3QT7>5ot65flM
zi2AqbaVU!m|GOWJ0R&4>U@ZP#EFK;}!ppMgkmT>fhy&OXujp9x_hLdE0H!o$RYgns
zza8~D*3p20LG-=<UjYAi8UMc=3|JWh;JhCm9){Qq#5~U3*a{gT|2wAe0o2OHNsRiw
zeZeqY3-<sIoG#QP{eIZbkR_$1-?LpN*}LXB1<zs<ivDxx&yXZ#W#46VEv<}2bk@Zi
zH!9YD=FuxH6woYD6C3k~<oz^9ptb<)4yWDvC*s<CLLj~2VynNPpumT?eg!k8?#p}k
zMBtn-_gbT?13-X1erUk_Z%6{*_c}nz%YuZJM{Ud8jS-IQyOR*IN7-Drwx6Jt_}7;a
zU_}4(4Zu!Kc<h$Gk534YM)hQaEZ>`gmIF%la#!P_zk88zz`CL=nZCSFu2%|RIl1<e
z3;vCi88AX-_~Li7BjN%|LG5=qs=WIxqW_cK1XKr6zU!e`>amgU2Kf`P&H=WxckdCf
zNd_c!Wc?jc1fX`j|GVL*eSE*8$dIytQbA67+%)f_K|;M3%$!jwB{90`fB%n0q{QXK
JszeL|{~yUs40Zqj

literal 0
HcmV?d00001

diff --git a/Images/least_squares_vertical.png b/Images/least_squares_vertical.png
new file mode 100644
index 0000000000000000000000000000000000000000..11629732bef9e432dae3cea704fb471f0dc616ee
GIT binary patch
literal 13512
zcmeIZbx;)0-#!Y<lF|qwxui<R(zSGlBHi8HNVg!MAPCYT2uOp7bayv`gmg(siNHPU
z``-8c&fNR|ojdb$bmr{t>GO%_dCu{rhMGJc4h0Sh3JRX0g3L1%6x5&KpD-2#d?UNr
zQwKi0w3C+BP?VO2X}G&s+c{dHpgblmdn2ZdCP^5z95?<Lo1D=W=MB0n+pqOkvMROx
zm}xPxF*x=<-!fmmPF!=R`-8UcSWD=1|BN$+pDfipFA|2;z6^sx=vOnFrJtcwVtQ}Q
z``AcWW4NZrmcLs>86chP<LI5=oZ&x(uSOCu#F(o*b?|3P)+=~TGZPfv_SKjvXjbjd
zUf@oyn;UPXJ3kR$H%)Yqm5cG`;t(!{u=f?kRzHcWigu$E1C-eKRI*MP+}d&phy{cm
zP~EO)*35TnVw3O9LsxK!uRZWbT{#4s+Vt32{9+Y};znp=Lh_y`MQ+60uhF>^Oi}h4
znB+HR&(WUOWjOrAop3+N6iZqTN^D+OCw~QbS<>RNPZrA@_tc6nwJ7zhq4*)avd)y=
zo5*#c^xoraH-g$~Z_bGEcjem6Q2J!VvYF0c7K+aW;;8tuY8F~&T-!lSIeDxH?Xc9s
z&1^#*FKzW2x(U_xU1jMEd_@RDSU&YE&`K}M!9U0UT;mTaqmSK6o^n$%IFu~A=enM{
zsw%=3E>0X~mM-R29KKGj;LuP|M16%p(aFlw4Cd?P=<FfvD@OmXhA=22zviTe{j1{X
zAV#mNssWRBakql;alkpa=*4khFqo*jrM2)g8M*&%4nB#|+j@Gs3UhM$`1o-6@N&4g
z+i-FV2?=p>@o@6+u!9=x9)8ZAX1?ss9t{80$^YGtjFpFlyPd12or^OJ*{_+oi<hSu
zJw0-u|M~M@^R)7{``?kAJ^p)HV1b;-UpTorxH$i(Z_rc}`K_>qov)Rnu8f_Nm9qyJ
zLmV#1Bl@rY|Kpec9q~U}>ius^UT*k*w)~G@{=20pCvpM*v7rCjt$*Kw?Gnck<@}$m
z7sq*zdz65J!u?cHM)J8Y>OmHEfQrs_uT3=;v1}M@ibCIjh*Oqbc3f3UYr1@)wZNEN
zR@uO*xhS=yVCr*0nfBA~&kWeVxKso_s*>5KtAi?9U*9pm%g9Jq<mq?Z8o3$n{Umm=
z_+r#=<U%FrV#p)MBVozBYWtE7l^BLfOqhZQDT*5BK$c;stf&L<R!l|&gb@Zu9jG1q
zA-$#$0#yj%89qe8evN{Sr5eJ+?s+jLh^(Hgaw-C9A|O&ExhnU5h`|#;P3Ysvzxbf$
z>yR-}l=2~nXFzrgx<&i{Pq#W&!oX&km?EhZk*kM_%Cu>vXA67nzI~!8YDpEx^l$6N
z4A@cwG}{Cl%)4Xj#l*$M+1S{YH;&}~ZDr^_Rz3WEkD&0AwfqKDFQxZZqv)qs8Jqj{
z_1qG}wq<+I+;3^4=O2d4@9zpuzAcNY_)4BM$unr;@zZ!SerjlFAID4_?BWXagmaQy
zNj;5A(w>HR_e7(2xf1top;B~)k|)bkKDuEhi|5<TXD*uxyAJEM@5!5i4~Bg3E@E>U
zM+JkD-G&uUZ{s#acAP7c(UVZHeKQcZR{9i5%ID?^?E4{qR+vtEKjXRSH0Sjh|7v!^
z-Yg;dgZ=~O?~%DeF<0|*5gt~uSx(<?ZTlm1R<!l+cCb6ujB3KVJKIPiYgE8G(-GoU
z`a*A02Z+59fd>g2!CONDnj6dpB-8Z2YeXJw>p$XBE-%4%F4R{u`3eu>%0-wKk}a!!
z%~Ytij)GcQ`xogUa4HpjwsnpPP05rY7g+0>g|p>G2A{Rd$k_E2x?!QxmL-u6p|HSo
z`akq^G_yRTsNdWdN)q$XtEWO#)}t_N`qxpxK9yiYH%9+#@rc*!RD5Om@#k#sR3{<X
zXO(^NOJhSHmk~nF>)TS~8|(2f4n^07{-iYXCqyNjX$zJ250A^2r_IPI!AGk7X9WY^
zd2`B78h%E43Q^|H3Kel<Dv<wO;?q@%+&#{HroF|5H_l1^aqyxOx7FeoIUAGonKAiy
zk=mSe#uZwqAx<9}M6odI<*rEJ@ZWAZi-qlwEeGKsew77h!6e(OpZ%tK`$J@df6)rr
zZH7LT>kW|^DZDOMs&8>o(KwjU3xZPBTV}WXxD3V%r(r9+GfN~I*<OeL==eqFni164
ziG`}#RK`kO$a=q&Q(HrzuG#DF%lbI=S&Hn6hjaYGam#3Yx86s_@jbYoShMjsr*P~|
z*yCIzcdsw^tPPvEmmKTwya`)Ywj7}1r-a$e>GZy<sT(P>JI#BTEq`Oyq80*@`7`<1
zMXbXPe<q+NGEq}#eN{o^q#^Vw5}DsWa^Wk?@Z56IqsQ5!>AO@t+HDPMS+<K{+H%(G
zxVQ81)6^rUFb3X$8G&E=LGw0-q&8}eF7_L)H*sIkXe%-cu9BE+T;AIp@=!OL_v91h
zJj-hcPPylMV<L%uC=sE?oPM0jGj01Vt(5g)$vxIw0-Q0`9ak6AM4R=+z`_)_Lik#9
zsuoN6p`l2n7b&LKkH)aOQ=HJQt}dsAy2z*~euo)0M#e5yuWDieLRTfhMU6zY=nH*v
zgB!oDsVceLXWbt=TnhA4X$1sga9;CK5LsKyQl1-Dit3B!dZnP?4!~x{y3APdD!QQF
z<ym%DS)QNYo%@$q4dASGTPjbX{pn@V*Q(Z~qs9?^LBivdz(J%TtYUK664z#0y<|>O
zfgwrzwH+(i;imde_8DaG!L*4|l|G+Squa$*$eXh8w-#4xIz!Sr<Tx06!qd2=hE!;B
z)HE2Mf2u4y3M?t$?dx$)if-+s52dDys3M-{rQPoAhE6N(RxH&~HJa<WPt{(8UFB;_
zZp7;6s1ro!E<~P771FYmR8W`=cadPF3rw=T-f@>bn^xB4<tFRF^vAK?a@MzhHzVyD
zM>;`UZ!7l-MiGa~Nh38A_ba%LDXnGE78id?QtNZd7(>YVHtA`s?rEqp2H|U5EcRS^
zqjxUSg~KVam~G$Nu1AMG^EYl%qId;Ly;k4Hboh(Pn28R^7#No%7nEOno!l+Kxa=lA
znsr7M6cprD7ZnxB#*y*aPxVof*5J=#o5J=7<ayfqiG!9LYz0`omeG@M&Vp(9e@@Nt
z%3V!8a+t`;%xVzk;Kls(i()8$wqR{wjx{~fQN*IzXT`*h^X|R>PkZZ@=Tm=@XKF-7
zt^3vMouxMaqG5a!W|S-YlG?Pbk*bp4FKTN?aq@s>Xh<M^xajjj(^S{}SFOAf)-~To
zU5e{3we79NKh(V8^)cBzNEfhFWfwHv;Qf>H+tXt6png5rMwL2PDKPLU#cs-BOryOM
zq`qHBA+yDL`1$)-6)Jj<?qfEc53v`i1z&Pf{AI@@A}E=;Xe${Hb`OSPH9A(63rAW%
z;_GD>mO703IT$&pTR!o2lasjhf-4ucTLuM3rmS`w=*4QK_SDK_ml`*Jmt$2*Ve#6V
zVNcW^==dSKa{IWikU-4iQbx-`0}_>a-pxQFF-@7;Xfygoh8Hr(^~+h4Z;;dK?@{nq
zJB@KdYN;qEoc1CQmBtm8htkR!nj=)%3Op|27EvDgn%X%krXjZnJ>>FxGnsv_L`Bjq
z&r*DR1qm$ztX3~m#yC3T!a`elmeb)qC-?B*4~XRkw_0gb()*%am}4#G8=H2h4mN0}
z^9yPgda5pJedboB8Jd_af~b*a&UuN~4AFXLaB<Kq@sQ`6T3zwcqa9tZv@zX_>KO>Q
z_H;}-gG9W4td6=o=vB66Yh^TWFYO#2I$vY6@EjGhui9$Rk;m^bb0}M|S~fpXRkPRN
zu0U_b>W#)sB-Uc`ESrX<?T5Fm+pn>erkkC~;#1YyyBF*9VTL4^?d5DAy+5{?AO97w
zS*82@t1~>0O;bq+hk$%cu*mu&jSEjfCPcC!C_YSI$h}T!^yT0aJ;xf(NpVbK+;{%H
z?@eFJ(igBU=-gww|M7aS=%BN|h3cB>i9bc6ScYce+i`V&=K7%d7h@}&9S$LNT4+iS
z+)Udvr&qIP%U%t4qxfcO#047hDeLWZoc8o!(D+BASo6>6taDA?@@!sahJ3wq<eKX;
zD2@MAqkv)Tgeudxtzxkvn81-2O)y0V6O?}A!Vr0u&*S3bt22`RsX}kye*3j>Z=|T6
z*wH~xyL*>Zq&X_J{TzP5@qW9qa#Qi(w-5Ux9X_u#kG6!HP#zyms5kViq6=B_7n`zs
zY^tqQT*s#|ahklulsZd7J!v9uBI+*oztA#hQBg9L!TmPsHP8r8$o?{jg+Fk$_snSG
zS|uaNZyOyFPbpAWu`w=B`(@NJAq3iVql78Z+=-)mLqkrZB)Ae~<TLS{^%-r-aS(30
zZ!4xy+n9SBac^>#i;c=FwI(M&UCCNsy59HY^^6OQqJ<BV*uN-kltH0i55!%>1Lj`|
zSd+&p@!E224liS((iOGH*w*=Uwz~_CL$dG&E$Ri9xD$LuZ70aq5?`pel4)^<F2hl`
zv6{5Cw4;2=1iR2<%Aaly+Q~c~I5gc*$$$op1uU{`@3G6hJslL+^o*csAUNn*P`JM6
z;lDT;^MGRDQ(c~pO5_N-NtJ(aS#IIx=02e#p(p99C|dujiZvV9<lbh|TRKRi*wD0A
zdS(6o*V%PRd>D~Y$Wfy?`q>xyn(Y|XtW`a)hr2n41Yrw=1I(D28~XT5xPNPBp%U24
z$)>O4(g?eTB;{7g+b6C1Vz2ic*7aZigmhUz(RZtb3rIhC3H$0FwY$6tV3VCIHHWc9
zIQY&Ao#qX0np|WY5#+D6C$^`15V*(kK@mDkQYP?>UPPHf7S*^}Gdx2*87I_B$zZR8
z7q|V*DSp}!LF;o2F)~LI#dKBFA#aIL059q8yZqiBPx~1X(o}BLc)m9~dNW-qGfH?=
zr;<;Kr4^-8A#C=G(t;GrR}=?x@4eR0^L0!d{j~)`qQAlMC#Km9iRCpzN7mK0l3DPI
z(exKb<peylP39<UwSEg2X6}X1Ylqx3U-{^Cp4G?$b$yTWkGedS>rQ=~?;`bBVv<rJ
z1;-+$2iTLe@+!U`0w#ou%>4E^Df_R-OWaM--ih^*AAT*RiV1#v{>R9{sFjwVE{F*2
zpof&o_s)tT{fH`*m`O<`Q*hj4qsDTSsPDe~=(L$8PmqH}3)-#cs(st?(TIojs|u^H
z6`tJI?prI-vKmmTN%ElYZ}$i2K9sC_zcwNMen0Z;pr@2qVaXs<KcFi@kyhGZ&K|Bt
zCp@=l{D@%CMsShDS+Iq=va*EsCnA$gwFMU~m`%iLO|vJzKshV-sXBB`CyuHevRQx6
z!(jb|6S1$_`gh?e^t;3pIl+9w^#mBz`p@}N(eBy{?An~HsNkiFcYF;}ZJ0cG9^4O(
zcd375pQZ+@Nd<cz+@~QZ=H`AnbFc1^pledcJ2Bhzyu%L99r*^<c%7v<I!5c88;^<5
zgcUz7i)gR!%JaB%%6z){$Asf%c#}2P>lD@2KE2Jj{hc*}8M^opy{5TYi3-@h5fV4O
zN2~UjLFAXE+(a1Sg#BUBO6Jv#_>+D|m$d@Klg9qHsp1Yk9GnWhr2>Ejv{RyAmL5B%
z?p1T<2myLEPjnQo!tfSvGEOIpt9>oBb;(|D*{Xf!fdhIJbl3Wje_rK&S1~%Kimw%B
zG#c4rU%8gv32(XkW3hoju6{~-KA+Zc>y?)dsWg!f8&XryK6-Jjbo9iDzbY+LPb;PU
z%X2BhVBh^qWoD0xS`wa9L=mFFf8X4LW{F%SEZP8Lve8LR$OEea9`xQc0YuCg+b)B4
zrPT{A`ZQ?G)-gTN_-lS0`k0LjC+$!<#>N1phT+13#JAXOxF=;cj>-rL^S5{sEde4V
zU@2<EU?~>e*ea;KSVUp0(E*Rydm=V<D4^|VM~6&11|y3k4u6XoKH{pGv5k$lR@FE=
zG{wsFS3T4e<s#OZ`JmF76>G&>8F80uwX>(5{_#xc=~@`tOfGs8>Wh3c>v*RUsMM><
z^2qg(IyJFY>+FW%koki&ShzWN3Ki4bUVXLP^>2qe9G0iL0`Bps%GaG&D8cAB^S<AI
z|Em7Y`qk@(25*2vYP{vK6~4A)`}MRNW&B`b2FM&wTtf(`r5#HRP8K`U2OP34Y0B?{
z-eE7aw*`g{jzQVzPWma(f);<5MDMJnn~)E6_ip+2NG;Xl(o&t5yJQ7Gd6W~=KdwmJ
z4>L_8(0N$dB`+^)G?Q@!=(=kdqVTt%Ta^fBXg*raZ5WzoS&8NC1}m#?xg2a$>D7WQ
z`B|bDRrJP_XS_kK7KQZC!7Hl;LY%0pc!|TBmhP};YzIcL#y&2nJi{fb#Dc`KX&Lt6
z-pB56LiMPAwi9y(!AMex6|G+NP_IuD+!*q<IE*+K2@bS|dG$drq=d=Zb%`>>4>usX
z_MKM**~ZJ>Y)1!oudo=}B~%_sV&RBC_WL<0$$#uI^5C92+^~~GfU?STa=lDA0+-$p
zO2Ae>8~vsJ;#PwNFER9`I;Q#$Yr}^QJrbq+W;_NJUGBwqvs`1=!)$^oW0Jwtd=)R1
zdDFZkS}mQ}M91?N&>3I^kt#n@^&SKT<2sF8M34ATR4|6BK$Z;XIBP~;-H$RMXR}az
zVf%Xc$p#%p$?<HN@(+{d@SR8gz6$%F!q*-N1vN2fNEUw_kXlh)avOQIOQ=d0Nt>%%
zY5-NOGC91T-^%-tkDh~)&)ic_Xz@54inbK~uGD23rt3?NsQHMAaz9{MmRG6VKrZn~
z<TInoKv?`<N|e>Rs5SKD*<(#KoVYOyh1O<TsyHgT8OjHbb98lsNjas6N@8puW^{0`
zh<VP3@tD#4f}G1MS@bh541R2^{6Le|%andxglHtJ{ZuN;@sJ;OgwLz+R6kBBeZ;?*
z1Etk_)*g2RBJ1tdU%<VJ4w<;_ObPrZn*G4Eu?#1tzji^tEet3WnaqfEZ;BMe17Vs+
zIF>Ngf8vf4&}AGN@8<mhQ(wi_D2zO$r%-?}<~l*%+BscF7^nl8M-<3BM@BMeK<KCi
z;!fGYt<4@NM?6BLleNUz!T`0d15LmDxN{T(<s%^KbodbAF`^QW@q?yy*Ewa6fQpoj
zVx@n{@gWp=?sqVi8SlHAGf?h8DnrY%@YhfU!~$sg9T1OfQ1%5gSZ$2KXh>-**8Jpu
zwo6J)eY2gPIFiQQS!PwkMSO&Xnsc!9^UX}9265jTL_k15XB1v?Qj+WROOi+4`*TDO
z9tZ^ey`*yasYrapjhavxxQJT1lmV5Cr(m+HHQecXNiv!##6Zg<34s8i>fYQFc)upp
z``4nKX6?M=QuB+is>}q6h{s?ac@5z`zNv`*B&Gva*EGA}haL)TI$*9X^rQZPh?TQh
zldr{JtcTyLzK2)!uhU^;$%a%AM(*NMtm#3F8oj$Sq<xDfCnuk39(@0Dce$3>)`l3J
z*$tie3b07f&7ZNcu>@+7R8Es6G`>oOP%Kv)w@~5mLf>eFx%tm*u^068^ycQ~5XjU;
z_{p%q@>^-_dt_{aHY1rMSt24Y6GY-fu$77ir!J^TacJY8GK4-W03_x3I1zvPX};WO
zmWa-bZJWBP>cNl3i;cl|({n_%^NL_qk0g(IhzY~mKFG#7?$>*4jl`0&0^Gf=4pw$M
zmEjn7N1~vtT;uYwQ{{RvE&my4lHyG@Jo7m8QMPYK;fCvvTKnk%=E;NT{{H^c&0&uh
zRcvDqK`ood#}vebktj>Fb++SDc6bn-N|~2Dz0Et60?q+{c0Q4^JomlYcz3)z-5gm1
zx546qjXqRrih|0}5fT$ugQEsSKpE8!i`(%>!<VgBBJS%7Yd!HUKN=!gIyyRBbz6f1
zN40vBsKKd;baki@6TU;Zx*kzJq~%PzJnAH1;JDtOuR+7W^}YDvL)5(;siYT)2MiI9
zkR~Za7|ZuD<j>!$)4RW?>Z93WOgR3{x3ea}k<T8mYO7Ei{vB8PQ+QLQGtW%iCxTjf
zf3Cu$l|`%M_rbyg555fumrgPT`{a>gSiW73sZ9YRj4V_&gTKAkiHqD+duO6RJzG@o
zi@jEr%2XxaTRS`-@r-gY2<vr}pd<Xe`iVhZX}=|fu;*j4q7Ew0=y%+ckHB7ww~r_(
zw)H~ssfBADX3NC`|0sB`d|5x)7~~G)(8vu*WVbl;hHhunNq689A2~o?T>LtiD%Q*r
z_FD4aD~o%;Ip9?GG#`UczmV=Zm>1GmUx(OWn<^6e2{i)(Nmrh)lFpM|mQ>KF5|VHC
zj;I=iSc4$+{9xhT<EIem9rSx-ZHMEzl4xgh<wnXv?`NEsYc}*@A+YU;2<SX!d=|8_
z0I@NULPS^=xp^&5DfIq4G02itp)Iv-AcZx9-zk=>F%%8c+z9^)PM=>q&V#4LDEMAm
zR$yF#xQ{Xvc(1wtS5NgH4PF(-&AYC#vC)M*o5R}1S(LyjJs7%98FQr|;MYziMW^qz
z^RJIr4M|DQ%?kUeRBftFW1-KLP$r*kj^-rZ4LG<(L`Tz#c(bvvC>5}Om{)>Osj}K}
zsX|y7OyLy4m#dhi7PHMSF6gAA7Mow_IHU0C%jKOz<tObACBIiZg-p~Q5=i`Y1wa`Q
zB4$`=ghCd@@x17@+{;Us8nCS87OO!x0wv=c;@yki+o3SqOEG!|2EH-dVZJx{cEesm
zl%>XyIhlZ-R}Xa=b_(7ZC+R;eH~a=%r=6s1NpAea5X3<b*{*vEZ5Pl=i~U-=NoJKa
z*Np*E6*a%r$OmCzziy77<p(Z{(bR?T9GJDSWkVMwO^;UEd2L2y6m`vi?|jPX`P@M-
z8(W`mnwkfJ{8s&(n`}#ujY|Q?YQU`mw()ztKe>4P{U0S)cv<l;Yl~?~7&rIv=ZY5l
ziI0lW_|%MXwEo+4Y{IdmLQ~@?`a9adA;12#G-F($K(#%7XA<=CBEr|;vX3EA^m@PM
z<PYVP?d({|rSm{-tVm+vHErna&Gp@JFRiVPjEu}X4kNA?=+`BeCcN_>M2T`$wy+;?
zGNX<X``+DLe0BcuXX(YEnFA?)j@Y2SSb=8Y^rTMh)FD<^Pfx-_UW|Kgrr(|xyWvst
zcb$wa?hhO`d@sOwT4>bKo1q8`hT_wTzfxRE6R_paAZnr;ge&FCZ4ngHF3YB$uZ>tu
zb}@|s-vdOnL!515`LhmZTsM6y%l%nf{Y?h2CfZP&FW;HD%)4R+-*K+B>XHj{acL}Y
z&HXXNa`ji{2=RRnHGEWQHAuEk+UT*R_N}PSX@N$_9cpZ6Upo}>av#eTo<;p|GdYA3
ztJI`5XsYPxoB)|sWJCm;ag#2`edWXfJ7xtKCgkl7)ymO*?0Oj#nyuiw>t##=8kr~Y
z6ny?`@dAb{2?7o&C^=jD2Yz)6r_dv9!ohQi^Er!Pusy)Psw|VX_AK#&!0pIEbk+d#
zRRl`UNbBA0i*O9Q1X>9W%8>L3;6GuH^heCXpeITpJXt#CP1#UKY}mQqQj?$8?o>O^
zSfkHj&nwaeR$mne2Vt(ucXZ#T6etfCqA;3(HbIZ&$lbK+BPtpOAue|P8x*VW@mzXu
z<*-c|Nc-SaV@EO;dLr@9Ol!pCLevNn^Q_u~lwj=aPJe#yOqXbh1m89-cFh{UIQY68
zlrJ}kNONTXfs)d!`mw|#l+bLr9dR9f*Ym^r`g+cm%Z^dXq0N_)pdao(q1r{EC;?->
zVlib}mBP;smztOTwy61ZRVlU90Yv!2VTmTH)Mm-kfBNknDGQVq-ZKueGpk8Ull-35
zDkBUe{lUj+uuwK=xk>9SNT47TzgTVwTwPtA_y&TB(#!Lj$=AB`0*r%VqA*kf<hs!`
z^ga$?FE;s|h7*VvY2<a-<OF)3@6SKcEG%l0gq!?;f~ezjH}0=a7((Fxz19w-C7>~O
zf1r95eh#%v-9ZAG(^B682@=G8Ozp~KecdKsx652S3^XW((!uDk^}-YaOg_r^L;Nr1
z+}}@ndwYX#ueJtL**o>ABJVv&E$L8}@|o>FQGn%ucs%k*aA^}0%3)Z4G{9ORBQHOi
z#=W-q&7;P8_#|Ae3j0<b9D8u!0NF-p8~8e2j^V>zp<33TKYJBht7^tdkR^J=r|7Q=
zH8k8<E8V7V=w%rV22$DMv;;xWx&FOFLPf<E6<Qkx%t52j>E+R!3AX9Wr~5u$D{Y~3
zbGmG)jC>?K2Nefajen!Sxw|w>Oh=j0<>dtZ+4<QWrz=q;pGf0A7yfwz$Mvqtq89^?
zqSm|{r#}_!N%QXsWyqW?i9*{G;7Xsku5M(Jxp_0)W`*#zzOdiPI#3<^H4mqj0vz$<
zwB*KP8@%?+FBgk7AAR{6bM6p}VnSzM(nu{pHmLo3e_nIO)(|?10*R(JDN9=XtWz<N
zYCb;xhC!Y~ui6sW#+)g7+{NIhG#KhJwkw=Hh}LgWD#l$(DomA6`5niX{-=YhAlt2m
zt0^i<Hd@Nw0*EwiE^)w!N(p912@`$J9SZdO=9}|{7w7wLzE=VfIhEa@q+Sf31sq{)
zr=2ql|3#Vj<w`g|Y^9{GpXiQo5TQ&FpVd^ABfzKsmm@!Ngb;uQ>Hg7*cK-p7&7n8>
zFUR$<zOvNyM(I%JJ#oJPR|KZ2jnpFEE0L=fiDJxKypoU-W^g_wjiXLI0xnB`x5x8Y
zaQZqM6ntx6QHzizYx27+v4vj(NV*OuLEh{D)HI&xY%BZx`F%1yZ)wSFPDq8oYPh(^
zCT<D17@S(|jCPnQ-MO;YL&Ls|Ldl|OSsl?gq6--=&mB)woCDj&AfKR-Zxq`9qWe+X
zc<p82y4bzQAA-9elFXfy7=Cjnj+-enNTd;K05D;0ec^?tE(!K!42t}uIAqj5i@_Br
z#e%u4+NGD987?e`ZTpn=wZvGHLCCBx@yl>p0Ph~6^<wD-xqLoTT2n%90*}=|QHz!%
z3ikRN(1{<kXRuiTh0;(mzq6ww3iqM=pWh%@F3ejzpxlt-cjnANZ-@l-oZI`P7Nu9f
ziN(|=2af47*mXo;gQS}f6XU9m?#zPD$(8f%M#tWMw<XZ2GBY_t>CBsZ?)G?8;!dUr
zcSdw)EDud*@!T6z&?Kk(OXBl44pv9bPqAt<QeuO}t1w;|ni04}xOyHg{dj~SX!7Le
z7w#WVZ%HVHJ&R5+Vm+9&-HMHy8y0a?Y#Tgxk_0!$iJ4G^#nw4lNW>XJD&qlIGd-IE
zF%zg@WNy^ryi8wXxf6oUrgZ=U-?-O$`STthhvwOLhYGVe<8P$8g(bmvw?GY)0@>}}
zG&KcZq36!TC2GQ5N4ML_djHs%hMDAF5TKOeQ}+$)UGpk2MBuo`yx_Fzgd!u2SKX@n
z*YmJII3UY=0JlM_(`?_9yh?#t7pDzdm--o1no73owb44c&}DO<r_*n(D26Xj@spYF
z83%0V)wR=i(8*8I{|<|C6!blU6&9E-!KkR+Yv}OirVRq(quK!=dNZ6#g3r5A17TKr
z`#7|0$`RM$d+n1YygADg4JhyrB7aF%{-(1T1C8jb(=ChrS5D29Em>3ULqTtu5wQdi
zH)>y-qS_k3Lt<o~ZWhstS@hs;ypQRQdvFKL<hj{5jQEd}(&1UZYM6D(^hsO4&<0(u
z5;4e`s>Gu|544WGN18|}P_9>FeI(yG8|ns!>o0v;jC|G{=#VcLfArlI1X|yRAt9no
z5^vXF%}zLbgYh9tOR^~o?0O^08RXw|6;g@)Rzx7qCX;9mr?W**r4#(+urTW`<lyl0
z*IIQG2WKqzY~Dks)L?@qao;JWue<3&>V+M%!>mdlsbxMkh$Kt^L}$UaHXs!n(9~$?
z_HWmi$aD@O-`(O*T=uE4bAYQUTOC^ykBI_A$#+M3go5Je9r?_l*0#!em_n~YOoJef
z5mgn00EZ75V&4^-(sgTX-?;CtG#<9ZArj8^W-ZKY6Saps6cP7;8|!adsed6N@&dT7
zOzSh5=*Z+J^>?4K2?*rbA_DrLfa9`)^Y)I+E<B8so2@XRl{Cr?3CXFi7gSv{T~lnc
z0Ey_FTtAqT{OKsxI$}-^z^L=X<-{H0+a>e5L}-9CNDP#}>`O@p^@cKqG`|TGSQne>
zxD2tB8a38Y7|3KOB1pk1HDm>@=K4f@ewkMO-FRnWp22!4P>E4!NjOv6+1uMw<xhO%
z0}LSe(CpnpG-1N_wk;N1sl)y9S;xjRJ>>g}8nBp=q=^nM{IA`eQ<7cNbnEzWb7s?d
zMMaO<b4@7~5mXSvN$JcM!tSJxA3xFt|0Sv#od{#++5-g1*KAwp4rBH=VZotY^lPvr
zvTB0>%jf3i^yljBC3a7qJRuOjgnh_kk`@d*e;Qq>Ph5$7@A1Ml5yhLWI|_izm^GkF
z|EPBhw*~Tp46H3g6xb>j<;tY(bP4JCSz8OsT$&dC7GZ}Vg(QT68H7A<kvqU_0GFf`
z52$~ILef$d7u_e)Gf)Uf`gb~Hg+IbI1yxm500)C8AiVj>lt|<XdVZKIHwXvmMG~Mo
zb_IFg(O3EMiRM*|`*JZXtj~*FFMb&vP7l8a(>PHBAj`*^r4`M9h~I7kTJs#4HjJD7
z&2(H+1Y!Ul%mLWs+7C0|4{Utb_rG9MsurgJM|#VmrBndsQf3+kg-eCB*aiREW;dv-
z1sopaBM`bHbO%-0H4DKNYW%cD>!01v2E4TDnG?Qqm1eIXZ?9vpiC-hLU;fe8t|&ap
zO1si27B<{6!+EvLLhpm`UaK9ENn(K88MOplfrx)ap_z&Qfr*}Q#nO$)zny{K^zbuC
z!elJZLB<jH2Q*sjS+qkW3oWC&;#zMat*CG1KidQW`u3Dsx54vC-EmZ~v^}x*x^AO4
zd*NrSg$|HJ=NF>JYXg}*6cB^JhP49^EvEinAGXeZ);XNF$+_9D8ASq90Y%IBs5eAJ
zMAgOa9cG0{#~TALbZU1+s9SzKF!nVAP|a3xq0Xrw#8#_z_i8Jr_a~i_=JCB;pTngA
zszM+SJ)SJN!$<71=#($!Nn<`a!Z#bYw<E^528hbW((<Dv*W&gZL!sMRk9}2+cT$KC
z))DCkAHl{lGN_{|x2=j=dz|FAvGw(Jz=isvca=<4TZS_PEb_YxRWl~m^2%HCzbw%q
z($Dd21g4TwQ_pWMmO+@}prb?0Ffv<Z<Ul*XC}j%+B2}9L!D{1nOX%1(AU11{A3rWK
z`09L7a}^9ii3Bpu+6$9AFq)}0KFSF=0lic_FD4c!e5N`&u0iY?4|cQs_;l_E005c7
zUMz*4(r*itj-uE(I5@yN0t>HdQ_u|&QaIa70ZADMKz1N$=&nFGF_wMT;*XA8`<3i#
zU7KaY71~arf0YV}c$ysNtEHpyQ#egpG(V{USa~jrxR?!*gj*Nq8rpic2Hij)<nW!K
zKDIZ|=Q3@<0h33>rhI~aCEWZy#$W77GrzIy;`UGN%~n{j;}4zqZe{tbyVVA=0)fD$
zgNpb_8FI_}o(5#g((!E;nKG}#r9kjpCd_96@Ki#JX!d5hfu!QOc*1t?Fg7@gpxAqP
zh(2sZBHvEMPFYE*dZz|NJDpP9ac0feotKxF06E?QQDtXmhe<iL%PhMIPNjNpA`u=A
z6;*8umYYoLu_m~uDYegw-rxSB<L>sNm8%fB!YJH40{z!Pc@G(36sUK8V$+4Hl$7<Z
z>hkjP3zo4SSawl<%K@i&hEzws_t|tcs1|IoLBtY-{*^DR(jhrdNa~k5Yq|a<;ucke
ziBGjXQ7|}^&TDm#J0>AMUX~5BbKjC#1w0~~BRQg7hJQ~@ON(jW0SId_h4liYHSynL
z(&u>RLHNcNw_6f-0ES|dMMdRPP3)%*H|=Dc2kBJH5g%cr-W{u=(RvTw{+?71?BI@8
zBEoF;JvKMg02m&mK&}V|Ol?tF2&1WbTZN^*eYS1q5E#RvXBd+<AmpKd?)g#+L7Fu3
zftwH^n1?5R{O`@;>CZaATmo+YUbz<lk`=y;Mp>$nZ}o4wg6eGq@{=P1OU*hQCM{IS
zO+Nc`q2}Dlgq*m=gTI)DCKM1EAk=yrv@&4nPi*(n22<U8^fdp6Tq^n-pt1tadUxBa
zq&^TLvF{)Gg#VJ0KW-p6uPGV%1g)BQ#a5V=U@Jj|c@ki)SAFq-Y8tdKFf&^N^=8WU
zAr|u1#2I+a2yYL_=Vq(TfOOG_c;lju`?cf<AfMzRjIlHdPo-Gbz9VS_kQ!WlJP*E~
zEx5D}pWZZuB8NEvv2SP5&J0~-f1TY#0ElyJ4$-!@d>q#7U&D|I`U@eL$EWOu!0YqN
z<24X|a}E0`La8KfFB$lRAGwOkLl{@YLEh?R!^7xI<F=@TV$M`G0@SsY6a4E^coRHA
zdmR!De2N;aXIG&WpnkBU0LW`U%;I-?zqXgAjNEV<X2h~5vuVAcl(h7Qg~}5oFI;I4
zulkUj*$+}Qc*vFxoL7709j8f@2il+(t(gDwUm<`pk~Q9cmTv_^8qZ}!$c&XR6O$ES
zR4FW49iKba*Dd$iSCXAg3&0SJuc=YXM8sD7jq4oee>D13+3n{_hX1)Z%%Og=nT(vJ
zI+pFz!U_O1X=pfBd+2Xswgr8DQF*`UpX^x@-(lBQ0#Z82T<-p+{sPQ?7Wc|)Y(@vN
z;z{MX6AF=Z8~M62HbmQ!Vu8`zS%6HrBT3QK%{A$(#<COzFss*Ss1lz3rTdiT%PW{P
zzNUae>-ximg(*&(fIejwjOPK)Lji$+1bwNg$6G$#8kLuo?N29{w<r#JRs7AabSMIO
zAjjC3pHy4`P6Hwbh}m(8iHR{Wgsqe(_NxF`&6XKFAR{|p^ca0YE9=J|JquPAqu#b`
zNum6z1!S=!7lrMr`RNc;w64P_QN5TVNxu_e2+lDp>WkE^VyLkSSR*~;mU$8weOH+G
zr3=t%tCpve)y0cHjJWnpnFQZ_0U>vQl_=kRcdEFrPr-_L66|h}G~(<H4OtvIgY7qo
zSTc5D@H&XkVFqZv(|2%+VI<aG)P3T%HIhZjstv#{xFBeA!79=%B&tIlF43tl2Jo}e
zK53B&P!DGy6bEeEysWK-Uy}(9)waGu-gVkD=4z({A>Rkc?j`}p-8a`4kC>R)O<D}&
zf1jFp0OiH;SBJMrnf@1m;pxC}E)!~eR$*vD#n*$*d4U^*B)sPH437S9bS}+j+8`vn
zoeenYe}sjhvk4?e#uatw^HUrmO5$umw-4Y}fhTa^QwwO*uZlOQ;u%9AjTuN08!h<f
z%SZ4DSN>#Zts(-QC+q$DKR<ky&30^FWgXWMUYFR`e4euhr{qLT7+8H-qV@SmiLjCA
z!wcA0TGd38pNEq-sR0-*R~7Q%y72S!Vt3%ouU`|g!6_a*kdL3)oPAryjLJ?zII;th
zDPAd0$#22YbY9FixS7Od8x>C$)FArIu-7UK<-hhSTC;hqaPD6LKZ)U;bnmS^Kw3GB
zh_GAWW{|VBtt>PP+l-7EuoK-<ykRx^|9bvw)rIpkO6~!talm@t8{L@%is1@oq=P9y
z12%svC@6$bHmGF@yQ>f7NYH+EHN6QEibh%;_)FUdw<Z8r8-Of>3k*WjUh#il6ZeIN
ze9fXEVOA*u7_RU$IWC5bCpd39%r)OwWQb8{Tj2Qt1nK<o7^b{W>NyEdBCQ5ft!R-E
z3XoMq8>U(!;4czqQ<|BlS<)3ip9A0bK?7iu?mv)@jjW~AwxE`1i1!m6xkdy;2}B$w
zJR~wvKnQ~JYC{CZ8>9~>fqN;%a69CT2@^oMo+D2Z*(8?{k*+;q`W`u<2nKN1RjzDH
zi0t_>2u&&QG-l)qS3$Up+#{xt`9DMapN;x|v8W@gP++K^K7L%UupRKctv~+vioOpm
z6x>m2m+HFh&sAL!It+NyAWoklEwE1@6c90hF}CDO*~<e@oVZ=UJt_=!m^daThMd=0
z#ofJ{7cw+nYu)<DQ4m<KFxGb7#%|?X?SEO*9=q0Y-W{X~F50#2_HaxBaxNGf+fe@u
zRLGB-^WQUjV%JE7I8?tRA>U4?^3)hPsB0_=<V63g^TA&%eZ9y3=?x}yII~Rf7^Dll
z1&CgWLgioDa|PuNFKoPj0fGtef+P%NL}!FifwHTSRQSKNqzJUskL_@z%U}U1l-=;%
u7z{a_2B3-mE^R=Q4y5Yx|Je|?w4eM>Q0FbM9g+XnMNw8wrd-N2?EeCmKY}^{

literal 0
HcmV?d00001


From 8c1e8d4efca90b9af073ea287bf140a567f140e0 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 4 May 2014 18:57:02 -0400
Subject: [PATCH 008/248] first commit

---
 .../cython_least_squares-checkpoint.ipynb     | 1255 ++++++++++
 benchmarks/ccy_classic_lstsqr.c               | 2203 +++++++++++++++++
 benchmarks/ccy_classic_lstsqr.pyx             |   10 +
 benchmarks/cython_least_squares.ipynb         | 1255 ++++++++++
 benchmarks/setup.py                           |    9 +
 5 files changed, 4732 insertions(+)
 create mode 100644 benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
 create mode 100644 benchmarks/ccy_classic_lstsqr.c
 create mode 100644 benchmarks/ccy_classic_lstsqr.pyx
 create mode 100644 benchmarks/cython_least_squares.ipynb
 create mode 100644 benchmarks/setup.py

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
new file mode 100644
index 0000000..ac28769
--- /dev/null
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -0,0 +1,1255 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:e0aeebc5816e30eb2937ee4b331a3885fbaf1583183823ad07fb79e368b90290"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](www.sebastianraschka.com)  \n",
+      "last updated: 05/04/2014\n",
+      "\n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)  \n",
+      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### All code was executed in Python 3.4"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I am really looking forward to your comments and suggestions to improve and  \n",
+      "extend this little collection! Just send me a quick note   \n",
+      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
+      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Implementing the least squares fit method for linear regression and speeding it up via Cython"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"sections\"></a>\n",
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#Sections"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Introduction](#introduction)\n",
+      "- [Least squares fit implementations](#implementations)\n",
+      "- [Generating sample data and benchmarking](#sample_data)\n",
+      "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
+      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"introduction\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Introduction"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Linear regression via the least squares method is the simplest approach to performing a regression analysis of a dependent and a explanatory variable. The objective is to find the best-fitting straight line through a set of points that minimizes the sum of the squared offsets from the line.  \n",
+      "The offsets come in 2 different flavors: perpendicular and vertical - with respect to the line.  \n",
+      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_vertical.png)  \n",
+      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_perpendicular.png)  \n",
+      "\n",
+      "Here, we will use the more common approach: minimizing the sum of the perpendicular offsets.\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In more mathematical terms, our goal is to compute the best fit to *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
+      "$f(x) = a\\cdot x + b$.  \n",
+      "Here, we assume that the y-component is  functionally dependent on the x-component.  \n",
+      "In a cartesian coordinate system, $b$ is the intercept of the straight line with the y-axis, and $a$ is the slope of this line."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In order to obtain the parameters for the linear regression line for a set of multiple points, we can re-write the problem as matrix equation  \n",
+      "$\\pmb X \\; \\pmb a = \\pmb y$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$\\Rightarrow\\Bigg[ \\begin{array}{cc}\n",
+      "x_1 & 1  \\\\\n",
+      "... & 1 \\\\\n",
+      "x_n & 1  \\end{array} \\Bigg]$\n",
+      "$\\bigg[ \\begin{array}{c}\n",
+      "a  \\\\\n",
+      "b \\end{array} \\bigg]$\n",
+      "$=\\Bigg[ \\begin{array}{c}\n",
+      "y_1   \\\\\n",
+      "...  \\\\\n",
+      "y_n  \\end{array} \\Bigg]$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "With a little bit of calculus, we can rearrange the term in order to obtain the parameter vector $\\pmb a = [a\\;b]^T$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$\\Rightarrow \\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "The more classic approach to obtain the slope parameter $a$ and y-axis intercept $b$ would be:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
+      "\n",
+      "\n",
+      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "where \n",
+      "\n",
+      "\n",
+      "$S_{xy} = \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})\\quad$ (covariance)\n",
+      "\n",
+      "\n",
+      "$\\sigma{_x}^{2} = \\sum_{i=1}^{n} (x_i - \\bar{x})^2\\quad$ (variance)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"implementations\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "## Least squares fit implementations"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### 1. The matrix approach"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First, let us implement the equation:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$\\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "which I will refer to as the \"matrix approach\"."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "def lin_lstsqr_mat(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### 2. The classic approach"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Next, we will calculate the parameters separately, using only standard library functions in Python, which I will call the \"classic approach\"."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
+      "\n",
+      "\n",
+      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def classic_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### 3. Using the lstsq numpy function"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For our convenience, `numpy` has a function that can also compute the leat squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def numpy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return np.linalg.lstsq(X,y)[0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### 4. Using the linregress scipy function"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The last approach is using `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.  The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import scipy.stats\n",
+      "\n",
+      "def scipy_lstsqr(x,y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    return scipy.stats.linregress(x, y)[0:2]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='sample_data'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Generating sample data and benchmarking"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In order to test our different least squares fit implementation, we will generate some sample data:\n",
+      "- 500 sample points for the x-component within the range [0,500)  \n",
+      "- 500 sample points for the y-component within the range [100,600)  \n",
+      "\n",
+      "where each sample point is multiplied by a random value within\n",
+      "the range [0.8, 12)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
+      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "#### Visualization"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To check how our dataset is distributed, and how the straight line, which we obtain via the least square fit method, we will plot it in a scatter plot.  \n",
+      "Note that we are using our \"matrix approach\" here for simplicity, but after plotting the data, we will check whether all of the four different implementations yield the same parameters."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline\n",
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "slope, intercept = lin_lstsqr_mat(x, y)\n",
+      "\n",
+      "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
+      "line_y = [slope*x_i + intercept for x_i in line_x]\n",
+      "\n",
+      "plt.figure(figsize=(8,8))\n",
+      "plt.scatter(x,y)\n",
+      "plt.plot(line_x, line_y, color='red', lw='2')\n",
+      "\n",
+      "plt.ylabel('y')\n",
+      "plt.xlabel('x')\n",
+      "plt.title('Linear regression via least squares fit')\n",
+      "\n",
+      "ftext = 'y = ax + b = {:.3f} + {:.3f}x'\\\n",
+      "        .format(slope, intercept)\n",
+      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "WARNING: pylab import has clobbered these variables: ['random']\n",
+        "`%matplotlib` prevents importing * from pylab and numpy\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105785f60>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "#### Comparing the results from the different implementations"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As mentioned above, let us confirm that the different implementation computed the same parameters (i.e., slope and y-axis intercept) as solution for the linear equation."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import prettytable\n",
+      "\n",
+      "params = [appr(x,y) for appr in [lin_lstsqr_mat, classic_lstsqr, numpy_lstsqr, scipy_lstsqr]]\n",
+      "\n",
+      "print(params)\n",
+      "\n",
+      "fit_table = prettytable.PrettyTable([\"\", \"slope\", \"y-intercept\"])\n",
+      "fit_table.add_row([\"matrix approach\", params[0][0], params[0][1]])\n",
+      "fit_table.add_row([\"classic approach\", params[1][0], params[1][1]])\n",
+      "fit_table.add_row([\"numpy function\", params[2][0], params[2][1]])\n",
+      "fit_table.add_row([\"scipy function\", params[3][0], params[3][1]])\n",
+      "\n",
+      "print(fit_table)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[array([   0.95181895,  107.01399744]), (0.95181895319126741, 107.01399744459181), array([   0.95181895,  107.01399744]), (0.95181895319126764, 107.01399744459175)]\n",
+        "+------------------+----------------+---------------+\n",
+        "|                  |     slope      |  y-intercept  |\n",
+        "+------------------+----------------+---------------+\n",
+        "| matrix approach  | 0.951818953191 | 107.013997445 |\n",
+        "| classic approach | 0.951818953191 | 107.013997445 |\n",
+        "|  numpy function  | 0.951818953191 | 107.013997445 |\n",
+        "|  scipy function  | 0.951818953191 | 107.013997445 |\n",
+        "+------------------+----------------+---------------+\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "#### Initial performance comparison"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For a first impression how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
+      "                     \"numpy function\",\"scipy function\"],\n",
+      "                    [lin_lstsqr_mat, classic_lstsqr, \n",
+      "                     numpy_lstsqr, scipy_lstsqr]):\n",
+      "    print(\"\\n{}: \".format(lab), end=\"\")\n",
+      "    %timeit appr(x, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "matrix approach: 1000 loops, best of 3: 270 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "classic approach: 100 loops, best of 3: 2.83 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "numpy function: 1000 loops, best of 3: 372 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "scipy function: 1000 loops, best of 3: 594 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "The timing above indicates, that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<a name=\"cython_nb\"></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Compiling the Python code via Cython in the IPython notebook"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
+      "Since we are working in an IPython notebook here, we can make use of the IPython magic: It will automatically convert it to C code, compile it, and load the function.  \n",
+      "Just to be thorough, let us also compile the other functions, which already use numpy objects."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "import numpy as np\n",
+      "import scipy.stats\n",
+      "cimport numpy as np\n",
+      "\n",
+      "def cy_lin_lstsqr_mat(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)\n",
+      "\n",
+      "def cy_classic_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)\n",
+      "\n",
+      "def cy_numpy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return np.linalg.lstsq(X,y)[0]\n",
+      "\n",
+      "def cy_scipy_lstsqr(x,y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    return scipy.stats.linregress(x, y)[0:2]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Comparing the compiled Cython code to the original Python code"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
+      "                     \"numpy function\",\"scipy function\"],\n",
+      "                    [(lin_lstsqr_mat, cy_lin_lstsqr_mat), \n",
+      "                     (classic_lstsqr, cy_classic_lstsqr),\n",
+      "                     (numpy_lstsqr, cy_numpy_lstsqr),\n",
+      "                     (scipy_lstsqr, cy_scipy_lstsqr)]):\n",
+      "    print(\"\\n\\n{}: \".format(lab))\n",
+      "    %timeit appr[0](x, y)\n",
+      "    %timeit appr[1](x, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "matrix approach: \n",
+        "1000 loops, best of 3: 274 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000 loops, best of 3: 260 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "\n",
+        "classic approach: \n",
+        "100 loops, best of 3: 2.82 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000 loops, best of 3: 212 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "\n",
+        "numpy function: \n",
+        "1000 loops, best of 3: 379 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000 loops, best of 3: 370 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "\n",
+        "scipy function: \n",
+        "1000 loops, best of 3: 608 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "100 loops, best of 3: 613 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to using numpy's functions. This is not surprising, since numpy is highly optmized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.  \n",
+      "However, we were able to speed up the \"classic approach\" quite significantly, roughly by 1500%.\n",
+      "\n",
+      "The following 2 code blocks are just to visualize our results in a bar plot."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs =  ['classic_lstsqr', 'cy_classic_lstsqr', \n",
+      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
+      "labels = ['classic approach','classic approach (cython)', \n",
+      "          'matrix approach', 'numpy function', 'scipy function']\n",
+      "\n",
+      "times = [timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
+      "         for f in funcs]\n",
+      "\n",
+      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline\n",
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "x_pos = np.arange(len(funcs))\n",
+      "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
+      "plt.xticks(x_pos, labels, rotation=45)\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.title('Performance of different least square fit implementations')\n",
+      "plt.show()\n",
+      "\n",
+      "x_pos = np.arange(len(funcs[1:]))\n",
+      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
+      "plt.xticks(x_pos, labels[1:], rotation=45)\n",
+      "plt.ylabel('relative performance gain')\n",
+      "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAsMwDPMcSwgMwzAMAOD/AFCftMrEW+2BAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105a7fe10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1066dcef0>"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"cython_bonus\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Bonus: How to use Cython without the IPython magic"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "IPython's notebook is really great for explanatory analysis and documentation, but what if we want to compile our Python code via Cython without letting IPython's magic doing all the work?  \n",
+      "These are the steps you would need."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 1. Creating a .pyx file containing the the desired code or function."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file ccy_classic_lstsqr.pyx\n",
+      "\n",
+      "def ccy_classic_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Writing ccy_classic_lstsqr.pyx\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 2. Creating a simple setup file"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file setup.py\n",
+      "\n",
+      "from distutils.core import setup\n",
+      "from distutils.extension import Extension\n",
+      "from Cython.Distutils import build_ext\n",
+      "\n",
+      "setup(\n",
+      "    cmdclass = {'build_ext': build_ext},\n",
+      "    ext_modules = [Extension(\"ccy_classic_lstsqr\", [\"ccy_classic_lstsqr.pyx\"])]\n",
+      ")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Writing setup.py\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "####3. Building and Compiling"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "!python3 setup.py build_ext --inplace"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "running build_ext\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "cythoning ccy_classic_lstsqr.pyx to ccy_classic_lstsqr.c\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "building 'ccy_classic_lstsqr' extension\r\n",
+        "creating build\r\n",
+        "creating build/temp.macosx-10.6-intel-3.4\r\n",
+        "/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -arch i386 -arch x86_64 -g -I/Library/Frameworks/Python.framework/Versions/3.4/include/python3.4m -c ccy_classic_lstsqr.c -o build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\u001b[1mccy_classic_lstsqr.c:2040:28: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_AsString'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {\r\n",
+        "\u001b[0;1;32m                           ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2037:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2104:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_IsTrue'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2159:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyIndex_AsSsize_t'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2188:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_FromSize_t'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1584:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_From_long'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1631:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is not\r\n",
+        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1731:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_int' is not\r\n",
+        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "8 warnings generated.\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\u001b[1mccy_classic_lstsqr.c:2040:28: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_AsString'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {\r\n",
+        "\u001b[0;1;32m                           ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2037:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2104:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_IsTrue'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2159:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyIndex_AsSsize_t'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2188:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_FromSize_t'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1584:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_From_long'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1631:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is not\r\n",
+        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1731:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_int' is not\r\n",
+        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "8"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " warnings generated.\r\n",
+        "/usr/bin/clang -bundle -undefined dynamic_lookup -arch i386 -arch x86_64 -g build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o -o /Users/sebastian/Github/python_reference/benchmarks/ccy_classic_lstsqr.so\r\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 4. Importing and running the code"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import ccy_classic_lstsqr\n",
+      "\n",
+      "%timeit classic_lstsqr(x, y)\n",
+      "%timeit cy_classic_lstsqr(x, y)\n",
+      "%timeit ccy_classic_lstsqr.ccy_classic_lstsqr(x, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "100 loops, best of 3: 2.9 ms per loop\n",
+        "1000 loops, best of 3: 212 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000 loops, best of 3: 207 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/benchmarks/ccy_classic_lstsqr.c b/benchmarks/ccy_classic_lstsqr.c
new file mode 100644
index 0000000..4813b5a
--- /dev/null
+++ b/benchmarks/ccy_classic_lstsqr.c
@@ -0,0 +1,2203 @@
+/* Generated by Cython 0.20.1 on Sun May  4 18:53:57 2014 */
+
+#define PY_SSIZE_T_CLEAN
+#ifndef CYTHON_USE_PYLONG_INTERNALS
+#ifdef PYLONG_BITS_IN_DIGIT
+#define CYTHON_USE_PYLONG_INTERNALS 0
+#else
+#include "pyconfig.h"
+#ifdef PYLONG_BITS_IN_DIGIT
+#define CYTHON_USE_PYLONG_INTERNALS 1
+#else
+#define CYTHON_USE_PYLONG_INTERNALS 0
+#endif
+#endif
+#endif
+#include "Python.h"
+#ifndef Py_PYTHON_H
+    #error Python headers needed to compile C extensions, please install development version of Python.
+#elif PY_VERSION_HEX < 0x02040000
+    #error Cython requires Python 2.4+.
+#else
+#define CYTHON_ABI "0_20_1"
+#include <stddef.h> /* For offsetof */
+#ifndef offsetof
+#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
+#endif
+#if !defined(WIN32) && !defined(MS_WINDOWS)
+  #ifndef __stdcall
+    #define __stdcall
+  #endif
+  #ifndef __cdecl
+    #define __cdecl
+  #endif
+  #ifndef __fastcall
+    #define __fastcall
+  #endif
+#endif
+#ifndef DL_IMPORT
+  #define DL_IMPORT(t) t
+#endif
+#ifndef DL_EXPORT
+  #define DL_EXPORT(t) t
+#endif
+#ifndef PY_LONG_LONG
+  #define PY_LONG_LONG LONG_LONG
+#endif
+#ifndef Py_HUGE_VAL
+  #define Py_HUGE_VAL HUGE_VAL
+#endif
+#ifdef PYPY_VERSION
+#define CYTHON_COMPILING_IN_PYPY 1
+#define CYTHON_COMPILING_IN_CPYTHON 0
+#else
+#define CYTHON_COMPILING_IN_PYPY 0
+#define CYTHON_COMPILING_IN_CPYTHON 1
+#endif
+#if CYTHON_COMPILING_IN_PYPY
+#define Py_OptimizeFlag 0
+#endif
+#if PY_VERSION_HEX < 0x02050000
+  typedef int Py_ssize_t;
+  #define PY_SSIZE_T_MAX INT_MAX
+  #define PY_SSIZE_T_MIN INT_MIN
+  #define PY_FORMAT_SIZE_T ""
+  #define CYTHON_FORMAT_SSIZE_T ""
+  #define PyInt_FromSsize_t(z) PyInt_FromLong(z)
+  #define PyInt_AsSsize_t(o)   __Pyx_PyInt_As_int(o)
+  #define PyNumber_Index(o)    ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
+                                (PyErr_Format(PyExc_TypeError, \
+                                              "expected index value, got %.200s", Py_TYPE(o)->tp_name), \
+                                 (PyObject*)0))
+  #define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \
+                                  !PyComplex_Check(o))
+  #define PyIndex_Check __Pyx_PyIndex_Check
+  #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
+  #define __PYX_BUILD_PY_SSIZE_T "i"
+#else
+  #define __PYX_BUILD_PY_SSIZE_T "n"
+  #define CYTHON_FORMAT_SSIZE_T "z"
+  #define __Pyx_PyIndex_Check PyIndex_Check
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
+  #define Py_TYPE(ob)   (((PyObject*)(ob))->ob_type)
+  #define Py_SIZE(ob)   (((PyVarObject*)(ob))->ob_size)
+  #define PyVarObject_HEAD_INIT(type, size) \
+          PyObject_HEAD_INIT(type) size,
+  #define PyType_Modified(t)
+  typedef struct {
+     void *buf;
+     PyObject *obj;
+     Py_ssize_t len;
+     Py_ssize_t itemsize;
+     int readonly;
+     int ndim;
+     char *format;
+     Py_ssize_t *shape;
+     Py_ssize_t *strides;
+     Py_ssize_t *suboffsets;
+     void *internal;
+  } Py_buffer;
+  #define PyBUF_SIMPLE 0
+  #define PyBUF_WRITABLE 0x0001
+  #define PyBUF_FORMAT 0x0004
+  #define PyBUF_ND 0x0008
+  #define PyBUF_STRIDES (0x0010 | PyBUF_ND)
+  #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES)
+  #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
+  #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
+  #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
+  #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE)
+  #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE)
+  typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
+  typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
+#endif
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
+          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyClass_Type
+#else
+  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
+          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyType_Type
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define Py_TPFLAGS_CHECKTYPES 0
+  #define Py_TPFLAGS_HAVE_INDEX 0
+#endif
+#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
+  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define Py_TPFLAGS_HAVE_VERSION_TAG 0
+#endif
+#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT)
+  #define Py_TPFLAGS_IS_ABSTRACT 0
+#endif
+#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE)
+  #define Py_TPFLAGS_HAVE_FINALIZE 0
+#endif
+#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
+  #define CYTHON_PEP393_ENABLED 1
+  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ? \
+                                              0 : _PyUnicode_Ready((PyObject *)(op)))
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
+  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
+  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
+  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
+#else
+  #define CYTHON_PEP393_ENABLED 0
+  #define __Pyx_PyUnicode_READY(op)       (0)
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
+  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
+  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
+  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
+#endif
+#if CYTHON_COMPILING_IN_PYPY
+  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
+#else
+  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \
+      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
+#endif
+#define __Pyx_PyString_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
+#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
+#else
+  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBaseString_Type            PyUnicode_Type
+  #define PyStringObject               PyUnicodeObject
+  #define PyString_Type                PyUnicode_Type
+  #define PyString_Check               PyUnicode_Check
+  #define PyString_CheckExact          PyUnicode_CheckExact
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define PyBytesObject                PyStringObject
+  #define PyBytes_Type                 PyString_Type
+  #define PyBytes_Check                PyString_Check
+  #define PyBytes_CheckExact           PyString_CheckExact
+  #define PyBytes_FromString           PyString_FromString
+  #define PyBytes_FromStringAndSize    PyString_FromStringAndSize
+  #define PyBytes_FromFormat           PyString_FromFormat
+  #define PyBytes_DecodeEscape         PyString_DecodeEscape
+  #define PyBytes_AsString             PyString_AsString
+  #define PyBytes_AsStringAndSize      PyString_AsStringAndSize
+  #define PyBytes_Size                 PyString_Size
+  #define PyBytes_AS_STRING            PyString_AS_STRING
+  #define PyBytes_GET_SIZE             PyString_GET_SIZE
+  #define PyBytes_Repr                 PyString_Repr
+  #define PyBytes_Concat               PyString_Concat
+  #define PyBytes_ConcatAndDel         PyString_ConcatAndDel
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
+  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
+#else
+  #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \
+                                         PyString_Check(obj) || PyUnicode_Check(obj))
+  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define PySet_Check(obj)             PyObject_TypeCheck(obj, &PySet_Type)
+  #define PyFrozenSet_Check(obj)       PyObject_TypeCheck(obj, &PyFrozenSet_Type)
+#endif
+#ifndef PySet_CheckExact
+  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
+#endif
+#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
+#if PY_MAJOR_VERSION >= 3
+  #define PyIntObject                  PyLongObject
+  #define PyInt_Type                   PyLong_Type
+  #define PyInt_Check(op)              PyLong_Check(op)
+  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
+  #define PyInt_FromString             PyLong_FromString
+  #define PyInt_FromUnicode            PyLong_FromUnicode
+  #define PyInt_FromLong               PyLong_FromLong
+  #define PyInt_FromSize_t             PyLong_FromSize_t
+  #define PyInt_FromSsize_t            PyLong_FromSsize_t
+  #define PyInt_AsLong                 PyLong_AsLong
+  #define PyInt_AS_LONG                PyLong_AS_LONG
+  #define PyInt_AsSsize_t              PyLong_AsSsize_t
+  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
+  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+  #define PyNumber_Int                 PyNumber_Long
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBoolObject                 PyLongObject
+#endif
+#if PY_VERSION_HEX < 0x030200A4
+  typedef long Py_hash_t;
+  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
+#else
+  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
+#endif
+#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
+  #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
+  #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
+  #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b)
+#else
+  #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0)))
+  #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1)))
+  #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1)))
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
+#endif
+#if PY_VERSION_HEX < 0x02050000
+  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),((char *)(n)))
+  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
+  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),((char *)(n)))
+#else
+  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),(n))
+  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a))
+  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),(n))
+#endif
+#if PY_VERSION_HEX < 0x02050000
+  #define __Pyx_NAMESTR(n) ((char *)(n))
+  #define __Pyx_DOCSTR(n)  ((char *)(n))
+#else
+  #define __Pyx_NAMESTR(n) (n)
+  #define __Pyx_DOCSTR(n)  (n)
+#endif
+#ifndef CYTHON_INLINE
+  #if defined(__GNUC__)
+    #define CYTHON_INLINE __inline__
+  #elif defined(_MSC_VER)
+    #define CYTHON_INLINE __inline
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_INLINE inline
+  #else
+    #define CYTHON_INLINE
+  #endif
+#endif
+#ifndef CYTHON_RESTRICT
+  #if defined(__GNUC__)
+    #define CYTHON_RESTRICT __restrict__
+  #elif defined(_MSC_VER) && _MSC_VER >= 1400
+    #define CYTHON_RESTRICT __restrict
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_RESTRICT restrict
+  #else
+    #define CYTHON_RESTRICT
+  #endif
+#endif
+#ifdef NAN
+#define __PYX_NAN() ((float) NAN)
+#else
+static CYTHON_INLINE float __PYX_NAN() {
+  /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and
+   a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is
+   a quiet NaN. */
+  float value;
+  memset(&value, 0xFF, sizeof(value));
+  return value;
+}
+#endif
+
+
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
+#else
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
+#endif
+
+#ifndef __PYX_EXTERN_C
+  #ifdef __cplusplus
+    #define __PYX_EXTERN_C extern "C"
+  #else
+    #define __PYX_EXTERN_C extern
+  #endif
+#endif
+
+#if defined(WIN32) || defined(MS_WINDOWS)
+#define _USE_MATH_DEFINES
+#endif
+#include <math.h>
+#define __PYX_HAVE__ccy_classic_lstsqr
+#define __PYX_HAVE_API__ccy_classic_lstsqr
+#ifdef _OPENMP
+#include <omp.h>
+#endif /* _OPENMP */
+
+#ifdef PYREX_WITHOUT_ASSERTIONS
+#define CYTHON_WITHOUT_ASSERTIONS
+#endif
+
+#ifndef CYTHON_UNUSED
+# if defined(__GNUC__)
+#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
+#     define CYTHON_UNUSED __attribute__ ((__unused__))
+#   else
+#     define CYTHON_UNUSED
+#   endif
+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
+#   define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+#   define CYTHON_UNUSED
+# endif
+#endif
+typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;
+                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
+
+#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
+#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
+#define __PYX_DEFAULT_STRING_ENCODING ""
+#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
+#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (    \
+    (sizeof(type) < sizeof(Py_ssize_t))  ||             \
+    (sizeof(type) > sizeof(Py_ssize_t) &&               \
+          likely(v < (type)PY_SSIZE_T_MAX ||            \
+                 v == (type)PY_SSIZE_T_MAX)  &&         \
+          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||       \
+                                v == (type)PY_SSIZE_T_MIN)))  ||  \
+    (sizeof(type) == sizeof(Py_ssize_t) &&              \
+          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||        \
+                               v == (type)PY_SSIZE_T_MAX)))  )
+static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
+static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
+#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
+#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
+#define __Pyx_PyBytes_FromString        PyBytes_FromString
+#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*);
+#if PY_MAJOR_VERSION < 3
+    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#else
+    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
+#endif
+#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_FromUString(s)  __Pyx_PyObject_FromString((char*)s)
+#define __Pyx_PyBytes_FromUString(s)   __Pyx_PyBytes_FromString((char*)s)
+#define __Pyx_PyByteArray_FromUString(s)   __Pyx_PyByteArray_FromString((char*)s)
+#define __Pyx_PyStr_FromUString(s)     __Pyx_PyStr_FromString((char*)s)
+#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s)
+#if PY_MAJOR_VERSION < 3
+static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
+{
+    const Py_UNICODE *u_end = u;
+    while (*u_end++) ;
+    return u_end - u - 1;
+}
+#else
+#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
+#endif
+#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
+#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
+#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
+#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None)
+#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
+#else
+#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
+#endif
+#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+static int __Pyx_sys_getdefaultencoding_not_ascii;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys = NULL;
+    PyObject* default_encoding = NULL;
+    PyObject* ascii_chars_u = NULL;
+    PyObject* ascii_chars_b = NULL;
+    sys = PyImport_ImportModule("sys");
+    if (sys == NULL) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+    if (default_encoding == NULL) goto bad;
+    if (strcmp(PyBytes_AsString(default_encoding), "ascii") == 0) {
+        __Pyx_sys_getdefaultencoding_not_ascii = 0;
+    } else {
+        const char* default_encoding_c = PyBytes_AS_STRING(default_encoding);
+        char ascii_chars[128];
+        int c;
+        for (c = 0; c < 128; c++) {
+            ascii_chars[c] = c;
+        }
+        __Pyx_sys_getdefaultencoding_not_ascii = 1;
+        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
+        if (ascii_chars_u == NULL) goto bad;
+        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
+        if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
+            PyErr_Format(
+                PyExc_ValueError,
+                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
+                default_encoding_c);
+            goto bad;
+        }
+    }
+    Py_XDECREF(sys);
+    Py_XDECREF(default_encoding);
+    Py_XDECREF(ascii_chars_u);
+    Py_XDECREF(ascii_chars_b);
+    return 0;
+bad:
+    Py_XDECREF(sys);
+    Py_XDECREF(default_encoding);
+    Py_XDECREF(ascii_chars_u);
+    Py_XDECREF(ascii_chars_b);
+    return -1;
+}
+#endif
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
+#else
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+static char* __PYX_DEFAULT_STRING_ENCODING;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys = NULL;
+    PyObject* default_encoding = NULL;
+    char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (sys == NULL) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+    if (default_encoding == NULL) goto bad;
+    default_encoding_c = PyBytes_AS_STRING(default_encoding);
+    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
+    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
+    Py_DECREF(sys);
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(sys);
+    Py_XDECREF(default_encoding);
+    return -1;
+}
+#endif
+#endif
+
+
+#ifdef __GNUC__
+  /* Test for GCC > 2.95 */
+  #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))
+    #define likely(x)   __builtin_expect(!!(x), 1)
+    #define unlikely(x) __builtin_expect(!!(x), 0)
+  #else /* __GNUC__ > 2 ... */
+    #define likely(x)   (x)
+    #define unlikely(x) (x)
+  #endif /* __GNUC__ > 2 ... */
+#else /* __GNUC__ */
+  #define likely(x)   (x)
+  #define unlikely(x) (x)
+#endif /* __GNUC__ */
+
+static PyObject *__pyx_m;
+static PyObject *__pyx_d;
+static PyObject *__pyx_b;
+static PyObject *__pyx_empty_tuple;
+static PyObject *__pyx_empty_bytes;
+static int __pyx_lineno;
+static int __pyx_clineno = 0;
+static const char * __pyx_cfilenm= __FILE__;
+static const char *__pyx_filename;
+
+
+static const char *__pyx_f[] = {
+  "ccy_classic_lstsqr.pyx",
+};
+
+/*--- Type declarations ---*/
+#ifndef CYTHON_REFNANNY
+  #define CYTHON_REFNANNY 0
+#endif
+#if CYTHON_REFNANNY
+  typedef struct {
+    void (*INCREF)(void*, PyObject*, int);
+    void (*DECREF)(void*, PyObject*, int);
+    void (*GOTREF)(void*, PyObject*, int);
+    void (*GIVEREF)(void*, PyObject*, int);
+    void* (*SetupContext)(const char*, int, const char*);
+    void (*FinishContext)(void**);
+  } __Pyx_RefNannyAPIStruct;
+  static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
+  static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/
+  #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;
+#ifdef WITH_THREAD
+  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
+          if (acquire_gil) { \
+              PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \
+              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
+              PyGILState_Release(__pyx_gilstate_save); \
+          } else { \
+              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
+          }
+#else
+  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
+          __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
+#endif
+  #define __Pyx_RefNannyFinishContext() \
+          __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
+  #define __Pyx_INCREF(r)  __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_DECREF(r)  __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_GOTREF(r)  __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_XINCREF(r)  do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0)
+  #define __Pyx_XDECREF(r)  do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0)
+  #define __Pyx_XGOTREF(r)  do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0)
+  #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0)
+#else
+  #define __Pyx_RefNannyDeclarations
+  #define __Pyx_RefNannySetupContext(name, acquire_gil)
+  #define __Pyx_RefNannyFinishContext()
+  #define __Pyx_INCREF(r) Py_INCREF(r)
+  #define __Pyx_DECREF(r) Py_DECREF(r)
+  #define __Pyx_GOTREF(r)
+  #define __Pyx_GIVEREF(r)
+  #define __Pyx_XINCREF(r) Py_XINCREF(r)
+  #define __Pyx_XDECREF(r) Py_XDECREF(r)
+  #define __Pyx_XGOTREF(r)
+  #define __Pyx_XGIVEREF(r)
+#endif /* CYTHON_REFNANNY */
+#define __Pyx_XDECREF_SET(r, v) do {                            \
+        PyObject *tmp = (PyObject *) r;                         \
+        r = v; __Pyx_XDECREF(tmp);                              \
+    } while (0)
+#define __Pyx_DECREF_SET(r, v) do {                             \
+        PyObject *tmp = (PyObject *) r;                         \
+        r = v; __Pyx_DECREF(tmp);                               \
+    } while (0)
+#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
+#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
+    PyTypeObject* tp = Py_TYPE(obj);
+    if (likely(tp->tp_getattro))
+        return tp->tp_getattro(obj, attr_name);
+#if PY_MAJOR_VERSION < 3
+    if (likely(tp->tp_getattr))
+        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
+#endif
+    return PyObject_GetAttr(obj, attr_name);
+}
+#else
+#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
+#endif
+
+static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/
+
+static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
+    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
+
+static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/
+
+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
+    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
+    const char* function_name); /*proto*/
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/
+#else
+#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
+#endif
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
+    PyListObject* L = (PyListObject*) list;
+    Py_ssize_t len = Py_SIZE(list);
+    if (likely(L->allocated > len)) {
+        Py_INCREF(x);
+        PyList_SET_ITEM(list, len, x);
+        Py_SIZE(list) = len+1;
+        return 0;
+    }
+    return PyList_Append(list, x);
+}
+#else
+#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
+#endif
+
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
+
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
+
+static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/
+
+static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/
+
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
+
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
+
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
+
+static int __Pyx_check_binary_version(void);
+
+typedef struct {
+    int code_line;
+    PyCodeObject* code_object;
+} __Pyx_CodeObjectCacheEntry;
+struct __Pyx_CodeObjectCache {
+    int count;
+    int max_count;
+    __Pyx_CodeObjectCacheEntry* entries;
+};
+static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
+static PyCodeObject *__pyx_find_code_object(int code_line);
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
+
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename); /*proto*/
+
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
+
+
+/* Module declarations from 'ccy_classic_lstsqr' */
+#define __Pyx_MODULE_NAME "ccy_classic_lstsqr"
+int __pyx_module_is_main_ccy_classic_lstsqr = 0;
+
+/* Implementation of 'ccy_classic_lstsqr' */
+static PyObject *__pyx_builtin_sum;
+static PyObject *__pyx_builtin_zip;
+static PyObject *__pyx_pf_18ccy_classic_lstsqr_ccy_classic_lstsqr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_x, PyObject *__pyx_v_y); /* proto */
+static char __pyx_k_x[] = "x";
+static char __pyx_k_y[] = "y";
+static char __pyx_k_sum[] = "sum";
+static char __pyx_k_x_i[] = "x_i";
+static char __pyx_k_y_i[] = "y_i";
+static char __pyx_k_zip[] = "zip";
+static char __pyx_k_main[] = "__main__";
+static char __pyx_k_test[] = "__test__";
+static char __pyx_k_slope[] = "slope";
+static char __pyx_k_var_x[] = "var_x";
+static char __pyx_k_x_avg[] = "x_avg";
+static char __pyx_k_y_avg[] = "y_avg";
+static char __pyx_k_cov_xy[] = "cov_xy";
+static char __pyx_k_y_interc[] = "y_interc";
+static char __pyx_k_ccy_classic_lstsqr[] = "ccy_classic_lstsqr";
+static char __pyx_k_Users_sebastian_Github_python_r[] = "/Users/sebastian/Github/python_reference/benchmarks/ccy_classic_lstsqr.pyx";
+static PyObject *__pyx_kp_s_Users_sebastian_Github_python_r;
+static PyObject *__pyx_n_s_ccy_classic_lstsqr;
+static PyObject *__pyx_n_s_cov_xy;
+static PyObject *__pyx_n_s_main;
+static PyObject *__pyx_n_s_slope;
+static PyObject *__pyx_n_s_sum;
+static PyObject *__pyx_n_s_test;
+static PyObject *__pyx_n_s_var_x;
+static PyObject *__pyx_n_s_x;
+static PyObject *__pyx_n_s_x_avg;
+static PyObject *__pyx_n_s_x_i;
+static PyObject *__pyx_n_s_y;
+static PyObject *__pyx_n_s_y_avg;
+static PyObject *__pyx_n_s_y_i;
+static PyObject *__pyx_n_s_y_interc;
+static PyObject *__pyx_n_s_zip;
+static PyObject *__pyx_int_2;
+static PyObject *__pyx_tuple_;
+static PyObject *__pyx_codeobj__2;
+
+/* "ccy_classic_lstsqr.pyx":2
+ * 
+ * def ccy_classic_lstsqr(x, y):             # <<<<<<<<<<<<<<
+ *     """ Computes the least-squares solution to a linear matrix equation. """
+ *     x_avg = sum(x)/len(x)
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_pw_18ccy_classic_lstsqr_1ccy_classic_lstsqr(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_18ccy_classic_lstsqr_ccy_classic_lstsqr[] = " Computes the least-squares solution to a linear matrix equation. ";
+static PyMethodDef __pyx_mdef_18ccy_classic_lstsqr_1ccy_classic_lstsqr = {__Pyx_NAMESTR("ccy_classic_lstsqr"), (PyCFunction)__pyx_pw_18ccy_classic_lstsqr_1ccy_classic_lstsqr, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_18ccy_classic_lstsqr_ccy_classic_lstsqr)};
+static PyObject *__pyx_pw_18ccy_classic_lstsqr_1ccy_classic_lstsqr(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+  PyObject *__pyx_v_x = 0;
+  PyObject *__pyx_v_y = 0;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("ccy_classic_lstsqr (wrapper)", 0);
+  {
+    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_x,&__pyx_n_s_y,0};
+    PyObject* values[2] = {0,0};
+    if (unlikely(__pyx_kwds)) {
+      Py_ssize_t kw_args;
+      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
+      switch (pos_args) {
+        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
+        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+        case  0: break;
+        default: goto __pyx_L5_argtuple_error;
+      }
+      kw_args = PyDict_Size(__pyx_kwds);
+      switch (pos_args) {
+        case  0:
+        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_x)) != 0)) kw_args--;
+        else goto __pyx_L5_argtuple_error;
+        case  1:
+        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_y)) != 0)) kw_args--;
+        else {
+          __Pyx_RaiseArgtupleInvalid("ccy_classic_lstsqr", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        }
+      }
+      if (unlikely(kw_args > 0)) {
+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "ccy_classic_lstsqr") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      }
+    } else if (PyTuple_GET_SIZE(__pyx_args) != 2) {
+      goto __pyx_L5_argtuple_error;
+    } else {
+      values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+      values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
+    }
+    __pyx_v_x = values[0];
+    __pyx_v_y = values[1];
+  }
+  goto __pyx_L4_argument_unpacking_done;
+  __pyx_L5_argtuple_error:;
+  __Pyx_RaiseArgtupleInvalid("ccy_classic_lstsqr", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("ccy_classic_lstsqr.ccy_classic_lstsqr", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __Pyx_RefNannyFinishContext();
+  return NULL;
+  __pyx_L4_argument_unpacking_done:;
+  __pyx_r = __pyx_pf_18ccy_classic_lstsqr_ccy_classic_lstsqr(__pyx_self, __pyx_v_x, __pyx_v_y);
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_pf_18ccy_classic_lstsqr_ccy_classic_lstsqr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_x, PyObject *__pyx_v_y) {
+  PyObject *__pyx_v_x_avg = NULL;
+  PyObject *__pyx_v_y_avg = NULL;
+  PyObject *__pyx_v_var_x = NULL;
+  PyObject *__pyx_v_cov_xy = NULL;
+  PyObject *__pyx_v_slope = NULL;
+  PyObject *__pyx_v_y_interc = NULL;
+  PyObject *__pyx_v_x_i = NULL;
+  PyObject *__pyx_v_y_i = NULL;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  Py_ssize_t __pyx_t_3;
+  PyObject *__pyx_t_4 = NULL;
+  PyObject *(*__pyx_t_5)(PyObject *);
+  PyObject *__pyx_t_6 = NULL;
+  PyObject *__pyx_t_7 = NULL;
+  PyObject *__pyx_t_8 = NULL;
+  PyObject *(*__pyx_t_9)(PyObject *);
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("ccy_classic_lstsqr", 0);
+
+  /* "ccy_classic_lstsqr.pyx":4
+ * def ccy_classic_lstsqr(x, y):
+ *     """ Computes the least-squares solution to a linear matrix equation. """
+ *     x_avg = sum(x)/len(x)             # <<<<<<<<<<<<<<
+ *     y_avg = sum(y)/len(y)
+ *     var_x = sum([(x_i - x_avg)**2 for x_i in x])
+ */
+  __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_INCREF(__pyx_v_x);
+  PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v_x);
+  __Pyx_GIVEREF(__pyx_v_x);
+  __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_1, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_t_3 = PyObject_Length(__pyx_v_x); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = PyInt_FromSsize_t(__pyx_t_3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_4 = __Pyx_PyNumber_Divide(__pyx_t_2, __pyx_t_1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_v_x_avg = __pyx_t_4;
+  __pyx_t_4 = 0;
+
+  /* "ccy_classic_lstsqr.pyx":5
+ *     """ Computes the least-squares solution to a linear matrix equation. """
+ *     x_avg = sum(x)/len(x)
+ *     y_avg = sum(y)/len(y)             # <<<<<<<<<<<<<<
+ *     var_x = sum([(x_i - x_avg)**2 for x_i in x])
+ *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
+ */
+  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __Pyx_INCREF(__pyx_v_y);
+  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_y);
+  __Pyx_GIVEREF(__pyx_v_y);
+  __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_4, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __pyx_t_3 = PyObject_Length(__pyx_v_y); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_4 = PyInt_FromSsize_t(__pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __pyx_t_2 = __Pyx_PyNumber_Divide(__pyx_t_1, __pyx_t_4); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __pyx_v_y_avg = __pyx_t_2;
+  __pyx_t_2 = 0;
+
+  /* "ccy_classic_lstsqr.pyx":6
+ *     x_avg = sum(x)/len(x)
+ *     y_avg = sum(y)/len(y)
+ *     var_x = sum([(x_i - x_avg)**2 for x_i in x])             # <<<<<<<<<<<<<<
+ *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
+ *     slope = cov_xy / var_x
+ */
+  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  if (PyList_CheckExact(__pyx_v_x) || PyTuple_CheckExact(__pyx_v_x)) {
+    __pyx_t_4 = __pyx_v_x; __Pyx_INCREF(__pyx_t_4); __pyx_t_3 = 0;
+    __pyx_t_5 = NULL;
+  } else {
+    __pyx_t_3 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_x); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_5 = Py_TYPE(__pyx_t_4)->tp_iternext;
+  }
+  for (;;) {
+    if (!__pyx_t_5 && PyList_CheckExact(__pyx_t_4)) {
+      if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_4)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_1 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_3); __Pyx_INCREF(__pyx_t_1); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_1 = PySequence_ITEM(__pyx_t_4, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else if (!__pyx_t_5 && PyTuple_CheckExact(__pyx_t_4)) {
+      if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_4)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_1 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_3); __Pyx_INCREF(__pyx_t_1); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_1 = PySequence_ITEM(__pyx_t_4, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else {
+      __pyx_t_1 = __pyx_t_5(__pyx_t_4);
+      if (unlikely(!__pyx_t_1)) {
+        PyObject* exc_type = PyErr_Occurred();
+        if (exc_type) {
+          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+          else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        }
+        break;
+      }
+      __Pyx_GOTREF(__pyx_t_1);
+    }
+    __Pyx_XDECREF_SET(__pyx_v_x_i, __pyx_t_1);
+    __pyx_t_1 = 0;
+    __pyx_t_1 = PyNumber_Subtract(__pyx_v_x_i, __pyx_v_x_avg); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_1);
+    __pyx_t_6 = PyNumber_Power(__pyx_t_1, __pyx_int_2, Py_None); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_6);
+    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+    if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_6))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+  }
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  __pyx_t_2 = 0;
+  __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_4, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __pyx_v_var_x = __pyx_t_2;
+  __pyx_t_2 = 0;
+
+  /* "ccy_classic_lstsqr.pyx":7
+ *     y_avg = sum(y)/len(y)
+ *     var_x = sum([(x_i - x_avg)**2 for x_i in x])
+ *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])             # <<<<<<<<<<<<<<
+ *     slope = cov_xy / var_x
+ *     y_interc = y_avg - slope*x_avg
+ */
+  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __Pyx_INCREF(__pyx_v_x);
+  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_x);
+  __Pyx_GIVEREF(__pyx_v_x);
+  __Pyx_INCREF(__pyx_v_y);
+  PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_v_y);
+  __Pyx_GIVEREF(__pyx_v_y);
+  __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_zip, __pyx_t_4, NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_6);
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  if (PyList_CheckExact(__pyx_t_6) || PyTuple_CheckExact(__pyx_t_6)) {
+    __pyx_t_4 = __pyx_t_6; __Pyx_INCREF(__pyx_t_4); __pyx_t_3 = 0;
+    __pyx_t_5 = NULL;
+  } else {
+    __pyx_t_3 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_t_6); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_5 = Py_TYPE(__pyx_t_4)->tp_iternext;
+  }
+  __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+  for (;;) {
+    if (!__pyx_t_5 && PyList_CheckExact(__pyx_t_4)) {
+      if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_4)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_6 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_3); __Pyx_INCREF(__pyx_t_6); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_6 = PySequence_ITEM(__pyx_t_4, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else if (!__pyx_t_5 && PyTuple_CheckExact(__pyx_t_4)) {
+      if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_4)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_6 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_3); __Pyx_INCREF(__pyx_t_6); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_6 = PySequence_ITEM(__pyx_t_4, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else {
+      __pyx_t_6 = __pyx_t_5(__pyx_t_4);
+      if (unlikely(!__pyx_t_6)) {
+        PyObject* exc_type = PyErr_Occurred();
+        if (exc_type) {
+          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+          else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        }
+        break;
+      }
+      __Pyx_GOTREF(__pyx_t_6);
+    }
+    if ((likely(PyTuple_CheckExact(__pyx_t_6))) || (PyList_CheckExact(__pyx_t_6))) {
+      PyObject* sequence = __pyx_t_6;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      Py_ssize_t size = Py_SIZE(sequence);
+      #else
+      Py_ssize_t size = PySequence_Size(sequence);
+      #endif
+      if (unlikely(size != 2)) {
+        if (size > 2) __Pyx_RaiseTooManyValuesError(2);
+        else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
+        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+      #if CYTHON_COMPILING_IN_CPYTHON
+      if (likely(PyTuple_CheckExact(sequence))) {
+        __pyx_t_1 = PyTuple_GET_ITEM(sequence, 0); 
+        __pyx_t_7 = PyTuple_GET_ITEM(sequence, 1); 
+      } else {
+        __pyx_t_1 = PyList_GET_ITEM(sequence, 0); 
+        __pyx_t_7 = PyList_GET_ITEM(sequence, 1); 
+      }
+      __Pyx_INCREF(__pyx_t_1);
+      __Pyx_INCREF(__pyx_t_7);
+      #else
+      __pyx_t_1 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_1);
+      __pyx_t_7 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_7);
+      #endif
+      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+    } else {
+      Py_ssize_t index = -1;
+      __pyx_t_8 = PyObject_GetIter(__pyx_t_6); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_8);
+      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+      __pyx_t_9 = Py_TYPE(__pyx_t_8)->tp_iternext;
+      index = 0; __pyx_t_1 = __pyx_t_9(__pyx_t_8); if (unlikely(!__pyx_t_1)) goto __pyx_L7_unpacking_failed;
+      __Pyx_GOTREF(__pyx_t_1);
+      index = 1; __pyx_t_7 = __pyx_t_9(__pyx_t_8); if (unlikely(!__pyx_t_7)) goto __pyx_L7_unpacking_failed;
+      __Pyx_GOTREF(__pyx_t_7);
+      if (__Pyx_IternextUnpackEndCheck(__pyx_t_9(__pyx_t_8), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_9 = NULL;
+      __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
+      goto __pyx_L8_unpacking_done;
+      __pyx_L7_unpacking_failed:;
+      __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
+      __pyx_t_9 = NULL;
+      if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index);
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_L8_unpacking_done:;
+    }
+    __Pyx_XDECREF_SET(__pyx_v_x_i, __pyx_t_1);
+    __pyx_t_1 = 0;
+    __Pyx_XDECREF_SET(__pyx_v_y_i, __pyx_t_7);
+    __pyx_t_7 = 0;
+    __pyx_t_6 = PyNumber_Subtract(__pyx_v_x_i, __pyx_v_x_avg); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_6);
+    __pyx_t_7 = PyNumber_Subtract(__pyx_v_y_i, __pyx_v_y_avg); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_7);
+    __pyx_t_1 = PyNumber_Multiply(__pyx_t_6, __pyx_t_7); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_1);
+    __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+    __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
+    if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  }
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  __pyx_t_2 = 0;
+  __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_4, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __pyx_v_cov_xy = __pyx_t_2;
+  __pyx_t_2 = 0;
+
+  /* "ccy_classic_lstsqr.pyx":8
+ *     var_x = sum([(x_i - x_avg)**2 for x_i in x])
+ *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
+ *     slope = cov_xy / var_x             # <<<<<<<<<<<<<<
+ *     y_interc = y_avg - slope*x_avg
+ *     return (slope, y_interc)
+ */
+  __pyx_t_2 = __Pyx_PyNumber_Divide(__pyx_v_cov_xy, __pyx_v_var_x); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_v_slope = __pyx_t_2;
+  __pyx_t_2 = 0;
+
+  /* "ccy_classic_lstsqr.pyx":9
+ *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
+ *     slope = cov_xy / var_x
+ *     y_interc = y_avg - slope*x_avg             # <<<<<<<<<<<<<<
+ *     return (slope, y_interc)
+ */
+  __pyx_t_2 = PyNumber_Multiply(__pyx_v_slope, __pyx_v_x_avg); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_4 = PyNumber_Subtract(__pyx_v_y_avg, __pyx_t_2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_v_y_interc = __pyx_t_4;
+  __pyx_t_4 = 0;
+
+  /* "ccy_classic_lstsqr.pyx":10
+ *     slope = cov_xy / var_x
+ *     y_interc = y_avg - slope*x_avg
+ *     return (slope, y_interc)             # <<<<<<<<<<<<<<
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __Pyx_INCREF(__pyx_v_slope);
+  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_slope);
+  __Pyx_GIVEREF(__pyx_v_slope);
+  __Pyx_INCREF(__pyx_v_y_interc);
+  PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_v_y_interc);
+  __Pyx_GIVEREF(__pyx_v_y_interc);
+  __pyx_r = __pyx_t_4;
+  __pyx_t_4 = 0;
+  goto __pyx_L0;
+
+  /* "ccy_classic_lstsqr.pyx":2
+ * 
+ * def ccy_classic_lstsqr(x, y):             # <<<<<<<<<<<<<<
+ *     """ Computes the least-squares solution to a linear matrix equation. """
+ *     x_avg = sum(x)/len(x)
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_XDECREF(__pyx_t_6);
+  __Pyx_XDECREF(__pyx_t_7);
+  __Pyx_XDECREF(__pyx_t_8);
+  __Pyx_AddTraceback("ccy_classic_lstsqr.ccy_classic_lstsqr", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_x_avg);
+  __Pyx_XDECREF(__pyx_v_y_avg);
+  __Pyx_XDECREF(__pyx_v_var_x);
+  __Pyx_XDECREF(__pyx_v_cov_xy);
+  __Pyx_XDECREF(__pyx_v_slope);
+  __Pyx_XDECREF(__pyx_v_y_interc);
+  __Pyx_XDECREF(__pyx_v_x_i);
+  __Pyx_XDECREF(__pyx_v_y_i);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyMethodDef __pyx_methods[] = {
+  {0, 0, 0, 0}
+};
+
+#if PY_MAJOR_VERSION >= 3
+static struct PyModuleDef __pyx_moduledef = {
+  #if PY_VERSION_HEX < 0x03020000
+    { PyObject_HEAD_INIT(NULL) NULL, 0, NULL },
+  #else
+    PyModuleDef_HEAD_INIT,
+  #endif
+    __Pyx_NAMESTR("ccy_classic_lstsqr"),
+    0, /* m_doc */
+    -1, /* m_size */
+    __pyx_methods /* m_methods */,
+    NULL, /* m_reload */
+    NULL, /* m_traverse */
+    NULL, /* m_clear */
+    NULL /* m_free */
+};
+#endif
+
+static __Pyx_StringTabEntry __pyx_string_tab[] = {
+  {&__pyx_kp_s_Users_sebastian_Github_python_r, __pyx_k_Users_sebastian_Github_python_r, sizeof(__pyx_k_Users_sebastian_Github_python_r), 0, 0, 1, 0},
+  {&__pyx_n_s_ccy_classic_lstsqr, __pyx_k_ccy_classic_lstsqr, sizeof(__pyx_k_ccy_classic_lstsqr), 0, 0, 1, 1},
+  {&__pyx_n_s_cov_xy, __pyx_k_cov_xy, sizeof(__pyx_k_cov_xy), 0, 0, 1, 1},
+  {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
+  {&__pyx_n_s_slope, __pyx_k_slope, sizeof(__pyx_k_slope), 0, 0, 1, 1},
+  {&__pyx_n_s_sum, __pyx_k_sum, sizeof(__pyx_k_sum), 0, 0, 1, 1},
+  {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
+  {&__pyx_n_s_var_x, __pyx_k_var_x, sizeof(__pyx_k_var_x), 0, 0, 1, 1},
+  {&__pyx_n_s_x, __pyx_k_x, sizeof(__pyx_k_x), 0, 0, 1, 1},
+  {&__pyx_n_s_x_avg, __pyx_k_x_avg, sizeof(__pyx_k_x_avg), 0, 0, 1, 1},
+  {&__pyx_n_s_x_i, __pyx_k_x_i, sizeof(__pyx_k_x_i), 0, 0, 1, 1},
+  {&__pyx_n_s_y, __pyx_k_y, sizeof(__pyx_k_y), 0, 0, 1, 1},
+  {&__pyx_n_s_y_avg, __pyx_k_y_avg, sizeof(__pyx_k_y_avg), 0, 0, 1, 1},
+  {&__pyx_n_s_y_i, __pyx_k_y_i, sizeof(__pyx_k_y_i), 0, 0, 1, 1},
+  {&__pyx_n_s_y_interc, __pyx_k_y_interc, sizeof(__pyx_k_y_interc), 0, 0, 1, 1},
+  {&__pyx_n_s_zip, __pyx_k_zip, sizeof(__pyx_k_zip), 0, 0, 1, 1},
+  {0, 0, 0, 0, 0, 0, 0}
+};
+static int __Pyx_InitCachedBuiltins(void) {
+  __pyx_builtin_sum = __Pyx_GetBuiltinName(__pyx_n_s_sum); if (!__pyx_builtin_sum) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_zip = __Pyx_GetBuiltinName(__pyx_n_s_zip); if (!__pyx_builtin_zip) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  return 0;
+  __pyx_L1_error:;
+  return -1;
+}
+
+static int __Pyx_InitCachedConstants(void) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
+
+  /* "ccy_classic_lstsqr.pyx":2
+ * 
+ * def ccy_classic_lstsqr(x, y):             # <<<<<<<<<<<<<<
+ *     """ Computes the least-squares solution to a linear matrix equation. """
+ *     x_avg = sum(x)/len(x)
+ */
+  __pyx_tuple_ = PyTuple_Pack(10, __pyx_n_s_x, __pyx_n_s_y, __pyx_n_s_x_avg, __pyx_n_s_y_avg, __pyx_n_s_var_x, __pyx_n_s_cov_xy, __pyx_n_s_slope, __pyx_n_s_y_interc, __pyx_n_s_x_i, __pyx_n_s_y_i); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple_);
+  __Pyx_GIVEREF(__pyx_tuple_);
+  __pyx_codeobj__2 = (PyObject*)__Pyx_PyCode_New(2, 0, 10, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple_, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_sebastian_Github_python_r, __pyx_n_s_ccy_classic_lstsqr, 2, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_RefNannyFinishContext();
+  return 0;
+  __pyx_L1_error:;
+  __Pyx_RefNannyFinishContext();
+  return -1;
+}
+
+static int __Pyx_InitGlobals(void) {
+  if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+  __pyx_int_2 = PyInt_FromLong(2); if (unlikely(!__pyx_int_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  return 0;
+  __pyx_L1_error:;
+  return -1;
+}
+
+#if PY_MAJOR_VERSION < 3
+PyMODINIT_FUNC initccy_classic_lstsqr(void); /*proto*/
+PyMODINIT_FUNC initccy_classic_lstsqr(void)
+#else
+PyMODINIT_FUNC PyInit_ccy_classic_lstsqr(void); /*proto*/
+PyMODINIT_FUNC PyInit_ccy_classic_lstsqr(void)
+#endif
+{
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannyDeclarations
+  #if CYTHON_REFNANNY
+  __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny");
+  if (!__Pyx_RefNanny) {
+      PyErr_Clear();
+      __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny");
+      if (!__Pyx_RefNanny)
+          Py_FatalError("failed to import 'refnanny' module");
+  }
+  #endif
+  __Pyx_RefNannySetupContext("PyMODINIT_FUNC PyInit_ccy_classic_lstsqr(void)", 0);
+  if ( __Pyx_check_binary_version() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #ifdef __Pyx_CyFunction_USED
+  if (__Pyx_CyFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  #ifdef __Pyx_FusedFunction_USED
+  if (__pyx_FusedFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  #ifdef __Pyx_Generator_USED
+  if (__pyx_Generator_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  /*--- Library function declarations ---*/
+  /*--- Threads initialization code ---*/
+  #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS
+  #ifdef WITH_THREAD /* Python build with threading support? */
+  PyEval_InitThreads();
+  #endif
+  #endif
+  /*--- Module creation code ---*/
+  #if PY_MAJOR_VERSION < 3
+  __pyx_m = Py_InitModule4(__Pyx_NAMESTR("ccy_classic_lstsqr"), __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
+  #else
+  __pyx_m = PyModule_Create(&__pyx_moduledef);
+  #endif
+  if (unlikely(!__pyx_m)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  Py_INCREF(__pyx_d);
+  __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME)); if (unlikely(!__pyx_b)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #if CYTHON_COMPILING_IN_PYPY
+  Py_INCREF(__pyx_b);
+  #endif
+  if (__Pyx_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+  /*--- Initialize various global constants etc. ---*/
+  if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT)
+  if (__Pyx_init_sys_getdefaultencoding_params() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  if (__pyx_module_is_main_ccy_classic_lstsqr) {
+    if (__Pyx_SetAttrString(__pyx_m, "__name__", __pyx_n_s_main) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+  }
+  #if PY_MAJOR_VERSION >= 3
+  {
+    PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    if (!PyDict_GetItemString(modules, "ccy_classic_lstsqr")) {
+      if (unlikely(PyDict_SetItemString(modules, "ccy_classic_lstsqr", __pyx_m) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+  }
+  #endif
+  /*--- Builtin init code ---*/
+  if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  /*--- Constants init code ---*/
+  if (unlikely(__Pyx_InitCachedConstants() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  /*--- Global init code ---*/
+  /*--- Variable export code ---*/
+  /*--- Function export code ---*/
+  /*--- Type init code ---*/
+  /*--- Type import code ---*/
+  /*--- Variable import code ---*/
+  /*--- Function import code ---*/
+  /*--- Execution code ---*/
+
+  /* "ccy_classic_lstsqr.pyx":2
+ * 
+ * def ccy_classic_lstsqr(x, y):             # <<<<<<<<<<<<<<
+ *     """ Computes the least-squares solution to a linear matrix equation. """
+ *     x_avg = sum(x)/len(x)
+ */
+  __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_18ccy_classic_lstsqr_1ccy_classic_lstsqr, NULL, __pyx_n_s_ccy_classic_lstsqr); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_d, __pyx_n_s_ccy_classic_lstsqr, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  if (__pyx_m) {
+    __Pyx_AddTraceback("init ccy_classic_lstsqr", __pyx_clineno, __pyx_lineno, __pyx_filename);
+    Py_DECREF(__pyx_m); __pyx_m = 0;
+  } else if (!PyErr_Occurred()) {
+    PyErr_SetString(PyExc_ImportError, "init ccy_classic_lstsqr");
+  }
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  #if PY_MAJOR_VERSION < 3
+  return;
+  #else
+  return __pyx_m;
+  #endif
+}
+
+/* Runtime support code */
+#if CYTHON_REFNANNY
+static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
+    PyObject *m = NULL, *p = NULL;
+    void *r = NULL;
+    m = PyImport_ImportModule((char *)modname);
+    if (!m) goto end;
+    p = PyObject_GetAttrString(m, (char *)"RefNannyAPI");
+    if (!p) goto end;
+    r = PyLong_AsVoidPtr(p);
+end:
+    Py_XDECREF(p);
+    Py_XDECREF(m);
+    return (__Pyx_RefNannyAPIStruct *)r;
+}
+#endif /* CYTHON_REFNANNY */
+
+static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
+    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
+    if (unlikely(!result)) {
+        PyErr_Format(PyExc_NameError,
+#if PY_MAJOR_VERSION >= 3
+            "name '%U' is not defined", name);
+#else
+            "name '%.200s' is not defined", PyString_AS_STRING(name));
+#endif
+    }
+    return result;
+}
+
+static void __Pyx_RaiseArgtupleInvalid(
+    const char* func_name,
+    int exact,
+    Py_ssize_t num_min,
+    Py_ssize_t num_max,
+    Py_ssize_t num_found)
+{
+    Py_ssize_t num_expected;
+    const char *more_or_less;
+    if (num_found < num_min) {
+        num_expected = num_min;
+        more_or_less = "at least";
+    } else {
+        num_expected = num_max;
+        more_or_less = "at most";
+    }
+    if (exact) {
+        more_or_less = "exactly";
+    }
+    PyErr_Format(PyExc_TypeError,
+                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                 func_name, more_or_less, num_expected,
+                 (num_expected == 1) ? "" : "s", num_found);
+}
+
+static void __Pyx_RaiseDoubleKeywordsError(
+    const char* func_name,
+    PyObject* kw_name)
+{
+    PyErr_Format(PyExc_TypeError,
+        #if PY_MAJOR_VERSION >= 3
+        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
+        #else
+        "%s() got multiple values for keyword argument '%s'", func_name,
+        PyString_AsString(kw_name));
+        #endif
+}
+
+static int __Pyx_ParseOptionalKeywords(
+    PyObject *kwds,
+    PyObject **argnames[],
+    PyObject *kwds2,
+    PyObject *values[],
+    Py_ssize_t num_pos_args,
+    const char* function_name)
+{
+    PyObject *key = 0, *value = 0;
+    Py_ssize_t pos = 0;
+    PyObject*** name;
+    PyObject*** first_kw_arg = argnames + num_pos_args;
+    while (PyDict_Next(kwds, &pos, &key, &value)) {
+        name = first_kw_arg;
+        while (*name && (**name != key)) name++;
+        if (*name) {
+            values[name-argnames] = value;
+            continue;
+        }
+        name = first_kw_arg;
+        #if PY_MAJOR_VERSION < 3
+        if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {
+            while (*name) {
+                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
+                        && _PyString_Eq(**name, key)) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    if ((**argname == key) || (
+                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
+                             && _PyString_Eq(**argname, key))) {
+                        goto arg_passed_twice;
+                    }
+                    argname++;
+                }
+            }
+        } else
+        #endif
+        if (likely(PyUnicode_Check(key))) {
+            while (*name) {
+                int cmp = (**name == key) ? 0 :
+                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
+                #endif
+                    PyUnicode_Compare(**name, key);
+                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                if (cmp == 0) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    int cmp = (**argname == key) ? 0 :
+                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
+                    #endif
+                        PyUnicode_Compare(**argname, key);
+                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                    if (cmp == 0) goto arg_passed_twice;
+                    argname++;
+                }
+            }
+        } else
+            goto invalid_keyword_type;
+        if (kwds2) {
+            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
+        } else {
+            goto invalid_keyword;
+        }
+    }
+    return 0;
+arg_passed_twice:
+    __Pyx_RaiseDoubleKeywordsError(function_name, key);
+    goto bad;
+invalid_keyword_type:
+    PyErr_Format(PyExc_TypeError,
+        "%.200s() keywords must be strings", function_name);
+    goto bad;
+invalid_keyword:
+    PyErr_Format(PyExc_TypeError,
+    #if PY_MAJOR_VERSION < 3
+        "%.200s() got an unexpected keyword argument '%.200s'",
+        function_name, PyString_AsString(key));
+    #else
+        "%s() got an unexpected keyword argument '%U'",
+        function_name, key);
+    #endif
+bad:
+    return -1;
+}
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
+    PyObject *result;
+    ternaryfunc call = func->ob_type->tp_call;
+    if (unlikely(!call))
+        return PyObject_Call(func, arg, kw);
+#if PY_VERSION_HEX >= 0x02060000
+    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+        return NULL;
+#endif
+    result = (*call)(func, arg, kw);
+#if PY_VERSION_HEX >= 0x02060000
+    Py_LeaveRecursiveCall();
+#endif
+    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+        PyErr_SetString(
+            PyExc_SystemError,
+            "NULL result without error in PyObject_Call");
+    }
+    return result;
+}
+#endif
+
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
+    PyErr_Format(PyExc_ValueError,
+                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
+}
+
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
+    PyErr_Format(PyExc_ValueError,
+                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
+                 index, (index == 1) ? "" : "s");
+}
+
+static CYTHON_INLINE int __Pyx_IterFinish(void) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyThreadState *tstate = PyThreadState_GET();
+    PyObject* exc_type = tstate->curexc_type;
+    if (unlikely(exc_type)) {
+        if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) {
+            PyObject *exc_value, *exc_tb;
+            exc_value = tstate->curexc_value;
+            exc_tb = tstate->curexc_traceback;
+            tstate->curexc_type = 0;
+            tstate->curexc_value = 0;
+            tstate->curexc_traceback = 0;
+            Py_DECREF(exc_type);
+            Py_XDECREF(exc_value);
+            Py_XDECREF(exc_tb);
+            return 0;
+        } else {
+            return -1;
+        }
+    }
+    return 0;
+#else
+    if (unlikely(PyErr_Occurred())) {
+        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
+            PyErr_Clear();
+            return 0;
+        } else {
+            return -1;
+        }
+    }
+    return 0;
+#endif
+}
+
+static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
+    if (unlikely(retval)) {
+        Py_DECREF(retval);
+        __Pyx_RaiseTooManyValuesError(expected);
+        return -1;
+    } else {
+        return __Pyx_IterFinish();
+    }
+    return 0;
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+    const long neg_one = (long) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(long) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long long)) {
+            return PyLong_FromUnsignedLongLong((unsigned long long) value);
+        }
+    } else {
+        if (sizeof(long) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(long long)) {
+            return PyLong_FromLongLong((long long) value);
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(long),
+                                     little, !is_unsigned);
+    }
+}
+
+#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func)             \
+    {                                                                     \
+        func_type value = func(x);                                        \
+        if (sizeof(target_type) < sizeof(func_type)) {                    \
+            if (unlikely(value != (func_type) (target_type) value)) {     \
+                func_type zero = 0;                                       \
+                PyErr_SetString(PyExc_OverflowError,                      \
+                    (is_unsigned && unlikely(value < zero)) ?             \
+                    "can't convert negative value to " #target_type :     \
+                    "value too large to convert to " #target_type);       \
+                return (target_type) -1;                                  \
+            }                                                             \
+        }                                                                 \
+        return (target_type) value;                                       \
+    }
+
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+    const long neg_one = (long) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(long) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to long");
+                return (long) -1;
+            }
+            return (long) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(long)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return (long) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (unlikely(Py_SIZE(x) < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to long");
+                return (long) -1;
+            }
+            if (sizeof(long) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(long) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong)
+            }
+        } else {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(long)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return +(long) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(long) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (sizeof(long) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong)
+            } else if (sizeof(long) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            long val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (long) -1;
+        }
+    } else {
+        long val;
+        PyObject *tmp = __Pyx_PyNumber_Int(x);
+        if (!tmp) return (long) -1;
+        val = __Pyx_PyInt_As_long(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+}
+
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+    const int neg_one = (int) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(int) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to int");
+                return (int) -1;
+            }
+            return (int) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(int)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return (int) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (unlikely(Py_SIZE(x) < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to int");
+                return (int) -1;
+            }
+            if (sizeof(int) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(int) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong)
+            }
+        } else {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(int)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return +(int) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(int) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (sizeof(int) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong)
+            } else if (sizeof(int) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            int val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (int) -1;
+        }
+    } else {
+        int val;
+        PyObject *tmp = __Pyx_PyNumber_Int(x);
+        if (!tmp) return (int) -1;
+        val = __Pyx_PyInt_As_int(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+}
+
+static int __Pyx_check_binary_version(void) {
+    char ctversion[4], rtversion[4];
+    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
+    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
+    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
+        char message[200];
+        PyOS_snprintf(message, sizeof(message),
+                      "compiletime version %s of module '%.100s' "
+                      "does not match runtime version %s",
+                      ctversion, __Pyx_MODULE_NAME, rtversion);
+        #if PY_VERSION_HEX < 0x02050000
+        return PyErr_Warn(NULL, message);
+        #else
+        return PyErr_WarnEx(NULL, message, 1);
+        #endif
+    }
+    return 0;
+}
+
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
+    int start = 0, mid = 0, end = count - 1;
+    if (end >= 0 && code_line > entries[end].code_line) {
+        return count;
+    }
+    while (start < end) {
+        mid = (start + end) / 2;
+        if (code_line < entries[mid].code_line) {
+            end = mid;
+        } else if (code_line > entries[mid].code_line) {
+             start = mid + 1;
+        } else {
+            return mid;
+        }
+    }
+    if (code_line <= entries[mid].code_line) {
+        return mid;
+    } else {
+        return mid + 1;
+    }
+}
+static PyCodeObject *__pyx_find_code_object(int code_line) {
+    PyCodeObject* code_object;
+    int pos;
+    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
+        return NULL;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
+        return NULL;
+    }
+    code_object = __pyx_code_cache.entries[pos].code_object;
+    Py_INCREF(code_object);
+    return code_object;
+}
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
+    int pos, i;
+    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
+    if (unlikely(!code_line)) {
+        return;
+    }
+    if (unlikely(!entries)) {
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (likely(entries)) {
+            __pyx_code_cache.entries = entries;
+            __pyx_code_cache.max_count = 64;
+            __pyx_code_cache.count = 1;
+            entries[0].code_line = code_line;
+            entries[0].code_object = code_object;
+            Py_INCREF(code_object);
+        }
+        return;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
+        PyCodeObject* tmp = entries[pos].code_object;
+        entries[pos].code_object = code_object;
+        Py_DECREF(tmp);
+        return;
+    }
+    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
+        int new_max = __pyx_code_cache.max_count + 64;
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
+            __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (unlikely(!entries)) {
+            return;
+        }
+        __pyx_code_cache.entries = entries;
+        __pyx_code_cache.max_count = new_max;
+    }
+    for (i=__pyx_code_cache.count; i>pos; i--) {
+        entries[i] = entries[i-1];
+    }
+    entries[pos].code_line = code_line;
+    entries[pos].code_object = code_object;
+    __pyx_code_cache.count++;
+    Py_INCREF(code_object);
+}
+
+#include "compile.h"
+#include "frameobject.h"
+#include "traceback.h"
+static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
+            const char *funcname, int c_line,
+            int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyObject *py_srcfile = 0;
+    PyObject *py_funcname = 0;
+    #if PY_MAJOR_VERSION < 3
+    py_srcfile = PyString_FromString(filename);
+    #else
+    py_srcfile = PyUnicode_FromString(filename);
+    #endif
+    if (!py_srcfile) goto bad;
+    if (c_line) {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #else
+        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #endif
+    }
+    else {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromString(funcname);
+        #else
+        py_funcname = PyUnicode_FromString(funcname);
+        #endif
+    }
+    if (!py_funcname) goto bad;
+    py_code = __Pyx_PyCode_New(
+        0,            /*int argcount,*/
+        0,            /*int kwonlyargcount,*/
+        0,            /*int nlocals,*/
+        0,            /*int stacksize,*/
+        0,            /*int flags,*/
+        __pyx_empty_bytes, /*PyObject *code,*/
+        __pyx_empty_tuple, /*PyObject *consts,*/
+        __pyx_empty_tuple, /*PyObject *names,*/
+        __pyx_empty_tuple, /*PyObject *varnames,*/
+        __pyx_empty_tuple, /*PyObject *freevars,*/
+        __pyx_empty_tuple, /*PyObject *cellvars,*/
+        py_srcfile,   /*PyObject *filename,*/
+        py_funcname,  /*PyObject *name,*/
+        py_line,      /*int firstlineno,*/
+        __pyx_empty_bytes  /*PyObject *lnotab*/
+    );
+    Py_DECREF(py_srcfile);
+    Py_DECREF(py_funcname);
+    return py_code;
+bad:
+    Py_XDECREF(py_srcfile);
+    Py_XDECREF(py_funcname);
+    return NULL;
+}
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyObject *py_globals = 0;
+    PyFrameObject *py_frame = 0;
+    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
+    if (!py_code) {
+        py_code = __Pyx_CreateCodeObjectForTraceback(
+            funcname, c_line, py_line, filename);
+        if (!py_code) goto bad;
+        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
+    }
+    py_globals = PyModule_GetDict(__pyx_m);
+    if (!py_globals) goto bad;
+    py_frame = PyFrame_New(
+        PyThreadState_GET(), /*PyThreadState *tstate,*/
+        py_code,             /*PyCodeObject *code,*/
+        py_globals,          /*PyObject *globals,*/
+        0                    /*PyObject *locals*/
+    );
+    if (!py_frame) goto bad;
+    py_frame->f_lineno = py_line;
+    PyTraceBack_Here(py_frame);
+bad:
+    Py_XDECREF(py_code);
+    Py_XDECREF(py_frame);
+}
+
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+    while (t->p) {
+        #if PY_MAJOR_VERSION < 3
+        if (t->is_unicode) {
+            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
+        } else if (t->intern) {
+            *t->p = PyString_InternFromString(t->s);
+        } else {
+            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+        }
+        #else  /* Python 3+ has unicode identifiers */
+        if (t->is_unicode | t->is_str) {
+            if (t->intern) {
+                *t->p = PyUnicode_InternFromString(t->s);
+            } else if (t->encoding) {
+                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
+            } else {
+                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+            }
+        } else {
+            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
+        }
+        #endif
+        if (!*t->p)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {
+    return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str));
+}
+static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
+    Py_ssize_t ignore;
+    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
+}
+static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+    if (
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+            __Pyx_sys_getdefaultencoding_not_ascii &&
+#endif
+            PyUnicode_Check(o)) {
+#if PY_VERSION_HEX < 0x03030000
+        char* defenc_c;
+        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
+        if (!defenc) return NULL;
+        defenc_c = PyBytes_AS_STRING(defenc);
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+        {
+            char* end = defenc_c + PyBytes_GET_SIZE(defenc);
+            char* c;
+            for (c = defenc_c; c < end; c++) {
+                if ((unsigned char) (*c) >= 128) {
+                    PyUnicode_AsASCIIString(o);
+                    return NULL;
+                }
+            }
+        }
+#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/
+        *length = PyBytes_GET_SIZE(defenc);
+        return defenc_c;
+#else /* PY_VERSION_HEX < 0x03030000 */
+        if (PyUnicode_READY(o) == -1) return NULL;
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+        if (PyUnicode_IS_ASCII(o)) {
+            *length = PyUnicode_GET_DATA_SIZE(o);
+            return PyUnicode_AsUTF8(o);
+        } else {
+            PyUnicode_AsASCIIString(o);
+            return NULL;
+        }
+#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
+        return PyUnicode_AsUTF8AndSize(o, length);
+#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
+#endif /* PY_VERSION_HEX < 0x03030000 */
+    } else
+#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII  || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */
+#if !CYTHON_COMPILING_IN_PYPY
+#if PY_VERSION_HEX >= 0x02060000
+    if (PyByteArray_Check(o)) {
+        *length = PyByteArray_GET_SIZE(o);
+        return PyByteArray_AS_STRING(o);
+    } else
+#endif
+#endif
+    {
+        char* result;
+        int r = PyBytes_AsStringAndSize(o, &result, length);
+        if (unlikely(r < 0)) {
+            return NULL;
+        } else {
+            return result;
+        }
+    }
+}
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+   int is_true = x == Py_True;
+   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
+   else return PyObject_IsTrue(x);
+}
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
+  PyNumberMethods *m;
+  const char *name = NULL;
+  PyObject *res = NULL;
+#if PY_MAJOR_VERSION < 3
+  if (PyInt_Check(x) || PyLong_Check(x))
+#else
+  if (PyLong_Check(x))
+#endif
+    return Py_INCREF(x), x;
+  m = Py_TYPE(x)->tp_as_number;
+#if PY_MAJOR_VERSION < 3
+  if (m && m->nb_int) {
+    name = "int";
+    res = PyNumber_Int(x);
+  }
+  else if (m && m->nb_long) {
+    name = "long";
+    res = PyNumber_Long(x);
+  }
+#else
+  if (m && m->nb_int) {
+    name = "int";
+    res = PyNumber_Long(x);
+  }
+#endif
+  if (res) {
+#if PY_MAJOR_VERSION < 3
+    if (!PyInt_Check(res) && !PyLong_Check(res)) {
+#else
+    if (!PyLong_Check(res)) {
+#endif
+      PyErr_Format(PyExc_TypeError,
+                   "__%.4s__ returned non-%.4s (type %.200s)",
+                   name, name, Py_TYPE(res)->tp_name);
+      Py_DECREF(res);
+      return NULL;
+    }
+  }
+  else if (!PyErr_Occurred()) {
+    PyErr_SetString(PyExc_TypeError,
+                    "an integer is required");
+  }
+  return res;
+}
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
+  Py_ssize_t ival;
+  PyObject *x;
+#if PY_MAJOR_VERSION < 3
+  if (likely(PyInt_CheckExact(b)))
+      return PyInt_AS_LONG(b);
+#endif
+  if (likely(PyLong_CheckExact(b))) {
+    #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+     #if CYTHON_USE_PYLONG_INTERNALS
+       switch (Py_SIZE(b)) {
+       case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0];
+       case  0: return 0;
+       case  1: return ((PyLongObject*)b)->ob_digit[0];
+       }
+     #endif
+    #endif
+  #if PY_VERSION_HEX < 0x02060000
+    return PyInt_AsSsize_t(b);
+  #else
+    return PyLong_AsSsize_t(b);
+  #endif
+  }
+  x = PyNumber_Index(b);
+  if (!x) return -1;
+  ival = PyInt_AsSsize_t(x);
+  Py_DECREF(x);
+  return ival;
+}
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
+#if PY_VERSION_HEX < 0x02050000
+   if (ival <= LONG_MAX)
+       return PyInt_FromLong((long)ival);
+   else {
+       unsigned char *bytes = (unsigned char *) &ival;
+       int one = 1; int little = (int)*(unsigned char*)&one;
+       return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0);
+   }
+#else
+   return PyInt_FromSize_t(ival);
+#endif
+}
+
+
+#endif /* Py_PYTHON_H */
diff --git a/benchmarks/ccy_classic_lstsqr.pyx b/benchmarks/ccy_classic_lstsqr.pyx
new file mode 100644
index 0000000..5def68b
--- /dev/null
+++ b/benchmarks/ccy_classic_lstsqr.pyx
@@ -0,0 +1,10 @@
+
+def ccy_classic_lstsqr(x, y):
+    """ Computes the least-squares solution to a linear matrix equation. """
+    x_avg = sum(x)/len(x)
+    y_avg = sum(y)/len(y)
+    var_x = sum([(x_i - x_avg)**2 for x_i in x])
+    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
+    slope = cov_xy / var_x
+    y_interc = y_avg - slope*x_avg
+    return (slope, y_interc)
\ No newline at end of file
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
new file mode 100644
index 0000000..ac28769
--- /dev/null
+++ b/benchmarks/cython_least_squares.ipynb
@@ -0,0 +1,1255 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:e0aeebc5816e30eb2937ee4b331a3885fbaf1583183823ad07fb79e368b90290"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](www.sebastianraschka.com)  \n",
+      "last updated: 05/04/2014\n",
+      "\n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)  \n",
+      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### All code was executed in Python 3.4"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I am really looking forward to your comments and suggestions to improve and  \n",
+      "extend this little collection! Just send me a quick note   \n",
+      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
+      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Implementing the least squares fit method for linear regression and speeding it up via Cython"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"sections\"></a>\n",
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#Sections"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Introduction](#introduction)\n",
+      "- [Least squares fit implementations](#implementations)\n",
+      "- [Generating sample data and benchmarking](#sample_data)\n",
+      "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
+      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"introduction\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Introduction"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Linear regression via the least squares method is the simplest approach to performing a regression analysis of a dependent and a explanatory variable. The objective is to find the best-fitting straight line through a set of points that minimizes the sum of the squared offsets from the line.  \n",
+      "The offsets come in 2 different flavors: perpendicular and vertical - with respect to the line.  \n",
+      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_vertical.png)  \n",
+      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_perpendicular.png)  \n",
+      "\n",
+      "Here, we will use the more common approach: minimizing the sum of the perpendicular offsets.\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In more mathematical terms, our goal is to compute the best fit to *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
+      "$f(x) = a\\cdot x + b$.  \n",
+      "Here, we assume that the y-component is  functionally dependent on the x-component.  \n",
+      "In a cartesian coordinate system, $b$ is the intercept of the straight line with the y-axis, and $a$ is the slope of this line."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In order to obtain the parameters for the linear regression line for a set of multiple points, we can re-write the problem as matrix equation  \n",
+      "$\\pmb X \\; \\pmb a = \\pmb y$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$\\Rightarrow\\Bigg[ \\begin{array}{cc}\n",
+      "x_1 & 1  \\\\\n",
+      "... & 1 \\\\\n",
+      "x_n & 1  \\end{array} \\Bigg]$\n",
+      "$\\bigg[ \\begin{array}{c}\n",
+      "a  \\\\\n",
+      "b \\end{array} \\bigg]$\n",
+      "$=\\Bigg[ \\begin{array}{c}\n",
+      "y_1   \\\\\n",
+      "...  \\\\\n",
+      "y_n  \\end{array} \\Bigg]$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "With a little bit of calculus, we can rearrange the term in order to obtain the parameter vector $\\pmb a = [a\\;b]^T$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$\\Rightarrow \\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "The more classic approach to obtain the slope parameter $a$ and y-axis intercept $b$ would be:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
+      "\n",
+      "\n",
+      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "where \n",
+      "\n",
+      "\n",
+      "$S_{xy} = \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})\\quad$ (covariance)\n",
+      "\n",
+      "\n",
+      "$\\sigma{_x}^{2} = \\sum_{i=1}^{n} (x_i - \\bar{x})^2\\quad$ (variance)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"implementations\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "## Least squares fit implementations"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### 1. The matrix approach"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First, let us implement the equation:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$\\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "which I will refer to as the \"matrix approach\"."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "def lin_lstsqr_mat(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### 2. The classic approach"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Next, we will calculate the parameters separately, using only standard library functions in Python, which I will call the \"classic approach\"."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
+      "\n",
+      "\n",
+      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def classic_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### 3. Using the lstsq numpy function"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For our convenience, `numpy` has a function that can also compute the leat squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def numpy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return np.linalg.lstsq(X,y)[0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### 4. Using the linregress scipy function"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The last approach is using `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.  The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import scipy.stats\n",
+      "\n",
+      "def scipy_lstsqr(x,y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    return scipy.stats.linregress(x, y)[0:2]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='sample_data'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Generating sample data and benchmarking"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In order to test our different least squares fit implementation, we will generate some sample data:\n",
+      "- 500 sample points for the x-component within the range [0,500)  \n",
+      "- 500 sample points for the y-component within the range [100,600)  \n",
+      "\n",
+      "where each sample point is multiplied by a random value within\n",
+      "the range [0.8, 12)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
+      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "#### Visualization"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To check how our dataset is distributed, and how the straight line, which we obtain via the least square fit method, we will plot it in a scatter plot.  \n",
+      "Note that we are using our \"matrix approach\" here for simplicity, but after plotting the data, we will check whether all of the four different implementations yield the same parameters."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline\n",
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "slope, intercept = lin_lstsqr_mat(x, y)\n",
+      "\n",
+      "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
+      "line_y = [slope*x_i + intercept for x_i in line_x]\n",
+      "\n",
+      "plt.figure(figsize=(8,8))\n",
+      "plt.scatter(x,y)\n",
+      "plt.plot(line_x, line_y, color='red', lw='2')\n",
+      "\n",
+      "plt.ylabel('y')\n",
+      "plt.xlabel('x')\n",
+      "plt.title('Linear regression via least squares fit')\n",
+      "\n",
+      "ftext = 'y = ax + b = {:.3f} + {:.3f}x'\\\n",
+      "        .format(slope, intercept)\n",
+      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "WARNING: pylab import has clobbered these variables: ['random']\n",
+        "`%matplotlib` prevents importing * from pylab and numpy\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105785f60>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "#### Comparing the results from the different implementations"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As mentioned above, let us confirm that the different implementation computed the same parameters (i.e., slope and y-axis intercept) as solution for the linear equation."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import prettytable\n",
+      "\n",
+      "params = [appr(x,y) for appr in [lin_lstsqr_mat, classic_lstsqr, numpy_lstsqr, scipy_lstsqr]]\n",
+      "\n",
+      "print(params)\n",
+      "\n",
+      "fit_table = prettytable.PrettyTable([\"\", \"slope\", \"y-intercept\"])\n",
+      "fit_table.add_row([\"matrix approach\", params[0][0], params[0][1]])\n",
+      "fit_table.add_row([\"classic approach\", params[1][0], params[1][1]])\n",
+      "fit_table.add_row([\"numpy function\", params[2][0], params[2][1]])\n",
+      "fit_table.add_row([\"scipy function\", params[3][0], params[3][1]])\n",
+      "\n",
+      "print(fit_table)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[array([   0.95181895,  107.01399744]), (0.95181895319126741, 107.01399744459181), array([   0.95181895,  107.01399744]), (0.95181895319126764, 107.01399744459175)]\n",
+        "+------------------+----------------+---------------+\n",
+        "|                  |     slope      |  y-intercept  |\n",
+        "+------------------+----------------+---------------+\n",
+        "| matrix approach  | 0.951818953191 | 107.013997445 |\n",
+        "| classic approach | 0.951818953191 | 107.013997445 |\n",
+        "|  numpy function  | 0.951818953191 | 107.013997445 |\n",
+        "|  scipy function  | 0.951818953191 | 107.013997445 |\n",
+        "+------------------+----------------+---------------+\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "#### Initial performance comparison"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For a first impression how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
+      "                     \"numpy function\",\"scipy function\"],\n",
+      "                    [lin_lstsqr_mat, classic_lstsqr, \n",
+      "                     numpy_lstsqr, scipy_lstsqr]):\n",
+      "    print(\"\\n{}: \".format(lab), end=\"\")\n",
+      "    %timeit appr(x, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "matrix approach: 1000 loops, best of 3: 270 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "classic approach: 100 loops, best of 3: 2.83 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "numpy function: 1000 loops, best of 3: 372 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "scipy function: 1000 loops, best of 3: 594 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "The timing above indicates, that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<a name=\"cython_nb\"></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Compiling the Python code via Cython in the IPython notebook"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
+      "Since we are working in an IPython notebook here, we can make use of the IPython magic: It will automatically convert it to C code, compile it, and load the function.  \n",
+      "Just to be thorough, let us also compile the other functions, which already use numpy objects."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "import numpy as np\n",
+      "import scipy.stats\n",
+      "cimport numpy as np\n",
+      "\n",
+      "def cy_lin_lstsqr_mat(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)\n",
+      "\n",
+      "def cy_classic_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)\n",
+      "\n",
+      "def cy_numpy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return np.linalg.lstsq(X,y)[0]\n",
+      "\n",
+      "def cy_scipy_lstsqr(x,y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    return scipy.stats.linregress(x, y)[0:2]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Comparing the compiled Cython code to the original Python code"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
+      "                     \"numpy function\",\"scipy function\"],\n",
+      "                    [(lin_lstsqr_mat, cy_lin_lstsqr_mat), \n",
+      "                     (classic_lstsqr, cy_classic_lstsqr),\n",
+      "                     (numpy_lstsqr, cy_numpy_lstsqr),\n",
+      "                     (scipy_lstsqr, cy_scipy_lstsqr)]):\n",
+      "    print(\"\\n\\n{}: \".format(lab))\n",
+      "    %timeit appr[0](x, y)\n",
+      "    %timeit appr[1](x, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "matrix approach: \n",
+        "1000 loops, best of 3: 274 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000 loops, best of 3: 260 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "\n",
+        "classic approach: \n",
+        "100 loops, best of 3: 2.82 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000 loops, best of 3: 212 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "\n",
+        "numpy function: \n",
+        "1000 loops, best of 3: 379 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000 loops, best of 3: 370 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "\n",
+        "scipy function: \n",
+        "1000 loops, best of 3: 608 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "100 loops, best of 3: 613 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to using numpy's functions. This is not surprising, since numpy is highly optmized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.  \n",
+      "However, we were able to speed up the \"classic approach\" quite significantly, roughly by 1500%.\n",
+      "\n",
+      "The following 2 code blocks are just to visualize our results in a bar plot."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs =  ['classic_lstsqr', 'cy_classic_lstsqr', \n",
+      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
+      "labels = ['classic approach','classic approach (cython)', \n",
+      "          'matrix approach', 'numpy function', 'scipy function']\n",
+      "\n",
+      "times = [timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
+      "         for f in funcs]\n",
+      "\n",
+      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline\n",
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "x_pos = np.arange(len(funcs))\n",
+      "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
+      "plt.xticks(x_pos, labels, rotation=45)\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.title('Performance of different least square fit implementations')\n",
+      "plt.show()\n",
+      "\n",
+      "x_pos = np.arange(len(funcs[1:]))\n",
+      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
+      "plt.xticks(x_pos, labels[1:], rotation=45)\n",
+      "plt.ylabel('relative performance gain')\n",
+      "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Populating the interactive namespace from numpy and matplotlib\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAsMwDPMcSwgMwzAMAOD/AFCftMrEW+2BAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105a7fe10>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1066dcef0>"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"cython_bonus\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Bonus: How to use Cython without the IPython magic"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "IPython's notebook is really great for explanatory analysis and documentation, but what if we want to compile our Python code via Cython without letting IPython's magic doing all the work?  \n",
+      "These are the steps you would need."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 1. Creating a .pyx file containing the the desired code or function."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file ccy_classic_lstsqr.pyx\n",
+      "\n",
+      "def ccy_classic_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Writing ccy_classic_lstsqr.pyx\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 2. Creating a simple setup file"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file setup.py\n",
+      "\n",
+      "from distutils.core import setup\n",
+      "from distutils.extension import Extension\n",
+      "from Cython.Distutils import build_ext\n",
+      "\n",
+      "setup(\n",
+      "    cmdclass = {'build_ext': build_ext},\n",
+      "    ext_modules = [Extension(\"ccy_classic_lstsqr\", [\"ccy_classic_lstsqr.pyx\"])]\n",
+      ")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Writing setup.py\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "####3. Building and Compiling"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "!python3 setup.py build_ext --inplace"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "running build_ext\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "cythoning ccy_classic_lstsqr.pyx to ccy_classic_lstsqr.c\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "building 'ccy_classic_lstsqr' extension\r\n",
+        "creating build\r\n",
+        "creating build/temp.macosx-10.6-intel-3.4\r\n",
+        "/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -arch i386 -arch x86_64 -g -I/Library/Frameworks/Python.framework/Versions/3.4/include/python3.4m -c ccy_classic_lstsqr.c -o build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\u001b[1mccy_classic_lstsqr.c:2040:28: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_AsString'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {\r\n",
+        "\u001b[0;1;32m                           ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2037:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2104:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_IsTrue'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2159:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyIndex_AsSsize_t'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2188:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_FromSize_t'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1584:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_From_long'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1631:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is not\r\n",
+        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1731:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_int' is not\r\n",
+        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "8 warnings generated.\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\u001b[1mccy_classic_lstsqr.c:2040:28: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_AsString'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {\r\n",
+        "\u001b[0;1;32m                           ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2037:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2104:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_IsTrue'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2159:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyIndex_AsSsize_t'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2188:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_FromSize_t'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1584:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_From_long'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1631:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is not\r\n",
+        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1731:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_int' is not\r\n",
+        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "8"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " warnings generated.\r\n",
+        "/usr/bin/clang -bundle -undefined dynamic_lookup -arch i386 -arch x86_64 -g build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o -o /Users/sebastian/Github/python_reference/benchmarks/ccy_classic_lstsqr.so\r\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 4. Importing and running the code"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import ccy_classic_lstsqr\n",
+      "\n",
+      "%timeit classic_lstsqr(x, y)\n",
+      "%timeit cy_classic_lstsqr(x, y)\n",
+      "%timeit ccy_classic_lstsqr.ccy_classic_lstsqr(x, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "100 loops, best of 3: 2.9 ms per loop\n",
+        "1000 loops, best of 3: 212 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000 loops, best of 3: 207 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/benchmarks/setup.py b/benchmarks/setup.py
new file mode 100644
index 0000000..eb0a3bc
--- /dev/null
+++ b/benchmarks/setup.py
@@ -0,0 +1,9 @@
+
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+setup(
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = [Extension("ccy_classic_lstsqr", ["ccy_classic_lstsqr.pyx"])]
+)
\ No newline at end of file

From b612a36ff26ee6a433679e588018eab22ae99b88 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 4 May 2014 19:11:16 -0400
Subject: [PATCH 009/248] fixed links and updated readme

---
 .../.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb  | 4 ++--
 benchmarks/cython_least_squares.ipynb                         | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index ac28769..9174704 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:e0aeebc5816e30eb2937ee4b331a3885fbaf1583183823ad07fb79e368b90290"
+  "signature": "sha256:14c3265ea9accf7a70bb5032b9e2b881a487f6b81b55999ef8578645edf4e3dd"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,7 +15,7 @@
       "[Sebastian Raschka](www.sebastianraschka.com)  \n",
       "last updated: 05/04/2014\n",
       "\n",
-      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)  \n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
      ]
     },
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index ac28769..9174704 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:e0aeebc5816e30eb2937ee4b331a3885fbaf1583183823ad07fb79e368b90290"
+  "signature": "sha256:14c3265ea9accf7a70bb5032b9e2b881a487f6b81b55999ef8578645edf4e3dd"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,7 +15,7 @@
       "[Sebastian Raschka](www.sebastianraschka.com)  \n",
       "last updated: 05/04/2014\n",
       "\n",
-      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)  \n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
      ]
     },

From cc9e405b4d7514a6f6982922e4557ffbf03b4b5b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 4 May 2014 19:11:37 -0400
Subject: [PATCH 010/248] fixed links and updated readme

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 82f14a1..589e610 100755
--- a/README.md
+++ b/README.md
@@ -7,6 +7,7 @@ Useful functions, tutorials, and other Python-related things
 ###Links to view the IPython Notebooks
 
 - [Python benchmarks via `timeit`](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
+- [Implementing the least squares fit method for linear regression and speeding it up via Cython](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)
 - [Benchmarks of different palindrome functions](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)
 - [A collection of not so obvious Python stuff you should know!](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)
 - [Python's scope resolution for variable names and the LEGB rule](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)

From b9786dd75ed6dc01c61b5fbfcc3a85c37ca99fca Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 4 May 2014 19:21:25 -0400
Subject: [PATCH 011/248] fixed links and updated readme

---
 README.md                                     |    4 +-
 .../cython_least_squares-checkpoint.ipynb     |   29 +-
 benchmarks/cython_least_squares.html          | 4928 +++++++++++++++++
 benchmarks/cython_least_squares.ipynb         |   29 +-
 4 files changed, 4981 insertions(+), 9 deletions(-)
 create mode 100644 benchmarks/cython_least_squares.html

diff --git a/README.md b/README.md
index 589e610..18dfea1 100755
--- a/README.md
+++ b/README.md
@@ -7,7 +7,9 @@ Useful functions, tutorials, and other Python-related things
 ###Links to view the IPython Notebooks
 
 - [Python benchmarks via `timeit`](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
-- [Implementing the least squares fit method for linear regression and speeding it up via Cython](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)
+-   
+- Implementing the least squares fit method for linear regression and speeding it up via Cython  
+  [[IPython Notebook](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)] [[HTML]()]
 - [Benchmarks of different palindrome functions](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)
 - [A collection of not so obvious Python stuff you should know!](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)
 - [Python's scope resolution for variable names and the LEGB rule](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index 9174704..d9dda4a 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:14c3265ea9accf7a70bb5032b9e2b881a487f6b81b55999ef8578645edf4e3dd"
+  "signature": "sha256:07d1097e63ed00fca1ea6848104a030744ac87d11e515ae48e01e53b7425d997"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -442,7 +442,14 @@
      "metadata": {},
      "source": [
       "<br>\n",
-      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "#### Visualization"
      ]
     },
@@ -514,7 +521,14 @@
      "metadata": {},
      "source": [
       "<br>\n",
-      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "#### Comparing the results from the different implementations"
      ]
     },
@@ -569,7 +583,14 @@
      "metadata": {},
      "source": [
       "<br>\n",
-      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "#### Initial performance comparison"
      ]
     },
diff --git a/benchmarks/cython_least_squares.html b/benchmarks/cython_least_squares.html
new file mode 100644
index 0000000..1e50e87
--- /dev/null
+++ b/benchmarks/cython_least_squares.html
@@ -0,0 +1,4928 @@
+<!DOCTYPE html>
+<html>
+<head>
+
+<meta charset="utf-8" />
+<title>Notebook</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    .clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0}
+.clearfix:after{clear:both}
+.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}
+.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
+article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}
+audio,canvas,video{display:inline-block;*display:inline;*zoom:1}
+audio:not([controls]){display:none}
+html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}
+a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
+a:hover,a:active{outline:0}
+sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}
+sup{top:-0.5em}
+sub{bottom:-0.25em}
+img{max-width:100%;width:auto\9;height:auto;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}
+#map_canvas img,.google-maps img{max-width:none}
+button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}
+button,input{*overflow:visible;line-height:normal}
+button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}
+button,html input[type="button"],input[type="reset"],input[type="submit"]{-webkit-appearance:button;cursor:pointer}
+label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer}
+input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}
+input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}
+textarea{overflow:auto;vertical-align:top}
+@media print{*{text-shadow:none !important;color:#000 !important;background:transparent !important;box-shadow:none !important} a,a:visited{text-decoration:underline} a[href]:after{content:" (" attr(href) ")"} abbr[title]:after{content:" (" attr(title) ")"} .ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""} pre,blockquote{border:1px solid #999;page-break-inside:avoid} thead{display:table-header-group} tr,img{page-break-inside:avoid} img{max-width:100% !important} @page {margin:.5cm}p,h2,h3{orphans:3;widows:3} h2,h3{page-break-after:avoid}}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;line-height:20px;color:#000;background-color:#fff}
+a{color:#08c;text-decoration:none}
+a:hover,a:focus{color:#005580;text-decoration:underline}
+.img-rounded{border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px rgba(0,0,0,0.1)}
+.img-circle{border-radius:500px;-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px}
+.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0}
+.row:after{clear:both}
+[class*="span"]{float:left;min-height:1px;margin-left:20px}
+.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}
+.span12{width:940px}
+.span11{width:860px}
+.span10{width:780px}
+.span9{width:700px}
+.span8{width:620px}
+.span7{width:540px}
+.span6{width:460px}
+.span5{width:380px}
+.span4{width:300px}
+.span3{width:220px}
+.span2{width:140px}
+.span1{width:60px}
+.offset12{margin-left:980px}
+.offset11{margin-left:900px}
+.offset10{margin-left:820px}
+.offset9{margin-left:740px}
+.offset8{margin-left:660px}
+.offset7{margin-left:580px}
+.offset6{margin-left:500px}
+.offset5{margin-left:420px}
+.offset4{margin-left:340px}
+.offset3{margin-left:260px}
+.offset2{margin-left:180px}
+.offset1{margin-left:100px}
+.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0}
+.row-fluid:after{clear:both}
+.row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.127659574468085%;*margin-left:2.074468085106383%}
+.row-fluid [class*="span"]:first-child{margin-left:0}
+.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%}
+.row-fluid .span12{width:100%;*width:99.94680851063829%}
+.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%}
+.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%}
+.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%}
+.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%}
+.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%}
+.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%}
+.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%}
+.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%}
+.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%}
+.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%}
+.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%}
+.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%}
+.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%}
+.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%}
+.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%}
+.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%}
+.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%}
+.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%}
+.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%}
+.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%}
+.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%}
+.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%}
+.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%}
+.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%}
+.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%}
+.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%}
+.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%}
+.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%}
+.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%}
+.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%}
+.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%}
+.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%}
+.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%}
+.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%}
+.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%}
+[class*="span"].hide,.row-fluid [class*="span"].hide{display:none}
+[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right}
+.container{margin-right:auto;margin-left:auto;*zoom:1}.container:before,.container:after{display:table;content:"";line-height:0}
+.container:after{clear:both}
+.container-fluid{padding-right:20px;padding-left:20px;*zoom:1}.container-fluid:before,.container-fluid:after{display:table;content:"";line-height:0}
+.container-fluid:after{clear:both}
+p{margin:0 0 10px}
+.lead{margin-bottom:20px;font-size:19.5px;font-weight:200;line-height:30px}
+small{font-size:85%}
+strong{font-weight:bold}
+em{font-style:italic}
+cite{font-style:normal}
+.muted{color:#999}
+a.muted:hover,a.muted:focus{color:#808080}
+.text-warning{color:#c09853}
+a.text-warning:hover,a.text-warning:focus{color:#a47e3c}
+.text-error{color:#b94a48}
+a.text-error:hover,a.text-error:focus{color:#953b39}
+.text-info{color:#3a87ad}
+a.text-info:hover,a.text-info:focus{color:#2d6987}
+.text-success{color:#468847}
+a.text-success:hover,a.text-success:focus{color:#356635}
+.text-left{text-align:left}
+.text-right{text-align:right}
+.text-center{text-align:center}
+h1,h2,h3,h4,h5,h6{margin:10px 0;font-family:inherit;font-weight:bold;line-height:20px;color:inherit;text-rendering:optimizelegibility}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#999}
+h1,h2,h3{line-height:40px}
+h1{font-size:35.75px}
+h2{font-size:29.25px}
+h3{font-size:22.75px}
+h4{font-size:16.25px}
+h5{font-size:13px}
+h6{font-size:11.049999999999999px}
+h1 small{font-size:22.75px}
+h2 small{font-size:16.25px}
+h3 small{font-size:13px}
+h4 small{font-size:13px}
+.page-header{padding-bottom:9px;margin:20px 0 30px;border-bottom:1px solid #eee}
+ul,ol{padding:0;margin:0 0 10px 25px}
+ul ul,ul ol,ol ol,ol ul{margin-bottom:0}
+li{line-height:20px}
+ul.unstyled,ol.unstyled{margin-left:0;list-style:none}
+ul.inline,ol.inline{margin-left:0;list-style:none}ul.inline>li,ol.inline>li{display:inline-block;*display:inline;*zoom:1;padding-left:5px;padding-right:5px}
+dl{margin-bottom:20px}
+dt,dd{line-height:20px}
+dt{font-weight:bold}
+dd{margin-left:10px}
+.dl-horizontal{*zoom:1}.dl-horizontal:before,.dl-horizontal:after{display:table;content:"";line-height:0}
+.dl-horizontal:after{clear:both}
+.dl-horizontal dt{float:left;width:160px;clear:left;text-align:right;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}
+.dl-horizontal dd{margin-left:180px}
+hr{margin:20px 0;border:0;border-top:1px solid #eee;border-bottom:1px solid #fff}
+abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #999}
+abbr.initialism{font-size:90%;text-transform:uppercase}
+blockquote{padding:0 0 0 15px;margin:0 0 20px;border-left:5px solid #eee}blockquote p{margin-bottom:0;font-size:16.25px;font-weight:300;line-height:1.25}
+blockquote small{display:block;line-height:20px;color:#999}blockquote small:before{content:'\2014 \00A0'}
+blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eee;border-left:0}blockquote.pull-right p,blockquote.pull-right small{text-align:right}
+blockquote.pull-right small:before{content:''}
+blockquote.pull-right small:after{content:'\00A0 \2014'}
+q:before,q:after,blockquote:before,blockquote:after{content:""}
+address{display:block;margin-bottom:20px;font-style:normal;line-height:20px}
+code,pre{padding:0 3px 2px;font-family:monospace;font-size:11px;color:#333;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+code{padding:2px 4px;color:#d14;background-color:#f7f7f9;border:1px solid #e1e1e8;white-space:nowrap}
+pre{display:block;padding:9.5px;margin:0 0 10px;font-size:12px;line-height:20px;word-break:break-all;word-wrap:break-word;white-space:pre;white-space:pre-wrap;background-color:#f5f5f5;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.15);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}pre.prettyprint{margin-bottom:20px}
+pre code{padding:0;color:inherit;white-space:pre;white-space:pre-wrap;background-color:transparent;border:0}
+.pre-scrollable{max-height:340px;overflow-y:scroll}
+form{margin:0 0 20px}
+fieldset{padding:0;margin:0;border:0}
+legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:19.5px;line-height:40px;color:#333;border:0;border-bottom:1px solid #e5e5e5}legend small{font-size:15px;color:#999}
+label,input,button,select,textarea{font-size:13px;font-weight:normal;line-height:20px}
+input,button,select,textarea{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif}
+label{display:block;margin-bottom:5px}
+select,textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{display:inline-block;height:20px;padding:4px 6px;margin-bottom:10px;font-size:13px;line-height:20px;color:#555;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;vertical-align:middle}
+input,textarea,.uneditable-input{width:206px}
+textarea{height:auto}
+textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{background-color:#fff;border:1px solid #ccc;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-webkit-transition:border linear .2s, box-shadow linear .2s;-moz-transition:border linear .2s, box-shadow linear .2s;-o-transition:border linear .2s, box-shadow linear .2s;transition:border linear .2s, box-shadow linear .2s}textarea:focus,input[type="text"]:focus,input[type="password"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="date"]:focus,input[type="month"]:focus,input[type="time"]:focus,input[type="week"]:focus,input[type="number"]:focus,input[type="email"]:focus,input[type="url"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="color"]:focus,.uneditable-input:focus{border-color:rgba(82,168,236,0.8);outline:0;outline:thin dotted \9;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6)}
+input[type="radio"],input[type="checkbox"]{margin:4px 0 0;*margin-top:0;margin-top:1px \9;line-height:normal}
+input[type="file"],input[type="image"],input[type="submit"],input[type="reset"],input[type="button"],input[type="radio"],input[type="checkbox"]{width:auto}
+select,input[type="file"]{height:30px;*margin-top:4px;line-height:30px}
+select{width:220px;border:1px solid #ccc;background-color:#fff}
+select[multiple],select[size]{height:auto}
+select:focus,input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
+.uneditable-input,.uneditable-textarea{color:#999;background-color:#fcfcfc;border-color:#ccc;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);cursor:not-allowed}
+.uneditable-input{overflow:hidden;white-space:nowrap}
+.uneditable-textarea{width:auto;height:auto}
+input:-moz-placeholder,textarea:-moz-placeholder{color:#999}
+input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999}
+input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999}
+.radio,.checkbox{min-height:20px;padding-left:20px}
+.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px}
+.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px}
+.radio.inline,.checkbox.inline{display:inline-block;padding-top:5px;margin-bottom:0;vertical-align:middle}
+.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px}
+.input-mini{width:60px}
+.input-small{width:90px}
+.input-medium{width:150px}
+.input-large{width:210px}
+.input-xlarge{width:270px}
+.input-xxlarge{width:530px}
+input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"]{float:none;margin-left:0}
+.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"]{display:inline-block}
+input,textarea,.uneditable-input{margin-left:0}
+.controls-row [class*="span"]+[class*="span"]{margin-left:20px}
+input.span12,textarea.span12,.uneditable-input.span12{width:926px}
+input.span11,textarea.span11,.uneditable-input.span11{width:846px}
+input.span10,textarea.span10,.uneditable-input.span10{width:766px}
+input.span9,textarea.span9,.uneditable-input.span9{width:686px}
+input.span8,textarea.span8,.uneditable-input.span8{width:606px}
+input.span7,textarea.span7,.uneditable-input.span7{width:526px}
+input.span6,textarea.span6,.uneditable-input.span6{width:446px}
+input.span5,textarea.span5,.uneditable-input.span5{width:366px}
+input.span4,textarea.span4,.uneditable-input.span4{width:286px}
+input.span3,textarea.span3,.uneditable-input.span3{width:206px}
+input.span2,textarea.span2,.uneditable-input.span2{width:126px}
+input.span1,textarea.span1,.uneditable-input.span1{width:46px}
+.controls-row{*zoom:1}.controls-row:before,.controls-row:after{display:table;content:"";line-height:0}
+.controls-row:after{clear:both}
+.controls-row [class*="span"],.row-fluid .controls-row [class*="span"]{float:left}
+.controls-row .checkbox[class*="span"],.controls-row .radio[class*="span"]{padding-top:5px}
+input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly]{cursor:not-allowed;background-color:#eee}
+input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly]{background-color:transparent}
+.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#c09853}
+.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#c09853}
+.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#c09853;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#a47e3c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e}
+.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#c09853;background-color:#fcf8e3;border-color:#c09853}
+.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#b94a48}
+.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#b94a48}
+.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#b94a48;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#953b39;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392}
+.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#b94a48;background-color:#f2dede;border-color:#b94a48}
+.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#468847}
+.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#468847}
+.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#468847;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#356635;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b}
+.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847}
+.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad}
+.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad}
+.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3}
+.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3a87ad;background-color:#d9edf7;border-color:#3a87ad}
+input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}
+.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;content:"";line-height:0}
+.form-actions:after{clear:both}
+.help-block,.help-inline{color:#262626}
+.help-block{display:block;margin-bottom:10px}
+.help-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle;padding-left:5px}
+.input-append,.input-prepend{display:inline-block;margin-bottom:10px;vertical-align:middle;font-size:0;white-space:nowrap}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input,.input-append .dropdown-menu,.input-prepend .dropdown-menu,.input-append .popover,.input-prepend .popover{font-size:13px}
+.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input{position:relative;margin-bottom:0;*margin-left:0;vertical-align:top;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append input:focus,.input-prepend input:focus,.input-append select:focus,.input-prepend select:focus,.input-append .uneditable-input:focus,.input-prepend .uneditable-input:focus{z-index:2}
+.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:13px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}
+.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546}
+.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}
+.input-prepend .add-on:first-child,.input-prepend .btn:first-child{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
+.input-append input,.input-append select,.input-append .uneditable-input{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}
+.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
+.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.input-prepend.input-append .btn-group:first-child{margin-left:0}
+input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
+.form-search .input-append .search-query,.form-search .input-prepend .search-query{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.form-search .input-append .search-query{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
+.form-search .input-append .btn{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
+.form-search .input-prepend .search-query{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
+.form-search .input-prepend .btn{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
+.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;*zoom:1;margin-bottom:0;vertical-align:middle}
+.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}
+.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}
+.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}
+.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}
+.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}
+.control-group{margin-bottom:10px}
+legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}
+.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0}
+.form-horizontal .control-group:after{clear:both}
+.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}
+.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}
+.form-horizontal .help-block{margin-bottom:0}
+.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px}
+.form-horizontal .form-actions{padding-left:180px}
+table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}
+.table{width:100%;margin-bottom:20px}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #ddd}
+.table th{font-weight:bold}
+.table thead th{vertical-align:bottom}
+.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}
+.table tbody+tbody{border-top:2px solid #ddd}
+.table .table{background-color:#fff}
+.table-condensed th,.table-condensed td{padding:4px 5px}
+.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}
+.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}
+.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
+.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
+.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
+.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
+.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;-moz-border-radius-bottomleft:0;border-bottom-left-radius:0}
+.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;-moz-border-radius-bottomright:0;border-bottom-right-radius:0}
+.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
+.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
+.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}
+.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}
+table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}
+.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}
+.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}
+.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}
+.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}
+.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}
+.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}
+.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}
+.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}
+.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}
+.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}
+.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}
+.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}
+.table tbody tr.success>td{background-color:#dff0d8}
+.table tbody tr.error>td{background-color:#f2dede}
+.table tbody tr.warning>td{background-color:#fcf8e3}
+.table tbody tr.info>td{background-color:#d9edf7}
+.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}
+.table-hover tbody tr.error:hover>td{background-color:#ebcccc}
+.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}
+.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}
+[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat;margin-top:1px}
+.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}
+.icon-glass{background-position:0 0}
+.icon-music{background-position:-24px 0}
+.icon-search{background-position:-48px 0}
+.icon-envelope{background-position:-72px 0}
+.icon-heart{background-position:-96px 0}
+.icon-star{background-position:-120px 0}
+.icon-star-empty{background-position:-144px 0}
+.icon-user{background-position:-168px 0}
+.icon-film{background-position:-192px 0}
+.icon-th-large{background-position:-216px 0}
+.icon-th{background-position:-240px 0}
+.icon-th-list{background-position:-264px 0}
+.icon-ok{background-position:-288px 0}
+.icon-remove{background-position:-312px 0}
+.icon-zoom-in{background-position:-336px 0}
+.icon-zoom-out{background-position:-360px 0}
+.icon-off{background-position:-384px 0}
+.icon-signal{background-position:-408px 0}
+.icon-cog{background-position:-432px 0}
+.icon-trash{background-position:-456px 0}
+.icon-home{background-position:0 -24px}
+.icon-file{background-position:-24px -24px}
+.icon-time{background-position:-48px -24px}
+.icon-road{background-position:-72px -24px}
+.icon-download-alt{background-position:-96px -24px}
+.icon-download{background-position:-120px -24px}
+.icon-upload{background-position:-144px -24px}
+.icon-inbox{background-position:-168px -24px}
+.icon-play-circle{background-position:-192px -24px}
+.icon-repeat{background-position:-216px -24px}
+.icon-refresh{background-position:-240px -24px}
+.icon-list-alt{background-position:-264px -24px}
+.icon-lock{background-position:-287px -24px}
+.icon-flag{background-position:-312px -24px}
+.icon-headphones{background-position:-336px -24px}
+.icon-volume-off{background-position:-360px -24px}
+.icon-volume-down{background-position:-384px -24px}
+.icon-volume-up{background-position:-408px -24px}
+.icon-qrcode{background-position:-432px -24px}
+.icon-barcode{background-position:-456px -24px}
+.icon-tag{background-position:0 -48px}
+.icon-tags{background-position:-25px -48px}
+.icon-book{background-position:-48px -48px}
+.icon-bookmark{background-position:-72px -48px}
+.icon-print{background-position:-96px -48px}
+.icon-camera{background-position:-120px -48px}
+.icon-font{background-position:-144px -48px}
+.icon-bold{background-position:-167px -48px}
+.icon-italic{background-position:-192px -48px}
+.icon-text-height{background-position:-216px -48px}
+.icon-text-width{background-position:-240px -48px}
+.icon-align-left{background-position:-264px -48px}
+.icon-align-center{background-position:-288px -48px}
+.icon-align-right{background-position:-312px -48px}
+.icon-align-justify{background-position:-336px -48px}
+.icon-list{background-position:-360px -48px}
+.icon-indent-left{background-position:-384px -48px}
+.icon-indent-right{background-position:-408px -48px}
+.icon-facetime-video{background-position:-432px -48px}
+.icon-picture{background-position:-456px -48px}
+.icon-pencil{background-position:0 -72px}
+.icon-map-marker{background-position:-24px -72px}
+.icon-adjust{background-position:-48px -72px}
+.icon-tint{background-position:-72px -72px}
+.icon-edit{background-position:-96px -72px}
+.icon-share{background-position:-120px -72px}
+.icon-check{background-position:-144px -72px}
+.icon-move{background-position:-168px -72px}
+.icon-step-backward{background-position:-192px -72px}
+.icon-fast-backward{background-position:-216px -72px}
+.icon-backward{background-position:-240px -72px}
+.icon-play{background-position:-264px -72px}
+.icon-pause{background-position:-288px -72px}
+.icon-stop{background-position:-312px -72px}
+.icon-forward{background-position:-336px -72px}
+.icon-fast-forward{background-position:-360px -72px}
+.icon-step-forward{background-position:-384px -72px}
+.icon-eject{background-position:-408px -72px}
+.icon-chevron-left{background-position:-432px -72px}
+.icon-chevron-right{background-position:-456px -72px}
+.icon-plus-sign{background-position:0 -96px}
+.icon-minus-sign{background-position:-24px -96px}
+.icon-remove-sign{background-position:-48px -96px}
+.icon-ok-sign{background-position:-72px -96px}
+.icon-question-sign{background-position:-96px -96px}
+.icon-info-sign{background-position:-120px -96px}
+.icon-screenshot{background-position:-144px -96px}
+.icon-remove-circle{background-position:-168px -96px}
+.icon-ok-circle{background-position:-192px -96px}
+.icon-ban-circle{background-position:-216px -96px}
+.icon-arrow-left{background-position:-240px -96px}
+.icon-arrow-right{background-position:-264px -96px}
+.icon-arrow-up{background-position:-289px -96px}
+.icon-arrow-down{background-position:-312px -96px}
+.icon-share-alt{background-position:-336px -96px}
+.icon-resize-full{background-position:-360px -96px}
+.icon-resize-small{background-position:-384px -96px}
+.icon-plus{background-position:-408px -96px}
+.icon-minus{background-position:-433px -96px}
+.icon-asterisk{background-position:-456px -96px}
+.icon-exclamation-sign{background-position:0 -120px}
+.icon-gift{background-position:-24px -120px}
+.icon-leaf{background-position:-48px -120px}
+.icon-fire{background-position:-72px -120px}
+.icon-eye-open{background-position:-96px -120px}
+.icon-eye-close{background-position:-120px -120px}
+.icon-warning-sign{background-position:-144px -120px}
+.icon-plane{background-position:-168px -120px}
+.icon-calendar{background-position:-192px -120px}
+.icon-random{background-position:-216px -120px;width:16px}
+.icon-comment{background-position:-240px -120px}
+.icon-magnet{background-position:-264px -120px}
+.icon-chevron-up{background-position:-288px -120px}
+.icon-chevron-down{background-position:-313px -119px}
+.icon-retweet{background-position:-336px -120px}
+.icon-shopping-cart{background-position:-360px -120px}
+.icon-folder-close{background-position:-384px -120px;width:16px}
+.icon-folder-open{background-position:-408px -120px;width:16px}
+.icon-resize-vertical{background-position:-432px -119px}
+.icon-resize-horizontal{background-position:-456px -118px}
+.icon-hdd{background-position:0 -144px}
+.icon-bullhorn{background-position:-24px -144px}
+.icon-bell{background-position:-48px -144px}
+.icon-certificate{background-position:-72px -144px}
+.icon-thumbs-up{background-position:-96px -144px}
+.icon-thumbs-down{background-position:-120px -144px}
+.icon-hand-right{background-position:-144px -144px}
+.icon-hand-left{background-position:-168px -144px}
+.icon-hand-up{background-position:-192px -144px}
+.icon-hand-down{background-position:-216px -144px}
+.icon-circle-arrow-right{background-position:-240px -144px}
+.icon-circle-arrow-left{background-position:-264px -144px}
+.icon-circle-arrow-up{background-position:-288px -144px}
+.icon-circle-arrow-down{background-position:-312px -144px}
+.icon-globe{background-position:-336px -144px}
+.icon-wrench{background-position:-360px -144px}
+.icon-tasks{background-position:-384px -144px}
+.icon-filter{background-position:-408px -144px}
+.icon-briefcase{background-position:-432px -144px}
+.icon-fullscreen{background-position:-456px -144px}
+.dropup,.dropdown{position:relative}
+.dropdown-toggle{*margin-bottom:-3px}
+.dropdown-toggle:active,.open .dropdown-toggle{outline:0}
+.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}
+.dropdown .caret{margin-top:8px;margin-left:2px}
+.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}
+.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
+.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}
+.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{text-decoration:none;color:#fff;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
+.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;outline:0;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
+.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}
+.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:default}
+.open{*z-index:1000}.open>.dropdown-menu{display:block}
+.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990}
+.pull-right>.dropdown-menu{right:0;left:auto}
+.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}
+.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}
+.dropdown-submenu{position:relative}
+.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;border-radius:0 6px 6px 6px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}
+.dropdown-submenu:hover>.dropdown-menu{display:block}
+.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;border-radius:5px 5px 5px 0;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}
+.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}
+.dropdown-submenu:hover>a:after{border-left-color:#fff}
+.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
+.dropdown .dropdown-menu .nav-header{padding-left:20px;padding-right:20px}
+.typeahead{z-index:1051;margin-top:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}
+.well-large{padding:24px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.well-small{padding:9px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}
+.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}
+.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}
+button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}
+.btn{display:inline-block;*display:inline;*zoom:1;padding:4px 12px;margin-bottom:0;font-size:13px;line-height:20px;text-align:center;vertical-align:middle;cursor:pointer;color:#333;text-shadow:0 1px 1px rgba(255,255,255,0.75);background-color:#f5f5f5;background-image:-moz-linear-gradient(top, #fff, #e6e6e6);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#e6e6e6));background-image:-webkit-linear-gradient(top, #fff, #e6e6e6);background-image:-o-linear-gradient(top, #fff, #e6e6e6);background-image:linear-gradient(to bottom, #fff, #e6e6e6);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e6e6e6;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);border:1px solid #ccc;*border:0;border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*margin-left:.3em;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}
+.btn:active,.btn.active{background-color:#ccc \9}
+.btn:first-child{*margin-left:0}
+.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}
+.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
+.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
+.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
+.btn-large{padding:11px 19px;font-size:16.25px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}
+.btn-small{padding:2px 10px;font-size:11.049999999999999px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}
+.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}
+.btn-mini{padding:0 6px;font-size:9.75px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.btn-block{display:block;width:100%;padding-left:0;padding-right:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
+.btn-block+.btn-block{margin-top:5px}
+input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}
+.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}
+.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;background-image:-moz-linear-gradient(top, #08c, #04c);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#04c));background-image:-webkit-linear-gradient(top, #08c, #04c);background-image:-o-linear-gradient(top, #08c, #04c);background-image:linear-gradient(to bottom, #08c, #04c);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#04c;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}
+.btn-primary:active,.btn-primary.active{background-color:#039 \9}
+.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#f89406;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}
+.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}
+.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;background-image:-moz-linear-gradient(top, #ee5f5b, #bd362f);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));background-image:-webkit-linear-gradient(top, #ee5f5b, #bd362f);background-image:-o-linear-gradient(top, #ee5f5b, #bd362f);background-image:linear-gradient(to bottom, #ee5f5b, #bd362f);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#bd362f;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}
+.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}
+.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;background-image:-moz-linear-gradient(top, #62c462, #51a351);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));background-image:-webkit-linear-gradient(top, #62c462, #51a351);background-image:-o-linear-gradient(top, #62c462, #51a351);background-image:linear-gradient(to bottom, #62c462, #51a351);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#51a351;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}
+.btn-success:active,.btn-success.active{background-color:#408140 \9}
+.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;background-image:-moz-linear-gradient(top, #5bc0de, #2f96b4);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));background-image:-webkit-linear-gradient(top, #5bc0de, #2f96b4);background-image:-o-linear-gradient(top, #5bc0de, #2f96b4);background-image:linear-gradient(to bottom, #5bc0de, #2f96b4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#2f96b4;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}
+.btn-info:active,.btn-info.active{background-color:#24748c \9}
+.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;background-image:-moz-linear-gradient(top, #444, #222);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#444), to(#222));background-image:-webkit-linear-gradient(top, #444, #222);background-image:-o-linear-gradient(top, #444, #222);background-image:linear-gradient(to bottom, #444, #222);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#222;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}
+.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}
+button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}
+button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}
+button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}
+button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}
+.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
+.btn-link{border-color:transparent;cursor:pointer;color:#08c;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}
+.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}
+.btn-group{position:relative;display:inline-block;*display:inline;*zoom:1;font-size:0;vertical-align:middle;white-space:nowrap;*margin-left:.3em}.btn-group:first-child{*margin-left:0}
+.btn-group+.btn-group{margin-left:5px}
+.btn-toolbar{font-size:0;margin-top:10px;margin-bottom:10px}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}
+.btn-group>.btn{position:relative;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.btn-group>.btn+.btn{margin-left:-1px}
+.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:13px}
+.btn-group>.btn-mini{font-size:9.75px}
+.btn-group>.btn-small{font-size:11.049999999999999px}
+.btn-group>.btn-large{font-size:16.25px}
+.btn-group>.btn:first-child{margin-left:0;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
+.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
+.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
+.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
+.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}
+.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}
+.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);*padding-top:5px;*padding-bottom:5px}
+.btn-group>.btn-mini+.dropdown-toggle{padding-left:5px;padding-right:5px;*padding-top:2px;*padding-bottom:2px}
+.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}
+.btn-group>.btn-large+.dropdown-toggle{padding-left:12px;padding-right:12px;*padding-top:7px;*padding-bottom:7px}
+.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
+.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}
+.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}
+.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}
+.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}
+.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}
+.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}
+.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}
+.btn .caret{margin-top:8px;margin-left:0}
+.btn-large .caret{margin-top:6px}
+.btn-large .caret{border-left-width:5px;border-right-width:5px;border-top-width:5px}
+.btn-mini .caret,.btn-small .caret{margin-top:8px}
+.dropup .btn-large .caret{border-bottom-width:5px}
+.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}
+.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}
+.btn-group-vertical>.btn{display:block;float:none;max-width:100%;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.btn-group-vertical>.btn+.btn{margin-left:0;margin-top:-1px}
+.btn-group-vertical>.btn:first-child{border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}
+.btn-group-vertical>.btn:last-child{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}
+.btn-group-vertical>.btn-large:first-child{border-radius:6px 6px 0 0;-webkit-border-radius:6px 6px 0 0;-moz-border-radius:6px 6px 0 0;border-radius:6px 6px 0 0}
+.btn-group-vertical>.btn-large:last-child{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
+.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.alert,.alert h4{color:#c09853}
+.alert h4{margin:0}
+.alert .close{position:relative;top:-2px;right:-21px;line-height:20px}
+.alert-success{background-color:#dff0d8;border-color:#d6e9c6;color:#468847}
+.alert-success h4{color:#468847}
+.alert-danger,.alert-error{background-color:#f2dede;border-color:#eed3d7;color:#b94a48}
+.alert-danger h4,.alert-error h4{color:#b94a48}
+.alert-info{background-color:#d9edf7;border-color:#bce8f1;color:#3a87ad}
+.alert-info h4{color:#3a87ad}
+.alert-block{padding-top:14px;padding-bottom:14px}
+.alert-block>p,.alert-block>ul{margin-bottom:0}
+.alert-block p+p{margin-top:5px}
+.nav{margin-left:0;margin-bottom:20px;list-style:none}
+.nav>li>a{display:block}
+.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eee}
+.nav>li>a>img{max-width:none}
+.nav>.pull-right{float:right}
+.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:20px;color:#999;text-shadow:0 1px 0 rgba(255,255,255,0.5);text-transform:uppercase}
+.nav li+.nav-header{margin-top:9px}
+.nav-list{padding-left:15px;padding-right:15px;margin-bottom:0}
+.nav-list>li>a,.nav-list .nav-header{margin-left:-15px;margin-right:-15px;text-shadow:0 1px 0 rgba(255,255,255,0.5)}
+.nav-list>li>a{padding:3px 15px}
+.nav-list>.active>a,.nav-list>.active>a:hover,.nav-list>.active>a:focus{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.2);background-color:#08c}
+.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{margin-right:2px}
+.nav-list .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
+.nav-tabs,.nav-pills{*zoom:1}.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;content:"";line-height:0}
+.nav-tabs:after,.nav-pills:after{clear:both}
+.nav-tabs>li,.nav-pills>li{float:left}
+.nav-tabs>li>a,.nav-pills>li>a{padding-right:12px;padding-left:12px;margin-right:2px;line-height:14px}
+.nav-tabs{border-bottom:1px solid #ddd}
+.nav-tabs>li{margin-bottom:-1px}
+.nav-tabs>li>a{padding-top:8px;padding-bottom:8px;line-height:20px;border:1px solid transparent;border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover,.nav-tabs>li>a:focus{border-color:#eee #eee #ddd}
+.nav-tabs>.active>a,.nav-tabs>.active>a:hover,.nav-tabs>.active>a:focus{color:#555;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent;cursor:default}
+.nav-pills>li>a{padding-top:8px;padding-bottom:8px;margin-top:2px;margin-bottom:2px;border-radius:5px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}
+.nav-pills>.active>a,.nav-pills>.active>a:hover,.nav-pills>.active>a:focus{color:#fff;background-color:#08c}
+.nav-stacked>li{float:none}
+.nav-stacked>li>a{margin-right:0}
+.nav-tabs.nav-stacked{border-bottom:0}
+.nav-tabs.nav-stacked>li>a{border:1px solid #ddd;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.nav-tabs.nav-stacked>li:first-child>a{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
+.nav-tabs.nav-stacked>li:last-child>a{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
+.nav-tabs.nav-stacked>li>a:hover,.nav-tabs.nav-stacked>li>a:focus{border-color:#ddd;z-index:2}
+.nav-pills.nav-stacked>li>a{margin-bottom:3px}
+.nav-pills.nav-stacked>li:last-child>a{margin-bottom:1px}
+.nav-tabs .dropdown-menu{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
+.nav-pills .dropdown-menu{border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.nav .dropdown-toggle .caret{border-top-color:#08c;border-bottom-color:#08c;margin-top:6px}
+.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#005580;border-bottom-color:#005580}
+.nav-tabs .dropdown-toggle .caret{margin-top:8px}
+.nav .active .dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
+.nav-tabs .active .dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}
+.nav>.dropdown.active>a:hover,.nav>.dropdown.active>a:focus{cursor:pointer}
+.nav-tabs .open .dropdown-toggle,.nav-pills .open .dropdown-toggle,.nav>li.dropdown.open.active>a:hover,.nav>li.dropdown.open.active>a:focus{color:#fff;background-color:#999;border-color:#999}
+.nav li.dropdown.open .caret,.nav li.dropdown.open.active .caret,.nav li.dropdown.open a:hover .caret,.nav li.dropdown.open a:focus .caret{border-top-color:#fff;border-bottom-color:#fff;opacity:1;filter:alpha(opacity=100)}
+.tabs-stacked .open>a:hover,.tabs-stacked .open>a:focus{border-color:#999}
+.tabbable{*zoom:1}.tabbable:before,.tabbable:after{display:table;content:"";line-height:0}
+.tabbable:after{clear:both}
+.tab-content{overflow:auto}
+.tabs-below>.nav-tabs,.tabs-right>.nav-tabs,.tabs-left>.nav-tabs{border-bottom:0}
+.tab-content>.tab-pane,.pill-content>.pill-pane{display:none}
+.tab-content>.active,.pill-content>.active{display:block}
+.tabs-below>.nav-tabs{border-top:1px solid #ddd}
+.tabs-below>.nav-tabs>li{margin-top:-1px;margin-bottom:0}
+.tabs-below>.nav-tabs>li>a{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-bottom-color:transparent;border-top-color:#ddd}
+.tabs-below>.nav-tabs>.active>a,.tabs-below>.nav-tabs>.active>a:hover,.tabs-below>.nav-tabs>.active>a:focus{border-color:transparent #ddd #ddd #ddd}
+.tabs-left>.nav-tabs>li,.tabs-right>.nav-tabs>li{float:none}
+.tabs-left>.nav-tabs>li>a,.tabs-right>.nav-tabs>li>a{min-width:74px;margin-right:0;margin-bottom:3px}
+.tabs-left>.nav-tabs{float:left;margin-right:19px;border-right:1px solid #ddd}
+.tabs-left>.nav-tabs>li>a{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
+.tabs-left>.nav-tabs>li>a:hover,.tabs-left>.nav-tabs>li>a:focus{border-color:#eee #ddd #eee #eee}
+.tabs-left>.nav-tabs .active>a,.tabs-left>.nav-tabs .active>a:hover,.tabs-left>.nav-tabs .active>a:focus{border-color:#ddd transparent #ddd #ddd;*border-right-color:#fff}
+.tabs-right>.nav-tabs{float:right;margin-left:19px;border-left:1px solid #ddd}
+.tabs-right>.nav-tabs>li>a{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.tabs-right>.nav-tabs>li>a:hover,.tabs-right>.nav-tabs>li>a:focus{border-color:#eee #eee #eee #ddd}
+.tabs-right>.nav-tabs .active>a,.tabs-right>.nav-tabs .active>a:hover,.tabs-right>.nav-tabs .active>a:focus{border-color:#ddd #ddd #ddd transparent;*border-left-color:#fff}
+.nav>.disabled>a{color:#999}
+.nav>.disabled>a:hover,.nav>.disabled>a:focus{text-decoration:none;background-color:transparent;cursor:default}
+.navbar{overflow:visible;margin-bottom:20px;*position:relative;*z-index:2}
+.navbar-inner{min-height:36px;padding-left:20px;padding-right:20px;background-color:#fafafa;background-image:-moz-linear-gradient(top, #fff, #f2f2f2);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#f2f2f2));background-image:-webkit-linear-gradient(top, #fff, #f2f2f2);background-image:-o-linear-gradient(top, #fff, #f2f2f2);background-image:linear-gradient(to bottom, #fff, #f2f2f2);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0);border:1px solid #d4d4d4;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 4px rgba(0,0,0,0.065);-moz-box-shadow:0 1px 4px rgba(0,0,0,0.065);box-shadow:0 1px 4px rgba(0,0,0,0.065);*zoom:1}.navbar-inner:before,.navbar-inner:after{display:table;content:"";line-height:0}
+.navbar-inner:after{clear:both}
+.navbar .container{width:auto}
+.nav-collapse.collapse{height:auto;overflow:visible}
+.navbar .brand{float:left;display:block;padding:8px 20px 8px;margin-left:-20px;font-size:20px;font-weight:200;color:#777;text-shadow:0 1px 0 #fff}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none}
+.navbar-text{margin-bottom:0;line-height:36px;color:#777}
+.navbar-link{color:#777}.navbar-link:hover,.navbar-link:focus{color:#333}
+.navbar .divider-vertical{height:36px;margin:0 9px;border-left:1px solid #f2f2f2;border-right:1px solid #fff}
+.navbar .btn,.navbar .btn-group{margin-top:3px}
+.navbar .btn-group .btn,.navbar .input-prepend .btn,.navbar .input-append .btn,.navbar .input-prepend .btn-group,.navbar .input-append .btn-group{margin-top:0}
+.navbar-form{margin-bottom:0;*zoom:1}.navbar-form:before,.navbar-form:after{display:table;content:"";line-height:0}
+.navbar-form:after{clear:both}
+.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:3px}
+.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}
+.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}
+.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0}
+.navbar-search{position:relative;float:left;margin-top:3px;margin-bottom:0}.navbar-search .search-query{margin-bottom:0;padding:4px 14px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
+.navbar-static-top{position:static;margin-bottom:0}.navbar-static-top .navbar-inner{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030;margin-bottom:0}
+.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{border-width:0 0 1px}
+.navbar-fixed-bottom .navbar-inner{border-width:1px 0 0}
+.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding-left:0;padding-right:0;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}
+.navbar-fixed-top{top:0}
+.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{-webkit-box-shadow:0 1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 1px 10px rgba(0,0,0,.1);box-shadow:0 1px 10px rgba(0,0,0,.1)}
+.navbar-fixed-bottom{bottom:0}.navbar-fixed-bottom .navbar-inner{-webkit-box-shadow:0 -1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,.1);box-shadow:0 -1px 10px rgba(0,0,0,.1)}
+.navbar .nav{position:relative;left:0;display:block;float:left;margin:0 10px 0 0}
+.navbar .nav.pull-right{float:right;margin-right:0}
+.navbar .nav>li{float:left}
+.navbar .nav>li>a{float:none;padding:8px 15px 8px;color:#777;text-decoration:none;text-shadow:0 1px 0 #fff}
+.navbar .nav .dropdown-toggle .caret{margin-top:8px}
+.navbar .nav>li>a:focus,.navbar .nav>li>a:hover{background-color:transparent;color:#333;text-decoration:none}
+.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#555;text-decoration:none;background-color:#e5e5e5;-webkit-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);-moz-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);box-shadow:inset 0 3px 8px rgba(0,0,0,0.125)}
+.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-left:5px;margin-right:5px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#ededed;background-image:-moz-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5));background-image:-webkit-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-o-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:linear-gradient(to bottom, #f2f2f2, #e5e5e5);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0);border-color:#e5e5e5 #e5e5e5 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e5e5e5;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075)}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#fff;background-color:#e5e5e5;*background-color:#d9d9d9}
+.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#ccc \9}
+.navbar .btn-navbar .icon-bar{display:block;width:18px;height:2px;background-color:#f5f5f5;-webkit-border-radius:1px;-moz-border-radius:1px;border-radius:1px;-webkit-box-shadow:0 1px 0 rgba(0,0,0,0.25);-moz-box-shadow:0 1px 0 rgba(0,0,0,0.25);box-shadow:0 1px 0 rgba(0,0,0,0.25)}
+.btn-navbar .icon-bar+.icon-bar{margin-top:3px}
+.navbar .nav>li>.dropdown-menu:before{content:'';display:inline-block;border-left:7px solid transparent;border-right:7px solid transparent;border-bottom:7px solid #ccc;border-bottom-color:rgba(0,0,0,0.2);position:absolute;top:-7px;left:9px}
+.navbar .nav>li>.dropdown-menu:after{content:'';display:inline-block;border-left:6px solid transparent;border-right:6px solid transparent;border-bottom:6px solid #fff;position:absolute;top:-6px;left:10px}
+.navbar-fixed-bottom .nav>li>.dropdown-menu:before{border-top:7px solid #ccc;border-top-color:rgba(0,0,0,0.2);border-bottom:0;bottom:-7px;top:auto}
+.navbar-fixed-bottom .nav>li>.dropdown-menu:after{border-top:6px solid #fff;border-bottom:0;bottom:-6px;top:auto}
+.navbar .nav li.dropdown>a:hover .caret,.navbar .nav li.dropdown>a:focus .caret{border-top-color:#333;border-bottom-color:#333}
+.navbar .nav li.dropdown.open>.dropdown-toggle,.navbar .nav li.dropdown.active>.dropdown-toggle,.navbar .nav li.dropdown.open.active>.dropdown-toggle{background-color:#e5e5e5;color:#555}
+.navbar .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#777;border-bottom-color:#777}
+.navbar .nav li.dropdown.open>.dropdown-toggle .caret,.navbar .nav li.dropdown.active>.dropdown-toggle .caret,.navbar .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}
+.navbar .pull-right>li>.dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right{left:auto;right:0}.navbar .pull-right>li>.dropdown-menu:before,.navbar .nav>li>.dropdown-menu.pull-right:before{left:auto;right:12px}
+.navbar .pull-right>li>.dropdown-menu:after,.navbar .nav>li>.dropdown-menu.pull-right:after{left:auto;right:13px}
+.navbar .pull-right>li>.dropdown-menu .dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right .dropdown-menu{left:auto;right:100%;margin-left:0;margin-right:-1px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
+.navbar-inverse .navbar-inner{background-color:#1b1b1b;background-image:-moz-linear-gradient(top, #222, #111);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#222), to(#111));background-image:-webkit-linear-gradient(top, #222, #111);background-image:-o-linear-gradient(top, #222, #111);background-image:linear-gradient(to bottom, #222, #111);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0);border-color:#252525}
+.navbar-inverse .brand,.navbar-inverse .nav>li>a{color:#999;text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.navbar-inverse .brand:hover,.navbar-inverse .nav>li>a:hover,.navbar-inverse .brand:focus,.navbar-inverse .nav>li>a:focus{color:#fff}
+.navbar-inverse .brand{color:#999}
+.navbar-inverse .navbar-text{color:#999}
+.navbar-inverse .nav>li>a:focus,.navbar-inverse .nav>li>a:hover{background-color:transparent;color:#fff}
+.navbar-inverse .nav .active>a,.navbar-inverse .nav .active>a:hover,.navbar-inverse .nav .active>a:focus{color:#fff;background-color:#111}
+.navbar-inverse .navbar-link{color:#999}.navbar-inverse .navbar-link:hover,.navbar-inverse .navbar-link:focus{color:#fff}
+.navbar-inverse .divider-vertical{border-left-color:#111;border-right-color:#222}
+.navbar-inverse .nav li.dropdown.open>.dropdown-toggle,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle{background-color:#111;color:#fff}
+.navbar-inverse .nav li.dropdown>a:hover .caret,.navbar-inverse .nav li.dropdown>a:focus .caret{border-top-color:#fff;border-bottom-color:#fff}
+.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#999;border-bottom-color:#999}
+.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
+.navbar-inverse .navbar-search .search-query{color:#fff;background-color:#515151;border-color:#111;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-webkit-transition:none;-moz-transition:none;-o-transition:none;transition:none}.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#ccc}
+.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#ccc}
+.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#ccc}
+.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#333;text-shadow:0 1px 0 #fff;background-color:#fff;border:0;-webkit-box-shadow:0 0 3px rgba(0,0,0,0.15);-moz-box-shadow:0 0 3px rgba(0,0,0,0.15);box-shadow:0 0 3px rgba(0,0,0,0.15);outline:0}
+.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e0e0e;background-image:-moz-linear-gradient(top, #151515, #040404);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404));background-image:-webkit-linear-gradient(top, #151515, #040404);background-image:-o-linear-gradient(top, #151515, #040404);background-image:linear-gradient(to bottom, #151515, #040404);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0);border-color:#040404 #040404 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#040404;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#040404;*background-color:#000}
+.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}
+.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;*zoom:1;text-shadow:0 1px 0 #fff}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}
+.breadcrumb>.active{color:#999}
+.pagination{margin:20px 0}
+.pagination ul{display:inline-block;*display:inline;*zoom:1;margin-left:0;margin-bottom:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}
+.pagination ul>li{display:inline}
+.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}
+.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}
+.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}
+.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;background-color:transparent;cursor:default}
+.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
+.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
+.pagination-centered{text-align:center}
+.pagination-right{text-align:right}
+.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:16.25px}
+.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
+.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
+.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-top-left-radius:3px;-moz-border-radius-topleft:3px;border-top-left-radius:3px;-webkit-border-bottom-left-radius:3px;-moz-border-radius-bottomleft:3px;border-bottom-left-radius:3px}
+.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;-moz-border-radius-topright:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;-moz-border-radius-bottomright:3px;border-bottom-right-radius:3px}
+.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.049999999999999px}
+.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:9.75px}
+.pager{margin:20px 0;list-style:none;text-align:center;*zoom:1}.pager:before,.pager:after{display:table;content:"";line-height:0}
+.pager:after{clear:both}
+.pager li{display:inline}
+.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
+.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}
+.pager .next>a,.pager .next>span{float:right}
+.pager .previous>a,.pager .previous>span{float:left}
+.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;background-color:#fff;cursor:default}
+.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}
+.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}
+.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box;outline:none}.modal.fade{-webkit-transition:opacity .3s linear, top .3s ease-out;-moz-transition:opacity .3s linear, top .3s ease-out;-o-transition:opacity .3s linear, top .3s ease-out;transition:opacity .3s linear, top .3s ease-out;top:-25%}
+.modal.fade.in{top:10%}
+.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}
+.modal-header h3{margin:0;line-height:30px}
+.modal-body{position:relative;overflow-y:auto;max-height:400px;padding:15px}
+.modal-form{margin-bottom:0}
+.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff;*zoom:1}.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0}
+.modal-footer:after{clear:both}
+.modal-footer .btn+.btn{margin-left:5px;margin-bottom:0}
+.modal-footer .btn-group .btn+.btn{margin-left:-1px}
+.modal-footer .btn-block+.btn-block{margin-left:0}
+.tooltip{position:absolute;z-index:1030;display:block;visibility:visible;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0)}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}
+.tooltip.top{margin-top:-3px;padding:5px 0}
+.tooltip.right{margin-left:3px;padding:0 5px}
+.tooltip.bottom{margin-top:3px;padding:5px 0}
+.tooltip.left{margin-left:-3px;padding:0 5px}
+.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}
+.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}
+.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}
+.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}
+.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}
+.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;background-color:#fff;-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);white-space:normal}.popover.top{margin-top:-10px}
+.popover.right{margin-left:10px}
+.popover.bottom{margin-top:10px}
+.popover.left{margin-left:-10px}
+.popover-title{margin:0;padding:8px 14px;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}
+.popover-content{padding:9px 14px}
+.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}
+.popover .arrow{border-width:11px}
+.popover .arrow:after{border-width:10px;content:""}
+.popover.top .arrow{left:50%;margin-left:-11px;border-bottom-width:0;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);bottom:-11px}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-bottom-width:0;border-top-color:#fff}
+.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-left-width:0;border-right-color:#999;border-right-color:rgba(0,0,0,0.25)}.popover.right .arrow:after{left:1px;bottom:-10px;border-left-width:0;border-right-color:#fff}
+.popover.bottom .arrow{left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);top:-11px}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-top-width:0;border-bottom-color:#fff}
+.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,0.25)}.popover.left .arrow:after{right:1px;border-right-width:0;border-left-color:#fff;bottom:-10px}
+.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0}
+.thumbnails:after{clear:both}
+.row-fluid .thumbnails{margin-left:0}
+.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}
+.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}
+a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}
+.thumbnail>img{display:block;max-width:100%;margin-left:auto;margin-right:auto}
+.thumbnail .caption{padding:9px;color:#555}
+.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}
+.media,.media .media{margin-top:15px}
+.media:first-child{margin-top:0}
+.media-object{display:block}
+.media-heading{margin:0 0 5px}
+.media>.pull-left{margin-right:10px}
+.media>.pull-right{margin-left:10px}
+.media-list{margin-left:0;list-style:none}
+.label,.badge{display:inline-block;padding:2px 4px;font-size:10.998px;font-weight:bold;line-height:14px;color:#fff;vertical-align:baseline;white-space:nowrap;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#999}
+.label{border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.badge{padding-left:9px;padding-right:9px;border-radius:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}
+.label:empty,.badge:empty{display:none}
+a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}
+.label-important,.badge-important{background-color:#b94a48}
+.label-important[href],.badge-important[href]{background-color:#953b39}
+.label-warning,.badge-warning{background-color:#f89406}
+.label-warning[href],.badge-warning[href]{background-color:#c67605}
+.label-success,.badge-success{background-color:#468847}
+.label-success[href],.badge-success[href]{background-color:#356635}
+.label-info,.badge-info{background-color:#3a87ad}
+.label-info[href],.badge-info[href]{background-color:#2d6987}
+.label-inverse,.badge-inverse{background-color:#333}
+.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}
+.btn .label,.btn .badge{position:relative;top:-1px}
+.btn-mini .label,.btn-mini .badge{top:0}
+@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0} to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.progress .bar{width:0;height:100%;color:#fff;float:left;font-size:12px;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top, #149bdf, #0480be);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));background-image:-webkit-linear-gradient(top, #149bdf, #0480be);background-image:-o-linear-gradient(top, #149bdf, #0480be);background-image:linear-gradient(to bottom, #149bdf, #0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}
+.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15)}
+.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}
+.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}
+.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0)}
+.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
+.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0)}
+.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
+.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0)}
+.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
+.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0)}
+.progress-warning.progress-striped .bar,.progress-striped .bar-warning{background-color:#fbb450;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
+.accordion{margin-bottom:20px}
+.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.accordion-heading{border-bottom:0}
+.accordion-heading .accordion-toggle{display:block;padding:8px 15px}
+.accordion-toggle{cursor:pointer}
+.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5}
+.carousel{position:relative;margin-bottom:20px;line-height:1}
+.carousel-inner{overflow:hidden;width:100%;position:relative}
+.carousel-inner>.item{display:none;position:relative;-webkit-transition:.6s ease-in-out left;-moz-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1}
+.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}
+.carousel-inner>.active{left:0}
+.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}
+.carousel-inner>.next{left:100%}
+.carousel-inner>.prev{left:-100%}
+.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}
+.carousel-inner>.active.left{left:-100%}
+.carousel-inner>.active.right{left:100%}
+.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#fff;text-align:center;background:#222;border:3px solid #fff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:.5;filter:alpha(opacity=50)}.carousel-control.right{left:auto;right:15px}
+.carousel-control:hover,.carousel-control:focus{color:#fff;text-decoration:none;opacity:.9;filter:alpha(opacity=90)}
+.carousel-indicators{position:absolute;top:15px;right:15px;z-index:5;margin:0;list-style:none}.carousel-indicators li{display:block;float:left;width:10px;height:10px;margin-left:5px;text-indent:-999px;background-color:#ccc;background-color:rgba(255,255,255,0.25);border-radius:5px}
+.carousel-indicators .active{background-color:#fff}
+.carousel-caption{position:absolute;left:0;right:0;bottom:0;padding:15px;background:#333;background:rgba(0,0,0,0.75)}
+.carousel-caption h4,.carousel-caption p{color:#fff;line-height:20px}
+.carousel-caption h4{margin:0 0 5px}
+.carousel-caption p{margin-bottom:0}
+.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:30px;color:inherit;background-color:#eee;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;color:inherit;letter-spacing:-1px}
+.hero-unit li{line-height:30px}
+.pull-right{float:right}
+.pull-left{float:left}
+.hide{display:none}
+.show{display:block}
+.invisible{visibility:hidden}
+.affix{position:fixed}
+@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}
+.visible-phone{display:none !important}
+.visible-tablet{display:none !important}
+.hidden-desktop{display:none !important}
+.visible-desktop{display:inherit !important}
+@media (min-width:768px) and (max-width:979px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-tablet{display:inherit !important} .hidden-tablet{display:none !important}}@media (max-width:767px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-phone{display:inherit !important} .hidden-phone{display:none !important}}.visible-print{display:none !important}
+@media print{.visible-print{display:inherit !important} .hidden-print{display:none !important}}@media (min-width:1200px){.row{margin-left:-30px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0} .row:after{clear:both} [class*="span"]{float:left;min-height:1px;margin-left:30px} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:1170px} .span12{width:1170px} .span11{width:1070px} .span10{width:970px} .span9{width:870px} .span8{width:770px} .span7{width:670px} .span6{width:570px} .span5{width:470px} .span4{width:370px} .span3{width:270px} .span2{width:170px} .span1{width:70px} .offset12{margin-left:1230px} .offset11{margin-left:1130px} .offset10{margin-left:1030px} .offset9{margin-left:930px} .offset8{margin-left:830px} .offset7{margin-left:730px} .offset6{margin-left:630px} .offset5{margin-left:530px} .offset4{margin-left:430px} .offset3{margin-left:330px} .offset2{margin-left:230px} .offset1{margin-left:130px} .row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0} .row-fluid:after{clear:both} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.564102564102564%;*margin-left:2.5109110747408616%} .row-fluid [class*="span"]:first-child{margin-left:0} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.564102564102564%} .row-fluid .span12{width:100%;*width:99.94680851063829%} .row-fluid .span11{width:91.45299145299145%;*width:91.39979996362975%} .row-fluid .span10{width:82.90598290598291%;*width:82.8527914166212%} .row-fluid .span9{width:74.35897435897436%;*width:74.30578286961266%} .row-fluid .span8{width:65.81196581196582%;*width:65.75877432260411%} .row-fluid .span7{width:57.26495726495726%;*width:57.21176577559556%} .row-fluid .span6{width:48.717948717948715%;*width:48.664757228587014%} .row-fluid .span5{width:40.17094017094017%;*width:40.11774868157847%} .row-fluid .span4{width:31.623931623931625%;*width:31.570740134569924%} .row-fluid .span3{width:23.076923076923077%;*width:23.023731587561375%} .row-fluid .span2{width:14.52991452991453%;*width:14.476723040552828%} .row-fluid .span1{width:5.982905982905983%;*width:5.929714493544281%} .row-fluid .offset12{margin-left:105.12820512820512%;*margin-left:105.02182214948171%} .row-fluid .offset12:first-child{margin-left:102.56410256410257%;*margin-left:102.45771958537915%} .row-fluid .offset11{margin-left:96.58119658119658%;*margin-left:96.47481360247316%} .row-fluid .offset11:first-child{margin-left:94.01709401709402%;*margin-left:93.91071103837061%} .row-fluid .offset10{margin-left:88.03418803418803%;*margin-left:87.92780505546462%} .row-fluid .offset10:first-child{margin-left:85.47008547008548%;*margin-left:85.36370249136206%} .row-fluid .offset9{margin-left:79.48717948717949%;*margin-left:79.38079650845607%} .row-fluid .offset9:first-child{margin-left:76.92307692307693%;*margin-left:76.81669394435352%} .row-fluid .offset8{margin-left:70.94017094017094%;*margin-left:70.83378796144753%} .row-fluid .offset8:first-child{margin-left:68.37606837606839%;*margin-left:68.26968539734497%} .row-fluid .offset7{margin-left:62.393162393162385%;*margin-left:62.28677941443899%} .row-fluid .offset7:first-child{margin-left:59.82905982905982%;*margin-left:59.72267685033642%} .row-fluid .offset6{margin-left:53.84615384615384%;*margin-left:53.739770867430444%} .row-fluid .offset6:first-child{margin-left:51.28205128205128%;*margin-left:51.175668303327875%} .row-fluid .offset5{margin-left:45.299145299145295%;*margin-left:45.1927623204219%} .row-fluid .offset5:first-child{margin-left:42.73504273504273%;*margin-left:42.62865975631933%} .row-fluid .offset4{margin-left:36.75213675213675%;*margin-left:36.645753773413354%} .row-fluid .offset4:first-child{margin-left:34.18803418803419%;*margin-left:34.081651209310785%} .row-fluid .offset3{margin-left:28.205128205128204%;*margin-left:28.0987452264048%} .row-fluid .offset3:first-child{margin-left:25.641025641025642%;*margin-left:25.53464266230224%} .row-fluid .offset2{margin-left:19.65811965811966%;*margin-left:19.551736679396257%} .row-fluid .offset2:first-child{margin-left:17.094017094017094%;*margin-left:16.98763411529369%} .row-fluid .offset1{margin-left:11.11111111111111%;*margin-left:11.004728132387708%} .row-fluid .offset1:first-child{margin-left:8.547008547008547%;*margin-left:8.440625568285142%} input,textarea,.uneditable-input{margin-left:0} .controls-row [class*="span"]+[class*="span"]{margin-left:30px} input.span12,textarea.span12,.uneditable-input.span12{width:1156px} input.span11,textarea.span11,.uneditable-input.span11{width:1056px} input.span10,textarea.span10,.uneditable-input.span10{width:956px} input.span9,textarea.span9,.uneditable-input.span9{width:856px} input.span8,textarea.span8,.uneditable-input.span8{width:756px} input.span7,textarea.span7,.uneditable-input.span7{width:656px} input.span6,textarea.span6,.uneditable-input.span6{width:556px} input.span5,textarea.span5,.uneditable-input.span5{width:456px} input.span4,textarea.span4,.uneditable-input.span4{width:356px} input.span3,textarea.span3,.uneditable-input.span3{width:256px} input.span2,textarea.span2,.uneditable-input.span2{width:156px} input.span1,textarea.span1,.uneditable-input.span1{width:56px} .thumbnails{margin-left:-30px} .thumbnails>li{margin-left:30px} .row-fluid .thumbnails{margin-left:0}}@media (min-width:768px) and (max-width:979px){.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0} .row:after{clear:both} [class*="span"]{float:left;min-height:1px;margin-left:20px} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:724px} .span12{width:724px} .span11{width:662px} .span10{width:600px} .span9{width:538px} .span8{width:476px} .span7{width:414px} .span6{width:352px} .span5{width:290px} .span4{width:228px} .span3{width:166px} .span2{width:104px} .span1{width:42px} .offset12{margin-left:764px} .offset11{margin-left:702px} .offset10{margin-left:640px} .offset9{margin-left:578px} .offset8{margin-left:516px} .offset7{margin-left:454px} .offset6{margin-left:392px} .offset5{margin-left:330px} .offset4{margin-left:268px} .offset3{margin-left:206px} .offset2{margin-left:144px} .offset1{margin-left:82px} .row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0} .row-fluid:after{clear:both} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.7624309392265194%;*margin-left:2.709239449864817%} .row-fluid [class*="span"]:first-child{margin-left:0} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.7624309392265194%} .row-fluid .span12{width:100%;*width:99.94680851063829%} .row-fluid .span11{width:91.43646408839778%;*width:91.38327259903608%} .row-fluid .span10{width:82.87292817679558%;*width:82.81973668743387%} .row-fluid .span9{width:74.30939226519337%;*width:74.25620077583166%} .row-fluid .span8{width:65.74585635359117%;*width:65.69266486422946%} .row-fluid .span7{width:57.18232044198895%;*width:57.12912895262725%} .row-fluid .span6{width:48.61878453038674%;*width:48.56559304102504%} .row-fluid .span5{width:40.05524861878453%;*width:40.00205712942283%} .row-fluid .span4{width:31.491712707182323%;*width:31.43852121782062%} .row-fluid .span3{width:22.92817679558011%;*width:22.87498530621841%} .row-fluid .span2{width:14.3646408839779%;*width:14.311449394616199%} .row-fluid .span1{width:5.801104972375691%;*width:5.747913483013988%} .row-fluid .offset12{margin-left:105.52486187845304%;*margin-left:105.41847889972962%} .row-fluid .offset12:first-child{margin-left:102.76243093922652%;*margin-left:102.6560479605031%} .row-fluid .offset11{margin-left:96.96132596685082%;*margin-left:96.8549429881274%} .row-fluid .offset11:first-child{margin-left:94.1988950276243%;*margin-left:94.09251204890089%} .row-fluid .offset10{margin-left:88.39779005524862%;*margin-left:88.2914070765252%} .row-fluid .offset10:first-child{margin-left:85.6353591160221%;*margin-left:85.52897613729868%} .row-fluid .offset9{margin-left:79.8342541436464%;*margin-left:79.72787116492299%} .row-fluid .offset9:first-child{margin-left:77.07182320441989%;*margin-left:76.96544022569647%} .row-fluid .offset8{margin-left:71.2707182320442%;*margin-left:71.16433525332079%} .row-fluid .offset8:first-child{margin-left:68.50828729281768%;*margin-left:68.40190431409427%} .row-fluid .offset7{margin-left:62.70718232044199%;*margin-left:62.600799341718584%} .row-fluid .offset7:first-child{margin-left:59.94475138121547%;*margin-left:59.838368402492065%} .row-fluid .offset6{margin-left:54.14364640883978%;*margin-left:54.037263430116376%} .row-fluid .offset6:first-child{margin-left:51.38121546961326%;*margin-left:51.27483249088986%} .row-fluid .offset5{margin-left:45.58011049723757%;*margin-left:45.47372751851417%} .row-fluid .offset5:first-child{margin-left:42.81767955801105%;*margin-left:42.71129657928765%} .row-fluid .offset4{margin-left:37.01657458563536%;*margin-left:36.91019160691196%} .row-fluid .offset4:first-child{margin-left:34.25414364640884%;*margin-left:34.14776066768544%} .row-fluid .offset3{margin-left:28.45303867403315%;*margin-left:28.346655695309746%} .row-fluid .offset3:first-child{margin-left:25.69060773480663%;*margin-left:25.584224756083227%} .row-fluid .offset2{margin-left:19.88950276243094%;*margin-left:19.783119783707537%} .row-fluid .offset2:first-child{margin-left:17.12707182320442%;*margin-left:17.02068884448102%} .row-fluid .offset1{margin-left:11.32596685082873%;*margin-left:11.219583872105325%} .row-fluid .offset1:first-child{margin-left:8.56353591160221%;*margin-left:8.457152932878806%} input,textarea,.uneditable-input{margin-left:0} .controls-row [class*="span"]+[class*="span"]{margin-left:20px} input.span12,textarea.span12,.uneditable-input.span12{width:710px} input.span11,textarea.span11,.uneditable-input.span11{width:648px} input.span10,textarea.span10,.uneditable-input.span10{width:586px} input.span9,textarea.span9,.uneditable-input.span9{width:524px} input.span8,textarea.span8,.uneditable-input.span8{width:462px} input.span7,textarea.span7,.uneditable-input.span7{width:400px} input.span6,textarea.span6,.uneditable-input.span6{width:338px} input.span5,textarea.span5,.uneditable-input.span5{width:276px} input.span4,textarea.span4,.uneditable-input.span4{width:214px} input.span3,textarea.span3,.uneditable-input.span3{width:152px} input.span2,textarea.span2,.uneditable-input.span2{width:90px} input.span1,textarea.span1,.uneditable-input.span1{width:28px}}@media (max-width:767px){body{padding-left:20px;padding-right:20px} .navbar-fixed-top,.navbar-fixed-bottom,.navbar-static-top{margin-left:-20px;margin-right:-20px} .container-fluid{padding:0} .dl-horizontal dt{float:none;clear:none;width:auto;text-align:left} .dl-horizontal dd{margin-left:0} .container{width:auto} .row-fluid{width:100%} .row,.thumbnails{margin-left:0} .thumbnails>li{float:none;margin-left:0} [class*="span"],.uneditable-input[class*="span"],.row-fluid [class*="span"]{float:none;display:block;width:100%;margin-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .span12,.row-fluid .span12{width:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .row-fluid [class*="offset"]:first-child{margin-left:0} .input-large,.input-xlarge,.input-xxlarge,input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .input-prepend input,.input-append input,.input-prepend input[class*="span"],.input-append input[class*="span"]{display:inline-block;width:auto} .controls-row [class*="span"]+[class*="span"]{margin-left:0} .modal{position:fixed;top:20px;left:20px;right:20px;width:auto;margin:0}.modal.fade{top:-100px} .modal.fade.in{top:20px}}@media (max-width:480px){.nav-collapse{-webkit-transform:translate3d(0, 0, 0)} .page-header h1 small{display:block;line-height:20px} input[type="checkbox"],input[type="radio"]{border:1px solid #ccc} .form-horizontal .control-label{float:none;width:auto;padding-top:0;text-align:left} .form-horizontal .controls{margin-left:0} .form-horizontal .control-list{padding-top:0} .form-horizontal .form-actions{padding-left:10px;padding-right:10px} .media .pull-left,.media .pull-right{float:none;display:block;margin-bottom:10px} .media-object{margin-right:0;margin-left:0} .modal{top:10px;left:10px;right:10px} .modal-header .close{padding:10px;margin:-10px} .carousel-caption{position:static}}@media (max-width:979px){body{padding-top:0} .navbar-fixed-top,.navbar-fixed-bottom{position:static} .navbar-fixed-top{margin-bottom:20px} .navbar-fixed-bottom{margin-top:20px} .navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding:5px} .navbar .container{width:auto;padding:0} .navbar .brand{padding-left:10px;padding-right:10px;margin:0 0 0 -5px} .nav-collapse{clear:both} .nav-collapse .nav{float:none;margin:0 0 10px} .nav-collapse .nav>li{float:none} .nav-collapse .nav>li>a{margin-bottom:2px} .nav-collapse .nav>.divider-vertical{display:none} .nav-collapse .nav .nav-header{color:#777;text-shadow:none} .nav-collapse .nav>li>a,.nav-collapse .dropdown-menu a{padding:9px 15px;font-weight:bold;color:#777;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px} .nav-collapse .btn{padding:4px 10px 4px;font-weight:normal;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px} .nav-collapse .dropdown-menu li+li a{margin-bottom:2px} .nav-collapse .nav>li>a:hover,.nav-collapse .nav>li>a:focus,.nav-collapse .dropdown-menu a:hover,.nav-collapse .dropdown-menu a:focus{background-color:#f2f2f2} .navbar-inverse .nav-collapse .nav>li>a,.navbar-inverse .nav-collapse .dropdown-menu a{color:#999} .navbar-inverse .nav-collapse .nav>li>a:hover,.navbar-inverse .nav-collapse .nav>li>a:focus,.navbar-inverse .nav-collapse .dropdown-menu a:hover,.navbar-inverse .nav-collapse .dropdown-menu a:focus{background-color:#111} .nav-collapse.in .btn-group{margin-top:5px;padding:0} .nav-collapse .dropdown-menu{position:static;top:auto;left:auto;float:none;display:none;max-width:none;margin:0 15px;padding:0;background-color:transparent;border:none;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none} .nav-collapse .open>.dropdown-menu{display:block} .nav-collapse .dropdown-menu:before,.nav-collapse .dropdown-menu:after{display:none} .nav-collapse .dropdown-menu .divider{display:none} .nav-collapse .nav>li>.dropdown-menu:before,.nav-collapse .nav>li>.dropdown-menu:after{display:none} .nav-collapse .navbar-form,.nav-collapse .navbar-search{float:none;padding:10px 15px;margin:10px 0;border-top:1px solid #f2f2f2;border-bottom:1px solid #f2f2f2;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1)} .navbar-inverse .nav-collapse .navbar-form,.navbar-inverse .nav-collapse .navbar-search{border-top-color:#111;border-bottom-color:#111} .navbar .nav-collapse .nav.pull-right{float:none;margin-left:0} .nav-collapse,.nav-collapse.collapse{overflow:hidden;height:0} .navbar .btn-navbar{display:block} .navbar-static .navbar-inner{padding-left:10px;padding-right:10px}}@media (min-width:979px + 1){.nav-collapse.collapse{height:auto !important;overflow:visible !important}}@font-face{font-family:'FontAwesome';src:url('../components/font-awesome/font/fontawesome-webfont.eot?v=3.2.1');src:url('../components/font-awesome/font/fontawesome-webfont.eot?#iefix&v=3.2.1') format('embedded-opentype'),url('../components/font-awesome/font/fontawesome-webfont.woff?v=3.2.1') format('woff'),url('../components/font-awesome/font/fontawesome-webfont.ttf?v=3.2.1') format('truetype'),url('../components/font-awesome/font/fontawesome-webfont.svg#fontawesomeregular?v=3.2.1') format('svg');font-weight:normal;font-style:normal}[class^="icon-"],[class*=" icon-"]{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em}
+[class^="icon-"]:before,[class*=" icon-"]:before{text-decoration:inherit;display:inline-block;speak:none}
+.icon-large:before{vertical-align:-10%;font-size:1.3333333333333333em}
+a [class^="icon-"],a [class*=" icon-"]{display:inline}
+[class^="icon-"].icon-fixed-width,[class*=" icon-"].icon-fixed-width{display:inline-block;width:1.1428571428571428em;text-align:right;padding-right:.2857142857142857em}[class^="icon-"].icon-fixed-width.icon-large,[class*=" icon-"].icon-fixed-width.icon-large{width:1.4285714285714286em}
+.icons-ul{margin-left:2.142857142857143em;list-style-type:none}.icons-ul>li{position:relative}
+.icons-ul .icon-li{position:absolute;left:-2.142857142857143em;width:2.142857142857143em;text-align:center;line-height:inherit}
+[class^="icon-"].hide,[class*=" icon-"].hide{display:none}
+.icon-muted{color:#eee}
+.icon-light{color:#fff}
+.icon-dark{color:#333}
+.icon-border{border:solid 1px #eee;padding:.2em .25em .15em;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.icon-2x{font-size:2em}.icon-2x.icon-border{border-width:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.icon-3x{font-size:3em}.icon-3x.icon-border{border-width:3px;border-radius:5px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}
+.icon-4x{font-size:4em}.icon-4x.icon-border{border-width:4px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.icon-5x{font-size:5em}.icon-5x.icon-border{border-width:5px;border-radius:7px;-webkit-border-radius:7px;-moz-border-radius:7px;border-radius:7px}
+.pull-right{float:right}
+.pull-left{float:left}
+[class^="icon-"].pull-left,[class*=" icon-"].pull-left{margin-right:.3em}
+[class^="icon-"].pull-right,[class*=" icon-"].pull-right{margin-left:.3em}
+[class^="icon-"],[class*=" icon-"]{display:inline;width:auto;height:auto;line-height:normal;vertical-align:baseline;background-image:none;background-position:0 0;background-repeat:repeat;margin-top:0}
+.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"]{background-image:none}
+.btn [class^="icon-"].icon-large,.nav [class^="icon-"].icon-large,.btn [class*=" icon-"].icon-large,.nav [class*=" icon-"].icon-large{line-height:.9em}
+.btn [class^="icon-"].icon-spin,.nav [class^="icon-"].icon-spin,.btn [class*=" icon-"].icon-spin,.nav [class*=" icon-"].icon-spin{display:inline-block}
+.nav-tabs [class^="icon-"],.nav-pills [class^="icon-"],.nav-tabs [class*=" icon-"],.nav-pills [class*=" icon-"],.nav-tabs [class^="icon-"].icon-large,.nav-pills [class^="icon-"].icon-large,.nav-tabs [class*=" icon-"].icon-large,.nav-pills [class*=" icon-"].icon-large{line-height:.9em}
+.btn [class^="icon-"].pull-left.icon-2x,.btn [class*=" icon-"].pull-left.icon-2x,.btn [class^="icon-"].pull-right.icon-2x,.btn [class*=" icon-"].pull-right.icon-2x{margin-top:.18em}
+.btn [class^="icon-"].icon-spin.icon-large,.btn [class*=" icon-"].icon-spin.icon-large{line-height:.8em}
+.btn.btn-small [class^="icon-"].pull-left.icon-2x,.btn.btn-small [class*=" icon-"].pull-left.icon-2x,.btn.btn-small [class^="icon-"].pull-right.icon-2x,.btn.btn-small [class*=" icon-"].pull-right.icon-2x{margin-top:.25em}
+.btn.btn-large [class^="icon-"],.btn.btn-large [class*=" icon-"]{margin-top:0}.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x,.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-top:.05em}
+.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x{margin-right:.2em}
+.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-left:.2em}
+.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{line-height:inherit}
+.icon-stack{position:relative;display:inline-block;width:2em;height:2em;line-height:2em;vertical-align:-35%}.icon-stack [class^="icon-"],.icon-stack [class*=" icon-"]{display:block;text-align:center;position:absolute;width:100%;height:100%;font-size:1em;line-height:inherit;*line-height:2em}
+.icon-stack .icon-stack-base{font-size:2em;*line-height:1em}
+.icon-spin{display:inline-block;-moz-animation:spin 2s infinite linear;-o-animation:spin 2s infinite linear;-webkit-animation:spin 2s infinite linear;animation:spin 2s infinite linear}
+a .icon-stack,a .icon-spin{display:inline-block;text-decoration:none}
+@-moz-keyframes spin{0%{-moz-transform:rotate(0deg)} 100%{-moz-transform:rotate(359deg)}}@-webkit-keyframes spin{0%{-webkit-transform:rotate(0deg)} 100%{-webkit-transform:rotate(359deg)}}@-o-keyframes spin{0%{-o-transform:rotate(0deg)} 100%{-o-transform:rotate(359deg)}}@-ms-keyframes spin{0%{-ms-transform:rotate(0deg)} 100%{-ms-transform:rotate(359deg)}}@keyframes spin{0%{transform:rotate(0deg)} 100%{transform:rotate(359deg)}}.icon-rotate-90:before{-webkit-transform:rotate(90deg);-moz-transform:rotate(90deg);-ms-transform:rotate(90deg);-o-transform:rotate(90deg);transform:rotate(90deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=1)}
+.icon-rotate-180:before{-webkit-transform:rotate(180deg);-moz-transform:rotate(180deg);-ms-transform:rotate(180deg);-o-transform:rotate(180deg);transform:rotate(180deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2)}
+.icon-rotate-270:before{-webkit-transform:rotate(270deg);-moz-transform:rotate(270deg);-ms-transform:rotate(270deg);-o-transform:rotate(270deg);transform:rotate(270deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=3)}
+.icon-flip-horizontal:before{-webkit-transform:scale(-1, 1);-moz-transform:scale(-1, 1);-ms-transform:scale(-1, 1);-o-transform:scale(-1, 1);transform:scale(-1, 1)}
+.icon-flip-vertical:before{-webkit-transform:scale(1, -1);-moz-transform:scale(1, -1);-ms-transform:scale(1, -1);-o-transform:scale(1, -1);transform:scale(1, -1)}
+a .icon-rotate-90:before,a .icon-rotate-180:before,a .icon-rotate-270:before,a .icon-flip-horizontal:before,a .icon-flip-vertical:before{display:inline-block}
+.icon-glass:before{content:"\f000"}
+.icon-music:before{content:"\f001"}
+.icon-search:before{content:"\f002"}
+.icon-envelope-alt:before{content:"\f003"}
+.icon-heart:before{content:"\f004"}
+.icon-star:before{content:"\f005"}
+.icon-star-empty:before{content:"\f006"}
+.icon-user:before{content:"\f007"}
+.icon-film:before{content:"\f008"}
+.icon-th-large:before{content:"\f009"}
+.icon-th:before{content:"\f00a"}
+.icon-th-list:before{content:"\f00b"}
+.icon-ok:before{content:"\f00c"}
+.icon-remove:before{content:"\f00d"}
+.icon-zoom-in:before{content:"\f00e"}
+.icon-zoom-out:before{content:"\f010"}
+.icon-power-off:before,.icon-off:before{content:"\f011"}
+.icon-signal:before{content:"\f012"}
+.icon-gear:before,.icon-cog:before{content:"\f013"}
+.icon-trash:before{content:"\f014"}
+.icon-home:before{content:"\f015"}
+.icon-file-alt:before{content:"\f016"}
+.icon-time:before{content:"\f017"}
+.icon-road:before{content:"\f018"}
+.icon-download-alt:before{content:"\f019"}
+.icon-download:before{content:"\f01a"}
+.icon-upload:before{content:"\f01b"}
+.icon-inbox:before{content:"\f01c"}
+.icon-play-circle:before{content:"\f01d"}
+.icon-rotate-right:before,.icon-repeat:before{content:"\f01e"}
+.icon-refresh:before{content:"\f021"}
+.icon-list-alt:before{content:"\f022"}
+.icon-lock:before{content:"\f023"}
+.icon-flag:before{content:"\f024"}
+.icon-headphones:before{content:"\f025"}
+.icon-volume-off:before{content:"\f026"}
+.icon-volume-down:before{content:"\f027"}
+.icon-volume-up:before{content:"\f028"}
+.icon-qrcode:before{content:"\f029"}
+.icon-barcode:before{content:"\f02a"}
+.icon-tag:before{content:"\f02b"}
+.icon-tags:before{content:"\f02c"}
+.icon-book:before{content:"\f02d"}
+.icon-bookmark:before{content:"\f02e"}
+.icon-print:before{content:"\f02f"}
+.icon-camera:before{content:"\f030"}
+.icon-font:before{content:"\f031"}
+.icon-bold:before{content:"\f032"}
+.icon-italic:before{content:"\f033"}
+.icon-text-height:before{content:"\f034"}
+.icon-text-width:before{content:"\f035"}
+.icon-align-left:before{content:"\f036"}
+.icon-align-center:before{content:"\f037"}
+.icon-align-right:before{content:"\f038"}
+.icon-align-justify:before{content:"\f039"}
+.icon-list:before{content:"\f03a"}
+.icon-indent-left:before{content:"\f03b"}
+.icon-indent-right:before{content:"\f03c"}
+.icon-facetime-video:before{content:"\f03d"}
+.icon-picture:before{content:"\f03e"}
+.icon-pencil:before{content:"\f040"}
+.icon-map-marker:before{content:"\f041"}
+.icon-adjust:before{content:"\f042"}
+.icon-tint:before{content:"\f043"}
+.icon-edit:before{content:"\f044"}
+.icon-share:before{content:"\f045"}
+.icon-check:before{content:"\f046"}
+.icon-move:before{content:"\f047"}
+.icon-step-backward:before{content:"\f048"}
+.icon-fast-backward:before{content:"\f049"}
+.icon-backward:before{content:"\f04a"}
+.icon-play:before{content:"\f04b"}
+.icon-pause:before{content:"\f04c"}
+.icon-stop:before{content:"\f04d"}
+.icon-forward:before{content:"\f04e"}
+.icon-fast-forward:before{content:"\f050"}
+.icon-step-forward:before{content:"\f051"}
+.icon-eject:before{content:"\f052"}
+.icon-chevron-left:before{content:"\f053"}
+.icon-chevron-right:before{content:"\f054"}
+.icon-plus-sign:before{content:"\f055"}
+.icon-minus-sign:before{content:"\f056"}
+.icon-remove-sign:before{content:"\f057"}
+.icon-ok-sign:before{content:"\f058"}
+.icon-question-sign:before{content:"\f059"}
+.icon-info-sign:before{content:"\f05a"}
+.icon-screenshot:before{content:"\f05b"}
+.icon-remove-circle:before{content:"\f05c"}
+.icon-ok-circle:before{content:"\f05d"}
+.icon-ban-circle:before{content:"\f05e"}
+.icon-arrow-left:before{content:"\f060"}
+.icon-arrow-right:before{content:"\f061"}
+.icon-arrow-up:before{content:"\f062"}
+.icon-arrow-down:before{content:"\f063"}
+.icon-mail-forward:before,.icon-share-alt:before{content:"\f064"}
+.icon-resize-full:before{content:"\f065"}
+.icon-resize-small:before{content:"\f066"}
+.icon-plus:before{content:"\f067"}
+.icon-minus:before{content:"\f068"}
+.icon-asterisk:before{content:"\f069"}
+.icon-exclamation-sign:before{content:"\f06a"}
+.icon-gift:before{content:"\f06b"}
+.icon-leaf:before{content:"\f06c"}
+.icon-fire:before{content:"\f06d"}
+.icon-eye-open:before{content:"\f06e"}
+.icon-eye-close:before{content:"\f070"}
+.icon-warning-sign:before{content:"\f071"}
+.icon-plane:before{content:"\f072"}
+.icon-calendar:before{content:"\f073"}
+.icon-random:before{content:"\f074"}
+.icon-comment:before{content:"\f075"}
+.icon-magnet:before{content:"\f076"}
+.icon-chevron-up:before{content:"\f077"}
+.icon-chevron-down:before{content:"\f078"}
+.icon-retweet:before{content:"\f079"}
+.icon-shopping-cart:before{content:"\f07a"}
+.icon-folder-close:before{content:"\f07b"}
+.icon-folder-open:before{content:"\f07c"}
+.icon-resize-vertical:before{content:"\f07d"}
+.icon-resize-horizontal:before{content:"\f07e"}
+.icon-bar-chart:before{content:"\f080"}
+.icon-twitter-sign:before{content:"\f081"}
+.icon-facebook-sign:before{content:"\f082"}
+.icon-camera-retro:before{content:"\f083"}
+.icon-key:before{content:"\f084"}
+.icon-gears:before,.icon-cogs:before{content:"\f085"}
+.icon-comments:before{content:"\f086"}
+.icon-thumbs-up-alt:before{content:"\f087"}
+.icon-thumbs-down-alt:before{content:"\f088"}
+.icon-star-half:before{content:"\f089"}
+.icon-heart-empty:before{content:"\f08a"}
+.icon-signout:before{content:"\f08b"}
+.icon-linkedin-sign:before{content:"\f08c"}
+.icon-pushpin:before{content:"\f08d"}
+.icon-external-link:before{content:"\f08e"}
+.icon-signin:before{content:"\f090"}
+.icon-trophy:before{content:"\f091"}
+.icon-github-sign:before{content:"\f092"}
+.icon-upload-alt:before{content:"\f093"}
+.icon-lemon:before{content:"\f094"}
+.icon-phone:before{content:"\f095"}
+.icon-unchecked:before,.icon-check-empty:before{content:"\f096"}
+.icon-bookmark-empty:before{content:"\f097"}
+.icon-phone-sign:before{content:"\f098"}
+.icon-twitter:before{content:"\f099"}
+.icon-facebook:before{content:"\f09a"}
+.icon-github:before{content:"\f09b"}
+.icon-unlock:before{content:"\f09c"}
+.icon-credit-card:before{content:"\f09d"}
+.icon-rss:before{content:"\f09e"}
+.icon-hdd:before{content:"\f0a0"}
+.icon-bullhorn:before{content:"\f0a1"}
+.icon-bell:before{content:"\f0a2"}
+.icon-certificate:before{content:"\f0a3"}
+.icon-hand-right:before{content:"\f0a4"}
+.icon-hand-left:before{content:"\f0a5"}
+.icon-hand-up:before{content:"\f0a6"}
+.icon-hand-down:before{content:"\f0a7"}
+.icon-circle-arrow-left:before{content:"\f0a8"}
+.icon-circle-arrow-right:before{content:"\f0a9"}
+.icon-circle-arrow-up:before{content:"\f0aa"}
+.icon-circle-arrow-down:before{content:"\f0ab"}
+.icon-globe:before{content:"\f0ac"}
+.icon-wrench:before{content:"\f0ad"}
+.icon-tasks:before{content:"\f0ae"}
+.icon-filter:before{content:"\f0b0"}
+.icon-briefcase:before{content:"\f0b1"}
+.icon-fullscreen:before{content:"\f0b2"}
+.icon-group:before{content:"\f0c0"}
+.icon-link:before{content:"\f0c1"}
+.icon-cloud:before{content:"\f0c2"}
+.icon-beaker:before{content:"\f0c3"}
+.icon-cut:before{content:"\f0c4"}
+.icon-copy:before{content:"\f0c5"}
+.icon-paperclip:before,.icon-paper-clip:before{content:"\f0c6"}
+.icon-save:before{content:"\f0c7"}
+.icon-sign-blank:before{content:"\f0c8"}
+.icon-reorder:before{content:"\f0c9"}
+.icon-list-ul:before{content:"\f0ca"}
+.icon-list-ol:before{content:"\f0cb"}
+.icon-strikethrough:before{content:"\f0cc"}
+.icon-underline:before{content:"\f0cd"}
+.icon-table:before{content:"\f0ce"}
+.icon-magic:before{content:"\f0d0"}
+.icon-truck:before{content:"\f0d1"}
+.icon-pinterest:before{content:"\f0d2"}
+.icon-pinterest-sign:before{content:"\f0d3"}
+.icon-google-plus-sign:before{content:"\f0d4"}
+.icon-google-plus:before{content:"\f0d5"}
+.icon-money:before{content:"\f0d6"}
+.icon-caret-down:before{content:"\f0d7"}
+.icon-caret-up:before{content:"\f0d8"}
+.icon-caret-left:before{content:"\f0d9"}
+.icon-caret-right:before{content:"\f0da"}
+.icon-columns:before{content:"\f0db"}
+.icon-sort:before{content:"\f0dc"}
+.icon-sort-down:before{content:"\f0dd"}
+.icon-sort-up:before{content:"\f0de"}
+.icon-envelope:before{content:"\f0e0"}
+.icon-linkedin:before{content:"\f0e1"}
+.icon-rotate-left:before,.icon-undo:before{content:"\f0e2"}
+.icon-legal:before{content:"\f0e3"}
+.icon-dashboard:before{content:"\f0e4"}
+.icon-comment-alt:before{content:"\f0e5"}
+.icon-comments-alt:before{content:"\f0e6"}
+.icon-bolt:before{content:"\f0e7"}
+.icon-sitemap:before{content:"\f0e8"}
+.icon-umbrella:before{content:"\f0e9"}
+.icon-paste:before{content:"\f0ea"}
+.icon-lightbulb:before{content:"\f0eb"}
+.icon-exchange:before{content:"\f0ec"}
+.icon-cloud-download:before{content:"\f0ed"}
+.icon-cloud-upload:before{content:"\f0ee"}
+.icon-user-md:before{content:"\f0f0"}
+.icon-stethoscope:before{content:"\f0f1"}
+.icon-suitcase:before{content:"\f0f2"}
+.icon-bell-alt:before{content:"\f0f3"}
+.icon-coffee:before{content:"\f0f4"}
+.icon-food:before{content:"\f0f5"}
+.icon-file-text-alt:before{content:"\f0f6"}
+.icon-building:before{content:"\f0f7"}
+.icon-hospital:before{content:"\f0f8"}
+.icon-ambulance:before{content:"\f0f9"}
+.icon-medkit:before{content:"\f0fa"}
+.icon-fighter-jet:before{content:"\f0fb"}
+.icon-beer:before{content:"\f0fc"}
+.icon-h-sign:before{content:"\f0fd"}
+.icon-plus-sign-alt:before{content:"\f0fe"}
+.icon-double-angle-left:before{content:"\f100"}
+.icon-double-angle-right:before{content:"\f101"}
+.icon-double-angle-up:before{content:"\f102"}
+.icon-double-angle-down:before{content:"\f103"}
+.icon-angle-left:before{content:"\f104"}
+.icon-angle-right:before{content:"\f105"}
+.icon-angle-up:before{content:"\f106"}
+.icon-angle-down:before{content:"\f107"}
+.icon-desktop:before{content:"\f108"}
+.icon-laptop:before{content:"\f109"}
+.icon-tablet:before{content:"\f10a"}
+.icon-mobile-phone:before{content:"\f10b"}
+.icon-circle-blank:before{content:"\f10c"}
+.icon-quote-left:before{content:"\f10d"}
+.icon-quote-right:before{content:"\f10e"}
+.icon-spinner:before{content:"\f110"}
+.icon-circle:before{content:"\f111"}
+.icon-mail-reply:before,.icon-reply:before{content:"\f112"}
+.icon-github-alt:before{content:"\f113"}
+.icon-folder-close-alt:before{content:"\f114"}
+.icon-folder-open-alt:before{content:"\f115"}
+.icon-expand-alt:before{content:"\f116"}
+.icon-collapse-alt:before{content:"\f117"}
+.icon-smile:before{content:"\f118"}
+.icon-frown:before{content:"\f119"}
+.icon-meh:before{content:"\f11a"}
+.icon-gamepad:before{content:"\f11b"}
+.icon-keyboard:before{content:"\f11c"}
+.icon-flag-alt:before{content:"\f11d"}
+.icon-flag-checkered:before{content:"\f11e"}
+.icon-terminal:before{content:"\f120"}
+.icon-code:before{content:"\f121"}
+.icon-reply-all:before{content:"\f122"}
+.icon-mail-reply-all:before{content:"\f122"}
+.icon-star-half-full:before,.icon-star-half-empty:before{content:"\f123"}
+.icon-location-arrow:before{content:"\f124"}
+.icon-crop:before{content:"\f125"}
+.icon-code-fork:before{content:"\f126"}
+.icon-unlink:before{content:"\f127"}
+.icon-question:before{content:"\f128"}
+.icon-info:before{content:"\f129"}
+.icon-exclamation:before{content:"\f12a"}
+.icon-superscript:before{content:"\f12b"}
+.icon-subscript:before{content:"\f12c"}
+.icon-eraser:before{content:"\f12d"}
+.icon-puzzle-piece:before{content:"\f12e"}
+.icon-microphone:before{content:"\f130"}
+.icon-microphone-off:before{content:"\f131"}
+.icon-shield:before{content:"\f132"}
+.icon-calendar-empty:before{content:"\f133"}
+.icon-fire-extinguisher:before{content:"\f134"}
+.icon-rocket:before{content:"\f135"}
+.icon-maxcdn:before{content:"\f136"}
+.icon-chevron-sign-left:before{content:"\f137"}
+.icon-chevron-sign-right:before{content:"\f138"}
+.icon-chevron-sign-up:before{content:"\f139"}
+.icon-chevron-sign-down:before{content:"\f13a"}
+.icon-html5:before{content:"\f13b"}
+.icon-css3:before{content:"\f13c"}
+.icon-anchor:before{content:"\f13d"}
+.icon-unlock-alt:before{content:"\f13e"}
+.icon-bullseye:before{content:"\f140"}
+.icon-ellipsis-horizontal:before{content:"\f141"}
+.icon-ellipsis-vertical:before{content:"\f142"}
+.icon-rss-sign:before{content:"\f143"}
+.icon-play-sign:before{content:"\f144"}
+.icon-ticket:before{content:"\f145"}
+.icon-minus-sign-alt:before{content:"\f146"}
+.icon-check-minus:before{content:"\f147"}
+.icon-level-up:before{content:"\f148"}
+.icon-level-down:before{content:"\f149"}
+.icon-check-sign:before{content:"\f14a"}
+.icon-edit-sign:before{content:"\f14b"}
+.icon-external-link-sign:before{content:"\f14c"}
+.icon-share-sign:before{content:"\f14d"}
+.icon-compass:before{content:"\f14e"}
+.icon-collapse:before{content:"\f150"}
+.icon-collapse-top:before{content:"\f151"}
+.icon-expand:before{content:"\f152"}
+.icon-euro:before,.icon-eur:before{content:"\f153"}
+.icon-gbp:before{content:"\f154"}
+.icon-dollar:before,.icon-usd:before{content:"\f155"}
+.icon-rupee:before,.icon-inr:before{content:"\f156"}
+.icon-yen:before,.icon-jpy:before{content:"\f157"}
+.icon-renminbi:before,.icon-cny:before{content:"\f158"}
+.icon-won:before,.icon-krw:before{content:"\f159"}
+.icon-bitcoin:before,.icon-btc:before{content:"\f15a"}
+.icon-file:before{content:"\f15b"}
+.icon-file-text:before{content:"\f15c"}
+.icon-sort-by-alphabet:before{content:"\f15d"}
+.icon-sort-by-alphabet-alt:before{content:"\f15e"}
+.icon-sort-by-attributes:before{content:"\f160"}
+.icon-sort-by-attributes-alt:before{content:"\f161"}
+.icon-sort-by-order:before{content:"\f162"}
+.icon-sort-by-order-alt:before{content:"\f163"}
+.icon-thumbs-up:before{content:"\f164"}
+.icon-thumbs-down:before{content:"\f165"}
+.icon-youtube-sign:before{content:"\f166"}
+.icon-youtube:before{content:"\f167"}
+.icon-xing:before{content:"\f168"}
+.icon-xing-sign:before{content:"\f169"}
+.icon-youtube-play:before{content:"\f16a"}
+.icon-dropbox:before{content:"\f16b"}
+.icon-stackexchange:before{content:"\f16c"}
+.icon-instagram:before{content:"\f16d"}
+.icon-flickr:before{content:"\f16e"}
+.icon-adn:before{content:"\f170"}
+.icon-bitbucket:before{content:"\f171"}
+.icon-bitbucket-sign:before{content:"\f172"}
+.icon-tumblr:before{content:"\f173"}
+.icon-tumblr-sign:before{content:"\f174"}
+.icon-long-arrow-down:before{content:"\f175"}
+.icon-long-arrow-up:before{content:"\f176"}
+.icon-long-arrow-left:before{content:"\f177"}
+.icon-long-arrow-right:before{content:"\f178"}
+.icon-apple:before{content:"\f179"}
+.icon-windows:before{content:"\f17a"}
+.icon-android:before{content:"\f17b"}
+.icon-linux:before{content:"\f17c"}
+.icon-dribbble:before{content:"\f17d"}
+.icon-skype:before{content:"\f17e"}
+.icon-foursquare:before{content:"\f180"}
+.icon-trello:before{content:"\f181"}
+.icon-female:before{content:"\f182"}
+.icon-male:before{content:"\f183"}
+.icon-gittip:before{content:"\f184"}
+.icon-sun:before{content:"\f185"}
+.icon-moon:before{content:"\f186"}
+.icon-archive:before{content:"\f187"}
+.icon-bug:before{content:"\f188"}
+.icon-vk:before{content:"\f189"}
+.icon-weibo:before{content:"\f18a"}
+.icon-renren:before{content:"\f18b"}
+code{color:#000}
+.border-box-sizing{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
+.corner-all{border-radius:4px}
+.hbox{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+.hbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none}
+.vbox{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+.vbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none}
+.hbox.reverse,.vbox.reverse,.reverse{-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
+.hbox.box-flex0,.vbox.box-flex0,.box-flex0{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none;width:auto}
+.hbox.box-flex1,.vbox.box-flex1,.box-flex1{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+.hbox.box-flex,.vbox.box-flex,.box-flex{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+.hbox.box-flex2,.vbox.box-flex2,.box-flex2{-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2}
+.box-group1{-webkit-box-flex-group:1;-moz-box-flex-group:1;box-flex-group:1}
+.box-group2{-webkit-box-flex-group:2;-moz-box-flex-group:2;box-flex-group:2}
+.hbox.start,.vbox.start,.start{-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start}
+.hbox.end,.vbox.end,.end{-webkit-box-pack:end;-moz-box-pack:end;box-pack:end;justify-content:flex-end}
+.hbox.center,.vbox.center,.center{-webkit-box-pack:center;-moz-box-pack:center;box-pack:center;justify-content:center}
+.hbox.align-start,.vbox.align-start,.align-start{-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
+.hbox.align-end,.vbox.align-end,.align-end{-webkit-box-align:end;-moz-box-align:end;box-align:end;align-items:flex-end}
+.hbox.align-center,.vbox.align-center,.align-center{-webkit-box-align:center;-moz-box-align:center;box-align:center;align-items:center}
+div.error{margin:2em;text-align:center}
+div.error>h1{font-size:500%;line-height:normal}
+div.error>p{font-size:200%;line-height:normal}
+div.traceback-wrapper{text-align:left;max-width:800px;margin:auto}
+body{background-color:#fff;position:absolute;left:0;right:0;top:0;bottom:0;overflow:visible}
+div#header{display:none}
+#ipython_notebook{padding-left:16px}
+#noscript{width:auto;padding-top:16px;padding-bottom:16px;text-align:center;font-size:22px;color:#f00;font-weight:bold}
+#ipython_notebook img{font-family:Verdana,"Helvetica Neue",Arial,Helvetica,Geneva,sans-serif;height:24px;text-decoration:none;color:#000}
+#site{width:100%;display:none}
+.ui-button .ui-button-text{padding:.2em .8em;font-size:77%}
+input.ui-button{padding:.3em .9em}
+.navbar span{margin-top:3px}
+span#login_widget{float:right}
+.nav-header{text-transform:none}
+.navbar-nobg{background-color:transparent;background-image:none}
+#header>span{margin-top:10px}
+.modal_stretch{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;height:80%}.modal_stretch .modal-body{max-height:none;flex:1}
+@media (min-width:768px){.modal{width:700px;margin-left:-350px}}.center-nav{display:inline-block;margin-bottom:-4px}
+.alternate_upload{background-color:none;display:inline}
+.alternate_upload.form{padding:0;margin:0}
+.alternate_upload input.fileinput{background-color:#f00;position:relative;opacity:0;z-index:2;width:295px;margin-left:163px;cursor:pointer;height:26px}
+ul#tabs{margin-bottom:4px}
+ul#tabs a{padding-top:4px;padding-bottom:4px}
+ul.breadcrumb a:focus,ul.breadcrumb a:hover{text-decoration:none}
+ul.breadcrumb i.icon-home{font-size:16px;margin-right:4px}
+ul.breadcrumb span{color:#5e5e5e}
+.list_toolbar{padding:4px 0 4px 0}
+.list_toolbar [class*="span"]{min-height:26px}
+.list_header{font-weight:bold}
+.list_container{margin-top:4px;margin-bottom:20px;border:1px solid #ababab;border-radius:4px}
+.list_container>div{border-bottom:1px solid #ababab}.list_container>div:hover .list-item{background-color:#f00}
+.list_container>div:last-child{border:none}
+.list_item:hover .list_item{background-color:#ddd}
+.list_item a{text-decoration:none}
+.list_header>div,.list_item>div{padding-top:4px;padding-bottom:4px;padding-left:7px;padding-right:7px;height:22px;line-height:22px}
+.item_name{line-height:22px;height:26px}
+.item_icon{font-size:14px;color:#5e5e5e;margin-right:7px}
+.item_buttons{line-height:1em}
+.toolbar_info{height:26px;line-height:26px}
+input.nbname_input,input.engine_num_input{padding-top:3px;padding-bottom:3px;height:14px;line-height:14px;margin:0}
+input.engine_num_input{width:60px}
+.highlight_text{color:#00f}
+#project_name>.breadcrumb{padding:0;margin-bottom:0;background-color:transparent;font-weight:bold}
+.ansibold{font-weight:bold}
+.ansiblack{color:#000}
+.ansired{color:#8b0000}
+.ansigreen{color:#006400}
+.ansiyellow{color:#a52a2a}
+.ansiblue{color:#00008b}
+.ansipurple{color:#9400d3}
+.ansicyan{color:#4682b4}
+.ansigray{color:#808080}
+.ansibgblack{background-color:#000}
+.ansibgred{background-color:#f00}
+.ansibggreen{background-color:#008000}
+.ansibgyellow{background-color:#ff0}
+.ansibgblue{background-color:#00f}
+.ansibgpurple{background-color:#f0f}
+.ansibgcyan{background-color:#0ff}
+.ansibggray{background-color:#808080}
+div.cell{border:1px solid transparent;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}div.cell.selected{border-radius:4px;border:thin #ababab solid}
+div.cell.edit_mode{border-radius:4px;border:thin #008000 solid}
+div.cell{width:100%;padding:5px 5px 5px 0;margin:0;outline:none}
+div.prompt{min-width:11ex;padding:.4em;margin:0;font-family:monospace;text-align:right;line-height:1.21429em}
+@media (max-width:480px){div.prompt{text-align:left}}div.inner_cell{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+div.input_area{border:1px solid #cfcfcf;border-radius:4px;background:#f7f7f7}
+div.prompt:empty{padding-top:0;padding-bottom:0}
+div.input{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+@media (max-width:480px){div.input{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}}div.input_prompt{color:#000080;border-top:1px solid transparent}
+div.input_area>div.highlight{margin:.4em;border:none;padding:0;background-color:transparent}
+div.input_area>div.highlight>pre{margin:0;border:0;padding:0;background-color:transparent;font-size:14px;line-height:1.21429em}
+.CodeMirror{line-height:1.21429em;height:auto;background:none;}
+.CodeMirror-scroll{overflow-y:hidden;overflow-x:auto}
+@-moz-document url-prefix(){.CodeMirror-scroll{overflow-x:hidden}}.CodeMirror-lines{padding:.4em}
+.CodeMirror-linenumber{padding:0 8px 0 4px}
+.CodeMirror-gutters{border-bottom-left-radius:4px;border-top-left-radius:4px}
+.CodeMirror pre{padding:0;border:0;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+pre code{display:block;padding:.5em}
+.highlight-base,pre code,pre .subst,pre .tag .title,pre .lisp .title,pre .clojure .built_in,pre .nginx .title{color:#000}
+.highlight-string,pre .string,pre .constant,pre .parent,pre .tag .value,pre .rules .value,pre .rules .value .number,pre .preprocessor,pre .ruby .symbol,pre .ruby .symbol .string,pre .aggregate,pre .template_tag,pre .django .variable,pre .smalltalk .class,pre .addition,pre .flow,pre .stream,pre .bash .variable,pre .apache .tag,pre .apache .cbracket,pre .tex .command,pre .tex .special,pre .erlang_repl .function_or_atom,pre .markdown .header{color:#ba2121}
+.highlight-comment,pre .comment,pre .annotation,pre .template_comment,pre .diff .header,pre .chunk,pre .markdown .blockquote{color:#408080;font-style:italic}
+.highlight-number,pre .number,pre .date,pre .regexp,pre .literal,pre .smalltalk .symbol,pre .smalltalk .char,pre .go .constant,pre .change,pre .markdown .bullet,pre .markdown .link_url{color:#080}
+pre .label,pre .javadoc,pre .ruby .string,pre .decorator,pre .filter .argument,pre .localvars,pre .array,pre .attr_selector,pre .important,pre .pseudo,pre .pi,pre .doctype,pre .deletion,pre .envvar,pre .shebang,pre .apache .sqbracket,pre .nginx .built_in,pre .tex .formula,pre .erlang_repl .reserved,pre .prompt,pre .markdown .link_label,pre .vhdl .attribute,pre .clojure .attribute,pre .coffeescript .property{color:#88f}
+.highlight-keyword,pre .keyword,pre .id,pre .phpdoc,pre .aggregate,pre .css .tag,pre .javadoctag,pre .phpdoc,pre .yardoctag,pre .smalltalk .class,pre .winutils,pre .bash .variable,pre .apache .tag,pre .go .typename,pre .tex .command,pre .markdown .strong,pre .request,pre .status{color:#008000;font-weight:bold}
+.highlight-builtin,pre .built_in{color:#008000}
+pre .markdown .emphasis{font-style:italic}
+pre .nginx .built_in{font-weight:normal}
+pre .coffeescript .javascript,pre .javascript .xml,pre .tex .formula,pre .xml .javascript,pre .xml .vbscript,pre .xml .css,pre .xml .cdata{opacity:.5}
+.cm-s-ipython span.cm-variable{color:#000}
+.cm-s-ipython span.cm-keyword{color:#008000;font-weight:bold}
+.cm-s-ipython span.cm-number{color:#080}
+.cm-s-ipython span.cm-comment{color:#408080;font-style:italic}
+.cm-s-ipython span.cm-string{color:#ba2121}
+.cm-s-ipython span.cm-builtin{color:#008000}
+.cm-s-ipython span.cm-error{color:#f00}
+.cm-s-ipython span.cm-operator{color:#a2f;font-weight:bold}
+.cm-s-ipython span.cm-meta{color:#a2f}
+.cm-s-ipython span.cm-tab{background:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);background-position:right;background-repeat:no-repeat}
+div.output_wrapper{position:relative;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+div.output_scroll{height:24em;width:100%;overflow:auto;border-radius:4px;-webkit-box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);-moz-box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);display:block}
+div.output_collapsed{margin:0;padding:0;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+div.out_prompt_overlay{height:100%;padding:0 .4em;position:absolute;border-radius:4px}
+div.out_prompt_overlay:hover{-webkit-box-shadow:inset 0 0 1px #000;-moz-box-shadow:inset 0 0 1px #000;box-shadow:inset 0 0 1px #000;background:rgba(240,240,240,0.5)}
+div.output_prompt{color:#8b0000}
+div.output_area{padding:0;page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}div.output_area .MathJax_Display{text-align:left !important}
+div.output_area .rendered_html table{margin-left:0;margin-right:0}
+div.output_area .rendered_html img{margin-left:0;margin-right:0}
+.output{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+@media (max-width:480px){div.output_area{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}}div.output_area pre{margin:0;padding:0;border:0;font-size:100%;vertical-align:baseline;color:#000;background-color:transparent;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;line-height:inherit}
+div.output_subarea{padding:.4em .4em 0 .4em;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+div.output_text{text-align:left;color:#000;line-height:1.21429em}
+div.output_stderr{background:#fdd;}
+div.output_latex{text-align:left}
+div.output_javascript:empty{padding:0}
+.js-error{color:#8b0000}
+div.raw_input_container{font-family:monospace;padding-top:5px}
+span.raw_input_prompt{}
+input.raw_input{font-family:inherit;font-size:inherit;color:inherit;width:auto;vertical-align:baseline;padding:0 .25em;margin:0 .25em}
+input.raw_input:focus{box-shadow:none}
+p.p-space{margin-bottom:10px}
+.rendered_html{color:#000;}.rendered_html em{font-style:italic}
+.rendered_html strong{font-weight:bold}
+.rendered_html u{text-decoration:underline}
+.rendered_html :link{text-decoration:underline}
+.rendered_html :visited{text-decoration:underline}
+.rendered_html h1{font-size:185.7%;margin:1.08em 0 0 0;font-weight:bold;line-height:1}
+.rendered_html h2{font-size:157.1%;margin:1.27em 0 0 0;font-weight:bold;line-height:1}
+.rendered_html h3{font-size:128.6%;margin:1.55em 0 0 0;font-weight:bold;line-height:1}
+.rendered_html h4{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1}
+.rendered_html h5{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1;font-style:italic}
+.rendered_html h6{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1;font-style:italic}
+.rendered_html h1:first-child{margin-top:.538em}
+.rendered_html h2:first-child{margin-top:.636em}
+.rendered_html h3:first-child{margin-top:.777em}
+.rendered_html h4:first-child{margin-top:1em}
+.rendered_html h5:first-child{margin-top:1em}
+.rendered_html h6:first-child{margin-top:1em}
+.rendered_html ul{list-style:disc;margin:0 2em}
+.rendered_html ul ul{list-style:square;margin:0 2em}
+.rendered_html ul ul ul{list-style:circle;margin:0 2em}
+.rendered_html ol{list-style:decimal;margin:0 2em}
+.rendered_html ol ol{list-style:upper-alpha;margin:0 2em}
+.rendered_html ol ol ol{list-style:lower-alpha;margin:0 2em}
+.rendered_html ol ol ol ol{list-style:lower-roman;margin:0 2em}
+.rendered_html ol ol ol ol ol{list-style:decimal;margin:0 2em}
+.rendered_html *+ul{margin-top:1em}
+.rendered_html *+ol{margin-top:1em}
+.rendered_html hr{color:#000;background-color:#000}
+.rendered_html pre{margin:1em 2em}
+.rendered_html pre,.rendered_html code{border:0;background-color:#fff;color:#000;font-size:100%;padding:0}
+.rendered_html blockquote{margin:1em 2em}
+.rendered_html table{margin-left:auto;margin-right:auto;border:1px solid #000;border-collapse:collapse}
+.rendered_html tr,.rendered_html th,.rendered_html td{border:1px solid #000;border-collapse:collapse;margin:1em 2em}
+.rendered_html td,.rendered_html th{text-align:left;vertical-align:middle;padding:4px}
+.rendered_html th{font-weight:bold}
+.rendered_html *+table{margin-top:1em}
+.rendered_html p{text-align:justify}
+.rendered_html *+p{margin-top:1em}
+.rendered_html img{display:block;margin-left:auto;margin-right:auto}
+.rendered_html *+img{margin-top:1em}
+div.text_cell{padding:5px 5px 5px 0;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+@media (max-width:480px){div.text_cell>div.prompt{display:none}}div.text_cell_render{outline:none;resize:none;width:inherit;border-style:none;padding:.5em .5em .5em .4em;color:#000}
+a.anchor-link:link{text-decoration:none;padding:0 20px;visibility:hidden}
+h1:hover .anchor-link,h2:hover .anchor-link,h3:hover .anchor-link,h4:hover .anchor-link,h5:hover .anchor-link,h6:hover .anchor-link{visibility:visible}
+div.cell.text_cell.rendered{padding:0}
+.widget-area{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-area .widget-subarea{padding:.44em .4em .4em 1px;margin-left:6px;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
+.widget-hlabel{min-width:10ex;padding-right:8px;padding-top:3px;text-align:right;vertical-align:text-top}
+.widget-vlabel{padding-bottom:5px;text-align:center;vertical-align:text-bottom}
+.widget-hreadout{padding-left:8px;padding-top:3px;text-align:left;vertical-align:text-top}
+.widget-vreadout{padding-top:5px;text-align:center;vertical-align:text-top}
+.slide-track{border:1px solid #ccc;background:#fff;border-radius:4px;}
+.widget-hslider{padding-left:8px;padding-right:5px;overflow:visible;width:348px;height:5px;max-height:5px;margin-top:11px;margin-bottom:10px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-hslider .ui-slider{border:0 !important;background:none !important;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-hslider .ui-slider .ui-slider-handle{width:14px !important;height:28px !important;margin-top:-8px !important}
+.widget-vslider{padding-bottom:8px;overflow:visible;width:5px;max-width:5px;height:250px;margin-left:12px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}.widget-vslider .ui-slider{border:0 !important;background:none !important;margin-left:-4px;margin-top:5px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-vslider .ui-slider .ui-slider-handle{width:28px !important;height:14px !important;margin-left:-9px}
+.widget-text{width:350px;margin:0 !important}
+.widget-listbox{width:364px;margin-bottom:0}
+.widget-numeric-text{width:150px;margin:0 !important}
+.widget-progress{width:363px}.widget-progress .bar{-webkit-transition:none;-moz-transition:none;-ms-transition:none;-o-transition:none;transition:none}
+.widget-combo-btn{min-width:138px;}
+.widget-box{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
+.widget-hbox{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+.widget-hbox-single{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;height:30px}
+.widget-vbox{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+.widget-vbox-single{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;width:30px}
+.widget-modal{overflow:hidden;position:absolute !important;top:0;left:0;margin-left:0 !important}
+.widget-modal-body{max-height:none !important}
+.widget-container{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
+.widget-radio-box{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;padding-top:4px}
+.docked-widget-modal{overflow:hidden;position:relative !important;top:0 !important;left:0 !important;margin-left:0 !important}
+body{background-color:#fff}
+body.notebook_app{overflow:hidden}
+@media (max-width:767px){body.notebook_app{padding-left:0;padding-right:0}}span#notebook_name{height:1em;line-height:1em;padding:3px;border:none;font-size:146.5%}
+div#notebook_panel{margin:0 0 0 0;padding:0;-webkit-box-shadow:0 -1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,0.1);box-shadow:0 -1px 10px rgba(0,0,0,0.1)}
+div#notebook{font-size:14px;line-height:20px;overflow-y:scroll;overflow-x:auto;width:100%;padding:1em 0 1em 0;margin:0;border-top:1px solid #ababab;outline:none;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
+div.ui-widget-content{border:1px solid #ababab;outline:none}
+pre.dialog{background-color:#f7f7f7;border:1px solid #ddd;border-radius:4px;padding:.4em;padding-left:2em}
+p.dialog{padding:.2em}
+pre,code,kbd,samp{white-space:pre-wrap}
+#fonttest{font-family:monospace}
+p{margin-bottom:0}
+.end_space{height:200px}
+.celltoolbar{border:thin solid #cfcfcf;border-bottom:none;background:#eee;border-radius:3px 3px 0 0;width:100%;-webkit-box-pack:end;height:22px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
+.ctb_hideshow{display:none;vertical-align:bottom;padding-right:2px}
+.celltoolbar>div{padding-top:0}
+.ctb_global_show .ctb_show.ctb_hideshow{display:block}
+.ctb_global_show .ctb_show+.input_area,.ctb_global_show .ctb_show+div.text_cell_input{border-top-right-radius:0;border-top-left-radius:0}
+.celltoolbar .button_container select{margin:10px;margin-top:1px;margin-bottom:0;padding:0;font-size:87%;width:auto;display:inline-block;height:18px;line-height:18px;vertical-align:top}
+.celltoolbar label{display:inline-block;height:15px;line-height:15px;vertical-align:top}
+.celltoolbar label span{font-size:85%}
+.celltoolbar input[type=checkbox]{margin:0;margin-left:4px;margin-right:4px}
+.celltoolbar .ui-button{border:none;vertical-align:top;height:20px;min-width:30px}
+.completions{position:absolute;z-index:10;overflow:hidden;border:1px solid #ababab;border-radius:4px;-webkit-box-shadow:0 6px 10px -1px #adadad;-moz-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad}
+.completions select{background:#fff;outline:none;border:none;padding:0;margin:0;overflow:auto;font-family:monospace;font-size:110%;color:#000;width:auto}
+.completions select option.context{color:#0064cd}
+#menubar .navbar-inner{min-height:28px;border-top:1px;border-radius:0 0 4px 4px}
+#menubar .navbar{margin-bottom:8px}
+.nav-wrapper{border-bottom:1px solid #d4d4d4}
+#menubar li.dropdown{line-height:12px}
+i.menu-icon{padding-top:4px}
+ul#help_menu li a{overflow:hidden;padding-right:2.2em}ul#help_menu li a i{margin-right:-1.2em}
+#notification_area{z-index:10}
+.indicator_area{color:#777;padding:4px 3px;margin:0;width:11px;z-index:10;text-align:center}
+#kernel_indicator{margin-right:-16px}
+.edit_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f040"}
+.command_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:' '}
+.kernel_idle_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f10c"}
+.kernel_busy_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f111"}
+.notification_widget{color:#777;padding:1px 12px;margin:2px 4px;z-index:10;border:1px solid #ccc;border-radius:4px;background:rgba(240,240,240,0.5)}.notification_widget.span{padding-right:2px}
+div#pager_splitter{height:8px}
+#pager-container{position:relative;padding:15px 0}
+div#pager{font-size:14px;line-height:20px;overflow:auto;display:none}div#pager pre{font-size:13px;line-height:1.21429em;color:#000;background-color:#f7f7f7;padding:.4em}
+.quickhelp{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+.shortcut_key{display:inline-block;width:20ex;text-align:right;font-family:monospace}
+.shortcut_descr{display:inline-block;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+span#save_widget{padding:0 5px;margin-top:12px}
+span#checkpoint_status,span#autosave_status{font-size:small}
+@media (max-width:767px){span#save_widget{font-size:small} span#checkpoint_status,span#autosave_status{font-size:x-small}}@media (max-width:767px){span#checkpoint_status,span#autosave_status{display:none}}@media (min-width:768px) and (max-width:979px){span#checkpoint_status{display:none} span#autosave_status{font-size:x-small}}.toolbar{padding:0 10px;margin-top:-5px}.toolbar select,.toolbar label{width:auto;height:26px;vertical-align:middle;margin-right:2px;margin-bottom:0;display:inline;font-size:92%;margin-left:.3em;margin-right:.3em;padding:0;padding-top:3px}
+.toolbar .btn{padding:2px 8px}
+.toolbar .btn-group{margin-top:0}
+.toolbar-inner{border:none !important;-webkit-box-shadow:none !important;-moz-box-shadow:none !important;box-shadow:none !important}
+#maintoolbar{margin-bottom:0}
+@-moz-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-webkit-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-moz-keyframes fadeIn{from{opacity:0} to{opacity:1}}@-webkit-keyframes fadeIn{from{opacity:0} to{opacity:1}}.bigtooltip{overflow:auto;height:200px;-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms}
+.smalltooltip{-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms;text-overflow:ellipsis;overflow:hidden;height:80px}
+.tooltipbuttons{position:absolute;padding-right:15px;top:0;right:0}
+.tooltiptext{padding-right:30px}
+.ipython_tooltip{max-width:700px;-webkit-animation:fadeOut 400ms;-moz-animation:fadeOut 400ms;animation:fadeOut 400ms;-webkit-animation:fadeIn 400ms;-moz-animation:fadeIn 400ms;animation:fadeIn 400ms;vertical-align:middle;background-color:#f7f7f7;overflow:visible;border:#ababab 1px solid;outline:none;padding:3px;margin:0;padding-left:7px;font-family:monospace;min-height:50px;-moz-box-shadow:0 6px 10px -1px #adadad;-webkit-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad;border-radius:4px;position:absolute;z-index:2}.ipython_tooltip a{float:right}
+.ipython_tooltip .tooltiptext pre{border:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;font-size:100%;background-color:#f7f7f7}
+.pretooltiparrow{left:0;margin:0;top:-16px;width:40px;height:16px;overflow:hidden;position:absolute}
+.pretooltiparrow:before{background-color:#f7f7f7;border:1px #ababab solid;z-index:11;content:"";position:absolute;left:15px;top:10px;width:25px;height:25px;-webkit-transform:rotate(45deg);-moz-transform:rotate(45deg);-ms-transform:rotate(45deg);-o-transform:rotate(45deg)}
+
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
+body {
+  overflow: visible;
+  padding: 8px;
+}
+
+div#notebook {
+  overflow: visible;
+  border-top: none;
+}
+
+@media print {
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+}
+</style>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration -->
+
+</head>
+<body>
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a href="www.sebastianraschka.com">Sebastian Raschka</a><br />last updated: 05/04/2014</p>
+<ul>
+<li><a href="https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb">Link to this IPython Notebook on GitHub</a><br /></li>
+<li><a href="https://github.com/rasbt/python_reference">Link to the GitHub repository</a></li>
+</ul>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="all-code-was-executed-in-python-3.4">All code was executed in Python 3.4</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+I am really looking forward to your comments and suggestions to improve and<br />extend this little collection! Just send me a quick note<br />via Twitter: <a href="https://twitter.com/rasbt">@rasbt</a><br />or Email: <script type="text/javascript">
+<!--
+h='&#x67;&#x6d;&#x61;&#x69;&#108;&#46;&#x63;&#x6f;&#x6d;';a='&#64;';n='&#98;&#108;&#x75;&#x65;&#x77;&#x6f;&#x6f;&#100;&#116;&#114;&#x65;&#x65;';e=n+a+h;
+document.write('<a h'+'ref'+'="ma'+'ilto'+':'+e+'">'+e+'<\/'+'a'+'>');
+// -->
+</script><noscript>&#98;&#108;&#x75;&#x65;&#x77;&#x6f;&#x6f;&#100;&#116;&#114;&#x65;&#x65;&#32;&#x61;&#116;&#32;&#x67;&#x6d;&#x61;&#x69;&#108;&#32;&#100;&#x6f;&#116;&#32;&#x63;&#x6f;&#x6d;</noscript>
+<hr>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="implementing-the-least-squares-fit-method-for-linear-regression-and-speeding-it-up-via-cython">Implementing the least squares fit method for linear regression and speeding it up via Cython</h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a name="sections"></a> <br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="sections">Sections</h1>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<ul>
+<li><a href="#introduction">Introduction</a></li>
+<li><a href="#implementations">Least squares fit implementations</a></li>
+<li><a href="#sample_data">Generating sample data and benchmarking</a></li>
+<li><a href="#cython_nb">Compiling the Python code via Cython in the IPython notebook</a></li>
+<li><a href="#cython_bonus">Bonus: How to use Cython without the IPython magic</a></li>
+</ul>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a name="introduction"></a> <br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="introduction">Introduction</h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#sections">back to top</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Linear regression via the least squares method is the simplest approach to performing a regression analysis of a dependent and a explanatory variable. The objective is to find the best-fitting straight line through a set of points that minimizes the sum of the squared offsets from the line.<br />The offsets come in 2 different flavors: perpendicular and vertical - with respect to the line.<br /><img src="https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_vertical.png" /><br /><img src="https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_perpendicular.png" /></p>
+<p>Here, we will use the more common approach: minimizing the sum of the perpendicular offsets.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In more mathematical terms, our goal is to compute the best fit to <em>n</em> points <span class="math">\((x_i, y_i)\)</span> with <span class="math">\(i=1,2,...n,\)</span> via linear equation of the form<br /><span class="math">\(f(x) = a\cdot x + b\)</span>.<br />Here, we assume that the y-component is functionally dependent on the x-component.<br />In a cartesian coordinate system, <span class="math">\(b\)</span> is the intercept of the straight line with the y-axis, and <span class="math">\(a\)</span> is the slope of this line.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In order to obtain the parameters for the linear regression line for a set of multiple points, we can re-write the problem as matrix equation<br /><span class="math">\(\pmb X \; \pmb a = \pmb y\)</span></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><span class="math">\(\Rightarrow\Bigg[ \begin{array}{cc} x_1 &amp; 1  \\ ... &amp; 1 \\ x_n &amp; 1  \end{array} \Bigg]\)</span> <span class="math">\(\bigg[ \begin{array}{c} a  \\ b \end{array} \bigg]\)</span> <span class="math">\(=\Bigg[ \begin{array}{c} y_1   \\ ...  \\ y_n  \end{array} \Bigg]\)</span></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>With a little bit of calculus, we can rearrange the term in order to obtain the parameter vector <span class="math">\(\pmb a = [a\;b]^T\)</span></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><span class="math">\(\Rightarrow \pmb a = (\pmb X^T \; \pmb X)^{-1} \pmb X^T \; \pmb y\)</span></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> The more classic approach to obtain the slope parameter <span class="math">\(a\)</span> and y-axis intercept <span class="math">\(b\)</span> would be:</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><span class="math">\(a = \frac{S_{x,y}}{\sigma_{x}^{2}}\quad\)</span> (slope)</p>
+<p><span class="math">\(b = \bar{y} - a\bar{x}\quad\)</span> (y-axis intercept)</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>where</p>
+<p><span class="math">\(S_{xy} = \sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y})\quad\)</span> (covariance)</p>
+<p><span class="math">\(\sigma{_x}^{2} = \sum_{i=1}^{n} (x_i - \bar{x})^2\quad\)</span> (variance)</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a name="implementations"></a> <br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="least-squares-fit-implementations">Least squares fit implementations</h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#sections">back to top</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="the-matrix-approach">1. The matrix approach</h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>First, let us implement the equation:</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><span class="math">\(\pmb a = (\pmb X^T \; \pmb X)^{-1} \pmb X^T \; \pmb y\)</span></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>which I will refer to as the &quot;matrix approach&quot;.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[1]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+
+<span class="k">def</span> <span class="nf">lin_lstsqr_mat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))])</span><span class="o">.</span><span class="n">T</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="the-classic-approach">2. The classic approach</h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Next, we will calculate the parameters separately, using only standard library functions in Python, which I will call the &quot;classic approach&quot;.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><span class="math">\(a = \frac{S_{x,y}}{\sigma_{x}^{2}}\quad\)</span> (slope)</p>
+<p><span class="math">\(b = \bar{y} - a\bar{x}\quad\)</span> (y-axis intercept)</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[4]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">def</span> <span class="nf">classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="n">x_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+    <span class="n">y_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="n">var_x</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
+    <span class="n">cov_xy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">y_i</span> <span class="o">-</span> <span class="n">y_avg</span><span class="p">)</span> <span class="k">for</span> <span class="n">x_i</span><span class="p">,</span><span class="n">y_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)])</span>
+    <span class="n">slope</span> <span class="o">=</span> <span class="n">cov_xy</span> <span class="o">/</span> <span class="n">var_x</span>
+    <span class="n">y_interc</span> <span class="o">=</span> <span class="n">y_avg</span> <span class="o">-</span> <span class="n">slope</span><span class="o">*</span><span class="n">x_avg</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">slope</span><span class="p">,</span> <span class="n">y_interc</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="using-the-lstsq-numpy-function">3. Using the lstsq numpy function</h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For our convenience, <code>numpy</code> has a function that can also compute the leat squares solution of a linear matrix equation. For more information, please refer to the <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html">documentation</a>.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[5]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">def</span> <span class="nf">numpy_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))])</span><span class="o">.</span><span class="n">T</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="using-the-linregress-scipy-function">4. Using the linregress scipy function</h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The last approach is using <code>scipy.stats.linregress()</code>, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively. The documentation for this function can be found <a href="http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html">here</a>.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[6]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">scipy.stats</span>
+
+<span class="k">def</span> <span class="nf">scipy_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">linregress</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a name='sample_data'></a> <br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="generating-sample-data-and-benchmarking">Generating sample data and benchmarking</h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#sections">back to top</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>In order to test our different least squares fit implementation, we will generate some sample data: - 500 sample points for the x-component within the range [0,500)<br />- 500 sample points for the y-component within the range [100,600)</p>
+<p>where each sample point is multiplied by a random value within the range [0.8, 12).</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[7]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">random</span>
+<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
+
+<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">x_i</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span><span class="o">/</span><span class="mi">10</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">500</span><span class="p">)]</span>
+<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_i</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span><span class="o">/</span><span class="mi">10</span> <span class="k">for</span> <span class="n">y_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">600</span><span class="p">)]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="visualization">Visualization</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>To check how our dataset is distributed, and how the straight line, which we obtain via the least square fit method, we will plot it in a scatter plot.<br />Note that we are using our &quot;matrix approach&quot; here for simplicity, but after plotting the data, we will check whether all of the four different implementations yield the same parameters.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[8]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="o">%</span><span class="k">pylab</span> <span class="n">inline</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+
+<span class="n">slope</span><span class="p">,</span> <span class="n">intercept</span> <span class="o">=</span> <span class="n">lin_lstsqr_mat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+
+<span class="n">line_x</span> <span class="o">=</span> <span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
+<span class="n">line_y</span> <span class="o">=</span> <span class="p">[</span><span class="n">slope</span><span class="o">*</span><span class="n">x_i</span> <span class="o">+</span> <span class="n">intercept</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">line_x</span><span class="p">]</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">line_x</span><span class="p">,</span> <span class="n">line_y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">&#39;red&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="s">&#39;2&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;y&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;x&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Linear regression via least squares fit&#39;</span><span class="p">)</span>
+
+<span class="n">ftext</span> <span class="o">=</span> <span class="s">&#39;y = ax + b = {:.3f} + {:.3f}x&#39;</span>\
+        <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">slope</span><span class="p">,</span> <span class="n">intercept</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figtext</span><span class="p">(</span><span class="o">.</span><span class="mi">15</span><span class="p">,</span><span class="o">.</span><span class="mi">8</span><span class="p">,</span> <span class="n">ftext</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s">&#39;left&#39;</span><span class="p">)</span>
+
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Populating the interactive namespace from numpy and matplotlib
+
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>
+WARNING: pylab import has clobbered these variables: [&apos;random&apos;]
+&#96;%matplotlib&#96; prevents importing * from pylab and numpy
+
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdUVMfbwPHv0lm6giAgooBRbNi7GCv2EnvXWOPPnhg1
+GksSxRhjoiYaE0SNsb4xEY0Fe+zGmijGLkZARRERFljYnfeP1Y0ELJRlAedzDkf2lpln7q48e++d
+O6MQQggkSZIkSSqSTIwdgCRJkiRJhiMTvSRJkiQVYTLRS5IkSVIRJhO9JEmSJBVhMtFLkiRJUhEm
+E70kSZIkFWEy0UtGd+jQIcqXL2/sMAqtSpUq8fvvv+drnSNHjuTTTz/N0b7e3t7s3bs3jyN6s/3y
+yy+UKlUKe3t7zp07Z5TPhFRwKeRz9FJ+8fb2JiQkhGbNmhk7FMmIypQpQ0hICE2bNjVI+QcOHKBf
+v378888/Bim/IPLx8eGrr76iffv2mdbNnDmT69ev8+OPPxohMqkgkGf0Ur5RKBQoFApjh6Gn0Wjy
+ZJvXJYRAfq+Wnsmrz5YQgtu3b+Pv758n5UlFj0z0ktEdOHCAUqVK6V97e3uzYMECqlatiqOjIz17
+9iQ1NVW/ftu2bQQEBODk5ESDBg3466+/9OuCg4Px9fXF3t6eihUr8uuvv+rXrVy5kgYNGjBhwgSc
+nZ2ZNWtWplhmzpxJ165d6devHw4ODqxatYrHjx/z7rvv4u7ujqenJ9OnT0er1QKg1WqZOHEiLi4u
+lC1bliVLlmBiYqJf36RJE6ZNm0aDBg2wsbHh5s2b/P3337Ro0YLixYtTvnx5Nm3apK9/+/btVKxY
+EXt7ezw9PVmwYAEADx48oF27djg5OVG8eHEaN26c4Xg9uxSemprKuHHj8PDwwMPDg/Hjx6NWq/XH
+2dPTky+//BJXV1fc3d1ZuXJllu/Jhg0bqFWrVoZlCxcupGPHjgAMHDiQ6dOnA/Do0SPatWtHiRIl
+KFasGO3btycqKirLcv9LCKF/z5ydnenRowePHj3Sr+/WrRslS5bE0dGRwMBAIiIiXnisvvzyS1Qq
+Fa1btyY6Oho7Ozvs7e25e/dupnpfdJwB5s+fr3+vV6xYgYmJCTdu3AB072dISIh+25UrV9KoUSP9
+67Fjx+Ll5YWDgwM1a9bk8OHD+nXZ/Wxdu3aNwMBAHB0dcXFxoWfPnpnakZqaip2dHRqNhqpVq+Ln
+5wf8+5nYuXMnc+fOZcOGDdjZ2VGtWrXXel+kIkZIUj7x9vYWe/fuzbR8//79wtPTM8N2derUETEx
+MSIuLk5UqFBBLFu2TAghxJkzZ0SJEiXEyZMnhVarFatWrRLe3t5CrVYLIYTYtGmTiImJEUIIsWHD
+BmFjYyPu3r0rhBAiNDRUmJmZiSVLlgiNRiOSk5MzxTJjxgxhbm4utmzZIoQQIjk5WXTq1EmMGDFC
+qFQqcf/+fVG7dm3x3XffCSGEWLp0qfD39xdRUVHi0aNHolmzZsLExERoNBohhBCBgYGidOnSIiIi
+Qmg0GhEfHy88PT3FypUrhUajEWfPnhXOzs7i0qVLQggh3NzcxOHDh4UQQsTHx4szZ84IIYSYPHmy
+GDFihEhPTxfp6en6bf57XKdPny7q1asnYmNjRWxsrKhfv76YPn26/jibmZmJGTNmiPT0dLF9+3ah
+VCpFfHx8puOgUqmEnZ2duHr1qn5ZzZo1xYYNG4QQQgwcOFBf7sOHD8XmzZtFcnKyePLkiejWrZvo
+1KlT1h+C/8T71VdfiXr16omoqCihVqvF8OHDRa9evfTbhoaGisTERKFWq8W4ceNEQECAft2LjtWB
+AwcyfJ6y8qJ9d+zYIVxdXcXFixdFUlKS6NWrl1AoFOL69etCCCGaNGkiQkJCMsTXsGFD/es1a9aI
+uLg4odFoxIIFC4Sbm5tITU0VQmT/s9WzZ08xZ84cIYQQqamp4siRIy9sz/Mx/vcYz5w5U/Tr1++l
+x0Mq2uQZvVQgjRkzBjc3N5ycnGjfvj3nzp0DYPny5QwfPpxatWqhUCjo378/lpaWHDt2DICuXbvi
+5uYGQPfu3fHz8+PEiRP6ct3d3Rk1ahQmJiZYWVllWXf9+vXp0KEDAI8fP2bHjh0sXLgQa2trXFxc
+GDduHOvXrwdg48aNjBs3Dnd3dxwdHZkyZUqGy/MKhYKBAwdSoUIFTExM2LlzJ2XKlGHAgAGYmJgQ
+EBBAly5d2LhxIwAWFhZcvHiRhIQEHBwc9GdgFhYWxMTEcOvWLUxNTWnQoEGWsa9du5aPP/4YZ2dn
+nJ2dmTFjRoZ7s+bm5nz88ceYmprSunVrbG1tuXz5cqZyrK2t6dixI+vWrQPg6tWrXL58WX9cAH07
+ixUrRufOnbGyssLW1papU6dy8ODBrN/Y//juu+/49NNPcXd3x9zcnBkzZvB///d/+rPagQMHYmNj
+o193/vx5njx58tJjJV7j9siL9t24cSODBw/G398fpVKZ5VWfl+nTpw9OTk6YmJgwYcIEUlNTMxzf
+7Hy2LCwsuHXrFlFRUVhYWFC/fv1sxfKMkLeM3ngy0UsF0rNkDbqkk5iYCEBkZCQLFizAyclJ/3Pn
+zh1iYmIAWL16NdWqVdOvu3DhAg8fPtSX9fwtghfx9PTU/x4ZGUlaWholS5bUlzlixAhiY2MBiImJ
+yVDm8/tmVWdkZCQnTpzIEP/atWu5d+8eAD///DPbt2/H29ubJk2acPz4cQA++OADfH19admyJT4+
+PsybNy/L2KOjoyldurT+tZeXF9HR0frXxYsXx8Tk3//2SqVSf2z/q3fv3vpEv3btWn0y/y+VSsXw
+4cPx9vbGwcGBwMBAHj9+/FrJ5datW3Tu3Fl/LPz9/TEzM+PevXtoNBomT56Mr68vDg4OlClTBoVC
+wYMHD156rF7Hi/b97/vp5eX12mUCfPHFF/j7++Po6IiTkxOPHz/WxwvZ+2x9/vnnCCGoXbs2lSpV
+IjQ0NFuxSNIzZsYOQJJex7NOfF5eXnz00UdMnTo10zaRkZEMGzaMffv2Ua9ePRQKBdWqVct0hv2q
+ep7fplSpUlhaWvLw4cMMCfKZkiVLZujdnVVP7+fL8/LyIjAwkPDw8Czrr1mzJr/++isajYbFixfT
+vXt3bt++ja2tLV988QVffPEFFy9epGnTptSuXZu33347w/7u7u7cunWLChUqAHD79m3c3d1f2uYX
+ad68ObGxsZw/f57169fz1VdfZdmuBQsWcOXKFU6ePEmJEiU4d+4c1atXRwjxyuPt5eVFaGgo9erV
+y7Tuxx9/JCwsjL1791K6dGni4+MpVqyY/v180bF6nQ6fL9q3ZMmS3L59W7/d878D2NjYkJSUpH/9
+/P3/Q4cOMX/+fPbt20fFihUBMsT7/DGDV3+2XF1dWb58OQBHjhyhefPmBAYGUrZs2Ve273kFqQOs
+ZBzyjF7KV2q1mpSUFP3P6/Y8fvbHcujQoSxbtoyTJ08ihCApKYnffvuNxMREkpKSUCgUODs7o9Vq
+CQ0N5cKFC9mK779noSVLlqRly5ZMmDCBJ0+eoNVquX79uv4Z5e7du/P1118THR1NfHw88+bNy/SH
+9fky27Vrx5UrV1izZg1paWmkpaXxxx9/8Pfff5OWlsZPP/3E48ePMTU1xc7ODlNTU0DXAfHatWsI
+IbC3t8fU1DTL5NCrVy8+/fRTHjx4wIMHD5g9ezb9+vXL1jF4xtzcnG7duvH+++/z6NEjWrRokaFN
+z9qVmJiItbU1Dg4OxMXFZety94gRI5g6dao+ocbGxhIWFqYv19LSkmLFipGUlJThy93LjpWrqysP
+Hz4kISEhyzpftm/37t1ZuXIlly5dQqVSZWpLQEAAmzdvJjk5mWvXrhESEqJ/v588eYKZmRnOzs6o
+1Wpmz579whjg1Z+tTZs2cefOHQAcHR1RKBRZvuev4ubmxq1bt+Tl+zeYTPRSvmrTpg1KpVL/M2vW
+rFc+dvf8+ho1avD999/zv//9j2LFiuHn58fq1asB8Pf3Z+LEidSrVw83NzcuXLhAw4YNsyzndep6
+ZvXq1ajVavz9/SlWrBjdunXTn8kNHTqUli1bUqVKFWrUqEHbtm0zJeHny7O1tSU8PJz169fj4eFB
+yZIlmTJlir5n/Jo1ayhTpgwODg4sX76cn376CdD1wG7RogV2dnbUr1+fUaNGERgYmCn+adOmUbNm
+TapUqUKVKlWoWbMm06ZNyzKW19G7d2/27t1Lt27dMrXpWVnjxo0jOTkZZ2dn6tevT+vWrV+7nrFj
+x9KhQwdatmyJvb099erV4+TJkwD079+f0qVL4+HhQaVKlfRXaZ550bEqX748vXr1omzZshQrVizL
+Xvcv2jcoKIhx48bRtGlTypUrl2nMh/Hjx2NhYYGrqyuDBg2ib9+++nVBQUEEBQVRrlw5vL29sba2
+znDpP7ufrVOnTlG3bl3s7Ozo2LEjixYtwtvbO8vj+LLj3a1bN0B326ZmzZov3E4qugw6YM7ly5cz
+PBJy48YNPvnkE/r27UuPHj2IjIzE29ubjRs34ujoCMDcuXNZsWIFpqamLFq0iJYtWxoqPEnKczt2
+7GDkyJHcunXL2KFIecTExIRr165l+5K5JBUUBj2jf+uttzh79ixnz57l9OnTKJVKOnfuTHBwMC1a
+tODKlSs0a9aM4OBgACIiItiwYQMRERHs3LmT9957T9/7VpIKopSUFLZv3056ejpRUVHMmjWLLl26
+GDssSZIkvXy7dL9nzx58fX0pVaoUYWFhDBgwAIABAwboBzXZsmULvXr1wtzcHG9vb3x9ffWX8SSp
+IBJCMHPmTIoVK0b16tWpWLEis2fPNnZYUh6Sndmkwi7fet2vX7+eXr16AXDv3j1cXV0BXceZZ48W
+RUdHU7duXf0+np6erz3CliQZg7W1tfwyWsTl5TDIkmQM+ZLo1Wo1W7duzfLZ39fpiPU8X19frl+/
+nucxSpIkSVJB5ePjw7Vr13K0b75cut+xYwc1atTAxcUF0J3FP+tZGhMTQ4kSJQDw8PDI8BzynTt3
+8PDwyFDW9evX9Y/2vIk/M2bMMHoMsv2y7bL9sv2y/fn7k5sT3HxJ9OvWrdNftgfo0KEDq1atAmDV
+qlV06tRJv3z9+vWo1Wpu3rzJ1atXqV27dn6EKEmSJElFksEv3SclJbFnzx6+//57/bLJkyfTvXt3
+QkJC9I/Xge456O7du+uHwfz2229lRxhJkiRJygWDJ3obG5sMYz2DbljIPXv2ZLn91KlTsxzeVNJp
+0qSJsUMwqje5/W9y20G2X7a/ibFDKLQMOmCOISgUCgpZyJIkSZKUK7nJfXIIXEmSJEkqwmSilyRJ
+kqQiTCZ6SZIkSSrCZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIkwmekmSJEkqwmSilyRJkqQiTCZ6
+SZIkSSrCZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIkwmekmSJEkqwmSilyRJkqQiTCZ6SZIkSSrC
+ZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIszM2AFIkiRJWYuNjWXHjh2YmJjQtm1bnJycjB2SVAgp
+hBDC2EFkh0KhoJCFLEmSlG03btygdu1AUlLqAOnY2p7nzJkjuLu750v9f/75JzNnzichIYn+/bvQ
+v3/ffKlXylpucp88o5ckSSqAJk78mEePRqDVfgRAauqHTJv2KStWfGvwuv/880/q1GlCSso0wINj
+xz7m8eMERo9+z+B1S3lP3qOXJEkqgKKi7qHV1tC/Tk+vwe3bdw1e78OHDwkMbEVKyhBgAtADlWo1
+8+d/Y/C6AU6cOMHIkWMZO/Z9/v7773yps6iTiV6SJKkAatWqMUrlF8Bj4CFK5Ve0bh1o8HqnTp1N
+QoInYPrcUpN8uWW6f/9+mjZtz7JlJVm8WEmtWo25ePGiwest6mSilyRJKoA+/ngy77zjg6lpCczM
+PBgwoA7jx482eL2XLt1Aq+0P/AB8A/yCiUkPxo4dZvC6P/poHirVV8BkhJhNUtJ4Pv98scHrLepk
+opckSSqAzM3NWb36O1JTVaSkJPHttwsxMTH8n+yGDWtgbb0bCAMOYGLyPs2a+TNx4liD152cnAIU
+178WwpnExGSD1qlSqRg+fCzly9chKKgr169fN2h9xiA740mSJBVgpqamr94olx49esSOHTtQKBSM
+HTuSs2ffY//+NoCCRo0aERa2AYVCYfA4hgzpyaRJE1GpbAAVSuVsBg9eZtA6O3fuw++/W5CSspCr
+Vw9Tp04Trlw5T7FixQxab36SiV6SJOkN9s8//1CzZiNUqiqAwMZmKqdOHcLc3BwhBK6urvmS5AHe
+e284arWab78dh5mZGR9/PJ+2bdsapK7k5GQ++mgWu3dvR4gngAVabX3U6gMcOHCALl26GKReY5DP
+0UuSJBnZX3/9RUxMDJUrV6ZkyZL5WnfPnoP5v//zRKOZDYCZ2VR69XrI6tXf5Wsc+UkIQZMmbTlx
+woLU1J3APcABENjZNWbt2g9p166dkaPMKDe5T96jlyRJMqIxYyZRt24Q3bvPw8+vCrt3787X+v/5
+5y4aTS396/T0WkRGRudrDPnt+vXr/PHHeVJTNwHDgCBgBebmg3Fze0KzZs2MHGHekolekiTJSA4f
+PsyKFT+jUl3k8eO9JCX9TNeuffLlquW6devx9q7C+fOnMDObDyQAj1Eqv6Zly0YGr9+YhBAoFKbo
+UuBXQD9MTafRo4eCP/44iLW1tZEjzFsy0UuSJBnJjRs3UCjqAo5PlzRCpUokMTHRoPXu3buXd9+d
+SGTkIpKSdiNEJAqFM6amJejevQKTJ080aP3G5uPjg79/WSwt3wV2Y2FxET8/d0JCluHg4GDs8PKc
+TPSSJElGUqVKFTSafcDNp0vW4eLijq2trUHr3bhxC8nJE4AmQFU0mp/x8ipPcnIioaFL86WnvzGZ
+mJiwb99WBg1yombN+fTuLThyJBwLCwtjh2YQste9JEmSkQQEBDBv3sd88EFVzMycsLaG7du3GLyX
+u4ODLaamd9Boni25g52dHebm5gattyCxs7Nj6dKFxg4jX8he95IkSUaWkJBAbGwspUqVypezyjt3
+7lC1al0SEjqQnl4Ca+tv+eWXH2nVqpXB65ZyJje5TyZ6SZKkN1B0dDQ//BBCUlIyXbt2platWq/e
+STIameglSZIkqQiTz9FLkiRJkpQlmeglSZIk6UWEgEWLIC7O2JHkmEz0kiRJkpQVrRZGj4axY6FD
+B93rQkg+XidJkiRJ/5WeDkOHwsqVYGEBkyZBPkwTbAgy0UuSJEnS89Rq6NsXNm0CpRK2bIHmzY0d
+VY4Vzq8nkiRJ0hsvKSmJLl36olQ64uzsxY8//pT7QlNS4J13dEne3h7Cwwt1kgf5eJ0kSZJUSPXo
+MYgtW1JITV0M3MTauiPh4Rtp2LBhzgpMTISOHWHfPiheHHbtgho18jTmnJKP10mSJElvnF27dpGa
+Og9wBmqRkjKY3bv35Kyw+Hho2VKX5N3c4ODBApPkc0smekmSJKlQcnAoBlzWv7a0vEzx4sWyX1Bs
+LDRtCseOgZcXHDoEFSvmXaBGJhN9Ieft7U1ERIRBytZoNIwaNQpfX1/8/PwICQl54bZz586lcuXK
+VKhQgYEDB6JWqwG4desWZmZmVKtWTf/z6NEjAI4ePUqDBg2oWLEiFStWZNKkSXkaf58+ffDw8MDE
+xASVSpVh3fHjx6latSpvvfUWrVq1IjY2Vh/T87F6eHhQ4wXf6lUqFT169MDPz48KFSrw22+/6det
+WbOGKlWqYG5uzjfffJPl/gcOHMDU1PSF6yVJermlSz9HqeyDufk4lMqOeHpeYdCgQdkrJDoaAgPh
+7Fnw89MleV9fwwRsLKKQKYQh50hoaKiYOXPmK7fz9vYWFy5cMEgMq1atEq1atRJCCBEbGys8PT3F
+rVu3Mm23a9cuUaVKFaFSqYQQQgwdOlQEBwcLIYS4efOmcHZ2zrL8CxcuiGvXrgkhhEhNTRUNGzYU
+P/744yvjGjBggDhw4MArt9u/f7+4f/++UCgUIikpSb9co9EIHx8fceTIESGEEJ9++qkYPHhwlmV0
+6tRJLFiwIMt1s2bNEsOGDRNCCHH16lXh5uYmEhMT9W2LiIgQ/fv3F998802mfRMSEkSdOnVE+/bt
+xZIlS17ZFkmSsnbu3Dnx+eefi++++048efIkezvfvClE2bJCgBCVKgkRE2OQGPNCbnLfG3VGP3/+
+fP73v//pX9+7dw83NzdSUlJyXfa9e/do2rQpNWvWpFKlSnz44Yf6dUOHDmXChAn67cqWLcuff/75
+0vKyM03lmjVrqFmzJn5+fnl6drhx40aGDRsGgLOzM506dWLTpk2Ztvvzzz9p1KgR1tbWAAQFBfHT
+T6/u/VqxYkV8fHwAsLCwICAggNu3b79yP4VC8VrHp0mTJri4uGRafvr0aaytralfvz4Aw4cPZ+PG
+jZm2u3//PuHh4fTr1y/L8jdu3Mjw4cMB8PX1pWbNmuzYsUPftgoVKmBiYpJlB5oJEyYwadIkihcv
+rl+WnJxM1apVCQsLA2Dfvn1UqFCBpKSkV7ZVMr5z584xYMAIevcewsGDB40dzhujatWqfPDBBwwb
+NgxbW9vX3/HyZWjUCG7c0N2LP3BAd2++CHqjnqMfMmQI/v7+fP755yiVSpYvX06fPn2wsrLKtG23
+bt24du1apuUKhYJjx45haWmZYbmjoyNbt27FxsaGtLQ0goKC2LVrF61atWLx4sXUqVOHLVu2sHjx
+YiZNmkSVKlVeGmtWyeFFYmNjOXXqFPfv36datWo0btyYypUrZ9jm0qVL9O7dO8v9W7Zsybx58zIt
+v337NqVLl9a/9vLy4p9//sm0XY0aNfj+++95+PAhDg4ObNy4kcjISP36hIQEatSogUKhoGfPnrz/
+/vuZyrh//z6bN29m+/btr9Xm7Byf//pvu5ydndFqtcTHx+Po6Khfvnr1alq1apXll4WsynnR8fmv
+HTt28OTJE7p06cLWrVv1X1qsra3ZuHEjLVu2xM3NjSFDhvDLL79gY2OT06ZK+eTs2bM0atSSpKQP
+AGu2bOnBzz+vJCgoyNihFXqJiYkMHPgeO3b8hp2dI4sWBdO9e7fcFfrnn9CiBdy/Dw0bwrZt4OCQ
+NwEXQG9UondycqJDhw6sXr2aIUOG8MMPP7Bv374st83qzPVl0tPTef/99zl27BhCCO7evcu5c+do
+1aoVVlZWbNy4kRo1atCmTRtGjBiRZRnbt2/no48+AiAuLg61Ws2vv/4KwOjRoxk8eHCW+7377rsA
+lChRgrZt23LgwIFMib5ChQqcPXs2W216XW+//TajRo2iZcuWWFlZ0axZM3bv3g2Au7s7UVFRODs7
+ExsbS4cOHXByctLHDPDkyRM6dOjA+++/T9WqVbOs45NPPmHz5s2ALsEePnxY/+191apVr/zilBOh
+oaFZfgHKjfj4eCZPnsyePbqewUKIDF9a3nrrLWbPnk39+vX5+uuvX3g8pILliy++JSnpQ0D3JVal
+KsGsWV/JRJ8HBg0axbZtaaSmXkSlus7Age9QurQXderUyVmBJ09CUBA8eqRL9r/8AkX8y/QblehB
+lzD79OmDi4sL/v7++kvH/9W1a1euX7+e5bpjx45lugrw5ZdfEh8fz8mTJ7GwsGD48OEZbglcvHgR
+BwcH7t69i0ajwdTUNFO5bdq0oU2bNoAueUVGRvLxxx+/sk3PJwohRJaXtSMiIujTp0+W+7do0YLP
+P/8803IvLy9u3bql74wWGRlJmTJlsixjzJgxjBkzBtBd0q74tMeqhYUFzs7OALi4uNCnTx+OHDmi
+T/QqlYp27doRFBTE+PHjX9jG6dOnM336dAAGDRrEoEGDaNy48Qu3f5nSpUtnuOLw4MEDTExMMpzN
+Hz9+nEePHunfj6w8Oz7PLr9HRkbStGnTTNs9/35cuHCBu3fvUrt2bX3d27Zt49GjR0ybNg3Q3Vpw
+dXV9rasDUsGQkqIGnr9sbKfvkGoIT5484eHDh3h4eGBubm6wegqCnTt3kJp6DnAD3EhNHUh4+O6c
+Jfrff4e2bXXPy3foABs2QBZXdIucPOgjkK/yIuS3335blCpVSmzdujUPItKZOHGiGD9+vBBCiDt3
+7ghXV1cxa9YsIYQQN27cEF5eXuLatWtiwIABYvLkya8s73U745UuXVoMHTpUCCHE/fv3hYeHR551
+zlu5cqVo1aqV0Gq14v79+y/sjCeEEDFPO7HExcWJ6tWriy1btuhjUqvVQgghkpKSRPPmzcWiRYuE
+EEIkJyeLpk2big8//DBbcQ0cOPC1OuMJIYRWqxUKhULfSU6IfzvjHT58WAghxCeffJKpM97QoUNf
+GdfMmTP1x/7KlSvC1dU1Qz1C6DoOvqyz3cCBAzN01tu8ebOoXr26iIuLExUrVhQ7dux4rXZKxhUe
+Hi6UypICfhawQyiVviIkJNQgdS1a9K2wsLAVSqWnKFGitPjrr78MUk9B4e5eTsABoZtGTghr6y45
+68C6c6cQ1ta6Qnr1EuLp36XCIje5741M9GvWrBHe3t55EM2/IiMjRe3atUWlSpVEUFCQ6NOnj5g1
+a5ZQq9Widu3aYt26dUIIXbLz9/cXO3fufGl5K1eu1H9ReBlvb28xdepUUaNGDeHr65tlD++c0mg0
+YuTIkcLHx0f4+PiI77//Xr9u2bJl4uOPP9a/rly5sqhYsaIoV66cWLx4sX755s2bRaVKlUTVqlWF
+v7+/+PDDD4VWqxVCCLFkyRJhamoqqlWrJgICAkRAQICYM2fOK+MaOHCgOHjw4Cu369y5s/D09BQm
+JibCw8NDBAUF6dcdPXpUVK5cWfj5+YmWLVuK+/fv69epVCrh4OAgLl++nKnMgIAA/ZeapKQk0a1b
+N+Hr6yveeustERYWpt9u7dq1wtPTU9jY2AgnJyfh6ekpLl26lGVbnr1nN2/eFKVKlRJXr14VQuh6
+7nt5eYmoqKhXtlUyvi1btogaNZqKqlUbix9+WGGQOs6cOfP0C8WNp4kvVHh6vmWQugqKX375RVhb
+lxBmZhNF0rhoAAAgAElEQVSFtXUn4etbJfu96zdvFsLcXJfkhwwRIj3dMMEaUG5y3xs5BO6QIUOo
+UKECEydOzKOoJEmSDC80NJTRo/eTlLT66RKBqakVCQmPUCqVeV7ftWvXOH/+PN7e3i8cTyI/nD59
+mt27d+Po6Ejfvn2z17t+zRoYOBA0Gt10swsXQjaeaiooCvQQuPHx8XTt2pUKFSrg7+/PiRMniIuL
+o0WLFpQrV46WLVsSHx+v337u3Ln4+flRvnx5wsPD8zSW6Ohoypcvz/Xr1xk1alSeli1JkmRoZcuW
+BY4BCU+X/I6trZP+0da89NNP66hatT6DB/9I48ZdGDducp7X8bpq1KjB5MmTGTFiRPaS/PLl0L+/
+LslPm1Zok3yu5dFVhRfq37+/CAkJEUIIkZaWJuLj48UHH3wg5s2bJ4QQIjg4WH8v9OLFi6Jq1apC
+rVaLmzdvCh8fH6HRaDKUlw8hS5IkFUharVaMGDFOKJWewsGhubCxcRbh4eF5Xk9KSoqwsrIX8OfT
+WwRxQqksJU6fPp3ndRnMggVCf2P/6QBehVlucp9BL90/fvyYatWqcePGjQzLy5cvz8GDB3F1deXu
+3bs0adKEv//+m7lz52JiYqIfbCYoKIiZM2dSt25d/b5y9jpJkt50586dIyYmhqpVq+Lu7p7n5UdH
+R+PrW43k5Hv6Zfb27Vi1agidOnXK8/rylBDwyScwY4bu9ZIlUASu4BbYS/c3b97ExcWFQYMGUb16
+dYYOHUpSUhL37t3D1dUVAFdXV+7d032YoqOj8fT01O/v6elJVFSUIUOUJEkqdAICAmjdurVBkjzo
+/i7b2loB654uOUda2omCP66DEDBpki7Jm5hAaGiRSPK5ZdDn6NPT0zlz5gxLliyhVq1ajBs3juDg
+4AzbvGo406zWzZw5U/97kyZNaNKkSV6FLEmS9Ma7ceMGtrYOxMb2BYZhYSFYuXLlC8fRKBC0Wl1S
+X7YMzMxg7VrolssR9IzowIEDHDhwIE/KMmii9/T0xNPTk1q1agG6QWjmzp2Lm5sbd+/exc3NjZiY
+GEqUKAGAh4dHhkFC7ty5g4eHR6Zyn0/0kiRJUt7RaDQ0bdqOqKhRwHBgDxYWgwgMbGTs0F4sPR0G
+D4YffwRLS/j5Z93AOIXYf09iZ82aleOyDHrp3s3NjVKlSnHlyhUA9uzZQ8WKFWnfvj2rVq0CdCPA
+Pbvn06FDB9avX49arebmzZtcvXpVP4KYJEmSZHhRUVHExSUixBjAEmiLqWkAZ86cMXZoWVOroWdP
+XZK3sYHt2wt9ks9rBh8Cd/HixfTp0we1Wo2Pjw+hoaFoNBq6d+9OSEgI3t7e+pnD/P396d69O/7+
+/piZmfHtt99maxY3SZIkKXecnJxIT08A7gCegIr09GsvnNzJqJKT4Z13YMcO3aQ0O3ZAvXrGjqrA
+eSMHzJEkSSpo1Go1oaGh3LwZSb16dejYsaPRYgkOXsAnnyxCo2mHuflhOnSoxZo13+fqxCsxMZF3
+3x3Nrl27cHQsztKln9O6deucB/nkCbRvDwcPgrMzhIdDtWo5L6+Ay03uk4lekiTJyDQaDYGBbTh7
+VqBSNcLGZi1jxvRgzpyZRovp0KFDnD17ljJlytCuXbtcX13t0qUv27drSE2dB/yNUtmXY8f25Gzm
+yUePoHVrOHECSpaEPXvA3z9X8RV0MtFLkiQVYlOmTGHevE0IcRkwBe5hbl6Gx48fGmTUO2OwtnYg
+JeU6oJvN0tx8HHPmePL+++9nr6D796FlSzh/HkqXhr174QWzkBYlBfY5ekmSJOnlQkNXEhy8BCHK
+oEvyAC4oFBaoVCpjhpanbGwcgFv61+bmN3FwcMheIVFREBioS/LlysGhQ29Eks8tmeglSZKMRKvV
+Mm7cdGAl8BewGrgNjMXHx49ixYoZM7w89eWXc1AqO6JQTMfKqhvu7rfo1avX6xdw8yY0agR//w1V
+qujmli9VKttxpKSkkJycnO39CjOZ6CVJkoxk9Oj3SUhQAWWBHcBSoBqwik2bVhapp4769+/Lzp3r
+mTbNhODghpw5c/j1J6j5+29dkr95E2rXhv374enoqq9Lo9HQv/9wbG0dsbNzomvXfqjV6hy0pPCR
+9+glSZKM4NSpU9Sp0wCtdhz/JvkY4F2aN2/E7t3bjBtgQXHunO6efGwsNG4M27aBnV22i5k7dz6f
+frodlSoMMMXauitjx9Zi7tycD0STn+Q9ekmSpFwKDw/HxyeA4sW96NNnqEHvjx87dozGjYPQagHe
+B7oB/YH3aNKkJjt3bjFY3YXK8ePw9tu6JB8UpHtOPptJPiEhga+++orly39CpXoPsAOUJCePZu/e
+owYJu6CRiV6SpDfeX3/9RadOfbhxYw5xcQfYvDmeAQNGGqy+GTO+IDl5LrohZrsC1VAo+lOsmAUb
+N67H1NT0FSVkX0JCAt26DaREibJUqdKAkydP5nkdeerAAWjeHOLjoXNn+PVXUCqzVURCQgIBAfWZ
+MuUYt25ZAb/r15maHsPbO/MQ60WRwUfGkyRJKuh27dqFRtMbaANASsq3/PZbOYPVp1KlAMWBhcAX
+wExcXBI4fvyQwUag69SpD0ePOpOauovY2D9o2rQtERGn8fLyMkh9ubJ9u27Eu5QU6NMHVq7UTVST
+TatXr+bu3QqkpGwA7gF1USjOYGvrgLX1JRYs+P1VRRQJ8oxekqQ3np2dHWZmt59bchulMvv3gV/X
+0KE9USonA4eBmiiVD1m8+DODzQ6nVqs5eHAXqanfAX5Ab6AF+/fvN0h9ufLzz9Cpky7JDxsGq1fn
+KMmD7oxerX52TF2BXVhaXmDFisFcvnyWUjnotV8YyUQvSdIbr3fv3ri6XsXSshcKxSyUyo588cWn
+BqtvwIB+fPnlB/j5vU+5clNYsmQm3bsbbkpVMzMzzMzM0Z3VAggUiqjX7/WeX1avhu7dIS0NJkzQ
+TTlrkvM01apVKywsVgH7gNtYWX1Ex47v0LVrVxwdHfMs7IJO9rqXJElCd/b3/fffExsbR1BQiwxT
+hBYFc+bM57PPvkelGoyV1Sl8fP7h1KmDWFlZGTs0naVL4b33dL/PmKH7yYPHC8PCwvjf/yaTkBBP
+mzZt+OGHRSizea+/IJBD4EqSJGWDEIL169ezf/9RvL09GDt2NDY2NsYOK0+Fh4czY8YCUlPVjBzZ
+lyFDBrN161b27z+Ep6cbI0aMKDhtnj8fJk369/fsDov7BpCJXpIkKRsmTZrGN9+EoVINxtLyGD4+
+Nzl9+veCc3abA7/99htjx07jyZME6tatzu7dv5Oc/DXggFI5ni++mMDIkcOMHWZGQsDMmTB7tu71
+t9/CSMM97VCYyUQvSZL0mtRqNTY29qSn3wZKAAJb24b89NOHdOjQwdjh5cipU6cIDGyLSrUKKIOp
+aVs0mlHA+Kdb7KFixRlcuHDEiFH+hxAwcSIsXKi7Dx8aCv37GzuqAis3uU8+XidJ0hslLS0NUABO
+T5coUChKFNrxzyMiIujRoz8q1SAgCACNpgHw/IA/Ksxy2HPdIDQa3f345cvB3BzWrdM9TpcNycnJ
+LF68hCtXImnYsCYDBgwoUkMG56UC9M5LkiQZno2NDQ0aNOX48aGkpk5EoTiGiclxAgOXGju0bLt0
+6RI1azYiObkREAnsBdKAeigU7yOEGeCIUjmbGTOWGDVWvfR0GDgQfvoJrKxg82bd3PLZkJaWRuPG
+rblwwYmUlCasW/cNJ06cZ+nShYaJuZCTl+4lSXrjJCQkMHz4eA4dOoanpwfff/8llStXNnZY2RIT
+E0OFClV5/DgQmI9uMhwPwBa4yOTJ/+POnYekpKgZMqQXrVq1yvMYhBCo1WosLS1fb4fUVOjZUzfK
+na0tbN0KOXi6Yf/+/XToMIHExNPonhKPx9zckwcPorG3t892eYWBHOtekiTpJdLT0zl+/DiHDh0i
+OTkZe3t71q0L4c6dCI4f313okjzA5MmzePKkJqAFQoB26Ka6PY6p6UQuXrzFjz8uZ9OmlTlO8klJ
+SXTu3AdLS1ucnNwJCQnVrwsJCUWpdESptKNmzSbcu3fvJSUBKhV06KBL8o6OsGdPjpK8rigVJibF
++TeF2WFiYkFKSkqOyivq5KV7SZKKtKSkJBo1CuLq1UcoFFYUK5bM8eN7cXNzM3ZouXLrVjRabS9g
+BnAdGIeu7wFoNM24fj0813UMGTKGHTu0qNX/oFbfZsyYdpQt6421tTVjxkwjJeU4UI7z56fQpUt/
+jhzZlXVBCQnQrh0cOgQuLrB7N1StmuO46tevj7n5CBSKrxHibSwsllK5chWDDR9c2MkzekmSirRP
+Pgnm0qVSJCb+yZMnfxAd3Y4xYyYbO6wcu3v3LleuXKFp07oolaFAOGCPbprbJCAdS8vvqVevRq7r
+Cg/fTWrqHHQdF6uSnDyE3bv3cvToUdLTuwIVAFPS06fzxx+Hsi4kLk43Oc2hQ+DhAb//nqskD+Dk
+5MTRo3tp0GAXnp696NBBRXj4L7Iz3gvIM3pJkoq0ixevk5LSmmfnNWlpbbh06SPjBpUDQgiGDx/L
+6tU/YmbmiIuLDUFBAWzZUhEhBJ6efty9WxKFwoxatWqzcOE3ua7Tyak4cXERQBlAYGkZgYtLXVxd
+XTE3/xW1WgOYAqdwcsriCsm9e9CiBfz1F5QpA3v36v7NA+XKlePQoe15UlZRJxO9JElFWt26Vdm7
+dy3Jyd0AcywtV1GrVu7OKPObEIIRI0YSEvI7Wu0tUlPtSUmZTalSJ0hKSgDA0tKSuLg40tLSKFGi
+RJ6c3S5bNp+OHXuh0fQATmFpGcn9+7707NmTatVWc+5cfYR4CyF2sGrVjxl3/ucf3Zn8lStQvrzu
+nrzHmzEtbEEje91LklSkpaWl0alTb/btO4BCYU7FiuXYuzesUPXOXrbse0aPnkJ6+lhg+tOlkTg5
+NSAu7o5B646IiODTTz/j55/3oVaPw9z8BsWK7ebcuaOcPHmShw8f0rBhQ/z8/P7d6fp1aNYMIiMh
+IAB27YISJQwaZ1EnB8yRJEl6AXNzc7Zt20hUVBTp6el4eXlhkosZ0fLb4cOH+fDDz0hPHwjsBiYB
+lkAYZcv6Grz+GzdusHlzOGr1NqAOaWkQH9+fdevWMX78+Mw7RETozuRjYqBuXd3c8k5OmbeT8o1M
+9JIkFXkKhQJPT09jh5FtBw8epHXrbiQnFwcaA7fRdYBzwtLyFmvWGHZI27Vr1zF06Iekpgp087nr
+pKe7kZiYlHmHM2egVSt48ED36FxYGNjZGTRG6dXkpXtJkoqM9PR0fvjhBy5dukbNmlXp27dvoe6J
+3apVV8LD26DrVT8O3aN0F7GyWsnevdupX7++QesvX74Oly9/CmwFLgNfADdQKody9Ohuqj7fe/7o
+UWjTBh4/1v37f/8H1tYGje9NIi/dS5L0xtNqtbRp05UjR56gUrXExmYx+/cfZ8WK3Pc+N5a0tHTA
+HEgGWgHf4uqaxM6dBwgICDB4/enp6YASXYKfCrTGycmMjRvXZkzy+/bpBsNJSoKuXXXD21pYGDw+
+6fUUnhtVkiRJL3HmzBmOHr2ISrUT+JCkpD2sXbuWu3fvGju0HHvvvb6YmIwFlgN2QCRduwYZNMnH
+xsZy7tw5EhIS+N//BmJjMxzYDwSgVKrZuXMjzZs3/3eHbdt0Z/BJSTBggG6CGpnkCxSZ6CVJKhKS
+kpIwNXVBdwYMYIeZmT2JiYnGDCtXTE1NsbDwAQ4CXwFHWbFitcFuX3733Q94eZUjMLAfHh4+VKpU
+gXnz/ke1asE0aLCWbds2ULt27X932LgROnfWjWH/3nuwYgUUpFnyJEBeupckqYioXr06lpbRmJh8
+jVbbBjOzlbi7F8Pb29vYoeVYXFwcpqb+/HtO5ktqqoq0tDQs8vis+dq1a4wfP5WUlD9ISfEFDtC5
+czcePLjDqFEjMu8QGgpDhoBWC5MmQXAwFOL+EEWZPKOXJKlIsLOz48iR3dSqtQ1n55YEBl7kwIHf
+CtY87K9p+/btdO8+iB079qHR/IZu+tl4zMwmUbNmozxP8gCXL1/GwqI68OyRvSZotVbExMRk3njJ
+Ehg8WJfkZ8+WSb6Ak73uJUmSXiImJoaDBw+iVCpp1arV60/JmkO6R9omoVJ9hEJxHyurL7G1dSIh
+4QF16jRm06ZQShhg8JnLly9TrVpjkpP/ALyAY9jYtOPBgyisrKz+3TA4GKZM0f2+YAFMmJDnsUiZ
+5Sb3yUQvSZL0AufOnSMwMAgh6iPEPcqU0XL8+F6USqXB6ixXrhZXr84FdB3eFIqpTJig5Ysvgg1W
+5zMLFy5m6tSZWFj4kp5+nY0bV9G2bVvdSiFg+nT47DPd2fuyZTBsWLbKT0xMRKPR4ODgYIDoizY5
+H70kSZIBvPvuOBIS5vDkyWYSEw9z9aoHS5YY9nG9tDQ1YKt/LYQtKSlqg9b5zPjxo7ly5Rzbt3/J
+rVuXMib58eN1Sd7UFH78MVtJXqPR0L//MJycSuDi4kGrVp1RqVQGaoX0XzLRS5IkZeH+/fvcvn0b
+qPd0iYKUlLpERkYbtN533+2FldUgdNPPrkOp/Ir+/XsatM7nlSpVigYNGvw7t7tGA0OHwtdf6x6b
+27QJ+vTJVplffbWYn3++THr6PdLS4vj9dzMmTfrYANHn3JEjR+jYsQ9t2/Zk165dxg4nT8lEL0mS
+9B+DB4/A3b0scXGJwGdAOnAPpTKUJk0MNxrd+vUb+eyzYDSaWExNu1Kp0tds3bo+4yNt+SktDfr2
+hZAQ3Sh3YWG6x+myaf/+E6hUQ9GNBWBBSsp7/P77yTwPN6eOHDlCy5adCQtryPbtLenSZRDbtm0z
+dlh5RiZ6SZKk58ycOYvQ0M1oNNfQaq8CEYASMzNvxo3rTteuXQ1S782bNxk8eBQpKQdJS3uARhNK
+TEwUjRs3Nkh9r5SSohvlbv163Xj1O3fqxrHPAV/fUlhY/A7o7jGbmh6ibNlSeRhs7nz55XeoVDOA
+kcBgVKqFBAd/a+yw8kzhe+5EkiTJgBYu/AZoD7g9XfIHYE5CwmOs83Ds9vT0dL7+egknTpynYkUf
+/P3fwty8NsnJz4aWfQeVahT37t3DI7/ncU9Kgk6ddHPIOznpppmtVSvHxc2YMYXffnube/caAdZY
+W19j0aKDeRdvLmm1gn8HWgIwQ6PRGiucPCcTvSRJEnD+/Hl69RpGQkIC8DsQDzgC27C1dcnTJA/Q
+rdsAwsPvolL1JCwsjLJlfyE9/R7wECgO/AmkULx48dcqT6vVcvHiRdLS0qhUqVLOn7V//BjatoUj
+R3RzyO/eDVWq5Kysp5ycnPjzz2Ps37+f9PR0AgMDC1TP+9GjB7FrV2+Sk5WAJUrlRCZM+NLYYeUZ
++XidJElvvEePHlG2bEXi4+cC+4BjwGOgNHCR0NBvGDhwYJ7VFxUVhY9PFVJTzwG9gb8AFf7+Vbh1
+6y5mZlVJSztJSMgSevXq8cryUlJSaNGiE2fPXsHExIqSJa04fHgXLi4upKen8+TJExwdHV89k9+D
+BxAUBKdPg6cn7N0L5crlRZMLvPDwcObMWUJ6uoZx4wbTtes7xg4pA/kcvSRJUg6lpKTQvXsftm27
+hRCngRTgfWAlJUq4sGhRMD16vDrZZseNGzeoXLkRKlV9wB1YCDzEyqoxn3zyLuXKlaNSpUqULVv2
+tcqbNesz5s07RXLyJsAUc/OJdO78CB8fDz7/fAEKhSleXj7s2fMrZcqUybqQmBho0QIuXgQfH91l
++0I8fHBRIxO9JElSDnXrNoCwsFuo1beBvwFL4CEWFmWIjr752pfOX8fOnTuZOnUeycnJJCQ8Ijr6
+IXAC8Hm6xVwmTHjEggWfZ6vczp378euvzYCBT5ccpnjxXjx8mPi0fD/gc/z9N3Px4onMBURGQvPm
+cO0a+PvrLte7u+eskZJByAFzJEmSckCr1fLrrxtQq8OAOsDbwFQsLOozevToPE3y69ato3373pw9
++z/+/juYR4/MsLIyRzcFLIAGa+tDlCnjle2yq1f3x9r6ZyANEJiZrScu7i7QBSgHKICJXLp0Co1G
+k3Hnq1ehUSNdkq9WDQ4elEm+iJFn9JIkvbGEEFhbO5Ca+hfgCazD3PwT3nuvNQsXLnz1Pe3XdObM
+GerUeZv09GnAB0+XHqFUqaEkJDxCiGpotTFUqlScAwd+y/Z4+qmpqbRu3ZUTJ85hYmKFp6c9V678
+iVZbEd0ZvSVwCBubLiQmxv6744ULujP5e/egfn347TdwdMyTNhcEp06dYvv2HTg42DNgwAAcC3Hb
+cpP7ZK97SZLeWAqFgqlTpzBvXmtUqlGYm5/HzU3B7Nmz8yzJA0ye/Bnp6XWAhOeWPsbOzpHTpw9y
+9OhRbG1tCQwMzNFse5aWluzdG8aVK1dQq9VUqFCBgIAGXLyYBlRDd1a/h6+//vrfnU6fhpYtIS4O
+mjaFLVvA1vYFNRQ+W7dupWfPIaSkDMLcPIIFC5Zy/vwxnJycjB1a/hOFTCEMWZKkAm79+vWiX79h
+YvLkaeLhw4e5KissLEyUKuUvHBxKiu7dB4rNmzcLX98aApYLcBEwQ8AiYWJSXPz888951ILM7ty5
+I6pXbyxMTEyFnV1xsWLFin9XHjokhL29ECBEu3ZCJCfnef33798Xbdt2E2XLVhOdO/cV0dHReV7H
+y3h7VxYQLnQD9QthadlHzJ8/P19jyEu5yX3y0r0kSW+U06dP8+mnC0lMTGbIkB706NE9z8o+c+YM
+DRsGkZy8FiiJQtEcU1NXTE3TSE3VAPOBdZia7mPixEHMmzc3z+p+ESFExqsTe/ZAx46gUkH37rBm
+DZibv7iAHLh79y7e3pVJTW0LDAF+pXTp7fz995mMU94aUPHiXsTF7edZR0eFYgZTpmj57LNP8qX+
+vCY740mSJL2Gv/76i8DAIH79tTZ79rzD4MGTWbFiZZ6Vv2vXLtTq/uimmN2EEE1JTz9LauoFwAdz
+84G4up5k/vyp2UrySUlJDB06Bj+/mjRt2oHLly+/9r4ZknxYmG4wHJUKBg2CtWvzPMkDDBgwktRU
+E2AF0BCYz927ppw+fTrP63qR9u3bYm09EfgHOIyV1XLatAnKt/oLEnmPXpKkN8by5StJShoNjAFA
+pSpJcPAkBg8emCflOzg4YGFxmuRk0CWYQHQ93gFm4OU1kmvXsp/s3nmnHwcPWpCS8g3Xrx+nXr2m
+XL587t8Z5v4jPj6egwcPYm5uzttvv60b1W/9et0ENRoNjB4NX30FJq93rqdSqYiOjsbd3R2lUvnK
+7S9fvg5oADVgBWjRaBIxN8CXihdZuvRLtNpxbNlSCxsbexYu/JoGDRrkW/0FiTyjlyTpjaHVasn4
+Z88kT28F9uvXDze3v7Gy6gncBb5EN8qeCkvLxTRqVCfbZapUKvbs2U5KymqgDkKMJS2tJvv3789y
++8jISN56K4B+/b6hZ885VKlSD9XixdC7ty7JT5mim3L2NZP8zp07KVHCi4CAFri4eLJlS9gr9wkI
+qIxC4QZ0AlYC7+Dqakn16tVft9m5Zm1tzerV3/H48V2io6/k6S2aQidPegnko0IYsiRJBcTZs2eF
+UuksYJmAn4VS6SuWLv0uT+t4/PixWLBggahQobowMXEU4CbAVtSp87ZISEjIdnmpqanCzMxSwIOn
+Hcu0wta2sfjll1+y3L51627C1HS2ftsJpnWFvkfaZ59lq+74+HhhY1NcwKGnRZwQSmVxcf/+/Zfu
+d/fuXeHjU0WYm7sJExMX4e1dQTx69ChbdUsZ5Sb3yUv3kiS9MQICAti7dyszZy4gKSmZIUM+ZsCA
+fnlah729PTY2NkRG2qDVRgNWmJlNw97+EnZ2dtkuz8LCgpEjRxMS0gqVaigWFscpWfIxrf4zZey1
+a9do3bor165FAqMAmMJc5miO6zb4+msYMyZbdd+4cQNTU3d099kBamNu7sPVq1dfeNsAwNXVlYiI
+P4iIiMDCwoIKFSrk6eOKUjbl4ReOfFEIQ5Yk6SW++WaZKFmynHBxKSOmTZslNBqNsUPKtWHDRgv4
+Un8iDRdEyZLlclyeVqsV338fInr0GCymTJku4uPjM60vU6aSUCgWChgvoIuYwwdCgNCA2NWjV47q
+vX//vrCychRw+Wk7bggrq2Lizp07OW5Lbh08eFD4+VUXTk6eokuXvuLx48dGiyU/5Sb3FbqsKRO9
+JBUdGzZsFEqlj4CTAi4IpbKmmDv3C2OHlWuLFy8RSmUzASkChDA1/VS8/Xb7XJWp0WiESqXKtDwy
+MlK0a9ddKBSWAoRQ8EQsoowQINQgvm3cTKSnp+e43h9+CBXW1s7CwaGpsLZ2EYsXL815I3Lp6tWr
+wsbGWcAvAm4KS8sBomXLzkaLJz8V6ERfunRpUblyZREQECBq1aolhBDi4cOHonnz5sLPz0+0aNEi
+w72bOXPmCF9fX/HWW2+JXbt2ZQ5YJnpJKjI6deorIOS5M989okqVRsYOK9fS0tJEUFAXYWNTWtjb
+BwhPz3IiMjIyx+X98MMKYWVlJ0xNLUSVKvVFVFSUEEKIuLg44eJSWpiYzBBgI0w4L1YwUAgQKSjE
+qY8/zpP23Lx5U+zatUtcv349T8rLqWXLlglr60HPfV5UwtTUPFdfZAqL3OQ+g9+jVygUHDhwgGLF
+iumXBQcH06JFCyZNmsS8efMIDg4mODiYiIgINmzYQEREBFFRUTRv3pwrV65g8pq9QyVJKlyKFbPH
+xOQ2Wu2zJZE4OtobM6Q8YWZmxvbt/8fFixdRqVRUrlxZ94hbDpw8eZIxYz4iJeUPwI+LF2fQuXM/
+TpzYy549e0hJqYhWOxNzyvAjtelBKkkoWNi4BR/NnJkn7fH29sa7AExZa2+v+7yAQPfY4j9YWNjI
+HPEK+XJ0xH8eXwkLC2PAgAEADBgwgF9//RWALVu2/D979xkfVfE1cPy3NduSUNIoodfQQeklgBTp
+HRA/SjQAACAASURBVEUQRaog2MEOSlP0ryKK8AARRDoISBEEDIQivQQQQq8hEAglfct5Xuwag4Ak
+2U1C8H5f6d07M2fWjzl7507h2WefRafTUaJECcqUKcOuXbtyIkSFQpEL3n33dby9v0erfQW1eiRm
+80g+/fR9t+o8efIkn376KV988QWXLl3yUKSZp1KpqFy5MrVr185ykgfYsWMHdnsXoDygxm5/h717
+twKg0WiAVLxIZilL6EkKt4CDn07kvfBfH7sJcJ06daJYsVsYDF2BjzGZWvLpp+Meu356Wo480T/1
+1FNoNBoGDRrEgAEDiImJITAwEHDOzoyJiQHg8uXL1K1bN61s0aJFc/V/VIVCkb1Kly7NoUO7mD17
+DlarjZ49w6lUqVKW6rJarXTr9hwrV64C+qDRWBk79kn27t1KqVKlPBt4DipUqBBa7QJSUmw4/2Tv
+okCBQgC0aNGCwj6j+C6+LM3kItfR8k27jox+++1cjTm7GI1Gdu8OZ8aMGVy+HEPz5tNp2bJlbof1
+yMv2RL9t2zYKFSrEtWvXaNGiBRUqVLjrc5VK9a+/xu732eh0w1GhoaGEhoZ6KlyFQpHDihUrxgcf
+uPcUD/Dhh2NZtWonzv3kh2K3w61bH/DJJ5MIC5vqdv25pWvXrkybNpddu2oDFbHb1zFnzlwAvO12
+Igv5ort0gut6AyuGDuODSRNzN+BsZjabGTFiRG6Hke3Cw8MJDw/3SF3ZnugLFXL+8vT396dz587s
+2rWLwMBArly5QlBQENHR0QQEBABQpEgRLly4kFb24sWLFClS5J46R3vovZNCoXh8rFq1EYcjACib
+dk2kHNeuncxynadOnaJ37yGcPBlFpUqV+fHHqQQHB3sg2gc7ceIEx44do0yZMlSsWBGNRsP69T8T
+FhbGhx9+xpUrcfTuPZBl076k8bhx6Pbvh+BgCm7cSL+yZR/egCJP+OdD7JgxY7JcV7a+o09MTOTO
+nTuA81CG9evXU6VKFTp06MDs2bMBmD17Np06dQKgQ4cOLFiwgNTUVM6cOcOJEyeoXbt2doaoUCge
+E4GBfkAw8CFwCjiCVjuG7t3bZKm+hIQEGjRowa5drYmN3UhEhC/ly9ehR48X6Nv3JQICyhAcXIkZ
+M2Z5rA/Tps2gWrUG9O49lVq1mjJp0leAc2Tzk0++4MqVlxFJQX99EgHde8L+/VCmDGzdCkqSVzyI
+5yb/3+v06dNSrVo1qVatmlSqVEnGjx8vIs7ldc2bN7/v8rpx48ZJ6dKlpXz58vLrr7/eU2c2h6xQ
+KLLJnTt37rsO3FMiIyPFYvEXjaaGgK+oVBZ5990PxeFwZKm+7du3i49PLdcyrvkCRV1LAasLlBLY
+KbBF9PpgWbx4idvxx8bGujanOeFq84IYDAXl7NmzEh0dLQaDn4BIcc7ISUqJgNwKDhbJ4XPeFbnD
+ndynnEevUCiyVUJCAp079+b339ch4qB//8F8993/smVJ1IULF1i1ahVarZYuXbpQsGDBLNcVGRlJ
+3brtSEyMApoCHwFlgDrAj8DTrjt/oHXr1axdu9it2CMjI2nQoCd37hxNu+brW49ffvmMJ554gnz5
+/CmRuoINvEAwF9mn9sK26mdqP/30v9SqeFwo59ErFIpH1uuvv0tEhBc2203s9iv8+OMfTJv2f9nS
+VnBwMEOGDGHAgAFuJXmAypUr06xZXUymlsBV4BjQCNAD0enuvIRO5/6f0pIlSwKxwAbXlR3YbCcp
+X748RqORWa+OIIKWBHOR7WoTkzt05cnWefd8davVypIlS/j+++85evTowwsoskx5olcoFNmqfPna
+REV9DdRzXZlB9+7bWLQoLDfDyhC73U5YWBhhYT+yffte4DvgFZzzmIcBycAU9u6N8MgRrOHh4XTs
+2JPUVCE11Ur+/PmpX78OP7zclwK9ekFcHBcqVODI2LG06tIlz64ft1qtNGnShsjIRByOisBK5s+f
+QYcOHXI7tEeWO7lPOb1OoVBki6SkJL799jsSE+NRqSIQqQcIev02SpUqmtvhZYhGo6F///4EBATQ
+s+fXJCc/D5iBF4H/odNBWNh0j52zHhoaSkTEb9Su3RSH4y2uX29P/JpP8FrdFhwO6NCB4IULCTYY
+PNJeblm0aBGHDqWSkBCBc2B5Ky+99BzXrimJPjsoiV6hUHhcamoqDRq05M8//UhO7gB8gl6/Hi+v
+FIKC4nnnna9yO8RMCQgIQKM5ByQBXYHK6HQ1iI6+8MBXBPHx8fTpM5CtW/8gKKgQ06ZNon79+v/a
+zvXr12ncuAUpKcWBd2nJOn62r8KEg/j27bEsWQI6nae7l+OuXr2K1VqVv98eV+fmzZjcDOmxpryj
+VygUHrdx40ZOnEghOXkpMBHYg822hbCwVzl4cDu+vr65HWKm1KlThzZtGmCx1MfL62VMppaMHz/+
+gUk+KSmJ4sWrsHx5IrGxazh8eDgtWnTk9OnTiAg2m+2+5X755RdSUioBCXRiCb/QHhNJzFLrufPd
+d49Fkgdo1KgRGs1i4CCQilb7IXXrNs3tsB5byhO9QqHwuMTERFSqQP5+liiDRqPnqaeecmvf99yi
+UqlYuPAHfvnlF86dO0fNms/RoEGDB97/4YdjuXHjInAY51B/BVJTV/HRR2NYtmwlycnx1K3blBUr
+5uHn55dWzuFwoFL50YvrzKYHWoSv8GN3zw70K5o3XndkxBNPPMG0aV/w8svNSUy8xRNPhLJ06U+5
+HdY9oqKiWLJkKVqthl69elE0j/43UCbjKRQKj7t69SrlylXj1q2PgYbo9V9Rs+YpduzY8NCyj4PG
+jdsTEREO7Me5JE9QqWqg010hNXUjUBad7k0aNjzLpk0r08pdvXqVCSXL80XiTdTAOJUvv9avweYt
+Gx/bE9rsdrvrcJ5Hy759+2jcuBXJyc+hVidjMq1g375tuXZugrK8TqFQPFICAgKIiFjPE0/MJyio
+I+3aJbFmjXvrzPOSKlXKotFUBVoBnwI90OlO43D0AioBeqzW0WzfHn5XuYCffuJLV5L/NrgsjjGj
+CN+84bFN8sAjmeQB3nrrYxISxmK3f4XV+j137gxk7NjPczusLFGG7hUKRbaoUqUKu3dvyu0wcsW4
+cR8SHt6SM2fspKRMQKXS4O3ty507ewAHzmes/RQo4DzFExEYOxY+/ND571OmMHTo0FyKPvdYrVaW
+Ll3KlStXaNSoEbVq1cq1WOLibgF/P707HKW5du1ErsXjjsf3Z6JCoVBkwu3bt9mxYwcnTrj/xzxf
+vnzs37+VMWOGotNZsNvnc/36bGy2o3h51cNk6ofJ9AyzZn3jTPIjRzqTvFoNYWHwH0zyNpuNpk3b
+0b//t4wadZLGjdvx44+5996+e/e2mEwfAFHAIUym8fTs2S7X4nGLe7vv5rw8GLJCoXjE7du3T/Ln
+Lyw+Pk+I0Rgogwe/muU98tPr1Km3wAzX3vUisFJKlqwk06ZNk2PHjonY7SJDhjg/1GpFFi3yQG/y
+piVLlojFUlfA5vquDorFUtAj/x2ywm63y8iRH0j+/EXFz6+4TJr0Za7E8Rd3cp8ydK9QKNyyf/9+
+Nm/ejJ+fHz169ECv1+d2SA8VHR3N+PGfEx0dS/v2TzF69GfExU0CegG3+PHH+nTo8CtPu7mPvNls
+QKW6zt9zqG5RpEhxBg4cCDYbvPgizJkDXl6wdCm0betmz/Ku2NhYHI4Q4K939hVJTLyF3W5Hq835
+VKVWq5k48WMmTvw4x9v2NGXWvUKhyLLFi5fQt+9QHI7uaLVHqVDBzvbtvz3Syf7GjRuEhNTi+vVO
+2GyVMZm+JCnpOCK3AefSP73+FSZOLMVrr73mVluRkZHUq9eMhISXASMm0/9YvXoRofXrQ69ezuRu
+NsPKldCsmfudy8OOHj3Kk0+Gkpi4DKiJVvsRNWvuY+fOjbkd2iNBmXWvUChyxeDBr5KUtJKUlCkk
+JGzg2DENixYtyu2w/tWSJUu4c+dJbLYvgZdITFwJmID5rjuuo9Oto3Llym63VaVKFXbt2sywYfEM
+GhTN77+vIrROHejUyZnkfX3ht9/+80keICQkhAULZlKwYC+02gLUrn2IlSvn5XZYjwVl6F6hUGTZ
+7duxOJeLAaix2UK4fv16bob0UFarFYfDku6KNxqNlXz5xpCS8gWpqdEMHjyEFi1aeKS9kJAQvvnm
+C+e/3LkDbdpAeDj4+cH69VCjhkfayYz4+HgiIiJQqVQ0adLkkdnEqH379sTGts/tMB47yhO9QqHI
+sgYNnkKnGwXEAztRqxfTpEmTLNUlIpw/f56oqCjsdrtH40yvffv26HSrgalABEZjL/r0eYHz54+x
+det8Tp2K5PPPx91TzuFwcODAAXbu3ElKSspdny1YsJDKlRtQsWJdpk+fcf+G4+KgRQsID0cKFeKd
++k0o3LYX1as3Ztu2bZ7v6ANER0dToUJNevacSI8en1C5cu1H/seZwk2emQ+Yc/JgyArFYys2Nlaa
+Nm0nWq1BChQoIosWLc5SPVarVTp2fFYMBn8xm4tLSMiTcu3aNQ9H+7cDBw5IaGh7CQmpJ2+99b6k
+pqb+6/1JSUnSsGErMZtLibd3FSlZsrJER0eLiMiKFSvEZAoWWCOwUUymsjJr1g93VxATI1KtmgiI
+o3hxaVa8okBtgR0Cc8VgKCCHDx++p90//vhDRo16T8aNGy8xMTEe6fszz/QTrXaka2a7Q3S6oTJo
+0AiP1K3IPu7kvjyXNZVEr1A8fr788msxmZoJJLmSzwjp1u35LNVltVrlzJkzcuvWLRERmTkzTAoX
+Li9+fsXl9dffEZvNluk6P/lkvBgMHQSsAg7RakdKp07PiYhI27bPCMxKt4RuhVSv3kjat39GKldu
+IO88P0Ds5cs7PyxXTtbPnClgETiVrsxwGTt23F1trlq1SozGAIEPRafrLwEBxeXKlStZ+k7Se+KJ
+5gK/pmt7kTRv3tnteh/k9u3bEh4eLnv37s21pXKPA3dynzJ0r1Aoct3u3ZEkJnYHDIAKq7U3e/ce
+ynQ9x44do1ixClSq1Ah//yK89NJAXnnlIy5f/oHY2HV8/30EH31077D8wxw6FEVycnuc05pU2Gyd
+OHLkOOBcQgdx6e6+yJEjkaxZE0L84WEM+HEe6uPHkcqV+bxDV/p9NNHVz/RlYvHyunulwquvfkhS
+0g/AGKzW/+PGjdZMnTot07H/U+PGtTEYvgdSgESMxhk0bvyk2/Xez/HjxylVqjIdOoyiceNutGvX
+I1tfyyjuT0n0CoUi11WpUhajcQ3gPL5Vo1lJxYrlMl1P+/bPcOXKmyQmXiA19SizZy8jMfFNoC5Q
+nsTEz1i06JdM11urViWMxiU4k6Og18+jRg3nrPxRo4ZjNk8AxgKfode/g05XgzL27kTwJiUlgd0q
+NcOrPsFH323n4sUhrn52A74FXkWj+YU+ffrc1WZCQjwQnPbvNlsxbt+Oz3Ts/zRu3IeEhqrR6/3R
+6QJo0yaAd9550+16/+n06dN07vw816+/ye3bO0hI+JPw8KuEhYV5vC3FQ3hwZCFH5MGQFQrFQyQn
+J7vegZcRH5+aEhxcQS5evJipOmw2m6hU6nQ7q4loNDVEpRog0EqgkEBlCQmpnen4UlNTpXXrLmI0
+BomXV4CYTEWkefOOsmXLFhEROXjwoAwc+Ir06/eyfPHFF1LfVF1i8BcBCaeB5NcYRK+3CMS4Yvuf
+gE5UqnxisfhLRETEPW0OH/62mEzNBY4JbBKTqZBs3rw5wzFbrVYZM2a8NGzYVnr3HnDP93njxg2J
+i4vL9HfxMA6HQ4YOfUMMBn8BH4Ez6V4TjJU33xzp8Tb/C9zJfXkuayqJXqF4PNntdtm7d69s375d
+EhMTs1SHv39xgcUCMwW+Er2+uKhU3gIfCZwXmCI+PkFp7+8zw+FwyOTJk8VgKCrwk8B0MZn8Zfv2
+7XfdlxQeLjfVGhGQtVSRgsb60rfvIDEafV0xOJOewdBFJk6c+MA5A6mpqTJs2Bvi719SihevLAsX
+LpJbt27JoUOH5MaNGw+Nt3fv/q55D8tFqx0lgYElsyWxpxcTEyOtW7cXjaaEQJxAW4EPBBwCN8Vs
+rik//fRTtsbwuFISvUKhUIjIzz//LCqVRaCNQE/Ras1iMAS7Eo0zwfr61svUk3F6tWo1E1iR7gn1
+S3n22Zf+vuH330XMZhGQg6XLSpe2PeV///tabDabjBz5gZjNNQV+Eo3mHfHzC5arV69muO21a9eK
+2VxQvL0risGQT+bOnffAe1NSUkSj0QvcTovVYnlaFi5cmKV+Z8SdO3ckOLi8qNWNBYa42r0gUEnA
+T7y88kn//sOUCXlZ5E7uUzbMUSgUj40tW3ag0fTFZpsCgM32GXb7x8BtwBdIwWaLxtvbO1P1bt68
+md9+20hMTAygSvdJAlFRR5k8eTI9fXwIHDIEkpPhueeo+sMPLE23R/uECWMoXrwoK1euoHBhP8aM
+2Y6/v3+G2o+Pj6dbt94kJKwAGgBHGDCgCU2aNKJo0aIZ7EX2bh++YcMGbt4sisMxEhgG3ACKAq8R
+HDyJnTt/p1ChQtnWvuLBlESvUCgeG5cuXcNmq5fuSkN8fApgs4WSkNAZs/k3mjWrQ/Xq1TNcZ1jY
+bIYNe4/ExJfQar1RqV5C5GvgIjCBw4fbs/2NlQy2ufZkHzgQpk51HjmbjkqlYsiQgQwZMjDT/Tp/
+/jxqdUGcSR6gEnp9CCdOnLhvotfr9fTs2YflyzuTmDgcrXYnZvOftGrVKtNtZ5TzR4QGaAF0AcoB
+FgoUsLFq1Rolyeci5VAbhUKRI5KTk/nyy685fPgkdepUY+jQIWg0mocXzIQff/yJwYMnkJi4CvDF
+aOzFoEHVqFu3BgcOHKJ8+bL06dMnU+0WKFCEuLhVQA2cM+6fpFQpDfHxt7l8uR3POaoQxotocLC4
+WGm6nz0BKtXDqs2U27dvExRUnKSk34HqwGmMxtr8+edeihcvft8yNpuNceM+47fftlGiRGE+/XQ0
+RYoU8Whc6d26dYsKFWpy7dqz2O0N8PKaRIMGBlauXIzZbM62dv8r3Ml9SqJXKBTZKiEhgXfeGU1Y
+2HySkqpht7fHZFpAmzbFWLx4jkfbEhHGjBnPZ59Nwmaz0qNHL2bN+tat0/QMBm9SUs4D+QHQ64cx
+blwJtm49QKEVGqbi7MNonmdZ5VMcitz6r/WdPHmSAwcOEBwcTJ06dTIcx8KFi+nXbwg6XTlSU6OY
+NGkcQ4cOynK/3HHgwAFmzvwRtVrFgAEvpB0AdOnSJV5//X3Onr1E06Z1+fjj9x/pkwzzEiXRKxSK
+R5LD4aBGjYYcOWLCbj8LHMc5vJuIwVCMkycPZstT5l9/I1QeeLLu1KkXv/4qpKRMBDagUr0OxDNK
+68V4axIAb/IBU00RvP56Mz755IMH1rVgwSL69RuKTtcQu/0Afft24dtvv8hwLNHR0Zw4cYKSJUsS
+HBz88ALZICIigtatu5CYOBywYzZ/S0TEemrkwuE8/yVu5b6szwHMHXkwZIXiP+vFFwcJFBD4XeDJ
+dLPV7WIyFZVTp07ldogPdefOHenZ80XJl6+waDT5RMX/5CM++KsjMlxnFqMxnwwcOFysVusD60lN
+TRWDwUfggKvoTTGbS8gff/yR6ZhsNpv83//9n7z66psSFhYmdrvdnS5miN1ul8GDX3WtjZ9218qD
+zp17Z3v7/3Xu5D5lZzyFQpHm2rVr7NmzxyOnme3du5f581fi3O61JnATGAPsRasdTqlSRShRooTb
+7TgcDs6ePcvVq1fdrut+LBYLCxbM4tixfWg1KiZxkdF8gh01gww1CF34I4mJcUyb9jVa7YPnN8fF
+xeFc6FTNdcUXjaY658+fz1Q8IkKXLr0ZMWIOX31VkGHDptG794AMlY2MjKRjx140atSOqVOnZ+oJ
+ccqU75gzZwdQB0i/WiCA+PikTPVBkcM89nMjh+TBkBWKPOGHH34UozG/+PhUF5OpgCxb9rNb9S1d
+ulS8vdsLtBDoKfB/AmVFp/OXLl16S2xsrNsxnzt3ToKCSotG4ydarbf07t0/255ukxMTZbpaKwKS
+gk66ME/M5rKydevWDJW32+0SGFhSYLbrSfigmEz+EhUVlak4Dh8+7DotL8lVT7wYDP5y5syZfy13
+4sQJsVj8RaX6UmCZmExVZOzYTx/a3r59+6Ro0fKug3jCBH4QKCewxbVjX0lZuHBRpvqgyDx3cl+e
+y5pKolcoPO/SpUtiNBYQOOJKHnvEZCqQpZ3Url69Kl269JFixUJEo/EV2CUwSqC2GAwF5fr16x6J
+OSUlRXx8gl2bszgE7ohO94RMn/5/HqlfxHkM78yZM2XmtGmS2K2bCEgiSCevVmKxVJEePV7I1AYw
+Bw8elMDAkuLllV8MBh+ZPz/zG9j88ccf4uNTI93QuYjFUk4iIyP/tdyYMR+LRvNqunKHxN+/5L+W
+iY+PlwIFigjME3hb4CXXd/29QEkxmQrLjBmzMt0HRea5k/uUdfQKhYJTp06h15cnKSnEdaUWGk0Q
+58+fJ1++fBmux2az0bjx05w61QirNQyVahwqVSMMhnyYzV6sW7eeAgUKuB2vzWajV6++3L6dAAzG
+uYmNBau1D9u27WHAgP5ut3HhwgVq1WqILeEJwlJ2YbRfxGE2c/6LL2glwoBixXj66aczNeGvatWq
+XL58ktjYWPLnz49Op8t0XFWqVMFsvk18/CQcjs5oNPMpWFBDuXL/fgiQM870Q/WOh8Z+4sQJrNZ8
+wLNAK6AxUA+TyQ+DIZWdOzdTpkyZTPdBkbOUd/QKhYJSpUqRmnocOOa6sg+bLZpixYplqp5jx45x
+8eJNrNb/AbURWY7ZXJolS2Zx5cppatas6ZF4R478kF9+OQhYgF9dV+3ASsqWzXjMEyZMxNs7EIPB
+l+7d+5KQkJD22fvvjyPpek/mJSbQ0X6ROAyMrNmAY0FBJCUlYTKZsjSrX61WExAQkKUkD2Aymdi6
+dT11627Ez+8pGjbcSUTEuocuY+vV61mMxnmoVJ8DSzCZnuO114b8a5mAgABSUy8DV4ECwFp0uj+Z
+MKElx48fUJJ8XuG5gYWckQdDVijyhFmzZovBkF98fGqKyVRAFi9emuk6oqKixGAIEkh0DQ+nitlc
+Qg4ePOjRWAMDywhsFvATCBKoJ1BSvL0LS3JycobqeP31N10rAvYKXBOttpP06tU/7fP2TdpJOBVF
+QGLwl6p8IwUKlBCLpbro9a+IyVRSPv54okf7ld0OHz4snTv3ltDQDjJt2owMvXZ4//2PxWQqISbT
+S2I2l5Y33ng3ByJV/JM7uU9ZR69QKNLExMRw7tw5SpUqhZ+fX6bKXr16lUaNWhMVFYlzsLAzRmMC
+tWs72LTpF9Rqzw0glixZjbNnvwL8gFeAQ5QoEcSePREULFjwoeXj4uLw9y+C3f42MNp19TT58jUh
+Lu4C3LjBlRo1CDp/nosU4imWcc4wFLs9Bqs1CjAB0ej15YiJuZCp1xt50datWzly5Ajly5cnNDQ0
+t8P5T3In9ylD9wqFIk1gYCC1a9fOdJK32WxUqFCHqKgngSTgOGr1Zp55JpB165Z5NMkDfPrp+xiN
+zwFr0GqrUrCgme3bN2YoyYNz4xmt1gxEpbt6DJPJDDExEBpK0PnzxPr40lwXx2ldM0JDi2A0lseZ
+5AEKodXm4+bNmx7t26OoYcOGDBo0SEnyeZQyGU+hULht+fLlxMXFAh/h/LNSAoejH8HBOry8vDze
+Xo8e3QkI8GfJkpX4+uZj6NCdmTo0pUSJEuj1KlJSdgKdcZ6yFsbUdz+Fxo0hKgoqVMBvwwaOFS4M
+QGxsLKVLVwaWAy1Rqf6PAgVMnD59mrlz5xIYGEifPn0wGAwe769C4Q4l0SsUCrc5j2/1BXYBnQAH
+sIVChZ7LtjZDQ0Oz/IRpMplYu/Zn2rbtQmLiRlQqGzPfHUmHSZPg3DmoXh3WrYOAgLRDaf39/Vm3
+bjk9e/bj8uUeVKhQkx49nqd9+74kJz+HwbCV77//kR07Nij7u/+DiLBt2zYuXbpEzZo1KVu2bG6H
+9J+ivKNXKBRu279/P/XqNSclRQW0BI5jMkVz48bZbHmivx+bzcbq1auJi4ujUaNGlC5dOu2z3377
+jZ9/Xk3+/D4MHz6UwMDAtDJXr17F/9o1dE8/DdHRULcurFkD+fP/a3sigtmcn6SkP4AKgGCxNGHW
+rFfo3r17Nvb0b8uXL2fgwFe5dSuWxo2fYtGiMPI/JO6cJiK8+OLLLFmyAY2mGlbrZubM+Z5u3brm
+dmh5irLXvUKhyHU//TRfjEZfUanUUqpUiJw7dy7H2k5NTZV69Z4Si6WOmM3PicnkJxs2bBARkZde
+GihQUGCSqFSDxN+/mMTExPxdeO9eET8/5y4yoaEit29nqE2r1SpqtVYgJW0TGpPpBZk+fXp2dPEu
+DodDFi9eLF5efgJbBeJErx8kzZt3yPa2M2vz5s1iNpcVuOP6nvaK0eibI/vzP07cyX15LmsqiV6h
+eHQ5HA5JTU3N0Tbj4+OlevXaArUF7K5kslaKFq0gGzduFPAV2J6WjNXqPjJp0iRn4W3bRHx9nR88
+/bRYb9+Wd98dLRUq1JEGDVrLrl277mkvJSUlbVlao0atRacbInBFYK2YTH5y/PjxbO2vzWaTdu16
+iE6XT2BAup3ubotWa8jWtrNi7ty5YrH0SBenQ3Q6s9y8eTO3Q8tT3Ml9yqx7hULhMSqVKssbwWTV
+wIGvEhmZDDTi74VETxIbe5nVq9cBOiAg7X6HI4iEhETYtAlatoRbt6BrV1i+nFffGc1XX/3OsWOf
+s21bD5o2bcOJEycAuH79Og0btsJotGA0+jBlylSWL/+JZs1iMJsrERz8OitWzH/oDnXumjlzJps2
+xWC1fgGc5u/d7o7h7Z2xVQc5qVatWtjtvwORAKhU0wkKKoqPj0/uBvZf4sEfHDkiD4asUOSq2UZO
+XwAAIABJREFUpKQk6ddvqPj7l5QyZWrKr7/+mtsh3eXOnTsyePCrUr16E3nmmX5y5cqVTJX38ysh
+8JNAUYEoAZuo1cMlNLSdjB8/QdTqqgLNBfYLfCfgI520RklWqZyPmM8/L+I6XtZi8Rc4l/b0qdMN
+k88++0xERFq16iI63csCqQInxGQqJps2bfL49/Eww4e/ITBRIFmgvqtvQ8RoDJQFCzK/d35OmDt3
+nhgMPqLX+0rRouXk6NGjuR1SnuNO7stzWVNJ9ApF5vTuPUAMhg4CxwVWidHoJwcOHMjtsETEOdTf
+oEFL8fLqJbBBdLo3pXjxEElMTMxwHRUqPCmwzHXQikVAK8HBFSQmJkZu3LghRYuWE42mskBhAYv0
+oLek4jyFboaXSeLSHbKTP38RgcNpid7Lq6989dVXIiJisfgJRKd9plK9I6NHj/H4d/IwYWFhYjLV
+FYgXSBSVqqOULh0ie/bsyfFYMsNqtUpsbGymDgFS/M2d3KcM3SsUj7nly5eTnPwdUA5oi9X6PGvX
+rs3tsAC4dOkSe/fuJyVlNtAcq/Uzbtwws3PnzgzX8f33kzCZBmIw7MNkakSpUhU4cmQXAQEB5M+f
+n8jInXz99WBef/0ZBukNzGMeOmx8xlu84VWNg5GRaXW9995bmEydgeloNG/h7b2JZ599FgA/vyBg
+j+tOB0bjPgoVCvLYd5FRzz//PJ06VcJgKIHFUpXixU8SHr6OWrVqpd1z9epVXnhhCA0btuW998aQ
+mpqa43H+k1arpWDBglk6H0DhHmUdvULxmDOZLMTHXwKKAKDVXsRiKZm7QbloNBpE7ICNv/8cpaLR
+aO57v81mY926dWlL6IoXL06TJk3Yv38b69evx2KpR/fu3TGbzWll8uXLx9ChQ7k9fjw+qbEAfMDH
+jOV1TLaKdy1He+ONEQQHF2bp0rUEBORn1KgdBAQ43+/PmjWZdu16oFI9jUp1ivLltfTt2zdD/YyO
+jmbEiJFcvHiNli0b8t57b2d5LoNareann2Zw/vyHxMfHU7Zs2bvqSkhI4Mknm3D58tPYbIPYt28a
+hw/3ZcWK+VlqT/EY8ODIQo7IgyErFLnqhx/miMlUROAT0eufl6JFy2XpnPns4HA4pG3b7mI0Pi0w
+X7y8XpBKlWpLSkrKPfempKS4ltA9KRZLTzGb/WTz5s0Za2jChLTD20fqA0SjeVvM5lrSs+eLmRpK
+PnnypMycOVOWLl163xjv59ixY6LR/DVDfoloNE2lc+fnMtxmZq1Zs0a8vRulm+WeKDqd+ZH5b67I
+Gndyn/JEr1A8hmJjYzl48CBBQUH07duH4sWDWb16HQULhjBo0Fe5eghLREQE3377A2q1ihEjBrBs
+2VwmTvycbduWEhJSijFjvr7vznJz587l4EEHiYk7AA2wkr59h3LmTOQ996YRgQ8+gHHjQKVCpk6l
+YZEi+Bw6RNmyI+nWrVumhpJLly5910Y8GdGlSy/s9jLANECF3d6GlSsLcvPmzfv+d1ixYgVjx36D
+3W5nxIh+9O3bJ1PtyT2bqihD5f95nvu9kTPyYMgKRY7asmWLWCz+4uvbWIzGwjJo0IhHZgLUxo0b
+xWQKEJgs8KWYTH6ybdu2DJX95JNPRK0ele5JNUZMpgIPLuBwiIwY4bxZoxGZO9dDvcgci6WgQMN0
+cSeLWm2SGzdu3HPv2rVrxWgs5JpcuEpMppIyZ07m4o6Pj5dixSqITveawHIxGttK+/Y9PdUdRS5x
+J/fluaypJHqF4t/5+xcXWO1KKrfEbC4vv/32W6bquHnzphw9elTi4+M9GlvTph0FfkiX9L6Vjh17
+Zais80dCcYEzAnbRal+Tpk3b3/9mm02kf39nI3q9yLJlHuxF5tSo0cg14/9tgTUCT0uVKnXue2+H
+Dr0E/i/d97Nc6tRpmek2Y2Ji5IUXhkiDBm3k3XdHZ/g1g+LR5U7uU4buFYrHiM1mIzb2AtDadcUH
+kYacOnWKp556KkN1zJu3gP79h6DV+iMSx88/z89w2YexWm2AOd0VM6mptgfca2XBggXExMTQsGFD
+mjVrxtixbzBqVCXsdjvVq9dlwYJF9ysIffvC/PlgNMLPP0OrVh6JPysWLJhB/frNuXXrJ+z2MEqV
+KsSOHdvve69erwMS0l2JR6fL/J/pgIAAwsK+y1rAisePB39w5Ig8GLJCkaNKlKgkMMv1RHhRTKZg
+2b59e4bKXrhwQYzGggKHXOV/F4vFz2NP9gsXLnI9la8UWCYmUxFZtWrVPffdvHlTAgPLiEpVW1Sq
+YWIwBMnMmWEi4twCNiEh4f4NJCWJdOzofBz29hbJ6GS9bHbnzh3Ztm2bREZG/utrlF27donJ5Ccw
+SWCyGI0Bsn79+hyM9G63b98Wm82Wa+0r/uZO7stzWVNJ9ArFv4uMjBR//+JisZQSvd5bxo+flOGy
+GzZsEF/fJumGjkUsltLy559/eiy+efPmS61azeSJJ5rLsvsMqTscDqlS5QmBGvL33vVHxGDw/fe5
+BvHxIi1aOIPOn1/kPvvU5wW7d++W3r0HSM+e/SQ8PDxXYjh37pxUqFBLtFqjeHlZZMaMWbkSh+Jv
+7uQ+5ZhaheIxlJqaytmzZ/Hz86NAgQIZLnf69GkqV65DUtJeoBhwCKOxCVeunMuxvclv3LhBQEBh
+7PZngTDXVRtgICioFCkpifTs2Z3Jkz/7e/34rVvQrh1s3QoBAfDbb1C1ao7E+ziqXr0hhw+3xm5/
+D4jCZGrKli2/3LUpjyJnuZP7sn1nPLvdTo0aNWjfvj3g/J+4RYsWlCtXjpYtW3Lz5s20eydMmEDZ
+smWpUKEC69evz+7QFIrHll6vp1y5chlO8mfOnCE8PByj0cj48R9hNNbC17cxRmMzZs2alqMHkGg0
+GlQqNbAK+B24BXRDpcrPlSuziYvbwuzZh3nrrQ+cBa5fh+bNnUm+aFGIiFCSvBscDgeRkX9gt4/E
+uTSvPA5He/7444/cDk2RRdme6L/++mtCQkLS1qpOnDiRFi1aEBUVRfPmzZk4cSIAR48eZeHChRw9
+epRff/2Vl19+GYfDkd3hKRT/eV98MZlKlWrTqdMHlClThWLFinDkyC6WLRvNiRMHeeaZHjkaj6+v
+L1279sTLqwjQGwhEowlH5G2gHlCKpKTPWbr0F7hyBUJDYe9eKF3ameSz+fS4nGS1Wvnhhx8YO3Ys
+GzduzJE21Wo1+fIFAX8l9lQ0mr0UKVIkR9pXZAMPvT64rwsXLkjz5s1l06ZN0q5dOxERKV++fNrp
+VNHR0VK+fHkRERk/frxMnDgxrWyrVq1kx44d99SZzSErFHmGw+GQS5cuydmzZ7O8Tj4qKkqMRn+B
+86534XvEaMz34MluOcRqtcqECZOkRo0GUqRIJSlWrLxoNIPTzR34WZqXrSlSpozzQkiIyKVLuRqz
+p9lsNmnUqLWYzaGiVo8Uk6mETJr0ZY60vXr1ajGZ/MTbu6dYLFWkTZtuYrfbc6Rtxf25k/uy9Yn+
+tddeY9KkSajVfzcTExNDYGAgAIGBgcTExABw+fJlihYtmnZf0aJFuXTpUnaGp1DkWVarlY4dn6VU
+qSpUrFiXOnWacfv27UzXc/r0afT6qkCw60ot1GpfoqOjPRpvZmm1WkqXLsHx4+e5dOl9zp8fgt3+
+IzpdX9TqUVQx9OOXWxfh5EmoUQM2b4bChXMkth9+mEPBgsEYjb507dqHhISEhxfKgg0bNrB/fwwJ
+CRtwOCaSmLiZd999F6vVmi3tpdemTRsOHfqDqVPbs2zZF/zyy8K7/o4r8pZsW0e/atUqAgICqFGj
+BuHh4fe9R6VS/ev2kw/6bPTo0Wn/HBoaSmhoqBuRKhR5z6BBL7N69RUcjkuAjkOHBvDqq+8wa9a3
+maqnTJkyJCfvAzYCzYDfUasTH4lh2kmTppGYOBno5LqSRMWKSxnc0Jv+izTorl6F+vVh9WpsFguv
+DnuDOXPmotPpef/9t3jtteEej2nLli28/PI7JCWtBEqwZs1Q+vcfzvz5Mz3eVlxcHCpVKZzb/QIU
+RURFUlJSlg/EyYysbPer8Jzw8PAH5s7MyrZEv337dlauXMmaNWtITk7m9u3b9OnTh8DAQK5cuUJQ
+UBDR0dFpJ0MVKVKECxcupJW/ePHiA//YpE/0CsV/zaZNm5g9+2ccjv8BBgBSUl5g5853MlVPbGws
+HTv2AkxAZzQaA0ajsHz5QgwGg8fjziznD/3083TMtA4IYsiC+XDjBjRrBitWgMXCR++NISxsL4mJ
+u4DbvP9+F4oUKUSPHt09GtO6db+RlNQfcM4+T06eyLp1DT3axl8aNGiAw/EKsBxogFb7OZUqVc/R
+iZGK3PPPh9gxY8ZkvTIPvkJ4oPDw8LR39G+99Vbau/gJEybIyJEjRUTkyJEjUq1aNUlJSZHTp09L
+qVKl7vveMYdCVigeWZ069RZoJ9Ar3Trzt6RLlz6Zqqdr1z6i0w0XcAgkisEQKhMnfpZNUWfesmXL
+XKfuzRGYJs298onVbHa+k2/Xzrk5jkv58rUFItK9w58mPXv283hMX375pRgMPdK1s1ZKlKji8Xb+
+EhERISVKVBaTKb80btxGoqOjs60txaPNndyXY1vg/jUMP2rUKHr06MHMmTMpUaIEixY5t7AMCQmh
+R48ehISEoNVq+e677zJ1qpRC8V+h1WqAJjif9KoBWvT6C0yZ8i+nuN3Hvn2RWK3/h3MJlZHk5J4c
+PLgnU3XcunWLCRMmcfbsZZo1q8+AAS957P/bzp07s3ChjsmTf+DJ27F8fDAZTUIy9OgBc+dCuuHr
+ggXzAycA59O1VnsCf3/3T+i7du0affu+zO7deyhWrDjfffcpRYrMIjq6I1ZrCbTaeUyd+qPb7TxI
+w4YN//10PoUiIzz4gyNH5MGQFQqP2r59uxiNfgJfC4wUvb6ALFmyJNP1tGnTXTSaD11PplYxGtvL
++PGfZrj85cuXXSfRPSswXfT66jJs2BuZjuOhVqxwHkwDIi++6Dyw5h927twpZrOfaLWviJdXX/Hz
+C5aLFy+61axzh766rlPgTohKNU3y5y8sZ8+elenTp8vnn38uhw4dEhHnKoFt27bJxo0b5c6dOxlu
+Y9euXbJgwQI5cuSIW7EqHn/u5L48lzWVRK9QOJN95869pX37ZzO0F/pfS/H+Wtoq4lz+GhxcXnx8
+aorZXEYaNmwpycnJGY6hVq0GArVdQ/8icF00Gq9Mn5RmtVolLi7u/ksE5893HjELIsOGifzLEq/j
+x4/Lp59+Kl9++eVd/cyKixcvysyZM0Wvz5+ufyI+Pi3u2Zs/MTFRnnwyVCyWSuLjU18KFSot586d
+e2gbb7zxrphMxcTbu6uYTIEybdoMt2L+p507d0qtWqESHFxJBgx4RRITEz1avyJnKYleoVA8UHx8
+vDRp0kYMhoLi5ZVf2rbtnpaMExMTZdu2bbJnz55Mr5PW680CbdO9r04RtVqfqTX406fPFL3eLDqd
+RcqUqSZnzpz5+8MZM0RUKmflo0Y5z5fPAb///ruYzX7i7d1KwEvguqt/NrFYKsvmfxyU88kn48Vg
+6CJgExDRaD6RVq26PrD+mJgYadq0jahU+dPVHSVeXj5y+/Ztt+O32WyyYMECMRjyC8wW2C8GQyfp
+2jVzczgUjxYl0SsUigcaNuwNMRh6CqQKJInR2EY++mhslupyOBwyefK3UqlSfdFq8wkECPxP4A+B
+zlKx4hMZrmvPnj1iMhUSOC7gELV6olSq5Dqn/auv/j5VZ9y4LMWaVUFBpQTWupp/Q6CswHgxGltL
+gwYt7znN7dlnXxL4Pt0Pnt1SsmT1+9admpoq5crVELW6q0DoXYcHmc3F5NSpU27FnpqaKo0bPy1e
+XoUEnktX/03Rag1Z3lhJkfvcyX3KDggKxWNux479JCf3A3SAgaSkvmzbti9LdU2ZMpVRo77jyJGP
+sdleBuJRqb5HpepE/vx72bJlbYbr2rlzJyLtgHKACofjDY4e3Y1j7Fh49VXnTV99Be++m6VYs0JE
+uHr1PBDqujIJrbYkTZpsZNKkdmza9AsajeauMvXr18BkmofzHHkHev1Mateucd/6jx8/zuXLd3A4
+vgYOAztcnyzGYHDctWlYZkVFRfHWW2+xe7eVlJSPgfQbKF1Dp8v9JZOKXOK53xs5Iw+GrFDkql69
+XhKt9g3Xk51DvLxekldeeTNLdVWsWFdgU7onxbflyScbyJw5cyQp3XK3jFixYoVYLDUEkl11bZb/
+GSzOilUq59D9v3j//Y/Ez6+UBAaWkQkTJnnsabVq1fqiVk90vZs/KyZTsGzduvWB99tsNnnmmRfF
+yyufGI1BUqNGQ7lx48Z97z1x4oQYjYVcfV4tUEDAKPnzF5E9e/ZkOeZ33hktRmOA6PXFBcYK3Bao
+IPCiwFdiMpWViRM/z3L9itznTu7Lc1lTSfQKxcOlpqbKvn375ODBg3L58mUpXjxEvL3riLd3TalQ
+oZbcvHkzS/VWq9ZYYHlaolepxsjAga9kqS673S4dOjwjFkuI+Fi6yndag7hecovMmycizlcFx44d
+k127dt317r9Nm44CRQV2CewTgyFEpkyZmqU4/unMmTNSsmRl8fIqIDqdSb744usMlbty5YqcO3fu
+X+c6OBwOadOmm5hMLQSmisHQWho1uvd1QGbs3r1bTKaiAlcFFgmECMQKXBeVqoEULlxOli5dmuX6
+FY8Gd3Kfch69QvGYuX79Og0btuLixQREUqlSpSSrVy/iwIEDaDQa6tati5eXV5bqXrlyJT169CMl
+pSWgwWRay+7dWwgJCQEgNTWVsLAwzp49T/36ddOOp34QEeH3DRsoMXYspbZsAb0eFi2Cjh1xOBz0
+6TOQn39eg04XiMFwk4iIdVy9epUmTbricHwJ9HLV9At1637Ljh2/Zqlf94vr2rVr+Pj4eHyXQKvV
+yjfffMvevUeoXr0CI0a8gl6vz3Q9s2f/SFjYEu7cieX48QASEn4GBHgH+BKDwZfSpUuycePKtPNF
+FHmXW7nPIz81clAeDFmhyFHPPTdAdLqhrqFnmxgM3eTddz/ySN1z5swVLy9/0WieEa22vDRv3i5t
+yNxqtUq9ek+JydRSYLSYTOUePukvJUWkRw/nk7zJJJJuqeC8efPEbH5SIN41ejBZatZsLDNnzhSN
+pqLAp+leIUyRp57q7JE+5gVTpkwVk6ms6wn+XQEfgVPy18l++fMXlgsXLiiT7x4j7uS+PJc1lUSv
+UPy7qlUb/eM9+lx5+ukebtdrs9nEYPAWOOyqN1nM5hDZsGGDiIisX7/e9c7d5vr8smi1hgeuzT93
+/LjsLRwsApJsMIhjy5a7Pv/ggw8FPkjXj0vi7R0gf/zxhxgMQQJ+rlnxb4tKZXLrHbenpKamyscf
+j5eWLbvJK6+8KXFxcdnSTokSVQW2pvtu2olGYxKLpbTky1dIdu7cmS3tKnKPO7lPmXWvUDxmqlcP
+Qa9fiPNAGCtG41Jq1arkdr2JiYnYbDYgxHXFC5WqStqRtrdu3UKlCubv09YCUat1JCYm3lPXnvBw
+osqHUPPyBa5joYmtKKNWrb/rnkqVQjCbVwN3AFCr51O+fAh16tThww/fQK9PRqebjcHwPTNmfMPG
+jRv54IOP2LMnc9v4elL37n2ZMGEz69d3Zfr0OOrVe4qUlBS36xURLl68mPZd37vNcA0GDerPnj2r
+iY4+Te3atd1uU/EY8dzvjZyRB0NWKHLUjRs3pHLlOmI2lxKTKVgaNWqd6RnxD1KmTDVRqz8T52E6
+O8Vo9JOoqCgREYmOjhZv7wCBnwTOiU73mtSo0fDeSuLiZKfOuaXtZYKkEpECF0Svt9w11OxwOKRz
+516uYeliAmZ5442RaZ/HxMRIZGSknD17VgICSohe309UqvfEZAqQNWvWeKS/mXH16lXR630FEtNW
+OHh715KNGze6Ve+dO3ekXr2nxGDwFy+vAtKuXQ+ZPPnbdEP334jZ7CeHDx/2UE8UjyJ3cl+ey5pK
+olcoHs5ms8nhw4flzz//dOs9bVxcnPTq1V/KlKklbdp0l4iICKlQ4QlRq7Xi7e0nP//881337969
+WypWrC358hWWFi06y9WrV++u8No1cVSvLgJyDpOUIcqVFK+KRmO8J9ayZWsIfCJwQOCkmEzFJCIi
+4q57PvpojGi1g9INY6+ScuUyvnGPp0RHR4uXV35xbkz015a59TO0RfG/GThwuHh59RawinPDo1by
+yScTZPbsH6Vp047Svv2zj8RrC0X2cif3KbPuFQrFfTkcDmrXbkpkZAVSU/uh0awjIGAOUVEH0Ol0
+6PX6zJ1Ud/kytGgBR49ySq2hqcOHC7wPVAY+oHXrQqxdu/yu9rVaHSLJODf7AYNhMJMmVWbYsGGE
+h4fz3XezOXBgPydO9MQ52xzgMIULd+PSpWMe+ibuz2q18u2333Ho0HFq1Ahh8OBBtGjRiZ07vUlO
+7o9Wu4FChZbz5597MZvNWW6nWrXGHDo0BmjquvITTz+9kjVrFnqkH4q8wZ3cp7yjVygU93Xx4kWO
+Hj1OaupUoA52+4ckJASwa9cuvLy8Mpfkz52Dxo3h6FGoVImLP80l1ihotVNQq/sQEuJg5crFAMyd
+Ow9//+JYLAXQ6/MB61yVxKPRbKV06dKsX7+etm2fYfHiJzlxogEwCdgCnMRofI2uXTt49Lv4J4fD
+QevWXXj33dWEhVVk1KhldO3ah9WrF/HSS0WpUWMcXbteZefO391K8gDly5dCq/1r2aDg5bWOkJDS
+7ndC8d/hoVGFHJMHQ1Yo8qQrV66Il1e+tOVtYBeLpfI9Q+d/WbFihbRt+4x069ZX9u7d+/cHx4+L
+FC3qHMuuVUskNlZERM6fPy/Lli2Tbdu2pQ3Zb9261bX//U6Ba6LTPSVarbf4+jYVk6mY9O07WBwO
+hzRq1NY1F+Cv4fq+YjQGSsGCxWTIkNckNTU1276Xffv2ScGCRVyz/lNc7SeJ0RgkJ06c8Hh70dHR
+Urx4RfHxqS0WS1WpXLmORw6/UeQt7uQ+bW7/0FAoFI+mwMBAOnbsyKpVbUlMfA6DYT0BASqOHDmC
+v78/5cuXT7t3/vwF9O//NomJHwM3Wbu2Fdu2baCaWu0cro+JgYYNYdUq8PUFIDg4mODg4Lva/O23
+DSQlvQg4Z41brWFYLDVZvPgd/P39qV69uuu6DTCmK9mQFi2srFjxUzZ+IxAWNpt+/V7G+ZpgEfDX
+RjdeaDTeJCUlZbluq9WKTqe753pQUBBHj+5h165daRse3e8+heKBPPiDI0fkwZAVijzLZrPJV19N
+lm7d+krRomXFbK4tJtMLYjL5ydq1a0VEZNGixWI0FhZYk+4Je6yM7dRdJH9+54UWLUTi4x/a3uTJ
+k11Hvv5VzwYJDq54z30//TRPTKYSAisFFovJVEh+/fVXj/c/vf3794uXV0HX1rtJAhUFPhQ4IBrN
+21K6dNUsjSTs379fgoMriEqlloCAErJt27ZsiF6R17mT+/Jc1lQSvUKR8+bMmSNmcxPXsjoR2CRB
+QaVlyZKlYjIFC1QS2JCWoBsxWBJ1Oue/dOggksHlfXfu3JGyZauJydRedLoRYjT6y6pVq+57748/
+/iS1ajWT2rVbyIoVK2TPnj0ybtw4+fbbb+XOnTue7L6IiEydOlUMhucFvAVOClwU6CAqVT5p1Ki1
+XL58OdN1JiYmSsGCRQXmur7bFeLtHSDXr1/3ePyKvM2d3KcM3SsUeYSIMGtWGFu27KJs2WK89toI
+tyd6ZVRMTAypqTX4e/5uTW7cuMKUKbNJTPwM5xGtg4HPaUkEP/M9Rivw7LMwezZkcKjZYrGwf/82
+5s2bx82bN2nZ8jeqVat233t79+5F797Ove6XL19Oo0ZtSE19Hr1+L1988T0HDmzD29vbzZ7/rVCh
+Qmi1R4BPgYZAI2Anw4cP4quvJmapzlOnTpGaagGec13pgFo9gSNHjtCoUSOPxK1Q5LnH4zwYskLh
+EYMHvyom0xMCU8Rg6CFVq9aTlJQUt+u1Wq1y+PBhiYqKeuCa+x07driOVz0iYBWt9jVp1OhpadGi
+i8As15P8bOlICUn+67G+f38RN05lywiHwyFjx34qKpWPwO9pIwpGY1f55ptv0u47ePCgPPVUJ6la
+tbF89NFYsVqtmW7LbrdL69ZdxGKpLkZjG9HpLDJx4kS34ndOePQViHbFfkOMxkA5fvy4W/UqHj/u
+5L48lzWVRK/4L4qPjxet1ijOY1nrC+QTtdpfpk+f7la9165dk4oVnxCLpbQYjYWlZctOD/zxMHNm
+mJhM+USt1kqdOs0lJiZGNm3aJEajv8A30os+Yv0r044YIeLGRj0HDhyQl14aKi++OER27Nhx33vs
+drsMHz5C9PpyrhnwF9MSvVo9Sj7++BMRETl79qx4eweISjVFYKOYTI1lyJBXsxSX3W6XNWvWSFhY
+mBw7dizL/Utv9OjxYjIVF5Opn5jNZWT48Lc9Uq/i8aIkeoXiMXf9+nXR6SwCJQW+Ebgm8J34+ARJ
+fAYmuT1Ijx4viF7/ijhPuksRo/FpGTfu0wfe73A47plwtmXLFvm+Zj2x/5Vl338/Lck7HA5Zs2aN
+fP/997J79+4MxbRnzx4xmfwExgv8P3tnHhZl9b7xe/aZd4YBZFUBE0TEfV9zScP9p7lruaWpaS5l
+kdpimaW2mZq54JqaW6amiAuaqKm57xsiGIobIoIyMMDM/ftjRoKvoCCoYedzXV7F+57znOeckvs9
+2/N8R0ly486dO3OUeTC7Vii8CCwg0J9AdwLXCOyhTufBv/76iyQ5ffp0ajSDsh3wi6MkORdglApP
+VFQUa9duRoPBjdWqvfzQR8LevXs5Z86ch/opEDxACL1A8IJjtVpZrVp9Ar7ZBIt0cKie70xlBw4c
+4IIFC7g7W5a48uXrENiXzeZ8du7ct2DOTZ36j0PZlrKtVit79HiTen1l6nQDKUml+NPG0LK+AAAg
+AElEQVRPcx5rrmvXfgR+yObTYjZr9n85ymzZsoUGQ1UCIwgE2+/69yPgSLW6BJct+4WbN29maGgo
+v//+e+p0vbPZu0gHB7eC9bEQhITMp1xuJPANgWuUyX6km1uZQn2gCf57FEb7xGE8gaAYIJPJsHRp
+CKpXbwyrNQmAI4D7yMy8AWdn58fWnzLle0ycOA0yWXMAX2Hw4B6YOnUSKlUKQEzMWmRk1AdggU63
+ETVq1MufUyTw5ZfA+PG2n2fOBN55J+v1/v37ERq6BykpJ2G78x6N996rigED+kGr1eZpNjXVbO/f
+A5yQlpaeo0x8fDyAQABjADQEcA2AAgaDGps2rcHAgSNw86YBMpkGWm00tNpMpKePhcUSCEn6DsHB
+7+Wvj4Vky5YtGDnyY1itngCCAQDkcJjNi3D69GnUq5fPsRYICkMRfnA8E4qhywJBkTF48Ejq9VUp
+l4+hXl+d/fq9/dg6u3btolJpyLaHbTvwdf78ed64cYN+flXp4FCZen1ZNm7cOs/88TmwWsng4Acb
+4uSiRQ8VWbt2LY3G/8uxAqHVuvDGjRuPNL1hwwZKkheBTQTCKUnluGjRzznKREVF2Zf3dxG4Qpms
+HT08vBkdHc3hw9+nWj3Evh1BKpUfs23bLhw8eAQ7dHidCxcuLlSin/xw8OBBVq/emDqdO4FP7WcI
+kuzjcJ+SVIrnzp17qj4IXiwKo32PrTl9+nTeuXPniRsoaoTQC/7LWK1Wrlu3jl9++SXXrFnzWMHa
+smULtVpn2tK8/iO4jo6NGBERQZJMS0vjoUOHeOLECVoslsc7YbGQQ4fSrqLkqlU5/Nu7dy9/++03
+7tu3zy7GOwlkUiabRh+fCvkS2ZUrV7FKlZdZsWIDzpu3INcyYWFhdHHxolyuYPXqLzM2NpYk2bJl
+VwIrsvU3nNWrN318v4qIv//+mwaDG4GlBN6yby8MI1CdwMeUyyuzV68BT/1jQ/Bi8VSF/qOPPqKf
+nx+7devGzZs3P/f/OYXQC4oLVquV0dHRPHfuHDOf8jWzvPD1rUZgHW3R3JbbZ7mb6eDgztv2mPMF
+IiOD7NvXpqAaDblxY9Yrq9XKnj3fpF5fjkbj/1GSXDlp0iSWKFGaMpmc5cvXfCqx4P/3d9KkSd9Q
+klrY9+3N1Gq7cMSI4CJvNy8WLFhAvf4N+0fGVfvY9yXwGpVKHT/++OPn/ntUUPx4qkJP2k64bt68
+mT169KCfnx/HjRvHqKioJ260MAihFxQH0tPT2bZtV+p0HtTry7By5XpPJqyFxMXFh7YobkcJ+BNQ
+UKcrwV27dhXcmNlMduliE3m9nty+PcfrsLAw6vWVCZjsIhdBZ+dSJPlE99ZJW3jYLVu28Pr16/mu
+k5GRwe7d+1Gl0lOtdmDLlq/RZDI9UftPwooVK2gwvJq1dQAcpFyu4ieffMpjx449Mz8ELxaF0b58
+pamVy+Xw9PSEh4cHFAoFEhMT0bVrVwQHBz+towMCQbFm8OCh2Lw5HqmpfyMlJQYXLtTBO+8U7O+L
+xWIptB9t27aGVjsGQGkAy6HTeWDz5rVo0qRJwQylpgKvvQb89pstKc22bUCLFjmKxMbGgqyHf5LN
+vIy7d28gIyMDSmXBzv2SxFtvjUCjRh3Qo8e3KFeuCiIiIvJVV6lUYtWqxYiPj8P165exdes66HS6
+x1V7ItLT07F3717s27cP6em2A4MdOnRAqVIJ0GheB/AdJKk3vvjiS0yc+EVWUh6B4JnyuC+BadOm
+sWbNmgwKCuKqVauy7tBaLBb6+vo+8RfGk5IPlwWC58qOHTuoULgQmJdtn3g//f1r56v+5s2bs5a7
+AwPr8NKlS0/sS0pKCrt3709JcqaLizcXLfqZGRkZDA0N5dKlS3n58uXHG0lOJps1s3XExYXMnoI2
+G9u3b6da7W5fQSBlsuksX77GE/kdHh5Ovb4CgWT7+G2li4vXE9l6Wty5c4cBATXp4FCNDg5VGRhY
+O+s8U3JyMidPnsJhw97lunXrnrOngheBwmjfY2uOHz8+z18GZ86ceeKGnxQh9IJ/O927v0mgA4HX
+CGTYl3A/YMeOrz+2bkxMjP0AWwSBDMrl37Js2cpFtqdrNpvZoMGrNBjq0GDoQb3e9dHL+HfukPXr
+20S+ZEkyj7/zly9fppubD9XqagQ0BAwsXdr/iffk586dS0kakO1DyUKZTEGz2cwJEyaxdOlAvvRS
+Vf7889Insl8YrFYrz58/z44de2Q73W+lWj2YQ4aMeub+CP4bPFWh/7chhF7wb6dPn8EEviIQRKAc
+gUpUq13yld1s9erVdHDolE3grFSrjUW2v79gwQJKUnMCmXb7v/OllyrnXvjWLZorViQBXtPoOPO9
+4Dz32jt16k2FYoLdZhoVikEcNGj4E/t54MAB+xW7v+0257Fs2cr8+uvvKUk1CRwhEEFJ8s4zu93T
+IDExkX5+1SiXuxDwoC1N7oP/Vuv58svtnpkvgv8WhdG+fO3RCwSC/PPee29Dr/8BQAsAXaDRXMfK
+lfNQsmTJh8rGxcXh44/HY9SoD7B37164u7uDPAcgzV4iCoAFRqOxwH7Yfjfk5Nq1a0hLqwtAYX9S
+H7duXXu4clwcLI0aQX32LC6gBOqaf8SHcw9h8OCRubYVG3sNFkt9+08aWCwtcPny9QL7/IC6deti
+4sRgqNWVoNf7wMNjEjZuXIklS36DyfQ9gJoAmsJkGodly9Y+cTsFITMzE1Wr1sGlSzJYrbEA+gNY
+BCADQAa02mWoXz/3THsCwfNECL1AUMTUqFEDe/ZsQ+/el9C9+y2Ehf2KTp06PVTu6tWrqFq1Hr7+
+OgkzZiShadM2CAmZh6ZNK8NgqA+dbhAkqSlmzJgGVT7TvALArFlzYTC4QqXSok2brrh3717Wu4YN
+G0KrXQEgBoAVSuUU1KvXKKeBmBigcWMoLl7EabkRTXAWVzEQJtM6LF26MNdDgi1aNIJONw22dLVJ
+kKSZCApq9FC5gjB69EjEx8fh1KlduHLlAipVqgSDQQ/gnw8Imew6jMZnk6r39OnTuHEjEUAnABKA
+8QDuAnCDVuuFevVM+OKLT56JLwJBgSi6hYVnQzF0WSDIlXHjPqFCMdIe3KUkgS8I9Kanpy9/+eUX
+zp49+7GJYK5fv87w8PCsKGvbt2+nJPkQOEvgPjWaPuzaNWfs+qlTZ1CtlqhQaFi7dlPeunXrn5fn
+zpGlS5MA43196a1vk21pOpEKhTrXmABms5nduvWlQqGmQqHmgAHDnkrsgJ07d1KncyXwOeXy0TQa
+3XOcAzhy5Ahff/0tdu3aj9v/5/pfYTl+/Dg1Gg8CtQncs4/JVwwMrMXLly+Lu/GCp0phtE9mN1Bs
+kMlkuS5JCgTFjZEj38ePP7oCWAxgIQDbDFij6YtJk6pj9OjRj6y/detWdOnSG0plZaSnn8eIEYMg
+l1swZYoawGf2Upfh7NwYd+5cyVHXYrHAbDZDkqR/Hp44AQQFAfHxQJMmuPPzzwis1wwJCb1hsdSG
+JE1Dz56VsGDBT3n6ZDabQRK3b9+GwWCAk5NTQYflsRw5cgTLl6+GRqPGoEEDULZsWQDA0aNH0bhx
+K5hMYwDoodN9gV9/nY927doVSbuZmZmoXbspTp1KgtV6A4ADVKoknDt3CH5+fkXShkCQF4XSvqL5
+1nh2FEOXBYJcCQ0NpVzuTEAiEJM1c5bLx/Lzzyc8sm5mZiYNBhcCe+z1blOSfDh69GhqtZ2zBWv5
+neXK5eOK219/kU5ONgdatSJTUkiSsbGxfP31t/jyy+341VffPHaWfu3aNQYG1qZO506VSs/33hv7
+0Ew3IyODP/wwnb16DeSXX05mamrq4/3LB717DyLwbbYViF9Zp86rRWL7AUlJSRw+/H3WqtWMvXr1
+ey5BkAT/TQqjfSJ7nUDwHLBarRg16mNYrV1gOxj3NoBpAGKg1S5E+/Zhj6yflJSE9PQMAC/bn7hA
+oaiLKlWqoEyZCFy92hpWqw9ksvUICVn9aGciImBt3x7ylBSsgwzBZ2Kw9MQJNGjQAN7e3ggJmQaL
+JX8HAnv3fhsXL76KzMxJAO4gJKQZGjWqjS5dumSV6dHjTWzZEgeTqQd0ui0IDW2PP//cCoVCkbfh
+fGA2Z+CfYD0AICEzM7NQNv8Xo9GIH3/8rkhtCgRPnSL84HgmFEOXBYKHaNiwhf2+eQKBNAKjCLjT
+xaUsN23a9Nj6VquVbm4+BNbYZ6+XqNN58NSpUzSZTFy6dClnzZrFCxcuPNpQWBitWi0JcClqUoEk
+Amvp4ODOa9eusX//t6lUaqlUSmzZstNjQ8k6O5cmcDnbrPoLjhkzLuv9uXPnqNW6ZAuTm0mDIYAH
+DhzI17g9ij/++IM6nQeBVQRCKUl+XLhwcaHtCgT/BgqjfcVONYXQC4o74eHhdpF/ibbMbg/uyzfl
+olzSvebFoUOHWKJEaRoMZanRGDlr1tyCObJmDa0qFQlwDjSUwZIl0EZjczZt2pxKZV3a0quaqdV2
+5bBhox9psmrVRgTm2+2kU5KaMyQkhJcuXWK5ctWoUGgJOBM52qrD3bt3F8z3PAgLC2P9+i1Zq1Zz
+IfKCF4rCaJ84jCcQPGUyMzMxduxnWLVqPQwGA+rWrYAlS34DMBPAh7Bd1zoBb+8kXLx4HBqN5rE2
+SWLlypU4fPgoXFyc8fbbb6NEiRL5d2rpUqB/f8BqxVQ0wPs4CeAigJIAUqFQ+IDUwmr9CkBfe6Vd
+qFz5Y5w69WeeZk+ePImmTVuDrAiLJQ516pTD1q1rUbVqA0RG9oTV+h6AOgBqAxgEhSIMnp6/IDLy
+eM6DgXmQkZGBhQsX4tKly6hTpya6du0KmUyW/34LBMWUwmifEHqB4Cnz7rtjMG/eQZhMPwC4ArX6
+DVgsLrBY3AC8DiAcMtkOXL4cCR8fn3zZ7NSpFzZvPgOzuTskKRzNmrkjNHT1Q6IXGxuLS5cuwd/f
+H15eXraHc+YAQ4cCAL7Tl0Bwyh4AGwCEAGgDhWIbyHhYrQMA3LM/l0Eun4i2bc9g48aVj/QtISEB
+Bw8ehNFoRIMGDZCZmQmdTg+r1Qxb6I4EKBSN4eycjtq1ayAkZCq8vb0f22er1YpXX+2AAwfSYDI1
+g16/GoMGtcUPP0zJ15jlZk8uF6FEBMUDcepeIHgGWK1Wzp4dwrp1g9i8eUfu27cvX/Xc3X3t99of
+7Ft/Qm/vQKrVXlQofKlQOPHXX3/Ntx/jxo23n9S/mxVyVq/35ZH/STYzZ8486nQudHRsTJ3OxRYX
+/ttvHzjBSSVK0dMzgErlGPsp/U1UqcqwV69eVCq97OcHqhJ4mUAjKpWOjImJKciQkbSNm4ODK4ED
+9qZTaTBU4ebNmwtkZ8+ePTQYAmnLH2C7aaBS6ZmYmFggO7dv32bjxm0olytpMLhy8eIlBaovEDwP
+CqN9xU41hdALnhfffz+dklSRwEYC8ylJrvnKL16mTOVse/GkSjWEX3wxkdu3b+evv/7Kq1ev5tuH
+mzdvUq02EPDJdoWONBjqMyIiIqvc1atXqdOVIHDRXuYMJyo0WSI/TNaBwEGqVK9To3GlXv8SNZoS
+bNu2C5csWUKNxpXAJwROEhhAoASrVGn0RONGkuvXr6dO50qDoTs1mvL09CzP/v3fZnR09GPr/vzz
+Ujo6elIuV1KhqJ/tg8lKnc69QONHki1adKBK9Y79EOQJSlJJ/vXXX0/aNYHgmSCEXiB4BpQpU4XA
+/mxC8znfffeDx9ZbsWIlJakUga+pVA6nq6s3r1+/nqPM/fv3+dFHn/G113rz66+/yzN5zKlTp2gw
+BBCoQuAz+/37GXR09GRSUlJWub1799LRsW6WIH6H0SRAi0zGwdqAbH3IpEZTgrt37+bnn0+kJHnS
+waELlUonymSuBLwJvEyttiK//356ocYvMjKSPXu+QY3Gm8AiyuXj6eRU8pFCvXfvXkpSSQJH7af5
+HQnMJRBDpXIMAwNr02KxFMgPjcaBwJ1sH17v8euvvy5U3wSCp01htE/coxcI8ontnnd61s8yWTqU
+ysff/e7Zswc8PNzx228b4ejojOHDD8DT0zPrfWZmJho3bo2zZ71gNrfCtm2/YN++I1i/fvlDtnx9
+faFS3QPwCYBNAOZALk/Hli1hOe65lytXDhkZlyDDYczCfLyNuUgHcHLMGCyfuRmAFbb9chPIDHh4
+eGDy5O9gNp8AUAbATSiV5VGihCNksusYOnQA3ntvxBON2wP8/f0RHr4HZnMYgMqwWgGT6QaWL1+O
+4ODgXOvs3LkTZnMfADXsT9ZDJusEJ6eJqFmzFpYt21jgfXZnZ3fcuHECQDMAVqjVJ+HuXvXJOyYQ
+/Nspwg+OZ0IxdFnwgjBv3gJKUlkCP1Mm+4Z6vWtWjPnCsHfvXhoMlbNdOTNRoymRZ1rbw4cPs2RJ
+P8rlKrq4eOV5NW3NytX8RaEmAZoA/vXZ50xPT2eNGi9Tq+1GYA4lqRH79BnEkydP0sGhQraZPuno
+2CDf197u3r3LJUuWcMGCBYyLi8vx7sSJE2zSpB0DAupSo8m+nUAqlSM5efLkPO3OmTOHktQu2zbF
+dpYuHZAvn/IiNDSUOp0rdbq3aDA0Zu3aTWk2mwtlUyB42hRG+4qdagqhFzxPVq/+lW3b9mCPHm/y
+5MmTD71PTU1ldHT0YwPLZCciIoJGY11mX07X6Tx5+fLlR9YzmUx5J1JJSyM7dSIBZkoSU7IF4UlJ
+SeGECV+yV6+BnDlzFi0WC1NSUujo6Ml/8qtHUK93ZXx8/GP9v3XrFkuX9qde356S1JOOjp48c+YM
+SVsIXQcHdwKzCPxJhaI8FYrqBLYRmE293pWRkZFZthYvXsJSpQLo4uLDkSODmZyczMqV61GvD6JG
+M5SS5FYk+efPnDnDWbNmceXKlULkBcUCIfQCwb+ArVu30mBwoV7vTb2+RL4FKSUlhd7eAVQqPyaw
+hxrNQNau3fTJs6GlpNji1QO2+PX5PGi2b98+OjuXolrtSAcHV4aHh+er3qhRwfbDbbYmZbLpbNGi
+I0ly9uzZ1On6ZfuIuU25XMvq1ZuxRYuOPHr0aJadrVu3UpK8COwlcIGS1JTBwZ8wNTWVS5Ys4fTp
+03nq1KmCj4dA8AIghF4geM7cvXuXBoMrgV12QdtHSXLJ14yYJOPi4vjaa28wMLA++/V7m3fv3s3x
+Pj09PX+HzpKSyCZNbKrq5kYeP57jdUZGBkePHsfSpSvQ378WN2zYkOO9xWJhfHx8gVLMdu7cl8CC
+bGK+h4GB9UmSCxYsoCR1zvbub2q1xlw/YgYNGk7g+2xlD/Gll6rl2w+B4EWmMNonokUIBEXApUuX
+IJeXAtDE/qQBVCpfXLx48bF1SSI6Ohp9+nTG1q2rsXjxbDg6OgIAUlJS0LZtN+h0Bmi1BowfPzHv
+oBl37gCvvgrs3g2UKmX7Z7VqOYp8+OGnmDNnH+LiVuHixYno0eMt7N+/P+u9XC6Hq6trgRLMtGnT
+FJL0I4DrAO5Bp5uMVq2aAgA6d+4MR8cTUCpHApgPSWqHDz8MzjWanbOzEQpFbLYnf8NodMi3HwKB
+IA+K7HPjGVEMXRb8B7h16xa1WqdsB81iqNWWeOwdb6vVyi5d+lCvL0+jsQP1eldu3749633//kOp
+1fYgkEogjpJUkStXruT69evZunU3duz4hi0hzI0bZJUqtqlw2bLkpUu5tufhUY7A6Wyz5gn84IMx
+heq71Wrlhx9+QpVKR4VCzR49+jMtLS3r/c2bNzlqVDC7devPxYuXZM3mjx07xt9//z0rCE9cXBxd
+Xb2pUr1FmWwcJcktx1gIBP9lCqN94nqdQFAEuLm5YejQgZg5szrU6gqwWmMxZcpElC5d+pH1QkND
+sWXLSaSknACgBbAdPXsOQHz83wCAHTt2Iy3tF/u7UjCZhmD27AU4dOg8TKYvASTj7NbWOOnuAG1s
+LBAQAGzfDjwId/s/SJIetpl3JQCAUnkdDg6lCtV3mUwGk+keLBaCJM6cOY+EhASUKmWz6+7ujmnT
+vslR5733xiEkZBmUyqrIzDyIJUvmokuXzjh16iAWL/4ZJlMqOnfehurVqxfKN4FAgOI3PS6GLgv+
+A7z//kfU6/2o1Q6kRlOGgwYNz1e9mTNnUqsdkm2GnU65XJG1H1+3bgv+kw3OSrW6Dz08yhMIJUD6
+IooxcLZVrlaNvHkzh32r1crjx4/zjz/+4J07d7hmzRrqdJ4EvqJS+Q5dXb3zvMaXX954oz+BEgQi
+7dfgxrFu3eZ5lj906BAlyTtb0Joj1OkcmZ6eXig/BIIXmcJo31Pbo09LS0O9evVQvXp1VKxYEePG
+jQMA3LlzB0FBQShfvjxatmyJu3fvZtWZPHky/P39UaFCBWzbtu1puSYQFJqzZ8+iceO28PWtjq5d
+38BPP4UgJeUA0tLmw2w+jKVLl+PKlSuPtVO7dm3I5aEAYgAQcvkMVKhQKysIzNy538HB4SPo9b1g
+MAShTJmTcHFxAaBEBZzDbjTBS0hElKs7sHMn4O6eZZsk3njjLTRs2AGvvTYeZctWxEsvvYQtW1Zh
+5Mg7GDfOFdu2/Y5Ro8ahevWmGDZsNFJSUgo0DgkJCVi5cgWAXgD8AcgAfIwjR/bmWefvv/+GUlkT
+gLP9SU1YrQokJiYWqG2BQJBPiu5742FSUlJI2k761qtXj3v27GFwcHBWuMkpU6ZwzBjb/uCZM2dY
+rVo1pqenMyYmhn5+frmeMn7KLgsEj+XmzZt0cipJmWwmgcNUqVpSocgeVpY0Gqvy8OHD+bI3Y8Ys
+qtV6ajQl+NJLlXjpf/bXr169ysWLF3PVqlVMSUnhokU/s4G2FG/BSAKMkKt4IJe97DVr1lCvr0Eg
+xe7Xcvr5/XOK/f79+/TyKk+lchyBHdRqe7Jp07YFutYXFRVFjcaFQH3+k2xmO52cSj9Ubv/+/UxK
+SmJkZCR1OrdsZwVW0s3Np8ChbAWC/xKF0b5nopopKSmsXbs2T58+zYCAAN64cYMkef36dQYE2KJc
+TZo0iVOmTMmq06pVK+7fv/9hh4XQC54zK1eupINDx2zCHk9AR2CVXexW0Nm5FO/du5dvm2lpabx5
+82b+RHbfPpoliQT4l4sHd2/dmmuxr7/+mkrl6Gx+3qVarc96v23bNhqNjXJsG2g0zll/P/NDRkYG
+vb0DCFSz/+lCQM81a9aQtG0dDB/+AXU6dxqNtejkVJJHjhzhkiXLqNUaqdN50tXV+6HMewKBICeF
+0b6ner3OarWievXq8PDwwCuvvIJKlSrh5s2b8PDwAAB4eHjg5s2bAIBr1679ky8bgJeXF+Li4p6m
+ewLBE6HVakHeAfDPNTeFIhOlS38CmUwDb+8J2L59IwwGQ456UVFRqF79ZWi1RpQvXxPHjh3LeqfR
+aODu7p7rtbMc/PEHEBQEtckEdOmCetdi0bhly1yLVqtWDRrNRgDxAACZbBEqVPjnup1CoQBpztaP
+TJCWAl2tUyqV2LVrM2rUMEKlioSHx2GsX/8LunTpAgAIDw/HokUbkZp6AcnJh3H37lR07twHffq8
+gTt3buDChYO4fj0aNWvWzHebAoGgYDzVU/dyuRzHjx9HUlISWrVqhZ07d+Z4L5PJHvmLLa93n3/+
+eda/N2vWDM2aNSsKdwWCfNGyZUuULv0FLl/uC7O5PvT6BRg0aDR++GEKLJbchTIjIwPNmrXFtWvD
+QG7ExYub0Lx5O8TEnIWTk1Ou7WzYsAEhISug12sxduxI1Lh2DejSBTCbgb59gQULAGXef4VbtWqF
+ESNex9Sp5aBSlYCTkwq//RaW9b5Ro0YoXZqIjn4L6ektIEmL0br1/8HV1bVA41G2bFkcPbo713fn
+z59HZmYLAA/62AlXrvQGSeh0Onh7exeoLYHgv0JERAQiIiKKxljRLSw8mi+++ILffvstAwICslJ0
+Xrt2LWvpfvLkyTmSW7Rq1SrXHNHP0GWBIE+SkpI4fvwE9ukzmIsWLX7skvv58+dpMPj+T9KYRty5
+c2eu5W2pbW3pXIGp7K1xoFWptFUcOpQswH52fHw8IyMjcz3VfvfuXb777ods3bobv/xySpGffN++
+fTv1+nIEbtv7/TN9fasUaRsCwX+BwmjfU1PN+Ph4JiYmkrQl32jcuDG3b9/O4ODgrL34yZMnP3QY
+z2w2Mzo6mr6+vrn+8hRCLyiO3Lx5kxqNo30/nwRSKEnePHHiRK7lK1VqSGAzAbIfFjETMpvIBweT
+BYyBb7FYuHbtWk6dOpV79uwpiu4UiA8++JhabQkajZXp4pJ3nwUCQd78K4X+5MmTrFGjBqtVq8Yq
+Varwm2++IUkmJCSwRYsW9Pf3Z1BQUNbHAEl+9dVX9PPzY0BAALds2ZK7w0LoBc+QpKQk9u07hH5+
+NRkU1OmhE/EF4YMPPqZeH0Cl8gPq9TXZq9eAPFcCAgPrE9jBYZiZtQSwtmbdAot8eno6X365JbXa
+alSrh1OSfPjttz88cR+elCtXrvDYsWNZN3EEAkHBKIz2yewGig0ymSzvWN8CQRFCEo0bt8bhwyVh
+Ng+DXL4TLi6zEBl5Is999ccRFhaGkydPwt/fH507d87zHEpIyHxcGT4WEzMSAABjVXp02rMD9erV
+y3dbqampqFWrEc6duwUgCrboerFQqQJx9248JEl6oj4IBIJnT2G0T4TAFQjyICEhAYcOHUB6+m0A
+SlitdWE2h2Pv3r1o167dE9ls3bo12rRp8+jT9SQGX/kbyEiAFcAP5auiTciMAok8AEyf/iOiolQA
+qsEm8gDgDYVCQnJyshB6geA/gsheJxDkgVqtBpkBwGR/QpBJUKvVBbaVnJyMRo2CoFJpIUnO+P77
+6bkXJIHRo4EvvwQUCsiXLcP7F06gadOm+W4rLi4OISEh2LhxGzIyOgA4BGCTvR9T4OnpAfdsEfQE
+AsGLjVi6FwgewcCB72DlymMwmfpBo9kFf/9oHDmyO99in5iYiDfffAcbNmwGabfYDeoAACAASURB
+VATwGwBnaLVB+PXXGWjfvv0/hS0W4O23gfnzAbUaWLkS6NSpQP6eOXMGDRu2QGZmS2RmnkJ6uhXA
+FAAjAVyG0eiJ48d3o2zZsgWyKxAIni+F0T4xoxf8J7h8+TIaNWqFEiW8UK9eC0RFReWr3rx5P+Lb
+b/uiV69DGDOmAvbv355vkSeJVq06IzTUAHIvgDEAOgJwQlraEISHR/xTOCMD6NPHJvI6HbBhQ4FF
+HgBGjfoY9+59ApNpCdLTj0AmU0Au7wqtNgk1atTHokXTsXDhIsyaNQtpaWmwWq2YOnUGmjbtgF69
+BiImJqbAbQoEgn83YkYveOExm80oV64qrl0bAKu1J+TytXB3n4lLl0491X1qW6pWP6SnJwB4EESn
+DYC3ASzF5Ml1MHbsGCAtDejZE/j9d8DBAQgNBZo0KVBbW7duxebN27Fixe+4dWs+gAf1F6Ft2y0I
+CZmKwYPfQVjYPgCDIZcfQOXKKWjSpCEWLvwTJtOHUChOw2ici3PnjmZFrxQIBP8OxIxeIHgEFy5c
+QFKSHFbrGABlYLW+B5PJiNOnT+coZzabER8fX2QfklqtFlZrOoAk+xMrgFgA70Ol2gJHRyPSExOB
+Dh1sIu/sDOzYUWCRnzVrLjp3HoLp011w+7YeMtl4AHcBXIVePx2dO7fG3r17ERa2BcA+AF/Cat2G
+s2ctmDPnJ5hMawF0hsUyHmlpzbB+/foi6b9AIPh3IIRe8MJjNBqRkZEA4L79SSoyMm7BaDRmlZk9
+OwRGowu8vQPg61sF0dHR+bKdnJyMN94YBB+fymjUqDXOnDmT9U6v12PYsOHQ61sAmArbbD4WQAKs
+1sH44v2VOOtTFggPt6WXjYgA6tQpcP/GjfsMJlMogLGwWg9ALr8GudwTGk0gRo58DQMG9EdY2A4A
+FgA+9loyWCw+sMW5z34DoGhXzKKiojBjxgzMmzcPSUlJj68gEAiKnkLc338uFEOXBc8Rq9VKq9XK
+3r0HUa+vQ+AL6vUN2KVLn6xgNcuXL6dC4UogigApl3/HwMA6+bLftGlbajR9CRynTDaLjo6evHnz
+Zo72ly1bxiFDRrJ9+44E1ATOsARu8xBqkQBTXFzICxeeuI8ajYFAQlZoXY1mKKdOncqbN2+yWbP2
+1Omc6ODgTsCPwGACVwisJyCxXr0mlKQGBH6nXP4lnZxKZoWoPnv2LGfOnMlly5YxNTW1wH7t37+f
+er0rNZohlKTOLF3an7dv337ifgoE/2UKo33FTjWF0AvyQ0ZGBgcOfIdqtZ46nSPHjfuMS5cu5Ycf
+juPPP/+clfv8/PnzVKv1BPpni0OfQZlMzszMzEe2ce/ePSqV2mx52ElJasdPP/2USUlJOcpGRUVR
+q3UloKQH/uZJVCYBRskc+MtXXxWqr1279qVW25nAOQJrKEmu3LZtGyXJk8Bwe9jdVQT0BLwJONr/
+fEmt1pnjxn3KRo3asmvXvoyKiiJJbtmyhZLkSq12EPX6FqxcuR5NJlOB/KpZsymBZVljo1IN4scf
+jy9UXwWC/ypC6AWC/+Gjjz6nJDW3i1wsJaka581b8FC5Dz8cR6CrPZd6ql2U/qCLi1dWmQMHDvCd
+d97j6NEfMjIykiT5yy/L2aHD65TJVNni11sJVKNOV5FubmWyRJMkw8LC6OgYRG+8zEg4kgBPowzL
+apx5/vz5QvXVZDKxf/+h9PAox4oV6zE8PJwlS/oSUBHIzBJarbYzVSpnAnMJXLYn1qnLvXv3PmTT
+2zuQwNasful07Th79uwC+eXjU5nA0WwfUNM4cOA7heqrQPBfpTDaJ/boBS8koaE7YDJ9DMAVgDdM
+ptHYuHHHQ+WsVgIIAFAVtghy/weZ7P+wfPkCAMAff/yBV15pj59+csUPPyhQq1YjBAePxaBBE7Bh
+QwuQdQE0APA+gOYAVEhNPYqEhBEYMGBkVjvlypWDd9ph7EEU/JGEo5DjFdzGtNWLERAQUKi+6nQ6
+LFo0CzduXMSZM3/Bw8MD9+4pAagBXH7QUwB/Qy7PBFAFQBkAR5GRcQnlypV7yGZiYry9HADIYDZX
+xa1b8QXyq127IOh04wEkADgPSfoR7du/+iRdFAgEhaEIPzieCcXQZcFzoGXLzpTJpmfNJpXKDzhk
+yMiHyp08eZKS5EpgNoFJVKu9ciwv16/fksDyLDsy2QSq1c4ETtufxdiXwV+yL4s3IHCPwCmWKlWe
+N2/etG0TnDrF+0YjCfAvhZ6lpBIMDd2Uwxer1cpdu3ZxxYoVvHjx4hP3PTY2llqtC4Hv7T59QKA+
+q1VryA0bNlCvd6GDgz8lyZm//bY2Vxvt2nWnWj2QQAqBk5Sk0ty9e3eB/EhNTWWvXgOo0Rjo4ODG
+qVOnP3GfBIL/OoXRvmKnmkLoBfnh9OnTNBo9qNP1oyR1o7t7GcbFxeVa9sCBAwwK6sz69Vtx9uyQ
+HBnlKld+mcD2bMvPs6lUGu0Cn0HAh8Db9mV7C4FuBMZSLg+iXK6lRlOCbT3KMNPJiQSY2qgR/9q+
+nfHx8Tl8sFqt7NnzTer1AXRw6EpJcuX69evz1Ver1cpvvpnKsmWr0d+/Nn/5ZTkHDRpBvb4agT5U
+qUqzbt2XaTabSdrOFpw9e5bJycl52kxMTOSrr3akQqGmg4MbFyxYlC9fBALB06Ew2icC5gheWK5e
+vYrQ0FAolUp06tQJLi4uBbYxdeoMfPrpAphMcwHchyS9iVdfbYDt26/DZOoFYAKAXwA8WJJeBZls
+KGxx8Q+gIeIRhlfhiDSgXTtgzRpAq83RBkn8+OOPGDNmFtLSjgHQATgIvb4NIiK24syZMyhfvjwa
+NGiQq4/Tp8/ERx+FwGQKyfJx9eo5SE1NxalTp1GhQgB69OgBubzgO3UkH52ARyAQPBMKpX1F8qnx
+DCmGLguKMVarlVOmfJdjtpyZmcnPP/+Kvr5VCXjYT+xbCKQTaM369RtTr+/HFgjnfUgkwNWQMS2X
+GbTFYmFQUEcqlS72Q4EPVg6slMlU1OlK0WB4g5JUhsHBn+TqY9WqjQlsy1Z3Drt16/+0h0YgEDxD
+CqN94jCeQPAIZDIZxox5H9HRxxEZeQgdO3ZA1659MWnSJNy+fRMuLhoAuwB4AygJne4oBg7sizaZ
+O7AJ7aCHCYvwCvooHdC991AsXrwk66ucJF55pRXCw08jMzMMwG4AZ+3tzgCpRWrqAdy/vwwm0xHM
+nBmCixcvPuSjXi8B+OegnEwWD4NB97SHRiAQFBOE0AsEBeCtt0Zi82Yr0tOvIDl5K0ymDDRvXhEe
+HnrI5WYolf7YNexdrEiPgwbpmKPywUD8hQzrYGzY8CqGD/8WEydOBgBs2LABf/11DkALAHUBfA+g
+PgAtSpb8CXq9CwAve8suUKvL4fr16w/5VK9eJQCDAUwCMBaSNB3BwSOe/mAIBIJigRB6gSCf3Lhx
+Axs3/g6z+VUAegDVkJY2BIGBfkhKug2r9Ri63huAnzNMUJKI6toVR/u1hEbbEVbrtwD6IyVlHb79
+9gcAQHR0NKzW+gDCAPwNoDeAT+HvH4iLF49DrU4HsAa2MLXhsFguomLFijl8Onz4MEJCVgD4GbZZ
+/UF4epZEYGBgkfX7wIEDCArqjAYNWmPOnHnijIxAUMxQPm8HBILnze7du3HgwAF4eXmhe/fuUCgU
+SEtLQ1paGpycnAAAx44dQ7NmbWAy1QQwG8BiAFuh0ZyFQuEJtdoXg9LCMAOjAABfaDzRdswYVN63
+D+SpbK3pYbFkAgBq1qwJtXoGMjOHAKgEQAOl0opNmw5CkiRs2/Y72rXrhjt3+sDBwRnr1v0KV1dX
+AEBqairOnTuHzZs3g2wPoIv9TyZiYrSwWCxQKBQoLKdOnULz5u1hMn0FwBOnTo2DyZSK0aNHPrau
+QCD4l1BUBwWeFcXQZcFz4sqVK+zUqTerVm3MYcNGMyUl5aEyU6fOoCT5UKV6j3p9Q7Zo0YFjx46n
+UqmlSqVnnTrNePv2bVat2ojAQvthNwuBNlSrK7JcuaqMiYnheJXuwUk4jsQo6vUuTEhI4OXLl2kw
+uBGYSeAPSlJTDh78z33+iROnUKXSU6FwpVZbgrNmzcrhn9Vq5b1793Jc+btw4QI9PMrSwaEy1WpH
+KpWBBNLszUewRInSRTaG778/hsD4bAf9/qKPT+Uisy8QCPJHYbSv2KmmEHpBfkhOTmbJkn5UKD4l
+8AeVynrU671YqVJDLl68hCSZnp5OlUqy34kngXRqtT7UagMI3CSQSaVyAB0dvQkYCERmE7wpbNGi
+Ne8lJ5PjxpEALQCHadwoSSW4cWNoli8nTpxg8+Yd6OHhR1/fGhw0aHhW4pvLly/TwcGNMtkIAjMp
+ST5cuHDxI/tWo0bjbMGA7lKhKEmt1p8ODp0pSa7cvHlzocbu+vXrPHfuHM1mMz/4YCxlsnHZ+r2H
+L71UtVD2BQJBwRFCLxD8D5s2baKDQzO7OP1KoAyBLQS2UpJe4ooVK5mYmEiVymAPdvMg8UoFApOy
+rrgB9QkMJdCLtsxvmQSuU6+vyDWrV5MjRtgqKhRMXbiQ58+f5/379x/yZ8CAYZSkxgRWUKUaRS+v
+8kxOTuYnn4ynQvFuNiHdzTJlHj1j1utdCFzPVucj9u3bjytXrmR0dPQTj5nVauXo0eOo0TjSYPBj
+qVLluHXrVur1rpTJviWwjJLkyzlzQp64DYFA8GQURvvEYTxBsSIxMRGtW3eBVmuEu3tZrF+/Ptdy
+tv3pNNgOsv0CYDKAVgBawmT6GnPnLoejoyP8/QOhUHwGIAlAGGSyK9Bq/wSQCaAngFMAPgLwE4Bo
+AAbI5WXw7oiu6BwWBvz4I6BW49bs2eixPhzdug3CuHETkJqamuVLeno6fv55PkymjQB6IiNjGpKS
+ymDbtm1IT8+AxeKQzXMjMjIyHjkG/v6BkMnW2H+6D71+C9q1a4sePXrA29sbsbGxSElJKeDIAmFh
+YZg7dx3M5ku4fz8KN24Mw4cfTsT+/X+ga9czaN36d8yfPwlDhgwqsG2BQPAcKcIPjmdCMXRZUIQE
+Bb1GtXoQbfnX91CS3Hns2LGHyqWmptLfvzrV6rcIvExgVrYZ8Fy2bt2VJHn16lXWq9eCarWeXl4B
+3Lp1K+vUaUat1pdAZQI17CsCtr15na45Z02fTvbo8SAvLe+tXUt39zJUKL4g8Ae12s5s06ZLDl8U
+CrU9brytmoNDO65atYpHjx61x9pfRmAnJak2x4+f+MgxOH/+PN3dX6LRWJU6nQf79h1Cq9XKEydO
+0N29DCWpFDUaB86ZM69AYztp0iQqFMHZximBGo1DgWwIBIKnQ2G0r9ipphD6/y7bt2+nLfXq3Swx
+UquHc+rUqSRtOegvXbrEW7dukbTFax81KpgNGrSgSuVEYAqBrylJrrmmZn1Aeno6hwwZQpVqKIF9
+BNwIdCFQgY3rNKWlXTtb40YjuWcP161bRweHltkEMo1KpS5HLPkuXfpQp2tLYCsVigl0c/PhnTt3
+SJIRERGsX78lK1VqyEmTvrUlwXkMKSkpPHz4cFbyG6vVylKlyhH42e7DRUqSJ0+cOJHv8V29ejX1
++prZPkh+ZkBArXzXFwgETw8h9IIXnhMnTthnvu4EDmTtoev1QVy8eDFjY2NZtmxl6vXeVKuNHDky
+OMdJ9cOHD3PAgGEcMGAYDx06lGc7sbGxXLduHefOnUtJ8rIf1IulTNadlV+qQEvz5jY1L1GCV9av
+Z/36r1KvL0G5vHa2vf4kKpXaHKf8zWYzg4M/Ya1azdmpU29evny5SMcnKSmJSqWU7WODNBh6ccmS
+JbmWP3jwIEeOfJ8ffDCWUVFRJG0fC716DaAkedHRsQGdnUvx+PHjReqnQCB4MoTQC154Jk78kgrF
+BwRW0RZf/l0CjVm1agOmpqayQYMgKhQT7GKbQL2+Mn/77bcCtbFt2zbq9a40GttTry/HmjUbUaWS
+qFY7skbZykytVcumoJ6eTD10iCVL+lEu/47AOQJeBN4ksJSS1Jj9+g15SiORO1arlQ4OrgT+zDqN
+r9f7cs+ePQ+V3bFjByXJjcBEyuUf0sHBnRcuXMiyc/LkSUZERDAxMfGZ9kEgEOSNEHrBC8/UqVOp
+0fSxi9ghAsNpNLrRZDJx5MhgAjoCcdlmtOP5ySefFqiNEiW8COyw10+hwVCZv//+OxMuXKC1Rg2b
+YW9vMjKShw8fpoNDlRz72TKZKxs2bMXvv5/GzMzMpzQSeRMWFkZJcqWjY0tKkhffeef9XMvVrfsq
+gRVZvstkn3Hw4BHP2FuBQFAQCqN94tS9oFjQt29fODn9CaXybQA7IUnrMW3at1iyZBnmz98JoCps
+oWQBwAy9fgfKlfPLl+3Lly8jMLAO7tyJA9DU/lSC2VwDYfPnw7FjR8iOHQPKlQP+/BPw94fRaERG
+RjwAk728BqQFer0ao0ePKpKodAWlTZs2iIw8juXLR2Hv3o2YOfO7rHe0fdQDAFJSTAA8sr3zQHKy
+6X/NCQSCFwSRj15QbIiPj8ePP/6EhIQkvPZaW2i1WrRu3QMm00QA9WC7PlcGMtlltGvXGOvXL89T
+cK1WK06ePImUlBT07DkQcXH9Qf4KoC+AUQCi4YM6+AMm+CEN1sBAyHfsAEqWBABs2bIFr73WG2az
+F4CuAEIBBEAuX4HU1PtQq9WP7IvFYsGKFSsQExODmjVrol27dkU1TA/1c+TIYISEzAYAvPnmIPj5
+lcGECUthMs0FcA+S1B9r1oSgTZs2T8UHgUBQeAqjfULoBcWSK1euIDCwJlJSmgFwBhAC4C6Aj1C3
+7jn89dcfkMlkudZNS0tDUNBrOHo0EqmpySCTAZgBRAHoAOAa/JGGHdDDG4k4Kjfi/LQv8foIW0a4
+EydOoGHDIJhM/QFsstepCKAllMoyuHXrOm7dugUfHx/odA+niyWJ//u/HoiIiIPJ1BSStBYjRvTA
+5MkTiniUgO++m4bPPlsNk+l3AHJIUieMG9cOCoUcc+cuhVqtxuefv4/XX+9V5G0LBIKio1DaV9h9
+g2dNMXRZ8BRYsWIFHRy62O/TVyTwKoEgOjmVzDpYlhcTJ06iTteRwNcEOhBwIHDKvmdtYmV48Doc
+SYC78TJdVQP4ww8/kCSPHDnCN954gwrFaAKJBDwJ6Ak4Uqn04auvtqNWa4ssZzR65HoYbv/+/dTr
+/QmY7W3eolptyPPw25kzZ/jDDz9wwYIFuUbdexTNmnUg8Fu2swQb2aBB6wLZEAgEz5/CaJ/YoxcU
+S5ydnUFeAmAEcBBAZygUEThz5jDKly//yLonT0YiNbU9bKlhW8CWja4FgH6oDX/sQjw8kYRtaIzW
+eB/3Fb8jKCgIH374KRo37ojffjsBiyUawI8AygOIBHAEMpkOu3btRlraX7h/PwrJyYvQvn23hyLd
+3b17F0qlD4AHy/uuUCodkJyc/JCv27dvR506TTF27EWMGLEW1as3wv379/M9TqVLu0OhOJ71s1x+
+HKVLu+e7vkAgeAEowg+OZ0IxdFlQhFitVqalpTEzM5NNmrShXt+UCsWHlKQy/O67afmy8fXX31GS
+WhGYS6CuPQDPMTZGVSbZp77r4UUN3CiTGbl27VqePHmSklSKwG37TN6Xtvj5O7LNlhdTqfTNcZdd
+kkoyNjY2R/u3b9+mo6MngaUEblKh+IJ+flVzDZTj61uNwKasuAFabbesAEH54e+//6arqzclqSsl
+qQdLlChdqHj4AoHg+VAY7RP56AXFhvnzF2DEiPdhNqcgIKA6Nm5cgb179yIuLg716y9E8+bN82Xn
+vfdGIiJiP3buHI/MTAsyMz3QEgqsQyokAMuhQj/oYJHdwYYNq9G+fTts2rQJKlUVAC52K4cA+AI4
+C8DWrkJxFuQdADcAeAI4DDIV7u45Z9AuLi7YuTMMr78+GLGxo1C1ak2sXh0KufzhBbbExATY9v8B
+QIa0tIqIj0/I95j5+Pjg3Lmj2LBhAwCgffsZD/kjEAhebMRhPEGxYMWKFXj99cEA/gBQC8DXKF9+
+DS5cOPJE9kgiJiYG6enp+CmoA767GgMNMjEfPhiCVwD5GqxZsxSdOnUCAMTGxiIwsBZMpi329tfA
+0fEdWCxERsZrkMlMMBh2o1+/3pg1awHU6kBkZJzGhAnj4O7uBn9/fzRo0KDAfvbo8SY2bDAjLW0W
+gL8hSe0RGroEr7zyyhP1WyAQFE/EYTzBC4+ra2kC3bMti1spl6sfeTjt3r177Nq1Lw0GN5YqVZ7r
+1q17uNAvv9Ail5MAp6ElZehHlUrPZcuWPVR07dp11OmcqNW60sXFi4cOHWJMTAynT5/On376KSvG
+fmRkJMPDw/nRR59RkkrTYHidklSGY8d+VuB+37t3jx069KJaraeTU0nOn7+wwDYEAkHxpzDaJ2b0
+gmKBWi0hI8MXwBEAGgCnoFTWh9l8L9clbwDo2rUvQkMzYDaPADASQBTq1q2FtWuXoHTp0sC8ecCQ
+IQCJUx06YujtdGh1Ggwc2B1HjpyAyZSG3r27o2HDhlk2MzIykJCQADc3t0cGxbFdrysPs/k0AC8A
+t6HVVsTp0/vh55e/QD4CgUDwgMJon9ijFzx30tPTcePGDXh6euYZaKZmzYY4cOAOgLoAqgH4HcHB
+7+Yp8gCwefMmmM07ALwCYBiARTh48Bf4+FTEaHk6vs1MsxWcPBlVxo7FnwAuXbqEmjUb4f79/rBa
+S2LRotcwadI4+Pn5oXbt2nBxcUF6ejosFstjhV6tLmkPqAMArtBo/HD9+nUh9AKB4NlSRKsKz4xi
+6LLgEWzdupUGgyslqRQNBldu27Yt13JXrlxh+fI1qFTqqVCoOXLk6Mfa9vDwJfCK/YT8gyV/Cz+G
+U9ax+CV1G+WoM3z4aMpkH2VtDwBBlMtL0WhsQ63WmVqtAyWpFB0c3Lhjx448205JSaGTU0kCa+y2
+ttHBwZ0JCQkFGyCBQCCgSGojKKbcuXOHer0rgd12MdxFvd41z8AxVquVt27dYmpqar7sr1q1moAT
+gZIE0ghYOQWjSYCZkLM/JtPVtUyOOv36vU3gh6zgMkAlAib7z1sJlLL/+w4aDG5MSkrKs/2DBw/S
+3f0lKpVaOjuXZERERL7HRiAQCLJTGO0TAXMEz42LFy9CofAB0Nj+pAkUCi9ERUXlWl4mk8HNzQ1a
+rTbHc7PZjNjYWJjN5hzPu3fvhm7d2gPQQoY2mIkmGIOpyADQC8uxGFXg5FQiR52+fbtBp/sGwGYA
+uwDUBvAgjO0rsF2dswJoDrncHdHR0Xn2r06dOrhxIxqJifFISIhD06ZN8ywrEAgETwsh9ILnhpeX
+F9LTY2CLUAcAl5Ge/je8vLweVS0H4eHhcHX1QrlytWEwuOLtt4ciMzMz631IyI9Q4CYWYjfewZ9I
+A9AJdfArdkGn64+ffpqSw17z5s2xbNlMVKgwASVL/g6Vaks2/34EUAW2vzaXkJ4eZzvU9whkMhkM
+BkOecfcFAoHgqVOEKwvPhGLosuARTJs2kzqdO43GNtTp3Dljxqx8101MTLQv/e+yL6fvJmBgmzZd
+aLVaSZK3rl7lr7Bdn7sHic3xO4FR1GqNPHnyZJ62zWYzZ82axebNW1Gp1FGnc6ejYylqNC52X904
+e3ZIofsvEAgE+aEw2ieu1wmeOxcuXEBkZCTKly+PgICAh95fuXIFy5cvR2amBd27d4O/vz8AYO/e
+vWjS5E1YrZHZSteCWh2NixdPwMfNDezaFbKwMNyFHG1RHvtRA8AGLF78E/r165erP5mZmWjatC2O
+HZMhNfVlSNIyDBz4f5g27RtERkbi4sWLqFChQpYfRUlcXBwSEhLg7++fa+Y7gUDw30QEzBEUezIy
+MhgXF0ez2Zzj+cWLF+no6EmVaigVindpMLjx6NGjJMn33gu2Z46Lts/oYwg4U6crxYtHj5KvvGI7
+eOfszA5e5QnIqdMZOW/evEf6Eh4eToOhOoFMu90bVKl0NJlMBeqTxWJhVFQUY2JislYYHsX7/9/e
+nQdEVb0NHP8OMwPMgOAOCCKGIiIIKq65YC6E5p7mXtpiWpbtZmbaT8W0zD1zqUxLLTX33dxywdwV
+LU1REUVxIZEBZmDO+8fQlK9LiwgyPJ+/nLuc+zy3iWfuveee88YQ5eJSQhUrVlWVLu2vjh49+q+O
+J4RwXPdT++Q9elHg9uzZQ0xMRzIyLICZefO+oGNH29Cz//vfx6Sl9cdqHQbAzZtBvPXWCAYM6M3G
+jTuATkAEUBM4ClQnokIagf37Q1wc+Pig3biRZSEhmM1m9Hr93z4vv3HjBk5OfsAf78mXQaPRYzKZ
+/vFV9o0bN2jevB3x8SdRKptHH63LypXf4eLicsft169fz/Tpi8jKOklWVinS0mbRsWPv/zzErxBC
+/EE644kCZTabefzxDly7NpmMjEtkZGykV68XOHfuHABXr/6O1VrxL3s8wu7d+3j66Y85fjwLuAws
+BSyAjvByyWzTmdHExUGFCrB9O4TYJoVxdnb+R53iHn30UWyT1swFzqDXv0lISBglS5b8mz3/9MYb
+Qzl8OACT6SwZGefYsUMxevS4W7ZRSvHtt/Pp3PkZPvxwNBZLS/6cNKc7p04d/cfHE0KIu5FCL/LF
+wYMHWbVqFefPn79l+YULF8jKcgI65C6phV5fg/j4eAC6dGmN0TgaOAz8irPzu5jNBm7e/Ins7J+A
+60BX3N1TaFKpLHvdrOiOHoWgIFuR/w+j0Hl5ebF582pCQz+jRIlGPPbYWdav/+Ef/UhYsmQJPXo8
+z9Kla8nK6ontroAzGRnd2L370C3bjh07nuef/5BFixqya1cpzOaVwB9z0i/F3//2/gpCCPGv5d0T
+hPxRCEMu8l5++U1lNPopT89oZTSWVitXrrSvS09PV66uHgric5+Hpyij0cf+fNpqtapx4z5VpUsH
+qBIl/HJ7wb+au+1pBRuVVuuijq1apawVK9pGvAsLUyo5Od/zHDVqjDIa5fUbaAAAIABJREFUKymY
+pjSaGgoG5I6ul6NcXHqoN98ccsv2Hh5eCn6xj8Kn1YYqvb6U8vSsrUqUKGfviyCEEPdT+6TXvXig
+du7cScuWPUlPPwB4Artxc2vNjRsp9nHqv/56Hv37v45OV5fs7AO88spzxMYOv2N7u3fvpmnTDmRm
+hmCb4KYMIU4X2F/KDZeUFKhdG9auhX9xm/1esX/77SKMRlcGDHiBgICAO253/fp1YmKeJC4uDtgN
+hAIpaDRhODuXRK+HSpVKsn37Wtzd3e37ubmVwmQ6DNjexXd27s/rrxenbdu2VKtWDQ8Pj/vOQQjh
+GO6n9kmhFw/Ut99+S79+y7h5c6F9mV5fjKFD38JoNNKlSxf8/f357bffOHLkCBUrViQiIuKebTZs
+GMWOHaeBQ1TnHBtoRFnSuB4WRomffoI8KJCrV6/mySf7kJExCCenqxQr9g0HDuykYsWKt23bsWMv
+Vq1yw2xeDOwHygOg0w2gXz8nevfuTc2aNdHpbu372r//a3z99UFMphHAr7i7v8ehQ7t55JFH7jt+
+IYRjkUIvHlrx8fHUqdMMk2k7UBn4Bo2mH1ptL5ycwNX1B37+eRtBQUG37XvlyhUOHDhAqVKlqFGj
+hv0ZucHgQWZmb+rQi7U8TglSWQv0MpYk8WrSbUPk/hfVqzfkyJG3gHYAODm9wyuvKD79dOxt2/r4
+BJGcvAz4HDgCjARO4Ob2Jvv377hjbmB7X/+DD0axdOk6SpcuyYQJ/6NGjRr3HbsQwvHcT+2Tznji
+gapWrRoTJozGxaUWRqMvzs4DUepFsrM/w2z+jLS0QQwdOvq2/fbs2UNgYCidO8fSuHEnunbta/+S
+KwVNWMpGmlOCVJZQk3aUx4SRixcv5knc6ekmwMv+2Wr1Ii3NdMdty5cvj0azDRgHNECjeRJ//4/Z
+tGnlXYs8gE6nY9SoD4iP38nWrSulyAshHggp9OKBe/75vly9epHjx3dRq1YdoLF9nVKVuHIl9bZ9
+unTpy40bk/n99x9JTz/GqlWHWLp0KQAfN3uMNVygGDeZhwdd+AUzA9FoTPj4+ORJzE8/3Rmj8RVg
+L7AOo/FjevTodMdtv/xyEp6eI/DwaI+7+xpq1Ajkl1/2ULdu3TyJRQgh7scDLfSJiYk0bdqUatWq
+ERoayqRJkwC4du0aLVq0ICgoiJYtW5Ka+ucf+tjYWCpXrkxwcDDr169/kOGJfDRlynSqVo0gLm4r
+Wu0QIB74CaPxf3Tu3Oq27S9cOA08nvvJgNnchFOnTsHixby0YTUGFF+7etAbKy5Gf4zGj1i48Os8
+uW0PMHToO7z1Vjv8/fsQFDSUr7+eTNOmTe+4bbVq1Thx4hBz5jzP4sWj2b17kwxfK4R4eNxfh/97
+u3jxojpw4IBSSqm0tDQVFBSkjh07pt566y310UcfKaWUGjNmjHrnnXeUUkrFx8er8PBwZTabVUJC
+ggoMDFQ5OTm3tPmAQxYPwOLFi5XRWFnBSQXXlJNTeQUuCtyVwVBS7d69+7Z9wsLqK41mvH0IWje3
+QHXk7beVcrJNUKNef10pq1UlJiaqHTt2qJSUlALITAgh8sf91L4HekXv7e1t70Ht7u5O1apVSUpK
+Yvny5fYJRZ5++mn7Ldlly5bRrVs39Ho9AQEBVKpUiT179jzIEEUeyszM5OjRoyQnJ9+yfOXKTZhM
+LwOVgJtYrWnAFiCNjIzZPP54h9vmkv/hh7n4+n6Om5s/zs6V+bZJCKFjx4LVCh98AB9/DBoNfn5+
+NGjQgNKlS9v3VUqxYsUKJk2axE8//fSg0xZCiIdavj2jP3PmDAcOHKBu3bpcunQJLy9bRycvLy8u
+XboE2EZJ++tc5H5+fiQlJeVXiOI+xMfH4+8fTIMGTxIQUJUhQ0bY1/n6lkGvPwJcAZpjK/j1cte2
+x2JxsQ95+4dHHnmEIUNe5bHHGrDk0Tq0Xb3CtmLcOBg+HO4ySp1Siu7dn6Nbt6G8/favREf34KOP
+xudxtrceLzZ2HKVK+VOypB/vvTcCq9X6wI4nhBD/Vr5ManPz5k06derExIkTKVas2C3rNBrNPYcW
+vdO64cOH2/8dFRVFVFRUXoUq/qN27XqQkvI+8CyQwqRJ9Tlx4hd++eUcZcuWpFSp46Sk1CMnpw6w
+GVvRLw2cIDv7mv2HH9iKZ82ajTh48DrDqEhrNgFgmTQJ/cCB94xj7969rFixmfT0o4AReJdhw6oy
+YMDzt3338sIXX3zFyJFzMJnWAjomTOhOyZLFeeONV/P8WEKIomPLli1s2bIlT9p64IXeYrHQqVMn
+evXqRfv27QHbVXxycjLe3t5cvHiRsmXLAuDr60tiYqJ93/Pnz+Pr63tbm38t9KLgKaVISDgK9Mxd
+UoaMjKYsX74Li2Uax4//jIfHXkqUcOPKlUFAFaAGUB2tdidTpky4ZRS42bO/4ODBOMbxIm8yhRyc
+eEFbji5BQUT/TSwpKSlotZWwFXkAP3S6YqSmpnL58mXi4+MJCAigevXqeZL7woWrMJneA2wT55hM
+I/j++0lS6IUQ9+X/X8SOGDHi7hv/jQd6614pxbPPPktISAiDBg2yL2/bti1z5swBYM6cOfYfAG3b
+tmXBggWYzWYSEhI4efIkderUeZAhijyg0Wjw9a0MLMtdcgOrdT0Wy0igMVbrG1gsDShf3gedbjnw
+AbAYZ+fLDBzYl+ee63NLe5s2bGMa8CZTMKPnKRbypdX/tuf4d1KrVi2s1oPASiALjWYipUp5sHnz
+VsLC6tGr1+fUrx/D0KH/y5Pcy5QpjpPT6b+ci1OULOmZJ20LIUSeyJPugHexfft2pdFoVHh4uIqI
+iFARERFqzZo16urVq6pZs2aqcuXKqkWLFur69ev2fUaNGqUCAwNVlSpV1Nq1a29r8wGHLP6jPXv2
+KE9Pb+XpWU8ZDN5KozEquJzba14pd/doNW3aNFWhQlVVrFiEcnMLVA0bRqvMzMxbG7JY1P6w6kqB
+MqFVMUxQME7pdB7q6tWr/yiW7du3Kx+fSsrJSadCQuqoQ4cOKVdXTwVHc+O5rAwGb/vEOffjxIkT
+ysPDS+n1Lyqd7mXl7l5GHTp06L7bFUKIv7qf2idD4Io8k5qaytGjRylTpgxTp85i9uytmEwD0Ov3
+4u29kfj4n9Hr9Rw8eBAXFxfCw8PtE9sAkJUF3bvDkiWkazS003jxo9Lg5JTF0qVzeOKJJ245XnZ2
+Nu+99yHff7+C4sU9+fTTETRp0sS+XimFRqMhISGBsLAmpKf/2eHP07Ml8+e/RkxMzH3nnZiYyIIF
+C7BarXTu3FnGqhdC5DkZ614UOKUU58+fx2w2U7FiRTQaDdOnz2Dduu1UqODD+++/c8srcLcxmaBT
+J9vMc8WLk7FkCStSUsjMzKR58+aUK1futl1eeeUtZs/+GZNpLHAGo/Eldu3adNvzd7PZjLd3Ra5f
+n4Zt7PoDGI0tOX58H/7+/nl6HoQQ4kGQQi8KVHZ2Np069WL9+o04OblSubI/mzevpESJEv+sgbQ0
+aNMGtm6FMmVg/Xr4mxnsAEqWLM/165uxva5nm3hm2DA3Pvhg2G3bxsXFERPTkcxMK5DBl1/OwN+/
+PGazmdq1a2M0Gm/bRwghHhb3U/vy5fU64dg+/XQSGzakkJmZCDhz/PjLDBjwJvPnz/77na9dg5gY
+2LMHypWDTZsgOBj489b73bi4uALX7J+12qsYDKXuuG3dunW5fPksycnJuLm5ER3dkePHL+Pk5I6H
+Ryq7dm26ZQwHIYRwFDKpjbgvixYtZvDg/5GR0Q1wBZwwm3uzb9/hv9/50iVo2tRW5CtWhO3bOaXX
+07NnX4oXL49Wq8fLq+Jd3yX93//exWjsDExCq30NT8/19hEX70Sn0+Hn58ekSVM5cqQ0N28e5saN
+OC5e7Er//m/+l/SFEOKhJ1f04h/bv38/n3wyjawsC/369SQgIIDevV/Eau0OrAaeAbTodCuoWrXy
+vRs7fx6aNYMTJ6BKFdi4kdNmMzVqNCAtTQu8BbzE5cubadOmC7/+evC25/TPPdeXcuW8WbRoJaVK
+efLaa7tvGXjnbuLjT5GZGQ1oAcjJieHXX1f/+xMihBCFgBR68Y8cOHCARo2iMZkGA+6sWfM0L73U
+E52uCfAx8ARQDVB4e8Nnn22+a1s5J06gadECp3PnIDzc9ky+bFmmvz2Emzc7AkuA13K3jsbJKZJ9
++/bdsUNeq1ataNXq9tnv7qVu3XBWrZqPydQDcMHZ+SsiI8P/VRtCCFFYyK178besVisDB76NyfQW
+8AbQD5NpCsuXb8JqPQxYgfXABzg7n+f48TsXZYDvPxxJcpVgnM6d46CrkcS5cyF3ZESTKROlvIF0
+4I9X4TLIyTlhHz3xTrGdPHmSs2fP/uOOKq+++jKPP+6Di4sfBoMf1arFM3XquH9+QoQQohCRQi/+
+1jPP9Gf37l/5c1hZAAOuru507NgUo7EGbm7dMBoH8eWXs3F3d79jO/HffEPUBx/gi2IzUTTJeoMn
+er5oX9+zZxeMxmlAV6AB0Adn55q0bt3ojiMkXr9+nVq1GhMR0Yzg4Dq0bt0Zi8Xyt/nodDoWL57H
+6dNHOHZsJ3v3bv3nbwgIIUQhI4Ve3NP58+f5/vvF5OTMAEYCC4FVuLq+xMCBz+Dq6oLFcgGLZRNl
+ypSkadMmd2znzPz5lO/dmzJYWY0frZjPDfU+R4/G2Wd7q1evHkuWfE2NGr/h7+9BmzapfPfdGBYs
++OKOve8HDnyHY8eqYTKdITPzHFu2pPPxxxP+cW7lypUjICDg1kF7hBDCwcgzenFP6enpaLUeQDTw
+FTAROECDBtVxcdEzb94GLJalQH2SkmLp2fNFNm1adksbv//wA6W798AdxSKa0R1fLDwNvEeJEj63
+FNro6Giio/9u6hqb/fuPYDaPw/Z71YWMjKfYvXsD48aNZ+/eo0REBPP666/i4uJi38dkMpGamoq3
+t7cUeCFEkSAD5oh7ys7OpkqVmpw925acnKexTVwzAReXkmi1lzCZtIA3kAbMolSp3ly5cta+/+/f
+fotrj564oJhDb55lNjkAeGAwGFi8eN5/Hoa2U6feLF/uQ3b2GMCKq2s3ypU7zsWL5cjIeBKDYTl1
+6uTw448rcXJyYvz4Sbz77hCcnIx4eZVm06YVBAYG3ucZEkKIB09GxhMP1IULF2jUKIbTpy9im152
+GvAKcBXYCrgAY4CFREaW4Oeff7Tt+P33ZD/1FDqlmEZZXuYCCi1wDSenchw+vI9q1arZj7N69Wr6
+9h3I9euXqFevCYsWfUWZMmXs681mM5mZmfYpbZOTk6lfvznXrjljtZp45JGSnDiRQGbmmdyYLLi5
+VWHHjh9IT0+nRYuumEzbgQpoNOMJCfmOo0d3P+jTJ4QQ9+1+ap/cuxT3ZLFYGDHiIxITfwM6AOuA
+48Bu4ElsBRWgAxpNAnPnTrN9/Oor6NoVnVKMpQ8v8QiK7sBkNJpGuLuXZuTI8aSmpgLwyy+/0Lnz
+01y6NAuzOYldu4Jo27a7PY7hw0fh5uZJqVI+1K4dxZUrV/D29ub48b2sWTOFzZvnMXfudLRaI+Cc
+u5cOJyd3zGYz+/btw2p9AqgAgFIvcfz4XvnRKIRweFLoxT0NGTKCefPisVh+BH4AXgKeBprnfjYB
+CphD48YNCQ4OhqlToU8fsFqZXq4ig6kNbASqA+NRSs+NGz+wZIkTLVq0RynFtm3bgDZAU8ATi2Us
+e/ZswWw28/jjbRgxYhrZ2afIzk7j0KHq9OzZDwAnJyfq169PZGQkISEhlC9fAr3+dWAPOt0QSpe2
+Ur16dSpUqIBWuxPIyM1sM2XK+N9ziF0hhHAEUujFPS1duhaTaSRQF9gGfAs8CrgDQUAAEIhe/znf
+fPM5fPQRvPyybedPPqHR+hUULzGSYsXaA19g+2EQB9TGbJ7O0aPxXLx4kZIlS+Lk9Cu2d/IBTuDq
+WowpUz5j06ZDwPNAOcAJi+VNdu/exaOPRmMwuGM0ejJ16nR0Oh3btq2hbdurBAb2p1Wrs+zcuREX
+FxfatGlDTEw4bm7V8fBojbt7bxYs+CK/TqMQQhQYeUYv7ujKlSssXbqUUaMmc+bMm0AvIAnbVfkJ
+oH7uv3XodCvZuGElTTZtgpEjURoN8S+/TNVPP0Wr1XLt2jWGDRvG55/vJDs7GziI7TfmTZydy3Hh
+QgIeHh40avQ4R49aMZsj0Onm0759c/bu/YWTJ6sDF4FVufstwGh8HYulIxbLBCABo7EZq1Z9TVRU
+1F1zUkqxe/duUlJSiIyMvOugPkII8bCRzngiTyUmJlKz5qOYTA3IyUklK2s7tiv649hmizsKlAHm
+oNePYP63M+i0YwdMmEA20M+lLt/pzdSu7cuMGZ8ycOC77Nu3h6tXG2C1JgFeQDNgJr161eLrr2cA
+ts52CxcuJCEhgalTv+TGjXpkZp7G1gHwJHADKItO9xM6HWRmnshtC5ycBtOqVTyNGzcmJiaG0NDQ
+fD1nQgjxIEmhF3lGKUXr1p1Yt64KVmts7tJ2aDRxKPU9ts54U4EmODsfonePVszUmmHWLLKArgxj
+KSOAbNzcGqPVniQ9fRA5OaFAb+Bd4CRa7Sbq1XuEbds23vY++5gxH/HBB8cwm+cAl4HaQHlcXbPR
+639j3boVdOv2PGfPjgVaAVY0mih0OhPQEL3+G5YvX0CzZs3y45QJIcQDJ73uRZ5QStG9+7OsW7cT
+q/Wvk7yYUCoWaIRtdLzReHruYd3KGczISoVZs8BgoKPWmaX8Md2rjqysWmRleZKT8x7QDtiORvMR
+YWEnGDr0ebZsWX/HQWuuXUvFbP7j/faywFKKFTvBl18OIiHhV+rXr8+XX07GaHwaN7deODvXRamz
+WCw7sFgmYDLN5qWXBj+4EyWEEIWIFHpht27dOlas2IPVOhj4BLgApKDVnsz99x9cqV09gqipU9F8
++y24u8PatVyNbIRWOwZbL/zf0GoXodXqcz8DVESns7Bt2wqGD3+Po0ePMn/+fPbt23dLHE88EYPR
++DmwCziPwfABnTt3oWvXrpQqVQqApk2bcvjwbiZPfozoaF/gOf581S+I69evPZiTJIQQhY0qZAph
+yIXG559/rozGvgqsCoYoKKZArzp16qbc3csojeZtBR+oUoZS6lqdOkqBUiVKKBUXp5RSKikpSVWv
+Xl/pdK7K2dlNTZ48VYWG1lUuLj0UzFRGY0PVq9cLSimlxo79VBmNPqpYsc7KaPRTw4ePviWWefO+
+UV5egcrDw0v16vWCysjIuGvcGzduVEajn4K9Ci4qg6Gd6tv3pQd3ooQQIp/dT+2TZ/TCbt++fTRq
+1IaMjG1AJWAilSp9xa5dG+jb9yV2744joGRxNrgoPA8ftk0vu2EDVK9+Szvp6em4urqi1Wq5efMm
+Y8Z8zIkTZ2nUKJKXXupPSkoKFSoEk5W1H5iObaKcVCZPHsXLL7/0n2KfPftL3nnnAzIy0mnfvgOz
+Zk3GYDDc5xkRQoiHg3TGE3li4cLv6dnzmdxX4PR4eXmxadNyOnbsxZkzDXE3N2Wd5gUi1RWUnx+a
+jRuhSpV/fZyDBw/SuHEP0tLCsD0SmAEk4+LSlbVrF9zzFTkhhCiKpDOeuG9nz56lT5/+ZGfvxPYa
+21QyM03cuHGDCxeyKGF+ly0MI1Jd4bRGx+mvvvpPRR7Ax8eH9PTz2EbLmwwEA1FkZb3GkiUr8iol
+IYQQSKEXueLj49HrawHh2Dq1PY3FYhvsxs9qYhtNCOMoxwimhUsprP7+/+k4V69e5Z13BuPk9BgQ
+CCTa1+l0iXh6uudBNkIIIf4g89ELACpUqEB29hEgBdtgOEewWm/SxNeXH7Mv4kMW+wmgnUsAJSp7
+Yjab/1G7WVlZ6PV6nJyciI+Pp1Gjlty86UJ29kBsV/LPAP2A83h6bmDAgD0PKEMhhCia5IpeAFCt
+WjVef70/RmM4Hh4xGAyPsWjEe7jHxOBjzuJsOT9eqeZNcs5Ozp51p3bt5gwfPvqu7V27do2GDaNx
+c/PAYCjGJ59M5JlnBpKaOgyLZRTwJVAL28Q4S9FolgCQkZFx1zaFEEL8e9IZT9ziyJEjnDlzhppK
+4du3L1y9Sk5UFGtffJEOvfpisWwC6gGXMBgi2Lt3EyEhIbe107p1FzZuLI3ZPAk4j9H4GHq9md9/
+/xGoDAwDxuZuHQV8h5PT50RF7WDTpmX5k6wQQhQS0hlP5JmwsDDalCyJb69ecPUq5pYtCTt7haee
+/RSLpQq2KWovAl7o9WGcOXPmlv1TUlI4fvw4O3b8hNn8LranQwGYTL0oU6Y0ev00bAPoDEKrDQA6
+YxtW1xOrtQWnTiXkY7ZCCOH4pNALAC5cuMCkSZNYMmAA1hYt4MYN6NyZNwKqcCqpIenpu4D92Iay
+HQocxmLZT1BQkL2NESNiKV++EnXrtiMtLQP4OXeNFYNhLy+80IOQkJ9xcSmDXu9PkyaBGAyngTTA
+irPzDGrXrpnPmQshhGOTW/eCU6dOERnZiKY3Q5ifvRkXrPzeoQOe339P8+jObNrUDduVN8A6NJqe
+aLUmnJy0ZGdnUKtWI4YOHUS3bq9gMu0GvIF30WgmYzS2RaM5S8WKOUREhJOQkESdOqG8997bWCwW
+goJqcuPGNcAZd3c3TpzYh4+PT4GdCyGEeBjJrXtxX4YOHc3jvzdiYfZWXLAyhQY8k+MKWi2NG9fG
+YJgFmIAsDIYZtG7dBL3eE7P5J6zW0fz8cwo9e/bDam2IrcgD/A+lTEye3IIZM14mNTWVBQvc+Omn
+Z/nss4O88MKrDBz4DhkZT2F7xe4Q2dm1+OKLOQV1GoQQwiFJoRfUOLifb9T36MkmlsEMpBFLl6/H
+3b08ZcuWICamDHq9F87OZWnUyErDhrXJyekMfA6sAN4nLa0TWVnL+fO9+FX4+DxCnz59KFasGKmp
+Plgs44EOZGQsYcmS7/jxx5+wWDoDpYEAMjM7sm/fsYI5CUII4aDkPfoiJjk5mVGjxpGUlEKbNs14
+5kYqb/9yEIAhvEEs5YHhwFTS0zPo3/8VVq6cz6xZk7FarZQqVYpvvvkGnW4lZvMebPPFewJd0Oni
+0WiqYzCEYLX+xvTpM0lJScm93aT5SxQalNJy/Xo2Gs23KFUPyMZgWEKtWo/m7wkRQggHJ8/oi5Br
+164REhLJ1avtyM4OY5h+CCMslwBY2Tyap3buxWTKwfaOe/vcvabSuPFKtm5dY2/HYrHQpEkrdu3a
+AvwOGAFwd2/D8OFRBAcH8/77Yzh+/Fes1izatGnL7t17SE5uQ05OFLaJbDyAMUAobm4+KJVB3brV
+WbNmMS4uLgghhPiTPKMX/8iSJUtIS6tFdvZ4RvEbIyyXsAJq5kye2LCWs2d/QaPRANa/7GXF2dn5
+lnb0ej3btq2hQYMmODu3B9ah1X6IwXCYPn36MH/+Mo4dCyYz8yJm8wWWLfuNMmVKEBq6A632RSAS
++Aooh4uLnqVLp7J37zo2blwuRV4IIfKYFHoHdPbsWb766iuWLFlCVlaWfbnFYgGrGxN5lSHEko2W
+p7XO8OyzAJQuXZrWraOAF4Cvgc9xchrK8OFv33YMnU7H5s2ref31+kRGjqN9+9/4+edtlCxZkri4
+/WRlPQdoATeys/ty8KCOEyeS8PDQoNOZgR24uj5NnTp1aNasGVWrVsXJSb6OQgiR1+TWvYP57rvv
+6NHjeZRqjk6XRHCwll27NmIwGEg8c4YtlavSKzuTLPT0cg7BrXt9vvzyM/v+SimGDh3KggWrKVbM
+jYkTR9GkSZN/FUNMTGc2bKhOTs772O4OdANCgccpVepJmjaN4sSJBOrXr8nHH4/E3V0mshFCiHuR
++egFYBv0pnz5alits4BOgEKni+HTT5/g5X79oFcvWLiQDCctA/2qUPKpjowaNQy9Xp+ncZw7d456
+9R7j2rWSZGXdwDZJzjogmeLFG3H9elKeHk8IIRydPKMv4qxWKzNnzqRjx6ewWsH2DBxAQ3Z2PX45
+eBg6dYKFC8HDA8PWLUw/dQiNRkPlyrUIDW3A+vXr8ywef39/fv31AJMnP4+LSzK22emOYjQ+S48e
+XfPsOEIIIf6eXNE7gF69XmDx4iNkZHgDx4EmwBQgCSN12eSWSb30G1CyJKxfD7Vq8dprg5kxYxcm
+03ggEaPxBbZuXU1kZOQ9j/Vvbdu2jUGDhpGa+judOrUmNnY4Op281SmEEP+G3LovwpKTkylfPojs
+7AvANWzPwgOAX/AAVuFKQ9LA2xs2bIDQUADKln2ElJRVQNXcloYxeLCV2NiRBZCFEEKIe5Fb90XY
+wYMHyc7WA26AP/A2cIVS1OFHytKQNM476ehbKZxt167Z9zMYjECK/bNOdxk3N0M+Ry+EEOJBk0Jf
+iMXFxbFs2TI0GmfgFeAIoMObLLayk1okcRItDazj+fKnJ4mJeZKdO3cCEBv7HgZDd+ATtNpX8PRc
+Rd++fVi+fDkTJ05k27ZtzJkzl9Kl/TEaS/DUU30wmUwFmK0QQoj/Qm7dF1LDh49m3LjpKBVKRsYu
+oBoQjz9l2UQSlUjnpIuBxllTSaZP7l6T6Nr1EPPnz+bKlStERNTj8uV0IIcqVSoSHFyFNWsOk53d
+EFiE1QoWy0rAH1fXAXTuXIqvv/68oFIWQogiS57RFzHnz5+ncuVwMjOPAV7AEGAy1V29WJF5Gn8U
+lurVidF4sunQG9g6580GNtCsmY6NG1fSo8dzfP+9OxbLp4DC2TkGpeKxWE5gG9L2FaAEMCL3qKdw
+c3uUmzeT8z9hIYQo4uQZfRGzf/9+rNay2Io8wGjqGMqwyzkFfxQ8+ij6bdt4YchAXF37ARWBnUAk
+27btYeHC7/j558NYLG2xTTbjhNkcjlIV+GPcetvz/qN/OeoJ0tOzWLVqVT5lKYQQIi/IFX0hc/r0
+aSIi6pGWZgbmAm2oxXjWa96kpFLQvDksXQpubmRkZFCqlC8ZGVGNMUnHAAARyUlEQVTAktwWduDm
+1onMTAs5OTGAH/ADcB2NxoRS84EWwKfAqNx/BwDfAB0pU2YDSUm/5PkgO0IIIe5OruiLkPHjp5Ce
+/jywBhhAQ5z5kTcoqRQp9euTk1vkATp37k1GRioQ+JcWKpCefoOcnG3YrvI3A4uAeSiVCbwGFAOW
+Ypt+tjRQDtgIOHP9umLevHn5lK0QQoj7JYW+kElLM2G1egH1acEs1mGb8HWRrhxBh9N5rFVnzGYz
+b745hFWrlgDuwBxgA3Aa24Q1Ltg67+mBWUAY0DJ32x8AM7AbF5fywHzgCjAJWIrV2ooLFy7kZ8pC
+CCHugxT6QqZHj44YjWNpx4esoA1GLMyiKU9lnyM1fR87d6YxcOBAPvtsOeAMjARygD5ATeAAtufw
+CwAFXPxL6+FoNM2Akbi4dKN8+QwiIsLRaLYDQcAiXF1X8Oijj+ZnykIIIe6DPKMvhHYNHEjtKVPQ
+ARPR8RoJKPxy13bH2XktZvO7wA1gBRCN7db7MWy3/H8BPsQ2YE4x4HXgQu62FwgOrk737h0ZNGgQ
+mZmZtGrVhQMHdqHVahk3biyvvPJS/iYshBBFnLxeV5TMnAn9+oFSxDq5McRqBHoD44CVQA+gA7bh
+cJcC84AvgTNABrY54gOAg0AMtiv6hthG1msFPIZe34OqVQ+zf/92tFotABkZGbi4uMic8UIIUQCk
+M15RMWECvPACKMUQJyNDrNOxzVS3DCgOdAWGAtOB34Ha2Ir8YeAZoCzwG7ZOeDGAN5CK7UdBKaAz
+8B4WyzR+++08p0+fth/aYDBIkRdCiEJI/nIXBkrByJHw2msAvOpkINZaEbgJTARM2Ap+CWy36w3A
+JqAD3t7nqF07HGfnj4HG/Pme/AhsPwJaYrttPxRoCwwGLFitWTg7O+dXhkIIIR6QB1ro+/bti5eX
+F2FhYfZl165do0WLFgQFBdGyZUtSU1Pt62JjY6lcuTLBwcF5Oj96oWexwIYNWDUaXtBVYJK1N9AM
+eB9YDvQHdvLqq0/i7j4DjWYkMAuDYTqTJo1lz57NrFixBNu79JdyG90EVAE+o0cPX4KCKuDqegH4
+EoOhHU2bNsLf3z//cxVCCJGnHmih79OnD2vXrr1l2ZgxY2jRogUnTpygWbNmjBkzBoBjx46xcOFC
+jh07xtq1axkwYABWq/VBhld4ODszvFYD2mpcmZndE2iH7fn7WGAfWu1sund/igkTJhAXt4U+fS7y
+1FN7+eGHL+jc+UkArly5hu0WfQBQAZgGdMDLqzzz5s3k4MHdvP12OO3b/8j77zdj2bL5aDSaAklX
+CCFEHlIPWEJCggoNDbV/rlKlikpOTlZKKXXx4kVVpUoVpZRSo0ePVmPGjLFvFx0drXbt2nVbe/kQ
+8kPnyJEjymDwUTBTQYSC3xV8ocBHabUe6qWXXldms/mu+1+6dEkZDCUVPKPAT4GbAj+l0birHTt2
+5GMmQggh/ov7qX26/P5hcenSJby8bGO0e3l5cemS7VbyhQsXqFevnn07Pz8/kpKS8ju8h9K5c+dw
+dg4jI+NZ4BDwCOBM8eJWPv10It26dbttSNrt27fz2Wdz0GqdaNasAc7Oj5CRMRvblfw6dLo4Fi/+
+hgYNGuR/QkIIIfJNvhf6v9JoNPe8PXy3dcOHD7f/OyoqiqioqDyO7OESGhqKxbIf2A9MBrzRaGLJ
+yanLK69M56OPphAX9yMeHh4AbNiwgXbtepKRMRTIYvHiN3Ifg/wKvAxEodc3loFvhBDiIbVlyxa2
+bNmSJ23le6H38vIiOTkZb29vLl68SNmyZQHw9fUlMTHRvt358+fx9fW9Yxt/LfRFgb+/P3PnzqBn
+z+YopScnx0pOzmDS0oYCioSEPowePY7Y2A+ZO3ceb701koyM8djeqYeMDD21ay/h6NGGODtXwmz+
+jVmzplGqVKkCzUsIIcSd/f+L2BEjRtx947+R76/XtW3bljlz5gAwZ84c2rdvb1++YMECzGYzCQkJ
+nDx5kjp16uR3eA+tjh07kJR0mlGjBlOiREms1qa5azRkZTXmxImzvPPO+wwY8AmXLxuwjVv/B3d8
+fPw4deooq1d/yunT8XTv3rUAshBCCJHfHugVfbdu3di6dStXrlyhfPnyfPjhhwwePJguXbowe/Zs
+AgIC+O677wAICQmhS5cuhISEoNPpmDZtmvT6/oukpCRq147i+vXKmM3uwHigDpCJ0fgVjz7akcGD
+B5OdfQ7bBDavYZu0JgujcRj9+8/Gx8cHHx+fAsxCCCFEfpMhcAuBM2fOEBZWk5s3G2N7re4m0Bw4
+jF6voWXLGIKDH2H8+AkolYZtdrq5ODkNpUKFkowf/4H9zokQQojCR4bAdXAvvvgmN2+GAzVyl7gD
+8ylevATLly9m8+atjB/vjFKB2IbB3YdGc5Nixczs3LlGirwQQhRhckVfCFSqVItTp54FYrHNMFcB
+J6fn6dLFkzNnzrN7dy+gJ5AONKNYsSSqVw9l+vRxhIaGFmToQggh8oBc0Tu4Bg0icXHZCwzHNhmN
+D6GhF5g5cyJpaTfBPkWtG9CHmJgW/PTTGinyQgghpNAXBpMnj6VGjXO4uLyNTvc7tWpFMGDA0wB0
+794eo/Ft4AiwE6Mxlh495Fa9EEIIG7l1X0gopVi0aBFPPz2AnJyn0OkS8fY+zb592/nkk0nMnDkX
+Z2dnhg17g+ee61vQ4QohhMhD91P7pNAXIpUq1eDUqf8BTwDg4tKNUaMieeONNwo2MCGEEA+UPKMv
+IlJTrwFV7Z+zsqpy+fLVggtICCHEQ08KfSESHd0CV9ch2KabPYjROJPHH29R0GEJIYR4iEmhL0Rm
+zJhIq1bOuLgEULx4KyZN+pCmTZv+/Y5CCCGKLHlGL4QQQjzk5Bm9EEIIIe5ICr0QQgjhwKTQCyGE
+EA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQ
+CyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjh
+wKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0Q
+QgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5M
+Cr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGE
+EA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA7soSv0a9euJTg4mMqVK/PRRx8VdDgPnS1b
+thR0CAWqKOdflHMHyV/y31LQIRRaD1Whz8nJ4eWXX2bt2rUcO3aM+fPnc/z48YIO66FS1L/sRTn/
+opw7SP6S/5aCDqHQeqgK/Z49e6hUqRIBAQHo9Xq6du3KsmXLCjosIYQQotB6qAp9UlIS5cuXt3/2
+8/MjKSmpACMSQgghCjeNUkoVdBB/WLx4MWvXrmXmzJkAzJs3j7i4OCZPnmzfplKlSpw6daqgQhRC
+CCHyXWBgIL/99tt/2leXx7HcF19fXxITE+2fExMT8fPzu2Wb/5qoEEIIURQ9VLfuIyMjOXnyJGfO
+nMFsNrNw4ULatm1b0GEJIYQQhdZDdUWv0+mYMmUK0dHR5OTk8Oyzz1K1atWCDksIIYQotB6qZ/RC
+CCGEyFsP1a37v/r++++pVq0aWq2W/fv337IuNjaWypUrExwczPr16+3L9+3bR1hYGJUrV+bVV1/N
+75AfqKIwkFDfvn3x8vIiLCzMvuzatWu0aNGCoKAgWrZsSWpqqn3d3b4HhVViYiJNmzalWrVqhIaG
+MmnSJKBonIPMzEzq1q1LREQEISEhvPvuu0DRyP2vcnJyqFGjBm3atAGKVv4BAQFUr16dGjVqUKdO
+HaBo5Z+amsqTTz5J1apVCQkJIS4uLu/yVw+p48ePq19//VVFRUWpffv22ZfHx8er8PBwZTabVUJC
+ggoMDFRWq1UppVTt2rVVXFycUkqpmJgYtWbNmgKJPa9lZ2erwMBAlZCQoMxmswoPD1fHjh0r6LDy
+3LZt29T+/ftVaGiofdlbb72lPvroI6WUUmPGjFHvvPOOUurO34OcnJwCiTuvXLx4UR04cEAppVRa
+WpoKCgpSx44dKzLnID09XSmllMViUXXr1lXbt28vMrn/4ZNPPlHdu3dXbdq0UUoVre9/QECAunr1
+6i3LilL+vXv3VrNnz1ZK2f4fSE1NzbP8H9pC/4f/X+hHjx6txowZY/8cHR2tdu3apS5cuKCCg4Pt
+y+fPn6/69euXr7E+KDt37lTR0dH2z7GxsSo2NrYAI3pwEhISbin0VapUUcnJyUopWyGsUqWKUuru
+3wNH0q5dO7Vhw4Yidw7S09NVZGSkOnr0aJHKPTExUTVr1kz9+OOP6oknnlBKFa3vf0BAgLpy5cot
+y4pK/qmpqapixYq3Lc+r/B/aW/d3c+HChVteuftjUJ3/v9zX19dhBtspygMJXbp0CS8vLwC8vLy4
+dOkScPfvgaM4c+YMBw4coG7dukXmHFitViIiIvDy8rI/wigquQO89tprjBs3DienP/8sF6X8NRoN
+zZs3JzIy0j6WSlHJPyEhgTJlytCnTx9q1qzJ888/T3p6ep7lX6C97lu0aEFycvJty0ePHm1/RiVs
+/wMI23m417lwlPN08+ZNOnXqxMSJEylWrNgt6xz5HDg5OXHw4EF+//13oqOj2bx58y3rHTn3lStX
+UrZsWWrUqHHXMd0dOX+AHTt24OPjQ0pKCi1atCA4OPiW9Y6cf3Z2Nvv372fKlCnUrl2bQYMGMWbM
+mFu2uZ/8C7TQb9iw4V/v8/8H1Tl//jx+fn74+vpy/vz5W5b7+vrmSZwF7Z8MJOSovLy8SE5Oxtvb
+m4sXL1K2bFngzt8DR/jvbbFY6NSpE7169aJ9+/ZA0TsHnp6etG7dmn379hWZ3Hfu3Mny5ctZvXo1
+mZmZ3Lhxg169ehWZ/AF8fHwAKFOmDB06dGDPnj1FJn8/Pz/8/PyoXbs2AE8++SSxsbF4e3vnSf6F
+4ta9+ssbgG3btmXBggWYzWYSEhI4efIkderUwdvbGw8PD+Li4lBKMXfuXPsfysKuKA8k1LZtW+bM
+mQPAnDlz7P9N7/Y9KMyUUjz77LOEhIQwaNAg+/KicA6uXLli71GckZHBhg0bqFGjRpHIHWx3MRMT
+E0lISGDBggU89thjzJ07t8jkbzKZSEtLAyA9PZ3169cTFhZWZPL39vamfPnynDhxAoCNGzdSrVo1
+2rRpkzf552WHgry0ZMkS5efnp1xdXZWXl5d6/PHH7etGjRqlAgMDVZUqVdTatWvty/fu3atCQ0NV
+YGCgGjhwYEGE/cCsXr1aBQUFqcDAQDV69OiCDueB6Nq1q/Lx8VF6vV75+fmpL774Ql29elU1a9ZM
+Va5cWbVo0UJdv37dvv3dvgeF1fbt25VGo1Hh4eEqIiJCRUREqDVr1hSJc3D48GFVo0YNFR4ersLC
+wtTYsWOVUqpI5P7/bdmyxd7rvqjkf/r0aRUeHq7Cw8NVtWrV7H/jikr+Sil18OBBFRkZqapXr646
+dOigUlNT8yx/GTBHCCGEcGCF4ta9EEIIIf4bKfRCCCGEA5NCL4QQQjgwKfRCCCGEA5NCL4QQQjgw
+KfRCCCGEA5NCL4QQQjgwKfRCCCGEA5NCL4S4p59//pnw8HCysrJIT08nNDSUY8eOFXRYQoh/SEbG
+E0L8rffff5/MzEwyMjIoX74877zzTkGHJIT4h6TQCyH+lsViITIyEoPBwK5duwr1lKBCFDVy614I
+8beuXLlCeno6N2/eJCMjo6DDEUL8C3JFL4T4W23btqV79+6cPn2aixcvMnny5IIOSQjxD+kKOgAh
+xMPt66+/xsXFha5du2K1WmnQoAFbtmwhKiqqoEMTQvwDckUvhBBCODB5Ri+EEEI4MCn0QgghhAOT
+Qi+EEEI4MCn0QgghhAOTQi+EEEI4MCn0QgghhAOTQi+EEEI4sP8Diwf1C+duoqkAAAAASUVORK5C
+YII=
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="comparing-the-results-from-the-different-implementations">Comparing the results from the different implementations</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As mentioned above, let us confirm that the different implementation computed the same parameters (i.e., slope and y-axis intercept) as solution for the linear equation.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[9]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">prettytable</span>
+
+<span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">appr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">appr</span> <span class="ow">in</span> <span class="p">[</span><span class="n">lin_lstsqr_mat</span><span class="p">,</span> <span class="n">classic_lstsqr</span><span class="p">,</span> <span class="n">numpy_lstsqr</span><span class="p">,</span> <span class="n">scipy_lstsqr</span><span class="p">]]</span>
+
+<span class="k">print</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
+
+<span class="n">fit_table</span> <span class="o">=</span> <span class="n">prettytable</span><span class="o">.</span><span class="n">PrettyTable</span><span class="p">([</span><span class="s">&quot;&quot;</span><span class="p">,</span> <span class="s">&quot;slope&quot;</span><span class="p">,</span> <span class="s">&quot;y-intercept&quot;</span><span class="p">])</span>
+<span class="n">fit_table</span><span class="o">.</span><span class="n">add_row</span><span class="p">([</span><span class="s">&quot;matrix approach&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span>
+<span class="n">fit_table</span><span class="o">.</span><span class="n">add_row</span><span class="p">([</span><span class="s">&quot;classic approach&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span>
+<span class="n">fit_table</span><span class="o">.</span><span class="n">add_row</span><span class="p">([</span><span class="s">&quot;numpy function&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span>
+<span class="n">fit_table</span><span class="o">.</span><span class="n">add_row</span><span class="p">([</span><span class="s">&quot;scipy function&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span>
+
+<span class="k">print</span><span class="p">(</span><span class="n">fit_table</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+[array([   0.95181895,  107.01399744]), (0.95181895319126741, 107.01399744459181), array([   0.95181895,  107.01399744]), (0.95181895319126764, 107.01399744459175)]
++------------------+----------------+---------------+
+|                  |     slope      |  y-intercept  |
++------------------+----------------+---------------+
+| matrix approach  | 0.951818953191 | 107.013997445 |
+| classic approach | 0.951818953191 | 107.013997445 |
+|  numpy function  | 0.951818953191 | 107.013997445 |
+|  scipy function  | 0.951818953191 | 107.013997445 |
++------------------+----------------+---------------+
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="initial-performance-comparison">Initial performance comparison</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>For a first impression how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the <code>timeit</code> module via IPython's <code>%timeit</code> magic.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[10]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">timeit</span>
+
+<span class="k">for</span> <span class="n">lab</span><span class="p">,</span><span class="n">appr</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="s">&quot;matrix approach&quot;</span><span class="p">,</span><span class="s">&quot;classic approach&quot;</span><span class="p">,</span>
+                     <span class="s">&quot;numpy function&quot;</span><span class="p">,</span><span class="s">&quot;scipy function&quot;</span><span class="p">],</span>
+                    <span class="p">[</span><span class="n">lin_lstsqr_mat</span><span class="p">,</span> <span class="n">classic_lstsqr</span><span class="p">,</span> 
+                     <span class="n">numpy_lstsqr</span><span class="p">,</span> <span class="n">scipy_lstsqr</span><span class="p">]):</span>
+    <span class="k">print</span><span class="p">(</span><span class="s">&quot;</span><span class="se">\n</span><span class="s">{}: &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lab</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">)</span>
+    <span class="o">%</span><span class="k">timeit</span> <span class="n">appr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+
+matrix approach: 1000 loops, best of 3: 270 µs per loop
+
+classic approach: 100 loops, best of 3: 2.83 ms per loop
+
+numpy function: 1000 loops, best of 3: 372 µs per loop
+
+scipy function: 1000 loops, best of 3: 594 µs per loop
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> The timing above indicates, that the &quot;classic&quot; approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br> <a name="cython_nb"></a></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="compiling-the-python-code-via-cython-in-the-ipython-notebook">Compiling the Python code via Cython in the IPython notebook</h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#sections">back to top</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Maybe we can speed things up a little bit via <a href="http://cython.org">Cython's C-extensions for Python</a>. Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.<br />Since we are working in an IPython notebook here, we can make use of the IPython magic: It will automatically convert it to C code, compile it, and load the function.<br />Just to be thorough, let us also compile the other functions, which already use numpy objects.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[15]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="o">%</span><span class="k">load_ext</span> <span class="n">cythonmagic</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[16]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="o">%%</span><span class="n">cython</span>
+<span class="k">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="k">import</span> <span class="nn">scipy.stats</span>
+<span class="k">cimport</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="k">def</span> <span class="nf">cy_lin_lstsqr_mat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))])</span><span class="o">.</span><span class="n">T</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">cy_classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="n">x_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+    <span class="n">y_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="n">var_x</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">**</span><span class="mf">2</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
+    <span class="n">cov_xy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">y_i</span> <span class="o">-</span> <span class="n">y_avg</span><span class="p">)</span> <span class="k">for</span> <span class="n">x_i</span><span class="p">,</span><span class="n">y_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)])</span>
+    <span class="n">slope</span> <span class="o">=</span> <span class="n">cov_xy</span> <span class="o">/</span> <span class="n">var_x</span>
+    <span class="n">y_interc</span> <span class="o">=</span> <span class="n">y_avg</span> <span class="o">-</span> <span class="n">slope</span><span class="o">*</span><span class="n">x_avg</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">slope</span><span class="p">,</span> <span class="n">y_interc</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">cy_numpy_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))])</span><span class="o">.</span><span class="n">T</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="mf">0</span><span class="p">]</span>
+
+<span class="k">def</span> <span class="nf">cy_scipy_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">linregress</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mf">0</span><span class="p">:</span><span class="mf">2</span><span class="p">]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="comparing-the-compiled-cython-code-to-the-original-python-code">Comparing the compiled Cython code to the original Python code</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[17]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">timeit</span>
+
+<span class="k">for</span> <span class="n">lab</span><span class="p">,</span><span class="n">appr</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="s">&quot;matrix approach&quot;</span><span class="p">,</span><span class="s">&quot;classic approach&quot;</span><span class="p">,</span>
+                     <span class="s">&quot;numpy function&quot;</span><span class="p">,</span><span class="s">&quot;scipy function&quot;</span><span class="p">],</span>
+                    <span class="p">[(</span><span class="n">lin_lstsqr_mat</span><span class="p">,</span> <span class="n">cy_lin_lstsqr_mat</span><span class="p">),</span> 
+                     <span class="p">(</span><span class="n">classic_lstsqr</span><span class="p">,</span> <span class="n">cy_classic_lstsqr</span><span class="p">),</span>
+                     <span class="p">(</span><span class="n">numpy_lstsqr</span><span class="p">,</span> <span class="n">cy_numpy_lstsqr</span><span class="p">),</span>
+                     <span class="p">(</span><span class="n">scipy_lstsqr</span><span class="p">,</span> <span class="n">cy_scipy_lstsqr</span><span class="p">)]):</span>
+    <span class="k">print</span><span class="p">(</span><span class="s">&quot;</span><span class="se">\n\n</span><span class="s">{}: &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lab</span><span class="p">))</span>
+    <span class="o">%</span><span class="k">timeit</span> <span class="n">appr</span><span class="p">[</span><span class="mi">0</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+    <span class="o">%</span><span class="k">timeit</span> <span class="n">appr</span><span class="p">[</span><span class="mi">1</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+
+
+matrix approach: 
+1000 loops, best of 3: 274 µs per loop
+1000 loops, best of 3: 260 µs per loop
+
+
+classic approach: 
+100 loops, best of 3: 2.82 ms per loop
+1000 loops, best of 3: 212 µs per loop
+
+
+numpy function: 
+1000 loops, best of 3: 379 µs per loop
+1000 loops, best of 3: 370 µs per loop
+
+
+scipy function: 
+1000 loops, best of 3: 608 µs per loop
+100 loops, best of 3: 613 µs per loop
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br> As we've seen before, our &quot;classic&quot; implementation of the least square method is pretty slow compared to using numpy's functions. This is not surprising, since numpy is highly optmized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.<br />However, we were able to speed up the &quot;classic approach&quot; quite significantly, roughly by 1500%.</p>
+<p>The following 2 code blocks are just to visualize our results in a bar plot.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[18]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">timeit</span>
+
+<span class="n">funcs</span> <span class="o">=</span>  <span class="p">[</span><span class="s">&#39;classic_lstsqr&#39;</span><span class="p">,</span> <span class="s">&#39;cy_classic_lstsqr&#39;</span><span class="p">,</span> 
+          <span class="s">&#39;lin_lstsqr_mat&#39;</span><span class="p">,</span> <span class="s">&#39;numpy_lstsqr&#39;</span><span class="p">,</span> <span class="s">&#39;scipy_lstsqr&#39;</span><span class="p">]</span>
+<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;classic approach&#39;</span><span class="p">,</span><span class="s">&#39;classic approach (cython)&#39;</span><span class="p">,</span> 
+          <span class="s">&#39;matrix approach&#39;</span><span class="p">,</span> <span class="s">&#39;numpy function&#39;</span><span class="p">,</span> <span class="s">&#39;scipy function&#39;</span><span class="p">]</span>
+
+<span class="n">times</span> <span class="o">=</span> <span class="p">[</span><span class="n">timeit</span><span class="o">.</span><span class="n">Timer</span><span class="p">(</span><span class="s">&#39;</span><span class="si">%s</span><span class="s">(x,y)&#39;</span> <span class="o">%</span><span class="k">f</span><span class="p">,</span> 
+                      <span class="s">&#39;from __main__ import </span><span class="si">%s</span><span class="s">, x, y&#39;</span> <span class="o">%</span><span class="k">f</span><span class="p">)</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
+         <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">funcs</span><span class="p">]</span>
+
+<span class="n">times_rel</span> <span class="o">=</span> <span class="p">[</span><span class="n">times</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">times</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))]</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[19]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="o">%</span><span class="k">pylab</span> <span class="n">inline</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
+
+<span class="n">x_pos</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">funcs</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">times</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;time in ms&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Performance of different least square fit implementations&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+
+<span class="n">x_pos</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">funcs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">times_rel</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">&quot;green&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">labels</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;relative performance gain&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Performance gain compared to the classic least square implementation&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Populating the interactive namespace from numpy and matplotlib
+
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFGfXB+DfLoiwVAEBXZpYEkBEbNgiS+yoSDS22Msr
+EjQaEyOmKCb2GhMTY4n62n01FkQwxrIalWI3kSCWgIC6ETtFYHfP9wcwH0sRMO4u6Lmvi4udnWdm
+zjPtzDxTVkREBMYYY288sb4DYIwxVj1wQmCMMQaAEwJjjLFCnBAYY4wB4ITAGGOsECcExhhjAF7z
+hKBQKNCpUydYWFhg2rRp+g5H73JyctCnTx9YWVlh0KBBFZaXy+VwcnISups2bYqTJ08CAIgIo0eP
+hrW1Ndq2bQsAWLVqFezt7WFhYYFHjx5ppxIvqWRdWOVdu3YNzZs3h4WFBb7//nuEhIRgzpw5lR6+
+quWrwtXVFUePHtXKuPVp69at6N69u+4nTNWMi4sLmZiYkJmZGdnb29OoUaMoMzPzpcb19ddfU//+
+/V9xhDXXpk2bqE2bNqRSqSpV/vjx4+To6Fhmv5MnT5KjoyNlZ2cTEVFeXh6ZmJjQH3/88crirQoX
+Fxc6evRouf1fVJdXZeTIkfTll19qdRr6MGbMGJo6dWqZ/XQxX1/E1dX1hctdHzZs2EAdO3asdPm/
+//6bRCJRpbdLbap2ZwgikQiRkZF49uwZLly4gHPnzlX56IKIoFarkZKSAnd395eKQ6lUvtRw1VlK
+SgqaNGkCsfjfL/aUlBS4urrCxMQEAHDv3j08f/78pee3Wq3+V/GIRCIQP2NZoZdZr1NSUuDh4aGF
+aFhx1WL91XNCKqVkxv/000+pd+/eREQUExND7dq1IysrK/L29ia5XC6U8/Pzoy+++II6dOhAJiYm
+NGzYMKpVqxYZGRmRmZkZHT16lHJzc2ny5MlUv359ql+/Pk2ZMoVyc3OJqOBIRyqV0sKFC8nBwYGG
+Dx9O4eHh9P7779OwYcPI3NycvLy8KCkpiebNm0d2dnbk7OxMhw8fFmJYv349ubu7k7m5Obm5udHq
+1auFfkXjX7p0KdnZ2VG9evVow4YNQv/s7GyaOnUqubi4kKWlJXXs2JFycnIqrHdJCQkJ5OfnR1ZW
+VuTp6UkRERFERDRz5kwyMjKiWrVqkZmZGa1fv77UsNnZ2TRy5EiqU6cOeXh40KJFizSO/lxcXOjI
+kSO0bt06MjY2JgMDAzIzM6MhQ4aQqakpiUQiMjMzo86dOxMR0V9//UVdunQha2treuutt+h///uf
+MK6RI0fShAkTqGfPnmRqakpHjx6l9PR06tevH9WtW5caNGhA3333nVB+1qxZNGDAABoxYgSZm5uT
+p6cnnTt3joiIhg0bRmKxWDizXLx4cam6lTySfdG04uLiqG3btmRlZUX16tWjiRMnUl5entB/ypQp
+ZGdnRxYWFuTl5UV//vknrV69WmN9CwwMLHP5lDUsEVFGRgb16dOHLCwsqE2bNvTll18KR5llHUH6
++fnRunXriIjoxo0b5O/vTzY2NmRra0tDhw6lx48fayy3hQsXkpeXFxkbG5NKpar0OuXv708GBgZk
+bGxM5ubmlJSUJJwJZWVlkbGxMYnFYjIzMyNzc3O6e/duqXEUP3Mq2g4WLVpEdevWpXr16tHevXvp
+4MGD1LhxY7K2tqb58+drLPf+/fvToEGDyNzcnFq0aEGXL18W+hffX6jVapo/fz41bNiQbGxsaODA
+gfTw4UONebhhwwZycnIia2trWrVqFcXHx5OXlxdZWVnRxIkTNeL++eefyd3dnerUqUPdu3enlJQU
+oZ9IJKKffvqJGjduTFZWVhQaGkpEBdtf8W2jTp06REQUGRlJzZs3JwsLC3JycqLw8HBhXE5OTsK2
+Y25uTjExMaXOMk6fPk2tWrUiS0tLat26NZ05c0ZjXfjqq6+oQ4cOZG5uTt26daOMjAwiIsrJyaGh
+Q4eSjY0NWVlZUevWrUmhUJS5rImIqmVCOHLkCBER3b59mzw9PWnmzJmUlpZGNjY2FB0dTUREv/32
+G9nY2AgV9/PzIxcXF0pISCCVSkX5+fk0atQo+uqrr4Rxf/XVV9SuXTu6f/8+3b9/n9q3by/0P378
+OBkaGlJYWBjl5eVRTk4OzZo1i4yNjenw4cOkVCppxIgR5OLiQvPmzSOlUklr166lBg0aCOM/ePAg
+3bp1i4iITpw4QRKJhC5cuKAx/lmzZpFSqaSoqCiSSCTChvvhhx+Sv78/3blzR9hgc3Nzy633/fv3
+S827vLw8atiwIc2fP5/y8/Pp2LFjZG5uTteuXSMiovDwcBo+fHi583769OnUqVMnevToEaWmppKn
+pyc5OTlpLJuijW/jxo0aK2xycrLGTiszM5McHR1p48aNpFKp6OLFi2Rra0sJCQlEVLCTsLS0FFbs
+7OxsatGiBX3zzTeUn59Pt27dIjc3N/r111+JiIRlER0dTWq1mmbMmEFt27YtM7ayFE8IKpXqhdM6
+f/48xcXFkUqlouTkZHJ3d6dvv/2WiIgOHTpELVu2pCdPnhARUWJiorATLLm+lfSiYQcNGkSDBg2i
+7Oxs+vPPP0kqldI777xDRGUnBJlMRj///DMRFSSEI0eOUF5eHt2/f586depEU6ZMEcq6uLiQj48P
+paWl0fPnz6u0TpWcVsl6yuXyCpuMipcv2g6++eYbYRuysbGhDz74gDIzM+nq1atkYmJCycnJRFSw
+3GvVqkW//PILKZVKWrJkCTVo0ICUSiURaS73b7/9ltq1a0fp6emUl5dHwcHBNGTIEI15GBISQrm5
+uXT48GEyMjKioKAgun//PqWnp5OdnR2dOHGCiIj27dtHjRo1osTERFKpVDRnzhxq3769UCeRSER9
++vShJ0+e0O3bt6lu3bp06NAhIiq9bRTNp6Lkf+XKFbK3t6d9+/YRUelth0iz2enBgwdkZWVFW7Zs
+IZVKRdu3b6c6deoIyc7Pz48aNWpE169fp5ycHJLJZBQWFkZERD/99BP16dOHcnJySK1W04ULF+jp
+06flLqtqlxBcXFzIzMyMrKysyMXFhUJDQyknJ4cWLFhQamfWvXt3+u9//0tEBSvtrFmzNPqPGjVK
+o023YcOGwkZARPTrr7+Sq6srERWsqEZGRsIZA1HBytitWzehOyIigszMzEitVhMR0dOnT0kkEgkb
+eElBQUG0YsUKYfwmJiYaC93Ozk7Y8ZiYmNCVK1dKjaOiehd38uRJcnBw0PhuyJAhwtHIrFmzaNiw
+YWXGSkQaO0UiojVr1mhs7MU3vpJHMCV3Wjt27BB2aEXGjx9Ps2fPJqKChDBy5EihX2xsLDk7O2uU
+nzdvHo0ePVqIvWvXrkK/oh1HWbGVpXhCqGhaJS1fvpzee+89IiI6evQoNWnShGJjY0u1+ZZc30o6
+duxYmcMqlUqqVauWkLiJiD7//PMXniGU3EkXt3fvXvLx8RG6XV1dNc5Gq7JOFU2r6GykZD0rcw2h
+ZHkTE5NS21B8fLxQvmXLlrR//34iKlju7dq1E/qp1WqqV68enTp1Sqhb0XJ3d3fXWAfu3LlDtWrV
+IpVKJczDO3fuCP1tbGw0zlr79+8vbK89evTQmL8qlYokEgndvn2biAoSwunTp4X+AwcOpAULFhBR
+5a4hTJ48mT7++GMiKnv5Fh/Hpk2byNfXV2P4du3a0caNG4moYPnMnTtX6Pfjjz9Sjx49iKig1aJ9
++/Zl7lvKYqjvJquSRCIR9u/fj3fffVfj+5SUFOzatQsHDhwQvlMqlRrlKrqL5M6dO3BxcRG6nZ2d
+cefOHaG7bt26MDIy0hjGzs5O+GxiYgJbW1uIRCKhGwAyMzNhYWGB6OhozJ49G9evX4darUZ2djaa
+NWsmDG9jY6PRfi+RSJCZmYmMjAw8f/4cDRs2LBVzZepdvH4l54GLiwvS09NfOF/KG97Z2blSw5Ul
+JSUFcXFxqFOnjvCdUqnEiBEjABQsZ6lUqlH+zp07GuVVKhU6deokdNvb2wufJRIJnj9/DrVaXeVr
+IhVNKykpCVOnTsX58+eRnZ0NpVKJVq1aAQDeffddTJw4EaGhoUhJSUG/fv2wZMkSmJubVzhdf3//
+MofNysqCUql86XmvUCgwefJknDp1Cs+ePYNarYa1tbVGmeLjrso6VaRonX8VbGxsSm1DxZetiYkJ
+MjMzhW5HR0eNOBwdHTW22yLJycl47733NNYHQ0NDKBQKobvkdMqbbkpKCiZPnoxPPvlEYxrp6enC
+vHRwcBC+l0gkyMrKKrfOcXFxCAsLw9WrV5GXl4fc3FwMHDiw3PLF3blzp9T64OLiojEPisdSvB7D
+hw9HamoqBg8ejMePH2PYsGGYO3cuDA3L3vVXu4vK5XF2dsbw4cPx6NEj4e/Zs2f47LPPhDIVrbT1
+69dHcnKy0H379m3Ur1+/3OGrshHk5uaif//++Oyzz/DPP//g0aNHCAgIqNSFIltbWxgbG+PGjRul
++lWm3sXrl5qaqjHNlJQUjQ3qRerVq4fbt28L3cU/V5WzszP8/PxKxf3DDz8IZYrPX2dnZzRo0ECj
+/NOnTxEZGVmqbFmqsqycnJxeOK2QkBB4eHjgxo0bePLkCebOnatx0XvSpEk4d+4cEhISkJSUhMWL
+F1c6hrKGtbOzg6GhYbnz3tTUFACQnZ0tfHfv3j3h8+effw4DAwP8+eefePLkCTZv3lzqIn3JeV3Z
+dao8ReOr7Hz/NwklNTVV+KxWq5GWlqax3RZxdnbGoUOHNOqVnZ2NevXqVXmazs7OWLNmjca4srKy
+hFusX6Ssun7wwQcICgpCWloaHj9+jAkTJgjLqKJ5I5VKkZKSovFdSkqKxgFVeQwNDTFz5kxcvXoV
+Z86cQWRkJDZt2lRu+RqTEIYNG4YDBw7g8OHDUKlUeP78OeRyucbRb8mdb8nuIUOGYM6cOcjIyEBG
+Rga+/vprDB8+vNxpVmZnXiQvLw95eXmwtbWFWCxGdHQ0Dh8+XKlhxWIxxowZg6lTp+Lu3btQqVSI
+iYlBXl5epepdpG3btpBIJFi0aBHy8/Mhl8sRGRmJwYMHVyqOgQMHYv78+Xj8+DHS0tLw/fffV7r+
+JfXu3RtJSUnYsmUL8vPzkZ+fj7NnzyIxMRFA6Xnbpk0bmJubY9GiRcjJyYFKpcKff/6Jc+fOlVm+
+JHt7e9y8ebNSsVU0rczMTJibm0MikSAxMRGrVq0SNtpz584hLi4O+fn5kEgkMDY2hoGBgRDDrVu3
+yp1uecOKxWL069cP4eHhyMnJQUJCAjZt2iRMs27dupBKpdi8eTNUKhXWr1+vUdfMzEyYmprCwsIC
+6enpQoIqT1XWqSLF5z8VNDULdX7w4AGePn36wmGrsi2VdP78eezduxdKpRLffvstjI2Ny9wxT5gw
+AZ9//rmQTO/fv4+IiIgqTasozgkTJmDevHlISEgAADx58gS7du164XDF50laWhry8/OF/pmZmahT
+pw6MjIwQHx+Pbdu2aSxfsVhc7vrbs2dPJCUlYfv27VAqldi5cycSExPRu3fvUnGXdPz4cfzxxx9Q
+qVQwNzdHrVq1hPW1LDUmITg6OmL//v2YN28e7Ozs4OzsjKVLl2rMiLKO8It/9+WXX6JVq1Zo1qwZ
+mjVrhlatWuHLL7+s9PDllQEAc3NzfPfddxg4cCCsra2xfft29O3b94XDFrdkyRJ4eXmhdevWsLGx
+wYwZM6BWq8utd1m3adaqVQsHDhxAdHQ06tati4kTJ2Lz5s1o0qRJufUpbtasWXBxcUGDBg3Qo0cP
+jBgxotzyFc0bMzMzHD58GDt27IBUKkW9evUwY8YM5OXllTm8WCxGZGQkLl26BDc3N9StWxfjx48X
+djQVTW/GjBmYM2cO6tSpg2XLlpUbMwAYGBi8cFpLlizBtm3bYGFhgfHjx2sk1KdPn2L8+PGwtraG
+q6srbG1thYcex44di4SEBNSpUwf9+vUrNf0XDbty5UpkZmbCwcEBY8aMwejRozXW7bVr12Lx4sWw
+tbVFQkICOnToIPSbNWsWLly4AEtLS/Tp0wf9+/d/4XKuyjpV1rwuvizefvttDBkyBG5ubrC2ttY4
+cymrfMlxldVdsl/fvn2xc+dOWFtbY+vWrdizZ0+ZO7XJkycjMDAQ3bp1g4WFBdq1a4f4+PhKTadk
+maCgIEyfPh2DBw+GpaUlvLy88Ouvv76wDkXfde7cGZ6ennBwcBCanH/88UfMnDkTFhYW+OabbzQe
+DJVIJPjiiy/QoUMHWFtbIy4uTmN8NjY2iIyMxNKlS2Fra4slS5YgMjJSo1mwvOWjUCgwYMAAWFpa
+wsPDAzKZ7IUHwSL6N6m7ElQqFVq1agVHR0eNNkug4OnRvn37ws3NDQDQv39/jR00Y2+qjRs34uef
+f8bvv/+u71D0avbs2bhx4wY2b96s71DeCFq/qLxixQp4eHjg2bNnZfb38/Or8mkdY+zNoOXjVVaC
+VpuM0tLSEBUVhXHjxpW7YHmBM1ZaRc17bwqeD7ql1SajAQMG4PPPP8fTp0+xZMmSUk1GJ06cQL9+
+/eDo6AipVIolS5bwI/KMMaYnWjtDiIyMhJ2dHXx8fMo9C2jRogVSU1Nx+fJlTJo0CUFBQdoKhzHG
+WEUq9fjaS5gxYwY5OjqSq6srOTg4kEQieeFrE4gKnjp88OBBqe+9vb0JAP/xH//xH/9V4c/b27tK
++22dvLpCLpcLL6gr7t69e8Ij7HFxceTi4lLm8EC1e8PGK1XylRuvm9e5fq9z3Yi4fjVdVfedOnt1
+RdGFodWrVwMAgoODsXv3bqxatQqGhoaQSCTYsWOHrsJhjDFWgk4Sgp+fH/z8/AAUJIIioaGhCA0N
+1UUIjDHGKlBjnlR+nclkMn2HoFWvc/1e57oBXL83jdafVH4V+NewGGOs6qq67+QzBMYYYwA4ITDG
+GCvECYExxhgATgiMMcYKcUJgjDEGgBMCY4yxQpwQGGOMAeCEwBhjrBAnBMYYYwA4ITDGGCvECYEx
+xhgATgiMMcYKcUJgjDEGgBMCY4yxQlpPCCqVCj4+PujTp0+Z/T/66CM0btwY3t7euHjxorbDYYwx
+Vg6tJ4QVK1bAw8ND+AnN4qKionDjxg1cv34da9asQUhIiLbDYYwxVg6tJoS0tDRERUVh3LhxZf5I
+Q0REBEaOHAkA8PX1xePHj6FQKLQZEmOMsXJoNSF8/PHHWLx4McTisieTnp4OJycnodvR0RFpaWna
+DIkxxlg5DLU14sjISNjZ2cHHxwdyubzcciXPHMpqWgKA8PBw4bNMJtP5b6GGhS3EvXs5Op3mv+Xg
+YIIFC6brOwzGmI7I5fIX7m8rorWEcObMGURERCAqKgrPnz/H06dPMWLECGzatEkoI5VKkZqaKnSn
+paVBKpWWOb7iCUEf7t3LgaurfmOoquTkcH2HwBjToZIHy7Nnz67S8FprMpo3bx5SU1Px999/Y8eO
+HXj33Xc1kgEABAYGCt/FxsbCysoK9vb22gqJMcbYC2jtDKGkoqag1atXAwCCg4MREBCAqKgoNGrU
+CKamptiwYYOuwmGMMVaCThKCn58f/Pz8ABQkguJWrlypixAYY4xVgJ9UZowxBoATAmOMsUKcEBhj
+jAHghMAYY6wQJwTGGGMAOCEwxhgrxAmBMcYYAE4IjDHGCnFCYIwxBoATAmOMsUKcEBhjjAHghMAY
+Y6wQJwTGGGMAOCEwxhgrxAmBMcYYAE4IjDHGCmk1ITx//hy+vr5o3rw5PDw8MGPGjFJl5HI5LC0t
+4ePjAx8fH8yZM0ebITHGGCuHVn8xzdjYGMePH4dEIoFSqUTHjh1x6tQpdOzYUaOcn58fIiIitBkK
+Y4yxCmi9yUgikQAA8vLyoFKpYG1tXaoMEWk7DMYYYxXQekJQq9Vo3rw57O3t4e/vDw8PD43+IpEI
+Z86cgbe3NwICApCQkKDtkBhjjJVBq01GACAWi3Hp0iU8efIE3bt3h1wuh0wmE/q3aNECqampkEgk
+iI6ORlBQEJKSkkqNJzw8XPgsk8k0xsEYY6zgmqxcLn/p4UWkw/aab775BiYmJvj000/LLdOgQQOc
+P39eo2lJJBLpvVlp1KhwuLqG6zWGqkpODsfGjeH6DoMxpidV3XdqtckoIyMDjx8/BgDk5OTgt99+
+g4+Pj0YZhUIhBBwfHw8iKvM6A2OMMe3SapPR3bt3MXLkSKjVaqjVagwfPhydO3fG6tWrAQDBwcHY
+vXs3Vq1aBUNDQ0gkEuzYsUObITHGGCuHTpuMXhY3Gb0cbjJi7M1WrZqMGGOM1RycEBhjjAHghMAY
+Y6wQJwTGGGMAOCEwxhgrxAmBMcYYAE4IjDHGCnFCYIwxBoATAmOMsUKcEBhjjAHghMAYY6wQJwTG
+GGMAOCEwxhgrxAmBMcYYAE4IjDHGCnFCYIwxBkCLCeH58+fw9fVF8+bN4eHhgRkzZpRZ7qOPPkLj
+xo3h7e2NixcvaiscxhhjFdDaT2gaGxvj+PHjkEgkUCqV6NixI06dOoWOHTsKZaKionDjxg1cv34d
+cXFxCAkJQWxsrLZCYowx9gJabTKSSCQAgLy8PKhUKlhbW2v0j4iIwMiRIwEAvr6+ePz4MRQKhTZD
+YowxVg6tJgS1Wo3mzZvD3t4e/v7+8PDw0Oifnp4OJycnodvR0RFpaWnaDIkxxlg5tNZkBABisRiX
+Ll3CkydP0L17d8jlcshkMo0yJX8AWiQSlTmu8PBw4bNMJis1HsYYe9PJ5XLI5fKXHl6rCaGIpaUl
+evXqhXPnzmnsyKVSKVJTU4XutLQ0SKXSMsdRPCEwxhgrreTB8uzZs6s0vNaajDIyMvD48WMAQE5O
+Dn777Tf4+PholAkMDMSmTZsAALGxsbCysoK9vb22QmKMMfYCWjtDuHv3LkaOHAm1Wg21Wo3hw4ej
+c+fOWL16NQAgODgYAQEBiIqKQqNGjWBqaooNGzZoKxzGGGMV0FpC8PLywoULF0p9HxwcrNG9cuVK
+bYXAGGOsCvhJZcYYYwA4ITDGGCvECYExxhgATgiMMcYKcUJgjDEGgBMCY4yxQpwQGGOMAahEQsjM
+zIRKpQIAXLt2DREREcjPz9d6YIwxxnSrwoTQqVMn5ObmIj09Hd27d8fmzZsxatQoHYTGGGNMlypM
+CEQEiUSCPXv24MMPP8SuXbvw559/6iI2xhhjOlSpawgxMTHYunUrevXqBaDgdw4YY4y9XipMCN9+
++y3mz5+P9957D56enrh58yb8/f11ERtjjDEdqvDldn5+fvDz8xO6GzZsiO+++06rQTHGGNO9ChPC
+2bNnMW/ePCQnJ0OpVAIo+FWzK1euaD04xhhjulNhQhg6dCiWLFmCpk2bQizmxxYYY+x1VeEevm7d
+uggMDISbmxtcXV2Fv8pITU2Fv78/PD090bRp0zKbmuRyOSwtLeHj4wMfHx/MmTOnypVgjDH271V4
+hjBr1iyMHTsWXbp0gZGREYCCJqN+/fpVOPJatWph+fLlaN68OTIzM9GyZUt07doV7u7uGuX8/PwQ
+ERHxklVgjDH2KlSYEP773//i2rVrUCqVGk1GlUkIDg4OcHBwAACYmZnB3d0dd+7cKZUQiKiqcTPG
+GHvFKkwI586dQ2JiIkQi0b+aUHJyMi5evAhfX1+N70UiEc6cOQNvb29IpVIsWbIEHh4e/2pajDHG
+qq7Cawjt27dHQkLCv5pIZmYm3n//faxYsQJmZmYa/Vq0aIHU1FRcvnwZkyZNQlBQ0L+aFmOMsZdT
+4RlCTEwMmjdvjgYNGqB27doAqnbbaX5+Pvr3749hw4aVubM3NzcXPvfs2RMffvghHj58CGtra41y
+4eHhwmeZTAaZTFap6TPG2JtCLpdDLpe/9PAVJoRDhw699MiJCGPHjoWHhwemTJlSZhmFQgE7OzuI
+RCLEx8eDiEolA0AzITDGGCut5MHy7NmzqzR8hQmhsreYluX06dPYsmULmjVrBh8fHwDAvHnzcPv2
+bQBAcHAwdu/ejVWrVsHQ0BASiQQ7dux46ekxxhh7eRUmhH+jY8eOFb4ILzQ0FKGhodoMgzHGWCXw
+o8eMMcYAcEJgjDFWqMKE8Msvv6Bx48awsLCAubk5zM3NYWFhoYvYGGOM6VCF1xA+++wzREZGlnq6
+mDHG2OulwjMEBwcHTgaMMfYGqPAMoVWrVhg0aBCCgoKq/HI7xhhjNUeFCeHJkycwMTHB4cOHNb7n
+hMAYY6+XChPCxo0bdRAGY4wxfSs3ISxcuBDTp0/HpEmTSvUTiUT8u8qMMfaaKTchFL2CumXLlhqv
+viaif/0qbMYYY9VPuQmhT58+AIBRo0bpKhbGGGN6xE8qM8YYA8AJgTHGWCFOCIwxxgBUIiFcu3YN
+nTt3hqenJwDgypUrmDNnjtYDY4wxplsVJoT//Oc/mDdvnvCUspeXF7Zv3671wBhjjOlWhQkhOzsb
+vr6+QrdIJEKtWrUqNfLU1FT4+/vD09MTTZs2LffZhY8++giNGzeGt7c3Ll68WMnQGWOMvUoVPqlc
+t25d3LhxQ+jevXs36tWrV6mR16pVC8uXL0fz5s2RmZmJli1bomvXrhovy4uKisKNGzdw/fp1xMXF
+ISQkBLGxsS9RFcYYY/9GhQlh5cqVGD9+PBITE1G/fn00aNAAW7durdTIHRwc4ODgAAAwMzODu7s7
+7ty5o5EQIiIiMHLkSACAr68vHj9+DIVCAXt7+5epD2OMsZdUYUJo2LAhjh49iqysLKjVapibm7/U
+hJKTk3Hx4kWN5icASE9Ph5OTk9Dt6OiItLQ0TgiMMaZjFSaER48eYdOmTUhOToZSqQRQ9XcZZWZm
+4v3338eKFStgZmZWqj8RaXTzqzEYY0z3KkwIAQEBaNeuHZo1awaxWFzldxnl5+ejf//+GDZsGIKC
+gkr1l0qlSE1NFbrT0tIglUpLlQsPDxc+y2QyyGSySsfAGGNvArlcDrlc/tLDV5gQcnNzsWzZspca
+ORFh7Nix8PDwwJQpU8osExgYiJUrV2Lw4MGIjY2FlZVVmc1FxRMCY4yx0koeLM+ePbtKw1eYED74
+4AOsWbNWHLvXAAAgAElEQVQGffr0Qe3atYXvra2tKxz56dOnsWXLFjRr1gw+Pj4AgHnz5uH27dsA
+gODgYAQEBCAqKgqNGjWCqakpNmzYUKUKMMYYezUqTAjGxsaYNm0a5s6dC7G44LEFkUiEW7duVTjy
+jh07Qq1WV1hu5cqVlQiVMcaYNlWYEJYuXYqbN2/C1tZWF/EwxhjTkwqfVG7cuDFMTEx0EQtjjDE9
+qvAMQSKRoHnz5vD39xeuIfBPaDLG2OunwoQQFBRU6nZRfk6AMcZePxUmBP4JTcYYezOUmxAGDBiA
+Xbt2wcvLq1Q/kUiEK1euaDUwxhhjulVuQlixYgUAIDIykl8twRhjb4By7zKqX78+AODHH3+Eq6ur
+xt+PP/6oswAZY4zpRoW3nR4+fLjUd1FRUVoJhjHGmP6U22S0atUq/Pjjj7h586bGdYRnz56hQ4cO
+OgmOMcaY7pSbED744AP07NkTYWFhWLhwoXAdwdzcHDY2NjoLkDHGmG6UmxAsLS1haWmJHTt26DIe
+xhhjelLhNQTGGGNvBk4IjDHGAHBCYIwxVogTAmOMMQBaTghjxoyBvb19ma+/AAp+/9PS0hI+Pj7w
+8fHBnDlztBkOY4yxF6jw5Xb/xujRozFp0iSMGDGi3DJ+fn6IiIjQZhiMMcYqQatnCO+88w7q1Knz
+wjIl35PEGGNMP/R6DUEkEuHMmTPw9vZGQEAAEhIS9BkOY4y90bTaZFSRFi1aIDU1FRKJBNHR0QgK
+CkJSUlKZZcPDw4XPMpkMMplMN0EyxlgNIZfLIZfLX3p4EWm5zSY5ORl9+vTBH3/8UWHZBg0a4Pz5
+87C2ttb4XiQS6b1padSocLi6hus1hqpKTg7Hxo3h+g6DMaYnVd136rXJSKFQCMHGx8eDiEolA8YY
+Y7qh1SajIUOG4MSJE8jIyICTkxNmz56N/Px8AEBwcDB2796NVatWwdDQEBKJhN+bxBhjeqTVhLB9
++/YX9g8NDUVoaKg2Q2CMMVZJ/KQyY4wxAJwQGGOMFeKEwBhjDAAnBMYYY4U4ITDGGAPACYExxlgh
+TgiMMcYAcEJgjDFWiBMCY4wxAJwQGGOMFeKEwBhjDAAnBMYYY4U4ITDGGAPACYExxlghTgiMMcYA
+cEJgjDFWSKsJYcyYMbC3t4eXl1e5ZT766CM0btwY3t7euHjxojbDYYwx9gJaTQijR4/GoUOHyu0f
+FRWFGzdu4Pr161izZg1CQkK0GQ5jjLEX0GpCeOedd1CnTp1y+0dERGDkyJEAAF9fXzx+/BgKhUKb
+ITHGGCuHXq8hpKenw8nJSeh2dHREWlqaHiNijLE3l6G+AyAijW6RSFRmufDwcOGzTCaDTCbTYlSM
+MVbzyOVyyOXylx5erwlBKpUiNTVV6E5LS4NUKi2zbPGEwBhjrLSSB8uzZ8+u0vB6bTIKDAzEpk2b
+AACxsbGwsrKCvb29PkNijLE3llbPEIYMGYITJ04gIyMDTk5OmD17NvLz8wEAwcHBCAgIQFRUFBo1
+agRTU1Ns2LBBm+Ewxhh7Aa0mhO3bt1dYZuXKldoMgTHGWCXxk8qMMcYAcEJgjDFWiBMCY4wxAJwQ
+GGOMFeKEwBhjDAAnBMYYY4U4ITDGGAPACYExxlghTgiMMcYAcEJgjDFWiBMCY4wxAJwQGGOMFeKE
+wBhjDAAnBMYYY4U4ITDGGANQDX5TmTHGKhIWthD37uXoO4xKc3AwwYIF0/UdRpVpPSEcOnQIU6ZM
+gUqlwrhx4zB9uuZMksvl6Nu3L9zc3AAA/fv3x5dffqntsBhjNci9ezlwdQ3XdxiVlpwcru8QXopW
+E4JKpcLEiRNx5MgRSKVStG7dGoGBgXB3d9co5+fnh4iICG2GwhhjrAJavYYQHx+PRo0awdXVFbVq
+1cLgwYOxf//+UuWISJthMMYYqwStJoT09HQ4OTkJ3Y6OjkhPT9coIxKJcObMGXh7eyMgIAAJCQna
+DIkxxlg5tNpkJBKJKizTokULpKamQiKRIDo6GkFBQUhKSipVLjw8XPgsk8kgk8leYaSMMVbzyeVy
+yOXylx5eqwlBKpUiNTVV6E5NTYWjo6NGGXNzc+Fzz5498eGHH+Lhw4ewtrbWKFc8ITDGGCut5MHy
+7NmzqzS8VpuMWrVqhevXryM5ORl5eXnYuXMnAgMDNcooFArhGkJ8fDyIqFQyYIwxpn1aPUMwNDTE
+ypUr0b17d6hUKowdOxbu7u5YvXo1ACA4OBi7d+/GqlWrYGhoCIlEgh07dmgzJMYYY+XQ+nMIPXv2
+RM+ePTW+Cw4OFj6HhoYiNDRU22EwxhirAL+6gjHGGAB+dQVjr4Wa9moHoOa+3uF1xgmBsddATXu1
+A1BzX+/wOuMmI8YYYwA4ITDGGCvECYExxhgATgiMMcYK8UVl9kbgu3AYqxgnBPZG4LtwGKsYNxkx
+xhgDwGcIrBA3qTDGOCEwANykwhjjJiPGGGOFOCEwxhgDwAmBMcZYIa0mhEOHDuHtt99G48aNsXDh
+wjLLfPTRR2jcuDG8vb1x8eJFbYbDGGPsBbSWEFQqFSZOnIhDhw4hISEB27dvx19//aVRJioqCjdu
+3MD169exZs0ahISEaCucai05Wa7vELTqda7f61w3gOv3ptFaQoiPj0ejRo3g6uqKWrVqYfDgwdi/
+f79GmYiICIwcORIA4Ovri8ePH0OhUGgrpGrrdV8pX+f6vc51A7h+bxqtJYT09HQ4OTkJ3Y6OjkhP
+T6+wTFpamrZCYowx9gJaSwgikahS5YjopYZjjDH2ipGWxMTEUPfu3YXuefPm0YIFCzTKBAcH0/bt
+24Xut956i+7du1dqXN7e3gSA//iP//iP/6rw5+3tXaX9ttaeVG7VqhWuX7+O5ORk1K9fHzt37sT2
+7ds1ygQGBmLlypUYPHgwYmNjYWVlBXt7+1LjunTpkrbCZIwxVkhrCcHQ0BArV65E9+7doVKpMHbs
+WLi7u2P16tUAgODgYAQEBCAqKgqNGjWCqakpNmzYoK1wGGOMVUBEVKIRnzHG2BuJn1RmjDEGgBMC
+e4PdvXsX06dPx82bN/HgwQMAgFqt1nNU/6/kyfvrcjJPRK9NXYqUVaeaWEdOCK+5oh2cUqnUcyTV
+T7169QAAW7duRWhoKC5dugSxWFwtNmQiEm7BvnPnDh49evRa3ZItEonw66+/YtmyZdi2bZu+w3kl
+RCIRzp49i+joaPz9998QiUTV6gCjMjghvKYePnyI9PR0iMViHDp0CJ9//jmWL1+u77CqjaINdeHC
+hZgwYQJkMhl69uyJkydP6n1DVigU+OmnnwAAv/32G/r27Yt3330Xe/fuxdOnT/UW16tQlOguX76M
+SZMmQaFQIDo6GsHBwfoO7aUV1eno0aPo27cvfvnlF7Ru3RoXL16EWCyuUUmBfyDnNZSVlYWlS5fC
+1NQUXl5eCAsLw5QpU7Bw4ULcu3cPc+fOhaHhm7noi47+xWIx8vLyYGRkBDs7O0yYMAG1a9fGkCFD
+8Msvv6Bt27YaR+m6jO/8+fM4c+YMFAoF4uLisHnzZly5cgXr169HVlYWAgMDYWFhodO4XhWRSIQT
+J05gy5YtWLFiBXr27IkbN25g3rx5mDBhgpAIaxKRSISEhATs3r0bO3bsQKdOneDt7Y3OnTvj2LFj
+aN68OdRqNcTi6n/8bRAeHh6u7yDYq2VkZIQnT57g2rVruHjxIt5//32MHz8egwYNwrJly3D9+nXI
+ZLIasYJqQ1FzxYYNG5CQkABfX18AgI+PD6ysrPDZZ5+hR48esLGx0WlcRQlIKpXCxMQEFy9ehEKh
+wKeffgpPT0+YmJhg69atICK4ubnB2NhYp/G9rJKJ9fLly5g7dy5cXFzg5+cHS0tLNGvWDFFRUYiK
+ikLfvn31GG3VqFQqqFQqLFiwAGfOnMFbb70FLy8vtG3bFqampujXrx969+6N+vXr6zvUSuGE8Boh
+IuFIxN3dHZaWljh16hRu3ryJli1bwsnJCb169cLs2bNx48YNdOvW7bVql65I0Y4pJiYGoaGh6NGj
+B5YuXQqFQoF33nkHBgYGaNGiBXJzc5GSkoI2bdpApVLpJHEWnbmIRCLcuXMHPj4+MDQ0xNmzZ6FQ
+KNCuXTu4u7vDwMAAW7ZsQUBAAMzNzbUe179VvF4pKSkgIjRv3hwdO3bEV199BTc3N7i7u8PKygo+
+Pj5o2bJlmQ+nVifF65SdnQ0TExPIZDL8888/SElJgbW1NaRSKXx9fWFhYQFTU1M0bNhQz1FXUpWe
+a2bVmlqtJiKiAwcO0OjRo4mI6MiRIzR58mRaunQp/f3330REdO/ePTpz5oy+wtSrxMREGjlyJK1e
+vZqIiNLT06lDhw70+eefU15eHhEVzLP//Oc/Oo2raNlFRUWRm5sbXbt2jbKzs2nv3r304Ycf0rJl
+y4SyZb3epbpSqVREVLBOtm/fngIDA2ns2LF09epVOnHiBDVq1Ih2796tMUzRvKiuiuKLjo6mLl26
+0KhRo+ibb74hIqLp06fT1KlT6dSpUxr1qO51KsIJ4TXz66+/kqenJx08eFD47uDBgzR16lSaO3cu
+3bp1S/i+pqyk/0bJOkZFRVHv3r1pyJAhwry4c+cOeXt706effiqUCw0Npbt37+o01qtXr5Knpyed
+OHFC+C47O5v27dtHo0aNokWLFhHR/+9kq/Pyy8nJET6npKSQh4cHnT9/nv744w/atGkTBQQE0L17
+92jPnj3k6OhICoWiWteHiCg/P1/4HB8fTx4eHhQZGUnnzp2j5s2b06RJk0itVtOHH35IH3/8MT16
+9EiP0b6cN/PK4mvs3LlzCA8PR0BAAJ4/fw5jY2MEBARALBYjIiJC45bK1725qHhdb926BYlEgs6d
+O0MqlWLt2rXYt28f+vXrBxcXF+FWwSIrV67UebxKpRIdO3ZEp06doFQqoVarYWJigi5dukAkEsHN
+zQ0AhCas6rr8FAoFtm/fjrFjxwrNWk5OTmjRogWAgtt9z58/j8OHD2P48OFo27Yt7Ozs9Blyhf75
+5x/h9mQjIyNkZ2ejS5cu6NWrFwDgwoULaNOmDU6fPo2vv/4aCoUCVlZWeo666t7Mq4qvCSrjfvm7
+d+/il19+AQDhomNcXBxkMhnmz58v7FTeBCKRCCKRCNHR0ejVqxemTp2KVq1awdLSEoMGDUJycjK2
+bduGlJQU1KtXD+3bt9fZQ1NlTUcikeDQoUOIjIyEoaEhjIyM8Ouvv+K///0vAgMD0bRpU63H9SrU
+rl0bPXv2RGZmJs6dOwdnZ2colUp88cUXAAAbGxtYW1sjKSkJAFC3bl0A1ftBrnv37qFPnz548OAB
+UlNTYWFhgaNHjwoPNIpEIvj7++PJkyewsbGBh4eHniN+OZwQaqjiG09ycrLw86SffPIJ6tSpIzxz
+EB8fj1GjRuHy5cuwtLTUS6z6dPv2bcyaNQtr167Ftm3bMGjQIAQGBqJJkybo168f0tPTNe4TL0oi
+ulB0C+bixYtx7NgxNGzYEMuXL8eyZcuwcuVKREVFYfr06ZBKpTqJ59/Kz89HdnY2rKys4OTkhPnz
+52PdunW4evUqli5diuTkZAwcOBAHDhzAtm3b0LlzZwAQboGujmc8+fn5AIBmzZrB2toaq1atwoIF
+C9C0aVMMHToUrVu3hlwuR2RkJKKiomrkWUFx/HK7GooK75iJiIhAeHg4pFIppFIpJk+ejL///hur
+Vq1CdnY2MjIyMGfOHPTp00ffIesElbjFMTMzEyEhIZg7dy6cnZ0BAJMmTYKBgQG+/fZbKBQKvd3V
+Eh0djalTp2LKlClYsmQJxo0bh169eiEjIwNLly6Fg4MD+vbti969e+vlmYiqyMvLg1wuh62tLZKS
+kpCSkoJhw4ZhyZIlMDIyQr9+/eDm5oY5c+bAysoKbdq0Qa9evap1vXJzc/H777/D0dERmZmZSEpK
+gr29PX777TcQEebPn4+1a9cKd4IFBwfXiGX1Qjq/asFemd9//51atmxJCoWC1q5dS2ZmZvTxxx9T
+cnIyqdVqunXrFqWkpBBRwQXI6n7R7lVQKpVERPTs2TPKz8+n3NxcCgoKou+//14os2PHDpo+fbq+
+QiS1Wk2pqakUFBRESUlJdPToUWrYsCENGTKEZs6cSc+ePStVviYsu927d1O7du2oQYMGtG/fPiIi
+un//Pk2aNImmTZtGV65c0Shf3ev19OlTOnDgAMlkMqpfvz4lJiYSEdHp06dp2rRpFBYWRg8fPiSi
+gov/RNW/ThXh5xBqsKysLHTp0gW3bt3CsmXLsH//fqxZswZHjhxBmzZt0KhRI41mohp71FIJt2/f
+Rl5eHszMzLBv3z5MmDABf/zxBwwMDDBq1CiEhYUhMTER586dw6pVqzBmzBg0adJEZ/FRsXvXRSIR
+LCws0KFDB2RlZWHSpEmIi4uDvb09PvnkExgZGcHb2xu1a9fWGKY6osJrISKRCM7Ozjhz5gxq166N
+bt26wczMDLa2tmjbti0OHjyIP//8E+3atROubVXXehUtq9q1ayM7Oxvz58+Hr68v2rdvj/r168PJ
+yQkWFha4fPky5HI5/Pz8YGRkBLFYXG3rVFmcEGqI4juUR48eQaVSQSqVwtHREatXr0aXLl0QEBCA
+3NxcxMTE4P3334e1tbUwfE1eSStjzpw5mDlzJlq3bo1Vq1Zh9OjRcHJywg8//ABnZ2d8/vnnePDg
+AZ4+fYrx48eje/fuOj+1F4lEuHLlCs6dOwcDAwPUr18f9+7dw5EjRzB+/Hjk5OTgjz/+wMSJE+Hk
+5KSzuP4tsViM3377DZs3b8aiRYsgFouxb98+GBsbw93dHUqlEl5eXvDx8akx9RKJRPjtt99gbW2N
+UaNGoW7duti9ezcMDQ3RuHFjGBkZwcjICD179oS9vf1r89Q/33Zag4hEIuzfvx8///wznjx5gg8+
++ACdO3dGq1atsG7dOuTn52Pnzp1YtmwZGjVqpO9wdaLoyezFixdDrVZj0KBBGDZsGAYPHoycnBzY
+2Nhg0aJFyMjIwLhx44ThSMeXzkQiEfbt24eZM2fCzc0NJiYmaNKkCYKDg+Hg4IAuXbogNTUV3377
+LZo2bVpj2qGL7uL65JNPsHz5cpiammL06NHIyclBZGQkzp49i3Xr1uH48eM15i6pojpNnjwZ33//
+Pbp37w4LCws8fPgQe/fuRWxsLC5duoQVK1agQYMG+g731dJfaxWrqsTERPL09KRLly7Rnj176Isv
+vqCvv/6a4uPj6ccff6SAgAA6cOAAEdX8tszKyMzMpD///JOIiOLi4ujp06cUFhZG7u7uwtO8ubm5
+FBUVRTKZjJKTk4WHunSh+DJ49uwZDR48mC5cuEBERCdOnKCwsDDatGkT3b9/n7Zt2yY8PV6Tll1e
+Xh5NnjyZoqOjiYjo+fPnQr/o6Ghavnw5HTp0SF/hvZTMzEzq1q0bHT16lIj+/wHAv//+m/73v/9R
+7969hWskrxtOCNVc0cp4584dio6Opp49ewr94uLiqHPnzhQXF0dE//90aE3aofwbt2/fpjFjxlBo
+aChJpVLhouWECROoffv2pFAoiKggKdy/f1+nsRWf/6dPn6bo6Ghq164dbdu2jYgKnnpdsmQJTZgw
+odRw1XnZlRXb8OHDS12kv3z5ssbTytW5XkVxFf1//PgxyWQy4SJy0QXjjIwMIipYn4rKV9c6vazX
+o+HrNaVWqyESiXDmzBkMHToULi4uMDY2xq5duwAAbdq0gaenJ65evQoAqFWrljBsTWhu+DfUajWc
+nJzQrVs3rF+/Hh988AG8vLwAAKtWrYK3tze6du2Kf/75B0ZGRrC1tQWg+6aia9euYdq0aWjWrBk+
+/fRTHDlyBCdOnIChoSFatmyJhw8f4unTp8KzEDXlomRKSgoSEhIAAGPGjEF+fj72798PADh79iwm
+TJiA69evC+VrQr0UCgUAwNLSEh06dEBYWBgePnwIExMTnDx5Er1798Y///yj8dxEda9TVfFF5WpM
+JBLhyJEjWLduHUJCQtCuXTtkZGTg6tWrOHbsGAwMDLB48WKEhITA0dGx2r/S4FUSiUQ4duwYYmNj
+8cUXX2DPnj3Izc2Fq6srTExM0KtXL9y8eRN2dnbC8wdFw+kqvsuXL2PgwIHo3bs3AgMDUbt2bWRl
+ZeHrr79GUlISFi5ciLCwMHh5edWIZUaF1zUiIyMxcuRI7Nq1C7dv30afPn1w9+5d7NixAzt27MDP
+P/+M2bNno1OnTvoOuUJFdYqKisJ//vMf/Prrr6hTpw78/f3xzz//YOrUqcjJycHcuXMRHh6OFi1a
+1Ihl9dL0fIbCSih5Crpx40YSiUS0Zs0aIiJSKBTC2zjHjRuncc3gTXPq1Cnq1q0bERW8odTPz4+2
+bt1K27Zto/fff194e6mu5k1ZTQhDhw6lFi1aCM8W5OXl0YULF2jv3r109uxZncb3Kvz111/Up08f
+SkxMpEePHlG7du3o66+/pszMTHrw4AHFxMQITS01pUklLi6O+vbtSzExMbRgwQIKCQmhrVu3UnZ2
+Nu3atYt++eUXOnnyJBHVnDq9LE4I1UzRypaeni60wW7fvp1q165Np0+f1ijzujwMU1kl65iRkUEj
+R46kc+fOEVHBm16HDx9O7777Lu3YsUNv8cXExNDOnTvp6tWrREQ0ZswY6tGjh7C8Sg5T3Zdd8bb1
+4OBgatmyJd28eZOICq5tderUiT766KNyh6tuil8z+OeffyggIID69u0r9P/pp58oODiYNm/erPGQ
+YE1YVv8WJ4RqpGhli4yMpDZt2lC3bt3om2++oYcPH9KuXbvIxsZGOFJ5kxTfCM+fP0/9+/env/76
+i5RKJa1bt478/PyE5Pno0SPh4p8uN96iaZ08eZKaNGlCAwcOpEGDBtG0adOIiGj06NEkk8nKTAo1
+waVLl+jZs2d0/vx5Gjp0KC1evJiSk5OJqCAp+Pr60l9//aXnKKum6KaDrVu3UpMmTWjdunVCv+++
++47GjBlD6enp+gpPLzghVDOxsbHUs2dPunDhAkVHR9PChQtpwoQJpFar6aeffiKJREKPHj3S6e2T
++lT8qCwpKYmysrJo4sSJ9Omnn1KfPn3o+PHjNGjQIOEOI33+KMmZM2eoe/fuwhlLYmIihYSE0A8/
+/EBERIGBgUIzUU1QNP9yc3NpypQp1KtXL3r27BnFxMTQxIkTadmyZcJvShTdeVMTKJVKun//PpmY
+mNDOnTuJiGjPnj3Uu3dvWr9+vVCu6LUvbxJOCNXIw4cPaeDAgdSiRQvhuz/++IOGDBlCR44cISIS
+jsreFEU7pYMHD5Kvry9du3aNiAreM7Nx40Z67733yMrKisaNG6e32Ips3bqVRCKRcKSZk5ND27dv
+p+Dg4BcOV91kZmZq/BgMUUET5qeffkoDBgygZ8+eUWxsLI0dO5YWLVpE2dnZwjukqmvdiq4nEf3/
+Dwzt27ePrK2tae/evUREtHfvXvL396e1a9fqJcbqgBOCHpXVJnnkyBFq1qwZzZ49W/hu4sSJNH/+
+fCL6/19tqq4bnjb89ddf9PbbbwvXUIp7/PgxXb16lWQymfDQly4UX3b3798XmoI2bNhAbm5uQgKP
+jo6mjh07UkZGhrDTrM6uXr1KISEhpFAo6OTJk7RixQqh3927d+njjz+mESNGUFZWFp0+fVp4MLA6
+u3r1Kn355ZdERJSQkECHDx8WlldUVBTVrl1beNBs9+7dFB8fr7dY9Y0Tgh4V7VCOHDlC8+bNo7Vr
+11JGRgbJ5XLq378/jRo1ik6cOEGenp7CU5NvgtTUVNq8ebPQffLkSRowYIDQXVZSHDFihE7nUdG0
+IyIiyM/Pj9555x1as2YNXbt2jXbu3EkSiYTGjRtHQUFBwhFodZednU1dunQRmk2OHTtG9vb2tHLl
+SiIqaGo5duwYNW3alAYNGlQjmi2fPHlCfn5+dPbsWXr69ClNmTKFxowZQ0ePHqWsrCwiIlq+fDmJ
+RCKKiooShnuTDriK4wfT9IQK73/+/fffMWHCBJiammLt2rX47rvvUKtWLUyaNAmxsbGYMWMG1q1b
+h3fffRdKpVLfYesEEWHz5s24fPkyAMDNzQ0PHjzA4cOHART8oMqxY8ewcOFCEBFu3ryJmzdv6vSH
+ZEQiEc6fP4+lS5dixYoVmDRpElJSUrBjxw706tULK1aswO+//46AgAAEBQVBpVJV618EAwATExMM
+HToU69evh1Qqhb+/Pw4ePIjly5fjhx9+gIGBAWrXro3OnTtj+vTpNeKFbiqVCtnZ2di+fTs++ugj
+fPzxx3B2dsYvv/yCmJgYAED79u3Rr18/jfq81s8avIh+89GbLTExkYYMGSI8Y5Cenk5TpkyhsLAw
+IiKSy+U0YsQIWrBggT7D1KmiZpUVK1bQrl27iKjgesGSJUto2rRptGDBApLL5eTp6UmHDx8Whnvw
+4IFO47x79y6NHj2a2rdvL3x3+vRp6tKlC8XGxhJRwTUFqVRKJ06c0GlsL6P4tRpjY2Py8/OjzMxM
+Iir4QflmzZrR2LFjyc7OTnhvUXU/ii6Kb+XKlWRoaCi8JiQvL49mzpxJY8eOpbFjx1Ljxo1r5DMh
+2sBPKusQFZ4VFL2S4uTJkzhx4gTS0tLQvn17SKVSeHt7Y+bMmQgMDMTbb78NS0tLHDt2DB07doRE
+ItF3FbSu6Cjt7t27+Pbbb+Hv7w97e3s4ODhAIpHg4MGDuH79OkJCQhAQEAClUgmxWAwTExOtxkXF
+Xj9e1E1EiImJQXZ2Nnx9feHk5ISYmBioVCq0bt0aXl5eqF+/Pt5++22NV5FXR0X1srGxgUwmg7W1
+NVasWIGWLVvCy8sLPXr0wNtvv43hw4ejU6dONeJtrEXx3b17F127dsXy5ctRu3ZtdOjQAZ06dYJE
+IoGpqSmGDh2Kd955R2OYNxX/hKaOFN+hpKenC80bp0+fxpYtW9C4cWMMHjwYWVlZGDBgAA4ePAip
+VG+AZkAAAB1rSURBVIq8vDwolco3IhmUNGvWLGzYsAHx8fFwcHAQvn/+/DmMjY1L7aS1pfh0Tp06
+hZycHNSuXRudOnXC7t27cfDgQUgkEgwaNAjjxo3D2rVr4efnp9WYXpXiO3aVSgUDAwMABe8qWr9+
+Pa5du4Y5c+ZovE5dV/P9VTt37hy6du2Kb775BhMnTtToV1Pr9Mrp5bzkDVT8lLxVq1Y0Y8YM+uKL
+L4QLdUFBQdSqVSvq0qULHTx4UGOYN4larda4WPnZZ5/RW2+9RRcuXNBoFtLHvDlw4AB5enrS6tWr
+qWnTpsLF1z179pC3tzd1796djh07RkRU6rbN6uzy5cvC5+J3Qt2+fZvCwsLovffeo+zs7Bq1Pqam
+ptKTJ0+EmIvqdeHCBTIwMNC4e4r9P24y0pGio8spU6Zg27ZtuHTpEvbs2YNLly4hNDQUbm5uuHfv
+Hlq1aoURI0a8MS+qK2o+K2r6Kapv0Q/fdO3aFUqlEvv370diYiIUCgWaNm2q8/mSkpKCTz75BP/7
+3/+gUCgQHx+PY8eOgYgwatQo2NraIjMzEwYGBmjVqlWNuOBKhWcHgwcPRlJSEjp37qwRt6WlJZo0
+aSI029WEdZGIoFAoMHXqVLRv3x516tSBWq2GgYEBVCoV6tevj169esHU1BQNGzbUd7jVDicEHVGp
+VEhISMC4ceOQnp6OtWvXYvXq1YiKisLx48cRHBwMQ0NDnD59GgqFAs2bNxdO319HDx8+xMOHD2Fp
+aYlDhw5h3bp1wm/uikQiiMViqFQqiMVitG3bFu7u7rCxscGmTZvQtWtXrV8zKEkkEqFbt27IyMhA
+WFgYTpw4AScnJ0ycOBHm5uYYOnQoHj16hKtXr6JNmzY6j68qihJB0Q6+WbNmOHv2LHx9fWFsbKyx
+47e0tISNjY2+Qq0ykUgEMzMzHD9+HAcPHkRQUJCwHRWtU1KpFA0bNqwR10F0jROCjojFYjg7Ows/
+6Tht2jR07NgRZ86cQXJyMtq0aYMOHTpALBbj3XffhYWFhb5D1pqsrCwsWrQICQkJePz4MT777DP0
+6tULy5cvR3p6Ovz9/SEWiyEWi4UzCFtbWzRo0AD9+/fX6fWUop2GsbExrK2tcf78eVhaWqJHjx74
++++/YWtri3feeQeNGzdGw4YN4efnBysrK53F9zKKbnd+/vw5xGIx6v9fe3ca1tSZ9gH8H0CpEZei
+FUG8BJFLKipQFFlUVERxgVarjLhQbBFFBYUyoi1DtVOnrSwK0xYVF1xaWVoVcUGEBKhQUKQjmyyj
+ogUFRcGwB5L7/YA5Bev0bWeQJPD8PhHIubjPSXLuPNv96OggIiICY8aMUepvzb/88guqq6sxbNgw
+WFtb49q1azAzM8OgQYO49xGbWvr7FL9dq4TohXF62WN1dXUQEUQiEfLz8yEUCpGXl4fw8HBMmDAB
+AODo6NhlALU3GjhwIMzNzfHkyROcOXMGW7Zswbp165CRkYGffvoJAQEB3JqLF7teeqIrpvPrx+Px
+ujyWSqXIzMzE3//+d3h6esLZ2Rlz5syBRCKBhoaGwiZyej4rSkYoFMLf3x9eXl5IS0uDi4sL9u7d
+i2fPnskxyj+n8/mIRCJ89NFH+Oyzz+Dn54e2tjaUlpbi/PnzAHrmfdMbsFlG3Yw6zVYoKCiApqYm
+dHR0ujwnNTUVoaGhaGpqgoeHB5ydnblje/O3FiLi+nMBICsrC2FhYZBIJPjiiy8wduxYPHr0CA4O
+Dpg9ezaCg4Pldj1ycnJw7Ngx/POf//zN6xITE4Pa2lro6enBwcFBKV43WYx5eXno168fdHR0uCnN
+n332GQwMDJCQkICrV69i3Lhx3BiOIpOd08OHDzF48GDweDzU19djy5YtmDhxIs6ePQuJRILY2FgY
+GhrKO1zl0IMD2H1C55IGM2bM4PY7lpHNoGloaOBqrfeFOutEv16bhIQEWrt2LRF1lO3YsmULhYSE
+0N27d4mIqKqqittwvid1nt109erV3xSle1ktImV47WTxXb58mbS1tcnV1ZV0dXW5Uh9VVVVUVFRE
+Tk5O5OjoKM9Q/5DO1zw+Pp5MTU1p0qRJ9Ne//pXbp+H+/ft0+PBhmj17NreAUdFfJ0XAEsIrUFZW
+RmZmZi8tdfyyG0hfeqNevnyZjI2Nuam1RB1TcX19fWn37t1cOWWinrsunfcoKCsro6ysLCooKKA5
+c+bQ06dPuzxXmaaTdnbz5k3auHEjt2r6+PHjpK+v/5vEu2rVKqqrq5NHiH9aYWEh2dnZUVFREf3y
+yy/cKn+RSMQ957vvvqNFixZRS0uLHCNVHordJlQS5eXl8PLy4h7X1NRg+PDhmDJlCgBw/eGNjY0v
+3Zhb0bsbulNOTg527tyJhQsXoqWlBQCwcOFC2Nvbo6Ki4jf9969aXV0dtm/fjtraWohEIgQFBcHD
+wwPBwcEQCoX44osvEBsbi9TUVEilUm6DdUUnu44SiQRisRg7d+5ESkoKGhoa0N7ejjVr1mDjxo0I
+CwuDVCoFACQlJSErKwttbW3yDP0/qqqqwu7duyGVSlFTU4PQ0FA8evQIQ4cOha6uLvz8/CAUCnHq
+1CnumEGDBqG+vp47R+b3sVlG3WDo0KHQ0tJCS0sLXn/9dWhqaiIpKQmvv/46dHV10a9fP6Snp+Pk
+yZOwsrKCqqpqn0gC9JK+9djYWNy4cQPLli3jbq7Z2dmwtLTErFmzoK2t3aMxtrS0YMqUKWhubsaD
+Bw+wbt06eHp6YubMmcjNzYWhoSF+/PFHCAQCjB07FqNHj+7R+P4XPB4PjY2N4PP5mD9/PvLy8lBV
+VQVjY2MMGTIET548QWlpKZYsWQIej4fm5masW7fuN2NeiuLx48cwMjKCWCzGsGHDoKmpibKyMtTV
+1WHMmDEYNWoUWltbUVdXBxsbG0gkElRWVsLFxaXH31dKS84tFKUmlUq7dCFYW1vTjBkziKijOJu3
+tzdt376dzp49S+PGjetSjK2369w1dvfuXSoqKuJ+9vT0pNDQUCLq2ODcyMiIKwjXk/HJ3L9/n06c
+OEEzZ84koVBIRB3jBatXr6aTJ0/+x+MUXUJCAllaWtInn3xCV69epfr6elq2bBnNnz+fAgICaOrU
+qXT69Gl5h/n/6nzNxWIxffDBB7R27Vpqa2uj5ORk8vT0pGXLllFUVBSNGzeOEhMT5RitcmNdRv8l
+et4kV1NTQ3FxMYCOukQqKipwdnaGt7c3HB0d0dLSgqSkJISFhcHe3l7hSyB3Jx6Ph3PnzmHp0qXY
+tm0bNmzYgObmZixevBgCgQB2dnZYt24d9uzZg2nTpsklxqSkJHh6esLMzAwuLi4IDg5GWloaVFVV
+MXfuXFRUVMglrv8GdZpaWllZiePHj2Pz5s0YPHgwjh49ioyMDBw/fhxaWlrIzc1FaGgolixZwh2r
+iDrHVVhYCBUVFfj4+IDP58PHxwe2trZYuXIlGhsbkZSUhJCQEMyfPx8SiUSOUSsxeWYjZda5NpGB
+gQG3jy4RkY2NDS1dupR7LBvQUoYZKd3pxx9/JHNzc6qurqbIyEjS0NAgHx8fKi8vJ6lUSnfu3OH2
+re3JayP7PyUlJbRw4UJuJlh1dTVFRESQk5MTXb16lXJycrjaRMpAdl45OTl06NAh8vf3J6KOUt1R
+UVHk7u5O8fHx1NTURM7OzrRlyxaqrq5W6PekLLZLly6Rvr4+5eXlUXt7OxUVFdGGDRtoy5YtJBaL
+KSUlhXx8fCg0NJQePXok56iVF0sI/4MbN27Q+PHj6eeffyaijv2OZTNWpk2bRnZ2dkRESrGz1KtQ
+VFREWVlZdPHiRbKwsKC8vDyysbGhBQsWcHsjy/TETamlpYVLzvfv36fAwECaMGECHT58mHvOo0eP
+aN++fTRv3jxuRy1FvmHKyGIUCAQ0evRocnNzIz6fT/n5+UTUcV4HDx4kV1dXam5upocPH9KqVauo
+qqpKnmH/ISUlJTRx4kRKT0/nfieVSqmoqIjee+898vT0JCKiEydOkL+/f4/vjdGbsIVpfwJR1xK5
+eXl5iI2Nxfjx41FZWYmYmBgYGhpix44dMDMzQ2ZmJqytreUZco/pfG1qa2vRr18/aGhoAAD8/Pxg
+aGiI9evXIyIiAlFRUfj222+7lFR+1drb25GRkYG7d+9CQ0MDhYWFWLJkCeLj41FXVwdHR0fMmjUL
+QMfgZVNTE8aMGdNj8XWH4uJi+Pj4ICAgADY2Nvj0008RFxeH6OhoGBsb49GjRxCLxdDV1QXQtdy1
+IqEXJiOUlpbi888/x9GjRyGRSCCRSNC/f3+0t7fj7t27aGpqgomJCQCgvr4egwYNklfoSo/NMvqT
+eDwekpKSUFZWBn19faSnp0MoFGLWrFnYvHkzysvLIRaL8dZbb2H06NF9qs46j8dDfHw8AgMDceTI
+EYjFYq6uz8mTJyESiRAdHY2goCCYmpr2aGwqKipoaGhAUFAQoqKisHHjRkyfPh06OjooKSlBSUkJ
+iAgGBgYYOHAgF/eLNydF0zk+gUCAhIQEEBHs7e1ha2uL2tpa+Pr6wt7eHvr6+lxpDSJSyJXInT8v
+t27dQmNjIzQ0NBAYGAgtLS1MnjwZqqqqSEpKQmxsLN555x2MHDmSK4Sorq4u5zNQbiwh/AmyG972
+7dsxe/ZsTJkyBba2tlixYgVMTExQVVWFkJAQuLi4QE9PjztGkW8o3YXH46GkpATr16/HV199hfHj
+x6O4uBi3bt2ChYUFhg4diosXL2Lr1q2YO3euXDa3GTJkCE6fPg1dXV3w+XwYGRlBV1cXBgYGuH79
+Ou7cuQMzM7MuxfMU/bWTlVWPiYmBh4cHtLW18a9//QsPHz7ElClTMHPmTDx79gwjR47s0uJR5POS
+TUbYvHkz5syZg/Hjx8PAwAARERG4d+8eRCIRPv74Yzg7O8PIyAgAq1XUbeTRT6Wsnj59SrNnz6bi
+4mKSSCSUk5NDJ06coKamJhIIBGRtbU1nzpwhIuXod+4OsvN88OABXbp0iRYsWMD9LTs7m+zs7LhB
+2+bmZu6Ynrg+nf/PgwcPuN8VFBTQxo0b6W9/+xsRdezZHBcXR6Wlpa88plehoKCARo8eTSEhIURE
+FBsbSx4eHrRv374uz1OW92Rubi6ZmJhw40wPHz6k69evU2FhITk7O5OXlxedP3+eiJTnnJSFciy7
+lBN6obugvb0dPB4PcXFxKC4uhqqqKgQCAerq6uDq6orDhw/DyMhIYafwdTdZAbTMzEwEBATg66+/
+xmuvvYa4uDgsX74cFhYWMDY25vYI6NevH3dsT31D5fF4uHDhAnbt2gVra2v0798fe/bswZo1a3Di
+xAksXboUBQUFiIuLU7oCaCKRCHw+H8bGxkhMTMTy5ctBRPjwww/R1taGK1eu4N69e1zLQJFbBZ29
+9tprMDExgUAgQGxsLIRCIQDA398fMTEx3PP6yuesJ7Euo/+AOnU1lJSUQEVFBcOHD4eenh4KCwux
+bNky+Pr6YsKECUhMTMS7774LLS0t7nhl+fD9L3g8HpKTk3Ho0CF4enrCysoKNTU1KCwshEAggKqq
+KoKCguDp6QldXd0e3wWOx+MhLS0NPj4++Pbbb1FRUYGIiAgUFhbCy8sLJiYmaG5uhqurK2xsbHok
+pu5ARLh9+zbc3d1haGiIkSNHQktLC7a2tvDz8wMAuLu7Y9q0adwAsjLh8/morq7GyZMn8c4778DN
+zQ18Ph9tbW3c4DHQd7pje5Q8myeKTFbZ8ty5c2Rubk5bt24lb2/vLsXXEhMTacKECV0KtfV2LzbR
+o6KiiMfj0cGDB4moYy5/cnIyrVu3jtzd3SkhIeGlx/UEiURCV65cofz8fEpMTCQLCwsqLCykqVOn
+0po1a7o8V9HXiLwsvsDAQHJycqLr169Ta2srERF5enqStrY2lZeXyyPMbiWbInzt2jWaOHEiXbly
+Rc4R9X4sIbxAVpKaqKMP3MzMjCoqKujjjz+mSZMm0erVqyknJ4caGhpowYIFcr3hyYPsPCsrK7kx
+gVOnTpG6ujplZGR0eY5sTUZP3mxlaz5aWlq6xLF69WrutfLz8+uyfkQZyM5FKBRSeHg4VwokJCSE
+HB0dKSkpiRISEmjNmjV069YteYbabdrb2+n69es0depUOnv2LBH1nc+ZvLAuo05qa2uxZ88eVFdX
+Y9KkSaioqMDSpUtRXl6OiIgIhIeHIzc3F0lJSbCyssL777+PiRMn9pmppfR8TOXChQvYuHEjzp49
+i3v37sHFxQVmZmZYtWoVrKysuD5r2ZhBTzbteTwezpw5g23btiE7Oxt8Ph+GhoZISkrCgAED8ODB
+AyQmJuL48eMwNjZW+GmlwK/XPSsrCx4eHmhqasK1a9fw5MkTbNq0Cc+ePUNKSgqOHTsGT09PzJgx
+o8txykpFRQVDhw6Fg4MDrKys+sznTJ7YoHInqqqq4PP5yMnJgYaGBpycnAB0DGbt27cPM2fOxKVL
+l1BdXc3Nj5bpC29SHo+H7OxsfP3119i/fz+qq6uRl5eHjz76CN988w2ePHkCBwcHVFZWYvDgwT02
+FZBeWBR39OhRuLq6QiQSYevWrTh48CDc3Nywf/9+lJaWwtfXV6kGkGXXfefOnYiOjsbkyZNx6tQp
+ZGZmIjIyEu7u7lBTU+PKrssSQW94T/L5fIwdO5Z73BvOSaHJr3GiOKRSKTdm0NDQQOHh4eTt7U0/
+/PADERFt2LCB7OzsKCUlhYyNjbm6RX2t+fr06VNydnamt956i/tdfn4+ubi4UHJyMhGRXPquZa9D
+VlYWffXVV7Rz507ub1FRUWRqako//fQTERE9e/aMO0aZXr/Lly+TqqoqBQUFEVHHRj3R0dHk7u5O
+e/fupfb2dq67TJnOi1EsrMvoORUVFQgEAty5cwdLlizB7du3cfPmTaipqcHb2xtpaWnIzs7G5s2b
+YWdnp/TN8T+CXmiiDxgwAJqamrh06RIeP34MW1tbjBgxAqmpqaivr8f06dOhoaEBFRWVHrs+sv+T
+kZGB9957D0+ePMHPP/8MQ0NDjBo1Cubm5lBTU4O/vz9WrFiBIUOGcHEp0+tnYGAAExMTBAcHQ1NT
+EyYmJnjzzTfR2NgIGxsbaGlpKeV5MQpGvvlIcZw7d45MTU3p0qVLRERUV1dHoaGh5OXlRRcvXiSi
+vle1VHaOycnJ9I9//IMiIyOppqaGUlNT6d133yU3NzdKS0sjY2Njbn9eecjKyqK5c+fSzZs3iYgo
+ICCANm3aREKhkMRiMRERVVRUyC2+7nT+/HkyMzOjqKgoeYfC9EIsIVDHStW3336b61aQ3UTq6uro
+yy+/pA0bNihFVcjuJEsG6enpNG7cOAoLCyMLCwsKDAykjIwMSk1NJSMjI7K2tuaum7z2G758+TKp
+qKhwK3VbW1spMDCQ3NzcKCUlpUvy7g2J/OzZs/Tmm29SZWUl19XJMN2BFQBBR3dRTU0NRCIRgF+b
+3GKxGL6+vti+fXuXRWd9gWxBXkREBLZt2wZvb2+cOXMGIpEICQkJsLW1xf79+zFu3DikpaUBgNz2
+G543bx5Onz6NQ4cO4bvvvkP//v0REBAAbW1tjBgxoksXSm/oTnn77beRlpYGHR0dhaxWyigvNoYA
+oH///mhoaEBpaSlGjBgBLS0tZGRkwNvbG/PmzVPK1Z7/DXreHy+VSsHj8ZCeno60tDRUVFTA2toa
+o0aNgomJCQIDA+Hk5AQjIyMMGTIEAoEA06dP71IUrqcZGRlBT08Pu3fvhrq6OszNzTFnzpxem8gH
+DhwIQPmnljKKhe2H8FxlZSUOHDgAoVAIGxsbxMXFITw8HIsWLZJ3aD2COg0gV1ZWYtSoUQA6tgU9
+efIkDA0NsWLFCjQ2NmL58uW4cOECRo0aBbFYjPb2drkmg87i4+OxY8cOJCcnQ0tLi32DZpg/gSWE
+TpqampCdnY3q6mro6enB0tKyzyyGkX3TvHjxIj755BPY29tDRUUFu3btQnp6OsLDw1FRUYGhQ4fC
+x8cHCxcuVNhvp48fP8Ybb7wh7zAYRumwhWmd8Pl8zJ49m3vcV5IB8GtdfX9/f8TGxuLYsWM4ffo0
+Hjx4gAMHDmDAgAE4cuQIDAwMMH/+fHmH+7tkyUBRExbDKCo2qPw7estqzz9CIpFwO5pVVFQgOTkZ
+R48eRVVVFTw8PGBubg4nJyeUlpbi0KFDXClwRabo8TGMomEtBAZAR9kOOzs78Hg87N27FyEhIbCy
+soK+vj7KyspQVlaGxYsXg4gwdepUuc0oYhjm1WGf6j7oxa4U2WN1dXW0trZCJBIhPz8fUqkUeXl5
+iIyM5LYqdHR0lFfYDMO8YmxQuY/pPC5SUFAATU1N6OjodHlOamoqQkND0dTUBA8PDzg7O3PHsm4Y
+hum9WELoY2Q39YSEBAQFBSE4OBgWFhbc32XbYjY2NoKIoKGh0acG1xmmL2MJoQ/697//DWdnZxw8
+eBBTpkzp8reX3fxZy4Bh+gY2y6gPKC8vh5eXF/dYVjdflgza29sBAI2NjS+dWcWSAcP0DSwh9AF6
+enpwc3PDnTt3AACTJ0+GpqYmUlJSIBaLoaamhvT0dOzZswctLS1gjUaG6ZtYl1EvRkSQSCTcFFEb
+GxuoqqpyK49v374NPp8PS0tL+Pn54ZtvvoG9vb2co2YYRl5YQuilOo8FFBcXc9NGZ82ahREjRiA2
+NhbJycm4cOECxGIxFi1apNDlKBiGefVYQuilOtcm8vb2RkxMDMzNzQEA06dPh5aWFn744QcAQGtr
+K9TV1dlsIobp41hC6MVyc3OxcuVKREdHw9TUFPfu3cOIESMwYMAAWFpaQkNDA8nJydxUU4Zh+ja2
+UrkXefEbvpqaGpYtW4b8/HwkJiYiJiYGhoaG2LFjB7KyspCZmQkALBkwDAOAzTLqdXg8HpKSkpCY
+mIjhw4ejubkZp06dgr6+PqKjo6Gvr4/c3FwAgLW1NahjG1U5R80wjCJgCaEX4fF4iI+Px4cffoi2
+tjbo6Ohg9+7dOHPmDP7yl7+gra0NV65cgYGBQZdj2JgBwzAASwi9Sm1tLcLCwvD9999j0aJFuHHj
+Br7//ntIpVIIhUKsX78egYGBmDVrFmsVMAzzG2wMQYm9OEVUtkdBXFwciouLoaqqCoFAgLq6Ori6
+uuLw4cMwMjJiyYBhmJdiLQQl1fmmXlJSgqdPn+KNN95AYGAg6uvrsXbtWhw7dgyRkZG4du0aBgwY
+wK1FANjUUoZhfotNO1VSEokEqqqqSEhIwK5duzBjxgxIpVJs3boV+vr6AIDLly/D19cXQUFBWLhw
+oZwjZhhG0bEWgpJpaGgA0LHD2bVr1/DJJ58gPj4eAwcOhFAoRGBgIG7cuIHGxkaEhYXhyy+/5FYg
+MwzD/B7WQlAitbW1CA4OhrGxMVauXIns7Gz0798fjx8/xvbt27Fv3z5ERkZCJBIhODgY2trabD8D
+hmH+MNZCUCKqqqrg8/nIycnBuXPnMG3aNJiZmSElJQX79u3DzJkzoauri2HDhqGxsREaGhrcsSwZ
+MAzz/2EJQQnIqpYOHjyYGyNISUnB6dOnAQAikQiffvopBAIBEhISsGnTJpiamrKWAcMwfwrrMlIC
+sumlAoEAra2tsLW1xYEDB1BeXo7FixfD3t4eH3zwAZqamrB8+XIsXbqUVS1lGOZPYwlBSSQkJCAw
+MBCff/45HBwc8OzZMxw5cgR3797FggULsGDBAla1lGGY/wnrMlIC9fX1OHz4MCIiIuDg4IC2tjYM
+GTIE77//PnR1dXHu3DlUV1dDXV0dACtHwTDMf4etVFYCKioqqKmpgUgkAvDrN3+xWAxfX19UVlZC
+S0tLniEyDNMLsBaCEhg4cCCcnZ2RmZmJoqIiqKmpISMjA6tWrcLjx48xZswYeYfIMEwvwMYQlERl
+ZSUOHDgAoVAIGxsbxMXFITw8HIsWLZJ3aAzD9BIsISiRpqYmZGdno7q6Gnp6erC0tGQDyAzDdBuW
+EJQYSwYMw3QnNqisxFgiYBimO7FBZYZhGAYASwgMwzDMcywhMAzDMABYQmAYhmGeYwmBYRiGAcAS
+AsMwDPMcSwgMwzAMAOD/AFCftMrEW+2BAAAAAElFTkSuQmCC
+"
+>
+</div>
+
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
+<div class="output_png output_subarea ">
+<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
+AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFNfXB/Dv0HvvSBFLbKhoVMQCBAt2xdiiGMXYEo2G
+FImxkGjsxhKNURNrLLEiYo0i6M+GGkuQCDZQkSKCUgV2Oe8fvjthAbvMIns+z+MjW+7cM3fKmTsz
+d1YgIgJjjDFWzWmoOgDGGGNMCpzwGGOMqQVOeIwxxtQCJzzGGGNqgRMeY4wxtcAJjzHGmFqQNOGl
+paWhffv2MDExwddffy1l1e+krl27YuPGjaoOo8pydXXF0aNHJatPQ0MDt27dqtQ6hg0bhqlTp1ba
+9I2NjZGYmPhKZaKiouDk5FQ5AVVzjRo1wvHjx9/6dBMTE6GhoYGSkpK3Pm1VGzt2LGbOnFkp09Z6
+0RdcXV2Rnp4OTU1NGBoaokuXLli2bBkMDQ1fubJVq1bBxsYG2dnZrxWsutm/f7+qQ6jSBEGAIAgV
+fjZs2DA4OTlhxowZrzVtHx8fBAYGYsSIEW8S4it73jy9DTk5OZU27TfxpsurqoqNjVV1CJJ61eW4
+bt06/P777zhx4oT43ooVKyorvBf38ARBQEREBHJycvD333/j/Pnzr5x9iQglJSVISkpC/fr1XytQ
+mUz2WuXYu6GqLd/KTDovws+CqJqq2jrKXgO9gKurKx09elR8/dVXX1H37t2JiOj06dPUunVrMjMz
+oyZNmlBUVJT4PW9vb/ruu++oTZs2pK+vT0OGDCFtbW3S0dEhIyMjOnr0KBUWFtKECRPIwcGBHBwc
+aOLEiVRYWEhERMeOHSNHR0eaO3cu2dnZUWBgIIWGhtKHH35IQ4YMIWNjY3J3d6eEhASaNWsW2djY
+kLOzMx0+fFiMYc2aNVS/fn0yNjYmNzc3WrlypfiZYvoLFy4kGxsbsre3p7Vr14qf5+fnU3BwMLm4
+uJCpqSm1bduWCgoKXjjfZV24cIGaNm1KxsbG1K9fP+rfvz9NmTKFiIgyMzOpW7duZG1tTebm5tS9
+e3e6d++eUhv+9ttvRES0du1aatOmDX311Vdkbm5ONWvWpAMHDjyz3jt37lCfPn3I2tqaLC0tady4
+cUREJJfLacaMGeTi4kI2NjY0dOhQevz4MRER3b59mwRBoLVr15KTkxNZWFjQihUrKCYmhtzd3cnM
+zEycjiImLy8vGjduHJmamlK9evWU1pWXaX/F8h06dCiVlJTQ7NmzqVatWmRpaUn9+/enzMxMscyG
+DRvI2dmZLC0t6ccffyy3biqsXLlSaV3r2bMnERHFxcWRt7c3mZmZUcOGDSk8PLzCtps8eTJpamqS
+np4eGRkZ0fjx44mISBAE+vXXX6lOnTpkZmZGn332mVK533//nerXr0/m5ubUuXNnSkpKeubyOXHi
+hLgOOTk50fr164mIaNiwYS+9fqxdu5bc3NzI2NiYatasSZs2bSIiouvXr1P79u3J1NSUrKysaMCA
+AWIZQRDo5s2bRPT8dby0Y8eOUY0aNcTXycnJFBAQQNbW1lSzZk1aunSp+NnZs2fJ09OTzMzMyN7e
+nsaNG0dFRUXi5xMnTiQbGxsyMTEhd3d3io2NfebyKquiskREGRkZ1KNHDzIxMaGWLVvSlClTqG3b
+tkT03zotl8vF6ZTerm7cuEG+vr5kaWlJVlZWNHjwYHr06JH4XRcXF5o7dy65u7uTnp4eyeXyV9r+
+XVxcxHV0+vTpr7T/8vb2ppCQEGrZsiWZmJhQr169xO2h7Hw9evSIgoKCyN7enhwdHWnKlCniZ4rt
+9IsvviAzMzOqVasWnTx5ktasWUNOTk5kY2Mjrn9ERE+ePKEvv/ySnJ2dydbWlsaMGSOuF8/bbz5r
+OSq2aWNjY2rQoAHt3r2biJ5uj3p6eqSpqUlGRkZkbm5OREQff/yxuA0QEa1atYpq165NFhYW1LNn
+T7p//7742Yu2ybJeKuEdOXKEiJ7uRBs2bEjTpk2je/fukaWlpbjT/euvv8jS0pIyMjLEheXi4kJx
+cXEkl8upuLiYhg0bRlOnThWnPXXqVGrdujU9ePCAHjx4QF5eXuLnx44dIy0tLQoJCaGioiIqKCig
+6dOnk56eHh0+fJhkMhkNHTqUXFxcaNasWSSTyWj16tVUs2ZNcfr79u2jW7duERFRdHQ0GRgY0N9/
+/600/enTp5NMJqP9+/eTgYGBuLJ/+umn5OvrS/fv3xdX8sLCwmfO94MHD8q1XWFhITk7O9PSpUtJ
+JpPRrl27SEdHR5zHhw8f0q5du6igoIBycnKoX79+1Lt3b7G8j48P/f7770T0dKXV1tam3377jUpK
+SmjFihXk4OBQ4TKTyWTUuHFjCg4Opvz8fHry5AmdPHmSiJ7ulGvXrk23b9+m3NxcCggIoMDAQCL6
+byMaO3YsFRYW0uHDh0lHR4d69+5NDx48oOTkZLKxsaHo6GgxJi0tLVq8eDHJZDL6888/ydTUVNwo
+X6b9Sy/fxYsXU+vWrSk5OZmKiopo9OjRNGjQICIiunr1KhkZGdGJEyeosLCQgoODSUtLq8KER0Tl
+1rWioiKqVasWzZ49m4qLiykyMpKMjY0pPj6+wvKl215BEATq0aMHPX78mO7cuUPW1tZ08OBBIiIK
+Cwuj2rVr07Vr10gul9PMmTPJy8urwmknJiaSsbExbd26lWQyGT18+JAuXbokxq3Y2J+3fuTm5pKJ
+iQklJCQQEVFqaipdvXqViIgGDhxIs2bNIqKn66Bi2SvmQZHwnrWOl1U64cnlcmrWrBnNmDGDiouL
+6datW+Tm5kaHDh0ioqcHeGfPniW5XE6JiYlUv359Wrx4MRERHTx4kJo3by4eYF27do1SUlIqXF5l
+Pa/sgAEDaMCAAZSfn0+xsbHk6OhI7dq1I6KKE17pZXvjxg06cuQIFRUV0YMHD6h9+/Y0ceJE8bsu
+Li7k4eFB9+7doydPnrzS9k+k3GF41f2Xt7c3OTo60tWrVykvL4/69u1LQ4YMqXC+evfuTWPGjKH8
+/HxKT0+nli1bigeYiu103bp1VFJSQlOmTCFHR0fxYOTw4cNkbGxMeXl5RPT0wKJXr16UlZVFOTk5
+1KNHD/r222/FdeF5+82KluP27dvFZfXnn3+SoaEhpaamEhHRunXrxIMThdLTOHr0KFlZWdHFixep
+sLCQxo8fT+3btxe/+7xtsiIvTHguLi5kZGREZmZm5OLiQp999hkVFBTQnDlzxB2lQufOncUjBR8f
+H5o+fXq5GSmduWvVqqXUSzl06BC5uroS0dOG1dHRUdoAp0+fTp06dRJfh4eHk5GREZWUlBARUXZ2
+NgmCIG4UZfXu3ZuWLFkiTl9fX19pQ7CxsRE3Vn19fbpy5Uq5abxovkuLjo4mR0dHpffatm37zA37
+4sWL4lEOUfmEV7t2bfGzvLw8EgSB0tLSyk3n1KlTZG1trTRvCh988AGtWLFCfB0fH0/a2tokl8vF
+jaj0EZSlpSVt27ZNfN23b19xB7Z27dpySbdly5a0cePGCuevbPuXXb7169dXSmD3798nbW1tkslk
+9P3334vJTzH/Ojo6z014pde148ePk52dndJ3Bg0aRKGhoRWW9/HxEXsBCoIgKCWP/v3709y5c4mI
+yN/fXylByuVyMjAwoDt37pSb9qxZsyggIOCl4i6t9PqRm5tLZmZmtHPnTsrPz1f63tChQ2nUqFFK
+vcHS83Dz5s3nruNllU54Z86cIWdn53LzM3z48ArLLlq0iPr06UNET3dedevWpTNnzpRbN58330RE
+kZGRFZaVyWSkra2tdOAyefLk5/bwKjqYUdi9ezd5eHiIr11dXZXO/LzK9q8oXzrhvcr+y8fHR0w0
+RE97RDo6OlRSUqI0X6mpqaSrq6vUO9+8eTP5+voS0dPttE6dOuJnV65cIUEQKD09XXzP0tKSLl++
+TCUlJWRoaCgeFBE93Z8oEvHz9ptEL16ORERNmzalPXv2iLE9L+EFBQXRpEmTxM9yc3NJW1tbPHtS
+0TY5Z86cZ9b9Utfw9uzZg6ysLCQmJmLZsmXQ09NDUlIStm/fDnNzc/HfyZMnkZqaKpZ90Z1d9+/f
+h4uLi/ja2dkZ9+/fF19bW1tDR0dHqYyNjY34t76+PqysrMTrLfr6+gCA3NxcAMCBAwfg6ekJS0tL
+mJubY//+/Xj48KFY3tLSEhoa/zWBgYEBcnNzkZGRgSdPnqBWrVrlYn6Z+S49f46OjkrvOTk5iddo
+8vPzMXr0aLi6usLU1BTe3t54/PjxM6/h2NnZKcVael5Lu3v3LlxcXJTmTSElJaVcm8tkMqSlpYnv
+2drain/r6+uXe52Xlye+Ljt/Li4uSElJAfDi9i+7fBMTE9GnTx+xXRs0aAAtLS2kpaUhJSUFNWrU
+UJp/S0vLipqpQvfv3y+3Prq4uCA5OfmZZSq6jld2GSjaPykpCRMmTBBjV8RW0fTv3bsHNze3F8b8
+vPXD0NAQf/75J3799Vc4ODige/fuiI+PBwDMmzcPRISWLVuiUaNGWLt2bblpP28df56kpCTcv39f
+af2fPXs20tPTAQAJCQno3r077O3tYWpqiu+++05c5h988AHGjRuHzz77DLa2thg9evRL30Tj6+tb
+YdkHDx5AJpMpLVtnZ+eXnp+0tDQMHDgQNWrUgKmpKQIDA5XWUUB5P/Yq239FXmX/VbZuZ2dnFBcX
+IyMjQ2maSUlJKC4uhr29vRjTmDFj8ODBA/E7Zbdh4On2V/q93NxcPHjwAPn5+WjevLk4rS5duijV
++az95rNs2LABHh4e4vRiY2PLtfGzlN1fGRoawtLSUmm7etY2WZHXHpbg7OyMwMBAZGVlif9ycnLw
+zTffiN950YV/BwcHpVuk79y5AwcHh2eWf5UbCQoLC9G3b1988803SE9PR1ZWFrp27fpSNwRYWVlB
+T08PN27cKPfZy8y3gr29fbkd3p07d8T5WLhwIRISEhATE4PHjx8jOjoa9LTX/dLzWREnJyfcuXMH
+crm83GcVtbmWlpbSBvEqys5fUlISHBwcXqr9yy5PZ2dnHDx4UKlt8/Pz4eDgAHt7e9y9e1f8bn5+
+/nM3mrLTdnBwwN27d5XqT0pKUkqizyv/Is7Ozli1apVS7Hl5efD09Cz3XScnJ9y8efOFsb9o/ejU
+qRMOHz6M1NRU1KtXDyNHjgTwdOe2atUqJCcnY+XKlfj000/LDad43jr+PE5OTqhZs6bSfGZnZyMi
+IgLA01vKGzRogBs3buDx48f48ccflW6dHz9+PM6fP4+4uDgkJCRg/vz5SvP8PBWVtbGxgZaWFu7c
+uSN+r/TfirvJ8/PzxfdKJ6fJkydDU1MTsbGxePz4MTZu3FjuVv/Ssb3K9v82lJ0vbW1tWFlZKX3H
+yckJurq6ePjwoRjT48eP8c8//7xyfVZWVtDX10dcXJw4rUePHr30nfVll2NSUhJGjRqF5cuXIzMz
+E1lZWWjUqJG4Dr9qjsjLy8PDhw/LHWi/rNdOeEOGDMHevXtx+PBhyOVyPHnyBFFRUUo7wLI77rKv
+Bw0ahJkzZyIjIwMZGRn44YcfEBgY+Mw6XyURFBUVoaioCFZWVtDQ0MCBAwdw+PDhlyqroaGBoKAg
+BAcHIyUlBXK5HKdPn0ZRUdFLzbeCl5cXNDU1sWzZMshkMuzZswfnzp0TP8/NzYW+vj5MTU2RmZmJ
+77///qXn73latWoFe3t7hISEID8/H0+ePMGpU6cAPG3zRYsWITExEbm5uZg8eTIGDhxYYW/wWUov
+h/T0dCxduhTFxcXYvn07rl27hq5du75W+48ZMwaTJ08WN/IHDx4gPDwcAPDhhx8iIiICJ0+eRFFR
+EaZNm/bcMUi2trZKO3lPT08YGBhg3rx5KC4uRlRUFCIiIjBw4MBnln9eUlK0g6ItxowZg1mzZiEu
+Lg4A8PjxY2zfvr3CcoMHD8aRI0ewfft2yGQyPHz4EJcvXy43zeetH+np6dizZw/y8vKgra0NQ0ND
+aGpqAgC2b9+Oe/fuAQDMzMwgCEK55fu8dfx5WrZsCWNjY8ybNw8FBQWQy+WIjY3F+fPnxZiNjY1h
+YGCAa9euYcWKFeJO7fz58zh79iyKi4thYGAAPT09Meayy6usZ5XV0NBAQEAAQkNDUVBQgLi4OGzY
+sEGs09raGo6Ojti4cSPkcjnWrFmjtFxzc3NhaGgIExMTJCcniwn4WV5l+39TRIQ//vgD//77L/Lz
+8zFt2jT069evXJKwt7dHp06dEBwcjJycHJSUlODmzZuvNf5PQ0MDI0eOxMSJE8UeYnJy8kvvO8su
+x7y8PAiCACsrK5SUlGDt2rVKQzVsbW1x7949FBcXK823YhsYNGgQ1q5di8uXL6OwsBCTJ0+Gp6fn
+M3vxL8oRr53watSogT179mDWrFmwsbGBs7MzFi5c+Nwj+LJjjKZMmYL3338fjRs3RuPGjfH+++9j
+ypQpL13+Wd8Bng6wXbp0Kfr37w8LCwts2bIFvXr1em7Z0hYsWAB3d3e0aNEClpaW+Pbbb1FSUvLM
++a5o56utrY1du3bh999/h7m5OTZt2oTu3buLp/EmTpyIgoICWFlZwcvLC126dHlmTC8z7woaGhrY
+u3cvbty4AWdnZzg5OWHbtm0AgKCgIAQGBqJ9+/Zwc3ODgYEBfv7555dqk4q+06pVK1y/fh3W1taY
+OnUqdu7cCXNz89dq/wkTJqBnz57o1KkTTExM0Lp1a8TExAAAGjRogOXLl+Ojjz6Cg4MDLCwsnnvK
+fMSIEYiLi4O5uTkCAgKgra2NvXv34sCBA7C2tsa4ceOwceNG1K1bt8LyEyZMwI4dO2BhYYGJEyc+
+sx0U89C7d29MmjQJAwcOhKmpKdzd3XHo0KEKyzk5OWH//v1YuHAhLC0t4eHhgStXrpSb5vPWj5KS
+EixatAiOjo6wtLTEiRMnxPFL58+fh6enJ4yNjdGrVy8sXboUrq6u5dr8Wev4s+YVADQ1NREREYFL
+ly7Bzc0N1tbWGDVqlNgDWLBgATZv3gwTExOMGjVK6YAiOzsbo0aNgoWFBVxdXWFlZSU+gKLs8irr
+eWWXLVuG3Nxc2NnZISgoCMOHD1faD61evRrz58+HlZUV4uLi0KZNG/Gz6dOn4++//4apqSl69OiB
+vn37PncbeJXtv6I2fNn9l+LvwMBADBs2DPb29igqKsLSpUsr/O6GDRtQVFSEBg0awMLCAv369RN7
+sq+y7wCAuXPnonbt2vD09ISpqSk6duyIhISElypbdjk2aNAAX375JVq3bg07OzvExsaibdu24vf9
+/PzQsGFD2NnZiad7S8fr5+eHGTNmoG/fvnBwcMDt27exdevW57bf8+IT6E3Pn1Wyu3fvYujQoUhP
+T4cgCBg1ahQ+//xzZGZmYsCAAUhKSoKrqyu2bdsGMzMzVYf7Qq1atcKnn36Kjz/+WNWhvLGKBo0y
+pmrVZb309fVFYGAggoKCVB1KtVHln6Wpra2NRYsW4erVqzhz5gyWL1+Of//9F3PmzBGPPPz8/DBn
+zhxVh1qh48ePIzU1FTKZDOvXr0dsbCz8/f1VHRZj7B1Qxfsj75wqn/Ds7OzQtGlTAICRkRHq16+P
+5ORkhIeHi72kjz/+GGFhYaoM85ni4+PRtGlTmJubY9GiRdixY8dr3yBS1VT2Y7AYex3Vab2sLvNR
+VVT5U5qlJSYmwtvbG7GxsXB2dkZWVhaAp0dBFhYW4mvGGGOsrCrfw1PIzc1F3759sWTJEhgbGyt9
+Vp2O6BhjjFWOF/5aQlVQXFyMvn37IjAwEL179wbw9HbW1NRU2NnZISUlRWlAp0LTpk3F270ZY4y9
+nCZNmuDSpUuqDuOtq/I9PCLCiBEj0KBBA6Xbw3v27In169cDANavXy8mwtIuX74sjumoyv+mT5+u
+8hiq0z9uT27LqvrvXWnP6tpRqPI9vJMnT+KPP/5A48aN4eHhAQCYPXs2QkJC0L9/f/z+++/isATG
+GGPsWap8wmvbtu0zB3UeOXJE4mgYY4y9q6r8KU114OPjo+oQqhVuz7eH2/Lt4vZUrXdqWMKrEgQB
+1Xj2GGOsUlTXfSf38BhjjKkFTniMMcbUAic8xhhjaqHK36VZmUJCQ5D66OV+qbi6szOzw5zQqvkA
+bsYYexvUOuGlPkqFa29XVYdRJSSGJao6BMYYq1R8SpMxxpha4ITHGGNMLXDCY4wxphY44THGGFML
+nPAYY4ypBU54jDHG1AInPMYYY2qBEx5jjDG1wAmPMcaYWuCExxhjTC1wwmOMMaYWOOExxhhTC5zw
+GGOMqQVOeIwxxtQCJzzGGGNqgRMeY4wxtcAJjzHGmFrghMcYY0wtcMJjjDGmFjjhMcYYUwuc8Bhj
+jKkFTniMMcbUAic8xhhjaoETHmOMMbXACY8xxpha4ITHGGNMLXDCY4wxphY44THGGFMLnPAYY4yp
+BU54jDHG1AInPMYYY2qBEx5jjDG1wAmPMcaYWuCExxhjTC1wwmOMMaYWOOExxhhTC5zwGGOMqQUt
+qSqKj4/HggULkJiYCJlMBgAQBAGRkZFShcAYY0yNSZbw+vXrh7Fjx+KTTz6BpqYmgKcJjzHGGJOC
+ZAlPW1sbY8eOlao6xhhjTIlk1/B69OiB5cuXIyUlBZmZmeK/FwkKCoKtrS3c3d3F90JDQ1GjRg14
+eHjAw8MDBw8erMzQGWOMVQOS9fDWrVsHQRCwYMECpfdv37793HLDhw/H+PHjMXToUPE9QRAQHByM
+4ODgSomVMcZY9SNZwktMTHytcu3atauwLBG9WUCMMcbUSqUnvKNHj8LPzw87d+6s8CaVgICA15ru
+zz//jA0bNuD999/HwoULYWZm9qahMsYYq8YqPeEdP34cfn5+2Lt371tLeGPHjsW0adMAAFOnTsWX
+X36J33//vcLvhoaGin/7+PjAx8fnletjjLHqLCoqClFRUaoOo9IJ9A6cG0xMTESPHj3wzz//vNJn
+giA899TnsInD4Nrb9W2G+s5KDEvEusXr3mgaIaEhSH2U+nYCesfZmdlhTugcVYfB2Gt50b7zXSXZ
+NTwAiIiIQFxcHJ48eSK+p+ipvYqUlBTY29sDAHbv3q10BydTndRHqXwA8f8SwxJVHQJjrAzJEt7o
+0aNRUFCAyMhIjBw5Etu3b0erVq1eWG7QoEGIjo5GRkYGnJyc8P333yMqKgqXLl2CIAioWbMmVq5c
+KcEcMMYYe5dJlvBOnTqFf/75B40bN8b06dPx5Zdfwt/f/4XltmzZUu69oKCgygiRMcZYNSbZwHN9
+fX0AgIGBAZKTk6GlpYXUVL7ewxhjTBqS9fC6d++OrKwsfP3112jevDkAYOTIkVJVzxhjTM1JlvAU
+N6f07dsX3bp1w5MnT3jsHGOMMclIlvAqGnhuamoKd3d32NjYSBUGY4wxNSVZwluzZg1Onz4NX19f
+AE8HOjZr1gy3b9/GtGnTlJ6VyRhjjL1tkiW84uJi/Pvvv7C1tQUApKWlITAwEGfPnkX79u054THG
+GKtUkt2leffuXTHZAYCNjQ3u3r0LS0tL6OjoSBUGY4wxNSVZD8/X1xfdunVD//79QUTYuXMnfHx8
+kJeXxzevMMYYq3SSJbzly5dj586dOHnyJADg448/Rt++fSEIAo4dOyZVGIwxxtSUZAlPEAR8+OGH
++PDDD6WqkjHGGBNJdg2PMcYYUyVOeIwxxtSCpAkvPz8f8fHxUlbJGGOMAZAw4YWHh8PDwwOdO3cG
+AFy8eBE9e/aUqnrGGGNqTrKEFxoairNnz8Lc3BwA4OHhgVu3bklVPWOMMTUnWcLT1tYuN95OQ4Mv
+ITLGGJOGZBmnYcOG2LRpE2QyGa5fv47x48fDy8tLquoZY4ypOckS3s8//4yrV69CV1cXgwYNgomJ
+CRYvXixV9YwxxtScZAPPDQ0NMWvWLMyaNUuqKhljjDGRZD28Dh064NGjR+LrzMxM8Y5NxhhjrLJJ
+lvAyMjKUblqxsLBAWlqaVNUzxhhTc5IlPE1NTSQlJYmvExMT+S5NxhhjkpHsGt6PP/6Idu3aoX37
+9gCA48ePY9WqVVJVzxhjTM1JlvD8/f1x4cIFnDlzBoIgYPHixbCyspKqesYYY2pOsoQHAEVFRbCw
+sIBMJkNcXBwAiD0+xhhjrDJJlvAmTZqEP//8Ew0aNICmpqb4Pic8xhhjUpAs4e3evRvx8fHQ1dWV
+qkrGGGNMJNltkrVq1UJRUZFU1THGGGNKJOvh6evro2nTpvDz8xN7eYIgYOnSpVKFwBhjTI1JlvB6
+9uxZ7vfvBEGQqnrGGGNqTrKEN2zYMKmqYowxxsqRLOElJCRg8uTJiIuLQ0FBAYCnPTz+EVjGGGNS
+kOymleHDh2PMmDHQ0tJCVFQUPv74YwwePFiq6hljjKk5yRJeQUEBOnToACKCi4sLQkNDsW/fPqmq
+Z4wxpuYkO6Wpp6cHuVyO2rVrY9myZXBwcEBeXp5U1TPGGFNzkiW8xYsXIz8/H0uXLsXUqVORnZ2N
+9evXS1U9Y4wxNSdZwmvZsiUAwNjYGOvWrZOqWsYYYwyAhAnv3LlzmDVrFhITEyGTyQA8vUvzypUr
+UoXAGGNMjUmW8AYPHowFCxagUaNG/MOvjDHGJCdZwrO2ti73pBXGGGNMKpIlvOnTp2PEiBHo0KED
+dHR0ADw9pRkQECBVCIwxxtSYZAlv/fr1iI+Ph0wmUzqlyQmPMcaYFCRLeOfPn8e1a9f4gdGMMcZU
+QrKE5+Xlhbi4ODRs2FCqKhl7p4WEhiD1Uaqqw6gS7MzsMCd0jqrDYO84yRLe6dOn0bRpU9SsWVPp
+9/B4WAJjFUt9lArX3q6qDqNKSAxLVHUIrBqQJOEREVatWgVnZ2cpqmOMMcbKkayH9+mnnyI2Nlaq
+6hhjjDElkowAFwQBzZs3R0xMzCuXDQoKgq2tLdzd3cX3MjMz0bFjR9StWxedOnXCo0eP3ma4jDHG
+qiHJHnly5swZtG7dGm5ubnB3d4e7uzsaN278wnLDhw/HwYMHld6bM2cOOnbsiISEBPj5+WHOHL6Y
+zRhj7Pl1TnejAAAgAElEQVQkO6V56NAhABCHJRDRS5Vr164dEhMTld4LDw9HdHQ0AODjjz+Gj48P
+Jz3GGGPPJVkPz9XVFY8ePUJ4eDj27t2Lx48fw9XV9bWmlZaWBltbWwCAra0t0tLS3mKkjDHGqiPJ
+enhLlizB6tWrERAQACLCkCFDMHLkSHz++edvNF1BEJ47mD00NFT828fHBz4+Pm9UH2OMVTdRUVGI
+iopSdRiVTrKE99tvv+Hs2bMwNDQEAISEhMDT0/O1Ep6trS1SU1NhZ2eHlJQU2NjYPPO7pRMeY4yx
+8sp2Br7//nvVBVOJJP2dntLP0HyTnwjq2bOn+Gvp69evR+/evd84NsYYY9WbZD284cOHo1WrVuIp
+zbCwMAQFBb2w3KBBgxAdHY2MjAw4OTnhhx9+QEhICPr374/ff/8drq6u2LZtmwRzwBhj7F1W6Qnv
+1q1bcHNzQ3BwMLy9vfG///0PgiBg3bp18PDweGH5LVu2VPj+kSNH3naojDHGqrFKT3j9+vXDhQsX
+4Ofnh6NHj6J58+aVXSVjjDFWTqUnPLlcjh9//BHx8fH46aeflMbfCYKA4ODgyg6BMcYYq/ybVrZu
+3QpNTU3I5XLk5OQgNzdX/JeTk1PZ1TPGGGMAJOjh1atXD19//TVcXFwwaNCgyq6OMcYYq5AkwxI0
+NTWxYMECKapijDHGKiTZOLyOHTtiwYIFuHv3LjIzM8V/jDHGmBQkG4e3detWCIKA5cuXK71/+/Zt
+qUJgjDGmxiRLeGV/8YAxxhiTkmSnNPPy8jBjxgyMHDkSAHD9+nVERERIVT1jjDE1J1nCGz58OHR0
+dHDq1CkAgIODA7777jupqmeMMabmJEt4N2/exKRJk6CjowMA4q8mMMYYY1KQLOHp6uqioKBAfH3z
+5k3o6upKVT1jjDE1J9lNK6GhofD398e9e/fw0Ucf4eTJk1i3bp1U1TPGGFNzkiW8Tp06oVmzZjh7
+9iyICEuXLoWVlZVU1TPGGFNzkiU8IkJ0dLT480DFxcXo06ePVNUzxhhTc5Jdw/v000+xcuVKNG7c
+GI0aNcLKlSvx6aefSlU9Y4wxNSdZD+/YsWOIi4uDhsbTHDts2DA0aNBAquoZY4ypOcl6eLVr18ad
+O3fE13fu3EHt2rWlqp4xxpiak6yHl52djfr166Nly5YQBAExMTFo0aIFevToAUEQEB4eLlUojDHG
+1JBkCe+HH34o954gCCAiCIIgVRiMMcbUlGQJz8fHR6qqGGOMsXIku4bHGGOMqRInPMYYY2pB0oSX
+n5+P+Ph4KatkjDHGAEiY8MLDw+Hh4YHOnTsDAC5evIiePXtKVT1jjDE1J1nCCw0NxdmzZ2Fubg4A
+8PDwwK1bt6SqnjHGmJqTLOFpa2vDzMxMuXINvoTIGGNMGpJlnIYNG2LTpk2QyWS4fv06xo8fDy8v
+L6mqZ4wxpuYkS3g///wzrl69Cl1dXQwaNAgmJiZYvHixVNUzxhhTc5INPI+Pj8esWbMwa9Ysqapk
+jDHGRJL18IKDg1GvXj1MnToVsbGxUlXLGGOMAZAw4UVFReHYsWOwsrLC6NGj4e7ujhkzZkhVPWOM
+MTUn6W2S9vb2mDBhAn799Vc0adKkwgdKM8YYY5VBsmt4cXFx2LZtG3bs2AFLS0sMGDAAP/30k1TV
+M8bUXEhoCFIfpao6DJWzM7PDnNA5qg5DJSRLeEFBQRg4cCAOHToER0dHqapljDEAQOqjVLj2dlV1
+GCqXGJao6hBURrKEd+bMGamqYowxxsqp9ITXr18/bN++He7u7uU+EwQBV65cqewQGGOMscpPeEuW
+LAEAREREgIiUPuNfOmeMMSaVSr9L08HBAQDwyy+/wNXVVenfL7/8UtnVM8YYYwAkHJZw+PDhcu/t
+379fquoZY4ypuUo/pblixQr88ssvuHnzptJ1vJycHLRp06ayq2eMMcYASJDwPvroI3Tp0gUhISGY
+O3eueB3P2NgYlpaWlV09Y4wxBkCChGdqagpTU1Ns3boVAJCeno4nT54gLy8PeXl5cHZ2ruwQGGOM
+Memu4YWHh6NOnTqoWbMmvL294erqii5dukhVPWOMMTUnWcKbMmUKTp8+jbp16+L27ds4evQoWrVq
+JVX1jDHG1JxkCU9bWxtWVlYoKSmBXC6Hr68vzp8/L1X1jDHG1JxkjxYzNzdHTk4O2rVrh8GDB8PG
+xgZGRkZSVc8YY0zNSZbwwsLCoK+vj0WLFmHTpk3Izs7G9OnT32iarq6uMDExgaamJrS1tRETE/OW
+omWMMVbdSJbwFL05TU1NDBs27K1MUxAEREVFwcLC4q1MjzHGWPVV6QnPyMjomc/MFAQB2dnZbzT9
+ss/nZIwxxipS6QkvNze30qYtCAI6dOgATU1NjB49GiNHjqy0uhhjjL3bJDulCQAnTpzAjRs3MHz4
+cDx48AC5ubmoWbPma0/v5MmTsLe3x4MHD9CxY0fUq1cP7dq1U/pOaGio+LePjw98fHxeuz7GGKuO
+oqKiEBUVpeowKp1kCS80NBTnz59HQkIChg8fjqKiIgwePBinTp167Wna29sDAKytrdGnTx/ExMQ8
+N+Exxhgrr2xn4Pvvv1ddMJVIsnF4u3fvRnh4OAwNDQEAjo6Ob3S6Mz8/Hzk5OQCAvLw8HD58uMIf
+mWWMMcYACXt4urq60ND4L7/m5eW90fTS0tLQp08fAIBMJsPgwYPRqVOnN5omY4yx6kuyhNevXz+M
+Hj0ajx49wqpVq7BmzRp88sknrz29mjVr4tKlS28xQsYYY9WZJAmPiDBgwABcu3YNxsbGSEhIwIwZ
+M9CxY0cpqmeMMcak6+F17doVsbGxfNqRMcaYSkhy04ogCGjevDk/+osxxpjKSNbDO3PmDP744w+4
+uLiId2oKgoArV65IFQJjjDE1JlnCO3TokFRVMcYYY+VIlvBcXV2lqooxxhgrR7KB54wxxpgqccJj
+jDGmFjjhMcYYUwuc8BhjjKkFTniMMcbUAic8xhhjaoETHmOMMbXACY8xxpha4ITHGGNMLXDCY4wx
+phY44THGGFMLnPAYY4ypBU54jDHG1AInPMYYY2qBEx5jjDG1wAmPMcaYWuCExxhjTC1wwmOMMaYW
+OOExxhhTC5zwGGOMqQVOeIwxxtQCJzzGGGNqgRMeY4wxtcAJjzHGmFrghMcYY0wtcMJjjDGmFjjh
+McYYUwuc8BhjjKkFTniMMcbUAic8xhhjaoETHmOMMbXACY8xxpha4ITHGGNMLXDCY4wxphY44THG
+GFMLnPAYY4ypBU54jDHG1AInPMYYY2qBEx5jjDG1wAmPMcaYWninE97BgwdRr1491KlTB3PnzlV1
+OIwxxqqwdzbhyeVyjBs3DgcPHkRcXBy2bNmCf//9V9VhvZbES4mqDqFa4fZ8e7gt3y5uT9V6ZxNe
+TEwMateuDVdXV2hra2PgwIHYs2ePqsN6LbwRvF3cnm8Pt+Xbxe2pWu9swktOToaTk5P4ukaNGkhO
+TlZhRIwxxqqydzbhCYKg6hAYY4y9QwQiIlUH8TrOnDmD0NBQHDx4EAAwe/ZsaGhoYNKkSeJ3mjZt
+isuXL6sqRMYYeyc1adIEly5dUnUYb907m/BkMhnee+89HD16FA4ODmjZsiW2bNmC+vXrqzo0xhhj
+VZCWqgN4XVpaWli2bBk6d+4MuVyOESNGcLJjjDH2TO9sD48xxhh7Fe/sTSuMMcbYq+CEx54rJSUF
+kyZNws2bN/Hw4UMAQElJiYqjUo2yJ0P45MjrISJuuzdUURtym74YJzz2XPb29gCATZs24bPPPsOl
+S5egoaGhdhsXEYlDYe7fv4+srCweGvMGBEHAoUOH8NNPP2Hz5s2qDuedJAgCzp07hwMHDuD27dsQ
+BEFtD0ZfFic89kyKjWfu3LkYM2YMfHx80KVLFxw/flytNq60tDT8+uuvAIC//voLvXr1wgcffIDd
+u3cjOztbxdG9WxQHDpcvX8b48eORlpaGAwcOYPTo0aoO7Z2haMOjR4+iV69e2LlzJ1q0aIGLFy9C
+Q0NDbbbL1/HO3qXJKo+i96ahoYGioiLo6OjAxsYGY8aMga6uLgYNGoSdO3fC09NTqedTHRERLly4
+gFOnTiEtLQ1nz57Fxo0bceXKFaxZswZ5eXno2bMnTExMVB3qO0EQBERHR+OPP/7AkiVL0KVLF9y4
+cQOzZs3CmDFjxAML9myCICAuLg47duzA1q1b0b59ezRp0gR+fn6IjIxE06ZNUVJSAg0N7s+UpRka
+Ghqq6iBY1aM45bR27VrExcWhVatWAAAPDw+YmZnhm2++gb+/PywtLVUcaeVRJHNHR0fo6+vj4sWL
+SEtLw1dffYWGDRtCX18fmzZtAhHBzc0Nenp6qg65Sip7UHT58mX8+OOPcHFxgbe3N0xNTdG4cWPs
+378f+/fvR69evVQYbdUml8shl8sxZ84cnDp1Cu+99x7c3d3h6ekJQ0NDBAQEoHv37nBwcFB1qFUS
+JzymRLFzOn36ND777DP4+/tj4cKFSEtLQ7t27aCpqYlmzZqhsLAQSUlJaNmyJeRyebU7mlT0cgVB
+wP379+Hh4QEtLS2cO3cOaWlpaN26NerXrw9NTU388ccf6Nq1K4yNjVUcddVTuh2TkpJARGjatCna
+tm2LqVOnws3NDfXr14eZmRk8PDzQvHlz2NraqjjqqqV0G+bn50NfXx8+Pj5IT09HUlISLCws4Ojo
+iFatWsHExASGhoaoVauWiqOumngcHisnPj4es2fPhpeXF0aNGoX79++jf//+8Pb2RmhoKLS1tXH0
+6FH8+eefWLVqlarDrRSKxH/gwAGMGzcOBw4cgJOTEw4dOoS//voLtWvXxhdffAHg6TU+3klXTHFq
+LSIiArNnz4aVlRWsra0RHByMjIwMjBgxAnPmzEHfvn3FMtX9NPmrUrTHwYMHsXDhQtSoUQO1atXC
+lClTEBISguLiYgQEBMDLy0tsN27DZyCm9kpKSpRe79+/n7p3706DBg2iW7duERHR/fv3qUmTJvTV
+V1+J3/vss88oJSVF0lildPXqVWrYsCFFR0eL7+Xn51NYWBgNGzaM5s2bR0REcrmciMq3ozorKCgQ
+/05KSqIGDRrQhQsX6J9//qENGzZQ165dKTU1lXbt2kU1atSgtLQ0br8yiouLxb9jYmKoQYMGFBER
+QefPn6emTZvS+PHjqaSkhD799FP64osvKCsrS4XRvhv4phU1R6U6+Ldu3YKBgQH8/Pzg6OiI1atX
+IywsDAEBAXBxcRFvf1ZYtmyZKkKWjEwmQ9u2bdG+fXvIZDKUlJRAX18fHTp0gCAIcHNzAwDxdC4f
+UT+VlpaGLVu2YMSIEeJpXicnJzRr1gzA06EuFy5cwOHDhxEYGAhPT0/Y2NioMuQqJz09XRwKpKOj
+g/z8fHTo0AHdunUDAPz9999o2bIlTp48iR9++AFpaWkwMzNTcdRVX/W68MJemSAI4qm7bt26ITg4
+GO+//z5MTU0xYMAAJCYmYvPmzUhKSoK9vT28vLyq5cDhiubJwMAABw8eREREBLS0tKCjo4NDhw5h
+/fr16NmzJxo1aqSiaKs2XV1ddOnSBbm5uTh//jycnZ0hk8nw3XffAQAsLS1hYWGBhIQEAIC1tTUA
+HjhdWmpqKnr06IGHDx/i7t27MDExwdGjR8WHPwiCAF9fXzx+/BiWlpZo0KCBiiN+N3DCY7hz5w6m
+T5+O1atXY/PmzRgwYAB69uyJunXrIiAgAMnJyUpjexRJsrpR3DI/f/58REZGolatWli0aBF++ukn
+LFu2DPv378ekSZPg6Oio6lCrpOLiYuTn58PMzAxOTk6YPXs2fvvtN1y9ehULFy5EYmIi+vfvj717
+92Lz5s3w8/MD8PRB8AD3kIGnbQgAjRs3hoWFBVasWIE5c+agUaNGGDx4MFq0aIGoqChERERg//79
+3Kt7RXzTihqiMhe0c3NzMXbsWPz4449wdnYGAIwfPx6amppYvHix2tyUceDAAQQHB2PixIlYsGAB
+PvnkE3Tr1g0ZGRlYuHAh7Ozs0KtXL3Tv3p1vCiijqKgIUVFRsLKyQkJCApKSkjBkyBAsWLAAOjo6
+CAgIgJubG2bOnAkzMzO0bNkS3bp143YspbCwECdOnECNGjWQm5uLhIQE2Nra4q+//gIRYfbs2Vi9
+erV4p/Do0aN5XXxFfA1PDZWUlEBTUxO5ubnQ09ODjo4OcnNzER4ejnHjxgEA2rZti4sXLwJAtU92
+RITk5GSsWrUK4eHhuHv3LogIly9fRn5+Pr7++mvs3btX6ftMmY6ODnJychAaGorU1FQsWrQIjo6O
++O677/DDDz9g586dCAwMxJIlS8Qy3I7KioqK8OTJE4wdOxYJCQmIjIzEe++9B319fYSFheG7777D
+N998g9GjR6OgoAD6+vrchq+Ix+GpkTt37qCoqAhGRkYICwvDmDFj8M8//0BTUxPDhg1DSEgIrl27
+hvPnz2PFihUICgpC3bp1VR12paBSY5sEQYCJiQnatGmDvLw8jB8/HmfPnoWtrS2+/PJL6OjooEmT
+JtDV1VUqw/679ikIApydnXHq1Cno6uqiU6dOMDIygpWVFTw9PbFv3z7ExsaidevW4gB9bsenFOui
+rq4u8vPzMXv2bLRq1QpeXl5wcHCAk5MTTExMcPnyZURFRcHb2xs6OjrQ0NDgNnxFnPDUyMyZMzFt
+2jS0aNECK1aswPDhw+Hk5ITly5fD2dkZkydPxsOHD5GdnY1Ro0ahc+fO1fp0iSAIuHLlCs6fPw9N
+TU04ODggNTUVR44cwahRo1BQUIB//vkH48aNg5OTk6rDrbI0NDTw119/YePGjZg3bx40NDQQFhYG
+PT091K9fHzKZDO7u7vDw8OB2fAZBEPDXX3/BwsICw4YNg7W1NXbs2AEtLS3UqVMHOjo60NHRQZcu
+XWBra1vtHvQgFT6lqQYUg3/nz5+PkpISDBgwAEOGDMHAgQNRUFAAS0tLzJs3DxkZGfjkk0/EctX5
+dIkgCAgLC8O0adPg5uYGfX191K1bF6NHj4adnR06dOiAu3fvYvHixWjUqFG1TvxvQnGH75dffolF
+ixbB0NAQw4cPR0FBASIiInDu3Dn89ttvOHbsGN/V+gyKNpwwYQJ+/vlndO7cGSYmJsjMzMTu3btx
+5swZXLp0CUuWLEHNmjVVHe67TbIRf0wlcnNzKTY2loiIzp49S9nZ2RQSEkL169en1NRUIiIqLCyk
+/fv3k4+PDyUmJooDqaubkpIScXBzTk4ODRw4kP7++28iIoqOjqaQkBDasGEDPXjwgDZv3kynTp0q
+V44pKyoqogkTJtCBAweIiOjJkyfiZwcOHKBFixbRwYMHVRXeOyE3N5c6depER48eJaL/HmBw+/Zt
+2rZtG3Xv3p3CwsJUGWK1wT28ai4zMxM//fSTeOH7wIEDmD17Nh49eoSAgADs3r0bNjY28PPzQ4sW
+LWBlZaXqkCsFleqhnTp1CtnZ2UhKSsK1a9fg4eEBLy8vnDt3DqdOnUJgYCAGDRoklgP4lnkFKtPT
+1dbWRmZmJqKiouDv7y9e57xy5Qp8fHzg7+8vlgO4HYH/2lDxv0wmQ1FRkTjc5cmTJ9DX14exsTH6
+9euHXr16QUdHh9vwLeATwdVYSUkJnJyc0KlTJ6xZswYfffQR3N3dAQArVqxAkyZN0LFjR6Snp0NH
+R0dMdlSNT2XGx8fj66+/RuPGjfHVV1/hyJEjiI6OhpaWFpo3b47MzExkZ2eL4w75poCKJSUlIS4u
+DgAQFBSE4uJi7NmzBwBw7tw5jBkzBtevXxe/z+1YXlpaGgDA1NQUbdq0QUhICDIzM6Gvr4/jx4+j
+e/fuSE9PVxqnyG34ZjjhVWMaGhqIjIzElStXsHfvXly+fBm//fYbMjMzAQC//PILOnbsqLRjAqrn
+EaTiR0cDAgLQoUMHODg4oGnTpmjWrBnGjRuHiRMnYvjw4QgMDISJiQnfFFABRY8kIiIC/v7++PDD
+DzFp0iTUq1cPNWvWxMqVK9GjRw8MHToUISEh4sEV+4+iDffv34/u3bujb9++OHz4MIYNG4YmTZqg
+TZs2mD9/PsaOHYtvv/0WNjY2vC6+RTzwvJpTPGvv0KFDOHr0KGbMmIFRo0ZBEATs2rULmzdvhra2
+drW8KaOiU0BDhgzBv//+i+joaBgZGaG4uBixsbFISkpCjRo18P7771fLtnhbrl27hm+++Qbz58+H
+ra0tunbtii5duiA4OBiFhYVISEiAubk53nvvPT4F9wwxMTGYNWsWQkJCEB0djaSkJLRt2xZ9+vTB
+vn37oKGhAWtra7Rr147b8C3ja3jVTNmddb169cSH9fr5+UEul+OPP/5AcnIyRo0aBW1tbQDVd4MS
+BAFnzpzBnTt30KhRI/zxxx8YMWIE+vXrh127dkFfXx8eHh7w8PAAUL1P574uxTr1+PFjLF68GPfv
+34e2tjbMzMywc+dODBw4EBkZGViyZAk8PT2VylbX9epVlL5ml5GRge+//x7a2trw9PSEp6cnVq5c
+iePHj6OkpAS9e/eGkZGRWA7gNnybeBxeNaHYqARBwN9//43x48ejcePGcHR0RFZWFubPn4+BAwfi
+vffeg6+vLz788EO0aNGi2vZmFPN14sQJBAUFISsrC8ePH0dMTAyWLVuGyMhILF++HAMGDBCTPsDX
+SSqiOB1saWkJV1dXXL9+HVlZWXB0dESNGjXEHwlu27at0k1P3I5PKdrhwYMHsLGxgSAI2LFjBwwM
+DNCsWTO8//77uH37Ns6cOYM2bdqIvzDB6+LbxwmvGih9JHj9+nW4ubnhzJkz+Oeff7B8+XJ069YN
+165dQ8OGDWFrawtdXV0YGBiI5avjRqX41fYZM2bgl19+weeffw53d3ecOHECSUlJmDlzJnbv3o36
+9evDwcFB1eFWSYqDhqKiIixYsACrVq3C6NGj4erqihMnTiA1NRV2dnZwcnLC0KFDq/0j6F6XXC5H
+ZmYmXFxcUKdOHQwcOBA1atTAli1bUFhYCA8PD7Rq1QoeHh6oUaOGqsOt1jjhVROKC+ETJ06En58f
+AgMD0bp1awiCgA0bNuDIkSPIyclBz549lRJcdUp2ZXurJ06cwPz589GyZUs0a9YMRkZGKCgowNmz
+Z9G9e3cMGjQIDg4O1baX+7ry8vKgoaEBTU1NAICmpiYaN26MGzduYMOGDRg5ciTs7e1x8OBBpKen
+o1mzZtDW1oaGhga35f8rLi4W2w8ADA0N0ahRI4waNQrvvfce+vTpAwMDA6xevRrFxcVo1qwZTE1N
+VRixmpBgrB+TwL///kv16tWjkydPlvvs0aNHdPXqVfLx8REHWlc3pQeHP3jwgPLz84mIaO3ateTm
+5kZHjhwhoqeDodu2bUsZGRkkk8lUFm9VdfXqVRo7diylpaXR8ePHacmSJeJnKSkp9MUXX9DQoUMp
+Ly+PTp48KT7UgP3n6tWrNGXKFCIiiouLo8OHD4vr4/79+0lXV1ccSL5jxw6KiYlRWazqhhPeO+ru
+3bu0ceNG8fXx48epX79+4uvi4mIiIqUnhAwdOlR8mkN1o5jP8PBw8vb2pnbt2tGqVasoPj6e/vzz
+TzIwMKBPPvmEevfuTbt371ZxtFVTfn4+dejQgdasWUNERJGRkWRra0vLli0jIiKZTEaRkZHUqFEj
+GjBgQLV9Is+bePz4MXl7e9O5c+coOzubJk6cSEFBQXT06FHKy8sjIqJFixaRIAi0f/9+sRw/yUca
+PMDjHUVE2LhxIy5fvgwAcHNzw8OHD3H48GEAT39UMzIyEnPnzgUR4ebNm7h582a1/fFSQRBw4cIF
+LFy4EEuWLMH48eORlJSErVu3olu3bliyZAlOnDiBrl27onfv3pDL5XxHZhn6+voYPHgw1qxZA0dH
+R/j6+mLfvn1YtGgRli9fDk1NTejq6sLPzw+TJk3i8WEVkMvlyM/Px5YtW/D555/jiy++gLOzM3bu
+3InTp08DALy8vBAQEKDUfnwaWCIqTrjsNShOxS1ZsoS2b99ORETZ2dm0YMEC+vrrr2nOnDkUFRVF
+DRs2pMOHD4vlHj58qJJ4pZCSkkLDhw8nLy8v8b2TJ09Shw4d6MyZM0REtGnTJnJ0dKTo6GhVhVll
+KXoY+/btIz09PfL29qbc3FwiIoqJiaHGjRvTiBEjyMbGRnxuJvdKlCnaY9myZaSlpUVjxowhoqfP
+G502bRqNGDGCRowYQXXq1KFz584plWHS4JtW3kGKI8OUlBQsXrwYvr6+sLW1hZ2dHQwMDLBv3z5c
+v34dY8eORdeuXSGTyaChoQF9fX0VR/72UJkxSvT/v8t2+vRp5Ofno1WrVnBycsLp06chl8vRokUL
+uLu7w8HBAfXq1YOFhYUqw69yFO1oaWkJHx8fWFhYYMmSJWjevDnc3d3h7++PevXqITAwEO3bt+eb
+UyqgaI+UlBR07NgRixYtgq6uLtq0aYP27dvDwMAAhoaGGDx4MNq1a6dUhkmDn7Tyjps+fTrWrl2L
+mJgY2NnZie8/efIEenp61XLwaul5+t///oeCggLo6uqiffv22LFjB/bt2wcDAwMMGDAAn3zyCVav
+Xg1vb28VR101lU5ccrlcvLMwKSkJa9asQXx8PGbOnInatWsrlQGq1zpVGc6fP4+OHTtixowZGDdu
+nNJn3IaqwT28d5CiNyMIAnx9fZGamopvv/0Wbdq0ga6uLvT19aGlpaU0GL06UcxTREQEJk6ciPfe
+ew/Tp08Xr0HR/1/fvHjxIubMmQMfHx+xl8uUKX4EV/GjonK5HBoaGjAzM0Pt2rVx48YNbN26FT17
+9oSWlpbY9tVtnXpT9+7dAwDo6OhAEATI5XLUqFEDnTt3Rt++fWFmZoZWrVqJ3+c2VA1OeO+AkpIS
+8WdENDQ0xA1F8cOuHTt2hEwmw549e3Dt2jWkpaWhUaNG1XqDSkpKwpdffolt27YhLS0NMTExiIyM
+BCAVHDkAABfkSURBVBFh2LBhsLKyQm5uLjQ1NfH+++9zsquA4oBo4MCBSEhIgJ+fn1I7mZqaom7d
+uuIp8+q8Pr0uIkJaWhqCg4Ph5eUFc3NzlJSUQFNTE3K5HA4ODujWrRsMDQ1Rq1YtVYer9jjhVWGZ
+mZnIzMyEqakpDh48iN9++w2xsbHigPLSR+Senp6oX78+LC0tsWHDBnTs2LFaXbMrSxAEdOrUCRkZ
+GeJDeJ2cnDBu3DgYGxtj8ODByMrKwtWrV9GyZctq3RavqmzPv3Hjxjh37hxatWoFPT09pcRmamoK
+S0tLVYVa5QmCACMjIxw7dgz79u1D7969xdPCiu3T0dERtWrV4uueVQAnvCoqLy8P8+bNQ1xcHB49
+eoRvvvkG3bp1w6JFi5CcnAxfX19oaGhAQ0ND7AFaWVmhZs2a6Nu3r9Kjw6oTxU5DT08PFhYWuHDh
+AkxNTeHv74/bt2/DysoK7dq1Q506dVCrVi14e3vDzMxM1WFXKYpnjD558gQaGhpwcHDAihUr4OLi
+wr2QV3D37l2kpaXB0tISXl5eiImJgYeHB4yNjcVtkoceVC1800oVtmvXLpw8eRJZWVnw9PTEqFGj
+kJaWhn79+sHLywszZ84UfxyytOp2JFl2fkq/3r17N3755Re0b98eK1euxI4dO+Dp6al0AwYrf5PE
+Dz/8gL///huGhoYYOnQo0tPTsWXLFmzZsoUfcfUMpde77OxsfPbZZxAEATY2Nvjmm28wdOhQBAQE
+YNSoUSqOlD0LJ7wqhojEawAAcObMGSxZsgRyuRxz5syBm5sb0tPT4e/vD19fXyxYsKBaJbdnOX/+
+PNavX4+ff/65XAL8888/kZWVBVdXV/j7+1e7hP82KNrkypUr0NbWhoODA0xNTREZGYmZM2eiVq1a
+2Lt3L/73v/+hdu3a4vVh9h9FG6akpMDExASCICAnJwcTJkxAo0aNEBYWBrlcjm3btqFOnTqqDpdV
+gH8PrwrS1NREREQEdu3ahTVr1iAvLw979+5FWFgYAgIC4OrqigMHDuDWrVvVesdeeqdbWFiI4uJi
+AP/1UhS9uAEDBohl+PitPMWOWvHL2h07dkRkZCTWr1+PDz74AA0bNkRmZibS09MRHByM8PBwTnal
+lO4dh4eHY/r06ZDL5fD398eYMf/X3r1HVVmlDxz/HkERRGUwQfFksZAlLh3IVBQxdTQVMafRAcfx
+TjkYFl6I8bJSSFdmeWGUJi+hJiMqSKnIoIhc1IQ83moEvGboKCipIYdA5QD790edEzj2K1fagcPz
++UvgvGvts9fr+7x772c/+zXi4+O5evUqLi4uxMXFcfnyZdzd3eXFqx6Su7qeMT6Y5s2bR0BAAACD
+Bw9m6NChFBYWsm3bNgoKCnB2dsbHx8ciH/B3794Fvl/0/+qrr9DpdDg4OJjOYTOysrKiqqqqzrWS
+7v2/jCO7pKQk4uPjiY2N5d1332Xq1Kl8/vnnODs706VLF5KSkmjVqhWlpaXmbnK9Yrynzpw5Q3R0
+NNu2bWPv3r0YDAZiYmIoKyvj6aef5pVXXuFvf/sbq1ev5v79+3If1kMS8OqhEydO8Pbbb+Pv78+9
+e/cA8Pf3Z8iQIVy7dq1OkLO0/1R37txh3rx5lJSUoNfrWb58OcHBwaxYsYKsrCzee+89duzYwcGD
+B6mpqXnoGqb4cVRSXV1NZWUlb7/9NhkZGXz33XdUVVUxceJEpk+fzurVq6mpqQEgLS2No0ePmkbS
+jd2NGzdYsmQJNTU13Lp1i6ioKL755hscHBzQarWEh4eTlZXF9u3bTde0bNmSsrIyU5+K+kWeFmb2
+sGmP69evk5ubS0BAAM2bNwdAp9MxcOBAfHx8LD6pIDw8nNLSUkpKSli/fj3w/RaNwsJC7O3t2bVr
+F6WlpTRr1oy+ffuaubX1W0VFBS1btmTTpk3MmDGD9PR0unXrRseOHencuTO5ubmm+8/FxYWMjIw6
+p5Y3Znfv3iUwMJAbN27g5OTEpEmTKCkpYdu2bYwdO5YOHTowceJEbt++TU1NDTU1Ndja2rJu3TrZ
+BlNfPaEaneIXqH2GW0FBgTpz5ozp3yEhISoqKkoppZROp1MeHh6mIsiWqHYR3f/+979qy5Ytqn//
+/iorK0sp9X3B7AkTJqi4uLifvE7UlZycrPr06aMiIyPVkSNHVFlZmQoICFDDhg1TCxYsUL169VI7
+d+40dzPrndr3VGVlpXr11VdVUFCQMhgMKj09XYWEhKiAgAC1efNm1alTJ5WammrG1opHIfvwzMy4
+EB4SEoJOp+Pw4cP06NEDJycnEhMT2bhxIwkJCSxdupRBgwaZu7lPlHH9MiIigokTJ9KqVSs2b95M
+hw4dcHV1paysjKKiIvr16/c/14m6yRWFhYUsW7aMSZMmUVVVxYEDB2jZsiWzZs3i4MGDXLp0iXfe
+eYdhw4aZrpV+rNuH+fn5tG3bFnd3d/7zn/+wf/9+pk2bxu9+9zsyMzMpKioiLCyMESNGmApAiPpN
+Ap4ZGYsfz58/n3379qHRaFi+fDkAI0eO5LXXXqN///6MHz8eb29viy04a3zYXrhwgUWLFhEZGUn3
+7t3p2LEj1dXVbNmyhY4dO+Li4mLaXG9kaX3xaxnPBTxy5AhKKcLCwnB1dcVgMJCamoq1tTUzZ85k
+7969XL16leeffx47Ozvpx1o0Gg2pqalMmDCBF198kS5duuDm5sbnn39OZmYmQUFBaLVaioqKqK6u
+plOnTtjb25u72eIXkFcSM2vTpg0ffvghJ0+eJCYmhpycHI4dO0ZISAgXL17E1dWVjh07mj5vSQ+m
++/fvm7LZrl69ytatW7l8+TJ5eXkAODk58ec//5lBgwaxePFiunTpwh/+8AeLzEz9tYwvDVlZWYwa
+NYojR47wwQcfkJeXR7t27fD398fb25tPP/0UjUbD6tWruXXrlozsHmB88fr73/9ObGwsv//977Gy
+ssLDw4MZM2Zw584dZs6cyaBBg3j++ecpLi6WAgcNiGw8/w3VHqGVlJTQtGlT05theHg47u7uTJs2
+jbVr17J582a2bt1a51gWS1JVVUV2djYFBQXY29uTn5/PqFGjSEpK4s6dO4wcOZKBAwcCcPPmTSoq
+KnjmmWfM2+h67ty5c8yePZsFCxbg6+vL4sWLSUxMJD4+nq5du/LNN99QWVmJVqsFkGo0P3gw6F+4
+cIGlS5fy8ccfU11dTXV1Nc2aNaOqqoqCggIqKirw8vICoKysjJYtW5qr6eIRSZbmb0yj0ZCUlMTG
+jRspLS1l3LhxDB48mJ49e7JhwwYMBgMJCQlERUVZbLADsLa2pk2bNrzzzjvk5uayadMmPD09sbW1
+ZevWrezfvx+DwcCQIUNo27at6ToZkdRVuz9Onz7N1atX2bNnD76+vkRERGBlZYW/vz8pKSl069at
+znUS7OoWKjh79iy2tra0bt2aw4cPEx8fz9ixY7GysiItLY3jx4/z1ltvAT++LEiwa1hkDe83pNFo
+OH/+PNOmTeOf//wnnTt35ty5c5w9exZvb28cHBzYu3cvs2bN4sUXX7TINbva36l169bs3LkTrVaL
+nZ0dHh4eaLVa3NzcOH78OF9//TXdu3evUwjbkvricTCuAyckJBAcHEz79u358ssvuX79Oj179qR/
+//6UlpbSrl27OiNk6ccfGRPH3njjDQYNGkTnzp1xc3Nj7dq1XLlyBb1ez1tvvcWYMWPw8PAAkASV
+huo3zgptlIxpzkVFRWrfvn1q+PDhpr/pdDo1ePBgpdPplFJK3b1713SNpaXc1/5ORUVFpt/l5eWp
+6dOnq4ULFyqllNLr9SoxMVFduHDBbG1tSPLy8tTTTz+tVq5cqZRSaseOHSo4OFitWrWqzucs7X56
+XE6dOqW8vLzU+fPnlVJKXb9+XR0/flzl5+erMWPGqNDQUPXvf/9bKSV92NDJlOYTZqwHmZOTw4IF
+C/jwww9p3rw5iYmJBAYG4u3tTdeuXU3ntjVt2tR0rSW+hWs0GlJSUli0aBF9+/alWbNmLFu2jIkT
+J7JlyxZGjx5NXl4eiYmJUoD3Z+j1euzs7OjatSupqakEBgailOLNN9/EYDBw4MABrly5YhrZWeL9
+9Dg0b94cLy8vMjMz2bFjB1lZWQDMnTuXhIQE0+eUpDs0eDKl+YRpNBrS09PZsGEDISEh+Pj4cOvW
+LfLz88nMzMTKyorly5cTEhKCVqs1TZVY4sNJo9Fw6NAhZs+ezdatW7l27Rpr164lPz+f0NBQvLy8
+uHv3LpMmTcLX19fcza23lFJcunSJqVOn4u7uTrt27XB2dmbAgAGEh4cDMHXqVHr37m1KUBE/zc7O
+juLiYuLi4vjTn/7ElClTsLOzw2AwmJJTQOq0WgKZiH4CHnwTLCwsZOvWrVy9ehWAwMBA/Pz8+Pbb
+b9m2bRvR0dH07t3b4t8ga2pqMBgMpuryu3fv5tChQ5w9e5ZJkyah1WqZMWMGQ4cORSll8f3xKGr3
+h0ajoVOnTvTs2ZOlS5fy5ZdfUllZSbdu3Rg6dCjvvfceV65coX379mZudcPQokULQkNDOXjwIKNH
+j6asrIw1a9ZI/1kis02mWjDjPH9hYaFpTW779u3KxsZGZWdn1/lMRUWF6WdLXB+orq5WSil17969
+Ot95woQJKjk5WSmlVHh4uOrcubP64osvzNbO+s7Yd1lZWSo6OtpUhm7lypVq5MiRKi0tTSUnJ6uJ
+Eyeqs2fPmrOpDVZVVZU6fvy46tWrl9q9e7dSStbsLI1MaT5m6oc08ZSUFKZPn87u3bu5cuUKf/3r
+X+nevTvjx4/Hx8fHtK5iXLOz1OkSjUbDrl27mDNnDjqdDjs7O9zd3UlLS8PW1paioiJSU1P517/+
+RdeuXWXbwUMY++To0aMEBwdTUVHBsWPHuH37Nq+//jqlpaVkZGQQGxtLSEgIL7zwQp3rxC/TpEkT
+HBwc8PPzq3P0lvSh5ZCN50+ATqdj0aJFLFmyhOLiYk6fPk1BQQFr1qzho48+IiwsjMLCQlq1amWR
+6c3qgQ32kydPZty4cej1elMf1NTUsG7dOi5cuEBYWJjp7D95SD+cTqcjMjKSZcuW4enpyfbt28nJ
+ycHT05OgoCCsra25desWTz31lPThYyL9aHkkS/MxKykpISoqiuLiYrp37w6AVqvl3XffJTMzk2nT
+puHn54eDg4OZW/pkaTQadDodJ06coEePHowdOxYAGxubOtVk9Ho9rVq1krfpn1FaWkp6ejppaWl4
+enoSGBhIkyZNSE9Pp7y8nNDQUBwdHc3dTIsi96LlkSnNX+nBB7WtrS2Ojo7s27ePmzdvMmDAAJyc
+nDh48CBlZWX069cPe3t7mjRpYpFvkMbvlJ2dzeTJk7l9+zZffPEF7u7udOjQgR49emBtbc3cuXMZ
+O3YsrVu3NvWBpfXF4+Tm5oaXlxcrVqzA0dERLy8vunTpQnl5Ob6+vjg7O0s/CvEzZErzVzI+4DMy
+Mjh27Bht27Zl1KhR5OXl8cEHH9CyZUuCgoKYPn060dHRFn/ED3w//bZgwQJWrlyJp6cnCxcupKSk
+hICAAHx9fWnatCmFhYV06NDB3E1tcFJSUli4cCEzZ85k8uTJ5m6OEA2K5S0g/YaMwe6zzz7jtdde
+o0WLFsTExBAdHU3Tpk0JDQ3l6NGjzJ8/nw0bNjBo0CCqqqrM3ewnrrS0lMzMTNLT0wFYuHAhbdq0
+ITY2ls8++wyllCnYyfvWoxkxYgSRkZG8//77puNphBC/jAS8X8FYG3Pt2rXMmTOHGTNmsGvXLvR6
+PcnJyQwYMIB169bRqVMnDh06BHxfNNnSDR06lJ07d7Jhwwa2bdtGs2bNWLBgAe3bt8fJyanOlJtM
+vz26l19+mUOHDuHi4iIFoIV4BJb/9H3MjKM6Y8mw/Px8bt68yf79+xk+fDharZY5c+YwbNgw3njj
+Dfr27YvBYCAuLs6URdcYvPzyy1hbW7Nw4UIqKyuZMmUKS5YskQD3mBhPkLDEdWAhnhRZw3sEtRNU
+aq9BZWdnExcXh7u7O2PHjqW8vJzAwEBSUlLo0KEDlZWVVFVV1an631gkJSUxf/580tPTcXZ2lhGJ
+EMJsJOA9AuPb9N69e4mMjGTIkCE0adKERYsWcfjwYaKjo7l27RoODg7Mnj0bf39/eQPn+wNca59p
+J4QQ5iBTmo/AePbY3Llz2bFjB7GxsezcuZOioiLWr1+Pra0tmzZtws3NjWHDhpm7ufWGTL8JIeoD
+SVp5BNXV1ej1euLj47l27Rrp6el8/PHH3Lhxg+DgYHr06MEf//hHLly4wIYNG6iqqpIHfC3SF0II
+c5IR3iOwsrJi8ODBaDQa/vGPf7By5Up8fHxwdXXl4sWLXLx4kZdeegmlFL169WoUGZlCCNFQyBP5
+Jzw4/Wb82cbGhvv376PX68nNzaWmpobTp08TExODh4cHACNHjjRXs4UQQvwESVp5iNrZmHl5eTg6
+OuLi4lLnMwcPHiQqKoqKigqCg4MZM2aM6VqZuhNCiPpHAt5DGINWcnIyy5cvZ8WKFXh7e5v+btyD
+V15ejlIKe3t7KX4shBD1nAS8n/DVV18xZswYPvroI3r27Fnnbw8LbjKyE0KI+k2yNH9w+fJlQkND
+TT8bq6IYg52xBmZ5eflDD2uVYCeEEPWbBLwfPPvss0yZMoWvv/4aAE9PTxwdHcnIyKCyshJra2sO
+Hz7MsmXLuHfvnhQ9FkKIBqbRT2kqpaiurjZtIfD19cXKyspUOeXSpUvY2dnRp08fwsPDWbNmDUOG
+DDFzq4UQQjyqRh3waq/FnTt3zrStYODAgTg5ObFjxw7S09NJSUmhsrKSESNGSLkwIYRooBp9wDPW
+xpwxYwYJCQn06NEDgH79+uHs7Mynn34KwP3797GxsZFsTCGEaKAadcADOHXqFOPGjSM+Pp7nnnuO
+K1eu4OTkhK2tLX369MHe3p709HTTVgQhhBANU6OrtPLgCM3a2pqAgAByc3NJTU0lISEBd3d35s+f
+z9GjR8nJyQGQYCeEEA1co3yKazQa0tLSSE1N5amnnuLu3bts374dV1dX4uPjcXV15dSpUwD07dsX
+pZRkZQohRAPX6AKeRqMhKSmJN998E4PBgIuLC0uWLGHXrl385S9/wWAwcODAAdzc3OpcI2t2QgjR
+sDW6gFdSUsLq1av55JNPGDFiBCdPnuSTTz6hpqaGrKwspk2bRkREBAMHDpRRnRBCWBCLX8N7cAuB
+8Yy6xMREzp07h5WVFZmZmdy5c4dJkyaxceNGPDw8JNgJIYSFsegRXu2gdf78eb799lvatm1LREQE
+ZWVlBAUFERsbS0xMDMeOHcPW1ta0Fw9k64EQQlgSi96WUF1djZWVFcnJySxatIgXXniBmpoaZs2a
+haurKwD79+8nLCyM5cuX4+/vb+YWCyGEeFIscoT33XffAd+fUH7s2DEiIyNJSkqiRYsWZGVlERER
+wcmTJykvL2f16tW8//77pgoqQgghLJPFjfBKSkpYsWIFXbt2Zdy4ceh0Opo1a8bNmzeZN28eq1at
+IiYmBr1ez4oVK2jfvr2cZyeEEI2AxY3wrKyssLOz48SJE+zZs4fevXvTvXt3MjIyWLVqFf3790er
+1dKmTRvKy8uxt7c3XSvBTgghLJfFBDzjqQetWrUyrdFlZGSwc+dOAPR6PYsXLyYzM5Pk5GRef/11
+nnvuORnZCSFEI2ExU5rG7QeZmZncv3+fAQMGsH79ei5fvsxLL73EkCFDePXVV6moqCAwMJDRo0fL
+qQdCCNGIWEzAA0hOTiYiIoKlS5fi5+dHaWkpmzZtoqCggOHDhzN8+HA59UAIIRopi5nSLCsrY+PG
+jaxduxY/Pz8MBgOtW7fmlVdeQavVsmfPHoqLi7GxsQGkXJgQQjQ2FlNppUmTJty6dQu9Xg/8OHKr
+rKwkLCyMwsJCnJ2dzdlEIYQQZmQxI7wWLVowZswYcnJyOHPmDNbW1mRnZzN+/Hhu3rzJM888Y+4m
+CiGEMCOLWsMrLCxk/fr1ZGVl4evrS2JiItHR0YwYMcLcTRNCCGFmFhXwACoqKtDpdBQXF/Pss8/S
+p08fSVARQghheQHvQRLshBBCgAUlrfwUCXRCCCHAgpJWhBBCiP+PBDwhhBCNggQ8IYQQjYIEPCGE
+EI2CBDwhhBCNggQ8IYQQjYIEPCGEEI3C/wElANR3gSZd6QAAAABJRU5ErkJggg==
+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a name="cython_bonus"></a> <br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="bonus-how-to-use-cython-without-the-ipython-magic">Bonus: How to use Cython without the IPython magic</h2>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#sections">back to top</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>IPython's notebook is really great for explanatory analysis and documentation, but what if we want to compile our Python code via Cython without letting IPython's magic doing all the work?<br />These are the steps you would need.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="creating-a-.pyx-file-containing-the-the-desired-code-or-function.">1. Creating a .pyx file containing the the desired code or function.</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[11]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="o">%%</span><span class="k">file</span> <span class="n">ccy_classic_lstsqr</span><span class="o">.</span><span class="n">pyx</span>
+
+<span class="k">def</span> <span class="nf">ccy_classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
+    <span class="n">x_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+    <span class="n">y_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+    <span class="n">var_x</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
+    <span class="n">cov_xy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">y_i</span> <span class="o">-</span> <span class="n">y_avg</span><span class="p">)</span> <span class="k">for</span> <span class="n">x_i</span><span class="p">,</span><span class="n">y_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)])</span>
+    <span class="n">slope</span> <span class="o">=</span> <span class="n">cov_xy</span> <span class="o">/</span> <span class="n">var_x</span>
+    <span class="n">y_interc</span> <span class="o">=</span> <span class="n">y_avg</span> <span class="o">-</span> <span class="n">slope</span><span class="o">*</span><span class="n">x_avg</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">slope</span><span class="p">,</span> <span class="n">y_interc</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Writing ccy_classic_lstsqr.pyx
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="creating-a-simple-setup-file">2. Creating a simple setup file</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[12]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="o">%%</span><span class="k">file</span> <span class="n">setup</span><span class="o">.</span><span class="n">py</span>
+
+<span class="kn">from</span> <span class="nn">distutils.core</span> <span class="kn">import</span> <span class="n">setup</span>
+<span class="kn">from</span> <span class="nn">distutils.extension</span> <span class="kn">import</span> <span class="n">Extension</span>
+<span class="kn">from</span> <span class="nn">Cython.Distutils</span> <span class="kn">import</span> <span class="n">build_ext</span>
+
+<span class="n">setup</span><span class="p">(</span>
+    <span class="n">cmdclass</span> <span class="o">=</span> <span class="p">{</span><span class="s">&#39;build_ext&#39;</span><span class="p">:</span> <span class="n">build_ext</span><span class="p">},</span>
+    <span class="n">ext_modules</span> <span class="o">=</span> <span class="p">[</span><span class="n">Extension</span><span class="p">(</span><span class="s">&quot;ccy_classic_lstsqr&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s">&quot;ccy_classic_lstsqr.pyx&quot;</span><span class="p">])]</span>
+<span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Writing setup.py
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="building-and-compiling">3. Building and Compiling</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[13]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="o">!</span>python3 setup.py build_ext --inplace
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+running build_ext
+cythoning ccy_classic_lstsqr.pyx to ccy_classic_lstsqr.c
+building &apos;ccy_classic_lstsqr&apos; extension
+creating build
+creating build/temp.macosx-10.6-intel-3.4
+/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -arch i386 -arch x86_64 -g -I/Library/Frameworks/Python.framework/Versions/3.4/include/python3.4m -c ccy_classic_lstsqr.c -o build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o
+ccy_classic_lstsqr.c:2040:28: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyObject_AsString&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
+<span class="ansigreen">                           ^
+</span>ccy_classic_lstsqr.c:2037:32: </span><span class="ansipurple">warning: </span>unused function
+      &apos;__Pyx_PyUnicode_FromString&apos; [-Wunused-function]</span>
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {
+<span class="ansigreen">                               ^
+</span>ccy_classic_lstsqr.c:2104:26: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyObject_IsTrue&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+<span class="ansigreen">                         ^
+</span>ccy_classic_lstsqr.c:2159:33: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyIndex_AsSsize_t&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
+<span class="ansigreen">                                ^
+</span>ccy_classic_lstsqr.c:2188:33: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyInt_FromSize_t&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
+<span class="ansigreen">                                ^
+</span>ccy_classic_lstsqr.c:1584:32: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyInt_From_long&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+<span class="ansigreen">                               ^
+</span>ccy_classic_lstsqr.c:1631:27: </span><span class="ansipurple">warning: </span>function &apos;__Pyx_PyInt_As_long&apos; is not
+      needed and will not be emitted [-Wunneeded-internal-declaration]</span>
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+<span class="ansigreen">                          ^
+</span>ccy_classic_lstsqr.c:1731:26: </span><span class="ansipurple">warning: </span>function &apos;__Pyx_PyInt_As_int&apos; is not
+      needed and will not be emitted [-Wunneeded-internal-declaration]</span>
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+<span class="ansigreen">                         ^
+</span>8 warnings generated.
+ccy_classic_lstsqr.c:2040:28: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyObject_AsString&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
+<span class="ansigreen">                           ^
+</span>ccy_classic_lstsqr.c:2037:32: </span><span class="ansipurple">warning: </span>unused function
+      &apos;__Pyx_PyUnicode_FromString&apos; [-Wunused-function]</span>
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {
+<span class="ansigreen">                               ^
+</span>ccy_classic_lstsqr.c:2104:26: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyObject_IsTrue&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+<span class="ansigreen">                         ^
+</span>ccy_classic_lstsqr.c:2159:33: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyIndex_AsSsize_t&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
+<span class="ansigreen">                                ^
+</span>ccy_classic_lstsqr.c:2188:33: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyInt_FromSize_t&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
+<span class="ansigreen">                                ^
+</span>ccy_classic_lstsqr.c:1584:32: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyInt_From_long&apos;
+      [-Wunused-function]</span>
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+<span class="ansigreen">                               ^
+</span>ccy_classic_lstsqr.c:1631:27: </span><span class="ansipurple">warning: </span>function &apos;__Pyx_PyInt_As_long&apos; is not
+      needed and will not be emitted [-Wunneeded-internal-declaration]</span>
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+<span class="ansigreen">                          ^
+</span>ccy_classic_lstsqr.c:1731:26: </span><span class="ansipurple">warning: </span>function &apos;__Pyx_PyInt_As_int&apos; is not
+      needed and will not be emitted [-Wunneeded-internal-declaration]</span>
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+<span class="ansigreen">                         ^
+</span>8 warnings generated.
+/usr/bin/clang -bundle -undefined dynamic_lookup -arch i386 -arch x86_64 -g build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o -o /Users/sebastian/Github/python_reference/benchmarks/ccy_classic_lstsqr.so
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="importing-and-running-the-code">4. Importing and running the code</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[20]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">ccy_classic_lstsqr</span>
+
+<span class="o">%</span><span class="k">timeit</span> <span class="n">classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="o">%</span><span class="k">timeit</span> <span class="n">cy_classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="o">%</span><span class="k">timeit</span> <span class="n">ccy_classic_lstsqr</span><span class="o">.</span><span class="n">ccy_classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+100 loops, best of 3: 2.9 ms per loop
+1000 loops, best of 3: 212 µs per loop
+1000 loops, best of 3: 207 µs per loop
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+    </div>
+  </div>
+</body>
+</html>
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index 9174704..d9dda4a 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:14c3265ea9accf7a70bb5032b9e2b881a487f6b81b55999ef8578645edf4e3dd"
+  "signature": "sha256:07d1097e63ed00fca1ea6848104a030744ac87d11e515ae48e01e53b7425d997"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -442,7 +442,14 @@
      "metadata": {},
      "source": [
       "<br>\n",
-      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "#### Visualization"
      ]
     },
@@ -514,7 +521,14 @@
      "metadata": {},
      "source": [
       "<br>\n",
-      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "#### Comparing the results from the different implementations"
      ]
     },
@@ -569,7 +583,14 @@
      "metadata": {},
      "source": [
       "<br>\n",
-      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "#### Initial performance comparison"
      ]
     },

From f6a1db2dfd98c5caa5836fc25d03ba7991164a2e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 4 May 2014 19:23:08 -0400
Subject: [PATCH 012/248] fixed links and updated readme

---
 README.md | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 18dfea1..1305f53 100755
--- a/README.md
+++ b/README.md
@@ -7,11 +7,9 @@ Useful functions, tutorials, and other Python-related things
 ###Links to view the IPython Notebooks
 
 - [Python benchmarks via `timeit`](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
--   
-- Implementing the least squares fit method for linear regression and speeding it up via Cython  
-  [[IPython Notebook](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)] [[HTML]()]
-- [Benchmarks of different palindrome functions](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)
-- [A collection of not so obvious Python stuff you should know!](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)
+- [Implementing the least squares fit method for linear regression and speeding it up via Cythonook](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
+- [Benchmarks of different palindrome functions](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
+- [A collection of not so obvious Python stuff you should know!](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)  
 - [Python's scope resolution for variable names and the LEGB rule](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
 
 ### Links to Markdown files

From fc9dcf61eff548bc014ad6779f4291ed7d731b86 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 4 May 2014 20:16:02 -0400
Subject: [PATCH 013/248] fixed links and updated readme

---
 .../cython_least_squares-checkpoint.ipynb                   | 6 +++---
 benchmarks/cython_least_squares.ipynb                       | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index d9dda4a..7c78e61 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:07d1097e63ed00fca1ea6848104a030744ac87d11e515ae48e01e53b7425d997"
+  "signature": "sha256:6fec0ff80302cd983b1b3ab4e6653aa12c291ed854c5efd69f28608d4f6af614"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,7 +12,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "[Sebastian Raschka](www.sebastianraschka.com)  \n",
+      "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
       "last updated: 05/04/2014\n",
       "\n",
       "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb)  \n",
@@ -414,7 +414,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In order to test our different least squares fit implementation, we will generate some sample data:\n",
+      "In order to test our different least squares fit implementation, we will generate some sample data:  \n",
       "- 500 sample points for the x-component within the range [0,500)  \n",
       "- 500 sample points for the y-component within the range [100,600)  \n",
       "\n",
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index d9dda4a..7c78e61 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:07d1097e63ed00fca1ea6848104a030744ac87d11e515ae48e01e53b7425d997"
+  "signature": "sha256:6fec0ff80302cd983b1b3ab4e6653aa12c291ed854c5efd69f28608d4f6af614"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,7 +12,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "[Sebastian Raschka](www.sebastianraschka.com)  \n",
+      "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
       "last updated: 05/04/2014\n",
       "\n",
       "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb)  \n",
@@ -414,7 +414,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In order to test our different least squares fit implementation, we will generate some sample data:\n",
+      "In order to test our different least squares fit implementation, we will generate some sample data:  \n",
       "- 500 sample points for the x-component within the range [0,500)  \n",
       "- 500 sample points for the y-component within the range [100,600)  \n",
       "\n",

From 7d6d3d35400869bed488e2e9f3c4dc33590cad23 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 4 May 2014 22:17:27 -0400
Subject: [PATCH 014/248] research reference added

---
 .../cython_least_squares-checkpoint.ipynb                   | 6 ++++--
 benchmarks/cython_least_squares.ipynb                       | 6 ++++--
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index 7c78e61..e28d5c5 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6fec0ff80302cd983b1b3ab4e6653aa12c291ed854c5efd69f28608d4f6af614"
+  "signature": "sha256:ed0de0773556cdc26dc372038810ad0e5322ec915298a90f2badf09740d856db"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -104,7 +104,9 @@
       "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_vertical.png)  \n",
       "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_perpendicular.png)  \n",
       "\n",
-      "Here, we will use the more common approach: minimizing the sum of the perpendicular offsets.\n"
+      "As Michael Burger summarizes it nicely in his article \"[Problems of Linear Least Square Regression - And Approaches to Handle Them](http://www.arsa-conf.com/archive/?vid=1&aid=2&kid=60101-220)\": \"the perpendicular offset method delivers a more precise result but is are more complicated to handle. Therefore normally the vertical offsets are used.\"  \n",
+      "Here, we will also use the method of computing the vertical offsets.\n",
+      "\n"
      ]
     },
     {
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index 7c78e61..e28d5c5 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6fec0ff80302cd983b1b3ab4e6653aa12c291ed854c5efd69f28608d4f6af614"
+  "signature": "sha256:ed0de0773556cdc26dc372038810ad0e5322ec915298a90f2badf09740d856db"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -104,7 +104,9 @@
       "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_vertical.png)  \n",
       "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_perpendicular.png)  \n",
       "\n",
-      "Here, we will use the more common approach: minimizing the sum of the perpendicular offsets.\n"
+      "As Michael Burger summarizes it nicely in his article \"[Problems of Linear Least Square Regression - And Approaches to Handle Them](http://www.arsa-conf.com/archive/?vid=1&aid=2&kid=60101-220)\": \"the perpendicular offset method delivers a more precise result but is are more complicated to handle. Therefore normally the vertical offsets are used.\"  \n",
+      "Here, we will also use the method of computing the vertical offsets.\n",
+      "\n"
      ]
     },
     {

From f5e08cde14ed95fd6174dcad848305821751c2c7 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 5 May 2014 01:44:10 -0400
Subject: [PATCH 015/248] section on growth rates for different sample sizes

---
 .../cython_least_squares-checkpoint.ipynb     | 86 ++++++++++++++++++-
 benchmarks/cython_least_squares.ipynb         | 86 ++++++++++++++++++-
 2 files changed, 170 insertions(+), 2 deletions(-)

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index e28d5c5..3032c66 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ed0de0773556cdc26dc372038810ad0e5322ec915298a90f2badf09740d856db"
+  "signature": "sha256:a091baac3cb82ac71775c03f73be1925fefe2d659bb7359de4bb88887fbe7d24"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -69,6 +69,7 @@
       "- [Least squares fit implementations](#implementations)\n",
       "- [Generating sample data and benchmarking](#sample_data)\n",
       "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
+      "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
       "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)"
      ]
     },
@@ -956,6 +957,89 @@
      ],
      "prompt_number": 19
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"sample_sizes\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Performance growth rates for different sample sizes"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the plot above, we've seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of the sample size on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs =  ['cy_classic_lstsqr', \n",
+      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
+      "labels = ['classic approach (cython)', 'matrix approach', \n",
+      "          'numpy function', 'scipy function']\n",
+      "orders_n = [10**n for n in range(1, 7)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for f in times_n.keys():\n",
+      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
+      "\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.legend(loc=2)\n",
+      "\n",
+      "\n",
+      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPUQAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10c6fb1d0>"
+       ]
+      }
+     ],
+     "prompt_number": 41
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index e28d5c5..3032c66 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ed0de0773556cdc26dc372038810ad0e5322ec915298a90f2badf09740d856db"
+  "signature": "sha256:a091baac3cb82ac71775c03f73be1925fefe2d659bb7359de4bb88887fbe7d24"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -69,6 +69,7 @@
       "- [Least squares fit implementations](#implementations)\n",
       "- [Generating sample data and benchmarking](#sample_data)\n",
       "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
+      "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
       "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)"
      ]
     },
@@ -956,6 +957,89 @@
      ],
      "prompt_number": 19
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"sample_sizes\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Performance growth rates for different sample sizes"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the plot above, we've seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of the sample size on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs =  ['cy_classic_lstsqr', \n",
+      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
+      "labels = ['classic approach (cython)', 'matrix approach', \n",
+      "          'numpy function', 'scipy function']\n",
+      "orders_n = [10**n for n in range(1, 7)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for f in times_n.keys():\n",
+      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
+      "\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.legend(loc=2)\n",
+      "\n",
+      "\n",
+      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVPX+P/DXzMCwDvu+rwkoLmmm4oKClpl7kgoIavXz\nqqVWtyxzKUut9N6+dvXe6qKU4J6muSSJoqQWmQsCBoICsoMIyjbAzPv3B5eJYUeFgeH9fDzm8Ziz\nfM55n3Xe8zmfc46AiAiMMcYYY6xLCVUdAGOMMcZYb8RJGGOMMcaYCnASxhhjjDGmApyEMcYYY4yp\nACdhjDHGGGMqwEkYY4wxxpgKcBKmxvLz8zF69GgYGBjg73//u6rDUbnKykpMnjwZRkZGePnll5sM\nX7duHYKDg1UQWc93+PBh2Nvbw8DAANeuXUO/fv1w/vz5dpfv6PjtlZ6eDqFQCLlc/sSnrWp/+9vf\n8PHHH3fpPOu3s0QiwfXr15/49Bseg5mZmZBIJKh/ilJz57P58+fDxMQEw4YNe+KxqIOYmBjY29s/\n0WnGxsbCw8PjiU6zN9NQdQBMmZOTEwoKCiASiaCnp4eJEyfiX//6F/T09Do8ra+//hoWFhZ48OBB\nJ0Ta8xw8eBAFBQUoLi6GUNj0/4dAIOj0GJycnLBjxw6MGzeu0+fVld5++21s374dkydPBgAkJCQo\nhq1btw5paWnYtWtXi+Ubjt8bhIaGwt7eHuvXr2/X+OHh4QgLC0NsbKyi37///e/OCq9Fjbfzk9bw\nGHRwcMDDhw8V3Y3PZ7GxsTh9+jRycnKgra3dKfG0JD09HS4uLqitrW32XKLORo0ahT///FPVYaiN\n3rX39AACgQDHjh3Dw4cPceXKFVy+fLnD/3aJCHK5HBkZGfD09HykOGprax+pXHeWkZGBp556qsWT\nZlc8t1ggEHTJfB5HR7c9ESEzMxNeXl6dFBHrDh53Oz9ubWTj81lGRgacnJweKQF7Uue37n4ssx6A\nWLfi5ORE0dHRiu63336bXnzxRSIiunTpEg0fPpyMjIxowIABFBMToxhvzJgxtGrVKvLx8SEdHR0K\nCgoiTU1NEovFpK+vT9HR0SSVSmnZsmVkY2NDNjY2tHz5cpJKpUREdPbsWbK1taVPP/2UrKysKDg4\nmNatW0cvvfQSBQUFkUQiIW9vb0pJSaENGzaQhYUFOTg4UFRUlCKGHTt2kKenJ0kkEnJxcaGvvvpK\nMax++lu2bCELCwuytramnTt3KoZXVFTQm2++SY6OjmRoaEgjR46kysrKNpe7saSkJBozZgwZGRlR\n37596ejRo0REtGbNGhKLxaSpqUn6+vq0Y8eOJmXXrl1LQUFBiu7W5tvashYWFtKkSZPIyMiITExM\naNSoUSSXyykoKIiEQiHp6OiQvr4+ff75501iaKksEdGVK1do0KBBJJFI6OWXX6aXX36ZPvjgAyIi\n2rlzJ40cOVJpWgKBgNLS0oiI6NixYzRw4EAyMDAge3t7WrdunWK8O3fukEAgoLCwMHJwcKAxY8YQ\nEVFYWBh5enqSsbExPffcc5SRkdEk3qqqKtLT0yOBQEB6enrk5uZGRESOjo50+vRpOnnypNJ6Hzhw\nYLPbzdHRUbHfr127tkP73ZgxY2jlypU0dOhQMjAwoKlTp1JxcbHSsslkMiIiKikpoQULFpC1tTXZ\n2trSBx98oBi2c+dOGjFiBK1YsYKMjIzI1dWVLly4QDt27CB7e3uysLCgb7/9VmnZ33rrLXJwcCBL\nS0tatGiRYp9tbX//6quvlI7NKVOmEBHRxo0bydXVlSQSCXl5edHhw4eJqG6f1tbWJpFIRPr6+mRs\nbExERCEhIYrtT0T09ddfk5ubG5mYmNCUKVMoJydHaV/4z3/+Q+7u7mRkZERLlixRDLt16xaNHj2a\nDA0NyczMjF5++eV2b+eWjrf6+BYtWkQTJ04kPT09pfNavdu3b9Po0aNJIpHQ+PHjaenSpYpjsH7b\n1dbWUkhIiNI6++qrr5TWSf3+/OOPP9KAAQPIyMiIRowYQfHx8Ur72Keffkre3t6kra1NMpmszXPq\n6tWrycfHhyQSCU2YMIGKioqIiMje3p4EAgHp6+uTvr4+/frrr02W7bfffqPBgweTgYEBWVpa0ptv\nvqkY9tJLL5GVlRUZGhrS6NGjKTExUWm9/e1vf6OJEyeSvr4+jRw5knJzc+mNN94gIyMj8vDwoKtX\nryot18aNG8nLy4uMjY1p/vz5VFVVRUR1+6GdnZ1i3OzsbJoxYwaZm5uTs7Mzbd26tUnc9Y4fP05e\nXl4kkUjI1taWNm/e3GSae/fuVawDfX19EovF5Ovrq9hnWjo+WjvP9TachHUzTk5OdPr0aSIiyszM\npL59+9KaNWsoKyuLTE1N6eTJk0RE9PPPP5OpqanipDBmzBhydHSkpKQkkslkVFNTQ6GhobR69WrF\ntFevXk3Dhw+nwsJCKiwspBEjRiiGnz17ljQ0NGjlypVUXV1NlZWVtHbtWtLW1qaoqCiqra2lefPm\nkaOjI23YsIFqa2vpm2++IWdnZ8X0jx8/Trdv3yYionPnzpGuri5duXJFafpr166l2tpaOnHiBOnq\n6lJJSQkRES1evJjGjh1LOTk5ipOjVCptcbkLCwubrLvq6mpydXWljRs3Uk1NDZ05c4YkEgklJycT\nEdG6desoODi4xXXfMAlra303t6z1J8aVK1fSokWLqLa2lmpra+mXX35R2r7N/RjVa6msVColBwcH\n+uKLL6i2tpYOHjxImpqaiu3XVhIWExNDCQkJREQUHx9PlpaW9MMPPxDRXz92ISEhVFFRQZWVlfTD\nDz+Qm5sb/fnnnySTyejjjz+mESNGtBh3w3k1Xs621nvj8Tu6340ZM4ZsbW0pMTGRysvLaebMmU1+\nyOsTrWnTptGiRYuooqKCCgoKaOjQoYoEeufOnaShoUHh4eEkl8vpgw8+IFtbW1q6dClVV1dTVFQU\nSSQSKi8vJyKi5cuX09SpU+n+/fv08OFDmjx5Mr333ntE1Pb+3vjYJCI6cOAA5ebmEhHRvn37SE9P\nj/Ly8oiIKDw8vMn2bTiN6OhoMjMzo6tXr5JUKqXXX3+dRo8erbR9Jk+eTKWlpZSZmUnm5uZ06tQp\nIiKaPXs2bdiwgYjq9rMLFy60uJ0abue2jreQkBAyNDSkixcvEhEpEoOGhg0bRm+99RZVV1fT+fPn\nSSKRKPaVxtuu8TprvE6uXLlCFhYWFBcXR3K5nL799ltycnKi6upqIqpLVgYNGkRZWVlUVVXVrnOq\nm5sb3bp1iyorK8nX15dWrlxJRETp6elKsTVn2LBhFBERQURE5eXlSonazp07qaysjKqrq2n58uVK\nf05CQkLIzMyMrly5QlVVVTRu3DhydHSkXbt2KfbLsWPHKsZ3dHQkb29vysrKouLiYvLx8VEk5w0T\nJplMRk8//TStX7+eampq6Pbt2+Ti4qLYDxqzsrJSnH9KSkqUzuUNE7t6Dx48IE9PT/r666+JqPXj\no7VzZG/DSVg34+joSPr6+mRkZESOjo60ZMkSqqyspE2bNjX5IXvuuecU/8x9fX1p7dq1SsNDQ0OV\n/im7uroqTjhERKdOnSInJyciqjuwxGKxomaMqO7HcMKECYruo0ePkr6+vuIfy4MHD0ggEFBpaWmz\nyzJt2jT6v//7P8X0dXR0lE5aFhYW9Ntvv5FMJiMdHR2lf6312lruhs6fP09WVlZK/ebMmaP4l9y4\npquxhsM7Mt/Gy7pmzRqaOnUqpaamNhmvrSSspbLnzp0jGxsbpX4Nk+i2krDGli1bRitWrCCiv37s\n7ty5oxj+/PPPU1hYmKJbJpORrq4uZWZmNju91pKwttZ7c+N3ZL/z9fVVnNyJ6mpnxGIxyeVypR/y\nvLw80tLSUvwbJyLavXu34gdt586d5O7urhgWHx9PAoGACgoKFP1MTU3p+vXrJJfLSU9PT2mZL168\nqEgOW9vfiZoem80ZOHAgHTlyRBFba0nYggUL6N1331UMKysrI01NTUXtpUAgUEquAgIC6NNPPyUi\nonnz5tFrr71GWVlZrcZTP536ZW7reAsJCaGQkJAWp5WRkUEaGhpUUVGh6Dd37twWE+jG66zxOlm0\naFGTxLZPnz50/vx5IqrbxxrWvrfnnPrJJ58ohm3fvp2ef/75ZmNrzujRo2nt2rXN/mFs6P79+yQQ\nCOjBgweK5XzttdcUw7/88kvy8vJSdMfHx5ORkZGi28nJSakm/sSJE+Tq6kpEygnTr7/+Sg4ODkrz\n3rBhA82fP7/ZuBwcHOirr75qcn5vLgmTyWQ0adIkWrx4MRFRm8dHa+fI3obbhHUzAoEAR44cwf37\n95Geno5//etf0NbWRkZGBg4cOABjY2PF58KFC8jLy1OUbesumJycHDg6Oiq6HRwckJOTo+g2NzeH\nWCxWKmNhYaH4rqOjAzMzM0XjWR0dHQBAWVkZAODkyZMYNmwYTE1NYWxsjBMnTuDevXuK8qampkrt\nsXR1dVFWVoaioiJUVVXB1dW1ScztWe6Gy9d4HTg6OiI7O7vV9dKctubb2rL+/e9/h5ubGyZMmABX\nV1d8+umn7Z5vS2VzcnJga2vbZNna67fffsPYsWNhYWEBIyMjfPXVV0rbBlDefzIyMrBs2TLFspua\nmgLAI63LR9GR/Q5Qjt3BwQE1NTUoKipSmmZGRgZqampgbW2tWK5FixahsLBQMY6lpaXSfIG646Jh\nv7KyMhQWFqKiogKDBw9WTGvixIlK82xpf2/Jd999h0GDBimml5CQ0GQbtSQ3N1dpf9DT04OpqanS\n9rKyslKKpb7R+2effQYiwtChQ9GvXz/s3LmzXfNs6XirP6cIBIJWz0k5OTkwNjZWrOf68o8qIyMD\nW7ZsUTpms7KylM5xjffxts4tDddZ/bZvr7CwMKSkpMDT0xNDhw7F8ePHAQAymQwrV66Em5sbDA0N\n4ezsDABK+07D/V9bW7vJ8dA4jsb7f8Nlbri89eu8/rNx40YUFBQ0G//333+PEydOwMnJCb6+vvj1\n119bXNZVq1ahvLwcW7duBYA2j4/HOUeqG747sodwcHBAcHAwvv766xbHaevuPhsbG6Snpysat2Zm\nZsLGxqbF8h25W1AqlWLmzJmIiIjA1KlTIRKJMH369HY1XDUzM4O2tjZSU1PRv39/pWHtWe56NjY2\nuHv3LohIEXtGRsYj3U7d2nzbWlZ9fX1s3rwZmzdvRmJiIsaNG4ehQ4di7Nixba7T5so+88wzsLGx\naZIAZWRkwM3NDUDdj25FRYViWOMkde7cuXjjjTdw6tQpiMVirFixokmS0vjOtNWrV2POnDntWFut\n64q7TjMzM5W+a2pqwszMDOXl5Yr+9vb20NLSwr179x77jjYzMzPo6OggKSkJ1tbWHS7feJ1kZGTg\ntddew5kzZzB8+HAIBAIMGjRIsU+199iuV15ejnv37jVJ3JtjaWmp2M8vXLgAf39/jBkzBi4uLm3O\n83GON2tra9y/fx8VFRXQ1dVVlBeJRO0q35iDgwNWrVqF999/v8VxGu/j7T23tDadlri5uWH37t0A\n6hKal156CcXFxTh48CCOHj2K6OhoODo6oqSkBCYmJo/VyL/x/t/wvF7P3t4ezs7OSElJadc0hwwZ\ngh9++AEymQxffvklAgIClOZTb+/evdi3bx9+//13xbZr6/ho6TynbneNtwfXhPUQQUFB+PHHHxEV\nFQWZTIaqqirExMQo/TA3Pogbd8+ZMwcff/wxioqKUFRUhI8++qjV52J15KRQXV2N6upqmJmZQSgU\n4uTJk4iKimpXWaFQiAULFuDNN99Ebm4uZDIZLl26hOrq6nYtd71hw4ZBV1cXn332GWpqahATE4Nj\nx45h9uzZ7V6Oeq3Nt61lPXbsGFJTU0FEMDAwgEgkUvzoW1paIi0trcX5Hj9+vElZkUiE4cOHQ0ND\nA1u3bkVNTQ0OHTqE33//XVFuwIABSExMxPXr11FVVYV169YpTbesrAzGxsYQi8WIi4vD7t27W/0h\nWbRoETZs2ICkpCQAQGlpKQ4cONDh9QjU1Sakp6d32p1kRISIiAjcvHkTFRUVWLNmDWbNmtVk+ayt\nrTFhwgS8+eabePjwIeRyOdLS0h7p+WRCoRCvvvoqli9frqhJy87Obvc+b2lpidu3byu6y8vLIRAI\nYGZmBrlcjp07dyo9tsPS0hJZWVmoqalRWu76dTpnzhzs3LkT169fh1Qqxfvvv49hw4bBwcGh2fk3\n3BYHDhxAVlYWAMDIyAgCgaBdSWpbx1tb29vR0RFDhgzB2rVrUVNTg19++QXHjh1rcfy2pvfqq6/i\nP//5D+Li4kBEKC8vx/Hjx1usvXqUc2o9c3NzCIXCVo/liIgIxb5haGioWK9lZWXQ0tKCiYkJysvL\nmySNHT1OiAjbt29HdnY2iouL8cknnzR7zhs6dCgkEgk+++wzVFZWQiaTISEhAZcvX24ybk1NDSIj\nI1FaWgqRSASJRNJscnz16lW8/vrrOHz4sKK2HGj7+GjpPNcbcRLWQ9jZ2eHIkSPYsGEDLCws4ODg\ngC1btigdsM3VZDXs98EHH2DIkCHo378/+vfvjyFDhuCDDz5od/mWxgEAiUSCrVu3IiAgACYmJtiz\nZw+mTp3aatmGNm/eDG9vbzzzzDMwNTXFe++9B7lc3uJyN3e7u6amJn788UecPHkS5ubmWLp0KXbt\n2oWnnnqqxeVpaXlbW99tLWtqairGjx8PiUSCESNGYMmSJRgzZgwA4L333sPHH38MY2Nj/OMf/2gS\nw61bt5otq6mpiUOHDiE8PBympqbYv38/ZsyYodj+Tz31FNasWQN/f3/06dMHo0aNUlrW7du3Y82a\nNTAwMMD69eubPKy28XqZNm0a3n33XcyePRuGhobw9vbGqVOnWl13LZk1axaAustzQ4YMaXG8htNq\n735X/z04OBihoaGwtrZGdXW14rJI43G/++47VFdXw8vLCyYmJpg1a5ai1rA9823o008/hZubG4YN\nGwZDQ0OMHz9eqZahtbILFy5EUlISjI2NMWPGDHh5eeGtt97C8OHDYWVlhYSEBIwcOVIxvp+fH/r2\n7QsrKyvFpamG8fr5+WH9+vWYOXMmbGxscOfOHezdu7fV9Vff7/Llyxg2bBgkEgmmTp2KrVu3wsnJ\nqdm4G07ncY83ANi9ezd+++03mJiY4KOPPkJISEiL82s8vcbdgwcPxjfffIOlS5fCxMQE7u7u+O67\n71qMoaPn1Ibz09XVxapVq+Dj4wNjY2PExcU1mf6pU6fQr18/SCQSrFixAnv37oWWlhbmzZsHR0dH\n2Nraol+/foqaz/YuZ3NxzZ07V3Fpz93dvdnzukgkwrFjx3Dt2jW4uLjA3Nwcr732WovPkYyIiICz\nszMMDQ3x9ddfIzIyssk0jxw5gpKSEowcORISiQQSiQSTJk0C0Prx0dJ5rjcSUGf9Pf0fJycnRaar\nqamJuLg4FBcX4+WXX1Y852X//v0wMjICAGzcuBE7duyASCTC1q1bMWHChM4Mj7Eea/78+bCzs2v3\nAz/V1dixYxEcHIwFCxaoOhTGupyzszPCwsJ65aU8ddDpNWECgQAxMTG4evWq4t/Cpk2bFFmxn58f\nNm3aBABISkrCvn37kJSUhJ9++gmLFy9Wy9eNMPYkdPL/px6F1wVjrCfqksuRjU+QR48eVVQ7h4SE\n4IcffgBQV7U5Z84caGpqwsnJCW5ubs1W8zLG2ne5p7fg9cAY64k6/e5IgUAAf39/iEQi/L//9//w\n6quvIj8/X3EruKWlJfLz8wHU3bLc8EWsdnZ2XXZLPGM9TXsfJaDuzp49q+oQGFOZO3fuqDoE9hg6\nPQm7cOECrK2tUVhYiPHjxze5fbk9jaUbcnNza/WOFMYYY4yx7mLAgAG4du1as8M6/XJk/TNCzM3N\nMX36dMTFxcHS0lJxR1Jubq7ibh9bW1vcvXtXUTYrK6vJc27S0tIUt2bzp/t81q5dq/IY+MPbpCd8\neLt0vw9vk+73Uadtcv369RZzpE5NwioqKhRPZS4vL0dUVBS8vb0xZcoUfPvttwCAb7/9FtOmTQMA\nTJkyBXv37kV1dTXu3LmDW7duYejQoZ0ZImOMMca6ieTUZGzbtw2/JvyKbfu2ITk1WdUhdapOvRyZ\nn5+P6dOnAwBqa2sRGBiICRMmYMiQIQgICEBYWJjiERUA4OXlhYCAAHh5eUFDQwPbt2/nBreMMcZY\nL5Ccmozws+HQdNNEgWYBCiwKEH42HKEIRR+3PqoOr1N0+nPCnjSBQIAeFnKvEBMTA19fX1WHwRrg\nbdI98XbpfnibdA/b9m1DjlkOEgoSkHE9AwOHDYSTkRMsCiywOGCxqsN7ZK3lLZyEMcYYY0zlNu7a\niFhhLCpqKqAl0oK3pTf0xfowyjPC8tnLVR3eI2stb1GbF3ibmJjg/v37qg6D9TDGxsYoLi5WdRiM\nMdar5TzMQVxWHCqsK6CnqYf+lv2hpaEFABALxSqOrvOoTRJ2//59riFjHcZtDhljTLVu3buFA0kH\nYOtoi5o7NRg4fCA0hHXpifSWFH5j/VQcYedRm8uRfJmSPQrebxhjTHX+yPkDx28dh5zkGGA5AH1E\nfRBzLQbV8mqIhWL4Pe3X4xvl94o2Yfxjyh4F7zeMMdb1iAhn08/ifMZ5AMBox9EY6zRWLa9O9Io2\nYYwxxhjr/mRyGY4mH8X1/OsQCoSY5D4Jg20GqzosleAkjDHGGGNdoqq2CvsT9+P2/dsQi8SY5TUL\n7qbuqg5LZTr9tUWs50pPT4dQKIRcLld1KIwxxnq4B9IH2Hl1J27fvw19sT5CB4b26gQM4CSMPQHh\n4eEYNWqUqsNgjDHWTeWX5eO/V/6L/PJ8mOmaYeGghbCR2Kg6LJXrFZcjk5MzcPp0GmpqhNDUlMPf\n3xV9+jh2WXn26ORyOYRC/q/AGGM91Z37d7A3YS+kMikcDB0wp98c6GjqqDqsbkHtf92SkzMQHp6K\nwsJxKCnxRWHhOISHpyI5OaNLygOAk5MTtmzZggEDBsDIyAizZ8+GVCpttgZJKBTi9u3bAIDQ0FAs\nXrwYL7zwAiQSCUaNGoW8vDwsW7YMxsbG8PT0xLVr15Tms2nTJvTt2xcmJiZYsGABpFIpAKBfv344\nduyYYtyamhqYmZm1+nb3xsLDw+Hq6goDAwO4uLhg9+7d+PPPP7Fo0SJcunQJEokEJiYmAIATJ06g\nb9++MDAwgJ2dHbZs2aKYzueffw4bGxvY2dlhx44dTZb5b3/7G1544QXo6+sjJiam3fExxhjrXuLz\n4xERHwGpTIq+5n0xb8A8TsAaUPuasNOn06Cl5Qfl33I/xMefwTPPtF2bFReXhoqKvx4U5+sLaGn5\nITr6TLtrwwQCAQ4cOIBTp05BS0sLPj4+CA8Ph7a2dptlDxw4gKioKHh5eeGFF17AsGHD8PHHH+OL\nL77AmjVr8Oabb+LMmTOK8Xfv3o2oqCjo6upi8uTJ+Pjjj7F+/XqEhIQgIiICL774IoC6JMnW1hYD\nBgxo1zKUl5dj2bJluHz5Mtzd3ZGfn4979+7Bw8MDX331Ff773/8iNjZWMf7ChQtx8OBB+Pj4oLS0\nVJFk/fTTT9iyZQvOnDkDJycnvPLKK03mtWfPHpw8eRLDhw9XJJGMMcZ6DiLCL5m/IPpONABguN1w\nTHCdoJaPoHgcal8TVlPT/CLKZO1bdLm8+fGqqzu26t544w1YWVnB2NgYkydPVqrBaolAIMCMGTMw\naNAgaGlpYfr06dDT00NQUBAEAgECAgJw9epVpfGXLl0KW1tbGBsbY9WqVdizZw8AIDAwEMePH0dZ\nWRkAYNeuXQgODu7QMgiFQty4cQOVlZWwtLSEl5cXADT7/BOxWIzExEQ8ePAAhoaGGDRoEABg//79\nWLBgAby8vKCrq4sPP/ywSdlp06Zh+PDhAAAtLa0OxcgYY0y15CTHsZRjiL4TDQEEmOg2Ec+5PccJ\nWDPUviZMU7Puzj5fX+X+FhZyLG7HS9m3bZOjsLBpf7G4Y3cMWllZKb7r6uoiJyenXeUsLCwU37W1\ntZW6dXR0FElVPXt7e8V3BwcHxXxsbGzg4+ODgwcPYtq0afjpp5/w5Zdftjt+PT097Nu3D5s3b8bC\nhQvh4+ODLVu2oE+f5p9k/P333+Pjjz/GypUr0b9/f2zatAnDhg1Dbm4unnnmGaUYGxIIBLCzs2t3\nXIwxxrqPalk1DiYdRMq9FGgINTDTcyY8zT1VHVa3pfY1Yf7+rpBKo5X6SaXR8PNz7ZLyrdHT00NF\nRYWiOy8v77GnmZmZqfTdxuavu0/qL0keOHAAI0aMgLW1dYemPWHCBERFRSEvLw8eHh549dVXATT/\n/sUhQ4bghx9+QGFhIaZNm4aAgAAAgLW1dZMYGWOM9Xxl1WUIvxaOlHsp0NXURciAEE7A2qD2SVif\nPo4IDXWDhcUZGBnFwMLiDEJD3drdnutxyzen/vLdgAEDkJiYiOvXr6Oqqgrr1q1rdryOTHf79u3I\nzs5GcXExPvnkE8yePVsxfPr06bhy5Qq2bt2KefPmdWjaBQUFOHLkCMrLy6GpqQk9PT2IRCIAgKWl\nJbKyslBTUwOgrtF/ZGQkSktLIRKJIJFIFOMGBAQgPDwcN2/eREVFRZPLkfwKIcYY63mKKooQdiUM\nOQ9zYKxtjIWDFsLe0L7tgr2c2l+OBOoSqcdJmh63fGMCgQACgQDu7u5Ys2YN/P39oauriw0bNuCb\nb75pMl5L3fX9Gn6fO3cuJkyYgJycHEybNg0ffPCBYri2tjZmzJiBffv2YcaMGe2OFah7VMQ///lP\nhISEQCAQYNCgQfj3v/8NAPDz80Pfvn1hZWUFkUiE7OxsRERE4PXXX4dMJoOHhwciIyMBAM8//zyW\nL1+OcePGQSQSYf369di9e3ery8gYY6z7yizNxJ4be1BZWwlbiS3mes+FnlhP1WH1CPwCbzXi7OyM\nsLAwjBs3rsVx1q9fj1u3buG7777rwshaJxQKkZqaChcXly6fN+83jDH26JIKk3Do5iHUymvRx7QP\nZnrNhFgXaQejAAAgAElEQVQkVnVY3Qq/wJsBAIqLi7Fjxw7s2rVL1aEwxhjr4S7dvYSotCgQCM/Y\nPIOJ7hMhFKh9K6cnitdWL/HNN9/AwcEBEydOxMiRIxX9IyMjIZFImny8vb27LDa+/MgYYz0HEeGn\n1J9wKu0UCAR/F3+84P4CJ2CPgC9Hsl6N9xvGGGu/GlkNDv95GEmFSRAJRJjmMQ3ell33p70n4suR\njDHGGHssFTUV2HNjD+4+uAttDW283PdlOBs7qzqsHo2TMMYYY4y16n7lfUTER+Be5T0YahkisH8g\nLPQs2i7IWsVJGGOMMcZalP0gG7tv7EZ5TTms9K0Q6B0IiZZE1WGpBU7CGGOMMdaslHspOJB4ADXy\nGrgauyKgbwC0NPidvk8KJ2GMMcYYa+JyzmUcTzkOAmGg1UBMfmoyREKRqsNSK3w/qRrx9fVFWFhY\np0w7MzMTEonkke8kjImJUXq5OGOMse6JiBB9OxrHUo6BQPB18sXUPlM5AesEnISpkc585Y+DgwMe\nPnzY6c/0WrduHYKDgzt1Howxxponk8tw+M/DiM2MhVAgxJQ+U+Dr5MvPc+wkveJyZHJqMk7/cRo1\nVANNgSb8B/ujj1ufLivPVK++Bo9PJIwx1ryq2irsS9iHOyV3IBaJEdA3AG4mbqoOS62pfU1Ycmoy\nws+Go9CyECVWJSi0LET42XAkpyZ3Sfl6d+/exYwZM2BhYQEzMzMsWbIEpqamSEhIUIxTUFAAPT09\n3Lt3r9VpHTlyBAMHDoShoSHc3NwQFRXVZJy0tDSMGzcOZmZmMDc3R1BQEEpLSxXDP/30U9jZ2cHA\nwAAeHh44c+YMACAuLg5DhgyBoaEhrKys8NZbbwEA0tPTIRQKIZfLAdS9Amn+/PmwtbWFiYkJpk+f\n3qH10dz8f/rpJ2zcuBH79u2DRCLBoEGDAADh4eFwdXWFgYEBXFxcFC/8lslkePvtt2Fubg5XV1ds\n27ZNKUZfX1988MEH8PHxgZ6eHu7cudOhGBljrLcorSrFjqs7cKfkDvTF+pg/cD4nYF1A7WvCTv9x\nGlruWohJj/mrpyYQvzcez4x8ps3ycb/EocKuAkiv6/Z18oWWuxair0S3uzZMJpPhxRdfhL+/PyIj\nIyESifD7778DACIiIrBp0yYAwJ49e+Dv7w9TU9OW44mLQ0hICL7//nv4+fkhJycHDx8+bHbcVatW\nYfTo0SgtLcXMmTOxbt06/POf/0RycjK2bduGy5cvw8rKCpmZmaitrQUALFu2DCtWrEBgYCAqKipw\n48aNZqcdHBwMAwMDJCUlQU9PD5cuXWrXugDQ4vxdXFzw/vvvIy0tTfGC8fLycixbtgyXL1+Gu7s7\n8vPzFUnqN998g+PHj+PatWvQ1dXFjBkzmtR0RURE4OTJk+jTp48iOWOMMfaXvLI8RMZH4mH1Q5jr\nmiOwfyCMtI1UHVavoPZJWA3VNNtfBlm7ysvR/A93tby63THExcUhNzcXn3/+OYTCuspHHx8faGho\nICAgQJGE7dq1CytXrmx1WmFhYVi4cCH8/PwAADY2Ns2O5+rqCldXVwCAmZkZVqxYgY8++ggAIBKJ\nIJVKkZiYCFNTUzg4OCjKicVi3Lp1C0VFRTAzM8Ozzz7bZNq5ubn46aefUFxcDENDQwDAqFGj2r0+\nWps/ETVp/C8UCnHjxg3Y2dnB0tISlpaWAID9+/djxYoVsLW1BQC8//77OHfunKKcQCBAaGgoPD09\nFdNhjDH2l7TiNOxP3A+pTApHQ0fM7jcbOpo6qg6r11D7JExToAmgrgarIQtdCyz2Xdxm+W3521Bo\nWdikv1gobncMd+/ehaOjY5Mk4Nlnn4WOjg5iYmJgZWWFtLQ0TJkypdVpZWVlYdKkSW3OMz8/H8uW\nLcMvv/yChw8fQi6Xw8TEBADg5uaGL774AuvWrUNiYiKee+45/OMf/4C1tTXCwsKwZs0aeHp6wtnZ\nGWvXrm0yv7t378LExESRgHVUa/NvTE9PD/v27cPmzZuxcOFC+Pj4YMuWLejTpw9yc3OV7rhsmMzV\n4zsyGWOsedfyruFo8lHISY5+Fv0wzWMaNIRqnxZ0K2pfNeA/2B/SW1KlftJbUvg97dcl5YG6RCAz\nMxMyWdPat5CQEERERGDXrl2YNWsWxOLWkzt7e3ukpqa2Oc/3338fIpEICQkJKC0txa5du5Qux82Z\nMwexsbHIyMiAQCDAu+++C6AuQdq9ezcKCwvx7rvv4qWXXkJlZWWTGIqLi5XamHVUS/NvruH8hAkT\nEBUVhby8PHh4eODVV18FAFhbWyMzM1MxXsPv9bghPmOMKSMinEs/hx/+/AFyksPH3gczPWdyAqYC\nap+E9XHrg9CxobAosIBRnhEsCiwQOja03e25Hrc8UFfjZW1tjZUrV6KiogJVVVW4ePEiACAoKAiH\nDh1CZGQk5s2b1+a0Fi5ciJ07d+LMmTOQy+XIzs5GcnLTmwTKysqgp6cHAwMDZGdn4/PPP1cMS0lJ\nwZkzZyCVSqGlpQVtbW2IRHXPf4mIiEBhYV3Nn6GhIQQCQZMaPGtra0ycOBGLFy9GSUkJampqcP78\n+Xavj9bmb2VlhfT0dMUlyYKCAhw5cgTl5eXQ1NSEnp6eYtyAgABs3boV2dnZuH//PjZt2tQk6XrU\n55oxxpg6ksll+DHlR5xNPwsBBHjB/QWMdx3Pf1hVhXqYlkLu7ouSmZlJ06ZNI1NTUzIzM6Nly5Yp\nhvn5+ZGzs3O7p3X48GHq378/SSQScnNzo6ioKCIi8vX1pbCwMCIiSkxMpMGDB5O+vj4NGjSItmzZ\nQvb29kREFB8fT0OHDiWJREImJiY0efJkys3NJSKioKAgsrCwIH19ferXrx8dOXKEiIju3LlDQqGQ\nZDIZEREVFxdTSEgIWVpakrGxMc2cObPVmM+ePduu+d+7d49GjhxJxsbGNHjwYMrNzaUxY8aQoaEh\nGRkZ0dixY+nmzZtERFRbW0srVqwgU1NTcnFxoW3btpFAIFDE2HB9tKS77zeMMfakSGultOv6Llp7\ndi2tP7eebhbeVHVIvUJrvzOC/43QYwgEgmZrN1rq3xMsXLgQtra2iobz7NGkp6fDxcUFtbW17W6E\n35P3G8YYa6+y6jJExkcitywXupq6mOs9F3YGdqoOq1do7XeGLwCrWHp6Og4dOoRr166pOhTGGGNq\nqLC8EJE3IlFSVQITHRME9Q+CiY6JqsNi6AVtwrqz1atXw9vbG++88w4cHR0V/Tds2ACJRNLk0567\nIlWpO8TN7RoYY+wvGSUZ2HF1B0qqSmBnYIeFgxZyAtaN8OVI1qvxfsMYU1eJBYk4dPMQZCSDh5kH\nZnrOhKZIU9Vh9Tp8OZIxxhjrJYgIl7IuISqt7pV2Q22H4nm35yEU8MWv7oaTMMYYY0xNyEmOU6mn\n8Fv2bwCACa4TMNxuODfV6KY4CWOMMcbUQI2sBoduHsLNopsQCUSY7jkd/Sz6qTos1gpOwhhjjLEe\nrqKmArtv7EbWgyxoa2hjTr85cDRybLsgUylOwhhjjLEerLiyGJHxkbhXeQ+GWoYI6h8Ecz1zVYfF\n2oFb6XUBJycnREdHY+PGjYr3Hj6qdevWITg4+AlFxhhjrCfLfpCNsCthuFd5D9b61njl6Vc4AetB\nOAnrAgKBAAKBAO+99x6++eabx55We/j6+iIsLOyx5qVq6rAMjDHWWZKLkhF+LRzlNeVwM3FD6MBQ\nSLQkqg6LdUCvuByZkZyMtNOnIaypgVxTE67+/nDs0/4XcD9u+Sepvc+06qw7YWpra6Gh0TW7Dd/N\nwxhjzYvLjsPJWydBIDxt/TQmuU+CSChSdVisg9S+JiwjORmp4eEYV1gI35ISjCssRGp4ODKSk7uk\nfD0iUrqUmJ6eDqFQiO+++w6Ojo4wNzfHhg0bOjTNqqoqBAUFwczMDMbGxhg6dCgKCgqwatUqxMbG\nYunSpZBIJHjjjTcAACtWrIClpSUMDQ3Rv39/JCYmAgDu3buHKVOmwNDQEM8++yxWr16NUaNGKeYj\nFAqxfft2uLu7o08byadQKMS///1vuLu7w8DAAGvWrEFaWhqGDx8OIyMjzJ49GzU1NQCAkpISvPji\ni7CwsICJiQkmT56M7OxsAGhxGRhjrDcjIvyc9jNO3DoBAmGs01hMfmoyJ2A9lNrXhKWdPg0/LS0g\nJkbRzw/Amfh4OD7zTNvl4+LgV1HxVw9fX/hpaeFMdHSHa8Oaq9m5cOECUlJSkJycjKFDh2LGjBnw\n8PBo1/S+/fZbPHjwAFlZWdDS0sK1a9ego6ODTz75BBcvXkRwcDAWLFgAADh16hRiY2Nx69YtGBgY\nIDk5GYaGhgCAJUuWQFdXF3l5ebh9+zaee+45uLi4KM3ryJEj+P3336Gjo9NmXFFRUbh69SoyMzMx\naNAg/PLLL9izZw9MTEwwfPhw7NmzB/PmzYNcLsfChQtx8OBB1NbWYsGCBVi6dCkOHz7c7DIwxlhv\nViuvxQ9//oCEggQIBUJM6TMFA60Gqjos9hjUviZM+L9alyb9ZbL2lZfLm+9fXf3IMTW0du1aaGlp\noX///hgwYACuX7/e7rJisRj37t3DrVu3IBAIMGjQIEgkf7UHaHjpUiwW4+HDh7h58ybkcjn69OkD\nKysryGQyHDp0CB999BF0dHTQt29fhISENLns+d5778HIyAhaWlptxvXOO+9AX18fXl5e8Pb2xsSJ\nE+Hk5AQDAwNMnDgRV69eBQCYmJhg+vTp0NbWhr6+Pt5//32cO3dOaVr8SiHGGAMqayoRER+BhIIE\naIm0EOgdyAmYGlD7mjC55v/ek+Xrq9zfwgJYvLjt8tu2AYWFTfuLxU8iPFhZWSm+6+rqory8vN1l\ng4ODcffuXcyePRslJSUICgrCJ598omiz1bDmbezYsVi6dCmWLFmCjIwMzJgxA5s3b0Z5eTlqa2th\nb2+vGNfBwaHJvBoOb4ulpaXiu46OTpPuvLw8AEBFRQVWrFiBU6dO4f79+wCAsrIyEJEidm4Xxhjr\n7UqqShAZH4nCikJIxBIE9g+Elb5V2wVZt6f2NWGu/v6IlkqV+kVLpXD18+uS8k9aw6REQ0MDa9as\nQWJiIi5evIhjx47hu+++azJevddffx2XL19GUlISUlJS8Pnnn8PCwgIaGhrIzMxUjNfwe3PzfVK2\nbNmClJQUxMXFobS0FOfOnQMRKWq/OAFjjPV2eWV5CLsShsKKQljoWeCVp1/hBEyNqH1NmGOfPkBo\nKM5ER0NYXQ25WAw3P792t+d63PINtefSWlvjNBweExMDU1NTeHl5QSKRQFNTEyJRXeNMS0tLpKWl\nKca9fPkyZDIZnn76aejq6kJbWxsikQhCoRAzZszAunXrsGPHDty5cwffffcdnJ2dO7x87Ym54fey\nsjLo6OjA0NAQxcXF+PDDD5XKNV4GxhjrTVKLU7E/cT+qZdVwMnLC7H6zoa2hreqw2BOk9jVhQF0i\nNW7xYvguX45xixd3OIF63PLAX88Ka1i701xNT1u1Pw2nkZeXh1mzZsHQ0BBeXl7w9fVV3H25bNky\nHDx4ECYmJli+fDkePHiA1157DSYmJnBycoKZmRn+/ve/AwD+9a9/oaysDFZWVliwYAHmz5+vlCx1\npEaqrWVqGP/y5ctRWVkJMzMzjBgxAhMnTlQat/EyMMZYb3E19yp239iNalk1vC28EdQ/iBMwNSSg\nHtbyWSAQNFtb1FJ/1nHh4eEICwtDbGysqkPpdLzfMMa6EyLCuYxziEmPAQCMdBgJP2c/bp7Rg7X2\nO6P2lyMZY4yxnkAml+FYyjFczbsKAQR4wf0FPGPb9qOUWM/VKy5H9jQTJ06ERCJp8tm0aVOXzL/x\nZdOGYmNjm43NwMCgS2JjjDF1JK2VYk/CHlzNuwpNoSZm95vNCVgvwJcjWa/G+w1jTNUeSh8i8kYk\n8sryoKeph7nec2FrYKvqsNgTwpcjGWOMsW6ooLwAkfGRKJWWwlTHFIH9A2GiY6LqsFgX4SSMMcYY\nU4H0knTsTdiLqtoq2BvYY473HOhq6qo6LNaFOAljjDHGulhCQQIO3zwMGcngaeaJGZ4zoCnSVHVY\nrItxEsYYY4x1ESLCxbsX8fPtnwEAw+yGYYLrBAgFfJ9cb8RJGGOMMdYF5CTHyVsn8XvO7wCA51yf\nw3D74SqOiqkSp97dlEQiQXp6eqdN38nJCdHR0Z02fcYYY3+pkdVgX8I+/J7zOzSEGpjlNYsTMMY1\nYd3Vw4cPO3X6rT0LrF56ejpcXFxQW1sLoZDzdcYYexTl1eXYfWM3sh9mQ0dDB3O858DB0EHVYbFu\noFckYcm3b+N0YiJqAGgC8O/bF31cXLqsfE/XGc/RkslkipeNM8aYurpXcQ8R8RG4X3UfRtpGCOof\nBDNdM1WHxboJta/eSL59G+FXrqCwXz+U9OuHwn79EH7lCpJv3+6S8vU+/fRT2NnZwcDAAB4eHjhz\n5gzkcjk2bNgANzc3GBgYYMiQIcjOzgYACIVC3P7fPEJDQ7Fo0SJMmDABBgYG8PX1RWZmJgBgyZIl\nePvtt5XmNWXKFHzxxRftji0uLg5DhgyBoaEhrKysFNMbPXo0AMDIyAgSiQS//fYbUlNTMWbMGBgZ\nGcHc3ByzZ89WTOfnn3+Gh4cHjIyM8Prrr2PMmDEICwsDUPc+Sh8fH7z55pswMzPDhx9+2KH1xxhj\nPc3d0rsIuxqG+1X3YSOxwStPv8IJGFOi9jVhpxMToTV4MGJKSv7q6eqK+PPn8Uw7Xogad/48KgYM\nAP5X3tfICFqDByM6IaHdtWHJycnYtm0bLl++DCsrK2RmZqK2thZbtmzB3r17cfLkSbi7uyM+Ph46\nOjrNTmP37t04ceIEhg4dinfeeQeBgYGIjY1FaGgopk2bhs8//xwCgQBFRUWIjo5WJD/tsWzZMqxY\nsQKBgYGoqKjAjRs3ANS9osjZ2RmlpaWKy5Fz5szB888/j3PnzqG6uhqXL18GABQVFWHmzJkIDw/H\n1KlT8eWXX+I///kPQkJC/lqXcXGYO3cuCgoKUF1d3e74GGOsp7lZeBPf3/wetfJauJu4Y1bfWRCL\nxKoOi3Uzal8TVtNCf1k730gvb2G8jqQQIpEIUqkUiYmJqKmpgYODA1xcXBAWFoZPPvkE7u7uAID+\n/fvDxKT5JyW/+OKLGDlyJMRiMT755BNcunQJ2dnZeOaZZ2BoaKhoZL93716MHTsW5ubm7Y5PLBbj\n1q1bKCoqgq6uLp599lkAzV+GFIvFSE9PR3Z2NsRiMUaMGAEAOHHiBPr164cZM2ZAJBJh+fLlsLKy\nUiprY2ODJUuWQCgUQltbu93xMcZYT/Jb1m/Yn7gftfJaDLYejDneczgBY81S+5qw+kff+RoZKfW3\nMDHBYmfnNstvS0hAYaOyANCRw8nNzQ1ffPEF1q1bh8TERDz33HPYsmUL7t69C1dX1zbLCwQC2NnZ\nKbr19PRgYmKCnJwc2NraYt68eYiIiIC/vz8iIiKwYsWKDkQHhIWFYc2aNfD09ISzszPWrl2LSZMm\nNTvuZ599htWrV2Po0KEwNjbGW2+9hfnz5yMnJ0cpRgCwt7dvtZsxxtQJEeHn2z/j4t2LAIBxzuMw\nymFUmzdBsd5L7WvC/Pv2hfSPP5T6Sf/4A359+3ZJ+Xpz5sxBbGwsMjIyIBAI8O6778Le3h6pqalt\nliUi3L17V9FdVlaG4uJi2NjYAACCgoJw5MgRXL9+HX/++SemTZvWodjc3Nywe/duFBYW4t1338VL\nL72EysrKZk8clpaW+Prrr5GdnY2vvvoKixcvRlpaGmxsbJRibBwzAD4RMcbUVq28FgeTDuLi3YsQ\nCoSY7jEdox1H83mPtUrtk7A+Li4IffppWCQkwCghARYJCQh9+ul2t+d63PIAkJKSgjNnzkAqlUJL\nSwva2trQ0NDAK6+8gtWrVyM1NRVEhPj4eBQXFzc7jRMnTuDChQuorq7G6tWrMXz4cNja2gIA7Ozs\nMGTIEMybNw8vvfQStLS02h0bAERERKCwsBAAYGhoCIFAAKFQCHNzcwiFQqSlpSnGPXDgALKysgDU\nNdgXCAQQiUR44YUXkJiYiMOHD6O2thZbt25FXl5eh+JgjLGeqLKmEruu70JiYSK0RFoI6h+EAVYD\nVB0W6wHU/nIkUJdIPc4jJR63vFQqxXvvvYebN29CU1MTPj4++Prrr2FhYQGpVIoJEyagqKgInp6e\nOHz4MADlWiOBQIC5c+fiww8/xKVLlzB48GBEREQozSMkJATz5s3D1q1bOxzfqVOn8NZbb6GiogJO\nTk7Yu3evIpFbtWoVfHx8UFtbi5MnT+Ly5ctYsWIFSktLYWlpia1bt8LJyQlAXYL2xhtvYP78+QgO\nDoaPj4/SMvA/QsaYuimpKkFEfASKKopgoGWAQO9AWOpbqjos1kMIqDMeAtWJBAJBsw3GW+qvDubP\nnw87OzusX7++xXFiY2MRFBSEjIyMLoysdWPHjkVwcDAWLFig6lBapM77DWOsc+U+zEXkjUiUVZfB\nUs8Sgf0DYaBloOqwWDfT2u9Mr6gJ6+naShJqamrwxRdf4NVXX+2iiNqPExzGmDq6de8WDiQdQLWs\nGi7GLgjoGwBtDb7rm3WM2rcJUwetXcq7efMmjI2NkZ+fj+XLlyv6Z2ZmQiKRNPkYGBgo2nR1Bb4E\nyRhTN1dyr2BPwh5Uy6oxwHIAAr0DOQFjj4QvR7Jejfcbxlh7ERFi0mNwLuMcAGC042iMdRrLfzZZ\nq/hyJGOMMfYYZHIZfkz5EdfyrkEoEGKS+yQMthms6rBYD8dJGGOMMdYKaa0U+xL34fb929AUaiKg\nbwDcTd1VHRZTA53eJkwmk2HQoEGYPHkyAKC4uBjjx4/HU089hQkTJqCkwTsdN27cCHd3d3h4eCAq\nKqqzQ2OMMcZa9UD6ADuu7sDt+7ehp6mH+YPmcwLGnphOT8L+7//+D15eXopr5ps2bcL48eORkpIC\nPz8/bNq0CQCQlJSEffv2ISkpCT/99BMWL14MuVze7vkYGxsrGrDzhz/t/RgbG3fKfs8Y6/nyy/Lx\n3yv/RX55Psx0zfDK06/ARmKj6rCYGunUJCwrKwsnTpzAK6+8omiUdvToUYSEhACoe8DoDz/8AAA4\ncuQI5syZA01NTTg5OcHNzQ1xcXHtnldxcTGIiD/86dCnpTcUMMZ6tzv372DH1R14IH0AB0MHLBi0\nAMY6/KeNPVmdmoStWLECn3/+OYTCv2aTn58PS8u6pwlbWloiPz8fAJq8ANrOzg7Z2dmdGR5jjDHW\nRHx+PCLiIyCVSeFl7oV5A+ZBV1NX1WExNdRpDfOPHTsGCwsLDBo0CDExMc2OU39JqCWtDWOMMcae\nJCLCL5m/IPpONABguN1wTHCdwL9FXSgjORlpp09DWFMDuaYmXP394dinj6rD6jSdloRdvHgRR48e\nxYkTJ1BVVYUHDx4gODgYlpaWyMvLg5WVFXJzc2FhYQEAsLW1xd27dxXls7KyFC+obmzdunWK776+\nvvD19e2sxWCMMdYLyEmOE7dO4HLOZQggwHNuz2GY3TBVh9WrZCQnIzU8HH5aWgARIBAgOjwcCA3t\nUYlYTExMi5VPjXXJw1rPnTuHzZs348cff8Q777wDU1NTvPvuu9i0aRNKSkqwadMmJCUlYe7cuYiL\ni0N2djb8/f2Rmpra5B+IQMAP12SMMfbkVMuqcTDpIFLupUBDqIEZnjPgZe6l6rB6nTPbtmFcYSHw\n4AGQmgr06weIxThjYYFxixerOrxH1lre0mXPCatPplauXImAgACEhYXByckJ+/fvBwB4eXkhICAA\nXl5e0NDQwPbt27kKmDHGWKcqqy7D7hu7kfMwBzoaOpjrPRf2hvaqDqtXEkqlQEYGkJ5eVxOWkQG4\nu0NYXa3q0DqN2ry2iDHGGOuIoooiRMZH4n7VfRhrGyOwfyDMdM1UHVbvVFKCM0uXYlz9u43t7QFn\nZ0Ao5JowxhhjTJ1klmZiz409qKythI3EBnO950JfrK/qsHqnGzeA48fhamSE6Nxc+PXtC5iYAACi\npVK4+fmpOMDOwzVhjDHGepWkwiQcunkItfJaPGX6FF7yeglikVjVYfU+Uilw/DgQH1/X7eGBDA8P\npF28CGF1NeRiMVz9/HpUo/zmtJa3cBLGGGOs1/g161ecSj0FAmGIzRC84P4ChIJOf3kMa+zuXeDQ\nIeD+fUBTE3j+eeDppwE1bAvOlyMZY4z1akSEU2mn8GvWrwAAfxd/+Nj78A1gXU0uB86fB86dq2t8\nb20NzJwJmPXOtnichDHGGFNrtfJaHLp5CEmFSRAJRJjmMQ3elt6qDqv3uX+/rvbr7t26Gi8fH2Dc\nOEAkUnVkKsNJGGOMMbVVUVOBvQl7kVmaCW0Nbbzc92U4GzurOqzehUjR+B5SKWBgAEyfXnf3Yy/H\nSRhjjDG1dL/yPiJvRKKoogiGWoYI7B8ICz0LVYfVu1RV1SVfN27UdXt5AS++COjyuzgBTsIYY4yp\noZyHOYiMj0R5TTks9SwR2D8QBloGqg6rd8nIAA4fBkpKALEYmDgRGDhQLRvfPypOwhhjjKmVlHsp\nOJB4ADXyGrgauyKgbwC0NLRUHVbvIZPVNbyPja27FGlrC8yYAZiaqjqyboeTMMYYY2rjj5w/cCzl\nGAiEgVYDMfmpyRAJe2/D7y5XXFzX+D4rq67Ga9QowNe3Vze+bw0nYYwxxno8IsKZO2cQmxkLABjj\nOAa+Tr78CIquQgRcvw6cOAFUVwOGhnWN752cVB1Zt8ZJGGOMsR5NJpfhSPIRxOfHQygQ4sWnXsTT\n1k+rOqzeo7ISOHYMSEys6+7bt67xvY6OauPqATgJY4wx1mNV1VZhX8I+3Cm5A7FIjFles+Bu6q7q\nsHqP9PS6y48PHtQ1vn/hBWDAAG58306chDHGGOuRSqtKEXkjEgXlBdAX6yPQOxDWEmtVh9U7yGTA\n2YxqTeYAACAASURBVLPAhQt1lyLt7Ooa3//vxdusfTgJY4wx1uPkl+Uj8kYkHkgfwFzXHIH9A2Gk\nbaTqsHqHe/eA778HcnLqarzGjAFGj+bG94+AkzDGGGM9yu37t7EvYR+kMikcDR0xu99s6Ghy+6NO\nRwRcvQqcPAnU1ABGRnW1Xw4Oqo6sx+IkjDHGWI9xPe86jiQfgZzk6GveF9M9p0NDyD9lna6iAvjx\nR+Dmzbpub29g0iRAW1u1cfVwvOcyxhjr9ogIsZmxOHPnDABghP0IjHcZz4+g6Aq3b9c9+f7hQ0BL\nqy756t+/U2aVnJyB06fTUFMjhKamHP7+rujTx7FT5tUdCIiIVB1ERwgEAvSwkBljjD0GOclxLOUY\nruRegQACTHSfiKG2Q1UdlvqTyYAzZ4CLF+suRdrb111+NDbulNklJ2cgPDwVQqEfMjPr3u9dUxON\n0FC3Hp2ItZa3cE0YY4yxbqtaVo0DiQdwq/gWNIQamOk5E57mnqoOS/0VFdU1vs/NBYTCuqfejxpV\n972TnD6dhocP/ZCSUve8V4EAcHHxQ3T0mR6dhLWGkzDGGGPdUll1GSLjI5FblgtdTV3M6TcH9ob2\nqg5LvREBf/wBnDpV1/je2Liu9su+c9d7ZSVw+bIQd+7UdRsZAdb/e9pIdXXnJX6qxkkYY4yxbqeo\noggR8REoqSqBiY4JAr0DYarLL4DuVBUVwNGjwJ9/1nUPGFD38FWtzn35eUpK3Wzz8+UQCgEXl7p3\nftc39xOL5Z06f1XiJIwxxli3klGSgb0Je1FZWwk7AzvM6TcHemI9VYel3tLS6hrfl5XV3fH44otA\nv36dOsuqKuCnn4Br1+q6fXxcUVQUDUNDP8U4Umk0/PzcOjUOVeKG+YwxxrqNxIJEHP7zMGrltfAw\n88BMz5nQFGmqOiz1VVsLREcDly7VdTs61r1426hzH3x761Zd7dfD/8/efQZHeWb5Av93t7LUQVlI\nBAkQQpGcg0TOIMAGg5j1vbf2Tu1O1e639cxW7YeZDzu2a6dqd+bDbNXdOzN3thDY2Jhgm5xEBpGs\ngLJQzqGzOr7P/XAaI88YlLrV3erzq5oqHky/7zPYQn/Oe97zGICgIGDzZmD5cqCurhnXrzfAZpMj\nJETCpk3+/3bku3ILhzDGGGNeJ4TAw7aHuNxwGQCwPGU5ts/dDrls6vYDeV1vL/Dll0B3NzXcb9gA\nrFnj0eZ7iwW4cgV49ozWM2YA+/YBcXEeu6XX8duRjDHGfJYkJFyuv4xH7Y8AAFtmb8HqGat5Bpin\nCAE8eULN9w4Hnfd48CA1YnlQQwNw7hyd9R0UBGzcCKxc6dHM5/M4hDHGGPMau9OOr6q+QlVfFRQy\nBfZn7kdOgmd7kQKayURJqLaW1osWAdu3e7T53mql6tfTp7ROSQEKC4H4eI/d0m9wCGOMMeYVZrsZ\nJ8pPoE3fhrCgMHyQ8wFSNane3tbUVVcHnD1LQSwsDNizB8jO9ugtGxup90urpfO9N2wAVq8O7OrX\ncBzCGGOMTbqBoQEUlxWjf6gf6lA1juUdQ3wkl0Y8wuEArl4FHtHjXqSmUvO9Wu2xW9psdMvSUlon\nJ1P1KyHBY7f0SxzCGGOMTap2fTtOlJ+AyW5CUlQSinKLoAxVentbU1N3N02+7+mh8tPGjR4vRTU1\n0RPPwUGqfuXnU7+/QuGxW/otDmGMMcYmTU1fDb58+SXskh1zY+bi/az3ERrk2WGgAUkI4PFjKkc5\nHEBsLDXfJyd77JY2G027eF1wS0qigltiosdu6fc4hDHGGJsUpe2luFB3AQICi5IWYfe83VDIuTzi\ndkYj9X7V19N6yRJg2zYgJMRjt2xupurXwAAV2davp6Mmufr1bhzCGGOMeZQQAtdfXcfdlrsAgILU\nAuTPyucRFJ5QW0tpyGQCwsOBvXuBTM8deG63AzduAA8fUvEtMZF6v16f+8jejUMYY4wxj3FIDpyr\nPofynnLIZXLsmbcHi6Yt8va2ph67nR49Pn5M69mzKQ2pVB67ZWsrFdz6+6n6tW4d9X9x9Wv0OIQx\nxhjzCIvDgs8qPkOTtgkhihAczj6MOTFzvL2tqaeri5rve3spAW3aBKxa9eYEbDez24GbN+mkIyHo\njcfCQo+2m01ZHMIYY4y5nc6iw/Gy4+g190IZokRRXhGSopK8va2pRQh6DnjtGuB00tk/Bw969Flg\nWxtVv/r6KOO9rn4FcZoYF/5tY4wx5lZdxi4UlxXDYDMgPiIex/KOQR3muZlUAclgoDTU0EDrZcuA\nrVuBYM8cdu5wALduAffuUfaLi6M3Hz180tGUxyGMMcaY2zQMNODzys9hc9qQqknF4ezDCA8O9/a2\nppbqahpDbzYDERF0AnZGhsdu19FBea+nh6pfa9bQ5Huufk0c/xYyxhhzixddL3C+5jwkISE3IRf7\n5u9DkJy/zbiNzUaHMD55Qus5c6gZS+mZQbcOB3D7NnD3LiBJNGqssBCYMcMjtwtI/NXBGGNsQoQQ\nKGkuwa2mWwCAtTPXYlPaJh5B4U6dndR839dHzfdbtgArVnis+b6zk6pf3d10i1WraNi+h552BiwO\nYYwxxsbNKTnxTe03eN71HDLIsDN9J5alLPP2tqYOIYD792kYl9MJxMcD773nsTH0Tidw5w5VwCQJ\niImh6tfMmR65XcDjEMYYY2xcrA4rvnj5BeoH6hEsD8Z7We8hI85zvUkBR68HzpwBXr2i9fLlVAHz\nUDmqq4uqX11dtF6xgqZdeHDQfsDjEMYYY2zMDFYDTpSfQKexE5HBkTiaexQpKn5Vzm2qqqj5fmgI\niIykclR6ukdu5XTSW48lJfTj6Gjq9U9N9cjt2DAcwhhjjI1Jr6kXx8uOQ2fVITY8FkV5RYgJj/H2\ntqYGmw24dAl49ozW6emUiKKiPHK7nh6qfnV00Hr5cmDzZq5+TRYOYYwxxkatSduEzyo+g8VhwQzV\nDBzJPYKI4Ahvb2tqaG8HvvqKzgEKCqK5X8uWeaT5XpKo+nXrFlW/NBrKemlpbr8VewcOYYwxxkal\noqcCZ6rOwCmcyIzLxIHMAwhW8OtyE/Y6Ed28ST9OTKTJ9wkJHrldby9Vv9rbab10KbWahYZ65Hbs\nHTiEMcYYeychBO633sfVxqsAgBUpK7Bt7jbIZXIv72wK0Omo+b6pidYrV9LzQA9MQpUkOu/x5k2a\nAaZWA3v30rgx5h0cwhhjjL2VJCRcrLuI0o5SAMC2OduwcvpKngHmDpWVwNdfAxYL9XwVFgJz53rk\nVn19VP1qa6P14sX0tDMszCO3Y6PEIYwxxtiPsjvtOF11GtV91VDIFDiQeQDZCdne3pb/s1qBixeB\nFy9oPW8eNWRFRrr9VpIEPHoEXL9O1S+VCtizx2MvWrIx4hDGGGPsr5hsJpysOIk2fRvCg8LxQc4H\nmKWZ5e1t+b+2Npp8PzhIjxy3baOmLA9UFgcGqPrV0kLrhQuB7du5+uVLOIQxxhj7gX5zP4rLizEw\nNABNmAZFuUWIj4z39rb8myTRIYy3btGPk5Ko+T7e/b+vQgCPHwPXrgF2Ox0tuWcPFdyYb+EQxhhj\n7Htt+jacKD8Bs92MaVHTUJRXhKgQz8yoChhaLY2eeF2SWr2aDmL0QPP94CBVv5qbaZ2XB+zYAYSH\nu/1WzA04hDHGGAMAVPdV48uXX8IhOZAek473s99HiIKndk5IeTnwzTfUB6ZUAvv3A7Nnu/02QgBP\nngBXr9K818hIqn7Nn+/2WzE34hDGGGMMj9sf42LdRQgILJ62GLvn7eYRFBNhsQAXLgBlZbSeP5/m\nQUS4f7CtVgucO/fmiMmcHGDnTo/cirkZhzDGGAtgQghcbbyK+633AQAb0zZi3cx1PIJiIlpbqfle\nq6XDtrdvp5kQbv49FQJ4+hS4cuVN9WvXLiAry623YR7EIYwxxgKUQ3LgbPVZVPRUQC6TY1/GPixI\nWuDtbfkvSQJu36aTsIUApk2j5vu4OLffSqej870bGmidlUUBzANTLpgHcQhjjLEANGQfwmcVn6FZ\n14xQRSgO5xzG7Gj39yoFjMFBar5vbaWK19q1wIYNgELh1tsIATx/Dly+TG1mEREUvrJ5fJtf4hDG\nGGMBRmvRorisGL3mXqhCVSjKLUJiVKK3t+WfhKC+rwsXKBWpVNR874GTsPV6qn7V19M6M5MCWBS/\nvOq3OIQxxlgA6TR0ori8GEabEQmRCTiWdwyqUJW3t+WfLBbg22/pDUiAngnu2eP2eRBCAN99B1y6\nRLcMD6fG+5wcj8x4ZZOIQxhjjAWI+oF6nKo8BZvThjRNGg7nHEZYEI9PH5fmZnr8qNMBISE0jGvh\nQrenIoOBjpesraV1RgawezdNu2D+j0MYY4wFgGedz/BN7TeQhIS8xDzsy9gHhdy9/UoBwemkxvs7\nd6hElZICHDgAxMa69TZCUIHtwgWqfoWFUc7Ly+Pq11TCIYwxxqYwIQRuNd1CSXMJAGDdzHXYmLaR\nR1CMx8AAjZ5ob6cktG4dUFDg9uZ7o5Hmu1ZX0zo9nZ5yqvip8ZTDIYwxxqYop+TE17Vf40XXC8gg\nw+55u7EkeYm3t+V/XjdlXbhAA7nUaqp+zXLvgeZCABUVdJuhISA0lEaMeeApJ/MRHMIYY2wKsjqs\n+LzyczQONiJYHoz3s9/HvFg+wXnMhoaoLFVZSevsbGrKcnPzvclEt6mqovXcuVT9UqvdehvmYziE\nMcbYFKO36lFcVoxuUzcigyNRlFeEZGWyt7flf5qaqPler6fm+127PNKUVVlJL1mazVT92rYNWLSI\nq1+BgEMYY4xNIT2mHhwvOw69VY/Y8FgcyzuG6PBob2/LvzidwM2bwL179Ixw+nR6/BgT49bbmM0U\nvl4X2WbPpuMlNRq33ob5MA5hjDE2RbwafIXPKz+HxWHBTPVMfJDzASKC+RTnMenvp+b7jg4qReXn\nA+vXu735vqqKHj+aTFRk27oVWLKEq1+BhkMYY4xNAWXdZThXfQ5O4URWfBb2z9+PYEWwt7flP16f\nB3TxImC3UznqwAFg5ky33sZsplu8nu+amgrs2wdEc7EyIHEIY4wxPyaEwN2Wu7j+6joAYNX0Vdg6\nZyuPoBgLs5kmor7uis/Npf6vMPcOsq2podsYjUBwMLBlC7BsGVe/AhmHMMYY81OSkHCh7gKedDyB\nDDJsm7sNK6ev9Pa2/EtjI3DmDI2mDw1903zvRkNDdOTQd9/RetYsqn65ucWM+SEOYYwx5odsThu+\nfPklavtrESQPwoHMA8iKz/L2tvyH0wncuAHcv0+PImfMoMePbn4uWFtL1S+DgapfmzYBK1Zw9YsR\nDmGMMeZnjDYjTpafRLuhHeFB4TiSewQz1e7tXZrS+vqo+b6zE5DLaer9unX0YzexWIDLl6nNDKCM\nV1jo9tONmJ/jEMYYY36k39yP42XHMWgZRHRYNIryihAXEeftbfkHIYCnTykd2e1U9TpwgBKSG9XX\nA+fP03ixoKA31S83Zjw2RXAIY4wxP9Gqa8XJipMw281IVibjaO5RRIVEeXtb/sFkomRUU0PrBQuA\nnTupD8xNrFbgyhXKeQCNFyssBOI4I7O34BDGGGN+oKq3CqerTsMhOTAvdh7ey3oPIYoQb2/LPzQ0\nUPO90UhvPO7eDeTkuPUWjY3AuXOATkcjxTZuBFat4uoXezcOYYwx5uMetj3E5frLEBBYmrwUO9N3\nQi7j7+4jcjiA69eBBw9oPWsWPX5044GMVitw9Srw5Amtk5OB/fuB+Hi33YJNYRzCGGPMRwkhcKXh\nCh60UYjYPHsz1sxYwzPARqOnh5rvu7upHLVhA7BmjVtLU69eUfVLq6XqV0GB22/BpjgOYYwx5oMc\nkgNnqs6gsrcSCpkC++bvQ16ie+dXTUlCAKWl1JzlcNAwroMHgZQUt93CZgOuXQMeP6b1tGnU+5WY\n6LZbsADBIYwxxnyM2W7GZxWfoUXXglBFKD7I+QBp0Wne3pbvM5moNFVbS+tFi4AdO+hwRjdpbgbO\nngUGB6nilZ8PrF3r9qMlWYDgEMYYYz5kcGgQxeXF6DP3QRWqwrG8Y0iITPD2tnxfXR2lI5MJCA8H\n9uwBstw3vNZup/ayR4+o2JaURNWvpCS33YIFIA5hjDHmIzoMHSguK4bJbkJiZCKK8oqgClV5e1u+\nzeGgzvhHj2idmkqd8W5svm9poQJbfz9Vv9avp/9x9YtNlMfaBy0WC1asWIGFCxciKysL//zP/wwA\nGBgYwJYtWzBv3jxs3boVWq32+898/PHHSE9Px/z583HlyhVPbY0xxnxObX8t/vT8TzDZTZgdPRv/\na9H/4gA2ku5u4P/8HwpgcjmweTPwN3/jtgBmt1Nr2Z/+RAEsIQH43/+bevw5gDF3kAkhhKcubjab\nERERAYfDgbVr1+I3v/kNzp8/j7i4OHz00Uf49NNPMTg4iE8++QQvX77E0aNHUVpaivb2dmzevBm1\ntbWQ/8VrJjKZDB7cMmOMTbqnHU/xTe03EBBYkLgAezP2QiHn7/JvJQQFr2vXqBIWG0vN98nJbrtF\nWxs93ezro3Me166l/q8gfn7ExuhducWj/zlFREQAAGw2G5xOJ6Kjo3H+/HmUlJQAAD788EMUFBTg\nk08+wblz53DkyBEEBwcjNTUVc+fOxePHj7Fy5UpPbpExxrxGCIEbr27gTssdAED+rHwUpBbwCIp3\nMRopHdXX03rJEmDbNrc13zscwM2bb871jo+n3i83vlzJ2Pc8GsIkScLixYvR0NCAv//7v0d2dja6\nu7uR6HqPNzExEd3d3QCAjo6OHwSu6dOno7293ZPbY4wxr3FKTpyrOYey7jLIZXLsnrcbi6ct9va2\nfFttLQUws5ma7/fuBTIz3Xb59na6fG/vm+pXQQFXv5jnePQ/LblcjhcvXkCn02Hbtm24efPmD/65\nTCZ759/43vbPfvnLX37/44KCAhQUFLhju4wxNiksDgtOVZ5C42AjQhQheD/rfaTHpnt7W77rdXNW\naSmtZ8+m5nul0i2XdziAkhLg3j1Akuisx8JCOvuRsbG6desWbt26NapfOyn5Xq1WY9euXXj69CkS\nExPR1dWFpKQkdHZ2IiGBXr1OSUlBa2vr959pa2tDylvqv8NDGGOM+RO9VY/ismJ0m7oRFRKFo7lH\nkax0Xy/TlNPVRZPve3upG37TJjqU0U2PbDs76VjJnh665OrV1HgfHOyWy7MA9JfFoV/96ldv/bUe\nezuyr6/v+zcfh4aGcPXqVSxatAh79+7Fn//8ZwDAn//8ZxQWFgIA9u7di88++ww2mw2vXr1CXV0d\nli9f7qntMcbYpOs2duP/Pvu/6DZ1Iy4iDn+7+G85gL2NEHTm43/9FwWwuDjgb/+WUpIbApjTSb1f\n//VfFMBiY4H/+T+BrVs5gLHJ47FKWGdnJz788ENIkgRJkvCTn/wEmzZtwqJFi3Do0CH84Q9/QGpq\nKk6dOgUAyMrKwqFDh5CVlYWgoCD8/ve/5+ZUxtiU0TjYiM8rPofVacUs9Sx8kPMBwoPDvb0t32Qw\nUHNWQwOtly1zazrq6qLLd3VRnlu5kgpsHL7YZPPoiApP4BEVjDF/813XdzhXcw6SkJAdn439mfsR\nJOdu7x9VXQ2cP0/N9xERwL59QEaGWy7tdAJ371L/lyQB0dHU+zVrllsuz9iP8tqICsYYC2RCCNxp\nuYMbr24AAFbPWI0ts7dwlf/H2GzA5cvA06e0njOHEpKbmu+7u6n61dlJ6+XLabarG4+VZGzMOIQx\nxpgHSELCt7Xf4mnnU8ggw/a527Fi+gpvb8s3dXZS831fHzXfb9kCrFjhlt4vSaK3Hm/dokqYRkPF\ntTQ+D535AA5hjDHmZjanDV9UfoG6gToEyYNwMPMgMuPdN89qyhCCpqLeuEEJKSGBJt+7ZklOVG8v\nvfnY0UHrpUsp34WGuuXyjE0YhzDGGHMjo82IE+Un0GHoQERwBI7kHMEM9Qxvb8v36PWUkF69ovXy\n5ZSQ3NAdL0mU7W7epGynVlP1a/bsCV+aMbfiEMYYY27SZ+7D8bLj0Fq0iAmPQVFuEWIjYr29Ld/z\n8iXw9dfA0BAQGUm9X+nuGVbb10e9X21ttF6yhF6s5OoX80UcwhhjzA1adC04WX4SQ44hpChTcDT3\nKCJDIr29Ld9iswGXLgHPntE6PZ1KVFFRE760JAEPH9KTTYcDUKnoVKO5cyd8acY8hkMYY4xNUGVP\nJc5Un4FDciAjNgPvZb2HYAUPnfqB9nbgq6+A/n46jHHrVpr/5Ybm+/5+qn69PnRl0SI60zssbMKX\nZsyjOIQxxtg4CSHwsO0hLjdcBgAsS16GHek7IJd57DAS//P69cSbN+nHiYnUfO86sm4ihAAePQKu\nX6fjJZVKqn656ckmYx7HIYwxxsZBEhIu11/Go/ZHAIAts7dg9YzVPANsOJ2Omu+bmmi9ciUN5wqa\n+LeegQHg3DmguZnWCxYA27cD4XwIAfMjHMIYY2yM7E47vqr6ClV9VVDIFCicX4jcxFxvb8u3VFZS\n873FQj1fhYVuadASAigtBa5epepXVBSwZ4/bhuozL6tpbMS1ykrYAQQD2JydjYwp/ForhzDGGBsD\ns92Mk+Un0apvRVhQGD7I+QCpmlRvb8t3WK3AxYvAixe0zsigZ4SRE39JYXCQql+vC2u5ucCOHXS6\nEfN/NY2N+H/PniF0yZLvf+7/PX2K/wFM2SDGIYwxxkZpYGgAxWXF6B/qhzpUjaK8IiRETry3acpo\na6PJ94OD9Mhx2zaakDrBR7RC0GlGV67QC5aRkcDu3UAmz7+dUq5VViJ0yRJoHQ60WK3ICA9H6JIl\nuF5RwSGMMcYCWbu+HSfKT8BkNyEpKglFuUVQhrrnXEO/J0nAnTtvTsZOSqLm+/j4CV9aq6XzvBsb\naZ2dDezc6ZbCGvMx3XY7yoxGaB0OAECbXI454eGweXlfnsQhjDHGRlDTV4MvX34Ju2THnOg5OJR9\nCKFBPP0TAKWkr74CWlpovXo1sHHjhJvvhaBxYleu0BPOiAhg1y4KYWzqEELglcWCEq0WT/R6mB0O\nBMlkmB4aihTXhN2pfMY6hzDGGHuH0vZSXKi7AAGBRUmLsHvebijkCm9vyzeUlwPffEMpSakE9u93\ny9lAOh319NfX0zozkx4/cvVr6hBCoNEVvlosFgBA1ty56KyoQNqaNQhyPcK2Pn2KTYsXe3OrHiUT\nQoh3/QKj0Yjw8HAoFArU1NSgpqYGO3bsQLAbzvcaD5lMhhG2zBhjEyaEwPVX13G35S4AoCC1APmz\n8nkEBUBvPF64AJSV0Xr+fGq+n2CHvBDUz3/pEuW68PA31S/+bZ8ahBBoGBrCLa0WbVYrACBcocAq\nlQorVCo0NTXhemUlbKAK2KYp8Hbku3LLiCFs8eLFuHv3LgYHB7FmzRosW7YMISEhKC4u9shmR8Ih\njDHmaQ7JgXPV51DeUw65TI498/Zg0bRF3t6Wb2hpocePWi0dtr19O7B48YRTkl5P1a+6OlrPn0/V\nLzecaMR8gBACdUNDKNFq0e4KXxEKBVarVFimUiFUPnUHHL8rt4z4OFIIgYiICPzhD3/Az372M3z0\n0UdYsGCB2zfJGGO+wOKw4LOKz9CkbUKIIgSHsg9hbgwfQAhJAm7fpuZ7IYDkZODAASAubkKXFYIK\nahcvUoEtLIwa73Nzufo1FQghUOsKXx2u8BWpUGC1Wo1lSiVCpnD4Go1R9YQ9ePAAxcXF+MMf/gAA\nkCTJo5tijDFv0Fl0KC4vRo+pB8oQJY7mHsU05TRvb8v7Bgep+tXaSslo7VpgwwZAMbHeOIOBWspq\namg9bx4NXlXyS6d+TwiBGrMZJTodOoeFrzVqNZZy+PreiCHsP/7jP/Dxxx9j//79yM7ORkNDAzZs\n2DAZe2OMsUnTZexCcVkxDDYD4iPiUZRXBE2Yxtvb8q7XZaoLF6hJS6Wi5vu0tAlftqKCLjs0RNWv\n7dvp6CGufvk3IQSqzGbc1mrRZaPhElEKBdaq1ViiVCKYw9cPjNgT5mu4J4wx5m4NAw04VXkKVqcV\nqZpUHM4+jPDgAD+E0GKhMlVFBa2zsqhMNcHDGY1G4NtvgaoqWs+dSz39KtUE98u8SgiBl67w1e0K\nX8qgIKxVq7E4Kiqgw9eEesJKS0vx61//Gk1NTXC4BqjJZDKUvX4rhjHG/NiLrhc4X3MekpCQm5CL\nffP3IUge4NN7mpvp8aNOB4SE0NlACxdOuExVWUkBzGwGQkNpoP6iRVz98meSEHhpMqFEp0OvK3yp\nhoWvoAAOX6MxYiVs3rx5+M1vfoOcnBzIh/1mpqamenpvP4orYYwxdxBC4HbzbdxsugkAWDtzLTal\nbQrsERROJzXe37lDzwxTUqj5PjZ2Qpc1mejRY2UlrefMoeqXWu2GPTOvkIRAhcmE21ot+ux2AIA6\nKAjr1Gos5PD1AxOqhMXHx2Pv3r1u3xRjjHmLU3Li27pv8azzGWSQYWf6TixLWebtbXnXwACd+9je\nTqWp9euB/PwJN9+/fEnVL5OJimrbtrllogXzEkkIlLvCV78rfGmCgrBOo8HCqCgo+F/smIxYCbty\n5Qo+//xzbN68GSEhdHiATCbDgQMHJmWDf4krYYyxibA6rPji5ReoH6hHsDwY72W9h4y4DG9vy3te\nT0i9eJFOx1arqfo1a9aELms2U/XrdUtZWhqwbx+gCfB3HfyVJATKjEbc1ukw4Apf0cHBWKdWYwGH\nr3eaUCXsz3/+M2pqauBwOH7wONJbIYwxxsbLYDXgRPkJdBo7ERkciSO5RzBdNd3b2/KeoSFqvn/9\nnDAnh0bUT7D5vrqaLms00jzXrVuBpUu5+uWPnELgO6MRd3Q6DLrCV0xwMNar1cjl8DVhI1bCMjIy\nUF1d7TN9ElwJY4yNR6+pF8fLjkNn1SEmPAbH8o4hJjzG29vynqYmar7X6+k54a5dQF7ehJLSrZRQ\nWwAAIABJREFU0BAV1F6/tzVrFlBYCERHu2fLbPI4hcALoxF3tFpoXS/lxQYHY71Gg9zISMh9JBP4\ngwlVwlavXo2XL18im4+uZ4z5qSZtEz6r+AwWhwXTVdNxJOcIIkMC9DRopxO4eRO4d48eRU6fTo8f\nYyYWSGtr6dghg4GqX5s3A8uXc/XL3zgkicKXTgedK3zFBQcjX6NBNocvtxuxEjZ//nw0NDQgLS0N\noaGh9CEvjqjgShhjbCwqeipwpuoMnMKJ+XHzcTDzIIIVwd7elnf091PzfUfHD5vvJ/Amm8VCB26/\neEHrmTOp+jXBTMcmmUOS8MxoxF2dDnpX+IoPCUG+Wo0sDl8TMqEDvJuamn7053lEBWPMlwkhcL/1\nPq42XgUArEhZgW1zt0EuC8BX54UAnj+nZ4V2O3XHHzhAiWkC6uqo+qXXA0FBwKZNwIoVE8p0bJLZ\nh4Uvgyt8JYSEIF+jQVZEhM+0IvmzCYUwX8MhjDE2EklIuFR/CY/bHwMAts7ZilXTVwXmNxSzmZLS\n6xH1eXl0QnZY2LgvabEAV64Az57Revp0qn5N8CxvNonskoSnBgPu6fXfh69EV/jK5PDlVhPqCWOM\nMX9id9pxuuo0qvuqoZApsD9zP3IScry9Le9obATOnKFGrdDQN833E9DQAJw/T8P0g4KAjRuBlSu5\n+uUvbJKEJwYD7ut0MDqdAIBpoaHIV6uRweFr0nEIY4xNGSabCScrTqJN34awoDAcyTmCWZqJzbvy\nSw4HNd/fv0+PImfOpIO3J/CaotUKXL0KPHlC65QUqn7Fx7tpz8yjbJKEUlf4MrnCV3JoKPI1GswL\nD+fw5SUcwhhjU8LA0ACOlx3HwNAANGEaFOUWIT4yABNCXx8133d2UnmqoABYt25CpapXr4Bz5wCt\nlgbob9gArF7N1S9/YJUklOr1uK/Xw+wKXymu8JXO4cvrRgxhp0+fxi9+8Qt0d3d//0xTJpNBr9d7\nfHOMMTYabfo2nCg/AbPdjGlR03A09yiUoUpvb2tyCQE8fQpcvkzN99HR1Hw/Y8a4L2mzUfWrtJTW\nyclU/UpIcNOemcdYJQmP9Ho80Osx5Apf00NDUaDRYA6HL58xYmP+nDlz8M033yAzM3Oy9vRO3JjP\nGBuuuq8ap1+ehl2yY27MXBzKPoQQRYi3tzW5TCZq1KqpofXChcCOHdQHNk5NTVT9Ghyk6ld+PrBm\nzYSPkmQeZnE68chgwMNh4WtmWBjyNRrMDgvj8OUFE2rMT0pK8pkAxhhjwz1uf4yLdRchILB42mLs\nSt8FhTzAUkJDAzXfG430xuPu3XT80DjZbMD168CjR7ROSqJ2ssREN+2XecSQ04lHej0e6vWwSBIA\nYJYrfKVx+PJZI4awpUuX4vDhwygsLPSJA7wZY0wIgWuN13Cv9R4AYGPaRqybuS6wvtE4HJSWHjyg\n9axZ9PhRrR73JVtagLNngYEB6vdav57aybj65buGnE480OvxSK+H1RW+UsPCUKDRIHWCZ4Ayzxsx\nhOl0OoSHh+PKlSs/+HkOYYwxb3BIDpytPouKngrIZXLsy9iHBUkLvL2tydXTQ8333d2UljZsoGeF\n4+yUt9uBGzeAhw+ptSwxkXq/pk1z876Z25hd4evxsPA1Ozwc+RoNZk1gBhybXDyslTHmN4bsQ/is\n4jM065oRqgjFoexDmBMzx9vbmjxCUJf8lStUCYuJAQ4epHkR49TaStWv/n7KcGvXUv8XV798k8np\nxAOdDo8NBthc4WuOK3zN5PDlk8bVE/bpp5/i5z//Of7hH/7hRy/4u9/9zn07ZIyxEWgtWhSXFaPX\n3AtliBJFeUVIikry9rYmj8lEaamujtaLFlHzfcj4XkL4y1Fi8fHU+5Wc7MY9M7cxOZ24p9Oh1GCA\n3RW+5rrC1wwOX37rrSEsKysLALBkyZIf9FkIIQKr74Ix5nWdhk4UlxfDaDMiITIBRblFUIeNv/fJ\n79TVUQAzmYDwcGDPHsD1Z/R4tLdTL39fH53jvXYtjRML4smRPsfocOCeXo8nw8LXvIgI5Gs0SJnA\n26/MN/DjSMaYT6sfqMepylOwOW1I06ThcM5hhAUFyN/87Xbg2rU3ryqmpVG5SqUa1+UcDqCkBLh7\nl6pfcXHU+zV9uhv3zNzC4HDgnk6HJwYDHK7veRmu8JXM4cuv8NmRjDG/9KzzGb6p/QaSkJCXmId9\nGfsCZwRFdzc13/f0ULPWpk3AqlXjbr7v6KBiWk8PVb/WrKF+fq5++Ra9w4G7Oh2eDQtf813haxqH\nrymHv/wYYz5HCIFbTbdQ0lwCAFg3cx02pm0MjFYIIajyde0ala5iY6n5fpzNWk7nm+qXJNHlCgsn\nNEifeYBuWPhyusJXVmQk1qvVSOLwNWVxCGOM+RSn5MTXtV/jRdcLyCDDrnm7sDR5qbe3NTmMRipX\n1dfTeskSYNu2cTffd3bS5bq7qfq1ahWwcSMQHOzGPbMJ0drtuKvT4bnRCKer5zo7MhLrNRokjvPf\nO/MfI4awmpoa/OxnP0NXVxcqKytRVlaG8+fP41/+5V8mY3+MsQBidVhxqvIUGgYbECwPxvvZ72Ne\n7Dxvb2ty1NZSYjKbqfl+715gnKeVOJ3AnTvA7dtU/YqJoerXzJlu3jMbt0FX+HoxLHzluMJXAoev\ngDFiY/769evxb//2b/i7v/s7PH/+HEII5OTkoLKycrL2+APcmM/Y1KS36nGi/AS6jF2IDI7E0dyj\nSFGNf/6V37Dbae7X61OyZ8+m5nvl+A4g7+6mNx+7umi9YgW1k/H3dd8wYLfjjk6H74xGSK7wlet6\n7BjH/5KmpAk15pvNZqxYseIHFwvmWjZjzI16TD0oLiuGzqpDbHgsjuUdQ3R4tLe35XldXdR839tL\n01FfN9+Po/dNkqjvq6SEKmHR0cC+fUBqqvu3zcau327HHa0WZSbT9+FrQVQU1ms0iOXvqQFrxBAW\nHx+P+tf9CQC+/PJLTOOzLBhjbvJq8BU+r/wcFocFM1QzcCT3CCKCI7y9Lc8Sgs4IunaNElNcHPDe\ne3Ra9jj09NCTzI4OWi9bBmzZwtUvX9Bns+G2TodykwlCCMhlMix0ha8YDl8Bb8THkQ0NDfjpT3+K\n+/fvIzo6GmlpaSguLkaql/56xY8jGZs6yrvLcbb6LJzCiaz4LOyfvx/Biin+jclgoOeFjY20XrYM\n2Lp1XN3ykkQT72/epCynVlP1a/ZsN++ZjVmvK3xV/EX4WqdWI5rDV0B5V24Z9bBWk8kESZKgHGef\ngrtwCGPM/wkhcK/1Hq41XgMArJy+ElvnbIVcNr4ZWH6juho4f56a7yMiKDFlZIzrUr29VP1qb6f1\nkiWU5XiagXf12Gy4rdWi0myGEAKKYeFLw+ErIE2oJ2xwcBD//d//jaamJjgcju8vyGdHMsbGQxIS\nLtZdRGlHKWSQYeucrVg1Y5W3t+VZNhtw+TLw9Cmt58yh1xXH8ZdaSQIePKDql8NB1a+9e+mSzHu6\nbTaUaLV4aTIBABQyGRarVFijUnH4Ym81YgjbuXMnVq1ahby8PMjlcj47kjE2bjanDadfnkZNfw2C\n5EE4kHkAWfHjPwPRL3R2UvN9Xx8132/ZQq8sjuPP0b4+4Nw5oLWV1osXU/WLz2/2ni6rFSU6HaqG\nha8lSiXWqNVQ83EEbAQjPo5cvHgxnj17Nln7GRE/jmTMP5lsJpwoP4F2QzvCg8JxJPcIZqqn8OAq\nIahh68YNathKSKDJ94mJY76UJNEQ/evXqfqlVFL1Kz3dA/tmo9JptaJEq0W12QwACBoWvlQcvtgw\nE+oJ+81vfgOVSoU9e/YgdFizQUxMjHt3OUocwhjzP/3mfhwvO45ByyCiw6JRlFeEuIg4b2/Lc/R6\nar5/9YrWK1YAmzePq/l+YIB6v1paaL1wIQ3RDw93437ZqHVYrbil1aJ2WPha6gpfSg5f7EdMqCcs\nLCwM//RP/4R//dd/hdx1cKxMJkPj6zd7GGPsHVp1rThZcRJmuxnJymQczT2KqJAob2/Lc16+BL7+\nGhgaAiIjqfdrHCUrIYDHj2mKhd0OREVR9WtegBwg4GvaLBaU6HSoc4WvYLkcy5RKrFapEMXhi43T\niJWwtLQ0lJaWIi7ON/7WypUwxvxHVW8VTledhkNyYF7sPLyX9R5CFFN0eJXNBly6BLxu30hPp7cf\no8YeOAcHqferqYnWeXnAjh1c/fKGVosFJVot6oeGAFD4Wq5UYrVajUiFwsu7Y/5gQpWw9PR0hPNX\nPmNsjB61PcKl+ksQEFiavBQ703dO3REU7e3UfD8wAAQFUbf8smVjbr4XAnjyBLh6lTJdZCSwZw8w\nf76H9s3eqsViwS2tFo2u8BXiCl+rOHwxNxoxhEVERGDhwoXYsGHD9z1hPKKCMfY2QghcabiCB20P\nAACb0jZh7cy1U/OtakkC7t2jeRGSRE33Bw9SE/4YabVU/XrdRpaTA+zcSePE2ORpGhpCiU6HV67w\nFSqXY4VKhZUqFSI4fDE3GzGEFRYWorCw8Ac/NyX/MGWMTZhDcuBM1RlU9lZCIVNg3/x9yEvM8/a2\nPEOnA776CmhupvWqVXT24xj7g4SgJ5iXL1P1KyIC2L0byJrikzt8iRACTa7Hjk0WCwAgbFj4Cufw\nxTxk1BPzfQX3hDHmm4bsQzhZcRItuhaEKkLxQc4HSItO8/a2PKOykprvLRbq+SosBObOHfNldDoa\noN/QQOusLGDXLnoMyTxPCIFXrvDVPCx8rXSFrzAOX8wNxtUT9v777+OLL75Abm7uj16wrKzMfTtk\njPm1waFBFJcXo8/cB1WoCkW5RUiMGvs8LJ9ntQIXLwIvXtA6I4NeWRxjahICeP6cql9WK1W/du0C\nsrM9sGf2V4QQaHT1fLW6wle4QoFVKhWWK5UcvtikeWslrKOjA8nJyWhubv6rBCeTyTBr1qxJ2eBf\n4koYY76lw9CBE+UnYLQZkRiZiKK8IqhCVd7elvu1tVHz/eAgzfvato0ObBxje4ZeT9Wv+npaz59P\njx/H8RIlGyMhBOqHhlCi1aLNagUARLwOXyoVQuVT9MUR5lUTGtb685//HJ9++umIPzdZOIQx5jvq\n+utwqvIU7JIds6Nn41D2IYQFTbEzdCQJuHMHKCmhHyclUfN9fPyYLiME8N13NMXCYqFxEzt3UgM+\nt9l6lhACda7w1T4sfK1WqbCMwxfzsAmFsEWLFuH58+c/+Lnc3FyUl5e7b4djwCGMMd/wtOMpvq37\nFpKQsCBxAfZm7IVCPsUe42i11Hz/elz96tXAxo1jbr43GKiFrLaW1hkZVP0ax/ndbAyEEKgxm1Gi\n06HTFb4iFQqsUauxVKlECIcvNgnG1RP2n//5n/j973+PhoaGH/SFGQwGrFmzxv27ZIz5BSEEbjbd\nxO3m2wCA9bPWY0Pqhqn31nR5OfDNN9S0pVQC+/cDs2eP6RJC0GUuXqQB+mFhNHQ1L4+rX54khEC1\n2YwSrRZdNhsAIGpY+Arm8MV8xFsrYTqdDoODg/jFL36BTz/99PsUp1QqERsbO6mbHI4rYYx5j1Ny\n4nzNeXzX/R3kMjl2pe/CkuQl3t6We1kswIULwOuXj+bPp+b7MQ7sMhopw1VX0zo9nQavqqZgu5yv\nEEKgyhW+ul3hSxkUhDUqFZZw+GJeMqHHkb6GQxhj3mFxWHCq8hQaBxsRogjB+1nvIz127Gci+rSW\nFnr8qNVS8/327cDixWMqWwlBEyy+/ZaqX6GhdJmFC7n65SmSEHhpMuG2ToceV/hSBQVhrVqNxVFR\nCOLwxbxoQscWMcaY3qpHcVkxuk3diAqJwtHco0hWJnt7W+4jScDt29R8LwSQnAwcOACM8cxck4nC\n18uXtJ4zh4poarUH9swgCYFKV/jqdYUvtSt8LeLwxfwAhzDG2Dt1G7tRXF4MvVWPuIg4FOUWITo8\n2tvbcp/BQap+tbZSqWrtWmDDBmCMs6JevqTHj2YzVb+2bh1zEY2NkiQEKkwm3NZq0We3A6DwtU6t\nxkIOX8yPcAhjjL1V42AjPq/4HFanFTPVM3Ek5wjCg8O9vS33EIL6vi5coOZ7lYqa79PGNuXfbKZL\nVFTQevZsqn5pNB7Yc4CThEC5K3z1u8KXJigI6zUaLIiKgoITL/MzHMIYYz/qu67vcL7mPJzCiez4\nbOzP3I8g+RT5I8NiobLV6+SUlUVd8+FjC5hVVXQZkwkICaHq1zjmt7IROIVAmdGIOzodBlzhKyY4\nGOvUauRx+GJ+bIr8icoYcxchBO623MX1V9cBAKtnrMaW2VumzgiK5mZ6/KjTUXLasWPMXfNDQzR2\n4vULlKmpwL59QPQUekrrC5xC4DtX+BocFr7Wu8KXfKr8N8kCFocwxtj3JCHh29pv8bTzKWSQYfvc\n7VgxfYW3t+UeTic13t+5Q48iU1Jo8n1MzJguU1NDg1eNRnqBcssWYNkyrn65k1MIvDAacUerhdbh\nAADEBQdjvUaDnMhIDl9syuAQxhgDANicNnxR+QXqBuoQJA/CwcyDyIzP9Pa23GNggM59bG+ntLR+\nPZCfP6bm+6EhOnLou+9oPXMmUFg45gzH3sEhSXhuNOKuTgfdsPCVr9Egm8MXm4I4hDHGYLQZcaL8\nBDoMHYgIjsCRnCOYoZ7h7W1NnBDAixf07NBmo1kRBw4As2aN6TJ1dXTotsFA1a9Nm4AVK7j65S4O\nScIzV/jSu8JXQkgI1qvVyOLwxaYwDmGMBbg+cx+Olx2H1qJFdFg0juUdQ2yE907FcJuhIeqar6yk\ndU4OHdgYNvoDxi0W4PJl4PXxuTNmUPXLi4eGTCn2YeHL4ApfiSEhyNdokBkRMXX6EBl7Cw5hjAWw\nFl0LTpafxJBjCCnKFBzNPYrIkEhvb2vimpqo+V6vp6FdO3eO+cDG+nqqfun1dF73xo3AypUAj6Ca\nOLsk4YnBgHs6HYxOJwAgyRW+5nP4YgGEQxhjAaqypxJnqs/AITmQEZuBg1kHEaII8fa2JsbpBG7e\nBO7do0eR06dT8/0YXlu0WoErV4CnT2k9fTpVv8Y4PJ/9CNuw8GVyha9poaHIV6uRweGLBSCPhrDW\n1lb8zd/8DXp6eiCTyfDTn/4U//iP/4iBgQEcPnwYzc3NSE1NxalTp6BxTTb8+OOP8cc//hEKhQK/\n+93vsHXrVk9ukbGA9KD1Aa40XIGAwLLkZdiRvgNymZ+XePr6qPrV0UEVr4ICasAfQ+mqsRE4d46m\nVygUVP1atYqrXxNlkySUGgy4Pyx8JYeGokCjQXp4OIcvFrA8eoB3V1cXurq6sHDhQhiNRixZsgRn\nz57Fn/70J8TFxeGjjz7Cp59+isHBQXzyySd4+fIljh49itLSUrS3t2Pz5s2ora2FfNifgHyAN2Pj\nJwkJVxqu4GHbQwDA5tmbsWbGGv/+JigE8OwZvbpot9Oo+gMH6PXFUbLZgKtXgdJSWicnU/UrIcFD\new4QVknCY70eD/R6mF3ha3poKPI1Gszl8MUChNcO8E5KSkJSUhIAICoqCpmZmWhvb8f58+dRUlIC\nAPjwww9RUFCATz75BOfOncORI0cQHByM1NRUzJ07F48fP8bKlSs9uU3GAoLdacdXVV+hqq8KCpkC\nhfMLkZuY6+1tTYzZTEO7qqponZdH/V9jaL5vagLOngW0Wqp+FRQAa9Zw9WsiLE4nHhsMeKDXY8gV\nvmaEhSFfrcYcDl+MfW/SesKamprw/PlzrFixAt3d3UhMTAQAJCYmoru7GwDQ0dHxg8A1ffp0tLe3\nT9YWGZuyzHYzTpafRKu+FWFBYfgg5wOkalK9va2JaWwEzpyhuRGhocCuXRTCRslmA65dAx4/pvW0\naVT9cv3RxMbB4nTioV6Ph3o9LJIEAJgZFoYCjQZpYWEcvhj7C5MSwoxGIw4ePIjf/va3UCqVP/hn\nMpnsnV+YP/bPfvnLX37/44KCAhQUFLhrq4xNOYNDgzhedhz9Q/1Qh6pRlFeEhEg/fs7mcAA3bgD3\n79N65kx6/DiGE7Obm6n6NThIFa/8fGDt2jHNbmXDDLnC16Nh4Ss1LAz5Gg1SOXyxAHPr1i3cunVr\nVL/W4yHMbrfj4MGD+MlPfoLCwkIAVP3q6upCUlISOjs7keBqvEhJSUFra+v3n21ra0NKSspfXXN4\nCGOMvV27vh0nyk/AZDchKSoJRblFUIYqR/6gr+rtpeb7zs436WndulE/O7TbgevXgUePqJUsMRHY\nvx9wdU2wMTIPC19WV/hKCw9HvlqN1DEehs7YVPGXxaFf/epXb/21Hm3MF0Lgww8/RGxsLP793//9\n+5//6KOPEBsbi5///Of45JNPoNVqf9CY//jx4+8b8+vr63/wtyhuzGdsdGr7a/FF5RewS3bMiZ6D\nQ9mHEBoU6u1tjY8QNDPi8mVKUtHRNHpi+vRRX6K1lapf/f2U2dato5cnufo1dmanE/d1Ojw2GGBz\nha/Z4eHI12gwawz9eIwFgnflFo+GsLt372L9+vXIy8v7Pkh9/PHHWL58OQ4dOoSWlpa/GlHx61//\nGn/84x8RFBSE3/72t9i2bduo/88wxsiTjif4tvZbCAgsTFqIPfP2QCH307RhMtHU1JoaWi9cCOzY\nQX1go2C30+iwBw8oyyUkUO9XcrIH9zxFmVzhq3RY+JrrCl8zOHwx9qO8FsI8gUMYY28nhMD1V9dx\nt+UuAKAgtQD5s/L9tyenoYGa741GeuNx9246fmiU2tqo+tXXR6PD1q6lJ5hBPKZ6TIwOB+7r9Sg1\nGGB3ha/0iAjkq9WYzuGLsXfy2ogKxtjkcUpOnKs5h7LuMshlcuyetxuLpy329rbGx+GgVxcf0jwz\nzJpFzfdq9ag/fuvWm8H58fFU/fqRFlP2DgZX+HoyLHzNi4hAvkaDlFFWIhljb8chjLEpwOKw4POK\nz/FK+wohihAcyj6EuTFzvb2t8enpAU6fBrq7qXlrw4YxDe5qb6fqV28vVb/WrKFLcPVr9PQOB+7p\ndHhqMMDh+hv8fFf4msbhizG34T+WGPNzOosOxeXF6DH1QBmixNHco5imnObtbY2dEDSy/soVKmXF\nxFDz/SjLVw4HcPs2cPcuIEl01mNh4Zh69wOe3uHAXZ0Oz4aFr8zISOSr1Uji8MWY23EIY8yPdRm7\nUFxWDIPNgPiIeBTlFUETNvp5WT7DZKLyVV0drRctoub7kNEdKN7ZSa1jPT1U/Vq9mqpfwcEe3PMU\nohsWvpyu8JUVGYl8jQaJo/x3wBgbOw5hjPmphoEGnKo8BavTilRNKg5nH0Z4sB/OZqqrowBmMgHh\n4cCePUBW1qg+6nRS9evOHap+xcRQ9WsMx0YGNK3djjs6HV4YjXAKAZlMhpzISKzXaJDA4Ysxj+MQ\nxpgfetH1AudrzkMSEnISclA4vxBBcj/7crbbqfn+0SNap6XR5FSValQf7+qi7NbVRdWvlSuBTZu4\n+jUag8PCl+QKX7lRUVivViOewxdjk8bP/tRmLLAJIXC7+TZuNt0EAKyZsQabZ2/2vxEU3d3UfN/T\nQw33mzYBq1aNqvne6aS+r5ISqn5FR1P1a9asSdi3nxtwha/vhoWvPFf4iuPwxdik4xDGmJ9wSk58\nW/ctnnU+gwwy7EjfgeUpy729rbERgipf165RJ31sLDXfj3Jyak8P9X51dtJ6+XJg8+ZRt44FrH67\nHbe1WpSbTJCEgFwmw8KoKKzTaBDLpUPGvIZDGGN+wOqw4ouXX6B+oB7B8mAczDqI+XHzvb2tsTEa\n6flhfT2tlywBtm0bVYKSJJr5desWVcI0GmDfPnqCyd6uz2bDbZ0O5SYThCt8LVIqsU6tRgyHL8a8\njkMYYz7OYDXgRPkJdBo7EREcgaO5RzFd5WdzF2pqgHPnALMZiIgA9u4F5o8uRPb2UnZrb6f10qXA\nli2jPrUoIPW6wlfFj4SvaA5fjPkMDmGM+bBeUy+Ky4uhtWgREx6DY3nHEBMe4+1tjZ7dTnO/Sktp\nPXs2Nd8rlSN+VJLovMcbN6j6pVZTdpszx8N79mM9NhtKtFq8NJshhIDCFb7WqtXQcPhizOdwCGPM\nRzVrm3Gy4iQsDgumq6bjSM4RRIZEentbo9fVRc33vb2AQvGm+X4ULxH09VH1q62N1osX05NLrn79\nuO7X4ctkAgAoZDIsVqmwVq2Gmo8KYMxn8VcnYz6ooqcCZ6rOwCmcmB83HwczDyJY4SeVDCGohHX9\nOpWw4uOp+T4pacSPShIdF3njBvXtq1RU/ZrrpycweVqn1YrbOh2qXOErSCbDYlflS8XhizGfx1+l\njPkQIQQetD3AlYYrAIDlKcuxfe52yGWjOzfR6wwGen2xsZHWy5YBW7eOanhXfz9Vv1pbab1oEVW/\nwsI8uF8/1WG1okSrRY3ZDIDC11KlEmvUaig5fDHmN/irlTEfIQkJl+ov4XH7YwDA1jlbsWr6Kv+Z\nAVZdDZw//6b5ft8+ICNjxI+9nlpx/Tq1kCmVNDR/3rxJ2LOfaXeFr1pX+AqWy7FUqcRqlYrDF2N+\niL9qGfMBdqcdp6tOo7qvGgqZAvsz9yMnIcfb2xodmw24fBl4+pTWc+fS9NSoqBE/OjBAL002N9N6\nwQJg+3Y6vYi90Wax4JZWi/qhIQAUvpa5wlcUhy/G/BZ/9TLmZSabCScrTqJN34awoDAcyTmCWRo/\nGf/e0UHN9/391Hy/ZQuwYsWIzfdC0AuTV69S9SsqiqpfoyicBZRWV/hqcIWvELkcy5VKrFKrEalQ\neHl3jLGJ4hDGmBcNDA3geNlxDAwNQB2qxrG8Y4iPjPf2tkYmBHD//pv5EQkJ1HyfmDjiR7Vaqn69\nekXr3Fxgxw56gslIs8WCEq0WjcPC1wqVCqtUKkRw+GJsyuAQxpiXtOnbcKL8BMx2M6ZFTcPR3KNQ\nho48P8vr9Hpqvn+dolasoLODRmi+F4KeWF65Qk8wIyOB3buBzMxJ2LOfaBoawi2tFk3SnTDVAAAg\nAElEQVQWCwAgdFj4CufwxdiUwyGMMS+o7qvG6ZenYZfsmBszF4eyDyFE4QcHIL58CXz9NTA0RCmq\nsBBITx/xY1ot9ey/fmkyOxvYuZMuEeiEEGhyPXZsdoWvMLkcK1UqrODwxdiUxiGMsUn2uP0xLtZd\nhIDA4mmLsSt9FxRyH/9Ga7MBFy8Cz5/TOj2dAtgIKUoI+sjly4DVSo8cd+2iEBbohBBodD12bHGF\nr3CFgsKXUokwDl+MTXkcwhibJEIIXGu8hnut9wAAG1I3YP2s9b4/gqK9nZrvBwaAoCCa+7Vs2YjN\n93o9Vb9en9edmUkBbBQvTU5pQgg0DA2hRKdD67DwtcpV+QqV+8lMOMbYhHEIY2wSOCQHzlafRUVP\nBeQyOfZm7MXCpIXe3ta7SRJw7x5w8yb9ODGRmu8TEt75MSGAFy+o+mWx0LiJ19UvX8+bniSEQP3Q\nEEq0WrRZrQCACIUCq1UqLOPwxVhA4hDGmIcN2YfwWcVnaNY1I1QRikPZhzAnxsdPodbpgK++ejPA\na9UqOvtxhJlUBgNVv+rqaJ2RQaMnArn6JYRArSt8dbjCV6RCgdVqNZYplQjh8MVYwOIQxpgHaS1a\nFJcVo9fcC2WIEkV5RUiKGvkMRa+qqAC++YbKWFFR1Ps1wuGNQgBlZdQ2ZrHQUUM7d9L4iUCtfgkh\nUGM2o0SnQ+ew8LVGrcZSDl+MMXAIY8xjOg2dKC4vhtFmREJkAopyi6AOU3t7W29ntQIXLgDffUfr\njAw6PXuE5nujkV6YrKmh9bx5VP1S+sG0DU8QQqDabEaJVosumw0AEKVQYK1ajSVKJYI5fDHGXDiE\nMeYB9QP1OFV5CjanDWmaNBzOOYywIB8+ibqtjZrvBwdp3te2bcCSJe8sYwlBRbMLF2hiRWgoDV1d\nsCAwq19CCLw0m3Fbq0W3K3wpg4KwVq3G4qgoDl+Msb/CIYwxN3ve+Rxf134NSUjIS8zD3oy9CJL7\n6JeaJAF37gAlJfTjpCRqvo9/99R+k4meWFZV0XruXCqaqVSTsGcfIwmBlyYTSnQ69LrCl2pY+Ari\n8MUYewsf/c7AmP8RQqCkuQS3mm4BANbNXIeNaRt9dwSFVkvN9y0ttF69Gti4ccTm+8pK4NtvAbOZ\nql/btgGLFgVe9UsSApUmE24PC1/qoCCsU6uxkMMXY2wUOIQx5gZOyYlvar/B867nkEGGXfN2YWny\nUm9v6+3Ky6mUZbVS89b+/cDs2e/8iMlEjx4rK2k9ezawbx+g9uE2N0+QhEC5yYTbWi367XYAgCYo\nCOs0GiyMioIi0NIoY2zcOIQxNkFWhxWnKk+hYbABwfJgvJf1HjLiMry9rR9nsVCSKiujdWYmddGP\ncHp2VRVlNpMJCAmhea0jtIxNOZIQKDMacVunw4ArfEUHB2OdWo0FHL4YY+PAIYyxCTBYDSguL0aX\nsQuRwZE4mnsUKaoUb2/rx7W00ONHrZaa73fsGPE5otlMYyfKy2mdlkbVL41mkvbsA5zDwtegK3zF\nBAdjvVqNXA5fjLEJ4BDG2Dj1mHpQXFYMnVWH2PBYFOUVISY8xtvb+muSRI33t2/TK43JycCBA0Bc\n3Ds/Vl1N1S+jkTLb1q3A0qWBU/1yCoEXRiPuaLXQOhwAgNjgYKzXaJAbGQl5oPxGMMY8hkMYY+PQ\npG3CZxWfweKwYIZqBo7kHkFE8Lsf6XnF4CCNnmhro/S0di2wYQPwjsOhh4aAS5fejAubNYuqXzE+\nmC89wSFJFL50Ouhc4SvOFb5yOHwxxtyIQxhjY1TeXY6z1WfhFE5kxmXiQOYBBCuCvb2tH3o9wv7C\nBWq+V6mo+pWa+s6P1dbS4FWDgapfmzcDy5cHRvXLIUl47gpfelf4ig8JQb5ajSwOX4wxD+AQxtgo\nCSFwr/UerjVeAwCsnL4SW+dshVzmY6MILBZ6jlhRQeusLGq+Dw9/50cuXaKDtwFg5kyqfsXGTsJ+\nvcwhSXhqNOLesPCVEBKCfI0GWRERvjtihDHm9ziEMTYKkpBwse4iSjtKIYMMW+dsxaoZq7y9rb/W\n3EzN9zodvca4YwewcOE7S1n19XTotl5PI8I2bQJWrACm+pgruyThqcGAe3o9DK7wlegKX5kcvhhj\nk4BDGGMjsDvt+PLll6jpr0GQPAgHMg8gKz7L29v6IaeTmu/v3KFHkSkpNPn+HY1cFgtw5Qrw7Bmt\np0+ns7pH6Nf3e7bX4Uung9HpBABMCw1FvlqNDA5fjLFJxCGMsXcw2Uw4UX4C7YZ2hAeF40juEcxU\nz/T2tn5oYICa79vbqeK1fj2Qn//O5vuGBqp+6XRU/dqwAVi1ampXv2yShFKDAfd1Ophc4Ss5NBT5\nGg3mhYdz+GKMTToOYYy9Rb+5H8fLjmPQMghNmAbH8o4hLsKHykRCUBPXxYuAzUaj6w8coNcZ38Jq\nBa5eBZ48oXVKClW/Rjgq0q9ZJQmlej3u6/Uwu8JXiit8pXP4Yox5EYcwxn5Eq64VJytOwmw3I1mZ\njKO5RxEVEuXtbb0xNESvMb58SeucHGD3biAs7K0fefUKOHeOZrUqFFT9Wr166la/rJKEx67wNeQK\nX9NDQ1Gg0WAOhy/GmA/gEMbYX6jqrcLpqtNwSA6kx6Tj/ez3EaII8fa23mhqouZ7vZ5O0N65E8jL\ne2vzvc0GXLsGPH5M62nT6KjIhITJ2/JksjideGQw4OGw8DUzLAz5Gg1mh4Vx+GKM+QwOYYwN86jt\nES7VX4KAwJJpS7Br3i7fGUHhdAI3bwL37tGjyBkz6PFjdPRbP9LcDJw9SzNbFQpqFVuz5p3tYn7L\n4nTioV6Ph3o9LJIEAJjlCl9pHL4YYz6IQxhjoBlgVxqu4EHbAwDAprRNWDtzre984+7ro+pXRwdV\nvAoKqAH/Lc8SbTbg+nXg0SNaJyVR71dS0uRtebIMDQtfVlf4Sg0LQ4FGg9R3zEZjjDFv4xDGAp5D\ncuBM1RlU9lZCLpNjX8Y+LEha4O1tESFohsSlS4DdTidnHzhA01TfoqWFql8DA5TR1q8H1q2betUv\ns9OJB3o9Hg8LX7PDw5Gv0WDWO3rjGGPMV3AIYwFtyD6EkxUn0aJrQagiFIdzDmN29Gxvb4uYzdR8\nX1VF67w86v96S8Cw24EbN4CHDym7JSZS9WvatEnc8yQwOZ14oNPhscEAmyt8zXGFr5kcvhhjfoRD\nGAtYWosWx8uOo8/cB1WoCkW5RUiMSvT2tkhjI3DmDB3iGBpKbz7m5r71l7e2UvWrv5+qX+vWjTgq\nzO+YnE7c1+lQOix8zXWFrxkcvhhjfohDGAtIHYYOnCg/AaPNiMTIRBTlFUEVqvL2tgCHg8pZ9+/T\neuZMevyo0bz1l9+8Sb9cCJr3tX8/kJw8iXv2MKPDgXt6Pf5/e3ceHNV15n38293at26QkIQkFqEN\nIYl9XwUYMLbZjAGz2DPxVMrJZPIms9hJvTWpmUmVY1xTMzXxJBlXzcTF5DXGxvECBoMBs5odAUZI\nLAItaEMsUrdaLanX8/5x2rJYzSKpJfF8/olu9+17T+ti9S/PffqcE3Y7bn/4yoyIYIbFQnJoaIBH\nJ4QQj05CmHjilNws4aPij3B5XQzpM4TlOcsJC+oGlZTr1/XM91ev6nLWjBm6pHWP5vvqal39un5d\n9+pPnar79YN6yX/Vdo+HgzYbJ+x2PEoBkOUPX0kSvoQQvUAv+XMtxIMpqClga8lWfMrHiIQRLMxa\niMkY4Ht2SkFBAXz5pW7s6tNHr/uYknLX3T0evUzk11/rl8bF6d6ve+ze4zT6w1dBu/A11B+++kv4\nEkL0IhLCxBNBKcWe8j3sr9gPwPRB05k5eGbgp6BwOPQijhcu6O2RI2H+fN0Hdhc1Nbr6de2arn5N\nnqxnvg8O7sIxdxKbx8PXNhsn7Xa8/vA1LDKS6WYziRK+hBC9kIQw0et5fV42X9jMN3XfYDQYeTbj\nWcYkjQn0sPQq2p9+Ck1N+huPzz2nlx+6C68X9u+HAwfA54PYWF39GjCgi8fcCaxuN1/bbJxqasKr\nFAaDgZzISKZbLCSEdKOVCoQQooNJCBO9WqunlY1FGyltKCXYGMzynOVkxGYEdlAej15H6MgRvT1o\nkG6+N5vvuvvVqzqr1dXp6tekSTBrVs+vfjX4w9fpduEr1x++4iV8CSGeABLCRK/V6Gxk/Zn11Dnq\niAqJYlXeKpKiA/y1wWvXdPN9XZ1uuJ85U68jdJfme69XV77279fVr759YdEindl6sga3m/02G980\nNeHzh6/hUVFMM5vpJ+FLCPEEkRAmeqW6pjrWF66n0dlIXEQcq/NW0yf83mssdjql4Phx2LFDV8L6\n9tXN98nJd929rk73ftXW6u0JE2D2bOjJGeWm280Bq5UzDkdb+BrhD19xPfmNCSHEI5IQJnqdsoYy\nPjj7AU6vk4HmgazMXUl4cADXEGxqgk2boKREb48eDU8/fddE5fPpbz3u26crYX366OrX4MFdO+SO\ndMPl4oDNxhmHA6UURoOBkVFRTLdY6NvT76kKIcRjkBAmepUzdWfYdH4TXuUlp18OS7KXEGQM4D/z\nkhJd0nI4IDwcFiyAYcPuuuu1a3rXmhq9PW4czJnTc6tf110u9ttsnG0XvkZFRzPVbJbwJYQQSAgT\nvYRSiq+vfM1XZV8BMCllEnPT5gZuCgq3WzffHz2qt1NT9VT2MXfOyu/z6Rnv9+zR1S+zWVe/hnST\nJSwf1jWXi/1WK0XNzSilMBkMjPSHrz4SvoQQoo2EMNHj+ZSPrRe3UlBbgAEDT6c/zYSUCYEbUF2d\nbr6/dk0v3jhrlp7Q6y6B8MYNXf2qqtLbY8bA3Ln3nCasW6vzh6/iduHr28qXRcKXEELcQUKY6NFc\nXhd/Lv4zF29eJMgYxNLspWT3yw7MYJTSla9du3TzfWysbr6/y0KOPp+eoWL3br1rTIyufqWlBWDc\nj+mq08k+m41zDgcAJoOBMTExTDGbMfeWNZSEEKITyF9I0WM1uZp4v/B9auw1RARHsDJ3JQPMAZq9\n1G7XzfeXLuntsWN1SesuDV03b+rqV2Wl3h41CubN0/O19iS1Tif7rFbONzcDEGQwMCY6milmMzES\nvoQQ4nvJX0rRI91ovsH6M+tpaG2gT1gf1gxfQ2xEbGAGc+GCDmDNzRARAQsXwtChd+x2e6EsOlrv\nmhHguWMfVo0/fF1oF77G+sNXtIQvIYR4YPIXU/Q4V2xX2FC4gRZPC8nRyazKW0VkSGTXD8Tt1vN+\nHT+ut9PS9FpC0dF37Fpfr3NaRYXeHjlSV7/CAzhzxsOqdjrZa7VS4g9fwUYj46KjmRwTQ5SELyGE\neGjyl1P0KMXXi/nk3Cd4fB6yYrNYOmwpIaYAzOFw9apuvr9+XTffP/UUTJx4R/O9UnDsmK5+ud0Q\nFaVnqcjK6vohP6rK1lb2Wa1camkBdPgaHx3NZLOZSJMpwKMTQoieS0KY6DEOVx5mx+UdKBTjksYx\nP2M+RsOdy/10KqXg8GH46is9n0S/frr5PjHxjl0bGnT1q7xcbw8fDvPn95zq1xV/+LrsD18h/vA1\nScKXEEJ0CAlhottTSvHl5S85UqUXvH5qyFNMGTCl6+cAs9v1StqlpXp73DjdfH/b9AtKwYkTsHMn\nuFwQGamrX3dpE+uWKlpb2Wu1UuYPX6FGIxNiYpgYE0OEhC8hhOgwEsJEt+b2uvn0/KcUXy/GZDCx\neOhi8hLyun4g58/rslZLi26+X7wYMjPv2M1qhc2bv8tpubnwzDP6Jd2ZUopyf+WrvLUV0OFroj98\nhUv4EkKIDichTHRbze5mNhRuoLKxkrCgMFbkrCC1T2rXDsLlgi+/hIICvZ2ergNYVNQtuykFJ0/q\nXV0uHbqee+6eKxR1G0opyvzhq8IfvsLaha8wCV9CCNFpJISJbqmhpYH3zrzHzZabmEPNrB6+mvjI\n+K4dRE2Nbr6/eROCgvRCjuPH39F8b7Pp6tfly3p72DB49ll9G7K7UkpR6r/tWOkPX+EmE5NiYhgf\nHS3hSwghuoCEMNHtVDdW837h+zjcDhIiE1g9fDUxoXeuudhplNKLOe7erZvv4+N1831Cwh27nT4N\n27eD06kb7p99FnJy7rpCUbeglOJSSwv7rFaqnE5Ah6/JMTGMj4kh1NjFX3QQQognmIQw0a1cvHmR\nj4o+wu1zk9YnjeU5ywkN6sKFFBsbdfN9WZnenjBBTz9xW/N9YyN8/jmUlOjtoUP17cfb7lJ2G0op\nSvzhq9ofviL84WuchC8hhAgICWGi2zhRc4KtF7eiUIxMHMmCzAWYjF14W6y4WCerlhZ9L3Hx4jum\ns1cKvvlGV79aW3X165lndAN+d6x+KaW46A9fNf7wFWkyMcVsZmx0NCESvoQQImAkhImAU0qxu2w3\nB64cACB/cD4zBs3ouikoXC7Ytg1OndLbGRk6gN3W1GW364x28aLezszUU0/cZYL8gFNKcb65mX1W\nK1ddLgCi2oWvYAlfQggRcBLCREB5fV42XdjEmbozGA1Gnst8jtH9R3fdAKqrdfN9fb1uvp87V8//\n1S4AKgWFhTqntbTohbbnz9eTr3a36pdSinP+8FXnD1/RQUFMiYlhjIQvIYToViSEiYBp9bTy4dkP\nKbOWEWIKYXnOctL7pnfNyX0+OHgQ9uzRPyck6Ob7+Fu/gdnUBFu26GnCQBfJFiyAmC78nsCDUEpR\n7A9f1/zhKyYoiKlmM6OjogiS8CWEEN2OhDARELZWG+sL13PNcY2okChW562mf3T/Ljq5DT755LvV\ntCdNgtmzdSXMTykoKoIvvoDmZggNhaef1gtvd6fql08pihwO9ttsXG8XvqaZzYyS8CWEEN2ahDDR\n5a42XWX9mfXYXXb6RfRj9fDVWMIsXXPys2d1aau1VX+VcckSSEu7ZReHA7Zu1X36oJ9euBDM5q4Z\n4oPwKcVZh4P9Vis33G4AzP7wNVLClxBC9AgSwkSXulx/mY1FG3F6nQwyD+LF3BcJD+6CFa2dTl3W\n+uYbvZ2VpZPVbc33xcU6gDkcEBIC8+bB6NHdp/rlU4pCf/i66Q9flqAgplssjIiKwtRdBiqEEOJ7\nSQgTXeb01dNsvrAZn/KRG5/L4qGLCTJ2wT/BqirdfN/QoOf7mjcPxoy5JVk1N+uMdvas3k5NhUWL\nwNJFBbrv41WKM01NHLDZqPeHr77BwUwzmxku4UsIIXokCWGi0yml2F+xnz3lewCYMmAKTw15qvOn\noPD54MAB2LdP/5yYqJvv+/W7Zbfz5/XUE99Wv+bMgbFju0f1y6sU3/jDV0O78DXdH76M3WGQQggh\nHkmnhrBXXnmFrVu3Eh8fT2FhIQD19fWsWLGCiooKBg8ezMaNG7H4yw1vvvkm7777LiaTibfffpu5\nc+d25vBEF/D6vGwt2crJ2pMYMDA/Yz7jk8d3/omtVt18f+WK3p4yBWbOvKX5vqVFTztx5ozeHjxY\nV7/69On84X0fr1KcbmrigNWK1eMBIC44mOkWC7mRkRK+hBCiFzAopVRnHfzAgQNERUXx8ssvt4Ww\n119/nbi4OF5//XXeeustGhoaWLt2LcXFxaxatYrjx49TXV3NU089xcWLFzHe1mBsMBjoxCGLDuTy\nuthYtJFL9ZcIMgbxwrAXGBo3tPNPfOaMbuxyOvVMqkuWwJAht+xy4YKufjU16TuUc+bcMT1YQHh8\nPk41NfG1zYatXfiaYbGQI+FLCCF6nPvllk6thE2bNo3y8vJbHtu8eTP79u0D4C/+4i/Iz89n7dq1\nbNq0iZUrVxIcHMzgwYNJT0/n2LFjTJw4sTOHKDpJk6uJ9WfWU9tUS0RwBKvyVpESk9K5J21t1Y1d\n35a2srP1pF4REbfssn27XngbYOBAPTl+376dO7Tv4/H5OOkPX43+8BUfEsJ0s5lhEr6EEKJX6vKe\nsLq6OhISEgBISEigrq4OgJqamlsCV0pKCtXV1V09PNEBrjuus75wPdZWK33D+7Jm+Br6hndyyrly\nRd9+tFp1aWv+fBg16pbSVkkJbN6slx8KCtLrck+YENjql7td+LL7w1dCSAgzLBayIyK6bukmIYQQ\nXS6gjfkGg+G+HzLyAdTzVFgr+ODsB7R4WkiJSWFl7koiQyK//4WPyufTjff79+sZVpOSdPN9bGzb\nLq2t8OWX3y0NOWCArn6126XLuX0+TtjtHLTZaPJ6AUj0h6+hEr6EEOKJ0OUhLCEhgatXr5KYmEht\nbS3x/mVikpOTqaysbNuvqqqK5OTkux7jn//5n9t+zs/PJz8/vzOHLB5Q0bUiPjn3CV7lZWjcUJZm\nLyXYFNx5J2xo0FNPVFXpctbUqbr53mRq2+XyZdi0CRobdfVr1iyYOBECNZepyx++DrULX/1DQ5lh\nNpMl4UsIIXq8vXv3snfv3gfat1Mb8wHKy8tZsGDBLY35sbGx/OIXv2Dt2rVYrdZbGvOPHTvW1ph/\n6dKlOz6UpDG/+1FKcbjqMDsu7wBgfPJ4nk5/GqOhk5KOUt8137tceiHH55/XX2/0czphxw4oKNDb\nycm6Pz8urnOG9H1cPh/H/eHL4Q9fSaGh5FssZISHS/gSQoheKmCN+StXrmTfvn3cuHGDAQMG8Otf\n/5pf/vKXLF++nD/+8Y9tU1QADBs2jOXLlzNs2DCCgoL4wx/+IB9MPYBP+fjy0pccrT4KwJwhc5g8\nYHLnXbuWFh2+vp1Vddgw3Xwf/t2s+6Wluvpls+mi2MyZMHlyYKpfTp+PY42NHG5spNkfvpL94Std\nwpcQQjzROr0S1tGkEtZ9uL1uPjn3CedunMNkMLEkewm58bmdd8KKCt18b7PpWVWfeQZGjGjrrHe5\nYOdOOH5c756UpHu//He8u1Sr18sxu53DjY20+MPXgLAwZpjNpEn4EkKIJ0bAKmGi92p2N/N+4ftU\nNVYRFhTGi7kvMtgyuHNO5vXC3r3w9df6VmRysm6+bzevRHm5rn41NOjqV36+np+1q6tfrV4vR+12\nDttstPp8AAwMCyPfYiE1LEzClxBCiDYSwsRDq2+p570z71HfUo851Mya4WvoF9nv+1/4KG7e1NWv\n6mpd8Zo+HWbMaGu+d7ngq6/gqL4bSv/+uvrlnwWly7R4vRxpbORoY2Nb+BocFsYMi4XBEr6EEELc\nhYQw8VCqGqt4v/B9mt3N9I/qz6q8VUSHRnf8iZTSM6pu26aTltmsm+8HDWrbpaJCV7/q63XFa8YM\n/QXJdl+O7HTN7cKX0x++UsPDmWE2M7hdn5oQQghxOwlh4oGdv3Gej4s/xu1zk943nWXDlhEaFNrx\nJ2pp0WsKFRfr7dxceO45CAsDwO2G3bvhyBGd1RIS9DcfExM7fij30uz1ctgfvlz+8DUkPJwZFguD\n/OMUQggh7kdCmHggx6qPsa1kGwrF6P6jeTbjWUzGTig5lZXBp5/qib1CQ3Xz/fDhbc33lZXw2Wf6\nLqXRqO9OTp/eddUvh9fLIZuN43Z7W/hK94evARK+hBBCPAQJYeK+lFLsKt3FwcqDAMwcPJPpg6Z3\nfI+T1wt79sDBg7q8NWCAvv3Ypw+gq1979sDhw/rp+Hjd+5WU1LHDuJcmj4dDjY0ct9tx+8NXRkQE\nM8xmUiR8CSGEeAQSwsQ9eXwePjv/GWevncVoMLIwayEjE0d2/Ilu3NAz39fW6opXfr4ub/m/2lhV\npatfN27op6dN0/1fQV3wr7fJ4+FgYyMn2oWvzIgIZlgsJId2wq1YIYQQTwwJYeKuWtwtfFj0IeXW\nckJNoSzPWU5a37SOPYlScPIkbN+uS10Wi556YsAAADwePTPFt8Wxfv109eseq1l1KLvHw9c2GwV2\nOx7//C5DIyKYbrGQJOFLCCFEB5AQJu5gbbWy/sx6rjdfJzokmtXDV5MY1cFd783Nuvn+3Dm9PXy4\n7v/y39qrqdGtYdev6+rXlCl65vvOrn41+sPXyXbhKzsykhlmM4kSvoQQQnQgCWHiFlebrrL+zHrs\nLjvxkfGszluNOczcsScpLdUJy27XzffPPQd5eYCufu3fr+dl9fkgNlZXv/zFsU5jaxe+vP7wNSwy\nkhkWCwkhIZ17ciGEEE8kCWGizaX6S2ws2ojL62KwZTAv5r5IWFAHNp17PHpuiUOH9PbAgbr53mIB\ndEvYZ59BXZ2ufk2aBLNmQXBwxw3hdla3mwM2G6ebmvAqhcFgIDcykukWC/ESvoQQQnQiCWECgFO1\np/j84uf4lI+8+DwWDV1EkLED/3lcv66b769e1Q33+fl6ZlWjEa8XDhzQFTCfT69GtHixzmidpaFd\n+PL5w1deVBTTzWb6SfgSQgjRBSSEPeGUUuyr2Mfe8r0ATB04ldmpsztuCgql4MQJ+PJLXQnr00c3\n36ekADqTffaZ/l+AiRNh9uzOq37V+8PXN+3C13B/+IqT8CWEEKILSQh7gnl9XrZc3MKpq6cwYOCZ\njGcYlzyu407gcMDmzXDhgt4eORLmz4fQULxe3fe1f7+eIqxPH1i0CAYP7rjTt3fT7Wa/1Uqhw4FP\nKYwGAyOjophmsRDbmfc7hRBCiHuQEPaEcnqcfFT8EZfqLxFsDOaFYS+QFZfVcSe4dEmXuJqa9Dce\nn3tOLz8EXLum+/Jra/Wu48fDU09BZxSibrhc7LfZKHQ4UP7wNSo6mmlmM30lfAkhhAggCWFPILvT\nzvrC9VxtukpkcCSr8laRHNNBk295PLBrl17YEfSC288/D2YzPp+e82vvXl39slh09Ss1tWNO3d51\nf/g6e5fw1UfClxBCiG5AQtgT5prjGuvPrMfmtBEbHsvq4avpG963gw5+TTff19Xp5vtZs2DyZDAa\nuX5dF8aqq/WuY8fCnDl6hoqOdM3lYp/VSnFzM0opTP7wNdVsxiLhSwghRDciIewJUm4t54OzH9Dq\naWVAzABW5q0kIjji8Q+sFBw/Djt26EpYbKyufiUn4/PB4YN63UePB8xmWLgQ0v8tW+cAAB/USURB\nVDp48v26b8OXwwGAyWBgdEwMU81mzF2xvpEQQgjxkOTT6Qlx9tpZPj33KV7lJTsum+eznyfY1AGV\noaYm2LQJSkr09ujR8PTTEBLCjRu6+lVV9d1Tc+e2TYrfIa46neyz2TjnD19BBgOj/ZWvGAlfQggh\nujH5lOrllFIcqjzEztKdAExMmcjctLkYDcbHP3hJiU5ZDgeEh8OCBTBsGD4fHD0MX32lq18xMbr6\nlZ7++Kf8Vo3TyT6rlQvNzYAOX2Ojo5liNhMt4UsIIUQPIJ9WvZhP+dhWso3jNccBmJc2j0kDJj3+\ngd1u2LkTjh3T26mpsGQJxMRw86YujF25op8aOVIXxjqq+lXtD18X/eEr2GhkbHQ0k2NiJHwJIYTo\nUeRTq5dye938ufjPXLh5gSBjEEuGLiEnPufxD1xXp5vvr10Dk6mt+V5h4NhR/cVItxuio3VhLDPz\n8U8JUNXayj6bjZJ24WucP3xFSfgSQgjRA8mnVy/kcDl4v/B9qu3VhAeFszJvJQPNj7kGkFJw9Kiu\ngHm9EBenm++Tkmho0HclKyr0riNG6OpXePjjv5fK1lb2Wq1cbmkBIMRoZHx0NJPMZiJNpsc/gRBC\nCBEgEsJ6mZvNN3nvzHs0tDZgCbOwZvga4iLiHu+gdru+x3jpkt4eOxbmzkUFh3D8mM5lbjdERek5\nWYcOffz3UdHayj6rldJ24WtCTAyTYmKIkPAlhBCiF5AQ1otU2irZcHYDze5mkqKTWJW3iqiQqMc7\n6IULOoA1N0NEhO6wHzoUq1U/XFamd8vL0ysSRTzmjBflLS3ss9ko84ev0HbhK1zClxBCiF5EQlgv\nce76OT4+9zEen4eMvhksy1lGiOkx1gFyu/Wi2ydO6O20NFi8GBUVTcEJPSWYywWRkbr6lZ396KdS\nSlHuv+1Y0doKQJjRyMSYGCZI+BJCCNFLSQjrBY5WHWX7pe0oFGP6j+HZzGcfbwqK2lrdfH/jhm6+\nf+opmDgRW6OBTf8PSkv1bjk58MwzOog9CqUUpf7bjlfaha9JZjMToqMJk/AlhBCiF5MQ1oMppdhZ\nupNDlYcAmJU6i2kDp2EwGB71gHDYP8GX1wv9+sHSpaiERE6d0oUxp1Pfcnz2WR3CHnXcl/23HSv9\n4SvcZGKSv/IVauyAOcyEEEKIbk5CWA/l8Xn49NynFF0vwmgwsihrESMSRzz6Ae12+PTT78pc48bB\n3Lk0tgSzef13PfnZ2TqART1Cq5lSikstLeyzWqlyOgGIMJmYHBPDOAlfQgghnjASwnqgFncLH5z9\ngApbBaGmUFbkrmBInyGPfsBz52DzZmhp0fcWFy1CZWTyzTewfTu0turpJp55BnJz4WELbUopLvrD\nV40/fEWaTEw2mxkXHU2IhC8hhBBPIAlhPYy11cp7Z97jRvMNYkJjWJ23moSohEc7mMul7zEWFOjt\n9HRYvBi7iuLzDXDxon44K0s330dHP9zhlVJcaG5mn81GbbvwNcVsZqyELyGEEE84CWE9SK29lvWF\n62lyNREfGc+a4WuICY15tIPV1Ojm+5s3ISgI5sxBjRvPmUID27bp6ldYmK5+5eU9XPVLKcX55mb2\nWa1cdbkAiDKZmGo2MyY6mmAJX0IIIYSEsJ6i5GYJHxV/hMvrItWSyorcFYQFPcKCjD4fHDoEu3fr\nn+PjYelSmiIT+PxDPS0YQEaGnhLsYapfSimKm5vZb7VS5w9f0UFBTDWbGR0VJeFLCCGEaEdCWA9w\nsvYkWy5uwad8DE8YzqKsRZiMjzB9Q2MjfPIJlJfr7QkTULOf4uyFYL74QreEhYbqSVdHjHjw6pdP\nKYodDvbbbFzzh6+YduErSMKXEEIIcQcJYd2YUoq95XvZV7EPgGkDpzErddajTUFRXAyff66TVlQU\nLFqEIymDLZ/qvnzQLWELF0LMA97h9ClFkT98XfeHL3NQENPMZkZK+BJCCCHuy6CUUoEexMMwGAz0\nsCE/Eq/Py+cXP+f01dMYDUaezXiWMUljHv5ALhds2wanTuntzExYtIii8ki2btWrEYWGwrx5MGrU\ng1W/fEpR6HCw32rlptsNgCUoiGkWCyOjojA96jxlQgghRC9zv9wilbBuyOlx8mHRh5Q2lBJsDGZ5\nznIyYjMe/kDV1br5vr5eN9/PnUtzzji2fmGgqEjvMmQILFoEZvP3H86nFGeamthvs1HvD199goOZ\nZjYzQsKXEEII8VCkEtbNNDobWX9mPXWOOiKDI1k9fDVJ0UkPdxCfDw4ehD179M8JCbB0KeduxrNl\nCzgcEBICc+fCmDHfX/3ytgtfDf7w1Tc4mOlmM3kSvoQQQoh7kkpYD1HXVMf6wvU0OhuJi4hjdd5q\n+oT3ebiD2Gy6+b6iQm9PmkTzpNls2xlEYaF+KDVV9371+Z5De5XidFMTB6xWrB4PALHBwUy3WMiL\njMQo4UsIIYR4ZFIJ6ybKGsr44OwHOL1OBpoH8mLui0QERzzcQc6ehS1b9CRfUVGwZAkXPGl8/jk0\nNUFwMMyZo1ckul9+8vh8OnzZbNj84SvOH75yJXwJIYQQD0wqYd3cmbozbDq/Ca/yMqzfMJ7Pfp4g\n40NcGqcTvvgCvvlGb2dl0TJ3Edv3R7Q9NGiQ7v3q2/feh/H4fJxqauLrduGrX0gIM8xmhkn4EkII\nITqUVMIC4MKlC+wq2IXL5+KK9Qpus5u4pDgmpUxibtrch5uCoqpKN983NOhS17x5XIwew+dbDNjt\n+qGnnoLx4+9d/fL4fJz0h69Gf/iKDwlhhsXCsIiIR5sSQwghhBBSCetOLly6wLo96whJD6GkvoSa\n8Bq8xV5+MvAnzEuf9+AH8vngwAHYt0//3L8/rc8u5cuCuLbZKAYO1NWv2Ni7H8Lt81Fgt3OwsRG7\nP3wl+MNXtoQvIYQQolNJCOtiuwp2YUozcfbaWW623MRoMJIzKYf6mvoHP4jVqpvvr1zR21OmcGnA\nTDZvDKKxUc9GMXs2TJgAd5sv1e3zccJu56DNRpPXC0D/0FBmmM1kSfgSQgghuoSEsC7W0NpAQU0B\nLZ4WgoxB5MXnYQ4z47K7HuwAZ87A1q26Dyw6GuczS/iyZAgnP9BPp6TA4sUQF3fnS10+H8ftdg7Z\nbDj84SspNJQZFguZ4eESvoQQQoguJCGsCxVdK+JY1TFakluIDI4kNz6X8OBwAEKMIfd/cWurDl/f\nzjORnU1Z7gI+2x6BzaarXzNnwqRJd1a/XD4fxxobOdTYSLM/fCX7w1eGhC8hhBAiICSEdQGf8rGr\ndBeHKg8xMHUgNRU15EzIaVuE21niZPbM2fc+wJUr+vaj1QrBwbhmz2fH9VGc+EiHp+RkXf3q1+/W\nlzn94etwu/CVEhpKvsVCmoQvIYQQIqDk25GdzOFy8OfiP1NmLcNoMDIvbR7mFjO7T+3G5XMRYgxh\n9ujZZKVn3flin0833u/fD0pBUhIVY5fy6f5YrFYwmSA/H6ZMubX61er1ctRu50hjIy3+8DUgLIx8\ni4UhYWESvoQQQogucr/cIiGsE1U1VrGxaCONzkaiQqJYnrOcgeaBD/bi+npd/aqqAoMB9/gp7PTM\n5FiBrp7176+rXwkJ372k1evlSGMjRxobafX5ABgUFsYMi4VUCV9CCCFEl5MpKrqYUoqTtSf5ouQL\nvMrLgJgBLM9ZTnRo9IO8+Lvme5cLYmKoGv88HxcMpqFBV79mzNDVL5POY7S0C19Of/ga7K98DQ4P\n78R3KoQQQohHJZWwDubxedh6cSunrurJusYnj2de2ry2/q/7amnR4evsWX2srBx2RzzH4dPhKAWJ\nibr6lZiod2/2ejnc2MixduErNTycfIuFQWFhnfL+hBBCCPHgpBLWRaytVjYWbaTGXkOwMZgFWQsY\nnjD8wV5cUaFvP9psEBJC7ahn+OjiCOobDBiNuvo1bZqufjm8Xg7bbByz23H5w1daeDgzLBYGSvgS\nQgghegQJYR3kcv1lPj73Mc3uZvqE9WFF7goSoxK//4VeL+zdC19/DUrhSUxmf9xSDhzri1K652vx\nYt0D5vB6OVRv43i78JXuD18DJHwJIYQQPYrcjnxMSikOVh7kq9KvUCgy+mbwfPbzbfN/3U3FhQtc\n3rULY0MDvqIi0mJjGdSvH9eHTmNj3Qyu15swGmHqVJg+HVrxcLCxkRN2O25/+MqMiGC62UyKhC8h\nhBCi25JvR3YSp8fJZ+c/49yNcwDMGDSD/MH59/0WYsWFC1xat47Z9fVw6RJ4vew0BtH49P/lbMss\nlNLzfS1eDDEJHg7abJyw2/H433NWRAQzLBaSQkO75D0KIYQQ4tFJT1gnuO64zodFH3Kj+QZhQWE8\nn/08mbGZ3/u6yxs2MOHwMaqrb6CUgcYwC8GWoRzad56Y8bOYOhVGT/Vw1GGjoOq78DXUH776S/gS\nQgghegUJYY+g+Hoxn53/DJfXRXxkPC/mvkjf8L73f1FtLezaRfOuPVy7YsUXnEiFaQiVTfH4Gssw\nZl1l2V96KIuy8furdrz+8DUsMpLpZjOJEr6EEEKIXkVC2EPwKR9flX7FwcqDAOTF57EgawEhpvus\n+9jQALt3t635WGtz0xSRzfZIMy6TCaO7juHGBCpHhvOxqsLbqDAYDORERjLdYiEh5HvWlBRCCCFE\njyQh7AHdvvzQ3LS5TEiecO/+r+ZmvdzQ8ePg9aKMJioSJ/BRn3BqjVdIGzkOT4iRpqRQDlwuJL5f\nHNlArj98xUv4EkIIIXo1CWEPoLqxmg+LPmxbfmjZsGUMsgy6+85uNxw5oqeccDrxYaCyzwh2uGZS\nXWOhKrIQ+9S/4JuoWrwWIz6DEUPSfEIKj/DXSUn0k/AlhBBCPBEkhH2PgpqCB1t+yOeDU6f0nF92\nO0pBVVg6O9VTXGlIpNXkwZFkJTo/Cnu6A2UaiFFBtDuEKEcd0wf3kwAmhBBCPEEkhN2Dx+fhi5Iv\nOFl7ErjP8kNKwYUL8NVXcP06SkG1SuIr4xwuuQZxI6KZxuSrRGW0kpCoiN0fSlD/cDzXrES6DIQb\nFal5fRlQ6wjAuxRCCCFEoEgIu4v2yw8FGYNYkLmAEYkj7tyxshJ27oQrV1AKalr6sMc0i5PRGVyN\nbsIeV0nSIB9piRBsNDA0IpKp48ezu6iIsLFj2w7jLChg9ujRXfgOhRBCCBFoMlnrbUobSvlz8Z/v\nv/zQjRuwaxecP49SUGuL4MuQ6RxIyORqTDMqxs3AgXrJoQFhoYyMiiI3MpJwk66iXSgt5auiIlxA\nCDA7J4esIUM67T0JIYQQIjBkxvwHcPvyQ+l901mavfTW5Yfsdt3zdeoUPo+PqvpgPo6exMHkTG5E\negiPUAwaCGlJJkZERzEyKkq+5SiEEEI8wSSEfY+7LT80Y/AMjAajfwcnHDwIhw/jdbo5bQ9jk3kE\nR5PScYYEER4OQwYZmJYawejoKNLDwzHeZ+kiIYQQQjwZJITdx+3LDy0ZuoSsuCz9pNcLJ07Avn00\ntjjZ44zky+h0SuLScAdHEBEBYwaH8mxmFMOjI4kwme5/MiGEEEI8USSE3cPtyw+tyFlBbESs/sbj\n2bN4du/mfIuTfd4ojoYkUhedhjMshj5hJuZlRLEoJ4r+YXK7UQghhBB3Jwt43+b25Ydy43NZmLWQ\nEFMI6vJlavfs4aSjmUO+KKoN/bnWZwit4bEMDorg+bxons4LJ9gktxuFEEII8eieuErYvZYfctTW\nUnjgACetNi47Q6h3hHAjajCEDCQ7JJrl46IYm2NCWr2EEEII8aDkdqRfdWM1G4s2YnPaiAqJ4vns\nF3A6wzh94gQXrt+g0Q4NdhNNoQMJC8omJ8LMwqmhZGcj4UsIIYQQD01CGLcuPxQTNZjB/aZyqaSc\npqpqmhoVjTYDIe4kgsNGk9W3LzNnGBg6VMKXEEIIIR7dEx3Cvl1+6Ejtaa4RRVhUNn2bwuFKFfYG\nD6ZaFzHNiajoKSQOjGXGDMjKkvAlhBBCiMf3xIaw+hYrvzv7KWcczTQQyTBvHH2u2nHXNRNb4sBi\nj6UhYSYxmUnk50NmpoQvIYQQQnScJy6E1blcbKst4aOKAhxeLxaHjyxrMEllTcQWNxHVZKZiyBzC\nc4aQnw8ZGRK+hBBCCNHxem0Iu1Bayq6iItyA8vlITk2lPjaWY9cvUWotI9ThJKe6kYmXFH2KnHi8\nMZSlziJ4VC75Mw2kp0v4EkIIIUTn6ZUh7EJpKetOnsQ9ciQVra3ccLtxnThBVJwJY7iLvPOXmFro\noE9FPK2GSCoGTccwbiwzZgeRlibhSwghhBCdr1dO1rqrqIiKPmaOHDuMz2DAhyI0LoTYfTv5y6o6\nohqyMKkUSgdMQk2azMw5YQwZIuFLCCGEEN1Djw1hZy+c41hkOKERYYS3NDG4qhSb1YmpsJoow3hu\n9J+KZ2o+0+ZHk5oq4UsIIYQQ3UuPDWFHz5zCNHMWwU432SWXCXV7sBgMlNuc3PjBPzBpYT8GD5bw\nJYQQQojuqUeGsKrWVhyWOIzHj5NpjiHU7cFlCMV64TKhJi8r/08/CV9CCCGE6NaMgR7A7bZv387Q\noUPJyMjgrbfeuus+GWvWcL2inAmnCxiwdQvugrOobwqZ0tSAJSJCAlgA7N27N9BDELeRa9I9yXXp\nfuSadD9PyjXpViHM6/XyN3/zN2zfvp3i4mI2bNjAuXPn7tjP+Jd/SeuMfI6qYOKancwKCmIOPs7b\nfPQfNTcAIxdPyn8wPYlck+5Jrkv3I9ek+3lSrkm3CmHHjh0jPT2dwYMHExwczIsvvsimTZvu2C8I\niExJ4ebMmbwXHMmXLX3Y0pKAylvIqz9Z1fUDF0IIIYR4SN2qJ6y6upoBAwa0baekpHD06NE79gtz\nthKsFN4gE1j6MnHFzwgJ8TF7dhpZWYO6cshCCCGEEI+kW03W+vHHH7N9+3b++7//G4D33nuPo0eP\n8p//+Z9t+5gSEvBduxaoIQohhBBCPLARI0Zw+vTpuz7XrSphycnJVFZWtm1XVlaSkpJyyz7eurqu\nHpYQQgghRIfrVj1hY8eOpaSkhPLyclwuFx9++CELFy4M9LCEEEIIITpct6qEBQUF8bvf/Y558+bh\n9Xr5q7/6K7KzswM9LCGEEEKIDtetesKEEEIIIZ4U3ep25Pd5kIlcxYOrrKxk5syZ5OTkkJuby9tv\nvw1AfX09c+bMITMzk7lz52K1Wtte8+abb5KRkcHQoUPZsWNH2+MFBQXk5eWRkZHBz372s7bHnU4n\nK1asICMjg4kTJ1JRUdH23P/+7/+SmZlJZmYmf/rTn7rgHfcsXq+XUaNGsWDBAkCuS6BZrVZeeOEF\nsrOzGTZsGEePHpVr0g28+eab5OTkkJeXx6pVq3A6nXJdutgrr7xCQkICeXl5bY8F+hqUlZUxYcIE\nMjIyePHFF3G73Z319h+P6iE8Ho9KS0tTZWVlyuVyqREjRqji4uJAD6tHq62tVadOnVJKKWW321Vm\nZqYqLi5Wr732mnrrrbeUUkqtXbtW/eIXv1BKKVVUVKRGjBihXC6XKisrU2lpacrn8ymllBo3bpw6\nevSoUkqp+fPnq23btimllPr973+vfvzjHyullPrggw/UihUrlFJK3bx5Uw0ZMkQ1NDSohoaGtp/F\nd/7t3/5NrVq1Si1YsEAppeS6BNjLL7+s/vjHPyqllHK73cpqtco1CbCysjKVmpqqWltblVJKLV++\nXK1bt06uSxfbv3+/OnnypMrNzW17LFDXwGq1KqWUWrZsmfrwww+VUkr96Ec/Uv/1X//V2b+GR9Jj\nQtihQ4fUvHnz2rbffPNN9eabbwZwRL3PokWL1M6dO1VWVpa6evWqUkoHtaysLKWUUr/5zW/U2rVr\n2/afN2+eOnz4sKqpqVFDhw5te3zDhg3q1VdfbdvnyJEjSin9wRUXF6eUUur9999XP/rRj9pe8+qr\nr6oNGzZ07hvsQSorK9Xs2bPV7t271XPPPaeUUnJdAshqtarU1NQ7HpdrElg3b95UmZmZqr6+Xrnd\nbvXcc8+pHTt2yHUJgLKysltCWCCvgc/nU3Fxccrr9SqllDp8+PAt+aE76TG3I+82kWt1dXUAR9S7\nlJeXc+rUKSZMmEBdXR0JCQkAJCQkUOefFqSmpuaWKUO+vQa3P56cnNx2bdpft6CgIMxmMzdv3rzn\nsYT2t3/7t/zrv/4rRuN3/4nKdQmcsrIy+vXrxw9+8ANGjx7ND3/4QxwOh1yTAOvbty9///d/z8CB\nA0lKSsJisTBnzhy5Lt1AIK9BfX09Foul7e9n+2N1Nz0mhBlkVe5O09TUxNKlS/ntb39LdHT0Lc8Z\nDAb53XexLVu2EB8fz6hRo1D3+N6MXJeu5fF4OHnyJH/913/NyZMniYyMZO3atbfsI9ek612+fJn/\n+I//oLy8nJqaGpqamnjvvfdu2UeuS+B15TXoade6x4SwB5nIVTw8t9vN0qVLeemll1i8eDGg/1/L\n1atXAaitrSU+Ph648xpUVVWRkpJCcnIyVVVVdzz+7WuuXLkC6A8ym81GbGysXM/7OHToEJs3byY1\nNZWVK1eye/duXnrpJbkuAZSSkkJKSgrjxo0D4IUXXuDkyZMkJibKNQmgEydOMHnyZGJjYwkKCuL5\n55/n8OHDcl26gUD9vUpOTqZv375YrVZ8Pl/bsZKTkzv3DT+qQN8PfVBut1sNGTJElZWVKafTKY35\nHcDn86mXXnpJ/fznP7/l8ddee63tnv2bb755R0Ol0+lUpaWlasiQIW0NlePHj1dHjhxRPp/vjobK\nb+/Zb9iw4ZaGytTUVNXQ0KDq6+vbfha32rt3b1tPmFyXwJo2bZq6cOGCUkqpf/qnf1KvvfaaXJMA\nO336tMrJyVHNzc3K5/Opl19+Wf3ud7+T6xIAt/eEBfoaLFu2TH3wwQdKKd0rJo35HeCLL75QmZmZ\nKi0tTf3mN78J9HB6vAMHDiiDwaBGjBihRo4cqUaOHKm2bdumbt68qWbPnq0yMjLUnDlzbvnD8sYb\nb6i0tDSVlZWltm/f3vb4iRMnVG5urkpLS1M//elP2x5vbW1Vy5YtU+np6WrChAmqrKys7bl3331X\npaenq/T0dLVu3bouec89zd69e9u+HSnXJbBOnz6txo4dq4YPH66WLFmirFarXJNu4K233lLDhg1T\nubm56uWXX1Yul0uuSxd78cUXVf/+/VVwcLBKSUlR7777bsCvQWlpqRo/frxKT09Xy5cvVy6Xq3N/\nCY9IJmsVQgghhAiAHtMTJoQQQgjRm0gIE0IIIYQIAAlhQgghhBABICFMCCGEECIAJIQJIYQQQgSA\nhDAhhBBCiACQECaEeKLk5+dTUFDwWMf4/PPPeeuttzpoREKIJ1VQoAcghBBdqSPWsVuwYAELFizo\noBEJIZ5UUgkTQgSUw+Hg2WefZeTIkeTl5fHRRx8B8Otf/5rx48eTl5fHq6++2rZ/fn4+f/d3f8e4\ncePIzs7m+PHjLFmyhMzMTH71q18BUF5eztChQ1mzZg3Dhg1j2bJltLS03HHuHTt2MHnyZMaMGcPy\n5ctxOBx37PP222+Tk5PDiBEjWLVqFQDr1q3jpz/9KQAjR45k1KhRjBo1ioiICA4cOIDD4eCVV15h\nwoQJjB49ms2bN99x3L1795Kfn8+yZcvIzs5mzZo1j//LFEL0KBLChBABtX37dpKTkzl9+jSFhYXM\nmzcPgJ/+9KccO3aMwsJCWlpa2LJlC6ArWaGhoRw/fpwf//jHLFq0iHfeeYezZ8+ybt06GhoaALh4\n8SI/+clPKC4uJiYmhj/84Q+3nPfGjRu88cYbfPXVVxQUFDBmzBj+/d///Y7xvfXWW5w+fZpvvvmG\nd955p20M3zp9+jSnTp3i17/+NePGjWPSpEm88cYbzJ49m6NHj7J7925ee+01mpub7zj26dOn+e1v\nf0txcTGlpaUcPHiwY36pQogeQUKYECKghg8fzs6dO/nlL3/J119/TUxMDAC7d+9m4sSJDB8+nN27\nd1NcXNz2moULFwKQm5tLbm4uCQkJhISEMGTIECorKwEYMGAAkyZNAmDNmjV8/fXXba9XSnHkyBGK\ni4uZPHkyo0aN4k9/+hNXrly56/hWrVrF+vXrMZlMd30PJSUlvP7662zcuJGgoCB27NjB2rVrGTVq\nFDNnzsTpdLaNq73x48eTlJSEwWBg5MiRlJeXP9ovUQjRI0lPmBAioDIyMjh16hRbt27lH//xH5k9\nezavv/46P/nJTygoKCA5OZl/+Zd/obW1te01oaGhABiNxrafv932eDzArdUqpdRd+8DmzJnD+++/\nf9/xbd26lf379/P555/zxhtvUFhYSPsld5uamlixYgX/8z//Q0JCQtvjn3zyCRkZGfc9dvuxm0ym\ntrELIZ4MUgkTQgRUbW0tYWFhrF69mn/4h3/g1KlTbYErNjaWpqamtj6xh3HlyhWOHDkCwPvvv8+0\nadPanjMYDEycOJGDBw9y+fJlQPemlZSU3HIMpRRXrlwhPz+ftWvXYrPZaGpqumWfV155hR/84AdM\nmTKl7bF58+bx9ttvt22fOnXqoccvhOj9pBImhAiowsJCXnvtNYxGI8HBwbzzzjuYzWZ++MMfkpub\nS2JiIhMmTLjra+/3TcesrCx+//vf88orr5CTk8OPf/zjW56Pi4tj3bp1rFy5EqfTCcAbb7xxS/XK\n6/Xy0ksvYbPZUErxs5/9DLPZ3HbeK1eu8PHHH1NSUsK7774LwB//+Ed+9atf8fOf/5zhw4fj8/kY\nMmTIHc35dxv7435rUwjRsxhU+7q6EEL0AuXl5SxYsIDCwsJAD0UIIe5JbkcKIXolqSoJIbo7qYQJ\nIYQQQgSAVMKEEEIIIQJAQpgQQgghRABICBNCCCGECAAJYUIIIYQQASAhTAghhBAiAP4/UZbOl670\nPUQAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10c6fb1d0>"
+       ]
+      }
+     ],
+     "prompt_number": 41
+    },
     {
      "cell_type": "markdown",
      "metadata": {},

From ad32c82ee9db5179ce910bb169e495a5dfb9ab91 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 5 May 2014 11:40:43 -0400
Subject: [PATCH 016/248] removed temp cython files

---
 .../cython_least_squares-checkpoint.ipynb     |   59 +-
 benchmarks/ccy_classic_lstsqr.c               | 2203 --------
 benchmarks/ccy_classic_lstsqr.pyx             |   10 -
 benchmarks/cython_least_squares.html          | 4928 -----------------
 benchmarks/cython_least_squares.ipynb         |    2 +-
 benchmarks/setup.py                           |    9 -
 6 files changed, 57 insertions(+), 7154 deletions(-)
 delete mode 100644 benchmarks/ccy_classic_lstsqr.c
 delete mode 100644 benchmarks/ccy_classic_lstsqr.pyx
 delete mode 100644 benchmarks/cython_least_squares.html
 delete mode 100644 benchmarks/setup.py

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index 3032c66..5e855ad 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a091baac3cb82ac71775c03f73be1925fefe2d659bb7359de4bb88887fbe7d24"
+  "signature": "sha256:379319350ffd7e7539cf8f11b3a7c15b6f1d1013bcb445b55714c76bf0855fa2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -714,7 +714,30 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "import numpy as np\n",
+      "import scipy.stats\n",
+      "cimport numpy as np\n",
+      "\n",
+      "def cy_lin_lstsqr_mat2(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg = sum(x)/len(x)\n",
+      "    cdef double y_avg = sum(y)/len(y)\n",
+      "    cdef double var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cdef double cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    cdef double slope = cov_xy / var_x\n",
+      "    cdef double y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -740,6 +763,16 @@
       "    y_interc = y_avg - slope*x_avg\n",
       "    return (slope, y_interc)\n",
       "\n",
+      "def cy_lin_lstsqr_mat2(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg = sum(x)/len(x)\n",
+      "    cdef double y_avg = sum(y)/len(y)\n",
+      "    cdef double var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cdef double cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    cdef double slope = cov_xy / var_x\n",
+      "    cdef double y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)\n",
+      "\n",
       "def cy_numpy_lstsqr(x, y):\n",
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    X = np.vstack([x, np.ones(len(x))]).T\n",
@@ -752,7 +785,27 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
+      "                     \"numpy function\",\"scipy function\"],\n",
+      "                    [(lin_lstsqr_mat, cy_lin_lstsqr_mat), \n",
+      "                     (classic_lstsqr, cy_classic_lstsqr),\n",
+      "                     (cy_lin_lstsqr_mat2, cy_lin_lstsqr_mat2),\n",
+      "                     (scipy_lstsqr, cy_scipy_lstsqr)]):\n",
+      "    print(\"\\n\\n{}: \".format(lab))\n",
+      "    %timeit appr[0](x, y)\n",
+      "    %timeit appr[1](x, y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     },
     {
      "cell_type": "markdown",
diff --git a/benchmarks/ccy_classic_lstsqr.c b/benchmarks/ccy_classic_lstsqr.c
deleted file mode 100644
index 4813b5a..0000000
--- a/benchmarks/ccy_classic_lstsqr.c
+++ /dev/null
@@ -1,2203 +0,0 @@
-/* Generated by Cython 0.20.1 on Sun May  4 18:53:57 2014 */
-
-#define PY_SSIZE_T_CLEAN
-#ifndef CYTHON_USE_PYLONG_INTERNALS
-#ifdef PYLONG_BITS_IN_DIGIT
-#define CYTHON_USE_PYLONG_INTERNALS 0
-#else
-#include "pyconfig.h"
-#ifdef PYLONG_BITS_IN_DIGIT
-#define CYTHON_USE_PYLONG_INTERNALS 1
-#else
-#define CYTHON_USE_PYLONG_INTERNALS 0
-#endif
-#endif
-#endif
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02040000
-    #error Cython requires Python 2.4+.
-#else
-#define CYTHON_ABI "0_20_1"
-#include <stddef.h> /* For offsetof */
-#ifndef offsetof
-#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#endif
-#ifndef DL_IMPORT
-  #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
-  #define DL_EXPORT(t) t
-#endif
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#ifdef PYPY_VERSION
-#define CYTHON_COMPILING_IN_PYPY 1
-#define CYTHON_COMPILING_IN_CPYTHON 0
-#else
-#define CYTHON_COMPILING_IN_PYPY 0
-#define CYTHON_COMPILING_IN_CPYTHON 1
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-#define Py_OptimizeFlag 0
-#endif
-#if PY_VERSION_HEX < 0x02050000
-  typedef int Py_ssize_t;
-  #define PY_SSIZE_T_MAX INT_MAX
-  #define PY_SSIZE_T_MIN INT_MIN
-  #define PY_FORMAT_SIZE_T ""
-  #define CYTHON_FORMAT_SSIZE_T ""
-  #define PyInt_FromSsize_t(z) PyInt_FromLong(z)
-  #define PyInt_AsSsize_t(o)   __Pyx_PyInt_As_int(o)
-  #define PyNumber_Index(o)    ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
-                                (PyErr_Format(PyExc_TypeError, \
-                                              "expected index value, got %.200s", Py_TYPE(o)->tp_name), \
-                                 (PyObject*)0))
-  #define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \
-                                  !PyComplex_Check(o))
-  #define PyIndex_Check __Pyx_PyIndex_Check
-  #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
-  #define __PYX_BUILD_PY_SSIZE_T "i"
-#else
-  #define __PYX_BUILD_PY_SSIZE_T "n"
-  #define CYTHON_FORMAT_SSIZE_T "z"
-  #define __Pyx_PyIndex_Check PyIndex_Check
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
-  #define Py_TYPE(ob)   (((PyObject*)(ob))->ob_type)
-  #define Py_SIZE(ob)   (((PyVarObject*)(ob))->ob_size)
-  #define PyVarObject_HEAD_INIT(type, size) \
-          PyObject_HEAD_INIT(type) size,
-  #define PyType_Modified(t)
-  typedef struct {
-     void *buf;
-     PyObject *obj;
-     Py_ssize_t len;
-     Py_ssize_t itemsize;
-     int readonly;
-     int ndim;
-     char *format;
-     Py_ssize_t *shape;
-     Py_ssize_t *strides;
-     Py_ssize_t *suboffsets;
-     void *internal;
-  } Py_buffer;
-  #define PyBUF_SIMPLE 0
-  #define PyBUF_WRITABLE 0x0001
-  #define PyBUF_FORMAT 0x0004
-  #define PyBUF_ND 0x0008
-  #define PyBUF_STRIDES (0x0010 | PyBUF_ND)
-  #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES)
-  #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
-  #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
-  #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
-  #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE)
-  #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE)
-  typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
-  typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
-#endif
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-  #define __Pyx_DefaultClassType PyClass_Type
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-  #define __Pyx_DefaultClassType PyType_Type
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define Py_TPFLAGS_CHECKTYPES 0
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define Py_TPFLAGS_HAVE_VERSION_TAG 0
-#endif
-#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT)
-  #define Py_TPFLAGS_IS_ABSTRACT 0
-#endif
-#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE)
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
-  #define CYTHON_PEP393_ENABLED 1
-  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ? \
-                                              0 : _PyUnicode_Ready((PyObject *)(op)))
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
-  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
-  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
-  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
-#else
-  #define CYTHON_PEP393_ENABLED 0
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
-#else
-  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \
-      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#define __Pyx_PyString_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
-#else
-  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBaseString_Type            PyUnicode_Type
-  #define PyStringObject               PyUnicodeObject
-  #define PyString_Type                PyUnicode_Type
-  #define PyString_Check               PyUnicode_Check
-  #define PyString_CheckExact          PyUnicode_CheckExact
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define PyBytesObject                PyStringObject
-  #define PyBytes_Type                 PyString_Type
-  #define PyBytes_Check                PyString_Check
-  #define PyBytes_CheckExact           PyString_CheckExact
-  #define PyBytes_FromString           PyString_FromString
-  #define PyBytes_FromStringAndSize    PyString_FromStringAndSize
-  #define PyBytes_FromFormat           PyString_FromFormat
-  #define PyBytes_DecodeEscape         PyString_DecodeEscape
-  #define PyBytes_AsString             PyString_AsString
-  #define PyBytes_AsStringAndSize      PyString_AsStringAndSize
-  #define PyBytes_Size                 PyString_Size
-  #define PyBytes_AS_STRING            PyString_AS_STRING
-  #define PyBytes_GET_SIZE             PyString_GET_SIZE
-  #define PyBytes_Repr                 PyString_Repr
-  #define PyBytes_Concat               PyString_Concat
-  #define PyBytes_ConcatAndDel         PyString_ConcatAndDel
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
-  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
-#else
-  #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \
-                                         PyString_Check(obj) || PyUnicode_Check(obj))
-  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define PySet_Check(obj)             PyObject_TypeCheck(obj, &PySet_Type)
-  #define PyFrozenSet_Check(obj)       PyObject_TypeCheck(obj, &PyFrozenSet_Type)
-#endif
-#ifndef PySet_CheckExact
-  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
-#endif
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#if PY_MAJOR_VERSION >= 3
-  #define PyIntObject                  PyLongObject
-  #define PyInt_Type                   PyLong_Type
-  #define PyInt_Check(op)              PyLong_Check(op)
-  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
-  #define PyInt_FromString             PyLong_FromString
-  #define PyInt_FromUnicode            PyLong_FromUnicode
-  #define PyInt_FromLong               PyLong_FromLong
-  #define PyInt_FromSize_t             PyLong_FromSize_t
-  #define PyInt_FromSsize_t            PyLong_FromSsize_t
-  #define PyInt_AsLong                 PyLong_AsLong
-  #define PyInt_AS_LONG                PyLong_AS_LONG
-  #define PyInt_AsSsize_t              PyLong_AsSsize_t
-  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
-  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
-  #define PyNumber_Int                 PyNumber_Long
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBoolObject                 PyLongObject
-#endif
-#if PY_VERSION_HEX < 0x030200A4
-  typedef long Py_hash_t;
-  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
-#else
-  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
-  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
-#endif
-#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
-  #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
-  #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
-  #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b)
-#else
-  #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \
-        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \
-        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \
-            (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0)))
-  #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \
-        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
-        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \
-            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1)))
-  #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \
-        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
-        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \
-            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1)))
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
-#endif
-#if PY_VERSION_HEX < 0x02050000
-  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),((char *)(n)))
-  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
-  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),((char *)(n)))
-#else
-  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),(n))
-  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a))
-  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),(n))
-#endif
-#if PY_VERSION_HEX < 0x02050000
-  #define __Pyx_NAMESTR(n) ((char *)(n))
-  #define __Pyx_DOCSTR(n)  ((char *)(n))
-#else
-  #define __Pyx_NAMESTR(n) (n)
-  #define __Pyx_DOCSTR(n)  (n)
-#endif
-#ifndef CYTHON_INLINE
-  #if defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifdef NAN
-#define __PYX_NAN() ((float) NAN)
-#else
-static CYTHON_INLINE float __PYX_NAN() {
-  /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and
-   a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is
-   a quiet NaN. */
-  float value;
-  memset(&value, 0xFF, sizeof(value));
-  return value;
-}
-#endif
-
-
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-
-#ifndef __PYX_EXTERN_C
-  #ifdef __cplusplus
-    #define __PYX_EXTERN_C extern "C"
-  #else
-    #define __PYX_EXTERN_C extern
-  #endif
-#endif
-
-#if defined(WIN32) || defined(MS_WINDOWS)
-#define _USE_MATH_DEFINES
-#endif
-#include <math.h>
-#define __PYX_HAVE__ccy_classic_lstsqr
-#define __PYX_HAVE_API__ccy_classic_lstsqr
-#ifdef _OPENMP
-#include <omp.h>
-#endif /* _OPENMP */
-
-#ifdef PYREX_WITHOUT_ASSERTIONS
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;
-                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (    \
-    (sizeof(type) < sizeof(Py_ssize_t))  ||             \
-    (sizeof(type) > sizeof(Py_ssize_t) &&               \
-          likely(v < (type)PY_SSIZE_T_MAX ||            \
-                 v == (type)PY_SSIZE_T_MAX)  &&         \
-          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||       \
-                                v == (type)PY_SSIZE_T_MIN)))  ||  \
-    (sizeof(type) == sizeof(Py_ssize_t) &&              \
-          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||        \
-                               v == (type)PY_SSIZE_T_MAX)))  )
-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromUString(s)  __Pyx_PyObject_FromString((char*)s)
-#define __Pyx_PyBytes_FromUString(s)   __Pyx_PyBytes_FromString((char*)s)
-#define __Pyx_PyByteArray_FromUString(s)   __Pyx_PyByteArray_FromString((char*)s)
-#define __Pyx_PyStr_FromUString(s)     __Pyx_PyStr_FromString((char*)s)
-#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s)
-#if PY_MAJOR_VERSION < 3
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
-{
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return u_end - u - 1;
-}
-#else
-#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
-#endif
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None)
-#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys = NULL;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    sys = PyImport_ImportModule("sys");
-    if (sys == NULL) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    if (default_encoding == NULL) goto bad;
-    if (strcmp(PyBytes_AsString(default_encoding), "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        const char* default_encoding_c = PyBytes_AS_STRING(default_encoding);
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (ascii_chars_u == NULL) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-    }
-    Py_XDECREF(sys);
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return 0;
-bad:
-    Py_XDECREF(sys);
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys = NULL;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (sys == NULL) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    if (default_encoding == NULL) goto bad;
-    default_encoding_c = PyBytes_AS_STRING(default_encoding);
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(sys);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(sys);
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-#ifdef __GNUC__
-  /* Test for GCC > 2.95 */
-  #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))
-    #define likely(x)   __builtin_expect(!!(x), 1)
-    #define unlikely(x) __builtin_expect(!!(x), 0)
-  #else /* __GNUC__ > 2 ... */
-    #define likely(x)   (x)
-    #define unlikely(x) (x)
-  #endif /* __GNUC__ > 2 ... */
-#else /* __GNUC__ */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-
-static PyObject *__pyx_m;
-static PyObject *__pyx_d;
-static PyObject *__pyx_b;
-static PyObject *__pyx_empty_tuple;
-static PyObject *__pyx_empty_bytes;
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm= __FILE__;
-static const char *__pyx_filename;
-
-
-static const char *__pyx_f[] = {
-  "ccy_classic_lstsqr.pyx",
-};
-
-/*--- Type declarations ---*/
-#ifndef CYTHON_REFNANNY
-  #define CYTHON_REFNANNY 0
-#endif
-#if CYTHON_REFNANNY
-  typedef struct {
-    void (*INCREF)(void*, PyObject*, int);
-    void (*DECREF)(void*, PyObject*, int);
-    void (*GOTREF)(void*, PyObject*, int);
-    void (*GIVEREF)(void*, PyObject*, int);
-    void* (*SetupContext)(const char*, int, const char*);
-    void (*FinishContext)(void**);
-  } __Pyx_RefNannyAPIStruct;
-  static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
-  static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/
-  #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;
-#ifdef WITH_THREAD
-  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
-          if (acquire_gil) { \
-              PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \
-              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
-              PyGILState_Release(__pyx_gilstate_save); \
-          } else { \
-              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
-          }
-#else
-  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
-          __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
-#endif
-  #define __Pyx_RefNannyFinishContext() \
-          __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
-  #define __Pyx_INCREF(r)  __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
-  #define __Pyx_DECREF(r)  __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
-  #define __Pyx_GOTREF(r)  __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
-  #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
-  #define __Pyx_XINCREF(r)  do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0)
-  #define __Pyx_XDECREF(r)  do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0)
-  #define __Pyx_XGOTREF(r)  do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0)
-  #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0)
-#else
-  #define __Pyx_RefNannyDeclarations
-  #define __Pyx_RefNannySetupContext(name, acquire_gil)
-  #define __Pyx_RefNannyFinishContext()
-  #define __Pyx_INCREF(r) Py_INCREF(r)
-  #define __Pyx_DECREF(r) Py_DECREF(r)
-  #define __Pyx_GOTREF(r)
-  #define __Pyx_GIVEREF(r)
-  #define __Pyx_XINCREF(r) Py_XINCREF(r)
-  #define __Pyx_XDECREF(r) Py_XDECREF(r)
-  #define __Pyx_XGOTREF(r)
-  #define __Pyx_XGIVEREF(r)
-#endif /* CYTHON_REFNANNY */
-#define __Pyx_XDECREF_SET(r, v) do {                            \
-        PyObject *tmp = (PyObject *) r;                         \
-        r = v; __Pyx_XDECREF(tmp);                              \
-    } while (0)
-#define __Pyx_DECREF_SET(r, v) do {                             \
-        PyObject *tmp = (PyObject *) r;                         \
-        r = v; __Pyx_DECREF(tmp);                               \
-    } while (0)
-#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
-#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_getattro))
-        return tp->tp_getattro(obj, attr_name);
-#if PY_MAJOR_VERSION < 3
-    if (likely(tp->tp_getattr))
-        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
-#endif
-    return PyObject_GetAttr(obj, attr_name);
-}
-#else
-#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
-#endif
-
-static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/
-
-static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
-    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
-
-static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/
-
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
-    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
-    const char* function_name); /*proto*/
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/
-#else
-#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
-#endif
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        Py_SIZE(list) = len+1;
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/
-
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/
-
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-static int __Pyx_check_binary_version(void);
-
-typedef struct {
-    int code_line;
-    PyCodeObject* code_object;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename); /*proto*/
-
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
-
-
-/* Module declarations from 'ccy_classic_lstsqr' */
-#define __Pyx_MODULE_NAME "ccy_classic_lstsqr"
-int __pyx_module_is_main_ccy_classic_lstsqr = 0;
-
-/* Implementation of 'ccy_classic_lstsqr' */
-static PyObject *__pyx_builtin_sum;
-static PyObject *__pyx_builtin_zip;
-static PyObject *__pyx_pf_18ccy_classic_lstsqr_ccy_classic_lstsqr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_x, PyObject *__pyx_v_y); /* proto */
-static char __pyx_k_x[] = "x";
-static char __pyx_k_y[] = "y";
-static char __pyx_k_sum[] = "sum";
-static char __pyx_k_x_i[] = "x_i";
-static char __pyx_k_y_i[] = "y_i";
-static char __pyx_k_zip[] = "zip";
-static char __pyx_k_main[] = "__main__";
-static char __pyx_k_test[] = "__test__";
-static char __pyx_k_slope[] = "slope";
-static char __pyx_k_var_x[] = "var_x";
-static char __pyx_k_x_avg[] = "x_avg";
-static char __pyx_k_y_avg[] = "y_avg";
-static char __pyx_k_cov_xy[] = "cov_xy";
-static char __pyx_k_y_interc[] = "y_interc";
-static char __pyx_k_ccy_classic_lstsqr[] = "ccy_classic_lstsqr";
-static char __pyx_k_Users_sebastian_Github_python_r[] = "/Users/sebastian/Github/python_reference/benchmarks/ccy_classic_lstsqr.pyx";
-static PyObject *__pyx_kp_s_Users_sebastian_Github_python_r;
-static PyObject *__pyx_n_s_ccy_classic_lstsqr;
-static PyObject *__pyx_n_s_cov_xy;
-static PyObject *__pyx_n_s_main;
-static PyObject *__pyx_n_s_slope;
-static PyObject *__pyx_n_s_sum;
-static PyObject *__pyx_n_s_test;
-static PyObject *__pyx_n_s_var_x;
-static PyObject *__pyx_n_s_x;
-static PyObject *__pyx_n_s_x_avg;
-static PyObject *__pyx_n_s_x_i;
-static PyObject *__pyx_n_s_y;
-static PyObject *__pyx_n_s_y_avg;
-static PyObject *__pyx_n_s_y_i;
-static PyObject *__pyx_n_s_y_interc;
-static PyObject *__pyx_n_s_zip;
-static PyObject *__pyx_int_2;
-static PyObject *__pyx_tuple_;
-static PyObject *__pyx_codeobj__2;
-
-/* "ccy_classic_lstsqr.pyx":2
- * 
- * def ccy_classic_lstsqr(x, y):             # <<<<<<<<<<<<<<
- *     """ Computes the least-squares solution to a linear matrix equation. """
- *     x_avg = sum(x)/len(x)
- */
-
-/* Python wrapper */
-static PyObject *__pyx_pw_18ccy_classic_lstsqr_1ccy_classic_lstsqr(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_18ccy_classic_lstsqr_ccy_classic_lstsqr[] = " Computes the least-squares solution to a linear matrix equation. ";
-static PyMethodDef __pyx_mdef_18ccy_classic_lstsqr_1ccy_classic_lstsqr = {__Pyx_NAMESTR("ccy_classic_lstsqr"), (PyCFunction)__pyx_pw_18ccy_classic_lstsqr_1ccy_classic_lstsqr, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_18ccy_classic_lstsqr_ccy_classic_lstsqr)};
-static PyObject *__pyx_pw_18ccy_classic_lstsqr_1ccy_classic_lstsqr(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
-  PyObject *__pyx_v_x = 0;
-  PyObject *__pyx_v_y = 0;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("ccy_classic_lstsqr (wrapper)", 0);
-  {
-    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_x,&__pyx_n_s_y,0};
-    PyObject* values[2] = {0,0};
-    if (unlikely(__pyx_kwds)) {
-      Py_ssize_t kw_args;
-      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
-      switch (pos_args) {
-        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
-        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
-        case  0: break;
-        default: goto __pyx_L5_argtuple_error;
-      }
-      kw_args = PyDict_Size(__pyx_kwds);
-      switch (pos_args) {
-        case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_x)) != 0)) kw_args--;
-        else goto __pyx_L5_argtuple_error;
-        case  1:
-        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_y)) != 0)) kw_args--;
-        else {
-          __Pyx_RaiseArgtupleInvalid("ccy_classic_lstsqr", 1, 2, 2, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-        }
-      }
-      if (unlikely(kw_args > 0)) {
-        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "ccy_classic_lstsqr") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-      }
-    } else if (PyTuple_GET_SIZE(__pyx_args) != 2) {
-      goto __pyx_L5_argtuple_error;
-    } else {
-      values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
-      values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
-    }
-    __pyx_v_x = values[0];
-    __pyx_v_y = values[1];
-  }
-  goto __pyx_L4_argument_unpacking_done;
-  __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("ccy_classic_lstsqr", 1, 2, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-  __pyx_L3_error:;
-  __Pyx_AddTraceback("ccy_classic_lstsqr.ccy_classic_lstsqr", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __Pyx_RefNannyFinishContext();
-  return NULL;
-  __pyx_L4_argument_unpacking_done:;
-  __pyx_r = __pyx_pf_18ccy_classic_lstsqr_ccy_classic_lstsqr(__pyx_self, __pyx_v_x, __pyx_v_y);
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_pf_18ccy_classic_lstsqr_ccy_classic_lstsqr(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_x, PyObject *__pyx_v_y) {
-  PyObject *__pyx_v_x_avg = NULL;
-  PyObject *__pyx_v_y_avg = NULL;
-  PyObject *__pyx_v_var_x = NULL;
-  PyObject *__pyx_v_cov_xy = NULL;
-  PyObject *__pyx_v_slope = NULL;
-  PyObject *__pyx_v_y_interc = NULL;
-  PyObject *__pyx_v_x_i = NULL;
-  PyObject *__pyx_v_y_i = NULL;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  Py_ssize_t __pyx_t_3;
-  PyObject *__pyx_t_4 = NULL;
-  PyObject *(*__pyx_t_5)(PyObject *);
-  PyObject *__pyx_t_6 = NULL;
-  PyObject *__pyx_t_7 = NULL;
-  PyObject *__pyx_t_8 = NULL;
-  PyObject *(*__pyx_t_9)(PyObject *);
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("ccy_classic_lstsqr", 0);
-
-  /* "ccy_classic_lstsqr.pyx":4
- * def ccy_classic_lstsqr(x, y):
- *     """ Computes the least-squares solution to a linear matrix equation. """
- *     x_avg = sum(x)/len(x)             # <<<<<<<<<<<<<<
- *     y_avg = sum(y)/len(y)
- *     var_x = sum([(x_i - x_avg)**2 for x_i in x])
- */
-  __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_INCREF(__pyx_v_x);
-  PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_v_x);
-  __Pyx_GIVEREF(__pyx_v_x);
-  __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_1, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_t_3 = PyObject_Length(__pyx_v_x); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_t_1 = PyInt_FromSsize_t(__pyx_t_3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_4 = __Pyx_PyNumber_Divide(__pyx_t_2, __pyx_t_1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_v_x_avg = __pyx_t_4;
-  __pyx_t_4 = 0;
-
-  /* "ccy_classic_lstsqr.pyx":5
- *     """ Computes the least-squares solution to a linear matrix equation. """
- *     x_avg = sum(x)/len(x)
- *     y_avg = sum(y)/len(y)             # <<<<<<<<<<<<<<
- *     var_x = sum([(x_i - x_avg)**2 for x_i in x])
- *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
- */
-  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __Pyx_INCREF(__pyx_v_y);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_y);
-  __Pyx_GIVEREF(__pyx_v_y);
-  __pyx_t_1 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_4, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_t_3 = PyObject_Length(__pyx_v_y); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_t_4 = PyInt_FromSsize_t(__pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __pyx_t_2 = __Pyx_PyNumber_Divide(__pyx_t_1, __pyx_t_4); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 5; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_v_y_avg = __pyx_t_2;
-  __pyx_t_2 = 0;
-
-  /* "ccy_classic_lstsqr.pyx":6
- *     x_avg = sum(x)/len(x)
- *     y_avg = sum(y)/len(y)
- *     var_x = sum([(x_i - x_avg)**2 for x_i in x])             # <<<<<<<<<<<<<<
- *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
- *     slope = cov_xy / var_x
- */
-  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  if (PyList_CheckExact(__pyx_v_x) || PyTuple_CheckExact(__pyx_v_x)) {
-    __pyx_t_4 = __pyx_v_x; __Pyx_INCREF(__pyx_t_4); __pyx_t_3 = 0;
-    __pyx_t_5 = NULL;
-  } else {
-    __pyx_t_3 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_x); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_5 = Py_TYPE(__pyx_t_4)->tp_iternext;
-  }
-  for (;;) {
-    if (!__pyx_t_5 && PyList_CheckExact(__pyx_t_4)) {
-      if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_4)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_1 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_3); __Pyx_INCREF(__pyx_t_1); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_1 = PySequence_ITEM(__pyx_t_4, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else if (!__pyx_t_5 && PyTuple_CheckExact(__pyx_t_4)) {
-      if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_4)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_1 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_3); __Pyx_INCREF(__pyx_t_1); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_1 = PySequence_ITEM(__pyx_t_4, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else {
-      __pyx_t_1 = __pyx_t_5(__pyx_t_4);
-      if (unlikely(!__pyx_t_1)) {
-        PyObject* exc_type = PyErr_Occurred();
-        if (exc_type) {
-          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
-          else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        }
-        break;
-      }
-      __Pyx_GOTREF(__pyx_t_1);
-    }
-    __Pyx_XDECREF_SET(__pyx_v_x_i, __pyx_t_1);
-    __pyx_t_1 = 0;
-    __pyx_t_1 = PyNumber_Subtract(__pyx_v_x_i, __pyx_v_x_avg); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_1);
-    __pyx_t_6 = PyNumber_Power(__pyx_t_1, __pyx_int_2, Py_None); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_6);
-    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-    if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_6))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-  }
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
-  __Pyx_GIVEREF(__pyx_t_2);
-  __pyx_t_2 = 0;
-  __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_4, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_v_var_x = __pyx_t_2;
-  __pyx_t_2 = 0;
-
-  /* "ccy_classic_lstsqr.pyx":7
- *     y_avg = sum(y)/len(y)
- *     var_x = sum([(x_i - x_avg)**2 for x_i in x])
- *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])             # <<<<<<<<<<<<<<
- *     slope = cov_xy / var_x
- *     y_interc = y_avg - slope*x_avg
- */
-  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __Pyx_INCREF(__pyx_v_x);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_x);
-  __Pyx_GIVEREF(__pyx_v_x);
-  __Pyx_INCREF(__pyx_v_y);
-  PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_v_y);
-  __Pyx_GIVEREF(__pyx_v_y);
-  __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_zip, __pyx_t_4, NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_6);
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  if (PyList_CheckExact(__pyx_t_6) || PyTuple_CheckExact(__pyx_t_6)) {
-    __pyx_t_4 = __pyx_t_6; __Pyx_INCREF(__pyx_t_4); __pyx_t_3 = 0;
-    __pyx_t_5 = NULL;
-  } else {
-    __pyx_t_3 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_t_6); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_5 = Py_TYPE(__pyx_t_4)->tp_iternext;
-  }
-  __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-  for (;;) {
-    if (!__pyx_t_5 && PyList_CheckExact(__pyx_t_4)) {
-      if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_4)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_6 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_3); __Pyx_INCREF(__pyx_t_6); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_6 = PySequence_ITEM(__pyx_t_4, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else if (!__pyx_t_5 && PyTuple_CheckExact(__pyx_t_4)) {
-      if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_4)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_6 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_3); __Pyx_INCREF(__pyx_t_6); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_6 = PySequence_ITEM(__pyx_t_4, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else {
-      __pyx_t_6 = __pyx_t_5(__pyx_t_4);
-      if (unlikely(!__pyx_t_6)) {
-        PyObject* exc_type = PyErr_Occurred();
-        if (exc_type) {
-          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
-          else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        }
-        break;
-      }
-      __Pyx_GOTREF(__pyx_t_6);
-    }
-    if ((likely(PyTuple_CheckExact(__pyx_t_6))) || (PyList_CheckExact(__pyx_t_6))) {
-      PyObject* sequence = __pyx_t_6;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      Py_ssize_t size = Py_SIZE(sequence);
-      #else
-      Py_ssize_t size = PySequence_Size(sequence);
-      #endif
-      if (unlikely(size != 2)) {
-        if (size > 2) __Pyx_RaiseTooManyValuesError(2);
-        else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
-        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-      #if CYTHON_COMPILING_IN_CPYTHON
-      if (likely(PyTuple_CheckExact(sequence))) {
-        __pyx_t_1 = PyTuple_GET_ITEM(sequence, 0); 
-        __pyx_t_7 = PyTuple_GET_ITEM(sequence, 1); 
-      } else {
-        __pyx_t_1 = PyList_GET_ITEM(sequence, 0); 
-        __pyx_t_7 = PyList_GET_ITEM(sequence, 1); 
-      }
-      __Pyx_INCREF(__pyx_t_1);
-      __Pyx_INCREF(__pyx_t_7);
-      #else
-      __pyx_t_1 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_1);
-      __pyx_t_7 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_7);
-      #endif
-      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-    } else {
-      Py_ssize_t index = -1;
-      __pyx_t_8 = PyObject_GetIter(__pyx_t_6); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_8);
-      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-      __pyx_t_9 = Py_TYPE(__pyx_t_8)->tp_iternext;
-      index = 0; __pyx_t_1 = __pyx_t_9(__pyx_t_8); if (unlikely(!__pyx_t_1)) goto __pyx_L7_unpacking_failed;
-      __Pyx_GOTREF(__pyx_t_1);
-      index = 1; __pyx_t_7 = __pyx_t_9(__pyx_t_8); if (unlikely(!__pyx_t_7)) goto __pyx_L7_unpacking_failed;
-      __Pyx_GOTREF(__pyx_t_7);
-      if (__Pyx_IternextUnpackEndCheck(__pyx_t_9(__pyx_t_8), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __pyx_t_9 = NULL;
-      __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-      goto __pyx_L8_unpacking_done;
-      __pyx_L7_unpacking_failed:;
-      __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-      __pyx_t_9 = NULL;
-      if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index);
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __pyx_L8_unpacking_done:;
-    }
-    __Pyx_XDECREF_SET(__pyx_v_x_i, __pyx_t_1);
-    __pyx_t_1 = 0;
-    __Pyx_XDECREF_SET(__pyx_v_y_i, __pyx_t_7);
-    __pyx_t_7 = 0;
-    __pyx_t_6 = PyNumber_Subtract(__pyx_v_x_i, __pyx_v_x_avg); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_6);
-    __pyx_t_7 = PyNumber_Subtract(__pyx_v_y_i, __pyx_v_y_avg); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_7);
-    __pyx_t_1 = PyNumber_Multiply(__pyx_t_6, __pyx_t_7); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_1);
-    __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-    __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
-    if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  }
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
-  __Pyx_GIVEREF(__pyx_t_2);
-  __pyx_t_2 = 0;
-  __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_4, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_v_cov_xy = __pyx_t_2;
-  __pyx_t_2 = 0;
-
-  /* "ccy_classic_lstsqr.pyx":8
- *     var_x = sum([(x_i - x_avg)**2 for x_i in x])
- *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
- *     slope = cov_xy / var_x             # <<<<<<<<<<<<<<
- *     y_interc = y_avg - slope*x_avg
- *     return (slope, y_interc)
- */
-  __pyx_t_2 = __Pyx_PyNumber_Divide(__pyx_v_cov_xy, __pyx_v_var_x); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 8; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_v_slope = __pyx_t_2;
-  __pyx_t_2 = 0;
-
-  /* "ccy_classic_lstsqr.pyx":9
- *     cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
- *     slope = cov_xy / var_x
- *     y_interc = y_avg - slope*x_avg             # <<<<<<<<<<<<<<
- *     return (slope, y_interc)
- */
-  __pyx_t_2 = PyNumber_Multiply(__pyx_v_slope, __pyx_v_x_avg); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_4 = PyNumber_Subtract(__pyx_v_y_avg, __pyx_t_2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_v_y_interc = __pyx_t_4;
-  __pyx_t_4 = 0;
-
-  /* "ccy_classic_lstsqr.pyx":10
- *     slope = cov_xy / var_x
- *     y_interc = y_avg - slope*x_avg
- *     return (slope, y_interc)             # <<<<<<<<<<<<<<
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 10; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __Pyx_INCREF(__pyx_v_slope);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_slope);
-  __Pyx_GIVEREF(__pyx_v_slope);
-  __Pyx_INCREF(__pyx_v_y_interc);
-  PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_v_y_interc);
-  __Pyx_GIVEREF(__pyx_v_y_interc);
-  __pyx_r = __pyx_t_4;
-  __pyx_t_4 = 0;
-  goto __pyx_L0;
-
-  /* "ccy_classic_lstsqr.pyx":2
- * 
- * def ccy_classic_lstsqr(x, y):             # <<<<<<<<<<<<<<
- *     """ Computes the least-squares solution to a linear matrix equation. """
- *     x_avg = sum(x)/len(x)
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_XDECREF(__pyx_t_6);
-  __Pyx_XDECREF(__pyx_t_7);
-  __Pyx_XDECREF(__pyx_t_8);
-  __Pyx_AddTraceback("ccy_classic_lstsqr.ccy_classic_lstsqr", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_x_avg);
-  __Pyx_XDECREF(__pyx_v_y_avg);
-  __Pyx_XDECREF(__pyx_v_var_x);
-  __Pyx_XDECREF(__pyx_v_cov_xy);
-  __Pyx_XDECREF(__pyx_v_slope);
-  __Pyx_XDECREF(__pyx_v_y_interc);
-  __Pyx_XDECREF(__pyx_v_x_i);
-  __Pyx_XDECREF(__pyx_v_y_i);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyMethodDef __pyx_methods[] = {
-  {0, 0, 0, 0}
-};
-
-#if PY_MAJOR_VERSION >= 3
-static struct PyModuleDef __pyx_moduledef = {
-  #if PY_VERSION_HEX < 0x03020000
-    { PyObject_HEAD_INIT(NULL) NULL, 0, NULL },
-  #else
-    PyModuleDef_HEAD_INIT,
-  #endif
-    __Pyx_NAMESTR("ccy_classic_lstsqr"),
-    0, /* m_doc */
-    -1, /* m_size */
-    __pyx_methods /* m_methods */,
-    NULL, /* m_reload */
-    NULL, /* m_traverse */
-    NULL, /* m_clear */
-    NULL /* m_free */
-};
-#endif
-
-static __Pyx_StringTabEntry __pyx_string_tab[] = {
-  {&__pyx_kp_s_Users_sebastian_Github_python_r, __pyx_k_Users_sebastian_Github_python_r, sizeof(__pyx_k_Users_sebastian_Github_python_r), 0, 0, 1, 0},
-  {&__pyx_n_s_ccy_classic_lstsqr, __pyx_k_ccy_classic_lstsqr, sizeof(__pyx_k_ccy_classic_lstsqr), 0, 0, 1, 1},
-  {&__pyx_n_s_cov_xy, __pyx_k_cov_xy, sizeof(__pyx_k_cov_xy), 0, 0, 1, 1},
-  {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
-  {&__pyx_n_s_slope, __pyx_k_slope, sizeof(__pyx_k_slope), 0, 0, 1, 1},
-  {&__pyx_n_s_sum, __pyx_k_sum, sizeof(__pyx_k_sum), 0, 0, 1, 1},
-  {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
-  {&__pyx_n_s_var_x, __pyx_k_var_x, sizeof(__pyx_k_var_x), 0, 0, 1, 1},
-  {&__pyx_n_s_x, __pyx_k_x, sizeof(__pyx_k_x), 0, 0, 1, 1},
-  {&__pyx_n_s_x_avg, __pyx_k_x_avg, sizeof(__pyx_k_x_avg), 0, 0, 1, 1},
-  {&__pyx_n_s_x_i, __pyx_k_x_i, sizeof(__pyx_k_x_i), 0, 0, 1, 1},
-  {&__pyx_n_s_y, __pyx_k_y, sizeof(__pyx_k_y), 0, 0, 1, 1},
-  {&__pyx_n_s_y_avg, __pyx_k_y_avg, sizeof(__pyx_k_y_avg), 0, 0, 1, 1},
-  {&__pyx_n_s_y_i, __pyx_k_y_i, sizeof(__pyx_k_y_i), 0, 0, 1, 1},
-  {&__pyx_n_s_y_interc, __pyx_k_y_interc, sizeof(__pyx_k_y_interc), 0, 0, 1, 1},
-  {&__pyx_n_s_zip, __pyx_k_zip, sizeof(__pyx_k_zip), 0, 0, 1, 1},
-  {0, 0, 0, 0, 0, 0, 0}
-};
-static int __Pyx_InitCachedBuiltins(void) {
-  __pyx_builtin_sum = __Pyx_GetBuiltinName(__pyx_n_s_sum); if (!__pyx_builtin_sum) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_zip = __Pyx_GetBuiltinName(__pyx_n_s_zip); if (!__pyx_builtin_zip) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  return 0;
-  __pyx_L1_error:;
-  return -1;
-}
-
-static int __Pyx_InitCachedConstants(void) {
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
-
-  /* "ccy_classic_lstsqr.pyx":2
- * 
- * def ccy_classic_lstsqr(x, y):             # <<<<<<<<<<<<<<
- *     """ Computes the least-squares solution to a linear matrix equation. """
- *     x_avg = sum(x)/len(x)
- */
-  __pyx_tuple_ = PyTuple_Pack(10, __pyx_n_s_x, __pyx_n_s_y, __pyx_n_s_x_avg, __pyx_n_s_y_avg, __pyx_n_s_var_x, __pyx_n_s_cov_xy, __pyx_n_s_slope, __pyx_n_s_y_interc, __pyx_n_s_x_i, __pyx_n_s_y_i); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple_);
-  __Pyx_GIVEREF(__pyx_tuple_);
-  __pyx_codeobj__2 = (PyObject*)__Pyx_PyCode_New(2, 0, 10, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple_, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_sebastian_Github_python_r, __pyx_n_s_ccy_classic_lstsqr, 2, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_RefNannyFinishContext();
-  return 0;
-  __pyx_L1_error:;
-  __Pyx_RefNannyFinishContext();
-  return -1;
-}
-
-static int __Pyx_InitGlobals(void) {
-  if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  __pyx_int_2 = PyInt_FromLong(2); if (unlikely(!__pyx_int_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  return 0;
-  __pyx_L1_error:;
-  return -1;
-}
-
-#if PY_MAJOR_VERSION < 3
-PyMODINIT_FUNC initccy_classic_lstsqr(void); /*proto*/
-PyMODINIT_FUNC initccy_classic_lstsqr(void)
-#else
-PyMODINIT_FUNC PyInit_ccy_classic_lstsqr(void); /*proto*/
-PyMODINIT_FUNC PyInit_ccy_classic_lstsqr(void)
-#endif
-{
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannyDeclarations
-  #if CYTHON_REFNANNY
-  __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny");
-  if (!__Pyx_RefNanny) {
-      PyErr_Clear();
-      __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny");
-      if (!__Pyx_RefNanny)
-          Py_FatalError("failed to import 'refnanny' module");
-  }
-  #endif
-  __Pyx_RefNannySetupContext("PyMODINIT_FUNC PyInit_ccy_classic_lstsqr(void)", 0);
-  if ( __Pyx_check_binary_version() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #ifdef __Pyx_CyFunction_USED
-  if (__Pyx_CyFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  #ifdef __Pyx_FusedFunction_USED
-  if (__pyx_FusedFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  #ifdef __Pyx_Generator_USED
-  if (__pyx_Generator_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  /*--- Library function declarations ---*/
-  /*--- Threads initialization code ---*/
-  #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS
-  #ifdef WITH_THREAD /* Python build with threading support? */
-  PyEval_InitThreads();
-  #endif
-  #endif
-  /*--- Module creation code ---*/
-  #if PY_MAJOR_VERSION < 3
-  __pyx_m = Py_InitModule4(__Pyx_NAMESTR("ccy_classic_lstsqr"), __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
-  #else
-  __pyx_m = PyModule_Create(&__pyx_moduledef);
-  #endif
-  if (unlikely(!__pyx_m)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  Py_INCREF(__pyx_d);
-  __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME)); if (unlikely(!__pyx_b)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #if CYTHON_COMPILING_IN_PYPY
-  Py_INCREF(__pyx_b);
-  #endif
-  if (__Pyx_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  /*--- Initialize various global constants etc. ---*/
-  if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT)
-  if (__Pyx_init_sys_getdefaultencoding_params() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  if (__pyx_module_is_main_ccy_classic_lstsqr) {
-    if (__Pyx_SetAttrString(__pyx_m, "__name__", __pyx_n_s_main) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  }
-  #if PY_MAJOR_VERSION >= 3
-  {
-    PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    if (!PyDict_GetItemString(modules, "ccy_classic_lstsqr")) {
-      if (unlikely(PyDict_SetItemString(modules, "ccy_classic_lstsqr", __pyx_m) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-  }
-  #endif
-  /*--- Builtin init code ---*/
-  if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  /*--- Constants init code ---*/
-  if (unlikely(__Pyx_InitCachedConstants() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  /*--- Global init code ---*/
-  /*--- Variable export code ---*/
-  /*--- Function export code ---*/
-  /*--- Type init code ---*/
-  /*--- Type import code ---*/
-  /*--- Variable import code ---*/
-  /*--- Function import code ---*/
-  /*--- Execution code ---*/
-
-  /* "ccy_classic_lstsqr.pyx":2
- * 
- * def ccy_classic_lstsqr(x, y):             # <<<<<<<<<<<<<<
- *     """ Computes the least-squares solution to a linear matrix equation. """
- *     x_avg = sum(x)/len(x)
- */
-  __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_18ccy_classic_lstsqr_1ccy_classic_lstsqr, NULL, __pyx_n_s_ccy_classic_lstsqr); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (PyDict_SetItem(__pyx_d, __pyx_n_s_ccy_classic_lstsqr, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  if (__pyx_m) {
-    __Pyx_AddTraceback("init ccy_classic_lstsqr", __pyx_clineno, __pyx_lineno, __pyx_filename);
-    Py_DECREF(__pyx_m); __pyx_m = 0;
-  } else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_ImportError, "init ccy_classic_lstsqr");
-  }
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  #if PY_MAJOR_VERSION < 3
-  return;
-  #else
-  return __pyx_m;
-  #endif
-}
-
-/* Runtime support code */
-#if CYTHON_REFNANNY
-static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
-    PyObject *m = NULL, *p = NULL;
-    void *r = NULL;
-    m = PyImport_ImportModule((char *)modname);
-    if (!m) goto end;
-    p = PyObject_GetAttrString(m, (char *)"RefNannyAPI");
-    if (!p) goto end;
-    r = PyLong_AsVoidPtr(p);
-end:
-    Py_XDECREF(p);
-    Py_XDECREF(m);
-    return (__Pyx_RefNannyAPIStruct *)r;
-}
-#endif /* CYTHON_REFNANNY */
-
-static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
-    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
-    if (unlikely(!result)) {
-        PyErr_Format(PyExc_NameError,
-#if PY_MAJOR_VERSION >= 3
-            "name '%U' is not defined", name);
-#else
-            "name '%.200s' is not defined", PyString_AS_STRING(name));
-#endif
-    }
-    return result;
-}
-
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *more_or_less;
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
-    }
-    PyErr_Format(PyExc_TypeError,
-                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                 func_name, more_or_less, num_expected,
-                 (num_expected == 1) ? "" : "s", num_found);
-}
-
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name,
-    PyObject* kw_name)
-{
-    PyErr_Format(PyExc_TypeError,
-        #if PY_MAJOR_VERSION >= 3
-        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
-        #else
-        "%s() got multiple values for keyword argument '%s'", func_name,
-        PyString_AsString(kw_name));
-        #endif
-}
-
-static int __Pyx_ParseOptionalKeywords(
-    PyObject *kwds,
-    PyObject **argnames[],
-    PyObject *kwds2,
-    PyObject *values[],
-    Py_ssize_t num_pos_args,
-    const char* function_name)
-{
-    PyObject *key = 0, *value = 0;
-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    while (PyDict_Next(kwds, &pos, &key, &value)) {
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-            continue;
-        }
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (**name == key) ? 0 :
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key);
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    PyErr_Format(PyExc_TypeError,
-    #if PY_MAJOR_VERSION < 3
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    return -1;
-}
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = func->ob_type->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-#if PY_VERSION_HEX >= 0x02060000
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-#endif
-    result = (*call)(func, arg, kw);
-#if PY_VERSION_HEX >= 0x02060000
-    Py_LeaveRecursiveCall();
-#endif
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-static CYTHON_INLINE int __Pyx_IterFinish(void) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyThreadState *tstate = PyThreadState_GET();
-    PyObject* exc_type = tstate->curexc_type;
-    if (unlikely(exc_type)) {
-        if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) {
-            PyObject *exc_value, *exc_tb;
-            exc_value = tstate->curexc_value;
-            exc_tb = tstate->curexc_traceback;
-            tstate->curexc_type = 0;
-            tstate->curexc_value = 0;
-            tstate->curexc_traceback = 0;
-            Py_DECREF(exc_type);
-            Py_XDECREF(exc_value);
-            Py_XDECREF(exc_tb);
-            return 0;
-        } else {
-            return -1;
-        }
-    }
-    return 0;
-#else
-    if (unlikely(PyErr_Occurred())) {
-        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
-            PyErr_Clear();
-            return 0;
-        } else {
-            return -1;
-        }
-    }
-    return 0;
-#endif
-}
-
-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
-    if (unlikely(retval)) {
-        Py_DECREF(retval);
-        __Pyx_RaiseTooManyValuesError(expected);
-        return -1;
-    } else {
-        return __Pyx_IterFinish();
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-    const long neg_one = (long) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long long)) {
-            return PyLong_FromUnsignedLongLong((unsigned long long) value);
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(long long)) {
-            return PyLong_FromLongLong((long long) value);
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func)             \
-    {                                                                     \
-        func_type value = func(x);                                        \
-        if (sizeof(target_type) < sizeof(func_type)) {                    \
-            if (unlikely(value != (func_type) (target_type) value)) {     \
-                func_type zero = 0;                                       \
-                PyErr_SetString(PyExc_OverflowError,                      \
-                    (is_unsigned && unlikely(value < zero)) ?             \
-                    "can't convert negative value to " #target_type :     \
-                    "value too large to convert to " #target_type);       \
-                return (target_type) -1;                                  \
-            }                                                             \
-        }                                                                 \
-        return (target_type) value;                                       \
-    }
-
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-  #include "longintrepr.h"
- #endif
-#endif
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-    const long neg_one = (long) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(long) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG)
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to long");
-                return (long) -1;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(long)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return (long) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (unlikely(Py_SIZE(x) < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to long");
-                return (long) -1;
-            }
-            if (sizeof(long) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong)
-            } else if (sizeof(long) <= sizeof(unsigned long long)) {
-                __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong)
-            }
-        } else {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(long)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return +(long) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(long) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (sizeof(long) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong)
-            } else if (sizeof(long) <= sizeof(long long)) {
-                __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong)
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            long val;
-            PyObject *v = __Pyx_PyNumber_Int(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-}
-
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-  #include "longintrepr.h"
- #endif
-#endif
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-    const int neg_one = (int) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(int) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG)
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to int");
-                return (int) -1;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(int)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return (int) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (unlikely(Py_SIZE(x) < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to int");
-                return (int) -1;
-            }
-            if (sizeof(int) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong)
-            } else if (sizeof(int) <= sizeof(unsigned long long)) {
-                __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong)
-            }
-        } else {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(int)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return +(int) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(int) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (sizeof(int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong)
-            } else if (sizeof(int) <= sizeof(long long)) {
-                __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong)
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            int val;
-            PyObject *v = __Pyx_PyNumber_Int(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-}
-
-static int __Pyx_check_binary_version(void) {
-    char ctversion[4], rtversion[4];
-    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
-    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
-        char message[200];
-        PyOS_snprintf(message, sizeof(message),
-                      "compiletime version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        #if PY_VERSION_HEX < 0x02050000
-        return PyErr_Warn(NULL, message);
-        #else
-        return PyErr_WarnEx(NULL, message, 1);
-        #endif
-    }
-    return 0;
-}
-
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = (start + end) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyObject *py_srcfile = 0;
-    PyObject *py_funcname = 0;
-    #if PY_MAJOR_VERSION < 3
-    py_srcfile = PyString_FromString(filename);
-    #else
-    py_srcfile = PyUnicode_FromString(filename);
-    #endif
-    if (!py_srcfile) goto bad;
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        #else
-        py_funcname = PyUnicode_FromString(funcname);
-        #endif
-    }
-    if (!py_funcname) goto bad;
-    py_code = __Pyx_PyCode_New(
-        0,            /*int argcount,*/
-        0,            /*int kwonlyargcount,*/
-        0,            /*int nlocals,*/
-        0,            /*int stacksize,*/
-        0,            /*int flags,*/
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,      /*int firstlineno,*/
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    Py_DECREF(py_funcname);
-    return py_code;
-bad:
-    Py_XDECREF(py_srcfile);
-    Py_XDECREF(py_funcname);
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyObject *py_globals = 0;
-    PyFrameObject *py_frame = 0;
-    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
-    if (!py_code) {
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) goto bad;
-        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
-    }
-    py_globals = PyModule_GetDict(__pyx_m);
-    if (!py_globals) goto bad;
-    py_frame = PyFrame_New(
-        PyThreadState_GET(), /*PyThreadState *tstate,*/
-        py_code,             /*PyCodeObject *code,*/
-        py_globals,          /*PyObject *globals,*/
-        0                    /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    py_frame->f_lineno = py_line;
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION < 3
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        #else  /* Python 3+ has unicode identifiers */
-        if (t->is_unicode | t->is_str) {
-            if (t->intern) {
-                *t->p = PyUnicode_InternFromString(t->s);
-            } else if (t->encoding) {
-                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
-            } else {
-                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
-            }
-        } else {
-            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
-        }
-        #endif
-        if (!*t->p)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str));
-}
-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-#if PY_VERSION_HEX < 0x03030000
-        char* defenc_c;
-        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-        if (!defenc) return NULL;
-        defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-        {
-            char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-            char* c;
-            for (c = defenc_c; c < end; c++) {
-                if ((unsigned char) (*c) >= 128) {
-                    PyUnicode_AsASCIIString(o);
-                    return NULL;
-                }
-            }
-        }
-#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/
-        *length = PyBytes_GET_SIZE(defenc);
-        return defenc_c;
-#else /* PY_VERSION_HEX < 0x03030000 */
-        if (PyUnicode_READY(o) == -1) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-        if (PyUnicode_IS_ASCII(o)) {
-            *length = PyUnicode_GET_DATA_SIZE(o);
-            return PyUnicode_AsUTF8(o);
-        } else {
-            PyUnicode_AsASCIIString(o);
-            return NULL;
-        }
-#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
-        return PyUnicode_AsUTF8AndSize(o, length);
-#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
-#endif /* PY_VERSION_HEX < 0x03030000 */
-    } else
-#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII  || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */
-#if !CYTHON_COMPILING_IN_PYPY
-#if PY_VERSION_HEX >= 0x02060000
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
-  PyNumberMethods *m;
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (PyInt_Check(x) || PyLong_Check(x))
-#else
-  if (PyLong_Check(x))
-#endif
-    return Py_INCREF(x), x;
-  m = Py_TYPE(x)->tp_as_number;
-#if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = PyNumber_Int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = PyNumber_Long(x);
-  }
-#else
-  if (m && m->nb_int) {
-    name = "int";
-    res = PyNumber_Long(x);
-  }
-#endif
-  if (res) {
-#if PY_MAJOR_VERSION < 3
-    if (!PyInt_Check(res) && !PyLong_Check(res)) {
-#else
-    if (!PyLong_Check(res)) {
-#endif
-      PyErr_Format(PyExc_TypeError,
-                   "__%.4s__ returned non-%.4s (type %.200s)",
-                   name, name, Py_TYPE(res)->tp_name);
-      Py_DECREF(res);
-      return NULL;
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-  #include "longintrepr.h"
- #endif
-#endif
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b)))
-      return PyInt_AS_LONG(b);
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-     #if CYTHON_USE_PYLONG_INTERNALS
-       switch (Py_SIZE(b)) {
-       case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0];
-       case  0: return 0;
-       case  1: return ((PyLongObject*)b)->ob_digit[0];
-       }
-     #endif
-    #endif
-  #if PY_VERSION_HEX < 0x02060000
-    return PyInt_AsSsize_t(b);
-  #else
-    return PyLong_AsSsize_t(b);
-  #endif
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-#if PY_VERSION_HEX < 0x02050000
-   if (ival <= LONG_MAX)
-       return PyInt_FromLong((long)ival);
-   else {
-       unsigned char *bytes = (unsigned char *) &ival;
-       int one = 1; int little = (int)*(unsigned char*)&one;
-       return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0);
-   }
-#else
-   return PyInt_FromSize_t(ival);
-#endif
-}
-
-
-#endif /* Py_PYTHON_H */
diff --git a/benchmarks/ccy_classic_lstsqr.pyx b/benchmarks/ccy_classic_lstsqr.pyx
deleted file mode 100644
index 5def68b..0000000
--- a/benchmarks/ccy_classic_lstsqr.pyx
+++ /dev/null
@@ -1,10 +0,0 @@
-
-def ccy_classic_lstsqr(x, y):
-    """ Computes the least-squares solution to a linear matrix equation. """
-    x_avg = sum(x)/len(x)
-    y_avg = sum(y)/len(y)
-    var_x = sum([(x_i - x_avg)**2 for x_i in x])
-    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])
-    slope = cov_xy / var_x
-    y_interc = y_avg - slope*x_avg
-    return (slope, y_interc)
\ No newline at end of file
diff --git a/benchmarks/cython_least_squares.html b/benchmarks/cython_least_squares.html
deleted file mode 100644
index 1e50e87..0000000
--- a/benchmarks/cython_least_squares.html
+++ /dev/null
@@ -1,4928 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-
-<meta charset="utf-8" />
-<title>Notebook</title>
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
-
-<style type="text/css">
-    .clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0}
-.clearfix:after{clear:both}
-.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}
-.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
-article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}
-audio,canvas,video{display:inline-block;*display:inline;*zoom:1}
-audio:not([controls]){display:none}
-html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}
-a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
-a:hover,a:active{outline:0}
-sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}
-sup{top:-0.5em}
-sub{bottom:-0.25em}
-img{max-width:100%;width:auto\9;height:auto;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}
-#map_canvas img,.google-maps img{max-width:none}
-button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}
-button,input{*overflow:visible;line-height:normal}
-button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}
-button,html input[type="button"],input[type="reset"],input[type="submit"]{-webkit-appearance:button;cursor:pointer}
-label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer}
-input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}
-input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}
-textarea{overflow:auto;vertical-align:top}
-@media print{*{text-shadow:none !important;color:#000 !important;background:transparent !important;box-shadow:none !important} a,a:visited{text-decoration:underline} a[href]:after{content:" (" attr(href) ")"} abbr[title]:after{content:" (" attr(title) ")"} .ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""} pre,blockquote{border:1px solid #999;page-break-inside:avoid} thead{display:table-header-group} tr,img{page-break-inside:avoid} img{max-width:100% !important} @page {margin:.5cm}p,h2,h3{orphans:3;widows:3} h2,h3{page-break-after:avoid}}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;line-height:20px;color:#000;background-color:#fff}
-a{color:#08c;text-decoration:none}
-a:hover,a:focus{color:#005580;text-decoration:underline}
-.img-rounded{border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px rgba(0,0,0,0.1)}
-.img-circle{border-radius:500px;-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px}
-.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0}
-.row:after{clear:both}
-[class*="span"]{float:left;min-height:1px;margin-left:20px}
-.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}
-.span12{width:940px}
-.span11{width:860px}
-.span10{width:780px}
-.span9{width:700px}
-.span8{width:620px}
-.span7{width:540px}
-.span6{width:460px}
-.span5{width:380px}
-.span4{width:300px}
-.span3{width:220px}
-.span2{width:140px}
-.span1{width:60px}
-.offset12{margin-left:980px}
-.offset11{margin-left:900px}
-.offset10{margin-left:820px}
-.offset9{margin-left:740px}
-.offset8{margin-left:660px}
-.offset7{margin-left:580px}
-.offset6{margin-left:500px}
-.offset5{margin-left:420px}
-.offset4{margin-left:340px}
-.offset3{margin-left:260px}
-.offset2{margin-left:180px}
-.offset1{margin-left:100px}
-.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0}
-.row-fluid:after{clear:both}
-.row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.127659574468085%;*margin-left:2.074468085106383%}
-.row-fluid [class*="span"]:first-child{margin-left:0}
-.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%}
-.row-fluid .span12{width:100%;*width:99.94680851063829%}
-.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%}
-.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%}
-.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%}
-.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%}
-.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%}
-.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%}
-.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%}
-.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%}
-.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%}
-.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%}
-.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%}
-.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%}
-.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%}
-.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%}
-.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%}
-.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%}
-.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%}
-.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%}
-.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%}
-.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%}
-.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%}
-.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%}
-.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%}
-.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%}
-.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%}
-.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%}
-.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%}
-.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%}
-.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%}
-.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%}
-.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%}
-.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%}
-.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%}
-.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%}
-.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%}
-[class*="span"].hide,.row-fluid [class*="span"].hide{display:none}
-[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right}
-.container{margin-right:auto;margin-left:auto;*zoom:1}.container:before,.container:after{display:table;content:"";line-height:0}
-.container:after{clear:both}
-.container-fluid{padding-right:20px;padding-left:20px;*zoom:1}.container-fluid:before,.container-fluid:after{display:table;content:"";line-height:0}
-.container-fluid:after{clear:both}
-p{margin:0 0 10px}
-.lead{margin-bottom:20px;font-size:19.5px;font-weight:200;line-height:30px}
-small{font-size:85%}
-strong{font-weight:bold}
-em{font-style:italic}
-cite{font-style:normal}
-.muted{color:#999}
-a.muted:hover,a.muted:focus{color:#808080}
-.text-warning{color:#c09853}
-a.text-warning:hover,a.text-warning:focus{color:#a47e3c}
-.text-error{color:#b94a48}
-a.text-error:hover,a.text-error:focus{color:#953b39}
-.text-info{color:#3a87ad}
-a.text-info:hover,a.text-info:focus{color:#2d6987}
-.text-success{color:#468847}
-a.text-success:hover,a.text-success:focus{color:#356635}
-.text-left{text-align:left}
-.text-right{text-align:right}
-.text-center{text-align:center}
-h1,h2,h3,h4,h5,h6{margin:10px 0;font-family:inherit;font-weight:bold;line-height:20px;color:inherit;text-rendering:optimizelegibility}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#999}
-h1,h2,h3{line-height:40px}
-h1{font-size:35.75px}
-h2{font-size:29.25px}
-h3{font-size:22.75px}
-h4{font-size:16.25px}
-h5{font-size:13px}
-h6{font-size:11.049999999999999px}
-h1 small{font-size:22.75px}
-h2 small{font-size:16.25px}
-h3 small{font-size:13px}
-h4 small{font-size:13px}
-.page-header{padding-bottom:9px;margin:20px 0 30px;border-bottom:1px solid #eee}
-ul,ol{padding:0;margin:0 0 10px 25px}
-ul ul,ul ol,ol ol,ol ul{margin-bottom:0}
-li{line-height:20px}
-ul.unstyled,ol.unstyled{margin-left:0;list-style:none}
-ul.inline,ol.inline{margin-left:0;list-style:none}ul.inline>li,ol.inline>li{display:inline-block;*display:inline;*zoom:1;padding-left:5px;padding-right:5px}
-dl{margin-bottom:20px}
-dt,dd{line-height:20px}
-dt{font-weight:bold}
-dd{margin-left:10px}
-.dl-horizontal{*zoom:1}.dl-horizontal:before,.dl-horizontal:after{display:table;content:"";line-height:0}
-.dl-horizontal:after{clear:both}
-.dl-horizontal dt{float:left;width:160px;clear:left;text-align:right;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}
-.dl-horizontal dd{margin-left:180px}
-hr{margin:20px 0;border:0;border-top:1px solid #eee;border-bottom:1px solid #fff}
-abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #999}
-abbr.initialism{font-size:90%;text-transform:uppercase}
-blockquote{padding:0 0 0 15px;margin:0 0 20px;border-left:5px solid #eee}blockquote p{margin-bottom:0;font-size:16.25px;font-weight:300;line-height:1.25}
-blockquote small{display:block;line-height:20px;color:#999}blockquote small:before{content:'\2014 \00A0'}
-blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eee;border-left:0}blockquote.pull-right p,blockquote.pull-right small{text-align:right}
-blockquote.pull-right small:before{content:''}
-blockquote.pull-right small:after{content:'\00A0 \2014'}
-q:before,q:after,blockquote:before,blockquote:after{content:""}
-address{display:block;margin-bottom:20px;font-style:normal;line-height:20px}
-code,pre{padding:0 3px 2px;font-family:monospace;font-size:11px;color:#333;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-code{padding:2px 4px;color:#d14;background-color:#f7f7f9;border:1px solid #e1e1e8;white-space:nowrap}
-pre{display:block;padding:9.5px;margin:0 0 10px;font-size:12px;line-height:20px;word-break:break-all;word-wrap:break-word;white-space:pre;white-space:pre-wrap;background-color:#f5f5f5;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.15);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}pre.prettyprint{margin-bottom:20px}
-pre code{padding:0;color:inherit;white-space:pre;white-space:pre-wrap;background-color:transparent;border:0}
-.pre-scrollable{max-height:340px;overflow-y:scroll}
-form{margin:0 0 20px}
-fieldset{padding:0;margin:0;border:0}
-legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:19.5px;line-height:40px;color:#333;border:0;border-bottom:1px solid #e5e5e5}legend small{font-size:15px;color:#999}
-label,input,button,select,textarea{font-size:13px;font-weight:normal;line-height:20px}
-input,button,select,textarea{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif}
-label{display:block;margin-bottom:5px}
-select,textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{display:inline-block;height:20px;padding:4px 6px;margin-bottom:10px;font-size:13px;line-height:20px;color:#555;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;vertical-align:middle}
-input,textarea,.uneditable-input{width:206px}
-textarea{height:auto}
-textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{background-color:#fff;border:1px solid #ccc;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-webkit-transition:border linear .2s, box-shadow linear .2s;-moz-transition:border linear .2s, box-shadow linear .2s;-o-transition:border linear .2s, box-shadow linear .2s;transition:border linear .2s, box-shadow linear .2s}textarea:focus,input[type="text"]:focus,input[type="password"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="date"]:focus,input[type="month"]:focus,input[type="time"]:focus,input[type="week"]:focus,input[type="number"]:focus,input[type="email"]:focus,input[type="url"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="color"]:focus,.uneditable-input:focus{border-color:rgba(82,168,236,0.8);outline:0;outline:thin dotted \9;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6)}
-input[type="radio"],input[type="checkbox"]{margin:4px 0 0;*margin-top:0;margin-top:1px \9;line-height:normal}
-input[type="file"],input[type="image"],input[type="submit"],input[type="reset"],input[type="button"],input[type="radio"],input[type="checkbox"]{width:auto}
-select,input[type="file"]{height:30px;*margin-top:4px;line-height:30px}
-select{width:220px;border:1px solid #ccc;background-color:#fff}
-select[multiple],select[size]{height:auto}
-select:focus,input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
-.uneditable-input,.uneditable-textarea{color:#999;background-color:#fcfcfc;border-color:#ccc;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);cursor:not-allowed}
-.uneditable-input{overflow:hidden;white-space:nowrap}
-.uneditable-textarea{width:auto;height:auto}
-input:-moz-placeholder,textarea:-moz-placeholder{color:#999}
-input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999}
-input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999}
-.radio,.checkbox{min-height:20px;padding-left:20px}
-.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px}
-.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px}
-.radio.inline,.checkbox.inline{display:inline-block;padding-top:5px;margin-bottom:0;vertical-align:middle}
-.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px}
-.input-mini{width:60px}
-.input-small{width:90px}
-.input-medium{width:150px}
-.input-large{width:210px}
-.input-xlarge{width:270px}
-.input-xxlarge{width:530px}
-input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"]{float:none;margin-left:0}
-.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"]{display:inline-block}
-input,textarea,.uneditable-input{margin-left:0}
-.controls-row [class*="span"]+[class*="span"]{margin-left:20px}
-input.span12,textarea.span12,.uneditable-input.span12{width:926px}
-input.span11,textarea.span11,.uneditable-input.span11{width:846px}
-input.span10,textarea.span10,.uneditable-input.span10{width:766px}
-input.span9,textarea.span9,.uneditable-input.span9{width:686px}
-input.span8,textarea.span8,.uneditable-input.span8{width:606px}
-input.span7,textarea.span7,.uneditable-input.span7{width:526px}
-input.span6,textarea.span6,.uneditable-input.span6{width:446px}
-input.span5,textarea.span5,.uneditable-input.span5{width:366px}
-input.span4,textarea.span4,.uneditable-input.span4{width:286px}
-input.span3,textarea.span3,.uneditable-input.span3{width:206px}
-input.span2,textarea.span2,.uneditable-input.span2{width:126px}
-input.span1,textarea.span1,.uneditable-input.span1{width:46px}
-.controls-row{*zoom:1}.controls-row:before,.controls-row:after{display:table;content:"";line-height:0}
-.controls-row:after{clear:both}
-.controls-row [class*="span"],.row-fluid .controls-row [class*="span"]{float:left}
-.controls-row .checkbox[class*="span"],.controls-row .radio[class*="span"]{padding-top:5px}
-input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly]{cursor:not-allowed;background-color:#eee}
-input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly]{background-color:transparent}
-.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#c09853}
-.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#c09853}
-.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#c09853;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#a47e3c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e}
-.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#c09853;background-color:#fcf8e3;border-color:#c09853}
-.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#b94a48}
-.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#b94a48}
-.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#b94a48;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#953b39;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392}
-.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#b94a48;background-color:#f2dede;border-color:#b94a48}
-.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#468847}
-.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#468847}
-.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#468847;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#356635;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b}
-.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847}
-.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad}
-.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad}
-.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3}
-.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3a87ad;background-color:#d9edf7;border-color:#3a87ad}
-input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}
-.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;content:"";line-height:0}
-.form-actions:after{clear:both}
-.help-block,.help-inline{color:#262626}
-.help-block{display:block;margin-bottom:10px}
-.help-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle;padding-left:5px}
-.input-append,.input-prepend{display:inline-block;margin-bottom:10px;vertical-align:middle;font-size:0;white-space:nowrap}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input,.input-append .dropdown-menu,.input-prepend .dropdown-menu,.input-append .popover,.input-prepend .popover{font-size:13px}
-.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input{position:relative;margin-bottom:0;*margin-left:0;vertical-align:top;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append input:focus,.input-prepend input:focus,.input-append select:focus,.input-prepend select:focus,.input-append .uneditable-input:focus,.input-prepend .uneditable-input:focus{z-index:2}
-.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:13px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}
-.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546}
-.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}
-.input-prepend .add-on:first-child,.input-prepend .btn:first-child{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
-.input-append input,.input-append select,.input-append .uneditable-input{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}
-.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
-.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.input-prepend.input-append .btn-group:first-child{margin-left:0}
-input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
-.form-search .input-append .search-query,.form-search .input-prepend .search-query{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.form-search .input-append .search-query{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
-.form-search .input-append .btn{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
-.form-search .input-prepend .search-query{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
-.form-search .input-prepend .btn{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
-.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;*zoom:1;margin-bottom:0;vertical-align:middle}
-.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}
-.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}
-.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}
-.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}
-.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}
-.control-group{margin-bottom:10px}
-legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}
-.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0}
-.form-horizontal .control-group:after{clear:both}
-.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}
-.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}
-.form-horizontal .help-block{margin-bottom:0}
-.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px}
-.form-horizontal .form-actions{padding-left:180px}
-table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}
-.table{width:100%;margin-bottom:20px}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #ddd}
-.table th{font-weight:bold}
-.table thead th{vertical-align:bottom}
-.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}
-.table tbody+tbody{border-top:2px solid #ddd}
-.table .table{background-color:#fff}
-.table-condensed th,.table-condensed td{padding:4px 5px}
-.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}
-.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}
-.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
-.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
-.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
-.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
-.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;-moz-border-radius-bottomleft:0;border-bottom-left-radius:0}
-.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;-moz-border-radius-bottomright:0;border-bottom-right-radius:0}
-.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
-.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
-.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}
-.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}
-table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}
-.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}
-.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}
-.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}
-.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}
-.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}
-.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}
-.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}
-.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}
-.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}
-.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}
-.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}
-.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}
-.table tbody tr.success>td{background-color:#dff0d8}
-.table tbody tr.error>td{background-color:#f2dede}
-.table tbody tr.warning>td{background-color:#fcf8e3}
-.table tbody tr.info>td{background-color:#d9edf7}
-.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}
-.table-hover tbody tr.error:hover>td{background-color:#ebcccc}
-.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}
-.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}
-[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat;margin-top:1px}
-.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}
-.icon-glass{background-position:0 0}
-.icon-music{background-position:-24px 0}
-.icon-search{background-position:-48px 0}
-.icon-envelope{background-position:-72px 0}
-.icon-heart{background-position:-96px 0}
-.icon-star{background-position:-120px 0}
-.icon-star-empty{background-position:-144px 0}
-.icon-user{background-position:-168px 0}
-.icon-film{background-position:-192px 0}
-.icon-th-large{background-position:-216px 0}
-.icon-th{background-position:-240px 0}
-.icon-th-list{background-position:-264px 0}
-.icon-ok{background-position:-288px 0}
-.icon-remove{background-position:-312px 0}
-.icon-zoom-in{background-position:-336px 0}
-.icon-zoom-out{background-position:-360px 0}
-.icon-off{background-position:-384px 0}
-.icon-signal{background-position:-408px 0}
-.icon-cog{background-position:-432px 0}
-.icon-trash{background-position:-456px 0}
-.icon-home{background-position:0 -24px}
-.icon-file{background-position:-24px -24px}
-.icon-time{background-position:-48px -24px}
-.icon-road{background-position:-72px -24px}
-.icon-download-alt{background-position:-96px -24px}
-.icon-download{background-position:-120px -24px}
-.icon-upload{background-position:-144px -24px}
-.icon-inbox{background-position:-168px -24px}
-.icon-play-circle{background-position:-192px -24px}
-.icon-repeat{background-position:-216px -24px}
-.icon-refresh{background-position:-240px -24px}
-.icon-list-alt{background-position:-264px -24px}
-.icon-lock{background-position:-287px -24px}
-.icon-flag{background-position:-312px -24px}
-.icon-headphones{background-position:-336px -24px}
-.icon-volume-off{background-position:-360px -24px}
-.icon-volume-down{background-position:-384px -24px}
-.icon-volume-up{background-position:-408px -24px}
-.icon-qrcode{background-position:-432px -24px}
-.icon-barcode{background-position:-456px -24px}
-.icon-tag{background-position:0 -48px}
-.icon-tags{background-position:-25px -48px}
-.icon-book{background-position:-48px -48px}
-.icon-bookmark{background-position:-72px -48px}
-.icon-print{background-position:-96px -48px}
-.icon-camera{background-position:-120px -48px}
-.icon-font{background-position:-144px -48px}
-.icon-bold{background-position:-167px -48px}
-.icon-italic{background-position:-192px -48px}
-.icon-text-height{background-position:-216px -48px}
-.icon-text-width{background-position:-240px -48px}
-.icon-align-left{background-position:-264px -48px}
-.icon-align-center{background-position:-288px -48px}
-.icon-align-right{background-position:-312px -48px}
-.icon-align-justify{background-position:-336px -48px}
-.icon-list{background-position:-360px -48px}
-.icon-indent-left{background-position:-384px -48px}
-.icon-indent-right{background-position:-408px -48px}
-.icon-facetime-video{background-position:-432px -48px}
-.icon-picture{background-position:-456px -48px}
-.icon-pencil{background-position:0 -72px}
-.icon-map-marker{background-position:-24px -72px}
-.icon-adjust{background-position:-48px -72px}
-.icon-tint{background-position:-72px -72px}
-.icon-edit{background-position:-96px -72px}
-.icon-share{background-position:-120px -72px}
-.icon-check{background-position:-144px -72px}
-.icon-move{background-position:-168px -72px}
-.icon-step-backward{background-position:-192px -72px}
-.icon-fast-backward{background-position:-216px -72px}
-.icon-backward{background-position:-240px -72px}
-.icon-play{background-position:-264px -72px}
-.icon-pause{background-position:-288px -72px}
-.icon-stop{background-position:-312px -72px}
-.icon-forward{background-position:-336px -72px}
-.icon-fast-forward{background-position:-360px -72px}
-.icon-step-forward{background-position:-384px -72px}
-.icon-eject{background-position:-408px -72px}
-.icon-chevron-left{background-position:-432px -72px}
-.icon-chevron-right{background-position:-456px -72px}
-.icon-plus-sign{background-position:0 -96px}
-.icon-minus-sign{background-position:-24px -96px}
-.icon-remove-sign{background-position:-48px -96px}
-.icon-ok-sign{background-position:-72px -96px}
-.icon-question-sign{background-position:-96px -96px}
-.icon-info-sign{background-position:-120px -96px}
-.icon-screenshot{background-position:-144px -96px}
-.icon-remove-circle{background-position:-168px -96px}
-.icon-ok-circle{background-position:-192px -96px}
-.icon-ban-circle{background-position:-216px -96px}
-.icon-arrow-left{background-position:-240px -96px}
-.icon-arrow-right{background-position:-264px -96px}
-.icon-arrow-up{background-position:-289px -96px}
-.icon-arrow-down{background-position:-312px -96px}
-.icon-share-alt{background-position:-336px -96px}
-.icon-resize-full{background-position:-360px -96px}
-.icon-resize-small{background-position:-384px -96px}
-.icon-plus{background-position:-408px -96px}
-.icon-minus{background-position:-433px -96px}
-.icon-asterisk{background-position:-456px -96px}
-.icon-exclamation-sign{background-position:0 -120px}
-.icon-gift{background-position:-24px -120px}
-.icon-leaf{background-position:-48px -120px}
-.icon-fire{background-position:-72px -120px}
-.icon-eye-open{background-position:-96px -120px}
-.icon-eye-close{background-position:-120px -120px}
-.icon-warning-sign{background-position:-144px -120px}
-.icon-plane{background-position:-168px -120px}
-.icon-calendar{background-position:-192px -120px}
-.icon-random{background-position:-216px -120px;width:16px}
-.icon-comment{background-position:-240px -120px}
-.icon-magnet{background-position:-264px -120px}
-.icon-chevron-up{background-position:-288px -120px}
-.icon-chevron-down{background-position:-313px -119px}
-.icon-retweet{background-position:-336px -120px}
-.icon-shopping-cart{background-position:-360px -120px}
-.icon-folder-close{background-position:-384px -120px;width:16px}
-.icon-folder-open{background-position:-408px -120px;width:16px}
-.icon-resize-vertical{background-position:-432px -119px}
-.icon-resize-horizontal{background-position:-456px -118px}
-.icon-hdd{background-position:0 -144px}
-.icon-bullhorn{background-position:-24px -144px}
-.icon-bell{background-position:-48px -144px}
-.icon-certificate{background-position:-72px -144px}
-.icon-thumbs-up{background-position:-96px -144px}
-.icon-thumbs-down{background-position:-120px -144px}
-.icon-hand-right{background-position:-144px -144px}
-.icon-hand-left{background-position:-168px -144px}
-.icon-hand-up{background-position:-192px -144px}
-.icon-hand-down{background-position:-216px -144px}
-.icon-circle-arrow-right{background-position:-240px -144px}
-.icon-circle-arrow-left{background-position:-264px -144px}
-.icon-circle-arrow-up{background-position:-288px -144px}
-.icon-circle-arrow-down{background-position:-312px -144px}
-.icon-globe{background-position:-336px -144px}
-.icon-wrench{background-position:-360px -144px}
-.icon-tasks{background-position:-384px -144px}
-.icon-filter{background-position:-408px -144px}
-.icon-briefcase{background-position:-432px -144px}
-.icon-fullscreen{background-position:-456px -144px}
-.dropup,.dropdown{position:relative}
-.dropdown-toggle{*margin-bottom:-3px}
-.dropdown-toggle:active,.open .dropdown-toggle{outline:0}
-.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}
-.dropdown .caret{margin-top:8px;margin-left:2px}
-.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}
-.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
-.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}
-.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{text-decoration:none;color:#fff;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
-.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;outline:0;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
-.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}
-.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:default}
-.open{*z-index:1000}.open>.dropdown-menu{display:block}
-.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990}
-.pull-right>.dropdown-menu{right:0;left:auto}
-.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}
-.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}
-.dropdown-submenu{position:relative}
-.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;border-radius:0 6px 6px 6px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}
-.dropdown-submenu:hover>.dropdown-menu{display:block}
-.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;border-radius:5px 5px 5px 0;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}
-.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}
-.dropdown-submenu:hover>a:after{border-left-color:#fff}
-.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
-.dropdown .dropdown-menu .nav-header{padding-left:20px;padding-right:20px}
-.typeahead{z-index:1051;margin-top:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}
-.well-large{padding:24px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.well-small{padding:9px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}
-.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}
-.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}
-button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}
-.btn{display:inline-block;*display:inline;*zoom:1;padding:4px 12px;margin-bottom:0;font-size:13px;line-height:20px;text-align:center;vertical-align:middle;cursor:pointer;color:#333;text-shadow:0 1px 1px rgba(255,255,255,0.75);background-color:#f5f5f5;background-image:-moz-linear-gradient(top, #fff, #e6e6e6);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#e6e6e6));background-image:-webkit-linear-gradient(top, #fff, #e6e6e6);background-image:-o-linear-gradient(top, #fff, #e6e6e6);background-image:linear-gradient(to bottom, #fff, #e6e6e6);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e6e6e6;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);border:1px solid #ccc;*border:0;border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*margin-left:.3em;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}
-.btn:active,.btn.active{background-color:#ccc \9}
-.btn:first-child{*margin-left:0}
-.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}
-.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
-.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
-.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
-.btn-large{padding:11px 19px;font-size:16.25px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}
-.btn-small{padding:2px 10px;font-size:11.049999999999999px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}
-.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}
-.btn-mini{padding:0 6px;font-size:9.75px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.btn-block{display:block;width:100%;padding-left:0;padding-right:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
-.btn-block+.btn-block{margin-top:5px}
-input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}
-.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}
-.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;background-image:-moz-linear-gradient(top, #08c, #04c);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#04c));background-image:-webkit-linear-gradient(top, #08c, #04c);background-image:-o-linear-gradient(top, #08c, #04c);background-image:linear-gradient(to bottom, #08c, #04c);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#04c;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}
-.btn-primary:active,.btn-primary.active{background-color:#039 \9}
-.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#f89406;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}
-.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}
-.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;background-image:-moz-linear-gradient(top, #ee5f5b, #bd362f);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));background-image:-webkit-linear-gradient(top, #ee5f5b, #bd362f);background-image:-o-linear-gradient(top, #ee5f5b, #bd362f);background-image:linear-gradient(to bottom, #ee5f5b, #bd362f);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#bd362f;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}
-.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}
-.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;background-image:-moz-linear-gradient(top, #62c462, #51a351);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));background-image:-webkit-linear-gradient(top, #62c462, #51a351);background-image:-o-linear-gradient(top, #62c462, #51a351);background-image:linear-gradient(to bottom, #62c462, #51a351);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#51a351;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}
-.btn-success:active,.btn-success.active{background-color:#408140 \9}
-.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;background-image:-moz-linear-gradient(top, #5bc0de, #2f96b4);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));background-image:-webkit-linear-gradient(top, #5bc0de, #2f96b4);background-image:-o-linear-gradient(top, #5bc0de, #2f96b4);background-image:linear-gradient(to bottom, #5bc0de, #2f96b4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#2f96b4;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}
-.btn-info:active,.btn-info.active{background-color:#24748c \9}
-.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;background-image:-moz-linear-gradient(top, #444, #222);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#444), to(#222));background-image:-webkit-linear-gradient(top, #444, #222);background-image:-o-linear-gradient(top, #444, #222);background-image:linear-gradient(to bottom, #444, #222);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#222;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}
-.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}
-button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}
-button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}
-button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}
-button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}
-.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
-.btn-link{border-color:transparent;cursor:pointer;color:#08c;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}
-.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}
-.btn-group{position:relative;display:inline-block;*display:inline;*zoom:1;font-size:0;vertical-align:middle;white-space:nowrap;*margin-left:.3em}.btn-group:first-child{*margin-left:0}
-.btn-group+.btn-group{margin-left:5px}
-.btn-toolbar{font-size:0;margin-top:10px;margin-bottom:10px}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}
-.btn-group>.btn{position:relative;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.btn-group>.btn+.btn{margin-left:-1px}
-.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:13px}
-.btn-group>.btn-mini{font-size:9.75px}
-.btn-group>.btn-small{font-size:11.049999999999999px}
-.btn-group>.btn-large{font-size:16.25px}
-.btn-group>.btn:first-child{margin-left:0;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
-.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
-.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
-.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
-.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}
-.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}
-.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);*padding-top:5px;*padding-bottom:5px}
-.btn-group>.btn-mini+.dropdown-toggle{padding-left:5px;padding-right:5px;*padding-top:2px;*padding-bottom:2px}
-.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}
-.btn-group>.btn-large+.dropdown-toggle{padding-left:12px;padding-right:12px;*padding-top:7px;*padding-bottom:7px}
-.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
-.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}
-.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}
-.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}
-.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}
-.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}
-.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}
-.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}
-.btn .caret{margin-top:8px;margin-left:0}
-.btn-large .caret{margin-top:6px}
-.btn-large .caret{border-left-width:5px;border-right-width:5px;border-top-width:5px}
-.btn-mini .caret,.btn-small .caret{margin-top:8px}
-.dropup .btn-large .caret{border-bottom-width:5px}
-.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}
-.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}
-.btn-group-vertical>.btn{display:block;float:none;max-width:100%;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.btn-group-vertical>.btn+.btn{margin-left:0;margin-top:-1px}
-.btn-group-vertical>.btn:first-child{border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}
-.btn-group-vertical>.btn:last-child{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}
-.btn-group-vertical>.btn-large:first-child{border-radius:6px 6px 0 0;-webkit-border-radius:6px 6px 0 0;-moz-border-radius:6px 6px 0 0;border-radius:6px 6px 0 0}
-.btn-group-vertical>.btn-large:last-child{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
-.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.alert,.alert h4{color:#c09853}
-.alert h4{margin:0}
-.alert .close{position:relative;top:-2px;right:-21px;line-height:20px}
-.alert-success{background-color:#dff0d8;border-color:#d6e9c6;color:#468847}
-.alert-success h4{color:#468847}
-.alert-danger,.alert-error{background-color:#f2dede;border-color:#eed3d7;color:#b94a48}
-.alert-danger h4,.alert-error h4{color:#b94a48}
-.alert-info{background-color:#d9edf7;border-color:#bce8f1;color:#3a87ad}
-.alert-info h4{color:#3a87ad}
-.alert-block{padding-top:14px;padding-bottom:14px}
-.alert-block>p,.alert-block>ul{margin-bottom:0}
-.alert-block p+p{margin-top:5px}
-.nav{margin-left:0;margin-bottom:20px;list-style:none}
-.nav>li>a{display:block}
-.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eee}
-.nav>li>a>img{max-width:none}
-.nav>.pull-right{float:right}
-.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:20px;color:#999;text-shadow:0 1px 0 rgba(255,255,255,0.5);text-transform:uppercase}
-.nav li+.nav-header{margin-top:9px}
-.nav-list{padding-left:15px;padding-right:15px;margin-bottom:0}
-.nav-list>li>a,.nav-list .nav-header{margin-left:-15px;margin-right:-15px;text-shadow:0 1px 0 rgba(255,255,255,0.5)}
-.nav-list>li>a{padding:3px 15px}
-.nav-list>.active>a,.nav-list>.active>a:hover,.nav-list>.active>a:focus{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.2);background-color:#08c}
-.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{margin-right:2px}
-.nav-list .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
-.nav-tabs,.nav-pills{*zoom:1}.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;content:"";line-height:0}
-.nav-tabs:after,.nav-pills:after{clear:both}
-.nav-tabs>li,.nav-pills>li{float:left}
-.nav-tabs>li>a,.nav-pills>li>a{padding-right:12px;padding-left:12px;margin-right:2px;line-height:14px}
-.nav-tabs{border-bottom:1px solid #ddd}
-.nav-tabs>li{margin-bottom:-1px}
-.nav-tabs>li>a{padding-top:8px;padding-bottom:8px;line-height:20px;border:1px solid transparent;border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover,.nav-tabs>li>a:focus{border-color:#eee #eee #ddd}
-.nav-tabs>.active>a,.nav-tabs>.active>a:hover,.nav-tabs>.active>a:focus{color:#555;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent;cursor:default}
-.nav-pills>li>a{padding-top:8px;padding-bottom:8px;margin-top:2px;margin-bottom:2px;border-radius:5px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}
-.nav-pills>.active>a,.nav-pills>.active>a:hover,.nav-pills>.active>a:focus{color:#fff;background-color:#08c}
-.nav-stacked>li{float:none}
-.nav-stacked>li>a{margin-right:0}
-.nav-tabs.nav-stacked{border-bottom:0}
-.nav-tabs.nav-stacked>li>a{border:1px solid #ddd;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.nav-tabs.nav-stacked>li:first-child>a{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
-.nav-tabs.nav-stacked>li:last-child>a{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
-.nav-tabs.nav-stacked>li>a:hover,.nav-tabs.nav-stacked>li>a:focus{border-color:#ddd;z-index:2}
-.nav-pills.nav-stacked>li>a{margin-bottom:3px}
-.nav-pills.nav-stacked>li:last-child>a{margin-bottom:1px}
-.nav-tabs .dropdown-menu{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
-.nav-pills .dropdown-menu{border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.nav .dropdown-toggle .caret{border-top-color:#08c;border-bottom-color:#08c;margin-top:6px}
-.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#005580;border-bottom-color:#005580}
-.nav-tabs .dropdown-toggle .caret{margin-top:8px}
-.nav .active .dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
-.nav-tabs .active .dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}
-.nav>.dropdown.active>a:hover,.nav>.dropdown.active>a:focus{cursor:pointer}
-.nav-tabs .open .dropdown-toggle,.nav-pills .open .dropdown-toggle,.nav>li.dropdown.open.active>a:hover,.nav>li.dropdown.open.active>a:focus{color:#fff;background-color:#999;border-color:#999}
-.nav li.dropdown.open .caret,.nav li.dropdown.open.active .caret,.nav li.dropdown.open a:hover .caret,.nav li.dropdown.open a:focus .caret{border-top-color:#fff;border-bottom-color:#fff;opacity:1;filter:alpha(opacity=100)}
-.tabs-stacked .open>a:hover,.tabs-stacked .open>a:focus{border-color:#999}
-.tabbable{*zoom:1}.tabbable:before,.tabbable:after{display:table;content:"";line-height:0}
-.tabbable:after{clear:both}
-.tab-content{overflow:auto}
-.tabs-below>.nav-tabs,.tabs-right>.nav-tabs,.tabs-left>.nav-tabs{border-bottom:0}
-.tab-content>.tab-pane,.pill-content>.pill-pane{display:none}
-.tab-content>.active,.pill-content>.active{display:block}
-.tabs-below>.nav-tabs{border-top:1px solid #ddd}
-.tabs-below>.nav-tabs>li{margin-top:-1px;margin-bottom:0}
-.tabs-below>.nav-tabs>li>a{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-bottom-color:transparent;border-top-color:#ddd}
-.tabs-below>.nav-tabs>.active>a,.tabs-below>.nav-tabs>.active>a:hover,.tabs-below>.nav-tabs>.active>a:focus{border-color:transparent #ddd #ddd #ddd}
-.tabs-left>.nav-tabs>li,.tabs-right>.nav-tabs>li{float:none}
-.tabs-left>.nav-tabs>li>a,.tabs-right>.nav-tabs>li>a{min-width:74px;margin-right:0;margin-bottom:3px}
-.tabs-left>.nav-tabs{float:left;margin-right:19px;border-right:1px solid #ddd}
-.tabs-left>.nav-tabs>li>a{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
-.tabs-left>.nav-tabs>li>a:hover,.tabs-left>.nav-tabs>li>a:focus{border-color:#eee #ddd #eee #eee}
-.tabs-left>.nav-tabs .active>a,.tabs-left>.nav-tabs .active>a:hover,.tabs-left>.nav-tabs .active>a:focus{border-color:#ddd transparent #ddd #ddd;*border-right-color:#fff}
-.tabs-right>.nav-tabs{float:right;margin-left:19px;border-left:1px solid #ddd}
-.tabs-right>.nav-tabs>li>a{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.tabs-right>.nav-tabs>li>a:hover,.tabs-right>.nav-tabs>li>a:focus{border-color:#eee #eee #eee #ddd}
-.tabs-right>.nav-tabs .active>a,.tabs-right>.nav-tabs .active>a:hover,.tabs-right>.nav-tabs .active>a:focus{border-color:#ddd #ddd #ddd transparent;*border-left-color:#fff}
-.nav>.disabled>a{color:#999}
-.nav>.disabled>a:hover,.nav>.disabled>a:focus{text-decoration:none;background-color:transparent;cursor:default}
-.navbar{overflow:visible;margin-bottom:20px;*position:relative;*z-index:2}
-.navbar-inner{min-height:36px;padding-left:20px;padding-right:20px;background-color:#fafafa;background-image:-moz-linear-gradient(top, #fff, #f2f2f2);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#f2f2f2));background-image:-webkit-linear-gradient(top, #fff, #f2f2f2);background-image:-o-linear-gradient(top, #fff, #f2f2f2);background-image:linear-gradient(to bottom, #fff, #f2f2f2);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0);border:1px solid #d4d4d4;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 4px rgba(0,0,0,0.065);-moz-box-shadow:0 1px 4px rgba(0,0,0,0.065);box-shadow:0 1px 4px rgba(0,0,0,0.065);*zoom:1}.navbar-inner:before,.navbar-inner:after{display:table;content:"";line-height:0}
-.navbar-inner:after{clear:both}
-.navbar .container{width:auto}
-.nav-collapse.collapse{height:auto;overflow:visible}
-.navbar .brand{float:left;display:block;padding:8px 20px 8px;margin-left:-20px;font-size:20px;font-weight:200;color:#777;text-shadow:0 1px 0 #fff}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none}
-.navbar-text{margin-bottom:0;line-height:36px;color:#777}
-.navbar-link{color:#777}.navbar-link:hover,.navbar-link:focus{color:#333}
-.navbar .divider-vertical{height:36px;margin:0 9px;border-left:1px solid #f2f2f2;border-right:1px solid #fff}
-.navbar .btn,.navbar .btn-group{margin-top:3px}
-.navbar .btn-group .btn,.navbar .input-prepend .btn,.navbar .input-append .btn,.navbar .input-prepend .btn-group,.navbar .input-append .btn-group{margin-top:0}
-.navbar-form{margin-bottom:0;*zoom:1}.navbar-form:before,.navbar-form:after{display:table;content:"";line-height:0}
-.navbar-form:after{clear:both}
-.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:3px}
-.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}
-.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}
-.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0}
-.navbar-search{position:relative;float:left;margin-top:3px;margin-bottom:0}.navbar-search .search-query{margin-bottom:0;padding:4px 14px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
-.navbar-static-top{position:static;margin-bottom:0}.navbar-static-top .navbar-inner{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030;margin-bottom:0}
-.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{border-width:0 0 1px}
-.navbar-fixed-bottom .navbar-inner{border-width:1px 0 0}
-.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding-left:0;padding-right:0;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}
-.navbar-fixed-top{top:0}
-.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{-webkit-box-shadow:0 1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 1px 10px rgba(0,0,0,.1);box-shadow:0 1px 10px rgba(0,0,0,.1)}
-.navbar-fixed-bottom{bottom:0}.navbar-fixed-bottom .navbar-inner{-webkit-box-shadow:0 -1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,.1);box-shadow:0 -1px 10px rgba(0,0,0,.1)}
-.navbar .nav{position:relative;left:0;display:block;float:left;margin:0 10px 0 0}
-.navbar .nav.pull-right{float:right;margin-right:0}
-.navbar .nav>li{float:left}
-.navbar .nav>li>a{float:none;padding:8px 15px 8px;color:#777;text-decoration:none;text-shadow:0 1px 0 #fff}
-.navbar .nav .dropdown-toggle .caret{margin-top:8px}
-.navbar .nav>li>a:focus,.navbar .nav>li>a:hover{background-color:transparent;color:#333;text-decoration:none}
-.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#555;text-decoration:none;background-color:#e5e5e5;-webkit-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);-moz-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);box-shadow:inset 0 3px 8px rgba(0,0,0,0.125)}
-.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-left:5px;margin-right:5px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#ededed;background-image:-moz-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5));background-image:-webkit-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-o-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:linear-gradient(to bottom, #f2f2f2, #e5e5e5);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0);border-color:#e5e5e5 #e5e5e5 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e5e5e5;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075)}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#fff;background-color:#e5e5e5;*background-color:#d9d9d9}
-.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#ccc \9}
-.navbar .btn-navbar .icon-bar{display:block;width:18px;height:2px;background-color:#f5f5f5;-webkit-border-radius:1px;-moz-border-radius:1px;border-radius:1px;-webkit-box-shadow:0 1px 0 rgba(0,0,0,0.25);-moz-box-shadow:0 1px 0 rgba(0,0,0,0.25);box-shadow:0 1px 0 rgba(0,0,0,0.25)}
-.btn-navbar .icon-bar+.icon-bar{margin-top:3px}
-.navbar .nav>li>.dropdown-menu:before{content:'';display:inline-block;border-left:7px solid transparent;border-right:7px solid transparent;border-bottom:7px solid #ccc;border-bottom-color:rgba(0,0,0,0.2);position:absolute;top:-7px;left:9px}
-.navbar .nav>li>.dropdown-menu:after{content:'';display:inline-block;border-left:6px solid transparent;border-right:6px solid transparent;border-bottom:6px solid #fff;position:absolute;top:-6px;left:10px}
-.navbar-fixed-bottom .nav>li>.dropdown-menu:before{border-top:7px solid #ccc;border-top-color:rgba(0,0,0,0.2);border-bottom:0;bottom:-7px;top:auto}
-.navbar-fixed-bottom .nav>li>.dropdown-menu:after{border-top:6px solid #fff;border-bottom:0;bottom:-6px;top:auto}
-.navbar .nav li.dropdown>a:hover .caret,.navbar .nav li.dropdown>a:focus .caret{border-top-color:#333;border-bottom-color:#333}
-.navbar .nav li.dropdown.open>.dropdown-toggle,.navbar .nav li.dropdown.active>.dropdown-toggle,.navbar .nav li.dropdown.open.active>.dropdown-toggle{background-color:#e5e5e5;color:#555}
-.navbar .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#777;border-bottom-color:#777}
-.navbar .nav li.dropdown.open>.dropdown-toggle .caret,.navbar .nav li.dropdown.active>.dropdown-toggle .caret,.navbar .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}
-.navbar .pull-right>li>.dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right{left:auto;right:0}.navbar .pull-right>li>.dropdown-menu:before,.navbar .nav>li>.dropdown-menu.pull-right:before{left:auto;right:12px}
-.navbar .pull-right>li>.dropdown-menu:after,.navbar .nav>li>.dropdown-menu.pull-right:after{left:auto;right:13px}
-.navbar .pull-right>li>.dropdown-menu .dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right .dropdown-menu{left:auto;right:100%;margin-left:0;margin-right:-1px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
-.navbar-inverse .navbar-inner{background-color:#1b1b1b;background-image:-moz-linear-gradient(top, #222, #111);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#222), to(#111));background-image:-webkit-linear-gradient(top, #222, #111);background-image:-o-linear-gradient(top, #222, #111);background-image:linear-gradient(to bottom, #222, #111);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0);border-color:#252525}
-.navbar-inverse .brand,.navbar-inverse .nav>li>a{color:#999;text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.navbar-inverse .brand:hover,.navbar-inverse .nav>li>a:hover,.navbar-inverse .brand:focus,.navbar-inverse .nav>li>a:focus{color:#fff}
-.navbar-inverse .brand{color:#999}
-.navbar-inverse .navbar-text{color:#999}
-.navbar-inverse .nav>li>a:focus,.navbar-inverse .nav>li>a:hover{background-color:transparent;color:#fff}
-.navbar-inverse .nav .active>a,.navbar-inverse .nav .active>a:hover,.navbar-inverse .nav .active>a:focus{color:#fff;background-color:#111}
-.navbar-inverse .navbar-link{color:#999}.navbar-inverse .navbar-link:hover,.navbar-inverse .navbar-link:focus{color:#fff}
-.navbar-inverse .divider-vertical{border-left-color:#111;border-right-color:#222}
-.navbar-inverse .nav li.dropdown.open>.dropdown-toggle,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle{background-color:#111;color:#fff}
-.navbar-inverse .nav li.dropdown>a:hover .caret,.navbar-inverse .nav li.dropdown>a:focus .caret{border-top-color:#fff;border-bottom-color:#fff}
-.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#999;border-bottom-color:#999}
-.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
-.navbar-inverse .navbar-search .search-query{color:#fff;background-color:#515151;border-color:#111;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-webkit-transition:none;-moz-transition:none;-o-transition:none;transition:none}.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#ccc}
-.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#ccc}
-.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#ccc}
-.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#333;text-shadow:0 1px 0 #fff;background-color:#fff;border:0;-webkit-box-shadow:0 0 3px rgba(0,0,0,0.15);-moz-box-shadow:0 0 3px rgba(0,0,0,0.15);box-shadow:0 0 3px rgba(0,0,0,0.15);outline:0}
-.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e0e0e;background-image:-moz-linear-gradient(top, #151515, #040404);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404));background-image:-webkit-linear-gradient(top, #151515, #040404);background-image:-o-linear-gradient(top, #151515, #040404);background-image:linear-gradient(to bottom, #151515, #040404);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0);border-color:#040404 #040404 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#040404;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#040404;*background-color:#000}
-.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}
-.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;*zoom:1;text-shadow:0 1px 0 #fff}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}
-.breadcrumb>.active{color:#999}
-.pagination{margin:20px 0}
-.pagination ul{display:inline-block;*display:inline;*zoom:1;margin-left:0;margin-bottom:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}
-.pagination ul>li{display:inline}
-.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}
-.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}
-.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}
-.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;background-color:transparent;cursor:default}
-.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
-.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
-.pagination-centered{text-align:center}
-.pagination-right{text-align:right}
-.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:16.25px}
-.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
-.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
-.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-top-left-radius:3px;-moz-border-radius-topleft:3px;border-top-left-radius:3px;-webkit-border-bottom-left-radius:3px;-moz-border-radius-bottomleft:3px;border-bottom-left-radius:3px}
-.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;-moz-border-radius-topright:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;-moz-border-radius-bottomright:3px;border-bottom-right-radius:3px}
-.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.049999999999999px}
-.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:9.75px}
-.pager{margin:20px 0;list-style:none;text-align:center;*zoom:1}.pager:before,.pager:after{display:table;content:"";line-height:0}
-.pager:after{clear:both}
-.pager li{display:inline}
-.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
-.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}
-.pager .next>a,.pager .next>span{float:right}
-.pager .previous>a,.pager .previous>span{float:left}
-.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;background-color:#fff;cursor:default}
-.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}
-.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}
-.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box;outline:none}.modal.fade{-webkit-transition:opacity .3s linear, top .3s ease-out;-moz-transition:opacity .3s linear, top .3s ease-out;-o-transition:opacity .3s linear, top .3s ease-out;transition:opacity .3s linear, top .3s ease-out;top:-25%}
-.modal.fade.in{top:10%}
-.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}
-.modal-header h3{margin:0;line-height:30px}
-.modal-body{position:relative;overflow-y:auto;max-height:400px;padding:15px}
-.modal-form{margin-bottom:0}
-.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff;*zoom:1}.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0}
-.modal-footer:after{clear:both}
-.modal-footer .btn+.btn{margin-left:5px;margin-bottom:0}
-.modal-footer .btn-group .btn+.btn{margin-left:-1px}
-.modal-footer .btn-block+.btn-block{margin-left:0}
-.tooltip{position:absolute;z-index:1030;display:block;visibility:visible;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0)}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}
-.tooltip.top{margin-top:-3px;padding:5px 0}
-.tooltip.right{margin-left:3px;padding:0 5px}
-.tooltip.bottom{margin-top:3px;padding:5px 0}
-.tooltip.left{margin-left:-3px;padding:0 5px}
-.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}
-.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}
-.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}
-.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}
-.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}
-.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;background-color:#fff;-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);white-space:normal}.popover.top{margin-top:-10px}
-.popover.right{margin-left:10px}
-.popover.bottom{margin-top:10px}
-.popover.left{margin-left:-10px}
-.popover-title{margin:0;padding:8px 14px;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}
-.popover-content{padding:9px 14px}
-.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}
-.popover .arrow{border-width:11px}
-.popover .arrow:after{border-width:10px;content:""}
-.popover.top .arrow{left:50%;margin-left:-11px;border-bottom-width:0;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);bottom:-11px}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-bottom-width:0;border-top-color:#fff}
-.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-left-width:0;border-right-color:#999;border-right-color:rgba(0,0,0,0.25)}.popover.right .arrow:after{left:1px;bottom:-10px;border-left-width:0;border-right-color:#fff}
-.popover.bottom .arrow{left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);top:-11px}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-top-width:0;border-bottom-color:#fff}
-.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,0.25)}.popover.left .arrow:after{right:1px;border-right-width:0;border-left-color:#fff;bottom:-10px}
-.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0}
-.thumbnails:after{clear:both}
-.row-fluid .thumbnails{margin-left:0}
-.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}
-.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}
-a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}
-.thumbnail>img{display:block;max-width:100%;margin-left:auto;margin-right:auto}
-.thumbnail .caption{padding:9px;color:#555}
-.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}
-.media,.media .media{margin-top:15px}
-.media:first-child{margin-top:0}
-.media-object{display:block}
-.media-heading{margin:0 0 5px}
-.media>.pull-left{margin-right:10px}
-.media>.pull-right{margin-left:10px}
-.media-list{margin-left:0;list-style:none}
-.label,.badge{display:inline-block;padding:2px 4px;font-size:10.998px;font-weight:bold;line-height:14px;color:#fff;vertical-align:baseline;white-space:nowrap;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#999}
-.label{border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.badge{padding-left:9px;padding-right:9px;border-radius:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}
-.label:empty,.badge:empty{display:none}
-a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}
-.label-important,.badge-important{background-color:#b94a48}
-.label-important[href],.badge-important[href]{background-color:#953b39}
-.label-warning,.badge-warning{background-color:#f89406}
-.label-warning[href],.badge-warning[href]{background-color:#c67605}
-.label-success,.badge-success{background-color:#468847}
-.label-success[href],.badge-success[href]{background-color:#356635}
-.label-info,.badge-info{background-color:#3a87ad}
-.label-info[href],.badge-info[href]{background-color:#2d6987}
-.label-inverse,.badge-inverse{background-color:#333}
-.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}
-.btn .label,.btn .badge{position:relative;top:-1px}
-.btn-mini .label,.btn-mini .badge{top:0}
-@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0} to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.progress .bar{width:0;height:100%;color:#fff;float:left;font-size:12px;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top, #149bdf, #0480be);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));background-image:-webkit-linear-gradient(top, #149bdf, #0480be);background-image:-o-linear-gradient(top, #149bdf, #0480be);background-image:linear-gradient(to bottom, #149bdf, #0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}
-.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15)}
-.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}
-.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}
-.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0)}
-.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
-.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0)}
-.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
-.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0)}
-.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
-.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0)}
-.progress-warning.progress-striped .bar,.progress-striped .bar-warning{background-color:#fbb450;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
-.accordion{margin-bottom:20px}
-.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.accordion-heading{border-bottom:0}
-.accordion-heading .accordion-toggle{display:block;padding:8px 15px}
-.accordion-toggle{cursor:pointer}
-.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5}
-.carousel{position:relative;margin-bottom:20px;line-height:1}
-.carousel-inner{overflow:hidden;width:100%;position:relative}
-.carousel-inner>.item{display:none;position:relative;-webkit-transition:.6s ease-in-out left;-moz-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1}
-.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}
-.carousel-inner>.active{left:0}
-.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}
-.carousel-inner>.next{left:100%}
-.carousel-inner>.prev{left:-100%}
-.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}
-.carousel-inner>.active.left{left:-100%}
-.carousel-inner>.active.right{left:100%}
-.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#fff;text-align:center;background:#222;border:3px solid #fff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:.5;filter:alpha(opacity=50)}.carousel-control.right{left:auto;right:15px}
-.carousel-control:hover,.carousel-control:focus{color:#fff;text-decoration:none;opacity:.9;filter:alpha(opacity=90)}
-.carousel-indicators{position:absolute;top:15px;right:15px;z-index:5;margin:0;list-style:none}.carousel-indicators li{display:block;float:left;width:10px;height:10px;margin-left:5px;text-indent:-999px;background-color:#ccc;background-color:rgba(255,255,255,0.25);border-radius:5px}
-.carousel-indicators .active{background-color:#fff}
-.carousel-caption{position:absolute;left:0;right:0;bottom:0;padding:15px;background:#333;background:rgba(0,0,0,0.75)}
-.carousel-caption h4,.carousel-caption p{color:#fff;line-height:20px}
-.carousel-caption h4{margin:0 0 5px}
-.carousel-caption p{margin-bottom:0}
-.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:30px;color:inherit;background-color:#eee;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;color:inherit;letter-spacing:-1px}
-.hero-unit li{line-height:30px}
-.pull-right{float:right}
-.pull-left{float:left}
-.hide{display:none}
-.show{display:block}
-.invisible{visibility:hidden}
-.affix{position:fixed}
-@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}
-.visible-phone{display:none !important}
-.visible-tablet{display:none !important}
-.hidden-desktop{display:none !important}
-.visible-desktop{display:inherit !important}
-@media (min-width:768px) and (max-width:979px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-tablet{display:inherit !important} .hidden-tablet{display:none !important}}@media (max-width:767px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-phone{display:inherit !important} .hidden-phone{display:none !important}}.visible-print{display:none !important}
-@media print{.visible-print{display:inherit !important} .hidden-print{display:none !important}}@media (min-width:1200px){.row{margin-left:-30px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0} .row:after{clear:both} [class*="span"]{float:left;min-height:1px;margin-left:30px} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:1170px} .span12{width:1170px} .span11{width:1070px} .span10{width:970px} .span9{width:870px} .span8{width:770px} .span7{width:670px} .span6{width:570px} .span5{width:470px} .span4{width:370px} .span3{width:270px} .span2{width:170px} .span1{width:70px} .offset12{margin-left:1230px} .offset11{margin-left:1130px} .offset10{margin-left:1030px} .offset9{margin-left:930px} .offset8{margin-left:830px} .offset7{margin-left:730px} .offset6{margin-left:630px} .offset5{margin-left:530px} .offset4{margin-left:430px} .offset3{margin-left:330px} .offset2{margin-left:230px} .offset1{margin-left:130px} .row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0} .row-fluid:after{clear:both} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.564102564102564%;*margin-left:2.5109110747408616%} .row-fluid [class*="span"]:first-child{margin-left:0} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.564102564102564%} .row-fluid .span12{width:100%;*width:99.94680851063829%} .row-fluid .span11{width:91.45299145299145%;*width:91.39979996362975%} .row-fluid .span10{width:82.90598290598291%;*width:82.8527914166212%} .row-fluid .span9{width:74.35897435897436%;*width:74.30578286961266%} .row-fluid .span8{width:65.81196581196582%;*width:65.75877432260411%} .row-fluid .span7{width:57.26495726495726%;*width:57.21176577559556%} .row-fluid .span6{width:48.717948717948715%;*width:48.664757228587014%} .row-fluid .span5{width:40.17094017094017%;*width:40.11774868157847%} .row-fluid .span4{width:31.623931623931625%;*width:31.570740134569924%} .row-fluid .span3{width:23.076923076923077%;*width:23.023731587561375%} .row-fluid .span2{width:14.52991452991453%;*width:14.476723040552828%} .row-fluid .span1{width:5.982905982905983%;*width:5.929714493544281%} .row-fluid .offset12{margin-left:105.12820512820512%;*margin-left:105.02182214948171%} .row-fluid .offset12:first-child{margin-left:102.56410256410257%;*margin-left:102.45771958537915%} .row-fluid .offset11{margin-left:96.58119658119658%;*margin-left:96.47481360247316%} .row-fluid .offset11:first-child{margin-left:94.01709401709402%;*margin-left:93.91071103837061%} .row-fluid .offset10{margin-left:88.03418803418803%;*margin-left:87.92780505546462%} .row-fluid .offset10:first-child{margin-left:85.47008547008548%;*margin-left:85.36370249136206%} .row-fluid .offset9{margin-left:79.48717948717949%;*margin-left:79.38079650845607%} .row-fluid .offset9:first-child{margin-left:76.92307692307693%;*margin-left:76.81669394435352%} .row-fluid .offset8{margin-left:70.94017094017094%;*margin-left:70.83378796144753%} .row-fluid .offset8:first-child{margin-left:68.37606837606839%;*margin-left:68.26968539734497%} .row-fluid .offset7{margin-left:62.393162393162385%;*margin-left:62.28677941443899%} .row-fluid .offset7:first-child{margin-left:59.82905982905982%;*margin-left:59.72267685033642%} .row-fluid .offset6{margin-left:53.84615384615384%;*margin-left:53.739770867430444%} .row-fluid .offset6:first-child{margin-left:51.28205128205128%;*margin-left:51.175668303327875%} .row-fluid .offset5{margin-left:45.299145299145295%;*margin-left:45.1927623204219%} .row-fluid .offset5:first-child{margin-left:42.73504273504273%;*margin-left:42.62865975631933%} .row-fluid .offset4{margin-left:36.75213675213675%;*margin-left:36.645753773413354%} .row-fluid .offset4:first-child{margin-left:34.18803418803419%;*margin-left:34.081651209310785%} .row-fluid .offset3{margin-left:28.205128205128204%;*margin-left:28.0987452264048%} .row-fluid .offset3:first-child{margin-left:25.641025641025642%;*margin-left:25.53464266230224%} .row-fluid .offset2{margin-left:19.65811965811966%;*margin-left:19.551736679396257%} .row-fluid .offset2:first-child{margin-left:17.094017094017094%;*margin-left:16.98763411529369%} .row-fluid .offset1{margin-left:11.11111111111111%;*margin-left:11.004728132387708%} .row-fluid .offset1:first-child{margin-left:8.547008547008547%;*margin-left:8.440625568285142%} input,textarea,.uneditable-input{margin-left:0} .controls-row [class*="span"]+[class*="span"]{margin-left:30px} input.span12,textarea.span12,.uneditable-input.span12{width:1156px} input.span11,textarea.span11,.uneditable-input.span11{width:1056px} input.span10,textarea.span10,.uneditable-input.span10{width:956px} input.span9,textarea.span9,.uneditable-input.span9{width:856px} input.span8,textarea.span8,.uneditable-input.span8{width:756px} input.span7,textarea.span7,.uneditable-input.span7{width:656px} input.span6,textarea.span6,.uneditable-input.span6{width:556px} input.span5,textarea.span5,.uneditable-input.span5{width:456px} input.span4,textarea.span4,.uneditable-input.span4{width:356px} input.span3,textarea.span3,.uneditable-input.span3{width:256px} input.span2,textarea.span2,.uneditable-input.span2{width:156px} input.span1,textarea.span1,.uneditable-input.span1{width:56px} .thumbnails{margin-left:-30px} .thumbnails>li{margin-left:30px} .row-fluid .thumbnails{margin-left:0}}@media (min-width:768px) and (max-width:979px){.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0} .row:after{clear:both} [class*="span"]{float:left;min-height:1px;margin-left:20px} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:724px} .span12{width:724px} .span11{width:662px} .span10{width:600px} .span9{width:538px} .span8{width:476px} .span7{width:414px} .span6{width:352px} .span5{width:290px} .span4{width:228px} .span3{width:166px} .span2{width:104px} .span1{width:42px} .offset12{margin-left:764px} .offset11{margin-left:702px} .offset10{margin-left:640px} .offset9{margin-left:578px} .offset8{margin-left:516px} .offset7{margin-left:454px} .offset6{margin-left:392px} .offset5{margin-left:330px} .offset4{margin-left:268px} .offset3{margin-left:206px} .offset2{margin-left:144px} .offset1{margin-left:82px} .row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0} .row-fluid:after{clear:both} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.7624309392265194%;*margin-left:2.709239449864817%} .row-fluid [class*="span"]:first-child{margin-left:0} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.7624309392265194%} .row-fluid .span12{width:100%;*width:99.94680851063829%} .row-fluid .span11{width:91.43646408839778%;*width:91.38327259903608%} .row-fluid .span10{width:82.87292817679558%;*width:82.81973668743387%} .row-fluid .span9{width:74.30939226519337%;*width:74.25620077583166%} .row-fluid .span8{width:65.74585635359117%;*width:65.69266486422946%} .row-fluid .span7{width:57.18232044198895%;*width:57.12912895262725%} .row-fluid .span6{width:48.61878453038674%;*width:48.56559304102504%} .row-fluid .span5{width:40.05524861878453%;*width:40.00205712942283%} .row-fluid .span4{width:31.491712707182323%;*width:31.43852121782062%} .row-fluid .span3{width:22.92817679558011%;*width:22.87498530621841%} .row-fluid .span2{width:14.3646408839779%;*width:14.311449394616199%} .row-fluid .span1{width:5.801104972375691%;*width:5.747913483013988%} .row-fluid .offset12{margin-left:105.52486187845304%;*margin-left:105.41847889972962%} .row-fluid .offset12:first-child{margin-left:102.76243093922652%;*margin-left:102.6560479605031%} .row-fluid .offset11{margin-left:96.96132596685082%;*margin-left:96.8549429881274%} .row-fluid .offset11:first-child{margin-left:94.1988950276243%;*margin-left:94.09251204890089%} .row-fluid .offset10{margin-left:88.39779005524862%;*margin-left:88.2914070765252%} .row-fluid .offset10:first-child{margin-left:85.6353591160221%;*margin-left:85.52897613729868%} .row-fluid .offset9{margin-left:79.8342541436464%;*margin-left:79.72787116492299%} .row-fluid .offset9:first-child{margin-left:77.07182320441989%;*margin-left:76.96544022569647%} .row-fluid .offset8{margin-left:71.2707182320442%;*margin-left:71.16433525332079%} .row-fluid .offset8:first-child{margin-left:68.50828729281768%;*margin-left:68.40190431409427%} .row-fluid .offset7{margin-left:62.70718232044199%;*margin-left:62.600799341718584%} .row-fluid .offset7:first-child{margin-left:59.94475138121547%;*margin-left:59.838368402492065%} .row-fluid .offset6{margin-left:54.14364640883978%;*margin-left:54.037263430116376%} .row-fluid .offset6:first-child{margin-left:51.38121546961326%;*margin-left:51.27483249088986%} .row-fluid .offset5{margin-left:45.58011049723757%;*margin-left:45.47372751851417%} .row-fluid .offset5:first-child{margin-left:42.81767955801105%;*margin-left:42.71129657928765%} .row-fluid .offset4{margin-left:37.01657458563536%;*margin-left:36.91019160691196%} .row-fluid .offset4:first-child{margin-left:34.25414364640884%;*margin-left:34.14776066768544%} .row-fluid .offset3{margin-left:28.45303867403315%;*margin-left:28.346655695309746%} .row-fluid .offset3:first-child{margin-left:25.69060773480663%;*margin-left:25.584224756083227%} .row-fluid .offset2{margin-left:19.88950276243094%;*margin-left:19.783119783707537%} .row-fluid .offset2:first-child{margin-left:17.12707182320442%;*margin-left:17.02068884448102%} .row-fluid .offset1{margin-left:11.32596685082873%;*margin-left:11.219583872105325%} .row-fluid .offset1:first-child{margin-left:8.56353591160221%;*margin-left:8.457152932878806%} input,textarea,.uneditable-input{margin-left:0} .controls-row [class*="span"]+[class*="span"]{margin-left:20px} input.span12,textarea.span12,.uneditable-input.span12{width:710px} input.span11,textarea.span11,.uneditable-input.span11{width:648px} input.span10,textarea.span10,.uneditable-input.span10{width:586px} input.span9,textarea.span9,.uneditable-input.span9{width:524px} input.span8,textarea.span8,.uneditable-input.span8{width:462px} input.span7,textarea.span7,.uneditable-input.span7{width:400px} input.span6,textarea.span6,.uneditable-input.span6{width:338px} input.span5,textarea.span5,.uneditable-input.span5{width:276px} input.span4,textarea.span4,.uneditable-input.span4{width:214px} input.span3,textarea.span3,.uneditable-input.span3{width:152px} input.span2,textarea.span2,.uneditable-input.span2{width:90px} input.span1,textarea.span1,.uneditable-input.span1{width:28px}}@media (max-width:767px){body{padding-left:20px;padding-right:20px} .navbar-fixed-top,.navbar-fixed-bottom,.navbar-static-top{margin-left:-20px;margin-right:-20px} .container-fluid{padding:0} .dl-horizontal dt{float:none;clear:none;width:auto;text-align:left} .dl-horizontal dd{margin-left:0} .container{width:auto} .row-fluid{width:100%} .row,.thumbnails{margin-left:0} .thumbnails>li{float:none;margin-left:0} [class*="span"],.uneditable-input[class*="span"],.row-fluid [class*="span"]{float:none;display:block;width:100%;margin-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .span12,.row-fluid .span12{width:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .row-fluid [class*="offset"]:first-child{margin-left:0} .input-large,.input-xlarge,.input-xxlarge,input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .input-prepend input,.input-append input,.input-prepend input[class*="span"],.input-append input[class*="span"]{display:inline-block;width:auto} .controls-row [class*="span"]+[class*="span"]{margin-left:0} .modal{position:fixed;top:20px;left:20px;right:20px;width:auto;margin:0}.modal.fade{top:-100px} .modal.fade.in{top:20px}}@media (max-width:480px){.nav-collapse{-webkit-transform:translate3d(0, 0, 0)} .page-header h1 small{display:block;line-height:20px} input[type="checkbox"],input[type="radio"]{border:1px solid #ccc} .form-horizontal .control-label{float:none;width:auto;padding-top:0;text-align:left} .form-horizontal .controls{margin-left:0} .form-horizontal .control-list{padding-top:0} .form-horizontal .form-actions{padding-left:10px;padding-right:10px} .media .pull-left,.media .pull-right{float:none;display:block;margin-bottom:10px} .media-object{margin-right:0;margin-left:0} .modal{top:10px;left:10px;right:10px} .modal-header .close{padding:10px;margin:-10px} .carousel-caption{position:static}}@media (max-width:979px){body{padding-top:0} .navbar-fixed-top,.navbar-fixed-bottom{position:static} .navbar-fixed-top{margin-bottom:20px} .navbar-fixed-bottom{margin-top:20px} .navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding:5px} .navbar .container{width:auto;padding:0} .navbar .brand{padding-left:10px;padding-right:10px;margin:0 0 0 -5px} .nav-collapse{clear:both} .nav-collapse .nav{float:none;margin:0 0 10px} .nav-collapse .nav>li{float:none} .nav-collapse .nav>li>a{margin-bottom:2px} .nav-collapse .nav>.divider-vertical{display:none} .nav-collapse .nav .nav-header{color:#777;text-shadow:none} .nav-collapse .nav>li>a,.nav-collapse .dropdown-menu a{padding:9px 15px;font-weight:bold;color:#777;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px} .nav-collapse .btn{padding:4px 10px 4px;font-weight:normal;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px} .nav-collapse .dropdown-menu li+li a{margin-bottom:2px} .nav-collapse .nav>li>a:hover,.nav-collapse .nav>li>a:focus,.nav-collapse .dropdown-menu a:hover,.nav-collapse .dropdown-menu a:focus{background-color:#f2f2f2} .navbar-inverse .nav-collapse .nav>li>a,.navbar-inverse .nav-collapse .dropdown-menu a{color:#999} .navbar-inverse .nav-collapse .nav>li>a:hover,.navbar-inverse .nav-collapse .nav>li>a:focus,.navbar-inverse .nav-collapse .dropdown-menu a:hover,.navbar-inverse .nav-collapse .dropdown-menu a:focus{background-color:#111} .nav-collapse.in .btn-group{margin-top:5px;padding:0} .nav-collapse .dropdown-menu{position:static;top:auto;left:auto;float:none;display:none;max-width:none;margin:0 15px;padding:0;background-color:transparent;border:none;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none} .nav-collapse .open>.dropdown-menu{display:block} .nav-collapse .dropdown-menu:before,.nav-collapse .dropdown-menu:after{display:none} .nav-collapse .dropdown-menu .divider{display:none} .nav-collapse .nav>li>.dropdown-menu:before,.nav-collapse .nav>li>.dropdown-menu:after{display:none} .nav-collapse .navbar-form,.nav-collapse .navbar-search{float:none;padding:10px 15px;margin:10px 0;border-top:1px solid #f2f2f2;border-bottom:1px solid #f2f2f2;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1)} .navbar-inverse .nav-collapse .navbar-form,.navbar-inverse .nav-collapse .navbar-search{border-top-color:#111;border-bottom-color:#111} .navbar .nav-collapse .nav.pull-right{float:none;margin-left:0} .nav-collapse,.nav-collapse.collapse{overflow:hidden;height:0} .navbar .btn-navbar{display:block} .navbar-static .navbar-inner{padding-left:10px;padding-right:10px}}@media (min-width:979px + 1){.nav-collapse.collapse{height:auto !important;overflow:visible !important}}@font-face{font-family:'FontAwesome';src:url('../components/font-awesome/font/fontawesome-webfont.eot?v=3.2.1');src:url('../components/font-awesome/font/fontawesome-webfont.eot?#iefix&v=3.2.1') format('embedded-opentype'),url('../components/font-awesome/font/fontawesome-webfont.woff?v=3.2.1') format('woff'),url('../components/font-awesome/font/fontawesome-webfont.ttf?v=3.2.1') format('truetype'),url('../components/font-awesome/font/fontawesome-webfont.svg#fontawesomeregular?v=3.2.1') format('svg');font-weight:normal;font-style:normal}[class^="icon-"],[class*=" icon-"]{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em}
-[class^="icon-"]:before,[class*=" icon-"]:before{text-decoration:inherit;display:inline-block;speak:none}
-.icon-large:before{vertical-align:-10%;font-size:1.3333333333333333em}
-a [class^="icon-"],a [class*=" icon-"]{display:inline}
-[class^="icon-"].icon-fixed-width,[class*=" icon-"].icon-fixed-width{display:inline-block;width:1.1428571428571428em;text-align:right;padding-right:.2857142857142857em}[class^="icon-"].icon-fixed-width.icon-large,[class*=" icon-"].icon-fixed-width.icon-large{width:1.4285714285714286em}
-.icons-ul{margin-left:2.142857142857143em;list-style-type:none}.icons-ul>li{position:relative}
-.icons-ul .icon-li{position:absolute;left:-2.142857142857143em;width:2.142857142857143em;text-align:center;line-height:inherit}
-[class^="icon-"].hide,[class*=" icon-"].hide{display:none}
-.icon-muted{color:#eee}
-.icon-light{color:#fff}
-.icon-dark{color:#333}
-.icon-border{border:solid 1px #eee;padding:.2em .25em .15em;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.icon-2x{font-size:2em}.icon-2x.icon-border{border-width:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.icon-3x{font-size:3em}.icon-3x.icon-border{border-width:3px;border-radius:5px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}
-.icon-4x{font-size:4em}.icon-4x.icon-border{border-width:4px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.icon-5x{font-size:5em}.icon-5x.icon-border{border-width:5px;border-radius:7px;-webkit-border-radius:7px;-moz-border-radius:7px;border-radius:7px}
-.pull-right{float:right}
-.pull-left{float:left}
-[class^="icon-"].pull-left,[class*=" icon-"].pull-left{margin-right:.3em}
-[class^="icon-"].pull-right,[class*=" icon-"].pull-right{margin-left:.3em}
-[class^="icon-"],[class*=" icon-"]{display:inline;width:auto;height:auto;line-height:normal;vertical-align:baseline;background-image:none;background-position:0 0;background-repeat:repeat;margin-top:0}
-.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"]{background-image:none}
-.btn [class^="icon-"].icon-large,.nav [class^="icon-"].icon-large,.btn [class*=" icon-"].icon-large,.nav [class*=" icon-"].icon-large{line-height:.9em}
-.btn [class^="icon-"].icon-spin,.nav [class^="icon-"].icon-spin,.btn [class*=" icon-"].icon-spin,.nav [class*=" icon-"].icon-spin{display:inline-block}
-.nav-tabs [class^="icon-"],.nav-pills [class^="icon-"],.nav-tabs [class*=" icon-"],.nav-pills [class*=" icon-"],.nav-tabs [class^="icon-"].icon-large,.nav-pills [class^="icon-"].icon-large,.nav-tabs [class*=" icon-"].icon-large,.nav-pills [class*=" icon-"].icon-large{line-height:.9em}
-.btn [class^="icon-"].pull-left.icon-2x,.btn [class*=" icon-"].pull-left.icon-2x,.btn [class^="icon-"].pull-right.icon-2x,.btn [class*=" icon-"].pull-right.icon-2x{margin-top:.18em}
-.btn [class^="icon-"].icon-spin.icon-large,.btn [class*=" icon-"].icon-spin.icon-large{line-height:.8em}
-.btn.btn-small [class^="icon-"].pull-left.icon-2x,.btn.btn-small [class*=" icon-"].pull-left.icon-2x,.btn.btn-small [class^="icon-"].pull-right.icon-2x,.btn.btn-small [class*=" icon-"].pull-right.icon-2x{margin-top:.25em}
-.btn.btn-large [class^="icon-"],.btn.btn-large [class*=" icon-"]{margin-top:0}.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x,.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-top:.05em}
-.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x{margin-right:.2em}
-.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-left:.2em}
-.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{line-height:inherit}
-.icon-stack{position:relative;display:inline-block;width:2em;height:2em;line-height:2em;vertical-align:-35%}.icon-stack [class^="icon-"],.icon-stack [class*=" icon-"]{display:block;text-align:center;position:absolute;width:100%;height:100%;font-size:1em;line-height:inherit;*line-height:2em}
-.icon-stack .icon-stack-base{font-size:2em;*line-height:1em}
-.icon-spin{display:inline-block;-moz-animation:spin 2s infinite linear;-o-animation:spin 2s infinite linear;-webkit-animation:spin 2s infinite linear;animation:spin 2s infinite linear}
-a .icon-stack,a .icon-spin{display:inline-block;text-decoration:none}
-@-moz-keyframes spin{0%{-moz-transform:rotate(0deg)} 100%{-moz-transform:rotate(359deg)}}@-webkit-keyframes spin{0%{-webkit-transform:rotate(0deg)} 100%{-webkit-transform:rotate(359deg)}}@-o-keyframes spin{0%{-o-transform:rotate(0deg)} 100%{-o-transform:rotate(359deg)}}@-ms-keyframes spin{0%{-ms-transform:rotate(0deg)} 100%{-ms-transform:rotate(359deg)}}@keyframes spin{0%{transform:rotate(0deg)} 100%{transform:rotate(359deg)}}.icon-rotate-90:before{-webkit-transform:rotate(90deg);-moz-transform:rotate(90deg);-ms-transform:rotate(90deg);-o-transform:rotate(90deg);transform:rotate(90deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=1)}
-.icon-rotate-180:before{-webkit-transform:rotate(180deg);-moz-transform:rotate(180deg);-ms-transform:rotate(180deg);-o-transform:rotate(180deg);transform:rotate(180deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2)}
-.icon-rotate-270:before{-webkit-transform:rotate(270deg);-moz-transform:rotate(270deg);-ms-transform:rotate(270deg);-o-transform:rotate(270deg);transform:rotate(270deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=3)}
-.icon-flip-horizontal:before{-webkit-transform:scale(-1, 1);-moz-transform:scale(-1, 1);-ms-transform:scale(-1, 1);-o-transform:scale(-1, 1);transform:scale(-1, 1)}
-.icon-flip-vertical:before{-webkit-transform:scale(1, -1);-moz-transform:scale(1, -1);-ms-transform:scale(1, -1);-o-transform:scale(1, -1);transform:scale(1, -1)}
-a .icon-rotate-90:before,a .icon-rotate-180:before,a .icon-rotate-270:before,a .icon-flip-horizontal:before,a .icon-flip-vertical:before{display:inline-block}
-.icon-glass:before{content:"\f000"}
-.icon-music:before{content:"\f001"}
-.icon-search:before{content:"\f002"}
-.icon-envelope-alt:before{content:"\f003"}
-.icon-heart:before{content:"\f004"}
-.icon-star:before{content:"\f005"}
-.icon-star-empty:before{content:"\f006"}
-.icon-user:before{content:"\f007"}
-.icon-film:before{content:"\f008"}
-.icon-th-large:before{content:"\f009"}
-.icon-th:before{content:"\f00a"}
-.icon-th-list:before{content:"\f00b"}
-.icon-ok:before{content:"\f00c"}
-.icon-remove:before{content:"\f00d"}
-.icon-zoom-in:before{content:"\f00e"}
-.icon-zoom-out:before{content:"\f010"}
-.icon-power-off:before,.icon-off:before{content:"\f011"}
-.icon-signal:before{content:"\f012"}
-.icon-gear:before,.icon-cog:before{content:"\f013"}
-.icon-trash:before{content:"\f014"}
-.icon-home:before{content:"\f015"}
-.icon-file-alt:before{content:"\f016"}
-.icon-time:before{content:"\f017"}
-.icon-road:before{content:"\f018"}
-.icon-download-alt:before{content:"\f019"}
-.icon-download:before{content:"\f01a"}
-.icon-upload:before{content:"\f01b"}
-.icon-inbox:before{content:"\f01c"}
-.icon-play-circle:before{content:"\f01d"}
-.icon-rotate-right:before,.icon-repeat:before{content:"\f01e"}
-.icon-refresh:before{content:"\f021"}
-.icon-list-alt:before{content:"\f022"}
-.icon-lock:before{content:"\f023"}
-.icon-flag:before{content:"\f024"}
-.icon-headphones:before{content:"\f025"}
-.icon-volume-off:before{content:"\f026"}
-.icon-volume-down:before{content:"\f027"}
-.icon-volume-up:before{content:"\f028"}
-.icon-qrcode:before{content:"\f029"}
-.icon-barcode:before{content:"\f02a"}
-.icon-tag:before{content:"\f02b"}
-.icon-tags:before{content:"\f02c"}
-.icon-book:before{content:"\f02d"}
-.icon-bookmark:before{content:"\f02e"}
-.icon-print:before{content:"\f02f"}
-.icon-camera:before{content:"\f030"}
-.icon-font:before{content:"\f031"}
-.icon-bold:before{content:"\f032"}
-.icon-italic:before{content:"\f033"}
-.icon-text-height:before{content:"\f034"}
-.icon-text-width:before{content:"\f035"}
-.icon-align-left:before{content:"\f036"}
-.icon-align-center:before{content:"\f037"}
-.icon-align-right:before{content:"\f038"}
-.icon-align-justify:before{content:"\f039"}
-.icon-list:before{content:"\f03a"}
-.icon-indent-left:before{content:"\f03b"}
-.icon-indent-right:before{content:"\f03c"}
-.icon-facetime-video:before{content:"\f03d"}
-.icon-picture:before{content:"\f03e"}
-.icon-pencil:before{content:"\f040"}
-.icon-map-marker:before{content:"\f041"}
-.icon-adjust:before{content:"\f042"}
-.icon-tint:before{content:"\f043"}
-.icon-edit:before{content:"\f044"}
-.icon-share:before{content:"\f045"}
-.icon-check:before{content:"\f046"}
-.icon-move:before{content:"\f047"}
-.icon-step-backward:before{content:"\f048"}
-.icon-fast-backward:before{content:"\f049"}
-.icon-backward:before{content:"\f04a"}
-.icon-play:before{content:"\f04b"}
-.icon-pause:before{content:"\f04c"}
-.icon-stop:before{content:"\f04d"}
-.icon-forward:before{content:"\f04e"}
-.icon-fast-forward:before{content:"\f050"}
-.icon-step-forward:before{content:"\f051"}
-.icon-eject:before{content:"\f052"}
-.icon-chevron-left:before{content:"\f053"}
-.icon-chevron-right:before{content:"\f054"}
-.icon-plus-sign:before{content:"\f055"}
-.icon-minus-sign:before{content:"\f056"}
-.icon-remove-sign:before{content:"\f057"}
-.icon-ok-sign:before{content:"\f058"}
-.icon-question-sign:before{content:"\f059"}
-.icon-info-sign:before{content:"\f05a"}
-.icon-screenshot:before{content:"\f05b"}
-.icon-remove-circle:before{content:"\f05c"}
-.icon-ok-circle:before{content:"\f05d"}
-.icon-ban-circle:before{content:"\f05e"}
-.icon-arrow-left:before{content:"\f060"}
-.icon-arrow-right:before{content:"\f061"}
-.icon-arrow-up:before{content:"\f062"}
-.icon-arrow-down:before{content:"\f063"}
-.icon-mail-forward:before,.icon-share-alt:before{content:"\f064"}
-.icon-resize-full:before{content:"\f065"}
-.icon-resize-small:before{content:"\f066"}
-.icon-plus:before{content:"\f067"}
-.icon-minus:before{content:"\f068"}
-.icon-asterisk:before{content:"\f069"}
-.icon-exclamation-sign:before{content:"\f06a"}
-.icon-gift:before{content:"\f06b"}
-.icon-leaf:before{content:"\f06c"}
-.icon-fire:before{content:"\f06d"}
-.icon-eye-open:before{content:"\f06e"}
-.icon-eye-close:before{content:"\f070"}
-.icon-warning-sign:before{content:"\f071"}
-.icon-plane:before{content:"\f072"}
-.icon-calendar:before{content:"\f073"}
-.icon-random:before{content:"\f074"}
-.icon-comment:before{content:"\f075"}
-.icon-magnet:before{content:"\f076"}
-.icon-chevron-up:before{content:"\f077"}
-.icon-chevron-down:before{content:"\f078"}
-.icon-retweet:before{content:"\f079"}
-.icon-shopping-cart:before{content:"\f07a"}
-.icon-folder-close:before{content:"\f07b"}
-.icon-folder-open:before{content:"\f07c"}
-.icon-resize-vertical:before{content:"\f07d"}
-.icon-resize-horizontal:before{content:"\f07e"}
-.icon-bar-chart:before{content:"\f080"}
-.icon-twitter-sign:before{content:"\f081"}
-.icon-facebook-sign:before{content:"\f082"}
-.icon-camera-retro:before{content:"\f083"}
-.icon-key:before{content:"\f084"}
-.icon-gears:before,.icon-cogs:before{content:"\f085"}
-.icon-comments:before{content:"\f086"}
-.icon-thumbs-up-alt:before{content:"\f087"}
-.icon-thumbs-down-alt:before{content:"\f088"}
-.icon-star-half:before{content:"\f089"}
-.icon-heart-empty:before{content:"\f08a"}
-.icon-signout:before{content:"\f08b"}
-.icon-linkedin-sign:before{content:"\f08c"}
-.icon-pushpin:before{content:"\f08d"}
-.icon-external-link:before{content:"\f08e"}
-.icon-signin:before{content:"\f090"}
-.icon-trophy:before{content:"\f091"}
-.icon-github-sign:before{content:"\f092"}
-.icon-upload-alt:before{content:"\f093"}
-.icon-lemon:before{content:"\f094"}
-.icon-phone:before{content:"\f095"}
-.icon-unchecked:before,.icon-check-empty:before{content:"\f096"}
-.icon-bookmark-empty:before{content:"\f097"}
-.icon-phone-sign:before{content:"\f098"}
-.icon-twitter:before{content:"\f099"}
-.icon-facebook:before{content:"\f09a"}
-.icon-github:before{content:"\f09b"}
-.icon-unlock:before{content:"\f09c"}
-.icon-credit-card:before{content:"\f09d"}
-.icon-rss:before{content:"\f09e"}
-.icon-hdd:before{content:"\f0a0"}
-.icon-bullhorn:before{content:"\f0a1"}
-.icon-bell:before{content:"\f0a2"}
-.icon-certificate:before{content:"\f0a3"}
-.icon-hand-right:before{content:"\f0a4"}
-.icon-hand-left:before{content:"\f0a5"}
-.icon-hand-up:before{content:"\f0a6"}
-.icon-hand-down:before{content:"\f0a7"}
-.icon-circle-arrow-left:before{content:"\f0a8"}
-.icon-circle-arrow-right:before{content:"\f0a9"}
-.icon-circle-arrow-up:before{content:"\f0aa"}
-.icon-circle-arrow-down:before{content:"\f0ab"}
-.icon-globe:before{content:"\f0ac"}
-.icon-wrench:before{content:"\f0ad"}
-.icon-tasks:before{content:"\f0ae"}
-.icon-filter:before{content:"\f0b0"}
-.icon-briefcase:before{content:"\f0b1"}
-.icon-fullscreen:before{content:"\f0b2"}
-.icon-group:before{content:"\f0c0"}
-.icon-link:before{content:"\f0c1"}
-.icon-cloud:before{content:"\f0c2"}
-.icon-beaker:before{content:"\f0c3"}
-.icon-cut:before{content:"\f0c4"}
-.icon-copy:before{content:"\f0c5"}
-.icon-paperclip:before,.icon-paper-clip:before{content:"\f0c6"}
-.icon-save:before{content:"\f0c7"}
-.icon-sign-blank:before{content:"\f0c8"}
-.icon-reorder:before{content:"\f0c9"}
-.icon-list-ul:before{content:"\f0ca"}
-.icon-list-ol:before{content:"\f0cb"}
-.icon-strikethrough:before{content:"\f0cc"}
-.icon-underline:before{content:"\f0cd"}
-.icon-table:before{content:"\f0ce"}
-.icon-magic:before{content:"\f0d0"}
-.icon-truck:before{content:"\f0d1"}
-.icon-pinterest:before{content:"\f0d2"}
-.icon-pinterest-sign:before{content:"\f0d3"}
-.icon-google-plus-sign:before{content:"\f0d4"}
-.icon-google-plus:before{content:"\f0d5"}
-.icon-money:before{content:"\f0d6"}
-.icon-caret-down:before{content:"\f0d7"}
-.icon-caret-up:before{content:"\f0d8"}
-.icon-caret-left:before{content:"\f0d9"}
-.icon-caret-right:before{content:"\f0da"}
-.icon-columns:before{content:"\f0db"}
-.icon-sort:before{content:"\f0dc"}
-.icon-sort-down:before{content:"\f0dd"}
-.icon-sort-up:before{content:"\f0de"}
-.icon-envelope:before{content:"\f0e0"}
-.icon-linkedin:before{content:"\f0e1"}
-.icon-rotate-left:before,.icon-undo:before{content:"\f0e2"}
-.icon-legal:before{content:"\f0e3"}
-.icon-dashboard:before{content:"\f0e4"}
-.icon-comment-alt:before{content:"\f0e5"}
-.icon-comments-alt:before{content:"\f0e6"}
-.icon-bolt:before{content:"\f0e7"}
-.icon-sitemap:before{content:"\f0e8"}
-.icon-umbrella:before{content:"\f0e9"}
-.icon-paste:before{content:"\f0ea"}
-.icon-lightbulb:before{content:"\f0eb"}
-.icon-exchange:before{content:"\f0ec"}
-.icon-cloud-download:before{content:"\f0ed"}
-.icon-cloud-upload:before{content:"\f0ee"}
-.icon-user-md:before{content:"\f0f0"}
-.icon-stethoscope:before{content:"\f0f1"}
-.icon-suitcase:before{content:"\f0f2"}
-.icon-bell-alt:before{content:"\f0f3"}
-.icon-coffee:before{content:"\f0f4"}
-.icon-food:before{content:"\f0f5"}
-.icon-file-text-alt:before{content:"\f0f6"}
-.icon-building:before{content:"\f0f7"}
-.icon-hospital:before{content:"\f0f8"}
-.icon-ambulance:before{content:"\f0f9"}
-.icon-medkit:before{content:"\f0fa"}
-.icon-fighter-jet:before{content:"\f0fb"}
-.icon-beer:before{content:"\f0fc"}
-.icon-h-sign:before{content:"\f0fd"}
-.icon-plus-sign-alt:before{content:"\f0fe"}
-.icon-double-angle-left:before{content:"\f100"}
-.icon-double-angle-right:before{content:"\f101"}
-.icon-double-angle-up:before{content:"\f102"}
-.icon-double-angle-down:before{content:"\f103"}
-.icon-angle-left:before{content:"\f104"}
-.icon-angle-right:before{content:"\f105"}
-.icon-angle-up:before{content:"\f106"}
-.icon-angle-down:before{content:"\f107"}
-.icon-desktop:before{content:"\f108"}
-.icon-laptop:before{content:"\f109"}
-.icon-tablet:before{content:"\f10a"}
-.icon-mobile-phone:before{content:"\f10b"}
-.icon-circle-blank:before{content:"\f10c"}
-.icon-quote-left:before{content:"\f10d"}
-.icon-quote-right:before{content:"\f10e"}
-.icon-spinner:before{content:"\f110"}
-.icon-circle:before{content:"\f111"}
-.icon-mail-reply:before,.icon-reply:before{content:"\f112"}
-.icon-github-alt:before{content:"\f113"}
-.icon-folder-close-alt:before{content:"\f114"}
-.icon-folder-open-alt:before{content:"\f115"}
-.icon-expand-alt:before{content:"\f116"}
-.icon-collapse-alt:before{content:"\f117"}
-.icon-smile:before{content:"\f118"}
-.icon-frown:before{content:"\f119"}
-.icon-meh:before{content:"\f11a"}
-.icon-gamepad:before{content:"\f11b"}
-.icon-keyboard:before{content:"\f11c"}
-.icon-flag-alt:before{content:"\f11d"}
-.icon-flag-checkered:before{content:"\f11e"}
-.icon-terminal:before{content:"\f120"}
-.icon-code:before{content:"\f121"}
-.icon-reply-all:before{content:"\f122"}
-.icon-mail-reply-all:before{content:"\f122"}
-.icon-star-half-full:before,.icon-star-half-empty:before{content:"\f123"}
-.icon-location-arrow:before{content:"\f124"}
-.icon-crop:before{content:"\f125"}
-.icon-code-fork:before{content:"\f126"}
-.icon-unlink:before{content:"\f127"}
-.icon-question:before{content:"\f128"}
-.icon-info:before{content:"\f129"}
-.icon-exclamation:before{content:"\f12a"}
-.icon-superscript:before{content:"\f12b"}
-.icon-subscript:before{content:"\f12c"}
-.icon-eraser:before{content:"\f12d"}
-.icon-puzzle-piece:before{content:"\f12e"}
-.icon-microphone:before{content:"\f130"}
-.icon-microphone-off:before{content:"\f131"}
-.icon-shield:before{content:"\f132"}
-.icon-calendar-empty:before{content:"\f133"}
-.icon-fire-extinguisher:before{content:"\f134"}
-.icon-rocket:before{content:"\f135"}
-.icon-maxcdn:before{content:"\f136"}
-.icon-chevron-sign-left:before{content:"\f137"}
-.icon-chevron-sign-right:before{content:"\f138"}
-.icon-chevron-sign-up:before{content:"\f139"}
-.icon-chevron-sign-down:before{content:"\f13a"}
-.icon-html5:before{content:"\f13b"}
-.icon-css3:before{content:"\f13c"}
-.icon-anchor:before{content:"\f13d"}
-.icon-unlock-alt:before{content:"\f13e"}
-.icon-bullseye:before{content:"\f140"}
-.icon-ellipsis-horizontal:before{content:"\f141"}
-.icon-ellipsis-vertical:before{content:"\f142"}
-.icon-rss-sign:before{content:"\f143"}
-.icon-play-sign:before{content:"\f144"}
-.icon-ticket:before{content:"\f145"}
-.icon-minus-sign-alt:before{content:"\f146"}
-.icon-check-minus:before{content:"\f147"}
-.icon-level-up:before{content:"\f148"}
-.icon-level-down:before{content:"\f149"}
-.icon-check-sign:before{content:"\f14a"}
-.icon-edit-sign:before{content:"\f14b"}
-.icon-external-link-sign:before{content:"\f14c"}
-.icon-share-sign:before{content:"\f14d"}
-.icon-compass:before{content:"\f14e"}
-.icon-collapse:before{content:"\f150"}
-.icon-collapse-top:before{content:"\f151"}
-.icon-expand:before{content:"\f152"}
-.icon-euro:before,.icon-eur:before{content:"\f153"}
-.icon-gbp:before{content:"\f154"}
-.icon-dollar:before,.icon-usd:before{content:"\f155"}
-.icon-rupee:before,.icon-inr:before{content:"\f156"}
-.icon-yen:before,.icon-jpy:before{content:"\f157"}
-.icon-renminbi:before,.icon-cny:before{content:"\f158"}
-.icon-won:before,.icon-krw:before{content:"\f159"}
-.icon-bitcoin:before,.icon-btc:before{content:"\f15a"}
-.icon-file:before{content:"\f15b"}
-.icon-file-text:before{content:"\f15c"}
-.icon-sort-by-alphabet:before{content:"\f15d"}
-.icon-sort-by-alphabet-alt:before{content:"\f15e"}
-.icon-sort-by-attributes:before{content:"\f160"}
-.icon-sort-by-attributes-alt:before{content:"\f161"}
-.icon-sort-by-order:before{content:"\f162"}
-.icon-sort-by-order-alt:before{content:"\f163"}
-.icon-thumbs-up:before{content:"\f164"}
-.icon-thumbs-down:before{content:"\f165"}
-.icon-youtube-sign:before{content:"\f166"}
-.icon-youtube:before{content:"\f167"}
-.icon-xing:before{content:"\f168"}
-.icon-xing-sign:before{content:"\f169"}
-.icon-youtube-play:before{content:"\f16a"}
-.icon-dropbox:before{content:"\f16b"}
-.icon-stackexchange:before{content:"\f16c"}
-.icon-instagram:before{content:"\f16d"}
-.icon-flickr:before{content:"\f16e"}
-.icon-adn:before{content:"\f170"}
-.icon-bitbucket:before{content:"\f171"}
-.icon-bitbucket-sign:before{content:"\f172"}
-.icon-tumblr:before{content:"\f173"}
-.icon-tumblr-sign:before{content:"\f174"}
-.icon-long-arrow-down:before{content:"\f175"}
-.icon-long-arrow-up:before{content:"\f176"}
-.icon-long-arrow-left:before{content:"\f177"}
-.icon-long-arrow-right:before{content:"\f178"}
-.icon-apple:before{content:"\f179"}
-.icon-windows:before{content:"\f17a"}
-.icon-android:before{content:"\f17b"}
-.icon-linux:before{content:"\f17c"}
-.icon-dribbble:before{content:"\f17d"}
-.icon-skype:before{content:"\f17e"}
-.icon-foursquare:before{content:"\f180"}
-.icon-trello:before{content:"\f181"}
-.icon-female:before{content:"\f182"}
-.icon-male:before{content:"\f183"}
-.icon-gittip:before{content:"\f184"}
-.icon-sun:before{content:"\f185"}
-.icon-moon:before{content:"\f186"}
-.icon-archive:before{content:"\f187"}
-.icon-bug:before{content:"\f188"}
-.icon-vk:before{content:"\f189"}
-.icon-weibo:before{content:"\f18a"}
-.icon-renren:before{content:"\f18b"}
-code{color:#000}
-.border-box-sizing{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
-.corner-all{border-radius:4px}
-.hbox{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-.hbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none}
-.vbox{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-.vbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none}
-.hbox.reverse,.vbox.reverse,.reverse{-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
-.hbox.box-flex0,.vbox.box-flex0,.box-flex0{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none;width:auto}
-.hbox.box-flex1,.vbox.box-flex1,.box-flex1{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-.hbox.box-flex,.vbox.box-flex,.box-flex{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-.hbox.box-flex2,.vbox.box-flex2,.box-flex2{-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2}
-.box-group1{-webkit-box-flex-group:1;-moz-box-flex-group:1;box-flex-group:1}
-.box-group2{-webkit-box-flex-group:2;-moz-box-flex-group:2;box-flex-group:2}
-.hbox.start,.vbox.start,.start{-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start}
-.hbox.end,.vbox.end,.end{-webkit-box-pack:end;-moz-box-pack:end;box-pack:end;justify-content:flex-end}
-.hbox.center,.vbox.center,.center{-webkit-box-pack:center;-moz-box-pack:center;box-pack:center;justify-content:center}
-.hbox.align-start,.vbox.align-start,.align-start{-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
-.hbox.align-end,.vbox.align-end,.align-end{-webkit-box-align:end;-moz-box-align:end;box-align:end;align-items:flex-end}
-.hbox.align-center,.vbox.align-center,.align-center{-webkit-box-align:center;-moz-box-align:center;box-align:center;align-items:center}
-div.error{margin:2em;text-align:center}
-div.error>h1{font-size:500%;line-height:normal}
-div.error>p{font-size:200%;line-height:normal}
-div.traceback-wrapper{text-align:left;max-width:800px;margin:auto}
-body{background-color:#fff;position:absolute;left:0;right:0;top:0;bottom:0;overflow:visible}
-div#header{display:none}
-#ipython_notebook{padding-left:16px}
-#noscript{width:auto;padding-top:16px;padding-bottom:16px;text-align:center;font-size:22px;color:#f00;font-weight:bold}
-#ipython_notebook img{font-family:Verdana,"Helvetica Neue",Arial,Helvetica,Geneva,sans-serif;height:24px;text-decoration:none;color:#000}
-#site{width:100%;display:none}
-.ui-button .ui-button-text{padding:.2em .8em;font-size:77%}
-input.ui-button{padding:.3em .9em}
-.navbar span{margin-top:3px}
-span#login_widget{float:right}
-.nav-header{text-transform:none}
-.navbar-nobg{background-color:transparent;background-image:none}
-#header>span{margin-top:10px}
-.modal_stretch{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;height:80%}.modal_stretch .modal-body{max-height:none;flex:1}
-@media (min-width:768px){.modal{width:700px;margin-left:-350px}}.center-nav{display:inline-block;margin-bottom:-4px}
-.alternate_upload{background-color:none;display:inline}
-.alternate_upload.form{padding:0;margin:0}
-.alternate_upload input.fileinput{background-color:#f00;position:relative;opacity:0;z-index:2;width:295px;margin-left:163px;cursor:pointer;height:26px}
-ul#tabs{margin-bottom:4px}
-ul#tabs a{padding-top:4px;padding-bottom:4px}
-ul.breadcrumb a:focus,ul.breadcrumb a:hover{text-decoration:none}
-ul.breadcrumb i.icon-home{font-size:16px;margin-right:4px}
-ul.breadcrumb span{color:#5e5e5e}
-.list_toolbar{padding:4px 0 4px 0}
-.list_toolbar [class*="span"]{min-height:26px}
-.list_header{font-weight:bold}
-.list_container{margin-top:4px;margin-bottom:20px;border:1px solid #ababab;border-radius:4px}
-.list_container>div{border-bottom:1px solid #ababab}.list_container>div:hover .list-item{background-color:#f00}
-.list_container>div:last-child{border:none}
-.list_item:hover .list_item{background-color:#ddd}
-.list_item a{text-decoration:none}
-.list_header>div,.list_item>div{padding-top:4px;padding-bottom:4px;padding-left:7px;padding-right:7px;height:22px;line-height:22px}
-.item_name{line-height:22px;height:26px}
-.item_icon{font-size:14px;color:#5e5e5e;margin-right:7px}
-.item_buttons{line-height:1em}
-.toolbar_info{height:26px;line-height:26px}
-input.nbname_input,input.engine_num_input{padding-top:3px;padding-bottom:3px;height:14px;line-height:14px;margin:0}
-input.engine_num_input{width:60px}
-.highlight_text{color:#00f}
-#project_name>.breadcrumb{padding:0;margin-bottom:0;background-color:transparent;font-weight:bold}
-.ansibold{font-weight:bold}
-.ansiblack{color:#000}
-.ansired{color:#8b0000}
-.ansigreen{color:#006400}
-.ansiyellow{color:#a52a2a}
-.ansiblue{color:#00008b}
-.ansipurple{color:#9400d3}
-.ansicyan{color:#4682b4}
-.ansigray{color:#808080}
-.ansibgblack{background-color:#000}
-.ansibgred{background-color:#f00}
-.ansibggreen{background-color:#008000}
-.ansibgyellow{background-color:#ff0}
-.ansibgblue{background-color:#00f}
-.ansibgpurple{background-color:#f0f}
-.ansibgcyan{background-color:#0ff}
-.ansibggray{background-color:#808080}
-div.cell{border:1px solid transparent;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}div.cell.selected{border-radius:4px;border:thin #ababab solid}
-div.cell.edit_mode{border-radius:4px;border:thin #008000 solid}
-div.cell{width:100%;padding:5px 5px 5px 0;margin:0;outline:none}
-div.prompt{min-width:11ex;padding:.4em;margin:0;font-family:monospace;text-align:right;line-height:1.21429em}
-@media (max-width:480px){div.prompt{text-align:left}}div.inner_cell{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-div.input_area{border:1px solid #cfcfcf;border-radius:4px;background:#f7f7f7}
-div.prompt:empty{padding-top:0;padding-bottom:0}
-div.input{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-@media (max-width:480px){div.input{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}}div.input_prompt{color:#000080;border-top:1px solid transparent}
-div.input_area>div.highlight{margin:.4em;border:none;padding:0;background-color:transparent}
-div.input_area>div.highlight>pre{margin:0;border:0;padding:0;background-color:transparent;font-size:14px;line-height:1.21429em}
-.CodeMirror{line-height:1.21429em;height:auto;background:none;}
-.CodeMirror-scroll{overflow-y:hidden;overflow-x:auto}
-@-moz-document url-prefix(){.CodeMirror-scroll{overflow-x:hidden}}.CodeMirror-lines{padding:.4em}
-.CodeMirror-linenumber{padding:0 8px 0 4px}
-.CodeMirror-gutters{border-bottom-left-radius:4px;border-top-left-radius:4px}
-.CodeMirror pre{padding:0;border:0;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-pre code{display:block;padding:.5em}
-.highlight-base,pre code,pre .subst,pre .tag .title,pre .lisp .title,pre .clojure .built_in,pre .nginx .title{color:#000}
-.highlight-string,pre .string,pre .constant,pre .parent,pre .tag .value,pre .rules .value,pre .rules .value .number,pre .preprocessor,pre .ruby .symbol,pre .ruby .symbol .string,pre .aggregate,pre .template_tag,pre .django .variable,pre .smalltalk .class,pre .addition,pre .flow,pre .stream,pre .bash .variable,pre .apache .tag,pre .apache .cbracket,pre .tex .command,pre .tex .special,pre .erlang_repl .function_or_atom,pre .markdown .header{color:#ba2121}
-.highlight-comment,pre .comment,pre .annotation,pre .template_comment,pre .diff .header,pre .chunk,pre .markdown .blockquote{color:#408080;font-style:italic}
-.highlight-number,pre .number,pre .date,pre .regexp,pre .literal,pre .smalltalk .symbol,pre .smalltalk .char,pre .go .constant,pre .change,pre .markdown .bullet,pre .markdown .link_url{color:#080}
-pre .label,pre .javadoc,pre .ruby .string,pre .decorator,pre .filter .argument,pre .localvars,pre .array,pre .attr_selector,pre .important,pre .pseudo,pre .pi,pre .doctype,pre .deletion,pre .envvar,pre .shebang,pre .apache .sqbracket,pre .nginx .built_in,pre .tex .formula,pre .erlang_repl .reserved,pre .prompt,pre .markdown .link_label,pre .vhdl .attribute,pre .clojure .attribute,pre .coffeescript .property{color:#88f}
-.highlight-keyword,pre .keyword,pre .id,pre .phpdoc,pre .aggregate,pre .css .tag,pre .javadoctag,pre .phpdoc,pre .yardoctag,pre .smalltalk .class,pre .winutils,pre .bash .variable,pre .apache .tag,pre .go .typename,pre .tex .command,pre .markdown .strong,pre .request,pre .status{color:#008000;font-weight:bold}
-.highlight-builtin,pre .built_in{color:#008000}
-pre .markdown .emphasis{font-style:italic}
-pre .nginx .built_in{font-weight:normal}
-pre .coffeescript .javascript,pre .javascript .xml,pre .tex .formula,pre .xml .javascript,pre .xml .vbscript,pre .xml .css,pre .xml .cdata{opacity:.5}
-.cm-s-ipython span.cm-variable{color:#000}
-.cm-s-ipython span.cm-keyword{color:#008000;font-weight:bold}
-.cm-s-ipython span.cm-number{color:#080}
-.cm-s-ipython span.cm-comment{color:#408080;font-style:italic}
-.cm-s-ipython span.cm-string{color:#ba2121}
-.cm-s-ipython span.cm-builtin{color:#008000}
-.cm-s-ipython span.cm-error{color:#f00}
-.cm-s-ipython span.cm-operator{color:#a2f;font-weight:bold}
-.cm-s-ipython span.cm-meta{color:#a2f}
-.cm-s-ipython span.cm-tab{background:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);background-position:right;background-repeat:no-repeat}
-div.output_wrapper{position:relative;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-div.output_scroll{height:24em;width:100%;overflow:auto;border-radius:4px;-webkit-box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);-moz-box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);display:block}
-div.output_collapsed{margin:0;padding:0;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-div.out_prompt_overlay{height:100%;padding:0 .4em;position:absolute;border-radius:4px}
-div.out_prompt_overlay:hover{-webkit-box-shadow:inset 0 0 1px #000;-moz-box-shadow:inset 0 0 1px #000;box-shadow:inset 0 0 1px #000;background:rgba(240,240,240,0.5)}
-div.output_prompt{color:#8b0000}
-div.output_area{padding:0;page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}div.output_area .MathJax_Display{text-align:left !important}
-div.output_area .rendered_html table{margin-left:0;margin-right:0}
-div.output_area .rendered_html img{margin-left:0;margin-right:0}
-.output{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-@media (max-width:480px){div.output_area{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}}div.output_area pre{margin:0;padding:0;border:0;font-size:100%;vertical-align:baseline;color:#000;background-color:transparent;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;line-height:inherit}
-div.output_subarea{padding:.4em .4em 0 .4em;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-div.output_text{text-align:left;color:#000;line-height:1.21429em}
-div.output_stderr{background:#fdd;}
-div.output_latex{text-align:left}
-div.output_javascript:empty{padding:0}
-.js-error{color:#8b0000}
-div.raw_input_container{font-family:monospace;padding-top:5px}
-span.raw_input_prompt{}
-input.raw_input{font-family:inherit;font-size:inherit;color:inherit;width:auto;vertical-align:baseline;padding:0 .25em;margin:0 .25em}
-input.raw_input:focus{box-shadow:none}
-p.p-space{margin-bottom:10px}
-.rendered_html{color:#000;}.rendered_html em{font-style:italic}
-.rendered_html strong{font-weight:bold}
-.rendered_html u{text-decoration:underline}
-.rendered_html :link{text-decoration:underline}
-.rendered_html :visited{text-decoration:underline}
-.rendered_html h1{font-size:185.7%;margin:1.08em 0 0 0;font-weight:bold;line-height:1}
-.rendered_html h2{font-size:157.1%;margin:1.27em 0 0 0;font-weight:bold;line-height:1}
-.rendered_html h3{font-size:128.6%;margin:1.55em 0 0 0;font-weight:bold;line-height:1}
-.rendered_html h4{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1}
-.rendered_html h5{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1;font-style:italic}
-.rendered_html h6{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1;font-style:italic}
-.rendered_html h1:first-child{margin-top:.538em}
-.rendered_html h2:first-child{margin-top:.636em}
-.rendered_html h3:first-child{margin-top:.777em}
-.rendered_html h4:first-child{margin-top:1em}
-.rendered_html h5:first-child{margin-top:1em}
-.rendered_html h6:first-child{margin-top:1em}
-.rendered_html ul{list-style:disc;margin:0 2em}
-.rendered_html ul ul{list-style:square;margin:0 2em}
-.rendered_html ul ul ul{list-style:circle;margin:0 2em}
-.rendered_html ol{list-style:decimal;margin:0 2em}
-.rendered_html ol ol{list-style:upper-alpha;margin:0 2em}
-.rendered_html ol ol ol{list-style:lower-alpha;margin:0 2em}
-.rendered_html ol ol ol ol{list-style:lower-roman;margin:0 2em}
-.rendered_html ol ol ol ol ol{list-style:decimal;margin:0 2em}
-.rendered_html *+ul{margin-top:1em}
-.rendered_html *+ol{margin-top:1em}
-.rendered_html hr{color:#000;background-color:#000}
-.rendered_html pre{margin:1em 2em}
-.rendered_html pre,.rendered_html code{border:0;background-color:#fff;color:#000;font-size:100%;padding:0}
-.rendered_html blockquote{margin:1em 2em}
-.rendered_html table{margin-left:auto;margin-right:auto;border:1px solid #000;border-collapse:collapse}
-.rendered_html tr,.rendered_html th,.rendered_html td{border:1px solid #000;border-collapse:collapse;margin:1em 2em}
-.rendered_html td,.rendered_html th{text-align:left;vertical-align:middle;padding:4px}
-.rendered_html th{font-weight:bold}
-.rendered_html *+table{margin-top:1em}
-.rendered_html p{text-align:justify}
-.rendered_html *+p{margin-top:1em}
-.rendered_html img{display:block;margin-left:auto;margin-right:auto}
-.rendered_html *+img{margin-top:1em}
-div.text_cell{padding:5px 5px 5px 0;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-@media (max-width:480px){div.text_cell>div.prompt{display:none}}div.text_cell_render{outline:none;resize:none;width:inherit;border-style:none;padding:.5em .5em .5em .4em;color:#000}
-a.anchor-link:link{text-decoration:none;padding:0 20px;visibility:hidden}
-h1:hover .anchor-link,h2:hover .anchor-link,h3:hover .anchor-link,h4:hover .anchor-link,h5:hover .anchor-link,h6:hover .anchor-link{visibility:visible}
-div.cell.text_cell.rendered{padding:0}
-.widget-area{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-area .widget-subarea{padding:.44em .4em .4em 1px;margin-left:6px;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
-.widget-hlabel{min-width:10ex;padding-right:8px;padding-top:3px;text-align:right;vertical-align:text-top}
-.widget-vlabel{padding-bottom:5px;text-align:center;vertical-align:text-bottom}
-.widget-hreadout{padding-left:8px;padding-top:3px;text-align:left;vertical-align:text-top}
-.widget-vreadout{padding-top:5px;text-align:center;vertical-align:text-top}
-.slide-track{border:1px solid #ccc;background:#fff;border-radius:4px;}
-.widget-hslider{padding-left:8px;padding-right:5px;overflow:visible;width:348px;height:5px;max-height:5px;margin-top:11px;margin-bottom:10px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-hslider .ui-slider{border:0 !important;background:none !important;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-hslider .ui-slider .ui-slider-handle{width:14px !important;height:28px !important;margin-top:-8px !important}
-.widget-vslider{padding-bottom:8px;overflow:visible;width:5px;max-width:5px;height:250px;margin-left:12px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}.widget-vslider .ui-slider{border:0 !important;background:none !important;margin-left:-4px;margin-top:5px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-vslider .ui-slider .ui-slider-handle{width:28px !important;height:14px !important;margin-left:-9px}
-.widget-text{width:350px;margin:0 !important}
-.widget-listbox{width:364px;margin-bottom:0}
-.widget-numeric-text{width:150px;margin:0 !important}
-.widget-progress{width:363px}.widget-progress .bar{-webkit-transition:none;-moz-transition:none;-ms-transition:none;-o-transition:none;transition:none}
-.widget-combo-btn{min-width:138px;}
-.widget-box{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
-.widget-hbox{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-.widget-hbox-single{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;height:30px}
-.widget-vbox{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-.widget-vbox-single{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;width:30px}
-.widget-modal{overflow:hidden;position:absolute !important;top:0;left:0;margin-left:0 !important}
-.widget-modal-body{max-height:none !important}
-.widget-container{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
-.widget-radio-box{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;padding-top:4px}
-.docked-widget-modal{overflow:hidden;position:relative !important;top:0 !important;left:0 !important;margin-left:0 !important}
-body{background-color:#fff}
-body.notebook_app{overflow:hidden}
-@media (max-width:767px){body.notebook_app{padding-left:0;padding-right:0}}span#notebook_name{height:1em;line-height:1em;padding:3px;border:none;font-size:146.5%}
-div#notebook_panel{margin:0 0 0 0;padding:0;-webkit-box-shadow:0 -1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,0.1);box-shadow:0 -1px 10px rgba(0,0,0,0.1)}
-div#notebook{font-size:14px;line-height:20px;overflow-y:scroll;overflow-x:auto;width:100%;padding:1em 0 1em 0;margin:0;border-top:1px solid #ababab;outline:none;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
-div.ui-widget-content{border:1px solid #ababab;outline:none}
-pre.dialog{background-color:#f7f7f7;border:1px solid #ddd;border-radius:4px;padding:.4em;padding-left:2em}
-p.dialog{padding:.2em}
-pre,code,kbd,samp{white-space:pre-wrap}
-#fonttest{font-family:monospace}
-p{margin-bottom:0}
-.end_space{height:200px}
-.celltoolbar{border:thin solid #cfcfcf;border-bottom:none;background:#eee;border-radius:3px 3px 0 0;width:100%;-webkit-box-pack:end;height:22px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
-.ctb_hideshow{display:none;vertical-align:bottom;padding-right:2px}
-.celltoolbar>div{padding-top:0}
-.ctb_global_show .ctb_show.ctb_hideshow{display:block}
-.ctb_global_show .ctb_show+.input_area,.ctb_global_show .ctb_show+div.text_cell_input{border-top-right-radius:0;border-top-left-radius:0}
-.celltoolbar .button_container select{margin:10px;margin-top:1px;margin-bottom:0;padding:0;font-size:87%;width:auto;display:inline-block;height:18px;line-height:18px;vertical-align:top}
-.celltoolbar label{display:inline-block;height:15px;line-height:15px;vertical-align:top}
-.celltoolbar label span{font-size:85%}
-.celltoolbar input[type=checkbox]{margin:0;margin-left:4px;margin-right:4px}
-.celltoolbar .ui-button{border:none;vertical-align:top;height:20px;min-width:30px}
-.completions{position:absolute;z-index:10;overflow:hidden;border:1px solid #ababab;border-radius:4px;-webkit-box-shadow:0 6px 10px -1px #adadad;-moz-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad}
-.completions select{background:#fff;outline:none;border:none;padding:0;margin:0;overflow:auto;font-family:monospace;font-size:110%;color:#000;width:auto}
-.completions select option.context{color:#0064cd}
-#menubar .navbar-inner{min-height:28px;border-top:1px;border-radius:0 0 4px 4px}
-#menubar .navbar{margin-bottom:8px}
-.nav-wrapper{border-bottom:1px solid #d4d4d4}
-#menubar li.dropdown{line-height:12px}
-i.menu-icon{padding-top:4px}
-ul#help_menu li a{overflow:hidden;padding-right:2.2em}ul#help_menu li a i{margin-right:-1.2em}
-#notification_area{z-index:10}
-.indicator_area{color:#777;padding:4px 3px;margin:0;width:11px;z-index:10;text-align:center}
-#kernel_indicator{margin-right:-16px}
-.edit_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f040"}
-.command_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:' '}
-.kernel_idle_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f10c"}
-.kernel_busy_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f111"}
-.notification_widget{color:#777;padding:1px 12px;margin:2px 4px;z-index:10;border:1px solid #ccc;border-radius:4px;background:rgba(240,240,240,0.5)}.notification_widget.span{padding-right:2px}
-div#pager_splitter{height:8px}
-#pager-container{position:relative;padding:15px 0}
-div#pager{font-size:14px;line-height:20px;overflow:auto;display:none}div#pager pre{font-size:13px;line-height:1.21429em;color:#000;background-color:#f7f7f7;padding:.4em}
-.quickhelp{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-.shortcut_key{display:inline-block;width:20ex;text-align:right;font-family:monospace}
-.shortcut_descr{display:inline-block;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-span#save_widget{padding:0 5px;margin-top:12px}
-span#checkpoint_status,span#autosave_status{font-size:small}
-@media (max-width:767px){span#save_widget{font-size:small} span#checkpoint_status,span#autosave_status{font-size:x-small}}@media (max-width:767px){span#checkpoint_status,span#autosave_status{display:none}}@media (min-width:768px) and (max-width:979px){span#checkpoint_status{display:none} span#autosave_status{font-size:x-small}}.toolbar{padding:0 10px;margin-top:-5px}.toolbar select,.toolbar label{width:auto;height:26px;vertical-align:middle;margin-right:2px;margin-bottom:0;display:inline;font-size:92%;margin-left:.3em;margin-right:.3em;padding:0;padding-top:3px}
-.toolbar .btn{padding:2px 8px}
-.toolbar .btn-group{margin-top:0}
-.toolbar-inner{border:none !important;-webkit-box-shadow:none !important;-moz-box-shadow:none !important;box-shadow:none !important}
-#maintoolbar{margin-bottom:0}
-@-moz-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-webkit-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-moz-keyframes fadeIn{from{opacity:0} to{opacity:1}}@-webkit-keyframes fadeIn{from{opacity:0} to{opacity:1}}.bigtooltip{overflow:auto;height:200px;-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms}
-.smalltooltip{-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms;text-overflow:ellipsis;overflow:hidden;height:80px}
-.tooltipbuttons{position:absolute;padding-right:15px;top:0;right:0}
-.tooltiptext{padding-right:30px}
-.ipython_tooltip{max-width:700px;-webkit-animation:fadeOut 400ms;-moz-animation:fadeOut 400ms;animation:fadeOut 400ms;-webkit-animation:fadeIn 400ms;-moz-animation:fadeIn 400ms;animation:fadeIn 400ms;vertical-align:middle;background-color:#f7f7f7;overflow:visible;border:#ababab 1px solid;outline:none;padding:3px;margin:0;padding-left:7px;font-family:monospace;min-height:50px;-moz-box-shadow:0 6px 10px -1px #adadad;-webkit-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad;border-radius:4px;position:absolute;z-index:2}.ipython_tooltip a{float:right}
-.ipython_tooltip .tooltiptext pre{border:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;font-size:100%;background-color:#f7f7f7}
-.pretooltiparrow{left:0;margin:0;top:-16px;width:40px;height:16px;overflow:hidden;position:absolute}
-.pretooltiparrow:before{background-color:#f7f7f7;border:1px #ababab solid;z-index:11;content:"";position:absolute;left:15px;top:10px;width:25px;height:25px;-webkit-transform:rotate(45deg);-moz-transform:rotate(45deg);-ms-transform:rotate(45deg);-o-transform:rotate(45deg)}
-
-    </style>
-<style type="text/css">
-    .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
-.highlight .c { color: #408080; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
-.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #FF0000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #00A000 } /* Generic.Inserted */
-.highlight .go { color: #888888 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #7D9029 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #A0A000 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
-    </style>
-
-
-<style type="text/css">
-/* Overrides of notebook CSS for static HTML export */
-body {
-  overflow: visible;
-  padding: 8px;
-}
-
-div#notebook {
-  overflow: visible;
-  border-top: none;
-}
-
-@media print {
-  div.cell {
-    display: block;
-    page-break-inside: avoid;
-  } 
-  div.output_wrapper { 
-    display: block;
-    page-break-inside: avoid; 
-  }
-  div.output { 
-    display: block;
-    page-break-inside: avoid; 
-  }
-}
-</style>
-
-<!-- Custom stylesheet, it must be in the same directory as the html file -->
-<link rel="stylesheet" href="custom.css">
-
-<!-- Loading mathjax macro -->
-<!-- Load mathjax -->
-    <script src="https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
-    <!-- MathJax configuration -->
-    <script type="text/x-mathjax-config">
-    MathJax.Hub.Config({
-        tex2jax: {
-            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
-            processEscapes: true,
-            processEnvironments: true
-        },
-        // Center justify equations in code and markdown cells. Elsewhere
-        // we use CSS to left justify single line equations in code cells.
-        displayAlign: 'center',
-        "HTML-CSS": {
-            styles: {'.MathJax_Display': {"margin": 0}},
-            linebreaks: { automatic: true }
-        }
-    });
-    </script>
-    <!-- End of mathjax configuration -->
-
-</head>
-<body>
-  <div tabindex="-1" id="notebook" class="border-box-sizing">
-    <div class="container" id="notebook-container">
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a href="www.sebastianraschka.com">Sebastian Raschka</a><br />last updated: 05/04/2014</p>
-<ul>
-<li><a href="https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb">Link to this IPython Notebook on GitHub</a><br /></li>
-<li><a href="https://github.com/rasbt/python_reference">Link to the GitHub repository</a></li>
-</ul>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="all-code-was-executed-in-python-3.4">All code was executed in Python 3.4</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<hr>
-I am really looking forward to your comments and suggestions to improve and<br />extend this little collection! Just send me a quick note<br />via Twitter: <a href="https://twitter.com/rasbt">@rasbt</a><br />or Email: <script type="text/javascript">
-<!--
-h='&#x67;&#x6d;&#x61;&#x69;&#108;&#46;&#x63;&#x6f;&#x6d;';a='&#64;';n='&#98;&#108;&#x75;&#x65;&#x77;&#x6f;&#x6f;&#100;&#116;&#114;&#x65;&#x65;';e=n+a+h;
-document.write('<a h'+'ref'+'="ma'+'ilto'+':'+e+'">'+e+'<\/'+'a'+'>');
-// -->
-</script><noscript>&#98;&#108;&#x75;&#x65;&#x77;&#x6f;&#x6f;&#100;&#116;&#114;&#x65;&#x65;&#32;&#x61;&#116;&#32;&#x67;&#x6d;&#x61;&#x69;&#108;&#32;&#100;&#x6f;&#116;&#32;&#x63;&#x6f;&#x6d;</noscript>
-<hr>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="implementing-the-least-squares-fit-method-for-linear-regression-and-speeding-it-up-via-cython">Implementing the least squares fit method for linear regression and speeding it up via Cython</h1>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a name="sections"></a> <br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="sections">Sections</h1>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<ul>
-<li><a href="#introduction">Introduction</a></li>
-<li><a href="#implementations">Least squares fit implementations</a></li>
-<li><a href="#sample_data">Generating sample data and benchmarking</a></li>
-<li><a href="#cython_nb">Compiling the Python code via Cython in the IPython notebook</a></li>
-<li><a href="#cython_bonus">Bonus: How to use Cython without the IPython magic</a></li>
-</ul>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a name="introduction"></a> <br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="introduction">Introduction</h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#sections">back to top</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Linear regression via the least squares method is the simplest approach to performing a regression analysis of a dependent and a explanatory variable. The objective is to find the best-fitting straight line through a set of points that minimizes the sum of the squared offsets from the line.<br />The offsets come in 2 different flavors: perpendicular and vertical - with respect to the line.<br /><img src="https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_vertical.png" /><br /><img src="https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_perpendicular.png" /></p>
-<p>Here, we will use the more common approach: minimizing the sum of the perpendicular offsets.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>In more mathematical terms, our goal is to compute the best fit to <em>n</em> points <span class="math">\((x_i, y_i)\)</span> with <span class="math">\(i=1,2,...n,\)</span> via linear equation of the form<br /><span class="math">\(f(x) = a\cdot x + b\)</span>.<br />Here, we assume that the y-component is functionally dependent on the x-component.<br />In a cartesian coordinate system, <span class="math">\(b\)</span> is the intercept of the straight line with the y-axis, and <span class="math">\(a\)</span> is the slope of this line.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>In order to obtain the parameters for the linear regression line for a set of multiple points, we can re-write the problem as matrix equation<br /><span class="math">\(\pmb X \; \pmb a = \pmb y\)</span></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><span class="math">\(\Rightarrow\Bigg[ \begin{array}{cc} x_1 &amp; 1  \\ ... &amp; 1 \\ x_n &amp; 1  \end{array} \Bigg]\)</span> <span class="math">\(\bigg[ \begin{array}{c} a  \\ b \end{array} \bigg]\)</span> <span class="math">\(=\Bigg[ \begin{array}{c} y_1   \\ ...  \\ y_n  \end{array} \Bigg]\)</span></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>With a little bit of calculus, we can rearrange the term in order to obtain the parameter vector <span class="math">\(\pmb a = [a\;b]^T\)</span></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><span class="math">\(\Rightarrow \pmb a = (\pmb X^T \; \pmb X)^{-1} \pmb X^T \; \pmb y\)</span></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> The more classic approach to obtain the slope parameter <span class="math">\(a\)</span> and y-axis intercept <span class="math">\(b\)</span> would be:</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><span class="math">\(a = \frac{S_{x,y}}{\sigma_{x}^{2}}\quad\)</span> (slope)</p>
-<p><span class="math">\(b = \bar{y} - a\bar{x}\quad\)</span> (y-axis intercept)</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>where</p>
-<p><span class="math">\(S_{xy} = \sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y})\quad\)</span> (covariance)</p>
-<p><span class="math">\(\sigma{_x}^{2} = \sum_{i=1}^{n} (x_i - \bar{x})^2\quad\)</span> (variance)</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a name="implementations"></a> <br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="least-squares-fit-implementations">Least squares fit implementations</h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#sections">back to top</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="the-matrix-approach">1. The matrix approach</h3>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>First, let us implement the equation:</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><span class="math">\(\pmb a = (\pmb X^T \; \pmb X)^{-1} \pmb X^T \; \pmb y\)</span></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>which I will refer to as the &quot;matrix approach&quot;.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[1]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
-
-<span class="k">def</span> <span class="nf">lin_lstsqr_mat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))])</span><span class="o">.</span><span class="n">T</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="the-classic-approach">2. The classic approach</h3>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Next, we will calculate the parameters separately, using only standard library functions in Python, which I will call the &quot;classic approach&quot;.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><span class="math">\(a = \frac{S_{x,y}}{\sigma_{x}^{2}}\quad\)</span> (slope)</p>
-<p><span class="math">\(b = \bar{y} - a\bar{x}\quad\)</span> (y-axis intercept)</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[4]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">def</span> <span class="nf">classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="n">x_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="n">y_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">var_x</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
-    <span class="n">cov_xy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">y_i</span> <span class="o">-</span> <span class="n">y_avg</span><span class="p">)</span> <span class="k">for</span> <span class="n">x_i</span><span class="p">,</span><span class="n">y_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)])</span>
-    <span class="n">slope</span> <span class="o">=</span> <span class="n">cov_xy</span> <span class="o">/</span> <span class="n">var_x</span>
-    <span class="n">y_interc</span> <span class="o">=</span> <span class="n">y_avg</span> <span class="o">-</span> <span class="n">slope</span><span class="o">*</span><span class="n">x_avg</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">slope</span><span class="p">,</span> <span class="n">y_interc</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="using-the-lstsq-numpy-function">3. Using the lstsq numpy function</h3>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>For our convenience, <code>numpy</code> has a function that can also compute the leat squares solution of a linear matrix equation. For more information, please refer to the <a href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html">documentation</a>.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[5]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">def</span> <span class="nf">numpy_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))])</span><span class="o">.</span><span class="n">T</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="using-the-linregress-scipy-function">4. Using the linregress scipy function</h3>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>The last approach is using <code>scipy.stats.linregress()</code>, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively. The documentation for this function can be found <a href="http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html">here</a>.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[6]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">scipy.stats</span>
-
-<span class="k">def</span> <span class="nf">scipy_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">linregress</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">:</span><span class="mi">2</span><span class="p">]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a name='sample_data'></a> <br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="generating-sample-data-and-benchmarking">Generating sample data and benchmarking</h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#sections">back to top</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>In order to test our different least squares fit implementation, we will generate some sample data: - 500 sample points for the x-component within the range [0,500)<br />- 500 sample points for the y-component within the range [100,600)</p>
-<p>where each sample point is multiplied by a random value within the range [0.8, 12).</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[7]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">random</span>
-<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span>
-
-<span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">x_i</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span><span class="o">/</span><span class="mi">10</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">500</span><span class="p">)]</span>
-<span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">y_i</span><span class="o">*</span><span class="n">random</span><span class="o">.</span><span class="n">randrange</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span><span class="o">/</span><span class="mi">10</span> <span class="k">for</span> <span class="n">y_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">600</span><span class="p">)]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="visualization">Visualization</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>To check how our dataset is distributed, and how the straight line, which we obtain via the least square fit method, we will plot it in a scatter plot.<br />Note that we are using our &quot;matrix approach&quot; here for simplicity, but after plotting the data, we will check whether all of the four different implementations yield the same parameters.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[8]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="o">%</span><span class="k">pylab</span> <span class="n">inline</span>
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
-
-<span class="n">slope</span><span class="p">,</span> <span class="n">intercept</span> <span class="o">=</span> <span class="n">lin_lstsqr_mat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-<span class="n">line_x</span> <span class="o">=</span> <span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">x</span><span class="p">))</span> <span class="o">+</span> <span class="mi">1</span><span class="p">]</span>
-<span class="n">line_y</span> <span class="o">=</span> <span class="p">[</span><span class="n">slope</span><span class="o">*</span><span class="n">x_i</span> <span class="o">+</span> <span class="n">intercept</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">line_x</span><span class="p">]</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">8</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">line_x</span><span class="p">,</span> <span class="n">line_y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">&#39;red&#39;</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="s">&#39;2&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;y&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;x&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Linear regression via least squares fit&#39;</span><span class="p">)</span>
-
-<span class="n">ftext</span> <span class="o">=</span> <span class="s">&#39;y = ax + b = {:.3f} + {:.3f}x&#39;</span>\
-        <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">slope</span><span class="p">,</span> <span class="n">intercept</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">figtext</span><span class="p">(</span><span class="o">.</span><span class="mi">15</span><span class="p">,</span><span class="o">.</span><span class="mi">8</span><span class="p">,</span> <span class="n">ftext</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s">&#39;left&#39;</span><span class="p">)</span>
-
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Populating the interactive namespace from numpy and matplotlib
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stderr output_text">
-<pre>
-WARNING: pylab import has clobbered these variables: [&apos;random&apos;]
-&#96;%matplotlib&#96; prevents importing * from pylab and numpy
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-
-
-<div class="output_png output_subarea ">
-<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
-AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdUVMfbwPHv0lm6giAgooBRbNi7GCv2EnvXWOPPnhg1
-GksSxRhjoiYaE0SNsb4xEY0Fe+zGmijGLkZARRERFljYnfeP1Y0ELJRlAedzDkf2lpln7q48e++d
-O6MQQggkSZIkSSqSTIwdgCRJkiRJhiMTvSRJkiQVYTLRS5IkSVIRJhO9JEmSJBVhMtFLkiRJUhEm
-E70kSZIkFWEy0UtGd+jQIcqXL2/sMAqtSpUq8fvvv+drnSNHjuTTTz/N0b7e3t7s3bs3jyN6s/3y
-yy+UKlUKe3t7zp07Z5TPhFRwKeRz9FJ+8fb2JiQkhGbNmhk7FMmIypQpQ0hICE2bNjVI+QcOHKBf
-v378888/Bim/IPLx8eGrr76iffv2mdbNnDmT69ev8+OPPxohMqkgkGf0Ur5RKBQoFApjh6Gn0Wjy
-ZJvXJYRAfq+Wnsmrz5YQgtu3b+Pv758n5UlFj0z0ktEdOHCAUqVK6V97e3uzYMECqlatiqOjIz17
-9iQ1NVW/ftu2bQQEBODk5ESDBg3466+/9OuCg4Px9fXF3t6eihUr8uuvv+rXrVy5kgYNGjBhwgSc
-nZ2ZNWtWplhmzpxJ165d6devHw4ODqxatYrHjx/z7rvv4u7ujqenJ9OnT0er1QKg1WqZOHEiLi4u
-lC1bliVLlmBiYqJf36RJE6ZNm0aDBg2wsbHh5s2b/P3337Ro0YLixYtTvnx5Nm3apK9/+/btVKxY
-EXt7ezw9PVmwYAEADx48oF27djg5OVG8eHEaN26c4Xg9uxSemprKuHHj8PDwwMPDg/Hjx6NWq/XH
-2dPTky+//BJXV1fc3d1ZuXJllu/Jhg0bqFWrVoZlCxcupGPHjgAMHDiQ6dOnA/Do0SPatWtHiRIl
-KFasGO3btycqKirLcv9LCKF/z5ydnenRowePHj3Sr+/WrRslS5bE0dGRwMBAIiIiXnisvvzyS1Qq
-Fa1btyY6Oho7Ozvs7e25e/dupnpfdJwB5s+fr3+vV6xYgYmJCTdu3AB072dISIh+25UrV9KoUSP9
-67Fjx+Ll5YWDgwM1a9bk8OHD+nXZ/Wxdu3aNwMBAHB0dcXFxoWfPnpnakZqaip2dHRqNhqpVq+Ln
-5wf8+5nYuXMnc+fOZcOGDdjZ2VGtWrXXel+kIkZIUj7x9vYWe/fuzbR8//79wtPTM8N2derUETEx
-MSIuLk5UqFBBLFu2TAghxJkzZ0SJEiXEyZMnhVarFatWrRLe3t5CrVYLIYTYtGmTiImJEUIIsWHD
-BmFjYyPu3r0rhBAiNDRUmJmZiSVLlgiNRiOSk5MzxTJjxgxhbm4utmzZIoQQIjk5WXTq1EmMGDFC
-qFQqcf/+fVG7dm3x3XffCSGEWLp0qfD39xdRUVHi0aNHolmzZsLExERoNBohhBCBgYGidOnSIiIi
-Qmg0GhEfHy88PT3FypUrhUajEWfPnhXOzs7i0qVLQggh3NzcxOHDh4UQQsTHx4szZ84IIYSYPHmy
-GDFihEhPTxfp6en6bf57XKdPny7q1asnYmNjRWxsrKhfv76YPn26/jibmZmJGTNmiPT0dLF9+3ah
-VCpFfHx8puOgUqmEnZ2duHr1qn5ZzZo1xYYNG4QQQgwcOFBf7sOHD8XmzZtFcnKyePLkiejWrZvo
-1KlT1h+C/8T71VdfiXr16omoqCihVqvF8OHDRa9evfTbhoaGisTERKFWq8W4ceNEQECAft2LjtWB
-AwcyfJ6y8qJ9d+zYIVxdXcXFixdFUlKS6NWrl1AoFOL69etCCCGaNGkiQkJCMsTXsGFD/es1a9aI
-uLg4odFoxIIFC4Sbm5tITU0VQmT/s9WzZ08xZ84cIYQQqamp4siRIy9sz/Mx/vcYz5w5U/Tr1++l
-x0Mq2uQZvVQgjRkzBjc3N5ycnGjfvj3nzp0DYPny5QwfPpxatWqhUCjo378/lpaWHDt2DICuXbvi
-5uYGQPfu3fHz8+PEiRP6ct3d3Rk1ahQmJiZYWVllWXf9+vXp0KEDAI8fP2bHjh0sXLgQa2trXFxc
-GDduHOvXrwdg48aNjBs3Dnd3dxwdHZkyZUqGy/MKhYKBAwdSoUIFTExM2LlzJ2XKlGHAgAGYmJgQ
-EBBAly5d2LhxIwAWFhZcvHiRhIQEHBwc9GdgFhYWxMTEcOvWLUxNTWnQoEGWsa9du5aPP/4YZ2dn
-nJ2dmTFjRoZ7s+bm5nz88ceYmprSunVrbG1tuXz5cqZyrK2t6dixI+vWrQPg6tWrXL58WX9cAH07
-ixUrRufOnbGyssLW1papU6dy8ODBrN/Y//juu+/49NNPcXd3x9zcnBkzZvB///d/+rPagQMHYmNj
-o193/vx5njx58tJjJV7j9siL9t24cSODBw/G398fpVKZ5VWfl+nTpw9OTk6YmJgwYcIEUlNTMxzf
-7Hy2LCwsuHXrFlFRUVhYWFC/fv1sxfKMkLeM3ngy0UsF0rNkDbqkk5iYCEBkZCQLFizAyclJ/3Pn
-zh1iYmIAWL16NdWqVdOvu3DhAg8fPtSX9fwtghfx9PTU/x4ZGUlaWholS5bUlzlixAhiY2MBiImJ
-yVDm8/tmVWdkZCQnTpzIEP/atWu5d+8eAD///DPbt2/H29ubJk2acPz4cQA++OADfH19admyJT4+
-PsybNy/L2KOjoyldurT+tZeXF9HR0frXxYsXx8Tk3//2SqVSf2z/q3fv3vpEv3btWn0y/y+VSsXw
-4cPx9vbGwcGBwMBAHj9+/FrJ5datW3Tu3Fl/LPz9/TEzM+PevXtoNBomT56Mr68vDg4OlClTBoVC
-wYMHD156rF7Hi/b97/vp5eX12mUCfPHFF/j7++Po6IiTkxOPHz/WxwvZ+2x9/vnnCCGoXbs2lSpV
-IjQ0NFuxSNIzZsYOQJJex7NOfF5eXnz00UdMnTo10zaRkZEMGzaMffv2Ua9ePRQKBdWqVct0hv2q
-ep7fplSpUlhaWvLw4cMMCfKZkiVLZujdnVVP7+fL8/LyIjAwkPDw8Czrr1mzJr/++isajYbFixfT
-vXt3bt++ja2tLV988QVffPEFFy9epGnTptSuXZu33347w/7u7u7cunWLChUqAHD79m3c3d1f2uYX
-ad68ObGxsZw/f57169fz1VdfZdmuBQsWcOXKFU6ePEmJEiU4d+4c1atXRwjxyuPt5eVFaGgo9erV
-y7Tuxx9/JCwsjL1791K6dGni4+MpVqyY/v180bF6nQ6fL9q3ZMmS3L59W7/d878D2NjYkJSUpH/9
-/P3/Q4cOMX/+fPbt20fFihUBMsT7/DGDV3+2XF1dWb58OQBHjhyhefPmBAYGUrZs2Ve273kFqQOs
-ZBzyjF7KV2q1mpSUFP3P6/Y8fvbHcujQoSxbtoyTJ08ihCApKYnffvuNxMREkpKSUCgUODs7o9Vq
-CQ0N5cKFC9mK779noSVLlqRly5ZMmDCBJ0+eoNVquX79uv4Z5e7du/P1118THR1NfHw88+bNy/SH
-9fky27Vrx5UrV1izZg1paWmkpaXxxx9/8Pfff5OWlsZPP/3E48ePMTU1xc7ODlNTU0DXAfHatWsI
-IbC3t8fU1DTL5NCrVy8+/fRTHjx4wIMHD5g9ezb9+vXL1jF4xtzcnG7duvH+++/z6NEjWrRokaFN
-z9qVmJiItbU1Dg4OxMXFZety94gRI5g6dao+ocbGxhIWFqYv19LSkmLFipGUlJThy93LjpWrqysP
-Hz4kISEhyzpftm/37t1ZuXIlly5dQqVSZWpLQEAAmzdvJjk5mWvXrhESEqJ/v588eYKZmRnOzs6o
-1Wpmz579whjg1Z+tTZs2cefOHQAcHR1RKBRZvuev4ubmxq1bt+Tl+zeYTPRSvmrTpg1KpVL/M2vW
-rFc+dvf8+ho1avD999/zv//9j2LFiuHn58fq1asB8Pf3Z+LEidSrVw83NzcuXLhAw4YNsyzndep6
-ZvXq1ajVavz9/SlWrBjdunXTn8kNHTqUli1bUqVKFWrUqEHbtm0zJeHny7O1tSU8PJz169fj4eFB
-yZIlmTJlir5n/Jo1ayhTpgwODg4sX76cn376CdD1wG7RogV2dnbUr1+fUaNGERgYmCn+adOmUbNm
-TapUqUKVKlWoWbMm06ZNyzKW19G7d2/27t1Lt27dMrXpWVnjxo0jOTkZZ2dn6tevT+vWrV+7nrFj
-x9KhQwdatmyJvb099erV4+TJkwD079+f0qVL4+HhQaVKlfRXaZ550bEqX748vXr1omzZshQrVizL
-Xvcv2jcoKIhx48bRtGlTypUrl2nMh/Hjx2NhYYGrqyuDBg2ib9+++nVBQUEEBQVRrlw5vL29sba2
-znDpP7ufrVOnTlG3bl3s7Ozo2LEjixYtwtvbO8vj+LLj3a1bN0B326ZmzZov3E4qugw6YM7ly5cz
-PBJy48YNPvnkE/r27UuPHj2IjIzE29ubjRs34ujoCMDcuXNZsWIFpqamLFq0iJYtWxoqPEnKczt2
-7GDkyJHcunXL2KFIecTExIRr165l+5K5JBUUBj2jf+uttzh79ixnz57l9OnTKJVKOnfuTHBwMC1a
-tODKlSs0a9aM4OBgACIiItiwYQMRERHs3LmT9957T9/7VpIKopSUFLZv3056ejpRUVHMmjWLLl26
-GDssSZIkvXy7dL9nzx58fX0pVaoUYWFhDBgwAIABAwboBzXZsmULvXr1wtzcHG9vb3x9ffWX8SSp
-IBJCMHPmTIoVK0b16tWpWLEis2fPNnZYUh6Sndmkwi7fet2vX7+eXr16AXDv3j1cXV0BXceZZ48W
-RUdHU7duXf0+np6erz3CliQZg7W1tfwyWsTl5TDIkmQM+ZLo1Wo1W7duzfLZ39fpiPU8X19frl+/
-nucxSpIkSVJB5ePjw7Vr13K0b75cut+xYwc1atTAxcUF0J3FP+tZGhMTQ4kSJQDw8PDI8BzynTt3
-8PDwyFDW9evX9Y/2vIk/M2bMMHoMsv2y7bL9sv2y/fn7k5sT3HxJ9OvWrdNftgfo0KEDq1atAmDV
-qlV06tRJv3z9+vWo1Wpu3rzJ1atXqV27dn6EKEmSJElFksEv3SclJbFnzx6+//57/bLJkyfTvXt3
-QkJC9I/Xge456O7du+uHwfz2229lRxhJkiRJygWDJ3obG5sMYz2DbljIPXv2ZLn91KlTsxzeVNJp
-0qSJsUMwqje5/W9y20G2X7a/ibFDKLQMOmCOISgUCgpZyJIkSZKUK7nJfXIIXEmSJEkqwmSilyRJ
-kqQiTCZ6SZIkSSrCZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIkwmekmSJEkqwmSilyRJkqQiTCZ6
-SZIkSSrCZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIkwmekmSJEkqwmSilyRJkqQiTCZ6SZIkSSrC
-ZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIszM2AFIkiRJWYuNjWXHjh2YmJjQtm1bnJycjB2SVAgp
-hBDC2EFkh0KhoJCFLEmSlG03btygdu1AUlLqAOnY2p7nzJkjuLu750v9f/75JzNnzichIYn+/bvQ
-v3/ffKlXylpucp88o5ckSSqAJk78mEePRqDVfgRAauqHTJv2KStWfGvwuv/880/q1GlCSso0wINj
-xz7m8eMERo9+z+B1S3lP3qOXJEkqgKKi7qHV1tC/Tk+vwe3bdw1e78OHDwkMbEVKyhBgAtADlWo1
-8+d/Y/C6AU6cOMHIkWMZO/Z9/v7773yps6iTiV6SJKkAatWqMUrlF8Bj4CFK5Ve0bh1o8HqnTp1N
-QoInYPrcUpN8uWW6f/9+mjZtz7JlJVm8WEmtWo25ePGiwest6mSilyRJKoA+/ngy77zjg6lpCczM
-PBgwoA7jx482eL2XLt1Aq+0P/AB8A/yCiUkPxo4dZvC6P/poHirVV8BkhJhNUtJ4Pv98scHrLepk
-opckSSqAzM3NWb36O1JTVaSkJPHttwsxMTH8n+yGDWtgbb0bCAMOYGLyPs2a+TNx4liD152cnAIU
-178WwpnExGSD1qlSqRg+fCzly9chKKgr169fN2h9xiA740mSJBVgpqamr94olx49esSOHTtQKBSM
-HTuSs2ffY//+NoCCRo0aERa2AYVCYfA4hgzpyaRJE1GpbAAVSuVsBg9eZtA6O3fuw++/W5CSspCr
-Vw9Tp04Trlw5T7FixQxab36SiV6SJOkN9s8//1CzZiNUqiqAwMZmKqdOHcLc3BwhBK6urvmS5AHe
-e284arWab78dh5mZGR9/PJ+2bdsapK7k5GQ++mgWu3dvR4gngAVabX3U6gMcOHCALl26GKReY5DP
-0UuSJBnZX3/9RUxMDJUrV6ZkyZL5WnfPnoP5v//zRKOZDYCZ2VR69XrI6tXf5Wsc+UkIQZMmbTlx
-woLU1J3APcABENjZNWbt2g9p166dkaPMKDe5T96jlyRJMqIxYyZRt24Q3bvPw8+vCrt3787X+v/5
-5y4aTS396/T0WkRGRudrDPnt+vXr/PHHeVJTNwHDgCBgBebmg3Fze0KzZs2MHGHekolekiTJSA4f
-PsyKFT+jUl3k8eO9JCX9TNeuffLlquW6devx9q7C+fOnMDObDyQAj1Eqv6Zly0YGr9+YhBAoFKbo
-UuBXQD9MTafRo4eCP/44iLW1tZEjzFsy0UuSJBnJjRs3UCjqAo5PlzRCpUokMTHRoPXu3buXd9+d
-SGTkIpKSdiNEJAqFM6amJejevQKTJ080aP3G5uPjg79/WSwt3wV2Y2FxET8/d0JCluHg4GDs8PKc
-TPSSJElGUqVKFTSafcDNp0vW4eLijq2trUHr3bhxC8nJE4AmQFU0mp/x8ipPcnIioaFL86WnvzGZ
-mJiwb99WBg1yombN+fTuLThyJBwLCwtjh2YQste9JEmSkQQEBDBv3sd88EFVzMycsLaG7du3GLyX
-u4ODLaamd9Boni25g52dHebm5gattyCxs7Nj6dKFxg4jX8he95IkSUaWkJBAbGwspUqVypezyjt3
-7lC1al0SEjqQnl4Ca+tv+eWXH2nVqpXB65ZyJje5TyZ6SZKkN1B0dDQ//BBCUlIyXbt2platWq/e
-STIameglSZIkqQiTz9FLkiRJkpQlmeglSZIk6UWEgEWLIC7O2JHkmEz0kiRJkpQVrRZGj4axY6FD
-B93rQkg+XidJkiRJ/5WeDkOHwsqVYGEBkyZBPkwTbAgy0UuSJEnS89Rq6NsXNm0CpRK2bIHmzY0d
-VY4Vzq8nkiRJ0hsvKSmJLl36olQ64uzsxY8//pT7QlNS4J13dEne3h7Cwwt1kgf5eJ0kSZJUSPXo
-MYgtW1JITV0M3MTauiPh4Rtp2LBhzgpMTISOHWHfPiheHHbtgho18jTmnJKP10mSJElvnF27dpGa
-Og9wBmqRkjKY3bv35Kyw+Hho2VKX5N3c4ODBApPkc0smekmSJKlQcnAoBlzWv7a0vEzx4sWyX1Bs
-LDRtCseOgZcXHDoEFSvmXaBGJhN9Ieft7U1ERIRBytZoNIwaNQpfX1/8/PwICQl54bZz586lcuXK
-VKhQgYEDB6JWqwG4desWZmZmVKtWTf/z6NEjAI4ePUqDBg2oWLEiFStWZNKkSXkaf58+ffDw8MDE
-xASVSpVh3fHjx6latSpvvfUWrVq1IjY2Vh/T87F6eHhQ4wXf6lUqFT169MDPz48KFSrw22+/6det
-WbOGKlWqYG5uzjfffJPl/gcOHMDU1PSF6yVJermlSz9HqeyDufk4lMqOeHpeYdCgQdkrJDoaAgPh
-7Fnw89MleV9fwwRsLKKQKYQh50hoaKiYOXPmK7fz9vYWFy5cMEgMq1atEq1atRJCCBEbGys8PT3F
-rVu3Mm23a9cuUaVKFaFSqYQQQgwdOlQEBwcLIYS4efOmcHZ2zrL8CxcuiGvXrgkhhEhNTRUNGzYU
-P/744yvjGjBggDhw4MArt9u/f7+4f/++UCgUIikpSb9co9EIHx8fceTIESGEEJ9++qkYPHhwlmV0
-6tRJLFiwIMt1s2bNEsOGDRNCCHH16lXh5uYmEhMT9W2LiIgQ/fv3F998802mfRMSEkSdOnVE+/bt
-xZIlS17ZFkmSsnbu3Dnx+eefi++++048efIkezvfvClE2bJCgBCVKgkRE2OQGPNCbnLfG3VGP3/+
-fP73v//pX9+7dw83NzdSUlJyXfa9e/do2rQpNWvWpFKlSnz44Yf6dUOHDmXChAn67cqWLcuff/75
-0vKyM03lmjVrqFmzJn5+fnl6drhx40aGDRsGgLOzM506dWLTpk2Ztvvzzz9p1KgR1tbWAAQFBfHT
-T6/u/VqxYkV8fHwAsLCwICAggNu3b79yP4VC8VrHp0mTJri4uGRafvr0aaytralfvz4Aw4cPZ+PG
-jZm2u3//PuHh4fTr1y/L8jdu3Mjw4cMB8PX1pWbNmuzYsUPftgoVKmBiYpJlB5oJEyYwadIkihcv
-rl+WnJxM1apVCQsLA2Dfvn1UqFCBpKSkV7ZVMr5z584xYMAIevcewsGDB40dzhujatWqfPDBBwwb
-NgxbW9vX3/HyZWjUCG7c0N2LP3BAd2++CHqjnqMfMmQI/v7+fP755yiVSpYvX06fPn2wsrLKtG23
-bt24du1apuUKhYJjx45haWmZYbmjoyNbt27FxsaGtLQ0goKC2LVrF61atWLx4sXUqVOHLVu2sHjx
-YiZNmkSVKlVeGmtWyeFFYmNjOXXqFPfv36datWo0btyYypUrZ9jm0qVL9O7dO8v9W7Zsybx58zIt
-v337NqVLl9a/9vLy4p9//sm0XY0aNfj+++95+PAhDg4ObNy4kcjISP36hIQEatSogUKhoGfPnrz/
-/vuZyrh//z6bN29m+/btr9Xm7Byf//pvu5ydndFqtcTHx+Po6Khfvnr1alq1apXll4WsynnR8fmv
-HTt28OTJE7p06cLWrVv1X1qsra3ZuHEjLVu2xM3NjSFDhvDLL79gY2OT06ZK+eTs2bM0atSSpKQP
-AGu2bOnBzz+vJCgoyNihFXqJiYkMHPgeO3b8hp2dI4sWBdO9e7fcFfrnn9CiBdy/Dw0bwrZt4OCQ
-NwEXQG9UondycqJDhw6sXr2aIUOG8MMPP7Bv374st83qzPVl0tPTef/99zl27BhCCO7evcu5c+do
-1aoVVlZWbNy4kRo1atCmTRtGjBiRZRnbt2/no48+AiAuLg61Ws2vv/4KwOjRoxk8eHCW+7377rsA
-lChRgrZt23LgwIFMib5ChQqcPXs2W216XW+//TajRo2iZcuWWFlZ0axZM3bv3g2Au7s7UVFRODs7
-ExsbS4cOHXByctLHDPDkyRM6dOjA+++/T9WqVbOs45NPPmHz5s2ALsEePnxY/+191apVr/zilBOh
-oaFZfgHKjfj4eCZPnsyePbqewUKIDF9a3nrrLWbPnk39+vX5+uuvX3g8pILliy++JSnpQ0D3JVal
-KsGsWV/JRJ8HBg0axbZtaaSmXkSlus7Age9QurQXderUyVmBJ09CUBA8eqRL9r/8AkX8y/QblehB
-lzD79OmDi4sL/v7++kvH/9W1a1euX7+e5bpjx45lugrw5ZdfEh8fz8mTJ7GwsGD48OEZbglcvHgR
-BwcH7t69i0ajwdTUNFO5bdq0oU2bNoAueUVGRvLxxx+/sk3PJwohRJaXtSMiIujTp0+W+7do0YLP
-P/8803IvLy9u3bql74wWGRlJmTJlsixjzJgxjBkzBtBd0q74tMeqhYUFzs7OALi4uNCnTx+OHDmi
-T/QqlYp27doRFBTE+PHjX9jG6dOnM336dAAGDRrEoEGDaNy48Qu3f5nSpUtnuOLw4MEDTExMMpzN
-Hz9+nEePHunfj6w8Oz7PLr9HRkbStGnTTNs9/35cuHCBu3fvUrt2bX3d27Zt49GjR0ybNg3Q3Vpw
-dXV9rasDUsGQkqIGnr9sbKfvkGoIT5484eHDh3h4eGBubm6wegqCnTt3kJp6DnAD3EhNHUh4+O6c
-Jfrff4e2bXXPy3foABs2QBZXdIucPOgjkK/yIuS3335blCpVSmzdujUPItKZOHGiGD9+vBBCiDt3
-7ghXV1cxa9YsIYQQN27cEF5eXuLatWtiwIABYvLkya8s73U745UuXVoMHTpUCCHE/fv3hYeHR551
-zlu5cqVo1aqV0Gq14v79+y/sjCeEEDFPO7HExcWJ6tWriy1btuhjUqvVQgghkpKSRPPmzcWiRYuE
-EEIkJyeLpk2big8//DBbcQ0cOPC1OuMJIYRWqxUKhULfSU6IfzvjHT58WAghxCeffJKpM97QoUNf
-GdfMmTP1x/7KlSvC1dU1Qz1C6DoOvqyz3cCBAzN01tu8ebOoXr26iIuLExUrVhQ7dux4rXZKxhUe
-Hi6UypICfhawQyiVviIkJNQgdS1a9K2wsLAVSqWnKFGitPjrr78MUk9B4e5eTsABoZtGTghr6y45
-68C6c6cQ1ta6Qnr1EuLp36XCIje5741M9GvWrBHe3t55EM2/IiMjRe3atUWlSpVEUFCQ6NOnj5g1
-a5ZQq9Widu3aYt26dUIIXbLz9/cXO3fufGl5K1eu1H9ReBlvb28xdepUUaNGDeHr65tlD++c0mg0
-YuTIkcLHx0f4+PiI77//Xr9u2bJl4uOPP9a/rly5sqhYsaIoV66cWLx4sX755s2bRaVKlUTVqlWF
-v7+/+PDDD4VWqxVCCLFkyRJhamoqqlWrJgICAkRAQICYM2fOK+MaOHCgOHjw4Cu369y5s/D09BQm
-JibCw8NDBAUF6dcdPXpUVK5cWfj5+YmWLVuK+/fv69epVCrh4OAgLl++nKnMgIAA/ZeapKQk0a1b
-N+Hr6yveeustERYWpt9u7dq1wtPTU9jY2AgnJyfh6ekpLl26lGVbnr1nN2/eFKVKlRJXr14VQuh6
-7nt5eYmoqKhXtlUyvi1btogaNZqKqlUbix9+WGGQOs6cOfP0C8WNp4kvVHh6vmWQugqKX375RVhb
-lxBmZhNF0rhoAAAgAElEQVSFtXUn4etbJfu96zdvFsLcXJfkhwwRIj3dMMEaUG5y3xs5BO6QIUOo
-UKECEydOzKOoJEmSDC80NJTRo/eTlLT66RKBqakVCQmPUCqVeV7ftWvXOH/+PN7e3i8cTyI/nD59
-mt27d+Po6Ejfvn2z17t+zRoYOBA0Gt10swsXQjaeaiooCvQQuPHx8XTt2pUKFSrg7+/PiRMniIuL
-o0WLFpQrV46WLVsSHx+v337u3Ln4+flRvnx5wsPD8zSW6Ohoypcvz/Xr1xk1alSeli1JkmRoZcuW
-BY4BCU+X/I6trZP+0da89NNP66hatT6DB/9I48ZdGDducp7X8bpq1KjB5MmTGTFiRPaS/PLl0L+/
-LslPm1Zok3yu5dFVhRfq37+/CAkJEUIIkZaWJuLj48UHH3wg5s2bJ4QQIjg4WH8v9OLFi6Jq1apC
-rVaLmzdvCh8fH6HRaDKUlw8hS5IkFUharVaMGDFOKJWewsGhubCxcRbh4eF5Xk9KSoqwsrIX8OfT
-WwRxQqksJU6fPp3ndRnMggVCf2P/6QBehVlucp9BL90/fvyYatWqcePGjQzLy5cvz8GDB3F1deXu
-3bs0adKEv//+m7lz52JiYqIfbCYoKIiZM2dSt25d/b5y9jpJkt50586dIyYmhqpVq+Lu7p7n5UdH
-R+PrW43k5Hv6Zfb27Vi1agidOnXK8/rylBDwyScwY4bu9ZIlUASu4BbYS/c3b97ExcWFQYMGUb16
-dYYOHUpSUhL37t3D1dUVAFdXV+7d032YoqOj8fT01O/v6elJVFSUIUOUJEkqdAICAmjdurVBkjzo
-/i7b2loB654uOUda2omCP66DEDBpki7Jm5hAaGiRSPK5ZdDn6NPT0zlz5gxLliyhVq1ajBs3juDg
-4AzbvGo406zWzZw5U/97kyZNaNKkSV6FLEmS9Ma7ceMGtrYOxMb2BYZhYSFYuXLlC8fRKBC0Wl1S
-X7YMzMxg7VrolssR9IzowIEDHDhwIE/KMmii9/T0xNPTk1q1agG6QWjmzp2Lm5sbd+/exc3NjZiY
-GEqUKAGAh4dHhkFC7ty5g4eHR6Zyn0/0kiRJUt7RaDQ0bdqOqKhRwHBgDxYWgwgMbGTs0F4sPR0G
-D4YffwRLS/j5Z93AOIXYf09iZ82aleOyDHrp3s3NjVKlSnHlyhUA9uzZQ8WKFWnfvj2rVq0CdCPA
-Pbvn06FDB9avX49arebmzZtcvXpVP4KYJEmSZHhRUVHExSUixBjAEmiLqWkAZ86cMXZoWVOroWdP
-XZK3sYHt2wt9ks9rBh8Cd/HixfTp0we1Wo2Pjw+hoaFoNBq6d+9OSEgI3t7e+pnD/P396d69O/7+
-/piZmfHtt99maxY3SZIkKXecnJxIT08A7gCegIr09GsvnNzJqJKT4Z13YMcO3aQ0O3ZAvXrGjqrA
-eSMHzJEkSSpo1Go1oaGh3LwZSb16dejYsaPRYgkOXsAnnyxCo2mHuflhOnSoxZo13+fqxCsxMZF3
-3x3Nrl27cHQsztKln9O6deucB/nkCbRvDwcPgrMzhIdDtWo5L6+Ay03uk4lekiTJyDQaDYGBbTh7
-VqBSNcLGZi1jxvRgzpyZRovp0KFDnD17ljJlytCuXbtcX13t0qUv27drSE2dB/yNUtmXY8f25Gzm
-yUePoHVrOHECSpaEPXvA3z9X8RV0MtFLkiQVYlOmTGHevE0IcRkwBe5hbl6Gx48fGmTUO2OwtnYg
-JeU6oJvN0tx8HHPmePL+++9nr6D796FlSzh/HkqXhr174QWzkBYlBfY5ekmSJOnlQkNXEhy8BCHK
-oEvyAC4oFBaoVCpjhpanbGwcgFv61+bmN3FwcMheIVFREBioS/LlysGhQ29Eks8tmeglSZKMRKvV
-Mm7cdGAl8BewGrgNjMXHx49ixYoZM7w89eWXc1AqO6JQTMfKqhvu7rfo1avX6xdw8yY0agR//w1V
-qujmli9VKttxpKSkkJycnO39CjOZ6CVJkoxk9Oj3SUhQAWWBHcBSoBqwik2bVhapp4769+/Lzp3r
-mTbNhODghpw5c/j1J6j5+29dkr95E2rXhv374enoqq9Lo9HQv/9wbG0dsbNzomvXfqjV6hy0pPCR
-9+glSZKM4NSpU9Sp0wCtdhz/JvkY4F2aN2/E7t3bjBtgQXHunO6efGwsNG4M27aBnV22i5k7dz6f
-frodlSoMMMXauitjx9Zi7tycD0STn+Q9ekmSpFwKDw/HxyeA4sW96NNnqEHvjx87dozGjYPQagHe
-B7oB/YH3aNKkJjt3bjFY3YXK8ePw9tu6JB8UpHtOPptJPiEhga+++orly39CpXoPsAOUJCePZu/e
-owYJu6CRiV6SpDfeX3/9RadOfbhxYw5xcQfYvDmeAQNGGqy+GTO+IDl5LrohZrsC1VAo+lOsmAUb
-N67H1NT0FSVkX0JCAt26DaREibJUqdKAkydP5nkdeerAAWjeHOLjoXNn+PVXUCqzVURCQgIBAfWZ
-MuUYt25ZAb/r15maHsPbO/MQ60WRwUfGkyRJKuh27dqFRtMbaANASsq3/PZbOYPVp1KlAMWBhcAX
-wExcXBI4fvyQwUag69SpD0ePOpOauovY2D9o2rQtERGn8fLyMkh9ubJ9u27Eu5QU6NMHVq7UTVST
-TatXr+bu3QqkpGwA7gF1USjOYGvrgLX1JRYs+P1VRRQJ8oxekqQ3np2dHWZmt59bchulMvv3gV/X
-0KE9USonA4eBmiiVD1m8+DODzQ6nVqs5eHAXqanfAX5Ab6AF+/fvN0h9ufLzz9Cpky7JDxsGq1fn
-KMmD7oxerX52TF2BXVhaXmDFisFcvnyWUjnotV8YyUQvSdIbr3fv3ri6XsXSshcKxSyUyo588cWn
-BqtvwIB+fPnlB/j5vU+5clNYsmQm3bsbbkpVMzMzzMzM0Z3VAggUiqjX7/WeX1avhu7dIS0NJkzQ
-TTlrkvM01apVKywsVgH7gNtYWX1Ex47v0LVrVxwdHfMs7IJO9rqXJElCd/b3/fffExsbR1BQiwxT
-hBYFc+bM57PPvkelGoyV1Sl8fP7h1KmDWFlZGTs0naVL4b33dL/PmKH7yYPHC8PCwvjf/yaTkBBP
-mzZt+OGHRSizea+/IJBD4EqSJGWDEIL169ezf/9RvL09GDt2NDY2NsYOK0+Fh4czY8YCUlPVjBzZ
-lyFDBrN161b27z+Ep6cbI0aMKDhtnj8fJk369/fsDov7BpCJXpIkKRsmTZrGN9+EoVINxtLyGD4+
-Nzl9+veCc3abA7/99htjx07jyZME6tatzu7dv5Oc/DXggFI5ni++mMDIkcOMHWZGQsDMmTB7tu71
-t9/CSMM97VCYyUQvSZL0mtRqNTY29qSn3wZKAAJb24b89NOHdOjQwdjh5cipU6cIDGyLSrUKKIOp
-aVs0mlHA+Kdb7KFixRlcuHDEiFH+hxAwcSIsXKi7Dx8aCv37GzuqAis3uU8+XidJ0hslLS0NUABO
-T5coUChKFNrxzyMiIujRoz8q1SAgCACNpgHw/IA/Ksxy2HPdIDQa3f345cvB3BzWrdM9TpcNycnJ
-LF68hCtXImnYsCYDBgwoUkMG56UC9M5LkiQZno2NDQ0aNOX48aGkpk5EoTiGiclxAgOXGju0bLt0
-6RI1azYiObkREAnsBdKAeigU7yOEGeCIUjmbGTOWGDVWvfR0GDgQfvoJrKxg82bd3PLZkJaWRuPG
-rblwwYmUlCasW/cNJ06cZ+nShYaJuZCTl+4lSXrjJCQkMHz4eA4dOoanpwfff/8llStXNnZY2RIT
-E0OFClV5/DgQmI9uMhwPwBa4yOTJ/+POnYekpKgZMqQXrVq1yvMYhBCo1WosLS1fb4fUVOjZUzfK
-na0tbN0KOXi6Yf/+/XToMIHExNPonhKPx9zckwcPorG3t892eYWBHOtekiTpJdLT0zl+/DiHDh0i
-OTkZe3t71q0L4c6dCI4f313okjzA5MmzePKkJqAFQoB26Ka6PY6p6UQuXrzFjz8uZ9OmlTlO8klJ
-SXTu3AdLS1ucnNwJCQnVrwsJCUWpdESptKNmzSbcu3fvJSUBKhV06KBL8o6OsGdPjpK8rigVJibF
-+TeF2WFiYkFKSkqOyivq5KV7SZKKtKSkJBo1CuLq1UcoFFYUK5bM8eN7cXNzM3ZouXLrVjRabS9g
-BnAdGIeu7wFoNM24fj0813UMGTKGHTu0qNX/oFbfZsyYdpQt6421tTVjxkwjJeU4UI7z56fQpUt/
-jhzZlXVBCQnQrh0cOgQuLrB7N1StmuO46tevj7n5CBSKrxHibSwsllK5chWDDR9c2MkzekmSirRP
-Pgnm0qVSJCb+yZMnfxAd3Y4xYyYbO6wcu3v3LleuXKFp07oolaFAOGCPbprbJCAdS8vvqVevRq7r
-Cg/fTWrqHHQdF6uSnDyE3bv3cvToUdLTuwIVAFPS06fzxx+Hsi4kLk43Oc2hQ+DhAb//nqskD+Dk
-5MTRo3tp0GAXnp696NBBRXj4L7Iz3gvIM3pJkoq0ixevk5LSmmfnNWlpbbh06SPjBpUDQgiGDx/L
-6tU/YmbmiIuLDUFBAWzZUhEhBJ6efty9WxKFwoxatWqzcOE3ua7Tyak4cXERQBlAYGkZgYtLXVxd
-XTE3/xW1WgOYAqdwcsriCsm9e9CiBfz1F5QpA3v36v7NA+XKlePQoe15UlZRJxO9JElFWt26Vdm7
-dy3Jyd0AcywtV1GrVu7OKPObEIIRI0YSEvI7Wu0tUlPtSUmZTalSJ0hKSgDA0tKSuLg40tLSKFGi
-RJ6c3S5bNp+OHXuh0fQATmFpGcn9+7707NmTatVWc+5cfYR4CyF2sGrVjxl3/ucf3Zn8lStQvrzu
-nrzHmzEtbEEje91LklSkpaWl0alTb/btO4BCYU7FiuXYuzesUPXOXrbse0aPnkJ6+lhg+tOlkTg5
-NSAu7o5B646IiODTTz/j55/3oVaPw9z8BsWK7ebcuaOcPHmShw8f0rBhQ/z8/P7d6fp1aNYMIiMh
-IAB27YISJQwaZ1EnB8yRJEl6AXNzc7Zt20hUVBTp6el4eXlhkosZ0fLb4cOH+fDDz0hPHwjsBiYB
-lkAYZcv6Grz+GzdusHlzOGr1NqAOaWkQH9+fdevWMX78+Mw7RETozuRjYqBuXd3c8k5OmbeT8o1M
-9JIkFXkKhQJPT09jh5FtBw8epHXrbiQnFwcaA7fRdYBzwtLyFmvWGHZI27Vr1zF06Iekpgp087nr
-pKe7kZiYlHmHM2egVSt48ED36FxYGNjZGTRG6dXkpXtJkoqM9PR0fvjhBy5dukbNmlXp27dvoe6J
-3apVV8LD26DrVT8O3aN0F7GyWsnevdupX7++QesvX74Oly9/CmwFLgNfADdQKody9Ohuqj7fe/7o
-UWjTBh4/1v37f/8H1tYGje9NIi/dS5L0xtNqtbRp05UjR56gUrXExmYx+/cfZ8WK3Pc+N5a0tHTA
-HEgGWgHf4uqaxM6dBwgICDB4/enp6YASXYKfCrTGycmMjRvXZkzy+/bpBsNJSoKuXXXD21pYGDw+
-6fUUnhtVkiRJL3HmzBmOHr2ISrUT+JCkpD2sXbuWu3fvGju0HHvvvb6YmIwFlgN2QCRduwYZNMnH
-xsZy7tw5EhIS+N//BmJjMxzYDwSgVKrZuXMjzZs3/3eHbdt0Z/BJSTBggG6CGpnkCxSZ6CVJKhKS
-kpIwNXVBdwYMYIeZmT2JiYnGDCtXTE1NsbDwAQ4CXwFHWbFitcFuX3733Q94eZUjMLAfHh4+VKpU
-gXnz/ke1asE0aLCWbds2ULt27X932LgROnfWjWH/3nuwYgUUpFnyJEBeupckqYioXr06lpbRmJh8
-jVbbBjOzlbi7F8Pb29vYoeVYXFwcpqb+/HtO5ktqqoq0tDQs8vis+dq1a4wfP5WUlD9ISfEFDtC5
-czcePLjDqFEjMu8QGgpDhoBWC5MmQXAwFOL+EEWZPKOXJKlIsLOz48iR3dSqtQ1n55YEBl7kwIHf
-CtY87K9p+/btdO8+iB079qHR/IZu+tl4zMwmUbNmozxP8gCXL1/GwqI68OyRvSZotVbExMRk3njJ
-Ehg8WJfkZ8+WSb6Ak73uJUmSXiImJoaDBw+iVCpp1arV60/JmkO6R9omoVJ9hEJxHyurL7G1dSIh
-4QF16jRm06ZQShhg8JnLly9TrVpjkpP/ALyAY9jYtOPBgyisrKz+3TA4GKZM0f2+YAFMmJDnsUiZ
-5Sb3yUQvSZL0AufOnSMwMAgh6iPEPcqU0XL8+F6USqXB6ixXrhZXr84FdB3eFIqpTJig5Ysvgg1W
-5zMLFy5m6tSZWFj4kp5+nY0bV9G2bVvdSiFg+nT47DPd2fuyZTBsWLbKT0xMRKPR4ODgYIDoizY5
-H70kSZIBvPvuOBIS5vDkyWYSEw9z9aoHS5YY9nG9tDQ1YKt/LYQtKSlqg9b5zPjxo7ly5Rzbt3/J
-rVuXMib58eN1Sd7UFH78MVtJXqPR0L//MJycSuDi4kGrVp1RqVQGaoX0XzLRS5IkZeH+/fvcvn0b
-qPd0iYKUlLpERkYbtN533+2FldUgdNPPrkOp/Ir+/XsatM7nlSpVigYNGvw7t7tGA0OHwtdf6x6b
-27QJ+vTJVplffbWYn3++THr6PdLS4vj9dzMmTfrYANHn3JEjR+jYsQ9t2/Zk165dxg4nT8lEL0mS
-9B+DB4/A3b0scXGJwGdAOnAPpTKUJk0MNxrd+vUb+eyzYDSaWExNu1Kp0tds3bo+4yNt+SktDfr2
-hZAQ3Sh3YWG6x+myaf/+E6hUQ9GNBWBBSsp7/P77yTwPN6eOHDlCy5adCQtryPbtLenSZRDbtm0z
-dlh5RiZ6SZKk58ycOYvQ0M1oNNfQaq8CEYASMzNvxo3rTteuXQ1S782bNxk8eBQpKQdJS3uARhNK
-TEwUjRs3Nkh9r5SSohvlbv163Xj1O3fqxrHPAV/fUlhY/A7o7jGbmh6ibNlSeRhs7nz55XeoVDOA
-kcBgVKqFBAd/a+yw8kzhe+5EkiTJgBYu/AZoD7g9XfIHYE5CwmOs83Ds9vT0dL7+egknTpynYkUf
-/P3fwty8NsnJz4aWfQeVahT37t3DI7/ncU9Kgk6ddHPIOznpppmtVSvHxc2YMYXffnube/caAdZY
-W19j0aKDeRdvLmm1gn8HWgIwQ6PRGiucPCcTvSRJEnD+/Hl69RpGQkIC8DsQDzgC27C1dcnTJA/Q
-rdsAwsPvolL1JCwsjLJlfyE9/R7wECgO/AmkULx48dcqT6vVcvHiRdLS0qhUqVLOn7V//BjatoUj
-R3RzyO/eDVWq5Kysp5ycnPjzz2Ps37+f9PR0AgMDC1TP+9GjB7FrV2+Sk5WAJUrlRCZM+NLYYeUZ
-+XidJElvvEePHlG2bEXi4+cC+4BjwGOgNHCR0NBvGDhwYJ7VFxUVhY9PFVJTzwG9gb8AFf7+Vbh1
-6y5mZlVJSztJSMgSevXq8cryUlJSaNGiE2fPXsHExIqSJa04fHgXLi4upKen8+TJExwdHV89k9+D
-BxAUBKdPg6cn7N0L5crlRZMLvPDwcObMWUJ6uoZx4wbTtes7xg4pA/kcvSRJUg6lpKTQvXsftm27
-hRCngRTgfWAlJUq4sGhRMD16vDrZZseNGzeoXLkRKlV9wB1YCDzEyqoxn3zyLuXKlaNSpUqULVv2
-tcqbNesz5s07RXLyJsAUc/OJdO78CB8fDz7/fAEKhSleXj7s2fMrZcqUybqQmBho0QIuXgQfH91l
-+0I8fHBRIxO9JElSDnXrNoCwsFuo1beBvwFL4CEWFmWIjr752pfOX8fOnTuZOnUeycnJJCQ8Ijr6
-IXAC8Hm6xVwmTHjEggWfZ6vczp378euvzYCBT5ccpnjxXjx8mPi0fD/gc/z9N3Px4onMBURGQvPm
-cO0a+PvrLte7u+eskZJByAFzJEmSckCr1fLrrxtQq8OAOsDbwFQsLOozevToPE3y69ato3373pw9
-+z/+/juYR4/MsLIyRzcFLIAGa+tDlCnjle2yq1f3x9r6ZyANEJiZrScu7i7QBSgHKICJXLp0Co1G
-k3Hnq1ehUSNdkq9WDQ4elEm+iJFn9JIkvbGEEFhbO5Ca+hfgCazD3PwT3nuvNQsXLnz1Pe3XdObM
-GerUeZv09GnAB0+XHqFUqaEkJDxCiGpotTFUqlScAwd+y/Z4+qmpqbRu3ZUTJ85hYmKFp6c9V678
-iVZbEd0ZvSVwCBubLiQmxv6744ULujP5e/egfn347TdwdMyTNhcEp06dYvv2HTg42DNgwAAcC3Hb
-cpP7ZK97SZLeWAqFgqlTpzBvXmtUqlGYm5/HzU3B7Nmz8yzJA0ye/Bnp6XWAhOeWPsbOzpHTpw9y
-9OhRbG1tCQwMzNFse5aWluzdG8aVK1dQq9VUqFCBgIAGXLyYBlRDd1a/h6+//vrfnU6fhpYtIS4O
-mjaFLVvA1vYFNRQ+W7dupWfPIaSkDMLcPIIFC5Zy/vwxnJycjB1a/hOFTCEMWZKkAm79+vWiX79h
-YvLkaeLhw4e5KissLEyUKuUvHBxKiu7dB4rNmzcLX98aApYLcBEwQ8AiYWJSXPz888951ILM7ty5
-I6pXbyxMTEyFnV1xsWLFin9XHjokhL29ECBEu3ZCJCfnef33798Xbdt2E2XLVhOdO/cV0dHReV7H
-y3h7VxYQLnQD9QthadlHzJ8/P19jyEu5yX3y0r0kSW+U06dP8+mnC0lMTGbIkB706NE9z8o+c+YM
-DRsGkZy8FiiJQtEcU1NXTE3TSE3VAPOBdZia7mPixEHMmzc3z+p+ESFExqsTe/ZAx46gUkH37rBm
-DZibv7iAHLh79y7e3pVJTW0LDAF+pXTp7fz995mMU94aUPHiXsTF7edZR0eFYgZTpmj57LNP8qX+
-vCY740mSJL2Gv/76i8DAIH79tTZ79rzD4MGTWbFiZZ6Vv2vXLtTq/uimmN2EEE1JTz9LauoFwAdz
-84G4up5k/vyp2UrySUlJDB06Bj+/mjRt2oHLly+/9r4ZknxYmG4wHJUKBg2CtWvzPMkDDBgwktRU
-E2AF0BCYz927ppw+fTrP63qR9u3bYm09EfgHOIyV1XLatAnKt/oLEnmPXpKkN8by5StJShoNjAFA
-pSpJcPAkBg8emCflOzg4YGFxmuRk0CWYQHQ93gFm4OU1kmvXsp/s3nmnHwcPWpCS8g3Xrx+nXr2m
-XL587t8Z5v4jPj6egwcPYm5uzttvv60b1W/9et0ENRoNjB4NX30FJq93rqdSqYiOjsbd3R2lUvnK
-7S9fvg5oADVgBWjRaBIxN8CXihdZuvRLtNpxbNlSCxsbexYu/JoGDRrkW/0FiTyjlyTpjaHVasn4
-Z88kT28F9uvXDze3v7Gy6gncBb5EN8qeCkvLxTRqVCfbZapUKvbs2U5KymqgDkKMJS2tJvv3789y
-+8jISN56K4B+/b6hZ885VKlSD9XixdC7ty7JT5mim3L2NZP8zp07KVHCi4CAFri4eLJlS9gr9wkI
-qIxC4QZ0AlYC7+Dqakn16tVft9m5Zm1tzerV3/H48V2io6/k6S2aQidPegnko0IYsiRJBcTZs2eF
-UuksYJmAn4VS6SuWLv0uT+t4/PixWLBggahQobowMXEU4CbAVtSp87ZISEjIdnmpqanCzMxSwIOn
-Hcu0wta2sfjll1+y3L51627C1HS2ftsJpnWFvkfaZ59lq+74+HhhY1NcwKGnRZwQSmVxcf/+/Zfu
-d/fuXeHjU0WYm7sJExMX4e1dQTx69ChbdUsZ5Sb3yUv3kiS9MQICAti7dyszZy4gKSmZIUM+ZsCA
-fnlah729PTY2NkRG2qDVRgNWmJlNw97+EnZ2dtkuz8LCgpEjRxMS0gqVaigWFscpWfIxrf4zZey1
-a9do3bor165FAqMAmMJc5miO6zb4+msYMyZbdd+4cQNTU3d099kBamNu7sPVq1dfeNsAwNXVlYiI
-P4iIiMDCwoIKFSrk6eOKUjbl4ReOfFEIQ5Yk6SW++WaZKFmynHBxKSOmTZslNBqNsUPKtWHDRgv4
-Un8iDRdEyZLlclyeVqsV338fInr0GCymTJku4uPjM60vU6aSUCgWChgvoIuYwwdCgNCA2NWjV47q
-vX//vrCychRw+Wk7bggrq2Lizp07OW5Lbh08eFD4+VUXTk6eokuXvuLx48dGiyU/5Sb3FbqsKRO9
-JBUdGzZsFEqlj4CTAi4IpbKmmDv3C2OHlWuLFy8RSmUzASkChDA1/VS8/Xb7XJWp0WiESqXKtDwy
-MlK0a9ddKBSWAoRQ8EQsoowQINQgvm3cTKSnp+e43h9+CBXW1s7CwaGpsLZ2EYsXL815I3Lp6tWr
-wsbGWcAvAm4KS8sBomXLzkaLJz8V6ERfunRpUblyZREQECBq1aolhBDi4cOHonnz5sLPz0+0aNEi
-w72bOXPmCF9fX/HWW2+JXbt2ZQ5YJnpJKjI6deorIOS5M989okqVRsYOK9fS0tJEUFAXYWNTWtjb
-BwhPz3IiMjIyx+X98MMKYWVlJ0xNLUSVKvVFVFSUEEKIuLg44eJSWpiYzBBgI0w4L1YwUAgQKSjE
-qY8/zpP23Lx5U+zatUtcv349T8rLqWXLlglr60HPfV5UwtTUPFdfZAqL3OQ+g9+jVygUHDhwgGLF
-iumXBQcH06JFCyZNmsS8efMIDg4mODiYiIgINmzYQEREBFFRUTRv3pwrV65g8pq9QyVJKlyKFbPH
-xOQ2Wu2zJZE4OtobM6Q8YWZmxvbt/8fFixdRqVRUrlxZ94hbDpw8eZIxYz4iJeUPwI+LF2fQuXM/
-TpzYy549e0hJqYhWOxNzyvAjtelBKkkoWNi4BR/NnJkn7fH29sa7AExZa2+v+7yAQPfY4j9YWNjI
-HPEK+XJ0xH8eXwkLC2PAgAEADBgwgF9//RWALVu2/D979xkfVfE1cPy3NduSUNIoodfQQeklgBTp
-HRA/SjQAACAASURBVEUQRaog2MEOSlP0ryKK8AARRDoISBEEDIQivQQQQq8hEAglfct5Xuwag4Ak
-2U1C8H5f6d07M2fWjzl7507h2WefRafTUaJECcqUKcOuXbtyIkSFQpEL3n33dby9v0erfQW1eiRm
-80g+/fR9t+o8efIkn376KV988QWXLl3yUKSZp1KpqFy5MrVr185ykgfYsWMHdnsXoDygxm5/h717
-twKg0WiAVLxIZilL6EkKt4CDn07kvfBfH7sJcJ06daJYsVsYDF2BjzGZWvLpp+Meu356Wo480T/1
-1FNoNBoGDRrEgAEDiImJITAwEHDOzoyJiQHg8uXL1K1bN61s0aJFc/V/VIVCkb1Kly7NoUO7mD17
-DlarjZ49w6lUqVKW6rJarXTr9hwrV64C+qDRWBk79kn27t1KqVKlPBt4DipUqBBa7QJSUmw4/2Tv
-okCBQgC0aNGCwj6j+C6+LM3kItfR8k27jox+++1cjTm7GI1Gdu8OZ8aMGVy+HEPz5tNp2bJlbof1
-yMv2RL9t2zYKFSrEtWvXaNGiBRUqVLjrc5VK9a+/xu732eh0w1GhoaGEhoZ6KlyFQpHDihUrxgcf
-uPcUD/Dhh2NZtWonzv3kh2K3w61bH/DJJ5MIC5vqdv25pWvXrkybNpddu2oDFbHb1zFnzlwAvO12
-Igv5ort0gut6AyuGDuODSRNzN+BsZjabGTFiRG6Hke3Cw8MJDw/3SF3ZnugLFXL+8vT396dz587s
-2rWLwMBArly5QlBQENHR0QQEBABQpEgRLly4kFb24sWLFClS5J46R3vovZNCoXh8rFq1EYcjACib
-dk2kHNeuncxynadOnaJ37yGcPBlFpUqV+fHHqQQHB3sg2gc7ceIEx44do0yZMlSsWBGNRsP69T8T
-FhbGhx9+xpUrcfTuPZBl076k8bhx6Pbvh+BgCm7cSL+yZR/egCJP+OdD7JgxY7JcV7a+o09MTOTO
-nTuA81CG9evXU6VKFTp06MDs2bMBmD17Np06dQKgQ4cOLFiwgNTUVM6cOcOJEyeoXbt2doaoUCge
-E4GBfkAw8CFwCjiCVjuG7t3bZKm+hIQEGjRowa5drYmN3UhEhC/ly9ehR48X6Nv3JQICyhAcXIkZ
-M2Z5rA/Tps2gWrUG9O49lVq1mjJp0leAc2Tzk0++4MqVlxFJQX99EgHde8L+/VCmDGzdCkqSVzyI
-5yb/3+v06dNSrVo1qVatmlSqVEnGjx8vIs7ldc2bN7/v8rpx48ZJ6dKlpXz58vLrr7/eU2c2h6xQ
-KLLJnTt37rsO3FMiIyPFYvEXjaaGgK+oVBZ5990PxeFwZKm+7du3i49PLdcyrvkCRV1LAasLlBLY
-KbBF9PpgWbx4idvxx8bGujanOeFq84IYDAXl7NmzEh0dLQaDn4BIcc7ISUqJgNwKDhbJ4XPeFbnD
-ndynnEevUCiyVUJCAp079+b339ch4qB//8F8993/smVJ1IULF1i1ahVarZYuXbpQsGDBLNcVGRlJ
-3brtSEyMApoCHwFlgDrAj8DTrjt/oHXr1axdu9it2CMjI2nQoCd37hxNu+brW49ffvmMJ554gnz5
-/CmRuoINvEAwF9mn9sK26mdqP/30v9SqeFwo59ErFIpH1uuvv0tEhBc2203s9iv8+OMfTJv2f9nS
-VnBwMEOGDGHAgAFuJXmAypUr06xZXUymlsBV4BjQCNAD0enuvIRO5/6f0pIlSwKxwAbXlR3YbCcp
-X748RqORWa+OIIKWBHOR7WoTkzt05cnWefd8davVypIlS/j+++85evTowwsoskx5olcoFNmqfPna
-REV9DdRzXZlB9+7bWLQoLDfDyhC73U5YWBhhYT+yffte4DvgFZzzmIcBycAU9u6N8MgRrOHh4XTs
-2JPUVCE11Ur+/PmpX78OP7zclwK9ekFcHBcqVODI2LG06tIlz64ft1qtNGnShsjIRByOisBK5s+f
-QYcOHXI7tEeWO7lPOb1OoVBki6SkJL799jsSE+NRqSIQqQcIev02SpUqmtvhZYhGo6F///4EBATQ
-s+fXJCc/D5iBF4H/odNBWNh0j52zHhoaSkTEb9Su3RSH4y2uX29P/JpP8FrdFhwO6NCB4IULCTYY
-PNJeblm0aBGHDqWSkBCBc2B5Ky+99BzXrimJPjsoiV6hUHhcamoqDRq05M8//UhO7gB8gl6/Hi+v
-FIKC4nnnna9yO8RMCQgIQKM5ByQBXYHK6HQ1iI6+8MBXBPHx8fTpM5CtW/8gKKgQ06ZNon79+v/a
-zvXr12ncuAUpKcWBd2nJOn62r8KEg/j27bEsWQI6nae7l+OuXr2K1VqVv98eV+fmzZjcDOmxpryj
-VygUHrdx40ZOnEghOXkpMBHYg822hbCwVzl4cDu+vr65HWKm1KlThzZtGmCx1MfL62VMppaMHz/+
-gUk+KSmJ4sWrsHx5IrGxazh8eDgtWnTk9OnTiAg2m+2+5X755RdSUioBCXRiCb/QHhNJzFLrufPd
-d49Fkgdo1KgRGs1i4CCQilb7IXXrNs3tsB5byhO9QqHwuMTERFSqQP5+liiDRqPnqaeecmvf99yi
-UqlYuPAHfvnlF86dO0fNms/RoEGDB97/4YdjuXHjInAY51B/BVJTV/HRR2NYtmwlycnx1K3blBUr
-5uHn55dWzuFwoFL50YvrzKYHWoSv8GN3zw70K5o3XndkxBNPPMG0aV/w8svNSUy8xRNPhLJ06U+5
-HdY9oqKiWLJkKVqthl69elE0j/43UCbjKRQKj7t69SrlylXj1q2PgYbo9V9Rs+YpduzY8NCyj4PG
-jdsTEREO7Me5JE9QqWqg010hNXUjUBad7k0aNjzLpk0r08pdvXqVCSXL80XiTdTAOJUvv9avweYt
-Gx/bE9rsdrvrcJ5Hy759+2jcuBXJyc+hVidjMq1g375tuXZugrK8TqFQPFICAgKIiFjPE0/MJyio
-I+3aJbFmjXvrzPOSKlXKotFUBVoBnwI90OlO43D0AioBeqzW0WzfHn5XuYCffuJLV5L/NrgsjjGj
-CN+84bFN8sAjmeQB3nrrYxISxmK3f4XV+j137gxk7NjPczusLFGG7hUKRbaoUqUKu3dvyu0wcsW4
-cR8SHt6SM2fspKRMQKXS4O3ty507ewAHzmes/RQo4DzFExEYOxY+/ND571OmMHTo0FyKPvdYrVaW
-Ll3KlStXaNSoEbVq1cq1WOLibgF/P707HKW5du1ErsXjjsf3Z6JCoVBkwu3bt9mxYwcnTrj/xzxf
-vnzs37+VMWOGotNZsNvnc/36bGy2o3h51cNk6ofJ9AyzZn3jTPIjRzqTvFoNYWHwH0zyNpuNpk3b
-0b//t4wadZLGjdvx44+5996+e/e2mEwfAFHAIUym8fTs2S7X4nGLe7vv5rw8GLJCoXjE7du3T/Ln
-Lyw+Pk+I0Rgogwe/muU98tPr1Km3wAzX3vUisFJKlqwk06ZNk2PHjonY7SJDhjg/1GpFFi3yQG/y
-piVLlojFUlfA5vquDorFUtAj/x2ywm63y8iRH0j+/EXFz6+4TJr0Za7E8Rd3cp8ydK9QKNyyf/9+
-Nm/ejJ+fHz169ECv1+d2SA8VHR3N+PGfEx0dS/v2TzF69GfExU0CegG3+PHH+nTo8CtPu7mPvNls
-QKW6zt9zqG5RpEhxBg4cCDYbvPgizJkDXl6wdCm0betmz/Ku2NhYHI4Q4K939hVJTLyF3W5Hq835
-VKVWq5k48WMmTvw4x9v2NGXWvUKhyLLFi5fQt+9QHI7uaLVHqVDBzvbtvz3Syf7GjRuEhNTi+vVO
-2GyVMZm+JCnpOCK3AefSP73+FSZOLMVrr73mVluRkZHUq9eMhISXASMm0/9YvXoRofXrQ69ezuRu
-NsPKldCsmfudy8OOHj3Kk0+Gkpi4DKiJVvsRNWvuY+fOjbkd2iNBmXWvUChyxeDBr5KUtJKUlCkk
-JGzg2DENixYtyu2w/tWSJUu4c+dJbLYvgZdITFwJmID5rjuuo9Oto3Llym63VaVKFXbt2sywYfEM
-GhTN77+vIrROHejUyZnkfX3ht9/+80keICQkhAULZlKwYC+02gLUrn2IlSvn5XZYjwVl6F6hUGTZ
-7duxOJeLAaix2UK4fv16bob0UFarFYfDku6KNxqNlXz5xpCS8gWpqdEMHjyEFi1aeKS9kJAQvvnm
-C+e/3LkDbdpAeDj4+cH69VCjhkfayYz4+HgiIiJQqVQ0adLkkdnEqH379sTGts/tMB47yhO9QqHI
-sgYNnkKnGwXEAztRqxfTpEmTLNUlIpw/f56oqCjsdrtH40yvffv26HSrgalABEZjL/r0eYHz54+x
-det8Tp2K5PPPx91TzuFwcODAAXbu3ElKSspdny1YsJDKlRtQsWJdpk+fcf+G4+KgRQsID0cKFeKd
-+k0o3LYX1as3Ztu2bZ7v6ANER0dToUJNevacSI8en1C5cu1H/seZwk2emQ+Yc/JgyArFYys2Nlaa
-Nm0nWq1BChQoIosWLc5SPVarVTp2fFYMBn8xm4tLSMiTcu3aNQ9H+7cDBw5IaGh7CQmpJ2+99b6k
-pqb+6/1JSUnSsGErMZtLibd3FSlZsrJER0eLiMiKFSvEZAoWWCOwUUymsjJr1g93VxATI1KtmgiI
-o3hxaVa8okBtgR0Cc8VgKCCHDx++p90//vhDRo16T8aNGy8xMTEe6fszz/QTrXaka2a7Q3S6oTJo
-0AiP1K3IPu7kvjyXNZVEr1A8fr788msxmZoJJLmSzwjp1u35LNVltVrlzJkzcuvWLRERmTkzTAoX
-Li9+fsXl9dffEZvNluk6P/lkvBgMHQSsAg7RakdKp07PiYhI27bPCMxKt4RuhVSv3kjat39GKldu
-IO88P0Ds5cs7PyxXTtbPnClgETiVrsxwGTt23F1trlq1SozGAIEPRafrLwEBxeXKlStZ+k7Se+KJ
-5gK/pmt7kTRv3tnteh/k9u3bEh4eLnv37s21pXKPA3dynzJ0r1Aoct3u3ZEkJnYHDIAKq7U3e/ce
-ynQ9x44do1ixClSq1Ah//yK89NJAXnnlIy5f/oHY2HV8/30EH31077D8wxw6FEVycnuc05pU2Gyd
-OHLkOOBcQgdx6e6+yJEjkaxZE0L84WEM+HEe6uPHkcqV+bxDV/p9NNHVz/RlYvHyunulwquvfkhS
-0g/AGKzW/+PGjdZMnTot07H/U+PGtTEYvgdSgESMxhk0bvyk2/Xez/HjxylVqjIdOoyiceNutGvX
-I1tfyyjuT0n0CoUi11WpUhajcQ3gPL5Vo1lJxYrlMl1P+/bPcOXKmyQmXiA19SizZy8jMfFNoC5Q
-nsTEz1i06JdM11urViWMxiU4k6Og18+jRg3nrPxRo4ZjNk8AxgKfode/g05XgzL27kTwJiUlgd0q
-NcOrPsFH323n4sUhrn52A74FXkWj+YU+ffrc1WZCQjwQnPbvNlsxbt+Oz3Ts/zRu3IeEhqrR6/3R
-6QJo0yaAd9550+16/+n06dN07vw816+/ye3bO0hI+JPw8KuEhYV5vC3FQ3hwZCFH5MGQFQrFQyQn
-J7vegZcRH5+aEhxcQS5evJipOmw2m6hU6nQ7q4loNDVEpRog0EqgkEBlCQmpnen4UlNTpXXrLmI0
-BomXV4CYTEWkefOOsmXLFhEROXjwoAwc+Ir06/eyfPHFF1LfVF1i8BcBCaeB5NcYRK+3CMS4Yvuf
-gE5UqnxisfhLRETEPW0OH/62mEzNBY4JbBKTqZBs3rw5wzFbrVYZM2a8NGzYVnr3HnDP93njxg2J
-i4vL9HfxMA6HQ4YOfUMMBn8BH4Ez6V4TjJU33xzp8Tb/C9zJfXkuayqJXqF4PNntdtm7d69s375d
-EhMTs1SHv39xgcUCMwW+Er2+uKhU3gIfCZwXmCI+PkFp7+8zw+FwyOTJk8VgKCrwk8B0MZn8Zfv2
-7XfdlxQeLjfVGhGQtVSRgsb60rfvIDEafV0xOJOewdBFJk6c+MA5A6mpqTJs2Bvi719SihevLAsX
-LpJbt27JoUOH5MaNGw+Nt3fv/q55D8tFqx0lgYElsyWxpxcTEyOtW7cXjaaEQJxAW4EPBBwCN8Vs
-rik//fRTtsbwuFISvUKhUIjIzz//LCqVRaCNQE/Ras1iMAS7Eo0zwfr61svUk3F6tWo1E1iR7gn1
-S3n22Zf+vuH330XMZhGQg6XLSpe2PeV///tabDabjBz5gZjNNQV+Eo3mHfHzC5arV69muO21a9eK
-2VxQvL0risGQT+bOnffAe1NSUkSj0QvcTovVYnlaFi5cmKV+Z8SdO3ckOLi8qNWNBYa42r0gUEnA
-T7y88kn//sOUCXlZ5E7uUzbMUSgUj40tW3ag0fTFZpsCgM32GXb7x8BtwBdIwWaLxtvbO1P1bt68
-md9+20hMTAygSvdJAlFRR5k8eTI9fXwIHDIEkpPhueeo+sMPLE23R/uECWMoXrwoK1euoHBhP8aM
-2Y6/v3+G2o+Pj6dbt94kJKwAGgBHGDCgCU2aNKJo0aIZ7EX2bh++YcMGbt4sisMxEhgG3ACKAq8R
-HDyJnTt/p1ChQtnWvuLBlESvUCgeG5cuXcNmq5fuSkN8fApgs4WSkNAZs/k3mjWrQ/Xq1TNcZ1jY
-bIYNe4/ExJfQar1RqV5C5GvgIjCBw4fbs/2NlQy2ufZkHzgQpk51HjmbjkqlYsiQgQwZMjDT/Tp/
-/jxqdUGcSR6gEnp9CCdOnLhvotfr9fTs2YflyzuTmDgcrXYnZvOftGrVKtNtZ5TzR4QGaAF0AcoB
-FgoUsLFq1Rolyeci5VAbhUKRI5KTk/nyy685fPgkdepUY+jQIWg0mocXzIQff/yJwYMnkJi4CvDF
-aOzFoEHVqFu3BgcOHKJ8+bL06dMnU+0WKFCEuLhVQA2cM+6fpFQpDfHxt7l8uR3POaoQxotocLC4
-WGm6nz0BKtXDqs2U27dvExRUnKSk34HqwGmMxtr8+edeihcvft8yNpuNceM+47fftlGiRGE+/XQ0
-RYoU8Whc6d26dYsKFWpy7dqz2O0N8PKaRIMGBlauXIzZbM62dv8r3Ml9SqJXKBTZKiEhgXfeGU1Y
-2HySkqpht7fHZFpAmzbFWLx4jkfbEhHGjBnPZ59Nwmaz0qNHL2bN+tat0/QMBm9SUs4D+QHQ64cx
-blwJtm49QKEVGqbi7MNonmdZ5VMcitz6r/WdPHmSAwcOEBwcTJ06dTIcx8KFi+nXbwg6XTlSU6OY
-NGkcQ4cOynK/3HHgwAFmzvwRtVrFgAEvpB0AdOnSJV5//X3Onr1E06Z1+fjj9x/pkwzzEiXRKxSK
-R5LD4aBGjYYcOWLCbj8LHMc5vJuIwVCMkycPZstT5l9/I1QeeLLu1KkXv/4qpKRMBDagUr0OxDNK
-68V4axIAb/IBU00RvP56Mz755IMH1rVgwSL69RuKTtcQu/0Afft24dtvv8hwLNHR0Zw4cYKSJUsS
-HBz88ALZICIigtatu5CYOBywYzZ/S0TEemrkwuE8/yVu5b6szwHMHXkwZIXiP+vFFwcJFBD4XeDJ
-dLPV7WIyFZVTp07ldogPdefOHenZ80XJl6+waDT5RMX/5CM++KsjMlxnFqMxnwwcOFysVusD60lN
-TRWDwUfggKvoTTGbS8gff/yR6ZhsNpv83//9n7z66psSFhYmdrvdnS5miN1ul8GDX3WtjZ9218qD
-zp17Z3v7/3Xu5D5lZzyFQpHm2rVr7NmzxyOnme3du5f581fi3O61JnATGAPsRasdTqlSRShRooTb
-7TgcDs6ePcvVq1fdrut+LBYLCxbM4tixfWg1KiZxkdF8gh01gww1CF34I4mJcUyb9jVa7YPnN8fF
-xeFc6FTNdcUXjaY658+fz1Q8IkKXLr0ZMWIOX31VkGHDptG794AMlY2MjKRjx140atSOqVOnZ+oJ
-ccqU75gzZwdQB0i/WiCA+PikTPVBkcM89nMjh+TBkBWKPOGHH34UozG/+PhUF5OpgCxb9rNb9S1d
-ulS8vdsLtBDoKfB/AmVFp/OXLl16S2xsrNsxnzt3ToKCSotG4ydarbf07t0/255ukxMTZbpaKwKS
-gk66ME/M5rKydevWDJW32+0SGFhSYLbrSfigmEz+EhUVlak4Dh8+7DotL8lVT7wYDP5y5syZfy13
-4sQJsVj8RaX6UmCZmExVZOzYTx/a3r59+6Ro0fKug3jCBH4QKCewxbVjX0lZuHBRpvqgyDx3cl+e
-y5pKolcoPO/SpUtiNBYQOOJKHnvEZCqQpZ3Url69Kl269JFixUJEo/EV2CUwSqC2GAwF5fr16x6J
-OSUlRXx8gl2bszgE7ohO94RMn/5/HqlfxHkM78yZM2XmtGmS2K2bCEgiSCevVmKxVJEePV7I1AYw
-Bw8elMDAkuLllV8MBh+ZPz/zG9j88ccf4uNTI93QuYjFUk4iIyP/tdyYMR+LRvNqunKHxN+/5L+W
-iY+PlwIFigjME3hb4CXXd/29QEkxmQrLjBmzMt0HRea5k/uUdfQKhYJTp06h15cnKSnEdaUWGk0Q
-58+fJ1++fBmux2az0bjx05w61QirNQyVahwqVSMMhnyYzV6sW7eeAgUKuB2vzWajV6++3L6dAAzG
-uYmNBau1D9u27WHAgP5ut3HhwgVq1WqILeEJwlJ2YbRfxGE2c/6LL2glwoBixXj66aczNeGvatWq
-XL58ktjYWPLnz49Op8t0XFWqVMFsvk18/CQcjs5oNPMpWFBDuXL/fgiQM870Q/WOh8Z+4sQJrNZ8
-wLNAK6AxUA+TyQ+DIZWdOzdTpkyZTPdBkbOUd/QKhYJSpUqRmnocOOa6sg+bLZpixYplqp5jx45x
-8eJNrNb/AbURWY7ZXJolS2Zx5cppatas6ZF4R478kF9+OQhYgF9dV+3ASsqWzXjMEyZMxNs7EIPB
-l+7d+5KQkJD22fvvjyPpek/mJSbQ0X6ROAyMrNmAY0FBJCUlYTKZsjSrX61WExAQkKUkD2Aymdi6
-dT11627Ez+8pGjbcSUTEuocuY+vV61mMxnmoVJ8DSzCZnuO114b8a5mAgABSUy8DV4ECwFp0uj+Z
-MKElx48fUJJ8XuG5gYWckQdDVijyhFmzZovBkF98fGqKyVRAFi9emuk6oqKixGAIEkh0DQ+nitlc
-Qg4ePOjRWAMDywhsFvATCBKoJ1BSvL0LS3JycobqeP31N10rAvYKXBOttpP06tU/7fP2TdpJOBVF
-QGLwl6p8IwUKlBCLpbro9a+IyVRSPv54okf7ld0OHz4snTv3ltDQDjJt2owMvXZ4//2PxWQqISbT
-S2I2l5Y33ng3ByJV/JM7uU9ZR69QKNLExMRw7tw5SpUqhZ+fX6bKXr16lUaNWhMVFYlzsLAzRmMC
-tWs72LTpF9Rqzw0glixZjbNnvwL8gFeAQ5QoEcSePREULFjwoeXj4uLw9y+C3f42MNp19TT58jUh
-Lu4C3LjBlRo1CDp/nosU4imWcc4wFLs9Bqs1CjAB0ej15YiJuZCp1xt50datWzly5Ajly5cnNDQ0
-t8P5T3In9ylD9wqFIk1gYCC1a9fOdJK32WxUqFCHqKgngSTgOGr1Zp55JpB165Z5NMkDfPrp+xiN
-zwFr0GqrUrCgme3bN2YoyYNz4xmt1gxEpbt6DJPJDDExEBpK0PnzxPr40lwXx2ldM0JDi2A0lseZ
-5AEKodXm4+bNmx7t26OoYcOGDBo0SEnyeZQyGU+hULht+fLlxMXFAh/h/LNSAoejH8HBOry8vDze
-Xo8e3QkI8GfJkpX4+uZj6NCdmTo0pUSJEuj1KlJSdgKdcZ6yFsbUdz+Fxo0hKgoqVMBvwwaOFS4M
-QGxsLKVLVwaWAy1Rqf6PAgVMnD59mrlz5xIYGEifPn0wGAwe769C4Q4l0SsUCrc5j2/1BXYBnQAH
-sIVChZ7LtjZDQ0Oz/IRpMplYu/Zn2rbtQmLiRlQqGzPfHUmHSZPg3DmoXh3WrYOAgLRDaf39/Vm3
-bjk9e/bj8uUeVKhQkx49nqd9+74kJz+HwbCV77//kR07Nij7u/+DiLBt2zYuXbpEzZo1KVu2bG6H
-9J+ivKNXKBRu279/P/XqNSclRQW0BI5jMkVz48bZbHmivx+bzcbq1auJi4ujUaNGlC5dOu2z3377
-jZ9/Xk3+/D4MHz6UwMDAtDJXr17F/9o1dE8/DdHRULcurFkD+fP/a3sigtmcn6SkP4AKgGCxNGHW
-rFfo3r17Nvb0b8uXL2fgwFe5dSuWxo2fYtGiMPI/JO6cJiK8+OLLLFmyAY2mGlbrZubM+Z5u3brm
-dmh5irLXvUKhyHU//TRfjEZfUanUUqpUiJw7dy7H2k5NTZV69Z4Si6WOmM3PicnkJxs2bBARkZde
-GihQUGCSqFSDxN+/mMTExPxdeO9eET8/5y4yoaEit29nqE2r1SpqtVYgJW0TGpPpBZk+fXp2dPEu
-DodDFi9eLF5efgJbBeJErx8kzZt3yPa2M2vz5s1iNpcVuOP6nvaK0eibI/vzP07cyX15LmsqiV6h
-eHQ5HA5JTU3N0Tbj4+OlevXaArUF7K5kslaKFq0gGzduFPAV2J6WjNXqPjJp0iRn4W3bRHx9nR88
-/bRYb9+Wd98dLRUq1JEGDVrLrl277mkvJSUlbVlao0atRacbInBFYK2YTH5y/PjxbO2vzWaTdu16
-iE6XT2BAup3ubotWa8jWtrNi7ty5YrH0SBenQ3Q6s9y8eTO3Q8tT3Ml9yqx7hULhMSqVKssbwWTV
-wIGvEhmZDDTi74VETxIbe5nVq9cBOiAg7X6HI4iEhETYtAlatoRbt6BrV1i+nFffGc1XX/3OsWOf
-s21bD5o2bcOJEycAuH79Og0btsJotGA0+jBlylSWL/+JZs1iMJsrERz8OitWzH/oDnXumjlzJps2
-xWC1fgGc5u/d7o7h7Z2xVQc5qVatWtjtvwORAKhU0wkKKoqPj0/uBvZf4sEfHDkiD4asUOSq2UZO
-XwAAIABJREFUpKQk6ddvqPj7l5QyZWrKr7/+mtsh3eXOnTsyePCrUr16E3nmmX5y5cqVTJX38ysh
-8JNAUYEoAZuo1cMlNLSdjB8/QdTqqgLNBfYLfCfgI520RklWqZyPmM8/L+I6XtZi8Rc4l/b0qdMN
-k88++0xERFq16iI63csCqQInxGQqJps2bfL49/Eww4e/ITBRIFmgvqtvQ8RoDJQFCzK/d35OmDt3
-nhgMPqLX+0rRouXk6NGjuR1SnuNO7stzWVNJ9ApF5vTuPUAMhg4CxwVWidHoJwcOHMjtsETEOdTf
-oEFL8fLqJbBBdLo3pXjxEElMTMxwHRUqPCmwzHXQikVAK8HBFSQmJkZu3LghRYuWE42mskBhAYv0
-oLek4jyFboaXSeLSHbKTP38RgcNpid7Lq6989dVXIiJisfgJRKd9plK9I6NHj/H4d/IwYWFhYjLV
-FYgXSBSVqqOULh0ie/bsyfFYMsNqtUpsbGymDgFS/M2d3KcM3SsUj7nly5eTnPwdUA5oi9X6PGvX
-rs3tsAC4dOkSe/fuJyVlNtAcq/Uzbtwws3PnzgzX8f33kzCZBmIw7MNkakSpUhU4cmQXAQEB5M+f
-n8jInXz99WBef/0ZBukNzGMeOmx8xlu84VWNg5GRaXW9995bmEydgeloNG/h7b2JZ599FgA/vyBg
-j+tOB0bjPgoVCvLYd5FRzz//PJ06VcJgKIHFUpXixU8SHr6OWrVqpd1z9epVXnhhCA0btuW998aQ
-mpqa43H+k1arpWDBglk6H0DhHmUdvULxmDOZLMTHXwKKAKDVXsRiKZm7QbloNBpE7ICNv/8cpaLR
-aO57v81mY926dWlL6IoXL06TJk3Yv38b69evx2KpR/fu3TGbzWll8uXLx9ChQ7k9fjw+qbEAfMDH
-jOV1TLaKdy1He+ONEQQHF2bp0rUEBORn1KgdBAQ43+/PmjWZdu16oFI9jUp1ivLltfTt2zdD/YyO
-jmbEiJFcvHiNli0b8t57b2d5LoNareann2Zw/vyHxMfHU7Zs2bvqSkhI4Mknm3D58tPYbIPYt28a
-hw/3ZcWK+VlqT/EY8ODIQo7IgyErFLnqhx/miMlUROAT0eufl6JFy2XpnPns4HA4pG3b7mI0Pi0w
-X7y8XpBKlWpLSkrKPfempKS4ltA9KRZLTzGb/WTz5s0Za2jChLTD20fqA0SjeVvM5lrSs+eLmRpK
-PnnypMycOVOWLl163xjv59ixY6LR/DVDfoloNE2lc+fnMtxmZq1Zs0a8vRulm+WeKDqd+ZH5b67I
-Gndyn/JEr1A8hmJjYzl48CBBQUH07duH4sWDWb16HQULhjBo0Fe5eghLREQE3377A2q1ihEjBrBs
-2VwmTvycbduWEhJSijFjvr7vznJz587l4EEHiYk7AA2wkr59h3LmTOQ996YRgQ8+gHHjQKVCpk6l
-YZEi+Bw6RNmyI+nWrVumhpJLly5910Y8GdGlSy/s9jLANECF3d6GlSsLcvPmzfv+d1ixYgVjx36D
-3W5nxIh+9O3bJ1PtyT2bqihD5f95nvu9kTPyYMgKRY7asmWLWCz+4uvbWIzGwjJo0IhHZgLUxo0b
-xWQKEJgs8KWYTH6ybdu2DJX95JNPRK0ele5JNUZMpgIPLuBwiIwY4bxZoxGZO9dDvcgci6WgQMN0
-cSeLWm2SGzdu3HPv2rVrxWgs5JpcuEpMppIyZ07m4o6Pj5dixSqITveawHIxGttK+/Y9PdUdRS5x
-J/fluaypJHqF4t/5+xcXWO1KKrfEbC4vv/32W6bquHnzphw9elTi4+M9GlvTph0FfkiX9L6Vjh17
-Zais80dCcYEzAnbRal+Tpk3b3/9mm02kf39nI3q9yLJlHuxF5tSo0cg14/9tgTUCT0uVKnXue2+H
-Dr0E/i/d97Nc6tRpmek2Y2Ji5IUXhkiDBm3k3XdHZ/g1g+LR5U7uU4buFYrHiM1mIzb2AtDadcUH
-kYacOnWKp556KkN1zJu3gP79h6DV+iMSx88/z89w2YexWm2AOd0VM6mptgfca2XBggXExMTQsGFD
-mjVrxtixbzBqVCXsdjvVq9dlwYJF9ysIffvC/PlgNMLPP0OrVh6JPysWLJhB/frNuXXrJ+z2MEqV
-KsSOHdvve69erwMS0l2JR6fL/J/pgIAAwsK+y1rAisePB39w5Ig8GLJCkaNKlKgkMMv1RHhRTKZg
-2b59e4bKXrhwQYzGggKHXOV/F4vFz2NP9gsXLnI9la8UWCYmUxFZtWrVPffdvHlTAgPLiEpVW1Sq
-YWIwBMnMmWEi4twCNiEh4f4NJCWJdOzofBz29hbJ6GS9bHbnzh3Ztm2bREZG/utrlF27donJ5Ccw
-SWCyGI0Bsn79+hyM9G63b98Wm82Wa+0r/uZO7stzWVNJ9ArFv4uMjBR//+JisZQSvd5bxo+flOGy
-GzZsEF/fJumGjkUsltLy559/eiy+efPmS61azeSJJ5rLsvsMqTscDqlS5QmBGvL33vVHxGDw/fe5
-BvHxIi1aOIPOn1/kPvvU5wW7d++W3r0HSM+e/SQ8PDxXYjh37pxUqFBLtFqjeHlZZMaMWbkSh+Jv
-7uQ+5ZhaheIxlJqaytmzZ/Hz86NAgQIZLnf69GkqV65DUtJeoBhwCKOxCVeunMuxvclv3LhBQEBh
-7PZngTDXVRtgICioFCkpifTs2Z3Jkz/7e/34rVvQrh1s3QoBAfDbb1C1ao7E+ziqXr0hhw+3xm5/
-D4jCZGrKli2/3LUpjyJnuZP7sn1nPLvdTo0aNWjfvj3g/J+4RYsWlCtXjpYtW3Lz5s20eydMmEDZ
-smWpUKEC69evz+7QFIrHll6vp1y5chlO8mfOnCE8PByj0cj48R9hNNbC17cxRmMzZs2alqMHkGg0
-GlQqNbAK+B24BXRDpcrPlSuziYvbwuzZh3nrrQ+cBa5fh+bNnUm+aFGIiFCSvBscDgeRkX9gt4/E
-uTSvPA5He/7444/cDk2RRdme6L/++mtCQkLS1qpOnDiRFi1aEBUVRfPmzZk4cSIAR48eZeHChRw9
-epRff/2Vl19+GYfDkd3hKRT/eV98MZlKlWrTqdMHlClThWLFinDkyC6WLRvNiRMHeeaZHjkaj6+v
-L1279sTLqwjQGwhEowlH5G2gHlCKpKTPWbr0F7hyBUJDYe9eKF3ameSz+fS4nGS1Wvnhhx8YO3Ys
-GzduzJE21Wo1+fIFAX8l9lQ0mr0UKVIkR9pXZAMPvT64rwsXLkjz5s1l06ZN0q5dOxERKV++fNrp
-VNHR0VK+fHkRERk/frxMnDgxrWyrVq1kx44d99SZzSErFHmGw+GQS5cuydmzZ7O8Tj4qKkqMRn+B
-86534XvEaMz34MluOcRqtcqECZOkRo0GUqRIJSlWrLxoNIPTzR34WZqXrSlSpozzQkiIyKVLuRqz
-p9lsNmnUqLWYzaGiVo8Uk6mETJr0ZY60vXr1ajGZ/MTbu6dYLFWkTZtuYrfbc6Rtxf25k/uy9Yn+
-tddeY9KkSajVfzcTExNDYGAgAIGBgcTExABw+fJlihYtmnZf0aJFuXTpUnaGp1DkWVarlY4dn6VU
-qSpUrFiXOnWacfv27UzXc/r0afT6qkCw60ot1GpfoqOjPRpvZmm1WkqXLsHx4+e5dOl9zp8fgt3+
-IzpdX9TqUVQx9OOXWxfh5EmoUQM2b4bChXMkth9+mEPBgsEYjb507dqHhISEhxfKgg0bNrB/fwwJ
-CRtwOCaSmLiZd999F6vVmi3tpdemTRsOHfqDqVPbs2zZF/zyy8K7/o4r8pZsW0e/atUqAgICqFGj
-BuHh4fe9R6VS/ev2kw/6bPTo0Wn/HBoaSmhoqBuRKhR5z6BBL7N69RUcjkuAjkOHBvDqq+8wa9a3
-maqnTJkyJCfvAzYCzYDfUasTH4lh2kmTppGYOBno5LqSRMWKSxnc0Jv+izTorl6F+vVh9WpsFguv
-DnuDOXPmotPpef/9t3jtteEej2nLli28/PI7JCWtBEqwZs1Q+vcfzvz5Mz3eVlxcHCpVKZzb/QIU
-RURFUlJSlg/EyYysbPer8Jzw8PAH5s7MyrZEv337dlauXMmaNWtITk7m9u3b9OnTh8DAQK5cuUJQ
-UBDR0dFpJ0MVKVKECxcupJW/ePHiA//YpE/0CsV/zaZNm5g9+2ccjv8BBgBSUl5g5853MlVPbGws
-HTv2AkxAZzQaA0ajsHz5QgwGg8fjziznD/3083TMtA4IYsiC+XDjBjRrBitWgMXCR++NISxsL4mJ
-u4DbvP9+F4oUKUSPHt09GtO6db+RlNQfcM4+T06eyLp1DT3axl8aNGiAw/EKsBxogFb7OZUqVc/R
-iZGK3PPPh9gxY8ZkvTIPvkJ4oPDw8LR39G+99Vbau/gJEybIyJEjRUTkyJEjUq1aNUlJSZHTp09L
-qVKl7vveMYdCVigeWZ069RZoJ9Ar3Trzt6RLlz6Zqqdr1z6i0w0XcAgkisEQKhMnfpZNUWfesmXL
-XKfuzRGYJs298onVbHa+k2/Xzrk5jkv58rUFItK9w58mPXv283hMX375pRgMPdK1s1ZKlKji8Xb+
-EhERISVKVBaTKb80btxGoqOjs60txaPNndyXY1vg/jUMP2rUKHr06MHMmTMpUaIEixY5t7AMCQmh
-R48ehISEoNVq+e677zJ1qpRC8V+h1WqAJjif9KoBWvT6C0yZ8i+nuN3Hvn2RWK3/h3MJlZHk5J4c
-PLgnU3XcunWLCRMmcfbsZZo1q8+AAS957P/bzp07s3ChjsmTf+DJ27F8fDAZTUIy9OgBc+dCuuHr
-ggXzAycA59O1VnsCf3/3T+i7du0affu+zO7deyhWrDjfffcpRYrMIjq6I1ZrCbTaeUyd+qPb7TxI
-w4YN//10PoUiIzz4gyNH5MGQFQqP2r59uxiNfgJfC4wUvb6ALFmyJNP1tGnTXTSaD11PplYxGtvL
-+PGfZrj85cuXXSfRPSswXfT66jJs2BuZjuOhVqxwHkwDIi++6Dyw5h927twpZrOfaLWviJdXX/Hz
-C5aLFy+61axzh766rlPgTohKNU3y5y8sZ8+elenTp8vnn38uhw4dEhHnKoFt27bJxo0b5c6dOxlu
-Y9euXbJgwQI5cuSIW7EqHn/u5L48lzWVRK9QOJN95869pX37ZzO0F/pfS/H+Wtoq4lz+GhxcXnx8
-aorZXEYaNmwpycnJGY6hVq0GArVdQ/8icF00Gq9Mn5RmtVolLi7u/ksE5893HjELIsOGifzLEq/j
-x4/Lp59+Kl9++eVd/cyKixcvysyZM0Wvz5+ufyI+Pi3u2Zs/MTFRnnwyVCyWSuLjU18KFSot586d
-e2gbb7zxrphMxcTbu6uYTIEybdoMt2L+p507d0qtWqESHFxJBgx4RRITEz1avyJnKYleoVA8UHx8
-vDRp0kYMhoLi5ZVf2rbtnpaMExMTZdu2bbJnz55Mr5PW680CbdO9r04RtVqfqTX406fPFL3eLDqd
-RcqUqSZnzpz5+8MZM0RUKmflo0Y5z5fPAb///ruYzX7i7d1KwEvguqt/NrFYKsvmfxyU88kn48Vg
-6CJgExDRaD6RVq26PrD+mJgYadq0jahU+dPVHSVeXj5y+/Ztt+O32WyyYMECMRjyC8wW2C8GQyfp
-2jVzczgUjxYl0SsUigcaNuwNMRh6CqQKJInR2EY++mhslupyOBwyefK3UqlSfdFq8wkECPxP4A+B
-zlKx4hMZrmvPnj1iMhUSOC7gELV6olSq5Dqn/auv/j5VZ9y4LMWaVUFBpQTWupp/Q6CswHgxGltL
-gwYt7znN7dlnXxL4Pt0Pnt1SsmT1+9admpoq5crVELW6q0DoXYcHmc3F5NSpU27FnpqaKo0bPy1e
-XoUEnktX/03Rag1Z3lhJkfvcyX3KDggKxWNux479JCf3A3SAgaSkvmzbti9LdU2ZMpVRo77jyJGP
-sdleBuJRqb5HpepE/vx72bJlbYbr2rlzJyLtgHKACofjDY4e3Y1j7Fh49VXnTV99Be++m6VYs0JE
-uHr1PBDqujIJrbYkTZpsZNKkdmza9AsajeauMvXr18BkmofzHHkHev1Mateucd/6jx8/zuXLd3A4
-vgYOAztcnyzGYHDctWlYZkVFRfHWW2+xe7eVlJSPgfQbKF1Dp8v9JZOKXOK53xs5Iw+GrFDkql69
-XhKt9g3Xk51DvLxekldeeTNLdVWsWFdgU7onxbflyScbyJw5cyQp3XK3jFixYoVYLDUEkl11bZb/
-GSzOilUq59D9v3j//Y/Ez6+UBAaWkQkTJnnsabVq1fqiVk90vZs/KyZTsGzduvWB99tsNnnmmRfF
-yyufGI1BUqNGQ7lx48Z97z1x4oQYjYVcfV4tUEDAKPnzF5E9e/ZkOeZ33hktRmOA6PXFBcYK3Bao
-IPCiwFdiMpWViRM/z3L9itznTu7Lc1lTSfQKxcOlpqbKvn375ODBg3L58mUpXjxEvL3riLd3TalQ
-oZbcvHkzS/VWq9ZYYHlaolepxsjAga9kqS673S4dOjwjFkuI+Fi6yndag7hecovMmycizlcFx44d
-k127dt317r9Nm44CRQV2CewTgyFEpkyZmqU4/unMmTNSsmRl8fIqIDqdSb744usMlbty5YqcO3fu
-X+c6OBwOadOmm5hMLQSmisHQWho1uvd1QGbs3r1bTKaiAlcFFgmECMQKXBeVqoEULlxOli5dmuX6
-FY8Gd3Kfch69QvGYuX79Og0btuLixQREUqlSpSSrVy/iwIEDaDQa6tati5eXV5bqXrlyJT169CMl
-pSWgwWRay+7dWwgJCQEgNTWVsLAwzp49T/36ddOOp34QEeH3DRsoMXYspbZsAb0eFi2Cjh1xOBz0
-6TOQn39eg04XiMFwk4iIdVy9epUmTbricHwJ9HLV9At1637Ljh2/Zqlf94vr2rVr+Pj4eHyXQKvV
-yjfffMvevUeoXr0CI0a8gl6vz3Q9s2f/SFjYEu7cieX48QASEn4GBHgH+BKDwZfSpUuycePKtPNF
-FHmXW7nPIz81clAeDFmhyFHPPTdAdLqhrqFnmxgM3eTddz/ySN1z5swVLy9/0WieEa22vDRv3i5t
-yNxqtUq9ek+JydRSYLSYTOUePukvJUWkRw/nk7zJJJJuqeC8efPEbH5SIN41ejBZatZsLDNnzhSN
-pqLAp+leIUyRp57q7JE+5gVTpkwVk6ms6wn+XQEfgVPy18l++fMXlgsXLiiT7x4j7uS+PJc1lUSv
-UPy7qlUb/eM9+lx5+ukebtdrs9nEYPAWOOyqN1nM5hDZsGGDiIisX7/e9c7d5vr8smi1hgeuzT93
-/LjsLRwsApJsMIhjy5a7Pv/ggw8FPkjXj0vi7R0gf/zxhxgMQQJ+rlnxb4tKZXLrHbenpKamyscf
-j5eWLbvJK6+8KXFxcdnSTokSVQW2pvtu2olGYxKLpbTky1dIdu7cmS3tKnKPO7lPmXWvUDxmqlcP
-Qa9fiPNAGCtG41Jq1arkdr2JiYnYbDYgxHXFC5WqStqRtrdu3UKlCubv09YCUat1JCYm3lPXnvBw
-osqHUPPyBa5joYmtKKNWrb/rnkqVQjCbVwN3AFCr51O+fAh16tThww/fQK9PRqebjcHwPTNmfMPG
-jRv54IOP2LMnc9v4elL37n2ZMGEz69d3Zfr0OOrVe4qUlBS36xURLl68mPZd37vNcA0GDerPnj2r
-iY4+Te3atd1uU/EY8dzvjZyRB0NWKHLUjRs3pHLlOmI2lxKTKVgaNWqd6RnxD1KmTDVRqz8T52E6
-O8Vo9JOoqCgREYmOjhZv7wCBnwTOiU73mtSo0fDeSuLiZKfOuaXtZYKkEpECF0Svt9w11OxwOKRz
-516uYeliAmZ5442RaZ/HxMRIZGSknD17VgICSohe309UqvfEZAqQNWvWeKS/mXH16lXR630FEtNW
-OHh715KNGze6Ve+dO3ekXr2nxGDwFy+vAtKuXQ+ZPPnbdEP334jZ7CeHDx/2UE8UjyJ3cl+ey5pK
-olcoHs5ms8nhw4flzz//dOs9bVxcnPTq1V/KlKklbdp0l4iICKlQ4QlRq7Xi7e0nP//881337969
-WypWrC358hWWFi06y9WrV++u8No1cVSvLgJyDpOUIcqVFK+KRmO8J9ayZWsIfCJwQOCkmEzFJCIi
-4q57PvpojGi1g9INY6+ScuUyvnGPp0RHR4uXV35xbkz015a59TO0RfG/GThwuHh59RawinPDo1by
-yScTZPbsH6Vp047Svv2zj8RrC0X2cif3KbPuFQrFfTkcDmrXbkpkZAVSU/uh0awjIGAOUVEH0Ol0
-6PX6zJ1Ud/kytGgBR49ySq2hqcOHC7wPVAY+oHXrQqxdu/yu9rVaHSLJODf7AYNhMJMmVWbYsGGE
-h4fz3XezOXBgPydO9MQ52xzgMIULd+PSpWMe+ibuz2q18u2333Ho0HFq1Ahh8OBBtGjRiZ07vUlO
-7o9Wu4FChZbz5597MZvNWW6nWrXGHDo0BmjquvITTz+9kjVrFnqkH4q8wZ3cp7yjVygU93Xx4kWO
-Hj1OaupUoA52+4ckJASwa9cuvLy8Mpfkz52Dxo3h6FGoVImLP80l1ihotVNQq/sQEuJg5crFAMyd
-Ow9//+JYLAXQ6/MB61yVxKPRbKV06dKsX7+etm2fYfHiJzlxogEwCdgCnMRofI2uXTt49Lv4J4fD
-QevWXXj33dWEhVVk1KhldO3ah9WrF/HSS0WpUWMcXbteZefO391K8gDly5dCq/1r2aDg5bWOkJDS
-7ndC8d/hoVGFHJMHQ1Yo8qQrV66Il1e+tOVtYBeLpfI9Q+d/WbFihbRt+4x069ZX9u7d+/cHx4+L
-FC3qHMuuVUskNlZERM6fPy/Lli2Tbdu2pQ3Zb9261bX//U6Ba6LTPSVarbf4+jYVk6mY9O07WBwO
-hzRq1NY1F+Cv4fq+YjQGSsGCxWTIkNckNTU1276Xffv2ScGCRVyz/lNc7SeJ0RgkJ06c8Hh70dHR
-Urx4RfHxqS0WS1WpXLmORw6/UeQt7uQ+bW7/0FAoFI+mwMBAOnbsyKpVbUlMfA6DYT0BASqOHDmC
-v78/5cuXT7t3/vwF9O//NomJHwM3Wbu2Fdu2baCaWu0cro+JgYYNYdUq8PUFIDg4mODg4Lva/O23
-DSQlvQg4Z41brWFYLDVZvPgd/P39qV69uuu6DTCmK9mQFi2srFjxUzZ+IxAWNpt+/V7G+ZpgEfDX
-RjdeaDTeJCUlZbluq9WKTqe753pQUBBHj+5h165daRse3e8+heKBPPiDI0fkwZAVijzLZrPJV19N
-lm7d+krRomXFbK4tJtMLYjL5ydq1a0VEZNGixWI0FhZYk+4Je6yM7dRdJH9+54UWLUTi4x/a3uTJ
-k11Hvv5VzwYJDq54z30//TRPTKYSAisFFovJVEh+/fVXj/c/vf3794uXV0HX1rtJAhUFPhQ4IBrN
-21K6dNUsjSTs379fgoMriEqlloCAErJt27ZsiF6R17mT+/Jc1lQSvUKR8+bMmSNmcxPXsjoR2CRB
-QaVlyZKlYjIFC1QS2JCWoBsxWBJ1Oue/dOggksHlfXfu3JGyZauJydRedLoRYjT6y6pVq+57748/
-/iS1ajWT2rVbyIoVK2TPnj0ybtw4+fbbb+XOnTue7L6IiEydOlUMhucFvAVOClwU6CAqVT5p1Ki1
-XL58OdN1JiYmSsGCRQXmur7bFeLtHSDXr1/3ePyKvM2d3KcM3SsUeYSIMGtWGFu27KJs2WK89toI
-tyd6ZVRMTAypqTX4e/5uTW7cuMKUKbNJTPwM5xGtg4HPaUkEP/M9Rivw7LMwezZkcKjZYrGwf/82
-5s2bx82bN2nZ8jeqVat233t79+5F797Ove6XL19Oo0ZtSE19Hr1+L1988T0HDmzD29vbzZ7/rVCh
-Qmi1R4BPgYZAI2Anw4cP4quvJmapzlOnTpGaagGec13pgFo9gSNHjtCoUSOPxK1Q5LnH4zwYskLh
-EYMHvyom0xMCU8Rg6CFVq9aTlJQUt+u1Wq1y+PBhiYqKeuCa+x07driOVz0iYBWt9jVp1OhpadGi
-i8As15P8bOlICUn+67G+f38RN05lywiHwyFjx34qKpWPwO9pIwpGY1f55ptv0u47ePCgPPVUJ6la
-tbF89NFYsVqtmW7LbrdL69ZdxGKpLkZjG9HpLDJx4kS34ndOePQViHbFfkOMxkA5fvy4W/UqHj/u
-5L48lzWVRK/4L4qPjxet1ijOY1nrC+QTtdpfpk+f7la9165dk4oVnxCLpbQYjYWlZctOD/zxMHNm
-mJhM+USt1kqdOs0lJiZGNm3aJEajv8A30os+Yv0r044YIeLGRj0HDhyQl14aKi++OER27Nhx33vs
-drsMHz5C9PpyrhnwF9MSvVo9Sj7++BMRETl79qx4eweISjVFYKOYTI1lyJBXsxSX3W6XNWvWSFhY
-mBw7dizL/Utv9OjxYjIVF5Opn5jNZWT48Lc9Uq/i8aIkeoXiMXf9+nXR6SwCJQW+Ebgm8J34+ARJ
-fAYmuT1Ijx4viF7/ijhPuksRo/FpGTfu0wfe73A47plwtmXLFvm+Zj2x/5Vl338/Lck7HA5Zs2aN
-fP/997J79+4MxbRnzx4xmfwExgv8P3tnHhZl9b7xe/aZd4YBZFUBE0TEfV9zScP9p7lruaWpaS5l
-kdpimaW2mZq54JqaW6amiAuaqKm57xsiGIobIoIyMMDM/ftjRoKvoCCoYedzXV7F+57znOeckvs9
-2/N8R0ly486dO3OUeTC7Vii8CCwg0J9AdwLXCOyhTufBv/76iyQ5ffp0ajSDsh3wi6MkORdglApP
-VFQUa9duRoPBjdWqvfzQR8LevXs5Z86ch/opEDxACL1A8IJjtVpZrVp9Ar7ZBIt0cKie70xlBw4c
-4IIFC7g7W5a48uXrENiXzeZ8du7ct2DOTZ36j0PZlrKtVit79HiTen1l6nQDKUml+NPG0LK+AAAg
-AElEQVRPcx5rrmvXfgR+yObTYjZr9n85ymzZsoUGQ1UCIwgE2+/69yPgSLW6BJct+4WbN29maGgo
-v//+e+p0vbPZu0gHB7eC9bEQhITMp1xuJPANgWuUyX6km1uZQn2gCf57FEb7xGE8gaAYIJPJsHRp
-CKpXbwyrNQmAI4D7yMy8AWdn58fWnzLle0ycOA0yWXMAX2Hw4B6YOnUSKlUKQEzMWmRk1AdggU63
-ETVq1MufUyTw5ZfA+PG2n2fOBN55J+v1/v37ERq6BykpJ2G78x6N996rigED+kGr1eZpNjXVbO/f
-A5yQlpaeo0x8fDyAQABjADQEcA2AAgaDGps2rcHAgSNw86YBMpkGWm00tNpMpKePhcUSCEn6DsHB
-7+Wvj4Vky5YtGDnyY1itngCCAQDkcJjNi3D69GnUq5fPsRYICkMRfnA8E4qhywJBkTF48Ejq9VUp
-l4+hXl+d/fq9/dg6u3btolJpyLaHbTvwdf78ed64cYN+flXp4FCZen1ZNm7cOs/88TmwWsng4Acb
-4uSiRQ8VWbt2LY3G/8uxAqHVuvDGjRuPNL1hwwZKkheBTQTCKUnluGjRzznKREVF2Zf3dxG4Qpms
-HT08vBkdHc3hw9+nWj3Evh1BKpUfs23bLhw8eAQ7dHidCxcuLlSin/xw8OBBVq/emDqdO4FP7WcI
-kuzjcJ+SVIrnzp17qj4IXiwKo32PrTl9+nTeuXPniRsoaoTQC/7LWK1Wrlu3jl9++SXXrFnzWMHa
-smULtVpn2tK8/iO4jo6NGBERQZJMS0vjoUOHeOLECVoslsc7YbGQQ4fSrqLkqlU5/Nu7dy9/++03
-7tu3zy7GOwlkUiabRh+fCvkS2ZUrV7FKlZdZsWIDzpu3INcyYWFhdHHxolyuYPXqLzM2NpYk2bJl
-VwIrsvU3nNWrN318v4qIv//+mwaDG4GlBN6yby8MI1CdwMeUyyuzV68BT/1jQ/Bi8VSF/qOPPqKf
-nx+7devGzZs3P/f/OYXQC4oLVquV0dHRPHfuHDOf8jWzvPD1rUZgHW3R3JbbZ7mb6eDgztv2mPMF
-IiOD7NvXpqAaDblxY9Yrq9XKnj3fpF5fjkbj/1GSXDlp0iSWKFGaMpmc5cvXfCqx4P/3d9KkSd9Q
-klrY9+3N1Gq7cMSI4CJvNy8WLFhAvf4N+0fGVfvY9yXwGpVKHT/++OPn/ntUUPx4qkJP2k64bt68
-mT169KCfnx/HjRvHqKioJ260MAihFxQH0tPT2bZtV+p0HtTry7By5XpPJqyFxMXFh7YobkcJ+BNQ
-UKcrwV27dhXcmNlMduliE3m9nty+PcfrsLAw6vWVCZjsIhdBZ+dSJPlE99ZJW3jYLVu28Pr16/mu
-k5GRwe7d+1Gl0lOtdmDLlq/RZDI9UftPwooVK2gwvJq1dQAcpFyu4ieffMpjx449Mz8ELxaF0b58
-pamVy+Xw9PSEh4cHFAoFEhMT0bVrVwQHBz+towMCQbFm8OCh2Lw5HqmpfyMlJQYXLtTBO+8U7O+L
-xWIptB9t27aGVjsGQGkAy6HTeWDz5rVo0qRJwQylpgKvvQb89pstKc22bUCLFjmKxMbGgqyHf5LN
-vIy7d28gIyMDSmXBzv2SxFtvjUCjRh3Qo8e3KFeuCiIiIvJVV6lUYtWqxYiPj8P165exdes66HS6
-x1V7ItLT07F3717s27cP6em2A4MdOnRAqVIJ0GheB/AdJKk3vvjiS0yc+EVWUh6B4JnyuC+BadOm
-sWbNmgwKCuKqVauy7tBaLBb6+vo+8RfGk5IPlwWC58qOHTuoULgQmJdtn3g//f1r56v+5s2bs5a7
-AwPr8NKlS0/sS0pKCrt3709JcqaLizcXLfqZGRkZDA0N5dKlS3n58uXHG0lOJps1s3XExYXMnoI2
-G9u3b6da7W5fQSBlsuksX77GE/kdHh5Ovb4CgWT7+G2li4vXE9l6Wty5c4cBATXp4FCNDg5VGRhY
-O+s8U3JyMidPnsJhw97lunXrnrOngheBwmjfY2uOHz8+z18GZ86ceeKGnxQh9IJ/O927v0mgA4HX
-CGTYl3A/YMeOrz+2bkxMjP0AWwSBDMrl37Js2cpFtqdrNpvZoMGrNBjq0GDoQb3e9dHL+HfukPXr
-20S+ZEkyj7/zly9fppubD9XqagQ0BAwsXdr/iffk586dS0kakO1DyUKZTEGz2cwJEyaxdOlAvvRS
-Vf7889Insl8YrFYrz58/z44de2Q73W+lWj2YQ4aMeub+CP4bPFWh/7chhF7wb6dPn8EEviIQRKAc
-gUpUq13yld1s9erVdHDolE3grFSrjUW2v79gwQJKUnMCmXb7v/OllyrnXvjWLZorViQBXtPoOPO9
-4Dz32jt16k2FYoLdZhoVikEcNGj4E/t54MAB+xW7v+0257Fs2cr8+uvvKUk1CRwhEEFJ8s4zu93T
-IDExkX5+1SiXuxDwoC1N7oP/Vuv58svtnpkvgv8WhdG+fO3RCwSC/PPee29Dr/8BQAsAXaDRXMfK
-lfNQsmTJh8rGxcXh44/HY9SoD7B37164u7uDPAcgzV4iCoAFRqOxwH7Yfjfk5Nq1a0hLqwtAYX9S
-H7duXXu4clwcLI0aQX32LC6gBOqaf8SHcw9h8OCRubYVG3sNFkt9+08aWCwtcPny9QL7/IC6deti
-4sRgqNWVoNf7wMNjEjZuXIklS36DyfQ9gJoAmsJkGodly9Y+cTsFITMzE1Wr1sGlSzJYrbEA+gNY
-BCADQAa02mWoXz/3THsCwfNECL1AUMTUqFEDe/ZsQ+/el9C9+y2Ehf2KTp06PVTu6tWrqFq1Hr7+
-OgkzZiShadM2CAmZh6ZNK8NgqA+dbhAkqSlmzJgGVT7TvALArFlzYTC4QqXSok2brrh3717Wu4YN
-G0KrXQEgBoAVSuUU1KvXKKeBmBigcWMoLl7EabkRTXAWVzEQJtM6LF26MNdDgi1aNIJONw22dLVJ
-kKSZCApq9FC5gjB69EjEx8fh1KlduHLlAipVqgSDQQ/gnw8Imew6jMZnk6r39OnTuHEjEUAnABKA
-8QDuAnCDVuuFevVM+OKLT56JLwJBgSi6hYVnQzF0WSDIlXHjPqFCMdIe3KUkgS8I9Kanpy9/+eUX
-zp49+7GJYK5fv87w8PCsKGvbt2+nJPkQOEvgPjWaPuzaNWfs+qlTZ1CtlqhQaFi7dlPeunXrn5fn
-zpGlS5MA43196a1vk21pOpEKhTrXmABms5nduvWlQqGmQqHmgAHDnkrsgJ07d1KncyXwOeXy0TQa
-3XOcAzhy5Ahff/0tdu3aj9v/5/pfYTl+/Dg1Gg8CtQncs4/JVwwMrMXLly+Lu/GCp0phtE9mN1Bs
-kMlkuS5JCgTFjZEj38ePP7oCWAxgIQDbDFij6YtJk6pj9OjRj6y/detWdOnSG0plZaSnn8eIEYMg
-l1swZYoawGf2Upfh7NwYd+5cyVHXYrHAbDZDkqR/Hp44AQQFAfHxQJMmuPPzzwis1wwJCb1hsdSG
-JE1Dz56VsGDBT3n6ZDabQRK3b9+GwWCAk5NTQYflsRw5cgTLl6+GRqPGoEEDULZsWQDA0aNH0bhx
-K5hMYwDoodN9gV9/nY927doVSbuZmZmoXbspTp1KgtV6A4ADVKoknDt3CH5+fkXShkCQF4XSvqL5
-1nh2FEOXBYJcCQ0NpVzuTEAiEJM1c5bLx/Lzzyc8sm5mZiYNBhcCe+z1blOSfDh69GhqtZ2zBWv5
-neXK5eOK219/kU5ONgdatSJTUkiSsbGxfP31t/jyy+341VffPHaWfu3aNQYG1qZO506VSs/33hv7
-0Ew3IyODP/wwnb16DeSXX05mamrq4/3LB717DyLwbbYViF9Zp86rRWL7AUlJSRw+/H3WqtWMvXr1
-ey5BkAT/TQqjfSJ7nUDwHLBarRg16mNYrV1gOxj3NoBpAGKg1S5E+/Zhj6yflJSE9PQMAC/bn7hA
-oaiLKlWqoEyZCFy92hpWqw9ksvUICVn9aGciImBt3x7ylBSsgwzBZ2Kw9MQJNGjQAN7e3ggJmQaL
-JX8HAnv3fhsXL76KzMxJAO4gJKQZGjWqjS5dumSV6dHjTWzZEgeTqQd0ui0IDW2PP//cCoVCkbfh
-fGA2Z+CfYD0AICEzM7NQNv8Xo9GIH3/8rkhtCgRPnSL84HgmFEOXBYKHaNiwhf2+eQKBNAKjCLjT
-xaUsN23a9Nj6VquVbm4+BNbYZ6+XqNN58NSpUzSZTFy6dClnzZrFCxcuPNpQWBitWi0JcClqUoEk
-Amvp4ODOa9eusX//t6lUaqlUSmzZstNjQ8k6O5cmcDnbrPoLjhkzLuv9uXPnqNW6ZAuTm0mDIYAH
-DhzI17g9ij/++IM6nQeBVQRCKUl+XLhwcaHtCgT/BgqjfcVONYXQC4o74eHhdpF/ibbMbg/uyzfl
-olzSvebFoUOHWKJEaRoMZanRGDlr1tyCObJmDa0qFQlwDjSUwZIl0EZjczZt2pxKZV3a0quaqdV2
-5bBhox9psmrVRgTm2+2kU5KaMyQkhJcuXWK5ctWoUGgJOBM52qrD3bt3F8z3PAgLC2P9+i1Zq1Zz
-IfKCF4rCaJ84jCcQPGUyMzMxduxnWLVqPQwGA+rWrYAlS34DMBPAh7Bd1zoBb+8kXLx4HBqN5rE2
-SWLlypU4fPgoXFyc8fbbb6NEiRL5d2rpUqB/f8BqxVQ0wPs4CeAigJIAUqFQ+IDUwmr9CkBfe6Vd
-qFz5Y5w69WeeZk+ePImmTVuDrAiLJQ516pTD1q1rUbVqA0RG9oTV+h6AOgBqAxgEhSIMnp6/IDLy
-eM6DgXmQkZGBhQsX4tKly6hTpya6du0KmUyW/34LBMWUwmifEHqB4Cnz7rtjMG/eQZhMPwC4ArX6
-DVgsLrBY3AC8DiAcMtkOXL4cCR8fn3zZ7NSpFzZvPgOzuTskKRzNmrkjNHT1Q6IXGxuLS5cuwd/f
-H15eXraHc+YAQ4cCAL7Tl0Bwyh4AGwCEAGgDhWIbyHhYrQMA3LM/l0Eun4i2bc9g48aVj/QtISEB
-Bw8ehNFoRIMGDZCZmQmdTg+r1Qxb6I4EKBSN4eycjtq1ayAkZCq8vb0f22er1YpXX+2AAwfSYDI1
-g16/GoMGtcUPP0zJ15jlZk8uF6FEBMUDcepeIHgGWK1Wzp4dwrp1g9i8eUfu27cvX/Xc3X3t99of
-7Ft/Qm/vQKrVXlQofKlQOPHXX3/Ntx/jxo23n9S/mxVyVq/35ZH/STYzZ8486nQudHRsTJ3OxRYX
-/ttvHzjBSSVK0dMzgErlGPsp/U1UqcqwV69eVCq97OcHqhJ4mUAjKpWOjImJKciQkbSNm4ODK4ED
-9qZTaTBU4ebNmwtkZ8+ePTQYAmnLH2C7aaBS6ZmYmFggO7dv32bjxm0olytpMLhy8eIlBaovEDwP
-CqN9xU41hdALnhfffz+dklSRwEYC8ylJrvnKL16mTOVse/GkSjWEX3wxkdu3b+evv/7Kq1ev5tuH
-mzdvUq02EPDJdoWONBjqMyIiIqvc1atXqdOVIHDRXuYMJyo0WSI/TNaBwEGqVK9To3GlXv8SNZoS
-bNu2C5csWUKNxpXAJwROEhhAoASrVGn0RONGkuvXr6dO50qDoTs1mvL09CzP/v3fZnR09GPr/vzz
-Ujo6elIuV1KhqJ/tg8lKnc69QONHki1adKBK9Y79EOQJSlJJ/vXXX0/aNYHgmSCEXiB4BpQpU4XA
-/mxC8znfffeDx9ZbsWIlJakUga+pVA6nq6s3r1+/nqPM/fv3+dFHn/G113rz66+/yzN5zKlTp2gw
-BBCoQuAz+/37GXR09GRSUlJWub1799LRsW6WIH6H0SRAi0zGwdqAbH3IpEZTgrt37+bnn0+kJHnS
-waELlUonymSuBLwJvEyttiK//356ocYvMjKSPXu+QY3Gm8AiyuXj6eRU8pFCvXfvXkpSSQJH7af5
-HQnMJRBDpXIMAwNr02KxFMgPjcaBwJ1sH17v8euvvy5U3wSCp01htE/coxcI8ontnnd61s8yWTqU
-ysff/e7Zswc8PNzx228b4ejojOHDD8DT0zPrfWZmJho3bo2zZ71gNrfCtm2/YN++I1i/fvlDtnx9
-faFS3QPwCYBNAOZALk/Hli1hOe65lytXDhkZlyDDYczCfLyNuUgHcHLMGCyfuRmAFbb9chPIDHh4
-eGDy5O9gNp8AUAbATSiV5VGihCNksusYOnQA3ntvxBON2wP8/f0RHr4HZnMYgMqwWgGT6QaWL1+O
-4ODgXOvs3LkTZnMfADXsT9ZDJusEJ6eJqFmzFpYt21jgfXZnZ3fcuHECQDMAVqjVJ+HuXvXJOyYQ
-/Nspwg+OZ0IxdFnwgjBv3gJKUlkCP1Mm+4Z6vWtWjPnCsHfvXhoMlbNdOTNRoymRZ1rbw4cPs2RJ
-P8rlKrq4eOV5NW3NytX8RaEmAZoA/vXZ50xPT2eNGi9Tq+1GYA4lqRH79BnEkydP0sGhQraZPuno
-2CDf197u3r3LJUuWcMGCBYyLi8vx7sSJE2zSpB0DAupSo8m+nUAqlSM5efLkPO3OmTOHktQu2zbF
-dpYuHZAvn/IiNDSUOp0rdbq3aDA0Zu3aTWk2mwtlUyB42hRG+4qdagqhFzxPVq/+lW3b9mCPHm/y
-5MmTD71PTU1ldHT0YwPLZCciIoJGY11mX07X6Tx5+fLlR9YzmUx5J1JJSyM7dSIBZkoSU7IF4UlJ
-SeGECV+yV6+BnDlzFi0WC1NSUujo6Ml/8qtHUK93ZXx8/GP9v3XrFkuX9qde356S1JOOjp48c+YM
-SVsIXQcHdwKzCPxJhaI8FYrqBLYRmE293pWRkZFZthYvXsJSpQLo4uLDkSODmZyczMqV61GvD6JG
-M5SS5FYk+efPnDnDWbNmceXKlULkBcUCIfQCwb+ArVu30mBwoV7vTb2+RL4FKSUlhd7eAVQqPyaw
-hxrNQNau3fTJs6GlpNji1QO2+PX5PGi2b98+OjuXolrtSAcHV4aHh+er3qhRwfbDbbYmZbLpbNGi
-I0ly9uzZ1On6ZfuIuU25XMvq1ZuxRYuOPHr0aJadrVu3UpK8COwlcIGS1JTBwZ8wNTWVS5Ys4fTp
-03nq1KmCj4dA8AIghF4geM7cvXuXBoMrgV12QdtHSXLJ14yYJOPi4vjaa28wMLA++/V7m3fv3s3x
-Pj09PX+HzpKSyCZNbKrq5kYeP57jdUZGBkePHsfSpSvQ378WN2zYkOO9xWJhfHx8gVLMdu7cl8CC
-bGK+h4GB9UmSCxYsoCR1zvbub2q1xlw/YgYNGk7g+2xlD/Gll6rl2w+B4EWmMNonokUIBEXApUuX
-IJeXAtDE/qQBVCpfXLx48bF1SSI6Ohp9+nTG1q2rsXjxbDg6OgIAUlJS0LZtN+h0Bmi1BowfPzHv
-oBl37gCvvgrs3g2UKmX7Z7VqOYp8+OGnmDNnH+LiVuHixYno0eMt7N+/P+u9XC6Hq6trgRLMtGnT
-FJL0I4DrAO5Bp5uMVq2aAgA6d+4MR8cTUCpHApgPSWqHDz8MzjWanbOzEQpFbLYnf8NodMi3HwKB
-IA+K7HPjGVEMXRb8B7h16xa1WqdsB81iqNWWeOwdb6vVyi5d+lCvL0+jsQP1eldu3749633//kOp
-1fYgkEogjpJUkStXruT69evZunU3duz4hi0hzI0bZJUqtqlw2bLkpUu5tufhUY7A6Wyz5gn84IMx
-heq71Wrlhx9+QpVKR4VCzR49+jMtLS3r/c2bNzlqVDC7devPxYuXZM3mjx07xt9//z0rCE9cXBxd
-Xb2pUr1FmWwcJcktx1gIBP9lCqN94nqdQFAEuLm5YejQgZg5szrU6gqwWmMxZcpElC5d+pH1QkND
-sWXLSaSknACgBbAdPXsOQHz83wCAHTt2Iy3tF/u7UjCZhmD27AU4dOg8TKYvASTj7NbWOOnuAG1s
-LBAQAGzfDjwId/s/SJIetpl3JQCAUnkdDg6lCtV3mUwGk+keLBaCJM6cOY+EhASUKmWz6+7ujmnT
-vslR5733xiEkZBmUyqrIzDyIJUvmokuXzjh16iAWL/4ZJlMqOnfehurVqxfKN4FAgOI3PS6GLgv+
-A7z//kfU6/2o1Q6kRlOGgwYNz1e9mTNnUqsdkm2GnU65XJG1H1+3bgv+kw3OSrW6Dz08yhMIJUD6
-IooxcLZVrlaNvHkzh32r1crjx4/zjz/+4J07d7hmzRrqdJ4EvqJS+Q5dXb3zvMaXX954oz+BEgQi
-7dfgxrFu3eZ5lj906BAlyTtb0Joj1OkcmZ6eXig/BIIXmcJo31Pbo09LS0O9evVQvXp1VKxYEePG
-jQMA3LlzB0FBQShfvjxatmyJu3fvZtWZPHky/P39UaFCBWzbtu1puSYQFJqzZ8+iceO28PWtjq5d
-38BPP4UgJeUA0tLmw2w+jKVLl+PKlSuPtVO7dm3I5aEAYgAQcvkMVKhQKysIzNy538HB4SPo9b1g
-MAShTJmTcHFxAaBEBZzDbjTBS0hElKs7sHMn4O6eZZsk3njjLTRs2AGvvTYeZctWxEsvvYQtW1Zh
-5Mg7GDfOFdu2/Y5Ro8ahevWmGDZsNFJSUgo0DgkJCVi5cgWAXgD8AcgAfIwjR/bmWefvv/+GUlkT
-gLP9SU1YrQokJiYWqG2BQJBPiu5742FSUlJI2k761qtXj3v27GFwcHBWuMkpU6ZwzBjb/uCZM2dY
-rVo1pqenMyYmhn5+frmeMn7KLgsEj+XmzZt0cipJmWwmgcNUqVpSocgeVpY0Gqvy8OHD+bI3Y8Ys
-qtV6ajQl+NJLlXjpf/bXr169ysWLF3PVqlVMSUnhokU/s4G2FG/BSAKMkKt4IJe97DVr1lCvr0Eg
-xe7Xcvr5/XOK/f79+/TyKk+lchyBHdRqe7Jp07YFutYXFRVFjcaFQH3+k2xmO52cSj9Ubv/+/UxK
-SmJkZCR1OrdsZwVW0s3Np8ChbAWC/xKF0b5nopopKSmsXbs2T58+zYCAAN64cYMkef36dQYE2KJc
-TZo0iVOmTMmq06pVK+7fv/9hh4XQC54zK1eupINDx2zCHk9AR2CVXexW0Nm5FO/du5dvm2lpabx5
-82b+RHbfPpoliQT4l4sHd2/dmmuxr7/+mkrl6Gx+3qVarc96v23bNhqNjXJsG2g0zll/P/NDRkYG
-vb0DCFSz/+lCQM81a9aQtG0dDB/+AXU6dxqNtejkVJJHjhzhkiXLqNUaqdN50tXV+6HMewKBICeF
-0b6ner3OarWievXq8PDwwCuvvIJKlSrh5s2b8PDwAAB4eHjg5s2bAIBr1679ky8bgJeXF+Li4p6m
-ewLBE6HVakHeAfDPNTeFIhOlS38CmUwDb+8J2L59IwwGQ456UVFRqF79ZWi1RpQvXxPHjh3LeqfR
-aODu7p7rtbMc/PEHEBQEtckEdOmCetdi0bhly1yLVqtWDRrNRgDxAACZbBEqVPjnup1CoQBpztaP
-TJCWAl2tUyqV2LVrM2rUMEKlioSHx2GsX/8LunTpAgAIDw/HokUbkZp6AcnJh3H37lR07twHffq8
-gTt3buDChYO4fj0aNWvWzHebAoGgYDzVU/dyuRzHjx9HUlISWrVqhZ07d+Z4L5PJHvmLLa93n3/+
-eda/N2vWDM2aNSsKdwWCfNGyZUuULv0FLl/uC7O5PvT6BRg0aDR++GEKLJbchTIjIwPNmrXFtWvD
-QG7ExYub0Lx5O8TEnIWTk1Ou7WzYsAEhISug12sxduxI1Lh2DejSBTCbgb59gQULAGXef4VbtWqF
-ESNex9Sp5aBSlYCTkwq//RaW9b5Ro0YoXZqIjn4L6ektIEmL0br1/8HV1bVA41G2bFkcPbo713fn
-z59HZmYLAA/62AlXrvQGSeh0Onh7exeoLYHgv0JERAQiIiKKxljRLSw8mi+++ILffvstAwICslJ0
-Xrt2LWvpfvLkyTmSW7Rq1SrXHNHP0GWBIE+SkpI4fvwE9ukzmIsWLX7skvv58+dpMPj+T9KYRty5
-c2eu5W2pbW3pXIGp7K1xoFWptFUcOpQswH52fHw8IyMjcz3VfvfuXb777ods3bobv/xySpGffN++
-fTv1+nIEbtv7/TN9fasUaRsCwX+BwmjfU1PN+Ph4JiYmkrQl32jcuDG3b9/O4ODgrL34yZMnP3QY
-z2w2Mzo6mr6+vrn+8hRCLyiO3Lx5kxqNo30/nwRSKEnePHHiRK7lK1VqSGAzAbIfFjETMpvIBweT
-BYyBb7FYuHbtWk6dOpV79uwpiu4UiA8++JhabQkajZXp4pJ3nwUCQd78K4X+5MmTrFGjBqtVq8Yq
-Varwm2++IUkmJCSwRYsW9Pf3Z1BQUNbHAEl+9dVX9PPzY0BAALds2ZK7w0LoBc+QpKQk9u07hH5+
-NRkU1OmhE/EF4YMPPqZeH0Cl8gPq9TXZq9eAPFcCAgPrE9jBYZiZtQSwtmbdAot8eno6X365JbXa
-alSrh1OSfPjttz88cR+elCtXrvDYsWNZN3EEAkHBKIz2yewGig0ymSzvWN8CQRFCEo0bt8bhwyVh
-Ng+DXL4TLi6zEBl5Is999ccRFhaGkydPwt/fH507d87zHEpIyHxcGT4WEzMSAABjVXp02rMD9erV
-y3dbqampqFWrEc6duwUgCrboerFQqQJx9248JEl6oj4IBIJnT2G0T4TAFQjyICEhAYcOHUB6+m0A
-SlitdWE2h2Pv3r1o167dE9ls3bo12rRp8+jT9SQGX/kbyEiAFcAP5auiTciMAok8AEyf/iOiolQA
-qsEm8gDgDYVCQnJyshB6geA/gsheJxDkgVqtBpkBwGR/QpBJUKvVBbaVnJyMRo2CoFJpIUnO+P77
-6bkXJIHRo4EvvwQUCsiXLcP7F06gadOm+W4rLi4OISEh2LhxGzIyOgA4BGCTvR9T4OnpAfdsEfQE
-AsGLjVi6FwgewcCB72DlymMwmfpBo9kFf/9oHDmyO99in5iYiDfffAcbNmwGabfYDeoAACAASURB
-VATwGwBnaLVB+PXXGWjfvv0/hS0W4O23gfnzAbUaWLkS6NSpQP6eOXMGDRu2QGZmS2RmnkJ6uhXA
-FAAjAVyG0eiJ48d3o2zZsgWyKxAIni+F0T4xoxf8J7h8+TIaNWqFEiW8UK9eC0RFReWr3rx5P+Lb
-b/uiV69DGDOmAvbv355vkSeJVq06IzTUAHIvgDEAOgJwQlraEISHR/xTOCMD6NPHJvI6HbBhQ4FF
-HgBGjfoY9+59ApNpCdLTj0AmU0Au7wqtNgk1atTHokXTsXDhIsyaNQtpaWmwWq2YOnUGmjbtgF69
-BiImJqbAbQoEgn83YkYveOExm80oV64qrl0bAKu1J+TytXB3n4lLl0491X1qW6pWP6SnJwB4EESn
-DYC3ASzF5Ml1MHbsGCAtDejZE/j9d8DBAQgNBZo0KVBbW7duxebN27Fixe+4dWs+gAf1F6Ft2y0I
-CZmKwYPfQVjYPgCDIZcfQOXKKWjSpCEWLvwTJtOHUChOw2ici3PnjmZFrxQIBP8OxIxeIHgEFy5c
-QFKSHFbrGABlYLW+B5PJiNOnT+coZzabER8fX2QfklqtFlZrOoAk+xMrgFgA70Ol2gJHRyPSExOB
-Dh1sIu/sDOzYUWCRnzVrLjp3HoLp011w+7YeMtl4AHcBXIVePx2dO7fG3r17ERa2BcA+AF/Cat2G
-s2ctmDPnJ5hMawF0hsUyHmlpzbB+/foi6b9AIPh3IIRe8MJjNBqRkZEA4L79SSoyMm7BaDRmlZk9
-OwRGowu8vQPg61sF0dHR+bKdnJyMN94YBB+fymjUqDXOnDmT9U6v12PYsOHQ61sAmArbbD4WQAKs
-1sH44v2VOOtTFggPt6WXjYgA6tQpcP/GjfsMJlMogLGwWg9ALr8GudwTGk0gRo58DQMG9EdY2A4A
-FgA+9loyWCw+sMW5z34DoGhXzKKiojBjxgzMmzcPSUlJj68gEAiKnkLc338uFEOXBc8Rq9VKq9XK
-3r0HUa+vQ+AL6vUN2KVLn6xgNcuXL6dC4UogigApl3/HwMA6+bLftGlbajR9CRynTDaLjo6evHnz
-Zo72ly1bxiFDRrJ9+44E1ATOsARu8xBqkQBTXFzICxeeuI8ajYFAQlZoXY1mKKdOncqbN2+yWbP2
-1Omc6ODgTsCPwGACVwisJyCxXr0mlKQGBH6nXP4lnZxKZoWoPnv2LGfOnMlly5YxNTW1wH7t37+f
-er0rNZohlKTOLF3an7dv337ifgoE/2UKo33FTjWF0AvyQ0ZGBgcOfIdqtZ46nSPHjfuMS5cu5Ycf
-juPPP/+clfv8/PnzVKv1BPpni0OfQZlMzszMzEe2ce/ePSqV2mx52ElJasdPP/2USUlJOcpGRUVR
-q3UloKQH/uZJVCYBRskc+MtXXxWqr1279qVW25nAOQJrKEmu3LZtGyXJk8Bwe9jdVQT0BLwJONr/
-fEmt1pnjxn3KRo3asmvXvoyKiiJJbtmyhZLkSq12EPX6FqxcuR5NJlOB/KpZsymBZVljo1IN4scf
-jy9UXwWC/ypC6AWC/+Gjjz6nJDW3i1wsJaka581b8FC5Dz8cR6CrPZd6ql2U/qCLi1dWmQMHDvCd
-d97j6NEfMjIykiT5yy/L2aHD65TJVNni11sJVKNOV5FubmWyRJMkw8LC6OgYRG+8zEg4kgBPowzL
-apx5/vz5QvXVZDKxf/+h9PAox4oV6zE8PJwlS/oSUBHIzBJarbYzVSpnAnMJXLYn1qnLvXv3PmTT
-2zuQwNasful07Th79uwC+eXjU5nA0WwfUNM4cOA7heqrQPBfpTDaJ/boBS8koaE7YDJ9DMAVgDdM
-ptHYuHHHQ+WsVgIIAFAVtghy/weZ7P+wfPkCAMAff/yBV15pj59+csUPPyhQq1YjBAePxaBBE7Bh
-QwuQdQE0APA+gOYAVEhNPYqEhBEYMGBkVjvlypWDd9ph7EEU/JGEo5DjFdzGtNWLERAQUKi+6nQ6
-LFo0CzduXMSZM3/Bw8MD9+4pAagBXH7QUwB/Qy7PBFAFQBkAR5GRcQnlypV7yGZiYry9HADIYDZX
-xa1b8QXyq127IOh04wEkADgPSfoR7du/+iRdFAgEhaEIPzieCcXQZcFzoGXLzpTJpmfNJpXKDzhk
-yMiHyp08eZKS5EpgNoFJVKu9ciwv16/fksDyLDsy2QSq1c4ETtufxdiXwV+yL4s3IHCPwCmWKlWe
-N2/etG0TnDrF+0YjCfAvhZ6lpBIMDd2Uwxer1cpdu3ZxxYoVvHjx4hP3PTY2llqtC4Hv7T59QKA+
-q1VryA0bNlCvd6GDgz8lyZm//bY2Vxvt2nWnWj2QQAqBk5Sk0ty9e3eB/EhNTWWvXgOo0Rjo4ODG
-qVOnP3GfBIL/OoXRvmKnmkLoBfnh9OnTNBo9qNP1oyR1o7t7GcbFxeVa9sCBAwwK6sz69Vtx9uyQ
-HBnlKld+mcD2bMvPs6lUGu0Cn0HAh8Db9mV7C4FuBMZSLg+iXK6lRlOCbT3KMNPJiQSY2qgR/9q+
-nfHx8Tl8sFqt7NnzTer1AXRw6EpJcuX69evz1Ver1cpvvpnKsmWr0d+/Nn/5ZTkHDRpBvb4agT5U
-qUqzbt2XaTabSdrOFpw9e5bJycl52kxMTOSrr3akQqGmg4MbFyxYlC9fBALB06Ew2icC5gheWK5e
-vYrQ0FAolUp06tQJLi4uBbYxdeoMfPrpAphMcwHchyS9iVdfbYDt26/DZOoFYAKAXwA8WJJeBZls
-KGxx8Q+gIeIRhlfhiDSgXTtgzRpAq83RBkn8+OOPGDNmFtLSjgHQATgIvb4NIiK24syZMyhfvjwa
-NGiQq4/Tp8/ERx+FwGQKyfJx9eo5SE1NxalTp1GhQgB69OgBubzgO3UkH52ARyAQPBMKpX1F8qnx
-DCmGLguKMVarlVOmfJdjtpyZmcnPP/+Kvr5VCXjYT+xbCKQTaM369RtTr+/HFgjnfUgkwNWQMS2X
-GbTFYmFQUEcqlS72Q4EPVg6slMlU1OlK0WB4g5JUhsHBn+TqY9WqjQlsy1Z3Drt16/+0h0YgEDxD
-CqN94jCeQPAIZDIZxox5H9HRxxEZeQgdO3ZA1659MWnSJNy+fRMuLhoAuwB4AygJne4oBg7sizaZ
-O7AJ7aCHCYvwCvooHdC991AsXrwk66ucJF55pRXCw08jMzMMwG4AZ+3tzgCpRWrqAdy/vwwm0xHM
-nBmCixcvPuSjXi8B+OegnEwWD4NB97SHRiAQFBOE0AsEBeCtt0Zi82Yr0tOvIDl5K0ymDDRvXhEe
-HnrI5WYolf7YNexdrEiPgwbpmKPywUD8hQzrYGzY8CqGD/8WEydOBgBs2LABf/11DkALAHUBfA+g
-PgAtSpb8CXq9CwAve8suUKvL4fr16w/5VK9eJQCDAUwCMBaSNB3BwSOe/mAIBIJigRB6gSCf3Lhx
-Axs3/g6z+VUAegDVkJY2BIGBfkhKug2r9Ri63huAnzNMUJKI6toVR/u1hEbbEVbrtwD6IyVlHb79
-9gcAQHR0NKzW+gDCAPwNoDeAT+HvH4iLF49DrU4HsAa2MLXhsFguomLFijl8Onz4MEJCVgD4GbZZ
-/UF4epZEYGBgkfX7wIEDCArqjAYNWmPOnHnijIxAUMxQPm8HBILnze7du3HgwAF4eXmhe/fuUCgU
-SEtLQ1paGpycnAAAx44dQ7NmbWAy1QQwG8BiAFuh0ZyFQuEJtdoXg9LCMAOjAABfaDzRdswYVN63
-D+SpbK3pYbFkAgBq1qwJtXoGMjOHAKgEQAOl0opNmw5CkiRs2/Y72rXrhjt3+sDBwRnr1v0KV1dX
-AEBqairOnTuHzZs3g2wPoIv9TyZiYrSwWCxQKBQoLKdOnULz5u1hMn0FwBOnTo2DyZSK0aNHPrau
-QCD4l1BUBwWeFcXQZcFz4sqVK+zUqTerVm3MYcNGMyUl5aEyU6fOoCT5UKV6j3p9Q7Zo0YFjx46n
-UqmlSqVnnTrNePv2bVat2ojAQvthNwuBNlSrK7JcuaqMiYnheJXuwUk4jsQo6vUuTEhI4OXLl2kw
-uBGYSeAPSlJTDh78z33+iROnUKXSU6FwpVZbgrNmzcrhn9Vq5b1793Jc+btw4QI9PMrSwaEy1WpH
-KpWBBNLszUewRInSRTaG778/hsD4bAf9/qKPT+Uisy8QCPJHYbSv2KmmEHpBfkhOTmbJkn5UKD4l
-8AeVynrU671YqVJDLl68hCSZnp5OlUqy34kngXRqtT7UagMI3CSQSaVyAB0dvQkYCERmE7wpbNGi
-Ne8lJ5PjxpEALQCHadwoSSW4cWNoli8nTpxg8+Yd6OHhR1/fGhw0aHhW4pvLly/TwcGNMtkIAjMp
-ST5cuHDxI/tWo0bjbMGA7lKhKEmt1p8ODp0pSa7cvHlzocbu+vXrPHfuHM1mMz/4YCxlsnHZ+r2H
-L71UtVD2BQJBwRFCLxD8D5s2baKDQzO7OP1KoAyBLQS2UpJe4ooVK5mYmEiVymAPdvMg8UoFApOy
-rrgB9QkMJdCLtsxvmQSuU6+vyDWrV5MjRtgqKhRMXbiQ58+f5/379x/yZ8CAYZSkxgRWUKUaRS+v
-8kxOTuYnn4ynQvFuNiHdzTJlHj1j1utdCFzPVucj9u3bjytXrmR0dPQTj5nVauXo0eOo0TjSYPBj
-qVLluHXrVur1rpTJviWwjJLkyzlzQp64DYFA8GQURvvEYTxBsSIxMRGtW3eBVmuEu3tZrF+/Ptdy
-tv3pNNgOsv0CYDKAVgBawmT6GnPnLoejoyP8/QOhUHwGIAlAGGSyK9Bq/wSQCaAngFMAPgLwE4Bo
-AAbI5WXw7oiu6BwWBvz4I6BW49bs2eixPhzdug3CuHETkJqamuVLeno6fv55PkymjQB6IiNjGpKS
-ymDbtm1IT8+AxeKQzXMjMjIyHjkG/v6BkMnW2H+6D71+C9q1a4sePXrA29sbsbGxSElJKeDIAmFh
-YZg7dx3M5ku4fz8KN24Mw4cfTsT+/X+ga9czaN36d8yfPwlDhgwqsG2BQPAcKcIPjmdCMXRZUIQE
-Bb1GtXoQbfnX91CS3Hns2LGHyqWmptLfvzrV6rcIvExgVrYZ8Fy2bt2VJHn16lXWq9eCarWeXl4B
-3Lp1K+vUaUat1pdAZQI17CsCtr15na45Z02fTvbo8SAvLe+tXUt39zJUKL4g8Ae12s5s06ZLDl8U
-CrU9brytmoNDO65atYpHjx61x9pfRmAnJak2x4+f+MgxOH/+PN3dX6LRWJU6nQf79h1Cq9XKEydO
-0N29DCWpFDUaB86ZM69AYztp0iQqFMHZximBGo1DgWwIBIKnQ2G0r9ipphD6/y7bt2+nLfXq3Swx
-UquHc+rUqSRtOegvXbrEW7dukbTFax81KpgNGrSgSuVEYAqBrylJrrmmZn1Aeno6hwwZQpVqKIF9
-BNwIdCFQgY3rNKWlXTtb40YjuWcP161bRweHltkEMo1KpS5HLPkuXfpQp2tLYCsVigl0c/PhnTt3
-SJIRERGsX78lK1VqyEmTvrUlwXkMKSkpPHz4cFbyG6vVylKlyhH42e7DRUqSJ0+cOJHv8V29ejX1
-+prZPkh+ZkBArXzXFwgETw8h9IIXnhMnTthnvu4EDmTtoev1QVy8eDFjY2NZtmxl6vXeVKuNHDky
-OMdJ9cOHD3PAgGEcMGAYDx06lGc7sbGxXLduHefOnUtJ8rIf1IulTNadlV+qQEvz5jY1L1GCV9av
-Z/36r1KvL0G5vHa2vf4kKpXaHKf8zWYzg4M/Ya1azdmpU29evny5SMcnKSmJSqWU7WODNBh6ccmS
-JbmWP3jwIEeOfJ8ffDCWUVFRJG0fC716DaAkedHRsQGdnUvx+PHjReqnQCB4MoTQC154Jk78kgrF
-BwRW0RZf/l0CjVm1agOmpqayQYMgKhQT7GKbQL2+Mn/77bcCtbFt2zbq9a40GttTry/HmjUbUaWS
-qFY7skbZykytVcumoJ6eTD10iCVL+lEu/47AOQJeBN4ksJSS1Jj9+g15SiORO1arlQ4OrgT+zDqN
-r9f7cs+ePQ+V3bFjByXJjcBEyuUf0sHBnRcuXMiyc/LkSUZERDAxMfGZ9kEgEOSNEHrBC8/UqVOp
-0fSxi9ghAsNpNLrRZDJx5MhgAjoCcdlmtOP5ySefFqiNEiW8COyw10+hwVCZv//+OxMuXKC1Rg2b
-YW9vMjKShw8fpoNDlRz72TKZKxs2bMXvv5/GzMzMpzQSeRMWFkZJcqWjY0tKkhffeef9XMvVrfsq
-gRVZvstkn3Hw4BHP2FuBQFAQCqN94tS9oFjQt29fODn9CaXybQA7IUnrMW3at1iyZBnmz98JoCps
-oWQBwAy9fgfKlfPLl+3Lly8jMLAO7tyJA9DU/lSC2VwDYfPnw7FjR8iOHQPKlQP+/BPw94fRaERG
-RjwAk728BqQFer0ao0ePKpKodAWlTZs2iIw8juXLR2Hv3o2YOfO7rHe0fdQDAFJSTAA8sr3zQHKy
-6X/NCQSCFwSRj15QbIiPj8ePP/6EhIQkvPZaW2i1WrRu3QMm00QA9WC7PlcGMtlltGvXGOvXL89T
-cK1WK06ePImUlBT07DkQcXH9Qf4KoC+AUQCi4YM6+AMm+CEN1sBAyHfsAEqWBABs2bIFr73WG2az
-F4CuAEIBBEAuX4HU1PtQq9WP7IvFYsGKFSsQExODmjVrol27dkU1TA/1c+TIYISEzAYAvPnmIPj5
-lcGECUthMs0FcA+S1B9r1oSgTZs2T8UHgUBQeAqjfULoBcWSK1euIDCwJlJSmgFwBhAC4C6Aj1C3
-7jn89dcfkMlkudZNS0tDUNBrOHo0EqmpySCTAZgBRAHoAOAa/JGGHdDDG4k4Kjfi/LQv8foIW0a4
-EydOoGHDIJhM/QFsstepCKAllMoyuHXrOm7dugUfHx/odA+niyWJ//u/HoiIiIPJ1BSStBYjRvTA
-5MkTiniUgO++m4bPPlsNk+l3AHJIUieMG9cOCoUcc+cuhVqtxuefv4/XX+9V5G0LBIKio1DaV9h9
-g2dNMXRZ8BRYsWIFHRy62O/TVyTwKoEgOjmVzDpYlhcTJ06iTteRwNcEOhBwIHDKvmdtYmV48Doc
-SYC78TJdVQP4ww8/kCSPHDnCN954gwrFaAKJBDwJ6Ak4Uqn04auvtqNWa4ssZzR65HoYbv/+/dTr
-/QmY7W3eolptyPPw25kzZ/jDDz9wwYIFuUbdexTNmnUg8Fu2swQb2aBB6wLZEAgEz5/CaJ/YoxcU
-S5ydnUFeAmAEcBBAZygUEThz5jDKly//yLonT0YiNbU9bKlhW8CWja4FgH6oDX/sQjw8kYRtaIzW
-eB/3Fb8jKCgIH374KRo37ojffjsBiyUawI8AygOIBHAEMpkOu3btRlraX7h/PwrJyYvQvn23hyLd
-3b17F0qlD4AHy/uuUCodkJyc/JCv27dvR506TTF27EWMGLEW1as3wv379/M9TqVLu0OhOJ71s1x+
-HKVLu+e7vkAgeAEowg+OZ0IxdFlQhFitVqalpTEzM5NNmrShXt+UCsWHlKQy/O67afmy8fXX31GS
-WhGYS6CuPQDPMTZGVSbZp77r4UUN3CiTGbl27VqePHmSklSKwG37TN6Xtvj5O7LNlhdTqfTNcZdd
-kkoyNjY2R/u3b9+mo6MngaUEblKh+IJ+flVzDZTj61uNwKasuAFabbesAEH54e+//6arqzclqSsl
-qQdLlChdqHj4AoHg+VAY7RP56AXFhvnzF2DEiPdhNqcgIKA6Nm5cgb179yIuLg716y9E8+bN82Xn
-vfdGIiJiP3buHI/MTAsyMz3QEgqsQyokAMuhQj/oYJHdwYYNq9G+fTts2rQJKlUVAC52K4cA+AI4
-C8DWrkJxFuQdADcAeAI4DDIV7u45Z9AuLi7YuTMMr78+GLGxo1C1ak2sXh0KufzhBbbExATY9v8B
-QIa0tIqIj0/I95j5+Pjg3Lmj2LBhAwCgffsZD/kjEAhebMRhPEGxYMWKFXj99cEA/gBQC8DXKF9+
-DS5cOPJE9kgiJiYG6enp+CmoA767GgMNMjEfPhiCVwD5GqxZsxSdOnUCAMTGxiIwsBZMpi329tfA
-0fEdWCxERsZrkMlMMBh2o1+/3pg1awHU6kBkZJzGhAnj4O7uBn9/fzRo0KDAfvbo8SY2bDAjLW0W
-gL8hSe0RGroEr7zyyhP1WyAQFE/EYTzBC4+ra2kC3bMti1spl6sfeTjt3r177Nq1Lw0GN5YqVZ7r
-1q17uNAvv9Ail5MAp6ElZehHlUrPZcuWPVR07dp11OmcqNW60sXFi4cOHWJMTAynT5/On376KSvG
-fmRkJMPDw/nRR59RkkrTYHidklSGY8d+VuB+37t3jx069KJaraeTU0nOn7+wwDYEAkHxpzDaJ2b0
-gmKBWi0hI8MXwBEAGgCnoFTWh9l8L9clbwDo2rUvQkMzYDaPADASQBTq1q2FtWuXoHTp0sC8ecCQ
-IQCJUx06YujtdGh1Ggwc2B1HjpyAyZSG3r27o2HDhlk2MzIykJCQADc3t0cGxbFdrysPs/k0AC8A
-t6HVVsTp0/vh55e/QD4CgUDwgMJon9ijFzx30tPTcePGDXh6euYZaKZmzYY4cOAOgLoAqgH4HcHB
-7+Yp8gCwefMmmM07ALwCYBiARTh48Bf4+FTEaHk6vs1MsxWcPBlVxo7FnwAuXbqEmjUb4f79/rBa
-S2LRotcwadI4+Pn5oXbt2nBxcUF6ejosFstjhV6tLmkPqAMArtBo/HD9+nUh9AKB4NlSRKsKz4xi
-6LLgEWzdupUGgyslqRQNBldu27Yt13JXrlxh+fI1qFTqqVCoOXLk6Mfa9vDwJfCK/YT8gyV/Cz+G
-U9ax+CV1G+WoM3z4aMpkH2VtDwBBlMtL0WhsQ63WmVqtAyWpFB0c3Lhjx448205JSaGTU0kCa+y2
-ttHBwZ0JCQkFGyCBQCCgSGojKKbcuXOHer0rgd12MdxFvd41z8AxVquVt27dYmpqar7sr1q1moAT
-gZIE0ghYOQWjSYCZkLM/JtPVtUyOOv36vU3gh6zgMkAlAib7z1sJlLL/+w4aDG5MSkrKs/2DBw/S
-3f0lKpVaOjuXZERERL7HRiAQCLJTGO0TAXMEz42LFy9CofAB0Nj+pAkUCi9ERUXlWl4mk8HNzQ1a
-rTbHc7PZjNjYWJjN5hzPu3fvhm7d2gPQQoY2mIkmGIOpyADQC8uxGFXg5FQiR52+fbtBp/sGwGYA
-uwDUBvAgjO0rsF2dswJoDrncHdHR0Xn2r06dOrhxIxqJifFISIhD06ZN8ywrEAgETwsh9ILnhpeX
-F9LTY2CLUAcAl5Ge/je8vLweVS0H4eHhcHX1QrlytWEwuOLtt4ciMzMz631IyI9Q4CYWYjfewZ9I
-A9AJdfArdkGn64+ffpqSw17z5s2xbNlMVKgwASVL/g6Vaks2/34EUAW2vzaXkJ4eZzvU9whkMhkM
-BkOecfcFAoHgqVOEKwvPhGLosuARTJs2kzqdO43GNtTp3Dljxqx8101MTLQv/e+yL6fvJmBgmzZd
-aLVaSZK3rl7lr7Bdn7sHic3xO4FR1GqNPHnyZJ62zWYzZ82axebNW1Gp1FGnc6ejYylqNC52X904
-e3ZIofsvEAgE+aEw2ieu1wmeOxcuXEBkZCTKly+PgICAh95fuXIFy5cvR2amBd27d4O/vz8AYO/e
-vWjS5E1YrZHZSteCWh2NixdPwMfNDezaFbKwMNyFHG1RHvtRA8AGLF78E/r165erP5mZmWjatC2O
-HZMhNfVlSNIyDBz4f5g27RtERkbi4sWLqFChQpYfRUlcXBwSEhLg7++fa+Y7gUDw30QEzBEUezIy
-MhgXF0ez2Zzj+cWLF+no6EmVaigVindpMLjx6NGjJMn33gu2Z46Lts/oYwg4U6crxYtHj5KvvGI7
-eOfszA5e5QnIqdMZOW/evEf6Eh4eToOhOoFMu90bVKl0NJlMBeqTxWJhVFQUY2JislYYHsX7/9/e
-nQdEVb0NHP8OMwPMgOAOCCKGIiIIKq65YC6E5p7mXtpiWpbtZmbaT8W0zD1zqUxLLTX33dxywdwV
-LU1REUVxIZEBZmDO+8fQlK9LiwgyPJ+/nLuc+zy3iWfuveee88YQ5eJSQhUrVlWVLu2vjh49+q+O
-J4RwXPdT++Q9elHg9uzZQ0xMRzIyLICZefO+oGNH29Cz//vfx6Sl9cdqHQbAzZtBvPXWCAYM6M3G
-jTuATkAEUBM4ClQnokIagf37Q1wc+Pig3biRZSEhmM1m9Hr93z4vv3HjBk5OfsAf78mXQaPRYzKZ
-/vFV9o0bN2jevB3x8SdRKptHH63LypXf4eLicsft169fz/Tpi8jKOklWVinS0mbRsWPv/zzErxBC
-/EE644kCZTabefzxDly7NpmMjEtkZGykV68XOHfuHABXr/6O1VrxL3s8wu7d+3j66Y85fjwLuAws
-BSyAjvByyWzTmdHExUGFCrB9O4TYJoVxdnb+R53iHn30UWyT1swFzqDXv0lISBglS5b8mz3/9MYb
-Qzl8OACT6SwZGefYsUMxevS4W7ZRSvHtt/Pp3PkZPvxwNBZLS/6cNKc7p04d/cfHE0KIu5FCL/LF
-wYMHWbVqFefPn79l+YULF8jKcgI65C6phV5fg/j4eAC6dGmN0TgaOAz8irPzu5jNBm7e/Ins7J+A
-60BX3N1TaFKpLHvdrOiOHoWgIFuR/w+j0Hl5ebF582pCQz+jRIlGPPbYWdav/+Ef/UhYsmQJPXo8
-z9Kla8nK6ontroAzGRnd2L370C3bjh07nuef/5BFixqya1cpzOaVwB9z0i/F3//2/gpCCPGv5d0T
-hPxRCEMu8l5++U1lNPopT89oZTSWVitXrrSvS09PV66uHgric5+Hpyij0cf+fNpqtapx4z5VpUsH
-qBIl/HJ7wb+au+1pBRuVVuuijq1apawVK9pGvAsLUyo5Od/zHDVqjDIa5fUbaAAAIABJREFUKymY
-pjSaGgoG5I6ul6NcXHqoN98ccsv2Hh5eCn6xj8Kn1YYqvb6U8vSsrUqUKGfviyCEEPdT+6TXvXig
-du7cScuWPUlPPwB4Artxc2vNjRsp9nHqv/56Hv37v45OV5fs7AO88spzxMYOv2N7u3fvpmnTDmRm
-hmCb4KYMIU4X2F/KDZeUFKhdG9auhX9xm/1esX/77SKMRlcGDHiBgICAO253/fp1YmKeJC4uDtgN
-hAIpaDRhODuXRK+HSpVKsn37Wtzd3e37ubmVwmQ6DNjexXd27s/rrxenbdu2VKtWDQ8Pj/vOQQjh
-GO6n9kmhFw/Ut99+S79+y7h5c6F9mV5fjKFD38JoNNKlSxf8/f357bffOHLkCBUrViQiIuKebTZs
-GMWOHaeBQ1TnHBtoRFnSuB4WRomffoI8KJCrV6/mySf7kJExCCenqxQr9g0HDuykYsWKt23bsWMv
-Vq1yw2xeDOwHygOg0w2gXz8nevfuTc2aNdHpbu372r//a3z99UFMphHAr7i7v8ehQ7t55JFH7jt+
-IYRjkUIvHlrx8fHUqdMMk2k7UBn4Bo2mH1ptL5ycwNX1B37+eRtBQUG37XvlyhUOHDhAqVKlqFGj
-hv0ZucHgQWZmb+rQi7U8TglSWQv0MpYk8WrSbUPk/hfVqzfkyJG3gHYAODm9wyuvKD79dOxt2/r4
-BJGcvAz4HDgCjARO4Ob2Jvv377hjbmB7X/+DD0axdOk6SpcuyYQJ/6NGjRr3HbsQwvHcT+2Tznji
-gapWrRoTJozGxaUWRqMvzs4DUepFsrM/w2z+jLS0QQwdOvq2/fbs2UNgYCidO8fSuHEnunbta/+S
-KwVNWMpGmlOCVJZQk3aUx4SRixcv5knc6ekmwMv+2Wr1Ii3NdMdty5cvj0azDRgHNECjeRJ//4/Z
-tGnlXYs8gE6nY9SoD4iP38nWrSulyAshHggp9OKBe/75vly9epHjx3dRq1YdoLF9nVKVuHIl9bZ9
-unTpy40bk/n99x9JTz/GqlWHWLp0KQAfN3uMNVygGDeZhwdd+AUzA9FoTPj4+ORJzE8/3Rmj8RVg
-L7AOo/FjevTodMdtv/xyEp6eI/DwaI+7+xpq1Ajkl1/2ULdu3TyJRQgh7scDLfSJiYk0bdqUatWq
-ERoayqRJkwC4du0aLVq0ICgoiJYtW5Ka+ucf+tjYWCpXrkxwcDDr169/kOGJfDRlynSqVo0gLm4r
-Wu0QIB74CaPxf3Tu3Oq27S9cOA08nvvJgNnchFOnTsHixby0YTUGFF+7etAbKy5Gf4zGj1i48Os8
-uW0PMHToO7z1Vjv8/fsQFDSUr7+eTNOmTe+4bbVq1Thx4hBz5jzP4sWj2b17kwxfK4R4eNxfh/97
-u3jxojpw4IBSSqm0tDQVFBSkjh07pt566y310UcfKaWUGjNmjHrnnXeUUkrFx8er8PBwZTabVUJC
-ggoMDFQ5OTm3tPmAQxYPwOLFi5XRWFnBSQXXlJNTeQUuCtyVwVBS7d69+7Z9wsLqK41mvH0IWje3
-QHXk7beVcrJNUKNef10pq1UlJiaqHTt2qJSUlALITAgh8sf91L4HekXv7e1t70Ht7u5O1apVSUpK
-Yvny5fYJRZ5++mn7Ldlly5bRrVs39Ho9AQEBVKpUiT179jzIEEUeyszM5OjRoyQnJ9+yfOXKTZhM
-LwOVgJtYrWnAFiCNjIzZPP54h9vmkv/hh7n4+n6Om5s/zs6V+bZJCKFjx4LVCh98AB9/DBoNfn5+
-NGjQgNKlS9v3VUqxYsUKJk2axE8//fSg0xZCiIdavj2jP3PmDAcOHKBu3bpcunQJLy9bRycvLy8u
-XboE2EZJ++tc5H5+fiQlJeVXiOI+xMfH4+8fTIMGTxIQUJUhQ0bY1/n6lkGvPwJcAZpjK/j1cte2
-x2JxsQ95+4dHHnmEIUNe5bHHGrDk0Tq0Xb3CtmLcOBg+HO4ySp1Siu7dn6Nbt6G8/favREf34KOP
-xudxtrceLzZ2HKVK+VOypB/vvTcCq9X6wI4nhBD/Vr5ManPz5k06derExIkTKVas2C3rNBrNPYcW
-vdO64cOH2/8dFRVFVFRUXoUq/qN27XqQkvI+8CyQwqRJ9Tlx4hd++eUcZcuWpFSp46Sk1CMnpw6w
-GVvRLw2cIDv7mv2HH9iKZ82ajTh48DrDqEhrNgFgmTQJ/cCB94xj7969rFixmfT0o4AReJdhw6oy
-YMDzt3338sIXX3zFyJFzMJnWAjomTOhOyZLFeeONV/P8WEKIomPLli1s2bIlT9p64IXeYrHQqVMn
-evXqRfv27QHbVXxycjLe3t5cvHiRsmXLAuDr60tiYqJ93/Pnz+Pr63tbm38t9KLgKaVISDgK9Mxd
-UoaMjKYsX74Li2Uax4//jIfHXkqUcOPKlUFAFaAGUB2tdidTpky4ZRS42bO/4ODBOMbxIm8yhRyc
-eEFbji5BQUT/TSwpKSlotZWwFXkAP3S6YqSmpnL58mXi4+MJCAigevXqeZL7woWrMJneA2wT55hM
-I/j++0lS6IUQ9+X/X8SOGDHi7hv/jQd6614pxbPPPktISAiDBg2yL2/bti1z5swBYM6cOfYfAG3b
-tmXBggWYzWYSEhI4efIkderUeZAhijyg0Wjw9a0MLMtdcgOrdT0Wy0igMVbrG1gsDShf3gedbjnw
-AbAYZ+fLDBzYl+ee63NLe5s2bGMa8CZTMKPnKRbypdX/tuf4d1KrVi2s1oPASiALjWYipUp5sHnz
-VsLC6tGr1+fUrx/D0KH/y5Pcy5QpjpPT6b+ci1OULOmZJ20LIUSeyJPugHexfft2pdFoVHh4uIqI
-iFARERFqzZo16urVq6pZs2aqcuXKqkWLFur69ev2fUaNGqUCAwNVlSpV1Nq1a29r8wGHLP6jPXv2
-KE9Pb+XpWU8ZDN5KozEquJzba14pd/doNW3aNFWhQlVVrFiEcnMLVA0bRqvMzMxbG7JY1P6w6kqB
-MqFVMUxQME7pdB7q6tWr/yiW7du3Kx+fSsrJSadCQuqoQ4cOKVdXTwVHc+O5rAwGb/vEOffjxIkT
-ysPDS+n1Lyqd7mXl7l5GHTp06L7bFUKIv7qf2idD4Io8k5qaytGjRylTpgxTp85i9uytmEwD0Ov3
-4u29kfj4n9Hr9Rw8eBAXFxfCw8PtE9sAkJUF3bvDkiWkazS003jxo9Lg5JTF0qVzeOKJJ245XnZ2
-Nu+99yHff7+C4sU9+fTTETRp0sS+XimFRqMhISGBsLAmpKf/2eHP07Ml8+e/RkxMzH3nnZiYyIIF
-C7BarXTu3FnGqhdC5DkZ614UOKUU58+fx2w2U7FiRTQaDdOnz2Dduu1UqODD+++/c8srcLcxmaBT
-J9vMc8WLk7FkCStSUsjMzKR58+aUK1futl1eeeUtZs/+GZNpLHAGo/Eldu3adNvzd7PZjLd3Ra5f
-n4Zt7PoDGI0tOX58H/7+/nl6HoQQ4kGQQi8KVHZ2Np069WL9+o04OblSubI/mzevpESJEv+sgbQ0
-aNMGtm6FMmVg/Xr4mxnsAEqWLM/165uxva5nm3hm2DA3Pvhg2G3bxsXFERPTkcxMK5DBl1/OwN+/
-PGazmdq1a2M0Gm/bRwghHhb3U/vy5fU64dg+/XQSGzakkJmZCDhz/PjLDBjwJvPnz/77na9dg5gY
-2LMHypWDTZsgOBj489b73bi4uALX7J+12qsYDKXuuG3dunW5fPksycnJuLm5ER3dkePHL+Pk5I6H
-Ryq7dm26ZQwHIYRwFDKpjbgvixYtZvDg/5GR0Q1wBZwwm3uzb9/hv9/50iVo2tRW5CtWhO3bOaXX
-07NnX4oXL49Wq8fLq+Jd3yX93//exWjsDExCq30NT8/19hEX70Sn0+Hn58ekSVM5cqQ0N28e5saN
-OC5e7Er//m/+l/SFEOKhJ1f04h/bv38/n3wyjawsC/369SQgIIDevV/Eau0OrAaeAbTodCuoWrXy
-vRs7fx6aNYMTJ6BKFdi4kdNmMzVqNCAtTQu8BbzE5cubadOmC7/+evC25/TPPdeXcuW8WbRoJaVK
-efLaa7tvGXjnbuLjT5GZGQ1oAcjJieHXX1f/+xMihBCFgBR68Y8cOHCARo2iMZkGA+6sWfM0L73U
-E52uCfAx8ARQDVB4e8Nnn22+a1s5J06gadECp3PnIDzc9ky+bFmmvz2Emzc7AkuA13K3jsbJKZJ9
-+/bdsUNeq1ataNXq9tnv7qVu3XBWrZqPydQDcMHZ+SsiI8P/VRtCCFFYyK178besVisDB76NyfQW
-8AbQD5NpCsuXb8JqPQxYgfXABzg7n+f48TsXZYDvPxxJcpVgnM6d46CrkcS5cyF3ZESTKROlvIF0
-4I9X4TLIyTlhHz3xTrGdPHmSs2fP/uOOKq+++jKPP+6Di4sfBoMf1arFM3XquH9+QoQQohCRQi/+
-1jPP9Gf37l/5c1hZAAOuru507NgUo7EGbm7dMBoH8eWXs3F3d79jO/HffEPUBx/gi2IzUTTJeoMn
-er5oX9+zZxeMxmlAV6AB0Adn55q0bt3ojiMkXr9+nVq1GhMR0Yzg4Dq0bt0Zi8Xyt/nodDoWL57H
-6dNHOHZsJ3v3bv3nbwgIIUQhI4Ve3NP58+f5/vvF5OTMAEYCC4FVuLq+xMCBz+Dq6oLFcgGLZRNl
-ypSkadMmd2znzPz5lO/dmzJYWY0frZjPDfU+R4/G2Wd7q1evHkuWfE2NGr/h7+9BmzapfPfdGBYs
-+OKOve8HDnyHY8eqYTKdITPzHFu2pPPxxxP+cW7lypUjICDg1kF7hBDCwcgzenFP6enpaLUeQDTw
-FTAROECDBtVxcdEzb94GLJalQH2SkmLp2fNFNm1adksbv//wA6W798AdxSKa0R1fLDwNvEeJEj63
-FNro6Giio/9u6hqb/fuPYDaPw/Z71YWMjKfYvXsD48aNZ+/eo0REBPP666/i4uJi38dkMpGamoq3
-t7cUeCFEkSAD5oh7ys7OpkqVmpw925acnKexTVwzAReXkmi1lzCZtIA3kAbMolSp3ly5cta+/+/f
-fotrj564oJhDb55lNjkAeGAwGFi8eN5/Hoa2U6feLF/uQ3b2GMCKq2s3ypU7zsWL5cjIeBKDYTl1
-6uTw448rcXJyYvz4Sbz77hCcnIx4eZVm06YVBAYG3ucZEkKIB09GxhMP1IULF2jUKIbTpy9im152
-GvAKcBXYCrgAY4CFREaW4Oeff7Tt+P33ZD/1FDqlmEZZXuYCCi1wDSenchw+vI9q1arZj7N69Wr6
-9h3I9euXqFevCYsWfUWZMmXs681mM5mZmfYpbZOTk6lfvznXrjljtZp45JGSnDiRQGbmmdyYLLi5
-VWHHjh9IT0+nRYuumEzbgQpoNOMJCfmOo0d3P+jTJ4QQ9+1+ap/cuxT3ZLFYGDHiIxITfwM6AOuA
-48Bu4ElsBRWgAxpNAnPnTrN9/Oor6NoVnVKMpQ8v8QiK7sBkNJpGuLuXZuTI8aSmpgLwyy+/0Lnz
-01y6NAuzOYldu4Jo27a7PY7hw0fh5uZJqVI+1K4dxZUrV/D29ub48b2sWTOFzZvnMXfudLRaI+Cc
-u5cOJyd3zGYz+/btw2p9AqgAgFIvcfz4XvnRKIRweFLoxT0NGTKCefPisVh+BH4AXgKeBprnfjYB
-CphD48YNCQ4OhqlToU8fsFqZXq4ig6kNbASqA+NRSs+NGz+wZIkTLVq0RynFtm3bgDZAU8ATi2Us
-e/ZswWw28/jjbRgxYhrZ2afIzk7j0KHq9OzZDwAnJyfq169PZGQkISEhlC9fAr3+dWAPOt0QSpe2
-Ur16dSpUqIBWuxPIyM1sM2XK+N9ziF0hhHAEUujFPS1duhaTaSRQF9gGfAs8CrgDQUAAEIhe/znf
-fPM5fPQRvPyybedPPqHR+hUULzGSYsXaA19g+2EQB9TGbJ7O0aPxXLx4kZIlS+Lk9Cu2d/IBTuDq
-WowpUz5j06ZDwPNAOcAJi+VNdu/exaOPRmMwuGM0ejJ16nR0Oh3btq2hbdurBAb2p1Wrs+zcuREX
-FxfatGlDTEw4bm7V8fBojbt7bxYs+CK/TqMQQhQYeUYv7ujKlSssXbqUUaMmc+bMm0AvIAnbVfkJ
-oH7uv3XodCvZuGElTTZtgpEjURoN8S+/TNVPP0Wr1XLt2jWGDRvG55/vJDs7GziI7TfmTZydy3Hh
-QgIeHh40avQ4R49aMZsj0Onm0759c/bu/YWTJ6sDF4FVufstwGh8HYulIxbLBCABo7EZq1Z9TVRU
-1F1zUkqxe/duUlJSiIyMvOugPkII8bCRzngiTyUmJlKz5qOYTA3IyUklK2s7tiv649hmizsKlAHm
-oNePYP63M+i0YwdMmEA20M+lLt/pzdSu7cuMGZ8ycOC77Nu3h6tXG2C1JgFeQDNgJr161eLrr2cA
-ts52CxcuJCEhgalTv+TGjXpkZp7G1gHwJHADKItO9xM6HWRmnshtC5ycBtOqVTyNGzcmJiaG0NDQ
-fD1nQgjxIEmhF3lGKUXr1p1Yt64KVmts7tJ2aDRxKPU9ts54U4EmODsfonePVszUmmHWLLKArgxj
-KSOAbNzcGqPVniQ9fRA5OaFAb+Bd4CRa7Sbq1XuEbds23vY++5gxH/HBB8cwm+cAl4HaQHlcXbPR
-639j3boVdOv2PGfPjgVaAVY0mih0OhPQEL3+G5YvX0CzZs3y45QJIcQDJ73uRZ5QStG9+7OsW7cT
-q/Wvk7yYUCoWaIRtdLzReHruYd3KGczISoVZs8BgoKPWmaX8Md2rjqysWmRleZKT8x7QDtiORvMR
-YWEnGDr0ebZsWX/HQWuuXUvFbP7j/faywFKKFTvBl18OIiHhV+rXr8+XX07GaHwaN7deODvXRamz
-WCw7sFgmYDLN5qWXBj+4EyWEEIWIFHpht27dOlas2IPVOhj4BLgApKDVnsz99x9cqV09gqipU9F8
-+y24u8PatVyNbIRWOwZbL/zf0GoXodXqcz8DVESns7Bt2wqGD3+Po0ePMn/+fPbt23dLHE88EYPR
-+DmwCziPwfABnTt3oWvXrpQqVQqApk2bcvjwbiZPfozoaF/gOf581S+I69evPZiTJIQQhY0qZAph
-yIXG559/rozGvgqsCoYoKKZArzp16qbc3csojeZtBR+oUoZS6lqdOkqBUiVKKBUXp5RSKikpSVWv
-Xl/pdK7K2dlNTZ48VYWG1lUuLj0UzFRGY0PVq9cLSimlxo79VBmNPqpYsc7KaPRTw4ePviWWefO+
-UV5egcrDw0v16vWCysjIuGvcGzduVEajn4K9Ci4qg6Gd6tv3pQd3ooQQIp/dT+2TZ/TCbt++fTRq
-1IaMjG1AJWAilSp9xa5dG+jb9yV2744joGRxNrgoPA8ftk0vu2EDVK9+Szvp6em4urqi1Wq5efMm
-Y8Z8zIkTZ2nUKJKXXupPSkoKFSoEk5W1H5iObaKcVCZPHsXLL7/0n2KfPftL3nnnAzIy0mnfvgOz
-Zk3GYDDc5xkRQoiHg3TGE3li4cLv6dnzmdxX4PR4eXmxadNyOnbsxZkzDXE3N2Wd5gUi1RWUnx+a
-jRuhSpV/fZyDBw/SuHEP0tLCsD0SmAEk4+LSlbVrF9zzFTkhhCiKpDOeuG9nz56lT5/+ZGfvxPYa
-21QyM03cuHGDCxeyKGF+ly0MI1Jd4bRGx+mvvvpPRR7Ax8eH9PTz2EbLmwwEA1FkZb3GkiUr8iol
-IYQQSKEXueLj49HrawHh2Dq1PY3FYhvsxs9qYhtNCOMoxwimhUsprP7+/+k4V69e5Z13BuPk9BgQ
-CCTa1+l0iXh6uudBNkIIIf4g89ELACpUqEB29hEgBdtgOEewWm/SxNeXH7Mv4kMW+wmgnUsAJSp7
-Yjab/1G7WVlZ6PV6nJyciI+Pp1Gjlty86UJ29kBsV/LPAP2A83h6bmDAgD0PKEMhhCia5IpeAFCt
-WjVef70/RmM4Hh4xGAyPsWjEe7jHxOBjzuJsOT9eqeZNcs5Ozp51p3bt5gwfPvqu7V27do2GDaNx
-c/PAYCjGJ59M5JlnBpKaOgyLZRTwJVAL28Q4S9FolgCQkZFx1zaFEEL8e9IZT9ziyJEjnDlzhppK
-4du3L1y9Sk5UFGtffJEOvfpisWwC6gGXMBgi2Lt3EyEhIbe107p1FzZuLI3ZPAk4j9H4GHq9md9/
-/xGoDAwDxuZuHQV8h5PT50RF7WDTpmX5k6wQQhQS0hlP5JmwsDDalCyJb69ecPUq5pYtCTt7haee
-/RSLpQq2KWovAl7o9WGcOXPmlv1TUlI4fvw4O3b8hNn8LranQwGYTL0oU6Y0ev00bAPoDEKrDQA6
-YxtW1xOrtQWnTiXkY7ZCCOH4pNALAC5cuMCkSZNYMmAA1hYt4MYN6NyZNwKqcCqpIenpu4D92Iay
-HQocxmLZT1BQkL2NESNiKV++EnXrtiMtLQP4OXeNFYNhLy+80IOQkJ9xcSmDXu9PkyaBGAyngTTA
-irPzDGrXrpnPmQshhGOTW/eCU6dOERnZiKY3Q5ifvRkXrPzeoQOe339P8+jObNrUDduVN8A6NJqe
-aLUmnJy0ZGdnUKtWI4YOHUS3bq9gMu0GvIF30WgmYzS2RaM5S8WKOUREhJOQkESdOqG8997bWCwW
-goJqcuPGNcAZd3c3TpzYh4+PT4GdCyGEeBjJrXtxX4YOHc3jvzdiYfZWXLAyhQY8k+MKWi2NG9fG
-YJgFmIAsDIYZtG7dBL3eE7P5J6zW0fz8cwo9e/bDam2IrcgD/A+lTEye3IIZM14mNTWVBQvc+Omn
-Z/nss4O88MKrDBz4DhkZT2F7xe4Q2dm1+OKLOQV1GoQQwiFJoRfUOLifb9T36MkmlsEMpBFLl6/H
-3b08ZcuWICamDHq9F87OZWnUyErDhrXJyekMfA6sAN4nLa0TWVnL+fO9+FX4+DxCnz59KFasGKmp
-Plgs44EOZGQsYcmS7/jxx5+wWDoDpYEAMjM7sm/fsYI5CUII4aDkPfoiJjk5mVGjxpGUlEKbNs14
-5kYqb/9yEIAhvEEs5YHhwFTS0zPo3/8VVq6cz6xZk7FarZQqVYpvvvkGnW4lZvMebPPFewJd0Oni
-0WiqYzCEYLX+xvTpM0lJScm93aT5SxQalNJy/Xo2Gs23KFUPyMZgWEKtWo/m7wkRQggHJ8/oi5Br
-164REhLJ1avtyM4OY5h+CCMslwBY2Tyap3buxWTKwfaOe/vcvabSuPFKtm5dY2/HYrHQpEkrdu3a
-AvwOGAFwd2/D8OFRBAcH8/77Yzh+/Fes1izatGnL7t17SE5uQ05OFLaJbDyAMUAobm4+KJVB3brV
-WbNmMS4uLgghhPiTPKMX/8iSJUtIS6tFdvZ4RvEbIyyXsAJq5kye2LCWs2d/QaPRANa/7GXF2dn5
-lnb0ej3btq2hQYMmODu3B9ah1X6IwXCYPn36MH/+Mo4dCyYz8yJm8wWWLfuNMmVKEBq6A632RSAS
-+Aooh4uLnqVLp7J37zo2blwuRV4IIfKYFHoHdPbsWb766iuWLFlCVlaWfbnFYgGrGxN5lSHEko2W
-p7XO8OyzAJQuXZrWraOAF4Cvgc9xchrK8OFv33YMnU7H5s2ref31+kRGjqN9+9/4+edtlCxZkri4
-/WRlPQdoATeys/ty8KCOEyeS8PDQoNOZgR24uj5NnTp1aNasGVWrVsXJSb6OQgiR1+TWvYP57rvv
-6NHjeZRqjk6XRHCwll27NmIwGEg8c4YtlavSKzuTLPT0cg7BrXt9vvzyM/v+SimGDh3KggWrKVbM
-jYkTR9GkSZN/FUNMTGc2bKhOTs772O4OdANCgccpVepJmjaN4sSJBOrXr8nHH4/E3V0mshFCiHuR
-+egFYBv0pnz5alits4BOgEKni+HTT5/g5X79oFcvWLiQDCctA/2qUPKpjowaNQy9Xp+ncZw7d456
-9R7j2rWSZGXdwDZJzjogmeLFG3H9elKeHk8IIRydPKMv4qxWKzNnzqRjx6ewWsH2DBxAQ3Z2PX45
-eBg6dYKFC8HDA8PWLUw/dQiNRkPlyrUIDW3A+vXr8ywef39/fv31AJMnP4+LSzK22emOYjQ+S48e
-XfPsOEIIIf6eXNE7gF69XmDx4iNkZHgDx4EmwBQgCSN12eSWSb30G1CyJKxfD7Vq8dprg5kxYxcm
-03ggEaPxBbZuXU1kZOQ9j/Vvbdu2jUGDhpGa+judOrUmNnY4Op281SmEEP+G3LovwpKTkylfPojs
-7AvANWzPwgOAX/AAVuFKQ9LA2xs2bIDQUADKln2ElJRVQNXcloYxeLCV2NiRBZCFEEKIe5Fb90XY
-wYMHyc7WA26AP/A2cIVS1OFHytKQNM476ehbKZxt167Z9zMYjECK/bNOdxk3N0M+Ry+EEOJBk0Jf
-iMXFxbFs2TI0GmfgFeAIoMObLLayk1okcRItDazj+fKnJ4mJeZKdO3cCEBv7HgZDd+ATtNpX8PRc
-Rd++fVi+fDkTJ05k27ZtzJkzl9Kl/TEaS/DUU30wmUwFmK0QQoj/Qm7dF1LDh49m3LjpKBVKRsYu
-oBoQjz9l2UQSlUjnpIuBxllTSaZP7l6T6Nr1EPPnz+bKlStERNTj8uV0IIcqVSoSHFyFNWsOk53d
-EFiE1QoWy0rAH1fXAXTuXIqvv/68oFIWQogiS57RFzHnz5+ncuVwMjOPAV7AEGAy1V29WJF5Gn8U
-lurVidF4sunQG9g6580GNtCsmY6NG1fSo8dzfP+9OxbLp4DC2TkGpeKxWE5gG9L2FaAEMCL3qKdw
-c3uUmzeT8z9hIYQo4uQZfRGzf/9+rNay2Io8wGjqGMqwyzkFfxQ8+ij6bdt4YchAXF37ARWBnUAk
-27btYeHC7/j558NYLG2xTTbjhNkcjlIV+GPcetvz/qN/OeoJ0tOzWLVqVT5lKYQQIi/IFX0hc/r0
-aSIi6pGWZgbmAm2oxXjWa96kpFLQvDksXQpubmRkZFCqlC8ZGVGNMUnHAAARyUlEQVTAktwWduDm
-1onMTAs5OTGAH/ADcB2NxoRS84EWwKfAqNx/BwDfAB0pU2YDSUm/5PkgO0IIIe5OruiLkPHjp5Ce
-/jywBhhAQ5z5kTcoqRQp9euTk1vkATp37k1GRioQ+JcWKpCefoOcnG3YrvI3A4uAeSiVCbwGFAOW
-Ypt+tjRQDtgIOHP9umLevHn5lK0QQoj7JYW+kElLM2G1egH1acEs1mGb8HWRrhxBh9N5rFVnzGYz
-b745hFWrlgDuwBxgA3Aa24Q1Ltg67+mBWUAY0DJ32x8AM7AbF5fywHzgCjAJWIrV2ooLFy7kZ8pC
-CCHugxT6QqZHj44YjWNpx4esoA1GLMyiKU9lnyM1fR87d6YxcOBAPvtsOeAMjARygD5ATeAAtufw
-CwAFXPxL6+FoNM2Akbi4dKN8+QwiIsLRaLYDQcAiXF1X8Oijj+ZnykIIIe6DPKMvhHYNHEjtKVPQ
-ARPR8RoJKPxy13bH2XktZvO7wA1gBRCN7db7MWy3/H8BPsQ2YE4x4HXgQu62FwgOrk737h0ZNGgQ
-mZmZtGrVhQMHdqHVahk3biyvvPJS/iYshBBFnLxeV5TMnAn9+oFSxDq5McRqBHoD44CVQA+gA7bh
-cJcC84AvgTNABrY54gOAg0AMtiv6hthG1msFPIZe34OqVQ+zf/92tFotABkZGbi4uMic8UIIUQCk
-M15RMWECvPACKMUQJyNDrNOxzVS3DCgOdAWGAtOB34Ha2Ir8YeAZoCzwG7ZOeDGAN5CK7UdBKaAz
-8B4WyzR+++08p0+fth/aYDBIkRdCiEJI/nIXBkrByJHw2msAvOpkINZaEbgJTARM2Ap+CWy36w3A
-JqAD3t7nqF07HGfnj4HG/Pme/AhsPwJaYrttPxRoCwwGLFitWTg7O+dXhkIIIR6QB1ro+/bti5eX
-F2FhYfZl165do0WLFgQFBdGyZUtSU1Pt62JjY6lcuTLBwcF5Oj96oWexwIYNWDUaXtBVYJK1N9AM
-eB9YDvQHdvLqq0/i7j4DjWYkMAuDYTqTJo1lz57NrFixBNu79JdyG90EVAE+o0cPX4KCKuDqegH4
-EoOhHU2bNsLf3z//cxVCCJGnHmih79OnD2vXrr1l2ZgxY2jRogUnTpygWbNmjBkzBoBjx46xcOFC
-jh07xtq1axkwYABWq/VBhld4ODszvFYD2mpcmZndE2iH7fn7WGAfWu1sund/igkTJhAXt4U+fS7y
-1FN7+eGHL+jc+UkArly5hu0WfQBQAZgGdMDLqzzz5s3k4MHdvP12OO3b/8j77zdj2bL5aDSaAklX
-CCFEHlIPWEJCggoNDbV/rlKlikpOTlZKKXXx4kVVpUoVpZRSo0ePVmPGjLFvFx0drXbt2nVbe/kQ
-8kPnyJEjymDwUTBTQYSC3xV8ocBHabUe6qWXXldms/mu+1+6dEkZDCUVPKPAT4GbAj+l0birHTt2
-5GMmQggh/ov7qX26/P5hcenSJby8bGO0e3l5cemS7VbyhQsXqFevnn07Pz8/kpKS8ju8h9K5c+dw
-dg4jI+NZ4BDwCOBM8eJWPv10It26dbttSNrt27fz2Wdz0GqdaNasAc7Oj5CRMRvblfw6dLo4Fi/+
-hgYNGuR/QkIIIfJNvhf6v9JoNPe8PXy3dcOHD7f/OyoqiqioqDyO7OESGhqKxbIf2A9MBrzRaGLJ
-yanLK69M56OPphAX9yMeHh4AbNiwgXbtepKRMRTIYvHiN3Ifg/wKvAxEodc3loFvhBDiIbVlyxa2
-bNmSJ23le6H38vIiOTkZb29vLl68SNmyZQHw9fUlMTHRvt358+fx9fW9Yxt/LfRFgb+/P3PnzqBn
-z+YopScnx0pOzmDS0oYCioSEPowePY7Y2A+ZO3ceb701koyM8djeqYeMDD21ay/h6NGGODtXwmz+
-jVmzplGqVKkCzUsIIcSd/f+L2BEjRtx947+R76/XtW3bljlz5gAwZ84c2rdvb1++YMECzGYzCQkJ
-nDx5kjp16uR3eA+tjh07kJR0mlGjBlOiREms1qa5azRkZTXmxImzvPPO+wwY8AmXLxuwjVv/B3d8
-fPw4deooq1d/yunT8XTv3rUAshBCCJHfHugVfbdu3di6dStXrlyhfPnyfPjhhwwePJguXbowe/Zs
-AgIC+O677wAICQmhS5cuhISEoNPpmDZtmvT6/oukpCRq147i+vXKmM3uwHigDpCJ0fgVjz7akcGD
-B5OdfQ7bBDavYZu0JgujcRj9+8/Gx8cHHx+fAsxCCCFEfpMhcAuBM2fOEBZWk5s3G2N7re4m0Bw4
-jF6voWXLGIKDH2H8+AkolYZtdrq5ODkNpUKFkowf/4H9zokQQojCR4bAdXAvvvgmN2+GAzVyl7gD
-8ylevATLly9m8+atjB/vjFKB2IbB3YdGc5Nixczs3LlGirwQQhRhckVfCFSqVItTp54FYrHNMFcB
-J6fn6dLFkzNnzrN7dy+gJ5AONKNYsSSqVw9l+vRxhIaGFmToQggh8oBc0Tu4Bg0icXHZCwzHNhmN
-D6GhF5g5cyJpaTfBPkWtG9CHmJgW/PTTGinyQgghpNAXBpMnj6VGjXO4uLyNTvc7tWpFMGDA0wB0
-794eo/Ft4AiwE6Mxlh495Fa9EEIIG7l1X0gopVi0aBFPPz2AnJyn0OkS8fY+zb592/nkk0nMnDkX
-Z2dnhg17g+ee61vQ4QohhMhD91P7pNAXIpUq1eDUqf8BTwDg4tKNUaMieeONNwo2MCGEEA+UPKMv
-IlJTrwFV7Z+zsqpy+fLVggtICCHEQ08KfSESHd0CV9ch2KabPYjROJPHH29R0GEJIYR4iEmhL0Rm
-zJhIq1bOuLgEULx4KyZN+pCmTZv+/Y5CCCGKLHlGL4QQQjzk5Bm9EEIIIe5ICr0QQgjhwKTQCyGE
-EA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQ
-CyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjh
-wKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0Q
-QgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5M
-Cr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGE
-EA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA7soSv0a9euJTg4mMqVK/PRRx8VdDgPnS1b
-thR0CAWqKOdflHMHyV/y31LQIRRaD1Whz8nJ4eWXX2bt2rUcO3aM+fPnc/z48YIO66FS1L/sRTn/
-opw7SP6S/5aCDqHQeqgK/Z49e6hUqRIBAQHo9Xq6du3KsmXLCjosIYQQotB6qAp9UlIS5cuXt3/2
-8/MjKSmpACMSQgghCjeNUkoVdBB/WLx4MWvXrmXmzJkAzJs3j7i4OCZPnmzfplKlSpw6daqgQhRC
-CCHyXWBgIL/99tt/2leXx7HcF19fXxITE+2fExMT8fPzu2Wb/5qoEEIIURQ9VLfuIyMjOXnyJGfO
-nMFsNrNw4ULatm1b0GEJIYQQhdZDdUWv0+mYMmUK0dHR5OTk8Oyzz1K1atWCDksIIYQotB6qZ/RC
-CCGEyFsP1a37v/r++++pVq0aWq2W/fv337IuNjaWypUrExwczPr16+3L9+3bR1hYGJUrV+bVV1/N
-75AfqKIwkFDfvn3x8vIiLCzMvuzatWu0aNGCoKAgWrZsSWpqqn3d3b4HhVViYiJNmzalWrVqhIaG
-MmnSJKBonIPMzEzq1q1LREQEISEhvPvuu0DRyP2vcnJyqFGjBm3atAGKVv4BAQFUr16dGjVqUKdO
-HaBo5Z+amsqTTz5J1apVCQkJIS4uLu/yVw+p48ePq19//VVFRUWpffv22ZfHx8er8PBwZTabVUJC
-ggoMDFRWq1UppVTt2rVVXFycUkqpmJgYtWbNmgKJPa9lZ2erwMBAlZCQoMxmswoPD1fHjh0r6LDy
-3LZt29T+/ftVaGiofdlbb72lPvroI6WUUmPGjFHvvPOOUurO34OcnJwCiTuvXLx4UR04cEAppVRa
-WpoKCgpSx44dKzLnID09XSmllMViUXXr1lXbt28vMrn/4ZNPPlHdu3dXbdq0UUoVre9/QECAunr1
-6i3LilL+vXv3VrNnz1ZK2f4fSE1NzbP8H9pC/4f/X+hHjx6txowZY/8cHR2tdu3apS5cuKCCg4Pt
-y+fPn6/69euXr7E+KDt37lTR0dH2z7GxsSo2NrYAI3pwEhISbin0VapUUcnJyUopWyGsUqWKUuru
-3wNH0q5dO7Vhw4Yidw7S09NVZGSkOnr0aJHKPTExUTVr1kz9+OOP6oknnlBKFa3vf0BAgLpy5cot
-y4pK/qmpqapixYq3Lc+r/B/aW/d3c+HChVteuftjUJ3/v9zX19dhBtspygMJXbp0CS8vLwC8vLy4
-dOkScPfvgaM4c+YMBw4coG7dukXmHFitViIiIvDy8rI/wigquQO89tprjBs3DienP/8sF6X8NRoN
-zZs3JzIy0j6WSlHJPyEhgTJlytCnTx9q1qzJ888/T3p6ep7lX6C97lu0aEFycvJty0ePHm1/RiVs
-/wMI23m417lwlPN08+ZNOnXqxMSJEylWrNgt6xz5HDg5OXHw4EF+//13oqOj2bx58y3rHTn3lStX
-UrZsWWrUqHHXMd0dOX+AHTt24OPjQ0pKCi1atCA4OPiW9Y6cf3Z2Nvv372fKlCnUrl2bQYMGMWbM
-mFu2uZ/8C7TQb9iw4V/v8/8H1Tl//jx+fn74+vpy/vz5W5b7+vrmSZwF7Z8MJOSovLy8SE5Oxtvb
-m4sXL1K2bFngzt8DR/jvbbFY6NSpE7169aJ9+/ZA0TsHnp6etG7dmn379hWZ3Hfu3Mny5ctZvXo1
-mZmZ3Lhxg169ehWZ/AF8fHwAKFOmDB06dGDPnj1FJn8/Pz/8/PyoXbs2AE8++SSxsbF4e3vnSf6F
-4ta9+ssbgG3btmXBggWYzWYSEhI4efIkderUwdvbGw8PD+Li4lBKMXfuXPsfysKuKA8k1LZtW+bM
-mQPAnDlz7P9N7/Y9KMyUUjz77LOEhIQwaNAg+/KicA6uXLli71GckZHBhg0bqFGjRpHIHWx3MRMT
-E0lISGDBggU89thjzJ07t8jkbzKZSEtLAyA9PZ3169cTFhZWZPL39vamfPnynDhxAoCNGzdSrVo1
-2rRpkzf552WHgry0ZMkS5efnp1xdXZWXl5d6/PHH7etGjRqlAgMDVZUqVdTatWvty/fu3atCQ0NV
-YGCgGjhwYEGE/cCsXr1aBQUFqcDAQDV69OiCDueB6Nq1q/Lx8VF6vV75+fmpL774Ql29elU1a9ZM
-Va5cWbVo0UJdv37dvv3dvgeF1fbt25VGo1Hh4eEqIiJCRUREqDVr1hSJc3D48GFVo0YNFR4ersLC
-wtTYsWOVUqpI5P7/bdmyxd7rvqjkf/r0aRUeHq7Cw8NVtWrV7H/jikr+Sil18OBBFRkZqapXr646
-dOigUlNT8yx/GTBHCCGEcGCF4ta9EEIIIf4bKfRCCCGEA5NCL4QQQjgwKfRCCCGEA5NCL4QQQjgw
-KfRCCCGEA5NCL4QQQjgwKfRCCCGEA5NCL4S4p59//pnw8HCysrJIT08nNDSUY8eOFXRYQoh/SEbG
-E0L8rffff5/MzEwyMjIoX74877zzTkGHJIT4h6TQCyH+lsViITIyEoPBwK5duwr1lKBCFDVy614I
-8beuXLlCeno6N2/eJCMjo6DDEUL8C3JFL4T4W23btqV79+6cPn2aixcvMnny5IIOSQjxD+kKOgAh
-xMPt66+/xsXFha5du2K1WmnQoAFbtmwhKiqqoEMTQvwDckUvhBBCODB5Ri+EEEI4MCn0QgghhAOT
-Qi+EEEI4MCn0QgghhAOTQi+EEEI4MCn0QgghhAOTQi+EEEI4sP8Diwf1C+duoqkAAAAASUVORK5C
-YII=
-"
->
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="comparing-the-results-from-the-different-implementations">Comparing the results from the different implementations</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>As mentioned above, let us confirm that the different implementation computed the same parameters (i.e., slope and y-axis intercept) as solution for the linear equation.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[9]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">prettytable</span>
-
-<span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">appr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">appr</span> <span class="ow">in</span> <span class="p">[</span><span class="n">lin_lstsqr_mat</span><span class="p">,</span> <span class="n">classic_lstsqr</span><span class="p">,</span> <span class="n">numpy_lstsqr</span><span class="p">,</span> <span class="n">scipy_lstsqr</span><span class="p">]]</span>
-
-<span class="k">print</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
-
-<span class="n">fit_table</span> <span class="o">=</span> <span class="n">prettytable</span><span class="o">.</span><span class="n">PrettyTable</span><span class="p">([</span><span class="s">&quot;&quot;</span><span class="p">,</span> <span class="s">&quot;slope&quot;</span><span class="p">,</span> <span class="s">&quot;y-intercept&quot;</span><span class="p">])</span>
-<span class="n">fit_table</span><span class="o">.</span><span class="n">add_row</span><span class="p">([</span><span class="s">&quot;matrix approach&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span>
-<span class="n">fit_table</span><span class="o">.</span><span class="n">add_row</span><span class="p">([</span><span class="s">&quot;classic approach&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span>
-<span class="n">fit_table</span><span class="o">.</span><span class="n">add_row</span><span class="p">([</span><span class="s">&quot;numpy function&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span>
-<span class="n">fit_table</span><span class="o">.</span><span class="n">add_row</span><span class="p">([</span><span class="s">&quot;scipy function&quot;</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">params</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]])</span>
-
-<span class="k">print</span><span class="p">(</span><span class="n">fit_table</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-[array([   0.95181895,  107.01399744]), (0.95181895319126741, 107.01399744459181), array([   0.95181895,  107.01399744]), (0.95181895319126764, 107.01399744459175)]
-+------------------+----------------+---------------+
-|                  |     slope      |  y-intercept  |
-+------------------+----------------+---------------+
-| matrix approach  | 0.951818953191 | 107.013997445 |
-| classic approach | 0.951818953191 | 107.013997445 |
-|  numpy function  | 0.951818953191 | 107.013997445 |
-|  scipy function  | 0.951818953191 | 107.013997445 |
-+------------------+----------------+---------------+
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="initial-performance-comparison">Initial performance comparison</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>For a first impression how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the <code>timeit</code> module via IPython's <code>%timeit</code> magic.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[10]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">timeit</span>
-
-<span class="k">for</span> <span class="n">lab</span><span class="p">,</span><span class="n">appr</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="s">&quot;matrix approach&quot;</span><span class="p">,</span><span class="s">&quot;classic approach&quot;</span><span class="p">,</span>
-                     <span class="s">&quot;numpy function&quot;</span><span class="p">,</span><span class="s">&quot;scipy function&quot;</span><span class="p">],</span>
-                    <span class="p">[</span><span class="n">lin_lstsqr_mat</span><span class="p">,</span> <span class="n">classic_lstsqr</span><span class="p">,</span> 
-                     <span class="n">numpy_lstsqr</span><span class="p">,</span> <span class="n">scipy_lstsqr</span><span class="p">]):</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&quot;</span><span class="se">\n</span><span class="s">{}: &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lab</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">)</span>
-    <span class="o">%</span><span class="k">timeit</span> <span class="n">appr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-
-matrix approach: 1000 loops, best of 3: 270 µs per loop
-
-classic approach: 100 loops, best of 3: 2.83 ms per loop
-
-numpy function: 1000 loops, best of 3: 372 µs per loop
-
-scipy function: 1000 loops, best of 3: 594 µs per loop
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> The timing above indicates, that the &quot;classic&quot; approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br> <a name="cython_nb"></a></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="compiling-the-python-code-via-cython-in-the-ipython-notebook">Compiling the Python code via Cython in the IPython notebook</h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#sections">back to top</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Maybe we can speed things up a little bit via <a href="http://cython.org">Cython's C-extensions for Python</a>. Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.<br />Since we are working in an IPython notebook here, we can make use of the IPython magic: It will automatically convert it to C code, compile it, and load the function.<br />Just to be thorough, let us also compile the other functions, which already use numpy objects.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[15]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="o">%</span><span class="k">load_ext</span> <span class="n">cythonmagic</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[16]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="o">%%</span><span class="n">cython</span>
-<span class="k">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="k">import</span> <span class="nn">scipy.stats</span>
-<span class="k">cimport</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="k">def</span> <span class="nf">cy_lin_lstsqr_mat</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))])</span><span class="o">.</span><span class="n">T</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">T</span><span class="p">))</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-
-<span class="k">def</span> <span class="nf">cy_classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="n">x_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="n">y_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">var_x</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">**</span><span class="mf">2</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
-    <span class="n">cov_xy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">y_i</span> <span class="o">-</span> <span class="n">y_avg</span><span class="p">)</span> <span class="k">for</span> <span class="n">x_i</span><span class="p">,</span><span class="n">y_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)])</span>
-    <span class="n">slope</span> <span class="o">=</span> <span class="n">cov_xy</span> <span class="o">/</span> <span class="n">var_x</span>
-    <span class="n">y_interc</span> <span class="o">=</span> <span class="n">y_avg</span> <span class="o">-</span> <span class="n">slope</span><span class="o">*</span><span class="n">x_avg</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">slope</span><span class="p">,</span> <span class="n">y_interc</span><span class="p">)</span>
-
-<span class="k">def</span> <span class="nf">cy_numpy_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">))])</span><span class="o">.</span><span class="n">T</span>
-    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">lstsq</span><span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">y</span><span class="p">)[</span><span class="mf">0</span><span class="p">]</span>
-
-<span class="k">def</span> <span class="nf">cy_scipy_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">scipy</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">linregress</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mf">0</span><span class="p">:</span><span class="mf">2</span><span class="p">]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="comparing-the-compiled-cython-code-to-the-original-python-code">Comparing the compiled Cython code to the original Python code</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[17]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">timeit</span>
-
-<span class="k">for</span> <span class="n">lab</span><span class="p">,</span><span class="n">appr</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">([</span><span class="s">&quot;matrix approach&quot;</span><span class="p">,</span><span class="s">&quot;classic approach&quot;</span><span class="p">,</span>
-                     <span class="s">&quot;numpy function&quot;</span><span class="p">,</span><span class="s">&quot;scipy function&quot;</span><span class="p">],</span>
-                    <span class="p">[(</span><span class="n">lin_lstsqr_mat</span><span class="p">,</span> <span class="n">cy_lin_lstsqr_mat</span><span class="p">),</span> 
-                     <span class="p">(</span><span class="n">classic_lstsqr</span><span class="p">,</span> <span class="n">cy_classic_lstsqr</span><span class="p">),</span>
-                     <span class="p">(</span><span class="n">numpy_lstsqr</span><span class="p">,</span> <span class="n">cy_numpy_lstsqr</span><span class="p">),</span>
-                     <span class="p">(</span><span class="n">scipy_lstsqr</span><span class="p">,</span> <span class="n">cy_scipy_lstsqr</span><span class="p">)]):</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&quot;</span><span class="se">\n\n</span><span class="s">{}: &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lab</span><span class="p">))</span>
-    <span class="o">%</span><span class="k">timeit</span> <span class="n">appr</span><span class="p">[</span><span class="mi">0</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-    <span class="o">%</span><span class="k">timeit</span> <span class="n">appr</span><span class="p">[</span><span class="mi">1</span><span class="p">](</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-
-
-matrix approach: 
-1000 loops, best of 3: 274 µs per loop
-1000 loops, best of 3: 260 µs per loop
-
-
-classic approach: 
-100 loops, best of 3: 2.82 ms per loop
-1000 loops, best of 3: 212 µs per loop
-
-
-numpy function: 
-1000 loops, best of 3: 379 µs per loop
-1000 loops, best of 3: 370 µs per loop
-
-
-scipy function: 
-1000 loops, best of 3: 608 µs per loop
-100 loops, best of 3: 613 µs per loop
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br> As we've seen before, our &quot;classic&quot; implementation of the least square method is pretty slow compared to using numpy's functions. This is not surprising, since numpy is highly optmized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.<br />However, we were able to speed up the &quot;classic approach&quot; quite significantly, roughly by 1500%.</p>
-<p>The following 2 code blocks are just to visualize our results in a bar plot.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[18]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">timeit</span>
-
-<span class="n">funcs</span> <span class="o">=</span>  <span class="p">[</span><span class="s">&#39;classic_lstsqr&#39;</span><span class="p">,</span> <span class="s">&#39;cy_classic_lstsqr&#39;</span><span class="p">,</span> 
-          <span class="s">&#39;lin_lstsqr_mat&#39;</span><span class="p">,</span> <span class="s">&#39;numpy_lstsqr&#39;</span><span class="p">,</span> <span class="s">&#39;scipy_lstsqr&#39;</span><span class="p">]</span>
-<span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="s">&#39;classic approach&#39;</span><span class="p">,</span><span class="s">&#39;classic approach (cython)&#39;</span><span class="p">,</span> 
-          <span class="s">&#39;matrix approach&#39;</span><span class="p">,</span> <span class="s">&#39;numpy function&#39;</span><span class="p">,</span> <span class="s">&#39;scipy function&#39;</span><span class="p">]</span>
-
-<span class="n">times</span> <span class="o">=</span> <span class="p">[</span><span class="n">timeit</span><span class="o">.</span><span class="n">Timer</span><span class="p">(</span><span class="s">&#39;</span><span class="si">%s</span><span class="s">(x,y)&#39;</span> <span class="o">%</span><span class="k">f</span><span class="p">,</span> 
-                      <span class="s">&#39;from __main__ import </span><span class="si">%s</span><span class="s">, x, y&#39;</span> <span class="o">%</span><span class="k">f</span><span class="p">)</span><span class="o">.</span><span class="n">timeit</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
-         <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">funcs</span><span class="p">]</span>
-
-<span class="n">times_rel</span> <span class="o">=</span> <span class="p">[</span><span class="n">times</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">times</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">times</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))]</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[19]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="o">%</span><span class="k">pylab</span> <span class="n">inline</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
-
-<span class="n">x_pos</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">funcs</span><span class="p">))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">times</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;time in ms&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Performance of different least square fit implementations&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-
-<span class="n">x_pos</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">funcs</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">times_rel</span><span class="p">,</span> <span class="n">align</span><span class="o">=</span><span class="s">&#39;center&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">&quot;green&quot;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">x_pos</span><span class="p">,</span> <span class="n">labels</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;relative performance gain&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Performance gain compared to the classic least square implementation&#39;</span><span class="p">)</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Populating the interactive namespace from numpy and matplotlib
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-
-
-<div class="output_png output_subarea ">
-<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
-AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFGfXB+DfLoiwVAEBXZpYEkBEbNgiS+yoSDS22Msr
-EjQaEyOmKCb2GhMTY4n62n01FkQwxrIalWI3kSCWgIC6ETtFYHfP9wcwH0sRMO4u6Lmvi4udnWdm
-zjPtzDxTVkREBMYYY288sb4DYIwxVj1wQmCMMQaAEwJjjLFCnBAYY4wB4ITAGGOsECcExhhjAF7z
-hKBQKNCpUydYWFhg2rRp+g5H73JyctCnTx9YWVlh0KBBFZaXy+VwcnISups2bYqTJ08CAIgIo0eP
-hrW1Ndq2bQsAWLVqFezt7WFhYYFHjx5ppxIvqWRdWOVdu3YNzZs3h4WFBb7//nuEhIRgzpw5lR6+
-quWrwtXVFUePHtXKuPVp69at6N69u+4nTNWMi4sLmZiYkJmZGdnb29OoUaMoMzPzpcb19ddfU//+
-/V9xhDXXpk2bqE2bNqRSqSpV/vjx4+To6Fhmv5MnT5KjoyNlZ2cTEVFeXh6ZmJjQH3/88crirQoX
-Fxc6evRouf1fVJdXZeTIkfTll19qdRr6MGbMGJo6dWqZ/XQxX1/E1dX1hctdHzZs2EAdO3asdPm/
-//6bRCJRpbdLbap2ZwgikQiRkZF49uwZLly4gHPnzlX56IKIoFarkZKSAnd395eKQ6lUvtRw1VlK
-SgqaNGkCsfjfL/aUlBS4urrCxMQEAHDv3j08f/78pee3Wq3+V/GIRCIQP2NZoZdZr1NSUuDh4aGF
-aFhx1WL91XNCKqVkxv/000+pd+/eREQUExND7dq1IysrK/L29ia5XC6U8/Pzoy+++II6dOhAJiYm
-NGzYMKpVqxYZGRmRmZkZHT16lHJzc2ny5MlUv359ql+/Pk2ZMoVyc3OJqOBIRyqV0sKFC8nBwYGG
-Dx9O4eHh9P7779OwYcPI3NycvLy8KCkpiebNm0d2dnbk7OxMhw8fFmJYv349ubu7k7m5Obm5udHq
-1auFfkXjX7p0KdnZ2VG9evVow4YNQv/s7GyaOnUqubi4kKWlJXXs2JFycnIqrHdJCQkJ5OfnR1ZW
-VuTp6UkRERFERDRz5kwyMjKiWrVqkZmZGa1fv77UsNnZ2TRy5EiqU6cOeXh40KJFizSO/lxcXOjI
-kSO0bt06MjY2JgMDAzIzM6MhQ4aQqakpiUQiMjMzo86dOxMR0V9//UVdunQha2treuutt+h///uf
-MK6RI0fShAkTqGfPnmRqakpHjx6l9PR06tevH9WtW5caNGhA3333nVB+1qxZNGDAABoxYgSZm5uT
-p6cnnTt3joiIhg0bRmKxWDizXLx4cam6lTySfdG04uLiqG3btmRlZUX16tWjiRMnUl5entB/ypQp
-ZGdnRxYWFuTl5UV//vknrV69WmN9CwwMLHP5lDUsEVFGRgb16dOHLCwsqE2bNvTll18KR5llHUH6
-+fnRunXriIjoxo0b5O/vTzY2NmRra0tDhw6lx48fayy3hQsXkpeXFxkbG5NKpar0OuXv708GBgZk
-bGxM5ubmlJSUJJwJZWVlkbGxMYnFYjIzMyNzc3O6e/duqXEUP3Mq2g4WLVpEdevWpXr16tHevXvp
-4MGD1LhxY7K2tqb58+drLPf+/fvToEGDyNzcnFq0aEGXL18W+hffX6jVapo/fz41bNiQbGxsaODA
-gfTw4UONebhhwwZycnIia2trWrVqFcXHx5OXlxdZWVnRxIkTNeL++eefyd3dnerUqUPdu3enlJQU
-oZ9IJKKffvqJGjduTFZWVhQaGkpEBdtf8W2jTp06REQUGRlJzZs3JwsLC3JycqLw8HBhXE5OTsK2
-Y25uTjExMaXOMk6fPk2tWrUiS0tLat26NZ05c0ZjXfjqq6+oQ4cOZG5uTt26daOMjAwiIsrJyaGh
-Q4eSjY0NWVlZUevWrUmhUJS5rImIqmVCOHLkCBER3b59mzw9PWnmzJmUlpZGNjY2FB0dTUREv/32
-G9nY2AgV9/PzIxcXF0pISCCVSkX5+fk0atQo+uqrr4Rxf/XVV9SuXTu6f/8+3b9/n9q3by/0P378
-OBkaGlJYWBjl5eVRTk4OzZo1i4yNjenw4cOkVCppxIgR5OLiQvPmzSOlUklr166lBg0aCOM/ePAg
-3bp1i4iITpw4QRKJhC5cuKAx/lmzZpFSqaSoqCiSSCTChvvhhx+Sv78/3blzR9hgc3Nzy633/fv3
-S827vLw8atiwIc2fP5/y8/Pp2LFjZG5uTteuXSMiovDwcBo+fHi583769OnUqVMnevToEaWmppKn
-pyc5OTlpLJuijW/jxo0aK2xycrLGTiszM5McHR1p48aNpFKp6OLFi2Rra0sJCQlEVLCTsLS0FFbs
-7OxsatGiBX3zzTeUn59Pt27dIjc3N/r111+JiIRlER0dTWq1mmbMmEFt27YtM7ayFE8IKpXqhdM6
-f/48xcXFkUqlouTkZHJ3d6dvv/2WiIgOHTpELVu2pCdPnhARUWJiorATLLm+lfSiYQcNGkSDBg2i
-7Oxs+vPPP0kqldI777xDRGUnBJlMRj///DMRFSSEI0eOUF5eHt2/f586depEU6ZMEcq6uLiQj48P
-paWl0fPnz6u0TpWcVsl6yuXyCpuMipcv2g6++eYbYRuysbGhDz74gDIzM+nq1atkYmJCycnJRFSw
-3GvVqkW//PILKZVKWrJkCTVo0ICUSiURaS73b7/9ltq1a0fp6emUl5dHwcHBNGTIEI15GBISQrm5
-uXT48GEyMjKioKAgun//PqWnp5OdnR2dOHGCiIj27dtHjRo1osTERFKpVDRnzhxq3769UCeRSER9
-+vShJ0+e0O3bt6lu3bp06NAhIiq9bRTNp6Lkf+XKFbK3t6d9+/YRUelth0iz2enBgwdkZWVFW7Zs
-IZVKRdu3b6c6deoIyc7Pz48aNWpE169fp5ycHJLJZBQWFkZERD/99BP16dOHcnJySK1W04ULF+jp
-06flLqtqlxBcXFzIzMyMrKysyMXFhUJDQyknJ4cWLFhQamfWvXt3+u9//0tEBSvtrFmzNPqPGjVK
-o023YcOGwkZARPTrr7+Sq6srERWsqEZGRsIZA1HBytitWzehOyIigszMzEitVhMR0dOnT0kkEgkb
-eElBQUG0YsUKYfwmJiYaC93Ozk7Y8ZiYmNCVK1dKjaOiehd38uRJcnBw0PhuyJAhwtHIrFmzaNiw
-YWXGSkQaO0UiojVr1mhs7MU3vpJHMCV3Wjt27BB2aEXGjx9Ps2fPJqKChDBy5EihX2xsLDk7O2uU
-nzdvHo0ePVqIvWvXrkK/oh1HWbGVpXhCqGhaJS1fvpzee+89IiI6evQoNWnShGJjY0u1+ZZc30o6
-duxYmcMqlUqqVauWkLiJiD7//PMXniGU3EkXt3fvXvLx8RG6XV1dNc5Gq7JOFU2r6GykZD0rcw2h
-ZHkTE5NS21B8fLxQvmXLlrR//34iKlju7dq1E/qp1WqqV68enTp1Sqhb0XJ3d3fXWAfu3LlDtWrV
-IpVKJczDO3fuCP1tbGw0zlr79+8vbK89evTQmL8qlYokEgndvn2biAoSwunTp4X+AwcOpAULFhBR
-5a4hTJ48mT7++GMiKnv5Fh/Hpk2byNfXV2P4du3a0caNG4moYPnMnTtX6Pfjjz9Sjx49iKig1aJ9
-+/Zl7lvKYqjvJquSRCIR9u/fj3fffVfj+5SUFOzatQsHDhwQvlMqlRrlKrqL5M6dO3BxcRG6nZ2d
-cefOHaG7bt26MDIy0hjGzs5O+GxiYgJbW1uIRCKhGwAyMzNhYWGB6OhozJ49G9evX4darUZ2djaa
-NWsmDG9jY6PRfi+RSJCZmYmMjAw8f/4cDRs2LBVzZepdvH4l54GLiwvS09NfOF/KG97Z2blSw5Ul
-JSUFcXFxqFOnjvCdUqnEiBEjABQsZ6lUqlH+zp07GuVVKhU6deokdNvb2wufJRIJnj9/DrVaXeVr
-IhVNKykpCVOnTsX58+eRnZ0NpVKJVq1aAQDeffddTJw4EaGhoUhJSUG/fv2wZMkSmJubVzhdf3//
-MofNysqCUql86XmvUCgwefJknDp1Cs+ePYNarYa1tbVGmeLjrso6VaRonX8VbGxsSm1DxZetiYkJ
-MjMzhW5HR0eNOBwdHTW22yLJycl47733NNYHQ0NDKBQKobvkdMqbbkpKCiZPnoxPPvlEYxrp6enC
-vHRwcBC+l0gkyMrKKrfOcXFxCAsLw9WrV5GXl4fc3FwMHDiw3PLF3blzp9T64OLiojEPisdSvB7D
-hw9HamoqBg8ejMePH2PYsGGYO3cuDA3L3vVXu4vK5XF2dsbw4cPx6NEj4e/Zs2f47LPPhDIVrbT1
-69dHcnKy0H379m3Ur1+/3OGrshHk5uaif//++Oyzz/DPP//g0aNHCAgIqNSFIltbWxgbG+PGjRul
-+lWm3sXrl5qaqjHNlJQUjQ3qRerVq4fbt28L3cU/V5WzszP8/PxKxf3DDz8IZYrPX2dnZzRo0ECj
-/NOnTxEZGVmqbFmqsqycnJxeOK2QkBB4eHjgxo0bePLkCebOnatx0XvSpEk4d+4cEhISkJSUhMWL
-F1c6hrKGtbOzg6GhYbnz3tTUFACQnZ0tfHfv3j3h8+effw4DAwP8+eefePLkCTZv3lzqIn3JeV3Z
-dao8ReOr7Hz/NwklNTVV+KxWq5GWlqax3RZxdnbGoUOHNOqVnZ2NevXqVXmazs7OWLNmjca4srKy
-hFusX6Ssun7wwQcICgpCWloaHj9+jAkTJgjLqKJ5I5VKkZKSovFdSkqKxgFVeQwNDTFz5kxcvXoV
-Z86cQWRkJDZt2lRu+RqTEIYNG4YDBw7g8OHDUKlUeP78OeRyucbRb8mdb8nuIUOGYM6cOcjIyEBG
-Rga+/vprDB8+vNxpVmZnXiQvLw95eXmwtbWFWCxGdHQ0Dh8+XKlhxWIxxowZg6lTp+Lu3btQqVSI
-iYlBXl5epepdpG3btpBIJFi0aBHy8/Mhl8sRGRmJwYMHVyqOgQMHYv78+Xj8+DHS0tLw/fffV7r+
-JfXu3RtJSUnYsmUL8vPzkZ+fj7NnzyIxMRFA6Xnbpk0bmJubY9GiRcjJyYFKpcKff/6Jc+fOlVm+
-JHt7e9y8ebNSsVU0rczMTJibm0MikSAxMRGrVq0SNtpz584hLi4O+fn5kEgkMDY2hoGBgRDDrVu3
-yp1uecOKxWL069cP4eHhyMnJQUJCAjZt2iRMs27dupBKpdi8eTNUKhXWr1+vUdfMzEyYmprCwsIC
-6enpQoIqT1XWqSLF5z8VNDULdX7w4AGePn36wmGrsi2VdP78eezduxdKpRLffvstjI2Ny9wxT5gw
-AZ9//rmQTO/fv4+IiIgqTasozgkTJmDevHlISEgAADx58gS7du164XDF50laWhry8/OF/pmZmahT
-pw6MjIwQHx+Pbdu2aSxfsVhc7vrbs2dPJCUlYfv27VAqldi5cycSExPRu3fvUnGXdPz4cfzxxx9Q
-qVQwNzdHrVq1hPW1LDUmITg6OmL//v2YN28e7Ozs4OzsjKVLl2rMiLKO8It/9+WXX6JVq1Zo1qwZ
-mjVrhlatWuHLL7+s9PDllQEAc3NzfPfddxg4cCCsra2xfft29O3b94XDFrdkyRJ4eXmhdevWsLGx
-wYwZM6BWq8utd1m3adaqVQsHDhxAdHQ06tati4kTJ2Lz5s1o0qRJufUpbtasWXBxcUGDBg3Qo0cP
-jBgxotzyFc0bMzMzHD58GDt27IBUKkW9evUwY8YM5OXllTm8WCxGZGQkLl26BDc3N9StWxfjx48X
-djQVTW/GjBmYM2cO6tSpg2XLlpUbMwAYGBi8cFpLlizBtm3bYGFhgfHjx2sk1KdPn2L8+PGwtraG
-q6srbG1thYcex44di4SEBNSpUwf9+vUrNf0XDbty5UpkZmbCwcEBY8aMwejRozXW7bVr12Lx4sWw
-tbVFQkICOnToIPSbNWsWLly4AEtLS/Tp0wf9+/d/4XKuyjpV1rwuvizefvttDBkyBG5ubrC2ttY4
-cymrfMlxldVdsl/fvn2xc+dOWFtbY+vWrdizZ0+ZO7XJkycjMDAQ3bp1g4WFBdq1a4f4+PhKTadk
-maCgIEyfPh2DBw+GpaUlvLy88Ouvv76wDkXfde7cGZ6ennBwcBCanH/88UfMnDkTFhYW+OabbzQe
-DJVIJPjiiy/QoUMHWFtbIy4uTmN8NjY2iIyMxNKlS2Fra4slS5YgMjJSo1mwvOWjUCgwYMAAWFpa
-wsPDAzKZ7IUHwSL6N6m7ElQqFVq1agVHR0eNNkug4OnRvn37ws3NDQDQv39/jR00Y2+qjRs34uef
-f8bvv/+u71D0avbs2bhx4wY2b96s71DeCFq/qLxixQp4eHjg2bNnZfb38/Or8mkdY+zNoOXjVVaC
-VpuM0tLSEBUVhXHjxpW7YHmBM1ZaRc17bwqeD7ql1SajAQMG4PPPP8fTp0+xZMmSUk1GJ06cQL9+
-/eDo6AipVIolS5bwI/KMMaYnWjtDiIyMhJ2dHXx8fMo9C2jRogVSU1Nx+fJlTJo0CUFBQdoKhzHG
-WEUq9fjaS5gxYwY5OjqSq6srOTg4kEQieeFrE4gKnjp88OBBqe+9vb0JAP/xH//xH/9V4c/b27tK
-+22dvLpCLpcLL6gr7t69e8Ij7HFxceTi4lLm8EC1e8PGK1XylRuvm9e5fq9z3Yi4fjVdVfedOnt1
-RdGFodWrVwMAgoODsXv3bqxatQqGhoaQSCTYsWOHrsJhjDFWgk4Sgp+fH/z8/AAUJIIioaGhCA0N
-1UUIjDHGKlBjnlR+nclkMn2HoFWvc/1e57oBXL83jdafVH4V+NewGGOs6qq67+QzBMYYYwA4ITDG
-GCvECYExxhgATgiMMcYKcUJgjDEGgBMCY4yxQpwQGGOMAeCEwBhjrBAnBMYYYwA4ITDGGCvECYEx
-xhgATgiMMcYKcUJgjDEGgBMCY4yxQlpPCCqVCj4+PujTp0+Z/T/66CM0btwY3t7euHjxorbDYYwx
-Vg6tJ4QVK1bAw8ND+AnN4qKionDjxg1cv34da9asQUhIiLbDYYwxVg6tJoS0tDRERUVh3LhxZf5I
-Q0REBEaOHAkA8PX1xePHj6FQKLQZEmOMsXJoNSF8/PHHWLx4McTisieTnp4OJycnodvR0RFpaWna
-DIkxxlg5DLU14sjISNjZ2cHHxwdyubzcciXPHMpqWgKA8PBw4bNMJtP5b6GGhS3EvXs5Op3mv+Xg
-YIIFC6brOwzGmI7I5fIX7m8rorWEcObMGURERCAqKgrPnz/H06dPMWLECGzatEkoI5VKkZqaKnSn
-paVBKpWWOb7iCUEf7t3LgaurfmOoquTkcH2HwBjToZIHy7Nnz67S8FprMpo3bx5SU1Px999/Y8eO
-HXj33Xc1kgEABAYGCt/FxsbCysoK9vb22gqJMcbYC2jtDKGkoqag1atXAwCCg4MREBCAqKgoNGrU
-CKamptiwYYOuwmGMMVaCThKCn58f/Pz8ABQkguJWrlypixAYY4xVgJ9UZowxBoATAmOMsUKcEBhj
-jAHghMAYY6wQJwTGGGMAOCEwxhgrxAmBMcYYAE4IjDHGCnFCYIwxBoATAmOMsUKcEBhjjAHghMAY
-Y6wQJwTGGGMAOCEwxhgrxAmBMcYYAE4IjDHGCmk1ITx//hy+vr5o3rw5PDw8MGPGjFJl5HI5LC0t
-4ePjAx8fH8yZM0ebITHGGCuHVn8xzdjYGMePH4dEIoFSqUTHjh1x6tQpdOzYUaOcn58fIiIitBkK
-Y4yxCmi9yUgikQAA8vLyoFKpYG1tXaoMEWk7DMYYYxXQekJQq9Vo3rw57O3t4e/vDw8PD43+IpEI
-Z86cgbe3NwICApCQkKDtkBhjjJVBq01GACAWi3Hp0iU8efIE3bt3h1wuh0wmE/q3aNECqampkEgk
-iI6ORlBQEJKSkkqNJzw8XPgsk8k0xsEYY6zgmqxcLn/p4UWkw/aab775BiYmJvj000/LLdOgQQOc
-P39eo2lJJBLpvVlp1KhwuLqG6zWGqkpODsfGjeH6DoMxpidV3XdqtckoIyMDjx8/BgDk5OTgt99+
-g4+Pj0YZhUIhBBwfHw8iKvM6A2OMMe3SapPR3bt3MXLkSKjVaqjVagwfPhydO3fG6tWrAQDBwcHY
-vXs3Vq1aBUNDQ0gkEuzYsUObITHGGCuHTpuMXhY3Gb0cbjJi7M1WrZqMGGOM1RycEBhjjAHghMAY
-Y6wQJwTGGGMAOCEwxhgrxAmBMcYYAE4IjDHGCnFCYIwxBoATAmOMsUKcEBhjjAHghMAYY6wQJwTG
-GGMAOCEwxhgrxAmBMcYYAE4IjDHGCnFCYIwxBkCLCeH58+fw9fVF8+bN4eHhgRkzZpRZ7qOPPkLj
-xo3h7e2NixcvaiscxhhjFdDaT2gaGxvj+PHjkEgkUCqV6NixI06dOoWOHTsKZaKionDjxg1cv34d
-cXFxCAkJQWxsrLZCYowx9gJabTKSSCQAgLy8PKhUKlhbW2v0j4iIwMiRIwEAvr6+ePz4MRQKhTZD
-YowxVg6tJgS1Wo3mzZvD3t4e/v7+8PDw0Oifnp4OJycnodvR0RFpaWnaDIkxxlg5tNZkBABisRiX
-Ll3CkydP0L17d8jlcshkMo0yJX8AWiQSlTmu8PBw4bNMJis1HsYYe9PJ5XLI5fKXHl6rCaGIpaUl
-evXqhXPnzmnsyKVSKVJTU4XutLQ0SKXSMsdRPCEwxhgrreTB8uzZs6s0vNaajDIyMvD48WMAQE5O
-Dn777Tf4+PholAkMDMSmTZsAALGxsbCysoK9vb22QmKMMfYCWjtDuHv3LkaOHAm1Wg21Wo3hw4ej
-c+fOWL16NQAgODgYAQEBiIqKQqNGjWBqaooNGzZoKxzGGGMV0FpC8PLywoULF0p9HxwcrNG9cuVK
-bYXAGGOsCvhJZcYYYwA4ITDGGCvECYExxhgATgiMMcYKcUJgjDEGgBMCY4yxQpwQGGOMAahEQsjM
-zIRKpQIAXLt2DREREcjPz9d6YIwxxnSrwoTQqVMn5ObmIj09Hd27d8fmzZsxatQoHYTGGGNMlypM
-CEQEiUSCPXv24MMPP8SuXbvw559/6iI2xhhjOlSpawgxMTHYunUrevXqBaDgdw4YY4y9XipMCN9+
-+y3mz5+P9957D56enrh58yb8/f11ERtjjDEdqvDldn5+fvDz8xO6GzZsiO+++06rQTHGGNO9ChPC
-2bNnMW/ePCQnJ0OpVAIo+FWzK1euaD04xhhjulNhQhg6dCiWLFmCpk2bQizmxxYYY+x1VeEevm7d
-uggMDISbmxtcXV2Fv8pITU2Fv78/PD090bRp0zKbmuRyOSwtLeHj4wMfHx/MmTOnypVgjDH271V4
-hjBr1iyMHTsWXbp0gZGREYCCJqN+/fpVOPJatWph+fLlaN68OTIzM9GyZUt07doV7u7uGuX8/PwQ
-ERHxklVgjDH2KlSYEP773//i2rVrUCqVGk1GlUkIDg4OcHBwAACYmZnB3d0dd+7cKZUQiKiqcTPG
-GHvFKkwI586dQ2JiIkQi0b+aUHJyMi5evAhfX1+N70UiEc6cOQNvb29IpVIsWbIEHh4e/2pajDHG
-qq7Cawjt27dHQkLCv5pIZmYm3n//faxYsQJmZmYa/Vq0aIHU1FRcvnwZkyZNQlBQ0L+aFmOMsZdT
-4RlCTEwMmjdvjgYNGqB27doAqnbbaX5+Pvr3749hw4aVubM3NzcXPvfs2RMffvghHj58CGtra41y
-4eHhwmeZTAaZTFap6TPG2JtCLpdDLpe/9PAVJoRDhw699MiJCGPHjoWHhwemTJlSZhmFQgE7OzuI
-RCLEx8eDiEolA0AzITDGGCut5MHy7NmzqzR8hQmhsreYluX06dPYsmULmjVrBh8fHwDAvHnzcPv2
-bQBAcHAwdu/ejVWrVsHQ0BASiQQ7dux46ekxxhh7eRUmhH+jY8eOFb4ILzQ0FKGhodoMgzHGWCXw
-o8eMMcYAcEJgjDFWqMKE8Msvv6Bx48awsLCAubk5zM3NYWFhoYvYGGOM6VCF1xA+++wzREZGlnq6
-mDHG2OulwjMEBwcHTgaMMfYGqPAMoVWrVhg0aBCCgoKq/HI7xhhjNUeFCeHJkycwMTHB4cOHNb7n
-hMAYY6+XChPCxo0bdRAGY4wxfSs3ISxcuBDTp0/HpEmTSvUTiUT8u8qMMfaaKTchFL2CumXLlhqv
-viaif/0qbMYYY9VPuQmhT58+AIBRo0bpKhbGGGN6xE8qM8YYA8AJgTHGWCFOCIwxxgBUIiFcu3YN
-nTt3hqenJwDgypUrmDNnjtYDY4wxplsVJoT//Oc/mDdvnvCUspeXF7Zv3671wBhjjOlWhQkhOzsb
-vr6+QrdIJEKtWrUqNfLU1FT4+/vD09MTTZs2LffZhY8++giNGzeGt7c3Ll68WMnQGWOMvUoVPqlc
-t25d3LhxQ+jevXs36tWrV6mR16pVC8uXL0fz5s2RmZmJli1bomvXrhovy4uKisKNGzdw/fp1xMXF
-ISQkBLGxsS9RFcYYY/9GhQlh5cqVGD9+PBITE1G/fn00aNAAW7durdTIHRwc4ODgAAAwMzODu7s7
-7ty5o5EQIiIiMHLkSACAr68vHj9+DIVCAXt7+5epD2OMsZdUYUJo2LAhjh49iqysLKjVapibm7/U
-hJKTk3Hx4kWN5icASE9Ph5OTk9Dt6OiItLQ0TgiMMaZjFSaER48eYdOmTUhOToZSqQRQ9XcZZWZm
-4v3338eKFStgZmZWqj8RaXTzqzEYY0z3KkwIAQEBaNeuHZo1awaxWFzldxnl5+ejf//+GDZsGIKC
-gkr1l0qlSE1NFbrT0tIglUpLlQsPDxc+y2QyyGSySsfAGGNvArlcDrlc/tLDV5gQcnNzsWzZspca
-ORFh7Nix8PDwwJQpU8osExgYiJUrV2Lw4MGIjY2FlZVVmc1FxRMCY4yx0koeLM+ePbtKw1eYED74
-4AOsWbNWHLvXAAAgAElEQVQGffr0Qe3atYXvra2tKxz56dOnsWXLFjRr1gw+Pj4AgHnz5uH27dsA
-gODgYAQEBCAqKgqNGjWCqakpNmzYUKUKMMYYezUqTAjGxsaYNm0a5s6dC7G44LEFkUiEW7duVTjy
-jh07Qq1WV1hu5cqVlQiVMcaYNlWYEJYuXYqbN2/C1tZWF/EwxhjTkwqfVG7cuDFMTEx0EQtjjDE9
-qvAMQSKRoHnz5vD39xeuIfBPaDLG2OunwoQQFBRU6nZRfk6AMcZePxUmBP4JTcYYezOUmxAGDBiA
-Xbt2wcvLq1Q/kUiEK1euaDUwxhhjulVuQlixYgUAIDIykl8twRhjb4By7zKqX78+AODHH3+Eq6ur
-xt+PP/6oswAZY4zpRoW3nR4+fLjUd1FRUVoJhjHGmP6U22S0atUq/Pjjj7h586bGdYRnz56hQ4cO
-OgmOMcaY7pSbED744AP07NkTYWFhWLhwoXAdwdzcHDY2NjoLkDHGmG6UmxAsLS1haWmJHTt26DIe
-xhhjelLhNQTGGGNvBk4IjDHGAHBCYIwxVogTAmOMMQBaTghjxoyBvb19ma+/AAp+/9PS0hI+Pj7w
-8fHBnDlztBkOY4yxF6jw5Xb/xujRozFp0iSMGDGi3DJ+fn6IiIjQZhiMMcYqQatnCO+88w7q1Knz
-wjIl35PEGGNMP/R6DUEkEuHMmTPw9vZGQEAAEhIS9BkOY4y90bTaZFSRFi1aIDU1FRKJBNHR0QgK
-CkJSUlKZZcPDw4XPMpkMMplMN0EyxlgNIZfLIZfLX3p4EWm5zSY5ORl9+vTBH3/8UWHZBg0a4Pz5
-87C2ttb4XiQS6b1padSocLi6hus1hqpKTg7Hxo3h+g6DMaYnVd136rXJSKFQCMHGx8eDiEolA8YY
-Y7qh1SajIUOG4MSJE8jIyICTkxNmz56N/Px8AEBwcDB2796NVatWwdDQEBKJhN+bxBhjeqTVhLB9
-+/YX9g8NDUVoaKg2Q2CMMVZJ/KQyY4wxAJwQGGOMFeKEwBhjDAAnBMYYY4U4ITDGGAPACYExxlgh
-TgiMMcYAcEJgjDFWiBMCY4wxAJwQGGOMFeKEwBhjDAAnBMYYY4U4ITDGGAPACYExxlghTgiMMcYA
-cEJgjDFWSKsJYcyYMbC3t4eXl1e5ZT766CM0btwY3t7euHjxojbDYYwx9gJaTQijR4/GoUOHyu0f
-FRWFGzdu4Pr161izZg1CQkK0GQ5jjLEX0GpCeOedd1CnTp1y+0dERGDkyJEAAF9fXzx+/BgKhUKb
-ITHGGCuHXq8hpKenw8nJSeh2dHREWlqaHiNijLE3l6G+AyAijW6RSFRmufDwcOGzTCaDTCbTYlSM
-MVbzyOVyyOXylx5erwlBKpUiNTVV6E5LS4NUKi2zbPGEwBhjrLSSB8uzZ8+u0vB6bTIKDAzEpk2b
-AACxsbGwsrKCvb29PkNijLE3llbPEIYMGYITJ04gIyMDTk5OmD17NvLz8wEAwcHBCAgIQFRUFBo1
-agRTU1Ns2LBBm+Ewxhh7Aa0mhO3bt1dYZuXKldoMgTHGWCXxk8qMMcYAcEJgjDFWiBMCY4wxAJwQ
-GGOMFeKEwBhjDAAnBMYYY4U4ITDGGAPACYExxlghTgiMMcYAcEJgjDFWiBMCY4wxAJwQGGOMFeKE
-wBhjDAAnBMYYY4U4ITDGGANQDX5TmTHGKhIWthD37uXoO4xKc3AwwYIF0/UdRpVpPSEcOnQIU6ZM
-gUqlwrhx4zB9uuZMksvl6Nu3L9zc3AAA/fv3x5dffqntsBhjNci9ezlwdQ3XdxiVlpwcru8QXopW
-E4JKpcLEiRNx5MgRSKVStG7dGoGBgXB3d9co5+fnh4iICG2GwhhjrAJavYYQHx+PRo0awdXVFbVq
-1cLgwYOxf//+UuWISJthMMYYqwStJoT09HQ4OTkJ3Y6OjkhPT9coIxKJcObMGXh7eyMgIAAJCQna
-DIkxxlg5tNpkJBKJKizTokULpKamQiKRIDo6GkFBQUhKSipVLjw8XPgsk8kgk8leYaSMMVbzyeVy
-yOXylx5eqwlBKpUiNTVV6E5NTYWjo6NGGXNzc+Fzz5498eGHH+Lhw4ewtrbWKFc8ITDGGCut5MHy
-7NmzqzS8VpuMWrVqhevXryM5ORl5eXnYuXMnAgMDNcooFArhGkJ8fDyIqFQyYIwxpn1aPUMwNDTE
-ypUr0b17d6hUKowdOxbu7u5YvXo1ACA4OBi7d+/GqlWrYGhoCIlEgh07dmgzJMYYY+XQ+nMIPXv2
-RM+ePTW+Cw4OFj6HhoYiNDRU22EwxhirAL+6gjHGGAB+dQVjr4Wa9moHoOa+3uF1xgmBsddATXu1
-A1BzX+/wOuMmI8YYYwA4ITDGGCvECYExxhgATgiMMcYK8UVl9kbgu3AYqxgnBPZG4LtwGKsYNxkx
-xhgDwGcIrBA3qTDGOCEwANykwhjjJiPGGGOFOCEwxhgDwAmBMcZYIa0mhEOHDuHtt99G48aNsXDh
-wjLLfPTRR2jcuDG8vb1x8eJFbYbDGGPsBbSWEFQqFSZOnIhDhw4hISEB27dvx19//aVRJioqCjdu
-3MD169exZs0ahISEaCucai05Wa7vELTqda7f61w3gOv3ptFaQoiPj0ejRo3g6uqKWrVqYfDgwdi/
-f79GmYiICIwcORIA4Ovri8ePH0OhUGgrpGrrdV8pX+f6vc51A7h+bxqtJYT09HQ4OTkJ3Y6OjkhP
-T6+wTFpamrZCYowx9gJaSwgikahS5YjopYZjjDH2ipGWxMTEUPfu3YXuefPm0YIFCzTKBAcH0/bt
-24Xut956i+7du1dqXN7e3gSA//iP//iP/6rw5+3tXaX9ttaeVG7VqhWuX7+O5ORk1K9fHzt37sT2
-7ds1ygQGBmLlypUYPHgwYmNjYWVlBXt7+1LjunTpkrbCZIwxVkhrCcHQ0BArV65E9+7doVKpMHbs
-WLi7u2P16tUAgODgYAQEBCAqKgqNGjWCqakpNmzYoK1wGGOMVUBEVKIRnzHG2BuJn1RmjDEGgBMC
-e4PdvXsX06dPx82bN/HgwQMAgFqt1nNU/6/kyfvrcjJPRK9NXYqUVaeaWEdOCK+5oh2cUqnUcyTV
-T7169QAAW7duRWhoKC5dugSxWFwtNmQiEm7BvnPnDh49evRa3ZItEonw66+/YtmyZdi2bZu+w3kl
-RCIRzp49i+joaPz9998QiUTV6gCjMjghvKYePnyI9PR0iMViHDp0CJ9//jmWL1+u77CqjaINdeHC
-hZgwYQJkMhl69uyJkydP6n1DVigU+OmnnwAAv/32G/r27Yt3330Xe/fuxdOnT/UW16tQlOguX76M
-SZMmQaFQIDo6GsHBwfoO7aUV1eno0aPo27cvfvnlF7Ru3RoXL16EWCyuUUmBfyDnNZSVlYWlS5fC
-1NQUXl5eCAsLw5QpU7Bw4ULcu3cPc+fOhaHhm7noi47+xWIx8vLyYGRkBDs7O0yYMAG1a9fGkCFD
-8Msvv6Bt27YaR+m6jO/8+fM4c+YMFAoF4uLisHnzZly5cgXr169HVlYWAgMDYWFhodO4XhWRSIQT
-J05gy5YtWLFiBXr27IkbN25g3rx5mDBhgpAIaxKRSISEhATs3r0bO3bsQKdOneDt7Y3OnTvj2LFj
-aN68OdRqNcTi6n/8bRAeHh6u7yDYq2VkZIQnT57g2rVruHjxIt5//32MHz8egwYNwrJly3D9+nXI
-ZLIasYJqQ1FzxYYNG5CQkABfX18AgI+PD6ysrPDZZ5+hR48esLGx0WlcRQlIKpXCxMQEFy9ehEKh
-wKeffgpPT0+YmJhg69atICK4ubnB2NhYp/G9rJKJ9fLly5g7dy5cXFzg5+cHS0tLNGvWDFFRUYiK
-ikLfvn31GG3VqFQqqFQqLFiwAGfOnMFbb70FLy8vtG3bFqampujXrx969+6N+vXr6zvUSuGE8Boh
-IuFIxN3dHZaWljh16hRu3ryJli1bwsnJCb169cLs2bNx48YNdOvW7bVql65I0Y4pJiYGoaGh6NGj
-B5YuXQqFQoF33nkHBgYGaNGiBXJzc5GSkoI2bdpApVLpJHEWnbmIRCLcuXMHPj4+MDQ0xNmzZ6FQ
-KNCuXTu4u7vDwMAAW7ZsQUBAAMzNzbUe179VvF4pKSkgIjRv3hwdO3bEV199BTc3N7i7u8PKygo+
-Pj5o2bJlmQ+nVifF65SdnQ0TExPIZDL8888/SElJgbW1NaRSKXx9fWFhYQFTU1M0bNhQz1FXUpWe
-a2bVmlqtJiKiAwcO0OjRo4mI6MiRIzR58mRaunQp/f3330REdO/ePTpz5oy+wtSrxMREGjlyJK1e
-vZqIiNLT06lDhw70+eefU15eHhEVzLP//Oc/Oo2raNlFRUWRm5sbXbt2jbKzs2nv3r304Ycf0rJl
-y4SyZb3epbpSqVREVLBOtm/fngIDA2ns2LF09epVOnHiBDVq1Ih2796tMUzRvKiuiuKLjo6mLl26
-0KhRo+ibb74hIqLp06fT1KlT6dSpUxr1qO51KsIJ4TXz66+/kqenJx08eFD47uDBgzR16lSaO3cu
-3bp1S/i+pqyk/0bJOkZFRVHv3r1pyJAhwry4c+cOeXt706effiqUCw0Npbt37+o01qtXr5Knpyed
-OHFC+C47O5v27dtHo0aNokWLFhHR/+9kq/Pyy8nJET6npKSQh4cHnT9/nv744w/atGkTBQQE0L17
-92jPnj3k6OhICoWiWteHiCg/P1/4HB8fTx4eHhQZGUnnzp2j5s2b06RJk0itVtOHH35IH3/8MT16
-9EiP0b6cN/PK4mvs3LlzCA8PR0BAAJ4/fw5jY2MEBARALBYjIiJC45bK1725qHhdb926BYlEgs6d
-O0MqlWLt2rXYt28f+vXrBxcXF+FWwSIrV67UebxKpRIdO3ZEp06doFQqoVarYWJigi5dukAkEsHN
-zQ0AhCas6rr8FAoFtm/fjrFjxwrNWk5OTmjRogWAgtt9z58/j8OHD2P48OFo27Yt7Ozs9Blyhf75
-5x/h9mQjIyNkZ2ejS5cu6NWrFwDgwoULaNOmDU6fPo2vv/4aCoUCVlZWeo666t7Mq4qvCSrjfvm7
-d+/il19+AQDhomNcXBxkMhnmz58v7FTeBCKRCCKRCNHR0ejVqxemTp2KVq1awdLSEoMGDUJycjK2
-bduGlJQU1KtXD+3bt9fZQ1NlTUcikeDQoUOIjIyEoaEhjIyM8Ouvv+K///0vAgMD0bRpU63H9SrU
-rl0bPXv2RGZmJs6dOwdnZ2colUp88cUXAAAbGxtYW1sjKSkJAFC3bl0A1ftBrnv37qFPnz548OAB
-UlNTYWFhgaNHjwoPNIpEIvj7++PJkyewsbGBh4eHniN+OZwQaqjiG09ycrLw86SffPIJ6tSpIzxz
-EB8fj1GjRuHy5cuwtLTUS6z6dPv2bcyaNQtr167Ftm3bMGjQIAQGBqJJkybo168f0tPTNe4TL0oi
-ulB0C+bixYtx7NgxNGzYEMuXL8eyZcuwcuVKREVFYfr06ZBKpTqJ59/Kz89HdnY2rKys4OTkhPnz
-52PdunW4evUqli5diuTkZAwcOBAHDhzAtm3b0LlzZwAQboGujmc8+fn5AIBmzZrB2toaq1atwoIF
-C9C0aVMMHToUrVu3hlwuR2RkJKKiomrkWUFx/HK7GooK75iJiIhAeHg4pFIppFIpJk+ejL///hur
-Vq1CdnY2MjIyMGfOHPTp00ffIesElbjFMTMzEyEhIZg7dy6cnZ0BAJMmTYKBgQG+/fZbKBQKvd3V
-Eh0djalTp2LKlClYsmQJxo0bh169eiEjIwNLly6Fg4MD+vbti969e+vlmYiqyMvLg1wuh62tLZKS
-kpCSkoJhw4ZhyZIlMDIyQr9+/eDm5oY5c+bAysoKbdq0Qa9evap1vXJzc/H777/D0dERmZmZSEpK
-gr29PX777TcQEebPn4+1a9cKd4IFBwfXiGX1Qjq/asFemd9//51atmxJCoWC1q5dS2ZmZvTxxx9T
-cnIyqdVqunXrFqWkpBBRwQXI6n7R7lVQKpVERPTs2TPKz8+n3NxcCgoKou+//14os2PHDpo+fbq+
-QiS1Wk2pqakUFBRESUlJdPToUWrYsCENGTKEZs6cSc+ePStVviYsu927d1O7du2oQYMGtG/fPiIi
-un//Pk2aNImmTZtGV65c0Shf3ev19OlTOnDgAMlkMqpfvz4lJiYSEdHp06dp2rRpFBYWRg8fPiSi
-gov/RNW/ThXh5xBqsKysLHTp0gW3bt3CsmXLsH//fqxZswZHjhxBmzZt0KhRI41mohp71FIJt2/f
-Rl5eHszMzLBv3z5MmDABf/zxBwwMDDBq1CiEhYUhMTER586dw6pVqzBmzBg0adJEZ/FRsXvXRSIR
-LCws0KFDB2RlZWHSpEmIi4uDvb09PvnkExgZGcHb2xu1a9fWGKY6osJrISKRCM7Ozjhz5gxq166N
-bt26wczMDLa2tmjbti0OHjyIP//8E+3atROubVXXehUtq9q1ayM7Oxvz58+Hr68v2rdvj/r168PJ
-yQkWFha4fPky5HI5/Pz8YGRkBLFYXG3rVFmcEGqI4juUR48eQaVSQSqVwtHREatXr0aXLl0QEBCA
-3NxcxMTE4P3334e1tbUwfE1eSStjzpw5mDlzJlq3bo1Vq1Zh9OjRcHJywg8//ABnZ2d8/vnnePDg
-AZ4+fYrx48eje/fuOj+1F4lEuHLlCs6dOwcDAwPUr18f9+7dw5EjRzB+/Hjk5OTgjz/+wMSJE+Hk
-5KSzuP4tsViM3377DZs3b8aiRYsgFouxb98+GBsbw93dHUqlEl5eXvDx8akx9RKJRPjtt99gbW2N
-UaNGoW7duti9ezcMDQ3RuHFjGBkZwcjICD179oS9vf1r89Q/33Zag4hEIuzfvx8///wznjx5gg8+
-+ACdO3dGq1atsG7dOuTn52Pnzp1YtmwZGjVqpO9wdaLoyezFixdDrVZj0KBBGDZsGAYPHoycnBzY
-2Nhg0aJFyMjIwLhx44ThSMeXzkQiEfbt24eZM2fCzc0NJiYmaNKkCYKDg+Hg4IAuXbogNTUV3377
-LZo2bVpj2qGL7uL65JNPsHz5cpiammL06NHIyclBZGQkzp49i3Xr1uH48eM15i6pojpNnjwZ33//
-Pbp37w4LCws8fPgQe/fuRWxsLC5duoQVK1agQYMG+g731dJfaxWrqsTERPL09KRLly7Rnj176Isv
-vqCvv/6a4uPj6ccff6SAgAA6cOAAEdX8tszKyMzMpD///JOIiOLi4ujp06cUFhZG7u7uwtO8ubm5
-FBUVRTKZjJKTk4WHunSh+DJ49uwZDR48mC5cuEBERCdOnKCwsDDatGkT3b9/n7Zt2yY8PV6Tll1e
-Xh5NnjyZoqOjiYjo+fPnQr/o6Ghavnw5HTp0SF/hvZTMzEzq1q0bHT16lIj+/wHAv//+m/73v/9R
-7969hWskrxtOCNVc0cp4584dio6Opp49ewr94uLiqHPnzhQXF0dE//90aE3aofwbt2/fpjFjxlBo
-aChJpVLhouWECROoffv2pFAoiKggKdy/f1+nsRWf/6dPn6bo6Ghq164dbdu2jYgKnnpdsmQJTZgw
-odRw1XnZlRXb8OHDS12kv3z5ssbTytW5XkVxFf1//PgxyWQy4SJy0QXjjIwMIipYn4rKV9c6vazX
-o+HrNaVWqyESiXDmzBkMHToULi4uMDY2xq5duwAAbdq0gaenJ65evQoAqFWrljBsTWhu+DfUajWc
-nJzQrVs3rF+/Hh988AG8vLwAAKtWrYK3tze6du2Kf/75B0ZGRrC1tQWg+6aia9euYdq0aWjWrBk+
-/fRTHDlyBCdOnIChoSFatmyJhw8f4unTp8KzEDXlomRKSgoSEhIAAGPGjEF+fj72798PADh79iwm
-TJiA69evC+VrQr0UCgUAwNLSEh06dEBYWBgePnwIExMTnDx5Er1798Y///yj8dxEda9TVfFF5WpM
-JBLhyJEjWLduHUJCQtCuXTtkZGTg6tWrOHbsGAwMDLB48WKEhITA0dGx2r/S4FUSiUQ4duwYYmNj
-8cUXX2DPnj3Izc2Fq6srTExM0KtXL9y8eRN2dnbC8wdFw+kqvsuXL2PgwIHo3bs3AgMDUbt2bWRl
-ZeHrr79GUlISFi5ciLCwMHh5edWIZUaF1zUiIyMxcuRI7Nq1C7dv30afPn1w9+5d7NixAzt27MDP
-P/+M2bNno1OnTvoOuUJFdYqKisJ//vMf/Prrr6hTpw78/f3xzz//YOrUqcjJycHcuXMRHh6OFi1a
-1Ihl9dL0fIbCSih5Crpx40YSiUS0Zs0aIiJSKBTC2zjHjRuncc3gTXPq1Cnq1q0bERW8odTPz4+2
-bt1K27Zto/fff194e6mu5k1ZTQhDhw6lFi1aCM8W5OXl0YULF2jv3r109uxZncb3Kvz111/Up08f
-SkxMpEePHlG7du3o66+/pszMTHrw4AHFxMQITS01pUklLi6O+vbtSzExMbRgwQIKCQmhrVu3UnZ2
-Nu3atYt++eUXOnnyJBHVnDq9LE4I1UzRypaeni60wW7fvp1q165Np0+f1ijzujwMU1kl65iRkUEj
-R46kc+fOEVHBm16HDx9O7777Lu3YsUNv8cXExNDOnTvp6tWrREQ0ZswY6tGjh7C8Sg5T3Zdd8bb1
-4OBgatmyJd28eZOICq5tderUiT766KNyh6tuil8z+OeffyggIID69u0r9P/pp58oODiYNm/erPGQ
-YE1YVv8WJ4RqpGhli4yMpDZt2lC3bt3om2++oYcPH9KuXbvIxsZGOFJ5kxTfCM+fP0/9+/env/76
-i5RKJa1bt478/PyE5Pno0SPh4p8uN96iaZ08eZKaNGlCAwcOpEGDBtG0adOIiGj06NEkk8nKTAo1
-waVLl+jZs2d0/vx5Gjp0KC1evJiSk5OJqCAp+Pr60l9//aXnKKum6KaDrVu3UpMmTWjdunVCv+++
-+47GjBlD6enp+gpPLzghVDOxsbHUs2dPunDhAkVHR9PChQtpwoQJpFar6aeffiKJREKPHj3S6e2T
-+lT8qCwpKYmysrJo4sSJ9Omnn1KfPn3o+PHjNGjQIOEOI33+KMmZM2eoe/fuwhlLYmIihYSE0A8/
-/EBERIGBgUIzUU1QNP9yc3NpypQp1KtXL3r27BnFxMTQxIkTadmyZcJvShTdeVMTKJVKun//PpmY
-mNDOnTuJiGjPnj3Uu3dvWr9+vVCu6LUvbxJOCNXIw4cPaeDAgdSiRQvhuz/++IOGDBlCR44cISIS
-jsreFEU7pYMHD5Kvry9du3aNiAreM7Nx40Z67733yMrKisaNG6e32Ips3bqVRCKRcKSZk5ND27dv
-p+Dg4BcOV91kZmZq/BgMUUET5qeffkoDBgygZ8+eUWxsLI0dO5YWLVpE2dnZwjukqmvdiq4nEf3/
-Dwzt27ePrK2tae/evUREtHfvXvL396e1a9fqJcbqgBOCHpXVJnnkyBFq1qwZzZ49W/hu4sSJNH/+
-fCL6/19tqq4bnjb89ddf9PbbbwvXUIp7/PgxXb16lWQymfDQly4UX3b3798XmoI2bNhAbm5uQgKP
-jo6mjh07UkZGhrDTrM6uXr1KISEhpFAo6OTJk7RixQqh3927d+njjz+mESNGUFZWFp0+fVp4MLA6
-u3r1Kn355ZdERJSQkECHDx8WlldUVBTVrl1beNBs9+7dFB8fr7dY9Y0Tgh4V7VCOHDlC8+bNo7Vr
-11JGRgbJ5XLq378/jRo1ik6cOEGenp7CU5NvgtTUVNq8ebPQffLkSRowYIDQXVZSHDFihE7nUdG0
-IyIiyM/Pj9555x1as2YNXbt2jXbu3EkSiYTGjRtHQUFBwhFodZednU1dunQRmk2OHTtG9vb2tHLl
-SiIqaGo5duwYNW3alAYNGlQjmi2fPHlCfn5+dPbsWXr69ClNmTKFxowZQ0ePHqWsrCwiIlq+fDmJ
-RCKKiooShnuTDriK4wfT9IQK73/+/fffMWHCBJiammLt2rX47rvvUKtWLUyaNAmxsbGYMWMG1q1b
-h3fffRdKpVLfYesEEWHz5s24fPkyAMDNzQ0PHjzA4cOHART8oMqxY8ewcOFCEBFu3ryJmzdv6vSH
-ZEQiEc6fP4+lS5dixYoVmDRpElJSUrBjxw706tULK1aswO+//46AgAAEBQVBpVJV618EAwATExMM
-HToU69evh1Qqhb+/Pw4ePIjly5fjhx9+gIGBAWrXro3OnTtj+vTpNeKFbiqVCtnZ2di+fTs++ugj
-fPzxx3B2dsYvv/yCmJgYAED79u3Rr18/jfq81s8avIh+89GbLTExkYYMGSI8Y5Cenk5TpkyhsLAw
-IiKSy+U0YsQIWrBggT7D1KmiZpUVK1bQrl27iKjgesGSJUto2rRptGDBApLL5eTp6UmHDx8Whnvw
-4IFO47x79y6NHj2a2rdvL3x3+vRp6tKlC8XGxhJRwTUFqVRKJ06c0GlsL6P4tRpjY2Py8/OjzMxM
-Iir4QflmzZrR2LFjyc7OTnhvUXU/ii6Kb+XKlWRoaCi8JiQvL49mzpxJY8eOpbFjx1Ljxo1r5DMh
-2sBPKusQFZ4VFL2S4uTJkzhx4gTS0tLQvn17SKVSeHt7Y+bMmQgMDMTbb78NS0tLHDt2DB07doRE
-ItF3FbSu6Cjt7t27+Pbbb+Hv7w97e3s4ODhAIpHg4MGDuH79OkJCQhAQEAClUgmxWAwTExOtxkXF
-Xj9e1E1EiImJQXZ2Nnx9feHk5ISYmBioVCq0bt0aXl5eqF+/Pt5++22NV5FXR0X1srGxgUwmg7W1
-NVasWIGWLVvCy8sLPXr0wNtvv43hw4ejU6dONeJtrEXx3b17F127dsXy5ctRu3ZtdOjQAZ06dYJE
-IoGpqSmGDh2Kd955R2OYNxX/hKaOFN+hpKenC80bp0+fxpYtW9C4cWMMHjwYWVlZGDBgAA4ePAip
-VG+AZkAAAB1rSURBVIq8vDwolco3IhmUNGvWLGzYsAHx8fFwcHAQvn/+/DmMjY1L7aS1pfh0Tp06
-hZycHNSuXRudOnXC7t27cfDgQUgkEgwaNAjjxo3D2rVr4efnp9WYXpXiO3aVSgUDAwMABe8qWr9+
-Pa5du4Y5c+ZovE5dV/P9VTt37hy6du2Kb775BhMnTtToV1Pr9Mrp5bzkDVT8lLxVq1Y0Y8YM+uKL
-L4QLdUFBQdSqVSvq0qULHTx4UGOYN4larda4WPnZZ5/RW2+9RRcuXNBoFtLHvDlw4AB5enrS6tWr
-qWnTpsLF1z179pC3tzd1796djh07RkRU6rbN6uzy5cvC5+J3Qt2+fZvCwsLovffeo+zs7Bq1Pqam
-ptKTJ0+EmIvqdeHCBTIwMNC4e4r9P24y0pGio8spU6Zg27ZtuHTpEvbs2YNLly4hNDQUbm5uuHfv
-Hlq1aoURI0a8MS+qK2o+K2r6Kapv0Q/fdO3aFUqlEvv370diYiIUCgWaNm2q8/mSkpKCTz75BP/7
-3/+gUCgQHx+PY8eOgYgwatQo2NraIjMzEwYGBmjVqlWNuOBKhWcHgwcPRlJSEjp37qwRt6WlJZo0
-aSI029WEdZGIoFAoMHXqVLRv3x516tSBWq2GgYEBVCoV6tevj169esHU1BQNGzbUd7jVDicEHVGp
-VEhISMC4ceOQnp6OtWvXYvXq1YiKisLx48cRHBwMQ0NDnD59GgqFAs2bNxdO319HDx8+xMOHD2Fp
-aYlDhw5h3bp1wm/uikQiiMViqFQqiMVitG3bFu7u7rCxscGmTZvQtWtXrV8zKEkkEqFbt27IyMhA
-WFgYTpw4AScnJ0ycOBHm5uYYOnQoHj16hKtXr6JNmzY6j68qihJB0Q6+WbNmOHv2LHx9fWFsbKyx
-47e0tISNjY2+Qq0ykUgEMzMzHD9+HAcPHkRQUJCwHRWtU1KpFA0bNqwR10F0jROCjojFYjg7Ows/
-6Tht2jR07NgRZ86cQXJyMtq0aYMOHTpALBbj3XffhYWFhb5D1pqsrCwsWrQICQkJePz4MT777DP0
-6tULy5cvR3p6Ovz9/SEWiyEWi4UzCFtbWzRo0AD9+/fX6fWUop2GsbExrK2tcf78eVhaWqJHjx74
-+++/YWtri3feeQeNGzdGw4YN4efnBysrK53F9zKKbnd+/vw5xGIx6v9fe3ca1tSZ9gH8H0CpEZei
-FUG8BJFLKipQFFlUVERxgVarjLhQbBFFBYUyoi1DtVOnrSwK0xYVF1xaWVoVcUGEBKhQUKQjmyyj
-ogUFRcGwB5L7/YA5Bev0bWeQJPD8PhHIubjPSXLuPNv96OggIiICY8aMUepvzb/88guqq6sxbNgw
-WFtb49q1azAzM8OgQYO49xGbWvr7FL9dq4TohXF62WN1dXUQEUQiEfLz8yEUCpGXl4fw8HBMmDAB
-AODo6NhlALU3GjhwIMzNzfHkyROcOXMGW7Zswbp165CRkYGffvoJAQEB3JqLF7teeqIrpvPrx+Px
-ujyWSqXIzMzE3//+d3h6esLZ2Rlz5syBRCKBhoaGwiZyej4rSkYoFMLf3x9eXl5IS0uDi4sL9u7d
-i2fPnskxyj+n8/mIRCJ89NFH+Oyzz+Dn54e2tjaUlpbi/PnzAHrmfdMbsFlG3Yw6zVYoKCiApqYm
-dHR0ujwnNTUVoaGhaGpqgoeHB5ydnblje/O3FiLi+nMBICsrC2FhYZBIJPjiiy8wduxYPHr0CA4O
-Dpg9ezaCg4Pldj1ycnJw7Ngx/POf//zN6xITE4Pa2lro6enBwcFBKV43WYx5eXno168fdHR0uCnN
-n332GQwMDJCQkICrV69i3Lhx3BiOIpOd08OHDzF48GDweDzU19djy5YtmDhxIs6ePQuJRILY2FgY
-GhrKO1zl0IMD2H1C55IGM2bM4PY7lpHNoGloaOBqrfeFOutEv16bhIQEWrt2LRF1lO3YsmULhYSE
-0N27d4mIqKqqittwvid1nt109erV3xSle1ktImV47WTxXb58mbS1tcnV1ZV0dXW5Uh9VVVVUVFRE
-Tk5O5OjoKM9Q/5DO1zw+Pp5MTU1p0qRJ9Ne//pXbp+H+/ft0+PBhmj17NreAUdFfJ0XAEsIrUFZW
-RmZmZi8tdfyyG0hfeqNevnyZjI2Nuam1RB1TcX19fWn37t1cOWWinrsunfcoKCsro6ysLCooKKA5
-c+bQ06dPuzxXmaaTdnbz5k3auHEjt2r6+PHjpK+v/5vEu2rVKqqrq5NHiH9aYWEh2dnZUVFREf3y
-yy/cKn+RSMQ957vvvqNFixZRS0uLHCNVHordJlQS5eXl8PLy4h7X1NRg+PDhmDJlCgBw/eGNjY0v
-3Zhb0bsbulNOTg527tyJhQsXoqWlBQCwcOFC2Nvbo6Ki4jf9969aXV0dtm/fjtraWohEIgQFBcHD
-wwPBwcEQCoX44osvEBsbi9TUVEilUm6DdUUnu44SiQRisRg7d+5ESkoKGhoa0N7ejjVr1mDjxo0I
-CwuDVCoFACQlJSErKwttbW3yDP0/qqqqwu7duyGVSlFTU4PQ0FA8evQIQ4cOha6uLvz8/CAUCnHq
-1CnumEGDBqG+vp47R+b3sVlG3WDo0KHQ0tJCS0sLXn/9dWhqaiIpKQmvv/46dHV10a9fP6Snp+Pk
-yZOwsrKCqqpqn0gC9JK+9djYWNy4cQPLli3jbq7Z2dmwtLTErFmzoK2t3aMxtrS0YMqUKWhubsaD
-Bw+wbt06eHp6YubMmcjNzYWhoSF+/PFHCAQCjB07FqNHj+7R+P4XPB4PjY2N4PP5mD9/PvLy8lBV
-VQVjY2MMGTIET548QWlpKZYsWQIej4fm5masW7fuN2NeiuLx48cwMjKCWCzGsGHDoKmpibKyMtTV
-1WHMmDEYNWoUWltbUVdXBxsbG0gkElRWVsLFxaXH31dKS84tFKUmlUq7dCFYW1vTjBkziKijOJu3
-tzdt376dzp49S+PGjetSjK2369w1dvfuXSoqKuJ+9vT0pNDQUCLq2ODcyMiIKwjXk/HJ3L9/n06c
-OEEzZ84koVBIRB3jBatXr6aTJ0/+x+MUXUJCAllaWtInn3xCV69epfr6elq2bBnNnz+fAgICaOrU
-qXT69Gl5h/n/6nzNxWIxffDBB7R27Vpqa2uj5ORk8vT0pGXLllFUVBSNGzeOEhMT5RitcmNdRv8l
-et4kV1NTQ3FxMYCOukQqKipwdnaGt7c3HB0d0dLSgqSkJISFhcHe3l7hSyB3Jx6Ph3PnzmHp0qXY
-tm0bNmzYgObmZixevBgCgQB2dnZYt24d9uzZg2nTpsklxqSkJHh6esLMzAwuLi4IDg5GWloaVFVV
-MXfuXFRUVMglrv8GdZpaWllZiePHj2Pz5s0YPHgwjh49ioyMDBw/fhxaWlrIzc1FaGgolixZwh2r
-iDrHVVhYCBUVFfj4+IDP58PHxwe2trZYuXIlGhsbkZSUhJCQEMyfPx8SiUSOUSsxeWYjZda5NpGB
-gQG3jy4RkY2NDS1dupR7LBvQUoYZKd3pxx9/JHNzc6qurqbIyEjS0NAgHx8fKi8vJ6lUSnfu3OH2
-re3JayP7PyUlJbRw4UJuJlh1dTVFRESQk5MTXb16lXJycrjaRMpAdl45OTl06NAh8vf3J6KOUt1R
-UVHk7u5O8fHx1NTURM7OzrRlyxaqrq5W6PekLLZLly6Rvr4+5eXlUXt7OxUVFdGGDRtoy5YtJBaL
-KSUlhXx8fCg0NJQePXok56iVF0sI/4MbN27Q+PHj6eeffyaijv2OZTNWpk2bRnZ2dkRESrGz1KtQ
-VFREWVlZdPHiRbKwsKC8vDyysbGhBQsWcHsjy/TETamlpYVLzvfv36fAwECaMGECHT58mHvOo0eP
-aN++fTRv3jxuRy1FvmHKyGIUCAQ0evRocnNzIz6fT/n5+UTUcV4HDx4kV1dXam5upocPH9KqVauo
-qqpKnmH/ISUlJTRx4kRKT0/nfieVSqmoqIjee+898vT0JCKiEydOkL+/f4/vjdGbsIVpfwJR1xK5
-eXl5iI2Nxfjx41FZWYmYmBgYGhpix44dMDMzQ2ZmJqytreUZco/pfG1qa2vRr18/aGhoAAD8/Pxg
-aGiI9evXIyIiAlFRUfj222+7lFR+1drb25GRkYG7d+9CQ0MDhYWFWLJkCeLj41FXVwdHR0fMmjUL
-QMfgZVNTE8aMGdNj8XWH4uJi+Pj4ICAgADY2Nvj0008RFxeH6OhoGBsb49GjRxCLxdDV1QXQtdy1
-IqEXJiOUlpbi888/x9GjRyGRSCCRSNC/f3+0t7fj7t27aGpqgomJCQCgvr4egwYNklfoSo/NMvqT
-eDwekpKSUFZWBn19faSnp0MoFGLWrFnYvHkzysvLIRaL8dZbb2H06NF9qs46j8dDfHw8AgMDceTI
-EYjFYq6uz8mTJyESiRAdHY2goCCYmpr2aGwqKipoaGhAUFAQoqKisHHjRkyfPh06OjooKSlBSUkJ
-iAgGBgYYOHAgF/eLNydF0zk+gUCAhIQEEBHs7e1ha2uL2tpa+Pr6wt7eHvr6+lxpDSJSyJXInT8v
-t27dQmNjIzQ0NBAYGAgtLS1MnjwZqqqqSEpKQmxsLN555x2MHDmSK4Sorq4u5zNQbiwh/AmyG972
-7dsxe/ZsTJkyBba2tlixYgVMTExQVVWFkJAQuLi4QE9PjztGkW8o3YXH46GkpATr16/HV199hfHj
-x6O4uBi3bt2ChYUFhg4diosXL2Lr1q2YO3euXDa3GTJkCE6fPg1dXV3w+XwYGRlBV1cXBgYGuH79
-Ou7cuQMzM7MuxfMU/bWTlVWPiYmBh4cHtLW18a9//QsPHz7ElClTMHPmTDx79gwjR47s0uJR5POS
-TUbYvHkz5syZg/Hjx8PAwAARERG4d+8eRCIRPv74Yzg7O8PIyAgAq1XUbeTRT6Wsnj59SrNnz6bi
-4mKSSCSUk5NDJ06coKamJhIIBGRtbU1nzpwhIuXod+4OsvN88OABXbp0iRYsWMD9LTs7m+zs7LhB
-2+bmZu6Ynrg+nf/PgwcPuN8VFBTQxo0b6W9/+xsRdezZHBcXR6Wlpa88plehoKCARo8eTSEhIURE
-FBsbSx4eHrRv374uz1OW92Rubi6ZmJhw40wPHz6k69evU2FhITk7O5OXlxedP3+eiJTnnJSFciy7
-lBN6obugvb0dPB4PcXFxKC4uhqqqKgQCAerq6uDq6orDhw/DyMhIYafwdTdZAbTMzEwEBATg66+/
-xmuvvYa4uDgsX74cFhYWMDY25vYI6NevH3dsT31D5fF4uHDhAnbt2gVra2v0798fe/bswZo1a3Di
-xAksXboUBQUFiIuLU7oCaCKRCHw+H8bGxkhMTMTy5ctBRPjwww/R1taGK1eu4N69e1zLQJFbBZ29
-9tprMDExgUAgQGxsLIRCIQDA398fMTEx3PP6yuesJ7Euo/+AOnU1lJSUQEVFBcOHD4eenh4KCwux
-bNky+Pr6YsKECUhMTMS7774LLS0t7nhl+fD9L3g8HpKTk3Ho0CF4enrCysoKNTU1KCwshEAggKqq
-KoKCguDp6QldXd0e3wWOx+MhLS0NPj4++Pbbb1FRUYGIiAgUFhbCy8sLJiYmaG5uhqurK2xsbHok
-pu5ARLh9+zbc3d1haGiIkSNHQktLC7a2tvDz8wMAuLu7Y9q0adwAsjLh8/morq7GyZMn8c4778DN
-zQ18Ph9tbW3c4DHQd7pje5Q8myeKTFbZ8ty5c2Rubk5bt24lb2/vLsXXEhMTacKECV0KtfV2LzbR
-o6KiiMfj0cGDB4moYy5/cnIyrVu3jtzd3SkhIeGlx/UEiURCV65cofz8fEpMTCQLCwsqLCykqVOn
-0po1a7o8V9HXiLwsvsDAQHJycqLr169Ta2srERF5enqStrY2lZeXyyPMbiWbInzt2jWaOHEiXbly
-Rc4R9X4sIbxAVpKaqKMP3MzMjCoqKujjjz+mSZMm0erVqyknJ4caGhpowYIFcr3hyYPsPCsrK7kx
-gVOnTpG6ujplZGR0eY5sTUZP3mxlaz5aWlq6xLF69WrutfLz8+uyfkQZyM5FKBRSeHg4VwokJCSE
-HB0dKSkpiRISEmjNmjV069YteYbabdrb2+n69es0depUOnv2LBH1nc+ZvLAuo05qa2uxZ88eVFdX
-Y9KkSaioqMDSpUtRXl6OiIgIhIeHIzc3F0lJSbCyssL777+PiRMn9pmppfR8TOXChQvYuHEjzp49
-i3v37sHFxQVmZmZYtWoVrKysuD5r2ZhBTzbteTwezpw5g23btiE7Oxt8Ph+GhoZISkrCgAED8ODB
-AyQmJuL48eMwNjZW+GmlwK/XPSsrCx4eHmhqasK1a9fw5MkTbNq0Cc+ePUNKSgqOHTsGT09PzJgx
-o8txykpFRQVDhw6Fg4MDrKys+sznTJ7YoHInqqqq4PP5yMnJgYaGBpycnAB0DGbt27cPM2fOxKVL
-l1BdXc3Nj5bpC29SHo+H7OxsfP3119i/fz+qq6uRl5eHjz76CN988w2ePHkCBwcHVFZWYvDgwT02
-FZBeWBR39OhRuLq6QiQSYevWrTh48CDc3Nywf/9+lJaWwtfXV6kGkGXXfefOnYiOjsbkyZNx6tQp
-ZGZmIjIyEu7u7lBTU+PKrssSQW94T/L5fIwdO5Z73BvOSaHJr3GiOKRSKTdm0NDQQOHh4eTt7U0/
-/PADERFt2LCB7OzsKCUlhYyNjbm6RX2t+fr06VNydnamt956i/tdfn4+ubi4UHJyMhGRXPquZa9D
-VlYWffXVV7Rz507ub1FRUWRqako//fQTERE9e/aMO0aZXr/Lly+TqqoqBQUFEVHHRj3R0dHk7u5O
-e/fupfb2dq67TJnOi1EsrMvoORUVFQgEAty5cwdLlizB7du3cfPmTaipqcHb2xtpaWnIzs7G5s2b
-YWdnp/TN8T+CXmiiDxgwAJqamrh06RIeP34MW1tbjBgxAqmpqaivr8f06dOhoaEBFRWVHrs+sv+T
-kZGB9957D0+ePMHPP/8MQ0NDjBo1Cubm5lBTU4O/vz9WrFiBIUOGcHEp0+tnYGAAExMTBAcHQ1NT
-EyYmJnjzzTfR2NgIGxsbaGlpKeV5MQpGvvlIcZw7d45MTU3p0qVLRERUV1dHoaGh5OXlRRcvXiSi
-vle1VHaOycnJ9I9//IMiIyOppqaGUlNT6d133yU3NzdKS0sjY2Njbn9eecjKyqK5c+fSzZs3iYgo
-ICCANm3aREKhkMRiMRERVVRUyC2+7nT+/HkyMzOjqKgoeYfC9EIsIVDHStW3336b61aQ3UTq6uro
-yy+/pA0bNihFVcjuJEsG6enpNG7cOAoLCyMLCwsKDAykjIwMSk1NJSMjI7K2tuaum7z2G758+TKp
-qKhwK3VbW1spMDCQ3NzcKCUlpUvy7g2J/OzZs/Tmm29SZWUl19XJMN2BFQBBR3dRTU0NRCIRgF+b
-3GKxGL6+vti+fXuXRWd9gWxBXkREBLZt2wZvb2+cOXMGIpEICQkJsLW1xf79+zFu3DikpaUBgNz2
-G543bx5Onz6NQ4cO4bvvvkP//v0REBAAbW1tjBgxoksXSm/oTnn77beRlpYGHR0dhaxWyigvNoYA
-oH///mhoaEBpaSlGjBgBLS0tZGRkwNvbG/PmzVPK1Z7/DXreHy+VSsHj8ZCeno60tDRUVFTA2toa
-o0aNgomJCQIDA+Hk5AQjIyMMGTIEAoEA06dP71IUrqcZGRlBT08Pu3fvhrq6OszNzTFnzpxem8gH
-DhwIQPmnljKKhe2H8FxlZSUOHDgAoVAIGxsbxMXFITw8HIsWLZJ3aD2COg0gV1ZWYtSoUQA6tgU9
-efIkDA0NsWLFCjQ2NmL58uW4cOECRo0aBbFYjPb2drkmg87i4+OxY8cOJCcnQ0tLi32DZpg/gSWE
-TpqampCdnY3q6mro6enB0tKyzyyGkX3TvHjxIj755BPY29tDRUUFu3btQnp6OsLDw1FRUYGhQ4fC
-x8cHCxcuVNhvp48fP8Ybb7wh7zAYRumwhWmd8Pl8zJ49m3vcV5IB8GtdfX9/f8TGxuLYsWM4ffo0
-Hjx4gAMHDmDAgAE4cuQIDAwMMH/+fHmH+7tkyUBRExbDKCo2qPw7estqzz9CIpFwO5pVVFQgOTkZ
-R48eRVVVFTw8PGBubg4nJyeUlpbi0KFDXClwRabo8TGMomEtBAZAR9kOOzs78Hg87N27FyEhIbCy
-soK+vj7KyspQVlaGxYsXg4gwdepUuc0oYhjm1WGf6j7oxa4U2WN1dXW0trZCJBIhPz8fUqkUeXl5
-iIyM5LYqdHR0lFfYDMO8YmxQuY/pPC5SUFAATU1N6OjodHlOamoqQkND0dTUBA8PDzg7O3PHsm4Y
-hum9WELoY2Q39YSEBAQFBSE4OBgWFhbc32XbYjY2NoKIoKGh0acG1xmmL2MJoQ/697//DWdnZxw8
-eBBTpkzp8reX3fxZy4Bh+gY2y6gPKC8vh5eXF/dYVjdflgza29sBAI2NjS+dWcWSAcP0DSwh9AF6
-enpwc3PDnTt3AACTJ0+GpqYmUlJSIBaLoaamhvT0dOzZswctLS1gjUaG6ZtYl1EvRkSQSCTcFFEb
-GxuoqqpyK49v374NPp8PS0tL+Pn54ZtvvoG9vb2co2YYRl5YQuilOo8FFBcXc9NGZ82ahREjRiA2
-NhbJycm4cOECxGIxFi1apNDlKBiGefVYQuilOtcm8vb2RkxMDMzNzQEA06dPh5aWFn744QcAQGtr
-K9TV1dlsIobp41hC6MVyc3OxcuVKREdHw9TUFPfu3cOIESMwYMAAWFpaQkNDA8nJydxUU4Zh+ja2
-UrkXefEbvpqaGpYtW4b8/HwkJiYiJiYGhoaG2LFjB7KyspCZmQkALBkwDAOAzTLqdXg8HpKSkpCY
-mIjhw4ejubkZp06dgr6+PqKjo6Gvr4/c3FwAgLW1NahjG1U5R80wjCJgCaEX4fF4iI+Px4cffoi2
-tjbo6Ohg9+7dOHPmDP7yl7+gra0NV65cgYGBQZdj2JgBwzAASwi9Sm1tLcLCwvD9999j0aJFuHHj
-Br7//ntIpVIIhUKsX78egYGBmDVrFmsVMAzzG2wMQYm9OEVUtkdBXFwciouLoaqqCoFAgLq6Ori6
-uuLw4cMwMjJiyYBhmJdiLQQl1fmmXlJSgqdPn+KNN95AYGAg6uvrsXbtWhw7dgyRkZG4du0aBgwY
-wK1FANjUUoZhfotNO1VSEokEqqqqSEhIwK5duzBjxgxIpVJs3boV+vr6AIDLly/D19cXQUFBWLhw
-oZwjZhhG0bEWgpJpaGgA0LHD2bVr1/DJJ58gPj4eAwcOhFAoRGBgIG7cuIHGxkaEhYXhyy+/5FYg
-MwzD/B7WQlAitbW1CA4OhrGxMVauXIns7Gz0798fjx8/xvbt27Fv3z5ERkZCJBIhODgY2trabD8D
-hmH+MNZCUCKqqqrg8/nIycnBuXPnMG3aNJiZmSElJQX79u3DzJkzoauri2HDhqGxsREaGhrcsSwZ
-MAzz/2EJQQnIqpYOHjyYGyNISUnB6dOnAQAikQiffvopBAIBEhISsGnTJpiamrKWAcMwfwrrMlIC
-sumlAoEAra2tsLW1xYEDB1BeXo7FixfD3t4eH3zwAZqamrB8+XIsXbqUVS1lGOZPYwlBSSQkJCAw
-MBCff/45HBwc8OzZMxw5cgR3797FggULsGDBAla1lGGY/wnrMlIC9fX1OHz4MCIiIuDg4IC2tjYM
-GTIE77//PnR1dXHu3DlUV1dDXV0dACtHwTDMf4etVFYCKioqqKmpgUgkAvDrN3+xWAxfX19UVlZC
-S0tLniEyDNMLsBaCEhg4cCCcnZ2RmZmJoqIiqKmpISMjA6tWrcLjx48xZswYeYfIMEwvwMYQlERl
-ZSUOHDgAoVAIGxsbxMXFITw8HIsWLZJ3aAzD9BIsISiRpqYmZGdno7q6Gnp6erC0tGQDyAzDdBuW
-EJQYSwYMw3QnNqisxFgiYBimO7FBZYZhGAYASwgMwzDMcywhMAzDMABYQmAYhmGeYwmBYRiGAcAS
-AsMwDPMcSwgMwzAMAOD/AFCftMrEW+2BAAAAAElFTkSuQmCC
-"
->
-</div>
-
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-
-
-<div class="output_png output_subarea ">
-<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
-AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFNfXB/Dv0HvvSBFLbKhoVMQCBAt2xdiiGMXYEo2G
-FImxkGjsxhKNURNrLLEiYo0i6M+GGkuQCDZQkSKCUgV2Oe8fvjthAbvMIns+z+MjW+7cM3fKmTsz
-d1YgIgJjjDFWzWmoOgDGGGNMCpzwGGOMqQVOeIwxxtQCJzzGGGNqgRMeY4wxtcAJjzHGmFqQNOGl
-paWhffv2MDExwddffy1l1e+krl27YuPGjaoOo8pydXXF0aNHJatPQ0MDt27dqtQ6hg0bhqlTp1ba
-9I2NjZGYmPhKZaKiouDk5FQ5AVVzjRo1wvHjx9/6dBMTE6GhoYGSkpK3Pm1VGzt2LGbOnFkp09Z6
-0RdcXV2Rnp4OTU1NGBoaokuXLli2bBkMDQ1fubJVq1bBxsYG2dnZrxWsutm/f7+qQ6jSBEGAIAgV
-fjZs2DA4OTlhxowZrzVtHx8fBAYGYsSIEW8S4it73jy9DTk5OZU27TfxpsurqoqNjVV1CJJ61eW4
-bt06/P777zhx4oT43ooVKyorvBf38ARBQEREBHJycvD333/j/Pnzr5x9iQglJSVISkpC/fr1XytQ
-mUz2WuXYu6GqLd/KTDovws+CqJqq2jrKXgO9gKurKx09elR8/dVXX1H37t2JiOj06dPUunVrMjMz
-oyZNmlBUVJT4PW9vb/ruu++oTZs2pK+vT0OGDCFtbW3S0dEhIyMjOnr0KBUWFtKECRPIwcGBHBwc
-aOLEiVRYWEhERMeOHSNHR0eaO3cu2dnZUWBgIIWGhtKHH35IQ4YMIWNjY3J3d6eEhASaNWsW2djY
-kLOzMx0+fFiMYc2aNVS/fn0yNjYmNzc3WrlypfiZYvoLFy4kGxsbsre3p7Vr14qf5+fnU3BwMLm4
-uJCpqSm1bduWCgoKXjjfZV24cIGaNm1KxsbG1K9fP+rfvz9NmTKFiIgyMzOpW7duZG1tTebm5tS9
-e3e6d++eUhv+9ttvRES0du1aatOmDX311Vdkbm5ONWvWpAMHDjyz3jt37lCfPn3I2tqaLC0tady4
-cUREJJfLacaMGeTi4kI2NjY0dOhQevz4MRER3b59mwRBoLVr15KTkxNZWFjQihUrKCYmhtzd3cnM
-zEycjiImLy8vGjduHJmamlK9evWU1pWXaX/F8h06dCiVlJTQ7NmzqVatWmRpaUn9+/enzMxMscyG
-DRvI2dmZLC0t6ccffyy3biqsXLlSaV3r2bMnERHFxcWRt7c3mZmZUcOGDSk8PLzCtps8eTJpamqS
-np4eGRkZ0fjx44mISBAE+vXXX6lOnTpkZmZGn332mVK533//nerXr0/m5ubUuXNnSkpKeubyOXHi
-hLgOOTk50fr164mIaNiwYS+9fqxdu5bc3NzI2NiYatasSZs2bSIiouvXr1P79u3J1NSUrKysaMCA
-AWIZQRDo5s2bRPT8dby0Y8eOUY0aNcTXycnJFBAQQNbW1lSzZk1aunSp+NnZs2fJ09OTzMzMyN7e
-nsaNG0dFRUXi5xMnTiQbGxsyMTEhd3d3io2NfebyKquiskREGRkZ1KNHDzIxMaGWLVvSlClTqG3b
-tkT03zotl8vF6ZTerm7cuEG+vr5kaWlJVlZWNHjwYHr06JH4XRcXF5o7dy65u7uTnp4eyeXyV9r+
-XVxcxHV0+vTpr7T/8vb2ppCQEGrZsiWZmJhQr169xO2h7Hw9evSIgoKCyN7enhwdHWnKlCniZ4rt
-9IsvviAzMzOqVasWnTx5ktasWUNOTk5kY2Mjrn9ERE+ePKEvv/ySnJ2dydbWlsaMGSOuF8/bbz5r
-OSq2aWNjY2rQoAHt3r2biJ5uj3p6eqSpqUlGRkZkbm5OREQff/yxuA0QEa1atYpq165NFhYW1LNn
-T7p//7742Yu2ybJeKuEdOXKEiJ7uRBs2bEjTpk2je/fukaWlpbjT/euvv8jS0pIyMjLEheXi4kJx
-cXEkl8upuLiYhg0bRlOnThWnPXXqVGrdujU9ePCAHjx4QF5eXuLnx44dIy0tLQoJCaGioiIqKCig
-6dOnk56eHh0+fJhkMhkNHTqUXFxcaNasWSSTyWj16tVUs2ZNcfr79u2jW7duERFRdHQ0GRgY0N9/
-/600/enTp5NMJqP9+/eTgYGBuLJ/+umn5OvrS/fv3xdX8sLCwmfO94MHD8q1XWFhITk7O9PSpUtJ
-JpPRrl27SEdHR5zHhw8f0q5du6igoIBycnKoX79+1Lt3b7G8j48P/f7770T0dKXV1tam3377jUpK
-SmjFihXk4OBQ4TKTyWTUuHFjCg4Opvz8fHry5AmdPHmSiJ7ulGvXrk23b9+m3NxcCggIoMDAQCL6
-byMaO3YsFRYW0uHDh0lHR4d69+5NDx48oOTkZLKxsaHo6GgxJi0tLVq8eDHJZDL6888/ydTUVNwo
-X6b9Sy/fxYsXU+vWrSk5OZmKiopo9OjRNGjQICIiunr1KhkZGdGJEyeosLCQgoODSUtLq8KER0Tl
-1rWioiKqVasWzZ49m4qLiykyMpKMjY0pPj6+wvKl215BEATq0aMHPX78mO7cuUPW1tZ08OBBIiIK
-Cwuj2rVr07Vr10gul9PMmTPJy8urwmknJiaSsbExbd26lWQyGT18+JAuXbokxq3Y2J+3fuTm5pKJ
-iQklJCQQEVFqaipdvXqViIgGDhxIs2bNIqKn66Bi2SvmQZHwnrWOl1U64cnlcmrWrBnNmDGDiouL
-6datW+Tm5kaHDh0ioqcHeGfPniW5XE6JiYlUv359Wrx4MRERHTx4kJo3by4eYF27do1SUlIqXF5l
-Pa/sgAEDaMCAAZSfn0+xsbHk6OhI7dq1I6KKE17pZXvjxg06cuQIFRUV0YMHD6h9+/Y0ceJE8bsu
-Li7k4eFB9+7doydPnrzS9k+k3GF41f2Xt7c3OTo60tWrVykvL4/69u1LQ4YMqXC+evfuTWPGjKH8
-/HxKT0+nli1bigeYiu103bp1VFJSQlOmTCFHR0fxYOTw4cNkbGxMeXl5RPT0wKJXr16UlZVFOTk5
-1KNHD/r222/FdeF5+82KluP27dvFZfXnn3+SoaEhpaamEhHRunXrxIMThdLTOHr0KFlZWdHFixep
-sLCQxo8fT+3btxe/+7xtsiIvTHguLi5kZGREZmZm5OLiQp999hkVFBTQnDlzxB2lQufOncUjBR8f
-H5o+fXq5GSmduWvVqqXUSzl06BC5uroS0dOG1dHRUdoAp0+fTp06dRJfh4eHk5GREZWUlBARUXZ2
-NgmCIG4UZfXu3ZuWLFkiTl9fX19pQ7CxsRE3Vn19fbpy5Uq5abxovkuLjo4mR0dHpffatm37zA37
-4sWL4lEOUfmEV7t2bfGzvLw8EgSB0tLSyk3n1KlTZG1trTRvCh988AGtWLFCfB0fH0/a2tokl8vF
-jaj0EZSlpSVt27ZNfN23b19xB7Z27dpySbdly5a0cePGCuevbPuXXb7169dXSmD3798nbW1tkslk
-9P3334vJTzH/Ojo6z014pde148ePk52dndJ3Bg0aRKGhoRWW9/HxEXsBCoIgKCWP/v3709y5c4mI
-yN/fXylByuVyMjAwoDt37pSb9qxZsyggIOCl4i6t9PqRm5tLZmZmtHPnTsrPz1f63tChQ2nUqFFK
-vcHS83Dz5s3nruNllU54Z86cIWdn53LzM3z48ArLLlq0iPr06UNET3dedevWpTNnzpRbN58330RE
-kZGRFZaVyWSkra2tdOAyefLk5/bwKjqYUdi9ezd5eHiIr11dXZXO/LzK9q8oXzrhvcr+y8fHR0w0
-RE97RDo6OlRSUqI0X6mpqaSrq6vUO9+8eTP5+voS0dPttE6dOuJnV65cIUEQKD09XXzP0tKSLl++
-TCUlJWRoaCgeFBE93Z8oEvHz9ptEL16ORERNmzalPXv2iLE9L+EFBQXRpEmTxM9yc3NJW1tbPHtS
-0TY5Z86cZ9b9Utfw9uzZg6ysLCQmJmLZsmXQ09NDUlIStm/fDnNzc/HfyZMnkZqaKpZ90Z1d9+/f
-h4uLi/ja2dkZ9+/fF19bW1tDR0dHqYyNjY34t76+PqysrMTrLfr6+gCA3NxcAMCBAwfg6ekJS0tL
-mJubY//+/Xj48KFY3tLSEhoa/zWBgYEBcnNzkZGRgSdPnqBWrVrlYn6Z+S49f46OjkrvOTk5iddo
-8vPzMXr0aLi6usLU1BTe3t54/PjxM6/h2NnZKcVael5Lu3v3LlxcXJTmTSElJaVcm8tkMqSlpYnv
-2drain/r6+uXe52Xlye+Ljt/Li4uSElJAfDi9i+7fBMTE9GnTx+xXRs0aAAtLS2kpaUhJSUFNWrU
-UJp/S0vLipqpQvfv3y+3Prq4uCA5OfmZZSq6jld2GSjaPykpCRMmTBBjV8RW0fTv3bsHNze3F8b8
-vPXD0NAQf/75J3799Vc4ODige/fuiI+PBwDMmzcPRISWLVuiUaNGWLt2bblpP28df56kpCTcv39f
-af2fPXs20tPTAQAJCQno3r077O3tYWpqiu+++05c5h988AHGjRuHzz77DLa2thg9evRL30Tj6+tb
-YdkHDx5AJpMpLVtnZ+eXnp+0tDQMHDgQNWrUgKmpKQIDA5XWUUB5P/Yq239FXmX/VbZuZ2dnFBcX
-IyMjQ2maSUlJKC4uhr29vRjTmDFj8ODBA/E7Zbdh4On2V/q93NxcPHjwAPn5+WjevLk4rS5duijV
-+az95rNs2LABHh4e4vRiY2PLtfGzlN1fGRoawtLSUmm7etY2WZHXHpbg7OyMwMBAZGVlif9ycnLw
-zTffiN950YV/BwcHpVuk79y5AwcHh2eWf5UbCQoLC9G3b1988803SE9PR1ZWFrp27fpSNwRYWVlB
-T08PN27cKPfZy8y3gr29fbkd3p07d8T5WLhwIRISEhATE4PHjx8jOjoa9LTX/dLzWREnJyfcuXMH
-crm83GcVtbmWlpbSBvEqys5fUlISHBwcXqr9yy5PZ2dnHDx4UKlt8/Pz4eDgAHt7e9y9e1f8bn5+
-/nM3mrLTdnBwwN27d5XqT0pKUkqizyv/Is7Ozli1apVS7Hl5efD09Cz3XScnJ9y8efOFsb9o/ejU
-qRMOHz6M1NRU1KtXDyNHjgTwdOe2atUqJCcnY+XKlfj000/LDad43jr+PE5OTqhZs6bSfGZnZyMi
-IgLA01vKGzRogBs3buDx48f48ccflW6dHz9+PM6fP4+4uDgkJCRg/vz5SvP8PBWVtbGxgZaWFu7c
-uSN+r/TfirvJ8/PzxfdKJ6fJkydDU1MTsbGxePz4MTZu3FjuVv/Ssb3K9v82lJ0vbW1tWFlZKX3H
-yckJurq6ePjwoRjT48eP8c8//7xyfVZWVtDX10dcXJw4rUePHr30nfVll2NSUhJGjRqF5cuXIzMz
-E1lZWWjUqJG4Dr9qjsjLy8PDhw/LHWi/rNdOeEOGDMHevXtx+PBhyOVyPHnyBFFRUUo7wLI77rKv
-Bw0ahJkzZyIjIwMZGRn44YcfEBgY+Mw6XyURFBUVoaioCFZWVtDQ0MCBAwdw+PDhlyqroaGBoKAg
-BAcHIyUlBXK5HKdPn0ZRUdFLzbeCl5cXNDU1sWzZMshkMuzZswfnzp0TP8/NzYW+vj5MTU2RmZmJ
-77///qXn73latWoFe3t7hISEID8/H0+ePMGpU6cAPG3zRYsWITExEbm5uZg8eTIGDhxYYW/wWUov
-h/T0dCxduhTFxcXYvn07rl27hq5du75W+48ZMwaTJ08WN/IHDx4gPDwcAPDhhx8iIiICJ0+eRFFR
-EaZNm/bcMUi2trZKO3lPT08YGBhg3rx5KC4uRlRUFCIiIjBw4MBnln9eUlK0g6ItxowZg1mzZiEu
-Lg4A8PjxY2zfvr3CcoMHD8aRI0ewfft2yGQyPHz4EJcvXy43zeetH+np6dizZw/y8vKgra0NQ0ND
-aGpqAgC2b9+Oe/fuAQDMzMwgCEK55fu8dfx5WrZsCWNjY8ybNw8FBQWQy+WIjY3F+fPnxZiNjY1h
-YGCAa9euYcWKFeJO7fz58zh79iyKi4thYGAAPT09Meayy6usZ5XV0NBAQEAAQkNDUVBQgLi4OGzY
-sEGs09raGo6Ojti4cSPkcjnWrFmjtFxzc3NhaGgIExMTJCcniwn4WV5l+39TRIQ//vgD//77L/Lz
-8zFt2jT069evXJKwt7dHp06dEBwcjJycHJSUlODmzZuvNf5PQ0MDI0eOxMSJE8UeYnJy8kvvO8su
-x7y8PAiCACsrK5SUlGDt2rVKQzVsbW1x7949FBcXK823YhsYNGgQ1q5di8uXL6OwsBCTJ0+Gp6fn
-M3vxL8oRr53watSogT179mDWrFmwsbGBs7MzFi5c+Nwj+LJjjKZMmYL3338fjRs3RuPGjfH+++9j
-ypQpL13+Wd8Bng6wXbp0Kfr37w8LCwts2bIFvXr1em7Z0hYsWAB3d3e0aNEClpaW+Pbbb1FSUvLM
-+a5o56utrY1du3bh999/h7m5OTZt2oTu3buLp/EmTpyIgoICWFlZwcvLC126dHlmTC8z7woaGhrY
-u3cvbty4AWdnZzg5OWHbtm0AgKCgIAQGBqJ9+/Zwc3ODgYEBfv7555dqk4q+06pVK1y/fh3W1taY
-OnUqdu7cCXNz89dq/wkTJqBnz57o1KkTTExM0Lp1a8TExAAAGjRogOXLl+Ojjz6Cg4MDLCwsnnvK
-fMSIEYiLi4O5uTkCAgKgra2NvXv34sCBA7C2tsa4ceOwceNG1K1bt8LyEyZMwI4dO2BhYYGJEyc+
-sx0U89C7d29MmjQJAwcOhKmpKdzd3XHo0KEKyzk5OWH//v1YuHAhLC0t4eHhgStXrpSb5vPWj5KS
-EixatAiOjo6wtLTEiRMnxPFL58+fh6enJ4yNjdGrVy8sXboUrq6u5dr8Wev4s+YVADQ1NREREYFL
-ly7Bzc0N1tbWGDVqlNgDWLBgATZv3gwTExOMGjVK6YAiOzsbo0aNgoWFBVxdXWFlZSU+gKLs8irr
-eWWXLVuG3Nxc2NnZISgoCMOHD1faD61evRrz58+HlZUV4uLi0KZNG/Gz6dOn4++//4apqSl69OiB
-vn37PncbeJXtv6I2fNn9l+LvwMBADBs2DPb29igqKsLSpUsr/O6GDRtQVFSEBg0awMLCAv369RN7
-sq+y7wCAuXPnonbt2vD09ISpqSk6duyIhISElypbdjk2aNAAX375JVq3bg07OzvExsaibdu24vf9
-/PzQsGFD2NnZiad7S8fr5+eHGTNmoG/fvnBwcMDt27exdevW57bf8+IT6E3Pn1Wyu3fvYujQoUhP
-T4cgCBg1ahQ+//xzZGZmYsCAAUhKSoKrqyu2bdsGMzMzVYf7Qq1atcKnn36Kjz/+WNWhvLGKBo0y
-pmrVZb309fVFYGAggoKCVB1KtVHln6Wpra2NRYsW4erVqzhz5gyWL1+Of//9F3PmzBGPPPz8/DBn
-zhxVh1qh48ePIzU1FTKZDOvXr0dsbCz8/f1VHRZj7B1Qxfsj75wqn/Ds7OzQtGlTAICRkRHq16+P
-5ORkhIeHi72kjz/+GGFhYaoM85ni4+PRtGlTmJubY9GiRdixY8dr3yBS1VT2Y7AYex3Vab2sLvNR
-VVT5U5qlJSYmwtvbG7GxsXB2dkZWVhaAp0dBFhYW4mvGGGOsrCrfw1PIzc1F3759sWTJEhgbGyt9
-Vp2O6BhjjFWOF/5aQlVQXFyMvn37IjAwEL179wbw9HbW1NRU2NnZISUlRWlAp0LTpk3F270ZY4y9
-nCZNmuDSpUuqDuOtq/I9PCLCiBEj0KBBA6Xbw3v27In169cDANavXy8mwtIuX74sjumoyv+mT5+u
-8hiq0z9uT27LqvrvXWnP6tpRqPI9vJMnT+KPP/5A48aN4eHhAQCYPXs2QkJC0L9/f/z+++/isATG
-GGPsWap8wmvbtu0zB3UeOXJE4mgYY4y9q6r8KU114OPjo+oQqhVuz7eH2/Lt4vZUrXdqWMKrEgQB
-1Xj2GGOsUlTXfSf38BhjjKkFTniMMcbUAic8xhhjaqHK36VZmUJCQ5D66OV+qbi6szOzw5zQqvkA
-bsYYexvUOuGlPkqFa29XVYdRJSSGJao6BMYYq1R8SpMxxpha4ITHGGNMLXDCY4wxphY44THGGFML
-nPAYY4ypBU54jDHG1AInPMYYY2qBEx5jjDG1wAmPMcaYWuCExxhjTC1wwmOMMaYWOOExxhhTC5zw
-GGOMqQVOeIwxxtQCJzzGGGNqgRMeY4wxtcAJjzHGmFrghMcYY0wtcMJjjDGmFjjhMcYYUwuc8Bhj
-jKkFTniMMcbUAic8xhhjaoETHmOMMbXACY8xxpha4ITHGGNMLXDCY4wxphY44THGGFMLnPAYY4yp
-BU54jDHG1AInPMYYY2qBEx5jjDG1wAmPMcaYWuCExxhjTC1wwmOMMaYWOOExxhhTC5zwGGOMqQUt
-qSqKj4/HggULkJiYCJlMBgAQBAGRkZFShcAYY0yNSZbw+vXrh7Fjx+KTTz6BpqYmgKcJjzHGGJOC
-ZAlPW1sbY8eOlao6xhhjTIlk1/B69OiB5cuXIyUlBZmZmeK/FwkKCoKtrS3c3d3F90JDQ1GjRg14
-eHjAw8MDBw8erMzQGWOMVQOS9fDWrVsHQRCwYMECpfdv37793HLDhw/H+PHjMXToUPE9QRAQHByM
-4ODgSomVMcZY9SNZwktMTHytcu3atauwLBG9WUCMMcbUSqUnvKNHj8LPzw87d+6s8CaVgICA15ru
-zz//jA0bNuD999/HwoULYWZm9qahMsYYq8YqPeEdP34cfn5+2Lt371tLeGPHjsW0adMAAFOnTsWX
-X36J33//vcLvhoaGin/7+PjAx8fnletjjLHqLCoqClFRUaoOo9IJ9A6cG0xMTESPHj3wzz//vNJn
-giA899TnsInD4Nrb9W2G+s5KDEvEusXr3mgaIaEhSH2U+nYCesfZmdlhTugcVYfB2Gt50b7zXSXZ
-NTwAiIiIQFxcHJ48eSK+p+ipvYqUlBTY29sDAHbv3q10BydTndRHqXwA8f8SwxJVHQJjrAzJEt7o
-0aNRUFCAyMhIjBw5Etu3b0erVq1eWG7QoEGIjo5GRkYGnJyc8P333yMqKgqXLl2CIAioWbMmVq5c
-KcEcMMYYe5dJlvBOnTqFf/75B40bN8b06dPx5Zdfwt/f/4XltmzZUu69oKCgygiRMcZYNSbZwHN9
-fX0AgIGBAZKTk6GlpYXUVL7ewxhjTBqS9fC6d++OrKwsfP3112jevDkAYOTIkVJVzxhjTM1JlvAU
-N6f07dsX3bp1w5MnT3jsHGOMMclIlvAqGnhuamoKd3d32NjYSBUGY4wxNSVZwluzZg1Onz4NX19f
-AE8HOjZr1gy3b9/GtGnTlJ6VyRhjjL1tkiW84uJi/Pvvv7C1tQUApKWlITAwEGfPnkX79u054THG
-GKtUkt2leffuXTHZAYCNjQ3u3r0LS0tL6OjoSBUGY4wxNSVZD8/X1xfdunVD//79QUTYuXMnfHx8
-kJeXxzevMMYYq3SSJbzly5dj586dOHnyJADg448/Rt++fSEIAo4dOyZVGIwxxtSUZAlPEAR8+OGH
-+PDDD6WqkjHGGBNJdg2PMcYYUyVOeIwxxtSCpAkvPz8f8fHxUlbJGGOMAZAw4YWHh8PDwwOdO3cG
-AFy8eBE9e/aUqnrGGGNqTrKEFxoairNnz8Lc3BwA4OHhgVu3bklVPWOMMTUnWcLT1tYuN95OQ4Mv
-ITLGGJOGZBmnYcOG2LRpE2QyGa5fv47x48fDy8tLquoZY4ypOckS3s8//4yrV69CV1cXgwYNgomJ
-CRYvXixV9YwxxtScZAPPDQ0NMWvWLMyaNUuqKhljjDGRZD28Dh064NGjR+LrzMxM8Y5NxhhjrLJJ
-lvAyMjKUblqxsLBAWlqaVNUzxhhTc5IlPE1NTSQlJYmvExMT+S5NxhhjkpHsGt6PP/6Idu3aoX37
-9gCA48ePY9WqVVJVzxhjTM1JlvD8/f1x4cIFnDlzBoIgYPHixbCyspKqesYYY2pOsoQHAEVFRbCw
-sIBMJkNcXBwAiD0+xhhjrDJJlvAmTZqEP//8Ew0aNICmpqb4Pic8xhhjUpAs4e3evRvx8fHQ1dWV
-qkrGGGNMJNltkrVq1UJRUZFU1THGGGNKJOvh6evro2nTpvDz8xN7eYIgYOnSpVKFwBhjTI1JlvB6
-9uxZ7vfvBEGQqnrGGGNqTrKEN2zYMKmqYowxxsqRLOElJCRg8uTJiIuLQ0FBAYCnPTz+EVjGGGNS
-kOymleHDh2PMmDHQ0tJCVFQUPv74YwwePFiq6hljjKk5yRJeQUEBOnToACKCi4sLQkNDsW/fPqmq
-Z4wxpuYkO6Wpp6cHuVyO2rVrY9myZXBwcEBeXp5U1TPGGFNzkiW8xYsXIz8/H0uXLsXUqVORnZ2N
-9evXS1U9Y4wxNSdZwmvZsiUAwNjYGOvWrZOqWsYYYwyAhAnv3LlzmDVrFhITEyGTyQA8vUvzypUr
-UoXAGGNMjUmW8AYPHowFCxagUaNG/MOvjDHGJCdZwrO2ti73pBXGGGNMKpIlvOnTp2PEiBHo0KED
-dHR0ADw9pRkQECBVCIwxxtSYZAlv/fr1iI+Ph0wmUzqlyQmPMcaYFCRLeOfPn8e1a9f4gdGMMcZU
-QrKE5+Xlhbi4ODRs2FCqKhl7p4WEhiD1Uaqqw6gS7MzsMCd0jqrDYO84yRLe6dOn0bRpU9SsWVPp
-9/B4WAJjFUt9lArX3q6qDqNKSAxLVHUIrBqQJOEREVatWgVnZ2cpqmOMMcbKkayH9+mnnyI2Nlaq
-6hhjjDElkowAFwQBzZs3R0xMzCuXDQoKgq2tLdzd3cX3MjMz0bFjR9StWxedOnXCo0eP3ma4jDHG
-qiHJHnly5swZtG7dGm5ubnB3d4e7uzsaN278wnLDhw/HwYMHld6bM2cOOnbsiISEBPj5+WHOHL6Y
-zRhj7Pl1TnejAAAgAElEQVQkO6V56NAhABCHJRDRS5Vr164dEhMTld4LDw9HdHQ0AODjjz+Gj48P
-Jz3GGGPPJVkPz9XVFY8ePUJ4eDj27t2Lx48fw9XV9bWmlZaWBltbWwCAra0t0tLS3mKkjDHGqiPJ
-enhLlizB6tWrERAQACLCkCFDMHLkSHz++edvNF1BEJ47mD00NFT828fHBz4+Pm9UH2OMVTdRUVGI
-iopSdRiVTrKE99tvv+Hs2bMwNDQEAISEhMDT0/O1Ep6trS1SU1NhZ2eHlJQU2NjYPPO7pRMeY4yx
-8sp2Br7//nvVBVOJJP2dntLP0HyTnwjq2bOn+Gvp69evR+/evd84NsYYY9WbZD284cOHo1WrVuIp
-zbCwMAQFBb2w3KBBgxAdHY2MjAw4OTnhhx9+QEhICPr374/ff/8drq6u2LZtmwRzwBhj7F1W6Qnv
-1q1bcHNzQ3BwMLy9vfG///0PgiBg3bp18PDweGH5LVu2VPj+kSNH3naojDHGqrFKT3j9+vXDhQsX
-4Ofnh6NHj6J58+aVXSVjjDFWTqUnPLlcjh9//BHx8fH46aeflMbfCYKA4ODgyg6BMcYYq/ybVrZu
-3QpNTU3I5XLk5OQgNzdX/JeTk1PZ1TPGGGMAJOjh1atXD19//TVcXFwwaNCgyq6OMcYYq5AkwxI0
-NTWxYMECKapijDHGKiTZOLyOHTtiwYIFuHv3LjIzM8V/jDHGmBQkG4e3detWCIKA5cuXK71/+/Zt
-qUJgjDGmxiRLeGV/8YAxxhiTkmSnNPPy8jBjxgyMHDkSAHD9+nVERERIVT1jjDE1J1nCGz58OHR0
-dHDq1CkAgIODA7777jupqmeMMabmJEt4N2/exKRJk6CjowMA4q8mMMYYY1KQLOHp6uqioKBAfH3z
-5k3o6upKVT1jjDE1J9lNK6GhofD398e9e/fw0Ucf4eTJk1i3bp1U1TPGGFNzkiW8Tp06oVmzZjh7
-9iyICEuXLoWVlZVU1TPGGFNzkiU8IkJ0dLT480DFxcXo06ePVNUzxhhTc5Jdw/v000+xcuVKNG7c
-GI0aNcLKlSvx6aefSlU9Y4wxNSdZD+/YsWOIi4uDhsbTHDts2DA0aNBAquoZY4ypOcl6eLVr18ad
-O3fE13fu3EHt2rWlqp4xxpiak6yHl52djfr166Nly5YQBAExMTFo0aIFevToAUEQEB4eLlUojDHG
-1JBkCe+HH34o954gCCAiCIIgVRiMMcbUlGQJz8fHR6qqGGOMsXIku4bHGGOMqRInPMYYY2pB0oSX
-n5+P+Ph4KatkjDHGAEiY8MLDw+Hh4YHOnTsDAC5evIiePXtKVT1jjDE1J1nCCw0NxdmzZ2Fubg4A
-8PDwwK1bt6SqnjHGmJqTLOFpa2vDzMxMuXINvoTIGGNMGpJlnIYNG2LTpk2QyWS4fv06xo8fDy8v
-L6mqZ4wxpuYkS3g///wzrl69Cl1dXQwaNAgmJiZYvHixVNUzxhhTc5INPI+Pj8esWbMwa9Ysqapk
-jDHGRJL18IKDg1GvXj1MnToVsbGxUlXLGGOMAZAw4UVFReHYsWOwsrLC6NGj4e7ujhkzZkhVPWOM
-MTUn6W2S9vb2mDBhAn799Vc0adKkwgdKM8YYY5VBsmt4cXFx2LZtG3bs2AFLS0sMGDAAP/30k1TV
-M8bUXEhoCFIfpao6DJWzM7PDnNA5qg5DJSRLeEFBQRg4cCAOHToER0dHqapljDEAQOqjVLj2dlV1
-GCqXGJao6hBURrKEd+bMGamqYowxxsqp9ITXr18/bN++He7u7uU+EwQBV65cqewQGGOMscpPeEuW
-LAEAREREgIiUPuNfOmeMMSaVSr9L08HBAQDwyy+/wNXVVenfL7/8UtnVM8YYYwAkHJZw+PDhcu/t
-379fquoZY4ypuUo/pblixQr88ssvuHnzptJ1vJycHLRp06ayq2eMMcYASJDwPvroI3Tp0gUhISGY
-O3eueB3P2NgYlpaWlV09Y4wxBkCChGdqagpTU1Ns3boVAJCeno4nT54gLy8PeXl5cHZ2ruwQGGOM
-Memu4YWHh6NOnTqoWbMmvL294erqii5dukhVPWOMMTUnWcKbMmUKTp8+jbp16+L27ds4evQoWrVq
-JVX1jDHG1JxkCU9bWxtWVlYoKSmBXC6Hr68vzp8/L1X1jDHG1JxkjxYzNzdHTk4O2rVrh8GDB8PG
-xgZGRkZSVc8YY0zNSZbwwsLCoK+vj0WLFmHTpk3Izs7G9OnT32iarq6uMDExgaamJrS1tRETE/OW
-omWMMVbdSJbwFL05TU1NDBs27K1MUxAEREVFwcLC4q1MjzHGWPVV6QnPyMjomc/MFAQB2dnZbzT9
-ss/nZIwxxipS6QkvNze30qYtCAI6dOgATU1NjB49GiNHjqy0uhhjjL3bJDulCQAnTpzAjRs3MHz4
-cDx48AC5ubmoWbPma0/v5MmTsLe3x4MHD9CxY0fUq1cP7dq1U/pOaGio+LePjw98fHxeuz7GGKuO
-oqKiEBUVpeowKp1kCS80NBTnz59HQkIChg8fjqKiIgwePBinTp167Wna29sDAKytrdGnTx/ExMQ8
-N+Exxhgrr2xn4Pvvv1ddMJVIsnF4u3fvRnh4OAwNDQEAjo6Ob3S6Mz8/Hzk5OQCAvLw8HD58uMIf
-mWWMMcYACXt4urq60ND4L7/m5eW90fTS0tLQp08fAIBMJsPgwYPRqVOnN5omY4yx6kuyhNevXz+M
-Hj0ajx49wqpVq7BmzRp88sknrz29mjVr4tKlS28xQsYYY9WZJAmPiDBgwABcu3YNxsbGSEhIwIwZ
-M9CxY0cpqmeMMcak6+F17doVsbGxfNqRMcaYSkhy04ogCGjevDk/+osxxpjKSNbDO3PmDP744w+4
-uLiId2oKgoArV65IFQJjjDE1JlnCO3TokFRVMcYYY+VIlvBcXV2lqooxxhgrR7KB54wxxpgqccJj
-jDGmFjjhMcYYUwuc8BhjjKkFTniMMcbUAic8xhhjaoETHmOMMbXACY8xxpha4ITHGGNMLXDCY4wx
-phY44THGGFMLnPAYY4ypBU54jDHG1AInPMYYY2qBEx5jjDG1wAmPMcaYWuCExxhjTC1wwmOMMaYW
-OOExxhhTC5zwGGOMqQVOeIwxxtQCJzzGGGNqgRMeY4wxtcAJjzHGmFrghMcYY0wtcMJjjDGmFjjh
-McYYUwuc8BhjjKkFTniMMcbUAic8xhhjaoETHmOMMbXACY8xxpha4ITHGGNMLXDCY4wxphY44THG
-GFMLnPAYY4ypBU54jDHG1AInPMYYY2qBEx5jjDG1wAmPMcaYWninE97BgwdRr1491KlTB3PnzlV1
-OIwxxqqwdzbhyeVyjBs3DgcPHkRcXBy2bNmCf//9V9VhvZbES4mqDqFa4fZ8e7gt3y5uT9V6ZxNe
-TEwMateuDVdXV2hra2PgwIHYs2ePqsN6LbwRvF3cnm8Pt+Xbxe2pWu9swktOToaTk5P4ukaNGkhO
-TlZhRIwxxqqydzbhCYKg6hAYY4y9QwQiIlUH8TrOnDmD0NBQHDx4EAAwe/ZsaGhoYNKkSeJ3mjZt
-isuXL6sqRMYYeyc1adIEly5dUnUYb907m/BkMhnee+89HD16FA4ODmjZsiW2bNmC+vXrqzo0xhhj
-VZCWqgN4XVpaWli2bBk6d+4MuVyOESNGcLJjjDH2TO9sD48xxhh7Fe/sTSuMMcbYq+CEx54rJSUF
-kyZNws2bN/Hw4UMAQElJiYqjUo2yJ0P45MjrISJuuzdUURtym74YJzz2XPb29gCATZs24bPPPsOl
-S5egoaGhdhsXEYlDYe7fv4+srCweGvMGBEHAoUOH8NNPP2Hz5s2qDuedJAgCzp07hwMHDuD27dsQ
-BEFtD0ZfFic89kyKjWfu3LkYM2YMfHx80KVLFxw/flytNq60tDT8+uuvAIC//voLvXr1wgcffIDd
-u3cjOztbxdG9WxQHDpcvX8b48eORlpaGAwcOYPTo0aoO7Z2haMOjR4+iV69e2LlzJ1q0aIGLFy9C
-Q0NDbbbL1/HO3qXJKo+i96ahoYGioiLo6OjAxsYGY8aMga6uLgYNGoSdO3fC09NTqedTHRERLly4
-gFOnTiEtLQ1nz57Fxo0bceXKFaxZswZ5eXno2bMnTExMVB3qO0EQBERHR+OPP/7AkiVL0KVLF9y4
-cQOzZs3CmDFjxAML9myCICAuLg47duzA1q1b0b59ezRp0gR+fn6IjIxE06ZNUVJSAg0N7s+UpRka
-Ghqq6iBY1aM45bR27VrExcWhVatWAAAPDw+YmZnhm2++gb+/PywtLVUcaeVRJHNHR0fo6+vj4sWL
-SEtLw1dffYWGDRtCX18fmzZtAhHBzc0Nenp6qg65Sip7UHT58mX8+OOPcHFxgbe3N0xNTdG4cWPs
-378f+/fvR69evVQYbdUml8shl8sxZ84cnDp1Cu+99x7c3d3h6ekJQ0NDBAQEoHv37nBwcFB1qFUS
-JzymRLFzOn36ND777DP4+/tj4cKFSEtLQ7t27aCpqYlmzZqhsLAQSUlJaNmyJeRyebU7mlT0cgVB
-wP379+Hh4QEtLS2cO3cOaWlpaN26NerXrw9NTU388ccf6Nq1K4yNjVUcddVTuh2TkpJARGjatCna
-tm2LqVOnws3NDfXr14eZmRk8PDzQvHlz2NraqjjqqqV0G+bn50NfXx8+Pj5IT09HUlISLCws4Ojo
-iFatWsHExASGhoaoVauWiqOumngcHisnPj4es2fPhpeXF0aNGoX79++jf//+8Pb2RmhoKLS1tXH0
-6FH8+eefWLVqlarDrRSKxH/gwAGMGzcOBw4cgJOTEw4dOoS//voLtWvXxhdffAHg6TU+3klXTHFq
-LSIiArNnz4aVlRWsra0RHByMjIwMjBgxAnPmzEHfvn3FMtX9NPmrUrTHwYMHsXDhQtSoUQO1atXC
-lClTEBISguLiYgQEBMDLy0tsN27DZyCm9kpKSpRe79+/n7p3706DBg2iW7duERHR/fv3qUmTJvTV
-V1+J3/vss88oJSVF0lildPXqVWrYsCFFR0eL7+Xn51NYWBgNGzaM5s2bR0REcrmciMq3ozorKCgQ
-/05KSqIGDRrQhQsX6J9//qENGzZQ165dKTU1lXbt2kU1atSgtLQ0br8yiouLxb9jYmKoQYMGFBER
-QefPn6emTZvS+PHjqaSkhD799FP64osvKCsrS4XRvhv4phU1R6U6+Ldu3YKBgQH8/Pzg6OiI1atX
-IywsDAEBAXBxcRFvf1ZYtmyZKkKWjEwmQ9u2bdG+fXvIZDKUlJRAX18fHTp0gCAIcHNzAwDxdC4f
-UT+VlpaGLVu2YMSIEeJpXicnJzRr1gzA06EuFy5cwOHDhxEYGAhPT0/Y2NioMuQqJz09XRwKpKOj
-g/z8fHTo0AHdunUDAPz9999o2bIlTp48iR9++AFpaWkwMzNTcdRVX/W68MJemSAI4qm7bt26ITg4
-GO+//z5MTU0xYMAAJCYmYvPmzUhKSoK9vT28vLyq5cDhiubJwMAABw8eREREBLS0tKCjo4NDhw5h
-/fr16NmzJxo1aqSiaKs2XV1ddOnSBbm5uTh//jycnZ0hk8nw3XffAQAsLS1hYWGBhIQEAIC1tTUA
-HjhdWmpqKnr06IGHDx/i7t27MDExwdGjR8WHPwiCAF9fXzx+/BiWlpZo0KCBiiN+N3DCY7hz5w6m
-T5+O1atXY/PmzRgwYAB69uyJunXrIiAgAMnJyUpjexRJsrpR3DI/f/58REZGolatWli0aBF++ukn
-LFu2DPv378ekSZPg6Oio6lCrpOLiYuTn58PMzAxOTk6YPXs2fvvtN1y9ehULFy5EYmIi+vfvj717
-92Lz5s3w8/MD8PRB8AD3kIGnbQgAjRs3hoWFBVasWIE5c+agUaNGGDx4MFq0aIGoqChERERg//79
-3Kt7RXzTihqiMhe0c3NzMXbsWPz4449wdnYGAIwfPx6amppYvHix2tyUceDAAQQHB2PixIlYsGAB
-PvnkE3Tr1g0ZGRlYuHAh7Ozs0KtXL3Tv3p1vCiijqKgIUVFRsLKyQkJCApKSkjBkyBAsWLAAOjo6
-CAgIgJubG2bOnAkzMzO0bNkS3bp143YspbCwECdOnECNGjWQm5uLhIQE2Nra4q+//gIRYfbs2Vi9
-erV4p/Do0aN5XXxFfA1PDZWUlEBTUxO5ubnQ09ODjo4OcnNzER4ejnHjxgEA2rZti4sXLwJAtU92
-RITk5GSsWrUK4eHhuHv3LogIly9fRn5+Pr7++mvs3btX6ftMmY6ODnJychAaGorU1FQsWrQIjo6O
-+O677/DDDz9g586dCAwMxJIlS8Qy3I7KioqK8OTJE4wdOxYJCQmIjIzEe++9B319fYSFheG7777D
-N998g9GjR6OgoAD6+vrchq+Ix+GpkTt37qCoqAhGRkYICwvDmDFj8M8//0BTUxPDhg1DSEgIrl27
-hvPnz2PFihUICgpC3bp1VR12paBSY5sEQYCJiQnatGmDvLw8jB8/HmfPnoWtrS2+/PJL6OjooEmT
-JtDV1VUqw/679ikIApydnXHq1Cno6uqiU6dOMDIygpWVFTw9PbFv3z7ExsaidevW4gB9bsenFOui
-rq4u8vPzMXv2bLRq1QpeXl5wcHCAk5MTTExMcPnyZURFRcHb2xs6OjrQ0NDgNnxFnPDUyMyZMzFt
-2jS0aNECK1aswPDhw+Hk5ITly5fD2dkZkydPxsOHD5GdnY1Ro0ahc+fO1fp0iSAIuHLlCs6fPw9N
-TU04ODggNTUVR44cwahRo1BQUIB//vkH48aNg5OTk6rDrbI0NDTw119/YePGjZg3bx40NDQQFhYG
-PT091K9fHzKZDO7u7vDw8OB2fAZBEPDXX3/BwsICw4YNg7W1NXbs2AEtLS3UqVMHOjo60NHRQZcu
-XWBra1vtHvQgFT6lqQYUg3/nz5+PkpISDBgwAEOGDMHAgQNRUFAAS0tLzJs3DxkZGfjkk0/EctX5
-dIkgCAgLC8O0adPg5uYGfX191K1bF6NHj4adnR06dOiAu3fvYvHixWjUqFG1TvxvQnGH75dffolF
-ixbB0NAQw4cPR0FBASIiInDu3Dn89ttvOHbsGN/V+gyKNpwwYQJ+/vlndO7cGSYmJsjMzMTu3btx
-5swZXLp0CUuWLEHNmjVVHe67TbIRf0wlcnNzKTY2loiIzp49S9nZ2RQSEkL169en1NRUIiIqLCyk
-/fv3k4+PDyUmJooDqaubkpIScXBzTk4ODRw4kP7++28iIoqOjqaQkBDasGEDPXjwgDZv3kynTp0q
-V44pKyoqogkTJtCBAweIiOjJkyfiZwcOHKBFixbRwYMHVRXeOyE3N5c6depER48eJaL/HmBw+/Zt
-2rZtG3Xv3p3CwsJUGWK1wT28ai4zMxM//fSTeOH7wIEDmD17Nh49eoSAgADs3r0bNjY28PPzQ4sW
-LWBlZaXqkCsFleqhnTp1CtnZ2UhKSsK1a9fg4eEBLy8vnDt3DqdOnUJgYCAGDRoklgP4lnkFKtPT
-1dbWRmZmJqKiouDv7y9e57xy5Qp8fHzg7+8vlgO4HYH/2lDxv0wmQ1FRkTjc5cmTJ9DX14exsTH6
-9euHXr16QUdHh9vwLeATwdVYSUkJnJyc0KlTJ6xZswYfffQR3N3dAQArVqxAkyZN0LFjR6Snp0NH
-R0dMdlSNT2XGx8fj66+/RuPGjfHVV1/hyJEjiI6OhpaWFpo3b47MzExkZ2eL4w75poCKJSUlIS4u
-DgAQFBSE4uJi7NmzBwBw7tw5jBkzBtevXxe/z+1YXlpaGgDA1NQUbdq0QUhICDIzM6Gvr4/jx4+j
-e/fuSE9PVxqnyG34ZjjhVWMaGhqIjIzElStXsHfvXly+fBm//fYbMjMzAQC//PILOnbsqLRjAqrn
-EaTiR0cDAgLQoUMHODg4oGnTpmjWrBnGjRuHiRMnYvjw4QgMDISJiQnfFFABRY8kIiIC/v7++PDD
-DzFp0iTUq1cPNWvWxMqVK9GjRw8MHToUISEh4sEV+4+iDffv34/u3bujb9++OHz4MIYNG4YmTZqg
-TZs2mD9/PsaOHYtvv/0WNjY2vC6+RTzwvJpTPGvv0KFDOHr0KGbMmIFRo0ZBEATs2rULmzdvhra2
-drW8KaOiU0BDhgzBv//+i+joaBgZGaG4uBixsbFISkpCjRo18P7771fLtnhbrl27hm+++Qbz58+H
-ra0tunbtii5duiA4OBiFhYVISEiAubk53nvvPT4F9wwxMTGYNWsWQkJCEB0djaSkJLRt2xZ9+vTB
-vn37oKGhAWtra7Rr147b8C3ja3jVTNmddb169cSH9fr5+UEul+OPP/5AcnIyRo0aBW1tbQDVd4MS
-BAFnzpzBnTt30KhRI/zxxx8YMWIE+vXrh127dkFfXx8eHh7w8PAAUL1P574uxTr1+PFjLF68GPfv
-34e2tjbMzMywc+dODBw4EBkZGViyZAk8PT2VylbX9epVlL5ml5GRge+//x7a2trw9PSEp6cnVq5c
-iePHj6OkpAS9e/eGkZGRWA7gNnybeBxeNaHYqARBwN9//43x48ejcePGcHR0RFZWFubPn4+BAwfi
-vffeg6+vLz788EO0aNGi2vZmFPN14sQJBAUFISsrC8ePH0dMTAyWLVuGyMhILF++HAMGDBCTPsDX
-SSqiOB1saWkJV1dXXL9+HVlZWXB0dESNGjXEHwlu27at0k1P3I5PKdrhwYMHsLGxgSAI2LFjBwwM
-DNCsWTO8//77uH37Ns6cOYM2bdqIvzDB6+LbxwmvGih9JHj9+nW4ubnhzJkz+Oeff7B8+XJ069YN
-165dQ8OGDWFrawtdXV0YGBiI5avjRqX41fYZM2bgl19+weeffw53d3ecOHECSUlJmDlzJnbv3o36
-9evDwcFB1eFWSYqDhqKiIixYsACrVq3C6NGj4erqihMnTiA1NRV2dnZwcnLC0KFDq/0j6F6XXC5H
-ZmYmXFxcUKdOHQwcOBA1atTAli1bUFhYCA8PD7Rq1QoeHh6oUaOGqsOt1jjhVROKC+ETJ06En58f
-AgMD0bp1awiCgA0bNuDIkSPIyclBz549lRJcdUp2ZXurJ06cwPz589GyZUs0a9YMRkZGKCgowNmz
-Z9G9e3cMGjQIDg4O1baX+7ry8vKgoaEBTU1NAICmpiYaN26MGzduYMOGDRg5ciTs7e1x8OBBpKen
-o1mzZtDW1oaGhga35f8rLi4W2w8ADA0N0ahRI4waNQrvvfce+vTpAwMDA6xevRrFxcVo1qwZTE1N
-VRixmpBgrB+TwL///kv16tWjkydPlvvs0aNHdPXqVfLx8REHWlc3pQeHP3jwgPLz84mIaO3ateTm
-5kZHjhwhoqeDodu2bUsZGRkkk8lUFm9VdfXqVRo7diylpaXR8ePHacmSJeJnKSkp9MUXX9DQoUMp
-Ly+PTp48KT7UgP3n6tWrNGXKFCIiiouLo8OHD4vr4/79+0lXV1ccSL5jxw6KiYlRWazqhhPeO+ru
-3bu0ceNG8fXx48epX79+4uvi4mIiIqUnhAwdOlR8mkN1o5jP8PBw8vb2pnbt2tGqVasoPj6e/vzz
-TzIwMKBPPvmEevfuTbt371ZxtFVTfn4+dejQgdasWUNERJGRkWRra0vLli0jIiKZTEaRkZHUqFEj
-GjBgQLV9Is+bePz4MXl7e9O5c+coOzubJk6cSEFBQXT06FHKy8sjIqJFixaRIAi0f/9+sRw/yUca
-PMDjHUVE2LhxIy5fvgwAcHNzw8OHD3H48GEAT39UMzIyEnPnzgUR4ebNm7h582a1/fFSQRBw4cIF
-LFy4EEuWLMH48eORlJSErVu3olu3bliyZAlOnDiBrl27onfv3pDL5XxHZhn6+voYPHgw1qxZA0dH
-R/j6+mLfvn1YtGgRli9fDk1NTejq6sLPzw+TJk3i8WEVkMvlyM/Px5YtW/D555/jiy++gLOzM3bu
-3InTp08DALy8vBAQEKDUfnwaWCIqTrjsNShOxS1ZsoS2b99ORETZ2dm0YMEC+vrrr2nOnDkUFRVF
-DRs2pMOHD4vlHj58qJJ4pZCSkkLDhw8nLy8v8b2TJ09Shw4d6MyZM0REtGnTJnJ0dKTo6GhVhVll
-KXoY+/btIz09PfL29qbc3FwiIoqJiaHGjRvTiBEjyMbGRnxuJvdKlCnaY9myZaSlpUVjxowhoqfP
-G502bRqNGDGCRowYQXXq1KFz584plWHS4JtW3kGKI8OUlBQsXrwYvr6+sLW1hZ2dHQwMDLBv3z5c
-v34dY8eORdeuXSGTyaChoQF9fX0VR/72UJkxSvT/v8t2+vRp5Ofno1WrVnBycsLp06chl8vRokUL
-uLu7w8HBAfXq1YOFhYUqw69yFO1oaWkJHx8fWFhYYMmSJWjevDnc3d3h7++PevXqITAwEO3bt+eb
-UyqgaI+UlBR07NgRixYtgq6uLtq0aYP27dvDwMAAhoaGGDx4MNq1a6dUhkmDn7Tyjps+fTrWrl2L
-mJgY2NnZie8/efIEenp61XLwaul5+t///oeCggLo6uqiffv22LFjB/bt2wcDAwMMGDAAn3zyCVav
-Xg1vb28VR101lU5ccrlcvLMwKSkJa9asQXx8PGbOnInatWsrlQGq1zpVGc6fP4+OHTtixowZGDdu
-nNJn3IaqwT28d5CiNyMIAnx9fZGamopvv/0Wbdq0ga6uLvT19aGlpaU0GL06UcxTREQEJk6ciPfe
-ew/Tp08Xr0HR/1/fvHjxIubMmQMfHx+xl8uUKX4EV/GjonK5HBoaGjAzM0Pt2rVx48YNbN26FT17
-9oSWlpbY9tVtnXpT9+7dAwDo6OhAEATI5XLUqFEDnTt3Rt++fWFmZoZWrVqJ3+c2VA1OeO+AkpIS
-8WdENDQ0xA1F8cOuHTt2hEwmw549e3Dt2jWkpaWhUaNG1XqDSkpKwpdffolt27YhLS0NMTExiIyM
-BCAVHDkAABfkSURBVBFh2LBhsLKyQm5uLjQ1NfH+++9zsquA4oBo4MCBSEhIgJ+fn1I7mZqaom7d
-uuIp8+q8Pr0uIkJaWhqCg4Ph5eUFc3NzlJSUQFNTE3K5HA4ODujWrRsMDQ1Rq1YtVYer9jjhVWGZ
-mZnIzMyEqakpDh48iN9++w2xsbHigPLSR+Senp6oX78+LC0tsWHDBnTs2LFaXbMrSxAEdOrUCRkZ
-GeJDeJ2cnDBu3DgYGxtj8ODByMrKwtWrV9GyZctq3RavqmzPv3Hjxjh37hxatWoFPT09pcRmamoK
-S0tLVYVa5QmCACMjIxw7dgz79u1D7969xdPCiu3T0dERtWrV4uueVQAnvCoqLy8P8+bNQ1xcHB49
-eoRvvvkG3bp1w6JFi5CcnAxfX19oaGhAQ0ND7AFaWVmhZs2a6Nu3r9Kjw6oTxU5DT08PFhYWuHDh
-AkxNTeHv74/bt2/DysoK7dq1Q506dVCrVi14e3vDzMxM1WFXKYpnjD558gQaGhpwcHDAihUr4OLi
-wr2QV3D37l2kpaXB0tISXl5eiImJgYeHB4yNjcVtkoceVC1800oVtmvXLpw8eRJZWVnw9PTEqFGj
-kJaWhn79+sHLywszZ84UfxyytOp2JFl2fkq/3r17N3755Re0b98eK1euxI4dO+Dp6al0AwYrf5PE
-Dz/8gL///huGhoYYOnQo0tPTsWXLFmzZsoUfcfUMpde77OxsfPbZZxAEATY2Nvjmm28wdOhQBAQE
-YNSoUSqOlD0LJ7wqhojEawAAcObMGSxZsgRyuRxz5syBm5sb0tPT4e/vD19fXyxYsKBaJbdnOX/+
-PNavX4+ff/65XAL8888/kZWVBVdXV/j7+1e7hP82KNrkypUr0NbWhoODA0xNTREZGYmZM2eiVq1a
-2Lt3L/73v/+hdu3a4vVh9h9FG6akpMDExASCICAnJwcTJkxAo0aNEBYWBrlcjm3btqFOnTqqDpdV
-gH8PrwrS1NREREQEdu3ahTVr1iAvLw979+5FWFgYAgIC4OrqigMHDuDWrVvVesdeeqdbWFiI4uJi
-AP/1UhS9uAEDBohl+PitPMWOWvHL2h07dkRkZCTWr1+PDz74AA0bNkRmZibS09MRHByM8PBwTnal
-lO4dh4eHY/r06ZDL5fD398eYMf/X3r1HVVmlDxz/HkERRGUwQfFksZAlLh3IVBQxdTQVMafRAcfx
-TjkYFl6I8bJSSFdmeWGUJi+hJiMqSKnIoIhc1IQ83moEvGboKCipIYdA5QD790edEzj2K1fagcPz
-+UvgvGvts9fr+7x772c/+zXi4+O5evUqLi4uxMXFcfnyZdzd3eXFqx6Su7qeMT6Y5s2bR0BAAACD
-Bw9m6NChFBYWsm3bNgoKCnB2dsbHx8ciH/B3794Fvl/0/+qrr9DpdDg4OJjOYTOysrKiqqqqzrWS
-7v2/jCO7pKQk4uPjiY2N5d1332Xq1Kl8/vnnODs706VLF5KSkmjVqhWlpaXmbnK9Yrynzpw5Q3R0
-NNu2bWPv3r0YDAZiYmIoKyvj6aef5pVXXuFvf/sbq1ev5v79+3If1kMS8OqhEydO8Pbbb+Pv78+9
-e/cA8Pf3Z8iQIVy7dq1OkLO0/1R37txh3rx5lJSUoNfrWb58OcHBwaxYsYKsrCzee+89duzYwcGD
-B6mpqXnoGqb4cVRSXV1NZWUlb7/9NhkZGXz33XdUVVUxceJEpk+fzurVq6mpqQEgLS2No0ePmkbS
-jd2NGzdYsmQJNTU13Lp1i6ioKL755hscHBzQarWEh4eTlZXF9u3bTde0bNmSsrIyU5+K+kWeFmb2
-sGmP69evk5ubS0BAAM2bNwdAp9MxcOBAfHx8LD6pIDw8nNLSUkpKSli/fj3w/RaNwsJC7O3t2bVr
-F6WlpTRr1oy+ffuaubX1W0VFBS1btmTTpk3MmDGD9PR0unXrRseOHencuTO5ubmm+8/FxYWMjIw6
-p5Y3Znfv3iUwMJAbN27g5OTEpEmTKCkpYdu2bYwdO5YOHTowceJEbt++TU1NDTU1Ndja2rJu3TrZ
-BlNfPaEaneIXqH2GW0FBgTpz5ozp3yEhISoqKkoppZROp1MeHh6mIsiWqHYR3f/+979qy5Ytqn//
-/iorK0sp9X3B7AkTJqi4uLifvE7UlZycrPr06aMiIyPVkSNHVFlZmQoICFDDhg1TCxYsUL169VI7
-d+40dzPrndr3VGVlpXr11VdVUFCQMhgMKj09XYWEhKiAgAC1efNm1alTJ5WammrG1opHIfvwzMy4
-EB4SEoJOp+Pw4cP06NEDJycnEhMT2bhxIwkJCSxdupRBgwaZu7lPlHH9MiIigokTJ9KqVSs2b95M
-hw4dcHV1paysjKKiIvr16/c/14m6yRWFhYUsW7aMSZMmUVVVxYEDB2jZsiWzZs3i4MGDXLp0iXfe
-eYdhw4aZrpV+rNuH+fn5tG3bFnd3d/7zn/+wf/9+pk2bxu9+9zsyMzMpKioiLCyMESNGmApAiPpN
-Ap4ZGYsfz58/n3379qHRaFi+fDkAI0eO5LXXXqN///6MHz8eb29viy04a3zYXrhwgUWLFhEZGUn3
-7t3p2LEj1dXVbNmyhY4dO+Li4mLaXG9kaX3xaxnPBTxy5AhKKcLCwnB1dcVgMJCamoq1tTUzZ85k
-7969XL16leeffx47Ozvpx1o0Gg2pqalMmDCBF198kS5duuDm5sbnn39OZmYmQUFBaLVaioqKqK6u
-plOnTtjb25u72eIXkFcSM2vTpg0ffvghJ0+eJCYmhpycHI4dO0ZISAgXL17E1dWVjh07mj5vSQ+m
-+/fvm7LZrl69ytatW7l8+TJ5eXkAODk58ec//5lBgwaxePFiunTpwh/+8AeLzEz9tYwvDVlZWYwa
-NYojR47wwQcfkJeXR7t27fD398fb25tPP/0UjUbD6tWruXXrlozsHmB88fr73/9ObGwsv//977Gy
-ssLDw4MZM2Zw584dZs6cyaBBg3j++ecpLi6WAgcNiGw8/w3VHqGVlJTQtGlT05theHg47u7uTJs2
-jbVr17J582a2bt1a51gWS1JVVUV2djYFBQXY29uTn5/PqFGjSEpK4s6dO4wcOZKBAwcCcPPmTSoq
-KnjmmWfM2+h67ty5c8yePZsFCxbg6+vL4sWLSUxMJD4+nq5du/LNN99QWVmJVqsFkGo0P3gw6F+4
-cIGlS5fy8ccfU11dTXV1Nc2aNaOqqoqCggIqKirw8vICoKysjJYtW5qr6eIRSZbmb0yj0ZCUlMTG
-jRspLS1l3LhxDB48mJ49e7JhwwYMBgMJCQlERUVZbLADsLa2pk2bNrzzzjvk5uayadMmPD09sbW1
-ZevWrezfvx+DwcCQIUNo27at6ToZkdRVuz9Onz7N1atX2bNnD76+vkRERGBlZYW/vz8pKSl069at
-znUS7OoWKjh79iy2tra0bt2aw4cPEx8fz9ixY7GysiItLY3jx4/z1ltvAT++LEiwa1hkDe83pNFo
-OH/+PNOmTeOf//wnnTt35ty5c5w9exZvb28cHBzYu3cvs2bN4sUXX7TINbva36l169bs3LkTrVaL
-nZ0dHh4eaLVa3NzcOH78OF9//TXdu3evUwjbkvricTCuAyckJBAcHEz79u358ssvuX79Oj179qR/
-//6UlpbSrl27OiNk6ccfGRPH3njjDQYNGkTnzp1xc3Nj7dq1XLlyBb1ez1tvvcWYMWPw8PAAkASV
-huo3zgptlIxpzkVFRWrfvn1q+PDhpr/pdDo1ePBgpdPplFJK3b1713SNpaXc1/5ORUVFpt/l5eWp
-6dOnq4ULFyqllNLr9SoxMVFduHDBbG1tSPLy8tTTTz+tVq5cqZRSaseOHSo4OFitWrWqzucs7X56
-XE6dOqW8vLzU+fPnlVJKXb9+XR0/flzl5+erMWPGqNDQUPXvf/9bKSV92NDJlOYTZqwHmZOTw4IF
-C/jwww9p3rw5iYmJBAYG4u3tTdeuXU3ntjVt2tR0rSW+hWs0GlJSUli0aBF9+/alWbNmLFu2jIkT
-J7JlyxZGjx5NXl4eiYmJUoD3Z+j1euzs7OjatSupqakEBgailOLNN9/EYDBw4MABrly5YhrZWeL9
-9Dg0b94cLy8vMjMz2bFjB1lZWQDMnTuXhIQE0+eUpDs0eDKl+YRpNBrS09PZsGEDISEh+Pj4cOvW
-LfLz88nMzMTKyorly5cTEhKCVqs1TZVY4sNJo9Fw6NAhZs+ezdatW7l27Rpr164lPz+f0NBQvLy8
-uHv3LpMmTcLX19fcza23lFJcunSJqVOn4u7uTrt27XB2dmbAgAGEh4cDMHXqVHr37m1KUBE/zc7O
-juLiYuLi4vjTn/7ElClTsLOzw2AwmJJTQOq0WgKZiH4CHnwTLCwsZOvWrVy9ehWAwMBA/Pz8+Pbb
-b9m2bRvR0dH07t3b4t8ga2pqMBgMpuryu3fv5tChQ5w9e5ZJkyah1WqZMWMGQ4cORSll8f3xKGr3
-h0ajoVOnTvTs2ZOlS5fy5ZdfUllZSbdu3Rg6dCjvvfceV65coX379mZudcPQokULQkNDOXjwIKNH
-j6asrIw1a9ZI/1kis02mWjDjPH9hYaFpTW779u3KxsZGZWdn1/lMRUWF6WdLXB+orq5WSil17969
-Ot95woQJKjk5WSmlVHh4uOrcubP64osvzNbO+s7Yd1lZWSo6OtpUhm7lypVq5MiRKi0tTSUnJ6uJ
-Eyeqs2fPmrOpDVZVVZU6fvy46tWrl9q9e7dSStbsLI1MaT5m6oc08ZSUFKZPn87u3bu5cuUKf/3r
-X+nevTvjx4/Hx8fHtK5iXLOz1OkSjUbDrl27mDNnDjqdDjs7O9zd3UlLS8PW1paioiJSU1P517/+
-RdeuXWXbwUMY++To0aMEBwdTUVHBsWPHuH37Nq+//jqlpaVkZGQQGxtLSEgIL7zwQp3rxC/TpEkT
-HBwc8PPzq3P0lvSh5ZCN50+ATqdj0aJFLFmyhOLiYk6fPk1BQQFr1qzho48+IiwsjMLCQlq1amWR
-6c3qgQ32kydPZty4cej1elMf1NTUsG7dOi5cuEBYWJjp7D95SD+cTqcjMjKSZcuW4enpyfbt28nJ
-ycHT05OgoCCsra25desWTz31lPThYyL9aHkkS/MxKykpISoqiuLiYrp37w6AVqvl3XffJTMzk2nT
-puHn54eDg4OZW/pkaTQadDodJ06coEePHowdOxYAGxubOtVk9Ho9rVq1krfpn1FaWkp6ejppaWl4
-enoSGBhIkyZNSE9Pp7y8nNDQUBwdHc3dTIsi96LlkSnNX+nBB7WtrS2Ojo7s27ePmzdvMmDAAJyc
-nDh48CBlZWX069cPe3t7mjRpYpFvkMbvlJ2dzeTJk7l9+zZffPEF7u7udOjQgR49emBtbc3cuXMZ
-O3YsrVu3NvWBpfXF4+Tm5oaXlxcrVqzA0dERLy8vunTpQnl5Ob6+vjg7O0s/CvEzZErzVzI+4DMy
-Mjh27Bht27Zl1KhR5OXl8cEHH9CyZUuCgoKYPn060dHRFn/ED3w//bZgwQJWrlyJp6cnCxcupKSk
-hICAAHx9fWnatCmFhYV06NDB3E1tcFJSUli4cCEzZ85k8uTJ5m6OEA2K5S0g/YaMwe6zzz7jtdde
-o0WLFsTExBAdHU3Tpk0JDQ3l6NGjzJ8/nw0bNjBo0CCqqqrM3ewnrrS0lMzMTNLT0wFYuHAhbdq0
-ITY2ls8++wyllCnYyfvWoxkxYgSRkZG8//77puNphBC/jAS8X8FYG3Pt2rXMmTOHGTNmsGvXLvR6
-PcnJyQwYMIB169bRqVMnDh06BHxfNNnSDR06lJ07d7Jhwwa2bdtGs2bNWLBgAe3bt8fJyanOlJtM
-vz26l19+mUOHDuHi4iIFoIV4BJb/9H3MjKM6Y8mw/Px8bt68yf79+xk+fDharZY5c+YwbNgw3njj
-Dfr27YvBYCAuLs6URdcYvPzyy1hbW7Nw4UIqKyuZMmUKS5YskQD3mBhPkLDEdWAhnhRZw3sEtRNU
-aq9BZWdnExcXh7u7O2PHjqW8vJzAwEBSUlLo0KEDlZWVVFVV1an631gkJSUxf/580tPTcXZ2lhGJ
-EMJsJOA9AuPb9N69e4mMjGTIkCE0adKERYsWcfjwYaKjo7l27RoODg7Mnj0bf39/eQPn+wNca59p
-J4QQ5iBTmo/AePbY3Llz2bFjB7GxsezcuZOioiLWr1+Pra0tmzZtws3NjWHDhpm7ufWGTL8JIeoD
-SVp5BNXV1ej1euLj47l27Rrp6el8/PHH3Lhxg+DgYHr06MEf//hHLly4wIYNG6iqqpIHfC3SF0II
-c5IR3iOwsrJi8ODBaDQa/vGPf7By5Up8fHxwdXXl4sWLXLx4kZdeegmlFL169WoUGZlCCNFQyBP5
-Jzw4/Wb82cbGhvv376PX68nNzaWmpobTp08TExODh4cHACNHjjRXs4UQQvwESVp5iNrZmHl5eTg6
-OuLi4lLnMwcPHiQqKoqKigqCg4MZM2aM6VqZuhNCiPpHAt5DGINWcnIyy5cvZ8WKFXh7e5v+btyD
-V15ejlIKe3t7KX4shBD1nAS8n/DVV18xZswYPvroI3r27Fnnbw8LbjKyE0KI+k2yNH9w+fJlQkND
-TT8bq6IYg52xBmZ5eflDD2uVYCeEEPWbBLwfPPvss0yZMoWvv/4aAE9PTxwdHcnIyKCyshJra2sO
-Hz7MsmXLuHfvnhQ9FkKIBqbRT2kqpaiurjZtIfD19cXKyspUOeXSpUvY2dnRp08fwsPDWbNmDUOG
-DDFzq4UQQjyqRh3waq/FnTt3zrStYODAgTg5ObFjxw7S09NJSUmhsrKSESNGSLkwIYRooBp9wDPW
-xpwxYwYJCQn06NEDgH79+uHs7Mynn34KwP3797GxsZFsTCGEaKAadcADOHXqFOPGjSM+Pp7nnnuO
-K1eu4OTkhK2tLX369MHe3p709HTTVgQhhBANU6OrtPLgCM3a2pqAgAByc3NJTU0lISEBd3d35s+f
-z9GjR8nJyQGQYCeEEA1co3yKazQa0tLSSE1N5amnnuLu3bts374dV1dX4uPjcXV15dSpUwD07dsX
-pZRkZQohRAPX6AKeRqMhKSmJN998E4PBgIuLC0uWLGHXrl385S9/wWAwcODAAdzc3OpcI2t2QgjR
-sDW6gFdSUsLq1av55JNPGDFiBCdPnuSTTz6hpqaGrKwspk2bRkREBAMHDpRRnRBCWBCLX8N7cAuB
-8Yy6xMREzp07h5WVFZmZmdy5c4dJkyaxceNGPDw8JNgJIYSFsegRXu2gdf78eb799lvatm1LREQE
-ZWVlBAUFERsbS0xMDMeOHcPW1ta0Fw9k64EQQlgSi96WUF1djZWVFcnJySxatIgXXniBmpoaZs2a
-haurKwD79+8nLCyM5cuX4+/vb+YWCyGEeFIscoT33XffAd+fUH7s2DEiIyNJSkqiRYsWZGVlERER
-wcmTJykvL2f16tW8//77pgoqQgghLJPFjfBKSkpYsWIFXbt2Zdy4ceh0Opo1a8bNmzeZN28eq1at
-IiYmBr1ez4oVK2jfvr2cZyeEEI2AxY3wrKyssLOz48SJE+zZs4fevXvTvXt3MjIyWLVqFf3790er
-1dKmTRvKy8uxt7c3XSvBTgghLJfFBDzjqQetWrUyrdFlZGSwc+dOAPR6PYsXLyYzM5Pk5GRef/11
-nnvuORnZCSFEI2ExU5rG7QeZmZncv3+fAQMGsH79ei5fvsxLL73EkCFDePXVV6moqCAwMJDRo0fL
-qQdCCNGIWEzAA0hOTiYiIoKlS5fi5+dHaWkpmzZtoqCggOHDhzN8+HA59UAIIRopi5nSLCsrY+PG
-jaxduxY/Pz8MBgOtW7fmlVdeQavVsmfPHoqLi7GxsQGkXJgQQjQ2FlNppUmTJty6dQu9Xg/8OHKr
-rKwkLCyMwsJCnJ2dzdlEIYQQZmQxI7wWLVowZswYcnJyOHPmDNbW1mRnZzN+/Hhu3rzJM888Y+4m
-CiGEMCOLWsMrLCxk/fr1ZGVl4evrS2JiItHR0YwYMcLcTRNCCGFmFhXwACoqKtDpdBQXF/Pss8/S
-p08fSVARQghheQHvQRLshBBCgAUlrfwUCXRCCCHAgpJWhBBCiP+PBDwhhBCNggQ8IYQQjYIEPCGE
-EI2CBDwhhBCNggQ8IYQQjYIEPCGEEI3C/wElANR3gSZd6QAAAABJRU5ErkJggg==
-"
->
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a name="cython_bonus"></a> <br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="bonus-how-to-use-cython-without-the-ipython-magic">Bonus: How to use Cython without the IPython magic</h2>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#sections">back to top</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>IPython's notebook is really great for explanatory analysis and documentation, but what if we want to compile our Python code via Cython without letting IPython's magic doing all the work?<br />These are the steps you would need.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="creating-a-.pyx-file-containing-the-the-desired-code-or-function.">1. Creating a .pyx file containing the the desired code or function.</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[11]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="o">%%</span><span class="k">file</span> <span class="n">ccy_classic_lstsqr</span><span class="o">.</span><span class="n">pyx</span>
-
-<span class="k">def</span> <span class="nf">ccy_classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; Computes the least-squares solution to a linear matrix equation. &quot;&quot;&quot;</span>
-    <span class="n">x_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-    <span class="n">y_avg</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">var_x</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x_i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">])</span>
-    <span class="n">cov_xy</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">([(</span><span class="n">x_i</span> <span class="o">-</span> <span class="n">x_avg</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">y_i</span> <span class="o">-</span> <span class="n">y_avg</span><span class="p">)</span> <span class="k">for</span> <span class="n">x_i</span><span class="p">,</span><span class="n">y_i</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)])</span>
-    <span class="n">slope</span> <span class="o">=</span> <span class="n">cov_xy</span> <span class="o">/</span> <span class="n">var_x</span>
-    <span class="n">y_interc</span> <span class="o">=</span> <span class="n">y_avg</span> <span class="o">-</span> <span class="n">slope</span><span class="o">*</span><span class="n">x_avg</span>
-    <span class="k">return</span> <span class="p">(</span><span class="n">slope</span><span class="p">,</span> <span class="n">y_interc</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Writing ccy_classic_lstsqr.pyx
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="creating-a-simple-setup-file">2. Creating a simple setup file</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[12]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="o">%%</span><span class="k">file</span> <span class="n">setup</span><span class="o">.</span><span class="n">py</span>
-
-<span class="kn">from</span> <span class="nn">distutils.core</span> <span class="kn">import</span> <span class="n">setup</span>
-<span class="kn">from</span> <span class="nn">distutils.extension</span> <span class="kn">import</span> <span class="n">Extension</span>
-<span class="kn">from</span> <span class="nn">Cython.Distutils</span> <span class="kn">import</span> <span class="n">build_ext</span>
-
-<span class="n">setup</span><span class="p">(</span>
-    <span class="n">cmdclass</span> <span class="o">=</span> <span class="p">{</span><span class="s">&#39;build_ext&#39;</span><span class="p">:</span> <span class="n">build_ext</span><span class="p">},</span>
-    <span class="n">ext_modules</span> <span class="o">=</span> <span class="p">[</span><span class="n">Extension</span><span class="p">(</span><span class="s">&quot;ccy_classic_lstsqr&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s">&quot;ccy_classic_lstsqr.pyx&quot;</span><span class="p">])]</span>
-<span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Writing setup.py
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="building-and-compiling">3. Building and Compiling</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[13]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="o">!</span>python3 setup.py build_ext --inplace
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-running build_ext
-cythoning ccy_classic_lstsqr.pyx to ccy_classic_lstsqr.c
-building &apos;ccy_classic_lstsqr&apos; extension
-creating build
-creating build/temp.macosx-10.6-intel-3.4
-/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -arch i386 -arch x86_64 -g -I/Library/Frameworks/Python.framework/Versions/3.4/include/python3.4m -c ccy_classic_lstsqr.c -o build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o
-ccy_classic_lstsqr.c:2040:28: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyObject_AsString&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
-<span class="ansigreen">                           ^
-</span>ccy_classic_lstsqr.c:2037:32: </span><span class="ansipurple">warning: </span>unused function
-      &apos;__Pyx_PyUnicode_FromString&apos; [-Wunused-function]</span>
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {
-<span class="ansigreen">                               ^
-</span>ccy_classic_lstsqr.c:2104:26: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyObject_IsTrue&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-<span class="ansigreen">                         ^
-</span>ccy_classic_lstsqr.c:2159:33: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyIndex_AsSsize_t&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-<span class="ansigreen">                                ^
-</span>ccy_classic_lstsqr.c:2188:33: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyInt_FromSize_t&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-<span class="ansigreen">                                ^
-</span>ccy_classic_lstsqr.c:1584:32: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyInt_From_long&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-<span class="ansigreen">                               ^
-</span>ccy_classic_lstsqr.c:1631:27: </span><span class="ansipurple">warning: </span>function &apos;__Pyx_PyInt_As_long&apos; is not
-      needed and will not be emitted [-Wunneeded-internal-declaration]</span>
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-<span class="ansigreen">                          ^
-</span>ccy_classic_lstsqr.c:1731:26: </span><span class="ansipurple">warning: </span>function &apos;__Pyx_PyInt_As_int&apos; is not
-      needed and will not be emitted [-Wunneeded-internal-declaration]</span>
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-<span class="ansigreen">                         ^
-</span>8 warnings generated.
-ccy_classic_lstsqr.c:2040:28: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyObject_AsString&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
-<span class="ansigreen">                           ^
-</span>ccy_classic_lstsqr.c:2037:32: </span><span class="ansipurple">warning: </span>unused function
-      &apos;__Pyx_PyUnicode_FromString&apos; [-Wunused-function]</span>
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {
-<span class="ansigreen">                               ^
-</span>ccy_classic_lstsqr.c:2104:26: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyObject_IsTrue&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-<span class="ansigreen">                         ^
-</span>ccy_classic_lstsqr.c:2159:33: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyIndex_AsSsize_t&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-<span class="ansigreen">                                ^
-</span>ccy_classic_lstsqr.c:2188:33: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyInt_FromSize_t&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-<span class="ansigreen">                                ^
-</span>ccy_classic_lstsqr.c:1584:32: </span><span class="ansipurple">warning: </span>unused function &apos;__Pyx_PyInt_From_long&apos;
-      [-Wunused-function]</span>
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-<span class="ansigreen">                               ^
-</span>ccy_classic_lstsqr.c:1631:27: </span><span class="ansipurple">warning: </span>function &apos;__Pyx_PyInt_As_long&apos; is not
-      needed and will not be emitted [-Wunneeded-internal-declaration]</span>
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-<span class="ansigreen">                          ^
-</span>ccy_classic_lstsqr.c:1731:26: </span><span class="ansipurple">warning: </span>function &apos;__Pyx_PyInt_As_int&apos; is not
-      needed and will not be emitted [-Wunneeded-internal-declaration]</span>
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-<span class="ansigreen">                         ^
-</span>8 warnings generated.
-/usr/bin/clang -bundle -undefined dynamic_lookup -arch i386 -arch x86_64 -g build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o -o /Users/sebastian/Github/python_reference/benchmarks/ccy_classic_lstsqr.so
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="importing-and-running-the-code">4. Importing and running the code</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[20]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">ccy_classic_lstsqr</span>
-
-<span class="o">%</span><span class="k">timeit</span> <span class="n">classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="o">%</span><span class="k">timeit</span> <span class="n">cy_classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-<span class="o">%</span><span class="k">timeit</span> <span class="n">ccy_classic_lstsqr</span><span class="o">.</span><span class="n">ccy_classic_lstsqr</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-100 loops, best of 3: 2.9 ms per loop
-1000 loops, best of 3: 212 µs per loop
-1000 loops, best of 3: 207 µs per loop
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-    </div>
-  </div>
-</body>
-</html>
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index 3032c66..ba478c5 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1359,4 +1359,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}
diff --git a/benchmarks/setup.py b/benchmarks/setup.py
deleted file mode 100644
index eb0a3bc..0000000
--- a/benchmarks/setup.py
+++ /dev/null
@@ -1,9 +0,0 @@
-
-from distutils.core import setup
-from distutils.extension import Extension
-from Cython.Distutils import build_ext
-
-setup(
-    cmdclass = {'build_ext': build_ext},
-    ext_modules = [Extension("ccy_classic_lstsqr", ["ccy_classic_lstsqr.pyx"])]
-)
\ No newline at end of file

From 067f823eeef96e70d54d9a8ac04f97907c48248d Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 5 May 2014 14:28:07 -0400
Subject: [PATCH 017/248] chart

---
 Images/cython_vs_chart.png | Bin 0 -> 40417 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Images/cython_vs_chart.png

diff --git a/Images/cython_vs_chart.png b/Images/cython_vs_chart.png
new file mode 100644
index 0000000000000000000000000000000000000000..a536ef2919a2e277f6c0aa77349aad119290c529
GIT binary patch
literal 40417
zcmce;Wmr|=+AR#YD3R_4N$CdZ5Rp#l?(UQh=}zee=?>}c?vU>8MZ=l6_j%8IUElxD
zU#uBV-p?IljA@9Rj2Pm3y!Q|g5QyU6z9>LIKq)~$K)S&}0axZo$PgeP-Vd0Gh{%bH
zh>*zH+ZdZ!8bLsOLo7>lQBoYl?%!)o(l&*Uk=Z9a07awyfPg;GB_sC@Kr<i-UP$Q0
zRFzly(h<lIO5KPhIOnHYSxL>IGy$y!nsKVq71_CklKgwc)Bb#RYkxXIwf4thZ!9g@
zfdL{Osr*YUWC=v{r?JGZF!wB5=xE7zR1lDOJ|Nw!ksTxP1yoeMb)ELxTS#)^76Y=V
zn+oe!cfkb4flWvV(X8&lCpvA)A*!9exNkL(f&ss+YuPkD?&-%LD6k2|-wu6t3YGU3
zl+D;IiQ}J)@%k;x2myMZL@N#L!yA4$LZD597bGmWC^Lc`B5$01+>361Pra98nV$Sd
zbqrRHL-s{1ZN{4^DYZ0saHoLA)tqr(%2Cf-K9StN+46GywPtW6I(v`(9yg0#lo~Pe
zi#y`=NbwYQ5RGaYFO`Qnb~aCnaW);7af+Lo*S?y}=#Im)NTFIHQFn%MIYxfF=a<E_
zeX~S{NTnWZlaZLUs6m1Ns7OC(HAwmCAEDIItfOvS`;BH}sJ&y!yQSCEqDXGS0zVlN
zL}~10Vqj>%zmk8BJ-KTU-9D->cI~g5r8Ax*|1$RF`Wh2OzN~^O7R&6`PIZNC2>Ntg
znnvCG*V3$3SavIL;Or=`ot#J5j<SOo<im8`4+3{!E<jSDz<m^StS7;4b%D5}Po#?r
zRrt9m#qpp3l8XFgVlnDiS>pmF_;R|<57FYube};2v(ZeM|8Bh=0)lJ5xqbrG$G{W`
zFYheH3?3r<BSeb3D~s8f5^s1*cTR;G?u0Mp#JjH@1Pc(b>rlKt$SHPkaIljkkaQqk
z5>T?>`$RuXU3zK=QC-#rB$^JQN@4;iu}-@Q(3lUx`uj#mYhU*)*lS;*bEt5?TU`P_
z65cOAO`|FEkdp<Kqu=%TwPZn1e+Ugg%Oa=7L=#~#g{~!{@sr4YZ%V9$@C=n6$RIeM
z#jsC0gB&4flQqr+rKrnYijdmHs)scaD6(Pd3Yqqv(f4ZI!xdc<Nu^tC{p%f+$GbMz
z+fK%HW)O*CfG-XS2OPqMNLS1ka_E?kCDFopABw&n#$e?U{_(#iWsH%<AqWiA)3;<I
z`lT!NAuveIPwnTYJ|$fwJv`<lCKsmXU-BcIbeJXnbzMycpBG#$X`DaRLudq+b!K#F
z_pNp#>91|sa>0y!*b7DIqgbD^9k%Oj)M|v$q%B9_^uh1mx=3-QZK2{P81FUNLOAES
z5If(yXt?mdivqC-Wv&atk&~nK1ULn-bm?_{?y~Aa(M$U+%tqRT-W}qx$)XQ`0a5kS
zHIC~W0aa1#$hV9<Qw2OT`l2)?*(Rw&idKra!9YXKOU>%wmOxDz{;zEkr&QiKzvbyZ
zdBxvj;2NRtkmM&^MqegM<fkZj{tlV3Jft+Izxj9rf0K(QJ*XU#+oPghrc^ZZ+fm~n
zfkl4tOLAF@+-KQ8QuhIu(mP2sUoaIh1u)BpXc8n6rfF7a42s#lyU*IrO3mueb{Eep
z1<jtD7nnnud(?T<J)2|C!kEI12Jeb(P0mdtO)yNu9B?(^ri;ktVoyd*@f_qHnjgv>
zRvrF1OgJ=|eZ+z5j_=enWRk$?$GyePG}#~19Y+~i;L7#tJHaJpt7Z3K1L6I|L&Upf
z<F{a8f5$e>-eAgM#y{6(s%Abqt6p5Eaa27gY1LF8#39=(_t*#~BUJ5Ipi(gTGv<|!
zl+LWhw?d*7aw@YD*2H*&^Mn6~*^lMxv4e>H>Fd^O(TC;h@go6~V%Eix55}Lb&>Ut2
z`$-lAjVs5qrgi^Lj4<`trtA4{7?8!H4paA<rpl*I{;q1fu-wAzbLxwd3Z|q(&mKgI
zbB;qUFfOnwz?Vvu3L0e_;WBnJb~0|=RdTSoqS&3?b)mP#q6>2j)52<?(~~rj%r?s3
zk)~Z3>Xk688!jHkvp6xPuP?7Zt<PSlw6eE6w0y9_t=FqRvP`wq8D+~z=5iPw0Vl5=
zr=74IBQ4x4lq?1<RMgm1d2i_+bU6HWY&`nr^ubZ{Wa0R3X>dq<y>|J)7w|y!S1@}V
zdld}j3<^m<l2BC)=E>)2=B=^PF*|9DwVb+}?wiV-dA7iVE4_Tn&y9~dL>xp1L_vIk
zCds2{Biu|D9n%wWM}Nj^#={O0w(I6g#+N%%kF-vnXZi<%&J?#;d+K!ytD-7(?w9T*
zJZ0~_&K+(w4(1P|?qKh+A6#I5L-&A`K)mp5AEXdV5eN|Nk<^K1@WK(15LeOuj!y2J
zbJe9yqRdApkR}O#%9hW@%$Dt@>(0}oui026t!WrLO*dc><1PF|5a7_&5GWAI6sC%H
zaisb8Z}6W4hfL8WvGQQcs2oxHP;P=7{w2;P@+PW2-mh5GSS18J0`(kVku+~jGfkbR
zk-LNoPPAIIFYu!wcRdR|DLqwgkXQBN_-SXn?Yb||(8dV%*qR!@>zE8S7Db1`8pCAB
z_2M>Tk*GX30v$^J7!0Ou7jMgF5Zl0>L9Ye1M+kg8$B2I)sKBczp%|#hr`S*s3D^45
zs4MBCfc(#WCw1GUj9TP_jRV+eDwa(aJQnz5@#Ml@^G(N#{N5j74%jZ@rR31DZ4{@H
z^^CX)ff5$gS*6fwql=EU9S70L!(P!hd#*c5yB?Z&&DUSb&C9tO43c-MBZdPnp{~5N
zBh|(<R_nWTdzUbuX%}g4%lwonD$F&kYPr2{)>E?BgXUuA3M!th(pQ?MoX+O@%WMYc
zM|I)^&P67WouDP4r~FP4iSWTs!T0jl276!oBX~HyXt%Mo8LMe&Xde_tXxm=mo@I9g
zq%9>DXVf+os@++wYh16M=~p$75{eUd6Ob&^TkGub9W*xTU6!$}+Ld>e>P=wHf-J>!
z+*IAJWVX^%UA&4=l>e4oms$RvDq2##(ZlwhKUv{#KDymM{j_xDR$+SP?<w?%S0Y_9
z4Bi6o9eX>IfCV4NOEWDjYRyB<+lVlTk9jcM_7%!3tWNaOyD6M((@U+l2cF0pA|HsY
z${UK-<~vNOxnUv)Bl%t_4w4@GMWPzgb<=+fnAuk!Vw@=Hk<B!g+SQ6L%*>}v{B`2q
zcGL(H8!pnZ^5%2xTbJ5VyQ!hr`?hyEE}b^cBQQr}=IKUy(<Qi^Q=bubfqkZ(;$C!R
zzrRzW0#%`DY0{9{e&}{H!hUN}SW#2Xxnlcga$)1I-67}Rnv7GEBgd*^>!Qc-iL&H;
zX}Lo~tBp?MmiOL}*4PUE?e5>BCSr$Iq1&g^HrOZFZe&)XHm~?A(-sqH#`=cys-dlF
zDsI_g?}oe9?D70F%XV+?{^{R^eD7L(vAqqSFN~O8ac|TXqhI7sD!b2<V>)6q#`pM$
zJxJY{U#iz-X9Y^MliRuPlg4!jb1!oJEAlI-Rx#RVJ>1XJr<?1mf~%;`Sl6Pwsh@t|
zUrbQV6@tg@GK2b(ygI!<y_#Rt?P%;btcHw)z{9HyrI$jZ)c#adfN*s{fiQ=HsMUao
zjxRXe6h}5KP87p^Sl+($zEC=$BNnuPldFO8F`emBhg=8&7yO=-4?7ofB8K4MruiV{
zsrD%nDGn5qIx&@JslQ3Dj=V_^aks?B%)H9Rd{GBewnhv&@!(ZVl;mDQ0NDXMY~0_N
zQRl|NfqCHmNZk7fAu|*a+Xy7M>1Il*j;hj9+y*vQ40?t(`bG?{R<=OS3jx9F$_@N!
zW#p(w;%a4S?ZEBI_wnBo+`!*&ml;2j{CmXFg72fMv>b_ujlB^GI|Ca7(?|aIBqSuf
z_J+pX3SYkd_jTYG-$zqNM_X=2Mi&<s1{YQa8+#K*=Fgu$GcvI-varwtPtZHKSv%^v
z(px)_{il)t+s+pw2LpRETSqe+Ym&Ei_4I9=9Qi(eeCz0c{{3g1My_W6uP1AV|4s{-
zAmiH|MrH;k#{X#>c$N3<Dz}`OtC6MZ7c(m(YX_hYe)i8Cy#Jp6|J?b1J^rsZ)&Ad`
ztel+x`_2D#=f7|AGQLgVe@*B=tM%_Huw4A_c^Us_>G|J>ZoN;0fDnWb|01O13VECk
zt4q*3+h6fp^0ak$MOU+f1IGZLX(eD)az9%H3LZsWUl;*pAW2Xd3Iz9SHJsGZKcdR6
z7l-q&dqwQ5lgs0?v6yznZK%wYxcb5B$FZ6k#aH)+S5KQ&_bv7h-DE)|Byez0em*48
zQgC;%X4NbzLV_qLAP}UGASuZ<)!MuN{PfKe5gcGVu>K>Z`ro?)vTg5x*WTV15AuoD
znOd<=uKaIf(U~5wZ!G|i#v(zIw&ykPu;~4_T~dL2q<<Zfz>yO|344jV3>$U*w;^G#
zOTz#3jq;5f6z~*#nm8N){hx*cp0;`Z?@%4#;O;3_=#$_P-bM~wy&rV>@2E)*P*5%<
zn=}~EvHnd0_!W2dpJDn5P61O&a%|9~r2EgD-zNB<)<OUOOz;zw5IYLaXZ!8q?<K|Z
z%q<ebiS*bEnm8GZpZ~iAb#H6wRJl@bwY)o;TB68WDx;>RrkY{+-!+?I0M=}_^S15r
zRHMz~9$UwQ*<!Bra3+cSzYW}k`ph$h{~#3XK7ERnWN>@FnLlx8T=zpb{j*2p9+`jy
zf|5+6yMH*U(DAqsrLFm1qRiubTq&dV>ldFpxEonugyb{->(iC>O4A?PXcqE++g2Rl
z^UdqK->{nN$S-E3OaBoVB!YdqhO?*2ZX|!kajva!hjG4!mKG14`jS_zTGPoOVz1vp
zaNf;7N?Ex6wXi87SjxB6{Ym0eW(7AyhS$Tvyq1erJf&7rgx|cAK`bQv+g8f4f{L$>
z%hjS3G)Qe<pKl+ZUtoNDtBr>7dbo_D|IM%`3Rs{gRf=lE<$5b*{=4-r^{PBpogSGU
zm*OY2^kwh=wPOR<_+!+jHx#q|e$+;;nO>`@b`?cJuHXA%I#YEUADD5B7*SCw@6&nr
z#ks{)o_H-0_;h9XZ#Hq{%*@Q`BT_J{dIHx~SZJtnxn^=^=32AM8BST5PJ0HX#QXk5
z%lzK=@89ny!8~3cbZ?sG@2KhLJvBKU&6R5n>nbQHET#4f!4l4ts8mRlsMK{3UF|Pa
z8K|qPe?O9N1D3;76|OPm>E&_l6^WStX)8*goTGqq#Sy*4f|*XPc*Q<}=5)37G0OXi
z>^ry1sb$QXz{^FH_v_=@#*i%6We8P<>u$11&L1=)hY{vqI*~ZcA3Z*_zdoN8%B16E
z$d0+JdG6i(P^r*<o)AH91ut3T=jS&q{FcvsdD;_L)<_lbeu@92xI2=3HK$?o`7g>i
z&sodJh*ZgQKe6}I@7&1E>)lkF3{IPj1RIL#ni@N}jrjQ_SYnU!lZLhW5f2%z4bVd`
z)Bug8tmR76&7ZxD$NBb`I<sj?_qGI>KH7?w%bYNVp~1l_mJ@hOMP|C{J=Fu&ao(#)
zp3{lK7RZ+S-BgchzyhcmlcnbD{z$8yKv9yym8!=AXW#ehPl$4)ay}WJlUiU#-t4%m
z`;)n({wcL6&E+d*Lf-V&Ef;;~G_1xW6Dm5MH?!v@;Iy;$*OzD!q>w#)aG1^$)Zl`4
zl4ET@A7Y?z_Q7;vgY}9!r}Z8aDBWzjKux;WCfq>J{d!VNjKE>KK-vsE%-BwzjbLbG
zq(+hbHF)lDUc#rC!acvRkZfD;dzdHP0LhcutoR397IH?$@nTb1%fF42+EW>AFAqmf
z;=G(GoHo|&hO!RuWAzQ6%-(jF4bD}QCk|WNRj<Pbvf;1&FwjFOY)DhMR6w}S>%;s2
z%6?C2XKuxhtq4x*<`a%fmy@OZE4RR$xh=v>sS-ndbi3HK6_>TL%wk~Cg~B08;Fd<k
zSXo&5BJlg){Q!K1>FYUXW!~2n_1BkYNePK#M8F+1w?D3&1tJledj}t}rHG6Q!bLWo
z`Vt9}3VlRU$g-F(SLR{5X5aM<*f!lua~S(`-i1)5EUKT&K2zU>_1(Jt8Egxm%5n~i
zo=vFKCh&4+z=ZR+&9fOAu3_O&g(@0+9ZkZq?pJdHomC51>?%KvYZ7B%Yq(4<o7cK8
zY%~&mTZ6gT<>Q{ftC{>KzORNt7|7rXHCM)1GQim)VnE);5~~Xlo$?lDfjV~7I7}x<
zW`3bl$vgq4VC;$zYbGoA&*4ONckAuy;~3A``Tm~X?bWz|!@;6Srl_gztnJg?MV+Jj
zCaHjGp>!(8!(bQ^)6zG%<hfEc)+5N#a5&;E>hSW*%VEZbp=cj_0;B7LSrr{C#;*_C
zak9Ht4eifmqErJ`Dw=lCISCB#?c6ECR|lr#nsc=(*+$!}?wF3T>Wo9Nh2PPH&Nsst
z>w{sR^PJ%EL<nZ+1rrB4=O}0)@vC-C&US3PA5)#!c{@w|{TIA$N^~w>@-)`J`&7R3
zX#PFGBb3CTbxa=z6L8K!AqK=hJZ`8{3V~?8n;AuB_8FA+>nTY(E;ZY$yg-_32Lgtc
z!{U-sNzT8v?*a;CkF-xhzI_S6J`zL-h5UKLD|%rXF&)zg9N;p2^7C{TjEO=z;wAQ=
zU^?FBZprFMBOB?$Y;+K#*026ngSrvFCG)c4Z86d9`g1Q-tTGO@k#5(1?Y>BO8xF8!
zaICVZyC1uxJG1i3u}b%}2jcNgoeU<}sAydN?)i4DA&FdCCz8dU2H30!2;X<gas&eI
zD(M-nn<0g1_Mz<LL|H$7{#2#brH4nT{#~!v4Ree^4S~%)!d9vt2?9a5adCCX7Zeu?
z{1U5GpM2<Af{G~rj?m^9EH_6H@Jo`h6~h1}y<6pcQjC}nykJlU#jU53c}ReDFGr$r
zFBe&VHlecdu6Z;px9(213|vM-uU_l=a=%wLJs3x&!NF*lgI%}W!UbZ?(_7&FOF5wZ
z^J^)^_Y!QeE@qlGA|~P=m$uQ9`<K?f5ax9x{4ZD-fG@#AheGTdN9JD_dQED7f_{HI
zUqL)9|HCLa)edMmx24M}a+c&6Sx>0h5@X9+PyYUTTDG4$`dl(mW_*(6P%clE7QGoW
z$kkBmqc4e(fT+8grl#e1$S>oY;i)zSpL~|)8p!%2=X{GEtm32)66?aJI2rCiFPBQQ
z>g)1a0oQZg$1c(-Tdpg4srDV!t<Hdi8_6TKj2<nQ70fXYr92*tF))_u_qt38^*2L-
zg+dH`nh?8a*%i8<QIysDF5q&i8TPHRP}es)pD@Fe7ZjsQaKS;gO0%6oHQLPpAxfu~
zFGX~!ny%VU7R9UYeZ4#lHdd3Y$>au+JW@<WVlrlo;ly$c1!Pupn~sCTuhS|e*V;Vg
z@t1aF+^g^kP%EulZ<O81<c>`;Jz>zN8EGSf55OgPF==j?uE$mV!Jk5VL$_!OTmDM@
zV(N?`Mt;`#?t|T0>vZ@n0Dpk%{Xh|rda*%)@WPkSlaU)LYsR=G{UhC|oEf+yIFL1d
zi6HY~T>O0Z2?aq`ibj8p$gnM)$3-D9z{mrqS%O!^9ef-jp4V#F?0iCZg&tOU0{x9=
zphB@wCK3v)_|!7FwMZd)OmzbNvB3R$e`BD3IHNlzc|kFNx8TeaeOwqxtiUPsIR7j+
zq5If%Wy1%X*|&3frAet3WRdB7Fg+P8B6iqFQwZb?6I>{!OtHk4h4eR_gwIqgkh0z%
zUb&1Eo#+J09y>!LcPIk4J9A>31|>|ZWHUd$P$=Kk{FsC{RSmKelEptsK2lXJR=TsT
z%~kt5Y2(koGrF6gqP>wX)So4k(YHfjf;{44H~!l(h7tUi{-@iqzWw*N?PP{R{CY{o
zFldydRlqlHJeD>nfySV}KkE7==Y;$tygA!3{{*Dh)hK5oI|+>(RG6+|_PbJ5&MCCV
z0pHJPv`=cS`ylnj7&0*+P!=*N_w+MpM$(LH55I5MAFnMWoC@(=9MLW%E%5c5k9h^+
z`Y*DB3s#<ExyHwSLYK4^&UVr<nZCC?mqDmwb~{KqcPHBkylAqu88H_Ythg@_lMiR-
z*U>|R7s|4aSi$vyI9Ms9t`%Giqt7AhgFyIK7yI$rYTYc1)3X-(Ps$oi5B30~`XkfF
zL%Ab0Sr=!E?(<brSIM1{3gM@XUOasyVP0!^Wm~LMI_noM7k!o`ltMCtw-kK-<GT4}
zjr)KviLt9(7Boee_}mY)C%XVQRTU?wXDU2avE4lJI7c|<ZBd4U2~ESkV8mV1?Bc?2
z^mKpJ%$Ls(_dlO(oA5n%OUC+_q>;7TMH4ep1Tfd^j=3WT(m$MX?b5z&G6~!a3ZzP$
z4&okG88>;Mn+z>!3|?-6loEeT6PJE%9|LS~nKEQQT2?{@I#L+&@k{Ptd}Js*uaO?6
z3m>^<K+P9+Ne}s)4b`%ZP`U5BB3dIXOvUm+>I>$IX#biw1?;?2a;r_x`PI|-0dQkx
z8X3Vgl$-f{Ya16<?Mm)uUB9~;HyB{q4`Mv2j%T|?osWv2MLUPcefMB;yBKCN)JhHn
zsZm(KJf?HzI#Z3nkjYVi;t1rY_FN#hx711^XZdwm7)ya%sc06x?Yt39y)Z<&XTd_w
z1!K~yaLIrqBdpb$4PCiMU#yYEoqj5WFd^6O$gwZ*?DX-WzJw~nWi$wz_hIFE*qOeH
zL0lNH!oP3Ha>S|p9Ygb7>N4N?rX^v6MNl)R_UZ-_&caW$Kg%*`X=pDzE!fR3ayq?<
z<SnYl1kU5VDm~hLOIot-Sn3w#%D7_fQl0$jC8|9EeDw&>3o}f-)(BX#OvG5(krA2a
z0okE1jy>LxY(Y7{;NVeV1u5S-|5fY{9|`Si-+MV(t*};WGHR>f#h{^0uX4T|eQkc4
z#zfC$r@aT(b)5s$!i)EpqktPf3JbN^7b2T$Q9DhL+%gRPRE*1Vf$3xUMu-p_ddt~X
z!&o5}zM|@MA%g~Vr&XzLRY$$+g+5M9aJ(`k!67!Cs;-TW&5tn}N|-97IQd`UWgk?3
z^oa4lSWW2Rbrq`dZQ6I>0Lel((iszC->2HX@7K|eqt1v-iDZ>DRr}{%<RD0}M0lq;
z4R0rmcmQY3R>&wqz3{KA%0@VNz4)Fc5=^s*2qHIw%J=B)FxL}AaotGX@uZMkA22II
z&Yz>JNJPs*Ia`fzK1{NBfzO;w6YgUxJ4q)oncAnqAMb~vUU+xf+D0D>?i8+*f>=a%
zejbH!3OF1Nduk98@Usu1zMU1)jtWjQWKY&&W=~D*&6XHAzl(<PNJLi<w|e06I&x40
zjFB#MZEj>=9cff9bpI#I!{3-&7^I(4F5qMmm3v+36c?5UXo%@IZ2KL<H8asVgZNI(
zAkX5@p>9`PGOhns;~_E{>{JHDu*XWgYq2Av)+C~9Ht%vvYH=HiIMZ?Me#BPw=Jd9`
zNZu0T<Jz$<5fXo^J5y0n>I!ejOJ6~LNcvdK^Glp%6y_Am=u0cBF#>JRV2%2%&Z4S%
z*a&t+XKEzH@B1ivkpmF$XN2uiIk>80>5*Uu2ZtEC_J>1Sdh3rX>ULYygTEIRL#+r2
z>(Jbra$O(ix}-lBeGc4%NPstH9GzCDTmD0iLL+M#HI?#qv|*rfz)GD{#i4W`)zc8%
zOOvw$3~z<<*et)pEe&?UU{H%1xJZC1C?pfJj<$4tBhu&Wf+k>xx=oQ_xDdE&BR5**
zIPsbTu<PxE@guF{)R@pfIzjXwQu=Uqi<(fgf*rQt)X7*HmGkw%Q2HxPP6m(qwecJJ
z)bD^=i9hb+Dknu5q=SE>^qg4_XsZf~-K`Z)$Ixj;pEw-ReO`<6BBwk#ozR$FJF0<8
zN%Ih~)v>SH?1fEC^fM-~s|^6^ph9>Vw#T3!|E<MRIrUgL^*gHCVTwh$kU~{gw0aWx
zhA~J2s#nF+Rr|+4W<snH_KS!1TK%Tg%JQ}Q@tw(EUN78KU>!_BUry9$*FZ{s)KqKk
zXRW~_Vo9a>s2TA-lyD4nEKp{(&L{)l26>C-&5jV}$?i!{4rIQ8L^@9>cx2Aptxp_j
z-^yb_KU4rBVaj5=J+2wA4$;a#6qC?z9w-n_FyBKH_+nNC1E;z0dP$o8^{HiVFGrBH
zi@3zo&B#q)@7d#&G>K?-=NBFu-FE<&$vQ<rsWb7L&7hmqOH<1~X=iRWk-cwf-kVa4
zK&56KTDv>w9ar&)*QxH`yQv?u_O^bN>;rSx^dU;VVMlUeZ#d4cCpqKlvM+Zl-Eu@{
zwLrZVq|>Ep&>u-S%OnhMQyjBXs;Y6hQtBtYJXBrt90VT6vs0A`Z5N(aEnyv9u5q7E
zwDNqszPP7pqFt-<+>00Q9Zqt8eo1=?s^xxgwQ*K9%Q7?>bk3Y#<0v7_gyxY@9$Tn&
zuxb<Noo}-VBkJbKGbTX!n5EoL7XiRYf(dW<5!1b1QgCy_7LlM0NVQWYwhR`ijP3fr
z%P_B*)HIzx<&AbixjY5&2t2uBTP9^}Aj0te6z$~qWbt;<3gV!*_$d#y1K`{hDXb=A
zx6_5=8LWlhgRF^L9+is+>zdo2jaR0<5iv0zs;lpsKnMuaOLgO|S8ZwjxBYW!%%0P=
z*x1lRenrVcSFNQ?6Q!PWzfxfvPFLy7Y;vSDQEXYE>MjZFt^zEaEEk4?Sc&<%f|-cn
z#XyV}Os*OT_iComzqrhRqW)}uiGq*)=Pj#|>Quq^p(XZw*(ZxDe-}p?S?Ro|2Gh<b
zmo;}{^t^-;<z#%x$;QbhgK}qMYSmiZ(M)0~s};&JX^US`1KA%cJm$+2kpDVNti2*4
z*f*J7jM=;}3lusT%Pfz)zipq(=+dId6!?5Q0xM?NFi<HgJ@w|=VL(0W6frJBVl5wR
zCYS67h(brS!=ov{)8w*e^dG#=+qDRJ@<_oyg+uLxcXfiCi-wc1WivIZy4`c+9=5x0
zGre|+5wIBaQwzewN8W+WSi+?%+P1}xQsPdEa3q4isFx^b=j70zwe{jux+XuKUWQW^
znJs?Gqm7J?Sz*Z~Z@1hqu|Q*&F?*}S(1L+T2-SJLz?oOqU;!N+zB`LumeU%uEncCi
zp<c=PE9TT6Oduwc=zZ$<QOp=o_QGK=Kj4{}!0U)8lPbEQ&~@iBes>h}Y7yz(i^XEK
z?hA5;T&1b(LHW%+uJp7nzXoc)%(!Jno889Px!OXQ_*+hY2o1#dFI_Ts2Z!t`W|lFB
z?8|}{m>567j}wc#5Dj;VEuy`!=g}uW{NnaG{d;@Ay8W5Q2DAE3JblXEM3ZHb(?<J{
zh%d8gNEg_@cbk|567J;2v-R6!9EFF9!1JxtamvlGvXU)d+E!6jRb4i)5IkwH$W6>a
zj?bMr*UO!nl6#yha$>~>oP!ywj&i*`ku7-t1}~rD6TP~t!W2tNf&O+^LA{<4ukVzu
z@|9z|j+c>)<+diuZx_(EN%DXOib==#IYWsSb4$_J=;>C6AM@q7?w3%`AqZ+rDA(}`
zKfe-B=0)xX?<c0O9G`FGFV}Bw*a{YE+bn_!=_C1&9+Se;Y8l>XU?0<6oL+@ZP{2Pb
z)Hc`72S+|`b`VVXqyf=I@cAR)AKULF&rnf1#&%SaYCnBqbv{x4h@A#?l@K2|SH}4H
zBtG%^$6V2cVZV-c@?4YEU|>Y)MwJ7|o9+&?LM@KZ)Hb!M@Msd5uU*!7y0&$BMxXpa
zHd1b2q0Y-DsnhC#VC8ey2MWozEy0JhbNW5a(^3Cnt1klKjOAs;sQZXxD9wwHrEg{O
z;OBfpg@(!yXe0fY;MEAbf$uR7%ZYbwM!C%W`F`+b{K@Go>Z<ZM&&5W<e0WRd*nUO2
z?b?y=SDJFN!P#ezrQt9R#>HL*VvD#>pW3|oopHVF1PE4-Tsx-H0SDgW4_F~DHAU%%
zZR~NcGWeqkQcg#?@e!)ilO}Vm!Ge<=95?XTsA;U0z?az7Hj#=TB1Y}jV%HP%it)_Z
zX4xM>kw~Vgb>l>jkKHci!)rA~C|wqD%e>7_(Cpk2LvICHBP1lL0Owz5ToFUi1Pgd*
zbX`_wwaKVv<Eo+JncjmelCs7f`K2-7x?kJ>WXWY<$dN`@!^vuh*y!@=wp6EG8kVy^
z<RdL@iCWEWQPHJC7+jg*d#D>9pC`N{NXG*fFHmUc`y@sU4ghw>UGo-5_000}d_+{y
z5x<cY$hhm4MNm`(d;9wP3R+jRVW)e`xNcRnJ`6P>;={9;Xmv5T3{-AVWvc#+w&!uS
z)M?c1yagI@*d4jSZ)mw0Yi@0Fsly|eS5;-Q=nJ<gl+miR-Pn~*MR<?k-$`<R9d_n%
zG*|X`eZpgYy>Af3S1-yc8g2h(MSLGCUl--0{G-dEAz`7I0My8f#ce3wP2i=`Yvmzl
zQy*;0hzzQvn51Zbc&bTSbLF9Boi0{YSFpc^FZ@a((CvDA(oi^>mY&h(?SGS#lT%XO
z`kCl*`H#V4001+;JmMl?(=Rny;p5M3HASaS|HXrUM@|39YwPBsqV;9>5|fscw>|Sb
zV<gM(KG@QScDUY31Do#G>uU4aHhAA{YqUYz^;B(cfPxgL$(-Ku@^t++`<<b;>9-6M
z@%>11kO9R|v>3*2V_;%D9r(h|*{sIKL}sSx1pTbx3Fx5SM+*@(hTOc>^Feb<j_`P2
zm`}J)vd>kwWJkU`p6-6;d0S{=n3tE;t`wxV?yu9?iLu&Z*L~*Y@)%z<t%$3bvZ>ND
z<OPe&Yu<C7c6{6$h9z?k*4XUcZ_-?;bRJD_e;zHM*RXEChY{2HhXXkUzKv3-j9b0h
za4_9v6?b)KkSddId(Q{D&u{s-NkYuG>9gpjQs=moGQwGvt<8&#m*;RTZVjo5XM$6k
z9@*E{^D~oF_QA=|Htpb{R_`Ztn35Eh4LNV>>ycMJcFX+<`R<^f4WHh~4>?%4nW%eB
zLa9>irU}NE+5M&WFKQ7@3i(8+ciXw})5as!zoKH+_s)lh85({?rCl-J@1Tkx)rq~5
zYOA}M4W`<(YDJMHl|%cIivd(sj1GjwZD27+N_x81ERDh{BHT!-z}ivWbMOZ;3idM3
z=6cx3Q6ATGL8~YI9fSKnj)R+<Q|2)KbxSb;z$sEfhFm_Y|AF|89v><pHq&!<NO{}<
zoACHP*7-A3nR+eU5{E}eCm%guE;O?MGH(jIjhikEjU4e@wkQQfq{my{=i4OoHV!UM
zGqlgghGs?Y6}#locJ!!XGy4iVJu{rf+_ZR{sp)8hO9rc4Y&ti(#kQTDn>rp(rZ(Ed
zixHz$;p0W#9M(HXW><0Y!_gFWi`nAYv2-572=(O~HFz;dc$-~_yzDga{>tr6lFq}e
z!#JZx720x|P!Q6egWXgGMKBM>VaS)bJNzLwqclIktv3#@^Y$7bZ5{!flrW-Pt;Tq<
z+6dr%o&Y|GFzNbWz0;p;SF+#{b${NwL+2Ij^7J}k$nDIyROPvRyjVjeo5Ab3bZu&l
zbDEhIE{9?o|CYhs)f0K$ZES4!hrRkhzH4$mY0~cvJ%3UCM8IY$=i0W-Mgm$uTE(;S
z(q64SEAV<)EUYme2l|y{!s+_Zxa!GKy78}C71VuYR9^oE2L*5;)348KuRm>M!X8z=
z?B-6ppZ+OSkW3B;m&Ply0?k!uU7hlrNj=?d&05S=mY}Zs78RL&{asrBm;ipuON)VF
zeaEbl7;@R4*m!w7dt(xG#Q%6EiACT!J)Sh2@supJ(=m8GUG!&qzuW`NO}+I>==tOz
zZ{P=9`-A+Xddq`arc(7Ig(e!f<Hfow0Y=`}zEm5V#d^fgjP%mIDV-CaSZN>q9L8^<
zCDuIlwCqc!^5WliUHuEN$QSFYPV-vd>>E~`@prweG=g$ot}<VhwvvMl?*{;|?tVKi
zk+A4=e=pOU@?wa#0!3`M<!a3Ru&8jU)(qYKgMHmDV6Uu4w=Qd~rh^JOS!NZO&TTt$
z;rzxU014x~ur5^5a-P;bUa^}lf$(;E&_O-3*_8XDWnq^Nec`zCnhw&_1*n_1=(~pi
zm}q2n4uIihFjpr44|R9G5uNFOf1JB9b$789ghcoNuzm|y6zR4gW0}k!JG1joEglrL
zVjl8KCMCDg8-GqB*V^wk5txWS@9daM{`d?~yB&OR!P_btw@@|Xz#LxAykB|1Ua2GR
z1Ym1pf%=JOKg%Crt~6UCt7v!+^5U=nj#JiYCYvhF7AmlSOSeZ+dq@e>mvFg0YjZU&
zDbrff3`RJ+`Sf|~Mny+^b}p|4ID<2|0M%KCz|I^a7Dbfa!T)kU?yW5TYq`#1{*5H4
z@P4NCHHa>t1FgEA%<j<68yl&7siSN!ExjI?G$?PC_%#kC4q!L<Z^bMAnM61<-_-~!
zYDI?AoEpy3ms&hefEvZmjdSW*pgyi9a&~RC6vjx@*!tki&LX@flbm9|E+fvxL14EX
z)Ap=EG}}mK_Qo-rLqf(pnM2N~1F7Zi<<4+-s#tGv1N`3>w<`q3mUU{MCZ~g`{C#!8
zd&CE}pl|lDeK6hK9^HXAq7+7IxUe|iWl}G1ax#<9zHwJw-MF<H<@YYToJ<{gfcp>-
z-X9m{^$w0h2B!fbHxl4nZ@dtSnS6j5f2B)LO)a^ys|{9N2WYzRU$VOZ8*~v<8QFZ+
z#-lzMe2-2kRe2a2-1yz5CzO4JfXg;oq%r4~oi?%U`p^QLXyPew0$y8@SJKp9^P1Dn
zS~3I4W{ziW-hL*Zh{t#Z4rW5ia^q1S_E_faOAv8&vQ^z<Y$#w_(Y7G8C+@WK3vqvE
zP~}x^rn*_{<mW==uBJTn(x=b%$A!+cPsKFfsty`d_GVV?Px8fxJf&`}wdSmrV$BiC
zgylxt`7ibh5Rb<Ax2`D{)pR^w1|7y<O>FdHTBULV4FkgCYs3GRYbBMbr*5thkDyn8
zdj+1)@6cse9ak#eBV4X|-=@E|T-(2_`kDW_p)~(J6$zL}02#?Pc4UaVLugnS8PZrR
zO-|AL`z{PS)MYclWP0_wKRdhlHtko|!Tqs^Gjl%zFAgPN-S#5pNrjE2!!Z-rbR9Vi
zWv`DhMqf9tr}<xhROjy0#>Zo4zRsHvd#h?H)f(){n2t9IMaKRMhBrS2yE5T!*kkhc
z8x$(;O{u>uOMg_qhl%k%g~nd<=yuA_r*NJ`|2Q%*(9xA&+QLiEN9f&O{)dmJ)<)-h
z2!{-)?`1PwQHCq>*E?9a7C;+DDbIIxdrFbPPRTTcQdDlZ$tA|n3j)Gd7G$)JynIaE
z+;_>S%D_vC-yHX%=(zYpJ!DbSN6lBK?!=zwvSsQVdy>M}N^L~@TK54+vvVQZjG+6a
zy9>gldgDSq`_gxUbWHDdcGA?7zS){Xqh!(df7eo=bzN)%U$pbO%^egbigZzg1`h5`
zkY%G%pEadg|2f_a10OGL?dRkLl)y$CZ<|S!znw!UsMGK6UYCPZQ2;HNBO0c<f4JOG
zf%MnuuU$W(+jdM3vcX=9x}Zpkr7fH*<DaJ?gD4&%k<>xOf>MnJA~cci#T=uO?C!vH
z?@s6-<j>)sBJeyNgBVthx%Gb8?CW7Jf4d=e-2aKK6<|J<!b>nm(LmMqf#8J4scpRr
z;lv4kqsv?eJ>c_|>1y@O^s6iQdR)NUyDOkK414Pw=^fcUyUnEi#Jb41!0QugH}Wg^
zNE}<!7Lr?4>!<8~m3Wx)!NH?LspLO)nOm?UV!Off5j-u!V+AnaILw=*ZF=2-EiJqe
z&^EVG{14Q!O(E!1U;u|ef-pDKA`#)qGpTg|fBu+Ns@=MjA@auZTPh&y*)kF&!Tq)x
z-(YLuwzEPrI^DQB<)p8AI9d2JJlgQ`P>^b~SmhulBt-gUw)%TX+OcIx2YvgWQHda`
zz6>uc?~Qxzy>-ZUFW`oD(dOc-#euUP#lFj=%*+P&TcSpEE0ZfM`sx~*v^BA)&ynp5
z5d+@=YWcS$NK?#<H-+)fHd>bp>cu?}#`lCctp7H0u6Zc>cOk*_&~{Ef0`zXD$7{#!
zMN`jczNeO5FJ=!b0<k6_m~h(d@O^i6b=8fa`guG#$f+1TAmPtOAii>$`fP=cKtDzQ
zCn0IV&uL@HtU(c!T~{S(TQ|mMaSdHZKRnwwtd{0%2+j(mQ!lXjIsPG*4W#Sbm+nBo
z48D_+Gp1%Dp!)#pibz#@)r+k&vI?!D?PlbfY&hn-O_yQe!FBii$!e+A+T$t~7m#}h
zu8^&B`zM{XJ(jzit)eE63-5IyFalY|-b|5V8G$&PCWr|`4M;s(+yqXSTKiK(gGHIg
zA^S*hVhoM;9FLY}X~^LJiA#zje2&Q5-L7`4P-Tzrb;tO1J0Lh{gSDi}n}~ghcSoMK
zi2La^szRNroe8=5&pZ74qduqT1wriyIg8A2Q0=lUjOE-4@i{a*+P~%*2E2h6UT+Jn
z`d*b|pLxC8j$WND4ySH=iqPe+9`1z&n<|vy8U+GwxEXU%os5%k#X=S1l;`crUSj~}
zDd3MSjH<EliN3I@Pt^If@U^!el~W8WZzczY>=xV{d0nOIwBN105_#=stu#BMVg9^J
zRvtFm@vSOM&&b$ENmqXSXR3L^9ZuUWJ*Z+R3@l*24(dSZ%kuV@hlSqQa~gau-pAvb
z4RkU2Qfc|F#ju48=SzHc^BI3+ew<aP&kG)bIuGmq$j93*lcLz2-l!!(Cf3XKM$duL
zOqocNepG@Rt9%WwyPPhsFXd)&RG3&vsvJ4LzdjQ2uIF5BT77O|{I|P89g#jq182Vs
zBHaKI3Q*V#t^xK6_FbCR6xA+L;xVs?B{$PVRb@lWq@xtxNWRIK&NkTdf4oU&cFEX5
z6Vx3U#h+Vs?7jifOpgTNbMfyHa=Y{%)O^1xxZyX(ePeCdkU*Ly=k@?IMOIMB14ftS
zMJ<0NhpllhC-S_WJl37(Iu&}V8Ccrm#PY_}no6hH963&-%KUM-AWs%jB^-<em6a8X
z2vUlU*JTpa#6;@pRFoA!4{_dKhy0#i7mp3#uR<hvP~~w?IMh5-q@91=_R>wyWj^m<
zJXPN`%?u^bURdeW7)t}R{pyb+`ufTcj*`9<V!MhZSIShYtGmcTkMxAWlf}pOipb21
z=j$_*u^yfb5<@29Bkz$~k)TLE_Z$5`RkCN=PJq<=8;~Hxf*?6BVP(z#;;g#u5!O?R
zjZM330s<NK5a9e|y)<3);cGpzRm`4Avt8Es)$5|++Cj`>;gZ!SE=YMx49Vwyy-Hdz
z&S(E;+4>~wUoWS-`NnyjPyY@wY{Pk<EY%%N=EAb*u!uQ@U`=BD%!V12;IekB4UN?Q
z7pVLMV@cMvFQ+*nt{zZ#W5{pGz-$*EeID&X958A){s79d)U8<lleKp59uF3cddrY#
zD=mt^#)L>JySt5`<&x&<YRZg|Z@57J-)KNBh^C5|jz2=Q_NKAGh;Wc?DHAvvuv~4q
zzeV5?=G#2oc@et|WZ&T}H*E?Y{HFL16-Q1FRbB(0anT3#HBo$EoaT-Nn-PnO)~}ko
zK}0{`$OPTJ6!N?ICX)I82FBuGx7zM*9g0(>laC}R>7XT-0%oBU>Vr=qz&mRj|JC1R
zfHa6swX!3n8xZ_B0fyM^vE6>V>dwA4^_x_}>sav1vEss*ABFQrc6Q7UCB-A*D+QWw
zz`IaZHsE0fqn@|DF2ZywgGC{dY}%jaBF`@RiO0*i8-NWD#iTul{-E<vjcr|O=9qeH
zfm%3=3`Oj&19!zf$LK%u1{uA1mw~8r21~6lL}(c=K$&1GB_;3xh>yN+>RhS1XiCRU
zmy|b!dLqJeV7a$hLO<Qi>s%$GH2?a~C`jN@ZPS;~E1WShfDgSQJXt5)st0*a&#S_E
z+m=vfGv>nIBOokJW^nodK?j|vcgSSeiJ;r2?S2;!O|i;AP8QSt$IStjH6KIl{&Q>T
zO`+1)2HC^mPX>r#u$onmOAu3@jupwEkSK+XjEwGt0>g&Ev9bTvKte#y-`{UI8@rYD
zHi`gzdnnc^`PZ_(A4NrOY;%i)E&sHd-?*U4HLkiP@!S~d&j>fl7N_=sLQ#^7U@8mP
zkT-~hg#>W+29jiX+-djvS>xsQT)Ze6<X>_BkiXr2JI!B2I-Ja9dG8sTkE;cqHjpRq
zmm_(UE15t>CAokk5U2lyb&QK_sgzs?Sl55O2K)LPebC0CO>Ij5aaa^bDXq`w2gGK?
zOlw{OCL2A;Tx5YKlviu}w$~T;F0Z~{s=P^f5uK8}aS1HeA`mNWFEA{XwtVeQgI3*%
z7l3r-9VBOL=2tMFP$J^APV3&w)@ZOkHVn&6UEOUCycHa%2!xQhDgFpCSoIqJG6S#A
zt{Rkn9y~DQTk}4d;Mndr@{jG>;^%)VJ==fB=v{BB)&1oTfI1ybWQ*LMuF&2bJ4-Qw
zi@bsZ+8z7LDO8|+?@-s5d$SJ^6JXX%5^I5A79q|GFEiNV6Xwr?RnqCeM4WvPJn8@P
zZbx9vgMZSwGS0}@m@;obi21AVqjK*ZEG5MoY|(MDty0n;Hc+9wA${40ddyZ*MYRKD
z`EI@%?OuFK?$=-xqHv>rs<hTFgsc;v`}Ov6t!C%LnIgcfEbYZ93E)weG~#Oib9|z8
z0(KH5<l}&S`v6*S57YX&@foZK2wyTcT3X$2g#(z<dKES-P1tb&^K}gNfh!YsMIk*z
z5hkn}Ci@Z`{>?btxD5Octs+@hxZHB#cRHM5x0uVqwr_#bXgGN*W3MMf%BIc9uj4+d
zKhB!U6$9tqBkYdbte0|Qes3hX#&VG*r#7wvp3%3~(9=Lq2<an1B^JNsLZx==k8Hfr
zfq`$jI^ymkMw<~tz_t;~MpPN}>o4QtdvZKAUybn?B!zf>A7JF*w`cbPp&X-}y-6W(
zS&e~2EJe=2qnYkA<BRj=C-GsdS9zkB>6tK+;49h~E%}I#aPOh}-W9})tAmj4F#kY8
zTSCHJ3g<}(v&OC8ma5!z_XifkVh5i;-8lq2ZT3q@d=-H>)x$+b+yiuxt~MfjgPirJ
zA+Rq7<R4<!dIw_2<U`qPRtZE+YxN}CNTRpl8Y7=h0nMQpky`+q36qZ18a`?J($~hx
zhVdn$J~~Un8=#_&?)y@Mr&u3m2R@uNkh5YBeSh79E^A0DmIzZaAL&UB98Nr9sB$ua
zzsrkRd#N^UMej;~J{>}A_bow(_Zs8b61md?B3w*FlP3a$s`NzE`|V0~L7!=5di7s5
z@B7#If06lYk};dlP;WwS0=0y)b?L3lE(M&App&+e1tyoXnMrP&`iU?eHpn~}V!IYd
zdSwNk<1zV_A2;)`qz!CwcGTj!wW89+ndF581{uPGO%1y|O0o6#df#uBr#FMQPe`SI
z3rLwY8rI1j+#NuPUxzWab(kMxx8Fev#hA8CsVe{>g6J(#D*diYmc+{`XuPzJBKTV!
za4`Y!=vegXc@*^$g%D<DV<LD4(+r6_3vMx(7KybR0>Po*g{3O2PAYv&;uDgvurPoW
z)~q`a@p?d*O8{}y@8X+pcm#}Ecu#7EV52`Qa-fct);9%|Z{{G)La+y5#b?cD{NcSv
z>p{htB#G*uirV2=?zvFbOZ?Kr2C`jN-C#LFb=JGjK^I6D2${V6G2kvlp%a<2N7Lw*
zQ1Y~(EfU9J-z^f!cW{0Q3R#p|+e+VV;}vCu3jKm{`@iG|lT6&9+7jMppXgED=0<WK
z0<ujNt8T_^m1Q{2YOwq_2Gz=SK%uz;fLOdEu(u>$LhjFxxBc*tWbwhfNmwVzU;2|b
zc@2n9PRP36)1khonv(C*ABX9)a=hAoZ(r_=|8NJoG6m!aWX+5$ghE+(ZzlEYowWk*
zC)=go2;@}RH0KiLD%WDT`(FRp_(`~|lQclKi*M5+)L6}x9JOEofOfnO`?;`)c-vWg
zVdtiQOBQ5)^02=89#6#39I~3(thK|g!U+3&0dUu`RnGIifnrwq``6AMKpsTEVf68p
z5Uwzdf($Ugc7f*uSy{dt4BDp_BL|VY@3~bWcct0eWeuz9ajBHsD(Kg-#S9}tS=B)N
zW%o*sIy(V8;i79STpC9y374q(lOh&eVj}GD9YVmjq@ZxY3Ag~ia}wqfMb9?2cFx(%
z*p$uDZ&xM-h55gogt*X*l_Eg?z5w*-eKV`_1#$LJT7xkrZUoASb~!-E_XDFn=^!R3
z_8sIrZp(QAukpw)*AA){8{Xc$a1uEYP!?9=qUv5)=Dlha|I6LhB+Jq@7OCs-c|Z%1
zj*z(S-fFB9&#m>+_eM{Zi^!*`mYmI;SY4mlKe`=qe|IG>+*9)M*+vH~W8#*ojGWKL
z%9`rTrmDqL`credCZ|Hz!~)s@>3vQh1p!)*a?w-Jwiql<k-ky)5R{)8VoXRox5!t=
zUHbYdp}w_(cnpxa-&^I8B2`fABzjpwqd%xdWHETL)&F|86}6#Ev*B|dFZNq_eQRB<
z!gc7c7qT#G@_4g>&BYAGvN7XIleN2@w%zCOy&II|UN|=okl96m-(k;rV0bjP852&&
z@pNYFT)Ej&3~YnI&N&g^5+~;<R|4EV>(6gkud5zc(jEb&ZCzqU<Gz$?fi0V%w5lO&
zsnt4C9(d^4Kt=2E@xEuZAIuSb*On(LMB)XCXRajx;xq~(WVi&SbV`r84lgg{vtV>%
zBH5LhRx8DRR*4~L#cy6NySbEkO2+hd{&p@D^Vrj2sVledY-~?x!cThHo426n3<&S&
zf=tinr)YiM=W@T>f~Tjb5P|ZQCVYD~jS?Id`Lc2TM@s^#eZD2S<9jI(_v?8#^2O-<
zy`6F`Cz>~28J%Y<po9$`!zt?1k(YQ6RTnBKLeUQR7oaeDaVZlf1GxA~sB&n~NkBRm
z4KyhGz1{m{M$KFt7F>(o#1`=@js%dRy5YST!QUUps9J&7)(IJYX+aZ(T-1hl!Y~gw
z6!0|nvV&p40T~z-#FS}uhTZsaSQv@WqY3TL-JPCLvd;A`!Et>g#w{VXzClZ76d6S)
zXQiP<0H{zSf`IYc-&3Wk9s_Wl2$1t-_ls1a6!_fe?vXO+wSM#luD-3OO-Ef75|Jd)
z)F7r~!=K?ewCdquD|91bB+MMZ{{!$bA%I6ja(-_o3Faw~IY*~ZGV)-at-IaR#!YYX
zFL9#H)g9{II-j)r&~fj!VE#My<jHp;mk#MqTTO9d$3#=P5gch%++B@<)Vbk_Qy0;=
z0-4FICPW_Rol4<_Ewl39(K~W}?y8^U7w5)a0vP8YvWa%R<GK~<qaTjT{$WW)q}_K0
zcqpKVK$W5HNkZvQbxnNO4-?~kFH2Yd5nMvY&bLZ<FG_caI=&NqYL#k|hnu0>Oo_|K
zQ<v6i8HQ}m8Vy0p6L$uF7O@yo_^KsLd5wjY#Guja^ew8sK&nv($0)j=$er#&b3siw
zPP#{LfmL4E3#j5&nq7iwPVQ$V#mKaH_T&qGL`c#Lk!-xVH(UGuZe$90THH6e^O)3r
zdU?y`5Kt3U8uNQTIJSg`e3!-e6~@p+y<z*!-_Pf(j~E#E1ZGhyK=E;l2ali02>84q
zcpRmyGWJoa<-k7U^?YH;${p-%k(_)w&PcOTUE?Ez!F=8xdpjd9<YOFL9Ei%s+Pctv
z%&sy<kRl87bsdK;-_58ek9CRZc&kw8J)>r0^!hybi6%dpE0$RTPd`ZihkAaKf-1^y
zvA3wk*m4dv5+s6I@}s4rnk6a~gK_3OGw=W?vKs;81->D{**mi0^P|<`)C5%yQ<*xY
zy&V5YX{oIWvkd=~3X`d+GO0oN+Q?5eyH+kby_4!Ps?!Evt{)?Nk)ge_-0vB$toD>l
z>nrh~xT!ZI1AJ05APW=2Ky;al`8#Y!yeXolCD%YTQP*Z5<y)HLsD5Ft4*(07ivqAf
ze-8Aj<V0#<ICaN6m=C(`Y++Ah+(&dk+O!=ji2`XLV4;c7^kL)~F3UZW&z*x&H8hb%
zaZZTTLtc%yW;g%9tX*MhMm`ok<kLKKrpt7BM@d|wabCeLc2Xj(*~GP#+_o6SOVK{s
zN(s0BDF)$X&=c&t;y`H?my+(#FUH1w01fyBdkmq@L8eDu`>RTsI`j%!OPMowj54&Z
zx=@GHTt=|x^TSQeIHn|rhwI5_fQ~>Es0Yxsfehdi2@-iYK~C43Dl^pWhNwXOI>c<?
zE8ayxhqOV(b7YQWO1<RF(5ds0?zqmNMPup}>@H*0ir*MNByaFP@yiwu-9}I35I(1*
z1;x&63piQV8^3g$u5;FSrZ0#^4yknwKj<Vx9)@^^Lk`*i>AHtLiqgqfeNx(afSUoz
zlUQN_QZbAkfN;(7sU{P=`_Ln#Jz_Ugw<oEN0#Hva>Z7TgjAI2PWNlR*r1ytiUz@PJ
zjwJjPsgxsOIosL@H88*?YwCuAzhlCR0|)ITaPck!LnTFOuxvW6(*F-vXBiMx7p`q$
zfT5(jyBnlCrMtUR8tLxt?oR3Ml5UVvq#LAl6ye*v=bS&EKZP0g%&b{^t>?L~YcbW<
zmI_Gq2O#KCl;*?DdA0wPj_|Jeqg{wWe3)Ea_Di3IIPc$d6!~H@I<UIHXhZocAQ&ze
zcxfZkrt<!^?QRnzVdLp*4K*kPL^<>;fl65o-gu<5#P8SjU-)8bp`-~DLtJKh1{P#c
zIut@oiVgnv?M~YtwK+A-;SHLJe^i-IZYg$s`g@ULgLHdJ+VZiuvc4+8)SHTqC!HH=
zAzgC|K{MuxHzg!5{N!jsbruN3j+N7L>iMtHND1tf@2Eg8WG^=eLJ&81_s8VsV#$VZ
zmDKOF3A@;1bn!xTz>V2HawwHX!Fw}yqBAM*w_6JxOJ-(yhWUyGb{4`{Cnm!Vnxw@7
zIOz)LG4^Mo-D>jNN!?XsZ@FS@tv2gv`)IG)mT7tx)bxy7Hsplq$p}Tl22+v8Bt3-~
z|M8MkyGZdiJdn6P6`y|X#QJ95*2GQc9lm`HCn?HJ+4zF+6D^v3#xgOvlfJg~VRF*Q
z%DFV^#~*h2`{YM<;kfikg<tI%y<lv7NPZWRcFT?_95lh>_Oj~KjMwWml#{@VNh=20
zQV6k%S15f5fq&c9{{;G;6NOd))wUq~i&!dJB4!Pnl=ZD<@xJH}o`ipZAvr+kbikwb
zqwExbZ!E_`QxJq@g#a=rce|l(DqX&3hUuN6Nk_o>NJ3o)4t5r3+NX~yWUts1guYE4
zos6KDTp=ZGalWJyARYKTit!MMJ)B)7RQ1bfLpI1*ZmAAZIYw`AiRvWT7pA49_2t|M
zTi)nmj_~TkS$)Jzz(Eo@+&<qJ5j8?05<q>K$4}kSPT$cf-S$i+slCECGg#4(?ytHU
z;BysH&|Bbx)68oqOSi+WBN(%nIHBI@7Ylup>{0_3`#*VfRaJ+)o;=39@!#XzHWpI`
zn7t@HZeWA=)KaA7--lB!(l@v^cDpi{nF`1vpQc@&R|K3+4JKf{l^nF)-)et3HiaKN
zq@Ij(_*^N6l8AT{O<A9%7Whwy2YP{x1-Hp2;kC^w-_NSq+ArYUmdIZQD4b2dis>g#
z&Q>d0bL_~E0{U(5!+)+{ZW>IBbWA^lPZnUSBfBtARBS##Bp7HrYqHnj@%W={D-H2i
zyUnb{$YbYb-Ts1AjVe*PvDNc!NVyxlmF)x|v&b2<NZ#@3v^GS>Ud*BKB^FwA`Cs%_
zpMlydxoDv4^H=lPtZ~ox@4|Il`o|+o(#Rt1q*kouwi-eeN3t9A+hd9Gk3x}I!E~ZM
zLm{95&Y!r%F8GHC6ws)|k*A7X1kC+DD-|g>w_<+DOp$WmNQ~k*Z^(?wyuN%*R}w?a
zSJL3NZTuW1#UgOjpTozuNQ&b*fcvs5^k>`{F9>I2wJ+~(Y4UCR5b_IP*Mb1hEp+eB
zt!i(g4@;Kv2{L*TLOf7lc(q)iKsCtE+5bljN@RBr-ypK-O9U>LFiV409@5G6IMI`0
ze)C5g@#NrDk+29s++A!g26IWS80yX+9xK#>cS>&CL8ZzJjjJ^RX)2zz25KY@lw8t+
z8I2xgZL@N!<bNb-LiW3d<DnVsV3m%kRAM4)cR~<E>Dzkyq$C2pYWbSd1rBVY^JNJF
zKiQKWht$h_L!<|Bu1Jj-<8qHn<FgtDmI^?cvaHE#$(@(TAz-t%1#u1k{h9LZC6Y6h
zj`aP4?3;ahp^~ZV@4XsX!&q3bm`9n-%n}5;#7J$w=7a69fC_rrXMU@VF%#((1yB9a
zs)*Qcu6;OmNUSF`+dH8F_^BTi!CfiOLwNs2m3Y5fLeHh00=E|GV}i!IaC1UVOYk7^
zMeYC0hAZg(#42CBx58^x;omzghmVXyFJy^++{)28RTy^qcF4^4L9e$OiY2+oS|n}t
zDXoC9^XDw~Z~EHRYylOcBPp`j!(E{{%O=7q|5_`lHNL1$!-l7byM$D|oi-6tl}tTx
zSM#xMa<1E%&_F&)+{GNjwed?fP@ga5PDz{p?KoaaMoLlgwr<rTRfH)q>M~dnHY9{)
z@ETaV+|=O7r%V;IW+SGgfJ9w1Mf3+&6N>A`8ttyXb#oWy<;7t@$+#qwS&>^?s#<Co
zXJMSFVlQF9)o)LLH-G9l`y@Fc@GSEnsh=jK-f$#nWNjSX&|}M)m2XSF;~(P)<ahE1
zk3AxPnrid;e&8}?YKfnK16>$7N#i>IZhmTZc7A^3-R4GHukBKga5Cb8ZF_E2bgU$g
z)%2(HvDxO8oP8NjvmYh8vzF}v1e!duWr}hTl{4PWBd{L<E{4)pnTXw7b3Ik=gKi6D
zdjHCW>^)-LxL_7vRehW|gxCeG?#{P5+JPgmFxQS=l017j48da6>4T0Gla%bAEr-V8
z&}4cFlq9}=aJ2j<5LL@iqYl=d^!vu*9_a+I3p>G#rwijgi3I7Ad<>V3v~I)#xn{Tm
zyvv4J6o!}Kg>=KzGEBe{vY+81bFOZw4H%;Ln|nW8KQ8$#{K5><)$CZ#pxrI4pY>Sb
zTI;n|Fb*`!nYhsLx3TAHS5}X)!U0W)deyE#<VkAN^W~{m;btGJ|6STFmy~j7t^efN
zYMbN`l9s8)hb72Xxvj(G;K<~N5cO{M|5WWLDebOVud1ajkjIFpioI|;OZ7wqt7BH6
zq0A5W6ne#cT*lZIPJ$XPs{zhJ-vE&9$#s@8WSSAiH3k_1!TYm7h*Y%H9wLDT$r{bx
zBn4Lh&ijhbnR+G02=`~4DAtFQDZY~QzI#^__Ij8J_|RGMf&O81irK*uVRRU^G10uc
z-vDYFh=NcVrz(mUZ-I+n_7U-e9_!;#(j9NU=Q{a>&>_>Y{+|m#jGhP1SDo21)=FrI
z2O$Thz?!4gE~8Oy>X~a(LHGhE<%SLMo~#0XyibGSy%H6kEz3rSkMiqhr}#UX9rV2>
zyYRZ)*XlZg^nuuf7EgwpQN@U=SOWXl;M)a;Tbdl~nQ-O~X~U-A{KZ_#Hi{u}osH01
zpPVP=Nq-?w^3?A8$%Rx1v7{8nw76NWxg9#I)XPf3X>W~z`)Z4F^OIEl>Q7-I7ksCT
z0a$IpR9msP?=?^(3+y8?AC!aP=<%Zfs0l3xbjc%wsHreq?0o|Ew$$8Q83-p;cnNZP
zWOV7N0vV2CWebySqCX9DO_1haYdDd|qL=yYb0*v|SJk!0T~hqgs<g;oGfqD_kOH~m
zIU97OUq5ik4`}qL(7^e|yOsHe>0H4jS=ni23l;L~)S+NSD*xuDBMH&heGMbwI4F;&
z>1;NS!eGELle(HkZ*Z3q$i}%VV|Peq(ym`T#B1oIB2ykWl|@TZ81aASPk2;wG3g9&
zr)a_t=sg%-%p==u-%Pi9Q19T{i<6~F(RXhr?mOj8Sc*j9HtXAjnmjve<&eLI#30)p
zzSltRd0iH%)zFNo>iVD)i#>N|xEl4r0taifh+<WU&g!AK@PzpLccN^o^v{~=RDmJ2
z$TSe~K-$h5J5~hv4#^NwHm_H?W-W9}tcdb5ASwOSriF5rBU!{5R3L^u`XbkFke<=Z
zX@k>bzll{(r7mXC>2;<5w?cjeCC0c~A+&7yPQ4i8zlyyMLM8}|L-sdr2V9)R(1isZ
z9$$+x%TDF#I|EbF4_6_?GZ)o}d%b!h!Wg!jZfJd6Jy|f;@i8;4FemeT)XoS4pV^uf
z`rK2jHNRW8Y9yxuq9M>WOS;kfJ-ehp@Q@i9S5XNOM<ObmPs3BmE{Tx#l|mK1`w;Pv
zX{m{FUQdfwQ^ks^CGjcr1hZB0ionm7c?+Wg&Mf7QnV3;5{EN9{<~JQ9*+4$7;!V6`
zh3iJE(VTw$R1I@^a?A7#1Q&f?D($-BHF;!2$gom<_Ze&+F6m^6Y3En|e|{ZvSBvFV
zUW|hLF1DV<%C^wTJf@^KHu|xRMhZbE+94<l^@}_FFGJci$3E#w2<bJ|%C|-5_2!#N
z$R(x8YKe08?^F`RJCBbSTs}+eN^;B49;jC=fHbqT35+<lU`q1DlbhEF-il=3z8OA;
zoIAcOr;wY<iiuj{B#rry`)^W9!;(~<9TmfgWGC71vfS3tCqU)z+>QS6;PcVV3UlYm
zwYi?Yx!zgNS}RK>RpQjdSu4>-m`PatSmqI?)-g=R9C_H}$kARSzxLsVd#$b3%3hv8
zsALtJhZCPK6`rUTG<LPSh<*htABMS?2BS?dmxY1!A(Olb?QLNe*b?-2`-krB`?-l<
zs2VRgpmPv3X}cD(OMDskp-s5@^S}dkkeQtxoaZgRb{oR4>etyM@^9Q-DtR+1go}*_
ztj?y92+rZEh2|XNN`EI+2&${wMcrl}g(?$>_4W}n31nh81J%Z=GykNEXd?PgW_ehq
z*7?}0n<h`%8?SWsL(BOu{bZ(PmvjCDClSRRXRO^HVOl*!QjUVNH;5l`1au;Mej*Gf
zEN=a9n_+Qt211dt!LoG5CYexbXHevn9V|$ZFa$LN$NY9eL2Jteo|g&|x<?dc7I6>A
zNf$vbRl<e)&hh(XeIclkDL-#79|<AGWkU{^G<<P7OJ<}+JBy?$E=jy?Ggw<!$k^VJ
zR&rvrYZ**IBGNTdx8Zm=kGVVV#5Kqdu{k(wf42zjaLa4CI9G7FT0!p{I#4NQE3zGo
z<I|G<+ET=BK|$ldDl0XVSAN_2^Rc5ZhS<uy=`}(=QH&h+ad2Mzk$@USVzD3Ng3?|E
zGDp)f)@Q#uom14r16SAE!a6k0sQ5LodF)-*#f4kqQ|$`xGDOfgDML$eXVdK$xOMh4
z&bX8^Dj9Yd{FKibzgOyXa}RlegJQ1~e|fdW>l<?Ss!nTZ$;8CJ;2Jy2M0{aCR^-;0
zU{-g=qZstM+6rO3BMWJ;V2eg4m+3uK%f|oq_}=!cE9C7P4$JG9s}?u_be1p8#{9_M
ziSY@AlL5j7MObLP$jxN*=>EDy3tC<Co4`n)h#wbM%kb_}8YVTGX|exSOJ2-)2UWdl
zoqq?j+I^wz!)}UIQ*f;od?d3f=5`-fUdm1-QG!|7xj3#V$-L!eQ&*qz*W~0^$N4fH
ziTbW@#|^tA_QqDYf#^_{?p$;{YhJPr0V1Vu{yaZ(%{Sqflt74&d|*jdivJE}Hisp1
z)c^=5H}CZOefl&jCbMS@GVDB-b)?u|-Y=WJAcC&n>$Bb6dmy|@Jw%3ClL;o*&Bfoh
z3rXc0$HT$paa!(^{aaZh^8R8)%6h9%%moLYO~y;cenOkS<c_RgidN%7y>hlH8>vSE
zq<P_WzECbEQo_N?FtVMcBC(V^uQ{i#tHm3m%45EZ;5;2IZqgyzDw_8#H!hUjF(TNm
z2%~Rg7ubJ<sp5pO&|5Uxs^p<+8YY++c66c(D*X+EN6i)T;S@oO4jAi@;TlV$=);v)
zd#Vu=oMs1;A-zUqr4avbVghI|M?v149fodGG+Mq0oeT2Ip<?YAyWIqX4WNM~>4*p)
z2t-QfdZ)za{_^3<XPmLGs-}m#LBo_mTHX7>s=N|E#2-7GR3*2RwYpitH*AG5djb1c
zQC@>zUe6vgdtZpFBwX}7H{V9aQ(Q(fl*CCLxhD)~MuGsLqViYeJ3Q=unNl|FuqLq1
z?nhk0;^&(pA}vJj>hH5!7iI`7ruwCRi_OC$v8&u+Vl0m1L)OULV;xeUDq8g1GPQ7>
zeqIWyJx>VW0%`FlC8jyjL~lX|de~{jZKp!cZ~l#<QXX3X4Q!8s>cD)G)5-7%Z@2L=
zop>U_cG%|jQ(f?)pp?wzsML~EO5<3ii`SwM9X1nT%{Hvaj#oI2E#}l2hFiowa4}L1
z(l6)oQLa)##+#OOsjP|9%fd~sgY{Lzh0$_Rp<2sw@8Xq?5t&|S1Z)lY2-vAW2oje8
z6*lG^=<v$8Eh`_<yM1U15u{xI`*Tnw`n#<&tnL>|PD;n&P+JnffI_155;rzDPr-r7
zV}0NB+3Yc3mSX}Pl3N%33DBv$0xc>_mX{@xbJa-$gjVY~c&`Jh)i^C9UR%+G%*ub>
z@B|G#hrE0sLplgXR??g!5pg2tu1pwIDw*GS31WP;vAd#%Fe|FXXk@}+6(oQT<?3m4
z;iB^zwURtxSk|6K1<}b~TzS0Onnp!<FN0LQhKc{TW@`Q)%_`cdrOavW2403bRu?TF
zbYMP^&kL%>brjD_Kb%6P!31$xT6!wX5bQmcYoqW?V-?u745EGeqPY1hDj{4~&4M9h
zXhoHzM-`X6D;%-ngUnyiOE46MU*l+QdbyB+;%E&xMcDUYS0l^v!CvCntdWSLA?}P(
z8~Qzqd6X#UrgLV)#-4RoGT&-E7H)XnR1{1O1~kT~q~+;XVkiBCZLHg@w)IpG*tcRM
zumA5Gz!Jox%5Alg00mw6BILCBfsXX<z!j0D3FtFMR&Tw#UOs;)^X(I{8{bJyE<zBQ
zF^h#!A_E;|C1P8fUX^ke`rFPQXgK|g8ml~=qvoQYFYiI})p)R5rZ_|p%+0StTI{wb
z8pb%*u&!v^ciwjhY++;RTqc5cN5N+g#zp^`ykUynktV%m@zs!$ECm@x5NJJ@$Vf>N
zQj6w)NO_(Yx}R+vd?CD+qAg><F1hS7Pa$h>!)#I4qK4x`Kdv?>6D??2{WQ;i($tBO
z_cZL~((rUBxi>Y5A+{N!;_Q7G9V+wPc5bV8v9*l)=%D*&VvpMv`hOk3ebWgqQsK*`
zHFW3<4qgwC_tS0lTGC(d<B<b5AQWKBoGi_$(MN}NVTI3-hF8DS*WT1ce6KJbY)mFt
zkC0A@y@&Fz++JrT+isy$ZZ;Ltq`p*&c`2ix7l64zSfW;^P@@F>F9xxCA)Wo7cu*`|
zMcCk`T+n<>J^M&&2B{1D_5lTZNwcLBnw<(0dr1&3$)qgo><RPzBJ+MRO;0epEhx=#
z5{=kYzHe!X$e`cibIlp3r^Wfe24@iw#U)Yn#haRcsHvm0eR`2CN9w2L1^gr$Lt2Hr
zi~>uKwNY}xk&k&e0=+5g<-gLhf(TxR_JvFDD;YFJ%E*)gEn`gJ0|(X_UaW5gARq!J
zVl|D;{W}55JVzo-GPq#R+m=%OhQ8k8Mks5OKA(>B5P_lHJ~d$Lh7{qb!?4?Ip)l0E
zBI%)tDru?ogFG#e$j|UyzA>+W*=9JZdT+teoqWXGxR7@=#lL)@aKcC&#XJF}NQJk>
zVYg%{UvQwNW{lmx95GRJrf{)0b9!;#E`u_*x<WlEABpDa`E!Tlgi<aJqMmyuMuCpg
zS6arrR6;9#M<p+feX&-D_YjtdoohydA<-i6rA3Cw8kTlWACd@4kQB9dBlU{es%jN(
zN~X0iR`QU=wthxuTsG#rI;%At_s!>yD9hMmw2*xFwUQg-#Cuolu%6@XMmwtUsC~c7
z6bJ+!%}*oV#Ws5LZG25)d`>+C1HG2uMq+ai(}!Cj1fP$0V>T>El2R+ZLn}gp?s5zD
zfd%?m8KXA#<o>35Ip?z7F+CbA--qpm61C`YrJd37rNAUD_^0=kUbnwn6n}xvU56jc
zRkxIjFkfvsDk>I!=nFS9(bl{?`f2n@d2(d^*|!{s-LhtHif)rKnN=PHB}dAQ(1_My
zOs0+sv}3aCadWm#Cl{CDyOn6ALf<G8zdCRn9oS_)QN7za*TGCc3jvp_52pD8EgQ0c
z2Q}fy!RI434)`#QF(BiJQ5I~7395~PQVjM*y^CuVP|=-4L;mpKMoYn<KRqRPPfULT
z5*|B2fiPxXDCVfBnW950GjNwkw}0(ePkW8n8qsD!VZ1~3Y1-p{rf`&+NoVS%J9QTL
zy53gA>8u8KDGNu$D9(<75H7{_oBb5zB^G0=wS^8c1j0a*NgIWyX8XgDk5}HY9|+!9
zj+^@RfwfbvM|V*#<YclLqZ_i=*bN$`wF*w_kfR6R!tTV0kwVN{A&Rdzu|gFyjLe3L
zf_RNpvq?F&=zk7GPuWgq@ar$lHNTyYB^$<bP_4EQgTb@S@_uC#wi-Z}?4#J*EH^9k
z4Jsj?A_v2iAYP0@S3*X!TQE6<h#b+;vMC|HWIwD%v7M$54iVTKFP%vXwr@oxPskVU
zOFSZo*Gh{^r!YA6zu?MjdZ|FX&TDe5hg3EC;+P@)kveG_ieh`7*=R}@`OXzQXUgYD
zFrJY^(6r;8U+X-m!Cl0FL*Os+tTFb#G1n%6=w2dgwxyX`uh|BC?vtD)zBw^=RdU*5
zdjt_aG<SR}I4srK=t3@*_3dMM=e#@#CpO;YUX9cU(eYGTNzx6S{R**qG0}ey8qDl^
zHR0=4b>XnkS*?-(W!S9xfDm4XSr}uPGdB3}_}Cme`pR6WRs6Tb9GcGU>Cqad5!i_4
zQX8Xr#lu;w0&z1(y|`2ur!7|$<^4?er32?`5zlhg&O(-ZeCOF-aw(;V)!K+xfP8A(
zzj3bFuIXK&sP6>oKewF+=SJveqh>z=7MU;T_6PPlNWi-c<XP=iU=RoHrkOVSc1;gy
zi!GU_BA{T%j)S}lSoK;!2z(EL8S`W>dA!b~3QPEmvW)fI*&-&$;X>}?XpEY%N;I>>
zG3AWrc@%JrvT$K6geZN}t*Fkl5zmE}V)9<m6}%g48M_S3zlW#i0jb%i>oMrW6e+?X
zXdgv%yA0H@Hfg5ts4~)>*l_t0jIR(rc2Bc?=Ac54<qs{ppP>BRmT%r>`~+ft%sVP|
zGBYYdkv`6_$k^axG-}e3PXWMJCI37h|2~9{Jn2Ik?cOc!M`LG3mD`dF5;?nLw(YCi
zGa6wHxLEe;j(sX8>wUQD{Xuk6L2SnCy(xj!17`=&dqRjPJ;Lu*WrmypMVwl9sp!I=
zefNph_4o&en6pvgu?9r%+Iy%Ed0qFVD65*hW3mLG9oT)we_$LR4E>@aZA{uInAQ>>
z(t2yBg3TX*ROzG6;2)fh<L4(sxJ(q<P4poa!aX!D<?>SR!k3#Lp>W86dFBHPf_9}A
z9kf`luUjIQSI{NbohB5X)kZ1qFGYw7f_un1pA{lH5q?pXx0&rsYssqiz#YrLeo@%V
z@qo6CuZBB=_+Kx`I}ypV*wY#zL?%H~5%aE_zb(}IV}=`v{?9A?rh?D5u3pqvV0>-8
zZ8sxyTYND61(!}jI<Km+=tKe$uh~dAK*!uX-vgf2+>9reGC@*B9>efO=lPG4Lt34Z
z@UU5?Ut-?F^r8*0`y1rLtcT#Mksfyek%s)nkJY?uZX_@}pYK8&g&=7GM|u7-Ic{ef
z+nn^Svx=v3tkW|p%|r$d5#EwoDNVvPBng=oE4lfXx1F5dsiWJvO$d-B0e&g&(huFw
zn|m6}5T>$(NSAMl<{R4n4$0V3I{>|OHt=ysZ#sv3arbQqd<W`snQ4BG9uC)spUJQ!
z0-Iek>%#elPgCYR7>{s}>jnb;8V5p-5OQTDK2G{borMS9NmejPjB_QFLUxu5b{35l
zh0UY~VxvP&-z&9fI9EhYBrPe>H-z#2MK_tpczGBQaYn|OpOp+&&29{RZc>2&1XY}C
zEX|Z26`~6H4l0R2`pt)~!|iTj^}}@B{UU4c1Hhxkjr|sXvW$rs`g%JQb>;bgnV3a{
z(l1gJAz=KZJ1nG-s124=fF^5w&&!^X6L@qQRa4cNBWe-M)?h$?VOwUIpf>*lS7p=M
zxmxT@wApJob0zEG@zYI3`+fN*1*MDY@olyUD5jbwDy1CKYn*eXON|}BpTZr%iBWC3
zvlfFCnSV@0Oq+FLqWB!5%Qf*p-@@K9Fg@WiuXjG0YgSduI<V5g1u|cf$db5By5L3p
zW&CUCfd*$=oc_zG!mx+3YD+HWRXSR)=pib+gw=p*$^!jecx)fMA7MJh0zn~nqv*_y
zwNC&6sY&HT9t;_yRYk3wQym7|*QF+AgZxo@FIr?WO#XC=FAr=%5QR&%h;vE_>YOQn
ztuAWuS1A0;!qor|<1e|3(-z7XFd9^!FuoA9;na-b@bTm44?g}Yzeh#IX>7GN8>%;0
ziT#lgKP9AviR|*fXcVH*Zr3QT2qts2M(>NGEt5{`=5y?uiL@1Tp&5BBNlSf>v3agl
z;0J|#i3_fqN}7P3m#Nck*vH!D`l`mRrm)+OKE)PdN-L@1g5rPwg~-|TnINed$Ha8C
zd_ysjfGe^opz<I18aWm+`^fV-@1d$owYg|tP+fmyG&jNtN)JZU4^k7sCmnR`8Ecvo
zs&v-*BXGvP=)Gx7f+&^EL<KTy7t}EpSLTN9)Ec$;P{m<X>k=?je*sxcF~29l;SRI6
zO68!LpLP2RU<r+6wPPBg>`NF9a`YuldIoWom{POT4x0mcHhL*BU92?gLw4x^-7ezO
zoZ`W>p*mC*zBNQSO(w=$Gn-M5Y&1e^ED?Eqt<vw<U*e$|d+WI1%?51gKLoZ=0HJh?
zJnN^T!_K)sAg23C|N7ja(8H#_VZon(X0mC3>e?LjXJ)bqDzxJK`3-I=nTfQVoI55l
zE)f#akk9Yg+owp%be3_*lZ5r*sG|S*XekG)9)giehWRtAuB+74ho8?#_>o#7`ad<x
z@o`zRcWxMF@lfffR3zStGkstzg&3!An;Sn{W?0?nv!cyzfn+85vysnHhBwdJO(Y^@
z4SlG*VZJlH|DCRL9*+ZV8%R_O2~KG2S3>IV__7cwkQ9kLOr1jg2c8&x%gW7n{}2o^
zv3TIWAOCs0;NYc6_#rLSHsOs=jcV@U4p5nx`OKvJ?aO*{K&E`@Z6W9fu!C<9PBf}-
zT;v^qe*6LgPC<~PEP&1PZ{xf@ME)<g7ZVNjJXVK)E;|r4HX7c~d>*T7Y-5j9PJNUz
z>O#Ga5O_4EE522RUk-OwvJ-xCQ%^U{fQ@{jRseJB5pIpchWwRqsl7%0vq2&UJCL%b
zCVHzx8hq?N0x~E=^+I0?cJ+pSLY}srqhKts>mE{*O)AhoglvGtl<6q|+fH3k;2#EM
z3{~E0yLj%l^1&#tz|dFv;+fM0Z?H<|Ec;>>vB#qLTVojrN>HqCXe8H#in&ql3ef%H
zcR$wbA$i|8yD|R{cxh8U3zlE6#i3#n*sU-9<EltHvH-n_wWFNpS1<+3oUwMIvaV$%
z{e1cfxsWV@?9@%2=IYg{F*+&(?A~5#k`uD_`_gk11SK~R^nWW6>35{`89Suf@~Q$_
z`ie+EB_RPyGm12{z}a;?2S}{Ms^XrUYQ}lwZrIzQ<U&l9-t}0RCE>~LAUD^G$LK2*
z$hQJK^V_;jc6Att%2%qp_)Jqz@0BbMlmj#@1B@-z22qmgl4K+b#TV|7Mb)S~S_DzO
z*g1Kkn8Dyw+_^AD{6xwtvkjQP_FF6E_(2ZpK)UpQMpx$mknREGxNuCkOV<c_nHXz`
zpQ3HyfXsTe1&I5HX=e-)Hj(nq9=Rx`KdW&cJ{}zLU7JfK-czlT@(oFkQm#Z1;>lkB
z#1?6O)E>Q2>#ELG<^?|Mnev-fgOW{d^`OaHaVUt0_v2V1anQg`iefG|;)&>vJK!uz
zO1<H9JwBy_LrdqIQQ&wB?Xe5(nOf-PQ<6*g<3z`OQErV6^L3ry(Ue+l3F2aa>NM+@
zh;9Lb;3Hmq3cIiJv{3;WOFQ-D00#ZWy-<n5#}ctA;5Ct7JURA-x&s)Hlb$R^3w$Da
zHjayc(-!x0MA;nUyg$VtUr4Gsk2sV#DsK2qv2gwh252)%JT8on%f+;xSUF0SuvRnk
zS{KG9BJZaVqteDZF5GY-YEf`wBwG+qPWmd&o9pr;wJ7_M5+nqH)D%H&LNdhUFDmc1
z;55JcW5=KBE0U9qm0<aQwdAI(%QfECe}K5&p`E=)8>s&*%$dQ&?;X<~J;NJ9qKXPK
zln?Z!t&X{|A+IH9o>RSR>T?A+kfwRH1Rt4t%0oO1+^KpC(B-+3-};}wb_*$Qv3F{u
zHw9cNEkOeb#h~q^+yL-@024q2jiX#ciiUdwk4D<(4{>uqdCMz^dpBtOXSe$Amk?!(
z3#ZT|yCC-`SpAE+_=L*e%Ip8XpZ|OQ1L%I!uYsHEn~>MhG#r}9)qov(h_!FI#{53?
z$e>~z7=8c25&%LbR{XO5{CVKdd6r2a!C&5qNk-uSU_op;Cm%OXPwo)NF_fb4A2q>k
z4mvM-hQeDg!F>gzqQC_Llgrq67vPs=f-690cU)JuYsQBQe>M-M%K8tQupI+;g+5>Y
z-T@|mVE48JEb}gN+S76G>S#<W^!@{f3=_qK8zB*8k{<q{xRECcZ^Z0>kOmM~1gcpy
z_U5;3RH=ZA1lVG|Va-4k{qgb~{oN_B{d2u)U;qCfA{%(A^#Cm2upqo@T<Ko_UB(;2
zf$%mX0hU6}u^Npk0r>xfi~s#4u~KxG@3}vKfw?RC-akhq!1w1)KNinI^wngpiIgt2
z#v1_=B9rvLY|VFGAOVhuMQiB27Xc9EfdG%S@g@Gsh?qC*`~1fJY>Hlm2E`!bFiiw7
zu>W)H0!!eV`!|p8!SFAj-Z~*bTbQ~u4TeZd%G~|W?|cLEjo0`VT>+|RA3(G?!4(fE
zDY*;H)n)#VJ^>I9fvE+K0exYdDS*1j`10QaUH-$DJXq8@+5dZQfXfUMgC!qKO~rJG
zq8eo<M1A1I_}`!Wkr?Vf`~}!X5(u~|0U9GGCkKGsL*Y-m4&KP@zjt?c=g4elI{)(*
z-x_i<ciRBw5RE|vKxpY1OggFf?;W$@xYc(7W<c@g;%FwzAm<GL{2wtnVBQ9{#4P_<
zfxIj*o5zsb01J*cw(m<KiG$J^V91@oO-FqNDqH!c?6yY=Z`Qy69vV&dR=DZy8Fc}o
zxR*TZh6lg?l5p8Amrtz#qaSb%H0}MUYaIArN9O;0WI}KIiZ_)oz-Ros%NmU5_ya_8
zy8!$i^OXx;%I(AdJUKc1+lLO|mYaAG%6BBMeIc+2tVTWD`&v`j|99QyRJ%Q3HmOVM
zKATH%cc4_$uN$KM-%S7Ckk<u+V4&ZlZ2<XsU_y$?As%S6%y4`_Tu=FdAxkVScd^lB
zfz1UdLN6KzMZ7q=|I^aKhNw=`vmoPUA<V!I-xcrO`z)*bpE10|a|hYSu5cE+D)R5O
z4oTbq<EId~=W9QdF9=-J186HEgogO!oYxx|EEdamZ@Z?B?T>L^k#qnOHVTCRvmqGh
zHiU{%i9wARH1h`yZwV{BKX%&EYw~mcV5c|4E<u`<a{lZ6l)T*!p*n-Sd`UNhr;Dvx
zqx(x4_M@#l7($*)P!_YEy1ssHT3T6{;8){M*Up<j)KCP)(Zb`T;1lJ}ci)SKM5Vao
zO%q^%_g?onJe)28+SQJ~w;333PBtENdgSl{gEp)E%g4B{Cm9j`jPS1Xr-*Q<DvLyv
zvslk3n?|ZFfd<2uRt7(cI{J)S<I4~{MXYR-K40Z(mGx5B?B7GYKfB3QGhcTBBZi|N
z*zpj(cl`bRXJFJQ?B{Dh*CRgf2YADiztSn^N8g*A%oqFsF6xHlCH0Vqc?J7TSt`Z;
zEyv_;L}z9ocTo6Hg~wY4ZYDH@vs}1Q*0q>Yq7m~&2nWq|K0!%v=0{G8$CuJaokAg(
zhoNsW@kxp-YHvONH(|K9_nTt_X#Vr5s61vGe56<csA<tx$inY#4O%sHIYD3y?2>}+
z;-+69o6C)OKjrQtWFF=}{$RG4zq?}UNcRbiz43>4Bl~8PS|p1>kt2^m$f=8L*jsV=
z11m`G@o(bgk2PVg)D)nX4RAwpB}oHALLJUO5*0?b+8q$@Vw_FZKO0H!x}WfVkf8yu
z5{KN0+0NgQ{9xY5EzETL+QbSRU@~!X|C!(MCzbP@(E$Gf77C$pmkJlzhjD+GqEt9D
zDQ^z^t>!Az{nhb*?%UjR%B&79={Z16L5kxmpnCZB?c2k{L++2nFW*BIrWRmGEDRHY
zSx}$<KXms?te6iwx8Kpb(F=uNAlXOfqSR92l<oFO;^jJ?Og3?q#VXuq>;(qUstx!L
z)t=-7S9en%caxdH&bd-A!lN?ooy9(|yei~(r~Q&&k6r*sn2mZo?CO6cgXU+X4`u7m
zftWhgISF800ph>t7UiHFYdd*#TXgV{Y44o^MXT6kYoK4?>*qNFFLL~?CgC5*_c{4h
z*(yw;INurO=o~^~Ulk!};_TQc*DQ=q==VyK<SCG*DBU(&jGcupR@1fAWNV@Ts?X$u
zfNJ|6fT#xm^CwFeK#M{)ERryGR;K^6<ZuUqu;Uo&=1WtDm`qq$7?@-)^Lk82<16wc
zhl7`07}Zp8I1v}bb~UPwt1_P-w2-(1>m9jP!x4~??l)CysLzn@dI1eN;;PT0IKmtM
zM<U=cL~sP?()$4Eb<C67rd5^}Cv5kd#x?;7+M5X<DZ^<C<M$xi@xcfwz3e(cFh;-P
zilxX4MROAPaoPlqBo{ekYTQ5J{CIuO61b@Xn^3g-0@p7DK-Ve~r`ZvhZ0L&oIkgLb
zFJ)ndUR0Bu&f9WB6l}V>|EtR0PdOWf^!^XW-Gc?(L~y#60=+3{rhTYN$tayX`WtY#
zU6KSX(bt93iuK4av43els)_<*3;)zq6}F@iyPkj#+<@+qKQPPu?&BpEvMPUj>Hv`X
z^A7ZP18bYg{ZX6PPx_H{$%m?L5&?d9ad?e8j>(!btmmnAuccp~6}!>TEvgn`8P~|<
zM{8ny!oN$5xNf-|tF2Qt1l?KGJOMoBXu>eZBftoYhX6*x=Hwz+0>Cvyhzb(|1?mWT
zAK3UaYvO4wy+c$7PwYm+<0Plm<DlrPliDLAvuFwg9d%8jSWh}<G#fEo{BPj8NMolq
zvp85VBH;+g@ZI0t$;->13Q$qO2Eme=NMUS?#>p|9?-z;fatLMD;M7D<Yj7c!B3aMF
ze~awRYM+!9L11zDkX5dvoI+0=QO5K6o<VVg>x&9o*9Y?0u|a>iPoSAE+8D$ho~4mM
z7l7C6Ie{HT=3P9HoZo1*QE_$U&@jbs3oA0v*4Dnf)j6;S-cbO^jn={y@lXYNE8773
zILh|nZRx^o3zDA>O~Z41`8f6U%11hOF}>be-Ei`NYc%W1WZ9yy61y7j-gKy6n&!yL
z>U`?+OVkPW{cw2Yvuveob2$e0^l_BDC}(Izn@Z(G)|t}Pk&@_0-j*=M;_aA?GpG;Q
zMQW$d$KA|~g1WqdRR&h{c9K-UgPtOqV&sO@X8_CEeQzWJxTMi<AzFp_hfxG{4w4)0
z5-i+tri;K!`Llm3C&Ho{EZKU{X18*@P}Hbb1z@1RW5h@IV2cHEZcxK)a6!wVp4o5-
z?5WBH0u*Tv7wZe3laZZ4En`werkk2op9@7;*aPBm|Ni<JbeJN$EXa*(ygm)hE8z<b
z2mdN2FNkY(UTGULyojBbb75xS>5y6-ba!AyvEOf|J%m8dlT!#R!QX77z+LNXrHHxk
zVMTjR-6fCq{c4uHZP*f{q5uWUxD&B{a}A4-Fh#t{!`+UER@K=}@$Uuh@LT+`*D*Tu
zs^3S(jWsY8Km)o(T4u`8`N?Nsc8_N`oQ`wh6u^_;xF(^2w)hIfBw4N`x8-D{*>H9c
zQ@G%cS}?*tfDelplFA((3#?orgrP(oK}u(4WL!Rw6<VgmgFDSq)MuinCokfTNEu2#
z@i6;Kmqp)gzsA@x5u(A+=stN^4A&EuSCan*?`|^f-<Zw;N)O^?Mkb~*D3(*`WO&MB
z5udw5U>SsHEDAIL%lN;CEdb&R*swcJab!i%K1f}kJgV|{d6|j`B%Bi@GEB+2Z+h7p
z$7S?-I3dPkzejZp7v33s<Nszi2!W3k)AQNwW($1r9rmJ9t?VOIO!CBU488Fv#+^x6
zpcbXs%Ndj5Sm}^<cx9|kIqCQEWbcQXYP(BuKxcrpHsp>a$AlLe@RS+ej^v%lbCr99
z3Rf_FW0+tNSPDmkR^G;Wl?vJO^rJQ+JHpr(NiuPyVeq!6z(`_0Kx00U=3sh^w~7?X
z@>_Y!jUcR+VqAo50)7=>bB`YxhDVS@OCd9oOfPA+ET`}k_#th9xtQ1^{)WEll#YND
zlTk6OFGRC;(*w{wp`=31*jkY!0RFxgK;}>PgsX**&ta8(f120;&gP5n0!Z91u)J^7
zyj%si_#H*6&0hd@jkc^IyNicL#HZfVSG5X{w*>O4O;lkepZ@p)Tad<qKlavF_==Cp
zM&f}Sk)NHt;$i+<0umy@2=&>p{kR=R%aaIkGsF|3XHk+^xvYp$70z#ms*2af&Y@_B
ztYY=<V?$$9YN`XSZTsuLh-e>GABx{)lt<eu7J080q6^9J62X~xtM~f-(@I2})gUE`
zc*DkkuNgY#67FVbf@}xXDm#C_@1q3czRz435o<n|%{$6;qh<oMlbn_4M`)FJps1!s
zVKJ<GgU-1Es7;pQ1hT&@A>~(P*rU_+Y>J1$mjtdDoRT+lxbfw2wh1}!2zgYoR~vbR
zEx-I4eB}^4P0$Y>g8rnRM)=+lzYk9JhRGSKk7-C31?4A?FlHpur_#cR!(BTmMcUBM
zYx|zDX5i3e{MIC3aZ`9lOz&+6oMn)d&>-un_<qZ-p&m;1Lh)_$L7sy_&xEF&-~Apc
z!R}bYzw#wrTZ3)uN@2rYj%jM)Sp%_xwgT?427hUK-6|d3j`TUKt?ET{!{M>Yo}jRW
zlB#KhUQZX;xTXzr2rZVF{|>I=pWQPR$(44E5{qOA%T!Bt)Wr+usavwpsuL59PoVOW
zjvXFzp}t<NrrD%J(5Fm;#TB}0ThYB*k%BuaewFHV-~m&;v7lf;U2$Xw+A*&zZ{TD<
z0`?*31VDxCi>WCe7)QSyUZyqw?gNf4)*QP<6IankB*)uO$@<kXMLcE|d$`6mopzuc
zz80y>__k=pLEjDO`)Dv6$|HEIzkXTa9rq8y2xhLH{P3?PJoSouO3RSPM>!_=n2<Dw
zT2qdTzKOC#TGCxCav`a{8oYccl=h@4SV=BtZ3_5RUqU*HZ<WVhqN@@gxj(H&z4ibz
zXs5Huv_>S4#3UNQq#83tRojAEMP)LwiojVpu!(93$7PismH}nAwZ)xkwi8rcn?Ihf
z(FZPFk7z9}-|UT!^3?TEhcHs8>EiZCXj{juCPJ;Tuv4ts9#jcj*&18sPL}B-6E^ri
zLq4neVQ!!tIsYzJV}ix4J-kskM8~+Qt@|A7{ectsM2DQfH;4LdUN&kBaV{Z9Q6A%F
z^38~93@M2iOq?1yvDM_PX`dh5Or@#0=FBQ(R;gP9SWNHjl(S`))t?)3t$gGANzn#w
z{_-tcO5;$dY46_F-+{cm)wgppYHu(^=fo)FxNM);>Go3Qa*4I6ZJ%lv^{%iZ+>&Q^
zJ6@fCDh?f1*9sf+_&tR)qjyk~$0ehGl_E#N4>g0NOXH5ixgId=j+{h_;1~`gOly48
z0qLNp(528oLtCW;Q&6_h5CREM5E$_TE!V%m&iUDj>gzM$U*YxnpY^OpAje?z%475@
za8l_0(J(FNG&V)j4n#!p;|rSTuvk8aPza=6h|R`QaKS7_rdaDCXVTs}7#15movdea
zgthjMMj<A;1e}|x@rUY$1zT|y4pJhsOV205Ioz|$@{)>l%%lMZhX_bPG5XmaxhG?B
z8c8`PW-`<AG;(FR5Gs7~!akLoz9r)){w>P49DChEGel2vTfs!E<*^#GWa($H{gl?Z
z5x9I~xE@;^!(O7j#Dmw4PSZquW{l<anKA5H9wGA=&(nFCY|=4u;kvMKux{uoEQAA&
z55<mIqU$D{eP>TBP<l2NFY-y3Iea!7gFDhn2?M^u?id-2-?vDj87nkeamJ4P$&en8
z`hI*x0uCWl$M5dmU!*Cdt_>X>rbZ(8(`l{E*KYIz;T9vbW>-6G+mq;uZ!iUEV5(eh
zhpcMc)g#S<zIKL7%q1j~Q^UXC#Ld5*rre`#l$H5t{o3-&k3g0t&KtNZqFPewR;n9?
z_Eo<Ba?&&)&p%2w8PxXxgrSFhONbz1`t%i9_URl!Vb3KaKfsLal<MTIVOw!4nEO=P
z{Bl633!Sd61eQiL5@S6@+kT*(Zcu(s>Iw`^p}GJD$U}!m<O`$dbK?%KNXg)f@A)Ap
zE9$->Y^6wis4a2%>r{moF{_yUx&3Prlhz_w6Ny5QYW`L)vh9D*IO5lv#c4-k6It(Y
zWz#dG59dF2L<S9Px!h*{3dOi1W3utT_?vJ4%Q)dF#iYL<@hkl-O%eIQKMDOCGLYRk
zxw46{aXqbFiiwh`PL%|?JJ`eP=)I#$oXbbl1v@ed?nX#FH5b+rrIyVGG3Inn0|T$G
z6hd<!v64&#)#}E^0%CwtKPUbwpPTnz)|HNe&ewRO4(IE@y}NIJ1oJme;6x@uJ}dkc
zO@-6*^(frc681jxV?Y?jE0et>(ohOi?XqF3)Hb~2ay$Up{aL6rH}*ZC99(Wz|3E<w
znwAaj4C;*Q-^cy7TasP7(p^zj%*~MS{5A8Kgf=OU*RM0Dam#u+JB6iTJk)}N+&f(^
z(}{%*p6L3*xRu=dY#_V9CLTr@uSb<rH$h<i44TAaOwAYPkn~+0oXwDbA-}wH`Dgi_
zs`WTAAtTG=Nq^cWwaPI@Y5lrgmJ}1XVNDGQG4MkSl!DP*;uGa-pWLMTj5h`{<)k!b
z$w5Y9VrJRNET7%=PFZB*l;;mD$3SK=>8ZVGz8sd+?b233X}$Rhvw%-07UJu*<d-Pm
zhokiTf!K?R6O+}(-ZiIM&0PGRQC*kQKXno+#vxONs(TXF|GY5i`Y<EL1Gnqtpi@kw
zO*N_Bt|1HxgJ?S+<;43PQD$_)x4S&9t?+zGzj?E(s|gbBf9X`e-U&F*dm0mgbUkDr
ztQC)uel2e>VJcU;_|DJmbh7y}^m2aj$52W=-aVui^?G%<kS)Emk@)xHxPQUS4UMu`
zFXh3cAyJ{JH&n+{B9K@JgKYzcxGSUCfEf#y&9Etg(JJ=27F3^$10%7E1NLZ(w2)~N
z9>|@|%0Uxw|Fb|Jg;V~$Qc>tOIY<0t<l8aRjq!pLWn?=}Wwct>RFGzj8*zWiE^$i!
zO#E~&Jj+}+?+T5t9~%4csm&n8`uOfUMPi>v10r^^Su-;Y_;q_02aBC4DMt_=+x|UN
zub;py8NnwGcj?Uw5d$~=(i+G)BqEA$Vp|^7b?{snn4=si7Pq!IT@|Ca)SDMsQo#89
z>_hgfg~;=UQFA+<-hZ)%D;+j6DWU}Z(e5AJ@@|Wk@|um%u5!F5dd#RHt}|NkRa7#U
zQ*Guf-CtRzy`i2k*_tVjDMYwM#kFn_R0JV=6*h^!yHF5}L#$A?Q26&E8(k8CVE@Sh
zP&x(#Cq$t$JQ&?p4CMXK><gw|t||9kHiQ2C1%E5q{Dzv-N|jU2g)t#_fA)5?QMIcv
zTeIt#E@vr68LzCT7eR(U{}orwvfs=P&vutQ{H0Zn?pk`Phu>nwvm|k^Qc#K)B)B1>
z%jV!PXEGwB++jwnu@JOSHW;b_RNPUl<Iz2|qE9I%7+oF;z#9QC1+@@PutMAZe(!J|
zrpW0Gy<2!FG<+Uu6wx9wiF6Iy<MZi~*}$w5#w$U><D@%A1%Y60+q$G%dOS#EhWd(r
z1!<e*q42x7g5T$Wzr~L@ekd8yeB58u<jqzCT*4y4E0G8Xe}9&#Mp7~WL-wGC)a3Ij
z(qrItm%$2lENbxYT8)?$VQ4*ae6HzYAqrk?V!zAS`Nu6vAdHfk$y@G|#oDqG<SXDC
z^}>!vTn)Me#DXwm*2@u9N=j<_X~+{C%UpEOWH=GAMR!>1luKYkqLfEmYcD~sgPo3u
z+S0Tv%R@S1=+`ABD(mpM(Nbf0&+|C2&vz`eTpiJU469_wM)5>cEN#@IXgU1_QKDrU
zXnVhS*EZe`X@?R^B2?CiI=#MwnBQtT9VmO3j#sdaXkj(4)Olf1jZqbvezlb)AdQP~
z3{}$+Nu(ScyI-yV?jmavriV8jqt;iL(tLh!_Y|Mac0#4_)d+QH`=urFY6O&!4pBlj
zHx)bwn$jTTgQu66DT%8z-#XBQA=EC3Xmki=Pb9O{K0m4In%BPrW%}V6;U(-(n?8vF
zK^wR8lSzxSmp7)Rg*etH<bw?S)Z&7dL3s;7no*`ezYzDmG^Nr^%s^N8Q!(yFI`^#X
z;=wb;(K@4GRB|B#sW<udHsSY{P13uf&WqbQ&a4PzygNA4xX?>~Dd7Ahm;uWM%$BEm
zoV#-!u;n{wsow6tS6#NZ|Ij|`eqTG=&lk5ozPet%R6n#FiQZF`cehEq@wXm^>Hj3Y
z$~fX9Y}aemPDvM)csu-ibR@B1Mw0ALbC{L@rPf=BqVL}(lr117p^m3x`y_|KY5Ge)
zly3#joS+mlZ+qOc@KD1WXuG(;g6x!<yP0~6s7Tc?5o6l9-%FUY2~+;H(1E$fAz<G=
zAE{*0`CN{oWm__-EGfOkaEq&c2~_HU28?|)?iDMQjZ;#BO%C>b#1lRu(IYuFC$gLM
zLSg^w%U>$y%#D{tY%B;W>ioGh7z`aXbc!)LFSNIN&)R+6OjmTV6;C9FJw%x5@?dF>
z8jd&Re^c0h>&IHsESDYkdvVEf!K}R$S-z(i7h!dIY15U_cg=87;61UYRM&W4VeQdN
z)Z(Zh$%;}m`jdW#BSBW*H~!YKaWho+sU0!E-DG9ZE2Z;o3oz5U;&cHr#$u!{R0tZW
zG)Ss#gb}0{>!sLJ+C+1iZyT#{$o?A#USdBy066a^cmTsSjg_x2Fk|U>orNu`KNcT_
z#B2fP)yQ*A3xyQM`Vb|UfB@{>PIccLC){G^L!!r$=6lv0s(}tcJ&vjV42(%w$>3k>
z%G*lMKQ&HFI(DD@$b3D%_n=Y;-tIl^eEYWZ_1yS6u|d%=ZX?m5swaN=ixal(NB7Jk
zHMMQXFluOTB8E|b&DuVgZC`CCi@C484W2|}v^gpY>m*8v<g>pX0+mM?3e0nRif8iq
zTi*=mZcts5A8phTe5Qns+6=q~3~{ZDG4J-~fpaR6ttG_&0L^BiHMJB_6i)<a8mldW
zfL@<>^A%0H?F8$d&Y|)l%b09*+TA5BG>e(V+0+7BShxzFW5P}x-Y)Ai!Wp1+Yf#8y
zm0m)6G=G*5@!Vs?oGb(E@;UtO*25Jdv961Lw~6$o<4B3_>^T|K%fs<yw4SIBj1&fG
z1T`8I$+)23-y8h@bmOf%&zeD|Fd|0<wmJ(oGW}jkiyb~Rr9=p%y?R3k@Otp9#LyRx
z3-ag+al;!v2a|{_xzDwj&^epRhF*)c62dDvb$1{tHCQm$^>LNMti44Os9lW2DszAX
zW%8q*FkcSh7f1cbTFe3*stUX$F2i8{ULhZg$xdtkw8fxM<3pjqyui;#QZ+4AD2D@$
z<$dgl8Av`!6uOf;Z8B{IvN;s-Ex*uFkX(UdT??&kM~4>6mf_-DZdj?texet$ZoIQ%
z9}QWkhV$Gj9r8!Um9aryqWoJ5dcUGgXmeOCb5;<8efZwq>;RT`MFibUe6-v2L*zt#
zNY+{Mu>%w;7dj5{Q!pnD1QpDRg9{^3%n81!-1c4WvNZU95e>Jj;3w>6Am!E{t18FY
z+2Qvu;L`MvTH>9uOdzXmPVa04i4xT>TYD8>PCg$e7ukKu9EF|leLqZaNZRITa*P%9
z%$@rdIimJX>x9zbiI(`_yno{PJaXY{cpmePSCb1RJ{v&zZP`+``R<%)-0)F|7g8qK
z0za_u)q4{J;Tew&j+kgOQry*Hr`iKY$b`s~qhshwnPWg6(Q}swmjngFOnq)H#}lKB
zU+`{=%EVsa#Q&BX{x&fqg=m?({A*KFeSco1jM!NsY^lVR7mkSDAYWZh`H!6Z?gD}Q
zJMeN%<uF{%rzh7+qPF8VDCg)7#QsnOxsSPnCCZn!yVrGDPtV^8O#OV9+oh(P$NM{m
zI(%@LS63dnqd2-Yc_i6nn5IpAOD%^FhH{XPP#zgroI8Max$x&}xDQ%L&TLT^I7(?K
zkKIdmCS5k_4QDpKZS{`K%Mz0aJd#5a+?(D<Qgt(E>ZwX~Zt}U;eOHtUY#%iietR19
z5Yqd35Ck$4{Y1`>3(=Q_w@5<l6c~?))O!{C6?-3+fX4sgP!D^7gTk;>LwS#(v-P}5
z<Pgh~t<;_l%aa}3gAF4={s`VZU%cL^+ZCll<{Ul!xZq>SXHS-Y9oocin@1eKLA6zv
z#CcUCTlxtm+i;&Jsf3o`Hw!wRjQxVKBmCpwq_gTxO$#u2WJr-azT5ufRrg8w&<1f{
zZKQ_FiJyHrPT&eavVHO7#z{bM-^ElN;{_%nMmt*jVvC}@=g<~Fq2U;^s1A6{?9Jfu
zwUYg`Y3nd`KkNH$hp!UFtQg?!5e$UGuRHkRGoe|OI-+SevmN93Da3=6U9L(W*D8#<
z=ag71de@nezV3WjJa-u>8E$f_<?d(Pbf=Yxi$JEBi$1Y4TW0qh<_(^-X|<jsw5(2-
zlGPi)hFC$)9IhpbchJG3Uq9@HuehuHb6$LrYpU5M@G3p`fH7%HJ^8&b6LHwOcNJ?o
z?(=&cZsV0V0cnDi=%Y{ly?x`nn(!1_8t%74SA4ou9(m>YuGwvBcDGI7#NFE(*(<#Q
zJF8}`6cgN{o?~XvIbKLpx83jg?kSs-!hXt5NM*6-|7-8M|JiQeJrRUjv1jek)?T$5
zL9FUPQKL3hMXg#DBlaF06eadUsaX%Lm7=ItjqXPgTaAZUO$g4Ne$V+I&g*qfeoFHB
z<TLK;%Kf>o>v~`B_lp~t*VBD)qQ-A!Y!T99x9@aR!agp~v0pBmRr&*S(Q~4G|3%?B
zR%Q?9Ig_6qBU-SBukK8U^;r%%3^r4H1(fSoY=5?Nw(B^st4)iMj5??l0PA`f#to*L
zxH*;NP+TGq9DZ-p?^Q_H%4^&2@w68^nhHOfQ#T@b;Bt5PbA8SVXZ^e+7>v}ojcxj3
z2&tRFglP(BAoYE969VYWtj*a_SZ~6hi_L~61N&8T^YmP)*`&KVpDrW1?q)PT_xp#(
zhV~R)r)94u4{b?BW+97<uhR?Mq%Xp<zII}YyZZ5YRRrq0veq3wo|o5|9O$^3TbZl?
z&D`(Uw2d*RT5BZt0bq6Xr?0#O@h)FWpncC<^!#y3>@aKb<%$T}C&UfwE8zJnnqvoD
zT+8`k+?WcyUIf~b9y_eiIp(Co0|9sI!3`ps+rr&a@=&l^9+n6HvJFJ!=&k?$Au75c
zYOk;Z_@}-|CoF~B0;=D^;2;Ue+i*6L=fQ-}{GTD)5EI#X|8MKJ4~em0-vUGAV*dZ3
z6JFNGgXGo&z-wT3zOY1Jtanf6LZ%gE2+hwxdrtNnuNQ0WUvMT}7PlxJZsd-+yasYO
zz%b{bB@xR7tz(GJTp7g=OF^{{picySd6S;_Uh`w1*h)DT2UG<pg?B*p=?;3}e-$@K
z^zI?FGyfO}MB^xtPY<07p4JxrF_9Te5U^Us0DXjlK=)zv!K!`B9#rin#bB{)GV~au
z@=Ha%61_nuELLEy2Uj2j4G4Lv9d_^eP2!Gl-mro&#}fD&?2e2V7m3fOi@_C^P>zg*
zEs6sR51}+E=B5@M#{*~JCsX@8_|NB@Y~WP?^JC>q+>Ps(+CZP>V3Nqy#rE}vPJo(d
z%{<mo)&6sAKez^v@CES7ai4}))qnTbgl>6@^#VRj4^3W&wo}f}J(P1&QM*r+8~z4;
z{XnGK_AGx#zi{9HeCdnOh&|Z5Y``$;KVy`F1DfW417+yt<>i)@nd#|nAWhv;$7=WX
zfkZE;?Z&L#z>1ZA90D*S2mkIi5pIwZzuO(>M@0qaFh%KEX#)l4neC*DAF#ToKv<&7
z@1>rUHJY@){tX-Yk2m01su?Q-vd4&D3Qzuw1dKWxhdtSoK9D;ES?|#z4pKJyu^)x^
zFI#74`1oLo!twkHaGIg#C~^*z^A^qQRdWET?_|K^3ZUD#7XXP-a-0o3BW42PALQJ@
z!VzK|4XV_LwVH~9nMi+^X@F<Q0^+_JKa)gc#!4;;te#410dDeP$pUes4B~?DDG^7*
zYTFE`E(?shmJh)Y;e&wmdI*tMRpQSekHViKqO|zm0~}D-@uq(aP*)xeM;!eaI{{!6
zavx7m06<vw5;FZIPAMwPnU93BC|d&7D};|_u0#Mek4L>z_c{TClmE`*yT=}<;uq|&
zE3eZxBS0Y;K#Q}qH=1aQOdKOO+meMsZ3PiMvMxj5=EjvtRuqQocjTWdh@k~iKj1)W
z&W_4(K*x8jT-vhh2$%!Sg3<DzBC=MIjj*5MeVc1#PjM<d{Cnt>pBKQ`^S08k>f~G*
z=-G<eiUf6*EgJ{Fw@aNf`wc*qa9iJqk)jYW7JbkPz(`P`bL}o8-+B((zNg%49aUFc
zeX8VMPdghVB(-*Jho<8+orQA<zmH)>;{i>L2^D>&Yc}T?dTMIMNHRzSkfFa2xYsfF
z30M?}f!w>TOwoSN*8aC6T(0WRWTs!L0A=ICtGFX(%HQWJByTF}RHDty-UC|Roq{oR
z66982Xn@D-y}KcxuMxq;$6|L>yoFv_2P~JsY4`%=2)^NTU}c`}MUe#u+z2A{$I{vK
zfV}hOSyrs5j%W#^{vrUq4z-(h<xOTL<F=xIcT#jHlp4Q&{d(?3^#(vcg}=Y@OhYL!
z?2f#hzmfRB+VJ@O{Mj?Yi2z?l@B9e3b;!xIm}Y%(4C%cyofbkVo%7R+B$Uh2$P)@u
zgkt;V4+B2x4VvQrsHt<4UANVZ*6`QsY;+rTavHUF-1%kFHJduRe^nQm%j~8bHuGW6
zI*W}9G^$`tUHiLhYkz@A!fPqpVOz$FmvdD53}!L-+f9pdQ8<!%`w^C0*;xA~PM9=o
zOnm7U(b1@EJ)#SGF^Cy;qNK7HuK{?b*-j=N^v_o*G_x!0)J@JBO{=HF-at4GRb-{F
zoERua04$#`Zq+0Pa7*7036XPr{dnp5rQbhy0hB*7joA3_Fja}WCs#oGcegY3j^Vkk
zK97kDRGzJ<9TK^zIjh<16S@VTjFr+f7fB?U<bM3r|7ANB5PaqmIZwiYD}$@K=5$<-
z1OG>oE$pH?q{#}=4_YZGd{jO@J_4&hNgkDM^Hm35+k2jb%_55|N%uu@mhkilfJ%6+
zBpeS2YLEOFS#`;ZF*J(8*Wg__y$5ZLqfeD++$U78vR%FBD8%crV`k9QYH^IG*Zm%I
zdMuZz&*xx1sY4Y#6*e7`ztsAC2uL5I$K?aV@ehqma{BvZdo7{CJ#O`}ADJ_#e0IoW
z-?ED_g#c*u9jt-QZ37q5I=yEdtfi%O4b*6!ht3JC)CB^p(@6_ILnRQO8M3MHY_oHK
zDlR`$^U2F}eG^B!x;7CxCF#S6)}QOgNva>no+s#~2GZrP&e+<&UdnSY*<CME@yfEU
zT?wA9y%Z5KCk|<MB|-elO8TmYd!$T?2KnSvEDyA22rLM9(q1X=bx#mdwYc>svwHcl
zj_I;5Zo5LFb_@)cJZbNLl!a_=Zkj~j>9ffAW=>OJM^+;o8prZfcWd&U*J3C7udE_P
z^wI}!iq8h5a1%ARzj{Wkf%$wXF++QwUYDq+k!>$C*MuwG=JR@%>)&jl3;X=*_TOk5
zAi;e4W*WNW1#ePRjbq_pED&1?shfl2n0T!<XG@+UxSq*kYZivGhghcOrMC$Qp^u$A
zf6)*MM1@BqboyvLoASi)95FfMrf$X>mm`mPixH|CLj|W}iVCh1wBw4MT+eqRVj;2t
zf03`9=y6u<u2nP-S+!}E1g2}u0~&Yv8Et|xy0T&p-<h)?q(U9P^{Y_Xq+8H-iSVe|
zPCO%MpzcUDbNsO|1vW~LIJovwe!L#SIWulmcIID5g~GxVfg$O5(oBIw7uCbQpG_tI
zx#p*>vSJa#2^F!GaKKT2ZhI($GX+zIJrm<X_A{3}(cTGLm$Vpjs~wbrf^vFboAe7i
zCMak8SLKo5=(O3xukDHqGhm|F^m;co_3w_r;M1uX*p%aaCLoOD@CI`X9wu+ARZpgj
zcm^O6i4o2NUrOptZ!pmsy$Ri}a8pcm$QXGkL^xp8Tx9DQ&-dVyiq78y4<y&lbROA!
z^o^i*>lvn^)JJKl&{?(siFK{K>xQvys;wsXG1)3+OfvSrL3I|TeZt<d7~<W1bVV`&
z$qcX2j?W+1x}2|H+5&SS>BrLx-8b*D1Ju>$@VLF1-#z)BOphQDW$vQnw+hj417n*L
zz;BpHPPTnFwja_MDMWZSFPAeYD0se4j4Uuqq<fZT57~kcsixtK<)}G~lUrccwO?Qq
zkH?b{3Di6Wo{A{ygnJA%dOAivY@dRec5^i5)Y%2MB4>|wj`XY1yG6517m8N%WN-s9
z;60nOqTZs-2AH8FMq3`o^78BlFqcjURSwfbcLK}R<l&5ZR1I%!5T^}62}nP-8d1V?
zA5rr_Xqs6xrTOC@K<vG!yZ(=a?pH;Cp;Gw#gh0UG?)PWc-U^rUwnCcRR*f5N?|qvF
z3see!)#|pXV(P*|C0a(-%i+&h{GHd{Mcc+s=J$WL2O7()=}gTl!=v@$^E(!eUH8hZ
z296Y+CMIt?Gfx~kJl=bV^;{O{!aqbQb^Q3YlPY|?YWy?p;;R2SQRv;oSP9+eCtCc>
z)F?KTn06CVd|t?AV4J?Ih|Q-d)RjRO;gz>O#|2?Oyn?y_8FE|k<6PY}ZSqKI=UDfG
zB_dAC)8+Ni1BFQ;8dy`v$NotAtLJ*R!%t!-i55yagV^5pOy`a1Qe{&HG)vXtPVkK;
zsrSOXlX7%!G$G7=C^l=kEa&3l;?!JRb-kMYyHvCzBZeQW`0GT8Yp?BLrRxU!UiAJE
zkg?vQ=`E?R)bnSmWmad|g>FdcCCluUa(wvW(ui`E_vV|GlAR^X4e<LqFtXG6IAVzd
z(&%@}?1~pTLjag^`P*BkWdNqF47*~rv&H?+)u~jkNg~uaoyQ>lGAjHl3*2vkR8>jM
zmo8eG&Qq<|qucaCt+R*-9@M}U!+#+DN{ToCMsY>9+=0%XQaRKYQ=+5q*gTy#;VgqY
zyjLRg1-(~g0jj5WpejF{`>CFogzIL~Ir^!dy)V(Zq3C!+_8QCPnmt!RcF9~dj``c=
zNauVMo5XkKptJ8Idotd|p%Q*;R!KMA#VmVc8kwEn-+CfqkF7{xtD%RYqW8BsRg)OS
zW3w%s8(3%~TZZ=t4o1-gwg-gH%F)nxWJ>_(OLV*wE#AY(I2gxXsbH?7M@4U>{FIyV
z@9W$28J@73gyy<e)*{Y**0M1{ob{GE9Dj*-q0E(AWz!sBiQd}}&EzpfN;&78ZiXe|
z*u+KJR@eX<bq$U|XKP8w(G~WhmyG@I8eO>k(hBv$Ses<#(^E@^TJeunMoleBl-T8}
z$}@+XyG(h%Xby$tR&>jD2CgaFZGB9k<BFdIOkGtQbk-IyD+Uk~K8dw%{bfXTfn5sk
z2-}gSF>QM4HDaA%Xev&-%+vZzY?HHIe}RCG+jV`YB665<Q{kbJuBjk{D%K_mY8K^N
zWm$Qd;1HgH-`ZFpJj6|M{T`TayMGhLbO}V5Wka_vRZ~%m>jpD^5SC9zZ@6ZjpOnz8
z2lAF2Ayo>|<pJezve@t&YWdtTNi73G>3PzftEo9|Jx~JLtaVz3{~JX*h17mH>Pj{j
z>hEWOj)ip*ntr%)wa%tRamB9X?8?$HC$+6wl^F;xS2x;^@IVYegyO%dbTj)Lk}5lh
zmo`5suJHO^0xBd;AkgJ(YRBM5FH*2Ua_TpfnDDwlknIPv#b6RxHf!gaLJfnqt4$Vz
zQ-^5ZaHakjRQ5WVwhZ#+PPxO|$d2(<)VjW&MK|$j63=z>7Un4Veo2)M?GwHSz#|N@
zUp=@&4S}w^0Va-2--!aeE_ptcFR5+V&Z~IIt4|$MPnsJmfd$58m~f))O?cz_>S6h|
z`7R1;0-|~gJWk=RI?1XfTpMGw3+z!+foG}*E@G=X!#Qsqo}b>Op5p$gu4wj#O9-xV
zk5sUFA=<9;48LeBOYYR29E-Pdx(jkLa?rc!vGtdH(t95?Izw~XVe$IBDtiU6P}>mp
zIVR>g5Wz%=MG}IthsX&Z!oP{DcQ30ox}K^WaEYF%b@t<*^?1BvKPY-REqUylo4K89
zJanfhu_chn-k3Zm-+_nhB@9$vQ~Woq_sG#UA@5v#s?^M3N=b@MIC1ViCSlD%idsn=
zUJeqdw=6ZHFX}Rf&7(|k{I^iNw$T`};A@;^lF-nHbnyvV=9Z82x6oQoZ)^0@F2OMT
z;<)pPETZFzFnnHC?(zKFbhBsho(GSU+Yir=sSn$(MP7D!k!2#;_4Q1i@rE`&mZmFG
z!$#3qFff5u$K4`__i3s<^faR}x%qo2!1Ig9>id(l%ggi!j#={d8=1c6Z<olvkSsg~
z3ysvw3@-7czotDeMBKicq>{QLtEB%$<2<q^)gjc>Fg9~`J{}i_p@}4Odxz)d$TLwL
zUjGSdP&FLTwfkn`r@J=#u+KD*6wib!@iZ_fO%<0g`^MYo_fQki-br3h=UJB5t4mcI
zQ7>z%m;NZ$HXdww*4SY6uVEK_$YbnAI$yqdx~jvV-0)cjt_hX7WlRK1Wfol4U8cg$
z8V)gt1#SyN+VJR$IKY~8OnE2$f#%VwcdJ_Ps5;OMBv0<vt6GZY&_T`bx^Bm<J;9Zc
zCTJ!VX)={KV8eJxj%>R}oZu)_XE3r^(JR`sZ_d+iu_(>5xirX+7$F}z{}`_=2hnyT
zGFHuO_tt<0cm9yrdTwJ)^%+X?0%S{(j!}#lBNQHxyBdE<T@bk^>egd~?qgPy&UH4p
zbnU7UC3F*kUe+rWuGq9JH0Ps4pOh@nFMW^&YFMe7VEj);ftHPpWKD%q6aD|q|7(W@
ae@cyEDERzqmiYq}c$perLpA8T#QhhaQz8}s

literal 0
HcmV?d00001


From e1c258f5532c7048024bbd7fb91d261991465d2c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 5 May 2014 15:34:22 -0400
Subject: [PATCH 018/248] section update

---
 .../cython_least_squares-checkpoint.ipynb     | 562 ++++++++++++++----
 benchmarks/cython_least_squares.ipynb         | 513 ++++++++++++++--
 2 files changed, 894 insertions(+), 181 deletions(-)

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index 5e855ad..8c47d64 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:379319350ffd7e7539cf8f11b3a7c15b6f1d1013bcb445b55714c76bf0855fa2"
+  "signature": "sha256:3b47add80317bb8ad369eacd5528c941bb1a2cbd92ae5b33aae66d56ae35c741"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -23,7 +23,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### All code was executed in Python 3.4"
+      "#### The code in this notebook was executed in Python 3.4.0"
      ]
     },
     {
@@ -70,7 +70,9 @@
       "- [Generating sample data and benchmarking](#sample_data)\n",
       "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
       "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
-      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)"
+      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
+      "- [Appendix I: Cython vs. Numba](#numba)\n",
+      "- [Appendix II: Cython with and without type declarations](#type_declarations)"
      ]
     },
     {
@@ -82,6 +84,18 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/cython_vs_chart.png)  \n",
+      "(Note that this chart just reflects my rather objective thoughts after experimenting with Cython, and it is not based on real numbers or benchmarks.)\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -264,7 +278,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
@@ -315,7 +329,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -351,7 +365,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
@@ -388,7 +402,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 39
     },
     {
      "cell_type": "markdown",
@@ -438,7 +452,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
@@ -493,31 +507,16 @@
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "WARNING: pylab import has clobbered these variables: ['random']\n",
-        "`%matplotlib` prevents importing * from pylab and numpy\n"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x105785f60>"
+        "<matplotlib.figure.Figure at 0x10f9e8a50>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
@@ -579,7 +578,7 @@
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -625,7 +624,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "matrix approach: 1000 loops, best of 3: 270 \u00b5s per loop"
+        "matrix approach: 10000 loops, best of 3: 163 \u00b5s per loop"
        ]
       },
       {
@@ -634,7 +633,7 @@
        "text": [
         "\n",
         "\n",
-        "classic approach: 100 loops, best of 3: 2.83 ms per loop"
+        "classic approach: 1000 loops, best of 3: 1.55 ms per loop"
        ]
       },
       {
@@ -643,7 +642,7 @@
        "text": [
         "\n",
         "\n",
-        "numpy function: 1000 loops, best of 3: 372 \u00b5s per loop"
+        "numpy function: 1000 loops, best of 3: 221 \u00b5s per loop"
        ]
       },
       {
@@ -652,7 +651,7 @@
        "text": [
         "\n",
         "\n",
-        "scipy function: 1000 loops, best of 3: 594 \u00b5s per loop"
+        "scipy function: 1000 loops, best of 3: 362 \u00b5s per loop"
        ]
       },
       {
@@ -663,7 +662,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 44
     },
     {
      "cell_type": "markdown",
@@ -702,7 +701,11 @@
      "source": [
       "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
       "Since we are working in an IPython notebook here, we can make use of the IPython magic: It will automatically convert it to C code, compile it, and load the function.  \n",
-      "Just to be thorough, let us also compile the other functions, which already use numpy objects."
+      "Just to be thorough, let us also compile the other functions, which already use numpy objects.\n",
+      "\n",
+      "**Note**  \n",
+      "Of course Cython has much more horsepower under its hood - more than I am showing in this article (for example, I am not using Cython's type definitions via `cdef` here). Here, I want to focus on how to speed up existing Python code by making only minimal changes to it.   \n",
+      "[In a later section - Appendix II](#type_declarations) We will see how static type declarations can further improve the performance via Cython."
      ]
     },
     {
@@ -713,31 +716,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "import numpy as np\n",
-      "import scipy.stats\n",
-      "cimport numpy as np\n",
-      "\n",
-      "def cy_lin_lstsqr_mat2(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg = sum(x)/len(x)\n",
-      "    cdef double y_avg = sum(y)/len(y)\n",
-      "    cdef double var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cdef double cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    cdef double slope = cov_xy / var_x\n",
-      "    cdef double y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The cythonmagic extension is already loaded. To reload it, use:\n",
+        "  %reload_ext cythonmagic\n"
+       ]
+      }
      ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "prompt_number": 45
     },
     {
      "cell_type": "code",
@@ -763,16 +752,6 @@
       "    y_interc = y_avg - slope*x_avg\n",
       "    return (slope, y_interc)\n",
       "\n",
-      "def cy_lin_lstsqr_mat2(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg = sum(x)/len(x)\n",
-      "    cdef double y_avg = sum(y)/len(y)\n",
-      "    cdef double var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cdef double cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    cdef double slope = cov_xy / var_x\n",
-      "    cdef double y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)\n",
-      "\n",
       "def cy_numpy_lstsqr(x, y):\n",
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    X = np.vstack([x, np.ones(len(x))]).T\n",
@@ -785,27 +764,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
-      "                     \"numpy function\",\"scipy function\"],\n",
-      "                    [(lin_lstsqr_mat, cy_lin_lstsqr_mat), \n",
-      "                     (classic_lstsqr, cy_classic_lstsqr),\n",
-      "                     (cy_lin_lstsqr_mat2, cy_lin_lstsqr_mat2),\n",
-      "                     (scipy_lstsqr, cy_scipy_lstsqr)]):\n",
-      "    print(\"\\n\\n{}: \".format(lab))\n",
-      "    %timeit appr[0](x, y)\n",
-      "    %timeit appr[1](x, y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "prompt_number": 46
     },
     {
      "cell_type": "markdown",
@@ -848,7 +807,7 @@
         "\n",
         "\n",
         "matrix approach: \n",
-        "1000 loops, best of 3: 274 \u00b5s per loop"
+        "10000 loops, best of 3: 165 \u00b5s per loop"
        ]
       },
       {
@@ -856,7 +815,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 260 \u00b5s per loop"
+        "10000 loops, best of 3: 166 \u00b5s per loop"
        ]
       },
       {
@@ -867,7 +826,7 @@
         "\n",
         "\n",
         "classic approach: \n",
-        "100 loops, best of 3: 2.82 ms per loop"
+        "1000 loops, best of 3: 1.59 ms per loop"
        ]
       },
       {
@@ -875,7 +834,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 212 \u00b5s per loop"
+        "10000 loops, best of 3: 127 \u00b5s per loop"
        ]
       },
       {
@@ -886,7 +845,7 @@
         "\n",
         "\n",
         "numpy function: \n",
-        "1000 loops, best of 3: 379 \u00b5s per loop"
+        "1000 loops, best of 3: 220 \u00b5s per loop"
        ]
       },
       {
@@ -894,7 +853,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 370 \u00b5s per loop"
+        "1000 loops, best of 3: 221 \u00b5s per loop"
        ]
       },
       {
@@ -905,7 +864,7 @@
         "\n",
         "\n",
         "scipy function: \n",
-        "1000 loops, best of 3: 608 \u00b5s per loop"
+        "1000 loops, best of 3: 361 \u00b5s per loop"
        ]
       },
       {
@@ -913,7 +872,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "100 loops, best of 3: 613 \u00b5s per loop"
+        "1000 loops, best of 3: 367 \u00b5s per loop"
        ]
       },
       {
@@ -924,7 +883,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 47
     },
     {
      "cell_type": "markdown",
@@ -958,20 +917,21 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 18
+     "prompt_number": 50
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%pylab inline\n",
-      "import matplotlib.pyplot as plt\n",
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
       "\n",
       "x_pos = np.arange(len(funcs))\n",
       "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
       "plt.xticks(x_pos, labels, rotation=45)\n",
       "plt.ylabel('time in ms')\n",
       "plt.title('Performance of different least square fit implementations')\n",
+      "plt.grid()\n",
       "plt.show()\n",
       "\n",
       "x_pos = np.arange(len(funcs[1:]))\n",
@@ -979,36 +939,30 @@
       "plt.xticks(x_pos, labels[1:], rotation=45)\n",
       "plt.ylabel('relative performance gain')\n",
       "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "plt.grid()\n",
       "plt.show()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAsMwDPMcSwgMwzAMAOD/AFCftMrEW+2BAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDPMCSwgMwzAMAOD/ANLifp9XPWcHAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x105a7fe10>"
+        "<matplotlib.figure.Figure at 0x10eeafd50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCFElSMATQghRJfw/5a9/wr1GWjoAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1066dcef0>"
+        "<matplotlib.figure.Figure at 0x10eeac310>"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 51
     },
     {
      "cell_type": "markdown",
@@ -1026,6 +980,13 @@
       "## Performance growth rates for different sample sizes"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1038,6 +999,8 @@
      "collapsed": false,
      "input": [
       "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
       "\n",
       "funcs =  ['cy_classic_lstsqr', \n",
       "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
@@ -1056,7 +1019,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
@@ -1074,7 +1037,7 @@
       "plt.ylabel('time in ms')\n",
       "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
       "plt.legend(loc=2)\n",
-      "\n",
+      "plt.grid()\n",
       "\n",
       "plt.title('Performance of least square fit implementations for different sample sizes')\n",
       "plt.show()"
@@ -1085,13 +1048,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPUQAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10c6fb1d0>"
+        "<matplotlib.figure.Figure at 0x10f9e1050>"
        ]
       }
      ],
-     "prompt_number": 41
+     "prompt_number": 34
     },
     {
      "cell_type": "markdown",
@@ -1407,6 +1370,373 @@
       }
      ],
      "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"numba\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Appendix I: Cython vs. Numba"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Like we did with Cython before, we will use the minimalist approach to Numba and see how they compare against each other.  \n",
+      "\n",
+      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up code. If you want to read more about Numba, see the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from numba import jit\n",
+      "\n",
+      "@jit\n",
+      "def nmb_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ImportError",
+       "evalue": "No module named 'llvm'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-4-505a644bdc72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnmb_lstsqr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m\"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \"\"\"\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtesting\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorators\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Re-export typeof\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/decorators.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msigutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mregistry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# -----------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/targets/registry.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescriptors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTargetDescriptor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdispatcher\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/targets/cpu.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpasses\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mee\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mImportError\u001b[0m: No module named 'llvm'"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# ... this section is still in progress"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"type_declarations\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Appendix II: Cython with and without type declarations"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the sections above, we have been using the simplest approach to Cython without using static type declarations and thereby neglecting one of its major strengths.  \n",
+      "Let us now see how we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First, our \"simple\" approach using Cython from the previous section:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "def cy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 54
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And now, the same code with static type declarations:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "def static_type_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 55
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "Now, let us see how the two functions (with and without static type declarations) compare against each other for different sample sizes."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs =  ['cy_lstsqr', 'static_type_lstsqr'] \n",
+      "labels = ['simple Cython', 'Cython w. type declarations']\n",
+      "orders_n = [10**n for n in range(1, 7)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 58
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for f in times_n.keys():\n",
+      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
+      "\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "\n",
+      "plt.title('Performance of a simple least square fit implementation')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkQLYhBEREREp4P8DGaETtWbn1E4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10eea0790>"
+       ]
+      }
+     ],
+     "prompt_number": 59
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "The improvement is pretty significant when static type declarations are used. One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation.\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
+      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 60
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs =  ['classic_lstsqr', 'cy_lstsqr', 'static_type_lstsqr', \n",
+      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
+      "labels = ['classic approach','classic approach (cython)', \n",
+      "          'classic approach (cython + type decl.)',\n",
+      "          'matrix approach', 'numpy function', 'scipy function']\n",
+      "\n",
+      "times = [timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
+      "         for f in funcs]\n",
+      "\n",
+      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 61
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "x_pos = np.arange(len(funcs[1:]))\n",
+      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
+      "plt.xticks(x_pos, labels[1:], rotation=90)\n",
+      "plt.ylabel('relative performance gain')\n",
+      "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "plt.grid()\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAG/CAYAAAAjJGJxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x106418090>"
+       ]
+      }
+     ],
+     "prompt_number": 63
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This is a pretty significant performance gain. The \"Cython + type declarations\" approach sped up our initial Python code 25 times."
+     ]
     }
    ],
    "metadata": {}
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index ba478c5..8c47d64 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a091baac3cb82ac71775c03f73be1925fefe2d659bb7359de4bb88887fbe7d24"
+  "signature": "sha256:3b47add80317bb8ad369eacd5528c941bb1a2cbd92ae5b33aae66d56ae35c741"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -23,7 +23,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### All code was executed in Python 3.4"
+      "#### The code in this notebook was executed in Python 3.4.0"
      ]
     },
     {
@@ -70,7 +70,9 @@
       "- [Generating sample data and benchmarking](#sample_data)\n",
       "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
       "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
-      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)"
+      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
+      "- [Appendix I: Cython vs. Numba](#numba)\n",
+      "- [Appendix II: Cython with and without type declarations](#type_declarations)"
      ]
     },
     {
@@ -82,6 +84,18 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/cython_vs_chart.png)  \n",
+      "(Note that this chart just reflects my rather objective thoughts after experimenting with Cython, and it is not based on real numbers or benchmarks.)\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -264,7 +278,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
@@ -315,7 +329,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -351,7 +365,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
@@ -388,7 +402,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 39
     },
     {
      "cell_type": "markdown",
@@ -438,7 +452,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
@@ -493,31 +507,16 @@
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "WARNING: pylab import has clobbered these variables: ['random']\n",
-        "`%matplotlib` prevents importing * from pylab and numpy\n"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x105785f60>"
+        "<matplotlib.figure.Figure at 0x10f9e8a50>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
@@ -579,7 +578,7 @@
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -625,7 +624,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "matrix approach: 1000 loops, best of 3: 270 \u00b5s per loop"
+        "matrix approach: 10000 loops, best of 3: 163 \u00b5s per loop"
        ]
       },
       {
@@ -634,7 +633,7 @@
        "text": [
         "\n",
         "\n",
-        "classic approach: 100 loops, best of 3: 2.83 ms per loop"
+        "classic approach: 1000 loops, best of 3: 1.55 ms per loop"
        ]
       },
       {
@@ -643,7 +642,7 @@
        "text": [
         "\n",
         "\n",
-        "numpy function: 1000 loops, best of 3: 372 \u00b5s per loop"
+        "numpy function: 1000 loops, best of 3: 221 \u00b5s per loop"
        ]
       },
       {
@@ -652,7 +651,7 @@
        "text": [
         "\n",
         "\n",
-        "scipy function: 1000 loops, best of 3: 594 \u00b5s per loop"
+        "scipy function: 1000 loops, best of 3: 362 \u00b5s per loop"
        ]
       },
       {
@@ -663,7 +662,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 44
     },
     {
      "cell_type": "markdown",
@@ -702,7 +701,11 @@
      "source": [
       "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
       "Since we are working in an IPython notebook here, we can make use of the IPython magic: It will automatically convert it to C code, compile it, and load the function.  \n",
-      "Just to be thorough, let us also compile the other functions, which already use numpy objects."
+      "Just to be thorough, let us also compile the other functions, which already use numpy objects.\n",
+      "\n",
+      "**Note**  \n",
+      "Of course Cython has much more horsepower under its hood - more than I am showing in this article (for example, I am not using Cython's type definitions via `cdef` here). Here, I want to focus on how to speed up existing Python code by making only minimal changes to it.   \n",
+      "[In a later section - Appendix II](#type_declarations) We will see how static type declarations can further improve the performance via Cython."
      ]
     },
     {
@@ -713,8 +716,17 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The cythonmagic extension is already loaded. To reload it, use:\n",
+        "  %reload_ext cythonmagic\n"
+       ]
+      }
+     ],
+     "prompt_number": 45
     },
     {
      "cell_type": "code",
@@ -752,7 +764,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 46
     },
     {
      "cell_type": "markdown",
@@ -795,7 +807,7 @@
         "\n",
         "\n",
         "matrix approach: \n",
-        "1000 loops, best of 3: 274 \u00b5s per loop"
+        "10000 loops, best of 3: 165 \u00b5s per loop"
        ]
       },
       {
@@ -803,7 +815,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 260 \u00b5s per loop"
+        "10000 loops, best of 3: 166 \u00b5s per loop"
        ]
       },
       {
@@ -814,7 +826,7 @@
         "\n",
         "\n",
         "classic approach: \n",
-        "100 loops, best of 3: 2.82 ms per loop"
+        "1000 loops, best of 3: 1.59 ms per loop"
        ]
       },
       {
@@ -822,7 +834,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 212 \u00b5s per loop"
+        "10000 loops, best of 3: 127 \u00b5s per loop"
        ]
       },
       {
@@ -833,7 +845,7 @@
         "\n",
         "\n",
         "numpy function: \n",
-        "1000 loops, best of 3: 379 \u00b5s per loop"
+        "1000 loops, best of 3: 220 \u00b5s per loop"
        ]
       },
       {
@@ -841,7 +853,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 370 \u00b5s per loop"
+        "1000 loops, best of 3: 221 \u00b5s per loop"
        ]
       },
       {
@@ -852,7 +864,7 @@
         "\n",
         "\n",
         "scipy function: \n",
-        "1000 loops, best of 3: 608 \u00b5s per loop"
+        "1000 loops, best of 3: 361 \u00b5s per loop"
        ]
       },
       {
@@ -860,7 +872,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "100 loops, best of 3: 613 \u00b5s per loop"
+        "1000 loops, best of 3: 367 \u00b5s per loop"
        ]
       },
       {
@@ -871,7 +883,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 47
     },
     {
      "cell_type": "markdown",
@@ -905,20 +917,21 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 18
+     "prompt_number": 50
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%pylab inline\n",
-      "import matplotlib.pyplot as plt\n",
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
       "\n",
       "x_pos = np.arange(len(funcs))\n",
       "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
       "plt.xticks(x_pos, labels, rotation=45)\n",
       "plt.ylabel('time in ms')\n",
       "plt.title('Performance of different least square fit implementations')\n",
+      "plt.grid()\n",
       "plt.show()\n",
       "\n",
       "x_pos = np.arange(len(funcs[1:]))\n",
@@ -926,36 +939,30 @@
       "plt.xticks(x_pos, labels[1:], rotation=45)\n",
       "plt.ylabel('relative performance gain')\n",
       "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "plt.grid()\n",
       "plt.show()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Populating the interactive namespace from numpy and matplotlib\n"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAsMwDPMcSwgMwzAMAOD/AFCftMrEW+2BAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDPMCSwgMwzAMAOD/ANLifp9XPWcHAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x105a7fe10>"
+        "<matplotlib.figure.Figure at 0x10eeafd50>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCFElSMATQghRJfw/5a9/wr1GWjoAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1066dcef0>"
+        "<matplotlib.figure.Figure at 0x10eeac310>"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 51
     },
     {
      "cell_type": "markdown",
@@ -973,6 +980,13 @@
       "## Performance growth rates for different sample sizes"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -985,6 +999,8 @@
      "collapsed": false,
      "input": [
       "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
       "\n",
       "funcs =  ['cy_classic_lstsqr', \n",
       "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
@@ -1003,7 +1019,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
@@ -1021,7 +1037,7 @@
       "plt.ylabel('time in ms')\n",
       "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
       "plt.legend(loc=2)\n",
-      "\n",
+      "plt.grid()\n",
       "\n",
       "plt.title('Performance of least square fit implementations for different sample sizes')\n",
       "plt.show()"
@@ -1032,13 +1048,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPUQAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVOX+B/DPDLIz7JvsCl4VV9IQxQWFXCqXXFBLBDW9\nppZbtyxTKXOrvLef3bxXCyXF3VLLJU2QUivJzA0MFQUUUFAUZZEZZr6/P+YyMayDMMyZme/79fIl\nZ+ac5zznfM+c+c5znvMcERERGGOMMcZYixLrugKMMcYYY8aIkzDGGGOMMR3gJIwxxhhjTAc4CWOM\nMcYY0wFOwhhjjDHGdICTMMYYY4wxHeAkzIDdvXsX/fv3h62tLf7xj3/oujo6V1ZWhuHDh8Pe3h7j\nx4+v8X5sbCyioqJ0UDP9t2/fPnh7e8PW1hbnz59H586d8dNPP2m8fGPn11RmZibEYjEUCkWzl61r\nr732Gj788MMWXWdlnCUSCS5cuNDs5Vf9DGZnZ0MikaByFKXazmdTpkyBo6MjQkJCmr0uhiA5ORne\n3t7NWubJkyfRoUOHZi3TmLXSdQWYOj8/P+Tn58PExATW1tYYNmwY/v3vf8Pa2rrRZW3cuBGurq54\n9OiRFmqqf/bu3Yv8/HwUFhZCLK75+0MkEmm9Dn5+fti0aRMGDRqk9XW1pDfffBPr16/H8OHDAQCX\nL19WvRcbG4uMjAxs3bq1zuWrzm8MYmJi4O3tjeXLl2s0f3x8POLi4nDy5EnVa//5z3+0Vb06VY9z\nc6v6GfTx8cHjx49V09XPZydPnsTx48eRm5sLCwsLrdSnLpmZmWjbti0qKipqPZcYsn79+uHPP//U\ndTUMhnEdPXpAJBLh4MGDePz4Mc6dO4ezZ882+tcuEUGhUCArKwsdO3Z8qnpUVFQ81XJClpWVhb/9\n7W91njRbYtxikUjUIutpisbGnoiQnZ2NwMBALdWICUFT49zU1sjq57OsrCz4+fk9VQLWXOc3oX+W\nmR4gJih+fn6UmJiomn7zzTfpxRdfJCKiX375hXr37k329vbUrVs3Sk5OVs03YMAAWrx4MYWGhpKl\npSVNmjSJTE1NyczMjGxsbCgxMZHKy8tp7ty55OHhQR4eHjRv3jwqLy8nIqITJ06Qp6cnrVmzhtzd\n3SkqKopiY2Np7NixNGnSJJJIJNSlSxe6evUqrVy5klxdXcnHx4eOHTumqsOmTZuoY8eOJJFIqG3b\ntrRhwwbVe5Xlr127llxdXal169a0efNm1fulpaW0YMEC8vX1JTs7O+rbty+VlZU1uN3VpaWl0YAB\nA8je3p46depE3377LRERLV26lMzMzMjU1JRsbGxo06ZNNZZdtmwZTZo0STVd33rr29aCggJ64YUX\nyN7enhwdHalfv36kUCho0qRJJBaLydLSkmxsbOjjjz+uUYe6liUiOnfuHAUFBZFEIqHx48fT+PHj\n6b333iMios2bN1Pfvn3VyhKJRJSRkUFERAcPHqTu3buTra0teXt7U2xsrGq+mzdvkkgkori4OPLx\n8aEBAwYQEVFcXBx17NiRHBwcaMiQIZSVlVWjvk+ePCFra2sSiURkbW1NAQEBRETk6+tLx48fpyNH\njqjt9+7du9caN19fX9Vxv2zZskYddwMGDKBFixZRcHAw2dra0siRI6mwsFBt2+RyORERPXz4kKZO\nnUqtW7cmT09Peu+991Tvbd68mfr06UPz588ne3t78vf3p9OnT9OmTZvI29ubXF1d6auvvlLb9oUL\nF5KPjw+5ubnRzJkzVcdsfcf7hg0b1D6bI0aMICKiVatWkb+/P0kkEgoMDKR9+/YRkfKYtrCwIBMT\nE7KxsSEHBwciIoqOjlbFn4ho48aNFBAQQI6OjjRixAjKzc1VOxb++9//Urt27cje3p5mz56teu/a\ntWvUv39/srOzI2dnZxo/frzGca7r81ZZv5kzZ9KwYcPI2tpa7bxW6caNG9S/f3+SSCT03HPP0Zw5\nc1SfwcrYVVRUUHR0tNo+27Bhg9o+qTyev/vuO+rWrRvZ29tTnz596OLFi2rH2Jo1a6hLly5kYWFB\ncrm8wXPqkiVLKDQ0lCQSCQ0ePJju3btHRETe3t4kEonIxsaGbGxs6Ndff62xbWfOnKEePXqQra0t\nubm50YIFC1TvjR07ltzd3cnOzo769+9Pqampavvttddeo2HDhpGNjQ317duX8vLy6I033iB7e3vq\n0KED/fHHH2rbtWrVKgoMDCQHBweaMmUKPXnyhIiUx6GXl5dq3pycHBo9ejS5uLhQmzZtaN26dTXq\nXenQoUMUGBhIEomEPD096ZNPPqlR5s6dO1X7wMbGhszMzCgsLEx1zNT1+ajvPGdsOAkTGD8/Pzp+\n/DgREWVnZ1OnTp1o6dKldPv2bXJycqIjR44QEdEPP/xATk5OqpPCgAEDyNfXl9LS0kgul5NMJqOY\nmBhasmSJquwlS5ZQ7969qaCggAoKCqhPnz6q90+cOEGtWrWiRYsWkVQqpbKyMlq2bBlZWFjQsWPH\nqKKigiZPnky+vr60cuVKqqiooC+++ILatGmjKv/QoUN048YNIiL68ccfycrKis6dO6dW/rJly6ii\nooIOHz5MVlZW9PDhQyIimjVrFg0cOJByc3NVJ8fy8vI6t7ugoKDGvpNKpeTv70+rVq0imUxGSUlJ\nJJFIKD09nYiIYmNjKSoqqs59XzUJa2h/17atlSfGRYsW0cyZM6miooIqKiro1KlTavGt7cuoUl3L\nlpeXk4+PD3366adUUVFBe/fuJVNTU1X8GkrCkpOT6fLly0REdPHiRXJzc6P9+/cT0V9fdtHR0VRa\nWkplZWW0f/9+CggIoD///JPkcjl9+OGH1KdPnzrrXXVd1bezof1eff7GHncDBgwgT09PSk1NpZKS\nEhozZkyNL/LKRGvUqFE0c+ZMKi0tpfz8fAoODlYl0Js3b6ZWrVpRfHw8KRQKeu+998jT05PmzJlD\nUqmUjh07RhKJhEpKSoiIaN68eTRy5Eh68OABPX78mIYPH07vvPMOETV8vFf/bBIR7dmzh/Ly8oiI\naNeuXWRtbU137twhIqL4+Pga8a1aRmJiIjk7O9Mff/xB5eXl9Prrr1P//v3V4jN8+HAqKiqi7Oxs\ncnFxoaNHjxIR0YQJE2jlypVEpDzOTp8+XWecqsa5oc9bdHQ02dnZ0c8//0xEpEoMqgoJCaGFCxeS\nVCqln376iSQSiepYqR676vus+j45d+4cubq6UkpKCikUCvrqq6/Iz8+PpFIpESmTlaCgILp9+zY9\nefJEo3NqQEAAXbt2jcrKyigsLIwWLVpERESZmZlqdatNSEgIJSQkEBFRSUmJWqK2efNmKi4uJqlU\nSvPmzVP7cRIdHU3Ozs507tw5evLkCQ0aNIh8fX1p69atquNy4MCBqvl9fX2pS5cudPv2bSosLKTQ\n0FBVcl41YZLL5fTMM8/Q8uXLSSaT0Y0bN6ht27aq46A6d3d31fnn4cOHaufyqoldpUePHlHHjh1p\n48aNRFT/56O+c6Sx4SRMYHx9fcnGxobs7e3J19eXZs+eTWVlZbR69eoaX2RDhgxR/TIPCwujZcuW\nqb0fExOj9kvZ399fdcIhIjp69Cj5+fkRkfKDZWZmpmoZI1J+GQ4ePFg1/e2335KNjY3qF8ujR49I\nJBJRUVFRrdsyatQo+r//+z9V+ZaWlmonLVdXVzpz5gzJ5XKytLRU+9VaqaHtruqnn34id3d3tdcm\nTpyo+pVcvaWruqrvN2a91bd16dKlNHLkSLp+/XqN+RpKwupa9scffyQPDw+116om0Q0lYdXNnTuX\n5s+fT0R/fdndvHlT9f7QoUMpLi5ONS2Xy8nKyoqys7NrLa++JKyh/V7b/I057sLCwlQndyJl64yZ\nmRkpFAq1L/I7d+6Qubm56tc4EdH27dtVX2ibN2+mdu3aqd67ePEiiUQiys/PV73m5OREFy5cIIVC\nQdbW1mrb/PPPP6uSw/qOd6Kan83adO/enQ4cOKCqW31J2NSpU+ntt99WvVdcXEympqaq1kuRSKSW\nXEVGRtKaNWuIiGjy5Mk0Y8YMun37dr31qSyncpsb+rxFR0dTdHR0nWVlZWVRq1atqLS0VPXayy+/\nXGcCXX2fVd8nM2fOrJHYtm/fnn766SciUh5jVVvfNTmnrlixQvXe+vXraejQobXWrTb9+/enZcuW\n1fqDsaoHDx6QSCSiR48eqbZzxowZqvc/++wzCgwMVE1fvHiR7O3tVdN+fn5qLfGHDx8mf39/IlJP\nmH799Vfy8fFRW/fKlStpypQptdbLx8eHNmzYUOP8XlsSJpfL6YUXXqBZs2YRETX4+ajvHGlsuE+Y\nwIhEIhw4cAAPHjxAZmYm/v3vf8PCwgJZWVnYs2cPHBwcVP9Onz6NO3fuqJZt6C6Y3Nxc+Pr6qqZ9\nfHyQm5urmnZxcYGZmZnaMq6urqq/LS0t4ezsrOo8a2lpCQAoLi4GABw5cgQhISFwcnKCg4MDDh8+\njPv376uWd3JyUuuPZWVlheLiYty7dw9PnjyBv79/jTprst1Vt6/6PvD19UVOTk69+6U2Da23vm39\nxz/+gYCAAAwePBj+/v5Ys2aNxuuta9nc3Fx4enrW2DZNnTlzBgMHDoSrqyvs7e2xYcMGtdgA6sdP\nVlYW5s6dq9p2JycnAHiqffk0GnPcAep19/HxgUwmw71799TKzMrKgkwmQ+vWrVXbNXPmTBQUFKjm\ncXNzU1svoPxcVH2tuLgYBQUFKC0tRY8ePVRlDRs2TG2ddR3vddmyZQuCgoJU5V2+fLlGjOqSl5en\ndjxYW1vDyclJLV7u7u5qdans9P7RRx+BiBAcHIzOnTtj8+bNGq2zrs9b5TlFJBLVe07Kzc2Fg4OD\naj9XLv+0srKysHbtWrXP7O3bt9XOcdWP8YbOLVX3WWXsNRUXF4erV6+iY8eOCA4OxqFDhwAAcrkc\nixYtQkBAAOzs7NCmTRsAUDt2qh7/FhYWNT4P1etR/fivus1Vt7dyn1f+W7VqFfLz82ut/9dff43D\nhw/Dz88PYWFh+PXXX+vc1sWLF6OkpATr1q0DgAY/H005RxoavjtST/j4+CAqKgobN26sc56G7u7z\n8PBAZmamqnNrdnY2PDw86ly+MXcLlpeXY8yYMUhISMDIkSNhYmKCl156SaOOq87OzrCwsMD169fR\ntWtXtfc02e5KHh4euHXrFohIVfesrKynup26vvU2tK02Njb45JNP8MknnyA1NRWDBg1CcHAwBg4c\n2OA+rW3ZZ599Fh4eHjUSoKysLAQEBABQfumWlpaq3quepL788st44403cPToUZiZmWH+/Pk1kpTq\nd6YtWbIEEydO1GBv1a8l7jrNzs5W+9vU1BTOzs4oKSlRve7t7Q1zc3Pcv3+/yXe0OTs7w9LSEmlp\naWjdunWjl6++T7KysjBjxgwkJSWhd+/eEIlECAoKUh1Tmn62K5WUlOD+/fs1EvfauLm5qY7z06dP\nIyIiAgMGDEDbtm0bXGdTPm+tW7fGgwcPUFpaCisrK9XyJiYmGi1fnY+PDxYvXox33323znmqH+Oa\nnlvqK6cuAQEB2L59OwBlQjN27FgUFhZi7969+Pbbb5GYmAhfX188fPgQjo6OTerkX/34r3per+Tt\n7Y02bdrg6tWrGpXZs2dP7N+/H3K5HJ999hkiIyPV1lNp586d2LVrF3777TdV7Br6fNR1njO0u8Y1\nwS1hemLSpEn47rvvcOzYMcjlcjx58gTJyclqX8zVP8TVpydOnIgPP/wQ9+7dw7179/DBBx/UOy5W\nY04KUqkUUqkUzs7OEIvFOHLkCI4dO6bRsmKxGFOnTsWCBQuQl5cHuVyOX375BVKpVKPtrhQSEgIr\nKyt89NFHkMlkSE5OxsGDBzFhwgSNt6NSfettaFsPHjyI69evg4hga2sLExMT1Ze+m5sbMjIy6lzv\noUOHaixrYmKC3r17o1WrVli3bh1kMhm++eYb/Pbbb6rlunXrhtTUVFy4cAFPnjxBbGysWrnFxcVw\ncHCAmZkZUlJSsH379nq/SGbOnImVK1ciLS0NAFBUVIQ9e/Y0ej8CytaEzMxMrd1JRkRISEjAlStX\nUFpaiqVLl2LcuHE1tq9169YYPHgwFixYgMePH0OhUCAjI+OpxicTi8WYPn065s2bp2pJy8nJ0fiY\nd3Nzw40bN1TTJSUlEIlEcHZ2hkKhwObNm9WG7XBzc8Pt27chk8nUtrtyn06cOBGbN2/GhQsXUF5e\njnfffRchISHw8fGpdf1VY7Fnzx7cvn0bAGBvbw+RSKRRktrQ562hePv6+qJnz55YtmwZZDIZTp06\nhYMHD9Y5f0PlTZ8+Hf/973+RkpICIkJJSQkOHTpUZ+vV05xTK7m4uEAsFtf7WU5ISFAdG3Z2dqr9\nWlxcDHNzczg6OqKkpKRG0tjYzwkRYf369cjJyUFhYSFWrFhR6zkvODgYEokEH330EcrKyiCXy3H5\n8mWcPXu2xrwymQzbtm1DUVERTExMIJFIak2O//jjD7z++uvYt2+fqrUcaPjzUdd5zhhxEqYnvLy8\ncODAAaxcuRKurq7w8fHB2rVr1T6wtbVkVX3tvffeQ8+ePdG1a1d07doVPXv2xHvvvafx8nXNAwAS\niQTr1q1DZGQkHB0dsWPHDowcObLeZav65JNP0KVLFzz77LNwcnLCO++8A4VCUed213a7u6mpKb77\n7jscOXIELi4umDNnDrZu3Yq//e1vdW5PXdtb3/5uaFuvX7+O5557DhKJBH369MHs2bMxYMAAAMA7\n77yDDz/8EA4ODvjnP/9Zow7Xrl2rdVlTU1N88803iI+Ph5OTE3bv3o3Ro0er4v+3v/0NS5cuRURE\nBNq3b49+/fqpbev69euxdOlS2NraYvny5TUGq62+X0aNGoW3334bEyZMgJ2dHbp06YKjR4/Wu+/q\nMm7cOADKy3M9e/asc76qZWl63FX+HRUVhZiYGLRu3RpSqVR1WaT6vFu2bIFUKkVgYCAcHR0xbtw4\nVauhJuutas2aNQgICEBISAjs7Ozw3HPPqbUy1LfstGnTkJaWBgcHB4wePRqBgYFYuHAhevfuDXd3\nd1y+fBl9+/ZVzR8eHo5OnTrB3d1ddWmqan3Dw8OxfPlyjBkzBh4eHrh58yZ27txZ7/6rfO3s2bMI\nCQmBRCLByJEjsW7dOvj5+dVa76rlNPXzBgDbt2/HmTNn4OjoiA8++ADR0dF1rq96edWne/TogS++\n+AJz5syBo6Mj2rVrhy1bttRZh8aeU6uuz8rKCosXL0ZoaCgcHByQkpJSo/yjR4+ic+fOkEgkmD9/\nPnbu3Alzc3NMnjwZvr6+8PT0ROfOnVUtn5puZ231evnll1WX9tq1a1fred3ExAQHDx7E+fPn0bZt\nW7i4uGDGjBl1jiOZkJCANm3awM7ODhs3bsS2bdtqlHngwAE8fPgQffv2hUQigUQiwQsvvACg/s9H\nXec5YyQiLf08ffLkCQYMGIDy8nJIpVKMHDkSq1atQmxsLL788ktVP4uVK1di2LBhAIBVq1Zh06ZN\nMDExwbp16zB48GBtVI0xgzBlyhR4eXlpPOCnoRo4cCCioqIwdepUXVeFsRbXpk0bxMXFGeWlPEOg\ntT5hFhYWOHHiBKysrFBRUYG+ffvi1KlTEIlEWLBgARYsWKA2f1paGnbt2oW0tDTk5OQgIiICV69e\nNbrRiBnTlLYu7+kj3heMMX2k1QynsrOlVCqFXC6Hg4MDgNpPmAcOHMDEiRNhamoKPz8/BAQE1NrE\nyxhT0uRyj7Hg/cAY00davTtSoVDgmWeeQUZGBl577TV06tQJe/fuxWeffYYtW7agZ8+eWLt2Lezt\n7ZGbm6v2EFYvL68Wux2eMX2k6VAChu7EiRO6rgJjOnPz5k1dV4E1gVaTMLFYjPPnz6OoqAhDhgxB\ncnIyXnvtNSxduhQAsGTJEixcuBBxcXG1Ll/br1tPT89ax0BhjDHGGBOabt264fz587W+1yIdruzs\n7PDCCy/g7NmzcHV1VV1GefXVV1WXHD09PXHr1i3VMrdv3651jJvc3FzVrdn8Txj/li1bpvM68D+O\niz7845gI8x/HRXj/DCkmFy5cqDM/0loSdu/ePTx8+BAAUFZWhh9++AFBQUFqg0ju27cPXbp0AQCM\nGDECO3fuhFQqxc2bN3Ht2jUEBwdrq3qsGVUdJJIJB8dFeDgmwsRxER5jiYnWLkfm5eUhOjoaCoUC\nCoUCUVFRCA8Px+TJk3H+/HmIRCK0adMGGzZsAAAEBgYiMjISgYGBaNWqFdavX8+dbRljjDFmsLQ2\nTpi2iEQi6FmVDV5ycjLCwsJ0XQ1WDcdFeDgmwsRxER5Dikl9eQsnYYwxxhhjWlJf3mIwI6E6Ojqq\nOvzzP/6n6T9HR0ddH7pak5ycrOsqsGo4JsLEcREeY4mJVoeoaEkPHjzgFjLWaCIR9ztkjDGmGwZz\nObKu1xmrDx83jDHGtKm+7xmDuRzJGGOMMaZPOAljzEAZS58KfcIxESaOi/AYS0w4CWOMMcYY0wHu\nE2YgwsLCEBUVhWnTpjV72dnZ2ejUqRMePXr0VB3Zk5OTERUVpfZYKqEw9uOGMcaYdnGfMCNQOeSC\nNvj4+ODx48dav5MwNjYWUVFRWl0HY4wxJhQGM0RFXdLTs3D8eAZkMjFMTRWIiPBH+/a+LV4G053K\nXyDGNhyFIY04bSg4JsLEcREeY4mJQbeEpadnIT7+OgoKBuHhwzAUFAxCfPx1pKdntWgZt27dwujR\no+Hq6gpnZ2fMnj0bTk5OuHz5smqe/Px8WFtb4/79+/WWdeDAAXTv3h12dnYICAjAsWPHasyTkZGB\nQYMGwdnZGS4uLpg0aRKKiopU769ZswZeXl6wtbVFhw4dkJSUBABISUlBz549YWdnB3d3dyxcuBCA\n8kGqYrEYCoUCAFBYWIgpU6bA09MTjo6OeOmllzTeF3Wt//vvv8eqVauwa9cuSCQSBAUFAQDi4+Ph\n7+8PW1tbtG3bFtu3bwcAyOVyvPnmm3BxcYG/vz8+//xztTqGhYXhvffeQ2hoKKytrXHz5s1G1ZEx\nxhjTNoNuCTt+PAPm5uFQv8kiHBcvJuHZZzVryUpJyUBpabhqOiwMMDcPR2JikkatYXK5HC+++CIi\nIiKwbds2mJiY4LfffgMAJCQkYPXq1QCAHTt2ICIiAk5OTvXUJQXR0dH4+uuvER4ejtzcXDx+/LjW\neRcvXoz+/fujqKgIY8aMQWxsLP71r38hPT0dn3/+Oc6ePQt3d3dkZ2ejoqICADB37lzMnz8fr7zy\nCkpLS3Hp0qVay46KioKtrS3S0tJgbW2NX375pcH9UKmu9bdt2xbvvvsuMjIysGXLFgBASUkJ5s6d\ni7Nnz6Jdu3a4e/euKkn94osvcOjQIZw/fx5WVlYYPXp0jZauhIQEHDlyBO3bt1clZ8bEGH5F6huO\niTBxXITHWGJi0EmYTFZ7Q59crnkDoEJR+7xSqWZlpKSkIC8vDx9//DHEYuUyoaGhaNWqFSIjI1VJ\n2NatW7Fo0aJ6y4qLi8O0adMQHq5MCj08PGqdz9/fH/7+/gAAZ2dnzJ8/Hx988AEAwMTEBOXl5UhN\nTYWTkxN8fHxUy5mZmeHatWu4d+8enJ2d0atXrxpl5+Xl4fvvv0dhYSHs7OwAAP369dNoXzS0fiKq\n0XlRLBbj0qVL8PLygpubG9zc3AAAu3fvxvz58+Hp6QkAePfdd/Hjjz+qlhOJRIiJiUHHjh1V5TDG\nGGNCYtBJmKlp5aUp9dddXRWYNUuzMj7/XIGCgpqvm5lp1rJy69Yt+Pr61kgCevXqBUtLSyQnJ8Pd\n3R0ZGRkYMWJEvWXdvn0bL7zwQoPrvHv3LubOnYtTp07h8ePHUCgUqmckBgQE4NNPP0VsbCxSU1Mx\nZMgQ/POf/0Tr1q0RFxeHpUuXomPHjmjTpg2WLVtWY323bt2Co6OjKgFrrPrWX521tTV27dqFTz75\nBNOmTUNoaCjWrl2L9u3bIy8vD97e3qp5qyZzlaq+b4yMpU+FPuGYCBPHRXiMJSYG3TwQEeGP8vJE\ntdfKyxMRHu7fYmV4e3sjOzsbcrm8xnvR0dFISEjA1q1bMW7cOJiZmTVY1vXr1xtc57vvvgsTExNc\nvnwZRUVF2Lp1q9rluIkTJ+LkyZPIysqCSCTC22+/DUCZIG3fvh0FBQV4++23MXbsWJSVldWoQ2Fh\noVofs8aqa/21dZwfPHgwjh07hjt37qBDhw6YPn06AKB169bIzs5WzVf170rG1hGfMcaYfjHoJKx9\ne1/ExATA1TUJ9vbJcHVNQkxMQKPubGxqGb169ULr1q2xaNEilJaW4smTJ/j5558BAJMmTcI333yD\nbdu2YfLkyQ2WNW3aNGzevBlJSUlQKBTIyclBenp6jfmKi4thbW0NW1tb5OTk4OOPP1a9d/XqVSQl\nJaG8vBzm5uawsLCAiYkJAGUfqoL/NfvZ2dlBJBLVaMFr3bo1hg0bhlmzZuHhw4eQyWT46aefNNoX\nDa3f3d0dmZmZqkuS+fn5OHDgAEpKSmBqagpra2vVvJGRkVi3bh1ycnLw4MEDrF69ukbSZezjfxnD\nr0h9wzERJo6L8BhNTEjP1FVlIW9KdnY2jRo1ipycnMjZ2Znmzp2rei88PJzatGmjcVn79u2jrl27\nkkQioYCAADp27BgREYWFhVFcXBwREaWmplKPHj3IxsaGgoKCaO3ateTt7U1ERBcvXqTg4GCSSCTk\n6OhIw4cPp7y8PCIimjRpErm6upKNjQ117tyZDhw4QEREN2/eJLFYTHK5nIiICgsLKTo6mtzc3MjB\nwYHGjBlTb51PnDih0frv379Pffv2JQcHB+rRowfl5eXRgAEDyM7Ojuzt7WngwIF05coVIiKqqKig\n+fPnk5OTE7Vt25Y+//xzEolEqjpW3R/1EfJxwxhjTP/V9z3DI+br2LRp0+Dp6anqOM+eTmZmJtq2\nbYuKiopGdcLX1+NGE8bSp0KfcEyEieMiPIYUk/q+Zwy6Y77QZWZm4ptvvsH58+d1XRXGGGOMtTCD\n7hMmZEvHNwwaAAAgAElEQVSWLEGXLl3w1ltvwdf3r/5lK1euhEQiqfFPk7sidUkI9eaO+OoM5Vek\nIeGYCBPHRXiMJSZ8OZIZNT5uGGOMaRM/wJsxI5Ss/qgIJgAcE2HiuAiPscSEkzDGGGOMMR3gy5HM\nqPFxwxhjTJv4ciRjjDHGmMBwEsaYgTKWPhX6hGMiTBwX4TGWmHASxmqVmZkJsVis9sxJxhhjjDUf\n7hPGaqXpCPTx8fGIi4vDyZMnW7B2zYePG8YYY9pk1CPmp19Px/Hfj0NGMpiKTBHRIwLtA9q3eBms\n8RQKRaMeQcQYY4zpE4P+hku/no74E/EocCvAQ/eHKHArQPyJeKRfT2+xMvz8/LB27Vp069YN9vb2\nmDBhAsrLyxEfH49+/fqpzSsWi3Hjxg0AQExMDGbNmoXnn38eEokE/fr1w507dzB37lw4ODigY8eO\nao878vPzw+rVq9GpUyc4Ojpi6tSpKC8vBwB07twZBw8eVM0rk8ng7OyMCxcuaLwf4uPj4e/vD1tb\nW7Rt2xbbt2/Hn3/+iZkzZ+KXX36BRCKBo6MjAODw4cPo1KkTbG1t4eXlhbVr16rK+fjjj+Hh4QEv\nLy9s2rSpxja/9tpreP7552FjY2M0fQK0hfef8HBMhInjIjzGEhODbgk7/vtxmLczR3Jm8l8vmgIX\nd17Es32f1aiMlFMpKPUqBTKV02F+YTBvZ47Ec4katYaJRCLs2bMHR48ehbm5OUJDQxEfHw8LC4sG\nl92zZw+OHTuGwMBAPP/88wgJCcGHH36ITz/9FEuXLsWCBQuQlJSkmn/79u04duwYrKysMHz4cHz4\n4YdYvnw5oqOjkZCQgBdffBGAMkny9PREt27dNNoHJSUlmDt3Ls6ePYt27drh7t27uH//Pjp06IAN\nGzbgyy+/VLscOW3aNOzduxehoaEoKipSJVnff/891q5di6SkJPj5+eHVV1+tsa4dO3bgyJEj6N27\ntyqJZIwxxgyRQbeEyUhW6+tyyDUuQ4HaO6ZLFVKNy3jjjTfg7u4OBwcHDB8+XKMHdotEIowePRpB\nQUEwNzfHSy+9BGtra0yaNAkikQiRkZH4448/1OafM2cOPD094eDggMWLF2PHjh0AgFdeeQWHDh1C\ncXExAGDr1q2IiorSuP6AspXu0qVLKCsrg5ubGwIDAwGg1uvcZmZmSE1NxaNHj2BnZ4egoCAAwO7d\nuzF16lQEBgbCysoK77//fo1lR40ahd69ewMAzM3NG1VHps5Ynr2mTzgmwsRxER5jiYlBt4SZikwB\nKFuvqnK1csWssFkalfH53c9R4FZQ43UzsZnG9XB3d1f9bWVlhdzcXI2Wc3V1Vf1tYWGhNm1paalK\nqip5e3ur/vbx8VGtx8PDA6Ghodi7dy9GjRqF77//Hp999pnG9be2tsauXbvwySefYNq0aQgNDcXa\ntWvRvn3tLYFff/01PvzwQyxatAhdu3bF6tWrERISgry8PDz77F8tkD4+PmrLiUQieHl5aVwvxhhj\nTJ8ZdEtYRI8IlF9Tv6RVfq0c4c+Et2gZtbG2tkZpaalq+s6dO00qDwCys7PV/vbw8FBNV16S3LNn\nD/r06YPWrVs3quzBgwfj2LFjuHPnDjp06IDp06cDUCZO1fXs2RP79+9HQUEBRo0ahcjISABA69at\na9SRaY+x9KnQJxwTYeK4CI+xxMSgk7D2Ae0RMzAGrvmusL9jD9d8V8QMjGnUnY3NUUZVlZfvunXr\nhtTUVFy4cAFPnjxBbGxsrfM1ptz169cjJycHhYWFWLFiBSZMmKB6/6WXXsK5c+ewbt06TJ48uVFl\n5+fn48CBAygpKYGpqSmsra1hYmICAHBzc8Pt27chkykv/cpkMmzbtg1FRUUwMTGBRCJRzRsZGYn4\n+HhcuXIFpaWlNS5H8lARjDHGjIlBX44ElElUU4eTaI4yKolEIohEIrRr1w5Lly5FREQErKyssHLl\nSnzxxRc15qtruvK1qn+//PLLGDx4MHJzczFq1Ci89957qvctLCwwevRo7Nq1C6NHj9a4roByqIh/\n/etfiI6OhkgkQlBQEP7zn/8AAMLDw9GpUye4u7vDxMQEOTk5SEhIwOuvvw65XI4OHTpg27ZtAICh\nQ4di3rx5GDRoEExMTLB8+XJs37693m1kT89Y+lToE46JMHFchMdYYsKDtRqINm3aIC4uDoMGDapz\nnuXLl+PatWvYsmVLC9asfmKxGNevX0fbtm11sn5jP24YY4xpFz/Am6GwsBCbNm3CjBkzdF0V1kKM\npU+FPuGYCBPHRXiMJSachBmBL774Aj4+Phg2bBj69u2ren3btm2QSCQ1/nXp0qXF6saXHxljjBkr\nvhzJjBofN4wxxrSJL0cyxhhjjAkMJ2GMGShj6VOhTzgmwsRxER5jiQknYYwxxhhjOsB9wphR4+OG\nMcaYNnGfMMYYY4wxgeEkTMv8/PyQmJiIVatWqZ63+LRiY2MRFRXVTDVjhs5Y+lToE46JMHFchMdY\nYsJJmJZVPornnXfeUXss0dOWpYmwsDDExcU1aV26ZgjbwBhjjNXH4J8dmZWejozjxyGWyaAwNYV/\nRAR82zfuOZDNUUZz0LTvkrYGQK2oqECrVi1zyPAgrk1nLM9e0yccE2HiuAiPscTEoFvCstLTcT0+\nHoMKChD28CEGFRTgenw8stLTW7QMIlK7lJiZmQmxWIwtW7bA19cXLi4uWLlyZaO27cmTJ5g0aRKc\nnZ3h4OCA4OBg5OfnY/HixTh58iTmzJkDiUSCN954AwAwf/58uLm5wc7ODl27dkVqaioA4P79+xgx\nYgTs7OzQq1cvLFmyBP369VOtRywWY/369WjXrh3aN5B4isVi/Oc//0G7du1ga2uLpUuXIiMjA717\n94a9vT0mTJgAmUwGAHj48CFefPFFuLq6wtHREcOHD0dOTg4A1LkNjDHGmCEx6JawjOPHEW5uDlS5\nthwOIOniRfg++6xmZaSkILy09K8XwsIQbm6OpMTERrWG1dayc/r0aVy9ehXp6ekIDg7G6NGj0aFD\nB43K++qrr/Do0SPcvn0b5ubmOH/+PCwtLbFixQr8/PPPiIqKwtSpUwEAR48excmTJ3Ht2jXY2toi\nPT0ddnZ2AIDZs2fDysoKd+7cwY0bNzBkyJAaD9M+cOAAfvvtN1haWjZYr2PHjuGPP/5AdnY2goKC\ncOrUKezYsQOOjo7o3bs3duzYgcmTJ0OhUGDatGnYu3cvKioqMHXqVMyZMwf79u2rdRtY4yUnJxvN\nr0l9wTERJo6LcKSnZ+H48QxcuXIRHTt2RUSEP9q399V1tbTGoFvCxP9rdanxulyueRkKRe2vS6VP\nVaeqli1bBnNzc3Tt2hXdunXDhQsXNF7WzMwM9+/fx7Vr1yASiRAUFASJRKJ6v+qlSzMzMzx+/BhX\nrlyBQqFA+/bt4e7uDrlcjm+++QYffPABLC0t0alTJ0RHR9e47PnOO+/A3t4e5ubmDdbrrbfego2N\nDQIDA9GlSxcMGzYMfn5+sLW1xbBhw/DHH38AABwdHfHSSy/BwsICNjY2ePfdd/Hjjz+qlcVDRzDG\nmPFIT89CfPx15OcPQmFhdxQUDEJ8/HWkp2fpumpaY9AtYQpTU+Uf1X7hKFxdgVmzNCvj88+BgoKa\nr5uZNbV6cHd3V/1tZWWFkpISjZeNiorCrVu3MGHCBDx8+BCTJk3CihUrVH22qra8DRw4EHPmzMHs\n2bORlZWF0aNH45NPPkFJSQkqKirg7e2tmtfHx6fGuqq+3xA3NzfV35aWljWm79y5AwAoLS3F/Pnz\ncfToUTx48AAAUFxcDCJS1Z37hTUN/7IXHo6JMHFchOH48QyIxeG4dAkoLQ2DTAaYm4cjMTHJYFvD\nDLolzD8iAonl5WqvJZaXwz88vEXLaC5Vk5JWrVph6dKlSE1Nxc8//4yDBw9iy5YtNear9Prrr+Ps\n2bNIS0vD1atX8fHHH8PV1RWtWrVCdna2ar6qf9e23uaydu1aXL16FSkpKSgqKsKPP/4IIlK1fnEC\nxhhjxuXePTF+/x0oLATkcqCsTPm6VGq4qYrhbhkA3/btERATgyRXVyTb2yPJ1RUBMTGN6svVHGUA\nml1aa2iequ8nJyfj0qVLkMvlkEgkMDU1hYmJCQBla1RGRoZq3rNnz+LMmTOQyWSwsrKChYUFTExM\nIBaLMXr0aMTGxqKsrAxpaWnYsmVLsyZAVetc9e/i4mJYWlrCzs4OhYWFeP/999WWq74NrPGMZZwd\nfcIxESaOi24RAb//DqSkKPDkCSCRAC4uybC1Vb5vZlZ7tyBDYNBJGKBMogbNmoWwefMwaNaspxpa\noqllVI4VVjW5qS3RaSj5qVrGnTt3MG7cONjZ2SEwMBBhYWGquy/nzp2LvXv3wtHREfPmzcOjR48w\nY8YMODo6ws/PD87OzvjHP/4BAPj3v/+N4uJiuLu7Y+rUqZgyZYpastSYhKyhbapa/3nz5qGsrAzO\nzs7o06cPhg0bpjZv9W1gjDFmeGQy4Ntvge++A9q08YeLSyKCgoDKHj/l5YkID/fXbSW1SGvPjnzy\n5AkGDBiA8vJySKVSjBw5EqtWrUJhYSHGjx+PrKws+Pn5Yffu3bC3twcArFq1Cps2bYKJiQnWrVuH\nwYMH16wwPztSq+Lj4xEXF4eTJ0/quiotgo8bxhjTjQcPgN27gbw8wNQUePFFwMIiC4mJGZBKxTAz\nUyA8XP/vjqzve0ZrHfMtLCxw4sQJWFlZoaKiAn379sWpU6fw7bff4rnnnsNbb72FNWvWYPXq1Vi9\nejXS0tKwa9cupKWlIScnBxEREbh69SrEYoNvrGOMMcaMytWrwDffAE+eAI6OwPjxgPI+Ll+9T7oa\nQ6sZjpWVFQBAKpVCLpfDwcEB3377LaKjowEA0dHR2L9/PwDlWFQTJ06Eqakp/Pz8EBAQgJSUFG1W\nT5CGDRsGiURS49/q1atbZP3VL5tWdfLkyVrrZlt54Z4JCvdzER6OiTBxXFqOQgGcOAFs365MwNq3\nB2bMqEzA/mIsMdHqEBUKhQLPPPMMMjIy8Nprr6FTp064e/euatgCNzc33L17FwCQm5uLkJAQ1bJe\nXl6qEdSNyZEjR3S6/ujoaFWSXF2/fv3w+PHjFq4RY4wxQ1BaCnz9NZCRAYhEwKBBQN++yr+NlVaT\nMLFYjPPnz6OoqAhDhgzBiRMn1N6vr9Wl8v3axMTEwM/PDwBgb2+P7t27N1udmfGpOlp25a8vQ5gO\nCwsTVH14GqrXhFIfnubplprOzQVWrEhGSQkQGBiGsWOB7Oxk/Pij4Z2/Kv/OzMxEQ7TWMb+65cuX\nw9LSEl9++SWSk5Ph7u6OvLw8DBw4EH/++afqctuiRYsAAEOHDsX777+PXr16qVeYO+azZsTHDWOM\naQ8RcO4ccPiwcuwvLy9g3Djgf0/OMwr1fc9orU/YvXv38PDhQwBAWVkZfvjhBwQFBWHEiBH46quv\nACiffzhq1CgAwIgRI7Bz505IpVLcvHkT165dQ3BwsLaqx5jBq/qrjAkDx0SYOC7aIZMBBw4oh5+Q\ny4FnnwViYjRLwIwlJlq7HJmXl4fo6GgoFAooFApERUUhPDwcQUFBiIyMRFxcnGqICgAIDAxEZGQk\nAgMD0apVK6xfv55HTWeMMcb00IMHwK5dwJ07fw0/0a2brmslPC12ObK58OVI1pz4uGGMseZV9/AT\nxkknlyPZ05NIJBp16Htafn5+SExM1Fr5jDHGjI9CASQl/TX8RIcOtQ8/wf7CSZgAPX78WHX3pzY0\ndFcqAGRmZkIsFkOhMNxndhk6Y+lToU84JsLEcWm60lJg2zbgp5+UQ06EhytbwCwsnq48Y4mJVoeo\nEIL0GzdwPDUVMgCmACI6dUL7tm1bvAx9pY1LdXK5XPWwccYYY/otJ0f5+KGiIsDaGhgzBjCSr8gm\nM+iWsPQbNxB/7hwKOnfGw86dUdC5M+LPnUP6jRstWsaaNWvg5eUFW1tbdOjQAUlJSVAoFFi5ciUC\nAgJga2uLnj17qganFYvFuPG/8mNiYjBz5kwMHjwYtra2CAsLQ3Z2NgBg9uzZePPNN9XWNWLECHz6\n6aca1y0lJQU9e/aEnZ0d3N3dVeX1798fgHIcNolEgjNnzuD69esYMGAA7O3t4eLiggkTJqjK+eGH\nH9ChQwfY29vj9ddfx4ABAxAXFwdA+TzK0NBQLFiwAM7Oznj//fc1rh97epVj1zDh4JgIE8fl6RAB\nv/8ObNqkTMC8vIC//715EjBjiYlBt4QdT02FeY8eSP7fUBkAAH9/XPzpJzyr4Z2XKT/9hNJu3YD/\nlRFmbw/zHj2QePmyRq1h6enp+Pzzz3H27Fm4u7sjOzsbFRUVWLt2LXbu3IkjR46gXbt2uHjxIiwt\nLWstY/v27Th8+DCCg4Px1ltv4ZVXXsHJkycRExODUaNG4eOPP4ZIJMK9e/eQmJioSn40MXfuXMyf\nPx+vvPIKSktLcenSJQDKRxS1adMGRUVFqud3Tpw4EUOHDsWPP/4IqVSKs2fPAlAORzJmzBjEx8dj\n5MiR+Oyzz/Df//5XbeT9lJQUvPzyy8jPz4dUKtW4fowxxoRHJgMOHQLOn1dOP/ssMGQI0Mqgs4rm\nZ9AtYbI6Xpc3YugLRR3zappGmJiYoLy8HKmpqZDJZPDx8UHbtm0RFxeHFStWoF27dgCArl27wtHR\nsdYyXnzxRfTt2xdmZmZYsWIFfvnlF+Tk5ODZZ5+FnZ2dqpP9zp07MXDgQLi4uGi8fWZmZrh27Rru\n3bsHKysr1eC4tV2GNDMzQ2ZmJnJycmBmZoY+ffoAAA4fPozOnTtj9OjRMDExwbx58+Du7q62rIeH\nB2bPng2xWAyLp+0kwBrFWPpU6BOOiTBxXBrnwQMgLk6ZgJmaAqNHAy+80LwJmLHExKBzVtP//R9m\nb6/2uqujI2a1aaNRGZ9fvoyCassDgJmGdQgICMCnn36K2NhYpKamYsiQIVi7di1u3boFf3//BpcX\niUTw8vJSTVtbW8PR0RG5ubnw9PTE5MmTkZCQgIiICCQkJGD+/Pka1kwpLi4OS5cuRceOHdGmTRss\nW7YML7zwQq3zfvTRR1iyZAmCg4Ph4OCAhQsXYsqUKcjNzVWrIwB4e3vXO80YY0z/8PATzcugW8Ii\nOnVC+e+/q71W/vvvCO/UqUXLmDhxIk6ePImsrCyIRCK8/fbb8Pb2xvXr1xtclohw69Yt1XRxcTEK\nCwvh4eEBAJg0aRIOHDiACxcu4M8//1Q9gUBTAQEB2L59OwoKCvD2229j7NixKCsrq/XuSTc3N2zc\nuBE5OTnYsGEDZs2ahYyMDHh4eKjVsXqdgbqfA8q0x1j6VOgTjokwcVwa1tLDTxhLTAw6CWvfti1i\nnnkGrpcvw/7yZbhevoyYZ55p1J2NTS3j6tWrSEpKQnl5OczNzWFhYYFWrVrh1VdfxZIlS3D9+nUQ\nES5evIjCwsJayzh8+DBOnz4NqVSKJUuWoHfv3vD09AQAeHl5oWfPnpg8eTLGjh0Lc3NzjbcNABIS\nElBQUAAAsLOzg0gkglgshouLC8RiMTIyMlTz7tmzB7dv3wag7LAvEolgYmKC559/Hqmpqdi3bx8q\nKiqwbt063Llzp1H1YIwxJkzVh5+IiGja8BPsLwZ9ORJQJlFNHU6iKWWUl5fjnXfewZUrV2BqaorQ\n0FBs3LgRrq6uKC8vx+DBg3Hv3j107NgR+/btA6DeaiQSifDyyy/j/fffxy+//IIePXogISFBbR3R\n0dGYPHky1q1b1+j6HT16FAsXLkRpaSn8/Pywc+dOVSK3ePFihIaGoqKiAkeOHMHZs2cxf/58FBUV\nwc3NDevWrVONZ7Znzx688cYbmDJlCqKiohAaGqq2DdwS1vKSk5ON5tekvuCYCBPHpW66Gn7CWGLC\njy0SuClTpsDLywvLly+vc56TJ09i0qRJyMrKasGa1W/gwIGIiorC1KlTdV2VehnqcQMYz0lMn3BM\nhInjUlPl8BNHjigfvu3lBURGAra2LbN+Q4pJfd8zBt8Spu8aShBkMhk+/fRTTJ8+vYVqpDlDTW70\nhaGcwAwJx0SYOC7qqg8/ERysHH6iJcfYNpaYGHSfMENQ36W8K1euwMHBAXfv3sW8efNUr2dnZ0Mi\nkdT4Z2trq+rT1RL4EiRjjOmXwsKaw088/3zLJmDGhC9HMqNmyMeNITXnGwqOiTBxXJSENPyEIcWE\nL0cyxhhjrFYKBZCcrLz7EVAOPzFqFN/92BK4JYwZNT5uGGPGrLQU+PprICNDOfxEeDgQGqr8mzUP\nbgljjDHGmJrqw0+MHQto+DAZ1kwMpmO+g4ODqhM7/+N/mv5zcHDQ9aGrNcby7DV9wjERJmOLCxFw\n9iywaZMyAfPyAv7+d0BICZixxMRgWsLqGm2eaZ8hdaBkjDFDJpMBBw8CFy4op3Ux/AT7i8H0CWOM\nMcZY3QoLgV27gLt3lcNPDB8OdO2q61oZvvryFoNpCWOMMcZY7dLTgX37hDH8BPuLwfQJY7pjLNfu\n9Q3HRXg4JsJkyHFRKICkJGDHDmUC1qEDMGOG8BMwQ45JVdwSxhhjjBmgkhLl8BM3biiHnIiIAPr0\n4eEnhIT7hDHGGGMGhoefEA7uE8YYY4wZASLg99+BI0cAuVw5/ERkJGBrq+uasdpwnzDWZMZy7V7f\ncFyEh2MiTIYSF5kM2L9fOQSFXA706gVMmaKfCZihxKQh3BLGGGOM6bnqw0+MGAF06aLrWrGGcJ8w\nxhhjTI9VHX7CyUk5/ISrq65rxSpxnzDGGGPMwCgUwIkTwMmTyumOHYGRIwELC93Wi2mO+4SxJjOW\na/f6huMiPBwTYdLHuJSUAAkJygRMJAKee07ZAd9QEjB9jMnT4JYwxhhjTI/cvg3s2cPDTxgC7hPG\nGGOM6QEi4OxZ4PvvlXc/ensD48bp592PxoT7hDHGGGN6TCZTDj1x4YJyulcvYPBgwMREt/ViTcN9\nwliTGcu1e33DcREejokwCT0uhYXAl18qEzBTU2DMGGDYMMNOwIQek+bCLWGMMcaYQPHwE4aN+4Qx\nxhhjAlPb8BOjRgHm5rqtF2s87hPGGGOM6YmSEuDrr4EbN5TDT0REAH36KP9mhoX7hLEmM5Zr9/qG\n4yI8HBNhElJcbt8GNmxQJmDW1kB0NBAaanwJmJBiok3cEsYYY4zpGA8/YZy4TxhjjDGmQzIZ8N13\nwMWLymkefsKwcJ8wxhhjTIAKC4Fdu4C7d5XDT4wYAXTpoutasZbCfcJYkxnLtXt9w3ERHo6JMOkq\nLn/+qez/dfeucviJ6dM5AatkLJ8VbgljjDHGWpBCASQlAadOKad5+AnjxX3CGGOMsRZSUgLs3Qvc\nvMnDTxgL7hPGGGOM6djt28Du3cCjR8rhJ8aNA/z8dF0rpkvcJ4w1mbFcu9c3HBfh4ZgIk7bjQgT8\n9huwebMyAfP2BmbO5ASsPsbyWeGWMMYYY0xLpFLg4EEefoLVjvuEMcYYY1pw/77y8iMPP2HcuE8Y\nY4wx1oL+/BPYtw8oL1cOPzF+PODqqutaMaHhPmGsyYzl2r2+4bgID8dEmJozLgoFcPw4sHOnMgHr\n2BGYMYMTsMYyls8Kt4QxxhhjzaDq8BNisXL4id69efgJVjfuE8YYY4w1EQ8/werCfcIYY4wxLagc\nfuLoUUAuB3x8lAmYRKLrmjF9wH3CWJMZy7V7fcNxER6OiTA9bVykUmXn+8OHlQlYSAgQHc0JWHMw\nls8Kt4QxxhhjjXT/PrBrF5CfD5iZKYef6NxZ17Vi+kZrfcJu3bqFyZMnIz8/HyKRCDNmzMAbb7yB\n2NhYfPnll3BxcQEArFy5EsOGDQMArFq1Cps2bYKJiQnWrVuHwYMH16ww9wljjDGmQ1WHn3B2BiIj\n+e5HVrf68hatJWF37tzBnTt30L17dxQXF6NHjx7Yv38/du/eDYlEggULFqjNn5aWhpdffhm//fYb\ncnJyEBERgatXr0IsVr9iykkYY4wxXVAogKQk4NQp5XRgIDByJGBurtt6MWGrL2/RWp8wd3d3dO/e\nHQBgY2ODjh07IicnBwBqrcyBAwcwceJEmJqaws/PDwEBAUhJSdFW9VgzMpZr9/qG4yI8HBNh0iQu\nJSXA1q3KBEwsVj56aNw4TsC0xVg+Ky3SMT8zMxN//PEHQkJCAACfffYZunXrhmnTpuHhw4cAgNzc\nXHh5eamW8fLyUiVtjDHGmK7cugVs2KAc/8vGBpg8GejTh8f/Yk2n9Y75xcXFGDt2LP7v//4PNjY2\neO2117B06VIAwJIlS7Bw4ULExcXVuqyojiM8JiYGfv8bgMXe3h7du3dHWFgYgL+yZ55u2elKQqkP\nT4chLCxMUPXhaaheE0p9eLr+6RMnkvHnn8C9e2GQy4HS0mQEBwN+fsKonyFPh+nx+avy78zMTDRE\nq4O1ymQyvPjiixg2bBjmzZtX4/3MzEwMHz4cly5dwurVqwEAixYtAgAMHToU77//Pnr16qVeYe4T\nxhhjTMukUuDgQeDiReV0SAjw3HOAiYlu68X0j076hBERpk2bhsDAQLUELC8vT/X3vn370OV/j5Qf\nMWIEdu7cCalUips3b+LatWsIDg7WVvVYM6qa/TPh4LgID8dEmKrH5f594MsvlQmYmRkwdiwwdCgn\nYC3JWD4rWrscefr0aSQkJKBr164ICgoCoByOYseOHTh//jxEIhHatGmDDRs2AAACAwMRGRmJwMBA\ntGrVCuvXr6/zciRjjDGmDVeuAPv3/zX8xPjxwP9GVGKs2fGzIxljjBk9Hn6CaQs/O5IxxhirQ3Ex\n8PXXyrsfxWJl36+QEL77kWmf1vqEMeNhLNfu9Q3HRXg4JsJz6xbwj38kq4afiI4GevfmBEzXjOWz\nwi1hjDHGjA4R8NtvwNGjQFkZ4OOjHHyVH77NWhL3CWOMMWZUpFLgu++AS5eU0zz8BNMm7hPGGGOM\nQaOxlSQAACAASURBVDn8xK5dQH6+cviJkSOBTp10XStmrLhPGGsyY7l2r284LsLDMdGtK1eAjRuV\nCZizMzB9ujIB47gIj7HEhFvCGGOMGTSFAkhMBE6fVk7z8BNMKLhPGGOMMYPFw08wXeM+YYwxxozO\nrVvA7t3A48fK4SfGjQN8fXVdK8b+wn3CWJMZy7V7fcNxER6OScsgAs6cATZvViZgPj7A3/9edwLG\ncREeY4kJt4QxxhgzGNWHn+jdG4iI4OEnmDBxnzDGGGMGgYefYELEfcIYY4wZtCtXgP37gfJy5fAT\n48cDLi66rhVj9eM+YazJjOXavb7huAgPx6T5KRTADz8oW8DKy5UtX9OnNy4B47gIj7HEhFvCGGOM\n6aXiYmDvXiAzk4efYPqJ+4QxxhjTOzz8BNMX3CeMMcaYQSACUlKAo0eVlyJ9fYGxYwGJRNc1Y6zx\nuE8YazJjuXavbzguwsMxaRqpFPjmG+DIEWUC1rs3MHly0xMwjovwGEtMuCWMMcaY4PHwE8wQcZ8w\nxhhjglZ1+AkXFyAykoefYPqD+4QxxhjTOwoFcPw48PPPyulOnYARIwBzc93Wi7Hmwn3CWJMZy7V7\nfcNxER6OieaKi4EtW5QJmFgMDBmi7ICvjQSM4yI8xhITbgljjDEmKDz8BDMW3CeMMcaYIPDwE8wQ\ncZ8wxhhjgiaVAt9+C1y+rJzu3RuIiABMTHRbL8a0ifuEsSYzlmv3+objIjwck9rduwd88YUyATMz\nU15+HDKk5RIwjovwGEtMuCWMMcaYzqSlAQcO8PATzDhxnzDGGGMtrrbhJ0aOVLaEMWZIuE8YY4wx\nwSguBvbuBTIzlcNPDB4M9OoFiES6rhnTtaz0dGQcPw6xTAaFqSn8IyLg2769rqulNdwnjDWZsVy7\n1zccF+HhmADZ2cCGDcoEzMYGiIkBQkJ0m4BxXIQhKz0d1+PjMSg/H0hJwaCCAlyPj0dWerquq6Y1\nnIQxxhjTOiLg11+B+Hjl+F++vsDMmYCPj65rxoQi4/hxhJeXA+fPA+npQEUFws3NkZGYqOuqaQ33\nCWOMMaZV1Yef6NMHCA/n4SdYFQ8fInnePIRlZiqnTU2BLl0AW1sk29sjbN48nVavKbhPGGOMMZ24\ndw/YtQsoKFB2uh81CggM1HWtmGA8eQKcPAn8+isU+fnKToJeXsom0lbKFEVhwHdr8OVI1mTcn0KY\nOC7CY2wxSUsDNm5UJmAuLsCMGcJMwIwtLoIglysfj7BuHXD6NCCXw3/oUCR27w60bYvk27cBAInl\n5fAPD9dxZbWHW8IYY4w1q+rDT3TuDIwYwcNPMCg7B169Cvzwg7KZFFB2EBw8GL6enkB6OpISE3Hx\n3j0oXF0REB5u0HdHcp8wxhhjzaa4GNizB8jK4uEnWDW5ucCxY8pbYwHAyQl47jmgfXuDPkC4Txhj\njDGty85WJmCPHysfuj1uHN/9yAAUFQFJScCFC8ppKytgwACgZ0+jvzuD+4SxJuP+FMLEcREeQ41J\nbcNP/P3v+pOAGWpcdK68HEhMBD77TJmAmZgob4194w1l82g9CZixxIRbwhhjjD01Hn6C1aBQAOfO\nASdOACUlytc6d1YeGA4Ouq2bwHCfMMYYY0+Fh59gaoiA69eV/b4KCpSveXsDQ4Yoh50wUtwnjDHG\nWLNKSwP271e2hLm4AOPHA87Ouq4V05k7d5TJ140bymkHByAiQpmVG3Cn+6biPmGsyYzl2r2+4bgI\njyHERKFQftfu3q1MwDp3BqZP1+8EzBDiojOPHwMHDigfCHrjBmBhoWz5mj0b6NTpqRMwY4kJt4Qx\nxhjTSPXhJ4YMAYKDuaHDKEmlyoHgTp8GZDLlAdGrF9C/v/LuR6YR7hPGGGOsQdnZytav4mIefsKo\nKRTKOx2TkpStYADQsaPy0qOTk27rJlDcJ4wxxthTIQLOnFFeglQoAD8/YOxYwMZG1zVjLS4jQ3kg\n3L2rnPb0VI7G6+ur23rpMe4TxprMWK7d6xuOi/DoW0ykUuDrr4Hvv1cmYH36AJMnG14Cpm9xaXH5\n+cC2bcDWrcoEzM4OGDMGePVVrSVgxhITbgljjDFWQ9XhJ8zNgZEjefgJo1NcrBzr69w5ZZOouTnQ\nrx8QEgK04vShOXCfMMYYY2p4+AkjJ5MBv/wCnDqlPAjE/8/enQe3fd93/n/i4AXgC1ESRYoUL12W\nRF0UKZKSKB625SuNvUnd2j97d2NPuslus9NuJzvjeLLHrPdo7Gk702Qz7iSts3Emma6dpHHceFvb\nsk2Qog5S92Xd4iGSIkXxwBcgCeL4/v74QLQs6yZAfAG8HzOZ6AvbxIf8CMCbn8/7+/pYoboampvB\n6Uz06JKO9IQJIYS4o3AYdu5Un7+g4ieeekoFsYo0YBhw9Kg6asjrVY+tWqWa7hctSuzYUpT0hIlZ\nS5e9+2Qj82I+Zp4Tnw9+9jNVgFmt8MQTqu0nHQowM8/LnOnqgh//GH7zG1WAFRbCCy/Ac88lpABL\nlzmRlTAhhEhz3d0q/0viJ9LQ8DB8+CGcPq2u3W51xuOGDRIANwekJ0wIIdKUYcDeveozWOIn0ozf\nDx4P7N+vJj8zE7Zvh61bISMj0aNLKdITJoQQ4nMCAXj3XThxQl3X16sFEKs0qaS2UEgFv7W2qr8E\nFotqun/wQam+E0BebmLW0mXvPtnIvJiPWebkyhX4u79TBVhWlrr78ZFH0rcAM8u8xJVhwPHj8MMf\nqqXPQABWrIA//mN48knTFWBpMSfISpgQQqSVEyfUecvT05CfD888I/ETKa+nB95/H/r61HVBgUq6\nX748seMS8esJ6+3t5Wtf+xpDQ0NYLBa++c1v8qd/+qeMjIzw7LPP0t3dTXl5OW+//Ta5ubkAfO97\n3+MnP/kJNpuNH/zgBzz66KNfHLD0hAkhxD27MX5i/Xq1AJIOdz+mrZERter16afq2uWChx6Cysr0\nXfZMgNvVLXErwi5fvszly5eprKzE5/NRXV3NO++8w//5P/+HvLw8XnrpJV577TVGR0d59dVXOXny\nJM8//zydnZ309fWxY8cOzpw5g/WGvyhShAkhxL3RdfjVr9RdkFYrPPYY1NbKzW8pa3JSNd13dqrq\nOyNDnTlVXy9VdwLcrm6JWym8ePFiKisrAXC5XKxZs4a+vj7effddXnjhBQBeeOEF3nnnHQB++9vf\n8txzz5GRkUF5eTkrVqygo6MjXsMTMZQue/fJRubFfBIxJ93d8KMfqf/XNHjxRairkwLseinzWgmF\n1FLn97+vbnuNRGDTJviTP1GN90lUgKXMnNzBnPSEdXV1cejQIerq6hgcHKSgoACAgoICBqOnsff3\n97Nly5aZ/6a4uJi+a/vXQggh7onET6QRw1Bbjh9+CKOj6rFly1Tf1+LFiR2buK24F2E+n4+nn36a\n73//+2ia9rl/ZrFYsNzm17Fb/bMXX3yR8vJyAHJzc6msrKS5uRn4rHqW67m9vsYs45HrZpqbm001\nHrlm5rF4P9/Wrc28+y689566/pf/spmHH4bWVnP9POQ6BtdXrtCs69DTQ0tXF8ybR/O///ewciUt\nHg+cOmWu8d7ldXMSv39d+3NXVxd3Etew1mAwyJe//GWeeOIJ/uzP/gyA1atX09LSwuLFixkYGODB\nBx/k1KlTvPrqqwC8/PLLADz++OO88sor1NXVfX7A0hMmhBC3dOUKvPWWCkLPyoKvfAXWrEn0qETM\njY6qMx6PH1fXTqfacqyqSuqm+9PnTrPzwE6CRpAMSwY7qnewasWqRA9rVhLSE2YYBn/0R39ERUXF\nTAEG8NRTT/Hmm28C8Oabb/KVr3xl5vH/+3//L9PT01y8eJGzZ89SW1sbr+GJGLq++hfmIfNiPvGe\nkxMn4G//VhVg+fnwjW9IAXY3kuq1MjWlth1/+ENVgNnt0NAAf/qnsHlz0hdgP/3kp5ybd45/PvfP\nXCm4wk8/+Smnz51O9NDiJm7bke3t7fz85z9nw4YNbNq0CVARFC+//DLPPPMMb7zxxkxEBUBFRQXP\nPPMMFRUV2O12Xn/99dtuVQohhFDCYfW5vHevupb4iRQUDqsjhjwemJhQj23YoI45mDcvsWOLkbd2\nvcWn2qeMXR5jzDeGHtDRVmp8dPCjpF8NuxU5O1IIIZKYrqvDt3t6JH4iJRmGOlz7ww/h6lX1WFmZ\nmuiiosSOLQYMw+Di2EU8XR7eeu8tpoqnsFvtLNGWUOwuJsOWQe7lXP7s//uzO38xk5KzI4UQIgV1\nd6sCzOdT8RPPPAMlJYkelYiZ/n744AO41uC9cKE6X2rVqqSvsg3D4PzoeTxdHnq9vQBkWbNYnLuY\nYncxdutn5UmmNXWXdJN381iYRlL1U6QRmRfzidWcGIaKg3rzTVWAlZfDv/23UoDdL9O9VsbH4R/+\nAX78Y1WAORzwxBPwrW/B6tVJXYAZhsHZq2d549Ab/Pzoz+n19pJjz+HhpQ/zP57+HxReKcRutdN1\nuAuAwNkAD1c9nNhBx9EdV8J8Ph85OTnYbDZOnz7N6dOneeKJJ8jIyJiL8QkhhLhOIKDOfjx5Ul3X\n16u2oCTuxxbXBAKwa5eqsEMhsNlgyxbVeJ+dnejRzYphGJy5egZPt4d+vR8AR4aDbSXbqCmqIcue\nBUCWLYuPDn7E8Mgw+UP5PPzgwynbDwZ30RNWVVXFrl27GB0dpb6+npqaGjIzM/nFL34xV2P8HOkJ\nE0KkK4mfSFGRCBw8CJ98An6/emzdOlVdz5+f2LHNkmEYnBo+hafbw2XfZQCcGU7qS+vZXLSZTFvq\nbjVeM6ueMMMwcDgcvPHGG3zrW9/ipZdeYuPGjTEfpBBCiFs7cUKtgE1Pq/iJZ59VLUIiiRkGnD2r\nmu6vXFGPlZSopvvi4sSObZYMw+DT4U/xdHkY9KuTcVyZLraXbqe6sJoMm+ymwV025u/Zs4df/OIX\nvPHGGwBEIpG4Dkokl5brEsCFeci8mM/9zInET8RfQl4rly+rpvsLF9T1/Pmq6X7NmqTu+YoYEU5e\nOUlrdytD/iEA3FlutpduZ9PiTXddfKXL+9cdi7C//uu/5nvf+x5f/epXWbt2LefPn+fBBx+ci7EJ\nIURauzF+4vHHoaYmqT+jha7Dxx/D4cNqJSw7G5qa1MTakzewIGJEOD50nNbuVoYnhgGYlzVPFV+F\nmz53t6P4jOSECSGECUn8RIqZnob2dti9G4JB1XRfU6MKsJycRI/uvkWMCMcGj9Ha3crVSZVjlpud\nS0NpA5WLK7FZbQkeYeLNqiess7OTP//zP6erq4tQKDTzBY8ePRrbUQohhJiJn9i5U/VrL10Kf/AH\n6mhAkYQiEbXq9cknahUM1JbjI4/AggWJHdsshCNhjg4epbW7ldGpUQDmZ8+nsayRDQUbpPi6S3dc\nCXvggQf4y7/8S9atW4f1unugy8vL4z22m5KVMPNJl737ZCPzYj53mpMb4ye2b4eHHpL4iXiL22vl\n/HnV9zWoGtNZsgQefVQl3iepcCTM4cuHaetpY2xqDICFOQtpLGtkfcF6rJbY/GVNpfevWa2ELVq0\niKeeeirmgxJCCPGZG+MnvvpVlcspktDQkCq+zp1T17m5Km5i3bqkbegLRUIcGjjErp5djAfGAchz\n5NFY1si6/HUxK77SzR1Xwj744APeeustduzYQWb0dhyLxcLv//7vz8kAbyQrYUKIVHP8OLz7rsRP\nJD2fT207Hjyo9pWzsqCxEerqkrbpPhQJcXDgILt6duENeAHId+bTWNZIxaIKKb7uwqxWwt58801O\nnz5NKBT63HZkooowIYRIFRI/kSKCQdXIt2uXqqSt1s+a7pO0mS8YDnJg4ADtPe3o06qXrcBZQFN5\nE2vy1mBJ0hU9s7njStiqVas4deqUaX7gshJmPqm0d59KZF7M5/o5uT5+wmZT+ZwSP5EY9/1aMQw4\nehQ++gi8apWIVatU031eXkzHOFemw9Ps79/P7t7d+KZ9ACx2LaaprInVeavnrBZIpfevWa2Ebdu2\njZMnT7J27dqYD0wIIdJRVxf86ldq98rthj/8Q4mfSDpdXfD++zAwoK4LC1XT/dKlCR3W/ZoOT9PR\n18Hu3t1MBCcAKNKKaCpr4oGFD5hmISbV3HElbPXq1Zw/f56lS5eSlaUO2ExkRIWshAkhkpXET6SA\n4WG1h3z6tLp2u1XT/YYNSbmMGQgF6OjrYM+lPTPF1xJtCc3lzaxYsEKKrxi4Xd1yxyKsq6vrpo9L\nRIUQQtw9iZ9Icn4/eDywf7+qoDMz1SRu3QoZyXcO4lRoin2X9rH30l4mQ5MAlLhLaCpvYvn85VJ8\nxdCsijCzkSLMfFJp7z6VyLyYx9AQvP027N/fwqpVzRI/YTK3fa2EQurOibY2VUlbLFBVBQ8+CC7X\nnI4zFiaDk+y9tJd9ffuYCk0BUDavjKbyJpbmLjVN8ZVK71+z6gkTQghx/66Pn8jNhW9+U+InkoJh\nqMnbuRPGVS4WK1eqpvv8/MSO7T5MBCdU8XVpH4FwAICluUtpKm+iPLc8sYNLY7ISJoQQcXBj/MSG\nDfDlL0v8RFLo6VFN93196rqgQDXdL1+e2HHdB/+0nz2X9tDR18F0eBqAZfOX0VTWRFlu8ib3JxNZ\nCRNCiDkk8RNJamREVc6ffqquXS7VuFdZmXTNe75pH7t7d9PZ10kwEgRgxYIVNJU1UTJPbsU1izsW\nYb/+9a95+eWXGRwcnKnkLBYL3muZKCLtpdLefSqReUmMG+MnnnkGiovVP5M5MaeW99+n2TCgs1Mt\nYWZkQH09bNuWdEuXekCnvbedA/0HZoqvBxY+QFNZE0vcSxI8uruXLq+VOxZhL730Er/73e9Ys2bN\nXIxHCCGSksRPJKFQSBVev/41FBWppcpNm1TTvdud6NHdE2/AS3tPOwcGDhCKhABYnbeaxrJGirSi\nBI9O3Mode8Lq6+tpb2+fq/HckfSECSHMRuInkoxhqMnauRNGR9Vjy5apvq/FixM7tns0PjXOrp5d\nHBw4SNgIA7Ambw1N5U0sdiXX95KqZtUTtnnzZp599lm+8pWvmOIAbyGEMJNr8RPDw+q8ZomfMLlL\nl1TTfW+vul60SBVfK1YkVdPe2NQYbd1tHL58mLARxoKFtYvW0ljWSIGrINHDE3fpjkXY+Pg4OTk5\nfPDBB597XIowcU267N0nG5mX+Dt+XK2ABYPqBrpnnrl9/ITMSQKNjqozHo8fV9dOp9p2rKqipbWV\n5pUrEzu+uzQ6OUpbjyq+IkYECxbW56+noayBfGfyRWfcSrq8Vu5YhP30pz+dg2EIIUTyCIfhgw9g\n3z51LfETJjY1Ba2tarLCYbDbVcr99u1q6TJJXJ24SltPG0cHj84UXxsLNtJQ1kCeIzkPCxe36Ql7\n7bXX+M53vsOf/MmffPE/slj4wQ9+EPfB3Yz0hAkhEknX1fZjb6+Kn3j8cdi8Oal2stJDOKyOGPJ4\nYEKdicjGjapZb968xI7tHgxPDNPa3cqxwWMYGFgtVjYUbKChtIGFDkn9TQb31RNWUVEBQHV19eeO\nMTAMwzTHGgghxFzq6lL5X37/F+MnhEkYhjpc+8MP4epV9Vh5uer7KkqeuwSv+K/Q2t3K8aHjM8XX\npsWbaChtYH7O/EQPT8SIJOaLWUuXvftkI/MSO4YBu3erlqLZxE/InMRZf7/aJ+7qUtcLF6pjhlat\nuu1SpZnmZdA3SGt3KyevnMTAwGaxsalwE9tLt5ObnZvo4c0ZM83JbElivhBC3KdAAN5557MQdYmf\nMKHxcVUhHz2qrh0OaG6G6mq1Z5wELvsu4+ny8Omw+otms9ioKqxie+l25mUnz/apuDeyEiaEELcw\nNARvvaV2tSR+woQCAdi1S6XkhkKq4NqyBRoaIDs70aO7K/16P54uD6evngbAbrVTXVhNfWk97qzk\nCowVNycrYUIIcY+OHYN33/0sfuLZZ2HBgkSPSgBqT/jAAWhpUQ16AOvWwY4dkJscW3Z93j483R7O\nXD0DQIY1g81Fm9lWsg0tS0vw6BLn9IUL7DxxgiCQAexYu5ZVy5Ylelhxc8cF9dOnT/Pwww+zdu1a\nAI4ePcr//J//M+4DE8mjpaUl0UMQNyHzcn/CYfinf1In2QSDKn7i3/yb2BRgMiezZBhw5gz8zd/A\ne++pAqykRE3QH/zBfRdgczkvveO9/Pzoz/nbg3/LmatnyLBmUF9Sz3/Y8h94bMVjaV+A/fTgQYbW\nrWOv18uVdev46cGDnL5wIdFDi5s7roR94xvf4C/+4i/4d//u3wGwfv16nnvuOf7zf/7PcR+cEELM\nJa9X3f0o8RMmdPmyarq/9oE8f75qul+zJikmqHusG0+3hwujavyZtkxql9SytXgrzkw5YBTgn44f\n58q6dfR7vVyenGSFYZBVXc1Hx4+n7GrYHYuwiYkJ6urqZq4tFgsZGRlxHZRILqlyB0uqkXm5Nxcv\nwq9+Fd/4CZmT++D1wscfw5EjaiUsJwcaG6GmRgWvxkC85sUwDLrHu2npaqFrrAuALFsWdcV1bCne\ngiPDEZfnTTZXpqfp0HU+HBvDPzkJwKKaGiYjETSbjekEjy+e7vg3eNGiRZw7d27m+le/+hWFhYVx\nHZQQQsyVa/ETO3eqPy9bBk8/fe/xEyLGpqehvV1NTjColiZra1UBlpOT6NHdlmEYXBy7iKfLQ/d4\nNwDZ9my2FG+hbkkdORnmHv9ciBgGZyYm2KfrXIwWXkYkQq7dzpKsLPIyMri2vpnKB1Hc8e7I8+fP\n881vfpPdu3czf/58li5dyi9+8QvKy8vnaIifJ3dHmk8q5bmkEpmXO7sxfqKhQR0nGK/4CZmTuxCJ\nwOHDavXL51OPVVSopvs43RkRq3kxDIPzo+fxdHno9aoDwnPsOar4Kq4j254cd2zG00Q4zEFdp1PX\nGQ+FAMi0WtnocpE7PMzvjh0jq7qarr17Kd+yhcCBA7xYVZXU25Gzujty+fLlfPTRR/j9fiKRCJqW\nvk2DQojUIfETJnT+vOr7GhxU18XFKum+tDSx47oDwzA4O3IWT5eHPr0PAEeGg63FW6ldUkuWPXnO\nqIyX/kCADq+X434/oWhBsjAjgxpNo9LlIttmg4ULycvI4KPjxxm+eJF8l4uHk7wAu5M7roSNjo7y\ns5/9jK6uLkLRqlXOjhRCJDOJnzCZoSFVfF1rfcnNVStfa9eauuneMAzOXD2Dp9tDv94PgDPDybaS\nbWwu2pz2xVcoEuHkxAQdXi+XAgFAfYavzMmhVtNYnpOTFscgzmol7Etf+hJbt25lw4YNWK1WOTtS\nCJG0wmH1Wb9vn7reuBG+/GWQe40SxOeDTz6BgwdVQ15Wlur5qquLWdN9PBiGwanhU3i6PVz2XQbA\nlemaKb4ybancxXRn3lCI/brOAV3HHw4DkGOzscnlokbTmC8vuBl3XAmrqqri4MGDczWeO5KVMPOR\nPhdzknn5PDPET8icRAWDKuV+1y7VgG+1qsloblZHDs2xu50XwzA4eeUkrd2tDPrVlqmWqVFfWk91\nYTUZtvQtLgzDoHtqig5d59TEBJHo5/TizExq3W7WO51k3EOzZSq9Vma1Evb888/z4x//mCeffJKs\nrM+WVhfI2r0QIknMRfyEuAuGoaImPv5YVcWgDtd+5BHIy0vs2G4jYkQ4eeUkni4PVyauAODOcrO9\ndDtVhVXYreZdtYu36UiEoz4fHbrO0LQKk7BaLKxzOql1uynJypLds9u440rYD3/4Q/7Tf/pP5Obm\nYo1WsRaLhQsJSrCVlTAhxN2S+AkTuXgR3n9fha4CFBbCY49Bgu60vxsRI8LxoeO0drcyPDEMwLys\neTSUNVC5uDKti6+rwSCdXi+HfT6mIhEAXDYbmzWNak1DM/F28ly7Xd1yxyJs6dKldHZ2kmeS31Kk\nCBNC3I2pKfjtb+cufkLcwvCwasQ7o85IxO2Ghx9W50GZdIUkYkQ4OniUtu42rk5eBSA3O5eGUlV8\n2ay2BI8wMQzD4NzkJPu8Xs5Fs70ASrOzqdU01jid2Ew6p4k0q+3IlStXkmPyYDyRWKm0d59K0nle\nro+fyM5W8ROrViV6VGk2J36/OmD7wAGV/ZWZCdu3w9atprsT4tq8hCNhjgweoa27jdGpUQAW5Cyg\nobSBDQUb0rb4mgyHORzdchwNBgGwWyxsiDbaF2bF/i7QdHmt3LEIczgcVFZW8uCDD870hCUyokII\nIW5H4icSLBSCvXuhrU2l4VosnzXdu1yJHt1NhSNhDvQfoK2njbGpMQAW5iyksayR9QXrsVrSc/n0\nciBAp65z1O8nGN1yzLXbqXG72eRy4bClZ1EaS3fcjvzpT3/6xf/IYuGFF16I15huS7YjhRA3Ew6r\nlqOODnUt8RNzzDDg+HHVgDc+rh5buVI13efnJ3ZstxCKhDg0cIhdPbsYD6gx5znyaCprYm3+2rQs\nvsKGwalotlf31NTM48tzcqh1u1mZk4NVthzvyax6wsxGijAhxI1ujJ944gmorjZty1Hq6e5WfV99\nKi2eggKVdL98eWLHdQuhSIgD/Qdo723HG1B3aeY782ksa6RiUUVaFl++UIgDPh/7dR09GsyeZbVS\nGd1yzMtM7+yz2bivnrA//MM/5Je//CXr16+/6Rc8evRo7EYoklq67N0nm3SZl2SKn0i5Obl6Va18\nXbv7QdPgoYfUMqQJ74AIhoMcGDhAe087+rQOQIGzgJy+HF5oeiHtohQMw+BSIECHrnPS7yccLRQW\nZWZSq2lscLnIStA8ptxr5RZuWYR9//vfB+B3v/vdFyq4dPuLKoQwH4mfSKCJCWhtVXu/kYja862v\nh23bVAO+yUyHp9nfv5/2nnb8QT8Aha5CmsqbWLVwFR6/J60+14KRCMf9fjp0nYHrjhNa43RSq2mU\nZ2en1c8jke64Hfmd73yH11577Y6PzRXZjhRCTE3BO+/AqVPqurFR9X2bcPEltYRCqvBqbVWTc7EH\nDwAAIABJREFUYLFAZaVa/dK0RI/uCwKhAJ39nezu3c1EcAKAIq2IprImHlj4QNoVGmPBIJ26zkGf\nj8nocUIOm41qTWOzpjFPsr3iYlY9YZs2beLQoUOfe2z9+vUcO3YsdiO8B1KECZHeBgfh7bfNFz+R\n0gwDTp5Uy46jKrqB5ctV31dBQWLHdhOBUICOvg529+5mMqTyrIrdxTSVNbFiwYq0Kr4Mw+DC1BQd\nXi9nJidnPj+LsrKoc7tZ63Bgl99e4uq+esL+5m/+htdff53z589/ri9M13Xq6+tjP0qRtNJl7z7Z\npOK8HD0K//iPyRs/kZRz0turmu57e9X1okWq+FqxwnR3PkyFpth3aR97Lu1hKqTu7Ctxl9Bc3syy\n+ctuWXwl5bzcQSAS4YjPR4fXy3A028tmsbDO5aLW7WZJHLK9YikV5+RmblmEPf/88zzxxBO8/PLL\nvPbaazNVnKZpLFy48K6++Ne//nXee+898vPzZ1bO/tt/+2/83d/9HYsWLQLgz//8z3niiScA+N73\nvsdPfvITbDYbP/jBD3j00Udn9c0JIVKDxE8kwOioWvk6cUJdO53qyIGqKtPt+04GJ9l7aS/7+vbN\nFF9l88poLm+mPLc8rVa+rkxP06nrHPb5mI5me7nt9pnjhJyS7WUqcY2oaGtrw+Vy8bWvfW2mCHvl\nlVfQNI1vf/vbn/t3T548yfPPP09nZyd9fX3s2LGDM2fOzJxXOTNg2Y4UIq14vWr78dIliZ+YE5OT\nKmh13z5V/drtquG+vh5MtnoyEZxgT+8eOvo6CIRVg/nS3KU0lTdRnlue2MHNoYhhcGZigg5d58J1\nxwmVZ2dT63az2uGQbK8EmtWxRbPR0NBAV1fXFx6/2WB++9vf8txzz5GRkUF5eTkrVqygo6ODLVu2\nxHOIQggTuz5+Yt48FT+xZEmiR5WiwmHYv18dNXTtg3zjRtV0P29eQod2I/+0nz2XVPE1HZ4GYPn8\n5TSVN1E6rzTBo5s7E+EwB3WdTl1nPJrtlWG1stHppNbtJt+Ed6qKz0vIrRD/+3//b372s5+xefNm\n/uqv/orc3Fz6+/s/V3AVFxfTdy34T5hauuzdJ5tknhfDgPZ2+Oijz+In/uAPwOFI9Mhmx5RzYhjq\nNtOdO9XdDgDl5fDYY1BYmNCh3cg37WN37246+zoJRlSf04oFK2gqa6JkXsl9f11Tzstt9AcCdHi9\nHPf7CUUXNRZmZFCjaVS6XGSnwJZjss3J/ZrzIuyP//iP+a//9b8C8F/+y3/hP/7H/8gbb7xx03/3\nVvv4L774IuXl5QDk5uZSWVk5M1ktLS0Acj2H14cPHzbVeOQ6ua+np2FkpJlTp6Crq4UNG+Bf/atm\nrFZzjG8214cPHzbVeFp+9Svo7KQ5J0ddj4zA5s00/+t/DRZL4scXva7eWk17bzu/fO+XhI0w5ZXl\nPLDwATJ6MljEIko2lMzq619jlu/3ZtehSIQ333+fU34/zupqALr27aM4M5MXH3+c5Tk5eDwe9ppk\nvOl8fe3PN9sJvFHcjy3q6uriySefvGmkxfX/7NVXXwXg5ZdfBuDxxx/nlVdeoa6u7vMDlp4wIVLW\n4CC89RaMjEj8RFyNj6tlxmsnnzgc0Nysmu1MtIriDXjZ1bOLgwMHCUXUdtvqvNU0ljVSpBUleHRz\nwxsKsV/XOaDr+KPZXtlWK1XRbK8FcneK6SWsJ+xmBgYGKIwucf/mN7+Zib946qmneP755/n2t79N\nX18fZ8+epba2dq6HJ4RIkOvjJxYvVv1fyRQ/kRQCAdV0v3evCl612WDLFmhoUFWvSYxPjc8UX2FD\nFR4ViypoLGtksWtxgkcXf4Zh0D01RYeuc2pigkj0A3xxZia1bjfrnU4yrNYEj1LEQlyLsOeeew6P\nx8Pw8DAlJSW88sortES3rywWC0uXLuVHP/oRABUVFTzzzDNUVFRgt9t5/fXX0+q24mTW0pIee/fJ\nJlnm5cb4icpK+L3fS834iYTNSSQCBw5AS4u6ywFg3TrYsQNyc+d+PLcwNjVGW3cbhy8fJmyEsWBh\n7aK1NJY1UuCKXyisWV4r05EIx/x+OrxeBqfVDQdWi4V10Ub7kqystPlcNMucxFtci7C///u//8Jj\nX//612/573/3u9/lu9/9bjyHJIQwEYmfiDPDgLNnVdjq8LB6rLRUha2a6JTzkckR2rrbODJ4hIgR\nwYKF9fnraSxrZJFzUaKHF3cj0eOEDuk6U9FsL5fNNpPtpclxQikr7j1hsSY9YUKkBomfiLPLl9US\n48WL6nrBAnjkEVi92jRV7tWJq7T1tHF08OhM8bWhYAMNZQ3kOfISPby4MgyDc5OTdOg6ZycmZh4v\nyc6mVtOocDqxmWSexOyYqidMCJHeboyfWL4cnn46+eMnTMPrhY8/hiNH1A84JweamqCmxjRN91f8\nV2jraePY4DEMDKwWK5sWb6KhrIEFOandCDgZDnPY56NT1xmJHidkt1hY73JRq2kUmiwQV8SXFGFi\n1tJl7z7ZmHFepqbgnXdULBVAY6O6KS9deozjOifT06q63b1b3d1gs0FtrfohRyMoEm3IP0Rrdysn\nhk58vvgqbWB+zvyEjWsuXiuD09N0eL0c9fsJRrccc+12atxuNrlcOExSIJuFGd+/4kGKMCHEnLgx\nfuL3fx8eeCDRo0oBkQgcPqxWv3w+9VhFhWq6N8ntpYO+QTzdHk5eOQmAzWJjU+EmtpduJzfbPDcG\nxFrYMDg1MUGH10v31NTM48tzcqh1u1mZkyPHCaU56QkTQsTdjfETzz4L8xO38JE6zp1TTfdDQ+q6\nuFg13Zea4+ieAX2A1u5WPh3+FFDFV3VRNfUl9czLNtdRSLHkC4U44POxX9fRo8cJZVmtVLpc1Gga\neZmZCR6hmEvSEyaESIhwGP75n6GzU12ncvzEnBochA8/VEUYqJiJHTtg7VpTNN336/14ujycvnoa\nALvVTnVhNfWl9biz3AkeXXwYhsGlQIAOXeek3084+qG7KDOTWk1jg8tFVrrsu4u7JkWYmLV02btP\nNomeF4mf+KJZz4nPp7YdDx1STffZ2Spota4OTBBjcMl7CU+Xh7MjZwHIsGawuWgz20q2oWVpCR7d\nrc1mXoKRCCf8fvbpOgOBAKBWPtY4ndRqGuXZ2WmT7RVLiX7/miuJf9UKIVLOhQsqfmJiQuInYiIY\nVA337e2qAd9qVXc7Njeb4rbS3vFePN0ezo2olbkMawa1S2rZVrINZ6YzwaOLj7FgkP26zkGfj4no\ncUIOm43q6HFC80xQFAvzk54wIUTMGAbs2qUWayR+IgYMQ0VNfPQR6Lp6bPVqtfWYl/gcre6xbjzd\nHi6MXgAg05ZJ3ZI6thRvScniyzAMLk5N0eH1cnpycuazqCgri1pNY53TiV22HMUNpCdMCBF3U1Pw\nm9/AadUGlHbxEzF38aIKW718WV0XFsJjj0F5eUKHZRgGXWNdeLo9dI11AZBly6KuWBVfjozUq7gD\nkQhHfD46vF6Go9leNouFtdFsryVpdJyQiC0pwsSspcvefbKZy3mR+Im7c1dzcuWKaro/c0Zdu91q\n5Wv9+oQ21BmGwcWxi3i6PHSPdwOQbc9mS/EW6pbUkZNhjiyy+3GreRmenqZD1zns8zEdzfZy2+1s\n1jSqXC5csuUYN+nyuSJ/g4QQsyLxEzHi96sDtg8cUNlfmZmq6X7LloTeTmoYBudHz+Pp8tDr7QUg\nx57D1pKt1C6pJduenbCxxUPEMDgzMUGHrnNhcnLm8fLsbGrdblY7HJLtJWJGesKEEPclFFK7ZRI/\nMUvBIOzbB21tEAio1a7qarWX63IlbFiGYXB25CyeLg99eh8AjgwH20q2UVNUQ5Y9tY7XmQiHOajr\ndOo649FsrwyrlY1OJzVuNwWS7SXuk/SECSFianwcfvnLz+InvvQlqKpK7/iJe2YYcOyYarofH1eP\nrVypwlYXLUrgsAxOXz2Np8vDgG8AAGeGUxVfS2rItKVWMdIfCNDh9XLc7ycU/aBckJFBraZR6XKR\nLccJiTiSIkzMWrrs3SebeM2LxE/cv5k56e5WSfd9aoWJxYtV8bVsWcLGZhgGp4ZP4en2cNmnbgZw\nZbqoL6mnuqg6pYqvsGFwwu+nw+vlUiBA1969LN26lQccDmo1jeU5OdJon2Dp8rkiRZgQ4q5I/EQM\njI+rOxg+Vcf4oGnw0EOwcWPCbiM1DIOTV07S2t3KoH9QDStTY3vpdqoKq8iwpc7+sjcU4oCuc0DX\n8UWzvbKtVtY6nfzRkiUskL10McekJ0wIcUc3xk80Nan/SfzEXZqYAI9HNdBFIqpxbvt22LpVNeAn\nQMSIcGLoBK3drVyZuAKAO8s9U3zZranxO7phGPREtxw/nZggEv38KMjMpNbtZoPTSYb8RRZxJD1h\nQoj7JvETsxAKQUcHtLaqStZiUc1zDz6oVsESIGJEOD50nNbuVoYnhgGYlzWPhrIGKhdXpkzxNR2J\ncCy65Tg4PQ2A1WJhrdNJrdtNqWR7CRNIjVebSKh02btPNrGYlyNH4He/k/iJe2YYcPIk7NwJo6Pq\nseXLaXE6aX7qqYQMKRwJc2zoGK3drYxMjgCQm51LY1kjGws2YrOmRgP6SDBIp65zSNeZimZ7uaLH\nCVVrGu6bZHvJe5j5pMucSBEmhPiCG+MnNm1Sd0BKy8xd6O1VP7xLl9R1fr5qul++XG1JzrFwJMyR\nwSO0dbcxOqUKwgU5C2gobWBDwYaUKL4Mw+Dc5CQdus65644TKsnOplbTqHA6scmqlzAh6QkTQnzO\n+Di8/ba6cU/iJ+7B6Kha+TpxQl07narpftOmhDTPhSIhDl8+zK6eXYxNjQGwMGchjWWNrC9Yj9WS\n/H1Qk+Ewh30+OnWdkehxQnaLhfXR44QKs1Iry0wkJ+kJE0LclRvjJ559FoqKEj0qk5ucVEGr+/ZB\nOAx2O2zbBvX1kIAiIBQJcWjgELt6djEeUPljeY48msqaWJu/NiWKr8HpaTq8Xo76/QSjW465djs1\nbjebXC4cku0lkoQUYWLW0mXvPtncy7zcGD+xYoVqwJf4idsIh9V+rcejCjFQURMPP6zOe7yJeL5W\nguEgBwcOsqtnF/q0DkC+M5+msibWLFqT9MVX2DA4NTFBh9dL99TUzOPLc3KodbtZmZNz38cJyXuY\n+aTLnEgRJkSak/iJe2QYcOqUOmR7RDW4U14Ojz0GhYVzPpxgOMj+/v2097bjm/YBUOAsoKm8iTV5\na5L+DkBfKMRBn4/9uo43epxQltVKpctFjaaRJ8cJiSQmPWFCpLHLl1X/17X4iaefVifniFvo61NJ\n993d6jovDx55RGV2zHGxMx2eprOvk929u/EH/QAUugppKm9i1cJVSV18GYZBXyBAh65zwu8nHH3P\nX5SZSa2mscHlIkt+SxBJQnrChBBfIPET92BsTJ3xeOyYunY41AHb1dXq7oU5FAgF6OxXxddEcAKA\nIq2I5vJmVi5YmdTFVzASUccJ6Tr9gQCgPsBWOxzUut0szc5O6u9PiBtJESZmLV327pPNreYlFIJ/\n/mfYv19dS/zEbUxNqWa5vXvVD85uhy1bVNp9dvY9f7nZvFamQlN09HWwp3cPkyHVg1bsLqaprIkV\nC1YkdXEyFgyyX9c56PMxET1OyGGzUeVysVnTyI3zX055DzOfdJkTKcKESCPXx0/Y7Z/FT4gbRCJw\n4AC0tIBfbfWxfr1qus/NndOhTIWm2HdpH3su7WEqpBrSS+eV0lTWxLL5y5K2+DIMg4tTU3R4vZy+\nLturKCuLWk1jndOJXbYcRYqTnjAh0sT18RO5ufDMMxI/8QWGAWfPqr6vYXWkD6Wlqul+yZI5Hcpk\ncJK9l/ay99JeAmG1NVeeW05TWRPlueVJW3wFIhGO+Hx0eL0MR7O9bNeOE9I0lshxQiLFSE+YEGlM\n4ifu0sCAKr4uXlTXCxaopvvVq+e06X4iOMGe3j109HXMFF9Lc5fSVK6Kr2Q1PD1Nh65zxOcjEM32\nctvtbNY0qlwuXDc5TkiIVCd/68WspcvefbJpaWlhy5ZmiZ+4E69XVahHjqgqNSdH/ZBqamLedH+7\n14p/2s/u3t109ncyHVYHTi+fv5ym8iZK55XGdBxzJWIYnJmYoEPXuXAtSw0oz86m1u1mlcNhiuOE\n5D3MfNJlTqQIEyJFjYzAj36kTtPJyVGrXxI/cZ1AANrbYc8edYuozQa1tdDYqH5gc8Q37VPFV18n\nwYjanlu5YCVN5U0Uu4vnbByxNBEOc8jno9PrZSya7ZVhtbLR6aTG7aZAsr2EAKQnTIiUdOQI/OM/\nqhv6CgtV/5fET0RFInDoEHzyCfhUuCkVFbBjh9qCnCN6QKe9t539/fsJRVShsmrhKhrLGlnintv+\ns1gZiGZ7HfP5CEXfpxdkZFCraVS6XGTLcUIiDUlPmBBpQuIn7uDcOdX3NTSkrouLVdN9ScmcDcEb\n8LKrZxcHBw7OFF+r81bTVNZEoTb3ifuzFTYMTkazvXqjxwlZLBZWOhzUahorcnKk0V6IW5AiTMxa\nuuzdm92N8RP5+S38i3/RnOhhmcPgoCq+zp9X17m5auVr7do5a7ofnxrn9V++Tqg0RNhQWVgViypo\nLGtksWvxnIwhlryhEAd0nQO6ji+a7ZVttbJJ06jRNBYkUeUv72Hmky5zIkWYECng/Hn49a8/Hz9x\n5kyiR2UCuq62HQ8dUk332dmq56u2VlWqc2B0cpRdPbs4fPkw56+eZ2nJUtblr6OxrJF8Z/6cjCFW\nDMOgJxCgw+vl04kJItEtloLMTGrdbtY7nWTKXR9C3DXpCRMiiRkGtLWpOkPiJ64zPa0a7tvb1Z+t\nVnW3Y1PTnP1wRiZHaOtu48jgESJGBAsW1hesp6G0gUXORXMyhliZjkQ45vfT4fUyOK3u3LRaLKyJ\nHidUKtleQtyS9IQJkYImJ+Gdd1T8hMWijjJsbEzz+IlIBI4eVec86rp6bPVqlfe1cOGcDOHqxFVa\nu1s5NnRspvjaWLCRhrIG8hx5czKGWBkJBunUdQ7pOlPRbC+nzcZmTaNa03BLtpcQsyKvIDFr6bJ3\nbyaXL8Nbb90+fiLt5uXCBdX3dfmyui4qgkcfhfLyOXn6K/4rtPW0cWzwGAYGVouVTYs30VDWwIIc\ndddlMsyJYRicm5ykQ9c5d91xQiXZ2dRqGmscjpQ7TigZ5iXdpMucSBEmRJI5fBh+9zuJn5hx5Qp8\n+OFnTXDz5qkzHtevn5Om+yH/EK3drZwYOvH54qu0gfk5yTMxU9eyvXSdkehxQnaLhfXRRvuirKwE\nj1CI1CM9YUIkiRvjJ6qqVPxE2u4I+f3qgO0DB9Q2ZFYWbN8OW7bMSSbHoG8QT7eHk1dOAmCz2NhU\nuIntpdvJzZ7bQ75nY3B6mk6vlyN+P8HolmOu3U6N280mlwuHZHsJMSvSEyZEkrsxfuJLX1JFWFoK\nBmHvXnUgZiDwWdN9czM4nXF/+gF9AE+3h1PDpwBVfFUXVVNfUs+87Hlxf/5YCBsGpycm6PB66Ypm\newEsy8mhVtN4wOHAKo32QsSdFGFi1tJl7z5RbhY/UVR05/8u5ebFMODYMdV0Pz6uHnvgAdV0vyj+\ndxv2efto7W7l9FV1EKfdamdz0Wa2lWzDneW+q6+R6DnxhUIc9PnYr+t4o8cJZVqtVLpc1Ggai9L0\nOKFEz4v4onSZEynChDCpm8VPPP30nB5raB7d3fD++9Dfr64XL1ZN98uWxf2pL3kv4enycHbkLAAZ\n1gw2F22mvrQeV6Yr7s8/W4Zh0Bc9TuiE3084ui2Sl5FBrdvNRpeLrBRrtBciWUhPmBAmNDkJv/mN\n6jW3WFS8VVPTnIW7m8fVq6rp/pTa+kPTVNP9hg1xz+LoGe/B0+Xh/KhK2c+0ZVJTVMO2km04M+O/\n7TlboUiE49HjhPoDAUC9f67KyaHW7WZpdrZkewkxB6QnTIgkcjfxEylvYgI8HujsVE33mZlQXw9b\nt6o/x1H3WDctXS1cHLsIqOKrbkkdW0u24sgwfwruWDDIfl3noM/HRPQ4IYfNRpXLxWZNIzeJjhMS\nItVJESZmLV327ufCjfETzz6r+sDuR1LOSygEHR3Q2gpTU2rpr6oKHnxQrYLFiWEYdI114en20DXW\nBUCWLYstxVvYUryFnIzY7AHHa04Mw+Di1BQdXi+nr8v2KszKok7TWOt0kiFbjreUlK+VFJcucyJF\nmBAmEArBP/2TSluANIyfMAw4cQJ27oSxMfXY8uWq76ugII5Pa3Bh9AKebg894z0AZNuz2VK8hbol\ndTErvuIlEIlwJJrtdSV6nJDNYmGty0WtprFEjhMSwtSkJ0yIBEv7+IneXtV0f+mSus7PV8XXihVx\ne0rDMDg/eh5Pl4deby8AOfYctpZspXZJLdn27Lg9dywMT0/Tqesc9vkIRLO9NLudGk2jyuXClTbV\nuxDmJz1hQpjU/cZPpISRERU3ceKEuna51Lbjpk1xa7o3DIOzI2fxdHno0/sAcGQ42FayjZqiGrLs\n5k2FjxgGZycn6fB6OT85OfN4WXY2dW43qxwObLLqJURSkSJMzFq67N3HkmGotqeWFvXnlStVA34s\n4ydMOy+Tk+qb7+iAcFil22/dqhrv43Q0jmEYnL56Gk+XhwHfAADODKcqvpbUkGmbm3ys+5mTiWvH\nCXm9jEWzvTKsVjY4ndS63RSkabZXLJn2tZLG0mVOpAgTYo7dGD/R3Jwm8RPhsLrb0eNRPwSLBSor\n4aGHwH13Yaf3yjAMPh3+lNbuVi771MHerkwX9SX1bC7aTIbNvHcKDkSzvY75fISiWxkLMjKo0TQq\nXS5y5DghIZKe9IQJMYdujJ94+um4tj6Zg2GonK8PP1RbkABLl6q+r8LCuDxlxIjw6ZVP8XR7GPIP\nAaBlamwv3U5VYZVpi6+wYXAymu3Ve91xQisdDmo1jRU5OdJoL0SSkZ4wIUzg+viJoiLV/3W/8RNJ\no68PPvhAJd4D5OWp4mvlyrgs/UWMCCeGTtDa3cqViSsAuLPcNJQ2sKlwE3arOd/y9FCI/brOAV3H\nF832yrZa2aRp1GgaCyTbS4iUZM53JJFU0mXv/n4lKn4iofMyNqaa7o8dU9cOh2q6r6qCOGyjRYwI\nxwaP0drdytXJqwDMy5pHQ1kDlYsrTVN8XT8nhmHQEwjQ4fXy6cQEkehvygWZmdS63ax3OsmUbK85\nIe9h5pMucxLXd6avf/3rvPfee+Tn53Ms+mY8MjLCs88+S3d3N+Xl5bz99tvkRpcDvve97/GTn/wE\nm83GD37wAx599NF4Dk+IuBsbU/ET/f2q6Pq931M3/6WsqSnYtQv27lXVp90OW7bA9u2QHfvYh3Ak\nzLEhVXyNTKqtzvnZ82koa2BjwUZsVvP1TU1HIhzz++nwehmMZntZLRbWRhvtSyXbS4i0EdeesLa2\nNlwuF1/72tdmirCXXnqJvLw8XnrpJV577TVGR0d59dVXOXnyJM8//zydnZ309fWxY8cOzpw5g/WG\n3wSlJ0wki3PnVPzE5KTadnz22bi1QCVeOKyW+lpaVN4GwPr16pzHOOy5hiNhjgweoa27jdGpUQAW\n5CygsayR9fnrTVl8jQSDdOo6h3SdqWi2l9NmY7OmUa1puCXbS4iUlLCesIaGBrq6uj732LvvvovH\n4wHghRdeoLm5mVdffZXf/va3PPfcc2RkZFBeXs6KFSvo6Ohgy5Yt8RyiEDE3F/ETpmEY6jbPDz+E\n4WH1WGkpPPYYLFkS86cLRUIcvnyYXT27GJtSyfp5jjwayxpZl78Oq8Vc23eGYXB+cpJ9us65644T\nKs7Kos7tZo3DgV22HIVIW3P+q9fg4CAF0WNICgoKGBwcBKC/v/9zBVdxcTF9fX1zPTxxH9Jl7/5u\n3Bg/8eCD0NiYmPiJuM/LwIBqur+oDrpmwQJ45BFYvTrm33AoEuLgwEF29ezCG/ACsMixiMayRtbm\nrzVd8TUVDnPY56ND1xkJBgGwWyxw5Ahff+IJiuKUhybuj7yHmU+6zElC178tFsttex9u9c9efPFF\nysvLAcjNzaWysnJmslpaWgDkeg6vDx8+bKrxJOp6YAD+1/9qweeDNWuaefppuHSpBY/HHOOL2bXf\nT/P0NBw9SsvFi5CZSfPXvw41NbS0tcHgYMyeb+dHOzkzcobJJZPo0zpdh7vIzc7lG7//DdYsWkOr\np5XWT1tN8/P5hw8/5PTEBJHKSoKRCF179+K02Xju0Uep0jR+/M47nNmzhyKTjFeu1fU1ZhmPXCf3\n9bU/37gTeDNxzwnr6uriySefnOkJW716NS0tLSxevJiBgQEefPBBTp06xauvvgrAyy+/DMDjjz/O\nK6+8Ql1d3ecHLD1hwoQOHYL33kvx+IlAANrbYc8eCAbVXY51ddDQEPO91mA4yP7+/bT3tuOb9gFQ\n4CygqbyJNXlrTNW4HjYMTk9M0OH10nVdtteynBxqNY0HHA6sJhqvEGJumSon7KmnnuLNN9/kO9/5\nDm+++SZf+cpXZh5//vnn+fa3v01fXx9nz56ltrZ2rocnxD25MX6iuhqeeCL+8RNzKhJRVeYnn4BP\nFUSsXQs7dsD8+TF9qunwNJ19nezu3Y0/6Aeg0FVIU3kTqxauMlXx5Q+HOaDr7Nd1vNHjhDKtVipd\nLmo0jUWZmQkeoRDC7OL6UfHcc8/h8XgYHh6mpKSE//7f/zsvv/wyzzzzDG+88cZMRAVARUUFzzzz\nDBUVFdjtdl5//XVTveGKW2tpSY+9+xuZPX4iJvNy7pzq+xpSqfMUF6um+5KSWY/veoFQgM5+VXxN\nBNXdlUu0JTSVN7FywUrTvBcYhkFf9DihE34/4ehvt3kZGdS63Wx0uciyWm/536fra8XsZF7MJ13m\nJK5F2N///d/f9PGdO3fe9PHvfve7fPe7343nkISIiZSPnxgcVMXX+fPqev58tfJVURHRw9evAAAg\nAElEQVTTpvup0BQdfR3s6d3DZGgSgGJ3Mc3lzSyfv9w0xVcoEuF49Dih/kAAUFsMqx0Oat1ulmZn\nm2asQojkIWdHCnEPUj5+QtfVtuOhQ+obzM5Wt3fW1sZ0j3UyOMm+vn3svbSXqZDqoyqdV0pTWRPL\n5i8zTUEzFgyyX9c56PMxET1OKMdmo9rlYrOmkSvHCQkh7sBUPWFCJKvJSfiHf4CzZxMfPxFz09Oq\n4b69Xf3ZalWFV1OTOnIoRiaCE+y9tJd9l/YRCKsVpfLccprKmijPLTdF8WUYBl1TU+zzejl9XbZX\nYVYWdZrGWqeTjNtsOQohxN2SIkzMWjrs3Q8MwFtvqT6wnBx4+mlYsSLRo7q9u5qXSASOHIGPP1ar\nYABr1qitx4ULYzaWieAEe3r3sK9vH9NhdVTPsvnLaCxrpDy3PGbPMxuBSISj0WyvK9HjhGwWCxUu\nF7WaRnEMjhNKh9dKMpJ5MZ90mRMpwoS4g5SNn7hwQfV9Xb6srouKVNN9WVnMnsI/7Wd37246+ztn\niq/l85fTVN5E6bzSmD3PbAxPT9Op6xz2+QhEjxPS7HZ1nJDLhSulbnUVQpiJ9IQJcQspGz9x5Yo6\nZujMGXU9b54643H9+pjtrfqmfbT3tLO/fz/BiEqMX7lgJU3lTRS7i2PyHLMRMQzOTk7S4fVyfnJy\n5vGy7Gxq3W5WOxzYTLA1KoRIftITJsQ9Mnv8xH3x+dQdBQcPqm3IrCwVtFpXBzFqMNcDOu29qvgK\nRVR21qqFq2gqb6JIK4rJc8zGRDjMIZ+PTq+XsWi2V4bVygank1q3mwLJ9hJCzCEpwsSspdre/fXx\nE/Pnq+3HZIyfmJmXYBD27oVdu1TqvdUKNTXQ3AxOZ0yeyxvwsqtnFwcHDs4UX2vy1tBY1kihlvgf\n3kA02+uYz0co+hvp/IwMajWNSpeLHJttTsaRaq+VVCHzYj7pMidShAkRlXLxE4YBR4/CRx/B+Lh6\n7IEH1CHbixbF5CnGpsbY1bOLQwOHCBsqwqFiUQWNZY0sdi2OyXPcr7BhcDKa7dV73XFCKx0OajWN\nFTk5prgbUwiRvqQnTAi+GD/R3Jzk8RNdXarpvr9fXS9eDI8+CsuWxeTLj06O0tbTxuHLh4kYESxY\nWJu/lsayRvKd+TF5jvulh0Ls13UO6Dq+aLZXttXKJk1js6axULK9hBBzSHrChLiNZIyfuKWrV1XT\n/alT6lrTVNP9hg1qG3KWRiZHaOtu48jgkZnia0PBBhpKG1jkjM3q2v0wDIPeQIAOr5eTExNEom94\n+ZmZ1LndrHc6yZRsLyGEyUgRJmYtmffuUyZ+YmICPB7o7FRN95mZtDgcNH/rWxCDZvOrE1dp7W7l\n2NAxIkYEq8VK5eJKGkobWOiIXZ7YvQpGIhzz++nwerkczfayWixUOJ3UahplJjtOKJlfK6lM5sV8\n0mVOpAgTaSkUgv/3/9SNgpDE8ROhEOzbB21tMDWl9k+rqlSc/4EDsy7Arviv0NrdyvGh4xgYWC1W\nNi3eRENZAwtyFsTom7h3o8EgnbrOQV1nKprt5bTZqI5uObqTbiKFEOlIesJE2hkbU9uPAwOq6Pry\nl6GyMtGjukeGASdOwM6d6hsCtYf6yCNQUDDrLz/kH6K1u5UTQycwMLBZbFQurmR76Xbm58yf9de/\nH4ZhcH5ykg5d5+x1xwkVZ2VR63ZT4XBgly1HIYTJSE+YEFE3xk88+6zqWU8qvb3w/vtw6ZK6zs9X\nTfcxaGS77LtMa3crJ6+cBMBmsVFVWEV9aT252YnZp50Khzns89Gp61wNquBXu8XCOpeLWreboqys\nhIxLCCFmS4owMWvJsHdvGKplyuNRf37gAfjqV5MsfmJkRK18nVQFEi4XPPSQWsa7yQrQvczLgD6A\np9vDqWHV0G+32qkqrGJ76XbcWe5YfQf3ZGh6mg6vl6N+P9PRLcd5djs1mkaVpuGYo2yvWEqG10o6\nknkxn3SZEynCRMq7MX7ioYdUULyJ+rVvb3JSBZh1dEA4rNLtt22D+vpZ93z1efvwdHs4c1UdYWS3\n2tlctJn6knq0LC0Wo78nEcPg1MQEHV4vXddley3LyaFW03jA4cCaNBMnhBC3Jz1hIqVdHz/hcKj4\nieXLEz2quxQOq7sdPR5ViFkssHGjqiLds1uduuS9hKfLw9mRswBkWDOoWVLDtpJtuDJdsRj9PfGH\nwxzQdfbrOt7ocUKZViuVLhc1msYiOU5ICJGkpCdMpKWDB9UdkEkXP2EY8OmnautxZEQ9tnSp6vua\n5flJPeM9eLo8nB89D0CmLZOaIlV8OTNjc4TRveiLZnsd9/sJR9+k8jIyqHW72ehykSWN9kKIFCZF\nmJg1s+3d3xg/sXkzPP54ksRP9PWppvueHnWdl6eKr5Ur73n/9Pp56RrrwtPl4eLYRQCybFnULqll\na8lWHBmOWH4HdxSKRDgR3XLsCwQA9ZviKoeDOrebpSbL9ools71WhCLzYj7pMifJ8LEkxF0bHYW3\n307C+ImxMbXydfy4unY61dlJVVVwnw3ohmFwcfQiLV0tdI93A6r42lK8hS3FW8jJmNu7EsavO05o\nInqcUI7NRlV0yzFXjhMSQqQZ6QkTKePsWdWAn1TxE1NTKmh13z61hGe3w5YtsH07ZGff15c0DIML\noxfwdHvoGVcratn2bLYWb6WuuI5s+/193fsdS9fUFB26zqmJiZnXbmFWFrWaxjqnkwzZchRCpDDp\nCRMpLSnjJ8JhlWjf0qKOHAJ1vuNDD91345phGJwbOYen28Mlr8oQy7HnsLVkK7VLaue0+ApEIhz1\n+ejQda5EjxOyWSxUuFzUahrFWVkpu+UohBB3S4owMWuJ3LufnFThq+fOJUn8hGHAmTPqkO3hYfVY\nWZnq+1qy5D6/pMGZq2fwdHvo1/sBcGQ4yLmUwzef/iZZ9rkLMx2enqZT1zns8xGIZntpdjubNY1q\nlwtXUjTmxU+69LkkG5kX80mXOUnvd0SR1Pr7Vf9X0sRPDAyopvuuLnW9YIE6Zmj16vuqGg3D4PTV\n03i6PAz4BgBwZjipL61nc9FmdrftnpMCLGIYnJ2cpMPr5fzk5MzjZdnZ1LrdrHY4sJm2KhZCiMSR\nnjCRlK6Pn1iyRMVPzJuX6FHdwvg4fPwxHDmirnNyVNP95s331XRvGAafDn+Kp8vDoH8QAFemi/oS\nVXxl2OamwX0iHOaQz0en18tYNNsrw2plg9NJjaaxWI4TEkII6QkTqSMYVMXXoUPq2tTxE4EAtLfD\n7t2qWrTZoK5O7ZfeR8NaxIhw8spJWrtbGfIPAaBlamwv3U5VYdWcFV8DgQAdus4xn49Q9I1lfkYG\ntZpGpctFThIeJySEEIlgxo8ukWTmau8+aeInIhFVJX7yCfh86rG1a2HHDnXb5r1+OSPCiaETtHa3\ncmXiCgDuLDcNpQ1sKtyE3Xrzl3Es5yVsGHzq97NP1+m97jihlQ4HtZrGipwcabS/C+nS55JsZF7M\nJ13mRIowkRSSIn7CMNQdAh9+CENqpYqSEtV0X1Jyz18uYkQ4NniM1u5Wrk5eBSA3O5eG0gY2Lt54\ny+IrlvRQaOY4IV802yv72nFCbjcLJdtLCCHum/SECVOLRFT0RGuryeMnBgfhgw/gvDoOiPnz1cpX\nRcU9N92HI2GODh6lraeNkUl1bNH87Pk0lDWwsWAjNmt8t/sMw6A3epzQyYkJItHXW35mJrWaxgaX\ni0zJ9hJCiLsiPWEiKU1MqNUvU8dP6Lradjx0SFWJ2dnQ2Ai1tffcqBaOhDkyeIS27jZGp0YBWJCz\ngMayRtbnr4978RWMRDjm99Ph9XI5mu1ltViocDqp1TTKUvg4ISGESAQpwsSsxWPv3vTxE9PTquG+\nvV3dLWC1qqb7xkY14HsQioQ4fPkwbd1tjAfGAchz5NFY1si6/HVYLfe36nS38zIaDNKp6xzy+ZiM\nbjk6bTaqNY3NmobblHc9JKd06XNJNjIv5pMucyLvrsJ0Dh6E995TofKmi5+IRFTUxMcfq1UwgDVr\n1NbjwoX39KVCkRAHBw6yq2cX3oAXgEWORTSVN1GxqOK+i6+7YRgG5ycn6dB1zk5OziyVF2dlUet2\nU+FwYJctRyGEiCvpCROmYfr4iQsXVNjqoMrmoqgIHntMJd7fg2A4yIGBA7T3tKNPq0Iu35lPU5kq\nvuK55TcVDnPY56NT17kaDAJgt1hY53RS43azRLK9hBAipqQnTJjejfETTz4JGzcmelRRQ0Pqjsez\nZ9X1vHlq5WvduntqUJsOT3Og/wDtve34plV0xWLXYprKmlidtzquxdfQ9DQdXi9H/X6mo8cJzbPb\nqdE0NmkaTsn2Ev9/e3ceHPV1JXr8291qrd2tFUlIAu0SiwAJUItdODJmMmO8xMZ2HNuVOM/PcTKu\nZBY7MzWTSiX1HEOlMm/iSWZcWTw4ydhx8pyZeIkxtrEQi4RAILOIXRsSQoDW7pbUUnff98dtd8Bs\nAiS6JZ1Plav4/fTrX9/WkdHh3vM7Vwhx20kSJm7Zra7dh2z7CadTb7BdV6eL7iMi9JMBZWVwA60Z\nhr3D7Gnfw67Tu3CNuABIs6ZRnllOQWLBuCVfWz/+mFS7ndr+fpov6u2VExWF3WqlIDoaoxTa31ZT\npc5lopG4hJ6pEhNJwkTQfNp+Yts2fVxYqNtPREYGd1yMjEBNDWzfrgvwjUa9Nrp6NcTEjPo2bo+b\n2vZaqtuqGRgZACDdmk55Vjn5Cfnjlny5vF72ORz8v/PnSfb3Kws3GllgsWC3WpkWHj4u7yuEEOLG\nSE2YCIortZ9YsSLI7SeUggMH4KOPoF8XylNQoDfZnjZt1LcZ8gzp5Ot0NYMevaH1DNsMyrPKyY3P\nHbfkq93f2+uQy4XX//9IotmM3WZjQUwMkbLkKIQQt53UhImQ8tn2Ew8+CDk5QR5Uc7NutnrmjD6e\nPl13us/OHvUtBkcG2d2+m5q2GoY8evlvZuxMVmetJjsue1ySL4/Px+GBAWr7+2l3uwH9P3xhdDR2\nm40c6e0lhBAhS5IwcctGu3avlG4/8ac/hVD7iQsXdNH9sWP62GbT03ILFox6Wm5gZICathp2t+3G\n7dWJUFZcFuWZ5WTFZY1LEtTn8bDX4aDO4WDA39srymRiocXCYquVeLOZyspKcqdATcVEMlXqXCYa\niUvomSoxkSRM3BafbT9RWqq7OwSt/cTAgC6637tXF6eFh+v10KVLR1107xp2Ud1WTW17LcNe3WE+\nJz6H8sxyMuNurG3FaCilaB4aotbh4OjAQGB6OzU8nDKbjaKYGMzS20sIISYMqQkT4+7i9hNmM9x9\ndxDbT3g8sHu3LrofGtKzXQsXwh13gMUyqls4h51Un65mz5k9geQrLyGPVZmrmBk7c8yHPOzz8YnT\nSa3DwfmLthOa699OKCMiQpYchRAiRElNmAiaEyfgzTd1vpOQoJcfg9J+Qik4fBg+/FAXowHk5em6\nr+TkUd3C4Xaw6/Qu9p7Zy4hPNzrNT8inPKucDFvGmA/5wvAwexwO6p1O3P7eXtawMBZbrSy0WLCG\nTBdbIYQQN0P+Fhe37Epr9yHVfqK1VRfdt7Xp45QUnXyNcjPKfnc/O1t3UtdRh8fnAaAwsZDyrHLS\nrGljOlSfUpwYHKS2v59Tg4OB85mRkdhtNmZFR2Ma5azXVKmpmEgkJqFJ4hJ6pkpMJAkTY25gQM9+\nnToV5PYT3d165quhQR9bLHowxcW699d19A31saN1B/s69uFVuvh9dtJsVmWuYrp1+pgOddDrZb9/\nO6Ee/3ZCZqORef4lx1TZTkgIISYdqQkTY+rMGXjjDejrC2L7icFBPQW3Z49+DNNshmXLYPlyXYB/\nHb1Dvexo3cH+jv14lRcDBuZMm8OqzFWkWFLGdKhn3W5qHQ4OOJ14/D/X8Waz3k7IYiFKensJIcSE\nJjVhYtyFRPsJrxdqa6GqSidiBoOe9frc53TrievoGexhe+t26s/W41M+DBgoSi5iVeYqkmNGVzc2\nqmEqxRGXi1qHg9aLthPKi4rCbrORFxUl2wkJIcQUIEmYuGUffliJy7U6eO0nlIIjR/TSY3e3Pped\nrQcxiqcAuge7qWqp4kDngUDyNT9lPqsyV5EUnTRmw3R4PNQ5HOx1OHD6e3tFGI2UWCyU2mwk3sB+\nlKMxVWoqJhKJSWiSuISeqRITScLELenp0bNfNluQ2k+0temi+9ZWfTxtmt5mKD//ukVoFwYusL1l\nOwc6D6BQGA1GilOLWTlzJYnRiWMyPKUUp/3bCTUMDODzT0knh4djt1qZb7EQLr29hBBiSpKaMHHT\njh/X+z9+2n7i4Yf1g4e3RW+vnvk6dEgfx8ToDbYXLbpu0f1513mqWqo4dO7QJcnXipkrSIhKGJPh\njfh8HHS5qO3v5+xFvb1mRUdjt1rJlO2EhBBiSpCaMDGmgtp+YmhIN1qtqdE1YGFhusv9ihVwnScI\nO52dVLVU0XC+AYXCZDAFkq/4qPgxGV7PyAh7HA72O50M+pccY0wmFlmtLLJaiZXeXkIIIfzkN4K4\nIVdqP+HxVBIZuXp839jrhbo6vdXQwIA+N38+VFRct/r/rPNsIPkCMBlMLJy+kBUzVxAbeetPDiil\nODU4SK3DwYnBwcC/eDIiIrDbbMyJjiYsCEuOU6WmYiKRmIQmiUvomSoxkSRMjFp7u95+6LPtJyor\nx/FNldLrnh98oDfbBsjM1EX3addulNrh6GBbyzaOXjgKQJgxjEXTF7F85nJsEdd/WvJ6hrxePvEv\nOXb5e3uZDAaKLBbsNhvp0ttLCCHENUhNmLiuz7afyMiA9etvQ/uJM2d00X1zsz5OTNRF94WF1yy6\nb+9vZ1vLNo53HQd08rU4bTHLZyzHGmG95WGd828n9InTybB/O6HYT7cTslqJkd5eQggh/KQmTNy0\nkRF4912or9fHt6X9RF8fbN0Kn3yij6OjobwcFi+GayQ4p/tOs61lGye7TwJgNpopTS9l2YxlWMJH\ntzn31fiU4tjAALUOB00XbSeUHRWF3WqlMDpaensJIYS4IZKEiavq6dHd78+evXb7iTFbu3e7YccO\nqK4Gj0cnXGVlsGrVNav+W/ta2da8jVM9pwAIN4VjT7ezNGMpMeExtzQkl9fLPoeDPQ4H/R69b2S4\n0cgCi4VSq5XkUXTgD5apUlMxkUhMQpPEJfRMlZgELQnLysrCZrNhMpkwm83U1tbS3d3Nww8/TEtL\nC1lZWfzud78jLi4uWEOc0m5r+wmfT693fvwxuFz6XFGRLrqPv/pTi829zWxr3kZTbxMAEaYIyjLK\nWJKxhGhz9C0Nqd3f2+uQy4XXP42caDZjt9lYEBNDpCw5CiGEuEVBqwnLzs6mrq6OhIQ/92V6/vnn\nSUpK4vnnn2fjxo309PSwYcOGS14nNWHjy+fThfZVVfp41iy4775xaj+hFJw8qeu+zp/X52bM0Oud\nGRlXeYmiqbeJbc3baOlrASAyLJKydJ18RZmjbno4Hp+PwwMD1Pb30+52A/rnrcC/nVCO9PYSQghx\ng66VtwQ1Cdu7dy+JiX/uTD5r1iy2bdtGSkoKZ8+eZfXq1Rw9evSS10kSNn6u1H5ixYrrNp6/OWfP\n6uSrsVEfx8fDnXfCnDlXfEOlFI09jWxr2UZrn+6OHxUWxZKMJZRllBEZdvNZYp/Hw16Hg30OBy5/\nb68ok4mFFguLrVbix3g7ISGEEFNHSCZhOTk5xMbGYjKZePrpp3nqqaeIj4+np6cH0L90ExISAseB\nAUsSNi4ubj8REwMPPKDbT4zGDa3dOxy66L6+Xs+ERUbqovvS0itW+yulONl9km0t22jrbwN08rVs\nxjLs6XYiwm6uDYRSiuahIWodDo4ODAR+plLDwymz2SiKicE8wbcTmio1FROJxCQ0SVxCz2SKSUg+\nHblz506mT5/O+fPnWbNmDbNmzbrk6waD4apLP1/+8pfJysoCIC4ujuLi4kCwKv1Nq+R4dMcff1zJ\n8eNw7txqvF5wuSopLYWcnNHfr76+/vrvt2wZ7NpF5a9/DV4vq3NyoKyMSgC3m9X+BOzT68vLyzne\ndZyfvfkzuga7yCrOItocTVRbFIVJhazMXHlTn/eDrVtpHBzEW1zMueFhmmtqMBgM3F1Rgd1q5WR1\nNX0GA+YQiY8cT67jev9jxqEyHjnWx58KlfHI8cQ+/vTPzZ+2V7qGkOgT9r3vfQ+LxcLPf/5zKisr\nSU1NpaOjgzvuuEOWI8fRbWk/4fPpVhNbt+pZMIDZs/XSY+Llm2QrpTh64ShVLVV0ODsAiDHHsHzm\nchanLSbcFH5Tw+gaGWFPfz/7nU7c/t5eFpOJxf7thKyynZAQQohxEHIzYQMDA3i9XqxWKy6Xiy1b\ntvDd736Xe+65h1dffZVvf/vbvPrqq9x3333BGN6U0N2tlx8/bT+xbp3eBWhMnTql6746O/Vxejrc\ndZfueP8ZSimOXDjCtuZtdLr09ZZwCytmrmDR9EWYTTdel+VTipODg9T293Pyot5eMyMjsVutzI6J\nwSSF9kIIIYIkKDNhTU1N3H///QB4PB6+9KUv8Y//+I90d3fz0EMP0draetUWFTITduvGuv1EZeVn\n1u7PndPbDJ04oY9jY/XMV1HRZUX3PuWj4XwDVS1VnHOdA8AabmXFzBUsnL7wppKvQa+X/U4nexwO\nevzbCZmNRubFxGC3WkmdItsJXRYXEXQSk9AkcQk9kykmITcTlp2dHaiNuFhCQgIffvhhEEY0NYx7\n+wmnU/f62rdPF91HRMDKlbBkyWVrnD7l49C5Q1S1VHFhQO8JGRsRy4qZKyiZXkKY8cZ/NM+63dQ6\nHBxwOvH4f+DjzWZKrVZKLBaipLeXEEKIEBISNWE3QmbCbs5n209UVMDy5WPUfmJkRHe537EDhofB\naNRbDJWX60ctL+JTPg52HqSqpYquwS4A4iLjWDlzJcWpxZiMN5YoeZXiiMtFrcNB69BQ4Hyev7dX\nXlSUbCckhBAiaEJuJkzcXrfSfuKalIIDB+Cjj6C/X58rLNSbbCclXXKp1+flQOcBqlqq6BnSbUfi\nI+NZlbmK+Snzbzj5cng81Dkc1DmdOPzbCUUYjZRYLJTabCRKby8hhBAhTpKwSUwpqKuD994Dr1c3\noX/oIbDZxuDmzc3w/vvQ0UFlczOrly7VRffZ2Zdc5vV5qT9bz/bW7fQO9QKQEJXAqsxVzEued0PJ\nl1KK0/7thBoGBvD5/2WRHB6O3WplvsVCuNE4Bh9ucphMNRWThcQkNElcQs9UiYkkYZPUZ9tP2O26\n/cQtl0VduKCL7o8d08c2m26r/9WvXrK26fF52N+xnx2tO+hz9wGQFJ3EqsxVFCUXYTSMPlka8fk4\n6HJR29/P2eFhAIwGA3P8hfaZsp2QEEKICUhqwiahcWk/4XLBtm2wd6+u8A8P18nX0qX6Tfw8Pg/7\nOvaxo3UH/W69RDktehrlWeXMmTbnhpKvnpERvZ2Q08mgfzuhaJOJRVYri61WYqW3lxBCiBAnNWFT\nyLFj8N//PXbtJ/B4oKYGtm8Ht1vPdi1aBHfcARZL4LIR7wh1HXXsbN2JY1g3ZU2JSaE8q5zZSbNH\nPVOllKJxaIja/n6ODw4GfnDTIyKw22zMjY4mTJYchRBCTAKShE0SY95+Qik4dEgX3ffqWi7y8nTd\nV3Jy4LJh7zA/e/NnDGUM4Rx2ApBqSaU8s5xZSbNGnXwNeb184l9y7PL39jIZDBRZLNhtNtKnSG+v\nsTRVaiomEolJaJK4hJ6pEhNJwiaBMW8/0dqqi+7b2/VxSopOvnJzA5cMe4fZ076HXad3cfjMYbKS\ns0izplGeWU5BYsGok69zw8PscTj4xOlk2L+dUGxYGIutVhZarcRIby8hhBCTlNSETXCfbT/x4IOX\nPaA4et3duuj+yBF9bLHA5z4HxcW69xfg9ripba+luq2agZEBANKt6azOWk1eQt6oki+fUhwbGKDW\n4aDpou2EsqOisFutFEZHS28vIYQQk4LUhE1CY9p+YnBQF93v2aNvZjbDsmV6Oi1cb5g95Blid9tu\natpqGPToxGmGbQblWeXkxueOKvlyeb3sczjY63DQ5+/tFW40ssBiodRqJTn85jbnFkIIISYiScIm\noJEReOcd+OQTfXzT7Sc8Hp14bdumK/kNBigp0UX3/mxucGSQ3e06+Rry6I70mbGZlGeVkx2XjcFg\nuO7afbu/t9chlwuv/18DiWYzdpuNBTExRMqS47iYKjUVE4nEJDRJXELPVImJJGETTHc3vPEGdHbe\nQvsJpfSS4wcfQI/uXk9Ojq77Sk0FYGBkgJq2Gna37cbtdQOQHZdNeVY5WXFZ130Lj8/H4YEBavv7\naXfr1xsMBgqjo7HbbORIby8hhBBTnNSETSBj0n6irQ22bNHF9wDTpunkKy8PDAZcwy6q26qpba9l\n2Ksbo+bE51CeWU5mXOZ1b9/36XZCDgcuf2+vKJNJbydktRIv2wkJIYSYQqQmbILz+eDjj3WrLrjJ\n9hM9PbrdxKFD+jgmRi87LlwIRiPOYSe7Tu9iT/seRny6RUReQh7lmeXMiJ1xzVsrpWgZGqLW4eDo\nRdsJpYaHY7fZmBcTg1l6ewkhhBCXkCQsxLlcuv1EY6Mu2brzTl0zP+qVvKEhnb3V1Oii+7Aw3eV+\nxQqIiMDhdrDr9C72ntkbSL4KEgsozywn3ZZ+zVsP+3wccDr5zfvvE7d4MaC3EyqKicFuszEjIkKW\nHINoqtRUTCQSk9AkcQk9UyUmkoSFsFtqP+H16i2Gtm3TjcRAF49VVEBsLP3ufnae2EpdRx0en35S\ncVbSLFZlriLNmnbNW3eNjLCnv5/9Tidun49ej4cMk4nFViuLrFassp2QEEIIcV1SExaClNL50+bN\nN9F+QildPPbBB9DVpc9lZurHJ9PS6BvqY0frDvZ17MOrdM3W7KTZlGeVk2pJvfCfGkwAABnASURB\nVOptfUpxcnCQ2v5+Tl7U22tmZCR2q5XZMTGYZNZLCCGEuMS18hZJwkLMZ9tPlJXpuvlRdXE4c0YX\n3Tc36+PERFizBgoL6XX3sb1lO/Vn6/EqLwYMzJk2h1WZq0ixXL26f9DrZb/TyR6Hgx7/dkJhBgPz\n/YX202U7ISGEEOKqJAmbID7bfuKee2DevFG8sK9PF90fOKCPo6OhvBwWL6ZnuJ/trTr58ikfBgwU\nJRexMnMlyTHJV73lWbebWoeDgy4XI/7thOLCwrDbbJRYLERdlBVOlbX7iUbiEnokJqFJ4hJ6JlNM\n5OnICeDi9hOJibr9RPLVcyTN7YYdO6C6WjdeNZlgyRJYuZIun4vtJ97mQOeBQPI1P2U+qzJXkRSd\ndMXbeZXiiMtFrcNB69BQ4HxeVBR2m428qCjZTkgIIYQYIzITFmSfbT8xezbce+912k/4fLBvn36h\ny6XPFRVBRQUXIrxUtVRxsPMgCoXRYGR+ynxWzlxJYnTiFW/n9HioczrZ63Dg8G8nFGE06t5eNhuJ\n0ttLCCGEuCkyExZijh1r4cMPT+F0Gjl82IfNlsu0aZnXbz+hFJw4oYvuz5/X52bMgLVrOR8fQVXL\nVg6dOxRIvkpSS1gxcwUJUQlXuJWizb/k2HDRdkLTwsOxW63Mt1iIkN5eQgghxLiRJOw2O3ashU2b\nTuJ2V3D4sF5RNBg+4p/+CZYvv0ZH+rNnddF9Y6M+jo+HNWvonJFAVet2Gk42oFCYDCaKU4tZmbmS\nuMi4y24z4vNxyL/k2HHRdkKzY2KwW61k3cR2QpNp7X4ykbiEHolJaJK4hJ6pEhNJwm6zLVtOce5c\nBY2NemLLZoO5cys4fnwrd955hSTM4YCtW6G+Xr8gMhLKyzk7ewbb2nZypO4IACaDiYXTF7Ji5gpi\nI2Mvu03PyAh7HQ72OZ0M+rcTijaZWGS1sthqJVZ6ewkhhBC3ldSE3UY9PfD1r1fS0bEagPR0yM0F\noxHi4ir51rdW//ni4WHYuRN27dJ9K0wmKC3lzMJ8tnXWcqzrGABhxjAWTV/E8pnLsUVc2khMKUXj\n0BC1/f0cHxwMfN/SIyKw22zMjY4mTJYchRBCiHEjNWFBppSeyHrvPejr8xEeDoWF+inIT4WH6zYQ\n+Hz64o8/1rNgAHPmcMY+h0rHAY43/BoAs9HM4rTFLJuxDGuE9ZL3c/t81Dud7Onv54K/t5fJYKDI\nYsFus5Euvb2EEEKIoJOZsHHmcsHbb8PRo/o4Lq6FtraTWCwVgWvc7o/48pfzKAzz6Lqvzk79hfR0\nziydx1bfSU52nwR08lWaXsqyGcuwhFsuea/zw8PUOhx84nQy7O/tZQsLo9RqZaHVSsyoOr7euKmy\ndj/RSFxCj8QkNElcQs9kionMhAXJ0aM6AXO5dCnXX/4lzJuXyfHj8NFHWxkeNhIe7uOu4jjy9myH\nkzrRIi6ODvscPojuoPHCZgDCTeHY0+0szVhKTHhM4D18SnFsYIBah4Omi7YTyo6Kwm61UhgdLb29\nhBBCiBAkM2HjwO3WS4/19fo4Oxvuuw9i/fXyLceOcerDDzE6nfiamsg1m8lMTISICDpK8vkgqZ9G\nZysAEaYIyjLKWJKxhGhzdOA9XF4v+xwO9joc9Pl7e4UbjSzwbyeUHB5+Wz+zEEIIIS4n2xbdRs3N\n8D//A729EBYGd96p93/8dDKq5dgxTr7yChXnzkFrK3i9fOj1EvvAWo7akzg1fBaAyLBIlmQsoSy9\njChzVOD+Z9xuavv7OeRy4fF/HxLNZkqtVootFiLHaclRCCGEEDdOliNvA49Hb99YU6ML8dPS4P77\nYdq0iy7y+Ti1aRNLdu2kvasTn/LRHx1OdGYiv22uxlpcSlRYlE6+MsqIDNNt8z0+Hw0DA9T299N2\nUW+vguho7FYruVFRN9zbayxNprX7yUTiEnokJqFJ4hJ6pkpMJAkbAx0det/Hc+d0u4lVq/R/l0xK\nNTbCli0M7NhOZ2crA3FGGhOMdEcO4rvQis+aS0V2BfZ0OxFh+unFfo+HvQ4HdQ4HLn9vryiTSW8n\nZLUSL9sJCSGEEBOWLEfeAp9Pt/KqrASvV7ec+MIXdP+vgHPn9DZDJ06glOKn7/6O3LhBOmMUGAwY\nDUZiI2LZE5XL//nP/4dSipahIWodDo4ODODzf9bU8HDsNhvzYmIwS28vIYQQYkKQ5chx0N2tZ79O\nn9bHdjusWQOBySmHQ2dn+/aBUvSqQXZlmXh7aTxpTS7uMIYRGxGLNcLKthEfhvkF7O3vp9bh4Nzw\nMABGg4GimBjsNhszIiKCuuQohBBCiLElSdgNUgrq6uD993Uje5sN7r1Xd74HdKf76mo9RTY8jGPE\nxd402JVjZiQqnIiOJDqTY/nPkwqjIYxho5EoewHRmQswdXUBYDGZWGy1sshqxToBthOaKmv3E43E\nJfRITEKTxCX0TJWYhP5v+BDicMAf//jndl7z5uneX1FRXNbp3jXs4kD8MNsLIhiIjSbcFE55xlJK\nvGVs+PgdvF9ZioMIBjHj2bGX8plRzIyMxG61MjsmBpPMegkhhBCTmtSEjdLhw/DOOzA4qJOuu++G\nuXP9Xzx5Utd9dXYyODLIkYh+qgoi6U2NJcwYhj3dzvIZyzGZIvmnN9+kdkYGZx09KBRGDOTHJrLi\nTAf/eP/9t/1zCSGEEGL8SE3YLRgchD/9CQ4e1Md5eXr50WpFby+0ZQucOsWQZ4iTvgvsyI/gbFYy\nJmMYpdMXsipzFUOGCCodDj5xXuDw4CCmyChyo2NIi4hgeng4ZoOBqHPng/o5hRBCCHF7SRJ2DadO\n6eXH/n5dcL92LSxaBAanA/64FerrGfa4aRrsYFd2GKdnpaFMJkpSi1mZuYrzKoL/7umncfBC4J7J\nYWHYYmJINJu5eMFxIve3nypr9xONxCX0SExCk8Ql9EyVmEgSdgUjI3p1sbZWH2dk6MaridZhqNwJ\nu3YxMjRAq7ON2jRoXJ7BSKSZouQiymau4rQ3gl93OegZ6QXAbDSywP+UY8/SpWzatw/DokWB93PX\n1VGxcGEwPqoQQgghgkRqwj6jvR3+8Afo6tKNV++4A5Yv9WH8ZD98/DGe/l7a+tvYH+/mePEMBmOj\nmZU0i6L0lTR5IzjgcjHi8wEQbzZj928nFHVR59ZjjY18dPgww+gZsIq5cynMyRm3zySEEEKI4JC9\nI0fB64WqKti+XT/oOG0afOF+xXTXSdiyBW/nWdod7TRE9HOkZAZ9KbFkx+WSOX0FLSqKpsHBwL1y\no6Ios9nIi4rCKE85CiGEEFOWJGHXcf68brx65ozeaHvJEqiYe5awrVvwnTpJh6ODY75zHFmQzvms\naaTEZpGcvIzTKppejweAcKORBRYLdquVaeETucLrxk2VtfuJRuISeiQmoUniEnomU0zk6cirUAp2\n74YPP9QbcMfFwf0V/WSe2orvF/V0ODo4NdDO0aIUzsxaQJQtk+lJds4ZrHSOKMBDwkVLjpGXbBYp\nhBBCCHF1U3YmrK9PP/nY2KiPF8518xfWnZj37ORc3xmaHK2cyI2naUEmI3HZRCUsxB0WH3iiMe+i\nJUfZTkgIIYQQVyLLkRdRCg4cgPfeg6EhiI708VBuHZlNH9N1oZWmniaa0qM5tjCPrqQ8wuPmERWV\nhAEDEUYjxRYLpVYrSVNsyVEIIYQQN06SML+BAd31vqEBUIrFtuPcqbYwcP4ETT1NtMUa2F82h9Op\nBUTEzWZaTAoGDCSazdhtNootFiKMxrH9QJPAZFq7n0wkLqFHYhKaJC6hZzLFRGrCgOPH4a23wOmE\nhKEz3Bu1hdhz9RzpbaI9fISa8gUcn1lAbGw+6ZbpGA1G8qOjKbNayZUlRyGEEEKMsUk/EzY8DO+/\nD3V1EDHUR5nzI4rMO+gYbKLFOMR2ewlHsgpIjssh3ZZOtCmMEosFu81Ggtk8jp9ECCGEEJPdlF2O\nbG3VrScc54fIatvBYuOHjESc5IjRzY6SBRzNziMtIYsMWwapEZGU2WzMlyVHIYQQQoyRKZeEeTxQ\nWQm7tnuZfqaOggvvEp14lIMWxe7Zs2nKziQ1KYuZsTOYa7Fht9nIiYyUJcebNJnW7icTiUvokZiE\nJolL6JlMMZlSNWGdnfCHNxXehmMUN76FOe4A+/PD+CR7Dh2Z00lMyWZ5fBZL4hKxW63Ey5KjEEII\nIYJg0syE+XxQXQ17/qedjONv4TTVcWqWmeaMNLrSE4lNy2XRtFxWJyQz32IhXJYchRBCCDHOJu1y\n5MYf/ojfv/cnfBgZ6fewwjeD/Pw4TuSacUyPpS81jpjMXO5Mm01F4nSyZMlRCCGEELfRtZKwCTsd\ntPGHP+LXH24hbelyshaVknHPat6fD39IdXO2KAMWL+CJ5Xfzcsld/K+MHLKlzcS4qaysDPYQxBVI\nXEKPxCQ0SVxCz1SJScglYZs3b2bWrFnk5+ezcePGK14Tf9dd/N83XifTvhSHNYqm7OmcnZZI9LIy\nGnv7efpz9/KfZetYn5ZDnNR8jbv6+vpgD0FcgcQl9EhMQpPEJfRMlZiEVBLm9Xr567/+azZv3kxD\nQwOvv/46R44cuew69fT/5nxuLvXRZnpjrXhNBkyeIVK7e5jefoG/SM3BLDVft01vb2+whyCuQOIS\neiQmoUniEnqmSkxCKlOpra0lLy+PrKwszGYzjzzyCH/84x8vu05FRGIsLqbbBzED/aSdO096lwPL\nkAeDzxeEkQshhBBC3JiQSsLa29uZMWNG4DgjI4P29vbLrjP6vMTEx6Hq6khyDBPpNWDAwMnqatZ/\n/i9v55AF0NzcHOwhiCuQuIQeiUlokriEnqkSk5B6OvLNN99k8+bN/PznPwfgN7/5Dbt37+bf/u3f\nAteYUlLwnTsXrCEKIYQQQozaggULrlrjFlLNWtPT0zl9+nTg+PTp02RkZFxyjbez83YPSwghhBBi\nzIXUcuTixYs5ceIEzc3NDA8P88Ybb3DPPfcEe1hCCCGEEGMupGbCwsLC+MlPfsLatWvxer189atf\nZfbs2cEelhBCCCHEmAupmjAhhBBCiKkipJYjr2U0TVzFjTl9+jR33HEHc+fOpaioiJdeegmA7u5u\n1qxZQ0FBAXfdddcl/VpefPFF8vPzmTVrFlu2bAmcr6urY968eeTn5/PNb34zcN7tdvPwww+Tn5/P\nkiVLaGlpCXzt1VdfpaCggIKCAn71q1/dhk88cXi9XkpKSli3bh0gMQkFvb29PPjgg8yePZs5c+aw\ne/duiUuQvfjii8ydO5d58+bx6KOP4na7JSZB8OSTT5KSksK8efMC54Idh6amJsrKysjPz+eRRx5h\nZGRkvD7+rVETgMfjUbm5uaqpqUkNDw+rBQsWqIaGhmAPa8Lr6OhQ+/fvV0op5XA4VEFBgWpoaFDP\nPfec2rhxo1JKqQ0bNqhvf/vbSimlDh8+rBYsWKCGh4dVU1OTys3NVT6fTymlVGlpqdq9e7dSSqnP\nf/7z6r333lNKKfXTn/5UPfPMM0oppX7729+qhx9+WCmlVFdXl8rJyVE9PT2qp6cn8Geh/ehHP1KP\nPvqoWrdunVJKSUxCwBNPPKF++ctfKqWUGhkZUb29vRKXIGpqalLZ2dlqaGhIKaXUQw89pDZt2iQx\nCYKqqiq1b98+VVRUFDgXrDj09vYqpZRav369euONN5RSSn3ta19T//Ef/zHe34abMiGSsF27dqm1\na9cGjl988UX14osvBnFEk9O9996rPvjgA1VYWKjOnj2rlNKJWmFhoVJKqR/84Adqw4YNgevXrl2r\nqqur1ZkzZ9SsWbMC519//XX19NNPB66pqalRSulfXElJSUoppV577TX1ta99LfCap59+Wr3++uvj\n+wEniNOnT6uKigq1detWdffddyullMQkyHp7e1V2dvZl5yUuwdPV1aUKCgpUd3e3GhkZUXfffbfa\nsmWLxCRImpqaLknCghkHn8+nkpKSlNfrVUopVV1dfUkOEUomxHLkaJu4ipvX3NzM/v37KSsro7Oz\nk5SUFABSUlLo9LcFOXPmzCUtQz6Nw2fPp6enB+JzcezCwsKIjY2lq6vrqvcS8Dd/8zf88Ic/xHjR\n1lsSk+Bqampi2rRpfOUrX2HhwoU89dRTuFwuiUsQJSQk8Hd/93fMnDmTtLQ04uLiWLNmjcQkRAQz\nDt3d3cTFxQX+Dr34XqFmQiRhBoMh2EOY1JxOJw888AA//vGPsVqtl3zNYDDI9/82euedd0hOTqak\npAR1lWdmJCa3n8fjYd++fXz9619n3759xMTEsGHDhkuukbjcXqdOneJf//VfaW5u5syZMzidTn7z\nm99cco3EJDTczjhMtHhPiCRsNE1cxc0ZGRnhgQce4PHHH+e+++4D9L9azp49C0BHRwfJycnA5XFo\na2sjIyOD9PR02traLjv/6WtaW1sB/Yusr6+PxMREielV7Nq1i7feeovs7Gy++MUvsnXrVh5//HGJ\nSZBlZGSQkZFBaWkpAA8++CD79u0jNTVV4hIke/fuZdmyZSQmJhIWFsYXvvAFqqurJSYhIlh/Z6Wn\np5OQkEBvby8+/17SbW1tpKenj+8HvlnBXg8djZGREZWTk6OampqU2+2Wwvwx4vP51OOPP66+9a1v\nXXL+ueeeC6zZv/jii5cVVLrdbtXY2KhycnICBZV2u13V1NQon893WUHlp2v2r7/++iUFldnZ2aqn\np0d1d3cH/iz+rLKyMlATJjEJvpUrV6pjx44ppZT67ne/q5577jmJSxDV19eruXPnqoGBAeXz+dQT\nTzyhfvKTn0hMguSzNWHBjsP69evVb3/7W6WUrhWTwvxb9Kc//UkVFBSo3Nxc9YMf/CDYw5kUtm/f\nrgwGg1qwYIEqLi5WxcXF6r333lNdXV2qoqJC5efnqzVr1lzyl8sLL7ygcnNzVWFhodq8eXPg/N69\ne1VRUZHKzc1Vzz77bOD80NCQWr9+vcrLy1NlZWWqqakp8LVXXnlF5eXlqby8PLVp06bb8pknksrK\nysDTkRKT4Kuvr1eLFy9W8+fPV/fff7/q7e2VuATZxo0b1Zw5c1RRUZF64okn1PDwsMQkCB555BE1\nffp0ZTabVUZGhnrllVeCHofGxkZlt9tVXl6eeuihh9Tw8PD4fhNukjRrFUIIIYQIgglREyaEEEII\nMdlIEiaEEEIIEQSShAkhhBBCBIEkYUIIIYQQQSBJmBBCCCFEEEgSJoQQQggRBJKECSGmlNWrV1NX\nV3dL93j77bfZuHHjGI1ICDFVhQV7AEIIcTuNxT5269atY926dWM0IiHEVCUzYUKIoHK5XPzVX/0V\nxcXFzJs3j9///vcAfP/738dutzNv3jyefvrpwPWrV6/mb//2byktLWX27Nns2bOH+++/n4KCAr7z\nne8A0NzczKxZs3jssceYM2cO69evZ3Bw8LL33rJlC8uWLWPRokU89NBDuFyuy6556aWXmDt3LgsW\nLODRRx8FYNOmTTz77LMAFBcXU1JSQklJCdHR0Wzfvh2Xy8WTTz5JWVkZCxcu5K233rrsvpWVlaxe\nvZr169cze/ZsHnvssVv/ZgohJhRJwoQQQbV582bS09Opr6/n4MGDrF27FoBnn32W2tpaDh48yODg\nIO+88w6gZ7IiIiLYs2cPzzzzDPfeey8vv/wyhw4dYtOmTfT09ABw/PhxvvGNb9DQ0IDNZuPf//3f\nL3nfCxcu8MILL/DRRx9RV1fHokWL+Jd/+ZfLxrdx40bq6+v55JNPePnllwNj+FR9fT379+/n+9//\nPqWlpSxdupQXXniBiooKdu/ezdatW3nuuecYGBi47N719fX8+Mc/pqGhgcbGRnbu3Dk231QhxIQg\nSZgQIqjmz5/PBx98wD/8wz+wY8cObDYbAFu3bmXJkiXMnz+frVu30tDQEHjNPffcA0BRURFFRUWk\npKQQHh5OTk4Op0+fBmDGjBksXboUgMcee4wdO3YEXq+UoqamhoaGBpYtW0ZJSQm/+tWvaG1tveL4\nHn30Uf7rv/4Lk8l0xc9w4sQJnn/+eX73u98RFhbGli1b2LBhAyUlJdxxxx243e7AuC5mt9tJS0vD\nYDBQXFxMc3PzzX0ThRATktSECSGCKj8/n/379/Puu+/yz//8z1RUVPD888/zjW98g7q6OtLT0/ne\n977H0NBQ4DUREREAGI3GwJ8/PfZ4PMCls1VKqSvWga1Zs4bXXnvtmuN79913qaqq4u233+aFF17g\n4MGDXLzlrtPp5OGHH+YXv/gFKSkpgfN/+MMfyM/Pv+a9Lx67yWQKjF0IMTXITJgQIqg6OjqIjIzk\nS1/6En//93/P/v37AwlXYmIiTqczUCd2I1pbW6mpqQHgtddeY+XKlYGvGQwGlixZws6dOzl16hSg\na9NOnDhxyT2UUrS2trJ69Wo2bNhAX18fTqfzkmuefPJJvvKVr7B8+fLAubVr1/LSSy8Fjvfv33/D\n4xdCTH4yEyaECKqDBw/y3HPPYTQaMZvNvPzyy8TGxvLUU09RVFREamoqZWVlV3zttZ50LCws5Kc/\n/SlPPvkkc+fO5Zlnnrnk60lJSWzatIkvfvGLuN1uAF544YVLZq+8Xi+PP/44fX19KKX45je/SWxs\nbOB9W1tbefPNNzlx4gSvvPIKAL/85S/5zne+w7e+9S3mz5+Pz+cjJyfnsuL8K439Vp/aFEJMLAZ1\n8by6EEJMAs3Nzaxbt46DBw8GeyhCCHFVshwphJiUZFZJCBHqZCZMCCGEECIIZCZMCCGEECIIJAkT\nQgghhAgCScKEEEIIIYJAkjAhhBBCiCCQJEwIIYQQIgj+P2WGO4pdoAcIAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10c6fb1d0>"
+        "<matplotlib.figure.Figure at 0x10f9e1050>"
        ]
       }
      ],
-     "prompt_number": 41
+     "prompt_number": 34
     },
     {
      "cell_type": "markdown",
@@ -1354,9 +1370,376 @@
       }
      ],
      "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"numba\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Appendix I: Cython vs. Numba"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Like we did with Cython before, we will use the minimalist approach to Numba and see how they compare against each other.  \n",
+      "\n",
+      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up code. If you want to read more about Numba, see the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from numba import jit\n",
+      "\n",
+      "@jit\n",
+      "def nmb_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ImportError",
+       "evalue": "No module named 'llvm'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-4-505a644bdc72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnmb_lstsqr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m\"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \"\"\"\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtesting\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorators\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Re-export typeof\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/decorators.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msigutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mregistry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# -----------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/targets/registry.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescriptors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTargetDescriptor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdispatcher\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/targets/cpu.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpasses\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mee\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mImportError\u001b[0m: No module named 'llvm'"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# ... this section is still in progress"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"type_declarations\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Appendix II: Cython with and without type declarations"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the sections above, we have been using the simplest approach to Cython without using static type declarations and thereby neglecting one of its major strengths.  \n",
+      "Let us now see how we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First, our \"simple\" approach using Cython from the previous section:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "def cy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 54
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And now, the same code with static type declarations:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "def static_type_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 55
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "Now, let us see how the two functions (with and without static type declarations) compare against each other for different sample sizes."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs =  ['cy_lstsqr', 'static_type_lstsqr'] \n",
+      "labels = ['simple Cython', 'Cython w. type declarations']\n",
+      "orders_n = [10**n for n in range(1, 7)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 58
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for f in times_n.keys():\n",
+      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
+      "\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "\n",
+      "plt.title('Performance of a simple least square fit implementation')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkQLYhBEREREp4P8DGaETtWbn1E4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10eea0790>"
+       ]
+      }
+     ],
+     "prompt_number": 59
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "The improvement is pretty significant when static type declarations are used. One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation.\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
+      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 60
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs =  ['classic_lstsqr', 'cy_lstsqr', 'static_type_lstsqr', \n",
+      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
+      "labels = ['classic approach','classic approach (cython)', \n",
+      "          'classic approach (cython + type decl.)',\n",
+      "          'matrix approach', 'numpy function', 'scipy function']\n",
+      "\n",
+      "times = [timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
+      "         for f in funcs]\n",
+      "\n",
+      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 61
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "x_pos = np.arange(len(funcs[1:]))\n",
+      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
+      "plt.xticks(x_pos, labels[1:], rotation=90)\n",
+      "plt.ylabel('relative performance gain')\n",
+      "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "plt.grid()\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAG/CAYAAAAjJGJxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x106418090>"
+       ]
+      }
+     ],
+     "prompt_number": 63
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This is a pretty significant performance gain. The \"Cython + type declarations\" approach sped up our initial Python code 25 times."
+     ]
     }
    ],
    "metadata": {}
   }
  ]
-}
+}
\ No newline at end of file

From 0bb5fd3e6b678586806982cfd9faf5c4b752d1b1 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 5 May 2014 19:31:23 -0400
Subject: [PATCH 019/248] appendices added

---
 .../cython_least_squares-checkpoint.ipynb     | 512 +++++++++++++++++-
 benchmarks/cython_least_squares.ipynb         | 512 +++++++++++++++++-
 2 files changed, 968 insertions(+), 56 deletions(-)

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index 8c47d64..fafb461 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3b47add80317bb8ad369eacd5528c941bb1a2cbd92ae5b33aae66d56ae35c741"
+  "signature": "sha256:caecd42da39e4b55b4dc50c985b30a04ee3eac4a88d143732c4441ecc28fc1e0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -72,7 +72,8 @@
       "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
       "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
       "- [Appendix I: Cython vs. Numba](#numba)\n",
-      "- [Appendix II: Cython with and without type declarations](#type_declarations)"
+      "- [Appendix II: Cython with and without type declarations](#type_declarations)\n",
+      "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)"
      ]
     },
     {
@@ -1400,7 +1401,14 @@
      "source": [
       "Like we did with Cython before, we will use the minimalist approach to Numba and see how they compare against each other.  \n",
       "\n",
-      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up code. If you want to read more about Numba, see the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)"
+      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up code. If you want to read more about Numba, see the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here is our \"classic\" approach in Python, where I removed the list comprehensions, since they caused errors in the Numba compilation."
      ]
     },
     {
@@ -1411,8 +1419,12 @@
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    x_avg = sum(x)/len(x)\n",
       "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
       "    slope = cov_xy / var_x\n",
       "    y_interc = y_avg - slope*x_avg\n",
       "    return (slope, y_interc)"
@@ -1420,7 +1432,14 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The Cython-compiled version of it:"
+     ]
     },
     {
      "cell_type": "code",
@@ -1430,8 +1449,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -1443,8 +1461,12 @@
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    x_avg = sum(x)/len(x)\n",
       "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
       "    slope = cov_xy / var_x\n",
       "    y_interc = y_avg - slope*x_avg\n",
       "    return (slope, y_interc)"
@@ -1452,7 +1474,14 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And now the Numba-compiled version:"
+     ]
     },
     {
      "cell_type": "code",
@@ -1465,37 +1494,95 @@
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    x_avg = sum(x)/len(x)\n",
       "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
+      "        \n",
       "    slope = cov_xy / var_x\n",
       "    y_interc = y_avg - slope*x_avg\n",
       "    return (slope, y_interc)"
      ],
      "language": "python",
      "metadata": {},
+     "outputs": [],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "Now, let us see how the different approaches compare against each other for different sample sizes."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs =  ['lstsqr', 'cy_lstsqr', 'nmb_lstsqr'] \n",
+      "orders_n = [10**n for n in range(1, 7)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for f in times_n.keys():\n",
+      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
+      "\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "\n",
+      "plt.title('Performance of a simple least square fit implementation')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
      "outputs": [
       {
-       "ename": "ImportError",
-       "evalue": "No module named 'llvm'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-4-505a644bdc72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnmb_lstsqr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m\"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \"\"\"\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtesting\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorators\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Re-export typeof\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/decorators.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msigutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mregistry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# -----------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/targets/registry.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescriptors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTargetDescriptor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdispatcher\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/targets/cpu.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpasses\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mee\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mImportError\u001b[0m: No module named 'llvm'"
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1031a8710>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "# ... this section is still in progress"
+      "Without making any modifications to the original code in order to account for the strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
      ]
     },
     {
@@ -1533,9 +1620,45 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "First, our \"simple\" approach using Cython from the previous section:"
+      "Here is our \"classic\" approach in Python:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 44
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The Cython-compiled version of it:"
      ]
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -1554,7 +1677,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 54
+     "prompt_number": 45
     },
     {
      "cell_type": "markdown",
@@ -1737,6 +1860,339 @@
      "source": [
       "This is a pretty significant performance gain. The \"Cython + type declarations\" approach sped up our initial Python code 25 times."
      ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"explicit_loops\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Appendix III: Cython performance after replacing list comprehensions by explicit for loops"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "List, set and dictionary comprehensions in Python do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also come with some small performance benefits.  \n",
+      "Does this also apply in Cython? Let's check it out."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "This is the code for our \"classic\" least squares approach that we have been using in the previous sections:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lstsqr_comprehensions(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 46
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "And here is a version where I replaced the list comprehensions by for-loops:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lstsqr_loops(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 48
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "Finally, the Cython versions of the two functions (with and without using list comprehensions) that we have defined above:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr_comprehensions(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 49
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr_loops(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 50
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "We will generate some sample data for different sample sizes and take a look at the results for the regular Python functions, and the Cython code separately."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs =  ['lstsqr_comprehensions', 'lstsqr_loops',\n",
+      "          'cy_lstsqr_comprehensions', 'cy_lstsqr_loops'] \n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 52
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.figure(figsize=(8,6))\n",
+      "plt.plot(orders_n, times_n['lstsqr_comprehensions'], alpha=0.5, \n",
+      "         label='list comprehensions', marker='o', lw=2)\n",
+      "plt.plot(orders_n, times_n['lstsqr_loops'], alpha=0.5, \n",
+      "         label='for-loops', marker='o', lw=2)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.title('Performance comparison list comprehensions and for-loops')\n",
+      "plt.show()\n",
+      "\n",
+      "plt.figure(figsize=(8,6))\n",
+      "plt.plot(orders_n, times_n['cy_lstsqr_comprehensions'], alpha=0.5, \n",
+      "         label='list comprehensions (Cython', marker='o', lw=2)\n",
+      "plt.plot(orders_n, times_n['cy_lstsqr_loops'], alpha=0.5, \n",
+      "         label='for-loops (Cython)', marker='o', lw=2)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.title('Performance comparison list comprehensions and for-loops in Cython')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAGJCAYAAACaQwrRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nURKDPTGQxwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10e8f8610>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAGJCAYAAABFDXDOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBAiY/8fzj2UNX7XML4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10565c6d0>"
+       ]
+      }
+     ],
+     "prompt_number": 71
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "As we can see in the first plot, the list comprehensions lead to a slightly increased performance in regular Python code.  \n",
+      "But the second plot is quite interesting: List comprehensions in Cython are significantly slower than the regular for-loop structures.\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "Let us do a quick comparison by how much we were able to improve the performance of the simple least square implementation using Cython so far:\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
+      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 72
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs =  ['lstsqr_comprehensions', 'cy_lstsqr_comprehensions', \n",
+      "          'cy_lstsqr_loops'] \n",
+      "labels = ['list comprehensions', 'list comprehensions (Cython)', \n",
+      "          'for-loops (Cython)']\n",
+      "\n",
+      "times = [timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
+      "         for f in funcs]\n",
+      "\n",
+      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 73
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.figure(figsize=(8,6))\n",
+      "x_pos = np.arange(len(funcs[1:]))\n",
+      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
+      "plt.xticks(x_pos, labels[1:], rotation=90)\n",
+      "plt.ylabel('relative performance gain')\n",
+      "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "ftext = 'For-loops in Cython are {:.2f}x faster then list comprehensions'\\\n",
+      "        .format(times[1]/times[2],1)\n",
+      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
+      "plt.xlim([-1,len(funcs[1:])])\n",
+      "plt.ylim([0,max(times_rel)*1.2])\n",
+      "plt.grid()\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAIBCAYAAABpxJfYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWRMRETVxLGsiIqIm7r+eoHMw3W5vGAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10e919d10>"
+       ]
+      }
+     ],
+     "prompt_number": 88
     }
    ],
    "metadata": {}
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index 8c47d64..fafb461 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3b47add80317bb8ad369eacd5528c941bb1a2cbd92ae5b33aae66d56ae35c741"
+  "signature": "sha256:caecd42da39e4b55b4dc50c985b30a04ee3eac4a88d143732c4441ecc28fc1e0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -72,7 +72,8 @@
       "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
       "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
       "- [Appendix I: Cython vs. Numba](#numba)\n",
-      "- [Appendix II: Cython with and without type declarations](#type_declarations)"
+      "- [Appendix II: Cython with and without type declarations](#type_declarations)\n",
+      "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)"
      ]
     },
     {
@@ -1400,7 +1401,14 @@
      "source": [
       "Like we did with Cython before, we will use the minimalist approach to Numba and see how they compare against each other.  \n",
       "\n",
-      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up code. If you want to read more about Numba, see the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)"
+      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up code. If you want to read more about Numba, see the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here is our \"classic\" approach in Python, where I removed the list comprehensions, since they caused errors in the Numba compilation."
      ]
     },
     {
@@ -1411,8 +1419,12 @@
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    x_avg = sum(x)/len(x)\n",
       "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
       "    slope = cov_xy / var_x\n",
       "    y_interc = y_avg - slope*x_avg\n",
       "    return (slope, y_interc)"
@@ -1420,7 +1432,14 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The Cython-compiled version of it:"
+     ]
     },
     {
      "cell_type": "code",
@@ -1430,8 +1449,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
+     "outputs": []
     },
     {
      "cell_type": "code",
@@ -1443,8 +1461,12 @@
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    x_avg = sum(x)/len(x)\n",
       "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
       "    slope = cov_xy / var_x\n",
       "    y_interc = y_avg - slope*x_avg\n",
       "    return (slope, y_interc)"
@@ -1452,7 +1474,14 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And now the Numba-compiled version:"
+     ]
     },
     {
      "cell_type": "code",
@@ -1465,37 +1494,95 @@
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    x_avg = sum(x)/len(x)\n",
       "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
+      "        \n",
       "    slope = cov_xy / var_x\n",
       "    y_interc = y_avg - slope*x_avg\n",
       "    return (slope, y_interc)"
      ],
      "language": "python",
      "metadata": {},
+     "outputs": [],
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "Now, let us see how the different approaches compare against each other for different sample sizes."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs =  ['lstsqr', 'cy_lstsqr', 'nmb_lstsqr'] \n",
+      "orders_n = [10**n for n in range(1, 7)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for f in times_n.keys():\n",
+      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
+      "\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "\n",
+      "plt.title('Performance of a simple least square fit implementation')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
      "outputs": [
       {
-       "ename": "ImportError",
-       "evalue": "No module named 'llvm'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-4-505a644bdc72>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnmb_lstsqr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m\"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \"\"\"\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtesting\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorators\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Re-export typeof\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/decorators.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msigutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mregistry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# -----------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/targets/registry.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdivision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtyping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcpu\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescriptors\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTargetDescriptor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnumba\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdispatcher\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numba/targets/cpu.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpasses\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mllvm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mee\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mImportError\u001b[0m: No module named 'llvm'"
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1031a8710>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "# ... this section is still in progress"
+      "Without making any modifications to the original code in order to account for the strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
      ]
     },
     {
@@ -1533,9 +1620,45 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "First, our \"simple\" approach using Cython from the previous section:"
+      "Here is our \"classic\" approach in Python:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 44
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The Cython-compiled version of it:"
      ]
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -1554,7 +1677,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 54
+     "prompt_number": 45
     },
     {
      "cell_type": "markdown",
@@ -1737,6 +1860,339 @@
      "source": [
       "This is a pretty significant performance gain. The \"Cython + type declarations\" approach sped up our initial Python code 25 times."
      ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"explicit_loops\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Appendix III: Cython performance after replacing list comprehensions by explicit for loops"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "List, set and dictionary comprehensions in Python do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also come with some small performance benefits.  \n",
+      "Does this also apply in Cython? Let's check it out."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "This is the code for our \"classic\" least squares approach that we have been using in the previous sections:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lstsqr_comprehensions(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 46
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "And here is a version where I replaced the list comprehensions by for-loops:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lstsqr_loops(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 48
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "Finally, the Cython versions of the two functions (with and without using list comprehensions) that we have defined above:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr_comprehensions(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
+      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 49
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr_loops(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    for x_i in x:\n",
+      "        var_x += (x_i - x_avg)**2\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 50
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "We will generate some sample data for different sample sizes and take a look at the results for the regular Python functions, and the Cython code separately."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs =  ['lstsqr_comprehensions', 'lstsqr_loops',\n",
+      "          'cy_lstsqr_comprehensions', 'cy_lstsqr_loops'] \n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 52
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plt.figure(figsize=(8,6))\n",
+      "plt.plot(orders_n, times_n['lstsqr_comprehensions'], alpha=0.5, \n",
+      "         label='list comprehensions', marker='o', lw=2)\n",
+      "plt.plot(orders_n, times_n['lstsqr_loops'], alpha=0.5, \n",
+      "         label='for-loops', marker='o', lw=2)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.title('Performance comparison list comprehensions and for-loops')\n",
+      "plt.show()\n",
+      "\n",
+      "plt.figure(figsize=(8,6))\n",
+      "plt.plot(orders_n, times_n['cy_lstsqr_comprehensions'], alpha=0.5, \n",
+      "         label='list comprehensions (Cython', marker='o', lw=2)\n",
+      "plt.plot(orders_n, times_n['cy_lstsqr_loops'], alpha=0.5, \n",
+      "         label='for-loops (Cython)', marker='o', lw=2)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time in ms')\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.title('Performance comparison list comprehensions and for-loops in Cython')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAGJCAYAAACaQwrRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nURKDPTGQxwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10e8f8610>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAGJCAYAAABFDXDOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBAiY/8fzj2UNX7XML4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10565c6d0>"
+       ]
+      }
+     ],
+     "prompt_number": 71
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "As we can see in the first plot, the list comprehensions lead to a slightly increased performance in regular Python code.  \n",
+      "But the second plot is quite interesting: List comprehensions in Cython are significantly slower than the regular for-loop structures.\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "Let us do a quick comparison by how much we were able to improve the performance of the simple least square implementation using Cython so far:\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
+      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 72
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs =  ['lstsqr_comprehensions', 'cy_lstsqr_comprehensions', \n",
+      "          'cy_lstsqr_loops'] \n",
+      "labels = ['list comprehensions', 'list comprehensions (Cython)', \n",
+      "          'for-loops (Cython)']\n",
+      "\n",
+      "times = [timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
+      "         for f in funcs]\n",
+      "\n",
+      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 73
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.figure(figsize=(8,6))\n",
+      "x_pos = np.arange(len(funcs[1:]))\n",
+      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
+      "plt.xticks(x_pos, labels[1:], rotation=90)\n",
+      "plt.ylabel('relative performance gain')\n",
+      "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "ftext = 'For-loops in Cython are {:.2f}x faster then list comprehensions'\\\n",
+      "        .format(times[1]/times[2],1)\n",
+      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
+      "plt.xlim([-1,len(funcs[1:])])\n",
+      "plt.ylim([0,max(times_rel)*1.2])\n",
+      "plt.grid()\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAIBCAYAAABpxJfYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWRMRETVxLGsiIqIm7r+eoHMw3W5vGAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10e919d10>"
+       ]
+      }
+     ],
+     "prompt_number": 88
     }
    ],
    "metadata": {}

From 58658aa7c148864d2caae7cec4bf5b0fdea8a959 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 6 May 2014 01:08:11 -0400
Subject: [PATCH 020/248] conclusion section

---
 .../cython_least_squares-checkpoint.ipynb     | 208 +++++++++++++++++-
 benchmarks/cython_least_squares.ipynb         | 208 +++++++++++++++++-
 2 files changed, 406 insertions(+), 10 deletions(-)

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index fafb461..2e97429 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:caecd42da39e4b55b4dc50c985b30a04ee3eac4a88d143732c4441ecc28fc1e0"
+  "signature": "sha256:b8a2adab4cfa8ac1064656d4b3e3121a2acc1060e034c98ce29986312939c9ac"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
-      "last updated: 05/04/2014\n",
+      "last updated: 05/06/2014\n",
       "\n",
       "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
@@ -73,7 +73,8 @@
       "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
       "- [Appendix I: Cython vs. Numba](#numba)\n",
       "- [Appendix II: Cython with and without type declarations](#type_declarations)\n",
-      "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)"
+      "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)\n",
+      "- [Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting](#showdown)"
      ]
     },
     {
@@ -1005,8 +1006,7 @@
       "\n",
       "funcs =  ['cy_classic_lstsqr', \n",
       "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
-      "labels = ['classic approach (cython)', 'matrix approach', \n",
-      "          'numpy function', 'scipy function']\n",
+      "\n",
       "orders_n = [10**n for n in range(1, 7)]\n",
       "times_n = {f:[] for f in funcs}\n",
       "\n",
@@ -1057,6 +1057,15 @@
      ],
      "prompt_number": 34
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "In this performance comparison for different sample sizes, we see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython. There are a lot of tweaks that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -2193,6 +2202,195 @@
       }
      ],
      "prompt_number": 88
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"showdown\"></a>\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To wrap it up, let us compare the Cython code of our simple least squares fit implementation to the Numpy and Scipy functions - after we made some improvements by adding static type declarations and explicit for loops."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        temp = (x_i - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import scipy.stats"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lin_lstsqr_mat(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def numpy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return np.linalg.lstsq(X,y)[0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def scipy_lstsqr(x,y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    return scipy.stats.linregress(x, y)[0:2]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs = ['cy_lstsqr', 'lin_lstsqr_mat',\n",
+      "          'numpy_lstsqr', 'scipy_lstsqr'] \n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('cy_lstsqr', 'Cython implementation'), \n",
+      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
+      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
+      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('performance gain relative to the slowest approach')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVMfXx7/30vsuZQFR6YKgYtdYEBQ1RBNjiYIVLDG2\nvBrzsytg10SNUYyRqDQ1ligxmkgsgDVRsCuioCAqxYqKCAjn/QO54cICC8Jimc/z7PPszp05c+bc\nO3PPTuWIiMBgMBgMBoPBeOfg61oBBoPBYDAYDEb1YI4cg8FgMBgMxjsKc+QYDAaDwWAw3lGYI8dg\nMBgMBoPxjsIcOQaDwWAwGIx3FObIMRgMBoPBYLyjMEdOSbx69QojR46EsbExeJ7H0aNH61qld5Kd\nO3fC1tYWqqqqGDlypNw4Pj4+6N69u5I1Y5S+NzExMeB5Hvfu3auyLGtrayxevLgWtCyLlZUVFi1a\npJS83lZ4nsfWrVvrWo06aSejo6NFz2np3wBw6dIltG3bFlpaWrCxsQEApKamolu3btDV1YWKikqt\n6/khoYzn0c3NDV9++WWt5qEsmCNXAh8fH/A8D57noaamBisrK4wbNw6PHj16Y9m//fYbtm3bhn37\n9iE9PR0fffRRDWj8YVFQUICRI0fCy8sLqampWL16tdx4HMeB4zil6hYeHg6e/3Crk7x706FDB6Sn\np8Pc3BwAcPz4cfA8j9u3b1cqLzY2FlOmTKlttQHUzfPyJty5c6faTo6Hhwd8fX3LhKenp6N///41\nod4b8Ta0kx07dhQ9twAwbdo0SCQSJCQk4MyZMwCAxYsX48GDB7hw4QLS0tKUrqc8Ro8eDXd397pW\n450gIiICK1eurGs1agTVulbgbcPV1RU7duzAq1evEBsbizFjxiA1NRX79u2rlry8vDyoq6vjxo0b\nsLCwQPv27d9Iv2J5HyL37t1DdnY2PD09RY1saYgIbJ/rqlNYWAgA1XJIy7s3MpmsTFxF7o2RkVGV\ndfjQqMlnXN59qgvehnZSTU2tjD0SExMxYsQINGzYUKRrmzZtYGtr+0a65ufnQ01N7Y1kMKqORCKp\naxVqDmIIjBgxgjw8PERhixYtIhUVFXr58iUREW3bto1cXFxIU1OTrKys6JtvvqHs7GwhfpcuXWjU\nqFE0Z84cMjc3JzMzM3JzcyOO44SPtbU1ERHl5eXR9OnTycLCgtTV1cnJyYm2bt0qyp/jOPrxxx/J\n29ubDAwMaNCgQbR582ZSVVWlqKgoatKkCWlpaZG7uzulpaXRkSNHyMXFhXR0dMjDw4Pu3r0ryLp5\n8yb17duX6tWrR9ra2tS0aVMKCwsT5delSxcaPXo0zZ8/n8zMzMjQ0JCGDx9Oz58/F8X79ddfqWXL\nlqSpqUlGRkbk6elJjx8/Fq7/+OOP5ODgQJqammRvb0+LFi2iV69eVWj/U6dOUefOnUlLS4ukUikN\nHjyYMjMziYho8+bNIhtyHEcxMTEK38fK7tvff/9NXbp0IUNDQzIwMKAuXbrQ6dOnRTKCgoLI0dGR\nNDU1ydDQkFxdXenOnTsUFRVVRjdfX99yy7lo0SKysbEhDQ0NMjExoZ49e1JOTo7IdhYWFqStrU09\ne/akkJAQ4jhOuJfF978kqampZWwyevRosrW1JS0tLbKxsaFZs2ZRbm6ucN3Pz4/s7Oxo+/bt5ODg\nQKqqqnTt2jV69uwZff3114IOLVq0oN27d5dbnvLuTbFd7t69S7du3SoTx93dvVyZlpaWtHDhQtHv\nuXPn0ldffUUGBgZkampK69ato5ycHBo/fjxJpVKysLCgtWvXiuRwHEerV6+mfv36kY6ODllYWNDq\n1atFcaysrGjRokXC77y8PPLz8yNra2vS1NQkZ2dn+vnnn8vIXbNmDQ0cOJB0dHTI0tKSdu/eTY8e\nPSIvLy/S09MjGxsb+u2330Tp0tPTacSIEWRiYkJ6enrUsWNHOnr0qHC92GYHDx6kzp07k7a2Njk5\nOdFff/0lyltee1JZ/R4xYkS5dYjjONqyZYsQ9969ezRo0CCSSCSkpaVFbm5uFBsbWyU9iSp/1kvS\npUuXGmsnvby85OZBVHn9quy59ff3L7e+V1Z3iuVt2bKFPD09SUdHh2bMmEFEir1bKmqb/fz8yugV\nEhIi1wZZWVnk4+NDZmZmpKGhQQ0aNKBvvvlGuK5Ie1idOlBc/vDwcOratavQNv36669lZJd8Hqva\nJhEVtYn9+vUjY2Nj0tTUJBsbG/ruu+/K2LPkPS/9sbKyEuLfuHGD+vXrRxKJhKRSKfXo0YMuXbqk\nsE1rE+bIlWDEiBHUvXt3UdiKFSuI4zh6/vw5bd68maRSKYWHh9OtW7fo6NGj1KxZMxo2bJgQv0uX\nLqSnp0fjxo2j+Ph4unz5Mj169Ii+/fZbsra2poyMDHrw4AEREX377bdkZGREu3btohs3btDixYuJ\n53k6fPiwII/jODIyMqLAwEC6efMm3bhxgzZv3kw8z5O7uzudPn2azp49S/b29tSpUydydXWlf//9\nl86fP0+Ojo40aNAgQdalS5coMDCQLl68SDdv3qQ1a9YIDmFJ/SUSCX3zzTeUkJBAf//9NxkaGtLc\nuXOFOJs2bSI1NTVauHChUMa1a9cK5fLz8yNLS0uKiIig5ORk+vPPP6lhw4YiGaVJS0sjPT09GjJk\nCF2+fJmOHz9OzZo1I1dXVyIiysnJoTNnzhDHcfTHH39QRkYG5eXllXsfSzpyity3PXv20M6dO+n6\n9et09epVGj16NBkaGtLDhw+JiCg2NpZUVVUpLCyMbt++TZcuXaKNGzfSnTt3KC8vjwIDA4njOMrI\nyKCMjAx6+vSpXN1+++030tfXp3379lFqaiqdP3+eVq9eLbzcIiIiSFVVlVatWkU3btygjRs3kkwm\nI57nq+TIFRYW0uzZs+n06dOUkpJCe/fuJXNzc/Lz8xPS+Pn5kba2Nrm5udHp06fpxo0b9OzZM3Jz\ncyN3d3c6ceIE3bp1izZs2EDq6uqi57Ik5d2bki/EgoIC2rt3L3EcR7GxsZSRkSFy/EtT2rmytLQk\niURCq1atoqSkJFq4cCHxPE89e/YUwpYsWUI8z9PVq1eFdBzHkaGhIa1du5Zu3LhBq1evJlVVVfr9\n99/LzWvEiBHk4uJCBw8epOTkZNq+fTtJJBLauHGjSK6ZmRmFhoZSUlISjR8/nnR0dKhHjx4UEhJC\nSUlJNGnSJNLR0RGeoRcvXlDjxo1pwIABFBcXR0lJSbRo0SLS0NCg+Ph4IvrvheLi4kKRkZGUmJhI\nvr6+pK+vL9jr3LlzxHEc7dmzR9SeVFa/s7KyyNXVlby8vITntLgOlXxxFhYWUtu2balFixZ04sQJ\nunTpEg0aNIikUqmQlyJ6Vvasl6Ym28nExES5eShSv0o/t+np6dSgQQOaOXMmZWRk0PPnzyk9PZ06\ndOhAQ4cOFep7YWFhpXWn2JGpX78+bd26lZKTk+nWrVsKv1sqapufP39OQ4YMoY4dOwr3tzxbT5o0\niVxcXOj06dOUmppKJ0+epF9++UW4Xll7WN06UFz+evXq0datW+n69es0Z84cUlFRoXPnzolkl3we\nq9omERF9+umn1L17d7pw4QKlpKRQVFQUbdu2Tbju5uZGY8aMIaKiPwvFNsvIyKCrV6+ShYUFjRw5\nkoiK/oCZmprS+PHj6fLly3T9+nWaNGkSGRkZ0f379xWyaW3CHLkSlHYArly5QjY2NvTRRx8RUdHL\npPQ/85iYGOI4jp48eUJERZXNwcGhjOzi3o9isrOzSUNDg3766SdRvL59+1LXrl2F3xzHCf8aiinu\nAblw4YIQ9t133xHHcXT27FkhbNWqVWRsbFxhmfv06SM8zMX6N2/eXBRn3Lhxgg2IiBo0aECTJk2S\nKy87O5u0tbUpMjJSFB4SEkISiaRcPebMmUMNGjSg/Px8IezChQvEcZzQY1HcCJw4caLCMpW+j4rc\nt9IUFBSQVCoVGpPdu3eTgYFBuQ5aWFgYcRxXoV5ERCtXrqRGjRqJylmSjh070tChQ0Vh3377bbV6\n5OTlbW9vL/z28/MjnucpNTVVCIuKiiJNTU3KysoSpfX19aXPP/+8XNny7k3JFyIR0bFjx4jjOEpJ\nSSlXTjHyHLm+ffsKvwsLC0lfX58+++wzUZhUKhX1ynEcR8OHDxfJHjx4MHXu3FluXjdv3iSe5ykh\nIUGUJiAgQFQvOI6jKVOmCL/v379PHMfR119/LYQ9fvyYOI6j/fv3E1HRfatfv36Znml3d3eaPHky\nEf1nsz179gjXMzIyiOM4+vvvv4lIsXtdTOn67eHhIbe3uOSL89ChQ8RxnOBcEhHl5uaSubk5zZ8/\nX2E9K3vW5VGT7aQ8FKlfpZ9borLPI5HYEShOV1ndKa4nJXubiRR/t1TWNo8aNYrc3NwqtUOfPn3I\nx8en0njFlG4PiapXB4rLP2/ePJH8Dh06iJzWks9jddskFxcX8vf3L/d66ftXTF5eHrm5uZGrq6vw\nR8fPz4/at28vildYWEi2trb0ww8/EFHVbVqTfLizs8shOjoaenp60NbWRtOmTWFnZ4ctW7bg/v37\nuH37NqZMmQI9PT3h88knn4DjOCQmJgoyWrVqVWk+iYmJyMvLg6urqyjc1dUVV65cEYW1bdu2THqO\n49C0aVPht6mpKQCgWbNmorCHDx8Kc2levHiBGTNmoEmTJjAyMoKenh7+/PNP0eRzjuPg4uIiysvc\n3BwZGRkAgMzMTNy5cwc9evSQW64rV64gJycH/fr1E9npq6++wtOnT/Hw4cNy07Vv3x6qqv9N22zW\nrBkMDAxw9epVuWkUQdH7duvWLQwbNgz29vYwMDCAgYEBsrKyBNv06NEDNjY2sLa2hre3N4KCgsot\nS0UMGjQI+fn5sLS0hK+vL8LDw/H8+XPhenx8PDp06CBK07Fjx2qVPSgoCO3atYOZmRn09PQwa9as\nMgsNTE1NUb9+feH3mTNnkJeXBwsLC5G9tmzZInrGlU3p55LjOJiYmIied47jIJPJcP/+fVHa0hPm\nO3ToUKaOFRMbGwsiQqtWrUTlX7JkSZnyl9TH2NgYKioqIn0kEgnU1dWRmZkJoMi26enpkEgkItnH\njx8vI7t58+bCd5lMBhUVFaEOloci9VsRrly5AiMjIzg6Ogph6urqaNeuXRm7VaRnZc+6IrxpO1ma\nmqxfpalK3Smpa1XeLRW1zVVh/Pjx2LVrF5o2bYrJkyfjwIEDojmXlbWH8vRRpA4UU7pOduzYsdw6\nWd02afLkyVi8eDHat2+PGTNm4NixY5UbBsC4ceNw9+5dRERECHMXz5w5g7i4OFH++vr6SElJEXSo\nzKa1CVvsUIr27dsjJCQEqqqqqFevnuBYFFeWH3/8Ue6qIAsLCwBFLxMdHZ0a1UmePJ7nRSvtir+X\nXAZfHEZE4DgO//vf/7B3716sWrUKDg4O0NbWxtSpU5GVlSWSXXqSMMdxwkT4yiiOt2vXLjRq1KjM\ndalUKjcdx3G18tAX61PZfevduzdkMhnWrVuHBg0aQE1NDZ06dUJeXh6AonsQGxuLEydO4NChQ1i/\nfj2mTZuGw4cPo2XLlgrrU69ePVy7dg1RUVE4cuQIFixYgOnTp+Pff/8VOVQVIW8xQn5+vuj3zp07\nMXHiRCxbtgxdunSBvr4+duzYgdmzZ4vilX62CgsLYWBggNjY2DJ51PUim9ITwjmOkxum6LMqj+K0\np06dgra2dhnZFelTno7FMgsLC9G4cWNERESUSVc6L3m2rqxcitbv6lLcjiiqZ00861WhptvdqlKV\nulNSV0XbKI7j3qhtLkmPHj1w+/ZtREZGIjo6GkOHDkXTpk1x+PBh8DxfaXtYTFXrQHlU1PZXt03y\n8fHBxx9/jAMHDiAqKgqenp7o27cvwsLCyk2zfPlyRERE4NSpU6J3FRHBw8MDa9euLZPGwMAAQOU2\nrU2YI1cKTU1NYZ+gkpiamqJBgwa4du0aRo0a9cb52NnZQUNDAzExMXBychLCY2JiRD1tNcmxY8cw\ndOhQDBgwAEBRBUlISKhwBWhpZDIZ6tevj8jISPTu3bvMdWdnZ2hqaiIpKQkff/yxwnKdnZ2xefNm\n0QquCxcuICsrC02aNFFYTmkUuW8PHz5EfHw8Vq5cKew/d+fOnTL/InmeR+fOndG5c2cEBATAyckJ\n27ZtQ8uWLYUGRd7LrjTq6uro2bMnevbsiQULFsDU1BS///47JkyYACcnJ5w4cQLjxo0T4p84cUKU\nXiaToaCgAJmZmcLqurNnz4riHD16FC1atMDkyZOFsFu3blWoFwC0adMGT548QU5ODpydnSuNXxWK\nbVRQUFCjcivj1KlT+Oqrr4TfJ0+eLLdsxb3pKSkp6NWrV43q0aZNG4SFhUFPTw8mJibVllOeHRWp\n3+rq6nj16lWF8p2dnYU60bhxYwBAbm4u/v33X0ycOLHKupb3rCtCTbeTitSv6lLdulOT7xZ1dXWF\n65dUKoWXlxe8vLzg6+uLjz76CPHx8TAzM1OoPXwTTp06JXo/VFQnW7duXe02yczMDD4+PvDx8YGn\npycGDx6Mn376Cbq6umXiRkREwM/PD5GRkbC3ty+jQ3BwMCwsLKChoVFufuXZtKbb0tK8c47c6dOn\nMXnyZKipqcHCwgKhoaGi4bjaZNGiRRg1ahSkUik+++wzqKmpIT4+HgcOHMD69esBKL71hba2Nr7+\n+mvMnTtXGCLatWsX9u7di0OHDtWK/g4ODoiIiEC/fv2go6ODlStXIi0tDWZmZkIcRfT38/PDuHHj\nYGpqiv79+6OwsBBRUVHw9vaGkZERZs2ahVmzZoHjOHTr1g2vXr3CpUuXcP78eSxdulSuzIkTJ2L1\n6tXw8fHBrFmz8PjxY4wfPx6urq5vPPRR2X2TSqUwMTHBhg0bYGNjgwcPHmDatGnQ0tISZPz++++4\ndesWOnfuDBMTE8TFxSE1NVV4uVhbWwvxOnbsCG1tbbk9BBs3bgQRoU2bNpBIJDh8+DCePXsmyJk6\ndSq++OILtG3bFp6enjh+/DjCw8NFzmG7du2gp6eHGTNmYObMmUhKSsL8+fNF+Tg6OmLTpk3Yu3cv\nnJ2dsW/fPuzZs6dSW3Xt2hUeHh7o168fli9fjqZNm+Lx48c4efIktLS0MHr06KrfgNdYWlqC53ns\n378fAwcOhIaGhvBvtjSln0F5z6SiYfv370dgYCB69OiBAwcOYMeOHdi1a5fcNHZ2dhg5ciTGjBmD\n5cuXo3379sjOzkZcXJzwXFSXIUOGYNWqVejVqxcWLVoEe3t7ZGRk4MiRI3ByckKfPn0UkmNsbAxd\nXV1ERkaicePG0NDQgFQqVah+W1tbIyoqCjdv3oS+vj4kEkmZ9rNbt25o27YtBg8ejMDAQOjr62PB\nggXIy8sTOUCVUdmzrgg13U6WV78qo7xnrWT4m9Sdmnq32NjYYNeuXbh69SpkMhn09fXl9lrNnj0b\nrVu3hpOTE3ieR3h4OPT09NCwYUPo6OhU2h6+KZs2bYKjoyNatWqF8PBw/PPPPwgMDJQbt1u3btWy\n68SJE9GrVy80atQIL1++xO7du9GwYUPBiStpzytXrmDo0KHw9/dHo0aNkJ6eDqBohMvExAQTJ07E\nxo0b0adPH8yZMwf169fHnTt38Ndff6F379746KOPKrRpraOcqXg1R1pamrAVyMyZM2nXrl01JtvH\nx6fMqtXSRERE0EcffUTa2tqkr69PzZs3pwULFgjXy5tA6e/vL5poTkSUn59PM2bMEJbVOzs7i1bV\nEJVdhk1UNGlaTU1NFBYWFkY8z4vCtm3bRjzPU0FBAREVTZLu2bMn6ejokLm5Ofn7+9OoUaNE20DI\n03/hwoXCVgDFbNmyhVxcXEhDQ4OMjIyod+/eooUDv/zyCzVv3pw0NTVJKpVS+/btaf369WXsUpJ/\n/vmHXF1dSUtLiyQSCQ0ZMkRYEURUNFGW5/lKFzvIu4+V3beYmBhh6b+joyP99ttvZGdnRwEBAURE\ndPToUeratSuZmJiQpqYmNWrUiJYtWybKY/LkySSTySrcfmT37t3UoUMHkkqlwhYRmzZtEsVZvXo1\nWVhYkJaWFnXv3r3M9ghERPv376fGjRuTlpYWderUiSIjI4nneWECfH5+Po0dO5YMDQ1JX1+fhgwZ\nQmvXrhU9I/KeSaKiVagzZswga2trUldXJzMzM/L09BStbi6NvHsTFRUlWg1IRLR8+XKysLAgFRWV\nCrcfKT25XN5k85L3pxhHR0fR6uji7Uc+//xz0tbWpnr16tGqVasqzKugoICWL19Ojo6OpK6uTsbG\nxuTm5iZqa+TVS1VV1TLbPWhqaopWuz58+JDGjRsn1HkLCwvq168fnT9/vlybyZMdGhpK1tbWpKqq\nKtRNRer3zZs3ydXVlXR1dSvcfiQtLY28vLxE24/ExcUJ1xXRU5FnvTQ12U6Wh7z6VXrVaumyKbLY\ngajyulNRG1add0vptvnRo0f0ySefkIGBQYXbjyxYsICaNGlCurq6ZGBgQG5ubiKdKmsPiapXB0pu\nP+Lm5iZsC1LZ/axOmzRhwgRq1KgRaWlpCe+okivaS9pT3hZKJbfAISJKSUmhIUOGkImJCWloaJCl\npSUNGzaMkpOTFbJpbcIRvbs7p/r5+aFFixb4/PPP61oVBqPWiI6ORteuXXHnzh3Uq1evrtV5pyj+\nZzx48OC6VoXB+OBJTk6GjY0Njh8/XmbRCaP6vLOrVlNSUnDw4EF8+umnda0Kg8FgMBgMRp1QZ47c\n2rVr0bp1a2hqapY5++/Ro0fo27cvdHV1YWVlhW3btomuP336FMOHD0dISAg7rJjxQVDZAgoGg8F4\nF2BtWc1TZ0Ore/bsAc/ziIyMRE5ODjZv3ixc8/b2BlA0WfbcuXPo1asXTp48CScnJ7x69QqfffYZ\nvv32W3Tt2rUuVGcwGAwGg8F4O1DKTLwKmDNnjmg35OfPn5O6ujrduHFDCBs+fLhwHl1oaCgZGRmR\nm5sbubm50fbt2+XKrVevHgFgH/ZhH/ZhH/ZhH/Z56z8uLi7V8qPqfI4cleoQvH79OlRVVWFnZyeE\nubi4CLs+Dxs2DA8ePEBUVBSioqIwcOBAuXLv3bsnLC9WxsfPz0+p6RWJX1Gc8q4pGi4v3pvaQJn2\nrqqM2rJ3VWypyD14m21e0894da8ze1c/PmtTak4Ga1Pe72e8Ova+cOFCtfyoOnfkSo+XP3/+HPr6\n+qIwPT09PHv2TJlqVRk3NzelplckfkVxyrumaLi8eMnJyZXqVFO8qb2rKqO27F3eNUXClGlvefnX\ndvrK4lf3OrN39eOzNqXmZLA25f1+xpVp7zrffmTOnDm4e/euMEfu3Llz6NSpE7Kzs4U433//PY4e\nPYq9e/cqLLe2jnxilI+Pjw+Cg4PrWo0PBmZv5cLsrXyYzZULs7dyKW3v6votb12PXKNGjfDq1SvR\nYbgXLlyo1jFN/v7+iI6OflMVGQri4+NT1yp8UDB7Kxdmb+XDbK5cmL2VS7G9o6Oj4e/vX205ddYj\nV1BQgPz8fAQEBODu3bsICgqCqqoqVFRU4O3tDY7j8Msvv+Ds2bPo3bs3Tp06JZz7pwisR47BYDAY\nDMa7wjvXI7dgwQJoa2tj2bJlCA8Ph5aWFhYtWgQAWLduHXJyciCTyTB06FCsX7++Sk4co25gvZ/K\nhdlbuTB7Kx9mc+XC7K1casreyjltXg7+/v7ldiVKpVKFDvhmMBgMBoPB+JCp88UOtUVFXZSGhoZ4\n/PixkjViMD5cpFIpHj16VNdqMBgMxltLdYdW66xHThn4+/vDzc2tzJLfx48fs/lzDIYSYcfyMBgM\nhnyio6PfaJj1g+yRYwshGAzl8j7Uuejo6BrZa4yhOMzmyoXZW7mUtvc7t9iBwWAwGAwGg/FmsB45\nBoNR67A6x2AwGBXDeuQYDAaDwWAwPjDea0eOnexQdXx8fNC9e/c6y9/a2hqLFy9WSl5WVlbC3oUf\nKjzPY+vWrXWtxjsBa0uUD7O5cmH2Vi7F9n7Tkx3ee0fufZy4+fDhQ0ybNg2Ojo7Q0tKCqakpunTp\ngrCwMBQUFCgk4/jx4+B5Hrdv3xaFcxxXpysMY2NjMWXKFKXkVddlrSp37twBz/M4evRoldN6eHjA\n19e3THh6ejr69+9fE+oxGAwGoxq4ubm9kSP3Xm8/Ul0SElJw6FAS8vN5qKkVwsPDFg4Olm+FzNTU\nVHTq1Anq6uqYP38+WrRoATU1NZw4cQLff/89XFxc0KxZM4XllR6Pr+t5TEZGRnWa/7tATd4jmUxW\nY7Led97HP4VvO8zmyoXZW7nUlL3f6x656pCQkILg4ETcv98VT5644f79rggOTkRCQspbIXP8+PHI\nz8/H2bNn4e3tDUdHR9ja2mL48OE4e/Ys7OzsEBwcDKlUipycHFHa+fPno1GjRkhOToarqyuAoqFM\nnufRtWtXIR4RYcOGDbC0tISBgQH69OmDzMxMkayQkBA4OTlBQ0MDDRo0wNy5c0W9gW5ubhgzZgwW\nLFgAc3NzGBkZYcSIEcjOzq6wfKWHO62srDBv3jyMGzcOEokEZmZm+Omnn/Dy5UtMmDABhoaGqF+/\nPgIDA0VyeJ7Hjz/+iP79+0NXVxf169fHjz/+WGHe+fn58Pf3h42NDbS0tNCkSRNs2LChjNy1a9di\n0KBB0NXVhZWVFfbs2YPHjx/D29sb+vr6sLW1xe7du0XpMjIy4OPjA5lMBn19fXTq1AnHjh0TrkdH\nR4PneRw6dAiurq7Q0dGBs7MzDhw4IMRp2LAhAMDd3R08z8PGxgYAcOvWLfTr1w8WFhbQ0dFBs2bN\nEB4eLqTz8fHBkSNHEBISAp7nRb16pYdW09LS4OXlBalUCm1tbbi7uyMuLq5KejIYDAZDidB7SkVF\nq+ja2rWHyc+PqEsX8eeTT4rCq/Px9DxcRp6fH1Fg4OEqlenhw4ekoqJCixYtqjBeTk4OSaVSCgkJ\nEcIKCgrI0tKSli9fTgUFBbR3717iOI5iY2MpIyODHj9+TEREI0aMIAMDAxo8eDBduXKFTp06RdbW\n1jRs2DDAEwgXAAAgAElEQVRB1r59+0hFRYWWLl1KN27coO3bt5NUKqW5c+cKcbp06UISiYS++eYb\nSkhIoL///psMDQ1FceRhZWUlKp+lpSVJJBJatWoVJSUl0cKFC4nneerZs6cQtmTJEuJ5nq5evSqk\n4ziODA0Nae3atXTjxg1avXo1qaqq0u+//15uXiNGjCAXFxc6ePAgJScn0/bt20kikdDGjRtFcs3M\nzCg0NJSSkpJo/PjxpKOjQz169KCQkBBKSkqiSZMmkY6ODj18+JCIiF68eEGNGzemAQMGUFxcHCUl\nJdGiRYtIQ0OD4uPjiYgoKiqKOI4jFxcXioyMpMTERPL19SV9fX3h3pw7d444jqM9e/ZQRkYGPXjw\ngIiILl26RIGBgXTx4kW6efMmrVmzhlRVVSkqKoqIiLKyssjV1ZW8vLwoIyODMjIyKC8vTyjPli1b\niIiosLCQ2rZtSy1atKATJ07QpUuXaNCgQSSVSoW8FNFTHu9DU1NsT4byYDZXLszeyqW0vavbTrIe\nuVLk58s3SUFB9U1VWCg/bV5e1WQmJiaisLAQTk5OFcbT1NTEsGHDEBQUJIQdPHgQaWlp8PX1Bc/z\nkEqlAAATExPIZDJIJBJR+uDgYDg5OaF9+/b46quvcOjQIeH60qVLMWDAAEyfPh12dnYYOHAg/P39\n8f333+PVq1dCPCsrK6xYsQKNGjVC9+7dMWjQIJEcRXF3d8fkyZNhY2ODWbNmQVdXFxoaGkLY9OnT\nYWBggCNHjojS9e7dGxMmTICdnR2+/vprDBw4EN9//73cPG7duoWwsDDs2LEDHh4esLS0xMCBAzFl\nyhSsWbNGFNfb2xvDhg2DjY0NAgIC8OLFCzg6OmL48OGwsbHB/Pnz8eLFC/zzzz8AgO3bt+PZs2f4\n9ddf0bJlS6EcHTp0wM8//yyS7e/vjx49esDW1hZLly7Fs2fPcObMGQCAsbExgKIj5mQymTAM3aRJ\nE4wfPx5NmzaFtbU1Jk6ciF69egk9bfr6+lBXV4eWlhZkMhlkMhnU1NTK2ODIkSM4c+YMtm7dig4d\nOqBJkyYIDQ2FpqYm1q1bp7CeDAaDwVAe7/UcufKO6KoINbVCueEqKvLDFYHn5adVV6+aTKrC3Kix\nY8eiSZMmSEhIgIODA4KCgtCnTx/BGagIR0dH0Yve3NwcGRkZwu+rV6/C29tblMbV1RUvX75EUlIS\nHBwcAAAuLi6iOObm5oiMjFS4DEDRgoSScjiOg4mJiWgeIMdxkMlkuH//vijtRx99JPrdoUMHzJs3\nT24+sbGxICK0atVKFP7q1SuoqoqrSUl9jI2NoaKiItJHIpFAXV1dGI4+c+YM0tPTRc4yAOTm5kJH\nR0cU1rx5c+G7TCaDioqKyPbyePHiBebPn499+/YhLS0NeXl5yM3NFQ2XK8KVK1dgZGQER0dHIUxd\nXR3t2rXDlStX3ljPdx02f0j5MJsrF2Zv5VJs7zc9ouu9d+SqioeHLYKDD8PNrZsQlpt7GD4+dnjt\nn1SZhIQimRoaYpndutlVSY69vT14nseVK1fw+eefVxjXyckJnTp1woYNGzB9+nT88ccf2L9/v0L5\nlO6tqc4mhRzHQV1dvUxYYWHVHWJ5+sgLq47sYorTnjp1Ctra2mVkV6RPeToWyywsLETjxo0RERFR\nJl3pvErbrKRu5fG///0Pe/fuxapVq+Dg4ABtbW1MnToVWVlZFaZTFCIqY4Pq6MlgMBiMshR3OAUE\nBFQrPRtaLYWDgyV8fOwgkx2BRBINmezIayeu+qtWa0qmoaEhPD09sXbtWjx9+rTM9fz8fLx48UL4\nPXbsWISGhmLDhg2oX78+PDw8hGvFL2J525VUtiWHs7MzYmJiRGExMTHQ1taGra1tlcpUm5w6dUr0\n++TJk3B2dpYbt7gnLiUlBTY2NqKPtbX1G+nRpk0b3Lx5E3p6emVkm5mZKSynvHt27NgxDB06FAMG\nDBCGVxMSEkT3UV1dXTTsLQ9nZ2c8fPgQ8fHxQlhubi7+/fdfNGnSRGE931fYHlvKh9lcuTB7K5ea\nsjdz5OTg4GCJ8eO7YvJkN4wf3/WNtx6pSZnr1q2DmpoaWrVqhW3btuHq1atITExEeHg42rRpg8TE\nRCHugAEDAAALFy7E6NGjRXIsLS3B8zz279+PzMxMkWNYWe/bzJkz8dtvv2HZsmW4fv06duzYgYCA\nAEydOlUYhiSiam2TUTqNPBmKhu3fvx+BgYG4ceMG1qxZgx07dmDq1Kly09jZ2WHkyJEYM2YMwsPD\nkZiYiAsXLmDTpk1Yvnx5lctRkiFDhsDa2hq9evXCwYMHkZycjH///RdLlizB77//rrAcY2Nj6Orq\nIjIyEunp6Xj8+DEAwMHBAREREThz5gyuXr2KL7/8EmlpaaLyWVtbIy4uDjdv3sSDBw/kOnXdunVD\n27ZtMXjwYJw8eRKXL1/G8OHDkZeXh3Hjxr2RDRgMBoNROzBH7h2jQYMGOHv2LD7//HP4+/ujVatW\n6NixI4KCgjBu3DhRj5OGhgaGDh0KIsLIkSNFckxNTbFkyRIsXboU9erVE4Zqy9skt2SYp6cnNm3a\nhJCQEDRt2hTffPMNJkyYAD8/P1H80nIU2YBXXprK4pQXNm/ePBw6dAjNmzfH0qVL8d1336FPnz7l\nptmwYQOmTJmCRYsWwdnZGR4eHggLC3vjXkYNDQ3ExMSgdevW8PX1hYODA/r374/Y2FhYWVlVWIaS\n8DyPwMBA7NixAw0aNBB6EVetWgVLS0u4u7vDw8MDDRo0wIABA0Typk6dCmNjY7i4uEAmk+HkyZNy\n84iIiICjoyN69eqFtm3bIjMzEwcPHoShoaHCer6vsPlDyofZXLkweyuXmrI3R9XpNnkHqGhe14d0\ngPfAgQNRUFCA3377ra5VUSo8zyM8PByDBw+ua1UY+LDqHIPBYFSH6raTrEfuPeXx48eIjIxERESE\n0o68YjDeZ9j8IeXDbK5cmL2VS03Z+71ftVrV7UfeF1q0aIFHjx5h+vTp6NSpU12rw2AwGAwGQw5v\nuv0IG1plMBi1DqtzDAaDUTFsaJXBYDAYDAbjA4M5cgwGg6EAbP6Q8mE2Vy7M3sqF7SPHYDAYDAaD\n8YHD5sgxGIxah9U5BoPBqBg2R47BYDAYDAbjA4M5cgwGg6EAbP6Q8mE2Vy7M3sqFzZFjMGqB6Oho\n8DyPe/fu1bUqtY6/vz/s7e3rWg0Gg8FgvAHv9Rw5Pz8/uRsCs/k6HxZ2dnYYNmyY6CzY8sjPz8fj\nx49hYmLy3pwpevz4cbi6uiI5ORkNGzYUwrOzs5Gbmys6R7W2YHWOwWAw5FO8IXBAQEC12sn3/mQH\nBkNRh+zVq1dQU1ODTCarZY3qhtINhI6ODnR0dOpIGwaDwWAAEDqcAgICqpWeDa3KISExAYHbA/HD\nrz8gcHsgEhIT3hqZbm5uGDNmDBYsWABzc3MYGRlhxIgRyM7OFuL4+Pige/fuonTh4eHg+f9ud/Gw\n2s6dO2FnZwcdHR30798fz58/x86dO+Hg4AB9fX188cUXePr0aRnZq1atgoWFBXR0dDBw4EA8fvwY\nQNE/C1VVVdy5c0eUf2hoKCQSCXJycuSWq7r6nD17Fp6enjA1NYWenh7atm2LyMhIkb2SkpIQEBAA\nnuehoqKC27dvC0Oof/75Jzp16gQtLS1s3LixzNDq8uXLIZVKkZKSIsicP38+ZDIZ0tPTy71PGRkZ\n8PHxgUwmg76+Pjp16oRjx46J4kRFRaFZs2bQ0tKCi4sLoqKiwPM8tmzZAgBITk4Gz/M4efKkKJ2d\nnZ2owq9evRotWrSAnp4ezM3N4e3tLeiWnJwMV1dXAIC1tTV4nkfXrl1FNi9JSEgInJycoKGhgQYN\nGmDu3LkoKCgQ2bOy5+99hc0fUj7M5sqF2Vu5sDlytURCYgKCo4Jx3/Q+npg9wX3T+wiOCn4jZ66m\nZe7atQtPnjxBTEwMfv31V+zbtw/Lli0TrnMcp1AvVFpaGkJDQxEREYG//voLx44dQ79+/RAcHIxd\nu3YJYYsXLxalO336NGJiYvD333/jzz//xPnz5zFq1CgARS96e3t7bNq0SZQmKCgIQ4YMgZaWVo3q\n8+zZM3h7eyM6Ohrnzp1Dz5498dlnn+HGjRsAgD179sDKygrffvst0tPTkZaWhvr16wvpp06dipkz\nZ+LatWvo3bt3GZ2mTZuGdu3awdvbGwUFBTh69CgWLlyIkJAQmJmZyS1HTk4O3N3dkZ2djQMHDuD8\n+fP45JNP0L17d1y7dg0AcO/ePfTu3Rtt2rTBuXPnsGLFCvzf//0fgMp7EEvfX47jsGLFCly+fBl7\n9uzB7du34eXlBQBo2LAhfv/9dwDAmTNnkJ6ejt27d8uVu3//fowaNQojRozAlStXsGLFCgQGBpb5\nl1jZ88dgMBgM5fFeD61Wh0Nxh6Bhr4Ho5Oj/AtWAi79eRJtObaol8/Tx03hR/wWQ/F+Ym70bDp89\nDAc7hyrLs7KywooVKwAAjRo1wqBBg3Do0CHMnz8fQNEQmiLj7Lm5uQgJCRHmSA0cOBDr169HRkYG\njIyMAABeXl44fPiwKB0RISwsDHp6egCAwMBA9OzZEzdv3oSNjQ2+/PJLrF69GnPnzgXHcbh27RpO\nnDiBtWvX1rg+Xbp0EclYsGAB/vjjD+zcuROzZs2CVCqFiooKdHV15Q6ZzpkzB7169RJ+FzuAJQkN\nDYWLiwsmTZqEffv2YdKkSfD09Cy3HNu3b8ezZ8/w66+/QkVFBQAwa9YsHDp0CD///DNWrVqFdevW\nQSaTISgoCDzPw9HREUuWLMGnn35aoY3k8fXXXwvfLS0tsXbtWrRq1QppaWkwNzeHVCoFAJiYmFQ4\nbLx06VIMGDAA06dPB1DU85eeno4ZM2Zg3rx5UFUtai4qe/7eV0rPtWXUPszmyoXZW7nUlL1Zj1wp\n8ilfbngBCuSGK0IhCuWG5xXmVVkWx3FwcXERhZmbmyMjI6PKsiwsLEQT3U1NTWFmZiY4TcVhmZmZ\nonROTk6CEwcAHTp0AABcvXoVADB8+HBkZmYKQ5y//PILWrduXUbvmtDn/v37GD9+PBo3bgypVAo9\nPT1cuXIFt2/fVsgGbdu2rTSOTCbD5s2bsX79ehgbG1fa+1Tc8yWRSKCnpyd8jh8/jsTERABFtmrb\ntq1ouLtjx44K6Vya6Oho9OzZEw0bNoS+vj46d+4MAKLhYEW4evWqMAxbjKurK16+fImkpCQhrKae\nPwaD8XaQkJiAFVtWYFn4shqbTsRQHsyRK4UapyY3XAUq1ZbJl2NmdV69WvLU1cXpOI5DYeF/ziLP\n82V65PLzyzqoamrisnIcJzespGyg7KT50hgZGWHAgAEICgpCfn4+QkND8eWXX1aYprr6+Pj44MSJ\nE/juu+9w/PhxnD9/Hs2bN0denmJOsqKT/aOjo6GiooKMjAw8efKkwriFhYVo3LgxLly4IPpcu3YN\nQUFBQjkqs2Oxk1fRvbx9+zY++eQT2NjYYPv27YiLi8PevXsBQGEbVAWO4yp9/t5X2Pwh5cNsXvsk\nJCZgw6ENOESHsCd+D+4a333j6UQMxaip55sNrZbCo5UHgqOC4WbvJoTl3siFj5dPtYZBASChftEc\nOQ17DZHMbu7d3lRduZiamuKff/4RhZ09e7bG5MfHx+PZs2dCr1zxZHwnJychztixY+Hu7o7169fj\n5cuX8Pb2rrH8S3Ls2DF89913wvy27OxsJCUloWnTpkIcdXV10YT9qnLo0CGsXLkS+/fvx9y5c+Hj\n44N9+/aVG79NmzbC0LOJiYncOE5OTggLC0NhYaHgsJ04cUIUpzjt3bt3hbDMzEzR7zNnzuDly5f4\n4YcfoKGhIYSVpNjxqswGzs7OiImJwfjx44WwmJgYaGtrw9bWtsK0DAbj3WTfv/twVfcqcl7l4GXB\nS1zMuIhWdq2qPfWHoXxYj1wpHOwc4OPuA1mmDJJ0CWSZMvi4V9+Jq2mZisx/8/DwwLVr17Bu3Tok\nJSUhKCgIO3furK76ZeA4DsOHD8eVK1dw9OhRTJgwAX369IGNjY0Qp2PHjnBwcMD//vc/eHt719o2\nFw4ODggPD8fly5dx/vx5eHt7o7CwUGQja2trHD9+HKmpqXjw4EGV9um5f/8+hg0bhmnTpqFHjx7Y\ntm0bjh07hh9++KHcNEOGDIG1tTV69eqFgwcPIjk5Gf/++y+WLFkiLDwYN24c7t+/jy+//BLx8fE4\nfPgwZs+eLZKjpaWFjh07Yvny5bh48SLi4uIwfPhwwWEDAHt7e3Ach++//x63bt1CREQEFixYIJJj\naWkJnuexf/9+ZGZmIisrS67eM2fOxG+//YZly5bh+vXr2LFjBwICAjB16lRhfpyi8y/fR9j8IeXD\nbF67PM19ihOpJ5Dzqmg3AamjFJYGluA4rlpTfxhVg82Rq0Uc7BwwfuB4TPaajPEDx9fIv5Kakilv\nRWrpsG7dumHhwoVYvHgxmjdvjujoaMybN6/MSsfK5JQX1rZtW3Tq1Andu3eHp6cnXFxcyqxSBYDR\no0cjLy9PoWHV6uqzefNmFBYWom3btujXrx8++eQTtGnTRhQnICAAT548gYODA0xNTZGamirIKk+X\nYnx8fGBtbS1M5LexscH69esxY8YMXLhwQW56DQ0NxMTEoHXr1vD19YWDgwP69++P2NhYWFlZAQDq\n1auHP/74A6dPn0aLFi0wZcoUrFq1qoysTZs2QVdXFx06dMDgwYMxduxYmJubC9ebNWuGNWvW4Oef\nf4azszNWrlyJH374QVQGU1NTLFmyBEuXLkW9evXQt29fubb09PTEpk2bEBISgqZNm+Kbb77BhAkT\nRBspK3qfGAzG283T3KcIPh+Ml69eoiAtG8Z/pcIlKgeaf9/C89sPqj31h6F83uuTHcorGttlvvr4\n+Pjg7t27OHjwYKVxp02bhsOHDyMuLk4Jmr0f8DyP8PBwDB48uK5VqVHehzoXHR3NeoiUDLN57ZD1\nMgshF0LwKOcRUmNvQGVnNCa/UsHZ7AJYtrRG5JMCdJs8D+49yl+dz3hzSj/f1W0n3+seOX9/fzZZ\ntg7IysrCmTNnEBQUhClTptS1OgwGg8F4TdbLLASfD8ajnEcAAFnifSziTGGQowGNHKBB/BNMtm0J\nSrxVx5p+OERHR7/RSVSsR45RJXx9fXH37l38/fff5cZxc3PD6dOn4e3tjY0bNypRu3cf1iPHYDBq\niycvnyDkfAgevyw6iUfnWS4cfzqNT1+8XgjF80CTJoChIaIlErhNnlyH2n54VLedZI4cg8GodVid\nYzDqlicvnyD4fDCevCzaPkn3aS6GXyBcPnURFllZOKStjfz69aGmoQEPbW3ctbND1xIr2Bm1Dxta\nZTAYjFqETdNQPszmNUMZJy7rJUacJ8gKNAGZDAHGxrg+aBBi6tXD/fbtEZCbCziwrUdqG7aPHIPB\nYDAYjAp5nPMYweeDkZVbtO1QkRMHmJAmACBeRweqw4cjJicHzzMzwenpoaGvL66lpaFrXSrOUBg2\ntMpgMGodVucYDOVT2onTf5qL4ecJxoVFThzU1PCtmRliW7YU0mjzPNro60N6+TImV+PsZ0b1YUOr\nDAaDwWAwAACPch6Jnbislxh+rvA/J05dHacGDMBVlf+On9TieTTT1QUHgO0i9+7AHDkGg8FQADZf\nS/kwm1eP0k6cwZOXGHGOYExaAABSV8eR/v0Rqa4OGxsbvIqNha6KCgwvX4YmzyM3Lg7dnJ3rsggf\nBGyOHIPBYDAYDBEPXzxEyIUQPM19CgAweJyD4RcAI7x24jQ08Gffvjjz+gxm4wYN4KmhAZ3kZCSm\npECmr49uLVvCocSRi4y3GzZHjsFg1DqszjEYtc/DFw8RfD4Yz/KeAQCkj19i6AUSnLgCDQ1E9O2L\nSyXPa9bWxkATE6jxbICurmFz5BjvFP7+/rC3txd+BwcHQ01NrcbzqSm5Pj4+6N69ew1o9OYQEVq1\naoWdO3cCAAoKCtC4cWP89ddfdawZg8GoKx68eCB24h7lYNj5QsGJy9fSwvbPPxc5cU10dOAlkzEn\n7h2H3T1GnVHyoHUvLy/cu3evDrWpmKoeDK+qqorQ0NBa0WXr1q3Izc3FF198AQBQUVHB7NmzMX36\n9FrJj1EEm6+lfJjNFePBiwcIOR8iOHGGD3Mw7ALBkNMGALzU0kL4Z5/huqamkKa1nh76mZhApUS7\nxuytXNgcuVokJSEBSYcOgc/PR6GaGmw9PGD5hpsj1obMd52SXciamprQLNHIvG0QUZW6vGtzKPGH\nH37AqFGjRGH9+/fHhAkTEBUVBXd391rJl8FgvH0U98Q9z3sOADB88ALDLgJSvsiJy9bRQXjv3kgr\n0b52lkjQVSKp0p9TxtsL65ErRUpCAhKDg9H1/n24PXmCrvfvIzE4GCkJCXUu083NDWPGjMGCBQtg\nbm4OIyMjjBgxAtnZ2QDkD/+Fh4eDL9FtXjykuXPnTtjZ2UFHRwf9+/fH8+fPsXPnTjg4OEBfXx9f\nfPEFnj59KqQrlr1q1SpYWFhAR0cHAwcOxOPHRWf2RUdHQ1VVFXfu3BHlHxoaColEgpycnArLVnoI\ntPj3yZMn0bJlS+jo6KB169aIjY0VpRszZgzs7Oygra0NW1tbzJ49G3l5eRXmtW3bNtja2kJLSwud\nO3fG/v37wfM8Tp48WWG6kly5cgU9e/aEVCqFrq4unJycEB4eDgCwsrJCQUEBfH19wfM8VF4v73/6\n9Cl8fX1hbm4OTU1NNGzYEFOnThVkvnz5EuPGjYNEIoGhoSHGjx+PmTNnioagr1+/jri4OPTt21ek\nj5aWFj7++GNBB0bN4+bmVtcqfHAwm1fM/ez7IifOqJQTl6Wnh029eomcuB6Ghugmlcp14pi9lUtN\n2Zv1yJUi6dAhdNPQAEp0eXYDcOTiRVi2aVM9madPo9uLF6Kwbm5uOHL4cJV75Xbt2oWRI0ciJiYG\nKSkp8PLygqWlJebPnw8ACv3DSktLQ2hoKCIiIvDo0SMMGDAA/fr1g5qaGnbt2oWnT5+if//+WLx4\nMZYuXSqkO336NHR0dPD333/jwYMHGDNmDEaNGoXdu3fDzc0N9vb22LRpE+bNmyekCQoKwpAhQ6Cl\npVWlcgJAYWEhZs2ahTVr1sDY2BhTpkzBwIEDcePGDaioqICIYGpqim3btsHU1BQXLlzA2LFjoaam\nBn9/f7ky4+LiMHToUMyePRvDhg3D1atXMXny5Cr/M/X29kazZs1w6tQpaGpq4tq1aygoKDp4OjY2\nFubm5li5ciUGDRokpJkzZw7OnTuHvXv3wtzcHKmpqbh69apwfebMmdi9ezfCwsLg4OCAoKAgrFu3\nDqampkKc6OhoGBsbw8rKqoxO7dq1w5o1a6pUDgaD8W5S7MRl5xf9kTd+8AJDLgBSlSIn7oG+PkI/\n/hhPX7e9HMfhUyMjtNTTqzOdGbUDc+RKwefnyw9//ZKulszCQvnhlfQcycPKygorVqwAADRq1AiD\nBg3CoUOHBEdOkeG83NxchISEwNDQEAAwcOBArF+/HhkZGTAyMgJQNGft8OHDonREhLCwMOi9bggC\nAwPRs2dP3Lx5EzY2Nvjyyy+xevVqzJ07FxzH4dq1azhx4gTWrl1b5XIW5/fDDz+gefPmAIp6E9u3\nb4+bN2/C3t4eHMdh4cKFQvyGDRsiMTERP/30U7mO3MqVK9GpUyfBXvb29khPT8e4ceOqpNvt27cx\ndepUODo6AoDIsTI2NgYAGBgYQCaTidK0aNECbV7/Iahfvz4++ugjAEB2djbWr1+PtWvX4tPXu6l/\n9913iI6ORlZWliDj+vXrsLS0lKuTlZUVUlJS8OrVK6iqsqpd00RHR7MeCyXDbC6fzOxMhJwP+c+J\nu5+NoZc4SF47cfckEoT36IEXr504FY5DfxMTOOnoVCiX2Vu51JS93+uhVX9//ypPJiwsZ4VjYYnd\nr6tKYTkrggrVq7Z3NsdxcHFxEYWZm5sjIyOjSnIsLCwEJw4ATE1NYWZmJjhxxWGZmZmidE5OToIT\nBwAdOnQAAKFXafjw4cjMzERkZCQA4JdffkHr1q3L6Kwopctrbm4OAKLyBgUFoV27djAzM4Oenh5m\nzZqF27dvlyszPj4e7du3F4WV/q0I3377LUaPHg13d3cEBATg3LlzlaYZP348du3ahaZNm2Ly5Mk4\ncOCA4HgnJSUhNzdXsGkxHTt2FDnnWVlZ0NXVlStfX18fAPDkyZMql4fBYLwblHbiTDJfO3Gvh1OT\nDQ0RUsKJU+d5DDY1rdSJY9Qd0dHR5XY+KMJ7/be9Ooax9fDA4eBgdCvhJR/OzYWdjw9QzcUJtgkJ\nRTJLLPs+nJsLu27dqixLvZTzx3EcCl/3+PE8X6ZHLl9OD2Pp7Tg4jpMbVliqJ7Gy3j4jIyMMGDAA\nQUFB6NatG0JDQ7F48eKKC1QBPM+LhjyLvxfrtXPnTkycOBHLli1Dly5doK+vjx07dmD27NkVyq2J\nCb5z5szBkCFDcODAARw5cgSLFy/GtGnTsGDBgnLT9OjRA7dv30ZkZCSio6MxdOhQNG3atEzPZ0VI\nJBI8e/ZM7rXinjuJRFK1wjAUgvVUKB9mczEZzzMQciEEL/KLpurIMrIx+PJ/PXHXjYywo1s3vHrt\nxGmpqGCITIb6Ci4kY/ZWLsX2dnNzg5ubGwICAqol573ukasOlg4OsPPxwRGZDNESCY7IZLDz8Xmj\nFaa1IVMeMpmszBYeZ8+erTH58fHxIieieHGAk5OTEDZ27Fj88ccfWL9+PV6+fAlvb+8ay780R48e\nRYsWLTB58mS0aNECtra2uHXrVoVpnJycyixq+OeffxTKr7QDaG1tjXHjxmHnzp0ICAjATz/9JFxT\nV1cX5syVRCqVwsvLC+vXr8f+/fsRExOD+Ph42NraQl1dHSdOnBDFP3HihChfe3t7pKSkyNUvJSUF\nVr/46b0AACAASURBVFZWbFiVwXgPSX+eLnLiTEs5cRdlMvxawonTVVGBj5mZwk4c492FtfhysHRw\nqHEnqyZkVrYFhoeHB5YvX45169ahZ8+eOHLkiLBpbE3AcRyGDx+OhQsX4uHDh5gwYQL69OkDmxJH\nuXTs2BEODg743//+hxEjRkDndXd+t27d0K5duzfqoSuNo6MjNm3ahL1798LZ2Rn79u3Dnj17Kkzz\nzTffoE2bNvDz88OQIUNw7do1rFy5UihfSdmTJk3ChAkThLBi2z9//hzTp0/HgAEDYGVlhSdPnuDA\ngQNwLnE2obW1NY4cOYKePXtCXV0dxsbGmD17Nlq3bg0nJyfwPI/w8HDo6emhYcOG0NHRwVdffYU5\nc+bA1NQUjRo1wsaNG3H9+nXRYocuXbrg4cOHSE5OLrPg4Z9//mH/qGsRNn9I+TCbF5H+PB2hF0L/\nc+LSn2PwFQ4Gr52402Zm+NPVFXjtxEnV1DDc1BTSKm6GzuytXNgcuQ8QeZvSlgzz8PDAwoULsXjx\nYjRv3hzR0dGYN29emeHJimRUFNa2bVt06tQJ3bt3h6enJ1xcXLBp06Yyeo4ePRp5eXn48ssvhbCb\nN28iPT290jwr+l06bOzYsRg2bBh8fX3RsmVLnDlzBv7+/hXKadmyJbZs2YItW7agWbNmWLZsmTAc\nWnIfu+vXr+Phw4dy9VVTU8OTJ08watQoODk54eOPP4a5uTm2bt0qxF+xYgXi4uJgbW0tOGJaWlqY\nN28eWrdujTZt2uDy5cv466+/hHmHS5cuxeeff45hw4ahXbt2ePr0KSZMmCBy3h0cHNC6dWvs3r1b\nVMacnBxERkZi6NChZWzGYDDeXdKepSHk/H89cWbpzzHkCg8DFR0QgJh69UROnExdHSPNzKrsxDHe\nXdhZqwyF8PHxwd27d3Hw4MFK406bNg2HDx9GXFycEjR7c0JDQzFy5Eg8evRIWDDwtuDv748tW7bg\nxo0bQtjWrVuxaNEiXLlyRQgLCwvDd999h4sXL9aFmpXC6hyDUXXSnqUh9EIocl4V7cNpnvYc3lc4\n6KsWOXGR9evjn44dgdd/QutraGCIqSm03mBxHqPuYGetMuqcrKwsnDlzBkFBQZgyZUpdq1Mu33//\nPeLi4nDr1i3s2LEDM2bMwMCBA986J648Bg8eDC0tLdFZq4sXL8by5cvrWDMGg1FT3Ht2T+TE1bv3\nTHDiCgH8bmkpcuJstbQw3MyMOXEfIMyRYyiEImeN9unTB126dEG/fv3e6iG+S5cu4dNPP0Xjxo2F\njYHlDRG/DZRn99jYWNFZq/Hx8fj444+Vrd4HBTuHUvl8qDYv7cRZ3H0Gr6s89FV18IrjsMPaGufb\ntxecOCcdHXjLZFAvZ6srRflQ7V1XsLNWGUpl8+bNlcZ5VxqBkJCQulZBYfz8/ODn51fXajAYDCVx\n9+ldhF0Mw8tXLwEA9e88w6B4HnpqOsjlOPxqbY1bbdsCr7ezaqGnh0+NjMCzc1M/WNgcOQaDUeuw\nOsdgVE4ZJy71KQZdU4Gemg5e8Dy22NjgbuvWghPXwcAA3cs5N5Xx7lHddlLhHrnIyEicP38ez58/\nF2VafNQRg8FgMBiM6nHn6R2EXQhDbkEuAKDB7SwMvK4GPTVtPFVRQZiNDe63aiU4cd2kUnQyMGBO\nHEOxOXITJ07EsGHDcPbsWdy5cwd37txBamoqUlNTa1s/BoPBeCt4V6YOvE98KDZPzUoVOXENb2dh\nYIIq9FS18UhVFZvs7HD/dU8cx3HoZWSEzhJJjTtxH4q93xaUOkduy5YtuHjxIho0aFAjmTIYDAaD\nwShy4sIvhgtOnGVKFr64rgpdNR2kq6sj3MYGz1u0ANTVwXMc+hkbo0k55y0zPkwUmiPXqFEjxMbG\nvjPbMwBsjhyD8TbB6hyDUZbbWbcRfjEceQV5AACr5CwMuKEGXTVtpGpoYIuNDV42bw6oq0OV4zBI\nJoO9tnYda82oLarbTpbryN28eVP4fvDgQezfvx8zZsyAmZmZKF7J45neJpgjx2C8PbA6x2CIKe3E\nWd96gv6J6tBV00ailha2W1sjv3lzQE0NGjyPwaamsGTnpr7X1PiGwHZ2dsJn3Lhx2LdvHzp16iQK\nt7e3fyOlGcojOTkZPM+XOTD+QyE6Oho8z+Pevf9n777DoyrTxo9/ZzLpddIbpEISQg1BmoaqgIIs\noNJEIthe3V3Xjr5Lta2+q/5cdXWtkaqCILIqSklognRIgUBIIxXSe5s5vz+GTDKhTcqUhOdzXbnk\nnJmc88ztZHLnKfeTB4h4tJWXl4ebmxu5ubkAZGRk4ObmxuXLl03cMvMh5g8ZX0+NeVZZlm4Sl16q\nTeKS7ezYEBysTeLsLSyI9fY2ShLXU+Ntrroq3tdN5NRq9U2/VCpVlzRCMLzevXtTUFDAbbfdZpL7\nv/baawQFBbX7+3JycpDL5ezdu7dL22PqeJib5cuXM3v2bPz8/AAICgpixowZYlW6IHSxrLIs1iWu\n0yZxIell3HcliTvm4MCm4GBUgwaBpSXOCgWLfHzwubJSVRCuRa/FDrm5udja2uLq6qo9V1JSQl1d\nHb6+vgZrnKmkpqezMzmZRsASmBgZSVgnh5ANcc32kMvleHp6Gu1+Xa2rh+W6Kh4NDQ1YWVl1QYuu\nplarAU1bDamkpIS1a9de1Tu5aNEiJk2axJtvvomDmFzN2LFjTd2EW05Pi3lmWSbrTq+jUd0IQOiF\nUmamW2NnZcd+Z2d29u4NAweCQoG7pSULvL1xVhivbn9Pi7e566p46/UbYvr06eTk5Oicy8nJYcaM\nGV3SCHOSmp5O3PHjXO7fn7L+/bncvz9xx4+T2mrOoCmvuX//fkaPHo2TkxNOTk4MHjyY3377DYBL\nly7x8MMP4+3tja2tLeHh4dodGdoOJTYfr1u3jgkTJmBnZ0dISAjffvut9l5jx47l8ccf17m/JEmE\nhITw+uuvX9W2N954g5CQEGxsbPD09GTy5MnU1dURFxfHsmXLyMrKQi6XI5fLtT0969evZ/jw4bi4\nuODh4cHUqVN1Nojv3bs3AOPGjUMul2vnZObk5DBr1iw8PDywtbUlJCSEf/7zn3rH8Xrx2LhxI1On\nTsXe3p6QkJCrdoGQy+V88MEHzJs3DxcXFxYuXAho5pGOHj0aOzs7/P39WbRoESUlJTpxe+WVV/Dw\n8MDJyYkHH3yQ999/H0tLS+1zVqxYQZ8+ffjuu+8IDw/H2tqa8+fPU1VVxdNPP42/vz/29vZERUWx\nZcsWvWKvT6w2btyIl5cXQ4YM0bnmyJEjsbe3v+pegiC0X0Zphm4Sl1bCzHRrbC3t2KFU6iRxvtbW\nLPLxMWoSJ3Rfer1Lzp07x8CBA3XODRgwgDNnzhikUaa0MzkZ66FDSSgrazkZEsLpvXsZ1sGaPYf3\n7qVm0CBodc2xQ4eyKympXb1yTU1N3HvvvSxatIjVq1cDmn1D7e3tqa2tZcyYMdjb27N+/XpCQkK4\ncOECRUVFN7zmiy++yD//+U8++eQTVq9ezfz58wkLC2Pw4ME88cQTPPbYY7z77rvY29sDsHv3brKz\ns1m8eLHOdTZv3sxbb73F+vXrGTRoEMXFxezZsweAOXPmkJqayrp16zh69CiA9noNDQ0sW7aMfv36\nUVFRwbJly7jnnntITk7G0tKS48ePExUVxebNmxk1ahQWVzaEfvLJJ6mrq2PXrl24uLiQnp5OYWGh\n3rG8niVLlvDWW2/xr3/9iy+++IJHHnmEUaNG6cwHXblyJatWreL1119HrVaze/du/vSnP/H222+z\nevVqSktLefHFF5k5c6Z2DsR7773HBx98wCeffMKIESP48ccfWbVq1VV1oPLy8vj4449Zs2YNSqUS\nb29vpk2bhkwm47vvvsPX15cdO3YwZ84cfvnlF8aPH3/D2F8vVgUFBdrH9+zZw/Dhw6+KhUwmY/jw\n4ezevZsFCxZ0OrbdXUJCguixMLKeEvOM0gzWJ67XJnF9zpcwI9MGG0s7/uvmxjE/PxgwABQKAm1s\nmOvlhbWBe+KvpafEu7voqnjrlch5enpy/vx5nV9mFy5cwN3dvdMNaK+KigomTpzImTNn+OOPP+jX\nr1+XXr/xOudVnSi8qL7O9za08zqVlZWUlZUxbdo0QkJCALT//eKLL8jMzOTChQva4e6AgICbXvOR\nRx5h7ty5ALz66qvs3r2bd999l9WrVzNjxgz++te/8s0332gTt88//5ypU6detXo5KysLb29vJk2a\nhEKhwN/fn0GDBmkft7e3x8LC4qrhzNjYWJ3jr776Cnd3d44ePcrIkSO17zFXV1ed783OzmbGjBna\nPzCae+466y9/+Qv33XefNh4ffPAB8fHxOu/9GTNm8OSTT2qPFy9ezNNPP81TTz2lPRcXF0dgYCCn\nT59m4MCBvPPOOzz77LPMnz8fgGeeeYbDhw+zadMmnfvX1dWxZs0a/P39Ac0P+qFDhygsLNSW/3n0\n0Uc5ePAgH3zwAePHj79p7G8Wq3PnzjFu3LhrxiMgIIBjx461L4iCIGill6azIXGDNonre66YP2XZ\nYm1px/ceHiT7+GiTuDA7O+7z8MDSBEmc0H3p9W5ZtGgRs2bNYtu2baSkpPDjjz8ya9asq3pljMHO\nzo6ff/6Z++67zyDlDCyvc96iE/eSX+d72zuzSqlU8sgjjzBp0iTuvvtu3nrrLc6dOwfAsWPHiIyM\nbPecxZEjR+ocjx49muTkZACsra2JjY3ls88+A6C4uJgffviBRx999KrrzJ49m8bGRgICAnj44YdZ\nu3atznZu13Py5ElmzJhBcHAwTk5O2uQzKyvrht/3t7/9jTfeeIMRI0awZMkS9u3bp9frvZnBgwdr\n/908j+7SpUs6z2m7QOLIkSO89957ODo6ar8iIyORyWScP3+e8vJy8vPzGTFihM73tT0G8PLy0iZx\nzdduaGjAz89P5/rr1q0jLS0NuHnsbxariooKHB0drxkPJycnylr3Tt/CRE+F8XX3mKeXpuv0xIWn\napI4hZU9G7y8NEncleHUgQ4OPODpadIkrrvHu7vpqnjr1SO3ZMkSLC0tef7558nJyaFXr1488sgj\nPPvss13SiPZQKBQG7QmcGBlJ3LFjjB06VHuu/tgxYmNiCOvAqkuAVEki7vhxrNtcc0JUVLuv9emn\nn/L000/z22+/sWPHDpYuXcqHH37YZXW62l7j8ccf55133iExMZFdu3bh6enJlClTrvo+X19fzp49\nS3x8PLt37+bVV1/lpZde4o8//tBJTFqrqanhrrvuIiYmhri4OLy8vJAkicjISBoabtxfGRsby+TJ\nk9m+fTvx8fFMmTKFGTNmsGbNmo6/eLhq4YJMJtMuOmjWPCzcTJIklixZcs3hRy8vL5qamrTXupm2\n11ar1Tg7O2uHpK/V1pvF/maxcnFxobKy8prtKS8vR6lU3rTdgiDoulBygQ1JG2hSa37+w88WMf2i\nPVjbs8bTk4teXpqeOAsLhjs5MdnVVeybKnSIXqm/XC7nhRdeIDU1lerqas6ePcvzzz9v8NV0phAW\nHExsVBSeSUm4JCXhmZREbFRUp1aYdvU1IyMjeeaZZ/j5559ZvHgxn376KUOHDiUlJUVbB0xfBw8e\n1Dn+/fffiYyM1B6HhIQwfvx4PvvsM7744gsWLVp03Q8bKysrJk2axFtvvUViYiI1NTVs3bpV+1jb\ncjVnzpyhqKiI119/nZiYGMLCwigpKdFJJpuTlWuVuvH29iY2Npavv/6azz//nHXr1unVC9jVoqOj\nSUpKIjg4+Kove3t7nJ2d8fX1vWpV6KFDh2567WHDhlFWVkZtbe1V126dIN8o9nDjWPXp04fMzMxr\n3j8rK4u+fft2ICo9j6ixZXzdNeZpJWk6SVy/FE0Sp7JxIM7bWyeJG+viYjZJXHeNd3dl1L1WQTMp\nPTU1laKiIp1ftOPHj+/QjT/88EPi4uJISkpi7ty52tWVoCmHsHjxYnbs2IG7uztvvvmmdh5Xa4Z6\n44cFB3d5aZCuuOaFCxf49NNPuffee/H39ycvL4+9e/cSHR3N3Llzefvtt7n33nt5++23CQ4OJj09\nneLiYh544IHrXvPLL78kPDycoUOHsnbtWg4dOsRHH32k85zHH3+c+fPno1areeSRRwDYsmULL7/8\nMvHx8fj4+PDFF18gSRLDhg3DxcWFXbt2UVlZqZ3DGBQUREFBAYcOHSI0NBR7e3sCAgKwtrbmX//6\nF88++yyZmZksWbJE5/+ru7s7Dg4O/Prrr0RERGBtbY1SqeTPf/4z99xzD3379qWuro7NmzfTu3dv\nbZmMl19+mSNHjrBz585OxVyfXs5Vq1Zx11138dxzz7FgwQIcHR05f/48mzZt4sMPP8TGxobnnnuO\n5cuXEx4ezrBhw/jpp5/YsWPHTf8YGj9+PBMnTmTmzJm8/fbbDBgwgNLSUn7//XdsbW155JFHbhr7\nm8VqzJgx11yFLEkShw8f5u233+5A5ATh1pRWksY3Sd+0SuIuMy3XgTpbR9Z4eVHi4QH9+4OFBZNd\nXRnh7GziFgvdnqSHffv2Sd7e3pJSqZTkcrmkVColCwsLKSgoSJ9vv6bNmzdLP/zwg/Q///M/Umxs\nrM5jc+bMkebMmSNVV1dL+/fvl5ydnaXk5GSd58TGxkpJSUnXvf6NXpqeL9vs5OfnSzNnzpT8/f0l\na2trydfXV3rsscekiooKSZIkqaCgQHrooYckd3d3ycbGRoqIiJC+/vprSZIkKSMjQ5LL5dKBAwe0\nxzKZTFq7dq00duxYycbGRgoODpY2bNhw1X0bGxslT09PaerUqdpzX331lSSXy6WsrCxJkjT/P0eN\nGiUplUrJzs5OGjBggPTll1/qXGPevHmSq6urJJPJpJUrV0qSJEmbNm2S+vTpI9nY2EhRUVHSnj17\nJIVCoW23JEnS6tWrpaCgIEmhUGjfc0899ZTUt29fydbWVnJzc5OmTp0qpaSkaL8nNjZW5/0ZHx8v\nyeVyKTc397rxaH3cLDQ0VNtWSZIkmUwmrVu37qoY7du3T5o4caLk6Ogo2dvbSxEREdIzzzwjNTU1\nSZIkSWq1Wnr55Zcld3d3ycHBQZo7d670xhtvSI6OjtprrFixQurTp89V166trZWWLFkiBQUFSVZW\nVpK3t7c0ZcoUKT4+Xq/Y3yxWRUVFko2NjXTs2DGd++7fv1+yt7eXKisrr2pTe3XXnzlBaI9zReek\nVQmrpOXxy6Xlu5dJGz96Sqp55UWp8LXXpH9+9pm0/IcfpOVpadLKjAzpZBf8XAk9S0c/J6+712pr\n0dHRzJs3j2effRalUklpaSmrVq3C1taWF154oVOJ5NKlS8nJydH2yFVXV+Pq6kpycjKhoaEALFy4\nEF9fX958800A7r77bk6dOkVAQACPP/64tpZXa2Kv1RvLzMwkODiY/fv3M2rUqBs+t7i4mF69evHt\nt98ybdo0I7Ww51u0aBGJiYkcOXLE1E3hsccew8LCgo8//lh7bvHixdja2vLhhx92+vriZ07o6c4V\nn+PbpG9RSSqQJPonFzG1wJFiOyfWenlR6+4O/fujsLDgPg8PwtvMhxWEjn5O6jW0ev78ef72t78B\nLUNNS5YsITAwsNOJXNtGnzt3DoVCoU3iAAYNGqQzlvzzzz/rde3Y2FgCAwMBzYTuwYMHi1U57dDU\n1ERRURErVqzA399fJHGdkJ+fz+bNmxk3bhwWFhZs27aNNWvWXDWMbSorV66kf//+/P3vf8fPz4+M\njAy2bt3apbUiW9dMav557k7HJ0+e1H4OmkN7boXj5nPm0p7rHa/Zuob4jHh6D+4NkoRsy0mcSu3J\nj+zFBk9PzuXng40NfRUK5np6kvXHHxSYUfu7W7x7yvHJkycpKyu77hxlfenVI9e7d29OnTqFUqmk\nX79+bNy4EXd3d/r27Ut5eXmnGtC2R27fvn088MAD5Ofna5/z2WefsX79euLj4/W+ruiRu7HMzExC\nQkLYt2/fdXvkEhISGD9+PMHBwaxZs+aqUiWC/i5dusTs2bM5ffo0dXV19OnTh7/85S8mKeFjCj3h\nZy5BFEs1uu4Q89SiVL5L/k7bEzco8TJTLjmR6eTKRg8PVG5uEBmJnaUl87288DPjfVO7Q7x7krbx\nNmiP3IwZM/j555+ZP38+ixYtYvz48SgUCm3h1M5o22gHBwcqKip0zpWXl1+3zpXQMYGBgddcCdra\n2LFjryq9IXSMp6dnu/4QEcyP+AVnfOYe87NFZ9mYvLEliTt9iSmXnTnr4s5WNzckd3fo1w8nKysW\neHnhYWWYfZm7irnHu6fpqnjrlci9//772n8///zzDB8+nMrKSiZPntzpBrRdedq3b1+amppIS0vT\nDq+eOnWK/v37d/pegiAIgtAVzhad5bvk71BLapAkhpy6xKQiZ066erLd1RWuJHGuVlY85OWFi+X1\nys0LQue0qxBcdnY2Bw8eJCAggLvvvrtTdeRUKhV1dXU0NTWhUqmor69HpVJhb2/PzJkzWbZsGTU1\nNezfv59t27Z1aK/HFStW6Iz9C4IgdJT4LDE+c435mctndJO4k4VMKnLhoLu3ThLnbW3NIm/vbpPE\nmWu8e6rmeCckJLBixYoOX0evTCw/P58xY8YQGhrKzJkzCQ0NJSYmhry8vA7f+NVXX8XOzo633nqL\ntWvXYmtrq61l9e9//5va2lo8PT158MEH+eSTT4iIiGj3PVasWCG6igVBEIQuk3I5hY0pG7VJXNTJ\nQiYVK9nt6cMeFxfw8IB+/ehta0ustzcOCr3LtQq3qLFjx3YqkdNrscP06dMJCAjgzTffxN7enurq\nal555RUyMjL48ccfO3xzQxKLHQTBfIifOaEnSL6UzPdnvtcmcUOPFzCh1JXt3n6cdnDQJHEREYTa\n2THbxPumCt1PRz8n9Urk3NzcyM/P19mHsr6+Hl9fX4qLi9t9U2O4UUBcXV0pLS01cosE4dalVCop\nKSkxdTMEocOuSuKO5TOu3J0fffw5Z2cHnp4QHk5/BwdmeHhgYQZbbgndS0cTOb3+XHB1dSUlJUXn\n3NmzZ81+M+3rzZFr3s9TfHXtV3x8vMnbcCt9dad494QkTswfMj5ziXnSpSSdJG7Y0XxiKjzY6Ndb\nk8R5eUF4ONFOTszsxkmcucT7VtFVc+T0Grx/8cUXufPOO1m8eDEBAQFkZmby1Vdf8eqrr3b4xsbQ\nmcAIgiAIQmJhIpvPbEZCQqaWiD6Wx6gqb77x8yff2lqTxIWFcYdSyXgXF4PtAS70XGPHjmXs2LGs\nXLmyQ9+v19AqwO7du1m3bh35+fn4+voyd+5cJkyY0KGbGoOYkyMIgiB0RtskbtjRfIbVePOtXy+K\nLC3B2xv69uVONzdGOzuburlCN2ewOXJNTU2EhYWRkpKCtRlXpG5LJHKCIAhCR50uPM2WM1u0Sdxt\nh3MZ1ODHt769KFcowMcHWd++THN3J0oUrBe6gMHmyCkUCuRyObW1tR1qmHDrEPMrjEvE27hEvI3P\nVDE/VXBKJ4kbfjiXfo3+rPPrrU3iLMLCuN/Ts0clceI9blxdFW+95sg988wzzJ49m5dffplevXrp\nzAEIDg7ukoYIgiAIgqmdLDjJ1rNbNUmcSs2Iw/kEq3uz3s+ferkcfH2x7NuXOV5ehNjamrq5gqDf\nHLnr7eAgk8luul+nqchkMpYvX66dRCgIgiAIN9I2iRv5Rx6+BPKDjx9NMhn4+WHTty/zvbzoZWNj\n6uZ2mdTULHbuvEBjoxxLSzUTJ4YQFhZg6mbdMhISEkhISGDlypWGmSPXXYk5coIgCIK+TuSf4MfU\nH7VJ3KhDubhZhPJfL2/UV5I4h7AwFnh749Wqpmp3l5qaRVxcGjU1E5DLwdkZ6ut3ERsbKpI5IzNo\nHblmubm5HDlyhNzc3HbfSOj5xPwK4xLxNi4Rb+MzVsyP5x/XJnFylZrRh3JxsOzLtuYkzt8fZXg4\ni3x8elQSB7Bz5wVqaiaQmAh79iRQVgbW1hPYteuCqZvW43XV+1uvRC47O5s77riDgIAA7rnnHgIC\nArjjjjvIysrqkkYIgiAIgikcyzumk8SNOpiL3CacXz29kGQy6NULzytJnKulpamb2+UKC+UkJoJa\nrfk6dw4kCRoaxPZi3YVe/6ceeughhg4dSnl5OZcuXaKsrIzo6GgWLlxo6PYJ3YiYi2hcIt7GJeJt\nfIaO+bG8Y2w7tw1Ak8T9nkO9fSR73Tw0T+jdG7+ICGJ9fHBU6LU2sFvJzoaTJ9Wo1ZpjT8+x9O8P\nMhlYWalN27hbQFe9v/WaI+fk5ERRUZHOXqsNDQ24ublRWVnZJQ3pamKxgyAIgnA9R/OO8t9z/wVA\n3qRi1MFcyp0GkujsonlCQADB4eHM8fLC6joL/rqzixdhzRrIy8vi5Mk07OwmMHgw2NmJOXLG1tnF\nDnq9O0eMGMHhw4d1zh05coSRI0e2+4bGtGLFCpHEGZGYQ2RcIt7GJeJtfIaK+ZHcI7pJ3O95FLoM\n1kniIvr1Y14PTeJyc2HtWmhoAHf3AEaODGXChN3U1Pw/PD13iyTOSJrf32PHjjX8XqvBwcHcfffd\nTJ06FX9/fy5evMjPP//MvHnzWLp0KaDpAVu1alWHGyIIgiAIhnY49zA/n/8Z0CRxI37P46J7FFl2\n9ponBAYypH9/prm5Ie+B+6bm5Wl64urrNcd2dvDkkwF4egaQkCAXnR/dkF5Dq7GxsS3f0Gp5bHNh\nYEmSkMlkfPXVV4ZpZQeI8iOCIAhCa62TOItGFcMO5pPpGUWBjZ3mCYGBjBw4kLuUSp3C9z1Ffj6s\nXg3NGzXZ2cHCheDlZdp2CRoG22u1uxKJnCAIgtDsj5w/+CXtF0CTxEUfKiDNcyjF1lcK+wYFMX7Q\nIO5wdu6RSVxBAXz9dUsSZ2urSeK8vU3bLqGFwevInTt3jtdee42nnnqK119/nXPnzrX7ZkLPmrOD\nTwAAIABJREFUJuYQGZeIt3GJeBtfV8X8UM6hliSuoYkhhy6R4hWtTeJkwcHcM2QIMS4uPTKJu3RJ\ntyfOxgYeeujqJE68x43LqHXk1q9fT1RUFImJidjb23P69GmioqJYt25dlzRCEARBEAzh4MWDbE/b\nDmiSuIF/FJHiPZRKK2sA5CEhzIyKYpiTkymbaTCXL2t64mpqNMfNSZyPj2nbJXQdvYZWg4KC+Prr\nr4mJidGe27dvHwsWLCAzM9OQ7eswMbQqCIJwa/v94u/8duE3ABQNTUQcKSHVewgNFprCvoqQEB4Y\nOpS+dnambKbBFBVBXBxUVWmOra1hwQLw9zdps4Tr6Gjeoteq1aqqqqtKjYwYMYLq6up239CYmsuP\niFU4giAIt5a2SVzo0TJSvKNQWWh+7VmHhjIvOpoAGxtTNtNgios1PXHNSZyVFTz4oEjizFFzHbmO\n0mto9dlnn+Xll1+m9soAe01NDa+88grPPPNMh29sDKKOnHGJ+RXGJeJtXCLextfRmB/IPtCSxNU3\nEnCsklTvIdokzr5PH2KHDeuxSVxJiSaJa67X35zE9ep14+8T73HjMmoduY8++ojCwkLef/99lEol\npaWlAHh7e/Pxxx8Dmi7B7OzsDjdEEARBEDprf/Z+dqbvBDRJnN/JWtK8BiG7UtjXuW9fHho2DLce\nuG8qQGmpJomrqNAcW1rCvHnQu7dp2yUYjl5z5PTN0s2p90vMkRMEQbi17Mvax66MXQBY1jXifrqB\nXI8I5DJNEuceFsaCYcNw7oH7pgKUlWnmxJWVaY4VCpg/H4KCTNosQU+ijlwbIpETBEG4dezN2svu\njN0AKGobcEpWU+TWV5vE+YSH8+CwYdhbWJiymQZTXq5J4q4MmKFQwNy5EBJi0mYJ7WDQxQ4AJ06c\nYN++fRQXF+vcSGzLJTRLSEgwq17Znk7E27hEvI1P35jvydxDfGY8ABa1DdickekkcQEREcwbNgzr\nHrhvKmiGUb/+uiWJs7CAOXPan8SJ97hxdVW89XpXf/rpp9x+++3Ex8fzj3/8g8TERN555x3S0tI6\n3QBBEARB6KiEzARtEqeobcAiVUGFMkSbxPWNjOTBHpzEVVZqkriSEs2xhQXMng2hoaZtl2A8eg2t\nhoSE8NVXXxETE6Nd7PDLL7+wYcMGVq9ebYx2tpsYWhUEQejZEjITSMhMAMCithHVeSvUjr20SdzA\n/v2ZPnQoFj1wtwbQlBaJi9PUiwOQyzVJXFiYSZsldJBB58g5OTlRcWUJjJubG5cuXUIul+Pq6qpd\nwWpuZDIZy5cvF3XkBEEQehhJkkjITGBP1h4AZDWNNKbbILf30yRxMhm3DRjAlCFDeuSWWwDV1Zok\n7vJlzbFcDvffDxERJm2W0AHNdeRWrlxpuL1W/f39ycjIAKBPnz5s3bqVffv2YW1t3e4bGpOoI2dc\nogaRcYl4G5eIt/FdK+aSJBGfGa9N4qhuojbDDgt7f20SN2bgwB6fxH39tW4SN2tW55M48R43LqPW\nkXvhhRc4c+YMQUFBLF++nFmzZtHQ0MC//vWvDt9YEARBENpDkiR2Z+xmX/Y+ANRVTdRkO2Bv56VJ\n2mQyJg8ezIhBg0zcUsOpqYHVq+HSJc2xTAYzZ0JkpGnbJZhOh8qP1NfX09DQgKOjoyHa1CXEHDlB\nEISeQ5IkdmXsYn/2fgAaq1TUXHTAycYTmUyGDJgeFcXggQNN21ADqq3V9MQVFGiOZTKYMQN68Eu+\npYg6cm2IRE4QBKFnaJvE1VdLVF10wNXaHZlMhgVw/7BhhPfgbqm6Ok1PXF6e5lgmg+nTYfBg07ZL\n6DodzVt65npswSTE/ArjEvE2LhFv40tISECSJHam79QmcTVVUJnjqE3irID5t93W45O4NWtakjiA\ne+/t+iROvMeNq6vi3TP3KREEQRC6PUmS2JG+g98v/g5AZZWc2lx7PKxckclk2EoSD44ciV94uIlb\najj19bB2LeTmtpybNg2GDDFdmwTzIoZWBUEQBLOSmpbKjqM7SLycyMXyiwQHB2Ph5E1Dri2eV5I4\nR0nioVGj8OjBRdMaGjRJXHZ2y7l77oFhw0zXJsFwDDq06urqes3znp6e7b6hIAiCIFxPaloqcfFx\nHLI6RKpjKjX+NRy7UEh5uoU2iXNVqVh8++09Polbt043iZsyRSRxwtX0SuQaGxuveU6lUnV5g4Tu\nS8yvMC4Rb+MS8TaO347+RpZrFjkVOZSeLaNR7Y+XfTCKOhUymQwvlYpFY8bg0qePqZtqMI2NsGED\nZGW1nJs0CYYPN+x9xXvcuIwyR+6OO+4AoLa2VvvvZjk5OYwcObJLGiEIgiAIdU11/JH7B+fJorTE\nhspcN7zqrPFwagAH6NXYyLzx47Ft727w3UhzEnelBj8Ad90F4tetcD03nCMXFxcHwBNPPMF//vMf\n7ditTCbDy8uLCRMmYGlpaZSGtpfYoksQBKH7KKsrY33ier75bjPpKhtkETFYyhxQyCyQJ59molri\n3adfwCooyNRNNZimJvjmG0hLazk3cSLcfrvp2iQYXme36NJrscPZs2cJ72argsRiB0EQhO4hpyKH\nDYkbqG6sZtu2VIqCo/G0skWODJlMRmNZOXeUV/D/Xn3d1E01mKYm+PZbOH++5dz48RATY7o2CcZl\n0MUOx48fJyUlBYDU1FRiYmIYN24cZ8+ebfcNhZ5LzK8wLhFv4xLxNoykS0nEnYyjpLGOs5Ib5ZIS\nP2snFJIFlWfP41VWwQSFFQ2NPXdOtkoFGzfqJnFjxxo/iRPvcePqqnjrlcj9/e9/x83NDYDnnnuO\n2267jZiYGJ588skuaYQgCIJwa5Ekib1Ze/ku5XvS1XYcbfKG7AZcatVYKSyxsbSid30Dgx2dsXNR\nYtXQYOomG0RzEpea2nIuJgbGjDFdm4TuRa+hVScnJyoqKqitrcXX15eCggIsLS1xc3OjtLTUGO1s\nNzG0KgiCYJ6a1E38eHYb8ZfOkYYr6moVvhfL8bV0JTn1PFJpKeGhodgqlWBlRf3x44R5ehL797+b\nuuldSqWC77+HKwNegGY+3IQJmi24hFtLR/MWvXZ28PDw4Pz58yQmJjJs2DCsra2prq4WiZIgCILQ\nLjWNNXx+eiMJlTWUSJ44F5bjXVSPp5073k0qJslkpBcUUCOX02BpiVVTE3YKBePuv9/UTe9SajVs\n2aKbxI0aJZI4of30GlpdunQp0dHRLF68mOeffx6AnTt3Mljs1iu0IuZXGJeIt3GJeHdeTtUlnjvy\nDVsq1ZQ3WOJ1oZBeJU0E2rpzT1k5T5SUEBMby7h//pPwQYMACB84kHF/+QsBPaj4b3MSl5TUcm7E\nCLjzTtMmceI9blxG3Ws1NjaW+++/H5lMhp2dHQAjR45kuKGrEwqCIAjdniRJ/JR/jn+n/UGNWo5t\neQ3u2UV4WDoxtknOhKJc7P39YdYscHYmAAgIC0OekNDjykep1bB1KyQmtpy77TZNwV/REyd0hN57\nrRYXF/PTTz9RUFDAiy++SG5uLpIk4e/vb+g2doiYIycIgmB6F+vq+E/GKQ4UngW1GmV+Kc6XKxks\ns+OB6kZ8GhtbZvfL9Rok6rYkCX78EU6caDk3bBjcfbdI4oSO5y16JXJ79uxh1qxZREdHc+DAASor\nK0lISOCdd95h27ZtHWqwoYlEThAEwXQqmprYUVLC1pwksiuyUdQ14pF1GY/KGubXK7itEWROTjBz\nJgQGmrq5BidJsG0bHD/ecm7oUJg6VSRxgoZB68g9/fTTfPPNN2zfvh2FQjMaO2LECP74449231Do\nucT8CuMS8TYuEW/9NKnV7Csr4/2L2XyTeYTs8iwcSqrodTaHmIvZvFauYngjyMLC4IknbpjE9ZSY\nSxL89JNuEjdkiPklcT0l3t2FUefIZWVlMXHiRJ1zlpaWqFQ9t0CjIAiCoD9JkkitqeHX0lIK6qpJ\nupREVW057heLiczM5K6SEoa7hKCwtNZsHnrbbeaVxRiIJMEvv8DRoy3nBg2CadNuiZcvGIFeQ6uj\nRo1i2bJlTJ48GaVSSWlpKb/99htvvPGG2WbwYmhVEATBOC43NLC9pIQLtbVUNVSTeCkRqaKCPuey\nuP1CCsPkDoQoQ5B5eMB994G3t6mbbBSSBL/+CocOtZwbMABmzOjx0wGFDjBoHbl3332XqVOncvfd\nd1NXV8djjz3Gtm3b2Lp1a7tvKAiCIPQMtSoVCWVlHKmsRC1JFNeWkHIpGWVBEeOPHSP8ch5hrqH4\nOflpxhKnTAErK1M32ygkCXbs0E3i+vcXSZzQ9fR6O40YMYJTp04RGRnJww8/THBwMEeOHOG2224z\ndPuEbsRce2d7KhFv4xLxbqGWJI5WVPBBbi5/VFSgliRyKnJJyT3BiBMneWTHLwwoKmCw1wD8PII1\nvXDTp7c7ieuuMZck2LULfv+95Vy/fpp1HeacxHXXeHdXRp0jV1ZWhp+fHy+99FKX3FQQBEHonrLq\n6viluJiCK3ufSkiklVxAyj7FY3sO4FVWirWFNQN9BmMf1FeTxCmVJm618UgSxMfD/v0t5yIiNCXy\nzDmJE7ovvebI2djYEBERwZgxYxgzZgwxMTG4ubkZo30dJpPJWL58OWPHju1xBSUFQRCMrfxKOZGk\n6mrtuSa1iqzLifQ7upsRRxORAY5WjgzwGoDVmPEwbhxYWJiu0SaQkKD5ahYWBg88cMuFQWiHhIQE\nEhISWLlypeHqyNXW1nLw4EH27NnD3r17OXz4MMHBwcTExPDRRx91qOGGJhY7CIIgdF6jWs3vFRXs\nLy+nUa3WnlepG6jPTGDE9p9xvVwOgIedB+GB0VjMug9CQkzVZJPZuxd272457tMHZs8GhV5jX8Kt\nzqAFgZtVV1dz4MABtm/fzueff46trS2FhYXtvqkxiETO+BJ64HY65kzE27hutXhLksSZmhp+Kymh\nrKlJ5zEfeT2q/XH0TTiColFThirAOYDA6InIZswAB4cuaUN3ivn+/bBzZ8txaCjMmdO9krjuFO+e\noG28Dbpq9cUXX2Tv3r3k5uYyatQoxowZw6FDh4iIiGj3DQVBEATzVnilnEhGba3OeW8rK0IbCylZ\n9w6e53IBkCGjr2c4Pn9aACNH3pLF0Q4c0E3igoNFT5xgPHr1yNnb2+Pj48PixYsZM2YMw4YNw9LS\n0hjt6zDRIycIgtA+tSoV8VfKibT+/LSzsGCciwuNZ3dTtu5T7MprAFDIFUSEjcbtwcfAz89UzTap\ngwc1teKaBQXBvHlg5r8iBTNk0KHVxsZGjhw5wr59+9i7dy8nTpwgMjKSmJgYli5d2qEGG5pI5ARB\nEPSjliSOVVayu6yM2lY79shlMoY5OnKHowPHtryP6tdfkKs1n6u2Clv6TZiD48w5YG1tqqab1B9/\naHZtaBYYqEnibpFSeUIXM8ocuZKSEvbs2cOuXbtYvXo1dXV1NFxZgm5uRCJnfGJ+hXGJeBtXT413\nZm0tv5SUUNjmszzY1pbJrq441lZy+N//i5R6VvuYo6M7kQ89j030cIMOpZpzzI8c0eyf2qx3b3jw\nwe6dxJlzvHsio86R++tf/0pCQgLnz58nOjqaMWPG8P333zNy5Mh231AQBEEwvbLGRn4rLSWlVTkR\nAKWlJZOUSsLs7Cg7e5Kjn6xCKi/VPu4SFMGAJ1di4eFp7CabjWPHdJO4Xr1g/vzuncQJ3ZdePXLN\n9dhGjBiBra2tMdrVaaJHThAE4WqNajX7y8s5UF5OU6vPSEu5nBhnZ0Y6OaEACn76lrQfvqRJ1ah9\njvv4aUTO/SuyW3gC2IkT0Hp3Sn9/WLDglh1dFrqQUYZWs7Ozyc3Nxc/Pj969e7f7ZsYkEjlBEIQW\nkiSRXF3NjtJSytuUExno4MBEpRInhQLKy8n+8j0yTu1BQvMZqrKxJiD2aUJH3G2KppuNkyc1SVzz\nrxZfX3joIbCxMW27hJ6ho3mLXhuG5OfnM2bMGEJDQ5k5cyahoaHExMSQl5fX7hsKPZfYp8+4RLyN\nqzvHu6C+nriCAjZdvqyTxPlYW7PIx4eZHh44KRRIKSmkvfE86acStElcrb83EUv/ZZIkzpxifvq0\nbhLn46PpietJSZw5xftW0FXx1iuRe+KJJxg0aBClpaXk5+dTWlrKkCFDeOKJJ7qkEYIgCELXq1Gp\n+G9REf/Jzyerrk573t7Cgnvd3XnUx4feNjbQ2EjTj1tJ+XA5OYXnAZBkMspGRjHylX/j6xdmqpdg\nFpKSYMuWliTO21uTxHWTmUZCD6fX0Kqbmxv5+flYtZrJWV9fj6+vL8XFxQZtYEeJoVVBEG5VKkni\naGUl8aWl1LXaVksukzHcyYkxzs7YNG/+eekSdd+sIyU5nor6CgDq7K2pnX4Pd49/HGvFrT35KzkZ\nvv8emsPo5QULF4KdnWnbJfQ8Bl216urqSkpKCoMHD9aeO3v2LEqlst03FARBEAwn/Uo5kcttyomE\nXikn4t78B7kkwfHjVP24iaTcE9Q1aXrsLgd44HjfPKb3n45cptegTY915oxuEufhoZkTJ5I4wZzo\n9VP64osvcuedd7JkyRI+/vhjXnrpJe68805eeOEFQ7dP6EbE/ArjEvE2LnOPd2ljI98UFrK6oEAn\niXO1tGSulxfzvbxakri6Oti0iZLvvuZE9mHqmupQW8g5NzKM3o88x+QBM8wiiTNlzM+ehY0bW5I4\nd3dNT5y9vcmaZHDm/h7vaboq3nr1yD366KOEhISwbt06Tp8+ja+vLxs2bGDChAld0ghBEAShYxrU\navaVl3OwTTkRqyvlREY4OaGQt0rKLl6E778nNzuZ8yWa+XDVLvacHz+Ie0Y/TB+3PsZ+CWbn3Dnd\nJM7NTZPEOTiYtl2CcC3tKj/SnYg5coIg9GSSJJF4pZxIZZtyIoOulBNxbL1ru1oNBw6g3r2b9OLz\n5FTkAJAX5svl26OYM2QBXg5exnwJZiktDTZsgOadylxdITYWnJxM2izhFtDlc+SWLl161UVlrbZi\nkSQJmUzGqlWr2n1TQRAEoePy6uv5paSEi61WogL4WVszxdUV/7Y1MSorYfNmmi6c58zlMxTXFtNk\npSB1VBhWAwazqP8cHK0djfgKzNOFC/DNNy1JnFKp6YkTSZxgzq6byF28eFEncWurOZEThGZinz7j\nEvE2LnOId7VKxa7SUk5UVen8ke1gYcFEpZJBDg5Xfy6fOwc//EBdRQmJhYlUN1ZT7ulMSkwEIUFR\nzAifgaWFee7UYMyYZ2RoeuKaOzddXDRJnLOzUW5vFszhPX4r6ap4XzeRi4uL6/TFBUEQhM5TSRKH\nKypIKCujvlU5EQuZjBFOTsS4uGAtb7M4oakJdu6EQ4eorK8k8VIi9eoGsgYFkDUokNGBdzAhaIL4\ngxzIzIT161uSOGdnTRLn4mLSZgmCXvSaI5eSkoKrqyve3t5UVlbyf//3f1hYWPDCCy9gZ4J12C+9\n9BIHDx4kMDCQL7/8EoXi6nxUzJETBKEnSKupYXtJCUWNjTrn+9rZMcnVFbdr7XtaXAybNkF+Pper\nL3Om6Ay1tgrOxPSjwseVaX2nMcRniJFegXnLyoJ166B5oa+Tk2ZOnKurSZsl3IIMukXXnDlzKC8v\nB+D5559n3759HDp0iMcff7zdN+ysU6dOkZeXx969ewkPD2fTpk1Gb4MgCIKhlTQ2sr6wkLWFhTpJ\nnJulJfO9vJjn5XXtJO7UKfjPf5Dy8sguzyb5cjKX/JUcvXcY9f4+LBi4QCRxV1y8qJvEOTpqeuJE\nEid0J3olcllZWYSFhaFWq9m8eTPfffcdmzZtYvv27YZu31UOHjzIpEmTAJg8eTIHDhwwehuEaxM1\niIxLxNu4jBXverWaHSUlfJSby7maGu15a7mcu1xdedLPjz7XGgmpr4fNm2HLFtT1dZwrPkdaeQbn\nbwslaXx/HJVeLI5aTJAyyCivoysYMuY5ObB2bUsS5+CgSeLc3Ax2S7MnPlOMy6h15GxsbKioqODM\nmTMEBATg4eFBY2MjdW1WTBlDaWkpPj4+ADg5OVFSUmL0NgiCIHQ1SZI4VVXFztJSqpqXTaIZbhns\n4MAEFxccrjGNBIC8PM1QakkJjapGki8nk2fdQMrUoVS5OhDgHMDs/rOxsxRbEgDk5sKaNZrcFzRF\nfhcu1BT9FYTuRq8euXnz5jF+/HgeeughFi5cCMDx48cJDg7u8I0//PBDoqOjsbGx4eGHH9Z5rKSk\nhBkzZuDg4EBgYCAbNmzQPubi4kJFhWY/wPLyclxFH7jZEKudjEvE27gMGe/c+nq+yM/nh6IinSSu\nl40Nj/r4MN3d/dpJnCTB77/DF19ASQm1jbWcKDjBGX8bjl1J4gZ5DWLBoAXdMokzRMzz83WTODs7\nTRLn4dHlt+p2xGeKcXVVvPXqkXvvvff49ddfsbKyYty4cQBYWFjw3nvvdfjGfn5+LF26lF9//ZXa\n2lqdx5566ilsbGy4dOkSJ06c4J577mHQoEH069ePUaNG8e6777JgwQJ+/fVXbr/99g63QRAEwZSq\nmprYWVrKyaoqnfOOCgV3KpUMsLe//qrS6mrYskVTwRYoqyvjVFkqySMDuRSsKew7Pmg8d/S+Q6xM\nvaKgAFav1uxQBi1JnKenadslCJ1h8p0dli5dSk5ODl999RUA1dXVuLq6kpycTGhoKAALFy7E19eX\nN998E9Ds/Xro0CECAgL46quvxKpVMyFqEBmXiLdxdWW8VZLEoYoK9l6jnMgoZ2fucHbGqm05kdbS\n0zXz4a4kgIVVhRyR5ZF8Rzi1TrYo5ApmhM8g0jOyS9prKl0Z88JC+PpraJ52aGurSeK8vbvk8j2C\n+Ewxrrbx7vKdHYylbaPPnTuHQqHQJnEAgwYN0pkU+Pbbb+t17djYWAIDAwHNkOzgwYO1QWu+njju\nuuOTJ0+aVXt6+rGId/eM97maGj786ScqmpoIHDECgMxDh+htbc1fp03D1dLy+t9/xx0QH0/C2rUA\njAkIILMsk08b8sgP9SHQyRZ7S3sCywK5nHIZrvQ0mUP8OnLcrLPX27IlgV9/BW9vzXFeXgKTJrUc\nm8vrNfVxM3NpT08/PnnyJAkJCWRmZtIZZtcjt2/fPh544AHy8/O1z/nss89Yv3498fHxel9X9MgJ\ngmBOihoa+LW0lPOtVqICeFhZMdnVlRBb2xtfoLQUvv9es9wSUEtqkqoziB+ipNRPM1fY096TeQPm\n4WIjKtk2u3wZ4uI0I9EA1tbw0EPg52fSZgnCVXpMj5yDg4N2MUOz8vJyHB3FPoCCIHQ/dSoVe8vL\nOVRRgbrV552NXM5YFxeGOTlhcbM5bElJsG2bdoZ+g6qBg1aXODChFw22VgCEuoZyX7/7sFHY3OhK\nt5SiIs1wauskbsECkcQJPYtc3yc2NDSwd+9evv32WwCqqqqoajNBtyPaTsLt27cvTU1NpF2ZwAua\nIsD9+/fv9L0Ew2rbPS8Yloi3cbU33pIkcaKykg9yc/m9vFybxMlkMoY6OvIXf39GODvfOIlraIAf\nf9SUFrmSxFU11bLFv4r42/20Sdww32HMGzCvxyVxnXmPFxdrkrjmX1NWVvDgg+Dv3zVt64nEZ4px\ndVW89eqRS0xM5N5778Xa2pqcnBxmz57Nnj17WL16tTaxay+VSkVjYyNNTU2oVCrq6+tRKBTY29sz\nc+ZMli1bxueff87x48fZtm0bBw8ebPc9VqxYwdixY7Xj0oIgCMZwsa6OX0pKyGuucXFFbxsbpri6\n4mNtffOLFBRoEriiIu2pImsVGwbKKL5SdkmGjMmhk7nN7zaxMrWVkhJNEldZqTm2tIT586FXL9O2\nSxCuJSEhoVNJnV5z5EaPHs3jjz/OQw89hFKppLS0lOrqavr06UNeXl6HbrxixQpWrVp11blly5ZR\nWlrKokWL2LFjB+7u7vzjH/9gzpw57bq+mCMnCIKxVTY1saO0lNNtRiucFAruUiqJvFE5kWaSBEeO\nwG+/teziDqT52fFtYBWNlpqBFCsLK+7rdx993fp2+evozkpLNXPiruwqqU3irqx7EwSz1dG8Ra9E\nTqlUUlJSgkwm0yZykiTh6upKaWlphxpsaCKREwTBWJrUag5WVLCvvJyGVuVEFDIZo52dGX2zciLN\namo0Q6lnz2pPqRUKDg105TfHQriSBDpZOzFvwDy8HUTtjNbKyjRJXFmZ5lihgHnzoBO16wXBaDqa\nt+g1Ry4gIICjR4/qnDty5Ah9+vRp9w2FnkvMrzAuEW/jula8JUnibHU1H+Xlsau0VCeJ62dvz5/9\n/BinVOqXxGVlwSef6CRxTZ7ubInx4DenS9okztfRl0ejHr0lkrj2vMfLyzXDqa2TuLlzRRLXHuIz\nxbiMOkfutddeY+rUqTz++OM0NDTwxhtv8Mknn/DZZ591SSMEQRC6m8sNDWwvKeFCm51pPK2smOLq\nStDNyok0U6th717Ys0czrHpF9dCBrPXIJ7+upRRThHsEMyJmYGVh1SWvoaeoqNAkcc0DRBYWMHs2\nhISYtl2CYAx615E7ceIEn376KVlZWfTu3ZtHH32UoUOHGrp9HSaTyVi+fLlY7CAIQpeqU6lIKCvj\ncGWlTjkRWwsLxrm4EO3oiFzfhQfl5ZodGrKyWs7Z2VE4cSRr6w5T2VCpPT2612gmBk8UixraqKzU\nDKcWF2uOm5O4vmLqoNBNNC92WLlypeHmyHVHYo6cIAhdITU9nZ3JydQDhfX1qLy9cWhViEwmkxHt\n6Mg4FxfsLCz0v/CZM5r5cK179AIDOTemPxsv/kqjuhEAuUzO1L5TifKJ6qJX1HNUVWmSuOaFvXI5\nPPAAhIebtFmC0CEGLQhcX19PXFwcJ0+epKqqSnszmUzG6tWr231ToWdKSEgQvZ9GJOJteElpaXx6\n7BiNgwdzcv9+rKOiaDp6lMFqNe69ehFoY8MUNze8rNox1NnYqFmReuRIyzm5HGnMGA6+4KiHAAAg\nAElEQVQGWrAj4yckNB/mNgobZkfOJkgZ1MWvrHu40Xu8uloznNo6ibv/fpHEdYb4TDGuroq3Xonc\nwoULOX36NNOmTcPLy0t7XnTxC4LQ3UmSRI1aTVFj41Vfvx04QPXAgVBTQ61ajTWgiI4mPzGRJ4cO\nJcLOrn2fg5cva2rDFRa2nHN2RjXjT/xUn8jxjOPa0662rswbMA93O/eue7E9RE0NrF6tCSdokrhZ\nsyAiwrTtEgRT0Gto1cXFhYyMDJRKpTHa1CXE0KogCK2pJYmypiaKGhu53CZhq1Wprvk9h/bupW7g\nQO2xXCajt7U1A86f57l779X/5pIEx4/D9u2aHrlm/fpRO3kiGzP+S3ppuvZ0b+fezOk/BztLu3a/\nzp6utlbTE1dQoDmWyWDmTBgwwLTtEoTOMujQakBAAPVtKpR3B2JnB0G49TRcp3etuLERVTs/JC0A\nW7kcOwsLHCws8LGywkYux7Y9vXB1dZp9UpOTW84pFDB5MiX9gliftJ6impbdGwZ6DeTesHtRyE2+\nFbbZqa3V9MS1TuJmzBBJnNC9GWxnh127dmmHDE6cOMHGjRv561//ire3bu2i8ePHd/jmhiR65IxP\nzK8wrls53pIkUaVSXbN3raLVbgj6spLLcbe0vOrrcnY2a0+exHroUDIPHSJwxAjqjx0jNiqKMH0K\nlF28CN9/31LcDMDTE+67j2zrOr5J+oaaxhrtQ+MCxxETECOmrVzR+j1eV6dJ4po3E5LJYPp0GDzY\ndO3raW7lzxRTaBvvLu+RW7x4sc6HiSRJ/O///u9Vz8vIyGj3TQVBEPShkiRKrtG7VtTYSH2r4rv6\nclQo8LhGwuZoYXHN5MkzNJRYuZxdSUkUZWTg6eDABH2SOEmC/fshPl5TJ65ZdDRMmsTpkjNsPbkV\nlaQZ0lXIFfwp/E/09+zf7td0K6ivh7VrW5I4gGnTRBInCCDKjwiCYAZqVSqKr9G7VtrUpFOrTR8W\nMhmu10jW3C0tsdZnh4XOqqyELVsgvWXOGzY2cO+9SBER7MnaQ0JmgvYhe0t75vSfQy9nsaP7tTQn\ncRcvtpybNg3MuIypIHSIQefITZ8+na1bt151fubMmWzevLndNxUE4dYjSRLlVxYbtP2qus5igxux\nkcvxsLK6KllzUSiwMNXQ5PnzmiSupmW4lF69YNYsmpwc2HpmM4mXErUPedh5MG/APJS23WchmTE1\nNMC6dbpJ3D33iCROEFrTq0fO0dGRysrKq84rlUpKm/dEMTOiR874xPwK4zLXeDeq1ZQ0NXG5oUF3\nsUFTE40dGA51USiu2btmf53hUEO5YbybmmDXLjh4sOWcTAZ33AFjx1LdVMs3Sd9wsaIlIwlRhnB/\n5P3YKGwM2/BuKDU1i+3bL/Djj6exth5IcHAI7u4BTJkCw4ebunU9l7l+pvRUBp8jB7B06VIAGhoa\nWLZsmc4N0tPTCQwMbPcNjUmsWhUEw7hR7bWypqZ2fxgpZDLcLC2vmr/mZmmJpTGGQzujuFhTGy6/\nZU9UHB01NTGCgrhcfZn1iesprWv5ozfaN5opoVOwkLdjJ4hbRGpqFl9+mUZq6gSKi+W4uIzl5Mld\n/PnPMHx4gKmbJwhdzmCrVgFiY2MBWL9+PfPnz2/5JpkMLy8vFi9eTGhoaIdvbkiiR04QOq8jtddu\nxN7C4pq9a84Khf77k5qTU6fgp580Y4DN+vaFP/0J7OxIL03nu+TvqGuqA0CGjLtC7mKE/wixMvU6\n3n9/NwkJ42k92BMSAkOH7ubJJ82zSoIgdAWD9MjFxcUBMGrUKB577LEONUwQBPPXlbXXZDIZymus\nDnWztGzfXqTmrL5ek8CdPt1yzsIC7roLbrsNZDKO5R3jp/M/oZY0w8lWFlbMiphFmHuYiRpt/urr\n4eBBuU4SFxysmWbY0GDmPbOCYCJ6LXYQSZygDzG/wrjaG29j1V5zVShQmPtwaAdo452XpxlKLSlp\nedDNDe67D3x8UEtqdl7Ywe8Xf9c+7GTtxNz+c/Fx9DF+w7uJ2lrN6tSKipZ5lDY2CfTuPRYAK6v2\nz68U2kd8hhuXUfdaFQSh+zB17bWeJis1lQs7d3I6JQX1tm2ENDYS4Ora8oTBg+Huu8HKigZVA5vP\nbOZs0Vntwz4OPswdMBcnaycTtL57qK6GNWs0OzYEB4dw8uQuwsIm0Dx6X1+/iwkTzHMajyCYmqgj\nJwjdTGp6OjuTk6lRq6lXqYjs0wcHP7/uXXvNTGWlppIWF8cEmQzOnoWSEnY1NRE6eDABfn4wdap2\nf6iK+go2JG4gv6pl0UO4ezgzI2ZiZWFlqpdg9iorNTs2XL7ccq5//ywuXbpAQ4McKys1EyaEEBYm\nFjoIPVtH8xaRyAlCK5IkoZIk1Gh6tlSShKr1v2/ymArNAgFDPZafnc3hs2eRDx2qnbvWdPQog8PC\ncO9184KyZll7zRypVJCTw+5332V8ZiZUVGh2a7hit48P4z/6CK70zOVX5rM+cT2VDS1lmkb1GsXE\n4InIZbduInwz5eXw9dcto9Ri2y3hVmbQgsAA8fHxrF69mtzcXPz9/XnwwQfNdp/VZqL8iHE09xCd\nSUwkYsAAJkZGEhYcjNQ26elEgmSsx9rbk2VsiefPI0VFoZIkyo4exSU6GkV0NBmnTukkcuZSe63b\nkCS4dEmzG0N6OmRlQUMD8tRUzSafQEJZGWNdXKBXL+SDB2uTuNSiVDalbKJR3QiAXCbnnj73MNRX\nVK29kdJSTRLXvA2tXK6p2NK/1S5lYs6WcYl4G1dzvDtbfkSvRO7zzz/nlVde4ZFHHmH48OFkZ2cz\nb948Vq1aZdYLIVasWGHqJvR4qenp/DkhAfnQoRQVFnK2Vy++i49nUG4urv7+pm5ej6NulYTJ0JTz\nsJfL8bSz4z4Pj+5Te80clJe3JG4ZGVBVddVT1K3jaGsLAweCqytqW1skSeJQziF+u/AbEpo/AGwU\nNjwQ+QDBypvsxXqLKyrSJHHNdeYtLOD++yE83LTtEgRTaO5wWrlyZYe+X6+h1T59+rBp0yYGDRqk\nPXf69GlmzpxJWlpah25saGJo1Tg+2raN7/39aTuF3v7UKYaNGWOSNnWWhUzW8gXIW/3b1I998fPP\nFA8YgPzKY81pnWdSEk9Om2b0WHUrdXWahK05eSsuvvHzXVzIsrYm7fhxJnh4gJVmntuu+nqCHlpA\nikU6R/OOap+utFEyb8A8POw9DPkqur3CQs2cuOpqzbFCAXPmgJmWJBUEozHo0GpJSQkRERE658LC\nwsx2ey7BeBrRvPlo8+ZTXek5ulZSIufqBKX1sc7jRn5M3vx6zNTUAQOIO34cRavNJuuPHWNCVJQJ\nW2Wmmpo0m3Q2J255eVe9T3XY2mqKljV/KZUEAKSmsnvXLuQNDaitrPAfczsHGo5xofSC9lt7OfVi\nTv852FvZG/xldWd5eZrVqbW1mmMrK5g7F4KCTNsuQejO9ErkRo8ezbPPPstbb72Fvb09VVVVvPzy\ny4waNcrQ7RPMnCUQ7eiIDLh46BCBI0ciBzyVSv4cGGjWSVF3FBYcTCywKymJlMRE+g0YwISoKMKC\nxVAekqSpX9GcuGVnQ2Pj9Z+vUEBAQEvi5u2tmW3fRkBYGAFhYSQkJDBk+CDWJ67nck3LEssBngOY\nHj4dhVxUc7qRixc1deLq6zXH1tYwfz707n397xFztoxLxNu4jFpH7pNPPmHOnDk4Ozvj6upKSUkJ\no0aNYsOGDZ1ugNC9TYyMJO74cayHDsVSLsdSJqP+2DHuiooSSZyBhAUHExYcTIKjo/jQLS3VnedW\nU3P958pk4Ovbkrj16qVJ5m4iNS2Vncd2cvj4YSoOVuAX4Ie7rzsAYwPHMiZgjHiv30RmJqxf37KT\nma0tLFig+d8hCELntKv8yMWLF8nLy8PX15deepQ6MCUxR854UtPT2ZWcTANgBUy4smpVELpcTY3u\nPLebTe9wc2tJ3AIDNRlEO6SmpfJV/Ff/v707j4+qvvoH/pnJvi9kI7uQEIggYSesgaiAsggIAkIA\neYSK2GJtbRWV8CDlsa3Y/kSl0goEJCDuAiqaEII2EFBA1kBYAmEJS/aFZJKZ3x/XmcmQBGYmM9/Z\nPu/XK69y7yz35HQ6Pbn33PNFWVgZzpSegVKlRGNhI3rf3xv/M/J/8EDoA8b/Lg6isBDYskW60g0A\nXl5AWhoQGmrZuIisjVnnyPXq1QuHDh1qsb9v3744ePBgK6+wPBZyRHZAoZAukaoLt2vX7t7n5uWl\nLdzuuw/w9zf60PWN9Vjy7yU45n1Ms+g9ALjIXTBcNRyvzH7F6Pd2FAUFwEcfQbNCg48PMHs2EBRk\n2biIrJFZb3Zo7c5UlUqFc+fOGXxAkThHTiz2V4hll/lWKoGrV7WF26VL2lM5rXFxkc60qYu3kJBW\n+9wMUXG7AvmX8/HT1Z9w7OYx3HaXirjyU+UI7xGOHiE94H3Lu13HcATHjwOffCL9VwoAfn5SEdd8\ndbN7scvPuBVjvsUSMkdu1qxZAID6+nqkpaXpVIoXLlzA/fffb/SBReAcOSIrp1JJY/2b97ndvt32\n8+VyICJCW7hFRkpDyEzgStUV5F3Kw/Ebx6FUSdWHHNIcOWe5M0K8QtC7Y284y53hKueSW3dz5Ajw\n+efak6eBgVIR5+dn2biIrJFZ58ipC6GVK1fi5Zdf1hRycrkcoaGhmDJlCgIN+fNKIF5aJbJS1dW6\nfW4VFXd/fnCwtnCLiQHc3U0WilKlxOlbp5F3KQ9FFUUtHleUKnD5wmVEJUXBSS4VjPVn6jFnxBwk\nxCWYLA57cvAgsH27djs4WOqJ8/GxXExEtsCsPXLffPMNRo8ebVRglsJCjshKNDRIS16pC7eSkrs/\n38dHt8/N19f0ITU14PC1w9hXvA+ldaUtHo/1j0VyZDK6dOiC02dPI+vnLDQoG+Aqd0Vq71QWcW3Y\ntw/45hvtdmioVMR5cbwe0T2ZtZCzRSzkxGN/hVhWm2+lErh8WVu4FRdru91b4+am2+cWFNTuPre2\nVNZXIv9yPg5eOahzAwMgrZHaI6QHBkYOREefji1ea7X5thJ79wJZWdrt8HBpxIiBNwrrYM7FYr7F\nujPfZr3ZgYioTSqVtHimunC7cEE79bU1Tk5Sb5u6cAsPN1mfW1ta639T83D2QN/wvugX0Q++bqY/\n+2fvVCogJwfYs0e7LzoamDHDpFfBiagNPCNHRIarqtIWbufOaVc/b0toqG6fm6v5bxa4V/9boEcg\nkiOT0TOsJ1ydePOCMVQq4LvvgP/+V7vvvvukZbcE/FdMZFd4Ro6IzKe+XjrTpi7cbty4+/P9/HT7\n3LzFjevQt/8tvkM85DK5sLjsjUoF7NwJHDig3RcfD0ydKk2FISIx9CrklEol/v3vf2PLli24ceMG\njh49itzcXFy7dg1Tp041d4xkI9hfIZZZ893UJPW2qQu3y5e1A8Fa4+4uFWzq4i0w0Gx9bm1R97/9\ndOUn1DXW6Twml8nRPaQ7BkYORLiPcetC8fOtpVQCX30FNJ8T360bMHmyXque6Y05F4v5FkvoWqtL\nly7Frl27sHjxYvzmN78BAERERGDx4sUs5IjsgUoFXL+uLdyKirQLY7bGyUlqhFIXbh07SjPeLOBq\n1VXkFefh2PVjLfrf3J3d0Te8L/pH9Gf/m4k0NUkz4o4e1e7r0QN47DGztzoSUSv06pGLjIzEoUOH\nEBwcjICAAJSVlUGpVCIwMBDl5eUi4jQYe+SI7qGiQrfPraam7efKZEBYmLZwi4626PUzlUol9b8V\n5+FC+YUWjwd6BGJg5EAkhSWx/82EmpqAjz8GTp7U7uvVCxg3zmJ1PJHdMGuPnFKphPcdPS41NTXw\n4YRHIttRV6fb53br1t2fHxCg2+fm6SkkzLtpaGrAkWtHsK94H27VtYw/xi8GyVHS/Df2v5mWQiGt\nm3rmjHZfv37AI48Iv4pORM3oVciNGTMGv//97/HWW28BkAq7V199FePGjTNrcO3FtVbFYn+FWPfM\nd2OjtFapunC7cuXuC857eur2uQUEmDxmY1XVV2nmv7XW/3Z/8P0YGDkQEb4RZovBkT/fDQ1AZqa0\nIIdacjLw8MPmLeIcOeeWwHyLJWStVbVVq1Zhzpw58Pf3h0KhgLe3Nx5++GFkZGQYfWARuNYqORSV\nCrh2TbfP7W4Lzjs7S6NA1IVbWJjVnVq5Vn0NeZek/rcmle5QYXdnd/Tp2Af9I/rDz52LeJpLfT3w\n4YfAxYvafcOGASNGWN3HhcgmmXWt1TuVlJSgqKgIUVFR6Nix5eRza8IeObJXRQUFOPv995ArFFA2\nNKBz586IUSql0yW1tW2/UCaThu+qC7eoKNPeYmgiKpUKZ0rPIO9SHs6Xn2/xeIB7gKb/zc3ZzQIR\nOo66OmDTJummZbXUVGDoUMvFRGSvzLpE1+9+9zs8+eST6N+/v1HBWQILObJqKpXUdHS3n4aGFvuK\nzp1D4a5dSJXJgMpK4PZtZDU2Ii4pCTFBQS2P06GDtnCLjW3feklmpmhS4EiJ1P92s/Zmi8ej/aKR\nHJmMhKAE9r8JUFMDbNwoneRVGz0aGDjQcjER2TOzDwR+7LHH4OnpiSeffBIzZsxAQgIXjSZddtNf\noVRKlyTbKKYMKbzafN7dLnnexdn8fKT+etYtp7wcKf7+SHV2Rvb581Ih5+WlLdw6dZIG81q56oZq\nTf9brUL3jKJcJkdicCIGRg5EpG+khSKU2M3nWw9VVUBGhu7c57Fjgb59xcbhSDm3Bsy3WELnyP3z\nn//EqlWrkJ2djc2bN2PgwIHo1KkTZsyYgRdeeKHdQRDpTak0XTHV1mNGFlkiyO8cyuvkBPj5QR4V\nBTzzDBASYjONSyXVJcgrzsPRkqMt+t/cnNzQJ1zqf/N397dQhI6pogLYsAEo/XVRDJkMmDABSEqy\nbFxE1Dqj1lq9fPky5syZg6ysLCjvNu3dgnhpVRxNz1Z9PZRyOToPHYqY++4z7Rks9b+bmu4dkK1w\ncbn3j6urznb2F19gZFWVNLTLwwPw8QHkcmSHhGDkwoWW/o3uSaVSobC0EHnFeThXdq7F4/7u/hgY\nORC9wnqx/80CSkulM3Hq8aByOTBpEtC9u2XjInIEZr+0Wl1djc8++wyZmZma04HWftcqmV/RqVMo\n/M1vpJ6tXz+AWR9/DLTVs2UrWimiDCm47vlcZ2ejzpx1Dg1F1vr1SHXTFjlZ9fWIS0015W9vcoom\nBX4p+QV5xXmt9r9F+UYhOSoZXYO6sv/NQm7elM7EVVVJ205OwJQpQNeulo2LiO5Or0JuypQp2Llz\nJ3r37o0ZM2Zgw4YNCA4ONndsZAPOZmUhVS4HlMrWe7ZMTSYzTSF1t8eNLLJEiElIAObMQXZWFn45\ncQIPJCYiLjVV2m+FqhuqceDyARy4cqBF/5sMMk3/W5RflIUi1J899w+VlEhn4tSLezg7A9OmAXFx\nlo3LnnNujZhvsYT2yPXt2xd///vfERMT0+4Dkn2RKxTS9Rf1JXYnJ8DJCXJXV6lfy9QFl5OT1RZZ\nosQkJCAmIQFyK/7SLakuwb7iffil5JdW+996d+yNAZED2P9mBa5cke5Orft1zrKrKzB9ujQbmois\nn1E9craAPXJiZL/zDkZevSoVczKZpsiylZ4tMh2VSoWzZWeRdykPZ8vOtnjc390fAyIGoHfH3ux/\nsxKXLklz4urrpW03N2DmTGnEIBGJZfIeua5du+LUqVMAgKg2/lctk8lwsfm4b3I4nR98UOrZajZY\n1hZ6tsh0GpWNUv/bpTzcqL3R4vFI30gkRyajW3A39r9ZkQsXgM2bpXuJAOnemVmzpJnRRGQ72jwj\nt3fvXgz9dXx3W2uAyWQyDB8+3GzBtQfPyIlTVFCAs816tjpbcc+WPbF0P0tNQw0OXDmAA5cPoEZR\no/OYDDJ0C+6G5Mhkm+h/04el821KhYXAli3aSTteXkBaGhAaatm47mRPObcFzLdYd+bb5GfkhjZb\ng+XGjRuYMmVKi+d8/PHHBh+Q7I8t9GyR6Vyvua7pf2tU6s7cc3VylfrfIgYgwCPAQhHS3Zw6BWzb\npp3k4+MDzJ4N2PJN5kSOTK8eOR8fH1Sp70lvJiAgAGVlZWYJrL14Ro7IdFQqFc6VnUNecR4KSwtb\nPO7n5ocBkVL/m7uzuwUiJH0cOwZ8+qn23iQ/P6mICwy0bFxEZKY5cufOnYNKpZK+xM/pDu88e/Ys\nPKx43UYASE9PR0pKCs8SERmpUdmIoyVHkVech+s111s8HuETgeSoZHQL6gYnuZMFIiR9HT4MfPGF\nZtwjAgOlIs4GVnEjsms5OTlttrDp465n5OTythuTQ0NDkZ6ejgULFhh9cHPiGTnx2F8hljnzXdNQ\ng4NXDiL/cn6r/W9dg7oiOSoZUb5RkDnIOBhb/nwfPAhs367dDg6WeuJ8fCwXkz5sOee2iPkWy+w9\ncgA0y28NGzYMubm5Br85EdmWGzU3sK94H46UHGm1/61XWC8MiByAQA9ei7MV+/YB33yj3Q4Lk+5O\n9fKyXExEZDqcI0fk4NT9b/uK9+FM6ZkWj/u6+WJAxAD0Ce/D/jcbs3cvkJWl3Y6IkObEWXlXDJFD\nMutaqwqFAu+++y727NmDW7duac7UyWQynqkjslHq/rd9xftQUlPS4vFwn3AkRyYjMTiR/W82RqUC\ndu8Gmn89R0cDTz4pDf0lIvuh13TO3//+9/jXv/6FYcOG4eDBg5g8eTKuX7+OESNGmDs+siHtadYk\nwxmb75qGGuy5sAf/2PcPfFHwhU4Rp+5/m5s0F0/3fho9QnuwiPuVrXy+VSrgu+90i7j77pPOxNla\nEWcrObcXzLdYpsq3XmfkPvnkE+Tl5SEmJgZLly7F4sWLMXr0aMyfPx/Lli0zSSBEZF53639zkbug\nV8deGBg5kP1vNkylAnbuBA4c0O6LjwemTpWWKiYi+6NXj1xAQABu3boFuVyOjh07orCwEJ6envD1\n9W11vpw1YI8ckdT/dr78PPIu5bXa/+bj6oMBkQPQp2MfeLiwccqWKZXAV18Bhw5p93XrBkyeDDjr\n9Sc7EVmSWXvkunbtioMHD6J///7o06cPli1bBh8fH0RGRhp8QCIyv0ZlI45dP4a8S3mt9r919O6I\n5Khk3B98Py+d2oGmJuCzz6SBv2o9egCPPQY48b9eIrumV4/cP//5Tzj/+ifdqlWr8NNPP2H79u14\n//33zRoc2Rb2V4jVWr5rFbXILcrFP/b9A5+f+rxF/1tChwTMSZqD+X3m44HQB1jEGcBaP9+NjcDH\nH+sWcb16ARMn2n4RZ605t1fMt1hCe+T69++v+XeXLl2Q1fx+diKyuJu1N6X+t2tHoFAqdB5zkbsg\nKSwJAyMHooNnBwtFSOagUAAffQScaXbVvF8/4JFHAAeZ00zk8NrskcvKytJrYvvIkSNNHpQpsEeO\n7FVBYQG+/+l7NCgbUHW7Cp6hnqj2rG7xPB9XH/SP6I++4X3Z/2aHGhqAzEzg/HntvkGDgIceYhFH\nZIuMrVvaLORiY2P1KuTON/8WsSIs5MheqFQq1DfVo1ZRi19O/4LMPZlQxipxtfoqqhuq0VjYiKTE\nJASFBwEAwrzDkByZjO4h3Xnp1E7V1wMffghcvKjdN3w4kJLCIo7IVpm8kLN1LOTE4zp9+lE0KVCr\nqG3zp0ZR02KfUiUN4c7/IR+1kbUAgPJT5fDv6g8A8Cr2wswJMzEwciBi/fX7I4wMYy2f77o6YNMm\n4PJl7b7UVGDoUMvFZC7WknNHwXyLJWSt1eYUCgX27duHK1eu4IknnkB1dTVkMhm8uGAfObAmZdNd\ni7LWfu7sYTOEEkqdbblMjjDvMCRGJ2J6j+nt/XXIytXUABkZQEmzG5FHjwYGDrRcTERkWXqdkTt6\n9CjGjx8PNzc3FBcXo7q6Gjt27EBGRga2bt0qIk6D8YwcGUqpUuJ2423prFhDy7Nirf3UN9ULic3V\nyRWeLp7Yv3c/6qPr4SJ3gZerFzp6d4SLkwtCrodg4dSFQmIhy6iqkoq4Gze0+8aOBfr2tVxMRGQ6\nZr20OnjwYCxYsABpaWkICAhAWVkZampqEB8fjytXrhgVsLmxkHNszfvK9P2pU9RBBfN/ZpxkTvB0\n8Wz1x8vVq8U+D2cPuDhJY/kLCguwfvd6uMVr11qqP1OPOSPmICEuweyxk2WUl0tFXGmptC2TARMm\nAElJlo2LiEzHrIVcQEAASktLIZPJNIWcSqVCYGAgysrKjArY3FjIiWeu/gqVSgWF8u59Za39qPvK\nzEkGWZtFWVs/rk6u7ephKygsQNbPWThx7AQSuycitXcqizgBLNU/VFoKbNgAVFRI23K5tFrD/fcL\nD0U49myJxXyLJbRHLiYmBgcPHkS/fv00+w4cOID4+HiDD0j2Rz0O4+TxkzhechwP9nnwroVFo7IR\ndYo6gxr+71wb1Fw8nD0MKsrcnd2F31iQEJeAhLgE5ITwS9fe3bwpFXHqlRCdnIApU4CuXS0bFxFZ\nD73OyG3fvh3z5s3DggUL8Oabb2LJkiVYs2YN1q5di1GjRomI02A8IyfGqTOn8O+sf0PWSQaFUiHd\nkXm6Fg/2eRBB4UFW0Vem74+HswfHdZDVKCmRLqfW1Ejbzs7AtGlAXJxl4yIi8zD7+JFDhw7h/fff\nR1FREaKjo/H000+jT58+Bh9QFBZyYvy/zP+HT+s/bbHfq9gL/Yb0a+UVxnGSObXaP3a3H2c5Vwon\n23TlCrBxozRqBABcXYEZM4DYWIuGRURmZLZLq42NjUhISMCJEyfw3nvvGRWcKVVWVuLBBx/EyZMn\nsX//fiQmJlo6JIemlCkhl8mhVCl15po1oanN18hl8jYvYbZVrLnIXTgb7Q7sZxFLVL4vXZLmxNX/\neuLazQ2YOROIijL7oa0OP+NiMd9imSrf9yzknJ2dIZfLUVdXBzc3t3s93ew8PbUBJHkAACAASURB\nVD2xc+dO/PGPf+QZNyvgInOBh7MHlColGlwa0MGjA1ycXBAcEIyHOj1kNX1lRLbg/Hlp2a2GBmnb\nwwOYNQsID7dsXERkvfS6tPruu+/iiy++wEsvvYSoqCid/xPu1KmTWQNsy9y5c/GHP/wB97dx6xYv\nrYrBcRhEplFYCGzZAjT+el+PlxeQlgaEhlo2LiISw6x3rS5atAgA8N1337U4aFNT25fQyP4lxCVg\nDuYg6+csNCgb4Cp3ReoIjsMgMsSpU8C2bYD669THB5g9GwgKsmxcRGT95Po8SalUtvpjaBG3evVq\n9O3bF+7u7pg7d67OY6WlpZg4cSK8vb0RGxuLzMxMzWNvvfUWRowYgTfffFPnNbw8Zx0S4hKwcOpC\nJIUlYeHUhSziBMnJybF0CA7FXPk+dgz46CNtEefvD8ydyyIO4GdcNOZbLFPlW+htfREREXj11Vfx\n7bffok59O9avnn32Wbi7u+P69es4dOgQHn30UfTs2ROJiYl4/vnn8fzzz7d4P146JSJbdvgw8MUX\ngPqrLDBQOhPn52fZuIjIdggt5CZOnAgAOHjwIIqLizX7a2pq8Omnn+L48ePw9PTE4MGDMWHCBGzc\nuBErV65s8T6PPPIIjhw5goKCAixYsACzZ89u9Xhz5sxB7K/36/v7+yMpKUlzh4i6Eua2abfVrCUe\ne99Ws5Z47H1bzRTvV1AAXL0qbV+4kAM/P+CFF1Lg42M9vy+3uc1t836fpKen48KFC2gPvefImdIr\nr7yCy5cvY926dQCkGXVDhgxBjXryJYBVq1YhJycHX375pVHH4M0ORGSt9u0DvvlGux0WJt2d6uVl\nuZiIyLKMrVvkZojlnu7sbauuroavr6/OPh8fH1Sp16Uhm9D8rwwyP+ZbLFPle+9e3SIuIkK6nMoi\nriV+xsVivsUyVb4NvrSqVOouRC6XG14L3llxent7o7KyUmdfRUUFfHx8DH5vIiJrpFIBu3cDubna\nfdHRwJNPSkN/iYiMoVcV9tNPPyE5ORmenp5wdnbW/Li4uBh10DvPyHXp0gWNjY0oLCzU7Dty5Ai6\nd+9u1Purpaen8y8MgdTX/0kM5lus9uRbpQJ27dIt4u67T1qxgUVc2/gZF4v5Fqt5z1x6errR76NX\nj1z37t0xfvx4zJw5E56enjqPxRqw+F9TUxMUCgWWLVuGy5cvY+3atXB2doaTkxOmT58OmUyGf//7\n3/j5558xduxY5OXloVu3bgb/UgB75IjIOqhUwM6dwIED2n3x8cDUqYCRfwsTkR0ya4/cxYsXsWLF\nCiQmJiI2NlbnxxDLly+Hp6cn3njjDWzatAkeHh5YsWIFAGn1iLq6OoSEhGDmzJlYs2aN0UUcWQbP\nforFfItlTL6VSmm8SPMirls3YNo0FnH64GdcLOZbLKE9chMnTsS3336L0aNHt+tg6enpbZ4+DAgI\nwGeffdau9ycishZNTcBnn0kDf9V69AAmTgSMaC0mImqVXpdWp06diq+++gpDhw5FaLOF/2QyGTIy\nMswaoLF4aZWILKWxEfjkE+DkSe2+Xr2AceNYxBFR68y61mpiYiISExNbPag1S09PR0pKChs4iUgY\nhUJacuvMGe2+/v2BMWMAK//KJCILyMnJaddlVosMBBaBZ+TEy8nJYdEsEPMtlj75bmgAMjOB8+e1\n+wYNAh56iEWcMfgZF4v5FuvOfJv8jFxubi6GDRsGAMjOzm7zDUaOHGnwQYmI7M3t28DmzcDFi9p9\nw4cDKSks4ojIfNo8I9e9e3cc+7VLNzY2ts3LqOeb/+lpRXhGjohEqasDNm4ErlzR7ktNBYYOtVxM\nRGRbjK1beGmViKgdamqAjAygpES7b/RoYOBAy8VERLbHptZaFYUrO4jFXIvFfIvVWr6rqoB167RF\nnEwm3ZnKIs40+BkXi/kWS53v9q7soNddqxUVFUhPT8eePXtw69YtzXqrMpkMF5s3hFiZ9iSGiOhu\nysulM3GlpdK2TAY89hjQs6dl4yIi26KerrFs2TKjXq/XpdWZM2fi0qVLeP755zFr1ixs3LgRf/vb\n3zB58mT8/ve/N+rA5sZLq0RkLqWlwIYNQEWFtC2XA5MnA/ffb9m4iMh2mbVHLjg4GCdPnkRQUBD8\n/PxQUVGBy5cvY9y4cfj555+NCtjcWMgRkTncuCGdiauqkradnKR1UxMSLBsXEdk2s/bIqVQq+Pn5\nAQB8fHxQXl6Ojh074kzziZfk8NhfIRbzLVZOTg6uXQPWr9cWcc7OwPTpLOLMhZ9xsZhvsYSutfrA\nAw8gNzcXqampGDJkCJ599ll4eXkhgd9eRGTnCgqK8P33Z3HgwC8oK1MiKqozgoJi4OoKzJgBxMZa\nOkIicmR6XVo9e/YsAKBz584oKSnByy+/jOrqaixdurTVpbusgUwmw9KlS7lEFxEZraCgCOvXF+L2\n7VT88gvQ1AQ0Nmahf/84PP98DKKiLB0hEdk69RJdy5Yt4xy55tgjR0Tt9c472SgoGIljx4Bfb9aH\nszMwcmQ2Xn6Zq9oQkemYfImu5v7zn/+0urKDm5sbIiMjMXDgQLi5uRl8cLIvXKdPLObb/IqK5Dh6\nFFCpgPLyHAQHp6BnT8DT065HcFoNfsbFYr7FMlW+9SrkMjIykJeXh7CwMERGRqK4uBjXrl1D3759\nUVRUBAD4/PPP0a9fv3YHRERkDfbvB44dU0L9B7KLC9CrF+DpCbi6Ki0bHBHRr/S6tPrss88iISEB\nv/3tbwFId7G+8847OHnyJN5++2385S9/wY4dO5CXl2f2gPXFS6tEZAyVCti9G8jNBW7eLMLhw4Xw\n9U1Fz56AmxtQX5+FOXPikJAQY+lQiciOmHWOnL+/P0pLSyGXay8nNDY2IigoCOXl5aivr0dwcDAq\nKysNDsBcWMgRkaGUSmDHDuCnn7T7nJ2L4O19FoAcrq5KpKZ2ZhFHRCZn1jlyoaGh+PLLL3X27dix\nA6GhoQCAuro6uLq6Gnxwsi+cQSQW821ajY3Atm26RVx8PPDiizFYvHgkkpKAhQtHsogTiJ9xsZhv\nsYTOkXv77bcxZcoUdO/eXdMjd/ToUWzbtg0AkJ+fj+eee84kAZlSeno6x48Q0T3V1wNbtgDnz2v3\nPfAAMGGCtHIDEZG5qMePGEvv8SM3b97Ezp07ceXKFYSHh+PRRx9Fhw4djD6wufHSKhHpo7oa+PBD\n4OpV7b6BA4FRo4BWbtYnIjILs/bI2SIWckR0L2VlwMaNQGmpdt+DDwKDB7OIIyKxzNojR6QP9leI\nxXy3T0kJ8J//aIs4mQwYPx4YMqT1Io75Fo85F4v5FktojxwRkT0pKgIyM4Hbt6VtZ2fg8ceBrl0t\nGxcRkaF4aZWIHEpBgXR3amOjtO3mBkyfDsTGWjQsInJwZr+02tDQgNzcXGzduhUAUF1djerqaoMP\nSERkKYcPA1u3aos4b29g7lwWcURku/Qq5I4ePYqEhATMnz8f8+bNAwDs2bNH828igP0VojHfhvnx\nR+Dzz6WhvwAQEAA89RQQFqbf65lv8ZhzsZhvsUyVb70Kud/85jdYtmwZTp06BRcXFwBASkoK9u7d\na5IgiIjMRaUCdu0CvvtOuy8sDJg3DwgMtFxcRESmoFePXEBAAEpLSyGTyRAQEICysjKoVCoEBgai\nrKxMRJwGk8lkWLp0KQcCEzkwpRL48kvpkqpaTIzUE+fubrm4iIjU1AOBly1bZr45cklJSVi7di36\n9eunKeTy8/OxaNEi5OfnGxW4ufFmByLHplBINzWcPq3d17WrdHeqM+/XJyIrY9abHV5//XWMHTsW\nr732GhoaGvCXv/wFjz/+OJYvX27wAcl+sb9CLOa7bXV10qDf5kVc797A1KnGF3HMt3jMuVjMt1hC\ne+TGjh2Lb775Bjdu3MDw4cNx8eJFfPbZZxg1apRJgiAiMpWqKmDdOuDiRe2+oUOBceMAOUegE5Gd\n4Rw5IrIbt25JZ+LKy7X7Ro0CkpMtFxMRkT7Meml14sSJLe5Qzc3NxeOPP27wAYmIzOHKFeCDD7RF\nnFwOTJrEIo6I7JtehdyePXuQfMe3YXJyMrKzs80SFNkm9leIxXxrnT8PrF8P1NRI2y4u0p2pDzxg\numMw3+Ix52Ix32IJXWvVw8MDNTU18PPz0+yrqamBq6urSYIgIjLWiRPAJ58ATU3StocHMGMGEBVl\n2biIiETQq0du7ty5uH37NtasWQM/Pz9UVFRg4cKFcHFxwfr16wWEaTj2yBHZv4MHgR07pKG/AODr\nC8ycCYSEWDYuIiJDmbVH7s0330RlZSUCAwMRHByMwMBAVFRU4K233jL4gERE7aVSAXv2ANu3a4u4\nDh2kJbdYxBGRI9GrkAsMDMSOHTtQXFys+c/t27cjICDA3PGRDWF/hViOmm+VCvj6a2D3bu2+8HCp\niPP3N99xHTXflsSci8V8iyW0R07NyckJQUFBqKurw7lz5wAAnTp1Mkkg5pCens4luojsSFMT8Nln\nwLFj2n2dOgFPPAG4uVkuLiIiY6mX6DKWXj1y33zzDebNm4erV6/qvlgmQ5O6w9jKsEeOyL40NABb\ntwJnz2r33X8/MHEil9wiIttnbN2iVyHXqVMnvPjii0hLS4Onp6dRAYrGQo7IftTWAh9+CFy+rN3X\nrx8wZgxXayAi+2DWmx3Ky8uxYMECmyniyDLYXyGWo+S7okIa9Nu8iEtJAR55RGwR5yj5tibMuVjM\nt1hC11qdN28ePvjgA5MckIhIXzduAP/5D3DzprQtkwGPPioVcjKZRUMjIrIKel1aHTJkCPLz8xET\nE4OwsDDti2Uy5ObmmjVAY/HSKpFtKy6WLqfW1UnbTk7Sklv332/ZuIiIzMGsPXJtDf2VyWSYPXu2\nwQcVgYUcke0qLJRubFAopG1XV2DaNOkOVSIie2TWQs4WsZATLycnh6NeBLLXfB89Ko0YUSqlbU9P\nabWG8HDLxmWv+bZmzLlYzLdYd+bb2LpF75v2S0pKsH//fty6dUvnQE899ZTBByUias3+/dKwXzV/\nf2DWLGnVBiIiakmvM3Kff/45Zs6cifj4eBw7dgzdu3fHsWPHMGTIEOxuPl7divCMHJHtUKmklRqa\nt9yGhEhn4nx9LRcXEZEoZh0/smTJEnzwwQc4dOgQvL29cejQIbz//vvo3bu3wQckImpOqZTWTG1e\nxEVFAXPnsogjIroXvQq5S5cuYerUqZptlUqFtLQ0ZGRkmC0wsj2cQSSWPeS7sRHYtg346Sftvi5d\ngLQ0wMPDcnG1xh7ybWuYc7GYb7GErrUaEhKCa9euISwsDLGxscjLy0NQUBCU6m5kIiID3b4NbNkC\nXLig3dezJzB+vDRqhIiI7k2vHrn/+7//Q1xcHB5//HFkZGRg/vz5kMlkeOGFF/D666+LiNNg7JEj\nsl7V1cCmTcC1a9p9ycnAww9z0C8ROSah40eKiopQU1ODxMREgw8oCgs5IutUVgZs3AiUlmr3PfQQ\nMGgQizgiclxmvdnhTjExMVZdxJFlsL9CLFvM97Vr0pJb6iJOJgMmTAAGD7b+Is4W823rmHOxmG+x\nhK61evjwYYwcORIBAQFwcXHR/Li6upokCHNJT0/nB5PIShQVAevWSZdVAcDZGXjiCaBXL8vGRURk\nSTk5OUhPTzf69XpdWu3WrRsef/xxTJ06FR533EoWFxdn9MHNiZdWiazHqVPAxx9Ld6kCgJsbMGMG\nEBNj2biIiKyFWXvkAgICUFpaCpm1X/tohoUckXU4dAj48ktp6C8AeHtLg37DwiwbFxGRNTFrj1xa\nWho+/PBDg9+cHAsvY4tl7flWqYAffwS++EJbxAUGAvPm2WYRZ+35tkfMuVjMt1hC58i99NJLGDhw\nIFauXImQkBDNfplMhuzsbJMEQkT2Q6UCvvsO+O9/tfvCwqQzcd7elouLiMje6HVpdejQoXB1dcXE\niRPh7u6ufbFMhnnz5pk1QGPx0iqRZTQ1SZdSjxzR7ouNBaZNA5p9fRARUTNm7ZHz8fHBzZs34ebm\nZlRwlsBCjkg8hUJacuv0ae2+bt2AyZOlu1SJiKh1Zu2RGzp0KE6cOGHwm5NjYX+FWNaW77o6ICND\nt4jr0weYMsU+ijhry7cjYM7FYr7FEtojFxsbi4cffhiTJk1q0SP3v//7vyYJhIhsV2WltOTW9eva\nfcOGASNGWP+gXyIiW6bXpdW5c+dCpVLpjB9Rb69bt86sARqLl1aJxLh1S1pyq7xcu2/0aGDgQMvF\nRERka4ytW+55Rq6pqQmRkZFYsmSJzo0ORERXrkhn4mprpW25HHjsMeCBBywbFxGRo7hnj5yTkxPe\ne+89q1+OiyyP/RViWTrf584B69drizgXF2m1Bnst4iydb0fEnIvFfIsldK3VtLQ0vPfeeyY5IBHZ\nvuPHgQ8/BBoapG0PD2D2bMBKV+wjIrJbevXIDR48GPn5+QgPD0dUVJSmV04mkyE3N9fsQRqDPXJE\n5nHgALBzp3a1Bl9fYNYsIDjYsnEREdkys86RW79+fZsHnT17tsEHFYGFHJFpqVRAbi6we7d2X1CQ\nVMT5+VkuLiIie2DWQs4WsZATLycnBykpKZYOw2GIzLdKBXz9NZCfr90XEQE8+STg6SkkBIvj51s8\n5lws5lusO/Nt1oHAKpUKH3zwAUaMGIEuXbpg5MiR+OCDD1goETmApibgk090i7jOnaWeOEcp4oiI\nrJVeZ+RWrFiBjIwMvPDCC4iOjsbFixfx1ltv4cknn8Qrr7wiIk6D8YwcUfvV1wMffQScPavd1707\nMHEi4ORkubiIiOyNWS+txsbGYs+ePYiJidHsKyoqwtChQ3Hx4kWDDyoCCzmi9qmpATZvBi5f1u7r\n3x8YM4arNRARmZpZL63W1tYiKChIZ1+HDh1w+/Ztgw9I9osziMQyZ77Ly4F163SLuBEjHLuI4+db\nPOZcLOZbLKFz5EaPHo2ZM2fi1KlTqKurw8mTJ5GWloZRo0aZJAhD5OfnY9CgQRg+fDhmzJiBxsZG\n4TEQ2bPr14EPPgBu3pS2ZTJg7Fhg+HDHLeKIiKyVXpdWKyoq8Nxzz2Hr1q1QKBRwcXHB1KlT8fbb\nb8Pf319EnBrXrl1DQEAA3Nzc8PLLL6NPnz6YPHlyi+fx0iqR4S5dki6n1tVJ205OwOTJQGKiZeMi\nIrJ3Jr+0unr1as2/b9y4gYyMDNTW1uLq1auora3Fxo0bhRdxABAWFgY3NzcAgIuLC5zYcU1kEmfO\nABkZ2iLO1VUaL8IijojIerVZyL388suaf/fu3RuAtO5qaGioVRRPRUVF+O677zBu3DhLh0K/Yn+F\nWKbM9y+/AJmZgEIhbXt5AXPmAJ06mewQNo+fb/GYc7GYb7FMlW/nth7o1KkTXnjhBSQmJkKhUGjm\nxqmX51L/+6mnntL7YKtXr8b69etx7NgxTJ8+HevWrdM8Vlpainnz5uG7775DUFAQVq5cienTpwMA\n3nrrLXz55ZcYO3YsXnjhBVRWViItLQ0bNmywiqKSyJbt2wd88412299fWq2hQwfLxURERPpps0eu\noKAAf/3rX1FUVIScnBwMHTq01TfY3Xy9nnv47LPPIJfL8e2336Kurk6nkFMXbf/5z39w6NAhPPro\no/jvf/+LxDuu6zQ2NmL8+PH4wx/+gJEjR7b9i7FHjuiuVCogOxvYu1e7LyREKuJ8fCwXFxGRIzLr\nHLnU1FRkZWUZFVhrXn31VRQXF2sKuZqaGgQGBuL48eOIi4sDAMyePRvh4eFYuXKlzms3btyI559/\nHj169AAAPPPMM5g6dWqLY7CQI2qbUgls3w78/LN2X3Q0MH064OFhubiIiByVsXVLm5dW1RobG/Hj\njz+ivr5ec5NBe90Z6OnTp+Hs7Kwp4gCgZ8+erV4/njVrFmbNmqXXcebMmYPY2FgAgL+/P5KSkjTr\nmqnfm9um2z58+DAWL15sNfHY+7ax+W5sBP73f3Nw8SIQGys93tiYg+howMPDen4/a9vm51v8tnqf\ntcRj79vqfdYSj71vHz58GOXl5bhw4QLaQ68zcj179sTOnTsRERHRroOp3XlGbu/evZg6dSquXr2q\nec7atWuxefNmgy7dNsczcuLl5ORoPqhkfsbk+/ZtYMsWoPn3RlISMG4cl9y6F36+xWPOxWK+xboz\n32Y7IwcATz75JMaNG4ff/va3iIqK0tzwAOCufWptuTNQb29vVFZW6uyrqKiADxt1bAq/AMQyNN/V\n1cCmTcC1a9p9gwYBDz3EQb/64OdbPOZcLOZbLFPlW69C7t133wUALFu2rMVj58+fN/igsjv+X6NL\nly5obGxEYWGh5vLqkSNH0L17d4Pfm4haKi0FNm4Eysq0+x56CBg82HIxERFR+8n1edKFCxdw4cIF\nnD9/vsWPIZqamnD79m00NjaiqakJ9fX1aGpqgpeXFyZNmoTXXnsNtbW1+OGHH/DVV1/p3QvXlvT0\ndJ1r/2RezLVY+ub72jVpyS11ESeXAxMmsIgzFD/f4jHnYjHfYqnznZOTg/T0dKPfR69CDgAUCgX2\n7t2LrVu3AgCqq6tRU1Nj0MGWL18OT09PvPHGG9i0aRM8PDywYsUKANJZv7q6OoSEhGDmzJlYs2YN\nunXrZtD73yk9PZ2nismhXbgArFsnXVYFAGdn4IkngF69LBoWERH9KiUlpV2FnF43Oxw9ehTjx4+H\nm5sbiouLUV1djR07diAjI0NT2Fkb3uxAju7UKeDjj4HGRmnb3V0aLxITY9m4iIioJbPOkRs8eDAW\nLFiAtLQ0BAQEoKysDDU1NYiPj8eVK1eMCtjcWMiRIzt0CPjyS2noLwB4e0uDfkNDLRsXERG1zti6\nRa9LqydOnGjRr+bp6Yk69eraVoo9cmIx12K1lm+VCvjhB+CLL7RFXGAgMG8ei7j24udbPOZcLOZb\nLKE9cjExMTh48KDOvgMHDiA+Pt7oA4vAHjlyJCoVsGsX8P332n0dOwJPPQUEBFguLiIiapuQHrnt\n27dj3rx5WLBgAd58800sWbIEa9aswdq1azFq1CijD25OvLRKjqSpSbqUeuSIdt999wHTpgEmWpCF\niIjMyKw9cgBw6NAhvP/++ygqKkJ0dDSefvpp9OnTx+ADisJCjhxFQwOwbRtw5ox2X7duwOTJ0l2q\nRERk/cxeyNkaFnLicXkXsXJycjBgQAo2bwYuXdLu79MHePRRaV4cmQ4/3+Ix52Ix32KZaokuvb7q\n6+vr8eqrryIuLg6enp6Ij4/HK6+8gtu3bxt8QJF4swPZs5oaadBv8yJu2DBg7FgWcUREtqK9Nzvo\ndUbuqaeewunTp7FkyRJER0fj4sWLWLFiBeLj4zUL31sbnpEje1VQUITPPz+LvDw5GhqU6NSpM4KC\nYjBmDDBggKWjIyIiY5j10mpgYCDOnj2LgGa3vpWWlqJz584oa754oxVhIUf26NSpIvztb4W4eDEV\nCoW0T6nMwh/+EIfx4znpl4jIVpn10mrHjh1RW1urs6+urg7h4eEGH5DsFy9jm49KJd3MsHTpWZw9\nKxVx5eU5kMuBpKRUFBeftXSIdo+fb/GYc7GYb7FMlW+97mmbNWsWxowZg0WLFiEqKgoXL17Eu+++\ni7S0NGRnZ2ueN3LkSJMERURaly5Js+GKioCKCu3fXk5OQFIS4OsLNDSwKY6IyBHpdWk1NjZWerJM\nptmnUql0tgHg/Pnzpo2uHXhplWzd9etAVhZQUKDdl5+fjdu3RyIqCoiK0o4XCQnJxsKF/EOKiMhW\nGVu36HVG7sKFCwa/sTVQr+zA26nJlpSXAzk50nDf5v+blsuBCRM648yZLHh7p2r219dnITU1Tnyg\nRETUbjk5Oe26zMo5cmQynEHUPjU1wN69wIED0koNzfXoAYwYIa2bWlBQhKysszhx4hckJj6A1NTO\nSEjgjQ7mxs+3eMy5WMy3WKaaI8e570QWVl8P7NsH/Pe/0r+bi48HUlOBsDDtvoSEGCQkxCAnR84v\nXSIiB8czckQW0tQEHDwI5OZKZ+Oai4wEHnwQ+LU9lYiI7BzPyBHZCJUKOHoUyM6W+uGaCw6WzsAl\nJAB33EtERETUAmcWkMlwBtHdqVTA6dPAmjXAp5/qFnF+fsBjjwHPPAN07apfEcd8i8V8i8eci8V8\niyV0jhwRtc/Fi9IsuIsXdfd7egJDhwL9+mlHiRAREenLrnvkli5dyvEjZFGtzYIDAFdXIDlZ+nF3\nt0xsRERkeerxI8uWLTPfWqu2iDc7kCWVlwO7dwO//KI7C87JCejTBxg2DPD2tlx8RERkXcy61iqR\nPthfId19+s03wNtv6w70lcmABx4AFi0CHnnENEUc8y0W8y0ecy4W8y0We+SIrEh9PZCXJ82Ca2jQ\nfay1WXBERESmwEurRO3Q2Aj89FPrs+CioqRZcDFcdIGIiO6Bc+SIBFIqpVlwu3e3nAUXEiKdgevS\nhbPgiIjIvNgjRybjCP0V6llw//oX8Nlnrc+C+81vxAz0dYR8WxPmWzzmXCzmWyz2yBEJdrdZcMOG\nAX37chYcERGJxR45onsoKZFmwZ0+rbtfPQtu0CDAzc0ysRERkX1gj1wr0tPTORCYjHa3WXB9+0or\nMnAWHBERtYd6ILCxeEaOTCYnJ8cuiuaaGuku1IMHgaYm7X6ZDOjRAxgxAggIsFx8avaSb1vBfIvH\nnIvFfIt1Z755Ro6one42C65LF+lO1NBQy8RGRETUGp6RI4fX2CidfcvNBWprdR/jLDgiIhKBZ+SI\nDMRZcEREZOs4R45MxlZmEKlUQEEBsGZNy1lw/v7AxIniZsG1h63k214w3+Ix52Ix32JxjhyRETgL\njoiI7Al75Mgh3G0W3KBB0jw4zoIjIiJLYY8cUSvKyqQeuKNHW58FN2wY4OVlufiIiIjagz1yZDLW\n1F9RXQ18/TWwerXuQF+ZDOjZE1i0CBgzxraLOGvKtyNgvsVjzsVivsVifYHyAQAAEfxJREFUjxxR\nK+rrpTlweXmcBUdERPbPrnvkli5dyiW6HMTdZsFFR0uz4KKjLRMbERFRW9RLdC1btsyoHjm7LuTs\n9FejZpRK6dLp7t1ARYXuY5wFR0REtsLYuoU9cmQyIvsrms+C+/xz3SLOlmbBtQf7WcRivsVjzsVi\nvsVijxw5rKIiaRbcpUu6+728pLtQ+/ThLDgiInIMvLRKNqOkRCrgzpzR3c9ZcEREZOs4R47s1t1m\nwfXrBwwdattjRIiIiIzFHjkyGVP3V1RXAzt3tj0L7rnngNGjHbeIYz+LWMy3eMy5WMy3WOyRI7t1\nt1lwCQnSnaghIZaJjYiIyJqwR46sRmMjcOAAsHcvZ8EREZFjYY8c2ax7zYJ78EEgPt5+x4gQEREZ\niz1yZDKGXu9XqYBTp4D33mt9FtykSdIsOA70bR37WcRivsVjzsVivsVijxzZNM6CIyIiaj/2yJFQ\n164BWVmtz4IbPBgYOJCz4IiIyPGwR46sWlkZkJ0tzYJrjrPgiIiIjMceOTKZ1q73q2fBvf22bhEn\nkwFJSZwF1x7sZxGL+RaPOReL+RaLPXJk1W7flmbB7dvHWXBERETmYtc9ckuXLkVKSgpSUlIsHY7D\nuNssuJgYaZRIVJRlYiMiIrI2OTk5yMnJwbJly4zqkbPrQs5OfzWrU1BQhF27zqKoSI7z55Xo2LEz\ngoJiNI+HhkoFXFwcx4gQERG1xti6hT1y1C6nThXhzTcL8fXXI/H998CtWyNx+HAhbt4sQkCAdhYc\nB/qaHvtZxGK+xWPOxWK+xWKPHFmFzMyzOH06VWefh0cqPD2zsWhRDJycLBQYERGRA+ClVWqXt97K\nQVZWCqqrpVEiUVHST4cOOVi8OMXS4REREdkEzpEji3B1VaJTJ6C0VFrQ3tVVu5+IiIjMiz1y1C4P\nPtgZXl5ZiIsDrlzJAQDU12chNbWzReNyBOxnEYv5Fo85F4v5Fos9cmQVEhJiMGcOkJWVjZs3f0FI\niBKpqXFISIi552uJiIiofdgjR0RERGRhHD9CRERE5GBYyJHJsL9CLOZbLOZbPOZcLOZbLFPlm4Uc\nERERkY1ijxwRERGRhbFHjoiIiMjBsJAjk2F/hVjMt1jMt3jMuVjMt1jskSMiIiJycOyRIyIiIrIw\n9sgRERERORgWcmQy7K8Qi/kWi/kWjzkXi/kWiz1yRERERA7O5nrkSkpKMGnSJLi6usLV1RWbN29G\nhw4dWjyPPXJERERkK4ytW2yukFMqlZDLpROJGzZswNWrV/HnP/+5xfNYyBEREZGtcJibHdRFHABU\nVlYiICDAgtFQc+yvEIv5Fov5Fo85F4v5Fsuhe+SOHDmCAQMGYPXq1Zg+fbqlw6FfHT582NIhOBTm\nWyzmWzzmXCzmWyxT5VtoIbd69Wr07dsX7u7umDt3rs5jpaWlmDhxIry9vREbG4vMzEzNY2+99RZG\njBiBN998EwDQs2dP7N+/H6+//jqWL18u8leguygvL7d0CA6F+RaL+RaPOReL+RbLVPkWWshFRETg\n1VdfxVNPPdXisWeffRbu7u64fv06PvzwQzzzzDM4ceIEAOD555/H7t278cILL0ChUGhe4+vri/r6\nemHx3017T5Ea+np9nn+357T1mL77LX0K3hTHN+Q9zJXvth7Td59I1vYZN/Zx5tv45/M7xXTvwe8U\n+/6Mi8y30EJu4sSJmDBhQou7TGtqavDpp59i+fLl8PT0xODBgzFhwgRs3LixxXscPnwYw4cPx8iR\nI7Fq1Sq8+OKLosK/K3v+QLa2v7XnXbhw4Z4xmQq/dMXmu7Xjm/v11lbIOXq+7/UcfqfwO8VQ9vwZ\nF5lvi9y1+sorr+Dy5ctYt24dAODQoUMYMmQIampqNM9ZtWoVcnJy8OWXXxp1jLi4OJw9e9Yk8RIR\nERGZU8+ePY3qm3M2Qyz3JJPJdLarq6vh6+urs8/HxwdVVVVGH6OwsNDo1xIRERHZAovctXrnSUBv\nb29UVlbq7KuoqICPj4/IsIiIiIhsikUKuTvPyHXp0gWNjY06Z9GOHDmC7t27iw6NiIiIyGYILeSa\nmppw+/ZtNDY2oqmpCfX19WhqaoKXlxcmTZqE1157DbW1tfjhhx/w1VdfYdasWSLDIyIiIrIpQgs5\n9V2pb7zxBjZt2gQPDw+sWLECAPDuu++irq4OISEhmDlzJtasWYNu3bqJDI+IiIjIptjcWqvt9ac/\n/Ql5eXmIjY3FBx98AGdni9zv4TAqKyvx4IMP4uTJk9i/fz8SExMtHZJdy8/Px+LFi+Hi4oKIiAhk\nZGTwM25GJSUlmDRpElxdXeHq6orNmze3GK9E5pGZmYnf/e53uH79uqVDsWsXLlxAv3790L17d8hk\nMnz00UcICgqydFh2LScnB6+//jqUSiV++9vf4rHHHrvr821yiS5jHTlyBFeuXEFubi66du2Kjz/+\n2NIh2T1PT0/s3LkTjz/+uFGLAZNhoqOjsXv3buzZswexsbH44osvLB2SXQsODsaPP/6I3bt3Y8aM\nGVi7dq2lQ3IITU1N2LZtG6Kjoy0dikNISUnB7t27kZ2dzSLOzOrq6rBq1Sp8/fXXyM7OvmcRBzhY\nIZeXl4dRo0YBAEaPHo0ff/zRwhHZP2dnZ/4PX6CwsDC4ubkBAFxcXODk5GThiOybXK79Cq2srERA\nQIAFo3EcmZmZmDp1aosb58g8fvzxRwwbNgxLliyxdCh2Ly8vDx4eHhg3bhwmTZqEkpKSe77GoQq5\nsrIyzUgTX19flJaWWjgiIvMoKirCd999h3Hjxlk6FLt35MgRDBgwAKtXr8b06dMtHY7dU5+Ne+KJ\nJywdikMIDw/H2bNnkZubi+vXr+PTTz+1dEh2raSkBIWFhdi+fTuefvpppKen3/M1NlnIrV69Gn37\n9oW7uzvmzp2r81hpaSkmTpwIb29vxMbGIjMzU/OYv7+/Zl5dRUUFAgMDhcZty4zNeXP861l/7cl3\nZWUl0tLSsGHDBp6R01N78t2zZ0/s378fr7/+OpYvXy4ybJtmbM43bdrEs3FGMDbfrq6u8PDwAABM\nmjQJR44cERq3rTI23wEBARg8eDCcnZ0xcuRIHD9+/J7HsslCLiIiAq+++iqeeuqpFo89++yzcHd3\nx/Xr1/Hhhx/imWeewYkTJwAAgwYNwvfffw8A+PbbbzFkyBChcdsyY3PeHHvk9GdsvhsbGzFt2jQs\nXboU8fHxosO2WcbmW6FQaJ7n6+uL+vp6YTHbOmNzfvLkSWRkZGDMmDE4c+YMFi9eLDp0m2Rsvqur\nqzXPy83N5feKnozNd79+/XDy5EkA0trynTt3vvfBVDbslVdeUc2ZM0ezXV1drXJ1dVWdOXNGsy8t\nLU315z//WbP9xz/+UTV06FDVzJkzVQqFQmi89sCYnI8ZM0YVHh6uSk5OVq1fv15ovLbO0HxnZGSo\nOnTooEpJSVGlpKSotm7dKjxmW2Zovvfv368aNmyYasSIEaqHH35YdenSJeEx2zpjvlPU+vXrJyRG\ne2Jovnfu3Knq06ePaujQoarZs2ermpqahMdsy4z5fL/zzjuqYcOGqVJSUlTnzp275zFsei6B6o4z\nPKdPn4azszPi4uI0+3r27ImcnBzN9l//+ldR4dklY3K+c+dOUeHZHUPzPWvWLA7SbgdD892/f3/s\n2bNHZIh2x5jvFLX8/Hxzh2d3DM33mDFjMGbMGJEh2hVjPt8LFy7EwoUL9T6GTV5aVbuzR6K6uhq+\nvr46+3x8fFBVVSUyLLvGnIvFfIvFfIvHnIvFfIslIt82XcjdWel6e3trbmZQq6io0NypSu3HnIvF\nfIvFfIvHnIvFfIslIt82XcjdWel26dIFjY2NKCws1Ow7cuQIunfvLjo0u8Wci8V8i8V8i8eci8V8\niyUi3zZZyDU1NeH27dtobGxEU1MT6uvr0dTUBC8vL0yaNAmvvfYaamtr8cMPP+Crr75iz5AJMOdi\nMd9iMd/iMediMd9iCc13e+/IsISlS5eqZDKZzs+yZctUKpVKVVpaqnrsscdUXl5eqpiYGFVmZqaF\no7UPzLlYzLdYzLd4zLlYzLdYIvMtU6k43IuIiIjIFtnkpVUiIiIiYiFHREREZLNYyBERERHZKBZy\nRERERDaKhRwRERGRjWIhR0RERGSjWMgRERER2SgWckREREQ2ioUcEdEd5syZg1dffdWk7/nMM8/g\n9ddfN+l7EhE5WzoAIiJrI5PJWix23V7vvfeeSd+PiAjgGTkiolZx9UIisgUs5IjIqrzxxhuIjIyE\nr68vunbtiuzsbABAfn4+kpOTERAQgPDwcDz33HNQKBSa18nlcrz33nuIj4+Hr68vXnvtNZw9exbJ\nycnw9/fHtGnTNM/PyclBZGQkVq5cieDgYNx3333YvHlzmzFt374dSUlJCAgIwODBg3H06NE2n/v8\n888jNDQUfn5+eOCBB3DixAkAupdrx40bBx8fH82Pk5MTMjIyAACnTp3CQw89hA4dOqBr167Ytm1b\nm8dKSUnBa6+9hiFDhsDX1xejRo3CrVu39Mw0EdkDFnJEZDUKCgrwzjvv4ODBg6isrMSuXbsQGxsL\nAHB2dsY///lP3Lp1C3l5ecjKysK7776r8/pdu3bh0KFD2LdvH9544w08/fTTyMzMxMWLF3H06FFk\nZmZqnltSUoJbt27hypUr2LBhA+bPn48zZ860iOnQoUOYN28e1q5di9LSUixYsADjx49HQ0NDi+d+\n++232Lt3L86cOYOKigps27YNgYGBAHQv13711VeoqqpCVVUVPvroI3Ts2BGpqamoqanBQw89hJkz\nZ+LGjRvYsmULFi5ciJMnT7aZs8zMTKxfvx7Xr19HQ0MD/v73vxucdyKyXSzkiMhqODk5ob6+HseP\nH4dCoUB0dDQ6deoEAOjduzf69+8PuVyOmJgYzJ8/H3v27NF5/Ysvvghvb28kJiaiR48eGDNmDGJj\nY+Hr64sxY8bg0KFDOs9fvnw5XFxcMGzYMDz66KPYunWr5jF10fX+++9jwYIF6NevH2QyGdLS0uDm\n5oZ9+/a1iN/V1RVVVVU4efIklEolEhISEBYWpnn8zsu1p0+fxpw5c/DRRx8hIiIC27dvx3333YfZ\ns2dDLpcjKSkJkyZNavOsnEwmw9y5cxEXFwd3d3dMnToVhw8fNiDjRGTrWMgRkdWIi4vDP/7xD6Sn\npyM0NBTTp0/H1atXAUhFz9ixY9GxY0f4+flhyZIlLS4jhoaGav7t4eGhs+3u7o7q6mrNdkBAADw8\nPDTbMTExmmM1V1RUhDfffBMBAQGan+Li4lafO2LECCxatAjPPvssQkNDsWDBAlRVVbX6u1ZUVGDC\nhAlYsWIFBg0apDnW/v37dY61efNmlJSUtJmz5oWih4eHzu9IRPaPhRwRWZXp06dj7969KCoqgkwm\nw5/+9CcA0viOxMREFBYWoqKiAitWrIBSqdT7fe+8C7WsrAy1tbWa7aKiIoSHh7d4XXR0NJYsWYKy\nsjLNT3V1NZ544olWj/Pcc8/h4MGDOHHiBE6fPo2//e1vLZ6jVCoxY8YMpKam4n/+5390jjV8+HCd\nY1VVVeGdd97R+/ckIsfCQo6IrMbp06eRnZ2N+vp6uLm5wd3dHU5OTgCA6upq+Pj4wNPTE6dOndJr\nnEfzS5mt3YW6dOlSKBQK7N27Fzt27MCUKVM0z1U//+mnn8aaNWuQn58PlUqFmpoa7Nixo9UzXwcP\nHsT+/fuhUCjg6empE3/z4y9ZsgS1tbX4xz/+ofP6sWPH4vTp09i0aRMUCgUUCgUOHDiAU6dO6fU7\nEpHjYSFHRFajvr4eL730EoKDg9GxY0fcvHkTK1euBAD8/e9/x+bNm+Hr64v58+dj2rRpOmfZWpv7\ndufjzbfDwsI0d8DOmjUL//rXv9ClS5cWz+3Tpw/Wrl2LRYsWITAwEPHx8Zo7TO9UWVmJ+fPnIzAw\nELGxsQgKCsIf//jHFu+5ZcsWzSVU9Z2rmZmZ8Pb2xq5du7BlyxZERESgY8eOeOmll1q9sUKf35GI\n7J9MxT/niMjB5OTkYNasWbh06ZKlQyEiaheekSMiIiKyUSzkiMgh8RIkEdkDXlolIiIislE8I0dE\nRERko1jIEREREdkoFnJERERENoqFHBEREZGNYiFHREREZKP+P8n938hzEeMBAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111098fd0>"
+       ]
+      }
+     ],
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "So, using a few tweaks, such as static type declarations and explicit for-loops instead of list comprehensions, we managed to increase the performance of our least squares fit implementation quite significantly - it outperforms the alternative functions in Numpy and Scipy now."
+     ]
     }
    ],
    "metadata": {}
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index fafb461..2e97429 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:caecd42da39e4b55b4dc50c985b30a04ee3eac4a88d143732c4441ecc28fc1e0"
+  "signature": "sha256:b8a2adab4cfa8ac1064656d4b3e3121a2acc1060e034c98ce29986312939c9ac"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
-      "last updated: 05/04/2014\n",
+      "last updated: 05/06/2014\n",
       "\n",
       "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
@@ -73,7 +73,8 @@
       "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
       "- [Appendix I: Cython vs. Numba](#numba)\n",
       "- [Appendix II: Cython with and without type declarations](#type_declarations)\n",
-      "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)"
+      "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)\n",
+      "- [Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting](#showdown)"
      ]
     },
     {
@@ -1005,8 +1006,7 @@
       "\n",
       "funcs =  ['cy_classic_lstsqr', \n",
       "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
-      "labels = ['classic approach (cython)', 'matrix approach', \n",
-      "          'numpy function', 'scipy function']\n",
+      "\n",
       "orders_n = [10**n for n in range(1, 7)]\n",
       "times_n = {f:[] for f in funcs}\n",
       "\n",
@@ -1057,6 +1057,15 @@
      ],
      "prompt_number": 34
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "In this performance comparison for different sample sizes, we see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython. There are a lot of tweaks that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -2193,6 +2202,195 @@
       }
      ],
      "prompt_number": 88
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"showdown\"></a>\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To wrap it up, let us compare the Cython code of our simple least squares fit implementation to the Numpy and Scipy functions - after we made some improvements by adding static type declarations and explicit for loops."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        temp = (x_i - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "import scipy.stats"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def lin_lstsqr_mat(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def numpy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return np.linalg.lstsq(X,y)[0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def scipy_lstsqr(x,y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    return scipy.stats.linregress(x, y)[0:2]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs = ['cy_lstsqr', 'lin_lstsqr_mat',\n",
+      "          'numpy_lstsqr', 'scipy_lstsqr'] \n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#%pylab inline\n",
+      "#import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('cy_lstsqr', 'Cython implementation'), \n",
+      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
+      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
+      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('performance gain relative to the slowest approach')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x111098fd0>"
+       ]
+      }
+     ],
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "So, using a few tweaks, such as static type declarations and explicit for-loops instead of list comprehensions, we managed to increase the performance of our least squares fit implementation quite significantly - it outperforms the alternative functions in Numpy and Scipy now."
+     ]
     }
    ],
    "metadata": {}

From 0511aa505891df4073ab7f8ab1da0d11712698d3 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 6 May 2014 14:37:15 -0400
Subject: [PATCH 021/248] img

---
 Images/cython_final_leastsqr.png | Bin 0 -> 53922 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Images/cython_final_leastsqr.png

diff --git a/Images/cython_final_leastsqr.png b/Images/cython_final_leastsqr.png
new file mode 100644
index 0000000000000000000000000000000000000000..9ae7432ba6ea289adee7bdc337873ddd33461256
GIT binary patch
literal 53922
zcmaHT2RN4P8~4+atg=#w5DH~fLZpmDC|hNxG9n{oCD~*oA+zk25m_Otkd%nXp4nL$
zDf)i*`~Lsm@qNcP-s63bH+mlK=en=!Jb&xF{V%F2P*X5dkVqtI#dC7%B+_O({I`{y
z4Bv^<s#V1Qw%W@mYLeq0S8`K7e7(cw+$DPwiIR@^Z&Pcp+aD5XKS@#UjHYAkblhEC
z&H9e|%9YmNN3LyBoA7vU(xJPH;kX?AReujg0pX%@=EVYjqYk+swZPEyW5**73P+z4
za6hh=$DjCoKi|860CMuN^)01?HJd4GOrn|>rx&L`#fjU0?>TATtUBuFS7ju$hl<Rz
z+Hq`P>hdQ1f%y9Dl2I`0zkjSdNnt<o&nt|C`qZ9s)&KkY`QRE>@qb?(c$eXOgYdsE
zDNhnoP*p5<UZUVq2=;pUQrI!Rz9mUv>T=56$8jg`)_ncCJ2NwLkGNfOkJzbGr^fp#
z${ruT`sB?CVPW9`C$^Ozy;=DJmX?+R2IVEYUkN-64`<wc_;T`-@;+_+q_5dmrY;9b
znDZtjCCN9NPxO;)CR({)S#|HeeED+T%a@x-$*HM{SBkAax3_x-1Sn-s{o+Un9BcXb
zk=oGh*R|~L2?9^KbT+fIv(HKHj2}x#O(o;!=MQ8#K@-9z<$U#9<@z#9kyW?Pz2D0m
zQd0Za@BLJxHv40&cJZQnD3?ObhYyq)85z12&V@aWto09u#Zo4Bv$4^{|Fy{qD|h*l
zAiF&|Ev>evhm~aWXVrF2GNilsmJXkwsOWBuROJk-!cA=-7Z-SXO6}9SRD*qex4YiE
z_rWVIEiLK(-;Lu5eJt~$ufq17yu%<>OcJu{I(F^=-GKuK2AZOdlHA`tWE!sa++Jcc
zxVg2p^+`^e<k=85>brMWUTY7>?dRm&%&(L4!t%Jq*7n!h16H-|?TjbyOjl*;<nCf&
zc_@6fZvXM)jK7y>vLedM%O%sZvWDw|Sn~4nPUCz1=PFH{*g3KEvUl!?N=iw|2i-LH
zBft4<DpNyOmz>mYckV;k36ZE5%6jrYf<i;p!W~vWr@z0U*LN?Ivd`y^x{6A6pcEZF
zy|Bq!@?$qUFD6^Yh*}w0TK+!!`uo_JtcuD^kf~5+upDQGC8Gt633=ciVGl`3Njc+(
zyAN|RGBP^<THvOowdyVk($mvBr=bz{;>C;cm|JfWrl)UmxJ4e(H)~5*J9gs}tH_BH
zyho27wX?V93Lf?fDJ(orOFKF`+VXSa%ln!dkGA&q_wV2P7g_i6H9i{_Tbr+Ef8X65
zLq$bp_WAjlS5Ax9)wQ%z($f!Gv?lK`H#ZM0D?6EzlG1`(+wkt4_wC!a8)|DkfAm%G
zH@tRQ<XM{Oj+|`IQt0aHYI|j_K|x7LKiZLUo{3q?`Pj#2($AWjni@WTe&n;0UA6g{
zG}F7^OC5$8*FHofXJjazJ$rU!VnQ*5U3z!U-__xek%58J(b3VSw{Bg)hfdVVHPABq
ztC6OvHufn_@%;IR$?Auw*(9He96jpqXSVqLBPWX;s{)P$MSp$ckM92ae^vBf-{z2&
zl@$>ZdVWOz_2-E%Y4Rc-9v%%XEuqJbAAgSv(bLzT_!M`Nfss+`><L4|e!CB)W-Xlg
zFJE#{Q&Wow3n#=&xu~nE1~=T<y?b|ZcD9O?%X0FgN4ti^R7)b?tyCVs-oAEI#QCRL
zeqkZ^;lqdV)`Uw1*VU}8b8LE>n?t6jr_Hcmh|hvEe0!$rxbyP#-h1my9Ue4?FTZ-;
zTXr|{O8mkPm6!Th#Ph|t5A^lDy%l;<5fKruYzAdoW=rkIT6GNX$&-<hnOayVO?}Ny
zij8GumUfd=Oi|Cz%X?mE*%2^e!>WLto9ec{78)E(b4geCGwy@DEw+VOOQL+uEVl5%
z;vyHyL~C2yfg3k&to<5$_Mt();5s!mtJovrMz*#-zBAjWf@94oC@9$a<qH=J3(ML}
zalhOIzA%65M{8<gBIE4r-1hRSf`s$WbeoFK>)Us-C1VA{ii$+V#l>3;@2_(=UNLY{
zzqHOKY$8|c;5RJRm4EHwu9dzDmt$t1_dh;&AUP#P-rCxlfraG?x0FBM{jcX+Q_e>+
zTk=+vmydiuqv^DsIbFK6vLaagMo0T@z^K)?d~X~vMFj=l>rGG3$OTkf;j@TY|MRDD
zoRN->vxDr?rAs5j!?M?}Ur$a*I77{R?D?%Xzs{EAoO98Qlenn2F*j#3*;SbJS#*7E
zD)*Xcq$^ui7Y{9Cq5t~Y64%wMSBVuNKI)IM*~ZR-5h=m-m-+d0L;WSTo@+}}3UvNn
z*lSDUEuPp#$$2W4^xqr8jj$YT6)tzxIs+K_@gbcz{)k)kl}F)~-oCzHvab}6kGCYX
zy|{FgS@h=9U6tn}kD$;gpoS!^t~$PU{Usvh{L}Bv`tp_hqN3EmKq}|Cflaiu_kPb5
zo0^)+mED<1WI18|dFb6k|1l>C*VVlJA;V%KA|i=ZRi}xQ){Bc1z162I*&K!=8F%Vl
z^rv{K^UKT2Z5bN8b`B0H_y4X(etIT-AvidgSobquvBn35gdQC+C_i`NR$uaQ%dg}#
z#)|9QbUW@XemwEy(WBFN%Za_r%&n-l-eRanoX3wJzg%LIaIp?sjAE47Kj+V%5AgGQ
zJ~Cpuw%9D4VO;COxi<x;kxNQSidn>zT>p)0R4}`=>c{BgTsk^BoScpC-zOZs@o87R
z?azAl`xnoiB}4TIcVRy-VHeO-jN|kfyJrH+e0yn9izpMs1=RZP^!Ga`DjK1wrFBR^
zAke!1jS6a7Vi3!TR&>OJf`TENw`}nr7He#1cuuU{+RqHVUbo*fCt5o?($I<2EG+oq
zr0>UT=NcR?@8RU+Jg21<uAO~Z{)BbUbNrOY$~;{0V_72&jJtQYPJb(yKskK!__2)3
z$`5XPdwYCR`qtH7ze2IAOG`^zP&p=0{C`e=`=^lJy?a-}c4%wwYlnxTH#<*HPEIC{
z<sTUBjd?8i*280SSJ_=bTULdaFJC?h4dt-=b6%sXH2>AB1O9R{S=!l&$;k(>s2uZ>
z7x3vBY9a(K$w)STY0uK}zRg-6nvlSetzQ;l-BWS~=b1-KOKWX?rT@c6OYFCip&=RK
zykYgb-rPH>cz~{}#5U<%PE^!hZyz6Zb#=NRcIkK=<#dG*HU>I68AV0KW9F>~T3cFZ
z;}e(!4K8B)nOC`QVqjuYz~8+>t!w@K`8hTu=i03I5Pp1UXy}utPtU!1^CmerSFm#m
zTj8xO>&&+Tg|>K2?aG4u{D$`Sa5O}(u~#KD;jXr*i|XA)RvGdSci$d*yW{ok2|64=
zzhw1{N0FS)+RHB@Q+c}IzkMrL>U2=OQc1daVq&80`I#-z;`Th(Oie#8jx`^Wl8TeK
zJ$_o&mxgiD#n{+5#UZUTG{dm+lvVHR@W)|c6TmPOmBgWcCVgL3)`uz~c;88im14;&
z7ov~(qXCW+w=X(ciT6T$GC+%biNPfo-CpA8J1<SP;N8RqBf6ownp%SW*eC!0)&Saj
zYZGb2F@6n@kdmByQB#uvO;x4j$M^4Nuhs=}BqmBzz7zd`ZHBw?*xP$s5UcoOzkm(*
zsOad{)%oFxh0zbU*Vi1PkD1f$-MhDC{r3uEzPVKmYbo86JER=FQW;TE(Vr~}?6hhv
zACiAcFFt*`XGknKUeZZMPCoo&b#+4E^iW@jNq#|rqO7c^+u8)LTKSuMztuZ(^u7C8
z>v#NR4mk8<Q>o3M$FAkxyZyBFwzrq3FLB&#&(t!#c~j3g>i5qHF3F~MYdF(-3^#g8
zY(1#`{FslKoetwsdCV_(@?^}b!oou$BI*om*b#L8$$C=Xo1$ciD*5M+E5~$=H|1N~
z*w0thddOqNY!hol!rz48WVSLeFg()aV`L6Piwb-2;7lpaWnJApwyZh{26A#hPRVD3
znEe|sJ;tqQ{hS~xZ`|+p+p5rcWMtIp)-Az5%P1D{V~36(*CKxy5WuOTqSDydNSCs-
zw3N~;{kL528+zV*CQjOxmsjgiK9v*|59z21FR+&4!&6jJQc@}!8a_Mqd-|x_Q(VPT
zBMua*0@HgoNk<`vA3c|>4e)^v;5Tq^oO!~tfB$JsL9yVZLmH{%m9zbo7g+0CIt=e0
z_L6e@^Az95ZJ}3dxtC>F=@u1lj`JHW=_EqCexm?Kki6rlhzKrW?e}b@|GAeg`pOe)
zGlPR1#l<Bf4;S&#usj2LbhM-2zdtT56$gwlG?wCb`8ui=Hj^yLkYSke>`G#OGgoLQ
z?Lk$YOTCA3c)49-{2A^SYkn0CVLl$v)6>IODEsWxy@vYwv{uJ=Ppo(PXX<PZ_#l0&
zPcrlm%{~dg*sqV9uR3%eU}9ogK;v6+p7^b!Y+%5K;uD&cl{KeO4=mADd`mDuM_2b;
zQBl#)+MUw7)Y`2shq;y%6nx;2kYGk*{`~dp*)ORo#y4*G9==jEu(AHT<lb-5ROP76
zD9?k7?|GlO4~re(;h_+@Txg+Xe#+n9zv06NPyB|1J%GPDIeN6-h1mbf&cVZ<f!_v;
zGcpd5o&(S%=K1bqr?59SD0h6Y_vrQKieWrLA|f8~@oXD^7B94n*ar^}3=MHMegKlk
zIUu<=H7TQZY)6xObR`GdWj`<PcC=giZxdO$Z)(1zU6`L6thM`6c&nc+GxM)X_sy>_
z_5yX<pbM0mGn{GpeP`<HVJa3;Dzr2qAtCn%4>slJ=Wp%)Io`4bM=GzV$fvmY1khaL
z;q&rT%*V)o{rZ*n>XjF&R!wc~{GVU;gU+t5lBg^DmA+t)S%ga+JV-v%Rp^Oo`Sa&b
zA<M5XL@241RaDd}-R=#pL>;~HC^B+8HeP5&(amq1&UN7`6Jw8u#hyNW>KA`EczA!<
zdz-%-_td&t>A#=6J$^AYn30}^g_4t~>2jln`KF3d3z2R0ot=C5`1m;Q9iZC*Y#kaE
zwc~k15o+F}a|bxT)rYVPa+NtPey_P3rA!7GpFk7g%ei!g7KLIn7Z;b9gam~#+KCxX
z|K2jK<QvzTUvrk5pS%<u{lYLML#g*rhNV{DVaCFbTE>4?=Q|56nW9Cl0^W!54p%+c
zk|-V-Ug*2<Q!SZ>LF2XRo$1b(Jrb9CMa>sR-s_e-3S<9-;NA^%zPuXFLRM8`J8ZM^
zLtjo#j@+K9Iab`Wq(n?@V{L8ioT4J_?ewP9!W~zpF9s~nbaNdzuodVvT_-nwK$k%$
zg8lv<wI7-Lu~sB`LHd3B;+WeN<0a$A<oRa;DkJsC3|Y_04T#bF{oVgJ@9wO@i_3)+
zG&D5WoOJ*vq2b}&kM*4&3)S1!$gMUW3@*0pIC9LQExpMhLzC~szIxljh0jb=8sF|s
z^_HD571@jO?Mp_@d|E|?I!?kt2B*S3(S@LB^t`GAIG(zdZc?c#F+L~{NuUxGQyRGj
zQhH@~sMXZe#Oy}McMIscqk#{D5ip8tGIHD!mT_}?Grj`cA#6J&Cvc^R3Pj<-n>VMh
zVh_F*+;}(??p6fqVr*pO-r3nH;?%ap8PiH%BOV;ak~&_%7c%rJO*PJ=sOZk#Z;TTf
z@xkvzuoNvNwpX#1PZV_|1f7a<Ug!mUot;$g2|k#4Lq&2*D<QSJH&Z?QwT8NmeD}fN
z;l6aMBZ`39Houpx<j$Vu!~sVClgH_`U7a(YlQcAU(N)%(I_;#{wyaaEvM@hi4g3_i
zWjoD&K0b>0`1tYeV!@i4nnslZ*I#z{#RFK|#Jh~N)wQ)-EIV=zht*Y8kxq|_W~+d{
zQ=tADKYRZ8X>@er8Bem3J2T8E-%EABI=_88BP+WFw8_TKj;`jaiHV$))$#W@Fhbbl
zRC|O-r`5J8o;#Orv(3yLH)FCRH>9EA+|M7qWlauX5oQ(^oQDqWz>auXV;2_}_Y#b;
zg(8QN>BxS5eoB?-V`{pp<_x8`cCw$6!*SHk(K|FPeHRQ)<S(aFyaN8vQfhx=>(;GR
z@89nLj*$HjaoC$ozVAcMF`R?(Zw0*IOa9v^cUQHwF;xEDILV{@l*G%+i&nK6V1|C^
znV7B269b;Ggy+vmQBhGfldlVIGzSc^N;xxtUsa8~5A)80xS^HKM0#cZh3rDS<QY{}
zn%KBF{vbof04vRl7cVB+@egD1RwuJ^z4`$m1d4CItC%=+=+HPoS<qxlstSvjxA&O~
z7k2XS@U%y0#P4Iw=*QJLqE>k?;6r;$5I3K6+S>Za#`=n(a;(_P#@7)mFZg9tio*ih
zawpY$Uu%TT?sjl+@Scj)<nxY<WCDa5n3{SRa%zoyetur)=uwSyDOY;cI0<fNX-!RD
zT(bU*==-K7Dkwg4^YahV(s;>svIRbKTifya_3LXxXXWIonwxh5ujLgN3nt0h*a!&-
z2s}?nP_uR${q`!gyS264KDY-PU)X8EG(97ux~67xLPA2~U`dxL7~!7d77XAjM2E#+
za-2HFww;Ex3NUHY!8c3Zz`bua)-0RiPU`I8>_VAY{?Ypo`?Eb@11H(f&+l}l+xqjQ
zB+qbu9nQmtsX@JDSml4NEH2iftQc8X1fcgM<>l?d8Jt^Q_6ZH8z5X%U8;t}C(Cw+O
z)VT%~H6SE58-JVx^j~Y{H2Kr>nk-GW<D*u;xKuE=I)62gU79T?C+C*rPqlCN!jw{k
zgoUeps9B7zUiAbBHtKq1{<*c4&h*d5@7WTEsofHHe^AlW)4zT9jsu{>u~hfU70P*u
zJJaM=R#v4_TS2C_(k7_Hh@udSxvmO<*z+_*NjUJmSskJz)G!<=F}FWrXdd#Pj6uJi
zr=|I#S{eZ?ol{b(`SWX0{uk=A2~KDy06uom%Uk`Yz#Y=muQ<$fF|x6-jd$d-M+h1Q
zqZ$p(57&!1EgX3D>Xnf5&+*KD_YU35mp#Uuru!?|afrQqe0=6^6&*`?cE+hWInns{
z%B+Hs(SE%20shvgK6oFBdt}eCJ9q9t_&ZP+xYu*^Lu3-DO~a>8XZ7@A=kzoqIb?l|
zfCI5jnIQOWb#ZY4bsT62<-%E(0p4JEeD&r{Dt&!@6qrQY;kwVCKf7!4X{EbexpIXI
zRRjpi3uW&Bonq8-0d|~OchO;DlYwH6PfcB|_Sgn=KreneT<u#y4*Bt3)d-zRzNGRS
z*`}*8ZE?}g#i))(XjLW7OMIveKbMw9moI;emtq0?^$823cZ^qXbQD9g{hVhkJ3c<H
z*~Rwe5O}3WQW6&ch&OooAFxPrDyk<e#rXa0`BQKG>07dN1kn%31r6W4MVV>Ad#nBV
z&6@!tjK)7F%&n{_B%KX<6dzCX+|294+qYZM2i}66wWM8Ohwit1X{w_hBykS-FX8TN
z-|Bc$2;VDnh$b11#}zJLj?QCjz{|{W-N$*lfykON+gnjTKP#}`lY?#r-gDt&%!xGA
z9P*A2F{dYHX9c6pROOyLdv@XI^`>1B?I%y3e5l>ND_q}4Me~^XWt3J1u%NS&%@1oJ
zgyR1iXU=S*WmI|m1LbRCeEbnm<FDVpzoe@r$#tJ@_;P=+#=AzF^zJ5f4MM#G?hgzM
zOvkk*dT*)xvp{+LR$nXxDn-6<b!bQCfKAt?)gRyA_~i?|ijcklE~xj4$m!C`X&zIH
z82hEfbh4o=B)qp6?811W63e^3+<nXR#tnr{n>NYG-TpbQ1_6XW>gm%*si{22E!zAd
z4qxGEyb54&AnZL~kDDo!%r<DTkCjqtcGKb((Y?D=;d~r2(xZro-43&T6-~?Ja&p+E
zX;|qn;(L>A{+wJ~5GRZ`ySllZ3fgxvf{L1&K<z$Mdu7mp7^S!t;uRAUwbHR{`yo83
z>*x?t<igLN4B6I4g34X!85y}RQ=gN2g$@QGYd<4nI7Ko0A*oWmKGVg=D`|Ta6ckz}
z$A@MVPW}C3-}?1yDnaKmwX*n{vYtP8$5B&-x{;idqgq#2H}d^EkA>w(`*|&`c9Cu=
zG^POUvQw_Bk8r`fZBFRW=w0j(1>Wezybs&Q%&heF>(}JWOujZ1`N<XB@%JFMw;de#
zd)y8jJ{*MlEY}@^YO<q)u|*^FM#Zt?-&7-f%h$A=+bt&Y59N10NMCrZp~j*eclZ26
z(Ju)0KY_0wfhl=+hY0BAxq}kQpj4Y08KE&)bH?#m{sa4!tfhx=sW&OrzpcMejX7pq
zvlZZn7TW5L+4RT!UFW+r`gSXE7gup`f)pj-j6d@C-(hZM_9!KVo0>&55QO+2ib-2h
zb~0`4*RKRn?^T?msmqLHNnibfgU@1X+m3zO*9OX()9#dZaN~xg<J`b&ZSU&U0Yz0+
zpP(RWXfy`bTXrO^l^0}bYpAQ+%nh7z1s8V6rudJW5qI%Ma1Tm<%WDTqEM|oG?^%3C
z0(D@KjIUku22b$Mk-oRa0N|x??i~5a7f<KUT>Q3~f-pvyjI1tw)VLv=<+41zsd>>a
zFi;L{7lM_5-s18y*~Md*mO&4Q%B31FxdRQIus@#NTi#13VvtD-k7(ZbkH>QIVxw|7
z#nYoVg3V5hj5R+Cd;MAh>X$4z%PH3q8#aA2vx8t9p)oNmgj55#I@*}6lgqZrh<(}k
zzyCT-Ngp|Kq<tZ2(lwYhd~#R0+qwj_B@iWEuhNnd^0HGqK{a-=NuGuPkeHY#WHWHO
z)NZ!7j8{NVyAI^*LdFE6zoA;}Le3%PBZ7S~U8kd#b20<F^6zTh%xI8ujIlEK((LGv
zd(=fo`jn-Hg({h)M&Im>a*5?P5OE9tq)Xf#Q3{EazQ4f=gq<h^J|^k1tQ=*bwfDq{
z6Dg@W2i)Xs8_QD7O3ED}$cImAYLsVGWbq{xP3!j^m#=wZe#_}s$DOstA)OV8g1bw@
z1)?e6P4h%Y*WK#E&1WV0-Aq5likG;o<OnJJAXc_5gC3fKw-`{bR4|)VK!wW+pFnX@
zu&c?FkdTm_9-f|Xg?5r%zcpGjo$dPcV>o3&vE3o7*~zuTxAa5wP+{jL+xc6JOPG%6
zrGFJx+1S{?)^gKe+)FVuJgm{d=Xz1dg`WxUuf3V0_p~DX{L2bBOoOwthF0PZU%7PP
z|3F^=vKv4VC#RtBz){oG)Et<Zp$;CN`yg03fZjO{h)-ai*xza7FBF3R@zLtaYEE`=
z6&qD=HnX(6`Yrz&`Oclbs5nH6Bk2}f@3XVB0~sUm=?lZkH%h9kN!CUc&l<hIT`9#S
zv0t|*K9ZhAV_&1Vl>rSdeDn^tQi{4a&Fb}kx76yRjKy~}h4S+HPtb-P|E#`zeB4Cn
zlpgR9EG^9r3%soE%6SK0?Wq?Es(V*||E~S`F}vw;Kloe0FMBE<I^+5UyvD~hF)69v
z?CFx{bm&z|jy+VP3#Zyj(dd}JSq3JEHGTi*nw}J`oGk8q@Xu!>^PIn)#|95jZ7NTK
za0MA_o&ReTo)YS_R0xinzUbiU>iQY?OX5R+R~Pd`CP8f_18r^Xnm%uKKFQ{*FaR8~
z>rQU6JpW{8L8+bbvi6a8%Whx7_Vu|a{J0le@@sBTRyjtL8l@2c$pZ`?qMS!wo)8qB
zg9nBoTER3dzSY0m+n!Y?jh_kvxbU?`hLjPlj%(R`cYpNYyo$lm2>;^j=vdX!QLOCf
z>@2aiGHXabNjR2C%q69z6`@=!2DhZu_37x;ZN@LsEi5{vXD|Xh?WUoLxaHl{)~0!T
zvTX-Y@>A*ir!K!T+XO9{0PVznMAQ2WVcdH(oGm1G%1If{k3;m|vePPkTv5u}SM&Ss
z_SH*!-#Yo91#Ej#(LcpjI{E0)BaeXrxt_{?#tZQ!gB$BP<TR>sGBS-<IPAfO8=9Nz
z!QKY3!O%B4yShwP=LW@GmIdI!>|$l5%FqmeZ=%-qYjG?|E$Qqz6vC`5e)q%#<@m9d
zLQ5SEHd@Hp)P#rtv0?4!geNqN&=+uf#Kgr3k_5@D3P(%6;sNUUo}<^Pz${5ni}&o^
zYYcT3z$_=vHO2R2_KO!nP<9B5l+J%Epz-QN+OxTZ(euhrg*IWsr9;_-d@XMWcQ!<N
z!w*ndR!4`4z%BSv*SHT-22hSK$~55P=VV3to1J+Hfjto6O<>9qJ}oTX<>_yp*pIc{
z-7Kgmd4+}VaL(i>U=a)q4{w3IaPQtd;uZi((&u17Jja|^C2o@gQGae}c~DSrl!jGo
z|6B9UJPuM-Z|^?I=GwYCGLj8cm!DG|I{|3$vNHwOKki~<JCeKzSPsi?7aiSZ(EJML
z6kqKxt*ry2quVkxDFCob%F4Ko9LZ5~7QqIQ_5bXmBrl&e-qls;`@T6O{XEnBSlESt
znykh7pOSiK<Daps84V2bg-;CD`X(nP%D~>s6f!`6fT2aPV+U;w1qB7+MiC8u+qP}5
zpdmmrHMx5A5Eqvxo3xv<oE+EL!2MgcY+;5Ps9$R5jT$14b`B?1sEVGP>AzWkGhd-5
zo|BjNjyR&PL`eTL-Nj*=d?~QhC~R>mAcuOr`SqPe>D;;JAeS)!F!Ht|Xl~qm&(Lil
zumh$ta&vRHKqUp#nFQM3Rb={}ZpbM<$Do2Qc~~qM?=eTex;<P+eHoA`BQvvgw(_rm
zwE=O7(DNi5=k{~+@_s>MQsfN%3^XEd_pzzz;IU&c93Got<0dC1$wDW4j-95Yq?8D0
zjj-^f0A2k2ptqfYUy-_L3)w|IJyzmU66GHfkP%fF9tj^`fLJgWeoa7NU@}zpg{38K
z-1x!1*pqjIA$&sLBs>=>$2oUDKiF4t4<0<YJwGH*SV-BI3VaBI1wX^j58~Q?l>LKP
zWb7iCEEf(o11I|VrRU{6>MF82PZ*W}+n+()2zU5ExRQ4{LyHF6tgME{%?myYMxr`L
zn)(`3UR7jutLC$ns*j9)IH7pv%#w<h?m=lQ1A}%`Fz7_kLRD2&#=0+UZEy38Yj-g)
z9PrvF>9`9JXl`j~Tl3;0r4$kgT&}@X1b)8S+Q5Jjj$i`F55duG#y@imuE8=CHmV{i
zKRdOL;7DRN`~R%3&%-Ji25Fa(L`30*?#s<V%*SN_ZG~Zgg|f=q-o0B7LBJRc5=`_A
z&Kxr{vrH$<148OS_`<Js=|?XMJ&&?`T--i_08_Dw?Lx(>=#jMUeeI1J3QcVbm>!D6
zTPSWs-2_`b%g}*Sh}z+aBfEL?=DDGFlmt~o{XxK@8q{$f-yx_D$sIdz7*%Q&#&=8p
zwetzA`y_5xK@k^tJTp5>Br2*9_kdaW4l;(#_a-Q*2!yyNa(y=yi4vmfMKK_FF?g&u
ztb88TSU-ab=N(d3C;=CBbci+yB8Gq4+S(dB2`VUN|D6i%egITvLbN5PruGH}fTos~
z7f(c3F0#!ov6e0bk7tq}FO(@A<l|eYWqdF;nx>_b8?BsK(~}M@h}e_o&Yg4j^xOi)
z^)rZ8_1<)H0-ZnX`&aO-nFZLa>F&Prqz*zdsGA(zC{|Wh5(&5~QSAHo?|^`sB~+(d
zJiH#;i3NK?dNyO*UvK;9y=lq)zi$rCfPNiV>jejCiaBw?`V`!={hY5X+LahKfJ4$V
z1F{qq?*#@0B_t+twkmLjo(8Sm6MPDv4&Jo<ClM0xzYMO3;cK~*{a~3gk7qzYK*REa
z<NVOJ@6FFbh<FNw3MVJ0hL0Z^sU!JoAYB-lnr_8=IF$AE_u&lJ;#+;X&3E^S-aq$C
zDCQq@qkePtgjC{G#odw20AJt39-cdEKW4}5iimO9bL!L`$C+oEQC?o0QOwLHQN6Ff
zX(<L*R7jz&<0s9*VAt8x0bL8SWet=Ip?hn${~%z|S$V%w=GT)S*%_aByjknd=<8P~
z>lz!k?=x{)YoN>uo;C^i5-;IE0Wx~cR44g@lf>>1(01gYchS<)s`2Pglq+?=NVXoG
zul&2d6PD#WP@;i>0TK$aa-0Ms>~O|Z4F*oWtDckN?*TFb^BL}nZQHt4lV6JE@?mdJ
zPi|M&>HT5*9>~#AM@=_a^5(2%X<I6#!h3|z{_@VuWyyPMubP&C-^v~5Wmcu`OzT3h
zFu@KddKYpiUY73!O`N*JHH)}?(Mn7p8yZ7Hgv9HUooyA~s2LM0;h^;MH%^<)ojVaM
z#U;E)xVg7MxKBg`Wr<~i5Y>9ho#<g&Tr-V{iaI2aR_+4*RhVj@1j80Gs$D)h^mKG`
zD{8pS$EFYOgw!bMy1ENenT+m#WHvaPa>i2qI-P7+8hyX%5!f#>I{$?I2*MUd{`13S
zCJUw~0@?eQf{NKP6r%u}JfowT3EctAWf19zbIQtc&?g`yd7^T1{BYm21!*#-BX42c
z%i9SYK3u&hxjfq+LcoBlx|e3VAsfth->NhQCM^MhAmn0qcM_IdYmd)?L<_4hHHjd}
zy#YQm(0EX1p@flreR0VX@g$?kwhX&35L=B$8bS%^0HhQOV*$U-2!fLC8&{|B4{DD=
z_NoB+Dx8;U5L^n4jSYk)&vZUQpc*YDCyy0SZG3W)Q$&OgjskG2FsZ7#8mR$V;*_Hy
z1qfFDp+gx>T(|^<=g1KnC_HbWmW@a_z^s;qzAEfEXA~>xL>DP&SO)_pG&0f`!cavh
zH4#NqI|=fzF9`xM0h<WU4d|Psm8Cu2`H~9<U&y*gOf4kL$A`>^W*-G-4a$qW9UPRG
z=!j797@*(L`&fMFK2U7>6ndqoRhJif+A*sxhJM#YYC^e{mX=og(`v`?&0WVJx^d_U
z6BwZ+0ZYKY>rv9@VR0s0cy?+u^;@3t78q9^Ah*@uzU@WLC8wlx88$eJV9wjOZ=X9%
zeMPgtUfu#^8q(RRt9$8^maflZB*-9N5oH>1ZI?5xPWI((2rU4h)Iu01ARKP9+(N2q
z90gXhq@?6DPuL)~sof$}O?N0KkQlrn<8U55N)MUQ3zBJ#^PAkfU$Xz}CR1pi5Ovy~
zZf4Nod)s{loq&kD!2r$9(NY6O=aQ6U#j@vEYVBFgn<PSkn!b}oVJ{SZNR2`mB-pT=
zoL6gbxrtEUjm|$D|C2+heEVHtSa`X8Mxl=hp!orOd}AO{usIF=tlEE4gt9cZUayn0
zT)rl>r;sMgp15toH_gKTr5D3nJjLzstpw7|jYkW{|Mp|=ng1C;d{9fI4g)Xywi7wT
z^gW{g;ap_@_cwkFy((n!V0_#RtBN|)UdUG%Tv;hSJu@?PnVJYPmTyA$5XW6DW!YTt
zOM-nj(TkCH85ihWAn^EbeTcD%$wFW3A!nrfN?yO_6c^tM5rd(opr8PhonJzsu*k@P
zAbukn<s3GrPoFOAdcU$^x}8i`&378D{Gx`2r!r60wV+9#R%$QcBE%ae&#@4{oU}_K
z7Tq>8Jf1qck%L2c(}V6U^R>Cs`)yqI)6voQF-QHsaQg0{)z#JK_(X}f2L}fSe*Lw+
zJD=ADLkNPv_I>;I{haM*M?S3{-x)+*1)y@Bf5+sw_S1atH`sk1$N}Io2$4Y2?a!Xm
zEz*?8X@D9iJh=}w0_q$n>PSS^>$O-Z7eZJTI)2<6Rw*#lLzpL_6##y_cI_e|O_`YJ
zfxsiBY&z6EUI~ZEt;mqw>?wKK^m!7=0*-K{(8rHC8)Cts(A3fCK2%(Q*g+Tq=y1?(
z@fNSE^}+r_a`N&-@Qh%54<AxO!l+u5gpawI1fD|%Lg%!Qd=OSeTzU<pcV-|tcU(}@
z$B&>(hsPsgW2unVA-MxnK(vK^Brhrm*M~edHWrq`1K4jyP!$dyJh<yyLH^k6J@1d~
z>Zkrg&+^3W3gu)>HhUy_*!KTC75`MK`ren{<iQkfTsLx3ul?vMq$FGyWo0V2^(CF@
z?&6*Bc<6KNS<@~GYF@s~EF&w63ihCKV~rAAMHa>a*jk`|*_|E6;J&+4PTpHPh6XdY
z(tm##K<O>`o3KUOL0FH@P`qz;y;6L_18WztkOvVF+Ch_xNa;AbxNyNygS{l%94|Hh
z?Z&e~0B@2z;BZxABLz<CVJv27M1&6<56TJHc>yqhhah)Bk+{1zj_?!j%yd-))`5V4
z4IkWnM9&Y!>}gcg`@;&LDm}fup>c6R5bFnk?^Z{`E`YM%C2U8M&1|0(3^Eq0u2<Dy
z8hIw~DE{>SU1xW1blG*j-!OQX_>SHi*S*ljs(}XwL8C}!AYyKR*YA?52q!u|J{Z^W
zA-wGVU$;Q!;}qZ$Re%)48@G~?Mf{4?{kHf|sCabVU}v_rudJ=_bK~3x<0IpLH@CY#
ztTd2|QMJnCUi>-##Zm3=wnu*qpT<<M&^(i(+J4fP?*n2yHHfW|NWjN6&(C-gkh<xa
zH2dk6UD2Gw)6mh#LBqbELo#sJR7b7?7z7k*63M{80NzR7!F{wG0s>U%-6VGuTg0O1
z2`d5M_syS$^DnT!kg?fTZ%f#Ws<C1ZKwQ6;oPx<p`(8KCXw$~(@XqO;QhJa}513&A
z0TfUaM`@xyLA*!nqjO_A>CU|aKouLAM<v<|(9O6tS#j>?Pk~}qoR)e(<GHEaN*Q_i
zZ6x^k0z?Cm{`==B0$$$e3uk!3IItnWXpws&nrGTGx627ZBlE!Al>Inh1i}Hdq(G+>
zI&s2RlW!XdF2APO&GOq5>V)Tsc*6FJZHtk(4TJ^MSMGFC>#)TUK9`L?9i`^NYi<AC
z`;aCsh3FkxIAmz;xQSxKhhK(+V$^4h@w?Wg-SxIMwzjn1Q86)a_LCa(2?$5uOBk`&
zuk(nhyfcPfnRpva$b)`62`qp_(yMT0Bni2$+L&IyP9YmhSOJ&e1D>6Y{yYYeU=Jdn
z%qOfNWKcjp)GM{y1{FuUfedga`&h6tPwn@QF)!WLo%gV?=yd(o>{;XvJqH(y*A-%K
zFc1z&zQ_VEH7hAY#h{qbi$-B5?j7neVFM8c8YywmNFH`hrV!PYK&T@IZ(RM6SL1k*
z8ZQm)>dvD_Pu&@>Xd7Kx&1a5LeS49uDU{!vIgRIp-ZsAWi1(3KuR9o2TwZ8ve>Wi(
zENeV?{}X5P;x*@yg~bmN=@u44`F5<lCtf7qd_JB0tCi4A(2b!D!jM0OGB7_HX{h})
z_4OL6^8lQnRRl7LR5;2X8vufWLA;4-eqz`<bVj0ZBKRkXvQ|^#?d@&WQzCpsuVnuk
zaRSerIU^&^S8(-Zm`+PYITA3ZevQx*UKQLFzlRTN5k=i}+L%RiTktU7J>1fNJFvm;
z0|$7FY7?l%<b3mZ-FE(9?!PxYAxHHtJ!33}Iy<5!DSSI>+~D<D9<R~92Jeu_3xz&s
zHtkPvYhe|qcA*Y!vi{NAr>S2GlYQvf{S8|0I4PH+x{><&dV=xE<m%0vx<cvZ_=Et)
z;Ex}{5PXJV>cJn)dHLtO9l)>aO7Gp8KSjuJAK>OrF#nP&!wS#1#cMs&4kz@5UMU?M
zn0hpt!Qo*~9E)(4N-)8i&jnypJ!05Oz(}&j#t8#?aj?Z1(!^#^MZEI>U|VJp!W_VF
zTOIUeM@i@K3Qz8w&uk+5^y!nIzkfAULGL`cN03}=zkV&Mc|}Dw+-urj`P)Woh0o4l
zbl_U`dA+6*KkYl&nNmx>39h=>_N>#a1H(VITuP;Se0zs!l*np{PQQPac~n5t{l&St
zDjfSf>s|?b6yi9Zy!+!t&e}EPLf{t={Ky8b6rqUSTl@JSC@5%G8g`|PjZM|xjTLFs
z?~X1}CV0&df9rACwlIqEO<2koO^FaQtF#*{ve&RR&V(;dl-~b)3IxjsE0zGr)LCT3
zf>@dlyW46)&oQDgpsx|Q4w#GpAb&T03nF&ODImG$gf;7r{>l&nH-lRc#_;v#_`{Er
zQc}F}6HI<BjFvdgb0Qf@9Ls9sEhMxY8&+SyaW-VNW#r_xLIm9Z_$u6+__3F$);PMH
z{QT#0`mo+_Z&j}QAd)hOCWCvv9biSsVN%oBEjDpcuwUUn+pbLV<Gld-4BkhJnPw+8
zWh5~~kVOoL<y_XdzemgSV`!E<mRZPcz)^WQI&gkVeMdv(<LJkSZpm9rV0B*`!%QT?
zJ<%s_1>-z}Jm)qk)i;hjVftERb%hWBu?2il1t7XMzEI!h4ikq9(oTYL`{ZN+(i-qC
z6H!43@0vI!2uYk};N8EUgr5BX=o;`oG&-8Ef$u`333O>AE33oGBgIfP@R=|pL8Vt>
zbH>{F{{zZ5kwCS4r8ic$q+NJ+7E+%OA(~t+%nW>iy-SENgaR^9?I{eF`7|bmvLoiW
zr4Q1{17pwb`+!^`Gvbdsc}9~=LqkL0QvOz`Bak4MJ{R9IEG>e&{1)9B%A^NjyJG~R
z+G_{B9Lpho=VwsyyFY&ENh3eS9vGQxkhuRaOWpmH8g9Pr(f9jn7Q1);yOsN<!uB=t
zGk0Hm^H1S{j4B3}lwzCqZ>R`7N_9tc1;$@OU|3VJNtWbjWvKP}N-*u)_tDw!2~~m9
z%%dd{N)9HOxr1Z0>Q`@_zINjnPo;YGx9QEgMX%0|gl)bZBf0a3RHI>Bv!lI|bbq@Q
zFBEO@`}gmQUMiw#Xl$%O(}xb`h5TY$p#-7JII4X8Ah7k>&bp*n1rq7f&^>`6W#dOl
zn}S%MpZP<tTx7LFsa&f*+D%MOL7}omQ4{Wo8aIYxY!T`D0~u_z&eq<(7V!@NydWru
zd}?KP*}oD*z~UjV;IsWBuH<Z$0uufTW0#Jw-j@ivU|4QI{%#SeQTGInL~gngm3Teh
zE;mQb;y6=ctSQ|=H}0y?WCZcvRac?-a)GW>iYw+vd<^&NopPYved&@|a(2+4wWWmA
zgiSlGta=IyWIO2iJNFju{D-RqUiMP?auI#=xa?luBU9XGV|g1Wj%|vf+EiCpa>{I%
z$z*%&`tl6zqZmrDyle#vmZFe<hp7T7oMIRERF8RvhF^I#b1{^D`_o4}r2Pg8+cRr@
zxp;WDHy=G(Ju#7xkSmyzl9FJl&-dx#9?@5g_@Mi=$Kc6mboqa|AN_D}Y)759yeey3
z+gx_kUXv?D46SC1l3n9dvd+ImcyxxRZ=78+mdzs8-bjc<3bAAJk1Kf*xyLPMPMTL$
ze_t9IRh3rw-{5C$Lr%xOv~Sz)K7;w6s(%K$E|NqI&qFcN329Z0tqzOaV;gLwe3?X2
z*QpQPSf|a%p}}}Tc|eeNP5=d!prH>>GuMeDG`+ELQ~BiVTW|h%Y>alNp`=`c_bwC!
z3X+gxtmhyTiL3n1(6M84H~Qa*Ev`{rA10$7$Vk`}H)47qHTiUEZiw~aD>VskLmvhf
zv;0Hm+9Y0l65dnTT+6hR*7ek$=7o3+c!e06jBlfhE!j*>E%$Tg;d$<6ON8-G)6=KV
z-*nH(F~+dkECY-(M2VQ(wr}5#`J?Q*#f(Fx|BjovP@DbyzKfQSP7tivOoBZB8G)xi
zz*7z{_F`}Fvz0sUzca)(YSj5lrrQ5QM{CV;vow>qt;+4&1KeT4yg&B&_DY?NQ(JiM
zfYFkkvb(u7g&9vX*{E_eGXoGyx!iIM`V5r0cT9$^n@AVc)T-eQ3Zpe#ejlQ?H6}L&
z7A50w^~3Gt;_RGawmv)Ug{C)GKl(yODsO9*Sp~j-otpY6wJ@G-E$Zp%6uGiGSb&|Z
zrI!zIb8J%drFqcYtdJ1Rd{au&=$B6n*H@pWyK#^88!%t=S)iq%+-jFv`0H{@`-Aho
z>~2*=P{SCJ;d)e(U5t#|Ffw-eeI(fKJ%*LHQq3+j0BLiZYmaPJf79Uq;cLqo$JxGf
z{4H<pM4bDq#hKCiOrx%waw?QxUP)#%ou_BZ(#E|KDJPGdm02!fu|0=&>~J{p1><$d
zmm^|BX=H6p1B8ePug%}SeIt$R{EGn+Pr()|1Ic922UQ}Xr=YN%xE_S%U*bDrVq&Ka
z4cQG{=eNb5y2qEyA5me^&Zn!Zdm66;BY8VqFMnfMUO_=tON$YP9_=VQGc$$1oCoL}
z#RqL?@>i=X$cz&DGt5dtO7RN{VoYIyE(Ndbd3H860zys5@jxwQ{Ek4dDBLue^XDmX
z_v-*ep^L$_zhwOSF#$rssk{K=b8?R0$HOh6A?4-g%OC=SYJ^v68VJtbU;B9oUm4?*
z0{_CL*@ZbSM$<R0zvv)pddhJ=03xXiVGltW4DflLYgieKFzU~lZk^;1NZO}1R<EAf
ze)i+=_wU*;o^5c@n%1X(bMk&le?lLblIE*Y;p`t<!sg>M&J*6~Fhsb~D&Jz38p1gz
zK6Bju@|mP`+v(00n~@=T-6C@hsi?FIN!<^b67!%Bh}o^$gu9G_?0LiB;^g5WL-22I
zVZj6b4|L<5B#QM~rSs=y!Rs+bGB@7BMGyo;atJYzco(`_B4!bZ9t@X>FPWgL5C{ID
zs}S)Bu@6y8Z**ap$ka$PcfK;G2U6!v-a$c8^N2(Cc}j{_irwbZ#>N~(Mh=~?3L#=l
zKUPCmf;dN=o}TXRM`Y^IrmGQygd<-CI6+KeqJxx*3<I6O3@2_F6xv#dCW!ov@cliy
zw7h&4B+F*9?GO?i1iJ-;mvEH8a3utJ-BLSl5c>nkSacdF9lo%fYoO)>RuK%q<{px7
zofR%85Hf|h?Ed`u{uft@X;M>DWnuv|Kr(6@8#4n{3Ee#UxMij$4G4m@`t;jtV@&Iz
zC*0;X%N`-Ahja4&c<|<wjP%By3x>rNto4H$TwJf(zGNMZ+grHBenhEyZ0y6%`|FQ$
zBARMsehr5IWPOjHOTn-Gf<z)xV4h^uH3_o5TNPA82g`eU^t`;hsy=_-MIyu(*#B%$
zLl8vYeDT<g8R(gbx8=Ox(u`MFlfz+cDY$Wg5Sbx6uP%HLeCfPofkYz{E&@v-h6$dp
zIr>*d?RJl?AWi=qv20R+Gd&1qNX&^_HGO?-tn~!ClKBH@$tf&ME4}_xqcpguF`T~z
zGgj3GxR>WHT&Rc4o0;4V9xn`R2is?}d&5f+QBg9|o;`aYF8a4(%x1Hd!CwwSJ3qBH
z&P6(nnbEJgh9LmRLti?|GVTNR6S*C2?TG!uz*TUUo@9l=<Hbl1k-|vN%+J*Sf<|A!
zM#)`!^T@NNX=Sh2oxhs>%bo=#M2ej=IBC0bfX-`W`~g#LoET%U?0tHAv59fbgOSKY
z{9~UdXx+d6m+u0PfWQs!tH%Dlxmila;`|;I?O8>4MgMoEs$>VIx|NrP1nBn<Rki)U
zP}Kw-bGTa#O-*v9rUys^k%n$(P{2Qozc`rk!4k)ul(;~)ZCe6V97J0^AvF<O8%rC>
ze-u$uEEbb@_(nE4A;kPUlzW#*ih$XKr|t#KB@PzgoCyMIVm1Tr1CG2P5hqUQ<e3W-
z!@lBU@HpUuhw~ev4wB}e-||KJqCm4exih3JVtCRv*H<HqnwnO<#$RL1iAwZ%cANjz
zwtX!vEu(i%9jtL#FRqaAdAT%KH8@~&n}3j-{nJP9xR9L}Kfc>hv}1?7$a#U^r($C$
z%dC4d1<qGIS**8Zr7&DsotHFBRXAd(y7TJ9jF=eW;M%&Gyf5?nPN9AiWdX!<{A)G~
zsO(GlP63pfn-B{0k$hOmG*yVYjbJb>v@cnVvOUku4Iz91xcH2X+5S7;*ne-TMlrA%
zsM^FTZnqW6R~?{uo>8@j-4|l!0aX7imKuQ*VVren>aUcWZ^*+QC1ONB3WwrP`pc;o
z)(A0pENLD@21a;q^U+;)99sn!9;Qw<h~x{eF5H*0?Fb6YOS*ER+<E4$c}BXCpR&&y
z#gPM7|A~;?vA6d`h>B2DxyLGggl5r1(9q;9&R5e$QMJ(R2yQUN7fwlxsf8RGY#42B
z+Rb*OIX+wX7l;!lZ=$2EdstZ5E>WO(!sH@S`A`pb+`2W({Gsv~SQ(W3!$Qa+6UPA)
zEXeF?dz+(WGzwU(0_rm~=DyluW@QzGl4lIz4zvDM7o^t@=xKl=d~XVu;|nK-$stG8
zqXp5@(|clUjp$48?s!#WcfFIg9r8M82!58FoZQeZ2)jwUa&@BOzgYln?VGyc)s8zA
z@5djp8&W<mZp(B{<j>_A|1Vl<4?>U}S7jaU^)6hs%e!rpZp)j+W2#-W%?A_bM4}fh
zG65eJ;{<+r<~LQUY*{hF9PCZWG=vDlQz7Hc5b3wX&?%JOp6;XtYhCUCyWvaAb@ud_
z!(BHJ#m_nVWhW*R_=8|D5Vi<WYT;@?kElfunNJx5nElT|hvpEVCUyblgu(XJ4;m_d
z!x&c$aEMo)NjH%mgEUnS2g3*fJVez8&?0P(*or2A;0Cjbd*Fu=>w!4rQz;iwh)2{A
z9R4LP<G{$sw;<I~jcH<UJk5ZJ)Dr?Alu=uZu(g=@A|Nn;4+P2hNWs7$Gz{av>6Wzr
zx}i*=8<P7Hm}LIi54AqKI>$&umK#^{1>u%2?Tn5yZx!3ygnHT-TKTMfKK%>5cJ6YD
zX^o&FlmMhBa3D;M-Rfh-5qx^$mO<3u`v6l-hX0eJ=7GbDI3uyf4DdlzZSOo(V@@fl
zidISfHGPSjuy)`u>;ar0@Jp<?JsE&2W25=Ed~WF79+-O~=9P4dZbk{QUbbT|!-g5g
zXR=$;($J_4<GmmR?HFl-!Ql{T9|UuW$@jO0FHN?A;}hgo%mj}`z!kD#@q%!x1|Y+)
zLTvPM@+Dk(Y}8<^B2n022ONT@wcoC9A1mu;q$H>NDz0eOA!CHNwF#2S*KXF~@elf~
z@hKc5e{lG&Pc#dUQ0&~oxNn#HQ|Bc))mUZyeOw=#GN<1P_vv?D%A)cn;_pBTwsQm3
zO2euMabfC;NPE*AbFoKN5w;sSC+Yn2oUYJZ(E#EC#E=*6o=hkF{FfN!1RRrx1VmYT
zxBs3mO2}95=<pi&PihXtMf{XGXlKAUQ)?c-%-W|THlDRx+%|M+YWP|3z6TxmWu`36
zjQlbVz3MZvWlQ>DbR%12GWNv_0b+$w*Pe=8Q_|1UI6~yNuvA1&h%ng@?XAKd?3dup
zkwkQy3hHXhr7WU)Vcn*5kHVeO=yADoVdQOl*^08w5XGiH*5-%ohHe(V66$FR6t`vH
z-xPZFpcm(jpyi1#+o2JkfdSAr$?*oOJ^1B<v^)T!chG}TYT8QbCpT7L^x@~D;;iBc
z0pZzw+qa$cRsZFhE-E<l_EOJ1vfM1mpIQ!c0mFH<UCd)>GqGa4`}R40Fuf>vBa2`D
z*TorqoxB3=y-L^c#DMTRCmrSoh?Tw8K4bjn&!2&@F`a+Hf84e!6Z&5IRJ9*;1UHeE
zW+iK%JF+=jNHmO!{jsnLx?<J6Ik&jLA|cK9LQr5;fxU*bZYMzm|4sF2s)FGz9U{}$
zcn}{=V=yOK)Y&;;J@wUtzN5N|)cZmFMELv<wcb1GgKVX+g9vq#co@R9%dTy-1o_+(
zIB7dJvCm7NT-N%`5z_PO83C&D95SinR+3t3XPyR8|0uTL7PIY5R=+JGWTeU}`%>JF
z$iO2@V}sdHG|}w<{bw)&t*)VAc?H64wIjcyPAX0L1=oY37d|R6irZ4(x?ybO!S_?M
zaQ>VYW6bol&0hx$P1=rMGxhpJrx<k7mOY@NfA<p!1#5IHLIcA{=|!$6BH!g%eb8>G
zM$m^+HTIg)?l&bl6#B>aC1+)E_0*9yUwK7pYp=FvUE9l&q$oai_~<ERhG?#ibTzKG
zb#-r!b{{1Xba!5~n%I^R-It1K-Go`4wmFeVSt9~eB?)Bu$E}hr%#0oeQ5%TfqDjw^
z4IdUd$KzR1bZ4LC$MnOQ-Kr5&4ZY<~8mn#)|A=u4>_K;E=?4wo4@dR!5Kmhvmm<=Y
zeuuCp`kg&C{3kzaeL0&LvAYPac%_s5X%%W_$&l*Q)I8I%)HfY%t3FlmEo+G3+0Noy
z?C{hMP!tyz-{(r%+sFmIP_j8vTxdg<7_yXX9`397dt>%ZZ4%#?x{&)fL?#?sS(0Vw
zWT?+X`((O%2t03<<Lor()$a&?bG|V9P1c5<gF|*`rpNw#J|#snkK5-SoIc0J*y<7^
zUMcOPERvwo_{3|>UcYoCHY#?pX~xuU?9fWsc0b2g;qd1F4K<U9v5xQG>!5?G^oT5S
zeBZg(@IsK2_N54Gx=4dem7&Ko)1A9mU#JXC9~SUiS{7BJ>(Cgs%6ZM#*P$}fX0fDF
ziLsB!V=_YJkiIo8UaYbz?!Er_cja=I<<@Q63V;8GU-{_6=yY#c@%iqnla5bg@T-ox
zEZ@YTAVHru!TgDd*6aPZU%to}_{yF0g~yg-;#$vLZvT_t2dYvBj5IyT9;9%8qS*G(
z)pf2kUtMcgj(qMr8W>6F&_k1xnNm9H>O^P4U^$s(Y({cwTES8AsIygcPxtIq)aCVk
zL~Z+V8JB>lDf5h*es-4c=xaVU$cnJ_9nL(=N!8BUq;iEiBRSa%sM(<XB=MXMm_u(N
z`1yN2)%yrSq|)nkMfJi%r|Xw^rc`s2)OyKJk9(_MPrY*W#<hC48#nGO|DfKo-9zR<
zSqe90d4;X^o?Pqu7akUuPGveyEwPxOhmVYmjK_X>|K1oMSihCr{&94?YR~+z-jDug
z`5Wt{J(be7^&w$h7N4FU%7y6c2J9MyK^G|R&;_3Y%~HFgZq3it?i_~fmYsbV_Hx(0
zy<IfiqU^a>ii3C1^B7?&1I{WD7Fafb<UI<o)KOY_KoQJl4$r4BY%e;|=jaKq@#f8&
z@VrccfiX!+%fwWlf9(UbLYX6xT#+9NrKHf=ZUBTM!!R&7SdEB)gQy=O>nBJknX*SB
zEyqm1kBkuh7y>)UhjJe{;B|jv@tJe|yz_i#i7gj6_`ANoc+|hFI}fn-u5;By_-ZH8
zgm62_&Ax{cZ5Y!oNY-6?Wk!qep@gl=Xt>TCS~?yYvz^`b5lmTj#4Bd(wO{C-!N?I|
z*$|R_S64K|3Joo-cc|e($P9oi!J>Y7?ZfU0liUk0M6G-LFa-x~zNPd#awIQ{iz_D$
zJodjbJZtWQU9DZybldXg;hD|^&B+6{Z@zvNSeO^KpyR!5=v;9gWVIem$Kez89wB$=
z?0b^cQePbv-#e8ct9X##QhYC6%|wOE-mlfsQobucQm%jAZ|3mvrL1p0|Dg>13Oh&;
zJub5q0+(O;fh%u`z9%~~Gn0{>eXZ%KaA$XS0W35b85!cSLGtqQC(6G+6nxVl{rA@e
zLLSEhB|gIM-9brt6+K#V{w=-aa_80Z-#^Om&MkQ^E7nNeUn+Ne`N;v%mZmGWD&B$6
z?LB6;>)pF|khHeD19&}(j&6MIFh$tP?#qOBjv3TIxAip)%T?nZmyE@`0D{#a0gL<u
zhGgDBF4&8Cz1=#ZUTzETc{8%It`cKcB_%M5RpIyay)2LCJ|8jn^~$aJ-23ae;8hO=
zHYYE{oF|&LBOuF-)WQ(tI)!ZQ0$k(9&ot?IOb8W#5kuT?LbxGH+&=XV&0DNXJ??82
z?=#mLKU%I;Y|cxC7VTON&leUJh@J~<MXi)Vck9+I!XsDa87@OD?C6LDnGwPR7TkV~
zGC|0>U)yOYs1579tA1trTP>J*Zh*Hk&sA(!prjbgR^65;>eZK#@fd$8lpjyG7}!en
zl{cq@$M)W*NFlB-WGv@TZcKfz6PA_+tzw|qY<2VL)Tnpi1DB!Pl(e^HW#c%q;SEmC
z&UP?{;ta17ng|}7^M%?h7k)BwkquBaaJka(H4GySD(5A}wBinyr@#5MBKm4kB0N0&
zNqD$09vFo?O?vfredwVe$m|G!YS-WocX#&~gT+t<He-YyNabsjS;R#HQ$=FG9EItz
zvDQ(!0LX4eUyMXTY=%afL7}ra241l&gkvp}P}Sf63^^)ucJz_@nwp`^!6rbLxZ&J4
zYZQ3=01j3oc75TjFk%;RvmZZwx{iv|B`p-~@<U=u@cu7KNCMA9+1Op@HUaEN%&x<S
zo<qpMA_R}|Xu_HLsBu9mMG8li7#oBJbsbcoeDTvs$=}ljgq81pBi@M^i7qhz!V~wq
zqrLs*^^eRr$eTXyT!Wr+P(Yw9;;$2)Od*bZGVyD$)hf-WZ|%;4cQ1^`k66}m?ceW?
z0#zvJ=ds@`B{H>u1CN~XpK{50Dt+yniIsW%3LD#j->YU}B|;}p{#cx=YTc?B`}y9)
zk~L8+AUv5Ma0W6qhua{6ar-RiYk}3DJ_UnUMB%_R?;oWS$hGfFq+fY#IOfuQvOp<A
zbD@DNL<o;8`SIqrb)WOpVI1}CRm3AA0t0WZ#J+<>H_3{WY&0H$LVA&%?H?30TF`uI
z%kz?wr|^g#AVm5za|%jWOkLesFj+^~otMh)WPiG2-C@5lqDVYH2$Qm?*Igq=`T3`O
z`d5l8*Mo?H3$~Vr92;WVX&qA)QIPH||Mp6T{6H#AqS?Ic^bqb>`AVM)6dsF8OumJ_
zHwAKwUs$li(_;1tn|OU%DU>3Ea@fja`?15?%nakX7sl40if|!!TH-`3Ew(1>zJ0lS
zcY>8QUS3h{uaUdpJ@2scG%Yf9>gQYUG>gY#-r-U#<>ki|d%_3X9?;N84oqM<a?0w<
z$|kVq+qsH~u-)OqP8+|*FesFO$6`rh(TLlw-|Dw~@gM`)%#K(z)t;AE1NTX2y5wwh
z3Y%OtA)l(sJnycl$oXT{_C>dzy^N-2S?&@B6Bd4cxv+>-BNmwk0%juw@Xl)vue%I}
zarVdJ?)GsA3x|U43{FhEe{x78XYCX;7vfn%80Rj#JNvy|$8a<y(qIUq3!fF0FK1<B
z4Ca*mfRO3~2^Ujzj}G_)<T5i`_ZaSFVfg^5<{O?}gH5d*CE|@;od;zoxYDu2V$egG
z=gScZv#*i@J<5*{z@WAJIH&--03!XXj@MB|9okv#Vkb{p`X=YLe~o#!as8#8t!+Yn
z{zCN3AwfZf49&h*y&n6=UthF)K&{APrf%uBQQN#*cs)@{x4WS3OV~pLgXnlMN=l!T
z;=<qBwWeeEZu=QzRO%fW^JX%@!`{LtCoS;7J|L%kYp(rv>aN|a$~QF`;uRH34sZ`f
zJ(0VqshOtz^v=?8$URQqqi(Ds;fq=?jG^p97>-7Nl)tiF*$}b8HgODs_xlG1wz2to
zc?r4waU=$<iBV%rG2QOn$%1@3fnLxb<V{S7=PcdqeXaFdx7+UcjZd)&>FIu$UX6nv
z5ApTjp+hZ-JTYP6mf^4GmzR0)Sft#_jcB6qK$e(jr|fKpDx|2S#3?4WnkfhY1hdKW
zOG_0NTH`qB*3L5^tZM&*slN`3a*O)MVGKYNL6DFT6b29wq&pN498y{u=}zgC?nW9!
zLXhq*Md?sdLb_o90THR+n)AHR`+fd8*LBpHd-lEe+H0*(EHyPrYwI1=37d=ubC3Sm
zExkJ07zqYDHp^e4%*^2+lW=~(Bph8(VeC1}y=qQpE`Zc-Ba$QhSZV)Ya9WEYlwxbq
zfCs7V66xY%Jldl6jFV9qRt(748G6+aI(CO6MbGxcp)v%qv;l<8sj<ZOjKSql^`mLB
zU{M8@T=~va>Xt$7OTn1fE^g*ArS+@OLHP^&uK~{0{_!yb_&Pyngb;95C}P~e3Jd)Q
z=>Ov2;n52VXDlunP`S>(-O@b({TM<>C>?%ZC6IZ5+};UCKldOVJQC8;0w1*^x8+zV
zjLAylTY&9z{8upk-2-&#0g@2iBnR(0*wcy{8ig5O?hz4509+A_cb|rfiwM>a0tfhu
ztm<9<BOMYseGIh)z|InDmqqY1pt*AfSsL&+p<58CRJc6kbHRB$<A@J*jJ(p)DB!7#
zG`ZMXr$j(cGzByT<q%KhziMac4g!(ZenQ7(f5vgB0@Q5~3HW_3gg|V&2<>O6j)LIc
zkAlSeE)~@v)N*Lm5M;Goa+mkeW^Yj889_(B8I--yYV`f_LkUe6v$D2E|4<w9)3mP^
zkiSf52R=p;Lf2pweA`e@m#;63C#c1J<3-L@OE0o=vP`@c(2^Q=byHkixN?5#ABY*R
zqeHf^ZM8^pYc?}C7=8ru*RNl3N`(o_4h^{~OmxsqV0`rGMV4(_KKwKpc**{UqL&Av
zN>*0IgND$xjD?4yRaIA)0cuJT?7Tb(%^7R(b3na{oM^d-ghTgadV6_oO$Ji61h|6e
zfWOf*F`@3hUD1^bArTpeD186^sr!0%Faqwz9thnaNQpt~FW`?r^CKSDxAFsY@zq@%
zDYBXn&bgq;i2iF}EAkE;xa`Kf0AHn3P*5-jp7oTYp4U+#BB?JAx);beu1is=NOC&H
zMCqQvL3wgEecwZ%8EtAC0;?+yoNqt?oeOLf={N)uUPT-f0kl6SCtpA=_5-?Uh(NJy
z>pBGT2ap)@00Kc~zcib3R1iK&7zO%4&-l0+I4i~jo0tjQqs-bEczD#-&dI2$pY*Ia
zF!J&qH}0Ex%{=DfQkhnR-;yFr57Kzlr+d%<S4ai7rRDYYM?a^hWq<(rgL6oT76JkM
zHKaw2ru)fC5*>OqIy#=E3c8dYextB`0g}EcOVG85ZH{~#Qk^I*DM>{0SRl_rf<Q11
zat$sZS1^i*=$1{+JywJCH4d0H(351(I0c%EUS(Zf5)(6XnT4{4(?k~kGqY&N=WDsD
zI7PWD`+a#UO-V=!QFrN2ZY$3V_i83`eZ2pqRG$S?=dPHW|LBdt-7q3O)5Z~&krHJn
zYnrgIgkB!h3(=-oUL<)+NON;W$TGpL^c|_LYqoOeGTfYSLw*0#4ZCA$b5q*Efdkmo
z7>~8<@4yxE%kw}E1AWb-wOdc0V!-`TU;9qo(x7aNqOZRn!N->jkS8Z-qbWq{;Cct0
z%RcOB4#?SPYs%NOAjJUI6ZFbEN97hOx~RT@n<ZL4=V1e48>l*e1JTL(-@ipu`Sp!K
z;;g0VTw6sXQw-uC11Wn=d#r&V!??Sf;uZ?;Kck9y5q$j5)BVB30a4!ao7~l4=jV3u
zr>^Y^i?|$62p_Vsm5+pKPfKPAy5PZ<kXyt6=q#7>9a_}0<+KC29pFt@1_Dr!H-xM5
z?+lNQN+>E40E`8hpdP|LBPJ_r2%W|Q;Rv*KR$$x-K%X{ugf$b|OA7$Rv-4daw96B)
z+Mp*nl~+TfH6d$p;f9j$f=2JCH(si^hfUzps^jX9%&q%nKO2MoM!34;qi)9qSoI;(
zrFVLwx4&X3C=9Nw`A6RNBEGrm?jCp4MNO2fc-o}Z?9saqkS`-Ma}$cv*lS~qb^@Xl
zEPbF-VJ^600q2+icH6%{J2KFB{YXAsD+Q)|UP62A*Wlz@W*q>97R@aL)!9*N>3VEa
z1(3=hcZPn+oRh~gAt51}o!HTVHt$&dq(tX;0lnl-D0uh*4h0SyXsm+U&Q_k#GcFk3
z6kaf8eMTh1-H5t#F#18IRpvr|`Y!LIeGX+-{@9J>UP*j>6&ss7WT6>(bO$ZXdz?#N
z%1z?U9&xq%JZ8}|5oDa`n>PhW(EuqJfP(H3(M{`x(Lu9mGaxU3{nDF3XrAcR4JHn=
zgAQXt8)s??B54^@%fmMjA3vHlUECq)kN%5k>bXjFQ1#sOXL54|v@*Ox8Iv;jw-yK0
zczGcZtxw=HFf)_5x-GmmPWDP^3Tx<U`iRcK61!V~K{n+Bl+y!TYLWEbUTJ!T%$W_W
zY8^N5Ski-h<%NvQ2woY8c+fs5F8`pgz2P@ZJV=tU(zLw$QpO?%5tQE8nM-3T-{@XB
zJ4RfJurF0p6Ixyg3|~3w9k?a7R$X*Q8(>1nB@mE?Ly^^_?6eF{Xt0)G4Uw%>SWs3b
zm8bOD)^-SFDH^p_WDq-`8<qtiv6ER@7csNidc^rHzXxv;{yt_Gag01k=`(#3UmqVR
z%jB@|zNt>0aLw9!fW~&$CcZPpvf1O)sw)Em0Sx0XJUyv0*~g-yS-|hQLr&fcpAT$Y
zI>_iCox_{^3mM;6NaO2%xrak9#=z3DsKoea*XJ^AYDyFILvGMUIDt|H^b}^t!q69Z
z*cgWS>`nk{yaRt=BqK!)QhT&VjGmbJ=g!6#DpJzDJzwwhb!$tR;yPZun=O4cH|x-v
zGiRpQ5zC4%uyJs1+?a=As}p(^kfG_itq_Ae5nY)IdOC^|hTp&5;Te*}pUv>1HKG65
zv=xd-@n}-E?9s%XU*74?cEbpJ0)l|4pX}3}r};N$6(D*YNiTyb8weC8eg;>zwo*Yj
z3JVXP_EI*iS9}T{8;mS0+z#Pzcjl=u0UdzdiSv5)ms07)s1?rAnG9;LGk(wsWM<x>
zlJ~*JHgR>;e~qPOgEK25b(g>8Lfg*C37rbA%>e6jz?*YtYarJ0*O0QO(~i2QXLHZA
z9kZ9yN7v&3gnU0WhlpdS+mAd%2Eu0YyQcw@(B%4rR(z+M+cQ_b@AC5f+uxAs4V|ZI
z-&Gx#56PQ-pi*yuDi57?g0%;b4S&Pi*aual^WSgZhEv*az)ll0cukWwWF;vkmj5n9
z<Xu+*lWpt%0mIysW_)L0pnb4Xk1Hvu|IQWGLv1MUjG>JWSW~6=T-n4hQ#-KVG(cky
z66C-C{&BdVv;pE6&Q2ruPmZUB4}T*${oj)Yjkz9_=sgL0%+dk+<3B6%yu6ZNHQ3l7
z#%l_K0BEs*WfEu?{A$S5b8Tc04(R8S%3u{5c*m(#jhwL#b|f2}@2YEUd8GcRy$f4o
zu2t62g+ZFAn6%NiW3`)+=;pEme@#uzeT!tftvd(I5i*_A^%BjILn`c|s&>+lkb3ha
zc}HFpA8eJ#Ek6GIxqfb>N{Y^YQP@UXhuF4dwOjb0pA%gPqVq7gJU05T+`z*_#RZ|4
z$geQtF<;Mkl@>Qd&Vi6GC)bNSs(uY+xi?UHdR1&sC-Lj*224(+Ha7UZpQCKp+L;H(
z0h>m>kA<K~1wgPgL*fzob9jI7gi@SHQMF$G?H^4o$3gb@<n>7169`)C#)gKQtIjE-
zqmP|Y%})(;C}m{0?=l44AtBM<G@0dqhzW{H{gvLtpcL8(olt)C3i-9lFK5knjZ)Vj
zX9~m{>NNs>>pig@RgdEfE@vZg^;jDFlyBjZl$!cnTbrDl`)*8M$4|1&Ef)d`3Z^)I
zT28b34JOcYg-bC2_P6ugi}jPvvztqV`#h`!wo9C9{n2aGa6B9mQ@;m0*q3kj)vh82
z=ctWc`Jiv>u0n_n$ttFW1$|8N4Ua#Fmugt!<98=(w)_gy#+RJNPPNJ;Sm7lLqU!xY
z_l9a%qk>Giekn>2x{{l5lK(9%BW0gpetc)7KyvsDeY&1-ON<f6`q-@@W@LI%US46^
z^vca!D+qc&&5{!V7d!+GIeF(FgV{jc%a2s<>kmO!Chg|NkM?>kE{*`22{a<j*8kwl
zh9rlZY191rMz?6~85DI=(QIfXSa<treCK(lc#9XpV^)YuVSSdOE#!uQm#3Y|dJRvx
zR?lEiJi}PQSgN7IP{rXcJ_p$=jY7HUdabAGEp11sv}?=Da7V`u>J~u@4m=H@8u&?f
zd&9z@!XI9Rov_qF)bBaROU$Rgat%g1w0kqK;?}o6xQRW*=vY|j-~au2Q{dJ`V^uim
z>j82wN?Kg*;o9D&XLDtn{0Xn~+xX9~c<wENQW5t$0Dp8Eybo^$`WLmQLEoxD#}V5}
z7Mm<?^#A`+VJEROw2jD_T{oge+;QY0&A%f<U3|Gk2glbtnr3DgF2@*>QMksR17=4@
z&qRfUUBVHAFH+~`;0SY{=@!A&JL7dci`IVvDF{q{yP-hrZ+1I))%b?-{l*n7FnkQA
zph3T*AZNZ~Hqoq0GKh?{-a|_tJcu-$|D-Zp$KuHK@yTy!c)0uaG=Fu)+<kF#i@VF&
z85&PlT-~u^VjhrYP|wEbiWh-8mt}6)!p+L@wLUTqG`yZiW9mWX#@HdhY2h2hEKB0~
z!X=5n;H}1QkUf7JnX!uV_H6|COgAM+XM8>ue(o(&tb2Qag8S#sj?aPfE5=jKIOJH<
zmNiX;q3N*r!`sp6qJQajNZ}1Xr)GLE;P~k4xcbdgEKGLq;Rh%?XH+aw8yIHiPBMh1
zmPN?L<`%hW*6hHJt#06`MNQ~>M1IV44?`ExP=3{%nurahR{1OZFBgD*^$uB5NS%GC
zD}|o=*Rz*{+3ygg*`W`ESz{N8A)FY&VsaYYU}t3&<_K+igREm1{DuYla}y!o+!fhZ
z;Is_%9R;w8feaM-zK$Ra1H~11ljs1*a_9Q9JGcw;U;V8Vrt!?;>fibh#V!gbNHbev
zEI)kM9}#Ad93Rh^X3m!M#L|7y!h)-70GS>gZ9Yx+=6*|%n!lglUnnr)lfP#IVTSq^
z$>Rr{oFg$iTWsvrOt3F4PtO;3&zu){F^F!<Nl-FA9P_wwn<VvfYgo)3s(S(nt<KK*
zR{0%7#Lu)TRaDKRN=jaLT-`h7HLnWTS~vUkVdB#mm^l_nN1{FD!Jxx=Za6u$FFHh(
z58hneyIQ37`gabc=A4%rX5Kp*!9g5P1X3|5D3p0d^uxlrh3(;K6N!cXzJIaUujqKc
zA?k;v*%4%iD^OMC|LMi>0H4OP=HEAmgbrkbZLM%WM5Wt@o)Du?E_$y|d{3tC78#D~
z9OEUlD~?myeK>^}Y*nB(2)?~lfz(EZ(uZ1>PRylu9&I`;?cUwBU*fR1-vBmwA)h~s
z{qni=N|2@l19}V)0~|mKa4nU!12~I#yb|bT7qImC<L#Wkdz}lJKG&o9qZ#XAlhWiw
zgRLGFIS+0fRlP0KQ}aUl(4vB2N-fRL&2Ga@b+GRDqIBVQ9r!U&cr4Q&d-wXY`kL*&
zsjkup9gJ7w+>73gZ?&UuEOW>au2GSE^8=I>1?S^xLZbO<XJVpx6!FbuQEERw*{R3R
z6h1_0Q_%eP5l-6!Q+=q2S&T;(6n4^T%FE;GX`Lql!3Xo@uP3!$C0C|kDeIOZ7fW%I
zvtNR*$Q?q1A0{|E`omxGFo4gZ57{@^G;htku<E1Zl6mohtw{H0ru_e0f(moVyFD}F
zo+d&VBsI4u(_=Rf_8<f92M?ny^p7+PQf#|uqzkPo2e@G>t26Yt-rh;I`N&<}2Rxo;
zz$m-3tLq@z=L{`;{^hoIALb(n%wjx)HC!(U=)Z1F__4scoksOpAcsU+=TT(DatBp<
zr>dGoTzwqjKt&*Cf9CD%?0oAaeIfVTB|!jOK^6j(oE)8Pw6hO1FR%b8v5s)AO}nQ_
z3?KTFkmK%)Zdr`>548kiZp@=mEsz7WU!13iym>?-Y*&XhoIny9J~A49EvAnfsr@P$
znMU^}=Gj^|c*-l<8{<R(5DJ$9KwnTue$wf%umL$_U(yp&r)7BU?)Q*!@ag&r<5U<%
z+>u=Q(+}#7M-cWLWOcXiwI@6jcVB*|g)ugkia`A~9;nd9JD$TQAfUH?H+*HlX&Lkd
z=JhRV=jZ1=-Q5`PL46FpxJ2lLytcNUMNyE)7toq&f53`GM+*oV`#Q|4aR`DevNE~F
z0-Fy!U+`{u_*fJ2LIa*EtJCYe#)gpBQf)1sZ=Z^nHD^55V<C_fEzbrH#bdqJbihx(
z4-dDuT^yC+&Amcz+9~lkgtO_kiQXR(j8Rp&Q6oWp+qFF8lo1C#K2?9nGk@6`r7wkr
zZ^w!je$Wk6$YODtiW%j?woR4~?u{Oum>2<DKafN{oEoL;q~wQ8I}`nh#@hyP7%<3-
zR7SPmwD)|wn<5*8n@mOgb^h%&x#^eJK9!6eb`lerMHzD>vtM)HlWENh5pb{$k&wT0
zJ=@eQt#eFWMn&gM``FZd_?MV=m*uYmj^}L(8wKPPv~@FAU(x%6Z^oYfNUhAe!2JO&
z_f>bR4#fXSH(<{`h3ClM9i_MDR(+YWR3Nul?0Zr-JH8ZO&uyt0$LH&@yYuhAkXlr<
zILmp=NC3;xD2$AojBWLwdvbyI)HpGHO4~!ISR1<SlhTCvQIw&2uJxf>7JNrX<<2b2
ztF`Gsk_p0P*Gap6Kog_}b;k9oxYT#OQ8|Jyn$x?#{tmy6jcvHFEGJ&dk_mZ~6ml_J
zF=ubR<M{|VITsR2tiA>h)Z5ozWGCo5>~ICDAt1UB9Drtbr!^Zcq6gXv7Me`<`}UNT
z0urALnYb^wDVtutaBB?Y33XI0eC2t%BP<fCRVU9In#S=QVgGsTP<ol1f}+e#K2OQX
z%|8Go(_hAh&b}ZCi<*nNkbGxJ!cWYfkjavxxD%b^#TF(&%#nioBr)u{;kWr8AEOJ?
zEZ8C=LH%xIT$7&0g%gC&yY2PWW2QcWLkbloh{AYZTdQ`uYXT=Df|9>dhWsJ=PIr=X
zI*UEUUhDZ#-5J!Y&D*?Lyl-cLfxSL9+1<RqRAIeqvp4&~k<cX%OBBOp7ISf;#7>wu
z9qk;0-F{a=r~{B;>Yd3FP!aa^g`;6=5Jx5i)h11Tkb(Vh`lyK6&}-4X^~8pAgNee3
zfc;f3<;(8JF$`HIA2b(dKkBtJJ?b8n<4(?}|GP@@LdKGx*Pdp^yJ=$zaZlH@Zv6+8
za!?N8?3xt$bsAv9CPh8`NE3WJl%l|<*krm^CCo2pzq;~y6mE*Fq@f1COM?2|rLNcP
z|L-IJXz}^9uFU-1zjN2Hkq76SF+@o)KLS=LrY~4cfjk(k<N}7C_3`b%%>T3G5xFwR
z&Wwnh+={5GIEs}xLQzWwqBzV%)1f6*wT|n7#J3}bUF2(}F)4UA>Fo`>$VEfZch2U2
zW&Sz3B?F{wUvy)3+NLQI8uFQ*zk68C1JDVgWh^4JMZ@ESb;aYym@}pI@1SigZHuv2
zjE^fSsz11RUGu-6QQ@d)FgC2eNlzi+NSKs5<-kgh^(_LnYXItbS+*+RK+vuzC@J|0
z5Gsrg0AL4XJ2}<WqxW{Kc>lZa^)pjP<<4B4DfW{FV}qDCEr?r5`}S+z(D5G$DVZ}a
zWM@w@CF&bLKi^*paB~NNU|U+_hK_We-)>tl^EF*x6Oow!oK%+j04*7*1CkcV=Jxi`
zl4Y;~=K#AT0MQCdOG!fG&{-ENcO<u`w>vFPB6q@zOg_?gOFAmiA2S4h$uZIQ80C@@
zy`)VV(KioORle(d6LSgGHa9A_t7O4#_47koCp1~(__EIr$u)t+#R$DYm`9Whd^sv#
zNXa-}ZI3fo)2yoB`=7kaMOS`|E>rOLZ7rs;1m0JRPT4WKbaL0H8XXlh_!SZo?%bv5
ztM|Ex-+Shdaf6yK#vUNiE5ZeTZDAD?MjdQAZ`2>i0}TaT?n%yCaswy=_U+UVsld4h
z1KRFJ_1Q&tZ^Fr#%|sy25`3b3KAt~|)QqxryN&w!-1U_Jdo6M>;Dh$|`T6Vd$9B&2
z7&mU*63>hoUh0)994mKXM$p%$H|_Aqb?AM+%kU=Pmq!T+Dd_<quVD4i4hZ`Tupx;<
zfZv_%I*)TmGA+XmKYe%7t<Ap*-x%?TGBIn&jlCHMlbFdKS1rBWl=LL0&@wshJ0eGH
z)*^7c&j{qx&G>n(?#+1By;uy0T4Qmia{xW~YcLD}qPHW^fT2+f68w2U;9dicdf%cv
zI}{z8JKiv*rcNp<eI8FoR5Dj8j&d)?I*~>9lCMrM*Ow3&$;-1j(qG4d(iMvVS#C{1
zDf-L7X8mQdfGWb?FX-7;xA6Jlh$j$Et>#+q2a0i$3&IE@kYq<Y&%Io4_<!;HoU3qb
zo0)m(Jp89;yj#Hb@5hwVpVpmtf|J94Hmj8yxYBlmFd&q{Cb{z{Az6m->@Xf~KBJHm
zI(uQdU$cRVYu(1i#%PD_3`dw{Q=lST_cRAK*?UpAtV;?PCVnX0-28-WOps8u=0}l+
zhXVXw8b=t^6!5;hdfbZ2ecF08@$0=1M8}i1_U}0?Ygo7a%tvfOlb+mr3l#_$6dHgJ
zi)^M4+5hyO(Q~M5oA-l$n(P^vslqz(vGQ`2x8w%gs;N=oR*83IBq;pgC$d&qe=c_O
z%Q9ZTBk>qF*LJVtw{>-eX;wsW*V`^`2IJMFQ?Ox}^sn!aTnWTugMu0j`vbu50DwF&
zk-H3|g};G4$RzMRn{3fNfJ59GCSn|8W6Hu}2^J*L#Jd;=Ml$1P1WVEPY0n&}UYBP7
z<v%h1M)*<o3Ny1Z@AM-Cj`Q}g+~U7uQb5a(4&4PKArA*G4gnci4*)^3fKN+BQ7+U@
z+i<=)4Y29d_AvEe`tJg<fifjZO=a(y4Xcle%KLW+2POw(k8nrGT?RL!t|z#UnDomh
zFwTVhxLfQ-KGq3_Zk4hpAtq~Xd0)1yXt#N%I^ADQ?{u1Qby1hhpjB5>3(Cr(e$sNN
zlr*D)PW>_~H#h(0)hGKgdt^z!$W7FK+AH$a&;xH?RV}P0ko%e!mLc`1dVD8iBE1dp
z1_?X6zFeHMtkKYL(f#h{553#?1lleDp4=fI=tyQaL`z@56bLG7vK+7b^zl7lbHfki
z>W)1o9bD8AC1=w7vALTTI}P+TVa0u+hk}i11X6cnuSBUu;qH<9jwSE2*-*ZEm0bFr
zJ|^b<i3JG>7QT>OU*Iz{09CI;2jx|RL*Bw>_t^Nj18@O>OOKXz^Mi;Ns$npR*@L+o
zP6Z1=^dy4tqTcA{Xb$WW6K_9x@=>v5JX5`pqF_i?zw=L)eTz1pZ*T_o!rSk!`Wx}Z
z#AL^e;(s=gI`7)>qeiezhOh#44fQ^k48F&Fn+9M8`gGRX&JCi;z`*eW`T0(3A$y3#
z;9o-bd1rxDS;*3DYFuUFI#W`~y_p%aiwlu!?9^_;pU&+M<@o$QC@Kdrwe$P7uZMP<
z^kZNLvzm*$gq>WRmphvF*_%D;PZ?>6t49&-X1=#>zTIeX1h}OUc<_S76$h}FfhqC@
zfY1N>tGCe^&w-k2Uiu43>ykk?TV`f4id&2t$9S1*4p(9x=9HQ8iTG$jMEn)^^*bIV
zY&<{z=L+eNxF2-`^0c(H^aW-N0S=FghDD(cxCe#icXTux7J-euuihJ4eQ{P@Rj!{N
zek;<{MY$HGyxnVw@v@s(A|UeHPDdB;98?B)3jMLUYZg;6F*};w^jF(xFgcBev&R6V
z16ulLfS1vp*-+Lz;NXaZTNH=}Q`uM)sV~s!`(X}jYgM(%rs9YxHNijK#vDQX0uI4B
z*Scz(qc<n?Vv4x4ifV6a{gX@fU+U)v-1Sp_+9<V;nX|PS$-M&PqD`!ln;0o)FJ(hq
z93gra0NWlW&T*rA<=Ps4HZ~G8rk)1gf#?igT_MuN#G0ioA>rlZYL(FB_`9CcQa*t3
zfU+N+;I4L>oRcFMi!U54cWCF2mzV0;A5HmvJ8P=g#nyGN1C`O4;@Ip#lQS55x}|q1
zEhcsq9BCN@1X7__z6X#$r^9kL1X4InY%P`FB)yqg=<-zg2{1rS7M~#fd3*bjphsLz
zR<Rh<lQ9t#AyJ1fgSx@Og84RmTBKnY^yhV3^e@ifYj9?hbcG!TjwU4|?!7H3DPf-)
zGyfn3@qfW0(t`W6uaEzwuDo*5&<mM{^n0eJk^=nM7&pEDy{o2f%|(tCctxH5HJ2DX
zXRg$8y*e3sci7!U+Q;Wr`TV>4IwXxTia}~%z|#YIOF*39%+#Hld_xeTYX_1zM69;X
zK-3SV-syz9Wb5<o5_yCSfmeI`dM<tobI9*J6FVULR9Qj);E%bO)Qojlep+Na1JrB>
zWh8ofuImrWE7+}@0y496+`@G!f&e@MdhR{ot^>i09^gMXmM~{y0!49CIBD|Z5W1yX
zB-qvTzBRMn?<$W@YjJ$%$t$g@?ylezDk?rkq8E&exh?xb8y^X)k=i9~yaY0r#&L+j
zbOY~y)i#7{OF0{%TS#O&|JPHsVb%R{=2(x#P=<TJw}U1De7UlwW-kaTT;YE<<`O_U
zAU08?Soqggk>Ijth@q%d-s9J=&wd`Wvu>kZ!M}KTh9_T)L~9{W?D+Wu*RDYCvR6`)
z{dc0z(iW-7rzCxnxH$d3ckd8DiHPrXicgUg!~Zxj{0XX4XqdmMN$t_l(~E`?BtZ7V
zveuUh%~pMO$3~S2xoBogx1X0cS~I04!y3;%(-X=*I5^2=d*QZ0=rmVMOUIuX_U|Uf
z=5|S8hBv-Tv=61_*An3qW`45PORCf_>noVINc}Q0py)dk!qTaMA_bfxZ-Sk{c%~p&
zbxG${&hiq$QNm?7hnU)D-5VO9%gC_!>kPXA(Xqdza$X;rJ9BrG+uVJd729>*bxtV%
zI4ep!f8xDdw9{$?EaXCV%3X#yzK-<uBjta<hI0VwPbE-|(ArB~_YFE!^MSms>jI{D
zox-~0|7Hn}(JtnOv~xrxm5uM8GK4zTR2!8hkwW)dGp@c-V6r+iZnt$+;?L{N)XkS#
z-Tb^95x=5^x|XkS_S1xN8ut%n21Zh7#qqhg$SI_^wDhDJ*{xns?V1B`8KRT$zx5p8
z#G)-E<g-K~VO1%3tn@((1H@bidfA5LqZWS>lN`(C_z8-bS0p6)__Jf`q<dL)19saO
zcm-1q=u%JY##sNPQ;UXpNQ%l**NJ@d#xYr&;h#ke4#>rK^7AFv>iF5Q>@bP`$Ou7q
z;17*p`=f)Yahd~-e`&&9d#e`9Ce7Q}c<+G$c({15tg?&|L%Q0xGM$smX>>&wPxqTA
zjtL{nZ5}J%)r?+korgRm>FLl4S|Ax-aa@Kt=RWq_xTMw~7J-u@`wfixro5EVPrvkg
zbL%88Hz2%3?Zef-8d{uR$A~X$x!IYRUmuvHvZk-~uW%Agkc=4RJ#bRFr*;jis^F56
zvgCf`-<$m-fw%Sscv+C?N`)JtcaiD(gQaQRI?b+%4EB~q{IPcfOboI_{_FZXhPc#7
ze=Yr?B(I{naTdFy|GGmWFYXy0p1KcfmLRt1*;Ae8IH7@|D!gjSs0~?Z+3OFVDfZvU
zdYc#Mh8orHPc}6&b6-pq<kiuJmRePnqKz#{|AMeLGvJ_lys_co;ai7a(cs>oGO$jV
znO;0%&S`~scPk}=KwiGHzFwcjwu&#i5XTu6ARb&$T!z?JqB+{cs>H&K;eHhJ{jCez
zLo>g>{*>MQ0Y_V~6--T`%eIK_g!$<nrqU(>f&?}+KuJ+iQ~T|6dF%r!=e^Ijp6HwU
zIZZ<9LWl-PVX7UFd<X*<H$7F*{X2jHiwa+RAaR~YMhdK*0`%82MqFpP<DsdI3zie!
zB1uJ09P`(4Yie%}b{^(@?n*TG`pDjC8WOAf)~&DC&uQzuTXtD|yor2zQ4w^aX8dp)
zpHF}g20T%*=K*>KHNF>^4ut^QsCkxmyBM-JaMJ67K#GHpZwiKflD23FAv=+ar?Ry?
zrY0~@0GXclADErIw&JvdK#el~9rB6!{kxlWi2lTZBk3%Wvc=`z(S^w4d-YEd_UhWr
z1!<l90v7Iz5<q%pfZ_lYj{vD<s}=ohcH^A$I=&BV6$22E1=-B0;!z28=OW`Vl3}V9
zjPYP|rwFjT%N_C`W)?xb>t2(3pN`HmA+4_=m3+s|vLU@mTgUCkP_)PdIZk0&@Icz@
zK9bQRr*vi!DNQ%5HQQTs=AJ&3iMo%YG-YErx*wJ0FyG@?<>xPVww57Wn}3g7oSDIO
zmZ@S8QI0@&!-1&|B4zZ{RkVt8Vd0sfA+3|Onk<<(kV3#i00RV9ib_fbhdk&!3@9#q
zKnx%Jq4X&=N$vxg4cYd5`*l)M%2M{o>%PrG11~L*IFX+u<DM|Yo6bJ%9nDs05V$i>
z*}i>IRZ&HxMF?J>pHsP0XGpo8dy}$`5}gwVpd`zv8bHCz4pcidkc1|%w}aIl5XVq^
z|54L`BL}F(0<c2H-HJg*04Wd1nx6uD7Q)J~$SMrKdmd;a+75(H?dR0>KkpP}{I`C%
zWV<u}+*`Yc)`EfThA2&nydC}TFWc<w`4lo80mh79NcEl~acUxK(jW14koizV=^z)e
z+-@6myvLm72<4RMKR9rT2i_uV?1k?ub(_wereXheV>e@gZGDV(f`xID1|UAcKyU6}
z9=lF|zj=FmgQH>sjMGd6R6V0B(|wZ>R=FO=>p>PZpXq^m4Gz5E)Eo^Q?|mTMR|2C9
z?XPA7br4=1M3|0*Pf#anX!iDeV#+v(x{2w+l!#ki*LLyg$EL+(<#Xi@5@_lysc10u
z&|YgGDrVdk6fqNWIcya#mTmd)9z(5ceETI91euQ?;o7;8@%neheF$gL;C%-CDq?7-
zj)9+F90WU%#{XxV2`voXInTSp+vo1!Rs_%>+O4<qlPc#!i~oUJoQ#`Q<2tj$ZvN5D
zN$-ASY6?Kkw@=pmvEx5RxDOUe5x#+{ft0Z;v7S<VHS)EOmT#H_3_bY4St&DR$)rPe
z@LBBdKTESrXS4r7e43Vj3;X?*WiPGWN5R8I<XPtofs9=&dzjuH19am*Qi%Qn$^HM(
zb7Ho^XQt+vWosrMs&NU#fT^k;?*A%6T#h4-;b@4QZ7@J_eS?zys&Bt|@H+4#^ghac
z8T;}3-<(g^{X5hOpQmatB5-azsCpc)K=Hd9|LEax4~d72$JM^84e1VKrH5Q8;%5R5
zGsRUC)0K2@^?WdGZ3paib+5+L*HEVQZE+vjb2^$UD2R%{Y#1CI902ftkePtnJ&ck8
zX5$s064wl!Y*!7T<+DIqbk|cq?8foamk%Q+|65a?aDcuoTwiGbe>L%r)-;9Q`)Z=r
zOWmp&LLimIec}wE{*XJWz^Oht@+;o$+UkM<Ez?h)CQq`(MK0R!7~|u6!{YO6rVw2}
ztGdfZr~CW+JF|;d!Ir1I|5d~n&Zejn6R`&*mnw>H8^_i%BDDu!k&*`S^S`#Q;<rLV
zKOxOI$lL$ZTYt4AKDKPq+qG&K0gXWpdTpoWijy<z(sc<RXa?*owh%TQS7SSIgu}Jx
z7<jn2&||>RlN~V7W5g^tpViS$$$thC+!G)Q<m#lf-%6!v2e#MlVaxR|7SqB{?!5za
zu7>wl9K!xXWgNbP<4H@ic3NkT5l4`Kz;99?A4xvJIlfR!Ghb?-(FBr)xK35>vC6M)
zVSlI30a;+1d)-gLR@OylpWzlwN@^KbzpAIKrnWclKfgQ2ZM_xx|Bj%Htn4xzxvwBt
z0p%u`Kf(MO4=5gLcq%NPBuYPrNCwYXpu)7dwWauo2+%%G$5mOd#r_7>-ylSopI|lx
zmyp$eRF@8*8PzQPfIRb?T}5e>p0sqgWvRXWb6X0E{QAa4Lu;>ta3Ufz!uk(1JB2is
z#1vS)k6M+SkwFnX+3^hnyF3*fHHmSAa)jy{h5WVE0VfC9>QA7<ey5b~;lpfYWCj3L
zpg9@<M*W~TzHk7Lx4<g^STR6b6bE;{V4G%umca+lX{-T^rxYRk1-o!~C~Oo8b+$x(
zsSi^kb{bp%YgbjM@^Dw=R40sBOERYu-9#xdg*Gel$C|`lmx^k)ZL8T{#LGSTxo@pc
z9QiKwEfMoAX4IAowb$%F=aa4VL1`Io$HmfZrb4;J(qfOh*Q>_9H$N|vkS|mL*8((^
zo8AH1W00sqBRQDd$_$N)b-YR)wZKzTDKBeI4^mr^AmSj)$;cq{s&b$i03cMn&~(T`
zp^*3{-t9Llyj^1Tan}Vj1leudg_`e6>_m+B_Cm*ABW3kfwy)d4`@1z;m>p&Usdekz
zQmnv{%8`oROc#WKZ{QkZ{(sxRjcUzw$VDqBZt9hlilS;2hR`ij+o7!UvwI&^Hj_Gy
zjXM~!))Dq{nJBB!{dwMxGJr@<d{2_fp=zOvtN+v7)z`=UrLZKv9=!hvC#V)Vzt%?x
zNTF&bbPa)SDg?bVE^z1pe&!wwfY}2m)Coz81~St6Zr`?g!nh!omSHYbQk=ZFDLp*U
z8_K0z^m<o!vPz1x*!}s@@Y~{}ZicqfJR;=)kL#Q?Ci=#&$LPwkKNb`KJU?(vf`o+4
zgC`&WovG^Mx=+Lkf^P+ya1QyvA!;@aXwM1I3?Og(-wsQ|t8J)ql0GRHLNc~wXPhU`
zPoQ<{*0+ZGJ3H$k+H--}D=Vq@$tx>02Y>!l<g-UuZs95ad|4<g+&E!IkEK8qPCivo
zJc|2~gw!%BExx|hVDtU39M==)|C%kU-A+;lQ2^2FJmeP6$mr=Ay%u<MQ~+u^Gu04T
z`gnq!^=>{=((T8O58L)qI3WQq@i@GCjh{DPl;X*SbWS4yMuY6&XsdGQ@6gQ;>Z6W;
zP{ZjAY;oWh+QSG4POH}N8-9qzazk5BPXP??f%*tDdZax#A$bN0E>N_xO~I}IPD_>~
zV$s?aNT;6e+xH^<u^k;Qy^C``m<hNibXahsTHFcD48IWZ@-`Pwv5)?oEYOH`Uw+ZY
z-#}^Je-E#9z99I2xd2$2<+Mu-2rlYXo|_aVQiJp_$VR`G6?@cw_h@d^<V>Fu2yMFb
zj_q^`LSHqdVRgEpGawT}^BN)11GGfOL-)=8Ex+MeWSm5#rktYTlB!<YeZHqh?QdTy
zi<f9da=#CWq8DY4WdMo5=fP61EPp`$Exr(w)TTQ+OL8wMoT+4W??)_6jg;1wLfpc4
z5j-ySs1TNryIrhXg2MnD0*$H%G@q+?2(K#-Tbhjlc^z1y3DSA&XvlB)(}No&g><2n
z`SfAr6b&=<uZvU*OHYJ@|Df`0=AZ6VXw&D^)o@=M%vx*k*d4oEjl13z{pHV2Dpi9C
zXwC%PXec-@WZcag|GcqX`_;c8iY+M9<lHuxSz4<$a##K3d;WJl`m3_{*xKO%0tPwO
zhb@4vdt}X&;DT*niv6tqh)esUOG$kNfvTV=f(1jXfl~0+Ey~A__bMtsOR{#=3bz0w
zF4apZK3;2(k%-NWgYMk#RaSN8htzko6j<)Jmw%OeD0a)W64(ALM1ztw@{2HM3!0-X
zWLBoZK+`u5#swSE4p6$EK+`3xuFhuX14LezzpFtA_vHu$K-fyP_~Eip%u~{-aw+*R
zW^?0TWn7<qU3K&3mV+~or9x(>`}#``{QER^oi{e2Gt0vi#^!Z1L8tk~oX?!n3~u|#
zT~t%F)s{dj@?TKuH}W0vrK?>8ckj^VmuP?gC#<Xf5xJPkdpE8}|KFQ?LX&THw#;o-
zhKQ8SD0W$QW3|wNzu-^+8g?%)c2$R9Ua;A*9MnaJ*>MQz((g@bqmwVZiWZyxIjB6^
ztC#$|YWCWGV7Z5rE8$f`x(*UaWNb9Q0ZFh|93Rr}{PdxSt};WZ#v3l#)Xt4KPAlOC
z;>29CAFAENXN+eGhsWD<sEIes1!ciCmQXYKnvXz{aN|L5G(Zqo7OG5PW$=wtqq)jO
za?~UoWTg`?#%I9wblfyJaPU)c0E^ax7UMw*9v-{hmKOvB+dWg_VkUln9zq_odYC2t
z#Iz#kw1$hpp7hCo=xTp`MB#%sAppk9Wr<_}e_hwf8#w<~znU9nd5M7}NsA<0Q756O
zKL6A>Xy8v#5!<(4UfIZ`;i&kioNa7)`Q#uK0Y<>}3DDabf1H|5Iq7b~8Ja{`gYP!!
z#s<+np?22vVG27pS9_xqFrb8wLlE+JG)Ic02ETugg|Wd!gUpH2(rL#M?3^E@2LGbF
z4;&M&yr0GrxVY^bvY)wBJX5|KdH)`slj5V&pG_t{84EAP8(uf3rRq<x^0}6;q|0Kt
z%v>?-QkqqPffRFg6(8tmaTJ5>e3s8AA6el}@7X-!<__PPegaapj}^64FYN4Ypioy}
zZtj=hv_>c4qz3Q&LlvwnTI_Ogh{b@Z5omvj!Q0>=jDw47b$mM#SqwafFZGH$HI@1K
z&OY}TjW}i=vy`Q_t(usJ=CKEf3+B|nQl?;SGgeTcywA<e?&nvp?UiyAy2V&xd6L6B
z#hc2Qty~^cXUGtuC1GJf0;EMri_Pm{;Vbp^5i83*I%g+CY@V*Zg=q{obO!Z{)ULoh
zlq={o5p=e;C-eXzKY2p(L>IDgvtV1B7wBH0KpAb#giYtK&x@%omV-9~FHYlA<CQ<o
zf6U@C^d0#3Cs6bpXM6j_6S*~DB^OwJy7n|EqDc&85O8^;g5yZwmyZuG4$hLf@Z?>>
ztKz|^)gFd{Drra(G5rs_p~H0rZKXlwv0Z`3EelQPiNu--U}f}L7UMZpRa?#J=B^gE
z>c+Qch|D?+e!hxoyk*mL+lV7-<^Iyg1JUevTwi+BNYwhnlAkAgB!`k)Uu<qI%s*?8
zCnTjTPBEDr)?XE_^3$hrXK{vD@E)JN?Q{JUg{|S3uA}Osm&vDgX4qy+8A%_>q*)lv
z-G&ElqnhwNJs}Nt)}8FwCH0vQ**4ngoVjq^w?Aa}{?ZgWr8%%U8W#4>ErEvz=MW`K
zg27z$XQ+&5O3E{cB1!QWUSReqo6}BBCOtEE;w?cJ2LU?Fqgv|#E*`@9&8go39sUcb
zrjFh)EY)j3uU)nJuCen)1W%;@J{u{6CBxn7R*!;GFs|eh<hhO=Z1;Pa_48-?VIg-t
zy*2>U`yI)nA0($1Kxchc*1}ytFh0Iev8YhJFl2iK8(XvurQ<OmYn+!KM?%_lJq33#
zGcb<OnVXwHz?JQaw>O*9Cug4mXc6)tY#y|IGX5@J@4?4OYhqyh+8ElBf0&rL1$nev
zXB<j>E*}H){;9Y>B{MVkUHX7KB=jT#H-xnS`;G0!5o3-+Ku8)jbFWp23;1-@X8Sw&
z=D^h};==E;G6oi^Ok?ryf68bYe{=ZA|I25)U8e)TN)ru_0m?#^nD{R!mpNlk6M7XD
zGiS?sgSu$^CDDI3N)P*2)K{xS?1d9=vlypJ9+7|ToDc0buCt-><Xl<VJDOn088w(@
zSx_?>_G`z0q6Q42Q^Cm#lt5^z_<(cPn8}B;5PPoUI~hn>gh4w7y`&@|31!46&&Gy?
zpHXqw)O5VDRp{CfEqI*AA$p^zzS0ShKYznT_ECNV;jpUYQ8FEasDFd3q7Zi%Ffx87
ze(m?D$^d=VQ!x!PLmln+>Q9zI%^6=0S_7Uus}Eod;Hyo)mML<~Hma^t(h~baNse%$
z`F4qRY2oCLwlP*?gTZ(IVFixOf?D1ZWfCY*d8{R>>n@-*!JuJ=$%>pXxB&8O<MCS$
zf#Uvplm5`PaAV_)|KXLB;cOye5{Fv#C#~VprY~(+OD`_+ZFHk^Y!U-{h7%6Tllu0n
ztCmFYqfyGRvI$9b2pcl8m#R)<%A+MqW|p_Wqo9b{LQ4yQTzonB(7S1mf-qGS!-|MU
z=*|g7M{nBpSNCvcrXP?UI<2xip1(MKC^Yk5ZeWJ8(`;}s4sO*e%N`T)`UhS*^k|ui
zunT0wCWGlnu(k=du`)9-8{-q1s*T(|KQ|W){B<$P7!*ln7{}xEE>L#-H)gli`SfVC
z*Z#o}5&k{jvtGji^9)lZLIwFWlfzd|%y5?nl<>PTvjVAHN4vYSmVW22J-w@|Z=y-&
zbm>2LKPt_qVb2ACJ!_FwTCK<d0bXcZ825y!vE{SaKzjYA#XyDF6~|TIly?Z?>pAR@
zPau&vpnwus1GQtnvj=O3EBW{46SIR=Wt|QFMl~gxwax2!M-_uhlHRhv&fH<R*p+J`
zg5H$Riw5YoqVF4usU+ZN8Rw^_a;3fDVX$#?Q*Tjlnw+u-d&lj=n0Or{H^0(jf_3@!
z?ShixJ!(fmZ1O1k>_fjlS|0pI2J)BulUhG1&WxQ*-P}^g3TtXo4Vjsf1xbO?H_iHN
zeGmve=#mR$#(f~y421bEVD{g9_PY;^*?Wg~?)InCp)+gTde`+kZ^1$kTPRWbg7Ti(
zI8xDt2qdJB%b6KN6FMdS0T(Tx;L&HluP>?Matrmor`H>siW@E+WtF{zxd$?ft2-qr
z)^n0iU){U%`ZWT9U^Dvr07UU?4sv~jeOEHCB~yvq57bt1Y>W*j<#zgyn>#+v<|A^5
z@;cuoaIF2GcAuscDqcb4HPm^~b3)6mU~$o4Ily}vHon6xauE>-v<5gB@2e7PmlAyr
z=z~#VVT8!^w*i>gcj1Bka&rC-v#^Y1tf?tGp0D*9&2LS|)gNu<Ay;i^xLFc2x||*U
z{(a4ar#4SIAKX+Xh+%lGi-TT2!nJHp!i2t9Z>};vrG(}2tM?~!4{xBQEN9(c!%2Jk
z`p`xJy6y=AY;^2WgRV*=BR><<Wv}UD%GOs}sNQMRmWxCQ)uqYp+BsSxXRco3CX*1%
z&CmO-dZvEa)*=`k{i*dI_C-oh>k}i|rkFb+4_-4Azb){sym8~I7=~Lw*rb8AHF4tJ
zgO!Ss$qR|RA2+Wf9-yu=rQY%5x%2Yr_|sQcU*pL92{ejJnWLUa>4~{+LeDw(Q)G97
zxy)hr@$vE-0pW(@nS<vVugC@yGT$oYNf^GSb)TD~xa9opv-foU+P6zqybtAfpnKN%
z<2JbAKh-3^LvHc2KBPEOW33y;pWFfwsYOMbEx5IUg)nnjxe$qig!9wv7}nTNw_wmY
z>et%pN*>D$+*QEH{Wp9RdGzY{E*;;hmu%I#Fh+g14x2VNWm&)X*Y#s(B23J7De7lc
zV*b(iPy^l}GD3e-tcI<#+0LK}4BrB3GnwbpSUxHF$A8pAjFV2(nBEqt*91=I!4OgY
z5_|jD!Ro^Bx|kTX(-~Yv`=+ZWNeBBQV_A0FtL!t*Nn!QBl~-=;5=2ow6rT6$Ytrem
zgUaAqmM6`*`b`XPZzX~WPp2x6ioT(GV#C(2gd9>fz)r#N{rAs3H!p8PB@Au40GOxk
z;oIl)l>9WzAC-`X=V`|cv#qm#4ptl<(1>Z(KbMpi@0OjH9wT1X>HV@#(Uq-&91EuW
zLCJ60z$w-&Hr!-r%h#8&z_b>p<>I(}1EwZT#eaPJR*7J3&GdhM?^<`^t!$8JE&{`N
zoXEkUff8^;@T0nU_4dhu^aEW@x=N+7u|_W&VqzB`Z@cnZyYSE}GqZ9eVM=+O8(JS@
zT^RA76F+C6QcqyAJNaxZLHE1^$x0k?WV*?2+DVD%q)UE`=i#vTpxmp;S&;A5X6V6w
zg|@6jZF{k>1A1}|9Di#S{db?mqPnHIxL(NU7T&6hi)#{{>yoM)8km?kgo)Ghdo^Ds
z)tmA2obp(xqK~4VI~Z{^zFGGhF;$@5T6@g(h+>|}<+;VK3W>e-WF0<`<aV6R3RF7X
z?3<~+3#Zh?R6T#Z+-c|&_)0C!Hjl90Q$wqsnAEaqR%>ZBe|n9CXcQ$<SF19vPIKuX
zf~U)0;CSuYgOb=O+FWcbNjwRutSA;;%bJr~LXYFC8X8kOdv@lXA#ZtaZs`wxHE8|X
z$T?@y^7Cy-fgDp}wQGK(U6b82!J`^(d3hz<v_`vI_`DifT3VW0KFSAnf1b|mwXTzX
z$8WXC7d<uIRbhI-fDQeMrY8dA$?RWp)jL89ZVS;$Ndy&r`axo|HfXLcYAa(_V3U<r
zbYe?VuYRfZUg|*{ZTxr6Vg~ID*8@Lx6{h*7UEj+Kbr7<h<{H;sKU0@AQg@Y%3hDpY
zsw*&(&pHE^6Q>8~XKzktn!iSF4cA?=w2BKYb2OCKZ%0aFZqMLQ^1YuWrN)=dV3G@b
zNW*@}v<y`63!vy3q8+MW))JU%gh5_`o&W%V47W>!5$&Yj!*?17v!jTkT6kxDNj29x
zG?!4U#8#V&ENsJ8UN+W>$8G0tRv1RP$2WxNlX|`j=L(|Nvp(8>OKkt3Bv0k4o);!p
z*4?=h3O2V&-J;BmNPg1r6B3zq%_6iR98e9=BKNaZ-nJ_kXjM3<PaiLbY`>Mp)NU(f
znF$N4zGYbZtp{llphRkOUr?I!*W-CGR9PloTbMjDWcfEYlQH|9xB1CGsYBDtv`{W?
zB<Erj)fj5)`!&~X_NqDoLPh4oO)z+l=!8BGdNc}XZ@|3dS)mFI8{3_gl@*#ru6rKK
zA*k#RVs%3$`T08#vY)<6hGP&_yMDx(C{1_N2Hz%j5=at_?S9=rfB7jQkgno#ARsl>
zH(C@+K64bcHFoPB@1TK`Q0y$5Wi$Yb0mt`+SwT{`w-6vjGp(rpaeB>pYkW3~`p*05
zwbczqM1PaHnMHf7rBr#DE49s7O@!#TCs;j-KhAjnw57Zgdsvd@z8c{S($aR?=o@We
z@)85<s@;)foLFs_=ezshRC6WfW)nCO5`$Y2r={J_*BZ622C)5)`&|rks?`c%i#n{J
zZVh_4Z~ke(yq+v%VyHT%Zob-9Gk)ppvv+>)9{(MJRsxgoj#$yT+qa*Z<X1lk)^y<5
z+xxQ0vPj9l_O9JCoX)63wFY$e=<5(YQd2D3x2}$t%KPtYu++NerB6^l&csiYc(dMF
zu5M_@=GLH~piIuw(Lmtmk7Z~!sCnIXOUk{dqfbP=eC=ZDa=0;xeNOxD%TPwRYpRTw
z@ZLgj``7R(uP;GYXeD3Gb_I6Rr#O|22__~aye}xY0UC0coVBv-X!)6nzf%1<CMMtM
z`Ed+0(Muk#_Yk~86q)iy=xS=L+g{Sg2kpWQQSfNwmw_1#Q{qii>7fU^N>qbMS+@JG
zz8=2$mjeSo2d)wbL^TTNkI--A8`q4g=&!%*`zm2n*54>GvFm*-C<MC8^|`h-#SV?D
zxA-e9m=Z_dys66y3L-fTgHbpOr8+MXdwhGP=l@t6ar4lyGUBj5d&UwylI;(hCDMA%
z9chwprZoQ|boA1>>W@iySI+r|Sjv=$fcMw`wBfu|zGgM^C}UZbLYVv>->?Ha!B5UA
z{OjHz<DcKy2nEX^Lg0El6}w6rNw4EPqL-~AorIWvl#k2UJmvXomeuXo*Pmyexfkx^
zEFYUG880qgI5|uHIXO^zxQ4zH1)Sor4m2A!gDm7OwMxW}BR*);OBvYFppRp&ZEhe<
z!sNi37QU+LQ^_FI%vy!v+g7iy-xgl**J1V7y%JmNK0nC11h}av2IlgBSAiio;-JSM
zgXhazv?6`>*_{o!LM|Qlv2jC55)GQ}UCQ3A<?DY>o>38F3LW5r0E079hHi|mn|bid
z_qZJN(M+EiX3*aBIdD0ydl?W__~Ghg`ISfKPKvlGxG=fmg><Erw#2AF&bVfa=LrWi
zMf|~X;L$G6&a9uSxhkdHkeZipf&;$*{{67zQxxY8+2f<Ov{cmnm=ZhthU4g>qCw|B
zq<Z>Bs)=0HeP6Ea?O$1TFbWRP2Opw$7m+9!hnihpeaG#q8=ty!>DQ$T)CGFiw|sVo
z!+TX)CXds8bN{^qKbtw>a7&Z%5{<)kleTJ29i2$9t-uA_iqw&}<K7cYDSBX7{OD}J
zHaD|GPt;d~+6T9Ktjd^Klko#CV?F9b+tf5?Fx?67_Ve+7M=zX4Ncc65jzVPQxL0g#
z9m41_y%|q1;UP-?v^_tOe8|s4rsLmOc{w0v^DAMnQ&kl?Eid1m!1#2c^j&0l2ui+8
z%%(Siak9aY!TlHoC*DLKa!=4+^0-(TT^lqpeyGZdozOe(Q7bD)&nfrNsje&Xaiv5E
z@&(3f|1)Pb5^sL{tM~|d8dh%ADcitL6rb$pe_x@jcX<E&G7XmlHb;V9LLkj(mhdrt
zV!{j4#nEsNKEZ1v`B&`i-$nLiI@&}g+g1ep=|0+-CiS^IodaRY2<lp#S1dEpk0a6}
zI%fuk_8uGub@f8MzERAi`1mmrjSrm&!~^<=R~(oxey|j>cC;>f{molWQahb<ciE1-
z5_@g((gd?qI~FBha_Tf#S;Wld)yPGW8d994DC|({E9_uk)OLU;2{U1d9XV3+yDJkj
zSvc-Pj#g!Ciq1bw`vz-WXXa+9uhC$=cj@}I?NddV#ENu;EI!Ygmlyi0=A91+LXCe@
zHNcIs2xe{=z6CrN>MZd+b`>Y}whnw5)SfRle_HUo8YgkD?S4a^7^!qQohLL?(DHq8
z|Ha%3gcN;_mK_9Njo4eHz5{7_9-$Z;zfv@+a#Oxb7zs`Z(bfS7hkNei>7r%n3rHGB
z*JrFcYOsg8LzPut`c@6N*9vIa54{tvcq1w!Cw?1Go8R|Z68?Kh#him=T#h%lP(jPx
zMrrFlBf)tG*3yB2?eAEokb?g&3q~0!@Yi9&S#(*QUN&70;?eS7fu1eeIW&CD6HI}z
zsAIOHSq`mYH?+$dN;iBgRW=7_9Gx5z|IN{0J!q$G7bEyFn=XK9HJzWa3`2}TWi<I)
zZdD>mwv39Onwnara2IJy53@;tSZsn*+yePS_Z!f}4<=@a>)KP@l)0rPgj!n-z7nf+
znbuU*gh_+&q7=mP{8#wr7eZ9DFyt0)H5^!@e0~Qo81|RWi)-y1+Q8cd3+2Z`#jdlG
z7~tnE1;nZ$LIScv#h)QLbW*YE>A_pv6Iw!43wPfx7Z|;{wTZ>h5Rd-o`y#lW`++7i
zv{$d?(F|g#+0#~9f-1whwtQo$)O30IcP&M{xVU&MOpyY^vwL24j*Ddc`{<N}|3GKt
zrN6&A9wc+#Md!9~o@n4$DYh!c@OSgw)4K4a7P?_IS2_#+Chm)#o{vf6MXdPOYC}#)
z`nH7~Fu3|`VQ3RBNPV%|_z8Z#qu}O6-@;J|l;$;;CF-x9;D%88KE>A!uOdGEb7jsm
zXBO{PJWBrPcO|5?Mgr-yQp@wB=eDO|#|yF>DJ0RSjVA_AJ24J%cE4QkQdGG-iAl_x
zjS7ib?bL8n$Bh-|*wQSz8F&?PH&&om`fJtMm=BJ^7RoH7tyJ8+sPp=I`cg^x$Kt+S
zmL2Rvm~s$%Zp$N?chz|pE7eC>O6r<|2;=Q@I-1k$i8q7bjePufqU|COe2EqSkVmst
zO2P6U9JrDNMJgZen2#gAkfrUW29a+!lw)5`9^-o|?gh#dd_?)ArE!re9D9=si+PlX
zoF<ldwyL62%QxZmr>OYB&YzkDrp)|xq_7F|d+T&fm(iYm%PT7n!Pg(1tpU}Q9Y&j@
z4bmWK<U~G@Fw%9|@^Or9davH43&Ax8o?8~48+9GHvZ(NA?0osecTDs&r#b4;+5mgN
z#2w5A?I@THgdSrIG9eg3Y3<aS&aEF!84I^&>zUAR)|Hy6%hjb#e+V1H9}THp>&xwX
z-!Ue(tLuTukomFG7M5%#F5lJ>gCK!MoQB78qw`J}aQV<<1lC7B;sdyh1-l(wgvmXh
z(r~m%8q-GUhz2#wpd+x;Au0s6;S9B>=#z}8xB2w*RYSRN+bo3LF`|XB`1ad$X`1rY
zqi4{Dg?yL)7-r!@+ym9ZYoH3bUY;KhkW>C7fogF~v(=KgrkkbrtLUHeYgt+Unx9{4
zi3MNpN2dCpc&#qI9mamuN!G3@Iu{w)-bKAdV&CF^<u1dNH-Tvhf1Nt|#`Rc+(VJx_
zE+EW{89VcKrm3t?tKrSNsI!!M|B~qq6|Cw5#>Y6<Xt6)|n_}O3{$ln0OAKnS>*?ms
zW6gS^^We{foiZZ3yf?KS+t=Is!_wdsJl5o>5*W*+qPA6^y}r_Vx#0no(@UqOo0-{}
z1%HZd_Jyd`sSl&qqgxleeqNdclau@Q%g&#<?X*7g_YI~pAdoT&k0{Zadb|wn@gt_?
zLEcM}3Yy2u-MRqbkMfS?s40f)$zS^Q3#YvN#$;dfj|uLV|F5zukEeS5x<^DIoMgyQ
zIU0o|GGr)2D%~QInIbZVNSRe~I7L!PXp%}q=Aux^kSQ`1GF~ckGL+Q29^HF?!~6d8
z_PL*1=?vfD`R-@$wbx#&&bWnsr(`8d2pbdS#nYD;kns$QwV(Xdu&1$^d3nGBkg?ho
z{1h)-d_jvSH;R3FA<GN3T92~-cYV2adMWo^#_w&{e|zZ0zEz95YJQ0=&xy*R1fjMb
z9r?^n8I_oPQMGW>Mj4M4hh`^c#LscsNZlBjOX51e93CqCD~q$0Zn}6(=lNX9+Ss>*
zeLjTfLLm)<35)Any}aastUrBhy<F+h>cej>7@180!-EGmomspq!R5*B2$>O?mD|@G
z@`?O3H0xFVy0L+>bm>;Wp;0=KtgKww<e@k5Nr>74!AXcSaw{k(V6-$bKVN9e@xiSy
z_ah?Mv^}TC&WSADmAtR}c#q4cRAbG`tV0iHam_fwsv9f#u1jBUDe_kToH)=$^A3(l
z5m5H=KEsIS&`1ll?rI3pI3!ki|G~xZ$HEHPGo=ypzfb1O#TvV3*>FI&mNb4u0TBSA
z?psYvmO45*@+@B-iPID0d-NpZ!ZGLSnb%)ulMNcS%^wf6i?`h~>N(~-QC8WQ@=A@{
zp8K)lPMdEW>>Q5*rhFd1a%whxpl%SUB;L0|4AZ|E(T6X^=xUogBxg%SGx4sb?A%H6
zklkgeA>@gLzNJ`ng<MGdt|SeRz0giN$KKLrTpnhh_LKecuK6KfhW!seoV`Eux@sq@
zNu0KcnR?ouw4u)j*RPe_+Ei<z=sw7@xL^$}$6JD-$RydQZfMHx+(I!l+|lasB-`}m
z>=XyLw&J97*el$-Y#nJt8qh(spkggI!zH2^1eN;J+&d<9RJnS3$D$Q2Jw&)CPmZRA
zXeVY;y~34G3;i517j7_DiC5*6c#T>xq{H|EPfXC6$1J^PMs=G%v_D<P=l=OJPeOdq
zI)k7z3n^g%stNbAnsOJ+??A-*B`jn{`{Irss<^ny7~-7pv|xY}7Dj(-ts=grG<nm#
z(UH_Z&8D5KA^XJIqfX}Y<NJBGb!{`MJ8*X-GBk^=%<Dqq+I3OV_nI1ZwAR!VXFF^)
zk4H918<&}xj)JgV7eF2zs2PDmHwLw~M2yI?F4tSa?{VlyQPr=|y#wkl867Dt9J6P?
zI{nF4>V8ov=e_#zyyf;v2iVuNdj84&$URZck%eMz5xjI$P>V6+thSb&ZLV$##Hk>H
zQ5?p*hS1)Y^2M*)Z)DW{oNajtw2FwE8iv$Nl~;z|y|hj&rs9_2I<LNK^0$(!o68S>
z*}UOk?*7y#<ra#<LtKOQR9(Zu4Hg&sN*aIpbic1&bXAF$9xgj`>miqV&!LSShhil<
zrX+izPbL;qsej-An}R}~uWI|CifRtiN{COdpzPGzW~fTsb$*OR2KFvhSl>xI7L2QN
zNn(|dxzL;EiOlBl<~$-i=R%cLZRPzBYvm&BIJ|p!mery4CG%Bzjs@I#B)NF@H9z}1
z2zlU87Z&Socy{Lg{j~|dc!wL3qfMBdD<Nz*7OMV(X)4csfPcfo@aOJ=T6gu07r?4j
z?x&AcWQ@RtE39YEnr@b+Eq<Bm{ZQI8djVw>w#Ye(M+^*FW8ReiTvf8lnyYWczfh&m
z3nE^wkS$3zD9&{W`aaN5#&%UzcmX>pPHbm;zpLy_Hq8BQbA*})xmTF#2!IQ86_R2#
zHsHu#RdlC4efA6rA`B=xRofhWHo;Fu4#4Cy=$C)0VlTS5d+{T;&l-RX4&phN>}G+e
zSBB>Tu2<&Y)Q)pbe-wA2_K$E^B~;S5I_=Hn`phH^{xtjO;txds1XLl2XxHsM{#%;T
zulrA$z=xSQoG5yJ<XPtbRiD^TLh0ZH#4N`krImL=-BNiK&FuKJ47B=!zNbja=qdC5
z1_H~EFOcemu<HT}pw$(S>ye*va&#PpNM1M03n7$TLOkRSzF;9zR){RlV``BqUltHo
z1Tt8O_ygNe*NGF5tlKu2J~c2iGb6rk-Ogj7^!&wrxej@asjHUxb{J({IWg#OR=s58
zfF8yCnS_hg;|t8{>YX71`#O7UREsv`J*jpX54dsX-n|~MK#p)f>o1S3U!VT;6sFN_
zV6siQv(yW!#@o*Od|4F)<UwrB8aAtdZ%O)Poz+}gwht$Vm<zvcFA(Kl-JbCI&%;ly
z(#tckVfEV4|GYjt=#r9_b~Zgdy+&;=-EB@1i+l4Uol{nQ?yCBZShmxoX&U+QuW4zk
z0!j(6C%eCJQDOE2!_U%M(lNr893M>FVT+rXnYqv23jV0D?BfhLN~1a5*~e7i&HUua
zd;hw+;^@7*Gy}uv=WZR~n?!CPrlhnKsSKRjn|?r4SkUXumfCW!PxbThd=jD9LBgua
z_a1R=m`O>xcW-<HXK+LFubrHIW~r0CX2_O?>ZQ7e3-+331Z$hOPA?_+Sjx#-b044l
zx)G8%O%1mbVs2Hu5-+|-Ir5dzqiHJx172)%NOsPWO9vYveE(U9Y2V#@g9~$<q;exN
zU&Iy=8Qd)-ovgq1Q2=z6E8jOPu1SCb^n3rYvEqj0pr}9&-Y8L7r^q(+88X0tL)@Sd
zDZVlud^T}E{BWXY5v+n2!bjW~W)BpH33`6MGR6z6QMDs%p0CpuF=4F@2uBn@yu0x7
zE{CT(#%XD03fAxC_}#x_VN2o`+)|wr+Ry6<V8BK(Z@?Nh>@L^UIAK)+5xl0fsWr4r
z{y+Y}Uv@GFHu`*}tlq<N?ugWCn&V~57$f2`O!R*dQMHd`h5W*n4k4>5p_>^e94^x(
zlQO8&y}X^hJBJD_p9Q)17?)7>2deU$93c?8n3vZ~^}f9B@A5=gF>Hrmh=!@hK-JQr
z!k@Fj8*M1-f4yEw<0!AFaA-bk#NcG%54P_uAxh?Tl04&5SJb|_9-+u?3LfvznPFWt
zZ<!BWJAUKuJ2g<_7x&|wImTJxbHqw1uc>hb<Q*a6FuE;_K3F}}THIIt8|4aRUbjxN
zn_ljRVzL=-$09R1x(b^&=T=wV?~nWS$w8X{x?0I8%$r?V(#-3Ib^n-umTN8zFvAij
zCwX7g(J|;8=gm7>1xLRgvF_Xo_)06c>~51OU&#6`^jGE#Tz{mdDG!3Y2C}u|cdu@b
zeNC&8`+cOk2_utfgEyNkQuz4zi1iYvn#72r+9w4g^a61TXN+SfSDvMRbbVb3Y$PeE
zK6f{(%B0|%tb1fTYrTCvEyMYpY@%g)b5bMpp}V0_NJ7s>Xrz;<5jfM@K^$s+{G2LM
z_*~pqs+*!^E1i^7e0I@R?T~D#80P3T%;<NZEm3|vHb+NVQBe>CH7z|o9th>bv@V#e
zWDvQ@x;;OaWl*D(+8&D~grz*Vn41uP$IF0su_QL|cbl^&%aaWyroVk-qTm_Tv$P~N
zMfdLUu$bOal!*M}Qbu4}r&%yJr8o1tV^{wANwT{t<c50&0XNcez85oduTW4BfLoWK
ztnC64TTh%kDHhyesb$;G&K{0oG<)~X;tgxp8_*;kY&v!7Q0Hg%s;f*TrKZXnG!I)J
z3sV@kNm2+735D6`knK1J9w|uY*^NmBsjwG+iL3KOOMtrc;mw;12AT!BCx;B-Nc^UO
zMQ*d*t-xQ3D!=?VZ{L;_6{EgWo>**wf~rU4NnZ;Z149!vTi&f*D;Q=|7G!m_DKu-7
zjEC)J%YfLF_dHRbuKI<g^+AaKS9X3TaU6n}@<zCMkvUO6x6BRZ_x4Kmwf+bX?^X0!
zqL&=><%<Xl+r69idyC(`P1A8RUGz`jLw=732;*d-@ccr#bQ`e?y6Bpi&d@hBWT8xB
zOR0mI+;KK7E!#ztBWw|(1{$+$nGB9MDFU6J7Cz3GkP;T5)}@mz8>DNRm>5&h?9frB
z9o`v*P+5a%`pORhGcM3A41A8;gKx5*44QaFmIdc!_LgVD<<;|DKS;jldKof0@rQQp
z^`JOmJ*yiq*Xf3Szs)DTu=0qjUJ>!ozb9#EjMIiSa+^2P;D!s>o(TA1))!;mi@1u-
zjmxP~w=V6nF5?0{CwnG7fzdhS&mSa`mbUB8-Q#vt9j(>&O-df<d=XO|ugM{pui!6~
zAcQgS@P){gi{G}Af850s1zWC5zP7oa%4j!ZBaG=*Uc49-!l9E%=cWI(erlq8p{lzP
zVxgWRA1vOAd&7$tnv{zukg1G>+Cj}qrr3QxKFVd0Jawulg3NL${k2Y0s-qN{gQv1R
zn5j`u?w3Uh>W++w!}+@eD&6jXzUDg<8rp-ATWvH!&#66nGFC4^{gjw5G(D=>`Fa0P
zVK8&BeqxZ3YttH<EC<&AI~tm!o*NDyE-pO}S)St^r@7~yX3L_B(=w{wi|gFsMw$A`
zFK_S8g`+oq9bqWOatf+16O_F(L5ujtANjirb0kuP8l}2UVjf~!YhIFzy?y6cS@7>H
zIqDV(IU>8Z1$%lsqNgy_$GZCl7ajmPNt%Znkka=MPC>&6axZ)oJ)ovo|47xfY}bWy
zGn?f(G8B&<wH`L-ch|BlK(~psmoRorumhys%oK=Q>Wm+y1<mr^-q>;W+n7%0cx}?H
zA_@-=MP*_M5C7ecf!L#CSC5egcK79@&)A=Ipi2be*iy)D&WG>@Wh<bj$>DaVi!12#
zDs8fl&MTbRQ?Nmj^Qr>(0?ur6b1kjAH-7^UzsndN9-g4gOj*>juz`m}+HS3Q*60db
zqVdHxk5@@1Y4P+I-A@-EnC?E;z}r9U`s6X4-q64t93y3BSE%FxMPB2A<5Eb#?yZ9-
z_c-wk!jPme-{U7^c5Jk=yLvkm<m1&Iwm0XbczQqBG}63=w%(>GPSi4R*6W<;7XI{S
zlOVqdjm8xHm=Cd4MM&YfjxtpQWE5#g_7%8zdDY+S=+ibI`G8%yRKUF|??lxT<KI|8
zX@n>P8qll?u5HEe18^Vf*7loxXD=x4cxmUU6*Sq)4<xMHl{>q&A-tj)?A}9B(z(eS
zmC%L8a;9*e>rVUb*OfUl+t1It7%e5hON~;i@^SF$E(wzw)~m)GD;V)Ok}!Hz*K5Ac
z)7;oQ%(=jxq(#sN<19nkuv1*{E6Y0w4O$p9R4-ogZuQ*ia!OF=PPO80K;<JXU7wK5
ztzw;TBh%5nv7ZzT3oV=)U%q4}ZLsn2w}a3&4~Am!+~GEb@xmm5^2Z<bxuI+_Hmp2+
zhp{ctwce!YxI+H=F<i&dEJy07ZfIy|4I4q2!4uEq`DYvyC(EhticYfyi*C7t_0l=O
zQ5C=N%o$^({4`B$E5i`G)H-~4)jVcqD%yR;#ar<aoe%Rq$W8}oX$>XqWAdG(wB|W6
z9&?G_S(S%8AxgY1<pKvMX9<Fop1FDIw_40rK)%1YZ7gchK{VAOt{uJ1j^P>WDc7OG
zQMY9J_>@A2yQqA}m6CX}<)U!iQ(aN95F$nQQ&L{Sc9cR%Q1+7DxuOpi6E&;y>4Q5A
zc)@Ll7XN$y1ZAeZDol@`m`LM@ZIWX?%3i^&GU=2<;WjkoK>FuG<JFqc`7c{u1IZ~b
zl#`>F<;?@Pf2uuDUB;aj(}vC1-nsqu0kLK{DL$R<W<W6Y^u2rc^qIm-(Pa;4XqrcG
zm%`L`*iy^baUO75L7TD0LVR6WNwl-$)^(T55+pUTx8Syh(AU<dblsgy%T8)JQ~TF`
z%U%7~s%th)fRmTnbSHDP$OZZiD+O1|uW#IL<97*C5UD1?=OG)bttn2+WXE<Wift6n
z1aX$bb~3~`EQ@*jYw}xd5XYii)=OVOf!s)X9rYK@ekTF+s^?$`xGb8zTwx2pwcO`y
ze8HGs8j_H_<m#-q_KwSe14th~aqXwkd~KUFYWOoQ@NQVMCN$BKC9~OYxvIo@L399J
zAX}YtcGRu1^l<vOT)W?g4hX#^Uwb5OQg_#`M<|5b^1XEXw-Vjy=WMHJj@3VB>mOOk
z2L^&r>(IzbqdD$O-{b*f1trkD!T>plY&p&eBe60Fsmj=v^9;JGS+<9dp2NZECEvL$
zbS<y#>>0F9{QRW^1gKGK?L^hdurG0dA*C4#LU)XCJ%!Be$M%A}=WHu!j(d_XHSKn>
zEYa3xI@X_wAe}FOkOoloAci2WxuDgNoh^s1>QyP3OjYbeS5$ioGN>)Ni>5!V87h>v
z2+Ec#e)-bFJ*RZp7Z<1BVG((Z5RAK>n$yH2B{?yk2N^|8WK)+uyV}xd%<YAXP!^j%
zzS-WKwMHi3shr~(I(_&LO#1zrDf*>}@qlH4Kiu)17@7YD5yBIY+9DP@3}7FFQR11F
zjg7^3|9}tud;f}x;+NdV-fW;Mt-jTx6CNI}XJA0Y&rWl%W#>?$4Hz1+I=3yjbj5K>
z^(&9e5gvvj&BGjwiLO0RurRVHi8}lUA<nF7D#r=8|1)jRYUb6uDXiSNKWR1Ca|ix8
zAD`*bgV1GK40TpK@pV72H(y)1;MmT+d;8g<%Xcl|V;X4M8MB|M_}$VI-3viYtSr^m
zvfYWk0TNn;?-N*vL$Ht}C$<|}j?28;c0qT>{_FGVuo$DT)G`$LWy^KFljkATAYq5s
zhJ>Be5WvXu=FQVHHD!;@v6?w@7VFSU(Q^T|1BZ_rpq8h5I^g9Cl6pqTZ*+<@H#Nny
z+o<O0j(8D{4K|F!L*I;&F-yf#IIUq;GM{zcc;7oas{PQnT%ErjrT+3~U+Z-R72Q@4
zKXzm{)d48d1_zy^Sv0gJHIuM&aZQf|=FGf!ac7hJhYwd640`#8k6sR5aKZ!s|5ZsC
zZ`bf}nt#UbvzXJ2{p-0p48B9mK1eJKGtSSy`v>zie}5jT{wBMjsBu`~p^Mwq*Jo$w
zcBNP&Vu#9H$@A4=sL-`Ns3JP4z%Yz{-wQ7lR`y(r0fB>~anUv;+BIDPzPdtOGmk#-
zmhGzSE46S%r^-C>-%Oar@5KcGcEILB5Y~#0b>dS+SWT-Jo}oJ*IGmK-+cYuocSHZz
zO)DDB>@JE01VoLbeV@)P*FOpyr<znIAqMV7$zv~FQ;(FV=KogA*gB!q89uo^Ejf8%
zR8*94rfpcwc`hxj18I2N9G#L}o``_f(HYD(cP|nj+egNu<^}qtnqn2fkc^z=J7l8V
z(O<(f(Coy(B#lE3tFKhIWWrIMzaq8g!$v%4#EE3J(hBOk!k)5VZgmzRp*t?z+}4xM
z#k&p+BA}UJfPT?36+y@nz>Z1B(D2gT1AA6S!$Y-saGTI+RS&H`_fE>AWA~2duv%Fm
zimXs<$%IbwJK3z7D6^sf|2VdUe?>HhWW%kl(vN)ej$7$nFf94@Z7%ZrTK_Aiu|X#J
z3fIen^s2mF^ZjP!FWg9P7oak!d|TNGt_TaGr{7GakJQkqTgF|Qf{Vd$Af__}X#!o@
zy0<)P8oRdfTB=eIS{l)N(a@<$NV=}5v#uMf`!YH?VA%#^>g6e>zu}czib9`|NV+g`
zRRZ{h_8Y#|pCO1nS`4Zw?aj}&V6W-ywIxZ=;CYh`4`GXWsP?)l9nzYE>m?*!yt$=G
z(vZ{K5@1W*o=i?=+I*B%+Nx=Ipkuk<rn4#XJk;20cwbXf6LR()_Et!2-DW0T2%gK>
zL@$xGFRZK#_8P#!QpH6I=>MFg<gK|ez4N5TL@SqUsQ@&iu3R|_WHxr?0!<`cHkjWP
zRP$3L^aRMHbZz8$mo?BF%&kU2TRr!%to5L=^|V`=)bI0;F~R|l!a3p~3}s;QLb3^b
zGBI&_34wX7cp*~w0PcqF?<ficg3+R{=E3MEyH}UA#l^pOuj1Z{t%q)tdN|X2by0Rc
zFv04gqiv7L#cgP6>c*56(IZ3SHblUk0=Z&Vv<oOf3<gJhi~Aq8i|uRBTzF5bY1}Wg
z0sRnqxlfKs`7_XAuLT0;5e~{8c06+A{{AI<;XXjzz+_z86m>&2iU_yoZq?L=B1&v*
zkcOCx?@4fgkz~XU@@$R-$>pC!xj%Oqp=}Vc3BWiSFkI*>O^)f`_iHBk-u3z!YvdQD
zNTl51YOwo;nkcTvTNNWy({MzUF3`)!{y{?2r?>Z*Da1m^4^9e#TaHVT4}^*U4Q@P`
z_<j_A`(pW(p;k7(@$<)9JFH9}-qK_vXoS{xhb*16n>Q(evjbKrBhO$glv7kWyHTN~
z-S63T-)R(zD?s5Uux47?o_iMz1YCZ7s||F+hN%L{3IxGljFK&B1E}<fHvRO=<v)Xn
z?(ZxkLnq>68rXDOgj)&@6l_?jB{A3AMNAf#9T0f}Av@c{hnMmhJ6ZZq>_>;jiTw#o
z#*yh0gp<x-FjI)fcUM;z8O5t{LMBbe5b82j08JDXs9j%G_Hty{Jh~w67`Jbsa|hRt
zA6dt(JmqBkl{>o*lBYoAa)y^~T#Qj86bi?XG2bpMv^*BQ&9{B&M_jJ1!NJsZ-ISZe
z)z?z}y1WxVta*}}-3hM+1_zWNl(82tUX&ZSmfR87dbd>#LQ9x9ah4xznaR!g=IYGy
zb1Lwd+Zc^Uc)jX}maC^f9>I?96QJ(Y+ShOPBiH9fPKnJ(>u3L_#gWgBE?4stL={YI
z*4}F(c>x+h>PVo*b!n%Q+fQzHh2jy;NFvSh*@KfcaO}=FIc|4ax_xumCXenvdwY+l
z-M0-W?ZxwGUCDp*+tl`biy;>k)0Qs~Q25H`AJ*d*aW)F9+g4&hhjzfyfgT=S-!@9=
zO!Ieid9q0lj$K$3p9fQ5vd%5+<;-40DUp@y_*dp|nw5!LwPah-_s+jV7{RyGHHLx7
z&!@n-E%lWRG=LPQZ)R@!X_bhC=!mnk7EIatzI?`Qe*>KCbVnTBR6=S#V6oS)vuBq1
zEWijE=u=evnTm;%^)SJ4>v}OCZc>G>BE{eyKw=D5&uwfW6B84#Za)R9JmM<`UE@X2
zM2D%>a-z8Ushy+w%Ga-+2~NLkEvs^jzxU0&mv^X;@}jPZVdh=^h`wow`roBY@_0gY
z^l7{}u|p?agA1N&^HF*-FwQlP@*pKe-@w4;&2yA<TyRl&I#w#&0+)If_yz(@rPP}g
z{RddO2<CNmZ)Q5y!II}*c*QM?$COmFH5NwmDJZ|g)&yBTlN92oQfDPrtxB>8iMLeP
zBBJWE1TI0u&l1SJK5A|F#jSv|i$RNt@Q1;z5fH@a?1>C6-R{uZZXvW`@$a<e{wnN7
z!YU_Z_$7^lu<Jox;$~_pC+abJWMmmA!M|I1QQ}+vGt<%3BwaFUP7QMCblnsvCLF0t
z?*m6odE)bXqj0cwHZ~v1xk759F1Aj_hHPzf>lHFbmoXkscK>si0rn#IL$l##O(8QI
z*9tYY4<DHs#mkgDR!Z<aauNW-_TFD~%e=j+^I2GoNh#zQcVXERR9?hd#`uvn6R|jf
za}W!>&z@ubZp64$BDd?Fr`fwA?j%F`&Zn1t97-Ncpf0U8y$}Krpd9rCg+vmPdasp`
z(D3w>yB_e}0WJt-oy_52Ad6^gGDFqRjc_aTToKIcEgk^fnys=de<#H6(d~XxAOmim
zGS*w!$!2CzKRoi5?Zj7o2TPGyu76fTtYet;vqFrt4-oGFnzS?*aCve~NhbrU)54&Y
zpU7f<mvv3Z;my%;nrn*;_9Gx6TDiBMDQx-ZwS5@D_yvMS2FeBq7WRNyL4bAmgUEAq
zm<eBx68GJ&r+21SU7hh%MM+5L5@I?*AyLf1f+s0a9F^nMiF!x0WZ}8R2(rs7ynN-#
zZGuKR6`n}sJ1C|ziMP;x3>^)5d}1RXdr0=)<f<3TfAWNm^(cLcp`#Ixx0gkT>YJIR
zWTmI2aUn1ghLOMqo4CkbtJ<qDt7j6gx}MMd&p(weR0iL<W4J`%Mvf=__<w9S&xRf1
z5b7YRb{Y=lViFSUaM&xsM#3QVjJ>_nbp^OCN3&>anS!vD)fFBd6wW@>VYz3|)_sdn
zyF=Vy9d<$LCoc!6P#XDU?!No(-o9kdTqdHjdG~ypC{QTp_KAV5M*MnuH``-w+&JHw
zr$duqr-#=^2rOJ!QTig}hh{iVXsKBqxw7ahu@+^>>!lACw<cFyxfH{!q*U+L(f2Fv
zN@eguUge&uSjiK<_Z%mio9)3o-{W^vORK>Z6~s_})oUg2&f~W=sL9J0e|Ux#N&T)x
z)e|3?&Iy8$Yl<6^nwq8P-Yu|lwHky|YoITAo~y4Ejkn)v{@rWhWlu)5_>HW%2V2>G
zOcm7^#eoE~&mb5Tap9RWf-6^+tXQK{v%!K!V?!L);;(OD5m8yt+qcWU=NjW2%Q8Qn
z=^WL|NVr65fpRRk%nlH@yynXHu4^1zS89q^t)zIj>0*~{m}k#_p~8)|u07eX_|x0s
zYx2eSZhYo<4+to>iDar(VfGw)U_~wSBj`lgZ;Zk-LWG)A`-)L7`IsT#HRbDrp~QK!
z-yL+qAUOD`GR>FBExjHSx7B}GeTtcK;J^wclke`{dAptQfSmk1CQc|n9i}O{bcHP!
zR$Y!^MnkOV$L%c~H5Cg#zGV~K%sD!lYwm{^TcGVC5v2xzq22#`wL&pOM7X|!Im@<;
z!Y?&%*~t<)ZIN*)VD!t5N#p<gXiBB-@K?PMv!?o#(~&p+sW~QYILEuiK#GUnv@H1X
z)nFj2^Y-mKCT<(eJ%dri1|RFCv+R>dGI1;L&WoQfu$Yz*JWwB!)k&w9KLv!ox@W;G
z-)9rIU*83Qx;(5sHB)`Gg!8M{89y`LzQ0mwN#IBekLQ`Vfqv{i<H0XNrG~v)rgYmE
z&3wD(32F}utW-H%->fLEu9nT9QltE>BhA-iWL1gh>N)Z@|CwFuW!=czazcGpJjy`#
zX7}nfYs#d<`>qLnl5PM(b$9hpKrH9SI%dI*^A;?4WbWrUdGV+jSSr77iI;uh$?XyM
z6NWc)r0EKL+SBkxud-x2ZmtlX|Ip@^wF&-})$<*lyq5P!D$;0+(UYQcmXK>>JD{i&
zvAfjBjt8a1Hl{~*e^i>d{rp*664q_^JSX&FZ>!}$D4ctQNEB6(BLa(Rp>J=wSepxf
z+s$qDDvC9y5JhwkMvCaKfBpK-vd4)!W;f<TQ?(O))oI8hzDJ#7E8EmyrEjqaHOV8N
zoZ+ojIGuOA#@P!5H<jix6rEn9F8n)PIOHGvg^5C;{vccc@~I$aD!H9J89!e~OKY<B
zuLZU$U!<hn?zy&(E^cX}6t6J$akB<pPnfY+-Cw3uAl*Co^+2NCrCp!uKMl8|aWrf*
z_W`*tTJl540nujGU&9UkajczIa|LJ1He*tPPVVoN<xr%EQtEA*1AkkRZvM=(r*{hU
zX1;cjtLMRqjbn4-9W9B@n)tf*+J7$NU=t2!Vt|cP)Unk@3+H_ft3Y<C!PEi61I$~z
zb5%KD)e%&eD4IvNQD*>-{p(=|L{i{|b+B+@eZqY)u`6NcvG}e5u}j@<BkqRk(0haZ
zbF3R1T;X@AzSXCEN4Kp|`w&n@$Md$7K~CkY<;a2LAcK2BQy-lC`P=U7td~^e`syP3
zUuX1AZthk&s!x_j={2V@nV4Wj3#$kUDqfgd@nhsmO-+_wNRYw<6hVGQKUap6h(%8|
z#s2xT*<4rZnrJ2n{mpM)2C@el%)c7|<R}_lJ8&6?TFd4hy1qlrsvgL~v;nR|i9}K~
z;^pI3$;Vd(9WL!Kyma^wmQ3bm1WU9^iP`SC&rw+e8Z~<Y<x^mcea51o=Jx#^5##-r
z!Q3bNm0V=#S&VX+i_D#MPU+k|Y>>XUhp($zfn#i<S$u4Es`#Oqxcju57<xe(TZ4}C
zi?Dsaw(P&Ifk{YJRhFW^@_zdpp*j*Os#zIF^f}|>C6@@|5;J!u?RSjH`|x_3krDfY
zlq7u`?C17|aw!=<ReV+7g{1f$OHGtG!_8~|ZG$y8vkgt(d}!QXa0Az=3YVZknp>@9
zsima}oxz-DUcp3ZAUi`&mUfQ^%YR=jzQv0V$=GvUxP1A1oI>EX9rVS?$?iLL(4X`>
zh0*Ev{Yn20&*woAbq)O9Yi(Q}RY9;Xw7>reJO+Qpg&=HDqt0WwlHWs6+Td8qpQHGQ
zCJ+oT|9@X+G%Q~?tT+6gDTA<uUrK`fSU`j6Jh+$temn7{T3j`KKD|&lBu=mZ&|Wk)
z^2I3}-H!6xR3#GF1&(h$U>4`J3kDLGFH|P+hM}S>hB@z8zxuByug1|#!$CcCyBa6j
zpe;fnRUg3cQ+0aB{_FcHZ~68Th6ocgt2M^=uB!RxC%wF7C93AvQZJ^s<Uh~Z?1<R^
zlR?8vd6Q+omw42}u~Qk@we*(*Hw&O5mk0h#$iJO;rQtVz({gPstxjUD1#92N?hIn9
z2d2*BfLUb%)sX@ZJ2wl32+)<mrfCc^cw12&TlNeLFffyMaIE7G-@Q>n-+lbA2j=x>
zPh2gWk@9E%u%t|(SGDT$Nba(?-ZyqMO)gcKbnk1O?jPUs%5&uT$dZ|#=NMIaZ!}!d
zOt~s^P=ish`!8b6p>JRQ{7+&{#w%YD)NSw;E(29F!?96<fcLjw6s`CDxivSpD5u{7
z1>N<HvFt^julfpddg5j#nvBYod<sV<L;~)OZh56Q`D3X->`_Kr&iB{r(>uTXSqa+|
zQA#JU7}CcEGf=J;j?;X|VMj6VC**YwHnQ=mzD+B~7UQPI8UVmK|2gj1W=+W{ST}m+
zq9M~YFp!c}TUN$~!QAc36CXW#^Rbz{_}x5wjZgp1t^Z!+MMg%JKf+q2JBMqtKlvBY
zMcKZNtOQ(G1{VS$z1$9bqyI-SMwGj0FXvYHIt>n`35(>TPsdm{yS!)N=H^Z{&X`9T
z1sVd#fJ;nF>?*`i@%q!C6Nhm|l5e})(q@`6G;t4-3^3rMpFjT!Hr%ME7;Ph6q(?jk
zcnl6h(Je)R>R^LNiC(m*tgS6W+TGRNeTq(B02q99lk0Xkk6o2>SPT<0XfK5WK+O|O
z*XoOkjScIV9rngz%1_eFaKhx~RxlJvaSB5#KiEJoU}w+tG(Ii`x)NEd7_AEc8=@P=
ziUkAXE*@dhHt;WEIgn<)4MY(_SVsE~&&WGf)Qf{{Pk4!aqdtQv`XJHSgT*-UItQSS
z)v-Ym$I8LQRf?C;(UY)2s*Nt>!z4|Q1(4QifqoX=BPANHxnt+f=eYh+2?^D-nktB#
zKEeX?o1JI|C#BGy%BE;67K3I#Ff=9#W-`w&5ByR0g8EL~-roKvx7AH_ZNMR5qL`bT
zcfy3U3!Ojnv|09tsi|69w+78mbQi%$%p67{++VH|@MBDe6C`@LJ7##UJr4B@ZjghB
zjR2{%fphp4mfm82f^KW|4qhuOt4mj|m|_<mM^*L$n&6@CJ*6hfOA5Su%c<~Vr4UpK
zRhpD&M7zO_^F)MJ5@!VLwG6fgQE_pMf#&ocaIJDYcN~9s=MD=*qg&vEd>zFdxMUsU
z@|#V{A6h;Wg6~KOY-amv5>m#Ep`J(>Z&<$YQ>AU%WH5fUsHn)!(gMsbbO%F9T`JF2
zpFH+sz$9>}!W%6^=r<GdmFrP&4Sc_}|5xmhb&j)hu2FD#Uy|j%U1oDfT3Q;s04S8*
z*W?yLahxiY_fU3o2O1ZGsJOwD+=#kIL_Ofg^|H(g4V1U@Kwgr75MeTZq$=#D-Mqca
zCI*_v07i7j+FyST7AJ+s?!B1m2;`;^<-LvQJl%3{m9;tcLBzNBtG#m}Y+8xwI#?!4
zAOvyyWv)qSpBZA<_5QzN*v3|6n_~fR>c(=0{OCsFoC1tVQrR=k*0~VRx_oVAb@iW9
z;n&Yqdt(JdW@9I%{{0tN3=~d1H{3i3l?MIl*iAZF78Vu{J-od3Cnz1;p0z<r>W>NR
z4JKK$W$Him<vl;@HA1a&KYNJAk*AZzSK}j>ZraYbY*`xQ4UHaJ6<Hp6np^Y6&)(Md
zzOv`Xo0y^3hrjE6!jo39GqXEVI5pCdF0|SCs$&VA`T)HIW6<SRb&;blG-v=ixn|{d
z-$O?Vy@%V9;nf=Hh$hGbEQAyUlrv?{mFK?s6@g&#022_&NKy<jj#ALS(b-2j_;bW2
z71+6-&C1#YGiA9MpUJ^6AduCQa3emT-}P>ljYlC!s`!(r=C~?|OI-D?Y?|ZkMS+2Y
zkYGzcUUqe4NzRWSe#b)Xy*aC=AjB>M1?DKR_k1>L9UKDEC8#ea)Xx54X}Z54OXoJY
zBZY;9$}@z8w1599S;>OZxpM1Ybr-E;vqgEk$tUm%om}m}cfbFpEqT|_?-JmDzwG$`
zCJss~%N4x(-=Aty4kV`wM0bLifer{fqOPHV1&<<Gl7wsE=;Sm8P5jQ3^uj~67Z%FQ
zb>%nyjcF~c_Vyhqe{?2EH|Bw@Y-~*}@;O6^%e(fQnm!=r!iG@9djPfZ{dRVDLlSei
zf7iJG<R!FmDT8FiHU<g5{O@{`(59u)4vEdBKUs4r1ohooP*m`1bNJu+&hLLt{6GG=
zPLu$pm}qUhGxDu!Q*9@NJrzFrE~YA(DO13}6pyYjl|0^V3C+*zJ3Auj3p$r7(QX^L
zPiW9G=VN5J`~kt;V03>5QpY61qPa%d8W5m%F*=$G-pA;un+lAk0sdcWhPyE700FTH
z#fvR{hvczE@`IR*A=k2OhqrRIwG|cHY;9K(B)qfJx(yMl<>|>|aT^J195fIR@r%Ki
zb^rRYZu5;9@TA0HeGSo3xNEbyb(bup<i|Lbofl>U4YfKF4Jz=gC=?JFUl7$n*lpiL
z&lZu}EUpaC#&f6iXJ^GU&lWc~3qWe(1wh5GRdUpK%(!^A^6c#FqcvMbI`MV)Q&USY
z-o_&=%s~O$vs4{JD(Gx;V8?@n;0u5*DdXRX*F3TTILj_4tW6czRg_m%m7w8+qt%CE
zrsH)pC=7xR+V%SK`W86l^wr*ogtv4y{S)4I6&-)VfrmjhA^ie%)WHxIqaUVMc|?RB
zlfb88I!)M4@P?&$ZfFhwOZonT2O9hL^QnFRC=R=1Dp+;*lars5lO;VIV8_Hs9HO#x
zh=%6IW`Qc5YE`tXIo%2$&ioU6LE(6BTBZfLjgAnzy#-TI_|stCcq<S*Wv|aH6g1Km
zFfYT6=^-ph!Xkp*N8QO;(8NiAN2}wuPUJG;nSjX^WHbyY%9E@}xz?)CPYHXP4BT2s
zg8|kPDSaEg>Lz~9jMzLsHUA?h5}=YI^mq34oyGo<LkE1#hg^v8*RYYpC;@GpyC;9t
zROTSGltRduI3tK5q66wHgKJ<YLX)O+4EWFsNl$b<6~xPzV5(E$4g^<V(ujto_~sQo
zsM`rqtr5+Lm+C*qxUqGSh4c2_-ZEt0Ogpq|p*2~TVWR_L<3S80NJ)#IJ$u&i=TKMI
zt*oKp;RsNys*j^h1!!5W74Ygc8~Fq1u@K!@Y&_R)Am$m}AdV!G;Jp*`btuOZ!iVAh
z{W;$Yj6kJ<05|~}iLQ-O6Wn#ZB683dxOC}Kc5GH($nDV3(3+7QT9c7@U`;LqptM07
z0edzSDPP|B`E?DYemVku|0VqVaO|50f99Zl$qh69BTqSUfGh)CzcvI@dobDZPFUd9
z!Cw0mNdfj^1m`+b<c%W7(S##YYa|yZy5xi~D0My2Py+Xh^UmG7-GM**13e?;{ifX#
zY;Y60VBk_WGqsXd2K1JmT8cwLf^!}TJCc>>?d_s__Uxeq;kryuPlMsei-)Y<DPCt>
zH}+wBNIvAoU~W9vULb%`9SqqT$kpyw{{_-}={0#LkoCC1yFiZ!9<GUvo2)Q+Qi4lz
zOK&X(vXglO$9pyaekL6Q?0UDxzk?0ZjU<I&`@sM49~n@AR>G%=EsLXi0J&-zC1T|g
zUF}n+Pm}xD_Vgq#$n-3fIeTi*d&tWCHr{XZKHEXr{+(&u%<=x7>O7cl%Xt^qT$AVc
z^}V_14FWQM?GQ%U$b(2wRR|SezrVI+_lTj1Nf_QQj-MbsMArFY`LF3R{t?KP@>j1i
zoJdGPk~TUySxSTz@G}J8oA3-d@hK_B^jU_+jgBb#;Emm{)ca9v_V)H#Ba4P8^2H=g
zF`A<QP$yDh>XXM%Y;5sLgdnJc%>9VyRiJGAzXJDM9s5^Y|EXRv+fjau+5;SjLfNUk
LM=MRk`qcjb_~k5?

literal 0
HcmV?d00001


From 667e4f6e327b33b8c24ac0fccfadb6277ab2c83f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 6 May 2014 14:38:02 -0400
Subject: [PATCH 022/248] minor fixes

---
 .../cython_least_squares-checkpoint.ipynb     | 270 ++++++++++-------
 benchmarks/cython_least_squares.ipynb         | 283 +++++++++++-------
 2 files changed, 344 insertions(+), 209 deletions(-)

diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
index 2e97429..0f94642 100644
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b8a2adab4cfa8ac1064656d4b3e3121a2acc1060e034c98ce29986312939c9ac"
+  "signature": "sha256:90a74ceed4e3dca395013883669de508e2ef94573e58cf24ce3ba49686ca5fe0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -132,7 +132,7 @@
      "source": [
       "In more mathematical terms, our goal is to compute the best fit to *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
       "$f(x) = a\\cdot x + b$.  \n",
-      "Here, we assume that the y-component is  functionally dependent on the x-component.  \n",
+      "We further have to assume that the y-component is  functionally dependent on the x-component.  \n",
       "In a cartesian coordinate system, $b$ is the intercept of the straight line with the y-axis, and $a$ is the slope of this line."
      ]
     },
@@ -280,7 +280,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 36
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -301,7 +301,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Next, we will calculate the parameters separately, using only standard library functions in Python, which I will call the \"classic approach\"."
+      "Next, we will calculate the parameters separately, using standard library functions in Python only, which I will call the \"classic approach\"."
      ]
     },
     {
@@ -331,7 +331,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 37
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -352,7 +352,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "For our convenience, `numpy` has a function that can also compute the leat squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
+      "For our convenience, `numpy` has a function that can computes the least squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
      ]
     },
     {
@@ -367,7 +367,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 38
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -388,7 +388,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The last approach is using `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.  The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
+      "Also scipy has a least squares function, `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.   \n",
+      "The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
      ]
     },
     {
@@ -404,7 +405,22 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 39
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Teaser"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "The following plot below should give you an impression of how the performance of the 4 different approaches will compare to each other at the end of this article."
+     ]
     },
     {
      "cell_type": "markdown",
@@ -433,12 +449,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In order to test our different least squares fit implementation, we will generate some sample data:  \n",
+      "In order to test our different least squares fit implementations, we will generate some sample data:  \n",
       "- 500 sample points for the x-component within the range [0,500)  \n",
       "- 500 sample points for the y-component within the range [100,600)  \n",
       "\n",
       "where each sample point is multiplied by a random value within\n",
-      "the range [0.8, 12)."
+      "the range [0.8, 12) to simulate some scatter in our dataset."
      ]
     },
     {
@@ -454,7 +470,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 40
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -476,15 +492,25 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "To check how our dataset is distributed, and how the straight line, which we obtain via the least square fit method, we will plot it in a scatter plot.  \n",
-      "Note that we are using our \"matrix approach\" here for simplicity, but after plotting the data, we will check whether all of the four different implementations yield the same parameters."
+      "To check how our dataset is distributed, and how the least squares regression line looks like, we will plot the results in a scatter plot.  \n",
+      "Note that we are only using our \"matrix approach\" to visualize the results - for simplicity. We expect all 4 approaches to produce similar results, which we will confirm after visualizing the data."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%pylab inline\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "from matplotlib import pyplot as plt\n",
       "\n",
       "slope, intercept = lin_lstsqr_mat(x, y)\n",
@@ -514,11 +540,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10f9e8a50>"
+        "<matplotlib.figure.Figure at 0x107806610>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -540,7 +566,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "As mentioned above, let us confirm that the different implementation computed the same parameters (i.e., slope and y-axis intercept) as solution for the linear equation."
+      "As mentioned above, let us now confirm that the different implementations computed the same parameters (i.e., slope and y-axis intercept) as solution of the linear equation."
      ]
     },
     {
@@ -580,7 +606,7 @@
        ]
       }
      ],
-     "prompt_number": 43
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -602,7 +628,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "For a first impression how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
+      "To get an initial impression of how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
      ]
     },
     {
@@ -626,7 +652,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "matrix approach: 10000 loops, best of 3: 163 \u00b5s per loop"
+        "matrix approach: 10000 loops, best of 3: 158 \u00b5s per loop"
        ]
       },
       {
@@ -635,7 +661,7 @@
        "text": [
         "\n",
         "\n",
-        "classic approach: 1000 loops, best of 3: 1.55 ms per loop"
+        "classic approach: 1000 loops, best of 3: 1.6 ms per loop"
        ]
       },
       {
@@ -644,7 +670,7 @@
        "text": [
         "\n",
         "\n",
-        "numpy function: 1000 loops, best of 3: 221 \u00b5s per loop"
+        "numpy function: 1000 loops, best of 3: 217 \u00b5s per loop"
        ]
       },
       {
@@ -653,7 +679,7 @@
        "text": [
         "\n",
         "\n",
-        "scipy function: 1000 loops, best of 3: 362 \u00b5s per loop"
+        "scipy function: 1000 loops, best of 3: 366 \u00b5s per loop"
        ]
       },
       {
@@ -664,14 +690,14 @@
        ]
       }
      ],
-     "prompt_number": 44
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "<br>\n",
-      "The timing above indicates, that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10."
+      "The timing above indicates that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10. However, we should keep in mind that this experiment was done for a fixed sample size n=500."
      ]
     },
     {
@@ -702,12 +728,12 @@
      "metadata": {},
      "source": [
       "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
-      "Since we are working in an IPython notebook here, we can make use of the IPython magic: It will automatically convert it to C code, compile it, and load the function.  \n",
-      "Just to be thorough, let us also compile the other functions, which already use numpy objects.\n",
+      "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  \n",
+      "Let us also compile the functions for the other 3 least squares approaches - those already use numpy objects and therefore we don't expect any performance gain here, but it is good to be thorough.\n",
       "\n",
       "**Note**  \n",
-      "Of course Cython has much more horsepower under its hood - more than I am showing in this article (for example, I am not using Cython's type definitions via `cdef` here). Here, I want to focus on how to speed up existing Python code by making only minimal changes to it.   \n",
-      "[In a later section - Appendix II](#type_declarations) We will see how static type declarations can further improve the performance via Cython."
+      "Of course Cython has much more horsepower under its hood - more than I am showing in this section of this article (for example, I am not using Cython's type definitions via `cdef` here, but we will get to it [in a later section - Appendix II](#type_declarations)). \n",
+      "The focus of this section should be on how to speed up existing Python code by making only minimal changes to it.   "
      ]
     },
     {
@@ -718,17 +744,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "The cythonmagic extension is already loaded. To reload it, use:\n",
-        "  %reload_ext cythonmagic\n"
-       ]
-      }
-     ],
-     "prompt_number": 45
+     "outputs": [],
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -766,7 +783,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 46
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -809,7 +826,7 @@
         "\n",
         "\n",
         "matrix approach: \n",
-        "10000 loops, best of 3: 165 \u00b5s per loop"
+        "10000 loops, best of 3: 159 \u00b5s per loop"
        ]
       },
       {
@@ -817,7 +834,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 166 \u00b5s per loop"
+        "10000 loops, best of 3: 159 \u00b5s per loop"
        ]
       },
       {
@@ -828,7 +845,7 @@
         "\n",
         "\n",
         "classic approach: \n",
-        "1000 loops, best of 3: 1.59 ms per loop"
+        "1000 loops, best of 3: 1.62 ms per loop"
        ]
       },
       {
@@ -836,7 +853,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 127 \u00b5s per loop"
+        "10000 loops, best of 3: 126 \u00b5s per loop"
        ]
       },
       {
@@ -847,7 +864,7 @@
         "\n",
         "\n",
         "numpy function: \n",
-        "1000 loops, best of 3: 220 \u00b5s per loop"
+        "1000 loops, best of 3: 218 \u00b5s per loop"
        ]
       },
       {
@@ -855,7 +872,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 221 \u00b5s per loop"
+        "1000 loops, best of 3: 220 \u00b5s per loop"
        ]
       },
       {
@@ -866,7 +883,7 @@
         "\n",
         "\n",
         "scipy function: \n",
-        "1000 loops, best of 3: 361 \u00b5s per loop"
+        "1000 loops, best of 3: 372 \u00b5s per loop"
        ]
       },
       {
@@ -874,7 +891,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 367 \u00b5s per loop"
+        "1000 loops, best of 3: 354 \u00b5s per loop"
        ]
       },
       {
@@ -885,7 +902,7 @@
        ]
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -893,8 +910,8 @@
      "source": [
       "<br>\n",
       "<br>\n",
-      "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to using numpy's functions. This is not surprising, since numpy is highly optmized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.  \n",
-      "However, we were able to speed up the \"classic approach\" quite significantly, roughly by 1500%.\n",
+      "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to the other approaches which use Numpy objects. This is not surprising, since Numpy is highly optimized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.  \n",
+      "However, we were able to speed up the \"classic approach\" quite significantly via Cython, roughly by 1500%.\n",
       "\n",
       "The following 2 code blocks are just to visualize our results in a bar plot."
      ]
@@ -919,14 +936,13 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 50
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "x_pos = np.arange(len(funcs))\n",
       "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
@@ -950,21 +966,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDPMCSwgMwzAMAOD/ANLifp9XPWcHAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGAAsITAMwzAvsYTAMAzDAAD+D41KT4VmEkzWAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10eeafd50>"
+        "<matplotlib.figure.Figure at 0x1079680d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFGfXBvB7qNJ7EaSIxgiKghWxYVBj16BGiSWoiSXR\naEyR1xjljbFrLNGYmESJPTHGGuyK+tl7I4JtsYIFlSqwy/n+8N2JC4iKw8yynN91eSWz7Mw8c+/O\nnJ3nmdkViIjAGGOMGTgjpRvAGGOMyYELHmOMsQqBCx5jjLEKgQseY4yxCoELHmOMsQqBCx5jjLEK\nQdaCl5qaihYtWsDW1hZffPGFnKsulzp06IBly5Yp3Qy95evri127dsm2PiMjI1y9erVM1xEVFYWv\nv/66zJZvY2MDlUr1SvPEx8fDy8urbBpk4GrXro19+/ZJvlyVSgUjIyMUFBRIvmylDRs2DN9++22Z\nLNvkRU/w9fXF3bt3YWxsDCsrK7Rv3x7z58+HlZXVK69s0aJFcHV1RXp6eqkaW9HExcUp3QS9JggC\nBEEo9m9RUVHw8vLCxIkTS7XssLAw9OvXD4MGDXqdJr6ykrZJChkZGWW27Nfxuq+Xvjp//rzSTZDV\nq76OsbGx+PXXX7F//37xsYULF5ZV8158hicIAjZv3oyMjAycPHkSx48ff+XqS0QoKChAcnIy/P39\nS9VQtVpdqvlY+aBvr29ZFp0X4e+C0E/69h5lpUAv4OvrS7t27RKnP//8c+rUqRMRER06dIiaNGlC\n9vb2VLduXYqPjxef17JlS/rqq6+oadOmZGFhQX379iVTU1MyMzMja2tr2rVrF+Xm5tLIkSPJw8OD\nPDw8aNSoUZSbm0tERHv27CFPT0+aNm0aubu7U79+/SgmJoZ69OhBffv2JRsbGwoMDKSkpCSaPHky\nubq6kre3N23fvl1sw+LFi8nf359sbGzIz8+PfvrpJ/Fv2uXPmjWLXF1dqXLlyrRkyRLx79nZ2TR6\n9Gjy8fEhOzs7atasGeXk5Lxwuws7ceIEBQUFkY2NDfXs2ZPeffddGjduHBERpaWlUceOHcnFxYUc\nHByoU6dOdPPmTZ0Mf/nlFyIiWrJkCTVt2pQ+//xzcnBwoKpVq9KWLVueu97r16/TO++8Qy4uLuTk\n5ETDhw8nIiKNRkMTJ04kHx8fcnV1pf79+9Pjx4+JiOjatWskCAItWbKEvLy8yNHRkRYuXEhHjx6l\nwMBAsre3F5ejbVNoaCgNHz6c7OzsqGbNmjrvlZfJX/v69u/fnwoKCmjKlClUrVo1cnJyonfffZfS\n0tLEeZYuXUre3t7k5OREkyZNKvLe1Prpp5903mtdunQhIqKEhARq2bIl2dvbU61atWjjxo3FZjd2\n7FgyNjamSpUqkbW1NY0YMYKIiARBoB9//JHeeOMNsre3p48//lhnvl9//ZX8/f3JwcGB3n77bUpO\nTn7u67N//37xPeTl5UW//fYbERFFRUW99PtjyZIl5OfnRzY2NlS1alVasWIFERFdunSJWrRoQXZ2\nduTs7Ey9evUS5xEEga5cuUJEJb/Hn7Vnzx6qUqWKOH3r1i2KiIggFxcXqlq1Ks2bN0/825EjRygk\nJITs7e2pcuXKNHz4cMrLyxP/PmrUKHJ1dSVbW1sKDAyk8+fPP/f1Kqy4eYmI7t+/T507dyZbW1tq\n1KgRjRs3jpo1a0ZE/76nNRqNuJxn96vLly9Tq1atyMnJiZydnalPnz706NEj8bk+Pj40bdo0CgwM\npEqVKpFGo3ml/d/Hx0d8j06YMOGVjl8tW7ak6OhoatSoEdna2lLXrl3F/aHwdj169IgGDhxIlStX\nJk9PTxo3bpz4N+1++umnn5K9vT1Vq1aNDhw4QIsXLyYvLy9ydXUV339ERE+ePKHPPvuMvL29yc3N\njYYOHSq+L0o6bj7vddTu0zY2NhQQEEDr1q0joqf7Y6VKlcjY2Jisra3JwcGBiIjef/99cR8gIlq0\naBFVr16dHB0dqUuXLnT79m3xby/aJwt7qYK3c+dOInp6EK1VqxaNHz+ebt68SU5OTuJBd8eOHeTk\n5ET3798XXywfHx9KSEggjUZD+fn5FBUVRV9//bW47K+//pqaNGlC9+7do3v37lFoaKj49z179pCJ\niQlFR0dTXl4e5eTk0IQJE6hSpUq0fft2UqvV1L9/f/Lx8aHJkyeTWq2mn3/+mapWrSou/++//6ar\nV68SEdHevXvJ0tKSTp48qbP8CRMmkFqtpri4OLK0tBTf7B999BG1atWKbt++Lb7Jc3Nzn7vd9+7d\nK5Jdbm4ueXt707x580itVtNff/1FZmZm4jY+ePCA/vrrL8rJyaGMjAzq2bMndevWTZw/LCyMfv31\nVyJ6+qY1NTWlX375hQoKCmjhwoXk4eFR7GumVqupTp06NHr0aMrOzqYnT57QgQMHiOjpQbl69ep0\n7do1yszMpIiICOrXrx8R/bsTDRs2jHJzc2n79u1kZmZG3bp1o3v37tGtW7fI1dWV9u7dK7bJxMSE\n5syZQ2q1mn7//Xeys7MTd8qXyf/Z13fOnDnUpEkTunXrFuXl5dGQIUMoMjKSiIguXLhA1tbWtH//\nfsrNzaXRo0eTiYlJsQWPiIq81/Ly8qhatWo0ZcoUys/Pp927d5ONjQ0lJiYWO/+z2WsJgkCdO3em\nx48f0/Xr18nFxYW2bt1KRETr16+n6tWr08WLF0mj0dC3335LoaGhxS5bpVKRjY0NrV69mtRqNT14\n8IBOnz4ttlu7s5f0/sjMzCRbW1tKSkoiIqKUlBS6cOECERH17t2bJk+eTERP34Pa1167DdqC97z3\neGHPFjyNRkP16tWjiRMnUn5+Pl29epX8/Pxo27ZtRPT0A96RI0dIo9GQSqUif39/mjNnDhERbd26\nlerXry9+wLp48SLduXOn2NersJLm7dWrF/Xq1Yuys7Pp/Pnz5OnpSc2bNyei4gves6/t5cuXaefO\nnZSXl0f37t2jFi1a0KhRo8Tn+vj4UHBwMN28eZOePHnySvs/ke4Jw6sev1q2bEmenp504cIFysrK\nou7du1Pfvn2L3a5u3brR0KFDKTs7m+7evUuNGjUSP2Bq99PY2FgqKCigcePGkaenp/hhZPv27WRj\nY0NZWVlE9PSDRdeuXenhw4eUkZFBnTt3pv/85z/ie6Gk42Zxr+OaNWvE1+r3338nKysrSklJISKi\n2NhY8cOJ1rPL2LVrFzk7O9OpU6coNzeXRowYQS1atBCfW9I+WZwXFjwfHx+ytrYme3t78vHxoY8/\n/phycnJo6tSp4oFS6+233xY/KYSFhdGECROKbMizlbtatWo6Zynbtm0jX19fInoarJmZmc4OOGHC\nBGrbtq04vXHjRrK2tqaCggIiIkpPTydBEMSdorBu3brR3LlzxeVbWFjo7Aiurq7izmphYUFnz54t\nsowXbfez9u7dS56enjqPNWvW7Lk79qlTp8RPOURFC1716tXFv2VlZZEgCJSamlpkOQcPHiQXFxed\nbdN66623aOHCheJ0YmIimZqakkajEXeiZz9BOTk50R9//CFOd+/eXTyALVmypEjRbdSoES1btqzY\n7Sucf+HX19/fX6eA3b59m0xNTUmtVtN///tfsfhpt9/MzKzEgvfse23fvn3k7u6u85zIyEiKiYkp\ndv6wsDDxLEBLEASd4vHuu+/StGnTiIioXbt2OgVSo9GQpaUlXb9+vciyJ0+eTBERES/V7mc9+/7I\nzMwke3t7Wrt2LWVnZ+s8r3///jR48GCds8Fnt+HKlSslvscLe7bgHT58mLy9vYtsz4ABA4qdd/bs\n2fTOO+8Q0dODV40aNejw4cNF3pslbTcR0e7du4udV61Wk6mpqc4Hl7Fjx5Z4hlfchxmtdevWUXBw\nsDjt6+ur0/PzKvu/dv5nC96rHL/CwsLEQkP09IzIzMyMCgoKdLYrJSWFzM3Ndc7OV65cSa1atSKi\np/vpG2+8If7t7NmzJAgC3b17V3zMycmJzpw5QwUFBWRlZSV+KCJ6ejzRFuKSjptEL34diYiCgoJo\nw4YNYttKKngDBw6kMWPGiH/LzMwkU1NTsfekuH1y6tSpz133S43hbdiwAQ8fPoRKpcL8+fNRqVIl\nJCcnY82aNXBwcBD/HThwACkpKeK8L7qy6/bt2/Dx8RGnvb29cfv2bXHaxcUFZmZmOvO4urqK/29h\nYQFnZ2dxvMXCwgIAkJmZCQDYsmULQkJC4OTkBAcHB8TFxeHBgwfi/E5OTjAy+jcCS0tLZGZm4v79\n+3jy5AmqVatWpM0vs93Pbp+np6fOY15eXuIYTXZ2NoYMGQJfX1/Y2dmhZcuWePz48XPHcNzd3XXa\n+uy2PuvGjRvw8fHR2TatO3fuFMlcrVYjNTVVfMzNzU38fwsLiyLTWVlZ4nTh7fPx8cGdO3cAvDj/\nwq+vSqXCO++8I+YaEBAAExMTpKam4s6dO6hSpYrO9js5ORUXU7Fu375d5P3o4+ODW7duPXee4sbx\nCr8G2vyTk5MxcuRIse3athW3/Js3b8LPz++FbS7p/WFlZYXff/8dP/74Izw8PNCpUyckJiYCAKZP\nnw4iQqNGjVC7dm0sWbKkyLJLeo+XJDk5Gbdv39Z5/0+ZMgV3794FACQlJaFTp06oXLky7Ozs8NVX\nX4mv+VtvvYXhw4fj448/hpubG4YMGfLSF9G0atWq2Hnv3bsHtVqt89p6e3u/9Pakpqaid+/eqFKl\nCuzs7NCvXz+d9yigexx7lf2/OK9y/Cq8bm9vb+Tn5+P+/fs6y0xOTkZ+fj4qV64stmno0KG4d++e\n+JzC+zDwdP979rHMzEzcu3cP2dnZqF+/vris9u3b66zzecfN51m6dCmCg4PF5Z0/f75Ixs9T+Hhl\nZWUFJycnnf3qeftkcUp9W4K3tzf69euHhw8fiv8yMjLw5Zdfis950cC/h4eHziXS169fh4eHx3Pn\nf5ULCXJzc9G9e3d8+eWXuHv3Lh4+fIgOHTq81AUBzs7OqFSpEi5fvlzkby+z3VqVK1cucsC7fv26\nuB2zZs1CUlISjh49isePH2Pv3r2gp2fdL72dxfHy8sL169eh0WiK/K24zE1MTHR2iFdRePuSk5Ph\n4eHxUvkXfj29vb2xdetWnWyzs7Ph4eGBypUr48aNG+Jzs7OzS9xpCi/bw8MDN27c0Fl/cnKyThEt\naf4X8fb2xqJFi3TanpWVhZCQkCLP9fLywpUrV17Y9he9P9q2bYvt27cjJSUFNWvWxIcffgjg6cFt\n0aJFuHXrFn766Sd89NFHRW6nKOk9XhIvLy9UrVpVZzvT09OxefNmAE8vKQ8ICMDly5fx+PFjTJo0\nSefS+REjRuD48eNISEhAUlISZsyYobPNJSluXldXV5iYmOD69evi8579f+3V5NnZ2eJjzxansWPH\nwtjYGOfPn8fjx4+xbNmyIpf6P9u2V9n/pVB4u0xNTeHs7KzzHC8vL5ibm+PBgwdimx4/foxz5869\n8vqcnZ1hYWGBhIQEcVmPHj166SvrC7+OycnJGDx4MBYsWIC0tDQ8fPgQtWvXFt/Dr1ojsrKy8ODB\ngyIftF9WqQte3759sWnTJmzfvh0ajQZPnjxBfHy8zgGw8IG78HRkZCS+/fZb3L9/H/fv38c333yD\nfv36PXedr1II8vLykJeXB2dnZxgZGWHLli3Yvn37S81rZGSEgQMHYvTo0bhz5w40Gg0OHTqEvLy8\nl9purdDQUBgbG2P+/PlQq9XYsGEDjh07Jv49MzMTFhYWsLOzQ1paGv773/++9PaVpHHjxqhcuTKi\no6ORnZ2NJ0+e4ODBgwCeZj579myoVCpkZmZi7Nix6N27d7Fng8/z7Otw9+5dzJs3D/n5+VizZg0u\nXryIDh06lCr/oUOHYuzYseJOfu/ePWzcuBEA0KNHD2zevBkHDhxAXl4exo8fX+I9SG5ubjoH+ZCQ\nEFhaWmL69OnIz89HfHw8Nm/ejN69ez93/pKKkjYHbRZDhw7F5MmTkZCQAAB4/Pgx1qxZU+x8ffr0\nwc6dO7FmzRqo1Wo8ePAAZ86cKbLMkt4fd+/exYYNG5CVlQVTU1NYWVnB2NgYALBmzRrcvHkTAGBv\nbw9BEIq8viW9x0vSqFEj2NjYYPr06cjJyYFGo8H58+dx/Phxsc02NjawtLTExYsXsXDhQvGgdvz4\ncRw5cgT5+fmwtLREpUqVxDYXfr0Ke968RkZGiIiIQExMDHJycpCQkIClS5eK63RxcYGnpyeWLVsG\njUaDxYsX67yumZmZsLKygq2tLW7duiUW4Od5lf3/dRERli9fjn/++QfZ2dkYP348evbsWaRIVK5c\nGW3btsXo0aORkZGBgoICXLlypVT3/xkZGeHDDz/EqFGjxDPEW7duvfSxs/DrmJWVBUEQ4OzsjIKC\nAixZskTnVg03NzfcvHkT+fn5Otut3QciIyOxZMkSnDlzBrm5uRg7dixCQkKeexb/ohpR6oJXpUoV\nbNiwAZMnT4arqyu8vb0xa9asEj/BF77HaNy4cWjQoAHq1KmDOnXqoEGDBhg3btxLz/+85wBPb7Cd\nN28e3n33XTg6OmLVqlXo2rVrifM+a+bMmQgMDETDhg3h5OSE//znPygoKHjudhd38DU1NcVff/2F\nX3/9FQ4ODlixYgU6deokduONGjUKOTk5cHZ2RmhoKNq3b//cNr3MtmsZGRlh06ZNuHz5Mry9veHl\n5YU//vgDADBw4ED069cPLVq0gJ+fHywtLfH999+/VCbFPadx48a4dOkSXFxc8PXXX2Pt2rVwcHAo\nVf4jR45Ely5d0LZtW9ja2qJJkyY4evQoACAgIAALFizAe++9Bw8PDzg6OpbYZT5o0CAkJCTAwcEB\nERERMDU1xaZNm7Blyxa4uLhg+PDhWLZsGWrUqFHs/CNHjsSff/4JR0dHjBo16rk5aLehW7duGDNm\nDHr37g07OzsEBgZi27Ztxc7n5eWFuLg4zJo1C05OTggODsbZs2eLLLOk90dBQQFmz54NT09PODk5\nYf/+/eL9S8ePH0dISAhsbGzQtWtXzJs3D76+vkUyf957/HnbCgDGxsbYvHkzTp8+DT8/P7i4uGDw\n4MHiGcDMmTOxcuVK2NraYvDgwTofKNLT0zF48GA4OjrC19cXzs7O4hdQFH69Citp3vnz5yMzMxPu\n7u4YOHAgBgwYoHMc+vnnnzFjxgw4OzsjISEBTZs2Ff82YcIEnDx5EnZ2dujcuTO6d+9e4j7wKvt/\ncRm+7PFL+//9+vVDVFQUKleujLy8PMybN6/Y5y5duhR5eXkICAiAo6MjevbsKZ7JvsqxAwCmTZuG\n6tWrIyQkBHZ2dmjTpg2SkpJeat7Cr2NAQAA+++wzNGnSBO7u7jh//jyaNWsmPj88PBy1atWCu7u7\n2N37bHvDw8MxceJEdO/eHR4eHrh27RpWr15dYn4lHsNKHF3UAwMGDCBXV1eqXbt2kb/NnDmTBEGg\nBw8eKNCy0mnUqBHFxsYq3QxJFDfgzJjSDOV9WdLFNax09P67NAcMGICtW7cWefzGjRvYsWOHzoCm\nPtq3bx9SUlKgVqvx22+/4fz582jXrp3SzWKMlQPEX0IgKb0veM2bN4eDg0ORx0ePHo3p06cr0KJX\nk5iYiKCgIDg4OGD27Nn4888/S32BiL4p66/BYqw0DOl9aSjboS8EKgcfIVQqFTp37ixedbRhwwbE\nx8dj9uzZqFq1Kk6cOAFHR0eFW8kYY0yfvfDLo/VNdnY2Jk+ejB07doiPlYOazRhjTGHlruBduXIF\nKpUKdevWBfD0Jt769evj6NGjOjd1AkD16tVfeGk5Y4wxXXXr1sXp06eVbobk9H4Mr7DAwECkpqbi\n2rVruHbtGqpUqYKTJ08WKXbA0+JI/7unQ5//TZgwQfE2GNI/zpOz1Nd/5SVP7X2hhkbvC15kZCRC\nQ0ORlJQELy+vIl+TZAiDuq/6g5ysZJyndDhLaXGeytL7Ls1Vq1aV+Pey/gVqxhhjhkHvz/Aqgqio\nKKWbYFA4T+lwltLiPJVVLm5LKC1BEGDAm8cYY2XCUI+dfIanB+Lj45VugkHhPKXDWUqL81QWFzzG\nGGMVAndpMsYY02Gox04+w2OMMVYhcMHTA9yvLy3OUzqcpbQ4T2VxwWOMMVYhVOgxvOiYaKQ8SpGx\nRfrL3d4dU2OmKt0MxpgeMNQxPL3/ppWylPIoBb7dfJVuhl5QrVcp3QTGGCtT3KWpB1SnVUo3waDw\nOIl0OEtpcZ7K4oLHGGOsQuCCpwd8g3yVboJBCQsLU7oJBoOzlBbnqSwueIwxxioELnh6gMfwpMXj\nJNLhLKXFeSqLCx5jjLEKgQueHuAxPGnxOIl0OEtpcZ7K4oLHGGOsQuCCpwd4DE9aPE4iHc5SWpyn\nsrjgMcYYqxC44OkBHsOTFo+TSIezlBbnqSwueIwxxioELnh6gMfwpMXjJNLhLKXFeSpL7wvewIED\n4ebmhsDAQPGxL774Av7+/qhbty4iIiLw+PFjBVvIGGOsPND7gjdgwABs3bpV57G2bdviwoULOHPm\nDGrUqIEpU6Yo1Dpp8BietHicRDqcpbQ4T2XpfcFr3rw5HBwcdB5r06YNjIyeNr1x48a4efOmEk1j\njDFWjuh9wXuRxYsXo0OHDko347XwGJ60eJxEOpyltDhPZZXrgjdp0iSYmZnhvffeU7opjDHG9JyJ\n0g0ordjYWMTFxWHXrl0lPi8qKgq+vr4AAHt7ewQFBYn96Ck3U4DT/46hac+0Kuq09tOnNh+e5uln\n6Ut7yvu0lr60JywsDPHx8YiNjQUA8XhpiAQiIqUb8SIqlQqdO3fGuXPnAABbt27FZ599hr1798LZ\n2fm58wmCgJI2L2pUFHy7+Urd3HJJtV6F2DmxSjeDMaYHXnTsLK/0vkszMjISoaGhSExMhJeXFxYv\nXowRI0YgMzMTbdq0QXBwMD766COlm/laeAxPWoU/SbPS4yylxXkqS++7NFetWlXksYEDByrQEsYY\nY+WZ3p/hVQR8H560tGMU7PVxltLiPJXFBY8xxliFwAVPD/AYnrR4nEQ6nKW0OE9lccFjjDFWIXDB\n0wM8hictHieRDmcpLc5TWVzwGGOMVQhc8PQAj+FJi8dJpMNZSovzVBYXPMYYYxUCFzw9wGN40uJx\nEulwltLiPJXFBY8xxliFwAVPD/AYnrR4nEQ6nKW0OE9lccFjjDFWIcj25dGJiYmYOXMmVCoV1Go1\ngKc/QbF79265mqC3eAxPWjxOIh3OUlqcp7JkK3g9e/bEsGHD8MEHH8DY2BjA04LHGGOMyUG2Lk1T\nU1MMGzYMjRs3RoMGDdCgQQPUr19frtXrNR7DkxaPk0iHs5QW56ks2Qpe586dsWDBAty5cwdpaWni\nP8YYY0wOAsn0O+6+vr7FdmFeu3atzNb5op+pjxoVBd9uvmW2/vJEtV6F2DmxSjeDMaYHXnTsLK9k\nG8NTqVRyrYoxxhgroswL3q5duxAeHo61a9cWe4YXERFR1k3Qe6rTKr5SU0Lx8fF8NZxEOEtpcZ7K\nKvOCt2/fPoSHh2PTpk1c8BhjjClGtjE8JfAY3svjMTzGmBaP4Ulg8+bNSEhIwJMnT8THxo8fL2cT\nGGOMVVCy3ZYwZMgQ/PHHH5g3bx6ICH/88QeSk5PlWr1e4/vwpMX3OkmHs5QW56ks2QrewYMHsXTp\nUjg6OmLChAk4fPgwEhMTXzjfwIED4ebmhsDAQPGxtLQ0tGnTBjVq1EDbtm3x6NGjsmw6Y4wxAyBb\nwbOwsAAAWFpa4tatWzAxMUFKSsoL5xswYAC2bt2q89jUqVPRpk0bJCUlITw8HFOnTi2TNsuFr9CU\nFl8FJx3OUlqcp7JkK3idOnXCw4cP8cUXX6B+/frw9fVFZGTkC+dr3rw5HBwcdB7buHEj3n//fQDA\n+++/j/Xr15dJmxljjBkO2Qre+PHj4eDggO7du0OlUuHixYuYOHFiqZaVmpoKNzc3AICbmxtSU1Ol\nbKrseAxPWjxOIh3OUlqcp7Jku0qzuBvP7ezsEBgYCFdX11IvVxCEEn91ISoqCr6+vgAAe3t7BAUF\nid0KKTdTgNP/dilqC4/c01pKrV87rd0ZtfmU12ktfWlPeZ4+ffq0XrWnvE/ra57x8fGIjY0FAPF4\naYhkuw+vY8eOOHToEFq1agXgadj16tXDtWvXMH78ePTv3/+586pUKnTu3Bnnzp0DANSsWRPx8fFw\nd3fHnTt30KpVK1y8eLHIfHwf3suT4j686JhopDx68bhsReBu746pMeV7bJlVXHwf3mvKz8/HP//8\nI3ZFpqamol+/fjhy5AhatGhRYsErrEuXLvjtt98wZswY/Pbbb+jWrVtZNZu9gpRHKfwB4n9U61VK\nN4ExVohsY3g3btwQix0AuLq64saNG3BycoKZmdlz54uMjERoaCgSExPh5eWFJUuWIDo6Gjt27ECN\nGjWwe/duREdHy7EJZYbH8KTFeUqncDcxez2cp7JkO8Nr1aoVOnbsiHfffRdEhLVr1yIsLAxZWVmw\nt7d/7nyrVq0q9vGdO3eWVVMZY4wZINkK3oIFC7B27VocOHAAwNPbCbp37w5BELBnzx65mqGX+D48\naXGe0tFe4MCkwXkqS7aCJwgCevTogR49esi1SsYYY0wk2xgeez4ec5IW5ykdHnOSFuepLC54jDHG\nKgRZC152dvZLfWF0RcNjTtLiPKXDY07S4jyVJVvB27hxI4KDg/H2228DAE6dOoUuXbrItXrGGGMV\nnGwFLyYmBkeOHBG/CDo4OBhXr16Va/V6jcecpMV5SofHnKTFeSpLtoJnampa5H47IyMeQmSMMSYP\n2SpOrVq1sGLFCqjValy6dAkjRoxAaGioXKvXazzmJC3OUzo85iQtzlNZshW877//HhcuXIC5uTki\nIyNha2uLOXPmyLV6xhhjFZxsBc/KygqTJ0/G8ePHcfz4cUyaNAmVKlWSa/V6jcecpMV5SofHnKTF\neSpLtoLXunVrPHr0SJxOS0sTr9hkjDHGyppsBe/+/fs6F604OjqW+18qlwqPOUmL85QOjzlJi/NU\nlmwFz9hOClWnAAAgAElEQVTYGMnJyeK0SqXiqzQZY4zJRraKM2nSJDRv3hx9+/ZF37590aJFC0ye\nPFmu1es1HnOSFucpHR5zkhbnqSzZfi2hXbt2OHHiBA4fPgxBEDBnzhw4OzvLtXrGGGMVnGwFDwDy\n8vLg6OgItVqNhIQEAECLFi3kbIJe4jEnaXGe0uExJ2lxnsqSreCNGTMGv//+OwICAmBsbCw+zgWP\nMcaYHGQreOvWrUNiYiLMzc3lWmW5oTqt4rMSCXGe0omPj+ezEglxnsqS7aKVatWqIS8vT67VMcYY\nYzpkO8OzsLBAUFAQwsPDxbM8QRAwb948uZqgt/hsRFqcp3T4bERanKeyZCt4Xbp0KfL7d4IgyLV6\nxhhjFZxsBS8qKkquVZU7POYkLc5TOjzmJC3OU1myjeElJSWhR48eCAgIQNWqVVG1alX4+fm91jKn\nTJmCWrVqITAwEO+99x5yc3Mlai1jjDFDI1vBGzBgAIYOHQoTExPEx8fj/fffR58+fUq9PJVKhZ9/\n/hknT57EuXPnoNFosHr1aglbLB8+G5EW5ykdPhuRFuepLNkKXk5ODlq3bg0igo+PD2JiYvD333+X\nenm2trYwNTVFdnY21Go1srOz4enpKWGLGWOMGRLZCl6lSpWg0WhQvXp1zJ8/H3/99ReysrJKvTxH\nR0d89tln8Pb2hoeHB+zt7dG6dWsJWywf/u5HaXGe0uHvfpQW56ks2S5amTNnDrKzszFv3jx8/fXX\nSE9Px2+//Vbq5V25cgVz5syBSqWCnZ0devbsiRUrVhTpJo2KioKvry8AwN7eHkFBQWK3QsrNFOD0\nv11g2gOl3NNaSq1fO63dGbX5vOo056k7/bp56sP06dOn9ao95X1aX/OMj49HbGwsAIjHS0MkEBEp\n3YjS+P3337Fjxw788ssvAIBly5bh8OHDWLBggfgcQRBQ0uZFjYqCbzffsm5quaBar0LsnNjXWgbn\n+S8p8mRMKS86dpZXsnVpHjt2DO+88w6Cg4MRGBiIwMBA1KlTp9TLq1mzJg4fPoycnBwQEXbu3ImA\ngAAJW8wYY8yQyNal2adPH8ycORO1a9eW5Idf69ati/79+6NBgwYwMjJCvXr1MHjwYAlaKj++b0xa\nnKd0+L4xaXGeypKt4Lm4uBT5ppXX9eWXX+LLL7+UdJmMMcYMk2wFb8KECRg0aBBat24NMzMzAE/7\niSMiIuRqgt7isxFpcZ7S4bMRaXGeypKt4P32229ITEyEWq3W6dLkgscYY0wOshW848eP4+LFi/yF\n0cXgMSdpcZ7S4TEnaXGeypLtKs3Q0FAkJCTItTrGGGNMh2xneIcOHUJQUBCqVq2q83t4Z8+elasJ\neovPRqTFeUqHz0akxXkqS5aCR0RYtGgRvL295VgdY4wxVoRsZ3gfffQRzp8/L9fqyhUec5KWoeQZ\nHRONlEcpirYh5WYK3Ku4K9oGAHC3d8fUmKlKN+O18RiesmQpeIIgoH79+jh69CgaNWokxyoZK/dS\nHqUo/1Vtp/Wji1i1XqV0E5gBkO0M7/Dhw1i+fDl8fHxgZWUFgMfwtPThgGJIOE/pcJbS4rM7ZclW\n8LZt2wYA4m0JhvjFpIwxxvSXbLcl+Pr64tGjR9i4cSM2bdqEx48fG/TPULwK/v02aXGe0uEspcW/\nh6cs2Qre3Llz0bdvX9y7dw+pqano27cv5s2bJ9fqGWOMVXCydWn+8ssvOHLkiDh+Fx0djZCQEHzy\nySdyNUFv8TiJtDhP6XCW0uIxPGXJdoYHQOc7NKX4iSDGGGPsZclWdQYMGIDGjRsjJiYGEyZMQEhI\nCAYOHCjX6vUaj5NIi/OUDmcpLR7DU1aZd2levXoVfn5+GD16NFq2bIn/+7//gyAIiI2NRXBwcFmv\nnjHGGAMgQ8Hr2bMnTpw4gfDwcOzatQv169cv61WWOzxOIi3OUzqcpbR4DE9ZZV7wNBoNJk2ahMTE\nRHz33Xc6998JgoDRo0eXdRMYY4yxsh/DW716NYyNjaHRaJCRkYHMzEzxX0ZGRlmvvlzgcRJpcZ7S\n4SylxWN4yirzM7yaNWviiy++gI+PDyIjI8t6dYwxxlixZLlK09jYGDNnzpRjVeUSj5NIi/OUDmcp\nLR7DU5ZstyW0adMGM2fOxI0bN5CWlib+Y4wxxuQg2zetrF69GoIgYMGCBTqPX7t2Ta4m6C1D+f02\nfcF5SoezlBb/Hp6yZCt4KpVK8mU+evQIH3zwAS5cuABBELB48WKEhIRIvh7GGGPln2xdmllZWZg4\ncSI+/PBDAMClS5ewefPm11rmyJEj0aFDB/zzzz84e/Ys/P39pWiq7PgTtLQ4T+lwltLisztlyfrV\nYmZmZjh48CAAwMPDA1999VWpl/f48WPs379f/HoyExMT2NnZSdJWxhhjhke2gnflyhWMGTMGZmZm\nACD+akJpXbt2DS4uLhgwYADq1auHDz/8ENnZ2VI0VXZ8r5O0OE/pcJbS4vvwlCXbGJ65uTlycnLE\n6StXrsDc3LzUy1Or1Th58iTmz5+Phg0bYtSoUZg6dSq++eYbnedFRUWJPzRrb2+PoKAgsVsh5WYK\ncPrfbhvtzi33tJZS69dOa3dGbT6vOs156k4bQp4pl1MUfz2lylMfpk+fPq1X7dFOx8fHIzY2FgAM\n+oe5BXr2u77K0Pbt2zFp0iQkJCSgTZs2OHDgAGJjY9GqVatSLS8lJQVNmjQRr/L8v//7P0ydOlVn\nXFAQBJS0eVGjouDbzbdU6zc0qvUqxM6Jfa1lcJ7/4jylJUWe7OW96NhZXsl2hte2bVvUq1cPR44c\nARFh3rx5cHZ2LvXy3N3d4eXlhaSkJNSoUQM7d+5ErVq1JGwxY4wxQyLbGB4RYe/evdi5cyd2796N\n/fv3v/Yyv//+e/Tp0wd169bF2bNnMXbsWAlaKj8eJ5EW5ykdzlJaPIanLNnO8D766CNcuXIFkZGR\nICL89NNP2LFjB3744YdSL7Nu3bo4duyYhK1kjDFmqGQreHv27EFCQgKMjJ6eVEZFRSEgIECu1es1\nvtdJWpyndDhLafF9eMqSrUuzevXquH79ujh9/fp1VK9eXa7VM8YYq+BkK3jp6enw9/dHy5YtERYW\nhoCAAGRkZKBz587o0qWLXM3QSzxOIi3OUzqcpbR4DE9ZsnVpFr4/Dvj30ldBEORqBmOMsQpKtoLH\nfdfPx+Mk0uI8pcNZSouPg8qSrUuTMcYYUxIXPD3A4yTS4jylw1lKi8fwlCVrwcvOzkZiYqKcq2SM\nMcYAyFjwNm7ciODgYLz99tsAgFOnTlX4qzO1eJxEWpyndDhLafEYnrJkK3gxMTE4cuQIHBwcAADB\nwcG4evWqXKtnjDFWwclW8ExNTWFvb6+7ciMeQgR4nERqnKd0OEtp8RiesmSrOLVq1cKKFSugVqtx\n6dIljBgxAqGhoXKtnjHGWAUnW8H7/vvvceHCBZibmyMyMhK2traYM2eOXKvXazxOIi3OUzqcpbR4\nDE9Zst14npiYiMmTJ2Py5MlyrZIxxhgTyXaGN3r0aNSsWRNff/01zp8/L9dqywUeJ5EW5ykdzlJa\nPIanLNkKXnx8PPbs2QNnZ2cMGTIEgYGBmDhxolyrZ4wxVsHJeplk5cqVMXLkSPz444+oW7dusV8o\nXRHxOIm0OE/pcJbS4jE8ZclW8BISEhATE4PatWtj+PDhCA0Nxa1bt+RaPWOMsQpOtoI3cOBA2Nvb\nY9u2bdi7dy8++ugjuLq6yrV6vcbjJNLiPKXDWUqLx/CUJdtVmocPH5ZrVYwxxlgRZV7wevbsiTVr\n1iAwMLDI3wRBwNmzZ8u6CXqPx0mkxXlKx5CyjI6JRsqjFKWbgdj1sYqu393eHVNjpiraBqWUecGb\nO3cuAGDz5s0gIp2/8S+dM8bkkvIoBb7dfJVuhuJU61VKN0ExZT6G5+HhAQD44Ycf4Ovrq/Pvhx9+\nKOvVlws8TiItzlM6nKW0OE9lyXbRyvbt24s8FhcX99rL1Wg0CA4ORufOnV97WYwxxgxXmXdpLly4\nED/88AOuXLmiM46XkZGBpk2bvvby586di4CAAGRkZLz2spRiSOMk+oDzlA5nKS3OU1llXvDee+89\ntG/fHtHR0Zg2bZo4jmdjYwMnJ6fXWvbNmzcRFxeHr776Ct99950UzWWMMWagyrxL087ODr6+vli9\nejV8fHxgaWkJIyMjZGVl4fr166+17E8//RQzZswo97+rx/360uI8pcNZSovzVJZs9+Ft3LgRn332\nGW7fvg1XV1ckJyfD398fFy5cKNXyNm/eDFdXVwQHB5d4M2dUVBR8fX0BAPb29ggKChK/3iflZgpw\n+t9uBu2bUe5pLaXWr53W5qjN51WnOU/daUPIM+VyiuKvJ+cp7bTWs/nEx8cjNjb26fP/d7w0RAIV\nvlegjNSpUwe7d+9GmzZtcOrUKezZswfLli3D4sWLS7W8sWPHYtmyZTAxMcGTJ0+Qnp6O7t27Y+nS\npeJzBEEocivEs6JGRfFlyv+jWq9C7JzY11oG5/kvzlNanKd0XibLFx07yyvZ+gJNTU3h7OyMgoIC\naDQatGrVCsePHy/18iZPnowbN27g2rVrWL16Nd566y2dYscYY4w9S7aC5+DggIyMDDRv3hx9+vTB\nJ598Amtra8mWX55vYud+fWlxntLhLKXFeSpLtoK3fv16WFpaYvbs2WjXrh2qV6+OTZs2SbLsli1b\nYuPGjZIsizHGmGGS7aIV7dmcsbExoqKi5FptucD35kiL85QOZyktzlNZZV7wrK2tn9vdKAgC0tPT\ny7oJjDHGWNl3aWZmZiIjI6PYf1zsnuJ+fWlxntLhLKXFeSpL1ju29+/fjyVLlgAA7t27h2vXrsm5\nesYYYxWYbAUvJiYG06ZNw5QpUwAAeXl56NOnj1yr12vcry8tzlM6nKW0OE9lyVbw1q1bh40bN8LK\nygoA4OnpiczMTLlWzxhjrIKTreCZm5vrfOdlVlaWXKvWe9yvLy3OUzqcpbQ4T2XJVvB69uyJIUOG\n4NGjR1i0aBHCw8PxwQcfyLV6xhhjFZws9+EREXr16oWLFy/CxsYGSUlJmDhxItq0aSPH6vUe9+tL\ni/OUDmcpLc5TWbLdeN6hQwecP38ebdu2lWuVjDHGmEiWLk1BEFC/fn0cPXpUjtWVO9yvLy3OUzqc\npbQ4T2XJdoZ3+PBhLF++HD4+PuKVmoIg4OzZs3I1gTHGWAUmW8Hbtm2bXKsqd7hfX1qcp3Q4S2lx\nnsqSreAZ8q/oMsYY03+yfrUYKx7360uL85QOZyktzlNZXPAYY4xVCFzw9AD360uL85QOZyktzlNZ\nXPAYY4xVCFzw9AD360uL85QOZyktzlNZXPAYY4xVCFzw9AD360uL85QOZyktzlNZXPAYY4xVCFzw\n9AD360uL85QOZyktzlNZ5bbg3bhxA61atUKtWrVQu3ZtzJs3T+kmMcYY02OyfbWY1ExNTTF79mwE\nBQUhMzMT9evXR5s2beDv7690014Z9+tLi/OUDmcpLc5TWeX2DM/d3R1BQUEAAGtra/j7++P27dsK\nt4oxxpi+KrcF71kqlQqnTp1C48aNlW5KqXC/vrQ4T+lwltLiPJVVbrs0tTIzM9GjRw/MnTsX1tbW\nRf4eFRUl/lKDvb09goKCEBYWBgBIuZkCnP63m0H7ZpR7Wkup9Wun4+PjAUDM51WnOU/daUPIM+Vy\niuKvJ+cp7bTWs/nEx8cjNjb26fMN+JdtBCIipRtRWvn5+ejUqRPat2+PUaNGFfm7IAgoafOiRkXB\nt5tvGbaw/FCtVyF2TuxrLYPz/BfnKS3OUzovk+WLjp3lVbnt0iQiDBo0CAEBAcUWO8YYY+xZ5bbg\nHThwAMuXL8eePXsQHByM4OBgbN26VelmlQr360uL85QOZyktzlNZ5XYMr1mzZigoKFC6GYwxxsqJ\ncnuGZ0j43hxpcZ7S4SylxXkqiwseY4yxCoELnh7gfn1pcZ7S4SylxXkqiwseY4yxCoELnh7gfn1p\ncZ7S4SylxXkqiwseY4yxCoELnh7gfn1pcZ7S4SylxXkqiwseY4yxCoELnh7gfn1pcZ7S4SylxXkq\niwseY4yxCoELnh7gfn1pcZ7S4SylxXkqiwseY4yxCoELnh7gfn1pcZ7S4SylxXkqiwseY4yxCoEL\nnh7gfn1pcZ7S4SylxXkqiwseY4yxCoELnh7gfn1pcZ7S4SylxXkqiwseY4yxCoELnh7gfn1pcZ7S\n4SylxXkqiwseY4yxCoELnh7gfn1pcZ7S4SylxXkqiwseY4yxCqFcF7ytW7eiZs2aeOONNzBt2jSl\nm1Nq3K8vLc5TOpyltDhPZZXbgqfRaDB8+HBs3boVCQkJWLVqFf755x+lm1UqKZdTlG6CQeE8pcNZ\nSovzVFa5LXhHjx5F9erV4evrC1NTU/Tu3RsbNmxQulml8iTzidJNMCicp3Q4S2lxnsoqtwXv1q1b\n8PLyEqerVKmCW7duKdgixhhj+qzcFjxBEJRugmQepTxSugkGhfOUDmcpLc5TWQIRkdKNKI3Dhw8j\nJiYGW7duBQBMmTIFRkZGGDNmjPicoKAgnDlzRqkmMsZYuVS3bl2cPn1a6WZIrtwWPLVajTfffBO7\ndu2Ch4cHGjVqhFWrVsHf31/ppjHGGNNDJko3oLRMTEwwf/58vP3229BoNBg0aBAXO8YYY89Vbs/w\nGGOMsVdRbi9aYYwxxl4FFzxWojt37mDMmDG4cuUKHjx4AAAoKChQuFXKKNwZwp0jpUNEnN1rKi5D\nzvTFuOCxElWuXBkAsGLFCnz88cc4ffo0jIyMKtzORUTirTC3b9/Gw4cPDerWGLkJgoBt27bhu+++\nw8qVK5VuTrkkCAKOHTuGLVu24Nq1axAEocJ+GH1ZXPDYc2l3nmnTpmHo0KEICwtD+/btsW/fvgq1\nc6WmpuLHH38EAOzYsQNdu3bFW2+9hXXr1iE9PV3h1pUv2g8OZ86cwYgRI5CamootW7ZgyJAhSjet\n3NBmuGvXLnTt2hVr165Fw4YNcerUKRgZGVWY/bI0yu1VmqzsaM/ejIyMkJeXBzMzM7i6umLo0KEw\nNzdHZGQk1q5di5CQEJ0zH0NERDhx4gQOHjyI1NRUHDlyBMuWLcPZs2exePFiZGVloUuXLrC1tVW6\nqeWCIAjYu3cvli9fjrlz56J9+/a4fPkyJk+ejKFDh4ofLNjzCYKAhIQE/Pnnn1i9ejVatGiBunXr\nIjw8HLt370ZQUBAKCgpgZMTnM4UZx8TExCjdCKZ/tF1OS5YsQUJCAho3bgwACA4Ohr29Pb788ku0\na9cOTk5OCre07GiLuaenJywsLHDq1Cmkpqbi888/R61atWBhYYEVK1aAiODn54dKlSop3WS9VPhD\n0ZkzZzBp0iT4+PigZcuWsLOzQ506dRAXF4e4uDh07dpVwdbqN41GA41Gg6lTp+LgwYN48803ERgY\niJCQEFhZWSEiIgKdOnWCh4eH0k3VS1zwmA7twenQoUP4+OOP0a5dO8yaNQupqalo3rw5jI2NUa9e\nPeTm5iI5ORmNGjWCRqMxuE+T2rNcQRBw+/ZtBAcHw8TEBMeOHUNqaiqaNGkCf39/GBsbY/ny5ejQ\noQNsbGwUbrX+eTbH5ORkEBGCgoLQrFkzfP311/Dz84O/vz/s7e0RHByM+vXrw83NTeFW65dnM8zO\nzoaFhQXCwsJw9+5dJCcnw9HREZ6enmjcuDFsbW1hZWWFatWqKdxq/cT34bEiEhMTMWXKFISGhmLw\n4MG4ffs23n33XbRs2RIxMTEwNTXFrl278Pvvv2PRokVKN7dMaAv/li1bMHz4cGzZsgVeXl7Ytm0b\nduzYgerVq+PTTz8F8HSMjw/SxdN2rW3evBlTpkyBs7MzXFxcMHr0aNy/fx+DBg3C1KlT0b17d3Ee\nQ+8mf1XaPLZu3YpZs2ahSpUqqFatGsaNG4fo6Gjk5+cjIiICoaGhYm6c4XMQq/AKCgp0puPi4qhT\np04UGRlJV69eJSKi27dvU926denzzz8Xn/fxxx/TnTt3ZG2rnC5cuEC1atWivXv3io9lZ2fT+vXr\nKSoqiqZPn05ERBqNhoiK5liR5eTkiP+fnJxMAQEBdOLECTp37hwtXbqUOnToQCkpKfTXX39RlSpV\nKDU1lfMrJD8/X/z/o0ePUkBAAG3evJmOHz9OQUFBNGLECCooKKCPPvqIPv30U3r48KGCrS0f+KKV\nCo6eOcG/evUqLC0tER4eDk9PT/z8889Yv349IiIi4OPjI17+rDV//nwlmiwbtVqNZs2aoUWLFlCr\n1SgoKICFhQVat24NQRDg5+cHAGJ3Ln+ifio1NRWrVq3CoEGDxG5eLy8v1KtXD8DTW11OnDiB7du3\no1+/fggJCYGrq6uSTdY7d+/eFW8FMjMzQ3Z2Nlq3bo2OHTsCAE6ePIlGjRrhwIED+Oabb5Camgp7\ne3uFW63/DGvghb0yQRDErruOHTti9OjRaNCgAezs7NCrVy+oVCqsXLkSycnJqFy5MkJDQw3yxuHi\ntsnS0hJbt27F5s2bYWJiAjMzM2zbtg2//fYbunTpgtq1ayvUWv1mbm6O9u3bIzMzE8ePH4e3tzfU\najW++uorAICTkxMcHR2RlJQEAHBxcQHAN04/KyUlBZ07d8aDBw9w48YN2NraYteuXeKXPwiCgFat\nWuHx48dwcnJCQECAwi0uH7jgMVy/fh0TJkzAzz//jJUrV6JXr17o0qULatSogYiICNy6dUvn3h5t\nkTQ02kvmZ8yYgd27d6NatWqYPXs2vvvuO8yfPx9xcXEYM2YMPD09lW6qXsrPz0d2djbs7e3h5eWF\nKVOm4JdffsGFCxcwa9YsqFQqvPvuu9i0aRNWrlyJ8PBwAE+/CB7gM2TgaYYAUKdOHTg6OmLhwoWY\nOnUqateujT59+qBhw4aIj4/H5s2bERcXx2d1r4gvWqmAqNCAdmZmJoYNG4ZJkybB29sbADBixAgY\nGxtjzpw5FeaijC1btmD06NEYNWoUZs6ciQ8++AAdO3bE/fv3MWvWLLi7u6Nr167o1KkTXxRQSF5e\nHuLj4+Hs7IykpCQkJyejb9++mDlzJszMzBAREQE/Pz98++23sLe3R6NGjdCxY0fO8Rm5ubnYv38/\nqlSpgszMTCQlJcHNzQ07duwAEWHKlCn4+eefxSuFhwwZwu/FV8RjeBVQQUEBjI2NkZmZiUqVKsHM\nzAyZmZnYuHEjhg8fDgBo1qwZTp06BQAGX+yICLdu3cKiRYuwceNG3LhxA0SEM2fOIDs7G1988QU2\nbdqk83ymy8zMDBkZGYiJiUFKSgpmz54NT09PfPXVV/jmm2+wdu1a9OvXD3PnzhXn4Rx15eXl4cmT\nJxg2bBiSkpKwe/duvPnmm7CwsMD69evx1Vdf4csvv8SQIUOQk5MDCwsLzvAV8X14Fcj169eRl5cH\na2trrF+/HkOHDsW5c+dgbGyMqKgoREdH4+LFizh+/DgWLlyIgQMHokaNGko3u0zQM/c2CYIAW1tb\nNG3aFFlZWRgxYgSOHDkCNzc3fPbZZzAzM0PdunVhbm6uMw/7d+xTEAR4e3vj4MGDMDc3R9u2bWFt\nbQ1nZ2eEhITg77//xvnz59GkSRPxBn3O8Snte9Hc3BzZ2dmYMmUKGjdujNDQUHh4eMDLywu2trY4\nc+YM4uPj0bJlS5iZmcHIyIgzfEVc8CqQb7/9FuPHj0fDhg2xcOFCDBgwAF5eXliwYAG8vb0xduxY\nPHjwAOnp6Rg8eDDefvttg+4uEQQBZ8+exfHjx2FsbAwPDw+kpKRg586dGDx4MHJycnDu3DkMHz4c\nXl5eSjdXbxkZGWHHjh1YtmwZpk+fDiMjI6xfvx6VKlWCv78/1Go1AgMDERwczDk+hyAI2LFjBxwd\nHREVFQUXFxf8+eefMDExwRtvvAEzMzOYmZmhffv2cHNzM7gvepALd2lWANqbf2fMmIGCggL06tUL\nffv2Re/evZGTkwMnJydMnz4d9+/fxwcffCDOZ8jdJYIgYP369Rg/fjz8/PxgYWGBGjVqYMiQIXB3\nd0fr1q1x48YNzJkzB7Vr1zbowv86tFf4fvbZZ5g9ezasrKwwYMAA5OTkYPPmzTh27Bh++eUX7Nmz\nh69qfQ5thiNHjsT333+Pt99+G7a2tkhLS8O6detw+PBhnD59GnPnzkXVqlWVbm75Jtsdf0wRmZmZ\ndP78eSIiOnLkCKWnp1N0dDT5+/tTSkoKERHl5uZSXFwchYWFkUqlEm+kNjQFBQXizc0ZGRnUu3dv\nOnnyJBER7d27l6Kjo2np0qV07949WrlyJR08eLDIfExXXl4ejRw5krZs2UJERE+ePBH/tmXLFpo9\nezZt3bpVqeaVC5mZmdS2bVvatWsXEf37BQbXrl2jP/74gzp16kTr169XsokGg8/wDFxaWhq+++47\nceB7y5YtmDJlCh49eoSIiAisW7cOrq6uCA8PR8OGDeHs7Kx0k8sEPXOGdvDgQaSnpyM5ORkXL15E\ncHAwQkNDcezYMRw8eBD9+vVDZGSkOB/Al8xrUaEzXVNTU6SlpSE+Ph7t2rUTxznPnj2LsLAwtGvX\nTpwP4ByBfzPU/letViMvL0+83eXJkyewsLCAjY0Nevbsia5du8LMzIwzlAB3BBuwgoICeHl5oW3b\ntli8eDHee+89BAYGAgAWLlyIunXrok2bNrh79y7MzMzEYkcG3JWZmJiIL774AnXq1MHnn3+OnTt3\nYu/evTAxMUH9+vWRlpaG9PR08b5DviigeMnJyUhISAAADBw4EPn5+diwYQMA4NixYxg6dCguXbok\nPp9zLCo1NRUAYGdnh6ZNmyI6OhppaWmwsLDAvn370KlTJ9y9e1fnPkXO8PVwwTNgRkZG2L17N86e\nPYtNmzbhzJkz+OWXX5CWlgYA+OGHH9CmTRudAxNgmJ8gtT86GhERgdatW8PDwwNBQUGoV68ehg8f\njowINqMAACAASURBVFGjRmHAgAHo168fbG1t+aKAYmjPSDZv3ox27dqhR48eGDNmDGrWrImqVavi\np59+QufOndG/f39ER0eLH67Yv7QZxsXFoVOnTujevTu2b9+OqKgo1K1bF02bNsWMGTMwbNgw/Oc/\n/4Grqyu/FyXEN54bOO137W3btg27du3CxIkTMXjwYAiCgL/++gsrV66EqampQV6UUVwXUN++ffHP\nP/9g7969sLa2Rn5+Ps6fP4/k5GRUqVIFDRo0MMgspHLx4kV8+eWXmDFjBtzc3NChQwe0b98eo0eP\nRm5uLpKSkuDg4IA333yTu+Ce4+jRo5g8eTKio6Oxd+9eJCcno1mzZnjnnXfw999/w8jICC4uLmje\nvDlnKDEewzMwhQ/WNWvWFL+sNzw8HBqNBsuXL8etW7cwePBgmJqaAjDcHUoQBBw+fBjXr19H7dq1\nsXz5cgwaNAg9e/bEX3/9BQsLCwQHByM4OBiAYXfnlpb2PfX48WPMmTMHt2/fhqmpKezt7bF27Vr0\n7t0b9+/fx9y5cxESEqIzr6G+r17Fs2N29+/fx3//+1+YmpoiJCQEISEh+Omnn7Bv3z4UFBSgW7du\nsLa2FucDOEMp8X14BkK7UwmCgJMnT2LEiBGoU6cOPD098fDhQ8yYMQO9e/fGm2++iVatWqFHjx5o\n2LChwZ7NaLdr//79GDhwIB4+fIh9+/bh6NGjmD9/Pnbv3o0FCxagV69eYtEHeJykONruYCcnJ/j6\n+uLSpUt4+PAhPD09UaVKFfFHgps1a6Zz0RPn+JQ2h3v37sHV1RWCIODPP/+EpaUl6tWrhwYNGuDa\ntWs4fPgwmjZtKv7CBL8XpccFzwA8+0nw0qVL8PPzw+HDh3Hu3DksWLAAHTt2xMWLF1GrVi24ubnB\n3NwclpaW4vyGuFNpf7V94sSJ+OGHH/DJJ58gMDAQ+/fvR3JyMr799lusW7cO/v7+8PDwULq5ekn7\noSEvLw8zZ87EokWLMGTIEPj6+mL//v1ISUmBu7s7vLy80L9/f4P/CrrS0mg0SEtLg4+PD9544w30\n7t0bVapUwapVq5Cbm4vg4GA0btwYwcHBqFKlitLNNWhc8AyEdiB81KhRCA8PR79+/dCkSRMIgoCl\nS5di586dyMjIQJcuXXQKnCEVu8Jnq/v378eMGTPQqFEj1KtXD9bW1sjJycGRI0fQqVMnREZGwsPD\nw2DPcksrKysLRkZGMDY2BgAYGxujTp06uHz5MpYuXYoPP/wQlStXxtatW3H37l3Uq1cPpqamMDIy\n4iz/Jz8/X8wPAKysrFC7dm0MHjwYb775Jt555x1YWlri559/Rn5+PurVqwc7OzsFW1xByHCvH5PB\nP//8QzVr1qQDBw4U+dujR4/owoULFBYWJt5obWievTn83r17lJ2dTURES5YsIT8/P9q5cycRPb0Z\nulmzZnT//n1Sq9WKtVdfXbhwgYYNG0apqam0b98+mjt3rvi3O3fu0Keffkr9+/enrKwsOnDggPil\nBuxfFy5coHHjxhERUUJCAm3fvl18P8bFxZG5ubl4I/mff/5JR48eVaytFQ0XvHLqxo0btGzZMnF6\n37591LNnT3E6Pz+fiEjnG0L69+8vfpuDodFu58aNG6lly5bUvHlzWrRoESUmJtLvv/9OlpaW9MEH\nH1C3bt1o3bp1CrdWP2VnZ1Pr1q1p8eLFRES0e/ducnNzo/nz5xMRkVqtpt27d1Pt2rWpV69eBvuN\nPK/j8ePH1LJlSzp27Bilp6fTqFGjaODAgbRr1y7KysoiIqLZs2eTIAgUFxcnzsff5CMPvsGjnCIi\nLFu2DGfOnAEA+Pn54cGDB9i+fTuApz+quXv3bkybNg1EhCtXruDKlSsG++OlgiDgxIkTmDVrFubO\nnYsRI0YgOTkZq1evRseOHTF37lzs378fHTp0QLdu3aDRaPiKzEIsLCzQp08fLF68GJ6enmjVqhX+\n/vtvzJ49GwsWLICxsTHMzc0RHh6OMWPG8P1hxdBoNMjOzsaqVavwySef4NNPP4W3tzfWrl2LQ4cO\nAQBCQ0MRERGhkx93A8tE4YLLSkHbFTd37lxas2YNERGlp6fTzJkz6YsvvqCpU6dSfHw81apVi7Zv\n3y7O9+DBA0XaK4c7d+7QgAEDKDQ0VHzswIED1Lp1azp8+DAREa1YsYI8PT1p7969SjVTb2nPMP7+\n+2+qVKkStWzZkjIzM4mI6OjRo1SnTh0aNGgQubq6it+byWclurR5zJ8/n0xMTGjo0KFE9PT7RseP\nH0+DBg2iQYMG0RtvvEHHjh3TmYfJgy9aKYe0nwzv3LmDOXPmoFWrVnBzc4O7uzssLS3x999/49Kl\nSxg2bBg6dOgAtVoNIyMjWFhYKNxy6VChe5Tof7/LdujQIWRnZ6Nx48bw8vLCoUOHoNFo0LBhQwQG\nBsLDwwM1a9aEo6Ojks3XO9ocnZycEBYWBkdHR8ydOxf169dHYGAg2rVrh5o1a6Jfv35o0aIFX5xS\nDG0ed+7cQZs2bTB79myYm5ujadOmaNGiBSwtLWFlZYU+ffqgefPmOvMwefA3rZRzEyZMwJIlS3D0\n6FG4u7uLjz958gSVKlUyyJtX/7+9e4/r+e4fP/749ImUUhNFGrqqCzeuzJxS5riIZptDuJjG5soy\nx7gcbkPjYsxp5JrDwjSnDhZpEeuAYcppF+VMuihySp8U+vTp/ftj+3xWxvea39inPj3vf0mf9+32\n+rxu717P1/H5KvudDh48yMOHD7GwsKBTp05s27aN+Ph4rKysGDRoECNHjiQsLIzOnTsbudQVU9nA\npdPpDDsLs7KyWL9+PefPn2fu3Lm4ubmVewZM6516GY4dO4aPjw//+te/GDNmTLnfSR0ah4zwKiH9\naEalUtG1a1du3rzJ9OnT8fb2xsLCAktLS8zNzcsdRjcl+u/03XffMWHCBJo0aUJISIhhDUr5ZX3z\n5MmTLFiwgC5duhhGuaI8/SW4+ktFdTodZmZm2NnZ4ebmxqVLl4iIiODtt9/G3NzcUPem9k79Udev\nXwegevXqqFQqdDodzs7O9OzZk/79+2NnZ0f79u0Nn5c6NA4JeJVAaWmp4RoRMzMzwx+K/mJXHx8f\nSkpKiI2N5dy5c+Tm5tKiRQuT/oPKyspi0qRJREVFkZubS1paGsnJySiKwvDhw6lTpw4PHjxArVbT\npk0bCXZPoe8QDR48mAsXLtC9e/dy9WRra8tf//pXw5S5Kb9P/78URSE3N5fg4GC8vLx45ZVXKC0t\nRa1Wo9PpcHJyws/Pj5o1a+Lq6mrs4lZ5EvAqsHv37nHv3j1sbW1JSEhg7dq1pKenGw6Ul+2Re3p6\n0qxZM+zt7fnmm2/w8fExqTW7J6lUKnr06MGdO3cMSXhfffVVxowZg42NDUOHDiUvL4+MjAzatWtn\n0nXxvJ4c+Xt4eHD06FHat29PjRo1ygU2W1tb7O3tjVXUCk+lUmFtbU1KSgrx8fG8++67hmlh/d9n\ngwYNcHV1lXXPCkACXgVVWFjIwoULOXPmDPfv32fKlCn4+fnxxRdfkJ2dTdeuXTEzM8PMzMwwAqxT\npw4uLi7079+/XOowU6JvNGrUqEHt2rU5fvw4tra2+Pr6kpmZSZ06dXjjjTdwd3fH1dWVzp07Y2dn\nZ+xiVyj6HKOPHj3CzMwMJycnVq1aRaNGjWQU8hyuXbtGbm4u9vb2eHl5kZaWRqtWrbCxsTH8TcrR\ng4pFNq1UYDExMRw6dIi8vDw8PT0JDAwkNzcXf39/vLy8mDt3ruFyyLJMrSf55Pcp+/P27dtZuXIl\nnTp1Ys2aNWzbtg1PT89yGzDEbzdJzJkzhxMnTlCzZk0CAgK4desWW7duZevWrZLi6hnKvncajYaP\nP/4YlUqFg4MDU6ZMISAggH79+hEYGGjkkopnkYBXwSiKYlgDADhy5AjLly9Hp9OxYMEC/vKXv3Dr\n1i18fX3p2rUrixcvNqng9izHjh0jPDycFStW/CYARkZGkpeXR+PGjfH19TW5gP8i6Ovk1KlTVKtW\nDScnJ2xtbUlOTmbu3Lm4uroSFxfHwYMHcXNzM6wPi1/p6/DGjRvUqlULlUpFQUEB48ePp0WLFuzY\nsQOdTkdUVBTu7u7GLq54CrkPrwJSq9V89913xMTEsH79egoLC4mLi2PHjh3069ePxo0bs3v3bq5c\nuWLSDXvZRvfx48dotVrg11GKfhQ3aNAgwzPSf/stfUOtv1nbx8eH5ORkwsPD6datG82bN+fevXvc\nunWL4OBgdu7cKcGujLKj4507dxISEoJOp8PX15ePPvqIiIgIrl27hpOTE5s2beLq1au4u7tLx6sC\nkre6gtE3TNOmTWPAgAEAdO/enR49epCdnc2WLVvIzMzE0dGRDh06mGQD//DhQ+DnRf9Lly6RmpqK\nnZ2d4R42PbVaTUlJSblnZbv3b+lHdrGxsURERBAeHs5nn33GyJEj+fHHH3F0dKRZs2bExsZSq1Yt\n8vPzjV3kCkX/Tp05c4bQ0FC2bNnCrl270Gq1hIWFUVBQwKuvvsoHH3zAP/7xD5YvX87jx4/lPayA\nJOBVQMeOHePTTz+ld+/ePHr0CIDevXvj4+PD9evXywU5U/ujun//PtOmTSMvLw+NRsOiRYsIDAxk\n8eLFpKSksGDBAqKioti3bx+lpaVPXcMUv45KdDodxcXFfPrppyQlJfHgwQNKSkoYNmwYo0ePZvny\n5ZSWlgKwd+9ejhw5YhhJV3U3b95k3rx5lJaWcufOHZYuXcqtW7ews7PD2dmZyZMnk5KSwtatWw3P\n2NjYUFBQYKhTUbFIa2FkT5v2uHHjBqdPn2bAgAHUqFEDgNTUVLp06UKHDh1MflPB5MmTyc/PJy8v\njzVr1gA/H9HIzs7G2tqa7du3k5+fT/Xq1fHy8jJyaSu2oqIibGxsWL9+PePGjSMxMZEWLVrQsGFD\nmjRpwunTpw3vn5OTE0lJSeVuLa/KHj58iL+/Pzdv3sTBwYGAgADy8vLYsmULgwcPpkGDBgwbNoy7\nd+9SWlpKaWkplpaWrF69Wo7BVFQvKUen+B3K3uGWmZmpnDlzxvDvoKAgZenSpYqiKEpqaqrStGlT\nQxJkU1Q2ie5///tfZePGjUqnTp2UlJQURVF+Tpj93nvvKZs2bXrmc6K8uLg4xdPTUwkJCVEOHjyo\nFBQUKAMGDFB69uypzJgxQ2nbtq0SExNj7GJWOGXfqeLiYuXDDz9URowYoWi1WiUxMVEJCgpSBgwY\noGzYsEFxc3NTEhISjFha8TzkHJ6R6RfCg4KCSE1N5cCBA7Ru3RoHBweio6NZt24dkZGRzJ8/n27d\nuhm7uC+Vfv1y1qxZDBs2jFq1arFhwwYaNGiAi4sLBQUF5OTk0LFjx988J8pvrsjOzmbhwoUEBARQ\nUlLC999/j42NDRMmTGDfvn1cvnyZuXPn0rNnT8OzUo/l6zAjI4O6devi7u7Of/7zH/bs2cOoUaN4\n5ZVXSE5OJicnh+DgYPz8/AwJIETFJgHPiPTJj6dPn87u3btRqVQsWrQIgD59+vDRRx/RqVMnhg4d\nSrt27Uw24ay+sb1w4QKzZ88mJCSEVq1a0bBhQ3Q6HRs3bqRhw4Y4OTkZDtfrmVpd/FH6ewEPHjyI\noigEBwfj4uKCVqslISEBc3Nzxo8fz65du7h27Rqvv/46VlZWUo9lqFQqEhISeO+993jzzTdp1qwZ\nrq6u/PjjjyQnJzNixAicnZ3JyclBp9Ph5uaGtbW1sYstfgfpkhiZvb09X375JcePHycsLIzDhw+T\nlpZGUFAQFy9exMXFhYYNGxo+b0oN0+PHjw272a5du8bmzZu5evUq6enpADg4ONC/f3+6devGnDlz\naNasGV27djXJnal/lL7TkJKSQt++fTl48CArVqwgPT2devXq0bt3b9q1a8e3336LSqVi+fLl3Llz\nR0Z2T9B3vP75z38SHh7O3/72N9RqNU2bNmXcuHHcv3+f8ePH061bN15//XVyc3MlwUElIgfP/0Rl\nR2h5eXlUq1bN0DOcPHky7u7ujBo1ilWrVrFhwwY2b95c7loWU1JSUsKhQ4fIzMzE2tqajIwM+vbt\nS2xsLPfv36dPnz506dIFgNu3b1NUVESjRo2MW+gK7ty5c0ycOJEZM2bg7e3NnDlziI6OJiIigubN\nm3Pr1i2Ki4txdnYGkGw0v3gy6F+4cIH58+fz9ddfo9Pp0Ol0VK9enZKSEjIzMykqKqJly5YAFBQU\nYGNjY6yii+ckuzT/ZCqVitjYWNatW0d+fj5Dhgyhe/futGnThrVr16LVaomMjGTp0qUmG+wAzM3N\nsbe3Z+7cuZw+fZr169fj4eGBpaUlmzdvZs+ePWi1Wnx8fKhbt67hORmRlFe2Pk6dOsW1a9fYuXMn\n3t7ezJo1C7VaTe/evYmPj6dFixblnpNgVz5RwdmzZ7G0tMTW1pYDBw4QERHB4MGDUavV7N27l6NH\nj/LJJ58Av3YWJNhVLrKG9ydSqVScP3+eUaNG8e9//5smTZpw7tw5zp49S7t27bCzs2PXrl1MmDCB\nN9980yTX7Mp+J1tbW2JiYnB2dsbKyoqmTZvi7OyMq6srR48e5cqVK7Rq1apcImxTqosXQb8OHBkZ\nSWBgIPXr1+enn37ixo0btGnThk6dOpGfn0+9evXKjZClHn+l3zg2ZswYunXrRpMmTXB1dWXVqlVk\nZWWh0Wj45JNPGDhwIE2bNgWQDSqV1Z+8K7RK0m9zzsnJUXbv3q306tXL8LvU1FSle/fuSmpqqqIo\nivLw4UPDM6a25b7sd8rJyTH8X3p6ujJ69Ghl5syZiqIoikajUaKjo5ULFy4YrayVSXp6uvLqq68q\nS5YsURRFUaKiopTAwEBl2bJl5T5nau/Ti3LixAmlZcuWyvnz5xVFUZQbN24oR48eVTIyMpSBAwcq\nY8eOVb777jtFUaQOKzuZ0nzJ9PkgDx8+zIwZM/jyyy+pUaMG0dHR+Pv7065dO5o3b264t61atWqG\nZ02xF65SqYiPj2f27Nl4eXlRvXp1Fi5cyLBhw9i4cSP9+vUjPT2d6OhoScD7P2g0GqysrGjevDkJ\nCQn4+/ujKAqTJk1Cq9Xy/fffk5WVZRjZmeL79CLUqFGDli1bkpycTFRUFCkpKQBMnTqVyMhIw+cU\n2e5Q6cmU5kumUqlITExk7dq1BAUF0aFDB+7cuUNGRgbJycmo1WoWLVpEUFAQzs7OhqkSU2ycVCoV\n+/fvZ+LEiWzevJnr16+zatUqMjIyGDt2LC1btuThw4cEBATg7e1t7OJWWIqicPnyZUaOHIm7uzv1\n6tXD0dGRzp07M3nyZABGjhxJ+/btDRtUxLNZWVmRm5vLpk2bePfddxk+fDhWVlZotVrD5hSQPK2m\nQCaiX4Ine4LZ2dls3ryZa9euAeDv74+vry/37t1jy5YthIaG0r59e5PvQZaWlqLVag3Z5Xfs2MH+\n/fs5e/YsAQEBODs7M27cOHr06IGiKCZfH8+jbH2oVCrc3Nxo06YN8+fP56effqK4uJgWLVrQo0cP\nFixYQFZWFvXr1zdyqSuHmjVrMnbsWPbt20e/fv0oKChg5cqVUn+myGiTqSZMP8+fnZ1tWJPbunWr\nYmFhoRw6dKjcZ4qKigw/m+L6gE6nUxRFUR49elTuO7/33ntKXFycoiiKMnnyZKVJkybKyZMnjVbO\nik5fdykpKUpoaKghDd2SJUuUPn36KHv37lXi4uKUYcOGKWfPnjVmUSutkpIS5ejRo0rbtm2VHTt2\nKIoia3amRqY0XzDll23i8fHxjB49mh07dpCVlcXf//53WrVqxdChQ+nQoYNhXUW/Zmeq0yUqlYrt\n27czZcoUUlNTsbKywt3dnb1792JpaUlOTg4JCQl88803NG/eXI4dPIW+To4cOUJgYCBFRUWkpaVx\n9+5dPv74Y/Lz80lKSiI8PJygoCDeeOONcs+J38fMzAw7Ozt8fX3LXb0ldWg65OD5S5Camsrs2bOZ\nN28eubm5nDp1iszMTFauXMlXX31FcHAw2dnZ1KpVyyS3NytPHLB///33GTJkCBqNxlAHpaWlrF69\nmgsXLhAcHGy4+08a6adLTU0lJCSEhQsX4uHhwdatWzl8+DAeHh6MGDECc3Nz7ty5Q506daQOXxCp\nR9MjuzRfsLy8PJYuXUpubi6tWrUCwNnZmc8++4zk5GRGjRqFr68vdnZ2Ri7py6VSqUhNTeXYsWO0\nbt2awYMHA2BhYVEum4xGo6FWrVrSm/4f8vPzSUxMZO/evXh4eODv74+ZmRmJiYkUFhYyduxYateu\nbeximhR5F02PTGn+QU821JaWltSuXZvdu3dz+/ZtOnfujIODA/v27aOgoICOHTtibW2NmZmZSfYg\n9d/p0KFDvP/++9y9e5eTJ0/i7u5OgwYNaN26Nebm5kydOpXBgwdja2trqANTq4sXydXVlZYtW7J4\n8WJq165Ny5YtadasGYWFhXh7e+Po6Cj1KMT/IFOaf5C+gU9KSiItLY26devSt29f0tPTWbFiBTY2\nNowYMYLRo0cTGhpq8lf8wM/TbzNmzGDJkiV4eHgwc+ZM8vLyGDBgAN7e3lSrVo3s7GwaNGhg7KJW\nOvHx8cycOZPx48fz/vvvG7s4QlQqpreA9CfSB7sffviBjz76iJo1axIWFkZoaCjVqlVj7NixHDly\nhOnTp7N27Vq6detGSUmJsYv90uXn55OcnExiYiIAM2fOxN7envDwcH744QcURTEEO+lvPR8/Pz9C\nQkL4/PPPDdfTCCF+Hwl4f4A+N+aqVauYMmUK48aNY/v27Wg0GuLi4ujcuTOrV6/Gzc2N/fv3Az8n\nTTZ1PXr0ICYmhrVr17JlyxaqV6/OjBkzqF+/Pg4ODuWm3GT67fm988477N+/HycnJ0kALcRzMP3W\n9wXTj+r0KcMyMjK4ffs2e/bsoVevXjg7OzNlyhR69uzJmDFj8PLyQqvVsmnTJsMuuqrgnXfewdzc\nnJkzZ1JcXMzw4cOZN2+eBLgXRH+DhCmuAwvxssga3nMou0Gl7BrUoUOH2LRpE+7u7gwePJjCwkL8\n/f2Jj4+nQYMGFBcXU1JSUi7rf1URGxvL9OnTSUxMxNHRUUYkQgijkYD3HPS96V27dhESEoKPjw9m\nZmbMnj2bAwcOEBoayvXr17Gzs2PixIn07t1beuD8fIFr2TvthBDCGGRK8zno7x6bOnUqUVFRhIeH\nExMTQ05ODmvWrMHS0pL169fj6upKz549jV3cCkOm34QQFYFsWnkOOp0OjUZDREQE169fJzExka+/\n/pqbN28SGBhI69atefvtt7lw4QJr166lpKREGvgypC6EEMYkI7znoFar6d69OyqVii+++IIlS5bQ\noUMHXFxcuHjxIhcvXuStt95CURTatm1bJXZkCiFEZSEt8jM8Of2m/9nCwoLHjx+j0Wg4ffo0paWl\nnDp1irCwMJo2bQpAnz59jFVsIYQQzyCbVp6i7G7M9PR0ateujZOTU7nP7Nu3j6VLl1JUVERgYCAD\nBw40PCtTd0IIUfFIwHsKfdCKi4tj0aJFLF68mHbt2hl+rz+DV1hYiKIoWFtbS/JjIYSo4CTgPcOl\nS5cYOHAgX331FW3atCn3u6cFNxnZCSFExSa7NH9x9epVxo4da/hZnxVFH+z0OTALCwufelmrBDsh\nhKjYJOD9onHjxgwfPpwrV64A4OHhQe3atUlKSqK4uBhzc3MOHDjAwoULefTokSQ9FkKISqbKT2kq\nioJOpzMcIfD29katVhsyp1y+fBkrKys8PT2ZPHkyK1euxMfHx8ilFkII8byqdMAruxZ37tw5w7GC\nLl264ODgQFRUFImJicTHx1NcXIyfn5+kCxNCiEqqygc8fW7McePGERkZSevWrQHo2LEjjo6OfPvt\ntwA8fvwYCwsL2Y0phBCVVJUOeAAnTpxgyJAhRERE8Nprr5GVlYWDgwOWlpZ4enpibW1NYmKi4SiC\nEEKIyqnKZVp5coRmbm7OgAEDOH36NAkJCURGRuLu7s706dM5cuQIhw8fBpBgJ4QQlVyVbMVVKhV7\n9+4lISGBOnXq8PDhQ7Zu3YqLiwsRERG4uLhw4sQJALy8vFAURXZlCiFEJVflAp5KpSI2NpZJkyah\n1WpxcnJi3rx5bN++nUGDBqHVavn+++9xdXUt94ys2QkhROVW5QJeXl4ey5cvZ9u2bfj5+XH8+HG2\nbdtGaWkpKSkpjBo1ilmzZtGlSxcZ1QkhhAkx+TW8J48Q6O+oi46O5ty5c6jVapKTk7l//z4BAQGs\nW7eOpk2bSrATQggTY9IjvLJB6/z589y7d4+6desya9YsCgoKGDFiBOHh4YSFhZGWloalpaXhLB7I\n0QMhhDAlJn0sQafToVariYuLY/bs2bzxxhuUlpYyYcIEXFxcANizZw/BwcEsWrSI3r17G7nEQggh\nXhaTHOE9ePAA+PmG8rS0NEJCQoiNjaVmzZqkpKQwa9Ysjh8/TmFhIcuXL+fzzz83ZFARQghhmkxu\nhJeXl8fixYtp3rw5Q4YMITU1lerVq3P79m2mTZvGsmXLCAsLQ6PRsHjxYurXry/32QkhRBVgciM8\ntVqNlZUVx44dY+fOnbRv355WrVqRlJTEsmXL6NSpE87Oztjb21NYWIi1tbXhWQl2Qghhukwm4Olv\nPahVq5ZhjS4pKYmYmBgANBoNc+bMITk5mbi4OD7++GNee+01GdkJIUQVYTJTmvrjB8nJyTx+/JjO\nnTuzZs0arl69yltvvYWPjw8ffvghRUVF+Pv7069fP7n1QAghqhCTCXgAcXFxzJo1i/nz5+Pr60t+\nfj7r168nMzOTXr160atXL7n1QAghqiiTmdIsKChg3bp1rFq1Cl9fX7RaLba2tnzwwQc4Ozuzc+dO\ncnNzsbCwACRdmBBCVDUmk2nFzMyMO3fuoNFogF9HbsXFxQQHB5OdnY2jo6MxiyiEEMKITGaEj5ET\naQAAALJJREFUV7NmTQYOHMjhw4c5c+YM5ubmHDp0iKFDh3L79m0aNWpk7CIKIYQwIpNaw8vOzmbN\nmjWkpKTg7e1NdHQ0oaGh+Pn5GbtoQgghjMykAh5AUVERqamp5Obm0rhxYzw9PWWDihBCCNMLeE+S\nYCeEEAJMaNPKs0igE0IIASa0aUUIIYT4v0jAE0IIUSVIwBNCCFElSMATQghRJUjAE0IIUSVIwBNC\nCFElSMATQghRJfw/5a9/wr1GWjoAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6gQJeEIIIeqE/weOwIO8Og7UhQAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10eeac310>"
+        "<matplotlib.figure.Figure at 0x1078eeb10>"
        ]
       }
      ],
-     "prompt_number": 51
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -993,7 +1009,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In the plot above, we've seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of the sample size on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
+      "In the previous section, we have seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of different sample sizes on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
      ]
     },
     {
@@ -1020,26 +1036,31 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 17
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "import matplotlib.pyplot as plt\n",
       "\n",
-      "plt.figure(figsize=(10,8))\n",
+      "labels = [('cy_classic_lstsqr', 'Cython implementation'), \n",
+      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
+      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
+      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
       "\n",
-      "for f in times_n.keys():\n",
-      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
       "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
       "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.ylabel('performance gain relative to the slowest approach')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
       "plt.legend(loc=2)\n",
       "plt.grid()\n",
-      "\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
       "plt.title('Performance of least square fit implementations for different sample sizes')\n",
       "plt.show()"
      ],
@@ -1049,20 +1070,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAIECAYAAAByl6h3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYFcfXx7+79N7kAqLSBUHFih1BUGM0MVGjYAVrRM2r\nMVFjA3tJ1BjF+JNYKGrsaDSRKAIqmihYoogo1UazYUNAOO8fhA0LF7hcQBTn8zw8Dzs7c+bM2ZnZ\nc6ctR0QEBoPBYDAYDMZ7CV/fCjAYDAaDwWAw5Ic5cwwGg8FgMBjvMcyZYzAYDAaDwXiPYc4cg8Fg\nMBgMxnsMc+YYDAaDwWAw3mOYM8dgMBgMBoPxHsOcubfEmzdvMHbsWDRq1Ag8z+P06dP1rdJ7yb59\n+2BlZQVFRUWMHTtWahwvLy/07t37LWvGKPtsoqKiwPM8Hjx4UG1ZFhYWWL58eR1oWR5zc3MsW7bs\nreT1rsLzPHbt2lXfatRLPxkZGSmqp2WvAeDatWtwcnKCmpoaLC0tAQB3796Fm5sbNDU1oaCgUOd6\nfki8jfro4uKCiRMn1mkebxPmzJXCy8sLPM+D53koKSnB3NwckydPxuPHj2ss+8CBA9i9ezeOHj2K\njIwMdOnSpRY0/rAoLCzE2LFj4eHhgbt372L9+vVS43EcB47j3qpuISEh4PkPtzlJezZdu3ZFRkYG\nTExMAABnz54Fz/O4c+dOlfJiYmIwY8aMulYbQP3Ul5pw7949uR0dd3d3eHt7lwvPyMjA4MGDa0O9\nGvEu9JPdunUT1VsAmDVrFnR1dZGQkICLFy8CAJYvX46HDx/i6tWrSE9Pf+t6SmP8+PFwdXWtbzXe\nC0JDQ7F27dr6VqPWUKxvBd41nJ2dsXfvXrx58wYxMTGYMGEC7t69i6NHj8olLz8/H8rKyrh9+zZM\nTU3RuXPnGulXIu9D5MGDB3j58iX69esn6mjLQkRgZ2FXn6KiIgCQyymt6NlIJJJycWV5NgYGBtXW\n4UOjNuu4tOdUH7wL/aSSklI5eyQmJmLMmDFo1qyZSNeOHTvCysqqRroWFBRASUmpRjIY1UdXV7e+\nVahdiCEwZswYcnd3F4UtW7aMFBQU6PXr10REtHv3bnJ0dCRVVVUyNzenr7/+ml6+fCnE79mzJ40b\nN47mz59PJiYmZGxsTC4uLsRxnPBnYWFBRET5+fk0e/ZsMjU1JWVlZbK3t6ddu3aJ8uc4jn766Sfy\n9PQkHR0dGjZsGG3fvp0UFRUpIiKCWrZsSWpqauTq6krp6el06tQpcnR0JA0NDXJ3d6f79+8LspKT\nk+nzzz+nxo0bk7q6OrVq1YqCg4NF+fXs2ZPGjx9PixcvJmNjY9LX16fRo0fTixcvRPF+/fVXateu\nHamqqpKBgQH169ePnjx5Itz/6aefyNbWllRVVcnGxoaWLVtGb968qdT+58+fpx49epCamhrp6enR\n8OHDKSsri4iItm/fLrIhx3EUFRUl83Os6rn9+eef1LNnT9LX1ycdHR3q2bMnXbhwQSQjICCA7Ozs\nSFVVlfT19cnZ2Znu3btHERER5XTz9vausJzLli0jS0tLUlFRIUNDQ+rbty/l5uaKbGdqakrq6urU\nt29fCgwMJI7jhGdZ8vxLc/fu3XI2GT9+PFlZWZGamhpZWlrS3LlzKS8vT7jv6+tL1tbWtGfPHrK1\ntSVFRUW6efMmPX/+nL766itBh7Zt29LBgwcrLE9Fz6bELvfv36eUlJRycVxdXSuUaWZmRkuXLhVd\nL1iwgL788kvS0dEhIyMj2rRpE+Xm5pKPjw/p6emRqakpbdy4USSH4zhav349DRo0iDQ0NMjU1JTW\nr18vimNubk7Lli0TrvPz88nX15csLCxIVVWVHBwc6H//+185uRs2bKChQ4eShoYGmZmZ0cGDB+nx\n48fk4eFBWlpaZGlpSQcOHBCly8jIoDFjxpChoSFpaWlRt27d6PTp08L9EpudOHGCevToQerq6mRv\nb09//PGHKG9p/UlV7XvMmDEVtiGO42jnzp1C3AcPHtCwYcNIV1eX1NTUyMXFhWJiYqqlJ1HVdb00\nPXv2rLV+0sPDQ2oeRFW3r6rqrZ+fX4Xtvaq2UyJv586d1K9fP9LQ0KA5c+YQkWzvlsr6Zl9f33J6\nBQYGSrVBTk4OeXl5kbGxMamoqFDTpk3p66+/Fu7L0h/K0wZKyh8SEkK9evUS+qZff/21nOzS9bG6\nfRJRcZ84aNAgatSoEamqqpKlpSV9//335exZ+pmX/TM3Nxfi3759mwYNGkS6urqkp6dHffr0oWvX\nrsls07qGOXOlGDNmDPXu3VsUtmbNGuI4jl68eEHbt28nPT09CgkJoZSUFDp9+jS1bt2aRo0aJcTv\n2bMnaWlp0eTJkyk+Pp6uX79Ojx8/pm+++YYsLCwoMzOTHj58SERE33zzDRkYGND+/fvp9u3btHz5\ncuJ5nsLDwwV5HMeRgYEB+fv7U3JyMt2+fZu2b99OPM+Tq6srXbhwgS5dukQ2NjbUvXt3cnZ2pr//\n/puuXLlCdnZ2NGzYMEHWtWvXyN/fn/755x9KTk6mDRs2CE5haf11dXXp66+/poSEBPrzzz9JX1+f\nFixYIMTZtm0bKSkp0dKlS4Uybty4USiXr68vmZmZUWhoKKWmptLvv/9OzZo1E8koS3p6OmlpadGI\nESPo+vXrdPbsWWrdujU5OzsTEVFubi5dvHiROI6j3377jTIzMyk/P7/C51jamZPluR06dIj27dtH\nt27dohs3btD48eNJX1+fHj16REREMTExpKioSMHBwXTnzh26du0abd26le7du0f5+fnk7+9PHMdR\nZmYmZWZm0rNnz6TqduDAAdLW1qajR4/S3bt36cqVK7R+/XrhBRcaGkqKioq0bt06un37Nm3dupUk\nEgnxPF8tZ66oqIjmzZtHFy5coLS0NDpy5AiZmJiQr6+vkMbX15fU1dXJxcWFLly4QLdv36bnz5+T\ni4sLubq6UnR0NKWkpNCWLVtIWVlZVC9LU9GzKf1SLCwspCNHjhDHcRQTE0OZmZki578sZR0sMzMz\n0tXVpXXr1lFSUhItXbqUeJ6nvn37CmErVqwgnufpxo0bQjqO40hfX582btxIt2/fpvXr15OioiId\nPny4wrzGjBlDjo6OdOLECUpNTaU9e/aQrq4ubd26VSTX2NiYgoKCKCkpiXx8fEhDQ4P69OlDgYGB\nlJSURNOmTSMNDQ2hDr169YpatGhBQ4YModjYWEpKSqJly5aRiooKxcfHE9F/LxVHR0cKCwujxMRE\n8vb2Jm1tbcFely9fJo7j6NChQ6L+pKr2nZOTQ87OzuTh4SHU05I2VPrlWVRURE5OTtS2bVuKjo6m\na9eu0bBhw0hPT0/ISxY9q6rrZanNfjIxMVFqHrK0r7L1NiMjg5o2bUrfffcdZWZm0osXLygjI4O6\ndu1KI0eOFNp7UVFRlW2nxJlp0qQJ7dq1i1JTUyklJUXmd0tlffOLFy9oxIgR1K1bN+H5VmTradOm\nkaOjI124cIHu3r1L586do19++UW4X1V/KG8bKCl/48aNadeuXXTr1i2aP38+KSgo0OXLl0WyS9fH\n6vZJRESffPIJ9e7dm65evUppaWkUERFBu3fvFu67uLjQhAkTiKj4B0OJzTIzM+nGjRtkampKY8eO\nJaLiH2FGRkbk4+ND169fp1u3btG0adPIwMCAsrOzZbJpXcOcuVKUdQLi4uLI0tKSunTpQkTFL5Sy\nv9CjoqKI4zh6+vQpERU3OFtb23KyS0ZBSnj58iWpqKjQzz//LIr3+eefU69evYRrjuOEXw8llIyE\nXL16VQj7/vvvieM4unTpkhC2bt06atSoUaVlHjhwoFChS/Rv06aNKM7kyZMFGxARNW3alKZNmyZV\n3suXL0ldXZ3CwsJE4YGBgaSrq1uhHvPnz6emTZtSQUGBEHb16lXiOE4YuSjpCKKjoystU9nnKMtz\nK0thYSHp6ekJHcrBgwdJR0enQictODiYOI6rVC8iorVr11Lz5s1F5SxNt27daOTIkaKwb775Rq6R\nOWl529jYCNe+vr7E8zzdvXtXCIuIiCBVVVXKyckRpfX29qbPPvusQtnSnk3plyIR0ZkzZ4jjOEpL\nS6tQTgnSnLnPP/9cuC4qKiJtbW369NNPRWF6enqi0TmO42j06NEi2cOHD6cePXpIzSs5OZl4nqeE\nhARRmkWLFonaBcdxNGPGDOE6OzubOI6jr776Sgh78uQJcRxHx44dI6Li59akSZNyI9Surq40ffp0\nIvrPZocOHRLuZ2ZmEsdx9OeffxKRbM+6hLLt293dXeqocemX58mTJ4njOMHBJCLKy8sjExMTWrx4\nscx6VlXXpVGb/aQ0ZGlfZestUfn6SCR2BkrSVdV2StpJ6VFnItnfLVX1zePGjSMXF5cq7TBw4EDy\n8vKqMl4JZftDIvnaQEn5Fy5cKJLftWtXkeNauj7K2yc5OjqSn59fhffLPr8S8vPzycXFhZydnYUf\nO76+vtS5c2dRvKKiIrKysqIff/yRiKpv09rmw12xXQGRkZHQ0tKCuro6WrVqBWtra+zcuRPZ2dm4\nc+cOZsyYAS0tLeHv448/BsdxSExMFGS0b9++ynwSExORn58PZ2dnUbizszPi4uJEYU5OTuXScxyH\nVq1aCddGRkYAgNatW4vCHj16JKytefXqFebMmYOWLVvCwMAAWlpa+P3330UL0jmOg6OjoygvExMT\nZGZmAgCysrJw79499OnTR2q54uLikJubi0GDBons9OWXX+LZs2d49OhRhek6d+4MRcX/lnG2bt0a\nOjo6uHHjhtQ0siDrc0tJScGoUaNgY2MDHR0d6OjoICcnR7BNnz59YGlpCQsLC3h6eiIgIKDCslTG\nsGHDUFBQADMzM3h7eyMkJAQvXrwQ7sfHx6Nr166iNN26dZOr7AEBAejUqROMjY2hpaWFuXPnltt8\nYGRkhCZNmgjXFy9eRH5+PkxNTUX22rlzp6iOv23K1kuO42BoaCiq7xzHQSKRIDs7W5S27CL6rl27\nlmtjJcTExICI0L59e1H5V6xYUa78pfVp1KgRFBQURPro6upCWVkZWVlZAIptm5GRAV1dXZHss2fP\nlpPdpk0b4X+JRAIFBQWhDVaELO1bFuLi4mBgYAA7OzshTFlZGZ06dSpnt8r0rKquy0JN+8my1Gb7\nKkt12k5pXavzbqmsb64OPj4+2L9/P1q1aoXp06fj+PHjojWYVfWH0vSRpQ2UULZNduvWrcI2KW+f\nNH36dCxfvhydO3fGnDlzcObMmaoNA2Dy5Mm4f/8+QkNDhbWMFy9eRGxsrCh/bW1tpKWlCTpUZdO6\nhm2AKEPnzp0RGBgIRUVFNG7cWHAuShrMTz/9JHW3kKmpKYDiF4qGhkat6iRNHs/zoh14Jf+X3iJf\nEkZE4DgO3377LY4cOYJ169bB1tYW6urqmDlzJnJyckSyyy4c5jhOWBxfFSXx9u/fj+bNm5e7r6en\nJzUdx3F1UvFL9KnquQ0YMAASiQSbNm1C06ZNoaSkhO7duyM/Px9A8TOIiYlBdHQ0Tp48ic2bN2PW\nrFkIDw9Hu3btZNancePGuHnzJiIiInDq1CksWbIEs2fPxt9//y1yqipD2gaFgoIC0fW+ffswdepU\nrFq1Cj179oS2tjb27t2LefPmieKVrVtFRUXQ0dFBTExMuTzqe+NN2UXiHMdJDZO1rkqjJO358+eh\nrq5eTnZl+lSkY4nMoqIitGjRAqGhoeXSlc1Lmq2rKpes7VteSvoRWfWsjbpeHWq7360u1Wk7pXWV\ntY/iOK5GfXNp+vTpgzt37iAsLAyRkZEYOXIkWrVqhfDwcPA8X2V/WEJ120BFVNb3y9sneXl54aOP\nPsLx48cRERGBfv364fPPP0dwcHCFaVavXo3Q0FCcP39e9K4iIri7u2Pjxo3l0ujo6ACo2qZ1DXPm\nyqCqqiqcI1QaIyMjNG3aFDdv3sS4ceNqnI+1tTVUVFQQFRUFe3t7ITwqKko04labnDlzBiNHjsSQ\nIUMAFDeShISESneGlkUikaBJkyYICwvDgAEDyt13cHCAqqoqkpKS8NFHH8ks18HBAdu3bxft7Lp6\n9SpycnLQsmVLmeWURZbn9ujRI8THx2Pt2rXC+XT37t0r92uS53n06NEDPXr0wKJFi2Bvb4/du3ej\nXbt2Qqci7YVXFmVlZfTt2xd9+/bFkiVLYGRkhMOHD2PKlCmwt7dHdHQ0Jk+eLMSPjo4WpZdIJCgs\nLERWVpaw6+7SpUuiOKdPn0bbtm0xffp0ISwlJaVSvQCgY8eOePr0KXJzc+Hg4FBl/OpQYqPCwsJa\nlVsV58+fx5dffilcnzt3rsKylYyqp6WloX///rWqR8eOHREcHAwtLS0YGhrKLaciO8rSvpWVlfHm\nzZtK5Ts4OAhtokWLFgCAvLw8/P3335g6dWq1da2orstCbfeTsrQveZG37dTmu0VZWVnm9qWnpwcP\nDw94eHjA29sbXbp0QXx8PIyNjWXqD2vC+fPnRe+Hytpkhw4d5O6TjI2N4eXlBS8vL/Tr1w/Dhw/H\nzz//DE1NzXJxQ0ND4evri7CwMNjY2JTTYceOHTA1NYWKikqF+VVk09ruS6Xx3jlzmZmZGDRoEJSV\nlaGsrIxdu3a9tWMMli1bhnHjxkFPTw+ffvoplJSUEB8fj+PHj2Pz5s0AZD8WQ11dHV999RUWLFgg\nTBft378fR44cwcmTJ+tEf1tbW4SGhmLQoEHQ0NDA2rVrkZ6eDmNjYyGOLPr7+vpi8uTJMDIywuDB\ng1FUVISIiAh4enrCwMAAc+fOxdy5c8FxHNzc3PDmzRtcu3YNV65cwcqVK6XKnDp1KtavXw8vLy/M\nnTsXT548gY+PD5ydnWs8DVLVc9PT04OhoSG2bNkCS0tLPHz4ELNmzYKampog4/Dhw0hJSUGPHj1g\naGiI2NhY3L17V3jBWFhYCPG6desGdXV1qSMFW7duBRGhY8eO0NXVRXh4OJ4/fy7ImTlzJr744gs4\nOTmhX79+OHv2LEJCQkQOYqdOnaClpYU5c+bgu+++Q1JSEhYvXizKx87ODtu2bcORI0fg4OCAo0eP\n4tChQ1XaqlevXnB3d8egQYOwevVqtGrVCk+ePMG5c+egpqaG8ePHV/8B/IuZmRl4nsexY8cwdOhQ\nqKioCL9qy1K2Dkqrk7KGHTt2DP7+/ujTpw+OHz+OvXv3Yv/+/VLTWFtbY+zYsZgwYQJWr16Nzp07\n4+XLl4iNjRXqhbyMGDEC69atQ//+/bFs2TLY2NggMzMTp06dgr29PQYOHCiTnEaNGkFTUxNhYWFo\n0aIFVFRUoKenJ1P7trCwQEREBJKTk6GtrQ1dXV3R0gYAcHNzg5OTE4YPHw5/f39oa2tjyZIlyM/P\nFzlBVVFVXZeF2u4nK2pfVVFRXSsdXpO2U1vvFktLS+zfvx83btyARCKBtra21NGrefPmoUOHDrC3\ntwfP8wgJCYGWlhaaNWsGDQ2NKvvDmrJt2zbY2dmhffv2CAkJwV9//QV/f3+pcd3c3OSy69SpU9G/\nf380b94cr1+/xsGDB9GsWTPBkSttz7i4OIwcORJ+fn5o3rw5MjIyABTPdBkaGmLq1KnYunUrBg4c\niPnz56NJkya4d+8e/vjjDwwYMABdunSp1KZvhbezNK/2KCwsFP7fsWMHrVixotZke3l5ldvNWpbQ\n0FDq0qULqaurk7a2NrVp04aWLFki3K9oUaWfn59o8TkRUUFBAc2ZM0fYcu/g4CDabUNUfos2UfFC\naiUlJVFYcHAw8TwvCtu9ezfxPC/Y7O7du9S3b1/S0NAgExMT8vPzo3HjxomOiJCm/9KlS4VjAkrY\nuXMnOTo6koqKChkYGNCAAQNEmwl++eUXatOmDamqqpKenh517tyZNm/eXM4upfnrr7/I2dmZ1NTU\nSFdXl0aMGCHsFCIqXjzL83yVGyCkPceqnltUVJRwLICdnR0dOHCArK2tadGiRUREdPr0aerVqxcZ\nGhqSqqoqNW/enFatWiXKY/r06SSRSCo9muTgwYPUtWtX0tPTE46P2LZtmyjO+vXrydTUlNTU1Kh3\n797ljk4gIjp27Bi1aNGC1NTUqHv37hQWFkY8zwuL4gsKCmjSpEmkr69P2traNGLECNq4caOojkir\nk0TFu1PnzJlDFhYWpKysTMbGxtSvXz/RrueySHs2ERERol2CRESrV68mU1NTUlBQqPRokrILzqUt\nQC/9fEqws7MT7ZouOZrks88+I3V1dWrcuDGtW7eu0rwKCwtp9erVZGdnR8rKytSoUSNycXGh/fv3\ni+SWbZeKiorljoJQVVUV7YJ99OgRTZ48WWjzpqamNGjQILpy5UqFNpMmOygoiCwsLEhRUVFom7K0\n7+TkZHJ2diZNTc1KjyZJT08nDw8P0dEksbGxwn1Z9JSlrpelNvvJipDWvsruZi1bNlk2QBBV3XYq\n68PkebeU7ZsfP35MH3/8Meno6FR6NMmSJUuoZcuWpKmpSTo6OuTi4iLSqar+kEi+NlD6aBIXFxfh\nyJCqnqc8fdKUKVOoefPmpKamJryjSu90L21PaccrlT4eh4goLS2NRowYQYaGhqSiokJmZmY0atQo\nSk1NlcmmdQ1H9P6errphwwYoKytj0qRJ9a0Kg1FnREZGolevXrh37x4aN25c3+q8V5T8Qh4+fHh9\nq8JgfPCkpqbC0tISZ8+eLbcRhVEz3rtpVqB4LdXEiRPx9OlT4dMqDAaDwWAwGB8i9XY0ycaNG9Gh\nQweoqqqW+1bg48eP8fnnn0NTUxPm5ubYvXu36L6joyP+/vtvLF26FEuWLHmbajMY9UJVmyoYDAbj\nfYD1ZXVDvY3MmZqaYsGCBQgLC0Nubq7o3pQpU6CqqoqsrCxcvnwZ/fv3h6OjI+zt7UW7HbW1tZGX\nl1cf6jMYbw0XF5e3vgO0oVCTY0oYDEbtYm5uzvqyOqLe18wtWLAA9+7dw/bt2wEAL1++hL6+PuLi\n4mBtbQ0AGDNmDBo3bowVK1bgwoUL+Pbbb6GgoAAlJSVs3bpV6plFpqamePDgwVstC4PBYDAYDIY8\nODo64sqVK3KlrfcvQJT1JW/dugVFRUXBkQOKC1hyOrSTkxOioqJw6tQphIWFVXj45IMHD4Stx3Xx\n5+vr+07KlzVdZfHe1bK9D7Z7l8vHbPfuypclHbNdzdKxPq9ubPcul+99s93Vq1fl9qXq3ZkrO3/+\n4sULaGtri8K0tLTw/Pnzt6lWlbi4uLyT8mVNV1m81NRUufKWlYZsO+D9th+zXf3IlyUds13N0rE+\nT/50DbnuNRTb1bszRyQemdPU1MSzZ89EYTk5OdDS0nqbalVJQ2+cdQmzXc1oyB1bXfM+1z1mu5ql\nY32e/Okact1rKLard2eu7Mhc8+bN8ebNG9EHdK9evSrXJ538/PwQGRlZUxU/OLy8vOpbhfcaZj/5\nYbaTH2Y7+WG2qxnMfvLj5eWFyMhI+Pn51UhOvW2AKCwsREFBARYtWoT79+8jICAAioqKUFBQgKen\nJziOwy+//IJLly5hwIABOH/+vPCdQFmoqw+3MxgMBoPBYNQ2NfFb6m1kbsmSJVBXV8eqVasQEhIC\nNTU1LFu2DACwadMm5ObmQiKRYOTIkdi8eXO1HDlGzWCjmTWD2U9+mO3kh9lOfpjtagazn/zUlu3q\n7Zw5Pz+/CocV9fT0ZPooOIPBYDAYDMaHTr2fM1dXcBwHX19fuLi4lFuAqK+vjydPntSPYgzGB4ie\nnh4eP35c32owGAzGO0dkZCQiIyOxaNEiuadZG7QzV1HR2Ho6BuPtwtocg8FgVM57uWaOwWAwysLW\n3sgPs538MNvVDGY/+akt2zFnjsFgMBgMBuM9pkFPs1a0Zo5N+TAYbxfW5hgMBkM6bM1cJbA1cwzG\nuwNrcwwGg1E5bM0co9bw8vJC79696y1/CwsLLF++/K3kZW5uLpxt+KHC8zx27dpV32oIsLU38sNs\nJz/MdjWD2U9+2Jq5D5hHjx5h1qxZsLOzg5qaGoyMjNCzZ08EBwejsLBQJhlnz54Fz/O4c+eOKJzj\nuHKfWHubxMTEYMaMGW8lr/oua3W5d+8eeJ7H6dOnq53W3d0d3t7e5cIzMjIwePDg2lCPwWAwGPVE\nvR0a/C6TkJCGkyeTUFDAQ0mpCO7uVrC1NXsnZN69exfdu3eHsrIyFi9ejLZt20JJSQnR0dH44Ycf\n4OjoiNatW8ssr+yQbn1PhRkYGNRr/u8DtfmMJBJJrcmqDer7g97vM8x28sNsVzOY/eSntmzXoEfm\n/Pz8qj2EmZCQhh07EpGd3QtPn7ogO7sXduxIREJCmtx61KZMHx8fFBQU4NKlS/D09ISdnR2srKww\nevRoXLp0CdbW1tixYwf09PSQm5srSrt48WI0b94cqampcHZ2BlA8rcnzPHr16iXEIyJs2bIFZmZm\n0NHRwcCBA5GVlSWSFRgYCHt7e6ioqKBp06ZYsGCBaFTQxcUFEyZMwJIlS2BiYgIDAwOMGTMGL1++\nrLR8Zac+zc3NsXDhQkyePBm6urowNjbGzz//jNevX2PKlCnQ19dHkyZN4O/vL5LD8zx++uknDB48\nGJqammjSpAl++umnSvMuKCiAn58fLC0toaamhpYtW2LLli3l5G7cuBHDhg2DpqYmzM3NcejQITx5\n8gSenp7Q1taGlZUVDh48KEqXmZkJLy8vSCQSaGtro3v37jhz5oxwPzIyEjzP4+TJk3B2doaGhgYc\nHBxw/PhxIU6zZs0AAK6uruB5HpaWlgCAlJQUDBo0CKamptDQ0EDr1q0REhIipPPy8sKpU6cQGBgI\nnudFo3tlp1nT09Ph4eEBPT09qKurw9XVFbGxsdXSk8FgMBiyExkZWeEXsWSGGiiVFa2yexs3hpOv\nL1HPnuK/jz8uDpfnr1+/8HLyfH2J/P3Dq1WmR48ekYKCAi1btqzSeLm5uaSnp0eBgYFCWGFhIZmZ\nmdHq1aupsLCQjhw5QhzHUUxMDGVmZtKTJ0+IiGjMmDGko6NDw4cPp7i4ODp//jxZWFjQqFGjBFlH\njx4lBQXjqHA6AAAgAElEQVQFWrlyJd2+fZv27NlDenp6tGDBAiFOz549SVdXl77++mtKSEigP//8\nk/T19UVxpGFubi4qn5mZGenq6tK6desoKSmJli5dSjzPU9++fYWwFStWEM/zdOPGDSEdx3Gkr69P\nGzdupNu3b9P69etJUVGRDh8+XGFeY8aMIUdHRzpx4gSlpqbSnj17SFdXl7Zu3SqSa2xsTEFBQZSU\nlEQ+Pj6koaFBffr0ocDAQEpKSqJp06aRhoYGPXr0iIiIXr16RS1atKAhQ4ZQbGwsJSUl0bJly0hF\nRYXi4+OJiCgiIoI4jiNHR0cKCwujxMRE8vb2Jm1tbeHZXL58mTiOo0OHDlFmZiY9fPiQiIiuXbtG\n/v7+9M8//1BycjJt2LCBFBUVKSIigoiIcnJyyNnZmTw8PCgzM5MyMzMpPz9fKM/OnTuJiKioqIic\nnJyobdu2FB0dTdeuXaNhw4aRnp6ekJcsekpD1q6mRGdG9WG2kx9mu5rB7Cc/pW1XE5esQY/MyUNB\ngXSTFBbKb6qiIulp8/OrJzMxMRFFRUWwt7evNJ6qqipGjRqFgIAAIezEiRNIT0+Ht7c3eJ6Hnp4e\nAMDQ0BASiQS6urqi9Dt27IC9vT06d+6ML7/8EidPnhTur1y5EkOGDMHs2bNhbW2NoUOHws/PDz/8\n8APevHkjxDM3N8eaNWvQvHlz9O7dG8OGDRPJkRVXV1dMnz4dlpaWmDt3LjQ1NaGioiKEzZ49Gzo6\nOjh16pQo3YABAzBlyhRYW1vjq6++wtChQ/HDDz9IzSMlJQXBwcHYu3cv3N3dYWZmhqFDh2LGjBnY\nsGGDKK6npydGjRoFS0tLLFq0CK9evYKdnR1Gjx4NS0tLLF68GK9evcJff/0FANizZw+eP3+OX3/9\nFe3atRPK0bVrV/zvf/8Tyfbz80OfPn1gZWWFlStX4vnz57h48SIAoFGjRgCKP0cnkUiEKemWLVvC\nx8cHrVq1goWFBaZOnYr+/fsLI27a2tpQVlaGmpoaJBIJJBIJlJSUytng1KlTuHjxInbt2oWuXbui\nZcuWCAoKgqqqKjZt2iSzngwGg8F4u7A1c2VQUiqSGq6gID1cFnheelpl5erJpGqslZo0aRJatmyJ\nhIQE2NraIiAgAAMHDhQcgsqws7MTvexNTEyQmZkpXN+4cQOenp6iNM7Oznj9+jWSkpJga2sLAHB0\ndBTFMTExQVhYmMxlAIo3KZSWw3EcDA0NResCOY6DRCJBdna2KG2XLl1E1127dsXChQul5hMTEwMi\nQvv27UXhb968gaKiuJmU1qdRo0ZQUFAQ6aOrqwtlZWVhavrixYvIyMgQOcwAkJeXBw0NDVFYmzZt\nhP8lEgkUFBREtpfGq1evsHjxYhw9ehTp6enIz89HXl6eaOpcFuLi4mBgYAA7OzshTFlZGZ06dUJc\nXFyN9ZQFtvZGfpjt5IfZrmYw+8lPbdmOOXNlcHe3wo4d4XBxcRPC8vLC4eVljX99lGqTkFAsU0VF\nLNPNzbpacmxsbMDzPOLi4vDZZ59VGtfe3h7du3fHli1bMHv2bPz22284duyYTPmUHbWR5+wbjuOg\nrKxcLqyoqPpOsTR9pIXJI7uEkrTnz5+Hurp6OdmV6VORjiUyi4qK0KJFC4SGhpZLVzavsjYrrVtF\nfPvttzhy5AjWrVsHW1tbqKurY+bMmcjJyak0nawQUTkbyKMng8FgMOoGNs1aBltbM3h5WUMiOQVd\n3UhIJKf+deTk381aWzL19fXRr18/bNy4Ec+ePSt3v6CgAK9evRKuJ02ahKCgIGzZsgVNmjSBu7u7\ncK/kZSztKJOqjutwcHBAVFSUKCwqKgrq6uqwsrKqVpnqkvPnz4uuz507BwcHB6lxS0bk0tLSYGlp\nKfqzsLCokR4dO3ZEcnIytLS0ysk2NjaWWU5Fz+zMmTMYOXIkhgwZIky1JiQkiJ6jsrKyaApcGg4O\nDnj06BHi4+OFsLy8PPz9999o2bKlzHrWBHZelfww28kPs13NYPaTH3bOnAzIs5sVKHa+fHx6Yfp0\nF/j49KrxsSS1KXPTpk1QUlJC+/btsXv3bty4cQOJiYkICQlBx44dkZiYKMQdMmQIAGDp0qUYP368\nSI6ZmRl4nsexY8eQlZUlcg6rGoX77rvvcODAAaxatQq3bt3C3r17sWjRIsycOVOYkiQiuY7QKJtG\nmgxZw44dOwZ/f3/cvn0bGzZswN69ezFz5kypaaytrTF27FhMmDABISEhSExMxNWrV7Ft2zasXr26\n2uUozYgRI2BhYYH+/fvjxIkTSE1Nxd9//40VK1bg8OHDMstp1KgRNDU1ERYWhoyMDDx58gQAYGtr\ni9DQUFy8eBE3btzAxIkTkZ6eLiqfhYUFYmNjkZycjIcPH0p17Nzc3ODk5IThw4fj3LlzuH79OkaP\nHo38/HxMnjy5RjZgMBgMhnRqYzdrg3fmGtpcftOmTXHp0iV89tln8PPzQ/v27dGtWzcEBARg8uTJ\nopEnFRUVjBw5EkSEsWPHiuQYGRlhxYoVWLlyJRo3bixM21Z0kG7psH79+mHbtm0IDAxEq1at8PXX\nX2PKlCnw9fUVxS8rR5ZDeqWlqSpORWELFy7EyZMn0aZNG6xcuRLff/89Bg4cWGGaLVu2YMaMGVi2\nbBkcHBzg7u6O4ODgGo82qqioICoqCh06dIC3tzdsbW0xePBgxMTEwNzcvNIylIbnefj7+2Pv3r1o\n2rSpMJq4bt06mJmZwdXVFe7u7mjatCmGDBkikjdz5kw0atQIjo6OkEgkOHfunNQ8QkNDYWdnh/79\n+8PJyQlZWVk4ceIE9PX1ZdazJjS09vo2YbaTH2a7msHsJz8l34+vqTPHvs3awBk6dCgKCwtx4MCB\n+lblrcLzPEJCQjB8+PD6VoWBD6vNMRgMhjywb7MyyvHkyROEhYUhNDT0rX0ei8GoKWztjfww28kP\ns13NYPaTn9qyHdvN2kBp27YtHj9+jNmzZ6N79+71rQ6DwWAwGIw6gk2zMhiMOoe1OQaDwagcNs3K\nYDAYDAaD8YHSoJ05eY8mYTAY9QNrr/LDbCc/zHY1g9lPfiIjI2vlaJIGvWaupsZhMBgMBoPBqEtK\njidZtGiR3DLYmjkGg1HnsDbHYDAYlcPWzDEYDAaDwWB8oDBnjsFgvDOwtTfyw2wnP8x2NYPZT37Y\nt1kZjDogMjISPM/jwYMH9a1KnePn5wcbG5v6VoPBYDAYNYStmWM0eKytrTFq1CjRt2MroqCgAE+e\nPIGhoWGdfoP0bXL27Fk4OzsjNTUVzZo1E8JfvnyJvLw80XdX6wrW5hgMBqNyatJPNujdrAwGIPuH\n4d+8eQMlJSVIJJI61qh+KNtJaGhoQENDo560YTAYDEZtwaZZpZCQmAD/Pf748dcf4b/HHwmJCe+M\nTBcXF0yYMAFLliyBiYkJDAwMMGbMGLx8+VKI4+Xlhd69e4vShYSEgOf/e9wlU2z79u2DtbU1NDQ0\nMHjwYLx48QL79u2Dra0ttLW18cUXX+DZs2flZK9btw6mpqbQ0NDA0KFD8eTJEwDF05SKioq4d++e\nKP+goCDo6uoiNzdXarnk1efSpUvo168fjIyMoKWlBScnJ4SFhYnslZSUhEWLFoHneSgoKODOnTvC\ndOrvv/+O7t27Q01NDVu3bi03zbp69Wro6ekhLS1NkLl48WJIJBJkZGRU+JwyMzPh5eUFiUQCbW1t\ndO/eHWfOnBHFiYiIQOvWraGmpgZHR0dERESA53ns3LkTAJCamgqe53Hu3DlROmtra9EW9vXr16Nt\n27bQ0tKCiYkJPD09Bd1SU1Ph7OwMALCwsADP8+jVq5fI5qUJDAyEvb09VFRU0LRpUyxYsACFhYUi\ne1ZV/2oCW3sjP8x28sNsJx8JCWnw9T2FyZN/hL//KSQkpFWdiCGCrZmTAXkODU5ITMCOiB3INsrG\nU+OnyDbKxo6IHTVy6Gpb5v79+/H06VNERUXh119/xdGjR7Fq1SrhPsdxMo1GpaenIygoCKGhofjj\njz9w5swZDBo0CDt27MD+/fuFsOXLl4vSXbhwAVFRUfjzzz/x+++/48qVKxg3bhyA4pe9jY0Ntm3b\nJkoTEBCAESNGQE1NrVb1ef78OTw9PREZGYnLly+jb9+++PTTT3H79m0AwKFDh2Bubo5vvvkGGRkZ\nSE9PR5MmTYT0M2fOxHfffYebN29iwIAB5XSaNWsWOnXqBE9PTxQWFuL06dNYunQpAgMDYWxsLLUc\nubm5cHV1xcuXL3H8+HFcuXIFH3/8MXr37o2bN28CAB48eIABAwagY8eOuHz5MtasWYP/+7//A1D1\nSGLZ58txHNasWYPr16/j0KFDuHPnDjw8PAAAzZo1w+HDhwEAFy9eREZGBg4ePChV7rFjxzBu3DiM\nGTMGcXFxWLNmDfz9/cudfVRV/WMwGA2fhIQ0LF+eiKioXoiLa4PMzF7YsSOROXRywA4NrgJ5jHMy\n9iRUbFQQmRr5X6AS8M+v/6Bj945y6XHh7AW8avIKSP0vzMXGBeGXwmFrbVtteebm5lizZg0AoHnz\n5hg2bBhOnjyJxYsXAyieTpNl3j0vLw+BgYHCmqmhQ4di8+bNyMzMhIGBAQDAw8MD4eHhonREhODg\nYGhpaQEA/P390bdvXyQnJ8PS0hITJ07E+vXrsWDBAnAch5s3byI6OhobN26sdX169uwpkrFkyRL8\n9ttv2LdvH+bOnQs9PT0oKChAU1NT6vTp/Pnz0b9/f+G6xAksTVBQEBwdHTFt2jQcPXoU06ZNQ79+\n/Sosx549e/D8+XP8+uuvUFBQAADMnTsXJ0+exP/+9z+sW7cOmzZtgkQiQUBAAHieh52dHVasWIFP\nPvmkUhtJ46uvvhL+NzMzw8aNG9G+fXukp6fDxMQEenp6AABDQ8NKp5BXrlyJIUOGYPbs2QCKRwAz\nMjIwZ84cLFy4EIqKxd1FVfWvJri4uNRYxocKs538MNtVny1bkpCW5gYA4HkXpKUBFhZuCA8/BVtb\ns/pV7j2ipO7V9NDgBj0yJw8FVCA1vBCFUsNloQhFUsPzi/KrLYvjODg6OorCTExMkJmZWW1Zpqam\nosXvRkZGMDY2FhynkrCsrCxROnt7e8GRA4CuXbsCAG7cuAEAGD16NLKysoTpzl9++QUdOnQop3dt\n6JOdnQ0fHx+0aNECenp60NLSQlxcHO7cuSOTDZycnKqMI5FIsH37dmzevBmNGjWqchSqZARMV1cX\nWlpawt/Zs2eRmJgIoNhWTk5Ooqnvbt26yaRzWSIjI9G3b180a9YM2tra6NGjBwCIpoZl4caNG8KU\nbAnOzs54/fo1kpKShLDaqn8MBuP95OxZIC7uv75LWxto2rT4//x85lbUB8zqZVDilKSGK0BBbpl8\nBWZW5pXlkqesLE7HcRyKiv5zGHmeLzcyV1BQ3klVUhKXleM4qWGlZQPlF9KXxcDAAEOGDEFAQAAK\nCgoQFBSEiRMnVppGXn28vLwQHR2N77//HmfPnsWVK1fQpk0b5OfL5ijLugEgMjISCgoKyMzMxNOn\nTyuNW1RUhBYtWuDq1auiv5s3byIgIEAoR1V2LHH0KnuWd+7cwccffwxLS0vs2bMHsbGxOHLkCADI\nbIPqwHFclfWvJrC1S/LDbCc/zHayc/o0cPIkwPPFbV5HB9DRicS/A/dQVq6dvuBDobbqXoOeZpUH\n9/bu2BGxAy42LkJY3u08eHl4yTUlCgAJTYrXzKnYqIhkurm61VRdqRgZGeGvv/4ShV26dKnW5MfH\nx+P58+fC6FzJAn17e3shzqRJk+Dq6orNmzfj9evX8PT0rLX8S3PmzBl8//33wnq3ly9fIikpCa1a\ntRLiKCsrixbxV5eTJ09i7dq1OHbsGBYsWAAvLy8cPXq0wvgdO3YUpqENDQ2lxrG3t0dwcDCKiooE\npy06OloUpyTt/fv3hbCsrCzR9cWLF/H69Wv8+OOPUFFREcJKU+J8VWUDBwcHREVFwcfHRwiLioqC\nuro6rKysKk3LYDAaNkRAZCQQFVV8bWlphdTUcLRu7Ya7d4vD8vLC4eZmXW86fsiwkbky2FrbwsvV\nC5IsCXQzdCHJksDLVX5HrrZlyrIezt3dHTdv3sSmTZuQlJSEgIAA7Nu3T171y8FxHEaPHo24uDic\nPn0aU6ZMwcCBA2FpaSnE6datG2xtbfHtt9/C09Ozzo7AsLW1RUhICK5fv44rV67A09MTRUVFIhtZ\nWFjg7NmzuHv3Lh4+fFitc3yys7MxatQozJo1C3369MHu3btx5swZ/PjjjxWmGTFiBCwsLNC/f3+c\nOHECqamp+Pvvv7FixQphM8LkyZORnZ2NiRMnIj4+HuHh4Zg3b55IjpqaGrp164bVq1fjn3/+QWxs\nLEaPHi04bQBgY2MDjuPwww8/ICUlBaGhoViyZIlIjpmZGXiex7Fjx5CVlYWcnBypen/33Xc4cOAA\nVq1ahVu3bmHv3r1YtGgRZs6cKayXk3U9prywtUvyw2wnP8x2lUMEnDr1nyMHAJ06mWHlSmuYmJxC\nmzaARHIKXl7WbL1cNamtusdG5qRga21bI+etLmVK26laNszNzQ1Lly7F8uXLMXv2bHz66adYuHAh\npk2bVi05FYU5OTmhe/fu6N27N3JycvDxxx9jy5Yt5XQdP348ZsyYIdMUq7z6bN++HZMmTYKTkxOM\njY0xa9Ys5ObmiuIsWrQIEydOhK2tLfLy8pCSkiLIqkiXEry8vGBhYSEs7re0tMTmzZvh7e0NV1dX\nqesAVVRUEBUVhfnz58Pb2xvZ2dkwNDREp06d8PHHHwMAGjdujN9++w3Tp09H27Zt0bx5c6xfvx5u\nbuLR2m3btmHChAno2rUrTE1NsXLlStH6tdatW2PDhg1YuXIlli1bhg4dOuDHH38U8gGKR2pXrFiB\nlStXYvr06XB2dsapU6fK2bJfv37Ytm0bVq5ciYULF8LQ0BBTpkwRHbYs63NiMBgNAyLgxAmg9AlJ\n1taAhwegqGiGli2Z8/YuwL4AwagWXl5euH//Pk6cOFFl3FmzZiE8PByxsbFvQbOGAc/zCAkJwfDh\nw+tblVpF1jYXGRnJRknkhNlOfpjtpEMEhIUBpVft2NoCX3wBYY0cwOxXE0rbjn0BgvFOkZOTg1u3\nbiEgIAAbNmyob3UYDAaDUU2IgN9/B0ovwW3RAhgyBFCQfz8go45gzhyjWsgypTZw4EBcuHABnp6e\nGDly5FvSjNEQYL/u5YfZTn6Y7cQQAUePAqUnVRwcgEGDpDtyzH7yU1u2Y9OsDAajzmFtjsF4Pygq\nAo4cAa5c+S+sVSvg888Bnm2ZrFNq0k+yR8NgMN4Z2Hlf8sNsJz/MdsUUFQGhoWJHztGxYkeu5Jvj\nUxdOrbXvmH9osG+zMhgMBoPBqBWKioCDB4F//vkvrG1bYODAih25HRE7cFHlIh7rPK6V75gz5KdB\nT7P6+vrCxcWl3Jw0m/JhMN4urM0xGO8uhYXAgQPAv19kBAB06AD07w9UtER6w68bEJ75O/i/bkFF\nQRlGes2g2NEGlqr28BnqIz0RQyqRkZGIjIzEokWL5O4nG7Qzx9bMMRjvBqzNMRjvJm/eAPv3Azdv\n/hfm5AT061exI5dfmI8RcwfDMPYSxqa/Qqa+CvIbG+GKghb4Dj3g+/WKt6N8A4OtmWMwGA0CtnZJ\nfpjt5OdDtd2bN8DevWJHrkuXyh253IJcBF8NhsbFmxj34CUUCwnZd1+jyStFuCoroigu+e0o30Bg\n32ZlMBgMBoMhFwUFwJ49QGLif2HdugHu7hU7cs/yniHknxAUxt9Ah0f54F8SVDRVoaTMo0BVGW8e\nvYF9a0vpiRl1CptmZTAYdQ5rcwzGu0NBAbB7N5BcahDN2Rlwda3YkXv46iGCrwZDNS4BdtEJSLhx\nD4PylfC6kJBlZgJSVYOFiQX+sbNHLx+2Zk4e2DQr473Dz88PNjY2wvWOHTugpKRU6/nUllwvLy/0\n7t27FjSqOUSE9u3bY9++fQCAwsJCtGjRAn/88Uc9a8ZgMN518vOBnTvFjpyrK9CrV8WO3IPnD7Dt\n8jZoXY5Di7M3wRPQwb49fteR4GCn7jhrZo8LBk1xtAiwKvN9acbbgTlzjHqj9JckPDw88ODBg3rU\npnKq+zF5RUVFBAUF1Ykuu3btQl5eHr744gsAgIKCAubNm4fZs2fXSX5vkw917VJtwGwnPx+K7fLy\ngJAQIDX1vzA3N6Bnz4rTJD9Jxo7L2yG5EAfrC4ngOR6tjFpBy6Y1ogYPRsjAgdhjaIgLbm642KoV\nXtfBj/KGDFszV4ekJSQg6eRJ8AUFKFJSgpW7O8xsbd85me87pYeTVVVVoaqqWo/aVA4RVWv4uy6n\nFX/88UeMGzdOFDZ48GBMmTIFERERcHV1rZN8GQzG+8vr18WO3L17/4X17l28Tq4i4rLicPDGAVj+\nlQDTm/ehyCuitVFraNu0xCYtLdyzsYEmEZ6mp0PD0RGGSkoIv34dtpZs3dzbho3MlSEtIQGJO3ag\nV3Y2XJ4+Ra/sbCTu2IG0BPkPQqwtmS4uLpgwYQKWLFkCExMTGBgYYMyYMXj58iUA6VOBISEh4Eud\n+Fgyvblv3z5YW1tDQ0MDgwcPxosXL7Bv3z7Y2tpCW1sbX3zxBZ49eyakK5G9bt06mJqaQkNDA0OH\nDsWTJ08AFP+6UFRUxL3SPQWAoKAg6OrqIjc3t9KylZ0OLbk+d+4c2rVrBw0NDXTo0AExMTGidBMm\nTIC1tTXU1dVhZWWFefPmIT8/v9K8du/eDSsrK6ipqaFHjx44duwYeJ7HuXPnKk1Xmri4OPTt2xd6\nenrQ1NSEvb09QkJCAADm5uYoLCyEt7c3eJ6Hwr8fM3z27Bm8vb1hYmICVVVVNGvWDDNnzhRkvn79\nGpMnT4auri709fXh4+OD7777TjQdfevWLcTGxuLzzz8X6aOmpoaPPvpI0OF9hX3jUX6Y7eSnodsu\nNxcIChI7ch99VLkjd/H+RRy4thfNT8fB9OZ9qCiooK1xW2i3bI+UL77A+dxcFPz7g9WgY0co/Ttz\nUXnvyyhLbdU9NjJXhqSTJ+GmogKUGvp0A3Dqn39g1rGjfDIvXIDbq1eiMDcXF5wKD6/26Nz+/fsx\nduxYREVFIS0tDR4eHjAzM8PixYsBQKapwPT0dAQFBSE0NBSPHz/GkCFDMGjQICgpKWH//v149uwZ\nBg8ejOXLl2PlypVCugsXLkBDQwN//vknHj58iAkTJmDcuHE4ePAgXFxcYGNjg23btmHhwoVCmoCA\nAIwYMQJqamrVKicAFBUVYe7cudiwYQMaNWqEGTNmYOjQobh9+zYUFBRARDAyMsLu3bthZGSEq1ev\nYtKkSVBSUoKfn59UmbGxsRg5ciTmzZuHUaNG4caNG5g+fXq1plABwNPTE61bt8b58+ehqqqKmzdv\norCwEAAQExMDExMTrF27FsOGDRPSzJ8/H5cvX8aRI0dgYmKCu3fv4kapUzq/++47HDx4EMHBwbC1\ntUVAQAA2bdoEIyMjIU5kZCQaNWoEc3Pzcjp16tQJGzZsqFY5GAxGw+bVKyA4GEhP/y/s44+Lz5KT\nBhHhdNppRCWehEPkDRjcewR1JXW0NmoN1TYdcLNvX+x/9AhUVAQAUOI4tNLUhPa/P1qV67pADKkw\nZ64MfEGB9PB/X9Ryyfy30pcLr2IESRrm5uZYs2YNAKB58+YYNmwYTp48KThzskzt5eXlITAwEPr6\n+gCAoUOHYvPmzcjMzISBgQGA4jVs4eHhonREhODgYGhpaQEA/P390bdvXyQnJ8PS0hITJ07E+vXr\nsWDBAnAch5s3byI6OhobN26sdjlL8vvxxx/Rpk0bAMWjip07d0ZycjJsbGzAcRyWLl0qxG/WrBkS\nExPx888/V+jMrV27Ft27dxfsZWNjg4yMDEyePLlaut25cwczZ86EnZ0dAIicq0aNGgEAdHR0IJFI\nRGnatm2Ljv/+KGjSpAm6dOkCAHj58iU2b96MjRs34pNPPgEAfP/994iMjEROTo4g49atWzAzM5Oq\nk7m5OdLS0vDmzRsoKr6fTTsyMrLBj5LUFcx28tNQbffyZfGIXGbmf2GffAK0by89PhHhj8Q/cCnl\nHFqHX4NuZg60lLXQ2qg1lDp1wVVnZxx+9AhFRLC0tET85cto4+yM7IsXod25M/JiY+HWrt3bKVwD\nobbqHptmLUNRBYs3i/791SGXTGkftgNQpFy93zAcx8HR0VEUZmJigszSLVUGTE1NBUcOAIyMjGBs\nbCw4ciVhWVlZonT29vaCIwcAXbt2BQBhdGn06NHIyspCWFgYAOCXX35Bhw4dyuksK2XLa2JiAgCi\n8gYEBKBTp04wNjaGlpYW5s6dizt37lQoMz4+Hp07dxaFlb2WhW+++Qbjx4+Hq6srFi1ahMuXL1eZ\nxsfHB/v370erVq0wffp0HD9+XHC+k5KSkJeXJ9i0hG7duokc9JycHGhqakqVr62tDQB4+vRptcvD\nYDAaFi9eAIGB/zlyHFf8ndWKHLnCokIciD+Ay4ln4Xj8CnQzc6Cnqoc2xm2g5OqGv3v0wKF/HTkA\naG5piZXdusEiPh6aKSmQXL8Or3bt2Hq5euL9/Pleh1i5uyN8xw64lfKUw/PyYO3lBci5YcEqIaFY\npoqKWKYcW7iVyziAHMeh6N+RP57ny43MFUgZaSx7VAfHcVLDisqMKFY16mdgYIAhQ4YgICAAbm5u\nCAoKwvLlyysvUCXwPC+a/iz5v0Svffv2YerUqVi1ahV69uwJbW1t7N27F/PmzatUbnWnVKUxf/58\njBgxAsePH8epU6ewfPlyzJo1C0uWLKkwTZ8+fXDnzh2EhYUhMjISI0eORKtWrcqNgFaGrq4unj9/\nLvVeyQierq5u9QrzDtEQR0feFsx28tPQbPf8ebEj9/Bh8TXHAZ99BlT0uzq/MB97ru/BvbtxaPvn\nVcIAh3MAACAASURBVKg/y4VEQwK7Rnbg+n6EKHt7RDx+LMQ3UlbGKCMjaCoqoqOtbfFwH0Muaqvu\nsZG5MpjZ2sLaywunJBJE6urilEQCay+vGu08rQuZ0pBIJOWO97h06VKtyY+Pjxc5EiUbBuzt7YWw\nSZMm4bfffsPmzZvx+vVreHp61lr+ZTl9+jTatm2L6dOno23btrCyskJKSkqlaezt7cttdPjrr79k\nyq+sE2hhYYHJkydj3759WLRoEX7++WfhnrKysrCGrjR6enrw8PDA5s2bcezYMURFRSE+Ph5WVlZQ\nVlZGdHS0KH50dLQoXxsbG6SlpUnVLy0tDebm5u/tFCuDwag5z54BO3b858jxPDBoUMWO3KuCVwi8\nEoj01Gto9/tlqD/LhamWKVoY2oMb+BnCbG0R8e9GNwBoqqoKL2NjaLJ+5p2COXNSMLO1RS8fH7hM\nn45ePj614nTVhsyqjsdwd3fHzZs3sWnTJiQlJSEgIEA4WLY24DgOo0ePRlxcHE6fPo0pU6Zg4MCB\nsCw1rN6tWzfY2tri22+/haenJzQ0NAAAbm5umDt3bq3pAgB2dna4du0ajhw5gqSkJKxfvx6HDh2q\nNM3XX3+N6Oho+Pr64tatWzhy5AjWrl0rlK+0bH9/f1HaEtu/ePFCOAYkJSUFly9fxvHjx+Hg4CDE\ntbCwwKlTp/DgwQM8/LdXnTdvHg4dOoSEhATcvn0bISEh0NLSQrNmzaChoYEvv/wS8+fPx2+//YaE\nhATMmjULt27dEunQs2dPPHr0CKmlD4r6l7/++uu9H2H4UM77qguY7eSnodju6VNg+3bg0aPia54H\nhgwBWrWSHj/ndQ62Xd6GZyk30fb3y1B5lQdzXXNYG9qChg7F4aZN8VepUw2s1dQwysgIamWWHTUU\n+9UHtWU75sy9R0g7uLZ0mLu7O5YuXYrly5ejTZs2iIyMxMKFC8tNVVYmo7IwJycndO/eHb1790a/\nfv3g6OiIbdu2ldNz/PjxyM/Px8SJE4Ww5ORkZGRkVJlnZddlwyZNmoRRo0bB29sb7dq1w8WLF+Hn\n51epnHbt2mHnzp3YuXMnWrdujVWrVglTo6XPubt16xYelfSIZfRVUlLC06dPMW7cONjb2+Ojjz6C\niYkJdu3aJcRfs2YNYmNjYWFhIexGVVNTw8KFC9GhQwd07NgR169fxx9//CGsQ1y5ciU+++wzjBo1\nCp06dcKzZ88wZcoUkQNva2uLDh064ODBg6Iy5ubmIiwsDCNHjixnMwaD0fB58qR4RK5kEE1BARg6\nFCg1cSIi+2U2tl7eijfJiXAMuwKlvALY6NvAXNIchSNGYK+BAa68eCHEd9DQgKeREZQrWAPOqF/e\nu2+zXrhwAdOnT4eSkhJMTU0RFBQkdVqJfZu1dvHy8sL9+/dx4sSJKuPOmjUL4eHhiI2NfQua1Zyg\noCCMHTsWjx8/FjYRvCv4+flh586duH37thC2a9cuLFu2DHFxcUJYcHAwvv/+e/zzzz/1oWaVsDbH\nYNQdjx8Xr5Er2fiuoAAMGwY0by49/r1n97Dzn51QT74Lh6gbUCgktDBsAUkjM+QNH45flZSQUups\n0HZaWhhgYAC+FtYbMyrmg/o2a7NmzRAREYGoqCiYm5vj8OHD9a0S419ycnJw8eJFBAQEYMaMGfWt\nToX88MMPiI2NRUpKCvbu3Ys5c+Zg6NCh75wjVxHDhw+Hmpqa6Nusy5cvx+rVq+tZMwaD8bZ5+LB4\narXEkVNUBDw9K3bkEh8nIvBKILRvpqBlRByUiji0NmoNibEVXo0ZgyAFBZEj101HB58wR+6d571z\n5oyNjaHy765QJSUl4XR9Rt0iy7dJBw4ciJ49e2LQoEHv9HTftWvX8Mknn6BFixbC4cHSpovfBSqy\ne0xMjOjbrPHx8fjoo4/etnq1Dlt7Iz/MdvLzvtouO7t4arVkX5qSEjB8OGBtLT3+tcxr2HVtF4yu\np6DF2ZtQ5hTRxrgN9Eyt8Mzr/9m78/ioyrPx/5+Z7Jnsy8xkIQlZCJCVTUEUEFSQRbYiO0ZAbWuf\nWq1abB8EpLY/fVr7bautdSMkhF2oGyJrWFQ2yQIJBEJIICEBspB9z/n9MWSSsGiY7HC9X6+85D6Z\nOfd9Ls8kV869RbGqvp6c6mrj6x9xduZRF5ef/NnfU+PXHdzze7NmZWWxc+fOFrsNiI6zatWqn3xN\nT/lAr169uqub0GrLli1j2bJlXd0MIUQ3c+WKoWv1+m6OWFjA3Llwi81hADicfZivz27DLykLv8RM\nrM2tCdeFY+vdm8LZs4kpLeVaXR1g+CNygosLg3tIb4Xowidz7777LoMHD8ba2pqnn366xfcKCwuZ\nOnUqdnZ2+Pn5sW7duhbfLykpYcGCBaxevVqezAlxF+nps3G7ksTOdD0tdnl5hidyjYmcpSXMm3fr\nRE5RFPac38PXZ7cReCQdv8RMNBYaBugHYBvQl8tz5/JJSYkxkVOrVEx3c7ujRK6nxa876fS9Wb/5\n5hsSExMpaza7RaVSGbdFulNeXl4sXbqUb7755qZN2J9//nmsra25cuUKCQkJTJgwgYiICPr3709d\nXR2zZs1i2bJlLTYgF0IIIe52ly4Z9lpt/LVpZWVI5Hr1uvm1DUoDX535iuPZR+n3bRq6jMs4WDkQ\npg3DIrgfF6dMIa6ggKrrC7Gbq1TM1GoJsrXtxCsS7aFVT+Z+9atfMX/+fI4fP052djbZ2dlcvHiR\nixcvmlzx1KlTmTx5costpMCwR+WWLVtYuXIltra2DB8+nMmTJxMbGwvAunXrOHLkCCtXruThhx9m\n48aNJrdBCNG99JSu+u5IYme6nhK7nBzDXquNiZy1NSxYcOtErq6hjs2pm0m4eITQvSnoMi7jYuNC\nhC4Ci4gBnJsyhZhmiZyVWs18vd6kRK6nxK876tQxc3FxcSQnJ9PrVndMG904DffMmTOYm5sT2GwE\nZ0REhPGC58+fz/z581t17qioKOMG6E5OTkRGRsrjYCG6SPMNpRs/zzeWm7/2Vt+X8u3LiYmJ3ao9\nPamcmJjYrdpzq/KVK3Du3CiqqyEzMx5LS1ixYhQeHje/fsfuHew5vwc7H3PCd58gMymLMmtnRviG\noh5yH9FqNfu//BKf++8HIO/IER51dsbX1/eujV93LANER0cTHR1NW7Vqnbk+ffpw7NixDlm6YenS\npWRnZxsH2B84cIAnn3yS3Nxc42s+/PBD1q5dy969e1t9XllnTojuQz5zQpguKwvi4qCmxlC2tTU8\nkdPrb35teU05a5LXkH81i/CdydgXluHt4E2AcwCqESM4PmQIXxQWGj+PjubmLNDrcb1hf27R+dry\nc/K2T+YyMjKM//7tb3/LvHnzWLJkCfob7p7mWzmZ4saG29nZUdJs+xAwrF/WuEq+EEIIca84fx7W\nroXaWkNZo4GnngKt9ubXFlUWEZscS/nVSwzYkYRtSSX+zv70cuiFauxYvgsJYUeznW3cLCyYr9fj\nKPus9ni3HTMXGBho/PrFL37Bl19+yYMPPtjieHtMQLhx/Zo+ffpQV1dHenq68VhSUhKhoaF3fO7l\ny5e3eJx5L8vMzEStVt+0yfy9Ij4+HrVazaVLlwCJx40uXbqEq6srOTk5AJw/fx5XV1euXr3aqe2Q\nz6vpJHam666xy8homcjZ2UFU1K0Tuctll/kk4ROq8rIZuC0B25JKgl2D8XHyhcmT2d2vHzsKC42v\n97Cy4mkPj3ZJ5Lpr/HqC+Ph44uPjWb58eZvOc9tkrqGh4Se/6uvrTa64vr6eqqoq6urqqK+vp7q6\nmvr6ejQaDdOmTeP111+noqKCgwcP8sUXX7R6nFxzy5cvN/ZR3+t8fHzIy8vjvvvu65L6//jHP9K7\nd+87fl92djZqtZr9+/e3a3u6Oh7dzbJly5g5cyZeXl4A9O7dm6lTp5o8W10I0Tbp6S0TOQcHePpp\ncHe/+bUXii+wKnEVXLrEgG0J2FTWEqoNxcPJG2XGDLb5+nLg2jXj632trYnS69HI0l7dwqhRo9qc\nzLUqJc/JycHGxgYXFxfjscLCQqqqqvD09DSp4pUrV7b4RbFmzRqWL1/O66+/zr/+9S8WLlyIVqvF\nzc2N999/n379+plUjynSMjLYlZJCLWABPBISQnAbu5M74px3Qq1Wo73Vn3M9RHuPt2qveNTU1GBp\nadkOLbpZw/VZZmp1xy4HWVhYyJo1a256Srlw4ULGjh3Ln//8Z+zs7Dq0DY3kjy/TSexM191il5YG\nGzdC4/MSR0dD12qzX8FGZwrOsDFlI3aX8gndfQKrOgjThePkoKV+5kz+6+DAiWZDl/rY2jLD3R2L\ndvy50t3i15O0V+xa9X9z8uTJZGdntziWnZ3N1KlTTa54+fLlNz3pa9zNwdnZma1bt1JWVkZmZiaz\nZs0yuZ47lZaRQfTx41wNDeVaaChXQ0OJPn6ctGZjCLvynAcPHmT48OE4ODjg4OBAZGQkO3bsAODK\nlSs8/fTT6PV6bGxs6Nu3r3FiyY3dio3luLg4xowZg62tLQEBAWzYsMFY16hRo3juueda1K8oCgEB\nAbz55ps3te1Pf/oTAQEBWFtbo9VqGTduHFVVVURHR/P666+TlZWFWq1GrVYbE/m1a9dy//334+Tk\nhLu7OxMnTmyxqbyPjw8ADz/8MGq12jhGMzs7m+nTp+Pu7o6NjQ0BAQH85S9/aXUcbxePTZs2MXHi\nRDQaDQEBATftFqFWq/nnP//JnDlzcHJy4qmnngJg586dDB8+HFtbW7y9vVm4cCGFzbo0FEXh97//\nPe7u7jg4ODBv3jz+/ve/Y9Fs0PHy5csJCgpi48aN9O3bFysrK86ePUtZWRkvvPAC3t7eaDQaBg4c\nyNatW1sV+9bEatOmTeh0OgYMGNDinMOGDUOj0dxUlxCi45w61TKRc3IydK3eKpFLykti/cn1OGbm\nEb4zGdsGMwZ4DMDJ2YPa+fNZb2fHiWZrw4bb2TFTq23XRE50D616MnfmzBnCw8NbHAsLC+PUqVMd\n0qj20tjNeieZ766UFKwGDSK+2SNpAgJI3r+fISZuNHxk/34qIiKg2TlHDRrE7pMn7+jpXF1dHU88\n8QQLFy4kJiYGMOwzqtFoqKysZOTIkWg0GtauXUtAQADnzp0jPz//R8/56quv8pe//IX333+fmJgY\n5s6dS3BwMJGRkfz85z/n2Wef5Z133kGj0QCwZ88eLly4wKJFi1qcZ8uWLbz11lusXbuWiIgICgoK\n2LdvHwCzZs0iLS2NuLg4jh07BmA8X01NDa+//jr9+/enpKSE119/nQkTJpCSkoKFhQXHjx9n4MCB\nbNmyhQceeMC448cvf/lLqqqq2L17N05OTmRkZHD58uVWx/J2lixZwltvvcU//vEPPv74YxYvXswD\nDzzQYnzoihUreOONN3jzzTdpaGhgz549TJkyhbfffpuYmBiKiop49dVXmTZtmnEsyd/+9jf++c9/\n8v777zN06FA+//xz3njjjZvGjF66dIl///vfxMbG4uzsjF6vZ9KkSahUKjZu3Iinpyc7d+5k1qxZ\nfP3114wePfpHY3+7WOXl5Rm/v2/fPu6/vkRBcyqVivvvv589e/aYNMzBFPHNli8Rd0ZiZ7ruEruU\nFPj0U7j+UB4XF8MTOUfHm1/73cXv2HFuB7r0PPp+m4aNmRURughsXLRUzZ3LOkUhq6LC+Pr7HBx4\nvBX7rJqiu8SvJ2r8HdHWcYetSua0Wi1nz55t8Qvt3LlzuLm5tanyjmZKH3TtbY7Xt+ED0HCb99bc\n4XlKS0u5du0akyZNIiAgAMD4348//pjMzEzOnTtn7PpuXDPoxyxevJjZs2cDhq7vPXv28M477xAT\nE8PUqVP59a9/zfr1643J20cffcTEiRNvmtWclZWFXq9n7NixmJub4+3tTUREhPH7Go0GMzOzm7o2\no6KiWpRXrVqFm5sbx44dY9iwYcZ7zMXFpcV7L1y4wNSpU41/ZDQ+wWur//mf/+FnP/uZMR7//Oc/\n2bt3b4t7f+rUqfzyl780lhctWsQLL7zA888/bzwWHR2Nn58fycnJhIeH89e//pWXXnqJuXPnAvDi\niy9y5MgRNm/e3KL+qqoqYmNj8fb2Bgwf8EOHDnH58mXj0kDPPPMM33//Pf/85z8ZPXr0T8b+p2J1\n5swZHn744VvGw9fXlx9++OHOgiiEuGMnTsDWrU2JnKurIZG7cUUwRVHYlbGLby9+i1dqNkFH0tFY\naAjXhWOl9aB8zhxiq6vJq2n6DTPCyYmHnZw6JJETbdf40GnFihUmn6NVz1oXLlzI9OnT+eKLL0hN\nTeXzzz9n+vTpNz2duRvcbqUdszaM2VLf5r13OtLK2dmZxYsXM3bsWMaPH89bb73FmTNnAPjhhx8I\nCQm54zGMw4YNa1EePnw4KSkpAFhZWREVFcWHH34IQEFBAf/973955plnbjrPzJkzqa2txdfXl6ef\nfpo1a9a02PrtdhITE5k6dSr+/v44ODgYE9CsrKwffd9vfvMb/vSnPzF06FCWLFnCgQMHWnW9PyUy\nMtL478ZxdVeuXGnxmhsnTRw9epS//e1v2NvbG79CQkJQqVScPXuW4uJicnNzGTp0aIv33VgG0Ol0\nxkSu8dw1NTV4eXm1OH9cXJxxxvdPxf6nYlVSUnLbpX8cHBy41vwpdQeTv+5NJ7EzXVfHLikJtmxp\nSuTc3Axdqzcmcg1KA5+nfc63Fw7il5hJ0JF0HK0cGeAxACsvH4oXLOCTqqoWidxYFxdGOzt3aCLX\n1fHrydordq16MrdkyRIsLCx4+eWXyc7OplevXixevJiXXnqpXRrRnTwSEkL0Dz8watAg47HqH34g\nasQIgk2YjQmQpihEHz+O1Q3nHDNw4B2f64MPPuCFF15gx44d7Ny5k6VLl/Luu++226KsN57jueee\n469//SsnTpxg9+7daLVaHn/88Zve5+npyenTp9m7dy979uxh5cqV/O53v+Pw4cMtkpPmKioqeOyx\nxxgxYgTR0dHodDoURSEkJISamh9/bhkVFcW4cePYvn07e/fu5fHHH2fq1KnGbd9MdeNkBpVKZZyI\n0Kixi7iRoigsWbLkll2ROp2OuusbWLfmh+mN525oaMDR0dHYPX2rtv5U7H8qVk5OTpSWlt6yPcXF\nxTg7O/9ku4UQpklIgM8/h8YfvVqtYUHgG+cc1dbXsjl1M2n5pwk8ko73qRxcbVzp794fM18/8mfM\nILa4mOJmP2+ecHVlgKzRek9o1ZM5tVrNK6+8QlpaGuXl5Zw+fZqXX365w2fZtZUp68wF+/sTNXAg\n2pMncTp5Eu3Jk0QNHNimmaftfc6QkBBefPFFtm3bxqJFi/jggw8YNGgQqampxnXCWuv7779vUf7u\nu+8ICQkxlgMCAhg9ejQffvghH3/8MQsXLrxtUmJpacnYsWN56623OHHiBBUVFXz22WfG7924lM2p\nU6fIz8/nzTffZMSIEQQHB1PYbGXyxvcBt1wGR6/XExUVxerVq/noo4+Ii4tr1dPA9jZ48GBOnjyJ\nv7//TV8ajQZHR0c8PT1vmi166NChnzz3kCFDuHbtGpWVlTedu3mS/GOxhx+PVVBQEJmZmbesPysr\niz59+pgQFdPIelWmk9iZrqtid+wYfPZZUyKn0xm6Vm9M5KrqqliTvIYzV07R78BpvE/loLfTE6oN\nxaxPMLkzZ/LJtWvGRM5MpeJJd/dOS+Tk3jNde60z1+rVAmtqakhLSyM/P7/FL9vRo0e3qQEdydTg\nBPv7t/uyIe1xznPnzvHBBx/wxBNP4O3tzaVLl9i/fz+DBw9m9uzZvP322zzxxBO8/fbb+Pv7k5GR\nQUFBAU8++eRtz/nJJ5/Qt29fBg0axJo1azh06BDvvfdei9c899xzzJ07l4aGBhYvXgzA1q1bee21\n19i7dy8eHh58/PHHKIrCkCFDcHJyYvfu3ZSWltK/f3/AsG5ZXl4ehw4dIjAwEI1Gg6+vL1ZWVvzj\nH//gpZdeIjMzkyVLlrRIFt3c3LCzs+Obb76hX79+WFlZ4ezszK9+9SsmTJhAnz59qKqqYsuWLfj4\n+BiX0Hjttdc4evQou3btalPMW/O084033uCxxx7jt7/9LfPnz8fe3p6zZ8+yefNm3n33Xaytrfnt\nb3/LsmXL6Nu3L0OGDOGrr75i586dP/kH0ejRo3nkkUeYNm0ab7/9NmFhYRQVFfHdd99hY2PD4sWL\nfzL2PxWrkSNH3nJ2sqIoHDlyhLffftuEyAkhfsyRI7BtW1PZwwPmzzds1dVcaXUpa5LXcLX4EiH7\nUnG7WICPow+9nXqjCgsj6/HHWZufT/X1HgRLtZpZWi3+NjadeDWiLdpjzBxKKxw4cEDR6/WKs7Oz\nolarFWdnZ8XMzEzp3bt3a97eJX7s0lp52d1Obm6uMm3aNMXb21uxsrJSPD09lWeffVYpKSlRFEVR\n8vLylAULFihubm6KtbW10q9fP2X16tWKoijK+fPnFbVarXz77bfGskqlUtasWaOMGjVKsba2Vvz9\n/ZV169bdVG9tba2i1WqViRMnGo+tWrVKUavVSlZWlqIoirJlyxblgQceUJydnRVbW1slLCxM+eST\nT1qcY86cOYqLi4uiUqmUFStWKIqiKJs3b1aCgoIUa2trZeDAgcq+ffsUc3NzY7sVRVFiYmKU3r17\nK+bm5sZ77vnnn1f69Omj2NjYKK6ursrEiROV1NRU43uioqJa3J979+5V1Gq1kpOTc9t4NC83CgwM\nNLZVURRFpVIpcXFxN8XowIEDyiOPPKLY29srGo1G6devn/Liiy8qdXV1iqIoSkNDg/Laa68pbm5u\nip2dnTJ79mzlT3/6k2Jvb288x/Lly5WgoKCbzl1ZWaksWbJE6d27t2Jpaano9Xrl8ccfV/bu3duq\n2P9UrPLz8xVra2vlhx9+aFHvwYMHFY1Go5SWlt7UpjvVUz9zQnSE779XlGXLmr4++EBRKipufl1B\nRYHy/77/f8ob3/xB2fq7ycrep0YqF36z0PCmL75Q0kpLlZXnzyvLMjKUZRkZyv+XlaVcrKzs3IsR\n7aYtPydV10/wowYPHsycOXN46aWXcHZ2pqioiDfeeAMbGxteeeUV0zPJDvRjY8hk02/Dumr+/v4c\nPHiQBx544EdfW1BQQK9evdiwYQOTJk3qpBbe/RYuXMiJEyc4evRoVzeFZ599FjMzM/79738bjy1a\ntAgbGxvefffdNp9fPnNCGHz7Lezc2VTu1QvmzgVr65avyyvLY03yGqqLCwnfmYxDYTnBbsHo7fTw\n0EOcuO8+thYU0HD9c2VnZsZ8vR5dBy1iLjpeW35OtmrQ29mzZ/nNb34DNHU7LVmyhL/97W8mVSp6\nhrq6OvLy8vjDH/6At7e3JHJtkJuby3vvvUdqaippaWn85S9/ITY29pYzg7vCihUr2LhxY4u9WT/7\n7DOWLVvWqe2QsTemk9iZrrNit39/y0TOxwfmzbs5kcu8lsmqhFXUFuYzYHsijkUVhGpDDYncY49x\ndMgQtjRL5JwtLFjk4dFliZzce6Zrr9i1asyco6OjcVabp6cnKSkpuLm5UV5e3i6N6CimLBp8L/mp\n2ZUHDx5k9OjR+Pv7t3mW6L3OzMyMzZs38/rrr1NVVUVQUBDvv/9+t1nex8PDg4KCAmO5d+/eP7ng\ntBCidRQF9u2D5r+3/fxgzhy4Mf86nX+azambsSwqIXxHEnaV9YTpInC0cUKZOJEDAQHsafZZ1Vpa\nMl+nw9681UPgRTfTOAmiLVrVzfrCCy9w3333MXfuXP7yl7/wf//3f5ibmzNu3Dg+/vjjNjWgo0g3\nqxDdh3zmxL1KUWDvXsNTuUb+/jB7NljcsLDp8dzjfJH2BZqCEiJ2JKOpUxGuC8fOxhFl2jR2enjw\nXXGx8fXeVlbM1emwub4zjujZ2vJzslXJ3I0OHDhAaWkp48aN67bLk0gyJ0T3IZ85cS9SFNi1yzBO\nrlFgIMyc2TKRUxSFby9+y66MXTjlXSN09wnsFUsi9BFY2zrQMHMmXzg6ktBsPUh/GxtmabVYdtPf\nweLOdfiYuUYXLlzg+++/x9fXl/Hjx3fbRE4I0TPJ2BvTSexM1xGxUxT45puWiVyfPjBr1s2J3I5z\nO9iVsQvXC/mE70zGSWXDAI8BWNs7Uzd/Ppvs7Vskcv00GuZ0o0RO7j3TtVfsWnUn5ObmMnLkSAID\nA5k2bRqBgYGMGDGCS5cutUsjhBBCiLuFosDXX0PzdcH79jU8kWs+tK2+oZ7/nv4v32d/j+7cZUL3\npuBi4UCkPhJLJ1dqoqJYZ2HBqWbj0yPt7Jjh7o55N0nkRPfQqm7WyZMn4+vry5///Gc0Gg3l5eX8\n/ve/5/z583z++eed0c47Jt2sQnQf8pkT9wpFgS+/hB9+aDrWvz9Mnw7Nh7bV1teyMWUjZwvP4pWa\nTdCRdNxt3enn3g+1qxuVc+cSV11NdnW18T3DHB15rIP3WRVdp8PHzLm6upKbm9ti38rq6mo8PT1b\nzIDrTlQqFcuWLbvlbFYXFxeKioq6pmFC3IOcnZ0pLCzs6mYI0aEaGuCLLwz7rTYKC4OpU6H5g7TK\n2krWnljLxeIL+CVl4ZeYiae9J0EuQaj0ekpnzya2vJwrzfaoHu3szEOOjpLI3YUaZ7OuWLGiY5O5\noKAgNm3aRGRkpPFYUlIS06dPJz093aSKO5o8CTBdfHy8LOfSBhI/00nsTCexM117xK6hwbDPalJS\n07GICJg8uWUiV1JdwprkNVwpu0zgkXS8T+Xg6+iLn5MfKh8fip58kpjiYopqa43vGe/qyn0ODm1q\nX0eSe890zWPXlrylVQvTvPrqqzz66KMsWrQIX19fMjMzWbVqFStXrjSpUiGEEOJu0dAAW7fCiRNN\nxwYMgEmTWiZyBRUFxCTFUFJRRN/v0tCfu0ygSyDeDt4QGMiVKVOILSqitK4OALVKxRQ3N8KvqNP9\nOgAAIABJREFU76MsxO20emmSPXv2EBcXR25uLp6ensyePZsxY8Z0dPtMJk/mhBBCdLT6evj0U0hN\nbTo2aBBMnAjNe0QvlV5iTfIaqipL6b8vFfeLhfR164vOTgchIWRPmEBcfj6V9fUAmKtUzNBqCba1\n7eQrEl2lQ8fM1dXVERwcTGpqKlZWViZV0hUkmRNCCNGR6uth82Y4darp2H33weOPt0zkMooyWH9y\nPfWVFYTtOYnL5RJCtaG42LjAoEFkjB7N+vx8ahoaALBSq5mt1eJnY9PJVyS6UoeuM2dubo5araay\nstKkCkTPI2sGtY3Ez3QSO9NJ7ExnSuzq6mDDhpaJ3NChNydyKVdSiEuOQykrI3J7Im5XyojURxoS\nuYce4tTDDxN39aoxkbM1M+Mpvb5HJXJy75muU/dmffHFF5k5cyavvfYavXr1ajGbxt/fv10aIoQQ\nQvQEtbWGRK75/L/hw+GRR1omckdzjrLt7DYsyyqJ2JmMc1k94foBaCw18OijJIaH81l+vvFpjIO5\nOQt0Otxu3LBViJ/QqjFzt9vpQaVSUX+9f7+7+bGlSYQQQghT1NbCunWQkdF07KGHYPTopkROURT2\nZ+1nb+ZebIsrCN+RhEuNGeG6cKwtbGDSJA4FBLC92XI9rhYWzNfpcLpxw1Zx1+u0pUl6IhkzJ4QQ\noj3V1MDatZCZ2XRs1CgYObJlIvd1+tccyTmCXUEpETuScVGsCdeFY2FpjTJtGvGenuy7ds14Dr2l\nJfN0OuzMW9VZJu5SnbY3a05ODkePHiUnJ8ekykTPIOMf2kbiZzqJnekkdqZrTeyqq2HNmpaJ3OjR\nhmSuMZGrb6jn01OfciTnCE5514jcnohWZUekPhILGw3K7Nls1+tbJHI+1tZE6fU9OpGTe890nbo3\n64ULF3jooYfw9fVlwoQJ+Pr68tBDD5GVldUujRBCCCG6q6oqQyJ34ULTsUcfhREjmso19TWsPbGW\nk1dO4nohn/CdyXhauhKmC8NMY0f9/PlsdXTkcEmJ8T1BtrbM1+mwbr7PlxAmaFU366hRo4iMjOTN\nN99Eo9FQVlbG0qVLSUhI6LYZuXSzCiGEaKuqKoiNheYdUmPHwrBhTeWK2grikuPIKc1Bd+4yfQ+e\nxtvOk0CXQFQODtTOm8dmIK2iwvieUI2Gqe7umMn2XOK6Dt+b1cHBgfz8/BZ7s9bU1ODq6kppaalJ\nFXc0SeaEEEK0RWUlxMRAbm7TsfHjDWvJNSquKiY2OZb8iny8UrMJOpKOn5Mfvo6+qFxcqJ43j3U1\nNWRWVRnfM9jenvGurqglkRPNdPiYuaFDh3LkyJEWx44ePcqw5n+aiLtGd33a2lNI/EwnsTOdxM50\nt4pdeTmsXt0ykZs4sWUid7X8Kh8nfEx++VV8EzMJOpJOH9c+hn1W9XoqoqJYXV3dIpF70NGRCXdZ\nIif3nuk6dZ05f39/xo8fz8SJE/H29ubixYts27aNOXPmsHTpUsCQUb7xxhvt0ighhBCiqzQmcleu\nGMoqFTzxhGG/1UbZJdnEJcdRWVtB4JF0ep26RD/3/mg1WujVi5InnySmuJj82lrjex51cWG4o2Mn\nX424F7SqmzUqKqrpDc0eAzYuHqwoCiqVilWrVnVMK00g3axCCCHuVGmpoWv16lVDWaWCKVMgIqLp\nNemF6Ww4uYG62mqCv0vDKyOfUG0ozjbOEBBAwbRpxBQWUlxXd/0cKia6ujLI3r4Lrkj0FB0+Zq4n\nkmROCCHEnSgpMTyRKygwlFUqmDYNwsKaXnPi8gm2nt4KdXX0j0/BI6eEcF049lb2EBJC3sSJxF69\nSvn1BfXNVCqmubsTotF0wRWJnqRT1pk7c+YMf/zjH3n++ed58803OXPmjEkVdqbly5dLX74JJGZt\nI/EzncTOdBI708XHx1NcDNHRTYmcWg0/+1nLRO5w9mE+PfUpquoawncm451bzgCPAYZEbtAgLkyY\nQPSVK8ZEzkKtZrZWe9cncnLvma5x94fly5e36TytSubWrl3LwIEDOXHiBBqNhuTkZAYOHEhcXFyb\nKu9oy5cvl628hBBC3FJaWhbvvbeHuLhEFi3aw5kzhrVTzcxgxgwICTG8TlEU9p7fy9fpX2NRWUPk\nN0l4FdQyQD8AWwtbePBB0seMIfbqVaoaGgCwVquZr9MRaGvbVZcneohRo0a1OZlrVTdr7969Wb16\nNSOarZB44MAB5s+fT2bz5bC7EelmFUIIcTtpaVlER6fT0DCGxETDDg91dbsZODCQ55/3JTjY8LoG\npYFtZ7dx7NIxrMqqiNiZjL7KnDBtGBZmFvDoo5yMiGBrfj7113/n2JmZMU+nQ29l1YVXKHqatuQt\nrZrNWlZWdtMyJEOHDqW8vNykSoUQQoiutGvXOerrx5CUZEjkACwtx+DsvIfgYF8A6hrq2HJqC6lX\nU7EtriB8RxKeDRpCdCGYmZnDpEn8EBTEl/n5xl/CTubmzNfrcbWw6KpLE/egVnWzvvTSS7z22mtU\nVlYCUFFRwe9//3tefPHFDm2c6Boy/qFtJH6mk9iZTmJ3ZwoK1CQkGBK5a9fiUashNBScnQ2/Fqvr\nqolLjiP1aip2BaVEfp2AL06EakMxs7CEGTM46O/PF80SOXdLSxZ6eNxziZzce6br1HXm3nvvPS5f\nvszf//53nJ2dKSoqAkCv1/Pvf/8bMDwevNB84zohhBCiG8rOhmPHGmhcAk6tNkx0cHYGS8sGymvK\nWZO8htyyXBzzrhG2+wR+Nh4EOAegsrREmTmT3S4uHLz+uxDA08qKeTodtrLPqugCrRoz19rMsTtN\nNpAxc0IIIW6UmQlr18KlS1kkJqZjbT2G8HBwcIDq6t1Mm+POoYp9FFQW4HqxgJD4FAId/Ojl0AuV\nrS0Ns2fzla0tPzTbytLP2prZOh1W6lYvECHETWSduVuQZE4IIURzZ8/Chg1wfS1fysuzcHE5h42N\nGkvLBiKGO3CofB+lNaXozl2m78HT9HXpg4e9B9jbUz9vHltUKlKajRcPtrVlhrs75pLIiTbqlGQu\nISGBAwcOUFBQ0KKy7rqFlyRzpouPj+9WT1l7Gomf6SR2ppPY/bjUVPj0U7i+BBz29vDUU+DmZoid\n/wB/1p5YS1VdFV6ncgg+co7+7v1xs3UDZ2dq589nQ00N6dfHjgNE2Nkx2c3trtpn1RRy75mueew6\nfDbrBx98wIsvvshjjz3Gtm3bGD9+PDt27GDy5MkmVSqEEEJ0lqQk+O9/ofH3pLMzLFgAVwrS2LB7\nF98d+47ig8X49fZl0NVyApMuEqYLx8naCbRaqubOZW1FBReqqoznvN/BgXEuLsZtLYXoSq16MhcQ\nEMCqVasYMWKEcQLE119/zbp164iJiemMdt4xeTInhBDi2DH48sumspubIZHLvZJG9N5oijyKSMtP\nQ1EaGPxNPmPrnRgWNAw7Szvw9qZs1izWFBeTV1NjPMcoJydGOjlJIifaVYd3szo4OFBSUgKAq6sr\nV65cQa1W4+LiYpzZ2t1IMieEEPe2776DHTuayjqdIZHTaOC9De9xqCCe4u8SsaxrwDu3An9LcwbZ\n92V4+HAICODa9OnEFBZS2DjtFRjn4sJQR8cuuBpxt+vwvVm9vb05f/48AEFBQXz22WccOHAAK1nd\n+q4kawa1jcTPdBI700nsmigKxMe3TOS8vCAqypDIASSmHMJ822Gml9TwYEI+C69UY5urcLW8EkJC\nuPqzn/FJQYExkVOrVExxc5NE7hbk3jNdp64z98orr3Dq1Cl69+7NsmXLmD59OjU1NfzjH/9ol0Z0\nlMa9WWVgphBC3BsUxZDEff990zE/P5g9G6ysru+zmrmXouPHeUKlwj+7nDOVCvbO9jyiUvFZg4pL\nEyey5upVKq7PljBTqZjh7k7fxkxQiHYUHx/f5qTOpKVJqqurqampwd7evk2VdyTpZhVCiHuLosBX\nXxnGyTUKDISZM8HCwpDIbU/fzuGcwxT95xuePJaGo4UajYUGlUpFobUd306YQemCKGoaGgCwVKuZ\nrdXS28ami65K3Cs6fDbrjaysrKSLVQghRLfR0GCYsZqc3HSsXz+YPh3MzaFBaeCLtC9IyEvAqrya\ngNwSfKydUdWroBZKde7UDBtJQnA/fK4ncjZmZszT6fCS33eim5NVDsVNZPxD20j8TCexM929HLu6\nOti0qWUiFxEBM2YYErn6hnq2nNpCQl4CNiWVDPg6gf4evTjt6IG31pt0/77Ujp/G+/6BOPj7A2Bv\nbs7Ter0kcq1wL997bdWpY+aEEEKI7qi21rCrQ3p607HBg2HCBFCpoK6hjk0pm0grSENTWEbEzmR8\nzFwI7hPMXusL/NrCgqTycqoqKwkODsbJ1RUXCwvm63Q4W1h03YUJcQdkOy8hhBA9UnW1YZ/VrKym\nYw88AI8+akjkauprWH9yPRlFGThcKSZ81wl8rXQEugRyprKS6IAAckeMIPP6YsB1x44xJjSUl4cM\nwd5cnnWIztXhS5O4uLjc8rhWqzWpUiGEEKItKishJqZlIvfww02JXFVdFbFJsWQUZeCcU0jEjiT8\nbTwJdAlEZW3NjpAQzj/4oDGRA3AZOhTnq1clkRM9TquSudpmCyY2P1bfuMmduKvI+Ie2kfiZTmJn\nunspdmVlEB0NOTlNxx57DEaONCRy5TXlrE5czcWSi7hnXiVs9wkC7Xzxd/ZHZWdH5YIFfG9ubtzV\n4dqxYzibmxOh0RhOIO7IvXTvtbdOGTP30EMPAVBZWWn8d6Ps7GyGDRvWLo0QQgghWqOkBFavhoIC\nQ1mlMoyPGzz4+verS4hNiuVqxVX0Z3MJ/i6NPs5BeDl4gaMjhbNnE1dXx7VmDylczM0Js7NDDVh2\n/iUJ0WY/OmYuOjoagJ///Of85z//MfblqlQqdDodY8aMwaKbDhCVMXNCCHF3KSw0dK1eu2Yoq1Qw\nZYph5ipAUWURMUkxFFUV4Z1ykcCj5wh2DcbD3gNcXbkwaxbrKyupqK8n/+JFEtPS6PPgg/SytkYF\nVP/wA1EDBxJ8fUarEJ2pw/dmPX36NH379jWpgq4iyZwQQtw9rl41JHKlpYaymRn87GeGteQA8ivy\niUmKoaSqmN4JmfglX6Cfez+0Gi14eHBi2jT+W15O/fXfC+YqFQPKy8nKyKAGwxO5MSEhksiJLtPh\nEyCOHz9OamoqAGlpaYwYMYKHH36Y06dPm1Sp6N5k/EPbSPxMJ7Ez3d0cu9xcWLWqKZEzNzdsz9WY\nyOWV5bEqYRUlVcUEHU6n94mLhGpD0Wq0KD4+7Js6lU/LyoyJnMbMjCi9ngkhIfxy0iQi7e355aRJ\nksiZ6G6+9zpae8WuVcnc//7v/+Lq6grAb3/7W+677z5GjBjBL3/5y3ZphBBCCHErFy8axshVVBjK\nlpYwb55hmy6A7JJsohOjqagqpd+B0/ik5RGmDcPV1pW6oCC2jh/P3vJy4/ncLS15xsMDb2vrLrga\nITpGq7pZHRwcKCkpobKyEk9PT/Ly8rCwsMDV1ZWioqLOaOcdk25WIYTo2c6fh3Xr4PqkU6ytDYmc\nt7ehnHktk7Un1lJXU0X/+BT0OcWE68JxsHKgIjSUDcOGkdX4ZsDfxoYn3d2xNjPrgqsR4sd1+N6s\n7u7unD17lhMnTjBkyBCsrKwoLy+XZEkIIUSHOHMGNm40bNUFoNHA/Pmg1xvKZwvOsiFlA0pVFWF7\nTqK9Uk6EPhI7SzsKBg1ibWQkBc0SuUH29ox3dcVMlh4Rd6FWdbMuXbqUwYMHs2jRIl5++WUAdu3a\nRWRkZIc2TnQNGf/QNhI/00nsTHc3xS4lBdavb0rkHBzg6aebErnUq6msP7keVUUlkd8kob9ayQCP\nAdhZ2pH14IN8FB5OwfU3q1QqHnVxYeKPJHJ3U+y6gsTPdJ26N2tUVBQzZsxApVJha2sLwLBhw7j/\n/vvbpRF3oqSkhEceeYRTp05x+PBh+vfv3+ltEEII0TESE+Gzz6Cx48fZGRYsMPwXIDEvkc9Of4Zl\neRXhO5JwLW8gQh+JjYUNyWPG8FmvXtQ3NABgoVYzzc2NfhpNF12NEJ2j1XuzFhQU8NVXX5GXl8er\nr75KTk4OiqLg3Th4oZPU1dVx7do1XnnlFV5++WVCQkJu+ToZMyeEED3LkSOwbVtT2c3NkMg5OFz/\nfs4Rtp3dhk1JJRE7knCpVhOhi8DSwpr4sWPZp9MZ32tnZsZsnQ4vK6tOvgohTNPhS5Ps27eP4OBg\n1q5dy8qVKwE4e/Ysv/jFL0yqtC3Mzc1xc3Pr9HqFEEJ0nG+/bZnI6fWGrtXGRO7ghYNsO7sNTWEZ\nA75OwK3GnEh9JGaWNmyZOLFFIqe1tGSxh4ckcuKe0apk7oUXXmD9+vVs374d8+sbEA8dOpTDhw93\naONE15DxD20j8TOdxM50PTV2igJ798LOnU3HvL0hKsow6UFRFPac38OujF04XClmwPZE3BqsidRH\nUmdjR8wTT3DCxcX43gAbGxbq9Tjdwe5EPTV23YXEz3Sdus5cVlYWjzzySItjFhYW1NfXm1zxu+++\ny+DBg7G2tubpp59u8b3CwkKmTp2KnZ0dfn5+rFu37pbnUMmsJCGE6LEUBXbsgH37mo75+RlmrVpb\nGxK57enb2Z+1H+ecQiJ2JOGmtiNCF8E1e0c+mjiRC46OxvcOtrdnrk4nS4+Ie06rJkD069eP7du3\nM27cOOOx3bt3ExYWZnLFXl5eLF26lG+++YbKysoW33v++eextrbmypUrJCQkMGHCBCIiIm6a7CBj\n4jrGqFGjuroJPZrEz3QSO9P1tNg1NMBXX8EPPzQdCwqCJ58ECwtoUBr4Iu0LEvIScMu6Sv99qbhb\nuRCiDeGCiysbxoyh8vrEBpVKxWPOzgx1cDDpj/yeFrvuRuJnuvaKXauSuXfeeYeJEycyfvx4qqqq\nePbZZ/niiy/47LPPTK546tSpABw7dozs7Gzj8fLycrZs2UJKSgq2trYMHz6cyZMnExsby5///GcA\nxo8fT1JSEmlpaTz33HM89dRTt6wjKioKPz8/AJycnIiMjDQGrvHRppSlLGUpS7lzy7t3x3PwICiK\noZyZGY+vL8yaNQozM9i9ZzcHLxxE8VPQp+dRufkQWVaOjIoM4YSHJ3+3tqbhxAn8hg7FQq2mV2oq\n1TY2qLrJ9UlZyq0pN/47MzOTtmr1bNacnBzWrFlDVlYWPj4+zJs3r11msv7v//4vOTk5rFq1CoCE\nhAQefPBBypttv/LOO+8QHx/P559/3urzymxW08XHxxtvOnHnJH6mk9iZrqfErq4ONm+G5lt7R0bC\nE0+AWg11DXVsStlEWkEa3qnZBB5JR2+np49rMPG+vux/4AG4PrHBzsyMOTodnm2c6NBTYtddSfxM\n1zx2Hb4DxLVr1/Dy8uJ3v/udSZX8mBsfiZeVleHQOH3pOnt7e0obd1gWQgjRI9XWGhYDPneu6diQ\nITB+PKhUUFNfw/qT68koPIdfYiZ+SVl42Xvh5xrElqAgTg4ZYuiDBXSWlszR6XA0b9WvMSHuaq36\nFOj1evr168fIkSMZOXIkI0aMwNXVtV0acGMWamdnR0lJSYtjxcXF2Nvbt0t94qfJX1htI/EzncTO\ndN09dtXVsHYtZGU1HRs+HB55xJDIVdVVEZccx8XiCwQeScf7VA4+jj5oXQKI6d+fi5GRcD1xC7Sx\nYYZWi5Va3S5t6+6x6+4kfqZrr9i16pNQVFTEX//6VxwdHfnHP/6Bj48PYWFhPP/8821uwI1P5vr0\n6UNdXR3p6enGY0lJSYSGht7xuZcvX96ib1oIIUTnq6iA1atbJnKjRzclcuU15axOXE32tQv0PXga\n71M59HbqjYM2mI8jIrg4cKAxkRvi4MAcna7dEjkhulp8fDzLly9v0zlaPWYODJMTvv32W7Zv385H\nH32EjY0Nly9fNqni+vp6amtrWbFiBTk5OXz44YeYm5tjZmbG7NmzUalUfPTRRxw/fpyJEyfy/fff\n069fv9ZfmIyZM5mMf2gbiZ/pJHam666xKyuDmBi4cqXp2NixMGyY4d8l1SXEJsVSUHqZ/vEpuF0s\nIMgliBptABtCQ6nq3x9UKlQqFWOdnbnfxBmrP6a7xq6nkPiZrr3GzLXqT5tXX32VoUOH0rdvXz7+\n+GMCAwM5dOgQeXl5JlUKsHLlSmxtbXnrrbdYs2YNNjY2vPnmmwD861//orKyEq1Wy7x583j//ffv\nKJETQgjR9YqLYdWqpkROpYJJk5oSuaLKIlYlrKLwWi5hO5Nxu1hAsGswVzyDiR0wwJjIWajVzNJq\nGeroKOuLCnELrXoyp9Fo8PDwYNGiRYwcOZIhQ4ZgcQera3cFlUrFsmXLGDVqlPzFIIQQnayw0NC1\nWlxsKKvVMGUKhIcbyvkV+cQkxVBZXEDYrmQc88vo696Pk159OBAWBv7+ANibmzNHq8VDtuYSd6n4\n+Hji4+NZsWKFyU/mWpXM1dbWcvToUQ4cOMD+/ftJSEggJCSEESNGsHTpUpMq7mjSzSqEEF3j6lVD\n12rjIgRmZvCzn0FjB0teWR6xSbHUXSskfEcS9iVV9NGGcsAnmJTQUPDxAUB/fcaqg8xYFfeADu9m\ntbCw4IEHHuCZZ55h8eLFTJs2jcOHD7Ny5UqTKhXdm0waaRuJn+kkdqbrLrHLzTV0rTYmchYWMHt2\nUyKXXZJNdGI0DQX5RH6dgENJNf4ekXzpH0JKZKQxketja8vTHh6dksh1l9j1VBI/07VX7FqVzP36\n178mPDwcLy8v3nnnHZycnPj0008pLCxsl0YIIYTo+S5ehOhow+xVMKztO28eBAYaypnXMolJisHs\nSj4Dvk7AvqIOr15D2BwQQnZkJHh6AnC/gwOz2nHpESHudq3qZm0cezZ06FBsbGw6o11tJt2sQgjR\neTIyYN06w8LAADY2hkTOy8tQPltwlg0pG7C9XEjYrmRsa1U49B7GV738qQoNBVdXVCoV41xcuP+G\nheOFuBe0JW+5o6VJLly4QE5ODl5eXvhcfxTeXckECCGE6BxpabBpk2GrLgCNBhYsAJ3OUE69msqn\nqZ/ikJNP6J6T2ChmKEEj2O3lS0NYGDg6YqlW8zN3d/rY2nbdhQjRBdpjAkSrnmHn5uYycuRIAgMD\nmTZtGoGBgYwYMYJLly6ZVGlnWb58uSRyJpDxD20j8TOdxM50XRW7kydhw4amRM7BARYubErkEvMS\n2ZSyCefMPMMTOSwo7P8IO3v1piEyEhwdcTA3Z6Fe32WJnNx3bSPxM13jOnNtXTS4Vcncz3/+cyIi\nIigqKiI3N5eioiIGDBjAz3/+8zZVLoQQoudKSIBPP4WGBkPZxcWQyDXu9ngk5wj/Pf1fdOm5hMSn\nYG2uISN8HMc8e0FkJNjZ4WFlxWIPD/Sy9IgQJmtVN6urqyu5ublYWloaj1VXV+Pp6UlBQUGHNtBU\nMmZOCCE6zuHD8PXXTWV3d0PXauM22gcvHGRXxi68U7MJPJKO2taZlLBHuOziZlhsztqaYFtbpru7\nYykTHYTo+KVJXFxcSE1NbXHs9OnTODs7m1SpEEKInuvAgZaJnIcHPP20IZFTFIU95/ew69xO/BLO\nE3gknToHHUcjx3HZXWd4ImdtzVAHB2ZqtZLICdEOWr2d16OPPsqSJUv497//ze9+9zseffRRXnnl\nlY5uX5ssX75c+vJNIDFrG4mf6SR2puuM2CkK7N5t+GrUqxc89RTY2hoSue3p29mfuY/AI+n4JWVR\n6ubL4YhHKXXXQkQEKisrxru6Ms7VFXU32ZpL7ru2kfiZrnHyQ1vHzLVqNcZnnnmGgIAA4uLiSE5O\nxtPTk3Xr1jFmzJg2Vd7R2hocIYQQBooC27cbulcb9e5tWBDY0hIalAa+SPuCxEvH6fvtafTnLnPZ\nsy+ng+5DcXWDkBAszc2Z4e5OkMxYFcKocdWNFStWmHyOO1qapCeRMXNCCNE+Ghrgyy/h+PGmY336\nwJNPgrk51DfUs/X0VlJzk+m/LxXXC/mc7T2QHJ8wVFot9OuHg4UFc7RameggxG20JW+57ZO5pUuX\n3nRiVbNH4oqioFKpeOONN0yqWAghRPdXXw9btxqWIGkUEgLTphn2XK1rqGNTyibSc1MI23MS+8sl\nJPQdTrEuEJWnJwQF4WFtzRytFnvZY1WIDnHbMXMXL17k4sWLZGdnG78ajzX/EncfGf/QNhI/00ns\nTNcRsaurg40bWyZykZEwfbohkaupr2HtibVk5JwkYkcSVvkVfB/2sCGR8/GBPn3oq9HwtF7frRM5\nue/aRuJnuvaK3W0/XdHR0e1SgRBCiJ6npgbWrzds09Xovvvg8cdBpYKquirikuO4nJdO5I5kqqvg\nSPgYzOy1qPz9wceHYY6OPOrs3G0mOghxt2rVmLnU1FRcXFzQ6/WUlpbyf//3f5iZmfHKK69g200H\nssp2XkIIYZqqKli7Fi5caDr24IMwZowhkSuvKWdN8hquXcogfEcShSpbkvoOx1bjCkFBqL28eNzF\nhSGyx6oQP6k9tvNqVTIXHh7Opk2bCA4O5rnnnuPMmTNYW1vj5uZGbGysSRV3NJkAIYQQd66iAtas\ngea7NY4ZAw89ZPh3SXUJsUmxVORkErEjiQxbN9KD7sfOxgn69sVKr2eGuzuB3fQPfSG6qw5fNDgr\nK4vg4GAaGhrYsmULGzduZPPmzWzfvt2kSkX3JuMf2kbiZzqJnenaI3alpRAd3TKRGzeuKZErqixi\nVcIqqrPOEb49gUTnXpztMww7W2cICcHR05OFHh49LpGT+65tJH6m6/Axc81ZW1tTUlLCqVOn8PX1\nxd3dndraWqqqqtqlEUIIIbrWtWsQEwOFhYaySgWTJsHAgYZyfkU+MUkxmJ3Pon98Kt969aPSoy/2\nNg4QGoqnTsccrRa7bjzRQYi7Vau6WV988UUOHDhAaWkpv/rVr/if//kfDh8+zLPPPku0ec0XAAAg\nAElEQVRSUlJntPOOSTerEEK0TkGBIZErLjaU1WqYOhXCwgzlvLI8YpNisUnPxO/bdOJ7h6F288fW\n1hHCwuin1zPNzQ0L2ZpLCJO1JW9p9aLB33zzDZaWljz88MMAHDt2jJKSEkaPHm1SxR1NkjkhhPhp\nV64YErmyMkPZzAxmzIC+fQ3l7JJs1iSvwel0Ju4/ZLPXPxyNUy9s7JwgPJwHPDx41Nm5xTqkQog7\n1+Fj5gDGjh1rTOQABg8e3G0TOdE2Mv6hbSR+ppPYmc6U2F26BKtWNSVyFhYwZ05TIpd5LZOYpBjc\nktOxS7rCN0GDsHP2wcbBBfWAAUzy8eExF5cen8jJfdc2Ej/TdeqYuZ5q+fLlsjSJEELcwoULEBcH\n1dWGspWVIZHz9TWUzxacZcPJ9XgnnKMyu5bvAyLR2euxdHTBKiKCJ729CbCx6boLEOIu0bg0SVvI\n3qxCCHGPyciAdeugttZQtrGB+fPB09NQTr2ayqcpm/E9nEZuiQ1ntL7o7fRYOLviNGAAc7y80Fpa\ndt0FCHEX6pC9WYUQQtx90tIMW3TV1xvKdnawYAFotYZyYl4in6VuJeD7s5yucyVPp0Ov0WHhrsNr\nwABme3jIjFUhuplWj5mrqalh//79bNiwAYCysjLKGgdaiLuKjH9oG4mf6SR2pmtN7E6ehA0bmhI5\nR0d4+ummRO5IzhE+T9lC7wNnOK7y4LKz3vBETu9J//vuI8rL665M5OS+axuJn+naK3atSuZOnDhB\ncHAwzz77LIsWLQJg3759xn8LIYTo3o4fh08/hYYGQ9nFxZDIuboaygcvHOSblM/x3pfOIRs/yuxd\n0dvpMffqxYNDhzJDp5OlR4Toplo1Zm748OE899xzLFiwAGdnZ4qKiigvLycoKIhLzZcK70ZkzJwQ\nQhgcOgTNN+zRag1j5OztQVEU9mbu5bszu3H97iI/OPhiZmmLzk6HeS8fJt53HwNlj1UhOlyHrzPn\n7OxMYWEhKpXKmMwpioKLiwtFRUUmVdzRJJkTQgg4cAB2724qe3gYEjlbW0Mitz19Owln92N99Con\nHLywsrBBq9Fi6x/Ak0OG4N/DtuYSoqfq8HXmfH19OXbsWItjR48eJSgoyKRKRfcm4x/aRuJnOomd\n6W6MnaLArl0tEzkfH3jqKUMi16A08Hna5yScjqfueDHJjt5YW9iitdPhEtyXRcOG3TOJnNx3bSPx\nM12nrjP3xz/+kYkTJ/Lcc89RU1PDn/70J95//30+/PDDdmmEEEKI9qMo8PXXcORI0zF/f5g1Cywt\nob6hnq2nt3I27SjFaTVcttNia2GLu0ZLr5AQZg8ciMbMrOsuQAhxR1q9zlxCQgIffPABWVlZ+Pj4\n8MwzzzBo0KCObp/JVCoVy5Ytk0WDhRD3lIYG+PxzSExsOhYcbNiiy9wc6hrq2JSyifNpCVw+D8UW\ntthZ2uGqcSc0MpIpYWEy0UGITtS4aPCKFSs6fm/WnkbGzAkh7jX19bBlC6SkNB0LDYWpUw17rtbU\n17D+5HoyT53kUo4FVWYW2Fva42KvZcTgwYzu27fHb80lRE/V4WPmqqur+c9//sMvfvEL5s+fz4IF\nC4z/FXcfGf/QNhI/00nsTLd7dzwbNrRM5AYMgGnTDIlcVV0VsUmxnEk5RVauNVVmFjhaOeLmoGPK\n8OGM6dfvnk3k5L5rG4mf6Tp1zNxTTz1FcnIykyZNQqfTGY/fqx98IYToLtLSsti+/Ryff56MlVUD\n/v4BuLn5cv/9MG4cqFRQXlNObNIaMtIucCXfElQqnKyd0Nu7M3PUKHp7eXX1ZQgh2qBV3axOTk6c\nP38eZ2fnzmhTu5BuViHE3S4tLYuPPkonLW0MJSWGY3V1u1m8OJCnnvJFpYKS6hJWJ8ZyJu0KpYWG\nP8BdbFzwtXNl7tixuLm5deEVCCEadfjerL6+vlRXV5tUgRBCiI7x1VfnOHVqDM13VgwKGkNFxR5U\nKl+KKov4JCGGtLPF1BQZEjlXG1f627sya8IENI6OXdRyIUR7uu2Yud27d7Nnzx727NnDggULmDJl\nCmvXrjUea/wSdx8Z/9A2Ej/TSexar7gYDh5UGxO5a9fiCQwEX1+oqVGTX5HPv45Hc+JMGTVFhj28\n3G3dGebozlNTpkgi14zcd20j8TNdh4+ZW7RoUYsxcYqi8Ic//OGm150/f75dGiKEEKJ1CgogJgaq\nqhqMx3x8wNvb8O9K83z+37EYMs9VY15ciwoV7hp3xjm68fDkyaisrLqo5UKIjiBLkwghRA+Slwex\nsVBeDvn5WSQnpxMWNgZ3d8P3r1avp/K+01QWWmFVWo0KFR627jzppifyiScMi80JIbqdDl+aZPLk\nybc8Pm3aNJMqFUIIcecuXoToaEMiB+Dh4cvSpYGEhOzBySketetaCoekU1FgjVVpNWqVGj8bN571\n6EXklCmSyAlxl2pVMne7sXF79+5t18aI7kHGP7SNxM90ErvbO3eusWvVULa2hgULwKd3FYrrKY7k\nfMTXyndUZ9djXVaFWqWmn5ULz/cOwm/iRJBdHW5L7ru2kfiZrlPWmVu6dCkANTU1vP766y0e/2Vk\nZODn59cujRBCCHF7p07B5s2GHR4ANBqYPx+Ky9JY+f5KzuVdJqOkFrtroLIqQ+PTi/s1LjzVLwzb\nESMMi80JIe5aPzpmLioqCoC1a9cyd+7cpjepVOh0OhYtWkRgYGCHN9IUMmZOCHE3SEyEzz6Dxh9n\njo6GJ3KurvDC0l8TfzEL1UMPo24wR61A4fEERlVV8cEvfo35/fd3beOFEK3WYevMRUdHA/DAAw/w\n7LPPmlRBV1q+fDmjRo1i1KhRXd0UIYS4Y4cOwfbtTWVXV0Mi5+gI+dWVbLt0BcuHx2FVXW8cMxPZ\n24/8AwckkROih4iPj29zd2urBlH0xEQOmpI5cWdk/EPbSPxMJ7EzUBTYt69lIqfXw8KFYGvfwFdX\nsll07P9n787jo67u/Y+/ZrJvkISQQBYSIBA2ZUcgggGsWMUFFRBFRLnWttperb33/lprCd3sr/da\n7/3VKldUELe617WgAhHKIiD7YiQBsgKBrGRfZn5/fMlmWCbfycxkeT8fDx7JnG8y3zMfhvDJOZ9z\nzkfU+gbifz6RK//mW4aeKSDcXk19SIjH+t4V6X3nHMXPvLS0NFJSUkhNTXXqebS0SUSkE7Hb4bPP\nYNu25rYBA2DhQjvHbZX8LSODHTl7CT2WT1BdAzTY6VN1jugzpwns14vaei+ievf13AsQEbfTPnMi\nIp2EzQYffQR79jS3DR4MKXNrWX+ukH8WZJJ/4gB9ss/g1WCj4ng2JVlnGDFiBJUhQeDlxZn0Y/z0\nrruYOWuW516IiLSbM3mLkjkRkU6gvh7eew8OH25uSxzRQO+UEnZVlJJe8A21R4/Q6+w5/OrrmHQ6\nh9u8I9gTNYAPcnNpsFjwAm65/nolciJdkFuSuY0bN7JmzRry8vKIjY1l0aJFzJw509RN3UHJnHmN\nc/hijuJnXk+NXW0tvPUWZGQYj+3YCZlwjoYrSiipq+Sb7D0EHD2OX2UNw87mcXVRIeMHTsR/wd3G\nYaz03Nh1BMXOOYqfeS1j5/ITIF544QUWLFhA//79ue222+jXrx933XUXzz//vKmbioiIoboaXn21\nOZEr8aumKDmfcyMLOVVZxJGDaYQc/Jb4gtPcnL6LW0rLmTztDvwffqQpkRORns2hkbkhQ4bwzjvv\nMHr06Ka2/fv3c9ttt5HR+BOok9HInIh0dhUVxjmrp05BtVc9meFFBI2sID4eTpXkUrT/K6JOn2Vi\nXgbxpYUMjhhK7Lz7sUyapI2ARboZl0+z9unTh5MnT+Lr69vUVlNTQ3R0NIWFhaZu7GpK5kSkMyst\nNY7nKiiykdOrjOzepQwaYiM6xs6JnANYDuxjXE4mowpy8Ld4kTTsaiIWP2jsUSIi3Y7Lp1mTk5P5\n2c9+RsX5053Ly8v5+c9/ztSpU03dVDo37RnkHMXPvJ4Su8JCePElO99UVbAzJo8TYcUMHW4jsn8t\nGfs3MmjjOubv28Lo01n09glkzPVLiHjkF5dM5HpK7FxBsXOO4meeW85mbbRixQruvPNOevfuTXh4\nOEVFRUydOpU33nijQzohItJTnDoFz75Ry37/IkrCqrBYYOQI8PcvomzDP7h53076VZQCEBEaTdK9\nj+EzdryHey0inVm7tibJyckhPz+f6Oho4uLiXNkvp2maVUQ6m6NZDfz+HyWc8DsHFjtWK4waBb5V\nGQz89DWG52Vhxfi5FTtsEoMf+A8sffp4uNci4g4ur5kbO3Yse1ruYnnehAkT2LVrl6kbu5qSORHp\nLGx2Ox8cOceKHSXU0ACAtzdcOdJOQsbnDF33Nv719Ua71ZuBN95NzK2LwcvLk90WETdyec3chVas\n2u12jh07Zuqm0rmp/sE5ip953TF2J6qqWL47n/+3s7ApkfPxge8Pr2HWP57iyk/eaErkfENCGfnI\nH4i5/b52J3LdMXbuotg5R/Ezzy01c/fccw9grFxdvHhxq4zxxIkTjBw5skM6ISLS3ZTW1/NZURHr\nMyv4Jr25Pczbhx/2P07gS3+ioeJcU7v/kOGM/fFv8QuL8EBvRaQru+Q0a2pqKgBPPvkkv/zlL5uS\nOavVSlRUFPPmzSM8PNwtHW0vTbOKiCfU2WxsKS1lS1kZx3NsTZsBW21WrmwI5l+sb1P0z7ew2W0A\n2C0WQq+/lTF3PIxF06oiPZbLa+bWrl3L9ddfb+oGrvAf//EfbNu2jYSEBF566SW8vdsOMCqZExF3\nstvtHK6s5LOiIkrq68nOguMnjGuR5cFcU1PP9JI/Upizr+l76kKCGLj0MRLHdN6jEUXEPVxeM9eZ\nErl9+/aRn5/Ppk2bGDZsGO+8846nu9TtqP7BOYqfeV01dqdra3n51CneLiigpL6eY8eMRC64xo+x\n+f2YV5DJlMx/bZXIVSUmMGbZcx2WyHXV2HUGip1zFD/z3LrPXGeybds2Zs+eDRhJ5qpVq7jzzjs9\n3CsR6YkqGxrYWFLCrnPnsNvt2O3w7VE4m+vF0OIwYkp8uKrkDXpXvUVZQxUANi8rNbNSmDHv3/D3\nCfDwKxCR7qBd+8x1Bk8++SQjRozglltuISMjg2XLlvHaa6+1+TpNs4qIq9jsdr4+d44NJSVUNTSc\nb4P0IxZ8MkOILwklvPQ0407/FS/v7djOr2Kt6B1IyN33cfXE27FaHJoYEZEewuXTrK7wzDPPMGHC\nBPz9/bnvvvtaXSsqKmLu3LkEBweTkJDQ6qSJ0NBQysrKACgtLe20CzBEpHs6UVXF/+bn80lhYVMi\n12CDgr0BDNgbTWJhOAOztjMh6wksXluaErmCpFgS/u33TJ80T4mciHQoh36i2Gw2nn/+eWbOnMkV\nV1wBwKZNm3jrrbdM3zgmJoYnnniC+++/v821hx56CH9/fwoKCnjttdf40Y9+xOHDhwGYOnUqX3zx\nBQDr1q3j6quvNt0HuTDVPzhH8TOvM8eupK6OtwoKWH3qFKdra5vag/HBf2skUfujCK2oY+SBNSQU\nP40tKBMsUO/jRc73rmL6T/6LkbFjXda/zhy7zk6xc47iZ15Hxc6hZG7ZsmW8+OKLPPDAA2RnZwNG\nMvbHP/7R9I3nzp3LLbfcQp/vHFVTUVHBe++9x29/+1sCAwNJTk7mlltu4ZVXXgFg9OjRREVFMX36\ndI4cOcLtt99uug8iIpdTZ7ORVlzMM3l5HK6oaGr3sVqZGhBG4GfR1GUGEVZ8gtG7/kwIb+EVchaL\nBcoiQjiz6DZun7+MfsH9PPgqRKQ7c2gBxKpVq9izZw99+/blxz/+MQADBw7skBMgvjs//O233+Lt\n7U1iYmJT2+jRo1tlr3/6058ceu4lS5aQkJAAGNOzY8aMISUlBWjOhvW47eOUlJRO1Z+u9ljx6x6P\n7XY7kZMm8VlREfv++U8AEiZPBsCybx9DvUL4NmsWhWdsVG97iuqCrVgGl+IfUs/eUyWcHtiXax74\nITcOuZ7Nmza7pf+NOkP8utLjxrbO0p+u9rixrbP0p6s8bvx89erVOMuhBRDR0dFkZmYSEBBAWFgY\nxcXFnDt3jhEjRpCTk+NUB5544glyc3NZtWoVAJs3b2b+/PmcPHmy6WtWrlzJ66+/zsaNGx1+Xi2A\nEBGzTtfW8o/CQk5UV7dq7+/nx/fDwwmq8OeVV6D6VAnDD78DZdvwisgkOBhq/X04On0UyTPvZUy/\nMR56BSLS1bh8AcT3v/99fvazn1F9/gebzWbjiSee4KabbjJ105a+2/Hg4OCmBQ6NSktLCQkJcfpe\n4pjv/pYv7aP4mefp2FU2NPBJYSEr8vNbJXKBXl7cFBHBA/3741fqz6pV4H30CON2/ZW6c5/j3ddI\n5Ir7h5F+xwxunfNztydyno5dV6bYOUfxM6+jYufQNOuf//xnlixZQmhoKHV1dQQHB3PdddexZs0a\npztgsVhaPR46dCj19fVkZGQ0TbXu27ePUaNGtfu5U1NTSTk/7SUicjE2u51d586xscVWIwBWi4VJ\nISGkhIbi7+VFbi68/nIdMYc+Iyp/M2csBwmNLMc/0MKxsQnYk5O574o7CfYN9uCrEZGuJC0tzemk\nrl37zJ0+fZqsrCzi4uLo37+/UzduaGigrq6O5cuXk5eXx8qVK/H29sbLy4uFCxdisVh44YUX2L17\nN3PmzGHbtm0MHz7c4efXNKuIOOJ4VRVri4parVAFGBwQwPXh4fT19QXg2DH48MUzJO59B++KdM5a\nDxERWQcR/hyePpwho2dyw5Ab8LLqfFURaT+Xn836r//6r9x9991MmjTJ1E0uJDU1ld/85jdt2n79\n619TXFzM/fffz+eff05ERAR//OMf233Kg5I5EbmUkro6PisubrVCFSDMx4fZYWEkBQY2zRx8c8TO\n5v+3h0Hpn1Jhy6LUK4PISDtlQ/vybfIwZo+6hQnREzzxMkSkm3BLMvf2228TGBjI3XffzV133UVS\nUpKpG7qLkjnzWq5KkvZT/MxzR+zqbDb+WVrKltJS6lv8jPC1WpnWuzdTevXC29pcTrx/RzXpT31M\nxOn9FHKUKq+T9I2xkp2cSOnIRBZccScDeg9waZ8dofedeYqdcxQ/81rGzuULIP7nf/6HnJwcnnvu\nObKzs5k8eTLjx4/nqaeeMnVTd0lNTVVhpogAxmKrQxUVPJOXx5clJa0SuSuDg3k4JoZpoaGtErk9\nH+eR9+v/Jez015xiL9XeJwkZFsiBueNhwgR+MOHBTpHIiUjXlZaWRmpqqlPPYeps1ry8PJYsWcL6\n9eux2WxOdcBVNDInIo1O1dSwtqiozVYj0ee3Gonz92/VbrfZ2ffcVorfWU+tvYQCDmL1qaVhWn9O\nTE3kiphxzBk6Bx8vH3e+DBHpxpzJWxxazQpQXl7O+++/zxtvvNE0LNgRq1lFRFylsqGBjSUl7Dp3\nrtUPySAvL2aFhTEmOBjrd1bU28+Vc/B3f6fkqwzKOUkh32INsnL21hGUDu3HdYOv46qYq9qsxBcR\n8RSHplnnzZtHVFQUzz//PDfddBNZWVl8+umnLFq0yNX9Ew/Q1LRzFD/zOip2NrudHWVl/CUvj51l\nZU2JnNViYUrv3vwkJoZxISFtEjlbxjEO/3QFZ7/6lkKOUkg6Ff1CyHlgAtXD41l05SImx07ulImc\n3nfmKXbOUfzMc+s+cxMmTOC//uu/iI+P75Cbuov2mRPpeY5XVfGPoiIKLrPVSCsNDTR8sZFvXtzC\nqYIaznCYakrIGjUA2y0J9Avtz52j7iQsIMxNr0JEegq37zPXlahmTqRnudRWI9eHhzM0IODCI2ol\nJdS/+Q6H1+VysugcBRyk3MfGN9OHE3p1OKMiR3LLsFvw9bpAEigi0kFcUjM3bNgwvvnmGwDi4uIu\neuPs7GxTNxYR6Qjt3WqklcOHqXv3Qw7uqiav9DSFpJMb1pvMmcMZcIUf1w6aRXJccqecVhURaXTR\nZG7lypVNn7/yyisX/Br9gOuetGeQcxQ/89oTu8atRj4vLqa0vr7VtSuDg7k2LIxe3hf5EVdXB+vW\nUbt1F/v228kpP0aJJZfdAwdy7uo4hg0J4I4RtzOkzxAnX5H76H1nnmLnHMXPvI6K3UWTuWnTpjV9\nfubMGebNm9fma9555x2nOyAi0l6namr4R1ERWQ5uNdJKQQG88w41OQXs3ltHVtVhzvpX8eWIsfQa\n24uJSREsHLWQPoF9XPwqREQ6hkM1cyEhIZw7d65Ne1hYGMXFxS7pmLMsFgvLli3TAgiRbqSyoYEN\nxcV8XV5+wa1GxgYHX3zGwG6H3bth7VqqyurYsbeCnJqDHO0bzLakJAaO9CZlVBK3Db8NP28/N70i\nEenpGhdALF++3DXHeR07dgy73c7o0aPZv39/q2uZmZnce++95Ofnm7qxq2kBhEj3YbPb2XXuHBtL\nSqhqaGhqt1osXNWrF9f07o2/1yUOuK+uho8+gkOHKC+HrfvOkNvwLdsTB3I0uj/DR1iYN/EaUhJS\nVD4iIh7hsuO8EhMTGTJkCJWVlSQmJrb6s3jxYpYtW2bqptK5ac8g5yh+5l0odserqliRn8+nhYWt\nErnEgAB+FB3N7PDwSydyubmwYgUcOkRpqZ2Ne45zyPcEH44fQ0ZsNGPH+PGTGQuYMXBGl07k9L4z\nT7FzjuJnnlv2mWs8qmv69Ols2rSpQ24oIuKIi201Eu7jw+xLbTXSyG6HLVtgwwaw2ThTWE/aoSPs\n7ufHjsHjwNeLaRPCeGj6QiKDIl38akREXEf7zIlIp3KprUam9+7N5EttNdKovBzefx8yMwHIOVXJ\n+sxv2Dg0jqy+ffHxgZuvHsy/TL2DAJ8AV74cERGHuPxs1rq6Op599lm+/PJLCgsLm0bsLBaLRuxE\nxGnpx47x+cGD5NTWklFZSf+EBCJa7G95ZXAw3wsLI+RiW420lJkJ770H50f00rML+agwn7TxIyj3\n98fPDx64PpnbxszCanHoREMRkU7NoZ9kP/vZz/jf//1fpk+fzq5du7j99tspKChgxowZru6fU1JT\nUzWXb4Ji5hzFzzH1Nhunamr44OBBlm3ZwrqEBNYVF1N4xRXsTU/nbE4O0X5+LO3fn9v69r18ItfQ\nAJ9/Dq+8AhUV2O12dmVm87/1ZXw6eiTl/v4EBXjz63m3c8fY73W7RE7vO/MUO+cofuY1rmRNTU11\n6nkcGpl799132bZtG/Hx8SxbtoxHHnmE66+/nh/84AcsX77cqQ64krPBERHn2e12iuvrKaitpaCu\njtPnPxbW1WGz29mxaxeVo0cbydh5ARMnEpKRwQPTpjm2KKG4GN5911jsANTbGthw4gSrwsM5GWac\npxoR0ps/3n0nif36u+R1ioiY0biFmjP5lEM1c2FhYRQWFmK1Wunfvz8ZGRkEBgbSq1evC+4/1xmo\nZk7E/SoaGoxkrUXidqaujtrzpRkXsn3TJqqvvBIACxDj50eCvz8Rhw7xyE03Xf6mhw7Bhx9CTQ0A\nVXVVvF14mtfDo6n2Nc5THRiWwH8umUdE7yCnX6OIiCu4vGZu2LBh7Nq1i0mTJjF+/HiWL19OSEgI\nsbGxpm4qIl1brc3GmcZRthaJW0WL0bXLsVgshHl7E+3jQ4O/P0FeXvT28sLv/OKGyx5rX1cHa9fC\n1183NRVWl/IcNXwRFQ/nR/TG95vE75bMJsD/EtuXiIh0YQ4lc//zP/+D9/l6lT//+c/86Ec/ory8\nnOeff96lnRPP0Dl7zulO8bPZ7RTW1TVPj55P3Irr69v1G2SQlxdRvr5E+vgQ6etLlK8vfX188LVa\nSZ88mdW7d+M3fjwntm8nYfJkar7+mlnjxl38CU+fhnfegTNnAGMq9zhl/KmXF9/UGtuMWPDiuoQ5\n/NuisTiybqKr607vO3dT7Jyj+Jnn8rNZW5o0aVLT50OHDmX9+vVO31hEOg+73c65xinSFonbmbo6\nGtqRtPlYrUT6+LRJ3IIusalv0qBBLAHWHzzI2ePHiQwOZta4cSQNGnShjhojcWvXQn09AA22Br4K\nreb/EcSpMuNHmi8hzBu+gKXzYrncLiYiIl3dRWvm1q9f71Dh8cyZMzu8Ux1BNXMiF1bd0NBqIUJj\n4lZ9ibq277JaLPTx8WlO2M5/DPP2dt0pClVVxpFchw83NVVbGvggwc7rhV6Ulhn37UUsi8cv4PY5\nIXThAx1EpIdxSc3c0qVLHfqhfPz4cVM3dofU1NSmVSIiPU29zcbZllOk5z+WnR/RclQvb+/WI20+\nPkT4+Fx+496OlJNjrFYtKWlqKurlw8uD7GzItNC4Dqs/47h/2g1cO9NbiZyIdAmN25M4QydASBuq\nf3COu+N3ua0/HOVvtRJ5PmmL8vVt+jzgUueedrA2sbPZjCO5Nm40Pj/v6KBQ1kQU8/VBC5WVYMFK\nIt9nyfcmkJzcM7M4/bs1T7FzjuJnXsvYuXw1KxinQGzfvp38/HwWLFhAeXk5FouFoCAt9RdxFzNb\nf3yXl8VC3/OjbC0Tt15eXp3roPlz54wjuY4da2qy+fny5ehQ1loK2LfPQnU1+BDEKMt8Fs2JZ/x4\nD/ZXRMRDHBqZO3DgADfffDN+fn7k5uZSXl7OJ598wpo1a3jzzTfd0c9208icdGUdsfUHQJiPT1M9\nW2PiFu7jg1dnStouJCPDSOTOH8kFUN2vL28Pt7G/vJB9+6C2FoLpz5WWO7nr9t6MGuXB/oqIOMmZ\nvMWhZC45OZkHH3yQxYsXExYWRnFxMRUVFQwZMoT8/HxTN3Y1JXPSFXTk1h8tFyI01rf5dpGlnFnp\n6WR+8QXW6mpsx48zGIiPiDAuWiycGTecNWFZ5BVVsH+/sZA1iisZ4XUTd93pw5AhHu2+iIjTXJ7M\nhYWFUVRUZGzyeT6Zs9vthIeHU1xcbOrGrqZkzjzVPzjnQvHr6K0/Wk2R+vgQ3C7Jd18AACAASURB\nVIU3UstKTydj9Wpm2WykbdpEip8f6+vrSRwzhviBAzk8bRjvVe/hTGE9Bw9CQ4OFwXyPwb5TuPtu\nC/Hxnn4FnYP+3Zqn2DlH8TPPrTVz8fHx7Nq1i4kTJza17dy5kyH6dVikSfqxY3xx6BAH9u1jc1ER\nwxITCYyJMbX1h8VioU/jKtIWiZtLt/5wt4oKyM0l85lnmJWTA2VlxvYjfn7M8vbmi3PnSL92EFuL\nd3L2rLEjidUWwJXcQWzgYBYtguhoT78IERHPc2hk7uOPP2bp0qU8+OCDPPXUUzz++OOsWLGClStX\nMnv2bHf0s900MiffZbPbqbfbqWv8aLO1fnyR6458TU5WFluPHMEybhy159939bt2MSYpiYi4uEv2\nq5e3d5sVpH3dvfWHqzU0QEGBscVIbq7xp6gIgLTt20mprm7+WouF+oQB/LlPHZVzhnL6NHzzDQTa\nIxnFnUT1Cueee6BvXw+9FhERF3D5yNycOXNYu3Ytzz//PNdccw3Z2dm8//77jNfSsW6lcWSpDvAB\nrh058sK78HcAu91OgwNJkqPXHfma9kxnttfO9HTqxo41Tig4z3vCBI7v29eUzPlZrW1ORnD31h9u\nU15uJGyNyVt+vnGW6gXYWiatQUFUDIrjQNUJTtd7UZYHRzOgr30Ew7iVvuG+LF4MoaFueh0iIl3A\nZZO5+vp6kpKSOHz4MM8995w7+iQekH7sGKu+/hrruHGc2L6dmKuu4q87dnBbbS0D4uOdSrS++7Hx\nT3caObW1mPos2bWLmKuuItjLi6jAQO6KiiKqM2790VEaGuDUqdbJW4vNfS/Kywv692dwbCzvfL6O\ngdWlbCzIIrrqKHtDQshPnMLJoxYGMoMBTCMq0sI990BIiOtfUlekuiXzFDvnKH7mue1sVm9vb6xW\nK1VVVfj5+Tl9Q3fSCRCO++LQIRg3jq1lZZRUVpJ/7hwkJZG5fTsTu9jf+8VYLBa8z//xudhHq9Wh\n69+95turFyUhIVgtFvKDgxl0PuOIDAhgaGCgh195Bysra54qbRx1c+RUid69ITYW4uKMj/36gbc3\n1RnppOVv56PcbE6UFxPSP5ATdjvBp3yZEHonESQRGwt33w0BAa5/eSIi7uS2EyCeffZZPvjgA37x\ni18QFxfXanRhkIum4Zylmrn2+e+PPqJg5Ei2lpa2avffv5/J06e75J5eHZBEtScR87JYXDYyln7s\nGKt378avRelBzddfs+RiB8Z3FfX1cPJkc+LWuFDhcry9jdUJLZO3CwypVdVV8csXfsmh4EPUNtRi\ntxuldNXnAkk4nkLywH9n4EBYuBB8fV3w+kREOgmX18w9/PDDAHz++edtbtzQzk1MpXPyAbwAH4sF\nq8WCF8Zh6r29vBgYENDho1ne5+/TXSQNGsQSYP3Bg9QCvsCsrpbI2e1GotZykcLJk8Y06uWEhRkJ\nW2PyFhVlTKNeRGl1Kdtzt/P1ya85UniEEnsZRUVVlJdboCqEWL9BeFl8GTYM7rjDyA1FROTCHPoR\naWvHlgrSNV07ciSrd+8mefx4TmzfTsLkycbI0tSpJPXr5+nudQlJgwaRNGhQ16kfqaszkrWWyVvj\nifWX4uMDMTHNyVtsLAQHO3TL0+Wn2ZqzlQMFB7DZjZ8rleU1nKytorayH3Xp3oT0mUCB7TgDg84x\nb94lc0Jpocu87zohxc45ip95bquZk56h5cjS2ePHiQwO7nojS3JxdruxKKHlIoVTp1odXn9R4eHN\nU6WxscaoWzu2TbHb7WSVZrElewtHi462uR5gj8Py9SD8+8ZgqcvGgoXgohiGJcYpkRMRcYBDNXNd\nkWrmpEerrTUWJrRM3lqcc3pRvr7GqFvL5M3kAg6b3cY3Z79hS/YW8s7ltbmeEJrAlaHJ/O6xXHJO\nRlNiX4/dq5Y+vX2ZkDiLIQNP8sgjKabuLSLS1bi8Zk5EOrHGVQMtFykUFDg26hYR0XqRQt++7Rp1\nu5C6hjr2nd7H1pytFFUVtbpmwcLwvsOZGjcVn6pYXn0VqqtyCPJNIogkBg82ugLg69s2ARQRkbaU\nzEkbqn9wjsvjV1MDeXmttweprLz89/n5tV6kEBPToXt9VNVVsTN/J1/lfkVFXetRQG+rN2P6jWFK\n7BT6BPbhxAl45Q3jpQwaNJj9+9czatQsKivTgBRqatYza1Zih/WtJ9C/W/MUO+cofuZ5rGbuu4sh\nrN3pyCGRzsZuh8LC1osUCgpanTRxQRaLMcrWcpFC375GewcrqS5he+52dp/cTW1Dbatr/t7+TIqZ\nxKSYSQT7GoskDh2C995rXiQbExPPrbfCt99u4PDh/URG2pg1K5GkpPgO76uISHfkUM3c119/zcMP\nP8y+ffuobnGGYmfemkQ1c9IlVVcbo26NyVtennH4/OUEBLRO3GJiwN/fpV09VX6KrTlbOVhwsGll\naqPefr2ZEjeFcf3H4evVvEHcV1/B2rXNuWhIiLEZsBZMi0hP50ze4lAyN2rUKG6++WYWLVpE4HeK\noRMSEkzd2NWUzEmnZ7fDmTOtFymcPevYqFtkZOtFCn36uGTUrW2X7ZwoOcGWnC1kFGW0uR4VFEXy\ngGRG9h2Jl9WrxffBF1/Ali3NXxsRAYsW6ZxVERFwQzLXq1cvSktLu9S5kkrmzFP9g3MuGr+qqtaL\nFPLyjKKxywkMbL1IITraqH9zI5vdxpEzR9iSs4X8c/ltrg8MHUjygGQGhw1u83OioQE+/BD27Wtu\ni42Fu+5qu1BW7z3zFDvzFDvnKH7mtYydy1ezzp07l3Xr1nH99debuolIT5CVnk7mF1+w/8gRbAcO\nMHjsWOL9/ZuTt8LCyz+J1Wrs49YyeQsLc8uo24XUNdSx99RetuZspbi6uNU1CxZG9B3B1LipxPSK\nueD319TAW29BZmZzW1KScaqDj48rey4i0nM4NDI3f/58PvroI6ZNm0ZUVFTzN1ssrFmzxqUdNMti\nsbBs2TJSUlL0G4N0vIYGYwVpZSVUVJB18CAZ777LLJvNOEXh3DnW19SQOGYM8RERF3+e4ODWK0z7\n9+8Uh5BW1lWyM28nX+V9RWVd65Wy3lZvxvYby5S4KYQHhF/0OcrL4fXXje3uGo0fDzfe6PTuJyIi\n3UZaWhppaWksX77ctdOsqampF/7m8wlTZ6RpVmmXhgZjU93zyVnTx4u1tVgIBLBhxw5mXmB7kA1B\nQcycONF4YLUayVrL5K13b4+Nul1ISXUJ23K2sfvkbupsda2uBXgHNK1MDfINuuTzFBXBK69AcYvB\nvJQUuOaaTvVyRUQ6DZdPs14smZPuqVvUP9TXXz4ha9n2neSsvawttuxJKykhJTQUfH2xRkTAddcZ\nyVv//p12bvHkuZNszdnKoTOH2qxMDfUPZUrsFMb2H9tqZerF5OUZI3KNB05YLDBnjjEqdznd4r3n\nIYqdeYqdcxQ/81y+z9ymTZuYPn06ABs2bLjoE8ycOdPpTohcVl2d46NmlZWOLSxwhsVibAcSFASB\ngdiys42E0MfHGG0bPhz8/LBFRcHUqa7ti0l2u53jJcfZkr2FzOLMNtf7BfcjOS6ZkZEjsVocmxc9\netSokas7P6jn42PUxyUldWTPRUSkpYtOs44aNYqDBw8CxvYjF1vJevz4cdf1zgmaZu3k6uocHzWr\nqDDOGnUli8VYWnk+OWv18UJtAQGtCr+y0tPJWL2aWS1Wma6vqSFxyRLiO1kmY7PbOFRwiK05WzlZ\nfrLN9UFhg0iOS2ZQ2KB2rWDfu9dYtdo4SBkQYKxYbTyeS0RELs7lW5N0RUrm3Mhub07OHB09q6u7\n/PM6w2o1Eq9LJWTfTc6cLObKSk8nc/16rLW12Hx9GTxrVqdK5GobaptWppZUl7S6ZsHCyMiRJMcl\n0z+kf7ue126Hf/4T1q9vbgsNNfaQu9TaDxERaaZk7gKUzLVf09Yahw9z5ZAhDJ46lfjoaMeSNHck\nZ46OmgUFGacfeKjSvrPVj1TUVrAjbwc783e2WZnqY/VhbP+xTImdQlhAWLuf22aDf/wDdu5sbuvX\nzzjVISSk/X3tbLHrShQ78xQ75yh+5rl1n7nS0lJSU1P58ssvKSwsbDqf1WKxkJ2dberG0rlkpaeT\nsWIFsw4fxlpQQMqhQ6x/5x243NYaZnl5OT5qFhjo0eSsqyquKmZrzlb2nNpDva2+1bVAn8CmlamB\nPoEXeYZLq6+Hd9+FI0ea2wYOhAULXH6SmIiItODQyNyiRYvIycnh0Ucf5Z577uGVV17hP//zP7n9\n9tv52c9+5o5+tptG5tpnw1//ysy8PNi6tXV7y601LsXb2/FRs8BA4wQDJWcukX8uny3ZWzh85jB2\nWv8bCPUPZWrcVMb2G4uPl/mVtVVV8Le/QVZWc9uoUXDrrcZbQURE2sflI3Pr1q3jyJEjREREYLVa\nufXWW5k4cSI33XRTp03mpH2sdXWt/xe2WsHHB2tQECQmXj5J8/VVcuZBdrudzOJMtmRv4XhJ20VJ\n/YP7kzwgmRF9Rzi8MvViSkvhtdegoKC5bcoUYwcWvQVERNzPoWTObrfTu3dvAEJCQigpKaF///4c\nPXrUpZ0T97H5+BgJ3OTJpOXlkTJoEFgs2CIjjUp2cZg760cabA0cOnOILdlbOF1xus31wWGDSR6Q\nzMDQgR1ytnJBAbz6KpSVNbddd13H7b6i2hvzFDvzFDvnKH7muXyfuZauvPJKNm3axKxZs7j66qt5\n6KGHCAoKIqkTrdQT5wy+9lrWr17NLH9/o57NYjG21pg1y9Ndkwuobahl98ndbMvZRmlNaatrVouV\nkX1HMjVuartXpl5KVha88Ubz/speXsa06hVXdNgtRETEBIdq5jLPn5I9ePBgTp8+zS9/+UvKy8tZ\ntmwZI0aMcHknzVDNXPt19q01xFiZ+lXeV+zM20lVfVWraz5WH8b1H8eUuCmE+od26H0PH4b33jMW\nPYBR8rhgAQwa1KG3ERHpsbQ1yQUomZPupKiqiK05W9l7au8FV6ZeFXMVE2Mmml6Zeik7dhjbjzT+\ncwoONmbe+/Xr8FuJiPRYLl8A8eKLL16w3sbPz4/Y2FgmT56MX4ud76VrU/2DczoyfnlleWzJ2cKR\nM0farEwN8w9jatxUxvQb49TK1Iux22HDBti8ubmtTx8jkQtr/5Z0DtF7zzzFzjzFzjmKn3lurZlb\ns2YN27Zto1+/fsTGxpKbm8upU6eYMGECWef3Jvj73//OREe2sHBSWVkZ1157LUeOHOGrr77qtNO8\nImbZ7XYyijLYkrOFEyUn2lyPDokmOS6Z4X2HO70y9WIaGuCjj4wjuhrFxhrHcwV2/OCfiIg4waFp\n1oceeoikpCR++tOfAsZ/Nn/96185cuQIf/nLX/jDH/7AJ598wrZt21ze4fr6ekpKSvi3f/s3fv7z\nnzNy5MgLfp2mWaWrabA1cLDgIFtytlBQUdDmemJ4IslxySSEXvys5I5QWwtvvw0tF6sPHQp33GHs\nQCMiIh3P5TVzoaGhFBUVYW1xsHh9fT0RERGUlJRQU1ND3759KWu5X4GL3XfffUrmpFuoqa9h98nd\nbM/dfsGVqaMiRzE1bir9gl1fpFZRYewhl5/f3DZ2LNx0k7FzjYiIuIYzeYtDP56joqL48MMPW7V9\n8sknREVFAVBVVYWvfmXvNtLS0jzdhS7N0fiV15az/th6nt7+NOsy17VK5Hy9fJkcO5mfXvVTbht+\nm1sSuaIiePHF1oncNdfAzTe7L5HTe888xc48xc45ip95HRU7h2rm/vKXvzBv3jxGjRrVVDN34MAB\n3n77bQB27NjBT37yk8s+zzPPPMPq1as5ePAgCxcuZNWqVU3XioqKWLp0KZ9//jkRERE8+eSTLFy4\nEICnn36aDz/8kDlz5vDYY481fY8rp5pEXKWwspCtOVvZd3pfm5WpQT5BXBV7FROjJxLgE+C2PuXn\nGyNyFRXGY4sFbrwRJkxwWxdERMQkh7cmOXv2LJ9++in5+flER0dz44030qdPn3bd7P3338dqtbJu\n3TqqqqpaJXONiduLL77Inj17uPHGG9m6detFFzhomlW6mtyyXLZkb+Gbs9+0WZkaHhDO1LipjI4a\n7ZKVqZeSkQFvvWXUyoFxqtsdd8CwYW7thohIj9bl9pl74oknyM3NbUrmKioqCA8P59ChQyQmJgJw\n7733Eh0dzZNPPtnm+2+44Qb27dtHfHw8Dz74IPfee2+br1Ey137pGel88fUX1Nnr8LH4cO34a0lK\n1KbBzrDb7RwtOsqW7C1klWa1uR4TEkPygGSGRQxz2crUS9m3Dz74AGw243FAACxcCAMGuL0rIiI9\nmsv3meto3+3st99+i7e3d1MiBzB69OiLziV/+umnDt1nyZIlJCQkAMYijjFjxjTt59L43HpsPF7z\n2hrW7l5L7xm9yT+Qj9ViZe0/1/KTJT9hyKAhfLXlK6xYSZ6ejJfVi22bt2G1WJl+zXSsFitbNm3B\nYrEwY8YMrBYrm7/cjNViZcaMGZ3i9bnzcVpaGg22Bo6XHKcmtoYzlWc4sfcEAAljEgCoy6zjiqgr\nWHDNAiwWi9v7u3FjGgcPQmGh8fjEiTSCgiA1NYW+fT0Xv8a2zvT32VUe7927l0ceeaTT9KcrPf7v\n//5v/f/gxGPFz9xjgNWrV9MROsXI3ObNm5k/fz4nT55s+pqVK1fy+uuvs3HjRlP30Mhc+/z1zb+S\nH5HPlpwtlHxTQugw4ziooNwgJl5tfv9ACxasFitWixUvq1fz55bmzy91rWV7Z7zWsm4zPSOdf+z8\nB5t3bMYeaSc6PpqI6Iim61aLlSsir2Bq3FSigqPM/2U5yWaDdevgq6+a26Ki4O67oVcvj3ULMH7I\nNf7Ak/ZR7MxT7Jyj+JnXMnZdfmQuODi4zbYmpaWlhISEuLNbPVqdva6pjqsxkQNooMGp57Vjp8He\nQIO9gTpbnVPP1Rk1JqtFJ4vYc2QP1kFWbKONOcuCwwWMYQzRcdGM7z+eybGT6e3f26P9ra83zlg9\nfLi5LSEB7rwT/P091q0m+g/BPMXOPMXOOYqfeR0VO4eTudraWrZv387JkydZsGAB5eXlgJGItdd3\nV6EOHTqU+vp6MjIymqZa9+3bx6hRo9r93C2lpqaSkpKiN5oDfCw+WLAQGRSJ3W7HZrdhx05IYAgJ\noQnY7LamPw22hubP7Q2XvNbdNSar6RnpMBBsdlvTtcCkQHxKfXh03qNuXZl6MdXV8Le/wYkTzW0j\nR8LcucaiBxERcb+0tLRWU69mODTNeuDAAW6++Wb8/PzIzc2lvLycTz75hDVr1vDmm286fLOGhgbq\n6upYvnw5eXl5rFy5Em9vb7y8vFi4cCEWi4UXXniB3bt3M2fOHLZt28bw4cPNvTBNs7ZLekY6qzeu\nxm+IHyf2niBhTAI1R2tYMmOJ6UUQdrsdO3ZTSeCF2l1xzdF+XOxao+3/3E51bDUAlUcrGTN5DP2C\n+xF+OpxH7nykQ/6OnFFWBq++CgUtDpaYPBlmzza2IeksNF1jnmJnnmLnHMXPPLdOs/7whz9k+fLl\nLF68mLDzJ2ynpKTwwAMPtOtmv/3tb/nNb37T9PjVV18lNTWVX//61zz77LPcf//9REZGEhERwYoV\nK0wnctJ+SYlJLGEJ63ev52zRWSILIpk1Y5ZTq1ktFkvTNCQAXh3U2U6iZbL6zMlnOBN5BoDc4lyi\nQ6IB8LX6erKLAJw5YyRypS0Ol/je92Dq1M6VyImIiDkOjcyFhYVRVFSExWIhLCyM4uJi7HY74eHh\nFBcXu6Of7aaROXGnliObjZwd2ewI2dnwxhtQVWU8tlrh1lvhyis91iUREbkAl4/MxcfHs2vXLiZO\nbF7VuHPnToYMGWLqpu6imjlxl5Yjm7W2Wnytvk6PbDrryBF4911j0QOAry8sWACDB3usSyIi8h1u\nq5n7+OOPWbp0KQ8++CBPPfUUjz/+OCtWrGDlypXMnj3bqQ64ikbmzFP9g3M6Q/x27oRPP4XGfwJB\nQbBoEfTv79FuXVZniF1XpdiZp9g5R/Ezr6Nq5qyOfNGcOXNYu3YtZ86c4ZprriE7O5v333+/0yZy\nIj2V3Q4bNsAnnzQncuHh8C//0vkTORERMccjmwa7g0bmpKex2eCjj2DPnua2mBi46y5jZE5ERDov\nl4/MzZ07l82bN7dq27RpE3fccYepm4pIx6qtNfaQa5nIDRkC996rRE5EpLtzKJn78ssvmTJlSqu2\nKVOmsGHDBpd0qqOkpqY6XVTYEylmznF3/Coq4OWX4dtvm9vGjDFOdfD1/M4o7aL3nnmKnXmKnXMU\nP/MaFz+kpqY69TwOrWYNCAigoqKC3r2bjyKqqKjAt5P/T+FscEQ6u+JiYw+5wsLmtunTYcYM7SEn\nItIVNO66sXz5ctPP4VDN3H333Ud1dTUrVqygd+/elJaW8uMf/xgfHx9Wr15t+uaupJo56e5OnoTX\nXoPzJ+thscANN0CLHYRERKSLcHnN3FNPPUVZWRnh4eH07duX8PBwSktLefrpp03dVESck5kJq1Y1\nJ3Le3jB/vhI5EZGeyKFkLjw8nE8++YTc3Nymjx9//HHT0V7Svaj+wTmujt/+/caIXG2t8djfH+65\nB7rD6Xd675mn2Jmn2DlH8TOvo2LnUM1cIy8vLyIiIqiqquLYsWMADBo0qEM64go6AUK6E7sdtm2D\nzz5rbuvVy9gMODLSc/0SERHz3HYCxNq1a1m6dCknT55s/c0WCw0NDU51wFVUMyfdid0O69bB9u3N\nbZGRRiLXq5fn+iUiIh3DmbzFoWRu0KBB/Pu//zuLFy8mMDDQ1I3cTcmcdBf19fD3v8PBg81t8fGw\ncKExxSoiIl2fyxdAlJSU8OCDD3aZRE6co/oH53Rk/Kqrjfq4lonciBFGjVx3TOT03jNPsTNPsXOO\n4mdeR8XOoWRu6dKlvPTSSx1yQxFxzLlzxorV48eb2yZNgjvuMFavioiIgIPTrFdffTU7duwgPj6e\nfv36NX+zxcKmTZtc2kGzLBYLy5Yt0wII6ZLOnDE2Ay4tbW679lpITtZmwCIi3UnjAojly5e7tmbu\nYhsDWywW7r33XlM3djXVzElXlZMDr78OVVXGY6sVbrkFRo/2bL9ERMR1XL4AoitSMmdeWlqaRjOd\n4Ez80tPh7beNRQ9gnK06fz4kJnZc/zozvffMU+zMU+yco/iZ1zJ2zuQtDlfenD59mq+++orCwsJW\nN7v//vtN3VhEWvv6a/j4Y2MbEoCgILj7boiO9my/RESkc3NoZO7vf/87ixYtYsiQIRw8eJBRo0Zx\n8OBBrr76ajZu3OiOfrabRuakq7DbIS0NvvyyuS083NhDLjzcY90SERE3cvnWJI8//jgvvfQSe/bs\nITg4mD179vD8888zbtw4UzcVEYPNBh991DqRi46GpUuVyImIiGMcSuZycnKYP39+02O73c7ixYtZ\ns2aNyzomnqM9g5zjaPzq6uBvf4Pdu5vbEhNhyRJjirUn0nvPPMXOPMXOOYqfeW49mzUyMpJTp07R\nr18/EhIS2LZtGxEREdhstg7phEhPU1lprFjNzW1uGz0abr4ZvLw81y8REel6HKqZ++Mf/0hiYiJ3\n3HEHa9as4Qc/+AEWi4XHHnuM3/3ud+7oZ7tpnznprEpKjD3kzp5tbps2DWbO1B5yIiI9jdv2mfuu\nrKwsKioqGDFihKmbuoMWQEhndOqUkciVlxuPLRb4/veNkx1ERKTncvkCiO+Kj4/v1ImcOEf1D865\nWPyOHzeO52pM5Ly8YN48JXIt6b1nnmJnnmLnHMXPPLeezbp3715mzpxJWFgYPj4+TX98fX07pBMi\n3d3Bg8aIXE2N8djfH+65B/Q7kYiIOMuhadbhw4dzxx13MH/+fAICAlpdS+ykW9NrmlU6i23bYN26\n5se9ehmbAUdFea5PIiLSubj8OK+wsDCKioqwdKHqbCVz4ml2O3z2mZHMNerb19gMuHdvz/VLREQ6\nH5fXzC1evJjXXnvN1A2k61H9g3PS0tJoaID33mudyA0YAPffr0TuUvTeM0+xM0+xc47iZ55b95n7\nxS9+weTJk3nyySeJjIxsardYLGzYsKFDOiLSXdTWwmuvwbFjzW3Dh8Ntt4GPj+f6JSIi3ZND06zT\npk3D19eXuXPn4u/v3/zNFgtLly51aQfN0jSruFt6ehYff5zJ9u1WKipsDBo0mIiIeCZONLYfsZpa\nOy4iIj2BM3mLQyNze/fu5ezZs/j5+Zm6iaekpqZq02Bxi/T0LJ59NoP09FlUVxtte/eu54c/hBtu\niNdmwCIickGNmwY7w6GxgmnTpnH48GGnbuQJjcmctI/qH9rHboeVKzM5cMBI5EpK0rBYYOTIWZSV\nZSqRawe998xT7MxT7Jyj+JmXlpZGSkoKqampTj2PQyNzCQkJXHfdddx2221tauZ+85vfONUBka6s\ntBT+/nc4eNBK41HFFguMGgV9+kBtreZWRUTEtRyqmbvvvvuw2+2ttiZpfLxq1SqXdtAs1cyJK9nt\nsH8/fPqpsRHwjh0bqKycSXAwDBsGwcHG10VGbuDHP57p2c6KiEin59KauYaGBmJjY3n88cdbLX4Q\n6akqKuDjj+HIkea2wYMHU1i4nsTEWU0LHWpq1jNrVufcVFtERLqPy84BeXl58dxzz+norh5E9Q8X\nl54Ozz7bOpELD4d///d4li1LpF+/DZw9+99ERm5gyZJEkpLiPdfZLkjvPfMUO/MUO+cofua5dZ+5\nxYsX89xzz/HQQw91yE1FupqaGli7Fvbsad0+cSJ873tg/K4TT1JSPGlpVi28ERERt3GoZi45OZkd\nO3YQHR1NXFxcU+2cxWJh06ZNLu+kGaqZk45y4oSxyKGkpLktJARuuQU66dHEIiLSxbj8bNbVq1df\n9Mb33nuvqRu7mpI5cVZ9Paxf3/pILoArroAbboCAAM/0S0REuh+XJ3NdYCHXlQAAHWhJREFUkZI5\n8xr3venJ8vPh/ffhzJnmtoAAmDMHRo689PcqfuYpduYpduYpds5R/MxrGTuXnwBht9tZtWoVr7zy\nCnl5ecTGxrJo0SLuu+++VtuViHR1Nhts3gxffknTvnEAQ4bAzTcb06siIiKdiUMjc7///e9Zs2YN\njz32GAMGDCA7O5unn36au+++m1/96lfu6Ge7aWRO2uvsWWM0Li+vuc3XF2bPhnHj0EkOIiLiMi6f\nZk1ISODLL78kPr55m4WsrCymTZtGdna2qRu7mpI5cZTdDjt2wBdfQF1dc/uAAXDrrcbWIyIiIq7k\nTN7i0FlDlZWVREREtGrr06cP1Y0nindSqamp2v/GhJ4Us9JSeOUV+Mc/mhM5Ly+49lpYssRcIteT\n4tfRFDvzFDvzFDvnKH7mpaWlkZaW5vTZrA4lc9dffz2LFi3im2++oaqqiiNHjrB48WJmz57t1M1d\nLTU1VUWZckF2O+zbB889B8eONbdHRcEPfgBXX03TSQ4iIiKukpKS4nQy59A0a2lpKT/5yU948803\nqaurw8fHh/nz5/OXv/yF0NBQpzrgKppmlYu50HFcFouRwF1zDXg7tCxIRESk47ikZu6ZZ57h4Ycf\nBiAjI4PExEQaGho4e/YsEREReHl5me+xGyiZkwtJT4ePPoLy8ua28HCjNm7AAM/1S0REejaX1Mz9\n8pe/bPp83LhxgHFOa1RUVKdP5MQ53bH+oaYGPvwQ3nijdSI3YQL88Icdm8h1x/i5i2JnnmJnnmLn\nHMXPPJefzTpo0CAee+wxRowYQV1dHS+99BJ2u71pX7nGz++///4O6YiIq2RlGVuOfPc4rptvNvaP\nExER6couOs2anp7On/70J7KyskhLS2PatGkXfIKNGze6tINmaZpV6uthwwbjOK6Wb4VRo+DGG3Uc\nl4iIdB4u32du1qxZrF+/3tQNPEXJXM928iS8917b47huvNFI5kRERDoTl+4zV19fz5YtW6ipqTF1\nA+l6unL9g80GmzbBypWtE7nERPjRj9yTyHXl+HmaYmeeYmeeYuccxc88l9fMNX2BtzdJSUmcPXuW\nmJiYDrmpiCsUFhq1cbm5zW0+PsZxXOPH6zguERHpnhyaZv3Tn/7E3/72N376058SFxfXtAgCYObM\nmS7toFmaZu057HbYuRM+/7z1cVxxcTB3ro7jEhGRzs8tZ7M23ui7jh8/burGrqZkrmcoK4MPPoDM\nzOY2Ly+YMQOmTtUpDiIi0jW4/GzWEydOcOLECY4fP97mj3Q/XaH+wW6H/fvh2WdbJ3JRUfDAA549\njqsrxK+zUuzMU+zMU+yco/iZ57aauUZ1dXVs376d/Px8FixYQHl5ORaLhaCgoA7piIijKiuN47gO\nH25us1iMkbgZM3Qcl4iI9CwOTbMeOHCAm2++GT8/P3JzcykvL+eTTz5hzZo1vPnmm+7oZ7tpmrV7\n+vZb4ySHlqc4hIUZtXE6jktERLoql9fMJScn8+CDD7J48WLCwsIoLi6moqKCIUOGkJ+fb+rGrqZk\nrnupqYF162D37tbtEybAddeBr69n+iUiItIRXF4zd/jwYe65555WbYGBgVRVVZm6qTN27NjB1KlT\nueaaa7jrrruor693ex+6u85W/5CVBStWtE7kgoPh7rthzpzOl8h1tvh1JYqdeYqdeYqdcxQ/8zoq\ndg4lc/Hx8ezatatV286dOxnigYMtBwwYwMaNG/nyyy9JSEjggw8+cHsfxD3q643tRlavhuLi5vaR\nI+HHP9a5qiIiIuDgNOvHH3/M0qVLefDBB3nqqad4/PHHWbFiBStXrmT27Nnu6OcFLVu2jLFjx3Lr\nrbe2uaZp1q7t1CnjOK6CguY2f//m47i0AbCIiHQnLq+ZA9izZw/PP/88WVlZDBgwgAceeIDx48eb\numlHyMrKYuHChWzevBkvL68215XMdU02G2zZAmlp0NDQ3D54MNxyC/Tq5bGuiYiIuIzLa+YAxo4d\ny3PPPcenn37KihUrTCVyzzzzDBMmTMDf35/77ruv1bWioiLmzp1LcHAwCQkJvPHGG03Xnn76aWbM\nmMFTTz0FQFlZGYsXL+bll1++YCInzvFU/UNhIbz0Eqxf35zI+fgYo3GLFnWdRE71I+YpduYpduYp\nds5R/Mxz6z5zNTU1/O53v+ONN94gPz+fmJgYFixYwK9+9Sv8/f0dvllMTAxPPPEE69ata7N44qGH\nHsLf35+CggL27NnDjTfeyOjRoxkxYgSPPvoojz76KAD19fXceeedLFu2zCM1e9Lx7HbYtQs++6z1\ncVyxscaWI336eK5vIiIinZ1D06z3338/3377LY8//jgDBgwgOzub3//+9wwZMoRVq1a1+6ZPPPEE\nubm5Td9bUVFBeHg4hw4dIjExEYB7772X6OhonnzyyVbf+8orr/Doo49yxRVXAPCjH/2I+fPnt31h\nFgv33ntv01FkoaGhjBkzhpSUFKA5G9Zjzz4eNy6FDz6A9euNxwkJKXh5Qe/eaYwaBTNndq7+6rEe\n67Ee67Eed8Tjxs9PnDgBwMsvv+zamrnw8HAyMzMJCwtraisqKmLw4MEUt1xm6KBf/epX5OXlNSVz\ne/bs4eqrr6aioqLpa/785z+TlpbGhx9+2O7nB9XMdXZ2Oxw8CJ98AtXVze2RkXDbbdCvn+f6JiIi\n4m4ur5nr378/lZWVrdqqqqqIjo42dVPLd5YilpeX0+s7BVEhISGcO3fO1POLc1r+1uAKlZXwzjvw\n7rvNiZzFAsnJ8IMfdP1EztXx684UO/MUO/MUO+cofuZ1VOwcqpm75557+P73v8/DDz9MXFwc2dnZ\nPPvssyxevJgNGzY0fd3MmTMduul3M8/g4GDKyspatZWWlhISEuLQ80nXcfQofPBB2+O4br0V4uM9\n1y8REZGuyqFp1sa6s5Yjana7vc0I2/Hjxx26qSM1c/fccw9xcXH84Q9/cOg5v8tisbBs2TJSUlKa\n5qnFc2prjeO4vv66dfv48cZxXH5+numXiIiIJ6WlpZGWlsby5ctdv89cR2hoaKCuro7ly5eTl5fH\nypUr8fb2xsvLi4ULF2KxWHjhhRfYvXs3c+bMYdu2bQwfPtzUvVQz13lkZ8P777c+xSE4GG6+GYYO\n9Vy/REREOgu37DPXEX77298SGBjI//2//5dXX32VgIAAfv/73wPw7LPPUlVVRWRkJIsWLWLFihWm\nEzlxTkfN4Tcex7VqVetEbsQI4ziu7prIqX7EPMXOPMXOPMXOOYqfeW6tmesoqamppKamXvBaWFgY\n77//vju7Iy506pQxGnf6dHObjuMSERHpeG6dZnUn1cx5hs0GW7fCxo2tj+MaNMhY5NBVTnEQERFx\nhy5XM+dOqplzv6IiYzQuJ6e5zccHvvc9mDhRo3EiIiIX02Vq5qRraO8cfuNxXM891zqRi42FH/4Q\nJk3qWYmc6kfMU+zMU+zMU+yco/iZ1yVr5qT7KSuDDz+EjIzmNqsVUlLg6quNz0VERMR1uvU0q2rm\nXKvxOK6qqua2yEiYOxf69/dcv0RERLoK1cxdgmrmXKeqykjiDh5sbrNYYMoUmDkTvDXeKyIi0i6q\nmZMOdak5/IwMePbZ1olcaCgsWWKc5KBETvUjzlDszFPszFPsnKP4maeaOXGr2lr47DNjoUNL48bB\n7Nk6jktERMRTNM0ql5WTY2w5UlTU3BYUZBzHlZTkuX6JiIh0F87kLd16ZC41NVULIJxQXw9pabBl\ni7H9SKPhw2HOHCOhExEREfMaF0A4QyNz0iQ9PYsvvsjkyJH9xMRcSXX1YCyW+Kbr/v5www1wxRU9\na9+49kpLS9MvECYpduYpduYpds5R/MxrGTuNzInT0tOzWL06A1/fWWRmWjl0KIW6uvWMGQMREfEM\nGgS33AK9e3u6pyIiItKSRuYEgL/+dQPZ2TP55hsoLW1u79VrA7/61UwdxyUiIuJCGpkTp9XVWSkr\na53IhYTApElWJk3yXL9ERETk0rTPnADg42MjMhL69oWSkjQSEoxtR8LDbZ7uWpejPZfMU+zMU+zM\nU+yco/iZ11Gx69bJXGpqqt5kDrr22sHU1q5n6FAYOhQSEqC2dj2zZg32dNdERES6rbS0NFJTU516\nDtXMSZP09CzWr8+kttaKr6+NWbMGk5QUf/lvFBEREac4k7comRMRERHxMJ3NKh1KU9POUfzMU+zM\nU+zMU+yco/iZp5o5EREREdE0q4iIiIinaZpVREREpIfq1smctiYxRzFzjuJnnmJnnmJnnmLnHMXP\nvLS0tA7ZmqRbnwDhbHBEREREXCklJYWUlBSWL19u+jlUMyciIiLiYaqZExEREemhlMxJG6p/cI7i\nZ55iZ55iZ55i5xzFzzztMyciIiIiqpkTERER8TTVzImIiIj0UErmpA3VPzhH8TNPsTNPsTNPsXOO\n4meeauYcoE2DRUREpDPriE2DVTMnIiIi4mGqmRMRERHpoZTMSRuamnaO4meeYmeeYmeeYuccxc88\n1cyJiIiIiGrmRERERDxNNXMiIiIiPZSSOWlD9Q/OUfzMU+zMU+zMU+yco/iZp5o5EREREVHNnIiI\niIinqWZOREREpIdSMidtqP7BOYqfeYqdeYqdeYqdcxQ/81QzJyIiIiLdu2Zu2bJlpKSkkJKS4unu\niIiIiLSRlpZGWloay5cvN10z162TuW760kRERKSb0QII6VCqf3CO4meeYmeeYmeeYuccxc881cyJ\niIiIiKZZRURERDxN06wiIiIiPZSSOWlD9Q/OUfzMU+zMU+zMU+yco/iZp5o5EREREVHNnIiIiIin\nqWZOREREpIdSMidtqP7BOYqfeYqdeYqdeYqdcxQ/81QzJyIiIiKqmRMRERHxNNXMiYiIiPRQSuak\nDdU/OEfxM0+xM0+xM0+xc47iZ55q5kRERESk69XMnT59mttuuw1fX198fX15/fXX6dOnT5uvU82c\niIiIdBXO5C1dLpmz2WxYrcaA4ssvv8zJkyf5P//n/7T5OiVzIiIi0lX0qAUQjYkcQFlZGWFhYR7s\nTfek+gfnKH7mKXbmKXbmKXbOUfzM69E1c/v27eOqq67imWeeYeHChZ7uTrezd+9eT3ehS1P8zFPs\nzFPszFPsnKP4mddRsXNrMvfMM88wYcIE/P39ue+++1pdKyoqYu7cuQQHB5OQkMAbb7zRdO3pp59m\nxowZPPXU/2/v3mOauvswgD/lJtcqqONmEDdwjrBh5nBTAfE2JcpQMhUMeNuEADo1izqDggsQw6bE\nJd42k02ZUpTFZFMxMgd4C8JIkBjBcVlkbiywAYMWtJRy3j/20teCvoODO+2B55M04bS/c37fPmnh\nyzk9pwcBAAEBASgtLUV6ejrS0tKkfAqjwl9//WXqEmSN+YnH7MRjduIxu+FhfuI9r+wkbeY8PT2x\nd+9ebNy4ccBjSUlJsLW1RXNzM86cOYOEhARUVVUBALZv346ioiJ8+OGH0Ol0hnWUSiW0Wq1k9T/p\n396tLHb7g13PlLvFmd3w/JvzMzvTbH8w6zG74a3H33ni1xvJr72Rkp2kzdyKFSsQEREx4OzTzs5O\nnD9/HmlpabC3t8ecOXMQERGBr7/+esA27ty5g7lz52L+/PnIysrCzp07pSrfyEh+cz548EDU3IM1\nkrMD5J0fszPN9p9HM8fsxI9jdsMbJ+f8Rkp2Jjmbdc+ePfjtt9/w1VdfAQAqKioQFBSEzs5Ow5is\nrCwUFxfju+++EzWHj48P6uvrn0u9RERERP+mgIAA0Z+hs3rOtQyKQqEwWtZoNFAqlUb3OTk5Qa1W\ni56jrq5O9LpEREREcmGSs1n77wx0dHRER0eH0X3t7e1wcnKSsiwiIiIi2TFJM9d/z9zUqVPR09Nj\ntDetsrIS/v7+UpdGREREJCuSNnN6vR6PHz9GT08P9Ho9tFot9Ho9HBwcEBkZiZSUFHR1deHmzZu4\ncOECYmNjpSyPiIiISHYkbeb6zlbNzMzE6dOnYWdnh4yMDADA0aNH8ejRI7zwwguIiYnB8ePH8cor\nr0hZHhEREZHsyO67WYdr165dKCkpgbe3N7788ktYWZnkHBBZ6ujowMKFC1FdXY3S0lL4+fmZuiTZ\nKCsrw7Zt22BtbQ1PT09kZ2fztTdITU1NiIyMhI2NDWxsbJCTkzPg8kb0/6lUKmzduhXNzc2mLkU2\nHjx4gMDAQPj7+0OhUODcuXOYMGGCqcuSleLiYqSnp6O3txcffPABli9fbuqSZOH27dvYvXs3AKCx\nsRFLly5FVlbW/11Hll/nJVZlZSUaGxtx/fp1TJs2Dd98842pS5IVe3t75Ofn49133xX9ZcCjlZeX\nF4qKinDt2jV4e3vj22+/NXVJsjFx4kTcunULRUVFWLNmDU6cOGHqkmRFr9cjLy8PXl5epi5FdkJD\nQ1FUVITCwkI2ckP06NEjZGVl4fLlyygsLGQjNwRvvfUWioqKUFRUhNmzZ2PFihX/uM6oauZKSkqw\nePFiAMCSJUtw69YtE1ckL1ZWVvyFJpKbmxvGjBkDALC2toalpaWJK5IPC4v//Zrq6OiAs7OzCauR\nH5VKhVWrVg048Yz+2a1btxASEoLk5GRTlyI7JSUlsLOzQ3h4OCIjI9HU1GTqkmSnu7sbZWVlCA4O\n/sexo6qZa2trM1zuRKlUorW11cQV0WjT0NCA77//HuHh4aYuRVYqKyvx5ptv4vDhw4iOjjZ1ObLR\nt1du9erVpi5Fdjw8PFBfX4/r16+jubkZ58+fN3VJstLU1IS6ujpcvHgRmzZtwr59+0xdkuxcvXoV\nCxcuHNRYWTZzhw8fxhtvvAFbW1ts2LDB6LHW1lasWLECjo6O8Pb2hkqlMjw2btw4w/Xs2tvb4eLi\nImnd5kJsfk8arf/lDye7jo4OrF27FqdOnRqVe+aGk11AQABKS0uRnp6OtLQ0Kcs2C2KzO3369Kjf\nKyc2OxsbG9jZ2QEAIiMjUVlZKWnd5kJsfs7OzpgzZw6srKwwf/583Lt3T+rSTW64f2vz8vKwcuXK\nQc0ly2bO09MTe/fuxcaNGwc8lpSUBFtbWzQ3N+PMmTNISEhAVVUVAGD27Nm4evUqAODKlSsICgqS\ntG5zITa/J43Wz8yJza6npwdRUVFITU2Fr6+v1GWbBbHZ6XQ6wzilUgmtVitZzeZCbHbV1dXIzs5G\nWFgYamtrsW3bNqlLNzmx2Wk0GsO469ev8307xPwCAwNRXV0N4O/vVH/ppZckrdscDOdvrU6nQ3l5\n+eD7FEHG9uzZI6xfv96wrNFoBBsbG6G2ttZw39q1a4WPPvrIsLxjxw4hODhYiImJEXQ6naT1mhsx\n+YWFhQkeHh7CrFmzhJMnT0parzkZanbZ2dnC+PHjhdDQUCE0NFQ4e/as5DWbi6FmV1paKoSEhAjz\n5s0T3n77beHhw4eS12wuxLxn+wQGBkpSo7kaanb5+fnCjBkzhODgYGHdunWCXq+XvGZzIua1d+TI\nESEkJEQIDQ0Vfv75Z0nrNSdisrt8+bKwdevWQc8h62sjCP32DtXU1MDKygo+Pj6G+wICAlBcXGxY\n/uSTT6Qqz+yJyS8/P1+q8szaULOLjY3lRbD/a6jZzZw5E9euXZOyRLMl5j3bp6ys7N8uz6wNNbuw\nsDCEhYVJWaJZE/PaS0xMRGJiolQlmi0x2S1ZsgRLliwZ9ByyPMzap//nQDQaDZRKpdF9Tk5OUKvV\nUpYlG8xPPGYnHrMTj9mJx+yGh/mJJ0V2sm7m+ne7jo6OhhMc+rS3txvOYCVjzE88ZicesxOP2YnH\n7IaH+YknRXaybub6d7tTp05FT08P6urqDPdVVlbC399f6tJkgfmJx+zEY3biMTvxmN3wMD/xpMhO\nls2cXq/H48eP0dPTA71eD61WC71eDwcHB0RGRiIlJQVdXV24efMmLly4wM8q9cP8xGN24jE78Zid\neMxueJifeJJmJ+7cDNNKTU0VFAqF0e3jjz8WBEEQWltbheXLlwsODg7C5MmTBZVKZeJqzQ/zE4/Z\nicfsxGN24jG74WF+4kmZnUIQRukFw4iIiIhGAFkeZiUiIiKiv7GZIyIiIpIxNnNEREREMsZmjoiI\niEjG2MwRERERyRibOSIiIiIZYzNHREREJGNs5oiIiIhkjM0cEVE/69evx969e5/rNhMSEpCenv5c\nt0lEBABWpi6AiMjcKBSKAV+OPVzHjh17rtsjIurDPXNERE/BbzokIrlgM0dEZiUzMxOTJk2CUqnE\ntGnTUFhYCAAoKyvDrFmz4OzsDA8PD2zZsgU6nc6wnoWFBY4dOwZfX18olUqkpKSgvr4es2bNwrhx\n4xAVFWUYX1xcjEmTJmH//v2YOHEipkyZgpycnGfWdPHiRUyfPh3Ozs6YM2cO7t69+8yx27dvh6ur\nK8aOHYvXXnsNVVVVAIwP3YaHh8PJyclws7S0RHZ2NgDg/v37WLRoEcaPH49p06YhLy/vmXOFhoYi\nJSUFQUFBUCqVWLx4MVpaWgaZNBGNFGzmiMhs/PTTTzhy5AjKy8vR0dGBgoICeHt7AwCsrKzw2Wef\noaWlBSUlJfjhhx9w9OhRo/ULCgpQUVGB27dvIzMzE5s2bYJKpcIvv/yCu3fvQqVSGcY2NTWhpaUF\njY2NOHXqFOLi4lBbWzugpoqKCrz33ns4ceIEWltbER8fj3feeQfd3d0Dxl65cgU3btxAbW0t2tvb\nkZeXBxcXFwDGh24vXLgAtVoNtVqNc+fOwd3dHQsWLEBnZycWLVqEmJgY/PHHH8jNzUViYiKqq6uf\nmZlKpcLJkyfR3NyM7u5uHDhwYMi5E5G8sZkjIrNhaWkJrVaLe/fuQafTwcvLCy+++CIA4PXXX8fM\nmTNhYWGByZMnIy4uDteuXTNaf+fOnXB0dISfnx9effVVhIWFwdvbG0qlEmFhYaioqDAan5aWBmtr\na4SEhGDp0qU4e/as4bG+xuuLL75AfHw8AgMDoVAosHbtWowZMwa3b98eUL+NjQ3UajWqq6vR29uL\nl19+GW5ubobH+x+6rampwfr163Hu3Dl4enri4sWLmDJlCtatWwcLCwtMnz4dkZGRz9w7p1AosGHD\nBvj4+MDW1harVq3CnTt3hpA4EY0EbOaIyGz4+Pjg0KFD2LdvH1xdXREdHY3ff/8dwN+Nz7Jly+Du\n7o6xY8ciOTl5wCFFV1dXw892dnZGy7a2ttBoNIZlZ2dn2NnZGZYnT55smOtJDQ0NOHjwIJydnQ23\nX3/99alj582bh82bNyMpKQmurq6Ij4+HWq1+6nNtb29HREQEMjIyMHv2bMNcpaWlRnPl5OSgqanp\nmZk92Sza2dkZPUciGh3YzBGRWYmOjsaNGzfQ0NAAhUKBXbt2Afj70h5+fn6oq6tDe3s7MjIy0Nvb\nO+jt9j87ta2tDV1dXYblhoYGeHh4DFjPy8sLycnJaGtrM9w0Gg1Wr1791Hm2bNmC8vJyVFVVoaam\nBp9++umAMb29vVizZg0WLFiA999/32iuuXPnGs2lVqtx5MiRQT9PIhp92MwRkdmoqalBYWEhtFot\nxowZA1tbW1haWgIANBoNnJycYG9vj/v37w/qUh9PHtZ82tmpqamp0Ol0uHHjBi5duoSVK1caxvaN\n37RpE44fP46ysjIIgoDOzk5cunTpqXvAysvLUVpaCp1OB3t7e6P6n5w/OTkZXV1dOHTokNH6y5Yt\nQ01NDU6fPg2dTgedTocff/wR9+/fH9RzJKLRic0cEZkNrVaL3bt3Y+LEiXB3d8eff/6J/fv3AwAO\nHDiAnJwcKJVKxMXFISoqymhv29OuC9f/8SeX3dzcDGfGxsbG4vPPP8fUqVMHjJ0xYwZOnDiBzZs3\nw8XFBb6+voYzT/vr6OhAXFwcXFxc4O3tjQkTJmDHjh0Dtpmbm2s4nNp3RqtKpYKjoyMKCgqQm5sL\nT09PuLu7Y/fu3U892WIwz5GIRgeFwH/riGiUKS4uRmxsLB4+fGjqUoiIho175oiIiIhkjM0cEY1K\nPBxJRCMFD7MSERERyRj3zBERERHJGJs5IiIiIhljM0dEREQkY2zmiIiIiGSMzRwRERGRjP0HYdrQ\n0/EX3NkAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10f9e1050>"
+        "<matplotlib.figure.Figure at 0x10846f5d0>"
        ]
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "<br>\n",
-      "In this performance comparison for different sample sizes, we see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython. There are a lot of tweaks that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches\n",
+      "In this performance comparison using different sample sizes, we can see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython.  \n",
+      "There are a lot of improvements that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches, after we tweaked it a little bit.\n",
       "<br>"
      ]
     },
@@ -1408,15 +1430,16 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Like we did with Cython before, we will use the minimalist approach to Numba and see how they compare against each other.  \n",
+      "Like we did with Cython before, we will use the minimalist approach to Numba and see how the two - Cython and Numba - compare against each other.  \n",
       "\n",
-      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up code. If you want to read more about Numba, see the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
+      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "<br>\n",
       "Here is our \"classic\" approach in Python, where I removed the list comprehensions, since they caused errors in the Numba compilation."
      ]
     },
@@ -1556,8 +1579,17 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "plt.figure(figsize=(10,8))\n",
       "\n",
@@ -1591,7 +1623,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Without making any modifications to the original code in order to account for the strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
+      "Without making any modifications to the original Python code - thereby we are not accounting for the particular strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
      ]
     },
     {
@@ -1622,7 +1654,7 @@
      "metadata": {},
      "source": [
       "In the sections above, we have been using the simplest approach to Cython without using static type declarations and thereby neglecting one of its major strengths.  \n",
-      "Let us now see how we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
+      "Now, let us see how and if we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
      ]
     },
     {
@@ -1649,7 +1681,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 44
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1666,7 +1698,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 23
     },
     {
      "cell_type": "code",
@@ -1686,7 +1719,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 45
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -1714,7 +1747,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 55
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1748,14 +1781,23 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 58
+     "prompt_number": 26
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "plt.figure(figsize=(10,8))\n",
       "\n",
@@ -1777,13 +1819,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkQLYhBEREREp4P8DGaETtWbn1E4AAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAH/CAYAAAASb3qiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB/IfaLkgtnNsZy4AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10eea0790>"
+        "<matplotlib.figure.Figure at 0x10ed0dd90>"
        ]
       }
      ],
-     "prompt_number": 59
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -1791,7 +1833,8 @@
      "source": [
       "<br>\n",
       "<br>\n",
-      "The improvement is pretty significant when static type declarations are used. One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation.\n",
+      "The improvement is pretty significant when static type declarations are used.  \n",
+      "One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation:\n",
       "<br>\n",
       "<br>"
      ]
@@ -1809,7 +1852,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 60
+     "prompt_number": 30
     },
     {
      "cell_type": "code",
@@ -1832,20 +1875,30 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 61
+     "prompt_number": 31
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "x_pos = np.arange(len(funcs[1:]))\n",
       "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
       "plt.xticks(x_pos, labels[1:], rotation=90)\n",
-      "plt.ylabel('relative performance gain')\n",
-      "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "plt.ylabel('rel. performance gain')\n",
+      "plt.title('Performance gain compared to the classic least square'\\\n",
+      "          '(n={})'.format(len(x)))\n",
       "plt.grid()\n",
       "plt.show()"
      ],
@@ -1855,13 +1908,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAG/CAYAAAAjJGJxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAHnCAYAAADuC8dRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVsKiSkREZCX/D2lbGZgZrg2GAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x106418090>"
+        "<matplotlib.figure.Figure at 0x10ed1da90>"
        ]
       }
      ],
-     "prompt_number": 63
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -1897,7 +1950,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "List, set and dictionary comprehensions in Python do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also come with some small performance benefits.  \n",
+      "In Python, we have those wonderful list, set, and dictionary comprehensions, which do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also additionally come with some small performance benefits.  \n",
       "Does this also apply in Cython? Let's check it out."
      ]
     },
@@ -2347,8 +2400,17 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "labels = [('cy_lstsqr', 'Cython implementation'), \n",
       "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index 2e97429..f1c57d5 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b8a2adab4cfa8ac1064656d4b3e3121a2acc1060e034c98ce29986312939c9ac"
+  "signature": "sha256:bbbdc8e390979922204161079c12c6b2020e8457991ef30ddd3766fb3c4503e6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -90,7 +90,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/cython_vs_chart.png)  \n",
+      "![Performance vs. Productivity for different programming languages](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/cython_vs_chart.png)  \n",
       "(Note that this chart just reflects my rather objective thoughts after experimenting with Cython, and it is not based on real numbers or benchmarks.)\n",
       "<br>\n",
       "<br>\n",
@@ -132,7 +132,7 @@
      "source": [
       "In more mathematical terms, our goal is to compute the best fit to *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
       "$f(x) = a\\cdot x + b$.  \n",
-      "Here, we assume that the y-component is  functionally dependent on the x-component.  \n",
+      "We further have to assume that the y-component is  functionally dependent on the x-component.  \n",
       "In a cartesian coordinate system, $b$ is the intercept of the straight line with the y-axis, and $a$ is the slope of this line."
      ]
     },
@@ -280,7 +280,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 36
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -301,7 +301,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Next, we will calculate the parameters separately, using only standard library functions in Python, which I will call the \"classic approach\"."
+      "Next, we will calculate the parameters separately, using standard library functions in Python only, which I will call the \"classic approach\"."
      ]
     },
     {
@@ -331,7 +331,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 37
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -352,7 +352,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "For our convenience, `numpy` has a function that can also compute the leat squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
+      "For our convenience, `numpy` has a function that can computes the least squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
      ]
     },
     {
@@ -367,7 +367,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 38
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -388,7 +388,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The last approach is using `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.  The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
+      "Also scipy has a least squares function, `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.   \n",
+      "The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
      ]
     },
     {
@@ -404,7 +405,33 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 39
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Teaser"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "The following plot below should give you an impression of how the performance of the 4 different approaches will compare to each other at the end of this article.\n",
+      "\n",
+      "![Performance of the final Cython implemenation of the least squares fit](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/cython_final_leastsqr.png)\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "markdown",
@@ -433,12 +460,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In order to test our different least squares fit implementation, we will generate some sample data:  \n",
+      "In order to test our different least squares fit implementations, we will generate some sample data:  \n",
       "- 500 sample points for the x-component within the range [0,500)  \n",
       "- 500 sample points for the y-component within the range [100,600)  \n",
       "\n",
       "where each sample point is multiplied by a random value within\n",
-      "the range [0.8, 12)."
+      "the range [0.8, 12) to simulate some scatter in our dataset."
      ]
     },
     {
@@ -454,7 +481,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 40
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -476,15 +503,25 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "To check how our dataset is distributed, and how the straight line, which we obtain via the least square fit method, we will plot it in a scatter plot.  \n",
-      "Note that we are using our \"matrix approach\" here for simplicity, but after plotting the data, we will check whether all of the four different implementations yield the same parameters."
+      "To check how our dataset is distributed, and how the least squares regression line looks like, we will plot the results in a scatter plot.  \n",
+      "Note that we are only using our \"matrix approach\" to visualize the results - for simplicity. We expect all 4 approaches to produce similar results, which we will confirm after visualizing the data."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%pylab inline\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "from matplotlib import pyplot as plt\n",
       "\n",
       "slope, intercept = lin_lstsqr_mat(x, y)\n",
@@ -514,11 +551,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10f9e8a50>"
+        "<matplotlib.figure.Figure at 0x107806610>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -540,7 +577,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "As mentioned above, let us confirm that the different implementation computed the same parameters (i.e., slope and y-axis intercept) as solution for the linear equation."
+      "As mentioned above, let us now confirm that the different implementations computed the same parameters (i.e., slope and y-axis intercept) as solution of the linear equation."
      ]
     },
     {
@@ -580,7 +617,7 @@
        ]
       }
      ],
-     "prompt_number": 43
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -602,7 +639,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "For a first impression how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
+      "To get an initial impression of how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
      ]
     },
     {
@@ -626,7 +663,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "matrix approach: 10000 loops, best of 3: 163 \u00b5s per loop"
+        "matrix approach: 10000 loops, best of 3: 158 \u00b5s per loop"
        ]
       },
       {
@@ -635,7 +672,7 @@
        "text": [
         "\n",
         "\n",
-        "classic approach: 1000 loops, best of 3: 1.55 ms per loop"
+        "classic approach: 1000 loops, best of 3: 1.6 ms per loop"
        ]
       },
       {
@@ -644,7 +681,7 @@
        "text": [
         "\n",
         "\n",
-        "numpy function: 1000 loops, best of 3: 221 \u00b5s per loop"
+        "numpy function: 1000 loops, best of 3: 217 \u00b5s per loop"
        ]
       },
       {
@@ -653,7 +690,7 @@
        "text": [
         "\n",
         "\n",
-        "scipy function: 1000 loops, best of 3: 362 \u00b5s per loop"
+        "scipy function: 1000 loops, best of 3: 366 \u00b5s per loop"
        ]
       },
       {
@@ -664,14 +701,14 @@
        ]
       }
      ],
-     "prompt_number": 44
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "<br>\n",
-      "The timing above indicates, that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10."
+      "The timing above indicates that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10. However, we should keep in mind that this experiment was done for a fixed sample size n=500."
      ]
     },
     {
@@ -702,12 +739,12 @@
      "metadata": {},
      "source": [
       "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
-      "Since we are working in an IPython notebook here, we can make use of the IPython magic: It will automatically convert it to C code, compile it, and load the function.  \n",
-      "Just to be thorough, let us also compile the other functions, which already use numpy objects.\n",
+      "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  \n",
+      "Let us also compile the functions for the other 3 least squares approaches - those already use numpy objects and therefore we don't expect any performance gain here, but it is good to be thorough.\n",
       "\n",
       "**Note**  \n",
-      "Of course Cython has much more horsepower under its hood - more than I am showing in this article (for example, I am not using Cython's type definitions via `cdef` here). Here, I want to focus on how to speed up existing Python code by making only minimal changes to it.   \n",
-      "[In a later section - Appendix II](#type_declarations) We will see how static type declarations can further improve the performance via Cython."
+      "Of course Cython has much more horsepower under its hood - more than I am showing in this section of this article (for example, I am not using Cython's type definitions via `cdef` here, but we will get to it [in a later section - Appendix II](#type_declarations)). \n",
+      "The focus of this section should be on how to speed up existing Python code by making only minimal changes to it.   "
      ]
     },
     {
@@ -718,17 +755,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "The cythonmagic extension is already loaded. To reload it, use:\n",
-        "  %reload_ext cythonmagic\n"
-       ]
-      }
-     ],
-     "prompt_number": 45
+     "outputs": [],
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -766,7 +794,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 46
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -809,7 +837,7 @@
         "\n",
         "\n",
         "matrix approach: \n",
-        "10000 loops, best of 3: 165 \u00b5s per loop"
+        "10000 loops, best of 3: 159 \u00b5s per loop"
        ]
       },
       {
@@ -817,7 +845,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 166 \u00b5s per loop"
+        "10000 loops, best of 3: 159 \u00b5s per loop"
        ]
       },
       {
@@ -828,7 +856,7 @@
         "\n",
         "\n",
         "classic approach: \n",
-        "1000 loops, best of 3: 1.59 ms per loop"
+        "1000 loops, best of 3: 1.62 ms per loop"
        ]
       },
       {
@@ -836,7 +864,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 127 \u00b5s per loop"
+        "10000 loops, best of 3: 126 \u00b5s per loop"
        ]
       },
       {
@@ -847,7 +875,7 @@
         "\n",
         "\n",
         "numpy function: \n",
-        "1000 loops, best of 3: 220 \u00b5s per loop"
+        "1000 loops, best of 3: 218 \u00b5s per loop"
        ]
       },
       {
@@ -855,7 +883,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 221 \u00b5s per loop"
+        "1000 loops, best of 3: 220 \u00b5s per loop"
        ]
       },
       {
@@ -866,7 +894,7 @@
         "\n",
         "\n",
         "scipy function: \n",
-        "1000 loops, best of 3: 361 \u00b5s per loop"
+        "1000 loops, best of 3: 372 \u00b5s per loop"
        ]
       },
       {
@@ -874,7 +902,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 367 \u00b5s per loop"
+        "1000 loops, best of 3: 354 \u00b5s per loop"
        ]
       },
       {
@@ -885,7 +913,7 @@
        ]
       }
      ],
-     "prompt_number": 47
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -893,8 +921,8 @@
      "source": [
       "<br>\n",
       "<br>\n",
-      "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to using numpy's functions. This is not surprising, since numpy is highly optmized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.  \n",
-      "However, we were able to speed up the \"classic approach\" quite significantly, roughly by 1500%.\n",
+      "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to the other approaches which use Numpy objects. This is not surprising, since Numpy is highly optimized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.  \n",
+      "However, we were able to speed up the \"classic approach\" quite significantly via Cython, roughly by 1500%.\n",
       "\n",
       "The following 2 code blocks are just to visualize our results in a bar plot."
      ]
@@ -919,14 +947,13 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 50
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "x_pos = np.arange(len(funcs))\n",
       "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
@@ -950,21 +977,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDPMCSwgMwzAMAOD/ANLifp9XPWcHAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGAAsITAMwzAvsYTAMAzDAAD+D41KT4VmEkzWAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10eeafd50>"
+        "<matplotlib.figure.Figure at 0x1079680d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCFElSMATQghRJfw/5a9/wr1GWjoAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6gQJeEIIIeqE/weOwIO8Og7UhQAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10eeac310>"
+        "<matplotlib.figure.Figure at 0x1078eeb10>"
        ]
       }
      ],
-     "prompt_number": 51
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -993,7 +1020,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In the plot above, we've seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of the sample size on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
+      "In the previous section, we have seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of different sample sizes on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
      ]
     },
     {
@@ -1020,26 +1047,31 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 17
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "import matplotlib.pyplot as plt\n",
       "\n",
-      "plt.figure(figsize=(10,8))\n",
+      "labels = [('cy_classic_lstsqr', 'Cython implementation'), \n",
+      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
+      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
+      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
       "\n",
-      "for f in times_n.keys():\n",
-      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
       "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
       "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
+      "plt.ylabel('performance gain relative to the slowest approach')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
       "plt.legend(loc=2)\n",
       "plt.grid()\n",
-      "\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
       "plt.title('Performance of least square fit implementations for different sample sizes')\n",
       "plt.show()"
      ],
@@ -1049,20 +1081,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAIECAYAAAByl6h3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0/EX3NkAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10f9e1050>"
+        "<matplotlib.figure.Figure at 0x10846f5d0>"
        ]
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "<br>\n",
-      "In this performance comparison for different sample sizes, we see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython. There are a lot of tweaks that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches\n",
+      "In this performance comparison using different sample sizes, we can see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython.  \n",
+      "There are a lot of improvements that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches, after we tweaked it a little bit.\n",
       "<br>"
      ]
     },
@@ -1408,15 +1441,16 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Like we did with Cython before, we will use the minimalist approach to Numba and see how they compare against each other.  \n",
+      "Like we did with Cython before, we will use the minimalist approach to Numba and see how the two - Cython and Numba - compare against each other.  \n",
       "\n",
-      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up code. If you want to read more about Numba, see the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
+      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "<br>\n",
       "Here is our \"classic\" approach in Python, where I removed the list comprehensions, since they caused errors in the Numba compilation."
      ]
     },
@@ -1556,8 +1590,17 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "plt.figure(figsize=(10,8))\n",
       "\n",
@@ -1591,7 +1634,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Without making any modifications to the original code in order to account for the strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
+      "Without making any modifications to the original Python code - thereby we are not accounting for the particular strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
      ]
     },
     {
@@ -1622,7 +1665,7 @@
      "metadata": {},
      "source": [
       "In the sections above, we have been using the simplest approach to Cython without using static type declarations and thereby neglecting one of its major strengths.  \n",
-      "Let us now see how we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
+      "Now, let us see how and if we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
      ]
     },
     {
@@ -1649,7 +1692,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 44
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1666,7 +1709,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 23
     },
     {
      "cell_type": "code",
@@ -1686,7 +1730,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 45
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -1714,7 +1758,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 55
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1748,14 +1792,23 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 58
+     "prompt_number": 26
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "plt.figure(figsize=(10,8))\n",
       "\n",
@@ -1777,13 +1830,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkQLYhBEREREp4P8DGaETtWbn1E4AAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAH/CAYAAAASb3qiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB/IfaLkgtnNsZy4AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10eea0790>"
+        "<matplotlib.figure.Figure at 0x10ed0dd90>"
        ]
       }
      ],
-     "prompt_number": 59
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -1791,7 +1844,8 @@
      "source": [
       "<br>\n",
       "<br>\n",
-      "The improvement is pretty significant when static type declarations are used. One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation.\n",
+      "The improvement is pretty significant when static type declarations are used.  \n",
+      "One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation:\n",
       "<br>\n",
       "<br>"
      ]
@@ -1809,7 +1863,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 60
+     "prompt_number": 30
     },
     {
      "cell_type": "code",
@@ -1832,20 +1886,30 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 61
+     "prompt_number": 31
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "x_pos = np.arange(len(funcs[1:]))\n",
       "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
       "plt.xticks(x_pos, labels[1:], rotation=90)\n",
-      "plt.ylabel('relative performance gain')\n",
-      "plt.title('Performance gain compared to the classic least square implementation')\n",
+      "plt.ylabel('rel. performance gain')\n",
+      "plt.title('Performance gain compared to the classic least square'\\\n",
+      "          '(n={})'.format(len(x)))\n",
       "plt.grid()\n",
       "plt.show()"
      ],
@@ -1855,13 +1919,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAG/CAYAAAAjJGJxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAHnCAYAAADuC8dRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVsKiSkREZCX/D2lbGZgZrg2GAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x106418090>"
+        "<matplotlib.figure.Figure at 0x10ed1da90>"
        ]
       }
      ],
-     "prompt_number": 63
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -1897,7 +1961,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "List, set and dictionary comprehensions in Python do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also come with some small performance benefits.  \n",
+      "In Python, we have those wonderful list, set, and dictionary comprehensions, which do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also additionally come with some small performance benefits.  \n",
       "Does this also apply in Cython? Let's check it out."
      ]
     },
@@ -2347,8 +2411,17 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
       "labels = [('cy_lstsqr', 'Cython implementation'), \n",
       "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",

From 3db65c76869ed380a0a31142e25de60369aeb96c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 6 May 2014 14:45:03 -0400
Subject: [PATCH 023/248] removed temp files

---
 .gitignore                                    |    1 +
 ...t_so_obvious_python_stuff-checkpoint.ipynb | 3969 -----------------
 .../palindrome_timeit-checkpoint.ipynb        |  194 -
 .../python_true_false-checkpoint.ipynb        |  578 ---
 ...cope_resolution_legb_rule-checkpoint.ipynb | 1092 -----
 .../timeit_test-checkpoint.ipynb              |  628 ---
 .../timeit_tests-checkpoint.ipynb             | 1883 --------
 .../cython_least_squares-checkpoint.ipynb     | 2461 ----------
 8 files changed, 1 insertion(+), 10805 deletions(-)
 delete mode 100644 .ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
 delete mode 100644 .ipynb_checkpoints/palindrome_timeit-checkpoint.ipynb
 delete mode 100644 .ipynb_checkpoints/python_true_false-checkpoint.ipynb
 delete mode 100644 .ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb
 delete mode 100644 .ipynb_checkpoints/timeit_test-checkpoint.ipynb
 delete mode 100644 .ipynb_checkpoints/timeit_tests-checkpoint.ipynb
 delete mode 100644 benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb

diff --git a/.gitignore b/.gitignore
index bb14e0e..6a5bebb 100755
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,4 @@
+*.ipynb_checkpoints/
 .DS_Store
 *.DS_Store
 *.pyc
diff --git a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb b/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
deleted file mode 100644
index 1e0ef3c..0000000
--- a/.ipynb_checkpoints/not_so_obvious_python_stuff-checkpoint.ipynb
+++ /dev/null
@@ -1,3969 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:0e1c6e74b301e23ea4146d660afb3f07765686c6c7fa4752f3a4495da7949787"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sebastian Raschka  \n",
-      "last updated: 05/03/2014 ([Changelog](#changelog))\n",
-      "\n",
-      "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### All code was executed in Python 3.4"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# A collection of not-so-obvious Python stuff you should know!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "I am really looking forward to your comments and suggestions to improve and  \n",
-      "extend this little collection! Just send me a quick note   \n",
-      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
-      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"sections\"></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Sections\n",
-      "- [The C3 class resolution algorithm for multiple class inheritance](#c3_class_res)\n",
-      "- [Assignment operators and lists - simple-add vs. add-AND operators](#pm_in_lists)\n",
-      "- [`True` and `False` in the datetime module](#datetime_module)\n",
-      "- [Python reuses objects for small integers - always use \"==\" for equality, \"is\" for identity](#python_small_int)\n",
-      "- [Shallow vs. deep copies if list contains other structures and objects](#shallow_vs_deep)\n",
-      "- [Picking `True` values from logical `and`s and `or`s](#false_true_expressions)\n",
-      "- [Don't use mutable objects as default arguments for functions!](#def_mutable_func)\n",
-      "- [Be aware of the consuming generator](#consuming_generator)\n",
-      "- [`bool` is a subclass of `int`](#bool_int)\n",
-      "- [About lambda-in-closures and-a-loop pitfall](#lambda_closure)\n",
-      "- [Python's LEGB scope resolution and the keywords `global` and `nonlocal`](#python_legb)\n",
-      "- [When mutable contents of immutable tuples aren't so mutable](#immutable_tuple)\n",
-      "- [List comprehensions are fast, but generators are faster!?](#list_generator)\n",
-      "- [Public vs. private class methods and name mangling](#private_class)\n",
-      "- [The consequences of modifying a list when looping through it](#looping_pitfall)\n",
-      "- [Dynamic binding and typos in variable names](#dynamic_binding)\n",
-      "- [List slicing using indexes that are \"out of range](#out_of_range_slicing)\n",
-      "- [Reusing global variable names and UnboundLocalErrors](#unboundlocalerror)\n",
-      "- [Creating copies of mutable objects](#copy_mutable)\n",
-      "- [Key differences between Python 2 and 3](#python_differences)\n",
-      "- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n",
-      "- [Abortive statements in `finally` blocks](#finally_blocks)\n",
-      "- [Assigning types to variables as values](#variable_types)\n",
-      "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n",
-      "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)\n",
-      "- [Metaclasses - What creates a new instance of a class?](#new_instance)\n",
-      "- [Else-clauses: \"conditional else\" and \"completion else\"](#else_clauses)\n",
-      "- [Interning of compile-time constants vs. run-time expressions](#string_interning)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='c3_class_res'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## The C3 class resolution algorithm for multiple class inheritance"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If we are dealing with multiple inheritance, according to the newer C3 class resolution algorithm, the following applies:  \n",
-      "Assuming that child class C inherits from two parent classes A and B, \"class A should be checked before class B\".\n",
-      "\n",
-      "If you want to learn more, please read the [original blog](http://python-history.blogspot.ru/2010/06/method-resolution-order.html) post by Guido van Rossum.\n",
-      "\n",
-      "(Original source: [http://gistroll.com/rolls/21/horizontal_assessments/new](http://gistroll.com/rolls/21/horizontal_assessments/new))"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class A(object):\n",
-      "    def foo(self):\n",
-      "        print(\"class A\")\n",
-      "\n",
-      "class B(object):\n",
-      "    def foo(self):\n",
-      "        print(\"class B\")\n",
-      "\n",
-      "class C(A, B):\n",
-      "    pass\n",
-      "\n",
-      "C().foo()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "class A\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So what actually happened above was that class `C` looked in the scope of the parent class `A` for the method `.foo()` first (and found it)!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I received an email containing a suggestion which uses a more nested example to illustrate Guido van Rossum's point a little bit better:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class A(object):\n",
-      "   def foo(self):\n",
-      "      print(\"class A\")\n",
-      "\n",
-      "class B(A):\n",
-      "   pass\n",
-      "\n",
-      "class C(A):\n",
-      "   def foo(self):\n",
-      "      print(\"class C\")\n",
-      "\n",
-      "class D(B,C):\n",
-      "   pass\n",
-      "\n",
-      "D().foo()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "class C\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here, class `D` searches in `B` first, which in turn inherits from `A` (note that class `C` also inherits from `A`, but has its own `.foo()` method) so that we come up with the search order: `D, B, C, A`. "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='pm_in_lists'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Assignment operators and lists - simple-add vs. add-AND operators"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python `list`s are mutable objects as we all know. So, if we are using the `+=` operator on `list`s, we extend the `list` by directly modifying the object directly.  \n",
-      "\n",
-      "However, if we use the assigment via `my_list = my_list + ...`, we create a new list object, which can be demonstrated by the following code:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_list = []\n",
-      "print('ID:', id(a_list))\n",
-      "\n",
-      "a_list += [1]\n",
-      "print('ID (+=):', id(a_list))\n",
-      "\n",
-      "a_list = a_list + [2]\n",
-      "print('ID (list = list + ...):', id(a_list))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ID: 4366496544\n",
-        "ID (+=): 4366496544\n",
-        "ID (list = list + ...): 4366495472\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Just for reference, the `.append()` and `.extends()` methods are modifying the `list` object in place, just as expected."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_list = []\n",
-      "print(a_list, '\\nID (initial):',id(a_list), '\\n')\n",
-      "\n",
-      "a_list.append(1)\n",
-      "print(a_list, '\\nID (append):',id(a_list), '\\n')\n",
-      "\n",
-      "a_list.extend([2])\n",
-      "print(a_list, '\\nID (extend):',id(a_list))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[] \n",
-        "ID (initial): 140704077653128 \n",
-        "\n",
-        "[1] \n",
-        "ID (append): 140704077653128 \n",
-        "\n",
-        "[1, 2] \n",
-        "ID (extend): 140704077653128\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='datetime_module'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## `True` and `False` in the datetime module\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable\u2014at least in some quarters.\"  \n",
-      "\n",
-      "(Original source: [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/))"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import datetime\n",
-      "\n",
-      "print('\"datetime.time(0,0,0)\" (Midnight) ->', bool(datetime.time(0,0,0)))\n",
-      "\n",
-      "print('\"datetime.time(1,0,0)\" (1 am) ->', bool(datetime.time(1,0,0)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\"datetime.time(0,0,0)\" (Midnight) -> False\n",
-        "\"datetime.time(1,0,0)\" (1 am) -> True\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='python_small_int'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Python reuses objects for small integers - use \"==\" for equality, \"is\" for identity\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This oddity occurs, because Python keeps an array of small integer objects (i.e., integers between -5 and 256, [see the doc](https://docs.python.org/2/c-api/int.html#PyInt_FromLong))."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 1\n",
-      "b = 1\n",
-      "print('a is b', bool(a is b))\n",
-      "True\n",
-      "\n",
-      "c = 999\n",
-      "d = 999\n",
-      "print('c is d', bool(c is d))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "a is b True\n",
-        "c is d False\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "(*I received a comment that this is in fact a CPython artefact and **must not necessarily be true** in all implementations of Python!*)\n",
-      "\n",
-      "So the take home message is: always use \"==\" for equality, \"is\" for identity!\n",
-      "\n",
-      "Here is a [nice article](http://python.net/%7Egoodger/projects/pycon/2007/idiomatic/handout.html#other-languages-have-variables) explaining it by comparing \"boxes\" (C language) with \"name tags\" (Python)."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This example demonstrates that this applies  indeed for integers in the range in -5 to 256:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('256 is 257-1', 256 is 257-1)\n",
-      "print('257 is 258-1', 257 is 258 - 1)\n",
-      "print('-5 is -6+1', -5 is -6+1)\n",
-      "print('-7 is -6-1', -7 is -6-1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "256 is 257-1 True\n",
-        "257 is 258-1 False\n",
-        "-5 is -6+1 True\n",
-        "-7 is -6-1 False\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### And to illustrate the test for equality (`==`) vs. identity (`is`):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 'hello world!'\n",
-      "b = 'hello world!'\n",
-      "print('a is b,', a is b)\n",
-      "print('a == b,', a == b)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "a is b, False\n",
-        "a == b, True\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We would think that identity would always imply equality, but this is not always true, as we can see in the next example:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = float('nan')\n",
-      "print('a is a,', a is a)\n",
-      "print('a == a,', a == a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "a is a, True\n",
-        "a == a, False\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='shallow_vs_deep'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Shallow vs. deep copies if list contains other structures and objects\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Shallow copy**:  \n",
-      "If we use the assignment operator to assign one list to another list, we just create a new name reference to the original list. If we want to create a new list object, we have to make a copy of the original list. This can be done via `a_list[:]` or `a_list.copy()`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "list1 = [1,2]\n",
-      "list2 = list1        # reference\n",
-      "list3 = list1[:]     # shallow copy\n",
-      "list4 = list1.copy() # shallow copy\n",
-      "\n",
-      "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\nlist4: {}\\n'\n",
-      "      .format(id(list1), id(list2), id(list3), id(list4)))\n",
-      "\n",
-      "list2[0] = 3\n",
-      "print('list1:', list1)\n",
-      "\n",
-      "list3[0] = 4\n",
-      "list4[1] = 4\n",
-      "print('list1:', list1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "IDs:\n",
-        "list1: 4346366472\n",
-        "list2: 4346366472\n",
-        "list3: 4346366408\n",
-        "list4: 4346366536\n",
-        "\n",
-        "list1: [3, 2]\n",
-        "list1: [3, 2]\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Deep copy**  \n",
-      "As we have seen above, a shallow copy works fine if we want to create a new list with contents of the original list which we want to modify independently.  \n",
-      "\n",
-      "However, if we are dealing with compound objects (e.g., lists that contain other lists, [read here](https://docs.python.org/2/library/copy.html) for more information) it becomes a little trickier.\n",
-      "\n",
-      "In the case of compound objects, a shallow copy would create a new compound object, but it would just insert the references to the contained objects into the new compound object. In contrast, a deep copy would go \"deeper\" and create also new objects   \n",
-      "for the objects found in the original compound object. \n",
-      "If you follow the code, the concept should become more clear:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from copy import deepcopy\n",
-      "\n",
-      "list1 = [[1],[2]]\n",
-      "list2 = list1.copy()    # shallow copy\n",
-      "list3 = deepcopy(list1) # deep copy\n",
-      "\n",
-      "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\n'\n",
-      "      .format(id(list1), id(list2), id(list3)))\n",
-      "\n",
-      "list2[0][0] = 3\n",
-      "print('list1:', list1)\n",
-      "\n",
-      "list3[0][0] = 5\n",
-      "print('list1:', list1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "IDs:\n",
-        "list1: 4377956296\n",
-        "list2: 4377961752\n",
-        "list3: 4377954928\n",
-        "\n",
-        "list1: [[3], [2]]\n",
-        "list1: [[3], [2]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='false_true_expressions'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Picking `True` values from logical `and`s and `or`s"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "** Logical `or`: **  \n",
-      "\n",
-      "`a or b == a if a else b`  \n",
-      "- If both values in `or` expressions are `True`, Python will select the first value (e.g., select `\"a\"` in `\"a\" or \"b\"`), and the second one in `and` expressions.  \n",
-      "This is also called **short-circuiting** - we already know that the logical `or` must be `True` if the first value is `True` and therefore can omit the evaluation of the second value.\n",
-      "\n",
-      "** Logical `and`: ** \n",
-      "\n",
-      "`a and b == b if a else a`  \n",
-      "- If both values in `and` expressions are `True`, Python will select the second value, since for a logical `and`, both values must be true.\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "result = (2 or 3) * (5 and 7)\n",
-      "print('2 * 7 =', result)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2 * 7 = 14\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='def_mutable_func'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Don't use mutable objects as default arguments for functions!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Don't use mutable objects (e.g., dictionaries, lists, sets, etc.) as default arguments for functions! You might expect that a new list is created every time when we call the function without providing an argument for the default parameter, but this is not the case: **Python will create the mutable object (default parameter) the first time the function is defined - not when it is called**, see the following code:\n",
-      "\n",
-      "(Original source: [http://docs.python-guide.org/en/latest/writing/gotchas/](http://docs.python-guide.org/en/latest/writing/gotchas/)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def append_to_list(value, def_list=[]):\n",
-      "    def_list.append(value)\n",
-      "    return def_list\n",
-      "\n",
-      "my_list = append_to_list(1)\n",
-      "print(my_list)\n",
-      "\n",
-      "my_other_list = append_to_list(2)\n",
-      "print(my_other_list)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1]\n",
-        "[1, 2]\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Another good example showing that demonstrates that default arguments are created when the function is created (**and not when it is called!**):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time\n",
-      "def report_arg(my_default=time.time()):\n",
-      "    print(my_default)\n",
-      "\n",
-      "report_arg()\n",
-      "\n",
-      "time.sleep(5)\n",
-      "\n",
-      "report_arg()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1397764090.456688\n",
-        "1397764090.456688"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='consuming_generator'></a>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Be aware of the consuming generator"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Be aware of what is happening when combining \"`in`\" checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gen = (i for i in range(5))\n",
-      "print('2 in gen,', 2 in gen)\n",
-      "print('3 in gen,', 3 in gen)\n",
-      "print('1 in gen,', 1 in gen) "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2 in gen, True\n",
-        "3 in gen, True\n",
-        "1 in gen, False\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Although this defeats the purpose of an generator (in most cases), we can convert a generator into a list to circumvent the problem. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gen = (i for i in range(5))\n",
-      "a_list = list(gen)\n",
-      "print('2 in l,', 2 in a_list)\n",
-      "print('3 in l,', 3 in a_list)\n",
-      "print('1 in l,', 1 in a_list) "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2 in l, True\n",
-        "3 in l, True\n",
-        "1 in l, True\n"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='bool_int'></a>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## `bool` is a subclass of `int`\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Chicken or egg? In the history of Python (Python 2.2 to be specific) truth values were implemented via 1 and 0 (similar to the old C). In order to avoid syntax errors in old (but perfectly working) Python code, `bool` was added as a subclass of `int` in Python 2.3.\n",
-      "\n",
-      "Original source: [http://www.peterbe.com/plog/bool-is-int](http://www.peterbe.com/plog/bool-is-int)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('isinstance(True, int):', isinstance(True, int))\n",
-      "print('True + True:', True + True)\n",
-      "print('3*True + True:', 3*True + True)\n",
-      "print('3*True - False:', 3*True - False)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "isinstance(True, int): True\n",
-        "True + True: 2\n",
-        "3*True + True: 4\n",
-        "3*True - False: 3\n"
-       ]
-      }
-     ],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='lambda_closure'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## About lambda-in-closures-and-a-loop pitfall"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Remember the section about the [\"consuming generators\"](consuming_generators)? This example is somewhat related, but the result might still come unexpected. \n",
-      "\n",
-      "(Original source: [http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html](http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html))\n",
-      "\n",
-      "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope of the list comprehension. Since the list is already constructed when we `for-loop` through the list, it will be set to the last value 4."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list = [lambda: i for i in range(5)]\n",
-      "for l in my_list:\n",
-      "    print(l())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4\n",
-        "4\n",
-        "4\n",
-        "4\n",
-        "4\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This, however, does not apply to generators:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_gen = (lambda: n for n in range(5))\n",
-      "for l in my_gen:\n",
-      "    print(l())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "4\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And if you are really keen on using lists, there is a nifty trick that circumvents this problem as a reader nicely pointed out in the comments: We can simply pass the loop variable `i` as a default argument to the lambdas."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list = [lambda x=i: x for i in range(5)]\n",
-      "for l in my_list:\n",
-      "    print(l())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "4\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='python_legb'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Python's LEGB scope resolution and the keywords `global` and `nonlocal`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There is nothing particularly surprising about Python's LEGB scope resolution (Local -> Enclosed -> Global -> Built-in), but it is still useful to take a look at some examples!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### `global` vs. `local`\n",
-      "\n",
-      "According to the LEGB rule, Python will first look for a variable in the local scope. So if we set the variable `x = 1` `local`ly in the function's scope, it won't have an effect on the `global` `x`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = 0\n",
-      "def in_func():\n",
-      "    x = 1\n",
-      "    print('in_func:', x)\n",
-      "    \n",
-      "in_func()\n",
-      "print('global:', x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in_func: 1\n",
-        "global: 0\n"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If we want to modify the `global` x via a function, we can simply use the `global` keyword to import the variable into the function's scope:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = 0\n",
-      "def in_func():\n",
-      "    global x\n",
-      "    x = 1\n",
-      "    print('in_func:', x)\n",
-      "    \n",
-      "in_func()\n",
-      "print('global:', x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in_func: 1\n",
-        "global: 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### `local` vs. `enclosed`\n",
-      "\n",
-      "Now, let us take a look at `local` vs. `enclosed`. Here, we set the variable `x = 1` in the `outer` function and set `x = 1` in the enclosed function `inner`. Since `inner` looks in the local scope first, it won't modify `outer`'s `x`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def outer():\n",
-      "       x = 1\n",
-      "       print('outer before:', x)\n",
-      "       def inner():\n",
-      "           x = 2\n",
-      "           print(\"inner:\", x)\n",
-      "       inner()\n",
-      "       print(\"outer after:\", x)\n",
-      "outer()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "outer before: 1\n",
-        "inner: 2\n",
-        "outer after: 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is where the `nonlocal` keyword comes in handy - it allows us to modify the `x` variable in the `enclosed` scope:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def outer():\n",
-      "       x = 1\n",
-      "       print('outer before:', x)\n",
-      "       def inner():\n",
-      "           nonlocal x\n",
-      "           x = 2\n",
-      "           print(\"inner:\", x)\n",
-      "       inner()\n",
-      "       print(\"outer after:\", x)\n",
-      "outer()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "outer before: 1\n",
-        "inner: 2\n",
-        "outer after: 2\n"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='immutable_tuple'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## When mutable contents of immutable tuples aren't so mutable"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As we all know, tuples are immutable objects in Python, right!? But what happens if they contain mutable objects? \n",
-      "\n",
-      "First, let us have a look at the expected behavior: a `TypeError` is raised if we try to modify immutable types in a tuple: "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tup = (1,)\n",
-      "tup[0] += 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "TypeError",
-       "evalue": "'tuple' object does not support item assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-41-c3bec6c3fe6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
-       ]
-      }
-     ],
-     "prompt_number": 41
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### But what if we put a mutable object into the immutable tuple? Well, modification works, but we **also** get a `TypeError` at the same time."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tup = ([],)\n",
-      "print('tup before: ', tup)\n",
-      "tup[0] += [1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "tup before:  ([],)\n"
-       ]
-      },
-      {
-       "ename": "TypeError",
-       "evalue": "'tuple' object does not support item assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-42-aebe9a31dbeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tup before: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('tup after: ', tup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "tup after:  ([1],)\n"
-       ]
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "However, **there are ways** to modify the mutable contents of the tuple without raising the `TypeError`, the solution is the `.extend()` method, or alternatively `.append()` (for lists):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tup = ([],)\n",
-      "print('tup before: ', tup)\n",
-      "tup[0].extend([1])\n",
-      "print('tup after: ', tup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "tup before:  ([],)\n",
-        "tup after:  ([1],)\n"
-       ]
-      }
-     ],
-     "prompt_number": 44
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tup = ([],)\n",
-      "print('tup before: ', tup)\n",
-      "tup[0].append(1)\n",
-      "print('tup after: ', tup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "tup before:  ([],)\n",
-        "tup after:  ([1],)\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Explanation\n",
-      "\n",
-      "**A. Jesse Jiryu Davis** has a nice explanation for this phenomenon (Original source: [http://emptysqua.re/blog/python-increment-is-weird-part-ii/](http://emptysqua.re/blog/python-increment-is-weird-part-ii/))\n",
-      "\n",
-      "If we try to extend the list via `+=` *\"then the statement executes `STORE_SUBSCR`, which calls the C function `PyObject_SetItem`, which checks if the object supports item assignment. In our case the object is a tuple, so `PyObject_SetItem` throws the `TypeError`. Mystery solved.\"*"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### One more note about the `immutable` status of tuples. Tuples are famous for being immutable. However, how comes that this code works?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_tup = (1,)\n",
-      "my_tup += (4,)\n",
-      "my_tup = my_tup + (5,)\n",
-      "print(my_tup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(1, 4, 5)\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "What happens \"behind\" the curtains is that the tuple is not modified, but every time a new object is generated, which will inherit the old \"name tag\":"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_tup = (1,)\n",
-      "print(id(my_tup))\n",
-      "my_tup += (4,)\n",
-      "print(id(my_tup))\n",
-      "my_tup = my_tup + (5,)\n",
-      "print(id(my_tup))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4337381840\n",
-        "4357415496\n",
-        "4357289952\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='list_generator'></a>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## List comprehensions are fast, but generators are faster!?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\"List comprehensions are fast, but generators are faster!?\" - No, not really (or significantly, see the benchmarks below). So what's the reason to prefer one over the other?\n",
-      "- use lists if you want to use the plethora of list methods  \n",
-      "- use generators when you are dealing with huge collections to avoid memory issues"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def plainlist(n=100000):\n",
-      "    my_list = []\n",
-      "    for i in range(n):\n",
-      "        if i % 5 == 0:\n",
-      "            my_list.append(i)\n",
-      "    return my_list\n",
-      "\n",
-      "def listcompr(n=100000):\n",
-      "    my_list = [i for i in range(n) if i % 5 == 0]\n",
-      "    return my_list\n",
-      "\n",
-      "def generator(n=100000):\n",
-      "    my_gen = (i for i in range(n) if i % 5 == 0)\n",
-      "    return my_gen\n",
-      "\n",
-      "def generator_yield(n=100000):\n",
-      "    for i in range(n):\n",
-      "        if i % 5 == 0:\n",
-      "            yield i"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### To be fair to the list, let us exhaust the generators:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def test_plainlist(plain_list):\n",
-      "    for i in plain_list():\n",
-      "        pass\n",
-      "\n",
-      "def test_listcompr(listcompr):\n",
-      "    for i in listcompr():\n",
-      "        pass\n",
-      "\n",
-      "def test_generator(generator):\n",
-      "    for i in generator():\n",
-      "        pass\n",
-      "\n",
-      "def test_generator_yield(generator_yield):\n",
-      "    for i in generator_yield():\n",
-      "        pass\n",
-      "\n",
-      "print('plain_list:     ', end = '')\n",
-      "%timeit test_plainlist(plainlist)\n",
-      "print('\\nlistcompr:     ', end = '')\n",
-      "%timeit test_listcompr(listcompr)\n",
-      "print('\\ngenerator:     ', end = '')\n",
-      "%timeit test_generator(generator)\n",
-      "print('\\ngenerator_yield:     ', end = '')\n",
-      "%timeit test_generator_yield(generator_yield)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "plain_list:     10 loops, best of 3: 22.4 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "listcompr:     10 loops, best of 3: 20.8 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "generator:     10 loops, best of 3: 22 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "generator_yield:     10 loops, best of 3: 21.9 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='private_class'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Public vs. private class methods and name mangling\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Who has not stumbled across this quote \"we are all consenting adults here\" in the Python community, yet? Unlike in other languages like C++ (sorry, there are many more, but that's one I am most familiar with), we can't really protect class methods from being used outside the class (i.e., by the API user).  \n",
-      "All we can do is to indicate methods as private to make clear that they are better not used outside the class, but it is really up to the class user, since \"we are all consenting adults here\"!  \n",
-      "So, when we want to mark a class method as private, we can put a single underscore in front of it.  \n",
-      "If we additionally want to avoid name clashes with other classes that might use the same method names, we can prefix the name with a double-underscore to invoke the name mangling.\n",
-      "\n",
-      "This doesn't prevent the class user to access this class member though, but he has to know the trick and also knows that it his own risk...\n",
-      "\n",
-      "Let the following example illustrate what I mean:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class my_class():\n",
-      "    def public_method(self):\n",
-      "        print('Hello public world!')\n",
-      "    def __private_method(self):\n",
-      "        print('Hello private world!')\n",
-      "    def call_private_method_in_class(self):\n",
-      "        self.__private_method()\n",
-      "        \n",
-      "my_instance = my_class()\n",
-      "\n",
-      "my_instance.public_method()\n",
-      "my_instance._my_class__private_method()\n",
-      "my_instance.call_private_method_in_class()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello public world!\n",
-        "Hello private world!\n",
-        "Hello private world!\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='looping_pitfall'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## The consequences of modifying a list when looping through it"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "It can be really dangerous to modify a list when iterating through it - this is a very common pitfall that can cause unintended behavior!  \n",
-      "Look at the following examples, and for a fun exercise: try to figure out what is going on before you skip to the solution!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = [1, 2, 3, 4, 5]\n",
-      "for i in a:\n",
-      "    if not i % 2:\n",
-      "        a.remove(i)\n",
-      "print(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 3, 5]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = [2, 4, 5, 6]\n",
-      "for i in b:\n",
-      "     if not i % 2:\n",
-      "         b.remove(i)\n",
-      "print(b)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[4, 5]\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "**The solution** is that we are iterating through the list index by index, and if we remove one of the items in-between, we inevitably mess around with the indexing, look at the following example, and it will become clear:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = [2, 4, 5, 6]\n",
-      "for index, item in enumerate(b):\n",
-      "    print(index, item)\n",
-      "    if not item % 2:\n",
-      "        b.remove(item)\n",
-      "print(b)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 2\n",
-        "1 5\n",
-        "2 6\n",
-        "[4, 5]\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='dynamic_binding'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Dynamic binding and typos in variable names\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Be careful, dynamic binding is convenient, but can also quickly become dangerous!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('first list:')\n",
-      "for i in range(3):\n",
-      "    print(i)\n",
-      "    \n",
-      "print('\\nsecond list:')\n",
-      "for j in range(3):\n",
-      "    print(i) # I (intentionally) made typo here!"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "first list:\n",
-        "0\n",
-        "1\n",
-        "2\n",
-        "\n",
-        "second list:\n",
-        "2\n",
-        "2\n",
-        "2\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='out_of_range_slicing'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## List slicing using indexes that are \"out of range\""
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As we have all encountered it 1 (x10000) time(s) in our live, the infamous `IndexError`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list = [1, 2, 3, 4, 5]\n",
-      "print(my_list[5])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "IndexError",
-       "evalue": "list index out of range",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-15-eb273dc36fdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But suprisingly, it is not raised when we are doing list slicing, which can be a really pain for debugging:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list = [1, 2, 3, 4, 5]\n",
-      "print(my_list[5:])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[]\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='unboundlocalerror'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Reusing global variable names and `UnboundLocalErrors`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Usually, it is no problem to access global variables in the local scope of a function:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_func():\n",
-      "    print(var)\n",
-      "\n",
-      "var = 'global'\n",
-      "my_func()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "global\n"
-       ]
-      }
-     ],
-     "prompt_number": 37
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And is also no problem to use the same variable name in the local scope without affecting the local counterpart: "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_func():\n",
-      "    var = 'locally changed'\n",
-      "\n",
-      "var = 'global'\n",
-      "my_func()\n",
-      "print(var)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "global\n"
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But we have to be careful if we use a variable name that occurs in the global scope, and we want to access it in the local function scope if we want to reuse this name:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_func():\n",
-      "    print(var) # want to access global variable\n",
-      "    var = 'locally changed' # but Python thinks we forgot to define the local variable!\n",
-      "    \n",
-      "var = 'global'\n",
-      "my_func()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "UnboundLocalError",
-       "evalue": "local variable 'var' referenced before assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-40-3afd870b7c35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m<ipython-input-40-3afd870b7c35>\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# want to access global variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'locally changed'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'var' referenced before assignment"
-       ]
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this case, we have to use the `global` keyword!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_func():\n",
-      "    global var\n",
-      "    print(var) # want to access global variable\n",
-      "    var = 'locally changed' # changes the gobal variable\n",
-      "\n",
-      "var = 'global'\n",
-      "\n",
-      "my_func()\n",
-      "print(var)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "global\n",
-        "locally changed\n"
-       ]
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='copy_mutable'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Creating copies of mutable objects\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's assume a scenario where we want to duplicate sub`list`s of values stored in another list. If we want to create independent sub`list` object, using the arithmetic multiplication operator could lead to rather unexpected (or undesired) results:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list1 = [[1, 2, 3]] * 2\n",
-      "\n",
-      "print('initially ---> ', my_list1)\n",
-      "\n",
-      "# modify the 1st element of the 2nd sublist\n",
-      "my_list1[1][0] = 'a'\n",
-      "print(\"after my_list1[1][0] = 'a' ---> \", my_list1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "initially --->  [[1, 2, 3], [1, 2, 3]]\n",
-        "after my_list1[1][0] = 'a' --->  [['a', 2, 3], ['a', 2, 3]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "In this case, we should better create \"new\" objects:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list2 = [[1, 2, 3] for i in range(2)]\n",
-      "\n",
-      "print('initially:  ---> ', my_list2)\n",
-      "\n",
-      "# modify the 1st element of the 2nd sublist\n",
-      "my_list2[1][0] = 'a'\n",
-      "print(\"after my_list2[1][0] = 'a':  ---> \", my_list2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "initially:  --->  [[1, 2, 3], [1, 2, 3]]\n",
-        "after my_list2[1][0] = 'a':  --->  [[1, 2, 3], ['a', 2, 3]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "And here is the proof:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for a,b in zip(my_list1, my_list2):\n",
-      "    print('id my_list1: {}, id my_list2: {}'.format(id(a), id(b)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "id my_list1: 4350764680, id my_list2: 4350766472\n",
-        "id my_list1: 4350764680, id my_list2: 4350766664\n"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='python_differences'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Key differences between Python 2 and 3\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,  \n",
-      "- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)\n",
-      "- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)\n",
-      "- [http://python3porting.com/differences.html](http://python3porting.com/differences.html)\n",
-      "- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html)  \n",
-      "etc.\n",
-      "\n",
-      "But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!\n",
-      "(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "### Unicode...\n",
-      "####- Python 2: \n",
-      "We have ASCII `str()` types, separate `unicode()`, but no `byte` type\n",
-      "####- Python 3: \n",
-      "Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#############\n",
-      "# Python 2\n",
-      "#############\n",
-      "\n",
-      ">>> type(unicode('is like a python3 str()'))\n",
-      "<type 'unicode'>\n",
-      "\n",
-      ">>> type(b'byte type does not exist')\n",
-      "<type 'str'>\n",
-      "\n",
-      ">>> 'they are really' + b' the same'\n",
-      "'they are really the same'\n",
-      "\n",
-      ">>> type(bytearray(b'bytearray oddly does exist though'))\n",
-      "<type 'bytearray'>\n",
-      "\n",
-      "#############\n",
-      "# Python 3\n",
-      "#############\n",
-      "\n",
-      ">>> print('strings are now utf-8 \\u03BCnico\\u0394\u00e9!')\n",
-      "strings are now utf-8 \u03bcnico\u0394\u00e9!\n",
-      "\n",
-      "\n",
-      ">>> type(b' and we have byte types for storing data')\n",
-      "<class 'bytes'>\n",
-      "\n",
-      ">>> type(bytearray(b'but also bytearrays for those who prefer them over strings'))\n",
-      "<class 'bytearray'>\n",
-      "\n",
-      ">>> 'string' + b'bytes for data'\n",
-      "Traceback (most recent call last):s\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "TypeError: Can't convert 'bytes' object to str implicitly"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### The print statement\n",
-      "Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ..."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> print 'Hello, World!'\n",
-      "Hello, World!\n",
-      ">>> print('Hello, World!')\n",
-      "Hello, World!\n",
-      "\n",
-      "# Python 3\n",
-      ">>> print('Hello, World!')\n",
-      "Hello, World!\n",
-      ">>> print 'Hello, World!'\n",
-      "  File \"<stdin>\", line 1\n",
-      "    print 'Hello, World!'\n",
-      "                        ^\n",
-      "SyntaxError: invalid syntax"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=\"\"` in Python 3:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> print \"line 1\", ; print 'same line'\n",
-      "line 1 same line\n",
-      "\n",
-      "# Python 3\n",
-      ">>> print(\"line 1\", end=\"\") ; print (\" same line\")\n",
-      "line 1 same line"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Integer division\n",
-      "This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed.  \n",
-      "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> 3 / 2\n",
-      "1\n",
-      ">>> 3 // 2\n",
-      "1\n",
-      ">>> 3 / 2.0\n",
-      "1.5\n",
-      ">>> 3 // 2.0\n",
-      "1.0\n",
-      "\n",
-      "# Python 3\n",
-      ">>> 3 / 2\n",
-      "1.5\n",
-      ">>> 3 // 2\n",
-      "1\n",
-      ">>> 3 / 2.0\n",
-      "1.5\n",
-      ">>> 3 // 2.0\n",
-      "1.0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### `xrange()`  \n",
-      "`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch!  \n",
-      "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      "> python -m timeit 'for i in range(1000000):' ' pass'\n",
-      "10 loops, best of 3: 66 msec per loop\n",
-      "\n",
-      "    > python -m timeit 'for i in xrange(1000000):' ' pass'\n",
-      "10 loops, best of 3: 27.8 msec per loop\n",
-      "\n",
-      "# Python 3\n",
-      "> python3 -m timeit 'for i in range(1000000):' ' pass'\n",
-      "10 loops, best of 3: 51.1 msec per loop\n",
-      "\n",
-      "> python3 -m timeit 'for i in xrange(1000000):' ' pass'\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 292, in main\n",
-      "    x = t.timeit(number)\n",
-      "  File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 178, in timeit\n",
-      "    timing = self.inner(it, self.timer)\n",
-      "  File \"<timeit-src>\", line 6, in inner\n",
-      "    for i in xrange(1000000):\n",
-      "NameError: name 'xrange' is not defined"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Raising exceptions\n",
-      "\n",
-      "Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> raise IOError, \"file error\"\n",
-      "Traceback (most recent call last):\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "IOError: file error\n",
-      ">>> raise IOError(\"file error\")\n",
-      "Traceback (most recent call last):\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "IOError: file error\n",
-      "\n",
-      "    \n",
-      "# Python 3    \n",
-      ">>> raise IOError, \"file error\"\n",
-      "  File \"<stdin>\", line 1\n",
-      "    raise IOError, \"file error\"\n",
-      "                 ^\n",
-      "SyntaxError: invalid syntax\n",
-      ">>> raise IOError(\"file error\")\n",
-      "Traceback (most recent call last):\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "OSError: file error"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Handling exceptions\n",
-      "\n",
-      "Also the handling of excecptions has slightly changed in Python 3. Now, we have to use the `as` keyword!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> try:\n",
-      "...     blabla\n",
-      "... except NameError, err:\n",
-      "...     print err, '--> our error msg'\n",
-      "... \n",
-      "name 'blabla' is not defined --> our error msg\n",
-      "\n",
-      "# Python 3\n",
-      ">>> try:\n",
-      "...     blabla\n",
-      "... except NameError as err:\n",
-      "...     print(err, '--> our error msg')\n",
-      "... \n",
-      "name 'blabla' is not defined --> our error msg"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### The `next()` function and `.next()` method\n",
-      "\n",
-      "Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> my_generator = (letter for letter in 'abcdefg')\n",
-      ">>> my_generator.next()\n",
-      "'a'\n",
-      ">>> next(my_generator)\n",
-      "'b'\n",
-      "\n",
-      "# Python 3\n",
-      ">>> my_generator = (letter for letter in 'abcdefg')\n",
-      ">>> next(my_generator)\n",
-      "'a'\n",
-      ">>> my_generator.next()\n",
-      "Traceback (most recent call last):\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "AttributeError: 'generator' object has no attribute 'next'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### In Python 3.x for-loop variables don't leak into the global namespace anymore"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
-      "\n",
-      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 1\n",
-      "print([i for i in range(5)])\n",
-      "print(i, '-> i in global')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0, 1, 2, 3, 4]\n",
-        "1 -> i in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In Python 2.x this would print  \n",
-      "`4 -> i in global`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='function_annotation'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Function annotations - What are those `->`'s in my Python code?\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Have you ever seen any Python code that used colons inside the parantheses of a function definition?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def foo1(x: 'insert x here', y: 'insert x^2 here'):\n",
-      "    print('Hello, World')\n",
-      "    return"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And what about the fancy arrow here?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def foo2(x, y) -> 'Hi!':\n",
-      "    print('Hello, World')\n",
-      "    return"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Q: Is this valid Python syntax?  \n",
-      "A: Yes!\n",
-      "    \n",
-      "    \n",
-      "Q: So, what happens if I *just call* the function?  \n",
-      "A: Nothing!\n",
-      "    \n",
-      "Here is the proof!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "foo1(1,2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello, World\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "foo2(1,2)    "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello, World\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**So, those are function annotations ... **  \n",
-      "- the colon for the function parameters  \n",
-      "- the arrow for the return value  \n",
-      "\n",
-      "You probably will never make use of them (or at least very rarely). Usually, we write good function documentations below the function as a docstring - or at least this is how I would do it (okay this case is a little bit extreme, I have to admit):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def is_palindrome(a):\n",
-      "    \"\"\"\n",
-      "    Case-and punctuation insensitive check if a string is a palindrom.\n",
-      "    \n",
-      "    Keyword arguments:\n",
-      "        a (str): The string to be checked if it is a palindrome.\n",
-      "        \n",
-      "    Returns `True` if input string is a palindrome, else False.\n",
-      "    \n",
-      "    \"\"\"\n",
-      "    stripped_str = [l for l in my_str.lower() if l.isalpha()]\n",
-      "    return stripped_str == stripped_str[::-1]\n",
-      "    "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "However, function annotations can be useful to indicate that work is still in progress in some cases. But they are optional and I see them very very rarely.\n",
-      "\n",
-      "As it is stated in [PEP3107](http://legacy.python.org/dev/peps/pep-3107/#fundamentals-of-function-annotations):\n",
-      "\n",
-      "1. Function annotations, both for parameters and return values, are completely optional.\n",
-      "\n",
-      "2. Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The nice thing about function annotations is their `__annotations__` attribute, which is dictionary of all the parameters and/or the `return` value you annotated."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "foo1.__annotations__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "{'y': 'insert x^2 here', 'x': 'insert x here'}"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "foo2.__annotations__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "{'return': 'Hi!'}"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**When are they useful?**"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Function annotations can be useful for a couple of things \n",
-      "- Documentation in general\n",
-      "- pre-condition testing\n",
-      "- [type checking](http://legacy.python.org/dev/peps/pep-0362/#annotation-checker)\n",
-      "     \n",
-      "..."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name=\"finally_blocks\"></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Abortive statements in `finally` blocks"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python's `try-except-finally` blocks are very handy for catching and handling errors. The `finally` block is always executed whether an `exception` has been raised or not as illustrated in the following example."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def try_finally1():\n",
-      "    try:\n",
-      "        print('in try:')\n",
-      "        print('do some stuff')\n",
-      "        float('abc')\n",
-      "    except ValueError:\n",
-      "        print('an error occurred')\n",
-      "    else:\n",
-      "        print('no error occurred')\n",
-      "    finally:\n",
-      "        print('always execute finally')\n",
-      "        \n",
-      "try_finally1()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in try:\n",
-        "do some stuff\n",
-        "an error occurred\n",
-        "always execute finally\n"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "But can you also guess what will be printed in the next code cell?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def try_finally2():\n",
-      "    try:\n",
-      "        print(\"do some stuff in try block\")\n",
-      "        return \"return from try block\"\n",
-      "    finally:\n",
-      "        print(\"do some stuff in finally block\")\n",
-      "        return \"always execute finally\"\n",
-      "    \n",
-      "print(try_finally2())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "do some stuff in try block\n",
-        "do some stuff in finally block\n",
-        "always execute finally\n"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Here, the abortive `return` statement in the `finally` block simply overrules the `return` in the `try` block, since **`finally` is guaranteed to always be executed.** So, be careful using abortive statements in `finally` blocks!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name=\"variable_types\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#Assigning types to variables as values"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I am not yet sure in which context this can be useful, but it is a nice fun fact to know that we can assign types as values to variables."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = str\n",
-      "a_var(123)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "'123'"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from random import choice\n",
-      "\n",
-      "a, b, c = float, int, str\n",
-      "for i in range(5):\n",
-      "    j = choice([a,b,c])(i)\n",
-      "    print(j, type(j))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 <class 'int'>\n",
-        "1 <class 'int'>\n",
-        "2.0 <class 'float'>\n",
-        "3 <class 'str'>\n",
-        "4 <class 'int'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='generator_rhs'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Only the first clause of generators is evaluated immediately"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The main reason why we love to use generators in certain cases (i.e., when we are dealing with large numbers of computations) is that it only computes the next value when it is needed, which is also known as \"lazy\" evaluation.\n",
-      "However, the first clause of an generator is already checked upon it's creation, as the following example demonstrates:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gen_fails = (i for i in 1/0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "division by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-18-29312e1ece8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Certainly, this is a nice feature, since it notifies us about syntax erros immediately. However, this is (unfortunately) not the case if we have multiple cases in our generator."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gen_succeeds = (i for i in range(5) for j in 1/0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('But obviously fails when we iterate ...')\n",
-      "for i in gen_succeeds:\n",
-      "    print(i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "division by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-20-8a83a1022971>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m<ipython-input-19-c54c53f2218a>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "But obviously fails when we iterate ...\n"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"splat_op\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "##Keyword argument unpacking syntax - `*args` and `**kwargs`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python has a very convenient \"keyword argument unpacking syntax\" (often also referred to as \"splat\"-operators). This is particularly useful, if we want to define a function that can take a arbitrary number of input arguments."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Single-asterisk (*args)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def a_func(*args):\n",
-      "    print('type of args:', type(args))\n",
-      "    print('args contents:', args)\n",
-      "    print('1st argument:', args[0])\n",
-      "\n",
-      "a_func(0, 1, 'a', 'b', 'c')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "type of args: <class 'tuple'>\n",
-        "args contents: (0, 1, 'a', 'b', 'c')\n",
-        "1st argument: 0\n"
-       ]
-      }
-     ],
-     "prompt_number": 55
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Double-asterisk (**kwargs)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def b_func(**kwargs):\n",
-      "    print('type of kwargs:', type(kwargs))\n",
-      "    print('kwargs contents: ', kwargs)\n",
-      "    print('value of argument a:', kwargs['a'])\n",
-      "    \n",
-      "b_func(a=1, b=2, c=3, d=4)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "type of kwargs: <class 'dict'>\n",
-        "kwargs contents:  {'d': 4, 'a': 1, 'c': 3, 'b': 2}\n",
-        "value of argument a: 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 56
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### (Partially) unpacking of iterables\n",
-      "Another useful application of the \"unpacking\"-operator is the unpacking of lists and other other iterables."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "val1, *vals = [1, 2, 3, 4, 5]\n",
-      "print('val1:', val1)\n",
-      "print('vals:', vals)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "val1: 1\n",
-        "vals: [2, 3, 4, 5]\n"
-       ]
-      }
-     ],
-     "prompt_number": 57
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"new_instance\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Metaclasses - What creates a new instance of a class?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Usually, it is the `__init__` method when we think of instanciating a new object from a class. However, it is the static method `__new__` (it is not a class method!) that creates and returns a new instance before `__init__()` is called. \n",
-      "More specifically, this is what is returned:  \n",
-      "`return super(<currentclass>, cls).__new__(subcls, *args, **kwargs)`  \n",
-      "\n",
-      "For more information about the `__new__` method, please see the [documentation](https://www.python.org/download/releases/2.2/descrintro/#__new__).\n",
-      "\n",
-      "As a little experiment, let us screw with `__new__` so that it returns `None` and see if `__init__` will be executed:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class a_class(object):\n",
-      "    def __new__(clss, *args, **kwargs):\n",
-      "        print('excecuted __new__')\n",
-      "        return None\n",
-      "    def __init__(self, an_arg):\n",
-      "        print('excecuted __init__')\n",
-      "        self.an_arg = an_arg\n",
-      "        \n",
-      "a_object = a_class(1)\n",
-      "print('Type of a_object:', type(a_object))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "excecuted __new__\n",
-        "Type of a_object: <class 'NoneType'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 53
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As we can see in the code above, `__init__` requires the returned instance from `__new__` in order to called. So, here we just created a `NoneType` object.  \n",
-      "Let us override the `__new__`, now and let us confirm that `__init__` is called now to instantiate the new object\":"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class a_class(object):\n",
-      "    def __new__(cls, *args, **kwargs):\n",
-      "        print('excecuted __new__')\n",
-      "        inst = super(a_class, cls).__new__(cls)\n",
-      "        return inst\n",
-      "    def __init__(self, an_arg):\n",
-      "        print('excecuted __init__')\n",
-      "        self.an_arg = an_arg\n",
-      "        \n",
-      "a_object = a_class(1)\n",
-      "print('Type of a_object:', type(a_object))\n",
-      "print('a_object.an_arg: ', a_object.an_arg)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "excecuted __new__\n",
-        "excecuted __init__\n",
-        "Type of a_object: <class '__main__.a_class'>\n",
-        "a_object.an_arg:  1\n"
-       ]
-      }
-     ],
-     "prompt_number": 54
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(5):\n",
-      "    if i == 1:\n",
-      "        print('in for')\n",
-      "else:\n",
-      "    print('in else')\n",
-      "print('after for-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in for\n",
-        "in else\n",
-        "after for-loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(5):\n",
-      "    if i == 1:\n",
-      "        break\n",
-      "else:\n",
-      "    print('in else')\n",
-      "print('after for-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "after for-loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"else_clauses\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Else-clauses: \"conditional else\" and \"completion else\""
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I would claim that the conditional \"else\" is every programmer's daily bread and butter. However, there is a second flavor of \"else\"-clauses in Python, which I will call \"completion else\" (for reason that will become clear later).  \n",
-      "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with.  \n",
-      "### Conditional else:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# conditional else\n",
-      "\n",
-      "a_list = [1,2]\n",
-      "if a_list[0] == 1:\n",
-      "    print('Hello, World!')\n",
-      "else:\n",
-      "    print('Bye, World!')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello, World!\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# conditional else\n",
-      "\n",
-      "a_list = [1,2]\n",
-      "if a_list[0] == 2:\n",
-      "    print('Hello, World!')\n",
-      "else:\n",
-      "    print('Bye, World!')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Bye, World!\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Why am I showing those simple examples? I think they are good to highlight some of the key points: It is **either** the code under the `if` clause that is executed, **or** the code under the `else` block, but not both.  \n",
-      "If the condition of the `if` clause evaluates to `True`, the `if`-block is exectured, and if it evaluated to `False`, it is the `else` block.  \n",
-      "\n",
-      "### Completion else\n",
-      "**In contrast** to the **either...or*** situation that we know from the conditional `else`, the completion `else` is executed if a code block finished.  \n",
-      "To show you an example, let us use `else` for error-handling:"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Completion else (try-except)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try:\n",
-      "    print('first element:', a_list[0])\n",
-      "except IndexError:\n",
-      "    print('raised IndexError')\n",
-      "else:\n",
-      "    print('no error in try-block')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "first element: 1\n",
-        "no error in try-block\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try:\n",
-      "    print('third element:', a_list[2])\n",
-      "except IndexError:\n",
-      "    print('raised IndexError')\n",
-      "else:\n",
-      "    print('no error in try-block')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "raised IndexError\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "In the code above, we can see that the code under the **`else`-clause is only executed if the `try-block` was executed without encountering an error, i.e., if the `try`-block is \"complete\".**  \n",
-      "The same rule applies to the \"completion\" `else` in while- and for-loops, which you can confirm in the following samples below."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Completion else (while-loop)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 0\n",
-      "while i < 2:\n",
-      "    print(i)\n",
-      "    i += 1\n",
-      "else:\n",
-      "    print('in else')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "in else\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 0\n",
-      "while i < 2:\n",
-      "    print(i)\n",
-      "    i += 1\n",
-      "    break\n",
-      "else:\n",
-      "    print('completed while-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Completion else (for-loop)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(2):\n",
-      "    print(i)\n",
-      "else:\n",
-      "    print('completed for-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "completed for-loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(2):\n",
-      "    print(i)\n",
-      "    break\n",
-      "else:\n",
-      "    print('completed for-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"string_interning\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Interning of compile-time constants vs. run-time expressions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This might not be particularly useful, but it is nonetheless interesting: Python's interpreter is interning compile-time constants but not run-time expressions (note that this is implementation-specific).\n",
-      "\n",
-      "(Original source: [Stackoverflow](http://stackoverflow.com/questions/15541404/python-string-interning))"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let us have a look at the simple example below. Here we are creating 3 variables and assign the value \"Hello\" to them in different ways before we test them for identity."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "hello1 = 'Hello'\n",
-      "\n",
-      "hello2 = 'Hell' + 'o'\n",
-      "\n",
-      "hello3 = 'Hell'\n",
-      "hello3 = hello3 + 'o'\n",
-      "\n",
-      "print('hello1 is hello2:', hello1 is hello2)\n",
-      "print('hello1 is hello3:', hello1 is hello3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello1 is hello2: True\n",
-        "hello1 is hello3: False\n"
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, how does it come that the first expression evaluates to true, but the second does not?  To answer this question, we need to take a closer look at the underlying byte codes:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import dis\n",
-      "def hello1_func():\n",
-      "    s = 'Hello'\n",
-      "    return s\n",
-      "dis.dis(hello1_func)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  3           0 LOAD_CONST               1 ('Hello')\n",
-        "              3 STORE_FAST               0 (s)\n",
-        "\n",
-        "  4           6 LOAD_FAST                0 (s)\n",
-        "              9 RETURN_VALUE\n"
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def hello2_func():\n",
-      "    s = 'Hell' + 'o'\n",
-      "    return s\n",
-      "dis.dis(hello2_func)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  2           0 LOAD_CONST               3 ('Hello')\n",
-        "              3 STORE_FAST               0 (s)\n",
-        "\n",
-        "  3           6 LOAD_FAST                0 (s)\n",
-        "              9 RETURN_VALUE\n"
-       ]
-      }
-     ],
-     "prompt_number": 39
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def hello3_func():\n",
-      "    s = 'Hell'\n",
-      "    s = s + 'o'\n",
-      "    return s\n",
-      "dis.dis(hello3_func)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  2           0 LOAD_CONST               1 ('Hell')\n",
-        "              3 STORE_FAST               0 (s)\n",
-        "\n",
-        "  3           6 LOAD_FAST                0 (s)\n",
-        "              9 LOAD_CONST               2 ('o')\n",
-        "             12 BINARY_ADD\n",
-        "             13 STORE_FAST               0 (s)\n",
-        "\n",
-        "  4          16 LOAD_FAST                0 (s)\n",
-        "             19 RETURN_VALUE\n"
-       ]
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "It looks like that `'Hello'` and `'Hell'` + `'o'` are both evaluated and stored as `'Hello'` at compile-time, whereas the third version   \n",
-      "`s = 'Hell'`  \n",
-      "`s = s + 'o'` seems to be not interned.  Let us quickly confirm the behavior with the following code:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(hello1_func() is hello2_func())\n",
-      "print(hello1_func() is hello3_func())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "True\n",
-        "False\n"
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Finally, to show that this hypothesis is the answer to this rather unexpected observation, let us `intern` the value manually:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sys\n",
-      "\n",
-      "print(hello1_func() is sys.intern(hello3_func()))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "True\n"
-       ]
-      }
-     ],
-     "prompt_number": 45
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"changelog\"></a>\n",
-      "<br>\n",
-      "<br>\n",
-      "<br>\n",
-      "<br>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Changelog"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### 05/03/2014\n",
-      "- new section: else clauses: conditional vs. completion\n",
-      "- new section: Interning of compile-time constants vs. run-time expressions\n",
-      "\n",
-      "#### 05/02/2014\n",
-      "- new section in Python 3.x and Python 2.x key differences: for-loop leak\n",
-      "- new section: Metaclasses - What creates a new instance of a class?  \n",
-      "\n",
-      "#### 05/01/2014\n",
-      "- new section: keyword argument unpacking syntax\n",
-      "\n",
-      "#### 04/27/2014\n",
-      "- minor fixes of typos  \n",
-      "- new section: \"Only the first clause of generators is evaluated immediately\""
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/.ipynb_checkpoints/palindrome_timeit-checkpoint.ipynb b/.ipynb_checkpoints/palindrome_timeit-checkpoint.ipynb
deleted file mode 100644
index ef0e33f..0000000
--- a/.ipynb_checkpoints/palindrome_timeit-checkpoint.ipynb
+++ /dev/null
@@ -1,194 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:6ea19109869c82ee989c8ea0599ec49401e74246a542ad0b7b05f6ef464bda19"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sebastian Raschka 04/2014\n",
-      "\n",
-      "#Timing different Implementations of palindrome functions"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import re\n",
-      "import timeit\n",
-      "import string\n",
-      "\n",
-      "# All functions return True if an input string is a palindrome. Else returns False.\n",
-      "\n",
-      "\n",
-      "\n",
-      "####\n",
-      "#### case-insensitive ignoring punctuation characters\n",
-      "####\n",
-      "\n",
-      "def palindrome_short(my_str):\n",
-      "    stripped_str = \"\".join(l.lower() for l in my_str if l.isalpha())\n",
-      "    return stripped_str == stripped_str[::-1]\n",
-      "\n",
-      "def palindrome_regex(my_str):\n",
-      "    return re.sub('\\W', '', my_str.lower()) == re.sub('\\W', '', my_str[::-1].lower())\n",
-      "\n",
-      "def palindrome_stringlib(my_str):\n",
-      "    LOWERS = set(string.ascii_lowercase)\n",
-      "    letters = [c for c in my_str.lower() if c in LOWERS]\n",
-      "    return letters == letters[::-1]\n",
-      "\n",
-      "LOWERS = set(string.ascii_lowercase)\n",
-      "def palindrome_stringlib2(my_str):\n",
-      "    letters = [c for c in my_str.lower() if c in LOWERS]\n",
-      "    return letters == letters[::-1]\n",
-      "\n",
-      "def palindrome_isalpha(my_str):\n",
-      "    stripped_str = [l for l in my_str.lower() if l.isalpha()]\n",
-      "    return stripped_str == stripped_str[::-1]\n",
-      "\n",
-      "\n",
-      "\n",
-      "####\n",
-      "#### functions considering all characters (case-sensitive)\n",
-      "####\n",
-      "\n",
-      "def palindrome_reverse1(my_str):\n",
-      "    return my_str == my_str[::-1]\n",
-      "\n",
-      "def palindrome_reverse2(my_str):\n",
-      "    return my_str == ''.join(reversed(my_str))\n",
-      "\n",
-      "def palindrome_recurs(my_str):\n",
-      "    if len(my_str) < 2:\n",
-      "        return True\n",
-      "    if my_str[0] != my_str[-1]:\n",
-      "        return False\n",
-      "    return palindrome(my_str[1:-1])\n",
-      "\n",
-      "\n",
-      "\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "test_str = \"Go hang a salami. I'm a lasagna hog.\"\n",
-      "\n",
-      "print('case-insensitive functions ignoring punctuation characters')\n",
-      "%timeit palindrome_short(test_str)\n",
-      "%timeit palindrome_regex(test_str)\n",
-      "%timeit palindrome_stringlib(test_str)\n",
-      "%timeit palindrome_stringlib2(test_str)\n",
-      "%timeit palindrome_isalpha(test_str)\n",
-      "\n",
-      "print('\\n\\nfunctions considering all characters (case-sensitive)')\n",
-      "%timeit palindrome_reverse1(test_str)\n",
-      "%timeit palindrome_reverse2(test_str)\n",
-      "%timeit palindrome_recurs(test_str)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "case-insensitive functions ignoring punctuation characters\n",
-        "100000 loops, best of 3: 15.3 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "100000 loops, best of 3: 19.9 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "100000 loops, best of 3: 13.5 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "100000 loops, best of 3: 8.58 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "100000 loops, best of 3: 9.42 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "\n",
-        "functions considering all characters (case-sensitive)\n",
-        "1000000 loops, best of 3: 508 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "100000 loops, best of 3: 3.08 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000000 loops, best of 3: 480 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/.ipynb_checkpoints/python_true_false-checkpoint.ipynb b/.ipynb_checkpoints/python_true_false-checkpoint.ipynb
deleted file mode 100644
index b2786a2..0000000
--- a/.ipynb_checkpoints/python_true_false-checkpoint.ipynb
+++ /dev/null
@@ -1,578 +0,0 @@
-{
- "metadata": {
-  "name": ""
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sebastian Raschka, 03/2014  \n",
-      "Code was executed in Python 3.4.0"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "###`True` and `False` in the `datetime` module\n",
-      "\n",
-      "Pointed out in a nice article **\"A false midnight\"** at [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/):\n",
-      "\n",
-      "*\"it often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that,   \n",
-      "unlike any other time value, midnight (i.e. datetime.time(0,0,0)) is False.  \n",
-      "A long discussion on the python-ideas mailing list shows that, while surprising,  \n",
-      "that behavior is desirable\u2014at least in some quarters.\"*"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import datetime\n",
-      "\n",
-      "print('\"datetime.time(0,0,0)\" (Midnight) evaluates to', bool(datetime.time(0,0,0)))\n",
-      "\n",
-      "print('\"datetime.time(1,0,0)\" (1 am) evaluates to', bool(datetime.time(1,0,0)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\"datetime.time(0,0,0)\" (Midnight) evaluates to False\n",
-        "\"datetime.time(1,0,0)\" (1 am) evaluates to True\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Boolean `True`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_true_val = True\n",
-      "\n",
-      "\n",
-      "print('my_true_val == True:', my_true_val == True)\n",
-      "print('my_true_val is True:', my_true_val is True)\n",
-      "\n",
-      "print('my_true_val == None:', my_true_val == None)\n",
-      "print('my_true_val is None:', my_true_val is None)\n",
-      "\n",
-      "print('my_true_val == False:', my_true_val == False)\n",
-      "print('my_true_val is False:', my_true_val is False)\n",
-      "\n",
-      "print(my_true_val\n",
-      "if my_true_val:\n",
-      "    print('\"if my_true_val:\" is True')\n",
-      "else:\n",
-      "    print('\"if my_true_val:\" is False')\n",
-      "    \n",
-      "if not my_true_val:\n",
-      "    print('\"if not my_true_val:\" is True')\n",
-      "else:\n",
-      "    print('\"if not my_true_val:\" is False')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my_true_val == True: True\n",
-        "my_true_val is True: True\n",
-        "my_true_val == None: False\n",
-        "my_true_val is None: False\n",
-        "my_true_val == False: False\n",
-        "my_true_val is False: False\n",
-        "\"if my_true_val:\" is True\n",
-        "\"if not my_true_val:\" is False\n"
-       ]
-      }
-     ],
-     "prompt_number": 83
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Boolean `False`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_false_val = False\n",
-      "\n",
-      "\n",
-      "print('my_false_val == True:', my_false_val == True)\n",
-      "print('my_false_val is True:', my_false_val is True)\n",
-      "\n",
-      "print('my_false_val == None:', my_false_val == None)\n",
-      "print('my_false_val is None:', my_false_val is None)\n",
-      "\n",
-      "print('my_false_val == False:', my_false_val == False)\n",
-      "print('my_false_val is False:', my_false_val is False)\n",
-      "\n",
-      "\n",
-      "if my_false_val:\n",
-      "    print('\"if my_false_val:\" is True')\n",
-      "else:\n",
-      "    print('\"if my_false_val:\" is False')\n",
-      "    \n",
-      "if not my_false_val:\n",
-      "    print('\"if not my_false_val:\" is True')\n",
-      "else:\n",
-      "    print('\"if not my_false_val:\" is False')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my_false_val == True: False\n",
-        "my_false_val is True: False\n",
-        "my_false_val == None: False\n",
-        "my_false_val is None: False\n",
-        "my_false_val == False: True\n",
-        "my_false_val is False: True\n",
-        "\"if my_false_val:\" is False\n",
-        "\"if not my_false_val:\" is True\n"
-       ]
-      }
-     ],
-     "prompt_number": 76
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## `None` 'value'"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_none_var = None\n",
-      "\n",
-      "print('my_none_var == True:', my_none_var == True)\n",
-      "print('my_none_var is True:', my_none_var is True)\n",
-      "\n",
-      "print('my_none_var == None:', my_none_var == None)\n",
-      "print('my_none_var is None:', my_none_var is None)\n",
-      "\n",
-      "print('my_none_var == False:', my_none_var == False)\n",
-      "print('my_none_var is False:', my_none_var is False)\n",
-      "\n",
-      "\n",
-      "if my_none_var:\n",
-      "    print('\"if my_none_var:\" is True')\n",
-      "else:\n",
-      "    print('\"if my_none_var:\" is False')\n",
-      "\n",
-      "if not my_none_var:\n",
-      "    print('\"if not my_none_var:\" is True')\n",
-      "else:\n",
-      "    print('\"if not my_none_var:\" is False')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my_none_var == True: False\n",
-        "my_none_var is True: False\n",
-        "my_none_var == None: True\n",
-        "my_none_var is None: True\n",
-        "my_none_var == False: False\n",
-        "my_none_var is False: False\n",
-        "\"if my_none_var:\" is False\n",
-        "\"if not my_none_var:\" is True\n"
-       ]
-      }
-     ],
-     "prompt_number": 62
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Empty String"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_empty_string = \"\"\n",
-      "\n",
-      "print('my_empty_string == True:', my_empty_string == True)\n",
-      "print('my_empty_string is True:', my_empty_string is True)\n",
-      "\n",
-      "print('my_empty_string == None:', my_empty_string == None)\n",
-      "print('my_empty_string is None:', my_empty_string is None)\n",
-      "\n",
-      "print('my_empty_string == False:', my_empty_string == False)\n",
-      "print('my_empty_string is False:', my_empty_string is False)\n",
-      "\n",
-      "\n",
-      "if my_empty_string:\n",
-      "    print('\"if my_empty_string:\" is True')\n",
-      "else:\n",
-      "    print('\"if my_empty_string:\" is False')\n",
-      "    \n",
-      "if not my_empty_string:\n",
-      "    print('\"if not my_empty_string:\" is True')\n",
-      "else:\n",
-      "    print('\"if not my_empty_string:\" is False')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my_empty_string == True: False\n",
-        "my_empty_string is True: False\n",
-        "my_empty_string == None: False\n",
-        "my_empty_string is None: False\n",
-        "my_empty_string == False: False\n",
-        "my_empty_string is False: False\n",
-        "\"if my_empty_string:\" is False\n",
-        "\"if my_empty_string:\" is True\n"
-       ]
-      }
-     ],
-     "prompt_number": 61
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Empty List\n",
-      "It is generally not a good idea to use the `==` to check for empty lists..."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_empty_list = []\n",
-      "\n",
-      "\n",
-      "print('my_empty_list == True:', my_empty_list == True)\n",
-      "print('my_empty_list is True:', my_empty_list is True)\n",
-      "\n",
-      "print('my_empty_list == None:', my_empty_list == None)\n",
-      "print('my_empty_list is None:', my_empty_list is None)\n",
-      "\n",
-      "print('my_empty_list == False:', my_empty_list == False)\n",
-      "print('my_empty_list is False:', my_empty_list is False)\n",
-      "\n",
-      "\n",
-      "if my_empty_list:\n",
-      "    print('\"if my_empty_list:\" is True')\n",
-      "else:\n",
-      "    print('\"if my_empty_list:\" is False')\n",
-      "    \n",
-      "if not my_empty_list:\n",
-      "    print('\"if not my_empty_list:\" is True')\n",
-      "else:\n",
-      "    print('\"if not my_empty_list:\" is False')\n",
-      "\n",
-      "\n",
-      "    \n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my_empty_list == True: False\n",
-        "my_empty_list is True: False\n",
-        "my_empty_list == None: False\n",
-        "my_empty_list is None: False\n",
-        "my_empty_list == False: False\n",
-        "my_empty_list is False: False\n",
-        "\"if my_empty_list:\" is False\n",
-        "\"if not my_empty_list:\" is True\n"
-       ]
-      }
-     ],
-     "prompt_number": 67
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## [0]-List"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_zero_list = [0]\n",
-      "\n",
-      "\n",
-      "print('my_zero_list == True:', my_zero_list == True)\n",
-      "print('my_zero_list is True:', my_zero_list is True)\n",
-      "\n",
-      "print('my_zero_list == None:', my_zero_list == None)\n",
-      "print('my_zero_list is None:', my_zero_list is None)\n",
-      "\n",
-      "print('my_zero_list == False:', my_zero_list == False)\n",
-      "print('my_zero_list is False:', my_zero_list is False)\n",
-      "\n",
-      "\n",
-      "if my_zero_list:\n",
-      "    print('\"if my_zero_list:\" is True')\n",
-      "else:\n",
-      "    print('\"if my_zero_list:\" is False')\n",
-      "    \n",
-      "if not my_zero_list:\n",
-      "    print('\"if not my_zero_list:\" is True')\n",
-      "else:\n",
-      "    print('\"if not my_zero_list:\" is False')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my_zero_list == True: False\n",
-        "my_zero_list is True: False\n",
-        "my_zero_list == None: False\n",
-        "my_zero_list is None: False\n",
-        "my_zero_list == False: False\n",
-        "my_zero_list is False: False\n",
-        "\"if my_zero_list:\" is True\n",
-        "\"if not my_zero_list:\" is False\n"
-       ]
-      }
-     ],
-     "prompt_number": 70
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## List comparison  \n",
-      "List comparisons are a handy way to show the difference between `==` and `is`.  \n",
-      "While `==` is rather evaluating the equality of the value, `is` is checking if two objects are equal.\n",
-      "The examples below show that we can assign a pointer to the same list object by using `=`, e.g., `list1 = list2`.   \n",
-      "a) If we want to make a **shallow** copy of the list values, we have to make a little tweak: `list1 = list2[:]`, or  \n",
-      "b) a **deepcopy** via `list1 = copy.deepcopy(list2)`\n",
-      "\n",
-      "Possibly the best explanation of shallow vs. deep copies I've read so far:\n",
-      "\n",
-      "*** \"Shallow copies duplicate as little as possible. A shallow copy of a collection is a copy of the collection structure, not the elements. With a shallow copy, two collections now share the individual elements.\n",
-      "Deep copies duplicate everything. A deep copy of a collection is two collections with all of the elements in the original collection duplicated.\"***\n",
-      "\n",
-      "(via [S.Lott](http://stackoverflow.com/users/10661/s-lott) on [StackOverflow](http://stackoverflow.com/questions/184710/what-is-the-difference-between-a-deep-copy-and-a-shallow-copy))"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "###a) Shallow vs. deep copies for simple elements  \n",
-      "List modification of the original list doesn't affect  \n",
-      "shallow copies or deep copies if the list contains literals."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from copy import deepcopy\n",
-      "\n",
-      "my_first_list = [1]\n",
-      "my_second_list = [1]\n",
-      "print('my_first_list == my_second_list:', my_first_list == my_second_list)\n",
-      "print('my_first_list is my_second_list:', my_first_list is my_second_list)\n",
-      "\n",
-      "my_third_list = my_first_list\n",
-      "print('my_first_list == my_third_list:', my_first_list == my_third_list)\n",
-      "print('my_first_list is my_third_list:', my_first_list is my_third_list)\n",
-      "\n",
-      "my_shallow_copy = my_first_list[:]\n",
-      "print('my_first_list == my_shallow_copy:', my_first_list == my_shallow_copy)\n",
-      "print('my_first_list is my_shallow_copy:', my_first_list is my_shallow_copy)\n",
-      "\n",
-      "my_deep_copy = deepcopy(my_first_list)\n",
-      "print('my_first_list == my_deep_copy:', my_first_list == my_deep_copy)\n",
-      "print('my_first_list is my_deep_copy:', my_first_list is my_deep_copy)\n",
-      "\n",
-      "print('\\nmy_third_list:', my_third_list)\n",
-      "print('my_shallow_copy:', my_shallow_copy)\n",
-      "print('my_deep_copy:', my_deep_copy)\n",
-      "\n",
-      "my_first_list[0] = 2\n",
-      "print('after setting \"my_first_list[0] = 2\"')\n",
-      "print('my_third_list:', my_third_list)\n",
-      "print('my_shallow_copy:', my_shallow_copy)\n",
-      "print('my_deep_copy:', my_deep_copy)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my_first_list == my_second_list: True\n",
-        "my_first_list is my_second_list: False\n",
-        "my_first_list == my_third_list: True\n",
-        "my_first_list is my_third_list: True\n",
-        "my_first_list == my_shallow_copy: True\n",
-        "my_first_list is my_shallow_copy: False\n",
-        "my_first_list == my_deep_copy: True\n",
-        "my_first_list is my_deep_copy: False\n",
-        "\n",
-        "my_third_list: [1]\n",
-        "my_shallow_copy: [1]\n",
-        "my_deep_copy: [1]\n",
-        "after setting \"my_first_list[0] = 2\"\n",
-        "my_third_list: [2]\n",
-        "my_shallow_copy: [1]\n",
-        "my_deep_copy: [1]\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### b) Shallow vs. deep copies if list contains other structures and objects\n",
-      "List modification of the original list does affect  \n",
-      "shallow copies, but not deep copies if the list contains compound objects."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_first_list = [[1],[2]]\n",
-      "my_second_list = [[1],[2]]\n",
-      "print('my_first_list == my_second_list:', my_first_list == my_second_list)\n",
-      "print('my_first_list is my_second_list:', my_first_list is my_second_list)\n",
-      "\n",
-      "my_third_list = my_first_list\n",
-      "print('my_first_list == my_third_list:', my_first_list == my_third_list)\n",
-      "print('my_first_list is my_third_list:', my_first_list is my_third_list)\n",
-      "\n",
-      "my_shallow_copy = my_first_list[:]\n",
-      "print('my_first_list == my_shallow_copy:', my_first_list == my_shallow_copy)\n",
-      "print('my_first_list is my_shallow_copy:', my_first_list is my_shallow_copy)\n",
-      "\n",
-      "my_deep_copy = deepcopy(my_first_list)\n",
-      "print('my_first_list == my_deep_copy:', my_first_list == my_deep_copy)\n",
-      "print('my_first_list is my_deep_copy:', my_first_list is my_deep_copy)\n",
-      "\n",
-      "print('\\nmy_third_list:', my_third_list)\n",
-      "print('my_shallow_copy:', my_shallow_copy)\n",
-      "print('my_deep_copy:', my_deep_copy)\n",
-      "\n",
-      "my_first_list[0][0] = 2\n",
-      "print('after setting \"my_first_list[0][0] = 2\"')\n",
-      "print('my_third_list:', my_third_list)\n",
-      "print('my_shallow_copy:', my_shallow_copy)\n",
-      "print('my_deep_copy:', my_deep_copy)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "my_first_list == my_second_list: True\n",
-        "my_first_list is my_second_list: False\n",
-        "my_first_list == my_third_list: True\n",
-        "my_first_list is my_third_list: True\n",
-        "my_first_list == my_shallow_copy: True\n",
-        "my_first_list is my_shallow_copy: False\n",
-        "my_first_list == my_deep_copy: True\n",
-        "my_first_list is my_deep_copy: False\n",
-        "\n",
-        "my_third_list: [[1], [2]]\n",
-        "my_shallow_copy: [[1], [2]]\n",
-        "my_deep_copy: [[1], [2]]\n",
-        "after setting \"my_first_list[0][0] = 2\"\n",
-        "my_third_list: [[2], [2]]\n",
-        "my_shallow_copy: [[2], [2]]\n",
-        "my_deep_copy: [[1], [2]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Some Python oddity:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 1\n",
-      "b = 1\n",
-      "print('a is b', bool(a is b))\n",
-      "True\n",
-      "\n",
-      "a = 999\n",
-      "b = 999\n",
-      "print('a is b', bool(a is b))\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "a is b True\n",
-        "a is b False\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb b/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb
deleted file mode 100644
index 70cbf9d..0000000
--- a/.ipynb_checkpoints/scope_resolution_legb_rule-checkpoint.ipynb
+++ /dev/null
@@ -1,1092 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:eb9ececefb4c35b16d0496c7f613e4a64bb35b2e9f79b7e049c5b434bd2e6654"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
-      "last updated: 04/28/2014\n",
-      "\n",
-      "- [Link to the containing GitHub Repository](https://github.com/rasbt/python_reference)\n",
-      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb)\n",
-      "\n",
-      "Note: The code in this IPython notebook was executed in Python 3.4.0"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "I am really looking forward to your comments and suggestions to improve and extend this tutorial! Just send me a quick note   \n",
-      "via Twitter: [@rasbt](https://twitter.com/rasbt)  \n",
-      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#A beginner's guide to Python's namespaces, scope resolution, and the LEGB rule"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download this IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb)."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"sections\"><a/>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Sections\n",
-      "- [Introduction to namespaces and scopes](#introduction)  \n",
-      "- [1. LG - Local and Global scopes](#section_1)  \n",
-      "- [2. LEG - Local, Enclosed, and Global scope](#section_2)  \n",
-      "- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3)  \n",
-      "- [Self-assessment exercise](#assessment)\n",
-      "- [Conclusion](#conclusion)  \n",
-      "- [Solutions](#solutions)\n",
-      "- [Warning: For-loop variables \"leaking\" into the global namespace](#for_loop)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Objectives\n",
-      "- Namespaces and scopes - where does Python look for variable names?\n",
-      "- Can we define/reuse variable names for multiple objects at the same time?\n",
-      "- In which order does Python search different namespaces for variable names?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"introduction\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Introduction to namespaces and scopes"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Namespaces"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object.  \n",
-      "Such a \"name-to-object\" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = \"Hello string\"`, we created a reference to the `\"Hello string\"` object, and henceforth we can access via its variable name `a_string`.\n",
-      "\n",
-      "We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., \n",
-      "\n",
-      "<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}</pre>  \n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example:\n",
-      "\n",
-      "<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}\n",
-      "b_namespace = {'name_a':object_3, 'name_b':object_4, ...}</pre>\n",
-      "\n",
-      "For example, everytime we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called \"scope\"), which we will discuss in more detail in the next section."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Scope"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of \"scope\". The \"scope\" in Python defines the \"hierarchy level\" in which we search namespaces for certain \"name-to-object\" mappings.  \n",
-      "For example, let us consider the following code:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 1\n",
-      "\n",
-      "def foo():\n",
-      "    i = 5\n",
-      "    print(i, 'in foo()')\n",
-      "\n",
-      "print(i, 'global')\n",
-      "\n",
-      "foo()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 global\n",
-        "5 in foo()\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here, we just defined the variable name `i` twice, once on the `foo` function."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- `foo_namespace = {'i':object_3, ...}`  \n",
-      "- `global_namespace = {'i':object_1, 'name_b':object_2, ...}`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So, how does Python now which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Tip:\n",
-      "If we want to print out the dictionary mapping of the global and local variables, we can use the\n",
-      "the functions `global()` and `local()"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#print(globals()) # prints global namespace\n",
-      "#print(locals()) # prints local namespace\n",
-      "\n",
-      "glob = 1\n",
-      "\n",
-      "def foo():\n",
-      "    loc = 5\n",
-      "    print('loc in foo():', 'loc' in locals())\n",
-      "\n",
-      "foo()\n",
-      "print('loc in global:', 'loc' in globals())    \n",
-      "print('glob in global:', 'foo' in globals())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "loc in foo(): True\n",
-        "loc in global: False\n",
-        "glob in global: True\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Scope resolution for variable names via the LEGB rule."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The \"scope\" defines on which hierarchy level Python searches for a particular \"variable name\" for its associated object. Now, the next question is: \"In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?\"  \n",
-      "To answer is: It uses the LEGB-rule, which stands for\n",
-      "\n",
-      "**Local -> Enclosed -> Global -> Built-in**, \n",
-      "\n",
-      "where the arrows should denote the direction of the namespace-hierarchy search order.  \n",
-      "\n",
-      "- *Local* can be inside a function or class method, for example.  \n",
-      "- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function.  \n",
-      "- *Global* refers to the uppermost level of the executing script itself, and  \n",
-      "- *Built-in* are special names that Python reserves for itself.  \n",
-      "\n",
-      "So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the global namespaces (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).\n",
-      "\n",
-      "**Note**:  \n",
-      "Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy\n",
-      "import math\n",
-      "import scipy\n",
-      "\n",
-      "print(math.pi, 'from the math module')\n",
-      "print(numpy.pi, 'from the numpy package')\n",
-      "print(scipy.pi, 'from the scipy package')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3.141592653589793 from the math module\n",
-        "3.141592653589793 from the numpy package\n",
-        "3.141592653589793 from the scipy package\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "(This is also why we have to be careful if we import modules via \"`from a_module import *`\", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"section_1\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## 1. LG - Local and Global scopes"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Example 1.1**  \n",
-      "As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes.  \n",
-      "What does the following code print?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global variable'\n",
-      "\n",
-      "def a_func():\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
-      "\n",
-      "a_func()\n",
-      "print(a_var, '[ a_var outside a_func() ]')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**a)**\n",
-      "<pre>raises an error</pre>\n",
-      "\n",
-      "**b)** \n",
-      "<pre>\n",
-      "global value [ a_var outside a_func() ]</pre>\n",
-      "\n",
-      "**c)** \n",
-      "<pre>global value [ a_var in a_func() ]  \n",
-      "global value [ a_var outside a_func() ]</pre>\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Here is why:\n",
-      "\n",
-      "We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously.\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Example 1.2**  \n",
-      "Now, let us define the variable `a_var` in the global and the local scope.  \n",
-      "Can you guess what the following code will produce?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global value'\n",
-      "\n",
-      "def a_func():\n",
-      "    a_var = 'local value'\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
-      "\n",
-      "a_func()\n",
-      "print(a_var, '[ a_var outside a_func() ]')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**a)**\n",
-      "<pre>raises an error</pre>\n",
-      "\n",
-      "**b)** \n",
-      "<pre>local value [ a_var in a_func() ]\n",
-      "global value [ a_var outside a_func() ]</pre>\n",
-      "\n",
-      "**c)** \n",
-      "<pre>global value [ a_var in a_func() ]  \n",
-      "global value [ a_var outside a_func() ]</pre>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Here is why:\n",
-      "\n",
-      "When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global value'\n",
-      "\n",
-      "def a_func():\n",
-      "    global a_var\n",
-      "    a_var = 'local value'\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
-      "\n",
-      "print(a_var, '[ a_var outside a_func() ]')\n",
-      "a_func()\n",
-      "print(a_var, '[ a_var outside a_func() ]')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "global value [ a_var outside a_func() ]\n",
-        "local value [ a_var inside a_func() ]\n",
-        "local value [ a_var outside a_func() ]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 1\n",
-      "\n",
-      "def a_func():\n",
-      "    a_var = a_var + 1\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
-      "\n",
-      "print(a_var, '[ a_var outside a_func() ]')\n",
-      "a_func()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "UnboundLocalError",
-       "evalue": "local variable 'a_var' referenced before assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-4-a6cdd0ee9a55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var outside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m<ipython-input-4-a6cdd0ee9a55>\u001b[0m in \u001b[0;36ma_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0ma_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_var\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var inside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'a_var' referenced before assignment"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 [ a_var outside a_func() ]\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"section_2\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## 2. LEG - Local, Enclosed, and Global scope\n",
-      "\n",
-      "\n",
-      "\n",
-      "Now, let us introduce the concept of the enclosed (E) scope. Following the order \"Local -> Enclosed -> Global\", can you guess what the following code will print?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Example 2.1**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global value'\n",
-      "\n",
-      "def outer():\n",
-      "    a_var = 'enclosed value'\n",
-      "    \n",
-      "    def inner():\n",
-      "        a_var = 'local value'\n",
-      "        print(a_var)\n",
-      "    \n",
-      "    inner()\n",
-      "\n",
-      "outer()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**a)**\n",
-      "<pre>global value</pre>\n",
-      "\n",
-      "**b)** \n",
-      "<pre>enclosed value</pre>\n",
-      "\n",
-      "**c)** \n",
-      "<pre>local value</pre>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Here is why:\n",
-      "\n",
-      "Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitely access a variable from the outer (enclosed) scope in order to modify its value.  \n",
-      "Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global value'\n",
-      "\n",
-      "def outer():\n",
-      "       a_var = 'local value'\n",
-      "       print('outer before:', a_var)\n",
-      "       def inner():\n",
-      "           nonlocal a_var\n",
-      "           a_var = 'inner value'\n",
-      "           print('in inner():', a_var)\n",
-      "       inner()\n",
-      "       print(\"outer after:\", a_var)\n",
-      "outer()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "outer before: local value\n",
-        "in inner(): inner value\n",
-        "outer after: inner value\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"section_3\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## 3. LEGB - Local, Enclosed, Global, Built-in\n",
-      "\n",
-      "To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our \"own\" length-funcion, which happens to bear the same name as the in-built `len()` function. What outcome do you excpect if we'd execute the following code?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Example 3**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global variable'\n",
-      "\n",
-      "def len(in_var):\n",
-      "    print('called my len() function')\n",
-      "    l = 0\n",
-      "    for i in in_var:\n",
-      "        l += 1\n",
-      "    return l\n",
-      "\n",
-      "def a_func(in_var):\n",
-      "    len_in_var = len(in_var)\n",
-      "    print('Input variable is of length', len_in_var)\n",
-      "\n",
-      "a_func('Hello, World!')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**a)**\n",
-      "<pre>raises an error (conflict with in-built `len()` function)</pre>\n",
-      "\n",
-      "**b)** \n",
-      "<pre>called my len() function\n",
-      "Input variable is of length 13</pre>\n",
-      "\n",
-      "**c)** \n",
-      "<pre>Input variable is of length 13</pre>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Here is why:\n",
-      "\n",
-      "Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration pruposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope first before it attempts"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name =\"assessment\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Self-assessment exercise"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 'global'\n",
-      "\n",
-      "def outer():\n",
-      "    \n",
-      "    def len(in_var):\n",
-      "        print('called my len() function: ', end=\"\")\n",
-      "        l = 0\n",
-      "        for i in in_var:\n",
-      "            l += 1\n",
-      "        return l\n",
-      "    \n",
-      "    a = 'local'\n",
-      "    \n",
-      "    def inner():\n",
-      "        global len\n",
-      "        nonlocal a\n",
-      "        a += ' variable'\n",
-      "    inner()\n",
-      "    print('a is', a)\n",
-      "    print(len(a))\n",
-      "\n",
-      "\n",
-      "outer()\n",
-      "\n",
-      "print(len(a))\n",
-      "print('a is', a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 59
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"conclusion\"\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Conclusion"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### A rule of thumb"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug.  \n",
-      "If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value.  \n",
-      "For example:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 2\n",
-      "\n",
-      "def a_func(some_var):\n",
-      "    return 2**3\n",
-      "\n",
-      "a_var = a_func(a_var)\n",
-      "print(a_var)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "8\n"
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name = \"solutions\">\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Solutions\n",
-      "\n",
-      "In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Example 1.1:', chr(int('01100011',2)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Example 1.2:', chr(int('01100001',2)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Example 2.1:', chr(int('01100011',2)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Example 3.1:', chr(int('01100010',2)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Execute to run the self-assessment solution\n",
-      "\n",
-      "sol = \"000010100110111101110101011101000110010101110010001010\"\\\n",
-      "\"0000101001001110100000101000001010011000010010000001101001011100110\"\\\n",
-      "\"0100000011011000110111101100011011000010110110000100000011101100110\"\\\n",
-      "\"0001011100100110100101100001011000100110110001100101000010100110001\"\\\n",
-      "\"1011000010110110001101100011001010110010000100000011011010111100100\"\\\n",
-      "\"1000000110110001100101011011100010100000101001001000000110011001110\"\\\n",
-      "\"1010110111001100011011101000110100101101111011011100011101000100000\"\\\n",
-      "\"0011000100110100000010100000101001100111011011000110111101100010011\"\\\n",
-      "\"0000101101100001110100000101000001010001101100000101001100001001000\"\\\n",
-      "\"0001101001011100110010000001100111011011000110111101100010011000010\"\\\n",
-      "\"1101100\"\n",
-      "\n",
-      "sol_str =''.join(chr(int(sol[i:i+8], 2)) for i in range(0, len(sol), 8))\n",
-      "for line in sol_str.split('\\n'):\n",
-      "    print(line)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 58
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name=\"for_loop\"></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Warning: For-loop variables \"leaking\" into the global namespace"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for a in range(5):\n",
-      "    if a == 4:\n",
-      "        print(a, '-> a in for-loop')\n",
-      "print(a, '-> a in global')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4 -> a in for-loop\n",
-        "4 -> a in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**This also applies if we explicitely defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = 1\n",
-      "for b in range(5):\n",
-      "    if b == 4:\n",
-      "        print(b, '-> b in for-loop')\n",
-      "print(b, '-> b in global')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4 -> b in for-loop\n",
-        "4 -> b in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 1\n",
-      "print([i for i in range(5)])\n",
-      "print(i, '-> i in global')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0, 1, 2, 3, 4]\n",
-        "1 -> i in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Why did I mention \"Python 3.x\"? Well, as it happens, the same code executed in Python 2.x would print:\n",
-      "\n",
-      "<pre>\n",
-      "4 -> i in global\n",
-      "<pre>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
-      "\n",
-      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/.ipynb_checkpoints/timeit_test-checkpoint.ipynb b/.ipynb_checkpoints/timeit_test-checkpoint.ipynb
deleted file mode 100644
index dd46362..0000000
--- a/.ipynb_checkpoints/timeit_test-checkpoint.ipynb
+++ /dev/null
@@ -1,628 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:5a2264b30b9632e14bd425a887a4455658fbdf9f8102fc5703ad982c3fa09b21"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sebastian Raschka  \n",
-      "last updated: 04/14/2014  \n",
-      "\n",
-      "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/timeit_test.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "# Python benchmarks via `timeit`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Sections\n",
-      "- [String operations](#string_operations)\n",
-      "    - [String formatting: .format() vs. binary operator %s](#str_format_bin)\n",
-      "    - [String reversing: [::-1] vs. `''.join(reversed())`](#str_reverse)\n",
-      "    - [String concatenation: `+=` vs. `''.join()`](#string_concat)\n",
-      "    - [Assembling strings](#string_assembly)  \n",
-      "- [List operations](#list_operations)\n",
-      "    - [List reversing: [::-1] vs. reverse() vs. reversed()](#list_reverse)\n",
-      "    - [Creating lists using conditional statements](#create_cond_list)\n",
-      "- [Dictionary operations](#dict_ops) \n",
-      "    - [Adding elements to a dictionary](#adding_dict_elements)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='string_operations'></a>\n",
-      "\n",
-      "# String operations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='str_format_bin'></a>\n",
-      "## String formatting: `.format()` vs. binary operator `%s`\n",
-      "\n",
-      "We expect the string .format() method to perform slower than %, because it is doing the formatting for each object itself, where formatting via the binary % is hard-coded for known types. But let's see how big the difference really is..."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def test_format():\n",
-      "    return ['{}'.format(i) for i in range(1000000)]\n",
-      "\n",
-      "def test_binaryop():\n",
-      "    return ['%s' %i for i in range(1000000)]\n",
-      "\n",
-      "%timeit test_format()\n",
-      "%timeit test_binaryop()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 400 ms per loop\n",
-        "1 loops, best of 3: 241 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='str_reverse'></a>\n",
-      "## String reversing: `[::-1]` vs. `''.join(reversed())`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def reverse_join(my_str):\n",
-      "    return ''.join(reversed(my_str))\n",
-      "    \n",
-      "def reverse_slizing(my_str):\n",
-      "    return my_str[::-1]\n",
-      "\n",
-      "\n",
-      "# Test to show that both work\n",
-      "a = reverse_join('abcd')\n",
-      "b = reverse_slizing('abcd')\n",
-      "assert(a == b and a == 'dcba')\n",
-      "\n",
-      "%timeit reverse_join('abcd')\n",
-      "%timeit reverse_slizing('abcd')\n",
-      "\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.4 GHz Intel Core Duo\n",
-      "# 8 GB 1067 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 1.28 \u00b5s per loop\n",
-        "1000000 loops, best of 3: 337 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='string_concat'></a>\n",
-      "## String concatenation: `+=` vs. `''.join()`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Strings in Python are immutable objects. So, each time we append a character to a string, it has to be created \u201cfrom scratch\u201d in memory. Thus, the answer to the question \u201cWhat is the most efficient way to concatenate strings?\u201d is a quite obvious, but the relative numbers of performance gains are nonetheless interesting."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def string_add(in_chars):\n",
-      "    new_str = ''\n",
-      "    for char in in_chars:\n",
-      "        new_str += char\n",
-      "    return new_str\n",
-      "\n",
-      "def string_join(in_chars):\n",
-      "    return ''.join(in_chars)\n",
-      "\n",
-      "test_chars = ['a', 'b', 'c', 'd', 'e', 'f']\n",
-      "\n",
-      "%timeit string_add(test_chars)\n",
-      "%timeit string_join(test_chars)\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 595 ns per loop\n",
-        "1000000 loops, best of 3: 269 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='string_assembly'></a>\n",
-      "## Assembling strings\n",
-      "\n",
-      "Next, I wanted to compare different methods string \u201cassembly.\u201d This is different from simple string concatenation, which we have seen in the previous section, since we insert values into a string, e.g., from a variable."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def plus_operator():\n",
-      "    return 'a' + str(1) + str(2) \n",
-      "    \n",
-      "def format_method():\n",
-      "    return 'a{}{}'.format(1,2)\n",
-      "    \n",
-      "def binary_operator():\n",
-      "    return 'a%s%s' %(1,2)\n",
-      "\n",
-      "%timeit plus_operator()\n",
-      "%timeit format_method()\n",
-      "%timeit binary_operator()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 764 ns per loop\n",
-        "1000000 loops, best of 3: 494 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000000 loops, best of 3: 79.3 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='list_operations'></a>\n",
-      "# List operations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='list_reverse'></a>\n",
-      "## List reversing - `[::-1]` vs. `reverse()` vs. `reversed()`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def reverse_func(my_list):\n",
-      "    new_list = my_list[:]\n",
-      "    new_list.reverse()\n",
-      "    return new_list\n",
-      "    \n",
-      "def reversed_func(my_list):\n",
-      "    return list(reversed(my_list))\n",
-      "\n",
-      "def reverse_slizing(my_list):\n",
-      "    return my_list[::-1]\n",
-      "\n",
-      "%timeit reverse_func([1,2,3,4,5])\n",
-      "%timeit reversed_func([1,2,3,4,5])\n",
-      "%timeit reverse_slizing([1,2,3,4,5])\n",
-      "\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.4 GHz Intel Core Duo\n",
-      "# 8 GB 1067 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 930 ns per loop\n",
-        "1000000 loops, best of 3: 1.89 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000000 loops, best of 3: 775 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='create_cond_list'></a>\n",
-      "## Creating lists using conditional statements\n",
-      "\n",
-      "In this test, I attempted to figure out the fastest way to create a new list of elements that meet a certain criterion. For the sake of simplicity, the criterion was to check if an element is even or odd, and only if the element was even, it should be included in the list. For example, the resulting list for numbers in the range from 1 to 10 would be \n",
-      "[2, 4, 6, 8, 10].\n",
-      "\n",
-      "Here, I tested three different approaches:  \n",
-      "1) a simple for loop with an if-statement check (`cond_loop()`)  \n",
-      "2) a list comprehension (`list_compr()`)  \n",
-      "3) the built-in filter() function (`filter_func()`)  \n",
-      "\n",
-      "Note that the filter() function now returns a generator in Python 3, so I had to wrap it in an additional list() function call."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def cond_loop():\n",
-      "    even_nums = []\n",
-      "    for i in range(100):\n",
-      "        if i % 2 == 0:\n",
-      "            even_nums.append(i)\n",
-      "    return even_nums\n",
-      "\n",
-      "def list_compr():\n",
-      "    even_nums = [i for i in range(100) if i % 2 == 0]\n",
-      "    return even_nums\n",
-      "    \n",
-      "def filter_func():\n",
-      "    even_nums = list(filter((lambda x: x % 2 != 0), range(100)))\n",
-      "    return even_nums\n",
-      "\n",
-      "%timeit cond_loop()\n",
-      "%timeit list_compr()\n",
-      "%timeit filter_func()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "100000 loops, best of 3: 14.4 \u00b5s per loop\n",
-        "100000 loops, best of 3: 12 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 23.9 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='dict_ops'></a>\n",
-      "# Dictionary operations "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='adding_dict_elements'></a>\n",
-      "## Adding elements to a Dictionary\n",
-      "\n",
-      "All three functions below count how often different elements (values) occur in a list.  \n",
-      "E.g., for the list ['a', 'b', 'a', 'c'], the dictionary would look like this:  \n",
-      "`my_dict = {'a': 2, 'b': 1, 'c': 1}`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import random\n",
-      "import copy\n",
-      "import timeit\n",
-      "\n",
-      "\n",
-      "\n",
-      "def add_element_check1(my_dict, elements):\n",
-      "    for e in elements:\n",
-      "        if e not in my_dict:\n",
-      "            my_dict[e] = 1\n",
-      "        else:\n",
-      "            my_dict[e] += 1\n",
-      "            \n",
-      "def add_element_check2(my_dict, elements):\n",
-      "    for e in elements:\n",
-      "        if e not in my_dict:\n",
-      "            my_dict[e] = 0\n",
-      "        my_dict[e] += 1            \n",
-      "\n",
-      "def add_element_except(my_dict, elements):\n",
-      "    for e in elements:\n",
-      "        try:\n",
-      "            my_dict[e] += 1\n",
-      "        except KeyError:\n",
-      "            my_dict[e] = 1\n",
-      "            \n",
-      "\n",
-      "random.seed(123)\n",
-      "rand_ints = [random.randrange(1, 10) for i in range(100)]\n",
-      "empty_dict = {}\n",
-      "\n",
-      "print('Results for 100 integers in range 1-10') \n",
-      "%timeit add_element_check1(copy.deepcopy(empty_dict), rand_ints)\n",
-      "%timeit add_element_check2(copy.deepcopy(empty_dict), rand_ints)\n",
-      "%timeit add_element_except(copy.deepcopy(empty_dict), rand_ints)\n",
-      "            \n",
-      "print('\\nResults for 1000 integers in range 1-10')            \n",
-      "rand_ints = [random.randrange(1, 10) for i in range(1000)]\n",
-      "empty_dict = {}\n",
-      "\n",
-      "%timeit add_element_check1(copy.deepcopy(empty_dict), rand_ints)\n",
-      "%timeit add_element_check2(copy.deepcopy(empty_dict), rand_ints)\n",
-      "%timeit add_element_except(copy.deepcopy(empty_dict), rand_ints)\n",
-      "\n",
-      "print('\\nResults for 1000 integers in range 1-1000')            \n",
-      "rand_ints = [random.randrange(1, 10) for i in range(1000)]\n",
-      "empty_dict = {}\n",
-      "\n",
-      "%timeit add_element_check1(copy.deepcopy(empty_dict), rand_ints)\n",
-      "%timeit add_element_check2(copy.deepcopy(empty_dict), rand_ints)\n",
-      "%timeit add_element_except(copy.deepcopy(empty_dict), rand_ints)\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Results for 100 integers in range 1-10\n",
-        "100000 loops, best of 3: 16.6 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "100000 loops, best of 3: 17.6 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "100000 loops, best of 3: 17.9 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Results for 1000 integers in range 1-10\n",
-        "10000 loops, best of 3: 135 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 125 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 105 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Results for 1000 integers in range 1-1000\n",
-        "10000 loops, best of 3: 122 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 123 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 104 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Conclusion\n",
-      "Interestingly, the `try-except` loop pays off if we have more elements (here: 1000 integers instead of 100) as dictionary keys to check. Also, it doesn't matter much whether the elements exist or do not exist in the dictionary, yet."
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb b/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
deleted file mode 100644
index d1bc680..0000000
--- a/.ipynb_checkpoints/timeit_tests-checkpoint.ipynb
+++ /dev/null
@@ -1,1883 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:75d807f509bd9f76b2e14a5a048cb44852a3318bcd0d95afc95d1c9b2904c078"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Sebastian Raschka  \n",
-      "last updated: 04/14/2014  \n",
-      "\n",
-      "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "I am really looking forward to your comments and suggestions to improve and extend this collection! Just send me a quick note   \n",
-      "via Twitter: [@rasbt](https://twitter.com/rasbt)  \n",
-      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "# Python benchmarks via `timeit`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"sections\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Sections\n",
-      "- [String operations](#string_operations)\n",
-      "    - [String formatting: .format() vs. binary operator %s](#str_format_bin)\n",
-      "    - [String reversing: [::-1] vs. `''.join(reversed())`](#str_reverse)\n",
-      "    - [String concatenation: `+=` vs. `''.join()`](#string_concat)\n",
-      "    - [Assembling strings](#string_assembly)  \n",
-      "    - [Testing if a string is an integer](#is_integer)\n",
-      "    - [Testing if a string is a number](#is_number)\n",
-      "- [List operations](#list_operations)\n",
-      "    - [List reversing: [::-1] vs. reverse() vs. reversed()](#list_reverse)\n",
-      "    - [Creating lists using conditional statements](#create_cond_list)\n",
-      "- [Dictionary operations](#dict_ops) \n",
-      "    - [Adding elements to a dictionary](#adding_dict_elements)\n",
-      "- [Comprehensions vs. for-loops](#comprehensions)\n",
-      "- [Copying files by searching directory trees](#find_copy)\n",
-      "- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
-      "- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='string_operations'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# String operations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='str_format_bin'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## String formatting: `.format()` vs. binary operator `%s`\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We expect the string .format() method to perform slower than %, because it is doing the formatting for each object itself, where formatting via the binary % is hard-coded for known types. But let's see how big the difference really is..."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def test_format():\n",
-      "    return ['{}'.format(i) for i in range(1000000)]\n",
-      "\n",
-      "def test_binaryop():\n",
-      "    return ['%s' %i for i in range(1000000)]\n",
-      "\n",
-      "%timeit test_format()\n",
-      "%timeit test_binaryop()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 400 ms per loop\n",
-        "1 loops, best of 3: 241 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='str_reverse'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## String reversing: `[::-1]` vs. `''.join(reversed())`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def reverse_join(my_str):\n",
-      "    return ''.join(reversed(my_str))\n",
-      "    \n",
-      "def reverse_slizing(my_str):\n",
-      "    return my_str[::-1]\n",
-      "\n",
-      "\n",
-      "# Test to show that both work\n",
-      "a = reverse_join('abcd')\n",
-      "b = reverse_slizing('abcd')\n",
-      "assert(a == b and a == 'dcba')\n",
-      "\n",
-      "%timeit reverse_join('abcd')\n",
-      "%timeit reverse_slizing('abcd')\n",
-      "\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.4 GHz Intel Core Duo\n",
-      "# 8 GB 1067 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 1.28 \u00b5s per loop\n",
-        "1000000 loops, best of 3: 337 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='string_concat'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## String concatenation: `+=` vs. `''.join()`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Strings in Python are immutable objects. So, each time we append a character to a string, it has to be created \u201cfrom scratch\u201d in memory. Thus, the answer to the question \u201cWhat is the most efficient way to concatenate strings?\u201d is a quite obvious, but the relative numbers of performance gains are nonetheless interesting."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def string_add(in_chars):\n",
-      "    new_str = ''\n",
-      "    for char in in_chars:\n",
-      "        new_str += char\n",
-      "    return new_str\n",
-      "\n",
-      "def string_join(in_chars):\n",
-      "    return ''.join(in_chars)\n",
-      "\n",
-      "test_chars = ['a', 'b', 'c', 'd', 'e', 'f']\n",
-      "\n",
-      "%timeit string_add(test_chars)\n",
-      "%timeit string_join(test_chars)\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 595 ns per loop\n",
-        "1000000 loops, best of 3: 269 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "<a name='string_assembly'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Assembling strings\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Next, I wanted to compare different methods string \u201cassembly.\u201d This is different from simple string concatenation, which we have seen in the previous section, since we insert values into a string, e.g., from a variable."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def plus_operator():\n",
-      "    return 'a' + str(1) + str(2) \n",
-      "    \n",
-      "def format_method():\n",
-      "    return 'a{}{}'.format(1,2)\n",
-      "    \n",
-      "def binary_operator():\n",
-      "    return 'a%s%s' %(1,2)\n",
-      "\n",
-      "%timeit plus_operator()\n",
-      "%timeit format_method()\n",
-      "%timeit binary_operator()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 764 ns per loop\n",
-        "1000000 loops, best of 3: 494 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000000 loops, best of 3: 79.3 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "<a name='is_integer'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Testing if a string is an integer"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def string_is_int(a_str):\n",
-      "    try:\n",
-      "        int(a_str)\n",
-      "        return True\n",
-      "    except ValueError:\n",
-      "        return False\n",
-      "\n",
-      "an_int = '123'\n",
-      "no_int = '123abc'\n",
-      "\n",
-      "%timeit string_is_int(an_int)\n",
-      "%timeit string_is_int(no_int)\n",
-      "%timeit an_int.isdigit()\n",
-      "%timeit no_int.isdigit()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 401 ns per loop\n",
-        "100000 loops, best of 3: 3.04 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000000 loops, best of 3: 92.1 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000000 loops, best of 3: 96.3 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='is_number'></a>\n",
-      "<br>\n",
-      "<br>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Testing if a string is a number"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def string_is_number(a_str):\n",
-      "    try:\n",
-      "        float(a_str)\n",
-      "        return True\n",
-      "    except ValueError:\n",
-      "        return False\n",
-      "    \n",
-      "a_float = '1.234'\n",
-      "no_float = '123abc'\n",
-      "\n",
-      "a_float.replace('.','',1).isdigit()\n",
-      "no_float.replace('.','',1).isdigit()\n",
-      "\n",
-      "%timeit string_is_number(an_int)\n",
-      "%timeit string_is_number(no_int)\n",
-      "%timeit a_float.replace('.','',1).isdigit()\n",
-      "%timeit no_float.replace('.','',1).isdigit()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 400 ns per loop\n",
-        "1000000 loops, best of 3: 1.15 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000000 loops, best of 3: 452 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000000 loops, best of 3: 394 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "<a name='list_operations'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "# List operations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='list_reverse'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## List reversing - `[::-1]` vs. `reverse()` vs. `reversed()`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def reverse_func(my_list):\n",
-      "    new_list = my_list[:]\n",
-      "    new_list.reverse()\n",
-      "    return new_list\n",
-      "    \n",
-      "def reversed_func(my_list):\n",
-      "    return list(reversed(my_list))\n",
-      "\n",
-      "def reverse_slizing(my_list):\n",
-      "    return my_list[::-1]\n",
-      "\n",
-      "%timeit reverse_func([1,2,3,4,5])\n",
-      "%timeit reversed_func([1,2,3,4,5])\n",
-      "%timeit reverse_slizing([1,2,3,4,5])\n",
-      "\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.4 GHz Intel Core Duo\n",
-      "# 8 GB 1067 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000000 loops, best of 3: 930 ns per loop\n",
-        "1000000 loops, best of 3: 1.89 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000000 loops, best of 3: 775 ns per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='create_cond_list'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Creating lists using conditional statements\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "In this test, I attempted to figure out the fastest way to create a new list of elements that meet a certain criterion. For the sake of simplicity, the criterion was to check if an element is even or odd, and only if the element was even, it should be included in the list. For example, the resulting list for numbers in the range from 1 to 10 would be \n",
-      "[2, 4, 6, 8, 10].\n",
-      "\n",
-      "Here, I tested three different approaches:  \n",
-      "1) a simple for loop with an if-statement check (`cond_loop()`)  \n",
-      "2) a list comprehension (`list_compr()`)  \n",
-      "3) the built-in filter() function (`filter_func()`)  \n",
-      "\n",
-      "Note that the filter() function now returns a generator in Python 3, so I had to wrap it in an additional list() function call."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def cond_loop():\n",
-      "    even_nums = []\n",
-      "    for i in range(100):\n",
-      "        if i % 2 == 0:\n",
-      "            even_nums.append(i)\n",
-      "    return even_nums\n",
-      "\n",
-      "def list_compr():\n",
-      "    even_nums = [i for i in range(100) if i % 2 == 0]\n",
-      "    return even_nums\n",
-      "    \n",
-      "def filter_func():\n",
-      "    even_nums = list(filter((lambda x: x % 2 != 0), range(100)))\n",
-      "    return even_nums\n",
-      "\n",
-      "%timeit cond_loop()\n",
-      "%timeit list_compr()\n",
-      "%timeit filter_func()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "100000 loops, best of 3: 14.4 \u00b5s per loop\n",
-        "100000 loops, best of 3: 12 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 23.9 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='dict_ops'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "# Dictionary operations "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='adding_dict_elements'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Adding elements to a Dictionary\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "All three functions below count how often different elements (values) occur in a list.  \n",
-      "E.g., for the list ['a', 'b', 'a', 'c'], the dictionary would look like this:  \n",
-      "`my_dict = {'a': 2, 'b': 1, 'c': 1}`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import random\n",
-      "import timeit\n",
-      "from collections import defaultdict\n",
-      "\n",
-      "\n",
-      "def add_element_check1(elements):\n",
-      "    d = dict()\n",
-      "    for e in elements:\n",
-      "        if e not in d:\n",
-      "            d[e] = 1\n",
-      "        else:\n",
-      "            d[e] += 1\n",
-      "    return d\n",
-      "            \n",
-      "def add_element_check2(elements):\n",
-      "    d = dict()\n",
-      "    for e in elements:\n",
-      "        if e not in d:\n",
-      "            d[e] = 0\n",
-      "        d[e] += 1            \n",
-      "    return d\n",
-      "        \n",
-      "def add_element_except(elements):\n",
-      "    d = dict()\n",
-      "    for e in elements:\n",
-      "        try:\n",
-      "            d[e] += 1\n",
-      "        except KeyError:\n",
-      "            d[e] = 1\n",
-      "    return d\n",
-      "            \n",
-      "def add_element_defaultdict(elements):\n",
-      "    d = defaultdict(int)\n",
-      "    for e in elements:\n",
-      "        d[e] += 1\n",
-      "    return d\n",
-      "\n",
-      "def add_element_get(elements):\n",
-      "    d = dict()\n",
-      "    for e in elements:\n",
-      "        d[e] = d.get(e, 1) + 1\n",
-      "    return d\n",
-      "\n",
-      "\n",
-      "random.seed(123)\n",
-      "\n",
-      "print('Results for 100 integers in range 1-10') \n",
-      "rand_ints = [random.randrange(1, 10) for i in range(100)]\n",
-      "%timeit add_element_check1(rand_ints)\n",
-      "%timeit add_element_check2(rand_ints)\n",
-      "%timeit add_element_except(rand_ints)\n",
-      "%timeit add_element_defaultdict(rand_ints)\n",
-      "%timeit add_element_get(rand_ints)\n",
-      "\n",
-      "print('\\nResults for 1000 integers in range 1-5')            \n",
-      "rand_ints = [random.randrange(1, 5) for i in range(1000)]\n",
-      "%timeit add_element_check1(rand_ints)\n",
-      "%timeit add_element_check2(rand_ints)\n",
-      "%timeit add_element_except(rand_ints)\n",
-      "%timeit add_element_defaultdict(rand_ints)\n",
-      "%timeit add_element_get(rand_ints)\n",
-      "\n",
-      "print('\\nResults for 1000 integers in range 1-1000')            \n",
-      "rand_ints = [random.randrange(1, 1000) for i in range(1000)]\n",
-      "%timeit add_element_check1(rand_ints)\n",
-      "%timeit add_element_check2(rand_ints)\n",
-      "%timeit add_element_except(rand_ints)\n",
-      "%timeit add_element_defaultdict(rand_ints)\n",
-      "%timeit add_element_get(rand_ints)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Results for 100 integers in range 1-10\n",
-        "10000 loops, best of 3: 28 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 26.2 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 26.5 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 22.8 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 33.3 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Results for 1000 integers in range 1-5\n",
-        "1000 loops, best of 3: 242 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 239 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 203 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 184 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 350 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Results for 1000 integers in range 1-1000\n",
-        "1000 loops, best of 3: 262 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 370 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 502 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 422 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 373 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Conclusion"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We see from the results that the `try-except` variant is faster than then the `if element in my_dict` alternative if we have a low number of unique elements (here: 1000 integers in the range 1-5), which makes sense: the `except`-block is skipped if an element is already added as a key to the dictionary. However, in this case the `collections.defaultdict` has even a better performance.  \n",
-      "However, if we are having a relative large number of unique entries(here: 1000 integers in range 1-1000), the `if element in my_dict` approach outperforms the alternative approaches."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"comprehensions\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Comprehesions vs. for-loops"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Comprehensions are not only shorter and prettier than ye goode olde for-loop,  \n",
-      "but they are also up to ~1.2x faster."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "n = 1000\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Set comprehensions"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def set_loop(n):\n",
-      "    a_set = set()\n",
-      "    for i in range(n):\n",
-      "        if i % 3 == 0:\n",
-      "            a_set.add(i)\n",
-      "    return a_set"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def set_compr(n):\n",
-      "    return {i for i in range(n) if i % 3 == 0}"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit set_loop(n)\n",
-      "%timeit set_compr(n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10000 loops, best of 3: 136 \u00b5s per loop\n",
-        "10000 loops, best of 3: 113 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## List comprehensions"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def list_loop(n):\n",
-      "    a_list = list()\n",
-      "    for i in range(n):\n",
-      "        if i % 3 == 0:\n",
-      "            a_list.append(i)\n",
-      "    return a_list"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def list_compr(n):\n",
-      "    return [i for i in range(n) if i % 3 == 0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit list_loop(n)\n",
-      "%timeit list_compr(n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10000 loops, best of 3: 129 \u00b5s per loop\n",
-        "10000 loops, best of 3: 111 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Dictionary comprehensions"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def dict_loop(n):\n",
-      "    a_dict = dict()\n",
-      "    for i in range(n):\n",
-      "        if i % 3 == 0:\n",
-      "            a_dict[i] = i\n",
-      "    return a_dict"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def dict_compr(n):\n",
-      "    return {i:i for i in range(n) if i % 3 == 0}"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit dict_loop(n)\n",
-      "%timeit dict_compr(n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10000 loops, best of 3: 121 \u00b5s per loop\n",
-        "10000 loops, best of 3: 127 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"find_copy\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Copying files by searching directory trees"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Executing `Unix`/`Linux` shell commands:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import subprocess\n",
-      "\n",
-      "def subprocess_findcopy(path, search_str, dest):    \n",
-      "    query = 'find %s -name \"%s\" -exec cp {} %s \\;' %(path, search_str, dest)\n",
-      "    subprocess.call(query, shell=True)\n",
-      "    return  "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Using Python's `os.walk()` to search the directory tree recursively and matching patterns via `fnmatch.filter()`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import shutil\n",
-      "import os\n",
-      "import fnmatch\n",
-      "\n",
-      "def walk_findcopy(path, search_str, dest):\n",
-      "    for path, subdirs, files in os.walk(path):\n",
-      "        for name in fnmatch.filter(files, search_str):\n",
-      "            try:\n",
-      "                shutil.copy(os.path.join(path,name), dest)\n",
-      "            except NameError:\n",
-      "                pass\n",
-      "    return"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 33
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "\n",
-      "def findcopy_timeit(inpath, outpath, search_str):\n",
-      "    \n",
-      "    shutil.rmtree(outpath)\n",
-      "    os.mkdir(outpath)\n",
-      "    print(50*'#')\n",
-      "    print('subprocsess call')\n",
-      "    %timeit subprocess_findcopy(inpath, search_str, outpath)\n",
-      "    print(\"copied %s files\" %len(os.listdir(outpath)))\n",
-      "    shutil.rmtree(outpath)\n",
-      "    os.mkdir(outpath)\n",
-      "    print('\\nos.walk approach')\n",
-      "    %timeit walk_findcopy(inpath, search_str, outpath)\n",
-      "    print(\"copied %s files\" %len(os.listdir(outpath)))\n",
-      "    print(50*'#')\n",
-      "\n",
-      "print('small tree')\n",
-      "inpath = '/Users/sebastian/Desktop/testdir_in'\n",
-      "outpath = '/Users/sebastian/Desktop/testdir_out'\n",
-      "search_str = '*.png'\n",
-      "findcopy_timeit(inpath, outpath, search_str)\n",
-      "\n",
-      "print('larger tree')\n",
-      "inpath = '/Users/sebastian/Dropbox'\n",
-      "outpath = '/Users/sebastian/Desktop/testdir_out'\n",
-      "search_str = '*.csv'\n",
-      "findcopy_timeit(inpath, outpath, search_str)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "small tree\n",
-        "##################################################"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "subprocsess call\n",
-        "1 loops, best of 3: 268 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "copied 13 files\n",
-        "\n",
-        "os.walk approach\n",
-        "100 loops, best of 3: 12.2 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "copied 13 files\n",
-        "##################################################\n",
-        "larger tree\n",
-        "##################################################\n",
-        "subprocsess call\n",
-        "1 loops, best of 3: 623 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "copied 105 files\n",
-        "\n",
-        "os.walk approach\n",
-        "1 loops, best of 3: 417 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "copied 105 files\n",
-        "##################################################\n"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I have to say that I am really positively surprised. The shell's `find` scales even better than expected!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='row_vectors'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Returning column vectors slicing through a numpy array"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Given a numpy matrix, I want to iterate through it and return each column as a 1-column vector.  \n",
-      "E.g., if I want to return the 1st column from matrix A below\n",
-      "\n",
-      "<pre>\n",
-      "A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])\n",
-      ">>> A\n",
-      "array([[1, 2, 3],\n",
-      "       [4, 5, 6],\n",
-      "       [7, 8, 9]])</pre>\n",
-      "\n",
-      "I want my result to be:\n",
-      "<pre>\n",
-      "array([[1],\n",
-      "       [4],\n",
-      "       [7]])</pre>\n",
-      "\n",
-      "with `.shape` = `(3,1)`\n",
-      "\n",
-      "\n",
-      "However, the default behavior of numpy is to return the column as a row vector:\n",
-      "\n",
-      "<pre>\n",
-      ">>> A[:,0]\n",
-      "array([1, 4, 7])\n",
-      ">>> A[:,0].shape\n",
-      "(3,)\n",
-      "</pre>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "\n",
-      "# 1st column, e.g., A[:,0,np.newaxis]\n",
-      "\n",
-      "def colvec_method1(A):\n",
-      "    for col in A.T:\n",
-      "        colvec = row[:,np.newaxis]\n",
-      "        yield colvec"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 83
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 1st column, e.g., A[:,0:1]\n",
-      "\n",
-      "def colvec_method2(A):\n",
-      "    for idx in range(A.shape[1]):\n",
-      "        colvec = A[:,idx:idx+1]\n",
-      "        yield colvec"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 82
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 1st column, e.g., A[:,0].reshape(-1,1)\n",
-      "\n",
-      "def colvec_method3(A):\n",
-      "    for idx in range(A.shape[1]):\n",
-      "        colvec = A[:,idx].reshape(-1,1)\n",
-      "        yield colvec"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 81
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 1st column, e.g., np.vstack(A[:,0]\n",
-      "\n",
-      "def colvec_method4(A):\n",
-      "    for idx in range(A.shape[1]):\n",
-      "        colvec = np.vstack(A[:,idx])\n",
-      "        yield colvec"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 79
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 1st column, e.g., np.row_stack(A[:,0])\n",
-      "\n",
-      "def colvec_method5(A):\n",
-      "    for idx in range(A.shape[1]):\n",
-      "        colvec = np.row_stack(A[:,idx])\n",
-      "        yield colvec"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 77
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 1st column, e.g., np.column_stack((A[:,0],))\n",
-      "\n",
-      "def colvec_method6(A):\n",
-      "    for idx in range(A.shape[1]):\n",
-      "        colvec = np.column_stack((A[:,idx],))\n",
-      "        yield colvec"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 74
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 1st column, e.g., A[:,[0]]\n",
-      "\n",
-      "def colvec_method7(A):\n",
-      "    for idx in range(A.shape[1]):\n",
-      "        colvec = A[:,[idx]]\n",
-      "        yield colvec"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 89
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def test_method(method, A):\n",
-      "    for i in method(A): \n",
-      "        assert i.shape == (A.shape[0],1), \"{}, {}\".format(i.shape, A.shape[0],1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 69
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "A = np.random.random((300, 3))\n",
-      "\n",
-      "for method in [\n",
-      "            colvec_method1, colvec_method2, \n",
-      "            colvec_method3, colvec_method4, \n",
-      "            colvec_method5, colvec_method6,\n",
-      "            colvec_method7]:\n",
-      "    print('\\nTest:', method.__name__)\n",
-      "    %timeit test_method(colvec_method2, A)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Test: colvec_method1\n",
-        "100000 loops, best of 3: 16.6 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Test: colvec_method2\n",
-        "10000 loops, best of 3: 16.1 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Test: colvec_method3\n",
-        "100000 loops, best of 3: 16.2 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Test: colvec_method4\n",
-        "100000 loops, best of 3: 16.4 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Test: colvec_method5\n",
-        "100000 loops, best of 3: 16.2 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Test: colvec_method6\n",
-        "100000 loops, best of 3: 16.8 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "Test: colvec_method7\n",
-        "100000 loops, best of 3: 16.3 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 91
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='numpy'></a>\n",
-      "<br>\n",
-      "<br>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Speed of numpy functions vs Python built-ins and std. lib."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import timeit\n",
-      "\n",
-      "samples = list(range(1000000))\n",
-      "\n",
-      "%timeit(sum(samples))\n",
-      "%timeit(np.sum(samples))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "100 loops, best of 3: 18.3 ms per loop\n",
-        "10 loops, best of 3: 136 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%timeit(list(range(1000000)))\n",
-      "%timeit(np.arange(1000000))\n",
-      "\n",
-      "# note that in Python range() is implemented as xrange()\n",
-      "# with lazy evaluation (generator)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "10 loops, best of 3: 82.6 ms per loop\n",
-        "100 loops, best of 3: 5.35 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import statistics\n",
-      "\n",
-      "%timeit(statistics.mean(samples))\n",
-      "%timeit(np.mean(samples))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 1.14 s per loop\n",
-        "10 loops, best of 3: 141 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file
diff --git a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb b/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
deleted file mode 100644
index 0f94642..0000000
--- a/benchmarks/.ipynb_checkpoints/cython_least_squares-checkpoint.ipynb
+++ /dev/null
@@ -1,2461 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:90a74ceed4e3dca395013883669de508e2ef94573e58cf24ce3ba49686ca5fe0"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
-      "last updated: 05/06/2014\n",
-      "\n",
-      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb)  \n",
-      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### The code in this notebook was executed in Python 3.4.0"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "I am really looking forward to your comments and suggestions to improve and  \n",
-      "extend this little collection! Just send me a quick note   \n",
-      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
-      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Implementing the least squares fit method for linear regression and speeding it up via Cython"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"sections\"></a>\n",
-      "<br>\n",
-      "<br>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#Sections"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- [Introduction](#introduction)\n",
-      "- [Least squares fit implementations](#implementations)\n",
-      "- [Generating sample data and benchmarking](#sample_data)\n",
-      "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
-      "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
-      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
-      "- [Appendix I: Cython vs. Numba](#numba)\n",
-      "- [Appendix II: Cython with and without type declarations](#type_declarations)\n",
-      "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)\n",
-      "- [Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting](#showdown)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"introduction\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/cython_vs_chart.png)  \n",
-      "(Note that this chart just reflects my rather objective thoughts after experimenting with Cython, and it is not based on real numbers or benchmarks.)\n",
-      "<br>\n",
-      "<br>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Introduction"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Linear regression via the least squares method is the simplest approach to performing a regression analysis of a dependent and a explanatory variable. The objective is to find the best-fitting straight line through a set of points that minimizes the sum of the squared offsets from the line.  \n",
-      "The offsets come in 2 different flavors: perpendicular and vertical - with respect to the line.  \n",
-      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_vertical.png)  \n",
-      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/least_squares_perpendicular.png)  \n",
-      "\n",
-      "As Michael Burger summarizes it nicely in his article \"[Problems of Linear Least Square Regression - And Approaches to Handle Them](http://www.arsa-conf.com/archive/?vid=1&aid=2&kid=60101-220)\": \"the perpendicular offset method delivers a more precise result but is are more complicated to handle. Therefore normally the vertical offsets are used.\"  \n",
-      "Here, we will also use the method of computing the vertical offsets.\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In more mathematical terms, our goal is to compute the best fit to *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
-      "$f(x) = a\\cdot x + b$.  \n",
-      "We further have to assume that the y-component is  functionally dependent on the x-component.  \n",
-      "In a cartesian coordinate system, $b$ is the intercept of the straight line with the y-axis, and $a$ is the slope of this line."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In order to obtain the parameters for the linear regression line for a set of multiple points, we can re-write the problem as matrix equation  \n",
-      "$\\pmb X \\; \\pmb a = \\pmb y$"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "$\\Rightarrow\\Bigg[ \\begin{array}{cc}\n",
-      "x_1 & 1  \\\\\n",
-      "... & 1 \\\\\n",
-      "x_n & 1  \\end{array} \\Bigg]$\n",
-      "$\\bigg[ \\begin{array}{c}\n",
-      "a  \\\\\n",
-      "b \\end{array} \\bigg]$\n",
-      "$=\\Bigg[ \\begin{array}{c}\n",
-      "y_1   \\\\\n",
-      "...  \\\\\n",
-      "y_n  \\end{array} \\Bigg]$"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "With a little bit of calculus, we can rearrange the term in order to obtain the parameter vector $\\pmb a = [a\\;b]^T$"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "$\\Rightarrow \\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "The more classic approach to obtain the slope parameter $a$ and y-axis intercept $b$ would be:"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
-      "\n",
-      "\n",
-      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "where \n",
-      "\n",
-      "\n",
-      "$S_{xy} = \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})\\quad$ (covariance)\n",
-      "\n",
-      "\n",
-      "$\\sigma{_x}^{2} = \\sum_{i=1}^{n} (x_i - \\bar{x})^2\\quad$ (variance)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"implementations\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Least squares fit implementations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### 1. The matrix approach"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "First, let us implement the equation:"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "$\\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "which I will refer to as the \"matrix approach\"."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "\n",
-      "def lin_lstsqr_mat(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### 2. The classic approach"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Next, we will calculate the parameters separately, using standard library functions in Python only, which I will call the \"classic approach\"."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
-      "\n",
-      "\n",
-      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def classic_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### 3. Using the lstsq numpy function"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "For our convenience, `numpy` has a function that can computes the least squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def numpy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return np.linalg.lstsq(X,y)[0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### 4. Using the linregress scipy function"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Also scipy has a least squares function, `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.   \n",
-      "The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import scipy.stats\n",
-      "\n",
-      "def scipy_lstsqr(x,y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    return scipy.stats.linregress(x, y)[0:2]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Teaser"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "The following plot below should give you an impression of how the performance of the 4 different approaches will compare to each other at the end of this article."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='sample_data'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Generating sample data and benchmarking"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In order to test our different least squares fit implementations, we will generate some sample data:  \n",
-      "- 500 sample points for the x-component within the range [0,500)  \n",
-      "- 500 sample points for the y-component within the range [100,600)  \n",
-      "\n",
-      "where each sample point is multiplied by a random value within\n",
-      "the range [0.8, 12) to simulate some scatter in our dataset."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
-      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "#### Visualization"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To check how our dataset is distributed, and how the least squares regression line looks like, we will plot the results in a scatter plot.  \n",
-      "Note that we are only using our \"matrix approach\" to visualize the results - for simplicity. We expect all 4 approaches to produce similar results, which we will confirm after visualizing the data."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from matplotlib import pyplot as plt\n",
-      "\n",
-      "slope, intercept = lin_lstsqr_mat(x, y)\n",
-      "\n",
-      "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
-      "line_y = [slope*x_i + intercept for x_i in line_x]\n",
-      "\n",
-      "plt.figure(figsize=(8,8))\n",
-      "plt.scatter(x,y)\n",
-      "plt.plot(line_x, line_y, color='red', lw='2')\n",
-      "\n",
-      "plt.ylabel('y')\n",
-      "plt.xlabel('x')\n",
-      "plt.title('Linear regression via least squares fit')\n",
-      "\n",
-      "ftext = 'y = ax + b = {:.3f} + {:.3f}x'\\\n",
-      "        .format(slope, intercept)\n",
-      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
-      "\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdUVMfbwPHv0lm6giAgooBRbNi7GCv2EnvXWOPPnhg1\nGksSxRhjoiYaE0SNsb4xEY0Fe+zGmijGLkZARRERFljYnfeP1Y0ELJRlAedzDkf2lpln7q48e++d\nO6MQQggkSZIkSSqSTIwdgCRJkiRJhiMTvSRJkiQVYTLRS5IkSVIRJhO9JEmSJBVhMtFLkiRJUhEm\nE70kSZIkFWEy0UtGd+jQIcqXL2/sMAqtSpUq8fvvv+drnSNHjuTTTz/N0b7e3t7s3bs3jyN6s/3y\nyy+UKlUKe3t7zp07Z5TPhFRwKeRz9FJ+8fb2JiQkhGbNmhk7FMmIypQpQ0hICE2bNjVI+QcOHKBf\nv378888/Bim/IPLx8eGrr76iffv2mdbNnDmT69ev8+OPPxohMqkgkGf0Ur5RKBQoFApjh6Gn0Wjy\nZJvXJYRAfq+Wnsmrz5YQgtu3b+Pv758n5UlFj0z0ktEdOHCAUqVK6V97e3uzYMECqlatiqOjIz17\n9iQ1NVW/ftu2bQQEBODk5ESDBg3466+/9OuCg4Px9fXF3t6eihUr8uuvv+rXrVy5kgYNGjBhwgSc\nnZ2ZNWtWplhmzpxJ165d6devHw4ODqxatYrHjx/z7rvv4u7ujqenJ9OnT0er1QKg1WqZOHEiLi4u\nlC1bliVLlmBiYqJf36RJE6ZNm0aDBg2wsbHh5s2b/P3337Ro0YLixYtTvnx5Nm3apK9/+/btVKxY\nEXt7ezw9PVmwYAEADx48oF27djg5OVG8eHEaN26c4Xg9uxSemprKuHHj8PDwwMPDg/Hjx6NWq/XH\n2dPTky+//BJXV1fc3d1ZuXJllu/Jhg0bqFWrVoZlCxcupGPHjgAMHDiQ6dOnA/Do0SPatWtHiRIl\nKFasGO3btycqKirLcv9LCKF/z5ydnenRowePHj3Sr+/WrRslS5bE0dGRwMBAIiIiXnisvvzyS1Qq\nFa1btyY6Oho7Ozvs7e25e/dupnpfdJwB5s+fr3+vV6xYgYmJCTdu3AB072dISIh+25UrV9KoUSP9\n67Fjx+Ll5YWDgwM1a9bk8OHD+nXZ/Wxdu3aNwMBAHB0dcXFxoWfPnpnakZqaip2dHRqNhqpVq+Ln\n5wf8+5nYuXMnc+fOZcOGDdjZ2VGtWrXXel+kIkZIUj7x9vYWe/fuzbR8//79wtPTM8N2derUETEx\nMSIuLk5UqFBBLFu2TAghxJkzZ0SJEiXEyZMnhVarFatWrRLe3t5CrVYLIYTYtGmTiImJEUIIsWHD\nBmFjYyPu3r0rhBAiNDRUmJmZiSVLlgiNRiOSk5MzxTJjxgxhbm4utmzZIoQQIjk5WXTq1EmMGDFC\nqFQqcf/+fVG7dm3x3XffCSGEWLp0qfD39xdRUVHi0aNHolmzZsLExERoNBohhBCBgYGidOnSIiIi\nQmg0GhEfHy88PT3FypUrhUajEWfPnhXOzs7i0qVLQggh3NzcxOHDh4UQQsTHx4szZ84IIYSYPHmy\nGDFihEhPTxfp6en6bf57XKdPny7q1asnYmNjRWxsrKhfv76YPn26/jibmZmJGTNmiPT0dLF9+3ah\nVCpFfHx8puOgUqmEnZ2duHr1qn5ZzZo1xYYNG4QQQgwcOFBf7sOHD8XmzZtFcnKyePLkiejWrZvo\n1KlT1h+C/8T71VdfiXr16omoqCihVqvF8OHDRa9evfTbhoaGisTERKFWq8W4ceNEQECAft2LjtWB\nAwcyfJ6y8qJ9d+zYIVxdXcXFixdFUlKS6NWrl1AoFOL69etCCCGaNGkiQkJCMsTXsGFD/es1a9aI\nuLg4odFoxIIFC4Sbm5tITU0VQmT/s9WzZ08xZ84cIYQQqamp4siRIy9sz/Mx/vcYz5w5U/Tr1++l\nx0Mq2uQZvVQgjRkzBjc3N5ycnGjfvj3nzp0DYPny5QwfPpxatWqhUCjo378/lpaWHDt2DICuXbvi\n5uYGQPfu3fHz8+PEiRP6ct3d3Rk1ahQmJiZYWVllWXf9+vXp0KEDAI8fP2bHjh0sXLgQa2trXFxc\nGDduHOvXrwdg48aNjBs3Dnd3dxwdHZkyZUqGy/MKhYKBAwdSoUIFTExM2LlzJ2XKlGHAgAGYmJgQ\nEBBAly5d2LhxIwAWFhZcvHiRhIQEHBwc9GdgFhYWxMTEcOvWLUxNTWnQoEGWsa9du5aPP/4YZ2dn\nnJ2dmTFjRoZ7s+bm5nz88ceYmprSunVrbG1tuXz5cqZyrK2t6dixI+vWrQPg6tWrXL58WX9cAH07\nixUrRufOnbGyssLW1papU6dy8ODBrN/Y//juu+/49NNPcXd3x9zcnBkzZvB///d/+rPagQMHYmNj\no193/vx5njx58tJjJV7j9siL9t24cSODBw/G398fpVKZ5VWfl+nTpw9OTk6YmJgwYcIEUlNTMxzf\n7Hy2LCwsuHXrFlFRUVhYWFC/fv1sxfKMkLeM3ngy0UsF0rNkDbqkk5iYCEBkZCQLFizAyclJ/3Pn\nzh1iYmIAWL16NdWqVdOvu3DhAg8fPtSX9fwtghfx9PTU/x4ZGUlaWholS5bUlzlixAhiY2MBiImJ\nyVDm8/tmVWdkZCQnTpzIEP/atWu5d+8eAD///DPbt2/H29ubJk2acPz4cQA++OADfH19admyJT4+\nPsybNy/L2KOjoyldurT+tZeXF9HR0frXxYsXx8Tk3//2SqVSf2z/q3fv3vpEv3btWn0y/y+VSsXw\n4cPx9vbGwcGBwMBAHj9+/FrJ5datW3Tu3Fl/LPz9/TEzM+PevXtoNBomT56Mr68vDg4OlClTBoVC\nwYMHD156rF7Hi/b97/vp5eX12mUCfPHFF/j7++Po6IiTkxOPHz/WxwvZ+2x9/vnnCCGoXbs2lSpV\nIjQ0NFuxSNIzZsYOQJJex7NOfF5eXnz00UdMnTo10zaRkZEMGzaMffv2Ua9ePRQKBdWqVct0hv2q\nep7fplSpUlhaWvLw4cMMCfKZkiVLZujdnVVP7+fL8/LyIjAwkPDw8Czrr1mzJr/++isajYbFixfT\nvXt3bt++ja2tLV988QVffPEFFy9epGnTptSuXZu33347w/7u7u7cunWLChUqAHD79m3c3d1f2uYX\nad68ObGxsZw/f57169fz1VdfZdmuBQsWcOXKFU6ePEmJEiU4d+4c1atXRwjxyuPt5eVFaGgo9erV\ny7Tuxx9/JCwsjL1791K6dGni4+MpVqyY/v180bF6nQ6fL9q3ZMmS3L59W7/d878D2NjYkJSUpH/9\n/P3/Q4cOMX/+fPbt20fFihUBMsT7/DGDV3+2XF1dWb58OQBHjhyhefPmBAYGUrZs2Ve273kFqQOs\nZBzyjF7KV2q1mpSUFP3P6/Y8fvbHcujQoSxbtoyTJ08ihCApKYnffvuNxMREkpKSUCgUODs7o9Vq\nCQ0N5cKFC9mK779noSVLlqRly5ZMmDCBJ0+eoNVquX79uv4Z5e7du/P1118THR1NfHw88+bNy/SH\n9fky27Vrx5UrV1izZg1paWmkpaXxxx9/8Pfff5OWlsZPP/3E48ePMTU1xc7ODlNTU0DXAfHatWsI\nIbC3t8fU1DTL5NCrVy8+/fRTHjx4wIMHD5g9ezb9+vXL1jF4xtzcnG7duvH+++/z6NEjWrRokaFN\nz9qVmJiItbU1Dg4OxMXFZety94gRI5g6dao+ocbGxhIWFqYv19LSkmLFipGUlJThy93LjpWrqysP\nHz4kISEhyzpftm/37t1ZuXIlly5dQqVSZWpLQEAAmzdvJjk5mWvXrhESEqJ/v588eYKZmRnOzs6o\n1Wpmz579whjg1Z+tTZs2cefOHQAcHR1RKBRZvuev4ubmxq1bt+Tl+zeYTPRSvmrTpg1KpVL/M2vW\nrFc+dvf8+ho1avD999/zv//9j2LFiuHn58fq1asB8Pf3Z+LEidSrVw83NzcuXLhAw4YNsyzndep6\nZvXq1ajVavz9/SlWrBjdunXTn8kNHTqUli1bUqVKFWrUqEHbtm0zJeHny7O1tSU8PJz169fj4eFB\nyZIlmTJlir5n/Jo1ayhTpgwODg4sX76cn376CdD1wG7RogV2dnbUr1+fUaNGERgYmCn+adOmUbNm\nTapUqUKVKlWoWbMm06ZNyzKW19G7d2/27t1Lt27dMrXpWVnjxo0jOTkZZ2dn6tevT+vWrV+7nrFj\nx9KhQwdatmyJvb099erV4+TJkwD079+f0qVL4+HhQaVKlfRXaZ550bEqX748vXr1omzZshQrVizL\nXvcv2jcoKIhx48bRtGlTypUrl2nMh/Hjx2NhYYGrqyuDBg2ib9+++nVBQUEEBQVRrlw5vL29sba2\nznDpP7ufrVOnTlG3bl3s7Ozo2LEjixYtwtvbO8vj+LLj3a1bN0B326ZmzZov3E4qugw6YM7ly5cz\nPBJy48YNPvnkE/r27UuPHj2IjIzE29ubjRs34ujoCMDcuXNZsWIFpqamLFq0iJYtWxoqPEnKczt2\n7GDkyJHcunXL2KFIecTExIRr165l+5K5JBUUBj2jf+uttzh79ixnz57l9OnTKJVKOnfuTHBwMC1a\ntODKlSs0a9aM4OBgACIiItiwYQMRERHs3LmT9957T9/7VpIKopSUFLZv3056ejpRUVHMmjWLLl26\nGDssSZIkvXy7dL9nzx58fX0pVaoUYWFhDBgwAIABAwboBzXZsmULvXr1wtzcHG9vb3x9ffWX8SSp\nIBJCMHPmTIoVK0b16tWpWLEis2fPNnZYUh6Sndmkwi7fet2vX7+eXr16AXDv3j1cXV0BXceZZ48W\nRUdHU7duXf0+np6erz3CliQZg7W1tfwyWsTl5TDIkmQM+ZLo1Wo1W7duzfLZ39fpiPU8X19frl+/\nnucxSpIkSVJB5ePjw7Vr13K0b75cut+xYwc1atTAxcUF0J3FP+tZGhMTQ4kSJQDw8PDI8BzynTt3\n8PDwyFDW9evX9Y/2vIk/M2bMMHoMsv2y7bL9sv2y/fn7k5sT3HxJ9OvWrdNftgfo0KEDq1atAmDV\nqlV06tRJv3z9+vWo1Wpu3rzJ1atXqV27dn6EKEmSJElFksEv3SclJbFnzx6+//57/bLJkyfTvXt3\nQkJC9I/Xge456O7du+uHwfz2229lRxhJkiRJygWDJ3obG5sMYz2DbljIPXv2ZLn91KlTsxzeVNJp\n0qSJsUMwqje5/W9y20G2X7a/ibFDKLQMOmCOISgUCgpZyJIkSZKUK7nJfXIIXEmSJEkqwmSilyRJ\nkqQiTCZ6SZIkSSrCZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIkwmekmSJEkqwmSilyRJkqQiTCZ6\nSZIkSSrCZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIkwmekmSJEkqwmSilyRJkqQiTCZ6SZIkSSrC\nZKKXJEmSpCJMJnpJkiRJKsJkopckSZKkIszM2AFIkiRJWYuNjWXHjh2YmJjQtm1bnJycjB2SVAgp\nhBDC2EFkh0KhoJCFLEmSlG03btygdu1AUlLqAOnY2p7nzJkjuLu750v9f/75JzNnzichIYn+/bvQ\nv3/ffKlXylpucp88o5ckSSqAJk78mEePRqDVfgRAauqHTJv2KStWfGvwuv/880/q1GlCSso0wINj\nxz7m8eMERo9+z+B1S3lP3qOXJEkqgKKi7qHV1tC/Tk+vwe3bdw1e78OHDwkMbEVKyhBgAtADlWo1\n8+d/Y/C6AU6cOMHIkWMZO/Z9/v7773yps6iTiV6SJKkAatWqMUrlF8Bj4CFK5Ve0bh1o8HqnTp1N\nQoInYPrcUpN8uWW6f/9+mjZtz7JlJVm8WEmtWo25ePGiwest6mSilyRJKoA+/ngy77zjg6lpCczM\nPBgwoA7jx482eL2XLt1Aq+0P/AB8A/yCiUkPxo4dZvC6P/poHirVV8BkhJhNUtJ4Pv98scHrLepk\nopckSSqAzM3NWb36O1JTVaSkJPHttwsxMTH8n+yGDWtgbb0bCAMOYGLyPs2a+TNx4liD152cnAIU\n178WwpnExGSD1qlSqRg+fCzly9chKKgr169fN2h9xiA740mSJBVgpqamr94olx49esSOHTtQKBSM\nHTuSs2ffY//+NoCCRo0aERa2AYVCYfA4hgzpyaRJE1GpbAAVSuVsBg9eZtA6O3fuw++/W5CSspCr\nVw9Tp04Trlw5T7FixQxab36SiV6SJOkN9s8//1CzZiNUqiqAwMZmKqdOHcLc3BwhBK6urvmS5AHe\ne284arWab78dh5mZGR9/PJ+2bdsapK7k5GQ++mgWu3dvR4gngAVabX3U6gMcOHCALl26GKReY5DP\n0UuSJBnZX3/9RUxMDJUrV6ZkyZL5WnfPnoP5v//zRKOZDYCZ2VR69XrI6tXf5Wsc+UkIQZMmbTlx\nwoLU1J3APcABENjZNWbt2g9p166dkaPMKDe5T96jlyRJMqIxYyZRt24Q3bvPw8+vCrt3787X+v/5\n5y4aTS396/T0WkRGRudrDPnt+vXr/PHHeVJTNwHDgCBgBebmg3Fze0KzZs2MHGHekolekiTJSA4f\nPsyKFT+jUl3k8eO9JCX9TNeuffLlquW6devx9q7C+fOnMDObDyQAj1Eqv6Zly0YGr9+YhBAoFKbo\nUuBXQD9MTafRo4eCP/44iLW1tZEjzFsy0UuSJBnJjRs3UCjqAo5PlzRCpUokMTHRoPXu3buXd9+d\nSGTkIpKSdiNEJAqFM6amJejevQKTJ080aP3G5uPjg79/WSwt3wV2Y2FxET8/d0JCluHg4GDs8PKc\nTPSSJElGUqVKFTSafcDNp0vW4eLijq2trUHr3bhxC8nJE4AmQFU0mp/x8ipPcnIioaFL86WnvzGZ\nmJiwb99WBg1yombN+fTuLThyJBwLCwtjh2YQste9JEmSkQQEBDBv3sd88EFVzMycsLaG7du3GLyX\nu4ODLaamd9Boni25g52dHebm5gattyCxs7Nj6dKFxg4jX8he95IkSUaWkJBAbGwspUqVypezyjt3\n7lC1al0SEjqQnl4Ca+tv+eWXH2nVqpXB65ZyJje5TyZ6SZKkN1B0dDQ//BBCUlIyXbt2platWq/e\nSTIameglSZIkqQiTz9FLkiRJkpQlmeglSZIk6UWEgEWLIC7O2JHkmEz0kiRJkpQVrRZGj4axY6FD\nB93rQkg+XidJkiRJ/5WeDkOHwsqVYGEBkyZBPkwTbAgy0UuSJEnS89Rq6NsXNm0CpRK2bIHmzY0d\nVY4Vzq8nkiRJ0hsvKSmJLl36olQ64uzsxY8//pT7QlNS4J13dEne3h7Cwwt1kgf5eJ0kSZJUSPXo\nMYgtW1JITV0M3MTauiPh4Rtp2LBhzgpMTISOHWHfPiheHHbtgho18jTmnJKP10mSJElvnF27dpGa\nOg9wBmqRkjKY3bv35Kyw+Hho2VKX5N3c4ODBApPkc0smekmSJKlQcnAoBlzWv7a0vEzx4sWyX1Bs\nLDRtCseOgZcXHDoEFSvmXaBGJhN9Ieft7U1ERIRBytZoNIwaNQpfX1/8/PwICQl54bZz586lcuXK\nVKhQgYEDB6JWqwG4desWZmZmVKtWTf/z6NEjAI4ePUqDBg2oWLEiFStWZNKkSXkaf58+ffDw8MDE\nxASVSpVh3fHjx6latSpvvfUWrVq1IjY2Vh/T87F6eHhQ4wXf6lUqFT169MDPz48KFSrw22+/6det\nWbOGKlWqYG5uzjfffJPl/gcOHMDU1PSF6yVJermlSz9HqeyDufk4lMqOeHpeYdCgQdkrJDoaAgPh\n7Fnw89MleV9fwwRsLKKQKYQh50hoaKiYOXPmK7fz9vYWFy5cMEgMq1atEq1atRJCCBEbGys8PT3F\nrVu3Mm23a9cuUaVKFaFSqYQQQgwdOlQEBwcLIYS4efOmcHZ2zrL8CxcuiGvXrgkhhEhNTRUNGzYU\nP/744yvjGjBggDhw4MArt9u/f7+4f/++UCgUIikpSb9co9EIHx8fceTIESGEEJ9++qkYPHhwlmV0\n6tRJLFiwIMt1s2bNEsOGDRNCCHH16lXh5uYmEhMT9W2LiIgQ/fv3F998802mfRMSEkSdOnVE+/bt\nxZIlS17ZFkmSsnbu3Dnx+eefi++++048efIkezvfvClE2bJCgBCVKgkRE2OQGPNCbnLfG3VGP3/+\nfP73v//pX9+7dw83NzdSUlJyXfa9e/do2rQpNWvWpFKlSnz44Yf6dUOHDmXChAn67cqWLcuff/75\n0vKyM03lmjVrqFmzJn5+fnl6drhx40aGDRsGgLOzM506dWLTpk2Ztvvzzz9p1KgR1tbWAAQFBfHT\nT6/u/VqxYkV8fHwAsLCwICAggNu3b79yP4VC8VrHp0mTJri4uGRafvr0aaytralfvz4Aw4cPZ+PG\njZm2u3//PuHh4fTr1y/L8jdu3Mjw4cMB8PX1pWbNmuzYsUPftgoVKmBiYpJlB5oJEyYwadIkihcv\nrl+WnJxM1apVCQsLA2Dfvn1UqFCBpKSkV7ZVMr5z584xYMAIevcewsGDB40dzhujatWqfPDBBwwb\nNgxbW9vX3/HyZWjUCG7c0N2LP3BAd2++CHqjnqMfMmQI/v7+fP755yiVSpYvX06fPn2wsrLKtG23\nbt24du1apuUKhYJjx45haWmZYbmjoyNbt27FxsaGtLQ0goKC2LVrF61atWLx4sXUqVOHLVu2sHjx\nYiZNmkSVKlVeGmtWyeFFYmNjOXXqFPfv36datWo0btyYypUrZ9jm0qVL9O7dO8v9W7Zsybx58zIt\nv337NqVLl9a/9vLy4p9//sm0XY0aNfj+++95+PAhDg4ObNy4kcjISP36hIQEatSogUKhoGfPnrz/\n/vuZyrh//z6bN29m+/btr9Xm7Byf//pvu5ydndFqtcTHx+Po6Khfvnr1alq1apXll4WsynnR8fmv\nHTt28OTJE7p06cLWrVv1X1qsra3ZuHEjLVu2xM3NjSFDhvDLL79gY2OT06ZK+eTs2bM0atSSpKQP\nAGu2bOnBzz+vJCgoyNihFXqJiYkMHPgeO3b8hp2dI4sWBdO9e7fcFfrnn9CiBdy/Dw0bwrZt4OCQ\nNwEXQG9UondycqJDhw6sXr2aIUOG8MMPP7Bv374st83qzPVl0tPTef/99zl27BhCCO7evcu5c+do\n1aoVVlZWbNy4kRo1atCmTRtGjBiRZRnbt2/no48+AiAuLg61Ws2vv/4KwOjRoxk8eHCW+7377rsA\nlChRgrZt23LgwIFMib5ChQqcPXs2W216XW+//TajRo2iZcuWWFlZ0axZM3bv3g2Au7s7UVFRODs7\nExsbS4cOHXByctLHDPDkyRM6dOjA+++/T9WqVbOs45NPPmHz5s2ALsEePnxY/+191apVr/zilBOh\noaFZfgHKjfj4eCZPnsyePbqewUKIDF9a3nrrLWbPnk39+vX5+uuvX3g8pILliy++JSnpQ0D3JVal\nKsGsWV/JRJ8HBg0axbZtaaSmXkSlus7Age9QurQXderUyVmBJ09CUBA8eqRL9r/8AkX8y/QblehB\nlzD79OmDi4sL/v7++kvH/9W1a1euX7+e5bpjx45lugrw5ZdfEh8fz8mTJ7GwsGD48OEZbglcvHgR\nBwcH7t69i0ajwdTUNFO5bdq0oU2bNoAueUVGRvLxxx+/sk3PJwohRJaXtSMiIujTp0+W+7do0YLP\nP/8803IvLy9u3bql74wWGRlJmTJlsixjzJgxjBkzBtBd0q74tMeqhYUFzs7OALi4uNCnTx+OHDmi\nT/QqlYp27doRFBTE+PHjX9jG6dOnM336dAAGDRrEoEGDaNy48Qu3f5nSpUtnuOLw4MEDTExMMpzN\nHz9+nEePHunfj6w8Oz7PLr9HRkbStGnTTNs9/35cuHCBu3fvUrt2bX3d27Zt49GjR0ybNg3Q3Vpw\ndXV9rasDUsGQkqIGnr9sbKfvkGoIT5484eHDh3h4eGBubm6wegqCnTt3kJp6DnAD3EhNHUh4+O6c\nJfrff4e2bXXPy3foABs2QBZXdIucPOgjkK/yIuS3335blCpVSmzdujUPItKZOHGiGD9+vBBCiDt3\n7ghXV1cxa9YsIYQQN27cEF5eXuLatWtiwIABYvLkya8s73U745UuXVoMHTpUCCHE/fv3hYeHR551\nzlu5cqVo1aqV0Gq14v79+y/sjCeEEDFPO7HExcWJ6tWriy1btuhjUqvVQgghkpKSRPPmzcWiRYuE\nEEIkJyeLpk2big8//DBbcQ0cOPC1OuMJIYRWqxUKhULfSU6IfzvjHT58WAghxCeffJKpM97QoUNf\nGdfMmTP1x/7KlSvC1dU1Qz1C6DoOvqyz3cCBAzN01tu8ebOoXr26iIuLExUrVhQ7dux4rXZKxhUe\nHi6UypICfhawQyiVviIkJNQgdS1a9K2wsLAVSqWnKFGitPjrr78MUk9B4e5eTsABoZtGTghr6y45\n68C6c6cQ1ta6Qnr1EuLp36XCIje5741M9GvWrBHe3t55EM2/IiMjRe3atUWlSpVEUFCQ6NOnj5g1\na5ZQq9Widu3aYt26dUIIXbLz9/cXO3fufGl5K1eu1H9ReBlvb28xdepUUaNGDeHr65tlD++c0mg0\nYuTIkcLHx0f4+PiI77//Xr9u2bJl4uOPP9a/rly5sqhYsaIoV66cWLx4sX755s2bRaVKlUTVqlWF\nv7+/+PDDD4VWqxVCCLFkyRJhamoqqlWrJgICAkRAQICYM2fOK+MaOHCgOHjw4Cu369y5s/D09BQm\nJibCw8NDBAUF6dcdPXpUVK5cWfj5+YmWLVuK+/fv69epVCrh4OAgLl++nKnMgIAA/ZeapKQk0a1b\nN+Hr6yveeustERYWpt9u7dq1wtPTU9jY2AgnJyfh6ekpLl26lGVbnr1nN2/eFKVKlRJXr14VQuh6\n7nt5eYmoqKhXtlUyvi1btogaNZqKqlUbix9+WGGQOs6cOfP0C8WNp4kvVHh6vmWQugqKX375RVhb\nlxBmZhNF0rhoAAAgAElEQVSFtXUn4etbJfu96zdvFsLcXJfkhwwRIj3dMMEaUG5y3xs5BO6QIUOo\nUKECEydOzKOoJEmSDC80NJTRo/eTlLT66RKBqakVCQmPUCqVeV7ftWvXOH/+PN7e3i8cTyI/nD59\nmt27d+Po6Ejfvn2z17t+zRoYOBA0Gt10swsXQjaeaiooCvQQuPHx8XTt2pUKFSrg7+/PiRMniIuL\no0WLFpQrV46WLVsSHx+v337u3Ln4+flRvnx5wsPD8zSW6Ohoypcvz/Xr1xk1alSeli1JkmRoZcuW\nBY4BCU+X/I6trZP+0da89NNP66hatT6DB/9I48ZdGDducp7X8bpq1KjB5MmTGTFiRPaS/PLl0L+/\nLslPm1Zok3yu5dFVhRfq37+/CAkJEUIIkZaWJuLj48UHH3wg5s2bJ4QQIjg4WH8v9OLFi6Jq1apC\nrVaLmzdvCh8fH6HRaDKUlw8hS5IkFUharVaMGDFOKJWewsGhubCxcRbh4eF5Xk9KSoqwsrIX8OfT\nWwRxQqksJU6fPp3ndRnMggVCf2P/6QBehVlucp9BL90/fvyYatWqcePGjQzLy5cvz8GDB3F1deXu\n3bs0adKEv//+m7lz52JiYqIfbCYoKIiZM2dSt25d/b5y9jpJkt50586dIyYmhqpVq+Lu7p7n5UdH\nR+PrW43k5Hv6Zfb27Vi1agidOnXK8/rylBDwyScwY4bu9ZIlUASu4BbYS/c3b97ExcWFQYMGUb16\ndYYOHUpSUhL37t3D1dUVAFdXV+7d032YoqOj8fT01O/v6elJVFSUIUOUJEkqdAICAmjdurVBkjzo\n/i7b2loB654uOUda2omCP66DEDBpki7Jm5hAaGiRSPK5ZdDn6NPT0zlz5gxLliyhVq1ajBs3juDg\n4AzbvGo406zWzZw5U/97kyZNaNKkSV6FLEmS9Ma7ceMGtrYOxMb2BYZhYSFYuXLlC8fRKBC0Wl1S\nX7YMzMxg7VrolssR9IzowIEDHDhwIE/KMmii9/T0xNPTk1q1agG6QWjmzp2Lm5sbd+/exc3NjZiY\nGEqUKAGAh4dHhkFC7ty5g4eHR6Zyn0/0kiRJUt7RaDQ0bdqOqKhRwHBgDxYWgwgMbGTs0F4sPR0G\nD4YffwRLS/j5Z93AOIXYf09iZ82aleOyDHrp3s3NjVKlSnHlyhUA9uzZQ8WKFWnfvj2rVq0CdCPA\nPbvn06FDB9avX49arebmzZtcvXpVP4KYJEmSZHhRUVHExSUixBjAEmiLqWkAZ86cMXZoWVOroWdP\nXZK3sYHt2wt9ks9rBh8Cd/HixfTp0we1Wo2Pjw+hoaFoNBq6d+9OSEgI3t7e+pnD/P396d69O/7+\n/piZmfHtt99maxY3SZIkKXecnJxIT08A7gCegIr09GsvnNzJqJKT4Z13YMcO3aQ0O3ZAvXrGjqrA\neSMHzJEkSSpo1Go1oaGh3LwZSb16dejYsaPRYgkOXsAnnyxCo2mHuflhOnSoxZo13+fqxCsxMZF3\n3x3Nrl27cHQsztKln9O6deucB/nkCbRvDwcPgrMzhIdDtWo5L6+Ay03uk4lekiTJyDQaDYGBbTh7\nVqBSNcLGZi1jxvRgzpyZRovp0KFDnD17ljJlytCuXbtcX13t0qUv27drSE2dB/yNUtmXY8f25Gzm\nyUePoHVrOHECSpaEPXvA3z9X8RV0MtFLkiQVYlOmTGHevE0IcRkwBe5hbl6Gx48fGmTUO2OwtnYg\nJeU6oJvN0tx8HHPmePL+++9nr6D796FlSzh/HkqXhr174QWzkBYlBfY5ekmSJOnlQkNXEhy8BCHK\noEvyAC4oFBaoVCpjhpanbGwcgFv61+bmN3FwcMheIVFREBioS/LlysGhQ29Eks8tmeglSZKMRKvV\nMm7cdGAl8BewGrgNjMXHx49ixYoZM7w89eWXc1AqO6JQTMfKqhvu7rfo1avX6xdw8yY0agR//w1V\nqujmli9VKttxpKSkkJycnO39CjOZ6CVJkoxk9Oj3SUhQAWWBHcBSoBqwik2bVhapp4769+/Lzp3r\nmTbNhODghpw5c/j1J6j5+29dkr95E2rXhv374enoqq9Lo9HQv/9wbG0dsbNzomvXfqjV6hy0pPCR\n9+glSZKM4NSpU9Sp0wCtdhz/JvkY4F2aN2/E7t3bjBtgQXHunO6efGwsNG4M27aBnV22i5k7dz6f\nfrodlSoMMMXauitjx9Zi7tycD0STn+Q9ekmSpFwKDw/HxyeA4sW96NNnqEHvjx87dozGjYPQagHe\nB7oB/YH3aNKkJjt3bjFY3YXK8ePw9tu6JB8UpHtOPptJPiEhga+++orly39CpXoPsAOUJCePZu/e\nowYJu6CRiV6SpDfeX3/9RadOfbhxYw5xcQfYvDmeAQNGGqy+GTO+IDl5LrohZrsC1VAo+lOsmAUb\nN67H1NT0FSVkX0JCAt26DaREibJUqdKAkydP5nkdeerAAWjeHOLjoXNn+PVXUCqzVURCQgIBAfWZ\nMuUYt25ZAb/r15maHsPbO/MQ60WRwUfGkyRJKuh27dqFRtMbaANASsq3/PZbOYPVp1KlAMWBhcAX\nwExcXBI4fvyQwUag69SpD0ePOpOauovY2D9o2rQtERGn8fLyMkh9ubJ9u27Eu5QU6NMHVq7UTVST\nTatXr+bu3QqkpGwA7gF1USjOYGvrgLX1JRYs+P1VRRQJ8oxekqQ3np2dHWZmt59bchulMvv3gV/X\n0KE9USonA4eBmiiVD1m8+DODzQ6nVqs5eHAXqanfAX5Ab6AF+/fvN0h9ufLzz9Cpky7JDxsGq1fn\nKMmD7oxerX52TF2BXVhaXmDFisFcvnyWUjnotV8YyUQvSdIbr3fv3ri6XsXSshcKxSyUyo588cWn\nBqtvwIB+fPnlB/j5vU+5clNYsmQm3bsbbkpVMzMzzMzM0Z3VAggUiqjX7/WeX1avhu7dIS0NJkzQ\nTTlrkvM01apVKywsVgH7gNtYWX1Ex47v0LVrVxwdHfMs7IJO9rqXJElCd/b3/fffExsbR1BQiwxT\nhBYFc+bM57PPvkelGoyV1Sl8fP7h1KmDWFlZGTs0naVL4b33dL/PmKH7yYPHC8PCwvjf/yaTkBBP\nmzZt+OGHRSizea+/IJBD4EqSJGWDEIL169ezf/9RvL09GDt2NDY2NsYOK0+Fh4czY8YCUlPVjBzZ\nlyFDBrN161b27z+Ep6cbI0aMKDhtnj8fJk369/fsDov7BpCJXpIkKRsmTZrGN9+EoVINxtLyGD4+\nNzl9+veCc3abA7/99htjx07jyZME6tatzu7dv5Oc/DXggFI5ni++mMDIkcOMHWZGQsDMmTB7tu71\nt9/CSMM97VCYyUQvSZL0mtRqNTY29qSn3wZKAAJb24b89NOHdOjQwdjh5cipU6cIDGyLSrUKKIOp\naVs0mlHA+Kdb7KFixRlcuHDEiFH+hxAwcSIsXKi7Dx8aCv37GzuqAis3uU8+XidJ0hslLS0NUABO\nT5coUChKFNrxzyMiIujRoz8q1SAgCACNpgHw/IA/Ksxy2HPdIDQa3f345cvB3BzWrdM9TpcNycnJ\nLF68hCtXImnYsCYDBgwoUkMG56UC9M5LkiQZno2NDQ0aNOX48aGkpk5EoTiGiclxAgOXGju0bLt0\n6RI1azYiObkREAnsBdKAeigU7yOEGeCIUjmbGTOWGDVWvfR0GDgQfvoJrKxg82bd3PLZkJaWRuPG\nrblwwYmUlCasW/cNJ06cZ+nShYaJuZCTl+4lSXrjJCQkMHz4eA4dOoanpwfff/8llStXNnZY2RIT\nE0OFClV5/DgQmI9uMhwPwBa4yOTJ/+POnYekpKgZMqQXrVq1yvMYhBCo1WosLS1fb4fUVOjZUzfK\nna0tbN0KOXi6Yf/+/XToMIHExNPonhKPx9zckwcPorG3t892eYWBHOtekiTpJdLT0zl+/DiHDh0i\nOTkZe3t71q0L4c6dCI4f313okjzA5MmzePKkJqAFQoB26Ka6PY6p6UQuXrzFjz8uZ9OmlTlO8klJ\nSXTu3AdLS1ucnNwJCQnVrwsJCUWpdESptKNmzSbcu3fvJSUBKhV06KBL8o6OsGdPjpK8rigVJibF\n+TeF2WFiYkFKSkqOyivq5KV7SZKKtKSkJBo1CuLq1UcoFFYUK5bM8eN7cXNzM3ZouXLrVjRabS9g\nBnAdGIeu7wFoNM24fj0813UMGTKGHTu0qNX/oFbfZsyYdpQt6421tTVjxkwjJeU4UI7z56fQpUt/\njhzZlXVBCQnQrh0cOgQuLrB7N1StmuO46tevj7n5CBSKrxHibSwsllK5chWDDR9c2MkzekmSirRP\nPgnm0qVSJCb+yZMnfxAd3Y4xYyYbO6wcu3v3LleuXKFp07oolaFAOGCPbprbJCAdS8vvqVevRq7r\nCg/fTWrqHHQdF6uSnDyE3bv3cvToUdLTuwIVAFPS06fzxx+Hsi4kLk43Oc2hQ+DhAb//nqskD+Dk\n5MTRo3tp0GAXnp696NBBRXj4L7Iz3gvIM3pJkoq0ixevk5LSmmfnNWlpbbh06SPjBpUDQgiGDx/L\n6tU/YmbmiIuLDUFBAWzZUhEhBJ6efty9WxKFwoxatWqzcOE3ua7Tyak4cXERQBlAYGkZgYtLXVxd\nXTE3/xW1WgOYAqdwcsriCsm9e9CiBfz1F5QpA3v36v7NA+XKlePQoe15UlZRJxO9JElFWt26Vdm7\ndy3Jyd0AcywtV1GrVu7OKPObEIIRI0YSEvI7Wu0tUlPtSUmZTalSJ0hKSgDA0tKSuLg40tLSKFGi\nRJ6c3S5bNp+OHXuh0fQATmFpGcn9+7707NmTatVWc+5cfYR4CyF2sGrVjxl3/ucf3Zn8lStQvrzu\nnrzHmzEtbEEje91LklSkpaWl0alTb/btO4BCYU7FiuXYuzesUPXOXrbse0aPnkJ6+lhg+tOlkTg5\nNSAu7o5B646IiODTTz/j55/3oVaPw9z8BsWK7ebcuaOcPHmShw8f0rBhQ/z8/P7d6fp1aNYMIiMh\nIAB27YISJQwaZ1EnB8yRJEl6AXNzc7Zt20hUVBTp6el4eXlhkosZ0fLb4cOH+fDDz0hPHwjsBiYB\nlkAYZcv6Grz+GzdusHlzOGr1NqAOaWkQH9+fdevWMX78+Mw7RETozuRjYqBuXd3c8k5OmbeT8o1M\n9JIkFXkKhQJPT09jh5FtBw8epHXrbiQnFwcaA7fRdYBzwtLyFmvWGHZI27Vr1zF06Iekpgp087nr\npKe7kZiYlHmHM2egVSt48ED36FxYGNjZGTRG6dXkpXtJkoqM9PR0fvjhBy5dukbNmlXp27dvoe6J\n3apVV8LD26DrVT8O3aN0F7GyWsnevdupX7++QesvX74Oly9/CmwFLgNfADdQKody9Ohuqj7fe/7o\nUWjTBh4/1v37f/8H1tYGje9NIi/dS5L0xtNqtbRp05UjR56gUrXExmYx+/cfZ8WK3Pc+N5a0tHTA\nHEgGWgHf4uqaxM6dBwgICDB4/enp6YASXYKfCrTGycmMjRvXZkzy+/bpBsNJSoKuXXXD21pYGDw+\n6fUUnhtVkiRJL3HmzBmOHr2ISrUT+JCkpD2sXbuWu3fvGju0HHvvvb6YmIwFlgN2QCRduwYZNMnH\nxsZy7tw5EhIS+N//BmJjMxzYDwSgVKrZuXMjzZs3/3eHbdt0Z/BJSTBggG6CGpnkCxSZ6CVJKhKS\nkpIwNXVBdwYMYIeZmT2JiYnGDCtXTE1NsbDwAQ4CXwFHWbFitcFuX3733Q94eZUjMLAfHh4+VKpU\ngXnz/ke1asE0aLCWbds2ULt27X932LgROnfWjWH/3nuwYgUUpFnyJEBeupckqYioXr06lpbRmJh8\njVbbBjOzlbi7F8Pb29vYoeVYXFwcpqb+/HtO5ktqqoq0tDQs8vis+dq1a4wfP5WUlD9ISfEFDtC5\nczcePLjDqFEjMu8QGgpDhoBWC5MmQXAwFOL+EEWZPKOXJKlIsLOz48iR3dSqtQ1n55YEBl7kwIHf\nCtY87K9p+/btdO8+iB079qHR/IZu+tl4zMwmUbNmozxP8gCXL1/GwqI68OyRvSZotVbExMRk3njJ\nEhg8WJfkZ8+WSb6Ak73uJUmSXiImJoaDBw+iVCpp1arV60/JmkO6R9omoVJ9hEJxHyurL7G1dSIh\n4QF16jRm06ZQShhg8JnLly9TrVpjkpP/ALyAY9jYtOPBgyisrKz+3TA4GKZM0f2+YAFMmJDnsUiZ\n5Sb3yUQvSZL0AufOnSMwMAgh6iPEPcqU0XL8+F6USqXB6ixXrhZXr84FdB3eFIqpTJig5Ysvgg1W\n5zMLFy5m6tSZWFj4kp5+nY0bV9G2bVvdSiFg+nT47DPd2fuyZTBsWLbKT0xMRKPR4ODgYIDoizY5\nH70kSZIBvPvuOBIS5vDkyWYSEw9z9aoHS5YY9nG9tDQ1YKt/LYQtKSlqg9b5zPjxo7ly5Rzbt3/J\nrVuXMib58eN1Sd7UFH78MVtJXqPR0L//MJycSuDi4kGrVp1RqVQGaoX0XzLRS5IkZeH+/fvcvn0b\nqPd0iYKUlLpERkYbtN533+2FldUgdNPPrkOp/Ir+/XsatM7nlSpVigYNGvw7t7tGA0OHwtdf6x6b\n27QJ+vTJVplffbWYn3++THr6PdLS4vj9dzMmTfrYANHn3JEjR+jYsQ9t2/Zk165dxg4nT8lEL0mS\n9B+DB4/A3b0scXGJwGdAOnAPpTKUJk0MNxrd+vUb+eyzYDSaWExNu1Kp0tds3bo+4yNt+SktDfr2\nhZAQ3Sh3YWG6x+myaf/+E6hUQ9GNBWBBSsp7/P77yTwPN6eOHDlCy5adCQtryPbtLenSZRDbtm0z\ndlh5RiZ6SZKk58ycOYvQ0M1oNNfQaq8CEYASMzNvxo3rTteuXQ1S782bNxk8eBQpKQdJS3uARhNK\nTEwUjRs3Nkh9r5SSohvlbv163Xj1O3fqxrHPAV/fUlhY/A7o7jGbmh6ibNlSeRhs7nz55XeoVDOA\nkcBgVKqFBAd/a+yw8kzhe+5EkiTJgBYu/AZoD7g9XfIHYE5CwmOs83Ds9vT0dL7+egknTpynYkUf\n/P3fwty8NsnJz4aWfQeVahT37t3DI7/ncU9Kgk6ddHPIOznpppmtVSvHxc2YMYXffnube/caAdZY\nW19j0aKDeRdvLmm1gn8HWgIwQ6PRGiucPCcTvSRJEnD+/Hl69RpGQkIC8DsQDzgC27C1dcnTJA/Q\nrdsAwsPvolL1JCwsjLJlfyE9/R7wECgO/AmkULx48dcqT6vVcvHiRdLS0qhUqVLOn7V//BjatoUj\nR3RzyO/eDVWq5Kysp5ycnPjzz2Ps37+f9PR0AgMDC1TP+9GjB7FrV2+Sk5WAJUrlRCZM+NLYYeUZ\n+XidJElvvEePHlG2bEXi4+cC+4BjwGOgNHCR0NBvGDhwYJ7VFxUVhY9PFVJTzwG9gb8AFf7+Vbh1\n6y5mZlVJSztJSMgSevXq8cryUlJSaNGiE2fPXsHExIqSJa04fHgXLi4upKen8+TJExwdHV89k9+D\nBxAUBKdPg6cn7N0L5crlRZMLvPDwcObMWUJ6uoZx4wbTtes7xg4pA/kcvSRJUg6lpKTQvXsftm27\nhRCngRTgfWAlJUq4sGhRMD16vDrZZseNGzeoXLkRKlV9wB1YCDzEyqoxn3zyLuXKlaNSpUqULVv2\ntcqbNesz5s07RXLyJsAUc/OJdO78CB8fDz7/fAEKhSleXj7s2fMrZcqUybqQmBho0QIuXgQfH91l\n+0I8fHBRIxO9JElSDnXrNoCwsFuo1beBvwFL4CEWFmWIjr752pfOX8fOnTuZOnUeycnJJCQ8Ijr6\nIXAC8Hm6xVwmTHjEggWfZ6vczp378euvzYCBT5ccpnjxXjx8mPi0fD/gc/z9N3Px4onMBURGQvPm\ncO0a+PvrLte7u+eskZJByAFzJEmSckCr1fLrrxtQq8OAOsDbwFQsLOozevToPE3y69ato3373pw9\n+z/+/juYR4/MsLIyRzcFLIAGa+tDlCnjle2yq1f3x9r6ZyANEJiZrScu7i7QBSgHKICJXLp0Co1G\nk3Hnq1ehUSNdkq9WDQ4elEm+iJFn9JIkvbGEEFhbO5Ca+hfgCazD3PwT3nuvNQsXLnz1Pe3XdObM\nGerUeZv09GnAB0+XHqFUqaEkJDxCiGpotTFUqlScAwd+y/Z4+qmpqbRu3ZUTJ85hYmKFp6c9V678\niVZbEd0ZvSVwCBubLiQmxv6744ULujP5e/egfn347TdwdMyTNhcEp06dYvv2HTg42DNgwAAcC3Hb\ncpP7ZK97SZLeWAqFgqlTpzBvXmtUqlGYm5/HzU3B7Nmz8yzJA0ye/Bnp6XWAhOeWPsbOzpHTpw9y\n9OhRbG1tCQwMzNFse5aWluzdG8aVK1dQq9VUqFCBgIAGXLyYBlRDd1a/h6+//vrfnU6fhpYtIS4O\nmjaFLVvA1vYFNRQ+W7dupWfPIaSkDMLcPIIFC5Zy/vwxnJycjB1a/hOFTCEMWZKkAm79+vWiX79h\nYvLkaeLhw4e5KissLEyUKuUvHBxKiu7dB4rNmzcLX98aApYLcBEwQ8AiYWJSXPz888951ILM7ty5\nI6pXbyxMTEyFnV1xsWLFin9XHjokhL29ECBEu3ZCJCfnef33798Xbdt2E2XLVhOdO/cV0dHReV7H\ny3h7VxYQLnQD9QthadlHzJ8/P19jyEu5yX3y0r0kSW+U06dP8+mnC0lMTGbIkB706NE9z8o+c+YM\nDRsGkZy8FiiJQtEcU1NXTE3TSE3VAPOBdZia7mPixEHMmzc3z+p+ESFExqsTe/ZAx46gUkH37rBm\nDZibv7iAHLh79y7e3pVJTW0LDAF+pXTp7fz995mMU94aUPHiXsTF7edZR0eFYgZTpmj57LNP8qX+\nvCY740mSJL2Gv/76i8DAIH79tTZ79rzD4MGTWbFiZZ6Vv2vXLtTq/uimmN2EEE1JTz9LauoFwAdz\n84G4up5k/vyp2UrySUlJDB06Bj+/mjRt2oHLly+/9r4ZknxYmG4wHJUKBg2CtWvzPMkDDBgwktRU\nE2AF0BCYz927ppw+fTrP63qR9u3bYm09EfgHOIyV1XLatAnKt/oLEnmPXpKkN8by5StJShoNjAFA\npSpJcPAkBg8emCflOzg4YGFxmuRk0CWYQHQ93gFm4OU1kmvXsp/s3nmnHwcPWpCS8g3Xrx+nXr2m\nXL587t8Z5v4jPj6egwcPYm5uzttvv60b1W/9et0ENRoNjB4NX30FJq93rqdSqYiOjsbd3R2lUvnK\n7S9fvg5oADVgBWjRaBIxN8CXihdZuvRLtNpxbNlSCxsbexYu/JoGDRrkW/0FiTyjlyTpjaHVasn4\nZ88kT28F9uvXDze3v7Gy6gncBb5EN8qeCkvLxTRqVCfbZapUKvbs2U5KymqgDkKMJS2tJvv3789y\n+8jISN56K4B+/b6hZ885VKlSD9XixdC7ty7JT5mim3L2NZP8zp07KVHCi4CAFri4eLJlS9gr9wkI\nqIxC4QZ0AlYC7+Dqakn16tVft9m5Zm1tzerV3/H48V2io6/k6S2aQidPegnko0IYsiRJBcTZs2eF\nUuksYJmAn4VS6SuWLv0uT+t4/PixWLBggahQobowMXEU4CbAVtSp87ZISEjIdnmpqanCzMxSwIOn\nHcu0wta2sfjll1+y3L51627C1HS2ftsJpnWFvkfaZ59lq+74+HhhY1NcwKGnRZwQSmVxcf/+/Zfu\nd/fuXeHjU0WYm7sJExMX4e1dQTx69ChbdUsZ5Sb3yUv3kiS9MQICAti7dyszZy4gKSmZIUM+ZsCA\nfnlah729PTY2NkRG2qDVRgNWmJlNw97+EnZ2dtkuz8LCgpEjRxMS0gqVaigWFscpWfIxrf4zZey1\na9do3bor165FAqMAmMJc5miO6zb4+msYMyZbdd+4cQNTU3d099kBamNu7sPVq1dfeNsAwNXVlYiI\nP4iIiMDCwoIKFSrk6eOKUjbl4ReOfFEIQ5Yk6SW++WaZKFmynHBxKSOmTZslNBqNsUPKtWHDRgv4\nUn8iDRdEyZLlclyeVqsV338fInr0GCymTJku4uPjM60vU6aSUCgWChgvoIuYwwdCgNCA2NWjV47q\nvX//vrCychRw+Wk7bggrq2Lizp07OW5Lbh08eFD4+VUXTk6eokuXvuLx48dGiyU/5Sb3FbqsKRO9\nJBUdGzZsFEqlj4CTAi4IpbKmmDv3C2OHlWuLFy8RSmUzASkChDA1/VS8/Xb7XJWp0WiESqXKtDwy\nMlK0a9ddKBSWAoRQ8EQsoowQINQgvm3cTKSnp+e43h9+CBXW1s7CwaGpsLZ2EYsXL815I3Lp6tWr\nwsbGWcAvAm4KS8sBomXLzkaLJz8V6ERfunRpUblyZREQECBq1aolhBDi4cOHonnz5sLPz0+0aNEi\nw72bOXPmCF9fX/HWW2+JXbt2ZQ5YJnpJKjI6deorIOS5M989okqVRsYOK9fS0tJEUFAXYWNTWtjb\nBwhPz3IiMjIyx+X98MMKYWVlJ0xNLUSVKvVFVFSUEEKIuLg44eJSWpiYzBBgI0w4L1YwUAgQKSjE\nqY8/zpP23Lx5U+zatUtcv349T8rLqWXLlglr60HPfV5UwtTUPFdfZAqL3OQ+g9+jVygUHDhwgGLF\niumXBQcH06JFCyZNmsS8efMIDg4mODiYiIgINmzYQEREBFFRUTRv3pwrV65g8pq9QyVJKlyKFbPH\nxOQ2Wu2zJZE4OtobM6Q8YWZmxvbt/8fFixdRqVRUrlxZ94hbDpw8eZIxYz4iJeUPwI+LF2fQuXM/\nTpzYy549e0hJqYhWOxNzyvAjtelBKkkoWNi4BR/NnJkn7fH29sa7AExZa2+v+7yAQPfY4j9YWNjI\nHPEK+XJ0xH8eXwkLC2PAgAEADBgwgF9//RWALVu2/D979xkfVfE1cPy3NduSUNIoodfQQeklgBTp\nHRA/SjQAACAASURBVEUQRaog2MEOSlP0ryKK8AARRDoISBEEDIQivQQQQq8hEAglfct5Xuwag4Ak\n2U1C8H5f6d07M2fWjzl7507h2WefRafTUaJECcqUKcOuXbtyIkSFQpEL3n33dby9v0erfQW1eiRm\n80g+/fR9t+o8efIkn376KV988QWXLl3yUKSZp1KpqFy5MrVr185ykgfYsWMHdnsXoDygxm5/h717\ntwKg0WiAVLxIZilL6EkKt4CDn07kvfBfH7sJcJ06daJYsVsYDF2BjzGZWvLpp+Meu356Wo480T/1\n1FNoNBoGDRrEgAEDiImJITAwEHDOzoyJiQHg8uXL1K1bN61s0aJFc/V/VIVCkb1Kly7NoUO7mD17\nDlarjZ49w6lUqVKW6rJarXTr9hwrV64C+qDRWBk79kn27t1KqVKlPBt4DipUqBBa7QJSUmw4/2Tv\nokCBQgC0aNGCwj6j+C6+LM3kItfR8k27jox+++1cjTm7GI1Gdu8OZ8aMGVy+HEPz5tNp2bJlbof1\nyMv2RL9t2zYKFSrEtWvXaNGiBRUqVLjrc5VK9a+/xu732eh0w1GhoaGEhoZ6KlyFQpHDihUrxgcf\nuPcUD/Dhh2NZtWonzv3kh2K3w61bH/DJJ5MIC5vqdv25pWvXrkybNpddu2oDFbHb1zFnzlwAvO12\nIgv5ort0gut6AyuGDuODSRNzN+BsZjabGTFiRG6Hke3Cw8MJDw/3SF3ZnugLFXL+8vT396dz587s\n2rWLwMBArly5QlBQENHR0QQEBABQpEgRLly4kFb24sWLFClS5J46R3vovZNCoXh8rFq1EYcjACib\ndk2kHNeuncxynadOnaJ37yGcPBlFpUqV+fHHqQQHB3sg2gc7ceIEx44do0yZMlSsWBGNRsP69T8T\nFhbGhx9+xpUrcfTuPZBl076k8bhx6Pbvh+BgCm7cSL+yZR/egCJP+OdD7JgxY7JcV7a+o09MTOTO\nnTuA81CG9evXU6VKFTp06MDs2bMBmD17Np06dQKgQ4cOLFiwgNTUVM6cOcOJEyeoXbt2doaoUCge\nE4GBfkAw8CFwCjiCVjuG7t3bZKm+hIQEGjRowa5drYmN3UhEhC/ly9ehR48X6Nv3JQICyhAcXIkZ\nM2Z5rA/Tps2gWrUG9O49lVq1mjJp0leAc2Tzk0++4MqVlxFJQX99EgHde8L+/VCmDGzdCkqSVzyI\n5yb/3+v06dNSrVo1qVatmlSqVEnGjx8vIs7ldc2bN7/v8rpx48ZJ6dKlpXz58vLrr7/eU2c2h6xQ\nKLLJnTt37rsO3FMiIyPFYvEXjaaGgK+oVBZ5990PxeFwZKm+7du3i49PLdcyrvkCRV1LAasLlBLY\nKbBF9PpgWbx4idvxx8bGujanOeFq84IYDAXl7NmzEh0dLQaDn4BIcc7ISUqJgNwKDhbJ4XPeFbnD\nndynnEevUCiyVUJCAp079+b339ch4qB//8F8993/smVJ1IULF1i1ahVarZYuXbpQsGDBLNcVGRlJ\n3brtSEyMApoCHwFlgDrAj8DTrjt/oHXr1axdu9it2CMjI2nQoCd37hxNu+brW49ffvmMJ554gnz5\n/CmRuoINvEAwF9mn9sK26mdqP/30v9SqeFwo59ErFIpH1uuvv0tEhBc2203s9iv8+OMfTJv2f9nS\nVnBwMEOGDGHAgAFuJXmAypUr06xZXUymlsBV4BjQCNAD0enuvIRO5/6f0pIlSwKxwAbXlR3YbCcp\nX748RqORWa+OIIKWBHOR7WoTkzt05cnWefd8davVypIlS/j+++85evTowwsoskx5olcoFNmqfPna\nREV9DdRzXZlB9+7bWLQoLDfDyhC73U5YWBhhYT+yffte4DvgFZzzmIcBycAU9u6N8MgRrOHh4XTs\n2JPUVCE11Ur+/PmpX78OP7zclwK9ekFcHBcqVODI2LG06tIlz64ft1qtNGnShsjIRByOisBK5s+f\nQYcOHXI7tEeWO7lPOb1OoVBki6SkJL799jsSE+NRqSIQqQcIev02SpUqmtvhZYhGo6F///4EBATQ\ns+fXJCc/D5iBF4H/odNBWNh0j52zHhoaSkTEb9Su3RSH4y2uX29P/JpP8FrdFhwO6NCB4IULCTYY\nPNJeblm0aBGHDqWSkBCBc2B5Ky+99BzXrimJPjsoiV6hUHhcamoqDRq05M8//UhO7gB8gl6/Hi+v\nFIKC4nnnna9yO8RMCQgIQKM5ByQBXYHK6HQ1iI6+8MBXBPHx8fTpM5CtW/8gKKgQ06ZNon79+v/a\nzvXr12ncuAUpKcWBd2nJOn62r8KEg/j27bEsWQI6nae7l+OuXr2K1VqVv98eV+fmzZjcDOmxpryj\nVygUHrdx40ZOnEghOXkpMBHYg822hbCwVzl4cDu+vr65HWKm1KlThzZtGmCx1MfL62VMppaMHz/+\ngUk+KSmJ4sWrsHx5IrGxazh8eDgtWnTk9OnTiAg2m+2+5X755RdSUioBCXRiCb/QHhNJzFLrufPd\nd49Fkgdo1KgRGs1i4CCQilb7IXXrNs3tsB5byhO9QqHwuMTERFSqQP5+liiDRqPnqaeecmvf99yi\nUqlYuPAHfvnlF86dO0fNms/RoEGDB97/4YdjuXHjInAY51B/BVJTV/HRR2NYtmwlycnx1K3blBUr\n5uHn55dWzuFwoFL50YvrzKYHWoSv8GN3zw70K5o3XndkxBNPPMG0aV/w8svNSUy8xRNPhLJ06U+5\nHdY9oqKiWLJkKVqthl69elE0j/43UCbjKRQKj7t69SrlylXj1q2PgYbo9V9Rs+YpduzY8NCyj4PG\njdsTEREO7Me5JE9QqWqg010hNXUjUBad7k0aNjzLpk0r08pdvXqVCSXL80XiTdTAOJUvv9avweYt\nGx/bE9rsdrvrcJ5Hy759+2jcuBXJyc+hVidjMq1g375tuXZugrK8TqFQPFICAgKIiFjPE0/MJyio\nI+3aJbFmjXvrzPOSKlXKotFUBVoBnwI90OlO43D0AioBeqzW0WzfHn5XuYCffuJLV5L/NrgsjjGj\nCN+84bFN8sAjmeQB3nrrYxISxmK3f4XV+j137gxk7NjPczusLFGG7hUKRbaoUqUKu3dvyu0wcsW4\ncR8SHt6SM2fspKRMQKXS4O3ty507ewAHzmes/RQo4DzFExEYOxY+/ND571OmMHTo0FyKPvdYrVaW\nLl3KlStXaNSoEbVq1cq1WOLibgF/P707HKW5du1ErsXjjsf3Z6JCoVBkwu3bt9mxYwcnTrj/xzxf\nvnzs37+VMWOGotNZsNvnc/36bGy2o3h51cNk6ofJ9AyzZn3jTPIjRzqTvFoNYWHwH0zyNpuNpk3b\n0b//t4wadZLGjdvx44+5996+e/e2mEwfAFHAIUym8fTs2S7X4nGLe7vv5rw8GLJCoXjE7du3T/Ln\nLyw+Pk+I0Rgogwe/muU98tPr1Km3wAzX3vUisFJKlqwk06ZNk2PHjonY7SJDhjg/1GpFFi3yQG/y\npiVLlojFUlfA5vquDorFUtAj/x2ywm63y8iRH0j+/EXFz6+4TJr0Za7E8Rd3cp8ydK9QKNyyf/9+\nNm/ejJ+fHz169ECv1+d2SA8VHR3N+PGfEx0dS/v2TzF69GfExU0CegG3+PHH+nTo8CtPu7mPvNls\nQKW6zt9zqG5RpEhxBg4cCDYbvPgizJkDXl6wdCm0betmz/Ku2NhYHI4Q4K939hVJTLyF3W5Hq835\nVKVWq5k48WMmTvw4x9v2NGXWvUKhyLLFi5fQt+9QHI7uaLVHqVDBzvbtvz3Syf7GjRuEhNTi+vVO\n2GyVMZm+JCnpOCK3AefSP73+FSZOLMVrr73mVluRkZHUq9eMhISXASMm0/9YvXoRofXrQ69ezuRu\nNsPKldCsmfudy8OOHj3Kk0+Gkpi4DKiJVvsRNWvuY+fOjbkd2iNBmXWvUChyxeDBr5KUtJKUlCkk\nJGzg2DENixYtyu2w/tWSJUu4c+dJbLYvgZdITFwJmID5rjuuo9Oto3Llym63VaVKFXbt2sywYfEM\nGhTN77+vIrROHejUyZnkfX3ht9/+80keICQkhAULZlKwYC+02gLUrn2IlSvn5XZYjwVl6F6hUGTZ\n7duxOJeLAaix2UK4fv16bob0UFarFYfDku6KNxqNlXz5xpCS8gWpqdEMHjyEFi1aeKS9kJAQvvnm\nC+e/3LkDbdpAeDj4+cH69VCjhkfayYz4+HgiIiJQqVQ0adLkkdnEqH379sTGts/tMB47yhO9QqHI\nsgYNnkKnGwXEAztRqxfTpEmTLNUlIpw/f56oqCjsdrtH40yvffv26HSrgalABEZjL/r0eYHz54+x\ndet8Tp2K5PPPx91TzuFwcODAAXbu3ElKSspdny1YsJDKlRtQsWJdpk+fcf+G4+KgRQsID0cKFeKd\n+k0o3LYX1as3Ztu2bZ7v6ANER0dToUJNevacSI8en1C5cu1H/seZwk2emQ+Yc/JgyArFYys2Nlaa\nNm0nWq1BChQoIosWLc5SPVarVTp2fFYMBn8xm4tLSMiTcu3aNQ9H+7cDBw5IaGh7CQmpJ2+99b6k\npqb+6/1JSUnSsGErMZtLibd3FSlZsrJER0eLiMiKFSvEZAoWWCOwUUymsjJr1g93VxATI1KtmgiI\no3hxaVa8okBtgR0Cc8VgKCCHDx++p90//vhDRo16T8aNGy8xMTEe6fszz/QTrXaka2a7Q3S6oTJo\n0AiP1K3IPu7kvjyXNZVEr1A8fr788msxmZoJJLmSzwjp1u35LNVltVrlzJkzcuvWLRERmTkzTAoX\nLi9+fsXl9dffEZvNluk6P/lkvBgMHQSsAg7RakdKp07PiYhI27bPCMxKt4RuhVSv3kjat39GKldu\nIO88P0Ds5cs7PyxXTtbPnClgETiVrsxwGTt23F1trlq1SozGAIEPRafrLwEBxeXKlStZ+k7Se+KJ\n5gK/pmt7kTRv3tnteh/k9u3bEh4eLnv37s21pXKPA3dynzJ0r1Aoct3u3ZEkJnYHDIAKq7U3e/ce\nynQ9x44do1ixClSq1Ah//yK89NJAXnnlIy5f/oHY2HV8/30EH31077D8wxw6FEVycnuc05pU2Gyd\nOHLkOOBcQgdx6e6+yJEjkaxZE0L84WEM+HEe6uPHkcqV+bxDV/p9NNHVz/RlYvHyunulwquvfkhS\n0g/AGKzW/+PGjdZMnTot07H/U+PGtTEYvgdSgESMxhk0bvyk2/Xez/HjxylVqjIdOoyiceNutGvX\nI1tfyyjuT0n0CoUi11WpUhajcQ3gPL5Vo1lJxYrlMl1P+/bPcOXKmyQmXiA19SizZy8jMfFNoC5Q\nnsTEz1i06JdM11urViWMxiU4k6Og18+jRg3nrPxRo4ZjNk8AxgKfode/g05XgzL27kTwJiUlgd0q\nNcOrPsFH323n4sUhrn52A74FXkWj+YU+ffrc1WZCQjwQnPbvNlsxbt+Oz3Ts/zRu3IeEhqrR6/3R\n6QJo0yaAd9550+16/+n06dN07vw816+/ye3bO0hI+JPw8KuEhYV5vC3FQ3hwZCFH5MGQFQrFQyQn\nJ7vegZcRH5+aEhxcQS5evJipOmw2m6hU6nQ7q4loNDVEpRog0EqgkEBlCQmpnen4UlNTpXXrLmI0\nBomXV4CYTEWkefOOsmXLFhEROXjwoAwc+Ir06/eyfPHFF1LfVF1i8BcBCaeB5NcYRK+3CMS4Yvuf\ngE5UqnxisfhLRETEPW0OH/62mEzNBY4JbBKTqZBs3rw5wzFbrVYZM2a8NGzYVnr3HnDP93njxg2J\ni4vL9HfxMA6HQ4YOfUMMBn8BH4Ez6V4TjJU33xzp8Tb/C9zJfXkuayqJXqF4PNntdtm7d69s375d\nEhMTs1SHv39xgcUCMwW+Er2+uKhU3gIfCZwXmCI+PkFp7+8zw+FwyOTJk8VgKCrwk8B0MZn8Zfv2\n7XfdlxQeLjfVGhGQtVSRgsb60rfvIDEafV0xOJOewdBFJk6c+MA5A6mpqTJs2Bvi719SihevLAsX\nLpJbt27JoUOH5MaNGw+Nt3fv/q55D8tFqx0lgYElsyWxpxcTEyOtW7cXjaaEQJxAW4EPBBwCN8Vs\nrik//fRTtsbwuFISvUKhUIjIzz//LCqVRaCNQE/Ras1iMAS7Eo0zwfr61svUk3F6tWo1E1iR7gn1\nS3n22Zf+vuH330XMZhGQg6XLSpe2PeV///tabDabjBz5gZjNNQV+Eo3mHfHzC5arV69muO21a9eK\n2VxQvL0risGQT+bOnffAe1NSUkSj0QvcTovVYnlaFi5cmKV+Z8SdO3ckOLi8qNWNBYa42r0gUEnA\nT7y88kn//sOUCXlZ5E7uUzbMUSgUj40tW3ag0fTFZpsCgM32GXb7x8BtwBdIwWaLxtvbO1P1bt68\nmd9+20hMTAygSvdJAlFRR5k8eTI9fXwIHDIEkpPhueeo+sMPLE23R/uECWMoXrwoK1euoHBhP8aM\n2Y6/v3+G2o+Pj6dbt94kJKwAGgBHGDCgCU2aNKJo0aIZ7EX2bh++YcMGbt4sisMxEhgG3ACKAq8R\nHDyJnTt/p1ChQtnWvuLBlESvUCgeG5cuXcNmq5fuSkN8fApgs4WSkNAZs/k3mjWrQ/Xq1TNcZ1jY\nbIYNe4/ExJfQar1RqV5C5GvgIjCBw4fbs/2NlQy2ufZkHzgQpk51HjmbjkqlYsiQgQwZMjDT/Tp/\n/jxqdUGcSR6gEnp9CCdOnLhvotfr9fTs2YflyzuTmDgcrXYnZvOftGrVKtNtZ5TzR4QGaAF0AcoB\nFgoUsLFq1Rolyeci5VAbhUKRI5KTk/nyy685fPgkdepUY+jQIWg0mocXzIQff/yJwYMnkJi4CvDF\naOzFoEHVqFu3BgcOHKJ8+bL06dMnU+0WKFCEuLhVQA2cM+6fpFQpDfHxt7l8uR3POaoQxotocLC4\nWGm6nz0BKtXDqs2U27dvExRUnKSk34HqwGmMxtr8+edeihcvft8yNpuNceM+47fftlGiRGE+/XQ0\nRYoU8Whc6d26dYsKFWpy7dqz2O0N8PKaRIMGBlauXIzZbM62dv8r3Ml9SqJXKBTZKiEhgXfeGU1Y\n2HySkqpht7fHZFpAmzbFWLx4jkfbEhHGjBnPZ59Nwmaz0qNHL2bN+tat0/QMBm9SUs4D+QHQ64cx\nblwJtm49QKEVGqbi7MNonmdZ5VMcitz6r/WdPHmSAwcOEBwcTJ06dTIcx8KFi+nXbwg6XTlSU6OY\nNGkcQ4cOynK/3HHgwAFmzvwRtVrFgAEvpB0AdOnSJV5//X3Onr1E06Z1+fjj9x/pkwzzEiXRKxSK\nR5LD4aBGjYYcOWLCbj8LHMc5vJuIwVCMkycPZstT5l9/I1QeeLLu1KkXv/4qpKRMBDagUr0OxDNK\n68V4axIAb/IBU00RvP56Mz755IMH1rVgwSL69RuKTtcQu/0Afft24dtvv8hwLNHR0Zw4cYKSJUsS\nHBz88ALZICIigtatu5CYOBywYzZ/S0TEemrkwuE8/yVu5b6szwHMHXkwZIXiP+vFFwcJFBD4XeDJ\ndLPV7WIyFZVTp07ldogPdefOHenZ80XJl6+waDT5RMX/5CM++KsjMlxnFqMxnwwcOFysVusD60lN\nTRWDwUfggKvoTTGbS8gff/yR6ZhsNpv83//9n7z66psSFhYmdrvdnS5miN1ul8GDX3WtjZ9218qD\nzp17Z3v7/3Xu5D5lZzyFQpHm2rVr7NmzxyOnme3du5f581fi3O61JnATGAPsRasdTqlSRShRooTb\n7TgcDs6ePcvVq1fdrut+LBYLCxbM4tixfWg1KiZxkdF8gh01gww1CF34I4mJcUyb9jVa7YPnN8fF\nxeFc6FTNdcUXjaY658+fz1Q8IkKXLr0ZMWIOX31VkGHDptG794AMlY2MjKRjx140atSOqVOnZ+oJ\nccqU75gzZwdQB0i/WiCA+PikTPVBkcM89nMjh+TBkBWKPOGHH34UozG/+PhUF5OpgCxb9rNb9S1d\nulS8vdsLtBDoKfB/AmVFp/OXLl16S2xsrNsxnzt3ToKCSotG4ydarbf07t0/255ukxMTZbpaKwKS\ngk66ME/M5rKydevWDJW32+0SGFhSYLbrSfigmEz+EhUVlak4Dh8+7DotL8lVT7wYDP5y5syZfy13\n4sQJsVj8RaX6UmCZmExVZOzYTx/a3r59+6Ro0fKug3jCBH4QKCewxbVjX0lZuHBRpvqgyDx3cl+e\ny5pKolcoPO/SpUtiNBYQOOJKHnvEZCqQpZ3Url69Kl269JFixUJEo/EV2CUwSqC2GAwF5fr16x6J\nOSUlRXx8gl2bszgE7ohO94RMn/5/HqlfxHkM78yZM2XmtGmS2K2bCEgiSCevVmKxVJEePV7I1AYw\nBw8elMDAkuLllV8MBh+ZPz/zG9j88ccf4uNTI93QuYjFUk4iIyP/tdyYMR+LRvNqunKHxN+/5L+W\niY+PlwIFigjME3hb4CXXd/29QEkxmQrLjBmzMt0HRea5k/uUdfQKhYJTp06h15cnKSnEdaUWGk0Q\n58+fJ1++fBmux2az0bjx05w61QirNQyVahwqVSMMhnyYzV6sW7eeAgUKuB2vzWajV6++3L6dAAzG\nuYmNBau1D9u27WHAgP5ut3HhwgVq1WqILeEJwlJ2YbRfxGE2c/6LL2glwoBixXj66aczNeGvatWq\nXL58ktjYWPLnz49Op8t0XFWqVMFsvk18/CQcjs5oNPMpWFBDuXL/fgiQM870Q/WOh8Z+4sQJrNZ8\nwLNAK6AxUA+TyQ+DIZWdOzdTpkyZTPdBkbOUd/QKhYJSpUqRmnocOOa6sg+bLZpixYplqp5jx45x\n8eJNrNb/AbURWY7ZXJolS2Zx5cppatas6ZF4R478kF9+OQhYgF9dV+3ASsqWzXjMEyZMxNs7EIPB\nl+7d+5KQkJD22fvvjyPpek/mJSbQ0X6ROAyMrNmAY0FBJCUlYTKZsjSrX61WExAQkKUkD2Aymdi6\ndT11627Ez+8pGjbcSUTEuocuY+vV61mMxnmoVJ8DSzCZnuO114b8a5mAgABSUy8DV4ECwFp0uj+Z\nMKElx48fUJJ8XuG5gYWckQdDVijyhFmzZovBkF98fGqKyVRAFi9emuk6oqKixGAIEkh0DQ+nitlc\nQg4ePOjRWAMDywhsFvATCBKoJ1BSvL0LS3JycobqeP31N10rAvYKXBOttpP06tU/7fP2TdpJOBVF\nQGLwl6p8IwUKlBCLpbro9a+IyVRSPv54okf7ld0OHz4snTv3ltDQDjJt2owMvXZ4//2PxWQqISbT\nS2I2l5Y33ng3ByJV/JM7uU9ZR69QKNLExMRw7tw5SpUqhZ+fX6bKXr16lUaNWhMVFYlzsLAzRmMC\ntWs72LTpF9Rqzw0glixZjbNnvwL8gFeAQ5QoEcSePREULFjwoeXj4uLw9y+C3f42MNp19TT58jUh\nLu4C3LjBlRo1CDp/nosU4imWcc4wFLs9Bqs1CjAB0ej15YiJuZCp1xt50datWzly5Ajly5cnNDQ0\nt8P5T3In9ylD9wqFIk1gYCC1a9fOdJK32WxUqFCHqKgngSTgOGr1Zp55JpB165Z5NMkDfPrp+xiN\nzwFr0GqrUrCgme3bN2YoyYNz4xmt1gxEpbt6DJPJDDExEBpK0PnzxPr40lwXx2ldM0JDi2A0lseZ\n5AEKodXm4+bNmx7t26OoYcOGDBo0SEnyeZQyGU+hULht+fLlxMXFAh/h/LNSAoejH8HBOry8vDze\nXo8e3QkI8GfJkpX4+uZj6NCdmTo0pUSJEuj1KlJSdgKdcZ6yFsbUdz+Fxo0hKgoqVMBvwwaOFS4M\nQGxsLKVLVwaWAy1Rqf6PAgVMnD59mrlz5xIYGEifPn0wGAwe769C4Q4l0SsUCrc5j2/1BXYBnQAH\nsIVChZ7LtjZDQ0Oz/IRpMplYu/Zn2rbtQmLiRlQqGzPfHUmHSZPg3DmoXh3WrYOAgLRDaf39/Vm3\nbjk9e/bj8uUeVKhQkx49nqd9+74kJz+HwbCV77//kR07Nij7u/+DiLBt2zYuXbpEzZo1KVu2bG6H\n9J+ivKNXKBRu279/P/XqNSclRQW0BI5jMkVz48bZbHmivx+bzcbq1auJi4ujUaNGlC5dOu2z3377\njZ9/Xk3+/D4MHz6UwMDAtDJXr17F/9o1dE8/DdHRULcurFkD+fP/a3sigtmcn6SkP4AKgGCxNGHW\nrFfo3r17Nvb0b8uXL2fgwFe5dSuWxo2fYtGiMPI/JO6cJiK8+OLLLFmyAY2mGlbrZubM+Z5u3brm\ndmh5irLXvUKhyHU//TRfjEZfUanUUqpUiJw7dy7H2k5NTZV69Z4Si6WOmM3PicnkJxs2bBARkZde\nGihQUGCSqFSDxN+/mMTExPxdeO9eET8/5y4yoaEit29nqE2r1SpqtVYgJW0TGpPpBZk+fXp2dPEu\nDodDFi9eLF5efgJbBeJErx8kzZt3yPa2M2vz5s1iNpcVuOP6nvaK0eibI/vzP07cyX15LmsqiV6h\neHQ5HA5JTU3N0Tbj4+OlevXaArUF7K5kslaKFq0gGzduFPAV2J6WjNXqPjJp0iRn4W3bRHx9nR88\n/bRYb9+Wd98dLRUq1JEGDVrLrl277mkvJSUlbVlao0atRacbInBFYK2YTH5y/PjxbO2vzWaTdu16\niE6XT2BAup3ubotWa8jWtrNi7ty5YrH0SBenQ3Q6s9y8eTO3Q8tT3Ml9yqx7hULhMSqVKssbwWTV\nwIGvEhmZDDTi74VETxIbe5nVq9cBOiAg7X6HI4iEhETYtAlatoRbt6BrV1i+nFffGc1XX/3OsWOf\ns21bD5o2bcOJEycAuH79Og0btsJotGA0+jBlylSWL/+JZs1iMJsrERz8OitWzH/oDnXumjlzJps2\nxWC1fgGc5u/d7o7h7Z2xVQc5qVatWtjtvwORAKhU0wkKKoqPj0/uBvZf4sEfHDkiD4asUOSq2UZO\nXwAAIABJREFUpKQk6ddvqPj7l5QyZWrKr7/+mtsh3eXOnTsyePCrUr16E3nmmX5y5cqVTJX38ysh\n8JNAUYEoAZuo1cMlNLSdjB8/QdTqqgLNBfYLfCfgI520RklWqZyPmM8/L+I6XtZi8Rc4l/b0qdMN\nk88++0xERFq16iI63csCqQInxGQqJps2bfL49/Eww4e/ITBRIFmgvqtvQ8RoDJQFCzK/d35OmDt3\nnhgMPqLX+0rRouXk6NGjuR1SnuNO7stzWVNJ9ApF5vTuPUAMhg4CxwVWidHoJwcOHMjtsETEOdTf\noEFL8fLqJbBBdLo3pXjxEElMTMxwHRUqPCmwzHXQikVAK8HBFSQmJkZu3LghRYuWE42mskBhAYv0\noLek4jyFboaXSeLSHbKTP38RgcNpid7Lq6989dVXIiJisfgJRKd9plK9I6NHj/H4d/IwYWFhYjLV\nFYgXSBSVqqOULh0ie/bsyfFYMsNqtUpsbGymDgFS/M2d3KcM3SsUj7nly5eTnPwdUA5oi9X6PGvX\nrs3tsAC4dOkSe/fuJyVlNtAcq/Uzbtwws3PnzgzX8f33kzCZBmIw7MNkakSpUhU4cmQXAQEB5M+f\nn8jInXz99WBef/0ZBukNzGMeOmx8xlu84VWNg5GRaXW9995bmEydgeloNG/h7b2JZ599FgA/vyBg\nj+tOB0bjPgoVCvLYd5FRzz//PJ06VcJgKIHFUpXixU8SHr6OWrVqpd1z9epVXnhhCA0btuW998aQ\nmpqa43H+k1arpWDBglk6H0DhHmUdvULxmDOZLMTHXwKKAKDVXsRiKZm7QbloNBpE7ICNv/8cpaLR\naO57v81mY926dWlL6IoXL06TJk3Yv38b69evx2KpR/fu3TGbzWll8uXLx9ChQ7k9fjw+qbEAfMDH\njOV1TLaKdy1He+ONEQQHF2bp0rUEBORn1KgdBAQ43+/PmjWZdu16oFI9jUp1ivLltfTt2zdD/YyO\njmbEiJFcvHiNli0b8t57b2d5LoNareann2Zw/vyHxMfHU7Zs2bvqSkhI4Mknm3D58tPYbIPYt28a\nhw/3ZcWK+VlqT/EY8ODIQo7IgyErFLnqhx/miMlUROAT0eufl6JFy2XpnPns4HA4pG3b7mI0Pi0w\nX7y8XpBKlWpLSkrKPfempKS4ltA9KRZLTzGb/WTz5s0Za2jChLTD20fqA0SjeVvM5lrSs+eLmRpK\nPnnypMycOVOWLl163xjv59ixY6LR/DVDfoloNE2lc+fnMtxmZq1Zs0a8vRulm+WeKDqd+ZH5b67I\nGndyn/JEr1A8hmJjYzl48CBBQUH07duH4sWDWb16HQULhjBo0Fe5eghLREQE3377A2q1ihEjBrBs\n2VwmTvycbduWEhJSijFjvr7vznJz587l4EEHiYk7AA2wkr59h3LmTOQ996YRgQ8+gHHjQKVCpk6l\nYZEi+Bw6RNmyI+nWrVumhpJLly5910Y8GdGlSy/s9jLANECF3d6GlSsLcvPmzfv+d1ixYgVjx36D\n3W5nxIh+9O3bJ1PtyT2bqihD5f95nvu9kTPyYMgKRY7asmWLWCz+4uvbWIzGwjJo0IhHZgLUxo0b\nxWQKEJgs8KWYTH6ybdu2DJX95JNPRK0ele5JNUZMpgIPLuBwiIwY4bxZoxGZO9dDvcgci6WgQMN0\ncSeLWm2SGzdu3HPv2rVrxWgs5JpcuEpMppIyZ07m4o6Pj5dixSqITveawHIxGttK+/Y9PdUdRS5x\nJ/fluaypJHqF4t/5+xcXWO1KKrfEbC4vv/32W6bquHnzphw9elTi4+M9GlvTph0FfkiX9L6Vjh17\nZais80dCcYEzAnbRal+Tpk3b3/9mm02kf39nI3q9yLJlHuxF5tSo0cg14/9tgTUCT0uVKnXue2+H\nDr0E/i/d97Nc6tRpmek2Y2Ji5IUXhkiDBm3k3XdHZ/g1g+LR5U7uU4buFYrHiM1mIzb2AtDadcUH\nkYacOnWKp556KkN1zJu3gP79h6DV+iMSx88/z89w2YexWm2AOd0VM6mptgfca2XBggXExMTQsGFD\nmjVrxtixbzBqVCXsdjvVq9dlwYJF9ysIffvC/PlgNMLPP0OrVh6JPysWLJhB/frNuXXrJ+z2MEqV\nKsSOHdvve69erwMS0l2JR6fL/J/pgIAAwsK+y1rAisePB39w5Ig8GLJCkaNKlKgkMMv1RHhRTKZg\n2b59e4bKXrhwQYzGggKHXOV/F4vFz2NP9gsXLnI9la8UWCYmUxFZtWrVPffdvHlTAgPLiEpVW1Sq\nYWIwBMnMmWEi4twCNiEh4f4NJCWJdOzofBz29hbJ6GS9bHbnzh3Ztm2bREZG/utrlF27donJ5Ccw\nSWCyGI0Bsn79+hyM9G63b98Wm82Wa+0r/uZO7stzWVNJ9ArFv4uMjBR//+JisZQSvd5bxo+flOGy\nGzZsEF/fJumGjkUsltLy559/eiy+efPmS61azeSJJ5rLsvsMqTscDqlS5QmBGvL33vVHxGDw/fe5\nBvHxIi1aOIPOn1/kPvvU5wW7d++W3r0HSM+e/SQ8PDxXYjh37pxUqFBLtFqjeHlZZMaMWbkSh+Jv\n7uQ+5ZhaheIxlJqaytmzZ/Hz86NAgQIZLnf69GkqV65DUtJeoBhwCKOxCVeunMuxvclv3LhBQEBh\n7PZngTDXVRtgICioFCkpifTs2Z3Jkz/7e/34rVvQrh1s3QoBAfDbb1C1ao7E+ziqXr0hhw+3xm5/\nD4jCZGrKli2/3LUpjyJnuZP7sn1nPLvdTo0aNWjfvj3g/J+4RYsWlCtXjpYtW3Lz5s20eydMmEDZ\nsmWpUKEC69evz+7QFIrHll6vp1y5chlO8mfOnCE8PByj0cj48R9hNNbC17cxRmMzZs2alqMHkGg0\nGlQqNbAK+B24BXRDpcrPlSuziYvbwuzZh3nrrQ+cBa5fh+bNnUm+aFGIiFCSvBscDgeRkX9gt4/E\nuTSvPA5He/7444/cDk2RRdme6L/++mtCQkLS1qpOnDiRFi1aEBUVRfPmzZk4cSIAR48eZeHChRw9\nepRff/2Vl19+GYfDkd3hKRT/eV98MZlKlWrTqdMHlClThWLFinDkyC6WLRvNiRMHeeaZHjkaj6+v\nL1279sTLqwjQGwhEowlH5G2gHlCKpKTPWbr0F7hyBUJDYe9eKF3ameSz+fS4nGS1Wvnhhx8YO3Ys\nGzduzJE21Wo1+fIFAX8l9lQ0mr0UKVIkR9pXZAMPvT64rwsXLkjz5s1l06ZN0q5dOxERKV++fNrp\nVNHR0VK+fHkRERk/frxMnDgxrWyrVq1kx44d99SZzSErFHmGw+GQS5cuydmzZ7O8Tj4qKkqMRn+B\n86534XvEaMz34MluOcRqtcqECZOkRo0GUqRIJSlWrLxoNIPTzR34WZqXrSlSpozzQkiIyKVLuRqz\np9lsNmnUqLWYzaGiVo8Uk6mETJr0ZY60vXr1ajGZ/MTbu6dYLFWkTZtuYrfbc6Rtxf25k/uy9Yn+\ntddeY9KkSajVfzcTExNDYGAgAIGBgcTExABw+fJlihYtmnZf0aJFuXTpUnaGp1DkWVarlY4dn6VU\nqSpUrFiXOnWacfv27UzXc/r0afT6qkCw60ot1GpfoqOjPRpvZmm1WkqXLsHx4+e5dOl9zp8fgt3+\nIzpdX9TqUVQx9OOXWxfh5EmoUQM2b4bChXMkth9+mEPBgsEYjb507dqHhISEhxfKgg0bNrB/fwwJ\nCRtwOCaSmLiZd999F6vVmi3tpdemTRsOHfqDqVPbs2zZF/zyy8K7/o4r8pZsW0e/atUqAgICqFGj\nBuHh4fe9R6VS/ev2kw/6bPTo0Wn/HBoaSmhoqBuRKhR5z6BBL7N69RUcjkuAjkOHBvDqq+8wa9a3\nmaqnTJkyJCfvAzYCzYDfUasTH4lh2kmTppGYOBno5LqSRMWKSxnc0Jv+izTorl6F+vVh9WpsFguv\nDnuDOXPmotPpef/9t3jtteEej2nLli28/PI7JCWtBEqwZs1Q+vcfzvz5Mz3eVlxcHCpVKZzb/QIU\nRURFUlJSlg/EyYysbPer8Jzw8PAH5s7MyrZEv337dlauXMmaNWtITk7m9u3b9OnTh8DAQK5cuUJQ\nUBDR0dFpJ0MVKVKECxcupJW/ePHiA//YpE/0CsV/zaZNm5g9+2ccjv8BBgBSUl5g5853MlVPbGws\nHTv2AkxAZzQaA0ajsHz5QgwGg8fjziznD/3083TMtA4IYsiC+XDjBjRrBitWgMXCR++NISxsL4mJ\nu4DbvP9+F4oUKUSPHt09GtO6db+RlNQfcM4+T06eyLp1DT3axl8aNGiAw/EKsBxogFb7OZUqVc/R\niZGK3PPPh9gxY8ZkvTIPvkJ4oPDw8LR39G+99Vbau/gJEybIyJEjRUTkyJEjUq1aNUlJSZHTp09L\nqVKl7vveMYdCVigeWZ069RZoJ9Ar3Trzt6RLlz6Zqqdr1z6i0w0XcAgkisEQKhMnfpZNUWfesmXL\nXKfuzRGYJs298onVbHa+k2/Xzrk5jkv58rUFItK9w58mPXv283hMX375pRgMPdK1s1ZKlKji8Xb+\nEhERISVKVBaTKb80btxGoqOjs60txaPNndyXY1vg/jUMP2rUKHr06MHMmTMpUaIEixY5t7AMCQmh\nR48ehISEoNVq+e677zJ1qpRC8V+h1WqAJjif9KoBWvT6C0yZ8i+nuN3Hvn2RWK3/h3MJlZHk5J4c\nPLgnU3XcunWLCRMmcfbsZZo1q8+AAS957P/bzp07s3ChjsmTf+DJ27F8fDAZTUIy9OgBc+dCuuHr\nggXzAycA59O1VnsCf3/3T+i7du0affu+zO7deyhWrDjfffcpRYrMIjq6I1ZrCbTaeUyd+qPb7TxI\nw4YN//10PoUiIzz4gyNH5MGQFQqP2r59uxiNfgJfC4wUvb6ALFmyJNP1tGnTXTSaD11PplYxGtvL\n+PGfZrj85cuXXSfRPSswXfT66jJs2BuZjuOhVqxwHkwDIi++6Dyw5h927twpZrOfaLWviJdXX/Hz\nC5aLFy+61axzh766rlPgTohKNU3y5y8sZ8+elenTp8vnn38uhw4dEhHnKoFt27bJxo0b5c6dOxlu\nY9euXbJgwQI5cuSIW7EqHn/u5L48lzWVRK9QOJN95869pX37ZzO0F/pfS/H+Wtoq4lz+GhxcXnx8\naorZXEYaNmwpycnJGY6hVq0GArVdQ/8icF00Gq9Mn5RmtVolLi7u/ksE5893HjELIsOGifzLEq/j\nx4/Lp59+Kl9++eVd/cyKixcvysyZM0Wvz5+ufyI+Pi3u2Zs/MTFRnnwyVCyWSuLjU18KFSot586d\ne2gbb7zxrphMxcTbu6uYTIEybdoMt2L+p507d0qtWqESHFxJBgx4RRITEz1avyJnKYleoVA8UHx8\nvDRp0kYMhoLi5ZVf2rbtnpaMExMTZdu2bbJnz55Mr5PW680CbdO9r04RtVqfqTX406fPFL3eLDqd\nRcqUqSZnzpz5+8MZM0RUKmflo0Y5z5fPAb///ruYzX7i7d1KwEvguqt/NrFYKsvmfxyU88kn48Vg\n6CJgExDRaD6RVq26PrD+mJgYadq0jahU+dPVHSVeXj5y+/Ztt+O32WyyYMECMRjyC8wW2C8GQyfp\n2jVzczgUjxYl0SsUigcaNuwNMRh6CqQKJInR2EY++mhslupyOBwyefK3UqlSfdFq8wkECPxP4A+B\nzlKx4hMZrmvPnj1iMhUSOC7gELV6olSq5Dqn/auv/j5VZ9y4LMWaVUFBpQTWupp/Q6CswHgxGltL\ngwYt7znN7dlnXxL4Pt0Pnt1SsmT1+9admpoq5crVELW6q0DoXYcHmc3F5NSpU27FnpqaKo0bPy1e\nXoUEnktX/03Rag1Z3lhJkfvcyX3KDggKxWNux479JCf3A3SAgaSkvmzbti9LdU2ZMpVRo77jyJGP\nsdleBuJRqb5HpepE/vx72bJlbYbr2rlzJyLtgHKACofjDY4e3Y1j7Fh49VXnTV99Be++m6VYs0JE\nuHr1PBDqujIJrbYkTZpsZNKkdmza9AsajeauMvXr18BkmofzHHkHev1Mateucd/6jx8/zuXLd3A4\nvgYOAztcnyzGYHDctWlYZkVFRfHWW2+xe7eVlJSPgfQbKF1Dp8v9JZOKXOK53xs5Iw+GrFDkql69\nXhKt9g3Xk51DvLxekldeeTNLdVWsWFdgU7onxbflyScbyJw5cyQp3XK3jFixYoVYLDUEkl11bZb/\nGSzOilUq59D9v3j//Y/Ez6+UBAaWkQkTJnnsabVq1fqiVk90vZs/KyZTsGzduvWB99tsNnnmmRfF\nyyufGI1BUqNGQ7lx48Z97z1x4oQYjYVcfV4tUEDAKPnzF5E9e/ZkOeZ33hktRmOA6PXFBcYK3Bao\nIPCiwFdiMpWViRM/z3L9itznTu7Lc1lTSfQKxcOlpqbKvn375ODBg3L58mUpXjxEvL3riLd3TalQ\noZbcvHkzS/VWq9ZYYHlaolepxsjAga9kqS673S4dOjwjFkuI+Fi6yndag7hecovMmycizlcFx44d\nk127dt317r9Nm44CRQV2CewTgyFEpkyZmqU4/unMmTNSsmRl8fIqIDqdSb744usMlbty5YqcO3fu\nX+c6OBwOadOmm5hMLQSmisHQWho1uvd1QGbs3r1bTKaiAlcFFgmECMQKXBeVqoEULlxOli5dmuX6\nFY8Gd3Kfch69QvGYuX79Og0btuLixQREUqlSpSSrVy/iwIEDaDQa6tati5eXV5bqXrlyJT169CMl\npSWgwWRay+7dWwgJCQEgNTWVsLAwzp49T/36ddOOp34QEeH3DRsoMXYspbZsAb0eFi2Cjh1xOBz0\n6TOQn39eg04XiMFwk4iIdVy9epUmTbricHwJ9HLV9At1637Ljh2/Zqlf94vr2rVr+Pj4eHyXQKvV\nyjfffMvevUeoXr0CI0a8gl6vz3Q9s2f/SFjYEu7cieX48QASEn4GBHgH+BKDwZfSpUuycePKtPNF\nFHmXW7nPIz81clAeDFmhyFHPPTdAdLqhrqFnmxgM3eTddz/ySN1z5swVLy9/0WieEa22vDRv3i5t\nyNxqtUq9ek+JydRSYLSYTOUePukvJUWkRw/nk7zJJJJuqeC8efPEbH5SIN41ejBZatZsLDNnzhSN\npqLAp+leIUyRp57q7JE+5gVTpkwVk6ms6wn+XQEfgVPy18l++fMXlgsXLiiT7x4j7uS+PJc1lUSv\nUPy7qlUb/eM9+lx5+ukebtdrs9nEYPAWOOyqN1nM5hDZsGGDiIisX7/e9c7d5vr8smi1hgeuzT93\n/LjsLRwsApJsMIhjy5a7Pv/ggw8FPkjXj0vi7R0gf/zxhxgMQQJ+rlnxb4tKZXLrHbenpKamyscf\nj5eWLbvJK6+8KXFxcdnSTokSVQW2pvtu2olGYxKLpbTky1dIdu7cmS3tKnKPO7lPmXWvUDxmqlcP\nQa9fiPNAGCtG41Jq1arkdr2JiYnYbDYgxHXFC5WqStqRtrdu3UKlCubv09YCUat1JCYm3lPXnvBw\nosqHUPPyBa5joYmtKKNWrb/rnkqVQjCbVwN3AFCr51O+fAh16tThww/fQK9PRqebjcHwPTNmfMPG\njRv54IOP2LMnc9v4elL37n2ZMGEz69d3Zfr0OOrVe4qUlBS36xURLl68mPZd37vNcA0GDerPnj2r\niY4+Te3atd1uU/EY8dzvjZyRB0NWKHLUjRs3pHLlOmI2lxKTKVgaNWqd6RnxD1KmTDVRqz8T52E6\nO8Vo9JOoqCgREYmOjhZv7wCBnwTOiU73mtSo0fDeSuLiZKfOuaXtZYKkEpECF0Svt9w11OxwOKRz\n516uYeliAmZ5442RaZ/HxMRIZGSknD17VgICSohe309UqvfEZAqQNWvWeKS/mXH16lXR630FEtNW\nOHh715KNGze6Ve+dO3ekXr2nxGDwFy+vAtKuXQ+ZPPnbdEP334jZ7CeHDx/2UE8UjyJ3cl+ey5pK\nolcoHs5ms8nhw4flzz//dOs9bVxcnPTq1V/KlKklbdp0l4iICKlQ4QlRq7Xi7e0nP//881337969\nWypWrC358hWWFi06y9WrV++u8No1cVSvLgJyDpOUIcqVFK+KRmO8J9ayZWsIfCJwQOCkmEzFJCIi\n4q57PvpojGi1g9INY6+ScuUyvnGPp0RHR4uXV35xbkz015a59TO0RfG/GThwuHh59RawinPDo1by\nyScTZPbsH6Vp047Svv2zj8RrC0X2cif3KbPuFQrFfTkcDmrXbkpkZAVSU/uh0awjIGAOUVEH0Ol0\n6PX6zJ1Ud/kytGgBR49ySq2hqcOHC7wPVAY+oHXrQqxdu/yu9rVaHSLJODf7AYNhMJMmVWbYsGGE\nh4fz3XezOXBgPydO9MQ52xzgMIULd+PSpWMe+ibuz2q18u2333Ho0HFq1Ahh8OBBtGjRiZ07vUlO\n7o9Wu4FChZbz5597MZvNWW6nWrXGHDo0BmjquvITTz+9kjVrFnqkH4q8wZ3cp7yjVygU93Xx4kWO\nHj1OaupUoA52+4ckJASwa9cuvLy8Mpfkz52Dxo3h6FGoVImLP80l1ihotVNQq/sQEuJg5crFAMyd\nOw9//+JYLAXQ6/MB61yVxKPRbKV06dKsX7+etm2fYfHiJzlxogEwCdgCnMRofI2uXTt49Lv4J4fD\nQevWXXj33dWEhVVk1KhldO3ah9WrF/HSS0WpUWMcXbteZefO391K8gDly5dCq/1r2aDg5bWOkJDS\n7ndC8d/hoVGFHJMHQ1Yo8qQrV66Il1e+tOVtYBeLpfI9Q+d/WbFihbRt+4x069ZX9u7d+/cHx4+L\nFC3qHMuuVUskNlZERM6fPy/Lli2Tbdu2pQ3Zb9261bX//U6Ba6LTPSVarbf4+jYVk6mY9O07WBwO\nhzRq1NY1F+Cv4fq+YjQGSsGCxWTIkNckNTU1276Xffv2ScGCRVyz/lNc7SeJ0RgkJ06c8Hh70dHR\nUrx4RfHxqS0WS1WpXLmORw6/UeQt7uQ+bW7/0FAoFI+mwMBAOnbsyKpVbUlMfA6DYT0BASqOHDmC\nv78/5cuXT7t3/vwF9O//NomJHwM3Wbu2Fdu2baCaWu0cro+JgYYNYdUq8PUFIDg4mODg4Lva/O23\nDSQlvQg4Z41brWFYLDVZvPgd/P39qV69uuu6DTCmK9mQFi2srFjxUzZ+IxAWNpt+/V7G+ZpgEfDX\nRjdeaDTeJCUlZbluq9WKTqe753pQUBBHj+5h165daRse3e8+heKBPPiDI0fkwZAVijzLZrPJV19N\nlm7d+krRomXFbK4tJtMLYjL5ydq1a0VEZNGixWI0FhZYk+4Je6yM7dRdJH9+54UWLUTi4x/a3uTJ\nk11Hvv5VzwYJDq54z30//TRPTKYSAisFFovJVEh+/fVXj/c/vf3794uXV0HX1rtJAhUFPhQ4IBrN\n21K6dNUsjSTs379fgoMriEqlloCAErJt27ZsiF6R17mT+/Jc1lQSvUKR8+bMmSNmcxPXsjoR2CRB\nQaVlyZKlYjIFC1QS2JCWoBsxWBJ1Oue/dOggksHlfXfu3JGyZauJydRedLoRYjT6y6pVq+57748/\n/iS1ajWT2rVbyIoVK2TPnj0ybtw4+fbbb+XOnTue7L6IiEydOlUMhucFvAVOClwU6CAqVT5p1Ki1\nXL58OdN1JiYmSsGCRQXmur7bFeLtHSDXr1/3ePyKvM2d3KcM3SsUeYSIMGtWGFu27KJs2WK89toI\ntyd6ZVRMTAypqTX4e/5uTW7cuMKUKbNJTPwM5xGtg4HPaUkEP/M9Rivw7LMwezZkcKjZYrGwf/82\n5s2bx82bN2nZ8jeqVat233t79+5F797Ove6XL19Oo0ZtSE19Hr1+L1988T0HDmzD29vbzZ7/rVCh\nQmi1R4BPgYZAI2Anw4cP4quvJmapzlOnTpGaagGec13pgFo9gSNHjtCoUSOPxK1Q5LnH4zwYskLh\nEYMHvyom0xMCU8Rg6CFVq9aTlJQUt+u1Wq1y+PBhiYqKeuCa+x07driOVz0iYBWt9jVp1OhpadGi\ni8As15P8bOlICUn+67G+f38RN05lywiHwyFjx34qKpWPwO9pIwpGY1f55ptv0u47ePCgPPVUJ6la\ntbF89NFYsVqtmW7LbrdL69ZdxGKpLkZjG9HpLDJx4kS34ndOePQViHbFfkOMxkA5fvy4W/UqHj/u\n5L48lzWVRK/4L4qPjxet1ijOY1nrC+QTtdpfpk+f7la9165dk4oVnxCLpbQYjYWlZctOD/zxMHNm\nmJhM+USt1kqdOs0lJiZGNm3aJEajv8A30os+Yv0r044YIeLGRj0HDhyQl14aKi++OER27Nhx33vs\ndrsMHz5C9PpyrhnwF9MSvVo9Sj7++BMRETl79qx4eweISjVFYKOYTI1lyJBXsxSX3W6XNWvWSFhY\nmBw7dizL/Utv9OjxYjIVF5Opn5jNZWT48Lc9Uq/i8aIkeoXiMXf9+nXR6SwCJQW+Ebgm8J34+ARJ\nfAYmuT1Ijx4viF7/ijhPuksRo/FpGTfu0wfe73A47plwtmXLFvm+Zj2x/5Vl338/Lck7HA5Zs2aN\nfP/997J79+4MxbRnzx4xmfwExgv8P3tnHhZl9b7xe/aZd4YBZFUBE0TEfV9zScP9p7lruaWpaS5l\nkdpimaW2mZq54JqaW6amiAuaqKm57xsiGIobIoIyMMDM/ftjRoKvoCCoYedzXV7F+57znOeckvs9\n2/N8R0ly486dO3OUeTC7Vii8CCwg0J9AdwLXCOyhTufBv/76iyQ5ffp0ajSDsh3wi6MkORdglApP\nVFQUa9duRoPBjdWqvfzQR8LevXs5Z86ch/opEDxACL1A8IJjtVpZrVp9Ar7ZBIt0cKie70xlBw4c\n4IIFC7g7W5a48uXrENiXzeZ8du7ct2DOTZ36j0PZlrKtVit79HiTen1l6nQDKUml+NPG0LK+AAAg\nAElEQVRPcx5rrmvXfgR+yObTYjZr9n85ymzZsoUGQ1UCIwgE2+/69yPgSLW6BJct+4WbN29maGgo\nv//+e+p0vbPZu0gHB7eC9bEQhITMp1xuJPANgWuUyX6km1uZQn2gCf57FEb7xGE8gaAYIJPJsHRp\nCKpXbwyrNQmAI4D7yMy8AWdn58fWnzLle0ycOA0yWXMAX2Hw4B6YOnUSKlUKQEzMWmRk1AdggU63\nETVq1MufUyTw5ZfA+PG2n2fOBN55J+v1/v37ERq6BykpJ2G78x6N996rigED+kGr1eZpNjXVbO/f\nA5yQlpaeo0x8fDyAQABjADQEcA2AAgaDGps2rcHAgSNw86YBMpkGWm00tNpMpKePhcUSCEn6DsHB\n7+Wvj4Vky5YtGDnyY1itngCCAQDkcJjNi3D69GnUq5fPsRYICkMRfnA8E4qhywJBkTF48Ejq9VUp\nl4+hXl+d/fq9/dg6u3btolJpyLaHbTvwdf78ed64cYN+flXp4FCZen1ZNm7cOs/88TmwWsng4Acb\n4uSiRQ8VWbt2LY3G/8uxAqHVuvDGjRuPNL1hwwZKkheBTQTCKUnluGjRzznKREVF2Zf3dxG4Qpms\nHT08vBkdHc3hw9+nWj3Evh1BKpUfs23bLhw8eAQ7dHidCxcuLlSin/xw8OBBVq/emDqdO4FP7WcI\nkuzjcJ+SVIrnzp17qj4IXiwKo32PrTl9+nTeuXPniRsoaoTQC/7LWK1Wrlu3jl9++SXXrFnzWMHa\nsmULtVpn2tK8/iO4jo6NGBERQZJMS0vjoUOHeOLECVoslsc7YbGQQ4fSrqLkqlU5/Nu7dy9/++03\n7tu3zy7GOwlkUiabRh+fCvkS2ZUrV7FKlZdZsWIDzpu3INcyYWFhdHHxolyuYPXqLzM2NpYk2bJl\nVwIrsvU3nNWrN318v4qIv//+mwaDG4GlBN6yby8MI1CdwMeUyyuzV68BT/1jQ/Bi8VSF/qOPPqKf\nnx+7devGzZs3P/f/OYXQC4oLVquV0dHRPHfuHDOf8jWzvPD1rUZgHW3R3JbbZ7mb6eDgztv2mPMF\nIiOD7NvXpqAaDblxY9Yrq9XKnj3fpF5fjkbj/1GSXDlp0iSWKFGaMpmc5cvXfCqx4P/3d9KkSd9Q\nklrY9+3N1Gq7cMSI4CJvNy8WLFhAvf4N+0fGVfvY9yXwGpVKHT/++OPn/ntUUPx4qkJP2k64bt68\nmT169KCfnx/HjRvHqKioJ260MAihFxQH0tPT2bZtV+p0HtTry7By5XpPJqyFxMXFh7YobkcJ+BNQ\nUKcrwV27dhXcmNlMduliE3m9nty+PcfrsLAw6vWVCZjsIhdBZ+dSJPlE99ZJW3jYLVu28Pr16/mu\nk5GRwe7d+1Gl0lOtdmDLlq/RZDI9UftPwooVK2gwvJq1dQAcpFyu4ieffMpjx449Mz8ELxaF0b58\npamVy+Xw9PSEh4cHFAoFEhMT0bVrVwQHBz+towMCQbFm8OCh2Lw5HqmpfyMlJQYXLtTBO+8U7O+L\nxWIptB9t27aGVjsGQGkAy6HTeWDz5rVo0qRJwQylpgKvvQb89pstKc22bUCLFjmKxMbGgqyHf5LN\nvIy7d28gIyMDSmXBzv2SxFtvjUCjRh3Qo8e3KFeuCiIiIvJVV6lUYtWqxYiPj8P165exdes66HS6\nx1V7ItLT07F3717s27cP6em2A4MdOnRAqVIJ0GheB/AdJKk3vvjiS0yc+EVWUh6B4JnyuC+BadOm\nsWbNmgwKCuKqVauy7tBaLBb6+vo+8RfGk5IPlwWC58qOHTuoULgQmJdtn3g//f1r56v+5s2bs5a7\nAwPr8NKlS0/sS0pKCrt3709JcqaLizcXLfqZGRkZDA0N5dKlS3n58uXHG0lOJps1s3XExYXMnoI2\nG9u3b6da7W5fQSBlsuksX77GE/kdHh5Ovb4CgWT7+G2li4vXE9l6Wty5c4cBATXp4FCNDg5VGRhY\nO+s8U3JyMidPnsJhw97lunXrnrOngheBwmjfY2uOHz8+z18GZ86ceeKGnxQh9IJ/O927v0mgA4HX\nCGTYl3A/YMeOrz+2bkxMjP0AWwSBDMrl37Js2cpFtqdrNpvZoMGrNBjq0GDoQb3e9dHL+HfukPXr\n20S+ZEkyj7/zly9fppubD9XqagQ0BAwsXdr/iffk586dS0kakO1DyUKZTEGz2cwJEyaxdOlAvvRS\nVf7889Insl8YrFYrz58/z44de2Q73W+lWj2YQ4aMeub+CP4bPFWh/7chhF7wb6dPn8EEviIQRKAc\ngUpUq13yld1s9erVdHDolE3grFSrjUW2v79gwQJKUnMCmXb7v/OllyrnXvjWLZorViQBXtPoOPO9\n4Dz32jt16k2FYoLdZhoVikEcNGj4E/t54MAB+xW7v+0257Fs2cr8+uvvKUk1CRwhEEFJ8s4zu93T\nIDExkX5+1SiXuxDwoC1N7oP/Vuv58svtnpkvgv8WhdG+fO3RCwSC/PPee29Dr/8BQAsAXaDRXMfK\nlfNQsmTJh8rGxcXh44/HY9SoD7B37164u7uDPAcgzV4iCoAFRqOxwH7Yfjfk5Nq1a0hLqwtAYX9S\nH7duXXu4clwcLI0aQX32LC6gBOqaf8SHcw9h8OCRubYVG3sNFkt9+08aWCwtcPny9QL7/IC6deti\n4sRgqNWVoNf7wMNjEjZuXIklS36DyfQ9gJoAmsJkGodly9Y+cTsFITMzE1Wr1sGlSzJYrbEA+gNY\nBCADQAa02mWoXz/3THsCwfNECL1AUMTUqFEDe/ZsQ+/el9C9+y2Ehf2KTp06PVTu6tWrqFq1Hr7+\nOgkzZiShadM2CAmZh6ZNK8NgqA+dbhAkqSlmzJgGVT7TvALArFlzYTC4QqXSok2brrh3717Wu4YN\nG0KrXQEgBoAVSuUU1KvXKKeBmBigcWMoLl7EabkRTXAWVzEQJtM6LF26MNdDgi1aNIJONw22dLVJ\nkKSZCApq9FC5gjB69EjEx8fh1KlduHLlAipVqgSDQQ/gnw8Imew6jMZnk6r39OnTuHEjEUAnABKA\n8QDuAnCDVuuFevVM+OKLT56JLwJBgSi6hYVnQzF0WSDIlXHjPqFCMdIe3KUkgS8I9Kanpy9/+eUX\nzp49+7GJYK5fv87w8PCsKGvbt2+nJPkQOEvgPjWaPuzaNWfs+qlTZ1CtlqhQaFi7dlPeunXrn5fn\nzpGlS5MA43196a1vk21pOpEKhTrXmABms5nduvWlQqGmQqHmgAHDnkrsgJ07d1KncyXwOeXy0TQa\n3XOcAzhy5Ahff/0tdu3aj9v/5/pfYTl+/Dg1Gg8CtQncs4/JVwwMrMXLly+Lu/GCp0phtE9mN1Bs\nkMlkuS5JCgTFjZEj38ePP7oCWAxgIQDbDFij6YtJk6pj9OjRj6y/detWdOnSG0plZaSnn8eIEYMg\nl1swZYoawGf2Upfh7NwYd+5cyVHXYrHAbDZDkqR/Hp44AQQFAfHxQJMmuPPzzwis1wwJCb1hsdSG\nJE1Dz56VsGDBT3n6ZDabQRK3b9+GwWCAk5NTQYflsRw5cgTLl6+GRqPGoEEDULZsWQDA0aNH0bhx\nK5hMYwDoodN9gV9/nY927doVSbuZmZmoXbspTp1KgtV6A4ADVKoknDt3CH5+fkXShkCQF4XSvqL5\n1nh2FEOXBYJcCQ0NpVzuTEAiEJM1c5bLx/Lzzyc8sm5mZiYNBhcCe+z1blOSfDh69GhqtZ2zBWv5\nneXK5eOK219/kU5ONgdatSJTUkiSsbGxfP31t/jyy+341VffPHaWfu3aNQYG1qZO506VSs/33hv7\n0Ew3IyODP/wwnb16DeSXX05mamrq4/3LB717DyLwbbYViF9Zp86rRWL7AUlJSRw+/H3WqtWMvXr1\ney5BkAT/TQqjfSJ7nUDwHLBarRg16mNYrV1gOxj3NoBpAGKg1S5E+/Zhj6yflJSE9PQMAC/bn7hA\noaiLKlWqoEyZCFy92hpWqw9ksvUICVn9aGciImBt3x7ylBSsgwzBZ2Kw9MQJNGjQAN7e3ggJmQaL\nJX8HAnv3fhsXL76KzMxJAO4gJKQZGjWqjS5dumSV6dHjTWzZEgeTqQd0ui0IDW2PP//cCoVCkbfh\nfGA2Z+CfYD0AICEzM7NQNv8Xo9GIH3/8rkhtCgRPnSL84HgmFEOXBYKHaNiwhf2+eQKBNAKjCLjT\nxaUsN23a9Nj6VquVbm4+BNbYZ6+XqNN58NSpUzSZTFy6dClnzZrFCxcuPNpQWBitWi0JcClqUoEk\nAmvp4ODOa9eusX//t6lUaqlUSmzZstNjQ8k6O5cmcDnbrPoLjhkzLuv9uXPnqNW6ZAuTm0mDIYAH\nDhzI17g9ij/++IM6nQeBVQRCKUl+XLhwcaHtCgT/BgqjfcVONYXQC4o74eHhdpF/ibbMbg/uyzfl\nolzSvebFoUOHWKJEaRoMZanRGDlr1tyCObJmDa0qFQlwDjSUwZIl0EZjczZt2pxKZV3a0quaqdV2\n5bBhox9psmrVRgTm2+2kU5KaMyQkhJcuXWK5ctWoUGgJOBM52qrD3bt3F8z3PAgLC2P9+i1Zq1Zz\nIfKCF4rCaJ84jCcQPGUyMzMxduxnWLVqPQwGA+rWrYAlS34DMBPAh7Bd1zoBb+8kXLx4HBqN5rE2\nSWLlypU4fPgoXFyc8fbbb6NEiRL5d2rpUqB/f8BqxVQ0wPs4CeAigJIAUqFQ+IDUwmr9CkBfe6Vd\nqFz5Y5w69WeeZk+ePImmTVuDrAiLJQ516pTD1q1rUbVqA0RG9oTV+h6AOgBqAxgEhSIMnp6/IDLy\neM6DgXmQkZGBhQsX4tKly6hTpya6du0KmUyW/34LBMWUwmifEHqB4Cnz7rtjMG/eQZhMPwC4ArX6\nDVgsLrBY3AC8DiAcMtkOXL4cCR8fn3zZ7NSpFzZvPgOzuTskKRzNmrkjNHT1Q6IXGxuLS5cuwd/f\nH15eXraHc+YAQ4cCAL7Tl0Bwyh4AGwCEAGgDhWIbyHhYrQMA3LM/l0Eun4i2bc9g48aVj/QtISEB\nBw8ehNFoRIMGDZCZmQmdTg+r1Qxb6I4EKBSN4eycjtq1ayAkZCq8vb0f22er1YpXX+2AAwfSYDI1\ng16/GoMGtcUPP0zJ15jlZk8uF6FEBMUDcepeIHgGWK1Wzp4dwrp1g9i8eUfu27cvX/Xc3X3t99of\n7Ft/Qm/vQKrVXlQofKlQOPHXX3/Ntx/jxo23n9S/mxVyVq/35ZH/STYzZ8486nQudHRsTJ3OxRYX\n/ttvHzjBSSVK0dMzgErlGPsp/U1UqcqwV69eVCq97OcHqhJ4mUAjKpWOjImJKciQkbSNm4ODK4ED\n9qZTaTBU4ebNmwtkZ8+ePTQYAmnLH2C7aaBS6ZmYmFggO7dv32bjxm0olytpMLhy8eIlBaovEDwP\nCqN9xU41hdALnhfffz+dklSRwEYC8ylJrvnKL16mTOVse/GkSjWEX3wxkdu3b+evv/7Kq1ev5tuH\nmzdvUq02EPDJdoWONBjqMyIiIqvc1atXqdOVIHDRXuYMJyo0WSI/TNaBwEGqVK9To3GlXv8SNZoS\nbNu2C5csWUKNxpXAJwROEhhAoASrVGn0RONGkuvXr6dO50qDoTs1mvL09CzP/v3fZnR09GPr/vzz\nUjo6elIuV1KhqJ/tg8lKnc69QONHki1adKBK9Y79EOQJSlJJ/vXXX0/aNYHgmSCEXiB4BpQpU4XA\n/mxC8znfffeDx9ZbsWIlJakUga+pVA6nq6s3r1+/nqPM/fv3+dFHn/G113rz66+/yzN5zKlTp2gw\nBBCoQuAz+/37GXR09GRSUlJWub1799LRsW6WIH6H0SRAi0zGwdqAbH3IpEZTgrt37+bnn0+kJHnS\nwaELlUonymSuBLwJvEyttiK//356ocYvMjKSPXu+QY3Gm8AiyuXj6eRU8pFCvXfvXkpSSQJH7af5\nHQnMJRBDpXIMAwNr02KxFMgPjcaBwJ1sH17v8euvvy5U3wSCp01htE/coxcI8ontnnd61s8yWTqU\nysff/e7Zswc8PNzx228b4ejojOHDD8DT0zPrfWZmJho3bo2zZ71gNrfCtm2/YN++I1i/fvlDtnx9\nfaFS3QPwCYBNAOZALk/Hli1hOe65lytXDhkZlyDDYczCfLyNuUgHcHLMGCyfuRmAFbb9chPIDHh4\neGDy5O9gNp8AUAbATSiV5VGihCNksusYOnQA3ntvxBON2wP8/f0RHr4HZnMYgMqwWgGT6QaWL1+O\n4ODgXOvs3LkTZnMfADXsT9ZDJusEJ6eJqFmzFpYt21jgfXZnZ3fcuHECQDMAVqjVJ+HuXvXJOyYQ\n/Nspwg+OZ0IxdFnwgjBv3gJKUlkCP1Mm+4Z6vWtWjPnCsHfvXhoMlbNdOTNRoymRZ1rbw4cPs2RJ\nP8rlKrq4eOV5NW3NytX8RaEmAZoA/vXZ50xPT2eNGi9Tq+1GYA4lqRH79BnEkydP0sGhQraZPuno\n2CDf197u3r3LJUuWcMGCBYyLi8vx7sSJE2zSpB0DAupSo8m+nUAqlSM5efLkPO3OmTOHktQu2zbF\ndpYuHZAvn/IiNDSUOp0rdbq3aDA0Zu3aTWk2mwtlUyB42hRG+4qdagqhFzxPVq/+lW3b9mCPHm/y\n5MmTD71PTU1ldHT0YwPLZCciIoJGY11mX07X6Tx5+fLlR9YzmUx5J1JJSyM7dSIBZkoSU7IF4UlJ\nSeGECV+yV6+BnDlzFi0WC1NSUujo6Ml/8qtHUK93ZXx8/GP9v3XrFkuX9qde356S1JOOjp48c+YM\nSVsIXQcHdwKzCPxJhaI8FYrqBLYRmE293pWRkZFZthYvXsJSpQLo4uLDkSODmZyczMqV61GvD6JG\nM5SS5FYk+efPnDnDWbNmceXKlULkBcUCIfQCwb+ArVu30mBwoV7vTb2+RL4FKSUlhd7eAVQqPyaw\nhxrNQNau3fTJs6GlpNji1QO2+PX5PGi2b98+OjuXolrtSAcHV4aHh+er3qhRwfbDbbYmZbLpbNGi\nI0ly9uzZ1On6ZfuIuU25XMvq1ZuxRYuOPHr0aJadrVu3UpK8COwlcIGS1JTBwZ8wNTWVS5Ys4fTp\n03nq1KmCj4dA8AIghF4geM7cvXuXBoMrgV12QdtHSXLJ14yYJOPi4vjaa28wMLA++/V7m3fv3s3x\nPj09PX+HzpKSyCZNbKrq5kYeP57jdUZGBkePHsfSpSvQ378WN2zYkOO9xWJhfHx8gVLMdu7cl8CC\nbGK+h4GB9UmSCxYsoCR1zvbub2q1xlw/YgYNGk7g+2xlD/Gll6rl2w+B4EWmMNonokUIBEXApUuX\nIJeXAtDE/qQBVCpfXLx48bF1SSI6Ohp9+nTG1q2rsXjxbDg6OgIAUlJS0LZtN+h0Bmi1BowfPzHv\noBl37gCvvgrs3g2UKmX7Z7VqOYp8+OGnmDNnH+LiVuHixYno0eMt7N+/P+u9XC6Hq6trgRLMtGnT\nFJL0I4DrAO5Bp5uMVq2aAgA6d+4MR8cTUCpHApgPSWqHDz8MzjWanbOzEQpFbLYnf8NodMi3HwKB\nIA+K7HPjGVEMXRb8B7h16xa1WqdsB81iqNWWeOwdb6vVyi5d+lCvL0+jsQP1eldu3749633//kOp\n1fYgkEogjpJUkStXruT69evZunU3duz4hi0hzI0bZJUqtqlw2bLkpUu5tufhUY7A6Wyz5gn84IMx\nheq71Wrlhx9+QpVKR4VCzR49+jMtLS3r/c2bNzlqVDC7devPxYuXZM3mjx07xt9//z0rCE9cXBxd\nXb2pUr1FmWwcJcktx1gIBP9lCqN94nqdQFAEuLm5YejQgZg5szrU6gqwWmMxZcpElC5d+pH1QkND\nsWXLSaSknACgBbAdPXsOQHz83wCAHTt2Iy3tF/u7UjCZhmD27AU4dOg8TKYvASTj7NbWOOnuAG1s\nLBAQAGzfDjwId/s/SJIetpl3JQCAUnkdDg6lCtV3mUwGk+keLBaCJM6cOY+EhASUKmWz6+7ujmnT\nvslR5733xiEkZBmUyqrIzDyIJUvmokuXzjh16iAWL/4ZJlMqOnfehurVqxfKN4FAgOI3PS6GLgv+\nA7z//kfU6/2o1Q6kRlOGgwYNz1e9mTNnUqsdkm2GnU65XJG1H1+3bgv+kw3OSrW6Dz08yhMIJUD6\nIooxcLZVrlaNvHkzh32r1crjx4/zjz/+4J07d7hmzRrqdJ4EvqJS+Q5dXb3zvMaXX954oz+BEgQi\n7dfgxrFu3eZ5lj906BAlyTtb0Joj1OkcmZ6eXig/BIIXmcJo31Pbo09LS0O9evVQvXp1VKxYEePG\njQMA3LlzB0FBQShfvjxatmyJu3fvZtWZPHky/P39UaFCBWzbtu1puSYQFJqzZ8+iceO28PWtjq5d\n38BPP4UgJeUA0tLmw2w+jKVLl+PKlSuPtVO7dm3I5aEAYgAQcvkMVKhQKysIzNy538HB4SPo9b1g\nMAShTJmTcHFxAaBEBZzDbjTBS0hElKs7sHMn4O6eZZsk3njjLTRs2AGvvTYeZctWxEsvvYQtW1Zh\n5Mg7GDfOFdu2/Y5Ro8ahevWmGDZsNFJSUgo0DgkJCVi5cgWAXgD8AcgAfIwjR/bmWefvv/+GUlkT\ngLP9SU1YrQokJiYWqG2BQJBPiu5742FSUlJI2k761qtXj3v27GFwcHBWuMkpU6ZwzBjb/uCZM2dY\nrVo1pqenMyYmhn5+frmeMn7KLgsEj+XmzZt0cipJmWwmgcNUqVpSocgeVpY0Gqvy8OHD+bI3Y8Ys\nqtV6ajQl+NJLlXjpf/bXr169ysWLF3PVqlVMSUnhokU/s4G2FG/BSAKMkKt4IJe97DVr1lCvr0Eg\nxe7Xcvr5/XOK/f79+/TyKk+lchyBHdRqe7Jp07YFutYXFRVFjcaFQH3+k2xmO52cSj9Ubv/+/UxK\nSmJkZCR1OrdsZwVW0s3Np8ChbAWC/xKF0b5nopopKSmsXbs2T58+zYCAAN64cYMkef36dQYE2KJc\nTZo0iVOmTMmq06pVK+7fv/9hh4XQC54zK1eupINDx2zCHk9AR2CVXexW0Nm5FO/du5dvm2lpabx5\n82b+RHbfPpoliQT4l4sHd2/dmmuxr7/+mkrl6Gx+3qVarc96v23bNhqNjXJsG2g0zll/P/NDRkYG\nvb0DCFSz/+lCQM81a9aQtG0dDB/+AXU6dxqNtejkVJJHjhzhkiXLqNUaqdN50tXV+6HMewKBICeF\n0b6ner3OarWievXq8PDwwCuvvIJKlSrh5s2b8PDwAAB4eHjg5s2bAIBr1679ky8bgJeXF+Li4p6m\newLBE6HVakHeAfDPNTeFIhOlS38CmUwDb+8J2L59IwwGQ456UVFRqF79ZWi1RpQvXxPHjh3LeqfR\naODu7p7rtbMc/PEHEBQEtckEdOmCetdi0bhly1yLVqtWDRrNRgDxAACZbBEqVPjnup1CoQBpztaP\nTJCWAl2tUyqV2LVrM2rUMEKlioSHx2GsX/8LunTpAgAIDw/HokUbkZp6AcnJh3H37lR07twHffq8\ngTt3buDChYO4fj0aNWvWzHebAoGgYDzVU/dyuRzHjx9HUlISWrVqhZ07d+Z4L5PJHvmLLa93n3/+\neda/N2vWDM2aNSsKdwWCfNGyZUuULv0FLl/uC7O5PvT6BRg0aDR++GEKLJbchTIjIwPNmrXFtWvD\nQG7ExYub0Lx5O8TEnIWTk1Ou7WzYsAEhISug12sxduxI1Lh2DejSBTCbgb59gQULAGXef4VbtWqF\nESNex9Sp5aBSlYCTkwq//RaW9b5Ro0YoXZqIjn4L6ektIEmL0br1/8HV1bVA41G2bFkcPbo713fn\nz59HZmYLAA/62AlXrvQGSeh0Onh7exeoLYHgv0JERAQiIiKKxljRLSw8mi+++ILffvstAwICslJ0\nXrt2LWvpfvLkyTmSW7Rq1SrXHNHP0GWBIE+SkpI4fvwE9ukzmIsWLX7skvv58+dpMPj+T9KYRty5\nc2eu5W2pbW3pXIGp7K1xoFWptFUcOpQswH52fHw8IyMjcz3VfvfuXb777ods3bobv/xySpGffN++\nfTv1+nIEbtv7/TN9fasUaRsCwX+BwmjfU1PN+Ph4JiYmkrQl32jcuDG3b9/O4ODgrL34yZMnP3QY\nz2w2Mzo6mr6+vrn+8hRCLyiO3Lx5kxqNo30/nwRSKEnePHHiRK7lK1VqSGAzAbIfFjETMpvIBweT\nBYyBb7FYuHbtWk6dOpV79uwpiu4UiA8++JhabQkajZXp4pJ3nwUCQd78K4X+5MmTrFGjBqtVq8Yq\nVarwm2++IUkmJCSwRYsW9Pf3Z1BQUNbHAEl+9dVX9PPzY0BAALds2ZK7w0LoBc+QpKQk9u07hH5+\nNRkU1OmhE/EF4YMPPqZeH0Cl8gPq9TXZq9eAPFcCAgPrE9jBYZiZtQSwtmbdAot8eno6X365JbXa\nalSrh1OSfPjttz88cR+elCtXrvDYsWNZN3EEAkHBKIz2yewGig0ymSzvWN8CQRFCEo0bt8bhwyVh\nNg+DXL4TLi6zEBl5Is999ccRFhaGkydPwt/fH507d87zHEpIyHxcGT4WEzMSAABjVXp02rMD9erV\ny3dbqampqFWrEc6duwUgCrboerFQqQJx9248JEl6oj4IBIJnT2G0T4TAFQjyICEhAYcOHUB6+m0A\nSlitdWE2h2Pv3r1o167dE9ls3bo12rRp8+jT9SQGX/kbyEiAFcAP5auiTciMAok8AEyf/iOiolQA\nqsEm8gDgDYVCQnJyshB6geA/gsheJxDkgVqtBpkBwGR/QpBJUKvVBbaVnJyMRo2CoFJpIUnO+P77\n6bkXJIHRo4EvvwQUCsiXLcP7F06gadOm+W4rLi4OISEh2LhxGzIyOgA4BGCTvR9T4OnpAfdsEfQE\nAsGLjVi6FwgewcCB72DlymMwmfpBo9kFf/9oHDmyO99in5iYiDfffAcbNmwGabfYDeoAACAASURB\nVATwGwBnaLVB+PXXGWjfvv0/hS0W4O23gfnzAbUaWLkS6NSpQP6eOXMGDRu2QGZmS2RmnkJ6uhXA\nFAAjAVyG0eiJ48d3o2zZsgWyKxAIni+F0T4xoxf8J7h8+TIaNWqFEiW8UK9eC0RFReWr3rx5P+Lb\nb/uiV69DGDOmAvbv355vkSeJVq06IzTUAHIvgDEAOgJwQlraEISHR/xTOCMD6NPHJvI6HbBhQ4FF\nHgBGjfoY9+59ApNpCdLTj0AmU0Au7wqtNgk1atTHokXTsXDhIsyaNQtpaWmwWq2YOnUGmjbtgF69\nBiImJqbAbQoEgn83YkYveOExm80oV64qrl0bAKu1J+TytXB3n4lLl0491X1qW6pWP6SnJwB4EESn\nDYC3ASzF5Ml1MHbsGCAtDejZE/j9d8DBAQgNBZo0KVBbW7duxebN27Fixe+4dWs+gAf1F6Ft2y0I\nCZmKwYPfQVjYPgCDIZcfQOXKKWjSpCEWLvwTJtOHUChOw2ici3PnjmZFrxQIBP8OxIxeIHgEFy5c\nQFKSHFbrGABlYLW+B5PJiNOnT+coZzabER8fX2QfklqtFlZrOoAk+xMrgFgA70Ol2gJHRyPSExOB\nDh1sIu/sDOzYUWCRnzVrLjp3HoLp011w+7YeMtl4AHcBXIVePx2dO7fG3r17ERa2BcA+AF/Cat2G\ns2ctmDPnJ5hMawF0hsUyHmlpzbB+/foi6b9AIPh3IIRe8MJjNBqRkZEA4L79SSoyMm7BaDRmlZk9\nOwRGowu8vQPg61sF0dHR+bKdnJyMN94YBB+fymjUqDXOnDmT9U6v12PYsOHQ61sAmArbbD4WQAKs\n1sH44v2VOOtTFggPt6WXjYgA6tQpcP/GjfsMJlMogLGwWg9ALr8GudwTGk0gRo58DQMG9EdY2A4A\nFgA+9loyWCw+sMW5z34DoGhXzKKiojBjxgzMmzcPSUlJj68gEAiKnkLc338uFEOXBc8Rq9VKq9XK\n3r0HUa+vQ+AL6vUN2KVLn6xgNcuXL6dC4UogigApl3/HwMA6+bLftGlbajR9CRynTDaLjo6evHnz\nZo72ly1bxiFDRrJ9+44E1ATOsARu8xBqkQBTXFzICxeeuI8ajYFAQlZoXY1mKKdOncqbN2+yWbP2\n1Omc6ODgTsCPwGACVwisJyCxXr0mlKQGBH6nXP4lnZxKZoWoPnv2LGfOnMlly5YxNTW1wH7t37+f\ner0rNZohlKTOLF3an7dv337ifgoE/2UKo33FTjWF0AvyQ0ZGBgcOfIdqtZ46nSPHjfuMS5cu5Ycf\njuPPP/+clfv8/PnzVKv1BPpni0OfQZlMzszMzEe2ce/ePSqV2mx52ElJasdPP/2USUlJOcpGRUVR\nq3UloKQH/uZJVCYBRskc+MtXXxWqr1279qVW25nAOQJrKEmu3LZtGyXJk8Bwe9jdVQT0BLwJONr/\nfEmt1pnjxn3KRo3asmvXvoyKiiJJbtmyhZLkSq12EPX6FqxcuR5NJlOB/KpZsymBZVljo1IN4scf\njy9UXwWC/ypC6AWC/+Gjjz6nJDW3i1wsJaka581b8FC5Dz8cR6CrPZd6ql2U/qCLi1dWmQMHDvCd\nd97j6NEfMjIykiT5yy/L2aHD65TJVNni11sJVKNOV5FubmWyRJMkw8LC6OgYRG+8zEg4kgBPowzL\napx5/vz5QvXVZDKxf/+h9PAox4oV6zE8PJwlS/oSUBHIzBJarbYzVSpnAnMJXLYn1qnLvXv3PmTT\n2zuQwNasful07Th79uwC+eXjU5nA0WwfUNM4cOA7heqrQPBfpTDaJ/boBS8koaE7YDJ9DMAVgDdM\nptHYuHHHQ+WsVgIIAFAVtghy/weZ7P+wfPkCAMAff/yBV15pj59+csUPPyhQq1YjBAePxaBBE7Bh\nQwuQdQE0APA+gOYAVEhNPYqEhBEYMGBkVjvlypWDd9ph7EEU/JGEo5DjFdzGtNWLERAQUKi+6nQ6\nLFo0CzduXMSZM3/Bw8MD9+4pAagBXH7QUwB/Qy7PBFAFQBkAR5GRcQnlypV7yGZiYry9HADIYDZX\nxa1b8QXyq127IOh04wEkADgPSfoR7du/+iRdFAgEhaEIPzieCcXQZcFzoGXLzpTJpmfNJpXKDzhk\nyMiHyp08eZKS5EpgNoFJVKu9ciwv16/fksDyLDsy2QSq1c4ETtufxdiXwV+yL4s3IHCPwCmWKlWe\nN2/etG0TnDrF+0YjCfAvhZ6lpBIMDd2Uwxer1cpdu3ZxxYoVvHjx4hP3PTY2llqtC4Hv7T59QKA+\nq1VryA0bNlCvd6GDgz8lyZm//bY2Vxvt2nWnWj2QQAqBk5Sk0ty9e3eB/EhNTWWvXgOo0Rjo4ODG\nqVOnP3GfBIL/OoXRvmKnmkLoBfnh9OnTNBo9qNP1oyR1o7t7GcbFxeVa9sCBAwwK6sz69Vtx9uyQ\nHBnlKld+mcD2bMvPs6lUGu0Cn0HAh8Db9mV7C4FuBMZSLg+iXK6lRlOCbT3KMNPJiQSY2qgR/9q+\nnfHx8Tl8sFqt7NnzTer1AXRw6EpJcuX69evz1Ver1cpvvpnKsmWr0d+/Nn/5ZTkHDRpBvb4agT5U\nqUqzbt2XaTabSdrOFpw9e5bJycl52kxMTOSrr3akQqGmg4MbFyxYlC9fBALB06Ew2icC5gheWK5e\nvYrQ0FAolUp06tQJLi4uBbYxdeoMfPrpAphMcwHchyS9iVdfbYDt26/DZOoFYAKAXwA8WJJeBZls\nKGxx8Q+gIeIRhlfhiDSgXTtgzRpAq83RBkn8+OOPGDNmFtLSjgHQATgIvb4NIiK24syZMyhfvjwa\nNGiQq4/Tp8/ERx+FwGQKyfJx9eo5SE1NxalTp1GhQgB69OgBubzgO3UkH52ARyAQPBMKpX1F8qnx\nDCmGLguKMVarlVOmfJdjtpyZmcnPP/+Kvr5VCXjYT+xbCKQTaM369RtTr+/HFgjnfUgkwNWQMS2X\nGbTFYmFQUEcqlS72Q4EPVg6slMlU1OlK0WB4g5JUhsHBn+TqY9WqjQlsy1Z3Drt16/+0h0YgEDxD\nCqN94jCeQPAIZDIZxox5H9HRxxEZeQgdO3ZA1659MWnSJNy+fRMuLhoAuwB4AygJne4oBg7sizaZ\nO7AJ7aCHCYvwCvooHdC991AsXrwk66ucJF55pRXCw08jMzMMwG4AZ+3tzgCpRWrqAdy/vwwm0xHM\nnBmCixcvPuSjXi8B+OegnEwWD4NB97SHRiAQFBOE0AsEBeCtt0Zi82Yr0tOvIDl5K0ymDDRvXhEe\nHnrI5WYolf7YNexdrEiPgwbpmKPywUD8hQzrYGzY8CqGD/8WEydOBgBs2LABf/11DkALAHUBfA+g\nPgAtSpb8CXq9CwAve8suUKvL4fr16w/5VK9eJQCDAUwCMBaSNB3BwSOe/mAIBIJigRB6gSCf3Lhx\nAxs3/g6z+VUAegDVkJY2BIGBfkhKug2r9Ri63huAnzNMUJKI6toVR/u1hEbbEVbrtwD6IyVlHb79\n9gcAQHR0NKzW+gDCAPwNoDeAT+HvH4iLF49DrU4HsAa2MLXhsFguomLFijl8Onz4MEJCVgD4GbZZ\n/UF4epZEYGBgkfX7wIEDCArqjAYNWmPOnHnijIxAUMxQPm8HBILnze7du3HgwAF4eXmhe/fuUCgU\nSEtLQ1paGpycnAAAx44dQ7NmbWAy1QQwG8BiAFuh0ZyFQuEJtdoXg9LCMAOjAABfaDzRdswYVN63\nD+SpbK3pYbFkAgBq1qwJtXoGMjOHAKgEQAOl0opNmw5CkiRs2/Y72rXrhjt3+sDBwRnr1v0KV1dX\nAEBqairOnTuHzZs3g2wPoIv9TyZiYrSwWCxQKBQoLKdOnULz5u1hMn0FwBOnTo2DyZSK0aNHPrau\nQCD4l1BUBwWeFcXQZcFz4sqVK+zUqTerVm3MYcNGMyUl5aEyU6fOoCT5UKV6j3p9Q7Zo0YFjx46n\nUqmlSqVnnTrNePv2bVat2ojAQvthNwuBNlSrK7JcuaqMiYnheJXuwUk4jsQo6vUuTEhI4OXLl2kw\nuBGYSeAPSlJTDh78z33+iROnUKXSU6FwpVZbgrNmzcrhn9Vq5b1793Jc+btw4QI9PMrSwaEy1WpH\nKpWBBNLszUewRInSRTaG778/hsD4bAf9/qKPT+Uisy8QCPJHYbSv2KmmEHpBfkhOTmbJkn5UKD4l\n8AeVynrU671YqVJDLl68hCSZnp5OlUqy34kngXRqtT7UagMI3CSQSaVyAB0dvQkYCERmE7wpbNGi\nNe8lJ5PjxpEALQCHadwoSSW4cWNoli8nTpxg8+Yd6OHhR1/fGhw0aHhW4pvLly/TwcGNMtkIAjMp\nST5cuHDxI/tWo0bjbMGA7lKhKEmt1p8ODp0pSa7cvHlzocbu+vXrPHfuHM1mMz/4YCxlsnHZ+r2H\nL71UtVD2BQJBwRFCLxD8D5s2baKDQzO7OP1KoAyBLQS2UpJe4ooVK5mYmEiVymAPdvMg8UoFApOy\nrrgB9QkMJdCLtsxvmQSuU6+vyDWrV5MjRtgqKhRMXbiQ58+f5/379x/yZ8CAYZSkxgRWUKUaRS+v\n8kxOTuYnn4ynQvFuNiHdzTJlHj1j1utdCFzPVucj9u3bjytXrmR0dPQTj5nVauXo0eOo0TjSYPBj\nqVLluHXrVur1rpTJviWwjJLkyzlzQp64DYFA8GQURvvEYTxBsSIxMRGtW3eBVmuEu3tZrF+/Ptdy\ntv3pNNgOsv0CYDKAVgBawmT6GnPnLoejoyP8/QOhUHwGIAlAGGSyK9Bq/wSQCaAngFMAPgLwE4Bo\nAAbI5WXw7oiu6BwWBvz4I6BW49bs2eixPhzdug3CuHETkJqamuVLeno6fv55PkymjQB6IiNjGpKS\nymDbtm1IT8+AxeKQzXMjMjIyHjkG/v6BkMnW2H+6D71+C9q1a4sePXrA29sbsbGxSElJKeDIAmFh\nYZg7dx3M5ku4fz8KN24Mw4cfTsT+/X+ga9czaN36d8yfPwlDhgwqsG2BQPAcKcIPjmdCMXRZUIQE\nBb1GtXoQbfnX91CS3Hns2LGHyqWmptLfvzrV6rcIvExgVrYZ8Fy2bt2VJHn16lXWq9eCarWeXl4B\n3Lp1K+vUaUat1pdAZQI17CsCtr15na45Z02fTvbo8SAvLe+tXUt39zJUKL4g8Ae12s5s06ZLDl8U\nCrU9brytmoNDO65atYpHjx61x9pfRmAnJak2x4+f+MgxOH/+PN3dX6LRWJU6nQf79h1Cq9XKEydO\n0N29DCWpFDUaB86ZM69AYztp0iQqFMHZximBGo1DgWwIBIKnQ2G0r9ipphD6/y7bt2+nLfXq3Swx\nUquHc+rUqSRtOegvXbrEW7dukbTFax81KpgNGrSgSuVEYAqBrylJrrmmZn1Aeno6hwwZQpVqKIF9\nBNwIdCFQgY3rNKWlXTtb40YjuWcP161bRweHltkEMo1KpS5HLPkuXfpQp2tLYCsVigl0c/PhnTt3\nSJIRERGsX78lK1VqyEmTvrUlwXkMKSkpPHz4cFbyG6vVylKlyhH42e7DRUqSJ0+cOJHv8V29ejX1\n+prZPkh+ZkBArXzXFwgETw8h9IIXnhMnTthnvu4EDmTtoev1QVy8eDFjY2NZtmxl6vXeVKuNHDky\nOMdJ9cOHD3PAgGEcMGAYDx06lGc7sbGxXLduHefOnUtJ8rIf1IulTNadlV+qQEvz5jY1L1GCV9av\nZ/36r1KvL0G5vHa2vf4kKpXaHKf8zWYzg4M/Ya1azdmpU29evny5SMcnKSmJSqWU7WODNBh6ccmS\nJbmWP3jwIEeOfJ8ffDCWUVFRJG0fC716DaAkedHRsQGdnUvx+PHjReqnQCB4MoTQC154Jk78kgrF\nBwRW0RZf/l0CjVm1agOmpqayQYMgKhQT7GKbQL2+Mn/77bcCtbFt2zbq9a40GttTry/HmjUbUaWS\nqFY7skbZykytVcumoJ6eTD10iCVL+lEu/47AOQJeBN4ksJSS1Jj9+g15SiORO1arlQ4OrgT+zDqN\nr9f7cs+ePQ+V3bFjByXJjcBEyuUf0sHBnRcuXMiyc/LkSUZERDAxMfGZ9kEgEOSNEHrBC8/UqVOp\n0fSxi9ghAsNpNLrRZDJx5MhgAjoCcdlmtOP5ySefFqiNEiW8COyw10+hwVCZv//+OxMuXKC1Rg2b\nYW9vMjKShw8fpoNDlRz72TKZKxs2bMXvv5/GzMzMpzQSeRMWFkZJcqWjY0tKkhffeef9XMvVrfsq\ngRVZvstkn3Hw4BHP2FuBQFAQCqN94tS9oFjQt29fODn9CaXybQA7IUnrMW3at1iyZBnmz98JoCps\noWQBwAy9fgfKlfPLl+3Lly8jMLAO7tyJA9DU/lSC2VwDYfPnw7FjR8iOHQPKlQP+/BPw94fRaERG\nRjwAk728BqQFer0ao0ePKpKodAWlTZs2iIw8juXLR2Hv3o2YOfO7rHe0fdQDAFJSTAA8sr3zQHKy\n6X/NCQSCFwSRj15QbIiPj8ePP/6EhIQkvPZaW2i1WrRu3QMm00QA9WC7PlcGMtlltGvXGOvXL89T\ncK1WK06ePImUlBT07DkQcXH9Qf4KoC+AUQCi4YM6+AMm+CEN1sBAyHfsAEqWBABs2bIFr73WG2az\nF4CuAEIBBEAuX4HU1PtQq9WP7IvFYsGKFSsQExODmjVrol27dkU1TA/1c+TIYISEzAYAvPnmIPj5\nlcGECUthMs0FcA+S1B9r1oSgTZs2T8UHgUBQeAqjfULoBcWSK1euIDCwJlJSmgFwBhAC4C6Aj1C3\n7jn89dcfkMlkudZNS0tDUNBrOHo0EqmpySCTAZgBRAHoAOAa/JGGHdDDG4k4Kjfi/LQv8foIW0a4\nEydOoGHDIJhM/QFsstepCKAllMoyuHXrOm7dugUfHx/odA+niyWJ//u/HoiIiIPJ1BSStBYjRvTA\n5MkTiniUgO++m4bPPlsNk+l3AHJIUieMG9cOCoUcc+cuhVqtxuefv4/XX+9V5G0LBIKio1DaV9h9\ng2dNMXRZ8BRYsWIFHRy62O/TVyTwKoEgOjmVzDpYlhcTJ06iTteRwNcEOhBwIHDKvmdtYmV48Doc\nSYC78TJdVQP4ww8/kCSPHDnCN954gwrFaAKJBDwJ6Ak4Uqn04auvtqNWa4ssZzR65HoYbv/+/dTr\n/QmY7W3eolptyPPw25kzZ/jDDz9wwYIFuUbdexTNmnUg8Fu2swQb2aBB6wLZEAgEz5/CaJ/YoxcU\nS5ydnUFeAmAEcBBAZygUEThz5jDKly//yLonT0YiNbU9bKlhW8CWja4FgH6oDX/sQjw8kYRtaIzW\neB/3Fb8jKCgIH374KRo37ojffjsBiyUawI8AygOIBHAEMpkOu3btRlraX7h/PwrJyYvQvn23hyLd\n3b17F0qlD4AHy/uuUCodkJyc/JCv27dvR506TTF27EWMGLEW1as3wv379/M9TqVLu0OhOJ71s1x+\nHKVLu+e7vkAgeAEowg+OZ0IxdFlQhFitVqalpTEzM5NNmrShXt+UCsWHlKQy/O67afmy8fXX31GS\nWhGYS6CuPQDPMTZGVSbZp77r4UUN3CiTGbl27VqePHmSklSKwG37TN6Xtvj5O7LNlhdTqfTNcZdd\nkkoyNjY2R/u3b9+mo6MngaUEblKh+IJ+flVzDZTj61uNwKasuAFabbesAEH54e+//6arqzclqSsl\nqQdLlChdqHj4AoHg+VAY7RP56AXFhvnzF2DEiPdhNqcgIKA6Nm5cgb179yIuLg716y9E8+bN82Xn\nvfdGIiJiP3buHI/MTAsyMz3QEgqsQyokAMuhQj/oYJHdwYYNq9G+fTts2rQJKlUVAC52K4cA+AI4\nC8DWrkJxFuQdADcAeAI4DDIV7u45Z9AuLi7YuTMMr78+GLGxo1C1ak2sXh0KufzhBbbExATY9v8B\nQIa0tIqIj0/I95j5+Pjg3Lmj2LBhAwCgffsZD/kjEAhebMRhPEGxYMWKFXj99cEA/gBQC8DXKF9+\nDS5cOPJE9kgiJiYG6enp+CmoA767GgMNMjEfPhiCVwD5GqxZsxSdOnUCAMTGxiIwsBZMpi329tfA\n0fEdWCxERsZrkMlMMBh2o1+/3pg1awHU6kBkZJzGhAnj4O7uBn9/fzRo0KDAfvbo8SY2bDAjLW0W\ngL8hSe0RGroEr7zyyhP1WyAQFE/EYTzBC4+ra2kC3bMti1spl6sfeTjt3r177Nq1Lw0GN5YqVZ7r\n1q17uNAvv9Ail5MAp6ElZehHlUrPZcuWPVR07dp11OmcqNW60sXFi4cOHWJMTAynT5/On376KSvG\nfmRkJMPDw/nRR59RkkrTYHidklSGY8d+VuB+37t3jx069KJaraeTU0nOn7+wwDYEAkHxpzDaJ2b0\ngmKBWi0hI8MXwBEAGgCnoFTWh9l8L9clbwDo2rUvQkMzYDaPADASQBTq1q2FtWuXoHTp0sC8ecCQ\nIQCJUx06YujtdGh1Ggwc2B1HjpyAyZSG3r27o2HDhlk2MzIykJCQADc3t0cGxbFdrysPs/k0AC8A\nt6HVVsTp0/vh55e/QD4CgUDwgMJon9ijFzx30tPTcePGDXh6euYZaKZmzYY4cOAOgLoAqgH4HcHB\n7+Yp8gCwefMmmM07ALwCYBiARTh48Bf4+FTEaHk6vs1MsxWcPBlVxo7FnwAuXbqEmjUb4f79/rBa\nS2LRotcwadI4+Pn5oXbt2nBxcUF6ejosFstjhV6tLmkPqAMArtBo/HD9+nUh9AKB4NlSRKsKz4xi\n6LLgEWzdupUGgyslqRQNBldu27Yt13JXrlxh+fI1qFTqqVCoOXLk6Mfa9vDwJfCK/YT8gyV/Cz+G\nU9ax+CV1G+WoM3z4aMpkH2VtDwBBlMtL0WhsQ63WmVqtAyWpFB0c3Lhjx448205JSaGTU0kCa+y2\nttHBwZ0JCQkFGyCBQCCgSGojKKbcuXOHer0rgd12MdxFvd41z8AxVquVt27dYmpqar7sr1q1moAT\ngZIE0ghYOQWjSYCZkLM/JtPVtUyOOv36vU3gh6zgMkAlAib7z1sJlLL/+w4aDG5MSkrKs/2DBw/S\n3f0lKpVaOjuXZERERL7HRiAQCLJTGO0TAXMEz42LFy9CofAB0Nj+pAkUCi9ERUXlWl4mk8HNzQ1a\nrTbHc7PZjNjYWJjN5hzPu3fvhm7d2gPQQoY2mIkmGIOpyADQC8uxGFXg5FQiR52+fbtBp/sGwGYA\nuwDUBvAgjO0rsF2dswJoDrncHdHR0Xn2r06dOrhxIxqJifFISIhD06ZN8ywrEAgETwsh9ILnhpeX\nF9LTY2CLUAcAl5Ge/je8vLweVS0H4eHhcHX1QrlytWEwuOLtt4ciMzMz631IyI9Q4CYWYjfewZ9I\nA9AJdfArdkGn64+ffpqSw17z5s2xbNlMVKgwASVL/g6Vaks2/34EUAW2vzaXkJ4eZzvU9whkMhkM\nBkOecfcFAoHgqVOEKwvPhGLosuARTJs2kzqdO43GNtTp3Dljxqx8101MTLQv/e+yL6fvJmBgmzZd\naLVaSZK3rl7lr7Bdn7sHic3xO4FR1GqNPHnyZJ62zWYzZ82axebNW1Gp1FGnc6ejYylqNC52X904\ne3ZIofsvEAgE+aEw2ieu1wmeOxcuXEBkZCTKly+PgICAh95fuXIFy5cvR2amBd27d4O/vz8AYO/e\nvWjS5E1YrZHZSteCWh2NixdPwMfNDezaFbKwMNyFHG1RHvtRA8AGLF78E/r165erP5mZmWjatC2O\nHZMhNfVlSNIyDBz4f5g27RtERkbi4sWLqFChQpYfRUlcXBwSEhLg7++fa+Y7gUDw30QEzBEUezIy\nMhgXF0ez2Zzj+cWLF+no6EmVaigVindpMLjx6NGjJMn33gu2Z46Lts/oYwg4U6crxYtHj5KvvGI7\neOfszA5e5QnIqdMZOW/evEf6Eh4eToOhOoFMu90bVKl0NJlMBeqTxWJhVFQUY2JislYYHsX7/9/e\nnQdEVb0NHP8OMwPMgOAOCCKGIiIIKq65YC6E5p7mXtpiWpbtZmbaT8W0zD1zqUxLLTX33dxywdwV\nLU1REUVxIZEBZmDO+8fQlK9LiwgyPJ+/nLuc+zy3iWfuveee88YQ5eJSQhUrVlWVLu2vjh49+q+O\nJ4RwXPdT++Q9elHg9uzZQ0xMRzIyLICZefO+oGNH29Cz//vfx6Sl9cdqHQbAzZtBvPXWCAYM6M3G\njTuATkAEUBM4ClQnokIagf37Q1wc+Pig3biRZSEhmM1m9Hr93z4vv3HjBk5OfsAf78mXQaPRYzKZ\n/vFV9o0bN2jevB3x8SdRKptHH63LypXf4eLicsft169fz/Tpi8jKOklWVinS0mbRsWPv/zzErxBC\n/EE644kCZTabefzxDly7NpmMjEtkZGykV68XOHfuHABXr/6O1VrxL3s8wu7d+3j66Y85fjwLuAws\nBSyAjvByyWzTmdHExUGFCrB9O4TYJoVxdnb+R53iHn30UWyT1swFzqDXv0lISBglS5b8mz3/9MYb\nQzl8OACT6SwZGefYsUMxevS4W7ZRSvHtt/Pp3PkZPvxwNBZLS/6cNKc7p04d/cfHE0KIu5FCL/LF\nwYMHWbVqFefPn79l+YULF8jKcgI65C6phV5fg/j4eAC6dGmN0TgaOAz8irPzu5jNBm7e/Ins7J+A\n60BX3N1TaFKpLHvdrOiOHoWgIFuR/w+j0Hl5ebF582pCQz+jRIlGPPbYWdav/+Ef/UhYsmQJPXo8\nz9Kla8nK6ontroAzGRnd2L370C3bjh07nuef/5BFixqya1cpzOaVwB9z0i/F3//2/gpCCPGv5d0T\nhPxRCEMu8l5++U1lNPopT89oZTSWVitXrrSvS09PV66uHgric5+Hpyij0cf+fNpqtapx4z5VpUsH\nqBIl/HJ7wb+au+1pBRuVVuuijq1apawVK9pGvAsLUyo5Od/zHDVqjDIa5fUbaAAAIABJREFUKymY\npjSaGgoG5I6ul6NcXHqoN98ccsv2Hh5eCn6xj8Kn1YYqvb6U8vSsrUqUKGfviyCEEPdT+6TXvXig\ndu7cScuWPUlPPwB4Artxc2vNjRsp9nHqv/56Hv37v45OV5fs7AO88spzxMYOv2N7u3fvpmnTDmRm\nhmCb4KYMIU4X2F/KDZeUFKhdG9auhX9xm/1esX/77SKMRlcGDHiBgICAO253/fp1YmKeJC4uDtgN\nhAIpaDRhODuXRK+HSpVKsn37Wtzd3e37ubmVwmQ6DNjexXd27s/rrxenbdu2VKtWDQ8Pj/vOQQjh\nGO6n9kmhFw/Ut99+S79+y7h5c6F9mV5fjKFD38JoNNKlSxf8/f357bffOHLkCBUrViQiIuKebTZs\nGMWOHaeBQ1TnHBtoRFnSuB4WRomffoI8KJCrV6/mySf7kJExCCenqxQr9g0HDuykYsWKt23bsWMv\nVq1yw2xeDOwHygOg0w2gXz8nevfuTc2aNdHpbu372r//a3z99UFMphHAr7i7v8ehQ7t55JFH7jt+\nIYRjkUIvHlrx8fHUqdMMk2k7UBn4Bo2mH1ptL5ycwNX1B37+eRtBQUG37XvlyhUOHDhAqVKlqFGj\nhv0ZucHgQWZmb+rQi7U8TglSWQv0MpYk8WrSbUPk/hfVqzfkyJG3gHYAODm9wyuvKD79dOxt2/r4\nBJGcvAz4HDgCjARO4Ob2Jvv377hjbmB7X/+DD0axdOk6SpcuyYQJ/6NGjRr3HbsQwvHcT+2Tznji\ngapWrRoTJozGxaUWRqMvzs4DUepFsrM/w2z+jLS0QQwdOvq2/fbs2UNgYCidO8fSuHEnunbta/+S\nKwVNWMpGmlOCVJZQk3aUx4SRixcv5knc6ekmwMv+2Wr1Ii3NdMdty5cvj0azDRgHNECjeRJ//4/Z\ntGnlXYs8gE6nY9SoD4iP38nWrSulyAshHggp9OKBe/75vly9epHjx3dRq1YdoLF9nVKVuHIl9bZ9\nunTpy40bk/n99x9JTz/GqlWHWLp0KQAfN3uMNVygGDeZhwdd+AUzA9FoTPj4+ORJzE8/3Rmj8RVg\nL7AOo/FjevTodMdtv/xyEp6eI/DwaI+7+xpq1Ajkl1/2ULdu3TyJRQgh7scDLfSJiYk0bdqUatWq\nERoayqRJkwC4du0aLVq0ICgoiJYtW5Ka+ucf+tjYWCpXrkxwcDDr169/kOGJfDRlynSqVo0gLm4r\nWu0QIB74CaPxf3Tu3Oq27S9cOA08nvvJgNnchFOnTsHixby0YTUGFF+7etAbKy5Gf4zGj1i48Os8\nuW0PMHToO7z1Vjv8/fsQFDSUr7+eTNOmTe+4bbVq1Thx4hBz5jzP4sWj2b17kwxfK4R4eNxfh/97\nu3jxojpw4IBSSqm0tDQVFBSkjh07pt566y310UcfKaWUGjNmjHrnnXeUUkrFx8er8PBwZTabVUJC\nggoMDFQ5OTm3tPmAQxYPwOLFi5XRWFnBSQXXlJNTeQUuCtyVwVBS7d69+7Z9wsLqK41mvH0IWje3\nQHXk7beVcrJNUKNef10pq1UlJiaqHTt2qJSUlALITAgh8sf91L4HekXv7e1t70Ht7u5O1apVSUpK\nYvny5fYJRZ5++mn7Ldlly5bRrVs39Ho9AQEBVKpUiT179jzIEEUeyszM5OjRoyQnJ9+yfOXKTZhM\nLwOVgJtYrWnAFiCNjIzZPP54h9vmkv/hh7n4+n6Om5s/zs6V+bZJCKFjx4LVCh98AB9/DBoNfn5+\nNGjQgNKlS9v3VUqxYsUKJk2axE8//fSg0xZCiIdavj2jP3PmDAcOHKBu3bpcunQJLy9bRycvLy8u\nXboE2EZJ++tc5H5+fiQlJeVXiOI+xMfH4+8fTIMGTxIQUJUhQ0bY1/n6lkGvPwJcAZpjK/j1cte2\nx2JxsQ95+4dHHnmEIUNe5bHHGrDk0Tq0Xb3CtmLcOBg+HO4ySp1Siu7dn6Nbt6G8/favREf34KOP\nxudxtrceLzZ2HKVK+VOypB/vvTcCq9X6wI4nhBD/Vr5ManPz5k06derExIkTKVas2C3rNBrNPYcW\nvdO64cOH2/8dFRVFVFRUXoUq/qN27XqQkvI+8CyQwqRJ9Tlx4hd++eUcZcuWpFSp46Sk1CMnpw6w\nGVvRLw2cIDv7mv2HH9iKZ82ajTh48DrDqEhrNgFgmTQJ/cCB94xj7969rFixmfT0o4AReJdhw6oy\nYMDzt3338sIXX3zFyJFzMJnWAjomTOhOyZLFeeONV/P8WEKIomPLli1s2bIlT9p64IXeYrHQqVMn\nevXqRfv27QHbVXxycjLe3t5cvHiRsmXLAuDr60tiYqJ93/Pnz+Pr63tbm38t9KLgKaVISDgK9Mxd\nUoaMjKYsX74Li2Uax4//jIfHXkqUcOPKlUFAFaAGUB2tdidTpky4ZRS42bO/4ODBOMbxIm8yhRyc\neEFbji5BQUT/TSwpKSlotZWwFXkAP3S6YqSmpnL58mXi4+MJCAigevXqeZL7woWrMJneA2wT55hM\nI/j++0lS6IUQ9+X/X8SOGDHi7hv/jQd6614pxbPPPktISAiDBg2yL2/bti1z5swBYM6cOfYfAG3b\ntmXBggWYzWYSEhI4efIkderUeZAhijyg0Wjw9a0MLMtdcgOrdT0Wy0igMVbrG1gsDShf3gedbjnw\nAbAYZ+fLDBzYl+ee63NLe5s2bGMa8CZTMKPnKRbypdX/tuf4d1KrVi2s1oPASiALjWYipUp5sHnz\nVsLC6tGr1+fUrx/D0KH/y5Pcy5QpjpPT6b+ci1OULOmZJ20LIUSeyJPugHexfft2pdFoVHh4uIqI\niFARERFqzZo16urVq6pZs2aqcuXKqkWLFur69ev2fUaNGqUCAwNVlSpV1Nq1a29r8wGHLP6jPXv2\nKE9Pb+XpWU8ZDN5KozEquJzba14pd/doNW3aNFWhQlVVrFiEcnMLVA0bRqvMzMxbG7JY1P6w6kqB\nMqFVMUxQME7pdB7q6tWr/yiW7du3Kx+fSsrJSadCQuqoQ4cOKVdXTwVHc+O5rAwGb/vEOffjxIkT\nysPDS+n1Lyqd7mXl7l5GHTp06L7bFUKIv7qf2idD4Io8k5qaytGjRylTpgxTp85i9uytmEwD0Ov3\n4u29kfj4n9Hr9Rw8eBAXFxfCw8PtE9sAkJUF3bvDkiWkazS003jxo9Lg5JTF0qVzeOKJJ245XnZ2\nNu+99yHff7+C4sU9+fTTETRp0sS+XimFRqMhISGBsLAmpKf/2eHP07Ml8+e/RkxMzH3nnZiYyIIF\nC7BarXTu3FnGqhdC5DkZ614UOKUU58+fx2w2U7FiRTQaDdOnz2Dduu1UqODD+++/c8srcLcxmaBT\nJ9vMc8WLk7FkCStSUsjMzKR58+aUK1futl1eeeUtZs/+GZNpLHAGo/Eldu3adNvzd7PZjLd3Ra5f\nn4Zt7PoDGI0tOX58H/7+/nl6HoQQ4kGQQi8KVHZ2Np069WL9+o04OblSubI/mzevpESJEv+sgbQ0\naNMGtm6FMmVg/Xr4mxnsAEqWLM/165uxva5nm3hm2DA3Pvhg2G3bxsXFERPTkcxMK5DBl1/OwN+/\nPGazmdq1a2M0Gm/bRwghHhb3U/vy5fU64dg+/XQSGzakkJmZCDhz/PjLDBjwJvPnz/77na9dg5gY\n2LMHypWDTZsgOBj489b73bi4uALX7J+12qsYDKXuuG3dunW5fPksycnJuLm5ER3dkePHL+Pk5I6H\nRyq7dm26ZQwHIYRwFDKpjbgvixYtZvDg/5GR0Q1wBZwwm3uzb9/hv9/50iVo2tRW5CtWhO3bOaXX\n07NnX4oXL49Wq8fLq+Jd3yX93//exWjsDExCq30NT8/19hEX70Sn0+Hn58ekSVM5cqQ0N28e5saN\nOC5e7Er//m/+l/SFEOKhJ1f04h/bv38/n3wyjawsC/369SQgIIDevV/Eau0OrAaeAbTodCuoWrXy\nvRs7fx6aNYMTJ6BKFdi4kdNmMzVqNCAtTQu8BbzE5cubadOmC7/+evC25/TPPdeXcuW8WbRoJaVK\nefLaa7tvGXjnbuLjT5GZGQ1oAcjJieHXX1f/+xMihBCFgBR68Y8cOHCARo2iMZkGA+6sWfM0L73U\nE52uCfAx8ARQDVB4e8Nnn22+a1s5J06gadECp3PnIDzc9ky+bFmmvz2Emzc7AkuA13K3jsbJKZJ9\n+/bdsUNeq1ataNXq9tnv7qVu3XBWrZqPydQDcMHZ+SsiI8P/VRtCCFFYyK178besVisDB76NyfQW\n8AbQD5NpCsuXb8JqPQxYgfXABzg7n+f48TsXZYDvPxxJcpVgnM6d46CrkcS5cyF3ZESTKROlvIF0\n4I9X4TLIyTlhHz3xTrGdPHmSs2fP/uOOKq+++jKPP+6Di4sfBoMf1arFM3XquH9+QoQQohCRQi/+\n1jPP9Gf37l/5c1hZAAOuru507NgUo7EGbm7dMBoH8eWXs3F3d79jO/HffEPUBx/gi2IzUTTJeoMn\ner5oX9+zZxeMxmlAV6AB0Adn55q0bt3ojiMkXr9+nVq1GhMR0Yzg4Dq0bt0Zi8Xyt/nodDoWL57H\n6dNHOHZsJ3v3bv3nbwgIIUQhI4Ve3NP58+f5/vvF5OTMAEYCC4FVuLq+xMCBz+Dq6oLFcgGLZRNl\nypSkadMmd2znzPz5lO/dmzJYWY0frZjPDfU+R4/G2Wd7q1evHkuWfE2NGr/h7+9BmzapfPfdGBYs\n+OKOve8HDnyHY8eqYTKdITPzHFu2pPPxxxP+cW7lypUjICDg1kF7hBDCwcgzenFP6enpaLUeQDTw\nFTAROECDBtVxcdEzb94GLJalQH2SkmLp2fNFNm1adksbv//wA6W798AdxSKa0R1fLDwNvEeJEj63\nFNro6Giio/9u6hqb/fuPYDaPw/Z71YWMjKfYvXsD48aNZ+/eo0REBPP666/i4uJi38dkMpGamoq3\nt7cUeCFEkSAD5oh7ys7OpkqVmpw925acnKexTVwzAReXkmi1lzCZtIA3kAbMolSp3ly5cta+/+/f\nfotrj564oJhDb55lNjkAeGAwGFi8eN5/Hoa2U6feLF/uQ3b2GMCKq2s3ypU7zsWL5cjIeBKDYTl1\n6uTw448rcXJyYvz4Sbz77hCcnIx4eZVm06YVBAYG3ucZEkKIB09GxhMP1IULF2jUKIbTpy9im152\nGvAKcBXYCrgAY4CFREaW4Oeff7Tt+P33ZD/1FDqlmEZZXuYCCi1wDSenchw+vI9q1arZj7N69Wr6\n9h3I9euXqFevCYsWfUWZMmXs681mM5mZmfYpbZOTk6lfvznXrjljtZp45JGSnDiRQGbmmdyYLLi5\nVWHHjh9IT0+nRYuumEzbgQpoNOMJCfmOo0d3P+jTJ4QQ9+1+ap/cuxT3ZLFYGDHiIxITfwM6AOuA\n48Bu4ElsBRWgAxpNAnPnTrN9/Oor6NoVnVKMpQ8v8QiK7sBkNJpGuLuXZuTI8aSmpgLwyy+/0Lnz\n01y6NAuzOYldu4Jo27a7PY7hw0fh5uZJqVI+1K4dxZUrV/D29ub48b2sWTOFzZvnMXfudLRaI+Cc\nu5cOJyd3zGYz+/btw2p9AqgAgFIvcfz4XvnRKIRweFLoxT0NGTKCefPisVh+BH4AXgKeBprnfjYB\nCphD48YNCQ4OhqlToU8fsFqZXq4ig6kNbASqA+NRSs+NGz+wZIkTLVq0RynFtm3bgDZAU8ATi2Us\ne/ZswWw28/jjbRgxYhrZ2afIzk7j0KHq9OzZDwAnJyfq169PZGQkISEhlC9fAr3+dWAPOt0QSpe2\nUr16dSpUqIBWuxPIyM1sM2XK+N9ziF0hhHAEUujFPS1duhaTaSRQF9gGfAs8CrgDQUAAEIhe/znf\nfPM5fPQRvPyybedPPqHR+hUULzGSYsXaA19g+2EQB9TGbJ7O0aPxXLx4kZIlS+Lk9Cu2d/IBTuDq\nWowpUz5j06ZDwPNAOcAJi+VNdu/exaOPRmMwuGM0ejJ16nR0Oh3btq2hbdurBAb2p1Wrs+zcuREX\nFxfatGlDTEw4bm7V8fBojbt7bxYs+CK/TqMQQhQYeUYv7ujKlSssXbqUUaMmc+bMm0AvIAnbVfkJ\noH7uv3XodCvZuGElTTZtgpEjURoN8S+/TNVPP0Wr1XLt2jWGDRvG55/vJDs7GziI7TfmTZydy3Hh\nQgIeHh40avQ4R49aMZsj0Onm0759c/bu/YWTJ6sDF4FVufstwGh8HYulIxbLBCABo7EZq1Z9TVRU\n1F1zUkqxe/duUlJSiIyMvOugPkII8bCRzngiTyUmJlKz5qOYTA3IyUklK2s7tiv649hmizsKlAHm\noNePYP63M+i0YwdMmEA20M+lLt/pzdSu7cuMGZ8ycOC77Nu3h6tXG2C1JgFeQDNgJr161eLrr2cA\nts52CxcuJCEhgalTv+TGjXpkZp7G1gHwJHADKItO9xM6HWRmnshtC5ycBtOqVTyNGzcmJiaG0NDQ\nfD1nQgjxIEmhF3lGKUXr1p1Yt64KVmts7tJ2aDRxKPU9ts54U4EmODsfonePVszUmmHWLLKArgxj\nKSOAbNzcGqPVniQ9fRA5OaFAb+Bd4CRa7Sbq1XuEbds23vY++5gxH/HBB8cwm+cAl4HaQHlcXbPR\n639j3boVdOv2PGfPjgVaAVY0mih0OhPQEL3+G5YvX0CzZs3y45QJIcQDJ73uRZ5QStG9+7OsW7cT\nq/Wvk7yYUCoWaIRtdLzReHruYd3KGczISoVZs8BgoKPWmaX8Md2rjqysWmRleZKT8x7QDtiORvMR\nYWEnGDr0ebZsWX/HQWuuXUvFbP7j/faywFKKFTvBl18OIiHhV+rXr8+XX07GaHwaN7deODvXRamz\nWCw7sFgmYDLN5qWXBj+4EyWEEIWIFHpht27dOlas2IPVOhj4BLgApKDVnsz99x9cqV09gqipU9F8\n+y24u8PatVyNbIRWOwZbL/zf0GoXodXqcz8DVESns7Bt2wqGD3+Po0ePMn/+fPbt23dLHE88EYPR\n+DmwCziPwfABnTt3oWvXrpQqVQqApk2bcvjwbiZPfozoaF/gOf581S+I69evPZiTJIQQhY0qZAph\nyIXG559/rozGvgqsCoYoKKZArzp16qbc3csojeZtBR+oUoZS6lqdOkqBUiVKKBUXp5RSKikpSVWv\nXl/pdK7K2dlNTZ48VYWG1lUuLj0UzFRGY0PVq9cLSimlxo79VBmNPqpYsc7KaPRTw4ePviWWefO+\nUV5egcrDw0v16vWCysjIuGvcGzduVEajn4K9Ci4qg6Gd6tv3pQd3ooQQIp/dT+2TZ/TCbt++fTRq\n1IaMjG1AJWAilSp9xa5dG+jb9yV2744joGRxNrgoPA8ftk0vu2EDVK9+Szvp6em4urqi1Wq5efMm\nY8Z8zIkTZ2nUKJKXXupPSkoKFSoEk5W1H5iObaKcVCZPHsXLL7/0n2KfPftL3nnnAzIy0mnfvgOz\nZk3GYDDc5xkRQoiHg3TGE3li4cLv6dnzmdxX4PR4eXmxadNyOnbsxZkzDXE3N2Wd5gUi1RWUnx+a\njRuhSpV/fZyDBw/SuHEP0tLCsD0SmAEk4+LSlbVrF9zzFTkhhCiKpDOeuG9nz56lT5/+ZGfvxPYa\n21QyM03cuHGDCxeyKGF+ly0MI1Jd4bRGx+mvvvpPRR7Ax8eH9PTz2EbLmwwEA1FkZb3GkiUr8iol\nIYQQSKEXueLj49HrawHh2Dq1PY3FYhvsxs9qYhtNCOMoxwimhUsprP7+/+k4V69e5Z13BuPk9BgQ\nCCTa1+l0iXh6uudBNkIIIf4g89ELACpUqEB29hEgBdtgOEewWm/SxNeXH7Mv4kMW+wmgnUsAJSp7\nYjab/1G7WVlZ6PV6nJyciI+Pp1Gjlty86UJ29kBsV/LPAP2A83h6bmDAgD0PKEMhhCia5IpeAFCt\nWjVef70/RmM4Hh4xGAyPsWjEe7jHxOBjzuJsOT9eqeZNcs5Ozp51p3bt5gwfPvqu7V27do2GDaNx\nc/PAYCjGJ59M5JlnBpKaOgyLZRTwJVAL28Q4S9FolgCQkZFx1zaFEEL8e9IZT9ziyJEjnDlzhppK\n4du3L1y9Sk5UFGtffJEOvfpisWwC6gGXMBgi2Lt3EyEhIbe107p1FzZuLI3ZPAk4j9H4GHq9md9/\n/xGoDAwDxuZuHQV8h5PT50RF7WDTpmX5k6wQQhQS0hlP5JmwsDDalCyJb69ecPUq5pYtCTt7haee\n/RSLpQq2KWovAl7o9WGcOXPmlv1TUlI4fvw4O3b8hNn8LranQwGYTL0oU6Y0ev00bAPoDEKrDQA6\nYxtW1xOrtQWnTiXkY7ZCCOH4pNALAC5cuMCkSZNYMmAA1hYt4MYN6NyZNwKqcCqpIenpu4D92Iay\nHQocxmLZT1BQkL2NESNiKV++EnXrtiMtLQP4OXeNFYNhLy+80IOQkJ9xcSmDXu9PkyaBGAyngTTA\nirPzDGrXrpnPmQshhGOTW/eCU6dOERnZiKY3Q5ifvRkXrPzeoQOe339P8+jObNrUDduVN8A6NJqe\naLUmnJy0ZGdnUKtWI4YOHUS3bq9gMu0GvIF30WgmYzS2RaM5S8WKOUREhJOQkESdOqG8997bWCwW\ngoJqcuPGNcAZd3c3TpzYh4+PT4GdCyGEeBjJrXtxX4YOHc3jvzdiYfZWXLAyhQY8k+MKWi2NG9fG\nYJgFmIAsDIYZtG7dBL3eE7P5J6zW0fz8cwo9e/bDam2IrcgD/A+lTEye3IIZM14mNTWVBQvc+Omn\nZ/nss4O88MKrDBz4DhkZT2F7xe4Q2dm1+OKLOQV1GoQQwiFJoRfUOLifb9T36MkmlsEMpBFLl6/H\n3b08ZcuWICamDHq9F87OZWnUyErDhrXJyekMfA6sAN4nLa0TWVnL+fO9+FX4+DxCnz59KFasGKmp\nPlgs44EOZGQsYcmS7/jxx5+wWDoDpYEAMjM7sm/fsYI5CUII4aDkPfoiJjk5mVGjxpGUlEKbNs14\n5kYqb/9yEIAhvEEs5YHhwFTS0zPo3/8VVq6cz6xZk7FarZQqVYpvvvkGnW4lZvMebPPFewJd0Oni\n0WiqYzCEYLX+xvTpM0lJScm93aT5SxQalNJy/Xo2Gs23KFUPyMZgWEKtWo/m7wkRQggHJ8/oi5Br\n164REhLJ1avtyM4OY5h+CCMslwBY2Tyap3buxWTKwfaOe/vcvabSuPFKtm5dY2/HYrHQpEkrdu3a\nAvwOGAFwd2/D8OFRBAcH8/77Yzh+/Fes1izatGnL7t17SE5uQ05OFLaJbDyAMUAobm4+KJVB3brV\nWbNmMS4uLgghhPiTPKMX/8iSJUtIS6tFdvZ4RvEbIyyXsAJq5kye2LCWs2d/QaPRANa/7GXF2dn5\nlnb0ej3btq2hQYMmODu3B9ah1X6IwXCYPn36MH/+Mo4dCyYz8yJm8wWWLfuNMmVKEBq6A632RSAS\n+Aooh4uLnqVLp7J37zo2blwuRV4IIfKYFHoHdPbsWb766iuWLFlCVlaWfbnFYgGrGxN5lSHEko2W\np7XO8OyzAJQuXZrWraOAF4Cvgc9xchrK8OFv33YMnU7H5s2ref31+kRGjqN9+9/4+edtlCxZkri4\n/WRlPQdoATeys/ty8KCOEyeS8PDQoNOZgR24uj5NnTp1aNasGVWrVsXJSb6OQgiR1+TWvYP57rvv\n6NHjeZRqjk6XRHCwll27NmIwGEg8c4YtlavSKzuTLPT0cg7BrXt9vvzyM/v+SimGDh3KggWrKVbM\njYkTR9GkSZN/FUNMTGc2bKhOTs772O4OdANCgccpVepJmjaN4sSJBOrXr8nHH4/E3V0mshFCiHuR\n+egFYBv0pnz5alits4BOgEKni+HTT5/g5X79oFcvWLiQDCctA/2qUPKpjowaNQy9Xp+ncZw7d456\n9R7j2rWSZGXdwDZJzjogmeLFG3H9elKeHk8IIRydPKMv4qxWKzNnzqRjx6ewWsH2DBxAQ3Z2PX45\neBg6dYKFC8HDA8PWLUw/dQiNRkPlyrUIDW3A+vXr8ywef39/fv31AJMnP4+LSzK22emOYjQ+S48e\nXfPsOEIIIf6eXNE7gF69XmDx4iNkZHgDx4EmwBQgCSN12eSWSb30G1CyJKxfD7Vq8dprg5kxYxcm\n03ggEaPxBbZuXU1kZOQ9j/Vvbdu2jUGDhpGa+judOrUmNnY4Op281SmEEP+G3LovwpKTkylfPojs\n7AvANWzPwgOAX/AAVuFKQ9LA2xs2bIDQUADKln2ElJRVQNXcloYxeLCV2NiRBZCFEEKIe5Fb90XY\nwYMHyc7WA26AP/A2cIVS1OFHytKQNM476ehbKZxt167Z9zMYjECK/bNOdxk3N0M+Ry+EEOJBk0Jf\niMXFxbFs2TI0GmfgFeAIoMObLLayk1okcRItDazj+fKnJ4mJeZKdO3cCEBv7HgZDd+ATtNpX8PRc\nRd++fVi+fDkTJ05k27ZtzJkzl9Kl/TEaS/DUU30wmUwFmK0QQoj/Qm7dF1LDh49m3LjpKBVKRsYu\noBoQjz9l2UQSlUjnpIuBxllTSaZP7l6T6Nr1EPPnz+bKlStERNTj8uV0IIcqVSoSHFyFNWsOk53d\nEFiE1QoWy0rAH1fXAXTuXIqvv/68oFIWQogiS57RFzHnz5+ncuVwMjOPAV7AEGAy1V29WJF5Gn8U\nlurVidF4sunQG9g6580GNtCsmY6NG1fSo8dzfP+9OxbLp4DC2TkGpeKxWE5gG9L2FaAEMCL3qKdw\nc3uUmzeT8z9hIYQo4uQZfRGzf/9+rNay2Io8wGjqGMqwyzkFfxQ8+ij6bdt4YchAXF37ARWBnUAk\n27btYeHC7/j558NYLG2xTTbjhNkcjlIV+GPcetvz/qN/OeoJ0tOzWLVqVT5lKYQQIi/IFX0hc/r0\naSIi6pGWZgbmAm2oxXjWa96kpFLQvDksXQpubmRkZFCqlC8ZGVGNMUnHAAARyUlEQVTAktwWduDm\n1onMTAs5OTGAH/ADcB2NxoRS84EWwKfAqNx/BwDfAB0pU2YDSUm/5PkgO0IIIe5OruiLkPHjp5Ce\n/jywBhhAQ5z5kTcoqRQp9euTk1vkATp37k1GRioQ+JcWKpCefoOcnG3YrvI3A4uAeSiVCbwGFAOW\nYpt+tjRQDtgIOHP9umLevHn5lK0QQoj7JYW+kElLM2G1egH1acEs1mGb8HWRrhxBh9N5rFVnzGYz\nb745hFWrlgDuwBxgA3Aa24Q1Ltg67+mBWUAY0DJ32x8AM7AbF5fywHzgCjAJWIrV2ooLFy7kZ8pC\nCCHugxT6QqZHj44YjWNpx4esoA1GLMyiKU9lnyM1fR87d6YxcOBAPvtsOeAMjARygD5ATeAAtufw\nCwAFXPxL6+FoNM2Akbi4dKN8+QwiIsLRaLYDQcAiXF1X8Oijj+ZnykIIIe6DPKMvhHYNHEjtKVPQ\nARPR8RoJKPxy13bH2XktZvO7wA1gBRCN7db7MWy3/H8BPsQ2YE4x4HXgQu62FwgOrk737h0ZNGgQ\nmZmZtGrVhQMHdqHVahk3biyvvPJS/iYshBBFnLxeV5TMnAn9+oFSxDq5McRqBHoD44CVQA+gA7bh\ncJcC84AvgTNABrY54gOAg0AMtiv6hthG1msFPIZe34OqVQ+zf/92tFotABkZGbi4uMic8UIIUQCk\nM15RMWECvPACKMUQJyNDrNOxzVS3DCgOdAWGAtOB34Ha2Ir8YeAZoCzwG7ZOeDGAN5CK7UdBKaAz\n8B4WyzR+++08p0+fth/aYDBIkRdCiEJI/nIXBkrByJHw2msAvOpkINZaEbgJTARM2Ap+CWy36w3A\nJqAD3t7nqF07HGfnj4HG/Pme/AhsPwJaYrttPxRoCwwGLFitWTg7O+dXhkIIIR6QB1ro+/bti5eX\nF2FhYfZl165do0WLFgQFBdGyZUtSU1Pt62JjY6lcuTLBwcF5Oj96oWexwIYNWDUaXtBVYJK1N9AM\neB9YDvQHdvLqq0/i7j4DjWYkMAuDYTqTJo1lz57NrFixBNu79JdyG90EVAE+o0cPX4KCKuDqegH4\nEoOhHU2bNsLf3z//cxVCCJGnHmih79OnD2vXrr1l2ZgxY2jRogUnTpygWbNmjBkzBoBjx46xcOFC\njh07xtq1axkwYABWq/VBhld4ODszvFYD2mpcmZndE2iH7fn7WGAfWu1sund/igkTJhAXt4U+fS7y\n1FN7+eGHL+jc+UkArly5hu0WfQBQAZgGdMDLqzzz5s3k4MHdvP12OO3b/8j77zdj2bL5aDSaAklX\nCCFEHlIPWEJCggoNDbV/rlKlikpOTlZKKXXx4kVVpUoVpZRSo0ePVmPGjLFvFx0drXbt2nVbe/kQ\n8kPnyJEjymDwUTBTQYSC3xV8ocBHabUe6qWXXldms/mu+1+6dEkZDCUVPKPAT4GbAj+l0birHTt2\n5GMmQggh/ov7qX26/P5hcenSJby8bGO0e3l5cemS7VbyhQsXqFevnn07Pz8/kpKS8ju8h9K5c+dw\ndg4jI+NZ4BDwCOBM8eJWPv10It26dbttSNrt27fz2Wdz0GqdaNasAc7Oj5CRMRvblfw6dLo4Fi/+\nhgYNGuR/QkIIIfJNvhf6v9JoNPe8PXy3dcOHD7f/OyoqiqioqDyO7OESGhqKxbIf2A9MBrzRaGLJ\nyanLK69M56OPphAX9yMeHh4AbNiwgXbtepKRMRTIYvHiN3Ifg/wKvAxEodc3loFvhBDiIbVlyxa2\nbNmSJ23le6H38vIiOTkZb29vLl68SNmyZQHw9fUlMTHRvt358+fx9fW9Yxt/LfRFgb+/P3PnzqBn\nz+YopScnx0pOzmDS0oYCioSEPowePY7Y2A+ZO3ceb701koyM8djeqYeMDD21ay/h6NGGODtXwmz+\njVmzplGqVKkCzUsIIcSd/f+L2BEjRtx947+R76/XtW3bljlz5gAwZ84c2rdvb1++YMECzGYzCQkJ\nnDx5kjp16uR3eA+tjh07kJR0mlGjBlOiREms1qa5azRkZTXmxImzvPPO+wwY8AmXLxuwjVv/B3d8\nfPw4deooq1d/yunT8XTv3rUAshBCCJHfHugVfbdu3di6dStXrlyhfPnyfPjhhwwePJguXbowe/Zs\nAgIC+O677wAICQmhS5cuhISEoNPpmDZtmvT6/oukpCRq147i+vXKmM3uwHigDpCJ0fgVjz7akcGD\nB5OdfQ7bBDavYZu0JgujcRj9+8/Gx8cHHx+fAsxCCCFEfpMhcAuBM2fOEBZWk5s3G2N7re4m0Bw4\njF6voWXLGIKDH2H8+AkolYZtdrq5ODkNpUKFkowf/4H9zokQQojCR4bAdXAvvvgmN2+GAzVyl7gD\n8ylevATLly9m8+atjB/vjFKB2IbB3YdGc5Nixczs3LlGirwQQhRhckVfCFSqVItTp54FYrHNMFcB\nJ6fn6dLFkzNnzrN7dy+gJ5AONKNYsSSqVw9l+vRxhIaGFmToQggh8oBc0Tu4Bg0icXHZCwzHNhmN\nD6GhF5g5cyJpaTfBPkWtG9CHmJgW/PTTGinyQgghpNAXBpMnj6VGjXO4uLyNTvc7tWpFMGDA0wB0\n794eo/Ft4AiwE6Mxlh495Fa9EEIIG7l1X0gopVi0aBFPPz2AnJyn0OkS8fY+zb592/nkk0nMnDkX\nZ2dnhg17g+ee61vQ4QohhMhD91P7pNAXIpUq1eDUqf8BTwDg4tKNUaMieeONNwo2MCGEEA+UPKMv\nIlJTrwFV7Z+zsqpy+fLVggtICCHEQ08KfSESHd0CV9ch2KabPYjROJPHH29R0GEJIYR4iEmhL0Rm\nzJhIq1bOuLgEULx4KyZN+pCmTZv+/Y5CCCGKLHlGL4QQQjzk5Bm9EEIIIe5ICr0QQgjhwKTQCyGE\nEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQ\nCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjh\nwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0Q\nQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5M\nCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGE\nEA5MCr0QQgjhwKTQCyGEEA5MCr0QQgjhwKTQCyGEEA7soSv0a9euJTg4mMqVK/PRRx8VdDgPnS1b\nthR0CAWqKOdflHMHyV/y31LQIRRaD1Whz8nJ4eWXX2bt2rUcO3aM+fPnc/z48YIO66FS1L/sRTn/\nopw7SP6S/5aCDqHQeqgK/Z49e6hUqRIBAQHo9Xq6du3KsmXLCjosIYQQotB6qAp9UlIS5cuXt3/2\n8/MjKSmpACMSQgghCjeNUkoVdBB/WLx4MWvXrmXmzJkAzJs3j7i4OCZPnmzfplKlSpw6daqgQhRC\nCCHyXWBgIL/99tt/2leXx7HcF19fXxITE+2fExMT8fPzu2Wb/5qoEEIIURQ9VLfuIyMjOXnyJGfO\nnMFsNrNw4ULatm1b0GEJIYQQhdZDdUWv0+mYMmUK0dHR5OTk8Oyzz1K1atWCDksIIYQotB6qZ/RC\nCCGEyFsP1a37v/r++++pVq0aWq2W/fv337IuNjaWypUrExwczPr16+3L9+3bR1hYGJUrV+bVV1/N\n75AfqKIwkFDfvn3x8vIiLCzMvuzatWu0aNGCoKAgWrZsSWpqqn3d3b4HhVViYiJNmzalWrVqhIaG\nMmnSJKBonIPMzEzq1q1LREQEISEhvPvuu0DRyP2vcnJyqFGjBm3atAGKVv4BAQFUr16dGjVqUKdO\nHaBo5Z+amsqTTz5J1apVCQkJIS4uLu/yVw+p48ePq19//VVFRUWpffv22ZfHx8er8PBwZTabVUJC\nggoMDFRWq1UppVTt2rVVXFycUkqpmJgYtWbNmgKJPa9lZ2erwMBAlZCQoMxmswoPD1fHjh0r6LDy\n3LZt29T+/ftVaGiofdlbb72lPvroI6WUUmPGjFHvvPOOUurO34OcnJwCiTuvXLx4UR04cEAppVRa\nWpoKCgpSx44dKzLnID09XSmllMViUXXr1lXbt28vMrn/4ZNPPlHdu3dXbdq0UUoVre9/QECAunr1\n6i3LilL+vXv3VrNnz1ZK2f4fSE1NzbP8H9pC/4f/X+hHjx6txowZY/8cHR2tdu3apS5cuKCCg4Pt\ny+fPn6/69euXr7E+KDt37lTR0dH2z7GxsSo2NrYAI3pwEhISbin0VapUUcnJyUopWyGsUqWKUuru\n3wNH0q5dO7Vhw4Yidw7S09NVZGSkOnr0aJHKPTExUTVr1kz9+OOP6oknnlBKFa3vf0BAgLpy5cot\ny4pK/qmpqapixYq3Lc+r/B/aW/d3c+HChVteuftjUJ3/v9zX19dhBtspygMJXbp0CS8vLwC8vLy4\ndOkScPfvgaM4c+YMBw4coG7dukXmHFitViIiIvDy8rI/wigquQO89tprjBs3DienP/8sF6X8NRoN\nzZs3JzIy0j6WSlHJPyEhgTJlytCnTx9q1qzJ888/T3p6ep7lX6C97lu0aEFycvJty0ePHm1/RiVs\n/wMI23m417lwlPN08+ZNOnXqxMSJEylWrNgt6xz5HDg5OXHw4EF+//13oqOj2bx58y3rHTn3lStX\nUrZsWWrUqHHXMd0dOX+AHTt24OPjQ0pKCi1atCA4OPiW9Y6cf3Z2Nvv372fKlCnUrl2bQYMGMWbM\nmFu2uZ/8C7TQb9iw4V/v8/8H1Tl//jx+fn74+vpy/vz5W5b7+vrmSZwF7Z8MJOSovLy8SE5Oxtvb\nm4sXL1K2bFngzt8DR/jvbbFY6NSpE7169aJ9+/ZA0TsHnp6etG7dmn379hWZ3Hfu3Mny5ctZvXo1\nmZmZ3Lhxg169ehWZ/AF8fHwAKFOmDB06dGDPnj1FJn8/Pz/8/PyoXbs2AE8++SSxsbF4e3vnSf6F\n4ta9+ssbgG3btmXBggWYzWYSEhI4efIkderUwdvbGw8PD+Li4lBKMXfuXPsfysKuKA8k1LZtW+bM\nmQPAnDlz7P9N7/Y9KMyUUjz77LOEhIQwaNAg+/KicA6uXLli71GckZHBhg0bqFGjRpHIHWx3MRMT\nE0lISGDBggU89thjzJ07t8jkbzKZSEtLAyA9PZ3169cTFhZWZPL39vamfPnynDhxAoCNGzdSrVo1\n2rRpkzf552WHgry0ZMkS5efnp1xdXZWXl5d6/PHH7etGjRqlAgMDVZUqVdTatWvty/fu3atCQ0NV\nYGCgGjhwYEGE/cCsXr1aBQUFqcDAQDV69OiCDueB6Nq1q/Lx8VF6vV75+fmpL774Ql29elU1a9ZM\nVa5cWbVo0UJdv37dvv3dvgeF1fbt25VGo1Hh4eEqIiJCRUREqDVr1hSJc3D48GFVo0YNFR4ersLC\nwtTYsWOVUqpI5P7/bdmyxd7rvqjkf/r0aRUeHq7Cw8NVtWrV7H/jikr+Sil18OBBFRkZqapXr646\ndOigUlNT8yx/GTBHCCGEcGCF4ta9EEIIIf4bKfRCCCGEA5NCL4QQQjgwKfRCCCGEA5NCL4QQQjgw\nKfRCCCGEA5NCL4QQQjgwKfRCCCGEA5NCL4S4p59//pnw8HCysrJIT08nNDSUY8eOFXRYQoh/SEbG\nE0L8rffff5/MzEwyMjIoX74877zzTkGHJIT4h6TQCyH+lsViITIyEoPBwK5duwr1lKBCFDVy614I\n8beuXLlCeno6N2/eJCMjo6DDEUL8C3JFL4T4W23btqV79+6cPn2aixcvMnny5IIOSQjxD+kKOgAh\nxMPt66+/xsXFha5du2K1WmnQoAFbtmwhKiqqoEMTQvwDckUvhBBCODB5Ri+EEEI4MCn0QgghhAOT\nQi+EEEI4MCn0QgghhAOTQi+EEEI4MCn0QgghhAOTQi+EEEI4sP8Diwf1C+duoqkAAAAASUVORK5C\nYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x107806610>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "#### Comparing the results from the different implementations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As mentioned above, let us now confirm that the different implementations computed the same parameters (i.e., slope and y-axis intercept) as solution of the linear equation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import prettytable\n",
-      "\n",
-      "params = [appr(x,y) for appr in [lin_lstsqr_mat, classic_lstsqr, numpy_lstsqr, scipy_lstsqr]]\n",
-      "\n",
-      "print(params)\n",
-      "\n",
-      "fit_table = prettytable.PrettyTable([\"\", \"slope\", \"y-intercept\"])\n",
-      "fit_table.add_row([\"matrix approach\", params[0][0], params[0][1]])\n",
-      "fit_table.add_row([\"classic approach\", params[1][0], params[1][1]])\n",
-      "fit_table.add_row([\"numpy function\", params[2][0], params[2][1]])\n",
-      "fit_table.add_row([\"scipy function\", params[3][0], params[3][1]])\n",
-      "\n",
-      "print(fit_table)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[array([   0.95181895,  107.01399744]), (0.95181895319126741, 107.01399744459181), array([   0.95181895,  107.01399744]), (0.95181895319126764, 107.01399744459175)]\n",
-        "+------------------+----------------+---------------+\n",
-        "|                  |     slope      |  y-intercept  |\n",
-        "+------------------+----------------+---------------+\n",
-        "| matrix approach  | 0.951818953191 | 107.013997445 |\n",
-        "| classic approach | 0.951818953191 | 107.013997445 |\n",
-        "|  numpy function  | 0.951818953191 | 107.013997445 |\n",
-        "|  scipy function  | 0.951818953191 | 107.013997445 |\n",
-        "+------------------+----------------+---------------+\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "#### Initial performance comparison"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To get an initial impression of how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
-      "                     \"numpy function\",\"scipy function\"],\n",
-      "                    [lin_lstsqr_mat, classic_lstsqr, \n",
-      "                     numpy_lstsqr, scipy_lstsqr]):\n",
-      "    print(\"\\n{}: \".format(lab), end=\"\")\n",
-      "    %timeit appr(x, y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "matrix approach: 10000 loops, best of 3: 158 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "classic approach: 1000 loops, best of 3: 1.6 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "numpy function: 1000 loops, best of 3: 217 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "scipy function: 1000 loops, best of 3: 366 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "The timing above indicates that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10. However, we should keep in mind that this experiment was done for a fixed sample size n=500."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name=\"cython_nb\"></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Compiling the Python code via Cython in the IPython notebook"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
-      "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  \n",
-      "Let us also compile the functions for the other 3 least squares approaches - those already use numpy objects and therefore we don't expect any performance gain here, but it is good to be thorough.\n",
-      "\n",
-      "**Note**  \n",
-      "Of course Cython has much more horsepower under its hood - more than I am showing in this section of this article (for example, I am not using Cython's type definitions via `cdef` here, but we will get to it [in a later section - Appendix II](#type_declarations)). \n",
-      "The focus of this section should be on how to speed up existing Python code by making only minimal changes to it.   "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "import numpy as np\n",
-      "import scipy.stats\n",
-      "cimport numpy as np\n",
-      "\n",
-      "def cy_lin_lstsqr_mat(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)\n",
-      "\n",
-      "def cy_classic_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)\n",
-      "\n",
-      "def cy_numpy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return np.linalg.lstsq(X,y)[0]\n",
-      "\n",
-      "def cy_scipy_lstsqr(x,y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    return scipy.stats.linregress(x, y)[0:2]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Comparing the compiled Cython code to the original Python code"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
-      "                     \"numpy function\",\"scipy function\"],\n",
-      "                    [(lin_lstsqr_mat, cy_lin_lstsqr_mat), \n",
-      "                     (classic_lstsqr, cy_classic_lstsqr),\n",
-      "                     (numpy_lstsqr, cy_numpy_lstsqr),\n",
-      "                     (scipy_lstsqr, cy_scipy_lstsqr)]):\n",
-      "    print(\"\\n\\n{}: \".format(lab))\n",
-      "    %timeit appr[0](x, y)\n",
-      "    %timeit appr[1](x, y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "matrix approach: \n",
-        "10000 loops, best of 3: 159 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 159 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "\n",
-        "classic approach: \n",
-        "1000 loops, best of 3: 1.62 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 126 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "\n",
-        "numpy function: \n",
-        "1000 loops, best of 3: 218 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 220 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "\n",
-        "scipy function: \n",
-        "1000 loops, best of 3: 372 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 354 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to the other approaches which use Numpy objects. This is not surprising, since Numpy is highly optimized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.  \n",
-      "However, we were able to speed up the \"classic approach\" quite significantly via Cython, roughly by 1500%.\n",
-      "\n",
-      "The following 2 code blocks are just to visualize our results in a bar plot."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "funcs =  ['classic_lstsqr', 'cy_classic_lstsqr', \n",
-      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
-      "labels = ['classic approach','classic approach (cython)', \n",
-      "          'matrix approach', 'numpy function', 'scipy function']\n",
-      "\n",
-      "times = [timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
-      "         for f in funcs]\n",
-      "\n",
-      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "x_pos = np.arange(len(funcs))\n",
-      "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
-      "plt.xticks(x_pos, labels, rotation=45)\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.title('Performance of different least square fit implementations')\n",
-      "plt.grid()\n",
-      "plt.show()\n",
-      "\n",
-      "x_pos = np.arange(len(funcs[1:]))\n",
-      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
-      "plt.xticks(x_pos, labels[1:], rotation=45)\n",
-      "plt.ylabel('relative performance gain')\n",
-      "plt.title('Performance gain compared to the classic least square implementation')\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGAAsITAMwzAvsYTAMAzDAAD+D41KT4VmEkzWAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1079680d0>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6gQJeEIIIeqE/weOwIO8Og7UhQAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1078eeb10>"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"sample_sizes\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Performance growth rates for different sample sizes"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the previous section, we have seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of different sample sizes on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs =  ['cy_classic_lstsqr', \n",
-      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
-      "\n",
-      "orders_n = [10**n for n in range(1, 7)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "labels = [('cy_classic_lstsqr', 'Cython implementation'), \n",
-      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
-      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
-      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
-      "\n",
-      "matplotlib.rcParams.update({'font.size': 12})\n",
-      "\n",
-      "fig = plt.figure(figsize=(10,8))\n",
-      "for lb in labels:\n",
-      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('performance gain relative to the slowest approach')\n",
-      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "plt.xscale('log')\n",
-      "plt.yscale('log')\n",
-      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAIECAYAAAByl6h3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0/EX3NkAAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10846f5d0>"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "In this performance comparison using different sample sizes, we can see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython.  \n",
-      "There are a lot of improvements that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches, after we tweaked it a little bit.\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"cython_bonus\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Bonus: How to use Cython without the IPython magic"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "IPython's notebook is really great for explanatory analysis and documentation, but what if we want to compile our Python code via Cython without letting IPython's magic doing all the work?  \n",
-      "These are the steps you would need."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### 1. Creating a .pyx file containing the the desired code or function."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%file ccy_classic_lstsqr.pyx\n",
-      "\n",
-      "def ccy_classic_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing ccy_classic_lstsqr.pyx\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### 2. Creating a simple setup file"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%file setup.py\n",
-      "\n",
-      "from distutils.core import setup\n",
-      "from distutils.extension import Extension\n",
-      "from Cython.Distutils import build_ext\n",
-      "\n",
-      "setup(\n",
-      "    cmdclass = {'build_ext': build_ext},\n",
-      "    ext_modules = [Extension(\"ccy_classic_lstsqr\", [\"ccy_classic_lstsqr.pyx\"])]\n",
-      ")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing setup.py\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "####3. Building and Compiling"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "!python3 setup.py build_ext --inplace"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "running build_ext\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "cythoning ccy_classic_lstsqr.pyx to ccy_classic_lstsqr.c\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "building 'ccy_classic_lstsqr' extension\r\n",
-        "creating build\r\n",
-        "creating build/temp.macosx-10.6-intel-3.4\r\n",
-        "/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -arch i386 -arch x86_64 -g -I/Library/Frameworks/Python.framework/Versions/3.4/include/python3.4m -c ccy_classic_lstsqr.c -o build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\u001b[1mccy_classic_lstsqr.c:2040:28: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_AsString'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {\r\n",
-        "\u001b[0;1;32m                           ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2037:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
-        "      '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
-        "\u001b[0;1;32m                               ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2104:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_IsTrue'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\r\n",
-        "\u001b[0;1;32m                         ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2159:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyIndex_AsSsize_t'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\r\n",
-        "\u001b[0;1;32m                                ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2188:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_FromSize_t'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
-        "\u001b[0;1;32m                                ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1584:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_From_long'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\r\n",
-        "\u001b[0;1;32m                               ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1631:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is not\r\n",
-        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
-        "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
-        "\u001b[0;1;32m                          ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1731:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_int' is not\r\n",
-        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
-        "static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\r\n",
-        "\u001b[0;1;32m                         ^\r\n",
-        "\u001b[0m"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "8 warnings generated.\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\u001b[1mccy_classic_lstsqr.c:2040:28: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_AsString'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {\r\n",
-        "\u001b[0;1;32m                           ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2037:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
-        "      '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
-        "\u001b[0;1;32m                               ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2104:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyObject_IsTrue'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\r\n",
-        "\u001b[0;1;32m                         ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2159:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyIndex_AsSsize_t'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\r\n",
-        "\u001b[0;1;32m                                ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:2188:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_FromSize_t'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
-        "\u001b[0;1;32m                                ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1584:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_PyInt_From_long'\r\n",
-        "      [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\r\n",
-        "\u001b[0;1;32m                               ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1631:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is not\r\n",
-        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
-        "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
-        "\u001b[0;1;32m                          ^\r\n",
-        "\u001b[0m\u001b[1mccy_classic_lstsqr.c:1731:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_int' is not\r\n",
-        "      needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
-        "static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\r\n",
-        "\u001b[0;1;32m                         ^\r\n",
-        "\u001b[0m"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "8"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " warnings generated.\r\n",
-        "/usr/bin/clang -bundle -undefined dynamic_lookup -arch i386 -arch x86_64 -g build/temp.macosx-10.6-intel-3.4/ccy_classic_lstsqr.o -o /Users/sebastian/Github/python_reference/benchmarks/ccy_classic_lstsqr.so\r\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### 4. Importing and running the code"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import ccy_classic_lstsqr\n",
-      "\n",
-      "%timeit classic_lstsqr(x, y)\n",
-      "%timeit cy_classic_lstsqr(x, y)\n",
-      "%timeit ccy_classic_lstsqr.ccy_classic_lstsqr(x, y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "100 loops, best of 3: 2.9 ms per loop\n",
-        "1000 loops, best of 3: 212 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 207 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"numba\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Appendix I: Cython vs. Numba"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Like we did with Cython before, we will use the minimalist approach to Numba and see how the two - Cython and Numba - compare against each other.  \n",
-      "\n",
-      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Here is our \"classic\" approach in Python, where I removed the list comprehensions, since they caused errors in the Numba compilation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Cython-compiled version of it:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "\n",
-      "def cy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And now the Numba-compiled version:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from numba import jit\n",
-      "\n",
-      "@jit\n",
-      "def nmb_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "        \n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "Now, let us see how the different approaches compare against each other for different sample sizes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs =  ['lstsqr', 'cy_lstsqr', 'nmb_lstsqr'] \n",
-      "orders_n = [10**n for n in range(1, 7)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "plt.figure(figsize=(10,8))\n",
-      "\n",
-      "for f in times_n.keys():\n",
-      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
-      "\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "\n",
-      "plt.title('Performance of a simple least square fit implementation')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1031a8710>"
-       ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Without making any modifications to the original Python code - thereby we are not accounting for the particular strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"type_declarations\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Appendix II: Cython with and without type declarations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the sections above, we have been using the simplest approach to Cython without using static type declarations and thereby neglecting one of its major strengths.  \n",
-      "Now, let us see how and if we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is our \"classic\" approach in Python:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Cython-compiled version of it:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "def cy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And now, the same code with static type declarations:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "def static_type_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "Now, let us see how the two functions (with and without static type declarations) compare against each other for different sample sizes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs =  ['cy_lstsqr', 'static_type_lstsqr'] \n",
-      "labels = ['simple Cython', 'Cython w. type declarations']\n",
-      "orders_n = [10**n for n in range(1, 7)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "plt.figure(figsize=(10,8))\n",
-      "\n",
-      "for f in times_n.keys():\n",
-      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
-      "\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "\n",
-      "plt.title('Performance of a simple least square fit implementation')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAH/CAYAAAASb3qiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB/IfaLkgtnNsZy4AAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10ed0dd90>"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "The improvement is pretty significant when static type declarations are used.  \n",
-      "One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation:\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
-      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "funcs =  ['classic_lstsqr', 'cy_lstsqr', 'static_type_lstsqr', \n",
-      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
-      "labels = ['classic approach','classic approach (cython)', \n",
-      "          'classic approach (cython + type decl.)',\n",
-      "          'matrix approach', 'numpy function', 'scipy function']\n",
-      "\n",
-      "times = [timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
-      "         for f in funcs]\n",
-      "\n",
-      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "x_pos = np.arange(len(funcs[1:]))\n",
-      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
-      "plt.xticks(x_pos, labels[1:], rotation=90)\n",
-      "plt.ylabel('rel. performance gain')\n",
-      "plt.title('Performance gain compared to the classic least square'\\\n",
-      "          '(n={})'.format(len(x)))\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAHnCAYAAADuC8dRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVsKiSkREZCX/D2lbGZgZrg2GAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10ed1da90>"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is a pretty significant performance gain. The \"Cython + type declarations\" approach sped up our initial Python code 25 times."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"explicit_loops\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Appendix III: Cython performance after replacing list comprehensions by explicit for loops"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In Python, we have those wonderful list, set, and dictionary comprehensions, which do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also additionally come with some small performance benefits.  \n",
-      "Does this also apply in Cython? Let's check it out."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "This is the code for our \"classic\" least squares approach that we have been using in the previous sections:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lstsqr_comprehensions(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 46
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "And here is a version where I replaced the list comprehensions by for-loops:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lstsqr_loops(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 48
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Finally, the Cython versions of the two functions (with and without using list comprehensions) that we have defined above:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "\n",
-      "def cy_lstsqr_comprehensions(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 49
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "\n",
-      "def cy_lstsqr_loops(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 50
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "We will generate some sample data for different sample sizes and take a look at the results for the regular Python functions, and the Cython code separately."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs =  ['lstsqr_comprehensions', 'lstsqr_loops',\n",
-      "          'cy_lstsqr_comprehensions', 'cy_lstsqr_loops'] \n",
-      "\n",
-      "orders_n = [10**n for n in range(1, 6)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 52
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.figure(figsize=(8,6))\n",
-      "plt.plot(orders_n, times_n['lstsqr_comprehensions'], alpha=0.5, \n",
-      "         label='list comprehensions', marker='o', lw=2)\n",
-      "plt.plot(orders_n, times_n['lstsqr_loops'], alpha=0.5, \n",
-      "         label='for-loops', marker='o', lw=2)\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
-      "plt.title('Performance comparison list comprehensions and for-loops')\n",
-      "plt.show()\n",
-      "\n",
-      "plt.figure(figsize=(8,6))\n",
-      "plt.plot(orders_n, times_n['cy_lstsqr_comprehensions'], alpha=0.5, \n",
-      "         label='list comprehensions (Cython', marker='o', lw=2)\n",
-      "plt.plot(orders_n, times_n['cy_lstsqr_loops'], alpha=0.5, \n",
-      "         label='for-loops (Cython)', marker='o', lw=2)\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
-      "plt.title('Performance comparison list comprehensions and for-loops in Cython')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAGJCAYAAACaQwrRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nURKDPTGQxwAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10e8f8610>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAGJCAYAAABFDXDOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBAiY/8fzj2UNX7XML4AAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10565c6d0>"
-       ]
-      }
-     ],
-     "prompt_number": 71
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "As we can see in the first plot, the list comprehensions lead to a slightly increased performance in regular Python code.  \n",
-      "But the second plot is quite interesting: List comprehensions in Cython are significantly slower than the regular for-loop structures.\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Let us do a quick comparison by how much we were able to improve the performance of the simple least square implementation using Cython so far:\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
-      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 72
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "funcs =  ['lstsqr_comprehensions', 'cy_lstsqr_comprehensions', \n",
-      "          'cy_lstsqr_loops'] \n",
-      "labels = ['list comprehensions', 'list comprehensions (Cython)', \n",
-      "          'for-loops (Cython)']\n",
-      "\n",
-      "times = [timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
-      "         for f in funcs]\n",
-      "\n",
-      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 73
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
-      "\n",
-      "plt.figure(figsize=(8,6))\n",
-      "x_pos = np.arange(len(funcs[1:]))\n",
-      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
-      "plt.xticks(x_pos, labels[1:], rotation=90)\n",
-      "plt.ylabel('relative performance gain')\n",
-      "plt.title('Performance gain compared to the classic least square implementation')\n",
-      "ftext = 'For-loops in Cython are {:.2f}x faster then list comprehensions'\\\n",
-      "        .format(times[1]/times[2],1)\n",
-      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
-      "plt.xlim([-1,len(funcs[1:])])\n",
-      "plt.ylim([0,max(times_rel)*1.2])\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAIBCAYAAABpxJfYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWRMRETVxLGsiIqIm7r+eoHMw3W5vGAAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10e919d10>"
-       ]
-      }
-     ],
-     "prompt_number": 88
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"showdown\"></a>\n",
-      "<br>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To wrap it up, let us compare the Cython code of our simple least squares fit implementation to the Numpy and Scipy functions - after we made some improvements by adding static type declarations and explicit for loops."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "\n",
-      "def cy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        temp = (x_i - x_avg)\n",
-      "        var_x += temp**2\n",
-      "        cov_xy += temp*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import scipy.stats"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lin_lstsqr_mat(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def numpy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return np.linalg.lstsq(X,y)[0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def scipy_lstsqr(x,y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    return scipy.stats.linregress(x, y)[0:2]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs = ['cy_lstsqr', 'lin_lstsqr_mat',\n",
-      "          'numpy_lstsqr', 'scipy_lstsqr'] \n",
-      "\n",
-      "orders_n = [10**n for n in range(1, 6)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f)\n",
-      "                              .repeat(repeat=3, number=1000)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "labels = [('cy_lstsqr', 'Cython implementation'), \n",
-      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
-      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
-      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
-      "\n",
-      "matplotlib.rcParams.update({'font.size': 12})\n",
-      "\n",
-      "fig = plt.figure(figsize=(10,8))\n",
-      "for lb in labels:\n",
-      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('performance gain relative to the slowest approach')\n",
-      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "plt.xscale('log')\n",
-      "plt.yscale('log')\n",
-      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x111098fd0>"
-       ]
-      }
-     ],
-     "prompt_number": 70
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "So, using a few tweaks, such as static type declarations and explicit for-loops instead of list comprehensions, we managed to increase the performance of our least squares fit implementation quite significantly - it outperforms the alternative functions in Numpy and Scipy now."
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file

From 3ef96e3874ab214f009a8b3f21f1e20b015847d6 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 6 May 2014 16:13:21 -0400
Subject: [PATCH 024/248] print fixes

---
 README.md                                     |    2 +-
 benchmarks/palindrome_timeit.ipynb            |   24 +-
 benchmarks/timeit_tests.ipynb                 |    2 +-
 not_so_obvious_python_stuff.ipynb             |   30 +-
 python_true_false.html                        | 2297 -----------------
 .../python_true_false.ipynb                   |   14 +-
 6 files changed, 40 insertions(+), 2329 deletions(-)
 delete mode 100644 python_true_false.html
 rename python_true_false.ipynb => tutorials/python_true_false.ipynb (96%)

diff --git a/README.md b/README.md
index 1305f53..c970e0e 100755
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ Useful functions, tutorials, and other Python-related things
 ###Links to view the IPython Notebooks
 
 - [Python benchmarks via `timeit`](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
-- [Implementing the least squares fit method for linear regression and speeding it up via Cythonook](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
+- [Implementing the least squares fit method for linear regression and speeding it up via Cython](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
 - [Benchmarks of different palindrome functions](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
 - [A collection of not so obvious Python stuff you should know!](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)  
 - [Python's scope resolution for variable names and the LEGB rule](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
diff --git a/benchmarks/palindrome_timeit.ipynb b/benchmarks/palindrome_timeit.ipynb
index ef0e33f..f1df349 100644
--- a/benchmarks/palindrome_timeit.ipynb
+++ b/benchmarks/palindrome_timeit.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6ea19109869c82ee989c8ea0599ec49401e74246a542ad0b7b05f6ef464bda19"
+  "signature": "sha256:3d878b64b4503fd987496df562af53903ad85d4cce103ea0a2e6c456519c03c7"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,8 +12,20 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Sebastian Raschka 04/2014\n",
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "last updated: 05/03/2014\n",
       "\n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb)  \n",
+      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
+      "\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
       "#Timing different Implementations of palindrome functions"
      ]
     },
@@ -178,14 +190,6 @@
       }
      ],
      "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
    "metadata": {}
diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index d1bc680..bbd8c17 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:75d807f509bd9f76b2e14a5a048cb44852a3318bcd0d95afc95d1c9b2904c078"
+  "signature": "sha256:132d5623e27eb721587599809f9870d397887ec873bbbdc95b25a05e710d160e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/not_so_obvious_python_stuff.ipynb b/not_so_obvious_python_stuff.ipynb
index 1e0ef3c..0a9632c 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0e1c6e74b301e23ea4146d660afb3f07765686c6c7fa4752f3a4495da7949787"
+  "signature": "sha256:7a22f6c91e4aab51a325c721dd7674622d1acc5b4a3a038ff512c736d83bbe4a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,11 +12,11 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Sebastian Raschka  \n",
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
       "last updated: 05/03/2014 ([Changelog](#changelog))\n",
       "\n",
-      "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)\n",
-      "\n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)  \n",
+      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
       "\n"
      ]
     },
@@ -307,14 +307,6 @@
      ],
      "prompt_number": 6
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -684,13 +676,13 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "** Logical `or`: **  \n",
+      "**Logical `or`:**  \n",
       "\n",
       "`a or b == a if a else b`  \n",
       "- If both values in `or` expressions are `True`, Python will select the first value (e.g., select `\"a\"` in `\"a\" or \"b\"`), and the second one in `and` expressions.  \n",
       "This is also called **short-circuiting** - we already know that the logical `or` must be `True` if the first value is `True` and therefore can omit the evaluation of the second value.\n",
       "\n",
-      "** Logical `and`: ** \n",
+      "**Logical `and`:** \n",
       "\n",
       "`a and b == b if a else a`  \n",
       "- If both values in `and` expressions are `True`, Python will select the second value, since for a logical `and`, both values must be true.\n"
@@ -3448,8 +3440,14 @@
      "metadata": {},
      "source": [
       "I would claim that the conditional \"else\" is every programmer's daily bread and butter. However, there is a second flavor of \"else\"-clauses in Python, which I will call \"completion else\" (for reason that will become clear later).  \n",
-      "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with.  \n",
-      "### Conditional else:"
+      "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with.  \n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "###Conditional else:"
      ]
     },
     {
diff --git a/python_true_false.html b/python_true_false.html
deleted file mode 100644
index 079d7cb..0000000
--- a/python_true_false.html
+++ /dev/null
@@ -1,2297 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-<meta charset="UTF-8">
-<title>[]</title>
-<style type="text/css">
-    .clearfix{*zoom:1;}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0;}
-.clearfix:after{clear:both;}
-.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0;}
-.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;}
-article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block;}
-audio,canvas,video{display:inline-block;*display:inline;*zoom:1;}
-audio:not([controls]){display:none;}
-html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%;}
-a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px;}
-a:hover,a:active{outline:0;}
-sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline;}
-sup{top:-0.5em;}
-sub{bottom:-0.25em;}
-img{max-width:100%;width:auto\9;height:auto;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic;}
-#map_canvas img,.google-maps img{max-width:none;}
-button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle;}
-button,input{*overflow:visible;line-height:normal;}
-button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0;}
-button,html input[type="button"],input[type="reset"],input[type="submit"]{-webkit-appearance:button;cursor:pointer;}
-label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer;}
-input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield;}
-input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none;}
-textarea{overflow:auto;vertical-align:top;}
-@media print{*{text-shadow:none !important;color:#000 !important;background:transparent !important;box-shadow:none !important;} a,a:visited{text-decoration:underline;} a[href]:after{content:" (" attr(href) ")";} abbr[title]:after{content:" (" attr(title) ")";} .ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:"";} pre,blockquote{border:1px solid #999;page-break-inside:avoid;} thead{display:table-header-group;} tr,img{page-break-inside:avoid;} img{max-width:100% !important;} @page {margin:0.5cm;}p,h2,h3{orphans:3;widows:3;} h2,h3{page-break-after:avoid;}}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;line-height:20px;color:#000000;background-color:#ffffff;}
-a{color:#0088cc;text-decoration:none;}
-a:hover,a:focus{color:#005580;text-decoration:underline;}
-.img-rounded{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;}
-.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0, 0, 0, 0.2);-webkit-box-shadow:0 1px 3px rgba(0, 0, 0, 0.1);-moz-box-shadow:0 1px 3px rgba(0, 0, 0, 0.1);box-shadow:0 1px 3px rgba(0, 0, 0, 0.1);}
-.img-circle{-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px;}
-.row{margin-left:-20px;*zoom:1;}.row:before,.row:after{display:table;content:"";line-height:0;}
-.row:after{clear:both;}
-.row:before,.row:after{display:table;content:"";line-height:0;}
-.row:after{clear:both;}
-[class*="span"]{float:left;min-height:1px;margin-left:20px;}
-.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px;}
-.span12{width:940px;}
-.span11{width:860px;}
-.span10{width:780px;}
-.span9{width:700px;}
-.span8{width:620px;}
-.span7{width:540px;}
-.span6{width:460px;}
-.span5{width:380px;}
-.span4{width:300px;}
-.span3{width:220px;}
-.span2{width:140px;}
-.span1{width:60px;}
-.offset12{margin-left:980px;}
-.offset11{margin-left:900px;}
-.offset10{margin-left:820px;}
-.offset9{margin-left:740px;}
-.offset8{margin-left:660px;}
-.offset7{margin-left:580px;}
-.offset6{margin-left:500px;}
-.offset5{margin-left:420px;}
-.offset4{margin-left:340px;}
-.offset3{margin-left:260px;}
-.offset2{margin-left:180px;}
-.offset1{margin-left:100px;}
-.row{margin-left:-20px;*zoom:1;}.row:before,.row:after{display:table;content:"";line-height:0;}
-.row:after{clear:both;}
-.row:before,.row:after{display:table;content:"";line-height:0;}
-.row:after{clear:both;}
-[class*="span"]{float:left;min-height:1px;margin-left:20px;}
-.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px;}
-.span12{width:940px;}
-.span11{width:860px;}
-.span10{width:780px;}
-.span9{width:700px;}
-.span8{width:620px;}
-.span7{width:540px;}
-.span6{width:460px;}
-.span5{width:380px;}
-.span4{width:300px;}
-.span3{width:220px;}
-.span2{width:140px;}
-.span1{width:60px;}
-.offset12{margin-left:980px;}
-.offset11{margin-left:900px;}
-.offset10{margin-left:820px;}
-.offset9{margin-left:740px;}
-.offset8{margin-left:660px;}
-.offset7{margin-left:580px;}
-.offset6{margin-left:500px;}
-.offset5{margin-left:420px;}
-.offset4{margin-left:340px;}
-.offset3{margin-left:260px;}
-.offset2{margin-left:180px;}
-.offset1{margin-left:100px;}
-.row-fluid{width:100%;*zoom:1;}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;}
-.row-fluid:after{clear:both;}
-.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;}
-.row-fluid:after{clear:both;}
-.row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.127659574468085%;*margin-left:2.074468085106383%;}
-.row-fluid [class*="span"]:first-child{margin-left:0;}
-.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%;}
-.row-fluid .span12{width:100%;*width:99.94680851063829%;}
-.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%;}
-.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%;}
-.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%;}
-.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%;}
-.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%;}
-.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%;}
-.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%;}
-.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%;}
-.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%;}
-.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%;}
-.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%;}
-.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%;}
-.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%;}
-.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%;}
-.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%;}
-.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%;}
-.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%;}
-.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%;}
-.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%;}
-.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%;}
-.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%;}
-.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%;}
-.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%;}
-.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%;}
-.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%;}
-.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%;}
-.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%;}
-.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%;}
-.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%;}
-.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%;}
-.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%;}
-.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%;}
-.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%;}
-.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%;}
-.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%;}
-.row-fluid{width:100%;*zoom:1;}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;}
-.row-fluid:after{clear:both;}
-.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;}
-.row-fluid:after{clear:both;}
-.row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.127659574468085%;*margin-left:2.074468085106383%;}
-.row-fluid [class*="span"]:first-child{margin-left:0;}
-.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%;}
-.row-fluid .span12{width:100%;*width:99.94680851063829%;}
-.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%;}
-.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%;}
-.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%;}
-.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%;}
-.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%;}
-.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%;}
-.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%;}
-.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%;}
-.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%;}
-.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%;}
-.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%;}
-.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%;}
-.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%;}
-.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%;}
-.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%;}
-.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%;}
-.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%;}
-.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%;}
-.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%;}
-.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%;}
-.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%;}
-.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%;}
-.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%;}
-.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%;}
-.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%;}
-.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%;}
-.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%;}
-.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%;}
-.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%;}
-.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%;}
-.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%;}
-.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%;}
-.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%;}
-.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%;}
-.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%;}
-[class*="span"].hide,.row-fluid [class*="span"].hide{display:none;}
-[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right;}
-.container{margin-right:auto;margin-left:auto;*zoom:1;}.container:before,.container:after{display:table;content:"";line-height:0;}
-.container:after{clear:both;}
-.container:before,.container:after{display:table;content:"";line-height:0;}
-.container:after{clear:both;}
-.container:before,.container:after{display:table;content:"";line-height:0;}
-.container:after{clear:both;}
-.container:before,.container:after{display:table;content:"";line-height:0;}
-.container:after{clear:both;}
-.container-fluid{padding-right:20px;padding-left:20px;*zoom:1;}.container-fluid:before,.container-fluid:after{display:table;content:"";line-height:0;}
-.container-fluid:after{clear:both;}
-.container-fluid:before,.container-fluid:after{display:table;content:"";line-height:0;}
-.container-fluid:after{clear:both;}
-p{margin:0 0 10px;}
-.lead{margin-bottom:20px;font-size:19.5px;font-weight:200;line-height:30px;}
-small{font-size:85%;}
-strong{font-weight:bold;}
-em{font-style:italic;}
-cite{font-style:normal;}
-.muted{color:#999999;}
-a.muted:hover,a.muted:focus{color:#808080;}
-.text-warning{color:#c09853;}
-a.text-warning:hover,a.text-warning:focus{color:#a47e3c;}
-.text-error{color:#b94a48;}
-a.text-error:hover,a.text-error:focus{color:#953b39;}
-.text-info{color:#3a87ad;}
-a.text-info:hover,a.text-info:focus{color:#2d6987;}
-.text-success{color:#468847;}
-a.text-success:hover,a.text-success:focus{color:#356635;}
-.text-left{text-align:left;}
-.text-right{text-align:right;}
-.text-center{text-align:center;}
-h1,h2,h3,h4,h5,h6{margin:10px 0;font-family:inherit;font-weight:bold;line-height:20px;color:inherit;text-rendering:optimizelegibility;}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#999999;}
-h1,h2,h3{line-height:40px;}
-h1{font-size:35.75px;}
-h2{font-size:29.25px;}
-h3{font-size:22.75px;}
-h4{font-size:16.25px;}
-h5{font-size:13px;}
-h6{font-size:11.049999999999999px;}
-h1 small{font-size:22.75px;}
-h2 small{font-size:16.25px;}
-h3 small{font-size:13px;}
-h4 small{font-size:13px;}
-.page-header{padding-bottom:9px;margin:20px 0 30px;border-bottom:1px solid #eeeeee;}
-ul,ol{padding:0;margin:0 0 10px 25px;}
-ul ul,ul ol,ol ol,ol ul{margin-bottom:0;}
-li{line-height:20px;}
-ul.unstyled,ol.unstyled{margin-left:0;list-style:none;}
-ul.inline,ol.inline{margin-left:0;list-style:none;}ul.inline>li,ol.inline>li{display:inline-block;*display:inline;*zoom:1;padding-left:5px;padding-right:5px;}
-dl{margin-bottom:20px;}
-dt,dd{line-height:20px;}
-dt{font-weight:bold;}
-dd{margin-left:10px;}
-.dl-horizontal{*zoom:1;}.dl-horizontal:before,.dl-horizontal:after{display:table;content:"";line-height:0;}
-.dl-horizontal:after{clear:both;}
-.dl-horizontal:before,.dl-horizontal:after{display:table;content:"";line-height:0;}
-.dl-horizontal:after{clear:both;}
-.dl-horizontal dt{float:left;width:160px;clear:left;text-align:right;overflow:hidden;text-overflow:ellipsis;white-space:nowrap;}
-.dl-horizontal dd{margin-left:180px;}
-hr{margin:20px 0;border:0;border-top:1px solid #eeeeee;border-bottom:1px solid #ffffff;}
-abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #999999;}
-abbr.initialism{font-size:90%;text-transform:uppercase;}
-blockquote{padding:0 0 0 15px;margin:0 0 20px;border-left:5px solid #eeeeee;}blockquote p{margin-bottom:0;font-size:16.25px;font-weight:300;line-height:1.25;}
-blockquote small{display:block;line-height:20px;color:#999999;}blockquote small:before{content:'\2014 \00A0';}
-blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eeeeee;border-left:0;}blockquote.pull-right p,blockquote.pull-right small{text-align:right;}
-blockquote.pull-right small:before{content:'';}
-blockquote.pull-right small:after{content:'\00A0 \2014';}
-q:before,q:after,blockquote:before,blockquote:after{content:"";}
-address{display:block;margin-bottom:20px;font-style:normal;line-height:20px;}
-code,pre{padding:0 3px 2px;font-family:monospace;font-size:11px;color:#333333;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;}
-code{padding:2px 4px;color:#d14;background-color:#f7f7f9;border:1px solid #e1e1e8;white-space:nowrap;}
-pre{display:block;padding:9.5px;margin:0 0 10px;font-size:12px;line-height:20px;word-break:break-all;word-wrap:break-word;white-space:pre;white-space:pre-wrap;background-color:#f5f5f5;border:1px solid #ccc;border:1px solid rgba(0, 0, 0, 0.15);-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}pre.prettyprint{margin-bottom:20px;}
-pre code{padding:0;color:inherit;white-space:pre;white-space:pre-wrap;background-color:transparent;border:0;}
-.pre-scrollable{max-height:340px;overflow-y:scroll;}
-form{margin:0 0 20px;}
-fieldset{padding:0;margin:0;border:0;}
-legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:19.5px;line-height:40px;color:#333333;border:0;border-bottom:1px solid #e5e5e5;}legend small{font-size:15px;color:#999999;}
-label,input,button,select,textarea{font-size:13px;font-weight:normal;line-height:20px;}
-input,button,select,textarea{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;}
-label{display:block;margin-bottom:5px;}
-select,textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{display:inline-block;height:20px;padding:4px 6px;margin-bottom:10px;font-size:13px;line-height:20px;color:#555555;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;vertical-align:middle;}
-input,textarea,.uneditable-input{width:206px;}
-textarea{height:auto;}
-textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{background-color:#ffffff;border:1px solid #cccccc;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-webkit-transition:border linear .2s, box-shadow linear .2s;-moz-transition:border linear .2s, box-shadow linear .2s;-o-transition:border linear .2s, box-shadow linear .2s;transition:border linear .2s, box-shadow linear .2s;}textarea:focus,input[type="text"]:focus,input[type="password"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="date"]:focus,input[type="month"]:focus,input[type="time"]:focus,input[type="week"]:focus,input[type="number"]:focus,input[type="email"]:focus,input[type="url"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="color"]:focus,.uneditable-input:focus{border-color:rgba(82, 168, 236, 0.8);outline:0;outline:thin dotted \9;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);}
-input[type="radio"],input[type="checkbox"]{margin:4px 0 0;*margin-top:0;margin-top:1px \9;line-height:normal;}
-input[type="file"],input[type="image"],input[type="submit"],input[type="reset"],input[type="button"],input[type="radio"],input[type="checkbox"]{width:auto;}
-select,input[type="file"]{height:30px;*margin-top:4px;line-height:30px;}
-select{width:220px;border:1px solid #cccccc;background-color:#ffffff;}
-select[multiple],select[size]{height:auto;}
-select:focus,input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px;}
-.uneditable-input,.uneditable-textarea{color:#999999;background-color:#fcfcfc;border-color:#cccccc;-webkit-box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.025);-moz-box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.025);box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.025);cursor:not-allowed;}
-.uneditable-input{overflow:hidden;white-space:nowrap;}
-.uneditable-textarea{width:auto;height:auto;}
-input:-moz-placeholder,textarea:-moz-placeholder{color:#999999;}
-input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999999;}
-input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999999;}
-input:-moz-placeholder,textarea:-moz-placeholder{color:#999999;}
-input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999999;}
-input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999999;}
-.radio,.checkbox{min-height:20px;padding-left:20px;}
-.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px;}
-.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px;}
-.radio.inline,.checkbox.inline{display:inline-block;padding-top:5px;margin-bottom:0;vertical-align:middle;}
-.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px;}
-.input-mini{width:60px;}
-.input-small{width:90px;}
-.input-medium{width:150px;}
-.input-large{width:210px;}
-.input-xlarge{width:270px;}
-.input-xxlarge{width:530px;}
-input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"]{float:none;margin-left:0;}
-.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"]{display:inline-block;}
-input,textarea,.uneditable-input{margin-left:0;}
-.controls-row [class*="span"]+[class*="span"]{margin-left:20px;}
-input.span12,textarea.span12,.uneditable-input.span12{width:926px;}
-input.span11,textarea.span11,.uneditable-input.span11{width:846px;}
-input.span10,textarea.span10,.uneditable-input.span10{width:766px;}
-input.span9,textarea.span9,.uneditable-input.span9{width:686px;}
-input.span8,textarea.span8,.uneditable-input.span8{width:606px;}
-input.span7,textarea.span7,.uneditable-input.span7{width:526px;}
-input.span6,textarea.span6,.uneditable-input.span6{width:446px;}
-input.span5,textarea.span5,.uneditable-input.span5{width:366px;}
-input.span4,textarea.span4,.uneditable-input.span4{width:286px;}
-input.span3,textarea.span3,.uneditable-input.span3{width:206px;}
-input.span2,textarea.span2,.uneditable-input.span2{width:126px;}
-input.span1,textarea.span1,.uneditable-input.span1{width:46px;}
-input,textarea,.uneditable-input{margin-left:0;}
-.controls-row [class*="span"]+[class*="span"]{margin-left:20px;}
-input.span12,textarea.span12,.uneditable-input.span12{width:926px;}
-input.span11,textarea.span11,.uneditable-input.span11{width:846px;}
-input.span10,textarea.span10,.uneditable-input.span10{width:766px;}
-input.span9,textarea.span9,.uneditable-input.span9{width:686px;}
-input.span8,textarea.span8,.uneditable-input.span8{width:606px;}
-input.span7,textarea.span7,.uneditable-input.span7{width:526px;}
-input.span6,textarea.span6,.uneditable-input.span6{width:446px;}
-input.span5,textarea.span5,.uneditable-input.span5{width:366px;}
-input.span4,textarea.span4,.uneditable-input.span4{width:286px;}
-input.span3,textarea.span3,.uneditable-input.span3{width:206px;}
-input.span2,textarea.span2,.uneditable-input.span2{width:126px;}
-input.span1,textarea.span1,.uneditable-input.span1{width:46px;}
-.controls-row{*zoom:1;}.controls-row:before,.controls-row:after{display:table;content:"";line-height:0;}
-.controls-row:after{clear:both;}
-.controls-row:before,.controls-row:after{display:table;content:"";line-height:0;}
-.controls-row:after{clear:both;}
-.controls-row [class*="span"],.row-fluid .controls-row [class*="span"]{float:left;}
-.controls-row .checkbox[class*="span"],.controls-row .radio[class*="span"]{padding-top:5px;}
-input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly]{cursor:not-allowed;background-color:#eeeeee;}
-input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly]{background-color:transparent;}
-.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#c09853;}
-.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#c09853;}
-.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#c09853;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#a47e3c;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #dbc59e;-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #dbc59e;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #dbc59e;}
-.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#c09853;background-color:#fcf8e3;border-color:#c09853;}
-.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#c09853;}
-.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#c09853;}
-.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#c09853;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#a47e3c;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #dbc59e;-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #dbc59e;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #dbc59e;}
-.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#c09853;background-color:#fcf8e3;border-color:#c09853;}
-.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#b94a48;}
-.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#b94a48;}
-.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#b94a48;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#953b39;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #d59392;-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #d59392;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #d59392;}
-.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#b94a48;background-color:#f2dede;border-color:#b94a48;}
-.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#b94a48;}
-.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#b94a48;}
-.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#b94a48;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#953b39;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #d59392;-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #d59392;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #d59392;}
-.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#b94a48;background-color:#f2dede;border-color:#b94a48;}
-.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#468847;}
-.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#468847;}
-.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#468847;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#356635;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7aba7b;-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7aba7b;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7aba7b;}
-.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847;}
-.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#468847;}
-.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#468847;}
-.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#468847;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#356635;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7aba7b;-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7aba7b;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7aba7b;}
-.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847;}
-.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad;}
-.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad;}
-.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7ab5d3;}
-.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3a87ad;background-color:#d9edf7;border-color:#3a87ad;}
-.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad;}
-.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad;}
-.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075);}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.075),0 0 6px #7ab5d3;}
-.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3a87ad;background-color:#d9edf7;border-color:#3a87ad;}
-input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b;}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7;}
-.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1;}.form-actions:before,.form-actions:after{display:table;content:"";line-height:0;}
-.form-actions:after{clear:both;}
-.form-actions:before,.form-actions:after{display:table;content:"";line-height:0;}
-.form-actions:after{clear:both;}
-.help-block,.help-inline{color:#262626;}
-.help-block{display:block;margin-bottom:10px;}
-.help-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle;padding-left:5px;}
-.input-append,.input-prepend{display:inline-block;margin-bottom:10px;vertical-align:middle;font-size:0;white-space:nowrap;}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input,.input-append .dropdown-menu,.input-prepend .dropdown-menu,.input-append .popover,.input-prepend .popover{font-size:13px;}
-.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input{position:relative;margin-bottom:0;*margin-left:0;vertical-align:top;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0;}.input-append input:focus,.input-prepend input:focus,.input-append select:focus,.input-prepend select:focus,.input-append .uneditable-input:focus,.input-prepend .uneditable-input:focus{z-index:2;}
-.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:13px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #ffffff;background-color:#eeeeee;border:1px solid #ccc;}
-.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546;}
-.input-prepend .add-on,.input-prepend .btn{margin-right:-1px;}
-.input-prepend .add-on:first-child,.input-prepend .btn:first-child{-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px;}
-.input-append input,.input-append select,.input-append .uneditable-input{-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px;}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0;}
-.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px;}
-.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0;}
-.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0;}
-.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px;}
-.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0;}
-.input-prepend.input-append .btn-group:first-child{margin-left:0;}
-input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px;}
-.form-search .input-append .search-query,.form-search .input-prepend .search-query{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.form-search .input-append .search-query{-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px;}
-.form-search .input-append .btn{-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0;}
-.form-search .input-prepend .search-query{-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0;}
-.form-search .input-prepend .btn{-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px;}
-.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;*zoom:1;margin-bottom:0;vertical-align:middle;}
-.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none;}
-.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block;}
-.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0;}
-.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle;}
-.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0;}
-.control-group{margin-bottom:10px;}
-legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate;}
-.form-horizontal .control-group{margin-bottom:20px;*zoom:1;}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0;}
-.form-horizontal .control-group:after{clear:both;}
-.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0;}
-.form-horizontal .control-group:after{clear:both;}
-.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right;}
-.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0;}.form-horizontal .controls:first-child{*padding-left:180px;}
-.form-horizontal .help-block{margin-bottom:0;}
-.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px;}
-.form-horizontal .form-actions{padding-left:180px;}
-table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0;}
-.table{width:100%;margin-bottom:20px;}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #dddddd;}
-.table th{font-weight:bold;}
-.table thead th{vertical-align:bottom;}
-.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0;}
-.table tbody+tbody{border-top:2px solid #dddddd;}
-.table .table{background-color:#ffffff;}
-.table-condensed th,.table-condensed td{padding:4px 5px;}
-.table-bordered{border:1px solid #dddddd;border-collapse:separate;*border-collapse:collapse;border-left:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}.table-bordered th,.table-bordered td{border-left:1px solid #dddddd;}
-.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0;}
-.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;}
-.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;}
-.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px;}
-.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px;}
-.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;-moz-border-radius-bottomleft:0;border-bottom-left-radius:0;}
-.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;-moz-border-radius-bottomright:0;border-bottom-right-radius:0;}
-.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;}
-.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;}
-.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9;}
-.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5;}
-table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0;}
-.table td.span1,.table th.span1{float:none;width:44px;margin-left:0;}
-.table td.span2,.table th.span2{float:none;width:124px;margin-left:0;}
-.table td.span3,.table th.span3{float:none;width:204px;margin-left:0;}
-.table td.span4,.table th.span4{float:none;width:284px;margin-left:0;}
-.table td.span5,.table th.span5{float:none;width:364px;margin-left:0;}
-.table td.span6,.table th.span6{float:none;width:444px;margin-left:0;}
-.table td.span7,.table th.span7{float:none;width:524px;margin-left:0;}
-.table td.span8,.table th.span8{float:none;width:604px;margin-left:0;}
-.table td.span9,.table th.span9{float:none;width:684px;margin-left:0;}
-.table td.span10,.table th.span10{float:none;width:764px;margin-left:0;}
-.table td.span11,.table th.span11{float:none;width:844px;margin-left:0;}
-.table td.span12,.table th.span12{float:none;width:924px;margin-left:0;}
-.table tbody tr.success>td{background-color:#dff0d8;}
-.table tbody tr.error>td{background-color:#f2dede;}
-.table tbody tr.warning>td{background-color:#fcf8e3;}
-.table tbody tr.info>td{background-color:#d9edf7;}
-.table-hover tbody tr.success:hover>td{background-color:#d0e9c6;}
-.table-hover tbody tr.error:hover>td{background-color:#ebcccc;}
-.table-hover tbody tr.warning:hover>td{background-color:#faf2cc;}
-.table-hover tbody tr.info:hover>td{background-color:#c4e3f3;}
-[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat;margin-top:1px;}
-.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png");}
-.icon-glass{background-position:0 0;}
-.icon-music{background-position:-24px 0;}
-.icon-search{background-position:-48px 0;}
-.icon-envelope{background-position:-72px 0;}
-.icon-heart{background-position:-96px 0;}
-.icon-star{background-position:-120px 0;}
-.icon-star-empty{background-position:-144px 0;}
-.icon-user{background-position:-168px 0;}
-.icon-film{background-position:-192px 0;}
-.icon-th-large{background-position:-216px 0;}
-.icon-th{background-position:-240px 0;}
-.icon-th-list{background-position:-264px 0;}
-.icon-ok{background-position:-288px 0;}
-.icon-remove{background-position:-312px 0;}
-.icon-zoom-in{background-position:-336px 0;}
-.icon-zoom-out{background-position:-360px 0;}
-.icon-off{background-position:-384px 0;}
-.icon-signal{background-position:-408px 0;}
-.icon-cog{background-position:-432px 0;}
-.icon-trash{background-position:-456px 0;}
-.icon-home{background-position:0 -24px;}
-.icon-file{background-position:-24px -24px;}
-.icon-time{background-position:-48px -24px;}
-.icon-road{background-position:-72px -24px;}
-.icon-download-alt{background-position:-96px -24px;}
-.icon-download{background-position:-120px -24px;}
-.icon-upload{background-position:-144px -24px;}
-.icon-inbox{background-position:-168px -24px;}
-.icon-play-circle{background-position:-192px -24px;}
-.icon-repeat{background-position:-216px -24px;}
-.icon-refresh{background-position:-240px -24px;}
-.icon-list-alt{background-position:-264px -24px;}
-.icon-lock{background-position:-287px -24px;}
-.icon-flag{background-position:-312px -24px;}
-.icon-headphones{background-position:-336px -24px;}
-.icon-volume-off{background-position:-360px -24px;}
-.icon-volume-down{background-position:-384px -24px;}
-.icon-volume-up{background-position:-408px -24px;}
-.icon-qrcode{background-position:-432px -24px;}
-.icon-barcode{background-position:-456px -24px;}
-.icon-tag{background-position:0 -48px;}
-.icon-tags{background-position:-25px -48px;}
-.icon-book{background-position:-48px -48px;}
-.icon-bookmark{background-position:-72px -48px;}
-.icon-print{background-position:-96px -48px;}
-.icon-camera{background-position:-120px -48px;}
-.icon-font{background-position:-144px -48px;}
-.icon-bold{background-position:-167px -48px;}
-.icon-italic{background-position:-192px -48px;}
-.icon-text-height{background-position:-216px -48px;}
-.icon-text-width{background-position:-240px -48px;}
-.icon-align-left{background-position:-264px -48px;}
-.icon-align-center{background-position:-288px -48px;}
-.icon-align-right{background-position:-312px -48px;}
-.icon-align-justify{background-position:-336px -48px;}
-.icon-list{background-position:-360px -48px;}
-.icon-indent-left{background-position:-384px -48px;}
-.icon-indent-right{background-position:-408px -48px;}
-.icon-facetime-video{background-position:-432px -48px;}
-.icon-picture{background-position:-456px -48px;}
-.icon-pencil{background-position:0 -72px;}
-.icon-map-marker{background-position:-24px -72px;}
-.icon-adjust{background-position:-48px -72px;}
-.icon-tint{background-position:-72px -72px;}
-.icon-edit{background-position:-96px -72px;}
-.icon-share{background-position:-120px -72px;}
-.icon-check{background-position:-144px -72px;}
-.icon-move{background-position:-168px -72px;}
-.icon-step-backward{background-position:-192px -72px;}
-.icon-fast-backward{background-position:-216px -72px;}
-.icon-backward{background-position:-240px -72px;}
-.icon-play{background-position:-264px -72px;}
-.icon-pause{background-position:-288px -72px;}
-.icon-stop{background-position:-312px -72px;}
-.icon-forward{background-position:-336px -72px;}
-.icon-fast-forward{background-position:-360px -72px;}
-.icon-step-forward{background-position:-384px -72px;}
-.icon-eject{background-position:-408px -72px;}
-.icon-chevron-left{background-position:-432px -72px;}
-.icon-chevron-right{background-position:-456px -72px;}
-.icon-plus-sign{background-position:0 -96px;}
-.icon-minus-sign{background-position:-24px -96px;}
-.icon-remove-sign{background-position:-48px -96px;}
-.icon-ok-sign{background-position:-72px -96px;}
-.icon-question-sign{background-position:-96px -96px;}
-.icon-info-sign{background-position:-120px -96px;}
-.icon-screenshot{background-position:-144px -96px;}
-.icon-remove-circle{background-position:-168px -96px;}
-.icon-ok-circle{background-position:-192px -96px;}
-.icon-ban-circle{background-position:-216px -96px;}
-.icon-arrow-left{background-position:-240px -96px;}
-.icon-arrow-right{background-position:-264px -96px;}
-.icon-arrow-up{background-position:-289px -96px;}
-.icon-arrow-down{background-position:-312px -96px;}
-.icon-share-alt{background-position:-336px -96px;}
-.icon-resize-full{background-position:-360px -96px;}
-.icon-resize-small{background-position:-384px -96px;}
-.icon-plus{background-position:-408px -96px;}
-.icon-minus{background-position:-433px -96px;}
-.icon-asterisk{background-position:-456px -96px;}
-.icon-exclamation-sign{background-position:0 -120px;}
-.icon-gift{background-position:-24px -120px;}
-.icon-leaf{background-position:-48px -120px;}
-.icon-fire{background-position:-72px -120px;}
-.icon-eye-open{background-position:-96px -120px;}
-.icon-eye-close{background-position:-120px -120px;}
-.icon-warning-sign{background-position:-144px -120px;}
-.icon-plane{background-position:-168px -120px;}
-.icon-calendar{background-position:-192px -120px;}
-.icon-random{background-position:-216px -120px;width:16px;}
-.icon-comment{background-position:-240px -120px;}
-.icon-magnet{background-position:-264px -120px;}
-.icon-chevron-up{background-position:-288px -120px;}
-.icon-chevron-down{background-position:-313px -119px;}
-.icon-retweet{background-position:-336px -120px;}
-.icon-shopping-cart{background-position:-360px -120px;}
-.icon-folder-close{background-position:-384px -120px;width:16px;}
-.icon-folder-open{background-position:-408px -120px;width:16px;}
-.icon-resize-vertical{background-position:-432px -119px;}
-.icon-resize-horizontal{background-position:-456px -118px;}
-.icon-hdd{background-position:0 -144px;}
-.icon-bullhorn{background-position:-24px -144px;}
-.icon-bell{background-position:-48px -144px;}
-.icon-certificate{background-position:-72px -144px;}
-.icon-thumbs-up{background-position:-96px -144px;}
-.icon-thumbs-down{background-position:-120px -144px;}
-.icon-hand-right{background-position:-144px -144px;}
-.icon-hand-left{background-position:-168px -144px;}
-.icon-hand-up{background-position:-192px -144px;}
-.icon-hand-down{background-position:-216px -144px;}
-.icon-circle-arrow-right{background-position:-240px -144px;}
-.icon-circle-arrow-left{background-position:-264px -144px;}
-.icon-circle-arrow-up{background-position:-288px -144px;}
-.icon-circle-arrow-down{background-position:-312px -144px;}
-.icon-globe{background-position:-336px -144px;}
-.icon-wrench{background-position:-360px -144px;}
-.icon-tasks{background-position:-384px -144px;}
-.icon-filter{background-position:-408px -144px;}
-.icon-briefcase{background-position:-432px -144px;}
-.icon-fullscreen{background-position:-456px -144px;}
-.dropup,.dropdown{position:relative;}
-.dropdown-toggle{*margin-bottom:-3px;}
-.dropdown-toggle:active,.open .dropdown-toggle{outline:0;}
-.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000000;border-right:4px solid transparent;border-left:4px solid transparent;content:"";}
-.dropdown .caret{margin-top:8px;margin-left:2px;}
-.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#ffffff;border:1px solid #ccc;border:1px solid rgba(0, 0, 0, 0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0, 0, 0, 0.2);-moz-box-shadow:0 5px 10px rgba(0, 0, 0, 0.2);box-shadow:0 5px 10px rgba(0, 0, 0, 0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;}.dropdown-menu.pull-right{right:0;left:auto;}
-.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #ffffff;}
-.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333333;white-space:nowrap;}
-.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{text-decoration:none;color:#ffffff;background-color:#0081c2;background-image:-moz-linear-gradient(top, #0088cc, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3));background-image:-webkit-linear-gradient(top, #0088cc, #0077b3);background-image:-o-linear-gradient(top, #0088cc, #0077b3);background-image:linear-gradient(to bottom, #0088cc, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0);}
-.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#ffffff;text-decoration:none;outline:0;background-color:#0081c2;background-image:-moz-linear-gradient(top, #0088cc, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0077b3));background-image:-webkit-linear-gradient(top, #0088cc, #0077b3);background-image:-o-linear-gradient(top, #0088cc, #0077b3);background-image:linear-gradient(to bottom, #0088cc, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0);}
-.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999999;}
-.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:default;}
-.open{*z-index:1000;}.open>.dropdown-menu{display:block;}
-.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990;}
-.pull-right>.dropdown-menu{right:0;left:auto;}
-.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000000;content:"";}
-.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px;}
-.dropdown-submenu{position:relative;}
-.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px;}
-.dropdown-submenu:hover>.dropdown-menu{display:block;}
-.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0;}
-.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#cccccc;margin-top:5px;margin-right:-10px;}
-.dropdown-submenu:hover>a:after{border-left-color:#ffffff;}
-.dropdown-submenu.pull-left{float:none;}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px;}
-.dropdown .dropdown-menu .nav-header{padding-left:20px;padding-right:20px;}
-.typeahead{z-index:1051;margin-top:2px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
-.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.05);-moz-box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.05);box-shadow:inset 0 1px 1px rgba(0, 0, 0, 0.05);}.well blockquote{border-color:#ddd;border-color:rgba(0, 0, 0, 0.15);}
-.well-large{padding:24px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;}
-.well-small{padding:9px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;}
-.fade{opacity:0;-webkit-transition:opacity 0.15s linear;-moz-transition:opacity 0.15s linear;-o-transition:opacity 0.15s linear;transition:opacity 0.15s linear;}.fade.in{opacity:1;}
-.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height 0.35s ease;-moz-transition:height 0.35s ease;-o-transition:height 0.35s ease;transition:height 0.35s ease;}.collapse.in{height:auto;}
-.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000000;text-shadow:0 1px 0 #ffffff;opacity:0.2;filter:alpha(opacity=20);}.close:hover,.close:focus{color:#000000;text-decoration:none;cursor:pointer;opacity:0.4;filter:alpha(opacity=40);}
-button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none;}
-.btn{display:inline-block;*display:inline;*zoom:1;padding:4px 12px;margin-bottom:0;font-size:13px;line-height:20px;text-align:center;vertical-align:middle;cursor:pointer;color:#333333;text-shadow:0 1px 1px rgba(255, 255, 255, 0.75);background-color:#f5f5f5;background-image:-moz-linear-gradient(top, #ffffff, #e6e6e6);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#e6e6e6));background-image:-webkit-linear-gradient(top, #ffffff, #e6e6e6);background-image:-o-linear-gradient(top, #ffffff, #e6e6e6);background-image:linear-gradient(to bottom, #ffffff, #e6e6e6);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#e6e6e6;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);border:1px solid #cccccc;*border:0;border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*margin-left:.3em;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333333;background-color:#e6e6e6;*background-color:#d9d9d9;}
-.btn:active,.btn.active{background-color:#cccccc \9;}
-.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333333;background-color:#e6e6e6;*background-color:#d9d9d9;}
-.btn:active,.btn.active{background-color:#cccccc \9;}
-.btn:first-child{*margin-left:0;}
-.btn:first-child{*margin-left:0;}
-.btn:hover,.btn:focus{color:#333333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position 0.1s linear;-moz-transition:background-position 0.1s linear;-o-transition:background-position 0.1s linear;transition:background-position 0.1s linear;}
-.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px;}
-.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);}
-.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:0.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none;}
-.btn-large{padding:11px 19px;font-size:16.25px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;}
-.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px;}
-.btn-small{padding:2px 10px;font-size:11.049999999999999px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;}
-.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0;}
-.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px;}
-.btn-mini{padding:0 6px;font-size:9.75px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;}
-.btn-block{display:block;width:100%;padding-left:0;padding-right:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;}
-.btn-block+.btn-block{margin-top:5px;}
-input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%;}
-.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255, 255, 255, 0.75);}
-.btn-primary{color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#006dcc;background-image:-moz-linear-gradient(top, #0088cc, #0044cc);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#0088cc), to(#0044cc));background-image:-webkit-linear-gradient(top, #0088cc, #0044cc);background-image:-o-linear-gradient(top, #0088cc, #0044cc);background-image:linear-gradient(to bottom, #0088cc, #0044cc);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);border-color:#0044cc #0044cc #002a80;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#0044cc;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#ffffff;background-color:#0044cc;*background-color:#003bb3;}
-.btn-primary:active,.btn-primary.active{background-color:#003399 \9;}
-.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#ffffff;background-color:#0044cc;*background-color:#003bb3;}
-.btn-primary:active,.btn-primary.active{background-color:#003399 \9;}
-.btn-warning{color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);border-color:#f89406 #f89406 #ad6704;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#f89406;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#ffffff;background-color:#f89406;*background-color:#df8505;}
-.btn-warning:active,.btn-warning.active{background-color:#c67605 \9;}
-.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#ffffff;background-color:#f89406;*background-color:#df8505;}
-.btn-warning:active,.btn-warning.active{background-color:#c67605 \9;}
-.btn-danger{color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#da4f49;background-image:-moz-linear-gradient(top, #ee5f5b, #bd362f);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));background-image:-webkit-linear-gradient(top, #ee5f5b, #bd362f);background-image:-o-linear-gradient(top, #ee5f5b, #bd362f);background-image:linear-gradient(to bottom, #ee5f5b, #bd362f);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);border-color:#bd362f #bd362f #802420;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#bd362f;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#ffffff;background-color:#bd362f;*background-color:#a9302a;}
-.btn-danger:active,.btn-danger.active{background-color:#942a25 \9;}
-.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#ffffff;background-color:#bd362f;*background-color:#a9302a;}
-.btn-danger:active,.btn-danger.active{background-color:#942a25 \9;}
-.btn-success{color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#5bb75b;background-image:-moz-linear-gradient(top, #62c462, #51a351);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));background-image:-webkit-linear-gradient(top, #62c462, #51a351);background-image:-o-linear-gradient(top, #62c462, #51a351);background-image:linear-gradient(to bottom, #62c462, #51a351);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);border-color:#51a351 #51a351 #387038;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#51a351;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#ffffff;background-color:#51a351;*background-color:#499249;}
-.btn-success:active,.btn-success.active{background-color:#408140 \9;}
-.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#ffffff;background-color:#51a351;*background-color:#499249;}
-.btn-success:active,.btn-success.active{background-color:#408140 \9;}
-.btn-info{color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#49afcd;background-image:-moz-linear-gradient(top, #5bc0de, #2f96b4);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));background-image:-webkit-linear-gradient(top, #5bc0de, #2f96b4);background-image:-o-linear-gradient(top, #5bc0de, #2f96b4);background-image:linear-gradient(to bottom, #5bc0de, #2f96b4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#2f96b4;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#ffffff;background-color:#2f96b4;*background-color:#2a85a0;}
-.btn-info:active,.btn-info.active{background-color:#24748c \9;}
-.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#ffffff;background-color:#2f96b4;*background-color:#2a85a0;}
-.btn-info:active,.btn-info.active{background-color:#24748c \9;}
-.btn-inverse{color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#363636;background-image:-moz-linear-gradient(top, #444444, #222222);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#444444), to(#222222));background-image:-webkit-linear-gradient(top, #444444, #222222);background-image:-o-linear-gradient(top, #444444, #222222);background-image:linear-gradient(to bottom, #444444, #222222);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);border-color:#222222 #222222 #000000;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#222222;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#ffffff;background-color:#222222;*background-color:#151515;}
-.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9;}
-.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#ffffff;background-color:#222222;*background-color:#151515;}
-.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9;}
-button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px;}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0;}
-button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px;}
-button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px;}
-button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px;}
-.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none;}
-.btn-link{border-color:transparent;cursor:pointer;color:#0088cc;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent;}
-.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333333;text-decoration:none;}
-.btn-group{position:relative;display:inline-block;*display:inline;*zoom:1;font-size:0;vertical-align:middle;white-space:nowrap;*margin-left:.3em;}.btn-group:first-child{*margin-left:0;}
-.btn-group:first-child{*margin-left:0;}
-.btn-group+.btn-group{margin-left:5px;}
-.btn-toolbar{font-size:0;margin-top:10px;margin-bottom:10px;}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px;}
-.btn-group>.btn{position:relative;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.btn-group>.btn+.btn{margin-left:-1px;}
-.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:13px;}
-.btn-group>.btn-mini{font-size:9.75px;}
-.btn-group>.btn-small{font-size:11.049999999999999px;}
-.btn-group>.btn-large{font-size:16.25px;}
-.btn-group>.btn:first-child{margin-left:0;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px;}
-.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px;}
-.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px;}
-.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px;}
-.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2;}
-.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0;}
-.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);*padding-top:5px;*padding-bottom:5px;}
-.btn-group>.btn-mini+.dropdown-toggle{padding-left:5px;padding-right:5px;*padding-top:2px;*padding-bottom:2px;}
-.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px;}
-.btn-group>.btn-large+.dropdown-toggle{padding-left:12px;padding-right:12px;*padding-top:7px;*padding-bottom:7px;}
-.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);}
-.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6;}
-.btn-group.open .btn-primary.dropdown-toggle{background-color:#0044cc;}
-.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406;}
-.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f;}
-.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351;}
-.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4;}
-.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222222;}
-.btn .caret{margin-top:8px;margin-left:0;}
-.btn-large .caret{margin-top:6px;}
-.btn-large .caret{border-left-width:5px;border-right-width:5px;border-top-width:5px;}
-.btn-mini .caret,.btn-small .caret{margin-top:8px;}
-.dropup .btn-large .caret{border-bottom-width:5px;}
-.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#ffffff;border-bottom-color:#ffffff;}
-.btn-group-vertical{display:inline-block;*display:inline;*zoom:1;}
-.btn-group-vertical>.btn{display:block;float:none;max-width:100%;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.btn-group-vertical>.btn+.btn{margin-left:0;margin-top:-1px;}
-.btn-group-vertical>.btn:first-child{-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0;}
-.btn-group-vertical>.btn:last-child{-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px;}
-.btn-group-vertical>.btn-large:first-child{-webkit-border-radius:6px 6px 0 0;-moz-border-radius:6px 6px 0 0;border-radius:6px 6px 0 0;}
-.btn-group-vertical>.btn-large:last-child{-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;}
-.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255, 255, 255, 0.5);background-color:#fcf8e3;border:1px solid #fbeed5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
-.alert,.alert h4{color:#c09853;}
-.alert h4{margin:0;}
-.alert .close{position:relative;top:-2px;right:-21px;line-height:20px;}
-.alert-success{background-color:#dff0d8;border-color:#d6e9c6;color:#468847;}
-.alert-success h4{color:#468847;}
-.alert-danger,.alert-error{background-color:#f2dede;border-color:#eed3d7;color:#b94a48;}
-.alert-danger h4,.alert-error h4{color:#b94a48;}
-.alert-info{background-color:#d9edf7;border-color:#bce8f1;color:#3a87ad;}
-.alert-info h4{color:#3a87ad;}
-.alert-block{padding-top:14px;padding-bottom:14px;}
-.alert-block>p,.alert-block>ul{margin-bottom:0;}
-.alert-block p+p{margin-top:5px;}
-.nav{margin-left:0;margin-bottom:20px;list-style:none;}
-.nav>li>a{display:block;}
-.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eeeeee;}
-.nav>li>a>img{max-width:none;}
-.nav>.pull-right{float:right;}
-.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:20px;color:#999999;text-shadow:0 1px 0 rgba(255, 255, 255, 0.5);text-transform:uppercase;}
-.nav li+.nav-header{margin-top:9px;}
-.nav-list{padding-left:15px;padding-right:15px;margin-bottom:0;}
-.nav-list>li>a,.nav-list .nav-header{margin-left:-15px;margin-right:-15px;text-shadow:0 1px 0 rgba(255, 255, 255, 0.5);}
-.nav-list>li>a{padding:3px 15px;}
-.nav-list>.active>a,.nav-list>.active>a:hover,.nav-list>.active>a:focus{color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.2);background-color:#0088cc;}
-.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{margin-right:2px;}
-.nav-list .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #ffffff;}
-.nav-tabs,.nav-pills{*zoom:1;}.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;content:"";line-height:0;}
-.nav-tabs:after,.nav-pills:after{clear:both;}
-.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;content:"";line-height:0;}
-.nav-tabs:after,.nav-pills:after{clear:both;}
-.nav-tabs>li,.nav-pills>li{float:left;}
-.nav-tabs>li>a,.nav-pills>li>a{padding-right:12px;padding-left:12px;margin-right:2px;line-height:14px;}
-.nav-tabs{border-bottom:1px solid #ddd;}
-.nav-tabs>li{margin-bottom:-1px;}
-.nav-tabs>li>a{padding-top:8px;padding-bottom:8px;line-height:20px;border:1px solid transparent;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0;}.nav-tabs>li>a:hover,.nav-tabs>li>a:focus{border-color:#eeeeee #eeeeee #dddddd;}
-.nav-tabs>.active>a,.nav-tabs>.active>a:hover,.nav-tabs>.active>a:focus{color:#555555;background-color:#ffffff;border:1px solid #ddd;border-bottom-color:transparent;cursor:default;}
-.nav-pills>li>a{padding-top:8px;padding-bottom:8px;margin-top:2px;margin-bottom:2px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px;}
-.nav-pills>.active>a,.nav-pills>.active>a:hover,.nav-pills>.active>a:focus{color:#ffffff;background-color:#0088cc;}
-.nav-stacked>li{float:none;}
-.nav-stacked>li>a{margin-right:0;}
-.nav-tabs.nav-stacked{border-bottom:0;}
-.nav-tabs.nav-stacked>li>a{border:1px solid #ddd;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.nav-tabs.nav-stacked>li:first-child>a{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;}
-.nav-tabs.nav-stacked>li:last-child>a{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px;}
-.nav-tabs.nav-stacked>li>a:hover,.nav-tabs.nav-stacked>li>a:focus{border-color:#ddd;z-index:2;}
-.nav-pills.nav-stacked>li>a{margin-bottom:3px;}
-.nav-pills.nav-stacked>li:last-child>a{margin-bottom:1px;}
-.nav-tabs .dropdown-menu{-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;}
-.nav-pills .dropdown-menu{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;}
-.nav .dropdown-toggle .caret{border-top-color:#0088cc;border-bottom-color:#0088cc;margin-top:6px;}
-.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#005580;border-bottom-color:#005580;}
-.nav-tabs .dropdown-toggle .caret{margin-top:8px;}
-.nav .active .dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff;}
-.nav-tabs .active .dropdown-toggle .caret{border-top-color:#555555;border-bottom-color:#555555;}
-.nav>.dropdown.active>a:hover,.nav>.dropdown.active>a:focus{cursor:pointer;}
-.nav-tabs .open .dropdown-toggle,.nav-pills .open .dropdown-toggle,.nav>li.dropdown.open.active>a:hover,.nav>li.dropdown.open.active>a:focus{color:#ffffff;background-color:#999999;border-color:#999999;}
-.nav li.dropdown.open .caret,.nav li.dropdown.open.active .caret,.nav li.dropdown.open a:hover .caret,.nav li.dropdown.open a:focus .caret{border-top-color:#ffffff;border-bottom-color:#ffffff;opacity:1;filter:alpha(opacity=100);}
-.tabs-stacked .open>a:hover,.tabs-stacked .open>a:focus{border-color:#999999;}
-.tabbable{*zoom:1;}.tabbable:before,.tabbable:after{display:table;content:"";line-height:0;}
-.tabbable:after{clear:both;}
-.tabbable:before,.tabbable:after{display:table;content:"";line-height:0;}
-.tabbable:after{clear:both;}
-.tab-content{overflow:auto;}
-.tabs-below>.nav-tabs,.tabs-right>.nav-tabs,.tabs-left>.nav-tabs{border-bottom:0;}
-.tab-content>.tab-pane,.pill-content>.pill-pane{display:none;}
-.tab-content>.active,.pill-content>.active{display:block;}
-.tabs-below>.nav-tabs{border-top:1px solid #ddd;}
-.tabs-below>.nav-tabs>li{margin-top:-1px;margin-bottom:0;}
-.tabs-below>.nav-tabs>li>a{-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px;}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-bottom-color:transparent;border-top-color:#ddd;}
-.tabs-below>.nav-tabs>.active>a,.tabs-below>.nav-tabs>.active>a:hover,.tabs-below>.nav-tabs>.active>a:focus{border-color:transparent #ddd #ddd #ddd;}
-.tabs-left>.nav-tabs>li,.tabs-right>.nav-tabs>li{float:none;}
-.tabs-left>.nav-tabs>li>a,.tabs-right>.nav-tabs>li>a{min-width:74px;margin-right:0;margin-bottom:3px;}
-.tabs-left>.nav-tabs{float:left;margin-right:19px;border-right:1px solid #ddd;}
-.tabs-left>.nav-tabs>li>a{margin-right:-1px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px;}
-.tabs-left>.nav-tabs>li>a:hover,.tabs-left>.nav-tabs>li>a:focus{border-color:#eeeeee #dddddd #eeeeee #eeeeee;}
-.tabs-left>.nav-tabs .active>a,.tabs-left>.nav-tabs .active>a:hover,.tabs-left>.nav-tabs .active>a:focus{border-color:#ddd transparent #ddd #ddd;*border-right-color:#ffffff;}
-.tabs-right>.nav-tabs{float:right;margin-left:19px;border-left:1px solid #ddd;}
-.tabs-right>.nav-tabs>li>a{margin-left:-1px;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0;}
-.tabs-right>.nav-tabs>li>a:hover,.tabs-right>.nav-tabs>li>a:focus{border-color:#eeeeee #eeeeee #eeeeee #dddddd;}
-.tabs-right>.nav-tabs .active>a,.tabs-right>.nav-tabs .active>a:hover,.tabs-right>.nav-tabs .active>a:focus{border-color:#ddd #ddd #ddd transparent;*border-left-color:#ffffff;}
-.nav>.disabled>a{color:#999999;}
-.nav>.disabled>a:hover,.nav>.disabled>a:focus{text-decoration:none;background-color:transparent;cursor:default;}
-.navbar{overflow:visible;margin-bottom:20px;*position:relative;*z-index:2;}
-.navbar-inner{min-height:36px;padding-left:20px;padding-right:20px;background-color:#fafafa;background-image:-moz-linear-gradient(top, #ffffff, #f2f2f2);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ffffff), to(#f2f2f2));background-image:-webkit-linear-gradient(top, #ffffff, #f2f2f2);background-image:-o-linear-gradient(top, #ffffff, #f2f2f2);background-image:linear-gradient(to bottom, #ffffff, #f2f2f2);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0);border:1px solid #d4d4d4;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 4px rgba(0, 0, 0, 0.065);-moz-box-shadow:0 1px 4px rgba(0, 0, 0, 0.065);box-shadow:0 1px 4px rgba(0, 0, 0, 0.065);*zoom:1;}.navbar-inner:before,.navbar-inner:after{display:table;content:"";line-height:0;}
-.navbar-inner:after{clear:both;}
-.navbar-inner:before,.navbar-inner:after{display:table;content:"";line-height:0;}
-.navbar-inner:after{clear:both;}
-.navbar .container{width:auto;}
-.nav-collapse.collapse{height:auto;overflow:visible;}
-.navbar .brand{float:left;display:block;padding:8px 20px 8px;margin-left:-20px;font-size:20px;font-weight:200;color:#777777;text-shadow:0 1px 0 #ffffff;}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none;}
-.navbar-text{margin-bottom:0;line-height:36px;color:#777777;}
-.navbar-link{color:#777777;}.navbar-link:hover,.navbar-link:focus{color:#333333;}
-.navbar .divider-vertical{height:36px;margin:0 9px;border-left:1px solid #f2f2f2;border-right:1px solid #ffffff;}
-.navbar .btn,.navbar .btn-group{margin-top:3px;}
-.navbar .btn-group .btn,.navbar .input-prepend .btn,.navbar .input-append .btn,.navbar .input-prepend .btn-group,.navbar .input-append .btn-group{margin-top:0;}
-.navbar-form{margin-bottom:0;*zoom:1;}.navbar-form:before,.navbar-form:after{display:table;content:"";line-height:0;}
-.navbar-form:after{clear:both;}
-.navbar-form:before,.navbar-form:after{display:table;content:"";line-height:0;}
-.navbar-form:after{clear:both;}
-.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:3px;}
-.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0;}
-.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px;}
-.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap;}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0;}
-.navbar-search{position:relative;float:left;margin-top:3px;margin-bottom:0;}.navbar-search .search-query{margin-bottom:0;padding:4px 14px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px;}
-.navbar-static-top{position:static;margin-bottom:0;}.navbar-static-top .navbar-inner{-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030;margin-bottom:0;}
-.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{border-width:0 0 1px;}
-.navbar-fixed-bottom .navbar-inner{border-width:1px 0 0;}
-.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding-left:0;padding-right:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px;}
-.navbar-fixed-top{top:0;}
-.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{-webkit-box-shadow:0 1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 1px 10px rgba(0,0,0,.1);box-shadow:0 1px 10px rgba(0,0,0,.1);}
-.navbar-fixed-bottom{bottom:0;}.navbar-fixed-bottom .navbar-inner{-webkit-box-shadow:0 -1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,.1);box-shadow:0 -1px 10px rgba(0,0,0,.1);}
-.navbar .nav{position:relative;left:0;display:block;float:left;margin:0 10px 0 0;}
-.navbar .nav.pull-right{float:right;margin-right:0;}
-.navbar .nav>li{float:left;}
-.navbar .nav>li>a{float:none;padding:8px 15px 8px;color:#777777;text-decoration:none;text-shadow:0 1px 0 #ffffff;}
-.navbar .nav .dropdown-toggle .caret{margin-top:8px;}
-.navbar .nav>li>a:focus,.navbar .nav>li>a:hover{background-color:transparent;color:#333333;text-decoration:none;}
-.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#555555;text-decoration:none;background-color:#e5e5e5;-webkit-box-shadow:inset 0 3px 8px rgba(0, 0, 0, 0.125);-moz-box-shadow:inset 0 3px 8px rgba(0, 0, 0, 0.125);box-shadow:inset 0 3px 8px rgba(0, 0, 0, 0.125);}
-.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-left:5px;margin-right:5px;color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#ededed;background-image:-moz-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5));background-image:-webkit-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-o-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:linear-gradient(to bottom, #f2f2f2, #e5e5e5);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0);border-color:#e5e5e5 #e5e5e5 #bfbfbf;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#e5e5e5;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#ffffff;background-color:#e5e5e5;*background-color:#d9d9d9;}
-.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#cccccc \9;}
-.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#ffffff;background-color:#e5e5e5;*background-color:#d9d9d9;}
-.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#cccccc \9;}
-.navbar .btn-navbar .icon-bar{display:block;width:18px;height:2px;background-color:#f5f5f5;-webkit-border-radius:1px;-moz-border-radius:1px;border-radius:1px;-webkit-box-shadow:0 1px 0 rgba(0, 0, 0, 0.25);-moz-box-shadow:0 1px 0 rgba(0, 0, 0, 0.25);box-shadow:0 1px 0 rgba(0, 0, 0, 0.25);}
-.btn-navbar .icon-bar+.icon-bar{margin-top:3px;}
-.navbar .nav>li>.dropdown-menu:before{content:'';display:inline-block;border-left:7px solid transparent;border-right:7px solid transparent;border-bottom:7px solid #ccc;border-bottom-color:rgba(0, 0, 0, 0.2);position:absolute;top:-7px;left:9px;}
-.navbar .nav>li>.dropdown-menu:after{content:'';display:inline-block;border-left:6px solid transparent;border-right:6px solid transparent;border-bottom:6px solid #ffffff;position:absolute;top:-6px;left:10px;}
-.navbar-fixed-bottom .nav>li>.dropdown-menu:before{border-top:7px solid #ccc;border-top-color:rgba(0, 0, 0, 0.2);border-bottom:0;bottom:-7px;top:auto;}
-.navbar-fixed-bottom .nav>li>.dropdown-menu:after{border-top:6px solid #ffffff;border-bottom:0;bottom:-6px;top:auto;}
-.navbar .nav li.dropdown>a:hover .caret,.navbar .nav li.dropdown>a:focus .caret{border-top-color:#333333;border-bottom-color:#333333;}
-.navbar .nav li.dropdown.open>.dropdown-toggle,.navbar .nav li.dropdown.active>.dropdown-toggle,.navbar .nav li.dropdown.open.active>.dropdown-toggle{background-color:#e5e5e5;color:#555555;}
-.navbar .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#777777;border-bottom-color:#777777;}
-.navbar .nav li.dropdown.open>.dropdown-toggle .caret,.navbar .nav li.dropdown.active>.dropdown-toggle .caret,.navbar .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#555555;border-bottom-color:#555555;}
-.navbar .pull-right>li>.dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right{left:auto;right:0;}.navbar .pull-right>li>.dropdown-menu:before,.navbar .nav>li>.dropdown-menu.pull-right:before{left:auto;right:12px;}
-.navbar .pull-right>li>.dropdown-menu:after,.navbar .nav>li>.dropdown-menu.pull-right:after{left:auto;right:13px;}
-.navbar .pull-right>li>.dropdown-menu .dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right .dropdown-menu{left:auto;right:100%;margin-left:0;margin-right:-1px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px;}
-.navbar-inverse .navbar-inner{background-color:#1b1b1b;background-image:-moz-linear-gradient(top, #222222, #111111);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#222222), to(#111111));background-image:-webkit-linear-gradient(top, #222222, #111111);background-image:-o-linear-gradient(top, #222222, #111111);background-image:linear-gradient(to bottom, #222222, #111111);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0);border-color:#252525;}
-.navbar-inverse .brand,.navbar-inverse .nav>li>a{color:#999999;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);}.navbar-inverse .brand:hover,.navbar-inverse .nav>li>a:hover,.navbar-inverse .brand:focus,.navbar-inverse .nav>li>a:focus{color:#ffffff;}
-.navbar-inverse .brand{color:#999999;}
-.navbar-inverse .navbar-text{color:#999999;}
-.navbar-inverse .nav>li>a:focus,.navbar-inverse .nav>li>a:hover{background-color:transparent;color:#ffffff;}
-.navbar-inverse .nav .active>a,.navbar-inverse .nav .active>a:hover,.navbar-inverse .nav .active>a:focus{color:#ffffff;background-color:#111111;}
-.navbar-inverse .navbar-link{color:#999999;}.navbar-inverse .navbar-link:hover,.navbar-inverse .navbar-link:focus{color:#ffffff;}
-.navbar-inverse .divider-vertical{border-left-color:#111111;border-right-color:#222222;}
-.navbar-inverse .nav li.dropdown.open>.dropdown-toggle,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle{background-color:#111111;color:#ffffff;}
-.navbar-inverse .nav li.dropdown>a:hover .caret,.navbar-inverse .nav li.dropdown>a:focus .caret{border-top-color:#ffffff;border-bottom-color:#ffffff;}
-.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#999999;border-bottom-color:#999999;}
-.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#ffffff;border-bottom-color:#ffffff;}
-.navbar-inverse .navbar-search .search-query{color:#ffffff;background-color:#515151;border-color:#111111;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-webkit-transition:none;-moz-transition:none;-o-transition:none;transition:none;}.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#cccccc;}
-.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#cccccc;}
-.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#cccccc;}
-.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#cccccc;}
-.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#cccccc;}
-.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#cccccc;}
-.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#333333;text-shadow:0 1px 0 #ffffff;background-color:#ffffff;border:0;-webkit-box-shadow:0 0 3px rgba(0, 0, 0, 0.15);-moz-box-shadow:0 0 3px rgba(0, 0, 0, 0.15);box-shadow:0 0 3px rgba(0, 0, 0, 0.15);outline:0;}
-.navbar-inverse .btn-navbar{color:#ffffff;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#0e0e0e;background-image:-moz-linear-gradient(top, #151515, #040404);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404));background-image:-webkit-linear-gradient(top, #151515, #040404);background-image:-o-linear-gradient(top, #151515, #040404);background-image:linear-gradient(to bottom, #151515, #040404);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0);border-color:#040404 #040404 #000000;border-color:rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.1) rgba(0, 0, 0, 0.25);*background-color:#040404;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#ffffff;background-color:#040404;*background-color:#000000;}
-.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000000 \9;}
-.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#ffffff;background-color:#040404;*background-color:#000000;}
-.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000000 \9;}
-.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}.breadcrumb>li{display:inline-block;*display:inline;*zoom:1;text-shadow:0 1px 0 #ffffff;}.breadcrumb>li>.divider{padding:0 5px;color:#ccc;}
-.breadcrumb>.active{color:#999999;}
-.pagination{margin:20px 0;}
-.pagination ul{display:inline-block;*display:inline;*zoom:1;margin-left:0;margin-bottom:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0, 0, 0, 0.05);-moz-box-shadow:0 1px 2px rgba(0, 0, 0, 0.05);box-shadow:0 1px 2px rgba(0, 0, 0, 0.05);}
-.pagination ul>li{display:inline;}
-.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#ffffff;border:1px solid #dddddd;border-left-width:0;}
-.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5;}
-.pagination ul>.active>a,.pagination ul>.active>span{color:#999999;cursor:default;}
-.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999999;background-color:transparent;cursor:default;}
-.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px;}
-.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px;}
-.pagination-centered{text-align:center;}
-.pagination-right{text-align:right;}
-.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:16.25px;}
-.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px;}
-.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px;}
-.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-top-left-radius:3px;-moz-border-radius-topleft:3px;border-top-left-radius:3px;-webkit-border-bottom-left-radius:3px;-moz-border-radius-bottomleft:3px;border-bottom-left-radius:3px;}
-.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;-moz-border-radius-topright:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;-moz-border-radius-bottomright:3px;border-bottom-right-radius:3px;}
-.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.049999999999999px;}
-.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:9.75px;}
-.pager{margin:20px 0;list-style:none;text-align:center;*zoom:1;}.pager:before,.pager:after{display:table;content:"";line-height:0;}
-.pager:after{clear:both;}
-.pager:before,.pager:after{display:table;content:"";line-height:0;}
-.pager:after{clear:both;}
-.pager li{display:inline;}
-.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px;}
-.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5;}
-.pager .next>a,.pager .next>span{float:right;}
-.pager .previous>a,.pager .previous>span{float:left;}
-.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999999;background-color:#fff;cursor:default;}
-.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000000;}.modal-backdrop.fade{opacity:0;}
-.modal-backdrop,.modal-backdrop.fade.in{opacity:0.8;filter:alpha(opacity=80);}
-.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#ffffff;border:1px solid #999;border:1px solid rgba(0, 0, 0, 0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 3px 7px rgba(0, 0, 0, 0.3);-moz-box-shadow:0 3px 7px rgba(0, 0, 0, 0.3);box-shadow:0 3px 7px rgba(0, 0, 0, 0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box;outline:none;}.modal.fade{-webkit-transition:opacity .3s linear, top .3s ease-out;-moz-transition:opacity .3s linear, top .3s ease-out;-o-transition:opacity .3s linear, top .3s ease-out;transition:opacity .3s linear, top .3s ease-out;top:-25%;}
-.modal.fade.in{top:10%;}
-.modal-header{padding:9px 15px;border-bottom:1px solid #eee;}.modal-header .close{margin-top:2px;}
-.modal-header h3{margin:0;line-height:30px;}
-.modal-body{position:relative;overflow-y:auto;max-height:400px;padding:15px;}
-.modal-form{margin-bottom:0;}
-.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;-webkit-box-shadow:inset 0 1px 0 #ffffff;-moz-box-shadow:inset 0 1px 0 #ffffff;box-shadow:inset 0 1px 0 #ffffff;*zoom:1;}.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0;}
-.modal-footer:after{clear:both;}
-.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0;}
-.modal-footer:after{clear:both;}
-.modal-footer .btn+.btn{margin-left:5px;margin-bottom:0;}
-.modal-footer .btn-group .btn+.btn{margin-left:-1px;}
-.modal-footer .btn-block+.btn-block{margin-left:0;}
-.tooltip{position:absolute;z-index:1030;display:block;visibility:visible;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0);}.tooltip.in{opacity:0.8;filter:alpha(opacity=80);}
-.tooltip.top{margin-top:-3px;padding:5px 0;}
-.tooltip.right{margin-left:3px;padding:0 5px;}
-.tooltip.bottom{margin-top:3px;padding:5px 0;}
-.tooltip.left{margin-left:-3px;padding:0 5px;}
-.tooltip-inner{max-width:200px;padding:8px;color:#ffffff;text-align:center;text-decoration:none;background-color:#000000;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
-.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid;}
-.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000000;}
-.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000000;}
-.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000000;}
-.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000000;}
-.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;background-color:#ffffff;-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0, 0, 0, 0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0, 0, 0, 0.2);-moz-box-shadow:0 5px 10px rgba(0, 0, 0, 0.2);box-shadow:0 5px 10px rgba(0, 0, 0, 0.2);white-space:normal;}.popover.top{margin-top:-10px;}
-.popover.right{margin-left:10px;}
-.popover.bottom{margin-top:10px;}
-.popover.left{margin-left:-10px;}
-.popover-title{margin:0;padding:8px 14px;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0;}.popover-title:empty{display:none;}
-.popover-content{padding:9px 14px;}
-.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid;}
-.popover .arrow{border-width:11px;}
-.popover .arrow:after{border-width:10px;content:"";}
-.popover.top .arrow{left:50%;margin-left:-11px;border-bottom-width:0;border-top-color:#999;border-top-color:rgba(0, 0, 0, 0.25);bottom:-11px;}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-bottom-width:0;border-top-color:#ffffff;}
-.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-left-width:0;border-right-color:#999;border-right-color:rgba(0, 0, 0, 0.25);}.popover.right .arrow:after{left:1px;bottom:-10px;border-left-width:0;border-right-color:#ffffff;}
-.popover.bottom .arrow{left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0, 0, 0, 0.25);top:-11px;}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-top-width:0;border-bottom-color:#ffffff;}
-.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0, 0, 0, 0.25);}.popover.left .arrow:after{right:1px;border-right-width:0;border-left-color:#ffffff;bottom:-10px;}
-.thumbnails{margin-left:-20px;list-style:none;*zoom:1;}.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0;}
-.thumbnails:after{clear:both;}
-.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0;}
-.thumbnails:after{clear:both;}
-.row-fluid .thumbnails{margin-left:0;}
-.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px;}
-.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0, 0, 0, 0.055);-moz-box-shadow:0 1px 3px rgba(0, 0, 0, 0.055);box-shadow:0 1px 3px rgba(0, 0, 0, 0.055);-webkit-transition:all 0.2s ease-in-out;-moz-transition:all 0.2s ease-in-out;-o-transition:all 0.2s ease-in-out;transition:all 0.2s ease-in-out;}
-a.thumbnail:hover,a.thumbnail:focus{border-color:#0088cc;-webkit-box-shadow:0 1px 4px rgba(0, 105, 214, 0.25);-moz-box-shadow:0 1px 4px rgba(0, 105, 214, 0.25);box-shadow:0 1px 4px rgba(0, 105, 214, 0.25);}
-.thumbnail>img{display:block;max-width:100%;margin-left:auto;margin-right:auto;}
-.thumbnail .caption{padding:9px;color:#555555;}
-.media,.media-body{overflow:hidden;*overflow:visible;zoom:1;}
-.media,.media .media{margin-top:15px;}
-.media:first-child{margin-top:0;}
-.media-object{display:block;}
-.media-heading{margin:0 0 5px;}
-.media>.pull-left{margin-right:10px;}
-.media>.pull-right{margin-left:10px;}
-.media-list{margin-left:0;list-style:none;}
-.label,.badge{display:inline-block;padding:2px 4px;font-size:10.998px;font-weight:bold;line-height:14px;color:#ffffff;vertical-align:baseline;white-space:nowrap;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#999999;}
-.label{-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;}
-.badge{padding-left:9px;padding-right:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px;}
-.label:empty,.badge:empty{display:none;}
-a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#ffffff;text-decoration:none;cursor:pointer;}
-.label-important,.badge-important{background-color:#b94a48;}
-.label-important[href],.badge-important[href]{background-color:#953b39;}
-.label-warning,.badge-warning{background-color:#f89406;}
-.label-warning[href],.badge-warning[href]{background-color:#c67605;}
-.label-success,.badge-success{background-color:#468847;}
-.label-success[href],.badge-success[href]{background-color:#356635;}
-.label-info,.badge-info{background-color:#3a87ad;}
-.label-info[href],.badge-info[href]{background-color:#2d6987;}
-.label-inverse,.badge-inverse{background-color:#333333;}
-.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a;}
-.btn .label,.btn .badge{position:relative;top:-1px;}
-.btn-mini .label,.btn-mini .badge{top:0;}
-@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}@-o-keyframes progress-bar-stripes{from{background-position:0 0;} to{background-position:40px 0;}}@keyframes progress-bar-stripes{from{background-position:40px 0;} to{background-position:0 0;}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);-moz-box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);box-shadow:inset 0 1px 2px rgba(0, 0, 0, 0.1);-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
-.progress .bar{width:0%;height:100%;color:#ffffff;float:left;font-size:12px;text-align:center;text-shadow:0 -1px 0 rgba(0, 0, 0, 0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top, #149bdf, #0480be);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));background-image:-webkit-linear-gradient(top, #149bdf, #0480be);background-image:-o-linear-gradient(top, #149bdf, #0480be);background-image:linear-gradient(to bottom, #149bdf, #0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0, 0, 0, 0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0, 0, 0, 0.15);box-shadow:inset 0 -1px 0 rgba(0, 0, 0, 0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width 0.6s ease;-moz-transition:width 0.6s ease;-o-transition:width 0.6s ease;transition:width 0.6s ease;}
-.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);}
-.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px;}
-.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite;}
-.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0);}
-.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}
-.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0);}
-.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}
-.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0);}
-.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}
-.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);}
-.progress-warning.progress-striped .bar,.progress-striped .bar-warning{background-color:#fbb450;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(0.25, rgba(255, 255, 255, 0.15)), color-stop(0.25, transparent), color-stop(0.5, transparent), color-stop(0.5, rgba(255, 255, 255, 0.15)), color-stop(0.75, rgba(255, 255, 255, 0.15)), color-stop(0.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);}
-.accordion{margin-bottom:20px;}
-.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
-.accordion-heading{border-bottom:0;}
-.accordion-heading .accordion-toggle{display:block;padding:8px 15px;}
-.accordion-toggle{cursor:pointer;}
-.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5;}
-.carousel{position:relative;margin-bottom:20px;line-height:1;}
-.carousel-inner{overflow:hidden;width:100%;position:relative;}
-.carousel-inner>.item{display:none;position:relative;-webkit-transition:0.6s ease-in-out left;-moz-transition:0.6s ease-in-out left;-o-transition:0.6s ease-in-out left;transition:0.6s ease-in-out left;}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1;}
-.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block;}
-.carousel-inner>.active{left:0;}
-.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%;}
-.carousel-inner>.next{left:100%;}
-.carousel-inner>.prev{left:-100%;}
-.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0;}
-.carousel-inner>.active.left{left:-100%;}
-.carousel-inner>.active.right{left:100%;}
-.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#ffffff;text-align:center;background:#222222;border:3px solid #ffffff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:0.5;filter:alpha(opacity=50);}.carousel-control.right{left:auto;right:15px;}
-.carousel-control:hover,.carousel-control:focus{color:#ffffff;text-decoration:none;opacity:0.9;filter:alpha(opacity=90);}
-.carousel-indicators{position:absolute;top:15px;right:15px;z-index:5;margin:0;list-style:none;}.carousel-indicators li{display:block;float:left;width:10px;height:10px;margin-left:5px;text-indent:-999px;background-color:#ccc;background-color:rgba(255, 255, 255, 0.25);border-radius:5px;}
-.carousel-indicators .active{background-color:#fff;}
-.carousel-caption{position:absolute;left:0;right:0;bottom:0;padding:15px;background:#333333;background:rgba(0, 0, 0, 0.75);}
-.carousel-caption h4,.carousel-caption p{color:#ffffff;line-height:20px;}
-.carousel-caption h4{margin:0 0 5px;}
-.carousel-caption p{margin-bottom:0;}
-.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:30px;color:inherit;background-color:#eeeeee;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;color:inherit;letter-spacing:-1px;}
-.hero-unit li{line-height:30px;}
-.pull-right{float:right;}
-.pull-left{float:left;}
-.hide{display:none;}
-.show{display:block;}
-.invisible{visibility:hidden;}
-.affix{position:fixed;}
-.clearfix{*zoom:1;}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0;}
-.clearfix:after{clear:both;}
-.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0;}
-.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;}
-@-ms-viewport{width:device-width;}.hidden{display:none;visibility:hidden;}
-.visible-phone{display:none !important;}
-.visible-tablet{display:none !important;}
-.hidden-desktop{display:none !important;}
-.visible-desktop{display:inherit !important;}
-@media (min-width:768px) and (max-width:979px){.hidden-desktop{display:inherit !important;} .visible-desktop{display:none !important ;} .visible-tablet{display:inherit !important;} .hidden-tablet{display:none !important;}}@media (max-width:767px){.hidden-desktop{display:inherit !important;} .visible-desktop{display:none !important;} .visible-phone{display:inherit !important;} .hidden-phone{display:none !important;}}.visible-print{display:none !important;}
-@media print{.visible-print{display:inherit !important;} .hidden-print{display:none !important;}}@media (min-width:1200px){.row{margin-left:-30px;*zoom:1;}.row:before,.row:after{display:table;content:"";line-height:0;} .row:after{clear:both;} .row:before,.row:after{display:table;content:"";line-height:0;} .row:after{clear:both;} [class*="span"]{float:left;min-height:1px;margin-left:30px;} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:1170px;} .span12{width:1170px;} .span11{width:1070px;} .span10{width:970px;} .span9{width:870px;} .span8{width:770px;} .span7{width:670px;} .span6{width:570px;} .span5{width:470px;} .span4{width:370px;} .span3{width:270px;} .span2{width:170px;} .span1{width:70px;} .offset12{margin-left:1230px;} .offset11{margin-left:1130px;} .offset10{margin-left:1030px;} .offset9{margin-left:930px;} .offset8{margin-left:830px;} .offset7{margin-left:730px;} .offset6{margin-left:630px;} .offset5{margin-left:530px;} .offset4{margin-left:430px;} .offset3{margin-left:330px;} .offset2{margin-left:230px;} .offset1{margin-left:130px;} .row{margin-left:-30px;*zoom:1;}.row:before,.row:after{display:table;content:"";line-height:0;} .row:after{clear:both;} .row:before,.row:after{display:table;content:"";line-height:0;} .row:after{clear:both;} [class*="span"]{float:left;min-height:1px;margin-left:30px;} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:1170px;} .span12{width:1170px;} .span11{width:1070px;} .span10{width:970px;} .span9{width:870px;} .span8{width:770px;} .span7{width:670px;} .span6{width:570px;} .span5{width:470px;} .span4{width:370px;} .span3{width:270px;} .span2{width:170px;} .span1{width:70px;} .offset12{margin-left:1230px;} .offset11{margin-left:1130px;} .offset10{margin-left:1030px;} .offset9{margin-left:930px;} .offset8{margin-left:830px;} .offset7{margin-left:730px;} .offset6{margin-left:630px;} .offset5{margin-left:530px;} .offset4{margin-left:430px;} .offset3{margin-left:330px;} .offset2{margin-left:230px;} .offset1{margin-left:130px;} .row-fluid{width:100%;*zoom:1;}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;} .row-fluid:after{clear:both;} .row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;} .row-fluid:after{clear:both;} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.564102564102564%;*margin-left:2.5109110747408616%;} .row-fluid [class*="span"]:first-child{margin-left:0;} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.564102564102564%;} .row-fluid .span12{width:100%;*width:99.94680851063829%;} .row-fluid .span11{width:91.45299145299145%;*width:91.39979996362975%;} .row-fluid .span10{width:82.90598290598291%;*width:82.8527914166212%;} .row-fluid .span9{width:74.35897435897436%;*width:74.30578286961266%;} .row-fluid .span8{width:65.81196581196582%;*width:65.75877432260411%;} .row-fluid .span7{width:57.26495726495726%;*width:57.21176577559556%;} .row-fluid .span6{width:48.717948717948715%;*width:48.664757228587014%;} .row-fluid .span5{width:40.17094017094017%;*width:40.11774868157847%;} .row-fluid .span4{width:31.623931623931625%;*width:31.570740134569924%;} .row-fluid .span3{width:23.076923076923077%;*width:23.023731587561375%;} .row-fluid .span2{width:14.52991452991453%;*width:14.476723040552828%;} .row-fluid .span1{width:5.982905982905983%;*width:5.929714493544281%;} .row-fluid .offset12{margin-left:105.12820512820512%;*margin-left:105.02182214948171%;} .row-fluid .offset12:first-child{margin-left:102.56410256410257%;*margin-left:102.45771958537915%;} .row-fluid .offset11{margin-left:96.58119658119658%;*margin-left:96.47481360247316%;} .row-fluid .offset11:first-child{margin-left:94.01709401709402%;*margin-left:93.91071103837061%;} .row-fluid .offset10{margin-left:88.03418803418803%;*margin-left:87.92780505546462%;} .row-fluid .offset10:first-child{margin-left:85.47008547008548%;*margin-left:85.36370249136206%;} .row-fluid .offset9{margin-left:79.48717948717949%;*margin-left:79.38079650845607%;} .row-fluid .offset9:first-child{margin-left:76.92307692307693%;*margin-left:76.81669394435352%;} .row-fluid .offset8{margin-left:70.94017094017094%;*margin-left:70.83378796144753%;} .row-fluid .offset8:first-child{margin-left:68.37606837606839%;*margin-left:68.26968539734497%;} .row-fluid .offset7{margin-left:62.393162393162385%;*margin-left:62.28677941443899%;} .row-fluid .offset7:first-child{margin-left:59.82905982905982%;*margin-left:59.72267685033642%;} .row-fluid .offset6{margin-left:53.84615384615384%;*margin-left:53.739770867430444%;} .row-fluid .offset6:first-child{margin-left:51.28205128205128%;*margin-left:51.175668303327875%;} .row-fluid .offset5{margin-left:45.299145299145295%;*margin-left:45.1927623204219%;} .row-fluid .offset5:first-child{margin-left:42.73504273504273%;*margin-left:42.62865975631933%;} .row-fluid .offset4{margin-left:36.75213675213675%;*margin-left:36.645753773413354%;} .row-fluid .offset4:first-child{margin-left:34.18803418803419%;*margin-left:34.081651209310785%;} .row-fluid .offset3{margin-left:28.205128205128204%;*margin-left:28.0987452264048%;} .row-fluid .offset3:first-child{margin-left:25.641025641025642%;*margin-left:25.53464266230224%;} .row-fluid .offset2{margin-left:19.65811965811966%;*margin-left:19.551736679396257%;} .row-fluid .offset2:first-child{margin-left:17.094017094017094%;*margin-left:16.98763411529369%;} .row-fluid .offset1{margin-left:11.11111111111111%;*margin-left:11.004728132387708%;} .row-fluid .offset1:first-child{margin-left:8.547008547008547%;*margin-left:8.440625568285142%;} .row-fluid{width:100%;*zoom:1;}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;} .row-fluid:after{clear:both;} .row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;} .row-fluid:after{clear:both;} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.564102564102564%;*margin-left:2.5109110747408616%;} .row-fluid [class*="span"]:first-child{margin-left:0;} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.564102564102564%;} .row-fluid .span12{width:100%;*width:99.94680851063829%;} .row-fluid .span11{width:91.45299145299145%;*width:91.39979996362975%;} .row-fluid .span10{width:82.90598290598291%;*width:82.8527914166212%;} .row-fluid .span9{width:74.35897435897436%;*width:74.30578286961266%;} .row-fluid .span8{width:65.81196581196582%;*width:65.75877432260411%;} .row-fluid .span7{width:57.26495726495726%;*width:57.21176577559556%;} .row-fluid .span6{width:48.717948717948715%;*width:48.664757228587014%;} .row-fluid .span5{width:40.17094017094017%;*width:40.11774868157847%;} .row-fluid .span4{width:31.623931623931625%;*width:31.570740134569924%;} .row-fluid .span3{width:23.076923076923077%;*width:23.023731587561375%;} .row-fluid .span2{width:14.52991452991453%;*width:14.476723040552828%;} .row-fluid .span1{width:5.982905982905983%;*width:5.929714493544281%;} .row-fluid .offset12{margin-left:105.12820512820512%;*margin-left:105.02182214948171%;} .row-fluid .offset12:first-child{margin-left:102.56410256410257%;*margin-left:102.45771958537915%;} .row-fluid .offset11{margin-left:96.58119658119658%;*margin-left:96.47481360247316%;} .row-fluid .offset11:first-child{margin-left:94.01709401709402%;*margin-left:93.91071103837061%;} .row-fluid .offset10{margin-left:88.03418803418803%;*margin-left:87.92780505546462%;} .row-fluid .offset10:first-child{margin-left:85.47008547008548%;*margin-left:85.36370249136206%;} .row-fluid .offset9{margin-left:79.48717948717949%;*margin-left:79.38079650845607%;} .row-fluid .offset9:first-child{margin-left:76.92307692307693%;*margin-left:76.81669394435352%;} .row-fluid .offset8{margin-left:70.94017094017094%;*margin-left:70.83378796144753%;} .row-fluid .offset8:first-child{margin-left:68.37606837606839%;*margin-left:68.26968539734497%;} .row-fluid .offset7{margin-left:62.393162393162385%;*margin-left:62.28677941443899%;} .row-fluid .offset7:first-child{margin-left:59.82905982905982%;*margin-left:59.72267685033642%;} .row-fluid .offset6{margin-left:53.84615384615384%;*margin-left:53.739770867430444%;} .row-fluid .offset6:first-child{margin-left:51.28205128205128%;*margin-left:51.175668303327875%;} .row-fluid .offset5{margin-left:45.299145299145295%;*margin-left:45.1927623204219%;} .row-fluid .offset5:first-child{margin-left:42.73504273504273%;*margin-left:42.62865975631933%;} .row-fluid .offset4{margin-left:36.75213675213675%;*margin-left:36.645753773413354%;} .row-fluid .offset4:first-child{margin-left:34.18803418803419%;*margin-left:34.081651209310785%;} .row-fluid .offset3{margin-left:28.205128205128204%;*margin-left:28.0987452264048%;} .row-fluid .offset3:first-child{margin-left:25.641025641025642%;*margin-left:25.53464266230224%;} .row-fluid .offset2{margin-left:19.65811965811966%;*margin-left:19.551736679396257%;} .row-fluid .offset2:first-child{margin-left:17.094017094017094%;*margin-left:16.98763411529369%;} .row-fluid .offset1{margin-left:11.11111111111111%;*margin-left:11.004728132387708%;} .row-fluid .offset1:first-child{margin-left:8.547008547008547%;*margin-left:8.440625568285142%;} input,textarea,.uneditable-input{margin-left:0;} .controls-row [class*="span"]+[class*="span"]{margin-left:30px;} input.span12,textarea.span12,.uneditable-input.span12{width:1156px;} input.span11,textarea.span11,.uneditable-input.span11{width:1056px;} input.span10,textarea.span10,.uneditable-input.span10{width:956px;} input.span9,textarea.span9,.uneditable-input.span9{width:856px;} input.span8,textarea.span8,.uneditable-input.span8{width:756px;} input.span7,textarea.span7,.uneditable-input.span7{width:656px;} input.span6,textarea.span6,.uneditable-input.span6{width:556px;} input.span5,textarea.span5,.uneditable-input.span5{width:456px;} input.span4,textarea.span4,.uneditable-input.span4{width:356px;} input.span3,textarea.span3,.uneditable-input.span3{width:256px;} input.span2,textarea.span2,.uneditable-input.span2{width:156px;} input.span1,textarea.span1,.uneditable-input.span1{width:56px;} input,textarea,.uneditable-input{margin-left:0;} .controls-row [class*="span"]+[class*="span"]{margin-left:30px;} input.span12,textarea.span12,.uneditable-input.span12{width:1156px;} input.span11,textarea.span11,.uneditable-input.span11{width:1056px;} input.span10,textarea.span10,.uneditable-input.span10{width:956px;} input.span9,textarea.span9,.uneditable-input.span9{width:856px;} input.span8,textarea.span8,.uneditable-input.span8{width:756px;} input.span7,textarea.span7,.uneditable-input.span7{width:656px;} input.span6,textarea.span6,.uneditable-input.span6{width:556px;} input.span5,textarea.span5,.uneditable-input.span5{width:456px;} input.span4,textarea.span4,.uneditable-input.span4{width:356px;} input.span3,textarea.span3,.uneditable-input.span3{width:256px;} input.span2,textarea.span2,.uneditable-input.span2{width:156px;} input.span1,textarea.span1,.uneditable-input.span1{width:56px;} .thumbnails{margin-left:-30px;} .thumbnails>li{margin-left:30px;} .row-fluid .thumbnails{margin-left:0;}}@media (min-width:768px) and (max-width:979px){.row{margin-left:-20px;*zoom:1;}.row:before,.row:after{display:table;content:"";line-height:0;} .row:after{clear:both;} .row:before,.row:after{display:table;content:"";line-height:0;} .row:after{clear:both;} [class*="span"]{float:left;min-height:1px;margin-left:20px;} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:724px;} .span12{width:724px;} .span11{width:662px;} .span10{width:600px;} .span9{width:538px;} .span8{width:476px;} .span7{width:414px;} .span6{width:352px;} .span5{width:290px;} .span4{width:228px;} .span3{width:166px;} .span2{width:104px;} .span1{width:42px;} .offset12{margin-left:764px;} .offset11{margin-left:702px;} .offset10{margin-left:640px;} .offset9{margin-left:578px;} .offset8{margin-left:516px;} .offset7{margin-left:454px;} .offset6{margin-left:392px;} .offset5{margin-left:330px;} .offset4{margin-left:268px;} .offset3{margin-left:206px;} .offset2{margin-left:144px;} .offset1{margin-left:82px;} .row{margin-left:-20px;*zoom:1;}.row:before,.row:after{display:table;content:"";line-height:0;} .row:after{clear:both;} .row:before,.row:after{display:table;content:"";line-height:0;} .row:after{clear:both;} [class*="span"]{float:left;min-height:1px;margin-left:20px;} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:724px;} .span12{width:724px;} .span11{width:662px;} .span10{width:600px;} .span9{width:538px;} .span8{width:476px;} .span7{width:414px;} .span6{width:352px;} .span5{width:290px;} .span4{width:228px;} .span3{width:166px;} .span2{width:104px;} .span1{width:42px;} .offset12{margin-left:764px;} .offset11{margin-left:702px;} .offset10{margin-left:640px;} .offset9{margin-left:578px;} .offset8{margin-left:516px;} .offset7{margin-left:454px;} .offset6{margin-left:392px;} .offset5{margin-left:330px;} .offset4{margin-left:268px;} .offset3{margin-left:206px;} .offset2{margin-left:144px;} .offset1{margin-left:82px;} .row-fluid{width:100%;*zoom:1;}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;} .row-fluid:after{clear:both;} .row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;} .row-fluid:after{clear:both;} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.7624309392265194%;*margin-left:2.709239449864817%;} .row-fluid [class*="span"]:first-child{margin-left:0;} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.7624309392265194%;} .row-fluid .span12{width:100%;*width:99.94680851063829%;} .row-fluid .span11{width:91.43646408839778%;*width:91.38327259903608%;} .row-fluid .span10{width:82.87292817679558%;*width:82.81973668743387%;} .row-fluid .span9{width:74.30939226519337%;*width:74.25620077583166%;} .row-fluid .span8{width:65.74585635359117%;*width:65.69266486422946%;} .row-fluid .span7{width:57.18232044198895%;*width:57.12912895262725%;} .row-fluid .span6{width:48.61878453038674%;*width:48.56559304102504%;} .row-fluid .span5{width:40.05524861878453%;*width:40.00205712942283%;} .row-fluid .span4{width:31.491712707182323%;*width:31.43852121782062%;} .row-fluid .span3{width:22.92817679558011%;*width:22.87498530621841%;} .row-fluid .span2{width:14.3646408839779%;*width:14.311449394616199%;} .row-fluid .span1{width:5.801104972375691%;*width:5.747913483013988%;} .row-fluid .offset12{margin-left:105.52486187845304%;*margin-left:105.41847889972962%;} .row-fluid .offset12:first-child{margin-left:102.76243093922652%;*margin-left:102.6560479605031%;} .row-fluid .offset11{margin-left:96.96132596685082%;*margin-left:96.8549429881274%;} .row-fluid .offset11:first-child{margin-left:94.1988950276243%;*margin-left:94.09251204890089%;} .row-fluid .offset10{margin-left:88.39779005524862%;*margin-left:88.2914070765252%;} .row-fluid .offset10:first-child{margin-left:85.6353591160221%;*margin-left:85.52897613729868%;} .row-fluid .offset9{margin-left:79.8342541436464%;*margin-left:79.72787116492299%;} .row-fluid .offset9:first-child{margin-left:77.07182320441989%;*margin-left:76.96544022569647%;} .row-fluid .offset8{margin-left:71.2707182320442%;*margin-left:71.16433525332079%;} .row-fluid .offset8:first-child{margin-left:68.50828729281768%;*margin-left:68.40190431409427%;} .row-fluid .offset7{margin-left:62.70718232044199%;*margin-left:62.600799341718584%;} .row-fluid .offset7:first-child{margin-left:59.94475138121547%;*margin-left:59.838368402492065%;} .row-fluid .offset6{margin-left:54.14364640883978%;*margin-left:54.037263430116376%;} .row-fluid .offset6:first-child{margin-left:51.38121546961326%;*margin-left:51.27483249088986%;} .row-fluid .offset5{margin-left:45.58011049723757%;*margin-left:45.47372751851417%;} .row-fluid .offset5:first-child{margin-left:42.81767955801105%;*margin-left:42.71129657928765%;} .row-fluid .offset4{margin-left:37.01657458563536%;*margin-left:36.91019160691196%;} .row-fluid .offset4:first-child{margin-left:34.25414364640884%;*margin-left:34.14776066768544%;} .row-fluid .offset3{margin-left:28.45303867403315%;*margin-left:28.346655695309746%;} .row-fluid .offset3:first-child{margin-left:25.69060773480663%;*margin-left:25.584224756083227%;} .row-fluid .offset2{margin-left:19.88950276243094%;*margin-left:19.783119783707537%;} .row-fluid .offset2:first-child{margin-left:17.12707182320442%;*margin-left:17.02068884448102%;} .row-fluid .offset1{margin-left:11.32596685082873%;*margin-left:11.219583872105325%;} .row-fluid .offset1:first-child{margin-left:8.56353591160221%;*margin-left:8.457152932878806%;} .row-fluid{width:100%;*zoom:1;}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;} .row-fluid:after{clear:both;} .row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0;} .row-fluid:after{clear:both;} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.7624309392265194%;*margin-left:2.709239449864817%;} .row-fluid [class*="span"]:first-child{margin-left:0;} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.7624309392265194%;} .row-fluid .span12{width:100%;*width:99.94680851063829%;} .row-fluid .span11{width:91.43646408839778%;*width:91.38327259903608%;} .row-fluid .span10{width:82.87292817679558%;*width:82.81973668743387%;} .row-fluid .span9{width:74.30939226519337%;*width:74.25620077583166%;} .row-fluid .span8{width:65.74585635359117%;*width:65.69266486422946%;} .row-fluid .span7{width:57.18232044198895%;*width:57.12912895262725%;} .row-fluid .span6{width:48.61878453038674%;*width:48.56559304102504%;} .row-fluid .span5{width:40.05524861878453%;*width:40.00205712942283%;} .row-fluid .span4{width:31.491712707182323%;*width:31.43852121782062%;} .row-fluid .span3{width:22.92817679558011%;*width:22.87498530621841%;} .row-fluid .span2{width:14.3646408839779%;*width:14.311449394616199%;} .row-fluid .span1{width:5.801104972375691%;*width:5.747913483013988%;} .row-fluid .offset12{margin-left:105.52486187845304%;*margin-left:105.41847889972962%;} .row-fluid .offset12:first-child{margin-left:102.76243093922652%;*margin-left:102.6560479605031%;} .row-fluid .offset11{margin-left:96.96132596685082%;*margin-left:96.8549429881274%;} .row-fluid .offset11:first-child{margin-left:94.1988950276243%;*margin-left:94.09251204890089%;} .row-fluid .offset10{margin-left:88.39779005524862%;*margin-left:88.2914070765252%;} .row-fluid .offset10:first-child{margin-left:85.6353591160221%;*margin-left:85.52897613729868%;} .row-fluid .offset9{margin-left:79.8342541436464%;*margin-left:79.72787116492299%;} .row-fluid .offset9:first-child{margin-left:77.07182320441989%;*margin-left:76.96544022569647%;} .row-fluid .offset8{margin-left:71.2707182320442%;*margin-left:71.16433525332079%;} .row-fluid .offset8:first-child{margin-left:68.50828729281768%;*margin-left:68.40190431409427%;} .row-fluid .offset7{margin-left:62.70718232044199%;*margin-left:62.600799341718584%;} .row-fluid .offset7:first-child{margin-left:59.94475138121547%;*margin-left:59.838368402492065%;} .row-fluid .offset6{margin-left:54.14364640883978%;*margin-left:54.037263430116376%;} .row-fluid .offset6:first-child{margin-left:51.38121546961326%;*margin-left:51.27483249088986%;} .row-fluid .offset5{margin-left:45.58011049723757%;*margin-left:45.47372751851417%;} .row-fluid .offset5:first-child{margin-left:42.81767955801105%;*margin-left:42.71129657928765%;} .row-fluid .offset4{margin-left:37.01657458563536%;*margin-left:36.91019160691196%;} .row-fluid .offset4:first-child{margin-left:34.25414364640884%;*margin-left:34.14776066768544%;} .row-fluid .offset3{margin-left:28.45303867403315%;*margin-left:28.346655695309746%;} .row-fluid .offset3:first-child{margin-left:25.69060773480663%;*margin-left:25.584224756083227%;} .row-fluid .offset2{margin-left:19.88950276243094%;*margin-left:19.783119783707537%;} .row-fluid .offset2:first-child{margin-left:17.12707182320442%;*margin-left:17.02068884448102%;} .row-fluid .offset1{margin-left:11.32596685082873%;*margin-left:11.219583872105325%;} .row-fluid .offset1:first-child{margin-left:8.56353591160221%;*margin-left:8.457152932878806%;} input,textarea,.uneditable-input{margin-left:0;} .controls-row [class*="span"]+[class*="span"]{margin-left:20px;} input.span12,textarea.span12,.uneditable-input.span12{width:710px;} input.span11,textarea.span11,.uneditable-input.span11{width:648px;} input.span10,textarea.span10,.uneditable-input.span10{width:586px;} input.span9,textarea.span9,.uneditable-input.span9{width:524px;} input.span8,textarea.span8,.uneditable-input.span8{width:462px;} input.span7,textarea.span7,.uneditable-input.span7{width:400px;} input.span6,textarea.span6,.uneditable-input.span6{width:338px;} input.span5,textarea.span5,.uneditable-input.span5{width:276px;} input.span4,textarea.span4,.uneditable-input.span4{width:214px;} input.span3,textarea.span3,.uneditable-input.span3{width:152px;} input.span2,textarea.span2,.uneditable-input.span2{width:90px;} input.span1,textarea.span1,.uneditable-input.span1{width:28px;} input,textarea,.uneditable-input{margin-left:0;} .controls-row [class*="span"]+[class*="span"]{margin-left:20px;} input.span12,textarea.span12,.uneditable-input.span12{width:710px;} input.span11,textarea.span11,.uneditable-input.span11{width:648px;} input.span10,textarea.span10,.uneditable-input.span10{width:586px;} input.span9,textarea.span9,.uneditable-input.span9{width:524px;} input.span8,textarea.span8,.uneditable-input.span8{width:462px;} input.span7,textarea.span7,.uneditable-input.span7{width:400px;} input.span6,textarea.span6,.uneditable-input.span6{width:338px;} input.span5,textarea.span5,.uneditable-input.span5{width:276px;} input.span4,textarea.span4,.uneditable-input.span4{width:214px;} input.span3,textarea.span3,.uneditable-input.span3{width:152px;} input.span2,textarea.span2,.uneditable-input.span2{width:90px;} input.span1,textarea.span1,.uneditable-input.span1{width:28px;}}@media (max-width:767px){body{padding-left:20px;padding-right:20px;} .navbar-fixed-top,.navbar-fixed-bottom,.navbar-static-top{margin-left:-20px;margin-right:-20px;} .container-fluid{padding:0;} .dl-horizontal dt{float:none;clear:none;width:auto;text-align:left;} .dl-horizontal dd{margin-left:0;} .container{width:auto;} .row-fluid{width:100%;} .row,.thumbnails{margin-left:0;} .thumbnails>li{float:none;margin-left:0;} [class*="span"],.uneditable-input[class*="span"],.row-fluid [class*="span"]{float:none;display:block;width:100%;margin-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;} .span12,.row-fluid .span12{width:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;} .row-fluid [class*="offset"]:first-child{margin-left:0;} .input-large,.input-xlarge,.input-xxlarge,input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;} .input-prepend input,.input-append input,.input-prepend input[class*="span"],.input-append input[class*="span"]{display:inline-block;width:auto;} .controls-row [class*="span"]+[class*="span"]{margin-left:0;} .modal{position:fixed;top:20px;left:20px;right:20px;width:auto;margin:0;}.modal.fade{top:-100px;} .modal.fade.in{top:20px;}}@media (max-width:480px){.nav-collapse{-webkit-transform:translate3d(0, 0, 0);} .page-header h1 small{display:block;line-height:20px;} input[type="checkbox"],input[type="radio"]{border:1px solid #ccc;} .form-horizontal .control-label{float:none;width:auto;padding-top:0;text-align:left;} .form-horizontal .controls{margin-left:0;} .form-horizontal .control-list{padding-top:0;} .form-horizontal .form-actions{padding-left:10px;padding-right:10px;} .media .pull-left,.media .pull-right{float:none;display:block;margin-bottom:10px;} .media-object{margin-right:0;margin-left:0;} .modal{top:10px;left:10px;right:10px;} .modal-header .close{padding:10px;margin:-10px;} .carousel-caption{position:static;}}@media (max-width:979px){body{padding-top:0;} .navbar-fixed-top,.navbar-fixed-bottom{position:static;} .navbar-fixed-top{margin-bottom:20px;} .navbar-fixed-bottom{margin-top:20px;} .navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding:5px;} .navbar .container{width:auto;padding:0;} .navbar .brand{padding-left:10px;padding-right:10px;margin:0 0 0 -5px;} .nav-collapse{clear:both;} .nav-collapse .nav{float:none;margin:0 0 10px;} .nav-collapse .nav>li{float:none;} .nav-collapse .nav>li>a{margin-bottom:2px;} .nav-collapse .nav>.divider-vertical{display:none;} .nav-collapse .nav .nav-header{color:#777777;text-shadow:none;} .nav-collapse .nav>li>a,.nav-collapse .dropdown-menu a{padding:9px 15px;font-weight:bold;color:#777777;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;} .nav-collapse .btn{padding:4px 10px 4px;font-weight:normal;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;} .nav-collapse .dropdown-menu li+li a{margin-bottom:2px;} .nav-collapse .nav>li>a:hover,.nav-collapse .nav>li>a:focus,.nav-collapse .dropdown-menu a:hover,.nav-collapse .dropdown-menu a:focus{background-color:#f2f2f2;} .navbar-inverse .nav-collapse .nav>li>a,.navbar-inverse .nav-collapse .dropdown-menu a{color:#999999;} .navbar-inverse .nav-collapse .nav>li>a:hover,.navbar-inverse .nav-collapse .nav>li>a:focus,.navbar-inverse .nav-collapse .dropdown-menu a:hover,.navbar-inverse .nav-collapse .dropdown-menu a:focus{background-color:#111111;} .nav-collapse.in .btn-group{margin-top:5px;padding:0;} .nav-collapse .dropdown-menu{position:static;top:auto;left:auto;float:none;display:none;max-width:none;margin:0 15px;padding:0;background-color:transparent;border:none;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none;} .nav-collapse .open>.dropdown-menu{display:block;} .nav-collapse .dropdown-menu:before,.nav-collapse .dropdown-menu:after{display:none;} .nav-collapse .dropdown-menu .divider{display:none;} .nav-collapse .nav>li>.dropdown-menu:before,.nav-collapse .nav>li>.dropdown-menu:after{display:none;} .nav-collapse .navbar-form,.nav-collapse .navbar-search{float:none;padding:10px 15px;margin:10px 0;border-top:1px solid #f2f2f2;border-bottom:1px solid #f2f2f2;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);} .navbar-inverse .nav-collapse .navbar-form,.navbar-inverse .nav-collapse .navbar-search{border-top-color:#111111;border-bottom-color:#111111;} .navbar .nav-collapse .nav.pull-right{float:none;margin-left:0;} .nav-collapse,.nav-collapse.collapse{overflow:hidden;height:0;} .navbar .btn-navbar{display:block;} .navbar-static .navbar-inner{padding-left:10px;padding-right:10px;}}@media (min-width:980px){.nav-collapse.collapse{height:auto !important;overflow:visible !important;}}@font-face{font-family:'FontAwesome';src:url('../components/font-awesome/build/assets/font-awesome/font/fontawesome-webfont.eot?v=3.1.0');src:url('../components/font-awesome/build/assets/font-awesome/font/fontawesome-webfont.eot?#iefix&v=3.1.0') format('embedded-opentype'),url('../components/font-awesome/build/assets/font-awesome/font/fontawesome-webfont.woff?v=3.1.0') format('woff'),url('../components/font-awesome/build/assets/font-awesome/font/fontawesome-webfont.ttf?v=3.1.0') format('truetype'),url('../components/font-awesome/build/assets/font-awesome/font/fontawesome-webfont.svg#fontawesomeregular?v=3.1.0') format('svg');font-weight:normal;font-style:normal;}[class^="icon-"],[class*=" icon-"]{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;}
-[class^="icon-"]:before,[class*=" icon-"]:before{text-decoration:inherit;display:inline-block;speak:none;}
-.icon-large:before{vertical-align:-10%;font-size:1.3333333333333333em;}
-a [class^="icon-"],a [class*=" icon-"],a [class^="icon-"]:before,a [class*=" icon-"]:before{display:inline;}
-[class^="icon-"].icon-fixed-width,[class*=" icon-"].icon-fixed-width{display:inline-block;width:1.2857142857142858em;text-align:center;}[class^="icon-"].icon-fixed-width.icon-large,[class*=" icon-"].icon-fixed-width.icon-large{width:1.5714285714285714em;}
-ul.icons-ul{list-style-type:none;text-indent:-0.7142857142857143em;margin-left:2.142857142857143em;}ul.icons-ul>li .icon-li{width:0.7142857142857143em;display:inline-block;text-align:center;}
-[class^="icon-"].hide,[class*=" icon-"].hide{display:none;}
-.icon-muted{color:#eeeeee;}
-.icon-light{color:#ffffff;}
-.icon-dark{color:#333333;}
-.icon-border{border:solid 1px #eeeeee;padding:.2em .25em .15em;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px;}
-.icon-2x{font-size:2em;}.icon-2x.icon-border{border-width:2px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;}
-.icon-3x{font-size:3em;}.icon-3x.icon-border{border-width:3px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px;}
-.icon-4x{font-size:4em;}.icon-4x.icon-border{border-width:4px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;}
-.icon-5x{font-size:5em;}.icon-5x.icon-border{border-width:5px;-webkit-border-radius:7px;-moz-border-radius:7px;border-radius:7px;}
-.pull-right{float:right;}
-.pull-left{float:left;}
-[class^="icon-"].pull-left,[class*=" icon-"].pull-left{margin-right:.3em;}
-[class^="icon-"].pull-right,[class*=" icon-"].pull-right{margin-left:.3em;}
-[class^="icon-"],[class*=" icon-"]{display:inline;width:auto;height:auto;line-height:normal;vertical-align:baseline;background-image:none;background-position:0% 0%;background-repeat:repeat;margin-top:0;}
-.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"]{background-image:none;}
-.btn [class^="icon-"].icon-large,.nav [class^="icon-"].icon-large,.btn [class*=" icon-"].icon-large,.nav [class*=" icon-"].icon-large{line-height:.9em;}
-.btn [class^="icon-"].icon-spin,.nav [class^="icon-"].icon-spin,.btn [class*=" icon-"].icon-spin,.nav [class*=" icon-"].icon-spin{display:inline-block;}
-.nav-tabs [class^="icon-"],.nav-pills [class^="icon-"],.nav-tabs [class*=" icon-"],.nav-pills [class*=" icon-"],.nav-tabs [class^="icon-"].icon-large,.nav-pills [class^="icon-"].icon-large,.nav-tabs [class*=" icon-"].icon-large,.nav-pills [class*=" icon-"].icon-large{line-height:.9em;}
-.btn [class^="icon-"].pull-left.icon-2x,.btn [class*=" icon-"].pull-left.icon-2x,.btn [class^="icon-"].pull-right.icon-2x,.btn [class*=" icon-"].pull-right.icon-2x{margin-top:.18em;}
-.btn [class^="icon-"].icon-spin.icon-large,.btn [class*=" icon-"].icon-spin.icon-large{line-height:.8em;}
-.btn.btn-small [class^="icon-"].pull-left.icon-2x,.btn.btn-small [class*=" icon-"].pull-left.icon-2x,.btn.btn-small [class^="icon-"].pull-right.icon-2x,.btn.btn-small [class*=" icon-"].pull-right.icon-2x{margin-top:.25em;}
-.btn.btn-large [class^="icon-"],.btn.btn-large [class*=" icon-"]{margin-top:0;}.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x,.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-top:.05em;}
-.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x{margin-right:.2em;}
-.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-left:.2em;}
-.icon-stack{position:relative;display:inline-block;width:2em;height:2em;line-height:2em;vertical-align:-35%;}.icon-stack [class^="icon-"],.icon-stack [class*=" icon-"]{display:block;text-align:center;position:absolute;width:100%;height:100%;font-size:1em;line-height:inherit;*line-height:2em;}
-.icon-stack .icon-stack-base{font-size:2em;*line-height:1em;}
-.icon-spin{display:inline-block;-moz-animation:spin 2s infinite linear;-o-animation:spin 2s infinite linear;-webkit-animation:spin 2s infinite linear;animation:spin 2s infinite linear;}
-@-moz-keyframes spin{0%{-moz-transform:rotate(0deg);} 100%{-moz-transform:rotate(359deg);}}@-webkit-keyframes spin{0%{-webkit-transform:rotate(0deg);} 100%{-webkit-transform:rotate(359deg);}}@-o-keyframes spin{0%{-o-transform:rotate(0deg);} 100%{-o-transform:rotate(359deg);}}@-ms-keyframes spin{0%{-ms-transform:rotate(0deg);} 100%{-ms-transform:rotate(359deg);}}@keyframes spin{0%{transform:rotate(0deg);} 100%{transform:rotate(359deg);}}.icon-rotate-90:before{-webkit-transform:rotate(90deg);-moz-transform:rotate(90deg);-ms-transform:rotate(90deg);-o-transform:rotate(90deg);transform:rotate(90deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=1);}
-.icon-rotate-180:before{-webkit-transform:rotate(180deg);-moz-transform:rotate(180deg);-ms-transform:rotate(180deg);-o-transform:rotate(180deg);transform:rotate(180deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2);}
-.icon-rotate-270:before{-webkit-transform:rotate(270deg);-moz-transform:rotate(270deg);-ms-transform:rotate(270deg);-o-transform:rotate(270deg);transform:rotate(270deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=3);}
-.icon-flip-horizontal:before{-webkit-transform:scale(-1, 1);-moz-transform:scale(-1, 1);-ms-transform:scale(-1, 1);-o-transform:scale(-1, 1);transform:scale(-1, 1);}
-.icon-flip-vertical:before{-webkit-transform:scale(1, -1);-moz-transform:scale(1, -1);-ms-transform:scale(1, -1);-o-transform:scale(1, -1);transform:scale(1, -1);}
-.icon-glass:before{content:"\f000";}
-.icon-music:before{content:"\f001";}
-.icon-search:before{content:"\f002";}
-.icon-envelope:before{content:"\f003";}
-.icon-heart:before{content:"\f004";}
-.icon-star:before{content:"\f005";}
-.icon-star-empty:before{content:"\f006";}
-.icon-user:before{content:"\f007";}
-.icon-film:before{content:"\f008";}
-.icon-th-large:before{content:"\f009";}
-.icon-th:before{content:"\f00a";}
-.icon-th-list:before{content:"\f00b";}
-.icon-ok:before{content:"\f00c";}
-.icon-remove:before{content:"\f00d";}
-.icon-zoom-in:before{content:"\f00e";}
-.icon-zoom-out:before{content:"\f010";}
-.icon-off:before{content:"\f011";}
-.icon-signal:before{content:"\f012";}
-.icon-cog:before{content:"\f013";}
-.icon-trash:before{content:"\f014";}
-.icon-home:before{content:"\f015";}
-.icon-file:before{content:"\f016";}
-.icon-time:before{content:"\f017";}
-.icon-road:before{content:"\f018";}
-.icon-download-alt:before{content:"\f019";}
-.icon-download:before{content:"\f01a";}
-.icon-upload:before{content:"\f01b";}
-.icon-inbox:before{content:"\f01c";}
-.icon-play-circle:before{content:"\f01d";}
-.icon-repeat:before,.icon-rotate-right:before{content:"\f01e";}
-.icon-refresh:before{content:"\f021";}
-.icon-list-alt:before{content:"\f022";}
-.icon-lock:before{content:"\f023";}
-.icon-flag:before{content:"\f024";}
-.icon-headphones:before{content:"\f025";}
-.icon-volume-off:before{content:"\f026";}
-.icon-volume-down:before{content:"\f027";}
-.icon-volume-up:before{content:"\f028";}
-.icon-qrcode:before{content:"\f029";}
-.icon-barcode:before{content:"\f02a";}
-.icon-tag:before{content:"\f02b";}
-.icon-tags:before{content:"\f02c";}
-.icon-book:before{content:"\f02d";}
-.icon-bookmark:before{content:"\f02e";}
-.icon-print:before{content:"\f02f";}
-.icon-camera:before{content:"\f030";}
-.icon-font:before{content:"\f031";}
-.icon-bold:before{content:"\f032";}
-.icon-italic:before{content:"\f033";}
-.icon-text-height:before{content:"\f034";}
-.icon-text-width:before{content:"\f035";}
-.icon-align-left:before{content:"\f036";}
-.icon-align-center:before{content:"\f037";}
-.icon-align-right:before{content:"\f038";}
-.icon-align-justify:before{content:"\f039";}
-.icon-list:before{content:"\f03a";}
-.icon-indent-left:before{content:"\f03b";}
-.icon-indent-right:before{content:"\f03c";}
-.icon-facetime-video:before{content:"\f03d";}
-.icon-picture:before{content:"\f03e";}
-.icon-pencil:before{content:"\f040";}
-.icon-map-marker:before{content:"\f041";}
-.icon-adjust:before{content:"\f042";}
-.icon-tint:before{content:"\f043";}
-.icon-edit:before{content:"\f044";}
-.icon-share:before{content:"\f045";}
-.icon-check:before{content:"\f046";}
-.icon-move:before{content:"\f047";}
-.icon-step-backward:before{content:"\f048";}
-.icon-fast-backward:before{content:"\f049";}
-.icon-backward:before{content:"\f04a";}
-.icon-play:before{content:"\f04b";}
-.icon-pause:before{content:"\f04c";}
-.icon-stop:before{content:"\f04d";}
-.icon-forward:before{content:"\f04e";}
-.icon-fast-forward:before{content:"\f050";}
-.icon-step-forward:before{content:"\f051";}
-.icon-eject:before{content:"\f052";}
-.icon-chevron-left:before{content:"\f053";}
-.icon-chevron-right:before{content:"\f054";}
-.icon-plus-sign:before{content:"\f055";}
-.icon-minus-sign:before{content:"\f056";}
-.icon-remove-sign:before{content:"\f057";}
-.icon-ok-sign:before{content:"\f058";}
-.icon-question-sign:before{content:"\f059";}
-.icon-info-sign:before{content:"\f05a";}
-.icon-screenshot:before{content:"\f05b";}
-.icon-remove-circle:before{content:"\f05c";}
-.icon-ok-circle:before{content:"\f05d";}
-.icon-ban-circle:before{content:"\f05e";}
-.icon-arrow-left:before{content:"\f060";}
-.icon-arrow-right:before{content:"\f061";}
-.icon-arrow-up:before{content:"\f062";}
-.icon-arrow-down:before{content:"\f063";}
-.icon-share-alt:before,.icon-mail-forward:before{content:"\f064";}
-.icon-resize-full:before{content:"\f065";}
-.icon-resize-small:before{content:"\f066";}
-.icon-plus:before{content:"\f067";}
-.icon-minus:before{content:"\f068";}
-.icon-asterisk:before{content:"\f069";}
-.icon-exclamation-sign:before{content:"\f06a";}
-.icon-gift:before{content:"\f06b";}
-.icon-leaf:before{content:"\f06c";}
-.icon-fire:before{content:"\f06d";}
-.icon-eye-open:before{content:"\f06e";}
-.icon-eye-close:before{content:"\f070";}
-.icon-warning-sign:before{content:"\f071";}
-.icon-plane:before{content:"\f072";}
-.icon-calendar:before{content:"\f073";}
-.icon-random:before{content:"\f074";}
-.icon-comment:before{content:"\f075";}
-.icon-magnet:before{content:"\f076";}
-.icon-chevron-up:before{content:"\f077";}
-.icon-chevron-down:before{content:"\f078";}
-.icon-retweet:before{content:"\f079";}
-.icon-shopping-cart:before{content:"\f07a";}
-.icon-folder-close:before{content:"\f07b";}
-.icon-folder-open:before{content:"\f07c";}
-.icon-resize-vertical:before{content:"\f07d";}
-.icon-resize-horizontal:before{content:"\f07e";}
-.icon-bar-chart:before{content:"\f080";}
-.icon-twitter-sign:before{content:"\f081";}
-.icon-facebook-sign:before{content:"\f082";}
-.icon-camera-retro:before{content:"\f083";}
-.icon-key:before{content:"\f084";}
-.icon-cogs:before{content:"\f085";}
-.icon-comments:before{content:"\f086";}
-.icon-thumbs-up:before{content:"\f087";}
-.icon-thumbs-down:before{content:"\f088";}
-.icon-star-half:before{content:"\f089";}
-.icon-heart-empty:before{content:"\f08a";}
-.icon-signout:before{content:"\f08b";}
-.icon-linkedin-sign:before{content:"\f08c";}
-.icon-pushpin:before{content:"\f08d";}
-.icon-external-link:before{content:"\f08e";}
-.icon-signin:before{content:"\f090";}
-.icon-trophy:before{content:"\f091";}
-.icon-github-sign:before{content:"\f092";}
-.icon-upload-alt:before{content:"\f093";}
-.icon-lemon:before{content:"\f094";}
-.icon-phone:before{content:"\f095";}
-.icon-check-empty:before{content:"\f096";}
-.icon-bookmark-empty:before{content:"\f097";}
-.icon-phone-sign:before{content:"\f098";}
-.icon-twitter:before{content:"\f099";}
-.icon-facebook:before{content:"\f09a";}
-.icon-github:before{content:"\f09b";}
-.icon-unlock:before{content:"\f09c";}
-.icon-credit-card:before{content:"\f09d";}
-.icon-rss:before{content:"\f09e";}
-.icon-hdd:before{content:"\f0a0";}
-.icon-bullhorn:before{content:"\f0a1";}
-.icon-bell:before{content:"\f0a2";}
-.icon-certificate:before{content:"\f0a3";}
-.icon-hand-right:before{content:"\f0a4";}
-.icon-hand-left:before{content:"\f0a5";}
-.icon-hand-up:before{content:"\f0a6";}
-.icon-hand-down:before{content:"\f0a7";}
-.icon-circle-arrow-left:before{content:"\f0a8";}
-.icon-circle-arrow-right:before{content:"\f0a9";}
-.icon-circle-arrow-up:before{content:"\f0aa";}
-.icon-circle-arrow-down:before{content:"\f0ab";}
-.icon-globe:before{content:"\f0ac";}
-.icon-wrench:before{content:"\f0ad";}
-.icon-tasks:before{content:"\f0ae";}
-.icon-filter:before{content:"\f0b0";}
-.icon-briefcase:before{content:"\f0b1";}
-.icon-fullscreen:before{content:"\f0b2";}
-.icon-group:before{content:"\f0c0";}
-.icon-link:before{content:"\f0c1";}
-.icon-cloud:before{content:"\f0c2";}
-.icon-beaker:before{content:"\f0c3";}
-.icon-cut:before{content:"\f0c4";}
-.icon-copy:before{content:"\f0c5";}
-.icon-paper-clip:before{content:"\f0c6";}
-.icon-save:before{content:"\f0c7";}
-.icon-sign-blank:before{content:"\f0c8";}
-.icon-reorder:before{content:"\f0c9";}
-.icon-list-ul:before{content:"\f0ca";}
-.icon-list-ol:before{content:"\f0cb";}
-.icon-strikethrough:before{content:"\f0cc";}
-.icon-underline:before{content:"\f0cd";}
-.icon-table:before{content:"\f0ce";}
-.icon-magic:before{content:"\f0d0";}
-.icon-truck:before{content:"\f0d1";}
-.icon-pinterest:before{content:"\f0d2";}
-.icon-pinterest-sign:before{content:"\f0d3";}
-.icon-google-plus-sign:before{content:"\f0d4";}
-.icon-google-plus:before{content:"\f0d5";}
-.icon-money:before{content:"\f0d6";}
-.icon-caret-down:before{content:"\f0d7";}
-.icon-caret-up:before{content:"\f0d8";}
-.icon-caret-left:before{content:"\f0d9";}
-.icon-caret-right:before{content:"\f0da";}
-.icon-columns:before{content:"\f0db";}
-.icon-sort:before{content:"\f0dc";}
-.icon-sort-down:before{content:"\f0dd";}
-.icon-sort-up:before{content:"\f0de";}
-.icon-envelope-alt:before{content:"\f0e0";}
-.icon-linkedin:before{content:"\f0e1";}
-.icon-undo:before,.icon-rotate-left:before{content:"\f0e2";}
-.icon-legal:before{content:"\f0e3";}
-.icon-dashboard:before{content:"\f0e4";}
-.icon-comment-alt:before{content:"\f0e5";}
-.icon-comments-alt:before{content:"\f0e6";}
-.icon-bolt:before{content:"\f0e7";}
-.icon-sitemap:before{content:"\f0e8";}
-.icon-umbrella:before{content:"\f0e9";}
-.icon-paste:before{content:"\f0ea";}
-.icon-lightbulb:before{content:"\f0eb";}
-.icon-exchange:before{content:"\f0ec";}
-.icon-cloud-download:before{content:"\f0ed";}
-.icon-cloud-upload:before{content:"\f0ee";}
-.icon-user-md:before{content:"\f0f0";}
-.icon-stethoscope:before{content:"\f0f1";}
-.icon-suitcase:before{content:"\f0f2";}
-.icon-bell-alt:before{content:"\f0f3";}
-.icon-coffee:before{content:"\f0f4";}
-.icon-food:before{content:"\f0f5";}
-.icon-file-alt:before{content:"\f0f6";}
-.icon-building:before{content:"\f0f7";}
-.icon-hospital:before{content:"\f0f8";}
-.icon-ambulance:before{content:"\f0f9";}
-.icon-medkit:before{content:"\f0fa";}
-.icon-fighter-jet:before{content:"\f0fb";}
-.icon-beer:before{content:"\f0fc";}
-.icon-h-sign:before{content:"\f0fd";}
-.icon-plus-sign-alt:before{content:"\f0fe";}
-.icon-double-angle-left:before{content:"\f100";}
-.icon-double-angle-right:before{content:"\f101";}
-.icon-double-angle-up:before{content:"\f102";}
-.icon-double-angle-down:before{content:"\f103";}
-.icon-angle-left:before{content:"\f104";}
-.icon-angle-right:before{content:"\f105";}
-.icon-angle-up:before{content:"\f106";}
-.icon-angle-down:before{content:"\f107";}
-.icon-desktop:before{content:"\f108";}
-.icon-laptop:before{content:"\f109";}
-.icon-tablet:before{content:"\f10a";}
-.icon-mobile-phone:before{content:"\f10b";}
-.icon-circle-blank:before{content:"\f10c";}
-.icon-quote-left:before{content:"\f10d";}
-.icon-quote-right:before{content:"\f10e";}
-.icon-spinner:before{content:"\f110";}
-.icon-circle:before{content:"\f111";}
-.icon-reply:before,.icon-mail-reply:before{content:"\f112";}
-.icon-folder-close-alt:before{content:"\f114";}
-.icon-folder-open-alt:before{content:"\f115";}
-.icon-expand-alt:before{content:"\f116";}
-.icon-collapse-alt:before{content:"\f117";}
-.icon-smile:before{content:"\f118";}
-.icon-frown:before{content:"\f119";}
-.icon-meh:before{content:"\f11a";}
-.icon-gamepad:before{content:"\f11b";}
-.icon-keyboard:before{content:"\f11c";}
-.icon-flag-alt:before{content:"\f11d";}
-.icon-flag-checkered:before{content:"\f11e";}
-.icon-terminal:before{content:"\f120";}
-.icon-code:before{content:"\f121";}
-.icon-reply-all:before{content:"\f122";}
-.icon-mail-reply-all:before{content:"\f122";}
-.icon-star-half-full:before,.icon-star-half-empty:before{content:"\f123";}
-.icon-location-arrow:before{content:"\f124";}
-.icon-crop:before{content:"\f125";}
-.icon-code-fork:before{content:"\f126";}
-.icon-unlink:before{content:"\f127";}
-.icon-question:before{content:"\f128";}
-.icon-info:before{content:"\f129";}
-.icon-exclamation:before{content:"\f12a";}
-.icon-superscript:before{content:"\f12b";}
-.icon-subscript:before{content:"\f12c";}
-.icon-eraser:before{content:"\f12d";}
-.icon-puzzle-piece:before{content:"\f12e";}
-.icon-microphone:before{content:"\f130";}
-.icon-microphone-off:before{content:"\f131";}
-.icon-shield:before{content:"\f132";}
-.icon-calendar-empty:before{content:"\f133";}
-.icon-fire-extinguisher:before{content:"\f134";}
-.icon-rocket:before{content:"\f135";}
-.icon-maxcdn:before{content:"\f136";}
-.icon-chevron-sign-left:before{content:"\f137";}
-.icon-chevron-sign-right:before{content:"\f138";}
-.icon-chevron-sign-up:before{content:"\f139";}
-.icon-chevron-sign-down:before{content:"\f13a";}
-.icon-html5:before{content:"\f13b";}
-.icon-css3:before{content:"\f13c";}
-.icon-anchor:before{content:"\f13d";}
-.icon-unlock-alt:before{content:"\f13e";}
-.icon-bullseye:before{content:"\f140";}
-.icon-ellipsis-horizontal:before{content:"\f141";}
-.icon-ellipsis-vertical:before{content:"\f142";}
-.icon-rss-sign:before{content:"\f143";}
-.icon-play-sign:before{content:"\f144";}
-.icon-ticket:before{content:"\f145";}
-.icon-minus-sign-alt:before{content:"\f146";}
-.icon-check-minus:before{content:"\f147";}
-.icon-level-up:before{content:"\f148";}
-.icon-level-down:before{content:"\f149";}
-.icon-check-sign:before{content:"\f14a";}
-.icon-edit-sign:before{content:"\f14b";}
-.icon-external-link-sign:before{content:"\f14c";}
-.icon-share-sign:before{content:"\f14d";}
-.border-box-sizing{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;}
-.corner-all{border-radius:4px;}
-.hbox{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;}
-.hbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;}
-.vbox{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;width:100%;}
-.vbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;}
-.reverse{-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;}
-.box-flex0{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;}
-.box-flex1{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;}
-.box-flex{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;}
-.box-flex2{-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;}
-.box-group1{-webkit-box-flex-group:1;-moz-box-flex-group:1;box-flex-group:1;}
-.box-group2{-webkit-box-flex-group:2;-moz-box-flex-group:2;box-flex-group:2;}
-.start{-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;}
-.end{-webkit-box-pack:end;-moz-box-pack:end;box-pack:end;}
-.center{-webkit-box-pack:center;-moz-box-pack:center;box-pack:center;}
-body{background-color:white;position:absolute;left:0px;right:0px;top:0px;bottom:0px;overflow:visible;}
-div#header{display:none;}
-#ipython_notebook{padding-left:16px;}
-#noscript{width:auto;padding-top:16px;padding-bottom:16px;text-align:center;font-size:22px;color:red;font-weight:bold;}
-#ipython_notebook img{font-family:Verdana,"Helvetica Neue",Arial,Helvetica,Geneva,sans-serif;height:24px;text-decoration:none;color:black;}
-#site{width:100%;display:none;}
-.ui-button .ui-button-text{padding:0.2em 0.8em;font-size:77%;}
-input.ui-button{padding:0.3em 0.9em;}
-.navbar span{margin-top:3px;}
-span#login_widget{float:right;}
-.nav-header{text-transform:none;}
-.navbar-nobg{background-color:transparent;background-image:none;}
-#header>span{margin-top:10px;}
-.modal-body{max-height:500px;}
-.center-nav{display:inline-block;margin-bottom:-4px;}
-.alternate_upload{background-color:none;display:inline;}
-.alternate_upload.form{padding:0;margin:0;}
-.alternate_upload input.fileinput{background-color:red;position:relative;opacity:0;z-index:2;width:295px;margin-left:163px;cursor:pointer;}
-.list_toolbar{padding:5px;height:25px;line-height:25px;}
-.toolbar_info{float:left;}
-.toolbar_buttons{float:right;}
-.list_header{font-weight:bold;}
-.list_container{margin-top:16px;margin-bottom:16px;border:1px solid #ababab;border-radius:4px;}
-.list_container>div{border-bottom:1px solid #ababab;}.list_container>div:hover .list-item{background-color:red;}
-.list_container>div:last-child{border:none;}
-.list_item:hover .list_item{background-color:#ddd;}
-.list_container>div>span,.list_container>div>div{padding:8px;}
-.list_item a{text-decoration:none;}
-input.nbname_input{height:15px;}
-.highlight_text{color:blue;}
-#project_name>.breadcrumb{padding:0px;margin-bottom:0px;background-color:transparent;font-weight:bold;}
-input.engine_num_input{height:20px;margin-bottom:2px;padding-top:0;padding-bottom:0;width:60px;}
-.ansibold{font-weight:bold;}
-.ansiblack{color:black;}
-.ansired{color:darkred;}
-.ansigreen{color:darkgreen;}
-.ansiyellow{color:brown;}
-.ansiblue{color:darkblue;}
-.ansipurple{color:darkviolet;}
-.ansicyan{color:steelblue;}
-.ansigray{color:gray;}
-.ansibgblack{background-color:black;}
-.ansibgred{background-color:red;}
-.ansibggreen{background-color:green;}
-.ansibgyellow{background-color:yellow;}
-.ansibgblue{background-color:blue;}
-.ansibgpurple{background-color:magenta;}
-.ansibgcyan{background-color:cyan;}
-.ansibggray{background-color:gray;}
-div.cell{border:1px solid transparent;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;width:100%;padding:5px 5px 5px 0px;margin:2px 0px 2px 7px;outline:none;}div.cell.selected{border-radius:4px;border:thin #ababab solid;}
-div.prompt{width:11ex;padding:0.4em;margin:0px;font-family:monospace;text-align:right;line-height:1.231em;}
-.celltoolbar{border:thin solid #CFCFCF;border-bottom:none;background:#EEE;border-top-right-radius:3px;border-top-left-radius:3px;width:100%;-webkit-box-pack:end;height:22px;}
-.no_input_radius{border-top-right-radius:0px;border-top-left-radius:0px;}
-.text_cell .ctb_prompt{display:none;}
-.code_cell .ctb_prompt{display:block;}
-.ctb_hideshow{display:none;vertical-align:bottom;padding-right:2px;}
-.celltoolbar>div{padding-top:0px;}
-.ctb_area{margin:0;padding:0;width:100%;}
-.ctb_show.ctb_hideshow,.ctb_show .ctb_hideshow{display:block;}
-.ctb_show .input_area,.ctb_show .ctb_hideshow+div.text_cell_input{border-top-right-radius:0px;border-top-left-radius:0px;}
-.ctb_show>.celltoolbar{border-bottom-right-radius:0px;border-bottom-left-radius:0px;}
-.button_container{margin-top:0;margin-bottom:0;}
-.ui-button{min-width:30px;}
-.celltoolbar .button_container select{margin:10px;margin-top:1px;margin-bottom:0px;padding:0;font-size:87%;width:auto;display:inline-block;height:18px;line-height:18px;vertical-align:top;}
-.celltoolbar label{display:inline-block;height:15px;line-height:15px;vertical-align:top;}
-.celltoolbar label span{font-size:85%;}
-.celltoolbar input[type=checkbox]{margin:0px;margin-left:4px;margin-right:4px;}
-.celltoolbar .ui-button{border:none;vertical-align:top;height:20px;}
-div.input{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;}
-div.input_area{border:1px solid #cfcfcf;border-radius:4px;background:#f7f7f7;}
-div.input_prompt{color:navy;border-top:1px solid transparent;}
-div.output_wrapper{margin-top:5px;position:relative;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;width:100%;}
-div.output_scroll{height:24em;width:100%;overflow:auto;border-radius:4px;-webkit-box-shadow:inset 0 2px 8px rgba(0, 0, 0, 0.8);-moz-box-shadow:inset 0 2px 8px rgba(0, 0, 0, 0.8);box-shadow:inset 0 2px 8px rgba(0, 0, 0, 0.8);}
-div.output_collapsed{margin:0px;padding:0px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;width:100%;}
-div.out_prompt_overlay{height:100%;padding:0px 0.4em;position:absolute;border-radius:4px;}
-div.out_prompt_overlay:hover{-webkit-box-shadow:inset 0 0 1px #000000;-moz-box-shadow:inset 0 0 1px #000000;box-shadow:inset 0 0 1px #000000;background:rgba(240, 240, 240, 0.5);}
-div.output_prompt{color:darkred;}
-.CodeMirror{line-height:1.231em;height:auto;background:none;}
-.CodeMirror-scroll{overflow-y:hidden;overflow-x:auto;}
-@-moz-document {.CodeMirror-scroll{overflow-x:hidden;}}.CodeMirror-lines{padding:0.4em;}
-.CodeMirror-linenumber{padding:0 8px 0 4px;}
-.CodeMirror-gutters{border-bottom-left-radius:4px;border-top-left-radius:4px;}
-.CodeMirror pre{padding:0;border:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;}
-.completions{position:absolute;z-index:10;overflow:hidden;border:1px solid #ababab;border-radius:4px;-webkit-box-shadow:0px 6px 10px -1px #adadad;-moz-box-shadow:0px 6px 10px -1px #adadad;box-shadow:0px 6px 10px -1px #adadad;}
-.completions select{background:white;outline:none;border:none;padding:0px;margin:0px;overflow:auto;font-family:monospace;font-size:110%;color:#000000;}
-.completions select option.context{color:#0064cd;}
-pre code{display:block;padding:0.5em;}
-.highlight-base,pre code,pre .subst,pre .tag .title,pre .lisp .title,pre .clojure .built_in,pre .nginx .title{color:black;}
-.highlight-string,pre .string,pre .constant,pre .parent,pre .tag .value,pre .rules .value,pre .rules .value .number,pre .preprocessor,pre .ruby .symbol,pre .ruby .symbol .string,pre .aggregate,pre .template_tag,pre .django .variable,pre .smalltalk .class,pre .addition,pre .flow,pre .stream,pre .bash .variable,pre .apache .tag,pre .apache .cbracket,pre .tex .command,pre .tex .special,pre .erlang_repl .function_or_atom,pre .markdown .header{color:#BA2121;}
-.highlight-comment,pre .comment,pre .annotation,pre .template_comment,pre .diff .header,pre .chunk,pre .markdown .blockquote{color:#408080;font-style:italic;}
-.highlight-number,pre .number,pre .date,pre .regexp,pre .literal,pre .smalltalk .symbol,pre .smalltalk .char,pre .go .constant,pre .change,pre .markdown .bullet,pre .markdown .link_url{color:#080;}
-pre .label,pre .javadoc,pre .ruby .string,pre .decorator,pre .filter .argument,pre .localvars,pre .array,pre .attr_selector,pre .important,pre .pseudo,pre .pi,pre .doctype,pre .deletion,pre .envvar,pre .shebang,pre .apache .sqbracket,pre .nginx .built_in,pre .tex .formula,pre .erlang_repl .reserved,pre .prompt,pre .markdown .link_label,pre .vhdl .attribute,pre .clojure .attribute,pre .coffeescript .property{color:#8888ff;}
-.highlight-keyword,pre .keyword,pre .id,pre .phpdoc,pre .aggregate,pre .css .tag,pre .javadoctag,pre .phpdoc,pre .yardoctag,pre .smalltalk .class,pre .winutils,pre .bash .variable,pre .apache .tag,pre .go .typename,pre .tex .command,pre .markdown .strong,pre .request,pre .status{color:#008000;font-weight:bold;}
-.highlight-builtin,pre .built_in{color:#008000;}
-pre .markdown .emphasis{font-style:italic;}
-pre .nginx .built_in{font-weight:normal;}
-pre .coffeescript .javascript,pre .javascript .xml,pre .tex .formula,pre .xml .javascript,pre .xml .vbscript,pre .xml .css,pre .xml .cdata{opacity:0.5;}
-.cm-s-ipython span.cm-variable{color:black;}
-.cm-s-ipython span.cm-keyword{color:#008000;font-weight:bold;}
-.cm-s-ipython span.cm-number{color:#080;}
-.cm-s-ipython span.cm-comment{color:#408080;font-style:italic;}
-.cm-s-ipython span.cm-string{color:#BA2121;}
-.cm-s-ipython span.cm-builtin{color:#008000;}
-.cm-s-ipython span.cm-error{color:#f00;}
-.cm-s-ipython span.cm-operator{color:#AA22FF;font-weight:bold;}
-.cm-s-ipython span.cm-meta{color:#AA22FF;}
-#menubar .navbar-inner{min-height:28px;border-top:1px;border-radius:0px 0px 4px 4px;}
-#menubar .navbar{margin-bottom:8px;}
-.nav-wrapper{border-bottom:1px solid #d4d4d4;}
-#menubar li.dropdown{line-height:12px;}
-body{background-color:#ffffff;}
-body.notebook_app{overflow:hidden;}
-span#notebook_name{height:1em;line-height:1em;padding:3px;border:none;font-size:146.5%;}
-div#notebook_panel{margin:0px 0px 0px 0px;padding:0px;-webkit-box-shadow:0 -1px 10px rgba(0, 0, 0, 0.1);-moz-box-shadow:0 -1px 10px rgba(0, 0, 0, 0.1);box-shadow:0 -1px 10px rgba(0, 0, 0, 0.1);}
-div#notebook{overflow-y:scroll;overflow-x:auto;width:100%;padding:5px 5px 15px 5px;margin:0px;border-top:1px solid #ababab;}
-div.ui-widget-content{border:1px solid #ababab;outline:none;}
-pre.dialog{background-color:#f7f7f7;border:1px solid #ddd;border-radius:4px;padding:0.4em;padding-left:2em;}
-p.dialog{padding:0.2em;}
-pre,code,kbd,samp{white-space:pre-wrap;}
-#fonttest{font-family:monospace;}
-p{margin-bottom:0;}
-.end_space{height:200px;}
-#notification_area{z-index:10;}
-.notification_widget{color:#777777;padding:1px 12px;margin:2px 4px;z-index:10;border:1px solid #ccc;border-radius:4px;background:rgba(240, 240, 240, 0.5);}
-div.output_area{padding:0px;page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;}
-div.output_area pre{font-family:monospace;margin:0;padding:0;border:0;font-size:100%;vertical-align:baseline;color:black;background-color:transparent;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;line-height:inherit;}
-div.output_subarea{padding:0.44em 0.4em 0.4em 1px;margin-left:6px;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;}
-div.output_text{text-align:left;color:#000000;font-family:monospace;line-height:1.231em;}
-div.output_stream{padding-top:0.0em;padding-bottom:0.0em;}
-div.output_stderr{background:#fdd;}
-div.output_latex{text-align:left;}
-.js-error{color:darkred;}
-div.raw_input{padding-top:0px;padding-bottom:0px;height:1em;line-height:1em;font-family:monospace;}
-span.input_prompt{font-family:inherit;}
-input.raw_input{font-family:inherit;font-size:inherit;color:inherit;width:auto;margin:-2px 0px 0px 1px;padding-left:1px;padding-top:2px;height:1em;}
-p.p-space{margin-bottom:10px;}
-div#pager_splitter{height:8px;}
-#pager-container{position:relative;padding:15px 0px;}
-div#pager{overflow:auto;display:none;}div#pager pre{font-size:13px;line-height:1.231em;color:#000000;background-color:#f7f7f7;padding:0.4em;}
-.shortcut_key{display:inline-block;width:15ex;text-align:right;font-family:monospace;}
-.shortcut_descr{display:inline-block;}
-div.quickhelp{float:left;width:50%;}
-.rendered_html{color:black;}.rendered_html em{font-style:italic;}
-.rendered_html strong{font-weight:bold;}
-.rendered_html u{text-decoration:underline;}
-.rendered_html :link{text-decoration:underline;}
-.rendered_html :visited{text-decoration:underline;}
-.rendered_html h1{font-size:197%;margin:.65em 0;font-weight:bold;}
-.rendered_html h2{font-size:153.9%;margin:.75em 0;font-weight:bold;}
-.rendered_html h3{font-size:123.1%;margin:.85em 0;font-weight:bold;}
-.rendered_html h4{font-size:100%;margin:0.95em 0;font-weight:bold;}
-.rendered_html h5{font-size:85%;margin:1.5em 0;font-weight:bold;}
-.rendered_html h6{font-size:77%;margin:1.65em 0;font-weight:bold;}
-.rendered_html ul{list-style:disc;margin:1em 2em;}
-.rendered_html ul ul{list-style:square;margin:0em 2em;}
-.rendered_html ul ul ul{list-style:circle;margin:0em 2em;}
-.rendered_html ol{list-style:decimal;margin:1em 2em;}
-.rendered_html ol ol{list-style:upper-alpha;margin:0em 2em;}
-.rendered_html ol ol ol{list-style:lower-alpha;margin:0em 2em;}
-.rendered_html ol ol ol ol{list-style:lower-roman;margin:0em 2em;}
-.rendered_html ol ol ol ol ol{list-style:decimal;margin:0em 2em;}
-.rendered_html hr{color:black;background-color:black;}
-.rendered_html pre{margin:1em 2em;}
-.rendered_html pre,.rendered_html code{border:0;background-color:#ffffff;color:#000000;font-size:100%;padding:0px;}
-.rendered_html blockquote{margin:1em 2em;}
-.rendered_html table,.rendered_html tr,.rendered_html th,.rendered_html td{border:1px solid black;border-collapse:collapse;margin:1em 2em;}
-.rendered_html td,.rendered_html th{text-align:left;vertical-align:middle;padding:4px;}
-.rendered_html th{font-weight:bold;}
-.rendered_html p{text-align:justify;}
-.rendered_html p+p{margin-top:1em;}
-span#save_widget{padding:0px 5px;margin-top:12px;}
-span#checkpoint_status,span#autosave_status{font-size:small;}
-@media (max-width:767px){span#save_widget{font-size:small;} span#checkpoint_status,span#autosave_status{font-size:x-small;}}@media (max-width:767px){span#checkpoint_status,span#autosave_status{display:none;}}@media (min-width:768px) and (max-width:979px){span#checkpoint_status{display:none;} span#autosave_status{font-size:x-small;}}div.text_cell{padding:5px 5px 5px 5px;}
-div.text_cell_input{color:#000000;border:1px solid #cfcfcf;border-radius:4px;background:#f7f7f7;}
-div.text_cell_render{outline:none;resize:none;width:inherit;border-style:none;padding:5px;color:#000000;}
-a.anchor-link:link{text-decoration:none;padding:0px 20px;visibility:hidden;}
-h1:hover .anchor-link,h2:hover .anchor-link,h3:hover .anchor-link,h4:hover .anchor-link,h5:hover .anchor-link,h6:hover .anchor-link{visibility:visible;}
-.toolbar{padding:0px 10px;margin-top:-5px;}.toolbar select,.toolbar label{width:auto;height:26px;vertical-align:middle;margin-right:2px;margin-bottom:0px;display:inline;font-size:92%;margin-left:0.3em;margin-right:0.3em;padding:0px;padding-top:3px;}
-.toolbar .btn{padding:2px 8px;}
-.toolbar .btn-group{margin-top:0px;}
-.toolbar-inner{border:none !important;-webkit-box-shadow:none !important;-moz-box-shadow:none !important;box-shadow:none !important;}
-#maintoolbar{margin-bottom:0px;}
-@-moz-keyframes fadeOut{from{opacity:1;} to{opacity:0;}}@-webkit-keyframes fadeOut{from{opacity:1;} to{opacity:0;}}@-moz-keyframes fadeIn{from{opacity:0;} to{opacity:1;}}@-webkit-keyframes fadeIn{from{opacity:0;} to{opacity:1;}}.bigtooltip{overflow:auto;height:200px;-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms;}
-.smalltooltip{-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms;text-overflow:ellipsis;overflow:hidden;height:80px;}
-.tooltipbuttons{position:absolute;padding-right:15px;top:0px;right:0px;}
-.tooltiptext{padding-right:30px;}
-.ipython_tooltip{max-width:700px;-webkit-animation:fadeOut 400ms;-moz-animation:fadeOut 400ms;animation:fadeOut 400ms;-webkit-animation:fadeIn 400ms;-moz-animation:fadeIn 400ms;animation:fadeIn 400ms;vertical-align:middle;background-color:#f7f7f7;overflow:visible;border:#ababab 1px solid;outline:none;padding:3px;margin:0px;padding-left:7px;font-family:monospace;min-height:50px;-moz-box-shadow:0px 6px 10px -1px #adadad;-webkit-box-shadow:0px 6px 10px -1px #adadad;box-shadow:0px 6px 10px -1px #adadad;border-radius:4px;position:absolute;z-index:2;}.ipython_tooltip a{float:right;}
-.ipython_tooltip .tooltiptext pre{border:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;font-size:100%;background-color:#f7f7f7;}
-.pretooltiparrow{left:0px;margin:0px;top:-16px;width:40px;height:16px;overflow:hidden;position:absolute;}
-.pretooltiparrow:before{background-color:#f7f7f7;border:1px #ababab solid;z-index:11;content:"";position:absolute;left:15px;top:10px;width:25px;height:25px;-webkit-transform:rotate(45deg);-moz-transform:rotate(45deg);-ms-transform:rotate(45deg);-o-transform:rotate(45deg);}
-
-    </style>
-<style type="text/css">
-    .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
-.highlight .c { color: #408080; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
-.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #FF0000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #00A000 } /* Generic.Inserted */
-.highlight .go { color: #888888 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #7D9029 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #A0A000 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
-    </style>
-
-
-<style type="text/css">
-/* Overrides of notebook CSS for static HTML export */
-body {
-  overflow: visible;
-  padding: 8px;
-}
-.input_area {
-  padding: 0.2em;
-}
-
-pre {
-    padding: 0.2em;
-    border: none;
-    margin: 0px;
-    font-size: 13px;
-}
-</style>
-
-<!-- Custom stylesheet, it must be in the same directory as the html file -->
-<link rel="stylesheet" href="custom.css">
-
-<script src="https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS_HTML" type="text/javascript"></script>
-<script type="text/javascript">
-init_mathjax = function() {
-    if (window.MathJax) {
-        // MathJax loaded
-        MathJax.Hub.Config({
-            tex2jax: {
-                inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-                displayMath: [ ['$$','$$'], ["\\[","\\]"] ]
-            },
-            displayAlign: 'left', // Change this to 'center' to center equations.
-            "HTML-CSS": {
-                styles: {'.MathJax_Display': {"margin": 0}}
-            }
-        });
-        MathJax.Hub.Queue(["Typeset",MathJax.Hub]);
-    }
-}
-init_mathjax();
-</script>
-</head>
-<body>
-
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Sebastian Raschka, 03/2014<br />Code was executed in Python 3.4.0</p>
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="true-and-false-in-the-datetime-module"><code>True</code> and <code>False</code> in the <code>datetime</code> module</h3>
-<p>Pointed out in a nice article <strong>&quot;A false midnight&quot;</strong> at <a href="http://lwn.net/SubscriberLink/590299/bf73fe823974acea/">http://lwn.net/SubscriberLink/590299/bf73fe823974acea/</a>:</p>
-<p><em>&quot;it often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that,<br />unlike any other time value, midnight (i.e. datetime.time(0,0,0)) is False.<br />A long discussion on the python-ideas mailing list shows that, while surprising,<br />that behavior is desirable—at least in some quarters.&quot;</em></p>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[17]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">datetime</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;datetime.time(0,0,0)&quot; (Midnight) evaluates to&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)))</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;datetime.time(1,0,0)&quot; (1 am) evaluates to&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">time</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">)))</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-&quot;datetime.time(0,0,0)&quot; (Midnight) evaluates to False
-&quot;datetime.time(1,0,0)&quot; (1 am) evaluates to True
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="boolean-true">Boolean <code>True</code></h2>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[83]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="n">my_true_val</span> <span class="o">=</span> <span class="bp">True</span>
-
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_true_val == True:&#39;</span><span class="p">,</span> <span class="n">my_true_val</span> <span class="o">==</span> <span class="bp">True</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_true_val is True:&#39;</span><span class="p">,</span> <span class="n">my_true_val</span> <span class="ow">is</span> <span class="bp">True</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_true_val == None:&#39;</span><span class="p">,</span> <span class="n">my_true_val</span> <span class="o">==</span> <span class="bp">None</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_true_val is None:&#39;</span><span class="p">,</span> <span class="n">my_true_val</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_true_val == False:&#39;</span><span class="p">,</span> <span class="n">my_true_val</span> <span class="o">==</span> <span class="bp">False</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_true_val is False:&#39;</span><span class="p">,</span> <span class="n">my_true_val</span> <span class="ow">is</span> <span class="bp">False</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="n">my_true_val</span>
-<span class="k">if</span> <span class="n">my_true_val</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_true_val:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_true_val:&quot; is False&#39;</span><span class="p">)</span>
-    
-<span class="k">if</span> <span class="ow">not</span> <span class="n">my_true_val</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_true_val:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_true_val:&quot; is False&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-my_true_val == True: True
-my_true_val is True: True
-my_true_val == None: False
-my_true_val is None: False
-my_true_val == False: False
-my_true_val is False: False
-&quot;if my_true_val:&quot; is True
-&quot;if not my_true_val:&quot; is False
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="boolean-false">Boolean <code>False</code></h2>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[76]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="n">my_false_val</span> <span class="o">=</span> <span class="bp">False</span>
-
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_false_val == True:&#39;</span><span class="p">,</span> <span class="n">my_false_val</span> <span class="o">==</span> <span class="bp">True</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_false_val is True:&#39;</span><span class="p">,</span> <span class="n">my_false_val</span> <span class="ow">is</span> <span class="bp">True</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_false_val == None:&#39;</span><span class="p">,</span> <span class="n">my_false_val</span> <span class="o">==</span> <span class="bp">None</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_false_val is None:&#39;</span><span class="p">,</span> <span class="n">my_false_val</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_false_val == False:&#39;</span><span class="p">,</span> <span class="n">my_false_val</span> <span class="o">==</span> <span class="bp">False</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_false_val is False:&#39;</span><span class="p">,</span> <span class="n">my_false_val</span> <span class="ow">is</span> <span class="bp">False</span><span class="p">)</span>
-
-
-<span class="k">if</span> <span class="n">my_false_val</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_false_val:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_false_val:&quot; is False&#39;</span><span class="p">)</span>
-    
-<span class="k">if</span> <span class="ow">not</span> <span class="n">my_false_val</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_false_val:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_false_val:&quot; is False&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-my_false_val == True: False
-my_false_val is True: False
-my_false_val == None: False
-my_false_val is None: False
-my_false_val == False: True
-my_false_val is False: True
-&quot;if my_false_val:&quot; is False
-&quot;if not my_false_val:&quot; is True
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="none-value"><code>None</code> 'value'</h2>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[62]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="n">my_none_var</span> <span class="o">=</span> <span class="bp">None</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_none_var == True:&#39;</span><span class="p">,</span> <span class="n">my_none_var</span> <span class="o">==</span> <span class="bp">True</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_none_var is True:&#39;</span><span class="p">,</span> <span class="n">my_none_var</span> <span class="ow">is</span> <span class="bp">True</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_none_var == None:&#39;</span><span class="p">,</span> <span class="n">my_none_var</span> <span class="o">==</span> <span class="bp">None</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_none_var is None:&#39;</span><span class="p">,</span> <span class="n">my_none_var</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_none_var == False:&#39;</span><span class="p">,</span> <span class="n">my_none_var</span> <span class="o">==</span> <span class="bp">False</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_none_var is False:&#39;</span><span class="p">,</span> <span class="n">my_none_var</span> <span class="ow">is</span> <span class="bp">False</span><span class="p">)</span>
-
-
-<span class="k">if</span> <span class="n">my_none_var</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_none_var:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_none_var:&quot; is False&#39;</span><span class="p">)</span>
-
-<span class="k">if</span> <span class="ow">not</span> <span class="n">my_none_var</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_none_var:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_none_var:&quot; is False&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-my_none_var == True: False
-my_none_var is True: False
-my_none_var == None: True
-my_none_var is None: True
-my_none_var == False: False
-my_none_var is False: False
-&quot;if my_none_var:&quot; is False
-&quot;if not my_none_var:&quot; is True
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="empty-string">Empty String</h2>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[61]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="n">my_empty_string</span> <span class="o">=</span> <span class="s">&quot;&quot;</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_string == True:&#39;</span><span class="p">,</span> <span class="n">my_empty_string</span> <span class="o">==</span> <span class="bp">True</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_string is True:&#39;</span><span class="p">,</span> <span class="n">my_empty_string</span> <span class="ow">is</span> <span class="bp">True</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_string == None:&#39;</span><span class="p">,</span> <span class="n">my_empty_string</span> <span class="o">==</span> <span class="bp">None</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_string is None:&#39;</span><span class="p">,</span> <span class="n">my_empty_string</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_string == False:&#39;</span><span class="p">,</span> <span class="n">my_empty_string</span> <span class="o">==</span> <span class="bp">False</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_string is False:&#39;</span><span class="p">,</span> <span class="n">my_empty_string</span> <span class="ow">is</span> <span class="bp">False</span><span class="p">)</span>
-
-
-<span class="k">if</span> <span class="n">my_empty_string</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_empty_string:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_empty_string:&quot; is False&#39;</span><span class="p">)</span>
-    
-<span class="k">if</span> <span class="ow">not</span> <span class="n">my_empty_string</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_empty_string:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_empty_string:&quot; is False&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-my_empty_string == True: False
-my_empty_string is True: False
-my_empty_string == None: False
-my_empty_string is None: False
-my_empty_string == False: False
-my_empty_string is False: False
-&quot;if my_empty_string:&quot; is False
-&quot;if my_empty_string:&quot; is True
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="empty-list">Empty List</h2>
-<p>It is generally not a good idea to use the <code>==</code> to check for empty lists...</p>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[67]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="n">my_empty_list</span> <span class="o">=</span> <span class="p">[]</span>
-
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_list == True:&#39;</span><span class="p">,</span> <span class="n">my_empty_list</span> <span class="o">==</span> <span class="bp">True</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_list is True:&#39;</span><span class="p">,</span> <span class="n">my_empty_list</span> <span class="ow">is</span> <span class="bp">True</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_list == None:&#39;</span><span class="p">,</span> <span class="n">my_empty_list</span> <span class="o">==</span> <span class="bp">None</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_list is None:&#39;</span><span class="p">,</span> <span class="n">my_empty_list</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_list == False:&#39;</span><span class="p">,</span> <span class="n">my_empty_list</span> <span class="o">==</span> <span class="bp">False</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_empty_list is False:&#39;</span><span class="p">,</span> <span class="n">my_empty_list</span> <span class="ow">is</span> <span class="bp">False</span><span class="p">)</span>
-
-
-<span class="k">if</span> <span class="n">my_empty_list</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_empty_list:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_empty_list:&quot; is False&#39;</span><span class="p">)</span>
-    
-<span class="k">if</span> <span class="ow">not</span> <span class="n">my_empty_list</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_empty_list:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_empty_list:&quot; is False&#39;</span><span class="p">)</span>
-
-
-    
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-my_empty_list == True: False
-my_empty_list is True: False
-my_empty_list == None: False
-my_empty_list is None: False
-my_empty_list == False: False
-my_empty_list is False: False
-&quot;if my_empty_list:&quot; is False
-&quot;if not my_empty_list:&quot; is True
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="list">[0]-List</h2>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[70]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="n">my_zero_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_zero_list == True:&#39;</span><span class="p">,</span> <span class="n">my_zero_list</span> <span class="o">==</span> <span class="bp">True</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_zero_list is True:&#39;</span><span class="p">,</span> <span class="n">my_zero_list</span> <span class="ow">is</span> <span class="bp">True</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_zero_list == None:&#39;</span><span class="p">,</span> <span class="n">my_zero_list</span> <span class="o">==</span> <span class="bp">None</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_zero_list is None:&#39;</span><span class="p">,</span> <span class="n">my_zero_list</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_zero_list == False:&#39;</span><span class="p">,</span> <span class="n">my_zero_list</span> <span class="o">==</span> <span class="bp">False</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_zero_list is False:&#39;</span><span class="p">,</span> <span class="n">my_zero_list</span> <span class="ow">is</span> <span class="bp">False</span><span class="p">)</span>
-
-
-<span class="k">if</span> <span class="n">my_zero_list</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_zero_list:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if my_zero_list:&quot; is False&#39;</span><span class="p">)</span>
-    
-<span class="k">if</span> <span class="ow">not</span> <span class="n">my_zero_list</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_zero_list:&quot; is True&#39;</span><span class="p">)</span>
-<span class="k">else</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="s">&#39;&quot;if not my_zero_list:&quot; is False&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-my_zero_list == True: False
-my_zero_list is True: False
-my_zero_list == None: False
-my_zero_list is None: False
-my_zero_list == False: False
-my_zero_list is False: False
-&quot;if my_zero_list:&quot; is True
-&quot;if not my_zero_list:&quot; is False
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="list-comparison">List comparison</h2>
-<p>List comparisons are a handy way to show the difference between <code>==</code> and <code>is</code>.<br />While <code>==</code> is rather evaluating the equality of the value, <code>is</code> is checking if two objects are equal. The examples below show that we can assign a pointer to the same list object by using <code>=</code>, e.g., <code>list1 = list2</code>.<br />a) If we want to make a <strong>shallow</strong> copy of the list values, we have to make a little tweak: <code>list1 = list2[:]</code>, or<br />b) a <strong>deepcopy</strong> via <code>list1 = copy.deepcopy(list2)</code></p>
-<p>Possibly the best explanation of shallow vs. deep copies I've read so far:</p>
-<p>*** &quot;Shallow copies duplicate as little as possible. A shallow copy of a collection is a copy of the collection structure, not the elements. With a shallow copy, two collections now share the individual elements. Deep copies duplicate everything. A deep copy of a collection is two collections with all of the elements in the original collection duplicated.&quot;***</p>
-<p>(via <a href="http://stackoverflow.com/users/10661/s-lott">S.Lott</a> on <a href="http://stackoverflow.com/questions/184710/what-is-the-difference-between-a-deep-copy-and-a-shallow-copy">StackOverflow</a>)</p>
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="a-shallow-vs.-deep-copies-for-simple-elements">a) Shallow vs. deep copies for simple elements</h3>
-<p>List modification of the original list doesn't affect<br />shallow copies or deep copies if the list contains literals.</p>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[11]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-
-<span class="n">my_first_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="n">my_second_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list == my_second_list:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="o">==</span> <span class="n">my_second_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list is my_second_list:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="ow">is</span> <span class="n">my_second_list</span><span class="p">)</span>
-
-<span class="n">my_third_list</span> <span class="o">=</span> <span class="n">my_first_list</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list == my_third_list:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="o">==</span> <span class="n">my_third_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list is my_third_list:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="ow">is</span> <span class="n">my_third_list</span><span class="p">)</span>
-
-<span class="n">my_shallow_copy</span> <span class="o">=</span> <span class="n">my_first_list</span><span class="p">[:]</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list == my_shallow_copy:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="o">==</span> <span class="n">my_shallow_copy</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list is my_shallow_copy:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="ow">is</span> <span class="n">my_shallow_copy</span><span class="p">)</span>
-
-<span class="n">my_deep_copy</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">my_first_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list == my_deep_copy:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="o">==</span> <span class="n">my_deep_copy</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list is my_deep_copy:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="ow">is</span> <span class="n">my_deep_copy</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;</span><span class="se">\n</span><span class="s">my_third_list:&#39;</span><span class="p">,</span> <span class="n">my_third_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_shallow_copy:&#39;</span><span class="p">,</span> <span class="n">my_shallow_copy</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_deep_copy:&#39;</span><span class="p">,</span> <span class="n">my_deep_copy</span><span class="p">)</span>
-
-<span class="n">my_first_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;after setting &quot;my_first_list[0] = 2&quot;&#39;</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_third_list:&#39;</span><span class="p">,</span> <span class="n">my_third_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_shallow_copy:&#39;</span><span class="p">,</span> <span class="n">my_shallow_copy</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_deep_copy:&#39;</span><span class="p">,</span> <span class="n">my_deep_copy</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-my_first_list == my_second_list: True
-my_first_list is my_second_list: False
-my_first_list == my_third_list: True
-my_first_list is my_third_list: True
-my_first_list == my_shallow_copy: True
-my_first_list is my_shallow_copy: False
-my_first_list == my_deep_copy: True
-my_first_list is my_deep_copy: False
-
-my_third_list: [1]
-my_shallow_copy: [1]
-my_deep_copy: [1]
-after setting &quot;my_first_list[0] = 2&quot;
-my_third_list: [2]
-my_shallow_copy: [1]
-my_deep_copy: [1]
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="b-shallow-vs.-deep-copies-if-list-contains-other-structures-and-objects">b) Shallow vs. deep copies if list contains other structures and objects</h3>
-<p>List modification of the original list does affect<br />shallow copies, but not deep copies if the list contains compound objects.</p>
-</div>
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[13]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="n">my_first_list</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">]]</span>
-<span class="n">my_second_list</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">],[</span><span class="mi">2</span><span class="p">]]</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list == my_second_list:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="o">==</span> <span class="n">my_second_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list is my_second_list:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="ow">is</span> <span class="n">my_second_list</span><span class="p">)</span>
-
-<span class="n">my_third_list</span> <span class="o">=</span> <span class="n">my_first_list</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list == my_third_list:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="o">==</span> <span class="n">my_third_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list is my_third_list:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="ow">is</span> <span class="n">my_third_list</span><span class="p">)</span>
-
-<span class="n">my_shallow_copy</span> <span class="o">=</span> <span class="n">my_first_list</span><span class="p">[:]</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list == my_shallow_copy:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="o">==</span> <span class="n">my_shallow_copy</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list is my_shallow_copy:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="ow">is</span> <span class="n">my_shallow_copy</span><span class="p">)</span>
-
-<span class="n">my_deep_copy</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">my_first_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list == my_deep_copy:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="o">==</span> <span class="n">my_deep_copy</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_first_list is my_deep_copy:&#39;</span><span class="p">,</span> <span class="n">my_first_list</span> <span class="ow">is</span> <span class="n">my_deep_copy</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;</span><span class="se">\n</span><span class="s">my_third_list:&#39;</span><span class="p">,</span> <span class="n">my_third_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_shallow_copy:&#39;</span><span class="p">,</span> <span class="n">my_shallow_copy</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_deep_copy:&#39;</span><span class="p">,</span> <span class="n">my_deep_copy</span><span class="p">)</span>
-
-<span class="n">my_first_list</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;after setting &quot;my_first_list[0][0] = 2&quot;&#39;</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_third_list:&#39;</span><span class="p">,</span> <span class="n">my_third_list</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_shallow_copy:&#39;</span><span class="p">,</span> <span class="n">my_shallow_copy</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;my_deep_copy:&#39;</span><span class="p">,</span> <span class="n">my_deep_copy</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-my_first_list == my_second_list: True
-my_first_list is my_second_list: False
-my_first_list == my_third_list: True
-my_first_list is my_third_list: True
-my_first_list == my_shallow_copy: True
-my_first_list is my_shallow_copy: False
-my_first_list == my_deep_copy: True
-my_first_list is my_deep_copy: False
-
-my_third_list: [[1], [2]]
-my_shallow_copy: [[1], [2]]
-my_deep_copy: [[1], [2]]
-after setting &quot;my_first_list[0][0] = 2&quot;
-my_third_list: [[2], [2]]
-my_shallow_copy: [[2], [2]]
-my_deep_copy: [[1], [2]]
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Some-Python-oddity:">Some Python oddity:<a class="anchor-link" href="#Some-Python-oddity:">&#182;</a></h2>
-</div>
-
-<div class="cell border-box-sizing code_cell vbox">
-<div class="input hbox">
-<div class="prompt input_prompt">
-In&nbsp;[1]:
-</div>
-<div class="input_area box-flex1">
-<div class="highlight"><pre><span class="n">a</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="n">b</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;a is b&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">(</span><span class="n">a</span> <span class="ow">is</span> <span class="n">b</span><span class="p">))</span>
-<span class="bp">True</span>
-
-<span class="n">a</span> <span class="o">=</span> <span class="mi">999</span>
-<span class="n">b</span> <span class="o">=</span> <span class="mi">999</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;a is b&#39;</span><span class="p">,</span> <span class="nb">bool</span><span class="p">(</span><span class="n">a</span> <span class="ow">is</span> <span class="n">b</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-
-<div class="vbox output_wrapper">
-<div class="output vbox">
-
-
-<div class="hbox output_area"><div class="prompt"></div>
-<div class="box-flex1 output_subarea output_stream output_stdout">
-<pre>
-a is b True
-a is b False
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-</body>
-</html>
diff --git a/python_true_false.ipynb b/tutorials/python_true_false.ipynb
similarity index 96%
rename from python_true_false.ipynb
rename to tutorials/python_true_false.ipynb
index b2786a2..fa8c446 100644
--- a/python_true_false.ipynb
+++ b/tutorials/python_true_false.ipynb
@@ -1,6 +1,7 @@
 {
  "metadata": {
-  "name": ""
+  "name": "",
+  "signature": "sha256:f6d59feb844af096c0581919fa052e8f29f4fa0aaa81752f5e50241ab084a0e9"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -11,7 +12,12 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Sebastian Raschka, 03/2014  \n",
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "last updated: 05/03/2014\n",
+      "\n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/python_true_false.ipynb)  \n",
+      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
+      "\n",
       "Code was executed in Python 3.4.0"
      ]
     },
@@ -392,8 +398,8 @@
       "\n",
       "Possibly the best explanation of shallow vs. deep copies I've read so far:\n",
       "\n",
-      "*** \"Shallow copies duplicate as little as possible. A shallow copy of a collection is a copy of the collection structure, not the elements. With a shallow copy, two collections now share the individual elements.\n",
-      "Deep copies duplicate everything. A deep copy of a collection is two collections with all of the elements in the original collection duplicated.\"***\n",
+      "***\"Shallow copies duplicate as little as possible. A shallow copy of a collection is a copy of the collection structure, not the elements. With a shallow copy, two collections now share the individual elements.\n",
+      "Deep copies duplicate everything. A deep copy of a collection is two collections with all of the elements in the original collection duplicated.\"***  \n",
       "\n",
       "(via [S.Lott](http://stackoverflow.com/users/10661/s-lott) on [StackOverflow](http://stackoverflow.com/questions/184710/what-is-the-difference-between-a-deep-copy-and-a-shallow-copy))"
      ]

From 2bd3c300f4604a6f3302488e977027aee9b8fde4 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 7 May 2014 03:04:41 -0400
Subject: [PATCH 025/248] plot fixes

---
 Images/cython_final_leastsqr.png      | Bin 53922 -> 53131 bytes
 benchmarks/cython_least_squares.ipynb |  19 +-
 benchmarks/palindrome_timeit.ipynb    | 150 ++++++--
 benchmarks/timeit_tests.ipynb         | 502 +++++++++++++++++++++++---
 4 files changed, 600 insertions(+), 71 deletions(-)

diff --git a/Images/cython_final_leastsqr.png b/Images/cython_final_leastsqr.png
index 9ae7432ba6ea289adee7bdc337873ddd33461256..036c5079074b2eef5d069a02d02f1204110181d0 100644
GIT binary patch
literal 53131
zcmZ_02Rzp6|37|9Lq>=qkwj%iD%nb8l)X0@schLR*%Dbzqe%8hB(k$95}A<^LRl%5
z^n1R~`Fy{>@Av=s{m<i^N9Q=W-}iOBuGj0it~)|qRe^?*g_1-f(I_d(X_82rT=36k
zimmucvd$|d{IS_pR!N%zfB8~ah2y`sJ1H8tl1NlLiGRqtKlrbcNc%}ja;LRDljf5>
z^tGG&nrnV^8+<OhmUZ>C`cn^j#uNG%_%(_y+@QBKy%}Lb_VUo>s0-)ALoS?DjyiOK
ze0~eRh8!bfgRI6=4$aQr+gt*S_03P~^RIZN_@#U(KQ^5r=3(=7Ae)t*o|$@UP@TCD
zGdruVJzp*Uh4}9*mX27<fB$vultnb_Ki@&!w2jhr@;~1%t$vrY`QPte3an=p`}dbb
z(K1f8@Pt=ZN=EHDwly>~^rP3l!>UPH_KDJp?h7L{SFT*)=Hs*MxO-H1!qdy^k<X8>
zl<%W7mIvg{p1nTpCF#3X)LqOkAn??xHIAjL`1tPKyY(wQpUQq+t*&2@d~QFaJ9N26
zSz-@8JqK^*!2t5w(kp#kPgM5W+S=B(wNa98B_%~Ue5iP&nk=rashQ&}C3BEbW1oq!
z@nWx@S?`P#;}+M+$gxkKwxmqgtw?HXYnxkG1V%6%9{>C<bAkVNr_A4m?_Y+TKh}~x
ze*E~M-|z3e50^6K{r#nzqV|x}($aDrK5Y5={t4j;XVz`e6O1`Vii#AT8}IT}i&Ip>
zcCg)wi`%57rA4aE*2=71k^K7MrQV~;tSs*H$>OC?dbg@eeEMYaBH*uoVlwN=lP9D0
zNmXcjDl1cN-NCBQHe1E8a7JR_silHO`Ic*Vo-=xSOwP{EFFcnHkjz`-4tlPy9W5}e
zid2Z%$78H!5ViN@j)H=M()^}vr)_Pyb@L5%hAwMUwUy@A{tkWi?3nwP!4T|&oV>in
z`r4ABs%ldp`L-M`!NZ5Q<>chN@cS*&(b3UWVDi$VyHIf?v*lG7ZS1{!+dVuyG&qfR
z9@sQAU9rr_$VfVs$l6_;a#NdEV3%IXMN4yY^-FBhCvu)ewR@|U*8T2M&B@A2C|2<G
zl{|ZsC1fiNOZZj$4;3f!^7BWP`i6&%v$C=(<EhVndfVHZZ@(*t_5wu|%ZbS0;bA$8
z8%%<S4jw${y)s2ZPj5e3ot%`Eq-bK2%+Jq1^Kzj&y`$qihyR@;CbskaPmg<iH5NW`
z;vfS9gNw5>NAyH+Oh(2*diqb3lU=B_mX3}nZ5^G~y1GDA$)O6FX6JA1f;G1?GY@~B
znd!R4amIFK>h<Hiyz^sYV?8gNExWtA_Uq~Cd4HRsXOr>A3nwWah`sgT!CooP#Z$N3
zrr)s5j@HQZ<{RBPe&R&7Qv4xCR#yHBGvaB6UpSYT*KHC$dX&S@&yPjSg{#VYRhiMT
zIhw5}O`b|qPwy}}Ik}66M`lzM_1G`Lnsr%sclXBy1**@VKQHlIy3p3%&eykbu`?y(
zO|nFMXz13@pFh9tdUS?IK;UM0xQOEje=$e1Gvl5;JU?9XX3E*5eeYIW{Ve<I*RRLv
z>1RwTu0En-IudTRdGqF5RaH{ixw-Mx)zV@T67T-}{=p+DS()IFt`KwNSSqo;e0+SG
zT3Vd=Yj)t-VQXva2)m0mHfQhOzu)uhq8cSNH5ZohqQSTI8}Ug=#%+ftnr?F9loHE>
zAH6T-BVv8=B2}NCuWw6JQ>gpbVI|_dU>#37jy%32Xx3de>d!aRRq5qA_|n7i-cO#n
z0mgw(bwS&%{`mSq)7jX>q&?x1gM;dg-2yxRq?~lz=l5r|k9ZqRO-&{Kf4tk;+7386
zI(EN%$9e47F~c&4WW^oL&LuXlSy+V3NlIKhg^wLeKXK)K__)5le%E;8jgV0>F|o;s
zi8Hrv-zL^HIy(B>kYi0^+)MW_hAfhvLd4IUJegVL^P~G+rdsQ(S3xBuB`s}jksIqP
zY8n*@35+Z!E@N@`#NNKW+iku-Ry9eK!&tC*AeK`h%XRwA<Lqpu9Xob>Td4UP`BflS
zDm%+RhUG*~hj@cCD=J$j{P*J685UtHiqEsNaoD`_7cbP&Nix21<-Eva$~<@HNbh@9
zZ%@yb%555rtGQ_|b8~Y>HhzU!Um076D%|+WU%rf&JMie<v5PxneHU&o58AM^r)qj{
z*}Ao@q;6%pv$ehb#`*K-6;)Jj&h{0mjr#pab}~u%Uc~G6kozwO2S-oo6}?CHgJ={y
z1C8yL)<axr^&-x-*T}4FZ53x{XRWNQ<z+V3EO~r(2TUE5K?{{#A1ZTvca4l(QBUvA
zw{PFF&_b?$89Y&LZx}D6P{5pi_GTJ-)s1@H0%IlC=C`?eh5wwE%uEHm9yZcM+<AQS
z3H!l>vr)_(4GY~FRF^JYdfO1ez#}4ZyTGhQRV`II1M8K7*3EO`MB+Xf|MPNkqXB=V
z_AoPdrAYbApsSWxweB4EN{MEZIMgyB8cj`2-7-3w`gOScOqt{8lh+SUGIXv?&dw?`
z9Wj20MU2hQ7jT>HiAYLL{=EKk<?5%lUBzW(nH*<ADT>jx&TedMe8VfeO_uPm{W4g3
z{fxeGfQgC8udgo`xQ`r(-oY+?9tY;Q<A@~3n<pv{6y@bZb_*DCO*((5aFea}MLiD>
zC&`{U!*ld#?8eV&_RC!l0~OCI?UVABGc+_jZr3kV;r~ZGS==r1zKFf@n>TOd<Ps7S
z<(ULcabjlhW}o!*Xt%Yt@~nvM_L!XXa`^rIOZtbG9`Evv%4V8w?mg}>EZW`Imw8w%
zg^pfBTbq%Gk59|VNeJH(Lp=Z9lMau6|5(scRo(IP_iu(~=Y;$Bm9w+6zl}Fi-K+TX
zb2{~Le!j}K9jp&0C#@=6K2hcyJUi=I9ugWFS6nQdZ(4QVVYFHWzlAGXTUoMGuPv63
zoBN@d>r__=6%#iPPsI0yQMD&ep5Ul(K3-Z{;!sghLFXh|N7t93vd^E|?@W%5%VLCF
z{q^lqh0l*8e>Q$EJkH7C<-OBLoZ<kEsp;u=8-IRBOo&ECMyBEI#!i^U#l1-^FBe1Y
zchC0b@^EkjVpZiNJGDAfq(X`^&fVGBxpH-)iH3u%+4*r{q1xR0{B)xF?w`1#Xkc(}
z<JVlF@WP49J=`uXE@_+!(drj29QZO^9)H!%?Gk#=f$~F#4q@!bZQs6q^2-;Moe{xk
z(We6g0~2oDIu(Cd?NEf=frLkWg=Si)THCINry1GURPfD2Uqz!!@9)<?@N1N+v9&d#
zzP_GYS~}%y%s!RD!NKcQN6f0vqq?j8e#?DPl3)G(b7gvRe*XNvefwm^1ai(ank8-7
zvZV)T;_^tPShBR=ePWx=gi>=zNlA^FF3%Wf7zir4J&(s?3NLV|{oyrm(9+b*^8fSe
z_?7qk$|@=udWEJMnCEUYU4fyr>_>WjT)%#ua!xgtQ>)NaYVf)J-h+n@b)x`2`Ija1
ziPU(nMuyVtJ<BHH&Y0q8@H|D*OBlWFEwO)MV)x!8iJb{pUkY=oO8Z`vLmYm$x%V0N
zL(ku$!fbJF08!-7fp@naG2O>EbcmjQ^~=zm3XcU<JVwUj$6S=Ow3*mxsy^T!TVMb=
z?X`=KT5M2b#0C+4^{8bNoyHiukfo*N^7rpg=K2bW>a|^-Y_&2sKZU8}GEj2JBHH+<
zvN-AJ(W69X!7n_VR)0sSHDD?>HWn>W%IAAjI3?eM<YW_`0r@I+c`F;6xcm26{$Qum
zJTZ|nv5b^8V$lp6Cm1z)$X!-vbvWjm*#n4U)SYoowjXuW+SOGwEcs)uPmQ}n{@2MX
z@6R({9B+uYP7@wZ^g!(UZ63j6$Ko-IdHDIyGwx$%R;;L~u%0~%Sde2@BmHS=iqZn_
z`yi&-z8h(0KYsiC?$cEEq-(--c_NpLOe)%5aFf--NEK#}){~&!tgNVo#9Su&w>dhw
zSr)HC(|mVlS~f=?_g!<+=>IVqV3x5@%;3lK($Zc2zt`62I#&{slK6u|!ouP!D#WD%
zHjES8zs<b;*2rX*z+?3O8PA3+*8y>_WiDq{1tldV>oy+Q+njwh0Rdwkr%#hzx^m^+
zbZWp6%6ISHF|4-Xdr<m}t^35Ua=U#UHkhg++8_>*v2haj&D7M?EK?^|1+7fgUH+Mw
zndlAQzLbqpT3}{j!Pz7|Z-rB)8WEsl_590<itYp+t?-!u5gfBrCn>aF(dcxbIfhu@
z&WiB3)uGp~RcsucmzbvCrYqnq*OhY}XnXVK5P+&-k$K>-x32bkYmMYDkNGFF%?`E;
zXnbB)<Z|B}*{!4P5jm^0G-7k$=cvH0($`p>gG@|!D8F<Sn#n9LFXsrE(jOc;la|vP
zn6}=TDr3e~b0%!Zfy`1*ue{3-CMPH5s;jH5O$C3hN4B)KaxgJ5xwyOU-k4ciTT{od
zmRq8vq-+@+yzjs9^WuS=N|oGrH4k_9J#un#r_P<D0z$Fve!Shz&aTL9PWN$U=EO*q
z&s}jhUQF{~wL;UXT4&briHR+H1dN{NElT51;za_VJ$uGIJTR~a)tXi$f{CTv>CAfn
z_@x8uA|fJNNyqI6wHd!pwlY{8d5vm6>N<5EeVTde)~#LL-Sskm*E#R|zHw#+8kGA1
zv?+i2GB0VrfB?;pg;C5*DXS^ILx;8+7#J`MnO|#BH4FH2!nD#e5G_989;)13U3X)&
z<Ta*^tb)R}lMchT?w#E?>F40FFwz)K&uM<;3Kbh0+sw?&N3@>n_GoNmz*zc*hV`fl
zGBPsjsSL?b>OI$in(sb%V2Sdg*uK3E(>QqU>C>k(U5|peRRA(`6MkH~Mk=u%+KpaG
zJoo&-vvUH+g+xToq9%ezF{0!!RRHhhz7JNo<z_|k-H-beHzCTrXHP_%M6>fP<qSnN
zHKq&!F^g*KSjMbAy@rOy{sSd;1DXkc(Zg@I1vvXr)6hIZ7wJ|24*_FPJa>*<-F67@
zX=Zjd(kfNlEhk~#=JoyUqZyh!2?+_o;`+OndmO#OBO=hP$>zU&2@%rK(ZORrPx#RG
z?pa##5!!`8)!UE7=k%0#wLBu{4DWfI^RjofUi|*O2^gX4u@b}id%|15qjtV7&&!fZ
z$cT}2dl(+)R*cHCe&3q&TuJN6`MS7+=RjK|I+DZ~u<&{PLZm~gN#WaQ_w7G^T>I`z
z&!usBIR%9qUkmf|Z{qn+heg^M#uqj2IB45fz_y8;{83(BRE66dWwgA8_0Lat%&ZD1
z(F@7d*Mwsgxq5Tr`Mo4NZ|Dy0@)NA4uI7#kN1gevjcuDBtzpMjgeE7mp$i?gA3QFJ
zCcyP_lFI@;Ct`7z2BRZpplyGVMfRMxx3};+XI4GVkZQ%$$P}YgX}|r6%1?oJdkW3i
zd3kwfda@ZX0ZEL~LEwi;(ta$cuSQG*A-`W&StZ=51q@4W8KvSm$ERLDprEB~UYclr
z>NF+`>P$Y}naU)l{fXnsQCSCvf*AnqmrJitj+*~m{)CQ7RZ?2|^h)26j(f)x7z?ci
zc)7W+MMv*QOG{J!kp7%$AR&qFjdN0mkXL&Se`Gfnem9O<hSi-+H3RY8t^C~Fr!ZOR
zSXc~sO<H|gi{0dZ#+CW&C8oK{nM&oR=jnycI(YD%vz}FTHu+?qE*kBV`Mg|I%KKX_
z5I5cK-J#*(6x8g}tSa}9*FCjtu3hmB`C>tzm``r4VLD{iD*|{+mFo9>&ki>6;KfC!
z7cXC?2mIZj*s<ftvDcBi`0&D!&n|XGj_Am321m{_Dl5=t!Koqb8v17xZSU#r4e`4k
zX=!H{^=ox*0;|I;;XW@h+T7H%_44J*;Zae+K|w)u`@o)NFXgJLrbtGNy$Z7gQXo<C
zDH<Cam-C%EcOX$&10y*&B;*kRT?-_{#br%Q*nlI}=AU&=-kzJ82?rv*q3dK)<-H5N
zO##^C(`~JtUMWKuMs$1k-q6X_-Tu4tLrp+UPmN;A^xJaRi&wu6@A3EdcXD#71>SuN
zF8Jtl>}oMz1VEMd_xT8{^@Xwe5FFGdw2lyv%DTEbvP`wqCP3+sHT=u&j62F1EuEbT
zhKBcxD!yVorins7IR)-|)Vht4V;)=mX?6<<Q@RPObSp7&i-JNk<%C}7fxVxmr}c7&
zBSn3_^H*>Dx-#|y45O{XNW;-vQ*);`KzzP;iNgmLyd-+Y_1IXA#7_y<3SP5icsg|R
zw#0>*8Sb74uy`_3@v~>n8|z-aE3Z!`zAw$g(e3@9+NM>LqpUNh>Q-$8s{N_J3{}3k
zxOgorY#T7vO}u4J5fP#%xs--2)DJwnD8CT4JZqJ!o1Zg$=-%DCwb*%6Q&T4ok7khF
zrMbeIH0U%L<B*B!o13>;SXdO5mI`FNx-DR2Zez3c#EDYj1-;uUxi19-TDMKNn7nWz
z!}$-4jHE<sqhn;`Vu{XG+AU}rg));>Q=?4{*boB|BQ^(J4I`a~G&%Z9R!fUv)c>~t
z3gs|0{g;7f+km>8F$%B91xQFp1c!!R0C&2j$faL;c^A<CW+)6C(UQL~^vwZ5h|Q`E
zB-c<^zlLh{^!Bc=ttDaUo6vOYoLO-|L!q)5zjUY3D=@Arwr;m-Tc3L1SOfwKfy?6i
z{6I@f%eAtyli)F<UNh-w(@!!pw}FnY18o--7uQcT#~j6(hk`{R40J3wT;;<AP7@j%
zOP`jW9t+S>X$)Ew;t7}-3_Rbl@A}QXC$EQhRk&S2Cm!#AYU%lNIb;9D!IDdB%k6@1
z0bp^cCVH~9Fg@tS!q9+2z;tciBt;lJvjL$aquR|+_n~=YWZwf3`+5NUF;qMGwjJi9
z)qcAMbFTCiIJvsM0$nV+&`5dwQuk)y4pG-B>f^_c3potGXp^~ChVc|M?ZqnTNf)$@
zHWdA1(eXLg!S;uF!aR=sApc_v3mBK)AF<E<rB|4+3Y^Do-n>Zz87&Bn%(5|Z7txK;
zcj?*K1QKrK2!n6snO3o&hctoYTU@zv1AK#3#-9~be5S83)z}5|NF!5~jX1L!>1Wlo
zw1QGnQjAxJy1REm(|O@Ky#tN!QEskL&Y$`B`FpV(L66Ra)sK#{1O4m2aN<C}mD`~%
zudV(5)1!)^*5X+``2?N!kzqCGK4ceo+(=B^h35<cqdLW#C~IV7WK-CiB<9+cqr)dE
zDtc|&D>yiq046jn$FIp9$S#ud)dRrT?BnBuxw#YTD(dl-b^XsD>!vU7^NkYcf1xEm
z#OTLF;25d&3c_1UI~}z3I)mPD3g8K|w6CbIpWoXei!%W7ByQF*VPWC=lcT;t*xod=
z8vnZz9!CI;!%(ghAiDCHAzk1uF7}2K+%~+Xt4lL)IW72ZVNc(pvE-;czjH?~TPOG6
zb{d)toW=MjPYwZgYCW@gZHuN;zgm1q9fUaY(0vAapyh6ihY;&ks3&PUC*(5jSTO8T
zy|1gGK_9YZ+cq%qv?3sV{F#uNswNi_|JdjIyb{RRc@>q`zP>w6(QJH!TV-!=5VFqm
zP23ki^9aC)223yqzhB?z1z6W6Vq~)Lc!WxWR?&l@J&1S9^OA9TxZIVXUMwsuk8^X+
z;iNL?Zl>R*n()c1y1sEoaf+pUr+~1qrv5KTLuvsVKMxP|_uooQWv8c47InT};r>PW
z?Af!#@1@x*@)%6^##f>}#wllfa*!$ZSTLRKdPENTvfW6|Zm>j&@gg)^(2}!wy&i0X
z<`5?xJprR31j!L!%hozfNkzq(G99vgvAn$exJCVDQ1`?3_Vz3ycFbtDs@i9*5|z^G
zb+V3Ke8Y9_+&N7hoqwcA4-ck%ha(Yr7a8{LJ7P$qC|80rgR<s4cre4IjGafU-1xoK
z%6Mlc1?uGO>`dAhh3d6&s_vei$7me{g>J9XgT9=v|MWT_lah{3BI>-gu~EL~eM`<A
zsACz=ebj-#^4<NGC%3C4iE3o6*x1+{H?Ji@6i~h=5`R#Fn>!pGLJ7Du>%nr*f`rG{
zTM&SIAwmy-Q6K~jP+Yt)+DXXV@(hpKzckjGBiBFCxYSVCahczJf6j}q34NhDpFU_7
z+x!3e#<M!xd*kZWt8E|rtx!FLVw$6sxdU42ts6IPC}u_u{)79+hhkp5ctJ6AUHhA4
zZucR~{0OX=BG$BCB4;h~`}p^S(h$uh0jKcrxVSh18(CRd{Zl271Ub(}J#1)@D+fGt
zl4Jk+CR<us+7{Fs3v^>pH>57>=z#+Vx}bIq*95SG`{vubNpnYT?4e~7FL|zGAtdd!
zlYzm)+ByV8Dbet1eU>=;d`>SQAFG(lW@z{M_B#JDD=vo^x=!LyhCwAiZDLZ@GZ28W
zFaLMr57AK|ESf{SNt+Wit*mW{<)mk3ZrQ<S<GWM4<l1#uBhs7ojdcI?40g@p_(9f(
zu7JTC0(?N#Cn+UG#;AI`%J-)*M(j;gM=eIWmzUSt`by{0pC6;?Xs4x*#Q$TU{0@}0
z8BctL@|S<fLGXVJ($6@ovi*47kd+ze1ueC;*ML%58XH5Pw(@Af&T!jK`dvi(Uw?P{
z{B?G5$%$jvnRA`Yk_-EY`h1v|N4<CNUXG5|R;B(`uZD1X!Y!zO^@@JqzG(F0_5?z-
zwD5X*0BbBwsUoPhzC51brZVDWur$)UHL}FR+PWv9iqG<#S4!P)yOr4^6FOCI=1o@D
z@>H^=_vESke9`WyGw+A0;Qgsl`n4sIRWM+6b{9IXyanVkVgeoL%{hML$dUj3%dtaz
zJ=qnWdOmu(XH^DX=3QA1=II_O{%ZE_Ghd`x?_9@-PE6-u!fbrqU_#8xD8BBI(&|4g
zSfHhkb>tS&!#Lb;aXfK)DV_a)eLkEd>bwaZ@s5#$3fs>H?!AJ7f_4MN8P9vytjxdb
z@^_3xh7A}$74qS`9rwK;XLNaFwdI6eKOJ@??UfT)qhW^SeY9PAditdTUg61x_V(Q8
zI%|*rKR<iGM?p@mwy&?O&E;~w!Vwu6cC>}Xm6cZ*cXV`gB<Sdn*y=#rSewUUWHxF+
znz}O8*psAubVYxt?bDC>*D{Nu4WTViaKH)b>gj1%{?tytbLS=+8XA7nN@h|K=zx{A
z^=aP33u}vGI2&g&l0?sbsr8oGMfI%gg2i`T$&RVWkO8Bo+IOmUxeiuR<H6hKm3!X3
zi=VsZ_#ekj?Vh38yt-CGpL}*<VGJCHEms=MeJf@@e;$L0RGLqGTV<KHn{DZP?@7kM
z!a_k*m$Jam*DWpPE7Q!>Tk4}fS-|QW0Pn7RCp33>fU|kGtPpeLC(A5XEK_fBAM+Xi
zyRZLwCZ0<Rv<^?n|E=b&6Ccc_T&TCEH(9tv<D9gGt+pKf_r&>L@a>6al^A%m{2!OE
z)I9)Qli#3Nj^jshQ4!tuBkS5@Ui*fg+vnP!kfzW#O7r}$mni=6GG%`8t%M8vM8j(^
zJo8`QJ{Oghg+XeMx_R?$#yO_V+qQAa$e113lc}1#sdMFp$3hBs(!>ix6AR{DyF$?R
zcqdIull$6S{%-g!O2#l8R@D~IhqVKgYQigd@Zc7JoxB~Z#G`6#!D@`O#{SMKW6$Pw
zc2*MgK2BC%4r`F|<CG1Jgv81ZSsND@VR5(F&0w?q`b8w1`!HZo)l})5ss6vVtE#Hn
z4whsOFDpkk_kEW<L{1)LSoJsTl$$hXwzi>tv9!B%lT65h#Q)t&nc02iIdn{Sf(4Am
zV^c<Ub*7Z>m#X4>ZWp0oY~1a`@os2nFUsx3)z3T8UMw6PX<^7jv5M`4`E(s1>bc#(
zCP<Pforvxucp%I`IjE=rm}HDQtd}JTd6{x4oTBFMhD68f*N@WEgP>7z2neWf@d*jh
z0Z;)vo%!=?^(e$~4abK!ZcsJFu*X1ltncYzgciaKe*`nE{k2)^m?{+&RY$4}I~Fmx
zpx_9MiA4bSaV)~!<Ck`RTU{l1)Y|XgHzu*tX8u2kwUvWvu8uFS1CD+gkvMsh2|^IC
z?saS-%SneQpqZ+KVRUqhmRlTI7%MRRiX2C!F&k=eqyX27o<DDb+#~-Pk}hG_W@}So
z7+;<3*^Yu>EWqIe9%GgAW`r&e=<am&Y6Gxduqdz@iS(kP0$#GdfdLum>qylNGV-mp
z9Uas=BQ~o%5Rq3@48QdTM5H-}UBTRZzh?^5>TDvbp&xueNlD3vz~<1&@f!|^i&toR
z3!_HP1a>JXdl~R8Kuc+EX%5Re$F!8GS6IK8<2(EzCNXY$;>_eiK%S<THwH8h56@fR
z*Y=xt;1vM$Ffuc92ZNc#Lo+94{PN1mC5#=&c=Gb{8jzQ)t*q$i-)3o~D=H|2h3;Te
z1~k0-ecqtBq~tL@=UM-mK=%L20#Fq1U==;3tgH;YDO>>qm(b0jYhq@4$tE2!skns=
z^84|@gOQ0zA&i#&5Zp-(Rs)Q;@a2n`w-8KAyjL$PD=I#ezp<OXbA@mKIqW`nXEdxx
zet6-0MzPtM_4C}^J0O#P3>$D5O<mn5H5<QjJA7`!wS_J=gF%&W_pTy91{ds>!57Zy
ziUko7+aaB6$jWZ=^6??eEr1SntnNWBuCB?}c%=ieU0}`O;WMjqeLYzkoGiy}>Esm@
zwD1E0rj>VldV08FB!!1#U_qcHoPnYup~#q+OwhD=n5K(%|5v-`{%?P&ufT*eARyou
zyqFmnsU`6Lh{pv@J8-~zV_gi6Xgs)S#*-(SSZEl<6h8;xztPk0Vq{bY^~v&Dnfk~3
zaR0(Wvqw0XkcfR+(=+*{L!UfpNuh46vn$ZQTea()l8;_#vXzv}!IsyF_Hpa$IS+Ck
zo^l^dN@{D3iiuHxzRSwW+IBOupdcDswtxTrQxp;|lN4BlV7vhkB{X9)QdCqFG$0TZ
zKIIzg_TAtV4<Q+@K_++pHls223*1r2wrf8j@Ib3%hLeeQzw}N`hC@(LE&L*a3W<Dr
zX`r<;BUKWR2@k|^3c|g)D{M`fmzO8x^W6?Ob{i15Y(EU~hdA}1mN&XrKfl`+e@KOB
z9Mylm8&U6*WS05!ExyBtAes;Z2z~}P-rT`~01B3~J=yv%-SfE%;GDzlBLvE|pFhX2
zap=L#*n9$TZK2xz5nmw)56OJ`O)|#+LHGH7!V04J37wMM|M!=Zv36!=X3#Ln-@kva
z|J-hmX|-<z9O+FY09-Q0eMb)`Du-AQPT4E;J0QnE`1#d8r}lpP`oU=C(!2NXLm^Cn
zb7Ijrux@E-nr&NgYMUExFg>*&61h6lMFysHR$ZMAk`N)Sm9JW1j)jh|Wlgsvy2<Hz
zWo0q8Q0r;Nu^pk66qLx%x%|mW^WfB3mAgX!{G_ol>-_vY#`!h0$L()UdG!ws-7x(I
zjt%d1m;dMsXL2yiwBzV8o`@*y+_}^I;>B$wCl{BUjYlC4>+9bg_99U-aWUR`<;;rh
zm0KZ+MF(l~;^0?s+XGU{*GOEQxdySfy;2PhU^oS|YQ~W1PoGX37#OQW&RK%=BLERA
zjiafqqq8eC9S}4e5^L-;YSO^K09sobl!X7*SmmDIX@qS36zKz2Q6~~8wk(8Rh&A%B
zJ}uKRFd%k7jyFMN0ln6C=()g!3l~NVI%(NN0u+~o9`Nn9actDITJVrcn@jhY<h}9W
z!HMT~Vq2N+ILPX|e?RK!HSjS%K=ac0IHh$?c7(P2oQ)Qb7>*x8Jm^Y5<$`TD=RSVC
z2GwQ^4oNf0Y!QD>e5?y{`H*SlsG=jnGby?QFP>3PNy)oF*WRt5tFL3lq>%t-{R|L@
z|KLkWzqanDiZ(bjU|+#qU0s@=l}B<PzmA;K-7T}eNDkXFrZ-pb)tfgvpex)Jv1dt<
z2{3gK_u@#?6v>KAA>v8gDjH$&si|g(-OnEN?(849_o}^ptLA6tnc1_ac%?SpN96v>
z>79)OAfiQ{1sdWGtSh&h6edxjS<P;LHB`Xq)2G*f&x!H%qP%>2U!-Y6@pHax+jBb<
z6ci9gimX4t$@wTdd;66ubIPBx4~hAF&zm7MwU3a=jyY(k8ADdX!INV@cdp%|J00Wf
z9}S|UM7dQ2p0bguLFYW0AHnmHis7OzY-((bNJ%HQrr&5ch_@%^#RW^tkfQl{yCj)_
zeW>Q!Mx8Oco;Tkfw-C`Iuxv)f{A*URJ;Xa4ddc_XymMfgN1w-t_hWHu*zWrvB-;)Y
zAAIgO8V%WEzqIr|$Pl(j-@q6Nc@eagW)q3uu!I8IB}41clP9-zu84Xr?(gpImW#HK
zuX2PmP!G{80vLM&?1<<D>+9=6khU$D<HB=FRL-^@FScsg>%RE$G)DXchzfdx0Ff-Z
z^zKRPdDw<Qun^7REkbHKjR+8tPr{V7fWDLXM!^|+_Ex|?oans@(X6KdQr@J>!~iRg
z7uIaV8AU&O#DRVjgk}#3WIrb-ISD5$tgB1ixYCmz{z<K#nO_Km@rdP%mo7!j%-Dh#
zY>Q!+x&65Z%S~fHSW*Xt$$6?{k8zDZ8!R=#_kbI)rYb^2)6TxGWi6-uM<#}nO4RSi
zxw#vV7t>%UAbnR0*;QUj6)lt4q;Lw$rYQQ>qY(V?-A^%Nd-9F+m~#ZK#1D^KeEs^h
zzvL1F{DZgfDSR<y3H*Z?f!xx$w%Cj0rUsXIBeit<5mg}CIUHf?_S*ulzzxFDT@vq2
z5eEhh`YC*BQE~D5*RN^Nd>4k>vz|N&ZD~;~x!g;M)DB@UAPyD|Qvh>2;X&B=q9iSb
z-27lE6*?}HH$6;F8dq0WKosJ4;A_nfPY1~@!*tqu=FAyUF){Foafmy@3-l~3lu#Xr
z)yGPmHZ)|3W)<56P*A?Qu5Ymt#z_0DoM0LJz1EJ=Io<ZlWa=Z81!|v33m60d*z6Eq
zF52g&se7g<Am;{fVmOo|w0NnP2>WGG>ewGPh#6LZ60;SI7`lB<5oaNDRLq|L*=<a|
zZ_McB>$?ArjR}DQI{`{^_o(Goz-jHF*?$9ux9VOx$kd1G?x8+=^VKZDn-XaE8e0GR
zKi3+}|4ARVkUfxj{9hEo;`e{^jPW9||HVLF|95Qh|M`Q6FGjKKKX71^Wak82fzo{C
zn-g<$R7OTagS!8anuS^gDTO!9%?eYYwRY8SGD9#niO`EU)pLFqS65ixWSpGoF;nN?
zLUgEu(HIdCffPa@UCO<C5je+h#d1z;$#q4O;&_lo6Zi*Up-0vK+hGYI=4Ej!uuydk
zjX-VQe2bfx!{TUy<qiZwN<3&!P!Y0IGfseqxtiW0CA>i|)<oA9zBPAB_=o;g4QOa=
zt~x0UA799S-IL$=1s#TzkdRO<aO16e_jnqXmX~E=1Id4um6fes`Rg+*|LXEfkA+Y?
zJ=f0dL_wi!Ebtvg&z=Q6e=btJzI?9Ib15xho@llIqGM27h-zD})d%c`?1Y$N^~?sE
zWLIymysqx;4qqLeRK#-XK73$9bPE=OEFcv8o<qfYfN|&>P)1+_IxUPySx~H^t(*os
zd-&*4Ako%X3{9S1*aCocD<Ofa#kk0v?6D?~rlw|_uPWjQi_6Q`wRuDET&Rm+AjQb>
z;IOcC^cakXYw__6z=eowK6m&)Nm4w2elyyb>r@nXG8E;k`a*+eTM0@Ai5ueN*yqnY
zGq>;FrMP(UBIz0s1!@;KQC>t)6?had?IDgEd?QO>X80^j1JBZye>|<9qUG6V<Jwq1
zpReD~TF!Fdi1ZGl`m+5WMA<ser*Pm8{8wfCF})AMKOhk!xyK<ayc4z)oPljczCT^Y
z#G9R6R%PLWQDDg^XlNQizHZ>15Z*$6u{9N;fCmeJ{w92_521od)qwI_Lf-UaTOuC}
zEdFctOjSrcK~d&XW&k7s&O3ePOuFH!7qAs*AeeYy`yIh_yfz^%Isg7{PpX9;MFdy@
zfcJrTc!I+~P(1uN6+;uKA>_eH$27v;x_V#QPaN?RDm68=$?53{v_1V2TYB^|3$)~8
z><|u}+}z}CYz`5o9G_mnLsScaG95-Lx05{A7CAt{gD@c`pkU~iyByqD_`5-Ss!=NZ
zV5(rVGw~Hnqi9HOV<2s?gh-_0wq2xvKdVfnk6=TJN=kM3AJ%QiR_=#%d;|JD2aI&Q
zQgBU8Kz2aHu0$F16m#zA7;oEBUe=q^lqZZ%$Z?%r7gXP}=GYuy`ytyc(aKcRjjV94
zYU|FwOj8%;u3O%Tu6xr|(7jC1M`)p>QxKAmTD8y-#1I|_NnJGfP^YMmySoV9YKWL7
zD!d6J>DK-GlmJR3!nh*RXvzw5updKFK%`o@1qie>z<@Ca&MmC|MS10YUR|Zv3S3~U
zMg$UtmJsB63)Dcy<%D-1F%HL0@5Nao453G$kHB+i^9ob9K4VzDMJ960BbHo0>EXlu
z=*<?0)`&WNBpp&qq4x0jA-wX`by~I3_a{+H*W%)KlFU&kqTb)Q2>?y_AwYX!0F<Xr
zkvvnv5ca%%#3T~Ce)|fif$#4xW%V8y1q8kUux!BiFu#1664C(a8tlFDS53qO!gLU_
zY@#NGNCluq?uxk{n(9oYwf<B;l}41lrb~f9`qck`8{U)3x6YLdzgmeEJZ8L)Qhon1
z$;|zk-NmBOLGkhN+#QezaAXs2Tccd=&p6oGQNu8+yTnV0LC=uk>f}V1BuXMU0O=#J
zJ)VS9;Q*uu=J9pd0Wee0-}J(yA<o10KMSXspxgoi0u2ofA5kMN0}p{8b~VK+XY^Nk
z9TTzZC!IO-iqa=F(z_ZFu}3)3zJQ-0YipjQkmr}*J-KQ7;w7fJ%V*;_7|-A42N(!{
zhG-=oU#UrHW9D8joF_tssEvXli4!(VaPVf1yoxq<qTP?Mz30^1v`baq=Ecs09R7I0
zS7y4ObaJ`WbaaxUCqx+;*E<a+y{ffij2#><ZPSsLPtuE?unOIEhbAHAxY<q=5jD$k
z3M4mXU^qd$Ri^1|OXK|o&1(#{+S*bxyTzqT+uhvUJUu=4A6Ae)Y%tQgfs>4Ex-2Lu
zY#Yq%SHcs!85tkqOrUQFxTzqQ)1n~n&hN6^=5re@`wUb|Aq3{ue*LQN>Y{6HZG}A-
zKeRP5aN-4J`)ISWoZRNQr|Lowm^je40#bkPeR}k%?c2O9!-f?)mrBAl#By?dk0}&v
zb`bbbbkLW|*v=%lXnts5ad|Pvr7k*GPgFtmwXO=+vj<noJaqI9p}m9IC2`LK2K642
zfu}2IR?X=BDe};Sgs3Qm^P%&1Nr+C{^%s#jj#LI_W?n4)P$LRqrU7V&yTh>^!4r%G
zB6ZWC0al~ZU&v3S8?g6IK$0B~PSU~##xXc`Z^@|4L4aCn@18xaQ4?SmA3vHS{7124
z2slCBIkH<CVQdPbu>Mpd22X4s2Fo6T<LWy{vKB_#0gQ(OJ$d^0P+4F&gJJ}imb#T+
z+C$wGrey5PD#AK)ZLi_UTXh#}TWTrL#cQc_0;Z2LQtdSq2u!}oR%u+dUnO4lq&!pi
zah^)!(fm_-ndiNxYM{T30k7!S_)CHJ5gZY*Jw2U2nog`?L`9=}!I!UntQ1d#p`bNw
zcNTY_r+}fnzW9oE@!Pk&r}jq(cDZxsb)4Y|ba{MH?)2%vma_;hi@MD+#>K}ktu0L`
ze|Z<5l0pavgwQ$jHhmib=P^3QiNM&g*F_eu%mIFJHYUKnG5#9BC!b#ZtZ6Y;hejTY
zpC#H90z#th^UPBn_c-IyF?G?{E&zqEVK&gQvE9ToQ|{PH>PHT8LUjGxJGB=Gd?Niy
zL`q^=(L|u$p*`ZH(je_{C;La9!Lvm?zy)MVUbxNe2Ik))BO`PEfr!do_QMP7?J@N4
zj}d_EN{Zjb9~@`-whsIp=ql-LDDAnTUaHaf^jtHqYKP}xVwyI*Y1%OoRCTn`lCpA}
zk6wFC+-w}r75U4pMesH(5R=d^w%U$-D3Kh2)L7G_+#ETXu<JdHY(nwF!iT+lDFN+C
zIbU<jHRp*Y4%}fd^IO#^ojj-8k6j|nc??c-%-WgWTqcCb1tKS=r^7HS>p&w>&$#}u
z+bS{R96buJsHBAP(6W(%0g(_O`XNXQ@3AUG7D4!s2IRxN3>JjknMIxUBQ%2nOGe5v
zDm${V@!QcD83cfhe}KIA>a1olUN#t@<skwG%n0)lM+4_~%=_IFm8a07*1r!q65=FE
ze{rQ#=Ekx)Vk_W37Y2&0iQoC_$AaLy+y75)5ogP3?Ul+9ROk6u$t&^tvDS;X++@fH
z71(PR7yWu<k|R1M59{22^*W19dN!V|t&L^Kx_XxAJOJ;aE|ZEO&d8HQqcw7fiyM;N
z_hZEl-?~Ld{f~K;tt%ZW-aK^$sAuK=MBR;T6dQuf%^rWrp9RZN`^VmRS*SDC)xUF0
z%uD^syIs#ReTVtpUU?Ar>1EEBNdh4E_32H~n?&9JgjyuZW7DQhMJSO$-7w-RQKO>w
z?O^-9=Nu9;dvAAM+h`Rl;;@zsNT&HxGCh;Wb^6z__w5DTk<l|bym<=I8azaL1o^Fx
zi({{>n{ef&4pQ{T&z~cJ9C_7W(z3rNZW>*Vli1@?^k>k0a`)sWxvq_a)Ru9h`&Zl!
zQ3SI_Y##g~K2}jGVEp8Kw!NDSPtKsKumU`Z9?`rvN101I^ADF5@!2pfQ!bMZ954Sy
zx^EHT)Y-}x^{|juRD6@Nvgnb-v@1_{%}3qO%=yA)G*8@va;bguNE;4JuflmlYIoz&
zz{=~APFsSfm=<3B$(Cki8MJJFa8f@v_eR8)EsJVu&9|FlEI#DhJa4|R9y7~3%D(!a
zOHtBp1y79SXI`t*Q5cgw`V?aIqAE_-M4ru2d&Q~X98&`nqKkb6V~NRZCGaGDb`3n3
zI5r~F-^B}(<*c7<DRen(ChV=K)7+^?40p_~J)d59wOV?#CxPlmx^aG9)MBSh^Nq5-
zp?h<(YBdf7F0IZj7Ss=PM&-QH%_rpRpgKBHkH4%t8re6I7JqJ;ahj4&ay|br$JleQ
zy<Ioo>DR!qx(ISogyxuYf*n&i?KTl&;e;lyz3$V*yVseP>yG#<=Ir%z#`l+W#pGRC
zo$Rg%pTPn)5izkbH`A0`XI;Lq@b~uCk!1}keT-^TH~I8{;AEJuzFc(l(I3P@Cp==v
zNeM2?AH8SE#HV{hA~nY<%X;1<KW(ie+vDX0XF&G!)#LLgPFQ?%H#K&@db~nR{Jr5m
z=ER_X=-i1NAXH~xpOK?DNAars_cV#rCgwHc{flETZcoBmJ;ytrf`Wb<i@=jxwuCr~
z?hr7mY5EYndGcY=;erS0ob!>%$*<L^?S9iYZ3}_>WoK_6GHUePE>M{!FD~auIXVKM
zuFnbEZ(r%?4PnBd-;k3)#30IZuK!-BDb3r1oe&78*yhO2m?WBbvV6vQmVWS2%EyKe
zUq{&L8k0m%q%>17Fuafut$pcecK-7a(}>@1;lp3agT!2-4Aa+6&%uBPjJ6QCtR<Z&
z#cXO|um?2ZL|QxUD&0y>zKJVBJ4p{8KMqEG-y9j46D~4mIqa$l8R$;TCf-vh9yzI~
zO{6TFPamJX<FReL7dTC6w$dm4n|kPV$F1M*BjUMv>e?GNPtDF=XNAgF|2;PMblmv(
zEvIkXYs1EjSy{D%uW6>--4~KOXp_fHT5wN9UGV+GN=G`4jEc%DF5F9K_D#qV$y>m{
zX~0~g+qZ9ro?ahIxggPkS_E5~NBXP6m158gt!>i#?p-~uQ=qpt9Y5jpNKbJ6iWOIh
zQbk)Ev&Dr?<j?hn70Bjd`A%yZvyPY9o$mW~^@XHQXu;>t;iJV{l$@PQkGyMa+yZ&J
z8Dz);m=#D0x56mz-MdG!qWv2Z9DHs1?~l7TLXRUqLjz-5k!vS4t0*0iP(2cG7LZ|K
z2l*nsiL__PtpdgG;5`<|j77*@C$tMFNvFnSsNm8fI$(D7FTgjVQV00>a)&DuSvLv%
z@`GelV*7RzOrd<kQdw>71nv)*#TJ&9<gkDAjf^ajzHe#e2^d?4=w)GV56_l{i1(pQ
z{YyX3mw%1~@C^cnBeG|>sS+3zL=O5*BH_jjsAV%oIY?{5$dT!QrSUE3aWt3=S-H9C
z2mwLXyuW{iFwsPv4-mK(N+qH$9rsV1JHFX(?!5pKtVAsGfFA_##es{d+r@=)*b%-S
z)E$ct8mRO|Co4K;llieByQV{S^jOSW?SZ_-POeVQG)J>_OIvPHXCzdjj-E*T+;9|L
z_-ye>Vn6ENrq4WivXyaed4B81wSd2Mm2Pu6t(8t>A53d@hq$>q*DYB#^M2qfyV$vp
zxWEZSrKqfY9Xts>BB45yZe3YhfTux0Nhxb<%bg<a7m0Ucfx!!1qYi7ah}$c~PMy2W
zPlVxuui`cj(o%H<+g)3-jAEDBhmq8X12;B4z6l{K<a4&;(i2lRZnK%g0|(>J(v~KB
z`0(LBDZ9baD;Bo4;qY`XEPkvbZY@G%I3-7<@`#PWG({%&LVfUdLRTp)EIfbj?c2A6
zng<{h1WZM65I7{t8KEj<gtrKC(THT?BUm+H#|Qp?F+Gpyv(SkX1UeN0cjR~t+UERo
z*_z1v5>@~%PGSxP&k<rN9-E$(l^Q6Icv?89c#hrUFwScM%(3kmS1&_#e_vo?l<h0T
z97P+Sw9Y#JqMF=8_$w%JO3m4tl}__a&9bftEzSBGzu)86qoZ#<Nx9Dwx@Yg1i7%<=
znwrU6CzWfbr`mQ1KDW4#VO^aZug+U}J%y97@ytK9C6caz!CruG3YM7lv8DA`(j7FA
z+S*eyb93aRt+e~1U<dC%abg#IToUDU9#Vtn6QaZE@YrBkq-!-bGDH*=_8oCy7AR`h
z`#ggnIAMVkqQjM53`80g{vU60XWH*S5E^XX=k2*xKhl8dJ_eHw3C%5F{3W)rbT!!d
z@R*opNV$ejILj{g@&c_X72Sb~0cA(XdAvdSGw%FK0W;Ek|Nfn{-zAT5en{ft;s~Ip
z9Y^&m%#{UD5E(b@B{>O}^HYmgTM0VrH~RI-2KXWZK%0O^nXHB@+^D9eri9%P9ykTz
z9f8${c^x_KZVdGFb>94p)X&Q8idTxCei;<ul2Y45QWO?mY7Aj2(l~*~Z9(>AW;%6Z
zaOgP;DeuH_AvxX<UbF9~Sdp^GKH?M5b0!xCbz;hSef6fK!0R4s+qBfrHu9+F58uAQ
z+FXZB4WrHF%aO1tm0OP<JH`Nli;WM3^!Kr0S4c)iMntz5h><EQi7=%|!KNAil3p4_
zT1nS%7mplGhBxC`WkN?`b~X<we0eVj1$uvdL&IjIXYS&bCEA+alMdWoD!J09%BZ_s
zCh7b4&kyF44y?FBIR-Eb@kj4&6Ra{M@lM0g6GGsPRs5==QzgUBofskl{UzZ5{gFvH
zO{wYN$iklx8z3j)Q0jhOC#o?hZ<v3x3C+%=7vgHB7e0pEDbnG)I_{#Q^EBN|CYE8E
zJGS=Oro+)1J}BsffuH+n*;hxu=P);6ax_1;J6(3cbouGLUB9OnnT~OiIUAE6x0Z?u
z|1+a9<#tVhv2|zGu<K<~{*s+{W@FnE-@ej!k=XGMTTYl!%F8P2L~a(2ge;^C)K@54
zGSVQA*Qpc95jN!LOk0rm{xVXvchq-<=C+_&H0<nCMn-#aPoy3p%(g_M0Sj$|j${cY
zfc969&V)IwuQ}rvY=$6143YNp@6r`$hhKWoV$H+D!)t*hA=JlfN~L~+<$446q}nQV
zdt5V-4=wKyn!LWM%?-ux=smG}1oY)8#6A7N&w@fi;#5<!he+2>&d&C~&!qjT#3g%Q
z*U)gr+MHw>?H+?H4&%TOW*34^hy&%uu}-DjsU3c^Lj;8ONa6umJ*MUiO)OE>JIZ(W
zZ}Cte0&<xu2D+)vgLQ0fEQ3agC=z0)DIN$}Y$6!N|7Ou#|0C>;tBb6#PC?Wh$}d5=
zRHiF;lKHPJfWT#fq8tC<`%FRsT|iK|zslz%R9GUNLD`2`S%{cx<J))dg3!F7qFf`$
z9W+bOL}V)nXBGJsfye+{{d?<&SsDvnR;vctIkcG~3!SYKoRiky&)Jn1%R7x<)l9K)
zx+>DqOEuk9-QCkLRqZFQn)IyTh<7`ti?p%+4MB2OiI<l>igV7`$Qzl%P9qE;f;$tf
z24kM;L6hz_)@F;zmRLe~#6`F32){o<$hC;~xtA60v|yx%vHP!)K@hq=X`kMmZ*&qj
zN4_U}&nRDTbc`W9CB!BODn%%+j-%Bs?+6cu@Vel^2q8U8G%rNWcP{H0<AP88(M#Q2
z9C<C@3CMs^CIn|4Z|x&`zv$H&I@TL(mn^}m@oa>m3hy-pUKBAikPsw7`I4QSTwKK2
zgt$)z3B4MLdZPNkC5b$*h{JF=q>G5%5?4QWZ&6E;gtv7edkZbRaLd10SLpt=K$w>2
zUmwGXtACf5Ng`E+?O|RwlRn>jlSJBMXh`AWp>X!8ytUNo>f7pm*==4#1kEmb_;7fh
z%B34&5Fq|v)OgX#OSp~d%DX2tgj9~E@d|o8B1rJ2w0NYNZ|)6CN@De0?bVa*hlYvI
zBhVa^YX5B_Y=;raeDuO*f@;B~Csf(R)l~sK;$Aj!zsl}I#V%qtLf{P<>l**v50R4!
z?<+RD5LqYmkWhr|>#;Dz)eY$Egagm90aXL}Z5jwPb@cDLx@uv55d@mJW`G--%}_@9
zIwXwY{S%qjDCVP^kVS`mFDQh~iCuBPo@2Q!a49cM(#G1FgP)%o_tpfhk8KTF8e2X-
z$0K4k!Y}F^`XjY|Lgsq5_gGqvwuy<|eD67@anI;iOrfEP?&HktN9OZ%LXH3bsc)48
zz&7(S8*0E6UVdgzGraRgsM&;u&S8fL8a`s;DE2S72=dc9Qp<#Ba{0__N+Q;lCHeW@
zXNn4z;5%X=T82^aDIeP(NK*HHBavD|L;DBw_l{KlVaVxlUen|aNq%CJ|G6Ve9M5w$
ze`WorD`8_o9mK7*%l5fihmgBqKnT|vcQFV*4`I;;Sm&b>hYR>&u2BMFzBR}tkQ45~
zTsFWeXqY^%RIZ!id$8s3oB6K{)aBV+qRvZAO%yJ!7(!qjPL`Ha4Sv^!6rwqpmXl5l
zLW?2_l6)(*XJObHa$unli_;)2);HA_hlY=CYb*8v*qzDiMKkjKK5#KocOm|I@|kQ;
zX`Kh70nY431$K3F!}T{O=bAGo+Kt+}^!SWp{XcY24^_C!e;rDui+rO}npNL4*Z5QW
zi-}RuGu=Iu*0`OrtLdjYOJHqn?V#>y2b{={6BAlPm*L@UiT%dE-KfxBUe$jrbm?|k
z_uIb(EG4~^3e-pLKbT03W;^Xz7S*kx+ob63FngHh!9P@=Rd<>>DpJYw{x!BfkNss2
zo&Kr~^&kIs?!6paulRn_;k-P$kpi~G_m_@o>NHYVTG~Dn2$QA+=`YUg1aqf8E5PtW
zbjQmJ?i{JhWCxB{#70Zga^1>OK1JGb!Y+Iz--z5XS>##S^pnfQu39o){r@oaTS2te
zv$f3)nHN^>mN3sp4e*FMs}y96t%MokM{m)Zrv{O~DZ9ie>inLCh1vRo<`>oB%nJ)Y
zw){(&F<_JUc=Q(gUe}!3e~sdG;p4Pv29vDZEY3R<MK7H#hcCaU_ViKBNte665gMv)
zGyOz(q<#uUW)w>p9M$yW8no$7AKwSov&-4~@$7Qg<E^fx`)Y4W^Y<qzu|1CgHA;KB
zFDN+pH4v6()8A(vqhlJnmnNEPj>V50u8Ixjx^-&PqaQm@ZOP!4KNWePsG{&lVS%ZP
zn_KL7PVPd^Lc!OU1-mY7Dt>r<|0a%aJGOJ3+P_^%G4R$%dxvC=%sycrI>ltAL({G%
zri)2dDbpSH%nonLJH@W=r$}yC+S@!3U$Tu9qIzLwz$i-4rK!H-`p(42)jz+SkMUD~
z<+4vtPGVk|{8nrKFt|V=>a{yP%m(hnwwF7i7I;zf8oY^!@NPP=j|RTMIPRUpX(SOn
z0x@UYTisiJku5BXU071<=FdkxXG)clbemh=oxG<$c|D}<y6>@L)q6yXOn3eWe|C(a
zXVzV9{QQX#HXWlzBQ6({VB8D9hf!Do6QA|kFxyyW!X4<5`M!+I%yeyDv8yxc5570|
zFU{EM=IPe~ODrxekv+r*&zw({PKNu5m%eT;&ZTrAVA(&dK!NOG&n&ag?C#hMhr78Q
zts*C<ADj+qnd_xur2JE!&Dl_%UGEp3-Rq;MsA^;8E7^NZY*AgGnIkZe?Q6>GN7}s3
z9v&zAR|XA@`t(Y5BoX!~Dk`!K+V?>v^BtLs*^rj0=|1SeHCL{>ER8pwPZEtqE`VJ^
z0yyW)MY=tEF3lggD)xlYRVl7Nz9fv{^I?+u<&r2FuLom^U$YW;9rO*0oxAq81qG4)
z9PTl2a0nh8WXX$p!np9}Vg9(l*+~}Sxn7sM4)37N+%K6lG&Yte<`Eq{Il05R6Zr@s
z2!ntUxS{qC!<SGeNmdsxuHeqwIP|vC(i4v!J^ID4heh?^dt=A`qQLD8hs-T4&mdz3
zw)9?xPgpn(Izjcw%0Z<AxOL!x52q-ouKF?Jvw6!FA{Yjp7SI%`>2-W$$0B}x{Qdip
zn%Q#M+OrDY7@93aT*Ji$$%UIdT2Fc;_K$RS(h`y`R&IM`{5tMN9mAbm<k5gq4&fDQ
z{(Ny%iMF&{S{n;vC!%hMBjL3^xKRdcW=|p<XAM&T#@bTHu})GEZhoANW{n`CZq?Pu
zSl_Lztn_yF!7Vmt+-SmQ!yve}_@m}8+lu(hr-jDlafs*@A&W-{;Uy)LAe&&9i{0l0
z$B+{Nk3k<LZU(4C`e|KQeMKwtact}~V94ADS0b=II5@J(f12*TLL8U7>4$8!acv!z
zebTY7!+FIb&Xqj3`ErZn7gxd5u=bfl&a%@7u{r%r>@lBe1Lc?)dqlQwaUSd9*jiR3
zJ=XK+OoF`JsbrtCJ6KybUc9im*>>qT3lbf~RVV`_ZuY0{y?o`$IWI3Uf;_)}e;bT+
zCq5(rL<k=k@S|$_N?}#g=*IVx(tp=oK|k9uh;f&Z09flAKWe7Sr&A`trkcLL*`|yf
z8a}*(z}_Q%pdd}#*nMlka3jLQ_n$m@2QYDy5bo5&^Z*$rzxXDJtZTF|Cnpjg^YQ@n
zS4&&F0iU+=8VD-_+z%h2flo|1ipwa(ufiQ?^$!ClKZh`xK12!N?*9bt1R*_L=J4UN
z3;*7groE1fb=%k%V*=KLKpXEv2^YT5K!J<UkN&3MLgiD$Q%C%Mm|PWS11pzYYGCli
zzUP^|yuH5eYwGjmgiXiYM}>iFlksY6@GyRYpYFj09htxD#~=e#=T05OX9NWNnKL6e
zWU1jzWEc_Ckbk63Y;t8))xb*+K|HTDTs0^W8`V!8`E}7cdIJ7n4nrCLR-n(#NJ*q{
zu}b)$3_CYFiI<2S9lc;-{)Emo`SC<EpNz}c`z@r7oa5&uW7|*VZ&MD56E!k=4&wcO
zl%2FUF>(vEzELrS-dpE6Mv|3`8ehIU=z_4X<hQOf#?`*};Oun6E*7Z@aO3<ksLOb7
zmc`3n=hy=7{+~xE4$pg#o15a)-?=`s02O?g1U^gv?iTsSfa#zyf7rimT53{TY9$sb
z&)^x^1J;k?f{8VpqxByFe@`Nk(S6u~VD-dJ>38p*jrbu!z4Yyy6&S+Zd-qV+=j7yq
zH|B7Rvb1wvJ~_rDV5q(D3tQUQ+nWePB81OB0OKTca(6HIF<4S^{E1qsDlP^F4#vKG
z4|#MCbdc`m37k9>GWqJxayWG>^;Cj0g?IQ3>dy&QHQW%a&T=$*W<vwy1HU8?2xR%K
z0{c=MU!hRIhA(Mjty#upwuc9C4#S0?yLRm&HVaYFQyW44;|<&4355g&`Mw`t@c$tZ
z%`WpMGu6Kh+8jIc?`@5<5DW0ZS6@?>7JvMhhU5O4Ds4Xn$=1zq!}X_(afRP@(n{pz
zeG;+fWLn8XWa}>Z__)*Ti|WbWyFc|oMb|6xK6l<ey7O9@4-8A6KD9tH1d>~>$@h%Q
z^Zgb&d?^`rlclf!r03}78dO~6LFRq(Y4g5`AX@2<cwrK5cicu+&2ML)HgIa8hZMRg
zl6GWpl&oN55qczPa%yTBM{W7#=%23zzmdZVo1MLkTY+8xTLxf6hzHR6`ky~f4dQwj
zY5Z;5w)rjJyMNz%w66+z`AYOs>b)XSCr3Pk@ERvS|B+U#@&EI!rZ79b{mq+#A5t_-
zinuO*UrqWaarN*|0QfFy7Etf3g#`za9Qf(HkvSGqa(K=&@h|TC=;`Q;0gWwe>=m)w
zfAlE5r_($;V{Tg70o*rR*h7m4pq!0X^{}|`xPDRGwc~_1&4Y=PbMc}#mU*ABb*86I
zl2L96V`ckkJ)`cobK1tD?t-9T`XT3Jv5{mAqPlOkK(_s?aB1+>84HU%F`wHls0;hV
zi=D-`9n7+ZMYB8ipN`Xa{mOcq=BpT_LYvV!l*q@7JEr4kDwaV1{ASfjozlPaUW*vp
zd&XeEq{K-73I|Up#%HAsfB4|_?XALscQP)~;*RYY$_^iDw)93~VG-GBS0pODBa}>_
z^-3<caDwWEV)UR<2XZOls;GvJ+ux-&#N7!#=>KO`4-xv>^cd+jd_IUlnS)9DsN`^|
z)9FX*GfZqfMfU1t2b6X?RxUM3WwJ|2N_N>E|K%g4_{wMQsyHhf+p1RgkJ-7P3x6N#
zqwQ)K8~b){Fz()+rkl&&?|7Q-vd*R4e#WwhN#F7vgT*Cr&1$>5&W-J$+~SjzEOykE
z4#LU!u=XQ2@ekp~K2+POt=?WEd(qrD#>f=rW6XMEE46Je<-6zA7xnd-1J)<l2N1^u
z85qN2nSZPeG_JU+R70z;#U|rlIkE~iMnOgO&TQM3Ek_XvBl27XMa6CCVO!Y?78Zmx
z{PSZFE%E7bSQ^6d{Z)9WbkKV=0NN%J_MTwRd%%eh7@hp|srTHWD{+aTg<D`!|5M8N
zzXXefI`K#;{hGZ!uFp3#HsWTSXI}t%2NI0qU%vSD<#geUNG^_GY&?1R@ETOPmtRdz
zo~|~VAKQ333ojce@Q1HIvJObq*qzosAJcEyz9??l!FM7tucpW}wZR}!TWH0KzKZed
z3?rrgRo96IVrm}g8oZx5G&bCGdnRh>YlrwBgIt1mB8$U;&vwd8@%}$ly=7F@?e;Z{
zD5!uUNGKr)8|m&A6p(I^MpRIcZYgPykOt|HE&=J1l<w{>2?6PP*7p3*8SguW_ZWV-
z-Er;fw_?t@=8~$f!d=?B7X>i~vXZ5(?J{V2M@KD(hjrs9eQuzaos5v1TJ{2<fowe?
zmHGhn1NRgVGe5vBcZZbaccG$S1Ffsj`JZQ?50aFVdjbg%00Qohz2<c%J|IH{;anSt
z#dyK}>gs$|__s?hkhs0bE_dCAL8l7u6U@l&X9{2V17|X~yxamII|w0(l-N^KQzQMC
zEG>)be2Sph1au-CjtXGdK&UIgy9!)C_K;~%UDweY1m80bi$Nc+^kAZ*sHEgI_(=TS
z7{-LYddQg5?YN072CRYL1iwL6g%%p%z|7evi}G%#NVwJzqI3RdM`dwdP;GTnei3NQ
zEv)MROsuH9d=*L>Xze56>B0gfzb=sGPz59X0^t{1iPm#PHs9D&nr}eDi%d{3eBg;K
zd>$g?ath<Ny>QE*4l{%a5$wPVPXkWS_&XJdMj`wmOCgW1jNHnj?;t`wy5zF)BirX?
zHw7)iDAwOHph4x%!N(}8st#wdAg`#r);Bp4^$>aaBLfcy7Z(?s0B6tu0iuqc({2l4
zQE@Q^KYttm*MIb`4)oxruNUn6Ha6mhNuaQp800h{R5Pyts76``BEJhDn|B`xyl`;f
zfpne@<vxQ}nM^$z+7$mTWdExULOo{@ki|g6(+9lpV51o&(P7~>NC-f*8|K<H*4QYD
zjF`5iH3H4!<6q6|FQT9bMXI$fA&o`@AE%`ETo}gk6P@=YL97)Bh3(b8@RcV_Z$SBO
z1g8cH#r+AB!d*yh!_sCEQdGzr0<=CRASbW&6V}(^bicwmve<3D1jE1aTBq_$6DT$i
zl?{hXcV@3V3y8rC6AEgO`Z8%(`~+Dn;Ffcd>bihGiPYD&fqh{Y+2x;`x*yl6(6JsM
zJtZCBku1t9VUtZ!nA&$lmObo-U2|}aD8cx}pd*5IbEY<Kj$aF3faVbU^Dtze_MV;<
zIKG2Kr_C`|7#)IlX}$is58v<fano-ZR_1)Vx3XPCF1SX@3LR5}%Qv#MvlQqwc9z8&
z=Ne8CW}j*WOQxpyRB%VXE|_9W(9K}9w~hC~k0&WFKl-IDGAe2T#<-oGYc82{j1nEu
zfy0_{yK+H!<h02O()L|Q(qLpZy0ff0dSTq!d~Ig0?!CLaI}GT00XZ{u<OIr-b<j>h
zcVP%haP*Cobu9p6z`<q-0(9#(Mz7!z30>r8WKDNkMkO7kZ))0Yl;kq9aTXwK?LkYQ
z!(d>#scF@*w3WEICl%vVfT723bTXx4Zf<tCgKfVdE5XZ0wA99!V*P%7^;<t7KgZ@@
zX!-!fAEdTK0V&qON|^oD#PByKX3ZadV5{?#X>A+q2960O*xzvwL7+sc8=;$^@Hg?!
z%VR*6xgaQ0$W{SQN>31>Pjd#DG63#KAs`She09<b=0+A5r?$vZRhU_tGVIN&Hd5+N
zTJ5nRy??(6e$%N}r-S$fCKG&8Zripx3W`6I>QC6IRH<(K#r!#M$9gH89`erci_Uj`
zliFI|%s~-R)N4&XyU_TmuJ&4g_xxMGZ7ZuKvHm4n?^koPf`UTdyC5rN+X|dYC>Piv
z$QBnD|DD|jUz=jfMd&AZ`j*Z<<5ZqWOG-Y5ehOJr@KF$wl6FH$bsG!o4cu0HQ&kEM
z9Z*SHuXN2r+yD=aJ^<4}CvP{}+L6(LciANt(5-Z*AnlhxIYTs<%h2XA-1tdb?U!j_
z>kz{qYHHTQr>FP%?BM#%n|K&#gWB3_vu$d^q#_bu%Cp};Fb{ti3cgHf-~_6x1?5WG
znf7f)(70OkMkd>eiHpya9{BXJfe5Di)Yq?X*ROsKtB~@SKs84k@~yJN)ZMDz;Ikc3
zRA}&on!RsF*VF5Ox8mnlvG0-H8gBS9wb8cAC1#3mo~EwBm4~wiK9@v6;`QUSPd|KM
z0BZ=J?O5_#9^_M0xjX@!tR{aY<DCp2?=#U^lvp6zJ3g0AS1Hca3y+PzN{Q%|-CC23
z?{j>}7&>TN3I~zg|E>@IqNbKQY+O`Y`WDFfnv26pa00YkYQuqe*q>VR2&xhd^KHF*
zH&W7@jr`|rn`1Hw)Aw~Y?ITp9ug$rng*fg?X)}=cl^4BAOCnRfj{XURzI6Bj0)p#i
zrlw1}+_DmZ<+Qo}_BJ*gpo#sk>k=5%R=eNBKQ=ao?3a*(!r-TR;G}Uedv!s!yu1ts
zA0<G?BHKNXAq{UD<!M(cVc}x}SYt|&PVNl{m)+Mk$!9CgsK1SmFSl|SUsP_Cn?yEo
zdC@RIB*D&3;Pi1p$0*>jNJTUll&DcziR*3l={6hO6j|eGPU1}|k?xXyV?AY<W4kx=
z_1ftE*RSu~6Dk=cNJz(bw!p4c|Gj^1B`>C+pd|<U$A?vY{x%aWcUzMQ&W>(*i1_bo
zdiO9qrgmrC)#{VlLoIsbe<Be2!9B>Eu|9j4<meD!$%aN$b7^S;KWk;Q>&6_RCdRU{
zAnruUy5elJ7>8x@x#D%ZJ3HCsd}ZQaR++t|nDL=nFm)h(yka?S`JuYzP6DEE4!bt?
z*JAd_OU;-2;kopziHTOYMrLyMCMGLY)vvQTPWc)M)^F`@OBpYKh0t`)X|wqKN|4I_
z0f!bLi;sIy2yXQG$0KjYkLO+84_vGEfJ}RV>r$}9&fA0@>CG88J@w+xUoRn-pIw2v
zWo2D>s~H%K3hZ+*u8~7fTh~ui2}BlH8$5FG1aTE~&6<Dz93qUZZiw9TisDPKjMXc+
zpwN4Pd%?WSPTNV)-WV&pz5NZ$K_%m*T2P#vGAb&P@u>Jk>3?vTuZIPY!oRzb!urdc
z#7KVN(TQl-183GRzUGt4re-Z4v9XH<1Ro2KL`aGA@$H3VRTE4+mEMmc2)YK<M)~On
zmWIE6f__-i&6D4s*WBstV~s}mKg;&rpt5Xwz3;G8cKvM>m-FJXSX6nrkiQem6!|73
z$UgDEFTCh8thQ^WM8OS>KTwG1KcOy{hAI@SGPb{$TEeCB=pzY36kVPE2O;Z|3>1p)
z^b(N3$op{IIvB2PZhi~%S&9;C_i$uraIpAxDLIFw@i2fqFpdu5Y_P9doxQr`2A)FT
z@_6wecwplN#0h$}yo%KvRrtmtS&t=g>z`c0JY_fv%@00`-evZjF@FXVVy=p6<?XFI
zj~{16bjVqn<@7lY<!S1a8mpcSw;^kA1%;=8BqB{3LGuCU>GU}uAa2&=`aMo7_}Gn}
zYY0;Q!tEvP5nhxCP15^4E9^BY-L~+$)z!+&i`T)BDMf{CbUNXuoYj(=G8N)mvAG`Z
z@UT=@x2?cmg?o=5U$$<mMGqEmT0yR;xw<?9QFd&5IQ8`q&|RiIP$Ss5cYu$)n8^D%
zI>S0D$>&<L_u3RUv!8AEM|`tRQR(Y6-<zt_RU*BKV_7N!i|g;hB&T9I{k1FE5}*0b
z9d7OBX@tKJ70oHCHr&I~q&zwp)w7;&M1$Ebht&jo6du=e9-ws1!{y+X-tu_--d3|r
zccQ~vix5RY)#R&l&Zo!AJ@MuCb2quUf4I8dBMEFuuvT`F`7<@-au!Czl=xJeM7#d3
zqoe+NUiXVLvYLySK32ai3FHlB5=$HW&TGa#x>s;bUoHLB#yZ8RN;dDxQnOGg5h^OE
z;g@=4T^57h$t4DUTPc(#CVn9BU})o=t}E|AmdkJBiHp2p$SBsA=Z<jh2iVFqcr_<0
zaow(2DI9jsqmlQ9^S@2e`5Tlj`W(jvncKBF!<*K2D=}%5H_%1P%^b8e*=C!rYuy**
z<`jCwq&u`8&SPS7@`KeIm4U&gbxWIYV1lwLmV=@kMzUb`y%B^pMP>?QNcAXzaE+ip
z4sX-_)xQs;{K{YXI}qz?{i+dFZfFf4mZX=t{<Eg$vxZT}tr4E$rtY8z{QS4>qLU@b
zxtVQvHn%jCnF4VxB_l&|cz9nSH*I~KJ*=bSf<Y6Hfk5C#iI4E?79T}91Yc<KcMlKE
z4zD90&xWy%+)fSqSEOHCzM{O0QrlY^w?aj53OiI-wzQ&uJiNioENc28W_iW1cr`<}
z0%fw?6XBklD;ye{tR`W3af&uIZ7UN)9#+xwO_I_Ih9;=!=rBM}{p@eHtRD_9yu-ER
zA2|^a!YfrjVxr`RVXP$PH)B%Q9sMD1>A(<u*3jg6d3Y$pId84}+F&j-sZTz$s<MH-
z_ftZG<r&qFeT`h`3RVD>MGm<D3NUmw0cBt8g~GFY|6-kjibnIqSm{#4GK#*AN>7dT
zFb{pg*xbBGMkM~<4Ky>eKnt6tSNDau52kp>iqg3Jt?i@3d5X#{fAtk8=K@{&7H|_1
zA?M=)qDmM$<Ty+5c#q5Z{C`iuL=2${-XEPV`z7_I>-t&>t>+II%jim-m!iDUyu955
zFtM|T1Ffu#(-Q3kz%QjIp>#Ac@z3AEJ-T6{1*56&P-e<XEzNxV%?Ji4?Et$@ZQR6w
zyD#e0$=JDEJ+?!W!+`ozs4xPp2v-WzsP6Dja$6&6W0N8eKReI~?l4!dZF$CPi($8w
zQ+v1<7S3~ja<Zb+cwi_8r~*)J01^BkRo+xoCLdnbMs_@XJTFK`T2l2k%ElDiicX*6
z+x0GVZ0wG0ge6^R>{m0hzg+W`E%&MCM)gbzU7R8DhBn(i-}?A`6i^{YKz-UfIz%%E
zhtCQrwJNRBdDJ8zh-0?X^%(rCtyit8BfN`nTSf|;SJx%Krc?dh3h*vr-xm~+;q&LW
z%X7tD{f&u9_!&`<<{unv2qGUwSC??{rHCMg1~nSIccl~myXOCkHN-YiS^w1n_>4})
z%+k~}_yiDV^{JrDx#mVVT86ycX;p)WKtRw}&de1Y+;#Mar5#i{FaZdz_PS>$4d*8o
z_2mq%t#>+;&)TS{skzTukpV`Zh8B(XLrp~}ohd%bj7EA_?KkrN{sdAcrWf|&3nV0@
z8f)|JTGVv-#s$xO$?0+qCua5J7G#bY?cXotA%l5}GO~0~o_fN;cL!yfO)ADktR1bC
z+9@Gn`Bhe(i)CeaGw;_p;>urJ&%);BM6$FLhidr+4kBJG@Og3-hHBM0JBFKq_1I@a
zVHHRX_a+)6<LRB<cAZ>s2}5)n<2&i(Do!DP!e#yK8?D(5a8$V5!d;TAq-Heq#u`To
ze|FoT;5HbawuEZ-tnOtP0{ZwDzyVj=6mT~C#n1bnVl?6IaxvhTH#sSEEfz11**OyP
zN$NTf#w&H*_g~pO`ZEt`vGy<-j>xApN_j_zn9a)UFvMsRsi5X00ST7%%2Vq-wOw|z
zms`4&>S}7@78ZZ5o9n-CY)M7l(_eVj>^?~a=U!gtgnDZS1{nygKcGN~HZ@&d#5g^q
z8ftI<)+-x{INsvsajhxKH<FJLB<_k;L1VVDOBI)cFTJKBXB58@ZBoBh*cATR5z!L1
zH8WjdX39uLGEvTaF=U)kSrz;5Gnq46?Y%M8dT~qO<Qtq|nw?JRghoD48!#2Ys=@%j
zCq?iyPpi3P{d(b<9;2a*&z(4Dm-Zh&06korjGkv=5-@07&5#On)@)iIEH$nD*pR1@
ztn{!@PjnwF8p@4(wOA>re>_ywd-dNE%=R^tB(AR#wk7#8iapVTFonjDIJ*yfyHvlX
z;CiIUlRc@ZV(aMd{}x`6z(2n^K2i3aC5AQ4Rx<whAvc5PHYGJOleGfUc2>Bv`eGt9
z7TrgW?lDELdug2z-q)}0nn4r-@wD69Ph8A$<ZklY{XE$(HFY`t`FCAE_3fv|>;H+8
zDNSJrf4%!|%WqoSW6@ii@dwisx{q{cBC=KH7Ch(}0>3_SU5b*sDSUCGsHoHw2j|c6
zBHRr$i0Mel9FSe0N`oE`_>usrbfht3(4$!@<VVX-3Afp-sS3B&SVd7-L$vBJv}Sfi
z+Rh}Z&_>R!_=xSybnX5{w0U9U46o~-*;P0cmF4RR$8gCaEOm6kFOTHEvU!8C6$&zh
z37xJ{3Y{$7*L7TuwA+gBtNfAiQu&<_Ts^pf@m|e0gr@(}eqnS9>~%YikMAPE(<H_I
z6*Z?c-y<&1%nPCS7oR|UW^E0Gib3Of;j8SV<~tAi4k<1F8!1n5D!{)2w20S{f<(|2
z1OT7_V+lh~5|B^NjgasP^R&9<<UMlkEi<JRt=5)`eXL2h>-0g?TGN7kba->i-YyP2
zy&oOtP2RrUG%JsbMKB4~m6`UD$ME6H*f9F`$;jq}THD%E2?{0v*?_cMhJ@H}TnZ>4
zDE$olT)E=1Lhr+^bru>wzI`+DCT&)TJeJaXo34tNLEc>Pfg+U4=6kyh>FUhkLfrUE
zh;3gon0uTkd;Dy!UqTGm=<`IWxY@ur$*x4fI558j9nN*6qY~&eOb@T4!IzlVHH(^B
zQkZy*A+U_-kx6^4mgi$ot<aNuv5U4ga;LCQfxzLTfk`8A#(R$MZ!xuNH79`=l8L7Y
z<4LJ|BAf3Dz==$vz*l(DJ*fRrVbMb;h*u^{%tfgwH|YPbW}Z~nwj<F?I*XN(rJBf7
z8geqb_vgr@nZU`iqaPCu<ImBrdVBN4byUfzmF|P^icrc);r~_71Ag&fAP57U&b3@c
z?~{?Gk0zhwugj$ilG?{f$J=kyTMhTcJWQu}R<;8=y6%eY$&$1RK7Rps$;!HE$}9T4
zntD@)Y)FL4B-cldKr&|W1urL)AL2;wsDt|59{CW2<bhqMZnd2$zg^+-f6C{35;nIX
zgDETzFMp{05&7+WB9H3ociIcF5p7cLU{PY?%H0i4^pn%Qqi9{WackvkIXQD@JM*Up
z6J<rOAG0YTEHm;}{0e}1E6oxw=yw|dY83>oGGJ2=wK`G?^R84Asn%1=T?QJavAsQ8
zHNo7R(+HV3Z;^K9hiN%m9%8!SW<pC)Q$UqDmFSf{m}Q~#`u!{a^tAhcw5G^Nv!5W$
z7F$mxVQ<Es+mrJ$w55kVBM~{Ae5IO48D9kUZq=XfVZ!j@NI5CsLV(VcA2)cc<NU9r
zD^HLvXWm_mB+DL9yQZ(I)X;VV-=C{a`+%u=qzelV10yVaos8(S-dn12Muxzt--pT?
zw~4u)YHFHJdfncO$a(CaWZ7h#>%g5Yz74QAk|5EY1R|Ojfb(2__@&5g?!Zsw{`)3R
zyxq&$l~^?|zR?64BW#2>DnC8_UWC+?>4!gq@BS3#AvDrbBrLw;IL)}y(ZM}#4VozP
zC5s~Dz9p*6@t&*_08=)HKl(F{tEAAdgX2Ha=`voxF%0H|9;POBKRNvOobb?b;GeG!
zxK%{i-$4<JM6b>rj?lP~Tv}mG=P4>ukWMvfZEa~||4TbpOey<&XXj!)nQKhQ&!?1i
zo@MHa*M5(Ph>M#}f;p*k#qKHMfjo49PpGUwPGJNUA4nX~OEqP|R1oBr2H^c*18Aif
zshK&&zxRG%7CmDK+1aYo`dU`caB}5)Pw&#3bjt~*&-3zbZA?Z>HZ&}yXO;*HCDQ#w
zjk!l)XeFubSQ+2^)^Mau@RSCN)VN0IcI$9~zjhGNgCN?4;d5}=tRKf_M})RLOw1}|
zL1tFmU9+$Byy|1Fj{K1mQgk+S80Kp%%=S-B*T2nP6WQACX~v<vWoeUFPVbT3T9b!0
zQULSHCX-L(eHuK2MyXk*9qd)^EKV0t^A90yQbFdHuO<y#GK%|?!$JhI&Nx6wF6eKF
zq$;QOr;_JR`FtkUtg-kx(m{ZNnI6VEKacf$6r2723~@ohfD*5NwXJlW8jXGj-UFV#
ziVIx6!Y4s(NRN78!o9%8<CIyA29Tbuk;;*Kl*rz(RWUpJ&E&GFcDpP&v6k6#?4nHx
zmOQ;WgUa5<;4OOkM<0)0ZR%(TPH8pK(uRgtY<@V=O!@SFFxky*@lbm0!Gx_bCkso+
zsIF5GJUT>iv3H30c|zOZd3~U3rolA(%cb|Am?Df*j)uL{f_`AmaM4F9yRkawvjpJ$
zlQFD)V&d~Cp9AKrcVKc9KKYzKH918i@{<QgtuV@-Dyt|c`~*5;_I(&n15Y!r%m3Bi
z*mj&pbV&&(pVQvrTBi4mX?Hhes`&fn-wehT(A8+)0eObjn>XQMJj(u#ia2>1%EJC}
ztZ9o>mKnG^J*oK%)8C$(!L}i!^{?sPQ#j{_A1}h1-6K4m9d-%tnrPEjr#I2>66hMr
zRcK^CR8?taD~-ME74U5A?H!xu%xV<n_3w-%+)Pzz%+u)0{GwP*6%zsxI2e$7U=5JQ
zurM&X6@ZUX^aX$yQ9`x!{Du8v((~A8iMNrc<v&P1NatFX+h)o~M){|wSIETQaycWa
z&P7*OAeYZvS?wI<l+@mtY2AwMV=Xlaf==3m$YqVaS730{m&oJA60%@aSF+^VX}u7)
zN=J|LP|`MAGRXZ--aHjbizfEh!qWXKyGTBlo%$UnVJKWNFqTXrF%q3|MV|#tGN&xE
zwFy}z_`g`flHU}0^Ph@GCs!$R@r*cnEac-OSzFGYhmK>bk#h!yO@F1Rnxv%^gC1Ug
z{j1Da<e5>Bqi$k+Gd@AbC>=iJFhM7xay{$;jm2q=hc;xql3~Z7hym7-6byA?nztAl
ziA0yf@sDxb$Z(n=9fmNFk~-cvbzImFE27D5=0D(yiX>`P$&FFf_6K7Sfini1BFze-
z%1SF-brpnO+}DPwO8r!c77Ws-&@UjB6G-s~&_AD{(E*i*^IkhOOjITzfoY#LqY_#?
z3?%*Y=FPWbLLWGM`nM`-#gx=>S%Cp>qGuW(SKm75c6RQNa?BVAyvOiZKzn9-q@x3V
z*3a=Iy#Dk|QH9-IbJ>~XG;PRxSO&@nkalSx(HPl$^cy!|y0_uuFDo$IV*B{_9oWqL
z>p7KkktLOSUjKJk!?1|P+x+&IqN1jsiRN9V6Ys$8_LbY1*bMnwOY9k^hml7kYaYcV
z%iXThQcEV*)-l}Jd~23VqBn;M&e|d)Bj-0ZGz+_-$%BO?hQbOgItJJj$U-WwfWSOR
z?pJ@Ks8Dy?+|!;rJ%)*59hE^lkv2XEsy~fE!~*Y^J=68<k1uwXW&0<1{C_jB*4s)y
z^3RoAU|`U1@aTzdwvBR#`&{<fKHPW-oJ^ZN&@df9f)5-9H5HY2`OL=vSQJ(!News5
zD_A59Q4kruZ=(CCa(MU~VS0GH^_|r&fyOZ92l1iJ<W9mh+Nk<}-#LnSN<mFU+}$0w
z_d;9atFumcvu!`FNW-mIj#AB+FWX>7W(?-shlhv3QW*)~3tjwG2A&G5zt`~ozmw|@
zS5|E)baX^SMW;pDXm{62U2buB6lSopt>aF2O5L``Z0fI#bLJ3v`l+b;gNlX*+znkO
zJuS@+N$qko<a5IN@XZ+x1W)kB0l%;;zT^6z&nch=@8iJNWW0%q$*ZB_B^L&*BW^UQ
z?{W}FYXZMS;pD?C#!cjlvl)D<Np6^!7@OwG3B-C?e%F{TFfUJkUi8l{t69BQPp*#1
zQr+1$KhQmj*(v}xBr9e61qHnYVxikLS-<MJi#HOMiK1=Q3PbeZ6YjqkNB@P1*)NHU
zhL6_#E#ccv`)h{j^0+^nHqNGk{(p!)mb=FH`fs31E$|t44Gu1cR>UHDCWsPUtM;ia
zNjYaSI+8i9U>bt@$&(D-IG}YQth|LP$#eh*8LceLR8+q6Gk@vokU~L_m+*M*HHQm0
z{b70JjYgx;*OX4JD|j<Ch7(g;J8*LSE;-{Jt`DF1Up2Lg%Pnlg>(bsrAO4oD3i%TX
zpxSknAaPHyNA%{@QnzO@Fjj?1)J%-%&e`66r9w$FZl5F{n-$aG;+UCFYjYY(SLBM9
zqt_zGE4D!5(R4zNp%=1|;T1V_Cle!Tati_ifs@P)96x@y-$k-@;Aac`oWyLI%bDFj
z4F4a-;gi|F&ZDN%Jl`mqW6S4Kt)}kH#T)y+e)DJ1Q@QO;_(?Dy1rLYI+tvwkOtc^T
z{%%RBqTV@bXJ>`Gx@|V)dRr6%0oeRBzWyTE3ebrRxSoK5Xmhgs3z*HZf{nnlza``U
znqceGe`PP9Nez|cf|SO?`sLMdy>RcTdAWy%XZ25x#~dU0PWA@|d%_=4W~O{RzwJ2F
z=f@}26&?mPq)1w5;nvptSEJ;H!<+<N34eR_SA8XF5<x^QAn+cqu%rZs2P`nvtG~L~
zvAJ)O2UR>@-X{N~KVo$Ai>0rxH(H&T|GSZ^>$c)dC>VG*s(k18SM6!gvDS*IIXEI>
zZpCp8Ke4=f*9w=q1I;@)b$s#*at6rUeCKdeIqqMIwozTyWb8*ni)UgIS6{X6(@ZxA
zOcPB1#hrGxEBsQWjnZaC%3Aa^u*r7bI)l4w2m?$TSGtpjJ4S<p;kY(Gz;R_{>qsoY
zu%{!?&`5JYuvP|cXBYCSHAs{$_oC~O@D_y1$-(JyIYdtKr{dVn>nF^94TpzVY|%XR
zhE!3j1_yd2CEqFk?oy4(#y%n77zzzR(JC8yVkswQg`25HT(0NQrx1P^dUmnn_64Q{
z!9N4&e^jK@3fZU?B;4mCEV>zrd`#3NzStX!q3OLbEN15}*To&3o`{`wavCh0Lnl-d
zR%iCNw!<c0xN`BDURoz*^JiD<V%94VhBs+S70XC!w*8xAsr<UnWeF)hP*Wp-k*QNI
zSfc<Iag*QfBfm!{o-xOHRJ4Ti`}ehW)a>k!EbTI)OF3^>kA{|-G8)?A3hXp2t{1JT
z;=!26wv{gd8TQo)_7KCStzDGF0tqQ3Ns)Il5F$N1JwaQ0Z)RpDYuFf0P*PfYF;wEw
zJhWBFRO3$sNT!TN>A#>HOn^T;Dc7u>{pLp+WfLihLR6H+{A@05Ab;9b)j(s|&&n(B
z9gxC!_@FcCdTmXbg!2#*Li20E0uiPl1tJVm_y(2)$DMkY9&(wBKwct&W`V2oeF!qh
zX3HuJVVRv5IJ>t;lcyo&C;^(<M@qRsPNn@-Vu-GD{xWc`by`R-P`h^4XdHk3@j=xj
zsqP1c^JB`PbHR-bI%-SULb4T2F7N`ro37i1Ei|D`Ncue|Hy5ehay;Mtg@pPhtKCGY
zDgLiz<P)tiS5@7e?v6VTNzh^T-!K00rYM?_eR|5`hE-YqYNJHqu=GL%r8t}O@Mr>^
zKf6lTYl97H%k6C|S#5Rc1-`~!2X%u!B3_WHA*FNh-_R3me8@1d1v0tIPera7eGd37
zTMNX2j6&|Zxsk6@%BRf_1N9gD?drj4;Y%kCbS9YbtBkMavc_qeRW@{b5<E%yjl+4c
zu}_-O75Qjv2%vWl`23lIoct3E2?9Cn0Q>hKP$Rhv2cr3JfN8oVz!(hhu|K)NeE#eB
z^hp-7n@=ejl*G*p2=Hg3Nv`P;MN1cLxaZ_Vkr4k~Df?+yUm~C6K!#iB*QA`QItn-W
z#;7_;h4Q-AHCJEz6|BuE4X3SjT53yip+3>IRL$6Oj`zj~APeoImM*N>!ypPmhi`1%
zD>%A&vrG+G+W%ALfaC^LfyRG?peOrLzO-j0)Z6vO-ps5P!PGVpd$o6H7#0u^-y9jG
zROEVxu>K-f$bT?9VkwZgBWv`EHmpKCo=;WSfB!JbWZ^k3wG9`*$jJU=e?P6NDi-uJ
ze|Fri;-E*5hi<dX{%YF_hRDD!?iSL=BXHp6{LFuoRmwtwo=*45=7*7)8c93V$hztA
zsrXC*ftPdh?s4AUQ!x?G`>$Moo~+5@so<d~q?V<xtyjt<)Ds!S^|%D9IJLkFkCo;g
zPW+O1<X=)|7X8bXXo0=uJMcTJb8|mo^bypv)G+S?j1?U{eYTkeO37oSHPiBP`i{D;
zE|_p5FZD={4PP)GfmPo3d$I}Z_{g><I2g;?;-`$`>g){Boz2<a{956#khtVRU+JB#
zfZ48=z`G>W8}me#e`z+SwVbB&iMx`p)zn&lhm-#hhtvS1ek&})jDbar%r$>dQNWi4
zxwVYZ^$(~jTEQy_daVVRH_ksQt*H?J?PoC5ogm~y4kMlxA{jRol^ah%Y1p&SSACa~
zjcs5|XH5y{#@yNkMoC;kX{lodO3!C#Xa`{x;D!=)a?H}vp3UW7?Ls^qtrzmQ^CQ!$
zwm9{ARx#UZN3#f)#va#ekJdOD+|dzMVdn~f!dGu%68Iqse2!&U^N_`u_`jzSDc@0{
z!v|>$3RKQ}u+6pfxB;xpk<vl<VHIGCf5(Km?wCSNO$`+7NJa`|JFtZC29TJB;D_!E
z&o3CKg~0y|W;$5}N61<shK-*7gp>KpkI$b!ZV5$232bZznonZ^F1@LdW4E<rWyo-X
ze}b}f$`ii-=Y`=NM|5>!%Q=_VcOQ9OziC`}9aVouAnR*7fmcrsP&p`|r8`C3O$LSp
z#4DLC*f4Lz2bMTIOiTwRJW|rq<FEm4r)K{y*t>$Aa~SMEiw6bCwR>Rg1yg$9J~$7`
z8rW-8(&BP&BI^J8XyZ<YXyelJ`0%D^4UK~^9{$MB)Y>{48_RIh>FGVY<7mgr{H@<p
zw-vTYWGY#izF<+cHr(yXB9vwGHwlQ54vYUeEW1!;PbfK6>(`6dWPC(a3D4QZt`<~i
z)vHG!9)(I>bJ*QS+7yQ6K7akHQTq*?_fuzxFTu(t&SnQ`77U)}ZOX*}Fb-nB9hs8X
zE(Z=vM#gsoWGDd+_H_<l9`Q#hi-|dxIB^e$J^%Ur70R~+0ZDUW;wag#9_dONR|oCw
z&#!u~LXx8W17ukNZrKgPP&zwhKPo~CJRs|Ysr{fwNrx2${R5+4NQos?@ba{%^={sH
zj*E9gSGS*p-|+lsw7<Z_CgL*Y6y6#O#=OBZvX3Zeo6;Vf6_D26J1Iv>gGEuAb<Vs{
zd?4+yPFH&XQ3Tn}#Sv1R`(dgg^t$bp<N=BS8DuBEu;yYm*X~Zr%m#|0L@q8F8@tw)
zshUg7ADVujMeYC6(4Mq^!_T-Dg0s;Sl#(LQzbYxJ^X$&>g`7l~{;ZIr<+GqU{TP12
z?V^J$@&`I`Nu`?M%_3(6Pq{XKBqSubvOWs}o!Msfga)k#d5qW8hVkLjvSn#ro^EwO
zC2x3q!n$Q)P^XoZ{v=n9`8O-xRW`?EM-`2);Vf);*+$q=Uc%mS`El+uBCC{=e<scM
zxF|V49(s+Jh~ZMlB#OHsx3A8;vwJyMpg{Ao_M>esU!R4oDo6+W-*F(GJlV)p2Y=4n
z*x2U7)?SC8_?a${tXD_^l9ZEsCX$cT7XyPwQN^pQXEiHbsr@B2wGSUpvT;R~O4!`F
z<5+FnzyRt(@gmJ=MEL4rSEORf@tGHE$&pN6S5+%pXViJ1H17E+eY}9iBmohIywGhr
z)yc!&p??<)kha)1@RMGwlO8j{t&AD~0gbwP+JBWa28M<{pn*gx*I=vN3UsRrpio1~
zsTOUo-VZ+q1q=dqf1K-Mc<8f)A#k*m8tcxTr8eT*k`-vy%#D6IP*ZPib26yt-W9&W
zie|R1{>!CqRiKdH7b<{=`Kv1wcu3Y%vAw837lH1~oe+R%C%#A&(wQ8o=I)B81weT5
zb8v8-`Rz$hj`h6ZLvW7ks21Oc)u-*Cf=9w;AUrk%$P64d_eSe4BP*>}xqe^%<7J*n
z7iQ_(>pprI8F?^Kt)a1-1)gG2QAe)ekNn+*U+L9j;Lg4gWGHRCYg#U<&9+h9f4+9#
ztJ3%39I1_N*bSgEDy>URU%g^^?ek*6t0yJhQ98|E8&qcDX7?UE82c}T!4hE*<a2Ry
z(i{;nWKzz}zWAy%R;aA(D6yf>c-e(D_>ovz{>t7~=Tg*a7|w$1z#KWrmiAX-Awq0^
z?CZ7RJ(8K>H`!ks7*C%5bzdJ8p+#r$mP_rv&-?bi>nT14+615SP11iD$B_C1ByB!T
zKSPud9NyAG!a$QOl{r|Ri-#f3!^bw!j}y=;ByDXC#Dja6Oh-<qUuSTdf<|{_=;Ni+
zO5AUE4b*ltUdGu^Uw<06$6helATKQP;e)Bx^BV$}6)zTIrJXY#s5Lp1pX8|OBw$k^
zQM)LFq&6e7k-Y9B$2(>BZ`YP2_*Ko@QIs-A`3BqV_~g)8by!t6pRtwKl2owKdC(C{
zi`x)4JafnP|5$Oe?|*!Yn_CQ@D#B89^wkfd%EJtJ1d0s2Uv5H$8`qJYvoKmCfJ;om
zxCV0dw4R<DnzKeX@@q)!w!MxW5Rw$7ERjp8Dq%O#!)7K4ts~I}MKk(;r7dT!*g!}8
zVG@0Jx776d&Bh2?eF}+g44voQ2EIs)d~0X}@MBO2V=K7HSAGP<5N3%LO2~|FAzMSQ
zvXGGYuDWA~4jVotIy!}qPKyh_bFZo*mD+hqV?_=R?air`)QxlE9q|ZK``=fDS$lKr
zvoQs&i}?o#%Bjh9-H!8&u8qDnqq>?g+&GeNabjgUIoI%K>@|h|eZE4jBu$so^~{5M
zz2Y~LY1sH)M5%R+AoQ7x7SCK<Dm6hW?{h9M{P{oPow;A{hvlKdl8xQHU)=5N0+n-%
zYG;dI<f{o4UHo|!f&c+SWsFN`^HXZvn@6HX8L8FE_K8?dxWxNTAMQ?hPnR$$UKc(`
zxqZ9T6gDV*lLYZ2<L+03ms@pTra=7+pW(+l#auE3lqfi=8#YTS>ekgcQkSJwvGE(m
zdz?gaB*JnU&u^%xtZ^T35_9-@96MYJ*w7>fnNllJGRr^mmX?oZ?%eldtP($8BeyzV
zdbaI*a(6K_S>m~%_WgJNqi~#g1or&kK`F>>=d^;ofD7>E5m8a7rNpT9Q2uXCG(_Pd
zL6V^mguhaLnQp4uzDcO~;M#9F629kRvvsE?(&=Ha$NHmoZ3kJVT<z@j<+l;lb|R|y
z&@H5`p|3LRR|@b&NNGuTMHtF#@kZY2^(%AEeUe*6#AWW&+&obhN;#k63$<j751fC#
z3Fv!a+lV(fpuBde7oE8)9uGH8CjcRd_m1O8W~?KwQZ-C8<)>WA&(<jt;&fd8NDOXk
zYZVN%BU0botPnzu=9X*8!|yBO-6F#UO)GI`2=OpT7)WE_(KUD4iK-uG`UY=p>hbN{
zL>cRA{VAT%=GzHLB64;Lxx>xBel2cII||wSm%`rY*d39=t-Ldkd3^C-EdXxmq^jzY
zF^3Ab^>0L^sC!8zui45<#mLF<@KpR#1l~wIzb_2#e2eE1fh4VxDA<{L?fR|CQNy+O
z@Ap)UMXsR$mlU5})06#bn#Qj5=Fi#wB<iNb=AOoaVM`WU$hv_KaN3V%JAL(r`~tne
zF9<5Psy(C6tUu0#tZb34%$S$~hO^r6q;nc?j1rA}UpC=bi#>5SN>LdBIlHUs?8CR!
z(LiPpTsvD_XsdK3wdd!5GDYt{xW0sT$M<ggV9rxlM<K-xR3<T>WX@>9XIQ%r8=I5;
zvmzF{vNEy?!2e_kt)8>>OSHt|4UrJe_s_SX+(Hh{Ae{xVznn_61g<B$rNS|h+uRL9
zC}s>!myXRHUWZTfI#+2}evE;^f|L7~NV;)S<l)K59?et`@uzdwfgH0E_U8>-DBLJ2
z82I5%a)zHqKb5)F-G+q;FfoRZi&s!m`9{gYa!Xy^?a{|u;z4Y7J2z5Xe*E^Ef|!6D
z*MOx9b-Ci(tqIu701}>sBK$>zRthFcCmFN!u3NCN=SF7@Bh0vb1Rh^Bq8%?2X64;j
z`dG0ab+Spi`7}KNALWqXu<Le`XQAiL#WrFpG$f=$U5_d(gvC9TqLT56p)hmZtu*ly
z;&N+`zH6Mcp-#W^%f~9MsX?-D;dCUi){N|5Y=<07sQhc37`WQ93(V{;VwF;UrZ=c$
z#Z~e$t*itky_%5QXsJ#W)eZeP?{WEixr)wpKNl8>qDj(zIk&Z5Yu)@lKBhH6{G%{o
zyg)}1bZYJk1|S`7ZKn<E>$|<QDshb>)I^^2fW1Yf6#1i%PfW0Z5>`#Ps}GFsMBWLj
za-J1(X1(e(@jNY!ot-Uo?S2QlP_e7)OOmgVk4hO>XL)>})^eGATIen?v8dP;A$uBw
z|EZXb$4&;!p-i>DJepg)uBvK~B-p_5@XhcBVEjB_m<X6(eXh9NxQP4{0+9FhasB=G
zHuN7vE@HbA7zK=ROYI8vOs;3&zKz(L^eryN)zKsp(PX@2V)`VY<J{dF%y`-Iu4}1R
zG*YxBP4Lm%kR$TfcGZKljNZ1t=T?)_Xy}QtpMo1iiH1kD6CQI7%Z#5Op4Z{<Kux-&
zzxM0)SkaWHlO7WU|8!IwI<glNwTF82|Gs5&ck+RWDLLn3_hwF$nB>8RNP2~rZLUE+
zbFLSE4tAgLO$9d{I-c&7mwbP1H}>J2<RWnFcTeUIyt*^+?*~J}$g#2KACD40jV8P{
zqNhJ9d8f$o_%Wmv7M-owHrVFOaa#K6(<ejWp?l9Mj2w`mUlVsH_O#TA>O(@MYks#R
z?whwb2Msp^<>h=93{o*M{A{dH=hT_8i(Q31RA}|CaLJs=R=j!}8U`9()^1094tTYt
zS>d7u%u=%XtV(fl80?!tg=Xn(qZfH-^TSWaSpT#dql)32BKNlQbG9!{Y>me>mFq`X
zUi}U^Fv(X`MQd;06G<E`5V2(K@Vepn>e(G{4_s#T;L3>9yzu6Oqs`u>#Beb|ZFYS8
zc1p|2%Dqz2YwU(klY}lm3HSF)g}%Jic%9=UZ@5Q1u<dpR-DBRbF1x!b*ZC5HU16Kv
z>G4qRE0X}YLfQSn+VvyG-riOVb_Y4mHcve(#1i;gj*Hn8XhP!SE0&FAMQsU$`N#FI
zn)M?vsKPy{S;w5dueW9;;1K`}K9lhEeLAjTkJon=pyuucrt4&?@lyg9=R+=ut0^gp
z`OestS@Lw&WZOC4Ol$hksW!vRsi`nCGu`rdfGXlTx<bmJBBSKHp7<2K7q<$)b0oVr
z*wE14G-~qU?@`U?VdRw0<fd?O=SK2d>qlFCR7_0d$auZ<R3B?Bm(ym`Jsz34Hr+Mv
zaHCzq6paV%LEc$LYVuRLg}ogtW4cHd6N10nrhr9yu$KNFGB%<rBV!GW#YycXWFE+s
zCwqi}S$)JCk*~o0s)}BbO`pQH(COhowsbU2fFDYJln1+!1$fYwh<H{!BGzIKRi?w^
ziTgTTs!@<jJ5<Q2{x)6UZEOg+{MV=`uFKl(?LDRTkb8f2Qv}xO_{|=raIl9@ik<Kr
zkA;}fv-URxt)dJ0rbVXB&-nVc^+v<^;Ix(sox`5wtvj`6PGa3r|1e6~8m;OJzjkUX
z(X*<sh~`<j#O4okPY3q*vp4%a!+!;{7F9eT6Rziy{dS{|Go}tc(ilx-g@y*JZvF`M
zhn@_z`VsAulTzgjTH3y`4y{OxDi9c!KN5VJKjKE4&k|IBeoi>xH0ANA08f|o8P3LY
zZY}Ki&ICv`sNfc8c<1y!u?k%byTsjkavN37>vAxDVch>aE&{1y+aJ*~$GLz1nWUtD
zolc5ENL18F>P^gySFqL?aj@a7qK58rY5}ESW8)aqB%Y*Its8=nC#6z;=4}_+Pnb+>
zl+6y?B^~-4DUD5r|3ln<8i40|q!iw#>U_r6+j&`=VK#g(<O>tn@-<K$naj$oR{Bb)
zZ=idmzpZ69A~3?W75v;>^W3V#`tR0;0;foi$nn@&a_ul{xz0wLmDLjP8QrekUHaSF
zx9*yqa~jyRA?4b+|6yK%zlZHs``{crYdezDSE_2g<LF|_ABaB9d{U|2XrnGV)cib$
z_ehBr51r8}X|j;aq$i=tYVN6v*7A6X%!58O?AL`^pS~e@Ev025zM#De1hO?EyS%(c
zpxAwL;KN;Q;h)R#ou^z>Qg}3fijNw5`?2ZSPtH{^)5YTINtNx+%Ho@c1Fy%#H7U!t
zJN^LXZt&Z4<RV(6skW**jSoR=3yB3cw*;zL5})^KUkc5&(n$Q*3GuP<F9hG=b@ts>
zmcYk-&_<!}rcDyfr2EZ2Nl4}aOLDgfE`Q$C)CBF|kcuN>TequgiFPpLo@2*Q(eC@c
zsbX!Q@%=l4pQRp>Au#X+_>L9B!ow`FED%&ryg#{KQ_;<R(^zra^rA;b%?|kyBc8;@
zHJzXIMd;)||If9I66w_>g^5P6zcT70w_FNTn1zAVZ?g{{7nc=N{G5pGC^6FDaA0|C
zyAkA7b|3e_ix>Lj{jm1_DkXQg!zgY9Vb04N<W^YgvK8_EXj|X%O0mS_GwR(85(GH&
z@MXJ-K3E|TfAbV!R%V7kJo$b#<!ZHOWzlPA1Ru=nhVPG-YIa9%^NzTS5v(Z9m!MFr
zADdLaHz5;VM)r?h0G<z;-Yol(9MKegnA-SPzD|C5ASY_YcDA^fjmq%p_SSb})iu$M
z&gJZZSlYoV;ytdVEuFX;N5^UFjvpxt9R_zZNmo9sX#7yR&@4}65_~x8B9Lg8nD0WU
zqpKDL&}ek$XjByV>vLE}lZdQYP1v4=4h$$|$b1#GwdDj@<Wmi)SIh=(vYpq9dl0#n
zLd#+m%Za&(lGr%8qcL-hGi0{5;?85PW@i$7#Wc3BuDkVq-w8lPEzY`O{4FB={pVY;
z8k+ClYA%Roo@N~6%60NG(mmSR*l?QYdB0E6X(h>_RhQ1F`IDyL2YKWIDhACq1%L5B
zgOOJc2uOXto_$@usA4K`-ZqL^ZOZf)5;|I~+^Ndo+oYLxhg-n$8;kIAi>!}qG4<Eu
z#xJTqyb+PtOh(pBU~4crC;lyLbuK0fr#C8(kJ_n|Uh-gd&hc3p6xE9|#PyV`(H~Wm
z;+Zcm>(r0*%Das+@@X=yRLv{xQ*H9qVLHieilmkG&uM#^-v~RSO4EB<pIi4uc2z5R
z@t1q4$@20U%Fm`}Obd;TRad%Ql`p%>UlQm^6jW4H*nSxXyx8sp{UZNZ4r?@&vY4W@
z^ptimmEz)ReNs7lNsF7YMeBv1qH@L-r!jHSQM2x*K;8Z5*gKg<&uX8APBA}Zb!Uy&
zU`ZQLs`N9d(Pnn~h+1OQiGAn3;_An$VC`r@D}XYy>T2e`R0TxNsnX&9&|&<M&2rd=
zLn{-uBtf9HU6sJ|_=|1pF#ZFUr-^Jq{?}@1W$Qka6g;etKS$%G;>|K;n`(L7#(t~)
zB8>I>(Nh8oLp5!zJgrRTg_@criUL_JP8^+UN1CR--Xx)@v2n{+Y2|q4jAtyit+RSe
zQwFb>e?3TWD(eqa%M_bg7x=(D9-QMt=V|T!$o<7<IZ<Z|fg!q^lc_SFUjKqu%a#;6
zJVamVy5`{#5fsoCWl^ZAL~=&oTpa5>I@IPA^N0_z^L}D%?Bur!-AQOm6zyO#d3c7*
z<e((qx$BDk2l^SGfM_P7_<igBrm?l62SjXmavO>Hp6@wN%Gkujo{IZuH(lS`r?y@}
zfr{k3_N#SKjCqk)0j0$AA*rw>v_{SCs>n#&3r%ir$DAK;J76PX;Hwur7TYMr0VS4o
zJr*v!=IZ7MajFHMKH|B&l|o)=SP44zT|}?><g`OfyHK~IRcz@6PpQ;%_0b_jZxUP^
zRaKpOE*1shw?q$VcLL4C!w~a-yqnsXdRo_G2hmpr;ssgwtT=ppv3&!Sj}pz&w|Q-D
z9dELqTzm7>wZdUr`_u1F_rLlm=RSX<tEvjO<Mpq<cNDg=hpie$*xpX$Mr*+L4@cbo
zMHl?|_e%mfl>`=^)s-b5MVvGX>)E*Zl}*D#lkgM|RQ*Nq3oEzFODmk!ox*o7pWn@%
z^o}o>7ERC*?3W7my}&p4DmWX4VE*N{7UDd*HI!YT_j~@ad}M2D$JO6IEP5mfH2Dl-
zBSj2&>UhL~)mb_$sXMo(a4j(fv>Ax63(po`oVhdC^$}XSyYJa-;FpzfKD1^c*k_FD
zdsGQq$?=hZHPCsM+1+-$BQ@u@a2}=<DY`yQdE4<LBjfkz;dXu!TaaJKPP4{X`$l*h
zUzB&_o~WlKb>w|BjQZPYloCyo!++DIvYf#7@OXpX1!pujS{sg?j+ii%l08R)3APm3
z&p1-Uf(o==yY3Fg0CX*4xaqbj#Y%b=r2H8>D(3R}QT9sjzOvoH!XsE1pD1Y)iVE`B
zNAr1{JycQGd+;Jq$}6cF&Fq?D^xnSv)tT^x(x+OdPBCq6J4wn^`tNIeRxq)dm6!J;
z8;wJw4M!(q@EW!JMy5<}{Q7BWV&dMTjH04FRH%j)7H(LiY@>3t5S=?7%D?ElI?DCH
zcqZzj|Kab;8z24XX(XefZ#wS>)%juGjUqhTYN+adJ`^lQW(SsDbTEtJF=<wvKS`*7
zd!&gU06&cm>+GF7d#HN8*qzu6LnC&Vd`~aytv=)lm=Zl=deiZv_Omqpm~JuYncR-2
z9NT77E!UbRLX7%xoAmhbThfF5i{FJmGh|N}L+(}j?e7(zmy&X>O1-LUDL&TCH60!u
zMXqN<I+=-1Y?h~jG!Xn!!kNw9PnkrLbhHG(ty?g-sJges%X{qtup$|J=b`UaDMY;K
zyvGMig?&;!yRC<xTPoHpZynrLrQth0F`An+KlqiB^3*s-Bl<eR%7I|gq%^+(g93K5
z0goSN^GbSgh;BR=^ot5$A)vs`@l;(a;M)AWUryo8o`m10aWyr6y|u0`_e5(ka!hiX
zf(J<37(2yEdcI2(x;=FZ40z;wtNxaEDkaKTHR184(}m^B80}__<7;mzd|PVOCSN_4
zP?OJ%Od>Kh4!asZ?A^L@Jn5y9_>yYzTkTtx%HA`~?(Vh^X5uoUsHqb>{zI8xTst@z
zx+qMlZAa5rq{m#Gx$Tn1g2eOZ)pT*{a9(Uj2fALn!22AmxjBLMVLC^6dy+7QQ`(^u
z5&Ep?*yATm1prpg2@7ue%FXq_`~wsADNNUqk26<q7QWN_V5_MA(4}B2X)?e4U0~{z
z_P3$oYbYG}_%HU3BtDn07YCC*Q%Mrqn=TuBdE$;H&9M14Ros;FFmWtE=a+p-UFFQ2
zc9uO2<QK{(EUh_YS;d{b_SANpD6XfHZX^$H3mhl2aa)p7t&Vt{5J<y41<%9By3V^|
z=gZ7)4M_hSP0i0YA3300fl11rHp(x5d2XdX>Nj@M)wi|n=@^kzDQCXa)^XRZO%4u5
zPju52Dx>JGGCa51BV3ADN-k^v;~qfXOO7~_Zv8oPJ-_OcHDa>t+W7eCVirxMUrF&7
ze4Nr(Lqi*!quQRpgOY$O)s*xMtP@zl2m!1tbm#dao3d3cHJ!?0W0S!T_|xtp4nCpL
zF-1a84Vv1do2Ew5TaHp93(L?e=JCs<grLd_^54G;L$!4n-3onuYc4&dyz}PtA*2@m
zl^>GI9i-T?c~lntK|<AYzAo?on@zT{Uf)67?t8Z+S|C>M`m^gR!-C)QqO~Doa5;i~
zX>V8v2nl@winlxSc%vtUfuR7)?O!P7E_+Djimj}41A66$f%W6UKsvq3;MH)NwVd4a
zwifX{Jg+W!ArYxK$AT^&^yY8hV$P_!{pAMczp%SKRnBW@k@l%4JD)md;rhtn<AdcL
zJ|UTMD_c+!wYd0GJjun_r)xh0AMQy+`sKwb+AnR_?90I_xP{VDUC-!fLfAX;2dr+A
zD=HquO)#;%5p$4`3(T{h726}!LkSz2C_zKf(A9lWxBi4x<OiOXAY)#a$_EY2_LOHd
z9bL^kH}C4V%{4p-2^-kGo23u=<!9w}WPUlR+DBil(Wzy9Vuj`7eaEw;YI2fa>yFbV
zSr}sO7um8ev1W5~n^=4a{&{ZkuXru?6|KC(ld?6^N?6<mtl>j&B7FurA8;8!3VdLT
zstxkYhPu1=IP|Ld%{C70=OzfXRv4=CzK<1u%c|gc-qIpsidJt!CD}NS=^LsOA2i6B
zd8fyb$4TF2=^Yj}*<C{FnTaB<up6*yc5^(9EFcIiL%uwiBG@l*Fn?JBY<Z9yDky-!
z#=#k>ZN0zK<{x@6<Md*tUQfuvDy)9U_>H$0`ANUTfmpOBzSf~!7=k31$0zT#29Kg`
z<6L}PII*}`U}Lw7xlde4@$X^9RfME2B@?&0FqZ|n#jMMoTh4Pt;`nH2I3oK%l5HA4
zN>YLTd%_pimu>!vR%`b#_<xVb@~iKz3l#?rbSS+c?G&{XD{80+m_VaE4gQ(sr})Qa
ze&A|ED(q8t+;V=oCe|aYDQoNi9tx_jn&mbc-Y2K0WtR{PDhHVf?Hc)N;82>o5(41r
z*0@{>6nl7TG~XTwuRW2mEIvhyT-FiL@2e5vS<2B-PfZ;e3OMeEKbUo^nI^DI-8a1*
zb^}txD;z>H>?x;0TLR0&5NoYQ=YY2b&i3QCi|XH2Vf_TexZ8@gSbk)vL~O;r?2w`O
z%iFX$XZMB*&Ggm-mjVd`6^iqze52i08}V{Coi8{;k_yR)*=|&vnN@a2`h5sj6tn`z
zEIy=O^zI_qlRUXR9j(_yd{0~$qEqbE&Z<U{Ekbuoytt41_lhw)xiKW<@dIK(0(jyu
z#9y=ytbZ!3L%lqHCL9%lGUZ4O2VwH6zgEq~*;zqHXZn=@Ir&fH(J<v~6{Hs?%riSg
z`(+W+F{>JAwR3Iq*JAUve-}QSNHz(@OYkBuB==5D+a54>XRg07AyceB#-hLXM#$~?
zn>!pt$Uor9VsI(SL{E<yhIj?86rgY{X{ZFpA#mxy0t2M!FPbPbq4&R--1C3C?X1qN
zLYc{Nvp(@0U$fofBxX#v=mDYN-4vk|!|KHRqL$urHB9GAUWGnGweyU)Pm|{5K@>d%
z)@Gf^k}!`lIw}esvK6crMjDu)6Jp-J-2m=bgIk!mp|;tOl6nRbkSQs=pnXpJ>eD6i
zwQ*cWYNzhm4ozWwn7T#6_l=C06}4iEYm#QcuZ>4sHXnSy4Z34FDHd3XXF@5filw`w
zzn>kOZSoe~yfcCBY5o7pG#^tdAHpge9;@fVTviB+-}w;8@*>5YmOkKM@}~Cuw<CZS
z<e>WieO2^-9YOx@7h$IFjZsGmZ+`y#_jQ`Yby`~Fzx@jL-Xs^`OL^f74zr4Rbg(;m
z3`W>6feeeoHV@I-T`liW6jjCT80xSe#*cZrZJiLZeR&J_QzTKO;%ee-x9z)xg>0e8
z%Vyg~0-;%1S*WOA;Y7#0s}_rAT$)N`nKjUGCao+ZV0ifnv_G%GyefY%4{2ax35FXn
zRJSOKN%G|aY6cyV#fs`3RrFO<K~&^#e>~@qrK;`Q7{$qj3ets^meV53#ZN`A@iG1V
zZm<{|bAopntO8z}u11;>D`;r=7Z<b1d}Twczc`HZ<Eai(&Rtrw3A^hSe{wz6;0}{6
zoA#5o-fryIX}=8KwJGpq=UYg+ZAAH?&kf4E4=7!{l)_*!476g!Q9OFNls!xkhG^)O
z*R7oX_3I`$hanfOg_b4!c>V?-A=&6|eKQ0?*}m;6gtF?7uVt1VU||xY8<i?vorFhL
z+ek7k1e1+^k&Qu_vI>Bg4CN8;P6v(RjmdKJn<&VwFR(Q1=hb=d6?yR4dnMw{!h5+c
zb^nQxyGeRio<TRc;EW&jJc{XaLro;vMTq|;ZJ_A6F>-uTXLI{yN$YpNIG%pj&pakw
zm4dJ9T5u;Q%VCajMED9kX&S-D$*-eB3I@n0z%?**9X@f<=hw&r`o1bPR!=2w<tvqD
z8f18>;H{>`d#oYqBKZ9yr^QBu>*nF4`+QSmfWKceRM2KXPJ@^4bKhN<mBK0?m?hc_
zrF62*`g70q^njW}QZ}nj<<9C%&$~Zd+BbW|wmfiLB=q5>9jYAY@MtxpbP}|i6NK`~
z;shR6?20cNP^2a*z9}3Ad00erwEbBs#rF_OC!0TQBF-@<k7Lu~6g4$I@10t45OPtz
zvOxXld*Bn^ahuy`+K*N7_1xA*zD>oOYo(=DIQA=n&Us4LlrvDcr;<NtKOg#kjeU7E
z)$7~-Mp4Fg17)mDgEAITLS-yPD4FLXGLtz}*tJ6%G#DCG$P_Y5gzOVi$xz8mWLD<r
zcYW%d^Zl;(egF7b);eb`**?Sb+|PYq_jO;_4sPF;eMt?ur77BW1-@r=b;)t@AMI59
zT9fKMjpMmxBV?0LrS0>w$z81HXk@kl2`qWoge{JFE2lwO&^_fin>e?je(6K}JINPy
zFOGRk>(5nZX*G?_W~49}q%*#WzZP|DYez<L@P#F671?VY>4m~bmA4-+%{082EB@gV
zkCQ_#ujKO|dQnX4v+X$QI0RUq#dM_&8=C3I>y9Rc=2aL{k1q>T<t)EmAlk=yH%-$k
z7f<HJPj=eDrHA5K+1EY5T|7+Nrk&1hVQ#b?+G!v_rF(w`5ea5r9RsJHKbMO$-l!EV
zvfS&imsy*lqe5G<lgwbQhj-5d>1=X_cE*_7<i3X%BCwfS5wLUZ_oRBc!CboEQ3DjA
zTAOLc9EW0=E33Eh@sMvvsXaDGOiW}TJ#spJTO?cmm9|ThOw-=Y0~w29U+$e_HP0}|
zhlS*)${|(g?B4XNaM;kuex1^nMbXxbcgi83Tpu!scI(yhav4QkVlAW=`}iKV%jGSY
zHn$wJJkd>|3|fAu-~{zl;KlgFtH7qBCChPY+odn8=}TxE;<U@n*(cqk7ndva-E&;V
zkCvtq8h&NgWE1C4U()$*7UABo)s<DQhc&eHyS@jGKQOI4c9dSNtz8y4&A`OM%Aq3N
zG^bEyF5PaOTc5AyeP*qcWpMdYXpBdIl*P!UG-H`BscFl3P}_wuxHzX+cj=wjYq1~Y
z?nTiqs+b<)mnr}G$g%!)O^A*X!xIUYsKK5ktwG8w_)<{-+n%^wB;0(P@RkRDMVuvt
zHrv_SqH_-CNj)DJ{>H(xnJ33QHaVS*2hhZc#?5wimao{?Z&H8vluAD*27JZtPHZd~
zOm7F~+?#?!<K&%qlD%{bEq^{87kE6EcFHd&m9eg-v8I3EFhy&Zw|0VddP;PZnQ8Ft
z9U+SgHzH_Mv6=_^saT%O4Jx_0xrDHJ#`CF9&K6ITAn;09a>BIClGv5Ckrrxe_(jWD
zOq5A;OjYxj-ub(_oc?94pw1z%w0(s|Rg&CuyuFbj=c$3ij%8=Lvs8QLhl~oTsp&U}
zzsMrAL_*oS&vj-U4-EwZeYd-~pkM!n^S{V`zP_NBF<^2;k@H?L3?awAKmeE!_7srY
z_H^=Q`plO>?+*4Ox<(;SXvg+a_nd9$k1bj_E8Y<MYPEe_n~1;r+_MrJl`p-q?=v#o
z$vkTwVR74jxxBT<xanFBGfb`#wDaFvy*+XD;5lq?;{$BJLKf4zhj}udzyERavzxXH
zZ&Z*6cg@vVC9i`LUEc<KZRF%Eo5V1cWPS75@}HM!Rljo{zUS2jYHCXfv+=k+zAGX4
zQvk&rJb%lW_4G4^$)+7&M_A$?o=?5D%{1P$6Bx*q$17^r?XDh4lDp{lB#qrLhq=J>
zfYKT7o3w(`K;rqIKChdv1nw%~WES*mzI_IwQsdxq@}VC+Gmvz*$SVs{v^)?^6oy=&
zv`gEb^Bz#iA&PX&aYLfqNy=#+jR7l+AVO$Oj=eNIG-Tox7hhDt6M2yz2ZHTxZ?<N%
zA-1T;tGazJg3m6SU_BR3WBFxf6s(@8yL(Bv^~Hd@@*CPOI*NkGAN(=Ls4}<1&Qz?=
zM9ic2bFzXvadhC)ETAg<-0L)^?tDNvbc5Ypq9nJVU?d!^tl`F(>~dSh-h%_C)L<=g
z!w$BfJ5oUkBoab1jyUcUARNUxS}nM%x2merN}`YQJ^xUgq;gb8PPyi~>ZJ}BGaf%%
zJ@Z5V-u-<_Q$LpI>4&^MLTv8O)*mK>AuG3yRnYSvE<k~6CnH8ljH}_Jy<+w13zuRx
zRu@}gQbHbjc1nvgHlzvtr%>Z+c@d;+Eg^Yb#_kP2BojM9t{fg)fCh{La3J!UHK7_p
zfvcenH868N<>;*)cG`nGZSbcwH8t(yuQ{h&@U68G{BHPTd~TBW=Z{ry^D_eizFlWt
zwVAQuv5`i=o<8X)@u1d;uQ6X&K<)iRp;r$<<|O<c0pXq&n1>#31f+>O;=S7jrqr;_
zPk@1+%k7S*vsTeh#=#_^kcANJ*`N5XBi5yKU}~PpR3nm_){tdWR^+u;|60J%u((bV
zGYb($MQc73sX5is2>;AI^Fuk1o~ecv1R1Y@&?h546YvRYrt`P}=cS(w4j<+S2n^(S
z1<mrbGg(&OOh3Okaite=?6z>bn%ab?gBJN~H&bnV4JBjSf0lTo@HDuQ-H^a5?Q$$?
zsBJ6l`T^OAV>MytZXPo*cv)Rt-9Ift$v&wON#ff{LM4`!DJf;g`8M@twi`LxX~&Z*
zu?pT9Mv|XO`)LsoY91atSlQT2{E1NnvXD@F;^4!iBw<m}6fg5TVl-cF1cTJgvr4}a
z$G!f(zG}+Kg;X|n8J*Kpm+ra|;OM8R+R2`gxOe{P&h4cFxX<*!(^p0IJ<{u};<1pK
zvo=oWCY+7nv<K`1>I$s#3JMi}{k0sDuNMwJdI|FdYWvxF4peoo;3^;uyM%_b1OOeq
zX3G|m(l1jWr}H%pDT|B8f9|BXOK}fJ`*&U3j8biyaN|SDw`I$gGO#$I$n!RxCJu4L
zgoFg2;oN5U!Wm;Kh!D18AQ7zd<jhPB3>XszwWwcXO)I0fw^(c-5J=HkC09~QfvyFF
z92md%Wl?u<D|cJtT`V7tHh~N>VGR#CZ9y|JMMVLe1@Q@NF!6>*cw^%9W=b}9(Bkrd
z*^T!a{;GcN_g6#8aKa#k@{OGM*dUyJ-8ry8V?T9Y2SEGqdV3HHEEDZ#*L*0>H?16>
z6{mc=l;TB0#ZQdu?}3G*Pe%v~<eaQsGZbnfsjtEXsG848_F(<}P%}Mp#kA7bpIB{1
zn>TPNfP%~qHC7Qq^}e&y%5?<837afhuL2y{6QN%5z-T>!gw4c%pgrF#3rWeES0^+7
zM0w<x;(i(<&_l&^O02Zi%L~pH!Bjf==g%Eups;o;1@=(hH0T%JqS6=Fd1*+%#UnH{
z)YZ#t4f&eUSR_CBVz#&B80gvEPo>Zzv6l<kr}|+dO+uN_mt@jWoLmhvn5FUBc4KO)
zJ@hlf+r_#tXg6A!MHQxy5&pscB`l1gXIO!Hc(SIds_Omtq^6(W?&c~owea`a%Ly25
zVIbhNNCNpSET++F62^BBu(5$wsVwi|n_4;RDv|{Y(I>eRz_4*rL|Rq#cy^$|l^c(g
zBQG%m&~o+Am)JzfRuoTl#Kdjo!%;aungr?XfG4b+*<qV?&w$TshmJHdS&G&b5l~|9
z4cF1mF5nDi;Bye(MA>HZPOuQRC6EZ<gH#;$bvQH`Pu3?yU_CoqT%pnp4qOf3$#5de
zSq2Aej#T3(i<b94H-J(kVQuyRlW&uEb@q&3c+@S-_i~Yy*fZHVvNHplF<z_8y^5*!
z7K5R0@zc7lKT-X@e>y>bCcJn{Br+Y=@$u0J$z5n#%W17pRc-qA_WsJBGdASB)3XA`
z>6{DmTzf~`bg2YR!V(uTCw@@XcAfep9TFNUiSO3$-d*IC2G|JVGh4*Pb8Q1Q{atG3
z_fsg>1a==ir=ujQdg>JG>(@P5eSL=x#d4qDeE0IXYuB>J$<f1(A0IekCU?^$ecdrS
z^vl@M@qUS!*Yl8iSk1%Jou1!D_{qYJ*j}$AhFGo1Nr2ed6wZir*RESIJhI}-ty`g+
z6EqDZq$t@6+<`4bRH$T=ED45^c-L*agmNfmh7e-wKHy0g2ky(ZK=nhL%}aZ{X}#R>
zFUCms>{sx!v-2*o&uv=xQ2PAu!|%SLJCbaSnrdok?Wj?^NKG?B)#5DR+IjBm^qsQ3
zg;f0%n8l7bsHirTC#N)SPViwQd@o>JZd?sdsZc`A6Y`sRYD_X~k_;}=S2s46-H|Q(
zm;=#|jV<|^E0~xcu16!Ui<fu1w6`;RSJ#8<oHwXLRARq|ViNedu7Lq_{!XnJiz3hE
zD3RfdW126ym62^1<y*#tyXB<Ay$AaND_JgoT|;}@k{p(r$|)+!WLQa#{%G_}Lp$9-
zC22?8L99fv>vR&DSk|R=gC9gY5~F^o#r;muYj2T5XTM*=P*?5UyR{VU^=DsIS^q9H
zBDVGrhAL3{EX=(H!9W5$!P#OHvtEo4%Mdz5DSFan-EmDP_>=K$OO={=zc+ow#zYE5
z1G<8K&*VyU^;4^(gsP#Y?uKzQ)cN;eP6`cv*tx8N_tl}&f{kttH>j80#@3I`e%<Sl
z6EHL);ZnF}T?&%79z(HD!Yp*}P8id>Vc-O|O_rE+IFgz^O%C98SM`+j^~FpN-9MQz
zIEo82lNBq8f~LoZ_%#yd8`th~7a>MG;8tbqMb>!TA(LlV>s>$8x;$VZ7cCF=CGX!C
zNp#(E8Nc<P2Ar*=$nu$S4a;VU1w$a=@$xP%y<HT__Rx3wsd+^I>qCMz%jr)dIw^V7
z$QYs1uHMJR#3+=S!|4aFUO;?!Q21ySLK(BHEW_QvlZKTX{9D>D+K@F=^Ij*b=fl@|
zs`A}2qNCgQ+2^JNgJbh+o#ecNML)|q&$5qn=2d+rd-{IoT~>qil%rcs;W7>u?iC{m
zO;r)S!x1IwrUx#b9f~mtRv5j`v#^npeFEE%kk&TD3DWl_o(7fGCSOxPMo=#xyPb)}
z=?9s87$9AMTJWQa4V0y(C0j;gjm<NJVx{&;^iZj3A?k_vF2A}_ODsRqRBOXSy_DT?
zkmDYiQd8@w;8cwqu3hK5JGaPK$sjdERKG{`7=_|!Grly$iGyv|I1sVXv#gZ?-s#U)
z8|G_|L@@Oz75I<d5OS7U^Icr<clXU;J|NaOf9><gR%D6@`|VxeH2N&gekE4-fvad%
zv^!f|r_%K}g<|e%JNdqCtAlr~zWK=uRC?Q5FAZnd*c5zC@OMRaYYi8o<cy3kTrr<Z
zNeFCxE>d$o`)EYc$9ulk6wCH|PGsG@SyG~DpNwRLY!;!GWB}PiLQUHSI>5_trlR-b
zHt>EUfqg?{ebds@r^I@w{51)RWn9YrQA~`%AaM+e<0kC@B5EBxNYp^*rFnRGAXbH7
zJjBzyekE--=D^A;w@(>XhF+l0KfaSg1tenghS)kQ^CJ04gmt#^vjy?x>tU(ZY~~$r
zbZ$MC5f)Dydrwb)HAOh2(1SrpsOlw!vho#T2S#@+q4rHw69Apm9|EL<&F%~?TN;y;
zc$ItzAVVe|JH~o%LL_`HtHrRRbe_b+^Jjfl-4uc#M+Fp-b@lYlK^_U+pPDCk4Nq*)
zuzkVH8$3A}f}X97O8DzyS@~U}JUry+6yS?V4Fj&2CW11GSCRZvS*s131#xpVy5G%A
z*aGH6T_+x%9qNk>xwwi!Yth*KL%_C+yNKGY#&nRru2I-~yh{xi`m&;eDG+3Q;>beY
zGm$snW_bU{56_)0D?dhGVzul&SV584ST%uDEBX;MiIDxp{#;Q15?wq$QrtN-M0f@h
z_FeFrBMh{7`S`{m6Brxdrqv~|i!$#%l~&SgaO?&Fzd)O?B1{y?N%Ss5PlrbMYb0sM
zpM&PYprM(W0Guv#jf_4H(=L_mkEEZCli#3|)YM!<RPu_`r1QUX=;%WHq8B8`w@66r
zT)RzFw7e|gw5jHq@P-!aT?b3q*g}q_A~vR`mYAIHr@Yafa60@aKGfcDNt_}kr(fYf
zx&%(FFi=b4lCkq(Ly+a(dr<T0l0Q2yXNj8X5)zTIO|E&HJynpDWLg;-`un<bfg|Sj
zrd-O?d6Ku<#p#5dU94P@V8@58(M~mWOG|&5w-qvwe*Wwc&-a@)03C#)wIcf`RUEBE
z+gOq1EqNUF3WF<Fi}1LWmR<@~Q?=DavMRitncyc>2WSyV!Z5UEB&A#uCe-2-`#yh?
z{`z;4DNM<hNjDe?pZ~7&yA`xBUBe~6f~>0nl!sCU$P>A|n%Z2}jF08Fqt1nDs*&X%
zibB16sG7Q7UMNN;jNQb;xL7tF?sDcp0%YSEWeQ7&#qp>hC=4tm1w(YZ3w|&8hL*Pe
z$a!NFdy1d1&dk{0<%|Bcn<np1&ri4dy+eRv;c$Ut3Kc?9sE4e;7a|1{RsxfmL9)m|
zdVJp8Wl@@2Chx7SWRL<>{3hEj$8B8*RyWWpAUs?lTn+*;NTVcE*ia$)C(u&(HLF#A
zbV8byZN6FO_59<e#=r3L-IYxPN{C<}TJ=ED9r2?ALxQ_6r#6~Czd$Ye+OKQ5;5W;_
zLUMIuI9Yq;?vag@2P4u?Ru*@L@jX3R$9p~t+TE7$MB!e3>GUe%L01I`$&&)+Qc9`c
z$4Gs(Sp;PN-D&XOb7UIw{g&_~o9R!CI&%DYxQWa2HDFJ_f-P6w$_tPFseWkE6umc@
zFBoIx2J>4dCU1B<E@RarUirpTGIs>mc&TR!A2%_fVV*Ma;lm}cD<$e_cu(0(go;Nf
zx~n$ryNb5n`fXauO}|+P^V^7RE!%0HIt-9gFF}OCB#-1j)nEPcC4s$^lw7CLsPs>U
zo|%nTudaOf=us!4OEtQs4RdSw_&yJ_U0tva3N>s>7>yCn|HKV7$G;Ta@(e4D{7Z+)
z|I#S1XStxg^AfDN&XE!47`9c>3l0v9JDrzs3N=M>2(kn9llM?wN1^0MJN(|F04rRx
z?;qycm3#2v<vBm!n)L4KNt=o=2Jcn|*D4I-y)nX;MDT0Lb27<qkE8NyLA$)y;4?)+
zntW@|Z6a=L#4#{}0SV$%hZqBNoYH=syupfbs%Q4Wj}a$`gXpFonf__Q%$zgLoCD(-
z)y~Bn1+yo6t+#J>tjQO){Jr)Z?z+%MhQOR;hAJfyrIBx^^XR*&s8Se#M&r*qr3a)L
zFnNDj$}aE(8HMW98z3N{Fm1s=B1~ARw`$K}Q&et|VBWqxIE)#o&(eZXrC_eyRpfQ`
z54?NAP>;c0rRC@b4b>R$|H^GR_l%KN{7!CEBVQ&@!9K<o9!X>v+;7Ez*2Np!j(0(`
zH~Z6~ydE>6u+~7p*Iov`o&7X<AtB6S&Wm?lf0Hk4{pXMY@(0O_moH!L{$e64E?y@V
zvLbr8rEnFpmjyS=d$^Ci!Ds`JcTt?*si~@86DbV-oQsu`MC3{x!|A5Dv%EJ$itbK#
z4>k^Xnw5s!p{bB}Es5uBd3GQ{&xtI+QG08{^eZws+Q-bm$_cs7nKL6lP(Z0^Ua{pP
zHaI;%?!VI<kL#4kyZ>ZUVr|0tu=s6K7E<*4AjTt1`Rp-p6{j+hzRV3GjVLf-B1gZ!
z{4LraBirNfXgaet2&z2e7)ooTgiNmX@1go-JXSaFpZ;-bp+!8O)LqSF$`wbY2OLT_
zxoo}y18hYVKJeR=FfVEO6Y-PnB+QpIrA6nrIY?WbGo|CwebEp#b+Z;hw+;v=B#`K@
zUvHl=&az(0vuqO+ssIA;^I$eC)U&8eIXV$z5{ZiWpR^_BiWgBoLh=K?y}{vve#u^@
zkMuUcIuUcOkw)kGBhjMRGx<T^)y-`m)cOrF%{lDs5@RZaDZ<xd&_Vj^xf`ith#h^?
zpRP<~vJnreb=V|lA*hhYO&c*rON^dE!JuTOi?$Y;ZX<v^IWs{@(VZ4h5>t-<lx=c8
zmQTv#aY^km{C=d`v~g|>g_8tFoQ63&WF#<$5?#v?y+E&LDP1%+m*gaY5Z9}=Uxfe`
zsYNGqXe=;&LZ3aT^17)0b<R);8|?7D+G!*^$1MhE@<Blz_rnb<bJ2+O`NIqBd!7Gw
z{|NBcstCEZWdQRqcv9bC3gW_J?RZvb;KRKc9@L4Gtq5zOFc#><zax=g%4sz<^wBWa
zVa+I=JQ*y)aQilqPgS2g-%F8@I#O6bMf-98wDH2X)XaMZsaY^B`q11=I5#;f%E1<k
zR0bsO0_L(?-KqszFmD38-_v7)b#-+G5DO<(2xZr<!WzdcjmUlsp@G{IViD9BrKbK`
z(M?eBbrtG=D)8{QLUGfA6>u807w>_s6ihv+C~Ji`ZDJ-#{RaY+ooOSCRDiC&@@h6?
zA^8ktRZAg<;cT|tpz$HM?_@`fB)m9(*kol9O@LP4D5~Zjn$nP^^DvWQz>HK}e7p%w
zp0swir+w1*YV;j64<DBOwi|v8YCn3bhhd3BoK8r&Nppp#1Y<{iU}NF)EhQ;!lc2xy
zPFe7%%DSp}<CHj5HEO;#I&i>Qn}=s`b~bJJ7Pe5exJ}c_sOLiYmyNXcOr0%pX;&iD
z+EHRFTCMT!!9*pl)iZ9l*ON(E=uG2m9)#4d(|g%Js7%A$zKu0IHn!*HmL=U~1UIbO
zxpdtv`DhboGY;kUu0II@IvI!CqrVfl-mx3yh9^#J1jM5nrlweFL9+IeYhSYlyMafd
zzgkZ(_+6q=<+}6#?rmg3xkhb|Vvi0lW|9FN^!1$tyCyHP_v=?-B_(rDd(|@ot-BEB
zV<_5>7#|;Ecy-wdC6H~+1#WjBbvGr<_5%`pW-QLj%X_cUp|iI)^i;!R!VnY&;uX;7
zetN25Q=)%o(ba?mXQ^qOm?~4+iPNPzaaU*sF|lph+7l`q6=6maJN+yFUA5w~P4+UH
zIy!Q*f%q%1qz#2V7GbIde=R`Nf<C=Bj+02RzEt`)IEd5ZvQf@StJ#U(wKDccqB3qK
zB)l=H>UqmbWPibPezPr&YkbD`-TU%HNmcdX70hA_u5rJ#XJ|d6(3$lb(_imSICB`M
z2Mi5}qFm*EOq>!Qq7N$$?NyDr5z7|v>v@R>6KQ6aAteR43D}sbs(p{>6`T*AYG{k~
z8K=FO5I+#C&J-{mk2;j~YJRIVxp~LW09tk2pDu&ouHsVR=4+KJKJp&8I{LD=?_2fU
z)KWp8y$J>z(dxSPr<(hNG}%S*M+;zZ_H2RjuMbfiYe}s^FMb9S(MApc=dR>e>XsGE
z_mVZ0p>=X7eygPHew=7E8U8(*VpGSdkKWlIInI)1?u1fRi1Rmz#H0J`KSe0O|BO+3
zZklj)ZVz3sv58SANNn`eTZ-uNwF)uCvMb0RStHaN5+6h&8QIxKU`Fk~)>utD%kuJh
z^|uF+&YgQ8b?Rz>TqKoxnhOD)9~kOJpYL`1`4wW$J>KLyG~K>qywwAC#=5VWHB?n!
zVsF=mEv$*bJk-<EMr4$Ow1P{l93LFkZ`iQ3I*P|^fg@RCW|lkZcNIdzpXGc|#7lq8
zI5o`l=5F(^FExKH*VH5h1ctm)UyQ#DoDrW$KZt7Fe~Pqg*ThO;62vh>=jLoLgfr>t
z2GeMJ1o`6#{Nn~&43+A4LD4<FLqFA)CbC@Km4u`8-h-E8rtT9YSQ#!4`gOf2D=h5L
z!-tj2BCk@ZwIMp`XF|MtDjV!&#P0a__Z;X57Ycr_S25mUY1yUfn;)hUe(=g|qYF$d
zXRABPRjyp!PI7Y#cw7Gd-#Y+0>Wd2uJ9eDu_HB0|Q<H>*z3$sxD>#DZ-1$sRO;-aD
zy7w-5R_i8}UOpblAARo5o!~FUy-Ec!y{e?yePLwb;7Zl9FJ^@^RH`OB=p5M_t}YpB
zmz*v?X26@kC%@vB6hnWb*6kJq-!ratjNAD#_Swc|Z%0km@x7jdR&Zs7<<x0(6uU3U
z1IYasegGNS*5=6*l=43VD%>O`3%N$-1}6hufx^6cIw)9(653;ytY@mf>ZwQ4&&E=&
z5mbrNHZ3d~sy)fZF+xV!wigd5x}Pzle;kNX-?{&4u=20#^Bqr~u-9|&icFU;W!}1q
zk2n$a2f4L-u6%s@V*hC6a==o-lBH6BaDOm>e?9Wz#aA`{@8SSr|KJGRSVL(?PCRO5
znVugF>ozyvd+<_;+}8OH+?^@-&bG`E5oH->@zdoI{G!w4g@vL#Lc~VE-8>{607Hlg
z{j|tH*YhkjfvyY;uPfED7ox81vE3QjWa0KxeylUR?54$;q4Ti{Jhzm-WUIv`_L`ik
z{pVyvU7@FW;!yz0NR%>r_4DU=4fj<(;+9pdpW-cQ;*UoPxJ#OP<eY8&Xf$%3Pw@`%
z#B6z|=w1{cW^9c5Q3tM;@Tr{Kbi~ZbXXKM($awef-+z3{XhqA>5@C&#5E2;5u$K^D
z3cF}|-}kP2WLAT_vwrKr+XbZg+I-o6Q3m9(c-`9*FR}V+ge}Z-5Z9yNsuIq)^~Uxr
zGMSv)A+YPgPF`NT!Y{=NQ{@r78}USnwh~X|<7WKoH--b)HrM7m930r|tEy|fJAW?R
zxEMjDCT}wp38{T$(7b-DD8u5sMaN(!77E~ZaSrs`2gSNh%eLoT`gao!;<SHTMRONw
z4PgrFR#KV}DA=GvECkgi6e&Hf03YJ`3%RsY8>18ynDSdGL0Mb~2&&dm{O^wSdt%ia
z7*s123}###eK|WTN-T?L(H1-ko4SQHRaN+0x%Rc(Zk?TFDqIZHtv3E@ak5`O3rhcU
zkP$n4S#K{sXJ1HXL1rdiPtoA-zn@2hIhcmkiWl)8RbhgL!Be{^E1@_Vt(8g6VKuRA
zG=0TQJjDsf9x+#<XXuAU;<mRKBiU2Pm66vp)_P^bgqggB#R+Q^`$QLo&A*3!`ua$w
z{2BI{`k>&$vFt<_H{`|7rMGt0m0ps6^nKX=Uqlf%;rTI?4R=s}KCJufSv(m4faSQq
zK=9f=rfJ>8%X#ikyc|Jsyqtn9#LJ1!DuBcpdpsuuv~d*~Z^l~R-p4`@JbZs8nd}v+
z=x)1{GPf;KaA}Mr%TAY_V!$VZ!-be1YkS2}A{WF^?f-qm<H_*94rMEAN-w*wLR>)E
zh2>?r&Dq4`=SMuIKWS_pobT}5y-<*v+R7s5F7kUPP2u;3E@`ts0pm*imn<%c4f{sU
ze;?P~w37l%tXy!{c0P}~X`VF<LII?J`E*bNLH(Ur<~sdFtcx_2Lw)tnK~^QWD_h}f
zJ@#5S7hmN3JX3xfD?4wck~~VjJUpCBshKQ!PE76|cmf+YG0L^G%YV=0$A+oK4|i&$
zHcpr00;@rTLB-HxTlU$62y@gl0pcYT?P#|!+>*%xFSjr?)1%j0S-ii-i*;p${f{Sb
z`hB4X-axc)ST_R${--Qt=saHL_87Biy104CTIk1V9z3YGn?L;c@y*la3qNzPLSkd_
zIINfaejMXKtnmH&wEbt1<cJ!JkJU&9t2bnEjfC;R<5fQNATHeD%S$|-*u3uxS4DL0
zo*iRA=L%QnDKpleSau!!H@|cL<MPs)KIUNQTHWvTvAoyH!oyQdJRXV%0HMj2`zjj|
zuqYpzp12L&;(YUl>22!qql3O6CJY%`&bSgwiR=|NNROs)3T!Q}7UF?~!0OdJOm%g8
zJv=t8Tp4`t-WGPgP1EJRzM=x6SXv8KQT&tiuXlMiC@U`w4Mi7%mlT}i!3qo1G38%O
zhZ1TjZ}eK~DgPaD>02RzCL_}yrMM@UfeGIqmMzzn07^m8C==ZEEqc1G=4HL|=I+Yn
zzQ@DHT=%9kpF5u;6?8WHeJ3vO<B};{-m54ajPDO!2z^x6cudgIp00ZE;N|QP{CpQ_
zimn?~S*5l?T%0*@{$%IR*?cmN<U$Xd6+Ap%lP-@1t(0k)SNgj;K@3hM^YDZgTsj<l
znndF4J)pYpXu%eH83*TZM01f9I3n=1`jvPQ@n3#+HY`^pI$&?$`=10WAm<9!03}<O
z=*UqhdTamu*8lmhv5N6eC@gj!itccXEq`{Zq2t>(!qbAV_Jr8sx3S&GwD5G(rE;CK
z3ioTn*D5{6MI^&Gmlf#bOPIq<b7tBA4M4^H(n|1*y=WKd-jgS+P@ZB{S8@8?4*0LL
zH+&?zroO(KN){d)DIT?kRNKLDm76f*(-~O7&-!1-VNBtK__Hhi*a2<;#t9K&c$M#v
zhr(7Q?9^U<{!B1P;D2PVIQ?Jr5E2Qz_$v4cX2Ed;K7U7hqV^s?qN{t~a~WsoEYT4C
zlU>!A&T<>mL&L*uK&n6kY|zyiFdrt>s)2ouqjKtkduRoUBoc|>c0h!WuqnQWT4>TA
zq=R>F->w&1+%_;E{(j`_3+c=k?;VSF+YJ@G&+n*nTNo(`e>Cx>5stsA*?IK^DPM29
zG+5NPi}v|*Eox_t))&wN*u;BKa9%1NDN3!-e+*_4DDsk7o&$P5etumsCeGz%-d{pd
z#CiHe!Dhq?5<8l}X2GGjh>ocF(V646^+NPFuN3^v+}y^G*=*-oe7`1k^i77#z-WHn
z{x|Qw2j(`9d9wy3dEQhEdZpH95}jo5H$S5jMJY20&XHC>`rjp|r#l7K;d7TmE)(43
zR}US*uDF++OcxtRKqec#huZexZ?PVEd&Q1F(o12FbaZg&7#wtz>g(=44^Z=3rmmr(
z{lql!k3TZ1B^Wyet$K@>bL`kLW*f-9!m^vZj+<COhjkxbaSYOs*twG%WiPY$yFY{p
z$=x!<3XI+G><Zd&m}FlG%Gdoy2Rvd@x&fP{q~xL4og6f7@%_NSp;bUMMK;LF9<%kq
zic{UGX1}3%;^;THD2L@bHWOeyp;AZcz+i+%V4xzT4y7HN#E3>eV7-;=1b<&iR^f94
z|8Fiq^L%GU+ajQa3lFZ9Z{A$mGukQjKWJfle4f7F@G<v)VIg((1xw>0*Nrv?;VcN>
z-7J{eaMo;#J^>a#!H=oLO1i;Wm9Yk0-p8@d`vU^@fPJVmKeXe=$QP4=r$cwp<$;Lo
zy@r9YUyJR(Du7A^RyBX_;Wm5x8e0TbMOhFSW#^Y)aW`nO5Lcn7qoWgm4N(BWjKiPU
zZxwAuUja;bAd7rZ^CkKU+XSwfv9Yqgd=sw_4W|TR9tAb@k=-KUs6~m^3;IG^1um)J
zRM-#OI1sadfEwaKwFP`XOen<xLMn0~v&b86$oHRiCJ;Q5>u0BYD*(%^8n(Xx4a^Fa
z?j;%`v9WxlvZ|`3=tklv?H?Q#GT)-hzhudhEo&pXu`^-%(4eqKULz*U<EysFrcGVo
zN$BlfT}*s1XeE}Qm*3jy#QO%S=Z&Lo$@@pp%S+d~1Ch7~B_*bvRH_((3@b0r2xA8J
zf#;SplFG_L!s?gHibqRnw|CfW6^Q)eSjgv%tKNoNf~l$sc&d3W?U6Zl^<D7mJGxt>
z_)K5zI@Ish2p&E#q>fHVINLay_1FIw1{C+=MbMz17gq5ycJ|f0`QkfdzdDV()V~@$
zP?`$%AT)o|jB_t}_gwyPEr3g4_iC7KRpE_3#XgR<`YfaGQ3?i4Tf~l#l-V<0UWhg<
zB+F0FPSNY@d3f{Fx68I8NEY<Qm~@~`1iW;p{l%{*&DnqLm)Nt1A0Vr$v9WQu2k2?R
zXg)y>jfPOPI!bYBExZ9qq_wgRs|l|{QW=y1rJ&k;-|h2ueUCQO5tr>8N@HVVVHn8{
zEA_R_rXCUfPn}{?W}Pty6BrNSU4Ro8s!k6cs`Evc1JGW?foq#O>CwUjjH4RveC<%3
zH`)$^gLs;6+PA6WQsusQu3}|nCDHwBxYkN_eB-%xx&_ZztS(+5f3o25V|7T#+(2&k
ze)mp1mjO`x{0GOO6S+BJ-41ogfW&<G@Igez+~4xxo6U#sh=2!k0MxPTgu%xB@lQn~
zeg5c^=t0H~c!UVNXAgXOYIT0>mXne9_vbK&7nPRQ<&|@e^Mr27K{VcZ1as1N^m-qp
z#X1)q(7ZhLuy}S62g*aMim(G1tck|KB77>>xKkR>M5%rB`E<hdTer4gg5dyk6Q6&R
zJ?&@4nDRv+0UlW3&a9J`mQF|ikTX<0@suJq<AQ~5=f{ou?u*!z7g_5^JmuOE`%N{A
zS-{=ztO&P3)0U$0OyTiQ?NiaQv5o^{L)zZo6;HhvIdn(nfGzb*)%E&mAo|j<%}&g;
zx~88h{%KkhkD#o%x!JN79C+wPQ!27SHz&rG|IGj>6HX1Grt=4zKYuQsv<l<K+Dq4Y
zqF4V1*7`U3y-3eL%;P@1?Mq9Gp^ThdBB)hfV%Qzy<Kquha!mgBhh7VfGf1sMEAnk?
zD|@;D4Ko+;hw$<BwZeKN`jX$ieS2zC&3XFMvx|x<C(C|gHUH)m8)e!R11T!YG1C=u
zbnA`sX0+2q4qSaWG2uRdhaOW>Qt}Xd7Exhgl}`I%rr+I^f3qwg>8T<YY5s`3<d2kC
zq92DZkQ|8cO$+5GB65WF?*zg&(U8bB$RD@-{o_mj$B%P&fF=Sx$hT>uvn@7JRuFbN
z8K^^{*gaxePs!f7^~RHmlg*0IQxUo~xj?02Tt%4jZMC<g2*m`AQ)=34;;ewP`OD^J
z9yHrj4Gg$R>*eHluo_i?xTEPXR=G;&_fbC&4_~F34klBNI$1cHR6S6m9XWdR0!SBd
zO%Q@2fPLU|^~8OEKgWUlQZPUPnZ*hKmPMCOA>e>!xfh@4T-eJEMyZfl=@b$RbxqA>
zn6HqsrLW)z3=`bxISlX`jjx8e-ku)9LxYI~gIwzJRh#Y|&oo~v@46nn#FpM%jP^XW
ze@7vP9WWw$OWutF?^#RR#~0~>Xoyi#-TL2dAf%LLU?6(r*s;)blYGkV-Mql)hb|K)
ztOTc&IPPj|4-s5G=Jpp}g9bzz0Np`??KcL_6rgV=bba9-qI@Mf`rX_o5Pmw~D=P0h
zAp!<ysD_~L64YG-EA-9H1qpg{nqd|rI$%Dq`F4Zl-Gi*GtO`fCy`77T<A+u360F}A
zC`vOjF)^&wZg&3kY&q6F7!I$XSxVS6W3%7O%i~_P=};Lof?Y>iccodp5QJ%-227uB
z?yA8lviWYnIRtckG#O)5e)*W|%OjSJ5eK{zWn^S5e=RQ9J2>pOwH2A09_1nxptDKn
zGb6DFE2k5}#xRTf%wGBn%;Rtvkpp?ZJeX0;d2Q3-{c!C>Hb)qY<3X$|ISadFqTOL_
zO&KZZ<|Hh%Cr!29O(MzVfv@=%=c0j4$wz+^*_@r7TXO7l!e`+7tZQZ#aqb*rzvpfk
zZQs|qtCarrvre<1Qove3=2gU<j*YpVZYx<wtPY%ZXb=zpI$&Wh-{abk^sFEqLcMsA
zpcoLG9wdBT!riL2gV+qwjrc)1)D0s*Ik<4)f+gY_A*e(u?7t0TX>bDIC>?&&+%Kg<
z#v1Ew@GiX1*(1Mv`J$_@-{8lO4h(d)nz~`&+T@sf4D1Oot(kD_q+6hi(ElSBbmt1Z
z?Z~BU-0RW%A?^~g$qEZb@SJu7wWE`8H6|!unYoC<=N+-CI8i7JY`1e_Q{&>|I)68s
zIwS<fR`yn`6;*WYU{#E!(pdhB1&-j70APdXks%V}GbIzR0Y7-Ezi^pPIwEOaLPe9S
zsT<8&Fqge%N<r%=`SD(>z-Qcg@Hn`=BQJg}YG;1OUhDu%s~owwE7U@Ne!u%`XpA>r
zJDkXEa83}q($=o&uy5ftO)V<oB|`l><;~<>+=wBzvYbLn(7YGJIw`BG<3Kyq)!m(u
zL<C~QVDMb!ea7zT*7!b)e?2rL`T@vaVoJ(#%!&|x4{o5y=9cF5PC}$hDn`<R=!j`v
zz3}OiF`-Zz-!?mkt@ad;MMhS(yrH3C{bF8o(7>acL{}Uf?9UzZo8do=6UJuvQ!cSY
z_4QYsU7WS!-{bQ(8PAQMi+FCl92^`zouNDg&l@`;PB}=BXqu8bkj_;jmLS`v5&I6|
zMyjBu>0^=0aDBewNJEm&;HsxMlmOK%o`>BmXQlv}Y%qxkK|_om<nV&NJC4S8rJOQR
zjS*fw*o5HIN1!<BMkj7?ECF`dF7f-FeBEWS?c3VgTA-rdlxY!#)p{W;tP7&g{XfcC
zZO*DwvV*o>42l<~umHika_7z^OrC8MU?uBD5hT`s;y33-{VS@rT!b_O(1Jv;mQbV8
u)wBMtMnOZu*Xh-`XMFKhAr=!qUI=n~68W+(B1x5muLEk@su}w&&;36MNWuO9

literal 53922
zcmaHT2RN4P8~4+atg=#w5DH~fLZpmDC|hNxG9n{oCD~*oA+zk25m_Otkd%nXp4nL$
zDf)i*`~Lsm@qNcP-s63bH+mlK=en=!Jb&xF{V%F2P*X5dkVqtI#dC7%B+_O({I`{y
z4Bv^<s#V1Qw%W@mYLeq0S8`K7e7(cw+$DPwiIR@^Z&Pcp+aD5XKS@#UjHYAkblhEC
z&H9e|%9YmNN3LyBoA7vU(xJPH;kX?AReujg0pX%@=EVYjqYk+swZPEyW5**73P+z4
za6hh=$DjCoKi|860CMuN^)01?HJd4GOrn|>rx&L`#fjU0?>TATtUBuFS7ju$hl<Rz
z+Hq`P>hdQ1f%y9Dl2I`0zkjSdNnt<o&nt|C`qZ9s)&KkY`QRE>@qb?(c$eXOgYdsE
zDNhnoP*p5<UZUVq2=;pUQrI!Rz9mUv>T=56$8jg`)_ncCJ2NwLkGNfOkJzbGr^fp#
z${ruT`sB?CVPW9`C$^Ozy;=DJmX?+R2IVEYUkN-64`<wc_;T`-@;+_+q_5dmrY;9b
znDZtjCCN9NPxO;)CR({)S#|HeeED+T%a@x-$*HM{SBkAax3_x-1Sn-s{o+Un9BcXb
zk=oGh*R|~L2?9^KbT+fIv(HKHj2}x#O(o;!=MQ8#K@-9z<$U#9<@z#9kyW?Pz2D0m
zQd0Za@BLJxHv40&cJZQnD3?ObhYyq)85z12&V@aWto09u#Zo4Bv$4^{|Fy{qD|h*l
zAiF&|Ev>evhm~aWXVrF2GNilsmJXkwsOWBuROJk-!cA=-7Z-SXO6}9SRD*qex4YiE
z_rWVIEiLK(-;Lu5eJt~$ufq17yu%<>OcJu{I(F^=-GKuK2AZOdlHA`tWE!sa++Jcc
zxVg2p^+`^e<k=85>brMWUTY7>?dRm&%&(L4!t%Jq*7n!h16H-|?TjbyOjl*;<nCf&
zc_@6fZvXM)jK7y>vLedM%O%sZvWDw|Sn~4nPUCz1=PFH{*g3KEvUl!?N=iw|2i-LH
zBft4<DpNyOmz>mYckV;k36ZE5%6jrYf<i;p!W~vWr@z0U*LN?Ivd`y^x{6A6pcEZF
zy|Bq!@?$qUFD6^Yh*}w0TK+!!`uo_JtcuD^kf~5+upDQGC8Gt633=ciVGl`3Njc+(
zyAN|RGBP^<THvOowdyVk($mvBr=bz{;>C;cm|JfWrl)UmxJ4e(H)~5*J9gs}tH_BH
zyho27wX?V93Lf?fDJ(orOFKF`+VXSa%ln!dkGA&q_wV2P7g_i6H9i{_Tbr+Ef8X65
zLq$bp_WAjlS5Ax9)wQ%z($f!Gv?lK`H#ZM0D?6EzlG1`(+wkt4_wC!a8)|DkfAm%G
zH@tRQ<XM{Oj+|`IQt0aHYI|j_K|x7LKiZLUo{3q?`Pj#2($AWjni@WTe&n;0UA6g{
zG}F7^OC5$8*FHofXJjazJ$rU!VnQ*5U3z!U-__xek%58J(b3VSw{Bg)hfdVVHPABq
ztC6OvHufn_@%;IR$?Auw*(9He96jpqXSVqLBPWX;s{)P$MSp$ckM92ae^vBf-{z2&
zl@$>ZdVWOz_2-E%Y4Rc-9v%%XEuqJbAAgSv(bLzT_!M`Nfss+`><L4|e!CB)W-Xlg
zFJE#{Q&Wow3n#=&xu~nE1~=T<y?b|ZcD9O?%X0FgN4ti^R7)b?tyCVs-oAEI#QCRL
zeqkZ^;lqdV)`Uw1*VU}8b8LE>n?t6jr_Hcmh|hvEe0!$rxbyP#-h1my9Ue4?FTZ-;
zTXr|{O8mkPm6!Th#Ph|t5A^lDy%l;<5fKruYzAdoW=rkIT6GNX$&-<hnOayVO?}Ny
zij8GumUfd=Oi|Cz%X?mE*%2^e!>WLto9ec{78)E(b4geCGwy@DEw+VOOQL+uEVl5%
z;vyHyL~C2yfg3k&to<5$_Mt();5s!mtJovrMz*#-zBAjWf@94oC@9$a<qH=J3(ML}
zalhOIzA%65M{8<gBIE4r-1hRSf`s$WbeoFK>)Us-C1VA{ii$+V#l>3;@2_(=UNLY{
zzqHOKY$8|c;5RJRm4EHwu9dzDmt$t1_dh;&AUP#P-rCxlfraG?x0FBM{jcX+Q_e>+
zTk=+vmydiuqv^DsIbFK6vLaagMo0T@z^K)?d~X~vMFj=l>rGG3$OTkf;j@TY|MRDD
zoRN->vxDr?rAs5j!?M?}Ur$a*I77{R?D?%Xzs{EAoO98Qlenn2F*j#3*;SbJS#*7E
zD)*Xcq$^ui7Y{9Cq5t~Y64%wMSBVuNKI)IM*~ZR-5h=m-m-+d0L;WSTo@+}}3UvNn
z*lSDUEuPp#$$2W4^xqr8jj$YT6)tzxIs+K_@gbcz{)k)kl}F)~-oCzHvab}6kGCYX
zy|{FgS@h=9U6tn}kD$;gpoS!^t~$PU{Usvh{L}Bv`tp_hqN3EmKq}|Cflaiu_kPb5
zo0^)+mED<1WI18|dFb6k|1l>C*VVlJA;V%KA|i=ZRi}xQ){Bc1z162I*&K!=8F%Vl
z^rv{K^UKT2Z5bN8b`B0H_y4X(etIT-AvidgSobquvBn35gdQC+C_i`NR$uaQ%dg}#
z#)|9QbUW@XemwEy(WBFN%Za_r%&n-l-eRanoX3wJzg%LIaIp?sjAE47Kj+V%5AgGQ
zJ~Cpuw%9D4VO;COxi<x;kxNQSidn>zT>p)0R4}`=>c{BgTsk^BoScpC-zOZs@o87R
z?azAl`xnoiB}4TIcVRy-VHeO-jN|kfyJrH+e0yn9izpMs1=RZP^!Ga`DjK1wrFBR^
zAke!1jS6a7Vi3!TR&>OJf`TENw`}nr7He#1cuuU{+RqHVUbo*fCt5o?($I<2EG+oq
zr0>UT=NcR?@8RU+Jg21<uAO~Z{)BbUbNrOY$~;{0V_72&jJtQYPJb(yKskK!__2)3
z$`5XPdwYCR`qtH7ze2IAOG`^zP&p=0{C`e=`=^lJy?a-}c4%wwYlnxTH#<*HPEIC{
z<sTUBjd?8i*280SSJ_=bTULdaFJC?h4dt-=b6%sXH2>AB1O9R{S=!l&$;k(>s2uZ>
z7x3vBY9a(K$w)STY0uK}zRg-6nvlSetzQ;l-BWS~=b1-KOKWX?rT@c6OYFCip&=RK
zykYgb-rPH>cz~{}#5U<%PE^!hZyz6Zb#=NRcIkK=<#dG*HU>I68AV0KW9F>~T3cFZ
z;}e(!4K8B)nOC`QVqjuYz~8+>t!w@K`8hTu=i03I5Pp1UXy}utPtU!1^CmerSFm#m
zTj8xO>&&+Tg|>K2?aG4u{D$`Sa5O}(u~#KD;jXr*i|XA)RvGdSci$d*yW{ok2|64=
zzhw1{N0FS)+RHB@Q+c}IzkMrL>U2=OQc1daVq&80`I#-z;`Th(Oie#8jx`^Wl8TeK
zJ$_o&mxgiD#n{+5#UZUTG{dm+lvVHR@W)|c6TmPOmBgWcCVgL3)`uz~c;88im14;&
z7ov~(qXCW+w=X(ciT6T$GC+%biNPfo-CpA8J1<SP;N8RqBf6ownp%SW*eC!0)&Saj
zYZGb2F@6n@kdmByQB#uvO;x4j$M^4Nuhs=}BqmBzz7zd`ZHBw?*xP$s5UcoOzkm(*
zsOad{)%oFxh0zbU*Vi1PkD1f$-MhDC{r3uEzPVKmYbo86JER=FQW;TE(Vr~}?6hhv
zACiAcFFt*`XGknKUeZZMPCoo&b#+4E^iW@jNq#|rqO7c^+u8)LTKSuMztuZ(^u7C8
z>v#NR4mk8<Q>o3M$FAkxyZyBFwzrq3FLB&#&(t!#c~j3g>i5qHF3F~MYdF(-3^#g8
zY(1#`{FslKoetwsdCV_(@?^}b!oou$BI*om*b#L8$$C=Xo1$ciD*5M+E5~$=H|1N~
z*w0thddOqNY!hol!rz48WVSLeFg()aV`L6Piwb-2;7lpaWnJApwyZh{26A#hPRVD3
znEe|sJ;tqQ{hS~xZ`|+p+p5rcWMtIp)-Az5%P1D{V~36(*CKxy5WuOTqSDydNSCs-
zw3N~;{kL528+zV*CQjOxmsjgiK9v*|59z21FR+&4!&6jJQc@}!8a_Mqd-|x_Q(VPT
zBMua*0@HgoNk<`vA3c|>4e)^v;5Tq^oO!~tfB$JsL9yVZLmH{%m9zbo7g+0CIt=e0
z_L6e@^Az95ZJ}3dxtC>F=@u1lj`JHW=_EqCexm?Kki6rlhzKrW?e}b@|GAeg`pOe)
zGlPR1#l<Bf4;S&#usj2LbhM-2zdtT56$gwlG?wCb`8ui=Hj^yLkYSke>`G#OGgoLQ
z?Lk$YOTCA3c)49-{2A^SYkn0CVLl$v)6>IODEsWxy@vYwv{uJ=Ppo(PXX<PZ_#l0&
zPcrlm%{~dg*sqV9uR3%eU}9ogK;v6+p7^b!Y+%5K;uD&cl{KeO4=mADd`mDuM_2b;
zQBl#)+MUw7)Y`2shq;y%6nx;2kYGk*{`~dp*)ORo#y4*G9==jEu(AHT<lb-5ROP76
zD9?k7?|GlO4~re(;h_+@Txg+Xe#+n9zv06NPyB|1J%GPDIeN6-h1mbf&cVZ<f!_v;
zGcpd5o&(S%=K1bqr?59SD0h6Y_vrQKieWrLA|f8~@oXD^7B94n*ar^}3=MHMegKlk
zIUu<=H7TQZY)6xObR`GdWj`<PcC=giZxdO$Z)(1zU6`L6thM`6c&nc+GxM)X_sy>_
z_5yX<pbM0mGn{GpeP`<HVJa3;Dzr2qAtCn%4>slJ=Wp%)Io`4bM=GzV$fvmY1khaL
z;q&rT%*V)o{rZ*n>XjF&R!wc~{GVU;gU+t5lBg^DmA+t)S%ga+JV-v%Rp^Oo`Sa&b
zA<M5XL@241RaDd}-R=#pL>;~HC^B+8HeP5&(amq1&UN7`6Jw8u#hyNW>KA`EczA!<
zdz-%-_td&t>A#=6J$^AYn30}^g_4t~>2jln`KF3d3z2R0ot=C5`1m;Q9iZC*Y#kaE
zwc~k15o+F}a|bxT)rYVPa+NtPey_P3rA!7GpFk7g%ei!g7KLIn7Z;b9gam~#+KCxX
z|K2jK<QvzTUvrk5pS%<u{lYLML#g*rhNV{DVaCFbTE>4?=Q|56nW9Cl0^W!54p%+c
zk|-V-Ug*2<Q!SZ>LF2XRo$1b(Jrb9CMa>sR-s_e-3S<9-;NA^%zPuXFLRM8`J8ZM^
zLtjo#j@+K9Iab`Wq(n?@V{L8ioT4J_?ewP9!W~zpF9s~nbaNdzuodVvT_-nwK$k%$
zg8lv<wI7-Lu~sB`LHd3B;+WeN<0a$A<oRa;DkJsC3|Y_04T#bF{oVgJ@9wO@i_3)+
zG&D5WoOJ*vq2b}&kM*4&3)S1!$gMUW3@*0pIC9LQExpMhLzC~szIxljh0jb=8sF|s
z^_HD571@jO?Mp_@d|E|?I!?kt2B*S3(S@LB^t`GAIG(zdZc?c#F+L~{NuUxGQyRGj
zQhH@~sMXZe#Oy}McMIscqk#{D5ip8tGIHD!mT_}?Grj`cA#6J&Cvc^R3Pj<-n>VMh
zVh_F*+;}(??p6fqVr*pO-r3nH;?%ap8PiH%BOV;ak~&_%7c%rJO*PJ=sOZk#Z;TTf
z@xkvzuoNvNwpX#1PZV_|1f7a<Ug!mUot;$g2|k#4Lq&2*D<QSJH&Z?QwT8NmeD}fN
z;l6aMBZ`39Houpx<j$Vu!~sVClgH_`U7a(YlQcAU(N)%(I_;#{wyaaEvM@hi4g3_i
zWjoD&K0b>0`1tYeV!@i4nnslZ*I#z{#RFK|#Jh~N)wQ)-EIV=zht*Y8kxq|_W~+d{
zQ=tADKYRZ8X>@er8Bem3J2T8E-%EABI=_88BP+WFw8_TKj;`jaiHV$))$#W@Fhbbl
zRC|O-r`5J8o;#Orv(3yLH)FCRH>9EA+|M7qWlauX5oQ(^oQDqWz>auXV;2_}_Y#b;
zg(8QN>BxS5eoB?-V`{pp<_x8`cCw$6!*SHk(K|FPeHRQ)<S(aFyaN8vQfhx=>(;GR
z@89nLj*$HjaoC$ozVAcMF`R?(Zw0*IOa9v^cUQHwF;xEDILV{@l*G%+i&nK6V1|C^
znV7B269b;Ggy+vmQBhGfldlVIGzSc^N;xxtUsa8~5A)80xS^HKM0#cZh3rDS<QY{}
zn%KBF{vbof04vRl7cVB+@egD1RwuJ^z4`$m1d4CItC%=+=+HPoS<qxlstSvjxA&O~
z7k2XS@U%y0#P4Iw=*QJLqE>k?;6r;$5I3K6+S>Za#`=n(a;(_P#@7)mFZg9tio*ih
zawpY$Uu%TT?sjl+@Scj)<nxY<WCDa5n3{SRa%zoyetur)=uwSyDOY;cI0<fNX-!RD
zT(bU*==-K7Dkwg4^YahV(s;>svIRbKTifya_3LXxXXWIonwxh5ujLgN3nt0h*a!&-
z2s}?nP_uR${q`!gyS264KDY-PU)X8EG(97ux~67xLPA2~U`dxL7~!7d77XAjM2E#+
za-2HFww;Ex3NUHY!8c3Zz`bua)-0RiPU`I8>_VAY{?Ypo`?Eb@11H(f&+l}l+xqjQ
zB+qbu9nQmtsX@JDSml4NEH2iftQc8X1fcgM<>l?d8Jt^Q_6ZH8z5X%U8;t}C(Cw+O
z)VT%~H6SE58-JVx^j~Y{H2Kr>nk-GW<D*u;xKuE=I)62gU79T?C+C*rPqlCN!jw{k
zgoUeps9B7zUiAbBHtKq1{<*c4&h*d5@7WTEsofHHe^AlW)4zT9jsu{>u~hfU70P*u
zJJaM=R#v4_TS2C_(k7_Hh@udSxvmO<*z+_*NjUJmSskJz)G!<=F}FWrXdd#Pj6uJi
zr=|I#S{eZ?ol{b(`SWX0{uk=A2~KDy06uom%Uk`Yz#Y=muQ<$fF|x6-jd$d-M+h1Q
zqZ$p(57&!1EgX3D>Xnf5&+*KD_YU35mp#Uuru!?|afrQqe0=6^6&*`?cE+hWInns{
z%B+Hs(SE%20shvgK6oFBdt}eCJ9q9t_&ZP+xYu*^Lu3-DO~a>8XZ7@A=kzoqIb?l|
zfCI5jnIQOWb#ZY4bsT62<-%E(0p4JEeD&r{Dt&!@6qrQY;kwVCKf7!4X{EbexpIXI
zRRjpi3uW&Bonq8-0d|~OchO;DlYwH6PfcB|_Sgn=KreneT<u#y4*Bt3)d-zRzNGRS
z*`}*8ZE?}g#i))(XjLW7OMIveKbMw9moI;emtq0?^$823cZ^qXbQD9g{hVhkJ3c<H
z*~Rwe5O}3WQW6&ch&OooAFxPrDyk<e#rXa0`BQKG>07dN1kn%31r6W4MVV>Ad#nBV
z&6@!tjK)7F%&n{_B%KX<6dzCX+|294+qYZM2i}66wWM8Ohwit1X{w_hBykS-FX8TN
z-|Bc$2;VDnh$b11#}zJLj?QCjz{|{W-N$*lfykON+gnjTKP#}`lY?#r-gDt&%!xGA
z9P*A2F{dYHX9c6pROOyLdv@XI^`>1B?I%y3e5l>ND_q}4Me~^XWt3J1u%NS&%@1oJ
zgyR1iXU=S*WmI|m1LbRCeEbnm<FDVpzoe@r$#tJ@_;P=+#=AzF^zJ5f4MM#G?hgzM
zOvkk*dT*)xvp{+LR$nXxDn-6<b!bQCfKAt?)gRyA_~i?|ijcklE~xj4$m!C`X&zIH
z82hEfbh4o=B)qp6?811W63e^3+<nXR#tnr{n>NYG-TpbQ1_6XW>gm%*si{22E!zAd
z4qxGEyb54&AnZL~kDDo!%r<DTkCjqtcGKb((Y?D=;d~r2(xZro-43&T6-~?Ja&p+E
zX;|qn;(L>A{+wJ~5GRZ`ySllZ3fgxvf{L1&K<z$Mdu7mp7^S!t;uRAUwbHR{`yo83
z>*x?t<igLN4B6I4g34X!85y}RQ=gN2g$@QGYd<4nI7Ko0A*oWmKGVg=D`|Ta6ckz}
z$A@MVPW}C3-}?1yDnaKmwX*n{vYtP8$5B&-x{;idqgq#2H}d^EkA>w(`*|&`c9Cu=
zG^POUvQw_Bk8r`fZBFRW=w0j(1>Wezybs&Q%&heF>(}JWOujZ1`N<XB@%JFMw;de#
zd)y8jJ{*MlEY}@^YO<q)u|*^FM#Zt?-&7-f%h$A=+bt&Y59N10NMCrZp~j*eclZ26
z(Ju)0KY_0wfhl=+hY0BAxq}kQpj4Y08KE&)bH?#m{sa4!tfhx=sW&OrzpcMejX7pq
zvlZZn7TW5L+4RT!UFW+r`gSXE7gup`f)pj-j6d@C-(hZM_9!KVo0>&55QO+2ib-2h
zb~0`4*RKRn?^T?msmqLHNnibfgU@1X+m3zO*9OX()9#dZaN~xg<J`b&ZSU&U0Yz0+
zpP(RWXfy`bTXrO^l^0}bYpAQ+%nh7z1s8V6rudJW5qI%Ma1Tm<%WDTqEM|oG?^%3C
z0(D@KjIUku22b$Mk-oRa0N|x??i~5a7f<KUT>Q3~f-pvyjI1tw)VLv=<+41zsd>>a
zFi;L{7lM_5-s18y*~Md*mO&4Q%B31FxdRQIus@#NTi#13VvtD-k7(ZbkH>QIVxw|7
z#nYoVg3V5hj5R+Cd;MAh>X$4z%PH3q8#aA2vx8t9p)oNmgj55#I@*}6lgqZrh<(}k
zzyCT-Ngp|Kq<tZ2(lwYhd~#R0+qwj_B@iWEuhNnd^0HGqK{a-=NuGuPkeHY#WHWHO
z)NZ!7j8{NVyAI^*LdFE6zoA;}Le3%PBZ7S~U8kd#b20<F^6zTh%xI8ujIlEK((LGv
zd(=fo`jn-Hg({h)M&Im>a*5?P5OE9tq)Xf#Q3{EazQ4f=gq<h^J|^k1tQ=*bwfDq{
z6Dg@W2i)Xs8_QD7O3ED}$cImAYLsVGWbq{xP3!j^m#=wZe#_}s$DOstA)OV8g1bw@
z1)?e6P4h%Y*WK#E&1WV0-Aq5likG;o<OnJJAXc_5gC3fKw-`{bR4|)VK!wW+pFnX@
zu&c?FkdTm_9-f|Xg?5r%zcpGjo$dPcV>o3&vE3o7*~zuTxAa5wP+{jL+xc6JOPG%6
zrGFJx+1S{?)^gKe+)FVuJgm{d=Xz1dg`WxUuf3V0_p~DX{L2bBOoOwthF0PZU%7PP
z|3F^=vKv4VC#RtBz){oG)Et<Zp$;CN`yg03fZjO{h)-ai*xza7FBF3R@zLtaYEE`=
z6&qD=HnX(6`Yrz&`Oclbs5nH6Bk2}f@3XVB0~sUm=?lZkH%h9kN!CUc&l<hIT`9#S
zv0t|*K9ZhAV_&1Vl>rSdeDn^tQi{4a&Fb}kx76yRjKy~}h4S+HPtb-P|E#`zeB4Cn
zlpgR9EG^9r3%soE%6SK0?Wq?Es(V*||E~S`F}vw;Kloe0FMBE<I^+5UyvD~hF)69v
z?CFx{bm&z|jy+VP3#Zyj(dd}JSq3JEHGTi*nw}J`oGk8q@Xu!>^PIn)#|95jZ7NTK
za0MA_o&ReTo)YS_R0xinzUbiU>iQY?OX5R+R~Pd`CP8f_18r^Xnm%uKKFQ{*FaR8~
z>rQU6JpW{8L8+bbvi6a8%Whx7_Vu|a{J0le@@sBTRyjtL8l@2c$pZ`?qMS!wo)8qB
zg9nBoTER3dzSY0m+n!Y?jh_kvxbU?`hLjPlj%(R`cYpNYyo$lm2>;^j=vdX!QLOCf
z>@2aiGHXabNjR2C%q69z6`@=!2DhZu_37x;ZN@LsEi5{vXD|Xh?WUoLxaHl{)~0!T
zvTX-Y@>A*ir!K!T+XO9{0PVznMAQ2WVcdH(oGm1G%1If{k3;m|vePPkTv5u}SM&Ss
z_SH*!-#Yo91#Ej#(LcpjI{E0)BaeXrxt_{?#tZQ!gB$BP<TR>sGBS-<IPAfO8=9Nz
z!QKY3!O%B4yShwP=LW@GmIdI!>|$l5%FqmeZ=%-qYjG?|E$Qqz6vC`5e)q%#<@m9d
zLQ5SEHd@Hp)P#rtv0?4!geNqN&=+uf#Kgr3k_5@D3P(%6;sNUUo}<^Pz${5ni}&o^
zYYcT3z$_=vHO2R2_KO!nP<9B5l+J%Epz-QN+OxTZ(euhrg*IWsr9;_-d@XMWcQ!<N
z!w*ndR!4`4z%BSv*SHT-22hSK$~55P=VV3to1J+Hfjto6O<>9qJ}oTX<>_yp*pIc{
z-7Kgmd4+}VaL(i>U=a)q4{w3IaPQtd;uZi((&u17Jja|^C2o@gQGae}c~DSrl!jGo
z|6B9UJPuM-Z|^?I=GwYCGLj8cm!DG|I{|3$vNHwOKki~<JCeKzSPsi?7aiSZ(EJML
z6kqKxt*ry2quVkxDFCob%F4Ko9LZ5~7QqIQ_5bXmBrl&e-qls;`@T6O{XEnBSlESt
znykh7pOSiK<Daps84V2bg-;CD`X(nP%D~>s6f!`6fT2aPV+U;w1qB7+MiC8u+qP}5
zpdmmrHMx5A5Eqvxo3xv<oE+EL!2MgcY+;5Ps9$R5jT$14b`B?1sEVGP>AzWkGhd-5
zo|BjNjyR&PL`eTL-Nj*=d?~QhC~R>mAcuOr`SqPe>D;;JAeS)!F!Ht|Xl~qm&(Lil
zumh$ta&vRHKqUp#nFQM3Rb={}ZpbM<$Do2Qc~~qM?=eTex;<P+eHoA`BQvvgw(_rm
zwE=O7(DNi5=k{~+@_s>MQsfN%3^XEd_pzzz;IU&c93Got<0dC1$wDW4j-95Yq?8D0
zjj-^f0A2k2ptqfYUy-_L3)w|IJyzmU66GHfkP%fF9tj^`fLJgWeoa7NU@}zpg{38K
z-1x!1*pqjIA$&sLBs>=>$2oUDKiF4t4<0<YJwGH*SV-BI3VaBI1wX^j58~Q?l>LKP
zWb7iCEEf(o11I|VrRU{6>MF82PZ*W}+n+()2zU5ExRQ4{LyHF6tgME{%?myYMxr`L
zn)(`3UR7jutLC$ns*j9)IH7pv%#w<h?m=lQ1A}%`Fz7_kLRD2&#=0+UZEy38Yj-g)
z9PrvF>9`9JXl`j~Tl3;0r4$kgT&}@X1b)8S+Q5Jjj$i`F55duG#y@imuE8=CHmV{i
zKRdOL;7DRN`~R%3&%-Ji25Fa(L`30*?#s<V%*SN_ZG~Zgg|f=q-o0B7LBJRc5=`_A
z&Kxr{vrH$<148OS_`<Js=|?XMJ&&?`T--i_08_Dw?Lx(>=#jMUeeI1J3QcVbm>!D6
zTPSWs-2_`b%g}*Sh}z+aBfEL?=DDGFlmt~o{XxK@8q{$f-yx_D$sIdz7*%Q&#&=8p
zwetzA`y_5xK@k^tJTp5>Br2*9_kdaW4l;(#_a-Q*2!yyNa(y=yi4vmfMKK_FF?g&u
ztb88TSU-ab=N(d3C;=CBbci+yB8Gq4+S(dB2`VUN|D6i%egITvLbN5PruGH}fTos~
z7f(c3F0#!ov6e0bk7tq}FO(@A<l|eYWqdF;nx>_b8?BsK(~}M@h}e_o&Yg4j^xOi)
z^)rZ8_1<)H0-ZnX`&aO-nFZLa>F&Prqz*zdsGA(zC{|Wh5(&5~QSAHo?|^`sB~+(d
zJiH#;i3NK?dNyO*UvK;9y=lq)zi$rCfPNiV>jejCiaBw?`V`!={hY5X+LahKfJ4$V
z1F{qq?*#@0B_t+twkmLjo(8Sm6MPDv4&Jo<ClM0xzYMO3;cK~*{a~3gk7qzYK*REa
z<NVOJ@6FFbh<FNw3MVJ0hL0Z^sU!JoAYB-lnr_8=IF$AE_u&lJ;#+;X&3E^S-aq$C
zDCQq@qkePtgjC{G#odw20AJt39-cdEKW4}5iimO9bL!L`$C+oEQC?o0QOwLHQN6Ff
zX(<L*R7jz&<0s9*VAt8x0bL8SWet=Ip?hn${~%z|S$V%w=GT)S*%_aByjknd=<8P~
z>lz!k?=x{)YoN>uo;C^i5-;IE0Wx~cR44g@lf>>1(01gYchS<)s`2Pglq+?=NVXoG
zul&2d6PD#WP@;i>0TK$aa-0Ms>~O|Z4F*oWtDckN?*TFb^BL}nZQHt4lV6JE@?mdJ
zPi|M&>HT5*9>~#AM@=_a^5(2%X<I6#!h3|z{_@VuWyyPMubP&C-^v~5Wmcu`OzT3h
zFu@KddKYpiUY73!O`N*JHH)}?(Mn7p8yZ7Hgv9HUooyA~s2LM0;h^;MH%^<)ojVaM
z#U;E)xVg7MxKBg`Wr<~i5Y>9ho#<g&Tr-V{iaI2aR_+4*RhVj@1j80Gs$D)h^mKG`
zD{8pS$EFYOgw!bMy1ENenT+m#WHvaPa>i2qI-P7+8hyX%5!f#>I{$?I2*MUd{`13S
zCJUw~0@?eQf{NKP6r%u}JfowT3EctAWf19zbIQtc&?g`yd7^T1{BYm21!*#-BX42c
z%i9SYK3u&hxjfq+LcoBlx|e3VAsfth->NhQCM^MhAmn0qcM_IdYmd)?L<_4hHHjd}
zy#YQm(0EX1p@flreR0VX@g$?kwhX&35L=B$8bS%^0HhQOV*$U-2!fLC8&{|B4{DD=
z_NoB+Dx8;U5L^n4jSYk)&vZUQpc*YDCyy0SZG3W)Q$&OgjskG2FsZ7#8mR$V;*_Hy
z1qfFDp+gx>T(|^<=g1KnC_HbWmW@a_z^s;qzAEfEXA~>xL>DP&SO)_pG&0f`!cavh
zH4#NqI|=fzF9`xM0h<WU4d|Psm8Cu2`H~9<U&y*gOf4kL$A`>^W*-G-4a$qW9UPRG
z=!j797@*(L`&fMFK2U7>6ndqoRhJif+A*sxhJM#YYC^e{mX=og(`v`?&0WVJx^d_U
z6BwZ+0ZYKY>rv9@VR0s0cy?+u^;@3t78q9^Ah*@uzU@WLC8wlx88$eJV9wjOZ=X9%
zeMPgtUfu#^8q(RRt9$8^maflZB*-9N5oH>1ZI?5xPWI((2rU4h)Iu01ARKP9+(N2q
z90gXhq@?6DPuL)~sof$}O?N0KkQlrn<8U55N)MUQ3zBJ#^PAkfU$Xz}CR1pi5Ovy~
zZf4Nod)s{loq&kD!2r$9(NY6O=aQ6U#j@vEYVBFgn<PSkn!b}oVJ{SZNR2`mB-pT=
zoL6gbxrtEUjm|$D|C2+heEVHtSa`X8Mxl=hp!orOd}AO{usIF=tlEE4gt9cZUayn0
zT)rl>r;sMgp15toH_gKTr5D3nJjLzstpw7|jYkW{|Mp|=ng1C;d{9fI4g)Xywi7wT
z^gW{g;ap_@_cwkFy((n!V0_#RtBN|)UdUG%Tv;hSJu@?PnVJYPmTyA$5XW6DW!YTt
zOM-nj(TkCH85ihWAn^EbeTcD%$wFW3A!nrfN?yO_6c^tM5rd(opr8PhonJzsu*k@P
zAbukn<s3GrPoFOAdcU$^x}8i`&378D{Gx`2r!r60wV+9#R%$QcBE%ae&#@4{oU}_K
z7Tq>8Jf1qck%L2c(}V6U^R>Cs`)yqI)6voQF-QHsaQg0{)z#JK_(X}f2L}fSe*Lw+
zJD=ADLkNPv_I>;I{haM*M?S3{-x)+*1)y@Bf5+sw_S1atH`sk1$N}Io2$4Y2?a!Xm
zEz*?8X@D9iJh=}w0_q$n>PSS^>$O-Z7eZJTI)2<6Rw*#lLzpL_6##y_cI_e|O_`YJ
zfxsiBY&z6EUI~ZEt;mqw>?wKK^m!7=0*-K{(8rHC8)Cts(A3fCK2%(Q*g+Tq=y1?(
z@fNSE^}+r_a`N&-@Qh%54<AxO!l+u5gpawI1fD|%Lg%!Qd=OSeTzU<pcV-|tcU(}@
z$B&>(hsPsgW2unVA-MxnK(vK^Brhrm*M~edHWrq`1K4jyP!$dyJh<yyLH^k6J@1d~
z>Zkrg&+^3W3gu)>HhUy_*!KTC75`MK`ren{<iQkfTsLx3ul?vMq$FGyWo0V2^(CF@
z?&6*Bc<6KNS<@~GYF@s~EF&w63ihCKV~rAAMHa>a*jk`|*_|E6;J&+4PTpHPh6XdY
z(tm##K<O>`o3KUOL0FH@P`qz;y;6L_18WztkOvVF+Ch_xNa;AbxNyNygS{l%94|Hh
z?Z&e~0B@2z;BZxABLz<CVJv27M1&6<56TJHc>yqhhah)Bk+{1zj_?!j%yd-))`5V4
z4IkWnM9&Y!>}gcg`@;&LDm}fup>c6R5bFnk?^Z{`E`YM%C2U8M&1|0(3^Eq0u2<Dy
z8hIw~DE{>SU1xW1blG*j-!OQX_>SHi*S*ljs(}XwL8C}!AYyKR*YA?52q!u|J{Z^W
zA-wGVU$;Q!;}qZ$Re%)48@G~?Mf{4?{kHf|sCabVU}v_rudJ=_bK~3x<0IpLH@CY#
ztTd2|QMJnCUi>-##Zm3=wnu*qpT<<M&^(i(+J4fP?*n2yHHfW|NWjN6&(C-gkh<xa
zH2dk6UD2Gw)6mh#LBqbELo#sJR7b7?7z7k*63M{80NzR7!F{wG0s>U%-6VGuTg0O1
z2`d5M_syS$^DnT!kg?fTZ%f#Ws<C1ZKwQ6;oPx<p`(8KCXw$~(@XqO;QhJa}513&A
z0TfUaM`@xyLA*!nqjO_A>CU|aKouLAM<v<|(9O6tS#j>?Pk~}qoR)e(<GHEaN*Q_i
zZ6x^k0z?Cm{`==B0$$$e3uk!3IItnWXpws&nrGTGx627ZBlE!Al>Inh1i}Hdq(G+>
zI&s2RlW!XdF2APO&GOq5>V)Tsc*6FJZHtk(4TJ^MSMGFC>#)TUK9`L?9i`^NYi<AC
z`;aCsh3FkxIAmz;xQSxKhhK(+V$^4h@w?Wg-SxIMwzjn1Q86)a_LCa(2?$5uOBk`&
zuk(nhyfcPfnRpva$b)`62`qp_(yMT0Bni2$+L&IyP9YmhSOJ&e1D>6Y{yYYeU=Jdn
z%qOfNWKcjp)GM{y1{FuUfedga`&h6tPwn@QF)!WLo%gV?=yd(o>{;XvJqH(y*A-%K
zFc1z&zQ_VEH7hAY#h{qbi$-B5?j7neVFM8c8YywmNFH`hrV!PYK&T@IZ(RM6SL1k*
z8ZQm)>dvD_Pu&@>Xd7Kx&1a5LeS49uDU{!vIgRIp-ZsAWi1(3KuR9o2TwZ8ve>Wi(
zENeV?{}X5P;x*@yg~bmN=@u44`F5<lCtf7qd_JB0tCi4A(2b!D!jM0OGB7_HX{h})
z_4OL6^8lQnRRl7LR5;2X8vufWLA;4-eqz`<bVj0ZBKRkXvQ|^#?d@&WQzCpsuVnuk
zaRSerIU^&^S8(-Zm`+PYITA3ZevQx*UKQLFzlRTN5k=i}+L%RiTktU7J>1fNJFvm;
z0|$7FY7?l%<b3mZ-FE(9?!PxYAxHHtJ!33}Iy<5!DSSI>+~D<D9<R~92Jeu_3xz&s
zHtkPvYhe|qcA*Y!vi{NAr>S2GlYQvf{S8|0I4PH+x{><&dV=xE<m%0vx<cvZ_=Et)
z;Ex}{5PXJV>cJn)dHLtO9l)>aO7Gp8KSjuJAK>OrF#nP&!wS#1#cMs&4kz@5UMU?M
zn0hpt!Qo*~9E)(4N-)8i&jnypJ!05Oz(}&j#t8#?aj?Z1(!^#^MZEI>U|VJp!W_VF
zTOIUeM@i@K3Qz8w&uk+5^y!nIzkfAULGL`cN03}=zkV&Mc|}Dw+-urj`P)Woh0o4l
zbl_U`dA+6*KkYl&nNmx>39h=>_N>#a1H(VITuP;Se0zs!l*np{PQQPac~n5t{l&St
zDjfSf>s|?b6yi9Zy!+!t&e}EPLf{t={Ky8b6rqUSTl@JSC@5%G8g`|PjZM|xjTLFs
z?~X1}CV0&df9rACwlIqEO<2koO^FaQtF#*{ve&RR&V(;dl-~b)3IxjsE0zGr)LCT3
zf>@dlyW46)&oQDgpsx|Q4w#GpAb&T03nF&ODImG$gf;7r{>l&nH-lRc#_;v#_`{Er
zQc}F}6HI<BjFvdgb0Qf@9Ls9sEhMxY8&+SyaW-VNW#r_xLIm9Z_$u6+__3F$);PMH
z{QT#0`mo+_Z&j}QAd)hOCWCvv9biSsVN%oBEjDpcuwUUn+pbLV<Gld-4BkhJnPw+8
zWh5~~kVOoL<y_XdzemgSV`!E<mRZPcz)^WQI&gkVeMdv(<LJkSZpm9rV0B*`!%QT?
zJ<%s_1>-z}Jm)qk)i;hjVftERb%hWBu?2il1t7XMzEI!h4ikq9(oTYL`{ZN+(i-qC
z6H!43@0vI!2uYk};N8EUgr5BX=o;`oG&-8Ef$u`333O>AE33oGBgIfP@R=|pL8Vt>
zbH>{F{{zZ5kwCS4r8ic$q+NJ+7E+%OA(~t+%nW>iy-SENgaR^9?I{eF`7|bmvLoiW
zr4Q1{17pwb`+!^`Gvbdsc}9~=LqkL0QvOz`Bak4MJ{R9IEG>e&{1)9B%A^NjyJG~R
z+G_{B9Lpho=VwsyyFY&ENh3eS9vGQxkhuRaOWpmH8g9Pr(f9jn7Q1);yOsN<!uB=t
zGk0Hm^H1S{j4B3}lwzCqZ>R`7N_9tc1;$@OU|3VJNtWbjWvKP}N-*u)_tDw!2~~m9
z%%dd{N)9HOxr1Z0>Q`@_zINjnPo;YGx9QEgMX%0|gl)bZBf0a3RHI>Bv!lI|bbq@Q
zFBEO@`}gmQUMiw#Xl$%O(}xb`h5TY$p#-7JII4X8Ah7k>&bp*n1rq7f&^>`6W#dOl
zn}S%MpZP<tTx7LFsa&f*+D%MOL7}omQ4{Wo8aIYxY!T`D0~u_z&eq<(7V!@NydWru
zd}?KP*}oD*z~UjV;IsWBuH<Z$0uufTW0#Jw-j@ivU|4QI{%#SeQTGInL~gngm3Teh
zE;mQb;y6=ctSQ|=H}0y?WCZcvRac?-a)GW>iYw+vd<^&NopPYved&@|a(2+4wWWmA
zgiSlGta=IyWIO2iJNFju{D-RqUiMP?auI#=xa?luBU9XGV|g1Wj%|vf+EiCpa>{I%
z$z*%&`tl6zqZmrDyle#vmZFe<hp7T7oMIRERF8RvhF^I#b1{^D`_o4}r2Pg8+cRr@
zxp;WDHy=G(Ju#7xkSmyzl9FJl&-dx#9?@5g_@Mi=$Kc6mboqa|AN_D}Y)759yeey3
z+gx_kUXv?D46SC1l3n9dvd+ImcyxxRZ=78+mdzs8-bjc<3bAAJk1Kf*xyLPMPMTL$
ze_t9IRh3rw-{5C$Lr%xOv~Sz)K7;w6s(%K$E|NqI&qFcN329Z0tqzOaV;gLwe3?X2
z*QpQPSf|a%p}}}Tc|eeNP5=d!prH>>GuMeDG`+ELQ~BiVTW|h%Y>alNp`=`c_bwC!
z3X+gxtmhyTiL3n1(6M84H~Qa*Ev`{rA10$7$Vk`}H)47qHTiUEZiw~aD>VskLmvhf
zv;0Hm+9Y0l65dnTT+6hR*7ek$=7o3+c!e06jBlfhE!j*>E%$Tg;d$<6ON8-G)6=KV
z-*nH(F~+dkECY-(M2VQ(wr}5#`J?Q*#f(Fx|BjovP@DbyzKfQSP7tivOoBZB8G)xi
zz*7z{_F`}Fvz0sUzca)(YSj5lrrQ5QM{CV;vow>qt;+4&1KeT4yg&B&_DY?NQ(JiM
zfYFkkvb(u7g&9vX*{E_eGXoGyx!iIM`V5r0cT9$^n@AVc)T-eQ3Zpe#ejlQ?H6}L&
z7A50w^~3Gt;_RGawmv)Ug{C)GKl(yODsO9*Sp~j-otpY6wJ@G-E$Zp%6uGiGSb&|Z
zrI!zIb8J%drFqcYtdJ1Rd{au&=$B6n*H@pWyK#^88!%t=S)iq%+-jFv`0H{@`-Aho
z>~2*=P{SCJ;d)e(U5t#|Ffw-eeI(fKJ%*LHQq3+j0BLiZYmaPJf79Uq;cLqo$JxGf
z{4H<pM4bDq#hKCiOrx%waw?QxUP)#%ou_BZ(#E|KDJPGdm02!fu|0=&>~J{p1><$d
zmm^|BX=H6p1B8ePug%}SeIt$R{EGn+Pr()|1Ic922UQ}Xr=YN%xE_S%U*bDrVq&Ka
z4cQG{=eNb5y2qEyA5me^&Zn!Zdm66;BY8VqFMnfMUO_=tON$YP9_=VQGc$$1oCoL}
z#RqL?@>i=X$cz&DGt5dtO7RN{VoYIyE(Ndbd3H860zys5@jxwQ{Ek4dDBLue^XDmX
z_v-*ep^L$_zhwOSF#$rssk{K=b8?R0$HOh6A?4-g%OC=SYJ^v68VJtbU;B9oUm4?*
z0{_CL*@ZbSM$<R0zvv)pddhJ=03xXiVGltW4DflLYgieKFzU~lZk^;1NZO}1R<EAf
ze)i+=_wU*;o^5c@n%1X(bMk&le?lLblIE*Y;p`t<!sg>M&J*6~Fhsb~D&Jz38p1gz
zK6Bju@|mP`+v(00n~@=T-6C@hsi?FIN!<^b67!%Bh}o^$gu9G_?0LiB;^g5WL-22I
zVZj6b4|L<5B#QM~rSs=y!Rs+bGB@7BMGyo;atJYzco(`_B4!bZ9t@X>FPWgL5C{ID
zs}S)Bu@6y8Z**ap$ka$PcfK;G2U6!v-a$c8^N2(Cc}j{_irwbZ#>N~(Mh=~?3L#=l
zKUPCmf;dN=o}TXRM`Y^IrmGQygd<-CI6+KeqJxx*3<I6O3@2_F6xv#dCW!ov@cliy
zw7h&4B+F*9?GO?i1iJ-;mvEH8a3utJ-BLSl5c>nkSacdF9lo%fYoO)>RuK%q<{px7
zofR%85Hf|h?Ed`u{uft@X;M>DWnuv|Kr(6@8#4n{3Ee#UxMij$4G4m@`t;jtV@&Iz
zC*0;X%N`-Ahja4&c<|<wjP%By3x>rNto4H$TwJf(zGNMZ+grHBenhEyZ0y6%`|FQ$
zBARMsehr5IWPOjHOTn-Gf<z)xV4h^uH3_o5TNPA82g`eU^t`;hsy=_-MIyu(*#B%$
zLl8vYeDT<g8R(gbx8=Ox(u`MFlfz+cDY$Wg5Sbx6uP%HLeCfPofkYz{E&@v-h6$dp
zIr>*d?RJl?AWi=qv20R+Gd&1qNX&^_HGO?-tn~!ClKBH@$tf&ME4}_xqcpguF`T~z
zGgj3GxR>WHT&Rc4o0;4V9xn`R2is?}d&5f+QBg9|o;`aYF8a4(%x1Hd!CwwSJ3qBH
z&P6(nnbEJgh9LmRLti?|GVTNR6S*C2?TG!uz*TUUo@9l=<Hbl1k-|vN%+J*Sf<|A!
zM#)`!^T@NNX=Sh2oxhs>%bo=#M2ej=IBC0bfX-`W`~g#LoET%U?0tHAv59fbgOSKY
z{9~UdXx+d6m+u0PfWQs!tH%Dlxmila;`|;I?O8>4MgMoEs$>VIx|NrP1nBn<Rki)U
zP}Kw-bGTa#O-*v9rUys^k%n$(P{2Qozc`rk!4k)ul(;~)ZCe6V97J0^AvF<O8%rC>
ze-u$uEEbb@_(nE4A;kPUlzW#*ih$XKr|t#KB@PzgoCyMIVm1Tr1CG2P5hqUQ<e3W-
z!@lBU@HpUuhw~ev4wB}e-||KJqCm4exih3JVtCRv*H<HqnwnO<#$RL1iAwZ%cANjz
zwtX!vEu(i%9jtL#FRqaAdAT%KH8@~&n}3j-{nJP9xR9L}Kfc>hv}1?7$a#U^r($C$
z%dC4d1<qGIS**8Zr7&DsotHFBRXAd(y7TJ9jF=eW;M%&Gyf5?nPN9AiWdX!<{A)G~
zsO(GlP63pfn-B{0k$hOmG*yVYjbJb>v@cnVvOUku4Iz91xcH2X+5S7;*ne-TMlrA%
zsM^FTZnqW6R~?{uo>8@j-4|l!0aX7imKuQ*VVren>aUcWZ^*+QC1ONB3WwrP`pc;o
z)(A0pENLD@21a;q^U+;)99sn!9;Qw<h~x{eF5H*0?Fb6YOS*ER+<E4$c}BXCpR&&y
z#gPM7|A~;?vA6d`h>B2DxyLGggl5r1(9q;9&R5e$QMJ(R2yQUN7fwlxsf8RGY#42B
z+Rb*OIX+wX7l;!lZ=$2EdstZ5E>WO(!sH@S`A`pb+`2W({Gsv~SQ(W3!$Qa+6UPA)
zEXeF?dz+(WGzwU(0_rm~=DyluW@QzGl4lIz4zvDM7o^t@=xKl=d~XVu;|nK-$stG8
zqXp5@(|clUjp$48?s!#WcfFIg9r8M82!58FoZQeZ2)jwUa&@BOzgYln?VGyc)s8zA
z@5djp8&W<mZp(B{<j>_A|1Vl<4?>U}S7jaU^)6hs%e!rpZp)j+W2#-W%?A_bM4}fh
zG65eJ;{<+r<~LQUY*{hF9PCZWG=vDlQz7Hc5b3wX&?%JOp6;XtYhCUCyWvaAb@ud_
z!(BHJ#m_nVWhW*R_=8|D5Vi<WYT;@?kElfunNJx5nElT|hvpEVCUyblgu(XJ4;m_d
z!x&c$aEMo)NjH%mgEUnS2g3*fJVez8&?0P(*or2A;0Cjbd*Fu=>w!4rQz;iwh)2{A
z9R4LP<G{$sw;<I~jcH<UJk5ZJ)Dr?Alu=uZu(g=@A|Nn;4+P2hNWs7$Gz{av>6Wzr
zx}i*=8<P7Hm}LIi54AqKI>$&umK#^{1>u%2?Tn5yZx!3ygnHT-TKTMfKK%>5cJ6YD
zX^o&FlmMhBa3D;M-Rfh-5qx^$mO<3u`v6l-hX0eJ=7GbDI3uyf4DdlzZSOo(V@@fl
zidISfHGPSjuy)`u>;ar0@Jp<?JsE&2W25=Ed~WF79+-O~=9P4dZbk{QUbbT|!-g5g
zXR=$;($J_4<GmmR?HFl-!Ql{T9|UuW$@jO0FHN?A;}hgo%mj}`z!kD#@q%!x1|Y+)
zLTvPM@+Dk(Y}8<^B2n022ONT@wcoC9A1mu;q$H>NDz0eOA!CHNwF#2S*KXF~@elf~
z@hKc5e{lG&Pc#dUQ0&~oxNn#HQ|Bc))mUZyeOw=#GN<1P_vv?D%A)cn;_pBTwsQm3
zO2euMabfC;NPE*AbFoKN5w;sSC+Yn2oUYJZ(E#EC#E=*6o=hkF{FfN!1RRrx1VmYT
zxBs3mO2}95=<pi&PihXtMf{XGXlKAUQ)?c-%-W|THlDRx+%|M+YWP|3z6TxmWu`36
zjQlbVz3MZvWlQ>DbR%12GWNv_0b+$w*Pe=8Q_|1UI6~yNuvA1&h%ng@?XAKd?3dup
zkwkQy3hHXhr7WU)Vcn*5kHVeO=yADoVdQOl*^08w5XGiH*5-%ohHe(V66$FR6t`vH
z-xPZFpcm(jpyi1#+o2JkfdSAr$?*oOJ^1B<v^)T!chG}TYT8QbCpT7L^x@~D;;iBc
z0pZzw+qa$cRsZFhE-E<l_EOJ1vfM1mpIQ!c0mFH<UCd)>GqGa4`}R40Fuf>vBa2`D
z*TorqoxB3=y-L^c#DMTRCmrSoh?Tw8K4bjn&!2&@F`a+Hf84e!6Z&5IRJ9*;1UHeE
zW+iK%JF+=jNHmO!{jsnLx?<J6Ik&jLA|cK9LQr5;fxU*bZYMzm|4sF2s)FGz9U{}$
zcn}{=V=yOK)Y&;;J@wUtzN5N|)cZmFMELv<wcb1GgKVX+g9vq#co@R9%dTy-1o_+(
zIB7dJvCm7NT-N%`5z_PO83C&D95SinR+3t3XPyR8|0uTL7PIY5R=+JGWTeU}`%>JF
z$iO2@V}sdHG|}w<{bw)&t*)VAc?H64wIjcyPAX0L1=oY37d|R6irZ4(x?ybO!S_?M
zaQ>VYW6bol&0hx$P1=rMGxhpJrx<k7mOY@NfA<p!1#5IHLIcA{=|!$6BH!g%eb8>G
zM$m^+HTIg)?l&bl6#B>aC1+)E_0*9yUwK7pYp=FvUE9l&q$oai_~<ERhG?#ibTzKG
zb#-r!b{{1Xba!5~n%I^R-It1K-Go`4wmFeVSt9~eB?)Bu$E}hr%#0oeQ5%TfqDjw^
z4IdUd$KzR1bZ4LC$MnOQ-Kr5&4ZY<~8mn#)|A=u4>_K;E=?4wo4@dR!5Kmhvmm<=Y
zeuuCp`kg&C{3kzaeL0&LvAYPac%_s5X%%W_$&l*Q)I8I%)HfY%t3FlmEo+G3+0Noy
z?C{hMP!tyz-{(r%+sFmIP_j8vTxdg<7_yXX9`397dt>%ZZ4%#?x{&)fL?#?sS(0Vw
zWT?+X`((O%2t03<<Lor()$a&?bG|V9P1c5<gF|*`rpNw#J|#snkK5-SoIc0J*y<7^
zUMcOPERvwo_{3|>UcYoCHY#?pX~xuU?9fWsc0b2g;qd1F4K<U9v5xQG>!5?G^oT5S
zeBZg(@IsK2_N54Gx=4dem7&Ko)1A9mU#JXC9~SUiS{7BJ>(Cgs%6ZM#*P$}fX0fDF
ziLsB!V=_YJkiIo8UaYbz?!Er_cja=I<<@Q63V;8GU-{_6=yY#c@%iqnla5bg@T-ox
zEZ@YTAVHru!TgDd*6aPZU%to}_{yF0g~yg-;#$vLZvT_t2dYvBj5IyT9;9%8qS*G(
z)pf2kUtMcgj(qMr8W>6F&_k1xnNm9H>O^P4U^$s(Y({cwTES8AsIygcPxtIq)aCVk
zL~Z+V8JB>lDf5h*es-4c=xaVU$cnJ_9nL(=N!8BUq;iEiBRSa%sM(<XB=MXMm_u(N
z`1yN2)%yrSq|)nkMfJi%r|Xw^rc`s2)OyKJk9(_MPrY*W#<hC48#nGO|DfKo-9zR<
zSqe90d4;X^o?Pqu7akUuPGveyEwPxOhmVYmjK_X>|K1oMSihCr{&94?YR~+z-jDug
z`5Wt{J(be7^&w$h7N4FU%7y6c2J9MyK^G|R&;_3Y%~HFgZq3it?i_~fmYsbV_Hx(0
zy<IfiqU^a>ii3C1^B7?&1I{WD7Fafb<UI<o)KOY_KoQJl4$r4BY%e;|=jaKq@#f8&
z@VrccfiX!+%fwWlf9(UbLYX6xT#+9NrKHf=ZUBTM!!R&7SdEB)gQy=O>nBJknX*SB
zEyqm1kBkuh7y>)UhjJe{;B|jv@tJe|yz_i#i7gj6_`ANoc+|hFI}fn-u5;By_-ZH8
zgm62_&Ax{cZ5Y!oNY-6?Wk!qep@gl=Xt>TCS~?yYvz^`b5lmTj#4Bd(wO{C-!N?I|
z*$|R_S64K|3Joo-cc|e($P9oi!J>Y7?ZfU0liUk0M6G-LFa-x~zNPd#awIQ{iz_D$
zJodjbJZtWQU9DZybldXg;hD|^&B+6{Z@zvNSeO^KpyR!5=v;9gWVIem$Kez89wB$=
z?0b^cQePbv-#e8ct9X##QhYC6%|wOE-mlfsQobucQm%jAZ|3mvrL1p0|Dg>13Oh&;
zJub5q0+(O;fh%u`z9%~~Gn0{>eXZ%KaA$XS0W35b85!cSLGtqQC(6G+6nxVl{rA@e
zLLSEhB|gIM-9brt6+K#V{w=-aa_80Z-#^Om&MkQ^E7nNeUn+Ne`N;v%mZmGWD&B$6
z?LB6;>)pF|khHeD19&}(j&6MIFh$tP?#qOBjv3TIxAip)%T?nZmyE@`0D{#a0gL<u
zhGgDBF4&8Cz1=#ZUTzETc{8%It`cKcB_%M5RpIyay)2LCJ|8jn^~$aJ-23ae;8hO=
zHYYE{oF|&LBOuF-)WQ(tI)!ZQ0$k(9&ot?IOb8W#5kuT?LbxGH+&=XV&0DNXJ??82
z?=#mLKU%I;Y|cxC7VTON&leUJh@J~<MXi)Vck9+I!XsDa87@OD?C6LDnGwPR7TkV~
zGC|0>U)yOYs1579tA1trTP>J*Zh*Hk&sA(!prjbgR^65;>eZK#@fd$8lpjyG7}!en
zl{cq@$M)W*NFlB-WGv@TZcKfz6PA_+tzw|qY<2VL)Tnpi1DB!Pl(e^HW#c%q;SEmC
z&UP?{;ta17ng|}7^M%?h7k)BwkquBaaJka(H4GySD(5A}wBinyr@#5MBKm4kB0N0&
zNqD$09vFo?O?vfredwVe$m|G!YS-WocX#&~gT+t<He-YyNabsjS;R#HQ$=FG9EItz
zvDQ(!0LX4eUyMXTY=%afL7}ra241l&gkvp}P}Sf63^^)ucJz_@nwp`^!6rbLxZ&J4
zYZQ3=01j3oc75TjFk%;RvmZZwx{iv|B`p-~@<U=u@cu7KNCMA9+1Op@HUaEN%&x<S
zo<qpMA_R}|Xu_HLsBu9mMG8li7#oBJbsbcoeDTvs$=}ljgq81pBi@M^i7qhz!V~wq
zqrLs*^^eRr$eTXyT!Wr+P(Yw9;;$2)Od*bZGVyD$)hf-WZ|%;4cQ1^`k66}m?ceW?
z0#zvJ=ds@`B{H>u1CN~XpK{50Dt+yniIsW%3LD#j->YU}B|;}p{#cx=YTc?B`}y9)
zk~L8+AUv5Ma0W6qhua{6ar-RiYk}3DJ_UnUMB%_R?;oWS$hGfFq+fY#IOfuQvOp<A
zbD@DNL<o;8`SIqrb)WOpVI1}CRm3AA0t0WZ#J+<>H_3{WY&0H$LVA&%?H?30TF`uI
z%kz?wr|^g#AVm5za|%jWOkLesFj+^~otMh)WPiG2-C@5lqDVYH2$Qm?*Igq=`T3`O
z`d5l8*Mo?H3$~Vr92;WVX&qA)QIPH||Mp6T{6H#AqS?Ic^bqb>`AVM)6dsF8OumJ_
zHwAKwUs$li(_;1tn|OU%DU>3Ea@fja`?15?%nakX7sl40if|!!TH-`3Ew(1>zJ0lS
zcY>8QUS3h{uaUdpJ@2scG%Yf9>gQYUG>gY#-r-U#<>ki|d%_3X9?;N84oqM<a?0w<
z$|kVq+qsH~u-)OqP8+|*FesFO$6`rh(TLlw-|Dw~@gM`)%#K(z)t;AE1NTX2y5wwh
z3Y%OtA)l(sJnycl$oXT{_C>dzy^N-2S?&@B6Bd4cxv+>-BNmwk0%juw@Xl)vue%I}
zarVdJ?)GsA3x|U43{FhEe{x78XYCX;7vfn%80Rj#JNvy|$8a<y(qIUq3!fF0FK1<B
z4Ca*mfRO3~2^Ujzj}G_)<T5i`_ZaSFVfg^5<{O?}gH5d*CE|@;od;zoxYDu2V$egG
z=gScZv#*i@J<5*{z@WAJIH&--03!XXj@MB|9okv#Vkb{p`X=YLe~o#!as8#8t!+Yn
z{zCN3AwfZf49&h*y&n6=UthF)K&{APrf%uBQQN#*cs)@{x4WS3OV~pLgXnlMN=l!T
z;=<qBwWeeEZu=QzRO%fW^JX%@!`{LtCoS;7J|L%kYp(rv>aN|a$~QF`;uRH34sZ`f
zJ(0VqshOtz^v=?8$URQqqi(Ds;fq=?jG^p97>-7Nl)tiF*$}b8HgODs_xlG1wz2to
zc?r4waU=$<iBV%rG2QOn$%1@3fnLxb<V{S7=PcdqeXaFdx7+UcjZd)&>FIu$UX6nv
z5ApTjp+hZ-JTYP6mf^4GmzR0)Sft#_jcB6qK$e(jr|fKpDx|2S#3?4WnkfhY1hdKW
zOG_0NTH`qB*3L5^tZM&*slN`3a*O)MVGKYNL6DFT6b29wq&pN498y{u=}zgC?nW9!
zLXhq*Md?sdLb_o90THR+n)AHR`+fd8*LBpHd-lEe+H0*(EHyPrYwI1=37d=ubC3Sm
zExkJ07zqYDHp^e4%*^2+lW=~(Bph8(VeC1}y=qQpE`Zc-Ba$QhSZV)Ya9WEYlwxbq
zfCs7V66xY%Jldl6jFV9qRt(748G6+aI(CO6MbGxcp)v%qv;l<8sj<ZOjKSql^`mLB
zU{M8@T=~va>Xt$7OTn1fE^g*ArS+@OLHP^&uK~{0{_!yb_&Pyngb;95C}P~e3Jd)Q
z=>Ov2;n52VXDlunP`S>(-O@b({TM<>C>?%ZC6IZ5+};UCKldOVJQC8;0w1*^x8+zV
zjLAylTY&9z{8upk-2-&#0g@2iBnR(0*wcy{8ig5O?hz4509+A_cb|rfiwM>a0tfhu
ztm<9<BOMYseGIh)z|InDmqqY1pt*AfSsL&+p<58CRJc6kbHRB$<A@J*jJ(p)DB!7#
zG`ZMXr$j(cGzByT<q%KhziMac4g!(ZenQ7(f5vgB0@Q5~3HW_3gg|V&2<>O6j)LIc
zkAlSeE)~@v)N*Lm5M;Goa+mkeW^Yj889_(B8I--yYV`f_LkUe6v$D2E|4<w9)3mP^
zkiSf52R=p;Lf2pweA`e@m#;63C#c1J<3-L@OE0o=vP`@c(2^Q=byHkixN?5#ABY*R
zqeHf^ZM8^pYc?}C7=8ru*RNl3N`(o_4h^{~OmxsqV0`rGMV4(_KKwKpc**{UqL&Av
zN>*0IgND$xjD?4yRaIA)0cuJT?7Tb(%^7R(b3na{oM^d-ghTgadV6_oO$Ji61h|6e
zfWOf*F`@3hUD1^bArTpeD186^sr!0%Faqwz9thnaNQpt~FW`?r^CKSDxAFsY@zq@%
zDYBXn&bgq;i2iF}EAkE;xa`Kf0AHn3P*5-jp7oTYp4U+#BB?JAx);beu1is=NOC&H
zMCqQvL3wgEecwZ%8EtAC0;?+yoNqt?oeOLf={N)uUPT-f0kl6SCtpA=_5-?Uh(NJy
z>pBGT2ap)@00Kc~zcib3R1iK&7zO%4&-l0+I4i~jo0tjQqs-bEczD#-&dI2$pY*Ia
zF!J&qH}0Ex%{=DfQkhnR-;yFr57Kzlr+d%<S4ai7rRDYYM?a^hWq<(rgL6oT76JkM
zHKaw2ru)fC5*>OqIy#=E3c8dYextB`0g}EcOVG85ZH{~#Qk^I*DM>{0SRl_rf<Q11
zat$sZS1^i*=$1{+JywJCH4d0H(351(I0c%EUS(Zf5)(6XnT4{4(?k~kGqY&N=WDsD
zI7PWD`+a#UO-V=!QFrN2ZY$3V_i83`eZ2pqRG$S?=dPHW|LBdt-7q3O)5Z~&krHJn
zYnrgIgkB!h3(=-oUL<)+NON;W$TGpL^c|_LYqoOeGTfYSLw*0#4ZCA$b5q*Efdkmo
z7>~8<@4yxE%kw}E1AWb-wOdc0V!-`TU;9qo(x7aNqOZRn!N->jkS8Z-qbWq{;Cct0
z%RcOB4#?SPYs%NOAjJUI6ZFbEN97hOx~RT@n<ZL4=V1e48>l*e1JTL(-@ipu`Sp!K
z;;g0VTw6sXQw-uC11Wn=d#r&V!??Sf;uZ?;Kck9y5q$j5)BVB30a4!ao7~l4=jV3u
zr>^Y^i?|$62p_Vsm5+pKPfKPAy5PZ<kXyt6=q#7>9a_}0<+KC29pFt@1_Dr!H-xM5
z?+lNQN+>E40E`8hpdP|LBPJ_r2%W|Q;Rv*KR$$x-K%X{ugf$b|OA7$Rv-4daw96B)
z+Mp*nl~+TfH6d$p;f9j$f=2JCH(si^hfUzps^jX9%&q%nKO2MoM!34;qi)9qSoI;(
zrFVLwx4&X3C=9Nw`A6RNBEGrm?jCp4MNO2fc-o}Z?9saqkS`-Ma}$cv*lS~qb^@Xl
zEPbF-VJ^600q2+icH6%{J2KFB{YXAsD+Q)|UP62A*Wlz@W*q>97R@aL)!9*N>3VEa
z1(3=hcZPn+oRh~gAt51}o!HTVHt$&dq(tX;0lnl-D0uh*4h0SyXsm+U&Q_k#GcFk3
z6kaf8eMTh1-H5t#F#18IRpvr|`Y!LIeGX+-{@9J>UP*j>6&ss7WT6>(bO$ZXdz?#N
z%1z?U9&xq%JZ8}|5oDa`n>PhW(EuqJfP(H3(M{`x(Lu9mGaxU3{nDF3XrAcR4JHn=
zgAQXt8)s??B54^@%fmMjA3vHlUECq)kN%5k>bXjFQ1#sOXL54|v@*Ox8Iv;jw-yK0
zczGcZtxw=HFf)_5x-GmmPWDP^3Tx<U`iRcK61!V~K{n+Bl+y!TYLWEbUTJ!T%$W_W
zY8^N5Ski-h<%NvQ2woY8c+fs5F8`pgz2P@ZJV=tU(zLw$QpO?%5tQE8nM-3T-{@XB
zJ4RfJurF0p6Ixyg3|~3w9k?a7R$X*Q8(>1nB@mE?Ly^^_?6eF{Xt0)G4Uw%>SWs3b
zm8bOD)^-SFDH^p_WDq-`8<qtiv6ER@7csNidc^rHzXxv;{yt_Gag01k=`(#3UmqVR
z%jB@|zNt>0aLw9!fW~&$CcZPpvf1O)sw)Em0Sx0XJUyv0*~g-yS-|hQLr&fcpAT$Y
zI>_iCox_{^3mM;6NaO2%xrak9#=z3DsKoea*XJ^AYDyFILvGMUIDt|H^b}^t!q69Z
z*cgWS>`nk{yaRt=BqK!)QhT&VjGmbJ=g!6#DpJzDJzwwhb!$tR;yPZun=O4cH|x-v
zGiRpQ5zC4%uyJs1+?a=As}p(^kfG_itq_Ae5nY)IdOC^|hTp&5;Te*}pUv>1HKG65
zv=xd-@n}-E?9s%XU*74?cEbpJ0)l|4pX}3}r};N$6(D*YNiTyb8weC8eg;>zwo*Yj
z3JVXP_EI*iS9}T{8;mS0+z#Pzcjl=u0UdzdiSv5)ms07)s1?rAnG9;LGk(wsWM<x>
zlJ~*JHgR>;e~qPOgEK25b(g>8Lfg*C37rbA%>e6jz?*YtYarJ0*O0QO(~i2QXLHZA
z9kZ9yN7v&3gnU0WhlpdS+mAd%2Eu0YyQcw@(B%4rR(z+M+cQ_b@AC5f+uxAs4V|ZI
z-&Gx#56PQ-pi*yuDi57?g0%;b4S&Pi*aual^WSgZhEv*az)ll0cukWwWF;vkmj5n9
z<Xu+*lWpt%0mIysW_)L0pnb4Xk1Hvu|IQWGLv1MUjG>JWSW~6=T-n4hQ#-KVG(cky
z66C-C{&BdVv;pE6&Q2ruPmZUB4}T*${oj)Yjkz9_=sgL0%+dk+<3B6%yu6ZNHQ3l7
z#%l_K0BEs*WfEu?{A$S5b8Tc04(R8S%3u{5c*m(#jhwL#b|f2}@2YEUd8GcRy$f4o
zu2t62g+ZFAn6%NiW3`)+=;pEme@#uzeT!tftvd(I5i*_A^%BjILn`c|s&>+lkb3ha
zc}HFpA8eJ#Ek6GIxqfb>N{Y^YQP@UXhuF4dwOjb0pA%gPqVq7gJU05T+`z*_#RZ|4
z$geQtF<;Mkl@>Qd&Vi6GC)bNSs(uY+xi?UHdR1&sC-Lj*224(+Ha7UZpQCKp+L;H(
z0h>m>kA<K~1wgPgL*fzob9jI7gi@SHQMF$G?H^4o$3gb@<n>7169`)C#)gKQtIjE-
zqmP|Y%})(;C}m{0?=l44AtBM<G@0dqhzW{H{gvLtpcL8(olt)C3i-9lFK5knjZ)Vj
zX9~m{>NNs>>pig@RgdEfE@vZg^;jDFlyBjZl$!cnTbrDl`)*8M$4|1&Ef)d`3Z^)I
zT28b34JOcYg-bC2_P6ugi}jPvvztqV`#h`!wo9C9{n2aGa6B9mQ@;m0*q3kj)vh82
z=ctWc`Jiv>u0n_n$ttFW1$|8N4Ua#Fmugt!<98=(w)_gy#+RJNPPNJ;Sm7lLqU!xY
z_l9a%qk>Giekn>2x{{l5lK(9%BW0gpetc)7KyvsDeY&1-ON<f6`q-@@W@LI%US46^
z^vca!D+qc&&5{!V7d!+GIeF(FgV{jc%a2s<>kmO!Chg|NkM?>kE{*`22{a<j*8kwl
zh9rlZY191rMz?6~85DI=(QIfXSa<treCK(lc#9XpV^)YuVSSdOE#!uQm#3Y|dJRvx
zR?lEiJi}PQSgN7IP{rXcJ_p$=jY7HUdabAGEp11sv}?=Da7V`u>J~u@4m=H@8u&?f
zd&9z@!XI9Rov_qF)bBaROU$Rgat%g1w0kqK;?}o6xQRW*=vY|j-~au2Q{dJ`V^uim
z>j82wN?Kg*;o9D&XLDtn{0Xn~+xX9~c<wENQW5t$0Dp8Eybo^$`WLmQLEoxD#}V5}
z7Mm<?^#A`+VJEROw2jD_T{oge+;QY0&A%f<U3|Gk2glbtnr3DgF2@*>QMksR17=4@
z&qRfUUBVHAFH+~`;0SY{=@!A&JL7dci`IVvDF{q{yP-hrZ+1I))%b?-{l*n7FnkQA
zph3T*AZNZ~Hqoq0GKh?{-a|_tJcu-$|D-Zp$KuHK@yTy!c)0uaG=Fu)+<kF#i@VF&
z85&PlT-~u^VjhrYP|wEbiWh-8mt}6)!p+L@wLUTqG`yZiW9mWX#@HdhY2h2hEKB0~
z!X=5n;H}1QkUf7JnX!uV_H6|COgAM+XM8>ue(o(&tb2Qag8S#sj?aPfE5=jKIOJH<
zmNiX;q3N*r!`sp6qJQajNZ}1Xr)GLE;P~k4xcbdgEKGLq;Rh%?XH+aw8yIHiPBMh1
zmPN?L<`%hW*6hHJt#06`MNQ~>M1IV44?`ExP=3{%nurahR{1OZFBgD*^$uB5NS%GC
zD}|o=*Rz*{+3ygg*`W`ESz{N8A)FY&VsaYYU}t3&<_K+igREm1{DuYla}y!o+!fhZ
z;Is_%9R;w8feaM-zK$Ra1H~11ljs1*a_9Q9JGcw;U;V8Vrt!?;>fibh#V!gbNHbev
zEI)kM9}#Ad93Rh^X3m!M#L|7y!h)-70GS>gZ9Yx+=6*|%n!lglUnnr)lfP#IVTSq^
z$>Rr{oFg$iTWsvrOt3F4PtO;3&zu){F^F!<Nl-FA9P_wwn<VvfYgo)3s(S(nt<KK*
zR{0%7#Lu)TRaDKRN=jaLT-`h7HLnWTS~vUkVdB#mm^l_nN1{FD!Jxx=Za6u$FFHh(
z58hneyIQ37`gabc=A4%rX5Kp*!9g5P1X3|5D3p0d^uxlrh3(;K6N!cXzJIaUujqKc
zA?k;v*%4%iD^OMC|LMi>0H4OP=HEAmgbrkbZLM%WM5Wt@o)Du?E_$y|d{3tC78#D~
z9OEUlD~?myeK>^}Y*nB(2)?~lfz(EZ(uZ1>PRylu9&I`;?cUwBU*fR1-vBmwA)h~s
z{qni=N|2@l19}V)0~|mKa4nU!12~I#yb|bT7qImC<L#Wkdz}lJKG&o9qZ#XAlhWiw
zgRLGFIS+0fRlP0KQ}aUl(4vB2N-fRL&2Ga@b+GRDqIBVQ9r!U&cr4Q&d-wXY`kL*&
zsjkup9gJ7w+>73gZ?&UuEOW>au2GSE^8=I>1?S^xLZbO<XJVpx6!FbuQEERw*{R3R
z6h1_0Q_%eP5l-6!Q+=q2S&T;(6n4^T%FE;GX`Lql!3Xo@uP3!$C0C|kDeIOZ7fW%I
zvtNR*$Q?q1A0{|E`omxGFo4gZ57{@^G;htku<E1Zl6mohtw{H0ru_e0f(moVyFD}F
zo+d&VBsI4u(_=Rf_8<f92M?ny^p7+PQf#|uqzkPo2e@G>t26Yt-rh;I`N&<}2Rxo;
zz$m-3tLq@z=L{`;{^hoIALb(n%wjx)HC!(U=)Z1F__4scoksOpAcsU+=TT(DatBp<
zr>dGoTzwqjKt&*Cf9CD%?0oAaeIfVTB|!jOK^6j(oE)8Pw6hO1FR%b8v5s)AO}nQ_
z3?KTFkmK%)Zdr`>548kiZp@=mEsz7WU!13iym>?-Y*&XhoIny9J~A49EvAnfsr@P$
znMU^}=Gj^|c*-l<8{<R(5DJ$9KwnTue$wf%umL$_U(yp&r)7BU?)Q*!@ag&r<5U<%
z+>u=Q(+}#7M-cWLWOcXiwI@6jcVB*|g)ugkia`A~9;nd9JD$TQAfUH?H+*HlX&Lkd
z=JhRV=jZ1=-Q5`PL46FpxJ2lLytcNUMNyE)7toq&f53`GM+*oV`#Q|4aR`DevNE~F
z0-Fy!U+`{u_*fJ2LIa*EtJCYe#)gpBQf)1sZ=Z^nHD^55V<C_fEzbrH#bdqJbihx(
z4-dDuT^yC+&Amcz+9~lkgtO_kiQXR(j8Rp&Q6oWp+qFF8lo1C#K2?9nGk@6`r7wkr
zZ^w!je$Wk6$YODtiW%j?woR4~?u{Oum>2<DKafN{oEoL;q~wQ8I}`nh#@hyP7%<3-
zR7SPmwD)|wn<5*8n@mOgb^h%&x#^eJK9!6eb`lerMHzD>vtM)HlWENh5pb{$k&wT0
zJ=@eQt#eFWMn&gM``FZd_?MV=m*uYmj^}L(8wKPPv~@FAU(x%6Z^oYfNUhAe!2JO&
z_f>bR4#fXSH(<{`h3ClM9i_MDR(+YWR3Nul?0Zr-JH8ZO&uyt0$LH&@yYuhAkXlr<
zILmp=NC3;xD2$AojBWLwdvbyI)HpGHO4~!ISR1<SlhTCvQIw&2uJxf>7JNrX<<2b2
ztF`Gsk_p0P*Gap6Kog_}b;k9oxYT#OQ8|Jyn$x?#{tmy6jcvHFEGJ&dk_mZ~6ml_J
zF=ubR<M{|VITsR2tiA>h)Z5ozWGCo5>~ICDAt1UB9Drtbr!^Zcq6gXv7Me`<`}UNT
z0urALnYb^wDVtutaBB?Y33XI0eC2t%BP<fCRVU9In#S=QVgGsTP<ol1f}+e#K2OQX
z%|8Go(_hAh&b}ZCi<*nNkbGxJ!cWYfkjavxxD%b^#TF(&%#nioBr)u{;kWr8AEOJ?
zEZ8C=LH%xIT$7&0g%gC&yY2PWW2QcWLkbloh{AYZTdQ`uYXT=Df|9>dhWsJ=PIr=X
zI*UEUUhDZ#-5J!Y&D*?Lyl-cLfxSL9+1<RqRAIeqvp4&~k<cX%OBBOp7ISf;#7>wu
z9qk;0-F{a=r~{B;>Yd3FP!aa^g`;6=5Jx5i)h11Tkb(Vh`lyK6&}-4X^~8pAgNee3
zfc;f3<;(8JF$`HIA2b(dKkBtJJ?b8n<4(?}|GP@@LdKGx*Pdp^yJ=$zaZlH@Zv6+8
za!?N8?3xt$bsAv9CPh8`NE3WJl%l|<*krm^CCo2pzq;~y6mE*Fq@f1COM?2|rLNcP
z|L-IJXz}^9uFU-1zjN2Hkq76SF+@o)KLS=LrY~4cfjk(k<N}7C_3`b%%>T3G5xFwR
z&Wwnh+={5GIEs}xLQzWwqBzV%)1f6*wT|n7#J3}bUF2(}F)4UA>Fo`>$VEfZch2U2
zW&Sz3B?F{wUvy)3+NLQI8uFQ*zk68C1JDVgWh^4JMZ@ESb;aYym@}pI@1SigZHuv2
zjE^fSsz11RUGu-6QQ@d)FgC2eNlzi+NSKs5<-kgh^(_LnYXItbS+*+RK+vuzC@J|0
z5Gsrg0AL4XJ2}<WqxW{Kc>lZa^)pjP<<4B4DfW{FV}qDCEr?r5`}S+z(D5G$DVZ}a
zWM@w@CF&bLKi^*paB~NNU|U+_hK_We-)>tl^EF*x6Oow!oK%+j04*7*1CkcV=Jxi`
zl4Y;~=K#AT0MQCdOG!fG&{-ENcO<u`w>vFPB6q@zOg_?gOFAmiA2S4h$uZIQ80C@@
zy`)VV(KioORle(d6LSgGHa9A_t7O4#_47koCp1~(__EIr$u)t+#R$DYm`9Whd^sv#
zNXa-}ZI3fo)2yoB`=7kaMOS`|E>rOLZ7rs;1m0JRPT4WKbaL0H8XXlh_!SZo?%bv5
ztM|Ex-+Shdaf6yK#vUNiE5ZeTZDAD?MjdQAZ`2>i0}TaT?n%yCaswy=_U+UVsld4h
z1KRFJ_1Q&tZ^Fr#%|sy25`3b3KAt~|)QqxryN&w!-1U_Jdo6M>;Dh$|`T6Vd$9B&2
z7&mU*63>hoUh0)994mKXM$p%$H|_Aqb?AM+%kU=Pmq!T+Dd_<quVD4i4hZ`Tupx;<
zfZv_%I*)TmGA+XmKYe%7t<Ap*-x%?TGBIn&jlCHMlbFdKS1rBWl=LL0&@wshJ0eGH
z)*^7c&j{qx&G>n(?#+1By;uy0T4Qmia{xW~YcLD}qPHW^fT2+f68w2U;9dicdf%cv
zI}{z8JKiv*rcNp<eI8FoR5Dj8j&d)?I*~>9lCMrM*Ow3&$;-1j(qG4d(iMvVS#C{1
zDf-L7X8mQdfGWb?FX-7;xA6Jlh$j$Et>#+q2a0i$3&IE@kYq<Y&%Io4_<!;HoU3qb
zo0)m(Jp89;yj#Hb@5hwVpVpmtf|J94Hmj8yxYBlmFd&q{Cb{z{Az6m->@Xf~KBJHm
zI(uQdU$cRVYu(1i#%PD_3`dw{Q=lST_cRAK*?UpAtV;?PCVnX0-28-WOps8u=0}l+
zhXVXw8b=t^6!5;hdfbZ2ecF08@$0=1M8}i1_U}0?Ygo7a%tvfOlb+mr3l#_$6dHgJ
zi)^M4+5hyO(Q~M5oA-l$n(P^vslqz(vGQ`2x8w%gs;N=oR*83IBq;pgC$d&qe=c_O
z%Q9ZTBk>qF*LJVtw{>-eX;wsW*V`^`2IJMFQ?Ox}^sn!aTnWTugMu0j`vbu50DwF&
zk-H3|g};G4$RzMRn{3fNfJ59GCSn|8W6Hu}2^J*L#Jd;=Ml$1P1WVEPY0n&}UYBP7
z<v%h1M)*<o3Ny1Z@AM-Cj`Q}g+~U7uQb5a(4&4PKArA*G4gnci4*)^3fKN+BQ7+U@
z+i<=)4Y29d_AvEe`tJg<fifjZO=a(y4Xcle%KLW+2POw(k8nrGT?RL!t|z#UnDomh
zFwTVhxLfQ-KGq3_Zk4hpAtq~Xd0)1yXt#N%I^ADQ?{u1Qby1hhpjB5>3(Cr(e$sNN
zlr*D)PW>_~H#h(0)hGKgdt^z!$W7FK+AH$a&;xH?RV}P0ko%e!mLc`1dVD8iBE1dp
z1_?X6zFeHMtkKYL(f#h{553#?1lleDp4=fI=tyQaL`z@56bLG7vK+7b^zl7lbHfki
z>W)1o9bD8AC1=w7vALTTI}P+TVa0u+hk}i11X6cnuSBUu;qH<9jwSE2*-*ZEm0bFr
zJ|^b<i3JG>7QT>OU*Iz{09CI;2jx|RL*Bw>_t^Nj18@O>OOKXz^Mi;Ns$npR*@L+o
zP6Z1=^dy4tqTcA{Xb$WW6K_9x@=>v5JX5`pqF_i?zw=L)eTz1pZ*T_o!rSk!`Wx}Z
z#AL^e;(s=gI`7)>qeiezhOh#44fQ^k48F&Fn+9M8`gGRX&JCi;z`*eW`T0(3A$y3#
z;9o-bd1rxDS;*3DYFuUFI#W`~y_p%aiwlu!?9^_;pU&+M<@o$QC@Kdrwe$P7uZMP<
z^kZNLvzm*$gq>WRmphvF*_%D;PZ?>6t49&-X1=#>zTIeX1h}OUc<_S76$h}FfhqC@
zfY1N>tGCe^&w-k2Uiu43>ykk?TV`f4id&2t$9S1*4p(9x=9HQ8iTG$jMEn)^^*bIV
zY&<{z=L+eNxF2-`^0c(H^aW-N0S=FghDD(cxCe#icXTux7J-euuihJ4eQ{P@Rj!{N
zek;<{MY$HGyxnVw@v@s(A|UeHPDdB;98?B)3jMLUYZg;6F*};w^jF(xFgcBev&R6V
z16ulLfS1vp*-+Lz;NXaZTNH=}Q`uM)sV~s!`(X}jYgM(%rs9YxHNijK#vDQX0uI4B
z*Scz(qc<n?Vv4x4ifV6a{gX@fU+U)v-1Sp_+9<V;nX|PS$-M&PqD`!ln;0o)FJ(hq
z93gra0NWlW&T*rA<=Ps4HZ~G8rk)1gf#?igT_MuN#G0ioA>rlZYL(FB_`9CcQa*t3
zfU+N+;I4L>oRcFMi!U54cWCF2mzV0;A5HmvJ8P=g#nyGN1C`O4;@Ip#lQS55x}|q1
zEhcsq9BCN@1X7__z6X#$r^9kL1X4InY%P`FB)yqg=<-zg2{1rS7M~#fd3*bjphsLz
zR<Rh<lQ9t#AyJ1fgSx@Og84RmTBKnY^yhV3^e@ifYj9?hbcG!TjwU4|?!7H3DPf-)
zGyfn3@qfW0(t`W6uaEzwuDo*5&<mM{^n0eJk^=nM7&pEDy{o2f%|(tCctxH5HJ2DX
zXRg$8y*e3sci7!U+Q;Wr`TV>4IwXxTia}~%z|#YIOF*39%+#Hld_xeTYX_1zM69;X
zK-3SV-syz9Wb5<o5_yCSfmeI`dM<tobI9*J6FVULR9Qj);E%bO)Qojlep+Na1JrB>
zWh8ofuImrWE7+}@0y496+`@G!f&e@MdhR{ot^>i09^gMXmM~{y0!49CIBD|Z5W1yX
zB-qvTzBRMn?<$W@YjJ$%$t$g@?ylezDk?rkq8E&exh?xb8y^X)k=i9~yaY0r#&L+j
zbOY~y)i#7{OF0{%TS#O&|JPHsVb%R{=2(x#P=<TJw}U1De7UlwW-kaTT;YE<<`O_U
zAU08?Soqggk>Ijth@q%d-s9J=&wd`Wvu>kZ!M}KTh9_T)L~9{W?D+Wu*RDYCvR6`)
z{dc0z(iW-7rzCxnxH$d3ckd8DiHPrXicgUg!~Zxj{0XX4XqdmMN$t_l(~E`?BtZ7V
zveuUh%~pMO$3~S2xoBogx1X0cS~I04!y3;%(-X=*I5^2=d*QZ0=rmVMOUIuX_U|Uf
z=5|S8hBv-Tv=61_*An3qW`45PORCf_>noVINc}Q0py)dk!qTaMA_bfxZ-Sk{c%~p&
zbxG${&hiq$QNm?7hnU)D-5VO9%gC_!>kPXA(Xqdza$X;rJ9BrG+uVJd729>*bxtV%
zI4ep!f8xDdw9{$?EaXCV%3X#yzK-<uBjta<hI0VwPbE-|(ArB~_YFE!^MSms>jI{D
zox-~0|7Hn}(JtnOv~xrxm5uM8GK4zTR2!8hkwW)dGp@c-V6r+iZnt$+;?L{N)XkS#
z-Tb^95x=5^x|XkS_S1xN8ut%n21Zh7#qqhg$SI_^wDhDJ*{xns?V1B`8KRT$zx5p8
z#G)-E<g-K~VO1%3tn@((1H@bidfA5LqZWS>lN`(C_z8-bS0p6)__Jf`q<dL)19saO
zcm-1q=u%JY##sNPQ;UXpNQ%l**NJ@d#xYr&;h#ke4#>rK^7AFv>iF5Q>@bP`$Ou7q
z;17*p`=f)Yahd~-e`&&9d#e`9Ce7Q}c<+G$c({15tg?&|L%Q0xGM$smX>>&wPxqTA
zjtL{nZ5}J%)r?+korgRm>FLl4S|Ax-aa@Kt=RWq_xTMw~7J-u@`wfixro5EVPrvkg
zbL%88Hz2%3?Zef-8d{uR$A~X$x!IYRUmuvHvZk-~uW%Agkc=4RJ#bRFr*;jis^F56
zvgCf`-<$m-fw%Sscv+C?N`)JtcaiD(gQaQRI?b+%4EB~q{IPcfOboI_{_FZXhPc#7
ze=Yr?B(I{naTdFy|GGmWFYXy0p1KcfmLRt1*;Ae8IH7@|D!gjSs0~?Z+3OFVDfZvU
zdYc#Mh8orHPc}6&b6-pq<kiuJmRePnqKz#{|AMeLGvJ_lys_co;ai7a(cs>oGO$jV
znO;0%&S`~scPk}=KwiGHzFwcjwu&#i5XTu6ARb&$T!z?JqB+{cs>H&K;eHhJ{jCez
zLo>g>{*>MQ0Y_V~6--T`%eIK_g!$<nrqU(>f&?}+KuJ+iQ~T|6dF%r!=e^Ijp6HwU
zIZZ<9LWl-PVX7UFd<X*<H$7F*{X2jHiwa+RAaR~YMhdK*0`%82MqFpP<DsdI3zie!
zB1uJ09P`(4Yie%}b{^(@?n*TG`pDjC8WOAf)~&DC&uQzuTXtD|yor2zQ4w^aX8dp)
zpHF}g20T%*=K*>KHNF>^4ut^QsCkxmyBM-JaMJ67K#GHpZwiKflD23FAv=+ar?Ry?
zrY0~@0GXclADErIw&JvdK#el~9rB6!{kxlWi2lTZBk3%Wvc=`z(S^w4d-YEd_UhWr
z1!<l90v7Iz5<q%pfZ_lYj{vD<s}=ohcH^A$I=&BV6$22E1=-B0;!z28=OW`Vl3}V9
zjPYP|rwFjT%N_C`W)?xb>t2(3pN`HmA+4_=m3+s|vLU@mTgUCkP_)PdIZk0&@Icz@
zK9bQRr*vi!DNQ%5HQQTs=AJ&3iMo%YG-YErx*wJ0FyG@?<>xPVww57Wn}3g7oSDIO
zmZ@S8QI0@&!-1&|B4zZ{RkVt8Vd0sfA+3|Onk<<(kV3#i00RV9ib_fbhdk&!3@9#q
zKnx%Jq4X&=N$vxg4cYd5`*l)M%2M{o>%PrG11~L*IFX+u<DM|Yo6bJ%9nDs05V$i>
z*}i>IRZ&HxMF?J>pHsP0XGpo8dy}$`5}gwVpd`zv8bHCz4pcidkc1|%w}aIl5XVq^
z|54L`BL}F(0<c2H-HJg*04Wd1nx6uD7Q)J~$SMrKdmd;a+75(H?dR0>KkpP}{I`C%
zWV<u}+*`Yc)`EfThA2&nydC}TFWc<w`4lo80mh79NcEl~acUxK(jW14koizV=^z)e
z+-@6myvLm72<4RMKR9rT2i_uV?1k?ub(_wereXheV>e@gZGDV(f`xID1|UAcKyU6}
z9=lF|zj=FmgQH>sjMGd6R6V0B(|wZ>R=FO=>p>PZpXq^m4Gz5E)Eo^Q?|mTMR|2C9
z?XPA7br4=1M3|0*Pf#anX!iDeV#+v(x{2w+l!#ki*LLyg$EL+(<#Xi@5@_lysc10u
z&|YgGDrVdk6fqNWIcya#mTmd)9z(5ceETI91euQ?;o7;8@%neheF$gL;C%-CDq?7-
zj)9+F90WU%#{XxV2`voXInTSp+vo1!Rs_%>+O4<qlPc#!i~oUJoQ#`Q<2tj$ZvN5D
zN$-ASY6?Kkw@=pmvEx5RxDOUe5x#+{ft0Z;v7S<VHS)EOmT#H_3_bY4St&DR$)rPe
z@LBBdKTESrXS4r7e43Vj3;X?*WiPGWN5R8I<XPtofs9=&dzjuH19am*Qi%Qn$^HM(
zb7Ho^XQt+vWosrMs&NU#fT^k;?*A%6T#h4-;b@4QZ7@J_eS?zys&Bt|@H+4#^ghac
z8T;}3-<(g^{X5hOpQmatB5-azsCpc)K=Hd9|LEax4~d72$JM^84e1VKrH5Q8;%5R5
zGsRUC)0K2@^?WdGZ3paib+5+L*HEVQZE+vjb2^$UD2R%{Y#1CI902ftkePtnJ&ck8
zX5$s064wl!Y*!7T<+DIqbk|cq?8foamk%Q+|65a?aDcuoTwiGbe>L%r)-;9Q`)Z=r
zOWmp&LLimIec}wE{*XJWz^Oht@+;o$+UkM<Ez?h)CQq`(MK0R!7~|u6!{YO6rVw2}
ztGdfZr~CW+JF|;d!Ir1I|5d~n&Zejn6R`&*mnw>H8^_i%BDDu!k&*`S^S`#Q;<rLV
zKOxOI$lL$ZTYt4AKDKPq+qG&K0gXWpdTpoWijy<z(sc<RXa?*owh%TQS7SSIgu}Jx
z7<jn2&||>RlN~V7W5g^tpViS$$$thC+!G)Q<m#lf-%6!v2e#MlVaxR|7SqB{?!5za
zu7>wl9K!xXWgNbP<4H@ic3NkT5l4`Kz;99?A4xvJIlfR!Ghb?-(FBr)xK35>vC6M)
zVSlI30a;+1d)-gLR@OylpWzlwN@^KbzpAIKrnWclKfgQ2ZM_xx|Bj%Htn4xzxvwBt
z0p%u`Kf(MO4=5gLcq%NPBuYPrNCwYXpu)7dwWauo2+%%G$5mOd#r_7>-ylSopI|lx
zmyp$eRF@8*8PzQPfIRb?T}5e>p0sqgWvRXWb6X0E{QAa4Lu;>ta3Ufz!uk(1JB2is
z#1vS)k6M+SkwFnX+3^hnyF3*fHHmSAa)jy{h5WVE0VfC9>QA7<ey5b~;lpfYWCj3L
zpg9@<M*W~TzHk7Lx4<g^STR6b6bE;{V4G%umca+lX{-T^rxYRk1-o!~C~Oo8b+$x(
zsSi^kb{bp%YgbjM@^Dw=R40sBOERYu-9#xdg*Gel$C|`lmx^k)ZL8T{#LGSTxo@pc
z9QiKwEfMoAX4IAowb$%F=aa4VL1`Io$HmfZrb4;J(qfOh*Q>_9H$N|vkS|mL*8((^
zo8AH1W00sqBRQDd$_$N)b-YR)wZKzTDKBeI4^mr^AmSj)$;cq{s&b$i03cMn&~(T`
zp^*3{-t9Llyj^1Tan}Vj1leudg_`e6>_m+B_Cm*ABW3kfwy)d4`@1z;m>p&Usdekz
zQmnv{%8`oROc#WKZ{QkZ{(sxRjcUzw$VDqBZt9hlilS;2hR`ij+o7!UvwI&^Hj_Gy
zjXM~!))Dq{nJBB!{dwMxGJr@<d{2_fp=zOvtN+v7)z`=UrLZKv9=!hvC#V)Vzt%?x
zNTF&bbPa)SDg?bVE^z1pe&!wwfY}2m)Coz81~St6Zr`?g!nh!omSHYbQk=ZFDLp*U
z8_K0z^m<o!vPz1x*!}s@@Y~{}ZicqfJR;=)kL#Q?Ci=#&$LPwkKNb`KJU?(vf`o+4
zgC`&WovG^Mx=+Lkf^P+ya1QyvA!;@aXwM1I3?Og(-wsQ|t8J)ql0GRHLNc~wXPhU`
zPoQ<{*0+ZGJ3H$k+H--}D=Vq@$tx>02Y>!l<g-UuZs95ad|4<g+&E!IkEK8qPCivo
zJc|2~gw!%BExx|hVDtU39M==)|C%kU-A+;lQ2^2FJmeP6$mr=Ay%u<MQ~+u^Gu04T
z`gnq!^=>{=((T8O58L)qI3WQq@i@GCjh{DPl;X*SbWS4yMuY6&XsdGQ@6gQ;>Z6W;
zP{ZjAY;oWh+QSG4POH}N8-9qzazk5BPXP??f%*tDdZax#A$bN0E>N_xO~I}IPD_>~
zV$s?aNT;6e+xH^<u^k;Qy^C``m<hNibXahsTHFcD48IWZ@-`Pwv5)?oEYOH`Uw+ZY
z-#}^Je-E#9z99I2xd2$2<+Mu-2rlYXo|_aVQiJp_$VR`G6?@cw_h@d^<V>Fu2yMFb
zj_q^`LSHqdVRgEpGawT}^BN)11GGfOL-)=8Ex+MeWSm5#rktYTlB!<YeZHqh?QdTy
zi<f9da=#CWq8DY4WdMo5=fP61EPp`$Exr(w)TTQ+OL8wMoT+4W??)_6jg;1wLfpc4
z5j-ySs1TNryIrhXg2MnD0*$H%G@q+?2(K#-Tbhjlc^z1y3DSA&XvlB)(}No&g><2n
z`SfAr6b&=<uZvU*OHYJ@|Df`0=AZ6VXw&D^)o@=M%vx*k*d4oEjl13z{pHV2Dpi9C
zXwC%PXec-@WZcag|GcqX`_;c8iY+M9<lHuxSz4<$a##K3d;WJl`m3_{*xKO%0tPwO
zhb@4vdt}X&;DT*niv6tqh)esUOG$kNfvTV=f(1jXfl~0+Ey~A__bMtsOR{#=3bz0w
zF4apZK3;2(k%-NWgYMk#RaSN8htzko6j<)Jmw%OeD0a)W64(ALM1ztw@{2HM3!0-X
zWLBoZK+`u5#swSE4p6$EK+`3xuFhuX14LezzpFtA_vHu$K-fyP_~Eip%u~{-aw+*R
zW^?0TWn7<qU3K&3mV+~or9x(>`}#``{QER^oi{e2Gt0vi#^!Z1L8tk~oX?!n3~u|#
zT~t%F)s{dj@?TKuH}W0vrK?>8ckj^VmuP?gC#<Xf5xJPkdpE8}|KFQ?LX&THw#;o-
zhKQ8SD0W$QW3|wNzu-^+8g?%)c2$R9Ua;A*9MnaJ*>MQz((g@bqmwVZiWZyxIjB6^
ztC#$|YWCWGV7Z5rE8$f`x(*UaWNb9Q0ZFh|93Rr}{PdxSt};WZ#v3l#)Xt4KPAlOC
z;>29CAFAENXN+eGhsWD<sEIes1!ciCmQXYKnvXz{aN|L5G(Zqo7OG5PW$=wtqq)jO
za?~UoWTg`?#%I9wblfyJaPU)c0E^ax7UMw*9v-{hmKOvB+dWg_VkUln9zq_odYC2t
z#Iz#kw1$hpp7hCo=xTp`MB#%sAppk9Wr<_}e_hwf8#w<~znU9nd5M7}NsA<0Q756O
zKL6A>Xy8v#5!<(4UfIZ`;i&kioNa7)`Q#uK0Y<>}3DDabf1H|5Iq7b~8Ja{`gYP!!
z#s<+np?22vVG27pS9_xqFrb8wLlE+JG)Ic02ETugg|Wd!gUpH2(rL#M?3^E@2LGbF
z4;&M&yr0GrxVY^bvY)wBJX5|KdH)`slj5V&pG_t{84EAP8(uf3rRq<x^0}6;q|0Kt
z%v>?-QkqqPffRFg6(8tmaTJ5>e3s8AA6el}@7X-!<__PPegaapj}^64FYN4Ypioy}
zZtj=hv_>c4qz3Q&LlvwnTI_Ogh{b@Z5omvj!Q0>=jDw47b$mM#SqwafFZGH$HI@1K
z&OY}TjW}i=vy`Q_t(usJ=CKEf3+B|nQl?;SGgeTcywA<e?&nvp?UiyAy2V&xd6L6B
z#hc2Qty~^cXUGtuC1GJf0;EMri_Pm{;Vbp^5i83*I%g+CY@V*Zg=q{obO!Z{)ULoh
zlq={o5p=e;C-eXzKY2p(L>IDgvtV1B7wBH0KpAb#giYtK&x@%omV-9~FHYlA<CQ<o
zf6U@C^d0#3Cs6bpXM6j_6S*~DB^OwJy7n|EqDc&85O8^;g5yZwmyZuG4$hLf@Z?>>
ztKz|^)gFd{Drra(G5rs_p~H0rZKXlwv0Z`3EelQPiNu--U}f}L7UMZpRa?#J=B^gE
z>c+Qch|D?+e!hxoyk*mL+lV7-<^Iyg1JUevTwi+BNYwhnlAkAgB!`k)Uu<qI%s*?8
zCnTjTPBEDr)?XE_^3$hrXK{vD@E)JN?Q{JUg{|S3uA}Osm&vDgX4qy+8A%_>q*)lv
z-G&ElqnhwNJs}Nt)}8FwCH0vQ**4ngoVjq^w?Aa}{?ZgWr8%%U8W#4>ErEvz=MW`K
zg27z$XQ+&5O3E{cB1!QWUSReqo6}BBCOtEE;w?cJ2LU?Fqgv|#E*`@9&8go39sUcb
zrjFh)EY)j3uU)nJuCen)1W%;@J{u{6CBxn7R*!;GFs|eh<hhO=Z1;Pa_48-?VIg-t
zy*2>U`yI)nA0($1Kxchc*1}ytFh0Iev8YhJFl2iK8(XvurQ<OmYn+!KM?%_lJq33#
zGcb<OnVXwHz?JQaw>O*9Cug4mXc6)tY#y|IGX5@J@4?4OYhqyh+8ElBf0&rL1$nev
zXB<j>E*}H){;9Y>B{MVkUHX7KB=jT#H-xnS`;G0!5o3-+Ku8)jbFWp23;1-@X8Sw&
z=D^h};==E;G6oi^Ok?ryf68bYe{=ZA|I25)U8e)TN)ru_0m?#^nD{R!mpNlk6M7XD
zGiS?sgSu$^CDDI3N)P*2)K{xS?1d9=vlypJ9+7|ToDc0buCt-><Xl<VJDOn088w(@
zSx_?>_G`z0q6Q42Q^Cm#lt5^z_<(cPn8}B;5PPoUI~hn>gh4w7y`&@|31!46&&Gy?
zpHXqw)O5VDRp{CfEqI*AA$p^zzS0ShKYznT_ECNV;jpUYQ8FEasDFd3q7Zi%Ffx87
ze(m?D$^d=VQ!x!PLmln+>Q9zI%^6=0S_7Uus}Eod;Hyo)mML<~Hma^t(h~baNse%$
z`F4qRY2oCLwlP*?gTZ(IVFixOf?D1ZWfCY*d8{R>>n@-*!JuJ=$%>pXxB&8O<MCS$
zf#Uvplm5`PaAV_)|KXLB;cOye5{Fv#C#~VprY~(+OD`_+ZFHk^Y!U-{h7%6Tllu0n
ztCmFYqfyGRvI$9b2pcl8m#R)<%A+MqW|p_Wqo9b{LQ4yQTzonB(7S1mf-qGS!-|MU
z=*|g7M{nBpSNCvcrXP?UI<2xip1(MKC^Yk5ZeWJ8(`;}s4sO*e%N`T)`UhS*^k|ui
zunT0wCWGlnu(k=du`)9-8{-q1s*T(|KQ|W){B<$P7!*ln7{}xEE>L#-H)gli`SfVC
z*Z#o}5&k{jvtGji^9)lZLIwFWlfzd|%y5?nl<>PTvjVAHN4vYSmVW22J-w@|Z=y-&
zbm>2LKPt_qVb2ACJ!_FwTCK<d0bXcZ825y!vE{SaKzjYA#XyDF6~|TIly?Z?>pAR@
zPau&vpnwus1GQtnvj=O3EBW{46SIR=Wt|QFMl~gxwax2!M-_uhlHRhv&fH<R*p+J`
zg5H$Riw5YoqVF4usU+ZN8Rw^_a;3fDVX$#?Q*Tjlnw+u-d&lj=n0Or{H^0(jf_3@!
z?ShixJ!(fmZ1O1k>_fjlS|0pI2J)BulUhG1&WxQ*-P}^g3TtXo4Vjsf1xbO?H_iHN
zeGmve=#mR$#(f~y421bEVD{g9_PY;^*?Wg~?)InCp)+gTde`+kZ^1$kTPRWbg7Ti(
zI8xDt2qdJB%b6KN6FMdS0T(Tx;L&HluP>?Matrmor`H>siW@E+WtF{zxd$?ft2-qr
z)^n0iU){U%`ZWT9U^Dvr07UU?4sv~jeOEHCB~yvq57bt1Y>W*j<#zgyn>#+v<|A^5
z@;cuoaIF2GcAuscDqcb4HPm^~b3)6mU~$o4Ily}vHon6xauE>-v<5gB@2e7PmlAyr
z=z~#VVT8!^w*i>gcj1Bka&rC-v#^Y1tf?tGp0D*9&2LS|)gNu<Ay;i^xLFc2x||*U
z{(a4ar#4SIAKX+Xh+%lGi-TT2!nJHp!i2t9Z>};vrG(}2tM?~!4{xBQEN9(c!%2Jk
z`p`xJy6y=AY;^2WgRV*=BR><<Wv}UD%GOs}sNQMRmWxCQ)uqYp+BsSxXRco3CX*1%
z&CmO-dZvEa)*=`k{i*dI_C-oh>k}i|rkFb+4_-4Azb){sym8~I7=~Lw*rb8AHF4tJ
zgO!Ss$qR|RA2+Wf9-yu=rQY%5x%2Yr_|sQcU*pL92{ejJnWLUa>4~{+LeDw(Q)G97
zxy)hr@$vE-0pW(@nS<vVugC@yGT$oYNf^GSb)TD~xa9opv-foU+P6zqybtAfpnKN%
z<2JbAKh-3^LvHc2KBPEOW33y;pWFfwsYOMbEx5IUg)nnjxe$qig!9wv7}nTNw_wmY
z>et%pN*>D$+*QEH{Wp9RdGzY{E*;;hmu%I#Fh+g14x2VNWm&)X*Y#s(B23J7De7lc
zV*b(iPy^l}GD3e-tcI<#+0LK}4BrB3GnwbpSUxHF$A8pAjFV2(nBEqt*91=I!4OgY
z5_|jD!Ro^Bx|kTX(-~Yv`=+ZWNeBBQV_A0FtL!t*Nn!QBl~-=;5=2ow6rT6$Ytrem
zgUaAqmM6`*`b`XPZzX~WPp2x6ioT(GV#C(2gd9>fz)r#N{rAs3H!p8PB@Au40GOxk
z;oIl)l>9WzAC-`X=V`|cv#qm#4ptl<(1>Z(KbMpi@0OjH9wT1X>HV@#(Uq-&91EuW
zLCJ60z$w-&Hr!-r%h#8&z_b>p<>I(}1EwZT#eaPJR*7J3&GdhM?^<`^t!$8JE&{`N
zoXEkUff8^;@T0nU_4dhu^aEW@x=N+7u|_W&VqzB`Z@cnZyYSE}GqZ9eVM=+O8(JS@
zT^RA76F+C6QcqyAJNaxZLHE1^$x0k?WV*?2+DVD%q)UE`=i#vTpxmp;S&;A5X6V6w
zg|@6jZF{k>1A1}|9Di#S{db?mqPnHIxL(NU7T&6hi)#{{>yoM)8km?kgo)Ghdo^Ds
z)tmA2obp(xqK~4VI~Z{^zFGGhF;$@5T6@g(h+>|}<+;VK3W>e-WF0<`<aV6R3RF7X
z?3<~+3#Zh?R6T#Z+-c|&_)0C!Hjl90Q$wqsnAEaqR%>ZBe|n9CXcQ$<SF19vPIKuX
zf~U)0;CSuYgOb=O+FWcbNjwRutSA;;%bJr~LXYFC8X8kOdv@lXA#ZtaZs`wxHE8|X
z$T?@y^7Cy-fgDp}wQGK(U6b82!J`^(d3hz<v_`vI_`DifT3VW0KFSAnf1b|mwXTzX
z$8WXC7d<uIRbhI-fDQeMrY8dA$?RWp)jL89ZVS;$Ndy&r`axo|HfXLcYAa(_V3U<r
zbYe?VuYRfZUg|*{ZTxr6Vg~ID*8@Lx6{h*7UEj+Kbr7<h<{H;sKU0@AQg@Y%3hDpY
zsw*&(&pHE^6Q>8~XKzktn!iSF4cA?=w2BKYb2OCKZ%0aFZqMLQ^1YuWrN)=dV3G@b
zNW*@}v<y`63!vy3q8+MW))JU%gh5_`o&W%V47W>!5$&Yj!*?17v!jTkT6kxDNj29x
zG?!4U#8#V&ENsJ8UN+W>$8G0tRv1RP$2WxNlX|`j=L(|Nvp(8>OKkt3Bv0k4o);!p
z*4?=h3O2V&-J;BmNPg1r6B3zq%_6iR98e9=BKNaZ-nJ_kXjM3<PaiLbY`>Mp)NU(f
znF$N4zGYbZtp{llphRkOUr?I!*W-CGR9PloTbMjDWcfEYlQH|9xB1CGsYBDtv`{W?
zB<Erj)fj5)`!&~X_NqDoLPh4oO)z+l=!8BGdNc}XZ@|3dS)mFI8{3_gl@*#ru6rKK
zA*k#RVs%3$`T08#vY)<6hGP&_yMDx(C{1_N2Hz%j5=at_?S9=rfB7jQkgno#ARsl>
zH(C@+K64bcHFoPB@1TK`Q0y$5Wi$Yb0mt`+SwT{`w-6vjGp(rpaeB>pYkW3~`p*05
zwbczqM1PaHnMHf7rBr#DE49s7O@!#TCs;j-KhAjnw57Zgdsvd@z8c{S($aR?=o@We
z@)85<s@;)foLFs_=ezshRC6WfW)nCO5`$Y2r={J_*BZ622C)5)`&|rks?`c%i#n{J
zZVh_4Z~ke(yq+v%VyHT%Zob-9Gk)ppvv+>)9{(MJRsxgoj#$yT+qa*Z<X1lk)^y<5
z+xxQ0vPj9l_O9JCoX)63wFY$e=<5(YQd2D3x2}$t%KPtYu++NerB6^l&csiYc(dMF
zu5M_@=GLH~piIuw(Lmtmk7Z~!sCnIXOUk{dqfbP=eC=ZDa=0;xeNOxD%TPwRYpRTw
z@ZLgj``7R(uP;GYXeD3Gb_I6Rr#O|22__~aye}xY0UC0coVBv-X!)6nzf%1<CMMtM
z`Ed+0(Muk#_Yk~86q)iy=xS=L+g{Sg2kpWQQSfNwmw_1#Q{qii>7fU^N>qbMS+@JG
zz8=2$mjeSo2d)wbL^TTNkI--A8`q4g=&!%*`zm2n*54>GvFm*-C<MC8^|`h-#SV?D
zxA-e9m=Z_dys66y3L-fTgHbpOr8+MXdwhGP=l@t6ar4lyGUBj5d&UwylI;(hCDMA%
z9chwprZoQ|boA1>>W@iySI+r|Sjv=$fcMw`wBfu|zGgM^C}UZbLYVv>->?Ha!B5UA
z{OjHz<DcKy2nEX^Lg0El6}w6rNw4EPqL-~AorIWvl#k2UJmvXomeuXo*Pmyexfkx^
zEFYUG880qgI5|uHIXO^zxQ4zH1)Sor4m2A!gDm7OwMxW}BR*);OBvYFppRp&ZEhe<
z!sNi37QU+LQ^_FI%vy!v+g7iy-xgl**J1V7y%JmNK0nC11h}av2IlgBSAiio;-JSM
zgXhazv?6`>*_{o!LM|Qlv2jC55)GQ}UCQ3A<?DY>o>38F3LW5r0E079hHi|mn|bid
z_qZJN(M+EiX3*aBIdD0ydl?W__~Ghg`ISfKPKvlGxG=fmg><Erw#2AF&bVfa=LrWi
zMf|~X;L$G6&a9uSxhkdHkeZipf&;$*{{67zQxxY8+2f<Ov{cmnm=ZhthU4g>qCw|B
zq<Z>Bs)=0HeP6Ea?O$1TFbWRP2Opw$7m+9!hnihpeaG#q8=ty!>DQ$T)CGFiw|sVo
z!+TX)CXds8bN{^qKbtw>a7&Z%5{<)kleTJ29i2$9t-uA_iqw&}<K7cYDSBX7{OD}J
zHaD|GPt;d~+6T9Ktjd^Klko#CV?F9b+tf5?Fx?67_Ve+7M=zX4Ncc65jzVPQxL0g#
z9m41_y%|q1;UP-?v^_tOe8|s4rsLmOc{w0v^DAMnQ&kl?Eid1m!1#2c^j&0l2ui+8
z%%(Siak9aY!TlHoC*DLKa!=4+^0-(TT^lqpeyGZdozOe(Q7bD)&nfrNsje&Xaiv5E
z@&(3f|1)Pb5^sL{tM~|d8dh%ADcitL6rb$pe_x@jcX<E&G7XmlHb;V9LLkj(mhdrt
zV!{j4#nEsNKEZ1v`B&`i-$nLiI@&}g+g1ep=|0+-CiS^IodaRY2<lp#S1dEpk0a6}
zI%fuk_8uGub@f8MzERAi`1mmrjSrm&!~^<=R~(oxey|j>cC;>f{molWQahb<ciE1-
z5_@g((gd?qI~FBha_Tf#S;Wld)yPGW8d994DC|({E9_uk)OLU;2{U1d9XV3+yDJkj
zSvc-Pj#g!Ciq1bw`vz-WXXa+9uhC$=cj@}I?NddV#ENu;EI!Ygmlyi0=A91+LXCe@
zHNcIs2xe{=z6CrN>MZd+b`>Y}whnw5)SfRle_HUo8YgkD?S4a^7^!qQohLL?(DHq8
z|Ha%3gcN;_mK_9Njo4eHz5{7_9-$Z;zfv@+a#Oxb7zs`Z(bfS7hkNei>7r%n3rHGB
z*JrFcYOsg8LzPut`c@6N*9vIa54{tvcq1w!Cw?1Go8R|Z68?Kh#him=T#h%lP(jPx
zMrrFlBf)tG*3yB2?eAEokb?g&3q~0!@Yi9&S#(*QUN&70;?eS7fu1eeIW&CD6HI}z
zsAIOHSq`mYH?+$dN;iBgRW=7_9Gx5z|IN{0J!q$G7bEyFn=XK9HJzWa3`2}TWi<I)
zZdD>mwv39Onwnara2IJy53@;tSZsn*+yePS_Z!f}4<=@a>)KP@l)0rPgj!n-z7nf+
znbuU*gh_+&q7=mP{8#wr7eZ9DFyt0)H5^!@e0~Qo81|RWi)-y1+Q8cd3+2Z`#jdlG
z7~tnE1;nZ$LIScv#h)QLbW*YE>A_pv6Iw!43wPfx7Z|;{wTZ>h5Rd-o`y#lW`++7i
zv{$d?(F|g#+0#~9f-1whwtQo$)O30IcP&M{xVU&MOpyY^vwL24j*Ddc`{<N}|3GKt
zrN6&A9wc+#Md!9~o@n4$DYh!c@OSgw)4K4a7P?_IS2_#+Chm)#o{vf6MXdPOYC}#)
z`nH7~Fu3|`VQ3RBNPV%|_z8Z#qu}O6-@;J|l;$;;CF-x9;D%88KE>A!uOdGEb7jsm
zXBO{PJWBrPcO|5?Mgr-yQp@wB=eDO|#|yF>DJ0RSjVA_AJ24J%cE4QkQdGG-iAl_x
zjS7ib?bL8n$Bh-|*wQSz8F&?PH&&om`fJtMm=BJ^7RoH7tyJ8+sPp=I`cg^x$Kt+S
zmL2Rvm~s$%Zp$N?chz|pE7eC>O6r<|2;=Q@I-1k$i8q7bjePufqU|COe2EqSkVmst
zO2P6U9JrDNMJgZen2#gAkfrUW29a+!lw)5`9^-o|?gh#dd_?)ArE!re9D9=si+PlX
zoF<ldwyL62%QxZmr>OYB&YzkDrp)|xq_7F|d+T&fm(iYm%PT7n!Pg(1tpU}Q9Y&j@
z4bmWK<U~G@Fw%9|@^Or9davH43&Ax8o?8~48+9GHvZ(NA?0osecTDs&r#b4;+5mgN
z#2w5A?I@THgdSrIG9eg3Y3<aS&aEF!84I^&>zUAR)|Hy6%hjb#e+V1H9}THp>&xwX
z-!Ue(tLuTukomFG7M5%#F5lJ>gCK!MoQB78qw`J}aQV<<1lC7B;sdyh1-l(wgvmXh
z(r~m%8q-GUhz2#wpd+x;Au0s6;S9B>=#z}8xB2w*RYSRN+bo3LF`|XB`1ad$X`1rY
zqi4{Dg?yL)7-r!@+ym9ZYoH3bUY;KhkW>C7fogF~v(=KgrkkbrtLUHeYgt+Unx9{4
zi3MNpN2dCpc&#qI9mamuN!G3@Iu{w)-bKAdV&CF^<u1dNH-Tvhf1Nt|#`Rc+(VJx_
zE+EW{89VcKrm3t?tKrSNsI!!M|B~qq6|Cw5#>Y6<Xt6)|n_}O3{$ln0OAKnS>*?ms
zW6gS^^We{foiZZ3yf?KS+t=Is!_wdsJl5o>5*W*+qPA6^y}r_Vx#0no(@UqOo0-{}
z1%HZd_Jyd`sSl&qqgxleeqNdclau@Q%g&#<?X*7g_YI~pAdoT&k0{Zadb|wn@gt_?
zLEcM}3Yy2u-MRqbkMfS?s40f)$zS^Q3#YvN#$;dfj|uLV|F5zukEeS5x<^DIoMgyQ
zIU0o|GGr)2D%~QInIbZVNSRe~I7L!PXp%}q=Aux^kSQ`1GF~ckGL+Q29^HF?!~6d8
z_PL*1=?vfD`R-@$wbx#&&bWnsr(`8d2pbdS#nYD;kns$QwV(Xdu&1$^d3nGBkg?ho
z{1h)-d_jvSH;R3FA<GN3T92~-cYV2adMWo^#_w&{e|zZ0zEz95YJQ0=&xy*R1fjMb
z9r?^n8I_oPQMGW>Mj4M4hh`^c#LscsNZlBjOX51e93CqCD~q$0Zn}6(=lNX9+Ss>*
zeLjTfLLm)<35)Any}aastUrBhy<F+h>cej>7@180!-EGmomspq!R5*B2$>O?mD|@G
z@`?O3H0xFVy0L+>bm>;Wp;0=KtgKww<e@k5Nr>74!AXcSaw{k(V6-$bKVN9e@xiSy
z_ah?Mv^}TC&WSADmAtR}c#q4cRAbG`tV0iHam_fwsv9f#u1jBUDe_kToH)=$^A3(l
z5m5H=KEsIS&`1ll?rI3pI3!ki|G~xZ$HEHPGo=ypzfb1O#TvV3*>FI&mNb4u0TBSA
z?psYvmO45*@+@B-iPID0d-NpZ!ZGLSnb%)ulMNcS%^wf6i?`h~>N(~-QC8WQ@=A@{
zp8K)lPMdEW>>Q5*rhFd1a%whxpl%SUB;L0|4AZ|E(T6X^=xUogBxg%SGx4sb?A%H6
zklkgeA>@gLzNJ`ng<MGdt|SeRz0giN$KKLrTpnhh_LKecuK6KfhW!seoV`Eux@sq@
zNu0KcnR?ouw4u)j*RPe_+Ei<z=sw7@xL^$}$6JD-$RydQZfMHx+(I!l+|lasB-`}m
z>=XyLw&J97*el$-Y#nJt8qh(spkggI!zH2^1eN;J+&d<9RJnS3$D$Q2Jw&)CPmZRA
zXeVY;y~34G3;i517j7_DiC5*6c#T>xq{H|EPfXC6$1J^PMs=G%v_D<P=l=OJPeOdq
zI)k7z3n^g%stNbAnsOJ+??A-*B`jn{`{Irss<^ny7~-7pv|xY}7Dj(-ts=grG<nm#
z(UH_Z&8D5KA^XJIqfX}Y<NJBGb!{`MJ8*X-GBk^=%<Dqq+I3OV_nI1ZwAR!VXFF^)
zk4H918<&}xj)JgV7eF2zs2PDmHwLw~M2yI?F4tSa?{VlyQPr=|y#wkl867Dt9J6P?
zI{nF4>V8ov=e_#zyyf;v2iVuNdj84&$URZck%eMz5xjI$P>V6+thSb&ZLV$##Hk>H
zQ5?p*hS1)Y^2M*)Z)DW{oNajtw2FwE8iv$Nl~;z|y|hj&rs9_2I<LNK^0$(!o68S>
z*}UOk?*7y#<ra#<LtKOQR9(Zu4Hg&sN*aIpbic1&bXAF$9xgj`>miqV&!LSShhil<
zrX+izPbL;qsej-An}R}~uWI|CifRtiN{COdpzPGzW~fTsb$*OR2KFvhSl>xI7L2QN
zNn(|dxzL;EiOlBl<~$-i=R%cLZRPzBYvm&BIJ|p!mery4CG%Bzjs@I#B)NF@H9z}1
z2zlU87Z&Socy{Lg{j~|dc!wL3qfMBdD<Nz*7OMV(X)4csfPcfo@aOJ=T6gu07r?4j
z?x&AcWQ@RtE39YEnr@b+Eq<Bm{ZQI8djVw>w#Ye(M+^*FW8ReiTvf8lnyYWczfh&m
z3nE^wkS$3zD9&{W`aaN5#&%UzcmX>pPHbm;zpLy_Hq8BQbA*})xmTF#2!IQ86_R2#
zHsHu#RdlC4efA6rA`B=xRofhWHo;Fu4#4Cy=$C)0VlTS5d+{T;&l-RX4&phN>}G+e
zSBB>Tu2<&Y)Q)pbe-wA2_K$E^B~;S5I_=Hn`phH^{xtjO;txds1XLl2XxHsM{#%;T
zulrA$z=xSQoG5yJ<XPtbRiD^TLh0ZH#4N`krImL=-BNiK&FuKJ47B=!zNbja=qdC5
z1_H~EFOcemu<HT}pw$(S>ye*va&#PpNM1M03n7$TLOkRSzF;9zR){RlV``BqUltHo
z1Tt8O_ygNe*NGF5tlKu2J~c2iGb6rk-Ogj7^!&wrxej@asjHUxb{J({IWg#OR=s58
zfF8yCnS_hg;|t8{>YX71`#O7UREsv`J*jpX54dsX-n|~MK#p)f>o1S3U!VT;6sFN_
zV6siQv(yW!#@o*Od|4F)<UwrB8aAtdZ%O)Poz+}gwht$Vm<zvcFA(Kl-JbCI&%;ly
z(#tckVfEV4|GYjt=#r9_b~Zgdy+&;=-EB@1i+l4Uol{nQ?yCBZShmxoX&U+QuW4zk
z0!j(6C%eCJQDOE2!_U%M(lNr893M>FVT+rXnYqv23jV0D?BfhLN~1a5*~e7i&HUua
zd;hw+;^@7*Gy}uv=WZR~n?!CPrlhnKsSKRjn|?r4SkUXumfCW!PxbThd=jD9LBgua
z_a1R=m`O>xcW-<HXK+LFubrHIW~r0CX2_O?>ZQ7e3-+331Z$hOPA?_+Sjx#-b044l
zx)G8%O%1mbVs2Hu5-+|-Ir5dzqiHJx172)%NOsPWO9vYveE(U9Y2V#@g9~$<q;exN
zU&Iy=8Qd)-ovgq1Q2=z6E8jOPu1SCb^n3rYvEqj0pr}9&-Y8L7r^q(+88X0tL)@Sd
zDZVlud^T}E{BWXY5v+n2!bjW~W)BpH33`6MGR6z6QMDs%p0CpuF=4F@2uBn@yu0x7
zE{CT(#%XD03fAxC_}#x_VN2o`+)|wr+Ry6<V8BK(Z@?Nh>@L^UIAK)+5xl0fsWr4r
z{y+Y}Uv@GFHu`*}tlq<N?ugWCn&V~57$f2`O!R*dQMHd`h5W*n4k4>5p_>^e94^x(
zlQO8&y}X^hJBJD_p9Q)17?)7>2deU$93c?8n3vZ~^}f9B@A5=gF>Hrmh=!@hK-JQr
z!k@Fj8*M1-f4yEw<0!AFaA-bk#NcG%54P_uAxh?Tl04&5SJb|_9-+u?3LfvznPFWt
zZ<!BWJAUKuJ2g<_7x&|wImTJxbHqw1uc>hb<Q*a6FuE;_K3F}}THIIt8|4aRUbjxN
zn_ljRVzL=-$09R1x(b^&=T=wV?~nWS$w8X{x?0I8%$r?V(#-3Ib^n-umTN8zFvAij
zCwX7g(J|;8=gm7>1xLRgvF_Xo_)06c>~51OU&#6`^jGE#Tz{mdDG!3Y2C}u|cdu@b
zeNC&8`+cOk2_utfgEyNkQuz4zi1iYvn#72r+9w4g^a61TXN+SfSDvMRbbVb3Y$PeE
zK6f{(%B0|%tb1fTYrTCvEyMYpY@%g)b5bMpp}V0_NJ7s>Xrz;<5jfM@K^$s+{G2LM
z_*~pqs+*!^E1i^7e0I@R?T~D#80P3T%;<NZEm3|vHb+NVQBe>CH7z|o9th>bv@V#e
zWDvQ@x;;OaWl*D(+8&D~grz*Vn41uP$IF0su_QL|cbl^&%aaWyroVk-qTm_Tv$P~N
zMfdLUu$bOal!*M}Qbu4}r&%yJr8o1tV^{wANwT{t<c50&0XNcez85oduTW4BfLoWK
ztnC64TTh%kDHhyesb$;G&K{0oG<)~X;tgxp8_*;kY&v!7Q0Hg%s;f*TrKZXnG!I)J
z3sV@kNm2+735D6`knK1J9w|uY*^NmBsjwG+iL3KOOMtrc;mw;12AT!BCx;B-Nc^UO
zMQ*d*t-xQ3D!=?VZ{L;_6{EgWo>**wf~rU4NnZ;Z149!vTi&f*D;Q=|7G!m_DKu-7
zjEC)J%YfLF_dHRbuKI<g^+AaKS9X3TaU6n}@<zCMkvUO6x6BRZ_x4Kmwf+bX?^X0!
zqL&=><%<Xl+r69idyC(`P1A8RUGz`jLw=732;*d-@ccr#bQ`e?y6Bpi&d@hBWT8xB
zOR0mI+;KK7E!#ztBWw|(1{$+$nGB9MDFU6J7Cz3GkP;T5)}@mz8>DNRm>5&h?9frB
z9o`v*P+5a%`pORhGcM3A41A8;gKx5*44QaFmIdc!_LgVD<<;|DKS;jldKof0@rQQp
z^`JOmJ*yiq*Xf3Szs)DTu=0qjUJ>!ozb9#EjMIiSa+^2P;D!s>o(TA1))!;mi@1u-
zjmxP~w=V6nF5?0{CwnG7fzdhS&mSa`mbUB8-Q#vt9j(>&O-df<d=XO|ugM{pui!6~
zAcQgS@P){gi{G}Af850s1zWC5zP7oa%4j!ZBaG=*Uc49-!l9E%=cWI(erlq8p{lzP
zVxgWRA1vOAd&7$tnv{zukg1G>+Cj}qrr3QxKFVd0Jawulg3NL${k2Y0s-qN{gQv1R
zn5j`u?w3Uh>W++w!}+@eD&6jXzUDg<8rp-ATWvH!&#66nGFC4^{gjw5G(D=>`Fa0P
zVK8&BeqxZ3YttH<EC<&AI~tm!o*NDyE-pO}S)St^r@7~yX3L_B(=w{wi|gFsMw$A`
zFK_S8g`+oq9bqWOatf+16O_F(L5ujtANjirb0kuP8l}2UVjf~!YhIFzy?y6cS@7>H
zIqDV(IU>8Z1$%lsqNgy_$GZCl7ajmPNt%Znkka=MPC>&6axZ)oJ)ovo|47xfY}bWy
zGn?f(G8B&<wH`L-ch|BlK(~psmoRorumhys%oK=Q>Wm+y1<mr^-q>;W+n7%0cx}?H
zA_@-=MP*_M5C7ecf!L#CSC5egcK79@&)A=Ipi2be*iy)D&WG>@Wh<bj$>DaVi!12#
zDs8fl&MTbRQ?Nmj^Qr>(0?ur6b1kjAH-7^UzsndN9-g4gOj*>juz`m}+HS3Q*60db
zqVdHxk5@@1Y4P+I-A@-EnC?E;z}r9U`s6X4-q64t93y3BSE%FxMPB2A<5Eb#?yZ9-
z_c-wk!jPme-{U7^c5Jk=yLvkm<m1&Iwm0XbczQqBG}63=w%(>GPSi4R*6W<;7XI{S
zlOVqdjm8xHm=Cd4MM&YfjxtpQWE5#g_7%8zdDY+S=+ibI`G8%yRKUF|??lxT<KI|8
zX@n>P8qll?u5HEe18^Vf*7loxXD=x4cxmUU6*Sq)4<xMHl{>q&A-tj)?A}9B(z(eS
zmC%L8a;9*e>rVUb*OfUl+t1It7%e5hON~;i@^SF$E(wzw)~m)GD;V)Ok}!Hz*K5Ac
z)7;oQ%(=jxq(#sN<19nkuv1*{E6Y0w4O$p9R4-ogZuQ*ia!OF=PPO80K;<JXU7wK5
ztzw;TBh%5nv7ZzT3oV=)U%q4}ZLsn2w}a3&4~Am!+~GEb@xmm5^2Z<bxuI+_Hmp2+
zhp{ctwce!YxI+H=F<i&dEJy07ZfIy|4I4q2!4uEq`DYvyC(EhticYfyi*C7t_0l=O
zQ5C=N%o$^({4`B$E5i`G)H-~4)jVcqD%yR;#ar<aoe%Rq$W8}oX$>XqWAdG(wB|W6
z9&?G_S(S%8AxgY1<pKvMX9<Fop1FDIw_40rK)%1YZ7gchK{VAOt{uJ1j^P>WDc7OG
zQMY9J_>@A2yQqA}m6CX}<)U!iQ(aN95F$nQQ&L{Sc9cR%Q1+7DxuOpi6E&;y>4Q5A
zc)@Ll7XN$y1ZAeZDol@`m`LM@ZIWX?%3i^&GU=2<;WjkoK>FuG<JFqc`7c{u1IZ~b
zl#`>F<;?@Pf2uuDUB;aj(}vC1-nsqu0kLK{DL$R<W<W6Y^u2rc^qIm-(Pa;4XqrcG
zm%`L`*iy^baUO75L7TD0LVR6WNwl-$)^(T55+pUTx8Syh(AU<dblsgy%T8)JQ~TF`
z%U%7~s%th)fRmTnbSHDP$OZZiD+O1|uW#IL<97*C5UD1?=OG)bttn2+WXE<Wift6n
z1aX$bb~3~`EQ@*jYw}xd5XYii)=OVOf!s)X9rYK@ekTF+s^?$`xGb8zTwx2pwcO`y
ze8HGs8j_H_<m#-q_KwSe14th~aqXwkd~KUFYWOoQ@NQVMCN$BKC9~OYxvIo@L399J
zAX}YtcGRu1^l<vOT)W?g4hX#^Uwb5OQg_#`M<|5b^1XEXw-Vjy=WMHJj@3VB>mOOk
z2L^&r>(IzbqdD$O-{b*f1trkD!T>plY&p&eBe60Fsmj=v^9;JGS+<9dp2NZECEvL$
zbS<y#>>0F9{QRW^1gKGK?L^hdurG0dA*C4#LU)XCJ%!Be$M%A}=WHu!j(d_XHSKn>
zEYa3xI@X_wAe}FOkOoloAci2WxuDgNoh^s1>QyP3OjYbeS5$ioGN>)Ni>5!V87h>v
z2+Ec#e)-bFJ*RZp7Z<1BVG((Z5RAK>n$yH2B{?yk2N^|8WK)+uyV}xd%<YAXP!^j%
zzS-WKwMHi3shr~(I(_&LO#1zrDf*>}@qlH4Kiu)17@7YD5yBIY+9DP@3}7FFQR11F
zjg7^3|9}tud;f}x;+NdV-fW;Mt-jTx6CNI}XJA0Y&rWl%W#>?$4Hz1+I=3yjbj5K>
z^(&9e5gvvj&BGjwiLO0RurRVHi8}lUA<nF7D#r=8|1)jRYUb6uDXiSNKWR1Ca|ix8
zAD`*bgV1GK40TpK@pV72H(y)1;MmT+d;8g<%Xcl|V;X4M8MB|M_}$VI-3viYtSr^m
zvfYWk0TNn;?-N*vL$Ht}C$<|}j?28;c0qT>{_FGVuo$DT)G`$LWy^KFljkATAYq5s
zhJ>Be5WvXu=FQVHHD!;@v6?w@7VFSU(Q^T|1BZ_rpq8h5I^g9Cl6pqTZ*+<@H#Nny
z+o<O0j(8D{4K|F!L*I;&F-yf#IIUq;GM{zcc;7oas{PQnT%ErjrT+3~U+Z-R72Q@4
zKXzm{)d48d1_zy^Sv0gJHIuM&aZQf|=FGf!ac7hJhYwd640`#8k6sR5aKZ!s|5ZsC
zZ`bf}nt#UbvzXJ2{p-0p48B9mK1eJKGtSSy`v>zie}5jT{wBMjsBu`~p^Mwq*Jo$w
zcBNP&Vu#9H$@A4=sL-`Ns3JP4z%Yz{-wQ7lR`y(r0fB>~anUv;+BIDPzPdtOGmk#-
zmhGzSE46S%r^-C>-%Oar@5KcGcEILB5Y~#0b>dS+SWT-Jo}oJ*IGmK-+cYuocSHZz
zO)DDB>@JE01VoLbeV@)P*FOpyr<znIAqMV7$zv~FQ;(FV=KogA*gB!q89uo^Ejf8%
zR8*94rfpcwc`hxj18I2N9G#L}o``_f(HYD(cP|nj+egNu<^}qtnqn2fkc^z=J7l8V
z(O<(f(Coy(B#lE3tFKhIWWrIMzaq8g!$v%4#EE3J(hBOk!k)5VZgmzRp*t?z+}4xM
z#k&p+BA}UJfPT?36+y@nz>Z1B(D2gT1AA6S!$Y-saGTI+RS&H`_fE>AWA~2duv%Fm
zimXs<$%IbwJK3z7D6^sf|2VdUe?>HhWW%kl(vN)ej$7$nFf94@Z7%ZrTK_Aiu|X#J
z3fIen^s2mF^ZjP!FWg9P7oak!d|TNGt_TaGr{7GakJQkqTgF|Qf{Vd$Af__}X#!o@
zy0<)P8oRdfTB=eIS{l)N(a@<$NV=}5v#uMf`!YH?VA%#^>g6e>zu}czib9`|NV+g`
zRRZ{h_8Y#|pCO1nS`4Zw?aj}&V6W-ywIxZ=;CYh`4`GXWsP?)l9nzYE>m?*!yt$=G
z(vZ{K5@1W*o=i?=+I*B%+Nx=Ipkuk<rn4#XJk;20cwbXf6LR()_Et!2-DW0T2%gK>
zL@$xGFRZK#_8P#!QpH6I=>MFg<gK|ez4N5TL@SqUsQ@&iu3R|_WHxr?0!<`cHkjWP
zRP$3L^aRMHbZz8$mo?BF%&kU2TRr!%to5L=^|V`=)bI0;F~R|l!a3p~3}s;QLb3^b
zGBI&_34wX7cp*~w0PcqF?<ficg3+R{=E3MEyH}UA#l^pOuj1Z{t%q)tdN|X2by0Rc
zFv04gqiv7L#cgP6>c*56(IZ3SHblUk0=Z&Vv<oOf3<gJhi~Aq8i|uRBTzF5bY1}Wg
z0sRnqxlfKs`7_XAuLT0;5e~{8c06+A{{AI<;XXjzz+_z86m>&2iU_yoZq?L=B1&v*
zkcOCx?@4fgkz~XU@@$R-$>pC!xj%Oqp=}Vc3BWiSFkI*>O^)f`_iHBk-u3z!YvdQD
zNTl51YOwo;nkcTvTNNWy({MzUF3`)!{y{?2r?>Z*Da1m^4^9e#TaHVT4}^*U4Q@P`
z_<j_A`(pW(p;k7(@$<)9JFH9}-qK_vXoS{xhb*16n>Q(evjbKrBhO$glv7kWyHTN~
z-S63T-)R(zD?s5Uux47?o_iMz1YCZ7s||F+hN%L{3IxGljFK&B1E}<fHvRO=<v)Xn
z?(ZxkLnq>68rXDOgj)&@6l_?jB{A3AMNAf#9T0f}Av@c{hnMmhJ6ZZq>_>;jiTw#o
z#*yh0gp<x-FjI)fcUM;z8O5t{LMBbe5b82j08JDXs9j%G_Hty{Jh~w67`Jbsa|hRt
zA6dt(JmqBkl{>o*lBYoAa)y^~T#Qj86bi?XG2bpMv^*BQ&9{B&M_jJ1!NJsZ-ISZe
z)z?z}y1WxVta*}}-3hM+1_zWNl(82tUX&ZSmfR87dbd>#LQ9x9ah4xznaR!g=IYGy
zb1Lwd+Zc^Uc)jX}maC^f9>I?96QJ(Y+ShOPBiH9fPKnJ(>u3L_#gWgBE?4stL={YI
z*4}F(c>x+h>PVo*b!n%Q+fQzHh2jy;NFvSh*@KfcaO}=FIc|4ax_xumCXenvdwY+l
z-M0-W?ZxwGUCDp*+tl`biy;>k)0Qs~Q25H`AJ*d*aW)F9+g4&hhjzfyfgT=S-!@9=
zO!Ieid9q0lj$K$3p9fQ5vd%5+<;-40DUp@y_*dp|nw5!LwPah-_s+jV7{RyGHHLx7
z&!@n-E%lWRG=LPQZ)R@!X_bhC=!mnk7EIatzI?`Qe*>KCbVnTBR6=S#V6oS)vuBq1
zEWijE=u=evnTm;%^)SJ4>v}OCZc>G>BE{eyKw=D5&uwfW6B84#Za)R9JmM<`UE@X2
zM2D%>a-z8Ushy+w%Ga-+2~NLkEvs^jzxU0&mv^X;@}jPZVdh=^h`wow`roBY@_0gY
z^l7{}u|p?agA1N&^HF*-FwQlP@*pKe-@w4;&2yA<TyRl&I#w#&0+)If_yz(@rPP}g
z{RddO2<CNmZ)Q5y!II}*c*QM?$COmFH5NwmDJZ|g)&yBTlN92oQfDPrtxB>8iMLeP
zBBJWE1TI0u&l1SJK5A|F#jSv|i$RNt@Q1;z5fH@a?1>C6-R{uZZXvW`@$a<e{wnN7
z!YU_Z_$7^lu<Jox;$~_pC+abJWMmmA!M|I1QQ}+vGt<%3BwaFUP7QMCblnsvCLF0t
z?*m6odE)bXqj0cwHZ~v1xk759F1Aj_hHPzf>lHFbmoXkscK>si0rn#IL$l##O(8QI
z*9tYY4<DHs#mkgDR!Z<aauNW-_TFD~%e=j+^I2GoNh#zQcVXERR9?hd#`uvn6R|jf
za}W!>&z@ubZp64$BDd?Fr`fwA?j%F`&Zn1t97-Ncpf0U8y$}Krpd9rCg+vmPdasp`
z(D3w>yB_e}0WJt-oy_52Ad6^gGDFqRjc_aTToKIcEgk^fnys=de<#H6(d~XxAOmim
zGS*w!$!2CzKRoi5?Zj7o2TPGyu76fTtYet;vqFrt4-oGFnzS?*aCve~NhbrU)54&Y
zpU7f<mvv3Z;my%;nrn*;_9Gx6TDiBMDQx-ZwS5@D_yvMS2FeBq7WRNyL4bAmgUEAq
zm<eBx68GJ&r+21SU7hh%MM+5L5@I?*AyLf1f+s0a9F^nMiF!x0WZ}8R2(rs7ynN-#
zZGuKR6`n}sJ1C|ziMP;x3>^)5d}1RXdr0=)<f<3TfAWNm^(cLcp`#Ixx0gkT>YJIR
zWTmI2aUn1ghLOMqo4CkbtJ<qDt7j6gx}MMd&p(weR0iL<W4J`%Mvf=__<w9S&xRf1
z5b7YRb{Y=lViFSUaM&xsM#3QVjJ>_nbp^OCN3&>anS!vD)fFBd6wW@>VYz3|)_sdn
zyF=Vy9d<$LCoc!6P#XDU?!No(-o9kdTqdHjdG~ypC{QTp_KAV5M*MnuH``-w+&JHw
zr$duqr-#=^2rOJ!QTig}hh{iVXsKBqxw7ahu@+^>>!lACw<cFyxfH{!q*U+L(f2Fv
zN@eguUge&uSjiK<_Z%mio9)3o-{W^vORK>Z6~s_})oUg2&f~W=sL9J0e|Ux#N&T)x
z)e|3?&Iy8$Yl<6^nwq8P-Yu|lwHky|YoITAo~y4Ejkn)v{@rWhWlu)5_>HW%2V2>G
zOcm7^#eoE~&mb5Tap9RWf-6^+tXQK{v%!K!V?!L);;(OD5m8yt+qcWU=NjW2%Q8Qn
z=^WL|NVr65fpRRk%nlH@yynXHu4^1zS89q^t)zIj>0*~{m}k#_p~8)|u07eX_|x0s
zYx2eSZhYo<4+to>iDar(VfGw)U_~wSBj`lgZ;Zk-LWG)A`-)L7`IsT#HRbDrp~QK!
z-yL+qAUOD`GR>FBExjHSx7B}GeTtcK;J^wclke`{dAptQfSmk1CQc|n9i}O{bcHP!
zR$Y!^MnkOV$L%c~H5Cg#zGV~K%sD!lYwm{^TcGVC5v2xzq22#`wL&pOM7X|!Im@<;
z!Y?&%*~t<)ZIN*)VD!t5N#p<gXiBB-@K?PMv!?o#(~&p+sW~QYILEuiK#GUnv@H1X
z)nFj2^Y-mKCT<(eJ%dri1|RFCv+R>dGI1;L&WoQfu$Yz*JWwB!)k&w9KLv!ox@W;G
z-)9rIU*83Qx;(5sHB)`Gg!8M{89y`LzQ0mwN#IBekLQ`Vfqv{i<H0XNrG~v)rgYmE
z&3wD(32F}utW-H%->fLEu9nT9QltE>BhA-iWL1gh>N)Z@|CwFuW!=czazcGpJjy`#
zX7}nfYs#d<`>qLnl5PM(b$9hpKrH9SI%dI*^A;?4WbWrUdGV+jSSr77iI;uh$?XyM
z6NWc)r0EKL+SBkxud-x2ZmtlX|Ip@^wF&-})$<*lyq5P!D$;0+(UYQcmXK>>JD{i&
zvAfjBjt8a1Hl{~*e^i>d{rp*664q_^JSX&FZ>!}$D4ctQNEB6(BLa(Rp>J=wSepxf
z+s$qDDvC9y5JhwkMvCaKfBpK-vd4)!W;f<TQ?(O))oI8hzDJ#7E8EmyrEjqaHOV8N
zoZ+ojIGuOA#@P!5H<jix6rEn9F8n)PIOHGvg^5C;{vccc@~I$aD!H9J89!e~OKY<B
zuLZU$U!<hn?zy&(E^cX}6t6J$akB<pPnfY+-Cw3uAl*Co^+2NCrCp!uKMl8|aWrf*
z_W`*tTJl540nujGU&9UkajczIa|LJ1He*tPPVVoN<xr%EQtEA*1AkkRZvM=(r*{hU
zX1;cjtLMRqjbn4-9W9B@n)tf*+J7$NU=t2!Vt|cP)Unk@3+H_ft3Y<C!PEi61I$~z
zb5%KD)e%&eD4IvNQD*>-{p(=|L{i{|b+B+@eZqY)u`6NcvG}e5u}j@<BkqRk(0haZ
zbF3R1T;X@AzSXCEN4Kp|`w&n@$Md$7K~CkY<;a2LAcK2BQy-lC`P=U7td~^e`syP3
zUuX1AZthk&s!x_j={2V@nV4Wj3#$kUDqfgd@nhsmO-+_wNRYw<6hVGQKUap6h(%8|
z#s2xT*<4rZnrJ2n{mpM)2C@el%)c7|<R}_lJ8&6?TFd4hy1qlrsvgL~v;nR|i9}K~
z;^pI3$;Vd(9WL!Kyma^wmQ3bm1WU9^iP`SC&rw+e8Z~<Y<x^mcea51o=Jx#^5##-r
z!Q3bNm0V=#S&VX+i_D#MPU+k|Y>>XUhp($zfn#i<S$u4Es`#Oqxcju57<xe(TZ4}C
zi?Dsaw(P&Ifk{YJRhFW^@_zdpp*j*Os#zIF^f}|>C6@@|5;J!u?RSjH`|x_3krDfY
zlq7u`?C17|aw!=<ReV+7g{1f$OHGtG!_8~|ZG$y8vkgt(d}!QXa0Az=3YVZknp>@9
zsima}oxz-DUcp3ZAUi`&mUfQ^%YR=jzQv0V$=GvUxP1A1oI>EX9rVS?$?iLL(4X`>
zh0*Ev{Yn20&*woAbq)O9Yi(Q}RY9;Xw7>reJO+Qpg&=HDqt0WwlHWs6+Td8qpQHGQ
zCJ+oT|9@X+G%Q~?tT+6gDTA<uUrK`fSU`j6Jh+$temn7{T3j`KKD|&lBu=mZ&|Wk)
z^2I3}-H!6xR3#GF1&(h$U>4`J3kDLGFH|P+hM}S>hB@z8zxuByug1|#!$CcCyBa6j
zpe;fnRUg3cQ+0aB{_FcHZ~68Th6ocgt2M^=uB!RxC%wF7C93AvQZJ^s<Uh~Z?1<R^
zlR?8vd6Q+omw42}u~Qk@we*(*Hw&O5mk0h#$iJO;rQtVz({gPstxjUD1#92N?hIn9
z2d2*BfLUb%)sX@ZJ2wl32+)<mrfCc^cw12&TlNeLFffyMaIE7G-@Q>n-+lbA2j=x>
zPh2gWk@9E%u%t|(SGDT$Nba(?-ZyqMO)gcKbnk1O?jPUs%5&uT$dZ|#=NMIaZ!}!d
zOt~s^P=ish`!8b6p>JRQ{7+&{#w%YD)NSw;E(29F!?96<fcLjw6s`CDxivSpD5u{7
z1>N<HvFt^julfpddg5j#nvBYod<sV<L;~)OZh56Q`D3X->`_Kr&iB{r(>uTXSqa+|
zQA#JU7}CcEGf=J;j?;X|VMj6VC**YwHnQ=mzD+B~7UQPI8UVmK|2gj1W=+W{ST}m+
zq9M~YFp!c}TUN$~!QAc36CXW#^Rbz{_}x5wjZgp1t^Z!+MMg%JKf+q2JBMqtKlvBY
zMcKZNtOQ(G1{VS$z1$9bqyI-SMwGj0FXvYHIt>n`35(>TPsdm{yS!)N=H^Z{&X`9T
z1sVd#fJ;nF>?*`i@%q!C6Nhm|l5e})(q@`6G;t4-3^3rMpFjT!Hr%ME7;Ph6q(?jk
zcnl6h(Je)R>R^LNiC(m*tgS6W+TGRNeTq(B02q99lk0Xkk6o2>SPT<0XfK5WK+O|O
z*XoOkjScIV9rngz%1_eFaKhx~RxlJvaSB5#KiEJoU}w+tG(Ii`x)NEd7_AEc8=@P=
ziUkAXE*@dhHt;WEIgn<)4MY(_SVsE~&&WGf)Qf{{Pk4!aqdtQv`XJHSgT*-UItQSS
z)v-Ym$I8LQRf?C;(UY)2s*Nt>!z4|Q1(4QifqoX=BPANHxnt+f=eYh+2?^D-nktB#
zKEeX?o1JI|C#BGy%BE;67K3I#Ff=9#W-`w&5ByR0g8EL~-roKvx7AH_ZNMR5qL`bT
zcfy3U3!Ojnv|09tsi|69w+78mbQi%$%p67{++VH|@MBDe6C`@LJ7##UJr4B@ZjghB
zjR2{%fphp4mfm82f^KW|4qhuOt4mj|m|_<mM^*L$n&6@CJ*6hfOA5Su%c<~Vr4UpK
zRhpD&M7zO_^F)MJ5@!VLwG6fgQE_pMf#&ocaIJDYcN~9s=MD=*qg&vEd>zFdxMUsU
z@|#V{A6h;Wg6~KOY-amv5>m#Ep`J(>Z&<$YQ>AU%WH5fUsHn)!(gMsbbO%F9T`JF2
zpFH+sz$9>}!W%6^=r<GdmFrP&4Sc_}|5xmhb&j)hu2FD#Uy|j%U1oDfT3Q;s04S8*
z*W?yLahxiY_fU3o2O1ZGsJOwD+=#kIL_Ofg^|H(g4V1U@Kwgr75MeTZq$=#D-Mqca
zCI*_v07i7j+FyST7AJ+s?!B1m2;`;^<-LvQJl%3{m9;tcLBzNBtG#m}Y+8xwI#?!4
zAOvyyWv)qSpBZA<_5QzN*v3|6n_~fR>c(=0{OCsFoC1tVQrR=k*0~VRx_oVAb@iW9
z;n&Yqdt(JdW@9I%{{0tN3=~d1H{3i3l?MIl*iAZF78Vu{J-od3Cnz1;p0z<r>W>NR
z4JKK$W$Him<vl;@HA1a&KYNJAk*AZzSK}j>ZraYbY*`xQ4UHaJ6<Hp6np^Y6&)(Md
zzOv`Xo0y^3hrjE6!jo39GqXEVI5pCdF0|SCs$&VA`T)HIW6<SRb&;blG-v=ixn|{d
z-$O?Vy@%V9;nf=Hh$hGbEQAyUlrv?{mFK?s6@g&#022_&NKy<jj#ALS(b-2j_;bW2
z71+6-&C1#YGiA9MpUJ^6AduCQa3emT-}P>ljYlC!s`!(r=C~?|OI-D?Y?|ZkMS+2Y
zkYGzcUUqe4NzRWSe#b)Xy*aC=AjB>M1?DKR_k1>L9UKDEC8#ea)Xx54X}Z54OXoJY
zBZY;9$}@z8w1599S;>OZxpM1Ybr-E;vqgEk$tUm%om}m}cfbFpEqT|_?-JmDzwG$`
zCJss~%N4x(-=Aty4kV`wM0bLifer{fqOPHV1&<<Gl7wsE=;Sm8P5jQ3^uj~67Z%FQ
zb>%nyjcF~c_Vyhqe{?2EH|Bw@Y-~*}@;O6^%e(fQnm!=r!iG@9djPfZ{dRVDLlSei
zf7iJG<R!FmDT8FiHU<g5{O@{`(59u)4vEdBKUs4r1ohooP*m`1bNJu+&hLLt{6GG=
zPLu$pm}qUhGxDu!Q*9@NJrzFrE~YA(DO13}6pyYjl|0^V3C+*zJ3Auj3p$r7(QX^L
zPiW9G=VN5J`~kt;V03>5QpY61qPa%d8W5m%F*=$G-pA;un+lAk0sdcWhPyE700FTH
z#fvR{hvczE@`IR*A=k2OhqrRIwG|cHY;9K(B)qfJx(yMl<>|>|aT^J195fIR@r%Ki
zb^rRYZu5;9@TA0HeGSo3xNEbyb(bup<i|Lbofl>U4YfKF4Jz=gC=?JFUl7$n*lpiL
z&lZu}EUpaC#&f6iXJ^GU&lWc~3qWe(1wh5GRdUpK%(!^A^6c#FqcvMbI`MV)Q&USY
z-o_&=%s~O$vs4{JD(Gx;V8?@n;0u5*DdXRX*F3TTILj_4tW6czRg_m%m7w8+qt%CE
zrsH)pC=7xR+V%SK`W86l^wr*ogtv4y{S)4I6&-)VfrmjhA^ie%)WHxIqaUVMc|?RB
zlfb88I!)M4@P?&$ZfFhwOZonT2O9hL^QnFRC=R=1Dp+;*lars5lO;VIV8_Hs9HO#x
zh=%6IW`Qc5YE`tXIo%2$&ioU6LE(6BTBZfLjgAnzy#-TI_|stCcq<S*Wv|aH6g1Km
zFfYT6=^-ph!Xkp*N8QO;(8NiAN2}wuPUJG;nSjX^WHbyY%9E@}xz?)CPYHXP4BT2s
zg8|kPDSaEg>Lz~9jMzLsHUA?h5}=YI^mq34oyGo<LkE1#hg^v8*RYYpC;@GpyC;9t
zROTSGltRduI3tK5q66wHgKJ<YLX)O+4EWFsNl$b<6~xPzV5(E$4g^<V(ujto_~sQo
zsM`rqtr5+Lm+C*qxUqGSh4c2_-ZEt0Ogpq|p*2~TVWR_L<3S80NJ)#IJ$u&i=TKMI
zt*oKp;RsNys*j^h1!!5W74Ygc8~Fq1u@K!@Y&_R)Am$m}AdV!G;Jp*`btuOZ!iVAh
z{W;$Yj6kJ<05|~}iLQ-O6Wn#ZB683dxOC}Kc5GH($nDV3(3+7QT9c7@U`;LqptM07
z0edzSDPP|B`E?DYemVku|0VqVaO|50f99Zl$qh69BTqSUfGh)CzcvI@dobDZPFUd9
z!Cw0mNdfj^1m`+b<c%W7(S##YYa|yZy5xi~D0My2Py+Xh^UmG7-GM**13e?;{ifX#
zY;Y60VBk_WGqsXd2K1JmT8cwLf^!}TJCc>>?d_s__Uxeq;kryuPlMsei-)Y<DPCt>
zH}+wBNIvAoU~W9vULb%`9SqqT$kpyw{{_-}={0#LkoCC1yFiZ!9<GUvo2)Q+Qi4lz
zOK&X(vXglO$9pyaekL6Q?0UDxzk?0ZjU<I&`@sM49~n@AR>G%=EsLXi0J&-zC1T|g
zUF}n+Pm}xD_Vgq#$n-3fIeTi*d&tWCHr{XZKHEXr{+(&u%<=x7>O7cl%Xt^qT$AVc
z^}V_14FWQM?GQ%U$b(2wRR|SezrVI+_lTj1Nf_QQj-MbsMArFY`LF3R{t?KP@>j1i
zoJdGPk~TUySxSTz@G}J8oA3-d@hK_B^jU_+jgBb#;Emm{)ca9v_V)H#Ba4P8^2H=g
zF`A<QP$yDh>XXM%Y;5sLgdnJc%>9VyRiJGAzXJDM9s5^Y|EXRv+fjau+5;SjLfNUk
LM=MRk`qcjb_~k5?

diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index f1c57d5..59ab236 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:bbbdc8e390979922204161079c12c6b2020e8457991ef30ddd3766fb3c4503e6"
+  "signature": "sha256:12829b0c696b932be7039bcfd54490f1918f4155c72cd73b7fcfe1b46387decb"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -2182,7 +2182,8 @@
      "source": [
       "<br>\n",
       "As we can see in the first plot, the list comprehensions lead to a slightly increased performance in regular Python code.  \n",
-      "But the second plot is quite interesting: List comprehensions in Cython are significantly slower than the regular for-loop structures.\n",
+      "But the second plot is quite interesting: List comprehensions in Cython are significantly slower than the regular for-loop structures.  \n",
+      "Apparently, Cython has no difficulties turning the explicit for-loop into for-loop in C with basic float arithmetic. However, the list comprehension version is constantly writing to memory while it is iterating and appending new values.\n",
       "<br>"
      ]
     },
@@ -2314,6 +2315,7 @@
       "    x_avg = sum(x)/len(x)\n",
       "    y_avg = sum(y)/len(y)\n",
       "    var_x = 0\n",
+      "    cov_xy = 0\n",
       "    for x_i, y_i in zip(x,y):\n",
       "        temp = (x_i - x_avg)\n",
       "        var_x += temp**2\n",
@@ -2405,7 +2407,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -2415,7 +2417,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -2434,7 +2437,7 @@
       "for lb in labels:\n",
       "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
       "plt.xlabel('sample size n')\n",
-      "plt.ylabel('performance gain relative to the slowest approach')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
       "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
       "plt.legend(loc=2)\n",
       "plt.grid()\n",
@@ -2449,13 +2452,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x111098fd0>"
+        "<matplotlib.figure.Figure at 0x10762ab00>"
        ]
       }
      ],
-     "prompt_number": 70
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
diff --git a/benchmarks/palindrome_timeit.ipynb b/benchmarks/palindrome_timeit.ipynb
index f1df349..afe4a0a 100644
--- a/benchmarks/palindrome_timeit.ipynb
+++ b/benchmarks/palindrome_timeit.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3d878b64b4503fd987496df562af53903ad85d4cce103ea0a2e6c456519c03c7"
+  "signature": "sha256:ab859015961b19530bb3c16fe1edf4e7f9baca715bdf8187c724e8c0c4cfc8ad"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://sebastianraschka.com)  \n",
-      "last updated: 05/03/2014\n",
+      "last updated: 05/06/2014\n",
       "\n",
       "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
@@ -39,11 +39,9 @@
       "\n",
       "# All functions return True if an input string is a palindrome. Else returns False.\n",
       "\n",
-      "\n",
-      "\n",
-      "####\n",
+      "#############################################################\n",
       "#### case-insensitive ignoring punctuation characters\n",
-      "####\n",
+      "############################################################\n",
       "\n",
       "def palindrome_short(my_str):\n",
       "    stripped_str = \"\".join(l.lower() for l in my_str if l.isalpha())\n",
@@ -68,9 +66,9 @@
       "\n",
       "\n",
       "\n",
-      "####\n",
+      "############################################################\n",
       "#### functions considering all characters (case-sensitive)\n",
-      "####\n",
+      "############################################################\n",
       "\n",
       "def palindrome_reverse1(my_str):\n",
       "    return my_str == my_str[::-1]\n",
@@ -83,15 +81,12 @@
       "        return True\n",
       "    if my_str[0] != my_str[-1]:\n",
       "        return False\n",
-      "    return palindrome(my_str[1:-1])\n",
-      "\n",
-      "\n",
-      "\n"
+      "    return palindrome(my_str[1:-1])"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
@@ -119,7 +114,7 @@
        "stream": "stdout",
        "text": [
         "case-insensitive functions ignoring punctuation characters\n",
-        "100000 loops, best of 3: 15.3 \u00b5s per loop"
+        "10000 loops, best of 3: 15.5 \u00b5s per loop"
        ]
       },
       {
@@ -127,7 +122,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "100000 loops, best of 3: 19.9 \u00b5s per loop"
+        "10000 loops, best of 3: 19.5 \u00b5s per loop"
        ]
       },
       {
@@ -135,7 +130,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "100000 loops, best of 3: 13.5 \u00b5s per loop"
+        "100000 loops, best of 3: 11.7 \u00b5s per loop"
        ]
       },
       {
@@ -143,7 +138,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "100000 loops, best of 3: 8.58 \u00b5s per loop"
+        "100000 loops, best of 3: 8.24 \u00b5s per loop"
        ]
       },
       {
@@ -151,7 +146,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "100000 loops, best of 3: 9.42 \u00b5s per loop"
+        "100000 loops, best of 3: 9.38 \u00b5s per loop"
        ]
       },
       {
@@ -162,7 +157,7 @@
         "\n",
         "\n",
         "functions considering all characters (case-sensitive)\n",
-        "1000000 loops, best of 3: 508 ns per loop"
+        "1000000 loops, best of 3: 507 ns per loop"
        ]
       },
       {
@@ -178,7 +173,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000000 loops, best of 3: 480 ns per loop"
+        "1000000 loops, best of 3: 581 ns per loop"
        ]
       },
       {
@@ -189,7 +184,120 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "funcs_s = ['palindrome_short', 'palindrome_regex', 'palindrome_stringlib', \n",
+      "         'palindrome_stringlib2', 'palindrome_isalpha'] \n",
+      "\n",
+      "funcs_ins = ['palindrome_reverse1', 'palindrome_reverse2', 'palindrome_recurs']\n",
+      "\n",
+      "times_s, times_ins = [], []\n",
+      "\n",
+      "for f in funcs_s:\n",
+      "    times_s.append(min(timeit.Timer('%s(test_str)' %f, \n",
+      "                      'from __main__ import %s, test_str' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))\n",
+      "    \n",
+      "for f in funcs_ins:\n",
+      "    times_ins.append(min(timeit.Timer('%s(test_str)' %f, \n",
+      "                      'from __main__ import %s, test_str' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('cy_lstsqr', 'Cython implementation'), \n",
+      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
+      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
+      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for f in funcs_s:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "x_pos = np.arange(len(times_s))\n",
+      "plt.bar(x_pos, times_s, align='center', alpha=0.5, color=\"green\")\n",
+      "plt.xticks(x_pos, funcs_s, rotation=90)\n",
+      "plt.ylabel('time per computation in ms')\n",
+      "plt.title('Performance of different case-sensitive palindrome functions')\n",
+      "plt.grid()\n",
+      "plt.show()\n",
+      "\n",
+      "x_pos = np.arange(len(times_ins))\n",
+      "plt.bar(x_pos, times_ins, align='center', alpha=0.5, color=\"blue\")\n",
+      "plt.xticks(x_pos, funcs_ins, rotation=90)\n",
+      "plt.ylabel('time per computation in ms')\n",
+      "plt.title('Performance of different case-insensitive palindrome functions')\n",
+      "plt.grid()\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAFrCAYAAAAHCiThAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDzH9gT2pJYOrAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105618160>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAacAAAFoCAYAAADzUTT5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8/8AVRvie0rr9RMAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1056baba8>"
+       ]
+      }
+     ],
+     "prompt_number": 29
     }
    ],
    "metadata": {}
diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index bbd8c17..199d744 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:132d5623e27eb721587599809f9870d397887ec873bbbdc95b25a05e710d160e"
+  "signature": "sha256:a2b1ea908157604888db3edc69f70ebb7f6f25d46bbb813b419c0879633a8962"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -67,7 +67,8 @@
       "- [Comprehensions vs. for-loops](#comprehensions)\n",
       "- [Copying files by searching directory trees](#find_copy)\n",
       "- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
-      "- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)"
+      "- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)\n",
+      "- [Cython vs. regular (C)Python](#cython)"
      ]
     },
     {
@@ -119,7 +120,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We expect the string .format() method to perform slower than %, because it is doing the formatting for each object itself, where formatting via the binary % is hard-coded for known types. But let's see how big the difference really is..."
+      "We expect the string `.format()` method to perform slower than %, because it is doing the formatting for each object itself, where formatting via the binary % is hard-coded for known types. But let's see how big the difference really is..."
      ]
     },
     {
@@ -128,21 +129,16 @@
      "input": [
       "import timeit\n",
       "\n",
-      "def test_format():\n",
-      "    return ['{}'.format(i) for i in range(1000000)]\n",
+      "n = 10000\n",
       "\n",
-      "def test_binaryop():\n",
-      "    return ['%s' %i for i in range(1000000)]\n",
+      "def test_format(n):\n",
+      "    return ['{}'.format(i) for i in range(n)]\n",
       "\n",
-      "%timeit test_format()\n",
-      "%timeit test_binaryop()\n",
+      "def test_binaryop(n):\n",
+      "    return ['%s' %i for i in range(n)]\n",
       "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
+      "%timeit test_format(n)\n",
+      "%timeit test_binaryop(n)"
      ],
      "language": "python",
      "metadata": {},
@@ -151,8 +147,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1 loops, best of 3: 400 ms per loop\n",
-        "1 loops, best of 3: 241 ms per loop"
+        "100 loops, best of 3: 5.67 ms per loop\n",
+        "100 loops, best of 3: 3.9 ms per loop"
        ]
       },
       {
@@ -165,6 +161,79 @@
      ],
      "prompt_number": 3
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['test_format', 'test_binaryop']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
+      "                      'from __main__ import %s, n' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 55
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('test_format', '.format() method'), \n",
+      "          ('test_binaryop', 'binary operator %')] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of different string formatting methods')\n",
+      "ftext = 'binary op. % is {:.2f}x faster than .format()'\\\n",
+      "        .format(times_n['test_format'][-1]\\\n",
+      "                /times_n['test_binaryop'][-1])\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIR8Y8ZXlAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10610c550>"
+       ]
+      }
+     ],
+     "prompt_number": 56
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -201,20 +270,15 @@
       "def reverse_slizing(my_str):\n",
       "    return my_str[::-1]\n",
       "\n",
+      "test_str = 'abcdefg'\n",
       "\n",
       "# Test to show that both work\n",
-      "a = reverse_join('abcd')\n",
-      "b = reverse_slizing('abcd')\n",
-      "assert(a == b and a == 'dcba')\n",
-      "\n",
-      "%timeit reverse_join('abcd')\n",
-      "%timeit reverse_slizing('abcd')\n",
+      "a = reverse_join(test_str)\n",
+      "b = reverse_slizing(test_str)\n",
+      "assert(a == b and a == 'gfedcba')\n",
       "\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.4 GHz Intel Core Duo\n",
-      "# 8 GB 1067 Mhz DDR3\n",
-      "#"
+      "%timeit reverse_join(test_str)\n",
+      "%timeit reverse_slizing(test_str)"
      ],
      "language": "python",
      "metadata": {},
@@ -223,8 +287,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000000 loops, best of 3: 1.28 \u00b5s per loop\n",
-        "1000000 loops, best of 3: 337 ns per loop"
+        "100000 loops, best of 3: 1.56 \u00b5s per loop\n",
+        "1000000 loops, best of 3: 362 ns per loop"
        ]
       },
       {
@@ -235,7 +299,84 @@
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['reverse_join', 'reverse_slizing']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "test_strings = (test_str*n for n in orders_n)\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for st,n in zip(test_strings, orders_n):\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(st)' %f, \n",
+      "                      'from __main__ import %s, st' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('reverse_join', '\"\".join(reversed(my_str))'), \n",
+      "          ('reverse_slizing', 'my_str[::-1]')] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of different string reversing methods')\n",
+      "max_perf = max( [times_n['reverse_join'][i]/times_n['reverse_slizing'][i]\n",
+      "                 for i in range(len(times_n['reverse_join']))] )\n",
+      "min_perf = min( [times_n['reverse_join'][i]/times_n['reverse_slizing'][i]\n",
+      "                 for i in range(len(times_n['reverse_join']))] )\n",
+      "ftext = 'my_str[::-1] is {:.2f}x to {:.2f}x faster than \"\".join(reversed(my_str))'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105b68be0>"
+       ]
+      }
+     ],
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -286,14 +427,7 @@
       "test_chars = ['a', 'b', 'c', 'd', 'e', 'f']\n",
       "\n",
       "%timeit string_add(test_chars)\n",
-      "%timeit string_join(test_chars)\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
+      "%timeit string_join(test_chars)"
      ],
      "language": "python",
      "metadata": {},
@@ -302,8 +436,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000000 loops, best of 3: 595 ns per loop\n",
-        "1000000 loops, best of 3: 269 ns per loop"
+        "1000000 loops, best of 3: 1.15 \u00b5s per loop\n",
+        "1000000 loops, best of 3: 436 ns per loop"
        ]
       },
       {
@@ -314,7 +448,79 @@
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['string_add', 'string_join']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 9)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(test_chars)' %f, \n",
+      "                      'from __main__ import %s, test_chars' %f)\n",
+      "                              .repeat(repeat=10, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 71
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('string_add', 'new_str += char'), \n",
+      "          ('string_join', '\"\".join(chars)')] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of different string reversing methods')\n",
+      "ftext = 'new_str += char is {:.2f}x faster than \"\".join(chars)'\\\n",
+      "        .format(times_n['string_add'][-1]\\\n",
+      "                /times_n['string_join'][-1])\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAIECAYAAAByl6h3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFNfXB/DvLB0EQRAUEEGQ3hVBUFywl6hRk1hQrFGM\nNZZYMIBGY0xsMUZjxYI9GktiBbGBXVARkGJHFCMYBUWE+/7By/xcacsufc7nefaRnXLvPbPDcpx7\nZpdjjDEQQgghhJA6SVTTAyCEEEIIIbKjZI4QQgghpA6jZI4QQgghpA6jZI4QQgghpA6jZI4QQggh\npA6jZI4QQgghpA6jZI7UeR8+fMDIkSOhp6cHkUiEs2fP1vSQ6qS9e/fC3NwcioqKGDlypNT7BQcH\no2XLlqU+L63tyMhI2NvbQ1lZGb6+vpUTRB0UGhoKJSWlmh5GrXX//n2IRCJERUXV9FCqhKmpKRYu\nXFglbYvFYowZM6ZK2ia1CyVzpFoMHz4cIpEIIpEISkpKMDU1RUBAAF6+fCl323/++Sd27tyJI0eO\nID09HW3btq2EEQtLfn4+Ro4ciYEDB+LRo0dYuXKlzG3NmDEDly5dKrftgIAAtG7dGvfu3cP+/fvl\njqEyWFhYICQkRO52FBUVsXXrVqm2HThwINLS0uTus74yMTFBeno62rRpU9NDkcsPP/wAMzOzYss5\njgPHcVXSZ1W2TWoXxZoeABEOb29v7NmzBx8+fMDVq1cxZswYPHr0CEeOHJGpvffv30NZWRlJSUkw\nMjKCh4eHXOMrak+I0tLSkJ2dje7du6Np06ZytaWhoQENDY0y22aMITk5GXPnzoWRkZHMfTHGkJ+f\nD0XFynkrq6w/fBzHobzPYy8au6qqKlRVVSul34oqKCgAAIhENfP/eml+50QiEfT19atpRITUTXRl\njlQbJSUl6Ovrw9DQEL1798bkyZNx7Ngx5ObmAgB27doFZ2dnqKmpwczMDNOmTUNOTg6/v1gsxujR\nozFv3jwYGhqiefPm8PHxwffff4/U1FSIRCK0aNECAJCXl4dZs2bB2NgYKioqsLOzw86dOyXGIxKJ\nsGrVKgwePBja2toYNmwYP+UVGRkJBwcHqKurw9fXF+np6Th9+jScnZ3RoEEDdO7cWeJqyr1799Cv\nXz8YGRlBQ0MDjo6O2L59u0R/RVMeCxYsQNOmTaGrqwt/f39kZ2dLbLd79260atUKampq0NPTQ48e\nPZCVlcWvX7VqFaytraGmpgZLS0ssWrQI+fn5ZR77ixcvwtvbG+rq6mjUqBGGDBmCjIwMAIXTfM2b\nNwdQmHCXNVX97t07BAQEQFtbG40aNcL48eP516/Ix9Osn7atoKCAM2fOQEFBAfn5+Rg2bBhEIhF/\nFSs5ORn9+/eHjo4OGjVqhK5du+L27dt82x+/Pi4uLlBVVUV4eDjy8vIQHByMFi1aQE1NDfb29li3\nbl2x13vNmjUYOnQotLS00KxZMyxevFji9UlJSUFISAh/Ffnhw4clHoe4uDh07doVOjo6aNCgAWxt\nbfnX29TUFPn5+RgxYgREIhEUFBRKHfupU6eKTbMWPY+KioKrqys0NDTQunVrXL16VWIM4eHhcHBw\ngJqaGlxcXHDu3DmIRCKEhYWVOOaPX5s9e/bA2toaKioqSEpKwps3bzB58mQYGxtDQ0MDrq6uOHDg\nAL+fl5cXxo4dW6w9GxsbfP/99/zzivwON23aFKampgCAgwcPwsXFBRoaGtDR0YG7uztiYmIAFJ9m\nLXq+d+9e9OrVCxoaGjA3N8eWLVskxnbv3j106dIFampqMDU1xR9//FHutGNkZCREIhGOHj2Ktm3b\nQl1dHW5uboiPj8fNmzfh5eUFDQ0NuLu7Iz4+XmLfa9euoUuXLtDU1IS+vj769+/Pnz+hoaH4/vvv\n8eDBA/7cmj9/Pr9vbm4uJk+eDF1dXTRp0gTffvutxO+0NO9nDx48QLdu3aCurg4TExOsWrWqWHxl\nHWdSxzFCqoG/vz/r3LmzxLKlS5cyjuPYmzdv2ObNm5mOjg7bvn07u3fvHjt79ixzdHRkQ4cO5bfv\n0KED09TUZAEBASw+Pp7dvn2bvXz5kk2fPp2ZmZmxZ8+esRcvXjDGGJs+fTrT1dVl+/btY0lJSWzR\nokVMJBKx8PBwvj2O45iuri5bvXo1S01NZUlJSWzz5s1MJBIxHx8fdvnyZXb9+nXWsmVL1q5dO+bt\n7c0uXbrEYmJimLW1Nfvqq6/4tm7dusVWr17Nbt68yVJTU9mqVauYoqIiO336tMT4tbW12bfffssS\nExPZiRMnWKNGjdi8efP4bTZt2sSUlJTYDz/8wMf422+/8XEFBQWx5s2bs7/++ovdv3+f/fPPP8zE\nxESijU89ffqUaWpqsiFDhrDbt2+z8+fPM0dHR+bt7c0YY+zt27fsypUrjOM4dvjwYfbs2TP2/v37\nEtuaMmUK09fXZ4cOHWKJiYls+vTpTEtLi7Vs2ZLfJigoiH9eWtvp6emM4zj2+++/s2fPnrG3b9+y\n9PR0ZmBgwMaPH89u377N7t69yyZOnMh0dXVZRkYGY4zxr4+7uzuLjIxk9+7dYxkZGczf3585OTmx\nkydPsvv377Pdu3czbW1ttnHjRonX28DAgG3YsIGlpqay1atXM47j+HPi5cuXzMzMjM2YMYM9e/aM\nPXv2jOXn55d4HBwcHNiQIUNYfHw8u3fvHjt69Cg7cuQIY4yxjIwMpqioyH799Ve+nbLGvnnzZqao\nqMi3XbRdhw4d2Pnz51lCQgLr3r07MzMzYx8+fGCMMfb48WOmpqbGxowZw+Lj41l4eDhzdXVlHMex\nsLCwUs+FoKAgpq6uzsRiMbt8+TJLSkpir1+/ZmKxmPn4+LALFy6we/fusXXr1jFlZWX+2Kxbt47p\n6Oiw3Nxcvq1Lly4xjuNYUlISP25ZfoefPn3KlJSU2M8//8zu37/PEhIS2M6dO9mtW7cYY4zdu3eP\ncRzHLly4IPG8RYsWbO/evSwlJYXNmTOHKSoqsrt37zLGGCsoKGBOTk7Mw8ODXblyhcXExLAePXqw\nhg0bsjFjxpR6fE6fPs04jmOurq7s9OnT7M6dO6xt27bM0dGReXl5sYiICBYfH8/atWvH3N3d+f3i\n4uJYgwYNWHBwMEtMTGS3b99mX3zxBbO0tGTv3r1jb9++ZbNmzWLNmjXjz4ns7GzGGGPNmzdnOjo6\n7KeffmLJyclsz549TElJSeLcLe/9rKCggLm4uLA2bdqwy5cvs5iYGNa5c2empaXFx1vecSZ1GyVz\npFr4+/uzTp068c/j4uJYixYtWNu2bRljhW9of/zxh8Q+Z86cYRzHsaysLMZY4R8CKyurYm0HBQUx\nCwsL/nl2djZTUVFha9askdju888/Z76+vvxzjuPY6NGjJbbZvHkz4ziOxcbG8st+/vlnxnEcu379\nOr9s+fLlTE9Pr8yY+/TpI/GHo0OHDszZ2Vlim4CAAP4YMMZYs2bN2MSJE0tsLzs7m6mrq7Pjx49L\nLN+yZQvT1tYudRyBgYGsWbNmLC8vj18WGxvLOI5jZ8+eZYwV/4NZkjdv3jBVVVW2YcMGieWtW7cu\nlsx9/HqU1vaniUdQUBDz8PCQ2KagoICZm5uzFStWMMb+9/qcP3+e3yY1NZWJRCKWmJgosW9ISIjE\n8eY4jk2ePFliGxsbGzZ79mz+uYWFBQsJCSn1GBRp2LAhCw0NLXW9oqIi27Jli8SyksZetPzTZI7j\nOHbjxg1+WVHiVJSszJkzh5mZmbGCggJ+m2PHjkmVzIlEIvbo0SN+2enTp5mqqip79eqVxLYjRoxg\nffv2ZYwxlpmZydTU1NjevXv59d988w3z9PTkn8v6O3z9+nXGcRy7f/9+iWMuLZlbvnw5v01+fj7T\n1NRk69atY4wxduLECcZxHEtJSeG3efnyJVNXV5cqmTt48CC/bO/evYzjOLZ//35+2YEDBxjHcXxC\n5u/vzwYOHCjR1rt375i6ujr766+/GGOMLViwgJmamhbrs3nz5qxPnz4Sy7p3784GDRrEGJPu/ezk\nyZMSiTVjhf+pKEr4GSv/OJO6jaZZSbWJjIyEpqYm1NXV4eDgAAsLC4SFhSEjIwMPHz7E1KlToamp\nyT969OgBjuOQnJzMt9GqVaty+0lOTsb79+/h7e0tsdzb2xtxcXESy0oqquY4Dg4ODvxzAwMDAICj\no6PEsn///Zevi8rJycGsWbNgb28PXV1daGpq4p9//pGYpuM4Dk5OThJ9NW3aFM+ePQMAPH/+HI8f\nP0aXLl1KjCsuLg5v375Fv379JI7TuHHj8N9//+Hff/8tdT8PDw+JujJHR0c0bNgQd+7cKXGfkqSk\npCA3Nxeenp4Sy728vMqtD5PGlStXcO3aNYnYtLS08ODBA4lzAADc3Nz4n69evQrGGFq1aiWx748/\n/lhsP2dnZ4nnhoaGeP78eYXHOn36dIwePRo+Pj4ICQnBjRs3pN7347GX5tNzpajWsOhcuXPnDtzc\n3CRq/KStGTUwMICxsTH//MqVK3j//j2MjIwkjl9YWBh//LS1tdG7d29s27YNQOG0365duzBs2DAA\nkOt32MnJCV27doW9vT369euHX3/9FY8fPy43jo9fy6K6uo+Pj56eHl92AQA6OjqwsrKS6hh9fOxL\n+/0HwJ87V65cwYEDByRi19PTQ25ubrFz8FMcxxU7Lz9+X5Dm/awoXgsLC369np6eRLyyHmdSN9AN\nEKTaeHh4YMuWLVBUVIShoSGfXBS9af3666/w8fEptl9RgTzHcRKF9ZWhpPZEIpHEH8min4tqnz5e\nxhgDx3GYMWMGDh06hOXLl8PKygrq6uqYNm0aXr16JdH2p8XeHMfxRejlKdpu3759sLS0LLZeR0en\nxP2kKcavDRhj6NSpE3777bdi6xo2bMj/rKCgIHEci45LdHQ01NXVJfb79IYGeY7/xwIDAzFkyBAc\nO3YMERERWLRoEWbOnIkFCxaUud+nYy9Naedg0VjluVHj03O+oKAADRs2LFaTB0ger2HDhuHzzz/H\nixcvcP78eWRnZ2PgwIES45Lld7ioRu3KlSs4deoU/vzzT8yaNQt79+5Fz549S42jvNeypGMk7e/B\nxzWMRe2UtKyoP8YYhg0bhlmzZhVrq1GjRuX2V1nn5ac+jlfW40zqBkrmSLVRVVWV+J9yEQMDAzRr\n1gwJCQkYNWqU3P1YWFhARUUFZ86cga2tLb/8zJkzElfcKtO5c+fg5+eHAQMGACh8k09MTKzQnaH6\n+vowNjbG8ePH0atXr2Lr7ezsoKqqipSUFHTr1k3qdu3s7LB582bk5eXxf5BiY2Px6tUr2NvbS92O\nubk5lJWVceHCBdjY2PDLL1y4UCl3gbZu3RqhoaEwMjKCioqK1PsVXel58OCB3H+UlJWVy72ZpIiZ\nmRkCAgIQEBCAxYsX45dffuGTuYq0IwtbW1vs2LEDBQUF/J2oFy9elKktNzc3ZGVl4e3bt7Czsyt1\nuy5duqBRo0bYtWsXIiIi8Nlnn/FJdmX8Dru5ucHNzQ2zZ89G9+7dsXnzZplfT1tbW2RkZCA1NZV/\nz8nMzMTdu3elujJaUa1bt0ZsbGyJ729FZD0npHk/s7W1xYsXL5CcnMxfnXvx4gXu3r1bbPahMo8z\nqT3qZDJ3+fJlTJkyBUpKSjAyMsLWrVsr7aMJSM1YuHAhRo0aBR0dHfTu3RtKSkqIj4/HsWPHsHbt\nWgCF/8uU5n/W6urqmDRpEubNm4fGjRvD0dER+/btw6FDh3Dq1KkqGb+VlRX++usv9OvXDxoaGli2\nbBmePn2KJk2a8NtIM/6goCAEBATAwMAA/fv3R0FBAU6fPo1BgwZBV1cXc+bMwZw5c8BxHDp27IgP\nHz7g1q1biImJkbgz82MTJkzAypUrMXz4cMyZMweZmZkYP348vL294eXlJXWMGhoaGDduHAIDA2Fg\nYABLS0ts3LgRd+/erZSPjpgwYQI2btyIPn36IDAwEMbGxnj8+DGOHj2KXr16lfr5gRYWFhg5ciTG\njBmDJUuWwMPDA9nZ2bh27RpevHiBmTNnltrnp6+JmZkZzp8/j0ePHkFNTQ26urrFEtXs7GzMnDkT\nAwYMgKmpKbKysnDs2DGJRMjMzAwRERHo1q0blJSUoKenJ+fRkTR+/HgsX74cAQEBmDJlCtLT0zF3\n7lwAFb9q5+vri06dOqFfv35YsmQJHBwckJmZiaioKKipqWH06NEACj87b/Dgwfj999+RmpqKP//8\nU6IdWX+Ho6OjcerUKXTt2hVNmjRBUlISbt68yfcrrY/b7dy5M5ycnDB06FCsXLkSSkpKmDt3LpSU\nlKrkc9fmzJmDNm3awM/PD5MnT4aenh7u37+PgwcPYvLkyTAzM0OLFi2Qnp6OixcvwsLCAhoaGlBT\nUyv3PUGa97NOnTrByckJfn5+WLVqFZSUlPDdd99JXE2MiopCeHi43MeZ1E51smbOxMQEp0+fxpkz\nZ2BqaoqDBw/W9JBIOcr78Eo/Pz/s2bMHR44cgbu7O9q0aYOQkBCJ2p7S2ihp+cKFCzFmzBhMmTIF\nDg4O2LFjB8LCwkqcAiqpvYouW758Of9RKZ06dUKzZs0wYMCAYlNln7bz6bJRo0YhNDQU+/btg4uL\nCzp06IDjx4/z/1kJDAzEsmXLsH79ejg7O6N9+/ZYuXJliR9GWkRfXx8nTpzA48eP4ebmhs8++4z/\ng1BejJ9avHgx+vbti6FDh8Ld3R3//fcfvvnmG6niLI++vj6io6Ohp6eHfv36wdraGn5+fnj06BEM\nDQ3LbGvdunWYOnUqFi5cCDs7O3Tq1Anbtm2Dubl5mX1+OtaQkBBkZWXBysoKBgYGePToUbF9FBUV\nkZWVhVGjRsHW1hbdunVD06ZNsWPHDn6bpUuX4tq1azA1NeXrq8o6DtIcr4+XGRoa4tChQ4iKioKL\niwumTp2KH374AQDK/My60n6HDh06hH79+mHq1KmwsbFBr169cPToUYkaLADw9/dHQkICtLW10b17\nd4l1sv4ON2zYEBcvXkSfPn1gaWmJUaNGwc/PD/PmzZP5+ADAgQMHoKGhgfbt26N3797o2bMnrKys\nyv1MP1l+/62trREVFYU3b96ga9eusLOzw9dff413795BW1sbANC3b1988cUX6NmzJ/T19fHzzz+X\n2fbHy6V5P/vrr7/QsGFDeHt7o3fv3ujVqxdcXV359dra2uUeZ1J3cawuFNOUISgoCC4uLujbt29N\nD4UQQmrM2bNnIRaLcevWrTKnS4Xq9evXMDY2xqJFi/DNN9/U9HAIqVR1Opl78OABBg0ahHPnzkkU\npxNCSH23Zs0aODk5wdDQEHfu3MHUqVOhq6tbb7/DtKIOHz4MBQUF2NjY4Pnz5wgJCcHFixeRmJiI\nxo0b1/TwCKlU1T7N+ttvv6F169ZQVVXFiBEjJNa9fPkSn3/+ORo0aABTU1OJT7hevnw5fHx8sHTp\nUgDAf//9h2HDhmHLli2UyBFCBOfhw4cYNGgQrK2tMX78eHTo0AF///13TQ+r1sjJycGMGTNgb2+P\nzz77DABw/vx5SuRIvVTtV+YOHDgAkUiE48eP4+3bt9i8eTO/btCgQQCAjRs34saNG+jZsyeioqIk\n7uABgA8fPqB3796YPn06fH19q3P4hBBCCCG1S7V9PPEnAgMD2fDhw/nnb968YcrKyhKfYD1s2DA2\na9asYvtu3bqV6erqMrFYzMRiMdu9e3exbQwNDRkAetCDHvSgBz3oQY9a/3BycpI5p6qxu1nZJxcE\n7969C0VFRYm7p5ycnIp9Yj8ADB06FC9evMDp06dx+vRpfPnll8W2SUtL42+Dl/cRFBRU7e2Ut21p\n6ytrrEKNu6aOX1nb1qXXuiJt1fRrXZlt0TlOccuyLZ3jFPfHy2NjY2XOqWosmfv0duw3b95AS0tL\nYpmmpiZev35dncMqkVgsrvZ2ytu2tPX379+Xug95x1AV7dR03JUVc0XbKmvbuvRaV6Stmn6tpRlD\nVbRT03HXxnO8rPX1OW46xyVR3HJgNWTu3LkS06zXr19n6urqEtv8/PPP7LPPPpOp/RoMrUb5+/vX\n9BBqhBDjFmLMjFHcQiPEuIUYM2MUtzx5S625MmdpaYkPHz5IfClxbGxshb5uiADDhw+v6SHUCCHG\nLcSYAYpbaIQYtxBjBihueVT73az5+fnIy8tDSEgInjx5gvXr10NRUREKCgoYNGgQOI7Dhg0bcP36\ndfTq1QvR0dES3wMprbry5eKEEEIIIfLkLdV+ZW7BggVQV1fHTz/9hO3bt0NNTQ0LFy4EAPz+++94\n+/Yt9PX14efnh7Vr18qUyAlZZGRkTQ+hRggxbiHGDFDcQiPEuIUYM0Bxy6Pav50+ODgYwcHBJa7T\n0dHBgQMHqndAhBBCCCF1WJ3+Oq+ylHW5slGjRsjMzKzmEREiOx0dHbx8+bKmh0EIIaSKyDPNKshk\njurpSF1D5ywhhNRvdapmjhBSOai+RFgobuEQYswAxS2Pep3MBQcHC/bkIIQQQkjtFxMTU+q9BNKi\naVZC6gA6ZwkhpH6jaVZCCCGEEIGiZI6QOkqoJQQUt7AIMW4hxgxQ3PKgZI4QGQQHB6Nly5Y1PQxC\nCCGEauZKk5j4AKdOpSAvTwQlpQJ06mQOK6vmMo+nsturSx4/fgwTExNERkbC29u7podTKYKDgxEW\nFoakpKRq6Y9q5gghpH6jmrlKlpj4AKGhycjI8EVWlhgZGb4IDU1GYuKDWtFeXVXeSVpQUICCggKp\n2jI1NcWZM2cqY1i1Sl5eXk0PgRBCSB1DyVwJTp1KgYpKR0RGgn9ER3fEt9+mIDgYFX5MnZqC6GjJ\n9lRUOiI8PEWm8YnFYowZMwYLFixA06ZNoaurC39/f2RnZ0tst2vXLjg7O0NNTQ1mZmaYNm0acnJy\nAADh4eFQUVHBu3fvAADv3r2Dqqoq2rdvz+9/8uRJqKio8PuU5fz58/Dy8oKWlha0tLTg7OyMEydO\nAABMTEwAAD4+PhCJRGjRogWA/01V7tmzB9bW1lBRUZH6ShfHceA4TqptZXXt2jV069YNDRs2hKam\nJtzd3XH58mWJbQ4dOgRra2s0aNAAPj4+SE5O5tdlZWXBz88PzZs3h7q6OqytrbFs2TKJ/YcPH47O\nnTtj1apVMDU1haqqKnJzc6UaH9WXCAvFLRxCjBmguOVByVwJ8vJKPiz5+bIdroKCkvd7/172w79v\n3z5kZWXhzJkz2LVrF44cOYKffvqJXx8aGorx48djxowZiI+Px9atW3Hq1CmMGzcOAODp6QmRSISz\nZ88CAC5cuAAtLS1cvXoVb9++BQBERESgTZs2UFdXL3MsHz58QO/evdG2bVvcuHEDN27cQEhICL/f\n9evXAQD79+9Heno6rly5wu+blpaGNWvWYNu2bYiPj4eRkZHMx+Rj3bt3h6amZpmPCxculLp/XFwc\nvL29oauri9OnTyM2NhbTp0+XuHL49OlTrF27Fjt37kRUVBRev36NkSNH8utzc3Ph4OCAgwcPIj4+\nHvPmzUNQUBBCQ0Ml+rp8+TIiIyNx+PBh3Lx5E0pKSpVyDAghhAiDYk0PoDZSUip5qk9BQbopwE+J\nRCXvp6wsW3tA4TTj0qVLAQCWlpb46quvcOrUKcyfPx9A4VWvxYsXY8iQIfz2q1atglgsxqpVq9Cw\nYUN4eHggPDwcXbp0QUREBHr37o3o6GicPXsWXbt2RUREBLp161buWF6/fo2srCx89tlnMDc3BwD+\nXwDQ09MDUPiduPr6+hL7vnv3Dtu2bYOxsXGFj0FZ07abNm3ik9LSGBoalrpu8eLFsLS0RFhYGL+s\n6IpikdzcXGzbtg26uroAgJkzZ2LQoEF4//49lJWVYWBggO+++47fvnnz5rh8+TJ27NiB4cOH88sV\nFBSwbdu2cpPmT4nF4gptX19Q3MIixLiFGDNAccuDkrkSdOpkjtDQcIjFHfllubnhGD7cAlZWFW8v\nMbGwPRUVyfY6drSQaXwcx8HJyUliWdOmTXH8+HEAQEZGBh4+fIipU6di2rRp/DaMMXAch+TkZLRq\n1Qo+Pj44fPgwgMKrcJMmTYKqqioiIiLQtm1bXL9+HUuWLCl3PDo6Ohg9ejS6du0KX19fdOjQAZ9/\n/jksLS3L3dfAwECqRK579+44f/48/zwnJwfdu3eHgoICv+zYsWPw8vLij4c8rl27hh49epS5jaGh\nIZ/IFfXJGMPz589hbGyMgoICLFmyBLt27cKTJ0/w7t075OXlwdTUVKIdGxubCidyhBBCSJF6Pc0q\n69d5WVk1x/DhFtDXj4C2diT09SP+P5GT7e7Tym4PAJSVlSWecxzHTwEW/fvrr78iNjaWf9y8eRNJ\nSUmwt7cHUFjDduPGDTx69AjXr19Hx44d4evri4iICJw5cwZKSkrw9PSUajzr1q3DtWvX0LlzZ5w5\ncwb29vZYt25duftpaGhI1f7GjRv5OGJiYmBoaCixLDY2Fq1ateK3l3eaVZq7ikp6DYD/Hf+lS5di\n8eLFmDJlCk6dOoXY2FiMHj26WE2crIkc1ZcIC8UtHEKMGRBu3CtWrJD767zq9ZU5eQ6OlVXzSv3o\nkMpurywGBgZo1qwZEhISMGrUqFK3c3d3h6qqKubPnw9LS0vo6+tDLBZj4MCBOHDgALy8vCpUv2Vn\nZwc7OztMnToVAQEBWLduHb7++ms+6cnPz5c5pk+nRBUVFWFkZFRs6rPIxo0b+Zs7pG3zY61atUJ4\neDh/NVMWZ8+eRffu3SWmVO/evVvlN24QQgipO5ydnSEWixESEiJzG/U6mauvGGPlXjVauHAhRo0a\nBR0dHfTu3RtKSkqIj4/HsWPHsHbtWgCFV5a8vLywZcsWBAQEACisa7O3t8f27dulPrFSUlKwbt06\n9O7dG8bGxkhLS8O5c+f4K2V6enpo0KABjh8/DhsbG6ioqEBHR0eOI1C+shI1acycORPu7u4YMmQI\npk2bBm1tbVy/fh3NmjWDh4eHVG1YW1tj27ZtiIyMhKGhIbZu3YrLly9XWuxUXyIsFLdwCDFmgOKW\nR72eZq1DUhuyAAAgAElEQVSvSvpYjk+X+fn5Yc+ePThy5Ajc3d3Rpk0bhISEFKtP8/HxQX5+Pnx9\nffllvr6+xZaVRUNDA8nJyRg4cCCsrKwwYMAAeHl54bfffgMAiEQirF69Gnv27EGzZs34JK86Pl5E\nVvb29oiMjERGRgY6dOgAFxcXLF++HIqKhf//KW3sHy+bN28eOnTogD59+sDT0xOvXr3CpEmTJLap\nzceAEEJI3UDfAEFIHVDSORsZGSnI/8lS3MIixLiFGDNAcdM3QBBCCCGECBRdmSPlCgsL4z9suCTx\n8fEyfU4ckR6ds4QQUr/J8z5PyRwp15s3b/D8+fNS1zdv3lzi895I5aNzlhBC6jeaZiVVqkGDBmjR\nokWpD0rkaoZQP5OJ4hYWIcYtxJgBilselMwRQgghhNRhNM1KSB1A5ywhhNRvNM1KCCGEECJQlMwR\nUkdRfYmwUNzCIcSYAYpbHvU6mQsODhbsyUEIIYSQ2i8mJkau75IHqGZOkMzMzDBmzBjMmTNH6n1E\nIhG2b9+OwYMHV6ivtLQ0ODg44ObNmzAyMip3+9DQUIwZMwZ5eXkV6qcy5Ofnw97eHsuWLUP37t2r\nvf+yCP2cJYSQ+o5q5gRk+PDhGDFiBIDCBOvs2bP8z2fOnEFoaCjMzMzKbOPq1auYOnVqhfpNT09H\n//79KzzeoKAgfPXVV1IlcjVNQUEBc+fOxXfffVfTQyGEEEKkRslcKRKTE7F692qs2LUCq3evRmJy\nYq1or6wvZpf2C9t1dXWhpqZWoX719fWhoqJSoX1evnyJ7du3Y8yYMRXaryoUFBSgoKCg3O369++P\nBw8e4PTp09UwKvkItYSA4hYWIcYtxJgBilselMyVIDE5EaGnQ5FhkIGsJlnIMMhA6OlQmROwymyv\nMqbaTE1NsXDhQv7569evMXbsWOjr60NVVRVubm44efKkxD4ikQhhYWESz9esWYOhQ4dCS0sLzZo1\nw+LFiyX22bt3LwwMDODi4iKxPCUlBQMGDICuri40NDTg5OSEv//+W2KbqKgouLq6QkNDA61bt8bV\nq1cl1o8ZMwYWFhZQV1eHubk55s6di/fv3/Prg4OD0bJlS+zZswfW1tZQUVFBUlIS4uLi0LVrV+jo\n6KBBgwawtbXF9u3b+f3U1NTQrVs3iWWEEEJIbaZY0wOojU5dOwWVliqIvB/5v4VKwM1dN+HWzq3C\n7V0+fxk5xjnA/f8tE7cUI/x6OKwsrCrUVnlX38q6clfaNiNHjsS1a9cQFhYGExMTrFmzBr169cLN\nmzdhZWUlsd/HQkJCsHDhQsyfPx9Hjx7FhAkT0KZNG/j6+gIAzpw5A3d3d4l90tPT4enpCScnJxw+\nfBiGhoaIi4uT+BaJgoICzJkzB6tWrYKenh6mTp2KL7/8EklJSVBQUABjDAYGBti5cycMDAwQGxuL\nsWPHQklJSaKINC0tDWvWrMG2bdugo6ODJk2awNPTE46OjoiOjoaqqioSEhKQn58vMUZ3d3esWrWq\nzGNYG4jF4poeQo2guIVFiHELMWaA4pYHJXMlyGMlF9/nI7/E5eUpQMnTe+8L3pe4vCybN2/+X7sf\nTRsW/ezt7Q1/f3+p20tOTsaff/6Jf/75B507dwYArFixAufOncOSJUuwcePGUvcdOHAgRo0aBQAY\nP348fvvtN5w6dYpP5u7evQsfHx+JfVavXg0FBQUcPHiQn+o1NTWV2IYxhhUrVsDZ2RlA4VU2Dw8P\npKamomXLluA4Dj/88AO/vYmJCZKTk7FmzRqJZO7du3fYtm0bjI2N+WUPHz7EtGnTYG1tXWLfRcse\nPHiADx8+QFGRfkUIIYTUbjTNWgIlTqnE5QqQ7TtIRaUcZmWRskztVaY7d+4AKEwCP+bt7Y24uLgy\n9y1KtooYGhri+fPn/PP//vsPmpqaEttcu3YNnp6eZdbscRwHJycn/nnTpk0BAM+ePeOXrV+/Hu7u\n7mjSpAk0NTUxZ84cPHz4UKIdAwMDiUQOAKZPn47Ro0fDx8cHISEhuHHjRrH+tbS0AABZWVmljrE2\noPoSYaG4hUOIMQMUtzzoskMJOrXqhNDToRC3FPPLcpNyMXzg8ApPiwJAonFhzZxKy//dQJCblIuO\nPh0rY7hVQpraPGVlyWSU4ziJq4Xa2tp4/fp1sW3Ka1skEklM6Rb9XNT23r17MWHCBPz000/o0KED\ntLS0sGfPHsydO1eiHQ0NjWJtBwYGYsiQITh27BgiIiKwaNEizJw5EwsWLOC3efXqFT9+QgghpLaj\nK3MlsLKwwnCf4dB/rg/tdG3oP9fHcB/ZErmqaK8y2dnZASisb/vY2bNn4eDgIFfbLVu2xP379yWW\ntWrVClFRUcjJyZG53bNnz8LFxQVTpkyBi4sLzM3Nce/ePan3NzMzQ0BAAPbu3YuQkBCsWbNGYv2D\nBw9gampa66dYqb5EWChu4RBizADFLY/a/deqBllZWFVqslXZ7Unr8uXLGDZsGLZt2wY3t+I3b5ib\nm+OLL77A+PHj8ccff/A3QNy5cwe7du2qUF+MMYmrbh06dJC4axYA30+fPn0QEhKCpk2bIi4uDoqK\niujWrZtU/VhbW2PTpk04dOgQ7OzscOTIERw4cKDc/bKzszFz5kwMGDAApqamyMrKwrFjx/iEtsjF\nixcF+6ZCCCGk7qErc/VcTk4OkpKS8Pbt21K32bBhA7p27Qo/Pz84OzsjOjoaR44cgaWlZYX6+vQu\n2f79++P58+e4fv06v6xJkyY4f/48NDU10aNHD9jb22PevHnF2imp7SJjx47F0KFDMWLECLi6uuLK\nlSsIDg4uNjX7aTuKiorIysrCqFGjYGtri27duqFp06bYsWMHv83bt29x/Phx+Pn5VSj2mkD1JcJC\ncQuHEGMGKG550Nd5CZChoSFmzZqFSZMmVXlfX3/9NRQUFIpNZdZW27Ztw88//4ybN2/W9FAklHTO\nRkZGCvIKIsUtLEKMW4gxAxS3PLkJJXMCkp2djQsXLqB79+4IDw+vll+ap0+fwt7eXurvZq1JRd/N\nunz5cqmnfKuLUM9ZQggRCkrmSkDJXHHBwcH47bffMGzYMCxbtqymh0MqQKjnLCGECIU87/P1umYu\nODhYsHPwJQkODsaLFy8okasnhHpuU9zCIsS4hRgzINy4V6xYIfGB97Ko13ezyntwCCGEEEKqkrOz\nM8RiMUJCQmRug6ZZCakD6JwlhJD6jaZZCSGEEEIESpDJnI6ODv85ZPSgR1146OjoFDuPhVpfQnEL\nixDjFmLMAMUtj3pdM1ealy9f1vQQqozQP6eHEEIIERpB1swRQgghhNQm8uQtgpxmJYQQQgipLyiZ\nq2eo5kA4hBgzQHELjRDjFmLMAMUtD0rmCCGEEELqMKqZI4QQQgipYVQzRwghhBAiUJTM1TNUcyAc\nQowZoLiFRohxCzFmgOKWByVzhBBCCCF1GNXMEUIIIYTUMKqZI4QQQggRKErm6hmqORAOIcYMUNxC\nI8S4hRgzQHHLg5I5QgghhJA6rF7XzAUFBUEsFtMXsBNCCCGkVoqMjERkZCRCQkJkrpmr18lcPQ2N\nEEIIIfUM3QBBeFRzIBxCjBmguIVGiHELMWaA4pYHJXOEEEIIIXUYTbMSQgghhNQwmmYlhBBCCBEo\nSubqGao5EA4hxgxQ3EIjxLiFGDNAccuDkjlCCCGEkDqMauYIIYQQQmoY1cwRQgghhAgUJXP1DNUc\nCIcQYwYobqERYtxCjBmguOVByRwhhBBCSB1GNXOEEEIIITWMaubqkRUrViAjI6Omh1Gq0NBQfPHF\nF5Xe7h9//IEVK1ZIvf3du3fh4+MDGxsbODg4YOTIkXj37l2p269atQo2NjZwdHSEi4sLv/ybb76B\njY0NnJ2d0a5dO1y7dq3CY09KSoKLiwtatWqFnTt3Vnj/Bw8eYP369RXerzSfnkPBwcGYMWNGpbX/\nqcjISIwYMQJnzpzBiBEjABSeJyEhIdiyZQtCQkKK7ZOWlgZfX99y27527Rr8/PykGgdjDO3bt8fj\nx4/L3G748OFYvXq1VG3KYvXq1fj555+rrH1CCPkUJXO1zMqVK/H8+fMS1xUUFJS5b2RkJLp3714V\nw+JxHCd3Gx8+fCi2bOzYsZgyZYrUbaioqGDFihWIj4/HzZs3cf/+ffzyyy8lbrt//37s27cPV69e\nxc2bN3HixAl+XY8ePXD79m3ExMRg9uzZ+Oqrryocz/79++Hl5YVr165h0KBBFd7/3r17WLduXYX3\ni4yMRH5+frHln55DlfGalaWk9svr09DQEBEREeW23apVK2zfvl1iWWn1JYcPH4aFhQWMjY3LbLMy\njkdJx73I6NGjsXbt2jL/cyELqicSDiHGDFDc8qBk7v+JRCL8+OOPaNOmDczNzbF//35+3aVLl+Dr\n64vWrVujdevW+OeffwAAs2fP5hOIPXv2QEFBAS9evABQmCScOnWq1P7WrVsHW1tbuLi4wMnJCYmJ\niVi4cCHS0tIwYMAAuLi4ID4+HsHBwfjiiy/QtWtX2NnZ4dWrV6W2WZl/tN+/f4/p06fDwcEBzs7O\n6N+/P4DCqx///fcfBg4cCHt7e7Rr1w7Pnj0DANy6dQve3t5o1aoV7OzssHLlSr694cOHY/To0fD2\n9kabNm2K9ffx1aOoqCi0atUKLi4usLe3x65du4pt37x5czg5OfFxW1tb48GDByXGsnTpUoSEhEBD\nQwMA0LhxY35dz549oaCgAADw8PDgr+q8ffsWTk5OOHToEAAgIiICNjY2yM7Olmg7LCwMK1aswN69\ne+Hi4oLU1FQsXboUbdq0gaurKzw9PREbGwsAyMnJwRdffAE7Ozs4Oztj4MCBAAqvDt65cwcuLi74\n8ssvAQCJiYno0aMH2rRpA2dnZ4SGhvJ9ikQihISEICAgAPPnz5cYT0nnEAA8efIEPXv2hI2NDXr1\n6oW3b98CAMLDw+Hp6QlXV1c4Ojpi9+7dfFtisRgzZ85E+/btYW5ujtmzZ5d4fJWVlaGtrc3/CwBq\nampo0KAB1NTUoKmpWWyf+/fvQ09Pj39+7NgxuLq6wsnJCZ06dUJKSgqAwjc5Nzc3iX02btwIV1dX\nWFtb48KFC3wb69evl0imnzx5gv79+8PJyQlOTk746aef+HW3b99Gx44dYWlpCX9/f375jh074OHh\nAVdXV7i6ukoknKamppg9ezbc3d0xbtw4JCYmom3btnB2doaDgwOWLl0KoPA/Gt7e3hLvIYQQUqVY\nPVXR0DiOY6tXr2aMMXbhwgVmZGTEGGMsMzOTubi4sKdPnzLGGEtLS2PGxsYsKyuLnTp1inXr1o0x\nxtjXX3/NvLy82K5du9j79++Zrq4ue/v2ban9NWzYkKWnpzPGGHv//j3LyclhjDFmamrK4uLi+O2C\ngoKYiYkJ+/fff8uN4fTp02z48OHFlmdmZjJnZ+cSH35+fiW2FRwczPr378/y8vIYY4zvf/PmzUxH\nR4c9fvyYMcbYmDFj2Ny5cxljjL1+/Zrl5ubyP9va2rKEhATGGGP+/v7Mzc2Nj7Ok/mbMmMEYY6x3\n795s586d/LqsrKwy487JyWF2dnbs8OHDJa7X0dFhixYtYp6enqx169Zs/fr1ZcZcJCEhgZmYmLBL\nly4xMzMzFhMTU+7YGWMsIyOD//nkyZPMw8ODMcbY/v37WdeuXYvFFRkZyVq3bs0vz8vLY66urvyx\n+++//5ilpSVLTExkjBWeq0uWLCn1eJR0DrVs2ZK9evWKMcZYly5d+GOQmZnJ8vPzGWOMpaen8+c2\nY4yJxWI2cOBAxhhjr169Ynp6eiw5ObnUfivi3r17TE9PjzHG2LNnz1jjxo1ZfHw8Y4yxjRs3Mnd3\nd8ZY4TlddGzu3bvHOI5jf//9N2OMsbCwMObl5cUYYyw/P59paWnxMRaN/5dffuGfv3jxgjFWeC62\nb9+e5ebmsvfv3zM7Ozt28uRJxhiT+D1LSEhgxsbG/HNTU1P2zTff8M8nTZrEfvzxR/55ZmYm//O6\ndevYyJEjZT4+hBDhkSclU6zpZLI2KbpS4u7ujrS0NLx//x5RUVG4d++exPSlSCRCSkoKPD09ceXK\nFeTl5SEqKgpLly7F3r17YWRkBHt7e6iqqpbal6+vL4YNG4bPPvsMPXv2hJmZWYnbcRyHnj17olGj\nRiWuj4mJ4euU3rx5g5cvX/I1Yf3790dgYCC0tbVx48aNCh2Lv//+G8uWLYOiYuEp8nH/Xl5eMDIy\nAlB4NevkyZMAgOzsbIwbNw43b96ESCRCWloaYmNjYWVlBY7jMGDAAKipqZXaJ/v/wk9fX1/88MMP\nSElJQefOnUu8klfkw4cPGDhwIDp27IhevXqVuE1+fj4eP36MCxcuICMjA15eXrCyskL79u35bXbt\n2oWdO3fi3Llz/DIrKyvMnz8fnp6eWLlyJX8lsKyxA8DVq1exaNEiZGZmQiQS4e7duwAAZ2dnxMfH\nY8KECRCLxejZs2exfYHCesCEhAT+fASAvLw8xMfHw9LSEgAkriaVh+M4dOvWDVpaWgAKz++iK1/P\nnz/HiBEjkJycDEVFRbx8+RKJiYn8MS+qj9TS0oKNjQ2Sk5Nhbm4udd/SuHTpEpycnGBtbQ2g8Cru\n+PHji10FBYAGDRqgR48efBzTpk0DALx48QIFBQV8jG/evEF0dDTCw8P5fXV1dfnj0bdvXygrKwMA\nXF1dkZKSgk6dOiE5ORmBgYFIS0uDkpIS0tPT8fz5c+jr6wMAhg0bxrfXoUMHzJw5Ezk5OfDx8YGP\njw+/zsjICKmpqZV2jAghpCw0zfqRouSraNrtw4cPYIzB0dERN27c4B8PHjyAq6sr1NTU4OjoiB07\ndsDQ0BBisZj/A9KpU6cy+9q/fz9++OEHZGdnw8fHB8eOHSt126LpwZI4Ozvz49qwYQPatGnDPw8M\nDAQAZGVlwdnZGS4uLsUeZRWXf5pkfHqcgMLEtqgGbs6cOTA0NERMTAxiYmLQpk0bibqhsuL42OTJ\nk3H48GE0btwYEydOxLx580rcLj8/H0OGDIGuri4+//zzUtszMTHhp98aN26Mzp074/Lly/z6AwcO\nIDAwECdOnJCYggUKC/ANDAzw6NEjqcb+/v17DBgwAL/++itu3bqFo0ePIjc3FwBgZmaGO3fuoHPn\nzjh16hScnJz4dR9jjEFPT0/inEtNTUWfPn34bRo0aFChOgsVFRX+ZwUFBb7mKyAgAL6+vrh16xZu\n3LgBY2Njidfs49f64/1qioqKCh+3goJCifWXHyvtHC7teAwaNAgTJkzA7du3cf36dSgqKkocjwYN\nGvA/9+vXD+fPn4e5uTkWL16MoUOH8uuq4m56qicSDiHGDFDc8qBkrhyenp5ISkqSONhXrlzhf+7Y\nsSOCgoLQsWNHKCsrw8jICKGhoejYsWOpbebn5yMlJQVubm747rvv0KVLF8TExAAovAKSlZXFb1uR\nPwilbautrY2YmBiJ5KDo8WlxeZFevXphxYoVyMvLAwD8+++/5fb/6tUrGBsbQyQS4fbt2xJXuSoy\n9rt378LMzAxff/01Jk2aJHG8ixQUFGD48OFQVFTEhg0bymx78ODBOHr0KIDCq4fnzp2Ds7MzAODI\nkSOYNm0aTpw4ARMTE4n9Dhw4gAsXLuD27ds4cuRIqQn3x2N/9+4d8vPz+SL833//nV/35MkTcByH\nPn36YNmyZcjIyEBmZia0tLQkaiGtrKygrq4u8dokJCTg9evXZcZZpLxziDHGL3v16hWaN28OADh5\n8iSSk5NLja2qeHh4IDY2FomJiQCALVu2wNXVVerkHwD09PTAcRx/jBo0aABPT08sX76c30bac9jU\n1BQAsHHjxhKT7SIpKSnQ19eHv78/vv/+e4n/IDx+/BgtWrSQevyEECIPSub+36c3DxQ919HRwaFD\nhxASEgJnZ2fY2tpi/vz5/B+5jh074tGjR3zy1qlTJ7x8+bLMqcH8/HyMGDECjo6OcHZ2Rnp6OsaO\nHQsAmDRpEkaMGAFXV1fEx8eD4zipb2zgOA5NmzatcOwlmTVrFkxNTfkregEBAXwfH4/n4+eBgYFY\nv349nJycEBISgg4dOhQbX1ljL1q/atUq2Nvbw9XVFatXr8bChQuLbX/06FGEhYXh9u3baNWqFaZO\nnYqJEyfy611cXJCeng4AmDp1Kh49egR7e3u4u7tj6NCh/Os1cuRI5OXloX///vzVyszMTNy/fx+T\nJ0/G7t27oaOjg927d2Ps2LFIS0src+xaWlqYP38+3Nzc0Lp1azRo0IBfd/PmTXh6esLZ2Rnu7u6Y\nM2cOmjRpAicnJ1hZWcHBwQFffvklFBUVcfjwYezatQtOTk6wt7fHhAkT+MS6qD2xWFzisSzvHPr4\n+eLFizF9+nS4uLhg7969xaaSK/OmmsOHD2PMmDHF2m7cuDG2bduGwYMHw8nJCTt27JBIZD8d+8dx\nF60TiUTw9vZGdHQ0v2779u24cOECfxPPpk2byo1rxYoV6Nu3L1q1aoV79+5J3KTxqT179sDR0RGu\nrq6YNGkSfv31V35dVFRUmf+hk0Vpr3d9J8S4hRgzQHHLo15/aHBQUBDEYrFgTxBCaqvLly/D39+f\nv9u2Mvz11184ePAgNm/eXGltyiI3Nxe2traIi4srs26WEEKAwmnWyMhIhISE0IcGlyQ4OFhwiRzV\nHAhHXY35yJEjGDRoEObOnSvT/qXF3bdvX6SkpODJkydyjE5+GzZswLhx4yo9kaurr7e8hBi3EGMG\nhBs3UJivyIPuZq1CH99p+rGJEydi5MiRNTAiQmper169Sr3zWF5nz56tknYr4ptvvqnpIRBCBKZe\nT7PW09AIIYQQUs/Qd7MSQgghhAgUJXP1jFBrDoQYtxBjBihuoRFi3EKMGaC45UHJHCGEEEJIHUY1\nc4QQQgghNYxq5gghhBBCBKrUjyb5+HsGy6KiolLu1ymR6hMZGSm4z9YDhBm3EGMGKG6hEWLcQowZ\noLjlUWoyt2fPHsyZM6fUS35FlwOXLl1KyRwhhBBCSA0ptWbO3NwcKSkp5TZgZWXFf0F2bUI1c4QQ\nQgipK+TJW+gGCEIIIYSQGlbtN0Ckpqbi/v37MnVIqhZ9To9wCDFmgOIWGiHGLcSYAYpbHlIlcwMH\nDkRUVBQAYPPmzbCzs4OtrS3VyhFCCCGE1DCpplkbN26MJ0+eQFlZGfb29vjjjz+gra2NPn36IDk5\nuTrGWWE0zUoIIYSQukKevKXUu1k/lpeXB2VlZTx58gSZmZnw8vICADx79kymTgkhhBBCSOWQaprV\nyckJP/74I+bPn4+ePXsCAB4/foyGDRtW6eBIxVHNgXAIMWaA4hYaIcYtxJgBilseUiVzGzduxM2b\nN/Hu3TssWLAAABAdHY0hQ4bIPQBCCCGEECI7+mgSQgghhJAaVuU1cwBw7tw53LhxA69fv+Y75DgO\nc+bMkaljQgghhBAiP6mmWSdOnIgBAwbg7NmzSEhIQHx8PP8vqV2o5kA4hBgzQHELjRDjFmLMAMUt\nD6muzG3fvh1xcXEwNDSUu0NCCCGEEFJ5pKqZc3R0REREBPT09KpjTJWCauYIIYQQUldU+XezXrly\nBYsWLcLgwYNhYGAgsc7b21umjqsaJXOEEEIIqSuq/LtZr127hn/++QcBAQEYMmSIxIPULlRzIBxC\njBmguIVGiHELMWaA4paHVDVzc+fOxZEjR9C5c2e5OySEEEIIIZVHqmlWExMTJCcnQ1lZuTrGVClo\nmpUQQgghdUWVT7POnz8fU6ZMwdOnT1FQUCDxqM2Cg4MFe9mWEEIIIbVfZGQkgoOD5WpDqmRu5MiR\nWLt2LYyMjKCoqMg/lJSU5Oq8qgUHB0MsFtf0MKqVUJNXIcYtxJgBiltohBi3EGMGhBs3ALmTOalq\n5lJTU+XqhBBCCCGEVA36blZCCCGEkBpWJTVz8+bNk6qBoKAgmTomhBBCCCHyKzWZW758OVJTU8t8\npKSkYOXKldU5XlIOodYcCDFuIcYMUNxCI8S4hRgzQHHLo9SauZycHFhYWJTbgIqKityDIIQQQggh\nsqGaOUIIIYSQGlblnzNHCCGEEEJqJ0rm6hmqORAOIcYMUNxCI8S4hRgzQHHLg5I5QgghhJA6jGrm\nCCGEEEJqWJXXzD1//hyvX78GAHz48AGbNm3Cli1bav13sxJCCCGE1HdSJXO9evVCcnIyAGDu3LlY\nunQpli9fjm+//bZKB0cqjmoOhEOIMQMUt9AIMW4hxgxQ3PKQ6rtZk5KS4OzsDADYvn07oqKioKmp\nCVtbW6xYsULuQRBCCCGEENlIVTOnp6eHx48fIykpCQMHDkRcXBzy8/PRsGFDvHnzpjrGWWFUM0cI\nIYSQukKevEWqK3PdunXDl19+iX///RdfffUVAODOnTswNjaWqVNCCCGEEFI5pKqZ27BhA3r27InR\no0djzpw5AIB///0XwcHBVTk2IgOqORAOIcYMUNxCI8S4hRgzQHHLQ6orc6qqqhg7dqzEMrFYLHfn\nhBBCCCFEPqXWzA0dOlRyQ44DADDG+J8BYOvWrVU4PNlRzRwhhBBC6ooq+Zw5c3NzWFhYwMLCAtra\n2vjrr7+Qn5+PZs2aIT8/HwcPHoS2trbMgyaEEEIIIfIrNZkLDg5GUFAQgoKCkJiYiL///hthYWFY\ntGgRwsLC8PfffyMhIaE6x0qkQDUHwiHEmAGKW2iEGLcQYwYobnlIdQPExYsX4eHhIbHM3d0d0dHR\ncg+AEEIIIYTITqrPmevQoQPc3NywYMECqKmpIScnB0FBQbh06RLOnj1bHeOsMKqZI4QQQkhdUeXf\nzRoaGooLFy5AS0sL+vr6aNiwIc6fP48tW7bI1CkhhBBCCKkcUiVzZmZmiI6ORkpKCg4dOoTk5GRE\nR0fDzMysqsdHKohqDoRDiDEDFLfQCDFuIcYMUNzykCqZK6Kqqgp9fX3k5+cjNTUVqampcg+AEEII\nIbjPkOQAACAASURBVITITqqauWPHjmHUqFF4+vSp5M4ch/z8/CobnDyoZo4QQgghdUWV18yNHz8e\n8+bNw5s3b1BQUMA/amsiRwghhBAiFFIlc1lZWRg7dizU1dWrejxETlRzIBxCjBmguIVGiHELMWaA\n4paHVMncqFGjsGnTJrk7I4QQQgghlUuqmrl27drh8uXLaN68OZo0afK/nTmOPmeOEEIIIURO8uQt\nUiVzoaGhpXbs7+8vU8dVjZI5QgghhNQVVZ7M1UVCTeYiIyMhFotrehjVTohxCzFmgOIWGiHGLcSY\nAYq7yu9mZYxh06ZN8PHxgaWlJXx9fbFp0yZBJkuEEEIIIbWJVFfmFi5ciK1bt2LatGkwMTHBw4cP\nsXz5cgwZMgSBgYHVMc4KE+qVOUIIIYTUPVU+zWpqaoozZ86gefPm/LIHDx6gffv2ePjwoUwdVzVK\n5gghhBBSV1T5NGtOTg709PQklunq6uLdu3cydUqqDn1Oj3AIMWaA4hYaIcYtxJgBilseUiVz3bp1\ng5+fHxISEvD27VvEx8dj2LBh6Nq1q9wDIIQQQgghspNqmvXVq1eYOHEidu/ejby8PCgpKeHLL7/E\nqlWroK2tXR3jrDCO4xAUFASxWCzIu2MIIYQQUvtFRkYiMjISISEh1fPRJPn5+Xjx4gX09PSgoKAg\nU4fVhWrmCCGEEFJXVHnN3JYtWxAbGwsFBQUYGBhAQUEBsbGx2LZtm0ydkqpDNQfCIcSYAYpbaIQY\ntxBjBihueUiVzM2bNw/NmjWTWGZsbIy5c+fKPQBCCCGEECI7qaZZdXR08OLFC4mp1Q8fPkBXVxev\nXr2q0gHKiqZZCSGEEFJXVPk0q42NDfbt2yex7MCBA7CxsZGpU0IIIYQQUjmkSuaWLFmCMWPGoH//\n/pgxYwb69euHUaNG4Zdffqnq8ZEKopoD4RBizADFLTRCjFuIMQMUtzykSubatWuHW7duoXXr1sjJ\nyUGbNm0QFxeHdu3ayT0AQgghhBAiuwp/NMmzZ89gaGhYlWOqFFQzRwghhJC6ospr5jIzMzF48GCo\nqanBwsICAHDo0CEEBgbK1CkhhBBCCKkcUiVz48aNg5aWFh48eAAVFRUAQNu2bbFr164qHRypOKo5\nEA4hxgxQ3EIjxLiFGDNAcctDUZqNwsPD8fTpUygpKfHLGjdujOfPn8s9AEIIIYQQIjupauYsLCxw\n9uxZGBoaQkdHB5mZmXj48CG6dOmChISE6hhnhVHNHCGEEELqiiqvmRs9ejQGDBiAiIgIFBQUIDo6\nGv7+/hg7dqxMnRJCCCGEkMohVTL33Xff4auvvsKECROQl5eHESNGoE+fPpgyZUpVj49UENUcCIcQ\nYwYobqERYtxCjBmguOUhVc0cx3GYPHkyJk+eLHeHhBBCCCGk8khVMxcREQFTU1O0aNECT58+xXff\nfQcFBQX8+OOPaNKkSXWMs8KoZo4QQgghdUWV18yNHz8eioqFF/G+/fZbfPjwARzH4euvv5apU0II\nIYQQUjmkSubS0tJgYmKCvLw8HD9+HH/88QfWrl2LCxcuVPX4SAVRzYFwCDFmgOIWGiHGLcSYAYpb\nHlLVzGlpaSE9PR1xcXGws7ODpqYmcnNzkZeXJ/cACCGEEEKI7KSqmfvpp5+wevVq5ObmYsWKFRg0\naBAiIiIwe/ZsXLp0qTrGWWFUM0cIIYSQukKevEWqZA4AEhMToaCgwH836927d5GbmwsHBweZOq5q\nlMwRQgghpK6o8hsgAMDKyopP5ADA0tKy1iZyQkY1B8IhxJgBiltohBi3EGMGKG55lFozZ21tzX9V\nV7NmzUrchuM4PHz4UO5BEEIIIYQQ2ZQ6zXru3Dm0b98eQNlZo1gsropxyY2mWQkhhBBSV1RLzVxd\nQ8kcIYQQQuoKefKWUqdZ582bV2rDRcs5jsP8+fNl6phUjcjIyFp7tbQqCTFuIcYMUNxCI8S4hRgz\nQHHLo9Rk7tH/tXfvcVHX+R7H38MAggKKl7wlUuGtbdPatItJGHZxsy09283ElFbbMk83206Zt6x1\nO2uubbV1slpXTSqrfXS0WtuAUfOYZl7a4x0NvKYliKCJMDPnDw6jCNgwP2aGH9/X8/GYh/BjZr6f\ntzPAh9/vM7/Zs0cOh6POG1Y1cwAAAAgfDrMCAACEWVAOs+7atcuvOzj//PMDWhgAAADW1XmeuZSU\nlJ+8dOvWLZS1wg+cp8ccJmaWyG0aE3ObmFkitxV17pnzeDyW7xwAAADBxcwcAABAmAVlZu6GG27Q\n0qVLJcl38uDaFl6+fHlACwMAAMC6Opu5kSNH+j6+9957a70OpyZpfDhPjzlMzCyR2zQm5jYxs0Ru\nK+ps5u6++27fx6NGjbK0CAAAAILD75m55cuXa/369Tp27JikUycNfuqpp4JaYKCYmQMAAHYRlJm5\n040fP17vvfeeBgwYoNjY2IAWAgAAQMOr8zxzp1uwYIHWr1+v999/X/Pnz692QePCeXrMYWJmidym\nMTG3iZklclvhVzPXpUsXRUdHW14MAAAADcuvmbmvvvpKv//97zV8+HC1b9++2tdSU1ODVpwVzMwB\nAAC7CPrM3Ndff61PPvlEK1asqDEzt2fPnoAWBgAAgHV+HWadOHGilixZoh9++EF79uypdkHjwsyB\nOUzMLJHbNCbmNjGzRG4r/GrmWrRooWuuucbyYgAAAGhYfs3MzZ07V2vWrNGkSZNqzMxFRPjVD4Yc\nM3MAAMAurPQtfjVzdTVsDodDbrc7oIWDjWYOAADYhZW+xa/dart27ar1snPnzoAWRfAwc2AOEzNL\n5DaNiblNzCyR2wq/Xs2anJxseSEAAAA0PL/fm9VuOMwKAADsIujnmUPjt21bgT7/fKfKyyMUFeXR\noEEXqEePruEuCwAABBl75pqAbdsK9PrrefJ607V3r0tJSWkqL8/W8OEp6tGjq5xOKSJCvn+rPnY4\nwl25dVVN7JYt36hXr4uNaGJNzCyZm7uKy+VSWlpauMsIOZNym/ocNzV3larnOHvmDPf55ztVWJiu\nb7+VjhyRDh2SpHTl5eWob9+6vyEcDtVo9IL9b0PeV15egd5+O08xMekqKYnQoUNp+utfs3XPPVKP\nHl1V1/dEMLcHe81t2wq0cGGeoqPTVVQUob170/Taa9m6804pJaWrPB75Ll6vQvp5MNf47rsCffVV\nnpzOytzbtqXpgw+y1a+f1KFD1xrPMSuXhrifhqrF4aj+i27TJnP2uteWu3v3ytxVz40zL/XZ3hD3\n0ZD3XVBQoKVL8xQZma7vvovQ0aNpWrkyWzfcIHXt2lUOhwK6SIHdzsqlPmvu2FH5c7xZs3QVF0fo\n4ME0zZ2brVGjZMTzvKH4tWdu165dmjhxojZs2KDS0tJTN3Y4tHv37qAWGCiT9szNnu3Shg1pys+v\nvj0mxqUrrkgLR0khsWZNjo4fv7bG9hYtctS3b83tTYGJmSVzcx8+XKCNG/MUFZXu21ZRka1LL01R\nu3ah+UUXjh+j339foHXrKhubqvUrKrLVu3eK2rZtmr/gTX2On5k7KUk6/3zpnHNy9MADTTd3bYK+\nZ2748OFKSUnRrFmzarw3K8IvKsqj6GgpPr76X3vNm3vUsqXkdlf+5Xfmv3bn8dR+Zh23u3GeyLoh\nmJhZMjf3zp07FRGRrtNP5+lwpGv79hy1bNk0mxpJ2r69MvfpP6ecznR9+21Ok23mTH2O15X75Mmm\nnbuh+dXMbd68WStXrpTT6Qx2PQjAoEEXaO/ebHXqlK78fJeSk9NUVpatUaNS1KNH7bc5/XBWVXNX\nW8MX7H+t3DY62qOTJys/PnLEpVat0iRJTqdHtZ3nuq4ZwWBub+j7jo31+H6xFxa61KZNmhwOKS7O\no9ataz9MF8rPg7XGG294dPhw5ccFBZVzoV6v1LatR5mZNQ/31nY5/TkX6CX093HqiXz6c9ykX/Bn\n5q7tUF3V88TK9oa4Dyv3XVTkUXFx5ccHDrjUsWPlc7xVK4+uuebUHtLaDtMG8xLsNWNiPKqoqFyn\nsNCliIjKxzo6ugnscfBTQ8yF+tXMpaamav369brsssssLYbg6NGjq0aNkrKzc/TDD9/onHM8Sk9P\nOeu8QdW8nNMpRUWFrtaGtG3bBZo7N1vNmqUrP19KTtZPNrF2Z2JmSRoypDJ3dHS6IiOl6OjK3EOG\npCgxMdzVBYfXK738skfff181UyV1/f9v6XbtPPrtb+t3f3X9oRAK9V371Vcrc0uVuZOTK+/jnHM8\nGjeuwctrFDp1OvW97fVWPtYmfG//6le1/0xLT08Jd2m24tfM3Lhx4/Tuu+9q2LBh1d6b1eFw6Jln\nnglqgYEyaWbOZNu2FSg7e6dOnoxQdLRH6elNfzjcxMySmbm3bSvQ3LmVw+FVTv2Cb7rZTc5t2nNc\nMjf3mYL+3qyjRo3yLVTF6/XK4XDor3/9a0ALBxvNHICmwNRfdKbmhrmC3szZkanNnEnnZDqdiblN\nzCyR2zQm5jYxs0TuoLyaNT8/X8nJyZIqT01Sl/PPPz+ghQEAAGBdnXvm4uPjVVJSIkmKqO2lgarc\n++U+/TXzjYipe+YAAID9GHWY9eDBgxo2bJiio6MVHR2thQsXqk2bNjWuRzMHAADswkrfYruTFbVr\n104rV65Ubm6uhg8frjlz5oS7pEbF5XKFu4SwMDG3iZklcpvGxNwmZpbIbYXtmrnTD/kePXpUiU31\nJFMAAAB+sN1hVknauHGjxo4dqyNHjuirr75SQkJCjetwmBUAANiFbQ6zvvzyy7rssssUExOj0aNH\nV/taYWGhhg4dqri4OCUnJysrK8v3tT/96U8aOHCgXnjhBUlS7969tXr1aj377LOaPn16KCMAAAA0\nKvVu5jweT7VLfXTu3FmTJk1SZmZmja+NGzdOMTExOnTokN5++23df//92rx5syTpkUceUW5urh57\n7DGVl5f7bpOQkKCysrL6RmjSmDkwh4mZJXKbxsTcJmaWyG2FX+/N+vXXX+vBBx/Uxo0bdeLECd/2\n+p6aZOjQoZKktWvXau/evb7tx44d04cffqhNmzapefPm6t+/v2655RbNnz9fM2bMqHYfGzZs0IQJ\nE+R0OhUVFaU333zT7/UBAACaGr+auXvuuUe/+tWv9Oabb6p58+aWFz3zmPD27dsVGRmplJRTb6zb\nu3fvWrvVvn37atmyZX6tM2rUKN+Jj1u1aqU+ffr4zi5ddd983jQ+r9rWWOoJ1eenZ28M9YTi87S0\ntEZVTyg/r9JY6uHxDs7nVdsaSz18HpzPqz7Oz8/X3LlzZYVfL4BISEhQcXFxtfdmtWLSpEnau3ev\n731dV6xYodtvv10HDhzwXWfOnDlauHChcnNzA1qDF0AAAAC7CPoLIIYOHaqlS5cGtEBtziw2Li5O\nR48erbatuLhY8fHxDbamKc78C94UJuY2MbNEbtOYmNvEzBK5rfDrMOuPP/6ooUOHasCAAWrfvr1v\nu8Ph0Lx58+q96Jl7+Lp3766Kigrl5eX5DrVu3LhRF110Ub3vGwAAwCR+HWadOnVq7Td2ODRlyhS/\nF3O73SovL9e0adO0b98+zZkzR5GRkXI6nbrrrrvkcDj0xhtvaN26dRoyZIhWrVqlXr16+X3/Z9bG\nYVYAAGAHtnlv1qlTp+qZZ56psW3y5MkqKipSZmam/vnPf6pt27b6wx/+oDvvvDPgtWjmAACAXYTk\npMG5ubkaPXq0rr/+emVmZionJ6fei02dOrXGeeomT54sSUpMTNTf//53lZaWKj8/31IjZzJmDsxh\nYmaJ3KYxMbeJmSVyW+FXM/fGG2/ojjvuUMeOHTVs2DB16NBBw4cP1+uvv265AAAAAATOr8Os3bp1\n0/vvv6/evXv7tn3zzTcaNmyY8vLyglpgoDjMCgAA7CLoM3Nt2rTRgQMHFB0d7dtWVlamTp066fDh\nwwEtHGw0cwAAwC6CPjPXv39/Pfroozp27JgkqbS0VBMmTNBVV10V0KKhMnXqVOOOwZuWt4qJuU3M\nLJHbNCbmNjGzZG7u2bNn13nWEH/51cy99tpr+uabb9SyZUudc845atWqlTZu3KjXXnvN0uLBNnXq\n1GpvjwIAANCY9OnTx3IzV69Tk+zZs0f79+9Xp06d1KVLF0sLBxuHWQEAgF0EZWbO6/X63qnB4/HU\neQcREX6f3SSkaOYAAIBdBGVmLiEhwfdxZGRkrZeoqKiAFkXwmDpzYGJuEzNL5DaNiblNzCyR24o6\n35t106ZNvo937dpleSEAAAA0PL9m5mbOnKkJEybU2D5r1iw9+uijQSnMKg6zAgAAuwj6eebi4+NV\nUlJSY3tiYqKKiooCWjjYaOYAAIBdBO08czk5OcrOzpbb7VZOTk61y5w5c6rN1aFxYObAHCZmlsht\nGhNzm5hZIrcVdc7MSVJmZqYcDofKysp07733+rY7HA61b99eL730kuUCAAAAEDi/DrNmZGRo/vz5\noainwXCYFQAA2EXQ387Lbo1cFRPfzgsAANiHy+UKzdt5FRcX65FHHtGll16qrl27qkuXLurSpYuS\nkpIsLR5sJr6dl6nNq4m5Tcwskds0JuY2MbNkbm5JoWnmxo0bp3Xr1mny5MkqLCzUSy+9pKSkJD38\n8MOWFgcAAIA1fs3MtWvXTlu2bFHbtm3VsmVLFRcXa9++fbr55pu1bt26UNRZb8zMAQAAuwj6zJzX\n61XLli0lVZ5z7siRI+rYsaN27NgR0KIAAABoGH41cxdffLGWL18uSbr66qs1btw4/fa3v1WPHj2C\nWhzqz9SZAxNzm5hZIrdpTMxtYmaJ3Fb41czNmTNHycnJkqQXX3xRMTExKi4u1rx58ywXAAAAgMD5\nNTO3evVqXX755TW2r1mzRv369QtKYVYxMwcAAOwibO/N2rp1axUWFga0cLDRzAEAALsI2gsgPB6P\n3G637+PTLzt27FBk5FnfDQxhwMyBOUzMLJHbNCbmNjGzRG4rztqNnd6sndm4RUREaOLEiZYLAAAA\nQODOepg1Pz9fkpSamqoVK1b4dv85HA61a9dOzZs3D0mRgeAwKwAAsIugz8zZEc0cAACwCyt9i19D\nbxkZGbUuKqlRn56k6r1ZTXp/VpfLZVTeKibmNjGzRG7TmJjbxMySublnz56tI0eOWLoPv5q5Cy64\noFrH+N133+mDDz7Q3XffbWnxYLP6xrUAAADB1KdPH6WlpWnatGkB30fAh1nXrl2rqVOnasmSJQEv\nHkwcZgUAAHYRlpm5iooKJSYm1nr+ucaAZg4AANhF0M4zVyU7O1s5OTm+y+LFi3XPPffoZz/7WUCL\nIng4T485TMwskds0JuY2MbNEbiv8mpm79957fS94kKQWLVqoT58+ysrKslwAAAAAAsepSQAAAMIs\n6KcmkaQjR47o448/1v79+9WpUyf98pe/VGJiYkCLAgAAoGH4NTOXk5Oj5ORk/fnPf9ZXX32lP//5\nz0pOTtbnn38e7PpQT8wcmMPEzBK5TWNibhMzS+S2wq89c+PGjdPrr7+u22+/3bdt0aJFevDBB7V1\n61bLRQAAACAwfs3MtWrVSocPH5bT6fRtKy8vV7t27SyftThYmJkDAAB2EfRTk2RkZOjll1+utu3V\nV1+t9W2+AAAAEDp+NXPr1q3ThAkT1LlzZ/Xr10+dO3fWY489pvXr12vAgAEaMGCAUlNTg10r/MDM\ngTlMzCyR2zQm5jYxs0RuK/yamRszZozGjBlz1uucfh46AAAAhAbnmQMAAAizkJxnbvny5Vq/fr2O\nHTsmSfJ6vXI4HHrqqacCWjgUpk6dqrS0NKWlpYW7FAAAgBpcLpflQ61+zcyNHz9et912m1asWKEt\nW7Zoy5Yt2rp1q7Zs2WJp8WCrauZMwsyBOUzMLJHbNCbmNjGzZG5uqbJfscKvPXMLFizQpk2b1KlT\nJ0uLAQAAoGH5NTN38cUXKycnR23btg1FTQ2CmTkAAGAXVvoWv5q5r776Sr///e81fPhwtW/fvtrX\nGuspSWjmAACAXQT9pMFff/21PvnkE91///26++67q13QuJg6c2BibhMzS+Q2jYm5TcwskdsKv2bm\nJk6cqCVLlui6666zvCAAAAAajl+HWZOSkpSXl6fo6OhQ1NQgOMwKAADsIuiHWZ955hk9/PDDOnDg\ngDweT7ULAAAAwsevZi4zM1OvvfaaOnfurMjISN8lKioq2PWhnpg5MIeJmSVym8bE3CZmlshthV8z\nc7t27bK8EAAAABpevd6b1ePx6ODBg2rfvr0iIvzaqRc2zMwBAAC7CPrM3NGjRzVy5EjFxMSoc+fO\niomJ0ciRI1VcXBzQogAAAGgYfr8367Fjx/S///u/On78uO/f8ePHB7s+1BMzB+YwMbNEbtOYmNvE\nzBK5rfBrZu4f//iHdu3apRYtWkiSunfvrrlz5+r888+3XAAAAAAC59fMXHJyslwul5KTk33b8vPz\nlZqaqt27dwezvoAxMwcAAOzCSt/i15653/zmN7ruuuv02GOPqWvXrsrPz9ef/vQnjRkzJqBFAQAA\n0DD8mpl76qmn9OSTT2rRokV67LHH9MEHH+iJJ57Q008/Hez6UE/MHJjDxMwSuU1jYm4TM0vktsKv\nPXMRERHKzMxUZmam5QVDaerUqUpLS1NaWlq4SwEAAKhhw4YNlhs6v2bmxo8fr7vuuktXXXWVb9v/\n/M//6L333tPs2bMtFRAszMwBAAC7sNK3+NXMtW3bVvv27VOzZs18206cOKEuXbro+++/D2jhYKOZ\nAwAAdhH0kwZHRETI4/FU2+bxeGiWGiFmDsxhYmaJ3KYxMbeJmSVyW+FXM3f11Vfr6aef9jV0brdb\nU6ZM0YABAywXAAAAgMD5dZh1z549GjJkiA4cOKCuXbtq9+7d6tixoxYvXqwuXbqEos56M+0w67a8\nbfr8689V7i1XlCNKg34xSD1SeoS7LAAA4Iegz8xJlXvj1qxZoz179qhLly66/PLLFRHh1469sDCp\nmduWt01vfP6GnClOOR1OOSOcKs8r16iBo2joAMAm+KPcbCFp5uzGpGbulXdf0bqYddpZtFNHth5R\nq56t5JBDLfe31DUDr1G0M1rNIpsp2hld+bHz1Mdn+9rp26MiouRwOMIdtU4ul8u4U9CYlrnqF92W\nTVvU62e9jPtFZ9rjXaUp5PZ6vfJ4PSr3lKvCU1HrZfvO7Xr/i/cVmRKpvd/sVZeLu8i9y607B9yp\nnik9q/0sjnJGKcLReHemBKopPNaBqMod9HeAQONW7i2X2+uuts0rr054Tqi4rLhB1nDI4VcD6G9z\nGO2MbpAfRqf/gt90cJMRv+CbemaP11PjsjVvq95e9raiUqJ0OOGw9rTZozmfz9GIihHqmdJTEY6I\napempKk/3nVpyNxVzVRdjVQoLl6d/Zf0mi/W6Pi5x6XvpSPFR3T0+6NSvPTHJX9U36v71rh+ZERk\ntQYv2hmtKGdUjW2nb69t25m3b+x/uKN27JlrAl559xV9HfO19h3dJ7fXLbfHLa+8arG3Ra0/BBqL\n03+wBNIc7i7YrfdXvq/m3Zv7fviU7SjTiGtGqPsF3Wtd82w/UH/q+RLobRtyzR07d2jh8oVq1q2Z\nvF6vvPKqbHuZbhtwmy4474JaGyG3113r9lqv66nHdYN0v7X9f/l+0Z2htue4Q44azZ0zwllzm6Pm\ntvpct7br1ee6/qy/c9dOvbPiHTXrduq0UGU7yjQ8dbi6XdDN9/zxyut7PlQ9r8728Zm38/c+QnW7\nb7/9Vh+v/liRKZG+RqxsR5nSf5Gujl061toslbvr3uvlTzMVbl9+8aVOnHuixvaYvTG64uorQlaH\nQw5fU9dQDWLVNqfDWWujyOHlSkE9zOr1evXtt98qKSlJkZH22ZFnUjO3LW+b5ubOrfYD/8ftP+qu\n1LuUnJyssooynXSf1En3SZW5T3180n2yzq+dvv2k+6TKPeVhTFi7+vyCbypMzCw1nl90oWbq490U\nczsdTkVGRNZ5ceW4VHpuqSIcEXI4HHJ73HJ73Wqxt4X6X9Nf5e7yRv3z2B9VR3hOb/B+2P+DVv9r\ntZp1ayanw6nWsa3V6kArI2e+g36Y9aKLLlJpaWlACyD4eqT00CiNUva6bG3+38268KILlX5teoN+\nI3i8np9sAOvTHJ50n7T8l7JHp859WDUrKEluueu6ie2ZkLm2PWuxzlh5nB5FOCJUuKVQiT0T5ZVX\nsc5YNXM2q7a3sCkx4fGuTTByV+1xOltD1dCXqIjK9ar2tp7NVfFX+f4oz9+Qr+Q+ySrbUaZRt9Zs\narxer8o95TUaPN/H/789kG0VnoqA/49/ildelbnLVOYu821b801l415yvERHth7Rz/v9XO27tVf2\numxjmrmGmBX8yWbO4XDokksu0bZt29SrVy9LiyF4eqT0UI+UHnKdE5wB0ghHhGIiYxQTGSM1++nr\n/5SqH0ZWmsOW0S3ldXrl9rp9f/VKUrOIZpV1/j+Hzj7/cbb5kLPd9qfmSgK97dluFxcZ5/uujXZG\nKzYyVg6HQ/HR8eoQ1+Gsh+/8PaRY53VDdL+1HobpcGrvc/4P+Uru8v+/6G6r/RddbYeAazvUa+V6\nwbjPM6/XwtlCHmflCdqdDqeiIqIkSbHOWMVHx/ueSw45fP9vtX1c9ZwK5LrhWOO7lt/paPzRygas\neZQ6teykCEeEWp9orV92+2VAjVVjn6U8/Y/yHwp/0DmHzlH6wNr/KHc4Ts0wt1CLBq3D4/XUaPJq\naxoDaSRr+2Pr9MZdkpwRTknSSc/JBs3V1Pk1M/f0009rwYIFGjVqlLp06eLbFehwOJSZmRmKOuvN\npMOspqrt8HLZjrImvXvexMxVtuVtU/a6bJ30nFR0RLTSL23Yvc+NkamPt6m5mzq3x12jGXzjgzf0\nffvvfYeV46LjFBcdp3MOnaMHbn8g3CWHVNBPTVK1p6e2v5hzc3MDWjjYaObMYOoveNMym8zUJdjE\nlwAAGgFJREFUx9vU3KahcT+F88zVwtRmzvTz9JjExMwSuU1jYm7TMlc17r6Zb8Ma95CeZ+7w4cP6\n+OOP9d133+l3v/ud9u3bJ6/Xq3PPPTeghQEAAII9820Cv/bMLVu2TP/2b/+myy67TCtXrlRJSYlc\nLpdeeOEFLV68OBR11pupe+YAAID9BP0wa58+fTRz5kwNGjRIiYmJKioq0okTJ5SUlKRDhw4FtHCw\n0cwBAAC7sNK3+PVa7YKCAg0aNKjatqioKLndTftcR3bkcrnCXUJYmJjbxMwSuU1jYm4TM0vktsKv\nZq5Xr176xz/+UW1bdna2fv7zn1suAAAAAIHz6zDrl19+qSFDhuiXv/ylFi1apIyMDC1evFgfffSR\n+vXrF4o6683hcGjKlClKS0tjoBIAADRKLpdLLpdL06ZNC/6pSfbt26cFCxaooKBASUlJGjFiRKN+\nJSszcwAAwC6CPjMnSZ07d9bjjz+uqVOn6oknnmjUjZzJmDkwh4mZJXKbxsTcJmaWyG2FX81cUVGR\nMjIyFBsbqw4dOigmJkYjRoxQYWGh5QIAAAAQOL8Os956662KjIzU9OnTlZSUpN27d2vy5Mk6efKk\nPvroo1DUWW8cZgUAAHYR9PPMtWzZUgcOHFDz5s19244fP66OHTuquLg4oIWDjWYOAADYRdBn5nr2\n7Kn8/Pxq2woKCtSzZ8+AFkXwMHNgDhMzS+Q2jYm5TcwskdsKv96b9dprr9X111+vkSNHqkuXLtq9\ne7cWLFigjIwMvfXWW/J6vXI4HMrMzLRcEAAAAPzn12HWqvO0ORwO37aqBu50ubm5DVudBRxmBQAA\ndhH0mTk7opkDAAB2EZLzzMEemDkwh4mZJXKbxsTcJmaWyG0FzRwAAICNcZgVAAAgzDjMCgAAYCi/\nm7ktW7bomWee0bhx4yRJW7du1TfffBO0whAYZg7MYWJmidymMTG3iZklclvhVzO3aNEipaamat++\nfZo3b54kqaSkRI8++qjlAgAAABA4v2bmevbsqXfeeUd9+vRRYmKiioqKVF5ero4dO+qHH34IRZ31\nxswcAACwi6DPzH3//fe6+OKLa944gpE7AACAcPKrG7v00ks1f/78atveffdd9evXLyhFIXDMHJjD\nxMwSuU1jYm4TM0vktsKv92Z96aWXdN111+nNN9/U8ePHdf3112v79u367LPPLBcAAACAwPl9nrlj\nx45pyZIlKigoUFJSkm666SbFx8cHu76AMTMHAADsgvdmrQXNHAAAsIugvwCioKBAmZmZuuSSS9St\nWzffpXv37gEtiuBh5sAcJmaWyG0aE3ObmFkitxV+zczddttt6tWrl6ZPn66YmBjLiwIAAKBh+HWY\ntWXLliosLJTT6QxFTQ2Cw6wAAMAugn6YdciQIVq2bFlACwAAACB4/GrmXnzxRd1333266aabNHr0\naN8lMzMz2PWhnpg5MIeJmSVym8bE3CZmlshthV8zc5mZmYqOjlavXr0UExPj2xXocDgsFwAAAIDA\n+TUzFx8fr3379ikhISEUNTUIh8OhKVOmKC0tTWlpaeEuBwAAoAaXyyWXy6Vp06YF9zxz/fv314IF\nC3TeeecFtEg48AIIAABgF0F/AcS1116rG264QTNmzNBbb72lt956S2+++abeeuutgBZF8DBzYA4T\nM0vkNo2JuU3MLJHbCr9m5lasWKFOnTrV+l6svAgCAAAgfHg7LwAAgDCz0rfUuWfu9FerejyeOu8g\nIsKvI7UAAAAIgjo7sdNfuRoZGVnrJSoqKiRFwn/MHJjDxMwSuU1jYm4TM0vktqLOPXObNm3yfbxr\n1y7LCwEAAKDh+TUzN3PmTE2YMKHG9lmzZunRRx8NSmFWMTMHAADswkrf4vdJg0tKSmpsT0xMVFFR\nUUALBxvNHAAAsIugnWcuJydH2dnZcrvdysnJqXaZM2eOrd4RwhTMHJjDxMwSuU1jYm4TM0vktuKs\n55nLzMyUw+FQWVmZ7r33Xt92h8Oh9u3b66WXXrJcAAAAAALn12HWjIwMzZ8/PxT1NBgOswIAALsI\n+sycHdHMAQAAuwj6e7PCPpg5MIeJmSVym8bE3CZmlshtBc0cAACAjXGYFQAAIMw4zAoAAGAomrkm\nhpkDc5iYWSK3aUzMbWJmidxW0MwBAADYGDNzAAAAYcbMHAAAgKFo5poYZg7MYWJmidymMTG3iZkl\ncltBMwcAAGBjzMwBAACEGTNzAAAAhqKZa2KYOTCHiZklcpvGxNwmZpbIbQXNHAAAgI0xMwcAABBm\nzMwBAAAYimauiWHmwBwmZpbIbRoTc5uYWSK3FTRzAAAANsbMHAAAQJgxMwcAAGAomrkmhpkDc5iY\nWSK3aUzMbWJmidxW0MwBAADYGDNzAAAAYcbMHAAAgKFo5poYZg7MYWJmidymMTG3iZklcltBMwcA\nAGBjTXpmbsqUKUpLS1NaWlq4ywEAAKjB5XLJ5XJp2rRpAc/MNelmrolGAwAATQwvgIAPMwfmMDGz\nRG7TmJjbxMwSua2gmQMAALAxDrMCAACEGYdZAQAADEUz18Qwc2AOEzNL5DaNiblNzCyR2wqaOQAA\nABtjZg4AACDMmJkDAAAwFM1cE8PMgTlMzCyR2zQm5jYxs0RuK2jmAAAAbIyZOQAAgDBjZg4AAMBQ\nNHNNDDMH5jAxs0Ru05iY28TMErmtoJkDAACwMWbmAAAAwoyZOQAAAEPRzDUxzByYw8TMErlNY2Ju\nEzNL5LaCZg4AAMDGmJkDAAAIM2bmAAAADEUz18Qwc2AOEzNL5DaNiblNzCyR2wqaOQAAABtjZg4A\nACDMmJkDAAAwFM1cE8PMgTlMzCyR2zQm5jYxs0RuK2jmAAAAbIyZOQAAgDBjZg4AAMBQNHNNDDMH\n5jAxs0Ru05iY28TMErmtoJkDAACwMWbmAAAAwoyZOQAAAEPRzDUxzByYw8TMErlNY2JuEzNL5LaC\nZg4AAMDGmJkDAAAIM2bmAAAADEUz18Qwc2AOEzNL5DaNiblNzCyR2wqaOQAAABtjZg4AACDMmJkD\nAAAwFM1cE8PMgTlMzCyR2zQm5jYxs0RuK2jmAAAAbIyZOQAAgDBjZg4AAMBQNHNNDDMH5jAxs0Ru\n05iY28TMErmtoJkDAACwMWbmAAAAwoyZOQAAAEPRzDUxzByYw8TMErlNY2JuEzNL5LaCZg4AAMDG\nmJkDAAAIM2bmAAAADEUz18Qwc2AOEzNL5DaNiblNzCyR2wqaOQAAABtjZg4AACDMmJkDAAAwFM1c\nE8PMgTlMzCyR2zQm5jYxs0RuK2jmAAAAbMy2M3NZWVl66KGHdOjQoVq/zswcAACwC+Nm5txutxYt\nWqSkpKRwlwIAABBWtmzmsrKydPvtt8vhcIS7lEaHmQNzmJhZIrdpTMxtYmaJ3FbYrpmr2it3xx13\nhLuURmnDhg3hLiEsTMxtYmaJ3KYxMbeJmSVyWxHSZu7ll1/WZZddppiYGI0ePbra1woLCzV06FDF\nxcUpOTlZWVlZvq/NmjVLAwcO1MyZM/X222+zV+4sjhw5Eu4SwsLE3CZmlshtGhNzm5hZIrcVIW3m\nOnfurEmTJikzM7PG18aNG6eYmBgdOnRIb7/9tu6//35t3rxZkvToo48qNzdXEyZM0ObNmzVv3jwN\nHjxYO3bs0MMPPxz0uhtq12997uenrhuK3dEm5m7I+2+o3HZ6rOtzX+F+rBtyDZ7j1q9rYm6e48Fj\nWu6QNnNDhw7VLbfcojZt2lTbfuzYMX344YeaPn26mjdvrv79++uWW27R/Pnza9zHH/7wBy1dulSf\nfvqpunfvrtmzZwe9bjs9KfLz8/1ew2oNwbifcOe20w/8xvhY1+e+wv1Y+1NDMO4n3Lkb43P8bF9v\nyrl5jldH7sCF5dQkTz/9tPbt26e//vWvkqT169fr6quv1rFjx3zXmTVrllwul/77v/87oDVSUlK0\nc+fOBqkXAAAgmHr37h3w/FxkA9filzPn3UpLS5WQkFBtW3x8vEpKSgJeIy8vL+DbAgAA2EVYXs16\n5s7AuLg4HT16tNq24uJixcfHh7IsAAAA2wlLM3fmnrnu3buroqKi2t60jRs36qKLLgp1aQAAALYS\n0mbO7XbrxIkTqqiokNvtVllZmdxut1q0aKFhw4Zp8uTJOn78uL744gstXrxYGRkZoSwPAADAdkLa\nzFW9WvX555/XggULFBsbq+eee06S9Je//EU//vijzjnnHI0YMUKvvfaaevXqFcryAAAAbCcsr2YN\npyeeeEKrVq1ScnKy3nrrLUVGhuU1ICF19OhRDRo0SFu2bNHq1at14YUXhrukkFizZo0efvhhRUVF\nqXPnzpo3b16Tf7wPHjyoYcOGKTo6WtHR0Vq4cGGNUwE1ZVlZWXrooYd06NChcJcSEvn5+erbt68u\nuugiORwOvffee2rbtm24ywoJl8ulZ599Vh6PR//+7/+uW2+9NdwlBdWXX36pJ598UpK0f/9+3XTT\nTZo1a1aYqwqNsWPHauvWrfJ6vXrjjTfUo0ePcJcUdG63W/fcc4/279+v8847T6+//rqcTmed17fd\n23lZsXHjRu3fv1/Lly9Xz5499f7774e7pJBo3ry5PvnkE/3617+u8eKTpiwpKUm5ublatmyZkpOT\n9dFHH4W7pKBr166dVq5cqdzcXA0fPlxz5swJd0khU/VWf0lJSeEuJaTS0tKUm5urnJwcYxq5H3/8\nUbNmzdKnn36qnJycJt/ISdIVV1yh3Nxc5ebm6qqrrtLQoUPDXVJIbNy4UaWlpVq+fLlmzJhhTAP7\n97//XRdccIFycnLUs2dPffjhh2e9vlHN3KpVq3TDDTdIkm688UatXLkyzBWFRmRkpDE/5E/XoUMH\nNWvWTJIUFRV11r9qmoqIiFPf0kePHlViYmIYqwmtrKwsI9/qb+XKlUpNTdXEiRPDXUrIrFq1SrGx\nsbr55ps1bNgwHTx4MNwlhczJkye1Zs0aDRgwINylhERiYqJKS0slVb7tZ7t27cJcUWjs2rVLvXv3\nliRdcsklWr58+Vmvb1QzV1RU5DvdSUJCggoLC8NcEUKhoKBA//znP3XzzTeHu5SQ2Lhxoy6//HK9\n/PLLuuuuu8JdTkhU7ZW74447wl1KSHXq1Ek7d+7U8uXLdejQoZ/8672pOHjwoPLy8rRkyRKNGTNG\nU6dODXdJIfP5559r0KBB4S4jZJKSktShQwf17NlTDz30kO6///5wlxQSF154oXJyciRVPuZFRUVn\nvb4tm7mXX35Zl112mWJiYjR69OhqXyssLNTQoUMVFxen5ORkZWVl+b7WqlUr3/nsiouL1bp165DW\nbVWguU9nx70WVnIfPXpUI0eO1N/+9jdb7Zmzkrl3795avXq1nn32WU2fPj2UZVsWaO4FCxbYeq9c\noLmjo6MVGxsrSRo2bJg2btwY0rqtCjR3YmKi+vfvr8jISF177bXatGlTqEsPmNWf44sWLdJtt90W\nqnIbTKC5V6xYoYqKCm3dulXvv/++HnvssVCXbkmguYcMGaKYmBilp6fr+PHj6tix41nXseU0eOfO\nnTVp0iQtXbpUP/74Y7WvjRs3TjExMTp06JDWr1+vm266Sb1799aFF16oq666SrNmzVJGRoaWLl2q\nq6++OkwJAhNo7tPZcWYu0NwVFRW68847NWXKFHXr1i1M1Qcm0Mzl5eWKioqSVLn3uaysLBzlByzQ\n3Fu2bNH69eu1YMEC7dixQw8//HBI3re5oQSau7S0VHFxcZKk5cuX62c/+1k4yg9YoLn79u2rF154\nQZK0YcMGXXDBBeEoPyBWfo6Xl5dr7dq1vrfCtJNAcxcXF/texNWmTRsVFxeHo/yAWXm8Z86cKUma\nNm2a0tPTz76Q18aefvpp76hRo3yfl5aWeqOjo707duzwbRs5cqT3P/7jP3yfP/74494BAwZ4R4wY\n4S0vLw9pvQ0lkNyDBw/2durUyXvllVd6586dG9J6G0p9c8+bN8/bpk0bb1pamjctLc377rvvhrxm\nq+qbefXq1d7U1FTvwIEDvddff713z549Ia+5IQTyHK/St2/fkNQYDPXN/cknn3h/8YtfeAcMGOC9\n5557vG63O+Q1N4RAHu9XXnnFm5qa6k1LS/Pu2rUrpPU2hEAyf/rpp96HHnoopHU2tPrmLi8v9/76\n17/2pqameq+44grvqlWrQl5zQ6hv7gMHDngHDhzoTU9P986YMeMn79+We+aqeM/Yy7R9+3ZFRkYq\nJSXFt613795yuVy+z//zP/8zVOUFTSC5P/nkk1CVFzT1zZ2RkWH7E0/XN3O/fv20bNmyUJYYFIE8\nx6usWbMm2OUFTX1zDx48WIMHDw5liUERyOP9wAMP6IEHHghViQ0ukMw33nijbrzxxlCVGBT1zR0Z\nGalFixaFssSgqG/uDh06+Gbm/GHLmbkqZ87HlJaWKiEhodq2+Ph4lZSUhLKsoCN3JRNym5hZIncV\ncp/S1HKbmFkid5WGzm3rZu7MTjcuLs73AocqxcXFvlewNhXkrmRCbhMzS+SuQu5TmlpuEzNL5K7S\n0Llt3cyd2el2795dFRUVysvL823buHGjLrroolCXFlTkrmRCbhMzS+SuQu6mm9vEzBK5qzR0bls2\nc263WydOnFBFRYXcbrfKysrkdrvVokULDRs2TJMnT9bx48f1xRdfaPHixbafm6pCbnNym5hZIje5\nm35uEzNL5A56bmuvzwiPKVOmeB0OR7XLtGnTvF6v11tYWOi99dZbvS1atPB27drVm5WVFeZqGw65\nzcltYmavl9zkbvq5Tczs9ZI72LkdXq8NTzwGAAAASTY9zAoAAIBKNHMAAAA2RjMHAABgYzRzAAAA\nNkYzBwAAYGM0cwAAADZGMwcAAGBjNHMAAAA2RjMHAGcYNWqUJk2a1KD3ef/99+vZZ59t0PsEAEmK\nDHcBANDYOByOGm+MbdWrr77aoPcHAFXYMwcAteCdDgHYBc0cgEbl+eef17nnnquEhAT17NlTOTk5\nkqQ1a9boyiuvVGJiojp16qTx48ervLzcd7uIiAi9+uqr6tatmxISEjR58mTt3LlTV155pVq1aqU7\n77zTd32Xy6Vzzz1XM2bMULt27XTeeedp4cKFdda0ZMkS9enTR4mJierfv7/+9a9/1XndRx55RO3b\nt1fLli118cUXa/PmzZKqH7q9+eabFR8f77s4nU7NmzdPkrR161Zdd911atOmjXr27KlFixbVuVZa\nWpomT56sq6++WgkJCbrhhht0+PBhP/+nATQVNHMAGo1t27bplVde0dq1a3X06FF99tlnSk5OliRF\nRkbqxRdf1OHDh7Vq1SplZ2frL3/5S7Xbf/bZZ1q/fr2+/PJLPf/88xozZoyysrK0e/du/etf/1JW\nVpbvugcPHtThw4e1f/9+/e1vf9PYsWO1Y8eOGjWtX79e9957r+bMmaPCwkLdd999+tWvfqWTJ0/W\nuO7SpUu1YsUK7dixQ8XFxVq0aJFat24tqfqh28WLF6ukpEQlJSV677331LFjR6Wnp+vYsWO67rrr\nNGLECH3//fd655139MADD2jLli11/p9lZWVp7ty5OnTokE6ePKmZM2fW+/8dgL3RzAFoNJxOp8rK\nyrRp0yaVl5crKSlJ559/viTp0ksvVb9+/RQREaGuXbtq7NixWrZsWbXb/+53v1NcXJwuvPBC/fzn\nP9fgwYOVnJyshIQEDR48WOvXr692/enTpysqKkqpqam66aab9O677/q+VtV4vf7667rvvvvUt29f\nORwOjRw5Us2aNdOXX35Zo/7o6GiVlJRoy5Yt8ng86tGjhzp06OD7+pmHbrdv365Ro0bpvffeU+fO\nnbVkyRKdd955uueeexQREaE+ffpo2LBhde6dczgcGj16tFJSUhQTE6Pbb79dGzZsqMf/OICmgGYO\nQKORkpKi2bNna+rUqWrfvr3uuusuHThwQFJl4zNkyBB17NhRLVu21MSJE2scUmzfvr3v49jY2Gqf\nx8TEqLS01Pd5YmKiYmNjfZ937drVt9bpCgoK9MILLygxMdF32bt3b63XHThwoB588EGNGzdO7du3\n13333aeSkpJasxYXF+uWW27Rc889p6uuusq31urVq6uttXDhQh08eLDO/7PTm8XY2NhqGQGYgWYO\nQKNy1113acWKFSooKJDD4dATTzwhqfLUHhdeeKHy8vJUXFys5557Th6Px+/7PfPVqUVFRTp+/Ljv\n84KCAnXq1KnG7ZKSkjRx4kQVFRX5LqWlpbrjjjtqXWf8+PFau3atNm/erO3bt+uPf/xjjet4PB4N\nHz5c6enp+s1vflNtrWuuuabaWiUlJXrllVf8zgnAPDRzABqN7du3KycnR2VlZWrWrJliYmLkdDol\nSaWlpYqPj1fz5s21detWv071cfphzdpenTplyhSVl5drxYoV+vjjj3Xbbbf5rlt1/TFjxui1117T\nmjVr5PV6dezYMX388ce17gFbu3atVq9erfLycjVv3rxa/aevP3HiRB0/flyzZ8+udvshQ4Zo+/bt\nWrBggcrLy1VeXq6vvvpKW7du9SsjADPRzAFoNMrKyvTkk0+qXbt26tixo3744QfNmDFDkjRz5kwt\nXLhQCQkJGjt2rO68885qe9tqOy/cmV8//fMOHTr4XhmbkZGh//qv/1L37t1rXPcXv/iF5syZowcf\nfFCtW7dWt27dfK88PdPRo0c1duxYtW7dWsnJyWrbtq0ef/zxGvf5zjvv+A6nVr2iNSsrS3Fxcfrs\ns8/0zjvvqHPnzurYsaOefPLJWl9s4U9GAGZwePmzDoBhXC6XMjIytGfPnnCXAgCWsWcOAADAxmjm\nABiJw5EAmgoOswIAANgYe+YAAABsjGYOAADAxmjmAAAAbIxmDgAAwMZo5gAAAGzs/wDxreAXv8Yx\nVAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1064c4dd8>"
+       ]
+      }
+     ],
+     "prompt_number": 73
     },
     {
      "cell_type": "markdown",
@@ -1092,6 +1298,13 @@
       "# Comprehesions vs. for-loops"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1126,6 +1339,13 @@
       "## Set comprehensions"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -1189,6 +1409,13 @@
       "## List comprehensions"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -1252,6 +1479,13 @@
       "## Dictionary comprehensions"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -1324,6 +1558,13 @@
       "# Copying files by searching directory trees"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1500,6 +1741,13 @@
       "# Returning column vectors slicing through a numpy array"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1774,6 +2022,13 @@
       "# Speed of numpy functions vs Python built-ins and std. lib."
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -1868,6 +2123,169 @@
      ],
      "prompt_number": 14
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"cython\"></a>\n",
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Cython vs regular (C)Python"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def py_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        temp = (x_i - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 62
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 58
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "\n",
+      "def cy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        temp = (x_i - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 63
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs = ['py_lstsqr', 'cy_lstsqr']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 64
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('py_lstsqr', 'regular Python (CPython)'), \n",
+      "          ('cy_lstsqr', 'Cython implementation')]\n",
+      "\n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "ftext = 'Cython % is {:.2f}x faster than (C)Python'\\\n",
+      "        .format(times_n['py_lstsqr'][-1]\\\n",
+      "                /times_n['cy_lstsqr'][-1])\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "plt.title('Performance of least square fit implementations in Cython and (C)Python')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbBQbOSIiIiIbxUaOiIiIyEb9PwzvsBND4hpQAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x108b50668>"
+       ]
+      }
+     ],
+     "prompt_number": 67
+    },
     {
      "cell_type": "code",
      "collapsed": false,

From 89761fa16876ea5dbd5a696c18bc267209a1fbdc Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 7 May 2014 10:54:53 -0400
Subject: [PATCH 026/248] legb image

---
 Images/scope_resolution_1.png | Bin 0 -> 44577 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Images/scope_resolution_1.png

diff --git a/Images/scope_resolution_1.png b/Images/scope_resolution_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..6bf48b0f7d730f96409da902141ccb23cc319ace
GIT binary patch
literal 44577
zcmZU5by!qg_x8ZR48qVM%?Q%nNDfj`f;7_Iol4DsbSoewDM+WJ#L!3ysDOad-AD-n
z-we<5zQ6anKK_9@bN1e6uXV3`uYK=zh}L|ngpW&&3jhG{m7mCK0RWgC0071$4kqeK
zxP(~_>O-`HoSdezoE%is)6Lew*#-b$Bd<)BQU%HqMJ&Wlu-&74XpftWt-!Uv8mFN4
z@iRC*N+Al@(XS;tIw^6*^T8!>*ZCun3)v}eln6zdWqvpmr)vR<g~_;--7cqvO#}Ab
z_~B<KV+(T6h^cxz4;Ui<I><BdV|_~Gx6o2JAybs4+B2t@TyG4ElIW*`!;np;%)!%7
zFL#2r@;p2QYdl4Wh5P6ugKgYQ%S+*WPeL=ROKp}(YD=~wlmk_`gw@{tX7WJh5t4{L
zx=(wvnqB{+Px~I_&JV0bT#_p<f-mkoVr@u6ZjKi*%EYl_3{miWo!8-OQDpV{w_>TP
zK0{wcOu2LQei$$veBe(Ydu@&*r-(&jS=^}f2Q#{?!)=!$h9&lyjc{5?+G%Sk6{D*D
zH^b!c)khfvM>!sZAM1R1UroHN(rd?Jd;?ps&>wyW@H>YcmX@o(1NOus_v))CW4stf
zAJ1J+w>Ang)U9Bf(Qe+>R7?piJ_3m=rJV-Z7`}6Qnfvlg^Ah8spTlClYTFx>Q{Fk~
z7<e0~tBG5=x$sz6yII=s_`A5HoCW|$`irCfbg}Wafcm>QyLyTHOEKQvA&&YReVLaL
zdUuPrlN6(Yx+YZ4&C>=d%p=6Z$0&^pg+e7gt!>4%<Q4yIj`}3UXz%UqF3!vA=jX@c
zC&=UGX~)a|=+PryJ^@|<0dCYC++HtSy)FE?UA>t8>Eu8C$lG{Xc{;dzJGi+*(fwLj
zy7_oZF*2eD`tQ#_&uQcD@P8w@di^^s)C76a-|+JD@bUh)Z&XuB^i^?92Y(x919=A*
z8&@yX7}BDA0+M(4|9{{7--!Ry((wOU3W<pPZ_EGr=HHf*yyyx1&xHQ5*4<T<xukI=
zdH-vAX<Xx5l`8-M22hrl)$zyJZNHadqVL1~JjjeA_0qIXe7M~sOd`(Z368lMLsSU-
z;)wL|>*Toe&+v1(MMC;l=Tv`I@h?7yJq#rAzh^>X30xuu&zaKz_pOcU=1XdAPPIxO
zrj{Mtwh5Q7oA~8=x&3VQDc=^^fy7s~OqmAkwQsZp<mz7SdOcmcxbP2W7=sHgLd1Y~
zzpMvfy1urqYybI-x(o}&Af)^2_upH@fP&^oxk##{wc`J2nICow%EqumQn{mlnS&ty
zn|#2!`EUPxMqifnK3w>pkw)OCZf#*{S>peUjDCPff*YE<_IKSNsQ4E1dmw|J+W+Ym
zNp(2h74m=g3-SR`-KJw0x^Dm90}Fe}BSNUH5~6&X#*kc8B^4FfMPS~2)D1RcFdNgV
zqZc0Cg2_tyHo=bVS4E>RdM3DJD~HQiyB}&gFp%dxI$Lj_+~nF^a@4TtOdwk_hLJ)4
z1nOPacNlhg=2U|s-h(WdgU)XrHJRT#j(7y@8-|Gx!)&ugTKb0Ln&wecwe5lF<;$&?
z2Rpk~yv83*63)u;H$?SD1f=5mQ)^f|<lOeTAAz!Z3L)_8D}pVa-_L?{fFxF_4@Kql
z{?r^u`GAhckZNJ@k|3QP#p16U?Rfj+sD5#?5o-BzC)@P)n8YRx)Ulr*>u4|T`v1;G
zEZacOR2bMQ?fGBQMJ+#gX9^-fxgl?rDvFTKvib<2RR9=NiTqqR%KgH&J!}m2fRD&{
zt5Qt6Vt*!7OB`i$#IXgG#XHJ$v66*Cl^p>3YVq31YdF&Npj41=8X{l4qemKBL<VE}
zYSFl`KIW4+--&vBGHRg3CQ*BVQM@}~GJ3PJ4wII-`?;c*$qLHa7{H9`1=*@z2ib3c
zQ<YK?dEp^XG>D2ukwW=l?b)QNRtm9Q`sP-`7(6bXDC;91^Aj0QGn34aaeuTlFEz&d
z#!jWWf`-Bn0+BK<`!?{**b=~I`=&clm^9y$Egn0=tFp+-E>Dsw{VbC^%o~2`ti?y`
z^LVz1_e=d^yDw-c4Y4Z96^hp=k5`aR{a!4=M_+oHKX^6J=G?q*adS1`I6pUS-r2d>
zXBjjG`yr4t${Z-5Qc_}HR%!26G4o9%_$Veu;dEtd=<yeXGEC6EA_$tsef!J9E^nUl
zNtO7_PE!qbuL`%AjKR?gFqJnq%UYwT*H)*H3yi`NpC4p~Q_3m1xVY{(M%PNzt)iq<
zrGit_4P&>6tPzSNx<=dG(;TY3nYSYa@Zb`gkh-bn)j7jwO{20`T;_WEfgK#vo;ZdK
z<wKSSYc}YTRNGUB3@EHro+gpVi5;0?X)?S&Rtt#!*VpL~x?LO7bbtd_UWZ-DGJ~J)
zDFJI3h&r|z_DZ@exfY0m;2<7wV)#W~GEPlF>2mwqxG4+rZd!W~b0{>`9-{`$1)2nq
zhiBq5PAz;GT6mZc;&b+w5iEX!d~Yj?dM4&Q&3{~NLPl7D&8u@5QDv1NN@{A&kh-Sj
zE`5$*8?V2O4`*w1oOu^H<XoMJ^--}6dnTAP&*Jc@%R!-@5I{Ji*42B<`m&!eyV6oN
zY9y7x(YdM)Q?=Z07X^*qJpe1rZ`d_gI;XnjifKa3Ab(w5axEHsB5f+`Q4Q^O3e*hP
z*g#s3!D_hn#z~LgM;IcltgJ{|k2an%-v-u-?B)v{D+dWpZ_`Nt$B@pHnlLD<9uqCR
zHYR36oWs>v1|$U%D*%%X5T#McTfRRTG5)@Mhp<Q#!X9k;y6;6s$$0<|llL9pv)O1z
z*TQsbqU7(qISpYWzyb8V{i6G`#`Tqp`3X!)i|vtl_`UVkuKfjcbiP4Qn{;E3Yg)h|
z;GEAX{3DoL{1(=M{V_T((fsts2L3i=N1Gb6=v`2>45axNARzWZH-tU1CB~pB0ZlG}
zlsNiyV#0a1RE!|o_=JQr!s*GRz}gD+t$}qCqjn**pUr<!F0L);iAv^Bo{Cs58Z?SP
zpzjfg$1zY<kvklmP~ymePx!>mH1D+&jPMp(HA`h`mOLTj<fMRYMWUj1^(_WL&t^C+
zwe64X5}l!DSzAnm=w?yv%u8Ri6PQO}Dw}y3m{2hoLMYvg#Gf%=dN@V0fDQ2h4QeHb
zdCJi?4>E>~8HGL~5Bnty$|8f_M!_k;xYDMyyLmjMLP1eXDK8Rpn_FmtiC9X_02*6_
zwfe9uUt>EuwVxw(2W|98exiV`1k(flUQ6z!wEidjRS%Ar)BqF{Gavf|{Z*%*PNSR%
z`^=YAw~aLQD25FIhuG7PWv1y<REanrD+dACX1N?_(Mwbs#N1#u_GPP99HF&ZGJ6c0
zei*Ha<+_&q5b#WU{MHW@XLVLkDsl5n&4WS%(ip%kEI%r=7XGp|Toz!dz=0y%Y($GW
ztTr@TRSxdK9Z&bICepMB$DBwPB*KG6@fLp)4xRTi!q$Okrc6suWm-}yE=EG3Vhs)#
zJ@blfYo_>rqbmkj@GGTnSSI2@gz&x|G@Ref2&P<*6x$vZMhBNP<e@&@xSblQ7^Zn&
zDO@(fg4kw|FqX3cFhhu5Z=`^<u2g?#hC4$6Ge9OAkL^dV!gTs<Mp|{U;VK$vkgQTY
zxkoo+q%Mb*EuW<DYyyag8Ru%mNj)~lhz&sF?1eR4|D_liPB!?Z=>`8&{*sKMB`yFV
z@*||JWfS%NT0cx}6NbyD4&Y-RfB2a1$Tad5MnXxEkjPs~!y)D~^qRe%fT0K!Q7|EM
zDf%|E*|kn$h0`;#T}Yj^GlTk8<u&5-c$ew735^&P3~#>smVLdSHlC<F&Sp~(w7@K)
zz@jWbGYOw6$lqkcKMn-;<_QwQS-ikk<X1@35+C+`>46SpM^Lvec$9j=^oIkszq4fR
zTMo^-lx)DbVktI8#O57TN8py2{zOxK^kEYm7ClR)+v&qyG+Ev7{zho(IGa8J9oqY)
z0!b*i8J6T`)0OC?$$y%VRRCBk(`KpMTfFNe79&o&)shIn1+Cze68@+pyCvMe7B7M*
z(iQhbGcXz%@&T?J1W(y&h|>eb-yF+x5Y}OsCqq+97|}5W<%O^r4?;BWN3z1X9(f8o
zGvkhvIGAQ`KSeLHPpF;*d(Zj8y}&}50$e#-vn2N<i*o;djeJ`2zEr63VXEE=+Gw^z
zaGniX%@~k(?OXaB?nZK12B`{J+qgPMMO)P`XaE-2=5Y*9S!uzUwJ~>n(=nre^j|6u
z@}E5;d}G^KVTERI1R|RMEv+Uk{7}wZ!D0$CIZ`;HKjvfHcedew^9g=YE+HqiF*ed$
zL~@igl``Mkv*o8}R6?GdG-w)20s=3+a#wg-w*k3>xFdNVsU==*MRMo=qWF>@ZrY1>
zZUn;pd)L_g#y8)*KpEdYv@AHd=-?`F6@#4|c0ADxA!q}oRHt>FiWWlHnD-bvIpe>w
zf!^VtjR8cG-}A8^fNu?g0|^bS^X`F0m}}UbQe;7_Iq0lX&=Sz2!W4@vb}usC5qT%T
zHoT1RfIk0+|8%IHv{>XD!l0WXCnJxkgStZvIT)WQq%%v&$LiMT$PQEmfW-;b=RTEX
z&R(AR#H&t=av7SLEC^LzJ3}rt7{rek^(!N}-LAftWr{mImG(H-Ja>rRn=|`bcg7g~
z0_)wE+mjJ5nPfTdke`*E+!L}QkQpPwUP?27+LA2XK-xi$+1a1({>-*6cB<a{b~N9i
zR>SwN2xKEatS7BTuv5E>+>1-K@#0QH;3|5h>b}m~INbjonUnKudV6l#+5hK(Nac(`
zq1F$YM0~=Ncj8HwG7ZM^Hq%|KR}ut6c@BN+k8tTC0>)<2E`*h{?Fjkudb9dY%!MiJ
z4i>mk!HuMX0v+VQYCIT9<L}wN?)tz=$?EU%{;&_-HpT*m*(L6EZU~mUHGBQB>C{Vq
zmG>g(gQ?cs)WYT5-kjU(P4gV8MVJ);17fZ%)K4ux7csx6nOssU#8!}`pn~?kW;aAF
zf`Ia`p|6%8o|*i+suWsWq?Q4^56J)Si{*4-)}s^eK<kUmo?EYvopx&t2O|=|2^Q?k
z5yn|T5dldNf%g&diSg^>N@Tf>jJiwEA>_PCi<d;!1Vw8<8amO4RZ<G7^{9&q=dk#s
zDx?uKVv0wDZ~SN^Q4#zy8)64dLzoj`7sR~(It-<IqR;nE`W?d^%RBH9dG#>*ibrWb
z{VnjE{6Ug^bgb_4Fp_?ktSZyn)&=#g#furuFF*44wdG7$pGuzsX*lBu&i&}-TdW71
zw0G^eg{o(R|3r4!{?ySM`1`P;i*_khzcXk}*G*kqBSeDBVP%jHo=;{HkS8D#s}RN&
zC7Fb0KK}t`t+1m~>)wTa`b@-pH7S*C8W&Yodn6Yphh}}*_9l=l9-ljYc&e}~R>b7A
zyGbVEk5N3suK0s&QBA{I&DzJ~;X@o;aOP=b=)6PU(L{1gnPX>;x@rnU>=Ky{uM+^y
z)Th+H%V0x=xJo)=OD=K-Pcr4R)AM^@>)lPt&DyAMYt*&WDfeV}A#gB*cy0jFaq8!>
zEGJ2B(9zN_(yRAw#<efD6q;0Mcg24koe(l(Rs@ZL>+Z3<Ogz{*2*f^PDy`g>uCw#@
zC%TL{YY==%vGSKw<DJY!%5$wI`#&-IKOcBl=WTp@bN-~F%4vBl@fRsrJm)glY+$%2
z$d2<!bE{wte{Uzv>}Y*m*T`lUY6se7(}4Vm?qQ0^{&NzFUV-_7HqAf9lmZ#QnuGEu
zsU>olfvKtNA>q@FJoN<NFS!u8$m!r3Mo-ext4*&cdYzh}l7$EYrXUl<8)4BKTakRZ
zY1KsE-Alg~a<Nb6b{Amch7mUJO^MSw7v<kWl_K7hbh{1Tzc+H3ycm^8yxHCPkb7NQ
zMz1vDc}98T(|%$wx9%j`c5}Xq$6>t=r2*}_H;QG|xuUlj%wJ$bee7OtL7>kg8o^#K
z$UjOSMX+(EJ(6zuO;00=<?P*la&cr9ETP$&j=+d>Wmu%TIO0M@S`9?4emluRBziPP
zclMIj&eq(!r1N0)=GRKVm$pM_r@Bf&8;#>czrGERk#tM5s^{@h$mqNo7Da56@j~0n
z09!#cX`^E{ogKZ4d*+8ruxktENG0c9%VYx@=!dLVFEDI#S8Bfyj*msfXnu73EA6uI
zn~={6O$`;F$STFoe!D?(B&ycAI{&J3@9-cD=Gt_I+JN&}@GnDUj<2*wop{PzOy?7?
z=)T-yoeRJAiBI%n__dZV_nJrv70swrE*e1<Nl^T2Fv#c41|KJA88bW&x_{{wxDrz3
z@|<?_zMiu|9mVZ9C~&pA@x8~72nCwzkC`n!PHr7eTYXIvx69nq79l-|gOI6?+@XFU
zujx3;qqsronOq$%?sH#s;*-zM7uHT7`*ncxFI6o3^&{dd3Buf8K@8F&{H(uXryQ;t
zX~;!uNYy!;7D;Iu<><u?L}*cI8)~KD1$mnJ93mf>Z9waDYm>XQB@VVEj+WQwWb_QR
zvwk#>dw$sucz+<jz0sgXT27OxZ*s*`n2zCW8sPDbT0%J>p&fcXnA8&Tpc0*0q4!EZ
z_>%%Q;Q^x+vrS^FzPN`bTYYn`xI+nv5d0hoe)qFMWCoLOmi3p~jiW(i=Cu5oBGww7
z+U}#ejS)P;A}1Y_ow{r}a9ihtZ4v)qVaG-4|MV+}>UU9s&h~S#$=o)1^-EQ8)}N7n
zoyQEfy!6dUvKx3ZCNK6r74YVz$QmLd`pOHgPZz9HNT<=Fg<z5&$a7&Q>AbjEZWy+6
zIh<yeL1GYxPiVQ<HJc(hw%)G0Hj;S$^KJ$$R^}A>VIg_;`%i~oZKMfHgi=SeN??wD
zWW-ExaGAfGCK^(MIf!{TgA0ntUBj_a5(ZZs+*vVzn=p=Nttg|vpD?7xg^@2y!{f6;
z^@bC@C1+)sL)7<AljtE(2}u&wpp-Y;s%fg?+~w1VFD&1<pXXQUP7C9a#u1!;7y4+h
zb}_T5IjvltVK?9Ah<7vURb=`Og(lQ&kl6x!u{0E3vsHC|zk=+iw|*|27aeLwM3q))
zM3dwl9QYfyU~LfY%byUjnRx$4@v-fJ?Uxf6w&8e);B#Kr^+>(5JHM9PWOY@B<VeM^
z3I-%}@=1$ZdS&UG&D1>nBX8-V8i3uHmHrj(_t-6Hj<xIq1^uDxx*Xx(hEqO5f9n!A
zddDM<yV!CEQ6wP@UlH7heE#vSsY4q~@bkQgE6AJ_C@ts`@8MS9@dNvsj#oJvzS4K_
z_wD+3i5lpHpQqbx29roll3}pyp-udl)VaQ%j;^TC_lBxtBl15*v+a-2j?;ufR_Vnz
zQ#G+C-%PrKI;O|Pj||!8M$cSCXT~uMdxA_h2qYa{9I2lxfm$nBI-47@b6aLvb1(ER
zCUS%5mM3FfPse3Hiv45t6w1SPg79FGawnSU`HL)7-WMhx^M|YbwGZhck;NW=*2(pM
z60`m0<e7C>2&QWl{TF+fcF=QO@NGn&aqevE)Y>plK+o$~*UBm<HT~(?>0ozf`@`KE
z_b*uVKaGRe=*p{t=Q8dysOXFEUY_lV5@t*JzC3R~dz1R&XcwJ~q5*{3hYn3lJZsDE
zqDSqGiISILRe%~P@hi@b^C7M0v&)+w<Sj6@;PU`Yu0J2oFFKNMyuNzZlkiFy=zRUA
zH`mr&R_sc>Qo5V}`@@wu!J@@eXTK5`*J`u2mnbqkDx8tq&^XNvwi&7^Xnb+6gOA<A
zJCD_0ZmmFFe{K)&r_xzm#=FG$VUby|{F+_!x$$|vog!!~@x+fQ0ydnb@8<Stk(x>k
z4>U$gC+VkVJ5x1*PR?eeW`q3S%ER;Cji?0Ptc+M>=;}>#&l)JkGM;74c4Mhv_`{3X
z)BoO9?a_TtE-Wj<6@PyQql)_cnlwtfuM^D>Y`UOjIq)mIxO+sL?+NKRI?snlGTHUa
z4WPso#H2~`vj+I5IVeg)$pXkVqrUKaDVT@wH8QoEgZwZYZ#4X=&@QAJyjvdboQkBv
zLC4bgvh3!h%|8x9+nCJ+<Vi?Ja=>)l^H0d3%{~-&htnv9jBCBNPj~c;3Mc^OAcyy1
z0O^g9$A6+F8$%#TcaumpZGg1)WAnd3i|W(`-DzO8oWrd^n;&yOfWC{Xmi~nw%B$x2
zl&Ggje_8wWal`!1zpD<K5V6gn=i~X^w-JW$-)p+4x&0%s;*Fs7$IPOqiF6bft1nK+
zU>%f;G|a!(Mz7KPB?rN~@evC(QAK^JW3YRCN$b_&B%2w2=rrgU3ER&HN^2xXUj7V6
zi+mhF2-Nv`3HPZlDr%5z0z?ern{EUMNc>Pgi=4+7PxQ{k9KiHW>Dk9Ank|$Wy#`?F
zXH_58<9lyOi(UuN6${<-IXeh|mF~vZyL~W1yWk__VMOgCdtQb0Wual`l@@d~cB8k(
ze005Mf_gIfaZQQh`QSgq@S?tn&?=Pe5L{GIbSgUiw(2`mX>~IcY9E?J?!<-@%0b`n
z55$$?G@7O{6zU9hqF(YyrL`-KAp-@HT-A!c+SwZsC<yrfMlbqo_@LM21lt<kby1Gy
z#u-O_1RRV>Yyu565Asy*_L8QQ<BKG4BMZb#YkF)7T!g}ojXY~j1>IB`WcfJK`#L*a
z<F&HSWx+%X3ypf~OOpfCj15uJAuM)w{Ka>9>uO`b5|@*oAi4=DXwvld!#go*r~U$3
z?4sRoa<ujpl&9<Hg=g?J283B6GpdliomuTe8epw6-%-Z^nP~_KgXs|8W$VYN6lEHt
z9>|JQoM-ho`AMeZ0Cm{_SW#O<b#@fLCZ7!^QfT?PewWT0wEhMIQaiJaDJ37k<wo0B
za0-(W!W5aVML95q2Kc6&Af0&JtN0miiW7cKTT9goK1a29F5Rii$k&(NeRp$s#AmKV
z5t**OG)|3mBl;0CYi#inCyv>6EH&dUx|q?sbu}yl4s*w=&-XA(306(#e|6plS{*)D
zri2bxiNct2WveQ?(L=6=V<ZSPGuE(Q55v9->n6H7Q<apI*s@cON0g3WO>WsNq9X>C
z4)>PH?Q#K%q$)B_RB04>?f2}`QW){5Q|k=Q>h41Fg$2APscJWmlm(8#o$<KM(a_DM
z;X5<9TDUPM`cAeHu!h=Zy=S?v3Qo<m7aB0`Z2MHnGtcWVx+rfG&5k=fqql+He%GNo
zQeuy_SHpl=X~%pUyq!`28d2cakZETqzxUA8q`woA4EZ&~THvDhEkC)i5{yxQW2>w#
zy-01?*q4~~btTGTAo)#}0P*8YVXluRIpSm(IYx^mb=EK|qO)FE4j^C|@A;*x@ed>2
zRLV|CowU@)4o-x<T=f+hR%kf*g1XxYb_s34dr#s0PuVc~<GwzZxa@Cum-x;xxb#ED
z6}?!?VAyFt*M+rf?bnSxKM-y_20p(~+)S~KmbSjI@7n7ey4b674($V7=o~Evrb)if
z)Eqhs#_NU&cq7}8%ovS5G1&r1#S+wx{cg`++!QqCylu@&IQ#BHsGs<>%}Ib-3i-$h
zQJ&Y+cm^F-Ks{BI4OBeA+%~mWg_E;<yG(IzCsI@h-`P}XMpOHlt}vD8OgMN340ejt
z8qJf{;RW6yvkdYOB{FLM4y{_wrlZ8LF9smFfHp~u(}^8rjXN=YR%dW3`S%k*;m3w$
zBQHC%5ie}vEBY(nA~1WO>^(Cv!B6hVC<^`7Xy%&#ab{KwEyD;{K$*QYbNPiA3#w8C
zv9(8pN9q_;pRCEkyD%mf;TS?HHV0_bc$x!Nh>KD@{T-8V&6Bp1NB}BD-@v7rSnV};
zIMo2LpmxyUwxn2W#}rH~@H#aa;x(Xj2p4)^mEeg*X#;rmajz`Y3Ox@#8W7*OCM`Rm
zIS7j^I=P=-JDlXU2N{F(E+>P5y_oM#Mo<cU@OOmNbV}E7n=R=G*EkF-j~O4c!yMU+
zkOj)8AHkVnNuWM-I)oxA<YK5z(&KMk_Aquh!Hhu#BbGBfg{0m&JqyL2FlB)x5^?j;
zns=+;>8RBiGiCXvVT3?Ow%NLIPFH|!uemmb9kH&p)SX38>(s?-D*nk$ge-267Jv>R
z{hWeYuZznZ!%`S|C-(q>(c>8PfH3$X$=ILA>@H$9e0|?YhlxFjMWL|@&D(%yDthm?
zu`JtL(94W~AW{~W29rReSkp3_gp>_?90O=>G;WE~_q_xqr%`tdy+Wkug6B~3WLp8L
zJ3Rse1LcNB9R4~=MgS7XhrtJM2DlTIhzXP2k!2dh-C!b}vJ{()oQlsF{u-J&Dg9(=
z!O_{ru<`3CIu*4>i_s$lyEIj6y_jT=g3MbHR6UrMX^(kQ0c`dwpeQkOTMX4_?7L{s
z)>*>~wff1DpTmgRBOHZLYh=3YE2CnH8lElPhCAOf-1!!(tx74N>QN|weVv*{2q5?p
zM!_e^CJ&E5gsNA{er)v~je3sKBiQUfV)Wd=NdOV)Ov(gM2<yoUO`w_;X7sUnIUb)g
zW_&Dq)hUC#uMjss{5?b`fu~tY_BAFHlvaQP1iTk9CITf2H9C~O&Qk9jloLrqZ8xoL
z;Z~#~fL<)wGB^ybu$he=eYBD?$iSgsL9J<>OrrYznq=p#-vCuQGmt$W7-aF$o(HOj
z3sitW$kH7-5G6mbD9?!YWYv=%bG-rTq$q2&XZ;?5^LV228%x4Id@K^H_)?ujDDGR4
zD1w>etpJBy!-Jn(R45}tAPE!+MCOjW@CW_Ia;oofp-Fw4ve-62Xq+Oc5#MkQnYqU)
z|5f9f=EHyuSHo_I{VNi-27uU44BK>aP3w>(J{XTM5};-{^&Ipx=7}|fppe+?s6(vv
z;kw}%ELkAw?`ESd#)-U`uMX6HF$R;%1%QwZ53`AO!L^E#<sgRk9MvWU?KTDPH1V{o
zzJ`c|zv*+ewg2)F#F5or6eGqa2NHS;1SC_3j+FXa5l2%jf}`iPE)7BN)TbMeT#K(P
zK{QSWGgNFC=yk+fSvw3{R-cL=3j<5h<OC+maW6E*)dEJ8)p0VZ>bDSdp0)7THn5U)
zD$hM7L*n%V)WPV|$(WHA8DtYv5uO63mciD((f{y1wIB0F-U5jHwoXB;xZ>jU6~Eov
z79qXO5rz;(9cPdU1EZsn1oX>LbpthSN+P>Aq4zVf;eitm>Pw>4!C>&h)vxpY`4+FO
z$)bfnV}6Zp3-nHniz+^>_R1B~deNa<CkT!E?+|drqNf7?lLsnt*)}n)_R2)>o$pDJ
z-VeyE;zb?Z7IxSTzD5b4dH!fmoxHkV0}W@xuhk-3It~-Unj#k>;RYnc=m=b|vdaAN
z3pw{g?o3t6Q6Bv~2=*IJ<DN3_;{Qb9qL@c0Kcgi4jCFEk(4dGGbvAtVT!AYddk7PU
zz@6M4s-m1HL;*A80)L{Io3+==yC*Le?KsJ9<>Xx?sOb$+`_=eSp9L+y1MDbdUp3@E
z@iiECEJ-XTlsXv?`1PB<JmmIj;Wk<$u%NPXl;b{8Id3EWvmznPnx+|w#N}P?LK_op
zJ;)|C1-t|YKNU+wR-a6}^jj!Ejw>zfXJVtsxDh{H1Zfs?Zmr51iWp<wBGSh(-Yqq#
z=yOHaLiMP`lJPSN*rqU_UHY}}m=MbRt&tBsJ39l@`<w60HXwp;`t0k0@#Nvy<L0r2
zdtb6?8vN9M2oM<;JbVs?q`}!q3}{D-P})E8S@Mvj@(;hcSs2qZ=%%N5d*E{*JBn}=
zMv<!`%72>+?{l!<o5Y8gP#G`fNda&y9cAH#4}))h=O*^!^Y!54`5PT_zu8WH?kZes
zFvONb3RnZZQgw?*oe8F4TxAhcz>D~TpCZ5BlvuM^VNB6PHS&i?Pngf~@k6DANh<kk
zTh(ZsG+GN3j8`Bjl)%p|a&l2pDU^U1S?Z`mb72y09LM~UlW(QkoxdeI!)PcdvuB_$
zE>9e6FpRxMg+qRehSb0KZCjqLG4ksNb)}+M8rE7_vk>M8uKW)PcTfRDp4Vqm9wSD(
zR0o>LjL(_c1p6{ypz7l1ocLY?Jh@d#IvlZEIaw>H`5y9+VXT*T&b0g?2<i(MXA!~X
zbuZUy#MPqs4!cdQ4qRL83Iq7~_*7O_&M^Hs`Ks}izG^I2=JtNb(f!=ZZy)rkjFr~{
ziyI{>^r_FnC85J41r!|mbdjJI1S!P_QyY{~w<yOCSRM1YFlLB2)%7{}#;f0+{oPU=
zrU+MD89T$i4j*(KLi)Zj+%~T$${rj+2x?lQ6mh&wnvj$2_vGRCEc;+oVmhuBA?)Y7
zKf-<{4<yjhP*Ejo0h&0{xQ(X1e?QG94BD*Ey{<U<4GJ}V0)dl97qZVea|6{Y#N4D#
zV`5@40>Z6^EBh^1RUQs<DYhFDj&N7$T;sR0v==c8vXIN7604`yq?@P(HYNeV=M4jU
zGh5kBMo*GrSuQHIFtU|Bn?gl#$3f6R!06~`dwY9)e0)$)5MNbwH3J=8-wX{seFXD~
zoSdA@-z7q~jz6~K*<d0eBUCK`2RRH6;gQX-FX`@jUi%BU5jmC+pZNGq{!xaPAlXR;
zDz{v}w07Os*Hgp&EODc4e|*bM{Ec~-ikUs0Ehce`vY=R;KSS5>nUP`VdyBPxh5IfJ
zpDH?9iC@~<ykWsnD`F@5;Q0IY9b(xERb2sNEtuvgkM7gLmA{^;@YdP;`RV-KK_DyY
zm{4!?;9vkBuWby2k{H;H&Gl%}6?^D0p^cnePtf^K(cFXRIujxSfV^YMb3Or)zd8dN
z=Q(qaq=^-<B*z6bL6#ySvc&g?-yjyBlhfcntaeql4h0d}oxJBSV8iqUa!fXeIV!5V
z=w7D|yS4a#E`M;G4rS@G*U?EsM;q!~5m8_TZJF*8p$mJ^EG-KJa#-=FPTg}~9GJzr
z(LF5vkh8tD)p9yz#1Qh=gG<tRt`R?NeiN>Mler{3^VTf*kseb{z<vvF;1ZtM&GB2^
zz^53`3wUtA0LED&Z(8c@NJa3z%<(%xAS=r#OR>FWV|EA`W7*(1al?bKc)L_?;O7fy
z9@6*_AHo!tvdr%LW`EgRRy~|;_8EmsZhy)M=IG(f;tK0+Vd3qddilb0BNI+S>Mbl;
zTtTsv@)9-~0#pTzB@{yJydxY_K_WK(<;lqI_Fyv8!bQKR^zV!>hgDed#4rGxYYn}l
zqhk#NW_}M?=0dhSm&rMRLoE+Qa*6TrHy49V;+F*VFGh5=fy9?3iv6f=lMq`xTl{|%
zQ%U%d4WH}Ci=G+1S?)QT9Gfyyhl@6uoMnD@3!kKCQ%HtBVaCKtr~b#5sD$w+(i{+F
zip*<L$^5KkDPoLOby)DB-tJ4)^9J02hrFM5>m>G@fgU}@>RI$JR+Gs&^>!CJZ<S;&
ziKl4Xo0e~`4$SIpN6Gb#5qku=SMwYDZNJBgi;JBdM}B}z5`5!TirifTtoiHCvagwM
z91(pF12Ebr%cGyg*P(VYr4Suk<5HEs+gf|1B*Bx~L)JWnVT0>gLffUseSc(>PFDT1
zw?pX~yX#QMpN@$*otrZgw#m6z$n+2~uj-rng<_vwh?+@%@s1K-E9P(SmaP+6k7xtu
zi;DpnX+FNgJ+}~=VaUw==t%abIg<Cn)U!l>y7%BW$07ijz=lSVTH7dH@O&cx`IW-#
za`Nq}9S?jD&m&0%D~dj>@Y=8Qq-d%3K?A->FD4Op!g^T+&<+X)k+Jor*w_=jN!GL(
zQPVD7{2m8#76iuk0)(bMwgvq@_^vxG=azV{fFuS`gS(xf5dUEeb|jGG@V)CyrSodD
z0pE7WhWH28%N<P-y+3BRJ0a~GIRQ1P7z<y!i=VVLVk}{%(GsZBj#~+1O3NG~2+hMD
z{vtiyEKIcIWd6k)O-C~IC41R^xCrBkUt8e5VIO{t1zsWGCHwc-5=BE47~lxVk!<rv
zbJeHUER|1*<&2AkxuoM99{#R*ElYNic+%#1^QSVl8mHBTI~n`-8x)@l%~|gd4*eRf
z`!@7G6IzC|3^KvAxsBarK&^Ji%c@WNFAuxGXkKEnMAl(ZJUDb#P*okaN|cKDz@5fx
zmheHyt@GN~zTkBISs@P*7P~xM30Z!kd?=V8WW)b<80oAff)p`5)7zS%8jL|unYP;6
z=jf8+j<UUc)I_}mcw)RzcirgINx=UTaU&25V}AjoK-F=e^S!+?x$*W*4Vm8!+>Q)l
z@1yqc0Q2|R!+w79vtj@?N}Cg6QP7LH&JR}n`Fknya)1Y?<V7v@8VR!4>AfJa@TS{4
z_B<IZ?AIdwssht&bmBlmem?S=erly^OR7<;AhLFnLYlCYpEWa%@4;eH?vN0AfVpDK
zo@8C##;W;O11nT*kiq~gAz$ueE-lBi>s?_9-(BSPV>`PKQx9wp1bOMjn2@O~bS>C4
zS|pH!GfROCHpZ)79fcxn(x(Oak97)XQ7~^2<n!qp`6)M%`msk)ZpDFuQk#}?Ypr>T
z?*qds<`ZT9=Y74Bi+F_3ERD5=AgkQo>4o%GaZADSetzdmJ&_vOVu<j_M`iYkuEN5A
zL)Z~}`h}S&7mlKmlHvzbs%T?saH%}125RE2G=mvP8|F^Z{!viCJZ<Em!2PWm|K)^-
zCzOlN{cCKJ`+&9_3X-WSw-P{w5m<gC0i}<BgyrYg%mBRO+H&uWsi&MZ$v7}IIN2LK
zg7Orp)Gc{EZkiahAPM_Lg=Fi2kowRVpE~rDg=NYDmDsqgq~udk!WRlYj|u6Jywb>@
zzkVg0t1j<p1hRNpC~v4CM2Oh1pZG!D^Q0gJ*uZ#sI6!-)6s5k55CbXmaWD%!0Ky{S
z%!Ocy5(^?1${@v4ba!tNj5u_Mv@E9T@ePsKi8NwLGjCk%@Xe!*ZDA@%uC?y48hFZo
zDmaqvgPa4QYVc?GU?V!hD$)7zx?ls8o~&NjKZ%mPRl@E%qlyi>I^#BJ{pe1)A(Vgi
zkR`Eniz$Y`FD^knnC5F{ezS0~DAnMj(TYv95VM|w@Ls}<Wc7OFpUe@VsgvLM;J!*s
zuO5>icnmhdueq5I{B`lsX?b&V(}2yBf3{Fpt~9KL2qT5#Ep)Je%_<9zi$A9H2raxE
zkHLue4Q@rakTpev`7Zo1?wWEAvGq@E<41i^xj!mnBobfr`6hOl)Yq-`nF`@BPQq5o
z8dYp;3S+muS3)N2?Ued$#`vEaqA|$f%P-3ma&i;J2&(#BzPBtWQkUN9)watMqGUT6
z$KVa?HU&!>9i4~Vo*x9Cf5Jm}o6deGG1+>Q3Uk{a<FY-@%PF#k7G*!Nl7&CGTtdk*
zj|B5LTTh9~Um?B&<-#U#;U1%00JftksstyF$|6S#SybsBs&+GPTn_A<)O(e8C>6Vr
z8$vIqlc>qdwUW&=4ua<~kKk%aVm}ou*Z1IH?#Co&Jw#94(+d99H#MHz=g+{*E9Mmd
zfYWovQfs+<8(NnFS1IK|+43_V+LjS!`ug`3nJDPp=L%qt!8ySnhcKhI2s&V(mKr+_
zB%>Y~2!~HtWle<|Er^&?MVWT)l`QqqpJ8&S;>VUpk>^{_1M<R(ptH-zTPO{S3WO`=
zjZ~^U6j7GUh;GK<$Li=ZvHxb3340^tXOOMak4;tgFiJf?C1(a*w>y}FLGZ;O!1QM_
zIUM#8bOHnd;`ae;`oXJRwiBs$Omz2@HkNNzH`y-w<S6iH#N|IcaI7$;2GCvX$Wy{Q
z$sOU`d2B$;@|3%x+0}kH&k|b|o+Ls&Y(9@iqCmLB0r)Jqg5_|rKv1&{LiTbK4(22h
zU7U<%j(lwOW9<IKi(bd^;5mQWXH2YE2pk#*%z&M7v`%T=1E|{M`<lLtO;2sImCUTB
zN>N(`XYQs4D_Fkg5U|*c7Ss%d^oH;^x{HfTO2$dOrj57zl4Eih;o%Dt1k6xk%!zQM
zdS*S8chpH*e)ZDxEPn&WTg2^J?u9Ot`N4;*E%uNn#xA`#S&~s1YU7d&osLO(haj=a
z8`q~h^jA<<AMNFiomd%2{33IeDee3n@%jx8epFEgR_VfGgx0TD_ndKQ)`nVe@02K7
z%Ei|Jr8GW(kSy-TRGiO5oZ*N9wbUwAS})grsh}n`gzT~m_-iNc8XuJfhbxK~LxiKh
zg5}xwUNH({Qi0E&B~v?T5Z-YP0c0lsAq1c=tCFlct_8x-G@PS1GQKn@@<eS4`nqBH
z)#8g;8G%sJCho&*1&m4R5p3V57zDj}*CXKqbV;Vzh152W&}R@M5PJ;x$`dCbU>u9x
z#87o7`{jGAL6REVZ{@$x3L~mMitR)<(raT(>iXm>^!_j*DoMxg^W@WhSe*1t0jE(U
z|J^K029h*V98=jAp=CH;pYnZh2CZ*de}?fT2<728<3<WS-7svy<ggTO6g$!LMg=!7
zFuY?MPUuo?@`-|>s}+@-9)(7BKNL@A7n0S?CDN7-f3x|D+j1|XNXR1drkZL!%}ZQd
z!1v62AtUTM>?f04JJSWsVP$!?|E#t6?D=1#kBt_yooAo`#eT)tq%U2peAI(-R<@Q9
zy>GRBG8a2rS-$8Fz7Qw!tuJjYcI}u;s&c_A7|2_3W5S)G-}){YuWf(w>9rQ@3@{bl
zH`R~IpW^P2NNC`^&~ZJX#L(vwi#@+XB*EL8<My19IWNAiG3DeLk&&kDl=6$^u3yP-
z>9-}1c3%W6fyqsjt`wg9o^0iA;xX!ISIA`ZKg+SQBV>+sZg=_9zcW{8TR$Ort)cWO
z;bHmAO$7N)wW%ac<fD|Jb+d#V8IinDyh+gajvA!tY1k$Peh*RD>(bfJ9v?!5!d}0O
zQPCa3G?-l$8UF7xp7)pqoqqS{?T+Q`Xtw_Rx;x*XUNM4&xtN0I8O!ZDWRw+O>n+9m
z6d#?!3IQx7-<-Ky*ubXy%jc|5w)SXePk&muIMFoS4jn5vQ4b}t9eAdojy6pC!m$PB
zl)Un5tpt%}Y;5WIt;k6f9QaUhP*B}<^FRK~n(M!OZ@yB@XIm#fnR0}TESaRH0eKnG
zC;!yHtG=I&kslYg@(OP<Cy3Cy&Htd(VFlEHk1}DW&-;yUc|rt3*%<L$heUmo030PM
zt13tVT;YA<OZWKyuz<^JAQ0HBi)gmEy}2g3AG71F4HFNF@GW@a%wA4LF}PS0SVth~
ze>q-w<OV7)tLjyZjBdh?2#h0$m-@{+5OJy44S749irB1)aje^5VZ-Aew>}u+M(vi&
z^Tyo_%)cW(G1X369Q<mZ;cf9h3S3~#Ig_8wc&rUdRVjRG9nBS6m^Nwcz;Z<be8+p<
z@%Q&w%s1Vv?bfnUQC^pY@tL}Uj=D0EXOGWIyT*X+)qjt<^a6Tew=Ma0H-BcnGZ--J
z8m!%WZ3tD7p&`0}^#1T4>u}a|fZ85>nBm@|!%BiZ1GJM;q&E)NWKZ?uXW!9^Tx4sj
zxb&OoQ_!vta*mGTFP3@n6H!A50m1Umw-Xcz;J2Sj`cha>d1mZ~k>SnT+KJkY5PtzE
z3C#oEfNARYjF`#2%RccJP0p&n_X84EKI7RejrILux^~&2xM6+I@+jR7#24j!k|ei>
z084Oik9F1GN2L`}i1Zy}*#`QRzmuxGL95A1Q`Kg-A3`oa#8Pl`l&oXNXKYoZxK=5D
zAS~nLJi4ggaG`~Fteo{W__@F;h=zfWU&Xqb{W)n$oSoOcdTC24ZmwLoaRO~rKk<f~
z=F+)3SGyRRPS>^3-%bkmOwlhUcRi~}IPr^Lte#RR-#)5tWG_F8QqsB_gPi&hFws%b
zmb+D!x|KSZ7%qH#Nc9-or$vkj#0Lngt$1)xieD?#L8^!AP{f5;^zN;SisG{Hy`Bhs
z8otBBJ<eU9#VqFgjZ##O1Kg?HMvaT5Nl2D6P@wsWr7eg+hT7GuWl>mF(5&XI>G*tW
zS;v*UXV~giyVRVkQBF(2jPd-{OhCv$GV2%YVfr`yspLCV&)fD+hs(-VR?<tpybYQ9
zGsOD^D@!UX?VD-fz;@#aVHo5*Uh=k3o9^nRWfo$rs?~bp(urcFU;U%vzcnVjpctsP
z|3SdQ6-qGxpJV2{+%66Dkr0*XVxU6c<Te+1u8&Z)#bO?Gj8x+Ck`Kdfe>{+MaHw}r
zHSuvM|K7Vi{HwP0V7F#;Xh{RM`(`HS+=C|6Ao8B1DWU|E*A3?5|3EjLCi(o=w8zC!
z`e;s&U4-Aw$`W3POJ64EEMqu_xY>5l{=!qpX@X*Xi7ehH4e#tnGk$+Q>De*#zW=>`
zNyU%+0TpK*8H}cpK(8N-ArHCDLuyvxdj$T2mUE3Rcr$U%WzxR8Gd+YdwAWJ+8f-sm
z-gd3;F0FE=+DCLA*BfHF8XHG8fUkJ7v>KUzvV!TaKFMV)N3iegzX?mczRv&6`r6Gj
zb4d9QgGN+uKfX|R0Bi288SBw<Oo{+GxqtrGuM3YdAGjt-xrRyxe7Se`S_CQsK_%ow
z;^r$4Oz(vsxSDzNk-s<y>7e!X@wxsjb1V4x)c{J=Kq7z$BSJdN$F>~4bokW+20rPf
zZ{~N;e@-K=vDj@mIW@29Do1B_h}tY^(`q#M6L5eg?B&qpzppTx1QjE-Iey-L{fb}~
ziL7caAs<DWMnZQ^I_RB;aZ=FL<9EeS-O8+H!M``b3pL2_KNm+USt2e&g>!#fyIJBs
z9dulwwu0SE528FVH#(*tK&^|HH8qZ9@1?oeXC;m=o!ro0WjgA_Ut_)PbW-mr_&n>u
zYc<_Td$9Gy^@EsSbElP?#(I-k&zCaQ=laGot^RUW3qSJ5fI*QAGLa^43b4!ZB6G!>
zwjw@Hf3S@0`Jt~RO%r?Hne%?p7z2a<F$3YwyZITv=)S}={NQP|xVvN{P(TwaAJ{rK
zI~%iebiCH$>^{tDv=y{eI)TderLK1C&2BHJMP29pSK>vBn@+Q-1o<S1ZtyE(n@+<=
z<7V*9g`dU6x;p<TVa}OaxP6n-d0IQ;etI=Mm#DuKF(};GaP!veMPC&&?ZwuX&0MD7
zJeBOc`)4El`u@iL{zl<=e`(BvkwzC}5PqO~*wa>x;@#gRPF27BO@<;xC@Nj;O(w>D
zxLz22*jUYrBuM-6!@PppRxGRjXxHW)g3%^V>@ir<6eCz8N7Ar{Yo!VoRbk}ZH#mr6
z6kEJ49o{Kmo5t#D4gGWfkukHhG}XBp%i~DWfL6ijhNa6hF1g4XLAmx1+EqVjs?97{
zKzceRMh#1?XYIGifvV!Z-j+RuL36KMw5*Kw5=5&%Qkz$3RkYOY&3rYPBs@<f@Qz=^
z(6OhUbK(iQvM#}>wRom}2*K{4jsXknQb7HEdu$(}PCQX{9cd6U*-bu4$q_pN2?+_i
zA9F372~9RkbuG6yS4aNX(q!~H?<pC_KcSA3Wb@!Of1*f2hwaj;>O}E)`kQxB_A?T`
zzipfy$Iw0TAt=J5OLK9G?q^`DbM~sor_0-t5tXD8=c?*CRvh;XR0>#}=gVQ}q8KCn
z@^0WmI}mvPuHYE;h8v1mX4(Tpf62t(to#XoWNC5cTKfxb#8Dk`y>k?FICEcRtRu$r
zOmi;jxC}BsJYeHfwE+J%mr<8nTUEsJlQdEI^t%C0?I-ry!=(LvYG2jfoFSIqPt~Xw
zlfT8NvtN;ez9iDl{q)(_l*w5~*2z~epkE9^oxV<^r+=877`Tekv7wmNYJ(XB^Ge8{
zDv~16qCAV#iJVe;nC|hB^k6<cyu5)z+O|WhQlO8lJo|27cB84ky@LZHa;LiU=FdiM
z?$j@Q+g_N9w#p{F?kK;fx1JEO(Yr&f<ke#H?d{8^f_!0PwL0=8mMGMDT7`kZ@NECZ
zKxR!cri{oZn6SIFjTf8r_304r*aeuBD+66wVlGR`7CJ7wb1UJu|9AO*XOxEha!i7#
zyoD-8)l|rtT}VWewiBtghK7cc68IMo!M0>4b4BIKm`6rGjuh!9Wqn#<+D@J5AbBz_
z=fB6qb+oS3!M~iUZ)#X;i5*j6+(P0d`s)E=KpdsmhkRy!3w8QYXH%|M`Pk<o0mKN=
zi3BYc8h_Bp{nhHjSwOVAJYQ>tI&$xpz8o6f?X;+c%7k`TdrxFWkbcQ&+UpI-t9%lt
zi=YWU$1ERUN+1kbg1Ls|=zlM6dqd1HQIi9tUDf;kKBb|5tLnP>R!cWb3RihfoRrUE
zXRXVuH+(FWd~|pl#T}mOMZc=7mY6D(%@R-$Me!#Cnm;$YVpwzhm%G3W^5bw$Q7WVj
z4WaP2>FJd(*Jo$G@<0PYSgY&LR=b=RwqJ6jYCeYan*{7N%%s2<f&Dnxrv6LVH}AkQ
zkpy(1%-sb3{S6EcFzLZTf7aeE;kUT?_s|f91I+xF!j%$eWd}nIut4^Z<+$_qgCGN9
z*%FM6!9G%oWYUyjvvy+EwQ_qihTwYKIm{{3@5M>{bi)2JlbcTxR>6Kmp~CPy#d(#Q
z%=MGpH%~UeS>&PZ@%W?x50}nXy*9#{F{)Dxf_^Xtu9f@f0{2sqpo6-P`0nVf+8A=4
zAI*2&m?sS7m>2URJmeRo4Aa2^p%u9|Cm+)2xSlPhVRiT-C^6rLU|}?Wl#4#Z#7?A_
zib)MCuWH4W1rY3hdL=hs1810FqK4xGFbQ_cH~05Deve6*U2p660BUiBkZ3)+28tL2
z`tiHFg>upwjKCBO=3s51i9Qd;YSc544!5AxB7Bec-Z0RitV`R)VDJsD*_8BgZ_DX)
zv1B3TIJU}B$17MyJEieWZ4=<uk*3Fp)jg{D0gh&9O^@EJlPRXf=|lkSJk!0bvA5dY
zm{?l+fBR?6tJmDkT%9-@@1cQ>@@WiOxN*1Wcfk&%$7HB)J|5yE=3IQ82nAgCYzPsP
zI{#{;0yR&=f2K4?ANDZZ5H(TmqgJV=&g;GAmBTgOkUxh@JyG;TRL~0~Mx=|2i~lKR
zH%<iuUk$PnZ0xXzZ_#zOH;157K8#670spd(Uc(iWWv}3eDr6&oO&p83_&ti?SH%9J
zI(jFco=riV{e7-FuCz_R`7h>=MeB<f|BMb*xbdpLFp|8C!umxR)TORks4gA%#j=Q=
z3dQWwt*4D^#vC=_3DT*aN}z+%UaM>&<VJTTa}F>5GDy;mBygo?R^pCjp`B=&fd_#^
ze|mq#zHjXQdsK9VIHIY2&!t&sh2^n0<IP3lEv4a1Gu%mr0<zz{LhuS_CJG*a9|f6A
z9nwlnh3rSd9wNfQ@42GRWtVcWyOZym1rq!5Ja*Hz&HuGlL1hqNGuM@yTboF}NIa_4
zc{#Lhl*Zgao^jdzl<TJR#N6&gmB?^9jjzH?=W9pp=Z=12C&~+<A`0LpemS;MuF5^S
z8jig3;E#$zS4a&Bp1%#LBIQ@T$j?&jGFfZ>5njtb$#L*Pv7<Mu?;TY44)9jU4WRfF
zrS#FEV{_hj&ZXJtsEaU|XaW$0RA7{whZbM#sZ`)D5`?CBfr$zipAHO2pVgY7Xl&ZV
z&%|Kj<eamcga-^+6n}YSq$iEq?ChQw*FFL)#EG4a{5gw{zAvy`49aOk24p|o-)fXq
zT;vGwnA=H{I%|+CBbH{koz161y_c{W$|9K*4Okl)4&wd0nG(cBT8?dGmb>3I@;Nhc
zcb9sbVa|PYIZgMJ325bhyd08^TXjwI;n(B%y*H=a?4nKy2jYpy$L&a?lA}JZXe?Yu
zU^}`f12wUnJAm%-{&lmplmhbOH_DsOVYwC0c&%s{7>IcG4>vY0_TU=?;7FRE!&>pN
zYMf0?b03JhlSj?GYTjPfyNXbeC%gcNE(0ZRh_2{{Q|~t{H;f~4OZ>P0xc=>VuIzme
zqhD1-xS>*6+`-V)?fk{!j-Ev43+q3Pw$3%k{lTwS0fjdMf>Hz|!hZ3nCqlpW4!t4%
zO(m8dpzZ1nlQMpieue8*iXkdrBXLZcdx7PeAbB=p<$uh0pV!n@9Mm{p=d3;N8s@!5
zBx-z}wPSfMQp`!RS<9RM!q8fx&tJiL9==CB7x#97=0E=nz%|SEGe$X68Ka0Rl9i*e
zsLH6~Q>(fHKohs=+1amY#3*eXO^pB|=Xus*G-1{nT20MH7^3sE9<Bf}nZ8<^<E2Q3
z{x<~%0mwAvHqq{>o$`USc*AAxYLk~obmaxS(J`%k3;S_2mQ~l!c7&+sv0Bb#T##Uv
z$yL_xPCRa(&cbMF<QPk1<eEtli8cdk54~}utNtKClFuyhYv$|rGyC+c_)Gl1zjK;4
zNuBH-dmAA2vW5$t%^)NN_(p}^4rHp#k)L^5`mI)(e40`3h^zbfsSVz46tu+{$h)0t
zmeV)&*x5Cz5sOz6X|~PDW7@0EGLy+CE_TY3l$qk*;-O6EOEU<(S-ww!T!GwgcUXI^
z9$)?UFBiQvjtH}kuKx1N#1Hf}VOk1YNGHaDh}XJ{MY9)a-;*2gFDiWNj{FI21NN5j
z)s5VYDB66os(Ai>CvOf%@V=OpxS#qx)t`ziX3->VhHxU2+MPDw>@T(`kA+9u$AI~^
zh3*dLA(8KBSrDz<B^@u%Ipd~e;opZO9mCF#0>S(kTC@f}NMOeKuK-^9t6r&Cx8UDd
zqVw*+v!{%BpZ*_HZygp@7qt)1Ffeq7bR!`hf=YKwNrOXor{oOXAe~auodOa=BOo9G
zBGTO@0wVDpp7;H|*T;WcaGkSb?X}ik>%Q+p*8zPk;dz{28u0NJKQcKw&5!^V6_f-v
z9`jen#rYFwUdPCNtIv!K?`#lrk7alrux7bdl{qi^E!g>^nH#P*!)kK=ugJDjSyb_f
zXbq4An`2^BJb8#N>xb4|YUzw`24UFO)I*NPgQyc@vd#D#7{ov~h!$pz_4J{~V(Y}B
zrxi~$8Mm4Qj*bmtRZ$nkm2FTJxpGdYg>>&H<$J_bfPc~O`gH#N>ayLfZfvodZ5Oof
z%HxJZuYv4o%YE_9Y-mz1UZ1en92qCRJ}8FU+Zyp=uk6)-Xk&UY2<^*{JJwHM`11c&
z0UCOAci-sN&SJ)RdUEN0W=BIl{U0bfCKS+hIDhxylz?5lBK#GJh}P|mKaJMze-x`n
zNLUnw;!l3vfjhR-(tok{ShuL}e*c$e0QfI!Kv$x=!d(W4Q2z_RMqZWVg}s9zSDwDW
zXZ9zZKvMI;yBa(;e=Y&-qftOGLe6i!^XGpq<1vQ!Jt{^lo=^1X1<aZ`8+f1m?_Yk8
z0RQ@1u~qN?ge^!UfEkU?zEJIw*uPTViLtplc+3KTq;&F8x*{^{ncu02uE$8@XKyGL
z9E|6U`xnLby>Kr@rp%+{gpB*v!dnF{jz`ewqrW0~1w3vu2r6(J{+{8PNnmSKJxJLg
zcOv=#h`00*hyh+vIfdX}0?sTSHd<MW8-NvRBR`!o8`_B#P;}&*ApDlaxHL{^k>!>i
zy)gW|KkacOTo6h%6gEaq-WocJO%2&cw8r2dJh<N+x`VCVALlHF{GcFbd<f@!*OZBy
zEH8OO(LAsA_`X*Fs<BZ3r$&R9jVj6voB`kuY>;|1jbf2mWsog-DMvK^rgi{IZ6NR%
zMgW?w;3(>*1|`_a0KSL=rD&`s9)4^Q0&D5`dT{`i=)Q|6Po%uiEr}}1KIwv!rRx!l
zmx&9ZG;o(zd)YN!Bq_HkMM;TnJ`?JYi!iqYwvl?$&{G~Bp<H_UsxVVWD^Axs-inyb
zAZEFU7C_ZJBBlv&7;&2m+Gf$^Z0*wc$&%Dob<JVyz@rQFA7S`SFSRpKn%^Q41lchp
z-x|#l)I>lzY5y?&qaW)?`;TmzNeJ<#Cj3QGTMd@8{a9VAIG#&{7V=z?hk(3o7l(u$
zuzOxsR&@b1P{PoD`shqevPPvjfREDUsn+_B)hi4=w4HVheHP0CeS?H5s5rgSTELh}
zHrI$r*DL`h!e|97+PUjGufZ<-6|59x_RZ|r{#+iVs4T8~i!M0c#J7N)?Wb&7$?kt$
zw+I9dMA-n;*IJwAMa968@m#mLBn++k8L*ao{Hs-X?fF9LsJ))BG$hdvAxGBMKT1q+
zW70wvS$jvR$9+f=fOxB7dZgM_V-}G8tRJczt@MhIUW*HDSGG%HuzHUJ1sJp>Xpe^W
zAc#h+Y#vfkZT!8yro}p%qd1w-l@r5}ZB)PZKOFOO99e}Vd18dxTGHjYD&bzlP^^)C
zvM{GDg+m?Z3pW514uu5un`i%y%0dw)$?Ox%!|1}xRzihCgDW~K|LD=a_*ch7+E3t>
z5|a76S}_|5^`jO1kbE2bxbYMaZ&XZTwiZ7#ou+c@y+JNvNeld4hDO2}Y+~R2N5~X#
zw64R<?W3T?bFq2aoI{oUv$4x<P5+h)ze(+C%Jw2cpd4?|mKKbS-djATqgmhtDH421
z`e}(&kp=s7lTX)|*_AJ0q`4zmBo%sOV}O1_DlITz#w(9XI}~n_!1)=Ds>;fwa8HA!
z1*%`m?akI)4kcn-#q91iS+f5_=g;Ct650&YM5fIm%Si3uZ~E16GnVJ?6C(D>@N5=%
z^)5Nbbom@gxjg-C3fRao-l}Mj#DCb4cXt8-c-Hp`@$r15<TNyB+YGAo4IaDKXN7lp
zGQ-dzKZT*lO$rkxK)~boVqo0_1sfmqP|hTiUh$wRJG;_IZ^p^^SA9);X-!85r5ohC
zKC&Ez9o)G_0F9qXdc8`-H1}Cs9OoK0_U8wOxM0&mu2ob7NfXq)ChDfZK<l%;+n<<*
z8(BE`y&Dv)<v&GB`CJFqdUOf2I<xrTW+Sv7q9_h9tW*BP8I(aR-r{8xSbN%QyHu4&
zEQH;Ef;kgR)?kzN7fc$cLts^c9P=S5wPpt0!6+(6?odoWc}h^)e>yBLc;Rc&TFRl{
zMn^z)AX|AW4cVf_#y3+tQ=&ZU|7&ON*}UwhG0tEctdG5jJ?M}Qio$^mSsz+yvuq{g
za)1+q`p<tPx{(NQ9DgQz3o-%?NrIXB*gspRZ<#1QLb&PdQQ8!c1HwIKLpHb@E2cd!
zXe61$Bwb;(N_x1Q08TlBws{joi2et4VDGkV8F2iiR-pd)=hTJBUXAdmZ}!@QxG3Kk
zHPtl6nsGh;*KacX`W^lgoZ(vfjeitPl6%@yfk2*@6V4Y31=${gT?o?TtGpkRHp@v+
z1bJK<k*f}4_(&)Mg%=@f|1^F)*bzkHIYG@{QN~t63ZMc3uNsN>J)Z~1-?X#Fj`s-?
zPD#nMg`gNtN7?Se+#@AJnyTROhJHYA(E5Kp_Ky~z&@pQ_M`;-`xg2v}X`QwtJjJ@k
zbGs_02(|y_tO(4RA%i&50QAGlDET)^DkxKfgpNQ`sVk;lBvlYL1$w`RqD_uXP}K&y
zQ{;T6F%ig9MJ$#xVXlfcSi&Gl=?^0@63YcJZn})Y>AZM?B!xVPm71fxj=h5F$JhZa
zd*wZE#G*h_3I&;?sLGT>&%of+81gVp4!;7DROMM+4wiHdwiM^|{k><oL2kU2VkBuO
zsQPXYgciswT{)w&HW_sf`qYl|3%7q63J}&XFtSVsPFF|PM8J4Z9J!KA2NJ=0dbv_g
zJRBs1D>H;$3X=L=xYm}-{lLVzBqp8N#W#q2YCh~0EP6nhf<=)TMp)7+buH8<PkAR&
zNTTb^hw|(5{N=E>$AVTvt+Bx>5L%NqFsD5UaB$S<W}Z3Muc1_aq|k!mUO?ZY<H7+(
z)-J4efDvt9PO`Xz31Y{os5iPG!tDW_zEdpM!{R*xKQ$x%m~Q&dqlCr15Vh5yRS{wF
zCxqt9qM-xlSiwfabAE3P&d0ZmWlhV{)ALZ0<4_jh7okru8Cn;hvxP#?3N`J)u9(z^
zCLIYM$JEMR?+gR<s8len7HA!$Z&iN@av6{Bz!m(q!ht29CRGtmT5K}5xQa9#LM(e@
zJ*RN5DcO*I%u}-RXl{3pPU+Q!i9QY=0)T9hHWVppwQuwsKlx$yrB1dA3c~=H>swvm
zk;8XG?gH><;$QIjYJDq5H=z1uz+Zq+g6n{2nW0jlq+HepMoC|j#1|@Y3^X_X&R9w;
z&W8iqPjp9|Dsq2*|J1H?NPnCZDdIW}JyezwEP@4vY(J63tOPOUPT?ce%$~m2{~zEi
zK>elS>A5{eA|<u8iG{+`i$cM2tvsV4S%%zz!iS!62#x`wBz9{Ee#h|W!$I@31+hKc
zY~c_*zIlT%=l~m9L8{y?Gn;9qc}HoZLhILNW|h{QY1G<s{M5NZVD9vtjAZUcPg<QQ
zLAFr0B<W45P<HmM;H9#9LkE|<erS>4fNC1cmcXRwZvd3(V`;hB`pfxWRnJY;&5fTG
zDMnQ5iz(EiY?@ujd+P?}cdi=SfV|Y}X@dX8vN{z*QO}8~D1Hc$Y+%z8T5f%=VaP|7
z+ne^)-8l1c4UXP5JYhAr14Ut?a9VLn{+T8oE_GxPBmQ-9`}g<b>=UV9OfN1P>*@LQ
z;|kwyy}h?`a;j-D(5H54pZ(bH=yBPO{Y#;)>y#p@>!W7H3<}J@K2%>E6=v^&C@hPh
zf5dnjd$Rv4w>IJfl(3ME2n;KoVnb>Y`p5W_krMLuMXimhfhlDSU?-&%?q0|U5zsjk
zD=#q~vP}FB`v9)Sr8+gf=qd8<YUQC+xGTEJ2qyYq-n=y}i^zWRH3W(}E!nOaFACsi
z%qgZ^o`MJ}%<RX`ub#iBO9;%`yHYA3fzA~Uau|&upX-a(_wF}3NgP^{q0LRN>+8)S
z-K6+{TtYyEh8oIAuS)Qd2#i8)yZY6hjRSYTi2URHit9nmCp<hn=*_3jEv~nJNA*y)
zZetI-_gjhp??;KQyoH4vU!Ujr=&2EbGw)#9{BM*cO05KS6)XW#6#!)!fT&3;IFYGc
z*p$|3ec&ik2+NXkg<%oU?FW>XfV3J~29a<usS3jkAyGHOv|7!x_6$iyPiqc~uu2<+
zG%*w5;NmWM{V@5~25vWMb(DIaogFSZD3w#A$f-tObf*j9Oh<)T@L>W7o{@1-kiZ;0
zfs-!$Z)mf`i+fHpYq0G6h$?&;g|ED*Kvq!5Hqoh*SIXMrZ$n7|i3`6gaJo4+y-M^&
zlz*td!V!TwX4jHjNJ-ghB(W>d%lRorX8k*PxIc-bGBh--S0GB%(@4B<dAM{@P*9LK
zf1k){*sM7E7u8ZmNZB$`F{Hd8>qvaO^3~gN@jJ(@_Ru%|Vx&y$V;r80I{+{TfN#*5
zt}<vq8RLsb%jKN~EWebxN?zQT3+_g_n!f}(jJAe`?T!kA*;Q4BdUhX+c`(N++TPAz
zexsD3i<+B$+^RyEAoWQ3@Vh;uIC~VT%~22tstSQ?HK>K1WeM1H-~Xxe@%HX3C@eGs
zfYXT*_iZMKYoL(h3RFgW4opszcbMlqx>NV@XetvOab$^zbQA)&{`UXj3Ha2w)5b-m
z-?59c84JGOM{z+AG2(<2lDyH9(Q6oz;*w*oeASwm*~$T(*jTyF%6N8pZyRtV^d_V8
zb5S?i^F}?o!Uiil0*4AlJb*BqN^Fp+#@Vd<GhckcTF%_AIuma!R9VRa?0RuMZX^Et
zD{$4FZ?O2rX-i2(1vCee)+ZlsC(mx>s=wxP2wY$ojKAT4KYjBdp!BlcMK3O1t3n>i
zc%c~|2i#c%%<28|EY3XrHixVSIc#Q<Q3Tb0R7gs&Kj*IP-0pwl<iO6j#V|xaWbTy<
zC87Zi8j#_-YlS>ZCk=;4ek&~(sc)cst^&0$5%i`19fHk`6d)1dop!AXsmt&Coiz{Q
z0~#od81${qOELc_0VupXKfdIVtVtiLB3kA%wIZ_}yt(7s1A6CzKt`W+i~)A9M#e|T
zS2}{TPHAs^de1;^+xS-;W8dCO(%qU&<e!=};lPiTb`*YCn&^W#>{$pgL>bx`e5HP5
zYi2dWsm}DaP`kJqG!>DCW}sn+EBivPqI`Sh{%D1O4rT%XqSDF8g;&)y00{b629HGG
zjiBH>6DB@S_x1XtX>Op+{=O_lZ=P$-%~USgf3J=Vv+leMKVfvP&mEzMMEQ-u4h^iv
zBs*53gK90G+Lx}lzTAfqaHUPOv0YGfP<!f{H)0&W75!Zkb8rvOF6e1cu4nBUlrngN
z9fr{Q*FH&A)9^j)&5${JF`O$yTvz9WK=5|R<#y;x3M=3Ihk)}h09KdFwDTGuF!X-K
zW_ui0m;V)G^7UC_aKLvp;V=EUD8I1R>C-S^i7~{JGl%!+Ew~2%0d-?I0O2m68P$=j
zcEIXCW@kd0Al0Fc<`=E0akei}ZWzRHaVd$;LWIsv0mns@g@1w9z1;LQJsrcVHFLF0
zyWRUP@5hcA!q{Nm9C-S7geeh*Fx#2P8yE9Er3aBw=@9+_e*5=#G8ivzEAs8bpSQs`
zdu4jH#$T%cDt*)WlPUxaL;YI5@2g8`C>@7V6}RxH4+z`?8+vBPY4v3#zKsvbrBaPE
z+rKNwAjeM-<Lh?(JW7NlLQX(tG}x(A-5aLJ%ZFkyuxr!?r)h6TizO~3+26x2`!xA<
zEsmlyxJ_;bd4dQ(F}p6hk8$PSZn8dh;c}vB@ab-*B2QDFrc(egY;{p5@K*pVP}_C6
zV3Qw6W)=KLFIZ^bsa8m&usnqQAVzF3Q?2sqq29cWu03mKonDy0A`eh!7-53kitT(e
z<Lih?rvJ)JzDKokZRn}y=cP>vGS3+z(=<R2M@o+8PlQiZuiMLXsOahB$43Mv8XBs1
zKir*c`0LFNoC2JoC%>56e}H)khxSp#VGWKy8~snZp5)N-Ql2Scw^i2G9-o{<UZ((~
zAEI(;u02nfb;pi+U;2sw!ccXINK8z`D^x-%TOi(`DEy~<c0K~)ae3#dKT>P{$^mfs
z1(}LfmBTBi??BXxU)88T(t{~WQKw1xC9Ia8_n5tpqyq&XcP<$CP(nvX3pAxD$F%$q
z0Iogg=6n873mFJsV|4;8ow^?F9B2`=c_8#&RRa($H;8!G9c%D8>!ifPZ8R|}t^XmP
zEa}wALW{$)HxT7^6RsQbh*)`<XotUNzJJuDYy9*-5{c&%@Sw2EjNB{3m>C8Uumt<w
zJX<L{D(>$4a_tja%x}p`_|2dE(%b<x2OvH8L|piu;7>VU!5LG09N@4cK#2zHXECj<
z&q;b!m@5L-qglnT+9zoM6!rlY&<l_P0s{b$Ja&LJtQAJm_Fc7U+HVZS|1kDYYu-l&
zFU!568EN0u8s_`VCJbtRZ$)ot@lziVouq$@<9eOiKRJHM_tMVxmPnCgat4W6j6O)f
zD@mqhNoa{Y-!7N}(sYJ=n}LKd{T_ae+94Z3mFHlj7|<s{RSv@dnqkt_u0O3Z%xF*~
zp;H)>0#k>Q1rzc)i0XzjzFoT%H4cjdE*a9f@rK7*P=ie4cra`?h?9$%*g%k8g@A%j
zBQ+U${yKlkXa`|?M8hNUc<=tgUJn70%0|Z6<8lg6Rq2qd1`Pl`$G@lj_#1&57&JxD
z-__CHKkeZb3DHM*wycerSo$UP|BnW;NWMDBWAbx%M9sz^*)ItKbw%aWWD;{H6oZ2o
zTkFkGUm&tZ3T2}4uyTPi-}VM78WXce+MGyJ$yYh{PAaV74`TkcJ@+?@x%!cR`?3iS
zNkX>`2+1R}<ojUQ?Y6H4t(Yo4;U)m0kH8WF^_Tkd6><?&%vDr$@Qa3`J~V3v7I=J&
zf>3)N4UbShu%W#9j;#CZnP2>C$b)>rB(#IZU4BAWEL&YMSubT%1f}K^JW31X$r&32
zi6Hc2`k)fJ#~NV)-469%9>z~`_#sdgB_zqmv!p1kDf%>n)#1TD8z6h{lTa{dArR&~
zjVS-oec5syA*YMBJrpG%4P6mj)gdY+#?E0=>4L{-6h9K+E*@nb&L==Tm+n+iQz|mZ
z&!k9@q8WT`oU@dZF%mB(99WE)EmgJe>Y_2xI|6Q>%m-sgzh_PaKIgeCSAgqF!O@T+
zyiKrtX8h-_T8dq7z$Na*_gPzyb)(JqV3Q4j>*nWusN_xLOTH~Q=sd$hO-X%aP@#H3
zR0{ix>ToMy$l+TOE-f218V5h>c#vm9S$z|fhfNnK0evX#)2JoP)AAM6L_qZgkY5-D
zZByXL=M=q+sm>rKbZncaha1CaImFA<ymmYWfUB;D>|wy?!JM+-(@?r<fR6HuhCqJY
z=Z`>zi~YXheZIasve#3ZD_AhZ7A&_Q=@K6fMDYpaAq55s%B#gq<L4;jMI-OWNGQ`n
zBA~j_#`{*=0IKhFs*rbbEY2uA3jDSjnGh*pGf{2OWIeR?+XS?+(y=)J#+AwzJR^95
z?*NMDQrG<diTr_hjySoi55(lH3c^?fTVKxGuJz*Cr?t>(B>5fmx|#7O`O1~ku^>Uq
z;BfS3r}B|NEAlh!bLcw;+nhYLr$1=pVuo};Ik~8*@^Iz!|2X;9U*Pi8w;1S>2G!3-
z;z>gpTj}|5!yc21?tKk7D@(>xnpfU9ZU9&o&SS8t6@)iBZvZ)F;BUDh5&aVuzR+hJ
z(+4^y!`4}R1fq_A0CV9WNgLEkEagTTh3w%nU*4<G3$TSdNe!bt>K3B~g)}<RhCpv;
z6Y9)x@$qepte1}jeG056*ilvawx%Qv91BRT3PQ+GGY9`uEy$&Sr6Sh{zLB(d+<Jsn
zzQkL`spzbT3N3onRxT1zGZ1q3^dxyj3v|xItE+dABc?wfSJ@hZ(NmI)@Vf{!_cSZ6
z3Vf~TW*sd2zW=J3R8SJssm#zQWthnxS47E>p`+X8G~Dj_SB)b?|KV82R_<d#H+T9~
z@Wk03miUXn{h5ltRbJjgYU8Zi-13_dXiH-_MR{<}^sPg~&5}HXyA8ZwnUl>H9^nRQ
ziYh6jXxc=y6BI|@GO5%8w})$->*rtoJ4Z>tIr0ss={HEr^=BH#D-UCL_^u-@HW(9~
z^Kmp=M_mZNXtXg^3x#58{1FlkXk2=eFEGdhQW$)drEF9vCF6O4*h~E(yuif<TiXBr
z*<cM;KtBo?wJ7q|e=<cuoh<j&?zB1`ppByOCZ;zG1o4A$XyQTe|5N4!aQm+b6NI?%
zB}pP|w&We8((w$S*1Q;<4b#Z+!4PwxhKsfDxw)LVyle_UBCBZBb4A{N&UnEV{f=R-
zYSzQO_xYs03Y#6$m`ukFJM8;kx30$>j{?WB<32;l=}U9B01B062)o$f8&4~S$3cd>
zlAQyZS{*2fdPu@_IszHkiVSV;_P(^X49OJBA4Saa8DcVm8q=C6jta~RMEN=z_VJ4F
z^^pzK-C_A;Y07hUVPSD}Law5VhUsB4wCmhh&*V&E`P<Wm0HkpKe(>gM(vAOM_5!}i
z59S6o_}zCpi<lzNX9A!W5sHgX3eKEX>u51(T`m{?L4pA|eUg#rOecYbU%&V$*Gg(@
z@f`54Dd!x;CJf%SY99+l!^9EEMUpd_27p*6>d^EP5v2LpsqO8;cr({z!+dj)hmfSd
z<6Dk)1dE=_ppmm}bCD0*J9nr;a-06k!EYb2UkrfIDKAu@dpG(YnFLLIMh#AcIh8jL
zPgZ*9T*9So(m4%6erP^{)$ux<!hXR+<>iGMRWr~aR+y;H|EVJ6QbgfZNkaIh08y2y
zR0GX_lEhI!Edj+0uyvFdgiTJ+?zX_kKzIbUW9TI1$mzwWqTLk*>CsFR@|h)#FxOY4
z?u~Xl76;v}>B;>rn6^}xwvAm&N!#yo`8A?~KTOy~s?7dvv>G!sGCEtUN=Qh!zrWw!
z-bOpIi+I5}l1;*NKw#j_uxykbj*Y}Q!tbazbMxB&7x2BcvEga^aq^rktw2ckYAVzu
zXSkKq@(m#^QB1hw)c8TG58agSvr&VWT|xw{1-tY0@jf^}rHt);e((Nl)k)h%!t3Ch
z??#`T0p1i3V|3BMd~;JY4r)>0coL})xBl0Mza8nN3#l$(5=ZSWvyxFzgqEQ3qXeO-
zY*Rrm!Wf}tRb71pNV-Mm9=Eo()&>2k(sw~RuwWmBC+CWw6cvwhK1`(Nm!n&~z#u0l
zr;u0>@~*ULu55I-a+n=WuN=w>drH$~5jh!uiJ!>hRBJe=)AF&RlH)Z3mz%xJZz#bt
zzE5JIGsx{vN9#T*mw@YvfM8Ig|Dolit<8zPN8ncSknHbOX8$KU(4oX!su$it)rGC@
zZZccjB|=<Y0=u*$@BWV4dYBBhmI!$X39ymp7MnkLK}+a_VfmA3zDY+=>&tX#&e#9C
zX%aggT1>6-=4H&r8xe!VuSL%;KlKEvS7xb{`LfIyckl$RSZt6!{Pj0!&3_x<|Lb(*
z8viZ%ZDLnw+vziG%>`VlgXMR2{f*nIpYu!)$^RZ7BV=~u(NzsZq~_gzrFRG$&%?26
z0wzdu_vrq89=<u_-O<?GOYYbo#m@L~Tc#(>h&Qr&DSNg1DWBH&Mt}Tjd4a0Rc}WZ4
z*x%rAQBzVpl$k&hX({(<Rry2r2|SFOi?%OgDY;Fv?EGYrXZWu#yIk#MUu)^;={g!}
z-6g0{ahtr6OdPYOcQtDw;5$8yGBbNE>)c{PN!9-DLJPYO)T-3DTv=K9h<cJ*;LTAc
zP8k{K&ua4I&sk^DxD42TLokUNbl>~ou8cT%>KDZL;dXV(A~NFPI^Ej%sS_DrQIh`$
zn(N-*kOtvh!tmQVSrPt=96h1M))H~U0K=~W!;ue{8G#+U&`XS`-udMBJ;yYE^A56W
z^=X1B?t48TSBDhjYQ#<-j_5xB`w2!>r3*G0TWHPn$)(<<zROYYf9*A#s<3jEOa0TM
z5#7Z9VK%;@>dC)X3J?45KX}}+^Px-Ax83#i2L`)Ztcg?7Xo$I(n+Lq6A$z@d`@m>H
zudVUc(uDnF+x#taF?y%2J0Ow}t}5@jUQp_|0_OE$JqQ;6Nf%G0votb){cxpEcWk97
z19#Zj37Y<Wx3j(hr6|6Z5dZmbTR~{oh`RDi;50na|N9`W`?P=vo&dQD)9L0nGtS75
zu~e239O4f!7n`@_bfRvS*G*SG!$Y_n*rzFwQ2HtRsQi~NZ`;xPfLlSqRvqo$pnaI&
z7A9}`yHuMimF^#W!LF$R`6`W}Il?Z^c6PnOoX*{Uw-bXdio+KbI!}mBYGPV>_5TfZ
z61T7uf9>MIn=}9G-vRN#%@$Zr8=ejcSml<ym;+s+-|mJ>RX2>`toCL=-ROGlr_FBj
z?j(Yi{{{EhX*%3p)RYiAIw0+(E*7~f>jwVf9n&CjhV<&H2A9!V3LRW;s}e7jQ1zFM
z!+!|1&ghv_z)tqwTbO)#L&hmVe|^2$^2G|YSa!WV5fr9y3Hmud<klLz*gc5_l@Q}?
zv$Fg1^$BxuZIs?=sp0^7t1ny%+qo1~8PJ~4G6&ow(Tkk6Dd)vDx2xl&f0su9B+;1R
z%Kk^K32;-vHPW9rlZx;l`B&yWbD!-c^woFPV+hhP_;6V{6stQ2TS1;3Yc59kUTBPN
z!)0u=x!v0u`-VDX$KC$P{n4wfFj+G6YI8WGdi|$sCW|wq1!40V$6_apD;XTTv&ZY~
zyEJ$6$=G<(30I$?&HoBHsX7q4yO#ff;(Ej(1)oYH*!<(<VHeUn{^4?aZlU5IR$Hk>
z1t=q+1?)o?2EIS~E9Kaf1Z7zG%;VyFH`kF}DSHTDnSuiv>m2AQDB$r_&*vC3<bu3i
z3H~v@p$6gfyi@2;x0;O?O8cjtxT+q*<;<mmV{vMq6Ees$r*@*w`B;ffPhZzwcOD#;
zxOcl1N^C(t&fuIEU&YS1V~p4gFsYB{sf~D}_vUuZA=_m^XBhkU{@ngbSNA&$f8}O3
z(LX^CiKf&zwE;b6l@Xszrr}z?0ifUziz`W^{o|3t6N6Qb1T~!W{!ecgP7zfpzZf!}
zzqz~gQgi9#wR#w+-RN(P$(Z;UhLV$^^UN7;W4ylNr_>c_i2m%EAq2^Y`qGx_^mvRi
z$jM)f2_lRBqqgd<|3~vjk`pTb+f&P&hWXRG9jzFPcC^>ijLi!Tolugy(bk*SJ#L!Y
zCG<w0Egm^jKsv4X+E}&8O=-xV+3n48y_t-vcW*f!op``W*VX!q=>oZiGudi>wq5g=
z(Ar-bkt*3=2h+U#D@%)o+rvOAj<Dv-dD*`mGx14nqswjVR`F}?GZF_*XP}2ZPF8yC
zA4|d~iqS;24|6J%r@0@w93)s~g_6z{9ySyr8irs#j`Zp`CqA5w52tNUyJ$oO>|K;r
zxxdso<5Sgp*?H9qYtx7ZIO8CqoaT+Vo%LOuOX;g>y3YgkSj1&_A|J)vKB_)rS)6p=
zH4F^A8yOiXl8c?IG1AMt8`O=HiNs^DMQ#-9y$$rL-{`%s(91mH>Ci>f6KY*Krh-46
zcbxy0;%?VzZ88qQ^D$m@y^BcQWl*tBC^-fp-7$_MWXIOM^ncXU1)i2{%Exc)q?~=-
zm(v$g8WL_RK5JpUc`&niNG}nJN@?Y)gChI0G(Sz{DjqE*q;(L!H}AGGh!5N*{>Pdb
z$RXs6{F;!XUCfrfc8Tll_CZ}py#k45Q!w(l{v)po?8Kel0F<E{&)x_;ATIk?5eUM{
zEPZ^0O)adl^L>9A$LJ6B1-I9*<mf#&vw3TsJl8r%E?BZ+5Z$vB8Xt2&R<^hV^J;5G
z_Yvb4mT4cJEE{0)eUa9PQeVVUVroS2nY+8khJL;*3WQ<ws{J$ZvuCe#XFB}?k6I4;
zoj&@$ORd51VZ6U$;-d-;dAt6z`^($yy_pe6U9GahcIBKsJ`Lw=f=V24`&8Zu<M*@$
zejGWc{%8eQK|YH#wCo$L+{_()TA>XS`Q}foskWC}6DQUi{(^_6XihqCXdyX^RhuV%
zWMsq1zf<N*cRJQYLlc964+qxroAAK*zE)_*JV<+O|35}N&d8J}6)ed_MOcgG$e!S3
zA-F%fH}+1&j1_(u#``NxmOG5KFC^EpvP6zan|MT72KALK`#*_w*8ssEcIY(TQu4ml
zJI@Gw-Bqp`H2=XtY}P5!Mg6^lgALZcv5j#~eI5&EsHNlB-x(*=`mfs*-{QDavMhR6
z&WVjEQ+`&k`CW~Q<41o@dl+`e2|Z<;m6GoBr@7zG2z+RK)m~M~b50a%TcM9j6+9uD
z4Q^UUO?#i}{!^kRTWcKHOhxk3H<64lGz|njJO$k(+N!FsU(sH;f8iYcnvd!If?Cv<
z;1^B3V8||lG)I#4U4I|jPTf0GZO=4Deb!7|xofJf?ouM2w$zl}vFvH9Lf9#*xHrG4
zw?CI@+TFm~8fzjR8jp7>9h^>F0%OBzjL3tA3{tPH)$U6rTOY4u07ib2D8K}wJS8Ga
zwv$mBpf+mw0i_KM#vKwpt4<zpUat(<bsSoEw+Wo4|30FjfzNT1nwmN;=$A)nFyj*#
zdTf`H?<XSe_ugJ0J)cE-jGJ&^^;Z~m&)=EhjTbT2F4krKP((d=m=m9W1J_fYH#VP%
zMVCCquZtbp$nb^#ZYTCdc#ay;!J>~v&(C>{qK37P$W%nFZc5t}M2oyMLoa_-<9wz#
zE#Y$jbOZoE!()O=9OJqMAb)vZhMvGk9e3d^1fn7x0e{aA0Hz8ct-<i;o810X-&VU7
zX!6ynzvd;6b*IMP{gyQuGGCQx$iI|2VWa}1_`jh!N5cpVWCUK1h{$b%3gy{(^eS>W
zWEhW?u1@l}QZ7aQTIS&=G5d+vY87n*7vaO{EaqWGz|Hk(M)0zYi!1ehHfJNpnWy}D
z{Yk5`{r6&ePh`4QXX$$5$Z#!z)^%+8_om$!B}m{BUgApj4SlH)xl>0A$dnjoHc&N`
zm7!0{n4_(y8#(tXaL>F6$fOFs($mwkpjZVcIQk8~`xRxdXb8Tl`6Q|{+v)_$CClp5
zk()mUZs@3!va5fifGR=32$1Qofe+AqWl>sSpO`|@(w^x72Z0-5lNv&|oD2tFTsJ~n
zMIOtXUqf-TKiN#uw%8D<-nAU~Jd?KG)-?U0>(MIlcXuNcJ>nE|v}HRcR)xR&xt&p1
zqM8vOBxtsy<88K&fw|H~84qrEki)fgINb?ukM+CRg)cTzk;5F_H~OOhL^i!rsPr9%
zEWts+*Q+BHsIZ@nPQ4%EllcZitizA80r`2tzkuNMtkK7*ysEjm_4r(n?=q?i&E*>2
zB5u2{oD;#_?LpHTgq}hKP&frJ86)1~Pb^kOE59Bc9kp?6xF?9L;y;|?yOro&xs?P@
z`Y(rKfPT0va>Bv7PZMaxIso3!qsC6p^&k&{*{(e<tIzT~*0_8?_~oIeiwDtIB{F#!
zw6w40@9+eL0h2%)n+VFpM2BihPw<+$p0(F4bCo4){snR36VWU)oC)Sj>V!b*U#uGp
zmknZ<Zm0Z1XCea;oQIi%@<WdD7!69&|GF=h#a8^^0)5mf|CNKQj(<y^0<U&cyKex_
zM`gE9)&5C`*8#0~0J(?g)1Y0ZC!OGcl$2K-FluQL+*1gQ>;vjz)U(>Q^^y!z6jOk7
z(TUr+3vL4LB?ABq9E84JSydaN9nx5UIi}mC$-DkCPI;d+aQ}GYy~N-~yJ88YbC;id
zz(U(cy*m3p=;G#JP;hVXZZe)9ex^|HwWrVNR;9|sc(&hflGyK*$NP?cKaZYYPo&ps
z2jc40Ic^>1h}WL|qz!mCMr9yghUsFc#UE^sb@JglwA?MEFSxS}!fJBEIqcu)&A@p;
zZR#jup-a5{>p}(Pt^c8pcBREqk@bgD_~p9HvWVd?qkbvEIP)GfvKO}qV%|T(_#Xlu
z<c=1hh);^$S5?}}V&~prgOXo)>3<Z;2PvygxADqYgbc6Tfub6n^!2W%GrBi_ePO{;
zJ#xjU7Y<Q!MO%Fba{}ED^o3(jzC7The*JD2`2LNhBYrQdq&!ApG$}S)e*-)g&VtMN
zLK6Mg6Z*HGy^rvNP9fqslIcPYbI30EScnjgW$z8Du83}(Y2f8gn|~ab{?HI6>_9E}
zhPn=xPE4%%0rDMo_R+1Z<xu{%@ZF(^>Bj*+qWN_K>f3s=v(#1J(c3MPPu+LH%N~86
zt|`lBI!`9-kc1@2&k1-p=(swck8i6p;(u^C2VKm5tGJ8K3(ObwJ?vxQDrXSiMbtTV
zde%R*Fc}!(>UKZ`C9a1Oa*kg*e->JM7Vx()#aAm-*Fp|lW{m!dd#&x-D%xSLwO^qA
zu$<vGF7eKSu2oG?==n{Zg+3M9faDS1e=Dbb4Ze1u4FZwjJT`Y~fP4K)H%;*R$<pU1
z4iFj|8ee_51?l!=exg8ez56K=g1>5!!_#@%2Xupywl06UL=uBu2(EbpigT(k?Y^OS
zDl=4K30<aD3Zxw9?Gysyw)}3V*92aLh{qPuLLsMkAJa+qZ+>`{O_Hu1Ms|;?wP(NR
zcTj_lPdkv41yp(yHR{y%2$e?}e6Kn#d+vX11R2n_Ap662u?aJIR*W_a$1(?bOqS2#
zuh@x(+(ep#&w7RZexIi!h5q6{@pOFUy6CT$d3d*hnNQ=pL81M;?TFI1<hR(tFIP~B
zUMOTOF|nDM+5u4)ge<AY_Lp+K7F?uBA+Qv`<@19SPoM<_3f32wlb2&bH(%-uPU>i|
zoWW3<L~cWKT)HWMiDB~=Tl_$=b~atcq7yv)*@QlUYK-_jDM3d?Q$OvxSCYZ_gxR#6
z!0~`uKLuV4$0fsXGy4lxhj6VjU{GU=8odLUTW-&FY`rbBm{Xs9;DlTk%)B)gKWbG_
z-+G{;q-(#Q-?p=$ttF^=C60(^#rX74`72nRu`+^*<sK$O<t+|X5<#BzyJX)CqQ6Ex
zphHj=ix?jS9>@M7F>Nhs@hMoN`>;3hu)p{2cg};ag~EmELGY)_7M9u5ySI^Vk6W>f
zTRTbVrc)1KVHh*C5O*5;9pS*8S1W(Nybels!2hO47wRpIo6*((cMU&SM1xI^WEqFs
zanSenZ1`-Pk+bj4>B-YsPwJzDhg4~B&1Eowj94?V0()ba{riwph2Ia?HG6M^4`=F3
z=>i2|hQ7+@Ljs2N9=v|1np5ephXMn*4Ez&I&&_Vd>X#69z+mzn4>80)nN`eF{%!Xe
zZ}WGUG%@mbS9=iIqNKYW`1Bkl#&$Et!%&#p>F=7h34Zq?m6yTU?RvJ5)4Szgo$gi#
z6iqqz$KO(VzvEgCxV^b9ebW77OX<VKj<S3>oSY3CQv7;gOsf+sPZnDLn-Cixpg2A8
z7F8|ULQyk|2WMxsL{x+RNKvo74mwYva_Kw+I%0<H?puIXR&S1T2WFz;p}k0}$F{)R
z;?l_zC(5Ui<k)O_B5qLiKX_(!{Sy|DIy#C&TJk=3v1NpD@ItCxyF{3#PT~A%+rcg}
zp&0Tx;bTT=`nYnh3G&wmv=9BbJ>UuS4<A<p6-E1y(rB&E%)dkCr0eSm4+K$X`_m^q
zA&Gd0;xD7TNIGR2EUoQ(FQj+;I-?ryR*?>o=nF=aPgGUbz9<@9Mtm)3+mAUXWIk}r
zL`nl7y}r|_L17PvnUQImn_$slOAOeP3q+tPjO1&5hi_Hng8givcaYVgwWP(=9Y?ED
zUS1v`#P|=WSH%yL;fr6xG$z}6k|3mVDb}FZ==CUshtD$yl#z1Ca~TAyvfS99Lf<Xf
zNO@l-(V;aw+>L7O9Z}8%brUsv6e8Qaz`ZOhyT~B^3oo22#g_|UfSnAyC7Qp23roqy
zkrP17C2@3UAckJ$9r@KU&*CQE_FBLor-~@UqLwFL$d?LC2jg{S2jRefwoJfvY3%Qw
z)PB5NTZ$=B$taQ!s6;tosPdK)e5OXoM-Lr<lb4e~oA))?dt}9CAUyWwGEltc`>9Cr
zus|&lV#cc3g~KGdNJikDQ3xz6$y%B4dtM=3E^gk{xQa)()cgS)Qha}k|3DB+;M-;1
zTw>@&z9p@Q69wkeXvZ1xDqmbFed-WFT<+Yq2JI~W;=|vx!1jY77Eca%H35Wl6n^<q
zXBS^PadsLK>vualYYmL5G7;R4H+z*cYaicllG3uE-lBptg9^E?f12n}<YqrW4m_AV
zl%9okNOJqIZ9rLD>T!HvK3;Xe(Zr>QSmk&^FFMVq+0&%>G`OMlc)eA|q#ErkkK<R`
zOFJ_IBnaZZgwyKBDZM8dkWAxWRRAScHNU8AHAY{8koLou>&r9qrAY}PCDxL7vK18E
z6@gv323merqL%b8O*5IXG7YK-8KPri5u+BMJ7K*i6K@Qx&A7*MR1D5z%q6SvNdI`u
z=vj%l7JNkw|BTT-L>Vvs#|u)?0to!H3J+ohaupElW)8rAJgL=sT>TOWV^SR|Cb?>1
zf;i5(WIi96O+H&Rv>`R;5)5EoNwJ86f0>5#pUT1CaU{9JjnC;n7m<#^jU!|3?Cd+u
zzcn!R9DwdbgCuKbY}tj>tFl=ESyiytm(lw`teQMpeUj1Ci=nTQp7p0hFjE=apLEmX
znzX6!*JU`gl9^HlfYpmj0UsE2e@E*REiL>q=1Cx=N}w2GTTpGw@|Y741%<z?k>M>6
z6#O(yv^HC05tHFd?Vv0T>=l+x0kU>y7C7uWO()5;{z7Z^mXi3Km1lZYAula5F+T9P
z1~jbogDCqy_ReL2#<#S;CcF;kjY=lTZ@{wEA{~?z;)N~6&_^|*5oG5k7q+T{c*e_d
zcwYEAl3|eOia#3q?Miu!!>$6ZL&TGtHH&hq1<8NLJPn+n^jKu1st{VfiaubSnEWuY
zF`o`eTRBN*y~%M>q#}Q`9#g5bjx!Nr)DqZvt9$Mi-Ku#oBe^cYQC`#2)~)m<c*Q%+
z`o!#nM_U}^M=o2^jKY8NMT1pP;FD9rJ~YXPqZkUp99jG%I5`O!7<%+T??@9Np**XH
zX(4sNE;GfE@Z`BYXm#K8qI9k+QTQUafS(!llB6F}dj17%CLmhMASMPdOlN2{)i%1K
z5L)W2n9b2@v)uKsnJ`qcaCL6c<4O(-a~#`Q2ENLJE_xi|dlZ@|erCmu?YWd^wRVoG
zn%}jbR|+J@R+u`^+mMh!%wg0x_4tPQSRie)IZ!OjvmkJyqNK-4iB_KsqehoDN24H_
zVyaSx<>94}mT(+)D>F=O-L{XRpI35aoOC$jVWK(Xp~0+xZd3<!T7^vj)#UIEgqZ*~
zpqdLl2>pvFF&~~jOADUjErBG@kLh7ziBqoLRv;RSl=$g?FetSi6mec$>MJ0mB~syd
zbV7@-e3stNO;ps~Hvipp7LaSgc>iq91*T=9Q7s>Xu%xJ;*a9~wX%r9XX#eQMEmIFR
zvR5`DCR$8Wr#T~HdmjBI#VJKOhX9D4Pi6D@pA3SAFg(zb-ZLo*s+MR$zN^3TJpNZo
z{d3OS(&2YBO}yAJ6}n$04Fh)kOM)c-e{!>GqrlKnarEDYF7o!;&d-a8Snb2c4wns{
z`E6Iz*yk+G%!F~{pnc9<Qt0X}(2%PN0i4U5Gl4f&EPPt^%efPTTb3?N7^IvrYfvm4
zh5^uw@WJ5+if7O!o?S^euV0}EMpZKB8$%o&TF7Bed4`u3#1oI;X6zMilFZLY%~z@V
zp9|??)z~y%Ji5Ewl_G105JH4N2Rijrf+16KH%WzRIuJu6>M|rvZAJ2{du2p4LW>{H
zE&m%;iuDg3xZXrZMsVs=`6o=AcBLpa>Gr;uM?W=qFF32m=lRdrqxCgE4uLVbCk)I$
z>4GE%2foCG>N841Oy545y_*2sG@|qHBb1=T1I8eEfi+{x%xm>L5VvrnxG(Js;3GzF
zO)9%}cOP=cYFMZV*%UGl`pCmLM^s=x!AWRA5E(WQ{pKJinIEWH1$bT`h2$i^m3dfi
z_%UX5ILKQw!8VB{st7}|njW!l4h+oe&U9AKh>C`HxcHJ<9B1A(1QFpBN*PAh0+qq&
z0c&zhS)4MYj7M3K0HLaY4LbEhHJ{w<q6ior+(hG2WJG0y;6^K8W)tQAJXoPQIUeul
zCKhT+$8`n4%f2XLrx${I!XP9{zQL3Wyb(Q4o=cGtIv}ZuQ&1<hB8?CK_L`80Ag4|e
zIU~vtuZ<vu;DL&<o+SZ0OL|?b6PU(}ni)$VIK=8UBN^53RO7cMTjq*n8A5)Sk3Oss
zpKCvUhV?1epP$|>ejzvo8NJd3jP_5&pz>nR!$GnK+AdeD7}3cZB}9Os-1NUOXsMZm
z)zh~UgyK=7$R3@4Kn>d#=H=;IMR}5>zblpYO%So4jKCz<(B<PD1>Mjvp&sTW5$M~}
zLO^_A8SUS2`SCWEtgwQo{kE)fzpI)7jjYOx$f_y(Vpi0p(7^t%xi=V`s7Tj--CjN3
z@i;}af=Dod@d#RUxS%wq+fn#nYpwp>f`uv<M=tnMdEW?Mjzq+-uqu1X;`^j_Z4Z~(
zfWq(1$_{2hF1<X9oLmKojJ(Y)cbep#PHyEmJk&${T@F?A8EKrC4YZIYw6JtS`Zh^6
z=8v<IM-8IZ--mR{#nfktlKGLx(Tt*?1k+_ZNscCR43223G^Ny?4wu4|XY9QCh;P!A
zQTF~q4&g3d)ggB2gd<*TOn+P6HhbNJ^n>YndwM(3y=k$t^6*f@o(kh|sOhIPH+nUw
zjisfk&^pKhALq3~UTK?$(SnM9Gg`<~LsQWiRL+E1aivjU46~KomPQ&*E0QZ%+67ZH
zDD75-(zQC;DzqbBiXVP=JR`gcti_Wo1IGcU=no;ki{~uZASosm30OkaojIhqCTg@e
z*|t_R8Pj^w!a(WGC|pesRVvEX6-^w~5El&_?3$>#j#+`AniS@E?NLs~rf-^(!#t|x
zn5?HlH0Gt4e00NsE&TE(G~6~|N{qba9<~hGRArP@WFliJkb+H9qRpy*Y8eq`2E1h*
zCUp+wv*x!9^#!&|{IE}`QUr`Ix6$?sh*b)f!ZhfgViYyJd!}aV)DSb+N@<T+q#TP=
z6>>oEdlr#Z4{RcgBR5~voe_oJuNpbzA(r}!N+Bm;rA~Tk#M*-@KbmzUk(A>M>l<;r
z1)^U?XM6N|sh6XyvO=oKh*i7>Hn224`B%~#dzoQpLqo~RrKI%DCLMNbqw08z){tze
zT6$%shCaTd3~8xa8Fu}&ubfa3OI}%NgxKh3!d>3ZBpP@jm>A{Md|I)3^ggks6BD=K
z4XqJV7jI1Qraz4UBR%xCMw&V-N-H_`*`lGwDlQa0oPg+N*Ki)?CAZz?D3ac6XlaQs
zV)<z*A84W08)nUGk2<=Xu%$*G*MY!%0w51F!OO>v1!f>~Y=Mw9(5N`&pebU$Pak9^
zWNPcH;l#t|h)h+JhH0|-Qpabbf%#S`>VINO_oHH1fVar_nTl!A-+dL|ipmaaqm)@m
zFL;q|DO!&}EbcTizm}_>dX`G!FkATXV$|1fs-YrwUfN>#KtmCe|2%}3OoKuR3~Q#8
zaZqWN1C|mpg$lKR29QXr6n&*vE_^gW_$REb0aNoEO$HbBkvkLnn@))%?viZ1%X#-V
z*pgeCZyeeZiFRBtemS?}(Cab${7scVFms*nO@y6Pabt<}!^?Kbq{ko>ibijkuQsUs
zrV=d&A^3Q_0@SoS-yzh07E7^kQG#NN`UcviwPD|jD8`m8-xLs$k4Uio)mf)+E=M1*
zr2#ddryA`_!3k?>W8<yziszY8F)#~W6X0@v2`vv)Cc@r&$t>@+L`w2Xb1Qq3{?kWM
z5VIi%rq#>{Zqd^#7@!yC5_n(~d=~q*`O_!=ckkRZJYu2nZ~AMn$=ZnHGAch)N=lIh
zkr}J6d{!wKQ3yVYdiMD2%gV4Wem?#$CEpPhyhZ5=T7ry|T9W2ZpE%(=;3-Vc;ml!>
zI|q~s$y8KSlp5eV?clWJ<=dN}P-s;kc{%uAr0`o$BQkP$DGJcZzMLALl&q5Npvn_a
zQ7SZyD#qstsX6}~&Smcb2*N-~?K=F4>3qFe=oZJT(^)7K(=i)iETsAj{{y+`d5X@Y
z?sp3JP#&(SY<6IHw>W~&EnPlSQ0A4(cOse*@9k(uy^E!C6Ri81l?`16C_ixbxscl<
zX%cAj)Fqz>|IQK3)7{G8OP)pcrpSb{Qr&n9QTO=NJE?6PI#6X>>t`s$#uGq3BIY_v
zhcUI&{^YP2x5iXCK?7XORMeyisQxIm^Hv)ygA$eY9!VCpK3&*(uHTB}R_=~{^y*B-
ztkY3R%yTTHLF;UiOHhCB=hXLjo#?PigYZzctp@2a;E<S8tac85B=j+xH>hU=(CSLh
z)or%SW#qYN2DiRi7g5;#L6`bGUmz($t)@no{rg^Lj7XmFe6;?3gg8&}(+j16=x}!$
zqT&u(?3f9yr<jtCseSaAqS?$y3$0F<m2LoHeFT(40$t{+4ZfGEeFk)h$+(S0tcQ|0
zuD7qr41Qbmy4E6?LjUz7Q@-4a-|XuqH5~@@yN#GZAdX}uSt*_HK}n#`*+H2#F&*=~
ziBE6FPVvApb?AahHKnvt_`<k>6fvNHh}8N{xfA%Y;hk;-7-Jd`>?aaZv>GMG&Jn-c
zd(EnwiB%~KExKw}0!4{|B*g8po(d}!43&qQu7rVvmwh)TgYP}=v6hyW0P?}Bpxf(r
zJ9)zXmyUJmyq7IPs>sSBaa6vwwG&~y!!K-!PyGTD6@(arkNz^7@xfnlUn{7)GA(pv
z9zPwL!6+Ih!Wg%Mcc@T)5ilP}mPehv>M3K^J5n2#?ALISS?Mmk{3AYQyRYa{GO!6v
z8nbZu=&4_YSF)j-9!U{(k2qjym=Dtg0ZU6VjMdfm{k6oSI86RauTgA-UbX(&0P*YQ
z=4RCMfg;`CAbMp?DfsukWj#v#p!h${GvDkJJDadDwG~qMF(z{$(M9I;qwxIeNbM%p
zz?@Hx$38H9p;rpyIyMGHq~(m!nrjD#;St3!+lfd@qYSBIp7{ngH-bj`T>@q@`y5Mi
zogXezZ8f14vBEFI8$=d}I|=g#ZR4%Yh*Wx&ydNvz#X%6y`lU6X3xZ9?$*aAER?bUx
zkh8}};w$7SCa0z|-$-xQc2BM*UteJ*<Rr_dQmz=tpX9$E-I2MRH8O6nmKXMKVx^(p
ziPF?r^|R5e<t)0nf7TPf@$D>TT=l!k!maUwpGh)(>S~Ty1W+OsWe^ZX;g+O)q77)W
z1D9AUbfS3YjQdRr+G^4*C%|VNw9C}PU4hS7=)*)@S0!Csc)T9f!t-AFEC>BN0ty>V
z#?jyvd046gLkhvUc^cfQQ}Itv^0M#q^(;btK_?Cuflg~vIhGjkqV<Qc>sMo<(DHS8
zxe?Wwp*w4Lm#XKt(3K)Sx2ZRg#eqC$)y_eoGgN0Y2BvjMqUi@oqB~xOMLqw-&=9$r
zY}YUNDTS~P70Ke!=D&j`VE|S^QOs?9V6QAc=zLSjg9cVuT)goN;3ct&pGEUKXI&f#
zjh_j(YT7YOxG2_fo{9-4OlF<EWE$TrN`i{2s|@i@sA3IDMj+yh2>Qc)o+RaVQ{|DJ
zv=<~Ay<B=9muS|=0nPfbYm`4rMpc2soAu3e-Y3k22v2s#AI|Sd(3pB_WkqV79c3Ij
zeacbwg1r8-gks_-JA(3aid<-1l>Be#%lx}To0HEc;x}0HH0@5mS|!&S#)Uk#$AKc}
z3q@rSdnOd4yt0dCYZ+mZ&MV2>FQnH5N#V6Jxy-K-%9YyCb5Ak;<aSbpFK1T<wcQ35
z>ixDGO@!I`RZIfxuMYXj7_!Gy!TJn#+X9-9bRGMoCbJT5{mVhgW`ES&xC+o6pBEm@
z%jST;?zTln>zZhDR5$<r85tQN+_E>n6)UW%(t8cXQE0RQeK$akn_Q>(qh^)FV-_J7
z)j>#1Pfbp4E9Q$GR=(_->4q1j5Pt0Pvd(x~{)?{~KkrMcE$0vU^X4s!15a{Hv89=M
zL56aUM09A@%`VF}p!!XMusutkZ<MIkbC6Y3?wXg(l8rG@Ht<j4(~hm!7pe?N{&&VN
z)6raB!+e+n!fR`}e?7(Y0Y9O&)Bt!k@vr%oe}A}IJ^DHr`^4zd5@Z+_!7CfxjrfMu
zHnXNcN-i_~PJn`<)Z?3-YxiJ1`;Mw?yb2@KosYmoJ<7f#$3yVjXKXL8tM|B`PM6LW
zVVZP|n3}grpdLhfz(|}mDu{?J6RjziJn0~be3pZa6>^BFXzlW)LO2<A!xWPHaCLDJ
zaCh+bZHxUI8=D<KRxqvx=l9O!aG%WSILZNmNVXpUmMKSO^xSS4xpWDY4JxU@cCMaU
zuxPB3n`LVhmxia4V22zw{D0Mb=U-Dz)NMi_w9s1+snTnx0#ZU3q=XKF5IRb4f(S_H
zU8G4<suV$_K7zE+r3nZqs5B`epazv93fzhBd++~n`9yxnnKLKZGka#Owbv*!rhr`&
z9@yA5-$9UVXWXp6&a!Z@U{c|OeHvm?vzDSvk3l=|GiZNJE4WbP#;4gq%j9HoN2ma&
z!Wk=2_LJFiqh(4hRm$~@kf`q3@J{yK)@0E>^Exm!V}2a;TIraF)r@|~4oP(JFZDl|
zw`Q}P<`=Cdrwi?CPK}>7ql}=iOdTen2uUVlAZHqL#t$CJ-zrrQ2xdl>$E#6QLm4Gi
zj?b<cy2YhsC|%cV34REoIY4YYdkWpu?Y8tX1bC;1{rRwx;gjVTK3k*)tnIrF-+L`=
zMpv^CJ%0lD<Q?kql8IGSf^#9jk{o2CZ=9hY1I8s@bba;l8<5Qy^+OlyAV?J=v}Yw)
z^zrS6<Q2CC3lA1zuCsIADm+6WN8Q{>2l2=3{zvP_>2sFGzicyH_6|HdL(YSbZnI_4
zw^-=b-lu0XPiCTRGm>%Yx3n)ud<1nV;3%KV{lG&L|FA?YQu!@Q<8t&1iB*ABUNYn0
zq-WD?J%o6oQmaWnTUdK4v5iNYneFV10GW9uKOL|kRq&`VL6u_9jv4(B;5$#S(LjgI
zG?D;trq#K6ll3WNn#FAm<6<k$MGgHldL8;d%?2~&HAc^rwUr)J>LRyycmK>)tHlq&
zBDScg=^$aJStVWXft{MwCCSb`nR+xn8~>rmCOr($5ZvM0yv5=jx4&NR>djQ4{soz!
zLz6|bpYRXl4L*ejmMk%o;A@C@nL!_9E9CYXzWpJGAl{JzBdF^D6RZ>&W8gE82bq_+
z^lPI6XGx2gSE(GY#_rv)Qdl1TTcLhZvGRB8vWeR7jW<uOt?{ki^7Yj{x(oQ{Rfkn`
z8-mc*M1hMfBcy4aI-bHs3L%}@;_Coq9205v6vy6<C!#y7u;RIH`J$~E0f~8t9FF0{
zVXw*UNa5z#HO67E7!kA|5*UO0sgz<&r3%X?5mNRSgXNJ2m^uMtQ-_wcd>FYX7B8xm
z{F^2%Tz&x3N*^aTWFadCFl787JH^GA1>jn=#e|_T{pCeP!$Zjy52@iL4DKogPy=M=
zb(y#Y6U+u{)8bCk)0X~Fd@O8FPIw(nzxjHD3o_!M)+v%7U?9E&7RB=dpv6jAyzNS&
zaEvKzi-z>ZLOH}?1SNi4ssB8JD>jhxM`lIk1y_iPG#IEH{D47LitsJ+qGNeuoWa~9
z6SYTyI+Mh?^SC-tNF)>Yh!v((EC7YWFvE&pFCJtvm<}vwjKU&!NS7AvuvwdYggmC<
z+PHD^0O!&!#Og00N%$vlj_6O0G<FURLlUee%ur`;F5h*GRIRevmT+e>>3`rtY-9>M
zJDEFM%QVc{k)Hp|`L@ACJ^9rii`j$M!Jg75*D?ye`b_=jge<5O>H{W&j%V-F(!@J%
zF>>maeu=+$#ZdD~nwhMEoIJ_DABYNubXX8@N>{uflRQK=_Vxh{dNLS-O=lN*;hE{G
z`~*N`1|!eJt@&Uo@1Z7Q{&+2cw^E^x6k~Yg=;zxk`0vQymq=bLV3rq12H_qLzQW?}
zQ51w<gQDo|ml}P_`Fz(hHzCyILP~+L_s#~@0p2<AcTSrq7GexDwyj-AIWdLhYRkRb
zG>;6bnVLh72(cgDi3dyqk{tF{|G~R(R!D_fCF;K<e9~r$rQ~x=9qs0(;}fAtJgCSb
zNEmtN4SL81s93xi8p(ZlPuPi89j=kZ?yNf%f_zXpWTlxIuWWd53)iDV2C#o*Hq=f5
zWfUw)MOj|<PR<*0(pAnl)}hEC@$OePH22R7YniXsA_v0|P(SCBrIPWI<U_^`w9Of5
zF_onlGq{6#E?~>rGTPN@=<S3lE(ZagHX0pL;yI=1KoOe>?{1cgIhn}1HnRJWVVsCm
zh`vN1R!zwqnnCqV$@$;U<CusHzEgUpnq!7R-8{6=1$iUCMQR$op9TPE!XG2iFPb0g
ztZFS73Uxl|D`l<<C%a#JF_%;~D|rW}BcoE6W8efyR9+R<uU8HZ39&SI6gU?POqG1P
zN>L`B!x5}ZB>5_M2fg;rP(sTK&O;UZh^P;Kehtmf+Ir5`2TIO9#1_D`6N1k7drR(|
z@J(Kjwm%V~*mhRC^}Z>P=_DgrJjtwoI*lHUw9_uNdRgNeL!7a5UqKF)R}0A0UZ&so
zuyQ`t-C^dGBGISk9DO(|TiP?V+2qECbjQ}L<-Yr)!r}2qX-IFEwD%<Z9KX5^FZ{M_
zYA=LfUNAhY!YU`yGjbQo??zfg7efLaswlCH(WMm6^Z4JwQu!CHY&Sh1+*5;G`oGX!
zCANp}OE1hQYsU*h8^sD|_@^mR0;V0cltjiQ0A8P}gJajrdQymDBNTFhgdg}^_KQu!
zK&828EXyRMCz9+%cXhSLN>3!qjhVeM9X;|;1;hVt39`-~8T>Zyu4)9asw0hkG!ALz
zP@wl>s;ycLsF#VaD$%QE)(2=im6cmdAUl4SB}^y?ctZlJ4WAo?*J|J`wdpW*ygF>J
z>|W^$&8K{oY_NkXE}hqC<w6QKrk^R1*!-9wk;uP}mMPEMfNnC0ys{Hx1=xYjaHZoC
zf+*X%MjPgiGooYZS>oj>gr&r5+<GjWh)i&h5Z$ls2NA{m8tv^z_0pl<cugmN%XLvY
zb{zuvE%KXVW<!Y8iVrq^ps1PrJ+O&uoi(n*4>QZlp%)!l7A{(j%KC2{<1<uG^)hHA
z3*U9QYM9&YhMh%SnUQbzILH60tyO9<n>4CektdLC_J`eZ{f_&!JhCWT=x%uJNy_-6
z@uwRHLjb!yIq9Ci-cvxyRajUk8ZnQf#YHv0$F4pQ>il-M%j@&<*}BlI<F(K6G-5#>
zmmf7X{=Q*t?AVM=n?Yuu_NoT%)IL)ssL*n9qNrQk<!&@SJ$QPp`t^733(^<Q=uFnC
z>59V-hqx3kCf=u6U<@;V8Zg9eF1a{;I2__L`+j77Fit?kfdr0X&bM3*RIO}OhNK0w
zgj+gEV_=90P=5~e!=4l+wiY_6fAOX)ELH-lu<l5$m9u*zT*qz`N__bYhujzNz11%c
z9L}%EDmc!4lMP2WuGOh4o_stcq&wRvMg*^}aH)oTcd1_fn{as4>*XHI;xvWBedq}V
zHAJ@G3*RW(r?5GE8S4FhHoJd+33Ye=2diU^{~~+MLXXttmc63{(M(|&@P{7gpcQ6R
zBYkYofBn#vEy3xDvtHh)uUFgq522o`dA+L0-OI;<eci_=oO*tLmS@gzGrdwqPP^mX
zmm~MFbABi?NWFe(&iB(_?QA5y$JbY?dm?z=`Vk6MyR{sTx7PmZghj5Ip)fkC*8#BR
z3t6nUhZAPL0>^vg6)pw8<%qvwim$DFyDeLGTvyNkZZ9-+pOU>j{cLi3>5ol2I41`8
z{Cs<Rv|(oC;+^`XgH?SM_I;|A(`pxg>MqhSgP;j`;^Uq&?$gw8ZM5ah3d;>KQtfS=
z4mzn^{xzQ6qZs-f`VLrPHW@YlXICem{Nt?0#BcY`))1E?xwm)Mx1BE@Qa)db3;Ofo
z9aWZ2TgXjbk}uMzpS@u6i_)^|{BQ-FPP9<EnlAo$CC$9)UGaSQr@2*^-fsy5Yv%ie
zPe!|m+!5X@GcUf**MDYy*xhs*x}r?O!EX{m*K-(t-m4P0^KdHI?$A2)z~zAN<kbK(
z=^%tXbGP(LSr0N__3k0ZG9f{aJCfNEAHEjPrr`75!ttxMSGF@Gh;Vc-y#Jws-|g9Z
zr`g7Psg?diU(5-9i#N^B-i6-Jzo1}xHXA9l61f>{@bWJFe9f^XZK81b*UEWo(De~Q
z%zlF9GasMyBbD$manQm0p~CO4zoB}*H}->LYwBkBioEQd{Dry0xu+}=<9TxG``#~p
zOWK4jbX0#&(>Vy+_>VytLqX*1D6YM!<%AQ~>OS~(eg&hJXmWyskw0yBQdqVQ;JDSX
z^TFXuq)+JEiPI~71@2L?Z5MaRKR?QCQ(_Q<=AG=NMvfS}sy;nl?eqXlLYtW67gS_4
z4##VE6CazL`u4xY-cX?6_NVxQ=Xr~4=5n|=Df!xtl@&G&82B_Q+)FHkdt&eb##6K_
z@ei;<1*O&XsqS?Tl4YZhP>}`s-!I>o-V#W7{!#9rgH6xz(e@5k{~zW@va_j7e`wv<
z3zQ_<eBJ8Y`0+ne&wnl3cAr*oT-b8`!FTY6`oq4|v<EZYmBv`0z7FJz?2na{#_)r+
zLJGg(j^%5JaTyo#s=Cr}=S<j#@-e%L9cxKgXUk<_@!}6TyL(l$d_u}RQa2L2$y;tK
ziQ6%r3qyCQekbD@SuVzZy%O%YIDg-#h@6&=x&6CLukDk!5}@Us!|La=2~+vfu)F*n
z1VY}6t|!K}n3ii(_;07S)bK|wfsfzC0quh4DmR+<`_6=UTI6Db7NMpbudCAaWOwAK
z7*Cl+wsXyhn6nR+i7foB6db5u3Yt=7)6?hcIy>3*dg@a!y!|YN>bFtn%a%jo);3ok
z5kl_ZE$v30fTwqld1D@3&_B)lsN1D+f4_fd<#g3kvKRV6+}UyN7c0=R<p3_o)R@0E
z08WS!v?uR!XmT6RyWsb<&{_@7Ix%^DRyCM^JezQ!d^@ml>Z=8s@v$!kx{^;QT)HF~
z&$iX4es0M|+x&|vZ2!mJ6Z3|i;GL3gkGBucK6LdSV?XB{d+?}bHzL9L?k_tAw1<Y!
z5;Fgss2}KY6(}+J)o%XPY7Bf2ps{q>mWqSglrg=!8|sV^OJkQ0@vp3fx-ySf9}rAq
zHgBM+zx;lv8+1{L;g$@I<G_W$$NFAgF4wlEtN%pJY?j2X%Cal^yzkw_&7IYM7Oilj
zOvb4oT$euH`IW;Dzm)*>&i)8W$5;<NCJQZpr}vZSZ(5tlr^z&iIlQHN;c-uWc|J{5
z21>5=d(YpKEv;U8q9j{hdp$|0iyr4^btf_+<)`|++r9TpK3#e@6*aJ9<=NA9Y4_{2
zdbe-Y5eL@wd<|NR`}&MB6{m@Ob)80xKQ?#}-6q@Uy(kkQ8`Kv=w(t$}+o$h0)8B2~
zO>0Z}Ux?ziV1vf=RiUFQ^~a3%VYAScfnS;`nAJjP+<$-3@sA&ub#jfFCHw_Bta}AQ
zDbDk;qncY!Mf#Fv7{4OpSUKqj2hK<lxvORthz%#6@h6iN)%?;JS;Xo^eQ}l5$qBGm
z?)<^g7PkKh)XcfyNTm`iqI|sC6MD~#rr2mXxms{veQN1$TxPnZc(^}yIrQV5m6}e%
zvH8-9_m%L|D;^6~S0gU&`t^jg*LeiT=nb#*-b%=AKKsa8tdZOX&HhXH#^g~l6ZpDd
zV5*To_Vq5;c%b0wW&gy%91+`8rZ9!3&Be#p6w<hUMyuw(SYeN9?N>i;)C3wpJjmD4
z^g3Q8P!$qjE&k>E_xnp)n44eKgSY83CLKVH-&n4g*fZY{OYVu4D5|OX+YwLtvz7xE
z=l<NAiEnvg6!?AphTY@czI{ZPb<oJS9&5+%bdTBW+h68-!6!(B)<ahmzIvS>)!5>l
z9^8xk&=>!+@U`7?%4u%z;xb3^wExolz~hnrj!BzC0tc#T57(9`@A2Z(t5=TdD?ew+
zjZa=SQ4~wnULy`V`$TDxEX`-q(E{YfyO4Kf{VOhm=DEG+e|o{ZI9?LIBNDMA>(7~R
zq4~_>SsQn);QQH`yznEI+1}rD6-?@d@i+gBes1YW2Ifd!K7YGcF5Cz#jhH3-dLFi-
z@12X1heXhOO`X*b!K*&)UHz%jc**&zk_5p*e0}%N$@$SyvuG@Tn9$j^mE&6pU#OFC
z_J}m)R~>TvY%qDS;J<-;$JB1yoK>Wts2FyBdIVe^s7-7Lk97pBEknkK@SAKe&VL`Y
zRR8CpdL_wHEoeWd_vB}XjWLDU8ua7w*`miIhM##xrFYK8-f}dI{!sC4cdv3Jqx`id
zx9VlXR{Sue`QWW}^KC1*Ei(%YC_`eu#ho}qxCTOdesZ?cwjb^-nvbTBe6c*1IR-%T
zX*$q3fz8|Vji*n2?;!EANxdhp?Dq;MDuRZpYA>JL3ONj=&G+3-EIMob`Qoz4<bguK
zcn%1w@Dn}1e4kEXPmj01=7g5juN)rV>bixS3hQs7G}~t|?!_?JaZ?UObJ=Wj)kwk3
zYUhAc4*Oq?`|iB}eR*gfs4wuMx1s{ZGMt?{kT|kjYCTF*`|@Smf0b(Y*1PsCuJ}ee
zeG`umx%>KC<ms^>t3w1n@fjUAhrL`cJGq3o?Lx#!zk5^@pGiO%@6V&>g;Bpbt~f+?
zOZm9^ou9=5Icj=~hukxVnV8(zjKkwz*uLp^lk>%5N44409}Q|Mjd=G_rIw<%#q%$t
zEX7v#BR&XlvNepJkPfHatng_+SPzPP=02GwHMC3d4n|B0Yt~jAdhB>-SWjpAl)L8K
zvf9fJ5f&9$(|xhz_x`SPyZna!Q5LIpXmfnHV9hS{#(QG+c!K5ZMwjnjcfxIfvm34d
zDN=h<_jYm?Jqs~#X_%+o)?4|UCeP>8EzQH$yc`wK_5#X1#ZSGUg;@*6&vblB5j#0K
zX*80lf2uBsGBmgUr#jPw^R1gwj%VGJ#xNjO86d?8ZC>wQKPqZN+IephGT_atH%S~1
zw3E`A^VfMtU<}wC&1g56_8Jt=0>QgbY={!(F*RjR!~Qe#KB$x;D&&M2z~dQc*H2hU
z!m1zLzTjYkXU<?h1$tfe@L$5q&gdLFDwO34uzX2nmFSn7QYcmoAW`<GAVjZ&Vh57p
zDxC;b>Jd3kr9jfNK3k7R2OHTYn(g<_qmU!K#?yE9huA((7Lm7roehD-pKk+T-5K`4
zFsUd2p`e0@i1z|pCt>DXjex(jiu|BfRM4A`nmLn=J)m^s$L6>xw9afZtFAB^;2Y#b
z1xhTpt<i-0YH`r(j=kn_6wSSZI>*TEvPA!Su(wkTGT-m`{CI$oB!WWaJ$^1@ox2f(
z8|At}#}#$#6b8G^Lz+gkbSM&9Wtsr_U9Ar=JF7|9Ca8W`EUb_K_hi*9{vLd^6goi5
z|2=HvN@=vXjRl&54O(sI%rtyWs+)Am<>%HUih}IbS|<;+Xmoi?QF%Vt;yIThK<|WM
zUqqX`5ca_{I`16rO5~CI;|GW}c!u-G>&xm4(drCy^wbF8AYlNpr?SsqT{B$$P?U-1
zz&|kJxa=GE9Fk_u>wjIKi&T*&f3FNN62l()?PGEWcO9*@!yN&$&r>CISAg!hcRl0E
zs#qzl@TY!isY(~<eNtMQy}v&m_G-T&2k>U80a29210NegSDc<&G+d~$m&UNdf#zNJ
z{PVptk6`G;siFqd{EEBR#5O?kaW1$=^cbMF6(S?nEV!Q%5lJ23^aOa=0*}Xig`|Q9
z@ziYnN<?^zm9mtsCFzqXMMNr4T#^i^%{7)oqQ7I%Bt@Pw<?WD{FJG?uPdrxT0IKG{
z(E}P&Fo91?rVD+d6f$bySd`2(hts8?@Lu#K4bS15D~X1h+t3Os0K()`!1ms?V)G$o
z<Y|SZk;PdsiIPEXE+mxxhbCim{G9ex>}?8+oR4uO3)xogE{4a{tS$h)%3V6L<;TAj
zwx@~TINhmx?mR>yM&(a2d}=BB`bbzh|9I{4bO^qJ>R2GG0Y}4%WFTP4$~tIcPnRA6
zpzR!MzyhO`S*`W~{Jbi5j(>~X;=wLz*rtHztEd4YCmSby3b)Akj4my4apZ3>vu0aH
zq=XSqMIRG;zM4#Vi=wv-z@7>Lqh&SrW<b1#Mr?SXKPzv1)~j_c^+sSVVQ%Z2)QU7V
zpM@K_{+d^z-D-)EtP5}diy5X$>-6lPU9U1`A41<`$Nv}xzHp&L`~6&+u6B^#O1pMA
zLt_$zMd4sKQp5Vr5lrPa(wH&?mj3tRR8)cyOFKp<g1k;s?CCAgDvBMUeVS0?Dl=9<
z%JF^|Mae%FhZH7*E!d`CmiJ#^PGA2@=QxJZI{Atq%HifzV9Pi{dm7EVr&{cz)-%pL
z+%V*&hL<~{-og00MbiU+IGf%p<Uo)a^EnuxoZ4Wo8Fi?&Ox*C}&lQFbV<9dyD{1%{
zh6VBD7Ae3hci)_?YW2$Oi20kTk+thaRmqoJHigVRws86<ql}J3P1D6`$YCT^lx=TQ
zNl8Dg4Si8w40KOfSjeoBWR7d}h>}gd+Om^Bed3Kz{cBk^cN?`h-<0-x8Gm0v`FU?k
z*lBnpEA-{e+>&r7F8He3B-E-v&qHo^PR(WRTsD(|BjFjYx_OURs=O5vqk1*L4=^?Z
zEd~s*bytYvK!0e<suuIz8m(59G^yYV2<;Gl2B0?NLzD(dcS5ykIFckt{(&=rcjDjg
zVjRvaUxM{-W78$WSA$E@S$;*8?7WRNi)jYp`b4$xzBG18lxm_>pr8_+Ddr-12j@Og
zE%?qUqD$l<GM^MqwfECP>uPk&Y-y9g+t62@Lw}bBb_(O2E@e+s6oC1HXEOS`y=xpm
zsV-`ciRdS$OEAmGO$Hlm@s)P%m2uhRoC;yUstW;AutCw7KF`rNl9w~~LMqcLm4`8E
zaDEs;mzkuiJ;Sp4*%Ebf$*9a~6P;|HCa+SbLaB#=S0P=+idINVu}9kf!y^yjwb>ij
zViS;?HMqtX0m&j5&rRDLeB!i54&y8v=n(c3;@DSbPisLhC(G0Jw07;=9*c^5UJd^(
zQE0M#BI#8t24^e+u6U7$pQ2TywX)T0YscsrLXKU>8AAVLI!k!p)ZJ7VfQyPqv~}~c
zYd$0Qn`V*YnD8Pi+Z?Ki?f8Uo+F$tcJ$0NY<~sr#-B^v-;IoS1rxk{W7rp+<9oBl&
z^Zq6u1(#vIJV_j;g^}G&_aAPY3EjnzYIxQPlPxs#3=2fox*4%@@1J#FXo`opbpd&?
z?hngW<g3wW4y`xz;jE147`9FmIY(8w=FTziC$ed-$s|wRED~Vj!bC7%5lIowRM7~d
zk4rQ2C8LpReeBsI^Y&`#P1`YA(AHrInuQ)_i@-KE3P7(B22w^~wZBIS8w!@s*M}2)
zg@Z~(T<4)<R#ZW@wj^K_rQL_F<R(?{N@%^V>`VA>eexY`)z<qg7_<01Dwq99yr-w~
zOr`$AS0VYq25uMxa~~wU(`aqR`=SPtFIOv;jpXP_9+z)V)b;Zv37Y2xvk*7zPLXYP
znEW-+4QH$wvN1}JJ}xh%Ih^CQ%5t!q%dOk9uVyl@ZpK@{>RHNr!qs+~-idJL?P8RD
z-E4_EX9Q{SmeR;9Ra_GrmmLBzsrKgA-P$wGXEb(p9HDZwTqg3t8jESj1QDv6vmdxU
zZsZz)@L_ndoR7YLNwAR~+gQf=-RV%_@l4Tm`ONESK~=o1)E$9Nf;y9CS^hxV4%n+=
zWReGDos3EG(WP5Fw}o*d5HfCBISD1{_Tz0leigH|%N84p;UOWgQ90-E^k`-g*$EVE
zuXpe1E7wR?)f{AW*l`i_F<j@w@ud7?qI=(1HG?kB$Uzc_vF#`7?4{|aAj4!6yI0q-
zQ11%32EFqjj#i5wnt!7OMoyB?W~PdABW5_ken>cX^Pg|YHK7#IE>gGcN*HdbQF*ic
z%pbBf%K~c7EblsTX$(E=A}6X(i(>)O*o*4np=2b9TUOp6AjK;?vAl9O-@;065XTVE
zhU&%25V_;4))1#(%@%YM%yIQFoDTJKn`F67<(wp&0v4DKDw?5BFmN2sE2&Kxkp!g(
zbmiswFO5{rlNTZ-3LDv?N+ZtLXdI$3QFAfWa$x%@KA00hYARYpZ`z`Z!5T4QvUf8f
zqoSIKK~!`A?&k{=@R*d@HL<9}e)@qfi~@o(m)bTk$qH|+`@j9_jg`4u0@a;Sg^H6>
zNIosd&{3uE=1UHd#e2$>*(bV&S&2Afz2PX*1^d@)5K}6KAN@&MYe_t(>T&LzriwI4
zj688s{=6)V)Dq8$?9RG7B$DQi1Sx?{S)`nmWY9s4`kBV3zV^Dy9qgORhLR=Cg76e~
zh!#vRFhRqeL!O7+o%xb+>98<Mc@#`w`EGQNeWDGm05fPPdTCbkP>;L(Gv6)V8|2UF
zQ%$*Fxp?JX<Em5X09<hPcy)a;E?H(Z5oS!toPL32)a>ly&~RhEZ=v=qdJtmHa3NZ7
zU)!txuXE`)c69Y6a?-b2Fl-cTV@sLd2I)G=A|VCTo@3*^2G70VC}Gm<P#%pOQU<Jl
znHU7ERGx5?s&Gz&f|5ptl2>YjtTD5S8AKaUq{lR*2n&#ISCX<m8SF>*iNV;+>PY6P
zq%D&&>=QL@vNDju60q&V;&EF;6ctZ0BT1UfGOiJ58GCUGAmoRWDQ#H!Nr_7n<Ym}f
z4@&wUF?|gAXDjxkK^F`uz!5OknM-jwb`+Tef&U#8uSP-88g3xE8Tr$~qfOxsG4U9J
zFEkzF%o`GunOCM&O3b7E?2VG6;;dvqNvZjgf<D}_Zyl{(LuUrl|6$reI~vr$IE2<f
zXp>VYQvs{jOmj6&;XgvmgLzc=R5BP0C0-+H#t4?15cq;eEd9(vY7a@)4@hOXP07-`
zm|BM{B+#4yJ-}7Oh6|(uvK2^OHdd>O<lL6y$&G@sah2-3k;JvSn6|jz5`<0@xyV8G
z#s&jy89DxT!3acO(t8Y2&x_OP_Wxug{4vHpvA-j$3Yi}PLd#$Ie&N<Owsub2a~ak=
zjf>7W;-%jti)YAhn}G19%F;{RU<6|_#VtCLwF8nQF+rglzii#eA6@ftC)&apmY1Uf
zv<fPpMNNr)TPJZ3PMB_SBGFW)$qc*S7hD=mZAwBdLaCWbvJD=PIQ1?6>sP#EO|A%&
zoht9;;{VscplozgbkIGBcN$vs5q#WFEpBwug(q6j=VUt0UC8Q_Ig!^(lEFr1>$B28
z%5&&Z+N|8^&d<@_yy!bp9sh{ERGc^*72%&jjwV3~q!sdunYcq@1f_Oh=8KSuiK}Jo
zl;wi|5~Cr!-mkDqpF%v1jQ%|t^_|AW>O!3dL;gg$xU)qa4{^2%k7^h#(r4BEDNDIX
zgG`W_1Rdeho+Pqu=q5xd-F4Oh(k9+GK|0hP#+j;hq9)f&H#2BL)a8*yE3!rluGrVI
zmT+3njZM`BGT?rjmXNE>h2n5VEZ&4L)3L%2#|RML+BH<lea0dvr!NEU4QM)MX%rk*
zyk|68zD0@}Er;`v)0Q*CZIK81f(*p`(Tr^Tq5_!_7SKRy+t%^;@f6n=via&YXsy}n
zn<H}apvLD`2|BRM@oR)YWOBN0dXYA9ag!{I4N@_(m4l?QGWHv1z7xF}n9UE0kBH<w
zBg_Sbm5ljvCpnbN@Ub4nCEWFTTPCXiVav*AJM>BDIcVZ)Of32h3L`#+a3zv~H7<{l
z?I5}q**LmPMPM9&Qk1{q$ek`7o$Mh_M%k~?sONuboML!V3bh3sJT2wrv-v5(-A4Z#
z19y|9ztVZP%n61vxR~3hB6TGHO5jMD$X}(6q8pM`GXK=9sYGuU##mkwZK`wy*w4S&
zuv8IVp8fJ6bByU@>r~y;y3AG(5yDwGY|N4AeW!Un8V-t-NddY!g9~6Y0ZN|$Mw5-h
zzP&7l>#C_?hf>I#039C>9F!F`+))*!<NsYzZ&E7z&A?ct!agxMNSNBW6d-s7s}=e<
z{v^2NM9BqVvHzL&H%gXb=Xl2Woa3*K2}m_MXV-ycAz-5ArBT1Z6<a|9n^_}P4Z@o?
z)IeGj?q0f}uWF@kMPU^*I)1I)KJmwo=WmA4We8@OKDP4MA5<1%t?qA_z>gV+D|bJU
z&K<EpiYR<J2U3d3_Aw?HlLz)<(79r>Vb|kk9{5f%E@0wx{Z*FAC{ZGBFOY)McR|zk
zRho)uIhWQWEaFG7h|v&~C@DA%P=@EIT;ZN|+fxH%jbuqF+H6yq`D)JGztT1E{U+h$
zU?MaBs^~y05nuNMQUs%D{f7D9OJW9Y-6_2b-XZ==gc1^<&y<-5@`Ay4{qK@FRU&k9
z#!3$*UQhf~iW7)jX>Wb&|GfykBny)MH$N!D|GiNu5=7enKaH%O!(a`V1g=t_5eWFx
M*D=;^(8R|4AKrzIApigX

literal 0
HcmV?d00001


From 15995ac8098a5ab31c4d6e1df7064269bf911626 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 7 May 2014 10:57:27 -0400
Subject: [PATCH 027/248] legb figure

---
 tutorials/scope_resolution_legb_rule.ipynb | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index 70cbf9d..cea54a9 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:eb9ececefb4c35b16d0496c7f613e4a64bb35b2e9f79b7e049c5b434bd2e6654"
+  "signature": "sha256:9cd21a4de985d02fd65a6682bfb5690a15932533ed143e684fc8403043dfbde2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -306,6 +306,17 @@
       "(This is also why we have to be careful if we import modules via \"`from a_module import *`\", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "![LEGB figure](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/scope_resolution_1.png)\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},

From 142318dca09b4840c903cbfadf0376cac9052ce7 Mon Sep 17 00:00:00 2001
From: Sebastian Raschka <raschkas@msu.edu>
Date: Wed, 7 May 2014 18:38:42 -0400
Subject: [PATCH 028/248] benchmark updates

---
 benchmarks/timeit_tests.ipynb | 1064 ++++++++++++++++++++++++++-------
 1 file changed, 844 insertions(+), 220 deletions(-)

diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index 199d744..40434a8 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a2b1ea908157604888db3edc69f70ebb7f6f25d46bbb813b419c0879633a8962"
+  "signature": "sha256:a6e11ece22a848e8c3b728870bda775773780d2fa909fb330ad64e4febec9f2a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,10 +12,11 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Sebastian Raschka  \n",
-      "last updated: 04/14/2014  \n",
+      "[Sebastian Raschka](http://sebastianraschka.com)\n",
+      "last updated: 05/07/2014  \n",
       "\n",
-      "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb)"
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb)  \n",
+      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb)  \n"
      ]
     },
     {
@@ -33,11 +34,16 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "\n",
-      "\n",
       "# Python benchmarks via `timeit`"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- Code was executed in Python 3.4.0"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -68,6 +74,9 @@
       "- [Copying files by searching directory trees](#find_copy)\n",
       "- [Returning column vectors slicing through a numpy array](#row_vectors)\n",
       "- [Speed of numpy functions vs Python built-ins and std. lib.](#numpy)\n",
+      "    - [`sum()` vs. `numpy.sum()`](#np_sum)\n",
+      "    - [`range()` vs. `numpy.arange()`](#np_arange)\n",
+      "    - [`statistics.mean()` vs. `numpy.mean()`](#np_mean)\n",
       "- [Cython vs. regular (C)Python](#cython)"
      ]
     },
@@ -147,8 +156,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "100 loops, best of 3: 5.67 ms per loop\n",
-        "100 loops, best of 3: 3.9 ms per loop"
+        "100 loops, best of 3: 4.01 ms per loop\n",
+        "1000 loops, best of 3: 1.82 ms per loop"
        ]
       },
       {
@@ -159,7 +168,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 131
     },
     {
      "cell_type": "code",
@@ -179,7 +188,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 55
+     "prompt_number": 132
     },
     {
      "cell_type": "code",
@@ -214,10 +223,16 @@
       "plt.xscale('log')\n",
       "plt.yscale('log')\n",
       "plt.title('Performance of different string formatting methods')\n",
-      "ftext = 'binary op. % is {:.2f}x faster than .format()'\\\n",
-      "        .format(times_n['test_format'][-1]\\\n",
-      "                /times_n['test_binaryop'][-1])\n",
+      "max_perf = max( f/b for f,b in zip(times_n['test_format'],\n",
+      "                                   times_n['test_binaryop']) )\n",
+      "min_perf = min( f/b for f,b in zip(times_n['test_format'],\n",
+      "                                   times_n['test_binaryop']) )\n",
+      "    \n",
+      "ftext = 'The binary op. % is {:.2f}x to {:.2f}x faster than .format()'\\\n",
+      "        .format(min_perf, max_perf)    \n",
       "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "\n",
       "plt.show()"
      ],
      "language": "python",
@@ -226,13 +241,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIR8Y8ZXlAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10610c550>"
+        "<matplotlib.figure.Figure at 0x107efc590>"
        ]
       }
      ],
-     "prompt_number": 56
+     "prompt_number": 135
     },
     {
      "cell_type": "markdown",
@@ -287,8 +302,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "100000 loops, best of 3: 1.56 \u00b5s per loop\n",
-        "1000000 loops, best of 3: 362 ns per loop"
+        "1000000 loops, best of 3: 1.33 \u00b5s per loop\n",
+        "1000000 loops, best of 3: 268 ns per loop"
        ]
       },
       {
@@ -299,7 +314,7 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -320,7 +335,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -346,7 +361,8 @@
       "\n",
       "fig = plt.figure(figsize=(10,8))\n",
       "for lb in labels:\n",
-      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "    plt.plot([n*len(test_str) for n in orders_n], \n",
+      "             times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
       "plt.xlabel('sample size n')\n",
       "plt.ylabel('time per computation in milliseconds [ms]')\n",
       "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
@@ -355,10 +371,11 @@
       "plt.xscale('log')\n",
       "plt.yscale('log')\n",
       "plt.title('Performance of different string reversing methods')\n",
-      "max_perf = max( [times_n['reverse_join'][i]/times_n['reverse_slizing'][i]\n",
-      "                 for i in range(len(times_n['reverse_join']))] )\n",
-      "min_perf = min( [times_n['reverse_join'][i]/times_n['reverse_slizing'][i]\n",
-      "                 for i in range(len(times_n['reverse_join']))] )\n",
+      "max_perf = max( j/s for j,s in zip(times_n['reverse_join'],\n",
+      "                                   times_n['reverse_slizing']) )\n",
+      "min_perf = min( j/s for j,s in zip(times_n['reverse_join'],\n",
+      "                                   times_n['reverse_slizing']) )\n",
+      "    \n",
       "ftext = 'my_str[::-1] is {:.2f}x to {:.2f}x faster than \"\".join(reversed(my_str))'\\\n",
       "        .format(min_perf, max_perf)\n",
       "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
@@ -370,13 +387,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nckREREQK9f8BT5GcuSZXa8EAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x105b68be0>"
+        "<matplotlib.figure.Figure at 0x105a6fe90>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -436,8 +453,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000000 loops, best of 3: 1.15 \u00b5s per loop\n",
-        "1000000 loops, best of 3: 436 ns per loop"
+        "1000000 loops, best of 3: 764 ns per loop\n",
+        "1000000 loops, best of 3: 321 ns per loop"
        ]
       },
       {
@@ -448,7 +465,7 @@
        ]
       }
      ],
-     "prompt_number": 70
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
@@ -456,19 +473,20 @@
      "input": [
       "funcs = ['string_add', 'string_join']\n",
       "\n",
-      "orders_n = [10**n for n in range(1, 9)]\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "test_chars_n = (test_chars*n for n in orders_n)\n",
       "times_n = {f:[] for f in funcs}\n",
       "\n",
-      "for n in orders_n:\n",
+      "for st,n in zip(test_chars_n, orders_n):\n",
       "    for f in funcs:\n",
-      "        times_n[f].append(min(timeit.Timer('%s(test_chars)' %f, \n",
-      "                      'from __main__ import %s, test_chars' %f)\n",
-      "                              .repeat(repeat=10, number=1000)))"
+      "        times_n[f].append(min(timeit.Timer('%s(st)' %f, \n",
+      "                      'from __main__ import %s, st' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 71
+     "prompt_number": 16
     },
     {
      "cell_type": "code",
@@ -478,7 +496,8 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [],
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -493,18 +512,23 @@
       "\n",
       "fig = plt.figure(figsize=(10,8))\n",
       "for lb in labels:\n",
-      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "    plt.plot([len(test_chars)*n for n in orders_n], \n",
+      "             times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
       "plt.xlabel('sample size n')\n",
       "plt.ylabel('time per computation in milliseconds [ms]')\n",
-      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
       "plt.legend(loc=2)\n",
       "plt.grid()\n",
       "plt.xscale('log')\n",
       "plt.yscale('log')\n",
-      "plt.title('Performance of different string reversing methods')\n",
-      "ftext = 'new_str += char is {:.2f}x faster than \"\".join(chars)'\\\n",
-      "        .format(times_n['string_add'][-1]\\\n",
-      "                /times_n['string_join'][-1])\n",
+      "plt.title('Performance of different string concatenation methods')\n",
+      "max_perf = max( a/j for a,j in zip(times_n['string_add'],\n",
+      "                                   times_n['string_join']) )\n",
+      "min_perf = min( a/j for a,j in zip(times_n['string_add'],\n",
+      "                                   times_n['string_join']) )\n",
+      "\n",
+      "ftext = '\"\".join(chars) is {:.2f}x to {:.2f}x faster than new_str += char'\\\n",
+      "        .format(min_perf, max_perf)\n",
       "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
       "plt.show()"
      ],
@@ -514,13 +538,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAIECAYAAAByl6h3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVAAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVEf3x7+7dAQEQVBEaSJdmgJBhaXYY/eNGhtiiRhf\nE2OJsQTQYDR5o8YSYwVFjUETE7tSxRYbCopIU4gNwQI2pJ7fH/vbCwsLLLgKi/N5nn3gzp0598yc\ne++enTkzwyMiAoPBYDAYDAZD7uE3tQIMBoPBYDAYDNnAHDsGg8FgMBiMFgJz7BgMBoPBYDBaCMyx\nYzAYDAaDwWghMMeOwWAwGAwGo4XAHDsGg8FgMBiMFgJz7BhNSllZGQICAqCnpwc+n4+EhISmVkku\n2bdvH8zNzaGoqIiAgACpywUHB8PCwqLW49pkx8fHw87ODsrKyvDx8ZFNJeSQ8PBwKCkpNbUaDCnh\n8/nYs2dPU6shc7Kzs8Hn83Hu3Ll3Ir+ltltLhTl2jHrx9/cHn88Hn8+HkpISTExMEBgYiKdPn761\n7D/++AO//fYbDh8+jNzcXHz00Ucy0PjDory8HAEBARg9ejTu3r2Ln3/+udGy5s2bhwsXLtQrOzAw\nEN26dcOdO3fw559/vnUdZEHnzp0REhLy1nIUFRWxc+dOqfKOHj0aDx48eOtrtiTu3bvX5D/S/Pz8\nMGnSpBrpubm5GDFiRBNoJDtqqxuDIUKxqRVgyAeenp6IjIxEWVkZLl++jKlTp+Lu3bs4fPhwo+SV\nlJRAWVkZGRkZ6NChA9zd3d9KP5G8D5EHDx7g1atX6N+/P9q3b/9Wslq1aoVWrVrVKZuIkJmZiUWL\nFqFDhw6NvhYRoby8HIqKsnkN8Xg8mcmpb912ke6qqqpQVVWVyXVbGs1x7Xt9ff2mVoHBePcQg1EP\nEydOJD8/P7G00NBQUlBQoDdv3hAR0W+//UYODg6kqqpKJiYm9NVXX9GrV6+4/F5eXjR58mRavHgx\ntW/fntq1a0cCgYB4PB73MTU1JSKikpIS+vrrr6lDhw6krKxMNjY2tGfPHrHr83g8Wrt2LY0ZM4Za\nt25No0aNorCwMFJUVKS4uDiys7MjNTU18vb2pocPH1JsbCw5ODhQq1atyM/Pj+7fv8/Jun37Ng0b\nNowMDQ1JXV2d7O3tKSIiQux6Xl5eNGXKFFq6dCm1a9eO2rRpQxMmTKCXL1+K5du7dy85OzuTqqoq\n6erqUv/+/enZs2fc+bVr15KlpSWpqqqShYUFhYaGUllZWZ3tf/78eerVqxepqamRjo4Offrpp5SX\nl0dERGFhYWJtyOPx6NSpUxLlFBUV0fTp06l169ako6NDgYGBtGDBAurcuTOXJygoiDuuLpvP51N8\nfHyN6+3YsYOIiDIyMmj48OGkra1NOjo61KdPH7p+/Tonu6p9HB0dSVlZmY4fP04lJSUUFBREpqam\npKqqSra2trRp06Ya9v7ll19o3LhxpKmpSUZGRvT999+L2ae6Xjk5ORLb4caNG9SnTx/S1tamVq1a\nkbW1NWdvY2PjGnWuTfdjx45x6dXrePbsWXJyciJ1dXVycXGhS5cuiekQHR1NdnZ2pKqqSo6OjpSQ\nkEA8Ho927dpVx51AFBUVRT179iR1dXVq3bo1eXl5UVZWFnf+xx9/JFNTU1JWViZzc3Nas2aNWHlj\nY2P69ttvadasWdSmTRsyMDCg2bNn17gH169fT9bW1qSiokL6+vo0YsQI7tzu3bvJ1dWVWrduTXp6\nejRw4EBKT08Xs5Wk55qI6OTJk+Th4UFqamrUoUMHmjRpEj158oQ7L3rXbNq0iTp16kRaWlo0ePBg\nevToEZenvud14sSJtT4TPB6Pdu/ezeV98OABjRo1irS1tUlNTY0EAgFdvnyZOx8XF0c8Ho+ioqKo\nV69epK6uTjY2NnTs2LE67SR6jiIjI8nc3JzU1dVp+PDh9OLFC4qMjKQuXbqQpqYmjRw5kgoLC8XK\n1vUura1ud+7cIR6PR5GRkTRw4EBSV1cnMzMzCg8PF5NdX32JiGJjY8ne3p5UVVWpa9euFBsbW6Pd\nQkNDyczMjFRUVKht27bUt29fKioqqrNNGO8P5tgx6mXixInUu3dvsbSffvqJeDwevXz5ksLCwkhH\nR4d27dpFd+7coYSEBOratSuNHz+ey+/l5UWampoUGBhIqampdOPGDXr69CnNnTuXTE1N6dGjR/T4\n8WMiIpo7dy7p6urS/v37KSMjg5YvX058Pp9iYmI4eTwej3R1dWnDhg10+/ZtysjIoLCwMOLz+eTt\n7U0XL16kxMREsrCwoJ49e5KnpydduHCBrl27RlZWVjRq1ChO1vXr12nDhg2UnJxMt2/fpnXr1nFf\n4lX119bWpq+++orS0tLo5MmT1KZNG1qyZAmXZ/v27aSkpETfffcdV8f169dz9QoKCiJjY2P666+/\nKDs7m44ePUqdOnUSk1Gdhw8fkqamJo0dO5Zu3LhBZ86coa5du5KnpycRCZ21S5cuEY/Ho0OHDtGj\nR4+opKREoqwvv/yS9PX16eDBg5SWlkZz584lLS0tsrCw4PIEBQVxx7XJzs3N5RytR48eUVFREeXm\n5pKBgQHNmDGDbty4Qenp6fTf//6XdHV1KT8/n4iIs4+bmxvFx8fTnTt3KD8/nyZOnEgODg4UFRVF\n2dnZ9Pvvv5O2tjZt27ZNzN4GBga0detWun37Nm3YsIF4PB53Tzx9+pRMTU1p3rx59OjRI3r06BGV\nl5dLbAd7e3saO3Yspaam0p07d+jYsWN0+PBhIiLKz88nRUVFWrt2LSenLt0lOXZ8Pp+8vLzozJkz\ndOvWLerfvz+ZmppyztO9e/dITU2Npk6dSqmpqRQTE0POzs41vjyrExUVRQoKCjR79mxKTk6mtLQ0\nCg8Pp7S0NCISOmNqamq0ZcsWyszMpF9//ZVUVVXF2tHY2Jh0dHRo5cqVlJmZSZGRkaSkpCSW59tv\nvyUNDQ3asGEDZWRk0LVr18Sc6LCwMDp8+DDdvn2brl27RoMHDyYLCwvuvrt69SrxeDw6cOCA2HMd\nExND6urqtH79esrMzKRLly6Rt7c3eXl5cbInTpxIrVu3pk8//ZRSUlLo/PnzZGpqKvYuqe95LSws\nJE9PTxo9ejRnQ5FuVdu4oqKCXF1dycnJic6ePUvXr1+nUaNGkY6ODqezyLFzcHCgEydOUGZmJk2a\nNIm0tLTEfrBVJygoiFq1akUff/wxXb9+nU6dOkVt27al3r1704ABAyg5OZnOnDlDBgYG9PXXX4u1\nbV3v0trqJnLszMzMaN++fZSVlUULFy4kRUVFzumWpr73798ndXV1CggIoNTUVIqKiiJ7e3uxdvvj\njz9IS0uLDh8+THfv3qVr167Rzz//zBy7ZgRz7Bj1Ur3HLiUlhczMzOijjz4iIuGXRfUellOnThGP\nx6OCggIiEjpGlpaWNWRX7SEiInr16hWpqKjQxo0bxfINGzaMfHx8uGMej0dTpkwRyyPqYUpKSuLS\nfvzxR+LxeJSYmMilrV69mvT09Oqs85AhQ2jq1KncsZeXFzk6OorlCQwM5NqAiKhjx4703//+V6K8\nV69ekbq6Op04cUIsfceOHaStrV2rHosXL6aOHTtSaWkpl5aUlEQ8Ho8SEhKIiLiX+tmzZ2uV8/Ll\nS1JVVaWtW7eKpXfr1q2GY1fVHrXJru6EBAUFkbu7u1ieiooKsV4jkX3OnDnD5bl9+zbx+XzOORER\nEhIi1t48Ho+++OILsTzW1tb0zTffcMedO3emkJCQWttAROvWrWv0ZFRFUVGR64UUIUl3UXp1x47H\n49HVq1e5tAsXLhCPx+O+YBcuXEimpqZUUVHB5Tl+/Hi9jl3Pnj1p0KBBtZ43MjIScxKIiGbPnk1m\nZmbcsbGxMQ0ZMkQsT//+/WnMmDFEVHmf/PTTT7VepzpPnjwhHo9H586dIyKiu3fvSuw59vLyErMX\nEVFOTo7YMztx4kQyMDAQ+3GycuVKat++fZ06VH9e/fz8aNKkSTXyVW3j6Oho4vF4lJqayp0vLi6m\n9u3b09KlS4mo0rE7cOAAl+fRo0fE4/Ho5MmTteoTFBREioqKYr2Rn3/+OSkoKHBOFBHRF198Qd26\ndeOOpXmX+vr61qib6DldvXo1l1ZeXk6ampq0efNmqeu7aNEiMjExEftRdPjwYbF2W7VqFXXp0kXs\nncRoXrDJEwypiI+Ph6amJtTV1WFvb4/OnTtj9+7dyM/Px7///ovZs2dDU1OT+wwYMAA8Hg+ZmZmc\nDBcXl3qvk5mZiZKSEnh6eoqle3p6IiUlRSzN1dW1Rnkejwd7e3vu2MDAAADQtWtXsbQnT55wMUCv\nX7/GggULYGdnB11dXWhqauLo0aP4999/xeQ6ODiIXat9+/Z49OgRACAvLw/37t1Dnz59JNYrJSUF\nRUVFGD58uFg7TZ8+Hc+fP8eTJ09qLefu7i4Wh9a1a1e0bt0aN2/elFhGEllZWSguLoaHh4dYeo8e\nPWQSC3Xp0iVcuXJFrG5aWlrIyckRuwcAoHv37tz/ly9fBhHBxcVFrOz3339fo5yjo6PYsaGhIfLy\n8hqs69y5czFlyhR4e3sjJCQEV69elbpsVd1ro/q9IopNFN0rN2/eRPfu3cViAqWJMU1MTKz1/nr+\n/Dnu378v8bnJzs7GmzdvON2qt2PV+zglJQXFxcW1XgcArl27hmHDhsHMzAxaWlowNjYGAOTk5NSp\n/6VLl7B69WoxO9va2oLH4yEjI4PLZ2VlJTbTuKp+gHTPqzSkpKRAV1cXVlZWXJqysjLc3NxqvGuq\ntpm+vj4UFBTEdJJEhw4d0KZNG+7YwMAA7dq1g66urlia6B5uyLu0Nqrqyefzoa+vL2bb2uorepfc\nvHkTrq6u4PMrXYMePXqIXWPUqFEoLS2FsbExJk2ahF27duHly5f16sZ4f7DJEwypcHd3x44dO6Co\nqAhDQ0PO0RC9NNauXQtvb+8a5UTB9TweTywoXxZIksfn88W+MEX/Kygo1EgjIvB4PMybNw8HDx7E\n6tWrYWlpCXV1dcyZMweFhYVisqtPzuDxeKioqJBKV1G+/fv3o0uXLjXO6+joSCwnTSB/c4CI4Ofn\nh/Xr19c417p1a+5/BQUFsXYUtcv58+ehrq4uVq76ZIi3af+qLF68GGPHjsXx48cRGxuL5cuXY/78\n+Vi2bFmd5arrXhu13YMiXWU1yaOxvE07vn79Gn369IGnpyfCw8NhYGAAIoKtrS1KSkrqLEtEWLBg\nAcaPH1/jnOgHGIAay8dUfwakfV4bi+i9UBVJdq+vzSTVQ1KaSI7orzTv0tpojG2rv1/qe98YGhri\n1q1biIuLQ2xsLJYtW4avv/4aFy5cgJGRUZ1lGe8H1mPHkApVVVWYmZmhU6dOYr1HBgYG6NixI27d\nugUzM7MaHxUVlQZdp3PnzlBRUcGpU6fE0k+dOiXWEydLTp8+jXHjxmHkyJGwt7eHqakp0tLSGvQF\nrK+vDyMjI5w4cULieVtbW6iqqiIrK0tiO1X9hVy93D///IPS0lIuLSkpCYWFhbCzs5NaP3Nzcygr\nK+Ps2bNi6WfPnpWJo9GtWzfcuHEDHTp0qFG3qj0U1RH14ubk5NQoZ2pq2iAdlJWVUV5eLlVeU1NT\nBAYGYt++fQgJCcHGjRsbJacx2NjY4NKlS2JfuP/880+95VxcXGq9v7S0tGBkZCTxuTEzM5N65q6N\njQ1UVVVrvU5qaioeP36M0NBQeHp6wtLSEk+fPhVzBkTORfU2FN0jku7/qj/S6rsfpXlelZWVUVZW\nVqccW1tbPHnyBKmpqVxacXExLly40KBnS1ZI+y6Vpm6SkKa+tra2uHjxoti9Wf2dIdKhb9++WLly\nJa5fv47Xr1/j77//brBOjHeDXDp2Fy9ehIeHB7y8vPDpp5826iZnyI7Q0FCsXbsWy5cvx40bN5CW\nloa//voL06dP5/KQMJ6zXlnq6uqYNWsWlixZgv379yM9PR3Lly/HwYMHsXDhwneiv6WlJf766y9c\nunQJN2/exLRp0/Dw4UMxfaXRPygoCJs2bcJ3332H1NRUpKSkYP369Xjy5Ak0NDSwcOFCLFy4EL/8\n8gvS0tKQkpKCvXv3YsGCBbXKnDlzJp4/fw5/f3+kpKTgzJkzGD9+PDw9PWsMkdRFq1atMH36dCxe\nvBiHDh1CWloa5s+fj/T0dJn0CM6cORPl5eUYMmQIzpw5g+zsbJw5cwaLFi3C+fPnay3XuXNnBAQE\nYOrUqdi1axcyMzORlJSE7du344cffqjzmtVtYmpqijNnzuDu3bt4/PixxHq9evUKn3/+OeLi4nDn\nzh1cvXoVx48fh62trZic2NhYPHz4EI8fP25Ea9TNjBkz8OjRIwQGBiI1NRVxcXFYtGgRgLqdmiVL\nluDYsWOYPXs2kpOTkZaWhvDwcKSnpwMAvvnmG6xbtw5bt25FRkYGNm3ahF9//VXsuanP1hoaGpgz\nZw6Cg4Pxyy+/ID09HUlJSVixYgUAwNjYGCoqKli7di2ysrIQExODL774QkxvPT09aGho4MSJE8jN\nzcWzZ88AAEuXLsXff/+NOXPm4Nq1a8jKysLx48cxZcoUFBcXS62jNM+rqakprly5gtu3b+Px48cS\nvyN8fX3h6uqKTz/9FOfOncONGzcwYcIElJSUIDAwsE4d3hXSvEulqZuIqm0iTX0DAwORn5+PadOm\nITU1FTExMdy9KWLbtm3YunUrkpKSkJOTg127duHFixewsbGRcWswGotcOnadOnVCXFwcTp06BRMT\nE/ZL4R3D4/Hq/MIZN24cIiMjcfjwYbi5ucHV1RUhISFi3fK1yZCUHhoaiqlTp+LLL7+Evb099uzZ\ng927d0scnpAkr6Fpq1evhrGxMby9veHn54eOHTti5MiRNYbTqsupnjZ58mSEh4dj//79cHJygpeX\nF06cOMH1cC5evBirVq3Cli1b4OjoiF69euHnn3+us2dKX18fJ0+exL1799C9e3cMGjQIXbt2xf79\n++utY3VWrFiBoUOHYvz48XBzc8Pz58/x+eefS1XP+tDX18f58+ehp6eH4cOHw8rKCuPGjcPdu3dh\naGhYp6zNmzdj9uzZCA0Nha2tLfz8/BAREQFzc/M6r1ld15CQEBQUFMDS0hIGBga4e/dujTKKiooo\nKCjA5MmTYWNjg379+qF9+/Ziq+r/9NNPuHLlCkxMTMSGCGtrB2naq2qaoaEhDh48iHPnzsHJyQmz\nZ8/Gd999BwB19qz17t0bR48exYULF+Du7g43NzdERERwPWSBgYFYunQpli9fDltbW/z4449YuXKl\n2GK20jyDy5Yt4xwMe3t79O3bl4tD1NPTw65duxAVFQU7OzvMnz8fP/30k1iPM5/Px4YNGxAZGYmO\nHTtyvbICgQCxsbFITk6Gp6cnHBwc8NVXX0FLS4t7Rup6T4iQ5nmdM2cO9PT04ODgAH19/Vp3ZPjr\nr79gZWWFgQMHwtXVFXl5eYiKihKLjWtMj7Y07wtJadK8S6vWzcDAgKubNO+++upraGiIQ4cO4eLF\ni9y9uXr1ajEZbdq0QVhYGLy9vWFjY4M1a9Zgy5YtUr2fGe8HHslDAE8dBAUFwcnJCUOHDm1qVRgM\nBqNRJCQkQCAQ4Pr162K9hwwGg9FQ5Nqxy8nJwZgxY3D69Gmx4HgGg8FozmzcuBEODg4wNDTEzZs3\nMXv2bOjq6r6zvT4ZDMaHQ5MOxa5fvx7dunWDqqpqjb3vnj59imHDhkFDQwMmJib47bffxM4/f/4c\nEyZMwI4dO5hTx2Aw5Ip///0XY8aMgZWVFWbMmAEvLy8cOXKkqdViMBgtgCbtsTtw4AD4fD5OnDiB\noqIihIWFcefGjBkDQBioefXqVQwcOBDnzp2DjY0NysrKMHjwYMydOxc+Pj5NpT6DwWAwGAxG8+Ld\nr4FcP4sXLyZ/f3/u+OXLl6SsrEwZGRlc2oQJE2jBggVERLRz507S1dUlgUBAAoGAfv/99xoyDQ0N\nCQD7sA/7sA/7sA/7sE+z/5ibm8vEp2oWs2KpWqdheno6FBUV0blzZy7NwcGBWw18/PjxePz4MeLi\n4hAXF4dPPvmkhswHDx5wyyE05ScoKKjJ5TWkjDR568tT2/mGpMu63eTVdu/bfg0911xt11Lt96E8\ne7LWoznYrr48jTnXXO3X0p69W7eysX59DPr2nYj162Nw61b2W9motvSsrCyZ+FTNwrGrPiX75cuX\n0NLSEkvT1NTEixcv3qdaMkEgEDS5vIaUkSZvfXlqO9+Q9Ozs7Hr1eNc0B9s1tNzb2q+h55qr7YCW\nab8P5dkDZGu/5mC7+vI05lxztV9LevbS0nKwZUsmYmN9oKHhj/x8H4SHZyItLUeq8g1NlwXNYlbs\n4sWLcf/+fS7G7urVq+jZsydevXrF5fnf//6HhIQEHDx4UCqZ8rIVE0My/v7+CA8Pb2o1GI2A2U6+\nYfaTb5j9ZEtwcCzOnfNBSQmgogJ06wYoKQH6+rGYMUO2Mf6y8luaZY9dly5dUFZWJrbpcVJSUoO3\neQkODkZ8fLwsVGS8Z/z9/ZtaBUYjYbaTb5j95BtmP9lQXg6cPAn88w8fom2Qi4uBp0+F/5eUyM59\nio+PR3BwsMzkNWmPXXl5OUpLSxESEoL79+9jy5YtUFRUhIKCAsaMGQMej4etW7ciMTERH3/8Mc6f\nPw9ra2upZLMeOwaDwWAwGA3lyRPgjz+ABw+Aixdj8fq1D5SVASsrQLQpCeuxq4Vly5ZBXV0dK1eu\nxK5du6CmpobQ0FAAwC+//IKioiLo6+tj3Lhx+PXXX6V26hjyD+tplV+Y7eQbZj/5htnv7UhKAjZt\nEjp1AGBmZg4trRh061bp1BUXx8DXt+4tD5sSxaa8eHBwcK3djzo6Ojhw4MD7VYjBYDAYDMYHR3Ex\ncOQIkJxcmaagAIwbZwxtbSA2NhYlJXwoK1fA17czLC2Nm07ZemgWkyfeBTweD0FBQRAIBDVmn7Rp\n0wbPnj1rGsUYjEago6ODp6LgDgaDwWDIjPv3gf37gapuga4uMHIk0L79u79+fHw84uPjERISIpOh\n2Bbt2NVWNRZ/x5A32D3LYDAYsoUIOHcOiIkBKioq052cgP79AWXl96tPi4ixYzAYLQ8W4yPfMPvJ\nN8x+0vHiBRARAURFVTp1KirCXrohQ96/UydLmjTGjsFgMBgMBuN9kpEBHDgAvH5dmWZkBIwYAejo\nNJ1esqJFD8XWFmPHhrUY8ga7ZxkMBuPtKCsDoqOBf/6pTOPxgF69AC8v4WSJpoDF2EkJi7FjtCTY\nPctgMBiN5/Fj4QSJ3NzKNE1NYPhwwNS06fSqCouxYzAYzRIW4yPfMPvJN8x+4hABV68K16ar6tRZ\nWgKBgc3HqZMlzLFjMBpBcHAwLCwsmloNBoPBYNTCmzfCHST+/hsoLRWmKSoCAwYAo0cD6upNq9+7\ngk2eqIW0tBxER2ehtJQPJaUK+PmZv9WChLKWJ0/cu3cPnTp1Qnx8PDw9PZtaHcY7pnpMK0O+YPaT\nb5j9hNy9K3TqCgoq09q2Fc56NTBoOr3eBy26xy44OLhR3dJpaTkID89Efr4PCgoEyM/3QXh4JtLS\nchqlh6zlySv1xQ5UVFSgoupiQnVgYmKCU6dOyUKtZkWp6Gclg8FgMBpMRQWQkACEhYk7dS4uwLRp\nzdOpi4+Pr3UXrsbQ4h27xvx6iY7OgoqKL+LjwX3On/fFV19lITgYDf7Mnp2F8+fF5amo+CImJqtR\n9RIIBJg6dSqWLVuG9u3bQ1dXFxMnTsSrV6/E8u3duxeOjo5QU1ODqakp5syZg9f/P787JiYGKioq\nePPmDQDgzZs3UFVVRa9evbjyUVFRUFFR4crUxZkzZ9CjRw9oaWlBS0sLjo6OOHnyJACgU6dOAABv\nb2/w+XyYmZkBqBzOjIyMhJWVFVRUVJCRkSFVG/B4PPB4PKnyNpYrV66gX79+aN26NTQ1NeHm5oaL\nFy+K5Tl48CCsrKygoaEBb29vZGZmcucKCgowbtw4GBsbQ11dHVZWVli1apVYeX9/f/Tu3Rvr1q2D\niYkJVFVVUVxc/E7r9a5hMT7yDbOffPMh2+/5c2DnTiA2tnJtOlVV4JNPgEGDACWlptWvNgQCgUwd\nOzYUK4HSUsn+bnl54/zgigrJ5UpKGu9X79+/HwEBATh16hRycnIwevRoGBsbY+nSpQCA8PBwfPXV\nV1i3bh169OiBu3fvYubMmcjPz8fOnTvh4eEBPp+PhIQE9OnTB2fPnoWWlhYuX76MoqIiqKmpITY2\nFq6urlCvJxChrKwMgwcPRkBAAHbu3AkAuHHjBlcuMTERzs7O+PPPP+Hh4QGFKnPKHzx4gI0bNyIi\nIgI6Ojpo165do9ukKv3798eZM2fqzHP8+HH06NFD4rmUlBR4enpi6NChiIuLg7a2Nq5cuSLWo/jw\n4UP8+uuv+O2336CgoICAgAAEBAQgISEBAFBcXAx7e3vMnTsXOjo6OHPmDKZPn442bdrA39+fk3Px\n4kVoaWnh0KFD4PP5UGqubx8Gg8FopqSlAX/9BRQVVaZ16iRcm65166bTqylgjp0ElJQkDwcqKEg3\nTFgdPl9yOWXlxskDhEORP/30EwCgS5cuGDVqFKKjoznHLjg4GCtWrMDYsWO5/OvWrYNAIMC6devQ\nunVruLu7IyYmBn369EFsbCwGDx6M8+fPIyEhAX379kVsbCz69etXry4vXrxAQUEBBg0aBHNzcwDg\n/gKAnp4eAOEevfr6+mJl37x5g4iICBgZGTW4Deoa2t2+fTuKqj7hEjA0NKz13IoVK9ClSxfs3r2b\nSxP1NIooLi5GREQEdHV1AQDz58/HmDFjUFJSAmVlZRgYGODrr7/m8hsbG+PixYvYs2ePmGOnoKCA\niIiIeh1oeYHF+Mg3zH7yzYdmv9JS4e4RVQdTeDzhunSengC/RY9LSoY5dhLw8zNHeHgMBAJfLq24\nOAb+/p1hadlweWlpQnkqKuLyfH07N0o/Ho8HBwcHsbT27dvjxIkTAID8/Hz8+++/mD17NubMmcPl\nISLweDzDK2BJAAAgAElEQVRkZmbCxcUF3t7eOHToEAAgNjYWs2bNgqqqKmJjY/HRRx8hMTERP/zw\nQ7366OjoYMqUKejbty98fHzg5eWFYcOGoUuXLvWWNTAwkMqpq94D9/r1a/Tv31+s969qD1z7t9y5\n+cqVKxgwYECdeQwNDTmnTnRNIkJeXh6MjIxQUVGBH374AXv37sX9+/fx5s0blJaWwsTEREyOtbV1\ni3HqGAwG432Rny9cm+7Ro8q01q2Fa9MZfxhzEyXyAfqy9WNpaQx//87Q14+FtnY89PVj/9+pa9yd\nImt5AKBcbSM7Ho/HDROK/q5duxZJSUncJzk5GRkZGbCzswMgjHm7evUq7t69i8TERPj6+sLHxwex\nsbE4deoUlJSU4OHhIZU+mzdvxpUrV9C7d2+cOnUKdnZ22Lx5c73lWrVqJZX8bdu2cfW4du0aDA0N\nxdKSkpLg4uLC5e/fvz80NTXr/Jw9e7bW60mzUKQkGwCV7f/TTz9hxYoV+PLLLxEdHY2kpCRMmTKl\nRgxdS3PqPuQYn5YAs5988yHYjwi4fFm4Nl1Vp87aGpg+/cN26oAW3mMnmjzRmK5pS0tjmS5HImt5\ndWFgYICOHTvi1q1bmDx5cq353NzcoKqqiqVLl6JLly7Q19eHQCDA6NGjceDAAfTo0aNB8V62traw\ntbXF7NmzERgYiM2bN2PatGmcA1ReXt7oOlUfNlVUVESHDh1qDI+K2LZtGzcxRFqZVXFxcUFMTAzX\ny9kYEhIS0L9/f7Fh1/T09Hc+6YPBYDBaKkVFwMGDQGpqZZqiItCvn3Dmqzy+XkVbismKFu/YtUSI\nqN7epNDQUEyePBk6OjoYPHgwlJSUkJqaiuPHj+PXX38FIOxx6tGjB3bs2IHAwEAAwjg4Ozs77Nq1\nCyEhIVLpk5WVhc2bN2Pw4MEwMjLCgwcPcPr0aa4HTU9PDxoaGjhx4gSsra2hoqICnXe803JdTps0\nzJ8/H25ubhg7dizmzJkDbW1tJCYmomPHjnB3d5dKhpWVFSIiIhAfHw9DQ0Ps3LkTFy9efOd1b2o+\ntBiflgazn3zTku2XkwP8+SdQWFiZpq8vXJuuWvi2XCHqgJL2O7c+2FCsHCJpqY/qaePGjUNkZCQO\nHz4MNzc3uLq6IiQkpEY8m7e3N8rLy+Hj48Ol+fj41Eiri1atWiEzMxOjR4+GpaUlRo4ciR49emD9\n+vUAAD6fjw0bNiAyMhIdO3bkHL73sWRJY7Gzs0N8fDzy8/Ph5eUFJycnrF69GoqKwt9CteleNW3J\nkiXw8vLCkCFD4OHhgcLCQsyaNUssT3NuAwaDwWgOVFQIlwkLDxd36lxdgalT5dupexfwqIXuLF5X\njBTbUJ0hb8jTPRsfH9+iew1aOsx+8k1Ls19hoXAHiX//rUxTUwOGDAGsrJpOr3eBrN7zLXoolsFg\nMBgMhnxy86Ywnq5quLSJiXDWq5ZWk6nV7GE9dox62b17N6ZPn17r+dTU1EatQ8eQHnbPMhiMD4XS\nUuD4ceDKlco0Ph8QCICePVvu2nSyes8zx45RLy9fvkReXl6t542NjcXWk2PIHnbPMhiMD4FHj4Rr\n0+XnV6Zpawt3kOjYsen0eh+woVgpeJvlThiVaGhoQENDo6nVYMgJLS3G50OD2U++kVf7EQGXLgEn\nTwJlZZXptrbCfV5VVZtOt3cNW+6kAbTU5U4YDAaDwWgpvH4N/P23cL9XEUpKQP/+gJOTfK5N1xBk\nvdwJG4plMOQAds8yGIyWyJ07wrXpXryoTGvXTrg23f9vM/7BwIZiGQwGg8FgyCXl5cCpU8Dp08Jh\nWBHu7oCfn3A3CUbjaKFzSxgMRlPxIexV2ZJh9pNv5MF+BQXCxYYTEiqdOnV14NNPhVuDMafu7WDN\nx2AwGAwG471w4wZw6BBQXFyZZmYGDBsGaGo2nV4tCdZj9wFiamqK5cuXN6gMn8/Hnj17GnytBw8e\nQFdXF/fv35cqf3h4OJSUlBp8HVlQXl4Oa2trHDt2rEmu31KQxxl5jEqY/eSb5mq/khLhBIn9+yud\nOj5fOOw6fjxz6mQJ67GTM/z9/cHj8RAWFgY+n4/4+Hh4enqCz+cjLi4Od+7cQUhICO7cuVOrjMuX\nL0NdXb1B183NzUXr1q0brG9QUBBGjRqFDh06NLjs+0ZBQQGLFi3C119/jf79+ze1OgwGg9EiePhQ\n6NA9eVKZpqMjXJuOrW0ve5hjVwtpmWmIvhKNUiqFEk8Jfi5+sOxs2eTy6to0XtrN5HV1dRt8Xf1G\n7LL89OlT7Nq1C+fOnWtwWVlTUVEBQNjzWBcjRozA559/jri4OHh7e78P1Voc8rqOFkMIs59805zs\nRwRcuABERQknS4iwtwc+/hhQUWk63VoyLXooNjg4uFGBpGmZaQiPC0e+QT4K2hUg3yAf4XHhSMtM\nq7/wO5Yni6nQJiYmCA0N5Y5fvHiBzz77DPr6+lBVVUX37t0RFRUlVobP52P37t1ixxs3bsT48eOh\npaWFjh07YsWKFWJl9u3bBwMDAzg5OYmlZ2VlYeTIkdDV1UWrVq3g4OCAI0eOiOU5d+4cnJ2d0apV\nK3Tr1g2XL18WOz916lR07twZ6urqMDc3x6JFi1BSUsKdDw4OhoWFBSIjI2FlZQUVFRVkZGQgJSUF\nffv2hY6ODjQ0NGBjY4Ndu3Zx5dTU1NCvXz+xNAaDwWA0jFevgD17hFuDiZw6ZWVhLN3w4cypq0p8\nfLxM191t0T12jW2o6CvRULFQQXx2fGWiEpC8Nxnde3ZvsLyLZy7itdFrILsyTWAhQExiTIN77err\nlaurR6+2PAEBAbhy5Qp2796NTp06YePGjfj444+RnJwMS0tLsXJVCQkJQWhoKJYuXYpjx45h5syZ\ncHV1hY+PDwDg1KlTcHNzEyuTm5sLDw8PODg44NChQzA0NERKSorYlmQVFRVYuHAh1q1bBz09Pcye\nPRuffPIJMjIyoKCgACKCgYEBfvvtNxgYGCApKQmfffYZlJSUxGz+4MEDbNy4EREREdDR0UG7du3g\n4eGBrl274vz581BVVcWtW7dQXvWnJAA3NzesW7euzjZk1E5z6S1gNA5mP/mmOdgvKws4cAB4+bIy\nrX174dp0jRgwavHIeoHiFu3YNZZSKpWYXo5yien1UYEKieklFSUS0+siLCysUm5FRY3/PT09MXHi\nRKnlZWZm4o8//sDRo0fRu3dvAMCaNWtw+vRp/PDDD9i2bVutZUePHo3JkycDAGbMmIH169cjOjqa\nc+zS09NrDGdu2LABCgoK+Pvvv6GmpgZA2INYFSLCmjVr4OjoCEDooLu7u+P27duwsLAAj8fDd999\nx+Xv1KkTMjMzsXHjRjHH7s2bN4iIiIBRlSCOf//9F3PmzIGVlZXEa4vScnJyUFZWBkU2757BYDCk\norwciI0Fzp4VT/fwAHx9Abal+PuhRQ/FNhYlnuRZmQpo3F3Jr6WZlfnKjZInS27evAlA6BBWxdPT\nEykpKXWWFTleIgwNDZGXl8cdP3/+HJrVpjpduXIFHh4enFMnCR6PBwcHB+64ffv2AIBHjx5xaVu2\nbIGbmxvatWsHTU1NLFy4EP/++6+YHAMDAzGnDgDmzp2LKVOmwNvbGyEhIbh69WqN62tpaQEACgoK\natWRUTvysI4Wo3aY/eSbprLf06fA9u3iTl2rVsC4cUCfPsype5+w7ggJ+Ln4ITwuHAILAZdWnFEM\n/9H+jZrwkGYkjLFTsagMKijOKIavt68s1H0nSBPLp6ws7pjyeDyxXkRtbW28qLpPDKTbMoXP54sN\n+4r+F8net28fZs6ciZUrV8LLywtaWlqIjIzEokWLxOS0atWqhuzFixdj7NixOH78OGJjY7F8+XLM\nnz8fy5Yt4/IUFhZy+jMYDAajbpKTgcOHhUuaiOjcGRg6FNDQaDq9PlRYj50ELDtbwt/bH/p5+tDO\n1YZ+nj78vRvn1L0LebLE1tYWgDAerioJCQmwt7d/K9kWFhbIzs4WS3NxccG5c+fw+vXrRstNSEiA\nk5MTvvzySzg5OcHc3LzO5V2qY2pqisDAQOzbtw8hISHYuHGj2PmcnByYmJiwYdhG0hxifBiNh9lP\nvnmf9isuFsbS/flnpVOnoCDsoRs7ljl1TQX75qoFy86WMnW8ZC1PWi5evIgJEyYgIiIC3bvXnPhh\nbm6O//znP5gxYwY2bdrETZ64efMm9u7d26BrEZFYb5yXl5fY7FsA3HWGDBmCkJAQtG/fHikpKVBU\nVES/fv2kuo6VlRW2b9+OgwcPwtbWFocPH8aBAwfqLffq1SvMnz8fI0eOhImJCQoKCnD8+HHOuRXx\nzz//sC83BoPBqIMHD4Rr0z19WpnWpo1wgoShYdPpxWA9di2e169fIyMjA0VFRbXm2bp1K/r27Ytx\n48bB0dER58+fx+HDh9GlS5cGXav6bNsRI0YgLy8PiYmJXFq7du1w5swZaGpqYsCAAbCzs8OSJUtq\nyJEkW8Rnn32G8ePHY9KkSXB2dsalS5cQHBxcY/i2uhxFRUUUFBRg8uTJsLGxQb9+/dC+fXuxHTWK\niopw4sQJjBs3rkF1Z1TCYrTkG2Y/+eZd249IGEe3dau4U+foCHz2GXPqmgM8ksXCaM2QumK5pInz\naskYGhpiwYIFmDVr1ju/1rRp06CgoFBjuLO5EhERgR9//BHJyclNrYoY8nTPNqcFUhkNh9lPvnmX\n9nv5Ujj0mpVVmaaiIlxs+C0jdxiQ3XueOXYfEK9evcLZs2fRv39/xMTEvJeX98OHD2FnZ4fk5ORm\nv61YeXk57OzssHr1aqmHhd8XH+o9y2AwmgcZGcBffwkXHhbRoYNw6FVHp+n0akkwx64emGNXk+Dg\nYKxfvx4TJkzAqlWrmlodRgP4UO9ZBoPRtJSVATExwPnzlWk8HtCjB+DtzZYxkSXMsasH5tgxWhLy\ndM+yoTz5htlPvpGl/Z48EU6QePiwMk1DQ7glmJmZTC7BqIKs3vNsViyDwWAwGAwOIiApCTh6VHxt\nui5dgCFDhAsPM5ovLbrHLigoiNuDrfq5FlptRguF3bMMBuN98OYNcOQIcP16ZZpobTpXV+EwLEO2\nxMfHIz4+HiEhIWwoti7YUCyjJcHuWQaD8a65dw/44w/g2bPKND094QSJdu2aTq8PBTYU+xbo6OhI\nXCuNwWiu6MjRtDMWoyXfMPvJN42xX0WFcG26uDjh/yKcnYF+/QDlpt/WnNEAPkjH7mnVVRUZzRL2\n5cJgMBjvnhcvhFuCVd2VUVUVGDQIqLYpD0NO+CCHYhkMBoPB+NBJSwP+/huounV3x47AiBGAtnbT\n6fWhwoZiGQwGg8FgNJiyMiAqCrhwoTKNxwN69QIEAoDPNhuVa5j5GM0Stl+l/MJsJ98w+8k39dkv\nPx/YskXcqdPSAiZOBHx8mFPXEmA9dgwGg8FgtHCIgMRE4PhxoLS0Mt3KChg8GFBXbzrdGLKFxdgx\nGAwGg9GCKSoCDh0Cbt6sTFNUBPr2Bbp1Y2vTNRdYjB2DwWAwGIw6+fdf4dp0hYWVafr6wgkSBgZN\npxfj3cFG0xnNEhbnI78w28k3zH7yjch+FRXAqVNAWJi4U9etGzB1KnPqWjKsx47BYDAYjBZEYaFw\nbbqcnMo0NTVhLJ21ddPpxXg/sBg7BoPBYDBaCKmpwMGDwrg6EcbGwPDhQOvWTacXo35YjB2DwWAw\nGAwAwpmuJ04Aly9XpvF4wnXpevViy5h8SDBTM5olLM5HfmG2k2+Y/eSPvDzh2nSXLwPZ2fEAhL1z\nkyYBXl7MqfvQYD12DAaDwWDIIURCZ+7ECeFuEiJsbIR7vaqpNZ1ujKZDLmPsnj9/Dj8/P6SmpuLC\nhQuwsbGpkYfF2DEYDAajpfL6tTCW7tatyjQlJaBfP8DZma1NJ4980DF26urqOHr0KObNm8ecNwaD\nwWB8UGRnC2e9Pn9emWZgAIwcCbRt22RqMZoJcjnyrqioCD09vaZWg/EOYXE+8guznXzD7Nd8qagA\n4uKAHTvEnTo3N+HadG3bMvsx5LTHjsFgMBiMD4mCAuEOEnfvVqapqwNDhgCWlk2nF6P50aQ9duvX\nr0e3bt2gqqqKSZMmiZ17+vQphg0bBg0NDZiYmOC3336TKIPHAglaJAKBoKlVYDQSZjv5htmv+ZGS\nAvz6q7hTZ2oKTJ9e06lj9mM0aY9dhw4dsGTJEpw4cQJFVVdTBPD5559DVVUVeXl5uHr1KgYOHAgH\nB4caEyVYjB2DwWAwWiIlJcDx40BiYmUanw94ewM9erBlTBiSadLbYtiwYRgyZAh0dXXF0l+9eoU/\n//wTy5Ytg7q6Onr06IEhQ4YgIiKCyzNgwACcPHkSU6dOxY4dO9636ox3DIsTkV+Y7eQbZr/mQW4u\nsHmzuFOnrQ0EBNS94DCzH6NZxNhV73VLT0+HoqIiOnfuzKU5ODiI3bBHjx6tV66/vz9MTEwAANra\n2nB0dOS6qUWy2HHzPL527Vqz0ocds2N2zI7fxzERsHFjPC5fBjp1Ep7Pzo7//6FXAVRVm5e+7Ljx\nx6L/s7OzIUuaxTp2S5Yswb179xAWFgYAOH36ND755BM8fPiQy7Nlyxbs2bMHcXFxUslk69gxGAwG\nQ5549Qr4+28gPb0yTUkJGDAAcHRka9O1dFrUOnbVK6KhoYHnVedyAygsLISmpub7VIvBYDAYjPfC\n7dvAgQPAixeVae3bAyNGAGx1L0ZD4De1AkDNma1dunRBWVkZMjMzubSkpCTY2dk1SG5wcLBYlydD\nfmB2k1+Y7eQbZr/3S3k5EB0NRESIO3UffQRMntxwp47ZT/6Ij49HcHCwzOQ1aY9deXk5SktLUVZW\nhvLychQXF0NRURGtWrXC8OHD8e2332Lr1q1ITEzEoUOHcP78+QbJl2VDMRgMBoMhS549A/bvB+7f\nr0xr1QoYOhSwsGg6vRjvF4FAAIFAgJCQEJnIa9IYu+DgYCxdurRG2rfffotnz54hICAAUVFR0NPT\nw4oVKzB69GipZbMYOwaDwWA0V65fBw4fBoqLK9PMzIBhwwAWdfRhIiu/pUmHYoODg1FRUSH2+fbb\nbwEAOjo6OHDgAF6+fIns7OwGOXWNwdTUFAC4WbTZ2dnw9vZGTk4OvL29JZYZOHAg7ty5U69sJycn\nFFd9eutg3rx5+P333+vMEx4ejv/85z9SyWsM169fx6BBg2o9v2nTJqxZs0ZqeefOnYOTkxP36dCh\nA1xcXCTmff36NUaNGgULCwtYW1vjyJEj3LkbN26gZ8+ecHJygo2NTaN+3YSHhyMjI6PB5QBg7Nix\n6NChA/h8Pl6/fi0xT0BAQJ3ne/fuzbWDvb09+Hw+bty4IZYnPj4eCgoK2LBhQ4N1zMjIgJOTE1xc\nXGpd1LsucnJysGXLlgaXq401a9YgPz+fOw4ODsa8efNkJr+5UlhYiB9++KGp1agTf3//Rt1jjLej\nuBj46y/hLhKirwU+H+jdGxg/njl1jLenWcTYvSsaE2PXkJ0sjhw5wjmEdXH16lWoqKjUmy8vLw9H\njhzBqFGjZKZjbZSVldV6zt7eHuXl5bhw4YLE85999hm+/PJLqa/l4eGBq1evch9XV1eMHTtWYt7/\n/e9/0NbWxpYtW3Do0CFMmTKFc5LmzZuHTz/9FFevXsWlS5cQFhaGy5cvS60HIHTs0qtOOWsAU6dO\n5ZZhkcShQ4fA5/PrtE9UVBTXDt999x3s7OzEYkdfvHiBBQsWYODAgY3S8c8//0SPHj1w5coVjBkz\npsHl79y5g82bNzfq2uXl5QDEY3x+/vln5OXlcccfyk4xz549w48//ljr+bqeP0D47nrX63PWZouG\nvDNFNmfUTVpaDjZsiEVISDzGjYtFdHQOd65NG2EsXY8espn1ymLs5A9Zx9i1eMdOtG5Mfejr6wMA\n2rZtCwBQUFCArq4uFBQU0KZNG4llTExMcPPmTQBAZmYmfH194eDgABcXF5w4cYLLV7UHx8TEBEFB\nQfDw8ICpqanYL+aIiAgMHTqUOy4pKcHcuXNhb28PR0dHjBgxAoBwFvHz588xevRo2NnZoWfPnnj0\n6BEAYW+bp6cnXFxcYGtri59//pmT5+/vjylTpsDT0xOurq4oKirCf/7zH9ja2sLR0VHMoRw1ahS2\nbdtWa7uKel3OnTsHFxcXODk5wc7ODnv37q2znfPy8nDy5EmMHz9e4vnIyEh89tlnAIDOnTujW7du\n3JqFRkZGKCgoAAC8fPkSPB4P+vr6KCoqgoODAw4ePAgAiI2NhbW1NV69eiUmOywsDFeuXMGsWbPg\n5OSE2NhYVFRUcG1sb2+PefPmoaKiQqJuAoGAuz+q8+TJEyxduhSrVq2Suit927ZtCAgIEEv76quv\nMH/+fLFFu6Wt3+7du7FmzRrs27cPTk5OuH37Nn766Se4urrC2dkZHh4eSEpKAiDsGa1qe1GP+Oef\nf46bN2/CyckJn3zyCQAgLS0NAwYMgKurKxwdHREeHs5dk8/nIyQkBK6urjXCKkJDQ/HgwQOMHDkS\nTk5OSE1NBQDcv38fAwcOhLW1NT7++GNu15mYmBh4eHjA2dkZXbt2Feu5FggEmD9/Pnr16gVzc3N8\n8803Ets0Pj4ejo6OmD59OhwcHODo6Ihbt25x53fs2AF3d3d069YNvr6+nJP/0UcfcT8SZsyYwTnb\nZWVlaNu2bY2dcUQQEWbMmAFra2s4OjqiV69eXDsWFBTAyckJPXv25Oowe/ZsfPTRR2LPuSR4PJ7M\nnOD79+9jxIgRcHBwgIODA1auXMmdu3HjBnx9fdGlSxdMnDiRS9+zZw/c3d3h7OwMZ2dnxMbGcudM\nTEzwzTffwM3NDdOnT5eJji2ZtLQchIdnIjHRB/HxAuTn++DatUw8fpwDBwfgs8+ADh2aWktGUyIQ\nCGQ7J4BaKO+jaiYmJpSSkkJERK6urrR9+3YiIrp58ybp6enR48ePiYiIx+PRq1evuDLz5s0jIqLs\n7GzS0NDgzg0cOJAOHjzIyQ8ODqYRI0ZQaWkpERE9efKEiIjCwsJIR0eH7t27R0REU6dOpUWLFhER\n0YsXL6i4uJj738bGhm7dukVERBMnTqTu3bvT69eviYjozz//pL59+3LXKygo4P5PS0sjMzMzifUO\nDg7m6jB48GD67bffJMqQxI8//kjDhg2r9bympibXbkREM2bMoFWrVhER0ePHj8nGxoY6dOhA6urq\n9Msvv3D5bt26RZ06daILFy6QqakpXbt2TaJ8gUBAR44c4Y5/+eUX8vPzo9LSUiopKSFfX1/auHFj\nnXWoak8Ro0aNoqNHj9Z6vjoPHz4kdXV1sboePXqURo0aRURE/v7+tGHDhgbXr6ptiIjy8/O5/6Oi\nosjd3Z2Iard9fHw8devWjUsvLS0lZ2dn7h56/vw5denShdLS0ri6/vDDD7XWs+ozQkQUFBREFhYW\nVFhYSEREffr0oS1bthAR0bNnz6i8vJyIiHJzc8nIyIjTSyAQ0OjRo4mIqLCwkPT09CgzM7PG9eLi\n4khJSYlrn9DQUBo7diwRESUkJNDAgQO55+Po0aPUo0cPIiJavHgxrVixgoiI7O3tqXv37vTw4UM6\nd+4ceXp61lq/xMREsra2rtGO2dnZpKenJ5ZXIBDQkCFDuDrWRVBQEIWHh9dIj46OJkdHR4mfNWvW\nSJQlEAjof//7H3csuucmTpxIvXr1ouLiYiopKSFbW1uKiooiosp3DZHw3jMyMuKOTUxM6PPPP6+3\nDgwhK1fG0JAhRF5elR8fH6IFC2KaWDNGc0NWfkuzWMdO3nnx4gWSkpIwadIkAOB+vf/zzz8Sh9RE\nvSPGxsbQ0dHBvXv30KVLF9y5cwcdqvx0O3LkCFatWgVFRaGZqvYc9ujRg8vr7u6OqKgoAMLt2KZP\nn47k5GTw+Xw8ePAASUlJsLS0BI/Hw8iRI6GmpgYAcHR0RGpqKmbOnAmBQCCmq5GREXJyKocLqkP/\n3yvl4+OD7777DllZWejduzdcXV3rbKuwsDCxHgNpEPVcTJw4EQEBAZgzZw5yc3MhEAjg4uICV1dX\nWFpaYunSpfDw8MDPP/8MBweHenUHhL1EkyZN4tp40qRJOHDgQIN6IiIjI6GiooL+/ftzsqmeXrud\nO3eif//+XM9cQUEBFixYgOjoaK58VRmNrd/ly5exfPlyPHv2DHw+n+uhqs321fVOT0/HrVu3xGJc\nS0tLkZqaii5dugCAWE9PffB4PPTr1w9aWloAADc3N2RlZQEQ9uZOmjQJmZmZUFRUxNOnT5GWlsbd\nU6K4Ui0tLVhbWyMzMxPm5uY1rmFpacm1j5ubGw4dOgRAOFSelJQENzc3rq6iHmBfX1+EhoZi7Nix\n0NPTg0AgQExMDO7cuQMfH59a62Nubo7S0lIEBATAx8cHH3/8scR2FPHpp5+Cz5c8ULJt2zasX78e\nAJCbmwtlZWUulvX7779Hv3794Ovri6tXr9aqT3VevnyJ8+fPIyYmhksT3XM8Hg9Dhw6FsrIyAMDZ\n2RlZWVnw8/NDZmYmFi9ejAcPHkBJSQm5ubnIy8vjRjYmTJggtQ4fKkTApUtAfDwfVUNuNTUBGxvA\nwKBFD5gxmpAWfWe973Xs6vsyF6Gqqsr9r6CgUGe8TW0yq8rg8/mcjIULF8LQ0BDXrl3DtWvX4Orq\nijdv3nB5W7Vqxf1vamqKmzdvonfv3oiOjoaDgwM3yUM0O6e+On3xxRc4dOgQ2rZti//+979YsmRJ\nrXn/+ecfPHv2DAMGDKg1T6dOnZCdnc3ZLScnBx07dgQAxMXFcU5Eu3bt4OPjg4SEBK7slStXYGBg\ngLt379apc/Uhrqp1lNaGVTl16hRiY2NhamoKMzMzAICdnZ3YEGB1wsPDxYZhb9y4gdzcXLi6usLU\n1BR//PEHgoKC8N1333F5pK2fiJKSEowcORJr167F9evXcezYMc6+ddm+KkQEPT09sRjJ27dvY8iQ\nIRh1XwYAACAASURBVFweDQ0NsTL1PXNV400VFBS4OK3AwED4+Pjg+vXruHr1KoyMjMTu3erPTW3x\nXXU9XwEBAVw9rl27xm3l89FHHyExMRFHjhyBr68vfHx8EB0djZiYGPj6+tZaFy0tLaSkpGD06NFI\nTk6Gra2tWExhdaq3VVUmT57M6TZ9+nQsW7aMO+7Xrx8AIDo6WmwiUtVPXROaaruvJdkiPj4eY8aM\nwcyZM3Hjxg0kJiZCUVFRzBZ11YMhXMZkxw5AGEUiDO3g8YBOnQAnJ0BNDVBWlhzy8bawGDv5g8XY\nNYCGxNi9DZqamnB0dOSCnVNTU5GUlAR3d/cGyTExMcG9e/e4448//hhr1qxBaWkpAGEcV30UFhbC\nyMiIm215+vTpWvPev38fPB4PQ4YMwapVq5Cfn49nz54BAO7du4dOnTpJjPOp+iWRnp4OU1NTTJs2\nDbNmzcKlS5dqvd727dsxYcKEWnssAGGvzKZNmwAIZ3hevnyZ+1KztbXFsWPHAAh7SU+fPg17e3sA\nwIEDB3D27FncuHEDhw8fxvHjxyXK19LS4nppAMDPzw87duxAWVkZSktLsWPHDvTp06dW/ST1yG3Y\nsAF3797FnTt3uFnSKSkpsLKykijj3LlzKCwsRP/+/bk0UZykSMbIkSOxdOlSLF68uEH1q6rXmzdv\nUF5eDiMjIwDAL7/8wp2rzfZaWlooLCzk8llaWkJdXR27du3i0m7duoUXVVdSrYPq7V3dwaj646Gw\nsBDGxsYAhJNMqi5QLqlsQxk0aBB27tyJ+/+/aFh5eTmuXLkCQOjgODs7Y8WKFejduzfc3d1x9uxZ\nXL9+vc7n+PHjx3j16hX69OmD77//Hq1bt8bt27ehpaWF169fv9XkAkn19fPzE3Oyq34kTWjS0NCA\nh4cHVq9ezaVJ+x4RrRCwbds2qWf1f+gQARcvAhs3AqLtP83MzKGiEgNnZ+FyJnw+UFwcA1/fmr3N\njA8TWcfYtWjHTtY8ePAATk5OYmkix2f37t3YtWsXHBwcMG7cOOzatUtsyEMavL298c8//3DHCxYs\ngImJCRwdHeHk5ITAwEBOXlWZVY8XL16MLVu2wMHBASEhIfDy8pKoLyCcaOHh4QFHR0e4ublh4cKF\naNeuHQCh8+Hn5ydRz6rXW7duHezs7ODs7IwNGzYgNDRUYpmioiJERkbWmCwACJeDyc3NBSCc+VpQ\nUICpU6di0KBB2LJlC9fLGBYWhq1bt8LR0RHu7u4YNWoU+vbti+zsbHzxxRf4/fffoaOjg99//x2f\nffYZHjx4UONa06ZNw9KlS7nJE9OmTUPXrl3h5OQEZ2dnODo6YurUqRLrMHz4cM7ZtbS0FHPMamvj\n6vUDhL11EydOlPq+aEj9qtpGS0sLS5cuRffu3dGtWzdoaGhw55KTkyXa3sHBAZaWlrC3t8cnn3wC\nRUVFHDp0CHv37oWDgwPs7Owwc+ZM7seGpDpU/TE1a9YsTJo0Cc7OzkhNTa3z3l2xYgXmzp0LJycn\n7Nu3r8ZwszTtVZf8Xr16ITQ0FIMHD4ajoyPs7e25YVpAOBxbUFCA7t27Q1FRERYWFtz/tXH37l30\n7t0bjo6OcHBwwIABA+Du7o42bdpg7NixsLe35yZPNBRZTZ7YtWsXzp49y03C2r59e53XEAgEWLNm\nDYYOHQoXFxfcuXMHemxPq3qp2ktXUiJM4/GAYcOMsWpVZ5ibx0JbOx76+rHw9+8MS0vjd6LH++jM\n+NBIy0zD+r3rsWbvGmz4fQPSMtOaWqU6adIFit8l73qB4rKyMujp6SE9PZ2LO3lb8vLyIBAIuJm2\nTcnAgQOxZMmSBvc6MhgMxoeEKJYuOrrSoQOAtm2FO0iwGa/yTVpmGn6N+hXZOtnooNUBeup6KM4o\nhr+3Pyw7W8r0Wi1igWJ55dGjR7C2tsb48eNl5tQBwiVXBg4ciMjISJnJbAzXr18Hn89vUqeOxYnI\nL8x28g2zn/Q8ewbs3Fmzl65nz6ZbxoTZT3YQEbbHbkeyejKevXmG9CfpKC0vhYqFCmISY+oX0ES0\n6Fmxohg7WXdNGxgYNHr3gvqoa1HT90X1ISoG40On6ozVquzYsQNdu3ZtAo0YTQkRcPkyEBXFeula\nKoVvCnEo/RCS8pJQbiSMlS0pL8GzN8+g30ofJRUl9UiQnvj4eJk65GwolsFgMBgMKSkoAP7+G6i6\nmySPJ9w5QiAA6gjJZMgBRIRruddwPPM4isuLcfHMRbw2eg01RTVY6VmhtWprAIB+nj5mfDJDpteW\nld/CbkEGg8FgMOqhrl66IUOA/598zpBjnhc/x6G0Q8h4WjkiZ25mjvx7+bDoZgEFvgIAoDijGL7e\ntS+D1NSwHjtGsyQ+Pp7N7pJTmO3kG2a/mshTLx2zX8MhIiQ/SsaxzGN4U1a5XmMbtTYYajUURflF\niEmMQUlFCZT5yvB19pX5xAn8H3t3Hh5lfe4N/Dsz2feQhGxkIQlJWISwBWWHsJMFbF8VFBe01WK5\nrLZvz/WqSNBztOe0ahdrbRFlO0ptKyRAJEDCQJR9ixpIyEIWkkDIvm8z8/7xNDMZITDJLM88k+/n\nurzM3CEzt/6c8c79/J77B3bsiIiIzEqjAS5cAA4f1u/S+foKe+nYpZO+lq4WHLh2AAV1+iNMHhz1\nIBJGJ8BeYQ94wiyFnLmwY0dERPQDjY1AejpQUqKLyWTAzJnAggXW1aWjwdNoNPiu5jt8VfgVOno7\ntHFvJ2+kxKYg3Cvc4jmxY2cAc90VS0REtoldOtvX2t2KA9cOIL9W/9jH+OB4LIpYBAeFg0Xzsdhd\nsevWrTPoCRwdHfHxxx+bLCFTYcdO2rhPRLq4dtI2nNfPFrp0w3n97kej0SDvdh4yCjPQ3tOujXs5\neSElJgWjvUeLmJ0FOnZffPEFXn311QFfpC+Bd9991yoLOyIiIkOwS2f72rrbcLDwIK7c1j/ZaVrQ\nNCyOWAxHO0eRMjO9ATt2kZGRKC4uvu8TxMTEoKDA+s5NY8eOiIju515duvnzAXt70VIjE8mrycPB\nwoN6XTpPR0+kxKYgwjtCxMz0mapu4c0TREQ07Gg0wMWLQpeuq0sX9/ERunQhIeLlRqbR3tOOjMIM\nfF/zvV58auBULIlcYnVdOlHPii0pKUFpaanRL040EJ53KF1cO2kbDuvX1ATs3g3s368r6vq6dC+8\nIO2ibjisnyGu3r6KP5/9s15R5+HogScmPoGkmCSrK+pMyaDC7rHHHsPJkycBAJ9++inGjx+PcePG\ncW8dERFJRt9eug8/BPrvNPLxAdavB5Ys4aVXqWvvace/rvwLf8/7O9p62rTxyQGTsWH6BkSNiBIx\nO8sw6FKsn58fKisr4eDggAkTJuCvf/0rvLy8kJKSgqKiIkvkOWgymQybN2/muBMiIkJTk7CXrn9B\nJ5MBDz0k3PHKgk76CmoLsP/afrR2t2pj7g7uSI5JxhifMSJmdm994062bNliuT12Xl5eaGxsRGVl\nJeLj41FZWQkAcHd3R0tLi9FJmAP32BEREffS2b6Ong4cKjqE3Fu5evFJ/pOwLGoZnO2dRcpscCw6\noHjSpEl45513UFpaipUrVwIAbty4AU9PT6MTILobzmKSLq6dtNnS+g3UpXvwQWDhQtvs0tnS+hni\nWt017C/Yj5ZuXZPJzcENSdFJiPGVzjFgpmRQYbdt2zZs2rQJDg4O+J//+R8AwKlTp/D444+bNTki\nIqLB0miAS5eAzMw7u3QpKUBoqHi5kWl09nbiUNEhXL55WS8+0X8ilkctl0yXzhw47oSIiGxGU5Nw\nt2v/7d+23qUbborqi5BekI7mrmZtzNXeFUkxSYj1jRUxM+NY/KzYnJwcXLp0CS0tLdoXl8lkePXV\nV41OgoiIyBjs0tm+zt5OHC4+jIvVF/XiE0ZOwIoxK+Bi7yJSZtbFoMJu48aN+OKLLzBnzhw4Ow/f\n9iZZznDbJ2JLuHbSJsX1a24W9tKxSyfN9TNEcX0x0gvS0dTVpI252rtiZfRKjPMbJ2Jm1segwm73\n7t3Iy8tDUFCQufMhIiIyiEYDXL4MHDqk36UbMUK445VdOunr6u3C4eLDuFB9QS8+3m88VoxZAVcH\nV5Eys14G7bGbOHEisrOz4evra4mcTIJ77IiIbNdAXboZM4CEhOHVpbNVJQ0lSC9IR2NnozbmYu+C\nlWNWYvzI8SJmZh4WPSv23LlzePvtt7F27Vr4+/vrfW/u3LlGJ2EOLOyIiGxPX5cuMxPo7NTF2aWz\nHd2qbhwpPoJzVef04mN9x2Jl9Eq4ObiJlJl5WfTmiQsXLiAjIwM5OTl37LGrqKgwOglzSU1N5ckT\nEmWr+0SGA66dtFnz+jU3C3e8FhbqYuzS6bPm9TNEaWMp0vLT0NDZoI052zljxZgVmDByAmQymYjZ\nmUffyROmYlBh99prr+HAgQNYvHixyV7YElJTU8VOgYiIjHSvLl1KChAWJl5uZBrdqm5klWThTOUZ\nvXiMTwySYpJstksHQNuA2rJli0mez6BLsaGhoSgqKoKDg4NJXtQSeCmWiEj62KWzfWWNZUgrSEN9\nR7025mTnhBVjVuCBkQ/YZJfubiy6x2779u04e/YsNm3adMceO7lcbnQS5sDCjohIujQaIDdXuOOV\nXTrb1KPqQdb1LJy5cQYa6P5/He0TjaToJLg7uouYneVZtLAbqHiTyWRQqVRGJ2EOLOykTer7RIYz\nrp20WcP6NTcDBw4A167px/u6dBK6eGRx1rB+hqhoqsC+/H2o66jTxpzsnLAsahkm+U8aNl26/ix6\n80RJSYnRL0RERHQvA3XpvL2FO17ZpZO+HlUPjpUew6mKU3pduqgRUUiOSYaHo4eI2dkGnhVLRESi\na2kR9tKxS2e7bjTfwL78fahtr9XGHBWOWBa1DHEBccOyS9efqeqWATfIbdq0yaAn2Lx5s9FJEBHR\n8NR3x+uf/6xf1Hl7A08/DSxfzqJO6nrVvThSfATbLm7TK+oivSOxYfoGTA6cPOyLOlMasGPn5uaG\nb7/99p4/rNFoMHXqVDQ2Nt7zz4mBHTtpk8o+EboT107aLLl+7NKZnrW9/yqbK7Evfx9ut9/WxhwU\nDlgauRRTAqewoOvH7Hvs2tvbERUVdd8ncHR0NDoJIiIaPjQa4Ntvga++unMvXUoKEB4uWmpkIr3q\nXhwvPY5vKr6BWqPWxkd7jUZKbAq8nLxEzM62cY8dERFZzEBduvh4YNEidulsQVVLFfbl70NNW402\n5qBwwOKIxZgWNI1dugFY9K5YIiIiY7BLZ/tUahVOlJ1ATnmOXpcu3CscKTEp8Hb2FjG74cM6pwvT\nsGfKc/PIsrh20maO9WtpAfbsAfbu1S/q4uOBn/2MRZ0pifX+u9l6E3+78DccLzuuLers5fZYMWYF\nnpr0FIs6C7Lpjl1qaqr2DDYiIrIsjQb47juhS9fRoYt7eQldutGjxcuNTEOlViGnPAcnyk7odenC\nPMOQEpuCEc4jRMxOGpRKpUkLcu6xIyIik2tpEU6PKCjQj0+fDixezL10tuBW6y3sy9+H6tZqbcxe\nbo+EiATMCJ7BvXSDZNE9djU1NXB2doa7uzt6e3uxc+dOKBQKrFu3zmrPiiUiIstjl872qdQqfFPx\nDY6XHodKoztWNMQjBKtiV8HHxUfE7MigqiwxMRFFRUUAgNdeew3vvvsu3n//fbzyyitmTY6GL+7T\nki6unbQZs36trcJeui+/1C/qpk8HNmxgUWcJ5n7/1bTVYNulbci+nq0t6uzkdlgauRTPTH6GRZ0V\nMKhjV1hYiLi4OADA7t27cfLkSbi7u2PcuHH4/e9/b9YEiYjIurFLZ/vUGjW+Kf8GylKlXpdulMco\nrIpdBV8XXxGzo/4M2mPn6+uLGzduoLCwEI899hjy8vKgUqng6emJ1tZWS+Q5aNxjR0Rkfq2twl66\n/Hz9+PTpwlw6zrCXvtttt7Evfx8qWyq1MTu5HRaEL8BDIQ9BLuOWLFOw6B67ZcuW4ZFHHkFdXR0e\nffRRAMCVK1cwatQooxMgIiLp0WiA778HMjLYpbNVao0apypO4VjpMfSqe7XxYPdgrIpdBT9XPxGz\no4EY1LHr7OzEjh074ODggHXr1sHOzg5KpRI3b97EY489Zok8B40dO2mztvMOyXBcO2kzZP0G6tJN\nmybc8counXhM9f6rba/Fvvx9uNF8QxtTyBRYMHoBZobMZJfODCzasXNycsLzzz+vF+MHNxHR8HKv\nLl1yMhARIV5uZBpqjRpnbpxB1vUsvS5dkHsQVsWuwkjXkSJmR4YYsGO3bt06/T/473k0Go1GbzbN\nzp07zZje0LFjR0RkOq2twMGDwNWr+nF26WxHXXsd0grSUN5Uro0pZArMC5+HWSGzoJArRMzO9pm9\nYxcZGakt4Gpra7Fjxw4kJSUhLCwMZWVlOHDgAJ566imjEyAiIus1UJfO01PYS8cunfRpNBqcqTyD\nrJIs9Kh7tPEAtwCsjl0Nfzd/EbOjwTJoj92SJUuwadMmzJkzRxv7+uuv8eabb+Lw4cNmTXCo2LGT\nNu7Tki6unbT1Xz926aRnsO+/+o56pOWnoaypTBuTy+SYGzYXc0LnsEtnQRbdY3f69Gk8+OCDerEZ\nM2bg1KlTRidARETWRaMB8vKELl17uy7OLp3t0Gg0OFd1DkeKj+h16fxd/bEqdhUC3QNFzI6MYVDH\nbt68eZg+fTreeustODs7o729HZs3b8aZM2dw4sQJS+Q5aOzYEREN3kBduqlTgSVL2KWzBQ0dDUgr\nSENpY6k2JpfJMSd0DuaGzWWXTiQW7dht374da9euhYeHB7y9vdHQ0IBp06bhs88+MzoBIiIS3726\ndMnJQGSkeLmRaWg0GpyvOo8jJUfQrerWxke6jsSq2FUIcg8SMTsyFYM6dn3Ky8tRVVWFwMBAhIWF\nmTMvo7FjJ23cpyVdXDvpaWsTunRXrgClpUqEh88HwC6dFA30/mvsbER6QTpKGkq0MRlkmB06G/PC\n58FOblCfh8zIoh27Pk5OThg5ciRUKhVKSoT/OCJE2GzxH//xHzh16hTCw8PxySefwM6O/0ESEQ1F\nXp5Q1LFLZ5s0Gg0uVl9EZnGmXpfOz8UPq2JXIdgjWMTsyBwM6tgdOnQIzz77LKqrq/V/WCaDSqUa\n4KfMIzc3F7/73e+wa9cuvP3224iIiLjr6Rfs2BERDax/l64/dulsR1NnE9IL0lHcUKyNySDDrNBZ\nmB8+n106K2OqusWgM0E2bNiATZs2obW1FWq1WvuXpYs6ADh16hSWLl0KQDjD9ptvvrF4DkREUpaX\nB/z5z/pFnacnsG4dkJTEok7q+rp0H577UK+o83XxxbNTnsWiiEUs6myYQSvb2NiI559/Xu/ECbE0\nNDQgMFC4DdvDwwP19fUiZ0TmwH1a0sW1s14DdemmTBG6dE5OXD+pO3j4IBoCGlBUX6SNySDDQyEP\nYUH4Atgr7EXMjizBoI7ds88+i08++cSkL/zBBx9g2rRpcHJywjPPPKP3vfr6eqxevRpubm4IDw/H\n559/rv2el5cXmpubAQBNTU0YMWKESfMiIrJFd+vSeXgATzwh7KdzchIvNzKeRqPB5ZuXkVaQplfU\n+Tj74JnJz2BJ5BIWdcOEQXvsZs+ejbNnzyIsLAwBAQG6H5bJhjzHbu/evZDL5cjMzERHRwc+/fRT\n7ffWrFkDANi2bRsuXbqElStX4uTJkxg3bhxyc3Px3nvvYceOHXj77bcRGRmJRx999M5/MO6xIyJC\nW5swwiQvTz/ev0tH0tbS1YL91/bjWt01bUwGGWaMmoGE0Qks6CTConfFPvfcc3juuefumsRQrV69\nGgBw/vx53LhxQxtva2vDl19+iby8PLi4uGDWrFlISUnBrl278M4772DSpEnw9/fH3LlzERYWhl//\n+tcDvsbTTz+N8PBwAEKnLy4uTnuJQalUAgAf8zEf87HNPh45cj4OHgTy8oTH4eHz4eEB+Psr4eEB\nODlZV758PLjH8+bNw3c13+GDLz5At6ob4XHhAIC6K3WYFToLy6KWWVW+fKz/uO/r0tJSmNKg5tiZ\nw+uvv47Kykptx+7SpUuYPXs22tratH/mvffeg1KpRHp6usHPy46dtCmVSu2bgKSFaye+gbp0kycD\nS5feu0vH9ZOG1u5WHLh2APm1+Xpx5xvOeHnNy3BQOIiUGQ2VRTt2Go0Gn376KXbt2oXKykqMGjUK\nTzzxBJ555hmjb6j44c+3trbCw8NDL+bu7o6WlhajXoeIaDi4ckW4QaLf78bw8BDudh0zRry8yDQ0\nGg2+r/keGYUZ6Ojt0Ma9nLywKnYVSlHKom6YM6iwe/vtt7Fz50788pe/RGhoKMrLy/Hb3/4WVVVV\neP31141K4IfVqZubm/bmiD5NTU1wd3c36nVIWtgxkC6unTja2/Hvy676cUO6dP1x/axXW3cbDlw7\ngKu1+gf5Tg+ajsWRi+GgcED4/HBxkiOrYVBht3XrVhw/flzvGLGlS5dizpw5Rhd2P+zYRUdHo7e3\nF0VFRYiKigIgDCWeMGHCoJ87NTUV8+fP5wcVEdk0dulsX15NHg4WHkR7j+6IEE9HT6TEpiDCO0LE\nzMhYSqVSb9+dsQzaYzdy5Ehcv34drq6u2lhraysiIiJQU1MzpBdWqVTo6enBli1bUFlZia1bt8LO\nzg4KhQJr1qyBTCbDxx9/jIsXLyIxMRGnTp3C2LFjDf8H4x47SeM+H+ni2llOe7uwl+777/Xjg+3S\n9cf1sy5t3W3IKMxA3m39VuzUwKlYErkEjnb606S5ftJl0ZMnli1bhieeeAL5+fno6OjA1atX8eST\nT2pPgBiKt956Cy4uLvjv//5v7N69G87Ozviv//ovAMCHH36Ijo4OjBw5Ek888QQ++uijQRV1RES2\n7upVYS5d/6LO3R14/HEgJYVjTGzBldtX8OG5D/WKOk9HT6ybuA5JMUl3FHVEgIEdu6amJmzcuBF/\n//vf0dPTA3t7ezzyyCP405/+BC8vL0vkOWgymQybN2/mpVgisikDdeni4oBly1jQ2YL2nnZ8VfgV\nvqv5Ti8+JXAKlkQugZMdF9mW9F2K3bJli0k6doMad6JSqVBbWwtfX18oFAqjX9yceCmWiGxFQUEZ\njh4tRkWFHAUFagQHR8LXV9jz7O4unBzBvXS2Ib82HweuHUBrd6s25uHogaToJIzx4SLbMoteit2x\nYwdyc3OhUCjg7+8PhUKB3Nxc7Nq1y+gEiO7GlBtJybK4dqZVUFCGjz8uwvHjC3H69Hw0NCzE5ctF\nqK0tQ1wcsGGDaYs6rp84Ono68OXVL7Hn+z16RV1cQBw2TN9gcFHH9SOD7ordtGkTLl++rBcbNWoU\nkpKSsG7dOrMkRkREwGefFSM3NwHd3bqYi0sCfHyysWpV2MA/SJJxre4a0gvS9Qo6dwd3JMUkIdon\nWsTMSIoMuhTr7e2N2tpavcuvvb298PHxQVNTk1kTHCpeiiUiKevqAjIzgQ8/VKKzc7427u8PREUB\nfn5K/OIX8wf8ebJ+nb2dOFR0CJdv6jdOJvlPwrKoZXC2dxYpMxKDRU+eGDt2LP75z3/i0Ucf1cb2\n7t1r9Xeqco4dEUnR9etAWhrQ2AjI5WoAgIMDEB0N+Pri34/VImZIxiqsK0R6QTpaunWnKrk5uCEp\nOgkxvjEiZkaWJsocu6+//horVqzA4sWLERERgeLiYhw9ehQZGRmYPXu2yZIxJXbspI2zmKSLazd0\nPT1AVhZw+rQuVltbhhs3ijB+fALs7YVYV1cWnn46CjExpr8Uy/Uzr87eTmQWZeLSzUt68QdGPoDl\nY5bDxd7FqOfn+kmXRTt2s2fPxnfffYfPPvsMN27cQHx8PP7whz8gJCTE6ASIiAi4cQPYuxeoq9PF\nnJ2BF14Ig50dkJ2dje5uORwc1EhIME9RR+ZRUFSAoxeOoqq1CldrriIoLAi+QULr1dXeFYnRiRjr\nZ91XwEg6Bj3u5NatWwgKCjJnTibBjh0RSUFvL3D8OPD110D/j6wxY4QxJjwmW9oKigqwLWsbKnwq\nUN1aDQDoLepF3Lg4zJs0DyvGrICrg+t9noWGA4uOO2loaMDatWvh7OysPb81PT3d6HNiiYiGs5s3\nga1bgZwcXVHn4CAUdGvXsqizBZ+f+By5Lrnaog4AnKOd4dXuhf8z/v+wqCOTM6iwe+GFF+Dh4YGy\nsjI4OgpHmDz00EPYs2ePWZMzVmpqKmf6SBTXTbq4dvenVgMnTghF3a1bunh4uDCXbsoUQCYTJzeu\nn2l09XZhf8F+nK46jS5Vlzbu5+KH6cHT4evma5bX5fpJj1KpRGpqqsmez6A9dllZWaiuroZ9385d\nAH5+fqipqTFZIuZgyn9RRESmUFsr7KWrrNTF7O2BRYuA+HjxCjoynZKGEqTlp6Gpqwnyf/dP7OX2\nGOMzBn4ufpDJZHCQO4icJVmLvukdW7ZsMcnzGbTHLioqCidOnEBQUBC8vb3R0NCA8vJyLFmyBPn5\n+SZJxNS4x46IrIlGA5w5Axw9Kuyr6zNqFLB6NeDjI15uZBpdvV04XHwYF6ovaGO1VbW4UXoD42aM\ng4NCKOa6Crvw9IKnERPFsSakY9G7Yp977jn8+Mc/xn/+539CrVbj1KlTePXVV/H8888bnQARka1r\naBDm0pWW6mIKBbBgATBzJiA3aFMMWbP+Xbo+LvYueGHRC7BrskP2pWx0q7vhIHdAwoIEFnVkNgZ1\n7DQaDf74xz/ir3/9K0pLSxEaGooXXngBL730EmRWet2AHTtp4ywm6eLa6Wg0wMWLwgkS/Y8ECwgQ\nunT+/uLlNhCu3+DcrUsHAGN9x2Jl9Eq4ObhZNB+un3RZtGMnk8nw0ksv4aWXXjL6BYmIhoOWFiA9\nHSgs1MXkcmD2bGDePKFjR9I2UJduxZgVGO833mobH2TbDOrYZWdnIzw8HBEREaiursZ//Md/DZ8G\neAAAIABJREFUQKFQ4J133kFAQIAl8hw0mUyGzZs380gxIrIojQb4/nsgIwPo6NDFfX2FLl1wsHi5\nkWl09XbhSMkRnK86rxcXq0tH0tZ3pNiWLVtM0rEzqLCLjY3F4cOHERoaijVr1kAmk8HJyQm1tbVI\nT083Oglz4KVYIrK0tjbg4EHgyhVdTCYDHnwQWLgQ6DdYgCTqbl06ZztnrIxeyS4dGcVUdYtBhZ2H\nhweam5vR09MDf39/7Ty7wMBA1PU//8aKsLCTNu4Tka7hunb5+cD+/UJx18fLC1i1SphPJxXDdf3u\nRypdOq6fdFl0j52Hhwdu3ryJvLw8jB8/Hu7u7ujq6kJPT4/RCRARSVlnJ/DVV0Burn586lRgyRLg\n3zPdScIG6tKtGLMCE0ZOYJeOrIpBhd3GjRsRHx+Prq4u/P73vwcAfPPNNxg7locWk3nwN07pGk5r\nV1wsjDFpbtbF3N2BlBTg36cvSs5wWr/7kUqXrj+uHxl0KRYACgoKoFAotGfFXrt2DV1dXXjggQfM\nmuBQ8VIsEZlLdzdw5Ahw7px+fOJEYPlywNlZnLzIdEoaSpBekI7GzkZtjF06MieL7rGTIhZ20sZ9\nItJl62tXVgbs2ycMHe7j4gIkJgLjxomXl6nY+vrdz0BduljfWCRGJ1pll66/4b5+Umb2PXaxsbHa\n48JCQkIGTKK8vNzoJMwlNTWV406IyCR6e4HsbODUKWGkSZ/YWCApCXB1FS83Mg126UgMfeNOTGXA\njl1OTg7mzJmjfdGBWGvRxI4dEZlKVRWwdy9w+7Yu5uQkXHadOFEYaULSJfUuHdkGXoq9DxZ2RGQs\nlQo4cQLIyQHUal08MlK4QcLDQ7zcyDSuN1xHWkEau3QkOrNfit20adOAL9IXl8lkePPNN41OguiH\nuE9Eumxl7WpqhC5ddbUu5uAgjDCZOtV2u3S2sn73Y6tduuGyfjSwAQu7ioqKe/6m0lfYERHZErVa\n2EeXnS107PqEhgrDhkeMEC83Mg126ciW8VIsEdG/1dcLXbqKCl3Mzk44DuzBBwG5XLzcyHjdqm4c\nKT6Cc1X6c2qk3qUj22D2S7ElJSUGPUFERITRSRARiUmjEWbSHTkC9D9QJygIWL0a8PMTLzcyDXbp\naLgYsGMnN+BXU5lMBlX/axVWhB07aeM+EemS2to1NQmnR/T/XVYuB+bNA2bPBhQK8XITg9TW736G\nW5fO1tZvODF7x07d/xYwIiIbo9EI57t+9RXQ1aWLjxwpdOkCA8XLjUxjoC7d8jHL8cDIB9ilI5tk\n03vsNm/ezAHFRHSH1lZg/36goEAXk8mAmTOBBQuEfXUkXcOtS0fS1jegeMuWLeadY7d06VJkZmYC\ngHZQ8R0/LJPhxIkTRidhDrwUS0R3k5cHHDwItLfrYiNGCF26AQ7ZIQlhl46kyuyXYp988knt188+\n++yASRCZA/eJSJe1rl1Hh1DQff+9fjw+Hli0SJhRR9a7fvczUJcuxicGidGJcHd0Fykzy5Lq+pHp\nDFjYPf7449qvn376aUvkQkRkFteuAenpwiXYPp6ewukRvLFf+tilI9IxeI/diRMncOnSJbS1tQHQ\nDSh+9dVXzZrgUPFSLBF1dQGZmcDFi/rxyZOBpUuF815JutilI1ti9kux/W3cuBFffPEF5syZA2dn\nZ6NflIjI3K5fF8aYNOqaOHBzA5KSgJgY8fIi07jecB3pBelo6GzQxtilIzKwY+ft7Y28vDwEBQVZ\nIieTYMdO2rhPRLrEXrueHuDoUeDMGf34+PHAypWAi4s4eUmF2Ot3P+zS3Zu1rx8NzKIdu5CQEDhw\nZzERWbmKCmDfPqCuThdzdhYKugkTxMuLTINdOqL7M6hjd+7cObz99ttYu3Yt/P399b43d+5csyVn\nDHbsiIaP3l5AqQS++UYYPNwnOlq49Oo+vJs4kscuHQ0HFu3YXbhwARkZGcjJybljj11F/9OyiYgs\n7OZNYO9e4NYtXczREVi2DIiLEwYPk3SVNpYiLT+NXToiAxnUsfPx8cGePXuwePFiS+RkEuzYSRv3\niUiXpdZOrQa+/lro1PU/AXH0aGGMiZeX2VOwSdby3utWdeNoyVGcrTyrF2eX7t6sZf1o8CzasXN1\ndcW8efOMfjEiIlOorRW6dJWVupi9vTBoOD6eXTqpu1uXzsnOCcujlmOi/0R26YjuwaCO3fbt23H2\n7Fls2rTpjj12crncbMkZgx07Ituj0QCnTwNZWcK+uj6jRglHgvn4iJcbGY9dOhrOTFW3GFTYDVS8\nyWQyqFQqo5MwB5lMhs2bN2P+/PlsSxPZgIYG4Y7XsjJdTKEAFiwAZs4ErPR3TDIQu3Q0XCmVSiiV\nSmzZssVyhV1paemA3wsPDzc6CXNgx07auE9Euky9dhqNcHJEZibQ3a2LBwQIXbofXEQgI1n6vccu\nnWnxs1O6LLrHzlqLNyKybc3NwhmvRUW6mFwOzJkDzJ0rdOxIutilIzI9g8+KlRp27IikS6MBvvsO\nyMgAOjt1cV9foUsXHCxebmS8gbp00T7RSIpOYpeOhiWLduyIiCylrQ04cAC4elUXk8mABx8EFi4U\n7n4l6WKXjsi8uN2YrJJSqRQ7BRoiY9YuPx/48EP9os7bG3jqKWDpUhZ1lmCu9163qhsZhRnYfnm7\nXlEX7RONF6e/iEkBk1jUmQA/O4kdOyISXWcn8NVXQG6ufnzaNGDxYuEkCZIudumILMegPXYlJSV4\n7bXXcPnyZbS2tup+WCZDeXm5WRMcKu6xI5KG4mIgLU24UaKPu7twekRUlHh5kfG4l47IcBbdY7d2\n7VpERUXhvffeu+OsWCKioejuBg4fBs6f149PnAgsXw7wo0ba2KUjEodBHTsPDw80NDRAIaHZAuzY\nSRtnMUmXIWtXViYMG27Q/T8frq5AYiIwdqx586N7M/a9163qRlZJFs5UntGLs0tnGfzslC6Lduzm\nzp2LS5cuYdq0aUa/IBENX729QHY2cOqUMNKkT2wskJQkFHckXQN16ZZFLcMkf94cQWQJBnXsXnzx\nRfz973/Hww8/rHdWrEwmw5tvvmnWBIeKHTsi61JVBezdC9y+rYs5OQErVgAPPCCMNCFpuleXLjE6\nER6OHiJlRiQdFu3YtbW1ITExET09Pbhx4wYAQKPR8LcvIrovlQo4cQLIyQHUal08MlK4QcKD/8+X\ntLLGMuzL38cuHZGV4MkTZJW4T0S6+q9dTY3Qpauu1n3fwQFYsgSYOpVdOmtk6HuPXTrrxM9O6TJ7\nx660tFR7RmxJScmATxAREWF0EkRkW9RqYR9ddrbQsesTFgasWiUMHSbpKmssQ1pBGuo76rUxdumI\nrMOAHTt3d3e0tLQAAOTyux9QIZPJoOr/qW1F2LEjEkddnXDHa0WFLmZnByQkADNmAAN8nJAEDNSl\nGzNiDJJiktilIzKCqeoWyV2KbW5uxqJFi3D16lWcOXMG48aNu+ufY2FHZFkaDXDuHHDkCNDTo4sH\nBQGrVwN+fuLlRsZjl47IvExVt0jud2cXFxdkZGTgxz/+MQs3G8bzDqWlqQnYtQvIyAAKC5UAhM7c\nggXAs8+yqJOSH773ulXd+KrwK2y/vF2vqBszYgw2TN+AuIA4FnVWhJ+dJLmzYu3s7ODr6yt2GkQE\noUt3+TJw6BDQ1aWLjxwpdOkCA8XLjYzHLh2R9EiusKPhgXd1Wb+WFmD/fuDaNV1MJgOeeGI+5s8X\n9tWR9MyfP1+7l+5s5VlooLsywr101o+fnWTRS7EffPABpk2bBicnJzzzzDN636uvr8fq1avh5uaG\n8PBwfP7559rvvf/++1iwYAHeffddvZ/hb4tE4sjLAz78UL+oGzECWL8eWLSIRZ2UlTWW4aPzH+FM\n5RltUedk54RVsauw9oG1LOqIrNygP37V/SeMYuA7Zu8mODgYmzZtQmZmJjo6OvS+9+KLL8LJyQk1\nNTW4dOkSVq5ciUmTJmHcuHF4+eWX8fLLL9/xfNxjZ7s4i8k6tbcL++i+/14/Hh8vFHQODlw7qerr\n0v394N8RHheujbNLJy18/5FBhd2FCxfw85//HLm5uejs7NTGBzvuZPXq1QCA8+fPa0+wAISTLb78\n8kvk5eXBxcUFs2bNQkpKCnbt2oV33nnnjudZsWIFcnNzUVBQgOeffx5PPfWUwTkQ0dBcuwakpwOt\nrbqYp6dwegTHWUrbQHvplkYu5c0RRBJjUGH31FNPITk5Gdu2bYOLi4vRL/rDTtu1a9dgZ2eHqKgo\nbWzSpEkD3t2TkZFh0Os8/fTT2iHLXl5eiIuL0/4m0/fcfGydj/ti1pLPcH7c1QX89rdKFBUB4eHC\n90tLlYiKAn72s/lwctL/8/Pnz7eq/Pl44Mez5sxC1vUs7DmwBwAQHheO8Lhw9BT3YHrIdEwOnGxV\n+fLx/R/z/Sedx31fl5aWwpQMmmPn4eGBpqYmk/3WtmnTJty4cQOffvopACAnJwePPPIIqvudO7R1\n61Z89tlnOHbs2JBeg3PsiIx3/bowbLipSRdzcwOSk4HoaPHyIuOxS0dkXSw6x2716tXIzMw0+sX6\n/DBxNzc3NDc368Wamprg7u5ustckaen/Gw1ZXk8P8NVXwI4d+kXdhAnAhg33Luq4dtatR9WDQ0WH\nBpxL11Rgul/iyfL4/iODLsV2dHRg9erVmDNnDvz9/bVxmUyGnTt3DvpFf/ihER0djd7eXhQVFWkv\nx+bm5mLChAmDfu7+UlNTta1pIjJMRYXQpaur08WcnYGVK4XCjqSrvKkc+/L36RV0jgpHLItaxi4d\nkUiUSqVJC3KDLsWmpqbe/YdlMmzevNngF1OpVOjp6cGWLVtQWVmJrVu3ws7ODgqFAmvWrIFMJsPH\nH3+MixcvIjExEadOncLYsWMNfv4f5sZLsUSG6+0FlErgm2+EwcN9oqOFS69ubqKlRkbqUfUg63oW\nztw4ozeXLmpEFJJjknnHK5EVkORZsampqXjzzTfviL3xxhtoaGjA+vXrceTIEfj6+uI3v/kNHnvs\nsSG/Fgs7IsNVVwN79wI1NbqYoyOwbBkQFycMHiZpYpeOSBosXtgdO3YMO3fuRGVlJUaNGoUnnngC\nCxcuNDoBc2FhJ23KfnfEkvmo1UBODnD8uPB1n9GjhTEmXl6Df06unXUYapeO6ydtXD/psujNEx9/\n/DEeffRRBAYG4uGHH0ZAQADWrl2Lv/3tb0YnYE6pqancSEo0gNu3gW3bgGPHdEWdvT2wYgXw5JND\nK+rIOpQ3leOj8x/h9I3T2qLOUeGIlJgUPP7A47z0SmRFlErlgFvehsKgjt2YMWPwz3/+E5MmTdLG\nvv32Wzz88MMoKioyWTKmxI4d0d1pNMDp00BWlrCvrk9ICLBqFeDjI15uZJx7demSopPg6eQpYnZE\ndC8WvRTr4+OD6upqODg4aGNdXV0ICgpCXf9b56wICzsifQUFZUhLK8alS3I0NakREREJX98wKBTA\nggXAzJmA3KAePlmj8qZypOWnoa5D95nMvXRE0mHRS7GzZs3CK6+8gra2NgBAa2srfvWrX2HmzJlG\nJ0B0N7yEblr5+WX4zW+KkJm5ENXV89HevhCXLxcBKMNPfwrMnm26oo5rZ1l9c+k+vfSpXlEXNSIK\nG6ZvwOTAyYMq6rh+0sb1I4Pm2H300Ud47LHH4OnpiREjRqC+vh4zZ87E559/bu78jMI5dkTC2a6/\n+U0xSksTtDGZDIiMTICPTzb8/cNEzI6MwS4dkfSJMseuT0VFBaqqqhAUFISQkBCTJWEOvBRLBFy5\nAhw4AGRnK9HZOR8A4OICxMYCHh6Al5cSv/jFfFFzpMHrUfUg+3q23s0RAPfSEUmZqeqWATt2Go1G\n+9ue+t+3zAUHByM4OFgvJuemHCKr09kpHAmWmys8lsuF9+uoUcIoE4VCiDs4qAd4BrImBUUFOHrh\nKHo0PWjpbEGnRyfsR9hrv++ocMTSqKWYHDC4y65EZHsGrMo8PHS3w9vZ2d31L3t7+4F+nMgo3Ccy\ndNevA3/5i66oA4CJEyMxdmwWoqJ0RV1XVxYSEiJN/vpcO9MqKCrA9mPbcdPvJs47nEcWsnD84nHU\nVtUC0O2lmxI4xSRFHddP2rh+NGDHLi8vT/t1SUmJRZIhoqHr6RFGmJw+rR+fOBFYsSIMZWVAVlY2\nurvlcHBQIyEhCjEx3F9n7Y5eOIqOkA4UVBWgo7cDAGAXZYfy0nKsX7CeXToi0mPQHrvf/e53+NWv\nfnVH/L333sMrr7xilsSM1XeOLW+eoOGguhr48kth6HAfZ2cgKQkYN068vMg4Xb1d+PmHP0ehR6Fe\nfITzCEzvno7/t+7/iZQZEZlK380TW7ZssdwcO3d3d7S0tNwR9/b2RkNDg9FJmANvnqDhQK0Gvv4a\nUCr1jwQbMwZITgbc3UVLjYxUVF+E/QX7cSTrCNpHtQMAFDIFokZEIcAtAP63/bHhkQ0iZ0lEpmL2\nmycAIDs7GxqNBiqVCtnZ2XrfKy4u1tuHR2RKPO/w/urqgL17gRs3dDF7e2DpUmDqVGGkiRi4dsbp\n6OlAZnEmLt+8DACIiIjA5SuX4f+AP6J9ouFo54iuwi4kLEi4zzMNDddP2rh+dM/Cbv369ZDJZOjq\n6sKzzz6rjctkMvj7++NPf/qT2RMkIn0aDXD+PHD4sLCvrk9ICLB6NTBihHi5kXGu3L6CjMIMtHa3\namOhYaFYErkEFaUV6KntgYPcAQkLEhATFSNipkRkrQy6FLtu3Trs2rXLEvmYDC/Fki1qaQHS0oD+\nRzTL5cKRYLNm8UgwqWrtbkVGYQau3L6iF58wcgKWRy2Hq4OrSJkRkaVY9KxYKWJhR7YmL08YNtzR\noYv5+QEPPwwEBoqXFw2dRqPBt7e+xaGiQ9o7XgHA3cEdidGJiPFlV45ouLDoWbFNTU14+eWXMWXK\nFISFhSEkJAQhISEIDQ01OgFzSk1N5UwfieK66XR0AP/6F/CPf+iKOpkMeOgh4Pnnra+o49oZpqmz\nCZ999xn25u/VK+qmBE7Bi/EvilbUcf2kjesnPUqlEqmpqSZ7PoPOin3xxRdRUVGBN954Q3tZ9re/\n/S1+9KMfmSwRczDlvygiMZSUAPv2Ac3Nupinp7CXLjxctLTICBqNBuerzuNIyRF0q7q1cS8nLyTH\nJCPCO0LE7IjI0vrGsm3ZssUkz2fQpVg/Pz9cvXoVvr6+8PT0RFNTEyorK5GUlISLFy+aJBFT46VY\nkrKeHuDoUeDMGf14XBywbBng5CROXmScuvY67L+2H6WNpdqYDDLEB8cjISIBDgoH8ZIjIlFZZNxJ\nH41GA09P4VBpd3d3NDY2IjAwEIWFhff5SSIarMpKYYxJba0u5uIiDBseO1a8vGjo1Bo1Tt84jezr\n2ehV92rjvi6+SI5JRqindW9rISLpMGiP3cSJE3HixAkAwOzZs/Hiiy/ihRdeQEwMN/aSeQzHfSIq\nlTBoeNs2/aIuOhrYsEE6Rd1wXLt7qWmrwbaL23C4+LC2qJPL5JgTOgcvTHvB6oo6rp+0cf3IoI7d\n1q1btV//4Q9/wKuvvoqmpibs3LnTbIkRDSe1tUKXrrJSF3NwEC67Tp4s3rBhGjqVWoWc8hzklOVA\npVFp4wFuAUiJSUGgu5Xd9UJENsGgPXZnzpzBjBkz7oifPXsW8fHxZknMWNxjR1Kg0QDnzgFHjugP\nGw4NFW6Q8PYWLzcausrmSqQXpONW2y1tTCFTYF74PMwKmQWFXCFidkRkjSy6x27RokV3PSt22bJl\nqK+vNzoJc0lNTdXebUJkbZqbhWHDxcW6mEIhDBueOZPDhqWoR9UDZakSJytOQgPdB/Qoj1FIiUmB\nn6ufiNkRkTVSKpUmvYR+z46dWq2GRqOBl5cXmpqa9L5XXFyMWbNmoaamxmTJmBI7dtJm6+cdfvcd\ncPAg0Nmpi/n7C126gADx8jIFW1+7gZQ1liGtIA31Hbpfdu3l9kiISEB8cDzkMmlU6sN1/WwF10+6\nLNKxs7Ozu+vXACCXy/Haa68ZnQDRcNLRIRR033+vi8lkQoduwQLAzqAeOlmTrt4uHC05inNV5/Ti\no71GIzkmGd7OvJ5ORJZzz45daWkpAGDu3LnIycnRVpIymQx+fn5wcXGxSJJDwY4dWZuiIuHSa/9d\nDV5eQpcuLEy8vGjoiuqLsL9gP5q6dFc0HBWOWBq1FJMDJkPGu16IyEA8K/Y+WNiRtejuFm6OOKff\n0MHkycJdr46O4uRFQ9fR04HM4kxcvnlZLx7tE43E6ER4OHqIlBkRSZVFb55Yt27dXRMAwJEnZBa2\nsk/kxg1hjEldnS7m6ioMG46NFS8vc7KVtRvIldtXkFGYgdbuVm3Mxd4Fy6OWY8LICZLv0tn6+tk6\nrh8ZVNhFRkbqVZI3b97Ev/71Lzz++ONmTY5IqlQq4MQJICcHUKt18dhYoahzdRUvNxqa1u5WZBRm\n4MrtK3rxCSMnYHnUcrg6cFGJSHxDvhR7/vx5pKam4sCBA6bOySR4KZbEcvu20KWrqtLFHB2Fy65x\ncRw2LDUajQbf3voWh4oOoaO3Qxt3d3BHYnQiYnx5Ag8RGU/0PXa9vb3w9va+63w7a8DCjixNowHO\nnAGOHgV6dceBIixMuEHCy0u83GhomjqbcODaARTW65+LPSVwCpZELoGTnZNImRGRrbHoHrusrCy9\nfSNtbW3Ys2cPxo8fb3QCRHcjtX0iTU3CHa8lJbqYQgEkJAAPPji8hg1Lbe3uRqPR4HzVeRwpOYJu\nVbc27uXkheSYZER4R4iYnXnZwvoNZ1w/Mqiwe/bZZ/UKO1dXV8TFxeHzzz83W2KmwJMnyNw0GmHY\ncEaG/rDhgAChS+fvL15uNDR17XXYf20/ShtLtTEZZIgPjkdCRAIcFA7iJUdENseiJ09IGS/Fkrm1\ntwMHDgBX+u2ll8mAWbOA+fM5bFhq1Bo1Tt84jezr2ehV666l+7r4IjkmGaGeoSJmR0S2zqKXYgGg\nsbERBw8eRFVVFYKCgrBixQp484RyGqYKC4VLr626iRfw9ha6dKH8/7/k1LTVIC0/DZUtldqYXCbH\nrJBZmBc+D3ZyVulEJA0G7fzJzs5GeHg4/vjHP+LcuXP44x//iPDwcBw9etTc+dEwZcq2tCl1dwtd\nuv/9X/2ibupU4IUXWNQB1rt2d6NSq6AsVeKv5/+qV9QFuAXgJ1N+goSIhGFX1Elp/ehOXD8y6BPr\nxRdfxN/+9jc88sgj2tg//vEP/PznP0d+fr7ZkiOyJhUVwhiTet0Z73BzA5KTgeho8fKioalsrkR6\nQTputd3SxhQyBeaFz8OskFlQyBUiZkdENDQG7bHz8vJCXV0dFArdB11PTw/8/PzQ2Nho1gSHinvs\nyFRUKkCpBL7+WrhZos/YsUBiIocNS02PqgfKUiVOVpyEBroFHeUxCikxKfBz9RMxOyIarix+pNgH\nH3yAl156SRv7y1/+ctejxohsSU2N0KWrrtbFHB2BFSuAiRM5bFhqyhrLkFaQhvoOXdvVXm6PhIgE\nxAfHQy4bRnNpiMgmGdSxmzVrFs6ePYuRI0ciODgYlZWVqKmpwYwZM7RjUGQyGU6cOGH2hA3Fjp20\niT2LSaMBTp8GsrL0hw2HhwOrVnHY8L2IvXZ309XbhaMlR3Gu6pxefLTXaCTHJMPbmTeC9bHG9SPD\ncf2ky6Idu5/85Cf4yU9+ct+EiGxBYyOwbx9QWqqL2dnphg3zP3VpKaovwv6C/WjqatLGHBWOWBq1\nFJMDJvOzi4hsCufYEf2bRgPk5gJffQV0denigYHCGJORI8XLjQavo6cDmcWZuHzzsl482icaidGJ\n8HD0ECkzIqI7WXyO3YkTJ3Dp0iW0tbUBEI7ckclkePXVV41OgkhsbW3CGJOrV3UxmQyYMweYN084\nHoyk48rtK8gozEBrt24mjYu9C5ZHLceEkRPYpSMim2VQYbdx40Z88cUXmDNnDpydnc2dE5FF94lc\nuwakp+vPpRsxQujShYRYJAWbIuYen9buVmQUZuDK7St68QkjJ2B51HK4OvAW5vvhHi1p4/qRQYXd\n7t27kZeXh6CgIHPnQ2QxXV1AZiZw8aJ+fNo0YMkSwIFHgkqGRqPBt7e+xaGiQ+jo7dDG3R3ckRid\niBjfGBGzIyKyHIP22E2cOBHZ2dnw9fW1RE4mIZPJsHnzZsyfP5+/vdAdysuFMSYNDbqYmxuQkgKM\nGSNeXjR4TZ1NOHDtAArrC/XiUwKnYEnkEjjZOYmUGRHR/SmVSiiVSmzZssUke+wMKuzOnTuHt99+\nG2vXroW/v7/e9+bOnWt0EubAmyfobnp7hWHD33yjP2x4/Hhg5UrAxUW01GiQNBoNzledx5GSI+hW\ndWvjXk5eSI5JRoR3hIjZERENjkVvnrhw4QIyMjKQk5Nzxx67iooKo5Mg+iFz7BO5dQv48kvh732c\nnIRhww88wDEmpmKJPT517XXYf20/ShtLtTEZZIgPjkdCRAIcFLyOPlTcoyVtXD8yqLB77bXXcODA\nASxevNjc+RCZnFoNnDoFZGcLx4P1iYgQLr16eoqXGw2OWqPG6RunkX09G71q3eRoXxdfJMckI9Qz\nVMTsiIjEZ9Cl2NDQUBQVFcFBQrvJeSmWAGEP3b59QFmZLmZnByxeDMTHs0snJTVtNUjLT0NlS6U2\nJpfJMStkFuaFz4Od3ODpTUREVsdUdYtBhd327dtx9uxZbNq06Y49dnK5dZ6tyMJueNNogMuXhWHD\n3brtVwgKEsaY+PGcd8lQqVXIKc9BTlkOVBpdyzXALQApMSkIdA8UMTsiItOwaGE3UPEmk8mg6n9t\ny4qwsJM2Y/aJtLUB+/cD+fm6mFwuDBueO5fDhs3NlHt8KpsrkV6Qjlttuo2RCpkC88LnYVbILCjk\nXExT4x4taeP6SZdFb54oKSkx+oWILCE/Xyjq/n1ACgDAxwd4+GEgOFi8vGhwelQ9UJYFkbZ2AAAg\nAElEQVQqcbLiJDTQfdCN8hiFlJgU+Lmy5UpEdDeDOitWrVbj1q1b8Pf3t9pLsH3YsRteurqAQ4eA\nS5f04/Hxwn46e3tx8qLBK2ssQ1pBGuo76rUxe7k9EiISEB8cD7nMuj97iIiGwqIdu+bmZvz85z/H\nnj170NvbCzs7Ozz22GP405/+BE/eUkgiKysThg03Nupi7u7AqlVAZKR4edHgdPV24WjJUZyrOqcX\nH+01GskxyfB29hYpMyIi6TDoV9+NGzeira0N33//Pdrb27V/37hxo7nzo2FKqVTe98/09gKHDwPb\nt+sXdRMmABs2sKgTiyFr90NF9UX48NyHekWdo8IRyTHJeHLSkyzqLGgo60fWg+tHBnXsDh06hJKS\nEri6CgdoR0dHY/v27YiI4GR3EsfNm8Kw4ZoaXczJCUhMFAo7koaOng5kFmfi8s3LevFon2gkRifC\nw9FDpMyIiKTJoD124eHhUCqVCA8P18ZKS0sxd+5clJeXmzO/IeMeO9ukVgMnTwLHjukPG46MFIYN\ne7AOkIwrt68gozADrd2t2piLvQuWRy3HhJETIOOQQSIaRiy6x+65557D4sWL8ctf/hJhYWEoLS3F\n+++/j5/85CdGJ0BkqIYGYS9d/98l7O2FmyOmT+ewYalo7W5FRmEGrty+ohefMHIClkcth6uDq0iZ\nERFJn0EdO7Vaje3bt+N///d/UV1djaCgIKxZswbr16+32t+q2bGTtv6zmDQa4OJFIDNTf9hwcLAw\nbNjXV5wc6e4GmqOl0Wjw7a1vcajoEDp6O7Rxdwd3JEYnIsY3xoJZ0kA4B03auH7SZdGOnVwux/r1\n67F+/XqjX9AUzp49i1/84hewt7dHcHAwdu7cCTs7Hidki1pbgfR04No1XUwuB+bNEwYOW/nUHfq3\nps4mHLh2AIX1hXrxKYFTsCRyCZzsnETKjIjIthjUsdu4cSPWrFmDmTNnamMnT57EF198gd///vdm\nTfBubt68CW9vbzg6OuLVV1/F1KlT8aMf/Ujvz7BjJ31XrwrDhtvbdTFfX2HYcFCQeHmR4TQaDc5X\nnceRkiPoVunarV5OXkiOSUaEN2/AIiICLHykmK+vLyorK+Ho6KiNdXZ2IiQkBLdv3zY6CWNs3rwZ\nkydPxqpVq/TiLOykq7NTOOM1N1c/PmMGsGgRhw1LRV17HdIL0lHWVKaNySBDfHA8EiIS4KBwEDE7\nIiLrYqq6xaALWXK5HGq1Wi+mVqtFL5zKyspw5MgRJCUliZoHmc7168Bf/gKkpSm1MQ8P4MkngeXL\nWdRJQfaxbJysOIm/nP+LXlHn6+KLZyY/g+VjlrOos2KcgyZtXD8yqLCbPXs2Xn/9dW1xp1KpsHnz\nZsyZM2fQL/jBBx9g2rRpcHJywjPPPKP3vfr6eqxevRpubm4IDw/H559/rv3e+++/jwULFuDdd98F\nIJyG8eSTT2LHjh1Q8FR3yevtFW6O2LEDaGrSxSdOFIYNc2SiNNS01SDjWgYOFx9Gr7oXACCXyTEn\ndA5emPYCQj1DRc6QiMi2GXQptqKiAomJiaiurkZYWBjKy8sRGBiI/fv3IyQkZFAvuHfvXsjlcmRm\nZqKjowOffvqp9ntr1qwBAGzbtg2XLl3CypUrcfLkSYwbN07vOXp7e5GcnIxf/epXWLhw4d3/wXgp\nVjKqq4Vhw/2v6js7C8OGx48XLy8ynEqtQk55DnLKcqDS6AYMBrgFICUmBYHugSJmR0Rk/Sy6xw4Q\nunRnz55FRUUFQkJCMGPGDMiNuCVx06ZNuHHjhrawa2trw4gRI5CXl4eoqCgAwFNPPYWgoCC88847\nej+7a9cuvPzyy3jggQcAAD/72c/wyCOP6P+DsbCzemo18PXXgFIpfN0nKkoYNuzuLlpqNAiVzZVI\nL0jHrbZb2phCpsC88HmYFTILCjk76kRE92PRcScAoFAo8NBDD+Ghhx4y+kUB3JH8tWvXYGdnpy3q\nAGDSpEl33S+wbt06rFu37r6v8fTTT2tPy/Dy8kJcXJx2vk/f8/KxOI/T05XIyQFcXYXHpaVKKBTA\nz342H1OnAn/4w++5Xlb+uFfVC024BicrTuL65esAgPC4cLRda8OskFlQX1dDEaawmnz52LDHfV9b\nSz58zPWz1cd9X5eWlsKUDO7YmdoPO3Y5OTl45JFHUF1drf0zW7duxWeffYZjx44N+vnZsbNOGg1w\n4YKwn66nRxcfNUoYNuzjIzxWKpXaNwFZn7LGMqQVpKG+o14bs5fbIyEiAe2F7Vi4YKGI2ZEx+N6T\nNq6fdFm8Y2dqP0zezc0Nzc3NerGmpia483qczWhpEYYNF/abUSuXA/PnA7Nn6w8b5geTderq7cLR\nkqM4V3VOLz7aazSSY5Lh7ewNjBIpOTIJvvekjetH9y3sNBoNrl+/jtDQUJOe7vDDo8iio6PR29uL\noqIi7eXY3NxcTJgwYcivkZqaivnz5/M/dCuQlwccOAB06E6Sgp+fMGw4kPvqJaGovgj7C/ajqUt3\n27KjwhFLIpdgSuAUqz1ekIjImimVSr3Ls8a676VYjUYDV1dXtLa2GnWzRB+VSoWenh5s2bIFlZWV\n2Lp1K+zs7KBQKLBmzRrIZDJ8/PHHuHjxIhITE3Hq1CmMHTt20K/DS7HWobMTyMgAvv1WP/7QQ8DC\nhQPPpePlBOvR0dOBzOJMXL55WS8e7RONxOhEeDh66MW5dtLG9ZM2rp90WexSrEwmw+TJk1FQUDCk\nAuuH3nrrLbz55pvax7t370ZqaireeOMNfPjhh1i/fj1GjhwJX19ffPTRRyZ5TRJHSQmwbx/Q/wq7\npyewahUwerR4eZHhrty+gozCDLR2t2pjLvYuWB61HBNGTmCXjojIyhh088Trr7+O3bt34+mnn0ZI\nSIi2qpTJZFi/fr0l8hw0duzE09MDHD0KnDmjH580STg9wonnvVu91u5WZBRm4MrtK3rxCSMnYHnU\ncrg6uIqUGRGRbbLozRNff/01wsPDcfz48Tu+Z62FHcA9dmKoqhKGDdfW6mIuLsKw4R/MmSYrpNFo\n8O2tb3Go6BA6enUbIt0d3JEYnYgY3xgRsyMisj0W32MnVezYWZZaDeTkAMeP6w8bjo4GkpMBN7fB\nPR/3iVheU2cTDlw7gML6Qr34lMApWBK5BE52hrVauXbSxvWTNq6fdFl83EldXR0OHjyImzdv4te/\n/jUqKyuh0WgwahRnGwx3dXVCl66yUhdzcACWLgWmTAG4Dcu6aTQanK86jyMlR9Ct6tbGvZy8kByT\njAjvCBGzIyKiwTCoY3f8+HH86Ec/wrRp0/DNN9+gpaUFSqUS7777Lvbv32+JPAeNHTvz02iAc+eA\nI0f0hw2HhAjDhkeMEC83Mkxdex3SC9JR1lSmjckgQ3xwPBIiEuCgcBAxOyKi4cOiZ8XGxcXhd7/7\nHRYtWgRvb280NDSgs7MToaGhqKmpMToJc2BhZ17NzUBaGlBcrIspFMCCBcDMmfrDhsn6qDVqnL5x\nGtnXs9Gr7tXGfV18kRyTjFDPUBGzIyIafix6KbasrAyLFi3Si9nb20OlUhmdgDnx5gnTKigow9Gj\nxSgvlyM/X42QkEj4+oYBAEaOFIYNBwSY5rW4T8T0CooKcPTCUdR31uNKzRX4BPvAN8gXACCXyTEr\nZBbmhc+Dndy4QeRcO2nj+kkb1096TH3zhEGf4GPHjsWhQ4ewbNkybSwrKwsPPPCAyRIxh9TUVLFT\nsBkFBWX4+OMilJUloK9JW1eXhcmTgaSkMCxcCJjwYBIysYKiAnyS/QlujbyFso4yaPw0qLhSgTjE\nYUL0BKTEpCDQnUeAEBFZWl8DasuWLSZ5PoMuxZ4+fRqJiYlYsWIF/vGPf2DdunXYv38/0tLSEB8f\nb5JETI2XYk3rz3/ORlbWQtTrznyHkxMwc2Y23niDB75bM41Gg02fbMIFxwt6I0xkkCGuIw6/e/53\nUMgVImZIREQWvRT74IMPIjc3F7t374abmxtCQ0Nx7tw53hE7jPT0yBERATQ0CDdNBAQAUVGAhwc3\n01mzqpYqZBZl4nz1eXSO6tTGPRw9EOMTg+D6YBZ1REQ2xOCLZ8HBwfi///f/ora2Fn5+fjxKaJix\nt1fDzU0o5hwdAV9haxYcHNT3/sEh4j4R4zR3NSOrJAu5t3IBAHIIBbid3A7hXuEIdg+GTCaDg9z0\nd71y7aSN6ydtXD8yqN3S0NCAdevWwdnZGQEBAXBycsITTzyB+v7X5axQamqqSTckDmeLFkWiqysL\nwcG6oq6rKwsJCZHiJkZ6ulXdOHb9GP505k/aog4AIiMj4V/jjxnBMzDKYxRkMhm6CruQMCVBxGyJ\niEipVJr0ngCD9titWrUKdnZ2eOuttxAaGory8nK88cYb6O7uRlpamsmSMSXusTO9goIyZGUVo7tb\nDgcHNRISIhETEyZ2WgRhfEnuzVxkX89GS3eL3vdifWOxOGIxaqtqkXUxC93qbjjIHZAwJQExUTwi\njIjIGlh0jp2npyeqq6vh4uKijbW3tyMwMBBNTU1GJ2EOLOxouLjecB2ZxZm42XpTLx7gFoClkUsx\n2nu0SJkREZGhTFW3GHQpNjY2FqWlpXqxsrIyxMbGGp0A0d3wEvr91bXX4fPvPseO3B16RZ27gztW\nxa7CT6f+VJSijmsnbVw/aeP6kUE3TyxcuBBLlizBk08+iZCQEJSXl2P37t1Yt24dPvnkE2g0Gshk\nMqxfv97c+RINe+097Theehznqs5BrdHdvGIvt8es0FmYGTKTR4EREQ1TBl2K7bvDpv+dsH3FXH/H\njh0zbXZGkMlk2Lx5M0+eIJuhUqtwruocjpce15tHBwBxAXFYOHohPBw9RMqOiIiGou/kiS1btlhu\nj50UcY8d2QqNRoOCugIcLj6M+g79O9HDPMOwNGopgtyDRMqOiIhMwaJ77IgsjftEBNUt1diRuwN7\nvt+jV9SNcB6BR8c/iqfjnra6oo5rJ21cP2nj+hFP9ySyQs1dzci+no3cm7nQQPcbnJOdE+aFzUN8\ncDxPjCAiojvwUiyRFelWdeNkxUl8U/4NetQ92rhcJsf0oOmYFz4PLvYu93gGIiKSIoueFUtE5qXR\naJB7KxdZJVl3DBiO8YnB4sjF8HXxFSk7IiKSCoP32F29ehVvvvkmXnzxRQBAfn4+vv32W7MlRsPb\ncNonUtpYir9d+Bv25e/TK+oC3ALw5KQnseaBNZIq6obT2tkirp+0cf3IoMLuH//4B+bOnYvKykrs\n3LkTANDS0oJXXnnFrMkR2bK69jrs+X4Ptl/ejurWam3czcENKTEp+OnUnyLCO0LEDImISGoM2mMX\nGxuLPXv2IC4uDt7e3mhoaEBPTw8CAwNRW1triTwHjXPsyFp19HTgRNkJnK08C9X/b+/Og6Mqsz6O\n/zp7yAJhCyQsAUKQRUE2ZUAmrBkGXIil6LwiiwKFuBfM6CAQCiiHGmScYlHHcQGRqLzjVAlqJSo0\ni8VuQApQSJCwmgCBbISQdPr9g5fWJiDZ6NtP5/upSpX3ubf7ns4x5OS55z7X6XCNB/gFqH/r/urf\npj8LDANAPWHJOnZNmjTRmTNn5Ofn51bYxcbGKjc3t9ZB3ArcPAFv46hwaNepXbIftVdaYPiO6Ds0\npN0QNQxpaFF0AAAreXQdu549e+qDDz5wG/v444/Vt2/fWgcAXI8v9Yk4nU79ePZHLd+5XF9mfulW\n1LVp2EaTek5ScudknynqfCl39RH5Mxv5Q5Xuil2yZImGDRumd955RxcvXtTw4cN16NAhpaen3+r4\nAKP9XPSz0jLT9NOFn9zGo0KiNKzDMHVu2rnSo/kAAKipKq9jV1xcrHXr1ik7O1tt2rTRyJEjFRER\ncavjqzEuxcJKhaWFWv/Teu35eU+lBYYHth2ovrF9FeDHakMAgCvqqm5hgWKgDpU5yq4sMHz8W112\nXHaN+9n81DumtxLjEllgGABQiUd77LKzszVx4kTdeeed6tixo+srISGh1gEA12Nan4jT6dTen/dq\nyY4l2nB0g1tRl9AkQVN7T9UfO/6xXhR1puUO7sif2cgfqnQt6KGHHlLnzp01b948hYSE3OqYAKNk\nX8hWWlaaThWechuPDovW8A7D1aFxB4siAwDUN1W6FNuwYUPl5eXJ39+ch45zKRa3Wl5Jnr7K+koH\nzx50Gw8PCtfgdoPVo0UP+dmq/HAXAEA95tFnxY4aNUobN27U4MGDa31CT0pJSWGBYtS5S+WXtCl7\nk7af2F5pgeHftf6d+rfur+CAYAsjBACY4uoCxXWlSjN2Z8+eVb9+/ZSQkKDmzZv/8mKbTe+++26d\nBVOXmLEzm91u97qC3FHh0O7Tu2U/atfFsotu+25vfruGth/qM2vR1YY35g5VR/7MRv7M5dEZu4kT\nJyooKEidO3dWSEiI6+Ssv4X6wOl06nDeYaVnpevsRfdH6LWObK2k+CS1imxlUXQAAPyiSjN2ERER\nOnnypCIjIz0RU51gxg51IacoR2lZaTpy/ojbeKOQRhrWfpi6NOvCHzgAgFrz6IzdHXfcoXPnzhlV\n2AG1UXS5SOt/Wq+M0xluCwwH+wdrYNuBuqvVXSwwDADwOlX6zTR48GAlJSVpwoQJio6OliTXpdiJ\nEyfe0gBRP1nVJ1LmKNPWE1u15dgWt7XobLK5FhgOCwrzeFwmocfHbOTPbOQPVSrsNm/erJiYmOs+\nG5bCDr7A6XRqX+4+fXPkG+WX5rvt69i4o4Z1GKbmYc1v8GoAALwDjxRDvXcs/5jSMtN0svCk23jz\nsOYa3mG44hvHWxQZAKC+uOU9dr++67WiouKGb+DnxwKsMNP5kvP6+sjX2n9mv9t4WGCYBrcbrDtb\n3skCwwAAo9zwt9avb5QICAi47ldgYKBHgkT9cyufd3ip/JK+yvpKS3csdSvqAvwCNKDNAD1717Pq\nFdOLoq6GeFal2cif2cgfbjhjt3//L7/wjhw5cqPDAGNUOCu0+9RubTi6odICw92ad9PQ9kPVKKSR\nRdEBAFB7VeqxW7RokaZPn15pfPHixXrxxRdvSWC1RY8drnI6ncrMy1R6VrrOXDzjtq9VZCsldUhS\n64atLYoOAIC6q1uqvEBxYWFhpfGoqCidP3++1kHcChR2kK4sMJyela6s81lu441CGmlo+6Hq2qwr\nCwwDACznkQWK169fL6fTKYfDofXr17vty8rK8voFi1NSUpSYmMiaPgaq7VpMRZeLtOGnDfru9HeV\nFhi+p+09urvV3SwwfIuwjpbZyJ/ZyJ957HZ7nfZG/uZvtokTJ8pms6m0tFRPPPGEa9xmsyk6OlpL\nliyps0BuhZSUFKtDgIeVV5Rr6/Gt2nxsc6UFhnvF9FJiXKLCg8ItjBAAgF9cnYCaO3dunbxflS7F\njh07Vh988EGdnNBTuBRbvzidTu0/s19fH/laFy5dcNvXIaqDkuKTWGAYAOC1PNpjZyIKu/rjeP5x\npWWl6UTBCbfxZg2aKSk+iQWGAQBer67qFhbqgleqSr/BhUsX9L8H/lfvZLzjVtSFBYZpVMIoTe0z\nlaLOAqyjZTbyZzbyB7rHYZzS8lJtPrZZ205sU3lFuWvc3+avu1vdrXva3qOQgBALIwQAwBpcioUx\nKpwV+u70d9rw0wYVlxW77evarKuGth+qqNAoi6IDAKDmPLLcCeAtri4wnFuc6zYeGxGrP8T/gQWG\nAQAQPXbwUlf7RHKLc7Xq+1Va9f0qt6KuYXBDPdj5QT3Z80mKOi9Dj4/ZyJ/ZyB+YsYNXKikr0bpD\n67T71G63BYaD/IN0T5srCwwH+gdaGCEAAN6HHjt4lfKKcm07sU2bszer1FHqGrfJpp4te2pQu0Es\nMAwA8Dn02MGnOJ1OHThzQF8d+arSAsPto9orqUOSosOjLYoOAAAz0GMHy50oOKF3M97VmgNrXEXd\n0T1H1bRBU/3P7f+jsXeMpagzCD0+ZiN/ZiN/YMYOlrlw6YK+OfKN9uXucxtvENhAd8ferUm9J8nf\nz9+i6AAAMA89dvC40vJSbTm2RVtPbGWBYQAARI8dDFThrFDG6Qyt/2l9pQWGuzTroqHth6pxaGOL\nogMAwHzG9djl5OSof//+GjRokJKSknTu3DmrQ0IVZOVl6a1db2ntobVuRV1MRIwm3jlRD3d92K2o\no0/EXOTObOTPbOQPxs3YNWvWTN9++60kacWKFXr77bf10ksvWRwVbuRM8RmlZ6XrcN5ht/HI4EgN\nbT9Utze/XTabzaLoAADwLUb32C1ZskRBQUGaMmVKpX302Fmr+HKxNmZv1K5Tu1ThrHCNB/kHaUCb\nAerXqh8LDAMA8P/qdY/d3r17NXnyZF24cEE7d+60Ohz8SnlFuXac3KFN2Zt0qfySa9wmm+5seacG\nxQ1SRHCEhRECAOC7PNpjt3TpUvXu3VshISGaMGGC2768vDyNHj1a4eHhiouLU2pqqmvfP/7xDw0a\nNEivvfaaJKl79+7avn275s+fr3nz5nnyI+AGri4wvGzHMqVnpbsVde0atdOU3lN0X6f7qlzU0Sdi\nLnJnNvJnNvIHj87YxcbGatasWUpLS1NJSYnbvmnTpikkJES5ubnKyMjQyJEj1b17d3Xp0kUvvPCC\nXnjhBUlSWVmZAgOvXMKLjIxUaWlppfPAs04WnFRaVpqO5R9zG2/aoKmGdxiujo070kcHAIAHWNJj\nN2vWLJ04cULvvfeeJKm4uFiNGzfW/v37FR8fL0kaN26cYmJi9Oqrr7q9dufOnZo+fbr8/f0VGBio\nd955R61atap0DpvNpnHjxikuLk6S1KhRI/Xo0UOJiYmSfvmrhu2abxddLtLl1pf1fc73OrrnqCQp\nrkecQgNCFXE6Qp2adNKQwUO8Jl622WabbbbZ9pbtq/999OhRSVduCK2LksySwu6VV17RyZMnXYVd\nRkaGBgwYoOLiX5bBWLx4sex2uz777LManYObJ+rej5k/6uvdX6ukvETH8o/JFmVTVMso135/m7/6\nxvbVwLYDFRoYamGkAACYpa7qFr86iKXarr0sV1RUpMjISLexiIgIFRYWejIs/IYfM3/Uexve0/cN\nvld6Rbq+b/C9du/frbOnzkqSOjftrGl9pykpPqlOirpf/0UDs5A7s5E/s5E/WHJX7LUVaXh4uAoK\nCtzG8vPzFRHB3ZPe4uvdXyurYZbOnDvjGguID9C5k+c0448z1LZRWwujAwAAkpfM2CUkJKi8vFyZ\nmZmusb1796pbt261Ok9KSgp/vdSRMmeZWoS3cG0H+wfrtqa3qW+rvrekqLvaiwDzkDuzkT+zkT/z\n2O12paSk1Nn7eXTGzuFwqKysTOXl5XI4HCotLVVAQIDCwsKUnJys2bNn69///re+++47rV27Vlu3\nbq3V+eryG1XfBdoC1Ti0sZo2aKrwoHC1jmwtfz9/BV8Mtjo0AACMlZiYqMTERM2dO7dO3s+jM3bz\n5s1TgwYNtHDhQq1atUqhoaFasGCBJGn58uUqKSlR8+bN9dhjj+nNN99U586dPRkefsPQXkN1OfOy\nujbrqrhGcfL381fp4VIN6TnklpyPmVZzkTuzkT+zkT94dMYuJSXlhrNoUVFR+u9//+vJcFANneI7\nabzG65vvvtHlissK8gvSkEFD1Cm+k9WhAQCA/2f0s2J/i81m05w5c1xTnAAAAN7GbrfLbrdr7ty5\n5q5j5wmsYwcAAExh9Dp2wM3QJ2Iucmc28mc28gcKOwAAAB/h05di6bEDAADejB67KqLHDgAAmIIe\nO/g0+kTMRe7MRv7MRv5AYQcAAOAjuBQLAABgMS7FVkFKSgrT0gAAwGvZ7fY6fbY9M3bwSna7nbuZ\nDUXuzEb+zEb+zMWMHQAAANwwYwcAAGAxZuwAAADghsIOXombXsxF7sxG/sxG/uDThR13xQIAAG/G\nXbFVRI8dAAAwBT12AAAAcENhB6/EJXRzkTuzkT+zkT9Q2AEAAPgIeuwAAAAsRo8dAAAA3Ph0Ycdy\nJ+Yib+Yid2Yjf2Yjf+ap6+VOAursnbxQXX6jAAAA6lpiYqISExM1d+7cOnk/euwAAAAsRo8dAAAA\n3FDYwSvRJ2Iucmc28mc28gcKOwAAAB9Bjx0AAIDF6LEDAACAGwo7eCX6RMxF7sxG/sxG/uDThR0L\nFAMAAG9W1wsU02MHAABgMXrsAAAA4IbCDl6JS+jmIndmI39mI3+gsAMAAPAR9NgBAABYjB47AAAA\nuKGwg1eiT8Rc5M5s5M9s5A8UdgAAAD6CHjsAAACL0WMHAAAANxR28Er0iZiL3JmN/JmN/IHCDgAA\nwEf4dI/dnDlzlJiYqMTERKvDAQAAqMRut8tut2vu3Ll10mPn04Wdj340AADgY7h5Aj6NPhFzkTuz\nkT+zkT9Q2AEAAPgILsUCAABYjEuxAAAAcENhB69En4i5yJ3ZyJ/ZyB8o7AAAAHwEPXYAAAAWo8cO\nAAAAbijs4JXoEzEXuTMb+TMb+QOFHQAAgI+gxw4AAMBi9NgBAADADYUdvBJ9IuYid2Yjf2Yjf6Cw\nAwAA8BHG9tilpqbqueeeU25u7nX302MHAABMUa977BwOh9asWaM2bdpYHQoAAIDXMLKwS01N1cMP\nPyybzWZ1KLhF6BMxF7kzG/kzG/mDcYXd1dm6MWPGWB0KbqE9e/ZYHQJqiNyZjfyZjfzBo4Xd0qVL\n1bt3b4WEhGjChAlu+/Ly8jR69GiFh4crLi5Oqamprn2LFy/WoEGDtGjRIn344YfM1tUDFy5csDoE\n1BC5Mxv5Mxv5g0cLu9jYWM2aNUsTJ06stG/atGkKCQlRbm6uPvzwQ02dOlUHDhyQJL344ovasGGD\npk+frgMHDmjlypUaMWKEDh8+rOeff96TH6Ha6npavCbvV53XVOXYmx1zo/3VHbeaN+Suuq+rbf6q\nu89bcyf5Zv7qy8+eVLexeUPubnZMTfZ5a/588WfvZsd408+eRwu70aNH6/7772A7Y1UAAAx9SURB\nVFeTJk3cxouLi/Xpp59q3rx5atCggfr376/7779fH3zwQaX3+Nvf/qa0tDR9+eWXSkhI0Ouvv+6p\n8GvEG/4HN7GwO3r06E3juNW8IXfVfZ03FHbekDvJN/NXX372JAq7quzz1vz54s/ezY7xpsLOkuVO\nXnnlFZ08eVLvvfeeJCkjI0MDBgxQcXGx65jFixfLbrfrs88+q9E54uPjlZWVVSfxAgAA3EodOnRQ\nZmZmrd8noA5iqbZr++OKiooUGRnpNhYREaHCwsIan6MuvjkAAAAmseSu2GsnCcPDw1VQUOA2lp+f\nr4iICE+GBQAAYDRLCrtrZ+wSEhJUXl7uNsu2d+9edevWzdOhAQAAGMujhZ3D4dClS5dUXl4uh8Oh\n0tJSORwOhYWFKTk5WbNnz9bFixe1ZcsWrV27VmPHjvVkeAAAAEbzaGF39a7XhQsXatWqVQoNDdWC\nBQskScuXL1dJSYmaN2+uxx57TG+++aY6d+7syfAAAACMZsldsVYpKCjQ0KFDdfDgQW3fvl1dunSx\nOiRUw44dO/T8888rMDBQsbGxWrlypQICLLn/BzWQk5Oj5ORkBQUFKSgoSKtXr6609BG8W2pqqp57\n7jnl5uZaHQqq4ejRo+rTp4+6desmm82mTz75RE2bNrU6LFSR3W7X/PnzVVFRoWeffVYPPPDAbx5f\nrwq78vJyXbhwQTNmzND06dPVtWtXq0NCNfz888+KiopScHCw/vrXv6pXr1568MEHrQ4LVVRRUSE/\nvysXCVasWKHTp0/rpZdesjgqVJXD4dBDDz2kY8eOadeuXVaHg2o4evSoZsyYoTVr1lgdCqqppKRE\nY8aM0X/+8x8FBgZW6TXGPSu2NgICAvgrxWAtWrRQcHCwJCkwMFD+/v4WR4TquFrUSVdmz6OioiyM\nBtWVmprK4xwN9u2332rgwIGaOXOm1aGgGrZu3arQ0FDde++9Sk5OVk5Ozk1fU68KO/iG7OxsffXV\nV7r33nutDgXVtHfvXt11111aunSpHn30UavDQRU5HA6tWbNGY8aMsToU1EBMTIyysrK0adMm5ebm\n6tNPP7U6JFRRTk6OMjMztW7dOk2aNEkpKSk3fY2Rhd3SpUvVu3dvhYSEaMKECW778vLyNHr0aIWH\nhysuLk6pqanXfQ/+6rRObfJXUFCgxx9/XCtWrGDGziK1yV/37t21fft2zZ8/X/PmzfNk2FDNc7dq\n1Spm67xATfMXFBSk0NBQSVJycrL27t3r0bhR89xFRUWpf//+CggI0ODBg7V///6bnsvIzvPY2FjN\nmjVLaWlpKikpcds3bdo0hYSEKDc3VxkZGRo5cqS6d+9e6UaJetRa6HVqmr/y8nI98sgjmjNnjjp2\n7GhR9Khp/srKylw9IpGRkSotLbUi/Hqtprk7ePCgMjIytGrVKh0+fFjPP/+81z+n2xfVNH9FRUUK\nDw+XJG3atIn+cgvUNHd9+vTRa6+9Jknas2ePOnTocPOTOQ32yiuvOMePH+/aLioqcgYFBTkPHz7s\nGnv88cedL730kmt7xIgRzpiYGGe/fv2c77//vkfjhbvq5m/lypXOJk2aOBMTE52JiYnOjz/+2OMx\n4xfVzd/27dudAwcOdA4aNMg5fPhw5/Hjxz0eM66oyb+dV/Xp08cjMeLGqpu/L774wtmrVy/nPffc\n4xw3bpzT4XB4PGZcUZOfvWXLljkHDhzoTExMdB45cuSm5zByxu4q5zWzbocOHVJAQIDi4+NdY927\nd5fdbndtf/HFF54KDzdR3fyNHTuWRau9SHXz17dvX23cuNGTIeIGavJv51U7duy41eHhJqqbvxEj\nRmjEiBGeDBE3UJOfvaeeekpPPfVUlc9hZI/dVdf2exQVFSkyMtJtLCIiQoWFhZ4MC1VE/sxG/sxF\n7sxG/szlidwZXdhdW/mGh4eroKDAbSw/P18RERGeDAtVRP7MRv7MRe7MRv7M5YncGV3YXVv5JiQk\nqLy8XJmZma6xvXv3qlu3bp4ODVVA/sxG/sxF7sxG/szlidwZWdg5HA5dunRJ5eXlcjgcKi0tlcPh\nUFhYmJKTkzV79mxdvHhRW7Zs0dq1a+nL8jLkz2zkz1zkzmzkz1wezV3t7/HwvDlz5jhtNpvb19y5\nc51Op9OZl5fnfOCBB5xhYWHOtm3bOlNTUy2OFtcif2Yjf+Yid2Yjf+byZO7q1bNiAQAAfJmRl2IB\nAABQGYUdAACAj6CwAwAA8BEUdgAAAD6Cwg4AAMBHUNgBAAD4CAo7AAAAH0FhBwAA4CMo7ADgGuPH\nj9esWbPq9D2nTp2q+fPn1+l7AsC1AqwOAAC8jc1mq/Sw7tp644036vT9AOB6mLEDgOvgaYsATERh\nB8CrLFy4UK1atVJkZKRuu+02rV+/XpK0Y8cO9evXT1FRUYqJidEzzzyjsrIy1+v8/Pz0xhtvqGPH\njoqMjNTs2bOVlZWlfv36qVGjRnrkkUdcx9vtdrVq1UqvvvqqmjVrpnbt2mn16tU3jGndunXq0aOH\noqKi1L9/f+3bt++Gx77wwguKjo5Ww4YNdccdd+jAgQOS3C/v3nvvvYqIiHB9+fv7a+XKlZKkH374\nQcOGDVOTJk102223ac2aNTc8V2JiombPnq0BAwYoMjJSSUlJOnfuXBW/0wB8EYUdAK/x448/atmy\nZdq1a5cKCgqUnp6uuLg4SVJAQID++c9/6ty5c9q6dau++eYbLV++3O316enpysjI0LZt27Rw4UJN\nmjRJqampOnbsmPbt26fU1FTXsTk5OTp37pxOnTqlFStWaPLkyTp8+HClmDIyMvTEE0/o7bffVl5e\nnqZMmaL77rtPly9frnRsWlqaNm/erMOHDys/P19r1qxR48aNJblf3l27dq0KCwtVWFioTz75RC1b\nttSQIUNUXFysYcOG6bHHHtOZM2f00Ucf6amnntLBgwdv+D1LTU3V+++/r9zcXF2+fFmLFi2q9vcd\ngO+gsAPgNfz9/VVaWqr9+/errKxMbdq0Ufv27SVJPXv2VN++feXn56e2bdtq8uTJ2rhxo9vr//zn\nPys8PFxdunTR7bffrhEjRiguLk6RkZEaMWKEMjIy3I6fN2+eAgMDNXDgQI0cOVIff/yxa9/VIuxf\n//qXpkyZoj59+shms+nxxx9XcHCwtm3bVin+oKAgFRYW6uDBg6qoqFCnTp3UokUL1/5rL+8eOnRI\n48eP1yeffKLY2FitW7dO7dq107hx4+Tn56cePXooOTn5hrN2NptNEyZMUHx8vEJCQvTwww9rz549\n1fiOA/A1FHYAvEZ8fLxef/11paSkKDo6Wo8++qhOnz4t6UoRNGrUKLVs2VINGzbUzJkzK112jI6O\ndv13aGio23ZISIiKiopc21FRUQoNDXVtt23b1nWuX8vOztZrr72mqKgo19eJEyeue+ygQYP09NNP\na9q0aYqOjtaUKVNUWFh43c+an5+v+++/XwsWLNDvfvc717m2b9/udq7Vq1crJyfnht+zXxeOoaGh\nbp8RQP1DYQfAqzz66KPavHmzsrOzZbPZ9Je//EXSleVCunTposzMTOXn52vBggWqqKio8vtee5fr\n+fPndfHiRdd2dna2YmJiKr2uTZs2mjlzps6fP+/6Kioq0pgxY657nmeeeUa7du3SgQMHdOjQIf39\n73+vdExFRYX+9Kc/aciQIXryySfdzvX73//e7VyFhYVatmxZlT8ngPqNwg6A1zh06JDWr1+v0tJS\nBQcHKyQkRP7+/pKkoqIiRUREqEGDBvrhhx+qtHzIry99Xu8u1zlz5qisrEybN2/W559/roceesh1\n7NXjJ02apDfffFM7duyQ0+lUcXGxPv/88+vOjO3atUvbt29XWVmZGjRo4Bb/r88/c+ZMXbx4Ua+/\n/rrb60eNGqVDhw5p1apVKisrU1lZmXbu3KkffvihSp8RACjsAHiN0tJSvfzyy2rWrJlatmyps2fP\n6tVXX5UkLVq0SKtXr1ZkZKQmT56sRx55xG0W7nrrzl27/9fbLVq0cN1hO3bsWL311ltKSEiodGyv\nXr309ttv6+mnn1bjxo3VsWNH1x2s1yooKNDkyZPVuHFjxcXFqWnTppoxY0al9/zoo49cl1yv3hmb\nmpqq8PBwpaen66OPPlJsbKxatmypl19++bo3alTlMwKof2xO/twDUM/Y7XaNHTtWx48ftzoUAKhT\nzNgBAAD4CAo7APUSlywB+CIuxQIAAPgIZuwAAAB8BIUdAACAj6CwAwAA8BEUdgAAAD6Cwg4AAMBH\n/B8B7aSAfFV1TAAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1064c4dd8>"
+        "<matplotlib.figure.Figure at 0x106fa92d0>"
        ]
       }
      ],
-     "prompt_number": 73
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -561,25 +585,28 @@
      "input": [
       "import timeit\n",
       "\n",
-      "def plus_operator():\n",
-      "    return 'a' + str(1) + str(2) \n",
+      "n = 1000\n",
+      "\n",
+      "def plus_operator(n):\n",
+      "    my_str = 'a'\n",
+      "    for i in range(n):\n",
+      "        my_str = my_str + str(1) + str(2)\n",
+      "    return my_str \n",
       "    \n",
-      "def format_method():\n",
-      "    return 'a{}{}'.format(1,2)\n",
+      "def format_method(n):\n",
+      "    my_str = 'a'\n",
+      "    for i in range(n):\n",
+      "        my_str = '{}{}{}'.format(my_str,1,2)\n",
       "    \n",
-      "def binary_operator():\n",
-      "    return 'a%s%s' %(1,2)\n",
-      "\n",
-      "%timeit plus_operator()\n",
-      "%timeit format_method()\n",
-      "%timeit binary_operator()\n",
+      "def binary_operator(n):\n",
+      "    my_str = 'a'\n",
+      "    for i in range(n):\n",
+      "        my_str = '%s%s%s' %(my_str,1,2)\n",
+      "    return my_str\n",
       "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
+      "%timeit plus_operator(n)\n",
+      "%timeit format_method(n)\n",
+      "%timeit binary_operator(n)"
      ],
      "language": "python",
      "metadata": {},
@@ -588,8 +615,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000000 loops, best of 3: 764 ns per loop\n",
-        "1000000 loops, best of 3: 494 ns per loop"
+        "1000 loops, best of 3: 869 \u00b5s per loop\n",
+        "1000 loops, best of 3: 686 \u00b5s per loop"
        ]
       },
       {
@@ -597,7 +624,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000000 loops, best of 3: 79.3 ns per loop"
+        "1000 loops, best of 3: 445 \u00b5s per loop"
        ]
       },
       {
@@ -608,7 +635,88 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['plus_operator', 'format_method', 'binary_operator']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 5)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
+      "                      'from __main__ import %s, n' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('plus_operator', 'my_str + str(1) + str(2)'), \n",
+      "          ('format_method', '\"{}{}{}\".format(my_str,1,2)'),\n",
+      "          ('binary_operator', '\"%s%s%s\" %(my_str,1,2)'),\n",
+      "          ] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], \n",
+      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of different string assembly methods')\n",
+      "\n",
+      "max_perf = max( p/b for p,b in zip(times_n['plus_operator'],\n",
+      "                                   times_n['binary_operator']) )\n",
+      "min_perf = min( p/b for p,b in zip(times_n['plus_operator'],\n",
+      "                                   times_n['binary_operator']) )\n",
+      "\n",
+      "ftext = '\"%s%s%s\" %(my_str,1,2) is {:.2f}x to'\\\n",
+      "        '{:.2f}x faster than my_str + str(1) + str(2)'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCRV2IiIiInlChZ2IiIhInvgHflbs0wMQQB4AAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105595650>"
+       ]
+      }
+     ],
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -654,14 +762,7 @@
       "%timeit string_is_int(an_int)\n",
       "%timeit string_is_int(no_int)\n",
       "%timeit an_int.isdigit()\n",
-      "%timeit no_int.isdigit()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
+      "%timeit no_int.isdigit()"
      ],
      "language": "python",
      "metadata": {},
@@ -670,8 +771,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000000 loops, best of 3: 401 ns per loop\n",
-        "100000 loops, best of 3: 3.04 \u00b5s per loop"
+        "1000000 loops, best of 3: 420 ns per loop\n",
+        "100000 loops, best of 3: 2.83 \u00b5s per loop"
        ]
       },
       {
@@ -679,7 +780,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000000 loops, best of 3: 92.1 ns per loop"
+        "10000000 loops, best of 3: 116 ns per loop"
        ]
       },
       {
@@ -687,7 +788,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000000 loops, best of 3: 96.3 ns per loop"
+        "10000000 loops, best of 3: 119 ns per loop"
        ]
       },
       {
@@ -698,7 +799,88 @@
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['string_is_int', 'isdigit']\n",
+      "t1 = '123'\n",
+      "t2 = '123abc'\n",
+      "isdigit_method = []\n",
+      "string_is_int_method = []\n",
+      "\n",
+      "for t in [t1,t2]:\n",
+      "    string_is_int_method.append(min(timeit.Timer('string_is_int(t)', \n",
+      "                      'from __main__ import string_is_int, t')\n",
+      "                              .repeat(repeat=3, number=1000000)))\n",
+      "    isdigit_method.append(min(timeit.Timer('t.isdigit()', \n",
+      "                      'from __main__ import t')\n",
+      "                              .repeat(repeat=3, number=1000000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 52
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "N = len(isdigit_method)\n",
+      "ind = np.arange(N)  # the x locations for the groups\n",
+      "width = 0.25       # the width of the bars\n",
+      "\n",
+      " \n",
+      "fig, ax = plt.subplots()\n",
+      "plt.bar(ind, \n",
+      "        [i for i in string_is_int_method], \n",
+      "        width,\n",
+      "        alpha=0.5,\n",
+      "        color='g',\n",
+      "        label='string_is_int(a_str)')\n",
+      "\n",
+      "plt.bar(ind + width, \n",
+      "        [i for i in isdigit_method], \n",
+      "        width,\n",
+      "        alpha=0.5,\n",
+      "        color='b',\n",
+      "        label='a_str.isdigit()')\n",
+      "    \n",
+      "ax.set_ylabel('time in microseconds')\n",
+      "ax.set_title('Time to check if a string is an integer')\n",
+      "ax.set_xticks(ind + width)\n",
+      "ax.set_xticklabels(['\"%s\"' %t for t in [t1, t2]])\n",
+      "plt.xlabel('test strings')\n",
+      "plt.xlim(-0.1,1.6)\n",
+      "#plt.ylim(0,15)\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEgCAYAAAC0MAQrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUUIghBACgBICIYSQlyghEEIIAQD8H2Cy7wDUzyzIAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x107b9de10>"
+       ]
+      }
+     ],
+     "prompt_number": 90
     },
     {
      "cell_type": "markdown",
@@ -746,14 +928,7 @@
       "%timeit string_is_number(an_int)\n",
       "%timeit string_is_number(no_int)\n",
       "%timeit a_float.replace('.','',1).isdigit()\n",
-      "%timeit no_float.replace('.','',1).isdigit()\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
+      "%timeit no_float.replace('.','',1).isdigit()"
      ],
      "language": "python",
      "metadata": {},
@@ -762,8 +937,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000000 loops, best of 3: 400 ns per loop\n",
-        "1000000 loops, best of 3: 1.15 \u00b5s per loop"
+        "1000000 loops, best of 3: 418 ns per loop\n",
+        "1000000 loops, best of 3: 1.28 \u00b5s per loop"
        ]
       },
       {
@@ -771,7 +946,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000000 loops, best of 3: 452 ns per loop"
+        "1000000 loops, best of 3: 500 ns per loop"
        ]
       },
       {
@@ -779,7 +954,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000000 loops, best of 3: 394 ns per loop"
+        "1000000 loops, best of 3: 427 ns per loop"
        ]
       },
       {
@@ -790,7 +965,90 @@
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 91
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = [\"string_is_number\", \"replace('.','',1).isdigit()\"]\n",
+      "t1 = '1.234'\n",
+      "t2 = '123abc'\n",
+      "isdigit_method = []\n",
+      "string_is_number_method = []\n",
+      "\n",
+      "for t in [t1,t2]:\n",
+      "    string_is_number_method.append(min(timeit.Timer('string_is_number(t)', \n",
+      "                      'from __main__ import string_is_number, t')\n",
+      "                              .repeat(repeat=3, number=1000000)))\n",
+      "    isdigit_method.append(min(timeit.Timer(\"t.replace('.','',1).isdigit()\", \n",
+      "                      'from __main__ import t')\n",
+      "                              .repeat(repeat=3, number=1000000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 96
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "N = len(isdigit_method)\n",
+      "ind = np.arange(N)  # the x locations for the groups\n",
+      "width = 0.25       # the width of the bars\n",
+      "\n",
+      " \n",
+      "fig, ax = plt.subplots()\n",
+      "\n",
+      "plt.bar(ind , \n",
+      "        [i for i in isdigit_method], \n",
+      "        width,\n",
+      "        alpha=0.5,\n",
+      "        color='b',\n",
+      "        label=\"a_str.replace('.','',1).isdigit()\")\n",
+      "\n",
+      "plt.bar(ind + width, \n",
+      "        [i for i in string_is_number_method], \n",
+      "        width,\n",
+      "        alpha=0.5,\n",
+      "        color='g',\n",
+      "        label='string_is_number(a_str)')\n",
+      "\n",
+      "    \n",
+      "ax.set_ylabel('time in microseconds')\n",
+      "ax.set_title('Time to check if a string is a number')\n",
+      "ax.set_xticks(ind + width)\n",
+      "ax.set_xticklabels(['\"%s\"' %t for t in [t2, t1]])\n",
+      "plt.xlabel('test strings')\n",
+      "plt.xlim(-0.1,1.6)\n",
+      "#plt.ylim(0,15)\n",
+      "plt.legend(loc='upper left')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEgCAYAAAC0MAQrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGGNsDCcExhhjAIB/AP0bxvnc1X/jAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10782cfd0>"
+       ]
+      }
+     ],
+     "prompt_number": 98
     },
     {
      "cell_type": "markdown",
@@ -846,11 +1104,10 @@
      "collapsed": false,
      "input": [
       "import timeit\n",
+      "import copy\n",
       "\n",
       "def reverse_func(my_list):\n",
-      "    new_list = my_list[:]\n",
-      "    new_list.reverse()\n",
-      "    return new_list\n",
+      "    return copy.deepcopy(my_list).reverse()\n",
       "    \n",
       "def reversed_func(my_list):\n",
       "    return list(reversed(my_list))\n",
@@ -858,15 +1115,12 @@
       "def reverse_slizing(my_list):\n",
       "    return my_list[::-1]\n",
       "\n",
-      "%timeit reverse_func([1,2,3,4,5])\n",
-      "%timeit reversed_func([1,2,3,4,5])\n",
-      "%timeit reverse_slizing([1,2,3,4,5])\n",
+      "n = 10\n",
+      "test_list = list([i for i in range(n)])\n",
       "\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.4 GHz Intel Core Duo\n",
-      "# 8 GB 1067 Mhz DDR3\n",
-      "#"
+      "%timeit reverse_func(test_list)\n",
+      "%timeit reversed_func(test_list)\n",
+      "%timeit reverse_slizing(test_list)"
      ],
      "language": "python",
      "metadata": {},
@@ -875,8 +1129,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000000 loops, best of 3: 930 ns per loop\n",
-        "1000000 loops, best of 3: 1.89 \u00b5s per loop"
+        "100000 loops, best of 3: 13.8 \u00b5s per loop\n",
+        "1000000 loops, best of 3: 1.44 \u00b5s per loop"
        ]
       },
       {
@@ -884,7 +1138,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000000 loops, best of 3: 775 ns per loop"
+        "1000000 loops, best of 3: 330 ns per loop"
        ]
       },
       {
@@ -895,7 +1149,91 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 104
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['reverse_func', 'reversed_func',\n",
+      "         'reverse_slizing']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    test_list = list([i for i in range(n)])\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(test_list)' %f, \n",
+      "                      'from __main__ import %s, test_list' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 117
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('reverse_func', 'copy.deepcopy(my_list).reverse()'), \n",
+      "          ('reversed_func', 'list(reversed(my_list))'),\n",
+      "          ('reverse_slizing', 'my_list[::-1]'),\n",
+      "          ] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], \n",
+      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of different list reversing approaches')\n",
+      "\n",
+      "max_perf = max( f/s for f,s in zip(times_n['reverse_func'],\n",
+      "                                   times_n['reverse_slizing']) )\n",
+      "min_perf = min( f/s for f,s in zip(times_n['reverse_func'],\n",
+      "                                   times_n['reverse_slizing']) )\n",
+      "\n",
+      "ftext = 'my_list[::-1] is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than copy.deepcopy(my_list).reverse()'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERERkZtgYkdERETkJv4/dDERlOXTSFkAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1069c7890>"
+       ]
+      }
+     ],
+     "prompt_number": 118
     },
     {
      "cell_type": "markdown",
@@ -941,62 +1279,139 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import timeit\n",
+      "import timeit\n",
+      "\n",
+      "def cond_loop(n):\n",
+      "    even_nums = []\n",
+      "    for i in range(n):\n",
+      "        if i % 2 == 0:\n",
+      "            even_nums.append(i)\n",
+      "    return even_nums\n",
+      "\n",
+      "def list_compr(n):\n",
+      "    even_nums = [i for i in range(n) if i % 2 == 0]\n",
+      "    return even_nums\n",
+      "    \n",
+      "def filter_func(n):\n",
+      "    even_nums = list(filter((lambda x: x % 2 != 0), range(n)))\n",
+      "    return even_nums\n",
+      "\n",
+      "%timeit cond_loop(n)\n",
+      "%timeit list_compr(n)\n",
+      "%timeit filter_func(n)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "10 loops, best of 3: 18.1 ms per loop\n",
+        "100 loops, best of 3: 15.3 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "10 loops, best of 3: 22.8 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 119
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['cond_loop', 'list_compr',\n",
+      "         'filter_func']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    test_list = list([i for i in range(n)])\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
+      "                      'from __main__ import %s, n' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 120
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
       "\n",
-      "def cond_loop():\n",
-      "    even_nums = []\n",
-      "    for i in range(100):\n",
-      "        if i % 2 == 0:\n",
-      "            even_nums.append(i)\n",
-      "    return even_nums\n",
+      "labels = [('cond_loop', 'explicit for-loop'), \n",
+      "          ('list_compr', 'list comprehension'),\n",
+      "          ('filter_func', 'lambda function'),\n",
+      "          ] \n",
       "\n",
-      "def list_compr():\n",
-      "    even_nums = [i for i in range(100) if i % 2 == 0]\n",
-      "    return even_nums\n",
-      "    \n",
-      "def filter_func():\n",
-      "    even_nums = list(filter((lambda x: x % 2 != 0), range(100)))\n",
-      "    return even_nums\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
       "\n",
-      "%timeit cond_loop()\n",
-      "%timeit list_compr()\n",
-      "%timeit filter_func()\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], \n",
+      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of different conditional list creation methods')\n",
       "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
+      "max_perf = max( f/c for f,c in zip(times_n['filter_func'],\n",
+      "                                   times_n['cond_loop']) )\n",
+      "min_perf = min( f/c for f,c in zip(times_n['filter_func'],\n",
+      "                                   times_n['cond_loop']) )\n",
+      "\n",
+      "ftext = 'the list comprehension is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than the lambda function'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "100000 loops, best of 3: 14.4 \u00b5s per loop\n",
-        "100000 loops, best of 3: 12 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 23.9 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4gAAAABJRU5ErkJggg==\n",
        "text": [
-        "\n"
+        "<matplotlib.figure.Figure at 0x106fe4290>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 122
     },
     {
      "cell_type": "markdown",
@@ -1066,6 +1481,7 @@
       "\n",
       "\n",
       "def add_element_check1(elements):\n",
+      "    \"\"\"if ele not in dict (v1)\"\"\"\n",
       "    d = dict()\n",
       "    for e in elements:\n",
       "        if e not in d:\n",
@@ -1075,6 +1491,7 @@
       "    return d\n",
       "            \n",
       "def add_element_check2(elements):\n",
+      "    \"\"\"if ele not in dict (v2)\"\"\"\n",
       "    d = dict()\n",
       "    for e in elements:\n",
       "        if e not in d:\n",
@@ -1083,6 +1500,7 @@
       "    return d\n",
       "        \n",
       "def add_element_except(elements):\n",
+      "    \"\"\"try-except\"\"\"\n",
       "    d = dict()\n",
       "    for e in elements:\n",
       "        try:\n",
@@ -1092,12 +1510,14 @@
       "    return d\n",
       "            \n",
       "def add_element_defaultdict(elements):\n",
+      "    \"\"\"defaultdict\"\"\"\n",
       "    d = defaultdict(int)\n",
       "    for e in elements:\n",
       "        d[e] += 1\n",
       "    return d\n",
       "\n",
       "def add_element_get(elements):\n",
+      "    \"\"\".get() method\"\"\"\n",
       "    d = dict()\n",
       "    for e in elements:\n",
       "        d[e] = d.get(e, 1) + 1\n",
@@ -1138,7 +1558,7 @@
        "stream": "stdout",
        "text": [
         "Results for 100 integers in range 1-10\n",
-        "10000 loops, best of 3: 28 \u00b5s per loop"
+        "100000 loops, best of 3: 16.8 \u00b5s per loop"
        ]
       },
       {
@@ -1146,7 +1566,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 26.2 \u00b5s per loop"
+        "100000 loops, best of 3: 18.2 \u00b5s per loop"
        ]
       },
       {
@@ -1154,7 +1574,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 26.5 \u00b5s per loop"
+        "100000 loops, best of 3: 17.1 \u00b5s per loop"
        ]
       },
       {
@@ -1162,7 +1582,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 22.8 \u00b5s per loop"
+        "100000 loops, best of 3: 14.7 \u00b5s per loop"
        ]
       },
       {
@@ -1170,7 +1590,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 33.3 \u00b5s per loop"
+        "10000 loops, best of 3: 20.8 \u00b5s per loop"
        ]
       },
       {
@@ -1180,7 +1600,7 @@
         "\n",
         "\n",
         "Results for 1000 integers in range 1-5\n",
-        "1000 loops, best of 3: 242 \u00b5s per loop"
+        "10000 loops, best of 3: 161 \u00b5s per loop"
        ]
       },
       {
@@ -1188,7 +1608,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 239 \u00b5s per loop"
+        "10000 loops, best of 3: 166 \u00b5s per loop"
        ]
       },
       {
@@ -1196,7 +1616,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 203 \u00b5s per loop"
+        "10000 loops, best of 3: 128 \u00b5s per loop"
        ]
       },
       {
@@ -1204,7 +1624,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "10000 loops, best of 3: 184 \u00b5s per loop"
+        "10000 loops, best of 3: 114 \u00b5s per loop"
        ]
       },
       {
@@ -1212,7 +1632,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 350 \u00b5s per loop"
+        "1000 loops, best of 3: 197 \u00b5s per loop"
        ]
       },
       {
@@ -1222,7 +1642,7 @@
         "\n",
         "\n",
         "Results for 1000 integers in range 1-1000\n",
-        "1000 loops, best of 3: 262 \u00b5s per loop"
+        "10000 loops, best of 3: 166 \u00b5s per loop"
        ]
       },
       {
@@ -1230,7 +1650,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 370 \u00b5s per loop"
+        "1000 loops, best of 3: 240 \u00b5s per loop"
        ]
       },
       {
@@ -1238,7 +1658,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 502 \u00b5s per loop"
+        "1000 loops, best of 3: 354 \u00b5s per loop"
        ]
       },
       {
@@ -1246,7 +1666,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 422 \u00b5s per loop"
+        "1000 loops, best of 3: 281 \u00b5s per loop"
        ]
       },
       {
@@ -1254,7 +1674,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000 loops, best of 3: 373 \u00b5s per loop"
+        "1000 loops, best of 3: 234 \u00b5s per loop"
        ]
       },
       {
@@ -1265,7 +1685,84 @@
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 123
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['add_element_check1', 'add_element_check2',\n",
+      "         'add_element_except', 'add_element_defaultdict',\n",
+      "         'add_element_get']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    elements = [random.randrange(1, 100) for i in range(n)]\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(elements)' %f, \n",
+      "                      'from __main__ import %s, elements' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 136
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('add_element_check1', 'if ele not in dict (v1)'), \n",
+      "          ('add_element_check2', 'if ele not in dict (v2)'),\n",
+      "          ('add_element_except', 'try-except'),\n",
+      "          ('add_element_defaultdict', 'defaultdict'),\n",
+      "          ('add_element_get', '.get() method')\n",
+      "          ] \n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,10))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], \n",
+      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of different methods to count elements in a dictionary')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAJ0CAYAAACfnG7yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7AAAABziG6SlHxRk0gPUAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x107ebb4d0>"
+       ]
+      }
+     ],
+     "prompt_number": 139
     },
     {
      "cell_type": "markdown",
@@ -1318,19 +1815,12 @@
      "collapsed": false,
      "input": [
       "import timeit\n",
-      "n = 1000\n",
-      "\n",
-      "#\n",
-      "# Python 3.4.0\n",
-      "# MacOS X 10.9.2\n",
-      "# 2.5 GHz Intel Core i5\n",
-      "# 4 GB 1600 Mhz DDR3\n",
-      "#"
+      "n = 1000"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 140
     },
     {
      "cell_type": "markdown",
@@ -1355,24 +1845,15 @@
       "    for i in range(n):\n",
       "        if i % 3 == 0:\n",
       "            a_set.add(i)\n",
-      "    return a_set"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
+      "    return a_set\n",
+      "\n",
       "def set_compr(n):\n",
       "    return {i for i in range(n) if i % 3 == 0}"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 21
+     "prompt_number": 141
     },
     {
      "cell_type": "code",
@@ -1388,8 +1869,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10000 loops, best of 3: 136 \u00b5s per loop\n",
-        "10000 loops, best of 3: 113 \u00b5s per loop"
+        "1000 loops, best of 3: 165 \u00b5s per loop\n",
+        "10000 loops, best of 3: 145 \u00b5s per loop"
        ]
       },
       {
@@ -1400,7 +1881,7 @@
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 142
     },
     {
      "cell_type": "markdown",
@@ -1430,7 +1911,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 143
     },
     {
      "cell_type": "code",
@@ -1442,7 +1923,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 24
+     "prompt_number": 144
     },
     {
      "cell_type": "code",
@@ -1458,8 +1939,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10000 loops, best of 3: 129 \u00b5s per loop\n",
-        "10000 loops, best of 3: 111 \u00b5s per loop"
+        "10000 loops, best of 3: 164 \u00b5s per loop\n",
+        "10000 loops, best of 3: 143 \u00b5s per loop"
        ]
       },
       {
@@ -1470,7 +1951,86 @@
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 145
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['list_loop', 'list_compr']\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
+      "                      'from __main__ import %s, n' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 152
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('list_loop', 'explicit for-loop'), \n",
+      "          ('list_compr', 'list comprehension')]\n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], \n",
+      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of explicit for-loops vs. list comprehensions')\n",
+      "\n",
+      "max_perf = max( l/c for l,c in zip(times_n['list_loop'],\n",
+      "                                   times_n['list_compr']) )\n",
+      "min_perf = min( l/c for l,c in zip(times_n['list_loop'],\n",
+      "                                   times_n['list_compr']) )\n",
+      "\n",
+      "ftext = 'the list comprehension is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than the explicit for-loop'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1055b1910>"
+       ]
+      }
+     ],
+     "prompt_number": 153
     },
     {
      "cell_type": "markdown",
@@ -1500,7 +2060,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 146
     },
     {
      "cell_type": "code",
@@ -1512,7 +2072,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 27
+     "prompt_number": 147
     },
     {
      "cell_type": "code",
@@ -1528,8 +2088,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10000 loops, best of 3: 121 \u00b5s per loop\n",
-        "10000 loops, best of 3: 127 \u00b5s per loop"
+        "10000 loops, best of 3: 159 \u00b5s per loop\n",
+        "10000 loops, best of 3: 151 \u00b5s per loop"
        ]
       },
       {
@@ -1540,7 +2100,7 @@
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 148
     },
     {
      "cell_type": "markdown",
@@ -2029,6 +2589,29 @@
       "[[back to top](#sections)]"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"np_sum\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## `sum()` vs. `numpy.sum()`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -2048,8 +2631,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "100 loops, best of 3: 18.3 ms per loop\n",
-        "10 loops, best of 3: 136 ms per loop"
+        "10 loops, best of 3: 85.8 ms per loop\n",
+        "10 loops, best of 3: 85.6 ms per loop"
        ]
       },
       {
@@ -2060,7 +2643,30 @@
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 155
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"np_arange\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## `range()` vs. `numpy.arange()`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
     },
     {
      "cell_type": "code",
@@ -2079,8 +2685,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10 loops, best of 3: 82.6 ms per loop\n",
-        "100 loops, best of 3: 5.35 ms per loop"
+        "10 loops, best of 3: 38.3 ms per loop\n",
+        "100 loops, best of 3: 2.28 ms per loop"
        ]
       },
       {
@@ -2091,7 +2697,30 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 156
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"np_mean\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## `statistics.mean()` vs. `numpy.mean()`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
     },
     {
      "cell_type": "code",
@@ -2106,22 +2735,17 @@
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 1.14 s per loop\n",
-        "10 loops, best of 3: 141 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
+       "ename": "ImportError",
+       "evalue": "No module named 'statistics'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-157-e3a554951c79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mstatistics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit (statistics.mean(samples))'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit (np.mean(samples))'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mImportError\u001b[0m: No module named 'statistics'"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 157
     },
     {
      "cell_type": "markdown",

From 936d1dc1ae49997f37edc237f799afcebe5cdba3 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 7 May 2014 21:57:53 -0400
Subject: [PATCH 029/248] updated for html print

---
 benchmarks/timeit_tests.ipynb              | 304 +++++++++++++++++++--
 tutorials/scope_resolution_legb_rule.ipynb |  16 +-
 2 files changed, 291 insertions(+), 29 deletions(-)

diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index 40434a8..1222ca9 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a6e11ece22a848e8c3b728870bda775773780d2fa909fb330ad64e4febec9f2a"
+  "signature": "sha256:7773fd07ffe611312feea432cbf6765914b236c0cdfbbb7c8de867f61a8b97ce"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -25,7 +25,7 @@
      "source": [
       "<hr>\n",
       "I am really looking forward to your comments and suggestions to improve and extend this collection! Just send me a quick note   \n",
-      "via Twitter: [@rasbt](https://twitter.com/rasbt)  \n",
+      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
       "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
       "<hr>"
      ]
@@ -2616,13 +2616,13 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import numpy as np\n",
+      "from numpy import sum as np_sum\n",
       "import timeit\n",
       "\n",
       "samples = list(range(1000000))\n",
       "\n",
       "%timeit(sum(samples))\n",
-      "%timeit(np.sum(samples))"
+      "%timeit(np_sum(samples))"
      ],
      "language": "python",
      "metadata": {},
@@ -2631,8 +2631,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10 loops, best of 3: 85.8 ms per loop\n",
-        "10 loops, best of 3: 85.6 ms per loop"
+        "10 loops, best of 3: 18.2 ms per loop\n",
+        "10 loops, best of 3: 138 ms per loop"
        ]
       },
       {
@@ -2643,7 +2643,89 @@
        ]
       }
      ],
-     "prompt_number": 155
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['sum', 'np_sum']\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    samples = list(range(n))\n",
+      "    times_n['sum'].append(min(timeit.Timer('sum(samples)', \n",
+      "                'from __main__ import samples')\n",
+      "                    .repeat(repeat=3, number=1000)))\n",
+      "    times_n['np_sum'].append(min(timeit.Timer('np_sum(samples)', \n",
+      "                'from __main__ import np_sum, samples')\n",
+      "                    .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('sum', 'in-built sum() function'), \n",
+      "          ('np_sum', 'numpy.sum() function')]\n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], \n",
+      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of explicit for-loops vs. list comprehensions')\n",
+      "\n",
+      "max_perf = max( n/i for i,n in zip(times_n['sum'],\n",
+      "                                   times_n['np_sum']) )\n",
+      "min_perf = min( n/i for i,n in zip(times_n['sum'],\n",
+      "                                   times_n['np_sum']) )\n",
+      "\n",
+      "ftext = 'the in-built sum() is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than the numpy.sum()'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHQAAQIgg2AEAAIQIgh0AAECI+H/3Q2KUDc+3ZgAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x106783160>"
+       ]
+      }
+     ],
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -2672,11 +2754,22 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%timeit(list(range(1000000)))\n",
-      "%timeit(np.arange(1000000))\n",
+      "from numpy import arange as np_arange\n",
+      "\n",
+      "n = 1000000\n",
+      "\n",
+      "def loop_range(n):\n",
+      "    for i in range(n):\n",
+      "        pass\n",
+      "    return\n",
       "\n",
-      "# note that in Python range() is implemented as xrange()\n",
-      "# with lazy evaluation (generator)"
+      "def loop_arange(n):\n",
+      "    for i in np_arange(n):\n",
+      "        pass\n",
+      "    return\n",
+      "\n",
+      "%timeit(loop_range(n))\n",
+      "%timeit(loop_arange(n))"
      ],
      "language": "python",
      "metadata": {},
@@ -2685,8 +2778,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "10 loops, best of 3: 38.3 ms per loop\n",
-        "100 loops, best of 3: 2.28 ms per loop"
+        "10 loops, best of 3: 50.9 ms per loop\n",
+        "10 loops, best of 3: 183 ms per loop"
        ]
       },
       {
@@ -2697,7 +2790,75 @@
        ]
       }
      ],
-     "prompt_number": 156
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['loop_range', 'loop_arange']\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(n)' %f, \n",
+      "                'from __main__ import %s, n' %f)\n",
+      "                    .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 38
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('loop_range', 'in-built range()'), \n",
+      "          ('loop_arange', 'numpy.arange()')]\n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], \n",
+      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of explicit for-loops vs. list comprehensions')\n",
+      "\n",
+      "max_perf = max( a/r for r,a in zip(times_n['loop_range'],\n",
+      "                                   times_n['loop_arange']) )\n",
+      "min_perf = min( a/r for r,a in zip(times_n['loop_range'],\n",
+      "                                   times_n['loop_arange']) )\n",
+      "\n",
+      "ftext = 'the in-built range() is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than numpy.arange()'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1050da390>"
+       ]
+      }
+     ],
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
@@ -2726,26 +2887,119 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import statistics\n",
+      "# note that the statistics module is only available\n",
+      "# in Python 3.4.0 or newer\n",
+      "\n",
+      "from statistics import mean as st_mean\n",
+      "from numpy import mean as np_mean\n",
+      "\n",
+      "def calc_mean(samples):\n",
+      "    return sum(samples)/len(samples)\n",
+      "\n",
+      "samples = list(range(1000000))\n",
+      "\n",
+      "%timeit(st_mean(samples))\n",
+      "%timeit(np_mean(samples))\n",
+      "%timeit(calc_mean(samples))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1 loops, best of 3: 1.13 s per loop\n",
+        "10 loops, best of 3: 141 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "10 loops, best of 3: 136 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 48
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "funcs = ['st_mean', 'np_mean', 'calc_mean']\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    samples = list(range(n))\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(samples)' %f, \n",
+      "                'from __main__ import %s, samples' %f)\n",
+      "                    .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 49
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('st_mean', 'statistics.mean()'), \n",
+      "          ('np_mean', 'numpy.mean()'),\n",
+      "          ('calc_mean', 'sum(samples)/len(samples)')]\n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], \n",
+      "             alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "plt.title('Performance of different approaches for calculating sample means')\n",
+      "\n",
+      "max_perf = max( s/c for s,c in zip(times_n['st_mean'],\n",
+      "                                   times_n['calc_mean']) )\n",
+      "min_perf = min( s/c for s,c in zip(times_n['st_mean'],\n",
+      "                                   times_n['calc_mean']) )\n",
+      "\n",
+      "ftext = 'using sum(samples)/len(samples) is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than statistics.mean()'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
       "\n",
-      "%timeit(statistics.mean(samples))\n",
-      "%timeit(np.mean(samples))"
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "ename": "ImportError",
-       "evalue": "No module named 'statistics'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-157-e3a554951c79>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mstatistics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit (statistics.mean(samples))'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit (np.mean(samples))'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mImportError\u001b[0m: No module named 'statistics'"
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFFf7N/DvrPTeXCwgSHGRtqAgVkBBsCciiRqJYkls\nUaxIFARieYIdwTxGomBEUzQafYydZo1gjSIiUlRMVKKCgoV2v3/wMj8WFtglKErO57q8dM/M3HPP\nmTOzxzNlOSIiMAzDMAzDMO89QUsnwDAMwzAMwzQP1rFjGIZhGIZpJVjHjmEYhmEYppVgHTuGYRiG\nYZhWgnXsGIZhGIZhWgnWsWMYhmEYhmklWMeuFSovL8ekSZNgYGAAgUCAkydPtnRK76Xdu3fD3Nwc\nCgoKmDRpkszLhYWFwdLSst7P9cVOTk6Gra0tlJSUMGDAgObZCKZZCQQC7Nq1q0XW/eWXX8LQ0BAC\ngQDff/99i+TQEH9/fwwcOLBZY0o7dpqqJffdu8Td3R2fffZZS6fBvEGsY9dC/P39IRAIIBAIoKio\nCFNTU0yfPh1Pnjz5x7F/+eUX/PDDDzh48CAePHiAXr16NUPG/y4VFRWYNGkSxowZg3v37iEyMrLJ\nsRYuXIjz5883Gnv69OlwcnJCbm4u9u7d+4+3oTlYWFggPDy8pdP41zt//jwiIiKwdetWPHjwAB9/\n/HFLp1QHx3HgOO6NxJWHp6cnJk6cWKf8wYMHGDVqVHOl9d56U/uJeXcotHQC/2aurq74+eefUV5e\njgsXLuCzzz7DvXv3cPDgwSbFKy0thZKSErKystCxY0f07NnzH+VXHe/f6M8//0RJSQkGDx6M9u3b\n/6NY6urqUFdXbzA2EeH27dtYsmQJOnbs2OR1EREqKiqgoNA8h/b78AXwb2inWVlZEAgEGDZs2D+K\n8ybriojwJt5331wxhUJhs8RhmHcdG7FrQYqKihAKhejQoQNGjBiBgIAAHDlyBK9fvwYA/Pjjj3Bw\ncICqqio6d+6M+fPn48WLF/zy7u7umDJlCkJCQtChQweYmJigf//+WLp0KXJyciAQCGBmZgYAKCsr\nQ1BQEIyMjKCsrAwbGxv88MMPEvkIBAJERUXhk08+gY6ODsaPH4+4uDgoKioiOTkZdnZ2UFNTw4AB\nA/DgwQMkJSXBwcEBGhoaGDhwIP78808+Vm5uLnx8fNCxY0eoq6vD3t4e8fHxEuurviSwbNkytG/f\nHvr6+pgwYQJKSkok5vvpp5/QvXt3qKqqwsDAAEOGDEFhYSE/PSoqClZWVlBVVUWXLl2wcuVKVFRU\nNFj3v//+O1xdXaGmpgY9PT2MGzcOBQUFAIC4uDiYmJgAqOp8N3Q5+9WrV5g+fTp0dHSgp6eHGTNm\n8PuvWs3LSbVjt2nTBikpKWjTpg0qKiowfvx4iUttt2/fxqhRo6Crqws9PT14e3vj+vXrfOya+8fR\n0REqKipISEhAWVkZwsLCYGZmBlVVVdja2mLLli119vd///tffPrpp9DS0oKxsTG+/vprif2TnZ2N\n8PBwfnT57t27Uuvh0qVLGDx4MAwNDaGpqYkePXrg6NGjEvOYmpoiODgYU6ZMgba2Ntq2bYslS5ZI\nfHHLOk9ISAhmzJgBAwMDuLm5AQAOHTqE7t27Q0VFBYaGhpg5c6bE8SJLjuXl5QgPD4e5uTlUVFRg\nZGSE2bNnS8xTVFRUb50BkKnuv/vuO3Tt2hWqqqrQ19eHm5sb7t+/L7Vu/f39MX78eFRWVkIgEKBN\nmzYAqjo8a9asgZmZGZSVlWFhYVFnZLm+upLm4sWLGDRoELS1taGpqQkXFxekpqYCkO14lqahY1fa\nJcHly5ejc+fO9cZrLA9/f38kJiZi+/btfJutPnYFAgF27tzJz9tY+weAx48f46OPPoKGhgbat2+P\nr776SqZLzitXruTbkFAoxKBBg/Dq1SuZ67L63B4cHAyhUAhdXV0sXboURITQ0FC0a9cOQqEQwcHB\nEsvJcvxII+85NDk5GQKBAIcPH0avXr2gpqYGZ2dnZGRk4I8//kCfPn2grq4OFxcXZGRkSCx78eJF\neHl5QVNTE0KhEKNGjZI4rzTXd0d6ejq8vb2hq6sLDQ0NWFtby9RmWwViWsSECRNo4MCBEmVr164l\njuOouLiYYmNjSVdXl+Lj4yk3N5dOnjxJ9vb29Omnn/Lzu7m5kaamJk2fPp0yMjLo+vXr9OTJE1qw\nYAF17tyZHj58SH///TcRES1YsID09fVpz549lJWVRStXriSBQEAJCQl8PI7jSF9fnzZt2kQ5OTmU\nlZVFsbGxJBAIqH///pSamkqXLl0iS0tL6tu3L7m6utL58+fpypUrZGVlRaNHj+ZjXbt2jTZt2kR/\n/PEH5eTkUFRUFCkoKFBSUpJE/jo6OjRv3jzKzMykY8eOkZ6eHoWEhPDzbNu2jRQVFWn58uX8NkZH\nR/PbFRoaSiYmJvTrr79SXl4eHTp0iDp16iQRo7a//vqLNDU1ady4cXT9+nU6ffo02dvbk6urKxER\nvXz5ktLS0ojjOPrf//5HDx8+pNLSUqmx5syZQ0KhkA4cOECZmZm0YMEC0tLSIktLS36e0NBQ/nN9\nsR88eEAcx9E333xDDx8+pJcvX9KDBw/I0NCQZsyYQdevX6dbt27RrFmzSF9fnwoKCoiI+P3j4uJC\nycnJlJubSwUFBTRhwgQSi8V0/PhxysvLo59++ol0dHRo69atEvvb0NCQvvvuO8rJyaFNmzYRx3F8\nm3jy5Al17tyZFi5cSA8fPqSHDx9SRUWF1HpITk6m7du3040bNygrK4uCg4NJSUmJbt26xc9jYmJC\nWlpaFBoaSrdu3aIdO3aQuro6RUZGNmme8PBwysrKooyMDLp69Sq1adOGb0uHDx+mTp06SRwvsuQ4\nfvx4EgqFFB8fTzk5OZSWliax7sbqjIgarfsLFy6QgoIC7dixg+7evUvXrl2jrVu3Un5+vtS6LSoq\nosjISFJQUOD3AxFRdHQ0qaqqUkxMDN2+fZs2b95MKioqEvtYWl1Jc/36dVJTU6NPPvmELl68SNnZ\n2fTzzz/TuXPniEi243nChAnk6enJf27s2HV3d6fPPvtMIo9ly5aRqakp/zk0NJQsLCz4z43lUVRU\nRK6urjRmzBi+rqqPXY7jaOfOnXLty+HDh5NIJKLk5GRKT0+niRMnko6OTp1zd02//PILaWlp0cGD\nB+nevXt05coVioyMpJcvX8pcl25ubqStrU1BQUGUlZVF27ZtI47jyNvbmxYtWkRZWVm0fft24jiO\nDh8+XGd/N3T81K73ppxDk5KSiOM46tatGyUlJdGNGzeoV69eZG9vT3369KHExETKyMigvn37kouL\nC79ceno6aWhoUFhYGGVmZtL169fpo48+oi5dutCrV6/kqp/Gvjvs7Oxo3LhxlJGRQbm5uXT48GE6\nePBgvdvUmrCOXQupfRJMT08nMzMz6tWrFxFVHaDffvutxDIpKSnEcRwVFhYSUVXjFolEdWLXPhmW\nlJSQsrIy/fe//5WYb+TIkTRgwAD+M8dxNGXKFIl5YmNjieM4unr1Kl+2evVq4jiOLl26xJetX7+e\nDAwMGtzmDz74QOKE4ubmRg4ODhLzTJ8+na8DIiJjY2OaNWuW1HglJSWkpqZGR48elSjfvn076ejo\n1JtHcHAwGRsbU1lZGV929epV4jiOTp48SUREubm5xHEcnTlzpt44xcXFpKKiQt99951EuZOTU52O\nXc39UV/s2l88oaGh1LNnT4l5KisrydzcnDZs2EBE/7d/Tp8+zc+Tk5NDAoGAMjMzJZYNDw+XqG+O\n4yggIEBinq5du9KXX37Jf7awsKDw8PB666AhYrGYVqxYwX82MTHhO8/VFi9eTMbGxnLPU/PYISLy\n8/OT+AIhItq/fz8JBAK6e/euTDlmZWURx3H0yy+/1Dt/Y3UmS93v3buXtLW16dmzZ/Wup7bY2FhS\nUFCQKDMyMqJFixZJlM2dO5fMzMz4z9LqSho/P786x2Jjah/Ptc9pDR27RE3r2MmSh6enJ02cOLHO\nfNI6dg3ty1u3bhHHcZSYmMhPLysrI2Nj4wY7duvWraMuXbpInF8aI+3c6OjoKDGPjY0N2dvbS5SJ\nxWJasGAB/1mW46dmvTf1HFrdsdu/fz9ftnv3buI4jvbu3cuX7du3jziOo5KSEiKqaiNjxoyRiPXq\n1StSU1OjX3/9td71NeW7Q1tbm+Li4uqN2ZqxS7EtKDk5GZqamlBTU4OdnR0sLCywc+dOFBQU4O7d\nu5g7dy40NTX5P0OGDAHHcbh9+zYfo3v37o2u5/bt2ygtLYWrq6tEuaurK9LT0yXKevToUWd5juNg\nZ2fHfzY0NAQA2NvbS5Q9fvyYH/J/8eIFgoKCYGtrC319fWhqauLQoUMSQ+4cx0EsFkusq3379nj4\n8CEA4NGjR8jPz4eXl5fU7UpPT8fLly/h4+MjUU/Tpk3Ds2fP8Pjx43qX69mzp8R9aPb29tDW1saN\nGzekLiNNdnY2Xr9+jd69e0uU9+nTp1nuC0pLS8PFixcltk1LSwt37tyRaAMA4OzszP/7woULICJ0\n795dYtn//Oc/dZZzcHCQ+NyhQwc8evRI7lwLCgowY8YMdO3aFbq6utDU1ER6enqd/V37QZ7evXsj\nPz8fxcXFcs1Tu53euHFDavsmIn6fNpbjpUuXAKDe9latoTqTpe69vLxgZmaGzp07Y+zYsYiJiam3\nrdbn2bNnuH//vtRtzsvL4y/7SasraS5evAgPD496p8tyPNfU2LHbVPLm0ZiG9mV1u6l5r7KCggKc\nnJwajDl69GiUlZXBxMQEEydORHx8PN92Zd0GaefGdu3aSZxzq8tqHq+yHD81NfUcWq1mjvV9LwDg\nc0xLS8O+ffsk1mVgYIDXr1/zx0dzfHcAwIIFCzBlyhT0798f4eHhuHz5coPb0pqwhydaUM+ePbF9\n+3YoKCigQ4cOfEejunFu3LgR/fv3r7Nc9c31HMdJ3JTfHKTFEwgEEjfRV/+7+l6fmmVEBI7jsHDh\nQhw4cADr16+HSCSCmpoa5s+fj6KiIonYtW/k5jgOlZWVMuVaPd+ePXvQpUuXOtN1dXWlLsdx3Bu5\nybu5ERE8PT0RHR1dZ5q2tjb/7zZt2kjUY3W9nDt3DmpqahLL1X4Y4p/Uf03+/v7Iz8/H6tWr0blz\nZ6ioqGDMmDEoLS2VO5YspLXTxvZpc+Uo7eGD6jqTpe7V1dVx4cIFnDlzBidOnMDmzZsRGBiIhIQE\ndOvWTa5cZCHLOaKxY0LW41keAoGgzjrLysoaXKa582hoX1arfcw01s46dOiAmzdvIikpCYmJiVi2\nbBkWLVqE8+fPw8jISOZtUFRUrJNH7TJZ8mlIU8+h0nKsridpZdXrISKMHz8eQUFBdWLp6+sDkH0f\nN3buCg4Oxrhx43DkyBEkJiZi5cqVCAwMxLJlyxrcptaAdexakIqKCv9wQ02GhoYwNjbGzZs3MXny\n5H+8HgsLCygrKyMlJQXW1tZ8eUpKisRIXHM6deoU/Pz84OvrC6DqwM7MzJTrCVOhUAgjIyMcPXpU\n6tOANjY2UFFRQXZ2NgYNGiRzXBsbG8TGxqKsrIw/CV29ehVFRUWwtbWVOY65uTmUlJRw5swZdO3a\nlS8/c+ZMszxN6uTkhLi4OHTs2BHKysoyL1c9invnzh0MHTr0H+WgpKTU6IMoQNX+Xr16Nb+fSkpK\nkJ2dLdG+iAjnzp2TWO7s2bMwMjKChoaGzPNIY2NjU+cBl5SUFHAcBxsbG5lyrO5UHT16VK7XYtTc\n17LWvUAgQL9+/dCvXz+Eh4fD2toau3btkrljp6WlBSMjI6SkpGDIkCES22xmZgYVFRWZ86/OOyEh\ngf+PWW3yHs+NHbvV89R+YOTSpUsNHjuy5KGkpITy8vKGN7geNdddfa48e/Ys/17J8vJyXLx4EVZW\nVg3GUVJSgre3N7y9vbFs2TIYGhpi//79mDlzJk6ePPmPz43S8gXkP36aeg5tKicnJ1y9elXq9161\n5vjuqNa5c2dMnz4d06dPx9dff401a9awjt276OHDh/Dx8YGSkhKUlJSwa9cuvqffmqxYsQKTJ0+G\nrq4uRowYAUVFRWRkZODIkSPYvHkzANlfL6CmpobZs2cjJCQEbdu2hb29Pfbs2YMDBw7gxIkTbyR/\nkUiEX3/9FT4+PlBXV8e6devw119/oV27dvw8suQfGhqK6dOnw9DQEKNGjUJlZSWSkpIwduxY6Ovr\nY/HixVi8eDE4joOHhwfKy8tx7do1XLlypc4TbtW++OILREZGwt/fH4sXL8bTp08xY8YMuLq6ok+f\nPjJvo7q6OqZNm4bg4GAYGhqiS5cu2Lp1K27dutUsr1b44osvsHXrVnzwwQcIDg6GkZER8vPzcfjw\nYQwbNqze9xNaWFhg0qRJ+Oyzz7Bq1Sr07NkTJSUluHjxIv7++28EBgbWu87a+6Rz5844ffo07t27\nxz/BKe2LVyQSIT4+Hn369EF5eTmWLl2KysrKOvv3ypUrCA8Px9ixY3HhwgVs3LgRy5cvl2seaW1m\n4cKF6NatG+bNm4fPP/8ceXl5mDVrFvz8/GBkZCRTjhYWFhg3bhxmzJiBV69eoWfPnnjy5AnOnTtX\n58nY+upMlrrfv38/cnNz0a9fP7Rt2xYXL17EvXv3+A6orL788kvMnz8flpaWcHNzQ2JiIjZv3oxv\nvvmmwbqSJjAwEC4uLhg3bhzmz58PHR0dXLp0CcbGxujZs6dMx3NtjR27np6emD59Ovbs2QMHBwfs\n2bMHp0+fho6OTr0xZcmjc+fOSEpKQk5ODrS0tKCjoyPz639q7ktLS0sMHz4cM2fOxLfffgsDAwOs\nXbsWz549a7DzuXXrVhARnJ2doaOjg4SEBDx//pzvKFpZWTXp3ChrmSzHT/UyGhoaTTqHNtXixYvR\no0cP+Pn5ISAgAAYGBsjLy8P+/fsREBCAzp07N8t3R3FxMRYtWgRfX1+YmpqisLAQR44ckfsYe1+9\nd/fYtW3bFmfOnEFSUhI++eQTxMTEtHRKTdLYSyL9/Pzw888/4+DBg3BxcUGPHj0QHh7Of0k1FENa\n+YoVK/DZZ59hzpw5sLOzw65du7Bz506pl3qlxZO3bP369fzrVzw9PWFsbAxfX986l3Rrx6ldNnny\nZMTFxWHPnj1wdHSEm5sbjh49yp+og4ODsW7dOsTExMDBwQH9+vVDZGRkg69MEAqFOHbsGPLz8+Hs\n7Izhw4fznd3GtrG2r7/+Gh9++CE+/fRTuLi44NmzZ5g5c6ZM29kYoVCIc+fOwcDAAD4+PrCysoKf\nnx/u3buHDh06NBhry5YtmDt3LlasWAEbGxt4enpix44dMDc3b3CdtXMNDw9HYWEhRCIRDA0Nce/e\nPanLxcbGorKyEj169ICPjw+GDBkCZ2fnOvUwe/Zs3LlzB87OzggICMCsWbMkOk2yzlObnZ0dDhw4\ngJMnT8LBwQHjx4/H8OHD+f8EyZpjbGwspk6diuDgYFhbW8PHxwd5eXly1Vljda+np4f//e9/GDx4\nMEQiEYKCghASEiL1pbq111PT9OnT8dVXX2HlypWwsbHB6tWrERERIRFH1pFjW1tbJCcno6CgAG5u\nbnB0dMT69ev546wpx3Njx+6ECRMwc+ZMzJw5E87Ozrh//z5mz57dYExZ8pg/fz4MDAwgFoshFApx\n9uxZmepA2vpiY2Nha2uLwYMHY8CAATAyMoKXl1eDI6J6enqIjY1F//79YW1tjQ0bNiAmJoY/1zbX\nuVFamazHT81lmnIOrY4jb5mVlRXOnj2L4uJieHt7w8bGBp9//jlevXrFd+ibo34UFRVRWFiIyZMn\nw9raGoMGDUL79u3/Nb88wtH7cLNRPaKioqCkpISpU6e2dCoMwzSic+fO+Oyzz7B48eJ/NA/DtJSK\nigpYWVnhww8/xOrVq1s6nTrY8cMA7+GlWKDqfqjPP/8chYWFSEtLa+l0GIaRgSz/h3yP/5/JtEKn\nTp3Cw4cP4ejoiOfPn2P9+vW4e/cu/P39Wzo1qdjxwwAteCk2OjoaTk5OUFFRqXMJ4smTJxg5ciQ0\nNDRgampa5xcSxGIxzp8/j+XLl/8rboRkmNZAlsuCzfHQCcM0l4qKCqxYsQIODg4YMGAA8vLykJSU\n9M7eq8WOHwZowRG7jh07IiQkBEePHsXLly8lps2cORMqKip49OgRLl++jKFDh0IsFsPa2lriSUYt\nLa06P9/EMMy7KTc3t1nmYZi3xd3d/b16/xk7fhjgHbjHLiQkBPn5+YiNjQVQ9QoCPT09pKenw8LC\nAkDVTbYdOnTAf/7zH6SmpmLhwoVo06YNFBUVsXXrVokHCqp17NhR4rdLGYZhGIZh3lXm5uZ1XiLf\nFC3+VGztfuWtW7egoKDAd+qAqkuv1b+Q0KNHD6SkpCAxMRFHjx6V2qkDgD///JN/JPpd/hMaGvpe\nrKOpMeRZTpZ5G5unoelNnfYu/XnTeTZX/KbEeZfaSkPTWVtp3nWwc8u78YedW2Sf902dW7Kzs5ul\nX9XiHbva9wQUFxdDS0tLokxTUxPPnz9/m2m9Ne7u7u/FOpoaQ57lZJm3sXkamt7QtMZeafGueNPt\npbniNyXOu9RWGprO2krzroOdW94N7Nwi+7xv6tzSXFr8UmxwcDDu37/PX4q9fPky+vbti5KSEn6e\nNWvW4OTJkzhw4IDMcd+Xn41i3g3+/v6Ii4tr6TSY9wBrK4w8WHthZNVc/ZZ3bsSuS5cuKC8vl7jO\nfPXqVbl+6qlaWFgYkpOT/2mKzL/Au/r6Aubdw9oKIw/WXpjGJCcnIywsrNnitdiIXUVFBcrKyhAe\nHo779+8jJiYGCgoKaNOmDcaOHQuO4/Ddd9/h0qVLGDZsGM6dOyfxe5yNYSN2DMMwDMO8L977Ebtl\ny5ZBTU0NERERiI+Ph6qqKlasWAEA+Oabb/Dy5UsIhUL4+flh8+bNcnXqGEZebGSXkRVrK4w8WHth\n3rYWe49dWFhYvUOPurq62Ldv39tNiGEYhmEY5j3X4g9PvCkNDWnq6enh6dOnbzkjhmHeJl1dXTx5\n8qSl02AYhpFJc12KfS9/K1ZWYWFhcHd3r/No8dOnT9n9dwzTyrGfV2IY5n2QnJzcrJfs/5UjduzB\nCoZp/d7UcZ6cnPxW3hHHtA6svTCyeu8fnmAYhmEYhmGaFxuxYximVWLHOcMw7xM2YscwDMMwDMNI\naNUdO/bLE/+MQCDArl27/lEMf39/DBw4sJkyen9dvnwZ7dq1w4sXLwAAp0+fhqmpKV6/ft3CmTHy\nYucURh6svTCNaTW/PPGm/dsuxebn56NTp05ITk6Gq6urXMt6enrC2NiY/73eao8ePYK2tjaUlZUb\njREfH4/x48ejsrJSovz58+eorKyEtra2XDm1NgMHDsTAgQMRGBjIl3l6emLw4MGYP39+C2bWerGH\nJ5h3AWsvjKzY607eoMzMOzhxIhtlZQIoKlbC09McIpHJOxOvIc35RSYUCv9xDE1NzWbI5P2Wnp6O\nlJSUOqOfkyZNwpIlSzBv3jz2ao73CPuSZuTB2gvztrXqS7FNkZl5B3Fxt1FQMACFhe4oKBiAuLjb\nyMy8807EO336NPr06QMtLS1oaWnBwcEBx44dQ6dOnQAA/fv3h0AggJmZGQAgNzcXPj4+6NixI9TV\n1WFvb4/4+Hg+nr+/PxITE7F9+3YIBAIIBAKcPHkSQN1Lsd999x26du0KVVVV6Ovrw83NDffv30dy\ncjLGjx/PLyMQCDBp0iQ+fu1LsT/99BO6d+8OVVVVGBgYYMiQISgsLGxw++pTHT8qKgpGRkbQ1NTE\ntGnTUFFRgejoaJiYmEBPTw9Tp05FWVmZxLJRUVGwsrKCqqoqunTpgpUrV6KiooKfvmvXLri4uEBH\nRwdt27bFsGHDkJWVxU/Py8uDQCDA7t27MWzYMKirq8Pc3Bzbt2+XWE98fDx69+6Ntm3bSpQPHz4c\n9+7dw+nTp+vdPoZhGIaRBxuxq+XEiWwoK3tA8rYID/zxRyKcneUfZUtNzcaLFx4SZe7uHkhISJR7\n1K68vBwjRozApEmT8P333wMArl+/DjU1NVy6dAndunXD3r170bt3b7Rp0wYAUFJSAk9PT4SHh0ND\nQwO//fYbJk6cCCMjI7i7u2Pjxo3Izc1Fhw4dEBkZCaDqjf21Xbx4EdOnT0dsbCzc3NxQVFSE1NRU\nAECfPn0QHR2NL774Ag8ePAAAqKqq8svWHI2KjY3F1KlTERoaip07d6KiogLJycmorKxscPsaruNU\nGBkZISEhAVlZWfjoo4+Ql5eHdu3a4dixY8jOzoavry8cHR0xbdo0AFX3X8bFxSEyMhIODg64ceMG\npk2bhlevXuGrr74CAJSWlmLp0qWwtrbGs2fPsHTpUgwdOhTp6elQVFTk1x8UFISIiAhs3LgRW7du\nxZQpU9C7d29YWloCAFJSUtCvX786eWtqasLGxgaJiYlSpzPvJnZpjZEHay/M28Y6drWUlUkfxKyo\naNrgZmWl9OVKS+WP9/z5cxQWFmL48OEwNzcHAP7v/Px8AFU/l1bzEqqtrS1sbW35z1988QVOnDiB\nXbt2wd3dHVpaWlBSUoKqqmqDl17v3r0LdXV1fPDBB9DU1ISxsbFEXC0tLQDSL9/WvDwcGhqKadOm\nYcmSJXyZjY0NgKpfBKlv+xqiqqqKmJgYKCgoQCQSwcPDA6mpqbh//z4UFRUhEong5eWFhIQETJs2\nDS9evMDq1auxb98+eHl5AQBMTEywbNkyBAQE8B07f39/ifXExsbCwMAAFy5cQK9evfjyWbNmwdfX\nFwCwbNkyREVFISkpie/YZWVlYdy4cVJzNzU1xa1btxrdRoZhGIaRRau+FNuUp2IVFSullrdpI728\nMQKB9OWUlOSPp6uriylTpsDb2xtDhgxBREREo52CFy9eICgoCLa2ttDX14empiYOHTqEu3fvyrVu\nLy8vmJmZoXPnzhg7dixiYmLw+PFjuWI8evQI+fn5fGeqtqZsHwB07doVCgr/938UQ0NDiEQiiVE1\nQ0NDPHr+TcEVAAAgAElEQVT0CEDVPW8vX76Ej48PNDU1+T/Tpk3Ds2fP+O26cuUKRo4cCTMzM2hp\nacHEpGqE9c4dycvoDg4O/L8FAgGEQiG/LgAoKiqq915DTU1N/jI0835goy+MPFh7YRrT3E/FtuoR\nu6ZUlKenOeLiEuDu/n+XT1+/ToC/vwVEIvlzyMysiqesLBnPw8NC/mAAtmzZgoCAABw7dgzHjx9H\nSEgIoqOjMWTIEKnzL1y4EAcOHMD69eshEomgpqaG+fPno6ioSK71qqur48KFCzhz5gxOnDiBzZs3\nIzAwEAkJCejWrVuTtkWa+rbv888/r3eZmp06oOrSr7Sy6id2q//es2cPunTpUieerq4uXrx4AS8v\nL7i6uiIuLg6GhoYgItjY2KC0tFRifiUlpXrXBQA6Ojp4/vy51NyLioqkXvpmGIZh/h2qf9M+PDy8\nWeK16hG7phCJTODvbwGhMBE6OskQChP/f6euaU+xNnc8oOrS5dy5c3Ho0CFMnjwZW7Zs4V9JUvPm\nfwA4deoU/Pz84OvrCzs7O3Tu3BmZmZkS970pKSmhvLy80fUKBAL069cP4eHhuHjxItq3b48ffviB\njwE0/FSuUCiEkZERjh49Kvf2NUTeJ0ptbGygoqKC7OxsmJmZ1fkjEAiQkZGBv//+GytWrICrqytE\nIhGePHnSpKeOLS0tkZeXJ3XanTt3pHYumXcXey8ZIw/WXpi3rVWP2DWVSGTSrK8jaa542dnZ2LJl\nC0aMGAEjIyP8+eefOHnyJJycnGBgYAANDQ0cPXoUXbt2hbKyMnR1dSESifDrr7/Cx8cH6urqWLdu\nHf766y+0a9eOj9u5c2ckJSUhJycHWlpa0NHRqTPitX//fuTm5qJfv35o27YtLl68iHv37sHa2pqP\nUT1fnz59oKamBnV19TrbEBoaiunTp8PQ0BCjRo1CZWUlkpOTMWbMGBQWFtbZvlOnTqF79+788h4e\nHnBxccHKlSv5Mnk7WxoaGli8eDEWL14MjuPg4eGB8vJyXLt2DVeuXMHXX38NExMTKCsrY+PGjZg3\nbx7y8vIQFBQkUyeydj5ubm44c+ZMnfmeP3+OGzdusEs1DMMwTLNhI3bvEXV1ddy+fRtjxoyBSCSC\nr68v+vbti+joaHAch02bNuHnn3+GsbEx3xlav349TExM0L9/f/5FxL6+vhIdlPnz58PAwABisRhC\noRBnz56ts249PT3873//w+DBgyESiRAUFISQkBBMnDgRAODs7IyAgABMnToVhoaGmDVrFoCq0bSa\n65o8eTLi4uKwZ88eODo6ws3NDUeOHIGCgoLU7at+4rZaTk4O/+SttPiylgUHB2PdunWIiYmBg4MD\n+vXrh8jISL6DamBggPj4eBw/fhy2trYIDAzE2rVrIRAI6sStrXaZn58fzp07h4KCAonyAwcOwNjY\nWO4XSjMti3XEGXmw9sK8beyXJxjmLfDy8oKHhwcWLVrEl3l4eGDw4MFYsGBBC2bWerHjnGGY90lz\nnbPYiB3DvAWrVq3Chg0bJH4rNicnB7Nnz27hzBh5sXumGHmw9sK8ba36HruwsDD+aROGaUkODg74\n66+/+M99+/ZFbm5uC2bEMAzDvAuSk5Ob9T8A7FIswzCtEjvOGYZ5n7BLsQzDMAzDMIwE1rFjGIaR\nA7tnipEHay/M28Y6dgzDMAzDMK0Eu8eOYZhWiR3nDMO8T9g9dgzDMAzDMIwE1rFjGIaRA7tnipEH\nay/M28Y6dgzTgMuXL6Ndu3YSLxY2NTXF69evWzgzhmEYhqmrVXfswsLC2P+WmH8kMDAQ8+bNg5qa\nGoCqFwtbWFhI/H4t8+/CXnjOyIO1F6YxycnJCAsLa7Z47OEJhqlHeno6HB0dcf/+fbRt25Yv37Vr\nF5YsWYKcnBxwHNeCGTINYcc5wzDvE/bwxBuUeTsTm37ahA0/bsCmnzYh83bmOxHP3d0dn332GZYt\nW4b27dtDX18fEyZMQElJCQDA398fAwcOlFgmPj4eAsH/7eawsDBYWlpi9+7dsLCwgLq6OkaNGoXi\n4mLs3r0bIpEIWlpa+Oijj/Ds2TN+uerY69evR8eOHaGuro6PP/4YT58+BVD1Pw4FBQXk5+dLrP/7\n77+Hjo4OXr58KVFeHS8qKgpGRkbQ1NTEtGnTUFFRgejoaJiYmEBPTw9Tp05FWVmZxLJRUVGwsrKC\nqqoqunTpgpUrV6KiooKfvmvXLri4uEBHRwdt27bFsGHDkJWVxU/Py8uDQCDA7t27MWzYMKirq8Pc\n3Bzbt2+vU3e9e/eW6NQBwPDhw3Hv3j2cPn264R3GtErsKgAjD9ZemLeNdexqybydibikOBQYFqCw\nXSEKDAsQlxTX5M5Yc8fbs2cPCgsLkZKSgh9//BEHDx5EREQEP12WEaS//voL33//PX799VccPnwY\np06dgo+PD+Li4rBnzx6+bOXKlRLLpaamIiUlBceOHcOhQ4dw5coVTJ48GUBVp9PS0hLbtm2TWCYm\nJgbjxo2DqqpqnTxSU1Nx6dIlJCQk4IcffsD27dsxdOhQXLhwAceOHUN8fDx27NiBrVu38suEhYVh\n7dq1iIiIwM2bNxEZGYlvv/0W4eHh/DylpaVYunQpLl++jBMnTqBNmzYYOnRonQ5iUFAQ/P39ce3a\nNYwZMwZTpkyR6ACmpKTAxcWlTt6ampqwsbFBYmJio3XNMAzDMG+TQksn8K45cfEElC2VkZyX/H+F\nisAfP/4B577OcsdLPZ2KF0YvgLz/K3O3dEfCpQSILERyxzM1NcXatWsBAF26dMHo0aNx4sQJfPXV\nVwAg0zDu69evsX37dujp6QEAPv74Y2zevBkPHz6Evr4+AGDMmDFISEiQWI6IsGPHDmhqagIANm3a\nBG9vb+Tk5MDMzAyff/45IiMjERISAo7jcPPmTZw5c6be+9FUVVURExMDBQUFiEQieHh4IDU1Fffv\n34eioiJEIhG8vLyQkJCAadOm4cWLF1i9ejX27dsHLy8vAICJiQmWLVuGgIAAvg78/f0l1hMbGwsD\nAwNcuHABvXr14stnzZoFX19fAMCyZcsQFRWFpKQkWFpaAgCysrIwbty4evfDrVu3Gq1rpvVh90wx\n8mDthXnb2IhdLWVUJrW8AhVSyxtTiUqp5aWVpXLH4jgOYrFYoqx9+/Z4+PChXHE6duzId+oAwNDQ\nEO3ateM7ddVljx49kljO2tqa79QBQO/evQEAN27cAACMHz8ejx49wtGjRwEA3333HZycnOrkXK1r\n165QUPi//1sYGhpCJBJBUVFRah7p6el4+fIlfHx8oKmpyf+ZNm0anj17hsePHwMArly5gpEjR8LM\nzAxaWlowMTEBANy5c0di/Q4ODvy/BQIBhEKhxDYXFRVJbG9NmpqaKCwslDqNYRiGYVoK69jVosgp\nSi1vgzZNiieop4qVBEpNiqekJLkcx3GorKzqPAoEgjojdrUvPwKQ6DhVx5BWVh23WmOjgfr6+vD1\n9UVMTAzKysrw/fff4/PPP693/pqduup1SiurzqP67z179uDq1av8n+vXryMrKwu6urp48eIFvLy8\n0KZNG8TFxSEtLQ1paWngOA6lpZKd6YbqEgB0dHTw/PlzqbkXFRVBV1e3wfpgWid2zxQjD9ZemLeN\nXYqtxbO7J+KS4uBu6c6Xvc56Df8x/k26dJppVHWPnbKlskQ8j/4ezZGuBKFQiN9//12i7NKlS80W\nPyMjA8+fP+dHsc6ePQugaiSv2tSpU9G/f39s3rwZr169wtixY+uNJ+8TpTY2NlBRUUF2djYGDRpU\nb45///03VqxYAZFIxOfZlCeNLC0tkZeXJ3XanTt3+Mu4DMMwDPOuYCN2tYgsRPDv7w/hIyF0HuhA\n+EgI//5N69Q1dzwiarCD4unpiZs3b+Kbb75BdnY2YmJisHv37iblLQ3HcRg/fjzS09Nx8uRJzJw5\nEx988AHMzMz4efr06QORSISFCxdi7NixUFdXBwB4eHhg8eLFdbZHHhoaGli8eDEWL16Mb775BpmZ\nmUhPT8ePP/6IoKAgAFX33CkrK2Pjxo3Izs5GQkICAgICZOpE1s7Hzc0NqampdeZ7/vw5bty4we6d\n+Zdi+52RB2svzNvGRuykEFmImtyRe5PxOI6r00GpWebp6Ynly5dj5cqVWLRoEUaMGIGlS5di1qxZ\nMsdoqKxHjx7o27cvBg4ciKKiIgwZMgRbtmypk+eUKVMwd+5cicuwOTk5/L1u/ySP4OBgtG/fHtHR\n0Zg/fz5UVVUhEon4ByYMDAwQHx+PL7/8Etu2bYO1tTXWr18PDw+POnFrq13m5+eHNWvWoKCgQOKV\nJwcOHICxsTFcXV3rxGAYhmGYlsReUMzIxN/fH/fv38fx48cbnTcwMBAJCQm4ePHiW8jszfLy8oKH\nhwcWLVrEl3l4eGDw4MFYsGBBC2bGNOZNHefJyclsFIaRGWsvjKzYC4plwH5S7O0qKipCWloaYmJi\nMHfu3JZOp1msWrUKGzZskPit2JycHMyePbuFM2MYhmFaA/aTYjJiI3bNa+LEibh//z6OHTtW7zzu\n7u5ITU3F2LFjJV4qzDAtgR3nDMO8T5rrnMU6dgzDtErsOGcY5n3CLsUyDMO0AHZ7ByMP1l6Yt411\n7BiGYRiGYVoJdimWYZhWiR3nDMO8T9ilWIZhGIZhGEYC69gxDMPIgd0zxciDtRfmbWMdO4ZhGIZh\nmFaC3WPHMEyrxI5zhmHeJ+weO+at279/P+zs7Fo6DZnk5eVBIBDg7NmzzRZz+fLlGD16tEzzCgQC\n7Nq1q9nWXdPly5fRrl07/tcw3nXNXRfx8fHo2bNns8VjGIZpTVjHjpFJZWUlAgMDERIS0tKptJg5\nc+bg+PHjuHDhgtTpbm5uiIqKeuN5BAYGYt68eVBTU3vj63oXffLJJ3j69Cl++eWXFlk/u2eKkQdr\nL8zbptDSCbyL7mRmIvvECQjKylCpqAhzT0+YiETvTLyWcPjwYTx+/Bg+Pj4tnUqL0dDQgK+vL6Ki\norB9+3aJaQUFBTh79ix27tz5RnNIT09HSkrKGxsNfB8IBAJMmDABGzduxKhRo1o6HYZhmHcKG7Gr\n5U5mJm7HxWFAQQHcCwsxoKAAt+PicCcz852Id/r0afTp0wdaWlrQ0tKCg4MDjh07Vu+lRwsLC4SH\nh/OfBQIBoqOjMXr0aGhoaMDU1BT79u3D06dPMXbsWGhpacHc3Bx79+6ViBMfH49hw4ZBQeH//i+Q\nn5+PUaNGoW3btlBVVYW5uTnWrFnDT9+1axdcXFygo6ODtm3bYtiwYcjKyuKnV+f8ww8/wNvbG+rq\n6rC2tsbp06dx9+5dDBo0CBoaGrCxscHp06f55ZKTkyEQCHDw4EH06NEDqqqqsLOzQ1JSUoN19/Dh\nQ/j7+0MoFEJLSwt9+/bFqVOn+OllZWWYN28ejI2NoaKigg4dOmDs2LESMUaOHIk9e/agtLRUonz/\n/v1wdHSEkZGR1HUXFxcjICAARkZGUFdXR7du3bBv3746dbF7924MGzYM6urqMDc3r9OBjI+PR+/e\nvdG2bVu+7NmzZ5g4cSLat28PFRUVdOrUCfPnz+enHz9+HO7u7tDX14eOjg7c3d2RlpYmEbcp7aI6\n5507d8LDwwNqamowNzfHTz/91OB+aKwuAGDlypUwNzeHiooKhEIhBg0ahFevXvHTP/zwQ5w6dQr3\n7t1rcF1vgru7+1tfJ/P+Yu2FedvYiF0t2SdOwENZGagxfO4BIPGPP2Di7Cx/vNRUeNS6F8rD3R2J\nCQlyj9qVl5djxIgRmDRpEr7//nsAwPXr16Gurl7vMhzHgeM4ibIVK1Zg1apV+M9//oO1a9fi008/\nRZ8+fTBu3DisWLECGzZswPjx4+Hu7g49PT0AwMmTJxEcHCwRZ8aMGXj16hUSEhKgo6ODnJwcPHjw\ngJ9eWlqKpUuXwtraGs+ePcPSpUsxdOhQpKenQ1FRkZ8vJCQE69atQ3R0NBYtWoQxY8bA0tISc+bM\nQVRUFBYvXoxPPvkEOTk5Eh3LefPmYcOGDTA3N8fq1asxfPhw3L59G+3atatTDy9fvkT//v1hY2OD\nI0eOQEdHBz/++CMGDhyIK1euwMrKClFRUdi9ezd27twJMzMzPHjwoE5H2cXFBS9fvsTvv/8OV1dX\nvnzfvn31jmYSEYYPHw6O4/Dzzz+jQ4cOOH78OMaMGYPDhw9jwIAB/LxBQUGIiIjAxo0bsXXrVkyZ\nMgW9e/eGpaUlACAlJQX9+vWTiB8cHIzLly/jwIEDaN++Pe7du4cbN27w00tKSvDFF19ALBajvLwc\n69atw6BBg5CVlcXv36a2C6Dq0vCaNWuwefNmfP/99xg3bhxEIhEcHByaVBd79+5FREQEdu3aBbFY\njMePHyMlJUUiTteuXaGtrY3ExERMmDBBar0zDMP8K1Er1dCmNTQtaf16otBQIjc3iT9J3t5V5XL+\nSfL2rhOLQkOr1iOnJ0+eEMdxlJycXGdabm4ucRxHZ86ckSi3sLCg8PBw/jPHcTR37lz+c0FBAXEc\nR7Nnz+bLnj59ShzH0W+//UZERM+fPyeO4+jgwYMSscViMYWFhcmc/+PHj4njODp79qxEzpGRkfw8\naWlpxHEcrVu3ji+7fPkycRxH6enpRESUlJREHMfRtm3b+HnKy8vJxMSEQkJCpNZHbGwsGRkZUXl5\nuURO/fv3pzlz5hARUUBAAA0YMKDR7dDS0qKYmBj+87Nnz0hFRYVu3rzJl3EcRzt37uTzVVFRoaKi\nIok4EydOpA8//FAi3/U12kVFRQVpamrSt99+y5cZGBhQdHS0RJwPPviA/P39G827ZlxdXV0+v+p8\n5W0X1TkvXbpUIn7v3r3p008/bXJdrFu3jrp06UJlZWUNboe9vT0tWbKk3ulv6vSWlJT0RuIyrRNr\nL4ysmuuc1aovxYaFhcl942pljZEkifI2bZqUQ6VAehVXKinJHUtXVxdTpkyBt7c3hgwZgoiICNy6\ndUvuOGKxmP+3gYEB2rRpA3t7e75MR0cHSkpKePToEQCgqKgIAKCpqSkRZ86cOVi5ciV69uyJoKAg\nicuaAHDlyhWMHDkSZmZm0NLSgomJCQDgzp079eZjaGgIABL5VJdV51OtV69e/L/btGmDHj16ID09\nXeo2p6Wl4cGDB9DR0YGmpib/5/Tp07h9+zYAYOLEibh27RosLCwwffp07N27F2VlZXViaWlpobCw\nkP/822+/oXPnzhDVMwKblpaG0tJSdOzYUWLdO3fu5NddreYol0AggFAolNjuoqKiOvthxowZ2LNn\nD+zs7DBnzhwcOXJE4pH53NxcfPrpp7C0tIS2tja0tbVRVFSEu3fvSsSRt11Uq7kfAKBPnz4N7ofG\n6mL06NEoKyuDiYkJJk6ciPj4eBQXF9eJVXs/MAzDvI+Sk5MRFhbWbPFa9aXYplSUuacnEuLi4FHj\nvoiE169h4e8PNOGBB/PMzKp4ysqS8Tw85I4FAFu2bEFAQACOHTuG48ePIyQkBNHR0Rg0aBAA1HkH\njrSOiaKUzmvtMo7jUFlZCaDqCx0Anj9/LjGPv78/Bg0ahCNHjiApKQmDBw/GyJEjsWPHDrx48QJe\nXl5wdXVFXFwcDA0NQUSwsbGpc39azXVXXzaWVladT32IqM5l52qVlZXo2rUrfv311zrTqp8uFYvF\nyM3NxfHjx5GUlISAgACEhITg999/l+hMFRUV8XUCNHwZtnrd2traUp+mVarVwa/9ueZ+AKr2Re39\n4OXlhbt37+Lo0aNITk6Gn58f7OzskJCQAIFAgGHDhkEoFOKbb76BsbExFBUV0bdv3wb3Q31ltfOR\npnYbrEmWuujQoQNu3ryJpKQkJCYmYtmyZVi0aBHOnz8vcQ9j7f3wtrB7phh5sPbCNMbd3R3u7u4S\n98P/E616xK4pTEQiWPj7I1EoRLKODhKFQlj4+zf5KdbmjgcANjY2mDt3Lg4dOoTJkydjy5YtEAqF\nAID79+/z8z169Ejic1Opq6ujffv2dUbaAKBdu3bw9/fH9u3b8d1332Hnzp0oLi5GRkYG/v77b6xY\nsQKurq4QiUR48uRJs74w9ty5c/y/y8vLkZqaCmtra6nzOjs7IycnB5qamjAzM5P4U/OePHV1dXz4\n4YeIjIzEhQsXkJGRgZMnT/LTHz9+jOLiYnTp0gUA8Pr1axw+fBgjR46sN08nJycUFhbi5cuXddZd\n38MW9bG0tEReXl6dcl1dXYwZMwabN2/Gb7/9hpSUFGRkZODx48fIyMhAUFAQBg4cCCsrKygrK9cZ\ndfsnau4HADh79ixsbGykzitrXSgpKcHb2xsRERG4du0aXrx4gf379/PTiQj37t3j9wPDMAxTpVWP\n2DWViUjUrK8jaa542dnZ2LJlC0aMGAEjIyP8+eefOHnyJJycnKCiooI+ffpg1apVsLKyQllZGZYs\nWQLlGiOF/4SbmxvOnz+PGTNm8GVffPEFhg4dii5duuDVq1fYu3cvOnXqBA0NDZiYmEBZWRkbN27E\nvHnzkJeXh6CgoHpH1JoiIiIC7dq1g6mpKdatW4fHjx9L5FfTuHHjsH79egwdOhQrVqyApaUlHj58\niMTERFhbW+ODDz7A6tWr0bFjR4jFYqipqeGHH36AgoKCROfh/PnzUFFR4V+Qe/z4cejq6qJ79+71\n5unh4QFPT0/4+Phg1apVsLOzw9OnT3H27FmoqqpiypQp9S5buyPs5uaGM2fOSJQtWbIETk5OsLa2\nhkAgQHx8PDQ1NdGpUyeoq6ujbdu22LJlC8zMzPD3338jMDAQqqqqjdavrLZt2wYrKyt0794d8fHx\n+P3337Fp0yap88pSF1u3bgURwdnZGTo6OkhISMDz588lOu0ZGRkoKipqkdGQ5ORkNgrDyIy1F+Zt\nYyN27xF1dXXcvn0bY8aMgUgkgq+vL/r27Yvo6GgAVV+wGhoa6N27Nz755BNMnToV7du3b5Z1+/n5\n4bfffkN5eblE+Zw5c2BnZwc3Nze8fPkShw8fBlB1j1Z8fDyOHz8OW1tbBAYGYu3atRDUuudQWkdP\n1rI1a9YgJCQEjo6OOHfuHPbv3y8x+lZzGWVlZaSkpMDJyQkTJ06ESCTCqFGjcOHCBZiamgIAtLW1\nsW7dOvTu3Rv29vbYv38/fvnlF/6JVKDqsquvry9/2XDfvn0NjtZVO3DgAHx8fDB37lx07doVw4YN\nw+HDh2FhYSHXdvv5+eHcuXMSI26qqqpYunQpnJyc4OzsjOvXr+Pw4cPQ1NTkX6GSnZ0Ne3t7TJo0\nCXPnzm22dgEAX3/9NbZs2QKxWIydO3di586dUp+IrdZYXejp6SE2Nhb9+/eHtbU1NmzYgJiYGPTv\n35+PsW/fPvTt2xedOnVqtu1gGIZpDdhvxTIyISJYW1sjLCxM5p/VelOSk5MxYMAA5Ofno0OHDm9t\nvc+fP4eJiQmOHTsGJycnVFRUoH379ti9ezfc3NzeWh5eXl7w8PDAokWL3to6pcnLy4OZmRlOnz6N\n3r17v7X1VlZWwsrKCitXroSvr2+987HjnGGY9wn7rVjmreI4DhEREVixYkVLp9JiNm7cCC8vLzg5\nOQEAnjx5gtmzZ9d5r9ybtmrVKmzYsOG9+a3Y5rZr1y7o6+s32KljGIb5t2Ijdsx7Jzk5GR4eHrh3\n795bHbFjJOXl5cHc3BynTp16qyN2snpTxzm7Z4qRB2svjKya65zFHp5g3jvu7u6oqKho6TT+9UxN\nTdl+YBiGecewETuGYVoldpwzDPM+YffYMQzDMAzDMBJYx45hGEYO8v5MIfPvxtoL87axjh3DMAzD\nMEwr8a+8x05PTw9Pnz59yxkxDPM26erq4smTJy2dBsMwjEya6x67f2XHjmEYhmEY5l3CHp5gmGbE\n7oNhZMXaCiMP1l6Yt4117BiGYRiGYVoJ1rFrov/9738IDAxs6TTk8vDhQ/Tq1aul04C7uzt+++23\nJi37+vVrODk5oaSkRKI8Pz8fPXr0AAAIBAK5f25L2pvhP/74Y6SmpjYpz+YSFxeHjz76qMnLL1y4\nELt37653+tChQ5GbmytzvJiYGIjFYtjb20MsFmPnzp1S5yMi9OrVCw4ODhCLxfD09MTt27f56QKB\nAGKxGI6OjnB0dER6errsGwWgqKgIq1atkmuZal9++SW/XkdHR6iqqiI6OlrqvLGxsRCLxbC1tcWI\nESPw9OlTvq08efIEY8eOhUgkgq2tLZYtWyZ3Lv/973/RtWtXdO/eHcXFxXIvv3//fqSlpcm9nDR3\n7txBTEyMRJmpqSlu3LjRLPHlsWHDBhQUFMg0b1xcHLKysvjPspybU1JScPz4cf7zn3/+iQEDBjQt\n2Ua87V+dqHmer+98ybRy1Eq14k1rsrlz51JcXFxLp0Hu7u7022+/NXn5iIgI+vrrryXKoqKiaMWK\nFURExHEclZSU/KMcr1y5Qu7u7v8oRnOIi4sjX1/fJi//559/kq2tbbPlk5ycTE+fPiUiovz8fDIw\nMKC8vDyp8z579oz/d2RkJA0fPpz//E/3UW5uLhkYGDR5+WoFBQWkpqZGDx8+rDPtxo0b1LFjR/r7\n77+JiGj58uU0bdo0fvrw4cMpMjKS//zgwQO519+1a1e6cOFCEzKvMmHCBIqOjm7SshUVFRKfk5KS\nyMnJSaLM1NSUrl+/3uT8mkqe9bq5udHBgwflih8aGkoLFixoSmrvvNrneWnnS+bd1Fz9llbb+5Gn\ngmp/SdT8/PDhQ/Lw8CA7Ozuys7OjefPmERFRbGws/4WblJREYrGYpk6dSvb29iQWiykjI4OPt3jx\nYrKwsCAXFxcKDAysc/KsFhYWRlZWVuTg4ECOjo5UVFTUYG65ubmkr69PX375JTk6OpKVlRWlpaXR\npEmTyM7OjlxcXPgvm7KyMjI0NKTi4mIiIiopKSFfX1+ytrYmsVhMH3/8MRER/fXXX9S/f3/q3r07\n2eKERUcAACAASURBVNjYUGBgIL/u0NBQGj16NA0ZMoQsLCzoo48+orS0NHJ3dydzc3NauHAhP6+b\nmxvNmTOHevToQRYWFrR48WJ+Ws2OXVFREU2ePJl69OhB9vb2FBAQwH/h1K6PwsJCIiLKy8sjS0tL\nibobOHAg/0VQs9Nw8+ZNGjx4MDk7O5NYLKbY2Fh+GY7jaOXKleTs7EwdOnSgX375hZ/2xRdf0Lff\nfst//vbbb6lr167k4OBA9vb2lJmZSURE8+fP52N7eHjQnTt35N43sbGx5OnpSSNGjCBra2saMGAA\n3b9/n59Ws2MXFxdHLi4u1L17dxowYACfx5kzZ6hbt27k4OBANjY29MMPP0jU95kzZ0gaExMTSk9P\nb7C+G2JnZ1dv7Jq++uormjx5Mv+Z4zi+LdaUnJxMlpaWVFRURERE/v7+FBQUVGe+IUOGkIKCAjk4\nOFCfPn2IiCgrK4sGDBhA9vb21K1bNzpy5Eijea1du5Y++OADqdN+/vlnGjp0KP/54sWLpKmpSUlJ\nSXTr1i0yNTWVupys2/Dxxx+TkpISWVlZkZ+fH5WXl5O3tzc5OTmRjY0NTZw4kUpLS4lI+v49evQo\n6enpkZGRETk4ONCOHTuIqP42EhsbSx4eHjRy5EiytbWlq1evSuRjbW1Nampq5ODgQB999BERVXWw\nli5dSr169SJTU1OJTuSCBQsabPtLliwhR0dHEolEdPr0aal1Vfu4unnzJi1fvpyvFwcHB7px4wad\nOHGCevXqRY6OjmRnZ0c//vgjERFt27aNNDQ0yMzMjBwcHOjEiRMSx8zNmzepZ8+eJBaLydbWltas\nWUPXrl2jdu3akVAoJAcHB4qIiOBzrnb27Fnq27cvicViEovFdPz4caqsrKTp06eTlZUVicVivt3V\n5ubmRvPnz6e+ffuSsbExTZs2jXbs2MHX4e7du/l5f//9d/582717d/682FBbiI2NpYEDB9Lo0aPJ\nxsaG+vTpU+95nkj6+ZJ5N/1rO3bnz5+nXr16kaurK40dO5bKysqkztdcHbt169bR1KlT+WnVX3a1\nO3aKiop05coVIiJasWIFjRs3joiIDhw4QGKxmF68eEGVlZXk4+NDzs7OdXJ4/Pgx6ejo0KtXr4iI\nqLi4mMrLyxvt2HEcR4cOHSIiotWrV5O2tjZ/wp4xYwYFBwcTEVFqaio5Ojrycfbu3Uve3t51tuvV\nq1f8SaG0tJQGDBjAf0GGhoaSpaUlPXv2jCoqKkgsFpOXlxeVlpZSSUkJCYVCun37NhFVdSa8vb2p\noqKCiouLyc7Ojv9fdc2O3eTJk/kvpIqKChozZgzFxMTUWx/VOnToQHfv3iUioqdPn5JIJOKnVXfs\nysrKqFu3bnTz5k0iqhpB6tKlC/9Fx3Ecbdq0iYiqRvw6duzIx7CxsaE//viD/6ytrc2fPEtLS+nF\nixdERPxoDhFRTEwMjRkzRu59ExsbS6qqqnTr1i0iIgoPD+fbVs12dvLkSRo6dCi9fv2aiIgOHTrE\nf7mMGDFCojNXs1O2ePFi+uqrr0gaU1NTSk9Pb7S+pUlKSqJOnTrxy0gzePBgateuHVlZWdGjR4/4\nco7j+I7Kl19+yW8TEdGyZcvI19eXtm/fTn379q0zskRU9WVVe8SuR48etG3bNiKqGm0zMDCggoKC\nBrfB1taW9u/fL3XarVu3SCgU/j/27ju86vps/Pj7JCF7D0IGWSSEnYQhQ/YQBMRinypVgaJW26K2\nl7VLiyCttY99alvH72mrLW6t+tiaALICYYYhhIABQgbZgZC91znf3x9fc5ITEjwhZyW5X9fFdeV8\nEr7fO+d8cvLJZ9y3cuXKFUWn0yk//elPFY1GoyQlJSn/+c9/lNmzZysPP/ywMnnyZGX58uX6AbKx\n34OidD7/HSoqKhRFURSdTqesXbtW+etf/6ooiqLcfffdPb6+3/ve9/R9WFFu3ke2bdumuLu7K7m5\nuT3GkpKS0uOMXccfbHl5eYq7u7v+j6Zv6vsdP+Pvv/9+r4Og3n6uuj8vVVVV+ufw6tWrSmhoqP45\n6L4C0PVn5sknn1RefPHFG563LVu2GPwh2vV9taKiQhkxYoSSmpqqKIr6WlRVVSlnzpxRxo4de8O1\nups/f77+uSgpKVGcnJyUZ599VlEU9X04NDRU/z0lJCQopaWl+q/t+n117Qvr1q3T94Vt27YpPj4+\nSlFRkaIoivL973/f4Ppd3+c7BAcHK4WFhT3GK2yHqQZ2A26PXVhYGAcOHODgwYNERETw+eefm/V+\nM2fO5IsvvuDnP/85O3bswM3Nrcevi42NJS4uDoDp06eTk5MDwIEDB7jvvvtwcXFBo9Gwfv36Ho8z\ne3t7Ex0dzdq1a3nzzTepq6vD3t7+G+Nzd3fnzjvvBCAhIYGRI0cyadIkAKZMmaLf13TlyhVCQkL0\n/y8+Pp6LFy/y+OOP8+mnn+Lo6AhAe3s7Tz/9NPHx8UydOpWvvvqK9PR0/f9btmwZHh4e2NnZMWnS\nJJYuXcqwYcNwdXUlNjZW/30DrF+/Hjs7O9zc3FizZg379++/If7ExET+8Ic/kJCQwJQpUzhz5gxZ\nWVnf+HyEhobq94bt3LmTFStW3HDty5cvc+nSJdasWUNCQgJz586lra2Nixcv6r9mzZo1APzwhz+k\npKSE1tbWHp+vhQsXsm7dOl577TWKiopwcXHR33vmzJlMnDiRP/7xj5w9e7bPrw3AnDlziImJAeCR\nRx7p8blKSkoiPT2d6dOnk5CQwK9+9SuKior08f32t7/lhRde4OTJk3h5eRk8V7m5uTdcr6u+9r8L\nFy6wfv16PvzwQ5ycnHr9up07d1JSUsLatWtZu3atvr2wsJDTp09z6NAhLly4YLA/7dlnn6WiooKn\nn36aDz/8EDu7G9+muv8M1dXVkZ6ezoYNGwAYO3Ys8fHxHD9+vNfYTp48yfXr11m5cmWPn4+JieGV\nV17hvvvuY+bMmfj5+QHqnimtVsvx48fZsGEDp0+f5pFHHmHVqlV9+h6602q1+p+FuLg4Dhw4oP/Z\nW7BgQa+vb9fn4mZ9BGD27NlERkb2eP+e3peg82ckPDwcHx8f/fW+qe8vX74cMHw/7K63n6vuysrK\n+Pa3v83EiRNZtmwZlZWVZGZmfmPs8+bN48033+S5557jwIEDvT5vXaWmpjJu3DhmzJgBqCkovL29\nGTVqFG1tbTz00EO89957N01L0bEvNigoiOHDh3PPPfcAMHnyZIqLi2ltbeXYsWNcuXKFO++8k4SE\nBJYvX46dnR05OTnodDqDvrB//36D9+Hbb79d//40Y8YM/fPb/X2rgzHvAcJ6MjPzef31G9/zb9WA\nG9iNGDFC/4tk2LBhRg1+vomDgwM6nU7/uLm5Wf/xjBkzOHv2LFOmTOHdd99lwYIFPV7D2dlZ/7G9\nvT3t7e3AjXlpenszsLOz4/jx4zz++OMUFRUxZcoUzp8/f9PYAINfqvb29gZx2NnZ6ePoLjIykgsX\nLrBkyRL27dtHXFwcLS0tvPzyy1RXV3Py5EnS09P51re+pb+nRqO54X7dH3e9X/fvW6PR9BjL559/\nTlpaGmlpaWRmZvLf//3fvT4fHbo+r//5z3+4++67b7iuoij4+/vrr52WlkZubq7B13Y8Xx39qLfn\n67PPPuO3v/0tDQ0NLFiwgF27dpGfn89TTz3FRx99xPnz5/nHP/5h8Pr05bUxpo8APPTQQ/rv5ezZ\ns+Tl5QHw4x//mKSkJAICAnjiiSfYtGnTDc/FzXzT891VVlYWK1as4O9//zuzZs266XVBfa0eeugh\njh49qm/r+OXj4eHBww8/bPC56upqCgoKcHZ2pqKi4huv31X377O3Pgfwz3/+k3Xr1t100HXfffdx\n4sQJjh8/zqJFiwgJCcHd3Z2wsDDCwsK4/fbbAVi9ejWlpaX6hMi38j188MEHHD16lCNHjnDu3Dl+\n9KMf0dTUBNz89e3+PfbWR0AdcPVVT+9tfe37ffm56skPf/hDFi5cyPnz50lLSyM0NNTgfr29zvfc\ncw9Hjhxh1KhR/P73v9f/cfFNPw898fT0JCMjgzVr1nDu3DnGjx/PtWvXevza7s9ZT+8ziqIwadIk\ng/en/Px8Jk+ezPvvv99rX+h+/Zu9z3eQvK62KzMzn7feyubKFdMd3hlwA7sO+fn57N27l7vuuqvf\n1xoxYgRtbW36v3o++OAD/efy8vJwd3fnvvvu449//COnT5/u07Xnz5/Pp59+SlNTEzqdjnfffbfH\nN6H6+nrKysqYO3cuW7ZsYcKECWRkZNw0tr6IiIiguLhY/7i4uBiNRsPdd9/Nyy+/zPXr16msrKSm\npoagoCAcHR0pLi42mBHtyxuDoii89957aLVaGhoa+OSTT3o8dbZq1SpefPFF/eC1vLycvLy8Xp+P\nDkVFRURGRtLa2sqpU6eYPXv2DdeOjY3F1dWV9957T9926dIl6urqbvja7rmmIiIi9DMTWq2WnJwc\npk2bxi9+8QvuuOMOzp49S11dHY6OjgQGBqLT6fjrX/9q9PPT3dGjR/UzeNu2bWPRokU3fM1dd93F\nO++8o38dtVotZ86cAdTZycjISB599FGefPJJg5OSRUVFREVF3fT+3/R8d8jNzWXp0qW8+uqrLF26\ntNfrlZeXU15ern/8ySef6E8tV1dX639Jtbe38+mnn5KQkKD/2g0bNvDoo4/y1ltvsWbNmh5Pi3p6\netLY2IhWqwXUAWJ8fDxvv/02ABcvXiQ9PV0/69JdU1MT//rXv3jooYdu+rxcvXoVUP+g2rx5Mz/7\n2c9ISUlhypQpuLm56U+MHjp0CD8/P3x9fY3+HrqrqanB398fNzc3ampqeP/99/XvFb29vp6enlRX\nV+uvcbM+8k08PT2pqakx6mtra2v73fd7+7nqiKXr91VTU0N4eDgAe/fuNZjt7v61XWVnZzN8+HDW\nr1/Pc889p3/evLy8ev1eZ86cyYULF/SzvVqtlurqasrLy2loaOCOO+7gxRdfxMvLq9cT5V3fK7v/\nMd5h1qxZZGVlGbz3dMR3s75wM93f5zt0vF8K27N7dw4lJYvo49Dipqw2sHvttdeYOnUqzs7O+uWT\nDpWVlaxevRp3d3ciIiL48MMPDT5fW1vLunXrePvtt002Y/eXv/yFJUuWMH36dBwcHPQ/RAcOHGDK\nlCn6qfK//e1vgPoXUNcftO4fdzy+6667WLp0KZMmTWLmzJmEhITg6el5Qww1NTWsXr2auLg4Jk6c\nSFBQEPfcc89NY7vZfbs/TkhIoLi4WJ8G5Pz588yaNYv4+HimT5/OM888Q1BQEE8++SRHjx5l4sSJ\nPPLIIyxevLjX63e/f/f2MWPG6O+xcuVK/dJMV3/+85+xt7fXp9C48847KSkp6fX5AHVQ7+zsTFhY\nGMnJycybN89g1qUjJgcHB5KSkvjoo4/0KSsef/xx2traeoy96+MFCxbo39jb29vZsGEDkyZNIj4+\nnqtXr/LYY48xYcIEvvOd7+iXbaKiom7ptQF1aeXpp59m/PjxpKSk8Je//OWGr5szZw4vvPACq1at\nIj4+nokTJ5KYmAjAq6++yoQJE5g8eTKvv/46L7zwgv7aqampPQ4Uu7rZ893VL3/5S6qqqti0aZM+\nXUhH2ojExES+//3vA1BaWsqyZcuIi4sjLi6OgwcP6gddly5dYsaMGfpUKE5OTvql2D//+c+0trby\n85//nIULF/Kd73yHRx999IY4fH19eeCBB5g4caJ+UP/+++/z3nvvERcXx4MPPsh7772nXz7t7rPP\nPmPs2LGMGTPGoD0pKUn/PYA6+zV+/HgmTpzItGnTeOKJJ/Svy7Zt29iwYQPx8fH86le/4rPPPuvT\n99DdunXrqKurY+zYsaxatYp58+bpP9fb67t27Vo++OADEhISeO+9927aR3r6+e0qLi6O2NhYJk6c\nyL333nvTWCdOnGh03+/pMagDpp5+rgCefPJJNmzYwOTJk7l48SK///3vefrpp0lISOCTTz7Rb3sB\nePTRR9m6dSsJCQkkJycbfJ+ffPIJkyZNYvLkyTz55JP6n6vVq1dz6tQpEhISeOmllwz+j6+vL599\n9hlPPfUUcXFxTJ06lTNnzlBYWMiSJUv0/Xb58uX6PxwSEhL0fwT09v12/5yPjw+JiYk8//zzxMfH\nM27cOLZu3YqiKDftC315nwfD90thW/LyICXFjvx8MOWEqtVKiv373//Gzs6O3bt309TUxLZt2/Sf\n++53vwvAP/7xD9LS0lixYgXHjh1j3LhxtLe3s2rVKp5++umb5h2ypann+vp63N3d0el0PPLII4SG\nhrJ161aLx/GTn/yEhIQE1q9fb/Z7LViwgJ/97Gc9Dub666WXXkKn0/HLX/6SH/7wh9xxxx2sXr3a\npPc4e/YsTz31VI973UztrbfeYseOHTfNN3erSktLWbx4cZ/zxAkhBqbu7/Nd3y+FbWhuhr174fRp\nOHlyP42N6ljm4MEBXlJs9erV3H333Tf8Rd3Q0MBnn33Gb37zG1xdXbn99tu5++67effddwH48MMP\nOXnyJL/5zW9YsGABH3/8sTXC75N169YxefJkxo8fT1tbm9USG//qV7/q13KhLWhpaeHjjz/Wz5z8\n7//+r8kHdaAeLvH39zdZ8teb+aaZlP54+eWX2bJli1muLYSwPV3f57u/Xwrru3gRXn8d/dJrVNQo\nIJmvz86ZhNVm7Dr8+te/pri4WD9jl5aWxuzZsw0yZb/88sukpKTolxSM0XECNSIiAlBP/cXHx+uz\ngHfsa5DH8hjU5TPpH/LYmMcdH9tKPPLYth9Lf5HHKSkpNDZCXd18Ll6EvDz18wD19Sk0N39FeXkd\nJ07sMcmMndUHdps2baKoqEg/sDt8+DD33nsvpaWl+q954403+OCDDzhw4IDR17WlpVhh+1JSUvQ/\nhELcjPQV0RfSX4Y2RVFn5/buhZaWznZ3d1i+HMaOhY4FG1ONWxz6fYV+6v5NuLu7U1tba9BWU1OD\nh4eHJcMSQ4y88QpjSV8RfSH9ZegqL4ekJMjPN2yfMgUWL4Ze0jb2m9UHdt33Fo0ePZr29nays7OJ\njo4GID09nQkTJlgjPCGEEEIIo2m1cPQoHDyoftzBzw/uugu+3iFmNnbmvXzvtFotzc3NtLe3o9Vq\naWlpQavV4ubmxj333MNzzz1HY2MjR44cISkpySBrvbG2bNlisL9BiN5IPxHGkr4i+kL6y9BSVAR/\n+xvs3985qLOzgzlz4Ac/6HlQl5KSYtJDblbbY7dly5YbUn5s2bKF5557jqqqKh566CH27t2Lv78/\nv//97/VlbYwle+xEX8g+GGEs6SuiL6S/DA2trZCcDCdPGuakCw6GVatgxIhvvoapxi1WPzxhLjKw\nE0IIIYS5ZWXB9u3QtZjJsGGwcCFMn67O2Blj0ByeEEIIIYQYaBoaYNcu6F5WOzoaVq4Eb2/rxNXr\nwM7YPW1OTk68+eabJgvIlLZs2cL8+fNlGlx8I1kuEcaSviL6QvrL4KMokJ4Ou3fD12WvAXB1hWXL\nYOLEzhQmxkhJSTHpXsxel2KdnJx45plnep0W7Jgy/OMf/9hjUXVrk6VY0Rfy5iuMJX1F9IX0l8Gl\nqkpNYZKba9g+aRIsXQpubrd+bbPvsRs1ahQ5OTnfeIHY2FgyMzP7HYipycBOCCGEEKag08Hx43Dg\nALS1dbZ7e6vLrl9nZ+sXOTzxDWRgJ4QQQoj+Ki1VZ+lKSjrbNBqYMQMWLABHR9Pcx1TjllvKY5eb\nm0teXl6/by6ErZBcU8JY0ldEX0h/Gbja2mDfPnjjDcNBXWAgPPKIuvRqqkGdKRk1sFuzZg3Hjh0D\nYNu2bYwfP55x48bZ7KEJIYQQQohbdeUK/O//wpEj6jIsgIMDLFoEjz4KISHWje9mjFqKDQgIoLi4\nGEdHRyZMmMDf/vY3vL29ufvuu8nOzrZEnH2m0WjYvHmznIoVQgghhFGammDPHkhLM2yPiFDLgfn5\nmf6eHadin3/+ecvtsfP29qa6upri4mJuu+02iouLAfDw8LDJE7Ege+yEEEIIYRxFgQsXYOdONT9d\nB2dnuOMOSEjoWwqTW2HRPXZxcXG8+OKLbN26lRUrVgBQVFSEl5dXvwMQwhbIPhhhLOkroi+kv9i+\nmhr46CP45BPDQd24cbBxI0yebP5BnSkZVXniH//4B5s2bcLR0ZGXXnoJgNTUVB544AGzBieEEEII\nYQ6KAqdOqQckWls72z08YMUKGDPGerH1h6Q7EUIIIcSQcv06JCZCYaFh+9SpsHixugRraRavFXv4\n8GHS0tKoq6vT31yj0fDMM8/0OwhzkZJiQgghhOjQ3q6edD18GLTaznZ/f/VwRHi45WOyWEmxrp54\n4gk+/vhj5syZg4uLi8Hn3n33XZMFY0oyYyf6Qsr+CGNJXxF9If3FdhQWqrN01693ttnbw+zZMGeO\nms7Emiw6Y/fee++RkZFBcHBwv28ohBBCCGEpLS2QnKzup+s6bgoNhVWrYPhw68VmDkbN2E2aNIn9\n+/fj7+9viZhMQmbshBBCiKEtMxN27IDa2s42R0d1H93UqWB3S/W3zMOitWJPnTrF7373O+6//34C\nAwMNPjd37tx+B2EOMrATQgghhqb6evjiC8jIMGyPiYGVK8EWs7VZdCn29OnT7Ny5k8OHD9+wx66w\n+5ESIQYg2QcjjCV9RfSF9BfLUhQ4exZ274bm5s52Nze4804YP35g5aS7FUYN7J599lm2b9/OkiVL\nzB2PEEIIIUSfVVZCUpJa57Wr+Hi1eoSrq3XisjSjlmLDwsLIzs7G0dHREjGZhCzFCiGEEIOfVgup\nqZCSoqYz6eDjo6YwiYqyWmh9YtGSYlu3buUnP/kJpaWl6HQ6g3+2bMuWLVLORQghhBikSkrgjTfU\n6hEdgzqNBm6/HX70o4ExqEtJSWHLli0mu55RM3Z2vRwb0Wg0aLtm+LMhMmMn+kL2wQhjSV8RfSH9\nxTxaW9UZutRUwxQmQUFqCpOgIKuFdsssengiNze33zcSQgghhOivnBzYvh2qqjrbHBxgwQKYOdO2\nUphYg9SKFUIIIYTNa2xUT7umpxu2R0WpKUx8fa0Tl6mYfY/dpk2bjLrA5s2b+x2EEEIIIURPFAXO\nn4fXXzcc1Lm4wLe+BWvXDvxBnSn1OmPn7u7OuXPnbvqfFUVhypQpVFdXmyW4/pAZO9EXsg9GGEv6\niugL6S/9U12tVo7IyjJsnzABli0Dd3frxGUOZt9j19jYSHR09DdewMnJqd9BCCGEEEJ00Ong5EnY\nv189KNHB01Nddh092nqx2TrZYyeEEEIIm3HtGiQmQnFxZ5tGA9OmwaJFMFjnkyx6Knag2rJlC/Pn\nz5dpcCGEEMLGtbfDoUNw5Ig6Y9chIEBNYTJypPViM6eUlBST5tyVGTshkH0wwnjSV0RfSH8xTn6+\nOktXUdHZZm8Pc+fC7Nnqx4OdzNgJIYQQYkBrblarRnz5pWF7WJhaDiwgwDpxDWQyYyeEEEIIi7t4\nEXbuhLq6zjYnJ1i8GKZOVffVDSUWnbErKyvDxcUFDw8P2tvbeeedd7C3t2ft2rW9lhsTQgghhOiu\nrk4d0F28aNg+ZgwsX66efBW3zqhR2cqVK8nOzgbg2Wef5Y9//CN/+tOfeOqpp8wanBCWYsqNq2Jw\nk74i+kL6SydFgdOn1UTDXQd17u5w771w330yqDMFo2bssrKyiI+PB+C9997j2LFjeHh4MG7cOP78\n5z+bNUAhhBBCDGzl5ZCUpB6S6GryZFiyRK0iIUzDqD12/v7+FBUVkZWVxZo1a8jIyECr1eLl5UV9\nfb0l4uwz2WMnhBBCWJdWC0ePqmlM2ts72/381MMRERFWC83mWHSP3bJly7j33nupqKjgvvvuA+DC\nhQuEhob2OwAhhBBCDD5FReos3bVrnW12dnD77Woak2HDrBfbYGbUjF1zczNvv/02jo6OrF27FgcH\nB1JSUrh69Spr1qyxRJx9JjN2oi8k15QwlvQV0RdDsb+0tqqlwE6cUPfVdQgOVhMNjxhhvdhsmUVn\n7JydnXnssccM2oZaRxVCCCHEzWVlwfbtUFPT2TZsGCxcCNOnqzN2wrx6nbFbu3at4Rd+nVBGURT9\nxwDvvPOOGcO7dRqNhs2bN0tJMSGEEMLMGhpg1y44f96wfdQoWLkSfHysE9dA0FFS7PnnnzfJjF2v\nA7stW7boB3Dl5eW8/fbb3HXXXYSHh5Ofn8/27dtZv349r7zySr+DMAdZihVCCCHMS1Hg3DnYvRsa\nGzvbXV1h2TKYOHHoJRq+VaYatxi1x+6OO+5g06ZNzJkzR9925MgRtm7dyp49e/odhDnIwE70xVDc\nByNujfQV0ReDub9UVanLrjk5hu2TJsHSpeDmZp24BiqL7rE7fvw4M2bMMGibPn06qamp/Q5ACCGE\nEAOHTqcejNi/H9raOtu9vdVl1+ho68UmjJyxmzdvHtOmTeM3v/kNLi4uNDY2snnzZk6cOMGhQ4cs\nEWefyYydEEIIYVpXr0JiIpSUdLZpNDBjBixYAI6O1ottoLPojN1bb73F/fffj6enJz4+PlRVVTF1\n6lQ++OCDfgcghBBCCNvW1gYHD8KxY+qMXYfAQDWFSUiI9WIThoyasetQUFBASUkJQUFBhIeHmzOu\nfpMZO9EXg3kfjDAt6SuiLwZDf7lyRU00XFnZ2ebgAPPmwaxZYG9vvdgGE4vO2HVwdnZm+PDhaLVa\ncnNzAYiKiup3EEIIIYSwLU1NsHcvnDlj2B4RoZYD8/OzSljiGxg1Y7dr1y4efvhhSktLDf+zRoNW\nqzVbcP0hM3ZCCCFE3ykKXLgAX3wBXcvBOzvDkiUwebKkMDEHi6Y7iYqK4uc//znr1q3D1dW13ze1\nBBnYCSGEEH1TWws7dkBmpmH7uHFw553g4WGduIYCU41bjCruUV1dzWOPPTZgBnVC9FVKSoq11ngN\nVwAAIABJREFUQxADhPQV0RcDpb8oCpw6Ba+/bjio8/CANWvg3ntlUDdQGDWwe/jhh/nnP/9p7liE\nEEIIYWHXr8O2bepMXUtLZ/vUqbBxI4wZY73YRN8ZtRQ7e/ZsTp48SXh4OCNGjOj8zxqN5LETQggh\nBqD2djhyBA4fhq7b5f391cMRNp78YtCx6B67t956q9cg1q9f3+8gzEEGdkIIIUTPCgvVRMPXr3e2\n2dnBnDnqP4c+5cwQpmDRgd1AJAM70ReDIdeUsAzpK6IvbK2/tLRAcrK6n67rr8jQUHWWLjDQerEN\ndRbNY6coCtu2bePdd9+luLiY0NBQHnzwQTZs2IBGzjwLIYQQNu/yZdi+XT352sHRERYtgmnT1Bk7\nMfAZNWP3wgsv8M477/DTn/6UsLAwCgoK+NOf/sQDDzzAr3/9a0vE2WcajYbNmzczf/58m/prSQgh\nhLCk+no1J11GhmF7TAysXAleXtaJS6hSUlJISUnh+eeft9xSbEREBAcPHjQoI5afn8+cOXMoKCjo\ndxDmIEuxQgghhjJFgbNnYc8etYpEBzc3NSfd+PGSaNiWWDSPXWNjI/7+/gZtfn5+NDc39zsAIWzB\nQMk1JaxP+oroC2v1l8pKeOcd+Pxzw0FdfLyawmTCBBnUDVZGDeyWLVvGgw8+yKVLl2hqauLixYus\nW7eOpUuXmjs+IYQQQhhJp4OjR+H//T+4cqWz3ccH1q6Fb30LpNbA4GbUUmxNTQ1PPPEE//rXv2hr\na2PYsGHce++9vPrqq3h7e1sizj6TpVghhBBDSWmpmsKka1l3jQZmzYL582HYMKuFJoxglXQnWq2W\n8vJy/P39sbe37/fNzUkGdkIIIYaCtjY4cABSUw1TmAQFqSlMgoOtF5swnkX32L399tukp6djb29P\nYGAg9vb2pKen8+677/Y7ACFsgeybEsaSviL6wtz9JTdXXXY9dqxzUOfgAEuWwPe/L4O6ocioPHab\nNm3i7NmzBm2hoaHcddddrF271iyBCSGEEKJnjY3qadduv5qJjFRn6Xx9rROXsD6jlmJ9fHwoLy83\nWH5tb2/Hz8+PmpoaswZ4q2QpVgghxGCjKPDVV7BrFzQ0dLa7uMAdd6inXuW068Bk0coTY8eO5dNP\nP+W+++7Tt/373/9m7Nix/Q5ACCGEEN+spkatHJGVZdg+YQIsWwbu7taJS9gWowZ2L730EsuXL+fj\njz8mKiqKnJwc9u3bx86dO80dnxAWYWv1HIXtkr4i+sIU/UWnU2u7JidDa2tnu6enWjli9Oj+xSis\nKzM7k32n95nsekYN7GbPns358+f54IMPKCoq4rbbbuMvf/kLI0eONFkgQgghhDBUVqamMCkq6mzT\naNTarosWgZOT9WIT/ZeZnckr779EbWG6ya7Z53Qn165dI3gAHLORPXZCCCEGqvZ2OHxY/afTdbYH\nBMCqVSDzKoPDT1/YSM3xL1iobeOBL4osl+6kqqqK+++/HxcXF6KjowFITEzk17/+db8DEEIIIUSn\n/Hz461/h4MHOQZ29PSxYAI89JoO6waCxrZF/X/w3ecf2s7qmkTFX6kx2baMGdj/4wQ/w9PQkPz8f\np6/nfWfOnMlHH31kskCEsCbJTSaMJX1F9EVf+ktzs3o4Yts2KC/vbA8Lgx/8AObNU3PUiYFLURQy\nyjJ4/eTrZF86xqSCKkLKmrFXTHeU2agukpycTGlpKcO61CMJCAigrKzMZIEIIYQQQ9WlS7BjB9R1\nmbhxcoLFi2HqVElhMhjUtdSxI2sHmdcuEH6+gLBz+Xxl74RDfTuunq5AtUnuY9Qeu+joaA4dOkRw\ncDA+Pj5UVVVRUFDAHXfcwaVLl0wSiKnJHjshhBC2rq4Odu6EixcN22NjYcUK9eSrGNgURSHtahp7\ncvYwrLSMMUczcatuwMneCWf8KTqXSbSbA+MOnrBcHrtHHnmE//qv/+K3v/0tOp2O1NRUnnnmGR57\n7LF+ByCEEEIMNYoCZ87A3r3qEmwHd3dYvhzGjpVZusGgsqmSpMwk8suziUzLI/RCIRoFgj2CifKJ\nwiE8Et+HN5Jz9iwcPGGSexo1Y6coCq+88gp/+9vfyMvLIywsjB/84Af8+Mc/RmOjPU9m7ERfSG4y\nYSzpK6IveuovFRWQlAR5eYZfO3myWuPVxcVi4Qkz0Sk6jhcd58CVA7gVlxF7NBOX+mZcHFyI9Y/F\n2yNAXWefNg3s1OMOFq08odFo+PGPf8yPf/zjft9QCCGEGIq0Wjh2TD3t2t7e2e7rq9Z3jYy0XmzC\ndK7VXyMxM5FrFflEfZlD8OVSAEZ6jiTCOwL76Bj1BffxMcv9jZqx279/PxEREURFRVFaWsovfvEL\n7O3tefHFFxkxYoRZAutNbW0tixcv5uLFi5w4cYJx48b1+HUyYyeEEMJWFBeriYavXetss7OD22+H\nuXOhy9lEMUC169o5nH+YwwWH8ckvY/Txyzg1tuLu6E6sXyweXgGwdGmvBX1NNW4xamA3ZswY9uzZ\nQ1hYGN/97nfRaDQ4OztTXl5OYmJiv4Poi/b2dqqrq/nZz37G008/zfjx43v8OhnYCSGEsJbMzHz2\n7cuhqcmOK1d02NmNws8vXP/54GA10bCF50aEmRTWFJKYmUh1RTExJ7MZfqUMDRoivCMY6TUSu3Hj\n1c2THh69XsOiS7ElJSWEhYXR1tbG7t279fnsgoKC+h1AXzk4OODv72/x+4rBTfZNCWNJXxHfJDMz\nn7feyqahYRHHj6fg6rqQ9vZk4uMhKCichQth+nT91ioxgLVqW9l/ZT8nCo8zPPcat53MZlhLG55O\nnozxH4Orz3B1QNfL6qI5GDWw8/T05OrVq2RkZDB+/Hg8PDxoaWmhra3N3PEJIYQQA8qOHTnk5Cyi\nrAxaW8HVFRwcFlFXt5/f/CbcXFurhIXlVOaQdDmJpvKrTDiehV9RBfYae6J8Ywj2CEaTkKAuvVr4\nNIxRfy888cQT3Hbbbdx///386Ec/AuDo0aOMHTv2lm/82muvMXXqVJydndmwYYPB5yorK1m9ejXu\n7u5ERETw4Ycf9ngNWz2RKwYemYERxpK+InrTkcJk/347OvL3e3vPZ9gwNX1JfLydDOoGgaa2Jv5z\n6T+8m/4OrukXmPb5KfyKKvB18WVayDRCRo5Ds3YtfOtbVjnibNSM3S9+8Qu+9a1vYW9vr68VGxoa\nyptvvnnLNw4JCWHTpk3s3r2bpqYmg89t3LgRZ2dnysrKSEtLY8WKFcTFxd1wUEL20AkhhLAF5eVq\nCpP8fNB1FHgFhg+H6GhwdAQnJ91NriAGggvXL7Azayfa62XEp17G+2o1DnYORPuPIdB9BJrbboNF\ni9SyIVZi1OEJc9q0aRNFRUVs27YNgIaGBnx9fcnIyNAPItevX09wcDAvvvgiAMuXLyc9PZ3w8HAe\ne+wx1q9ff8N15fCE6AvZNyWMJX1FdNXeDocPw5EjajoTgPLyfC5dymbcuEXU1qYQETGflpZkvve9\naGJjw29+QWGT6lrq2Jm1k0tlFwjNKCTybB52Wh0BrgHE+MXgGPj1aZiwsFu+h9kPT4wZM0ZfLmzk\nyJG9BlFQUNCvALp/E5cvX8bBwUE/qAOIi4szKKS8c+dOo679ve99j4iICAC8vb2Jj4/XvyF3XE8e\ny2OAs2fP2lQ88lgey2PbfxwRMZ+kJDh9uvOxnR1ERl5hypTrNDXt58KFc1RXn2Hy5GBiYxfZVPzy\n+JsfK4rCm5+9yamSU4wO8WTy0UxyMgq5YOfAmgkz8HcfToqbG4wdy/yvB3XGXr/j47zumar7qdcZ\nu8OHDzNnzpwbguiuI9Bb1X3G7vDhw9x7772Ulpbqv+aNN97ggw8+4MCBA0ZfV2bshBBCmENjo1oK\nLC3NsD00VM07GxhonbiEaVU1VZF0OYm88mzC0/MJO1+ARlEIcg9ilO8oHEJGwt13myxnjdln7DoG\nddD/wdvNdP8m3N3dqa2tNWirqanB4ya5X4QQQghzUxQ4dw5271YHdx2cnNTqUFOnSn3XwUCn6DhR\ndIL9V/bjcrWcKUczcatpxNnBmVi/WHw8AmD+fJg5E+ztrR3uDXod2G3atKnX0WNHu0ajYevWrf0K\noPvJ1tGjR9Pe3k52drZ+OTY9PZ0JEyb06z5C3ExKSopZ/4ARg4f0laGpshK2b4fcXMP2cePgzjt7\nzzsr/WVgKWsoIzEzkdKKfCLTrhBysQiN0qUcWESkupfOhvPp9jqwKywsvGk6kY6B3a3SarW0tbXR\n3t6OVqulpaUFBwcH3NzcuOeee3juued48803OXPmDElJSaSmpvb5Hlu2bGH+/PnyQyWEEOKWaLVw\n9CgcOmRY39XLC1asgNGjrRebMB2tTsvhgsMczj+MZ9F1pqVexrm+GbdhbsT6x+Lp4Q9LlphlWjYl\nJeWmW976ymqnYrds2XLDbN+WLVt47rnnqKqq4qGHHmLv3r34+/vz+9//njVr1vTp+rLHTgghRH8U\nFKizdB056UD9nT5jBixYoKYwEQNfUW0RiZmJVFYWM+pUDkHZV9GgIdw7nDCvMOxiRsPKleDtbdY4\nzF4rNrf7fHMvoqKi+h2EOcjATgghxK1oboZ9++DLLw3bg4PVwxFWqKYpzKBV28qBKwc4XnQcv/wy\nRh/PwrGpFU8nT2L9YnHz8odly2DSJItsnjT7wM7Ozs6oILQdiXtsjAzsRF/IPhhhLOkrg5eiQEYG\n7NoF9fWd7Y6OsHAh3HZb3+u7Sn+xTblVuSRlJtFQdY2Y41kE5F/HTmNHlE8UIR4haCZMUDdPurtb\nLCazn4rtmjlbCCGEGMyqq2HHDsjKMmyPjVVruHt5WScuYVpNbU3sydlDWukZAnOuMe5UNsNa2vFx\n9iHWPxZnb3912XXMGGuHesusXnnCXDQaDZs3b5bDE0IIIXql08Hx43DgALS1dbZ7eKgDujFjJIXJ\nYHHx+kV2ZO2gvbKc0amX8S2uVMuB+UYT6BaIZsoU9YCEheu7dhyeeP755827FLt06VJ2794NGOa0\nM/jPGg2HDh3qdxDmIEuxQgghbqa4WK3vevVqZ5tGA9OmqUuvzs7Wi02YTn1rPTuzdnKhLIOQSyVE\nnc7Fvl3bWQ7Mb7iawsTKZwbMvhS7bt06/ccPP/xwr0EIMRjIPhhhLOkrA19LC+zfDydPqvvqOgQG\nqocjQkNNdy/pL9ajKArp19LZnb0bTUUFCUcz8SqrwdHekZiAMQS4D4fp09VR/CA64tzrwO6BBx7Q\nf/y9733PErEIIYQQZnXpEuzcCV0LHA0bphYSmDHDJgsJiFtQ3VxNUmYSueVZjMwoJCI9HzutjiD3\nIKJ8ohg2IlgtB2bKUbyNMHqP3aFDh0hLS6OhoQHoTFD8zDPPmDXAWyVLsUIIITrU1qoDukuXDNuj\no9VEwz4+1olLmJZO0XGq+BTJV5JxLKtgzNFM3CvrO8uBufnBnDnqP4de57aswuxLsV098cQTfPzx\nx8yZMwcXC28q7A+pPCGEEEObTgenTkFyMrS2dra7uanZLMaPl8MRg8X1huskZiZSXJVPxNk8Rn5V\niEZRCPUMJdI7EvvQkeosXWCgtUM1YJXKEz4+PmRkZBAcHGyyG5ubzNiJvpB9MMJY0lcGjqtX1cMR\nxcWG7VOmwOLFljn8KP3F/LQ6LUcKjnAo/xDuVyuJPXoJ19qmznJgbr7qProZM/qeiNCCLDpjN3Lk\nSBwH0cZCIYQQg1drK6SkqGlMuqZkDQhQU5SFh1stNGFixbXFJGYmUl5VTNSZK4RcKv66HFiEWg4s\nMko98erra+1QLcaoGbtTp07xu9/9jvvvv5/AblOYc+fONVtw/SEzdkIIMfRkZamJhqurO9scHGDu\nXLj9djkcMVi0ads4kHeA1MJUfIorGH0sE+eGFjwcPRjjPwY3D181J92UKQNmrd2iM3anT59m586d\nHD58+IY9doWFhf0OQgghhOiPujq1FFhGhmF7ZKQ6S+fnZ524hOldqbpC0uUk6qquEXsqmxE517DT\n2BHpM4pQz1A0sbHqi+7pae1QrcKoGTs/Pz8++ugjlixZYomYTEJm7ERfyD4YYSzpK7ZFUeD0adi3\nD5qbO9tdXeGOOyAuzroTNtJfTKe5vZm9OXs5XfIlAfnlxBy/jGNzGz7OPoz2G42Ll596ImbChAEz\nS9eVRWfs3NzcmDdvXr9vZmlyKlYIIQavsjL1cET3haP4eHVQ5+pqnbiE6WWWZ7L98nZaqssZfzyL\ngIJyHOwcGOUXywj3EWgmTYJly9TjzgOMVU7FvvXWW5w8eZJNmzbdsMfOzkZPmMiMnRBCDE5tbXDo\nEBw9ang4ws9PXYGLjLRebMK0Glob+CL7C766dp4R2VeJPpWDQ2s7/q7+xPjG4OQboCYijI21dqj9\nZqpxi1EDu94GbxqNBq1W2+8gzEEGdkIIMfjk5sL27VBZ2dlmbw+zZ9tkzllxixRF4dy1c+zK3oVS\nVUnsscv4lFap5cB8Y/B39UczbZqat2aQFPW16FJsbm5uv28khC2TfTDCWNJXrKOhAXbvhnPnDNvD\nwtT6rgEB1onrm0h/6bvq5mq2X95OTnkWIZeKiTyTi327jhHuIxjlM4phAV8X9ZWp2R4ZNbCLiIgw\ncxhCCCHEjRQFzp6FPXugqamz3dlZzWYxefKA3CcveqAoCqdKTrEvdx8OFVUkHM3E83otzg7OjA4c\nja+rH8ycCQsWqAV+RY+MrhU70MhSrBBCDGzl5eqya16eYfuECeo+eXd3q4QlzKC8sZzEzEQKK/MI\nO19A+Ll87HQKIR4hRPlEYT8iSC0HFhJi7VDNxqJLsUIIIYSltLfDkSNw+DB03cbt46Puk4+Otl5s\nwrS0Oi3HCo+RkpeC6/Vqphy9hHtVA67DXIkdHouXm6+aXXr2bMkubaRBPbCTdCfCWLIPRhhL+op5\n5eerKUzKyzvb7OzUFbj58wfeCpz0l96V1JWQmJlIWXUxEWfzGJlRiJ2iIcwrnHDvcOxGhqnlwIYP\nt3aoZmXqdCeDfmAnhBDC9jU1wd69cOaMYXtIiLpPfsQI68QlTK9N20ZKXgqpRal4llYy7WgmLnVN\neDh6EOsfi7ubDyxcCNOnq6P6Qa5jAur55583yfWM2mOXm5vLs88+y9mzZ6mvr+/8zxoNBQUFJgnE\n1GSPnRBC2D5FgfPn1ROvDQ2d7U5OsGgRTJ06JH63Dxl51XkkZSZRXXONUadzCc4sUcuBeUeq5cCi\notRZOh8fa4dqcRbdY3f//fcTHR3Nyy+/fEOtWCGEEOJWVFbCjh2Qk2PYPnasWhlqiJb6HJRa2lvY\nm7uXL0u+xK+wgttSL+PU2IK3szexfrG4ePio5UISEuSYcz8ZNWPn6elJVVUV9gNo46LM2Im+kH0w\nwljSV/pPq4Vjx+DgQfWgRAdPz0FTREBP+gtcrrjM9svbaaqpIPpkNoG517DX2DPKdxRB7kFoxo5V\nX3gPD2uHalUWnbGbO3cuaWlpTJ06td83FEIIMXQVFqqHI8rKOts0GnU71YIF6hKsGBwaWhvYlb2L\n89fOMTzvOhOPZzGspQ0/Fz9G+43GycsXli+HceNkls6EjJqx27hxI//617+45557DGrFajQatm7d\natYAb5XM2AkhhO1obobkZPjyS3VfXYegIPVwRHCw9WITpqUoCl+VfcUX2V+gra4i5vhl/AsrGGY3\njBi/GAJcA9DEx8PSpeDqau1wbYZFZ+waGhpYuXIlbW1tFBUVAeoLp5ERthBCiJtQFLhwAb74Arqc\nvcPRUZ2hGyIHH4eMmuYadmTt4HJ5JkGXSxn1ZQ4ObVoC3QKJ9o1mmK8/rFwJMTHWDnXQMmpg99Zb\nb5k5DPOQPHbCWLIPRhhL+orxqqth5064fNmwffRodQXO29s6cVnSUOkviqLwZcmX7Mvdh11VNXHH\nMvG5Wo2TvROxgePxdfGFadNg8WJZb+/GYnns8vLy9DVic3Nze71AVFSUyYIxNcljJ4QQlqfTwYkT\nsH8/tLV1tnt4qKddx46VLVWDSUVjBYmZiRRU5RF6oYiIs1ewb9cR4hFCpE8kDgGBagqT8HBrh2qT\nLJbHzsPDg7q6OgDsepkn12g0aLvWe7EhssdOCCEsr6REPRxRWtrZptGo+egWLQJnZ+vFJkxLq9OS\nWpRKSl4KThU1xB69hGd5nVoOzC8WL1cfmDUL5s0beCVDrMBU4xajDk8MRDKwE0IIy2lpgQMH1Jm6\nrm+9w4erhyNGjrRebML0SutKScxM5GpNMeHnCwg7l4+9DsK8wtRyYEHB6iydnIoxmkUPTwgx2A2V\nfTCi/6Sv3CgzU000XFvb2ebgoNZ2nTlzaNduH2z9pV3XzsG8gxwtPIp7WTVTj2biVt2Au6M7Y/zH\n4O7ipb7ws2YN7RfeimRgJ4QQ4pbU1qqnXS9eNGwfNUrNN+vra524hHkU1BTw+aXPqa4tIzLtCqEX\ni7DHjgifKEZ6jkQTFqbO0gUEWDvUIU2WYoUQQvSJTqfmo0tOVpdgO7i5wbJlMGGCHI4YTFraW9iX\nu49TJafwLq0i9mgmLvXNeDl5Eesfi6ubt7qBcto0yV3TD7IUK4QQwuKuXlUPRxQXG7ZPngxLloCU\nEx9csiqy2H55Ow11FcSeyiEoq1QtB+Y3Wi0HFh2tbqIcCrlrBog+z9jpdDqDx72dmLU2mbETfTHY\n9sEI8xmqfaW1Va3tmpqqzth18PdXf69LJoueDdT+0tjWyK7sXZy7dg7/gnJijl/GqbG1sxyYu5c6\nPRsXJ9OzJmLRGbvTp0/z+OOPk56eTnNzs0EQtpruRAghhGlkZ8P27WrC4Q729jB3Ltx+u3pQQgwO\niqKQcT2DL7K+oLW2inEnsxl+pYxhdsOI9h/LcLfhaMaPVzNMu7tbO1zRA6Nm7CZMmMCqVat48MEH\nce1W160jibGtkRk7IYTon/p62LULvvrKsD0iQq0K5e9vlbCEmdS21LLj8g4yyy8RmFtG9MkshrW0\nd5YD8/JRB3Tjxlk71EHJonnsPD09qampGVC1YWVgJ4QQt0ZR4MwZ2LsXuizS4OKi1m2X1bfBRVEU\nzpSeYU/OHqipYXTqZfyKK3Gyd2K032j8XP0gPl598WUTpdlYdCl29erV7N69m2XLlvX7hpYktWKF\nsQbqPhhheYO9r1y/rh6OKCgwbI+LgzvuUE++CuPZen+pbKokMTORvKorBGeWMOrLXOzbtQR7BBPl\nE4WD79ebKEeNsnaog5bFasV21dTUxOrVq5kzZw6BgYH6do1GwzvvvGOyYExNasUKIYRx2tvh0CE4\nehS6bp329VWXXW24LLi4BTpFR2phKgfyDjCsqpb4Y5l4X6vBxcGF2BGxeLv4wG23qWlMHB2tHe6g\nZrFasV31NkDSaDRs3rzZJIGYmizFCiGEca5cUWfpKis72+zsYPZsmDNHynwONlfrr5KYmUhpTTGh\nGYVEns3DXqsw0msk4V7h2A8PVBMNh4VZO9QhRWrFfgMZ2AkhxM01NsLu3ZCebtg+cqS6+jZ8uHXi\nEubRrmvnUP4hjhQcwbWiltgjl/CorMfd0Z1Yv1g8XLzU0fzcuXLU2QosnqD4wIEDvPPOOxQXFxMa\nGsqDDz7IwoUL+x2AELbA1vfBCNsxGPqKoqiDuT171MFdB2dnWLwYpkyRwxGmYiv9pbCmkM8zP6ey\nrozw9HzCzhdgj4Zw70hGeo3ELjgE7r4bRoywdqiin4wa2L355ps888wzPPLII0yfPp2CggLuv/9+\ntm7dyqOPPmruGIUQQphIRYWak+7KFcP28ePVfLMeHtaJS5hHq7aV5NxkThafxKOsmqlHM3Gtaews\nB+biCfPnw6xZUg5skDBqKTYmJoZPP/2UuLg4fdu5c+e45557yM7ONmuAt0qWYoUQolN7u3ow4vBh\n9eMO3t6wYgXExFgvNmEe2ZXZJGUmUV9fSeSZXEIuFeOAPVE+UQR7BKOJiFD30vn5WTtUgYX32Pn5\n+VFaWopjl5MxLS0tBAcHU1FR0e8gzEEGdkIIocrPV2fprl/vbLOzg5kzYd48OfQ42DS2NbI7ezfp\n19LxKa4kNvUyzvXN+Lr4MtpvNM6unmph36lTZc3dhphq3GLUvOvtt9/OU089RUNDAwD19fU8/fTT\nzJo1q98BCGELTJlDSAxuA6mvNDVBYiJs22Y4qAsJgUcfVX+3y6DOvCzZXxRFIaMsg9dPvk5GwZeM\nOXKJuL3n8GjUMtZ/LBOHT8R57ETYuBGmTZNB3SBl1B67v/71r6xZswYvLy98fX2prKxk1qxZfPjh\nh+aOTwghRB8piloGbNcu+PrvcUAdxC1apP5Ol+1Ug0tdSx07snZwqfwS/vnXiTuehWNTK8PdhhPt\nG42juxfceSdMnCgDukGuT+lOCgsLKSkpITg4mJEjR5ozrn6TpVghxFBUVQU7dkD37c9jxqhlPj09\nrROXMA9FUUi7msaenD3o6mqJOZ5FQP51HO0dGe03Gn9Xf/VkzPLlUjbExpl9j52iKPrasDqdrtcL\n2Nnon30ysBNCDCVaLaSmwsGD0NbW2e7pqf5OHzPGerEJ86hsqiQpM4krVbmMyLnGqFPZDGtp7ywH\n5uWjnoyRF39AMPvAzsPDg7q6OqD3wZtGo0HbtfaMDZGBnegLW8k1JWyfLfaVoiK1csS1a51tGo1a\nEWrhQnBysl5sQ505+otO0XGi6AT7r+zHvraO0ccy8S2pUsuB+cfi7ewNkyerxX2dnU16b2E+Zk9Q\nnJGRof84Nze33zcSQghhWs3NsH8/nDql7qvrMGKEWjkiJMR6sQnzuFZ/jcTMRIpriwi5WEzUmSvY\nt2sZ6TmSCO8I7P381RdfivsOWUbtsfuf//kfnn766RvaX375ZZ566imzBNZfMmMnhBisFAUuXoQv\nvoCvF1YAtabrggUwY4Ycjhhs2nXtHM4/zOGCwzhX1RF7NBOv67W4DXNjjP8YPJw91Rc0HpuXAAAg\nAElEQVR+wQI56jxAWTSPXddl2a58fHyoqqrqdxDmoNFo2Lx5M/Pnz7e5ZRMhhLhVNTWwcydkZhq2\nx8So26m8va0TlzCfotoiPr/0OeV11xiZUUjE2TzsdRDhHaGWAxseqJYDCw21dqjiFqSkpJCSksLz\nzz9v/oHd/v37URSFu+66i+3btxt8Licnh9/+9rfk5+f3OwhzkBk70Re2uG9K2CZr9RWdDk6eVJde\nW1s7293d1SwW48ZJFgtb1J/+0qptZf+V/ZwoOoFbRS1jjmbiXlmPp5MnsX6xuDl7wJw56j8Ho0u/\nCxtl9j12AA899BAajYaWlhYefvhhg5sHBgby6quv9jsAIYQQN1daqiYaLi01bJ86FRYvlv3xg1FO\nZQ5Jl5Oora8gMj2fkV8V4oAdUb4xajmwkBB1li4w0NqhChtj1FLs2rVreffddy0Rj8nIjJ0QYqBr\nbYUDB+D4ccPDEcOHq/vjbTydqLgFTW1N7MnZQ9rVNLyuVhN7LBPX2qbOcmDO7upRZ9lIOehYdI/d\nQCQDOyHEQJaZqe6lq6npbHNwUGu7zpoF9vbWi02Yx8XrF9mRtYOm+mqiTucSklmCg50D0b7RBLoF\noomMhFWrwNfX2qEKM7DIUmyHmpoatmzZwsGDB6moqNAnLNZoNBQUFPQ7CCGsTfbYCWOZu6/U1amn\nXS9cMGyPioKVK+V3+kBjTH+pa6ljZ9ZOLpZfxLeoggmpl3FuaCHANYAYvxgcXT3UnHSTJ8tGSvGN\njBrYbdy4kcLCQp577jn9suwf/vAHvv3tb5s7PiGEGBJ0Ojh9Gvbtg5aWznZXV1i2TEp8DkaKonD2\n6ll25+xGW1/HmFPZjMi5ppYDGz5BLQc2erQ6opdacMJIRi3FBgQEcPHiRfz9/fHy8qKmpobi4mLu\nuusuzpw5Y4k4+0yWYoUQA8W1a2rliKIiw/aEBFiyRB3cicGlqqmKpMtJ5FbmEJB3nZgTWTg2txHk\nHsQo31E4uHuqx50nTJAR/RBh0aVYRVHw8vIC1Jx21dXVBAUFkZWV1e8AhBBiqGprU2u7Hjumzth1\n8PdXJ2kiIqwWmjATnaLjZPFJknOT0dTXM+F4Fv4F5Tg7OBMbOA4fFx91enbZMnBzs3a4YgAyamA3\nadIkDh06xKJFi5g9ezYbN27Ezc2N2NhYc8cnhEXIHjthLFP1lexs2LEDuuZ4t7dXU5LNni1pyQaL\nrv2lrKGMxMxEimoKCcq6yqgvc3Bobe8sB+bto47oR4+2btBiQDPqreONN97Qf/yXv/yFZ555hpqa\nGt555x2zBSaEEINRfT3s3g3nzxu2h4erKUz8/a0TlzAfrU7LkYIjHMo/xLDaeuKOZuJztRq3YW7E\nBk3C08lTkhIKkzFqj92JEyeYPn36De0nT57ktttuM0tg/SV77IQQtkRRIC0N9u6FpqbOdhcX9cBj\nfLxspRpMMrMz2Xd6H9cbr3Px+kWGB/sTV9tC5JlcHNoVwr3DCfMKw87PX01hIuvuQ55N1Ir19fWl\nsrKy30GYgwzshBC24vp12L4duldgnDQJli6VrVSDTWZ2Jv9M/ifFAcUU1RbhXdvKjN1lTHL2ZYT/\nCLUcmJO7mpBw/nwYNszaIQsbYJHDEzqdTn8TXdedvai1Yh1kE4gYJGSPnTBWX/pKezscPgxHjoBW\n29nu8/VWqlGjzBOjsB5FUXj7wNt8WXMEt8PX8L1QzWStgrO/Mw4aBxJGJKAZMUItBxYcbO1wxSB0\n05FZ14Fb90GcnZ0dzz77rHmiEkKIAe7KFXWWrqKis83ODm6/HebOlUmawai4tphd2bs4+dUhxnyV\nx4PlzWRUtTDD3ZnUaxoaRjqgWbhQPR0jpUOEmdx0KTYvLw+AuXPncvjwYf3snUajISAgAFcbTq4k\nS7FCCGtobIQ9e+DsWcP2kSPVWTqp2T741LbUkpybTPq1dBybWqnc9C4P1tehQYOzgzOO9o60ujnz\nScRYfv3Bf6wdrrBRFlmKjfh6M6eUDRNCiJtTFDh3Tj3x2tjY2e7kpB52nDpVDkcMNm3aNo4VHuNI\nwRHa21oIzSwhIu0Kl1s1OLY64uLhgmJvR1WQD1UaV2Iix1o7ZDEEGLVJbu3atTe0ab5+h5KUJ2Iw\nkD12wlg99ZWKCjUnXW6u4deOH6/mmfXwsFx8wvwURSHjegZ7c/ZS01KDd2kVMSeycatuwN/Vn/ag\nWCKqayiljb32Gqa4BhEXFMm5kJHWDl0MAUYN7EaNGmUwRXj16lX+7//+jwceeMCswQkhhC3TauHo\nUTh0SD0o0cHLC1askDyzg1HHPrrC2kKc6psZfyqHgPzruA1zIzowDh8XHzx82kitqGDRiBEU5uUx\nLSKC5JYWohctsnb4YggwKt1JT7788ku2bNnC9u3bTR2TScgeOyGEORUUqPVdr1/vbNNoYOZMNYOF\no6PVQhNm0HUfnZ1Wx8jzBYR9VYCzzp5In0iC3IPQODnBvHkwYwb52dnkJCdj19qKztGRUYsWES7V\nmsRNWDSPXU/a29vx8fHpMb+dLZCBnRDClDIz89m3L4eGBjtyc3U4OIzC3z9c//ngYLVyRFCQFYMU\nJtd1H12bthX/wgpGnczGtb6FUM9Qwr3DcbBzUJMSLlki6+7illnk8ESH5ORk/Z46gIaGBj766CPG\njx/f7wBuxS9+8QtSU1OJiIjgn//8p+TTE/0me+zEzWRm5vPWW9nU1Czi5MkU3N0X0t6eTHw8BAeH\ns3Ah3Habms5EDA7d99G51jQy9mQ2vsWV+Lv6MyokDpdhLjBiBCxfDmFhPV5H3luEpRk1Inr44YcN\nBnZubm7Ex8fz4Ycfmi2w3qSnp1NSUsKhQ4f43e9+x6effsqaNWssHocQYuj45JMcLlxYRE1N5146\nB4dFNDTsZ+PGcLy8rBufMK2u++jsW9uJOpdP6IUiPOxd9fvocHGBRYtg8mQZ0QubYtTAriOfnS1I\nTU1l6dKlACxbtoxt27bJwE70m/xFLXpSUwPJyXDkiB3NzWqbt/d8HB0hJgZiYuxkUDeI1LXUkXwl\nmbNXz4KiEJhbRtTpHNybFSJ9YtR9dHZ2MGUKLFwIRuRylfcWYWlGr2FWV1ezY8cOSkpKCA4OZvny\n5fj4+Jgzth5VVVUR9PUmFk9PT5utVSuEGLhaWtTTrseOqTN0dnZqSUWNBkJC1HrtDg7g6Ki7+YXE\ngNCmbSO1KJUjBUdo1bbiXllPzPEsvMtq1X10oV/vowsLgzvvlI2UwqYZNX+8f/9+IiIieOWVVzh1\n6hSvvPIKERER7Nu375Zv/NprrzF16lScnZ3ZsGGDwecqKytZvXo17u7uREREGCz5ent7U1tbC0BN\nTQ2+vr63HIMQHVJSUqwdgrABOh2cOQOvvmqYwiQqahTe3slMmwYODik4OEBLSzKLFkmx14FMURS+\nKvuK106+xv4r+1EaGohJvcyUpC8ZVT+MaSHTGOU7CgcvH7jnHtiwoc+DOnlvEZZm1Izdxo0b+fvf\n/869996rb/vkk094/PHHuXTp0i3dOCQkhE2bNrF7926amppuuJ+zszNlZWWkpaWxYsUK4uLiGDdu\nHLNmzeLll19m7dq17N69m9mzZ9/S/YUQoqvcXLVqxLVrhu3BwbBhQzjNzZCcvJ/GxnMMH65j0aJo\nYmPDe76YsHkldSXsyt5FQU0BGp1CcFYpkWdy8dY5ET3863109vYwY4Za3NfJydohC2EUo9KdeHt7\nU1FRgX2XosVtbW0EBARQXV3drwA2bdpEUVER27ZtA9QTt76+vmRkZBAdHQ3A+vXrCQ4O5sUXXwTg\n5z//+f9v786jm7zO/IF/JUve933BC+CVxTgLJCSEGAxJSMikpU0LPSUhtElO0zJpp8tMf0lYSnsy\nnSZt2izNNF1IQkMCczqTkOYEEmwDWcAhOCYsNraxDRhjYxsv8iJb0vv74yLJsiVbsqVX0uvv5xyf\nWK9e6b1yLtKje5/7XBw5cgSZmZn429/+ZndVrEqlwoMPPmjZFi06OhpFRUWWfAfztyje5m3ent63\nr1wBfv/7cly8CGRlifsbG8sRGgo8+mgxCguBgwd9p728PbXbvfpe/P6t36Ousw5ZRVmIau3GwP8c\nQYRuCF+bcwNSwlNwsKkJSEtD8Y9+BMTH+1T7eVs5t82/m9cxvPrqq/LVsdu0aROys7Px+OOPW479\n4Q9/QG1tLZ5//vkpNeDJJ59Ec3OzJbCrrKzEkiVL0NfXZznnt7/9LcrLy/HOO+84/bysY0dE4+nr\nAw4eBI4dE1OwZlotsGQJcMst4ndShmHjMI5cPILD5w9jyDiEwH49Zh87h+Rzbbb16GJigDvvBPLy\nuLkvyUrWOnbHjx/Hyy+/jP/6r/9CWloampub0dbWhptuugm33XabpUGHDh1yuQGqUf9wdDodIiMj\nbY5FRET4bCFkUoby8nLLtylSNoMBqKgQOXTmla6A+AwvKhKLHcerMcu+4l8kScLpK6fxwbkP0DXY\nBZXRhPTTF5FV1YREbTRmpy1EqDbUYxE9+wvJzanA7uGHH8bDDz887jmjAzRnjY5Ow8PDLYsjzLq7\nuxHBat5ENAWSBJw+DXz4IXD1qu19M2eKQZrkZO+0jTxjZB4dAMQ2dyK7og4J/Spkx80TeXQAMGcO\ncMcdQHS0F1tL5B5OBXYbNmzwWANGB4S5ubkwGAyoq6uz5NhVVVVh3rx5Lj/31q1bUVxczG9LNCH2\nEWVrbgbefx+4cMH2eHy82AUqN9f5WTf2Fd/Xq+9FaUMpvrj8BSRICO4dQPZn9Ui52C32dU1NEZ89\nCQmifMmsWR5rC/sLTaS8vNwm726qnN4r9tChQ6isrLTkvkmSBJVKhf/3//7fpC5sNBoxPDyMbdu2\nobm5Ga+88go0Gg0CAgKwbt06qFQq/PnPf8bx48exevVqfPrppygoKHD+hTHHjmja6+4WI3Rffml7\nPCQEWLZM1JkdsSaM/JzBZMCnFz615NGpDUZkfHkemScvIj0sBVnRWSKPLihIdICFC9kByGfImmO3\nadMm7N69G7fddhtCQkKmfFEA2L59O37xi19Ybu/cuRNbt27F5s2b8dJLL2Hjxo1ITExEfHw8Xn75\nZZeCOiJXMQ9GWfR64KOPgE8/tdaiA8Rn+E03AbfdJoK7yWBf8T2j8+ggSUhoasfsz+qQZgrH7OQb\nRB4dIBIpV6wAwsNlaRv7C8nNqRG7mJgYnDp1CqmpqXK0yS04Ykeu4JuvMphMQGUlUFoqVr2ONGeO\n+Dyfak1z9hXf0tLbgvfr3kdTdxMAILSrDzlH6zCjfQizY2cjNuTa//DUVODuu4EZM2RtH/sLOctd\ncYtTgV1hYSFKS0sRHx8/5QvKhYEd0fRSXw/s32+/wPCddwKZrCWsKLohHQ6cO2DJo9MMGZD1RSOy\nzrZhZmQmUiNSRR5daKiI6K+7juVLyKfJOhX7l7/8BQ8//DC+9a1vISkpyea+pUuXTrkRnsLFE0TK\nd+WKCOhqa22PR0aKz/P58/l5riSj8+ggSUiuu4zZxxswU5uIzJSF0AZoAbVa5NAVF09+3p1IBl5Z\nPPHyyy/j8ccfR0RExJgcuwujl5n5CI7YkSs4XeJ/+vqA8nLg889tCwwHBopyZIsXe6bAMPuKd0iS\nhDPtZ7C/fr/IowMQcaUHORV1mKnTYnbsbGseXVaWWO06aiDCG9hfyFmyjtg98cQTePfdd7Fy5cop\nX5CIaCoMBuDoUVFgWK+3HlepxGzb8uWy5cWTTEbn0WkHhjDreANmN/UgO2Y2YpOu5dFFRop6dHPn\ncpiWpi2nRuwyMjJQV1eHwMBAOdrkFhyxI1IWSQJOnRLlS0ZvUT1rlvg8Z4FhZRmdR6cySUitbkbu\niWZkh86w5tEFBIgdI267TQzZEvkhWRdP7NixAxUVFXjqqafG5Nip1eopN8ITGNgRKcfFi8C+ffYL\nDN9xB5CTwwEaJTGYDDhy8QgONR0SeXQAoi93IfdoHXKN0ciMyhR5dICoLn3XXVNf7kzkZbIGdo6C\nN5VKBaPROOVGeIJKpcKWLVu4eIKcwjwY39TVJUboTp60PR4aKurLXn+9/PVl2Vc8x5xH90H9B7g6\nKPZ9C9INYvaxehS0mmzz6GJjRR5dTo4XWzwx9heaiHnxxLZt2+TLsTt37tyUL+QNW7du9XYTiGgS\n9Hrg8GHgyJGxBYZvvlnMuAUHe6995H4tvS3YV78PjV2NAAC10YT0kxdQUN2O3MiZ1jy6wEBg6VLR\nETROfYQR+TTzANS2bdvc8nxObykGACaTCa2trUhKSvLZKVgzTsUS+R+TCTh+HCgrG1tgeO5cUb4k\nJsY7bSPP0A3pUNpQisqWSkgQ79lxFzpQ8HkTCtRJ1jw6QNSuWblSLJIgUhhZV8X29PTgBz/4Ad58\n800YDAZoNBqsXbsWzz//PKKioqbcCCKiujpRj66tzfZ4WpooMJyR4Z12kWeY8+gONx2G3iiWN4d0\n9yPns3Mo7AlGZtR8ax5dUpLYNYJVpokm5NSw26ZNm9DX14eTJ0+iv7/f8t9NmzZ5un1EsnBncUhy\nTVsbsHOn+BkZ1EVFAV/7GvDd7/pWUMe+MjWSJOHMlTN4seJFfHjuQ+iNegQMGzHr83O4a/853CXN\nQnZstgjqgoNFQPfoo34b1LG/kNycGrF7//33ce7cOYSFhQEAcnNzsWPHDsyaNcujjSMi5dLprAWG\nR84+BAaKHLqbb/ZMgWHynsu6y3i/7n1LHh0kCYkNbSg80Yo5QTMQm3DtM0WlEitjSkrEShkicppT\ngV1ISAiuXLliCewAoL29HcE+nr3MLcXIWewj8jEYxKKIw4fHFhi+/nqx2tWXCwyzr7jOXh5dWKcO\ncz9rwgJ9NFKi5kGtujaBlJ4uVrumpnqxxe7D/kIT8cqWYr/85S/x6quv4sc//jEyMzPR2NiI3/3u\nd1i/fj2eeuoptzXGnbh4gsi3jFdgePZsUY/OB3aAIjcymAw4evEoDjUdsuTRafTDmPVFExY2A1kj\n69GFh4uFEYWFLEpI05KsdexMJhN27NiBv//972hpaUFqairWrVuHjRs3Wlcr+RgGduQK1pryrAsX\nRIHhixdtjyckiIAuO9t/PsvZVyYmSRKq26uxv36/pR6dyiQhubYFi6p7kRsyA2GB12aA1GrgppuA\n4mIgKMh7jfYQ9hdylqyrYtVqNTZu3IiNGzdO+YJENH1cvQocODC2wHBYmPgcv+EG8blOyjEmjw5A\nZFs3rqu8jAWmBMRGjZhinTVLTLsmJMjfUCKFcmrEbtOmTVi3bh1uueUWy7FPPvkEu3fvxnPPPefR\nBk4WR+yIvGdw0FpgeOTmNAEBwOLFwJIlLDCsNLohHcoaynC85bgljy5wYAj5lRewqD0IqRGp1jy6\n6GhRwyY/33+Gaok8TNap2Pj4eDQ3NyNoxDD54OAg0tPTceXKlSk3whMY2BHJz2QSq1zLyoD+ftv7\n5s0TixxZYFhZ7OXRqYwmpFdfwq3nDJgVmmbNo9NoRFR/661c8kw0iuxTsSaTyeaYyWRi4ESKwTyY\nqZEka4Hh0d/1ZswQgzPp6d5pm7uxrwj28ugAIKa5E4tPdmOuKhFhEdZKCigoEB0hOtoLrfUe9heS\nm1OB3ZIlS/Dkk0/iN7/5DdRqNYxGI7Zs2YLbbrvN0+2bEpY7IfK81lYR0NXX2x6PihKLHOfO5Wyb\n0lzWXca+un1o6GqwHAvWDaKoqhULu8MRFzrTenJ8vMijmz3bCy0l8n1eKXdy4cIFrF69Gi0tLcjM\nzMT58+eRkpKCvXv3It1Hv4ZzKpbIs3Q6MeV6/LhtgeGgIFFg+KabONumNH1DfShtKLXJo1MbjMg+\n04rbzquQFppkzaMLCgJuv110hIAAL7aayD/ImmMHAEajERUVFbhw4QLS09Nx0003Qe3Dy9kY2BF5\nxvCwtcDw0JD1uEolVrkuWyZWvZJyGEwGVDRX4GDjQUseHSQJiRc6UVyjR446wZpHBwALFgArVgAR\nEd5pMJEfkj2w8zcM7MgVzIOZmCSJsiUffgh0d9vel50t6tElJnqnbXKaTn1FkiTUdNRgf/1+dA50\nWo6Hdvfj1pM9KOqLsNajA4CUFLG3q4/O5HjDdOovNDWyLp4gount/HlRYLi52fZ4YqK1wDApS6uu\nFe/XvW+TRxcwZEBh9VUsuaRBbFASVIHXkidDQ4Hly8WecD48k0M0HXDEjogcunpVjNCdOmV7PCxM\nTLnyc1x57OXRQZKQ0dSFkjoT0gNirHl0KhVw440iqAsJ8V6jiRRAthE7SZLQ0NCAjIwMaDQc4COa\nDgYHgUOHgKNHbQsMazTAzTeLxREK3P1pWjOajDjafNQ2jw5AZGcfVlQPoWAgHFrNiDy6zEyx2jU5\n2QutJSJHJhyxkyQJYWFh0Ol0Pr1YYjSO2JErmAcjGI2iwHB5+dgCw/PniwLD06wM2RhK6yuO8ui0\ng8O45ewAFrZqEK4NtT4gIkLMv8+bxzo2TlBafyHPkW3ETqVS4brrrkNNTQ0KCgqmfEEi8j2SBNTW\ninp07e2296Wni7qyM2Z4p23kOa26Vuyr34dzV89ZjqlMEgoa+7CsCYhHBFTaa8GbeT+4pUuBwEAv\ntZiIJuLU3OqyZcuwatUqbNiwAenp6ZaoUqVSYePGjZ5u46SxQDE5azr3kdZWsTDi3Dnb49HRosDw\nnDkcmBlJCX2lb6gPZY1l+PzS59Y8OgCJ7QNYdVZCpj7UmkcHADk5wF13AXFxXmitf1NCfyHP8kqB\nYnPHVNl5dy8rK3NbY9yJU7FE49PpgNJSoLJybIHhpUtFXVmm1SqLozy64P4h3HFOhfmtsK1HFxMj\nArrcXEb3RB7GOnYTYGBHrphOeTDDw8CnnwIffTS2wPCNNwLFxSwwPB5/7CuSJOFsx1nsq99nk0en\nNpqw6IIJSxpNCMeI6VWtVqyQueUWRvdT5I/9hbxD9jp2HR0d+Oc//4nLly/jZz/7GZqbmyFJEmYw\n8YbIL0gS8OWXonxJT4/tfTk5Ytp1OhQYnm7s5dEBwKwrRtxVDyQMBEClGvFRMHeuWBwRFSVzS4nI\nHZwasTt48CC+9rWv4cYbb8THH3+M3t5elJeX49lnn8XevXvlaKfLOGJHZNXUJPLoLl2yPZ6YKBZG\ncH925XGURxfdb8I99QGY3W60zaNLTBTlS2bO9EJriUjWqdiioiI888wzWLFiBWJiYnD16lUMDg4i\nIyMDbW1tU26EJzCwIwI6O8UI3enTtsfDwkRN2euuY4FhpTGajGJf16aDGDQMWo4HDBux8lIwrm/Q\nIxAj/qcHB4tq0wsXsjMQeZGsU7FNTU1YsWKFzTGtVgvjyMqlRH5MaXkwAwPA4cP2CwwvXgwsWcIC\nw5Plq33FnEe3v34/OgY6Rt6B6zuDsKzWgIjBYQAjdo247jpRnJBJlR7jq/2FlMupwK6goADvv/8+\n7rrrLsuxAwcOYP78+R5rGBG5zmgEjh0TBYYHBmzvY4Fh5Wrra8O+un2ov1pvc3xGvxb31KuR3D5o\nW9UgLQ24+27xXyJSFKemYo8cOYLVq1fj7rvvxp49e7B+/Xrs3bsXb7/9NhYtWiRHO13GqViaTiQJ\nOHsW+OCDsQWGMzJEHh0/w5Wnf7gfZQ1lOHbpmE0eXZgxAKubw5DX0AP1yLfBsDBgxQqgqIjlS4h8\njOzlTpqbm7Fz5040NTUhIyMD3/72t316RSwDO5ouLl8WCyMaGmyPx8SIla4FBfwMVxpHeXQqCVje\nFYObzvQicMhgfYBaDSxaJGrZBAfL32AimpBX6tiZTCa0t7cjISHBbrFiX8LAjlzhj3kwvb2iwPAX\nX4wtMHz77eJznCXI3M+bfUWSJNR21mJf3T7bPDoA84aiccdZIyLbe20fNHOmWO3KWjZe4Y/vLeQd\nsi6euHr1Kv71X/8Vu3fvxvDwMLRaLe6//3784Q9/QGxs7JQb4SncUoyUaHgY+OQT4OOPbQsMq9Wi\nwPDttzMXXokc5dElS+G492IoUutabb9wR0WJOXgO2RL5NK9sKfaVr3wFGo0G27dvR0ZGBs6fP4/N\nmzdjaGgIb7/9ttsa404csSOlkSTgxAngwIGxBYZzc8W0a0KCd9pGnmPOo/u85XOYJJPleIgqEHd3\nJWDuqTaoh4atD9BoxI4RS5YAgYF2npGIfJGsU7FRUVFoaWlBaGio5Vh/fz9SUlLQ3d095UZ4AgM7\nUpLGRpFH19JiezwpSQzKzJrllWaRBxlNRnx26TOUN5bb5tFBhSXGNCw51YugzlHvv3l5Ym/XmBiZ\nW0tEUyXrVGx+fj4aGxsxZ84cy7Gmpibk5+dPuQFEvsBX82A6OkSB4TNnbI+Hh4sCw0VFrCkrN0/3\nlfHy6PI0yVh1To3ocxdtHxQXJ/LosrM91i6aHF99byHlciqwW758Oe644w488MADSE9Px/nz57Fz\n506sX78ef/3rXyFJElQqFTZu3Ojp9hJNCwMDwKFDQEXF2ALDt9wC3HorCwwrkaM8ujhtFO5rj0f6\nl01QGUasdg0MFEmVN98MBATI3Foi8kVOTcWav22MTMw1B3MjlZWVubd1U8CpWPJH4xUYLiwUBYa5\nN7vy9A/3o7yxHMcuHbPJowtSB+Iu40wUVrUgoGtUYmVhoUisjIiQubVE5AleKXfiTxjYkT+RJKCm\nRhQY7rCdfWOBYQUbL4/u5uBs3F49iODGC7YPSk4Wu0ZkZMjcWiLyJFlz7IiUzpt5MC0tYmFEY6Pt\ncRYY9k3u6CuSJKGusw776vehvd92q5DZoTOw+nIkYg7X2M7Dh4SIxMobbmBipR9hjh3JjYEdkZf0\n9IgCw1VVtgWGg4NF2tTChSwwrERX+q5gX/0+1HXW2RyPDY7BvfosZB2qhUo3YnGESiWCueXLgRGV\nCYiI7OFULJHMhoasBYaHR5QfU6tFMHf77fz8ViKHeXQBQVgRMhfXf9GKgIvNttGr1LsAACAASURB\nVA9KTxfTrikpMreWiOTGqVgiPyNJYnTuwAGxHdhIeXli2jU+3jttI88xmow4dukYyhvLMWCwrohR\nQYWF0XOxvAEIPlFpO2wbHi46RGEh5+GJyCVOB3ZnzpzBnj170NraihdffBHV1dUYGhpCYWGhJ9tH\nJAtP58E4KjCcnAzccQcLDPsTV/pKbUet3Ty6mZGZuEeXgvh9VbbLn9VqUbrk9ttZz0YhmGNHcnMq\nsNuzZw8ee+wxrFmzBm+88QZefPFF9Pb24uc//zk+/PBDT7eRyG91dIiVrtXVtsfDw0XpkgULmAev\nRA7z6EJicXfQPMw+chaqy0dsHzR7tigyzGFbIpoCp3Ls8vPz8eabb6KoqAgxMTG4evUqhoeHkZKS\ngvb29oke7hXMsSNvGhgADh4UBYZN1nQqaLXWAsPcxlN5+of7cbDxID679NmYPLplcTdi4ekuBJw8\nZfug6GixDVheHqddiaYxWXPsrly5YnfKVc2hBiIbRiPw2WciqBtdYHjBAjFKFxnpnbaR54yXR3d9\n4gKUtIUh9P8+EytnzDQa4LbbRKSv1Xqh1USkRE4Fdtdffz1ef/11PPjgg5Zjb731FhYtWuSxhhHJ\naap5MOYCw/v3A52dtvdlZooCw6mpU2sj+YbRfcVRHl1WdBbuUeUhofRzYPTMxpw5IrkyOlqGFpM3\nMceO5OZUYPf8889j5cqV+Mtf/oL+/n7ccccdOHv2LPbv3+/p9k3J1q1bUVxczH9U5FGXLomFEU1N\ntsdjY8XCxvx8zrAp0ZW+K9hfvx+1nbU2x2OCY3BX3E3IPdYAVc0+2wclJIg8Oq6WIaJrysvLUV5e\n7rbnc7qOXV9fH9599100NTUhIyMD99xzDyJ8eI9C5tiRp/X0iNIlVVW2x80Fhhct4r7sSlJTV4MP\nP/8QfYY+NHY2IiA+AHEpcZb7gwKCcHvqLbipcRgBnx4BDAbrg4OCgOJidgoicoh7xU6AgR15ytCQ\nKC78ySdjCwwvWgQsXcoCw0pTU1eDP334J3SldKGpuwkGkwGGOgOK5hQhITUB1yUXoUSfhrDSw0B3\nt+2Di4qAFSvEUmgiIgdkDeyampqwbds2VFZWQqfT2TTi7NmzU26EJzCwI1c4kwdjMonRudLSsQWG\n8/PFtGtcnP3Hkn9q729HdXs1Xtr9Ei7FXwIAdFV3ITpf5Mald6Tj2TX/jsRDnwMNDbYPTk0Vu0bM\nmCF3s8mHMMeOnCXrqtj7778fBQUF2L59O4KDg6d8USJ/09Ag8uguX7Y9npwsFkbMnOmddpF7SZKE\n5t5mVLdXo7q92rIgonPQdkVMsCYYeWGZuL1aQuLr/7CtaRMaKkborruOyZVEJDunRuyioqLQ2dmJ\nAD/KDeGIHblDe7soMFxTY3s8IkKULiksZIFhf2c0GdHQ1YDq9mrUtNegd6h3zDkVH1WgN+AKUmv1\niFGFIqlfjVwTkGuKwcK5C8VJKpWYiy8uBkJC5H0RROT3ZB2xW716NQ4ePIjly5dP+YJE/qC/X9Si\n++yzsQWGb71VlB5jgWH/pTfoUdtZi+r2atR21EJv1Ns9T6vWIjs2GzOy41G/43XcGxGGuOZOBPXr\n8aHOAM3ia0O1WVlitWtSknwvgojIDqdG7Nrb27F48WLk5uYiMTHR+mCVCn/961892sDJ4ogducKc\nB2MwWAsMDw5a71epRIHh5ctZYNhf9ep7UdNRg+r2ajRcbYBRMto9L1Qbiry4POTH52NWzCxoB/Qo\n3bIFi45/ju7WZnzS149bwkIRExGDIzPSsXzbNmDuXE67kl3MsSNnyTpit3HjRgQGBqKgoADBwcGW\ni6v4RkYKIUnAmTNi2nV0geGsLJFHl5LilabRFJgXP1S3V+Niz0WH50UHR6MgvgD58flIj0qH2mAU\nG/zu2w3U10N96hTCJSA8MQ0JXV1Ii4kB0tOhLiwE5s2T8RUREY3PqcCurKwMzc3NiORQBSlMTU0T\n/ud/6nHypBrd3aWYNWs24uMzAYgVritXcgtPf+Jo8YM9KeEpyI/PR358PhLDEqECgMZGoPwdEeXr\nrdOzJnMipVqN4txcsVomJAQm1rWhCXC0juTmVGBXWFiIjo4OBnbk9wwGoK1N7BZRUdGEd96pw+Bg\nieX+L744gJtvBr7+9UwsXMhasv7AmcUPAKBWqZEZlYn8+HzkxechOvjadl5XrgBHDgBffjm2Bt01\ns5cswYHaWpSkpYk9XgEc0OuRXVJi93wiIm9xKrBbvnw57rzzTjz00ENIupYcbJ6K3bhxo0cbSDRZ\nRqM1iDP/tLWJ4wBQUVFvCeq6usoRE1OMrKwSzJhRiptvzvRiy2kiri5+yI/PR05cDkK110bY+vqA\nI0eAEydEx7AnLk4sey4sRGZMDFBTg9IDB3Di9GkUzpmD7JISZObleegVklIwx47k5lRgd/jwYaSm\nptrdG5aBHfkCo1EMvLS0WIO4y5etQZw9JpO1TklUFLBwoXnHCNYv8UWTXvwQoBV3DA8DJ0+KKtP1\n9bbLnc1CQoD580VAl5ZmMwefmZeHzLw8qPlBTUQ+jFuKkd8xmUR9uZEjcZcv227NOZ7YWLEpwKef\nlsJoXI7wcMvsGgAgMbEUjz3G0j6+wNnFDzHBMZZ8ufSodKhV14JzSQKamkQwd/q0Td6cRUCASKQs\nLARycjj/TkRe4fFVsSNXvZrsfbO9Rs3qrORBJhPQ0WEN4FpaxM/IPVrHExMjgjjzT0oKYN48Zf78\n2dix4wA0GmuelF5/ACUl2R54JeSMKS1+GLnCpb1dBHMnTjjMm0NGhgjm5s5lQWEiUgyHI3YRERHo\nvbYhpqPgTaVSwTjeXJcXccTO/0iSKDUyciSupQUYGnLu8dHRInAbGcRNtGixpqYJBw7U4/TpE5gz\npxAlJbORl8f8OjkZTAY0djVagjndkM7ueQ4XP5j19VmnWh3lzcXGioKEhYUi6p8E5kyRK9hfyFke\nH7E7deqU5fdz585N+UJEI0kScPXq2CDO3kyZPZGRY0fiwsJcb0deXiby8jJRXq7mm6+MBg2DqOus\nc2nxQ25cLkK0o0bWDAax31tVFVBX5zhvbt48EczNmMHaNUSkaE7l2D3zzDP4yU9+Mub4b3/7W/zb\nv/2bRxo2VRyx8x2SBHR1WYM3cyA3cmeH8YSHizx2cwCXmiqOkX+Z8uIHM0kCzp8XwdypU47z5nJz\nxegc8+aIyA+4K25xKrAbOS07UkxMDK5evTrlRngCAzvvkCSgp8d2JO7SJWBgwLnHh4XZjsSlpgIR\nEZ5tM3nOlBc/2DxZu8iZO3FCfFOwJz1dBHPMmyMiPyPLlmKlpaWQJAlGoxGlpaU299XX17Ng8TQn\nSUBv79jp1L4+5x4fGjp2OjUy0jszZcyDcQ+3LX4w6++35s01N9t/opgYa95cbKybXolj7CvkCvYX\nktu4gd3GjRuhUqmg1+vxne98x3JcpVIhKSkJzz//vMcbOFpPTw9WrFiBM2fO4OjRo5gzZ47sbZiu\nenttp1IvXQJ09vPcxwgJsV3YkJoqascx3cn/TWbxQ358PqKCoxw8oQE4e1YEc7W1jvPm5s4VAR3z\n5oiILJyail2/fj1ef/11OdozIYPBgK6uLvz0pz/FT37yE8ydO9fueZyKnZq+vrHTqXZm4+0KCho7\nnRodzc9eJRk0DKK2oxY1HTVTW/xgZs6bO3FC5M3ZS8AMCBD5cua8OY1T9dWJiPyCLFOxZr4S1AGA\nRqNBfHy8t5uhKP39tlOply45Lv01WmCg7aKG1FQxG8YgTnl69D2oaReLHxq7Gh0ufgjThiE3Ltfx\n4oeROjqseXOO8nVnzLDmzU1Uv4aIaJrjV95pZmBg7HSqozz00bTasdOpcXHKCOKYBzOWJEk2ix+a\nex3kuEEsfihIKEB+fD5mRM6wv/jBrL9fjMpVVQEXHSyoiImx7NOKuLgpvhL3Yl8hV7C/kNxkDexe\neOEF7NixAydPnsS6devwt7/9zXJfZ2cnvvOd7+CDDz5AfHw8nn76aaxbtw4A8Lvf/Q7vvPMOVq9e\njR//+MeWx9hNtiaLwUHbIK6lRRQAdoZGM7bYb3w8wI1GlE2SJFzsuWgJ5joGOhyemxqRasmXSwhN\nGP/fozlv7sQJkTdnr7B5cLA1by49XRnfGIiIZCbrXrH/+7//C7VajX379mFgYMAmsDMHcX/5y19Q\nWVmJe+65B5988onDxREPPfQQc+xG0OvFfqkjR+I6HH8m29BogKQk25G4hAQGcdOFwWRAw9UGVLdX\no6ajZtzFD1nRWWLnh7g8x4sfzCQJuHBBBHMnT9rPm1OrrXlzubnMmyOiaUvWOnbu9tRTT+HixYuW\nwK6vrw+xsbE4deoUsrPFPp0PPvggUlNT8fTTT495/N13342qqipkZmbi0UcfxYMPPjjmHCUHdkND\n9oM4Z15uQID9II71W6cX8+KH6vZq1HbWYshof982rVqLnLgc5MfnIyc2x/Hih5E6O637tI6XN1dY\nKHaEYN4cEZG8iyfcbXTDz549C41GYwnqAGDBggUoLy+3+/j33nvPqets2LABWVlZAIDo6GgUFRVZ\nch3Mz+3rt2+9tRiXLwN795ajsxOIjS3GlStAQ4O4PytLnN/YOPa2SgXcfHMxUlKAlpZyxMUBX/lK\nMTQa8fw9PcD11/vW6/XW7eeee84v+4crt/uG+pA4NxHV7dUoKyuDCSZkFWUBABq/aAQAZBVlIUwb\nhqH6IWREZWDt6rXQBmhRXl6Oozjq+Pn37QMaGlCs1QIXLqC8UTxf8bV/f+WNjUB4OIrXrAEWLED5\nl18C/f0ovhbU+cLfx9nbI9+XfKE9vO3bt9lfeNvRbfPvjdfeL93FJ0bsDh8+jG984xtoaWmxnPPK\nK6/gjTfeQFlZ2aSu4Y8jdgYD0NpqOxJ35Yr9Ml6jqdVi5G3kSFxSEme2nFVerrwEZ48tfjAzGES+\nnLne3Hh5c4WFQEaGIvLmlNhXyHPYX8hZihqxCw8PR09Pj82x7u5uRCh4LymDAWhrsw3i2tqcC+JU\nKtsgLiUFSE4Wq1ZpcpTyxuuxxQ/WC4iVrOZ9Wu3tFadWi7y5wkIgL09x3y6U0ldIHuwvJDevvOOO\n/gDJzc2FwWBAXV2dZTq2qqoK8+bN80bz3M5oFEHbyBWqra32BzhGU6lEtYeRI3HJyaJ+HBHgwcUP\nI3V2WuvNOVpanZZmrTcXFjaJV0JERFMla2BnNBoxPDwMg8EAo9EIvV4PjUaDsLAwrFmzBps3b8af\n//xnHD9+HHv37sWnn346pett3boVxcXFsn5jMpnE9OnIkbjWVjFC5wx7QVxQkGfbTP43XeLs4ofA\ngEDLzg9OL34wGxgQo3InTohdIeyJjrbWm5smhcP9ra+Qd7G/0ETKy8tt8u6mStYcu61bt+IXv/jF\nmGObN2/G1atXsXHjRksdu//8z//E2rVrJ30tOXLsTCagvd22Ttzly8DwsHOPj40dWysuONijTSYH\n/OHNd+TODw1dDTBJ9uftw7RhyIvPs+z8oFG78P3NaLTmzZ09a39YOSjImjeXmamIvDlX+ENfId/B\n/kLO8utyJ3Jwd2AnSaKkyMiRuJYW54O46GjbkbiUFLGPOZEjrix+iA2JteTLOb34wXohoLnZmjfX\n3z/2HLUayM621ptjQicRkVv59eIJXydJIo1odBA3ZH+2a4yoqLH7p7JUFznD44sfRrp61Zo356ia\ndWqqCObmzWPeHBGRH1B0YOdMjp0kic83c/BmDuT0eueuERExdiQuPNw97Sf5eHO6RJbFD2YDA8Dp\n02J0zlHeXFSUNW8uIcH1aygcp9bIFewvNBF359gpPrAbSZKA7u6xI3H2KjbYEx4+NohTcEUW8iBZ\nFj+YGY1AXZ0I5mpqHOfNzZkjRuemYd4cEZG3mAegtm3b5pbnU3SO3W9+cwAFBbMRHJxpCeLspQ/Z\nExpqG8Slpoogjp93NFmyLH4wkyTxzaWqSuzT6ihvbvZsEczl5TFvjojIi7h4YgIqlQq33y7BYDiA\noqJsxMdnOjw3JGRsEBcZySCOpka2xQ8jdXWJnLmqKsd5cykp1rw55g0QEfkELp5wkkZTgoaGUktg\nFxw8dmFDdDSDuOnOXXkwJslkWfxQ017j2cUPZoOD1ry5pib750RGipy5BQuYNzdFzJkiV7C/kNwU\nHdg1NW1FWlox0tPV+PrXRRAXE8MgjtzLYDLg3NVzlmCub7jP7nluWfxgZs6bO3FC5M3Zq4AdGGjN\nm8vKYscnIvJBfl2gWE4qlQqbN0tQqYDExFI89thybzeJFGRgeAC1nWLxQ11n3biLH3Jic8Tih7gc\nBGumUIHanDd34oTIm+uzE0CqVKLeXGEhkJ/PvDkiIj/BqVgnqFSAXn8AJSXZ3m4KKUCPvseSL9fY\n1Tju4gfzFOvMmJmTW/wwkjlv7sQJsdWJPSkpIpibP595c0RE05iiA7vExFKUlGQjL8/xwgkiwH4e\njCRJuNJ/xRLMXeq95PDxsSGxKIgvQH58PtIi0ya/+MHMnDd34gTQ2Gj/HHPeXGEhkJg4teuR05gz\nRa5gfyG5KTqw4/QruWrk4ofq9mp0DnQ6PDctIs0yMhcfGj/5xQ9mRiNQXy+Cuerq8fPmCgtF3px6\nigEkEREpiqJz7BT60siNaupqsO/YPlzWXUbHQAcikyIRlmh/6yy1So2Z0TPF4of4PEQGRU69AZIk\nCiya6805ypubPduaNxcYOPXrEhGRT2GOnROc2VKMlEuSJPQP90M3pLP56Rvug25Ih9pztSj9vBTD\nmcMwaUxABGA4YUDRnCLEp8YDcPPih5G6u615c1eu2D8nOdmaN8ctToiIFImrYp3EETtlkiQJeqN+\nTLBmE7gN9VkCOEcLHACg4qMK9M8QOzJ0VXchOj8aABDTEoONaza6b/GDmV5vmzdnr39GRFjz5pKS\n3HNdcivmTJEr2F/IWRyxI0UZMg6NCcwc/RglO3udToIJ1qAvMCAQ6ZHpiA+NR3pQOu7Nu9ct14DJ\nJPLmzPu0Dg+PPScwECgoEMHczJnMmyMiokljYEceYzAZ7AZp5qnQkT+O6sBNVYgmBOGB4QgLDEN4\nYLjNj3ROQl9qHwIDAqHN1FoWPwQHTHG6VZKAy5eteXM63dhzVCpg1ixRPJh5c36Foy/kCvYXkhsD\nO3KJSTKhb6jPbnA2ejp0wDDgkTYEBgSOCdLCA8MRprUN3sICw8adRg1ZGoIdZTsQmGMNqvS1epQs\nK5lcw3p6rHlzbW32z0lKEsEc8+aIiMgDmGNHkCQJA4YBu3lqo3/6h/shwf1/V41aMyYwGxO4XRt1\nCwxw3+hWTV0NDhw/gNMnT2POvDkoub4Eedl5zj+BXg+cOSNG58bLm5s/X0y1Jie7re3kHcyZIlew\nv5CzmGNH45IkySZvbaLp0PEWGUyWWqW2CdbsTYeaf4ICgqZeB24S8rLzkJedh/JEF958TSbg3DkR\nzFVX28+b02pF3tyCBcybIyIi2Sh6xG7Lli2KK3cybBx2mKc2esRt2GQn4HCDUG2o41G1EYFcqDbU\nK8GaR0gS0Noqgrkvv3ScNzdzpgjmCgqYN0dERBMylzvZtm2bW0bsFB3Y+ctLM5qMYwI1R1OheqPe\nI20I1gTbzVMb/ROqDUWAOsAjbfBJPT0ikKuqcpw3l5hozZuLdEPRYiIimnY4FevjTJIJA8MDTk2F\n9g/3e6QNWrXWYZ7a6FE2bYDWI23wFzZ5MEND1ry5hgb7eXPh4SKQW7BALIhQysgkTYg5U+QK9heS\nGwM7F0iShEHDoFNTof3D/R7JWwtQBYybqzZyxC0wIFA5U6Ee0lRTg/oPP8SJU6dgKi/H7NhYZPb2\nOs6by88XwdysWcybIyIin8OpWMDuIgNHU6HuKo5r01aobIK18aZDgzXBDNamQpLEStbeXjRVVaFu\n1y6U6PVimnVoCAcMBmQXFSEzXmwpBpUKyMqy5s0FBXm1+UREpEycinXCMzufwcL5C5GcljzudKin\ni+NONB0aqg2FWsXRnymRJGBwEOjtFT86nf3fe3sBgwEAUF9RgZJ+22nwEo0GpQ0NyJwzx7q1F/Pm\niIjITyg6sHvX8C7+b8//2WzqPlVBAUHjToWaR9wmKo5LTpIkYGBg4mBNp7MEbM5Sm6xT5eVdXShO\nSACSkqDOyQG+9z3mzZFdzJkiV7C/kNwUH3losjVoONcwbmCnUWvG3cFg5EibO4vjTmuSBPT3Txys\n9fYCRjdPfwcGAhERMMXHi4USQUFATIwYnVOpYEpIYFBHRER+SdE5ditfW4nAgEBEt0Tj/nvudzgV\n6q3iuIpkDtjsBWgjf9fp3B+wBQWJ1aoREdafkbfNv1/Lk2uqqUHdjh0oGZE3d0CvR/aGDcjMc2H3\nCSIioilijp0Thg4MIbUoFYvSF+G+/Pu83Rz/ZjLZBmyORtp0OnGuOwUFTRysRUS4XBA4My8P2LAB\npQcOQD00BFNgILJLShjUERGRbMwFit1F0SN2W8q2QF+rx4ZlG1zb/3M6MZmAvr6Jc9j6+twfsAUH\n2w/QRt4OD5dlBwfmwZCz2FfIFewv5CyO2DkhsS0RJctc3NRdKUwmEZw5ylsz/67T2S/AOxUhIeMH\na+bftdO7KDIREZG7KXrETpEvzWi0jrCNNy3a1+f+gC00dPxgzXxbo+jvC0RERG7HETulMRrHz1sz\n/97f7/6ALSzMcd6a+TYDNiIiIp/HT2pPMxhsV4I6Gmnrd/N+sSqVGGFzJoctIMC91/ZDzIMhZ7Gv\nkCvYX0huDOwmy2CYOFjT6TwTsIWFTZzDFhbGgI2IiGiaYY7daMPDEwdrvb1iNwT3Nth2FM3RSFtY\nGDefJyIiUhjm2Dmh9MUXMXvFClGXbGjIuRy2wUH3NkKttuaojTctyoCNiIiIpkjZI3b33YcD/f3I\nnjcPme7eyN0csE1UODc0lAGbH2AeDDmLfYVcwf5CzuKInTO6ulACoLSmBpkLFzr3mIAAx6NrI38P\nDeV+okRERORTFB3YbW1sRHF0NNTBwSJgmyhYi4gQxXUZsE07/EZNzmJfIVewv9BEuKWYk1QqFaRN\nm4CgIJSmpWH5448zYCMiIiKf5K6pWGUnf8XG4oBGg9mrVjGoo3G589sSKRv7CrmC/YXkpuip2NLE\nRGSXlIhVsUREREQKp+ypWGW+NCIiIlIYTsUSERERkQ0GdkRgHgw5j32FXMH+QnJjYEdERESkEMyx\nIyIiIvIy5tgRERERkQ0GdkRgHgw5j32FXMH+QnJjYEdERESkEMyxIyIiIvIy5tgRERERkQ0GdkRg\nHgw5j32FXMH+QnJjYEdERESkEIrOsduyZQuKi4tRXFzs7eYQERERjVFeXo7y8nJs27bNLTl2ig7s\nFPrSiIiISGG4eILIjZgHQ85iXyFXsL+Q3BjYERERESkEp2KJiIiIvIxTsURERERkg4EdEZgHQ85j\nXyFXsL+Q3BjYERERESkEc+yIiIiIvIw5dkRERERkg4EdEZgHQ85jXyFXsL+Q3BjYERERESkEc+yI\niIiIvIw5dkRERERkg4EdEZgHQ85jXyFXsL+Q3BjYERERESkEc+yIiIiIvIw5dkRERERkg4EdEZgH\nQ85jXyFXsL+Q3BjYERERESmE3+XYVVRU4Ic//CG0Wi3S0tLw2muvQaPRjDmPOXZERETkL6Ztjl1G\nRgbKyspw8OBBZGVl4e233/Z2k4iIiIh8gt8FdsnJyQgKCgIAaLVaBAQEeLlFpATMgyFnsa+QK9hf\nSG5+F9iZNTU14YMPPsC9997r7aaQAnzxxRfebgL5CfYVcgX7C8lN1sDuhRdewI033ojg4GA89NBD\nNvd1dnbiq1/9KsLDw5GVlYVdu3ZZ7vvd736HZcuW4dlnnwUA9PT04IEHHsCrr77KETtyi66uLm83\ngfwE+wq5gv2F5CZrYJeWloannnoKGzduHHPf97//fQQHB6OtrQ1///vf8b3vfQ+nT58GAPzoRz9C\nWVkZfvzjH8NgMGDt2rXYsmULcnJy5Gy+R8gxTO+Oa0z2OVx5nDPnTnTOePcrYUrE06/BXc8/mefx\npb7iant8Ed9bXDuX7y3lfvH8fG+ZmKyB3Ve/+lXcd999iIuLszne19eHf/zjH9i+fTtCQ0Nx6623\n4r777sPrr78+5jl27dqFiooKbN++HcuWLcPu3bvlar5H8M3XtXM99ebb2Ng44bV9Ad98nT/XU2++\n7CvuvQbfW3wD31ucP9fXAzuvlDt58skn0dzcjL/97W8AgMrKSixZsgR9fX2Wc37729+ivLwc77zz\nzqSukZ2djfr6ere0l4iIiMiTZs+ejbq6uik/z9gCcDJQqVQ2t3U6HSIjI22ORUREoLe3d9LXcMcf\nh4iIiMifeGVV7OhBwvDwcPT09Ngc6+7uRkREhJzNIiIiIvJrXgnsRo/Y5ebmwmAw2IyyVVVVYd68\neXI3jYiIiMhvyRrYGY1GDA4OwmAwwGg0Qq/Xw2g0IiwsDGvWrMHmzZvR39+Pjz76CHv37sX69evl\nbB4RERGRX5M1sDOvev31r3+NnTt3IiQkBL/61a8AAC+99BIGBgaQmJiIb3/723j55ZdRUFAgZ/OI\niIiI/JpXVsV6S09PD1asWIEzZ87g6NGjmDNnjrebRD6soqICP/zhD6HVapGWlobXXnsNGo1X1huR\nj2ttbcWaNWsQGBiIwMBAvPHGG2PKOhGNtGvXLjz++ONoa2vzdlPIhzU2NmLhwoWYN28eVCoVdu/e\njfj4+HEf47dbik1GaGgo3nvvPXz9618fs4CDaLSMjAyUlZXh4MGDyMrKwttvv+3tJpGPSkhIwMcf\nf4yysjJ861vfwiuvvOLtJpEPMxqN2LNnDzIyMrzdFPIDxcXFKCsrQ2lp6YRBHTDNAjuNRuPUH4UI\nAJKTkxEUFAQA0Gq13L6OHFKrrW+lPT09iImJ8WJryNft2rUL3/jGN8YsDfvm+wAACW1JREFUJCSy\n5+OPP8bSpUvxxBNPOHX+tArsiCajqakJH3zwAe69915vN4V8WFVVFW666Sa88MILWLdunbebQz7K\nPFr3zW9+09tNIT+QmpqK+vp6HDp0CG1tbfjHP/4x4WP8MrB74YUXcOONNyI4OBgPPfSQzX2dnZ34\n6le/ivDwcGRlZWHXrl12n4PflKaPqfSXnp4ePPDAA3j11Vc5YjcNTKWvLFiwAEePHsUvf/lLbN++\nXc5mkxdMtq/s3LmTo3XT0GT7S2BgIEJCQgAAa9asQVVV1YTX8stM8LS0NDz11FPYt28fBgYGbO77\n/ve/j+DgYLS1taGyshL33HMPFixYMGahBHPspo/J9heDwYC1a9diy5YtyMnJ8VLrSU6T7SvDw8PQ\narUAgMjISOj1em80n2Q02b5y5swZVFZWYufOnaitrcUPf/hDPPfcc156FSSXyfYXnU6H8PBwAMCh\nQ4cwd+7ciS8m+bEnn3xS2rBhg+W2TqeTAgMDpdraWsuxBx54QPqP//gPy+1Vq1ZJqamp0uLFi6Ud\nO3bI2l7yLlf7y2uvvSbFxcVJxcXFUnFxsfTWW2/J3mbyDlf7ytGjR6WlS5dKy5Ytk+644w7pwoUL\nsreZvGMyn0NmCxculKWN5Dtc7S/vvfeedMMNN0i33Xab9OCDD0pGo3HCa/jliJ2ZNGrU7ezZs9Bo\nNMjOzrYcW7BgAcrLyy2333vvPbmaRz7G1f6yfv16FsmeplztK4sWLcLBgwflbCL5iMl8DplVVFR4\nunnkY1ztL6tWrcKqVatcuoZf5tiZjc5R0Ol0iIyMtDkWERGB3t5eOZtFPor9hZzFvkLOYl8hV8jR\nX/w6sBsd+YaHh6Onp8fmWHd3NyIiIuRsFvko9hdyFvsKOYt9hVwhR3/x68BudOSbm5sLg8GAuro6\ny7GqqirMmzdP7qaRD2J/IWexr5Cz2FfIFXL0F78M7IxGIwYHB2EwGGA0GqHX62E0GhEWFoY1a9Zg\n8+bN6O/vx0cffYS9e/cyT2qaY38hZ7GvkLPYV8gVsvYXd630kNOWLVsklUpl87Nt2zZJkiSps7NT\n+spXviKFhYVJmZmZ0q5du7zcWvI29hdyFvsKOYt9hVwhZ39RSRILuhEREREpgV9OxRIRERHRWAzs\niIiIiBSCgR0RERGRQjCwIyIiIlIIBnZERERECsHAjoiIiEghGNgRERERKQQDOyIiIiKFYGBHRDTK\nhg0b8NRTT7n1Ob/3ve/hl7/8pVufk4hoNI23G0BE5GtUKtWYzbqn6o9//KNbn4+IyB6O2BER2cHd\nFonIHzGwIyKf8utf/xozZsxAZGQk8vPzUVpaCgCoqKjA4sWLERMTg9TUVGzatAnDw8OWx6nVavzx\nj39ETk4OIiMjsXnzZtTX12Px4sWIjo7G2rVrLeeXl5djxowZePrpp5GQkICZM2fijTfecNimd999\nF0VFRYiJicGtt96KL7/80uG5P/rRj5CUlISoqCgUFhbi9OnTAGynd++9915ERERYfgICAvDaa68B\nAKqrq7Fy5UrExcUhPz8fe/bscXit4uJibN68GUuWLEFkZCTuvPNOdHR0OPmXJiIlYmBHRD6jpqYG\nL774Io4dO4aenh7s378fWVlZAACNRoPf//736OjowKeffooDBw7gpZdesnn8/v37UVlZiSNHjuDX\nv/41Hn74YezatQvnz5/Hl19+iV27dlnObW1tRUdHBy5duoRXX30VjzzyCGpra8e0qbKyEt/5znfw\nyiuvoLOzE48++ij+5V/+BUNDQ2PO3bdvHw4fPoza2lp0d3djz549iI2NBWA7vbt371709vait7cX\nu3fvRkpKCkpKStDX14eVK1fi29/+Nq5cuYI333wTjz32GM6cOePwb7Zr1y7s2LEDbW1tGBoawjPP\nPOPy352IlIOBHRH5jICAAOj1epw6dQrDw8PIyMjArFmzAADXX389Fi1aBLVajczMTDzyyCM4ePCg\nzeN/9rOfITw8HHPmzMH8+fOxatUqZGVlITIyEqtWrUJlZaXN+du3b4dWq8XSpUtxzz334K233rLc\nZw7C/vSnP+HRRx/FwoULoVKp8MADDyAoKAhHjhwZ0/7AwED09vbizJkzMJlMyMvLQ3JysuX+0dO7\nZ8+exYYNG7B7926kpaXh3XffxcyZM/Hggw9CrVajqKgIa9ascThqp1Kp8NBDDyE7OxvBwcH4xje+\ngS+++MKFvzgRKQ0DOyLyGdnZ2XjuueewdetWJCUlYd26dWhpaQEggqDVq1cjJSUFUVFReOKJJ8ZM\nOyYlJVl+DwkJsbkdHBwMnU5nuR0TE4OQkBDL7czMTMu1RmpqasKzzz6LmJgYy8/Fixftnrts2TL8\n4Ac/wPe//30kJSXh0UcfRW9vr93X2t3djfvuuw+/+tWvcMstt1iudfToUZtrvfHGG2htbXX4NxsZ\nOIaEhNi8RiKafhjYEZFPWbduHQ4fPoympiaoVCr8+7//OwBRLmTOnDmoq6tDd3c3fvWrX8FkMjn9\nvKNXuV69ehX9/f2W201NTUhNTR3zuIyMDDzxxBO4evWq5Uen0+Gb3/ym3ets2rQJx44dw+nTp3H2\n7Fn85je/GXOOyWTCt771LZSUlOC73/2uzbVuv/12m2v19vbixRdfdPp1EtH0xsCOiHzG2bNnUVpa\nCr1ej6CgIAQHByMgIAAAoNPpEBERgdDQUFRXVztVPmTk1Ke9Va5btmzB8PAwDh8+jH/+85+4//77\nLeeaz3/44Yfx8ssvo6KiApIkoa+vD//85z/tjowdO3YMR48exfDwMEJDQ23aP/L6TzzxBPr7+/Hc\nc8/ZPH716tU4e/Ysdu7cieHhYQwPD+Ozzz5DdXW1U6+RiIiBHRH5DL1ej5///OdISEhASkoK2tvb\n8fTTTwMAnnnmGbzxxhuIjIzEI488grVr19qMwtmrOzf6/pG3k5OTLSts169fj//+7/9Gbm7umHNv\nuOEGvPLKK/jBD36A2NhY5OTkWFawjtbT04NHHnkEsbGxyMrKQnx8PH7605+Oec4333zTMuVqXhm7\na9cuhIeHY//+/XjzzTeRlpaGlJQU/PznP7e7UMOZ10hE049K4tc9IppmysvLsX79ely4cMHbTSEi\nciuO2BEREREpBAM7IpqWOGVJRErEqVgiIiIiheCIHREREZFCMLAjIiIiUggGdkREREQKwcCOiIiI\nSCEY2BEREREpxP8HC5XlsuQ4w4kAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10a23e908>"
        ]
       }
      ],
-     "prompt_number": 157
+     "prompt_number": 51
     },
     {
      "cell_type": "markdown",
diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index cea54a9..b2af45c 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9cd21a4de985d02fd65a6682bfb5690a15932533ed143e684fc8403043dfbde2"
+  "signature": "sha256:134ba1fb10441f1756c0ab8a2ecef528f9c1cfd7543d8df415e2ae855f6891f9"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -27,7 +27,7 @@
      "source": [
       "<hr>\n",
       "I am really looking forward to your comments and suggestions to improve and extend this tutorial! Just send me a quick note   \n",
-      "via Twitter: [@rasbt](https://twitter.com/rasbt)  \n",
+      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
       "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
       "<hr>"
      ]
@@ -50,7 +50,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<a name=\"sections\"><a/>\n",
+      "<a name=\"sections\"></a>\n",
       "<br>\n",
       "<br>"
      ]
@@ -59,7 +59,15 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Sections\n",
+      "## Sections  "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "\n",
       "- [Introduction to namespaces and scopes](#introduction)  \n",
       "- [1. LG - Local and Global scopes](#section_1)  \n",
       "- [2. LEG - Local, Enclosed, and Global scope](#section_2)  \n",

From 0c27e44346ad07dd912866b3de49d9a84a59671c Mon Sep 17 00:00:00 2001
From: Sebastian Raschka <raschkas@msu.edu>
Date: Thu, 8 May 2014 14:57:48 -0400
Subject: [PATCH 030/248] numba performance

---
 benchmarks/cython_least_squares.ipynb |   2 +-
 benchmarks/timeit_tests.ipynb         | 575 +++++++++++++++++++++++---
 2 files changed, 524 insertions(+), 53 deletions(-)

diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index 59ab236..1899265 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:12829b0c696b932be7039bcfd54490f1918f4155c72cd73b7fcfe1b46387decb"
+  "signature": "sha256:f31afc5ec11e69936e14bd2ddb07709f1548a2bf4e92e6915a6fa94869a056ab"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index 1222ca9..621e604 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7773fd07ffe611312feea432cbf6765914b236c0cdfbbb7c8de867f61a8b97ce"
+  "signature": "sha256:72a4e6f875d6b68497036a926caa242f8bcd993b827ce9b6dd576c414b944cd5"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -77,7 +77,8 @@
       "    - [`sum()` vs. `numpy.sum()`](#np_sum)\n",
       "    - [`range()` vs. `numpy.arange()`](#np_arange)\n",
       "    - [`statistics.mean()` vs. `numpy.mean()`](#np_mean)\n",
-      "- [Cython vs. regular (C)Python](#cython)"
+      "- [Cython vs. regular (C)Python](#cython)\n",
+      "- [Numba vs regular (C)Python & Numpy](#numba)"
      ]
     },
     {
@@ -2887,61 +2888,66 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# note that the statistics module is only available\n",
-      "# in Python 3.4.0 or newer\n",
+      "# The statistics module has been added to\n",
+      "# the standard library in Python 3.4\n",
       "\n",
-      "from statistics import mean as st_mean\n",
-      "from numpy import mean as np_mean\n",
+      "import timeit\n",
+      "import statistics as stats\n",
+      "import numpy as np\n",
       "\n",
       "def calc_mean(samples):\n",
       "    return sum(samples)/len(samples)\n",
       "\n",
-      "samples = list(range(1000000))\n",
+      "def np_mean(samples):\n",
+      "    return np.mean(samples)\n",
+      "\n",
+      "def np_mean_ary(np_array):\n",
+      "    return np.mean(np_array)\n",
+      "\n",
+      "def st_mean(samples):\n",
+      "    return stats.mean(samples)\n",
+      "\n",
+      "n = 1000000\n",
+      "samples = list(range(n))\n",
+      "samples_array = np_arange(n)\n",
+      "\n",
+      "assert(st_mean(samples) == np_mean(samples)\n",
+      "       == calc_mean(samples) == np_mean_ary(samples_array))\n",
       "\n",
-      "%timeit(st_mean(samples))\n",
+      "%timeit(calc_mean(samples))\n",
       "%timeit(np_mean(samples))\n",
-      "%timeit(calc_mean(samples))"
+      "%timeit(np_mean_ary(samples_array))\n",
+      "%timeit(st_mean(samples))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 loops, best of 3: 1.13 s per loop\n",
-        "10 loops, best of 3: 141 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10 loops, best of 3: 136 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
+       "ename": "ImportError",
+       "evalue": "No module named 'statistics'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-15-6e75a2d67720>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtimeit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mstatistics\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mImportError\u001b[0m: No module named 'statistics'"
        ]
       }
      ],
-     "prompt_number": 48
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "funcs = ['st_mean', 'np_mean', 'calc_mean']\n",
+      "funcs = ['st_mean', 'np_mean', 'calc_mean', 'np_mean_ary']\n",
       "orders_n = [10**n for n in range(1, 6)]\n",
       "times_n = {f:[] for f in funcs}\n",
       "\n",
       "for n in orders_n:\n",
       "    samples = list(range(n))\n",
       "    for f in funcs:\n",
+      "        if f == 'np_mean_ary':\n",
+      "            samples = np.arange(n)\n",
       "        times_n[f].append(min(timeit.Timer('%s(samples)' %f, \n",
       "                'from __main__ import %s, samples' %f)\n",
       "                    .repeat(repeat=3, number=1000)))"
@@ -2958,8 +2964,10 @@
       "import matplotlib.pyplot as plt\n",
       "\n",
       "labels = [('st_mean', 'statistics.mean()'), \n",
-      "          ('np_mean', 'numpy.mean()'),\n",
-      "          ('calc_mean', 'sum(samples)/len(samples)')]\n",
+      "          ('np_mean', 'numpy.mean() on list'),\n",
+      "          ('np_mean_ary', 'numpy.mean() on array'),\n",
+      "          ('calc_mean', 'sum(samples)/len(samples)')\n",
+      "          ]\n",
       "\n",
       "matplotlib.rcParams.update({'font.size': 12})\n",
       "\n",
@@ -2976,14 +2984,14 @@
       "plt.title('Performance of different approaches for calculating sample means')\n",
       "\n",
       "max_perf = max( s/c for s,c in zip(times_n['st_mean'],\n",
-      "                                   times_n['calc_mean']) )\n",
+      "                                   times_n['np_mean_ary']) )\n",
       "min_perf = min( s/c for s,c in zip(times_n['st_mean'],\n",
-      "                                   times_n['calc_mean']) )\n",
+      "                                   times_n['np_mean_ary']) )\n",
       "\n",
-      "ftext = 'using sum(samples)/len(samples) is {:.2f}x to '\\\n",
-      "        '{:.2f}x faster than statistics.mean()'\\\n",
+      "ftext = 'using numpy.mean() on np.arrays is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than statistics.mean() on lists'\\\n",
       "        .format(min_perf, max_perf)\n",
-      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "plt.figtext(.14,.15, ftext, fontsize=11, ha='left')\n",
       "\n",
       "plt.show()"
      ],
@@ -3024,6 +3032,39 @@
       "[[back to top](#sections)]"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here, we implement a linear regression via least squares fitting (with vertical offsets) by solving to fit *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
+      "$f(x) = a\\cdot x + b$.  \n",
+      "\n",
+      "Therefore we calculate the following parameters as follows:\n",
+      "\n",
+      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
+      "\n",
+      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)\n",
+      "\n",
+      "where \n",
+      "\n",
+      "\n",
+      "$S_{xy} = \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})\\quad$ (covariance)\n",
+      "\n",
+      "\n",
+      "$\\sigma{_x}^{2} = \\sum_{i=1}^{n} (x_i - \\bar{x})^2\\quad$ (variance)\n",
+      "\n",
+      "I have described the approach in more detail in this [IPython notebook](http://sebastianraschka.com/IPython_htmls/cython_least_squares.html)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "**First, the implementation in Python (CPython)**:"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -3046,7 +3087,16 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 62
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "**And now, adding type definitions and compiling the code via Cython**:"
+     ]
     },
     {
      "cell_type": "code",
@@ -3057,7 +3107,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 58
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -3083,7 +3133,83 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 63
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "**A small visual proof of concept that our least squares fit works as intended:**"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "n = 500\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "\n",
+      "slope, intercept = cy_lstsqr(x, y)\n",
+      "\n",
+      "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
+      "line_y = [slope*x_i + intercept for x_i in line_x]\n",
+      "\n",
+      "plt.figure(figsize=(8,8))\n",
+      "plt.scatter(x,y)\n",
+      "plt.plot(line_x, line_y, color='red', lw='2')\n",
+      "\n",
+      "plt.ylabel('y')\n",
+      "plt.xlabel('x')\n",
+      "plt.title('Linear regression via least squares fit')\n",
+      "\n",
+      "ftext = 'y = ax + b = {:.3f} + {:.3f}x'\\\n",
+      "        .format(slope, intercept)\n",
+      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAgIAAAH4CAYAAAA4pIUuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB2H+zOtjC0adAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10699bb10>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "**Eventually, the benchmark:**:"
+     ]
     },
     {
      "cell_type": "code",
@@ -3109,7 +3235,277 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 64
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "labels = [('py_lstsqr', 'regular Python (CPython)'), \n",
+      "          ('cy_lstsqr', 'Cython implementation')]\n",
+      "\n",
+      "\n",
+      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=2)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "max_perf = max( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
+      "                                   times_n['cy_lstsqr']) )\n",
+      "min_perf = min( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
+      "                                   times_n['cy_lstsqr']) )\n",
+      "\n",
+      "ftext = 'Using Cython is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than regular (C)Python'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.title('Performance of least square fit implementations in Cython and (C)Python')\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10ca5b4d0>"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"numba\"></a>\n",
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Numba vs regular (C)Python & Numpy"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here, we implement a linear regression via least squares fitting (with vertical offsets) by solving to fit *n* points $(x_i, y_i)$ with $i=1,2,...n,$ via linear equation of the form  \n",
+      "$f(x) = a\\cdot x + b$.  \n",
+      "\n",
+      "\n",
+      "\n",
+      "$\\Rightarrow \\pmb a = (\\pmb X^T \\; \\pmb X)^{-1} \\pmb X^T \\; \\pmb y$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I have described the approach in more detail in this [IPython notebook](http://sebastianraschka.com/IPython_htmls/cython_least_squares.html)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Matrix equation "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In order to obtain the parameters for the linear regression line for a set of multiple points, we can re-write the problem as matrix equation  \n",
+      "$\\pmb X \\; \\pmb a = \\pmb y$  \n",
+      "\n",
+      "$\\Rightarrow\\Bigg[ \\begin{array}{cc}\n",
+      "x_1 & 1  \\\\\n",
+      "... & 1 \\\\\n",
+      "x_n & 1  \\end{array} \\Bigg]$\n",
+      "$\\bigg[ \\begin{array}{c}\n",
+      "a  \\\\\n",
+      "b \\end{array} \\bigg]$\n",
+      "$=\\Bigg[ \\begin{array}{c}\n",
+      "y_1   \\\\\n",
+      "...  \\\\\n",
+      "y_n  \\end{array} \\Bigg]$  \n",
+      "\n",
+      "With a little bit of calculus, we can rearrange the term in order to obtain the parameter vector  \n",
+      "$\\pmb a = [a\\;b]^T$  \n",
+      "\n",
+      "We will implement this matrix equation in  \n",
+      "- `py_mat_lstsqr()` and  \n",
+      "- `numba_mat_lstqrs()`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Classic approach"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the more \"classic\" approach that is often found in statistics textbooks, we calculate the following parameters as follows:\n",
+      "\n",
+      "$a = \\frac{S_{x,y}}{\\sigma_{x}^{2}}\\quad$  (slope)\n",
+      "\n",
+      "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)\n",
+      "\n",
+      "where \n",
+      "\n",
+      "\n",
+      "$S_{xy} = \\sum_{i=1}^{n} (x_i - \\bar{x})(y_i - \\bar{y})\\quad$ (covariance)\n",
+      "\n",
+      "\n",
+      "$\\sigma{_x}^{2} = \\sum_{i=1}^{n} (x_i - \\bar{x})^2\\quad$ (variance)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We will implement this \"classic\" approach in\n",
+      "- `py_lstsqr()` and  \n",
+      "- `numba_lstqrs()`"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "def py_mat_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from numba import jit\n",
+      "\n",
+      "@jit\n",
+      "def numba_mat_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def py_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        temp = (x_i - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "@jit\n",
+      "def numba_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        temp = (x_i - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Verifying that both approaches yield the same results"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "n = 500\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "\n",
+      "np.testing.assert_array_almost_equal(py_lstsqr(x, y),\n",
+      "                                     py_mat_lstsqr(x, y), decimal=6)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Visual checking the least square fit"
+     ]
     },
     {
      "cell_type": "code",
@@ -3121,14 +3517,82 @@
      "metadata": {},
      "outputs": []
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "slope, intercept = py_mat_lstsqr(x, y)\n",
+      "\n",
+      "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
+      "line_y = [slope*x_i + intercept for x_i in line_x]\n",
+      "\n",
+      "plt.figure(figsize=(7,6))\n",
+      "plt.scatter(x,y)\n",
+      "plt.plot(line_x, line_y, color='red', lw='2')\n",
+      "\n",
+      "plt.ylabel('y')\n",
+      "plt.xlabel('x')\n",
+      "plt.title('Linear regression via least squares fit')\n",
+      "\n",
+      "ftext = 'y = ax + b = {:.3f} + {:.3f}x'\\\n",
+      "        .format(slope, intercept)\n",
+      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAdQAAAGQCAYAAAAX7pEWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nImIDFaqIiIgNVKgiIiI2UKGKiIjY4C99KgLU7MAmZwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10ca76cd0>"
+       ]
+      }
+     ],
+     "prompt_number": 45
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs = ['py_mat_lstsqr', 'numba_mat_lstsqr', \n",
+      "         'py_lstsqr', 'numba_lstsqr']\n",
+      "\n",
+      "orders_n = [10**n for n in range(1, 6)]\n",
+      "times_n = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x = np.asarray([x_i*np.random.randint(8,12)/10 for x_i in range(n)])\n",
+      "    y = np.asarray([y_i*np.random.randint(10,14)/10 for y_i in range(n)])\n",
+      "    for f in funcs:\n",
+      "        times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
+      "                      'from __main__ import %s, x, y' %f)\n",
+      "                              .repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 28
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import matplotlib.pyplot as plt\n",
       "\n",
-      "labels = [('py_lstsqr', 'regular Python (CPython)'), \n",
-      "          ('cy_lstsqr', 'Cython implementation')]\n",
+      "labels = [('py_mat_lstsqr', 'matrix equation in reg. (C)Python & Numpy'), \n",
+      "          ('numba_mat_lstsqr', 'matrix equation in Numba'),\n",
+      "          ('py_lstsqr', '\"classic least squares in reg. (C)Python'),\n",
+      "          ('numba_lstsqr', 'classic least squares in Numba'),]\n",
       "\n",
       "\n",
       "matplotlib.rcParams.update({'font.size': 12})\n",
@@ -3143,11 +3607,18 @@
       "plt.grid()\n",
       "plt.xscale('log')\n",
       "plt.yscale('log')\n",
-      "ftext = 'Cython % is {:.2f}x faster than (C)Python'\\\n",
-      "        .format(times_n['py_lstsqr'][-1]\\\n",
-      "                /times_n['cy_lstsqr'][-1])\n",
-      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
-      "plt.title('Performance of least square fit implementations in Cython and (C)Python')\n",
+      "\n",
+      "#max_perf = max( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
+      "#                                   times_n['numba_lstsqr']) )\n",
+      "#min_perf = min( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
+      "#                                   times_n['numba_lstsqr']) )\n",
+      "#\n",
+      "#ftext = 'Using Numba is {:.2f}x to '\\\n",
+      "#        '{:.2f}x faster than regular (C)Python and Numpy'\\\n",
+      "#        .format(min_perf, max_perf)\n",
+      "\n",
+      "plt.figtext(.14,.15, ftext, fontsize=11, ha='left')\n",
+      "plt.title('Performance of least square fit implementations in Numba and (C)Python+Numpy')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -3156,13 +3627,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbBQbOSIiIiIbxUaOiIiIyEb9PwzvsBND4hpQAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnwAAAIECAYAAACDnPP6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x108b50668>"
+        "<matplotlib.figure.Figure at 0x103b0fb10>"
        ]
       }
      ],
-     "prompt_number": 67
+     "prompt_number": 33
     },
     {
      "cell_type": "code",

From e0456a37720d4653132e758cf3fe40524948db82 Mon Sep 17 00:00:00 2001
From: Sebastian Raschka <raschkas@msu.edu>
Date: Thu, 8 May 2014 18:59:11 -0400
Subject: [PATCH 031/248] temporary update

---
 benchmarks/timeit_tests.ipynb | 226 ++++++++++++++++++++++++++++------
 1 file changed, 188 insertions(+), 38 deletions(-)

diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index 621e604..601214e 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:72a4e6f875d6b68497036a926caa242f8bcd993b827ce9b6dd576c414b944cd5"
+  "signature": "sha256:e000047e484bbe1e2fa97fe4904e21d20d5a2b3caa66f25b574f10db0e4225a6"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -78,7 +78,7 @@
       "    - [`range()` vs. `numpy.arange()`](#np_arange)\n",
       "    - [`statistics.mean()` vs. `numpy.mean()`](#np_mean)\n",
       "- [Cython vs. regular (C)Python](#cython)\n",
-      "- [Numba vs regular (C)Python & Numpy](#numba)"
+      "- [Numba vs. Cython vs. regular (C)Python & NumPy](#numba)"
      ]
     },
     {
@@ -3263,10 +3263,10 @@
       "                                   times_n['cy_lstsqr']) )\n",
       "min_perf = min( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
       "                                   times_n['cy_lstsqr']) )\n",
-      "\n",
       "ftext = 'Using Cython is {:.2f}x to '\\\n",
       "        '{:.2f}x faster than regular (C)Python'\\\n",
       "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
       "plt.title('Performance of least square fit implementations in Cython and (C)Python')\n",
       "plt.show()"
      ],
@@ -3297,7 +3297,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "# Numba vs regular (C)Python & Numpy"
+      "# Numba vs. Cython vs. regular (C)Python & Numpy"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
      ]
     },
     {
@@ -3356,15 +3363,16 @@
       "$\\pmb a = [a\\;b]^T$  \n",
       "\n",
       "We will implement this matrix equation in  \n",
-      "- `py_mat_lstsqr()` and  \n",
-      "- `numba_mat_lstqrs()`"
+      "- Python/CPython: `py_mat_lstsqr()`   \n",
+      "- Numba: `numba_mat_lstsqrs()`  \n",
+      "- Cython: `cy_mat_lstsqr()`"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Classic approach"
+      "### \"Classic\" approach "
      ]
     },
     {
@@ -3391,8 +3399,18 @@
      "metadata": {},
      "source": [
       "We will implement this \"classic\" approach in\n",
-      "- `py_lstsqr()` and  \n",
-      "- `numba_lstqrs()`"
+      "- Python/CPython: `py_lstsqr()`  \n",
+      "- Numba: `numba_lstsqrs()`  \n",
+      "- Cython: `cy_lstsqrs()`  "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<br>"
      ]
     },
     {
@@ -3400,7 +3418,34 @@
      "collapsed": false,
      "input": [
       "import numpy as np\n",
-      "\n",
+      "import scipy.stats\n",
+      "from numba import jit"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Matrix equation:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
       "def py_mat_lstsqr(x, y):\n",
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    X = np.vstack([x, np.ones(len(x))]).T\n",
@@ -3409,14 +3454,12 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 59
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from numba import jit\n",
-      "\n",
       "@jit\n",
       "def numba_mat_lstsqr(x, y):\n",
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
@@ -3426,7 +3469,28 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 24
+     "prompt_number": 60
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "def cy_mat_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### \"Classic\" approach:"
+     ]
     },
     {
      "cell_type": "code",
@@ -3449,7 +3513,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 25
+     "prompt_number": 61
     },
     {
      "cell_type": "code",
@@ -3473,13 +3537,72 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 62
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "def cy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
+      "    x_avg = sum(x)/len(x)\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for x_i, y_i in zip(x,y):\n",
+      "        temp = (x_i - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y_i - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 63
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### NumPy and SciPy libraries:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def numpy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return np.linalg.lstsq(X,y)[0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def scipy_lstsqr(x,y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    return scipy.stats.linregress(x, y)[0:2]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 71
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Verifying that both approaches yield the same results"
+      "### Verifying that the different approaches yield the same results"
      ]
     },
     {
@@ -3493,12 +3616,27 @@
       "x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
       "y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
       "\n",
-      "np.testing.assert_array_almost_equal(py_lstsqr(x, y),\n",
-      "                                     py_mat_lstsqr(x, y), decimal=6)"
+      "np.testing.assert_array_almost_equal(\n",
+      "        py_lstsqr(x, y), py_mat_lstsqr(x, y), decimal=6)\n",
+      "np.testing.assert_array_almost_equal(\n",
+      "        numpy_lstsqr(x,y), py_lstsqr(x, y), decimal=6)\n",
+      "np.testing.assert_array_almost_equal(\n",
+      "        scipy_lstsqr(x,y), py_lstsqr(x, y), decimal=6)\n",
+      "\n",
+      "print('ok')"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "ok\n"
+       ]
+      }
+     ],
+     "prompt_number": 80
     },
     {
      "cell_type": "markdown",
@@ -3556,6 +3694,13 @@
      ],
      "prompt_number": 45
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Benchmarking:"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -3564,8 +3709,9 @@
       "import random\n",
       "random.seed(12345)\n",
       "\n",
-      "funcs = ['py_mat_lstsqr', 'numba_mat_lstsqr', \n",
-      "         'py_lstsqr', 'numba_lstsqr']\n",
+      "funcs = ['py_mat_lstsqr', 'numba_mat_lstsqr', 'cy_mat_lstsqr', \n",
+      "         'py_lstsqr', 'numba_lstsqr', 'cy_lstsqr',\n",
+      "         'numpy_lstsqr', 'scipy_lstsqr']\n",
       "\n",
       "orders_n = [10**n for n in range(1, 6)]\n",
       "times_n = {f:[] for f in funcs}\n",
@@ -3581,7 +3727,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 28
+     "prompt_number": 66
     },
     {
      "cell_type": "code",
@@ -3589,10 +3735,14 @@
      "input": [
       "import matplotlib.pyplot as plt\n",
       "\n",
-      "labels = [('py_mat_lstsqr', 'matrix equation in reg. (C)Python & Numpy'), \n",
+      "labels = [('py_mat_lstsqr', 'matrix equation in reg. (C)Python & NumPy'), \n",
       "          ('numba_mat_lstsqr', 'matrix equation in Numba'),\n",
-      "          ('py_lstsqr', '\"classic least squares in reg. (C)Python'),\n",
-      "          ('numba_lstsqr', 'classic least squares in Numba'),]\n",
+      "          ('cy_mat_lstsqr', 'matrix equation in Cython & NumPy'),\n",
+      "          ('py_lstsqr', '\"classic\" least squares in reg. (C)Python'),\n",
+      "          ('numba_lstsqr', '\"classic\" least squares in Numba'),\n",
+      "          ('cy_lstsqr', '\"classic\" least squares in Cython'),\n",
+      "          ('numpy_lstsqr', 'least squares via np.linalg.lstsq()'),\n",
+      "          ('scipy_lstsqr', 'least_squares via scipy.stats.linregress()'),]\n",
       "\n",
       "\n",
       "matplotlib.rcParams.update({'font.size': 12})\n",
@@ -3608,17 +3758,17 @@
       "plt.xscale('log')\n",
       "plt.yscale('log')\n",
       "\n",
-      "#max_perf = max( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
-      "#                                   times_n['numba_lstsqr']) )\n",
-      "#min_perf = min( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
-      "#                                   times_n['numba_lstsqr']) )\n",
-      "#\n",
-      "#ftext = 'Using Numba is {:.2f}x to '\\\n",
-      "#        '{:.2f}x faster than regular (C)Python and Numpy'\\\n",
-      "#        .format(min_perf, max_perf)\n",
+      "max_perf = max( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
+      "                                   times_n['cy_lstsqr']) )\n",
+      "min_perf = min( py/nu for py,nu in zip(times_n['py_lstsqr'],\n",
+      "                                   times_n['cy_lstsqr']) )\n",
+      "\n",
+      "ftext = 'Using Cython is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than regular (C)Python'\\\n",
+      "        .format(min_perf, max_perf)\n",
       "\n",
       "plt.figtext(.14,.15, ftext, fontsize=11, ha='left')\n",
-      "plt.title('Performance of least square fit implementations in Numba and (C)Python+Numpy')\n",
+      "plt.title('Performance of least square fit implementations')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -3627,13 +3777,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnwAAAIECAYAAACDnPP6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAICkHiAvheE4HrYHAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x103b0fb10>"
+        "<matplotlib.figure.Figure at 0x10a753b90>"
        ]
       }
      ],
-     "prompt_number": 33
+     "prompt_number": 69
     },
     {
      "cell_type": "code",

From f3b534c3f5256699a2915e4fe91199e041601a0a Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 8 May 2014 21:04:11 -0400
Subject: [PATCH 032/248] fixed statistics module dependend benchmark

---
 benchmarks/timeit_tests.ipynb | 60 +++++++++++++++++++++++++++--------
 1 file changed, 46 insertions(+), 14 deletions(-)

diff --git a/benchmarks/timeit_tests.ipynb b/benchmarks/timeit_tests.ipynb
index 601214e..1099508 100644
--- a/benchmarks/timeit_tests.ipynb
+++ b/benchmarks/timeit_tests.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:e000047e484bbe1e2fa97fe4904e21d20d5a2b3caa66f25b574f10db0e4225a6"
+  "signature": "sha256:a4749ce2a9f843d9846081abaa9265690ebabfaf5a0aa18877f65945b1f56805"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -2909,7 +2909,7 @@
       "\n",
       "n = 1000000\n",
       "samples = list(range(n))\n",
-      "samples_array = np_arange(n)\n",
+      "samples_array = np.arange(n)\n",
       "\n",
       "assert(st_mean(samples) == np_mean(samples)\n",
       "       == calc_mean(samples) == np_mean_ary(samples_array))\n",
@@ -2923,17 +2923,38 @@
      "metadata": {},
      "outputs": [
       {
-       "ename": "ImportError",
-       "evalue": "No module named 'statistics'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-15-6e75a2d67720>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtimeit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mstatistics\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mImportError\u001b[0m: No module named 'statistics'"
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "100 loops, best of 3: 26.2 ms per loop\n",
+        "1 loops, best of 3: 144 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "100 loops, best of 3: 3.21 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1 loops, best of 3: 1.12 s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -2955,7 +2976,18 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 49
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -3001,13 +3033,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSCEY2BEREREpxP8HC5XlsuQ4w4kAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXuIfaui5S+SyaUwAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10a23e908>"
+        "<matplotlib.figure.Figure at 0x1058d4f28>"
        ]
       }
      ],
-     "prompt_number": 51
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",

From 791a4fc5471f044b4f5f78f2b69728e591337645 Mon Sep 17 00:00:00 2001
From: Sebastian Raschka <raschkas@msu.edu>
Date: Fri, 9 May 2014 20:28:22 -0400
Subject: [PATCH 033/248] cython_least_square V2

---
 benchmarks/cython_least_squares.ipynb | 1804 +++++--------------------
 1 file changed, 302 insertions(+), 1502 deletions(-)

diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index 1899265..5fc6944 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f31afc5ec11e69936e14bd2ddb07709f1548a2bf4e92e6915a6fa94869a056ab"
+  "signature": "sha256:c63d806ef3ce407085d530f46ff34fdb5ed6bb79e32545e1ea8d5134d46bf946"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
-      "last updated: 05/06/2014\n",
+      "last updated: 05/09/2014\n",
       "\n",
       "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)"
@@ -67,14 +67,14 @@
      "source": [
       "- [Introduction](#introduction)\n",
       "- [Least squares fit implementations](#implementations)\n",
+      "    - [1. The matrix approach in (C)Python and NumPy](#matrix_approach)\n",
+      "    - [2. The classic approach in (C)Python, Cython, and Numba](#classic_approach)\n",
+      "    - [3. Using the `numpy.linalg.lstsq` function](#numpy_func)\n",
+      "    - [4. Using the `scipy.stats.linregress` function](#scipy_func)\n",
       "- [Generating sample data and benchmarking](#sample_data)\n",
-      "- [Compiling the Python code via Cython in the IPython notebook](#cython_nb)\n",
-      "- [Performance growth rates for different sample sizes](#sample_sizes)\n",
-      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)\n",
-      "- [Appendix I: Cython vs. Numba](#numba)\n",
-      "- [Appendix II: Cython with and without type declarations](#type_declarations)\n",
-      "- [Appendix III: Cython performance after replacing list comprehensions by explicit for loops](#explicit_loops)\n",
-      "- [Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting](#showdown)"
+      "    - [Performance growth rates: (C)Python vs. Cython vs. Numba](#performance1)\n",
+      "    - [Performance growth rates: NumPy and SciPy library functions](#performance2)\n",
+      "- [Bonus: How to use Cython without the IPython magic](#cython_bonus)"
      ]
     },
     {
@@ -102,7 +102,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Introduction"
+      "# Introduction"
      ]
     },
     {
@@ -220,7 +220,7 @@
      "metadata": {},
      "source": [
       "\n",
-      "## Least squares fit implementations"
+      "# Least squares fit implementations"
      ]
     },
     {
@@ -234,6 +234,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "<a name='matrix_approach'></a>\n",
       "<br>\n",
       "<br>"
      ]
@@ -242,7 +243,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### 1. The matrix approach"
+      "### 1. The matrix approach in (C)Python and NumPy"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
      ]
     },
     {
@@ -266,13 +274,20 @@
       "which I will refer to as the \"matrix approach\"."
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Matrix approach implemented in NumPy and (C)Python"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import numpy as np\n",
       "\n",
-      "def lin_lstsqr_mat(x, y):\n",
+      "def py_matrix_lstsqr(x, y):\n",
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
       "    X = np.vstack([x, np.ones(len(x))]).T\n",
       "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
@@ -286,6 +301,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "<a name='classic_approach'></a>\n",
       "<br>\n",
       "<br>"
      ]
@@ -294,7 +310,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### 2. The classic approach"
+      "### 2. The classic approach in (C)Python, Cython, and Numba"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
      ]
     },
     {
@@ -314,19 +337,40 @@
       "$b = \\bar{y} - a\\bar{x}\\quad$ (y-axis intercept)"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Note: I refrained from using list comprehensions and convenience functions such as `zip()` in\n",
+      "order to maximize the performance for the Cython compilation into C code in the later sections.\n",
+      "    "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Implemented in (C)Python"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def classic_lstsqr(x, y):\n",
+      "def py_classic_lstsqr(x, y):\n",
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
+      "    len_x = len(x)\n",
+      "    x_avg = sum(x)/len_x\n",
       "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for i in range(len_x):\n",
+      "        temp = (x[i] - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y[i] - y_avg)\n",
       "    slope = cov_xy / var_x\n",
       "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
+      "    return (slope, y_interc) "
      ],
      "language": "python",
      "metadata": {},
@@ -337,32 +381,22 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### 3. Using the lstsq numpy function"
+      "#### Implemented in Cython"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "For our convenience, `numpy` has a function that can computes the least squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
+      "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
+      "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  "
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def numpy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return np.linalg.lstsq(X,y)[0]"
+      "%load_ext cythonmagic"
      ],
      "language": "python",
      "metadata": {},
@@ -370,47 +404,81 @@
      "prompt_number": 3
     },
     {
-     "cell_type": "markdown",
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "def cy_classic_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, var_x, cov_xy,\\\n",
+      "         slope, y_interc, x_i, y_i\n",
+      "    cdef int len_x\n",
+      "    len_x = len(x)\n",
+      "    x_avg = sum(x)/len_x\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for i in range(len_x):\n",
+      "        temp = (x[i] - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y[i] - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
      "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
+     "outputs": [],
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### 4. Using the linregress scipy function"
+      "#### Implemented in Numba"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Also scipy has a least squares function, `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.   \n",
-      "The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
+      "Like we did with Cython before, we will use the minimalist approach to Numba and see how the two - Cython and Numba - compare against each other.  \n",
+      "\n",
+      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import scipy.stats\n",
+      "from numba import jit\n",
       "\n",
-      "def scipy_lstsqr(x,y):\n",
+      "@jit\n",
+      "def numba_classic_lstsqr(x, y):\n",
       "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    return scipy.stats.linregress(x, y)[0:2]"
+      "    len_x = len(x)\n",
+      "    x_avg = sum(x)/len_x\n",
+      "    y_avg = sum(y)/len(y)\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for i in range(len_x):\n",
+      "        temp = (x[i] - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y[i] - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "<a name='numpy_func'></a>\n",
       "<br>\n",
       "<br>"
      ]
@@ -419,25 +487,42 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Teaser"
+      "### 3. Using the `numpy.linalg.lstsq` function"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<br>\n",
-      "The following plot below should give you an impression of how the performance of the 4 different approaches will compare to each other at the end of this article.\n",
-      "\n",
-      "![Performance of the final Cython implemenation of the least squares fit](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/cython_final_leastsqr.png)\n",
-      "<br>"
+      "[[back to top](#sections)]"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<a name='sample_data'></a>\n",
+      "For our convenience, `numpy` has a function that can computes the least squares solution of a linear matrix equation. For more information, please refer to the [documentation](http://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.lstsq.html)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def numpy_lstsqr(x, y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    X = np.vstack([x, np.ones(len(x))]).T\n",
+      "    return np.linalg.lstsq(X,y)[0]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='scipy_func'></a>\n",
       "<br>\n",
       "<br>"
      ]
@@ -446,7 +531,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Generating sample data and benchmarking"
+      "### 4. Using the `scipy.stats.linregress` function"
      ]
     },
     {
@@ -460,28 +545,55 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In order to test our different least squares fit implementations, we will generate some sample data:  \n",
-      "- 500 sample points for the x-component within the range [0,500)  \n",
-      "- 500 sample points for the y-component within the range [100,600)  \n",
-      "\n",
-      "where each sample point is multiplied by a random value within\n",
-      "the range [0.8, 12) to simulate some scatter in our dataset."
+      "Also scipy has a least squares function, `scipy.stats.linregress()`, which returns a tuple of 5 different attributes, where the 1st value in the tuple is the slope, and the second value is the y-axis intercept, respectively.   \n",
+      "The documentation for this function can be found [here](http://docs.scipy.org/doc/scipy-0.13.0/reference/generated/scipy.stats.linregress.html)."
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import random\n",
-      "random.seed(12345)\n",
+      "import scipy.stats\n",
       "\n",
-      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
-      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
+      "def scipy_lstsqr(x,y):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    return scipy.stats.linregress(x, y)[0:2]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='sample_data'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Generating sample data and benchmarking"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
     },
     {
      "cell_type": "markdown",
@@ -511,20 +623,26 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%pylab inline"
+      "%matplotlib inline"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "from matplotlib import pyplot as plt\n",
+      "import random\n",
+      "\n",
+      "random.seed(12345)\n",
       "\n",
-      "slope, intercept = lin_lstsqr_mat(x, y)\n",
+      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
+      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]\n",
+      "\n",
+      "slope, intercept = py_matrix_lstsqr(x, y)\n",
       "\n",
       "line_x = [round(min(x)) - 1, round(max(x)) + 1]\n",
       "line_y = [slope*x_i + intercept for x_i in line_x]\n",
@@ -551,11 +669,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x107806610>"
+        "<matplotlib.figure.Figure at 0x10c2c0e90>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -586,7 +704,7 @@
      "input": [
       "import prettytable\n",
       "\n",
-      "params = [appr(x,y) for appr in [lin_lstsqr_mat, classic_lstsqr, numpy_lstsqr, scipy_lstsqr]]\n",
+      "params = [appr(x,y) for appr in [py_matrix_lstsqr, py_classic_lstsqr, numpy_lstsqr, scipy_lstsqr]]\n",
       "\n",
       "print(params)\n",
       "\n",
@@ -605,24 +723,25 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[array([   0.95181895,  107.01399744]), (0.95181895319126741, 107.01399744459181), array([   0.95181895,  107.01399744]), (0.95181895319126764, 107.01399744459175)]\n",
-        "+------------------+----------------+---------------+\n",
-        "|                  |     slope      |  y-intercept  |\n",
-        "+------------------+----------------+---------------+\n",
-        "| matrix approach  | 0.951818953191 | 107.013997445 |\n",
-        "| classic approach | 0.951818953191 | 107.013997445 |\n",
-        "|  numpy function  | 0.951818953191 | 107.013997445 |\n",
-        "|  scipy function  | 0.951818953191 | 107.013997445 |\n",
-        "+------------------+----------------+---------------+\n"
+        "[array([   0.95181895,  107.01399744]), (0.9518189531912674, 107.01399744459181), array([   0.95181895,  107.01399744]), (0.95181895319126764, 107.01399744459175)]\n",
+        "+------------------+--------------------+--------------------+\n",
+        "|                  |       slope        |    y-intercept     |\n",
+        "+------------------+--------------------+--------------------+\n",
+        "| matrix approach  |   0.951818953191   |   107.013997445    |\n",
+        "| classic approach | 0.9518189531912674 | 107.01399744459181 |\n",
+        "|  numpy function  |   0.951818953191   |   107.013997445    |\n",
+        "|  scipy function  |   0.951818953191   |   107.013997445    |\n",
+        "+------------------+--------------------+--------------------+\n"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "<a name='performance1'></a>\n",
       "<br>\n",
       "<br>"
      ]
@@ -631,15 +750,21 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "\n",
-      "#### Initial performance comparison"
+      "# Performance growth rates: (C)Python vs. Cython vs. Numba"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "To get an initial impression of how the performances of the different least squares implementations compare against each other, let us do a quick benchmark using the `timeit` module via IPython's `%timeit` magic."
+      "Now, finally let us take a look at the effect of different sample sizes on the relative performances for each approach."
      ]
     },
     {
@@ -647,84 +772,88 @@
      "collapsed": false,
      "input": [
       "import timeit\n",
+      "import random\n",
+      "random.seed(12345)\n",
+      "\n",
+      "funcs =  ['py_classic_lstsqr', 'cy_classic_lstsqr', 'numba_classic_lstsqr']\n",
+      "\n",
+      "orders_n = [10**n for n in range(2, 5)]\n",
+      "perf1 = {f:[] for f in funcs}\n",
+      "\n",
+      "for n in orders_n:\n",
+      "    x_list = ([x_i*np.random.randint(8,12)/10 for x_i in range(n)])\n",
+      "    y_list = ([y_i*np.random.randint(10,14)/10 for y_i in range(n)])\n",
+      "    for f in funcs:\n",
+      "        perf1[f].append(timeit.Timer('%s(x_list,y_list)' %f, \n",
+      "                      'from __main__ import %s, x_list, y_list' %f).timeit(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "labels = [ ('py_classic_lstsqr', '\"classic\" least squares in reg. (C)Python'),\n",
+      "           ('cy_classic_lstsqr', '\"classic\" least squares in Cython'),\n",
+      "           ('numba_classic_lstsqr','\"classic\" least squares in Numba')\n",
+      "           ]\n",
+      "\n",
+      "plt.rcParams.update({'font.size': 12})\n",
       "\n",
-      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
-      "                     \"numpy function\",\"scipy function\"],\n",
-      "                    [lin_lstsqr_mat, classic_lstsqr, \n",
-      "                     numpy_lstsqr, scipy_lstsqr]):\n",
-      "    print(\"\\n{}: \".format(lab), end=\"\")\n",
-      "    %timeit appr(x, y)"
+      "fig = plt.figure(figsize=(10,8))\n",
+      "for lb in labels:\n",
+      "    plt.plot(orders_n, perf1[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "plt.xlabel('sample size n')\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=4)\n",
+      "plt.grid()\n",
+      "plt.xscale('log')\n",
+      "plt.yscale('log')\n",
+      "max_perf = max( py/cy for py,cy in zip(perf1['py_classic_lstsqr'],\n",
+      "                                   perf1['cy_classic_lstsqr']) )\n",
+      "min_perf = min( py/cy for py,cy in zip(perf1['py_classic_lstsqr'],\n",
+      "                                   perf1['cy_classic_lstsqr']) )\n",
+      "ftext = 'Using Cython is {:.2f}x to '\\\n",
+      "        '{:.2f}x faster than regular (C)Python'\\\n",
+      "        .format(min_perf, max_perf)\n",
+      "plt.figtext(.14,.75, ftext, fontsize=11, ha='left')\n",
+      "plt.title('Performance of least square fit implementations')\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "matrix approach: 10000 loops, best of 3: 158 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "classic approach: 1000 loops, best of 3: 1.6 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "numpy function: 1000 loops, best of 3: 217 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "scipy function: 1000 loops, best of 3: 366 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIgXiHzlDZFWOcLMWAAAAAElFTkSuQmCC\n",
        "text": [
-        "\n"
+        "<matplotlib.figure.Figure at 0x10cfbd6d0>"
        ]
       }
      ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "The timing above indicates that the \"classic\" approach (Python's standard library functions only) is significantly worse in performance than the other implemenations - roughly by a magnitude of 10. However, we should keep in mind that this experiment was done for a fixed sample size n=500."
-     ]
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "<a name='performance2'></a>\n",
       "<br>\n",
-      "<br>\n",
-      "<a name=\"cython_nb\"></a>"
+      "<br>"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Compiling the Python code via Cython in the IPython notebook"
+      "# Performance growth rates: NumPy and SciPy library functions"
      ]
     },
     {
@@ -738,289 +867,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
-      "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  \n",
-      "Let us also compile the functions for the other 3 least squares approaches - those already use numpy objects and therefore we don't expect any performance gain here, but it is good to be thorough.\n",
+      "Okay, now that we have seen that Cython improved the performance of our Python code and that the matrix equation (using NumPy) performed even better, let us see how they compare against the in-built NumPy and SciPy library functions.  \n",
       "\n",
-      "**Note**  \n",
-      "Of course Cython has much more horsepower under its hood - more than I am showing in this section of this article (for example, I am not using Cython's type definitions via `cdef` here, but we will get to it [in a later section - Appendix II](#type_declarations)). \n",
-      "The focus of this section should be on how to speed up existing Python code by making only minimal changes to it.   "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "import numpy as np\n",
-      "import scipy.stats\n",
-      "cimport numpy as np\n",
-      "\n",
-      "def cy_lin_lstsqr_mat(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)\n",
-      "\n",
-      "def cy_classic_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)\n",
-      "\n",
-      "def cy_numpy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return np.linalg.lstsq(X,y)[0]\n",
-      "\n",
-      "def cy_scipy_lstsqr(x,y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    return scipy.stats.linregress(x, y)[0:2]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Comparing the compiled Cython code to the original Python code"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "for lab,appr in zip([\"matrix approach\",\"classic approach\",\n",
-      "                     \"numpy function\",\"scipy function\"],\n",
-      "                    [(lin_lstsqr_mat, cy_lin_lstsqr_mat), \n",
-      "                     (classic_lstsqr, cy_classic_lstsqr),\n",
-      "                     (numpy_lstsqr, cy_numpy_lstsqr),\n",
-      "                     (scipy_lstsqr, cy_scipy_lstsqr)]):\n",
-      "    print(\"\\n\\n{}: \".format(lab))\n",
-      "    %timeit appr[0](x, y)\n",
-      "    %timeit appr[1](x, y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "matrix approach: \n",
-        "10000 loops, best of 3: 159 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 159 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "\n",
-        "classic approach: \n",
-        "1000 loops, best of 3: 1.62 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "10000 loops, best of 3: 126 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "\n",
-        "numpy function: \n",
-        "1000 loops, best of 3: 218 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 220 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "\n",
-        "scipy function: \n",
-        "1000 loops, best of 3: 372 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000 loops, best of 3: 354 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "As we've seen before, our \"classic\" implementation of the least square method is pretty slow compared to the other approaches which use Numpy objects. This is not surprising, since Numpy is highly optimized and uses compiled C/C++ and Fortran code already. This explains why there is no significant difference if we used Cython to compile the numpy-objects-containing functions.  \n",
-      "However, we were able to speed up the \"classic approach\" quite significantly via Cython, roughly by 1500%.\n",
-      "\n",
-      "The following 2 code blocks are just to visualize our results in a bar plot."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "funcs =  ['classic_lstsqr', 'cy_classic_lstsqr', \n",
-      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
-      "labels = ['classic approach','classic approach (cython)', \n",
-      "          'matrix approach', 'numpy function', 'scipy function']\n",
-      "\n",
-      "times = [timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
-      "         for f in funcs]\n",
-      "\n",
-      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "x_pos = np.arange(len(funcs))\n",
-      "plt.bar(x_pos, times, align='center', alpha=0.5)\n",
-      "plt.xticks(x_pos, labels, rotation=45)\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.title('Performance of different least square fit implementations')\n",
-      "plt.grid()\n",
-      "plt.show()\n",
-      "\n",
-      "x_pos = np.arange(len(funcs[1:]))\n",
-      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
-      "plt.xticks(x_pos, labels[1:], rotation=45)\n",
-      "plt.ylabel('relative performance gain')\n",
-      "plt.title('Performance gain compared to the classic least square implementation')\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAFhCAYAAABwNN3iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGAAsITAMwzAvsYTAMAzDAAD+D41KT4VmEkzWAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1079680d0>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAFhCAYAAAALEB8uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFOfaBvB7qALSqyBFNEZQFKyIDWOJXYMaJZagJpZE\nj8YUOcYoJ8YWNZZoTEyixJ5jjI0gNkQ9KvZOBNtiBbtUhV2e7w+/nbCAqDjMLMvzuy6vZJadmXfu\n3Zln531ndgUiIjDGGGMGzkjpBjDGGGNy4ILHGGOsUuCCxxhjrFLggscYY6xS4ILHGGOsUuCCxxhj\nrFKQteClp6ejdevWsLGxweeffy7nqiukLl26YOXKlUo3Q2/5+Phg9+7dsq3PyMgIV65cKdd1RERE\n4Kuvviq35VtbW0OlUr3SPAkJCfD09CyfBhm4evXqYd++fZIvV6VSwcjICAUFBZIvW2mjRo3CN998\nUy7LNnnRE3x8fHDnzh0YGxvDysoKnTt3xqJFi2BlZfXKK1u6dClcXFyQkZFRpsZWNrGxsUo3Qa8J\nggBBEEr8W0REBDw9PTF16tQyLTs0NBSDBg3CsGHDXqeJr6y0bZJCZmZmuS37dbzu66Wvzp07p3QT\nZPWqr2N0dDR+/fVX7N+/X3xsyZIl5dW8F5/hCYKAmJgYZGZm4sSJEzh27NgrV18iQkFBAVJTU+Hn\n51emhqrV6jLNxyoGfXt9y7PovAh/F4R+0rf3KCsDegEfHx/avXu3OP3ZZ59Rt27diIjo0KFD1Lx5\nc7Kzs6MGDRpQQkKC+Lw2bdrQl19+SS1atCALCwsaOHAgmZqakpmZGVWtWpV2795NT58+pbFjx5K7\nuzu5u7vTuHHj6OnTp0REtGfPHvLw8KBZs2aRm5sbDRo0iKKioqhPnz40cOBAsra2poCAAEpJSaHp\n06eTi4sLeXl50Y4dO8Q2LFu2jPz8/Mja2pp8fX3pp59+Ev+mXf7cuXPJxcWFqlWrRsuXLxf/npOT\nQ+PHjydvb2+ytbWlli1bUm5u7gu3u6jjx49TYGAgWVtbU9++fendd9+lSZMmERHRgwcPqGvXruTs\n7Ez29vbUrVs3unHjhk6Gv/zyCxERLV++nFq0aEGfffYZ2dvbU40aNWjbtm3PXe+1a9fonXfeIWdn\nZ3J0dKTRo0cTEZFGo6GpU6eSt7c3ubi40ODBg+nx48dERHT16lUSBIGWL19Onp6e5ODgQEuWLKEj\nR45QQEAA2dnZicvRtikkJIRGjx5Ntra2VKdOHZ33ysvkr319Bw8eTAUFBTRjxgyqWbMmOTo60rvv\nvksPHjwQ51mxYgV5eXmRo6MjTZs2rdh7U+unn37Sea/16NGDiIiSkpKoTZs2ZGdnR3Xr1qUtW7aU\nmN3EiRPJ2NiYqlSpQlWrVqUxY8YQEZEgCPTjjz/SG2+8QXZ2dvTxxx/rzPfrr7+Sn58f2dvb09tv\nv02pqanPfX32798vvoc8PT3pt99+IyKiiIiIl35/LF++nHx9fcna2ppq1KhBq1evJiKiixcvUuvW\nrcnW1pacnJyoX79+4jyCINDly5eJqPT3eGF79uyh6tWri9M3b96ksLAwcnZ2pho1atDChQvFvx0+\nfJiCg4PJzs6OqlWrRqNHj6a8vDzx7+PGjSMXFxeysbGhgIAAOnfu3HNfr6JKmpeI6N69e9S9e3ey\nsbGhpk2b0qRJk6hly5ZE9M97WqPRiMspvF9dunSJ2rZtS46OjuTk5EQDBgygR48eic/19vamWbNm\nUUBAAFWpUoU0Gs0r7f/e3t7ie3TKlCmvdPxq06YNRUZGUtOmTcnGxoZ69uwp7g9Ft+vRo0c0dOhQ\nqlatGnl4eNCkSZPEv2n3008++YTs7OyoZs2adODAAVq2bBl5enqSi4uL+P4jInry5Al9+umn5OXl\nRa6urjRy5EjxfVHacfN5r6N2n7a2tiZ/f3/auHEjET3bH6tUqULGxsZUtWpVsre3JyKi999/X9wH\niIiWLl1KtWrVIgcHB+rRowfdunVL/NuL9smiXqrg7dq1i4ieHUTr1q1LkydPphs3bpCjo6N40N25\ncyc5OjrSvXv3xBfL29ubkpKSSKPRUH5+PkVERNBXX30lLvurr76i5s2b0927d+nu3bsUEhIi/n3P\nnj1kYmJCkZGRlJeXR7m5uTRlyhSqUqUK7dixg9RqNQ0ePJi8vb1p+vTppFar6eeff6YaNWqIy//r\nr7/oypUrRES0d+9esrS0pBMnTugsf8qUKaRWqyk2NpYsLS3FN/tHH31Ebdu2pVu3bolv8qdPnz53\nu+/evVssu6dPn5KXlxctXLiQ1Go1/fnnn2RmZiZu4/379+nPP/+k3NxcyszMpL59+1KvXr3E+UND\nQ+nXX38lomdvWlNTU/rll1+ooKCAlixZQu7u7iW+Zmq1murXr0/jx4+nnJwcevLkCR04cICInh2U\na9WqRVevXqWsrCwKCwujQYMGEdE/O9GoUaPo6dOntGPHDjIzM6NevXrR3bt36ebNm+Ti4kJ79+4V\n22RiYkLz588ntVpNv//+O9na2oo75cvkX/j1nT9/PjVv3pxu3rxJeXl5NGLECAoPDyciovPnz1PV\nqlVp//799PTpUxo/fjyZmJiUWPCIqNh7LS8vj2rWrEkzZsyg/Px8io+PJ2tra0pOTi5x/sLZawmC\nQN27d6fHjx/TtWvXyNnZmeLi4oiIaNOmTVSrVi26cOECaTQa+uabbygkJKTEZatUKrK2tqZ169aR\nWq2m+/fv06lTp8R2a3f20t4fWVlZZGNjQykpKURElJaWRufPnyciov79+9P06dOJ6Nl7UPvaa7dB\nW/Ce9x4vqnDB02g01LBhQ5o6dSrl5+fTlStXyNfXl7Zv305Ezz7gHT58mDQaDalUKvLz86P58+cT\nEVFcXBw1atRI/IB14cIFun37domvV1GlzduvXz/q168f5eTk0Llz58jDw4NatWpFRCUXvMKv7aVL\nl2jXrl2Ul5dHd+/epdatW9O4cePE53p7e1NQUBDduHGDnjx58kr7P5HuCcOrHr/atGlDHh4edP78\necrOzqbevXvTwIEDS9yuXr160ciRIyknJ4fu3LlDTZs2FT9gavfT6OhoKigooEmTJpGHh4f4YWTH\njh1kbW1N2dnZRPTsg0XPnj3p4cOHlJmZSd27d6d///vf4nuhtONmSa/j+vXrxdfq999/JysrK0pL\nSyMioujoaPHDiVbhZezevZucnJzo5MmT9PTpUxozZgy1bt1afG5p+2RJXljwvL29qWrVqmRnZ0fe\n3t708ccfU25uLs2cOVM8UGq9/fbb4ieF0NBQmjJlSrENKVy5a9asqXOWsn37dvLx8SGiZ8GamZnp\n7IBTpkyhjh07itNbtmyhqlWrUkFBARERZWRkkCAI4k5RVK9evWjBggXi8i0sLHR2BBcXF3FntbCw\noDNnzhRbxou2u7C9e/eSh4eHzmMtW7Z87o598uRJ8VMOUfGCV6tWLfFv2dnZJAgCpaenF1vOwYMH\nydnZWWfbtN566y1asmSJOJ2cnEympqak0WjEnajwJyhHR0f673//K0737t1bPIAtX768WNFt2rQp\nrVy5ssTtK5p/0dfXz89Pp4DdunWLTE1NSa1W03/+8x+x+Gm338zMrNSCV/i9tm/fPnJzc9N5Tnh4\nOEVFRZU4f2hoqHgWoCUIgk7xePfdd2nWrFlERNSpUyedAqnRaMjS0pKuXbtWbNnTp0+nsLCwl2p3\nYYXfH1lZWWRnZ0cbNmygnJwcnecNHjyYhg8frnM2WHgbLl++XOp7vKjCBS8xMZG8vLyKbc+QIUNK\nnHfevHn0zjvvENGzg1ft2rUpMTGx2HuztO0mIoqPjy9xXrVaTaampjofXCZOnFjqGV5JH2a0Nm7c\nSEFBQeK0j4+PTs/Pq+z/2vkLF7xXOX6FhoaKhYbo2RmRmZkZFRQU6GxXWloamZub65ydr1mzhtq2\nbUtEz/bTN954Q/zbmTNnSBAEunPnjviYo6MjnT59mgoKCsjKykr8UET07HiiLcSlHTeJXvw6EhEF\nBgbS5s2bxbaVVvCGDh1KEyZMEP+WlZVFpqamYu9JSfvkzJkzn7vulxrD27x5Mx4+fAiVSoVFixah\nSpUqSE1Nxfr162Fvby/+O3DgANLS0sR5X3Rl161bt+Dt7S1Oe3l54datW+K0s7MzzMzMdOZxcXER\n/9/CwgJOTk7ieIuFhQUAICsrCwCwbds2BAcHw9HREfb29oiNjcX9+/fF+R0dHWFk9E8ElpaWyMrK\nwr179/DkyRPUrFmzWJtfZrsLb5+Hh4fOY56enuIYTU5ODkaMGAEfHx/Y2tqiTZs2ePz48XPHcNzc\n3HTaWnhbC7t+/Tq8vb11tk3r9u3bxTJXq9VIT08XH3N1dRX/38LCoth0dna2OF10+7y9vXH79m0A\nL86/6OurUqnwzjvviLn6+/vDxMQE6enpuH37NqpXr66z/Y6OjiXFVKJbt24Vez96e3vj5s2bz52n\npHG8oq+BNv/U1FSMHTtWbLu2bSUt/8aNG/D19X1hm0t7f1hZWeH333/Hjz/+CHd3d3Tr1g3JyckA\ngG+//RZEhKZNm6JevXpYvnx5sWWX9h4vTWpqKm7duqXz/p8xYwbu3LkDAEhJSUG3bt1QrVo12Nra\n4ssvvxRf87feegujR4/Gxx9/DFdXV4wYMeKlL6Jp27ZtifPevXsXarVa57X18vJ66e1JT09H//79\nUb16ddja2mLQoEE671FA9zj2Kvt/SV7l+FV03V5eXsjPz8e9e/d0lpmamor8/HxUq1ZNbNPIkSNx\n9+5d8TlF92Hg2f5X+LGsrCzcvXsXOTk5aNSokbiszp0766zzecfN51mxYgWCgoLE5Z07d65Yxs9T\n9HhlZWUFR0dHnf3qeftkScp8W4KXlxcGDRqEhw8fiv8yMzPxxRdfiM950cC/u7u7ziXS165dg7u7\n+3Pnf5ULCZ4+fYrevXvjiy++wJ07d/Dw4UN06dLlpS4IcHJyQpUqVXDp0qVif3uZ7daqVq1asQPe\ntWvXxO2YO3cuUlJScOTIETx+/Bh79+4FPTvrfuntLImnpyeuXbsGjUZT7G8lZW5iYqKzQ7yKotuX\nmpoKd3f3l8q/6Ovp5eWFuLg4nWxzcnLg7u6OatWq4fr16+Jzc3JySt1pii7b3d0d169f11l/amqq\nThEtbf4X8fLywtKlS3Xanp2djeDg4GLP9fT0xOXLl1/Y9he9Pzp27IgdO3YgLS0NderUwYcffgjg\n2cFt6dKluHnzJn766Sd89NFHxW6nKO09XhpPT0/UqFFDZzszMjIQExMD4Nkl5f7+/rh06RIeP36M\nadOm6Vw6P2bMGBw7dgxJSUlISUnB7Nmzdba5NCXN6+LiAhMTE1y7dk18XuH/115NnpOTIz5WuDhN\nnDgRxsbGOHfuHB4/foyVK1cWu9S/cNteZf+XQtHtMjU1hZOTk85zPD09YW5ujvv374ttevz4Mc6e\nPfvK63NycoKFhQWSkpLEZT169Oilr6wv+jqmpqZi+PDhWLx4MR48eICHDx+iXr164nv4VWtEdnY2\n7t+/X+yD9ssqc8EbOHAgtm7dih07dkCj0eDJkydISEjQOQAWPXAXnQ4PD8c333yDe/fu4d69e/j6\n668xaNCg567zVQpBXl4e8vLy4OTkBCMjI2zbtg07dux4qXmNjIwwdOhQjB8/Hrdv34ZGo8GhQ4eQ\nl5f3UtutFRISAmNjYyxatAhqtRqbN2/G0aNHxb9nZWXBwsICtra2ePDgAf7zn/+89PaVplmzZqhW\nrRoiIyORk5ODJ0+e4ODBgwCeZT5v3jyoVCpkZWVh4sSJ6N+/f4lng89T+HW4c+cOFi5ciPz8fKxf\nvx4XLlxAly5dypT/yJEjMXHiRHEnv3v3LrZs2QIA6NOnD2JiYnDgwAHk5eVh8uTJpd6D5OrqqnOQ\nDw4OhqWlJb799lvk5+cjISEBMTEx6N+//3PnL60oaXPQZjFy5EhMnz4dSUlJAIDHjx9j/fr1Jc43\nYMAA7Nq1C+vXr4darcb9+/dx+vTpYsss7f1x584dbN68GdnZ2TA1NYWVlRWMjY0BAOvXr8eNGzcA\nAHZ2dhAEodjrW9p7vDRNmzaFtbU1vv32W+Tm5kKj0eDcuXM4duyY2GZra2tYWlriwoULWLJkiXhQ\nO3bsGA4fPoz8/HxYWlqiSpUqYpuLvl5FPW9eIyMjhIWFISoqCrm5uUhKSsKKFSvEdTo7O8PDwwMr\nV66ERqPBsmXLdF7XrKwsWFlZwcbGBjdv3hQL8PO8yv7/uogIq1atwt9//42cnBxMnjwZffv2LVYk\nqlWrho4dO2L8+PHIzMxEQUEBLl++XKb7/4yMjPDhhx9i3Lhx4hnizZs3X/rYWfR1zM7OhiAIcHJy\nQkFBAZYvX65zq4arqytu3LiB/Px8ne3W7gPh4eFYvnw5Tp8+jadPn2LixIkIDg5+7ln8i2pEmQte\n9erVsXnzZkyfPh0uLi7w8vLC3LlzS/0EX/Qeo0mTJqFx48aoX78+6tevj8aNG2PSpEkvPf/zngM8\nu8F24cKFePfdd+Hg4IC1a9eiZ8+epc5b2Jw5cxAQEIAmTZrA0dER//73v1FQUPDc7S7p4Gtqaoo/\n//wTv/76K+zt7bF69Wp069ZN7MYbN24ccnNz4eTkhJCQEHTu3Pm5bXqZbdcyMjLC1q1bcenSJXh5\necHT0xP//e9/AQBDhw7FoEGD0Lp1a/j6+sLS0hLff//9S2VS0nOaNWuGixcvwtnZGV999RU2bNgA\ne3v7MuU/duxY9OjRAx07doSNjQ2aN2+OI0eOAAD8/f2xePFivPfee3B3d4eDg0OpXebDhg1DUlIS\n7O3tERYWBlNTU2zduhXbtm2Ds7MzRo8ejZUrV6J27dolzj927Fj88ccfcHBwwLhx456bg3YbevXq\nhQkTJqB///6wtbVFQEAAtm/fXuJ8np6eiI2Nxdy5c+Ho6IigoCCcOXOm2DJLe38UFBRg3rx58PDw\ngKOjI/bv3y/ev3Ts2DEEBwfD2toaPXv2xMKFC+Hj41Ms8+e9x5+3rQBgbGyMmJgYnDp1Cr6+vnB2\ndsbw4cPFM4A5c+ZgzZo1sLGxwfDhw3U+UGRkZGD48OFwcHCAj48PnJycxC+gKPp6FVXavIsWLUJW\nVhbc3NwwdOhQDBkyROc49PPPP2P27NlwcnJCUlISWrRoIf5typQpOHHiBGxtbdG9e3f07t271H3g\nVfb/kjJ82eOX9v8HDRqEiIgIVKtWDXl5eVi4cGGJz12xYgXy8vLg7+8PBwcH9O3bVzyTfZVjBwDM\nmjULtWrVQnBwMGxtbdGhQwekpKS81LxFX0d/f398+umnaN68Odzc3HDu3Dm0bNlSfH67du1Qt25d\nuLm5id29hdvbrl07TJ06Fb1794a7uzuuXr2KdevWlZpfqcewUkcX9cCQIUPIxcWF6tWrV+xvc+bM\nIUEQ6P79+wq0rGyaNm1K0dHRSjdDEiUNODOmNEN5X5Z2cQ0rG73/Ls0hQ4YgLi6u2OPXr1/Hzp07\ndQY09dG+ffuQlpYGtVqN3377DefOnUOnTp2UbhZjrAIg/hICSel9wWvVqhXs7e2LPT5+/Hh8++23\nCrTo1SQnJyMwMBD29vaYN28e/vjjjzJfIKJvyvtrsBgrC0N6XxrKdugLgSrARwiVSoXu3buLVx1t\n3rwZCQkJmDdvHmrUqIHjx4/DwcFB4VYyxhjTZy/88mh9k5OTg+nTp2Pnzp3iYxWgZjPGGFNYhSt4\nly9fhkqlQoMGDQA8u4m3UaNGOHLkiM5NnQBQq1atF15azhhjTFeDBg1w6tQppZshOb0fwysqICAA\n6enpuHr1Kq5evYrq1avjxIkTxYod8Kw40v/f06HP/6ZMmaJ4GwzpH+fJWerrv4qSp/a+UEOj9wUv\nPDwcISEhSElJgaenZ7GvSTKEQd1X/UFOVjrOUzqcpbQ4T2XpfZfm2rVrS/17ef8CNWOMMcOg92d4\nlUFERITSTTAonKd0OEtpcZ7KqhC3JZSVIAgw4M1jjLFyYajHTj7D0wMJCQlKN8GgcJ7S4SylxXkq\niwseY4yxSoG7NBljjOkw1GMnn+ExxhirFLjg6QHu15cW5ykdzlJanKeyuOAxxhirFCr1GF5kVCTS\nHqXJ2CL95WbnhplRM5VuBmNMDxjqGJ7ef9NKeUp7lAafXj5KN0MvqDaplG4CY4yVK+7S1AOqUyql\nm2BQeJxEOpyltDhPZXHBY4wxVilwwdMDPoE+SjfBoISGhirdBIPBWUqL81QWFzzGGGOVAhc8PcBj\neNLicRLpcJbS4jyVxQWPMcZYpcAFTw/wGJ60eJxEOpyltDhPZXHBY4wxVilwwdMDPIYnLR4nkQ5n\nKS3OU1lc8BhjjFUKXPD0AI/hSYvHSaTDWUqL81QWFzzGGGOVAhc8PcBjeNLicRLpcJbS4jyVpfcF\nb+jQoXB1dUVAQID42Oeffw4/Pz80aNAAYWFhePz4sYItZIwxVhHofcEbMmQI4uLidB7r2LEjzp8/\nj9OnT6N27dqYMWOGQq2TBo/hSYvHSaTDWUqL81SW3he8Vq1awd7eXuexDh06wMjoWdObNWuGGzdu\nKNE0xhhjFYjeF7wXWbZsGbp06aJ0M14Lj+FJi8dJpMNZSovzVFaFLnjTpk2DmZkZ3nvvPaWbwhhj\nTM+ZKN2AsoqOjkZsbCx2795d6vMiIiLg4+MDALCzs0NgYKDYj552Iw049c8YmvZMq7JOaz99avPh\naZ4uTF/aU9GntfSlPaGhoUhISEB0dDQAiMdLQyQQESndiBdRqVTo3r07zp49CwCIi4vDp59+ir17\n98LJyem58wmCgNI2L2JcBHx6+Ujd3ApJtUmF6PnRSjeDMaYHXnTsrKj0vkszPDwcISEhSE5Ohqen\nJ5YtW4YxY8YgKysLHTp0QFBQED766COlm/laeAxPWkU/SbOy4yylxXkqS++7NNeuXVvssaFDhyrQ\nEsYYYxWZ3p/hVQZ8H560tGMU7PVxltLiPJXFBY8xxlilwAVPD/AYnrR4nEQ6nKW0OE9lccFjjDFW\nKXDB0wM8hictHieRDmcpLc5TWVzwGGOMVQpc8PQAj+FJi8dJpMNZSovzVBYXPMYYY5UCFzw9wGN4\n0uJxEulwltLiPJXFBY8xxlilwAVPD/AYnrR4nEQ6nKW0OE9lccFjjDFWKcj25dHJycmYM2cOVCoV\n1Go1gGc/QREfHy9XE/QWj+FJi8dJpMNZSovzVJZsBa9v374YNWoUPvjgAxgbGwN4VvAYY4wxOcjW\npWlqaopRo0ahWbNmaNy4MRo3boxGjRrJtXq9xmN40uJxEulwltLiPJUlW8Hr3r07Fi9ejNu3b+PB\ngwfiP8YYY0wOAsn0O+4+Pj4ldmFevXq13Nb5op+pjxgXAZ9ePuW2/opEtUmF6PnRSjeDMaYHXnTs\nrKhkG8NTqVRyrYoxxhgrptwL3u7du9GuXTts2LChxDO8sLCw8m6C3lOdUvGVmhJKSEjgq+EkwllK\ni/NUVrkXvH379qFdu3bYunUrFzzGGGOKkW0MTwk8hvfyeAyPMabFY3gSiImJQVJSEp48eSI+Nnny\nZDmbwMpRZFQk0h6lKd0MveBm54aZUTOVbgZjrBDZCt6IESOQm5uL+Ph4fPjhh1i/fj2aNWsm1+r1\nmqGM4aU9StOLM2Z9yFO1SaXo+qXCY07S4jyVJdt9eAcPHsSKFSvg4OCAKVOmIDExEcnJyS+cb+jQ\noXB1dUVAQID42IMHD9ChQwfUrl0bHTt2xKNHj8qz6YwxxgyAbAXPwsICAGBpaYmbN2/CxMQEaWkv\n7v4aMmQI4uLidB6bOXMmOnTogJSUFLRr1w4zZ1bsriOlz0YMDecpHT4bkRbnqSzZCl63bt3w8OFD\nfP7552jUqBF8fHwQHh7+wvlatWoFe3t7nce2bNmC999/HwDw/vvvY9OmTeXSZsYYY4ZDtoI3efJk\n2Nvbo3fv3lCpVLhw4QKmTp1apmWlp6fD1dUVAODq6or09HQpmyo7/i5NaXGe0uHvfpQW56ks2S5a\nKenGc1tbWwQEBMDFxaXMyxUEodRfXYiIiICPjw8AwM7ODoGBgWK3QtqNNODUP11g2gOl3NNaSq1f\nO63dGbX5vOo056k7/bp56sP0qVOn9Ko9FX1aX/NMSEhAdHQ0AIjHS0Mk2314Xbt2xaFDh9C2bVsA\nz8Ju2LAhrl69ismTJ2Pw4MHPnVelUqF79+44e/YsAKBOnTpISEiAm5sbbt++jbZt2+LChQvF5uP7\n8F6eFPfhcZ7/4PsaWUVmqPfhydalmZ+fj7///hsbNmzAhg0bkJSUBEEQcPjwYcyaNeuVltWjRw/8\n9ttvAIDffvsNvXr1Ko8mM8YYMyCyFbzr16+L424A4OLiguvXr8PR0RFmZmbPnS88PBwhISFITk6G\np6cnli9fjsjISOzcuRO1a9dGfHw8IiMj5diEcsNjTtLiPKXDY07S4jyVJdsYXtu2bdG1a1e8++67\nICJs2LABoaGhyM7Ohp2d3XPnW7t2bYmP79q1q7yayhhjzADJVvAWL16MDRs24MCBAwCe3U7Qu3dv\nCIKAPXv2yNUMvcT3jUmL85QO3zcmLc5TWbIVPEEQ0KdPH/Tp00euVTLGGGMi2cbw2PPxmJO0OE/p\n8JiTtDhPZXHBY4wxVinIWvBycnJe6gujKxsec5IW5ykdHnOSFuepLNkK3pYtWxAUFIS3334bAHDy\n5En06NFDrtUzxhir5GQreFFRUTh8+LD4RdBBQUG4cuWKXKvXazzmJC3OUzo85iQtzlNZshU8U1PT\nYvfbGRnxECJjjDF5yFZx6tati9WrV0OtVuPixYsYM2YMQkJC5Fq9XuMxJ2lxntLhMSdpcZ7Kkq3g\nff/99zh//jzMzc0RHh4OGxsbzJ8/X67VM8YYq+RkK3hWVlaYPn06jh07hmPHjmHatGmoUqWKXKvX\nazzmJC3OUzo85iQtzlNZshW89u3b49GjR+L0gwcPxCs2GWOMsfImW8G7d++ezkUrDg4OFf6XyqXC\nY07S4jzUAiASAAAgAElEQVSlw2NO0uI8lSVbwTM2NkZqaqo4rVKp+CpNxhhjspGt4kybNg2tWrXC\nwIEDMXDgQLRu3RrTp0+Xa/V6jcecpMV5SofHnKTFeSpLtl9L6NSpE44fP47ExEQIgoD58+fDyclJ\nrtUzxhir5GQreACQl5cHBwcHqNVqJCUlAQBat24tZxP0Eo85SYvzlA6POUmL81SWbAVvwoQJ+P33\n3+Hv7w9jY2PxcS54jDHG5CBbwdu4cSOSk5Nhbm4u1yorDNUpFZ+VSIjzlE5CQgKflUiI81SWbBet\n1KxZE3l5eXKtjjHGGNMh2xmehYUFAgMD0a5dO/EsTxAELFy4UK4m6C0+G5EW5ykdPhuRFuepLNkK\nXo8ePYr9/p0gCHKtnjHGWCUnW8GLiIiQa1UVDo85SYvzlA6POUmL81SWbGN4KSkp6NOnD/z9/VGj\nRg3UqFEDvr6+r7XMGTNmoG7duggICMB7772Hp0+fStRaxhhjhka2gjdkyBCMHDkSJiYmSEhIwPvv\nv48BAwaUeXkqlQo///wzTpw4gbNnz0Kj0WDdunUStlg+fDYiLc5TOnw2Ii3OU1myFbzc3Fy0b98e\nRARvb29ERUXhr7/+KvPybGxsYGpqipycHKjVauTk5MDDw0PCFjPGGDMkshW8KlWqQKPRoFatWli0\naBH+/PNPZGdnl3l5Dg4O+PTTT+Hl5QV3d3fY2dmhffv2ErZYPvzdj9LiPKXD3/0oLc5TWbJdtDJ/\n/nzk5ORg4cKF+Oqrr5CRkYHffvutzMu7fPky5s+fD5VKBVtbW/Tt2xerV68u1k0aEREBHx8fAICd\nnR0CAwPFboW0G2nAqX+6wLQHSrmntZRav3ZauzNq83nVac5Td/p189SH6VOnTulVeyr6tL7mmZCQ\ngOjoaAAQj5eGSCAiUroRZfH7779j586d+OWXXwAAK1euRGJiIhYvXiw+RxAElLZ5EeMi4NPLp7yb\nWiGoNqkQPT/6tZbBef5DijwZU8qLjp0VlWxdmkePHsU777yDoKAgBAQEICAgAPXr1y/z8urUqYPE\nxETk5uaCiLBr1y74+/tL2GLGGGOGRLYuzQEDBmDOnDmoV6+eJD/82qBBAwwePBiNGzeGkZERGjZs\niOHDh0vQUvnxfWPS4jylw/eNSYvzVJZsBc/Z2bnYN628ri+++AJffPGFpMtkjDFmmGQreFOmTMGw\nYcPQvn17mJmZAXjWTxwWFiZXE/QWn41Iy1DyjIyKRNqjNKWbgehN0Uo3AW52bpgZNVPpZrw2PrtT\nlmwF77fffkNycjLUarVOlyYXPMZKlvYojS8C+n+qTSqlm8AMgGwF79ixY7hw4QJ/YXQJeMxJWpyn\ndDhLafEYnrJku0ozJCQESUlJcq2OMcYY0yHbGd6hQ4cQGBiIGjVq6Pwe3pkzZ+Rqgt7iT9DS4jyl\nw1lKi8/ulCVLwSMiLF26FF5eXnKsjjHGGCtGti7Njz76CD4+PsX+Mf7uR6lxntLhLKXF36WpLFkK\nniAIaNSoEY4cOSLH6hhjjLFiZBvDS0xMxKpVq+Dt7Q0rKysAPIanxeMk0uI8pcNZSovH8JQlW8Hb\nvn07AIi3JRjiF5MyxhjTX7KN4fn4+ODRo0fYsmULtm7disePH/MY3v/jcRJpcZ7S4SylxWN4ypKt\n4C1YsAADBw7E3bt3kZ6ejoEDB2LhwoVyrZ4xxlglJ1uX5i+//ILDhw+L43eRkZEIDg7Gv/71L7ma\noLd4nERanKd0OEtp8RiesmQ7wwOg8x2aUvxEEGOMMfayZKs6Q4YMQbNmzRAVFYUpU6YgODgYQ4cO\nlWv1eo3HSaTFeUqHs5QWj+Epq9y7NK9cuQJfX1+MHz8ebdq0wf/+9z8IgoDo6GgEBQWV9+oZY4wx\nADIUvL59++L48eNo164ddu/ejUaNGpX3KiscHieRFucpHc5SWjyGp6xyL3gajQbTpk1DcnIyvvvu\nO5377wRBwPjx48u7CYwxxlj5j+GtW7cOxsbG0Gg0yMzMRFZWlvgvMzOzvFdfIfA4ibQ4T+lwltLi\nMTxllfsZXp06dfD555/D29sb4eHh5b06xhhjrESyXKVpbGyMOXPmyLGqConHSaTFeUqHs5QWj+Ep\nS7bbEjp06IA5c+bg+vXrePDggfiPMcYYk4Ns37Sybt06CIKAxYsX6zx+9epVuZqgt1SnVPxJWkKc\np3Q4S2klJCTwWZ6CZCt4KpVK8mU+evQIH3zwAc6fPw9BELBs2TIEBwdLvh7GGGMVn2xdmtnZ2Zg6\ndSo+/PBDAMDFixcRExPzWsscO3YsunTpgr///htnzpyBn5+fFE2VHX+ClhbnKR3OUlp8dqcsWb9a\nzMzMDAcPHgQAuLu748svvyzz8h4/foz9+/eLX09mYmICW1tbSdrKGGPM8MhW8C5fvowJEybAzMwM\nAMRfTSirq1evwtnZGUOGDEHDhg3x4YcfIicnR4qmyo7vdZIW5ykdzlJafB+esmQbwzM3N0dubq44\nffnyZZibm5d5eWq1GidOnMCiRYvQpEkTjBs3DjNnzsTXX3+t87yIiAjxh2bt7OwQGBgodiuk3UgD\nTv3TbaPdueWe1lJq/dpp7c6ozedVpzlP3WlDyDPtUprir6dUeerD9KlTp/SqPdrphIQEREdHA4BB\n/zC3QIW/66sc7dixA9OmTUNSUhI6dOiAAwcOIDo6Gm3bti3T8tLS0tC8eXPxKs///e9/mDlzps64\noCAIKG3zIsZFwKeXT5nWb2hUm1SInh/9WsvgPP/BeUpLijzZy3vRsbOiku0Mr2PHjmjYsCEOHz4M\nIsLChQvh5ORU5uW5ubnB09MTKSkpqF27Nnbt2oW6detK2GLGGGOGRLYxPCLC3r17sWvXLsTHx2P/\n/v2vvczvv/8eAwYMQIMGDXDmzBlMnDhRgpbKj8dJpMV5SoezlBaP4SlLtjO8jz76CJcvX0Z4eDiI\nCD/99BN27tyJH374oczLbNCgAY4ePSphKxljjBkq2Qrenj17kJSUBCOjZyeVERER8Pf3l2v1eo3v\ndZIW5ykdzlJafB+esmTr0qxVqxauXbsmTl+7dg21atWSa/WMMcYqOdkKXkZGBvz8/NCmTRuEhobC\n398fmZmZ6N69O3r06CFXM/QSj5NIi/OUDmcpLR7DU5ZsXZpF748D/rn0VRAEuZrBGGOskpKt4HHf\n9fPxOIm0OE/pcJbS4uOgsmTr0mSMMcaUxAVPD/A4ibQ4T+lwltLiMTxlyVrwcnJykJycLOcqGWOM\nMQAyFrwtW7YgKCgIb7/9NgDg5MmTlf7qTC0eJ5EW5ykdzlJaPIanLNkKXlRUFA4fPgx7e3sAQFBQ\nEK5cuSLX6hljjFVyshU8U1NT2NnZ6a7ciIcQAR4nkRrnKR3OUlo8hqcs2SpO3bp1sXr1aqjValy8\neBFjxoxBSEiIXKtnjDFWyclW8L7//nucP38e5ubmCA8Ph42NDebPny/X6vUaj5NIi/OUDmcpLR7D\nU5ZsN54nJydj+vTpmD59ulyrZIwxxkSyneGNHz8ederUwVdffYVz587JtdoKgcdJpMV5SoezlBaP\n4SlLtoKXkJCAPXv2wMnJCSNGjEBAQACmTp0q1+oZY4xVcrJeJlmtWjWMHTsWP/74Ixo0aFDiF0pX\nRjxOIi3OUzqcpbR4DE9ZshW8pKQkREVFoV69ehg9ejRCQkJw8+ZNuVbPGGOskpPtopWhQ4eif//+\n2L59Ozw8PORabYWgOqXiT9IS4jylY0hZRkZFIu1RmqJtSLuRBrfqboq2wc3ODTOjZiraBqXIVvAS\nExPlWhVjjBWT9igNPr18lG3EKeW7iVWbVIquX0nlXvD69u2L9evXIyAgoNjfBEHAmTNnyrsJek/p\nHcDQcJ7S4SylxXkqq9wL3oIFCwAAMTExICKdv/EvnTPGGJNLuV+04u7uDgD44Ycf4OPjo/Pvhx9+\nKO/VVwh8r5O0OE/pcJbS4jyVJdtVmjt27Cj2WGxs7GsvV6PRICgoCN27d3/tZTHGGDNc5d6luWTJ\nEvzwww+4fPmyzjheZmYmWrRo8drLX7BgAfz9/ZGZmfnay1IK9+tLi/OUDmcpLc5TWeVe8N577z10\n7twZkZGRmDVrljiOZ21tDUdHx9da9o0bNxAbG4svv/wS3333nRTNZYwxZqDKvUvT1tYWPj4+WLdu\nHby9vWFpaQkjIyNkZ2fj2rVrr7XsTz75BLNnz67wv6vH/frS4jylw1lKi/NUlmz34W3ZsgWffvop\nbt26BRcXF6SmpsLPzw/nz58v0/JiYmLg4uKCoKCgUr+QNSIiAj4+PgAAOzs7BAYGil/vk3YjTee+\nGO2bUe5pLaXWr53W5qjN51WnOU/daUPIM+1SmuKvJ+cp7bRW4XwSEhIQHR397Pn/f7w0RAIVvVeg\nnNSvXx/x8fHo0KEDTp48iT179mDlypVYtmxZmZY3ceJErFy5EiYmJnjy5AkyMjLQu3dvrFixQnyO\nIAjFboUoLGJchPI3ouoJ1SYVoudHv9YyOM9/cJ7S4jyl8zJZvujYWVHJ1hdoamoKJycnFBQUQKPR\noG3btjh27FiZlzd9+nRcv34dV69exbp16/DWW2/pFDvGGGOsMNkKnr29PTIzM9GqVSsMGDAA//rX\nv1C1alXJll+Rb2Lnfn1pcZ7S4SylxXkqS7aCt2nTJlhaWmLevHno1KkTatWqha1bt0qy7DZt2mDL\nli2SLIsxxphhku2iFe3ZnLGxMSIiIuRabYXA9+ZIi/OUDmcpLc5TWeVe8KpWrfrc7kZBEJCRkVHe\nTWCMMcbKv0szKysLmZmZJf7jYvcM9+tLi/OUDmcpLc5TWbLesb1//34sX74cAHD37l1cvXpVztUz\nxhirxGQreFFRUZg1axZmzJgBAMjLy8OAAQPkWr1e4359aXGe0uEspcV5Kku2grdx40Zs2bIFVlZW\nAAAPDw9kZWXJtXrGGGOVnGwFz9zcXOc7L7Ozs+Vatd7jfn1pcZ7S4SylxXkqS7aC17dvX4wYMQKP\nHj3C0qVL0a5dO3zwwQdyrZ4xxlglJ8t9eESEfv364cKFC7C2tkZKSgqmTp2KDh06yLF6vcf9+tLi\nPKXDWUqL81SWbDeed+nSBefOnUPHjh3lWiVjjDEmkqVLUxAENGrUCEeOHJFjdRUO9+tLi/OUDmcp\nLc5TWbKd4SUmJmLVqlXw9vYWr9QUBAFnzpyRqwmMMcYqMdkK3vbt2+VaVYXD/frS4jylw1lKi/NU\nlmwFz5B/RZcxxpj+k/WrxVjJuF9fWpyndDhLaXGeyuKCxxhjrFLggqcHuF9fWpyndDhLaXGeyuKC\nxxhjrFLggqcHuF9fWpyndDhLaXGeyuKCxxhjrFLggqcHuF9fWpyndDhLaXGeyuKCxxhjrFLggqcH\nuF9fWpyndDhLaXGeyqqwBe/69eto27Yt6tati3r16mHhwoVKN4kxxpgek+2rxaRmamqKefPmITAw\nEFlZWWjUqBE6dOgAPz8/pZv2yrhfX1qcp3Q4S2lxnsqqsGd4bm5uCAwMBABUrVoVfn5+uHXrlsKt\nYowxpq8qbMErTKVS4eTJk2jWrJnSTSkT7teXFucpHc5SWpynsipsl6ZWVlYW+vTpgwULFqBq1arF\n/h4RESH+UoOdnR0CAwMRGhoKAEi7kQac+qebQftmlHtaS6n1a6cTEhIAQMznVac5T91pQ8gz7VKa\n4q8n5ynttFbhfBISEhAdHf3s+Qb8yzYCEZHSjSir/Px8dOvWDZ07d8a4ceOK/V0QBJS2eRHjIuDT\ny6ccW1hxqDapED0/+rWWwXn+g/OUFucpnZfJ8kXHzoqqwnZpEhGGDRsGf3//EosdY4wxVliFLXgH\nDhzAqlWrsGfPHgQFBSEoKAhxcXFKN6tMuF9fWpyndDhLaXGeyqqwY3gtW7ZEQUGB0s1gjDFWQVTY\nMzxDwvfmSIvzlA5nKS3OU1lc8BhjjFUKXPD0APfrS4vzlA5nKS3OU1lc8BhjjFUKXPD0APfrS4vz\nlA5nKS3OU1lc8BhjjFUKXPD0APfrS4vzlA5nKS3OU1lc8BhjjFUKXPD0APfrS4vzlA5nKS3OU1lc\n8BhjjFUKXPD0APfrS4vzlA5nKS3OU1lc8BhjjFUKXPD0APfrS4vzlA5nKS3OU1lc8BhjjFUKXPD0\nAPfrS4vzlA5nKS3OU1lc8BhjjFUKXPD0APfrS4vzlA5nKS3OU1lc8BhjjFUKXPD0APfrS4vzlA5n\nKS3OU1lc8BhjjFUKXPD0APfrS4vzlA5nKS3OU1lc8BhjjFUKFbrgxcXFoU6dOnjjjTcwa9YspZtT\nZtyvLy3OUzqcpbQ4T2VV2IKn0WgwevRoxMXFISkpCWvXrsXff/+tdLPKJO1SmtJNMCicp3Q4S2lx\nnsqqsAXvyJEjqFWrFnx8fGBqaor+/ftj8+bNSjerTJ5kPVG6CQaF85QOZyktzlNZFbbg3bx5E56e\nnuJ09erVcfPmTQVbxBhjTJ9V2IInCILSTZDMo7RHSjfBoHCe0uEspcV5KksgIlK6EWWRmJiIqKgo\nxMXFAQBmzJgBIyMjTJgwQXxOYGAgTp8+rVQTGWOsQmrQoAFOnTqldDMkV2ELnlqtxptvvondu3fD\n3d0dTZs2xdq1a+Hn56d00xhjjOkhE6UbUFYmJiZYtGgR3n77bWg0GgwbNoyLHWOMseeqsGd4jDHG\n2KuosBetMMYYY6+CCx4r1e3btzFhwgRcvnwZ9+/fBwAUFBQo3CplFO0M4c6RsiEizu41lZQhZ/pi\nXPBYqapVqwYAWL16NT7++GOcOnUKRkZGlW7nIiLxVphbt27h4cOHBnVrjNwEQcD27dvx3XffYc2a\nNUo3p0ISBAFHjx7Ftm3bcPXqVQiCUGk/jL4sLnjsubQ7z6xZszBy5EiEhoaic+fO2LdvX6XaudLT\n0/Hjjz8CAHbu3ImePXvirbfewsaNG5GRkaFw6yoW7QeH06dPY8yYMUhPT8e2bdswYsQIpZtWYWgz\n3L17N3r27IkNGzagSZMmOHnyJIyMjCrNflkWFfYqTVZ+tGdvRkZGyMvLg5mZGVxcXDBy5EiYm5sj\nPDwcGzZsQHBwsM6ZjyEiIhw/fhwHDx5Eeno6Dh8+jJUrV+LMmTNYtmwZsrOz0aNHD9jY2Cjd1ApB\nEATs3bsXq1atwoIFC9C5c2dcunQJ06dPx8iRI8UPFuz5BEFAUlIS/vjjD6xbtw6tW7dGgwYN0K5d\nO8THxyMwMBAFBQUwMuLzmaKMo6KiopRuBNM/2i6n5cuXIykpCc2aNQMABAUFwc7ODl988QU6deoE\nR0dHhVtafrTF3MPDAxYWFjh58iTS09Px2WefoW7durCwsMDq1atBRPD19UWVKlWUbrJeKvqh6PTp\n05g2bRq8vb3Rpk0b2Nraon79+oiNjUVsbCx69uypYGv1m0ajgUajwcyZM3Hw4EG8+eabCAgIQHBw\nMKysrBAWFoZu3brB3d1d6abqJS54TIf24HTo0CF8/PHH6NSpE+bOnYv09HS0atUKxsbGaNiwIZ4+\nfYrU1FQ0bdoUGo3G4D5Nas9yBUHArVu3EBQUBBMTExw9ehTp6elo3rw5/Pz8YGxsjFWrVqFLly6w\ntrZWuNX6p3COqampICIEBgaiZcuW+Oqrr+Dr6ws/Pz/Y2dkhKCgIjRo1gqurq8Kt1i+FM8zJyYGF\nhQVCQ0Nx584dpKamwsHBAR4eHmjWrBlsbGxgZWWFmjVrKtxq/cT34bFikpOTMWPGDISEhGD48OG4\ndesW3n33XbRp0wZRUVEwNTXF7t278fvvv2Pp0qVKN7dcaAv/tm3bMHr0aGzbtg2enp7Yvn07du7c\niVq1auGTTz4B8GyMjw/SJdN2rcXExGDGjBlwcnKCs7Mzxo8fj3v37mHYsGGYOXMmevfuLc5j6N3k\nr0qbR1xcHObOnYvq1aujZs2amDRpEiIjI5Gfn4+wsDCEhISIuXGGz0Gs0isoKNCZjo2NpW7dulF4\neDhduXKFiIhu3bpFDRo0oM8++0x83scff0y3b9+Wta1yOn/+PNWtW5f27t0rPpaTk0ObNm2iiIgI\n+vbbb4mISKPREFHxHCuz3Nxc8f9TU1PJ39+fjh8/TmfPnqUVK1ZQly5dKC0tjf7880+qXr06paen\nc35F5Ofni/9/5MgR8vf3p5iYGDp27BgFBgbSmDFjqKCggD766CP65JNP6OHDhwq2tmLgi1YqOSp0\ngn/lyhVYWlqiXbt28PDwwM8//4xNmzYhLCwM3t7e4uXPWosWLVKiybJRq9Vo2bIlWrduDbVajYKC\nAlhYWKB9+/YQBAG+vr4AIHbn8ifqZ9LT07F27VoMGzZM7Ob19PREw4YNATy71eX48ePYsWMHBg0a\nhODgYLi4uCjZZL1z584d8VYgMzMz5OTkoH379ujatSsA4MSJE2jatCkOHDiAr7/+Gunp6bCzs1O4\n1frPsAZe2CsTBEHsuuvatSvGjx+Pxo0bw9bWFv369YNKpcKaNWuQmpqKatWqISQkxCBvHC5pmywt\nLREXF4eYmBiYmJjAzMwM27dvx2+//YYePXqgXr16CrVWv5mbm6Nz587IysrCsWPH4OXlBbVajS+/\n/BIA4OjoCAcHB6SkpAAAnJ2dAfCN04WlpaWhe/fuuH//Pq5fvw4bGxvs3r1b/PIHQRDQtm1bPH78\nGI6OjvD391e4xRUDFzyGa9euYcqUKfj555+xZs0a9OvXDz169EDt2rURFhaGmzdv6tzboy2ShkZ7\nyfzs2bMRHx+PmjVrYt68efjuu++waNEixMbGYsKECfDw8FC6qXopPz8fOTk5sLOzg6enJ2bMmIFf\nfvkF58+fx9y5c6FSqfDuu+9i69atWLNmDdq1awfg2RfBA3yGDDzLEADq168PBwcHLFmyBDNnzkS9\nevUwYMAANGnSBAkJCYiJiUFsbCyf1b0ivmilEqIiA9pZWVkYNWoUpk2bBi8vLwDAmDFjYGxsjPnz\n51eaizK2bduG8ePHY9y4cZgzZw4++OADdO3aFffu3cPcuXPh5uaGnj17olu3bnxRQBF5eXlISEiA\nk5MTUlJSkJqaioEDB2LOnDkwMzNDWFgYfH198c0338DOzg5NmzZF165dOcdCnj59iv3796N69erI\nyspCSkoKXF1dsXPnThARZsyYgZ9//lm8UnjEiBH8XnxFPIZXCRUUFMDY2BhZWVmoUqUKzMzMkJWV\nhS1btmD06NEAgJYtW+LkyZMAYPDFjohw8+ZNLF26FFu2bMH169dBRDh9+jRycnLw+eefY+vWrTrP\nZ7rMzMyQmZmJqKgopKWlYd68efDw8MCXX36Jr7/+Ghs2bMCgQYOwYMECcR7OUVdeXh6ePHmCUaNG\nISUlBfHx8XjzzTdhYWGBTZs24csvv8QXX3yBESNGIDc3FxYWFpzhK+L78CqRa9euIS8vD1WrVsWm\nTZswcuRInD17FsbGxoiIiEBkZCQuXLiAY8eOYcmSJRg6dChq166tdLPLBRW6t0kQBNjY2KBFixbI\nzs7GmDFjcPjwYbi6uuLTTz+FmZkZGjRoAHNzc5152D9jn4IgwMvLCwcPHoS5uTk6duyIqlWrwsnJ\nCcHBwfjrr79w7tw5NG/eXLxBn3N8RvteNDc3R05ODmbMmIFmzZohJCQE7u7u8PT0hI2NDU6fPo2E\nhAS0adMGZmZmMDIy4gxfERe8SuSbb77B5MmT0aRJEyxZsgRDhgyBp6cnFi9eDC8vL0ycOBH3799H\nRkYGhg8fjrffftugu0sEQcCZM2dw7NgxGBsbw93dHWlpadi1axeGDx+O3NxcnD17FqNHj4anp6fS\nzdVbRkZG2LlzJ1auXIlvv/0WRkZG2LRpE6pUqQI/Pz+o1WoEBAQgKCiIc3wOQRCwc+dOODg4ICIi\nAs7Ozvjjjz9gYmKCN954A2ZmZjAzM0Pnzp3h6upqcF/0IBfu0qwEtDf/zp49GwUFBejXrx8GDhyI\n/v37Izc3F46Ojvj2229x7949fPDBB+J8htxdIggCNm3ahMmTJ8PX1xcWFhaoXbs2RowYATc3N7Rv\n3x7Xr1/H/PnzUa9ePYMu/K9De4Xvp59+innz5sHKygpDhgxBbm4uYmJicPToUfzyyy/Ys2cPX9X6\nHNoMx44di++//x5vv/02bGxs8ODBA2zcuBGJiYk4deoUFixYgBo1aijd3IpNtjv+mCKysrLo3Llz\nRER0+PBhysjIoMjISPLz86O0tDQiInr69CnFxsZSaGgoqVQq8UZqQ1NQUCDe3JyZmUn9+/enEydO\nEBHR3r17KTIyklasWEF3796lNWvW0MGDB4vNx3Tl5eXR2LFjadu2bURE9OTJE/Fv27Zto3nz5lFc\nXJxSzasQsrKyqGPHjrR7924i+ucLDK5evUr//e9/qVu3brRp0yYlm2gw+AzPwD148ADfffedOPC9\nbds2zJgxA48ePUJYWBg2btwIFxcXtGvXDk2aNIGTk5PSTS4XVOgM7eDBg8jIyEBqaiouXLiAoKAg\nhISE4OjRozh48CAGDRqE8PBwcT6AL5nXoiJnuqampnjw4AESEhLQqVMncZzzzJkzCA0NRadOncT5\nAM4R+CdD7X/VajXy8vLE212ePHkCCwsLWFtbo2/fvujZsyfMzMw4QwlwR7ABKygogKenJzp27Ihl\ny5bhvffeQ0BAAABgyZIlaNCgATp06IA7d+7AzMxMLHZkwF2ZycnJ+Pzzz1G/fn189tln2LVrF/bu\n3QsTExM0atQIDx48QEZGhnjfIV8UULLU1FQkJSUBAIYOHYr8/Hxs3rwZAHD06FGMHDkSFy9eFJ/P\nORaXnp4OALC1tUWLFi0QGRmJBw8ewMLCAvv27UO3bt1w584dnfsUOcPXwwXPgBkZGSE+Ph5nzpzB\n1q1bcfr0afzyyy948OABAOCHH35Ahw4ddA5MgGF+gtT+6GhYWBjat28Pd3d3BAYGomHDhhg9ejTG\njcpl3zUAACAASURBVBuHIUOGYNCgQbCxseGLAkqgPSOJiYlBp06d0KdPH0yYMAF16tRBjRo18NNP\nP6F79+4YPHgwIiMjxQ9X7B/aDGNjY9GtWzf07t0bO3bsQEREBBo0aIAWLVpg9uzZGDVqFP7973/D\nxcWF34sS4hvPDZz2u/a2b9+O3bt3Y+rUqRg+fDgEQcCff/6JNWvWwNTU1CAvyiipC2jgwIH4+++/\nsXfvXlStWhX5+fk4d+4cUlNTUb16dTRu3Nggs5DKhQsX8MUXX2D27NlwdXVFly5d0LlzZ4wfPx5P\nnz5FSkoK7O3t8eabb3IX3HMcOXIE06dPR2RkJPbu3YvU1FS0bNkS77zzDv766y8YGRnB2dkZrVq1\n4gwlxmN4BqbowbpOnTril/W2a9cOGo0Gq1atws2bNzF8+HCYmpoCMNwdShAEJCYm4tq1a6hXrx5W\nrVqFYcOGoW/fvvjzzz9hYWGBoKAgBAUFATDs7tyy0r6nHj9+jPnz5+PWrVswNTWFnZ0dNmzYgP79\n++PevXtYsGABgoODdeY11PfVqyg8Znfv3j385z//gampKYKDgxEcHIyffvoJ+/btQ0FBAXr16oWq\nVauK8wGcoZT4PjwDod2pBEHAiRMnMGbMGNSvXx8eHh54+PAhZs+ejf79++PNN99E27Zt0adPHzRp\n0sRgz2a027V//34MHToUDx8+xL59+3DkyBEsWrQI8fHxWLx4Mfr16ycWfYDHSUqi7Q52dHSEj48P\nLl68iIcPH8LDwwPVq1cXfyS4ZcuWOhc9cY7PaHO4e/cuXFxcIAgC/vjjD1haWqJhw4Zo3Lgxrl69\nisTERLRo0UL8hQl+L0qPC54BKPxJ8OLFi/D19UViYiLOnj2LxYsXo2vXrrhw4QLq1q0LV1dXmJub\nw9LSUpzfEHcq7a+2T506FT/88AP+9a9/ISAgAPv370dqaiq++eYbbNy4EX5+fnB3d1e6uXpJ+6Eh\nLy8Pc+bMwdKlSzFixAj4+Phg//79SEtLg5ubGzw9PTF48GCD/wq6stJoNHjw4AG8vb3xxhtvoH//\n/qhevTrWrl2Lp0+fIigoCM2aNUNQUBCqV6+udHMNGhc8A6EdCB83bhzatWuHQYMGoXnz5hAEAStW\nrMCuXbuQmZmJHj166BQ4Qyp2Rc9W9+/fj9mzZ6Np06Zo2LAhqlatitzcXBw+fBjdunVDeHg43N3d\nDfYst6yys7NhZGQEY2NjAICxsTHq16+PS5cuYcWKFfjwww9RrVo1xMXF4c6dO2jYsCFMTU1hZGTE\nWf6//Px8MT8AsLKyQr169TB8+HC8+eabeOedd2BpaYmff/4Z+fn5aNiwIWxtbRVscSUhw71+TAZ/\n//031alThw4cOFDsb48ePaLz589TaGioeKO1oSl8c/jdu3cpJyeHiIiWL19Ovr6+tGvXLiJ6djN0\ny5Yt6d69e6RWqxVrr746f/48jRo1itLT02nfvn20YMEC8W+3b9+mTz75hAYPHkzZ2dl04MAB8UsN\n2D/Onz9PkyZNIiKipKQk2rFjh/h+jI2NJXNzc/FG8j/++IOOHDmiWFsrGy54FdT169dp5cqV4vS+\nffuob9++4nR+fj4Rkc43hAwePFj8NgdDo93OLVu2UJs2bahVq1a0dOlSSk5Opt9//50sLS3pgw8+\noF69etHGjRsVbq1+ysnJofbt29OyZcuIiCg+Pp5cXV1p0aJFRESkVqspPj6e6tWrR/369TPYb+R5\nHY8fP6Y2bdrQ0aNHKSMjg8aNG0dDhw6l3bt3U3Z2NhERzZs3jwRBoNjYWHE+/iYfefANHhUUEWHl\nypU4ffo0AMDX1xf379/Hjh07ADz7Uc34+HjMmjULRITLly/j8uXLBvvjpYIg4Pjx45g7dy4WLFiA\nMWPGIDU1FevWrUPXrl2xYMEC7N+/H126dEGvXr2g0Wj4iswiLCwsMGDAACxbtgweHh5o27Yt/vrr\nL8ybNw+LFy+GsbExzM3N0a5dO0yYMIHvDyuBRqNBTk4O1q5di3/961/45JNP4OXlhQ0bNuDQoUMA\ngJCQEISFhenkx93AMlG44LIy0HbFLViwgNavX09ERBkZGTRnzhz6/PPPaebMmZSQkEB169alHTt2\niPPdv39fkfbK4fbt2zRkyBAKCQkRHztw4AC1b9+eEhMTiYho9erV5OHhQXv37lWqmXpLe4bx119/\nUZUqVahNmzaUlZVFRERHjhyh+vXr07Bhw8jFxUX83kw+K9GlzWPRokVkYmJCI0eOJKJn3zc6efJk\nGjZsGA0bNozeeOMNOnr0qM48TB580UoFpP1kePv2bcyfPx9t27aFq6sr3NzcYGlpib/++gsXL17E\nqFGj0KVLF6jVahgZGcHCwkLhlkuHityjRP//u2yHDh1CTk4OmjVrBk9PTxw6dAgajQZNmjRBQEAA\n3N3dUadOHTg4OCjZfL2jzdHR0RGhoaFwcHDAggUL0KhRIwQEBKBTp06oU6cOBg0ahNatW/PFKSXQ\n5nH79m106NAB8+bNg7m5OVq0aIHWrVvD0tISVlZWGDBgAFq1aqUzD5MHf9NKBTdlyhQsX74cR44c\ngZubm/j4kydPUKVKFYO8ebXwNv3v/9q787Cqy/Tx4+/DQREEIVFIJJMBRr10MHJDMNdQlGwxMcYF\ntRwMc0XH5UolHU1zK2nSDLUoFxZDEVFUFjUxwaVGwV2RQVDckIOgcjh8fn/UOYHZd/KXduBwv/4S\nOZ/res5zfXjuZ72fgwe5d+8eFhYWdO/enS1btpCYmIiVlRVvvfUWY8aMISIigh49ehi51DVT1cCl\n0+kMOwtzc3NZv349Z8+eZcGCBbi5uVV7BkzrnXoajh49iq+vL//6178YP358td9JHRqHjPBqIf1o\nRqVS0atXL65du8asWbPw8fHBwsICS0tLzM3Nqx1GNyX677Rjxw4mT55Mq1atCAsLM6xBKT+vb/7w\nww8sXryYnj17Gka5ojr9Jbj6S0V1Oh1mZmbY2dnh5ubGhQsXiIqK4tVXX8Xc3NxQ96b2Tv1RV65c\nAaB+/fqoVCp0Oh3Ozs7069ePN998Ezs7O7p06WL4vNShcUjAqwUqKysN14iYmZkZ/lD0F7v6+vpS\nUVFBfHw8Z86cobCwkHbt2pn0H1Rubi5Tp04lJiaGwsJCMjMzSU1NRVEURo0aRZMmTbh79y5qtZqO\nHTtKsHsEfYcoMDCQc+fO0adPn2r1ZGtry1//+lfDlLkpv0//vxRFobCwkNDQULy9vXnmmWeorKxE\nrVaj0+lwcnLC39+fhg0b4urqauzi1nkS8Gqw27dvc/v2bWxtbUlKSmLt2rVkZWUZDpRX7ZF7eXnR\npk0b7O3t+frrr/H19TWpNbuHqVQq+vbty82bNw1JeJ977jnGjx+PjY0Nw4YNo6ioiOzsbDp37mzS\ndfG4Hh75e3h4cOTIEbp06UKDBg2qBTZbW1vs7e2NVdQaT6VSYW1tTVpaGomJibz++uuGaWH932fz\n5s1xdXWVdc8aQAJeDVVaWsqSJUs4deoUd+7cYfr06fj7+/Pxxx+Tn59Pr169MDMzw8zMzDACbNKk\nCS4uLrz55pvVUoeZEn2j0aBBAxo3bsyxY8ewtbXFz8+PnJwcmjRpwksvvYS7uzuurq706NEDOzs7\nYxe7RtHnGL1//z5mZmY4OTmxevVqnn/+eRmFPIa8vDwKCwuxt7fH29ubzMxMPD09sbGxMfxNytGD\nmkU2rdRgcXFxpKenU1RUhJeXF8HBwRQWFhIQEIC3tzcLFiwwXA5Zlan1JB/+PlV/3rp1K6tWraJ7\n9+6sWbOGLVu24OXlVW0Dhvj1Jon58+dz/PhxGjZsSFBQENevX2fz5s1s3rxZUlz9hqrvnUaj4b33\n3kOlUuHg4MD06dMJCgpi0KBBBAcHG7mk4rdIwKthFEUxrAEAHD58mJUrV6LT6Vi8eDF/+ctfuH79\nOn5+fvTq1Ytly5aZVHD7LUePHiUyMpJPP/30VwEwOjqaoqIiWrZsiZ+fn8kF/CdBXycnTpygXr16\nODk5YWtrS2pqKgsWLMDV1ZWEhAQOHjyIm5ubYX1Y/EJfh1evXqVRo0aoVCpKSkqYNGkS7dq1Y9u2\nbeh0OmJiYnB3dzd2ccUjyH14NZBarWbHjh3ExcWxfv16SktLSUhIYNu2bQwaNIiWLVuya9cuLl26\nZNINe9VG98GDB2i1WuCXUYp+FPfWW28ZnpH+26/pG2r9zdq+vr6kpqYSGRlJ7969adu2Lbdv3+b6\n9euEhoayfft2CXZVVB0db9++nbCwMHQ6HX5+frz77rtERUWRl5eHk5MTGzZs4PLly7i7u0vHqwaS\nt7qG0TdMM2fOZPDgwQD06dOHvn37kp+fz6ZNm8jJycHR0ZGuXbuaZAN/79494KdF/wsXLpCRkYGd\nnZ3hHjY9tVpNRUVFtWdlu/ev6Ud28fHxREVFERkZyYcffsiYMWP4/vvvcXR0pE2bNsTHx9OoUSOK\ni4uNXeQaRf9OnTp1ivDwcDZt2sTOnTvRarVERERQUlLCc889x9tvv80//vEPVq5cyYMHD+Q9rIEk\n4NVAR48e5YMPPmDAgAHcv38fgAEDBuDr68uVK1eqBTlT+6O6c+cOM2fOpKioCI1Gw9KlSwkODmbZ\nsmWkpaWxePFiYmJi2LdvH5WVlY9cwxS/jEp0Oh3l5eV88MEHpKSkcPfuXSoqKhgxYgTjxo1j5cqV\nVFZWArBnzx4OHz5sGEnXddeuXWPhwoVUVlZy8+ZNVqxYwfXr17Gzs8PZ2Zlp06aRlpbG5s2bDc/Y\n2NhQUlJiqFNRs0hrYWSPmva4evUqJ0+eZPDgwTRo0ACAjIwMevbsSdeuXU1+U8G0adMoLi6mqKiI\nNWvWAD8d0cjPz8fa2pqtW7dSXFxM/fr18fb2NnJpa7aysjJsbGxYv349EydOJDk5mXbt2tGiRQta\ntWrFyZMnDe+fk5MTKSkp1W4tr8vu3btHQEAA165dw8HBgaCgIIqKiti0aROBgYE0b96cESNGcOvW\nLSorK6msrMTS0pLPP/9cjsHUVE8pR6f4Hare4ZaTk6OcOnXK8O+QkBBlxYoViqIoSkZGhtK6dWtD\nEmRTVDWJ7n//+1/lm2++Ubp3766kpaUpivJTwuzhw4crGzZs+M3nRHUJCQmKl5eXEhYWphw8eFAp\nKSlRBg8erPTr10+ZPXu20qlTJyUuLs7Yxaxxqr5T5eXlyjvvvKOMHj1a0Wq1SnJyshISEqIMHjxY\n+eqrrxQ3NzclKSnJiKUVj0PO4RmZfiE8JCSEjIwMDhw4QIcOHXBwcCA2NpZ169YRHR3NokWL6N27\nt7GL+1Tp1y/nzp3LiBEjaNSoEV999RXNmzfHxcWFkpISCgoK6Nat26+eE9U3V+Tn57NkyRKCgoKo\nqKhg79692NjYMHnyZPbt28fFixdZsGAB/fr1Mzwr9Vi9DrOzs2natCnu7u785z//Yffu3YwdO5Zn\nnnmG1NRUCgoKCA0Nxd/f35AAQtRsEvCMSJ/8eNasWezatQuVSsXSpUsBGDhwIO+++y7du3dn2LBh\ndO7c2WQTzuob23PnzjFv3jzCwsLw9PSkRYsW6HQ6vvnmG1q0aIGTk5PhcL2eqdXFH6W/F/DgwYMo\nikJoaCguLi5otVqSkpIwNzdn0qRJ7Ny5k7y8PF588UWsrKykHqtQqVQkJSUxfPhwXn75Zdq0aYOr\nqyvff/89qampjB49GmdnZwoKCtDpdLi5uWFtbW3sYovfQbokRmZvb89nn33GsWPHiIiI4NChQ2Rm\nZhISEsL58+dxcXGhRYsWhs+bUsP04MEDw262vLw8Nm7cyOXLl8nKygLAwcGBN998k969ezN//nza\ntGlDr169THJn6h+l7zSkpaXxxhtvcPDgQT799FOysrJ49tlnGTBgAJ07d+bbb79FpVKxcuVKbt68\nKSO7h+g7Xv/85z+JjIzkb3/7G2q1mtatWzNx4kTu3LnDpEmT6N27Ny+++CKFhYWS4KAWkYPnf6Kq\nI7SioiLq1atn6BlOmzYNd3d3xo4dy+rVq/nqq6/YuHFjtWtZTElFRQXp6enk5ORgbW1NdnY2b7zx\nBvHx8dy5c4eBAwfSs2dPAG7cuEFZWRnPP/+8cQtdw505c4YpU6Ywe/ZsfHx8mD9/PrGxsURFRdG2\nbVuuX79OeXk5zs7OAJKN5mcPB/1z586xaNEivvzyS3Q6HTqdjvr161NRUUFOTg5lZWW0b98egJKS\nEmxsbIxVdPGYZJfmn0ylUhEfH8+6desoLi5m6NCh9OnTh44dO7J27Vq0Wi3R0dGsWLHCZIMdgLm5\nOfb29ixYsICTJ0+yfv16PDw8sLS0ZOPGjezevRutVouvry9NmzY1PCcjkuqq1seJEyfIy8tj+/bt\n+Pj4MHfuXNRqNQMGDCAxMZF27dpVe06CXfVEBadPn8bS0hJbW1sOHDhAVFQUgYGBqNVq9uzZw5Ej\nR3j//feBXzoLEuxqF1nD+xOpVCrOnj3L2LFj+fe//02rVq04c+YMp0+fpnPnztjZ2bFz504mT57M\nyy+/bJJrdlW/k62tLXFxcTg7O2NlZUXr1q1xdnbG1dWVI0eOcOnSJTw9PaslwjalungS9OvA0dHR\nBAcH06xZM3788UeuXr1Kx44d6d69O8XFxTz77LPVRshSj7/QbxwbP348vXv3plWrVri6urJ69Wpy\nc3PRaDS8//77DBkyhNatWwPIBpXa6k/eFVon6bc5FxQUKLt27VL69+9v+F1GRobSp08fJSMjQ1EU\nRbl3757hGVPbcl/1OxUUFBj+LysrSxk3bpwyZ84cRVEURaPRKLGxscq5c+eMVtbaJCsrS3nuueeU\n5cuXK4qiKDExMUpwcLDyySefVPucqb1PT8rx48eV9u3bK2fPnlUURVGuXr2qHDlyRMnOzlaGDBmi\nTJgwQdmxY4eiKFKHtZ1MaT5l+nyQhw4dYvbs2Xz22Wc0aNCA2NhYAgIC6Ny5M23btjXc21avXj3D\ns6bYC1epVCQmJjJv3jy8vb2pX78+S5YsYcSIEXzzzTcMGjSIrKwsYmNjJQHv/6DRaLCysqJt27Yk\nJSUREBCAoihMnToVrVbL3r17yc3NNYzsTPF9ehIaNGhA+/btSU1NJSYmhrS0NABmzJhBdHS04XOK\nbHeo9WRK8ylTqVQkJyezdu1aQkJC6Nq1Kzdv3iQ7O5vU1FTUajVLly4lJCQEZ2dnw1SJKTZOKpWK\n/fv3M2XKFDZu3MiVK1dYvXo12dnZTJgwgfbt23Pv3j2CgoLw8fExdnFrLEVRuHjxImPGjMHd3Z1n\nn30WR0dHevTowbRp0wAYM2YMXbp0MWxQEb/NysqKwsJCNmzYwOuvv86oUaOwsrJCq9UaNqeA5Gk1\nBTIR/RQ83BPMz89n48aN5OXlARAQEICfnx+3b99m06ZNhIeH06VLF5PvQVZWVqLVag3Z5bdt28b+\n/fs5ffo0QUFBODs7M3HiRPr27YuiKCZfH4+jan2oVCrc3Nzo2LEjixYt4scff6S8vJx27drRt29f\nFi9eTG5uLs2aNTNyqWuHhg0bMmHCBPbt28egQYMoKSlh1apVUn+myGiTqSZMP8+fn59vWJPbvHmz\nYmFhoaSnp1f7TFlZmeFnU1wf0Ol0iqIoyv3796t95+HDhysJCQmKoijKtGnTlFatWik//PCD0cpZ\n0+nrLi0tTQkPDzekoVu+fLkycOBAZc+ePUpCQoIyYsQI5fTp08Ysaq1VUVGhHDlyROnUqZOybds2\nRVFkzc7UyJTmE6b8vE08MTGRcePGsW3bNnJzc/n73/+Op6cnw4YNo2vXroZ1Ff2analOl6hUKrZu\n3cr06dPJyMjAysoKd3d39uzZg6WlJQUFBSQlJfH111/Ttm1bOXbwCPo6OXz4MMHBwZSVlZGZmcmt\nW7d47733KC4uJiUlhcjISEJCQnjppZeqPSd+HzMzM+zs7PDz86t29ZbUoemQg+dPQUZGBvPmzWPh\nwoUUFhZy4sQJcnJyWLVqFV988QWhoaHk5+fTqFEjk9zerDx0wH7kyJEMHToUjUZjqIPKyko+//xz\nzp07R2hoqOHuP2mkHy0jI4OwsDCWLFmCh4cHmzdv5tChQ3h4eDB69GjMzc25efMmTZo0kTp8QqQe\nTY/s0nzCioqKWLFiBYWFhXh6egLg7OzMhx9+SGpqKmPHjsXPzw87Ozsjl/TpUqlUZGRkcPToUTp0\n6EBgYCAAFhYW1bLJaDQaGjVqJL3p/6G4uJjk5GT27NmDh4cHAQEBmJmZkZycTGlpKRMmTKBx48bG\nLqZJkXfR9MiU5h/0cENtaWlJ48aN2bVrFzdu3KBHjx44ODiwb98+SkpK6NatG9bW1piZmZlkD1L/\nndLT0xk5ciS3bt3ihx9+wN3dnebNm9OhQwfMzc2ZMWMGgYGB2NraGurA1OriSXJ1daV9+/YsW7aM\nxo0b0759e9q0aUNpaSk+Pj44OjpKPQrxP8iU5h+kb+BTUlLIzMykadOmvPHGG2RlZfHpp59iY2PD\n6NGjGTduHOHh4SZ/xQ/8NP02e/Zsli9fjoeHB3PmzKGoqIjBgwfj4+NDvXr1yM/Pp3nz5sYuaq2T\nmJjInDlzmDRpEiNHjjR2cYSoVUxvAelPpA923333He+++y4NGzYkIiKC8PBw6tWrx4QJEzh8+DCz\nZs1i7dq19O7dm4qKCmMX+6krLi4mNTWV5ORkAObMmYO9vT2RkZF89913KIpiCHbS33o8/v7+hIWF\n8dFHHxmupxFC/D4S8P4AfW7M1atXM336dCZOnMjWrVvRaDQkJCTQo0cPPv/8c9zc3Ni/fz/wU9Jk\nU9e3b1/i4uJYu3YtmzZton79+syePZtmzZrh4OBQbcpNpt8e32uvvcb+/ftxcnKSBNBCPAbTb32f\nMP2oTp8yLDs7mxs3brB792769++Ps7Mz06dPp1+/fowfPx5vb2+0Wi0bNmww7KKrC1577TXMzc2Z\nM2cO5eXljBo1ioULF0qAe0L0N0iY4jqwEE+LrOE9hqobVKquQaWnp7Nhwwbc3d0JDAyktLSUgIAA\nEhMTad68OeXl5VRUVFTL+l9XxMfHM2vWLJKTk3F0dJQRiRDCaCTgPQZ9b3rnzp2EhYXh6+uLmZkZ\n8+bN48CBA4SHh3PlyhXs7OyYMmUKAwYMkB44P13gWvVOOyGEMAaZ0nwM+rvHZsyYQUxMDJGRkcTF\nxVFQUMCaNWuwtLRk/fr1uLq60q9fP2MXt8aQ6TchRE0gm1Yeg06nQ6PREBUVxZUrV0hOTubLL7/k\n2rVrBAcH06FDB1599VXOnTvH2rVrqaiokAa+CqkLIYQxyQjvMajVavr06YNKpeLjjz9m+fLldO3a\nFRcXF86fP8/58+d55ZVXUBSFTp061YkdmUIIUVtIi/wbHp5+0/9sYWHBgwcP0Gg0nDx5ksrKSk6c\nOEFERAStW7cGYODAgcYqthBCiN8gm1YeoepuzKysLBo3boyTk1O1z+zbt48VK1ZQVlZGcHAwQ4YM\nMTwrU3dCCFHzSMB7BH3QSkhIYOnSpSxbtozOnTsbfq8/g1daWoqiKFhbW0vyYyGEqOEk4P2GCxcu\nMGTIEL744gs6duxY7XePCm4yshNCiJpNdmn+7PLly0yYMMHwsz4rij7Y6XNglpaWPvKyVgl2QghR\ns0nA+1nLli0ZNWoUly5dAsDDw4PGjRuTkpJCeXk55ubmHDhwgCVLlnD//n1JeiyEELVMnZ/SVBQF\nnU5nOELg4+ODWq02ZE65ePEiVlZWeHl5MW3aNFatWoWvr6+RSy2EEOJx1emAV3Ut7syZM4ZjBT17\n9sTBwYGYmBiSk5NJTEykvLwcf39/SRcmhBC1VJ0PePrcmBMnTiQ6OpoOHToA0K1bNxwdHfn2228B\nePDgARYWFrIbUwghaqk6HfAAjh8/ztChQ4mKiuKFF14gNzcXBwcHLC0t8fLywtramuTkZMNRBCGE\nELVTncu08vAIzdzcnMGDB3Py5EmSkpKIjo7G3d2dWbNmcfjwYQ4dOgQgwU4IIWq5OtmKq1Qq9uzZ\nQ1JSEk2aNOHevXts3rwZFxcXoqKicHFx4fjx4wB4e3ujKIrsyhRCiFquzgU8lUpFfHw8U6dORavV\n4uTkxMKFC9m6dStvvfUWWq2WvXv34urqWu0ZWbMTQojarc4FvKKiIlauXMmWLVvw9/fn2LFjbNmy\nhcrKStLS0hg7dixz586lZ8+eMqoTQggTYvJreA8fIdDfURcbG8uZM2dQq9WkpqZy584dgoKCWLdu\nHa1bt5ZgJ4QQJsakR3hVg9bZs2e5ffs2TZs2Ze7cuZSUlDB69GgiIyOJiIggMzMTS0tLw1k8kKMH\nQghhSkz6WIJOp0OtVpOQkMC8efN46aWXqKysZPLkybi4uACwe/duQkNDWbp0KQMGDDByiYUQQjwt\nJjnCu3v3LvDTDeWZmZmEhYURHx9Pw4YNSUtLY+7cuRw7dozS0lJWrlzJRx99ZMigIoQQwjSZ3Aiv\nqKiIZcuW0bZtW4YOHUpGRgb169fnxo0bzJw5k08++YSIiAg0Gg3Lli2jWbNmcp+dEELUASY3wlOr\n1VhZWXH06FG2b99Oly5d8PT0JCUlhU8++YTu3bvj7OyMvb09paWlWFtbG56VYCeEEKbLZAKe/taD\nRo0aGdboUlJSiIuLA0Cj0TB//nxSU1NJSEjgvffe44UXXpCRnRBC1BEmM6WpP36QmprKgwcP6NGj\nB2vWrOHy5cu88sor+Pr68s4771BWVkZAQACDBg2SWw+EEKIOMZmAB5CQkMDcuXNZtGgRfn5+FBcX\ns379enJycujfvz/9+/eXWw+EEKKOMpkpzZKSEtatW8fq1avx8/NDq9Via2vL22+/jbOzM9u3b6ew\nsBALCwtA0oUJIURdYzKZVszMzLh58yYajQb4ZeRWXl5OaGgo+fn5ODo6GrOIQgghjMhkRngNtSO4\nUQAAALFJREFUGzZkyJAhHDp0iFOnTmFubk56ejrDhg3jxo0bPP/888YuohBCCCMyqTW8/Px81qxZ\nQ1paGj4+PsTGxhIeHo6/v7+xiyaEEMLITCrgAZSVlZGRkUFhYSEtW7bEy8tLNqgIIYQwvYD3MAl2\nQgghwIQ2rfwWCXRCCCHAhDatCCGEEP8XCXhCCCHqBAl4Qggh6gQJeEIIIeoECXhCCCHqBAl4Qggh\n6gQJeEIIIeqE/weOwIO8Og7UhQAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1078eeb10>"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"sample_sizes\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Performance growth rates for different sample sizes"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the previous section, we have seen how the different implemantations perform for a fixed sample size n=500. Now, let us take a look at the effect of different sample sizes on the relative performances for each approach. We will consider the sample sizes 10, 100, 1000, 10000, 100000, and 1000000."
+      "Note that we are now passing `numpy.arrays` to the NumPy, SciPy, and (C)Python matrix functions (not to the Cython implemtation though!), since they are optimized for it."
      ]
     },
     {
@@ -1031,48 +880,54 @@
       "import random\n",
       "random.seed(12345)\n",
       "\n",
-      "funcs =  ['cy_classic_lstsqr', \n",
-      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
+      "funcs =  ['py_matrix_lstsqr', 'cy_classic_lstsqr', \n",
+      "          'numpy_lstsqr', 'scipy_lstsqr']\n",
+      "\n",
+      "orders_n = [10**n for n in range(2, 5)]\n",
+      "perf2 = {f:[] for f in funcs}\n",
       "\n",
-      "orders_n = [10**n for n in range(1, 7)]\n",
-      "times_n = {f:[] for f in funcs}\n",
       "\n",
       "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    x_list = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
+      "    y_list = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
+      "    x_ary = np.asarray(x_list)\n",
+      "    y_ary = np.asarray(y_list)\n",
       "    for f in funcs:\n",
-      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
+      "        if f != 'cy_classic_lstsqr':\n",
+      "            perf2[f].append(timeit.Timer('%s(x_ary,y_ary)' %f, \n",
+      "                      'from __main__ import %s, x_ary, y_ary' %f).timeit(1000))\n",
+      "        else:\n",
+      "            perf2[f].append(timeit.Timer('%s(x_list,y_list)' %f, \n",
+      "                      'from __main__ import %s, x_list, y_list' %f).timeit(1000))"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "labels = [('cy_classic_lstsqr', 'Cython implementation'), \n",
-      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
-      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
-      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
+      "labels = [ ( 'py_matrix_lstsqr', '\"matrix\" least squares in reg. (C)Python and NumPy'),\n",
+      "           ('cy_classic_lstsqr', '\"classic\" least squares in reg. Cython'),\n",
+      "           ('numpy_lstsqr', 'in NumPy'),\n",
+      "           ('scipy_lstsqr','in SciPy')\n",
+      "           ]\n",
       "\n",
-      "matplotlib.rcParams.update({'font.size': 12})\n",
+      "plt.rcParams.update({'font.size': 12})\n",
       "\n",
       "fig = plt.figure(figsize=(10,8))\n",
       "for lb in labels:\n",
-      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
+      "    plt.plot(orders_n, perf2[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
       "plt.xlabel('sample size n')\n",
-      "plt.ylabel('performance gain relative to the slowest approach')\n",
-      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
-      "plt.legend(loc=2)\n",
+      "plt.ylabel('time per computation in milliseconds [ms]')\n",
+      "#plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
+      "plt.legend(loc=4)\n",
       "plt.grid()\n",
       "plt.xscale('log')\n",
       "plt.yscale('log')\n",
-      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
+      "plt.title('Performance of least square fit implementations')\n",
       "plt.show()"
      ],
      "language": "python",
@@ -1081,23 +936,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnMAAAIECAYAAAByl6h3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0/EX3NkAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/B+P+oR2lLvXHQAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10846f5d0>"
+        "<matplotlib.figure.Figure at 0x10a114a90>"
        ]
       }
      ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "In this performance comparison using different sample sizes, we can see that our Cython approach is actually not so fast anymore. However, this is just the simplest approach to using Cython.  \n",
-      "There are a lot of improvements that can be made. In a [later section](#showdown) we will come back to this comparison and see how the Cython version of our simple least squares implementation holds up against the other approaches, after we tweaked it a little bit.\n",
-      "<br>"
-     ]
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -1112,7 +957,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "## Bonus: How to use Cython without the IPython magic"
+      "# Bonus: How to use Cython without the IPython magic"
      ]
     },
     {
@@ -1381,7 +1226,7 @@
      "input": [
       "import ccy_classic_lstsqr\n",
       "\n",
-      "%timeit classic_lstsqr(x, y)\n",
+      "%timeit py_classic_lstsqr(x, y)\n",
       "%timeit cy_classic_lstsqr(x, y)\n",
       "%timeit ccy_classic_lstsqr.ccy_classic_lstsqr(x, y)"
      ],
@@ -1422,1051 +1267,6 @@
       "<br>\n",
       "<br>"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Appendix I: Cython vs. Numba"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Like we did with Cython before, we will use the minimalist approach to Numba and see how the two - Cython and Numba - compare against each other.  \n",
-      "\n",
-      "Numba is using the [LLVM compiler infrastructure](http://llvm.org) for compiling Python code to machine code. Its strength is to work with NumPy arrays to speed-up the code. If you want to read more about Numba, please see refer to the original [website and documentation](http://numba.pydata.org/numba-doc/0.13/index.html)."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Here is our \"classic\" approach in Python, where I removed the list comprehensions, since they caused errors in the Numba compilation."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Cython-compiled version of it:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "\n",
-      "def cy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And now the Numba-compiled version:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from numba import jit\n",
-      "\n",
-      "@jit\n",
-      "def nmb_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "        \n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "Now, let us see how the different approaches compare against each other for different sample sizes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs =  ['lstsqr', 'cy_lstsqr', 'nmb_lstsqr'] \n",
-      "orders_n = [10**n for n in range(1, 7)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "plt.figure(figsize=(10,8))\n",
-      "\n",
-      "for f in times_n.keys():\n",
-      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
-      "\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "\n",
-      "plt.title('Performance of a simple least square fit implementation')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAH4CAYAAAACdDpdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x1031a8710>"
-       ]
-      }
-     ],
-     "prompt_number": 32
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Without making any modifications to the original Python code - thereby we are not accounting for the particular strengths of Numba (Numpy) and Cython (static type declarations), we see that Cython performs significantly better than Numba."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"type_declarations\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Appendix II: Cython with and without type declarations"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the sections above, we have been using the simplest approach to Cython without using static type declarations and thereby neglecting one of its major strengths.  \n",
-      "Now, let us see how and if we can further improve the Cython implementation of our \"classic least squares approach\" by adding those type declarations."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is our \"classic\" approach in Python:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The Cython-compiled version of it:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "def cy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And now, the same code with static type declarations:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "def static_type_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "Now, let us see how the two functions (with and without static type declarations) compare against each other for different sample sizes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs =  ['cy_lstsqr', 'static_type_lstsqr'] \n",
-      "labels = ['simple Cython', 'Cython w. type declarations']\n",
-      "orders_n = [10**n for n in range(1, 7)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "plt.figure(figsize=(10,8))\n",
-      "\n",
-      "for f in times_n.keys():\n",
-      "    plt.plot(orders_n, times_n[f], alpha=0.5, label=f, marker='o', lw=2)\n",
-      "\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "\n",
-      "plt.title('Performance of a simple least square fit implementation')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAH/CAYAAAASb3qiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nB/IfaLkgtnNsZy4AAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10ed0dd90>"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "The improvement is pretty significant when static type declarations are used.  \n",
-      "One more experiment to see by how much we could improve our \"classic least squares\" code via Cython compared to the initial Python implementation:\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
-      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "funcs =  ['classic_lstsqr', 'cy_lstsqr', 'static_type_lstsqr', \n",
-      "          'lin_lstsqr_mat', 'numpy_lstsqr', 'scipy_lstsqr']\n",
-      "labels = ['classic approach','classic approach (cython)', \n",
-      "          'classic approach (cython + type decl.)',\n",
-      "          'matrix approach', 'numpy function', 'scipy function']\n",
-      "\n",
-      "times = [timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
-      "         for f in funcs]\n",
-      "\n",
-      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "x_pos = np.arange(len(funcs[1:]))\n",
-      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
-      "plt.xticks(x_pos, labels[1:], rotation=90)\n",
-      "plt.ylabel('rel. performance gain')\n",
-      "plt.title('Performance gain compared to the classic least square'\\\n",
-      "          '(n={})'.format(len(x)))\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAHnCAYAAADuC8dRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVsKiSkREZCX/D2lbGZgZrg2GAAAAAElFTkSuQmCC\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10ed1da90>"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is a pretty significant performance gain. The \"Cython + type declarations\" approach sped up our initial Python code 25 times."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"explicit_loops\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Appendix III: Cython performance after replacing list comprehensions by explicit for loops"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In Python, we have those wonderful list, set, and dictionary comprehensions, which do not only look prettier and are easier to read (at least most of the time) than nested loop structures, but they also additionally come with some small performance benefits.  \n",
-      "Does this also apply in Cython? Let's check it out."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "This is the code for our \"classic\" least squares approach that we have been using in the previous sections:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lstsqr_comprehensions(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 46
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "And here is a version where I replaced the list comprehensions by for-loops:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lstsqr_loops(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 48
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Finally, the Cython versions of the two functions (with and without using list comprehensions) that we have defined above:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "\n",
-      "def cy_lstsqr_comprehensions(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = sum([(x_i - x_avg)**2 for x_i in x])\n",
-      "    cov_xy = sum([(x_i - x_avg)*(y_i - y_avg) for x_i,y_i in zip(x,y)])\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 49
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "\n",
-      "def cy_lstsqr_loops(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg, y_avg, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    for x_i in x:\n",
-      "        var_x += (x_i - x_avg)**2\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        cov_xy += (x_i - x_avg)*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 50
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "We will generate some sample data for different sample sizes and take a look at the results for the regular Python functions, and the Cython code separately."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs =  ['lstsqr_comprehensions', 'lstsqr_loops',\n",
-      "          'cy_lstsqr_comprehensions', 'cy_lstsqr_loops'] \n",
-      "\n",
-      "orders_n = [10**n for n in range(1, 6)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 52
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plt.figure(figsize=(8,6))\n",
-      "plt.plot(orders_n, times_n['lstsqr_comprehensions'], alpha=0.5, \n",
-      "         label='list comprehensions', marker='o', lw=2)\n",
-      "plt.plot(orders_n, times_n['lstsqr_loops'], alpha=0.5, \n",
-      "         label='for-loops', marker='o', lw=2)\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
-      "plt.title('Performance comparison list comprehensions and for-loops')\n",
-      "plt.show()\n",
-      "\n",
-      "plt.figure(figsize=(8,6))\n",
-      "plt.plot(orders_n, times_n['cy_lstsqr_comprehensions'], alpha=0.5, \n",
-      "         label='list comprehensions (Cython', marker='o', lw=2)\n",
-      "plt.plot(orders_n, times_n['cy_lstsqr_loops'], alpha=0.5, \n",
-      "         label='for-loops (Cython)', marker='o', lw=2)\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time in ms')\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "plt.xlim([0,max(orders_n) + max(orders_n) * 0.1])\n",
-      "plt.title('Performance comparison list comprehensions and for-loops in Cython')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAGJCAYAAACaQwrRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nURKDPTGQxwAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10e8f8610>"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAGJCAYAAABFDXDOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8TXf++PFXQoJE9kgiO0ntgtZSFLG2lNYoKtagqr/B\nTKtT7bSKKqodVBedfnVUSmyli30pGqNUaRGqxJpYEksESSSyfn5/nMltQja5N7n3JO/n4+Eh5957\nznmf9z03n5zz/tzPx0oppRBCCCGERbI2dwBCCCGEKJo01EIIIYQFk4ZaCCGEsGDSUAshhBAWTBpq\nIYQQwoJJQy2EEEJYMGmoi3Ht2jU6d+6Mo6Mjr732mrnDEUXYu3cvjRo1qtB9hoaGsmTJEgBWrFjB\nk08+WaH7tyT5c1FR+vTpw/Llyyt0n6YUGBjIrl27Cn0uPT2dfv364ezszPPPP2/0vmbMmMGIESOM\n3o6pWNJ7Fx4ezttvv23uMEpU6RrqwMBA7OzscHBwwMvLi9GjR3P37t0ybWvx4sV4eHiQnJzMv/71\nLxNHKkylU6dOnDp1qkL3aWVlhZWVFQDDhg1j+/btJa6jl18KDyt/LirKli1bLKrxeVjF5WzdunVc\nv36dpKQk1qxZY5J9WRJj37uVK1fSunVrHBwc8Pb2pk+fPuzbt6/E9SIiIujUqVOBx8xx7pZFpWuo\nrays2LRpEykpKRw+fJhff/2VWbNmPdQ2lFLk5uYSFxdH48aNyxRHdnZ2mdYTD0fyXL4kvxUvLi6O\nBg0aYG398L+eC3u/KtOYVgsWLOCVV15h6tSpXL9+nUuXLjFhwgQ2bNhg7tDKVaVrqPPz9vbmqaee\n4vfffwfgwIEDdOjQARcXF1q2bMmePXsMrw0NDWXq1Kk88cQT2NvbM2rUKJYtW8YHH3yAg4MDu3fv\nJjMzk5dffhkfHx98fHx45ZVXyMzMBCAqKgpfX18++OAD6taty5gxY3jnnXcYNGgQI0aMwNHRkZCQ\nEM6cOcN7772Hp6cnAQEB/PDDD4YYli5dSpMmTXB0dCQoKIjFixcbnsvb/oIFC/D09MTb25uIiAjD\n8+np6bz66qsEBgbi7OxMp06duHfvXonHfb9Lly4xYMAAPDw8cHd3Z9KkSQDk5uYya9YsAgMD8fT0\nZNSoUSQnJwMQGxuLtbU1ERER+Pv74+bmxueff86hQ4cICQnBxcXFsB3Q/rLt2LEjkyZNwtnZmcaN\nG7N79+6HykNenseOHUtUVBR+fn6G17z//vv4+vri6OhIo0aNDNvOyMgo8f0rKr/Fyf+XulKKV155\nBU9PT5ycnAgJCeHEiRMsXryYlStXGs6nZ599ttBtnThxgp49e+Lm5oaXlxfvvfdeqWP/17/+hYeH\nB97e3nz//fds2bKFBg0a4Obmxty5cw37mDFjBgMHDmTIkCE4Ojry2GOPcezYMcPzgYGBfPDBB4SE\nhODg4EBubm6J51BsbCxPPPEEjo6OPPnkk9y8edPwXEmfu2nTphW67r179xg+fDju7u64uLjQtm1b\nbty4YVgv73a7UqrEc3PZsmUEBARQp04d5syZY9j/wYMHad26NU5OTnh5efHqq68W+r7cvn2bvn37\n4uHhgaurK/369ePKlSulOg6A5cuXExAQgLu7e4H932/69Om8++67rFmzBgcHB5YuXVqq4/vyyy8J\nCAigR48eRW47z4YNG2jatCkuLi507dq1wN2okydPEhoaiouLC82aNWPjxo2G58LDw3nppZfo1asX\njo6OhIaGcvHiRcPzhZ33hcn/3kVERPDEE0/w2muv4erqSv369dm2bVuh6925c4fp06fz2Wef0b9/\nf2rVqkW1atV4+umnef/997l69Sr29vYkJSUZ1jl8+DAeHh78/vvvvPTSS/z88884ODjg6upqeE1S\nUhJ9+/bF0dGRxx9/nPPnzxue279/P23atMHZ2Zm2bdvy888/FziO4t5zk1KVTGBgoNq5c6dSSqmL\nFy+qpk2bqmnTpqnLly8rNzc3tXXrVqWUUj/88INyc3NTiYmJSimlunTpogICAtQff/yhcnJyVFZW\nlgoPD1dvv/22Ydtvv/22at++vbpx44a6ceOG6tChg+H5H3/8UVWvXl298cYbKjMzU6Wnp6vp06er\nmjVrqh07dqjs7Gw1cuRIFRAQoObMmaOys7PVF198oerVq2fY/ubNm9X58+eVUkrt2bNH2dnZqcOH\nDxfY/vTp01V2drbasmWLsrOzU7dv31ZKKfXXv/5Vde3aVcXHx6ucnBz1888/q4yMjCKP+8aNGw/k\nLjs7W4WEhKjJkyertLQ0de/ePbVv3z6llFJLlixRwcHB6sKFCyo1NVUNGDBAjRgxQiml1IULF5SV\nlZX6f//v/6mMjAy1Y8cOZWtrq/r3769u3Lihrly5ojw8PNSePXuUUkotXbpUVa9eXS1cuFBlZ2er\nNWvWKCcnJ5WUlFTqPOTP848//qh8fX2VUkqdOnVK+fn5qYSEBKWUUnFxcercuXOlfv+Kyu/9QkND\n1ZIlSwzH88QTTyillNq2bZt67LHH1J07dwzx5MVy//l0v+TkZOXl5aUWLFigMjIyVEpKivrll19K\nHfu7775rOK/c3NzU0KFDVWpqqjpx4oSqVauWio2NVUopNX36dGVjY6O++eYblZ2drebNm6fq1aun\nsrOzlVJKBQQEqFatWqnLly+re/fuleqzExQUpM6cOaPS09NVaGioeuONN5RSqlTrBgcHF7ru559/\nrvr166fS09NVbm6uOnz4sEpOTn4g/6U5N1988UV17949FR0drWrUqKFOnTqllFLq8ccfV5GRkUop\npe7evasOHDhQ6Htz8+ZN9e2336r09HSVkpKiBg0apPr37294vrjjOHHihKpdu7bau3evysjIUJMn\nT1bVq1dXu3btKnRfM2bMMMRf2uMbNWqU4TN7v+nTp6vhw4crpZSKiYlR9vb2aufOnSo7O1t98MEH\nKjg4WGVlZanMzEwVFBSk3nvvPZWVlaV2796tHBwcVExMjFJKqVGjRikHBwfDcfz9738v1Xl/v/s/\nOzY2Nuo///mPys3NVf/+97+Vt7d3oett3bpVVa9eXeXk5BT6vFJK9enTR/373/82LL/88svqb3/7\nm1JKqYiICEO8eUaNGqXc3NzUoUOHVHZ2tho2bJgaMmSIUkp7z52dnVVkZKTKyclRq1atUi4uLobf\nU8W956ZW6RrqgIAAVbt2beXs7KwCAgLUhAkTVHp6upo7d26Bk18ppZ588kn11VdfKaW0k2f69OkF\nng8PD1dTp041LAcFBRl+4Sil1Pbt21VgYKBSSvtlaWtrqzIyMgzPT58+XfXq1cuwvGHDBlW7dm2V\nm5urlNJ+MVtZWRlO7vv1799fffTRR4bt16pVq8BJ6uHhoX755ReVk5OjatWqpY4dO/bANko67vz2\n79+v6tSpU+gHoVu3bgU+ADExMcrGxkbl5OQYflnEx8cbnndzc1Nff/21Yfm5555TCxcuVEppH877\nP4xt27ZVy5cvL1Ue7s9z/ob6zJkzysPDQ+3cuVNlZmYW2E5J719R+S1MUQ31rl27VIMGDdSBAwce\nyOP959P9Vq5cqR599NFCnytN7PefVwcPHjS8/rHHHlPr169XSmnnZfv27Q3P5ebmqrp166qffvpJ\nKaX9sbt06VLD86X57MyePdvw3Geffaaeeuopo9f98ssvVYcOHQo9r/PnvzTn5pUrVwzPt23bVq1Z\ns0YppVTnzp3V9OnTC/3DtThHjhxRLi4uBeIp6jjeeecdFRYWZnju7t27ytbWtsiGOn/DWtrju3Dh\nQpGx5t/ezJkz1fPPP294Ljc3V/n4+KioqCj13//+V3l5eRVYNywsTM2YMUMppTVq+Y8jNTVVVatW\nTV2+fFnt3r27yPP+fvd/doKDgwvkxsrKSl27du2B9SIjIx+I736rV69WHTt2VEppFx5eXl7q0KFD\nhn3d31CHh4ercePGGZa3bNmiGjVqpJRSatmyZapdu3YFXt++fXsVERFhOI6i3nNTq3S3vq2srFi/\nfj23bt0iNjaWTz/9lJo1axIXF8fatWtxcXEx/Nu3bx9Xr141rJv/9mlh4uPjCQgIMCz7+/sTHx9v\nWK5Tpw62trYF1vHw8DD8XKtWLdzd3Q2dF2rVqgVAamoqAFu3buXxxx/Hzc0NFxcXtmzZUuBWipub\nW4G6lZ2dHampqSQmJnLv3j2CgoIeiLk0x53n0qVLBAQEFFobS0hIeODYs7OzuXbtmuExT0/PAsd6\n/3L+Tn0+Pj4Fth8QEEBCQkKp8lBYnvMEBwezcOFCZsyYgaenJ2FhYYbtlvT+FZXfh9GtWzcmTpzI\nhAkT8PT0ZPz48aSkpJRq3UuXLlG/fv1CnytN7PefV/fnP/+x+Pr6Gn62srLC19e3wPbyfxZKcw55\neXkVui9j1h0xYgRPPvkkQ4YMwcfHh9dff73QGmxpzs38+8j/vi5ZsoTTp0/TuHFj2rZty+bNmx/Y\nPkBaWhrjx48nMDAQJycnunTpwp07dwrUf4s6jvj4+AL5trOzw83NrdD9FKY0x5f3fq1YsQIHBwcc\nHBx4+umnH9hWfHw8/v7+hmUrKyv8/Py4cuUKCQkJD/wODAgIMJwXeedJHnt7e1xdXYmPj6dr165l\nPu/vf2+AQj93bm5uJCYmkpubW+S2nn32Wf744w9iY2P54YcfcHJyonXr1sXuv6jPyf25goL5uD/2\n+z9jplTpGuqi+Pv7M2LECG7dumX4l5KSwpQpUwyvKan3n7e3N7GxsYblixcv4u3tXeT6D9ObMCMj\ng+eee44pU6Zw/fp1bt26RZ8+fUrVEcTd3Z2aNWty9uzZB54rzXHn8fPz4+LFi+Tk5DzwXGHHXr16\n9QIn+cPIX98D7Re6t7d3qfJQUl7DwsLYu3cvcXFxWFlZ8frrrxd5DPnfP1OZNGkSv/76K3/88Qen\nT582fGOgpLj9/f0L1MfyM3Xsly5dMvycm5vL5cuXizyXH+Ycup8x61avXp1p06Zx4sQJ9u/fz6ZN\nm1i2bNkDrzPm3AwODmblypXcuHGD119/nYEDB5Kenv7A6+bPn8/p06c5ePAgd+7cYc+ePSjtjmSJ\n+/D29i6Q77S0tGJrmfefJ6U5vvzfQEhJSSElJaXQPzp8fHyIi4szLCuluHTpEr6+voY48x9TXFyc\n4Y/qvNfmSU1NJSkpyXDeFHXem0r79u2pUaMG3333XZGvqVmzJoMGDSIyMpLIyEhGjhxpeO5he3ff\nnysomI+KVGUa6uHDh7Nx40Z27NhBTk4O9+7dIyoqqkCDcf+H7v7lsLAwZs2aRWJiIomJicycObPY\nrxmU5kOcJzMzk8zMTNzd3bG2tmbr1q3s2LGjVOtaW1szZswYJk+eTEJCAjk5Ofz8889kZmaW6rjz\ntGvXjrp16/LGG2+QlpbGvXv32L9/v+HYP/zwQ2JjY0lNTeXNN99kyJAhD9UzNX8+rl+/zscff0xW\nVhZr167l1KlT9OnTx6g8AJw+fZrdu3eTkZFBjRo1qFmzJtWqVTMcw8O8f2Xx66+/8ssvv5CVlYWd\nnV2B/Xt6ehbZEAP07duXhIQEPvroIzIyMkhJSeHgwYPlEvtvv/3Gd999R3Z2NgsXLqRmzZo8/vjj\nhb62LJ8dU6z7448/cvz4cXJycnBwcMDGxsaQy/yMOTcjIyMNHdScnJywsrIqdL3U1FRq1aqFk5MT\nSUlJvPPOOw+8pqjjeO6559i0aRP79u0jMzOTadOmFXtVWNjvHWM/e3kGDRrE5s2b2b17N1lZWcyf\nP5+aNWvSoUMH2rZti52dHR988AFZWVlERUWxadMmhgwZYlh/y5YthuN4++23ad++PT4+PsWe96bi\n5OTEzJkzmTBhAuvXryctLY2srCy2bt1q+GMcYOTIkSxdupQNGzYU+Ix4eXlx+fJlsrKyDI8V9zu6\nd+/enD59mlWrVpGdnc2aNWs4deoUffv2LdX6plRlGmpfX1/Wr1/PnDlz8PDwwN/fn/nz5xd7pXb/\nd+ymTp1K69atCQkJISQkhNatWzN16tRSr1/UawAcHBz4+OOPGTx4MK6urqxateqBnsHF/UU4b948\nmjdvTps2bXBzc+Of//wnubm5RR53Yb8orK2t2bhxI2fPnsXf3x8/Pz++/vprAMaMGcOIESPo3Lkz\n9evXx87Ojk8++aRUsRX2mnbt2nHmzBnq1KnD22+/zTfffIOLi0uZ85D3WEZGBv/85z+pU6cOdevW\nJTEx0dBz+mHfv9LK/z4nJyfz4osv4urqSmBgIO7u7obBcsaOHcsff/yBi4sLAwYMeGA7tWvX5ocf\nfmDjxo3UrVuXBg0aEBUVVabYizsWKysrnn32WdasWYOrqysrVqzg22+/LfIX68N+dvLnw5h1r127\nxqBBg3BycqJJkyaEhoYW+seJMefm9u3badasGQ4ODrzyyiusXr2aGjVqPPC6l19+mfT0dNzd3enQ\noQO9e/cuNuf5j6Np06YsWrSIoUOH4u3tjaura7Fltvt/bxj72cu/vYYNGxIZGcmkSZOoU6cOmzdv\nZuPGjVSvXh1bW1s2btzI1q1bqVOnDhMnTmT58uU0aNDAsJ2hQ4fyzjvv4ObmxpEjR4iMjASKP+9L\nG1tpjmfy5MksWLCAWbNmGc6nzz77jL/85S+G13Ts2BFra2see+yxAnnu1q0bTZs2xcvLy1CSLG7/\nbm5ubNq0ifnz5+Pu7s68efPYtGlTgR7jRb3npmalyulPgjFjxrB582Y8PDw4fvw4AK+99hqbNm3C\n1taWoKAgli5dipOTU3nsXliwiIgIlixZwt69e80dSpX0zjvvcPbsWYsZHUrow+jRo/H19eXdd981\ndygl6tGjB0OHDmXMmDHmDsUkyu2KevTo0Q98H65Xr16cOHGC6OhoGjRoYLjSEUJUnIq6XScqF72c\nN4cOHeLw4cMmGX7VUpRbQ92pUydcXFwKPNazZ09DXaVdu3Zcvny5vHYvLJhehu2rrCT/oiz0cN6M\nGjWKnj17snDhQuzt7c0djsmU261v0EbN6devn+HWd379+vUjLCyMoUOHltfuhRBCCN0zS2ey2bNn\nY2trK420EEIIUYLqFb3DiIgItmzZUuQUb6B9fy3/l8qFEEKIyqxFixYcPXq00Ocq9Ip627Zt/Otf\n/2L9+vXUrFmzyNfFx8cbBhOQf2X7N336dLPHoPd/kkPJo6X8kxxW/hxGR0cX2SaWW0MdFhZGhw4d\niImJwc/Pjy+//JJJkyaRmppKz549adWqFX/961/La/dVXv6RjETZSA5NQ/JoPMmh8fScw3K79b1q\n1aoHHqss32kTQgihDzExcezceY4//rjKokW76dEjiIYNA0pe0YJUmZHJqprw8HBzh6B7kkPTkDwa\nT3JYNjExcUREnCUhoRsNG77BjRvdiIg4S0xMXMkrW5By/XpWWVlZWWGBYQkhhNCRRYt2c/FiN37/\nHWrVgqZNwcoKPDx289e/djN3eAUU1+5VeK9vY7i6unLr1i1zhyFEpeLi4kJSUlK5bT8qKorQ0NBy\n235VIDksm2vXrDl8GLKyIDExigYNQrG1hcxMfd1M1lVDfevWLbnSFsLELH20KSHK4rff4LffcsnK\nAldXsLeHvGnsbW2Lnr3MEunq1rfcEhfC9ORzJSqT3FzYvh1++QUSE+NITDxLw4bdyft7NCNjF+Hh\nwRbXoay4z6E01EJUcfK5EpVFejqsWwfnzkG1atC3L9jZxbFr1zkyM62xtc2le3fL7PUtDbUQokjl\n/bmS+qrxJIclS0yEVavg5k3tNvfzz4O//5/PW3oOi/sc6quibsECAwPZvXs3AHPmzGHcuHFmjsh8\nAgMDix0itjw0a9aM//73vxW6zxs3btC4cWMyMjLKZfszZsxgxIgR5bJtISqTs2fhP//RGmkvLxg3\nrmAjrXfSUJtI/g45b775Jl988UWJ64SGhrJkyZLyDMsszDEd3u+//07nzp0rdJ9z585l9OjR1KhR\nw/DY9u3b6dy5M46Ojnh4eBAaGsrGjRtL3FZUVBR+fn4FHqssnbws+SpGLySHhVMKDhyAFSvg3j1o\n3BjGjAFn5wdfq+ccSkNtRnr8RZydnW3uECxCRkYGy5YtY/jw4YbH1q1bx+DBgwkPD+fKlStcv36d\nmTNnlqqhLoyUeYQoWnY2bNgA27ZpDXaXLjB48J89uyuTStFQx8TEsWjRbhYujGLRot1lGnXGFNvI\nk/+W5b179xg+fDju7u64uLjQtm1brl+/zltvvcXevXuZOHEiDg4O/O1vfyt0Wz/99BMdOnTAxcUF\nf39/vvrqKwDu3LnDyJEj8fDwIDAwkNmzZxt+sUdERNCxY0cmT56Mi4sLwcHB7N+/n6VLl+Lv74+n\npyfLli0z7CM8PJyXXnqJXr164ejoSGhoKBcvXjQ8b21tzWeffcYjjzxCw4YNAdi0aRMtW7bExcWF\njh07PjDn+JEjR2jRogXOzs4MGTKkwO3h4tYNDAxk/vz5ha6bmJhI3759cXFxwc3NrcAVdP7b7RkZ\nGbz88sv4+Pjg4+PDK6+8QmZmJqBdufr6+rJgwQI8PT3x9vYmIiLCsJ0tW7bQtGlTHB0d8fX1Zf78\n+YW+L7/88gvOzs54e3sDWqM6efJkpk2bxpgxY3BwcACgc+fOLF68mMzMTNzc3Pj9998N27h+/Tr2\n9vZcvHiR3r17Ex8fj4ODA46OjiQkJGBlZUVmZiajRo3C0dGRZs2a8dtvvxnWP3nyJKGhobi4uNCs\nWbMCfxCEh4czYcIE+vbti6OjI48//jjnz58v9FjKW1RUlFn2W5lIDgu6exeWLYMjR8DGBgYNgq5d\nobhrHz3nUPcNdd4QcTdudOP27dAyDRFnim3kl//W71dffUVycjKXL18mKSmJ//u//6NWrVrMnj2b\nTp06sWjRIlJSUvj4448f2E5cXBx9+vTh73//O4mJiRw9epSWLVsCMGnSJFJSUrhw4QJ79uxh2bJl\nLF261LDuwYMHadGiBUlJSYSFhTF48GAOHz7MuXPniIyMZOLEiaSlpRlev3LlSqZNm0ZiYiItW7Zk\n2LBhBWJZv349hw4d4o8//uDIkSOMHTuWL774gqSkJMaPH88zzzxDVlYWoDVaa9euZfv27Vy4cIFj\nx44ZGsOS1rWysipy3fnz5+Pn50diYiLXr1/nvffeKzTns2fP5uDBg0RHRxMdHc3BgweZNWuW4bXX\nrl0jOTmZ+Ph4lixZwoQJE7hz5w4AY8eOZfHixSQnJ3PixAm6dSt89KLjx48b/mgBiImJ4fLlywwc\nOLDQ19va2jJkyBAiIyMNj61atYoePXrg7+/Ptm3b8Pb2JiUlheTkZOrWrYtSig0bNhAWFsadO3d4\n5plnmDhxIgBZWVn069ePp556ihs3bvDJJ58wbNgwTp8+bdj+mjVrmDFjBrdu3SI4OJi33nqr0NiE\n0JOrV2HxYrh4ERwdYfRobcSxykxXA54UZufOc9So0Z2Cfyx159ix3bRpU7ou+AcPniMtrbthOTQU\natTozq5du8vUjT9v2jLQfkHfvHmTM2fO0Lx5c1q1avXAa4uycuVKevbsyfPPPw9oI7O5urqSk5PD\nmjVriI6Oxt7eHnt7e1599VWWL19umPikXr16jBo1CoDBgwcze/Zspk2bho2NDT179sTW1pazZ88S\nEhICQN++fXniiScAraFzcnLiypUr+Pj4APDPf/4T5/8VfhYvXsz48eNp06YNACNHjmTOnDkcOHCA\nTp06YWVlxd/+9je8vLwA6Nevn2Ge1ZLWBYpc19bWloSEBGJjYwkKCqJjx45F5u3TTz/F3d0dgOnT\npzN+/HhmzpwJgI2NDdOmTcPa2prevXtTu3ZtYmJiaNu2Lba2tpw4cYLmzZvj5OT0wPuV5/bt24ar\nZoCbN28CULdu3SLfz5EjRzJ48GDmzp0LwPLly3njjTeAos+DTp068dRTTwEwfPhwFi5cCMCBAwe4\ne/euYf2uXbvSt29fVq1axfTp0wEYMGAArVu3BmDYsGFMnjy5yNjKk55rg5ZCcqg5eRK++w4yM8HX\nV+vZne9jWCw951D3V9RZWYUfQk5O6Q8tN7fw15pimLkRI0bw5JNPMmTIEHx8fHj99dcL1HmLq1Nf\nvnyZ+vXrP/B4YmIiWVlZBAT8+UeEv78/V65cMSx7enoafq5VqxYAderUKfBYamqqIQZfX1/Dc/b2\n9ri6uhIfH294LH9Hp7i4OObPn4+Li4vh3+XLlwu8Pq+hvX9fxqz72muvERwcTK9evQgKCuL9998v\nNG/x8fEP5Cb/9t3c3LC2/vO9tbOzM+zjm2++YcuWLQQGBhIaGsqBAwcK3YerqyspKSkFtgmQkJBQ\n6OsB2rVrR61atYiKiuLUqVOcO3eOZ555psjXQ8H30c7Ojnv37pGbm0t8fPwDnc8CAgIMx2llZfXA\nOZB3jELojVLw3//CmjVaI92iBYSHl76R1jvdX1Hb2GhDwd3/x5KHRy6lne560aJcbtx48HFTDDNX\nvXp1pk2bxrRp0wy3shs2bMiYMWNK7Ezm5+fHwYMHH3jc3d0dGxsbYmNjady4MQAXL14s0Ng+DKUU\nly5dMiynpqaSlJRkqL9CwT8o/P39eeutt3jzzTdLvY+89R923fz7rV27NvPmzWPevHmG29Jt27al\na9euBdbx9vZ+IDf5j6U4rVu35vvvvycnJ4dPPvmEwYMHF6jX5wkJCeHDDz80LDds2BA/Pz/WrVvH\nq6++WuT2R40aRWRkJJ6engwaNAjb//V8KexcKO788Pb25tKlSyilDK+Li4ujUaNGpTrOimTp31/V\ng6qcw6wsWL8efv9dq0H36AEdOhRfjy6MnnOo+yvqHj2CyMgo+J3djIxddO8eVKHbKEpUVBTHjx8n\nJycHBwcHbGxsqFatGqBdLZ07d67IdYcNG8bOnTtZu3Yt2dnZ3Lx5k+joaKpVq8bgwYN56623SE1N\nJS4ujg8//LBAD+SHtWXLFvbt20dmZiZvv/027du3N9z2vt+4ceP4/PPPOXjwIEop7t69y+bNm4u9\nYsu7tfubivxFAAAgAElEQVSw6+a/Jbxp0ybOnj2LUgpHR0eqVatW4Mo4T1hYGLNmzSIxMZHExERm\nzpxZqu8jZ2VlsWLFCu7cuUO1atVwcHAwvFf3a9OmDbdv3y5wBbtgwQLeffddIiIiSE5OJjc3l59+\n+onx48cb1hs+fDjffvstK1asYOTIkYbHPT09uXnzJsnJyYUe+/3atWuHnZ0dH3zwAVlZWURFRbFp\n0yaGDBlS4rpC6EVyMixdqjXStrYQFgYdOz58I613um+oGzYMIDw8GA+P3Tg7R+Hhsfuhx3E1xTby\ny9+x6erVqwwaNAgnJyeaNGlCaGioodH4+9//zrp163B1deXll19+YDt+fn5s2bKF+fPn4+bmRqtW\nrTh27BgAn3zyCfb29tSvX59OnToxbNgwRo8e/cD+88dUXLxDhw7lnXfewc3NjSNHjhTo9HT/uo89\n9hhffPEFEydOxNXVlUceeYRly5YVuY/88Riz7tmzZ+nZsycODg506NCBCRMm0KVLlwfWmTp1Kq1b\ntyYkJISQkBBat27N1KlTS5WLyMhI6tWrh5OTE4sXL2bFihWFvs7W1pbw8PACeXruuedYs2YNX375\nJT4+Pnh5eTFt2jT69+9veI2fnx+PPvoo1tbWhj4BAI0aNSIsLIz69evj6upq6PVd1Ptoa2vLxo0b\n2bp1K3Xq1GHixIksX76cBg0aPJC30hx3edLrVYwlqYo5vHxZ6zQWHw8uLvDCC/C/07tM9JxDGUJU\nMHr0aHx9fXn33XfNHYquJCYm0qlTJ44ePVpg0JOSjB07Fh8fH0PnNnOTz5WwNMeOad+Rzs6GwEDt\n+9F2duaOqnzJEKKiWPJLumzc3d05efLkQzXSsbGxfPvtt4wdO7YcI7Msev7+qqWoKjlUCnbuhG+/\n1Rrp1q1hxAjTNNJ6zqE01MIsQ35WRW+//TbNmzdnypQpBXqlCyEgIwNWr4affgJra3j6aW32qyK6\niVQpcutbiCpOPlfC3G7d0ma+un4datXSRhor5JuplVpxn0Pdfz1LCCGEfsXGwtdfQ1oauLvD0KHg\n6mruqCyL3PoWQpQrPdcGLUVlzeFvv2ljdqelwSOPaD27y6uR1nMO5YpaCCFEhcrJge3bIW88pw4d\ntIFMChkWQSA1aiGqPPlciYqUng5r18L581pHsX794H9zDVVpUqMWQghhdjduaJ3GkpLA3h6GDIH7\nhqwXhZAbDSYSExNDy5YtcXR05NNPPzV6e6GhoSxZssQEkZlOWFgY69evL5dtx8bGYm1tTW6u8eOr\n3+8f//gHn3/+ucm3K0pHz7VBS1EZcnjmDPznP1oj7eUFL75YsY20nnMoDbWJfPDBB3Tv3p3k5GTD\nnMHGsLTvNh87doxjx47x7LPPGh5LSEhg7NixeHt74+joSOPGjZkxY0aBea6LEhgYyO7du8szZIN/\n/OMfzJkzxzDntRCi4igF+/fDypXad6WbNIExY8DJydyR6Yc01CYSFxdHkyZNyrRuTk6OiaMxvf/7\nv/8rMOlHUlIS7du3JyMjgwMHDpCcnMwPP/zAnTt3ip1oJE9F1kW9vLxo1KgRGzZsqJD9iYL0PMay\npdBrDrOztZmvduzQGuzQUO070v+bNK5C6TWHUEka6pizMSxas4iFqxeyaM0iYs7GVOg2unXrRlRU\nFBMnTsTR0ZGzZ89y584dRo4ciYeHB4GBgcyePdvQMEVERNCxY0cmT56Mu7s777zzTrHbV0oxa9Ys\nAgMD8fT0ZNSoUQVmWdqwYQNNmzbFxcWFrl27curUKcNzgYGBzJ07l6ZNm+Lq6sqYMWPIyMgAtLGq\n+/bti4uLC25ubnTu3LnIxnPbtm0FJsBYsGABTk5OREZG4u/vD4Cvry8ffvghzZs3Z8KECfzjH/8o\nsI1nnnmGhQsXMnLkSC5evEi/fv1wcHBg3rx5htdERkYSEBBAnTp1mDNnjuHxjIwMXn75ZXx8fPDx\n8eGVV14hMzMT0G5p+fr6smDBAjw9PfH29iYiIqLAvkNDQ9m8eXOxeRZCmE5qKnz1FRw9CjY2WgMd\nGlr1Zr4yBd031DFnY4j4MYIbnje47XWbG543iPgx4qEaWmO3sXv3bjp16sSiRYtITk4mODiYSZMm\nkZKSwoULF9izZw/Lli1j6dKlhnUOHjxIUFAQ169fL3Fu5qVLl/LVV18RFRXF+fPnSU1NNdxeP336\nNEOHDuXjjz8mMTGRPn360K9fP7Kzsw3rr1y5kh07dnDu3DlOnz7NrFmzAJg/fz5+fn4kJiZy/fp1\n3nvvvUJvt9+9e5cLFy7QsGFDw2M7d+5kwIABRcYcHh7OqlWrDA1/YmIiu3btYtiwYSxbtgx/f382\nbdpESkpKgQZ93759nD59ml27djFz5kxiYrT3YPbs2Rw8eJDo6Giio6M5ePCg4TgArl27RnJyMvHx\n8SxZsoQJEyZw584dw/ONGjUiOjq62DyL8qHn2qCl0FsOExLgiy/g0iVwdNRudTdtat6Y9JbD/HTf\n63vnbzup8UgNomKj/nzQBo6tPkabJ9qUahsHfzpImm8axGrLoYGh1HikBrsO76JhcMNi180vr1HK\nyclhzZo1REdHY29vj729Pa+++irLly9nzJgxAHh7ezNhwgQAatasWex2V6xYwauvvkpgYCAA7733\nHs2aNWPp0qWsWbOGvn370r17d0Crx3700Ufs37+fzp07Y2VlxcSJEw1zS7/11ltMmjSJd999F1tb\nWxISEoiNjSUoKIiOHTsWuv/bt28D4ODgYHgsKSmJunXrFhlzmzZtcHJyYteuXfTo0YPVq1fTtWtX\n6tSpU+yxTp8+nRo1ahASEkKLFi2Ijo6mYcOGrFy5kk8//RR3d3fD68aPH2+YgcrGxoZp06ZhbW1N\n7969qV27NjExMbRt29YQe95xCCHKzx9/wHffQVYW+PpqPbtr1zZ3VPqm+yvqLFV4B6EcSl/3zaXw\nnsaZuZkPFUve1WhiYiJZWVkFJl7w9/fnypUrhmW/fN0dX3rpJRwcHHBwcGDu3LkPbDchIeGBbWVn\nZ3Pt2jUSEhIMt57zYvDz8ytyX/7+/sTHxwPw2muvERwcTK9evQgKCuL9998v9LicnZ0BSElJMTzm\n5uZm2E5RRo4caZivOTIy0jAPd3G8vLwMP9vZ2ZGamgpAfHz8AznIv383Nzes842WkH/dvNjzjkNU\nLD3XBi2FHnKoFOzZow0HmpUFLVpAeLjlNNJ6yGFRdH9FbWNlA2hXwfl52Hnw19C/lmobi64t4obn\njQcet7UuW48Hd3d3bGxsiI2NpXHjxgBcvHgRX19fw2vy32L+/PPPi/36kLe3N7GxsYblixcvUr16\ndby8vPD29ub48eOG55RSXLp0yXAFnff6/D97e3sDULt2bebNm8e8efM4ceIE3bp1o02bNnTr1q3A\n/u3t7QkKCiImJoYOHToA0KNHD7777jumT59eZO/04cOH07x5c6Kjozl16hT9+/cv9PhLIy8H+fOZ\ndxylcfLkSVrKqApClIusLPj+ezhxQqtB9+wJ7dtLPdpUdH9F3eOxHmScySjwWMaZDLo/2r1CtwF/\n3vquVq0agwcP5q233iI1NZW4uDg+/PDDAr2mH0ZYWBgffvghsbGxpKam8uabbzJkyBCsra0ZNGgQ\nmzdvZvfu3WRlZTF//nxq1qxpaFCVUnz22WdcuXKFpKQkZs+ezZAhQwDYtGkTZ8+eRSmFo6Mj1apV\no1oRc8r16dOHPXv2GJYnT55McnIyo0aNMvwhcOXKFV599VXDHw6+vr60bt2akSNHMnDgwALzNnt6\nepaqd3j+HMyaNYvExEQSExOZOXNmqa7Q8+zZs4fevXuX+vXCdPRcG7QUlpzDO3fgyy+1RrpGDW1S\njQ4dLK+RtuQclkT3DXXD4IaEdw3H47oHzled8bjuQXjX8IeqLZtiG1DwKvGTTz7B3t6e+vXr06lT\nJ4YNG8bo0aMNr3uYK8oxY8YwYsQIOnfuTP369bGzs+OTTz7RYm/YkMjISCZNmkSdOnXYvHkzGzdu\npHr16oZ9DR061HB7+5FHHmHq1KkAnD17lp49e+Lg4ECHDh2YMGFCgZ7d+b344ousWLHCsOzi4sL+\n/fuxsbGhXbt2ODo60qNHD5ydnQkODja8btSoURw/fvyBRvWf//wns2bNwsXFhQULFjyQv/tNnTqV\n1q1bExISQkhICK1btzYcR0nrJiQkcPLkyQJX9EII412+rHUaS0jQJtN44QVtcg1hWjLWdyVXr149\nlixZ8sDt7LIYNmwYgwcPLjDoSUn27t3L8OHDiYuLM3r/ZfWPf/yD4OBgXnrpJbPFYMnkcyXKIjoa\nNmzQJtioV0/7+pWdnbmj0i8Z61uYRP4r6tLIyspi4cKFjBs3rpwiKp3839MWQhgnNxd27YJ9+7Tl\ntm3hySe1CTZE+dD9rW9hmU6ePImLiwvXrl3j5ZdfNnc4woz0XBu0FJaSw4wMWL1aa6StraFvX+jT\nRx+NtKXksCzkirqSu3Dhgln227hx4wJfjxJC6FtSkjbz1Y0bUKsWDB6s3fIW5U9q1EJUcfK5EiW5\ncEH7fnR6OtSpA2FhWucxYTpSoxZCCFEmhw7B1q1abbpBA3juOe1rWKLiSI1aCFGu9FwbtBTmyGFO\nDmzerP3LzYWOHbXhQPXaSOv5PJQraiGEEAWkpcHatdot72rV4JlntCFBhXnoqkbt6urKrVu3zBCR\nEJWXi4sLSUlJ5g5DWIgbN7ROY0lJ2jjdQ4Zok2uI8lVcjVpXDbUQQojyc+YMrFunfQ2rbl2tkXZy\nMndUVUNx7Z7UqCspPddjLIXk0DQkj8Yr7xwqBfv3w8qVWiPdtKk2h3RlaqT1fB6WW0M9ZswYPD09\nad68ueGxpKQkevbsSYMGDejVq5fMDyyEEGaWna3NfLVjh9Zgd+0KAweCjY25IxN5yu3W9969e6ld\nuzYjR440zKY0ZcoU3N3dmTJlCu+//z63bt0qdP5lufUthBDlLzVVG2ns8mWtYf7LX6BJE3NHVTWZ\nrUYdGxtLv379DA11o0aN2LNnD56enly9epXQ0FBOnTr1UAELIYQwXkKC1mksOVm7xR0WBl5e5o6q\n6rKYGvW1a9fw9PQEtPmIr127VpG7r1L0XI+xFJJD05A8Gs/UOTxxQptDOjkZ/Pxg3LjK30jr+Tw0\n2/eoH3ZOZiGEEMZRCvbsgbw2q1UrePppqC4jali0Cn178m55e3l5kZCQgIeHR5GvDQ8PJzAwEABn\nZ2datmxJaGgo8OdfRrJc/HIeS4lHlqvmct5jlhKPXpfzlHX9Dh1C+f572LJFWx4/PpTHH4c9eyzj\n+Kract7PsbGxlKRCa9RTpkzBzc2N119/nblz53L79m3pTCaEEOXszh2tHn31qjYE6MCB8Mgj5o5K\n5GeWGnVYWBgdOnQgJiYGPz8/li5dyhtvvMEPP/xAgwYN2L17N2+88UZ57b7Ku/+vcPHwJIemIXk0\nnjE5vHQJFi/WGmlXV3jhharZSOv5PCy3W9+rVq0q9PGdO3eW1y6FEELkc/QobNyoTbBRvz4MGqTN\nJS30RYYQFUKISiY3F3bu1EYbA2jbFp58UptgQ1gmmY9aCCGqiHv34JtvtHG7ra2hTx9o3drcUQlj\nyFjflZSe6zGWQnJoGpJH45U2hzdvwn/+ozXSdnYwcqQ00nn0fB7KFbUQQlQC589rc0inp4OHhzbS\nmIuLuaMSpiA1aiGE0DGl4NAh2LZNq003bAgDBmhfwxL6ITVqIYSohHJyYOtW+PVXbfmJJ6BbN602\nLSoPeTsrKT3XYyyF5NA0JI/GKyyHaWmwfLnWSFevrl1F9+ghjXRR9HweyhW1EELozPXr2khjt26B\ngwM8/zz4+po7KlFepEYthBA6EhOjff0qMxO8vWHIEHB0NHdUwlhSoxZCCJ1TCvbtg127tJ+bNYNn\nnwUbG3NHJsqbVDMqKT3XYyyF5NA0JI/G27Uriu++00YbU0rrMPbcc9JIPww9n4dyRS2EEBYsJUX7\n6pW9Pdjawl/+Ao0bmzsqUZGkRi2EEBYqPh5Wr4bkZHB21gYx8fQ0d1SiPEiNWgghdOb332H9esjK\nAn9/rWe3vb25oxLmIDXqSkrP9RhLITk0Dcnjw1EKfvwR1q3TGulHH4WAgChppI2k5/NQGmohhLAQ\nmZnw9dewZw9YWcFTT0G/fjI9ZVUnNWohhLAAt29rg5hcuwY1a8LAgRAcbO6oREWRGrUQQliwixdh\nzRq4exfc3LROY+7u5o5KWAq59V1J6bkeYykkh6YheSzekSPw1VdaIx0UBC+88GAjLTk0np5zKFfU\nQghhBrm58MMP8PPP2vLjj0OvXjKphniQ1KiFEKKC3bun9eo+e1brKPb001rvblF1SY1aCCEsxM2b\nWqexxESws9O+Hx0QYO6ohCWTmyyVlJ7rMZZCcmgaksc/nTsHX3yhNdKenvDii6VrpCWHxtNzDuWK\nWgghyplScPAgbN+u1aYbNdLG7K5Rw9yRCT2QGrUQQpSjnBzYsgV++01b7tRJm/3Kysq8cQnLIjVq\nIYQwg7Q07fvRcXFQvbo2f3Tz5uaOSuiN1KgrKT3XYyyF5NA0qmoer12DxYu1RtrBAUaPLnsjXVVz\naEp6zqFcUQshhImdOgXffquN3e3jA0OGaI21EGUhNWohhDARpeCnn2D3bu3n5s3hmWfAxsbckQlL\nJzVqIYQoZ1lZsGEDHD+uLXfvDk88IZ3GhPGkRl1J6bkeYykkh6ZRFfKYkgIREVojbWur3eru1Ml0\njXRVyGF503MO5YpaCCGMcOUKrF6tNdbOztrMV56e5o5KVCZSoxZCiDI6fhzWr4fsbG2EscGDwd7e\n3FEJPZIatRBCmJBSWoexvXu15Ucf1SbWqFbNvHGJyklq1JWUnusxlkJyaBqVLY8ZGdogJnv3ajXo\n3r2hX7/ybaQrWw7NQc85lCtqIYQopdu3tZmvrl2DmjVh0CAICjJ3VKKykxq1EEKUQlycdiWdlgZu\nbjB0qPa/EKYgNWohhDDC4cOwebM2wUZwMAwcqF1RC1ERpEZdSem5HmMpJIemoec85ubCtm3aQCY5\nOdC+vXYlXdGNtJ5zaCn0nEO5ohZCiEKkp8O6dXDunNZRrG9faNXK3FGJqkhq1EIIcZ/ERK3T2M2b\n2vein38e/P3NHZWozKRGLYQQpXTuHKxdC/fugZeXNhyos7O5oxJVmdSoKyk912MsheTQNPSSR6Xg\nwAGIjNQa6caNYcwYy2ik9ZJDS6bnHMoVtRCiysvJ0Xp1Hz6sLXfpAqGhMvOVsAxSoxZCVGl372rf\nj754EapXh/79oVkzc0clqhqpUQshRCGuXdM6jd2+DY6OWj3a29vcUQlRkNSoKyk912MsheTQNCw1\nj6dOwZIlWiPt4wPjxlluI22pOdQTPefQLA31e++9R9OmTWnevDlDhw4lIyPDHGEIIaogpeC//9Xm\nkM7MhJAQGD0aHBzMHZkQhavwGnVsbCzdunXj5MmT1KhRg+eff54+ffowatSoP4OSGrUQohxkZWnz\nR//+u9ZRrHt36NhROo0J87OoGrWjoyM2NjakpaVRrVo10tLS8PHxqegwhBBVTHKydhUdHw+2tvDc\nc9CwobmjEqJkFX7r29XVlVdffRV/f3+8vb1xdnamR48eFR1GpafneoylkByahiXk8coV+OILrZF2\ncYEXXtBXI20JOdQ7Peewwhvqc+fOsXDhQmJjY4mPjyc1NZUVK1ZUdBhCiCri+HFYuhRSUiAwUOs0\n5uFh7qiEKL0Kv/X966+/0qFDB9z+N5HrgAED2L9/P8OGDSvwuvDwcAIDAwFwdnamZcuWhIaGAn/+\nZSTLxS/nsZR4ZLlqLuc9VtH779IllF27IDJSWx44MJTevWHvXvPmQz7PspwnKiqK2NhYSlLhncmi\no6MZNmwYhw4dombNmoSHh9O2bVsmTJjwZ1DSmUwIYYSMDPj2W4iJAWtreOopaNNGOo0Jy1Vcu1fh\nt75btGjByJEjad26NSEhIQC8+OKLFR1GpXf/X+Hi4UkOTaOi83jrlvb96JgYqFULhg+Htm313UjL\nuWg8PefQLCOTTZkyhSlTpphj10KISiw2Fr7+GtLSwN0dwsLgf1U2IXRLxvoWQlQKv/2mTayRmwvB\nwTBwINSsae6ohCgdi/oetRBCmFJuLmzbBgcPassdOkCPHlptWojKQE7lSkrP9RhLITk0jfLMY3q6\nNn/0wYNQrZo281WvXpWvkZZz0Xh6zqFcUQshdCkxEVauhKQksLfXZr7y8zN3VEKYntSohRC6c+YM\nrFunfQ3Ly0vrNObkZO6ohCg7qVELISoFpeDAAdixQ/u5SRPtdretrbkjE6L8VLJKjsij53qMpZAc\nmoap8pidrc18tX271kh36QKDBlWNRlrORePpOYdyRS2EsHipqbBmDVy6BDY22lV006bmjkqIiiE1\naiGERbt6FVatgjt3wNFRq0fXrWvuqIQwLalRCyF06eRJbczurCzw9dV6dteube6ohKhYUqOupPRc\nj7EUkkPTKEselYI9e7Tb3VlZ0KIFhIdX3UZazkXj6TmHckUthLAoWVnw/fdw4oQ2kUaPHtpoY3qe\nVEMIY0iNWghhMZKTtXp0QgLUqAHPPQcNGpg7KiHKn9SohRAW7/JlWL1a6+Ht6qp1GqtTx9xRCWF+\nUqOupPRcj7EUkkPTKE0eo6MhIkJrpOvVgxdekEY6PzkXjafnHMoVtRDCbHJzYdcu2LdPW27TBp56\nSptgQwihkRq1EMIsMjLgm2/g9GlttqvevbWGWoiqSGrUQgiLcuuWNvPVjRtQqxYMHqzd8hZCPEhq\n1JWUnusxlkJyaBr35/HCBVi8WGuk69SBceOkkS6JnIvG03MO5YpaCFFhfv0VtmzRatMNGmhfv6pR\nw9xRCWHZpEYthCh3OTmwbRscOqQtd+wI3btrtWkhhNSohRBmlJ4OX3+t3fKuVg2eeUYbElQIUTry\n92wlped6jKWQHBrvxg2YMiWKCxe0cbrDw6WRLgs5F42n5xzKFbUQolycOQPr1kFKCjRrps185eRk\n7qiE0B+pUQshTEop+Pln+OEH7eemTeHZZ8HW1tyRCWG5pEYthKgQ2dmwaRMcPaotd+0KnTvLzFdC\nGKPEGnVqaio5OTkAxMTEsGHDBrKysso9MGEcPddjLIXk8OGkpsJXX2mNtI2NNohJly6wZ0+UuUPT\nPTkXjafnHJbYUHfu3JmMjAyuXLnCk08+yfLlywkPD6+A0IQQepGQAF98AZcuaXXosWOhSRNzRyVE\n5VBijbpVq1YcOXKETz75hPT0dKZMmUKLFi2Ijo4uv6CkRi2EbvzxB3z3HWRlgZ8fPP+81sNbCFF6\nRteof/75Z1asWMGSJUsAyM3NNV10QghdUgr27IG8O4otW0LfvlBder4IYVIl3vpeuHAh7733Hn/5\ny19o2rQp586do2vXrhURmzCCnusxlkJyWLTMTFi7VmukraygVy+tZ3dhjbTk0XiSQ+PpOYcl/u3b\npUsXunTpYlgOCgri448/LteghBCW684dWL1aq0vXqAEDB8Ijj5g7KiEqrxJr1IcOHWLOnDnExsaS\nnZ2trWRlxbFjx8ovKKlRC2GRLl3SGum7d8HVFcLCtBmwhBDGKa7dK7GhbtCgAfPmzaNZs2ZY5xtB\nPzAw0KRBFghKGmohLM7Ro7BxozbBRv36MGiQNpe0EMJ4xbV7Jdao69SpwzPPPEP9+vUJDAw0/BOW\nTc/1GEshOdTk5sKOHfD991oj3bYtDBtW+kZa8mg8yaHx9JzDEmvU06dPZ+zYsfTo0QPb/40BaGVl\nxYABA8o9OCGEed27B998o43bbW0NffpA69bmjkqIqqXEW9/Dhg0jJiaGpk2bFrj1vXTp0vILSm59\nC2F2SUmwapU2A1atWtr3o+VmmhDlw6gadcOGDTl16hRWFThYrzTUQpjXhQvaHNLp6eDhoXUac3Ex\nd1RCVF5G1ag7dOjAH3/8YfKgRPnScz3GUlTVHB46BMuXa410gwbacKDGNNJVNY+mJDk0np5zWGKN\n+ueff6Zly5bUq1ePGjVqAOX/9SwhRMXLyYGtW+HXX7XlJ56Abt202rQQwnxKvPUdGxtb6OPy9Swh\nKo+0NO1Wd2ysNrrYM89ASIi5oxKi6jCqRm0O0lALUXGuX9c6jd26pU2mMWQI+PqaOyohqhajatRC\nn/Rcj7EUVSGHp0/DkiVaI+3tDS++aPpGuirksbxJDo2n5xzKPDdCVEFKwf79sHOn9nOzZtqkGjY2\n5o5MCHE/ufUtRBWTna0NBZo3pXy3btCpkzYLlhDCPIy69f3NN9/wyCOP4OjoiIODAw4ODjg6Opo8\nSCFE+UtJgYgIrZG2tdUGMencWRppISxZiQ31lClT2LBhA8nJyaSkpJCSkkJycnJFxCaMoOd6jKWo\nbDmMj4cvvoDLl8HJCcaMgcaNy3+/lS2P5iA5NJ6ec1hiQ+3l5UVjE3+ab9++zcCBA2ncuDFNmjTh\nwIEDJt2+EKKgEydg6VJITgZ/f63TmJeXuaMSQpRGiTXqv//971y9epX+/fubbFKOUaNG0aVLF8aM\nGUN2djZ3797Fycnpz6CkRi2ESSgFUVGwZ4+23KoVPP209l1pIYTlMOp71OHh4YaN5FfWSTnu3LlD\nq1atOH/+fJGvkYZaCONlZsJ338HJk1oN+sknoV07qUcLYYksasCTo0ePMn78eJo0aUJ0dDSPPfYY\nH330EXZ2dn8GJQ210aKioggNDTV3GLqm5xzevg2rV8PVq1CzJgwcCMHB5olFz3m0FJJD41l6Dotr\n94q8Afb+++/z+uuvM2nSpEI3+PHHH5cpmOzsbA4fPsynn35KmzZtePnll5k7dy4zZ84s8Lrw8HDD\nMKXOzs60bNnSkOS8TgGyXPTy0aNHLSoePS7nsZR4Sru8Zk0UP/4IXl6huLmBv38Uly9DcLB54jl6\n9KHJ3WgAACAASURBVKhZ81EZluXzXPk+z3k/FzVMd35FXlFv3LiRfv36ERERUeC2t1IKKysrRo0a\nVeLGC3P16lXat2/PhQsXAPjpp5+YO3cumzZt+jMouaIWokyOHIFNm7QJNurXh0GDtLmkhRCWrUxX\n1P369QP+rFGbipeXF35+fpw+fZoGDRqwc+dOmjZtatJ9CFHV5ObCDz/Azz9ry+3aaTVpaxkkWAjd\nM8vH+JNPPmHYsGG0aNGCY8eO8eabb5ojjErt/ts94uHpJYf37sHKlVojbW0N/fpB796W00jrJY+W\nTHJoPD3n0Cxf0mjRogWHDh0yx66FqFRu3tRmvkpMBDs7baSxgABzRyWEMCUZ61sInTp/HtauhfR0\n8PCAsDBwcTF3VEKIsjBqrO+YmBi6d+9uqCMfO3aMWbNmmTZCIUSpKQUHD0JkpNZIN2wIY8dKIy1E\nZVViQz1u3DjmzJljGJWsefPmrFq1qtwDE8bRcz3GUlhiDnNytF7dW7ZoHcg6dYIhQ6BGDXNHVjRL\nzKPeSA6Np+ccllijTktLo127doZlKysrbGTSWiEqXFoafP01xMZqQ4A++yw0b27uqIQQ5a3EhrpO\nnTqcPXvWsLxu3Trq1q1brkEJ4+V9uV6UnSXl8Pp1rdPYrVvg4KBdRfv4mDuq0rGkPOqV5NB4es5h\niZ3Jzp07x4svvsj+/ftxcXGhXr16rFixwjBqWLkEJZ3JhDCIiYFvvtHG7vb21hppmRJeiMrFqM5k\nQUFB7Nq1i8TERGJiYti3b1+5NtLCNPRcj7EU5s6hUrB3rzZmd2YmNGsGo0frr5E2dx4rA8mh8fSc\nwxJvfd+6dYtly5YRGxtLdnY2YNxY30KIkmVlwYYNcPy4tty9OzzxhMx8JURVVOKt7/bt29O+fXua\nN2+OtbW10WN9lyooufUtqrCUFO0q+soVsLWFAQOgUSNzRyWEKE9GTXP56KOPcvjw4XIJrCjSUIuq\nKj5e6zSWkgLOztogJp6e5o5KCFHejKpRDx06lMWLF5OQkEBSUpLhn7Bseq7HWIqKzuHvv8OXX2qN\ndEAAjBtXORppOReNJzk0np5zWGKNumbNmrz22mvMnj0b6/+N8m9lZcX58+fLPTghqgKl4Mcf4b//\n1ZYffRSefhqqVTNvXEIIy1Dire969epx6NAh3N3dKyomufUtqozMTPj2Wzh1Suso9tRT0LatdBoT\noqop03zUeR555BFqyczzQpjc7dtaPfraNahZEwYNgqAgc0clhLA0Jdao7ezsaNmyJS+++CKTJk1i\n0qRJ/O1vf6uI2IQR9FyPsRTlmcO4OFi8WGuk3dy0enRlbaTlXDSe5NB4es5hiVfU/fv3p3///gUe\ns5L7ckKU2eHDsHmzNsFGcDAMHKhdUQshRGFkPmohKkhuLuzYAQcOaMuPPw69eoF1ife1hBCVXZlq\n1IMGDWLt2rU0L2R6HisrK44dO2a6CIWo5O7dg7Vr4dw5rTd3377QqpW5oxJC6EGRV9Tx8fF4e3sT\nFxf3QCtvZWVFQEBA+QUlV9RGi4qK0vVsMZbAVDm8eRNWrtT+t7eH558Hf3/j49MLOReNJzk0nqXn\nsEwDnnh7ewPw2WefERgYWODfZ599Vj6RClHJnDsHX3yhNdKenlqnsarUSAshjFdijbpVq1YcOXKk\nwGPNmzfneN5sAeURlFxRC51TCn75BbZv135u1Egbs9vW1tyRCSEsUZlq1P/+97/57LPPOHfuXIE6\ndUpKCh07djR9lEJUEjk5Wq/uvCHyO3eGrl1lEBMhRNkUeUV9584dbt26xRtvvMH7779vaOkdHBxw\nc3Mr36Dkitpoll6P0YOy5PDuXfj6a+170tWrQ//+2jzSVZmci8aTHBrP0nNYpitqJycnnJycWL16\ndbkFJkRlcu2aNtLY7dvg4KDNfPW/rh5CCFFm8j1qIUzg1CltzO7MTPDxgSFDtMZaCCFKw6ixvoUQ\nRVMKfvoJdu3SlkNCoF8/sLExb1xCiMpDxkSqpPQ8rq2lKCmHWVnaVfSuXVpHsR494C9/kUb6fnIu\nGk9yaDw951CuqIUog+RkWL0a4uO1r1w99xw0bGjuqIQQlZHUqIV4SFeuaI10Sgo4O8PQoeDhYe6o\nhBB6JjVqIUzk+HFYvx6ysyEwEAYPBjs7c0clhKjMpEZdSem5HmMp8udQKa0W/c03WiP92GMwYoQ0\n0qUh56LxJIfG03MO5YpaiBJkZMB332lfwbK2hqeegjZtZKQxIUTFkBq1EMW4dUsbxOT6dahZU7vV\nXb++uaMSQlQ2UqMWogzi4mDNGkhLA3d3baSxch49VwghHiA16kpKz/UYS/DbbzBjRhRpaRAcDC+8\nII10Wcm5aDzJofH0nEO5ohYin9xcbWrKX37ROpC1bw89e2q1aSGEMAepUQvxP+npsHYtnD8P1apB\n377QqpW5oxJCVAVSoxaiBImJWqexmzfB3h6efx78/c0dlRBCSI260tJzPaainT0L//mP1kh7ecG4\ncVojLTk0Dcmj8SSHxtNzDuWKWlRZSsGBA7Bjh/Zz48bapBq2tuaOTAgh/iQ1alElZWfD5s3/v707\nj26ruvYH/pUseR5kyfEQybFsWXYGJ7HJSPgBDm4IFBICSchQoCF9PEpb2rR9Hd5qu1Zf14KE1RFW\nWavrteW5tIUQhkLCkIYMJpiQAEn8mhJe4siSLc+OZFmWrPme3x+3OUFkwI5s3Stpf/7y1ZGsox3H\n2+fufe8BTp4Uj2++GWhspJuYEEKkQTVqQj7F6xWvj+7qErekXLMGmDNH6lkRQqbCmXNnsP/4foRY\nCGqFGl9Y8AXUVifWVndUo05SiVyPmUr9/cB//7eYpPPzgQcfvHKSphhODopj7CiG1+bMuTNoPtSM\nvml9eN/+PoZKhtB8qBlnzp2RemoTQitqkjI++QR45RUgFAIMBrGzOy9P6lkRQqaC0+fE7/f/HhaN\nBa4uF3wuH2ZhFjLMGThw4kBCraqpRk2SHmPA4cPAoUPi8fz5wKpVgIr+TCUkaUSECLpGutDubMdZ\nx1mcHzuPo61H4Tf4AQAFGQWYVzIPaco0aPo12LZxm8QzjkY1apKyQiFx/+h//lNsFPvCF4Bly6hp\njJBk4A16cc55DmcdZ3HOeQ6BSICPZaoyYcg1QF2khjZLC3Wamo+lKxPr0g7JEnUkEsHChQthMBiw\nZ88eqaaRtFpaWtDY2Cj1NCTldgM7dwK9vUBGBrB2LVBTM/7XUwwnB8UxdhRDEWMMA94BnHWcxVnH\nWfS4e8BwcRU6LXsaanQ1qNHVoLygHO2l7Wg+1Ay1WQ1bmw3GeiMC7QE0LW+S8FNMnGSJ+sknn8Ts\n2bMxOjoq1RRIEuvuFpO0xwMUFoo7XxUXSz0rQshEBSNBWIetOOs4i3ZnO9wBNx9LU6ShsrASZq0Z\nNboaFGYVRr22troWW7AFB04cwHnneRQPFqNpeVNC1acBiWrU3d3d2LJlC370ox/hV7/61SUraqpR\nk1j84x/A7t3itdJGo7iHdHa21LMihIyXy+8SE7OjHVaXFWEhzMfy0vNg1omJuaqwCulpiXUa+0pk\nV6P+9re/jZ///Odwu92f/2RCxkkQgIMHgdZW8XjRIuC228QNNggh8iUwAfYRO28EG/QO8jEFFNDn\n6fkp7dLcUihSrMkk7on69ddfR3FxMRoaGujawCmUajWtQAB4+WXg7FlxS8rbbxcTdSxSLYZTheIY\nu2SMoS/ki2oE84V9fCwjLQMmrQk1uhpUa6uRm54b8/slcgzjnqiPHDmC3bt3480334Tf74fb7cYD\nDzyAZ599Nup5W7ZsgdFoBABoNBrU19fzIF9I8HR85eO2tjZZzWcqj/fsacGBA4BG04isLGDGjBZ4\nvQAQ2/e/QOrPl+jHbW1tsppPIh4nw//nm2++GUNjQ3jh9Rdgd9uRY84BA4OtzQYAWHD9AtToauD4\nxIGSnBI0zWma1Pe/QC7xuPC1zWbD55H0Oup33nkHv/jFL6hGTa6ZzQbs2gWMjQHTpolNY1qt1LMi\nhABAWAhHNYK5/C4+plQoYdQYeSOYLlsn4UylJ7sa9aelWq2BTJ6PPgLefFOsTZvN4uVXmZlSz4qQ\n1OYOuNHuEGvNHcMdCAkhPpajzolqBMtU0X/Y8aA7kyWplgSux3yeSAT4+9+BDz4Qj5ctE29kopzk\nO9cncwzjieIYOznHUGACekd7+bXN/Z7+qPGy3DLeCDY9b7pkizM5xxCQ+YqakInw+YAXXwQ6OsRu\n7lWrgPp6qWdFSGrxh/2wOC38lPZYaIyPpaelo6qwCjW6Gpi1ZuRl0A31Y0UrapIwhoaA558HnE4g\nN1fcVKO8XOpZEZL8GGNw+Bx81dw10gWBCXy8MLNQTMw6M4waI1RKWgNOFK2oScJrbwdeekm8DKus\nDNi4ESgokHpWhCSvsBBGp6uTX9vs9Dn5mFKhREVBBT+lXZRdRP1GU4gSdZKSez1mvBgD3n8fePtt\n8evZs4E1a4D0ONyMKFliKDWKY+ziFUNP0MMbwSzDFgQjQT6WpcrijWCmQhOy1FlTPp/JlMg/h5So\niWyFw8DrrwP/ugwXjY3AzTfTzleETBbGGPo8ffyUdu9ob9R4SU4JXzXr8/VQKia5Y5OMC9WoiSx5\nPMALLwB2O6BWA3ffLa6mCSGxCYQD6Bju4I1gnqCHj6mUKlQVVvFrmwsyqb4UL1SjJgmlr0/c+Wpk\nRKxDb9wo1qUJIdfG6XPyTS5sLhsiLMLHCjIK+CntSk1l1L7NRB4oUSepRK3HnD4N/O1vQCgkdnRv\n2CB2eEshUWMoNxTH2E00hhEhArvbzk9pnx87z8cUUKA8v5yf0i7OKU6JRrBE/jmkRE1kgTHg8GHg\n0CHxuL4euPNOQEU/oYSMizfo5ZtcWIYt8If9fCxTlYlqbTXf5CJbTfu+JhKqURPJhULAq68CH38s\nNoqtWAFcfz01jRFyNYwxDHgH+Kq5x90Dhou/N6dlT+PXNpfnlyNNSfu9yhnVqIlsjYyI9ei+PiAj\nA1i3TrxvNyHkUqFIKKoRzB1w87E0RRoqCyt5I1hhVqGEMyWTiRJ1kkqEeozdLnZ2ezzijlebNok7\nYMlFIsQwEVAcY+Pyu7Bzz07k1ebB6rIiLIT5WF56XtQmF+lpcbjBQIJK5J9DStREEm1twJ494gYb\nlZXA+vVANpXNCIHABHS7u/kp7UHvIGw9NhinGQEA+jw9bwQrzS1NiUawVEc1ahJXggDs3w8cOSIe\nL14MrFwpbrBBSKryhXy8Eeyc8xx8YR8fy0jLgElr4o1guekSXQZBphTVqIksBALi/brb28UtKb/4\nRWDhQqlnRUj8McYwNDbEV832EXtUI5guS8cbwSoKKqgRLMVRok5ScqvHOJ3izldDQ0BWFnDvveIp\nbzmTWwwTFcVRFBbCsA5b+SYXLr+LjykVShgLjPyUti5bF/VaimHsEjmGlKjJlLNagV27xL2kp00T\nm8a0WqlnRcjUcwfcfJOLjuEOhIQQH8tR50Q1gmWqMiWcKZEzqlGTKfXhh8Bbb4m16ZoaYO1a8TIs\nQpKRwAT0jvbyU9r9nv6o8bLcMr5qnp43nRrBCEc1ahJ3kQiwd6+YqAHghhuApiaxNk1IMvGH/bA4\nLfza5rHQGB9TK9UwaU0wa80w68zIz8iXcKYkUVGiTlJS1mPGxoAXXxRPeatUwOrVwLx5kkwlJolc\n05KTZIsjYwwOn4NvctE50gmBCXxck6nhq2ajxgiVMvZfs8kWQykkcgwpUZNJNTQEPPccMDwsbqax\ncSNgMEg9K0JiExbC6HR18kYwp8/Jx5QKJSoKKnhyLsouolPaZFJRjZpMmrNngZdfFi/DKisTm8by\n6UwfSVCeoIc3glmGLQhGgnwsS5XFG8FMhSZkqbMknClJBlSjJlOKMfEGJvv3i1/PmQOsWQOoaVtb\nkkAYY+jz9PFGsN7R3qjxkpwSvmrW5+uhVFDDBYkPStRJKl71mHBYvBXo//6veHzLLcCNNybHzleJ\nXNOSEznHMRAORG1y4Ql6+JhKqUJVYRXf5KIgs0Cyeco5hokikWNIiZpcM49H3Pmqu1tcPd9zDzBr\nltSzIuTqnD4nbwSzuWyIsAgfK8go4Ke0KzWVUKfRaSEiPapRk2vS1yfeacztBgoKxHp0aanUsyLk\nUhEhArvbzk9pnx87z8cUUMCQb+CntItziqkRjEiCatRkUn38MfDqq0AoBMyYAWzYAOTkSD0rQi7y\nBr18kwvLsAX+sJ+PZaoyUa2t5ptcZKtp2zYib5Sok9RU1GMYA1pagHfeEY8bGoA77hCvlU5GiVzT\nkpN4xJExhgHvAF8197h7oja5mJY9jW9yUZ5fnnCbXNDPYuwSOYZJ+iuWTLZgUFxFnz4tNordeiuw\ndGlyNI2RxBSKhNAx3MGvbXYH3HwsTZEGo+biJheFWYUSzpSQ2FCNmnyukRGxHt3fL96ne/16oLpa\n6lmRVOTyu/i1zVaXFWEhzMfy0vOiNrlIT0uXcKaETAzVqMk1s9vFzm6vV9zxavNmoKhI6lmRVCEw\nAd3ubn5Ke9A7GDWuz9PzVXNpbik1gpGkRIk6SU1GPaatTbxGOhIBqqrElXRWCt2AKZFrWnIy0Tj6\nQj7eCHbOeQ6+sI+PZaRlwKQ18Uaw3PTcKZix/NDPYuwSOYaUqMklBEG8y9iRI+LxkiXAypW08xWZ\nGowxDI0N8Wubu0a6ohrBtFlavmquKKhIuEYwQmJFNWoSxe8X79fd3i4m5jvuABYskHpWJNmEhTCs\nw1beCObyu/jYZze50GXrJJwpIfFBNWoyLg6H2DR2/jyQnQ3cey9gNEo9K5Is3AE3bwTrGO5ASAjx\nsRx1TlQjWKYqU8KZEiIvlKiT1ETrMR0d4h7SPh9QXCzeaawwxa9oSeSalhwITEDvaC92vbEL2eZs\n9Hv6o8bLcsv4tc36PD01gl0F/SzGLpFjSIk6xTEGfPghsHevWJuurRXv2Z2RIfXMSCLyh/2wOC28\nEcwb8sI2YIOxzAi1Ug2T1gSz1gyzzoz8DNoDlZDxoBp1CotEgLfeAj76SDy+8UZx9yta2JDxYozB\n4XPwRrDOkU4ITODjmkwNrzUbNUaolLQ2IORyqEZNLjE2BuzaBdhs4i1AV68G5s2TelYkEUSECDpH\nOvm1zU6fk499thGsKLuITmkTEiNK1EnqavWYwUGxaWx4GMjLAzZuBPT6+M4vESRyTWuyeYIe3ghm\nGbYgGAnysSxVFm8EMxWakKWOvtie4hg7imHsEjmGlKhTzJkz4uVXwSAwfbqYpPOpVEg+gzGGPk8f\nXzX3jvZGjZfklPBGMEO+AUoFXWRPyFShGnWKYAx47z3gwAHx67o64K67ALVa6pkRuQiEA1GbXHiC\nHj6mUqpQqankp7QLMgsknCkhyYdq1CkuHAZ27wb+8Q/x+JZbxMYxKh0Sp8/JT2nbXDZEWISP5Wfk\n88RcqamEOo3+qiNECpSok9SFeszoqLipRk8PkJ4O3H03MGuW1LNLDIlc07qSiBCB3W3np7TPj53n\nYwooUJ5fzpNzcU7xpDSCJWMc441iGLtEjiEl6iTW2ysmabcb0GjEm5iUlEg9KxJv3qCXb3JhGbbA\nH/bzsUxVJqq11XyTi2x1toQzJYRcDtWok9Q//wm8+qp42nvGDGDDBiAnR+pZkXhgjGHAO8BXzT3u\nnqhNLqZlT+Nd2uX55bTJBSEyQDXqFMIYcOgQcPiweHzddeLGGmn0uziphSKhqEYwd8DNx9IUaTBq\njPyUdmFWit8blpAEE/dEbbfb8cADD2BwcBAKhQL//u//jm9+85vxnkZSCgaBv/0N+OQTwGZrwVe/\n2oglS6hp7FrJvabl8rt4I5jVZUVYCPOxvPS8qE0u0tPSJZun3OOYCCiGsUvkGMY9UavVavz6179G\nfX09PB4PFixYgBUrVmAWdTjFxOUSb2IyMABkZgIrVgBLl0o9KzKZBCag293NT2kPegejxvV5er5q\nLs0tpTuCEZIkJK9Rr1mzBo8++iiampr4Y1SjnpiuLuCFFwCvF9DpxKaxoiKpZ0Umgy/k441g55zn\n4Av7+Fh6WjqqtdV8k4vc9FwJZ0oIiYVsa9Q2mw0nT57EkiVLpJxGQjtxAnjjDXGDDZMJWLcOyMr6\n/NcReWKMYWhsiG9y0TXSFdUIps3S8lVzRUEFNYIRkgIkS9Qejwfr1q3Dk08+idxcWglMlCAA+/YB\nR4+Kx0uXArfeCij/dSfHRK7HyEW8YhgWwrC5bPyUtsvv4mNKhRLGgouNYLps3ZTPZ7LRz2LsKIax\nS+QYSpKoQ6EQ1q5di/vuuw9r1qy57HO2bNkCo9EIANBoNKivr+dBbmlpAYCUPd63rwXvvAOo1Y1I\nSwNKSlqQmQkolRef39bWJpv5JurxBVPx/b1BL0rrSnHWcRb7D+5HhEVgrDcCAPpP9cNQYMA9t9+D\nqsIqHG09ioArAF25TlbxGe9xW1ubrOaTiMf0/1ne/5+vdT4tLS2w2Wz4PHGvUTPG8OUvfxk6nQ6/\n/vWvLz8pqlFfkcMhNo2dPw9kZ4vXR1dUSD0r8nkEJqB3tJevmvs9/VHjZbllfJMLfZ6eGsEISTFX\ny3txT9Stra246aabMG/ePP7LaPv27bjtttsuTooS9WVZLMCLLwJ+v3iHsU2bxDuOEXnyh/2wOC28\nEcwb8vIxtVKNqsIqnpzzM2gLM0JSmawS9XhQoo7GGPDBB8Df/y7WpmfOBO65R7x395W0JHA9Ri4m\nGkPGGBw+B7+2uXOkEwIT+LgmU8NrzUaNESplatxviH4WY0cxjJ3cYyjbrm/y+SIR4M03gePHxeOb\nbgKWL6ebmMhFRIigc6STn9J2+px8TKlQoqKggifnouwiOqVNCJkwWlHLmNcL7NoFdHYCKpW4f/Tc\nuVLPiniCHr5qtgxbEIwE+ViWKgtmnRlmrRnV2mpkqelaOULI56MVdQIaGBCbxlwuIC8P2LgR0Oul\nnlVqYoyhz9PHr23uGe2JGi/JKeG36zTkG6BUKCWaKSEkGVGilqH/+z/glVfEe3fr9WKSzsub2PeQ\nez1G7gLhAHa+vhOFswpx1nEWnqCHj6mUKlRqKnkjmCaTOvquhn4WY0cxjF0ix5AStYwwBrS2AgcP\nil/PnQusXg2o1VLPLDU4fU5+StvmssFis8CoMQIA8jPyea25UlMJdRr9oxBC4oNq1DIRCgG7dwOn\nTomNYk1NwA03UNPYVIoIEdjddt4Idn7sPB9TQAFDvoEn5+KcYmoEI4RMGapRy9zoKLBzJ9DTI15y\ntXYtUFsr9ayS01hoLKoRzB/287FMVSbf5KJaW42c9BwJZ0oIISJK1BLr6RGT9OioePOSTZvEm5nE\nKpHrMZOJMYYB7wBvBOt2d0dtcjEtexpvBCvPL4/a5IJiODkojrGjGMYukWNIiVpCp04Br70GhMPi\nbUDvvRfIoUVczEKREDqGO9DuFFfO7oCbj6Up0mDUXNzkojCrUMKZEkLI56MatQQYExvG3n1XPF6w\nAPjiF4E02rHwmrn8Ln5K2+qyIiyE+Vhuei5PzFWFVUhPu8ot3QghRAJUo5aRQAD429/ES7CUSmDl\nSmDxYmoamyiBCeh2d/NGsEHvYNS4Pk/PL58qyy2jRjBCSMKiRB1HLpd4E5OBASAzE1i/HjCZpua9\nErkecyW+kA/nnOf4Jhe+sI+Ppael80Yws86M3PTY9zhPxhhKgeIYO4ph7BI5hpSo46SzE3jhBWBs\nDCgqEpvGdDqpZyVvjDEMjQ3xRrCuka6oRjBtlpaf0q4oqIhqBCOEkGRBNeo4OHECeOMNcYON6mpg\n3TpxRU0uFRbCsLls/JS2y+/iY5/d5EKXTX/pEEKSA9WoJSII4taUx46Jx9dfD6xYIdamyUXugJs3\ngnUMdyAkhPhYjjqHXz5VVViFTBX9hUMISS2UqKeIzwe89BJgsYjd3HfeCTQ0xO/95VyPEZiA3tFe\nvmru9/RHjZfllvHkrM/TS9YIJucYJhKKY+wohrFL5BhSop4C58+LTWMOh3hd9IYNwIwZUs9KWv6w\nHxanBe3OdrQ72uENefmYWqlGVWEV79LOz8iXcKaEECIvVKOeZOfOiStpvx8oLRV3vtKk4OZKjDE4\nfA5+SrtzpBMCE/i4JlPDa81GjREqJf3NSAhJXVSjjgPGxFr03/8ufj1rFnD33eK9u1NFRIigc6ST\nn9J2+px87NONYGadGdOyp9G1zYQQMg6UqCdBOCx2dZ88KR7ffDPQ2CjtTUziVY/xBD1RjWCBSICP\nZamyYNaZ+SYXWeqsKZ/PZErkmpacUBxjRzGMXSLHkBJ1jLxe8frori5ApQLWrAHq6qSe1dRhjKHP\n08evbe4Z7YkaL8kp4Y1ghnwDlApqcSeEkFhQjToG/f1i09jICJCfL9ajp0+XelaTLxAORG1y4Ql6\n+JhKqUKlppKf0tZkpmBBnhBCYkQ16kl05kwn9u+3wG5X4vRpATNmmFBfX4ENG4C8PKlnN3mcPic/\npW1z2RBhET6Wn5HPG8EqNZVQp6klnCkhhCQ3StQTcOZMJ5qbz6G/vwlWq/hYb+8BPPIIkJdXIe3k\nPmOi9ZiIEIHdbeeNYOfHzvMxBRQozy/nq+aSnJKUaARL5JqWnFAcY0cxjF0ix5AS9QS89poFn3zS\nBNe/7mpZVQWUlzfhnXcOYs4ceSXq8RgLjfFVs2XYAn/Yz8cyVZl8k4tqbTVy0mmjbEIIkQLVqMeB\nMeDDD4Gf/rQFY2ONUKuBmTMvbqqh0bRg27ZGSec4HowxDHgHeCNYt7s7apOLouwifkq7PL+cNrkg\nhJA4oRp1DJxO4LXXxN2vAAHFxeLGGp++Pjo9XbjSyyUXioRgdVn5KW13wM3H0hRpMGqM/JS2Nksr\n4UwJIYRcDiXqK7hwA5MDB4BQSLwV6Ne/bkJr6wGkpzfx5wUCB9DUVC3hTC/l8ruwc89O5NXmkFMO\nXwAAEZhJREFUweqyIiyE+Vhuei5fNVcVViE9LYXuyDJBiVzTkhOKY+wohrFL5BhSor4Mh0NcRXd1\nicdz5wK33w5kZ1eguho4cOAggkEl0tMFNDVVo7ZW2vq0wAR0u7v5qnnQOwhbjw3GaUYAgD5Pz69t\nLsstS4lGMEIISRZUo/4UQbi4ig6HgdxccdermTPjPpXP5Qv5cM55jm9y4Qv7+Fh6WjpMhSZ+Sjs3\nPVfCmRJCCPk8VKMeh/PnxVW03S4ez58P3HYbkCWTu14yxjA0NsS7tO1ue9QmF9osLT+lPaNgBm1y\nQQghSSLlf5sLAvD++8ChQ+IqOi8PWLUKqKmRemZAWAjD5rLxU9ouv4uPKRVKfkewGl0NdNm6qNcm\ncj1GLiiGk4PiGDuKYewSOYYpnaiHhoBXXwV6/nW76vp6YOVKaVfR7oA7apOLkBDiYznqHL7JhUlr\nQqYqU7qJEkIIiYuUrFELAnDkiLiKjkTE+3SvWgWYzVP2llfEGEPPaA+/trnP0xc1XpZbxhvB9Hl6\nagQjhJAkRDXqTxkcFFfRvb3i8XXXAbfeCmTGcXHqD/thcVp4I5g35OVjaqUaVYVVvBEsPyM/fhMj\nhBAiOymTqCMR4L33gHfeEb8uKBBX0dVxugTaMebgtebOkc6oRjBNpobXmo0a46Q0giVyPUYuKIaT\ng+IYO4ph7BI5himRqAcGxFV037/OKi9cCKxYAWRkTN17RoQIOkc6eXJ2+px8TKlQoqKggq+ap2VP\no1PahBBCLiupa9SRCNDaChw+LH6t0QCrV4ubaUwFT9AT1QgWiAT4WJYqizeCVWurkaWWyXVfhBBC\nJJeSNer+fnEV3d8vHi9aBHzhC7Gvos+cO4P9x/cjxEJQQYV5s+ZBKBDQ7mhHz2hP1HOLc4r5KW1D\nvgFKhTK2NyeEEJJykm5FHYmIK+h33xW7uwsLxVV0ZWXs8zpz7gz+cOAPGDOMwelzwuFzYOzMGOpn\n16NoehFUShW/ttmsM0OTqYn9Ta9RItdj5IJiODkojrGjGMZO7jFMmRV1X5+4ih4YEI+XLAGamqJ3\nupqoiBBBz2gPOoY78PtXf4/eol6woYvBzKnNQcQRweaVm1GpqYQ6TR3jpyCEEEIuSooVdTgsrqJb\nW8VVtFYrrqKNxom/N2MMDp8DHcMdsDgtsLlsvNZ8tPUoAoYA8jPyoc3SQpetQ446B4UDhdi2cdvE\n34wQQghBkq+oe3vFVfTgIKBQAEuXArfcMrFV9FhoDB3DHTw5jwRGosaLsotQVVgFoUyAMEO45PKp\ndCVtFUkIIWRqJGyiDoeBlhbxDmOCAOh0wF13ATNmjOO1QhhdI108Mfd7+sFw8S+ZbHU2qgqrUFVY\nBVOhCQWZBQAAk8KE5kPNUJkvhi3QHkDT8qZL3kNqcq/HJAKK4eSgOMaOYhi7RI5hQibq7m5xp6uh\nIXEVvWwZsHw5oL5CeZgxhkHvICzDFnQMd6DT1Rl1D+00RRoqNBU8MZfmll72uuba6lpswRYcOHEA\nQSGIdGU6mpY3oba6dqo+KiGEkBSXUDXqUOjiKpoxoKhIXEWXl1/6PUYDozwxdwx3wBP0RI2X5JTA\npDWhqrAKFQUV1ARGCCFEMglboz5zphP791sQCinh8Qjw+01QqSqgUAA33AA0Nl5cRQcjQXS6Only\nHvQORn2vvPQ8npirCquQm54b/w9ECCGETJAkiXrv3r3Ytm0bIpEI/u3f/g0/+MEPLnnOmTOdaG4+\nB5WqCVareLo7HD6A5cuBhx+uwHS9gL7RPlh6xcRsH7EjwiL89WqlGkaNkSfnVLtNZyLXY+SCYjg5\nKI6xoxjGLpFjGPdEHYlE8I1vfAP79++HXq/HokWLsHr1asyaNSvqeZs2/Q90JY3oHXsagUgICkGN\nav0i+LVv4ciIFlabFb6wjz9fAQX0eXqxzqw1oTy/HGnKtHh/PNloa2tL2B9KuaAYTg6KY+wohrFL\n5BjGPVF/8MEHqK6uhvFfFzlv3LgRr7322iWJ+p+eNxDxtUKz0IAMTRDpBcP4uOt5jPYVIWeoHoC4\n65Sp0AST1oRKTSXdP/tTXC6X1FNIeBTDyUFxjB3FMHaJHMO4J+qenh6Uf6r7y2Aw4NixY5c8LzTP\nAuSFMcJs0OsMAABVVSaEPj/uMN8Bk9aEwszClDqdTQghJPXEPVGPO7GqI0BZBLC7UIj/hyxokRYZ\nwo2zK7FIv2hqJ5kEbDab1FNIeBTDyUFxjB3FMHaJHMO4X5519OhR/PSnP8XevXsBANu3b4dSqYxq\nKFMWZIG5/fGcFiGEECKZ+fPno62t7bJjcU/U4XAYtbW1OHDgAKZPn47Fixfj+eefv6RGTQghhBAJ\nTn2rVCr89re/xcqVKxGJRPCVr3yFkjQhhBByBbK8MxkhhBBCREqpJ/BZe/fuxcyZM2E2m/HEE09I\nPR1J2e12LF++HHPmzEFdXR2eeuopAIDT6cSKFStQU1ODW2+9Neqyg+3bt8NsNmPmzJnYt28ff/z4\n8eOYO3cuzGYzvvWtb/HHA4EANmzYALPZjKVLl6KzszN+HzCOIpEIGhoasGrVKgAUw4lyuVxYt24d\nZs2ahdmzZ+PYsWMUwwnavn075syZg7lz52Lz5s0IBAIUw3HYunUrSkpKMHfuXP5YvOL2pz/9CTU1\nNaipqcGzzz47xZ/0KpiMhMNhZjKZmNVqZcFgkM2fP5+dPn1a6mlJpq+vj508eZIxxtjo6Cirqalh\np0+fZt/73vfYE088wRhjbMeOHewHP/gBY4yxjz/+mM2fP58Fg0FmtVqZyWRigiAwxhhbtGgRO3bs\nGGOMsdtvv5299dZbjDHGnn76afbII48wxhjbuXMn27BhQ1w/Y7z88pe/ZJs3b2arVq1ijDGK4QQ9\n8MAD7I9//CNjjLFQKMRcLhfFcAKsViurrKxkfr+fMcbYvffey5qbmymG43D48GF24sQJVldXxx+L\nR9wcDgerqqpiw8PDbHh4mH8tBVkl6iNHjrCVK1fy4+3bt7Pt27dLOCN5ueuuu9jbb7/NamtrWX9/\nP2NMTOa1tbWMMcYef/xxtmPHDv78lStXsvfff5/19vaymTNn8seff/559vDDD/PnHD16lDEm/gIu\nKiqK18eJG7vdzpqamtjBgwfZnXfeyRhjFMMJcLlcrLKy8pLHKYbj53A4WE1NDXM6nSwUCrE777yT\n7du3j2I4TlarNSpRxyNuzz33HPvqV7/KX/Pwww+z559/foo+4dXJ6tT35W6G0tPTI+GM5MNms+Hk\nyZNYsmQJBgYGUFJSAgAoKSnBwMAAAKC3txcGg4G/5kL8Pvu4Xq/ncf10zFUqFQoKCuB0OuP1seLi\n29/+Nn7+859Dqbz4404xHD+r1Ypp06bhwQcfxHXXXYeHHnoIXq+XYjgBWq0W3/3udzFjxgxMnz4d\nGo0GK1asoBheo6mOm8PhuOL3koKsEjXdZezyPB4P1q5diyeffBJ5eXlRYwqFguJ2Fa+//jqKi4vR\n0NBwxS3kKIZXFw6HceLECXzta1/DiRMnkJOTgx07dkQ9h2J4dRaLBb/5zW9gs9nQ29sLj8eDv/zl\nL1HPoRhem1SIm6wStV6vh91u58d2uz3qL5pUFAqFsHbtWtx///1Ys2YNAPEvyP7+fgBAX18fiouL\nAVwav+7ubhgMBuj1enR3d1/y+IXXdHV1ARB/IY+MjECr1cbls8XDkSNHsHv3blRWVmLTpk04ePAg\n7r//forhBBgMBhgMBixaJN4RcN26dThx4gRKS0sphuP00UcfYdmyZdDpdFCpVLjnnnvw/vvvUwyv\n0VT//9XpdLLKR7JK1AsXLkR7eztsNhuCwSBeeOEFrF69WuppSYYxhq985SuYPXs2tm3bxh9fvXo1\n/vSnPwEQuxIvJPDVq1dj586dCAaDsFqtaG9vx+LFi1FaWor8/HwcO3YMjDH8+c9/xl133XXJ93rp\npZfQ1NQU5085tR5//HHY7XZYrVbs3LkTt9xyC/785z9TDCegtLQU5eXlOHv2LABg//79mDNnDlat\nWkUxHKeZM2fi6NGj8Pl8YIxh//79mD17NsXwGsXj/++tt96Kffv2weVyYXh4GG+//TZWrlwpwaeF\nvLq+GWPszTffZDU1NcxkMrHHH39c6ulI6t1332UKhYLNnz+f1dfXs/r6evbWW28xh8PBmpqamNls\nZitWrIjqRHzssceYyWRitbW1bO/evfzxjz76iNXV1TGTycQeffRR/rjf72fr169n1dXVbMmSJcxq\ntcbzI8ZVS0sL7/qmGE5MW1sbW7hwIZs3bx67++67mcvlohhO0BNPPMFmz57N6urq2AMPPMCCwSDF\ncBw2btzIysrKmFqtZgaDgT3zzDNxi9szzzzDqqurWXV1NWtubo7L570cuuEJIYQQImOyOvVNCCGE\nkGiUqAkhhBAZo0RNCCGEyBglakIIIUTGKFETQgghMkaJmhBCCJExStSEpKjGxkYcP348pu+xZ8+e\nlN+OlpCpppJ6AoQQaUzGPZJXrVrF9/gmhEwNWlETIhNerxd33HEH6uvrMXfuXLz44osAgJ/97GdY\nvHgx5s6di4cffpg/v7GxEd/5znewaNEizJo1Cx9++CHuvvtu1NTU4Cc/+QkAcde1mTNn4r777sPs\n2bOxfv16+Hy+S9573759WLZsGRYsWIB7770XXq/3kuc89dRTmDNnDubPn4/NmzcDAJqbm/Hoo48C\nAOrr69HQ0ICGhgZkZ2fj3XffhdfrxdatW7FkyRJcd9112L179yXft6WlBY2NjVi/fj1mzZqF++67\nL/ZgEpJMJLsnGiEkyksvvcQeeughfjwyMsIYY8zpdPLH7r//frZnzx7GGGONjY3shz/8IWOMsSef\nfJKVlZWx/v5+FggEmMFgYE6nk1mtVqZQKNiRI0cYY4xt3bqV/eIXv+CvP378OBsaGmI33XQTGxsb\nY4wxtmPHDvazn/3skvlNnz6dBYPBqLk1Nzezb3zjG1HP2717N7vppptYKBRi//mf/8n+8pe/MMYY\nGx4eZjU1Nczr9UY9/9ChQ6ygoID19PQwQRDY9ddfz1pbW68lhIQkJVpREyIT8+bNw9tvv40f/vCH\naG1tRX5+PgDg4MGDWLp0KebNm4eDBw/i9OnT/DUXNq2pq6tDXV0dSkpKkJ6ejqqqKr7zT3l5Oa6/\n/noAwH333YfW1lb+esYYjh49itOnT2PZsmVoaGjAs88+y3cT+uz8Nm/ejL/+9a9IS0u77Gdob2/H\n97//fezatQsqlQr79u3Djh070NDQgOXLlyMQCETtSHTB4sWLMX36dCgUCtTX18Nms11bEAlJQlSj\nJkQmzGYzTp48iTfeeAM//vGP0dTUhO9///v4+te/juPHj0Ov1+O//uu/4Pf7+WsyMjIAAEqlkn99\n4TgcDgOI3uedMXbZuvSKFSvw3HPPXXV+b7zxBg4fPow9e/bgsccew6lTp6L2+PZ4PNiwYQP+8Ic/\noKSkhD/+yiuvwGw2X/V7f3ruaWlpfO6EEKpREyIbfX19yMzMxJe+9CX8x3/8B06ePMmTsk6ng8fj\n4XXriejq6sLRo0cBAM899xxuvPFGPqZQKLB06VK89957sFgsAMRaeXt7e9T3YIyhq6sLjY2N2LFj\nB0ZGRuDxeKKes3XrVjz44IO44YYb+GMrV67EU089xY9Pnjw54fkTkupoRU2ITJw6dQrf+973oFQq\noVar8bvf/Q4FBQV46KGHUFdXh9LSUixZsuSyr71aB3dtbS2efvppbN26FXPmzMEjjzwSNV5UVITm\n5mZs2rQJgUAAAPDYY49FrYIjkQjuv/9+jIyMgDGGb33rWygoKODv29XVhZdffhnt7e145plnAAB/\n/OMf8ZOf/ATbtm3DvHnzIAgCqqqqLmkou9zcY+1GJySZ0DaXhCQxm82GVatW4dSpU1JPhRByjejU\nNyFJjlanhCQ2WlETQgghMkYrakIIIUTGKFETQgghMkaJmhBCCJExStSEEEKIjFGiJoQQQmSMEjUh\nhBAiY/8fzj2UNX7XML4AAAAASUVORK5CYII=\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10565c6d0>"
-       ]
-      }
-     ],
-     "prompt_number": 71
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "As we can see in the first plot, the list comprehensions lead to a slightly increased performance in regular Python code.  \n",
-      "But the second plot is quite interesting: List comprehensions in Cython are significantly slower than the regular for-loop structures.  \n",
-      "Apparently, Cython has no difficulties turning the explicit for-loop into for-loop in C with basic float arithmetic. However, the list comprehension version is constantly writing to memory while it is iterating and appending new values.\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Let us do a quick comparison by how much we were able to improve the performance of the simple least square implementation using Cython so far:\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "x = [x_i*random.randrange(8,12)/10 for x_i in range(500)]\n",
-      "y = [y_i*random.randrange(8,12)/10 for y_i in range(100,600)]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 72
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "funcs =  ['lstsqr_comprehensions', 'cy_lstsqr_comprehensions', \n",
-      "          'cy_lstsqr_loops'] \n",
-      "labels = ['list comprehensions', 'list comprehensions (Cython)', \n",
-      "          'for-loops (Cython)']\n",
-      "\n",
-      "times = [timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f).timeit(1000)\n",
-      "         for f in funcs]\n",
-      "\n",
-      "times_rel = [times[0]/times[i+1] for i in range(len(times[1:]))]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 73
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#%pylab inline\n",
-      "#import matplotlib.pyplot as plt\n",
-      "\n",
-      "plt.figure(figsize=(8,6))\n",
-      "x_pos = np.arange(len(funcs[1:]))\n",
-      "plt.bar(x_pos, times_rel, align='center', alpha=0.5, color=\"green\")\n",
-      "plt.xticks(x_pos, labels[1:], rotation=90)\n",
-      "plt.ylabel('relative performance gain')\n",
-      "plt.title('Performance gain compared to the classic least square implementation')\n",
-      "ftext = 'For-loops in Cython are {:.2f}x faster then list comprehensions'\\\n",
-      "        .format(times[1]/times[2],1)\n",
-      "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
-      "plt.xlim([-1,len(funcs[1:])])\n",
-      "plt.ylim([0,max(times_rel)*1.2])\n",
-      "plt.grid()\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAIBCAYAAABpxJfYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWRMRETVxLGsiIqIm7r+eoHMw3W5vGAAAAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10e919d10>"
-       ]
-      }
-     ],
-     "prompt_number": 88
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"showdown\"></a>\n",
-      "<br>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Final Comparison: Cython vs. NumPy vs. SciPy for least squares fitting"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To wrap it up, let us compare the Cython code of our simple least squares fit implementation to the Numpy and Scipy functions - after we made some improvements by adding static type declarations and explicit for loops."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext cythonmagic"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%cython\n",
-      "\n",
-      "def cy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    cdef double x_avg, y_avg, temp, var_x, cov_xy, slope, y_interc, x_i, y_i\n",
-      "    x_avg = sum(x)/len(x)\n",
-      "    y_avg = sum(y)/len(y)\n",
-      "    var_x = 0\n",
-      "    cov_xy = 0\n",
-      "    for x_i, y_i in zip(x,y):\n",
-      "        temp = (x_i - x_avg)\n",
-      "        var_x += temp**2\n",
-      "        cov_xy += temp*(y_i - y_avg)\n",
-      "    slope = cov_xy / var_x\n",
-      "    y_interc = y_avg - slope*x_avg\n",
-      "    return (slope, y_interc)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "import scipy.stats"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def lin_lstsqr_mat(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return (np.linalg.inv(X.T.dot(X)).dot(X.T)).dot(y)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def numpy_lstsqr(x, y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    X = np.vstack([x, np.ones(len(x))]).T\n",
-      "    return np.linalg.lstsq(X,y)[0]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def scipy_lstsqr(x,y):\n",
-      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
-      "    return scipy.stats.linregress(x, y)[0:2]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "import random\n",
-      "random.seed(12345)\n",
-      "\n",
-      "funcs = ['cy_lstsqr', 'lin_lstsqr_mat',\n",
-      "          'numpy_lstsqr', 'scipy_lstsqr'] \n",
-      "\n",
-      "orders_n = [10**n for n in range(1, 6)]\n",
-      "times_n = {f:[] for f in funcs}\n",
-      "\n",
-      "for n in orders_n:\n",
-      "    x = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
-      "    y = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
-      "    for f in funcs:\n",
-      "        times_n[f].append(min(timeit.Timer('%s(x,y)' %f, \n",
-      "                      'from __main__ import %s, x, y' %f)\n",
-      "                              .repeat(repeat=3, number=1000)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%pylab inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import matplotlib.pyplot as plt\n",
-      "\n",
-      "labels = [('cy_lstsqr', 'Cython implementation'), \n",
-      "          ('lin_lstsqr_mat', 'numpy matrix equation'),\n",
-      "          ('numpy_lstsqr', 'numpy.linalg.lstsq()'), \n",
-      "          ('scipy_lstsqr', 'scipy.stats.linregress()')] \n",
-      "\n",
-      "matplotlib.rcParams.update({'font.size': 12})\n",
-      "\n",
-      "fig = plt.figure(figsize=(10,8))\n",
-      "for lb in labels:\n",
-      "    plt.plot(orders_n, times_n[lb[0]], alpha=0.5, label=lb[1], marker='o', lw=3)\n",
-      "plt.xlabel('sample size n')\n",
-      "plt.ylabel('time per computation in milliseconds [ms]')\n",
-      "plt.xlim([1,max(orders_n) + max(orders_n) * 10])\n",
-      "plt.legend(loc=2)\n",
-      "plt.grid()\n",
-      "plt.xscale('log')\n",
-      "plt.yscale('log')\n",
-      "plt.title('Performance of least square fit implementations for different sample sizes')\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAIECAYAAACdVcNJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVMfXx7/30lnpsICodEFQsXcRFDVYYixRsGKLEWNe\njYldAbsmaoxiTIiFYjdKjCZiA+wFLFFEFBTEQlGxIVLP+wdyf1xYYEFYLPN5nn2e3bkz5545d2bu\n2akcEREYDAaDwWAwGB8cfG0rwGAwGAwGg8GoGsyRYzAYDAaDwfhAYY4cg8FgMBgMxgcKc+QYDAaD\nwWAwPlCYI8dgMBgMBoPxgcIcOQaDwWAwGIwPFObIKYi8vDyMGTMGhoaG4HkeJ06cqG2VPkh2794N\na2trKCsrY8yYMTLjeHl5oXv37grWjFHy2URGRoLneTx8+LDSsiwtLbFkyZIa0LI0FhYWWLx4sULu\n9b7C8zy2bdtW22rUSjsZEREhKqclfwPAtWvX0KZNG2hoaMDKygoAkJycjG7duqFOnTpQUlKqcT0/\nJRRRHl1cXPDVV1/V6D0UBXPkiuHl5QWe58HzPFRUVGBhYYGJEyfi6dOn7yz7zz//xPbt23HgwAGk\npKSgffv21aDxp0V+fj7GjBkDDw8PJCcnY82aNTLjcRwHjuMUqltISAh4/tOtTrKeTYcOHZCSkgJT\nU1MAwKlTp8DzPO7du1ehvKioKEydOrWm1QZQO+XlXbh//36VnRw3NzeMHj26VHhKSgoGDhxYHeq9\nE+9DO9mxY0dRuQWA6dOnQ1dXF3Fxcbh48SIAYMmSJXj8+DGuXr2KR48eKVxPWYwbNw6urq61rcYH\nQWhoKFatWlXbalQLyrWtwPuGs7Mzdu3ahby8PERFRWH8+PFITk7GgQMHqiQvJycHqqqquH37NszM\nzNCuXbt30q9I3qfIw4cPkZmZCXd3d1EjWxIiAtvnuvIUFBQAQJUc0rKejVQqLRVXnmdjYGBQaR0+\nNaqzjMt6TrXB+9BOqqiolLJHfHw8Ro0ahQYNGoh0bd26Naytrd9J19zcXKioqLyTDEbl0dXVrW0V\nqg9iCIwaNYrc3NxEYYsXLyYlJSV68+YNERFt376dnJycSF1dnSwsLOi7776jzMxMIX6XLl1o7Nix\nNHfuXDI1NSUTExNycXEhjuOEj6WlJRER5eTk0IwZM8jMzIxUVVXJwcGBtm3bJro/x3H0yy+/kKen\nJ+no6NCQIUNo8+bNpKysTOHh4dS4cWPS0NAgV1dXevToER0/fpycnJxIIpGQm5sbPXjwQJB1584d\n6t+/P9WtW5c0NTWpSZMmFBwcLLpfly5daNy4cbRgwQIyMTEhfX19GjlyJL169UoUb8eOHdSiRQtS\nV1cnAwMDcnd3p4yMDOH6L7/8QnZ2dqSurk62tra0ePFiysvLK9f+Z8+epc6dO5OGhgbp6enR0KFD\nKS0tjYiINm/eLLIhx3EUGRkp93Os6LkdPnyYunTpQvr6+qSjo0NdunShCxcuiGQEBASQvb09qaur\nk76+Pjk7O9P9+/cpPDy8lG6jR48uM5+LFy8mKysrUlNTIyMjI+rZsydlZWWJbGdmZkaamprUs2dP\nCgwMJI7jhGdZ9PyLk5ycXMom48aNI2tra9LQ0CArKyuaPXs2ZWdnC9d9fHzIxsaGdu7cSXZ2dqSs\nrEw3b96kly9f0rfffivo0Lx5c9q7d2+Z+Snr2RTZ5cGDB3T37t1ScVxdXcuUaW5uTosWLRL9njdv\nHn399deko6NDxsbGtH79esrKyiJvb2/S09MjMzMzWrdunUgOx3G0Zs0aGjBgAEkkEjIzM6M1a9aI\n4lhYWNDixYuF3zk5OeTj40OWlpakrq5Ojo6O9Ntvv5WSu3btWho8eDBJJBIyNzenvXv30tOnT8nD\nw4O0tLTIysqK/vzzT1G6lJQUGjVqFBkZGZGWlhZ17NiRTpw4IVwvstmRI0eoc+fOpKmpSQ4ODvTv\nv/+K7i2rPamofo8aNarMOsRxHG3dulWI+/DhQxoyZAjp6uqShoYGubi4UFRUVKX0JKq4rBenS5cu\n1dZOenh4yLwHUcX1q6Jy6+vrW2Z9r6juFMnbunUrubu7k0QioZkzZxKRfO+W8tpmHx+fUnoFBgbK\ntMHz58/Jy8uLTExMSE1NjerXr0/fffedcF2e9rAqdaAo/yEhIdS1a1ehbdqxY0cp2cXLY2XbJKLC\nNnHAgAFkaGhI6urqZGVlRT/++GMpexZ/5iU/FhYWQvzbt2/TgAEDSFdXl/T09KhHjx507do1uW1a\nkzBHrhijRo2i7t27i8JWrlxJHMfRq1evaPPmzaSnp0chISF09+5dOnHiBDVt2pRGjBghxO/SpQtp\naWnRxIkTKTY2lq5fv05Pnz6l77//niwtLSk1NZUeP35MRETff/89GRgY0J49e+j27du0ZMkS4nme\njh07JsjjOI4MDAzI39+f7ty5Q7dv36bNmzcTz/Pk6upKFy5coEuXLpGtrS116tSJnJ2d6fz583Tl\nyhWyt7enIUOGCLKuXbtG/v7+9N9//9GdO3do7dq1gkNYXH9dXV367rvvKC4ujg4fPkz6+vo0b948\nIc6mTZtIRUWFFi1aJORx3bp1Qr58fHzI3NycQkNDKTExkf755x9q0KCBSEZJHj16RFpaWjRs2DC6\nfv06nTp1ipo2bUrOzs5ERJSVlUUXL14kjuPo77//ptTUVMrJySnzORZ35OR5bvv27aPdu3fTrVu3\n6MaNGzRu3DjS19enJ0+eEBFRVFQUKSsrU3BwMN27d4+uXbtGGzdupPv371NOTg75+/sTx3GUmppK\nqamp9OLFC5m6/fnnn6StrU0HDhyg5ORkunLlCq1Zs0Z4uYWGhpKysjKtXr2abt++TRs3biSpVEo8\nz1fKkSsoKKA5c+bQhQsXKCkpifbv30+mpqbk4+MjpPHx8SFNTU1ycXGhCxcu0O3bt+nly5fk4uJC\nrq6udPr0abp79y79/vvvpKqqKiqXxSnr2RR/Iebn59P+/fuJ4ziKioqi1NRUkeNfkpLOlbm5Oenq\n6tLq1aspISGBFi1aRDzPU8+ePYWwpUuXEs/zdOPGDSEdx3Gkr69P69ato9u3b9OaNWtIWVmZ/vrr\nrzLvNWrUKHJycqIjR45QYmIi7dy5k3R1dWnjxo0iuSYmJhQUFEQJCQnk7e1NEomEevToQYGBgZSQ\nkECTJ08miUQilKHXr19To0aNaNCgQRQdHU0JCQm0ePFiUlNTo9jYWCL63wvFycmJwsLCKD4+nkaP\nHk3a2tqCvS5fvkwcx9G+fftE7UlF9fv58+fk7OxMHh4eQjktqkPFX5wFBQXUpk0bat68OZ0+fZqu\nXbtGQ4YMIT09PeFe8uhZUVkvSXW2k/Hx8TLvIU/9KlluU1JSqH79+jRr1ixKTU2lV69eUUpKCnXo\n0IGGDx8u1PeCgoIK606RI1OvXj3atm0bJSYm0t27d+V+t5TXNr969YqGDRtGHTt2FJ5vWbaePHky\nOTk50YULFyg5OZnOnDlDf/zxh3C9ovawqnWgKP9169albdu20a1bt2ju3LmkpKREly9fFskuXh4r\n2yYREfXt25e6d+9OV69epaSkJAoPD6ft27cL111cXGj8+PFEVPhnochmqampdOPGDTIzM6MxY8YQ\nUeEfMGNjY/L29qbr16/TrVu3aPLkyWRgYEDp6ely2bQmYY5cMUo6ADExMWRlZUXt27cnosKXScl/\n5pGRkcRxHD179oyICiubnZ1dKdlFvR9FZGZmkpqaGv3666+ieP3796euXbsKvzmOE/41FFHUA3L1\n6lUh7McffySO4+jSpUtC2OrVq8nQ0LDcPPfr108ozEX6N2vWTBRn4sSJgg2IiOrXr0+TJ0+WKS8z\nM5M0NTUpLCxMFB4YGEi6urpl6jF37lyqX78+5ebmCmFXr14ljuOEHouiRuD06dPl5qnkc5TnuZUk\nPz+f9PT0hMZk7969pKOjU6aDFhwcTBzHlasXEdGqVauoYcOGonwWp2PHjjR8+HBR2Pfff1+lHjlZ\n97a1tRV++/j4EM/zlJycLISFh4eTuro6PX/+XJR29OjR9MUXX5QpW9azKf5CJCI6efIkcRxHSUlJ\nZcopQpYj179/f+F3QUEBaWtr0+effy4K09PTE/XKcRxHI0eOFMkeOnQode7cWea97ty5QzzPU1xc\nnCiNn5+fqF5wHEdTp04VfqenpxPHcfTtt98KYRkZGcRxHB08eJCICp9bvXr1SvVMu7q60pQpU4jo\nfzbbt2+fcD01NZU4jqPDhw8TkXzPuoiS9dvNzU1mb3HxF+fRo0eJ4zjBuSQiys7OJlNTU1qwYIHc\nelZU1mVRne2kLOSpXyXLLVHp8kgkdgSK0lVUd4rqSfHeZiL53y0Vtc1jx44lFxeXCu3Qr18/8vLy\nqjBeESXbQ6Kq1YGi/M+fP18kv0OHDiKntXh5rGqb5OTkRL6+vmVeL/n8isjJySEXFxdydnYW/uj4\n+PhQu3btRPEKCgrI2tqafv75ZyKqvE2rk093dnYZREREQEtLC5qammjSpAlsbGywdetWpKen4969\ne5g6dSq0tLSET69evcBxHOLj4wUZLVu2rPA+8fHxyMnJgbOzsyjc2dkZMTExorA2bdqUSs9xHJo0\naSL8NjY2BgA0bdpUFPbkyRNhLs3r168xc+ZMNG7cGAYGBtDS0sI///wjmnzOcRycnJxE9zI1NUVq\naioAIC0tDffv30ePHj1k5ismJgZZWVkYMGCAyE5ff/01Xrx4gSdPnpSZrl27dlBW/t+0zaZNm0JH\nRwc3btyQmUYe5H1ud+/exYgRI2BrawsdHR3o6Ojg+fPngm169OgBKysrWFpawtPTEwEBAWXmpTyG\nDBmC3NxcmJubY/To0QgJCcGrV6+E67GxsejQoYMoTceOHauU94CAALRt2xYmJibQ0tLC7NmzSy00\nMDY2Rr169YTfFy9eRE5ODszMzET22rp1q6iMK5qS5ZLjOBgZGYnKO8dxkEqlSE9PF6UtOWG+Q4cO\npepYEVFRUSAitGzZUpT/pUuXlsp/cX0MDQ2hpKQk0kdXVxeqqqpIS0sDUGjblJQU6OrqimSfOnWq\nlOxmzZoJ36VSKZSUlIQ6WBby1G95iImJgYGBAezt7YUwVVVVtG3btpTdytOzorIuD+/aTpakOutX\nSSpTd4rrWpl3S3ltc2Xw9vbGnj170KRJE0yZMgWHDh0SzbmsqD2UpY88daCIknWyY8eOZdbJqrZJ\nU6ZMwZIlS9CuXTvMnDkTJ0+erNgwACZOnIgHDx4gNDRUmLt48eJFREdHi+6vra2NpKQkQYeKbFqT\nsMUOJWjXrh0CAwOhrKyMunXrCo5FUWX55ZdfZK4KMjMzA1D4MpFIJNWqkyx5PM+LVtoVfS++DL4o\njIjAcRx++OEH7N+/H6tXr4adnR00NTUxbdo0PH/+XCS75CRhjuOEifAVURRvz549aNiwYanrenp6\nMtNxHFcjhb5In4qeW58+fSCVSrF+/XrUr18fKioq6NSpE3JycgAUPoOoqCicPn0aR48exYYNGzB9\n+nQcO3YMLVq0kFufunXr4ubNmwgPD8fx48excOFCzJgxA+fPnxc5VOUhazFCbm6u6Pfu3bvxzTff\nYPny5ejSpQu0tbWxa9cuzJkzRxSvZNkqKCiAjo4OoqKiSt2jthfZlJwQznGczDB5y6ositKePXsW\nmpqapWSXp09ZOhbJLCgoQKNGjRAaGloqXcl7ybJ1RfmSt35XlaJ2RF49q6OsV4bqbncrS2XqTnFd\n5W2jOI57p7a5OD169MC9e/cQFhaGiIgIDB8+HE2aNMGxY8fA83yF7WERla0DZVFe21/VNsnLywuf\nffYZDh06hPDwcLi7u6N///4IDg4uM82KFSsQGhqKs2fPit5VRAQ3NzesW7euVBodHR0AFdu0JmGO\nXAnU1dWFfYKKY2xsjPr16+PmzZsYO3bsO9/HxsYGampqiIyMhIODgxAeGRkp6mmrTk6ePInhw4dj\n0KBBAAorSFxcXLkrQEsilUpRr149hIWFoU+fPqWuOzo6Ql1dHQkJCfjss8/kluvo6IjNmzeLVnBd\nvXoVz58/R+PGjeWWUxJ5ntuTJ08QGxuLVatWCfvP3b9/v9S/SJ7n0blzZ3Tu3Bl+fn5wcHDA9u3b\n0aJFC6FBkfWyK4mqqip69uyJnj17YuHChTA2NsZff/2FSZMmwcHBAadPn8bEiROF+KdPnxall0ql\nyM/PR1pamrC67tKlS6I4J06cQPPmzTFlyhQh7O7du+XqBQCtW7fGs2fPkJWVBUdHxwrjV4YiG+Xn\n51er3Io4e/Ysvv76a+H3mTNnysxbUW96UlISevfuXa16tG7dGsHBwdDS0oKRkVGV5ZRlR3nqt6qq\nKvLy8sqV7+joKNSJRo0aAQCys7Nx/vx5fPPNN5XWtayyLg/V3U7KU7+qSlXrTnW+W1RVVeWuX3p6\nevDw8ICHhwdGjx6N9u3bIzY2FiYmJnK1h+/C2bNnRe+H8upkq1atqtwmmZiYwMvLC15eXnB3d8fQ\noUPx66+/ok6dOqXihoaGwsfHB2FhYbC1tS2lw5YtW2BmZgY1NbUy71eWTau7LS3JB+fIXbhwAVOm\nTIGKigrMzMwQFBQkGo6rSRYvXoyxY8dCT08Pn3/+OVRUVBAbG4tDhw5hw4YNAOTf+kJTUxPffvst\n5s2bJwwR7dmzB/v378fRo0drRH87OzuEhoZiwIABkEgkWLVqFR49egQTExMhjjz6+/j4YOLEiTA2\nNsbAgQNRUFCA8PBweHp6wsDAALNnz8bs2bPBcRy6deuGvLw8XLt2DVeuXMGyZctkyvzmm2+wZs0a\neHl5Yfbs2cjIyIC3tzecnZ3feeijouemp6cHIyMj/P7777CyssLjx48xffp0aGhoCDL++usv3L17\nF507d4aRkRGio6ORnJwsvFwsLS2FeB07doSmpqbMHoKNGzeCiNC6dWvo6uri2LFjePnypSBn2rRp\n+PLLL9GmTRu4u7vj1KlTCAkJETmHbdu2hZaWFmbOnIlZs2YhISEBCxYsEN3H3t4emzZtwv79++Ho\n6IgDBw5g3759Fdqqa9eucHNzw4ABA7BixQo0adIEGRkZOHPmDDQ0NDBu3LjKP4C3mJubg+d5HDx4\nEIMHD4aamprwb7YkJcugrDIpb9jBgwfh7++PHj164NChQ9i1axf27NkjM42NjQ3GjBmD8ePHY8WK\nFWjXrh0yMzMRHR0tlIuqMmzYMKxevRq9e/fG4sWLYWtri9TUVBw/fhwODg7o16+fXHIMDQ1Rp04d\nhIWFoVGjRlBTU4Oenp5c9dvS0hLh4eG4c+cOtLW1oaurW6r97NatG9q0aYOhQ4fC398f2traWLhw\nIXJyckQOUEVUVNblobrbybLqV0WUVdaKh79L3amud4uVlRX27NmDGzduQCqVQltbW2av1Zw5c9Cq\nVSs4ODiA53mEhIRAS0sLDRo0gEQiqbA9fFc2bdoEe3t7tGzZEiEhITh37hz8/f1lxu3WrVuV7PrN\nN9+gd+/eaNiwId68eYO9e/eiQYMGghNX3J4xMTEYPnw4fH190bBhQ6SkpAAoHOEyMjLCN998g40b\nN6Jfv36YO3cu6tWrh/v37+Pff/9Fnz590L59+3JtWuMoZipe9fHo0SNhK5BZs2bRnj17qk22l5dX\nqVWrJQkNDaX27duTpqYmaWtrU7NmzWjhwoXC9bImUPr6+oommhMR5ebm0syZM4Vl9Y6OjqJVNUSl\nl2ETFU6aVlFREYUFBwcTz/OisO3btxPP85Sfn09EhZOke/bsSRKJhExNTcnX15fGjh0r2gZClv6L\nFi0StgIoYuvWreTk5ERqampkYGBAffr0ES0c+OOPP6hZs2akrq5Oenp61K5dO9qwYUMpuxTn3Llz\n5OzsTBoaGqSrq0vDhg0TVgQRFU6U5Xm+wsUOsp5jRc8tMjJSWPpvb29Pf/75J9nY2JCfnx8REZ04\ncYK6du1KRkZGpK6uTg0bNqTly5eL7jFlyhSSSqXlbj+yd+9e6tChA+np6QlbRGzatEkUZ82aNWRm\nZkYaGhrUvXv3UtsjEBEdPHiQGjVqRBoaGtSpUycKCwsjnueFCfC5ubk0YcIE0tfXJ21tbRo2bBit\nW7dOVEZklUmiwlWoM2fOJEtLS1JVVSUTExNyd3cXrW4uiaxnEx4eLloNSES0YsUKMjMzIyUlpXK3\nHyk5uVzWZPPiz6cIe3t70eroou1HvvjiC9LU1KS6devS6tWry71Xfn4+rVixguzt7UlVVZUMDQ3J\nxcVF1NbIqpfKysqltntQV1cXrXZ98uQJTZw4UajzZmZmNGDAALpy5UqZNpMlOygoiCwtLUlZWVmo\nm/LU7zt37pCzszPVqVOn3O1HHj16RB4eHqLtR6Kjo4Xr8ugpT1kvSXW2k2Uhq36VXLVaMm/yLHYg\nqrjulNeGVeXdUrJtfvr0KfXq1Yt0dHTK3X5k4cKF1LhxY6pTpw7p6OiQi4uLSKeK2kOiqtWB4tuP\nuLi4CNuCVPQ8q9ImTZo0iRo2bEgaGhrCO6r4ivbi9pS1hVLxLXCIiJKSkmjYsGFkZGREampqZG5u\nTiNGjKDExES5bFqTcEQf7s6pPj4+aN68Ob744ovaVoXBqDEiIiLQtWtX3L9/H3Xr1q1tdT4oiv4Z\nDx06tLZVYTA+eRITE2FlZYVTp06VWnTCqDof7KrVpKQkHDlyBH379q1tVRgMBoPBYDBqhVpz5Nat\nW4dWrVpBXV291Nl/T58+Rf/+/VGnTh1YWFhg+/btousvXrzAyJEjERgYyA4rZnwSVLSAgsFgMD4E\nWFtW/dTa0Oq+ffvA8zzCwsKQlZWFzZs3C9c8PT0BFE6WvXz5Mnr37o0zZ87AwcEBeXl5+Pzzz/H9\n99+ja9eutaE6g8FgMBgMxvuBQmbilcPcuXNFuyG/evWKVFVV6fbt20LYyJEjhfPogoKCyMDAgFxc\nXMjFxYV27twpU27dunUJAPuwD/uwD/uwD/uwz3v/cXJyqpIfVetz5KhEh+CtW7egrKwMGxsbIczJ\nyUnY9XnEiBF4/PgxwsPDER4ejsGDB8uU+/DhQ2F5sSI+Pj4+Ck0vT/zy4pR1Td5wWfHe1QaKtHdl\nZdSUvStjS3mewfts8+ou41W9zuxd9fisTak+GaxN+bjLeFXsffXq1Sr5UbXuyJUcL3/16hW0tbVF\nYVpaWnj58qUi1ao0Li4uCk0vT/zy4pR1Td5wWfESExMr1Km6eFd7V1ZGTdm7rGvyhCnS3rLuX9Pp\nK4pf1evM3lWPz9qU6pPB2pSPu4wr0t61vv3I3Llz8eDBA2GO3OXLl9GpUydkZmYKcX766SecOHEC\n+/fvl1tuTR35xCgbLy8vbNmypbbV+GRg9lYszN6Kh9lcsTB7K5aS9q6q3/Le9cg1bNgQeXl5osNw\nr169WqVjmnx9fREREfGuKjLkxMvLq7ZV+KRg9lYszN6Kh9lcsTB7K5Yie0dERMDX17fKcmqtRy4/\nPx+5ubnw8/PDgwcPEBAQAGVlZSgpKcHT0xMcx+GPP/7ApUuX0KdPH5w9e1Y4908eWI8cg8FgMBiM\nD4UPrkdu4cKF0NTUxPLlyxESEgINDQ0sXrwYALB+/XpkZWVBKpVi+PDh2LBhQ6WcOEbtwHo/FQuz\nt2Jh9lY8zOaKhdlbsVSXvRVz2rwMfH19y+xK1NPTk+uAbwaDwWAwGIxPmVpf7FBTlNdFqa+vj4yM\nDAVrxGB8uujp6eHp06e1rQaDwWC8t1R1aLXWeuQUga+vL1xcXEot+c3IyGDz5xgMBcKO5WEwGAzZ\nREREvNMw6yfZI8cWQjAYiuVjqHMRERHVstcYQ36YzRULs7diKWnvD26xA4PBYDAYDAbj3WA9cgwG\no8ZhdY7BYDDKh/XIMRgMBoPBYHxifNSOHDvZofJ4eXmhe/futXZ/S0tLLFmyRCH3srCwEPYu/FTh\neR7btm2rbTU+CFhboniYzRULs7diKbL3u57s8NE7ch/jxM0nT55g+vTpsLe3h4aGBoyNjdGlSxcE\nBwcjPz9fLhmnTp0Cz/O4d++eKJzjuFpdYRgVFYWpU6cq5F61ndfKcv/+ffA8jxMnTlQ6rZubG0aP\nHl0qPCUlBQMHDqwO9RgMBoNRBVxcXN7Jkfuotx+pKnFxSTh6NAG5uTxUVArg5mYNOzvz90JmcnIy\nOnXqBFVVVSxYsADNmzeHiooKTp8+jZ9++glOTk5o2rSp3PJKjsfX9jwmAwODWr3/h0B1PiOpVFpt\nsj52PsY/he87zOaKhdlbsVSXvT/qHrmqEBeXhC1b4pGe3hXPnrkgPb0rtmyJR1xc0nsh09vbG7m5\nubh06RI8PT1hb28Pa2trjBw5EpcuXYKNjQ22bNkCPT09ZGVlidIuWLAADRs2RGJiIpydnQEUDmXy\nPI+uXbsK8YgIv//+O8zNzaGjo4N+/fohLS1NJCswMBAODg5QU1ND/fr1MW/ePFFvoIuLC8aPH4+F\nCxfC1NQUBgYGGDVqFDIzM8vNX8nhTgsLC8yfPx8TJ06Erq4uTExM8Ouvv+LNmzeYNGkS9PX1Ua9e\nPfj7+4vk8DyPX375BQMHDkSdOnVQr149/PLLL+XeOzc3F76+vrCysoKGhgYaN26M33//vZTcdevW\nYciQIahTpw4sLCywb98+ZGRkwNPTE9ra2rC2tsbevXtF6VJTU+Hl5QWpVAptbW106tQJJ0+eFK5H\nRESA53kcPXoUzs7OkEgkcHR0xKFDh4Q4DRo0AAC4urqC53lYWVkBAO7evYsBAwbAzMwMEokETZs2\nRUhIiJDOy8sLx48fR2BgIHieF/XqlRxaffToETw8PKCnpwdNTU24uroiOjq6UnoyGAwGQ4HQR0p5\nWSvv2rp1x8jHh6hLF/GnV6/C8Kp83N2PlZLn40Pk73+sUnl68uQJKSkp0eLFi8uNl5WVRXp6ehQY\nGCiE5efnk7m5Oa1YsYLy8/Np//79xHEcRUVFUWpqKmVkZBAR0ahRo0hHR4eGDh1KMTExdPbsWbK0\ntKQRI0Z+6IEfAAAgAElEQVQIsg4cOEBKSkq0bNkyun37Nu3cuZP09PRo3rx5QpwuXbqQrq4ufffd\ndxQXF0eHDx8mfX19URxZWFhYiPJnbm5Ourq6tHr1akpISKBFixYRz/PUs2dPIWzp0qXE8zzduHFD\nSMdxHOnr69O6devo9u3btGbNGlJWVqa//vqrzHuNGjWKnJyc6MiRI5SYmEg7d+4kXV1d2rhxo0iu\niYkJBQUFUUJCAnl7e5NEIqEePXpQYGAgJSQk0OTJk0kikdCTJ0+IiOj169fUqFEjGjRoEEVHR1NC\nQgItXryY1NTUKDY2loiIwsPDieM4cnJyorCwMIqPj6fRo0eTtra28GwuX75MHMfRvn37KDU1lR4/\nfkxERNeuXSN/f3/677//6M6dO7R27VpSVlam8PBwIiJ6/vw5OTs7k4eHB6WmplJqairl5OQI+dm6\ndSsRERUUFFCbNm2oefPmdPr0abp27RoNGTKE9PT0hHvJo6csPoampsieDMXBbK5YmL0VS0l7V7Wd\nZD1yJcjNlW2S/Pyqm6qgQHbanJzKyYyPj0dBQQEcHBzKjaeuro4RI0YgICBACDty5AgePXqE0aNH\ng+d56OnpAQCMjIwglUqhq6srSr9lyxY4ODigXbt2+Prrr3H06FHh+rJlyzBo0CDMmDEDNjY2GDx4\nMHx9ffHTTz8hLy9PiGdhYYGVK1eiYcOG6N69O4YMGSKSIy+urq6YMmUKrKysMHv2bNSpUwdqampC\n2IwZM6Cjo4Pjx4+L0vXp0weTJk2CjY0Nvv32WwwePBg//fSTzHvcvXsXwcHB2LVrF9zc3GBubo7B\ngwdj6tSpWLt2rSiup6cnRowYASsrK/j5+eH169ewt7fHyJEjYWVlhQULFuD169c4d+4cAGDnzp14\n+fIlduzYgRYtWgj56NChA3777TeRbF9fX/To0QPW1tZYtmwZXr58iYsXLwIADA0NARQeMSeVSoVh\n6MaNG8Pb2xtNmjSBpaUlvvnmG/Tu3VvoadPW1oaqqio0NDQglUohlUqhoqJSygbHjx/HxYsXsW3b\nNnTo0AGNGzdGUFAQ1NXVsX79ern1ZDAYDIbi+KjnyJV1RFd5qKgUyAxXUpIdLg88LzutqmrlZFIl\n5kZNmDABjRs3RlxcHOzs7BAQEIB+/foJzkB52Nvbi170pqamSE1NFX7fuHEDnp6eojTOzs548+YN\nEhISYGdnBwBwcnISxTE1NUVYWJjceQAKFyQUl8NxHIyMjETzADmOg1QqRXp6uiht+/btRb87dOiA\n+fPny7xPVFQUiAgtW7YUhefl5UFZWVxNiutjaGgIJSUlkT66urpQVVUVhqMvXryIlJQUkbMMANnZ\n2ZBIJKKwZs2aCd+lUimUlJREtpfF69evsWDBAhw4cACPHj1CTk4OsrOzRcPl8hATEwMDAwPY29sL\nYaqqqmjbti1iYmLeWc8PHTZ/SPEwmysWZm/FUmTvdz2i66N35CqLm5s1tmw5BheXbkJYdvYxeHnZ\n4K1/Umni4gplqqmJZXbrZlMpOba2tuB5HjExMfjiiy/Kjevg4IBOnTrh999/x4wZM/D333/j4MGD\nct2nZG9NVTYp5DgOqqqqpcIKCirvEMvSR1ZYVWQXUZT27Nmz0NTULCW7PH3K0rFIZkFBARo1aoTQ\n0NBS6Ureq6TNiutWFj/88AP279+P1atXw87ODpqampg2bRqeP39ebjp5IaJSNqiKngwGg8EoTVGH\nk5+fX5XSs6HVEtjZmcPLywZS6XHo6kZAKj3+1omr+qrV6pKpr68Pd3d3rFu3Di9evCh1PTc3F69f\nvxZ+T5gwAUFBQfj9999Rr149uLm5CdeKXsSytiupaEsOR0dHREZGisIiIyOhqakJa2vrSuWpJjl7\n9qzo95kzZ+Do6CgzblFPXFJSEqysrEQfS0vLd9KjdevWuHPnDrS0tErJNjExkVtOWc/s5MmTGD58\nOAYNGiQMr8bFxYmeo6qqqmjYWxaOjo548uQJYmNjhbDs7GycP38ejRs3llvPjxW2x5biYTZXLMze\niqW67M0cORnY2ZnD27srpkxxgbd313feeqQ6Za5fvx4qKipo2bIltm/fjhs3biA+Ph4hISFo3bo1\n4uPjhbiDBg0CACxatAjjxo0TyTE3NwfP8zh48CDS0tJEjmFFvW+zZs3Cn3/+ieXLl+PWrVvYtWsX\n/Pz8MG3aNGEYkoiqtE1GyTSyZMgbdvDgQfj7++P27dtYu3Ytdu3ahWnTpslMY2NjgzFjxmD8+PEI\nCQlBfHw8rl69ik2bNmHFihWVzkdxhg0bBktLS/Tu3RtHjhxBYmIizp8/j6VLl+Kvv/6SW46hoSHq\n1KmDsLAwpKSkICMjAwBgZ2eH0NBQXLx4ETdu3MBXX32FR48eifJnaWmJ6Oho3LlzB48fP5bp1HXr\n1g1t2rTB0KFDcebMGVy/fh0jR45ETk4OJk6c+E42YDAYDEbNwBy5D4z69evj0qVL+OKLL+Dr64uW\nLVuiY8eOCAgIwMSJE0U9Tmpqahg+fDiICGPGjBHJMTY2xtKlS7Fs2TLUrVtXGKota5Pc4mHu7u7Y\ntGkTAgMD0aRJE3z33XeYNGkSfHx8RPFLypFnA15ZaSqKU1bY/PnzcfToUTRr1gzLli3Djz/+iH79\n+pWZ5vfff8fUqVOxePFiODo6ws3NDcHBwe/cy6impobIyEi0atUKo0ePhp2dHQYOHIioqChYWFiU\nm4fi8DwPf39/7Nq1C/Xr1xd6EVevXg1zc3O4urrCzc0N9evXx6BBg0Typk2bBkNDQzg5OUEqleLM\nmTMy7xEaGgp7e3v07t0bbdq0QVpaGo4cOQJ9fX259fxYYfOHFA+zuWJh9lYs1WVvjqrSbfIBUN68\nrk/pAO/BgwcjPz8ff/75Z22rolB4nkdISAiGDh1a26ow8GnVOQaDwagKVW0nWY/cR0pGRgbCwsIQ\nGhqqsCOvGIyPGTZ/SPEwmysWZm/FUl32/uhXrVZ2+5GPhebNm+Pp06eYMWMGOnXqVNvqMBgMBoPB\nkMG7bj/ChlYZDEaNw+ocg8FglA8bWmUwGAwGg8H4xGCOHIPBYMgBmz+keJjNFQuzt2Jh+8gxGAwG\ng8FgfOKwOXIMBqPGYXWOwWAwyofNkWMwGAwGg8H4xGCOHIPBYMgBmz+keJjNFQuzt2Jhc+QYjBog\nIiICPM/j4cOHta1KjePr6wtbW9vaVoPBYDAY78BHPUfOx8dH5obAbL7Op4WNjQ1GjBghOgu2LHJz\nc5GRkQEjI6OP5kzRU6dOwdnZGYmJiWjQoIEQnpmZiezsbNE5qjUFq3MMBoMhm6INgf38/KrUTn70\nJzswGPI6ZHl5eVBRUYFUKq1hjWqHkg2ERCKBRCKpJW0YDAaDAUDocPLz86tSeja0KoO4+Dj47/TH\nzzt+hv9Of8TFx703Ml1cXDB+/HgsXLgQpqamMDAwwKhRo5CZmSnE8fLyQvfu3UXpQkJCwPP/e9xF\nw2q7d++GjY0NJBIJBg4ciFevXmH37t2ws7ODtrY2vvzyS7x48aKU7NWrV8PMzAwSiQSDBw9GRkYG\ngMJ/FsrKyrh//77o/kFBQdDV1UVWVpbMfFVVn0uXLsHd3R3GxsbQ0tJCmzZtEBYWJrJXQkIC/Pz8\nwPM8lJSUcO/ePWEI9Z9//kGnTp2goaGBjRs3lhpaXbFiBfT09JCUlCTIXLBgAaRSKVJSUsp8Tqmp\nqfDy8oJUKoW2tjY6deqEkydPiuKEh4ejadOm0NDQgJOTE8LDw8HzPLZu3QoASExMBM/zOHPmjCid\njY2NqMKvWbMGzZs3h5aWFkxNTeHp6SnolpiYCGdnZwCApaUleJ5H165dRTYvTmBgIBwcHKCmpob6\n9etj3rx5yM/PF9mzovL3scLmDykeZnPFwuytWNgcuRoiLj4OW8K3IN04Hc9MniHdOB1bwre8kzNX\n3TL37NmDZ8+eITIyEjt27MCBAwewfPly4TrHcXL1Qj169AhBQUEIDQ3Fv//+i5MnT2LAgAHYsmUL\n9uzZI4QtWbJElO7ChQuIjIzE4cOH8c8//+DKlSsYO3YsgMIXva2tLTZt2iRKExAQgGHDhkFDQ6Na\n9Xn58iU8PT0RERGBy5cvo2fPnvj8889x+/ZtAMC+fftgYWGB77//HikpKXj06BHq1asnpJ82bRpm\nzZqFmzdvok+fPqV0mj59Otq2bQtPT0/k5+fjxIkTWLRoEQIDA2FiYiIzH1lZWXB1dUVmZiYOHTqE\nK1euoFevXujevTtu3rwJAHj48CH69OmD1q1b4/Lly1i5ciX+7//+D0DFPYglny/HcVi5ciWuX7+O\nffv24d69e/Dw8AAANGjQAH/99RcA4OLFi0hJScHevXtlyj148CDGjh2LUaNGISYmBitXroS/v3+p\nf4kVlT8Gg8FgKI6Pemi1KhyNPgo1WzVEJEb8L1AF+G/Hf2jdqXWVZF44dQGv670GEv8X5mLrgmOX\njsHOxq7S8iwsLLBy5UoAQMOGDTFkyBAcPXoUCxYsAFA4hCbPOHt2djYCAwOFOVKDBw/Ghg0bkJqa\nCgMDAwCAh4cHjh07JkpHRAgODoaWlhYAwN/fHz179sSdO3dgZWWFr776CmvWrMG8efPAcRxu3ryJ\n06dPY926ddWuT5cuXUQyFi5ciL///hu7d+/G7NmzoaenByUlJdSpU0fmkOncuXPRu3dv4XeRA1ic\noKAgODk5YfLkyThw4AAmT54Md3f3MvOxc+dOvHz5Ejt27ICSkhIAYPbs2Th69Ch+++03rF69GuvX\nr4dUKkVAQAB4noe9vT2WLl2Kvn37lmsjWXz77bfCd3Nzc6xbtw4tW7bEo0ePYGpqCj09PQCAkZFR\nucPGy5Ytw6BBgzBjxgwAhT1/KSkpmDlzJubPnw9l5cLmoqLy97FScq4to+ZhNlcszN6KpbrszXrk\nSpBLuTLD85EvM1weClAgMzynIKfSsjiOg5OTkyjM1NQUqamplZZlZmYmmuhubGwMExMTwWkqCktL\nSxOlc3BwEJw4AOjQoQMA4MaNGwCAkSNHIi0tTRji/OOPP9CqVatSeleHPunp6fD29kajRo2gp6cH\nLS0txMTE4N69e3LZoE2bNhXGkUql2Lx5MzZs2ABDQ8MKe5+Ker50dXWhpaUlfE6dOoX4+HgAhbZq\n06aNaLi7Y8eOculckoiICPTs2RMNGjSAtrY2OnfuDACi4WB5uHHjhjAMW4SzszPevHmDhIQEIay6\nyh+DwWAw3h3WI1cCFU5FZrgSlKosky/DX1blVaskT1VVnI7jOBQU/M9Z5Hm+VI9cbm5pB1VFRZxX\njuNkhhWXDZSeNF8SAwMDDBo0CAEBAejWrRuCgoJKDc/Koir6eHl54f79+/jxxx9haWkJdXV1eHh4\nICdHPidZ3sn+ERERUFJSQmpqKp49ewZDQ8My4xYUFKBRo0YIDQ0tdU1TU1PIR0V2LHLyynuW9+7d\nQ69evTBq1Cj4+vrC0NAQycnJcHNzk9sGlYHjuArL38dKREQE67FQMMzmiiEuPg5/n/sbsTGxaNGs\nBdxaulVptIhROaqrfDNHrgRuLd2wJXwLXGxdhLDs29nw8vCqcsGOq1c4R07NVk0ks5trt3dVVybG\nxsY4d+6cKOzSpUvVJj82NhYvX74UeuWKJuM7ODgIcSZMmABXV1ds2LABb968gaenZ7XdvzgnT57E\njz/+KMxvy8zMREJCApo0aSLEUVVVFU3YryxHjx7FqlWrcPDgQcybNw9eXl44cOBAmfFbt24tDD0b\nGRnJjOPg4IDg4GAUFBQIDtvp06dFcYrSPnjwQAhLS0sT/b548SLevHmDn3/+GWpqakJYcYocr4ps\n4OjoiMjISHh7ewthkZGR0NTUhLW1dblpGQzGh0lcfBw2HN6AG1o3kMFnwMjACFvCt8ALVX/nMRQL\nG1otgZ2NHbxcvSBNk0I3RRfSNCm8XN+tQFenTHnmv7m5ueHmzZtYv349EhISEBAQgN27d1dV/VJw\nHIeRI0ciJiYGJ06cwKRJk9CvXz9YWVkJcTp27Ag7Ozv88MMP8PT0rLFtLuzs7BASEoLr16/jypUr\n8PT0REFBgchGlpaWOHXqFJKTk/H48eNK7dOTnp6OESNGYPr06ejRowe2b9+OkydP4ueffy4zzbBh\nw2BpaYnevXvjyJEjSExMxPnz57F06VJh4cHEiRORnp6Or776CrGxsTh27BjmzJkjkqOhoYGOHTti\nxYoV+O+//xAdHY2RI0cKDhsA2NraguM4/PTTT7h79y5CQ0OxcOFCkRxzc3PwPI+DBw8iLS0Nz58/\nl6n3rFmz8Oeff2L58uW4desWdu3aBT8/P0ybNk2YHyfv/MuPEdYzpHiYzWuefWf3IUYrBtn52dC0\n1cS11GtQtVHFsUvHKk7MeCfYHLkaxM7GDt6DvTHFYwq8B3tXy7+S6pIpa0VqybBu3bph0aJFWLJk\nCZo1a4aIiAjMnz+/1ErHiuSUFdamTRt06tQJ3bt3h7u7O5ycnEqtUgWAcePGIScnB1999VW15EtW\n2ObNm1FQUIA2bdpgwIAB6NWrF1q3bi2K4+fnh2fPnsHOzg7GxsZITk4WZJWlSxFeXl6wtLQUJvJb\nWVlhw4YNmDlzJq5evSozvZqaGiIjI9GqVSuMHj0adnZ2GDhwIKKiomBhYQEAqFu3Lv7++29cuHAB\nzZs3x9SpU7F69epSsjZt2oQ6deqgQ4cOGDp0KCZMmABTU1PhetOmTbF27Vr89ttvcHR0xKpVq/Dz\nzz+L8mBsbIylS5di2bJlqFu3Lvr37y/Tlu7u7ti0aRMCAwPRpEkTfPfdd5g0aZJoI2V5nxODwXj/\nSXmVgjPJZ5B1PwPaRx/A8PB91D+diczkJ1Waw82oHT7qkx3KyhrbZb7qeHl54cGDBzhy5EiFcadP\nn45jx44hOjpaAZp9HPA8j5CQEAwdOrS2ValWPoY6x+ZrKR5m85rjwYsHCP4vGMd3/wOzhEQMfpGL\nu69V4dDYEuE5eVBr0xWzp3zcK9Frm5Llu6rt5Ec9R87X11fmEV2MmuX58+e4desWAgICsHbt2tpW\nh8FgMBjFSHqWhG3XtiE7PxuWz4D+yTmwIWU8f50N7bTn6KwkQfoz1suuKIqO6KoqrEeOUSlGjx6N\nBw8e4PDhw2XGcXFxwYULF+Dp6YmNGzcqULsPH9Yjx2AwapI7GXew/dp25BbkAkTQ/iUMY5LfIPN1\nJgiEHIkE2u064bq1LVymTKltdT8pqtpOMkeOwWDUOKzOMRi1z60nt7ArZhfyCvIAIjS59ACaYYlw\nz8krjKCvDzg6AkpKOC6VomuxFeyMmqeq7SRb7MBgMBhywM6hVDzM5tVHTFoMdlzfgbyCPHAFhOYX\n78P9qT4cGtpjy5s3WKuri4kFBfBPT8eWx49h3a1mtsdi/I/qKt8f9Rw5BoPBYDA+da6mXEXozVAQ\nCFwBoeWFZLi9kkJdRR1vVPNwrksXxHTogGfXruGVnR20nz5FexXZm+Mz3j/Y0CqDwahxWJ1jMGqH\n6IfROHDrQKETl1+Atufvw/W1MdSUC/ejXK2sjPDPP8eLtxuGG6qowFEigfH16/CuwtnPjKrDVq0y\nGAwGg8EQOHf/HA7FHwIA8PkF6HDmPpxzTKCqXHjay8tWrXD62TPBiQMAiVLhcZRsF7kPBzZHjsFg\nMOSAzddSPMzmVedE0on/OXF5+eh8+j665JhCVanQiXvarh02OTnhdV6ekEZy7Ros1dXBAajaSeCM\nylBd5Zs5cgwGg8FgfCQQEY7dOYbjd48DAJRy8uB66gE659WFilLhvLfUzp2xycEBGXl5sLKyQkF0\nNOw1NWH0dl5cdnQ0ujk61loeGJWDzZFjMBg1DqtzDEbNQ0QISwjDufvnAADKOXnoevoh2pIZlPjC\nIdN7rq7YZmGBNwUFhXE4Di1fv8bdhATkoLAnrpujI+yKnZ3NUAxs+xHGB4Wvry9sbW2F31u2bIFK\nDaySqi65Xl5e6N69ezVo9O4QEVq2bIndu3cDAPLz89GoUSP8+++/tawZg8GoLYgIB28fFJw4lTe5\n6HHqEdqhnuDE3e7eHcHFnDg1nscIExO4OzjAu29fTOnbF959+zIn7gODOXKMWqP4QeseHh54+PBh\nLWpTPpU9GF5ZWRlBQUE1osu2bduQnZ2NL7/8EgCgpKSEOXPmYMaMGTVyP0YhbL6W4mE2l48CKkDo\nzVBEPYwCAKhm5eCz0yloxZmB5wpf89c++wzbzcyQ+9aJkygpwcvEBObq6oIcZm/FwvaRq0GS4uKQ\ncPQo+NxcFKiowNrNDeZ2du+dzA+d4l3I6urqUC/WoLxvEFGlurxrcijx559/xtixY0VhAwcOxKRJ\nkxAeHg5XV9cauS+DwXj/yC/Ix97YvYhJjwEAqGVmo9eZdDRVrlf455PjcKFXL/xrZCS0SbrKyhhh\nYgIDtlfcRwHrkStBUlwc4rdsQdf0dLg8e4au6emI37IFSXFxtS7TxcUF48ePx8KFC2FqagoDAwOM\nGjUKmZmZAGQP/4WEhIDn//eYi4Y0d+/eDRsbG0gkEgwcOBCvXr3C7t27YWdnB21tbXz55Zd48eKF\nkK5I9urVq2FmZgaJRILBgwcjIyMDQOE/C2VlZdy/f190/6CgIOjq6iIrK6vcvJUcAi36febMGbRo\n0QISiQStWrVCVFSUKN348eNhY2MDTU1NWFtbY86cOcjJKX/h/Pbt22FtbQ0NDQ107twZBw8eBM/z\nOHPmTLnpihMTE4OePXtCT08PderUgYODA0JCQgAAFhYWyM/Px+jRo8HzPJTeLud/8eIFRo8eDVNT\nU6irq6NBgwaYNm2aIPPNmzeYOHEidHV1oa+vD29vb8yaNUs0BH3r1i1ER0ejf//+In00NDTw2Wef\nCTowqh8XF5faVuGTg9m8fPIK8rAzZqfgxKm/zELfM4/RVNms8M8kzyOib1/8Y2goOHFSVVWMMTWV\n6cQxeyuW6rI365ErQcLRo+impgYU6/LsBuD4f//BvHXrqsm8cAHdXr8WhXVzccHxY8cq3Su3Z88e\njBkzBpGRkUhKSoKHhwfMzc2xYMECAJBr+O/Ro0cICgpCaGgonj59ikGDBmHAgAFQUVHBnj178OLF\nCwwcOBBLlizBsmXLhHQXLlyARCLB4cOH8fjxY4wfPx5jx47F3r174eLiAltbW2zatAnz588X0gQE\nBGDYsGHQ0NCoVD4BoKCgALNnz8batWthaGiIqVOnYvDgwbh9+zaUlJRARDA2Nsb27dthbGyMq1ev\nYsKECVBRUYGvr69MmdHR0Rg+fDjmzJmDESNG4MaNG5gyZUqlhk0BwNPTE02bNsXZs2ehrq6Omzdv\nIv/tXkxRUVEwNTXFqlWrMGTIECHN3LlzcfnyZezfvx+mpqZITk7GjRs3hOuzZs3C3r17ERwcDDs7\nOwQEBGD9+vUwNjYW4kRERMDQ0BAWFhaldGrbti3Wrl1bqXwwGIwPk5z8HOy4vgN3Mu4AADSev0a/\n889gp1K30IlTUsK/ffvigo6OkKaemhqGGRtD4+2fS8bHAXPkSsDn5soOL7ZhYqVlvp2TUCq8gp4j\nWVhYWGDlypUAgIYNG2LIkCE4evSo4MjJM5yXnZ2NwMBA6OvrAwAGDx6MDRs2IDU1FQYGBgAK56wd\nO3ZMlI6IEBwcDC0tLQCAv78/evbsiTt37sDKygpfffUV1qxZg3nz5oHjONy8eROnT5/GunXrKp3P\novv9/PPPaNasGYDC3sR27drhzp07sLW1BcdxWLRokRC/QYMGiI+Px6+//lqmI7dq1Sp06tRJsJet\nrS1SUlIwceLESul27949TJs2Dfb29gAgcqwMDQ0BADo6OpBKpaI0zZs3R+u3fwjq1auH9u3bAwAy\nMzOxYcMGrFu3Dn3f7qb+448/IiIiAs+fPxdk3Lp1C+bm5jJ1srCwQFJSEvLy8qCszKp2dRMREcF6\nLBQMs7lssvOyse3aNiQ9TwIASDIy8cWFF7BRNQXHcchXVkbo55/j2tu2GgCsNTQwRCqFKl/2QByz\nt2KpLnt/1EOrvr6+lZ5MWFDGnIGCd/gHU1BGxSlQrdyWixzHwcnJSRRmamqK1NTUSskxMzMTnDgA\nMDY2homJieDEFYWlpaWJ0jk4OAhOHAB06NABAIRepZEjRyItLQ1hYWEAgD/++AOtWrUqpbO8lMyv\nqakpAIjyGxAQgLZt28LExARaWlqYPXs27t27V6bM2NhYtGvXThRW8rc8fP/99xg3bhxcXV3h5+eH\ny5cvV5jG29sbe/bsQZMmTTBlyhQcOnRIcLwTEhKQnZ0t2LSIjh07ipzz58+fo06dOjLla2trAwCe\nPXtW6fwwGIwPg6zcLARdDRKcuDpPXmLghZewVSt04nJVVbGjXz+RE+cokcCzAieOUXtERESU2fkg\nDx/13/aqGMbazQ3HtmxBt2Je8rHsbNh4eQFVXJxgHRdXKFNNTSyzW7dKy1It4fxxHIeCtz1+PM+X\n6pHLldHDWHI7Do7jZIYVlOhJrKi3z8DAAIMGDUJAQAC6deuGoKAgLFmypPwMlQPP86Ihz6LvRXrt\n3r0b33zzDZYvX44uXbpAW1sbu3btwpw5c8qVW9lhVFnMnTsXw4YNw6FDh3D8+HEsWbIE06dPx8KF\nC8tM06NHD9y7dw9hYWGIiIjA8OHD0aRJk1I9n+Whq6uLly9fyrxW1HOnq6tbucww5IL1VCgeZnMx\nmTmZCP4vGCmvUgAA2ukvMDA6C+bqJgCAN+rq2NanD+5JJEKaVlpa6GVgAF6Odo/ZW7EU2dvFxQUu\nLi7w8/OrkhzmnpfA3M4ONl5eOC6VIkJXF8elUth4eb3TCtOakCkLqVRaaguPS5cuVZv82NhYkRNR\ntDjAwcFBCJswYQL+/vtvbNiwAW/evIGnp2e13b8kJ06cQPPmzTFlyhQ0b94c1tbWuHv3brlpHBwc\nShC5cD8AACAASURBVC1qOHfunFz3K+kAWlpaYuLEidi9ezf8/Pzw66+/CtdUVVWFOXPF0dPTg4eH\nBzZs2ICDBw8iMjISsbGxsLa2hqqqKk6fPi2Kf/r0adF9bW1tkZSUJFO/pKQkWFhYsGFVBuMj5GX2\nS2y5skVw4nRSnmFwdDbM1Qvn0L7S1MTmvn1FTpyzri56y+nEMT5cWIsvA3M7u2p3sqpDZkVbYLi5\nuWHFihVYv349evbsiePHjwubxlYHHMdh5MiRWLRoEZ48eYJJkyahX79+sCq2eWTHjh1hZ2eHH374\nAaNGjYLkbaPSrVs3tG3b9p166Epib2+PTZs2Yf/+/XB0dMSBAwewb9++ctN89913aN26NXx8fDBs\n2DDcvHkTq1atEvJXXPbkyZMxadIkIazI9q9evcKMGTMwaNAgWFhY4NmzZzh06BAcix1pY2lpiePH\nj6Nnz55QVVWFoaEh5syZg1atWsHBwQE8zyMkJARaWlpo0KABJBIJvv76a8ydOxfGxsZo2LAhNm7c\niFu3bokWO3Tp0gVPnjxBYmJiqQUP586dY/+oaxA2f0jxMJsX8uzNMwRdDcLTrKcAAL2HGRj8Xz5M\n1Y0AABlaWghyd0dGsUVlPfX10b7YQgd5YPZWLGyO3CeIrE1pi4e5ublh0aJFWLJkCZo1a4aIiAjM\nnz+/1PBkeTLKC2vTpg06deqE7t27w93dHU5OTti0aVMpPceNG4ecnBx89dVXQtidO3eQkpJS4T3L\n+10ybMKECRgxYgRGjx6NFi1a4OLFi/D19S1XTosWLbB161Zs3boVTZs2xfLly4Xh0OL72N26dQtP\nnjyRqa+KigqePXuGsWPHwsHBAZ999hlMTU2xbds2If7KlSsRHR0NS0tLwRHT0NDA/Pnz0apVK7Ru\n3RrXr1/Hv//+K8w7XLZsGb744guMGDECbdu2xYsXLzBp0iSR825nZ4dWrVph7969ojxmZWUhLCwM\nw4cPL2UzBoPx4fI06yk2X94sOHGG95/C4z+CqXrhoqpUXV1s7NVLcOJ4jsMXhoaVduIYHy7srFWG\nXHh5eeHBgwc4cuRIhXGnT5+OY8eOITo6WgGavTtBQUEYM2YMnj59KiwYeF/w9fXF1q1bcfv2bSFs\n27ZtWLx4MWJiYoSw4OBg/Pjjj/jvv/9qQ80KYXWOwag86ZnpCLoahJc5hVNajJOe4MubPAzV9AAA\nyYaG2NqtG968/ROqzHH4UiqFnaZmrenMqDrsrFVGrfP8+XNcvHgRAQEBmDp1am2rUyY//fQToqOj\ncffuXezatQszZ87E4MGD3zsnriyGDh0KDQ0N0VmrS5YswYoVK2pZMwaDUV2kvErB5iubBSfO7O4T\nDLmpJDhx8cbGCHJzE5w4NZ7HcGNj5sR9gjBHjiEX8pw12q9fP3Tp0gUDBgx4r4f4rl27hr59+6JR\no0bCxsCyhojfB8qye1RUlOis1djYWHz22WeKVu+Tgp1DqXg+VZvff3EfW65swevcwo3k68en48tb\nytBXK1yRft3MDNu6dkXu250Qis5NtajCxuvF+VTtXVuws1YZCmXz5s0VxvlQGoHAwMDaVkFufHx8\n4OPjU9tqMBgMBZH0LAlbr21FTn7hhvGWt9LR/64atNUKRwwumpvjn44dQW+3jNJRVsZIdm7qJw2b\nI8dgMGocVucYjIpJeJqAHdd3ILegcP9P29g09EvWRB3VOiAAJ6ysEN6uHfDWaTNSVcUIY2Nosy2H\nPgqq2k6yp89gMBgMRi0T9zgOu2J2IZ/yASI0upGOPg8kkKhKQAAONWyI861bA2+dNrO356ZqsnNT\nP3nYHDkGg8GQgw9l6sDHxKdi85i0GOyM2Sk4cU2upeHzh1qQqEqQD2CfgwPOt2kjOHHWGhoYZWJS\n7U7cp2Lv9wU2R47BYDAYjA+cKylX8NfNv0AggAjNr6Si5xNdqKuoI5fjsLtxY9xq1gx4e06qo0SC\n/oaGUGbnpjLewubIMRiMGofVOQajNFEPo3Dg1oHCH0RofSkVbhl6UFNWwxuex/amTZHUpIngxLXU\n0mJHbn3EsDlyDMb/s3ff4VFWaePHvzOT3ie9kYQkkEACofcSQAWlKEVAiiKwuq8ur65eP3V1keKq\nq++q767uu64VaQKyoIIgAiH0DgkkQCAJSUiBkN7LzDy/Px4yKRBInUkm53NdXvCcZJ7nzG0Ybs65\nzzmCIAidxPEbx9mTtAcAhU5i6NlbjC9yxsLMghKVivX9+nEzLAzuJG2jnZwY7+T0wG2ghK5HjM12\nESkpKSiVyrsOjO8qoqOjUSqVZGZmAiIeDWVmZuLi4kJGRgYA169fx8XFhdu3bxu5Zx2HqB8yPFON\n+aHUQ7VJnFbHyNM3mVDkgoXKgnwzM74ZOLBeEveIszMT1Op2T+JMNd4dVVvFWyRyXYSfnx83b95k\nyJAhRnn+X/7yF7p3797s16Wnp6NUKjl06FCb9sfY8ehoVqxYwZw5c/Dx8QGge/fuTJ8+ndWrVxu5\nZ4JgOiRJYn/yfqKuRwGg1OoYe+oWkaVumKvMyTY355tBg8jr1QsUCv25qSPEuanCfYip1XtISE5m\nX3w81YA58FBYGCGBgR3uns2hVCpxd3c32PPaWlvXV7VVPKqqqrCwsGiDHt1Np9MBcl/bU15eHuvX\nr79rdHLx4sVMnDiR999/Hzs7u3btQ2cQGRlp7C50OaYUc0mS2JO0hxPpJwBQarSMP3mb4ZXuqJQq\nblhasnHQIMqDgwH53NRZbm6E2toarI+mFO/OoK3iLUbkGkhITmbNuXPcDg+nIDyc2+HhrDl3joTk\n5A5xzyNHjjBy5EgcHBxwcHCgX79+/PbbbwBkZ2fz7LPP4unpibW1NaGhofoTGRpOJdZcb9iwgQkT\nJmBjY0NQUBCbN2/WPysyMpLnn3++3vMlSSIoKIh33333rr699957BAUFYWVlhbu7O5MmTaKiooI1\na9bw9ttvk5qailKpRKlU6kd6Nm7cyNChQ3FycsLNzY0pU6bUOyDez88PgHHjxqFUKgm8k/ymp6cz\nc+ZM3NzcsLa2JigoiL/97W9NjmNj8fjhhx+YMmUKtra2BAUF3XUKhFKp5NNPP2XevHk4OTnxzDPP\nALB3715GjhyJjY0Nvr6+LF68mLy8vHpxe/PNN3Fzc8PBwYEFCxbw97//HfM6u7GvXLmSHj16sGXL\nFkJDQ7G0tOTatWuUlJTw0ksv4evri62tLQMGDGD79u1Nin1TYvXDDz/g4eFB//79691z+PDh2Nra\n3vUsQRCaR5Ikdl7dqU/iVFUaHj6RzYgqOYlLtLZm7ZAh+iSu5txUQyZxQuclRuQa2Bcfj+XAgUQX\nFNQ2BgVx4dAhBrewPuHUoUOURURAnXtGDhzI/ri4Zo3KaTQapk2bxuLFi1m7di0gnxtqa2tLeXk5\nY8eOxdbWlo0bNxIUFERSUhI5OTn3vedrr73G3/72Nz7//HPWrl3L/PnzCQkJoV+/fvz+97/nueee\n4+OPP8b2zgdKVFQUaWlpLFmypN59tm3bxgcffMDGjRuJiIggNzeXgwcPAjB37lwSEhLYsGEDZ86c\nAdDfr6qqirfffpvevXtTVFTE22+/zeTJk4mPj8fc3Jxz584xYMAAtm3bxogRI1Dd2TfphRdeoKKi\ngv379+Pk5ERycjK3bt1qciwb88Ybb/DBBx/wj3/8g6+//pqlS5cyYsQIevToof+eVatWsXr1at59\n9110Oh1RUVE88cQTfPjhh6xdu5b8/Hxee+01ZsyYoa+B+OSTT/j000/5/PPPGTZsGD///DOrV6++\nq+YlMzOTf/3rX6xbtw61Wo2npydTp05FoVCwZcsWvL292bt3L3PnzmX37t2MHz/+vrFvLFY3b97U\nf/3gwYMMHTr0rlgoFAqGDh1KVFQUCxcubHVsO7vo6GgxYmFgphBznaTjxys/cuHWBQDMqjRMPJHL\nQK0HSoWSOFtbtg8ejNbfHwAblYoFHh543zlH1ZBMId6dSVvFu9MlckVFRTz00ENcvnyZkydP0rt3\n7za9f3Uj7dpWFJnqGnltVTPvU1xcTEFBAVOnTiUoKAhA/+vXX39NSkoKSUlJeHt7A+B/54PhfpYu\nXcpTTz0FwDvvvENUVBQff/wxa9euZfr06fz3f/83mzZt0iduX331FVOmTMHT07PefVJTU/H09GTi\nxImYmZnh6+tLRESE/uu2traoVKq7pjMXLVpU7/rbb7/F1dWVM2fOMHz4cFxdXQFwdnau99q0tDSm\nT59O3759gdqRu9ZatmwZs2bN0sfj008/5cCBA/USuenTp/PCCy/or5csWcJLL73Eiy++qG9bs2YN\nAQEBXLhwgb59+/LRRx/xyiuvMH/+fAD++Mc/curUKbZu3Vrv+RUVFaxbtw5fX19A/oN+4sQJbt26\nhYODfNbi7373O44fP86nn37K+PHjHxj7B8Xq6tWrjBs37p7x8Pf35+zZs80LoiAIAGh1Wv5z+T9c\nun0JAPOKah47kUs/yQOFQsFpe3t2DR6M1K0bIJ+butDDA9d2KtcQTFOnm1q1sbFh165dzJo1q132\npWrs2GFVK56lbOS1zf2jqlarWbp0KRMnTuSxxx7jgw8+4OrVqwCcPXuWsLAwfRLXVMOHD693PXLk\nSOLj4wGwtLRk0aJFfPnllwDk5uby448/8rvf/e6u+8yZM4fq6mr8/f159tlnWb9+PSUlJQ98fkxM\nDNOnTycwMBAHBwd98pmamnrf17388su89957DBs2jDfeeIPDhw836f0+SL9+/fS/r6mjy87Orvc9\nDRdInD59mk8++QR7e3v9f2FhYSgUCq5du0ZhYSFZWVkMGzas3usaXgN4eHjok7iae1dVVeHj41Pv\n/hs2bCAxMRF4cOwfFKuioiLs7e3vGQ8HBwcK6o5Od2FipMLwOnPMNToNm+M365M4i/Iqph3Po5/k\nAQoFhxwd+WXoUH0S52ZhwWIvL6MmcZ053p1RW8W7043ImZmZ6Udp2sNDYWGsOXuWyIED9W2VZ8+y\naMwYQlqw6hIgQZJYc+4clg3uOWHAgGbf64svvuCll17it99+Y+/evSxfvpzPPvuszTZcbXiP559/\nno8++oiLFy+yf/9+3N3defTRR+96nbe3N1euXOHAgQNERUXxzjvv8Prrr3Py5Ml6iUldZWVlPPLI\nI4wZM4Y1a9bg4eGBJEmEhYVRVXX/8cpFixYxadIkfv31Vw4cOMCjjz7K9OnTWbduXcvfPNy1cEGh\nUOgXHdSwbVC3IkkSb7zxxj2nHz08PNBoNPp7PUjDe+t0OhwdHfVT0vfq64Ni/6BYOTk5UVxcfM/+\nFBYWolarH9hvQRBqVWmr2BS3ieR8uQ7asrSSx08W0EvhDgoFe5ydOTFoEHh5AeLcVKF1Ot2IXHsL\nCQxk0YABuMfF4RQXh3tcHIsGDGjVCtO2vmdYWBh//OMf2bVrF0uWLOGLL75g4MCBXLp0Sb8PWFMd\nP3683vWxY8cICwvTXwcFBTF+/Hi+/PJLvv76axYvXtxoQmJhYcHEiRP54IMPuHjxImVlZfz000/6\nr2m12nrff/nyZXJycnj33XcZM2YMISEh5OXl1Usma5KVhq8F8PT0ZNGiRXz33Xd89dVXbNiwoUmj\ngG1t0KBBxMXFERgYeNd/tra2ODo64u3tfdeq0BMnTjzw3oMHD6agoIDy8vK77l03Qb5f7OH+serR\nowcpKSn3fH5qaio9e/ZsQVRMj9hjy/A6Y8wrNZWsv7Ben8RZlVQw82QRvRTu6BQKfnRz48SQIfok\nLtDamqfb4dzUluiM8e7MOv1Zq5999hlr1qwhLi6Op556Sr+6EuTtEJYsWcLevXtxdXXl/fff19dx\n1dVemyOGBAa2+dYgbXHPpKQkvvjiC6ZNm4avry+ZmZkcOnSIQYMG8dRTT/Hhhx8ybdo0PvzwQwID\nA0lOTiY3N5fZs2c3es9vvvmG0NBQBg4cyPr16zlx4gT//Oc/633P888/z/z589HpdCxduhSA7du3\n86c//YkDBw7g5eXF119/jSRJDB48GCcnJ/bv309xcbG+hrF79+7cvHmTEydOEBwcjK2tLf7+/lha\nWvKPf/yDV155hZSUFN544416/19dXV2xs7Njz5499OrVC0tLS9RqNX/4wx+YPHkyPXv2pKKigm3b\ntuHn56ffJuNPf/oTp0+fZt++fa2KeVNGOVevXs0jjzzCq6++ysKFC7G3t+fatWts3bqVzz77DCsr\nK1599VVWrFhBaGgogwcP5pdffmHv3r0P3Fpk/PjxPPTQQ8yYMYMPP/yQPn36kJ+fz7Fjx7C2tmbp\n0qUPjP2DYjV27Nh7rkKWJIlTp07x4YcftiBygtD1lFeXs/7CejKK5X9QWxeWMetMOUEqN6oVCra6\nu5MwcCC4uQHQ29aWGeLcVKGVjPbT4+Pjw/Lly1m8ePFdX3vxxRexsrIiOzubDRs28F//9V9cunTp\nru/ramc32trakpiYyNy5cwkJCWHWrFmMGjWKzz77DGtraw4ePEh4eDhz586ld+/eLFu2TL8FBdw7\n8f3rX//KF198QUREBBs2bGDDhg316sQAnnjiCZycnJg0aZJ+w9jCwkKuXbtGdbW8PMTZ2Zlvv/2W\ncePG0bt3b/73f/+XL7/8Ul9EP336dJ588kkmT56Mu7s7//M//4Orqyvr169n7969hIeH89prr/HR\nRx/VS26USiX//Oc/2bJlC926dWNgnenpl19+mT59+jB27FjKy8vZvXu3/ms3b94kucH2Lg3f/4Ou\nG2trKDIykqioKC5cuMCYMWOIiIjglVdewcHBQb+9yMsvv8wf/vAHXnrpJQYMGMCpU6d49dVXsayz\nMk2hUNzzeT///DMzZszgj3/8I7169WLKlCns3r2b4DtbFTwo9g+K1cyZM8nOzubcuXP1nnvs2DFK\nSkqYMWPGA2PQFYj6IcPrTDEvrSplTcwafRJnU1DK3NPlBKlcqFAqWe/lRcLgwfokboC9PbPc3DpU\nEteZ4m0K2ireCsnI2dDy5ctJT0/Xj8iVlpbi7OxMfHy8/i+qZ555Bm9vb95//30AHnvsMWJjY/H3\n9+f555/X7+VV1/1qxsQB3vK+aYGBgRw5coQRI0bc93tzc3Pp1q0bmzdvZurUqQbqoelbvHgxFy9e\n5PTp08buCs899xwqlYp//etf+rYlS5ZgbW3NZ5991ur7iz9zgikrqixibexacsrk7Z7s8kqYe64K\nXzNn+dxUb29u9u8Pd+pNRzk6GuTILaFzaennpNEXOzTs9NWrVzEzM9MncQARERH15pJ37drVpHsv\nWrSIgIAAQC7o7tevn/gXRzNoNBpycnJYuXIlvr6+IolrhaysLLZt28a4ceNQqVTs2LGDdevW3TWN\nbSyrVq0iPDycP//5z/j4+HD9+nV++uknLl++3GbPqLtnUs2f5850HRMTw8svv9xh+tMVrmvaOkp/\n7nVdUFHAn7/5MyVVJQT0C8DhdjH+P6WQaGaPXbA7a729OafTQUICAcOG8YizM1Xnz3Owg/S/7nVN\nW0fpj6lfx8TEUFBQ0GiNclN1uBG5w4cPM3v2bLKysvTf8+WXX7Jx40YOHDjQ5PuKEbn7S0lJISgo\niMOHDzc6IhcdHc348eMJDAxk3bp1d21VIjRddnY2c+bM4cKFC1RUVNCjRw+WLVt218bKpsoU/sxF\ni81SDa6jxzy3LJe1sWsprCwEwCm7iKdidXiYO5Ftbs46X1+K+/UDBwcUCgXTXFzo38hWPx1BR4+3\nqWkYb5MZkbOzs6OoqKheW2FhYaP7XAktExAQcM+VoHVFRkbetfWG0DLu7u7N+oeI0PGIv+AMryPH\nPLs0m7Wxaympkld/u2QVMvciuFk4kW5pyQZfX8r79QM7O1R3zk3t1cGP3OrI8TZFbRVvo1dZNqwR\n6NmzJxqNRr/ZKUBsbCzh4eGG7pogCIIg3CWrOIs1MWv0SZx7ZiHzLipws3AkycqKtd26Ud6/P9jZ\nYaGUz03t6Emc0HkZLZHTarVUVFSg0WjQarVUVlai1WqxtbVlxowZvP3225SVlXHkyBF27NjRorMe\nV65cWW/uXxAEoaXEZ4nhdcSYpxel813sd5RVlwHgfaOQefFKXCwciLexYaOfH1UDBoCtLTYqFYs8\nPelubW3kXjdNR4y3KauJd3R0NCtXrmzxfYyWyL3zzjvY2NjwwQcfsH79eqytrfV7Wf3f//0f5eXl\nuLu7s2DBAj7//HN69erV7GesXLlSDBULgiAIbSKlIIW1sWup0MjbOnVLLWDuFRVO5vacsbdnq58f\n2v79wdoaRzMzFnt64l1niyFBuJfIyMhWJXJGX+zQXsRiB0HoOMSfOaGzS8xLZHPcZqp18t6ZAdfz\nmZlogZ25LUccHdnv7Q0REWBpiau5OQs9PXE0M3oZutCJdNrFDsagFvv3CIJBifNahc4sISeBLfFb\n0EryArEeifk8kWyBjYUtv6nVHPf2hr59wcICb0tLFohzUwUDajSRa2pNmqWlJV999VWbdagt1Uyt\nNpxezcvLM06HTJxYum5YIt6GJeJteB0h5nHZcWy7vA2dJK/gD72ax+Op1lhaWPOTqysxnp5yEmdu\nTndra+a6u2OpNPo6whbpCPHuSmriHR0d3ar6xEYTuS1btvDmm28+cHryo48+6tCJnCAIgiC0RMzN\nGH668hMSEkgSfa7kMznDBpWFNZvd3Ejw9IQ+fcDMjF62tswU56YKLVAz4LRq1aoWvb7RGrmgoCCS\nkpIeeIOQkBASEhJa9PD2JGpyBEEQhJY6nXGaX679Il9IEv0v5TMpyxbJwppN7u6keHjISZxKxQB7\ne6a4uKAUJTtCK7Q0b+mSix0EQRAEoTHHbxxnT9Ie+UKSGHwxj4dv21NtbsV6Dw+yPD0hPByUSkY6\nOvKQqLsW2kBL85YWjQEnJye3+mwwwfSIPYgMS8TbsES8Dc/QMZckiUOph+olccNj83jktgNlljZ8\n4+VFlre3Pol72NmZh52dTSaJEz/jhtVW8W5SIjd37lyOHTsGwLfffktYWBi9e/fusLVxgiAIgtAc\nkiSx//p+oq5HAaDQSYw+l8uEPEcKrGz4xtOTXG9vCAtDoVIxzdWVkY6ORu61IDRxatXNzY2MjAws\nLCwIDw/n3//+N05OTjz++OP1jtLqSBQKBStWrLjnqlVBEARBqCFJEr8m/srJjJMAKLQ6Is/lM6rY\niSxrGza4u1Pu7Q0hIaiUyk5xbqrQedSsWl21alX71cg5OTlRUFBARkYGQ4YMISMjAwB7e3uKi4ub\n32sDEDVygiAIwoPoJB07r+7kXNY5AJRaHRPO5jO8VM11axs2u7tT5eMDPXtioVQy192dwE5y5FZT\nJSSksm9fEtXVSszNdTz0UBAhIf7G7laX0641chEREbz//vusXr2ayZMnA5Ceno6jGFYW6hD1FYYl\n4m1YIt6G194x10k6frzyY20Sp9Ey8bScxF2xtWOjhwdV3bpBz57YqFQ84+lpkkncmjWJXLkynkOH\n4Pbt8axZk0hCQqqxu2byDFoj9/XXX3PhwgUqKip45513ADh+/Djz589vk04IgiAIgiFpdVq2XtrK\nhVsXAFBVa5l8qoAh5c6ct3fgBzc3tH5+EByMw51zU31M8NzUffuSKC6eQFwcXL8OeXlgaTmB/fsf\nvP2Y0DGI7UcEQRCELkWj07A5bjPX8q4BYFalYerpIvpUqTnm5MQ+tRoCAsDf3+TPTX3jjWhOnoyk\n5q9LGxsYPBjU6mhefjnSqH3ratr9rNXDhw9z/vx5iouL9Q9TKBS8+eabzX6ooTR2RJcgCILQNVVp\nq9gUt4nk/GQAzCuqeeJMCb2q1exzduaYoyMEBkK3bnhbWjLfwwNbEz03NT4eLl7U6ZM4a2v5tDGF\nAiwsdMbtXBfS2iO6mjQit2zZMrZs2cLo0aOxblAfsG7duhY/vD2JETnDE+f0GZaIt2GJeBteW8e8\nQlPBxosbSStMA8C8vIpZZ8oI1jiyw82NGDs7CA4GH59Of27qg8TFwbZtkJ2dSkxMIvb2E1Cro+nZ\nM5LKyv0sWhQsFjy0s4Y/3+06Ird+/Xri4+Px9vZu9gMEQRAEwdjKq8tZd2EdmcWZAFiWVvLk2XIC\nJDU/eLhxxcYGevYELy9CbWyY5eZmsuemXrgA27eDJIGrqz+RkaBWR3H9+gXc3XVMmCCSuM6kSSNy\nffv2JSoqCldXV0P0qU2IETlBEAQBoLSqlLWxa7lVegsAq5IK5p6txAsnvvfwIMXKCkJDwcOD/vb2\nTDXhc1NjYuCnn9BPp7q5wTPPgJ2dcfsltPNZq6dPn+a9995j3rx5eHh41PvamDFjmv1QQxCJnCAI\nglBUWcTa2LXklOUAYF1Uzrxz1Tgrndjg4UGmlRX06gVuboxwdORhEz439dw52LGjNolzd5eTOLG3\nccfQrlOrZ8+eZdeuXRw+fPiuGrkbN240+6GCaRI1RIYl4m1YIt6G19qYF1QU8F3Md+RX5ANgW1DG\n/Bgttiq1fOSWhQWEhYGLCw+p1Yxycmqjnnc8Z87Azp21156e8PTT8irVGuJn3LDaKt5NSuTeeust\ndu7cycMPP9zqBwqCIAhCe8sty+W72O8oqiwCwD6vlAWxEkoLZ7728KDIwgLCw1E4OzPFxYWB9vZG\n7nH7OXUKdu2qvfbykpM4E9vbuMtq0tSqn58fiYmJWFhYGKJPbUJMrQqCIHRN2aXZrI1dS0lVCQDq\n3DLmXdBRZenMBg8Pyu4kcSq1mplubvQ24bnFEyfg119rr318YMECkcR1RO16RNfq1at5+eWXycrK\nQqfT1fuvI1u5cqU4VkcQBKELySrOYk3MGn0S53K7lIWxEsXWrnzn6UmZpSX07YuFszPzPTxMOok7\ndqx+EufrCwsXiiSuo4mOjmblypUtfn2TRuSUjSzBVigUaLXaFj+8PYkROcMT9RWGJeJtWCLeG3SW\nqAAAIABJREFUhtfcmN8ovMGGixuo0FQA4H6rlHlxkGXvxlY3N7QWFtC3LzaOjsz38DDJI7dqHDkC\n+/bVXvv5wfz5cL+3LH7GDcug+8glJyc3+8aCIAiCYCgpBSlsvLiRKm0VAN5Zpcy9pCDR0Z0dLi5I\nd0biHBwdWejhgVsnKhVqrkOHICqq9trfX07iTPgtd2nirFVBEAShU0vMS2RT3CY0Og0A3TJKmH1F\nyQW1J3udneVhqL59cbmTxDmZmxu5x+1DkuDgQahbUdS9Ozz1lEjiOoM2r5Fbvnx5k26wYsWKZj9U\nEARBENrClZwrfH/xe30SF5BewpzLKk64+shJnJUV9OuHl1rNYi8vk07iDhyon8QFBsK8eSKJM3WN\njsjZ2dlx4cKF+75YkiQGDhxIQUFBu3SuNcSInOGJ+grDEvE2LBFvw3tQzOOy49h2eRs6SV54F5xS\nzBOJ5uz38OG8vb1c1R8RQYCjI095eJjsuamSJNfDHT1a2xYcDHPmQHPyVvEzbljtXiNXVlZGcHDw\nA29gacLFooIgCELHFHMzhp+u/ISE/Bdf6PVipiRb8IuXL5dtbeXjCvr2JdTJyaTPTZUk+O03OH68\ntq1nT5g9G8yaVAUvdHaiRk4QBEHoVE5nnOaXa7/or8OTinkkxZLt3t24bm0tHxzaty/91Gqmubqa\n7LmpkiRvL3LyZG1bSAg8+aRI4jqjdl212lmtXLmSyMhIMVQsCIJgIo7dOMZvSb/pr/tfLWZMhg2b\nfH3JtLQEe3vo25fhLi48YsLnpkqSfFrD6dO1bb16waxZoFIZr19C80VHR7dqz1sxIie0GVFfYVgi\n3oYl4m14dWMuSRKHUg9xIOUAdxoYklDCkJt2bPLxJcfcHBwdoU8fJri6MsrR0aSTuJ074ezZ2raw\nMJgxo3VJnPgZNyyD7iMnCIIgCMYiSRL7r+/nSNqRmgaGXyohIs+Btb4+FJmZgVqNIjycKe7uJn1u\nqk4HO3bA+fO1bX36wPTpYKJlgMIDiBE5QRAEocOSJIlfE3/lZMbJmgbGxBXTo1DN917elKlU4OKC\nKiyMmSZ+5JZOBz/9BLGxtW0REfD44yKJMwXtOiKXnZ2NtbU19vb2aDQa1q5di0qlYuHChY0e3yUI\ngiAIraGTdOy8upNzWecAUOgkxl0sxqfMlXXenlQpleDmhkXv3szx9CTIhA8R1elg+3a4eLG2rV8/\nmDZNJHFdXZP+90+ZMoXExEQA3nrrLT766CM++eQTXnnllXbtnNC5tKZYU2g+EW/DEvE2nITEBD7d\n9CmPLXuMz3/4nJzMHBRaHQ/HluBS4c5GTy85iXN3xzo8nKe9vEw6idNq4T//qZ/EDRjQ9iNx4mfc\nsNoq3k36Ebh27Rr9+vUDYP369ezatYuoqCg2bdrUJp0QBEEQBJCTuG+jvuWg8iA37W5S5lvGxbjz\nDNt/AyuNJ1vdPdAqFODlhX14OM96eeFrZWXsbrebmiQuPr62bdAgmDoVTHQth9BMTaqRc3V1JT09\nnWvXrjF37lzi4+PRarU4OjpSUlJiiH42m6iREwRB6Hw+2fgJB5UHKaiQTwxSaXTMuFiNhTacjEEj\n5W/y8cGlVy+TPjcV5CTuhx/gypXatiFD4NFHRRJnitq1Rm7SpEnMnj2b3Nxc5syZA8ClS5fw9fVt\n9gMFQRAE4V5ultzkUNohCjzlJM5Mo2NWrIZiu1DOq/3xBejWDa/QUBZ4emJrwhumaTSwZQtcvVrb\nNmwYTJwokjihviZNrX711VdMnjyZpUuX8uabbwKQm5vLypUr27NvQicj6isMS8TbsES821dcdhxf\nn/uaSk0l2qxS1HtuEPD3a8QWe3HK0QsFCvD3x793b57pAknc5s31k7iRI9s/iRM/44bVVvFu0oic\nlZUVzz//fL02sWmgIAiC0Fo6Scf+5P0cvSGf+O5lpcZm80WCbdw45u6LyswORfJNHMY9REjv3sxy\nc8PchJdpVlfDpk2QlFTbNno0jB8vRuKEe2u0Rm7hwoX1v/HOT5AkSfV2y167dm07dq/lFAoFK1as\nEEd0CYIgdFDl1eVsvbSVpPzarCX/66NIRdZcHTeOQhsbufHqNQb5+vL3l14y2XNTAaqq4Pvv4fr1\n2raxYyEyUiRxpqzmiK5Vq1a1qEau0X/WBAUFERwcTHBwME5OTvz4449otVq6deuGVqvlp59+wsnJ\nqVWdb281Z60KgiAIHcutklt8cfYLfRKn0EmMvK4lp9yehKnTqHBSY2lhiaWjE0GDBqFMSzP5JG7j\nxvpJ3Lhx8n8m/LYF5BnO1pSqNTq1WvemjzzyCL/88gujR4/Wtx05coTVq1e3+MGC6RHn9BmWiLdh\niXi3nUu3L/HjlR+p0lYBYFVSwcgEFYk2vlz1LsTWzAwUCgqSk+kfGopfZSW3LSyM3Ov2U1kJGzZA\nWlpt24QJ8pSqIYmfccNqq3g3qUbuxIkTDBs2rF7b0KFDOX78eKs7IAiCIHQNOknHgesHOJx2WN/m\nmlaEOtedKGcPJIUCOysr8pRK3G1s8NNo8K+sJFmjoUdwsBF73n4qKuQk7saN2raHH5YXNwhCUzRp\nH7mxY8cyePBg3nnnHaytrSkrK2PFihWcPHmSQ4cOGaKfzSb2kRMEQeg4KjQV/OfSf7iWdw0ARZUG\nu6vVlFkEoLW4cyqDQkGupSXXc3NxdHfHXKtFUqmwy8vjvx9+mJDAQCO+g7ZXUQHr1kFGRm3bxIkw\nfLjx+iQYT0vzliYlctevX2fevHmcOXMGtVpNfn4+gwYNYuPGjXTv3r1FHW5vIpETBEHoGG6X3mZT\n3CZyy3MBqM7VoLlpg4W1ByrlnW1ErKwI6tuXScHB5KWnsz8+nirAApgQFmZySVx5uZzEZWbWtj36\nKAwdarw+CcbVrolcjbS0NDIzM/Hy8sLf37/ZDzMkkcgZnqivMCwRb8MS8W6ZKzlX2H55O5XaSip0\nKgpv6DAvd0Bt6aTfAUHt7s7EESMIcXSstyuCqca8rAzWroWbN2vbJk+GwYON1ycw3Xh3VA3j3a4n\nO9SwsrLC3d0drVZLcnIyAIEm9q8kQRAEofUkSeJg6kGiU6LRoiCr3IaKG1q8cMDWyhYAC6WSMeHh\nDOvXDzMT3huurtJSOYm7dUu+VihgyhQYONC4/RI6ryaNyP36668sWbKErKys+i9WKNBqte3WudYQ\nI3KCIAjGUampZNvlbVzJTSAbW27lmuN0swxPK1csVPLq0wgrKx56+GHsXVyM3FvDKSmRk7jsbPla\noYBp06B/f+P2S+gY2nVqNTAwkNdee42nn34am5oNGjs4kcgJgiAYXm5ZLt/Hfc/1siKSNU4oM8tw\nK9LgZuOGSqnCu6qKR3v2pNuYMdBFRuEAiovhu+8gJ0e+VijgiScgIsK4/RI6jpbmLU36U1RQUMDz\nzz/faZI4wTjEOX2GJeJtWCLeD3Y19yqfnfmKo2UScaXO2FzNw6dUiYetB44SPF5Zye8mTaJbZGST\nkjhTiXlREaxZUz+JmzGj4yVxphLvzqKt4t2kRG7JkiV88803bfJAQRAEwbRIksSBlIN8ePEXDmtc\nKbulwyvxFl5mTrhZqRlVVMQytZr+zzyDws/P2N01qMJCOYnLlRfsolTCrFnQp49RuyWYkCZNrY4a\nNYpTp07h7++Pp6dn7YsVCrGPnCAIQhdWpa3i8/gd/JqXR2WVAre0HGxLq3G3dadPlZaJpaW4TJwI\nffsau6sGV1AgT6fm58vXNUlc797G7ZfQMbVrjdyaNWsafegzzzzT7IcagkjkBEEQ2ldScTbvxv1G\ncqUGm4JSXG7kYquwIMTckSkFRfRwcYGZM0GtNnZXDS4/X07iCgrka5UKnnwSQkON2y+h4zLIPnKd\niUjkDE/sQWRYIt6GJeJdq1Kn4/sbl9iQeh5NdTXOGXnY55XgYmbD9Eolw0tKUY0ZA2PHtmpBQ2eN\neV6enMQVFsrXKhXMmQM9exq3Xw/SWePdWRl0HzlJkvj2229Zt24dGRkZ+Pr6smDBAp599tl6mzcK\ngiAIpkuSJGJKSvjq+nni85KxKKvEO/U2lpUaxlSrmFVciZ2dHTz7LHSxWrgaublyEldUJF+bmcHc\nuWCiR8UKHUCTRuTeffdd1q5dy6uvvoqfnx9paWl88sknzJ8/nz//+c+G6GezKRQKVqxYQWRkpPgX\nhiAIQitlVFayIyebqIyLZJfewiG7CPXNfHzKSnmm0owQLCE8XN7d1srK2N01ipwcOYkrLpavzczg\nqacgKMi4/RI6tujoaKKjo1m1alX7Ta0GBARw8ODBesdypaamMnr0aNLS0pr9UEMQU6uCIAitV6zR\nsD8/n+MFOcRlx1FRWohrWg4u+YWMzcnmCQsPLK3t5DOm+vaV99bogrKz5c1+S0rka3NzmDcPOuhx\n5EIH1K77yJWVleHq6lqvzcXFhYqKimY/UDBdYg8iwxLxNqyuFm+NTsfRwkI+zcjgQM4NzmaeRcq5\nje+VdIYkJfDCjRs8aeWDpV93+P3v5U3R2jiJ6ywxv3VLHomrSeIsLGDBgs6XxHWWeJuKtop3k2rk\nJk2axIIFC3j//ffx9/cnJSWFt956i4kTJ7ZJJwRBEISOQZIkrpWX82teHrnV1aQXpZOccw3nzDzC\nklMYnJHEIHs/vFwCYPRoeUGDSmXsbhvNzZvySFxZmXxdk8R10RJBwQiaNLVaWFjIsmXL2Lx5M9XV\n1ZibmzN79mw+/fRTnJycDNHPZhNTq4IgCM2TU1XFr3l5JJaXo5V0XM29Sv7tNIKvpjEy6RLdy0oJ\ncwvD0b2bfDRBQICxu2xUWVlyEldeLl9bWspJXLduxu2X0DkZZPsRrVZLTk4Orq6uqDr4v8BEIicI\ngtA0FVotBwsLOVlUhE6SqNBUEpcdh+WNNMadO0ev2xmoLewJcwvDMmKAvKDB2trY3TaqjAxYtw5q\nKoysrGDhQvDxMW6/hM6rXWvkvvvuO2JjY1GpVHh4eKBSqYiNjWXdunXNfqBgukR9hWGJeBuWKcZb\nkiTOFRfzaUYGxwsL0UkSBRUFxKSdpO/JYyzdt5vw7HR8bT2J8BuC5czZ8tEEBkriOmrM09Plkbia\nJM7aGp5+uvMncR013qbKoDVyy5cvJyYmpl6br68vU6dOZeHChW3SEUEQBMFwblRUsDsvj8zKSgAk\nILM4g/LEkzx78DAehQUoUNDDpSdePQeimDULXFyM2+kOIC0NNmyAO2HDxkZO4uqcXikIBtWkqVW1\nWk1OTk696VSNRoOLiwuFNVtXdzBialUQBOFuRRoN+/LzuVCzxBLQSToyci7T49huhsbEowDMleaE\neYTjNGEyjBvXpRc01EhNlZO4qir52tZWTuI8PIzbL8E0tOvJDr169WLr1q3MmTNH37Z9+3Z69erV\n7AcKgiAIhqfR6ThWVMThwkKqdTp9u1ZXhTY5mmm/7MCxQE7u7C3sCQsejtWTT3W+PTTayfXrsHEj\nVFfL13Z2chLn7m7cfglCk0bkjhw5wmOPPcbDDz9MYGAgSUlJ7Nu3j127djFq1ChD9LPZxIic4Ylz\n+gxLxNuwOmu8JUniSlkZv+Xnk1+ThdzhThlm+78m+Nh5lDr589LTzpMeI6ehevwJed7QiDpKzJOS\n4PvvQaORr+3t4ZlnoMH2qp1eR4l3V2HQs1ZHjRrFxYsX2bhxI+np6QwZMoS///3vdBNrrAVBEDqs\n7DvbiSTX7I9xh4eFBb5FiWjW/wN1Ri4AChQEuofgO+d3KAYM6LInNDSUmAibNtUmcQ4OchInygWF\njqLZ24/cunULb2/v9uxTmxAjcoIgdFXlWi3RBQWcLi5GV+dz0FqlYoyjPaXHN6P78T9YVMgjdOZK\nc0L6jMN14XOmN8zUClevwubNoNXK146OchLn7GzcfgmmqV1H5PLz83nxxRfZunUrZmZmlJWV8fPP\nP3Pq1Cn+8pe/NPuhgiAIQtvT3dlOJKqggLKa7AP5L4jB9vYMMpc4v2Ylludi9V+zs7Aj9PEl2E2a\nJhY01JGQAFu21CZxTk5yEqdWG7dfgtBQk/aR+/3vf4+DgwOpqalYWloCMHz4cDZt2tSunRM6F7EH\nkWGJeBtWR493akUFX2RmsjM3t14S193amt97e9Ov4AYXVj9XL4lzdg8g4v99hN3k6R0yiTNWzC9f\nrj8Sp1bDokWmn8R19J9xU2PQfeT2799PVlYW5ubm+jY3Nzeys7PbpBOCIAhCyxRqNPyWl0d8aWm9\ndiczMx5xdqaXtTUJu9eRtW0tZnUSPO/B4+mx6FUUtraG7nKHFh8P//kP1CzsdXaWR+IcHY3bL0Fo\nTJNq5IKDgzl06BDe3t6o1Wry8/NJS0vjkUce4cqVK4boZ7OJGjlBEExZtU7H0cJCjhYV1dtOxFyp\nZLSjI8MdHFCWlnDh36spjDuj/7rSwpLAuf+F77jHxYKGBi5ehO3ba5M4Fxc5iXNwMG6/hK6hXWvk\nli5dyqxZs/jLX/6CTqfj+PHjvPnmmzz//PPNfqAgCILQcpIkcamsjN/y8iisWUp5Rx87Ox5Sq3E0\nM6P0UiyXvnyP0vxb+q8rPL3o+8JqnPx6GLrbHV5sLPz4I9T8PerqKidx9vbG7ZcgPEiTauRef/11\n5syZwx/+8Aeqq6t59tlnefzxx3n55Zfbu39CJyLqKwxLxNuwOkK8b1VV8d3Nm/yQnV0vifOytGSx\nlxcz3dxwBHK2refCR6/VS+KUI0YyfNXXnSqJM1TMY2LqJ3Hu7nJNXFdL4jrCz3hXYtAaOYVCwUsv\nvcRLL73UJg8VBEEQmq5Mq+VAQQFniovrTb3YqFRMUKvpb2eHUqGA7GzSv/07yVeOo5Pk+cEqawuc\n5y1h4KjZKMRU6l3OnYMdO2qTOA8P+cQGUToodBZNqpGLiooiICCAwMBAsrKyeP3111GpVLz//vt4\nGuGk4Ndff53jx48TEBDAN998g5nZ3fmoqJETBKGz00kSp4uLiS4ooLzOQgWlQsEQe3sinZywUqlA\nktCeOknSpn+RmZ+q/74if0/Clr5JULe+xuh+h3fmDOzcWXvt6SkncUY+0ELoolqatzRpavWFF17Q\nJ0uvvPIKGo0GhULBc8891+wHtlZsbCyZmZkcOnSI0NBQtm7davA+CIIgtLfk8nI+z8xkd25uvSQu\nyNqa//L2ZpKLi5zElZZSsX4NF7/9qz6J06mU5IwbyqjX/ymSuEacOlU/ifP2lmviRBIndDZNSuQy\nMzPx8/OjurqaPXv28O9//5vPP/+co0ePtnf/7nL8+HEmTpwIwKRJk4zSB+HeRH2FYYl4G5ah4p1f\nXc3m7GzW3rxJdlWVvt3Z3JynPDxY4OGBm4WF3JiURNH/fsD56E0UVBQAUKK2pejpuUydvxpnm859\njlR7xfz4cdi1q/bax0ceibO2bpfHdRriM8WwDFoj5+DgwM2bN4mPjycsLAx7e3sqKyupbnAAsyHk\n5+fj5eWl71deXp7B+yAIgtDWqnQ6jhQWcqywEE2d6RULpZIxjo4Mc3DATHnn394aDezfz629P5KQ\nm6Cvh0vv7YvfzMWMDhwn6uEacfQo7N1be92tG8yfD1ZWxuuTILRGk0bkli1bxpAhQ5g3bx4vvPAC\nAEePHqVXr14tfvBnn33GoEGDsLKy4tlnn633tby8PKZPn46dnR0BAQF8//33+q85OTlRVFQEQGFh\nIc7i0LsOIzIy0thd6FJEvA2rveItSRIXS0r4LCODQwUF9ZK4CDs7lvn4MMrJqTaJu30b3ZdfkPTL\nei7nXEYn6aiyMufKxEEMWfI2Y4LGm0wS19YxP3y4fhLn5wcLFogkrob4TDGstop3k0bkXn/9dZ54\n4glUKhXBwcEA+Pr68tVXX7X4wT4+Pixfvpw9e/ZQXl5e72svvvgiVlZWZGdnc/78eSZPnkxERAS9\ne/dmxIgRfPzxxyxcuJA9e/YwatSoFvdBEATBmDIrK/k1L4+0iop67T6Wljzq7Ixv3QxDkuDsWap2\n7eRyZiz5FfkA5Po4k/PwSGYNeQZXG3HgfWMOHoQDB2qvAwJg3jyomaUWhM6qSSNyACEhIfokDqBn\nz5706dOnxQ+ePn06jz/+OC4u9Ws4SktL2bZtG++88w42NjaMHDmSxx9/nHXr1gEQERGBh4cHY8aM\n4fLly8ycObPFfRDalqivMCwRb8Nqy3iXarX8nJPDl1lZ9ZI4O5WKJ1xdWerlVT+JKyuDzZsp2baJ\nc2knyK/IR6dScm1IMFVzZvHMqBdNMolri5hLkpzA1U3iuncXSdy9iM8Uw2r3GrnQ0FD98VvdunW7\n5/coFArS0tJa1YGGS22vXr2KmZlZvaQxIiKi3hv+8MMPm3TvRYsWERAQAMhTsv369dMPZdbcT1y3\n3XVMTEyH6o+pX4t4d754jx47llNFRXyzezfVkkTAsGEApJ08SW8bG5ZNmYKlUln/9cnJRP/P/5Cf\nl4mLTTE6ScfxkgpS+/oz97EFjPUfy8GDB40en/a4rtHS148dG0lUFKxbJ18HBEQSFAReXtEcO2b8\n99fRrmt0lP6Y+nVMTAzR0dGkpKTQGo3uI3f48GFGjx5d76H3UtOxllq+fDnp6el8++23+ufOnj2b\nrKws/fd8+eWXbNy4kQN1/0n1AGIfOUEQOpLEsjJ+zcsjp8EisZ42Nkx0dsbF3Lz+C7RaiIpCd/Qo\nKfnJpBWmAZAR6sONoaE80edJQl1DDdX9TkeSYN8+eXFDjeBgmDsX7rH1qCAYXZuftVqTxEH7FkA2\n7LSdnZ1+MUONwsJC7LvaWSmCIJiE3Opq9uTlcbWsrF67i7k5k5yd6XGvjctycmDbNqpvpHI55zJ5\n5XlUWZmTMDIUqWcPloQ/hZutm4HeQecjSfDbb/I2IzV69oTZs0USJ5ieRn+kly9f3mh2WNOuUChY\nvXp1qzrQcHVVz5490Wg0JCYm6qdXY2NjCQ8Pb9VzhPYXHR3drkm/UJ+It2E1N96VOh2HCgo4UVSE\nts7nqKVSyVgnJ4Y6OKBquLpUkuD8edi9m9LSAuKy4yjXlJPn48yVUaH4+4Qxs/dMrMy6xjLLlvyM\nSxL8+iucPFnbFhoKTz4JKlXb9s/UiM8Uw2qreDeayN24ceO+S9hrErmW0mq1VFdXo9Fo0Gq1VFZW\nYmZmhq2tLTNmzODtt9/mq6++4ty5c+zYsYPjdf9p1UQrV64kMjJS/GAKgmAwkiQRW1LCvvx8Suqc\nyKBQKOhvZ8d4Jyfs7jUsVF4OP/8Mly9zu/Q2V3KuUK3QkTwkmPRePoz2H8O47uNQKpQGfDediyTJ\nG/2ePl3b1rs3zJwpkjih44qOjr5vCduDNOms1fawcuXKu0bzVq5cydtvv01+fj6LFy9m7969uLq6\n8te//pW5c+c26/6iRk4QBENLr6hgd14eGZWV9dq7WVnxqLMz3paW937h9euwfTtSYSEpBSmkFqZS\n6mjD5bG9qXRV80ToE4S5hxngHXRekgQ7dsC5c7VtYWEwY4ZI4oTOoaV5S6OJXHJycpNuEBgY2OyH\nGoJI5ARBMJRijYb9+fnElJTUa7c3M+NhtZo+trb3nsHQauV9MY4eRaOt5vLty+SW55IR4k3S4CAc\n7VyZGz4XDzsPA72TzkmnkwczY2Jq2/r0genTQSkGMIVOos0TOWUTfvoVCgXaOlMHHYlI5AxP1FcY\nloi3Yd0r3hqdjpPFxRwsKKBKp9O3qxQKRjg6MtrREYvGPktzc+E//4HMTMqqy4jLjqNQWc2VkSHk\n+rkSpA5iVu9ZWJt33QNAm/IzrtPBTz9BbGxtW0QEPP64SOKaS3ymGFbDeLf5qlVdnQ8lQRAEoZYk\nSVwtL2dPXh55DbYTCb2znYi64XYitS+Wh45274aqKnLKcrh8+zK3vRy4MiqCKhtLRnQbwUOBD4l6\nuAfQ6WD7drh4sbatf3+YOlUkcULXYbQaufamUChYsWKFWOwgCEKbyqmq4te8PBIbHC3oZmHBo87O\nBFrfZwStvBx27oT4eCRJIrUwleSiVJIHBpLe2xdzlQXTQqbRx6Plp+Z0FVotbNsG8fG1bQMHwpQp\nYCJHzQpdRM1ih1WrVrXt1OrEiRPZs2cPUH9PuXovVig4dOhQsx9qCGJqVRCEtpCQnMy++HjKdDpS\nKipQenvj4uur/7qVUsk4tZpB9vZ3bydSV2qqnHkUFqLRabiSc4U08zIujelFiYs9TlZOzAmbg5e9\nlwHeVeem1cLWrXD5cm3b4MHw2GMiiRM6rzafWn366af1v1+yZEmjDxWEGqK+wrBEvNuXRqfj5LVr\nrDl7lqr+/Yk7ehT7gQPRnDlDP0nCzc+PgXZ2jFOrsb3fskitVj6x/fBhkCTKq8u5mH2RxEAnEgf3\nQmemortTd54MexIb83tsDtyF3bMuUQM//AAJCbVtQ4fCpEkiiWst8ZliWO2+j9z8+fP1v1+0aFGr\nHyQIgtARVep05FRXc7uqSv61upqc6mryNRpOHD9OWUQEVFToN/U1GzSIgvh4/jx0KJ6NbSdSIy9P\nHoVLT5cvy/OILbpG/OggcvzkQ+6H+Q7jkaBHRD1cE2g0sGULXL1a2zZ8ODzyiEjihK6ryTVyhw4d\n4vz585SWlgK1GwK/+eab7drBlhJTq4Ig1JAkiVKttl6iVvNrkUbT6OtOHDpERd+++mtLpZIgKyt6\nXL3KH6dOvd8D5WWUu3ZBVRWSJHGj6AbnLPO5PDqUSltLzJRmTO05lQjPiLZ8qyaruho2b4bExNq2\nkSPhoYdEEieYhjafWq1r2bJlbNmyhdGjR2N9v0LeDkac7CAIXYskSRRoNPdM2MqbuVWSQqHATqHA\nxtwcG6USO5UKN3NzVAoF9x2Hq6iQFzTExQGg1Wm5nH+Vkz1tuRHeFxQKHCwdmBs+F29775a/2S6k\nuho2bYKkpNq2MWNg3DiRxAmdn0FOdlCr1cTHx+Pt3Xk+dMSInOGJ+grD6srx1koSuQ3Sp/j9AAAg\nAElEQVQStdtVVeRqNFQ3c+sklUKBs7k5bubmuNb51dXcnOSUFNacO4flwIGknDhBwLBhVJ49y6IB\nAwi512boaWnyVGpBAQDl1eWcqbzOqWHdKHa1B8Df0Z8nw57EzsKu1XEwddHR0YwYEcn338uHX9SI\njISxY0US19a68meKMbT7PnJ1devWDQsLi2bfXBAEoTVq6tca1rDlazTomvmBZ6FU1kvUan5V3xll\nu5eQwEAWAfvj4si5fh13Ozsm3CuJ0+nkBQ2HDsnTqkB+eT5R6gIuRYagNZcXQwzxGcLEoImolOLM\nqKaoroYNG+QFvzXGj5dH4wRBkDVpRO706dO89957zJs3Dw+P+kfFjOmgf6LEiJwgdA6SJFGm0921\n2OD2A+rXGmOrUtVP2CwscDU3x0Glap+V9vn58ijcjRuA/H7Sqm6zM0TB7QA3AFQKFZN7TmaA14C2\nf74JSkhIZffuJE6cUFJUpCMwMAhXV38eeghGjTJ27wShfbTriNzZs2fZtWsXhw8fvqtG7sadDy9B\nEIT7kSSJQo3mrtq12y2oXwNwMjOrl6jVJG42hjwh/cIF+OUXqKwE5Hq485b57B3sQKWdFQD2FvbM\nCZ+Dr4Pv/e4k3JGQkMpXXyWSkDCBoiK5LSZmPy++CKNG+Ru3c4LQATUpkXvrrbfYuXMnDz/8cHv3\nR+jERH2FYXXUeGslibx7LDbIqa5udv2aUqHApcFUqJu5OS7m5o2fYdpO6sW7okJekXrhgv7r5boq\nfvOrJibQBUkpj/x1c+jG7LDZ2FvaG7SvndmuXUlcuTKB4mIoKIjGySmS0NAJ5OdHASKRa08d9TPF\nVLX7PnJ12draMnbs2FY/zNDEqlVBaD9VNfuvNahhy2uD+rWa39+vfs1obtyQD7u/s6ABINdKYnOo\ngmwnW33bQK+BPNrjUcyUTfqYFYCyMjh2TElxcW1bjx7g4wNVVWKfPcE0GWTV6po1azh16hTLly+/\nq0ZO2UFPJhY1coLQNvT7rzWoYStsQf2azZ0tPGoStXavX2sDqQkJJO3bh7KyEl1aGkFaLf4uLoA8\nXXzNz44t/sVozOTPQpVCxaM9HmWQ9yBjdrvTKS2Fdevg55+jKCsbD0DPnlCzWYK7exQvvDDeiD0U\nhPbV0rylSYlcY8maQqFA24LaFkMQiZwgNF1N/dq99l8ra4P6tZrEzaD1a20gNSGBxDVrmKDTwZUr\nUFjIfo2G4H798PXxJjrcjsP2efrvt7OwY3bYbPwc/YzY686ntBS++w6ysyEnJ5XY2ER6956A151j\nZysr97NoUTAhIWJqVTBd7ZrIpaSkNPq1gICAZj/UEEQiZ3iivsKwWhLvmvq1eyVsnbl+rc1JEmRl\nEfXBB4xPTobiYqILCoh0cgLgVy8Psn4/glQpX/8SH3sf5oTPwcHSwVi97pSKi2HtWrh9W75WKKBv\n31SyspK4dOkCvXv3ZcKEIJHEGYD4DDcsg+4j11GTNUEQ7q2q7v5rdWrYWlO/1jBh65D1a61RVQXJ\nyfJBnteuQXExyoQEeWFDDYWCEi9XjrmUoqyTxPX37M/knpNFPVwzFRXJI3G5ufK1QgEzZkCfPv6A\nP9HRSpFYCMIDNPms1c5GjMgJpiohOZl98fFUAzqdjn4hITj6+NSrYWuL+rWaqdGOXL/WaoWFcuJ2\n9ap8dECDuEWdOsX4sjJQKJCcnLjlak1CdRY/O9lg/8RglAolk4InMdh7sOnGqJ0UFspJXN6dmWml\nEmbOhLAw4/ZLEIylXadWOyORyAmGIEkSWklCC2hqfi9J+t9r7nytblvd9nu9pm57w6+np6Vx+NIl\nlAMGUKHTUS1JaM6coV9ICK7dujWpz45mZndtltsZ69daRJIgI0NO3BIS4Natxr/XxoZUa2tOHzmE\nX1UhWeW3KakqIdbeAd3M4XgEyUdtBTgFGKz7pqKgQE7i8u8MaiqVMGsW9O5t3H4JgjG169RqZyW2\nHzGMmhGiyxcv0qtPHx4KC7v3OZStUDdhuitRMmAida97GdKpS5coi4gArZaCM2dwGjQIs0GDuB4b\nWy+RUyoUOJuZ3bVZrqsp1K81V2WlPGWakCBPmZaWNv697u7yUsmQEPDxoTzpKjtuHqE0M5PbRfk4\n+NiQaysx0cGN5wY+h6OVo+Heh4nIz5eTuJrdW1QqePJJCA29+3tFzZZhiXgbVk28W7v9iMknckL7\nupKUxN9OnsRs4EDS8/Op7NGDk8eOMbG4GB8/vzZNpATQNZi+UyoU2CiVuFlZMV6t1idszqZWv9Zc\n+fm1U6YpKdDYyluVCrp3l5O3Hj1ArdZ/Ka0wjb/8/BfSe9yCHq4UXjFDEepEN1sPnMudRRLXAnl5\nsGYN+hMbVCqYM0cOvyB0VTUDTqtWrWrR65uUyCUnJ/PWW28RExNDSUmJvl2hUJCWltaiBwumYf+l\nSyT16oVUVgZ9+nCtvBzCwth4/jyDHbrG6j2VQoFKocCs7q932mva6rbf1Xaf9ob3Utjbk29nh1Kh\nwGL8eCyVShSAu60tY+6sqOySdDpIT6+dMq1ZAnkvtrZy5tCzJwQGgqVlvS9nFmcSdT2KxLxEcspz\n9O3qUDVBzkH42PugvdUxt13qyHJy5JG4ms1+zcxg7lwIDm78NWJ0yLBEvA2rreLdpERu3rx5BAcH\n8/HHH9911qrQtVVz73l9bTuMBt0v+blXwnRXQtTERKo591IpFAYtcp/brx9rzp3DcuBAfVvl2bNM\nGNAFD2OvqICkJDlxS0yUjwVojKdn7ZSpt7e8PLKB7NJsDlw/wOWcy/o2JUoUKPCy98LP0Q8rM/n8\nVAulRZu/HVN2+7acxNWMA5ibw1NPyXm0IAit06RE7tKlSxw9ehRVVyiGFprFHHA3N0cCbp86hefQ\noSgBtZUVY52cmpxIPWgUy9AJU0cVEhjIImB/XByXLl6kd58+TBgwoM1rEjus3NzaKdPUVHkk7l7M\nzOQp05AQecrUsfFp0NyyXKJToonLjkOi9h8kChQ8OvhRkpOScQxwJCUmhYB+AVReq2TCuAlt/c5M\nVna2nMTVlCaam8P8+dCUXa1EzZZhiXgblkHPWh0zZgznz59n0CBx5IxQ30NhYaTfGSGytrIiwNqa\nyrNnWTR4MCF16o2EthMSGEhIYCDR9vam/6Gr1cpnm9Ykbzk5jX+vvX3tlGn37mBx/1GzgooCDqYc\nJPZWLDqpfkIY5hZGZEAkbrZuJCQmsP/cfnLycnDPdmfCuAmEBIe0xbszeTdvypv91gyWWljISZy/\n2NtXENpMk7YfefHFF9m8eTMzZsyod9aqQqFg9erV7drBlhLbjxhOQnIy++PjqQIsgAntsGpV6ELK\ny+Wp0pop07ob8jbk7V2bvHl53XPKtKHiymIOpx3mbOZZtFL9WreeLj0Z3308nnaerX0XXV5WlpzE\nlZfL15aWsGABNHGXHEHoctp1+5HS0lKmTJlCdXU16enpgLwdhJjqEqB2hEgQWkSS5JG2mlG3Gzca\nnzI1N5cLq2qmTO3tm/yY/9/enYdFWe7/A3/PsO+LAgKyqARqpJSlaYoImllaaWVa4kKn+qb1q07L\nOS0qHu3q6nvazmnzHMtySTRbvqUtWsAorrQgleICCijuIjuyzDy/Px5hGBYdhpn7meX9ui6ufO5n\nmOfDp4k+Pvfnue+6pjrsKN2B3LJcNOsMF/7tH9Af46LHIcKPVYY5lJUBa9boa3B3dyA1FQgPVzYu\nIntkVCH3ySefWDgMy+A6cmKxv0Ism863Viv3uLUUb+XlXb/Wz09/1y06Wi7muuFS8yXsOr4Le07s\nQaO20eBchG8Ekvslo19Av6u+j03nW6Djx4G1a+Xl+wDAw0Mu4sLCuv9ezLlYzLdYFl9Hrri4uHWP\n1aNHj3b5Bv2t+E4M15EjsiK1tfop06Ii/f/p21Op5Fs3LcVbSIhRU6btNWobsffEXuw8vhOXmg2n\nZ0O9Q5HcLxkxgTGcWTCj0lK5iGu8XC97egKzZ8sPDRNR53q6jlyXPXI+Pj6ovrzgj7qLleBVKhW0\nXS20qTD2yBEpTJLkRxZb7rqdOCGPdcbVFRgwQJ4yjYkBvL1Nvmyzrhm/nPwFOSU5qG0y3MUhyDMI\nyf2SMbD3QBZwZlZcDKxbpy/ivLzkIq5NWzURXQH3Wm2HhRyRApqb5f+jtxRvLfswdcbfXy7cYmPl\nxxide7bRjFanRd7pPGwv2Y6qhiqDc4EegUiKTkJ8cDzUKgfbokyAY8fkIq6pST729gbmzAGCgpSN\ni8iWcK9VUhz7K8SymnzX1Mh7mB46JO9p2tjY+etUKvmRxZYp06Agk6ZM29NJOvx+5ndsK96Gi5cu\nGpzzc/PD2OixGBoyFE7qnq2DaTX5tjJFRUBGhlzDA/LzJ3PmAL179/y9mXOxmG+xhK4jR0TUSpLk\nBcJa7rqVlXX9Wjc3eaq0ZcrU09OMYUg4cO4Asouzcb7OcH05b1dvJEYl4obQG+Cs5q85SzlyBNiw\nQV/E+frKRVyvXsrGReRIOLVKRFfX1CTPn7UUb1VVXb82MFA/ZRoZKe+MbkaSJOHwhcPILs7G6ZrT\nBuc8nD0wOnI0hocPh4tT955upe45dAj47DP5AWRAfrh47lyA64ATmYZTq0RkXlVV+inTY8f0DVDt\nqdVywdYyZdqrl1mmTNuTJAnHKo4h61gWTlSdMDjn5uSGURGjcHPfm+Hm7Gb2a5OhggJg40b9cn/+\n/nIR5++vaFhEDqnbhZyu3UKdXT3RSo6H/RVimT3fkiQvx3/okHzX7dSprl/r4SFPlcbGyv/08DBf\nHJ0orSxF1rEsFFcUG4y7qF0wou8I3BJxCzxcLBsDP9+y/fuBL77QF3GBgfJ06hW2szUZcy4W8y2W\n0B65X3/9FY8//jjy8/Nxqc12Oda8/AgRGaGxUX5AoWXKtKam69f27q2fMo2IkO/EWdjJ6pPIOpaF\nwvJCg3EnlRNuCr8JoyNHw9vV9KVKqHv++AP46it9Ederl1zE+foqGxeRIzOqRy4+Ph533nknZs2a\nBc92zcotiwZbG/bIEXWhslJfuB07pu9Ub0+tlndSaJkyDQwUFuLZ2rPIPpaNgvMFhiGp1Li+z/VI\njEqEn7sFbgFRl/Lzgf/7P/1SgL17y0VcN3ZJI6IrsOg6cr6+vqisrLSpBTRZyBFdptMBJ0/qp0zP\nnOn6tZ6e8h6msbHyAr3u7uLiBHCh7gI0xRr8efZPSND/96uCCkNChmBs9FgEeogrKEmWlwd8842+\niAsOlhf77cG6zUTUjkUfdpg6dSq2bNmC2267rdsXUBL3WhWL/RViXTHfDQ3yAl+HD8sPLNTWdv46\nQP6/cmysPG0aHi5kyrS9iksV2F6yHftO74NOMuzDvTboWiRFJyHIS9nVZR318/3rr8CmTfrjkBC5\niPPysvy1HTXnSmG+xbL4Xqtt1dfXY+rUqRgzZgxC2uy3olKpsHr1apMvbmnca5UcysWL+inT4mL9\nuhDtOTkB/frpp0wVfNSwuqEaOaU5+PXkr9BKhvHG9orFuOhxCPUJVSg6+vln4Ntv9cehoUBqqlmX\nAyRyeBbba7WtrgoilUqFxYsXm3RhS+PUKtmrkkOHUPTTT1A3NEBXW4sB4eGIqq8Hzp3r+pu8vPSF\nW//+8kK9CqprqsOO0h3ILctFs86wR69/QH+Mix6HCL8IhaIjANi7F/j+e/1xWJhcxFn4AWUih8W9\nVtthIUdWT6uVnxptapL/2fbP7f95+c8lR4+icOtWpKhU8kMLzc3IbG5GTEICotrvidSnj37KNCzM\nImu7ddel5kvYfXw3dp/YjUat4VZeEb4RSO6XjH4B/RSKjlrs2gVs3ao/7tsXmDVLeMskkUOx+ILA\n2dnZWL16NcrKytC3b1/MmjULycnJ3b4g2S+77K9oX2x1UWAZfa7tn9utyWiMotxcpNTVAQA0FRVI\n8vdHirMzso4dQ1SfPvLdtthY+YEFSyzsZaJGbSP2ntiLXcd3ob653uBcqHcokvslIyYwxqofqLLL\nz3cnduwAfvpJfxwZCTz4oDI3cR0l59aC+RZL6DpyH374IV588UX85S9/wYgRI1BaWooHHngA//jH\nP/DII4/0OAiiHtFqe15gdXXOytZJVLcv/lxdgV69oI6OBp5/Xj62Is26Zvxy8hfklOSgtsnwgYsg\nzyAk90vGwN4DrbqAcyTbtgHZ2frjqCi5iLOyjxURtWHU1Oo111yDzz//HEOHDm0d+/333zFt2jQU\nFhZe4TuVw6lVcVp7tpqaoHNxwYDx4xEVF2f4Ip3OvAVW239aWbFlNLUacHGR/y/Z/p+djbm4IOvr\nr5FcXS1/r4eHvP6DSoWs4GAkz5+v9E/USqvTIu90HraXbEdVg+G+rIEegUiKTkJ8cDzUKu4MYw0k\nCdBo5EKuRb9+wMyZLOKIRLHo1Gp5eTkGDRpkMBYXF4eLFy92+4JkX0oKClD48stIUavlgkqrRebm\nzcCwYYgKDLT9YkulumJh1aNzTk7d7lsbEBSEzE8+QUqbea7MhgbEpKSY+yc3iU7S4Y8zf0BTrMHF\nS4a/H/zc/DA2eiyGhgyFk9pJoQipPUkCsrKAnBz92IABwIwZ8keViKybUXfk7rzzTkRGRuK1116D\nl5cXampq8MILL6C4uBib2i4wZEV4R06MrHffRfIXXwCS1NqzBQBZXl5IvukmMUG0FFs9LazMVGxZ\nWsmhQyjKzMTvBw5gyODBGJCS0vEOqGCSJOHAuQPILs7G+brzBue8Xb0xJnIMhoUNg7O629s7Ww17\n7B+SJODHH+WHG1rExMhFnLMV/Kuyx5xbM+ZbrPb5tugdueXLl2PGjBnw8/NDYGAgysvLMWrUKGRk\nZHT7gmRf1M3N8jRfuztu6vZ34NoXW+Ysuqyw2LKkqLg4RMXFQW0Fv3QlScLhC4eRXZyN0zWnDc55\nOHtgdORoDA8fDhcn3tqxNpIEbNkC7NmjH4uNBaZPt44ijoiM063lR44fP46TJ08iLCwMERHWvcYT\n78iJkfXee0jev18+cHKSv9RqZIWEIPnRR/VFl4MVW/ZOkiQcqziGrGNZOFF1wuCcm5MbRkWMws19\nb4abs7Lr1VHnJEleIy43Vz82cCBw333yf6pEJJ7Z15GTJKn1STLdFZZJUCuwnY8xWMiJUXLoEAo7\n69maO1fx6T6yjNLKUmQdy0JxRbHBuIvaBSP6jsCoiFHwdOHS/9ZKkoDNm+Wtt1oMHgzccw+LOCIl\nmVq3dFmF+fr6tv7Z2dm50y8XdsI6vKi4OMTMnYus4GC8ff48soKDWcQJ0pO9+Uxxsvok1v6+Fivz\nVhoUcU4qJ9zc92Y8efOTGN9/vN0WcaLzbQmSBHzzjWERFx8P3HuvdRZx9pBzW8J8i2WufHfZCbG/\nZboMwNGjR81yMbJP1tSzReZ3tvYsso9lo+B8gcG4WqXG9X2uR2JUIvzcrWfxYeqcTgd8/TWQn68f\nGzIEuPtuuc2ViGyTUT1yr7/+Op599tkO42+++Sb++te/WiSwnuLUKlHPXKi7AE2xBn+e/RMS9P8t\nqaDCkJAhGBs9FoEegQpGSMbS6YCvvgL++EM/lpAA3Hknizgia2HRvVZ9fHxQXV3dYTwgIMBq15JT\nqVRYvHgxkpKSeJeIqBsqL1ViW8k27Du9DzrJsD/22qBrkRSdhCCvIIWio+7SaoEvvwTaTLJg2DBg\n8mQ+f0RkDTQaDTQaDZYsWWL+Qi4rKwuSJGHKlCnYvHmzwbmioiIsW7YMJSUl3Y9aAN6RE49rEIll\n7nxXN1QjpzQHv578FVrJcPmY2F6xGBc9DqE+oWa7nq2xxc+3Vgt8/jlQ0GZW/KabgNtvt40izhZz\nbsuYb7GErCOXlpYGlUqFhoYGPPTQQwYXCwkJwTvvvNPtCxKRdalrqsPO0p3ILctFk67J4Fz/gP4Y\nFz0OEX7WvdwQddTcDGzcCBw6pB8bMQK47TbbKOKIyDhGTa2mpqZizZo1IuIxG96RI7qyS82XsPv4\nbuw5sQcN2gaDcxG+EUjul4x+Af0Uio56orkZ2LABOHJEPzZyJHDrrSziiKyVRXvkbBELOaLONWob\nsffEXuw6vgv1zfUG50K9Q5HcLxkxgTGt60iSbWlqAtavB4qK9GOjRwMpKSziiKyZ2deRa6uyshJP\nP/00brjhBkRFRSEiIgIRERGIjIzs9gXJfnENIrG6m+9mXTP2nNiDf+35FzKPZRoUcUGeQbj/2vvx\nyLBHcE2va1jEdcIWPt9NTUBGhmERl5hou0WcLeTcnjDfYll8Hbm2FixYgOPHj2PRokWt06z//Oc/\ncc8995glCCKyHK1Oi7zTedhesh1VDVUG5wI9ApEUnYT44HioVVyHwpY1NgLr1gHFxfqxceOAsWMV\nC4mIBDBqajUoKAgFBQXo3bs3/Pz8UFlZibKyMkyZMgW//fabiDi7jVOr5Oh0kg5/nPkDmmINLl4y\nXCbIz80PY6PHYmjIUDiprXBJf+qWhgbg00+B0lL9WEoKMGaMcjERUfdY5KnVFpIkwc9PXrndx8cH\nFRUVCA0NxZG2nbREZBUkScKBcweQXZyN83XnDc55u3pjTOQYDAsbBme1Uf/5k5W7dEku4o4f149N\nmADccotyMRGROEbNpQwZMgTbt28HAIwePRoLFizA//zP/yCO+2lSG+yvEKt9viVJwuELh/GfX/+D\njQc2GhRxHs4emNB/Ap4c8SRG9B3BIs4E1vj5vnQJWLPGsIibONF+ijhrzLk9Y77FEtojt2LFitY/\n/+tf/8KLL76IyspKrF692ixBEJHpJEnCsYpjyDqWhRNVJwzOuTm5YVTEKNzc92a4ObspFCFZQn09\nsHo1cOqUfuz224Hhw5WLiYjEM6pHbu/evRgxYkSH8dzcXAy30t8a7JEje3Wo8BB++vUnNElNqL5U\nDefezmjwNlwHzkXtghF9R2BUxCh4ungqFClZSl2dXMSdPq0fmzwZuPFG5WIiop5RZK/VwMBAlJeX\nd/uiIrCQI3t08MhBfJj5IZqjm1FaWYry+nI0FzYjYXACeof1hpPKCTeG3YgxUWPg7eqtdLhkAbW1\nchF35ox8rFIBU6YAN9ygbFxE1DMWedhBp9O1vqlOZ7h5dlFREZyd2WdDetynz3RanRY1jTWoaaxB\ndWO1/s8N1QZjmZmZqO1bC5wGKg5WwH+gP5xjnHHs6DFMHDYRiVGJ8HP3U/rHsUvW8PmuqQFWrQLO\nnZOPVSrgrruAhARFw7IYa8i5I2G+xTJXvq9YibUt1NoXbWq1Gi+99FKPAyCyZw3NDR0KtJbirO1Y\nXVOdUe+nhbbDWIhXCIZEDsGUuCnmDp+sSHW1XMSdv/wMi0oFTJ0KDBmibFxEpKwrTq0WX15ZMjEx\nETk5Oa1351QqFYKCguDpab29N5xaJUuRJAl1TXVd3j1rO96obTTrtXN35KIxshGuTq7wcfVBpF8k\nvFy9EHw2GPOnzzfrtch6VFbKRVxLJ4taDUybBsTHKxsXEZmPRaZWo6OjAQClbVeZJLJTxk5v1jTW\nQCfprv6G3aCCCl6uXvBx9YG3qze8Xb3h4yb/ue1YWWgZPt3+Kdyu0T+B2nCkASnjUswaD1mPigq5\niLt4eU1ntRq4915g8GBl4yIi62DUww6pqakdv/Hyxn3WugQJ78iJZ639Feae3uwOZ7WzQTHWUpy1\nH/N08TR6i6xDhYeQ+VsmDvx5AIPjByPlhhTExXBNR0tT4vN98aJcxFVUyMdOTsB99wEDBwoNQzHW\n+jvFXjHfYrXPt0V3dhgwYIDBBU6fPo0vvvgCDz74YLcvSGQOSk5vAoC7s7tRBZqbk5vZN6CPi4lD\nXEwcNMH8pWvPysuBTz4Bqi5vj+vkBNx/PxAbq2hYRGRljLoj15lffvkF6enp2Lx5s7ljMgvekbNN\ntjC96e3qDRcnF7Nem6itCxfkIq5l1SdnZ2DGDCAmRtGwiMiCLLqOXGeam5sREBDQ6fpy1oCFnDht\nF6h1Ublg/LDxHab6rGl6s6sCzcvVy+jpTSJLOXdOnk6tqZGPXVyAmTOB/v2VjYuILMuiU6uZmZkG\n00O1tbVYv349rr322m5fsKeqqqowfvx4FBQUYO/evRjMjl9FFRwpwAdbPwD6A8X7itF7cG9s/2I7\nRg0dBd8QX8WnN71dveHu7G726U1rwH4WsUTk++xZuYirrZWPXVyABx8ELj935nD4GReL+RZLyDpy\nLR566CGD/xF6eXkhISEBGRkZPQ6guzw9PfHdd9/hueee4x03K7D1l6343et34AxQUVmBqotVQIg8\nftPom7r9fi3Tm8YUaJzeJHty+rS8Y0Pd5ZvSrq5yERcVpWxcRGTdjCrkWtaTswbOzs7o3bu30mHQ\nZZJKgpPKCVpJC/+B/q3j7ReuvdL0ZttxTm8aj39zFsuS+T51Si7i6uvlYzc3YNYsICLCYpe0CfyM\ni8V8i2WufBu9x1ZFRQW+/fZbnDx5EmFhYbj99tsREBBgliDIdrmoXODn7gedpIOrkytcnVzh5uSG\nkKYQzBk6x+6nN4l6qqwMWLMGuHRJPnZ3l4u4vn2VjYuIbINRtz6ysrIQHR2Nf//73/j555/x73//\nG9HR0fjpp5+6dbF3330XN954I9zd3TFv3jyDc+Xl5Zg6dSq8vb0RHR1tMG371ltvYdy4cXjjjTcM\nvoeFgfLGDxuPuKo4JPRJgGeZJ2ICYxB8Nhhzxs1Bv4B+CPIKgoeLB/9dWYBGo1E6BIdiiXyfOCHf\niWsp4jw8gNmzWcS14GdcLOZbLHPl26g7cgsWLMB///tfTJ8+vXVs48aNePzxx3Hw4EGjLxYeHo6F\nCxdiy5YtqG+ZQ2hzDXd3d5w9exZ5eXm44447MHToUAwePBhPP/00nn766Q7vxx455cXFxGEu5iLz\nt0ycLz+P4LPBSBnHBWqJrqa0FPj0U6ChQT729ARSU4HQUGXjIiLbYtTyI/7+/isKHnQAACAASURB\nVLhw4QKcnJxax5qamhAUFISKliXHu2HhwoU4ceIEPv74YwDyU7CBgYHYv38/Yi4vlDRnzhyEhYXh\n1Vdf7fD9t99+O/Lz8xEVFYVHH30Uc+bM6fiDqVSYM2dO6zZj/v7+SEhIaJ2TbqmEecxjHvNY9PH6\n9Rr89BPQt698fOqUBhMnAtOmWUd8POYxjy1/3PLnlucQVq1aZbl15J544gnExMTgySefbB3797//\njSNHjuCdd97p9kVffvlllJWVtRZyeXl5GD16NGpbnrkH8Oabb0Kj0eCbb77p9vsDXEeOiKzTsWPA\nunVAU5N87O0tT6cGBysbFxEpy9S6RW3Mi3777Tc8++yzCA8Px/DhwxEeHo5nnnkGeXl5GDNmDMaM\nGYPExMRuBdtWTU0NfH19DcZ8fHysdrFh6lzbv2WQ5THfYpkj30VF8nRqSxHn4wPMncsiriv8jIvF\nfItlrnwb1SP38MMP4+GHH77ia7rTzN6+4vT29kZVy4aCl1VWVsLHx8fo9yQismZHjgAbNgDNzfKx\nry8wZw7Qq5eycRGRbTOqkJs7d65ZL9q+6IuNjUVzczMKCwtbe+Ty8/MRHx/fo+ukp6cjKSmpdV6a\nLIt5Fov5Fqsn+T50CPjsM0B7eXlFPz+5iAsMNE9s9oqfcbGYb7Ha9sz15O6c0Xutbt++HXl5ea19\nbJIkQaVS4cUXXzT6YlqtFk1NTViyZAnKysqwYsUKODs7w8nJCTNnzoRKpcKHH36I3377DZMnT8bu\n3bsxaNAg034w9sgRkRUoKAA+/1xfxPn7y9Op/v5X/DYicjAW7ZF74okncN999yEnJwcFBQUoKCjA\nwYMHUVBQ0K2LLV26FJ6ennjttdewdu1aeHh44JVXXgEAvP/++6ivr0dwcDBmzZqF5cuXm1zEkTLY\nXyEW8y2WKfk+cADYuFFfxAUGAvPmsYgzFj/jYjHfYgntkVu7di3279+PsLCwHl0sPT0d6enpnZ4L\nCAjAV1991aP3JyKyFn/+CXz5JaDTyce9esnTqe2e6yIi6hGjplaHDBmCrKwsm9rjlFOrRKSU338H\nvvoKaPkV1Lu3XMTx+S0i6oqpdYtRd+Q++ugjPPzww3jggQcQEhJicK47y46IxocdiEi0vDzgm2/0\nRVxQkFzEeXsrGxcRWSchDzssX74cTz75JHx8fODh4WFw7vjx4yZf3JJ4R048jUbDolkg5lssY/L9\n66/Apk3645AQebFfLy/Lxmav+BkXi/kWq32+LXpH7qWXXsLmzZsxYcKEbl+AiMgR/Pwz8O23+uM+\nfeQiztNTuZiIyP4ZdUcuMjIShYWFcHV1FRGTWfCOHBGJsncv8P33+uOwMCA1FWg3gUFE1CWLLj/y\nj3/8A0899RROnToFnU5n8EVE5Mh27TIs4vr2le/EsYgjIhGMKuTS0tKwfPlyhIeHw9nZufXLxcXF\n0vH1SHp6OtfFEYi5Fov5FquzfO/YAWzdqj+OiJDvxLm7i4vLnvEzLhbzLVZLvjUaTZdLsxnDqB65\no0ePmnwBJfUkMUREV7J9O5CVpT+OigIeeABwc1MuJiKyPS2rayxZssSk7zd6iy4A0Ol0OHPmDEJC\nQqBWG3UzTzHskSMiS5AkYNs2oO3Ni379gJkzARtqIyYiK2PRHrmqqirMnj0b7u7uCA8Ph7u7O2bP\nno3KyspuX5CIyFZJknwXrm0RN2CAfCeORRwRKcHovVZra2vx559/oq6urvWfTzzxhKXjIxvC/gqx\nmG+xsrM1+OknICdHPxYTA8yYAVh5u7DN4mdcLOZbLKF7rf7www84evQovC6vahkbG4tPPvkE/fv3\nN0sQRETW6tChEvz4YxG2bPkdWq0O/fsPQO/eUYiNBaZPB5yN+i1KRGQZRvXIRUdHQ6PRIDo6unWs\nuLgYiYmJKC0ttWR8JlOpVFi8eDG36CIikx06VIJPPilEaWkKysrksebmTEybFoMnn4yCk5Oy8RGR\n7WvZomvJkiUm9cgZVcgtW7YMq1atwjPPPIOoqCgUFxfjrbfeQmpqKhYuXGhS4JbGhx2IqKfeeScL\nO3Yk48wZ/VhQEJCYmIUnnkhWLjAisjsWfdjhxRdfxAsvvICNGzfimWeewRdffIG//e1vePnll7t9\nQbJf7K8Qi/m2rKYmIDdX3VrEVVRoEBwMDBoEaLXW/dS+veBnXCzmWyyhPXJqtRppaWlIS0szy0WJ\niKxZfT2wbh1w4YJ+95rAQLmIU6kAV1fuakNE1sGoqdUnnngCM2fOxKhRo1rHdu3ahc8++wxvv/22\nRQM0FadWicgU1dXAmjXA2bPA+fMl2LevEAMGpCA6Wi7iGhoyMXduDOLiopQOlYjsiKl1i1GFXO/e\nvVFWVga3NkuWX7p0CRERETh37ly3LyoCCzki6q7ycmD1aqCiQj82cGAJysuL0NiohqurDikpA1jE\nEZHZWbRHTq1WQ6cznErQ6XQslMgA+yvEYr7N69Qp4KOP9EWcWg1MmwbMmBGF+fOTkZAAzJ+fzCJO\nIH7GxWK+xTJXvo0q5EaPHo2XX365tZjTarVYvHgxxowZY5YgLCU9PZ0fTCK6quJi4JNPgNpa+djF\nRV7od8gQJaMiIkeg0Wh6tDe8UVOrx48fx+TJk3Hq1ClERUWhtLQUoaGh2LRpEyIiIky+uCVxapWI\njHHwIPD550Bzs3zs7i5vuRUZqWxcRORYLNojB8h34XJzc3H8+HFERERgxIgRUKut9xF8FnJEdDV5\necA338h7qAKAjw8waxYQEqJsXETkeCzaIwcATk5OGDlyJKZPn46RI0dadRFHyuA0tljMd8/s3Al8\n/bW+iAsMBNLSui7imG/xmHOxmG+xhK4jR0RkLyQJ+PFHYNcu/VifPvKdOG9v5eIiIjKF0VOrtoZT\nq0TUnk4HbNokT6m2iI6WH2xwd1csLCIiy02tSpKEo0ePormlE5iIyAY1NQGffWZYxA0cKN+JYxFH\nRLbKqEa3+Ph49sTRVbG/Qizm23iXLgFr18pPqLa4/npg+nTA2cgGE+ZbPOZcLOZbLGHryKlUKlx/\n/fU4dOiQWS5IRCRSTY28RlxJiX7slluAO++UF/0lIrJlRvXIvfzyy1i7di3mzp2LiIiI1nlclUqF\ntLQ0EXF2m0qlwuLFi5GUlISkpCSlwyEiBVy8KO+bWl6uH5swQS7kiIisgUajgUajwZIlSyy3jlxL\nIaRSqTqcy87O7vZFReDDDkSO7cwZeTq1ulo+VquBKVPkKVUiImtj8QWBbQ0LOfE0Gg3vfgrEfHet\ntBRYt07ujQPkPrh775UfbjAV8y0ecy4W8y1W+3ybWrcYvY7chQsX8O233+L06dN4/vnnUVZWBkmS\n0Ldv325flIjIUg4flp9ObXnQ3s0NmDlTXmaEiMjeGHVHbtu2bbjnnntw4403YufOnaiuroZGo8Eb\nb7yBTZs2iYiz23hHjsjx5OfLuzXodPKxl5e8vEhoqLJxERFdjUWnVhMSEvD6669j/PjxCAgIwMWL\nF3Hp0iVERkbi7NmzJgVsaSzkiBzLnj3ADz/ojwMCgNRUeestIiJrZ9G9VktKSjB+/HiDMRcXF2i1\n2m5fkOwX1yASi/mWSRKQmWlYxIWEyPummrOIY77FY87FYr7FEraOHAAMGjQIP7T9LQkgMzMT1113\nnVmCICIyhU4HbN4M5OToxyIjgblzAR8fxcIiIhLGqKnVPXv2YPLkybj99tuxceNGpKamYtOmTfj6\n668xfPhwEXF2G6dWiexbczPw5ZfAgQP6sdhY4L77ABcX5eIiIjKFxZcfKSsrw9q1a1FSUoLIyEjM\nmjXLqp9YZSFHZL8aGoD164Fjx/RjQ4fKuzU4OSkXFxGRqYSsI6fT6XD+/HkEBQV1ujiwNWEhJx7X\nIBLLUfNdWwt8+ilw8qR+7OabgYkTAUv+WnLUfCuJOReL+RbLXOvIGdUjd/HiRaSmpsLDwwN9+vSB\nu7s7Zs2ahfK2+95YofT0dDZvEtmRigpg5UrDIi4lxfJFHBGRpWg0GqSnp5v8/Ubdkbv77rvh7OyM\npUuXIjIyEqWlpVi0aBEaGxvx9ddfm3xxS+IdOSL7cvasvOVWVZV8rFIBkycDw4YpGxcRkTlYdGrV\nz88Pp06dgqenZ+tYXV0dQkNDUVlZ2e2LisBCjsh+nDghT6fW18vHTk7APfcAgwcrGxcRkblYdGp1\n4MCBKC4uNhgrKSnBwJ5sXEh2h9PYYjlKvgsLgVWr9EWcq6u8W4PoIs5R8m1NmHOxmG+xzJVvo/Za\nTU5Oxq233orZs2cjIiICpaWlWLt2LVJTU7Fy5UpIkgSVSoW0tDSzBEVEBAB//gl89RXQsva4p6dc\nxIWFKRsXEZG1MGpqteWpirZPqrYUb21lZ2ebN7oe4NQqkW3LzQW+/17euQEA/PzkLbd691Y2LiIi\nSxCy/IgtYSFHZJskCdi2DWg76xAUJBdxvr6KhUVEZFEW7ZEjMgb7K8Syx3xLknwXru2P1rcvMG+e\n8kWcPebb2jHnYjHfYgntkSMisjStVu6H+/NP/diAAcD998sPOBARUUecWiUixTU2Ahs2AEVF+rH4\neGDqVG65RUSOwdS6hXfkiEhRdXXAunXyWnEthg8HJk3ibg1ERFdjdI9cQUEB/vGPf2DBggUAgIMH\nD+L333+3WGBke9hfIZY95LuqCvj4Y8MiLinJOos4e8i3rWHOxWK+xTJXvo0q5DZu3IjExESUlZVh\n9erVAIDq6mr89a9/NUsQROR4zp8HPvoIOHdOPlapgNtvlws5ayviiIislVE9cgMHDsT69euRkJCA\ngIAAXLx4EU1NTQgNDcX58+dFxNlt7JEjsl4nT8r7ptbVycdOTnI/XHy8snERESnFoj1y586dw5Ah\nQzqMq9VcvYSIuufoUWD9evkBBwBwcQFmzJCfUCUiou4xqhK74YYbsGbNGoOxDRs2YPjw4RYJimwT\n+yvEssV8HzgAfPqpvojz8ADmzLGNIs4W823rmHOxmG+xhK4j984772DChAn46KOPUFdXh1tvvRWH\nDx/G1q1bzRKEpaSnpyMpKal1izEiUs6vvwKbN+u33PL1lXdrCApSNi4iIiVpNJoeFXVGryNXW1uL\nzZs3o6SkBJGRkbjjjjvg4+Nj8oUtjT1yRNZBkoCcHCArSz/Wq5dcxPn7KxcXEZE14V6r7bCQI1Ke\nJAFbtgB79ujHwsKABx8EvLyUi4uIyNpYdK/VkpISpKWl4frrr8c111zT+hUbG9vtC5L9Yn+FWNae\n75Ytt9oWcf36yT1xtljEWXu+7RFzLhbzLZbQHrn77rsPgwYNwtKlS+Hu7m6WCxOR/WpqAjZuBA4f\n1o8NHgxMmwY4cz8ZIiKzMWpq1c/PD+Xl5XCyoU0PObVKpIz6ennLrePH9WPDhgF33AFwxSIios5Z\ndGp18uTJ2LZtW7ffnIgcS3W1vOVW2yIuMRGYPJlFHBGRJRh1R+78+fMYOXIkYmNjERwcrP9mlQor\nV660aICm4h058TQaDZd6Ecja8l1eDqxeDVRU6Mduuw24+WblYjIna8u3I2DOxWK+xWqfb4vu7JCW\nlgZXV1cMGjQI7u7urRdTcUNEIgJw6pS85VZtrXysVgN33QUMHapsXERE9s6oO3I+Pj4oKyuDr6+v\niJjMgnfkiMQoLgYyMoCGBvnYxQW47z6AD7UTERnPonfkhgwZggsXLthUIUdElnfwIPD550Bzs3zs\n7g488AAQGalsXEREjsKo9uPk5GRMnDgRr776KlauXImVK1fio48+str+OFIG1yASS+l85+UBGzbo\nizgfH2DePPst4pTOtyNizsVivsUSuo5cTk4OwsLCOt1bNS0tzSyBEJHt2LkT+PFH/XFgoLzlVkCA\ncjERETkibtFFREaTJLmA27VLP9anDzBrFuDtrVxcRES2zuw9cm2fStXpdF2+gZqLQxE5BJ0O2LRJ\nnlJtER0NzJgh98YREZF4XVZhbR9scHZ27vTLxcVFSJBkG9hfIZbIfDc1AZ99ZljEDRwo34lzlCKO\nn2/xmHOxmG+xLN4jt3///tY/Hz161CwXIyLbc+mSvLxISYl+7PrrgSlTuFsDEZHSjOqRe/311/Hs\ns892GH/zzTfx17/+1SKB9RR75Ih6rqZGXuj39Gn92C23AOPHA1wPnIjIfEytW4xeELi6urrDeEBA\nAC5evNjti4rAQo6oZy5eBNaskbfeajFhglzIERGReZlat1xxYiQrKwuZmZnQarXIysoy+FqxYgUX\nCCYD7K8Qy5L5PnMGWLlSX8SpVPKWW45cxPHzLR5zLhbzLZaQdeTS0tKgUqnQ0NCAhx56qHVcpVIh\nJCQE77zzjlmC6I7c3Fw89dRTcHFxQXh4OFavXg1nZ6OWwyMiI5SWAuvWyb1xAODsDNx7r/xwAxER\nWRejplZTU1OxZs0aEfFc1enTpxEQEAA3Nze8+OKLGDZsGO65554Or+PUKlH3HT4sP53asluDmxsw\nc6a8zAgREVmORfdatZYiDgD69OnT+mcXFxc4OTkpGA2R/cjPB77+Wl4vDgC8vOTlRUJDlY2LiIi6\nZrOLB5SUlODHH3/ElClTlA6FLmN/hVjmzPeePcBXX+mLOH9/4KGHWMS1xc+3eMy5WMy3WObKt9BC\n7t1338WNN94Id3d3zJs3z+BceXk5pk6dCm9vb0RHRyMjI6P13FtvvYVx48bhjTfeAABUVVVh9uzZ\nWLVqFe/IEfWAJAGZmcAPP+jHgoPlIi4wULm4iIjIOEL3Wv3qq6+gVquxZcsW1NfX4+OPP249N3Pm\nTADARx99hLy8PNxxxx3YtWsXBg8ebPAezc3NuPPOO/Hss88iOTm5y2uxR47oynQ64NtvgV9/1Y9F\nRso9cR4eysVFROSILLqOnLktXLgQJ06caC3kamtrERgYiP379yMmJgYAMGfOHISFheHVV181+N41\na9bg6aefxnXXXQcAeOyxxzB9+vQO12AhR9S15mbgyy+BAwf0Y7GxwH33Adx5j4hIPIs+7GBu7QM9\nfPgwnJ2dW4s4ABg6dGin88epqalITU016jpz585F9OXH7fz9/ZGQkICkpCQA+rlpHpvveN++fXjq\nqaesJh57PzY13w0NwOLFGpw+DURHy+fVag1CQgAXF+v5+aztmJ9v8cctY9YSj70ft4xZSzz2frxv\n3z5UVFSguLgYPWEVd+RycnIwffp0nDp1qvU1K1aswLp165CdnW3SNXhHTjyNRtP6QSXLMyXftbXA\np58CJ0/qx26+GZg4kVtuXQ0/3+Ix52Ix32K1z7dN35Hz9vZGVVWVwVhlZSV8fHxEhkU9xF8AYnU3\n3xUV8pZbFy7ox1JSgNGjWcQZg59v8ZhzsZhvscyVb7VZ3qWbVO3+rxEbG4vm5mYUFha2juXn5yM+\nPl50aER26dw5ecutliJOpQKmTAHGjGERR0Rky4QWclqtFpcuXUJzczO0Wi0aGhqg1Wrh5eWFadOm\nYdGiRairq8OOHTuwadMmo3vhupKenm4w90+WxVyLZWy+T5yQi7iWm95OTvJDDcOGWS42e8TPt3jM\nuVjMt1gt+dZoNEhPTzf5fYQWckuXLoWnpydee+01rF27Fh4eHnjllVcAAO+//z7q6+sRHByMWbNm\nYfny5Rg0aFCPrpeens5bxeTQCguBVauA+nr52NUVePBBoN2qPkREpJCkpKQeFXKKPOwgAh92IEf3\n55/ybg1arXzs6SlvuRUWpmxcRETUkU097EBElpWbC3z/vbxzAwD4+QGpqUDv3srGRURE5qXIww6i\nsEdOLOZarM7yLUmARgN8952+iAsKkrfcYhHXM/x8i8eci8V8i2WuHjm7viPXk8QQ2RpJku/C5ebq\nx/r2BR54QJ5WJSIi65OUlISkpCQsWbLEpO9njxyRHdBq5X64P//Ujw0YANx/v/yAAxERWTf2yBE5\nqMZGYMMGoKhIPxYfD0ydKi81QkRE9suue+RILPZXiKXRaFBXB6xebVjEDR8O3HMPizhz4+dbPOZc\nLOZbLHPl267vyLWsI8e15Mge1dYCH38s79rQIikJGDuWuzUQEdkKjUbTo6KOPXJENubQoRL83/8V\nYfduNRobdejffwCCgqIwaZJ8N46IiGwPe+SIHMDBgyV4881CHDuWgqYmeez33zPx3HPA8OFRygZH\nRETCsUeOzIb9FZZ16hSwbFkRDh+Wi7iKCg3UaiAhIQXHjxdd/Q2oR/j5Fo85F4v5Fos9ckQOoroa\nyMoC9u0Dzp/X/93LyQlISAB8fYHGRv6djIjIEbFHjshKNTUBe/YAOTnyEiMAkJubhfr6ZISHA1FR\ngIuLPB4cnIX585OVC5aIiHqEPXKd4FOrZIskCThwAPjxR6CiwvBccvIAnDiRCX//lNaxhoZMpKTE\nCI6SiIjMgU+tdoF35MTTaDQsmnvo5Enghx+A0lLD8eBgYOJEebeGQ4dKkJlZhAMHfsfgwUOQkjIA\ncXF80MHS+PkWjzkXi/kWq32+eUeOyIZVVwOZmXIfXFuenkByMnDDDYD6chtcXFwU4uKioNGo+UuX\niMjB8Y4ckYKamoDdu+U+uJblRAD5QYYRI4DERMDdXbn4iIhIDN6RI7IhkgTs3y/3wVVWGp4bOBCY\nMAHo1UuZ2IiIyHZwzQIyG65BZJyyMmDlSuDzzw2LuJAQYPZsYMYM44o45lss5ls85lws5lssriNH\nZGOqquQ+uPx8w3EvL7kP7vrr9X1wRERExrDrHrnFixdz+RFSXFMTsHOn/NW+D+7mm4ExY9gHR0Tk\nqFqWH1myZIlJPXJ2XcjZ6Y9GNkKSgD//lPvgqqoMzw0aJPfBBQYqExsREVkXU+sWTuSQ2bC/Qu/E\nCeCjj4AvvjAs4vr0AebMAe6/v+dFHPMtFvMtHnMuFvMtFnvkiKxQZaXcB/f774bjXl5ASoq8Nyr7\n4IiIyFw4tUpkBo2NwK5dnffBjRwp98G5uSkXHxERWTeuI0ekAEkC/vgD+Omnjn1wgwfLfXABAcrE\nRkRE9o+TPGQ2jtZfcfw48OGHwJdfduyDmzsXmD7dskWco+Vbacy3eMy5WMy3WOyRI1JIZaV8B+6P\nPwzHvb3lPrihQ9kHR0REYrBHjshIjY3Ajh1yL1xzs37c2Vnugxs9mn1wRERkGvbIdSI9PZ0LAlOP\nSZK8G0NmJlBdbXju2muB8ePZB0dERKZpWRDYVLwjR2aj0WjsrmguLQV++AE4edJwPDQUuO02ICpK\nmbgA+8y3NWO+xWPOxWK+xWqfb96RIzKjigp5R4b9+w3Hvb3lO3BDhwIqlTKxERERteAdOaI2Ghrk\nPrjduzv2wY0aJffBuboqFx8REdkn3pEj6gFJAvbtk/vgamoMz8XHy3fh/P2ViY2IiKgrXCSBzMZW\n1yAqKQH++1/g668Ni7iwMCAtDbj3Xuss4mw137aK+RaPOReL+RaL68gR9dDFi3If3IEDhuM+PvId\nuCFD2AdHRETWjT1y5HAaGoCcHLkPTqvVjzs7A7fcIn+xD46IiERijxzRVeh0ch9cVlbHPrjrrpPv\nwvn5KRMbERGRKdgjR2Zjzf0VxcVyH9w33xgWceHhwEMPAffcY3tFnDXn2x4x3+Ix52Ix32KxR47I\nCOXlch9cQYHhuK+vfAfuuuvYB0dERLbLrnvkFi9ezC26HFRDA7B9O7Bnj2EfnIuL3AM3ahT74IiI\nSHktW3QtWbLEpB45uy7k7PRHoyvQ6YC8PLkPrrbW8NyQIfJdOF9fZWIjIiLqiql1C3vkyGyU7q84\ndgz4z3+ATZsMi7i+fYG//AWYNs2+ijil8+1omG/xmHOxmG+x2CNHdNmFC3If3MGDhuN+fvIduPh4\n9sEREZF94tQq2axLl+Q+uL17O/bBjR4t98G5uCgXHxERkbG4jhw5DJ0O+O03uQ+urs7w3NChQEqK\nfU2hEhERdYU9cmQ2Ivorjh4Fli8HNm82LOIiIoCHHwamTnWcIo79LGIx3+Ix52Ix32KxR44cyoUL\nwNatwKFDhuN+fsCECcC117IPjoiIHA975Miq1dfr++B0Ov24q6vcBzdyJPvgiIjI9rFHjuyKTgf8\n+iuQnd2xDy4hQe6D8/FRJjYiIiJrwR45MhtzzfcXFQEffAB8+61hERcZCTzyCHD33SziAPaziMZ8\ni8eci8V8i8UeObI758/LfXCHDxuO+/sDt94KDBrEPjgiIqK22CNHiquvB7ZtA3JzO/bBJSYCN98M\nOPOvHEREZMfYI0c2R6vV98HV1+vHVSrg+uuB5GTA21u5+IiIiKwde+TIbLoz319YKK8H9913hkVc\nVJTcB3fnnSzirob9LGIx3+Ix52Ix32KxR45s0rlzch/ckSOG4wEBch/cwIHsgyMiIjKWXffILV68\nGElJSUhKSlI6HIdXVyf3wf38s2EfnJub3Ac3YgT74IiIyPFoNBpoNBosWbLEpB45uy7k7PRHsyla\nLfDLL4BG07EP7oYbgHHjOIVKRERkat3CHjkym7bz/ZIkT59+8AHw/feGRVx0NPDoo8CUKSzieoL9\nLGIx3+Ix52Ix32KxR46s1tmzch9cYaHheGCg3AcXF8c+OCIiInPg1Cr12KFDJfjppyLU1qpx7JgO\nzs4D0KtXVOt5Nzdg7Fhg+HD2wREREXWG68iRIg4dKsHHHxfi/PkUFBcDzc1Ac3MmEhKAoKAoDBsm\n98F5eSkdKRERkf1hjxz1yOefFyE/PwWFhcD58xoAgLNzCqqqivA//wNMnswizlLYzyIW8y0ecy4W\n8y0We+TIKqjVajQ06I89PIABA4ABA9QICVEuLiIiIkfAHjnqkffey8LPPyfjxAl5V4bwcECtBoKD\nszB/frLS4REREdkE9siRIsaPH4DS0kyEhaXA1VUea2jIREpKjLKBEREROQD2yFGPxMVFIS0tBn37\nZuH8+bcRHJyFuXNjEBcXdfVvph5hP4tYzLd4zLlYzLdY7JEjqxEXF4W4ik2AbAAADuVJREFUuCho\nNGpuh0ZERCQQe+SIiIiIFMYtuoiIiIgcDAs5Mhv2V4jFfIvFfIvHnIvFfItlrnyzkCMiIiKyUeyR\nIyIiIlIYe+SIiIiIHAwLOTIb9leIxXyLxXyLx5yLxXyLxR45IiIiIgdncz1yZ86cwbRp0+Dq6gpX\nV1esW7cOvXr16vA69sgRERGRrTC1brG5Qk6n00Gtlm8krlq1CqdOncLf//73Dq9jIUdERES2wmEe\ndmgp4gCgqqoKAQEBCkZDbbG/QizmWyzmWzzmXCzmWyyH7pHLz8/HiBEj8O6772LmzJlKh0OX7du3\nT+kQHArzLRbzLR5zLhbzLZa58i20kHv33Xdx4403wt3dHfPmzTM4V15ejqlTp8Lb2xvR0dHIyMho\nPffWW29h3LhxeOONNwAAQ4cOxd69e7Fs2TIsXbpU5I9AV1BRUaF0CA6F+RaL+RaPOReL+RbLXPkW\nWsiFh4dj4cKFSEtL63BuwYIFcHd3x9mzZ/Hpp5/isccew4EDBwAATz/9NLKzs/HMM8+gqamp9Xt8\nfX3R0NAgLP4r6ekt0u5+vzGvv9Jrujpn7LjSt+DNcf3uvIel8t3VOWPHRLK2z7ip55lv01/P3ynm\new/+TrHvz7jIfAst5KZOnYq77rqrw1OmtbW1+PLLL7F06VJ4enrilltuwV133YU1a9Z0eI99+/Zh\n7NixSE5Oxptvvonnn39eVPhXZM8fyM7GO3tdcXHxVWMyF/7SFZvvzq5v6e+3tkLO0fN9tdfwdwp/\np3SXPX/GReZbkadWX375ZZSVleHjjz8GAOTl5WH06NGora1tfc2bb74JjUaDb775xqRrxMTEoKio\nyCzxEhEREVnS0KFDTeqbc7ZALFelUqkMjmtqauDr62sw5uPjg+rqapOvUVhYaPL3EhEREdkCRZ5a\nbX8T0NvbG1VVVQZjlZWV8PHxERkWERERkU1RpJBrf0cuNjYWzc3NBnfR8vPzER8fLzo0IiIiIpsh\ntJDTarW4dOkSmpubodVq0dDQAK1WCy8vL0ybNg2LFi1CXV0dduzYgU2bNiE1NVVkeEREREQ2RWgh\n1/JU6muvvYa1a9fCw8MDr7zyCgDg/fffR319PYKDgzFr1iwsX74cgwYNEhkeERERkU2xub1We+pv\nf/sbdu/ejejoaKxcuRLOzoo87+EwqqqqMH78eBQUFGDv3r0YPHiw0iHZtdzcXDz11FNwcXFBeHg4\nVq9ezc+4BZ05cwbTpk2Dq6srXF1dsW7dug7LK5FlZGRk4Mknn8TZs2eVDsWuFRcX46abbkJ8fDxU\nKhU+++wz9O7dW+mw7JpGo8GyZcug0+nw//7f/8Pdd999xdfb5BZdpsrPz8fJkyexfft2DBw4EJ9/\n/rnSIdk9T09PfPfdd7j33ntN2gyYuicyMhLZ2dnYtm0boqOj8fXXXysdkl0LCgrCzp07kZ2djQce\neAArVqxQOiSHoNVqsXHjRkRGRiodikNISkpCdnY2srKyWMRZWH19Pd588018//33yMrKumoRBzhY\nIbd7925MnDgRAHDbbbdh586dCkdk/5ydnfkfvkB9+vSBm5sbAMDFxQVOTk4KR2Tf1Gr9r9CqqioE\nBAQoGI3jyMjIwPTp0zs8OEeWsXPnTiQmJuKll15SOhS7t3v3bnh4eGDKlCmYNm0azpw5c9XvcahC\n7uLFi61Lmvj6+qK8vFzhiIgso6SkBD/++COmTJmidCh2Lz8/HyNGjMC7776LmTNnKh2O3Wu5G3f/\n/fcrHYpDCAsLQ1FREbZv346zZ8/iyy+/VDoku3bmzBkUFhZi8+bNePjhh5Genn7V77HJQu7dd9/F\njTfeCHd3d8ybN8/gXHl5OaZOnQpvb29ER0cjIyOj9Zy/v3/renWVlZUIDAwUGrctMzXnbfFvz8br\nSb6rqqowe/ZsrFq1infkjNSTfA8dOhR79+7FsmXLsHTpUpFh2zRTc7527VrejTOBqfl2dXWFh4cH\nAGDatGnIz88XGretMjXfAQEBuOWWW+Ds7Izk5GTs37//qteyyUIuPDwcCxcuRFpaWodzCxYsgLu7\nO86ePYtPP/0Ujz32GA4cOAAAGDVqFH766ScAwJYtWzB69GihcdsyU3PeFnvkjGdqvpubmzFjxgws\nXrwY11xzjeiwbZap+W5qamp9na+vLxoaGoTFbOtMzXlBQQFWr16NSZMm4ciRI3jqqadEh26TTM13\nTU1N6+u2b9/O3ytGMjXfN910EwoKCgDIe8sPGDDg6heTbNjLL78szZ07t/W4pqZGcnV1lY4cOdI6\nNnv2bOnvf/976/Fzzz0njRkzRpo1a5bU1NQkNF57YErOJ02aJIWFhUkjR46UPvnkE6Hx2rru5nv1\n6tVSr169pKSkJCkpKUnasGGD8JhtWXfzvXfvXikxMVEaN26cdOutt0rHjx8XHrOtM+V3SoubbrpJ\nSIz2pLv5/u6776Rhw4ZJY8aMkebMmSNptVrhMdsyUz7f7733npSYmCglJSVJR48eveo1bHpdAqnd\nHZ7Dhw/D2dkZMTExrWNDhw6FRqNpPf7f//1fUeHZJVNy/t1334kKz+50N9+pqalcSLsHupvv4cOH\nY9u2bSJDtDum/E5pkZuba+nw7E538z1p0iRMmjRJZIh2xZTP9/z58zF//nyjr2GTU6st2vdI1NTU\nwNfX12DMx8cH1dXVIsOya8y5WMy3WMy3eMy5WMy3WCLybdOFXPtK19vbu/VhhhaVlZWtT6pSzzHn\nYjHfYjHf4jHnYjHfYonIt00Xcu0r3djYWDQ3N6OwsLB1LD8/H/Hx8aJDs1vMuVjMt1jMt3jMuVjM\nt1gi8m2ThZxWq8WlS5fQ3NwMrVaLhoYGaLVaeHl5Ydq0aVi0aBHq6uqwY8cObNq0iT1DZsCci8V8\ni8V8i8eci8V8iyU03z19IkMJixcvllQqlcHXkiVLJEmSpPLycunuu++WvLy8pKioKCkjI0PhaO0D\ncy4W8y0W8y0ecy4W8y2WyHyrJImLexERERHZIpucWiUiIiIiFnJERERENouFHBEREZGNYiFHRERE\nZKNYyBERERHZKBZyRERERDaKhRwRERGRjWIhR0RERGSjWMgREbUzd+5cLFy40Kzv+dhjj2HZsmVm\nfU8iImelAyAisjYqlarDZtc99cEHH5j1/YiIAN6RIyLqFHcvJCJbwEKOiKzKa6+9hr59+8LX1xcD\nBw5EVlYWACA3NxcjR45EQEAAwsLC8MQTT6Cpqan1+9RqNT744ANcc8018PX1xaJFi1BUVISRI0fC\n398fM2bMaH29RqNB37598eqrryIoKAj9+vXDunXruoxp8+bNSEhIQEBAAG655Rb88ccfXb726aef\nRkhICPz8/DBkyBAcOHAAgOF07ZQpU+Dj49P65eTkhNWrVwMADh48iAkTJqBXr14YOHAgNm7c2OW1\nkpKSsGjRIowePRq+vr6YOHEiLly4YGSmicgesJAjIqtx6NAhvPfee/jll19QVVWFrVu3Ijo6GgDg\n7OyMf/3rX7hw4QJ2796NzMxMvP/++wbfv3XrVuTl5WHPnj147bXX8PDDDyMjIwOlpaX4448/kJGR\n0fraM2fO4MKFCzh58iRWrVqFRx55BEeOHOkQU15eHh566CGsWLEC5eXlePTRR3HnnXeisbGxw2u3\nbNmCnJwcHDlyBJWVldi4cSMCAwMBGE7Xbtq0CdXV1aiursZnn32G0NBQpKSkoLa2FhMmTMCsWbNw\n7tw5rF+/HvPnz0dBQUGXOcvIyMAnn3yCs2fPorGxEa+//nq3805EtouFHBFZDScnJzQ0NGD//v1o\nampCZGQk+vfvDwC44YYbMHz4cKjVakRFReGRRx7Btm3bDL7/+eefh7e3NwYPHozrrrsOkyZNQnR0\nNHx9fTFp0iTk5eUZvH7p0qVwcXFBYmIi7rjjDmzYsKH1XEvR9d///hePPvoobrrpJqhUKsyePRtu\nbm7Ys2dPh/hdXV1RXV2NgoIC6HQ6xMXFoU+fPq3n20/XHj58GHPnzsVnn32G8PBwbN68Gf369cOc\nOXOgVquRkJCAadOmdXlXTqVSYd68eYiJiYG7uzumT5+Offv2dSPjRGTrWMgRkdWIiYnB22+/jfT0\ndISEhGDmzJk4deoUALnomTx5MkJDQ+Hn54eXXnqpwzRiSEhI6589PDwMjt3d3VFTU9N6HBAQAA8P\nj9bjqKio1mu1VVJSgjfeeAMBAQGtXydOnOj0tePGjcPjjz+OBQsWICQkBI8++iiqq6s7/VkrKytx\n11134ZVXXsGoUaNar7V3716Da61btw5nzpzpMmdtC0UPDw+Dn5GI7B8LOSKyKjNnzkROTg5KSkqg\nUqnwt7/9DYC8fMfgwYNRWFiIyspKvPLKK9DpdEa/b/unUC9evIi6urrW45KSEoSFhXX4vsjISLz0\n0ku4ePFi61dNTQ3uv//+Tq/zxBNP4JdffsGBAwdw+PBh/POf/+zwGp1OhwceeAApKSn4y1/+YnCt\nsWPHGlyruroa7733ntE/JxE5FhZyRGQ1Dh8+jKysLDQ0NMDNzQ3u7u5wcnICANTU1MDHxweenp44\nePCgUct5tJ3K7Owp1MWLF6OpqQk5OTn49ttvcd9997W+tuX1Dz/8MJYvX47c3FxIkoTa2lp8++23\nnd75+uWXX7B37140NTXB09PTIP6213/ppZdQV1eHt99+2+D7J0+ejMOHD2Pt2rVoampCU1MTfv75\nZxw8eNCon5GIHA8LOSKyGg0NDXjhhRcQFBSE0NBQnD9/Hq+++ioA4PXXX8e6devg6+uLRx55BDNm\nzDC4y9bZum/tz7c97tOnT+sTsKmpqfjPf/6D2NjYDq8dNmwYVqxYgccffxyBgYG45pprWp8wba+q\nqgqPPPIIAgMDER0djd69e+O5557r8J7r169vnUJteXI1IyMD3t7e2Lp1K9avX4/w8HCEhobihRde\n6PTBCmN+RiKyfyqJf50jIgej0WiQmpqK48ePKx0KEVGP8I4cERERkY1iIUdEDolTkERkDzi1SkRE\nRGSjeEeOiIiIyEaxkCMiIiKyUSzkiIiIiGwUCzkiIiIiG8VCjoiIiMhG/X9xqo7C5o2nRAAAAABJ\nRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10762ab00>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "So, using a few tweaks, such as static type declarations and explicit for-loops instead of list comprehensions, we managed to increase the performance of our least squares fit implementation quite significantly - it outperforms the alternative functions in Numpy and Scipy now."
-     ]
     }
    ],
    "metadata": {}

From 604053e9b86940587e728e6c050928ff05d9f6ff Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 10 May 2014 12:05:43 -0400
Subject: [PATCH 034/248] upd cython article

---
 benchmarks/cython_least_squares.ipynb | 57 +++++++++++++--------------
 1 file changed, 28 insertions(+), 29 deletions(-)

diff --git a/benchmarks/cython_least_squares.ipynb b/benchmarks/cython_least_squares.ipynb
index 5fc6944..c71c137 100644
--- a/benchmarks/cython_least_squares.ipynb
+++ b/benchmarks/cython_least_squares.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c63d806ef3ce407085d530f46ff34fdb5ed6bb79e32545e1ea8d5134d46bf946"
+  "signature": "sha256:5d04280c23460c2481423dabf313f3ad28f40fb4ad915967d83e3a08231c8b3d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -295,7 +295,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -375,7 +375,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -401,7 +401,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -429,7 +429,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -472,7 +472,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -516,7 +516,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -562,7 +562,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -628,7 +628,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -669,11 +669,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAH4CAYAAACi3S9CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10c2c0e90>"
+        "<matplotlib.figure.Figure at 0x10a57ca10>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -735,7 +735,7 @@
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -777,7 +777,7 @@
       "\n",
       "funcs =  ['py_classic_lstsqr', 'cy_classic_lstsqr', 'numba_classic_lstsqr']\n",
       "\n",
-      "orders_n = [10**n for n in range(2, 5)]\n",
+      "orders_n = [10**n for n in range(1, 7)]\n",
       "perf1 = {f:[] for f in funcs}\n",
       "\n",
       "for n in orders_n:\n",
@@ -790,7 +790,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
@@ -832,13 +832,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIgXiHzlDZFWOcLMWAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n40wV6QAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10cfbd6d0>"
+        "<matplotlib.figure.Figure at 0x10a5b13d0>"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -880,13 +880,12 @@
       "import random\n",
       "random.seed(12345)\n",
       "\n",
-      "funcs =  ['py_matrix_lstsqr', 'cy_classic_lstsqr', \n",
+      "funcs =  ['cy_classic_lstsqr', 'py_matrix_lstsqr',\n",
       "          'numpy_lstsqr', 'scipy_lstsqr']\n",
       "\n",
-      "orders_n = [10**n for n in range(2, 5)]\n",
+      "orders_n = [10**n for n in range(1, 7)]\n",
       "perf2 = {f:[] for f in funcs}\n",
       "\n",
-      "\n",
       "for n in orders_n:\n",
       "    x_list = [x_i*random.randrange(8,12)/10 for x_i in range(n)]\n",
       "    y_list = [y_i*random.randrange(10,14)/10 for y_i in range(n)]\n",
@@ -895,24 +894,24 @@
       "    for f in funcs:\n",
       "        if f != 'cy_classic_lstsqr':\n",
       "            perf2[f].append(timeit.Timer('%s(x_ary,y_ary)' %f, \n",
-      "                      'from __main__ import %s, x_ary, y_ary' %f).timeit(1000))\n",
+      "                'from __main__ import %s, x_ary, y_ary' %f).timeit(1000))\n",
       "        else:\n",
       "            perf2[f].append(timeit.Timer('%s(x_list,y_list)' %f, \n",
-      "                      'from __main__ import %s, x_list, y_list' %f).timeit(1000))"
+      "                'from __main__ import %s, x_list, y_list' %f).timeit(1000))\n"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 35
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "labels = [ ( 'py_matrix_lstsqr', '\"matrix\" least squares in reg. (C)Python and NumPy'),\n",
-      "           ('cy_classic_lstsqr', '\"classic\" least squares in reg. Cython'),\n",
+      "labels = [ ('cy_classic_lstsqr', '\"classic\" least squares in Cython (numpy arrays)'),\n",
+      "           ('py_matrix_lstsqr', '\"matrix\" least squares in (C)Python and NumPy'),\n",
       "           ('numpy_lstsqr', 'in NumPy'),\n",
-      "           ('scipy_lstsqr','in SciPy')\n",
+      "           ('scipy_lstsqr','in SciPy'),\n",
       "           ]\n",
       "\n",
       "plt.rcParams.update({'font.size': 12})\n",
@@ -936,13 +935,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/B+P+oR2lLvXHQAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAnYAAAIECAYAAACUvmMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10a114a90>"
+        "<matplotlib.figure.Figure at 0x10a59ce90>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",

From 9166f5d3ba8a4ffe6d4be89d77c8241a60f4ec33 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 11 May 2014 12:33:58 -0400
Subject: [PATCH 035/248] surprise

---
 funstuff/happy_mothers_day.ipynb | 119 +++++++++++++++++++++++++++++++
 1 file changed, 119 insertions(+)
 create mode 100644 funstuff/happy_mothers_day.ipynb

diff --git a/funstuff/happy_mothers_day.ipynb b/funstuff/happy_mothers_day.ipynb
new file mode 100644
index 0000000..85ef519
--- /dev/null
+++ b/funstuff/happy_mothers_day.ipynb
@@ -0,0 +1,119 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:eeeed0cea91d7503e20b21f6a2e90637fbd3a9ae171e49fc645e45d9e2f77b8c"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Sebastian Raschka\n",
+      "last updated: 05/11/2014\n",
+      "\n",
+      "- Link to the GitHub Repository"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Happy Mother's Day!"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x = np.arange(-1,1.001,0.001)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "y1 = (x**2)**(1.0/3) + np.sqrt(1-x**2)\n",
+      "y2 = (x**2)**(1.0/3) - np.sqrt(1-x**2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "plt.rcParams.update({'font.size': 12})\n",
+      "fig = plt.figure(figsize=(8,8))\n",
+      "plt.plot(x, y1, lw=15, color='r')\n",
+      "plt.plot(x, y2, lw=15, color='r')\n",
+      "plt.xlim(-1.2, 1.2)\n",
+      "plt.title(\"Happy Mother's Day\")\n",
+      "\n",
+      "ftext = 'x = np.arange(-1,1.001,0.001)'\\\n",
+      "        '\\ny1 = (x**2)**(1.0/3) + np.sqrt(1-x**2) '\\\n",
+      "        '\\ny2 = (x**2)**(1.0/3) - np.sqrt(1-x**2)'\\\n",
+      "        '\\n\\n\\n[inspired by a MATLAB impl. by TU Delft]'\n",
+      "plt.figtext(.32,.45, ftext, fontsize=11, ha='left')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAesAAAHtCAYAAAA5qla8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERu7/AP8FKy4MQovVAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105422790>"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Source: Equation was adopted from a [MATLAB implementation](https://twitter.com/tudelft/status/465500616891330561/photo/1) by researches from Delft University of Technology]"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 5e75fec086321629d92f9e67d02b42a778787301 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 11 May 2014 12:38:08 -0400
Subject: [PATCH 036/248] Happy Mother's Day

---
 README.md                        | 1 +
 funstuff/happy_mothers_day.ipynb | 5 +++--
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index c970e0e..a6a6686 100755
--- a/README.md
+++ b/README.md
@@ -11,6 +11,7 @@ Useful functions, tutorials, and other Python-related things
 - [Benchmarks of different palindrome functions](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
 - [A collection of not so obvious Python stuff you should know!](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)  
 - [Python's scope resolution for variable names and the LEGB rule](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
+- [Happy Mother's Day](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)
 
 ### Links to Markdown files
 - [A thorough guide to SQLite database operations in Python](./sqlite3_howto/README.md)
diff --git a/funstuff/happy_mothers_day.ipynb b/funstuff/happy_mothers_day.ipynb
index 85ef519..f076f11 100644
--- a/funstuff/happy_mothers_day.ipynb
+++ b/funstuff/happy_mothers_day.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:eeeed0cea91d7503e20b21f6a2e90637fbd3a9ae171e49fc645e45d9e2f77b8c"
+  "signature": "sha256:1e7add170bf70122859ff2c20df61a8aa1e1cd07c56d24b3aef9b53159412581"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,7 +15,8 @@
       "Sebastian Raschka\n",
       "last updated: 05/11/2014\n",
       "\n",
-      "- Link to the GitHub Repository"
+      "- Link to [this IPython Notebook](https://github.com/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb) on GitHub\n",
+      "- Link to the [GitHub repository](https://github.com/rasbt/python_reference/tree/master/funstuff)"
      ]
     },
     {

From 829a260c9a8557403f54cef949f0bff3f2ea5bc3 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 11 May 2014 12:44:26 -0400
Subject: [PATCH 037/248] Happy Mother's Day

---
 funstuff/happy_mothers_day.ipynb | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/funstuff/happy_mothers_day.ipynb b/funstuff/happy_mothers_day.ipynb
index f076f11..9a3acd6 100644
--- a/funstuff/happy_mothers_day.ipynb
+++ b/funstuff/happy_mothers_day.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:1e7add170bf70122859ff2c20df61a8aa1e1cd07c56d24b3aef9b53159412581"
+  "signature": "sha256:22eebfc6362172b18bd5f7cf4997f70cd1453ec5eccf2c962aa66119c70cc82d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,13 +12,21 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Sebastian Raschka\n",
+      "Sebastian Raschka  \n",
       "last updated: 05/11/2014\n",
       "\n",
       "- Link to [this IPython Notebook](https://github.com/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb) on GitHub\n",
       "- Link to the [GitHub repository](https://github.com/rasbt/python_reference/tree/master/funstuff)"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},

From 31a40c29bbaf93d8de59dd834683c1eae958d03e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 11 May 2014 13:13:47 -0400
Subject: [PATCH 038/248] link upd.

---
 funstuff/happy_mothers_day.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/funstuff/happy_mothers_day.ipynb b/funstuff/happy_mothers_day.ipynb
index 9a3acd6..de8f0db 100644
--- a/funstuff/happy_mothers_day.ipynb
+++ b/funstuff/happy_mothers_day.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:22eebfc6362172b18bd5f7cf4997f70cd1453ec5eccf2c962aa66119c70cc82d"
+  "signature": "sha256:c47e61a194d5b7fe9015e4a6494048a41f535f22a9a874def78f1a7fc13b1243"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,11 +12,11 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Sebastian Raschka  \n",
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
       "last updated: 05/11/2014\n",
       "\n",
       "- Link to [this IPython Notebook](https://github.com/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb) on GitHub\n",
-      "- Link to the [GitHub repository](https://github.com/rasbt/python_reference/tree/master/funstuff)"
+      "- Link to the [GitHub repository](https://github.com/rasbt/python_reference)"
      ]
     },
     {

From 061975b72aa424bd28b01860b1a49ddfc8e831fe Mon Sep 17 00:00:00 2001
From: Sebastian Raschka <raschkas@msu.edu>
Date: Mon, 12 May 2014 15:25:31 -0400
Subject: [PATCH 039/248] reorganized Readme

---
 README.md                        | 43 ++++++++++-----
 useful_scripts/fix_tab_csv.ipynb | 94 ++++++++++++++++++++++++++++++++
 2 files changed, 124 insertions(+), 13 deletions(-)
 create mode 100644 useful_scripts/fix_tab_csv.ipynb

diff --git a/README.md b/README.md
index a6a6686..8dcdf81 100755
--- a/README.md
+++ b/README.md
@@ -4,16 +4,33 @@ Python Tutorials and References
 Useful functions, tutorials, and other Python-related things
 
 
-###Links to view the IPython Notebooks
-
-- [Python benchmarks via `timeit`](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
-- [Implementing the least squares fit method for linear regression and speeding it up via Cython](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
-- [Benchmarks of different palindrome functions](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
-- [A collection of not so obvious Python stuff you should know!](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)  
-- [Python's scope resolution for variable names and the LEGB rule](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
-- [Happy Mother's Day](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)
-
-### Links to Markdown files
-- [A thorough guide to SQLite database operations in Python](./sqlite3_howto/README.md)
-- [Unit testing in Python - Why we want to make it a habit](./tutorials/unit_testing.md)
-- [Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks](./tutorials/installing_scientific_packages.md)
+###Links to view the IPython Notebooks and Markdown files
+
+<br>
+<br>
+<br>
+
+**// Python tips and tutorials**  
+
+- A collection of not so obvious Python stuff you should know! [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)  
+- Python's scope resolution for variable names and the LEGB rule [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
+- A thorough guide to SQLite database operations in Python [[Markdown]](./sqlite3_howto/README.md)
+- Unit testing in Python - Why we want to make it a habit [[Markdown]](./tutorials/unit_testing.md)
+- Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown]](./tutorials/installing_scientific_packages.md)
+
+**// benchmarks**
+
+- Python benchmarks via `timeit` [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
+- Implementing the least squares fit method for linear regression and speeding it up via Cython [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
+- Benchmarks of different palindrome functions [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
+
+
+
+**// other**
+
+- Happy Mother's Day [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)
+
+
+**// useful snippets**
+
+- convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)
diff --git a/useful_scripts/fix_tab_csv.ipynb b/useful_scripts/fix_tab_csv.ipynb
new file mode 100644
index 0000000..496f89f
--- /dev/null
+++ b/useful_scripts/fix_tab_csv.ipynb
@@ -0,0 +1,94 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:996358a25da6fc77c66d183e79209307af06bd2f9abb0656d3bb70cfc2fe597a"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Sebastian Raschka 05/09/2014"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Fixing CSV files"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We have a directory `../CSV_files_raw/` with CSV files where some of them have 'tab-separated' and some of them 'comma-separated' columns.  \n",
+      "Here, we will 'fix' them, i.e., have them all comma-separated, and save them to a new directory `../CSV_fixed`."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First, we create a dictionary with the file basenames as keys. The values are lists of the file paths to the raw and new fixed CSV files. e.g., \n",
+      "\n",
+      "    {\n",
+      "    'abc.csv': ['../CSV_files_raw/abc.csv', '../CSV_fixed/abc.csv'], \n",
+      "    'def.csv': ['../CSV_files_raw/def.csv', '../CSV_fixed/def.csv'], \n",
+      "    ...\n",
+      "    }"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "import os\n",
+      "\n",
+      "raw_dir = '../CSV_files_raw/'\n",
+      "fixed_dir = '../CSV_fixed'\n",
+      "\n",
+      "if not os.path.exists(fixed_dir):\n",
+      "    os.mkdir(fixed_dir)\n",
+      "\n",
+      "f_dict = {os.path.basename(f):[os.path.join(raw_dir, f),\n",
+      "                                  os.path.join(fixed_dir, f)]\n",
+      "             for f in os.listdir(raw_dir) if f.endswith('.csv')} "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, we can replace the tabs with commas for the new files very easily:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for f in f_dict.keys():\n",
+      "    with open(f_dict[f][0], 'r') as raw, open(f_dict[f][1], 'w') as fixed:\n",
+      "        for line in raw:\n",
+      "            line = line.strip().split('\\t')\n",
+      "            fixed.write(','.join(line) + '\\n')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From df97f1b70c7e5c535a7689b8454a07ba71aed81a Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 13 May 2014 22:41:40 -0400
Subject: [PATCH 040/248] sorting csvs

---
 README.md                               |   3 +-
 tutorials/scope_resolution_legb_rule.md | 579 +++++++++++++++++++++++
 tutorials/sorting_csvs.ipynb            | 585 ++++++++++++++++++++++++
 3 files changed, 1166 insertions(+), 1 deletion(-)
 create mode 100644 tutorials/scope_resolution_legb_rule.md
 create mode 100644 tutorials/sorting_csvs.ipynb

diff --git a/README.md b/README.md
index 8dcdf81..0d2e24c 100755
--- a/README.md
+++ b/README.md
@@ -17,13 +17,14 @@ Useful functions, tutorials, and other Python-related things
 - A thorough guide to SQLite database operations in Python [[Markdown]](./sqlite3_howto/README.md)
 - Unit testing in Python - Why we want to make it a habit [[Markdown]](./tutorials/unit_testing.md)
 - Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown]](./tutorials/installing_scientific_packages.md)
+- Sorting CSV files using the Python csv module [](./tutorials/http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb?create=1)
 
 **// benchmarks**
 
 - Python benchmarks via `timeit` [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
 - Implementing the least squares fit method for linear regression and speeding it up via Cython [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
 - Benchmarks of different palindrome functions [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
-
+- (#marking)
 
 
 **// other**
diff --git a/tutorials/scope_resolution_legb_rule.md b/tutorials/scope_resolution_legb_rule.md
new file mode 100644
index 0000000..6722604
--- /dev/null
+++ b/tutorials/scope_resolution_legb_rule.md
@@ -0,0 +1,579 @@
+# A Beginner's Guide to Python's Namespaces, Scope Resolution, and the LEGB Rule #
+
+
+This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download it as IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb).
+
+<br>
+<br>
+
+## Objectives
+- Namespaces and scopes - where does Python look for variable names?
+- Can we define/reuse variable names for multiple objects at the same time?
+- In which order does Python search different namespaces for variable names?
+
+<br>
+<br>
+
+## Sections  
+- [Introduction to namespaces and scopes](#introduction)  
+- [1. LG - Local and Global scopes](#section_1)  
+- [2. LEG - Local, Enclosed, and Global scope](#section_2)  
+- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3)  
+- [Self-assessment exercise](#assessment)
+- [Conclusion](#conclusion)  
+- [Solutions](#solutions)
+- [Warning: For-loop variables "leaking" into the global namespace](#for_loop)
+
+<a name="introduction"></a>
+<br>
+<br>
+
+##Introduction to Namespaces and Scopes
+
+<br>
+
+###Namespaces
+<br>
+
+Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object.  
+Such a "name-to-object" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = "Hello string"`, we created a reference to the `"Hello string"` object, and henceforth we can access via its variable name `a_string`.
+
+We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., 
+
+<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}</pre>  
+
+
+Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example:
+
+<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}
+b_namespace = {'name_a':object_3, 'name_b':object_4, ...}</pre>
+
+For example, every time we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called "scope"), which we will discuss in more detail in the next section.
+
+<br>
+<br>
+
+### Scope
+
+
+In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of "scope". The "scope" in Python defines the "hierarchy level" in which we search namespaces for certain "name-to-object" mappings.  
+For example, let us consider the following code:
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">foo</span>():
+    i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">5</span>
+    <span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;in foo()&#39;</span>)
+<span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;global&#39;</span>)
+
+foo()
+</pre></div>
+
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">1 global</span>
+<span style="color: #888888">5 in foo()</span>
+</pre></div>
+
+<br>
+<br>
+Here, we just defined the variable name `i` twice, once on the `foo` function.
+
+- `foo_namespace = {'i':object_3, ...}`  
+- `global_namespace = {'i':object_1, 'name_b':object_2, ...}`
+
+So, how does Python now which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section.
+
+<br>
+### Tip:
+If we want to print out the dictionary mapping of the global and local variables, we can use the
+the functions `global()` and `local()
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">#print(globals()) # prints global namespace</span>
+<span style="color: #888888">#print(locals()) # prints local namespace</span>
+
+glob <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">foo</span>():
+    loc <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">5</span>
+    <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;loc in foo():&#39;</span>, <span style="background-color: #fff0f0">&#39;loc&#39;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">locals</span>())
+
+foo()
+<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;loc in global:&#39;</span>, <span style="background-color: #fff0f0">&#39;loc&#39;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">globals</span>())    
+<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;glob in global:&#39;</span>, <span style="background-color: #fff0f0">&#39;foo&#39;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">globals</span>())
+</pre></div>
+
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">loc in foo(): True</span>
+<span style="color: #888888">loc in global: False</span>
+<span style="color: #888888">glob in global: True</span>
+</pre></div>
+
+<br>
+<br>
+
+### Scope resolution for variable names via the LEGB rule.
+
+We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The "scope" defines on which hierarchy level Python searches for a particular "variable name" for its associated object. Now, the next question is: "In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?"  
+To answer is: It uses the LEGB-rule, which stands for
+
+**Local -> Enclosed -> Global -> Built-in**, 
+
+where the arrows should denote the direction of the namespace-hierarchy search order.  
+
+- *Local* can be inside a function or class method, for example.  
+- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function.  
+- *Global* refers to the uppermost level of the executing script itself, and  
+- *Built-in* are special names that Python reserves for itself.  
+
+So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the global namespaces (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).
+
+**Note**:  
+Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">import</span> <span style="color: #0e84b5; font-weight: bold">numpy</span>
+<span style="color: #008800; font-weight: bold">import</span> <span style="color: #0e84b5; font-weight: bold">math</span>
+<span style="color: #008800; font-weight: bold">import</span> <span style="color: #0e84b5; font-weight: bold">scipy</span>
+
+<span style="color: #007020">print</span>(math<span style="color: #333333">.</span>pi, <span style="background-color: #fff0f0">&#39;from the math module&#39;</span>)
+<span style="color: #007020">print</span>(numpy<span style="color: #333333">.</span>pi, <span style="background-color: #fff0f0">&#39;from the numpy package&#39;</span>)
+<span style="color: #007020">print</span>(scipy<span style="color: #333333">.</span>pi, <span style="background-color: #fff0f0">&#39;from the scipy package&#39;</span>)
+</pre></div>
+
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">3.141592653589793 from the math module</span>
+<span style="color: #888888">3.141592653589793 from the numpy package</span>
+<span style="color: #888888">3.141592653589793 from the scipy package</span>
+</pre></div>
+<br>
+<br>
+(This is also why we have to be careful if we import modules via "`from a_module import *`", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)
+
+<br>
+<a name='section1'></a>
+<br>
+<br>
+![LEGB figure](../Images/scope_resolution_1.png)
+<br>
+<br>
+
+<a name="section_1"></a>
+<br>
+<br>
+
+## 1. LG - Local and Global scopes
+
+<a name='example1.1'></a>
+**Example 1.1**  
+As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes.  
+What does the following code print?
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global variable&#39;</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
+    <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
+
+a_func()
+<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
+</pre></div>
+
+**a)**
+<pre>raises an error</pre>
+
+**b)** 
+<pre>
+global value [ a_var outside a_func() ]</pre>
+
+**c)** 
+<pre>global value [ a_var in a_func() ]  
+global value [ a_var outside a_func() ]</pre>
+
+[[go to solution](#solutions)]
+
+### Here is why:
+
+We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously.
+<br>
+<br>
+
+<a name='example1.2'></a>
+**Example 1.2**  
+Now, let us define the variable `a_var` in the global and the local scope.  
+Can you guess what the following code will produce?
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global value&#39;</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
+    a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local value&#39;</span>
+    <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
+
+a_func()
+<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
+</pre></div>
+
+**a)**
+<pre>raises an error</pre>
+
+**b)** 
+<pre>local value [ a_var in a_func() ]
+global value [ a_var outside a_func() ]</pre>
+
+**c)** 
+<pre>global value [ a_var in a_func() ]  
+global value [ a_var outside a_func() ]</pre>
+
+
+[[go to solution](#solutions)]
+
+### Here is why:
+
+When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope.
+
+<br>
+However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate:
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global value&#39;</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
+    <span style="color: #008800; font-weight: bold">global</span> a_var
+    a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local value&#39;</span>
+    <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
+
+<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
+a_func()
+<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
+</pre></div>
+
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">**a)**</span>
+<span style="color: #888888">&lt;pre&gt;raises an error&lt;/pre&gt;</span>
+
+<span style="color: #888888">**b)** </span>
+<span style="color: #888888">&lt;pre&gt;</span>
+<span style="color: #888888">global value [ a_var outside a_func() ]&lt;/pre&gt;</span>
+
+<span style="color: #888888">**c)** </span>
+<span style="color: #888888">&lt;pre&gt;global value [ a_var in a_func() ]  </span>
+<span style="color: #888888">global value [ a_var outside a_func() ]&lt;/pre&gt;</span>
+</pre></div>
+
+But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first):
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
+    a_var <span style="color: #333333">=</span> a_var <span style="color: #333333">+</span> <span style="color: #0000DD; font-weight: bold">1</span>
+    <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
+
+<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
+a_func()
+</pre></div>
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #333333">---------------------------------------------------------------------------</span>
+<span style="color: #FF0000; font-weight: bold">UnboundLocalError</span>                         Traceback (most recent call last)
+<span style="color: #333333">&lt;</span>ipython<span style="color: #333333">-</span><span style="color: #007020">input</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">4</span><span style="color: #333333">-</span>a6cdd0ee9a55<span style="color: #333333">&gt;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>()
+      <span style="color: #0000DD; font-weight: bold">6</span> 
+      <span style="color: #0000DD; font-weight: bold">7</span> <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
+<span style="color: #333333">----&gt;</span> <span style="color: #0000DD; font-weight: bold">8</span> a_func()
+
+<span style="color: #333333">&lt;</span>ipython<span style="color: #333333">-</span><span style="color: #007020">input</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">4</span><span style="color: #333333">-</span>a6cdd0ee9a55<span style="color: #333333">&gt;</span> <span style="color: #000000; font-weight: bold">in</span> a_func()
+      <span style="color: #0000DD; font-weight: bold">2</span> 
+      <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
+<span style="color: #333333">----&gt;</span> <span style="color: #0000DD; font-weight: bold">4</span>     a_var <span style="color: #333333">=</span> a_var <span style="color: #333333">+</span> <span style="color: #0000DD; font-weight: bold">1</span>
+      <span style="color: #0000DD; font-weight: bold">5</span>     <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
+      <span style="color: #0000DD; font-weight: bold">6</span> 
+
+<span style="color: #FF0000; font-weight: bold">UnboundLocalError</span>: local variable <span style="background-color: #fff0f0">&#39;a_var&#39;</span> referenced before assignment
+
+<span style="color: #0000DD; font-weight: bold">1</span> [ a_var outside a_func() ]
+</pre></div>
+
+<br>
+<br>
+
+<a name="section_2"></a>
+<br>
+<br>
+
+## 2. LEG - Local, Enclosed, and Global scope
+
+
+
+Now, let us introduce the concept of the enclosed (E) scope. Following the order "Local -> Enclosed -> Global", can you guess what the following code will print?
+
+<a name='example2'></a>
+**Example 2.1**
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global value&#39;</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">outer</span>():
+    a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;enclosed value&#39;</span>
+    
+    <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">inner</span>():
+        a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local value&#39;</span>
+        <span style="color: #007020">print</span>(a_var)
+    
+    inner()
+
+outer()
+</pre></div>
+**a)**
+<pre>global value</pre>
+
+**b)** 
+<pre>enclosed value</pre>
+
+**c)** 
+<pre>local value</pre>
+
+[[go to solution](#solutions)]
+
+### Here is why:
+
+Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope.
+
+Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitly access a variable from the outer (enclosed) scope in order to modify its value.  
+Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet).
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global value&#39;</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">outer</span>():
+       a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local value&#39;</span>
+       <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;outer before:&#39;</span>, a_var)
+       <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">inner</span>():
+           <span style="color: #008800; font-weight: bold">nonlocal</span> a_var
+           a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;inner value&#39;</span>
+           <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;in inner():&#39;</span>, a_var)
+       inner()
+       <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&quot;outer after:&quot;</span>, a_var)
+outer()
+</pre></div>
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">outer before: local value</span>
+<span style="color: #888888">in inner(): inner value</span>
+<span style="color: #888888">outer after: inner value</span>
+</pre></div>
+
+<a name="section_3"></a>
+<br>
+<br>
+<br>
+
+
+## 3. LEGB - Local, Enclosed, Global, Built-in
+
+To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our "own" length-function, which happens to bear the same name as the in-built `len()` function. What outcome do you expect if we'd execute the following code?
+
+<a name='example3'></a>
+
+**Example 3**
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global variable&#39;</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">len</span>(in_var):
+    <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;called my len() function&#39;</span>)
+    l <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">0</span>
+    <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> in_var:
+        l <span style="color: #333333">+=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+    <span style="color: #008800; font-weight: bold">return</span> l
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>(in_var):
+    len_in_var <span style="color: #333333">=</span> <span style="color: #007020">len</span>(in_var)
+    <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Input variable is of length&#39;</span>, len_in_var)
+
+a_func(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
+</pre></div>
+
+**a)**
+<pre>raises an error (conflict with in-built `len()` function)</pre>
+
+**b)** 
+<pre>called my len() function
+Input variable is of length 13</pre>
+
+**c)** 
+<pre>Input variable is of length 13</pre>
+
+[[go to solution](#solutions)]
+
+### Here is why:
+
+Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration purposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope first before it attempts
+
+
+<a name ="assessment"></a>
+<br>
+<br>
+
+# Self-assessment exercise
+
+Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global&#39;</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">outer</span>():
+    
+    <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">len</span>(in_var):
+        <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;called my len() function: &#39;</span>, end<span style="color: #333333">=</span><span style="background-color: #fff0f0">&quot;&quot;</span>)
+        l <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">0</span>
+        <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> in_var:
+            l <span style="color: #333333">+=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+        <span style="color: #008800; font-weight: bold">return</span> l
+    
+    a <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local&#39;</span>
+    
+    <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">inner</span>():
+        <span style="color: #008800; font-weight: bold">global</span> <span style="color: #007020">len</span>
+        <span style="color: #008800; font-weight: bold">nonlocal</span> a
+        a <span style="color: #333333">+=</span> <span style="background-color: #fff0f0">&#39; variable&#39;</span>
+    inner()
+    <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;a is&#39;</span>, a)
+    <span style="color: #007020">print</span>(<span style="color: #007020">len</span>(a))
+
+outer()
+
+<span style="color: #007020">print</span>(<span style="color: #007020">len</span>(a))
+<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;a is&#39;</span>, a)
+</pre></div>
+
+<a name="conclusion"
+<br>
+<br>
+
+[[go to solution](#solutions)]
+
+# Conclusion
+
+I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes.
+
+#### A rule of thumb
+
+In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug.  
+If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value.  
+For example:
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">2</span>
+
+<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>(some_var):
+    <span style="color: #008800; font-weight: bold">return</span> <span style="color: #0000DD; font-weight: bold">2</span><span style="color: #333333">**</span><span style="color: #0000DD; font-weight: bold">3</span>
+
+a_var <span style="color: #333333">=</span> a_func(a_var)
+<span style="color: #007020">print</span>(a_var)
+</pre></div>
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">8</span>
+</pre></div>
+
+
+<a name = "solutions"></a>
+<br>
+<br>
+
+## Solutions
+
+In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Example 1.1:&#39;</span>, <span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(<span style="background-color: #fff0f0">&#39;01100011&#39;</span>,<span style="color: #0000DD; font-weight: bold">2</span>)))
+</pre></div>
+
+[[back to example 1.1](#example1.1)]
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Example 1.2:&#39;</span>, <span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(<span style="background-color: #fff0f0">&#39;01100001&#39;</span>,<span style="color: #0000DD; font-weight: bold">2</span>)))
+</pre></div>
+
+[[back to example 1.2](#example1.2)]
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Example 2:&#39;</span>, <span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(<span style="background-color: #fff0f0">&#39;01100011&#39;</span>,<span style="color: #0000DD; font-weight: bold">2</span>)))
+</pre></div>
+
+[[back to example 2](#example2)]
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Example 3:&#39;</span>, <span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(<span style="background-color: #fff0f0">&#39;01100010&#39;</span>,<span style="color: #0000DD; font-weight: bold">2</span>)))
+</pre></div>
+
+[[back to example 3](#example3)]
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Solution to the self-assessment exercise</span>
+sol <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&quot;000010100110111101110101011101000110010101110010001010&quot;</span>\
+<span style="background-color: #fff0f0">&quot;0000101001001110100000101000001010011000010010000001101001011100110&quot;</span>\
+<span style="background-color: #fff0f0">&quot;0100000011011000110111101100011011000010110110000100000011101100110&quot;</span>\
+<span style="background-color: #fff0f0">&quot;0001011100100110100101100001011000100110110001100101000010100110001&quot;</span>\
+<span style="background-color: #fff0f0">&quot;1011000010110110001101100011001010110010000100000011011010111100100&quot;</span>\
+<span style="background-color: #fff0f0">&quot;1000000110110001100101011011100010100000101001001000000110011001110&quot;</span>\
+<span style="background-color: #fff0f0">&quot;1010110111001100011011101000110100101101111011011100011101000100000&quot;</span>\
+<span style="background-color: #fff0f0">&quot;0011000100110100000010100000101001100111011011000110111101100010011&quot;</span>\
+<span style="background-color: #fff0f0">&quot;0000101101100001110100000101000001010001101100000101001100001001000&quot;</span>\
+<span style="background-color: #fff0f0">&quot;0001101001011100110010000001100111011011000110111101100010011000010&quot;</span>\
+<span style="background-color: #fff0f0">&quot;1101100&quot;</span>
+
+sol_str <span style="color: #333333">=</span><span style="background-color: #fff0f0">&#39;&#39;</span><span style="color: #333333">.</span>join(<span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(sol[i:i<span style="color: #333333">+</span><span style="color: #0000DD; font-weight: bold">8</span>], <span style="color: #0000DD; font-weight: bold">2</span>)) <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">0</span>, <span style="color: #007020">len</span>(sol), <span style="color: #0000DD; font-weight: bold">8</span>))
+<span style="color: #008800; font-weight: bold">for</span> line <span style="color: #000000; font-weight: bold">in</span> sol_str<span style="color: #333333">.</span>split(<span style="background-color: #fff0f0">&#39;</span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\n</span><span style="background-color: #fff0f0">&#39;</span>):
+    <span style="color: #007020">print</span>(line)
+</pre></div>
+
+[[back to self-assessment exercise](#assessment)]
+
+
+
+
+<br>
+<br>
+<a name="for_loop"></a>
+
+## Warning: For-loop variables "leaking" into the global namespace
+
+In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">for</span> a <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>):
+    <span style="color: #008800; font-weight: bold">if</span> a <span style="color: #333333">==</span> <span style="color: #0000DD; font-weight: bold">4</span>:
+        <span style="color: #007020">print</span>(a, <span style="background-color: #fff0f0">&#39;-&gt; a in for-loop&#39;</span>)
+<span style="color: #007020">print</span>(a, <span style="background-color: #fff0f0">&#39;-&gt; a in global&#39;</span>)
+</pre></div>
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">4 -&gt; a in for-loop</span>
+<span style="color: #888888">4 -&gt; a in global</span>
+</pre></div>
+
+**This also applies if we explicitely defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">b <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #008800; font-weight: bold">for</span> b <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>):
+    <span style="color: #008800; font-weight: bold">if</span> b <span style="color: #333333">==</span> <span style="color: #0000DD; font-weight: bold">4</span>:
+        <span style="color: #007020">print</span>(b, <span style="background-color: #fff0f0">&#39;-&gt; b in for-loop&#39;</span>)
+<span style="color: #007020">print</span>(b, <span style="background-color: #fff0f0">&#39;-&gt; b in global&#39;</span>)
+</pre></div>
+
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">4 -&gt; b in for-loop</span>
+<span style="color: #888888">4 -&gt; b in global</span>
+</pre></div>
+
+However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):
+
+`Input:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #007020">print</span>([i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)])
+<span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span>)
+</pre></div>
+`Output:`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">[0, 1, 2, 3, 4]</span>
+<span style="color: #888888">1 -&gt; i in global</span>
+</pre></div>
+
+Why did I mention "Python 3.x"? Well, as it happens, the same code executed in Python 2.x would print:
+
+<pre>
+4 -> i in global
+</pre>
+
+This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:
+
+"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope."
\ No newline at end of file
diff --git a/tutorials/sorting_csvs.ipynb b/tutorials/sorting_csvs.ipynb
new file mode 100644
index 0000000..9aaf2f2
--- /dev/null
+++ b/tutorials/sorting_csvs.ipynb
@@ -0,0 +1,585 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:c498612c4d414160d8e015bc6858051328e51cd00e56b3215dca53652ee7c936"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "last updated: 05/13/2014\n",
+      "\n",
+      "- Open in [IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb?create=1)  \n",
+      "- Link to this [IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)  \n",
+      "- Link to the GitHub Repository [`python_reference`](https://github.com/rasbt/python_reference)\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I am looking forward to comments or suggestions, please don't hesitate to contact me via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Sorting CSV files using the Python `csv` module"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "I wanted to summarize a way to sort CSV files by just using the [`csv` module](https://docs.python.org/3.4/library/csv.html) and other standard library Python modules   \n",
+      "(you probably also want to consider using the [pandas](http://pandas.pydata.org) library if you are working with very large CSV files - I am planning to make this a separate topic)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "<hr>\n",
+      "## Sections\n",
+      "- [Reading in a CSV file](#reading)\n",
+      "- [Printing the CSV file contents](#printing)\n",
+      "- [Sorting the CSV file](#sorting)\n",
+      "- [Marking min/max values in particular columns](#marking)\n",
+      "- [Writing out the modified table to as a new CSV file](#writing)\n",
+      "<hr>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Objective:\n",
+      "\n",
+      "Let us assume that we have an [example CSV](../Data/test.csv) file formatted like this:\n",
+      "    \n",
+      "<pre>name,column1,column2,column3\n",
+      "abc,1.1,4.2,1.2\n",
+      "def,2.1,1.4,5.2\n",
+      "ghi,1.5,1.2,2.1\n",
+      "jkl,1.8,1.1,4.2\n",
+      "mno,9.4,6.6,6.2\n",
+      "pqr,1.4,8.3,8.4</pre>\n",
+      "\n",
+      "And we want to sort particular columns and eventually mark min- of max-values in the table.\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='sections'></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='reading'></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "##Reading in a CSV file"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Because we will be iterating over our CSV file a couple of times, let us read in the CSV file using the `csv` module and hold the contents in memory using a Python list object (note: be careful with very large CSV files and possible memory issues associated with this approach).\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import csv\n",
+      "\n",
+      "def csv_to_list(csv_file, delimiter=','):\n",
+      "    \"\"\" \n",
+      "    Reads in a CSV file and returns the contents as list,\n",
+      "    where every row is stored as a sublist, and each element\n",
+      "    in the sublist represents 1 cell in the table.\n",
+      "    \n",
+      "    \"\"\"\n",
+      "    csv_content = []\n",
+      "    with open(csv_file, 'r') as csv_con:\n",
+      "        reader = csv.reader(csv_con, delimiter=delimiter)\n",
+      "        for row in reader:\n",
+      "            csv_content.append(row)\n",
+      "    return csv_content"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "csv_cont = csv_to_list('../Data/test.csv')\n",
+      "\n",
+      "print('first 3 rows:')\n",
+      "for row in range(3):\n",
+      "    print(csv_cont[row])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "first 3 rows:\n",
+        "['name', 'column1', 'column2', 'column3']\n",
+        "['abc', '1.1', '4.2', '1.2']\n",
+        "['def', '2.1', '1.4', '5.2']\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='printing'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "##Printing the CSV file contents"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Also, let us define a short function that prints out the CSV file to the standard output screen in a slightly prettier format:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def print_csv(csv_content):\n",
+      "    \"\"\" Prints CSV file to standard output.\"\"\"\n",
+      "    print(50*'-')\n",
+      "    for row in csv_content:\n",
+      "        print('\\t'.join(row))\n",
+      "    print(50*'-')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "csv_cont = csv_to_list('../Data/test.csv')\n",
+      "\n",
+      "print('\\n\\nOriginal CSV file:')\n",
+      "print_csv(csv_cont)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Original CSV file:\n",
+        "--------------------------------------------------\n",
+        "name\tcolumn1\tcolumn2\tcolumn3\n",
+        "abc\t1.1\t4.2\t1.2\n",
+        "def\t2.1\t1.4\t5.2\n",
+        "ghi\t1.5\t1.2\t2.1\n",
+        "jkl\t1.8\t1.1\t4.2\n",
+        "mno\t9.4\t6.6\t6.2\n",
+        "pqr\t1.4\t8.3\t8.4\n",
+        "--------------------------------------------------\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='sorting'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "##Sorting the CSV file"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Using the very handy [`operator.itemgetter`](https://docs.python.org/3.4/library/operator.html#operator.itemgetter) function, we define a function that returns a CSV file contents sorted by a particular column (column index or column name)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import operator\n",
+      "\n",
+      "def sort_by_column(csv_cont, col, reverse=False):\n",
+      "    \"\"\" \n",
+      "    Sorts CSV contents by column name (if col argument is type <str>) \n",
+      "    or column index (if col argument is type <int>). \n",
+      "    \n",
+      "    \"\"\"\n",
+      "    header = csv_cont[0]\n",
+      "    body = csv_cont[1:]\n",
+      "    if isinstance(col, str):  \n",
+      "        col_index = header.index(col)\n",
+      "    else:\n",
+      "        col_index = col\n",
+      "    body = sorted(body, \n",
+      "           key=operator.itemgetter(col_index), \n",
+      "           reverse=reverse)\n",
+      "    body.insert(0, header)\n",
+      "    return body"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To see how (and if) it works, let us sort the CSV file in [../Data/test.csv](../Data/test.csv) by the column name \"column3\"."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "csv_cont = csv_to_list('../Data/test.csv')\n",
+      "\n",
+      "print('\\n\\nOriginal CSV file:')\n",
+      "print_csv(csv_cont)\n",
+      "\n",
+      "print('\\n\\nCSV sorted by column \"column3\":')\n",
+      "csv_sorted = sort_by_column(csv_cont, 'column3')\n",
+      "print_csv(csv_sorted)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Original CSV file:\n",
+        "--------------------------------------------------\n",
+        "name\tcolumn1\tcolumn2\tcolumn3\n",
+        "abc\t1.1\t4.2\t1.2\n",
+        "def\t2.1\t1.4\t5.2\n",
+        "ghi\t1.5\t1.2\t2.1\n",
+        "jkl\t1.8\t1.1\t4.2\n",
+        "mno\t9.4\t6.6\t6.2\n",
+        "pqr\t1.4\t8.3\t8.4\n",
+        "--------------------------------------------------\n",
+        "\n",
+        "\n",
+        "CSV sorted by column \"column3\":\n",
+        "--------------------------------------------------\n",
+        "name\tcolumn1\tcolumn2\tcolumn3\n",
+        "abc\t1.1\t4.2\t1.2\n",
+        "ghi\t1.5\t1.2\t2.1\n",
+        "jkl\t1.8\t1.1\t4.2\n",
+        "def\t2.1\t1.4\t5.2\n",
+        "mno\t9.4\t6.6\t6.2\n",
+        "pqr\t1.4\t8.3\t8.4\n",
+        "--------------------------------------------------\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='marking'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Marking min/max values in particular columns"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To visualize minimum and maximum values in certain columns if find it quite useful to add little symbols to the cells (most people like to highlight cells with colors in e.g., Excel spreadsheets, but CSV doesn't support colors, so this is my workaround - please let me know if you figured out a better approach, I would be looking forward to your suggestion)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def mark_minmax(csv_cont, col, mark_max=True, marker='*'):\n",
+      "    \"\"\"\n",
+      "    Sorts a list of CSV contents by a particular column \n",
+      "    (see sort_by_column function).\n",
+      "    Puts a marker on the maximum value if mark_max=True,\n",
+      "    or puts a marker on the minimum value mark_max=False\n",
+      "    (modifies input CSV content list).\n",
+      "    \n",
+      "    \"\"\"\n",
+      "    \n",
+      "    sorted_csv = sort_by_column(csv_cont, col, reverse=mark_max)\n",
+      "    if isinstance(col, str):  \n",
+      "        col_index = sorted_csv[0].index(col)\n",
+      "    else:\n",
+      "        col_index = col\n",
+      "    sorted_csv[1][col_index] += marker\n",
+      "    return None"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def mark_all_col(csv_cont, mark_max=True, marker='*'):\n",
+      "    \"\"\"\n",
+      "    Marks all maximum (if mark_max=True) or minimum (if mark_max=False)\n",
+      "    values in all columns of a CSV contents list - except the first column.\n",
+      "    Returns a new list that is sorted by the names in the first column\n",
+      "    (modifies input CSV content list).\n",
+      "    \n",
+      "    \"\"\"\n",
+      "    for c in csv_cont[0][1:]:\n",
+      "        mark_minmax(csv_cont, c, mark_max, marker)\n",
+      "    marked_csv = sort_by_column(csv_cont, 0, False)\n",
+      "    return marked_csv"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import copy\n",
+      "csv_cont = csv_to_list('../Data/test.csv')\n",
+      "\n",
+      "csv_marked = copy.deepcopy(csv_cont)\n",
+      "mark_all_col(csv_marked, mark_max=True, marker='*')\n",
+      "mark_all_col(csv_marked, mark_max=False, marker='^')\n",
+      "print_csv(csv_marked)\n",
+      "\n",
+      "print('^: min-value\\n*: max-value')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "--------------------------------------------------\n",
+        "name\tcolumn1\tcolumn2\tcolumn3\n",
+        "abc\t1.1^\t4.2\t1.2^\n",
+        "def\t2.1\t1.4\t5.2\n",
+        "ghi\t1.5\t1.2\t2.1\n",
+        "jkl\t1.8\t1.1^\t4.2\n",
+        "mno\t9.4*\t6.6\t6.2\n",
+        "pqr\t1.4\t8.3*\t8.4*\n",
+        "--------------------------------------------------\n",
+        "^: min-value\n",
+        "*: max-value\n"
+       ]
+      }
+     ],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='writing'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Writing out the modified table to as a new CSV file"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "After the sorting and maybe marking of minimum and maximum values, we likely want to write out the modified data table as CSV file again."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def write_csv(dest, csv_cont):\n",
+      "    \"\"\" Writes a comma-delimited CSV file. \"\"\"\n",
+      "\n",
+      "    with open(dest, 'w') as out_file:\n",
+      "        writer = csv.writer(out_file, delimiter=',')\n",
+      "        for row in csv_cont:\n",
+      "            writer.writerow(row)\n",
+      "\n",
+      "write_csv('../Data/test_marked.csv', csv_marked)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 34
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let us read in the written CSV file to confirm that the formatting is correct:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "csv_cont = csv_to_list('../Data/test_marked.csv')\n",
+      "\n",
+      "print('\\n\\nWritten CSV file:')\n",
+      "print_csv(csv_cont)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "Written CSV file:\n",
+        "--------------------------------------------------\n",
+        "name\tcolumn1\tcolumn2\tcolumn3\n",
+        "abc\t1.1^\t4.2\t1.2^\n",
+        "def\t2.1\t1.4\t5.2\n",
+        "ghi\t1.5\t1.2\t2.1\n",
+        "jkl\t1.8\t1.1^\t4.2\n",
+        "mno\t9.4*\t6.6\t6.2\n",
+        "pqr\t1.4\t8.3*\t8.4*\n",
+        "--------------------------------------------------\n"
+       ]
+      }
+     ],
+     "prompt_number": 35
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From db164b70b9bd961fea3eaf40ccbdf50b18dbdae8 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 13 May 2014 22:42:29 -0400
Subject: [PATCH 041/248] sorting csvs

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0d2e24c..ef89f87 100755
--- a/README.md
+++ b/README.md
@@ -17,7 +17,7 @@ Useful functions, tutorials, and other Python-related things
 - A thorough guide to SQLite database operations in Python [[Markdown]](./sqlite3_howto/README.md)
 - Unit testing in Python - Why we want to make it a habit [[Markdown]](./tutorials/unit_testing.md)
 - Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown]](./tutorials/installing_scientific_packages.md)
-- Sorting CSV files using the Python csv module [](./tutorials/http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb?create=1)
+- Sorting CSV files using the Python csv module [IPython nb](./tutorials/http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb?create=1)
 
 **// benchmarks**
 

From ac10148e8b322d763ea88e7e57289b0da0e7e02b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 13 May 2014 22:43:03 -0400
Subject: [PATCH 042/248] sorting csvs

---
 README.md | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index ef89f87..28d72c5 100755
--- a/README.md
+++ b/README.md
@@ -10,6 +10,8 @@ Useful functions, tutorials, and other Python-related things
 <br>
 <br>
 
+
+
 **// Python tips and tutorials**  
 
 - A collection of not so obvious Python stuff you should know! [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)  
@@ -17,14 +19,17 @@ Useful functions, tutorials, and other Python-related things
 - A thorough guide to SQLite database operations in Python [[Markdown]](./sqlite3_howto/README.md)
 - Unit testing in Python - Why we want to make it a habit [[Markdown]](./tutorials/unit_testing.md)
 - Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown]](./tutorials/installing_scientific_packages.md)
-- Sorting CSV files using the Python csv module [IPython nb](./tutorials/http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb?create=1)
+- Sorting CSV files using the Python csv module [[IPython nb](./tutorials/http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb?create=1)]
+
+
 
 **// benchmarks**
 
 - Python benchmarks via `timeit` [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
 - Implementing the least squares fit method for linear regression and speeding it up via Cython [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
 - Benchmarks of different palindrome functions [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
-- (#marking)
+
+
 
 
 **// other**

From e20369a6a1b89d290dcfe430b8ec5e431dcfed5f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 13 May 2014 22:44:46 -0400
Subject: [PATCH 043/248] sorting csvs

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 28d72c5..aa31d3b 100755
--- a/README.md
+++ b/README.md
@@ -19,7 +19,7 @@ Useful functions, tutorials, and other Python-related things
 - A thorough guide to SQLite database operations in Python [[Markdown]](./sqlite3_howto/README.md)
 - Unit testing in Python - Why we want to make it a habit [[Markdown]](./tutorials/unit_testing.md)
 - Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown]](./tutorials/installing_scientific_packages.md)
-- Sorting CSV files using the Python csv module [[IPython nb](./tutorials/http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb?create=1)]
+- Sorting CSV files using the Python csv module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
 
 

From cb155d03111bcad41142ef13155a47d7aabee76b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 13 May 2014 22:57:30 -0400
Subject: [PATCH 044/248] added data files for csv file sorting article

---
 test.csv        | 7 +++++++
 test_marked.csv | 7 +++++++
 2 files changed, 14 insertions(+)
 create mode 100644 test.csv
 create mode 100644 test_marked.csv

diff --git a/test.csv b/test.csv
new file mode 100644
index 0000000..7c8d315
--- /dev/null
+++ b/test.csv
@@ -0,0 +1,7 @@
+name,column1,column2,column3
+abc,1.1,4.2,1.2
+def,2.1,1.4,5.2
+ghi,1.5,1.2,2.1
+jkl,1.8,1.1,4.2
+mno,9.4,6.6,6.2
+pqr,1.4,8.3,8.4
diff --git a/test_marked.csv b/test_marked.csv
new file mode 100644
index 0000000..86dfd82
--- /dev/null
+++ b/test_marked.csv
@@ -0,0 +1,7 @@
+name,column1,column2,column3
+abc,1.1^,4.2,1.2^
+def,2.1,1.4,5.2
+ghi,1.5,1.2,2.1
+jkl,1.8,1.1^,4.2
+mno,9.4*,6.6,6.2
+pqr,1.4,8.3*,8.4*

From e43265cb579a623c3b733958032c4fff5bbfd516 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 13 May 2014 23:44:33 -0400
Subject: [PATCH 045/248] datafiles

---
 Data/test.csv                |  7 +++++++
 Data/test_marked.csv         |  7 +++++++
 tutorials/sorting_csvs.ipynb | 13 +++++--------
 3 files changed, 19 insertions(+), 8 deletions(-)
 create mode 100644 Data/test.csv
 create mode 100644 Data/test_marked.csv

diff --git a/Data/test.csv b/Data/test.csv
new file mode 100644
index 0000000..7c8d315
--- /dev/null
+++ b/Data/test.csv
@@ -0,0 +1,7 @@
+name,column1,column2,column3
+abc,1.1,4.2,1.2
+def,2.1,1.4,5.2
+ghi,1.5,1.2,2.1
+jkl,1.8,1.1,4.2
+mno,9.4,6.6,6.2
+pqr,1.4,8.3,8.4
diff --git a/Data/test_marked.csv b/Data/test_marked.csv
new file mode 100644
index 0000000..86dfd82
--- /dev/null
+++ b/Data/test_marked.csv
@@ -0,0 +1,7 @@
+name,column1,column2,column3
+abc,1.1^,4.2,1.2^
+def,2.1,1.4,5.2
+ghi,1.5,1.2,2.1
+jkl,1.8,1.1^,4.2
+mno,9.4*,6.6,6.2
+pqr,1.4,8.3*,8.4*
diff --git a/tutorials/sorting_csvs.ipynb b/tutorials/sorting_csvs.ipynb
index 9aaf2f2..04e99ab 100644
--- a/tutorials/sorting_csvs.ipynb
+++ b/tutorials/sorting_csvs.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c498612c4d414160d8e015bc6858051328e51cd00e56b3215dca53652ee7c936"
+  "signature": "sha256:683cfd7e6b5fd1dca9cd2028b294ec8ae44a175b613eb3c037d365a28957a987"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -133,12 +133,9 @@
       "    in the sublist represents 1 cell in the table.\n",
       "    \n",
       "    \"\"\"\n",
-      "    csv_content = []\n",
       "    with open(csv_file, 'r') as csv_con:\n",
       "        reader = csv.reader(csv_con, delimiter=delimiter)\n",
-      "        for row in reader:\n",
-      "            csv_content.append(row)\n",
-      "    return csv_content"
+      "        return list(reader)"
      ],
      "language": "python",
      "metadata": {},
@@ -169,7 +166,7 @@
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -215,7 +212,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -248,7 +245,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",

From ed0eb89f62135e64fd0e6df324c0c78b9725dfdd Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 14 May 2014 01:34:47 -0400
Subject: [PATCH 046/248] added conversion to float

---
 tutorials/sorting_csvs.ipynb | 102 ++++++++++++++++++++++++++++++-----
 1 file changed, 90 insertions(+), 12 deletions(-)

diff --git a/tutorials/sorting_csvs.ipynb b/tutorials/sorting_csvs.ipynb
index 04e99ab..237db52 100644
--- a/tutorials/sorting_csvs.ipynb
+++ b/tutorials/sorting_csvs.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:683cfd7e6b5fd1dca9cd2028b294ec8ae44a175b613eb3c037d365a28957a987"
+  "signature": "sha256:13c10e46fea51bcbbf203261897bb4d8ea2087e39925e077d452778f78d62b1e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -56,6 +56,7 @@
       "## Sections\n",
       "- [Reading in a CSV file](#reading)\n",
       "- [Printing the CSV file contents](#printing)\n",
+      "- [Converting numeric cells to floats](#floats)\n",
       "- [Sorting the CSV file](#sorting)\n",
       "- [Marking min/max values in particular columns](#marking)\n",
       "- [Writing out the modified table to as a new CSV file](#writing)\n",
@@ -140,7 +141,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -166,7 +167,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -212,7 +213,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -245,7 +246,76 @@
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='floats'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Converting numeric cells to floats"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To avoid problems with the sorting approach that can occur when we have negative values in some cells, let us define a function that converts all numeric cells into float values."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def convert_cells_to_floats(csv_cont):\n",
+      "    \"\"\" \n",
+      "    Converts cells to floats if possible\n",
+      "    (modifies input CSV content list).\n",
+      "    \n",
+      "    \"\"\"\n",
+      "    for row in range(len(csv_cont)):\n",
+      "        for cell in range(len(csv_cont[row])):\n",
+      "            try:\n",
+      "                csv_cont[row][cell] = float(csv_cont[row][cell])\n",
+      "            except ValueError:\n",
+      "                pass "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('first 3 rows:')\n",
+      "for row in range(3):\n",
+      "    print(csv_cont[row])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "first 3 rows:\n",
+        "['name', 'column1', 'column2', 'column3']\n",
+        "['abc', 1.1, 4.2, 1.2]\n",
+        "['def', 2.1, 1.4, 5.2]\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -304,7 +374,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -360,7 +430,7 @@
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -425,7 +495,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 28
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
@@ -447,7 +517,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 29
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -484,7 +554,7 @@
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -533,7 +603,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 34
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -573,7 +643,15 @@
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}

From 8f0944c7cfe389623062058fa365e4a04ce46592 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 14 May 2014 01:51:10 -0400
Subject: [PATCH 047/248] added conversion to float

---
 Data/test.csv                |  4 +-
 Data/test_marked.csv         | 10 ++---
 tutorials/sorting_csvs.ipynb | 81 ++++++++++++++++++------------------
 3 files changed, 48 insertions(+), 47 deletions(-)

diff --git a/Data/test.csv b/Data/test.csv
index 7c8d315..74aa781 100644
--- a/Data/test.csv
+++ b/Data/test.csv
@@ -1,7 +1,7 @@
 name,column1,column2,column3
 abc,1.1,4.2,1.2
 def,2.1,1.4,5.2
-ghi,1.5,1.2,2.1
-jkl,1.8,1.1,4.2
+ghi,1.5,1.2,-2.1
+jkl,1.8,-1.1,4.2
 mno,9.4,6.6,6.2
 pqr,1.4,8.3,8.4
diff --git a/Data/test_marked.csv b/Data/test_marked.csv
index 86dfd82..69ef554 100644
--- a/Data/test_marked.csv
+++ b/Data/test_marked.csv
@@ -1,7 +1,7 @@
 name,column1,column2,column3
-abc,1.1^,4.2,1.2^
+abc,1.1*,4.2,1.2
 def,2.1,1.4,5.2
-ghi,1.5,1.2,2.1
-jkl,1.8,1.1^,4.2
-mno,9.4*,6.6,6.2
-pqr,1.4,8.3*,8.4*
+ghi,1.5,1.2,-2.1*
+jkl,1.8,-1.1*,4.2
+mno,9.4,6.6,6.2
+pqr,1.4,8.3,8.4
diff --git a/tutorials/sorting_csvs.ipynb b/tutorials/sorting_csvs.ipynb
index 237db52..e0ff384 100644
--- a/tutorials/sorting_csvs.ipynb
+++ b/tutorials/sorting_csvs.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:13c10e46fea51bcbbf203261897bb4d8ea2087e39925e077d452778f78d62b1e"
+  "signature": "sha256:7ce6d9e0e1dc3da5c31fc5f3a5ab7687870a76cd4adaedd6da95bc6451755b12"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -141,7 +141,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 1
     },
     {
      "cell_type": "code",
@@ -167,7 +167,7 @@
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -207,13 +207,14 @@
       "    \"\"\" Prints CSV file to standard output.\"\"\"\n",
       "    print(50*'-')\n",
       "    for row in csv_content:\n",
+      "        row = [str(e) for e in row]\n",
       "        print('\\t'.join(row))\n",
       "    print(50*'-')"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -238,15 +239,15 @@
         "name\tcolumn1\tcolumn2\tcolumn3\n",
         "abc\t1.1\t4.2\t1.2\n",
         "def\t2.1\t1.4\t5.2\n",
-        "ghi\t1.5\t1.2\t2.1\n",
-        "jkl\t1.8\t1.1\t4.2\n",
+        "ghi\t1.5\t1.2\t-2.1\n",
+        "jkl\t1.8\t-1.1\t4.2\n",
         "mno\t9.4\t6.6\t6.2\n",
         "pqr\t1.4\t8.3\t8.4\n",
         "--------------------------------------------------\n"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -291,7 +292,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 5
     },
     {
      "cell_type": "code",
@@ -310,12 +311,12 @@
        "text": [
         "first 3 rows:\n",
         "['name', 'column1', 'column2', 'column3']\n",
-        "['abc', 1.1, 4.2, 1.2]\n",
-        "['def', 2.1, 1.4, 5.2]\n"
+        "['abc', '1.1', '4.2', '1.2']\n",
+        "['def', '2.1', '1.4', '5.2']\n"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -374,7 +375,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -393,6 +394,7 @@
       "print_csv(csv_cont)\n",
       "\n",
       "print('\\n\\nCSV sorted by column \"column3\":')\n",
+      "convert_cells_to_floats(csv_cont)\n",
       "csv_sorted = sort_by_column(csv_cont, 'column3')\n",
       "print_csv(csv_sorted)"
      ],
@@ -410,8 +412,8 @@
         "name\tcolumn1\tcolumn2\tcolumn3\n",
         "abc\t1.1\t4.2\t1.2\n",
         "def\t2.1\t1.4\t5.2\n",
-        "ghi\t1.5\t1.2\t2.1\n",
-        "jkl\t1.8\t1.1\t4.2\n",
+        "ghi\t1.5\t1.2\t-2.1\n",
+        "jkl\t1.8\t-1.1\t4.2\n",
         "mno\t9.4\t6.6\t6.2\n",
         "pqr\t1.4\t8.3\t8.4\n",
         "--------------------------------------------------\n",
@@ -420,9 +422,9 @@
         "CSV sorted by column \"column3\":\n",
         "--------------------------------------------------\n",
         "name\tcolumn1\tcolumn2\tcolumn3\n",
+        "ghi\t1.5\t1.2\t-2.1\n",
         "abc\t1.1\t4.2\t1.2\n",
-        "ghi\t1.5\t1.2\t2.1\n",
-        "jkl\t1.8\t1.1\t4.2\n",
+        "jkl\t1.8\t-1.1\t4.2\n",
         "def\t2.1\t1.4\t5.2\n",
         "mno\t9.4\t6.6\t6.2\n",
         "pqr\t1.4\t8.3\t8.4\n",
@@ -430,7 +432,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -489,13 +491,13 @@
       "        col_index = sorted_csv[0].index(col)\n",
       "    else:\n",
       "        col_index = col\n",
-      "    sorted_csv[1][col_index] += marker\n",
+      "    sorted_csv[1][col_index] = str(sorted_csv[1][col_index]) + marker\n",
       "    return None"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 12
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -509,7 +511,7 @@
       "    (modifies input CSV content list).\n",
       "    \n",
       "    \"\"\"\n",
-      "    for c in csv_cont[0][1:]:\n",
+      "    for c in range(1, len(csv_cont[0])):\n",
       "        mark_minmax(csv_cont, c, mark_max, marker)\n",
       "    marked_csv = sort_by_column(csv_cont, 0, False)\n",
       "    return marked_csv"
@@ -517,21 +519,21 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "import copy\n",
+      "\n",
       "csv_cont = csv_to_list('../Data/test.csv')\n",
       "\n",
       "csv_marked = copy.deepcopy(csv_cont)\n",
-      "mark_all_col(csv_marked, mark_max=True, marker='*')\n",
-      "mark_all_col(csv_marked, mark_max=False, marker='^')\n",
+      "convert_cells_to_floats(csv_marked)\n",
+      "mark_all_col(csv_marked, mark_max=False, marker='*')\n",
       "print_csv(csv_marked)\n",
-      "\n",
-      "print('^: min-value\\n*: max-value')"
+      "print('*: min-value')"
      ],
      "language": "python",
      "metadata": {},
@@ -542,19 +544,18 @@
        "text": [
         "--------------------------------------------------\n",
         "name\tcolumn1\tcolumn2\tcolumn3\n",
-        "abc\t1.1^\t4.2\t1.2^\n",
+        "abc\t1.1*\t4.2\t1.2\n",
         "def\t2.1\t1.4\t5.2\n",
-        "ghi\t1.5\t1.2\t2.1\n",
-        "jkl\t1.8\t1.1^\t4.2\n",
-        "mno\t9.4*\t6.6\t6.2\n",
-        "pqr\t1.4\t8.3*\t8.4*\n",
+        "ghi\t1.5\t1.2\t-2.1*\n",
+        "jkl\t1.8\t-1.1*\t4.2\n",
+        "mno\t9.4\t6.6\t6.2\n",
+        "pqr\t1.4\t8.3\t8.4\n",
         "--------------------------------------------------\n",
-        "^: min-value\n",
-        "*: max-value\n"
+        "*: min-value\n"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -603,7 +604,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -633,17 +634,17 @@
         "Written CSV file:\n",
         "--------------------------------------------------\n",
         "name\tcolumn1\tcolumn2\tcolumn3\n",
-        "abc\t1.1^\t4.2\t1.2^\n",
+        "abc\t1.1*\t4.2\t1.2\n",
         "def\t2.1\t1.4\t5.2\n",
-        "ghi\t1.5\t1.2\t2.1\n",
-        "jkl\t1.8\t1.1^\t4.2\n",
-        "mno\t9.4*\t6.6\t6.2\n",
-        "pqr\t1.4\t8.3*\t8.4*\n",
+        "ghi\t1.5\t1.2\t-2.1*\n",
+        "jkl\t1.8\t-1.1*\t4.2\n",
+        "mno\t9.4\t6.6\t6.2\n",
+        "pqr\t1.4\t8.3\t8.4\n",
         "--------------------------------------------------\n"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 14
     },
     {
      "cell_type": "code",

From efa50ac899e6b5b2e46d66f17823353babfaea13 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 14 May 2014 08:55:21 -0400
Subject: [PATCH 048/248] batch processing csvs

---
 tutorials/sorting_csvs.ipynb | 100 ++++++++++++++++++++++++++++++++++-
 1 file changed, 98 insertions(+), 2 deletions(-)

diff --git a/tutorials/sorting_csvs.ipynb b/tutorials/sorting_csvs.ipynb
index e0ff384..df1b182 100644
--- a/tutorials/sorting_csvs.ipynb
+++ b/tutorials/sorting_csvs.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7ce6d9e0e1dc3da5c31fc5f3a5ab7687870a76cd4adaedd6da95bc6451755b12"
+  "signature": "sha256:f56b7081a6e5b63610100fcfa0a226c7a0184dfe0d63128614a7a68555653428"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -60,6 +60,7 @@
       "- [Sorting the CSV file](#sorting)\n",
       "- [Marking min/max values in particular columns](#marking)\n",
       "- [Writing out the modified table to as a new CSV file](#writing)\n",
+      "- [Batch processing CSV files](#batch)\n",
       "<hr>\n",
       "<br>\n",
       "<br>"
@@ -646,10 +647,105 @@
      ],
      "prompt_number": 14
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name='batch'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Batch processing CSV files"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Usually, CSV files never come alone, but we have to process a whole bunch of similar formatted CSV files from some output device.  \n",
+      "For example, if we want to process all CSV files in a particular input directory and want to save the processed files in a separate output directory, we can use a simple list comprehension to collect tuples of input-output file names."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": [],
+     "input": [
+      "import os\n",
+      "\n",
+      "in_dir = '../Data'\n",
+      "out_dir = '../Data/processed'\n",
+      "csvs = [\n",
+      "    (os.path.join(in_dir, csv), \n",
+      "        os.path.join(out_dir, csv))\n",
+      "    for csv in os.listdir(in_dir) \n",
+      "    if csv.endswith('.csv')\n",
+      "    ]\n",
+      "\n",
+      "for i in csvs:\n",
+      "    print(i)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "('../Data/test.csv', '../Data/processed/test.csv')\n",
+        "('../Data/test_marked.csv', '../Data/processed/test_marked.csv')\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "Next, we can summarize the processes we want to apply to the CSV files in a simple function and loop over our file names:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def process_csv(csv_in, csv_out):\n",
+      "    \"\"\" \n",
+      "    Takes an input- and output-filename of an CSV file\n",
+      "    and marks minimum values for every column.\n",
+      "    \n",
+      "    \"\"\"\n",
+      "    csv_cont = csv_to_list(csv_in)\n",
+      "    csv_marked = copy.deepcopy(csv_cont)\n",
+      "    convert_cells_to_floats(csv_marked)\n",
+      "    mark_all_col(csv_marked, mark_max=False, marker='*')\n",
+      "    write_csv(csv_out, csv_marked)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "for inout in csvs:\n",
+      "    process_csv(inout[0], inout[1])"
+     ],
      "language": "python",
      "metadata": {},
      "outputs": []

From ce93c395968eebdb482912ab5445f83eab7b4e5c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 18 May 2014 19:20:21 -0400
Subject: [PATCH 049/248] Creating a table of contents with internal links in
 IPython Notebooks and Markdown documents

---
 README.md                                 |   1 +
 tutorials/table_of_contents_ipython.ipynb | 224 ++++++++++++++++++++++
 2 files changed, 225 insertions(+)
 create mode 100644 tutorials/table_of_contents_ipython.ipynb

diff --git a/README.md b/README.md
index aa31d3b..26facab 100755
--- a/README.md
+++ b/README.md
@@ -34,6 +34,7 @@ Useful functions, tutorials, and other Python-related things
 
 **// other**
 
+- Creating a table of contents with internal links in IPython Notebooks and Markdown documents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]   
 - Happy Mother's Day [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)
 
 
diff --git a/tutorials/table_of_contents_ipython.ipynb b/tutorials/table_of_contents_ipython.ipynb
new file mode 100644
index 0000000..88df370
--- /dev/null
+++ b/tutorials/table_of_contents_ipython.ipynb
@@ -0,0 +1,224 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:3c2a446633991d2794356f74854558a9fa03bb10b4498bc2f618afc26fedddcb"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "last updated: 05/18/2014\n",
+      "\n",
+      "- [Link to this IPython notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)  \n",
+      "- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='top'></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# Creating a table of contents with internal links in IPython Notebooks and Markdown documents"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Many people have asked me how I create the table of contents in my IPython notebooks and Markdown documents with links. Well, no (IPython) magic is involved, it is just a little bit of HTML, but I thought it might be worthwhile to write up this little tutorial.  \n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For example, [click this link](#bottom) to jump to the bottom of the page."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## The two components to create an internal link"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "So how does it work?  Basically, all you need are those two components:    \n",
+      "1. the destination\n",
+      "2. an internal hyperlink to the destination"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "###1. The destination"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To define the destination (i.e., the section on the page or the cell you want to jump to), you just need to insert an empty HTML anchor tag and give it an **`id`**,   \n",
+      "e.g., **`<a id='the_destination'></a>`**  \n",
+      "\n",
+      "This anchor tag will be invisible if you render it as Markdown in the IPython notebook.  \n",
+      "Note that it would also work if we use the **`name`** attribute instead of **`id`**, but since the **`name`** attribute is not supported by HTML5 anymore, I would suggest to just use the **`id`** attribute, which is also shorter to type."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "###2. The internal hyperlink"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now we have to create the hyperlink to the **`<a id='the_destination'></a>`** anchor tag that we just created.  \n",
+      "We can either do this in ye goode olde HTML where we put a fragment identifier in form of a hash mark (`#`) in front of the name,   \n",
+      "for example,  **`<a href='#the_destination'>Link to the destination'</a>`**\n",
+      "\n",
+      "Or alternatively, we can just use the slightly more convenient Markdown syntax:  \n",
+      "**`[Link to the destination](#the_destination)`**\n",
+      "\n",
+      "**That's all!**\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### One more piece of advice"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Of course it would make sense to place the empty anchor tags for you table of contents just on top of each cell that contains a heading.  \n",
+      "E.g.,   "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`<a id='section2'></a>`  \n",
+      "`###Section 2`  \n",
+      "`some text ...`  "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And I did this for a very long time ... until I figured out that it wouldn't render the Markdown properly if you convert you IPython notebook to HTML (for example, via the print preview option).  \n",
+      "\n",
+      "Instead of "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "###Section 2"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "it would be rendered as"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "`###Section 2`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "which is certainly not what we want (note that it looks normal in the IPython notebook, but not in the converted HTML version).\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[Click this link and jump to the top of the page](#top)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "You can't see it, but this cell contains a  \n",
+      "`<a id='bottom'></a>`  \n",
+      "anchor tag just below this text.\n",
+      "<a id='bottom'></a>"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From bdf3ef28b922999f03a33e3080a57b6abc1a870b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 18 May 2014 19:23:26 -0400
Subject: [PATCH 050/248] Creating a table of contents with internal links in
 IPython Notebooks and Markdown documents

---
 tutorials/table_of_contents_ipython.ipynb | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tutorials/table_of_contents_ipython.ipynb b/tutorials/table_of_contents_ipython.ipynb
index 88df370..ac7705a 100644
--- a/tutorials/table_of_contents_ipython.ipynb
+++ b/tutorials/table_of_contents_ipython.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3c2a446633991d2794356f74854558a9fa03bb10b4498bc2f618afc26fedddcb"
+  "signature": "sha256:4b74fea5930fc3c6164ec6082de5aa8dc334699cf4ad1d5775c8062e59d92027"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -48,7 +48,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Many people have asked me how I create the table of contents in my IPython notebooks and Markdown documents with links. Well, no (IPython) magic is involved, it is just a little bit of HTML, but I thought it might be worthwhile to write up this little tutorial.  \n"
+      "Many people have asked me how I create the table of contents with internal links for my IPython notebooks and Markdown documents on GitHub.   \n",
+      "Well, no (IPython) magic is involved, it is just a little bit of HTML, but I thought it might be worthwhile to write this little how-to tutorial."
      ]
     },
     {

From d2b526cd430885b4e37610a1d63c91e29ec420d1 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 18 May 2014 19:24:32 -0400
Subject: [PATCH 051/248] moved csv files to the correct location

---
 Data/test.csv        |  4 ++--
 Data/test_marked.csv | 10 +++++-----
 test.csv             |  7 -------
 test_marked.csv      |  7 -------
 4 files changed, 7 insertions(+), 21 deletions(-)
 delete mode 100644 test.csv
 delete mode 100644 test_marked.csv

diff --git a/Data/test.csv b/Data/test.csv
index 74aa781..7c8d315 100644
--- a/Data/test.csv
+++ b/Data/test.csv
@@ -1,7 +1,7 @@
 name,column1,column2,column3
 abc,1.1,4.2,1.2
 def,2.1,1.4,5.2
-ghi,1.5,1.2,-2.1
-jkl,1.8,-1.1,4.2
+ghi,1.5,1.2,2.1
+jkl,1.8,1.1,4.2
 mno,9.4,6.6,6.2
 pqr,1.4,8.3,8.4
diff --git a/Data/test_marked.csv b/Data/test_marked.csv
index 69ef554..86dfd82 100644
--- a/Data/test_marked.csv
+++ b/Data/test_marked.csv
@@ -1,7 +1,7 @@
 name,column1,column2,column3
-abc,1.1*,4.2,1.2
+abc,1.1^,4.2,1.2^
 def,2.1,1.4,5.2
-ghi,1.5,1.2,-2.1*
-jkl,1.8,-1.1*,4.2
-mno,9.4,6.6,6.2
-pqr,1.4,8.3,8.4
+ghi,1.5,1.2,2.1
+jkl,1.8,1.1^,4.2
+mno,9.4*,6.6,6.2
+pqr,1.4,8.3*,8.4*
diff --git a/test.csv b/test.csv
deleted file mode 100644
index 7c8d315..0000000
--- a/test.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-name,column1,column2,column3
-abc,1.1,4.2,1.2
-def,2.1,1.4,5.2
-ghi,1.5,1.2,2.1
-jkl,1.8,1.1,4.2
-mno,9.4,6.6,6.2
-pqr,1.4,8.3,8.4
diff --git a/test_marked.csv b/test_marked.csv
deleted file mode 100644
index 86dfd82..0000000
--- a/test_marked.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-name,column1,column2,column3
-abc,1.1^,4.2,1.2^
-def,2.1,1.4,5.2
-ghi,1.5,1.2,2.1
-jkl,1.8,1.1^,4.2
-mno,9.4*,6.6,6.2
-pqr,1.4,8.3*,8.4*

From b79dc17e0a13a0765ad70a97ed8faa0a98b28529 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 18 May 2014 19:35:27 -0400
Subject: [PATCH 052/248] fixed typos

---
 tutorials/table_of_contents_ipython.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tutorials/table_of_contents_ipython.ipynb b/tutorials/table_of_contents_ipython.ipynb
index ac7705a..6390393 100644
--- a/tutorials/table_of_contents_ipython.ipynb
+++ b/tutorials/table_of_contents_ipython.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4b74fea5930fc3c6164ec6082de5aa8dc334699cf4ad1d5775c8062e59d92027"
+  "signature": "sha256:19987eb5b732fc336836589fd9ab146141494c84bb74435e60e69fab53bf0781"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -164,9 +164,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "And I did this for a very long time ... until I figured out that it wouldn't render the Markdown properly if you convert you IPython notebook to HTML (for example, via the print preview option).  \n",
+      "And I did this for a very long time ... until I figured out that it wouldn't render the Markdown properly if you convert the IPython Notebook into HTML (for example, for printing via the print preview option).  \n",
       "\n",
-      "Instead of "
+      "But instead of "
      ]
     },
     {

From d51771eb9376f2129450279da9b75802d48bfc0b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 19 May 2014 12:47:17 -0400
Subject: [PATCH 053/248] images for clarity

---
 Images/ipython_links_ex.png               | Bin 0 -> 93970 bytes
 Images/ipython_links_format.png           | Bin 0 -> 150759 bytes
 Images/ipython_links_overview.png         | Bin 0 -> 24323 bytes
 Images/ipython_links_remedy.png           | Bin 0 -> 10566 bytes
 tutorials/table_of_contents_ipython.ipynb |  33 +++++++++++++++++++---
 5 files changed, 29 insertions(+), 4 deletions(-)
 create mode 100644 Images/ipython_links_ex.png
 create mode 100644 Images/ipython_links_format.png
 create mode 100644 Images/ipython_links_overview.png
 create mode 100644 Images/ipython_links_remedy.png

diff --git a/Images/ipython_links_ex.png b/Images/ipython_links_ex.png
new file mode 100644
index 0000000000000000000000000000000000000000..6b3dacfce11af014afb01650a3dcd449cb6a3a15
GIT binary patch
literal 93970
zcmZU)19W9gvp*bjCdtGT+qP{^Y}+<Y?Bq=BWMbR4C$??d$v@BY-tXRbt#7Zj*V?<g
zs;jEIPIpzE-wyvJC;lA{3l0PX<h!JVh$09GD9zU`1q1cfQZ!}A^z{;MAuRk$QdpSa
zm!qAjg|!I?2nAY2EUz@UAX3O?<TM2=CW$#*ETkyS#a@)CO#KLSN|<OEoTXb!M)+^w
zt|Q?K_?dM*k`3xTeHa^hl3`9T0Zh*(0R$M)c1DM=A|y7n>*2DS8NVr%eQHEy@*2n}
z+Uzjh(DK0o+bzp>Fd}i7p^So+Cr!LY!Ed|;-@xuBT~gmgxtCL)<3A1#%+-!;KUfFw
zLVZo_bjwQo7$gGHs!B|DkZX!hLL|MVXjo-3Z;2hc{~#i>b7J6p>}Av~4=BN6o-RXd
z!6CmoAxzrSakO`9&{BCsNdAbNzz_4wQH~4V4@0d}`{YQJb{U;z)1}Q;T~;T)EMkm7
zjnjt_7K1=GENm2i1`99kv^ztOAd6HmVNEJdx^FG{NhGZ{rx6>x$C)~Go#lX7U+YRA
zG@V?j+5tfnFR*E#HkJwE_9$>wQYM=T-WS<@URQ}3;e<afwEDi-+Q^Jwy9=p@bM#qV
zzQD4@3B@Izbnj!Lk!j_b?fLNQ1(d|iVk<|wJ^pKLVC^L|oIpU}QU19>LDDiXzg8;K
zLRsBeT~>z6$j*k&z}U{vgwEZ@{)-v}gvXuh>(R!<*?_>^#@g12%bl0#Uka|T=YN{%
zi3t8Bakk<mQkVTjAZ+JoLcmJLLdQVF2S-3az~g9a%B3hG_CM@jZ@fh2&d&B+^z?3S
zZgg(Ubaswr^o*RGob(J#^h`{&Ulg=X9=6T~?zFZ}#Qz!O{~Jfd#L39f!rs}!&X(Yx
zaSaUZT%37{i2f1uzt?~CG;z21KS{Pu|6|sdf%O0M&@<98(Esn)U#vX;v~vBja5u45
z7qPH0v32^A!N<YC!1FKt|F7r&B>oqt#{Y4$v$Fp;=YRG54<`@(KL-4lLI0Vpe_Ow%
ziw};6{(q;Q4=(DE5E%qS07OzmP}v>y%m>;}S0(j<x#Mwzi8=Ky3keLCHHb(SlBU?I
zCs=f#^`y0{GQeihg|^xCw@P(QE(H-x4wQt%rAeI3Wyd7DPv@t{#QVhF*pSSLaa{Ey
z$?dwAJpU!{Vauh*q}K+D^XL1cS71QE*1hLA{*g036H`D>kD#-&GXWhP!u9nvT)<9D
zOiWtY_0>BBr@xU00|vx(_F}SF&*&o8=j+ziR!wzx>(MgTCj$Twyvl&R_;f8!y7Fd|
z`cTRN7hmT)Dt;bcXF~CJ5WDG|?<G~F6Mz22-A3&r_x9?Ffd>!xEG;W5Dk1_wDSlWM
zSrJNqdcAs578q@{n#b~>Yw&UlSm^k?tr`8LuKyur^*-3UY=2-`TkCP!(-~Uh-VU4J
zpa=!Y`A*1Smp*Y|(Pxux&HL7uC?;o+Tlc}m%^j_$shL(1FKc(p?B9ux?V5m(KcU)H
zdk<q>_GZ(DcA#hHb%}4gYT0Mq`LTH2yJwa;E<gG4*eL(_@L;H~50dMouAVY0FVU=U
z$?>$kNDqE`plA9^ODlc0&YIR+4O=o(CUdk)2vlKxXg1%^+~!kTGn<SN7{4~7F-@b6
zM;?yt9fw-IWMDxHl1_smLV$%)O;!fNj-L!h2dNDwgAOUA)1F47*!tXzYENljNQq>*
z+{CQ&JH1O9^dAzrWU!e2XJ;pe*|x9#Ek%7e3M9`~6LTpKu34$J7d8c6ITnD~b+1N`
zeam2Yv4`*LYNvj1%b}aq+DbMY<VJ#!Tic5h4+9TcKu78fCdhBN&`rgL6>@-o0RKCU
zEchzF%9g<?s(R<^8z2&_=)3y(`1sN3DR-$;HVo`MsIahbbWBJkN=s4_dS3P(zx&&+
zWj95~9Zx=eVVMh+u$+Uo+W`fGivEn~J7wuM0W&pxY;3GqrJ(J^Aq^y%ScH-Cvg<D&
z7r;{={>~x)Cnq|+uC<@^)*pMy%op$wKaoGgT~}B3^^J{&7i%p~8;_l#G_C^aMG*~;
zx>PBYpH)8RR?TlWW8dwAgMx5Wn0eugk&%%Zj0V5Sq_cgOPNL~~d%pdFjcv8F@)je-
zG0`XTf*TL(rlc^)A&b2z9oSV;0-KxjP7tQrCoCLBD(mFvIN06U*@?%?%S%?dLm^9o
zrd@!F=@^AL(F~cLU*rS8)&<}$>H&BWa5(%poerYz<KzEMMEl|?Z6$z=HLBs3<mdN0
zzZ_<HO?YNsxhz)dJS{YJn(VB~9cQO3W9!FnQdhO_;5)Cqx^>=WHlJ3u+0Uz}MSri|
zb<m85l|fOlry5Nuwm!A9=m2c;c|S|*F@fu4+z0^pYhe{<es$hnv^lVUiZJ`6;Jpmc
zU+q_ULvkJ;7n2T;>|YBzy}oXQBKjN&rwkzmW!yB{@_4S=W1F7lvP|;aeuKNYWemk<
zO0?KrdJBQccU1i`J*d#$-OLtUN!8c2cD?hmD2y+~y6gV@lDxek{~b~ezWPqZ_f%^$
z;_qJ>aJUz+$T~|qyP=6p?&<Y)o6fbgT3W5fzuwUUI-=s@=}*sR86R4{gI2XB@AslM
z*48W@K#a*3%1K{@_8~m4zi;n%Mf|MYq%qxHN-IyF7*(#_y4BvCzOP4lA-bRMf%sd`
z4OZ73ub1Mgu55;ehJ(#?1B+hKL^U6Wi(O(;j!d2~ma9!Ref)rjx}{bVc;DBy%~WT$
z%kU#Eh31zDx6XH)x7Fk>YmhNV?@@6Op(lYbJM2V3l$WDS%|k?q-RI|IZe9NBZ8>#@
zp3wuxpk4l*-Hl9IxC~_d<>a!{^QKJ%2j-WR<FTR`vX<E*zI)2IYaoE-99iAxv29b-
zz#u0)FQ%~mp;UrxQb1^_s)Pf?f3=EJzn1e4i=T2*FJx3fLBUe13pJkS5n=p5xklYD
zHMLNn?_HjRj10^DH=4fZ2rzJAV)eEUq_kBpq#wRPBt2Ng-=t+@deS0I3E~XcWc7K;
zJsSh_8fF7A*b)ux^<jDl=g6kjHj|610GLjNMcZ)&kP~>l;Y{hqpT)%#NAhw6i2_{V
zZLk?qk+v^d_C1j54VLrjPiSh|pgsh)UPIi~sekSpn>xnNa8bzffpPptOQokik_SHp
ze=1}08$V>?@Ofj>x8KEW@6>O~<KqS&2b$d0pu!(2E6e!^Js-cXAf^{Ibx>uE&WbDJ
zOP|lzn^4Kfc@*>b*Y~!zwoXZd%-5^$Ks`Sn9_Bj&CZQl>q9$f3bh`8SsjuWDB^l{A
zO-m;7V{+}Whe9%sRBn=@qP}T+Ul08H@V`j2s$|5y?Zf|I=zJcx+c_@E9{iiioa28r
zU*$Xhjb)?>qTCe(i8VzQ2?{FodU&?&dg$Ioz>s5V?h-;2NnPsIVcBN3+EUUVlqQ|5
z`Yk*w6PcrV#q;{&vIB1J4#Ytl6O(8Wsdv&HJ$NV)&-*-YtUAtQ_|#a?A-5KINIx-P
z%YwH@HZKSk4b=oPCf8(*hgI#f_NU@Fw--{%Guf4DN#sJUvb7aUk1+Wzhx?+tVNur#
zs`ZUl;*FlO)#Y?y8+-E!YKEi3`(}jvdM1)SH#Zkd<BWLoeZuW(Oz&gHT5$pOe(Z50
zQZy2s5`JOC-`yuHOY0Q}&+`g&_7b8N|7i=lW`+H|=~5lPp(xuE4*2o$NU_m{KVANq
zR#r90-~RcY!t3>*u1B#u6e9+51$5g8KPk0*+xoc7i%<Vx<@G&m+X&XO?U)BihSxk`
z&6v-gg`(*YIh|90&_1DfvD6?3Jdccg!sz2153^l}sQKU~0?V;rsuO^?5UnTjYXKa7
zmw?aL-@o05VsKOVe7GVaBWt2(F+h`KclBc4;*4OGC3Bi><e1Rc^L5_jvfjb_hR0<D
z+>c_9Ljfzh>#M1v-qs|Y_m$5HD6z~Wp`;fTKo2{<DZd7vzOa~-ClwA7vdsC3T{L&a
zZ8_T{U(ec0Q*Ex;yoT)uu05}+q|;dUKXrMt!|nCIa)F5(i7%q?oVoNq+wjfj>z8c!
z3@WUtvtwhJ@>^GjU2l+T$j%4j>Ed$Eer+!u62xH|W~X-zuNT6$x_B3_FN@z>`EQ1a
zn?tsqUP8V%yYEXWiQkXu&3?*_GIm|je+F<r5ytPwUOL4d%A;cNeDtG;*;lng_VVW(
z>9d=Z?(Cqc;T%TNxBDUb9tmR_bhQP7SdTT`J5(vj`-h8<z%C4YlCH?n@B;v!W&_uV
z+&zYaopT>1OJ4gq2$nrcvptiFjW>v&SBOSRc%Tcbn;))9jXRJFf*`?3f;3sF{*!<w
zDUlF|)?+0FL#G%pIM5<MI+<l)MSSJ5g~L-acO?T)W|pwJu9g}<-+i_`n9jIeF>pLy
z;b$TFZ6Di;8Me_$B^a&`$DT9&Ea8svemxa6`Nio4a4otic0n&+K(B1bvT7}>!%IOy
z%c}6V;2Cu8E@`s1VK1BAhIM6`TGil8f%JICy8=&#RoO>vl+vu%h=;`}SyPzF+qFD^
z37#1v7JRX7xy}p-T897e2-w}tehM0gOV;+JtTg?}|9lO+#*Lty!>e#Ky&*0xIRg6L
zprE3or#&SzRW`Q_eQ`uD^*LsJyxY<kJ(@~U*dH_=aQNJPkFL&q{0>@loFjL#8Jnc^
zhlYGPIAagjHF3lp;w^=70DoS7iIiL+{=x@1IGL1pdYut%by`3&Dz8sk6lq5zpOQF3
zA!pZBC*%q?AeXXO0)^>EDsaBmf!$SPuY(gEboVJCx2N~H`57o6SW2>><2)^zGIad3
zVwTN%hdM<8OdhV$mP48Xi*NxW<{bI`R&}$OJrg~>B2H7n22&2f;JDLl*Lh6FjEj5y
zY(Xe+QOaudcYg3I&KVHDY=P!~n+ATvumyXb6<|$wQ^K?Akur(oR&KMjvK%ypVe9nb
z<woVOK{!2Aetjx9v7+D{=m~_DH3m&q2aFL$r3ZpS(?drDHnwW|P5qgk6YDPODJTgn
zWFB(Ve2(i(ZT@jgJ;IO;^=B}XqxHwZC3c|Q@F2om<p${6!HCULdlMeJE*nadZrv0}
z*35Ei2jR7`S9P_1qu_-CSdt%qjHlg>adrV!uS&+C#xLYdCJx#+ik38bz)ie6{XAa&
zB<b?P<i#gw=gax0h@xWLh}E~SC*#Zast4Wr-*sE)DZ1Oa6+gJ2!ie5$y_2lea_*<A
zS!gLJ7@jR0BqE^}Bqykf9Icl})eK=eycRQk;4(PBENwb3#yYa{Fa!h0nq}sn)Hm*q
zia&N0fvD*jP}aeL>}Qy`IeE=ZUUD?Ub8wK{B{QsYnHptuXntN-V!NsAWI;FWY}+(6
zpXNkUlvpxOpAq2EkLSPE<SLS73j4iq-q&OiN$`$Z%)IZ_R(4!)1t~t+_H+kCaHJ!s
z8e^;3?z7pg1RZSPf!lgMKtu8ZIq^B7d0*F;*sOx5H^Vb@`F|^<+yL`sn6KU`Y5K{5
z^=m0_d)A)}7T%Y`jbq{4A6YhV?G~WY+P|C2oGV2}BvCf#wzD~=GwIv7s-gTkXQ)-n
z8AbrzJ*8+8oS%%Q(B+*QfREd3*=A!VP0!@pXmBS05&!yLwSdRUz|ZNUy-~w#J4<&W
z3n0c2m|x`q5SdL2;H83R+0{!-)H^O!v69O_)<E$WEU*I|4Q(5ta*cP)B8LOD0>Tnb
zqkkQSP2LIR%4oLC`<?QVz4`j>4MgmEAQ(4av2HZU>Bg}z`}_Bah{FB34fVlklTOAp
z<KvGENVp7kKeQ!_!Cg7jp+Rovqu+!@nT=?us3|4O$200&CT@<;S{M7tdTeUk_Tzz<
z;?(goYYTsx)1;3bp&$m!n(3oI2M|)>5B7JGf-RQR*CQu&(}2ewF;YXYrn}ku*cU2x
zoc=+o&%J#(1-j1`Sf`h$<Qx?4GD&%qaptrDc&3OduV*VN(nP;x$|ldOHiCs%RrU?{
zT?Hj^azIgm0$=4wC0UvG{N#bzEt`e9*dKTV*PYczI}=Of&M}KoDl`O#R;y=S+tzI;
zW*3!|imr~)Nv`MMXLPLcY%g9fR1Q01^+x7YL3vNk8`8tbV|hLoXUY$Y6EZW%a}0YE
zNx9>50aiTnCnG^g?KJ03-=31`Pe)B>Y8U5xIzhfjaFtq0aaF@6WS_9H9Y_di&Q+*e
z`waXN-eI&ol5KLmW>CF6MJqWmq8%fgf|2+UtK3`?Bdfx4@iL!=1V)Qxz14#JG$i>P
zSE_y6!5Raw;)|?{8a8!?pY(R{#u#zfnN9kj7w=5~VM0#2R|FAi#Xi$<9s@R(v0b;W
z_lnp@Sd#kXuM8}lJE`t+BOva259k<=6$EjbME6qdeH^LRZ_~6MGW2=JaX)~g5<Y|v
zzyUglD!_xOc7BCl&#PuS;>#B?4{QMX#}69Ct#(iv7cv(yYBQKU5Ss6#+MmmlzNRcY
z4Opadhe=wtjAN&46D7Lm0tq*zA8fX39EF<&pyqvLrKNxT3CaEIA>P@}lt(9Iq60tn
zAR_o^xD(60JvcIAm3jFdQztR~J?Y)xVG{WIaN%qqYUCAZ_p*S1HBkLR<PIZ4!y%9!
zd}lCfLw*=!!%gSgak22bzzhUAY!rKL{Q_rn{d&(wwG*-R>y1Y&1d5nQtnX<1?hdmD
zjNP@*D=dxLv-A?XzZRxLBq#2dCw(T$@76t6D=7Ys3wy}2qAR=$jh3q2<{52<zjG|T
z$#(L|%o_@8XE+%q&`x$_@&4oFq<@gjCCPHijFnF^(uB;d<gBErBM{Md)w*61QzJ$k
z1G}XzoX(ol(j+;2m5X>JTUUDsVouhZx>i9J;4^O4x{rf(K$Vkr6d|C%Sj!4*pob*U
zEpny(6|IB(Mi;h~Z)z;8@ghqEKe!#kSMta^C^+YpdXF$GYggP`eeaC$nc)(RYB}Vp
z$Q(z7FC_@fOu~|NT~ov90H1Du+G%ZU4?5>~6W}dH72rya*Oq^aLn@rADwvf8Z7VV*
z);))vzT+hZ-&^w=k1AchMefyZi2Jm2od77a1ZFFtsA4jjn-xdf%4MXL#7{SRY|QL(
zu!6tWnXK49ay6iG9D<G^V3j~BJFND1{j$>V>YTft-zJHlLVmES>R=OV>FpIV5*5Nj
z_LhEKcM$J9;{(RG7Sp+Q>omMj#Xk%w3na)$GoiSc$_h5zi^zG=WFGGvgGO(I$W@&b
z^7-?J<4nD9|0uH93Fn#~0Snzzi^F(6)rCCRMbu<s&F{L-KzmqaSY=mE;;*J$JD^06
zd=~_KOa$atf-(6oxXZoE0V=uKDeNb}lSt#aO!Ts$0l9D6?v?HBI9AQ&zMDRef-9qr
zje&Ud=hwaG1k-$?HswjIVHho7WO7n|Dm}aowTNUz6_k$Xdw=^l?}*ciZ5w49Et6C~
z)`E6Mo?n-rKGbBXNu`m+X)cr&@Uym*<vkrn-E!|55gKvQi{5NfDynX(ec7^P53{>=
zD55{!G6A00;>Yh_iQx53gTaN%38no8$U^vUF|37`wxk<ao9QdFcW~~f@I7{2oYkIt
zm88K25NQIcGPGKv8sB%%yzKj|$Mzu#RVv^Jl~?;qMojjJ31`mk{w^ZK?f@DEMVC)~
z&Asf!H8z<&uiV8$`r4j1u1ka59Bx^L(Y#x(vs7M}QH#>Zqxy4;fmsaLA&>rYRi3e$
z;cGz<CAv;6wTt3N%-#LM!?%&2`GW-foaqnlxDUo7R?tP=3e+J?x)iK%^QR`+rGZc-
zI>B7)H@HySVU@EZ3vN`qr?}c99iC}eV}7|ZQ|iwf$1?_0tJ_H~wu)OB&@|F)BPGU)
z8m&C4zpeVfs?5@&oAjLKcX8_>NxN612-~1e`w;o9OO5tTT}ywc525%pZb|Tsll*YU
z4K?7HF6YGM@hl|~;GAv;KQ-u)z(<2UdS8!adwP}I@X^;Hqd$xvlRQ_}DMz8D)hXXp
z)=`Nzx=mD4i65ao{Mwt^q|DLaAt6381v5qEe9?cAB`b>`n)(le&6crjul3DKwa`xm
zMf_bEeqNk*&*%p-BW2o>E3qiB2<8bgXk<>S7sC_-l}!B(Ie_F~)*qL~88l?$ry?gz
z7QwQ=V<GpQa_CBT(tniCRo+5XD{F89KvIr2!aYm@Hp?p@G^eF2rAG4<#zdmEgsb85
z1!Qa$jR$WZ$C*pjO$c^v)=(Rs^8vFfZ`PnCO?F7HV`=91@ueUcnqJNOW(W1kB;+E|
zvIQ!3@G*@Y=t1=kS_@=2pTrrdy*4<(%g$u<XcIa8n4Wu?@p2nCvjp%o^4BsP00^j7
zM8h7kO1z_tRt}Hn<f7`K?9D);3j}o@?o~DVBGga!k9!rYS*Z1?R5p$#ln<)To$s@F
z(>W2QbL?)!gmlQvg#`qRF?YsNS|Lm|o_cckxR%AL3(Kv`EdDGt$JRUrbuQHw{XC}|
znotVb*hQr|;8iwN-Q3Nwd^|Gmb4h1w5J_gYO&@HHfairv89g9HM#gd6hVk<%0ztvA
zRH&qQ9O<$+_~Lb1Gj5;<#{j;KZ|Cb_`t{X;rT1P>GzIVZ_@rD>!tvo~tLxicO~n&X
zuVSbt0v!w0If|y#=0QGi%vEM}J2XW6`Yfam(?C^@r^ta#8>jLE^*|mRTg`*(dBy+W
zn_=+k%^88?&D=@85~9=nwCXjsrq|WZL2Zrwe1F&9xqfrULh%@1qqv%fl;$^wlYN0f
zb!=-Lup1>TfZ6U?=^#6vJFK415vw(I<<R73EegF2C9^_eolRY(W%`G;j&=I`X-?3?
za>&>uf2PL*V&=|D*@fP^?nIwZ>);K;SHbo?Nn$&6Bsr*aVo1OJCZV75cDSwP+Iv~{
zc>k}1I>LKlJ)@FN%6U1HF8QB0x)ftgrXnIU?&;&XGNttF=Ok5!+uM){!amqcIp0P(
z$!moJ_*HSavc>{v{O0Gd>Gq@tT@lfcyt+0-mHTtkPeV_~a!S>VRDnBrdNks|#*5vh
zXSBk%gh!;7DDZ;lL8-&`Tp5uoc)CS?jwrmzKjCi8apZSb9;Uunxx{@0AlBx`Lb68W
zhIxIAWd!T_@loS)T4R=)(-QW`(CR7iLKP4C&i4;S=T^`)Rk>&$sn<}bg(hUR7Y2pp
zqpfUdDUvx3d}hghC*=Ca*Waf8<mPo$+OBM@Po7H(o3i`w)Vtkp+nYp|oc{G5f@+6^
zo5kD&;tqSh4P>+Bm8=Kie~#Tt5K5GO1E{Ueg{OtcWw9i1sJfcTcUgF8iPPWI#ZQk~
zAs%x_nndj0SIcc%9}><`Gqd8g{Dg%vzC%c0i)_PJ*3%fU*-iybrHnZV;}Ze6+wxcH
zD0B}{iYNS*Zl$O+UmK{$_&dqW4X!Ny>=p+#s+}7Xh$dUu9$;++6{fZJ6!UP~f$$xO
z*&21ld&sP9Mi3>A{n;pg@SYEn^qyN%<4R@xuB@@XYWC^%<7CabQTM^~O!I}h(vpk9
z%tU)g&f{&0)T!2Cww>k81_003((300kBhBcB+b<UJ+1d_E_HO7rRfIJCZ)w8uw#MN
zfXFJ#ZGWkgj6xAt;{w3_c+iB!vdH^LJWn+BbbTjfmL*5W<arvRJ=*cZB7D97YZZDO
z>!xGeo2J!v-MzcY$tx|y2wK=-29oW)==e7~HvDFV?d=Isi*qa2;ZN=N$fBaHtjBT|
zwmV2?AWzMAEd(|qUdlng5e$BB%Gw#^D<aMmj7F8z&IVUu*rx5E?S--fPQQ6}{gcPT
zMXBp~ZdJqm8KK#Jv4!2&uGO&Dk7FtD`Wx@Ps@2$d^hh`Dwqd`%NsURC06AX&w;R%y
z=4d7IX~}j<dZUS%U;20bg;Z}xvuc7knTCU++|=_CZ##v^Z`;9!dnn3dr)^g`*y#Fb
zh&Uc5(v-SO3gGn6098kpb{gYZf3deGr$sFN=F1*6b5Qnm^&=!5KF=}5_zMQmeZ)!p
z?hgExQ4lXueyU9`XZVj@qcT`sVfVKI!-RPk!=W6pggIBkk@9b8&#vXc$|x*=+rPR&
zkRI0+@;XCxq_{9k9@ThV09$<1J(3$ErU>4B+NXZ*q|`tKXNfUzppB4DIyCItAD)^T
zFno1pY<$mGCVW~j%H3N&@Z_%w*6WNid)chf-wDMKqs&6+1^cY%JciGDwu|MCQf^HY
zYL@LXVXXGMT|mvgBN!3q{5srY0RiPBV`YqFAt?sTpl5;ZW@*MuNYIg$skiJ~H-5S=
z)IL4`>mv5A#kJtvHRDkcqD=qYGI{;hcpp`rzYF+XK{^oeimp%en=Yh1A&p0$X1G~i
z?+hQuxER|J@Nnjvd@x-;ruY}_E-gP(lE?snh7@w7@4IC<5{{n_o0B`oY7e_U>^l0!
zl=8M^gNuh|yQh!kjDf$3kVcNBKQ8h00#k|^=HkGcU->`fjDjn8Bq{pre^p&n4m|R|
zqLWO~`O^DK=o36I)0Q3f2>(W{_TK3z``h;8sa7(i3|U?E$2YI{W?WETj$P|zt!Qle
zY-tZA47M}7Zh((>LrG-eFuM*HxD{QNMK9m=dcz)sQjPh-bm)89aA!7xBfABofrP9h
z2dB#q#d&WS2qb<FL-vdJDJdFbJZ^-!9ezBIOmvMpcjXn3gMG5C6=}2WKs_%wp!YtR
zQ)9P(t=L~mnIR`=kC(YN`u>>~PIrf0gSVX#9f%FES&X(3owSfzgj&u`*$b77B#xmr
z+YXTV1f}&x)2x!FW@66!ro_l12XG$_-=`z8*)CZSK!3Oml||3th8v@aUCGnziq2Et
z3NKVOLL&S22<^Q4*{R(jcUbrK2^&pHtZCuOhj#V&sf?~XcJ*0zTu7bkyA_BzxFi1K
ziw1cnj>bvC58hyD`XR>0Z9l(gxAHPp2d{FDBHTd?EFttYUl%Y=#C<!#(WC;UFnsqd
z(A#2WEE{ZE9c!Kj<zB-f&P9n))o(~}O)=Y!GrJys$Fbj=*^-S&IJf^?N1%)8H4$z4
zB{I#R7L*s-&`jFHo$Na^gxV{ypgiD}TeHsQhK|&jQ>iVNqqyQ2d3j!r6_W1Em_Wi9
zy)k6Z!|vxbw;?R#)^-NUv$*tCb&1?M?jYN$bFL4yT>T=3tJzP;*2e5nlFMfuD!Sf)
zC4L<Cs;fQ8Yb#`=dOeARtM*lk1}$HQKK-4I6=<O$nUUm*Jz>l~R$j2B4f1PQP++eE
zWb9e7xIQm)rK;8@^G$fnN|S+>bQC1vZ9t*0Uldcrm;;m>sI)fMuTC>6FXZYOkb1*J
z3@&7sni#bVLDvFF1K7HJogU_*(!sQ_+sxF&+6M;=%qJ|hW9YRBKRY;QDE{uuC_J^J
z0e`XV**Y-cgepf>!;f`YEpv`kmwnys9k1^gLWw~@1Uf{*u^TA8J4yK!VB~?8oZe3I
z+kM-YtWo+yf<np!-=6-^RbW9EBNg)ILJ@SARF5z7!Splt-9oWFA)sKK`8P-s@$3&h
zRYDeq31c4YtuS~|@kfw{M3&h31wz;fWwwBBrfBlRVxz{!Mu%OO>}^bwM8UGaSC60s
zImsj(E25$aJ=qI`1YBXdgh1WUU*C>SG0wleh0jE=z1eXY5Fp7V9EiNn+m7+Buwa2C
zy~b`boSLhp|7Bj)hw{gTzEj49{%PmwN`!txfLLMcd$UgcTd*Y}1B8~0$(aV<0g`aV
z_%Dy|;~c^ntyK^A@DFnavVo+uhZX1C#0&s_%^fD*Lt3I7ia`dx1JIu(KDwr>f}UCE
zc-?z3*<pp8UE<=`7H1Jd;+kR<XgM^0y!dm9(Z<0r^x;v!4s32F&vS9dm=OZD!=5dI
zL%+)FMar<h!L-&^iKoR0Prqa0Bf-9~r|V!+-w%ynwOhGn`z1UiL-8ndl(ZdD*bBUu
z8&5qhL>w6%FY)yL@qew0kUJ8Zj8$^mnwi7F7HG!$n(&wXjx}JLcVC_%uB_uNiPD~G
zGw#MHL6a(i3&D2i&iqRj0PR+^Zezg1(XlZgtolNIb<BC33Y5#ri0f!gBu&E_idN~>
zYIH*66<>_rW!wX3yO##K??*v;Y&U)M1FY|{v>r>kM+H{k8IR9btqgxP;e0Z9Sx|L-
z$zSW@75hN};kSHC4+wW=FLFQOx2YEhP#dD67__M-vIx)KkH)%E1T3BpIsIJvN|%%V
zkI8L=918MBlAdZm*<;89_{f<kZ_A%W431z7%kgnaoPmMFP|bJNPxx#)4q40UF>RZj
z5Y*@gIrXh*uctBXhXWa&Jnx=AE8C14Yaq$|>!3=vky7L_IfHLr<Ze4z-o6QxfMm}?
z3z>{@3iRd?aN20{apMAcTKeh@26DYovSN`ZL4VqHKlMV!B!=;g2GO|V^R@%Oxy$xd
z>cmCs@pOLIYw>D`U6ypObY_N|`E|Rv&LQD{&*P|;rYniA+;5@g;Svc7*#Td%a)K-U
z&45GD@z*Ie*DoReHf5$Sul{ub;R~|Ek(kfA4wZ#5wathh)De2Bus$RG#{onJ|2ga9
zM<3%vYSzR<S>;4FR_V=O@nLB9n|RAGP%hDYC9L_88JR=N4bK4@8qy_lQW2mJNyZY{
zNWxnNx_-&_3|}%WaVDvmJfFfi>-TzG(MiR(`aqG#oqm$bnp#@y!sZXTwUowHippl@
zF^A_N&UG)Jocdlz3k4!Y4)M#w$T)anYT9KHv6&yMTN=w6>fI`WseG}p*#7fg7JZUr
z{`0zk>YJ)$sJrrdbK@KPg|I86S;4#7Ja>HQ<>*2#rRy6N@#)rRM~o7(@=Yb6ZoqyZ
z&~89Wb)0}bc35yPo#_ed&vEEy_)?q$O<I!3@&a{=V9Ux0+H6T*V!$bVdf-gj;`QXF
z7y9#0hhp=y!Pm-k;QsAE&k?GbtiMm{OJ$nwMx!U2@BO3ClDt8CsEfw%aV)Ew(|xH1
zMTN}}2H=edvKQ8zQ0*lr9bLGVw*?+!Z)bTNO|0OL-`aX~d7vut;$?aFMPD?9&6y~F
zaKDOdJ^7^Pfzt`O+jXvmy8Gqk^_3TTsvv!(@a2BqWjoU5^SGGxUJia7vn2sw=OGfu
zhYJ5(2I_5ksqyY@Yn8qm@P4A={l-0MKTUupoB8hkx?g91JmbCQGAs0!5b*X4Nhy}%
zGg%=WrP+hjQUVdsrQ5D*B8nU?B_x63JMkME-``A^ejp@sUcIXd&uzVZYKk6c)9h@^
z)%na{We7SJ_sveyAKeH?e}U!cB{A52Yh-;%5{o81Gk7c+BP~(1!%21>TXHB{@pt8;
z+wQ)T%<90pX3+=MkBcyxyP8r`f(t5szY6U)bE2)z*AvdaPOZ^=CW{2fYZm*I=oa#^
zHQ@867%&10dIJ<Plb)O_05=+>I2&gN4XHf$h$`p!<2#jwMJem*_uQp7QQYrK#&oOH
zG6Ll<5`~4H8Al2SqgvFqSePeAA98_2A-Jkw_sr*Wc!~5E#VLAT4peCu_YdEHLQ0pA
z&nd~}9*c0zqwk4e%W~F7+0I4=F1a=L1WnS8V%7zfHcFVUZ7he%WDQVggdLG1)Yeo$
zw<HW$FbprZ&w)NBvp5Re8-NB+r-ex13Q-|A{zZn?SVY&<k(G>MbVQ$DEwn4d5@Sr%
z-+iu${iAEqaI@5ZLsE=!-k>B9QDd2%@iqz@WSOUMfvSVrjbHLj0#sRB0u;J_#fPS-
zv;2cH5y9ct3ugb2*w2A!%<OV;p{vnW1S3J!6IjR6Y;)pyq+^-=>8{|??=_6E#rbE_
zIIH}CD<(P-X98Nz7^OPmLO-bCiikSJ>;$YZRrB~30YiS8B-3%4JO=gw8LI)>lgzSA
zFo}}j;?42h*V8|mfg2?ozzk*lJ@#lzOn{FEKZcK|{ViJE2y2MJ>jxIkQ&Y#&bJvU<
z=5YxhE{ClQnc3E1J?QDTx#RXR$695~iQo_|Bo0-bGm!65%r#x$asJoxdmgzwLo;ya
z-)5qoqd=E`WD~pXittl?IE-<}%`3Q}6r_h`p$s&oUQksIG!;+1UM<>Tr+=(ehu_q1
z@9M~t$r40=yknd;2Vh5V+i4l_*`Bf`8`8F{Q~}PY^2FWR8>7^+mF~H1R7Df#H%qw2
zQe_kP7(RCgvMI86pVJ-5!t$^6IS0F6hCfA4opViOenm?@E+kKLu>NkcjBKW#SDNS=
z|CqdSJJQdME`YylvPdY(m|5b%#XnRVz@?nU$ZfrPaQC2^QP**&_pcefeY_n{7(B5U
zYrLWR<m$-b(aT~R7g=P_XLjKi5>7NZVN?|l4T)k@)!R2fCmvq6M6;!Isb;}NVYhf!
z`A|5&<>mhPyLJd>VY!O=G$G1AEpx3RIG{ww*1=KqxLytOd7FIxbU|G8^TIuhC?2@x
z+LI8NXnEwQo*Qtg4VPhrid3y$&4p1~Skf7)B-VnNjkmU3oD8pS7I<QtN(VVX3>P)@
z6L`puCd+kBxvi``L)%x-{V58ULHi3P|9;7N5#tYKUZx?`An)uXU8qYT<=MfdVs}Az
zzCyQ#URM3tT9h{2zu2w=iY>5()VzxHx__Wu@iL%aNU+pDwyyu+z%n@Dn*Cf3%uf%_
z{^2E2>V_<rBr8tO{D<_b(nzPD$+^<X`40w+VFcA4)G7}4#PskV($qY^FI{6AZ72T4
zlQBV(3C`-UO=XVPmTDnIhgmosP;y_ivZO1vfZjRRa_--(2mLFs4wZve*3AYNi3<{J
zY{ZVzUIv7@ZWg@FDe156z&{$Ne1ps2(tqxjN<2E*s*%m)4U?7-a?CXX`Rn&{T!mvA
zLIk0FWD}OV7Jnp!vMP3)5X&piV|kduXmsc33E|OncC~Eg^zx`#d{nu!(&-Djw)Ixj
zT{2QV*lmeQ|F+MrUS~-N2M6~%t^o#-=Y_<*JQWm47SV)y6ko=fp8vG!u+bCG;BKm_
zr;lUmS1V+)jsy1iP9xR{9`2@@VwgqDgIl*HPuJ}yS_|zzz_@P3e*jvT#6}3RiO}}<
zw3kCSS|4Z)4XjVw7@HAoo42ZG6qSt6O)LxB0uGGw3-?bTGTKQP_pB-ul(a?X&WC>j
zd9`$Vb=-VGxF7Ju+-0!%ZjTXfx}k@(ul$OjO$tI4bPiK?RS<A_#AJ%@SQZv@b4#HD
zSscl#vGC4>MKxd%5)*Fn4SwD?iOtBkto;k{n{fIUe#C}2+u5RR<V4Kn{&euY3Ggkw
z@vJ*H*V$U@k;L|}<~;jzt;k{3>ob-dc9Ho22JhvfV|XGxQiZv2SH}&2N6*`Yj`(@Q
zPL^#4xP*4UZise288MrdW~->n`R3Mf3Z?D3KAw^kAN=Wg1w1;w=zsrwVbpfIt#~Tp
z{s0Yr_He(NHk;a9jrn}rA$OQOyd3Dr?t*Ue-ond@T-*;x$|--{Ros#l0v+{{+i=jB
zYQ4Am+|x<3i0ZN|nR?9&#eY;y#J|z)d0pjycDY=T#cp?U2dt8J?XTDPR)pWanz6Z;
zA)McI9u79SXS>_hxt)#ySU$8K5<J}O+}GCdX)5md?#6*Pz6DMBh10L2D2UjfMY<b%
z?{D1n*QPpNMNPS-Gq3xA*1`181+R8)_Abj_{)c^h_eI`Mx67ukTi(l_Qd1?_744>Z
zm#TlgcL<e(v;nHhSVE*Isg){-^kDZmQx=mX^mfk(ad#KL9j$8pp7L%yOWMNwdzSA3
zXZc4FK!gowzEbA`j|70MY~3@=AA;I0>iVF~-HlghXj<<EeAIh?TS&U)!zTVpX6x<P
z`MfW2DMls56?$Eo!$Lz6)P5_$$MlBUwV*$HeBqMzU7lX#^@K<7p4|^>^mhF9_(Q5}
zypg5Vbig;C)5+`}hYI|p{cp3uidabRZvM!&oPcB37!EkTxvV4Xp-%J{sA=22)z!JX
z8})&r(zRkfuH7n>>$aeZ)|nYDXy0h+oK`<>891}5ZN)U{_;>Mijo<+DgG~1Kd5B8V
z-CSVy9UfXBgP*d8vW2OE3@dG3)NDT%YnhZ7n2xreUGS6^kbrcf;fUfkCY(~1+*fv+
zt@w}cx0z29YF&O9x|=pTDFsr86Ynnn{4%`@yG!gtT_aeJj_)=Ao8;rlG)%l>+M5sC
zlU~>>T<w#*5Ae6%ujiL6i$i)}AusNYd)Ia9<1OcOo3UydhIHj^5}T_Bwp3F(2eWF|
z&}$w3JI19^`#>V?Mo6vB1{&oRUFVY`%13zBd;jXj#zn{N48<k?=4V*Riyq+RJ3XCG
zsGHq2=q0ooh6jd@OXOn~z`M%rLX_Rp4Y!ijLCpQ5>1(fUM&<aVv>)|Uw{9h#!uu)W
zq45(-Yvl)Sa#-&1tOtlG1fw^qib<%#>{P_r8xbBss)4-@wMQr-t`IxWESu8{A<lh^
zWRh9jM~Al6BgM>62nUhsTwGHuiBqh&t^u}SKM^J}hP{Yi{L1`sr4%k`kmRa=gT%R!
zEw7>tF&J`|VQw+d=N0h}u&7f$n=yVnoJL8#2)6t`nr(h*=2sA+H3df!P#E=4?C@EA
zy#qWnn~lO?D+GH8xfud4yneI3xS0RfEuz7SAk{6)1w;W0l8=&rIBX3c+Ti6rvG-lz
z04av?BeCW>C1}<Yk$?91`XD<KiykK%|GwR&tPW-+IFZi3JAZoUKX_d`JjG^Ohkq7P
zyQrJ80y*n6+VS*1{qbMNMhE9+*(y~sn4J`y%ujvJn*#QksnH_T9dbD4A3!)SqnW%N
zh{Ai|MK-Xj%j@5B>si!aGTA^ueC$^@-10p^H;2k~livi)wQS|k=MQ@gjaP-oB8AZ1
z<deaF@yJvZ497T0ewIAdmNw@-FYmHPZdc#X8qPW9=GYg+fMy`1E*(abt+9kk@T$Bz
zcxP3|JXG3<V8WMI3{=={3L2ZyU&#>4kffEZJUl}t&YIposOO0<{KJfHuaQc7W#t5f
znVH$^f%{&?Q&&p^M*1jMjk{bc?B&_<&_p5K86DJD)s=9RJ8GO2yxCd|XT>uV2@O5y
zB!;kD_jkwxTfq7@LOw}wUb0k9aQL>l!AndGD2_FbfEY`4&0ru}@JNb$qvd*|tFxzY
zwY4Tog^05u1qUj-PC|5kF%t86J<d|i%63T0Kj7>O1${*h8rju7C@3iV*Yl=y{&g$c
zgL^KT`HWmtn(^?#t=}l@L^PNUHh3cBVtEXNn7G7(&5j6<urp!{47*Vf!P|}Svsbrf
z(hkksnE35Xh_QKn97}6fh2GqctUCg$NE@A0*qjix@Mem)yi72sjcf$W>_0G2%JJND
zuq1z8r=+3=MW~ux=2V=vJW{P;{i_uZoD5Yc8XjJZJm6)vP-cB>nG&~w;|C6MRG7VZ
z8k(FEoP$EnuryH6a~O$BJaBqUc7jl*$x;&xLDfZHT$G(IGcziz`iC{Wg*Zvmb${Hg
zr-d3yuVL<Q%%1_cck&V6L{r?5cms9+rimd`tOwfGx{Sf?#%7MXL-ZlYsr<(kpJn|e
zQCflII8DA^2v~}ow~_)cB-olCVKA^l$!18I3G4$RA%qEvWVZ5RW|IVBfx^F&k%Q4k
z+YuTOws%t3=$ED=-=p7C(lJ7}atsn!9*s<GafT}m0K)j_djok@03_iJeMhYyELCg4
zzCnK|c##K-DKWqtXN?pD?C&u8voFe+qY9XJdDqL=?*2U>Kp@G!SI4L<z>scbb_Bv~
zqbjVruuy(OA1W{-#yzZi8}_de!o<WArMT>CnfCBY65q!c`;DF3wP#@9&GsmRA|@yM
zs7lAmvSY#p_wm!td?{=%`ihT>2x8kH&VX}&EH_yeMcoZ_TExKL;oiIUdT6Zg{@)<y
z2{pyJyOt@prey2-)Lzwy<KCsPMAL~y|3sMDzs#s~3icYB(|?X)-|67bcTzQ>^l=TF
zSgmGhe|CCEGPLS5)L(nGk4djbIpdE9S%l22dF{Aaor21&9?e3S5dl%^zf-ZCGIe~+
z&#^`2EWZ^2W15NpFuHOWM!iGHq8jZ-d4l}4#@Z%0<apGx-SeYKSqAweVuaKRruG|0
ztsJb;Tgobh)?k@7@*1Mlx_oQFd(8}gcTe_c=u}qNpbH(cb^ZzcaNuOprg-hEBl>E6
ziFwSf_Iaau)O#bDMza@aWJD!BnAzB)3D~J%3KHaXxTrzDD=SHa!}i!^Q;UC7d#v1Q
zw+*T-sUC(IZ3&s)xembkeuND>cy@-Fu;8puKmp<8=z+d7Mes3-CL{~#)gjny?+IsW
zDwfw-vf5865m~srPuSj?2RjRoay9dyS+1EDD3KM&frTCuwWNPmaU>$-O)^gk6`TGu
zkn6<vduYg4fhU8@PzNS5qWdlivrmGx>5(wvhGVL8(UcAWrkdsc`~KX#%fD{%6HW$B
zUqgT9K2{u$k@dYBny>pfj@f<Sr%odSZX04_?qU%K*KkjVyDApl-<V$d?4{64wR*xR
zai@WPaRL&q2veZ&)o7L{9wd6dN;lG;skoB;=0IY%eknbKk(&l~?vdW&k)Vt-2^_ND
ziX5meCKnI!l0)^+M3<Z;wdH88Ns5b32dF}ngcoSBsi_4Z-e?m-QlmcfeSN^lV8QQI
zsojPHd-5A4!>Qp!F`ztKqXRnzIE)B*9${D~1=#X+j`=ZGKQ*+M{d0hFyH81;7FkRB
zf6gB#)Reg&Z2d-EcwT3Ww?$r@BmP31ysNH%VDE){nmrv#uQAAH2=^x&b?;({v2p@l
z{+44J_lxRF`hp?Dh}mfGX!!`T(b0s7VHX8;cz2o$a#f~ahw^^Mqand)APJ=gAg{%v
zXtMEwmN;xTr#PriN_P`hqQik4IyKm*^txwC{SZbAOXYMJ4u>VRzbDMhP~-D2Z}-Ba
zKD@1luVg}~&IoxR{KCmbw50A;YC<{e-e}W|{&7*hR_hC-ILGN3kBpaHGcUEoA=*M;
ze4Lsn{f}8ns41ujV{)JQUiW2s@1t`nkD8!!mVb(h5V=b|f5^8?<5{*%7F8w$&96SN
z8S1H1(CNhmcE;eHm0k`J1)%&0v+!_5=#kcP6$<xZBqWsBMU@GatB>Vw<=}^!v@02G
zEameiVa@Wx2*8u0q~^?&{=;N>dIgj8S3=N6iN1w$w37mOa&{z+e9FKZX`1520~3Q#
zgzkesPAk3(mo<DlyrRg0q+FGj7$r11nYF4dYfF!8bwHXI?Z`^CZ1lSnRpl#jGFVrQ
zwOVVh`WZU%+|k?yOL$#FF&f1>t4OWHtwcXDzW8rKDN{Pd``b@dfR?x1(GmYWuy4@0
zc25S3$w5r8J;aHM_3vzY!304f1ZRN?ju~@9UI2kU4q@CeSATnBvMT`xPZW+udo1p=
zG!O!FXD1}Js2Q(BSz?ho$V2KoQBXv_Vj?C<b#V1t)`hQi0S#540BdqaRw<)|ob<iZ
zj^r1FLg=M{OoWJCMaWhBJtuK?s5S)eTfCAs8}~R3YKBF3tx2TnVJ>&_iCf|3K#RmD
zyHzF`ZuMZ@f_-5aIbjosd2I#=m3RC6fD!7S9s-PhY|vbRg)Xs=z<uM(%nbR9#jYr$
z0v^@&?&@x{Pj4XF0lxg>hr|1&P5brRAp9-x5MZ}^;z{&@GQfEJ^9ZFU&H8=!Wi!1S
zb`+a_qhzq@m~7Xx+*4NU!;A?inP^aw>%V{%qvFvvIW;(~#yJ(eyHAnMv=i<(@9wd}
zR4=i0DTh^_qP=Ad62V^zym+r;HQ?*IzEJ6u&0FDk6hCp<lVb$Bve8uY0JL7ji#TqK
zM>%iY=Q1mbc*X-F=;x9S@t654ySgfb_1EvpyRK(axuVrzm>+<b{j(ZiNxBFE%QqEW
zJ`3}vr+Oiu9RTh1*T6)N2T@G=%e-En>+<Zc;16spi(<38-v1er4|)*MyKz6my0zPT
zV&OI)d?B2rA6eRwhLaat=!VwS9(zKMUD?3L0lRq5mdjE{`0LL<E5^(T>EkzaTknta
zv3++#X<kiFTqma8M5|kz{ralr{P}Vw)de?$^-3|Gt~}6ro4{RC?w!L^Ydl+Gs=VL)
zQ;@EftU{6-TbH*=K6}>9YWGz44e>T=<Vkg9Wd|Ei9Q<rPT3YN`8=B(FsR)_8aoH%5
z_}}$FWm#I321}kqY{OdZU&pw~;MhJ;=1q1)bglE@#>!r`rUq{pthH>sc~9lZ{1KEJ
zd-4X5_%Sdy>0vw{xhbKJ^1dnE*E+Z~)GK-OTH%USrcn1(;alUwBYN|*A(B7D>ZN}m
zZOS78^Xupsd`*-Px!(J1L^f5f&%NMbu*v`$wBtn2`wn5yV2duCk*cIBnZ=jR7m0k;
z-k*nJnyk}}4Mv>Uo4P=>N9}Lvs5!4AY%={j+l8pbvzo%rEG80bOuJ+G!2#FaaFtCL
zhaMsAqX{x5I^>P>_IlfI$|pe~`VK!HF&@6c_-B@;yI=TLNRo`GQ@7fEX$N>Lh!ER0
z82bdAToA9)>DN*Y&?Xv!>RI@ah6p?BERraHZMb5UHP`El5K4Z+O5{s)<hy{tD)g8a
zVD-~f!oVTr8WBeyryol5JdJvp5{IA@XKf5Kqqe9ym})|}D!Ea!5n@Th8`c=eGY1g$
z*5rnzH^D*}Oy`)}T#8Fc;h>v{Z9&2vI2Dl^ajCHBuFVEm%pI4OpLJ`dO|;lLK#6&J
z)^zp?<JP(-D7z5_rFYn=r*RhYSaC0#(y5*iOppYJZq3V`1S&M5i=ps>cf2nxviM0K
zkmN*o8l5g*uwooI#`+()!8|cL8YzrFH2r$d%X%TbE#_WW+3`>QX=d+IpVNXUd(K;Y
zge6AOGB8|hPxCi&;6a!+Og^UXK63WgSkD|9YQz%~y^7Gvv<zAtX`Okv5thsL5m)NR
zqgJ~8{u^q50rfs0XU%n>gzdDsC>Or2*f<@PD<WB<3~%(u<;bBFHHN@PeK?o1c5$j~
zYmtLN5VD-IBAxpB35{!U%xs;Z!`3hCEHkZ2os5{JKL$KqUAP$Ivcg(M<WMCDPPp?j
z2uUovkW}}&!;e?U{Bq1@#%Zk;o2r334?naU2q0DF&x;NCHFS}HyrEhxkIFX+^o56{
z1o?I#t5%*h&YnbKtxy7O#7pv9!+&h`6_+Svaa|0?Avw#fNLEUabXHe@mcB5;hD0rC
z8RL(P5o)e;y8^dJwz{dIXTu8Zx*Nf%-kSw$N~;UJ{lNtKo|X^@tan62RB-T=YCbt}
zW?r@D9a+9-rMCT83r>IRAWGgGFN@%0c~4&}rJbgw^-C{)3SzmS7kKMP!;|$W#16~D
z_gz>nCrFEXUoniP^A?l&-J@(ZfUq&MgE|lryEo)xtI?13{=F+^6zf4c;(3B?0oqcX
zQvOWtj3Fic`YLdw=XK+e55XMo^6XZ=aHkMTOIhe!(!LXGY~q+qR8S~(QC&+b#-Kl{
zRJgm=BZwdsP8&j#KC?>&7f&CB)tR8LAXNlW2Zim=-l#i<HxDg%+ja(QVbsz`MK8W^
zdJ8soIgD$~UeGy#^H<dXW2H$7ZUG9{8l&s<FtuSqRJ4Lzf<Y^;A)d7I)O56H1giK8
z!=fW&UlSIAW^X|#N27qN;XyH)%*+_lpFf^%t?t8&hE8)T7vM^zzB#+>G>rQ8hk`<v
z3@V7}dyn1Vh(uRg$As)NBiFqC;&!b*Q($XOIo~!U<;G$l=Ej>oGnTh2&9Q|3osJX!
zgQ`tTiI+G;AL#_QcTvx>KXG>V>Sn{VPUsoiF$;tCZ%;6Uq#+6%tV}4iW_8;}AeZaa
zpJDUzRNTleTRQ;=o8a(C*$Bj;#vRu>%vf0hyh0`$Hqh;yMav)PcoCyXe1B;r#K54>
zHk(*;<L~eexXaA2^N9L5F<V-tF+>bDU^W$qWZ#WR#J5xL=gqOd59q7WQ?-hlH`kz$
z*7rSKsAwD&xx@sti#Rtp=QNK+!aEXO?^K$Cd1PpO_k9+?I6z|P3l#r7GJd*0&kJ)%
z*wIueF%+WH@U)vnm+v%%S9wmz4qZ-mG{kVR>DKszg9#>O{Ad^M>V&gIIUcrmVMnzd
z2}GS2Z-G0Cs`LK=n?Pj0!}dNeGTG;1X%QqPEf%e$PigBgZoN2*{i6l71oj$osr5{!
ze$vU8(nX_e+Qgrq8c9&-#(~;?%pjP0F+?TjNF))kc2#!b5EY`+lVZ3)6N#P0%_t@~
ziACz;B2*)HTvQS0X0iSJMsgK;2z`TtC}A01(*EriWTB&>6Qvy^=wm(ilf!RioV4U<
z<R*pT6@L6`+hp{z2g?^`n!dED%7am=+(gDkBYt!U>j>_tX>n$iJ)ix(uDJux9q2|F
zKOkNGUMw`L_2#vmH$PJjSWc%FJ0FA<D^_6s{P~FD7e;Ke{B|Fx$N%o_z}3{H8srJk
zogBG|<diDwdr`!%xByH))l^rQo|8cCs|Q&rk$eR~)Xz#Lm*7DsJ7YcvdKx=oD?3Iz
zJD9#|($R_|YQ#i^nN8F2+~&hm?2*M4zm&L0s#L_IpI_G~4vJ(BgjoeiRILaBbxqO3
z4m+D1N<X=ntyIy_)}JAM`LuE<c}+Zx19q|;u3{K2aCUwY^5TL~O0Gq9(?)EqgzCJ-
zjM#9hM+`Im5R#}iF)Jq)-2{L*4yq7>Lmo}yWU~A^ayqm1RU|m2ab+@-V5L=5sa6aL
zW@zogLxjsu2ty?OL%EG9hFsBHc1~7{3Y=3*002M$Nkl<Z9n^yaa(~k}U{aX(vwPcd
z^Tj#vaN6C>_C%0d%^*!iy|ku5Knminf$7vs%cR~~7(pn5`eph$$59zKo8U8_tFck!
zAgij{FdP$uRRu9d33os!%P)8;KP+KXAE_rwCew6KE5R(DAC*`wWQb!UM_5FQ8L51q
zUF1v}$!1(oKrV(mAv^@%SuD4M)9)T~%Juv}MUvxMz%t3?A~h)%!=!IJH_;0yL&+g$
z5oBdoEt7T2NQ%Z{a?n9+Qw!gD6F)?eq+1fVPUUjtH<s}`y7~zU!)T-!A($(=`Y?rr
zhG7ABN+eMiFDWqwAzXOT$8dyiO<Y|9x$wl;Ftb{mnG{7jIGlVC)p6!<w}sSQx(9->
zh`NKp!K2(M5ra5#>sv}Xm`4!OXnCZ7{WFH(zrL&wm(NMWAz9XC@m`u#T1`$-)8iU~
z*AQuFC!ZtaxMWH$iZn=x3srQUiXK6WCDc(QO*@*qP(Koe>*iD6kYiwwCX^D`R&Da{
z;nuP`eoQ0z-U>Mm64<A5SO+iPv)*wm=~BRt|1|oYZ*Ix~OQ0k{uBL+X=FRhgm9x4g
z;x)3fd-<|D*kRj9_%>eq`Bm#9uyhlMTG{EE_!aVS&fiBQiR254<#Z^X10;$AqJdMw
zHctOC_>w!=@x-|m^9vl$mmNnAN-7OQ?36MR7)OxlVV;rPHyF#oImo!;HoBO14m;W?
zP1$sD%A?MrJ&H1i<6)VqPZVFexV{RqJ@Fh-abaF;DD9%XuTV@&i=vf@VQp!o@+!Y*
zWY-J$RTPlMv#fLu0C5Gfv>HRLeOXuyCg5nFWCwvHgLM^{#j#^Xa1gX<T0-D3gPd(|
zA^7qm&0PTe`f}hErbVJ#rg}(=PTHWJksXJ24%$ZErxBpF53`Mf-Oz|}x~Z@$WmxHq
zg|ke3@f#>7$>g+9$JQ#Ze%7^@G)hefXZm48aNrUtIGyC=iJ%Z05loO}zN#;$?X7%o
zGEXf~@bJoP#yA3!itP(A03Xi}191(zs(LuUxV4Fcw3(cPR%p9fN4<+yws(-Gf-(uj
zeFMX!IqSr-=*2y!qm<M1A%~npCO;-k1oadF7pJJLd>=AfByqz&(l?zT7Rz_gN$`@b
zwPx0_pP5YO`w^!fOc`oC>95N6vd+D1=LkP42l&n!2&U=-3F=q;>m@y+sJ9nN6zOJt
zGPyHCCXLiTgvb5-i1f4HNG#FGy7sX>0#kNL)89nXgy=ByA@MM;Py${u0cB*A?~rlB
zY3EJvIFi7sogMX@3Wu@0RQ6-NBh`0=V8g1gJ)#d-cZRWy^15ye@<W)*_YlT5^=i!U
zn#qscFn5m(@%?J-Xvaw+`+N)g)@&+I>jUmE3M9dK;wD|K;bhuvIt>Z!@1*B6sL|+i
z0ymkA2@xmb6VOWDr4>25cTa#KRFO8jOvorl>3(r6kn3>F)MySr%s<7Izplzh9Fks|
z%2Bw~5<-1>8`C$8sVb^Gft_-Y*&97~^J}lcD<BX!vXX|=8e}{<m7`9pe4M&Er&F`<
znf9nm`Kx^5IGxItdW2y}<YCHGK8h=_S2UwK!5clRt*W=`!)Ru@BRXi>EG|oR(X;Y5
z+!^T`#yY8u3R4>Cs2QC##uK+_>!UE0XY%HQ(|*UKk0OZ{hQnm}%HM=Do~5bYnc8RT
ztvI5E@(m||>HX_{OV>?xWsu27?@Z;2lM=0zPIY25ww>)_qg9sTOGi#+$myzE`_65B
z9Xhj2wp;ny_NiVbOawOpDodu2IOQXlO0p`hM?nq^oHpxTz@~nwwrY{XX(#hm9hgFW
zMER?YqO<BPn(JMu&I)5}w!@*Fp4C2IHOxD<{YLdwnMz{Y?a;{Sv*KJ;uIi&aWRA)9
z3$;(qzs)K`b&w4Ad~;_Nu+BGPx%G7^;8Ni1QsAsY?06iOzB<}Y6UkLSZDf{{xtUI~
z;dzv==<Co;mbjj)Z(e@2G#BUW<fL3Lrw!tUG=-A0+)EFq4egxbWF}GeMeX%g$~v+s
z|48CgQeV4%ve)9MbU=oakiWiL$K%9*`#DXMyh@7*_Ujjz+W9)3VgZ4fxz8s|0f&!z
zlhd8eJ0DwkRyZsR5Vi-|fhm^va9K09)pE6iswZ!`BoDD%NqdTBV~AHM$VtM*Gvcv%
zYc(pl#5<cCpCc&;%BQSBG7f=V8|4+5DbeWX9>i9%_Qs|aK_-=QqY5sQrxAeKsU{H;
z7me)1FbZdfDa+voYOcIJwWWb_rV-?4MWK|Ym$N8KYNX6)H8(!zQWjLpbk7%eV)dd_
zM03iO$c@!~lv#9eYA6A7ZdN=6%E6RRbt5yCLShP&yD3KsmfVB#pezER*!379?6zGE
zXkw%07jfSrWh#=r4O8%(78}Z?>Rt+fqfMPHjohnC0ha;<S%QT#$(<r<Sg>koX~8qk
zJcGBq<t<1}O+AXskx=pZU9GtJihLwf7%rjnR$6<AN>0FKw41e&o07{ZGFn!Yhym)X
z{B>J1E~R<_&*Z`5JKK5OR}zBy`?!&r@*A#TWl+Y{#Z|aya%@qdlp9efpGUCTUfG4R
z&SAX&ssbz`_;r)>TDPSZ?E)39T;6&?I!Y^=u%pe3)l@yGsP5oVW!*?l3Pl@rwN_Fe
zthK!tyPNuvNARN9f^vir?B*(BAyptox%xx=L<)ti1q7v5a*FBMNhpjO#a}izA~ueP
zY7o36e~>=S5D_~;fJHsA)l0K*_57qUx=hjQaf@*&a86U;Bqvj+-xiCH?d|P&|NGw$
zUF9_8k{jY`%im~ZZ`G14By%tia;0w5-UbX&zeQjrr=|0B0)#dpXLGftlFCdW<aoRk
zglfgCi>n~h(&KT%vNT*oJ*bQFVyKKaic1$|Vs=_Mm&}LYAy+g)0sSI!Q;yJf1a(}Q
z$)}E%R1CDLqLsQ@YQraSC8@5Z*C>>(X1$irNT3c&Ja!ytMktj#mr!5qvc;K52^zts
z${s9WSU9<=OzQQlnU{ntT2x&*H`PofHLYyx9OQ}+^{=QOw44g3>-M!%`81MBvIJC4
z6Xn;|;o-74zbFK;DgB}t3cEip1<p+hOt}n8t0i(pMMXx_JtD#}Q*8YYP(!?ywyCP9
zIo#FJZS+%0slA<=6pTGw3ZKif4M#YL57MMd2W3vOV*26LxoDySpFn1mpw&+;-+nTV
z9!~FusVgNd=5Q+WJ@{-X=1}KpDU}i9sfC_9+D)AtS~2BPuZQ5YgBs866mQKZXjOCi
z_`tJewB8Vn8)qjOOQ_+&9bB@e`Q!*L_j8JQh)@zw?x~b@{`SsRTr)2P@4YY@TgtkS
zPen@UW+ibMe_u%hepcOu71UrqP|=RPtn+kQLw#X$jgi%T_?kQ_i&7h(N}MtdsBJ)+
z61qTjZVno^R%ed_Q$8g;UQ_F-vfRK!Pqmn=C1a|SaEVrCU8w9w0kv7uXC_*z7iXm9
zdns|6C2c<IsyIwyEGVcv<x@_@Qwh0qsVv7v8!!-8r)6uIw~-=(rgVJ-xM=FZ*kx-i
z8S9=a(`Gqc6KI{<)5K%Q>Zk?1fXbw0jg&c2Ni~fiCJDOq!@|hDIlPgxbk5l+qNz_a
zKn_c5&ni>u8>0l@XqM4WmP_|i0PW>{G?$!<s6FkR^dnA7EpeSof&beSIIDnV<yq(B
zD=<@70qFTydAH6k1<q3noKxfNM5jPj4rZ^G_Tqj{#vL#IMA^yTaC?4*1=htc$h~zb
z;8I|cC~#IeEdMqLJT&d;?DWtyNh^|*W3Yn9)Q57_?e+al=;lc|%V-rKl{-Z@?P)?8
z)eRD;s}&Izh6{K)wB%67bA8+g*g)ZXA@#Y;oX|JT>ZN}vPnllC&7j9}O--HH!HuqK
zmS*r2pNU-`&9<~qr9yiL)42In+kVD#0>1G>#*6LxxfF0IaIA56MyaFyUj6<3+&dOY
z9ZrIcZ{#-d_^jOYXquO4K^`}5=5pVewsBO~cXAhc6fUP8)*`Ai)bFgvyy;0eK-)|A
zKPih0nMkI}!i#&`a1mDnrKWJ0mH{N|>Y%Qb_SBV9zbTAmZDN`bZW6v^b|Ut0NBbeF
z6l7C}s=2AlY>b`D4YzSzp={=E_(ZB14AGp*qG_@C^M)GK2S=C<rOBZqI8519rK}%>
zM&Pp9)DPp1{9v}Hj}{$d#^h?6dC|VTtvs1$VNnXr)aaz0F3hD#mDLN9ZIjO^o!!h`
z3cQ&pFy&K1X}8Ojm6ahjmI7;D<+MpzZsxQk3{wj{pMyG&8$TuKUO-^VqApdxG}P&$
zxG7j0HxsE>70*3iH!Mw~Nt$*%^hzBSHMuMPK#PgjMm2@MY^=w#yPB|_resDa<SwVd
zwM?oRq|qkRZ`ReJh$d4mDvHBq0#_PAYgTGF*6wJb`Hp5h!*tK@ZRJL79)`%B?%6bZ
zvV>|A0^2%*-}(a`Jijm=DdD5|B~8i{O;5+gh2+vg!f?&pBvf+ca%){buD^h$N=h2B
zuFQjrXC|VN3ky2XLT;jIrxWV_x)g9J;7@@m=dhejpWu`)8IL~tsJS{7a#*ZA#SQr5
z-Y(pE<uva6??XAcm?rM#522ZjXs*23xOTNLf|~Amw3ReMjd*G~7$JBLO_<1$siOTL
znmpkS|GdmNgbxg$gBtRS$w3{i0qt8k)*YItJi2;TB3fyyYLwOwuAZBKebwEVpBIgx
zuqe!+p*0!DOQcnX7_Q<S;+crOv=&;(eCAS3qMD{dmU8!fGj+9SqzcK2G@nA?)QQ$z
z)O0VWibN>QvMkI=#Iz)?2(p$U{Q11-U<mB~xD=RT3Y--gR^UMOf8I@ld^@VTjVTVP
z0fZ3<Bzvl*g~esG02yPfAB-APBC+JOJlv8a)rMgS!6-AT;1n&FoKUt*o^<rlZWPrD
zq*6dt1(^>SBp@Ynx;MbM5ma~BRo20?3!^bVGm5A1v>|~qr<p0?Mg|p1p_;bRq>-DG
z*^+MRPel>@_SN)Ije&}*M8pVAR|lyIq5XwBX?--Ehp-AL8(H5XPHlU5#949k0Ok?c
zw1a;{yYsoaxO{esQIBxtSX12N-Qvz`3Y-<dvJ@L{^RvHs8N2f4m>+R;v7~1uE={^s
z5^7uf<(6|3=cRm&i4}s4_%?xp@-q5a!ZqH@q>B8*2zJt)(h*}3<lvNckf5Uc<&;i&
z$-IZs^U<V{zl{IM9E<8Ff0=JldREcImnje_#LAS%5bLTq{BP<M%PFo*gxF_O*fD;T
zR*MpWZX*9T#&b#<FON6GmB$3-1;%xsT?$Mk1<op9O=UyR*|G=>f)d9$C}BYMh^Gm%
zKqLJx1I~=3J%|Q`8N=~c)Bsd7nH>Q;3vdp2GB00zYoWlFqnrfFs)iHi$i06Y#dDoY
zfvKXvlndRD*DwtbGnjazt357C>8x=%>!;Ifs%U_pjE-><hRYzoEMJZ}W}>N}iNgC1
zZmgEp`Gn5I8T12@31f~MAP%dGTZATZYLj(6te5aY0O0&t_^{7Mt{!T?lkqbEW#Tdu
zft9r7WvNp40<t4{`O-3;{JLmhP*yK>FQ==xQ(gV<6(;k%U7Ttwzaw#u-1{{_(Ou_K
zV9F>k<tCDk*D{SKJBSEbV^cn*bEbWc-zZJDtlimwQf|3vqnX+Q%8L$A{`DGH`zm-`
zeG%<sh0=QD+Se<wlR8!lvtyaTD4yNXs9Z6~Q+Jwa5=Q2D!?_KnjRJRVq7Gx|ULFXM
z#yy1f1iv84sCucIP)W1AvgROrSxH=-tl{Z1{jy~~GHlEp%Zlgr;zsOm>@%FtFj29V
zHq~ROD>g(^y89`_S6;D{rET5cfZcT-RPt0F9paEqo53SPLq`4<$M~{z*+IY%`1R8)
zjjTTG<*6+KtTx)|>ZAScIOe0%gkIRyMpMcWGy&`}N1kb2LRKxc$y%nMhgipu(Lwz9
zsWL8H$i6w%E(nyen_k63J7gQ4+bX%2vD0i*TEDvyRaEDYs!2q!7YA5J>AQ7v0i=`*
zAW^i3AHk`wKuhn+buI;_k^)n{N_e7;({w}tHO#^K`s=ToWGh#$q=4P2@(4E>Uc!Sa
z_8w~Gkx35AG^;R$1I>#ki+gy0fRY3un+Gzq>mVFCjxr5sopBh+lW4kR6P|+mL<;#k
zY41Cc((?Tr<Y^p~uWqlwhBDa&k7QiR!U#;-L@h!8-m+jG`_zSS>T<;q6oY76JB0@e
zbn(!P@R%rsa#Qv5yZdMxi#E6jY;x_iv%Q&d?z$|O_kE}z3P%##R?QPpTDv{Q4)^XF
z4_050jq4XCW5>Z}`qQce8=?)-JUoTq(aFu;LCBG?pF)0}&(}^R)eSVgqvsIH$-3#M
z!x^fnI*~?87PInGQ5Z*~y{uzxb0_xGPI~lEAJ*<^M+E&M!+Y31hi6J+U3Ah*RvOJp
z6RZ!E@n8hjy@d9?dRa~{?V;CqkK!8E&jl;<THT*1rocHjB|Or8XAtS^!itIt^ViqM
zvz+KOCDhSVdk#0FemEG{EJ~vQoae9^OQliBqb1R{4(d42717wfRu+vT$mwn*I1FoO
z4(5`%iMV=E2D~&Ulg6_HuUnGFfk_Jx1hPh^*KuIsJR~3{Ar3b#OUEjndAE|P4BL4k
zN(wdcvtzjF+8c`7cm|?Q!YShED|2#b%7%Jmi8K^=+p26HRN+BC^|G#|u2`&gSJTbT
zPsA)*Ye-9u#XB#_#R!jgD(x7;a_Vpm@$d=(Z7y}t_K*u}qeg!h?ROO<hw;3{QQWjL
zmnL#TOxPt<kX@D&MQ*JZOL)YXRyt)0r-a8+>ClA3v~p3%v-2W&3eW{q+sH|YFo$lm
zvTeaUkl>b!^Dr$s2nSjQa2f4y%_XfCOiMr!ZFjXZ&Tw$JIWDW2+-FKs9Nx7e3tmn^
zUFTBZY*FBxn-W?=h`SP(B~AE@j0|IrM&}nwxGZ^1AJql^yrmJLoaO{`I(3j|zO{33
zZ{ne(NwhdRL{*3Z0#p#?T8-pXvUo((Fl}u$whPFDIIW{e84f<3K0Qc)+E&trT^#r`
zsIMh8hGbeBt>uY0I(bD_7(zKc8erLEij3t@4}mR=C%oj6i;1L~gg}nVi3AG|v;wFV
zM{~RtR|xxQx<*!0y9g31s06E{$99$X@WdQ)IOL2*S%*|k@fxXbHj^jb$V9WuIE#zS
z<4HCNG*J`J?LE@zN+mGr-~yc#vw@1Ohp17%h8p!dIL$2W@#3<%DaP_c4vz|pVf_lJ
zqo$+DYB^0#p#tpI{mnd&Zv=U?(i+b)J<KbE+nS!+UXMYZ%A#}q_LG(}JsmdOkH>hf
z<J@V9Naws!T&s@#%IEakbuI<Y1_jQ$RYD1^CF9Z&S7kijNJmgfS4wIJ(sR;tIJqyv
zol!fG%7M5lVvUizD4wMG64dKf;3xa)C7cQs@tiqvV>V23pis)4LUcGNXPqin<x3Sp
z$9U=7I>~dS_MkBRIe@AXDSKiXsWenIbP)vlxg{sT0GwHU^R+|y>2w)o+|TK#QLSKM
zI=_%bn|iZ&*2yQkQ$rYCC$Ok~YP(M6k$D=WQF(HVrD&|1bJCA#D=TPJEf_~?9o7_&
zlIZxdaGJT1YVHs#E|m*grzpz!Bu`U2Wo}2uh?#K4JsqdE%ZYBTb185l6gV#cO9Nca
z5xSp9v(BwJ8tj@$30U6D&dMDpYNKAG1*fC$9eJfqXTjD!+pE97|Gf<#?|P!~#!Kt^
zoqH5`bB~|^daLtV=YMbg&eT=PqZX`=Rj%bYCZisck=KdlVdDjUQ0#|B?NJ;HR(d}A
z-p3XCh%*txCI2z*)OWAE{GF|f^|N^$RTmrRUe7xUoU<}4e@bc~1P%sGdUkUqZBtn%
zVkw)^VWthVw(ts90IB7`Q*R?E%pSt4yBf_&!6~$vE^D4Gv=sXHEp^=f(`8O<PLymW
zU@-ZYJwmxu9c5X)oB~P!zN4fasoZ{}Z9O_oxJEYLWodK**TmJj4xU6&-O_JX?d$*=
zFS`FA9xvSAZ%=6l;%Vh{BBsqhuYRuZRZ(T*NIku%rb<IhY=oIE+k6|j7m|yaMrA^N
z{&`f^cN=-3rrqV0?eKE7Rwr%Sc%zgR>O}4)T8ouYRmt)E-<k4~8J}0FHqp|@sW0u{
zucMiuy?hVaKBd#H6{n=S4SlqZ5$`*K%@pAG<Mai`!#{r;#lOx6sj8tP<+a7jKkl*K
zfz8Lgo|_anXVn@+E<43&Y-}{tlkna8^%E?lW)RP#++U$7n)h6ig8?3!70RO<UOL!@
z`8)`sn@4>`b5PgRbzo<G6t3VZ+zUGzuxMc}GC~INmn}6|N*h}`W~;81N33%|6;qbA
zfOfH}8@u4)Dp`JfFrMDufz|VqafpNKV0|A#sF!phR|8+!O?3jQF;rBwp^|49UO}7g
z^|VF5gU6Izu`C-4d2-I5*Hv;uGzI=rKPYL%b+o0`Mqu4m+lT8{<RCjP3>$aW;vj|R
zN?%>mj#_TeUS1H-RnS)Sv5ndbna{n5gVYB*#FfA0wD{1(qvj*RJF$lwvEx(Xam|7h
zbanOOPp{V_EGhz5Elk6ql4ewMVRb2K(M8ZaT+xaa>M6|U%HcD#MzLf@DtbD4k)Dx=
z%V}Mrm%3k#)VJDopcgG8A-I5tYwRs;M+%QDOCU0~^>k2otBc3L_v7XZfi3$eLnMuE
zp=lov?U+6Ad=)o$NAoE4LClz*Y!3K%okIS^)Hv2J6$4aU6~^tN{rUk9b-O4rTQe^a
zPrY7?)MOsJ#)Xi|+73Lwu?5%6;CYjg;aEgE>?hr-_|fR1PTHkZW${wyY;%<dO9~QD
z)6mTWJR%^=9Asm8<Xg1SHS6TN-%g#pvds;+oV1ve7J=t?HX<V{5v%99Y8mGy938(_
z=PZY1fvLN@8&5s;6iQ1=F@5@U9sm(x1i)eJoU++>h>Dua=cGZLlFYz}*(v5>6$^OY
zUjm;8xSH7H@nRKEtqB`sC$8w`2{-A8qmEQ@X)EfwMlmNl8k-KZp@%}{Hg1?Mp@L)<
z2Wbjd(VA$8Zw?2p1{#?qTux(w{mk<~9hEMrl&KST`lxoWxG)-zy;e)RT8WsG5{g5#
z05Ol}9rkdAFpUE$f#uD}io|+yh1)8q{7J>lAs#w0hw2TR_fb8A<>*k=9)d=BXCL*e
zc+dsa5u!qd2^cY`t?lAjjKR2tN004f{C;vFRkZh2#S?g{sFtvFW(sx5deG{nov*wo
z{NRbhNH3roMs@`LwzY}oY_#ec%)>7tP(x+a89XFIU#r7h86D+{=EA%<!yWA-eG;^>
zTl!`^22C38Lrgb1Foc&5b>K<@aSjjRShuGQEfn-u@rbj0+AYhV8b&`)I7;BkWIk6I
zy<Eun1HnQI4;B0v%*spPX7LDPU#zmW$1IRUCq!cbRVkY4+p&WRz{8|TCxv};bK(&?
zJitxk<k+Ym*G9s7X{hn)#i@Aw<w~0A>7l{KV7zv)o#ut25lp*kY1xSc$Oyc&zms&1
zMj1`}G<SRPR)T3~O9y`ZY9o($165!8F^8%fG32_A#aSPJ=(sV?1_jPpz!EXU#!KT`
zpp}@IXaGy{Ao+W#yY%4NGDIfQMiq6D8rnSAMQ)&t(~WSd1L(ZJPM*5+n+;8vU65$F
zs8CK12FRK1qHV0FH`ns$uqez=3uEW@@H8B5ePNl*p1Mk2tmF|~IvIzv8?H8Tg^bh2
za0V98{?&}s5InuNlR8RSJgvvWW4z+1ju1u7`2kL4Xl90hBL&h~<lK6wj9E){g$(L#
zP0xrh2erm>IvGpl)Opm$N+*XUj%0v?y^e+kb##SRI733iu!w^^IWCAgP_YOiIMnlK
zvS_Z}iQ|eSFeH;Bk<-DkO9`qYLxb2+-NQKKJPDBDylU%?4#ZI1vw7@TVP>Q`Vl0Gu
zYa=|kHJ0ic4b<LWK}#62H`YLXutkLl<^#|{5KT=`dj?n@ZIJP!5X>dZCQgGB6KL*-
zgFcC1yqMgSRuAiRa#269gy%v^TYr!Wy$MW{PL+}s(-TlvuX7!HIDHS}^c<+8u2&nU
zkS#;O<UBLU9r48Nu72c@#xkIoN7anYhk0=Al1#KP??JxzD7Gbuc_vZ4BZ>LUr9HF4
zM4mL%LSB?}J#8y$qTU$wTM<P{dbFBLnu*R*0L~yMC;d82>E)Ehb>|NS&RM`Rpu|t0
z04qN~-~0)z?B-t&rx#7+6r^6z$^q3xT`QfQbC6ouNgN;}+`3cEGrP2nrmeHz0MZaa
zLHSltej_=TxE>wFRmN>MS=394Wap&?84ieWqf*dKkPwHUGYq9>70UrCnN|g-A?@U<
zvdC4)3WK)%=;Ue1$PRG}&GRKZ`I*~o;wbNF;~u^aavK5<aZllt{X`RII+&rj!d~Xx
z(B6+ea%<BGaFPY7jz)$<aL~TUUj7XeVD`|gO*4-`%cCAtH1)D#Sa%(5CYxZa8V*qt
z{xu%-8bp1s1)MH*wfCU3ow{8-eMbN?LZp*I=`hi^pGv6R1gbs`@HBEhqH7BUSi!94
z0NdE31~W|?K{Qc%X#@xXWGU(Ep}y8ks(@$;8O)DBINRS&&a{nnW47d6MwxyT=>#=x
z0g51qOFu*HD5FyA0PB}SaO$9S*&2d*5w}fI-<540CP&BivW~La(Zp$92R|N(5kc5O
z{kk?z`Df+D8|7H(tYsxf(XJxv$vT&j1C@$h4$pQhY4i}VBM4~0C?+Qy5gm!S8T_ab
zAQNfuaSqEfK*jg%;??;BR;uYasn+1E=r|j5?bJ|}^}m-+kTwKsP}!9sEi&l{E^$N*
zHo8Xk3o3miV<v{3TV`OisaHBl9AD;ngvjJf5wyvdx=0LDe!3Tcm<L@LuYyy?G;&Na
zvej<F9Gxp>Yc^l^a|)GDyYW(Iu;yiqJ`^)leo7;KFjGDY4Wq)9lZ{IBmB#{QzQ%E;
zV6%By15PL^y)4x!#Bf~F6XWq>1n(dMeB%FS@4cVnxUziD1MdnRC|H6p^xlh<p=`I>
z`|g=}Z~DdVzV$zB#QX>Q`~0{sb|ZFTBW8E!#mw~Xj_ICOx1^ROMNfhjh5!ifs|qN1
z6in^sdlN~P&>%&sDUy=vN<>v<-n@D9X5N$Mp7TB5!`jEgkbzI*P-!~Cqv--U)Sb(x
z>~UvcyXpv&rhZ9yX}i9A*UoX?>F(mZ)b}*0qtv`Nj%i$<egjj`F2sS_(0x-}qs}Ru
znFQJr*CsrhcOO>J1ikV0(wbq5r0yW>uz6(1My><Q1vHB`Cymh`Ke1IP>Op-|yShHb
zQHmc;Np&T1{pUgVUf8h|`j>}hrt>Dtmv01~NrB}Lqb7|Wt3*M?GwCLrUnlvQ9D(Er
ze0@fMch}P^(m?bEcW?28Zj2JTqWCvy3cj#X0#YHl9hx0m%K*!TY=V#LuyZ_Wkp`i+
zvQN_IAHRsk7DqM6*7_=;hznjzo!tAl+ErC;N<+C{aZ5(*PWOW$_{S*@Q32jrYjKDE
zc719rJQ1F_HSzmB&Z@+%U)Jy5)vUd=xCmMI9!E(kSC3;9pH3T3MZ@xikblYkMAU=S
z*&VWr{j-)z;M%JsZwO-ej?K;6e|l%Y{_M3zm<rPD+@%4V!eAUja24Y+-yY0zR4>H{
zxN(fOGAn6r2eEO)pmtfp=2D)QZoSy9(lH)yUK4UN*pC$2qGd!bM&33sR(ZlUvAT${
ztgDCVVKg*j7h58>Yg-w1C3vO4DOD3cC^b(4S4)=|%MBU1V|daAFr-vNO*5^2ApAn%
zY3s%lS<iB&06P!?F)Zi!%Q4k(&(C9}mi=mJalx+i&ynIj&&B`Ek$GnY&)6o;C7W3W
z{r)_{*&pNNXL1C-3M25uH7s9PMZ#fw|AP<gwO3!Y5`vn2p%p{`oy6$<?$wa9#~^xe
zp^sZ^75jtJFg&lFfShscC?kL!d%;18^Sw5oU|yIJ4wF3MpU#ci!6px<GY@>A31h4b
z-O6b50s+EhIVl*ar!eY5@q%G`C)lJxyk9{yw+;Z1fxK*M?m#Y~Y(=SagW%{m_7DAc
ziXi08Fdy`S4JsutSrCY$jT!cDXZq~Q$f64@p1`17j`t4xzl|@YSsB{d_3lY*H!Cja
zIt^{B4iDoh*sA7%S*t<l&cHlT0{zRm)=@l%%g%{#8Nn9CaE)8bzI~+Dsz^KLkKtG0
z0Zd*)as(b_1U|j?d6XkRXI)P)8H@#C`}xm*?mpF&mQ*TnykWn)I&R1JSHVk_3P5rO
zttDnr;0S^rsAQrQ$(Vf-?x;hX$&iDmsuTevl=e@KRl6h*uiz%%T1)~9JW|`pdNU3#
z>N`hj>=2l%J~S2~ziaWzwf4#tzho_7nYM%9iK0TicA(l`1Y~-td&>4g+w#iRLbB}4
z+4hDqJ6a!r#13y6xS<&Y*)=?OqLm@c*m~&aE_t93tXshsiD4lVH2GojD5j&0&90`|
zH|W>%(As?KK!r^s9Ot=?S`uayqe&gwQe^oYa|3MD;oam`1O{<qc){K{Tx$pFav}AT
zlR+XpS&}30^&EjGu3<^gs5CY;Ieb-4j%S1Nx8DM}-4n$XkD!W1$R$XU(x0E)WFI5M
ziV+kpe<1>DKg<WBS_zFtalOZ!z$*mYGtE-!57$Drn3Zig;IpdHNVL4$Q5&#1(B>k&
z+frL-jV$-3Kxc0T+*txfXATxmE$g!Rw*ve@M0nrWU1}Gu4A=mmo|pEOfB+x15m4wq
zIa*<D-4m99yWR;Aw5Da0pywJvr^Cw72<%`Q=^4fDR>G3;PR}Gj%nS#vl<OYF5Z2EX
zD`1&AOuZ?x?&RXWADEtENty*+Oo@GTuGcbIp1w@7*BONMGW`?SQRVSia$Hl%KgkjJ
zQjNe<v0*)EgoXeT4u$5>BD~BvGGxn6B>R@MFrN}dpZcdXTT9@EMD-$AfoGP+uEaLM
z1!T;UfRG_w&;s9Z>oh%%5gm(LY0|#zN;=Ql%F`L;W$ahmiklPWOD$w$Irr<RzTEmc
zPzH7JVu_ALcrBsJ1+G*YFyR>lF_0q32kj^mk8oWArQ+h}yKgej6q_jR$jOk6@jd2@
za*z>1>mtCZca0Ak#pJ%^2z)h1;He6%q%G$2Jyc(W!1}yxN^nbPKTKJFj`hDErj7M=
z65F`n_w~m-%<l(1n~CRnn6{F29^VLfYmCR&-k;Qx9)w3JckAE%a_{KGO};jCZ||dh
zOKj`Wj(oheefwXcd{}>NiN8PfJ)tYQp2U6H<+r(hyMLI!-5u+1)N%fSYuo&cMhR8i
zxpZ7&RkfG++pmB3JUU=qS$}T7O&ycip8wr#Uu$E1bFM8NmvD~6@9Uv93DsP`{d=M9
ziQg0Jt^cn6tS=9H?E2%ruD_qEz)E~5{D(~0S$=nZ0IK5&7r&=Rss+2&xwf!{*No1y
zByo^O?(YbMRk(_tpcqs#w_zYvn9CsC?&E}R_$6^t_4Q8@P>Xf`5z0mr%RgM9A-yA>
zGr-@r(dYVSwX-K;7doS^>Nuf8VnJvnY-Pg&GQ7EE)ZV%@WT#-J&<fKp{<Zn-`@gmS
zQ{Vjqv^`<pAha>E>-oh$rmJVdW<j6(#oz9Z^*7>37#yE*F_`|QdkFHlb-~IYN@X2;
zcMQ3fJA}*J+u$GRecnCK-2?9J_s_kIjj@Y1{3@<}e#rLNjwd#;jk|U3HRHayw)mq@
zQC5d=w&?pbp;T=oB?%D^+C#X{n4{EQcQ8K_pI!gEAT0fOnDxxIW8+L1KG%TXuf*2;
zW3}xLv^`P6rBpT&Zf?id{Ld3Ny8GiT3{#8%=FH4Y=kfEOzF}-wLE_AsTgR;me)YP%
zxc&C>kd*`rZAX2vJN#pKluF8r?D=2@sizj~WBAA?5gfDFn{M{Z+cD@@b`tB>(KTkB
z(N%jMFJCJYFt;GjGUpboHZzMjH?#N7_1W#oWqWaVx#h%{>~zNzfxN{Q2@#J6>|hZ%
z!3r>l?RXiRc>sxnaUmr8wv<{HYyww@=Am6N`?zz?{_^E2yG@<4a_menEVK<RSRo$2
zD8}+P@C=q=VDA89_QCBbJBBy#4_Ah)X-gH?vugkOgC2s5Ni{}nUURU(UWTK+y&D0&
zoMzvDuG+Gh0R3>qH*P7mtF0mDofLaQ9*~NxI6>0{d;?Fjp2(8DzORgIHxbJsq@^??
z?t-D4HWj-z2~@OOFfa%(E7@Pb{x+~*RZPyCr1Pq+E+&vT&CWmxGZKy4_prMupgg}Q
z*Ky0so+bxD9>*7vK%>YO?SJ`wxBcv;ItvfOJr&4>F(KPNg1V*zJjXb~Fx(P`HmBT{
zXXfnc$h>Wbi6N7j8-=yv2Ink;u|Yx4mHdnFk`X=7IpPMlz^D*s#_!!yVzJqI2R&I`
z1?mBH&ZL5y8JkFC1|I%7JF>mZGFM{u!HqE7XxZ2cSM4m!5{=mWf;chcd0qyKmw^<h
zg-IfeLuUbF;uPan$qj7ge7gz$?&uBMdARm7z{$nn>nZ`5JPlTF)21R4lvEHry=td<
z*7|4Twy!4F+KAm;#kq5m=dG3d^)c)bZ@yUXuycb5)M@&k1so-Y)8sa(=8oZ%X+?;q
zf~lM3#Vodp;{=6&a&ydb3Au_9>v)(z_d=XY$+GbXJn`UXAKfmx81Y;E@|VA~l9CeV
z{nOi~l%U~JV#v~Ra~vPjGREgZynz{He;5Lq&<HKcg*%gWWM>7*Au??R0d%2z7QXa=
zjlucU047Rlv99nCW;1C|;$`dVo3vr}bwdVKg7LhnGGGPit9Gh&!oCUp$l2?o7Qt&b
zL7f43rd9!RHUKH;CT6e~ArNDG75V&Tc*tvta%_M%;psb5j#s`fvTVnIEewoI+D$N6
zQM5nNqU^<s83H5LP4b6ECTU5g*;`k__Qrvr-Rd5<S+4Kp?WNB1IR(PL6o70Y{oPiQ
zZvT9K$c9K1F$&JBtT@k+5#A){VjIwlI{F?!5cUG`=qDA|IQ34F0Vs>fECA~4^sIft
zcuWIGZ5fzH7?;8*vEVcY<rN%bQd|Nc+YAMd-r^a&IEG<5XoNXp0&VG?+fjsMtz|>S
zbL#dqbTXATADyyKdeB1He*qXs1TA0(OcLb5pbh%T@hu3W$;mnUpet%`9<8ym%(!(k
zNsn$TvQ8*#CSZQp4uwrOLSDHQZ-IIH?%_)7MYxWDgR6({tF1eV1{s0(3fda(!-?Hx
zRze`5l81bJV-#ARY3txx#6Gc%w>6B1b$3INU4)mcm%!&A;k_Lo_`H*xXx~0mZr8}F
z)WMkb4ox{Qa-0{=KC&K3(EYY~+^(aYD*_=0=gcs5jPXBOpJyWo&Rv^`6~&9A@tz^D
zzMvq7m(!}9M-Ymru%BzL3Fccdfa7<sgsfq6vF)$Ov^m1e>i{f^<LvKl&)SQ1d0-01
z@IsU0u_tOJ2!F9@WbmTS&snu!Ul}8aU*w033p7Yaw!wU{77nqIMz--Z{`zcv;u@B(
z0v8q*+TZ@|-<m#E{6)#F_EG!Q^{D+fuWn}Yv%F)8r+8N_HL}-2WA;GCcKZ4V=<zgb
zVFGycF<Fq$q1Lk!oJI?%=AlVNqv%(}KM$OOaB;A?H>;>n1ZQX7J2ufmcFZve^LY3s
z1Pa{*urzDMC4~sc;|K0bnn4b4%3yYuWx)gylc$jFW#Ft<@$?1wY>1~Z!FZE{xZev!
zOD5TThG9gV0Iy{LO{-9YdReX(!r>&Zqjx=I@ZpWxJ2r2%yvgf}(`?glHbN7iEMCG2
zq3S7Rg*jljYCzkk*!k;&q$=}P^L1!+)uihB-%myC*#2tEi7}CimqFua*lLV^qh-uN
zE~s>8$^<kvG=V0zXxktr6so@gp>umA#dd)u%V3~q5PD-Qku(llNE;?#?owJNP>X!K
zNP4r~Xtj$_-vk}C4PH>jVVP^LD_JF>Yo^uW9StFnaGfG(bHKLcBj^?x+jN&kEEJC6
zwS_Vo&2oUT5OZmHAPpwhFgUdomnJR5com}=i49_f>kky<S_#}_t4#7mFmqeD7vpF!
zB|t^QL@`2^pLv8p9|BpJyjrfk3-drtRiWkbWZmTY8nTvM1|;b&^%ViId=qeS<+#jB
zyP%utACU)l6@IT6FqbTnk)*(t2Kk}W2&Y|GRtgDeqr_<CBSfbFD(`>?Ov}@7IoHH9
z(T5<b;KivTVfOE<pxlAWtA;>$F=}KWwD!O@!Oi9Vk?<o8L0twj1usi6cg)Yq+w2Zc
z{<+TxJaG+60!xO*yu3U+aNvOZ)Ndb$B|73ICQg*M=NPt~S%gn$V%GY23vCv^6e>EE
zSt3naCvWDxuxWx<aG8<;qLbHe$qB4V(qm14vufv!TmyBCpaoe-s2qfeREr4wSr^Ih
zJOm|5Q<az(l&VYoO=LV~=_3e)=upJPp2K8O_|7~+O%yS4-n{wnp@cxASCEoxf#sPp
zA}i)Fos2z&OwMVD`(&Gn^Y$*GuHqDz&=N19C5RhKOE|HBE^@4LC{80F^k$u+%>o!r
zSGgu-WezZLA}rZvu>}dLF^$j^Pt`ovlgoJm2)oG{aw)R>2tl(Wx@e^~TV+C&L4Oma
zzY83v%+yOPnJ4(2%kr}n?NoF)3M+~u%xCEbmrE+JG?tCn)>wKqf&LddKP4*@VN0qr
z1au)nLY!Y>g9w4<SN3et$Ed%n2%knY(HnOrSS+Rz1weEG<FJGvP>Qf=w4z1&mdjX4
z@Lq-HxflMeI;dZI&=?C4`c*8^#--7Kha2T(Bl;X=H4=?-fjGeEw_b0K{em{5glDBm
z_(O5i%yKtKxRXG{7rUk$_f;V;Au&LdK#?PgA7vMk4O92D$XyFdHeNVcoNp3=(}8dm
z50~nBwmn!uxYl`oN4XYJ-w4`LM16*sWhJDAXvZT6`Cgt;g_4OfC`O1x3ExEzl*hSr
zf0dMX0ceb<d*-;N8Mdj8Ob>caG+reLYNdZuNorVb7(VsqslpnxJoE*X-z!Z7-PFWa
zjc%O&iK4_ycSSm@wcubMJLTh>gxjL36EK=!{O|BuYG1-q=M=1InF%(>q%TG55EY3Y
zLY`-LTD7V65>sZ0o`j~|GD;`VS^TzrA*@do31A(s^j*?}bnG(cRmw0C#|gJ30Z29K
z#odIG1}It_9jEiCEqBe-RlRfxQG^y>xpt2>Ms-AsL-URzM|3M{S8b`HuwLwze0S&6
zcTukh*XDbt*u-_KoqNV{)zL{s6O)Oc6aH<Heu%2YKTbSg8Uyu3Cv(@%G3txk#;1ge
zJHON#$|^e@9gZ%DbJDcqGRr~8=Ak_=uZ@ewUE`uQwM~6<n7Xy|==Yj}>9wz6S9=_%
zeVo?4blf;|DtEn9apR+YbNz|e!XaFVcfT}tP8jO`vQgJ5?OtP`D^~Em>gs%IpAT1r
zsnbx1$4h<Cy;r+F&QJApO~`KVWbE^cm*$P<l>HJ`jw;9Bnf(0J2s~AR^{JjDe<VjB
zIReQM_>2*F;x??r{%oXSY5ypm!YtmpqL)#Lp>c&-J}L8!)OwWxl9Ns>cnp*t-L1#Y
z8Q=}2<<~N65UJwp%N*)=nlv4~-XC+3%_WoNk*}F;)V$uhQh;3l#h{7p$fxVwsQ%;L
z=4nW4YQf`f{ByJ?opDWP`5Z@o^$zhhwy)lmJ)SFiDjJq1jax#jU4uV~bhEl}{h!B!
z1$U*rfw!s@Z_{tj4%i67>BZd@wjD!oOZynC2n$vOr*Vqh%6Otip#$zApm_$vWhI8%
zT2izJ(pPK*FIqjZedE|$l-8=JGh{`T@WW#_QNoERc7!Ti>QT|bV3d$oN?xD<HVQ3m
zl-XG}j(OP}usvwWAPV9jHl04`Y63tDBDl$W$$HaRhOGx1irUQy<iLTRYIiUQ7vovX
zfjfO3h(kH9wbtA8pP=S^>^Rv#0Zwi$aP=Ch3oSOk;8NBH@s_4zdy&jlMrfU1b}89p
zLI`i!yJWMfAVG!NmcyYMc8E#rcH*^HcIHWJe`9m2CJyrn4B=RNtP0y+vLr{~%Q^y2
zT*LB(RU{Izk3as{PM$pJ!5R=^THKcbOL!UW>i3t&(S$IBle+3M*qBWi#vMH_wXOlw
zeDzSB)#RtzIKtu|-tDoHsvHc*Kp)27ILCm#0F)tumoW|ROoT}+<MBdzw(TXULdlwf
z(R^#vPLZ}t3=3_f@M^?WUz$pqu--X)jWkn>aBdxMAkEaBad0#kTOoWGwQm&|g?xgl
zdq-nd38l>qQcl$(sDNu(H{j1H1WbDR6!p;zF}lhtxF5=!EIf?m1#wc2dHt`Z|6?Rq
z`1QF#Tg6D*Nj4c7cH`U?rHwBZbqzxA8bHP{0%?&tWjGGz=|eF_<TMN-Go2C3#!%D&
z3}8=<c-Dbsz@yd5^~CUCN$|>P(Ts<09Dl`s{AMGb$V_KXxd-z6vOd1aet+FY;PJ1E
zK4SzuZOXtXh3v;a{;~ViAwIoK3Q&Z%uZ`RD2dj)^D9-!03ry1ifO=hc22}*e3?qzI
zfel;+5wBcxuk0*g37BCgz_C=}ZMlO`r~n=MZE#Q1KqHjh1%=ERMIEexrHC}1%?KZP
z-~`}XL4!g{+KEj8w3isXRd@<PSWX};D#7EN1Yr8o&LBAh(W3Bpy#W5{2ssS%5sIRa
z$)NAI!D793sG59+q#GMwwm*Au6G8{C9C)fGv^oi8dB{TaAB(b`0bewoo@qZkR%H_i
z->iawegAL;;8~y#P(Yj_YmC5+akQ&?KIdX7_6@GJitBkDKxzsEh25}Q9Kf9(;X0dY
zitVQye+$58aRt|ZsKUn31eK;{8`nB5ZY`Arqp!Ci`8PQN&;AHJl_jBuWktyJ*;xl`
zP-Yw{SXvUExzcZMb<Wy10S)GWEM5VjJOgFSt<gn$9>nw!1WPxF<hQPe-IA#mj7teF
z_-b+ic9Z>PN}33CG7T*A`bVNR3>{1juKF;p<~i(I)hr8-LEu-4=JGG62P`Cb0NRYW
ztER|=Qw<bg6qrFi_OdKA809^zLv<=cfE0r9k(VcswF*!Y5ak^Jt7vD9oNJgRXAq4@
zX19v~Ad3J9)*(1+SdNBhPdWRtAO(C5l=~g}|2t6GqGR4g3bIs|ftMih+YP$A00=>_
z2t3$QisggezBIIGZ|*6vcL~c$QK~J3<8}n<Z9<IB;JPn@-E;>U(;!@2btT!(?O%Yq
zzL(4~Va5&E6Ju6j-DJC&T}id?ft)YLmF|!Cv;T-Ddzc)7dn54F1lGMSeEQoU)G|YW
z6=jN52#MU_@((K=RX)-{&<45Mg_99(NH`YZw&YHieMuUe+}g^FBib3NTL43l(t;r$
zs3WD)GxVx%%(?D87q!kMo0YIX!YavUzCgBNQPXIGJ5T|if=i4L9x{ngX#yK2N5CR|
zaGdjepGEr;gaJ9|+85M9E_DudLOGLt!fvVmr7Z1~eoPoJxwQREFcRLf^ZCm*Er;o<
z1DuKzYyhq4Cg}3r2)z0dvMAH%906;oBlo%NZyFa+B4C;TB{~cgTwnk#%MixNk{p3A
z;|M%ef#ut?zKjFsNuS5ib0@_fy`5F0vI2`Alo;btTa{3jpuAu4yZWWRZ}+>n<n}Mc
z2s~2)%byTWdgLD0dHr_$&nI;qI-kk|WQ|v0?XNGGBzPpDKJz%L38}7niP!e2@3iH6
zeiOHU>cq(($r1PyN8qUnti-<ilW2zE;ZIt_Rty`!5$ruHES;_s>^zS5tRA=j4p<m^
z@Bro$-lBlv`xQmfZ>lULfc1VgHr^hEUSytSpqNHCL7#j-uf!DLFj_9o6Dm`RXH(10
z^<`*ul2Dvkw~X6GI`<kfAwO!-^3Got`p3@9E|3M;oXyJLrcY6$2*$A$-unBM`w9~r
z2BW|vR#-#l69k-pU&esAgNVeq_B>(-72c98$q{%`Bk+jR`SEp2pwVT#WXi$k!gJPM
z^FuHYln`@uy=~lr7}V>T<eTANzj$`Ss__P@gs^kn!KggL@p5`;QtyDLeIBNQ8$b<C
z>;RXAK$*r!G(^IQ+Ug=pBaPN|APfO;OfuxZ2yc38SJ)PykJ&_EUo$a&jRXh}!GY92
zZ}tL|A^jZNMLgRf=u7gU8~FpYDOF&&zI(9TF5eunOQS2ct0=|VFOS*DeHD(O;B<S$
z_U{PVUIgpV=(H_mWLZV(lKtD|VcU!GewbA32XP_ZBHm8?@uL{)D+pi~6-);f@}qbg
zJE6iE1yuSbNi!5=+y`~b0)fFZ2yMlqwIYlQpeE_Q6SiLB3SZh;W@BU1_F-Gpw$>Hf
zY;@Y1!Flb&gDC_20#Ju6Jg5u6GWHRaJw6u2=s#imx0cw_%sh+|D>w*J?CrKmJJD2X
z)rAYzH8zLc5RD4kTayC6VS~6awqV=Kv+cc`<5thTIMD=$J(##|ao%qeg^$w7r_gT_
zh`8)N4_;zEzNabK(${4Kp16kPquV<>J8kRMt=7@eVOzFr!M?o~SBM+#DqKk;^YBOk
zNl?h(@bHA4A6SGwq>}p*w_CTy>@xH&jbNmPNkCCX($yI#X(CwFUx5!wd|6n}tq;DX
zJUETSuU?J^Z%cKy^$?)ifbm{hTZGmEwBN^s{_eqQOI@5pprkTkvk6FDv`&Ca$9KSC
zG&l*bRfc^7famGfsQvpl>Z}t0YTs1c0`Zu|@%|0N4sZp0SSf+d8L$idPt>{9He~;J
zbJ`A(0HYEZ!Wi@{?ew>r{n-f3*Y_9OyO&3-2-bw_9T7_<C>VMx$K4JsmJPuO5X2C_
zjPZPc;9$|q>;!970BBXY4VPwCZChQo{pR8*p3k6dEzPk1{PB=&L7RJ_Hpkw&I1I2g
z%Lc<xvLLAUZ!Ndifkgb@ziGD~1Y$95XA@k#fOm8^IJOu7)FR*$!_cq9>EjB4;blN6
zW)>HT1570_c-A%}yuil-K~1;N@Ra4(kkF$z!~V|?`fbP7GON!<hXXWwqN&(9K>TIm
z*JVN_uk(u?fhVqE`46yYW&Y-G{$@jZnSZ<%wg2w*dYgb>DHAvUAX-j6T9Q}10Mr12
zXa|!gm%M(#90Ue<q-mHAgi<e7L_aRNI8paX2$6-Ct`woN6*`fOf>o<!oi_opJrCDn
zAxpWOxePMt<Xa3nkrfc@^7zT6SY46}l}WxuSc2s&fc=7fVTHCdXhNcQ!Z4YEmb6uA
zzEJhH(w+oBIp}j00bgGOEA<KhZa|U0`4OXd729AzD5hTu8#)ADY;a)0#_%*AVd*&t
zOri)_!Yaa}5#qwKd_0^W^$*amEVR47|7ey}U-_^ype2DzyG9DK&6Nf4c4dO(pSCmv
z{t)9-LBE7w>jmO*Xh%7jfk;Z5mg}%&TcATxsYPR5M%Yz?y3aw7iP~m~<1_Jw!gU9I
zN~V?KO^gRthyutY#aY-o$5z0brCTl@&fLsZn<Ap18Vz`wmjQf-j;E{vaBov?9Ielt
z=Cf^MUbW=k<On?U2&Dbbl!vbU7%EC&$=EA;nL~#T{V}}^+JNjwvL%HODx&~6T?%-P
z5p73kY7vSQW);Ffv?+6}Ta;mD9wE~Ly~qGsK^+sR02jX~KqjE6$p?Ftg&>J?+yZr#
zYAXPoK#UCwV76)rSD8oP%o3(U?}%GmL{JhI!^9WCn?MjV^yS}3A2tP-lPQI9l>h)h
z07*naRBTaE>WtDC*AE~TBG@el!xe%Wr3l-aII_Zght<kyNTUeSY0fPZcRp>WKzuh0
z{q7unnL`MbU~HS@{0b`)$I=vi71x7UJ|keuL{Ff03(yY5daxpl1KQ{m%ftd~Nz+i!
zw6F}^Q(tH)XhhBMgf#$m%|yV8`bO{xfpTZKPQg1w2`tTS1vLH?kcuk!!?fho(lZw!
zz6d@|un3Knv^n8A1Q(G&7K_6)8d?zAo)CigHk#;e$oZDJZz4eyzgQVGDGzixK89y0
z*~Hgx1fJNEkmsFp&y%EIS_Vph`C6B@&GXPpV5vy3$v)-$Q+$d#MMXF$!Kp+8BZIWI
z3Y(-!F2+*1{5=X1567f-)Qk>i{<=1GjBs1}?e=M>>x;MDN8XDXMwlisi4MS$xe4;O
zTqdoK7uHMl{Bx-twW)2QB~d?BQ&=xih^W4b>WNB58kneEL}#Mn3-iQlRve=Qwy<Xc
zty0~c&%>N~*P<~{9c^=M@mrKC4jaaI9mD?~-HZB2zZ``N{dD8cK99CXz4gY*T_D??
z(Vw<-4z=U@h5$=}d`w|Vx+ZozAx3p~j}(CJaBSM}kVRvnqI2A@sLq4-Cm!eDb5Y4W
zpvlBT-1mn)$69wEsqH^HS#rZ8kH8ZVSh~6EADYAm!+TggVC8y`*dt$2@_=Vz1SBy0
z7m>c-E8deJ!ISt5+}qD~{kZ$btFNcSe>NmEC5C+A<u%U%Y*1|->K&zqbe>Od@z1@s
zmk;kKwJPDO;-5p`b?(IT>4oI9{=4;bYVW}H=W@MqC-hqK>*-grJo*TDulz?p?xBxx
z4<)rl`632U_Z;$D_fQN3N<6CXx{>|^dAD&6RyugUNFx)X`+nu_#Nap~yZ@-Hom{M)
zop=LB2$}gjMGvg6?Ij+sM5~Kv9e#21gdfuQ#1Q(Ra<}gfs`MHA{PU@r7&85yIIU=d
zW3@f;7*)AnUH@>uP1H!o#OZHe|Ec@wx2^B@{(1La^>yD}q2i3#Fa-UlXtVZt$9UiM
zZ0MdUazWqk9-CN~@9Lk9A0en*2{#teZWG(_kJG33&udF{B&-yrpw+g24%P9$``063
zrDsuXd;4@oudF|p`^_%@Tz<Xe=jR)NFVu#mNvIp9Am8_{4%tOKd#l)tYA`OV{Qh*0
zT_=gdUJ?)t0s#>DzbJS^&=(IOOvLV}6!m#{x(cwXbPq;t2wLQ<G}5|5rCfw<Y8IpW
z9k{$RGf6YgvgyOiLoTkar*Fb$Sh|Rxdyy3N!?Y(CzZN|oTp2K!aIF{b--5EK@bJwb
zP_$wjfr~4Q{cI9jjF>lPuvN_f2aqkPpFXOd`ltt0lZ-=w3y77HBm*QsP?n}e1fsaV
z3q@4}b?(Q+4X&o98$mcE(ul2Pp=L?N?9;MV!Ym78yeMuYL~~eD$>Go+f`SJ7)C_^r
zcYqn>VH*;sd4#@+l|kbr2ZQWL5qR6xuOYmpvX!-y@St@FY8z}<Lc@o!jQ{!)$r3Q8
z4|5$#SW&=vbbCaHGe%p=Dy?PZB>jlUp2g(VXAa{kdY)106w#k38DCD_7zVPDZaIu+
zA9lwgY;a?Yo$5#Ngf4JhS{kkrGpbBTqGr~1Q8G)CDUNb!VbR{dK4yd1_xiY>QG&QL
zxzFO(SKUeMk5lwP%i9p+(Mr6f;yy)_t!&sXc?eF6N^FOHI2m$r7RamI2D`>5U3db3
zV)U|%uPVxPeUK?mOMLyOrMAY<Q9N^fAp*x2v1tzR?8zcA$30x)0NE4!7<)(c<Cf*%
z(5Q2od#d31EU1~~2<hpcu!~^ss)_3yB0+_C(dOsC#er=rpkHD7&m&>oJt4bJTx=tD
z)?|6~5s<^{PcC-_6D|GAzx<19??*rSkteM9wTi*%g}oKz5*)ArChSqRP2;`lA*0MH
zNfT~^JNgi8j{=TwgE<-{kT;hnFBeZ>j0yUOYol;p71+mQR4E02bQ4Iy5)V%g6Jddg
zpa&#av1C6!QU*)ItQC+AEDkK8<yMHCfyDgrur!0g`pHY1?S+PX0H4Gj@}P+tS-Iz~
zG2v$kEN(!+3^6J9*X7#3HiztRL$M9gpE=kRc9KM51Pw8hIwyb-2sd?w1QW8I^}|cP
z9kAvw6Ksk0CV?Y}Gra=%fw-&0Q7(3eb}*I_Qas<b388%l7(^|&roy5;dyb&)E4N1M
zeL!F*wwI71i*!>M*gL^oiT5fEVbVd{VtJ6QzYGJsCUQS9g&~BZFkC0L7m`OW=7dy)
zW0d@2kotddvd#&*+l<X=@MEH?S!S|*`*^Ks-h9+NY-e!o{qoI5(sbq7Z%_4F3c%d}
zFPf`^bN1X;vfaRqAAql3>Cz;0M1wOIkJ=qgd?xq}FkxeCTf#rUtpR$GX}@@>&ZZ;N
z_77*rNcaJTF!!_qI-X^CzK(Azx8eRV`>1Emj^h2y&;;i`br45-v_9YE29&ll%=5Ad
zArNwofq-Sh*m12pViP=5F{Rt$h4U6(%y-D}^8>Euja@}{6FQz(kJQ;hC~6(N5L<a(
z-Y46QD1m~2d|v<#)7lfZ%Up9BUg!g)CVRJK0*qP_{QrW1MC=B*JOOA6fqG1kC!n09
z5<fmzVVz_=I!|geVf>cpLwm=J4Z=SrE_d;bHMjF}z8SHDTMEf~grHtcvy+YacD^lQ
z%RolbR+p@dbZUO_HA4S)^7FwX@K~3Gi+HF0`mg`m{nK~9#CY(+2!dU;c^GOvdXrAj
z>Zlb~injD|Ys7W}SQH>roKU4a96y9rAWBx190b%hvg7<7c81;c#Wsh4s;MfrA3w*#
zgD}Z}0PhD!D&U6Ftv5SB9y<jjH{$aD^Aj~T2KciV7yd!$Y2;B<QBd^?s>LgZYHSyE
z1*$|gSa=aOgJVo$e!K>C%ogwgVt!}>XVf>bY_C)QaDBkq5h8a`(W-c}UnT8TNE!k{
zOGz6B5l*iisj*|=qt20J;V7houYu*d!t$iCq0C-@IU$Epzr)0n)^Vw4#)C!RHJn{e
zg#<9mv8z@=jq{^pmAuWt2msso#S1kE-gyZBa-h;F0>D~mZ<5068rf!6VXLU8ugwV1
z9gO3vyNX%Xq}q>8Y@)pkv=q{lRTS9soO_0);tOQg*-F-&Faib@&tkA;)N_sVT5nOR
zh$46uQp6M_kA%et9?0!m%ItdwOYA)OwWhjK`~HD4G}I}pBs0x-_LtdJCiu2S@L$_X
z$&DDa2GW(e(dNGF0F!k>+xM~^R*fj<5%Vkm+w(AIY(q#YK}Ie)1vila=<VwvmsU*)
zLrU}D48!yL{lnE(kFe{5q_GT4n>gUbbkTs|A7Shdl9o$tYC%}YK6!^@oTrU(AL9j5
z&wXCd(i~wcZ)g-^!~L*hlmX?KK=3_}5L;$!h7t7g^wz+@vB-0ql9_Ekd%gxIg&P_R
zqX{13h4l(A&^|KaDDzMubU{@W`L-X7oO)2FlyJ1wM8=?co{<Z@Xj6H{>Y?{h2Ax9)
zWLLBT@xCShKI8~I77fcS`#51%R+jzhSHE(G1bz24FC~n)*h2m_CeD5Yir5JT2qF3L
z^L19360`r~tzLWY$_Rphn+ekUJURHf5oo)>K#6Ifga=}l9D70F2cv)&dC{|ESmr@5
zw3&%%``@lkTOFynRN`<*wF0o*3Zb5oU^ak&uVm@b%W~r&OM^ICgOYLXYb2Ey^e+Sb
zOHABlOQnzbv_A{x>OGdB3kZoL2)PP!94_-F-45tfRLu(8*<M>@+rTn~!C}>Lo*-{-
zk>=&ou^^h&67}K;jORhqpSm;v7<JXY2MD&Kb=W=x3H}oVtg=}*RDpCyki%@l(lh2B
za=p2WQkJcGFk~&GQQHUptOKp)KmBgOc2wj6ML=^ypxnMPY=7UIVtYya+6Sm~lDGU;
zCdf^(v9^;s4BVM)glT9X>d!pHgdvvCrCj4XXL<ptX4y|^YXX8qWddRdSn4s!35Ci9
z4kDYQCcP4;2GEXxw7PS=va7_}I!EnowBzF-?1u&-cCkHfFYhU3$&1I7_l7*A!cNQq
z(}=P3<o0>XFyY%Wh+F{Q_4^A$Q1;|lFnt*~%Dfe@bQM!ZkbI4OAm_!xaSee_6D;$<
zhtXyVFB&bqrLpRTvj{CuAJ^4{c6&=ij6g#4+7j*b+gFEKLc<^n1V!G`G5RYujC8K=
zUoVb;Vasz_j<m$jgQiUI5-sscwKUgqloK!GmPJ8wOdmO%Tkw-wm|TB*c@W{pQk1p@
zvq6b=c@ld__{wALc+^(t^DJq+CY8gHB&}NF%Pg=N>Y(=iX`au3WpE8i4a-g2hyL@0
zdI_cUa8I1QpdA4;#m!Snq-EZGzPnlO{z>d3S}H2vTq<^lR>E%T0ACg(th8*qg>5Jc
z&0;HWE-zb)lZl{|2xl3XvQCnn?gM{Ri$Idm*ufoGE~$u4M$0y3$PvCs26N1;PME5$
zv@K;ik<gX>M*UUML#|9M43IPQ<_J{Du_|j-+;{a~*Qw&#rUBn@t;{Sk6tA*Pv4%2)
z3yGD-r10ygEgA52ei?FgiWv1e$Y{L{Y*P+P*LD(qRw1l39=bM-op4^V6Db{*Fl1_1
zHathQLtDA@S6O!W>P-rL(zXo7qUI6(je|C@O-_L&POK%S@BNaEPC`Q4bser9>I%jn
z=K{m&(ftU9LO%j%L&|VN6<2g!iQlzR*XG6<p(DGUSXEQ$l*{zPIn+VzS0D7U6QvG!
z27$$WVYm9Aekrq(>gnF;9DKO;l<QIVNDvSST&0MUjZfn$JELrpx}RFo3U;Bf(z#`8
z6F$vR0`cOay1W1ChyLkW{Ob`^MYg?Fw(CAR=LUVr;I}F%lu2+8L1A=W8So2O&g<o&
zdnBQ(vJ>#H#x+?UZ3G^>z|u`pS)ab^VY}sPWr_9mP-J7{&_lDnhz59BU`Lh#PqOgb
zy9x9~oink0O?1tNWJ!*|*L?&YyTE$10oD8Q^UuUT?dV7A=~u1x(;mA-f&4{{ZQ?b4
z-t#54@kRAB+3v%Sz!wT@xL-H!Jv^SUl2+v&3{>kWT;`{5jk|0%<#-LHSt!v&2QK>Z
zV$#p^TkhibvEeAwZwegj%0%<1McI<&{tpu#lh3l@zG9QUwfB<Am^DcjO8vW+hHVaS
zP$6Ez_vnMZXA!adPV<PR;zY>6hNkaI!Cr=4=3z=4Zu(BVeWe6we^%+neSW%q!Y#+T
z_<ja<2rmP5u7hRV?|21(%Z?Mr$S%8$-~kU?mg(Q=n<0{g<bCdxWKj}<yu*0iBp^Kp
z?{D!#^Zo!~!DE^Wgk?i5GeM$+F@ozk5t3gi-gHj|dlAG$Mta%jZgc)H_Rq2no|zC{
z;3!K&!4v{(8vFX^;@-3CEqT}P-)Kw|y68RU8&yhfd#Dk3EJpW-I#WU=<dU;d`1CTe
zNBCuyAZ7*PzDL|%Jwd?5xYTa<MC`)gvhCaAxr0w#AGMsKeA^BsN+-CZtoRbC$)gzb
zOKp~PUpL@ke+iG&B9r9i*rGj0@No-Sa|%eeHHD`w54%tM(3GX+=GuYET%Zrr*2BYo
z5KmURCTIJkZ3WWsGVlV$+I@fkI&`=O+!L?{;19zHifirSoeEeVX~|9#S9chD(=FWR
z=P|lAVQahEIpGZQtMJzU7Kp@V49w#kUra!CHXgz219Nt4JBDh6-5;9AEFbTY>?<GQ
z4L!Q6%vLZcx01>%4_lP*KyTnBE6!M@55N{+gn0kbpe-?}Uf5Y_!`)+eBvb8`?ZvjV
z7_(oWAF>%@9$(vAZev4H>jQUCM*8-T+ooZ*z{XROZzl)@Zzi4EF|K)tI#JoH5M*f>
z#Sd=IvrD(bI3WV|#-1|k0s`=><_O{%2m{v|z_Z(n=T5XX0|S#bh!;-tI|J5?H*x)E
zVs|?S6r~f)+&7L?TSXv^7tE;DH^S4uvIuNo*3sHD_f8Y<h|>bEpX^BTwhqB)A<Cbv
zcuQ{(EM6(DG1{J*3tI~w<i@%p4CgVsg5i2U>=2^r5r>*!F>NFO*;F2IF{0;M=dqWP
zf^9r%1IuZ401vL%FG?^i+`KblpTLFxJkLcAHl;$adVOH>e%BHv$iBe#5OChnIqtm6
zFXAn2gEj}}q`i5#3OEUNRV0ue*?Ui||MYJBnOHCJ`^L4CTOTw6Ph7*2jY|TmtE<a`
z!Jz%}m%sGVd}%xXW#he4f`}H3>Dz%Z<TGK*d7u{obJk##PlvhS+?{DV4oB4@9<I}P
zlFoEMv%q9(@1Fq^#KTEen-9oFbE$X61s=EbOqtT53qU9j%@7l#81LS81Y#6H)drC7
z2pFcbr2QI&)<$^(n=y3n#O<AlKN~q`%k((}wBgvUpe;{M**m>4`wu5K+3Z-v-kXfu
ze|)~yhMBy#Nrv;&W7Y6#O#?m1x6o+B+NR^yz#IKGP=+o9<_Js&hnZ+MJI3sGG{t^+
zIA|k4G?c3An`FE>cXh;Wq75~W_+k;yYDq3eV=z2bbfg&1*Bu1%mQ4j1hUpIirzmyR
zPLak-o~}E9LKo6Ww1J!dgR5f}qu+PvODbATV;MpoZ>LPv7lD$5Rex|6_yN}lEzA`8
z#mGixx2Qje6GR}`-57^a%MfH3@e$q;CG$wjw3Fc3esgBfmT~)6=P`qE+t1=ID}t7$
z0Ks~`W769Crz}VSb`28eLi?1xe4yNV7$aTh3Vk18?Atk45rU-vA$*Y+#h7qr2u{IQ
z1W0ue1^P{2`+-*s5-&P46UQS8jt!9QZoGCEuMJslS%LGw^>dF_(Q=iL1f>$sa;6JP
zAIEcCLmcQBn7u+0guJ?=(0+TaALl_j<C<YNd3LHRa!F>AZD+2HSbk9eri@(s+xPn{
zuQbo<^HN9*5(hu0%txMc;6W2Bx$lb^fhR7obQuy@(yp4Cn(V-V18a;8Y)9vB*P9Jo
zO#x`<7_YK?usI^#>m_Yh6Iu*@8%sg^xDKGP0PX7<f~A~^ya{1c%$u@;bXt4CN{tCI
zjwh^=VCZ5zSO?LVGI4>oLqexy7Ggfg#N{97sm>r@;B(uH0BE8?vBU(Vz{5$lnL3Y-
zWtum59GHf{q8A|ETcF0Sa)6v@X4Ck+>+#+V@O>FrLp5!8Gbv{gFb6m1yX9+5746_9
z>PHAkSV+@*3CP0?<asM8a8jX>Ofn&hS@x(X_`8_p?{<zWLTjlgB)~X38|^5=e)WEr
zji+SVtMvDFe-w>t+1fyW9|VZG8_fWR6sUM5X(jOnXl-Q*(o#xUvz;~pu1V?31Qn3S
zR!gILFkPF$O=SXwXefc<f+bWX*CasfA+*|ja2G=8*FYY)2LUz>+`;pP8AN`Cwk1fO
zMy@FbC`TESJ1=#am&%m$I+n4F+>ACe2f1PwsPi1Ovs?669y>2MTn>{IkoJIMfrf}V
zBgA;8p~=>QVH)CG1!M|R2$#IA3ev6wXnPP$nSim4<zS!C%EQ1q<`8C!XqIIN^&%iE
z!dsQV=jC!qg6G>owxIz$(86P7bIugTfZIjXgqLKukUVf&E+d<XZ19$p@*r<(D6wsD
zjZFh(sOEWcS`vLpmM1>~Ph4P0SjlD;2ta{+<j7rm8Ew}yFo#F*BkU!)pp3JCIP~yF
zT*3W3!z7;LGs**;%ER6Vd0Z=+(JnAIn|VVD&s2gYmWj)J5*&@Zip)7MR&YA;RusB@
zj)z|8X{8m*1@0hfmMrS&0hL=@wvRCA<lnFyO+)yU(ZHmmxk20D?JDqK3mTfhf^}r7
zxlZ_tCaOT2m*J<<@}&ac<CX4dt3*2jyx>A@X2DhoN-)J|E{!N8CPrT%s&aO*b1Y{o
zS;~|mfKRthK+PN=_aZnRCZy7X%^^TEab~f*MF30*h9G(vW$S4}U>6{ass-u5K^p<@
z^KUFG)%;dMo&<)}_k3(tOORcu4??*YkoqiwEl0dwGzbI$iXJLzj6@$Z&UhAL+oKyq
z9xSkrB_n+hzF;0Z<7Jk#I}qr?wJ8R07IjSFE(>U3Lcvd=ZOQeY$xRh@N&>2-chUwR
zP!!EI?;vc5tB!_7G(Ur!uNoYclHaBZ&&Jq~bFWJoYuUSG<rJ`0LRSzI2d$vb%8Je9
zS&L&kjkEO50-nNs=h!TSo?cdC2-{RP%G=EQ&e^hV49$V3EA()CWFM+T7%%f|gy@H`
zbrU=X?SO;#)G>yfN1<ARCzK~?bDq4EN;u<F^%81%xE}r>{w3jKROHT=Mk0C^hfZgM
zXFmYXf|QYflkj8yIxRx`^k8e^YM+E4a>@NxJ#7@$NX7fjCw2A8RE(JfK!}v?!)Psv
zxy$0Wa6Qrl)Ux`cB&-gfM1B9emMvKbB27Ft-~H~<%Fs`aRbLz>jCXQ(J|E+w$*eYY
z9l{WaB1YZSAH~19KYW*Vp_oH;WSQ@&2oIkoMuLK~xYTAM2o(R7a8dp^xa*}UU4v_q
z?-~mI*0s@`+pgj8@M!F0qlA$9u5nYH6$V#$CA#b|aqeigt9_l{YY1Et72J8)E=-$V
z28r}Ks;_pverarcJeupyy-u$E)OpmNjuGBX_eK45xIDJG=YjnmXqEQXx}xh-Jr}Uf
zxTdm0_fvOtiEGmUsjs?s>c8r0{MBF8(Y@9E(<3RYnZT#X^7KaFsS2#8ciG7^Cr9A(
zjer2Zvb7@TlI1CkfL<a`tw_W2-~5T42xDBG(K=?W%)z!Go0MOK8kSw+K}Acv&!}?W
z!__A-joz<p)aUheg|v2W`%mo<U5*$`H!ebU`|*4m)$os#1}X%)N@8F1o&O(Lv@8<(
zTE&aK^S*ENSJ}BA)E^xq%|#`GW_pulnQTChQq%|0v}{y#J$^(0SL)0K8`VqPrX~OV
z?z=*(CRwg-R5ltnk8SqTwLLl+^(I-K#t1xd8<s9o5293i|DQMMyth^#v1#vM!|Eh9
zYzy|OI6>6EC-&<$tP4vR$>r(0NfHXY>a>G#99zXCo~u;sQVZa-`d}5v#XxeUdjk6u
zab#?ZvrmTEK>}B0OPb)J6%~tURYV;lyH_6+H7P6u+R3CN$brXZYOo*7!z2Lh3U;1O
z5=|&Z%%e?NwLzE#1UbNT?^qMGPc$^LuYE!QZw2vU3XvJ5-YBt**%<B50k0^;V>e3X
zpA48wPvgl8)Bjr-&}Dd+{YX#>VTII3(e6xO9}+bVYh}k=Ax~f{Ob_|ABg$l5hbW8H
zFD+k01tS}dCfvN1(=2ne+>B$xx<p*!9XznzP^KuPhqaG8ZW^kUDaFuXxYn|B7K42|
z*DD&DIQ>!hP5;oOo$s5o24EYajM2Dg89g{SVQ<4wpmTS_4__k)2Zn6OnBC0Q{auuO
zi?JUgxLDlrlNi|-*fxxfQcLGWFjDHP#&Upo(M8Vdu7~GDDb=pDj@TIYO0k+F*wnN%
z8=>AL_Ok%}9l_?Lm)Iy{(aOE=!owIW2Af8EiqF)N@i(V?tZkeie4IBi*}r%xicvu_
zOyj65NwSHFd7>K}gHoO;+-ZROC;Fll#=n<<{0wYt3bB!pOKMtAR%|`-1-UP*CMPHD
z?Afz+;=~CnDk^e9Oje3YSOre-*8a!yVYCR@gVLd<3EI`$<8Cr(cw}d|(K?PN4g)(u
zs`BX6;_aFQB)CldqCq@;xXC}dGU(EQ)nG&o;E5Z<h`orxxe!Bp!y3*fh7c?Pqcq3%
z8azr91lBI4W;k3?b34Xs&Y@%r89bdMBzc&LXV`!L%{nxS8T<QF0~RO%Do~JOElj+!
z!W@?eah?g@56}6F2!mw;V*lUM{Un&kMZjQ&#{~6Z^N^<@aUDHihjwntaT9D$9h5m>
zqQ(gREv4=)gp|1I2XV>&=w!WZ3ar{SjQ&AKTZ6G1!+T@U!+Z^rn(Tl0?iQ;gLBv0Q
zGDxxvH&F0t!9)SB={fL3Ly@TUM47x$?+gLI*o#N*7DoF+q%`Y6h{-w7fgm{u)@cB^
zK_LO&C5+iwzDHs(Hx#AWI53O1XW;k(N+KqTpB)K;=bE<X_EcIaf$ZnHXJAN(*atVm
zI8<;*aNP%*@~nMi$~u6VAV)}{R%mCS{s|(`n@AMGO|%;bawYVrDonRtU=M|S|LH56
zZ5bM(Uz0{nwzGoyxcw8<GHs0Q79a?U>C47ApT|q$0#1(?cb7TXM=!zPV_b{mF$5t7
zQ4i-V0852)0PVdS@Tj!=3jlEEa2n)e?61V;H;Aw;=NY&$1jGTDL@M|32B2YSv-#Nw
zZ(abCY!h7N`v)qVu#%>gTpemwKAG~wmxP`+h39E>)V}kb@3>E0s=r-CY5}lVJIirt
z12afP7~SX?x5dmXdlgz06&c`hp4l7Z0z9^}*uJ^H!UAl_0s`@KXjsIdC2vnBF6;)t
zohxWp>Es9e;n4~^xTDnG020s!Zb`25&EzD!gBR&L&(+wDN+1My>yD7SFUCYo$;yG#
zX%p=&kO?Mk-#=c1yWewni$ty*4_hh^KFSE^t+Q%e<yT1l@aC~i02Mvv0KJB5dBA@N
zRgAQXQJ@W+&VC4PD~H!}9W*${0gxUC^jSgl{~&da!5#M6j(q!|bIK0ytgsVxc}^oh
zr6K1c30a|3ISKvE>$?i<Vn^6g!I(ARsdTP?W{@%reS3e2y|fRg1HyI)IDrhwT8=KG
z1#Sahb`)@EKG>;7-1Wb`G6vKl7x+jzd{r=5(80Z1%I$bV08MumU?tgg&_1icF&)Hf
zHbQj3cBoXA(TY@xK$VN(U<t$Z4K&5u;Kb^nbCGvfUP);)lXy4lI9}sDf)=!^zQ8ik
z_@woY${UC$my{Sb&XV#wCu(g+5PmE)rYZIX%M0x%jNg<TA~0X<gGHm0;P7L+EA4nw
zfc%sQLFi#75a1C!k6ZAL{^+@CD}ycJH@Bj^5WtdQLf^l&*tP;7m8LgK-%&&zWza!V
zqJ6~kaESI^+Eq&Ze4sJFH#lGSTpU)BO}07>BL*+4KYOv(c|FA<;ff?yvOKX7c;ZV!
zEdy1gRsGdp{gqW#R^Ig#YTwXE)P8$41neQ(vH?Wi=$haSE|r`tHmK!taf{h<j^#rs
zZ^l_BMJBF5ff^NdaS2|gTCh14>?=pRh~kc}U}+F!BBrC>Gk+|dH)bIknF6Qd=_FD`
z04Rg87!b4+yaReZo5}1T2!L3ZM3KT|{1AkF4}$AB2}sAsfK}iQmn$h3cXuUkW|_Sz
zNql;iC99H)9)MOP&ct}@!mzc1jcNo{AEaM-EJ1&FY1C@T=~n<|X_Pg|3=?w#2!=vJ
z#88<*og6fW+pt``+cpIqP7yBkG;0S3)&p5zCCe@e%Y3kBf-VTpwHG0<$~A>pFKa2v
z3JN0=o=vb%*Pyc*1FKYvU=VU#f+!7aSrb~+FhX<(7_Kg~tVt$(ri2si>$nkAB0&}w
z7&Ad3LJ$N7xmLXp8bO8EBAB>jJ4HCvFx!+^BbOJ7vgf4G|J?MrT>|y52muavS&$T{
zzK8%7grY<+0kj-t_U0Bkw$&;yXj&p>L-A9DrhE;$p$|y%x&=X7&Tm0gwt$c?Lc5Z0
za+YPRfToM+I^_ucOvXhVSVfG%09Z27{G<RT6@#lheJw1DJGl<w%9O20xU5Q;RIib|
zw}<*VXE(41VaUn>!M<~C%t`>=*6_?IYmk<%b%L#|saDBlp_|fA<j*tbB^3HulriFD
z;@NPv6Xt-!2>J9M{>aFNAR~#O5fWI6BdcQhCmI$NJxIbbN!T$5Sq6COyIzih1L)1q
zv*(x|1Zz-zR%y-_a@V-Zera^Wgp6G2pG70ew_R1a$f>xaEJ0zRZ7*|-&MAA5c<428
zMRzg{uBexZQU|umrL(6!P60RllA}CUJF*Yy;aC53E@T7OME{tymw59p!=rQz21`*D
z3;v+}qP3C1TNF4lCNtOB@6oHc2}B)NXjg4Z`%+zvrFIDxpcjOe*ScmE8U2yjt`9oD
zDyl!S^{CTw^-E*aF%mY)`KaH5Wr*V}N>*!8GV4$w)3sgSMeQQmXHf;sa{?{*X1V?p
z#!Z+ir7aUqPC!nb!kwG`=>9rwntc)y!a@0tfIQCa4F0sQb7(o6jUd#u#<43ZcCwY_
za~Vt08i?~`8+7-V%F?ieCzJ4@ORk8sP0xz#n7Mkf@ob0|Ih_}WvN4ICnyxupoa|j&
z!Em{8_6|?}eL^GfR0Y;XIIKVAx_xZUpVG$tj#XbZNyU5qC`FS_0z#AO^AwTmi630X
zZME~DW<G2G=Q;mF9rM&q{NUb6=m;y9EMJ`wc&Y;HVTaMTZ~4o~hdu6DswctqMZS?g
zOJ;o5{x4#zp2j)9i0eqU`Rt6q6ZaB6%%vwj=)PA`$9aMa9mD-kmKy9b!`Lcz!|h#x
zeIXo~VF?w5#<q~Sx_~>C(mhR(S!E~2cj-FUVN~cNP*4m3iZ=@oKUdAMDh{W^vW69l
zOB6AKJx4BjEd@3za~Su9%vUJTCSvMv?EO*c0UDq%qabl{VOR=8K~qc&-$q5$FTEJF
z>xsSdN*rbb!bV(}&!>;*=nh*m0lOu5CZFF`PM;s7d)?E_7QnQP!sELc+to(po?u<I
zE*LH9FyL?8TkmK&9|WN&;F{z`D=5H5h7n$6eUJS6>VK+^zN;kG^|yI@?(OrptFHh3
zes$dTwToF_-#^!b+DJSm@w?jfkN3}={CsjF@JtFU|AFv@RX7}WBT`yg>V%e%#xgF?
z@UIIObPTM(w+@tAKW^z#gwV$~#xWY_0aq})fB}3oEBCIzx`qpWTV08bjDQ{LT(vhb
z$hNkP*&K0w0gU|TIuQa;ummu&ZUb-B)jw(dU~&$@ijcVyvyWgD*w<9%Y)Z{wgWm6k
zYd!}+=(VtY|4@bH6JY)Rt%x0l)nNxl_I^^6{h@2nUf*ACRRq^|j^T~kS#C>nFb~kK
zm?P@ph!<r_8@Qq%Udj--9~p}`6{p+3((aFsRofK&@$;}Se2aD_$wYIiYsS8HsNC9t
z1N7k}m<Z3<7<}=vugS2iF&xGocnADiBVMw&c<m<%Il^lw>g2JpsPz)pcNo@%4k)O@
z9Q($hN-Kvx<xXFO3`IaXu;F|YXvXyp;u~=gtj;Z13uAl&`kM**C|a2v@W^-F30ZRl
z8Xwp-wpEgTi{uoUKpHmTxjYYaV9(Za+bev5Bm^Dv@2_rZ;&=U4*=XDP?GIBYQKj9e
z{zlsp&y%=6@w@6I%hMTwXHsBkD12cxGc#jvzx}owoge(*2QFxuokZ@Z!6Z;lx-fF+
z;bl_UkN56k>zHjPZIrN8DkpI5pBeBHxN3_)qMtAsw^!%bYZVz_qDJh6Jrx#ATd`j^
zPm(ID6t)4_2=cOQ0?f`;086jo4sUK5vRF>Oi67|}JWeu#%N4&5VHw9LyoVs&+g)SU
z4rXcr_uLfxOgkz!W4j`q7J?vkVaDF-Sg_sTrarhnVtEJyaWg2n?AXeL<;7AEEV%$i
z7aU%w3ofjcc)(sH=|eYsR4sTJ1yXfMJOFu?BG{@C&hG<!oS~g1uvNE11W5zTJg}`8
zqcb2;4EE9z(=%tC@qYqvsY`so_-TKim<vd=mH{tVb>6CVBZSIx1NI42FfZ+|f(I*P
z{~xK@QW56DCKVKrBC9c%3_uVX;t^Cf9x-C1KvsCTf!vW`*4{ZcY!eK0I@E?;Xq6Mx
zD}?Gr0_%%h5;FwJ@--NNXI5bOW1pFs>3FmBTi;#b$zK5h{|RiHInb|U;~^Rbztlm-
zm`?^5?7w)m-YviKpqf#VhKw}Px~Sll0^{{VfT&GnO0F5TTX?m!6r06UwI4bddC~Il
zGJ;-kUNO1%cfegWJ&hIv8dFRJ;)YTY(?Kh^t1JxhyTI*q#zBh%K$X|48fb-z5Yb~G
zwM+3}i8){&S%coaG+;S(WmZVav)&1Ugz?ywl?H4-u!Q$XI6IBozL5204ofQ;+jD_f
zP#rHIOIWt$LpyW&_Mo+l2yqR!7=ou3&8QHzhb)d8AvKuL@ii<hS7E#u15Z><5{oRx
zD8%wr)IFk@DMsK4WxfqgsroEX=Sy4{!l@Eq>IB|R!6NF>$W{@Q7&`&U2B2ukwek50
z%dN}=<T}l`$81kU4v2U{p?Ggp!6&7~w>+Bi7=|-J-9uccKr1a2*OV;2B};My9(@F!
zI1cM!yC?&@w5)XA-u3nM*3i)4zWbV&{MT*JmD~U+F4S``<aiO#e0kV%Mp$Nr`KP>p
z%0;NmHChHmm^h+INkbD5&PUm!%g~Gl3CZY#p<x$*#8D<Uhai}ML$Dmm<~&|LL$F2c
z`+qKn&{#auH-TJ7Sqf#~i4p~gQd$WcBsvp^FT%5^u%TNJ*~LK5D_^2;V~+ZUW1P(l
z!IFzmQ#g_04+U+|tQNc=n{7+<J&O<+301HXVADJoehR<O+@7#>Y+_pwDvNk9m9s_^
zJEJTGm2RpGfYb<msQ}j`jM)^|UJd3<biraLkU$E6Z4)cS9713J1e=Hvg7OK8M`e3c
z7|saeR0w@@fU%uoOo>u*S(%sMzp4Q(FCj1b6@~HCfLxDq&8ZTu)E@%lRmC~$gW#6r
zD&?Hq+GI(Nz~>r)XWmPAS3q%jiQk2r(&Uk|UvLJ8n_}XKE=Kh<D+EQ*#8(QYWhV(l
zEh6lVcZ{FDO86qRBdm^(Z&G{y`GgV|rYMZS8X_jJ5$YLfKC-rm4n|~lsv`nI6-`WG
zghXGG!O~B4{c~!&&gWx@e7YM2`3kh^`a&~mNtdCr_TjdvP1RMqiN~s*j-h#nV{+g1
zLtiNC+w+BnwySUMIKC@j+izRfuW|NLnR$WLe*gXB)j2o*T(fJ7W75`ks^9J_Ula@J
z(e$t}p}1@0K)-)?+fAsG_|>(UxZytE)pxb67vE>Knb<^Pe-gKE^!;J_lGvWtpN(2d
zZhP1fc;*F`F2o5SKEKSchKIc&U)6e=-0lUiCP5`c?l*x|_pX#)8xSGd^%8NroD{hC
zJa|8yblOnx`Ol9h7ImC=?zN`xek7j5r;+h?dZNh}eC|3v)fXC6*Vg*A_Ja)@3(NIw
zC!g!Px8O6spp!h?#eX>bC8B!kKNm_ks?57uMR~<zDp16|UuFpO3uCuYQCpu$J3}3z
zu-&Bb8(H^xZvw2_;eK+wkkt1ne&4lQ44)enJ<y+31d2rYxm%<mZB*3O{hAZi%>8J3
zMALG=Hr;tRpWm(+P&cB8(Kz{aG@qRothucp6n%;gn!{bu(p^QnwJ-6Xj+Mr&d+TXU
zYmMBk>uGrEvyM{+?^~D*6wBz>_uF&lSl7Px=^Ppp+4U0p>K(76xMu$8w~C+8)wR2}
z-LLC^3i0pXC$Z`3`}($BW63`cH3HA94NJZEg_VCLdI;`r!^`y^2I>oNhsWb-*k;JG
zf`{-Np0PVn%xon#Zk{9+3heD7g;pkk(Pb=4-fkYou8gk%gSX<{WN*pDCMDkXVU~O8
ztSPVIiVtEm7i~)lUa&>5OC#`c>ETmqFolSWvpkg7E(L>*QpIa&D2$gl;~fho59T;_
zG^Mm!i7i===xb<8p(xi$KG8;ia5q_CCNY?m6JIE9`C-wf(6*LTtJtWzNpB@y<}5s$
zoiHZk;<-#i0(8UZ@JZ{GvlmU{Rg8;j8RL5gUcHO`bB-pshhsA_*30Ox2_=e~2pBWj
zuVmxW)VtO<;j-i`16R;Kg)1?e92L*UHzF?Hau$!ImeU_yA$1u}4?^{92&|Zvcqt^O
z_^^4@g&rw8wL)VQb~A~0bs7ViC}4DMd0WRZ&PyO^{IryHf`zfZ3S{CYc)9_`YJnv_
z>sd!DBc3e1Tq3+|#0a{;_cq`X3)sz4<s>28XJB~JE^)o}&d!Fnll!P^n1o5=UoVZh
zXQhjA4?<%d#eV6;A@@b+*3w%2(F;%aE(#U4=x&DaXzG2Z)Mlb^W>{?qDj?B(Y3%rk
zQ)JPuwvE~d`zvrPj4~d1I8?^**oGCxgJUE_Wc31qZ=AT-Yh)lAr~m4+koUT0dKQ+j
zqmJU$RoIfhjP{@P<mbjC@XUG%eZi!+>ZMDUT<^sJ?^7zT@?am|9kkHdK^tN+s8o?m
zp#Tu*+eGMN+qO)E#Vf!X&fXk#yh*MS6aEu$S}CLko8|$^L_k!*i5}s>O6S4J<KZpg
zp*nkQ&`#Y(C}1oWwTwWbD@-&AjZJvbu9B@Mi|>VCw`LGr)l7;BBKlJa*j-&BZz5@~
z<nnI_LS=#qA)%H+Mw$N#Hi<#v68o1jtqc!dD;TO_c-R;5Bwp;DwbzeS+c$tRoTJaJ
zq$t~3%|u414nuoWfPf=J8?8aX!g2ff`mkLgIJ^d2*?;{~jYR~*=wGr7FjoVb2n3D)
z;zX5=jZWHcu7<6WxVp)S3}{s%HpewK0;Zh?Llwp_ucREn%<Q?{rPk6J5#x&u0~|d?
zKrj*6WEUbnGJDFd-<r15ee?F0uhf(IC&#Ws>r#d{atg2F7*skn2;XjE_y&PI{I}oU
zVkDli-<|2VFoFi%gfv_D3Zc7sc`sPF37j31#0g5E@j^I?C$np0#<r5^VvKPKBh0(t
zRo?;)jq)1iA(-m$!d@jHycc7BfMD>|RJiO(aPg6&y9wG)k5mxEJYl;UOGzA&MryMO
zYXcT?hL=!rVV-S+Ppt{BuQ<W3fj^t&Wp|>fz|OWncLP+U0k5!l!fp{D9m1=uVD<@u
z$@6(m66tPY2+bmb$^V&pH^*YIUx1(E1-C+)v?@Ix1bjEumsmZX*f!2pjFhY^UUgvS
zE`sLEa84}qOz~LQfB8ne)smM$f|N%rSspL~&+w9vyGIcYtEHvI{nK~9D2c-qTEQMN
zq0GlTHqunIruSPW?9C%pju}7~cLf%Kg3<z)_DWiX-oOhyq_0EI@;s@YzOg@O^Gudf
z_>X?}TqQ2}Ig)9j-2k|3AX&m9Z_~8AJo^EuxQ@fObOMam2iIYf0MT6xAzzfVV84J4
zr3_)$MCz^m(40&`8khq^#GfNXLZPUUKgNEH;K;^1sj`y{KI(U{Bw+vU<R+U%&|L+0
zwGXV4mSXB>AAS1X3!8c1)5w{)WPkD6W}9Xb-xyu8pPj68JWuCfrf7h&B?awFtE#5W
zMJ_ef90@ai`qCyhA&?vNfrJrE;BOr$bIh$J2xtWvOUvkhkZ!+dRiL+tBKXC>F3m#A
zmI<^4u}dfjSUD8Sp<5USEV`!(;f!V?pu!30dX#hV=#HR$oAyt&hHVwiuD$@RRoBA#
zHvz)^4j|VfAo7oseryy52st=>x*H|4I01lFED_r(bL{QQB&EfBS%^zi++TxuVs|x_
z+w+b2XwNg24x2?OeF+lieh9joA-tGdxmThUQYnOiLF9$vqrVAet_4Cy(cLVgI_aEC
z2pggEYdOA7bT?>2g05tU?uKy}d15|7I>7xM<Qe<U@mkvs-H`&Yl?0_6x|^RJuR@3e
z@MS#qg6Zpp{o>HJpuG-t(G<rYY%Jv2A<rT>w7t+39YrWAXXHgV!5XUaY!%8QWk%W*
z0QLeF&lP|D?EOmq-CzWs*(ITGC&+>VrKF_98X6iL9?RFf<PLoI@({V{;90`8KEy+J
zj$C`=y!8rE&&6fbLc-IF1Eg?b8J5dKuH1KnFl}O?aC9(Y!Au9bAl8N((6-zVe->|9
zsrjY6iM0%rRuY85g%>PKK-pxVUSSe1^6+J&!ENH)1xygB`+^#D!2~L%Oi|&KC{~A8
zW{ipZ9?YUsfL)I_6<LG_{Ti8d@;E-9KCK`el$uIeY_`F&D%z765BqN}4#UZnX*Kz&
z_D^SrtqDQb1c}~TFtEnZQl4j$i(9IOJ~u(P66O0X7$r*33f^HUtO>mhkEU2WPu&F6
z3Q9ej<<%I1Nem3RP^#>r&Sjzj`dAKEGI`IiL=!`+LY)Nt7)DcbY!obg;|O~Rk#RVw
zb|D;9rxzB|HkMHpXqahen*qi-6`|+R-4t0WOZPC7Hk0cUD}|7BsgNmN$G#*oz*?>~
z2SMHq0b&|dKU)wy{e&ZJXB<Wm(BlXUjn8J5=&V9X#Re-XOL;NH3Jx;E{vhkr5E|%~
zHM$#N)y|<A_Q3-tjYZ1{u~kS)7a<@nu3e*^3mqp`4cYQkN2%hp1RvtwFG>SPBisyZ
zM9djEm=%=7_8LonkM0J_AAW}zkFAV<fup<em^fs|)Qdye%SEx%htSpYQpa;O$vG<#
zR-BgSHs+W-lrc!ff9~Cn<extpfiH!ZFtOKq3CS*^w`VKr_$1m;3CkH7Kt)U9Wwa4s
z6Z_Jdel6p+5~60xAi?qc?}~(<U<C8ns00AjWRbAbc4-)*fXTux!o0v(3Oxx&=~%%b
zWY5xU5J1w)tl}AcWxw)Z5?);$+XqJMI_>Y|E#AnRKF;KD^*CM+s7K|3#zC8YRu-9)
z1X7it1&-xxIR(7+l{!qeH-SlYjZrj-?6p2>Yy>e7bYq(3Q~-OD0H11K_8QF3j`dXZ
zH#)E23Zej>78sW0YcBZ*C%~WOB5-wn6@Od>)zC45f6OzH)lZERf81pEAqyTIjd!kG
zW~BP#70&?g&P&_UzI0EV-Ha+45A{>$(b%g<1Jrhx<woacoAxV1&<e|N4_d-DI;;C3
zTM)7a8>J{~(4rWpVknh$4}>uj<&XO=VXgktwzMWkcZ02th8&NWpc~ROb;or*et*;_
zook6_ZH~U_xD`GH!gaLSYtR|G==kR|V88VtEq*2*U9Yr1@vZT&x)<Fh1Y$j!a<Y8(
z2z)67mYWz{iyn5rXs#su^fy27Gw~QrqkFA+6J3)*=Sn=!0~=hobA6lsZY7Oa;BA|~
z#?zJ955La(&&0Ow_dU_(kyt0OEp3;S^~Y-cxf6fiuZ_g^62EU$|5NQHocDgmCDu=@
z^QU}&)O+|S_5alVZZu|p%C&yoj{Q;ytVg*iN=zXlxUX7y+z<MnI`O`0<GcQP9`|^!
zZ$J6_^NqllT3{tURO{c8`Yjzwn#Bwrtjl*Mtr*_&-7L$}1YNj|cMQAnp{=D>gnear
zR9>SQ+Xmx+mTf8nWWQNtnb3rtXK{YPE)WVc&XP$?5YKHdSw}k)$3-4v3B=L{TZ~u_
zHY)0u!gZF=xMbs~-+<;N@wzr@qYuM#1|dtVYFN&$z(b#RcL}$?{v_AFi0!OmjnZZT
zTg(*ppCER+#3J^KGuX0*Fd}a!3B`V#3lCdHfJ|g#-zgFY@&-l5=Cf^KW9XoX4iGH9
zWbMOq+>abPO#Eb9PsBpl9>s6JlV$k;p28xW76Gk=H!9fEI-u6sf;TZyOWvsJ`fVDI
z6vBoIv2&@!xnE(H#L|KlmnwexjfyYeb#GL3-i^Myo$L1d+x*YOW3}y3ufcEU>-f3x
ztoo9SMB<IsGEVflN^Vh6QGu-iQ}de8lGknqEY!s-<JMGDAjBrWvCm-G{sg+1?~0lR
zBdd(^f4DJffB9;?5%O!Nz<Fg?l{kTwm7PT(aJDsr{n=iXLdvK<tF0}x0MLXM5+aBS
z=B=9(b`)lZA|~1eFjdQP-|s56tL@`1Tg^9))>w7svi+~W?<Un0nP~_ft^!(c5{jEn
zvcW`F((KK{6?m(lsJSvsLItJhBF#L8+fDpaF!4MIF`5TwZ3@iKI1CR*pp)r=f_Z;q
zf%Sk>l6@<JjcG6Z=lz3|7Q)tCk3eg=4Xgr3z~bzjU7v{C&-ax$uJyg!gSMUE<qIui
z76mkMoD4T@P)+w?0~U0nRJ`Wcfy&@<j|hZ|M^2fs#UFo%gcBJ|@&asC85qOQBS>?B
zZdBnU5HDGIS-@W3Tjqp)3w?ZPOR;@?b<m~}=x;#3voJYpf4DMaVQfNM;S&?Tx=IK`
z|0uW$ITRG1De!9pG&a-VtNLc*b`TG11S5SmFPi3#h}A)Tv&_Bh;teDo{8WtFa&BA!
zmzP_VXH~f=cAkVD4aA(bfIZ7D%(p$IWao)416xS3w_7Le*tQbeTtbE*?4og~arRbb
zLyZ%$y!>2y{Xmes0iJ;R)HCOL>-|aG?r+{`+r#Yh>m)z_cm$qVFX10|^|QBJzkc0O
z(PU?5yW|!UViH=pFf^2rOyU-Jn5}3JB}_miSm<T)9e|QXZ`*P_Ui|<}E6NM33C01b
zW@UM)usAT`MX1N`Y>d(&CTln}ZMX2a2~aqI_a}sq2w<No!qZcO$Lbu~TUH>~Hf6`{
z_jm{EYKkmpHHPp2`-DanLc^+tcrOQO6vXTI&*z4~O#v=Nv#1E>TOoZr*9jjv6Kz$7
zYYfI0?#z-XBF}o@u~JH}2E0vTqJX-~`Vn4zfEN|8t<+Z?cp*b{d=YPXO*x)Cz&<jp
zx4>jkiS}4Q7K&yxo-Xwr=z_8YDUahm=z20Z=mMN!n8=;C?he8j`EGSVfVl%v>mpxd
z1TPn`bMh9FKM*ZZwq`jf1biI=&o+z!y8@<+1@KWt!2*|J?L*RfMez!5!<&{#P_#I~
zHgQibc13If4N#2f6|i5()7Aw@_6|b11Z>g-nol_-i#>o#L#w<1wiVb1yi~+oq5Ofp
z+>bT{XLt!%Ng)md?o%GN$lqLGOxY)F+BNzq58~?loK2vaZo|kOL0DewA-5w=jRu5%
z4t;v}%BUUKQEr(_G5f#Hj@V9!AyHv1orEYQYtER@Yv26rr#D&SVMgFftzr2WD50bW
zsj!f&D&^&XN714v{`QVL<2?9gTj8P_o|v`xY>NHk#{;$#>`*D%kdWB|m$pyE?LWL6
zbb_lIuZ<>8IuqS5o}Y?9ULNw=5(%_S=PesylFs3A%0+{bS1gx@dj}d$O~w+i3t#~>
z;IjN=X}Hr6L}_@Dyd_X&DLD;=630uIhKoOl;LJ!{wR)CETY@CCAREpKz{Tmg876TQ
z?FIlTZ~b!e_buZ7SLT}&TXO6lE{)p{j#XP58D<JuD=KJtS3@bO!&dBC*A&a%bP)Q$
zJNS&Tj4VU2X2F0{v$p)*%cQ-8Cb$R9Vg`ZJA66n$`6MN)@yJ3HI0GBT4pLzCAy6U+
z-5t>J3;_k1UIcy4GA$KvWo`-q>1elm&~QS-zyk0>;;SU}Sb_ZrJd>RF?_B7$vp2>N
z20#%I=2MK_>+o;+i&UZW187NmfllNDsI6kTnKm<TeF#To1TQ7ij?m<lX+R=aW-?sl
z77W0)0ZxqHS<57M6;Ky1WF^4O2pZfDmXFI*z#D+UOmR=<xRz1`cMe*bme#s&VwYXQ
z1Bn$Btu)7_LtBo@FY1>E=}dl|<mVST0$&LY%YW+?7sGhDZ5t?J$fMSZ_9=$zz3bzS
zE+w0Al3TpJbHLXKL>S`X6{QR>V<w$42cspRc`4z9vZw%vKzF}rk}l)AjsTP#XQ?8M
zq7(`jQOo3kbkD#ZHi~9b4X$PxceOyLr3f-*4VEiClgW3PrNw)9NI!-^%13)rwqs>j
zW`5fg0#VqX6(-dpu6hX@A*SW_pJ$sO2o6TTOm9a}fa0_uf>h{vrP_*v_*W+7Y+=p#
zr%c!!25k&3d?cm%pir0?%fwV%>W)f=HjB_QBWQ*tIyY^_8K)s|Pge(Kp-&DVR0Y1o
z{m+EX%Z!tGdk#U7?Q#*y23F0rL>P|@gmeYtfn2gVPMkuRP`;A@;F>tM@LmzZUKm0L
zIS?XV%hoo~@F~#dG$7B_^{D^=KmbWZK~xy#=}(MtRfcDAXHD^22113(%rTy7L%61M
zT~Qll+?sfq=>AFgj-go=(l0ItEzhIKnW3)2odj1|r9ZOQDL!<LC3c+iDD@i{3ipI4
zRA!!gATO>caBdMf5M55imIivC?MiVLWI3MCxl|aYWO<4s@Z~nTyI0>I!w_V;N_?<H
zLnEQ1qBp7b`9jXOa{=ACyI;5);eXV;V!VW*QPDBN<<Nw~0|~Dqte2i)Uz>9k`6GcM
zA&|m)=?*w7R1J@wnC+QJ5)N}QdakcJh`Q>7ut&7&@JTuk4ZD4+F94@3DWaiKkGz;X
zC}5~4Y?!0L;ghCR&HYvr+NV!#cYWs5YnE%_SY4~GP3hcKbhtbB+IJl*$cO6cI=!~m
zuA6;{*Y2O&Z&Tx-6KmXrol_D1O22gy1v3lV<fkvwxrMt@JMLcbTVpK#E~OExL{O?O
zL*V);?3U{jztt~S=%cR3-=}TryBbydI^Wu`dfVBi^J!@-8X@hJhN@5XxQG`}7J|(`
z#{XnjvOM7t_|gh2J)C;@k|j9;Piq8{G%-(p<h@twlRxi6pHXj5zxdzX8;*@~MJt*=
zu|$Z=QV57&=2#YJ`K97-9UPs8!2w&AU+HsxYMF7r;`dei#U#2>(fOr03;_%1$AIr^
zH5=8v?=}X*X#?vjC}`Y^OI+KLaIsoyKBzd2<pC8O5Eg{^XBCA!2`PWSvQZ1}-2M)(
zZ4nLZj|%%AhJxphj$?zPdvTiLy}hE8`K+Sr@Oz?hRTrg+OEdHSC10PPHHMF4|1)jF
z(!=X7-E^_~es>#zs_)?vKaW9Kv4PdZ^xc5+<1BGvcNF1Y6R?GuIpXm~tP}V^As8%q
z1Fy6U+hte`#zHgD2;-&0l`b!u(o{`hKM^KJ)Fz?^(F&=b;NTdxr96!DBM23J*8*!8
zAtMi302_~NPOLL9F2|jxPa!bMOssVFXKqJqkigg|4|yTR;&iTQn7BtJN7a(9lMFQ<
z-iFZutWpaB(A5~j6_~4Klk7cV>^};>VO<2xF#?+Y<#KEUh6i~k3s|mbxhBD<d8bd&
zu?%C6>L+MgF_5F!>GG+k_{S@F2m4`2s353%fFNWob+lZP(9@Dg6H|<%ntr2rA5*!m
zRmB4m=ePhZ%9-}CVbgZ@BGzoKZ?g5Mf2`G9oT4yYXIsX-AYyDkE4)}nU{@%_E*562
z)bD%(1Gysm!{s4chT$NGz@Y)`Q3|C=A-?9!^)c%vMzIx-WjcK<rv8H14e(IPo}_0%
zFC$%B4i`BObY&E1#Ufn)@81g34qX3u{$dC`ac&7C$4b-A+>BTW_D0$JL=$tZEo5Uj
zHOle2Do#=KKOt<Ilh`ivd0s-;G_|}PWxUT4t2)N{WK-0&$gY`2_>E{{M4zM3pu{kq
z$1y>ZZG3tJJWJxIw>>kz^iWDuxP1At>(k-Ghp|g|O5rLK<T<cBp>u;a$b)v68kH5$
z%V1y?y$sGC%flW0;?7e0*E55FCG~z>u@(S}-+!eE=m0T(@JjvWY`@JRgl>`vMnr$p
z^X~Nxh%wt4vb%;C9OR)MI*=<&))ZpEiV*_i%-U@P9iO^1Xx-RWsu73^avj4MKa1%4
zAHP}WIJPb^;TjN%*U{*@!6IdV+mi8kAMW#CK!vi*B-aFx7#u`Ed~|)@t^hwcfWRFf
z?bm*?^IT{RSwTs@<-z~bI<ag=VK)ddffVC^gm!iUmFQBcE(F^E=dZ&kJ??NynRes$
zIO)0&=oq0zcsRnu%0xg!CZ?dNfexAB43hmPf)EWbcIm=MLCMqFK4O1&F^u3B3j&_C
zSgIYY=A{6OMJ|H3e=uwjz@AHZJ0%F3=r42Ljr|r3=@a0hs!H+L;pFJzg)j@Assx<d
zAc9LtI$k|eV>>D`t%a9Kh%xUVaJnBS!jYX7b_A-Hw%(|vf`?jxDyJW6nLS+dFm*42
z>G;{n&GsC1ZW7@9Hh8b?cx1B>)OwlVL$EfW7gKmTp94A(BY8+~h!kp2^Hh;+BbzKo
zvgyshmG&=iX33ymiu0t0AnpzX{AO-$l>1T4eXimAhv4*flVGH_G}Es3Od;5EY<EqH
zebPK+r(s^$%-GF>`<Pr!#lyUr)Mg|#S@)uK4{!3%V;KQ&_CJ=U9&aNOR4UWc({}Ub
zP4{m-y^K6~d!Q@v>1EQ%-10%|gnbiw8AbL_uslq~z})~H$`}&`H3$G;it}Erg0(qC
zDy|$jl79YtwcS97lvd{3%h08q++A+Fm`uY6qVGOmXZyi2okw#Msb2~x?FAmrpB}BU
zG&GMDsA~S*OPehNBzy;X{(kgAtyQ3PX(3cgdoON<eF4UYzj(3Ea$@8)?3%V+fJGPN
z38To+J&iZ5GFWIo0L-bSP6e*`zHrQr@2Rr=zzCWEnV#HIfIy4cF6dX3Yj9hAxqatg
zDa3$GV)~YcW->T7ZM}deFLg&<UuVP$P*G$*d9K2NAj`m*{rU4%P@zO^70R3+0#Z#w
zyBY@Y+0AhmI*10KuPW^%&Zt80q@gWkGnlo&D3AyC;_eb~QK<-*MLW2?%#Ok!@vVau
z7Wn_!d($AhuIt`!_1t}Xp63}2pfLjkH~^eUQ<f<`S$dYDDbLS#Ipw5ce@H&$>-(5`
zl~kot&yiE*q@41zpPnVt7A0!n1P~+$fSA$f2GD5qJdd~MxjVoAK6f`Rng9urAOw<k
zljwWzIs5Fh&pv0bv-Vp5^<N1qHiQ-?99l9MsUwZKRt4DfCUA%{pbOuCUPhc$Tk7-e
z-|WwGAXMFtBfAR0O>xb@IR5aFLc4(nS7kpD2;0c(xT7ZD{w=gWt-M@rPC{+Ni^OSk
z)S1TX*8u4CGUrGD`tYqM%J6W8>~UUFg`^l0`@#eo(#Y(ZRf4UX<t?TaW+){Nvb|Z$
zWjwyaG2>{?ePr#>*j_^@?%!E#Pj3aB%6&NqKz5NA!64VRw>F30*<@^5CMsgiB|lk2
z8&{H#a$ZVGJ`yXR?Fc-yOG3XC6bRJQ%Lu`~@%BQXiC(6IhqwamL8$1HE#o%CTR#tV
zJ&amE$P({D`y}dpkjhFJr&X3kzlJ6AGMZ046eeQZd<zb(ojjPrT_vEoX{p9a2p9wV
z;Jy+&WHG!<1CixG4nWizP}9ozoW#CtEmzPQQh+$T&r+y!X3ZW4IlThs(OJ;qG=S?r
z$zic70Yi>(TphS1VSNVCg!19VdITP=HI_W*>F**-kQF+x6>QWMlDwWKH)1K!0nx;8
zDNIVJOjv#hAC;1pW=9AU%4Ndz0%ombpJ-PU0yD#TGU%HCuVN<%qrn|Rpf^_MT4;Fz
zWd1lDUlKO!EQv+Gli&cBxr~G8d%9q5EklD)CZZuUvjWCI46Tb$?4V4NIXlHcK~h;l
zF0t$z<)t77)l@VoEdxgwhaHS-2Y}NLASc|<H42KrZd=we9-wF&n(Zvw>I^(&CjiSH
z0`*_QeHug%OPdpvLd)6&0H68rQ;9xk4}wG5S`*<`Lfsd0{}a$u--E2LkTyrar>!Gh
zuR>v?kRMU%gn^#OMkuz}GVoS{UNnPQJB|ib$8#Vdth|lY+!IeRGmG{(%W`)*0%eh4
zy@`n+lMF(&`AH-sAtMl=;xy6G@V%-ylWPjlWd8a6Y^U#+ul3KJmk<Sni5k$$w1Cc@
zV1necl#yyLH?~~+;x-caQ1%%aytNus`YpNDQ<z*~>_W>lQi2e0RJ>HkVPbpe1E+9Q
zG=d{6AZ)b65lx6_LG&QXB`*pYXU3<p>T8=c71_rsd2n~LloI4nFa;e$*W4%7z-ry1
zaFGDkHEA-bjb&5-!4%v%I7r)<S?<V2luFo%c()v&Q#&ZWZT+I@jS{rUR&C@%PywYC
zQ~Y8!_PR9$iWpaQ-RgjvQ(MXaB;D52pon%D-O2$rC75M!*8)~TU48dOV=r6S0&OPI
zPqig&Q8*_pMNw9q-ACi0!tS6B_ez?Z&MUj2Y(s$)L1Pz;gX(L%MH!>Su+E0XHo+U_
z886*uU4Up_azOv9k2--UVq}lgvRB%c#z5n$zPYi+wx#jZb*b%H@MX8r#BiL9^lC%Y
zM7lmbTj~p@F6?)~eN;T6`n7{)tN5=pD$(~s{g0LVGXkFrf#oNL9yDJxSNzZD?|%R0
z&%gy82p4Y{70yQ!O|5Fbwnw)m0p@Ty?2q2Awtanl_hl8qq6gRUowITCF{&56ZS!~S
z_t&KFcP*P{L~w0^$I2fQ-L|%E{(Y-nfUELjqI$k;?#tcU@b&NXxuFrWGy1#gZvO4J
zX}WJdceE7nef&@z*XF&A;ce&MG@k0m$CXbq79ZDUZ2!lPz~@3>ef(wIs_{jom$`L?
zv2U>v`0|c`)-|8o5~U2dx3w<Bb7AQX>fgjOxZ~fwJZMebQ_dK!9rB(XB^kw})+&?%
zAP8??8pNZdj50y#8=S(mPL_a385^ImSKjTiU!EJZn;2(hf4bK)0t`aN^-og#b$pUz
zHy<;HM@#k=UmU{)AvV$FPUo9s42Kl-ys1pFyza*P`B5eMK56OIg^@dYTlBI1+}eMq
zf2te3J^FX_ci+CRyLsQoZTI^>>T`3O`W-8u?Fc;7u!fJj^jix=395izW_Va!=0(^U
zZqdtVvQ1*pJ`KH$jNNdwI79p#+uyqu(8~zlGm5cx7~^bJL51UIKaT5K^fI+X_pjpN
z6HodcpcJWi2RreiwIZ~ND{}0~t+~**48tanXfHvha_$P$Fog0xOF{xs%fyG$tO(E4
z0>ZB!=RhjK%X`a11Y8f>Qev|GLBy+h2BF$F2Ym`$?QLQpSctP1_LtyxPq3S0e!18;
zM?iBn_%6J9xZt0}!#07Sy$pWqaAScjfjjF5A`k*fFaXc^(`3N8gzJBNInIvY{TxOB
zcVUOC#>Up!HD=@qvb6Ojd$VKS{vDpgW^95L6?wLg#10Cyma{?B%Ax|6_bdkk_|?`~
zm=TQj@~j<$`MYg1_6Q!mk>Rj)5Lmno52%*Ja~S7E1B{u-wgGu)?HIKtAR;^9*%}9D
zCPTck9qp~lwNb8Tl_2hC=`Snv=+)mHAA#umt_sn=qrZRLKHvD}WBm4e`TR7;-b?$j
zI=7F&L+d5Hy_@&;tEs8!&h#?SlN7*Ia0PmqtuO$HUPhQH(aY?O(97gQ_Y%LdWKF}%
zc4!O96To}rLGOY$8-bN>Q!qL-_srW%`-@CjXQuILt>C#*@(9txlvU+gP0G6cAHQz5
zx-CUkk+^2Z05B@yLm0!ZOtD=A{APpQS_TmMTJr?%_X7C4pi#j~n3J?-C$5cK2kZwE
z2-LZFf{NKTiH*9F__=0ypRVJf>O`YiVc%mlnRephpuuBjFYJQbs(HY!jgstP1!;m8
za~s@UuY53QsqjHPQl4h71EA${5!h+KBfu#kP%h${FQn}fJeRG|w_E`T+e4*8cpb+P
zgulKpVwdQ17)(<l*Qr3`dGJ^jc?tGkP7GKr!g@yuxVK9qmccbN<|W&k(CG-=`T>C1
zc3uGaxcn9L{Xz4fT_SCnc;i<Yw<5fJN?kSwJmM_)rVjEs)`F7~tAq^j(e}!FyZf<P
zUxpF*TrUaz4bt*YB{MVAK_K+0;ti`eZjO@9YQeUNUIy3xAl|%xJl5khogyLC$7bv_
z@PQe*+ChMeNkB#3BcaAsav-vUj}Byg*v_CqT>!wi2jRCBjY4jC<u1hD4#i1^V{lkx
z(v{)z-cgYOQk<l%EC+KyU+)LEBoL}&00larMPeoz3~nl$!pkMv7cmzIav`Wi2&jRI
zSRk^YwPC)ag~+2uT{NC75cu+xrQ)t{M0<Q{M?Q5(6oH4Wl01Hid{<Uxom+4Q^=}Fx
zD#xP;^Fny#Qt^5TF)yyJ9blj2nBNK*Q;9d~5qzX?uw5LthC6RtWwvcC&jh9c!X6<k
zHi$yt4I)?-q(Qi<RNxj><QQD#I<`V`s8WfbZUEXb0;*id`S-3*S_5sB6QQ7h<{fxd
z1&?qgrnUs*5c~U~5%@xASpIG7>1D);<%{+y_Up|{!<Lo_X&%d^EAUS#8;<B@hS4?@
zj-$8wS-_*hS1IFj7|^I<%sjn}@JDj>p8#wcCZuO46L^+orpw%n#?=oPvlUF((|{Il
zc8pmE7^LmwpX+A1lbo9DQiqAhcmY?txUodje2nmuvm?t^PuA*1+7i>I7&g=Cn;;BY
zjtM5P!Z8Zb5hMW|wVfr`-U#@k8bFg*z#@rEu)Z|QrOR4p8K+oDmxLA4z1N#Y-SW2q
zfv_%E0769`K~Xg8KhfXJ<DQqm63JhNGV>rXMzC`U0wR`EVZJmz4)%cye~S7A2&q0O
zq??sQ6z;1jfSAgyxSfpKvNNR9UOw;%ygD|AhSsa)Hphu`YMphWC}gxWOd-rjAwBB%
zUg)a@m(U{bP98viPh+27-Vyl1c?s`yE5(MXcrq2{or}G5-}xck%)ih*mPnIDcrIU8
zbS|nVT9RNw1?_mvPW82flrE;YGe?iYccpmWfD7<jMQy1a<+-~76Q<(Y#MdNff&_#w
zj&6k?>YLyf()1KFD2$O3oQj=u8QNzFuHw8@Cm4j^QlC}V;i%x3!XyVNU<%ixv{m#Q
zEl?OFeOKRvEmHqE{l=%mO0mt+(nPik8|99pqRyedXuKp}G*)q3zXYAIPZ|rpy7ttT
z>Wj!v+x&p5El-o9ZE8mxT>-vL6=Nkf0v{fMFPy+qNRN*<{P5+*w!}stHUgjL5%@yb
zu>1{>#v@_nH6OoSpOY-9Wb5%oS&g+M*(fa06n!rwvoF(VC>u}&+IG~zEb#_dMs6;m
zQt<?JoA=%Aw=@qGX;Vuq6Yo}}{rG=(tMgIYwX~HzN^SV^QM50<j-z6U93$4&yLHj`
z?bCkx^Ts}7BXGA7_*}ypHeZytW%*A{AT4)#`X=lsfw!s2srGLOc1>7bv|r;v6HCL+
zx_sM10QDKN#$4&2bKK+4?JNKSFlldcOd=sMPwg)w6HJ2r=4>w+S<*;Sk>)ZcYmGCH
zeWnR-TTNjKadISufV$T$&5%a22PGp2!x*NMF+{)D61Jy-Da7H8Tu(@JS*Azfm_O4x
zW!vinpMc62%e}HmOF&4dD2uVYn!bo)MH!8Y^GTl$rOE^W;ECAfJ|GV7Nmv$Sgm*%h
zi&yq%g%rudxN}D_^g4Jw>F;$Q2CL~IJ5ZSgCGnCKVe}SV%>wo_8NkEDP$sZE9YZjS
zUMG`nviVh#-fWd+Xb5%#3|ek!s@OfhO!j?@ea1%Mvl@XfguwFNKw+>1ZP>kgx5Hw2
zgGnJCszcaTetKry#TBN5(RzGmku|lAIM@LuV%t`mhwxdptH27L+i7^+z)>N%j-4BF
zTvke5C7$&>1Xnh=sMk(*TQ@L)J;kYZxn}{xG-<m?v(=84wy+w6`&1XCo6-)x+ZMKT
z>`0UFP^G30xuD$?0%*4tq(H;6WX<4{w$_8AA`tuN)k$b;QplP!LGbY+fw?*M-6JqV
zxF-O>D}DZ1(<IP~Tnt9!H^lg(Sifuii?)h%K0tu+2zabn7z(boPmpyd(W*GNV&P_|
z=j_!Fh8$}_VLo&&;HWMSE!yKiBnI)Gu7C-v%uTlQus_t-<XH`_zbl=SmV@EHHb2E$
zNrBY{Jx?LHv}HLgz>NLL3pG{_EJE6%>`<{18-dSq1ilbULa$MEc6QqN^XE;Uu1LM2
zOv1C23pdub0urVoP(-@c3UG2hDaD@O$;8JXJ&hr|qBO%+F%k>Vor%l;RLdxsssh`N
zYach63$j(xhIE2$D}gMOQYVhwgYpdKgDV=HShA-9mOj>)V=wM2v0S*X2B+7+EkQ~L
zr@J&afmP#Dk}VI0gkuP&7Y<d}Gj(_z3C>LhV45lxg{E;B336>0n5m^G##C9%<>jlF
zckt%8t$;84<`d;sOVWtLfLHT?Ku9y2<(L7O8j8^bSI~%Xc)EOjBVeow3bXBdN6IV=
zfng`9#YCxc1Fz}6hC+J*pzAnewFdzvvc7)4@2bnT$Dp=Zf_dWo_9@^BA#xi+2uR>~
zISdEGFk%EHYP}iR68nga!2KS9FO-J0Yu7GtMIo!Jt8?SzHLox^=#JhXu_w!y$?<7>
z1Jw9gmQY*BJJ$lPY6TvxVFXzZ98^{CCN+cG8Y7hEDj_GS84Ca*(-8)E&5(a>Ai!6q
z!AhZZh`v{zvN76U0<%@1O3Yg&uF$!YB|s2j6QIsR8Ee+h@-G9;Dt}=P61-IVIvB2B
z5b84k1mYXz!Gsr`OE31dLWJQZmXgXsBN~({G_7hlsituIO_Fo46yB$`>3REq-srS_
z(5-|?gO!9JJ#i&$Yd{4G>5rfZf=TGDBvyg~+CF`A!2Y&l1=^P!E8#o?aCq$kPQXgi
zC3eU}@Xo>NAc~tb`k$JxYS*FS5o76#JMzddw1}{Vi2|yXVfrU8qe@J<a>Ee&^BIr8
z7tTv~r|}vfZ(s`ymKjVsOiUQxgN(}aq8t$ffPb>}$OxZ}y-1l}WP5iQB7{;pI3&RY
z<f2#VuXyUGu*8y~T**uW$`#-O2ro@vO<sKlumLDg5EjBaX@?*WqP$Uhu53W7j%F9+
zw<vp+FUP|mbRD9z5l%^Ol@$b_$o_<dQhNcV3jN``Y+T}_ayB(-YAgeFoihj&!4Cva
zP>L`0ZxLZM!!e>F7Ui;|YXLkeoR#mh@MTG8p~8X*QV~Ek#MB8m6s%S{wz_r3Xqt1?
zz`s=s_tu??AB#Kfi*1XIz$Y1jFO<NF_73`Gq6Q_J3dS4aHxHJt<F&Hr{W?Y>D7sC*
zKe~QE^P)Wg@bOOPiryw+Ac3Od&*7wwlXxFKpYNkPAnH$aAN=Oc!b0s!Bzb}8#tKa^
zaw>OPe{9gBNT2u-{oAdwqb}l3eTv%cG|fAWn?Kis_;h0%bsZ1loUwC!HY4zb5?C6d
z%{Z*+$@AF^VXRdzw47jII3lLQje+q3HG2Q;-vi;i`Cdid6RthKFM69!u(5sPi`(j6
zE}Mn+hwA$m3p>0}R-IURct_w1C$OT2+CQ+fBt*Cf^>7Y}B9v-fF>qI~3qfOU`?usd
zYT!Zm(65o*xUM`4J60N@G6V+`_L58>@lMj5Cu6g@%rSkFD<oG)vn-Zp1-SXMSk|eC
z&2yHerhaL;qy(Wddf%-Go*+Z}9LD}ijQyL7pMRUT%lJQm9ZQJvv`~s=%lLH1LS|@e
zSOtU)Z7%X=<`rdH((1DHVPmUt=U;~=28v~z33r>k!z`^+bk4{%E9(qvY7##LW8lvt
z1pWz_D8`{2-UEmG7P(6Au?&)UL%j0JQ2kM528<kx)g*@iVsW>^SoLCu%wWkYA*$tX
zIS`FI75}W<X<uyH!!rV3D5JZyD^D-u(C(30ABG)a9L&<Gn=?#K0;6F=7{~2D!?t%L
z^fE&j-G?x=4}fv1I$QxCIw{rhwl!a$BCAX~$ra*Y6_~`ER%F-O;LVzjw{IURw3E;%
zC&N3RxC$hpeGaw+Bgi<@>hKJ95MS6%;)ugL3T-|-V<$VN$#PTda9{78>#?TEbt}Mf
zwR%0pUf5IQ`gMvrhj$eN6(HRgLD}Ld--^eOC}ZoGST^8S_9nQk#~N}iBWczpSy*$n
zDH3u()v`vGnKt-e3P>;iX4{ng;F&7x?HacPC~fM<0;BX>*9R8uFTT0WlJSnddc4c>
z%d_ps)*R~?oVEdkoami9pp%(UXb)Lt8VQKL-Ww(@TB0@Lp*)L+QL%!TJ4l&ToM%sz
zr`Sp2Fdu^-URh(_CH>lZR;KMOFjyF-;ksY3^FTW)$mUZ>e4$c&O<*q^BmQuBG0t{Z
zgsg?J&CNwnaSs(rrPORiB&Nv4n|hjKw-C5}2#=+_p6@pg+nLTe+lw#^PfWW65)A~l
zD;Y&L_e#kxT5uF73CKDxhM(h6O~gxlkmIMoo3#-LznA#OeiB=ZEyvrljJ<g4@A{H>
zc;@Suz9(OZ;Ah=M*-4`5Wt6Z&+q4`BiC!j@U`=pXZLPumkHMKaW8DBFcW=pu(j>v9
zZ&F5_E6~e43cXA^%m|uX*WjfpBe1vt?A7JAQL@XRwUFwmj!AL0b<(P<ax8I)oOOfq
zOo%+oL)$1JDMA=Vi0-hsr$_}x(u)~VYc+zqx;D6IyWyLfM(az!tCzt)X+hVaSh;{k
zcM~mV4*sg$l^OQxJMDIP1c(BHv<L6pD3d%SZzS=DS@3I}1!omP<C#Y5%O^|B7?d~T
zI0c%zXNd2MgL^7u!vrY5+YUNkbTOSX_Eq2(t6;w>5mM5yI)N@cQlDk-5Y#=#cuf<4
z-8VdkL7m`a1lARV+cM`B#a}LCGDtcw{c8tMnvKD|9IVz+0?XS;STRjDp}nwEynlJj
zo<2~5M|Q@paJ_M8oNc7pYL##YFsq>Gvm}2|VE7O$6l<L85&~u&!Mlp1K!Dj!_O)Z+
zpCq7M^f%qWC$_<0ah=?gBk+#xsYtc=NPl(}Tv(WX3Y5DIPW45OX@YHInCn=<Ly9sD
zTm(uS`Z_o?X}`HV?t<D|iQ^nZMH@w1t>Y!81RS#Q{jTk8UwVo3F$3_0(6Ib9rjxoV
zCkJ7}r;4|o+yHDj#RT3C$a9dldi-3R{rGr~ZQE94B?vMxiC$+pC~sjg*rIL{c+OA4
z5Q^ii1|buyk)U7!gZ13*3adgm+=Q`o5P=krKwM=KZZAo9NjNh|(WP0EfETcyZM#Sd
z)sMEIrJI7Gg@jir1p?7CGfNP2I#e?0c6xrC)1+7yAWzZ4s1yLhsN?iS2q|T~8ANc+
zW2n!i&q{odfroNCkc4@16;_d!>?&}LeuAO%5h7X!>0HTcNmfmJ;_b>|NwmUJ6-T3`
z(w|iN9Y=fo?<Q;}+DT2~s{ISV)bEgT>nuy)JSICUJJMbOY-O4JX8#n4OF`-5LCmI4
ziCkw1{mg+Eu@=?|m7P^-b`ox{TEMo0ur$yWlE@}oJet!I2>EM3L*fzCJD{++idHwl
zIPZhluNN&hIXM}R=&Fkvkk>Q~0PlWqU<TozMmCS}8RUQ^5=Mw_C&YCqV^B`glI=&s
z9H2b`XcOaB5vp^R3r|^PRy_A#2{963i-69CHbiBU#x-0Un6Xj3vE{sgV&&l<fiHxH
z<==`*2iDNg5J9;IgRgyZ)4vX}oTrz$3{X<(xjem$G7*cq_k$Z#Rtt~QD5!67TeV^9
z39}@sVwp2Fh2{esKq;f<St=?aX+GPppyiCRtY{>&PB$5z6|<*IKH{g+gq!Bv2^`Z8
z<;zblj=71b$uUI2(;k+K$-o%|_LO!Z4AcM`PZ6NZIP5Q4q6|<k7Y%G3?QaBaqyp_~
z8qGjdGr0(kHJ}xX<PKCD$Rd|%M)<H)G=^E~hnUohgyVpNppPl`Uh}9eGjVg-FA_gN
zJ}wYi)Bq+d6+0a|hRb-YtjWrjtc*5lOU}$Wg$nU0x*l*6h*xEbW?74XR2r~CG)uuF
z6i##v6#X@j`V9!}bnH=KY;~DvgXEF6)}eXQhe3jf4NT!tWe9m`MAB*t_`VK@m#C|g
zxOWmrf|&?{vcNT~Ki4|Ljy~rAOT1)++c@D#m2iwLC@T<s)Od`-yjX-F&CrVidtV<f
zA(08@Bg8evWqhB&RIosUyWsZ{tE%2ioHAA(;t_bDMtA0)EB={~2Bb+D$1L#MK5DF7
z-Mdu$sD07&G9F4GK_<|n{?q?@SQB~U3g;w^LmiU#AY_%&oudReER|Bh3!^1%OkGhO
zwITWy(Z&RA>4X2Mr?#ldGHZs3wQgH5aEnwA#Hqu6sXyw60H`XOFq-s=2NY&Yum@_p
zb7{ZR8daB#`mW>MG1O39eOF65w~B*WXk(;5p87`1E>%$)F6|XXjE>PZPV25m7&Yxv
zar^ny@hY3o;i+nb*^>QGn6W^^mFG4%K*!Ks*G~sHb&UkqtnL0>YF|IxZaU@0mTPl;
z;95k(qh9E@uc*5Et!K&M;dCKv^!PH3O)ypwD2H~lU;W5b3@F-StUUZ9@W2F?CWD(d
zkukZ`?_j#<(R0&_`g-Dm@3-(nK|6XLKfF)N8)1!tN#!cgM$n=Th?Up~#75xrJOX}Z
z-?wWBv?eD`Hrm_}PJ9X8ih<CXLM6Y64FOF*&<rG8kS_Ws0VP4>zf1UNsiWU2(ffQt
zO$j*Jj#MZ37kQiB-kqR<O@9RwP!Vcf%cK#OIQLRKDqeMUoxVhW7YhAO^f3~y(M4WP
zWs{b+;R^eG;?d}O{;+wLn>}c5VQes;q)5Av*0H&W>@b>U$DdDjGu?yVw)o?9oFC`S
zZD>g=&Gq9-@O%Yht#<FnzmGd-Z2!YE0{7j9C86OoqsTStfs*h*`L_T1R=dS#z_5vz
zXkajGzq$k#isTL*qp%X-I`7BxltQMJHEa-l1nMg5$~s2cL9jxr@S>l>i<U<W9%c!6
z%E3DEkk4V;P*$8#>=>ymwIpc7kshYq38H=#R>Nw*gnN=rCYg{E(Gd@Z7YH#Sbv@_0
z(H*w)9kS!3Thompd*{Zq<9Qz?15F9s<&y{zO|)6;D!LZgQIw5Y%cC234lhAjoQ)w@
zM(#<3@D%lwL3nHe$`<<B2E1Wz9giUPcE#yU;mL9#L0VoR1iHEIG?pcoTl(!Cm>>3I
zfNp7n?gm?19|Ap|@EqA#9ETTPEwxQA?AK@dY=pSNRt(Z{Nu;EQ!Uj{3V{aIT|4ZkY
zWsGF6o5x^|X^my5VF-trfs0Uo^<vQSP;$$$iZX^*@mNyTjm-=;s&s5vx~@}DIwz#S
zbI*Ck>4%nm5_pasl~3_y#lubi%hLl6^A=C=x3WuzIO!1QT%yipl2Z)8>#p?jN<2G=
zjZQ1+E;0(G()JAJQRt9+VX<8tV_MEBgh}B{F2sqx=|0@Ae>4{>pOX=|?+f?0-+tR&
z`t#2}f7=i=w~pE%GiD!lB9(LCiw-oF*v`^4ymWJR>`cG)AW(`iFwbL*9mHT<1uaYx
z5B(UEqXrK33EcL-X<Bux0@pgnF&57<I}@Q=$+9U7=NAz|bL)8ekPO{p3rwUqd+w1^
zJa{2%BFV(B34q>DN~&e}o(Aw()nH&>M}YL;$y!SW;6#zI*+bTwN2`<UAK&S=a|onT
zgk%UTQ7gvU3V=|R7{$BsMpXhby?%Y%#+coMxYp}1Fb|SS>*~b1eFesb8wiqg@IP?`
z5O))7y&pJ3&!AlWOZLO(s%;xy%Tred?PC8tUMdn~j7(W^4R|a{3x?(4RfG_N`Z5ON
z10=jqDy{d(-1EJss`*Va2{fw$(ql<@UP0)do|v(Pq>yb*N9f=!d<34a*UpaEquX+A
z7?{O0Chu~L+M=`xAz)wHTVknP*Cjx;qEH^f##Wt`WL<b2bI2Xo4u~@kV|FFNr5D<r
zp|Ic$adr-;gFJ^x(DYP5N7Ku==faUy1dw(oS#&;NoZ}I4z2KZC*SSCRwTJ9Lxxhif
z)E%4-+ZedAL|!)6*<Qi8i95b`WXSPziIE~DCDmToo6m~{FCMUsD>vaYBQwvRK3iqN
zt=@VOy5Wlb`I3yleOVGpO;=f5T(rOZ%fEF0^j$@7!G+m*JJA}()3atbz|E+HcstAU
zqjD8s<BjmT{qWgpa6lwjSW2*e&x1L_1K+b0XWwOeK54p^Fsd%_mY(N<o<Km=BX9~(
zw}!z92{c$*mSf*MSVVd)T<Q?>eQhr+223uMX=Ye8F71Ba)M=R+a7>jU6yS?uVtoJU
za$Nkbnlso6ez4{eK!5>a!aljPkYH!LVtDlOC9H7Ew}CrS(uy1e&2wOzzOp;dj$InV
zU7rXvBEe3R6=qv~p?&Q@k*y&d-?=#rn?e$O7A;LYfz}$Ib-PYG-+Z##^5R#aen|&J
z89*}j0Spzr%wDeJn~xNe*Ds7fUv-|o5j<)t3dkPD8+HW1=B|QdOXS>09t7E%ee+Nm
z-c6`)_L6c8Sv-uURF)oRmk^dOKUQIN1!#2SA-n=+D}%B9fNSYQm|g?^UkN;+m%N8B
zJW@{n#8mqg<N3($Qu_u1<Q&+uMg;mZ4VlpJtXMP2X?wtqo$3H!i!e`2hO%dO0VIiJ
z3c`C@$;;!{=SS@cND!ahmPLxPMEik+4r$f~=K<dyslezRXMH68xB|4~S<d+cf>v5J
z%9YJ=|4wx<&b%}d(cY9jNF{jU+}GHjFVP6x7Y)lV0aJL}|LkW!bKW6+_a%|<fAmxp
zp)Dhp%4Ak)tk1TeUmRi^iBEAc^Rl<UeWL?+cZTg_`7_CKrk*rV(=28F?u~YG)@8U<
zQNw6X|H_i19C{U=bJz-Se`5qYv@PET`omD6pjGY8cdqnhmOUwWx@3D%mX>KIa$?G=
zrR1^<LU``2hqMm>=ik3Q4yz_uABtcLM=$l08Jf&Em0-GH>0D)5s$6?BGZ640xRmrE
z2UNCFY1Q%lRP&&<kdfzLZ3bEhOB#e@JsESZw~yMj?ga7}hU|a&O0GjzFR-l9N>DZ%
z#JTM$4cU+1YPWi{wVE6<<wS(Iyp=a_{~xGAgTii9Pd1u45c=()-IaMpOY95)u}^I)
zMgs)Y3Mev+CJOzit*5409EkAO&JNi!bUV3asaApJl)$o4nxj0OEZzuH%CQQEs0S5}
zuq*;!ww?$umCsDTQ}TZT%0Q#dwey$z5%#OFn&w#x%Vtsx+8>aku>}-)4NF#O!5Ij2
z2AdbkqOGxPt;wN1mf+udyvW`?*XN)fd9bj`E+<bPurId^RhltrXw~d{`*aU9Jj>R=
z_#L~@hsKy>Ux7#LS{LR3Fl)k~sRVku`x^W6B^rUx)Jy2qcG-WF+TFRO8C+#c5iDR9
zd{7wMQ#o3UG=pK5TsbTaG*RTSb}oH{jW9epgn~$0lifo$4$-<Oeor=F*>vR2mTd~T
z1d^S7ObXRkQ9BX<!sv)<MfM}9mVzM&qoZY@+H>cl)500Wv)svMa>%|TLw;a5hbD%J
zrVKEy@S4{tg!7Vc6ozPqWt#dVH@R$1vK3{5mr^|$xs~Qi@<IYZ+tkt+8e%%<SNxoe
z-M$|(aC`WK=wU=IID<e_zC^8w7#gQ(=sLMXmm^RPQe~b=WTP}ir3p(#peXO5xUn*@
zTRBL9&dotOj&9vn<P7llPM@+HO|y(#p~o8A8NM&`J(qsS?xmt@5tKvUWYbc^t6Zz_
zZ23eMjDb{-N7#$}(4C(H{5f<O!{lHdvy5LGDD)CuNa=z`a6yiS2aPlZ8`u829Cr^k
zj@8Ly<x4XH_g!GE^M+N4+kh4XV<}<fu}0dW2Tl`HMGv^rMCqsV0<kUP-{V;hCY4`y
zdTlQHf%V6D=!3%^ahy9QGBI@S;B3LU-H9U;%nLS6q`+wq?3F=T$Eh9Pw)XhGsF#|g
zexK_5Yf_zCeT@i!+xie7%+;P3P{DMM^p8#j<KfQego<xl=TV<G#)t!>`|XZ!+w|@B
zvbpbW9I2xAf-%)7MGjVn^h?*T&QLT){<|9=H+ZTTjD-$$`-4sHn9bv&x+=jrZ&mbT
zsdMl@PK^(rGw7o`Joe{HI0B!6z|sS#hcS5AzDQ$wYifOwt}1pdu@U(Ej6h^<bALMQ
zwF(I<|0WN_7hW_%Q_f@W7-89`GCVqMZ(bO%<4wZ^AqPg}&AnJ#U9mR2NOvj=*tYs%
z7Wk;5OvZADcaj<TPGuRR@lC>M?zMcB2wrCS*E4wMwxktoE+cp<wG^DfyV#4d+hdU2
za%ppAes}eS?VsQ4wqx+jvx2?nvZyekJESGsXaJ44l@KIWZ*~Lrg`0RJ@2vpWz*8DA
zxjF4j*QrbW(7(HFze8P}+qI)c^nKGmw=wqTOFIJh#ptfl^TKLwF5(5m*z32kkg%Vh
z?z35FSiWBz7`MAHkdLk4HNy!0deewKU6bxSZ~JNqh`l*xlf>}tsZO_6CeS4IkA8&O
zK@98TBa_6a!MzU0lI%=9-Q(5+uIEV%vjcb)o5(<OWKXfx6Y%@fqaC)EmP$67CCkN7
z{rs*18v=IFiShXNU=tv8&wg{N&$8fv+J|Q<l~}hDoC3|zDa#YLgh%T|JYJnW6Gj$x
zYakZydLM3hyl8E>`Na{R3p}8jEHdp(_G8TxmJchyGrNmyeSY3P=$Ns?yNj&^+g4le
zl(T)!Bbl<GOu3ApT*MaCK_Z9(mOe{~Blg&iBHMwnc?Pa_#r&mZh3uIf`8Ew);pkOT
zgzYZ0Zmv;G26F^)k0P&?kRlP=PA0~7dFuvnN|ch6!Z9&myspA2fEsOsz#97TDDH;a
ztFs5l1df5{_mvV;2+IZD(i2xFtT;Eto`5IoRMUVhl0l~oZ{|(-q{I&|c80BF(y1nE
z(0immtKqz@+>;`lB#yfZX?|CMe%Bp$o_Ep8`EPmXog0j8{lbjEeGyn*FfHTC{;R+G
zD>pbl`q7Wz1rH+N*LZ#JL)c8B?VN2Jw?Eid<h*w`;eMJ2RI`e4_!sA=F<b-06rBq^
zMmtMW9I&(+7(h3ZtsNmX1Uo_@fy0O3GP=RUomj!J45dP4S+f1-w>xYP5P-Ve1iR2X
z?F3dSZuW%KWLpqZ0UoP=I^K!baovGU^VxS8ftU|0p*%m)E~90HU@z#T%bf)67K16;
zjyLhIU%SCf1h9y3d#8zvG7Wh)LBDrxgS}v466%(D2QIye5XdGu!y%|zUOCp`0&)d8
zgB!yB+rwn3LFk+TU>aVHcU)PE7@*4$1X~3HMR+9B$ICaToWL4KK(`|hCwgb?AOdR`
zoLEV2I<SmnGWn23j8<m|#_mNp^}q+UqrS+>6PD1v#vJx+Mgh^nvf&k#>W190DKx<A
zcrZtS1>|5aOix*}CMaK?roZb;OZGSCChf1kx7}tYrtF{2j*^2g#eRdpdkI*^Fv2PW
zFJ2e4EvMkZn!xzqf&ebAfTAX1XZpg-h}eZZ)FW_T1eU)-1-)+Dw$1%?1bUGj@(KzL
zkbtJqAIS=(K;vh4!#`4(V1IYC*Rr8*DZmpLFBck<K+DP^C`shSn+366h+7^{y>o|a
z*(48N0s^iQbo!2Jc&2)09HM%i)nz)KFqH~2;Pe2XEGfvaZIvn3$?`G-EJO<Z6vc?F
z+Qs<R8!OW+B?Gto;)1<?bI!hwR&^C&0b!L(EUEx!v1;a`sXdF9(?#Asc@2fi&q`Xe
zX2|Cj;et}q1}$d_X*(^1Cll|S5|gTIgAw2;;L#dL^y1JAcS4dEPaBhHSt+C&+gY1w
zr(ugw5%wraZ38%E)oS%*xH<i9AH;K@{6V5$L(nJ}U^cWWO5Qq+2dx=8oN|D+*)T4&
zfgZ0zQ%WYA&mbuMO1zZvo{FYs8DX{sfuEhWY`ycrI3<xB_9uT@VQnN>y$M8QFRZLN
zXqx#jJShKRcNf=Gnr%0`Cy5wHwp_e-Nt|av*Xw#5`x6_12QvZ*|AF#pPvvb^5>iKw
z9C82j-D_TYs83%Vu`|8%wi7&&Fbkq15nRB<;-Ls+B*#H1vTivbI8R+0bK1dnuvf`U
z&}Al(LU4+C>#w2>oFqS<c#%Q~tyK`(!ZZnQbpx!<zg*GMCKd2zq74%o)63FMj0AJI
z%N2?v@L?DbWii4~6FrQ;5OhJXhanL6vkIYM3H!=92<=$pJX#NnC+8HMvX)m)p0fW<
zmaz%YCuh^YiwMhdmSHQ{)lQ)$?SXc=5HMyJOQup%YYCe)hTsq%lOqol3<IINOh1O8
zidhGSAWx~IZ$XI4?lwyO1Oyu^J-lv&r~sWfeii0SgkT*okU7Ap^C0<)LDG+n&DscB
zS}~9VF{kDOvW%mn6O36IW08ii6&<taS<+Dzr4`Ks-H_0YWBkH^a1|PqKtBcL3bAA*
z;>XHa`f6+irBD*tMP57wyf_Bvv$9$j0?nvksqHgA$ChuOb|%G+|F{vjuU<kgpmfsc
z@4^;IlaMV%^dkxc7QRU8h|;<XM5y|5+Y93(t|z5W7r;@%P_O~*cg6h{6hf}=z`ais
z;Epg%V+5XM$+ZPLklGd=Nt08M3e{1*Kp9%}u|gl*aVQyUfPmr<WUbhhs#%V%uzZ4=
z4`Mo&lL#}p={dv|;f{p)Qd%&%>(!vNJ!dSZ5?AH0RNBMo-F9g;YD3p7x)U|#jq8d>
z)OC2*zwl$iifJs=Pk~04ImXk#xQ@_-8(-?EJ&lp-di<1!S@`kM&}qL;tPd^0wN2`g
zYa5M8=M|5YAZG5eTy4fF(*DNyvt0s8b@VCZzs~<@%B}w0-pbv!-(KsZf8A>v_gc@N
zExLa6cfWu0r-u8}jnb_aKdP;J>C3H_f^VPXn9XfGsNeThV5!so!Tz)l@q-$G*m*wv
z2>5#*w8XU3%=EKBvrCduLN%xmEF;}^x7lq~8*U;>_~}?`dpYO?ua0)@MjBC#NQ1%o
z{r=!sUtP5~_6KK(v~&A8d_8RqUXX5!JBd4Oq}ibJQRk*wu>bb<{5gFeefzF<bXOyN
z3NG89M74d1Y;z~MbzY6ht$yCxzHyWei<H~jzSYk=_0b>muzpG*xUVI6+iP@hFM0-V
z{})90YtW*%>!<}Z90m6JH@&twMgUcelzu#AT29@MA}wm8aE|%t-y1uEFSoi7Xp5V6
zN3Mk>EK8_cz1>`kv2ZB#sPlffifii*oA0_w5e3n-Fwqq?&U(nBi|A>huhmB)s800Q
z>o93@V%F%x0f54ITk-ci_zWhn`%W!Q7XLlaNd5j`pDt2#Hmaius3*s_qo8gTw_WYH
zeZg^RS4Ca%^`no~chU6->A$gG?Xx4gp4w0!6cXk8piQox>T9?E?LXP5<+O_SE7ZuJ
z!w<3ltvY&kQOq~$=opoabFe|j`D23Zx9`_6+OBKTz45}z*HwGE$NpLN+x4k(tL<P6
zf@=!CYy6|{cW}hR`S-rqu<qs3ZXdv48RM2(ZbaSixf?P%GHw5Svftux)yw|Es>3d|
z4BFec^>+h#(D<Lc*l*_u+8xH5mVy^hRKZI;+*;O%3TB>%Ng*<VJqRDAI6!4QR`7Ki
zHk>gqPzsr0B}bb1IlBS1iI$5?+_*4QHQfZ>DlB9SJDaF#X7IR;Oh>XX)1gZ+I9%@|
zEg0cONpO(oV9QaM&;;iZosCS<El{*1veflL!7&hEOAB-EX>3=k7}lGqqf}tZAd|*%
zN?kQc7?SKuvKOUMUww^ZskV--Nl6pZpe{ajbr@=2ILR@5&#`2jg2mz#!OQbpzwBhQ
zT*DIAC*Q*m7%YX<s4YFT;viEvRO0%w{q1kMtr|~X%G#102dXem`wC7kfSo}$!Ew&L
zh5;>^_4Wk!<=B-mvPt7@1lm!KLqlV&P$h*RjbS^}K7}P|wXbVRp)X38Ci-Jh$7p#j
z!KeGAC9#&E6W9&AfUYQ1X#_836356D&<<Rs32L1(ytN}x)5LQgSZ$opmEe`lk*CNl
zohNt>5I1;o<J>)4A@=iB^O&O-5?ieLFFV~l_ekTaWv{4)^lY6Yk%dy7tukKRYB$EZ
z=kqvNhPkFx?u){lbgZ5|Es>SR?I!FPojf;P@W!`71Edh79Gn_j%1fx|zUjTErTjRy
z)VG_)Sw7=bqLFny^R%P!n&2EdR#ZP>u0v<xZev1O;TmBo;kiqq!;bog@e+Kd32$>I
zPAVNUk1bPl()zAv{p7VtcmK5PU#0^}ZKiu4#$Kz`bE3so7+D%^%2ukboFcegLHybl
z2+n(%(Z8EI_ti^yFPEUVfF{oL^t3fMH`~sgI~n{;_W--?OyoXrRb@QzdXr12$Y{L2
zwCLVay5V|&)J_Swp+4EI5EP;0ReKksv^d5U*sLTAb)<e$rkN2uh1kZyOeNXwnk*<?
zMtDL(R;CA(^OcolT8IiidAHvhfh}Z{BI{JkFb|FrNrcdrh;Jh+&J(*z>@lcpF13%7
zA%>VZ9;kKzT8{z^P+r3mq@Mcjb2YXf#sxhftq8R-gpl6F$xMO@QOFFzC(n0f*U7+h
z`kRy8cDirgo`juYoXI<eJ5LiiQ}`|*9A{c5EIT{R(#1i=wbg<dyUDq#a8O(&7`hdf
zi|vJp)<QJ$K?2Iv{va84GVr)2apo>q7xH+x|LxP2whn8?tEc+K?~IcJY!(A7df@Qb
zl)Vj`##3axxeWWl(9ArH5{Y&i3ZP5`#-G1X?F5aSAL4o!*!K9P6}t$$<I(Lo?m8<9
zNQM!fwl30k?SwYxoS+{y1Z*=w!lYE&QJZ6jx90(kn6Q5C?<m4a><*J$b0(09_JOdq
z!_(7P2DJqDR{U6L2!NAd!rDp8m4>sSkO{wn+jIr*aEN{!f<a;&UbX-GXrGmrWLOc7
z-*Zs)JXV`!7xBn$B>_kY<A1uD`@q<tXIMVtQbh_jU0XSU=fm*o=RtcTZnaA{=SfJh
z2BanfAvtYr;PoCQQOVQWvh6(dNK0J*2!cc&+78ZxY&E(@0t~Y&0DC*YslB|Xko!1e
z|L|VFt)`^eUwyUO@snK!G(F8ToWcWKO%|r~wPky=IZVuHf=hc=4NcE!yw52(hSKr0
zOmKhm*e8LR4OT9NG;NJAa(qBLLvvv65N<U%UwTLi(#*L1*-N!n2Xv>OXX9sR!aKOO
zrZ$q2EXCVFGB9=XT*Tqnn&#Ll@()x}_f3Ro0gj{;Fn9YTxjqNwzAp(s?wTcNRQme*
z?1?9yaG$|3!S-a{#DDgDrOkmaYUS;vvXz{4<zf<ujA=TC-p7RN!aMeU8(dDjp+r#E
zk5+YXM*(j69Q(@NLcDtf+-9Z%zAPk<Ux6JYn@byS?LsuSB?Lnl`jfBhD-Lqf@i0He
zvCr(tu`b%pCnw&wVHr5xI%c_r(AMnF=a>MczZapllZRjjX}!Kfoz_k?E-*`50hSIk
z5mlt&&GV@KK8yyBG-TQHwB3VdQeTsA-#S?6gpO!&+J+X$=0m@LDim;@--o8=Fc19>
z9^hBbj94K_MHk7RIJ2B!-+7|kk{Bjo+A5g5N^YS<4LJz3XMtj*C9a_<P1@I=s)qR>
z-i~$7+SkajSP!0T7U49?a;~T-*S@|tkFf%53y_v(Tt2`TcI6}3QtfL{&+I5kv7f@D
zRe<&vhh~AicbdX9T1NuR8((|0*e#Pv5U4%ivTC?LUwxzqzN?UZ<3J%aHPXOBHbR!4
zWjv|heX11gamL;z9hj0%XnQl7+)45cDluyk_`Nx_;qN_D;pl}rX5;NoUaYYU(vWq)
z&Go(Ks+>o71h~d!IM6P@6*jiQL`PsW1A0vWevyKQQ(n{Ug{cmGe~^3Bz{C`gwxK-7
zo`+@Pv7JTM2(!fvG`cc8&MQ2N`|#-Q!E=2BeznE4kp1v*DQpyDmXVci-+hdJqlKuH
zLzD9^nSk24w(H!pGM>R|o{>Xn{Z&9n{+q+)C`|!R{wzW|2MEs!%Tc|ol$hkn-6eJy
z!I#ZTtDhW?E#L|p5V9{GD6>a+bN;6Qy+A_0;{%XDrdcxBol%7AliRZ`o$>tEkxEO(
zD?LS9@(e3d<j9^9``W&I`|+_p8$httWs^}!<`EKuj4`$?5Ms)~HG=RJV@4BWw!fhe
z{xzNx0MlD@<L#H1{QX_FVPHc(Zqr@tL;UA~EeZV?NQ+V$WDoZM06+jqL_t)MR`nNu
z@fTKJUJiRgpy+9zT=Z|C0cAkfq6D0p|2b$&O1&inH`=XThsWtE+)48H3u@2@dc9c>
z5fFkR;j0i}82~ZoP>~bipvvSR@w_MV<{E{BPU`joORQAh>|4>Gra+dTU<tv?7Oh8Y
zrobzJ3*^y8Jet=e>iru{V^$2#NgCG>K-Mc=GcF5DE}Bvt%asg-$K?DP;MH6!1P$It
zI8XxDRm`Lo)25bc3kb2xKrg!JLoIYJB|s}Ofj^9~M62hmo{1I_BH!RDeT_qlDCJl!
zWrb1GbqaSik6ORR^7jUL{KUFC%kNy;8AT&mW6}$ae-f1ZVqAi4<pIrwDM1Vu=}^(+
z!#;8D`Y?on3$~B+V5qfL0uD_qq33UeX%D;=5=@J&0w5AvZsap*cEW%<4PRACDAgWE
zOG=AhLy%9nOxLWA(4?}6K$x+gUKq1#SQa>?Tc*_NI~q+c<A~m9kMAJSDnf7+A(w|B
zR&K>wyt2aHtuTi9XjFPU)Su(PMpBu;)u=DO{9wo`c?mV-gCk>XN|97$g_(}GOHY$1
zt(6g{jGHG0(`3v92?)CpUP{^E*|J$~CSnR$XH1gNihG&dGmKk2K;aCYi7wb}YtVYu
zXXfl(lFur?VIF3N6z=UD+IkJ(Wu<c)B2r*Gnv3vvA~lrSev^<h)@<%xO<@PC1U#&0
zimN=2S|MzOA}OA8Y8lS$h1UyOA~18S2*>i&b^Gz@0gg|$ml|_nbtTOinS-hkx((z?
z9DsN2IO8nH%qTWn5mqWaTo?Hr+W<E|x2J$KZr~IV)Q=!|(-4p18{1tBXB%3!@@+0M
z?zKc;<imo3n(R2!wB!^lXo}@#A=+gwT6PupDphM`N@R+<?+@Fb`|c(DBp1lDurWw|
zuv}hYQfDDBQn1nR47h=m!B)#b2{qYB2GAB1k|GLY*$YIUt9874^=r`iC!J0I^j*b$
z@<%ozolrD09-g4~1J3w>I#`;ZY-}=)3mAG64eIb#G(DD~vUN#VBr(Z!iv#KxVU^Y#
z^^0s+n#isV&Lf+PY(1h(zQLPy8=z55B6Uvfi<C}w6TKDHUteVN5ga6uC7eL1bkU8I
z>_D=m$PT1_CnJ#8W!0s>A=&7h?Ip?CBxHowb?YG%tU`FSIcAa`U>UUogLYuQQ{GWg
z$>@GWpIg{1*-(7nG%m{s(goTP1S3h95{iuevJEOJshlS1Oa$FCwWs22SbVDA>W}V-
z#)zLA#gF+oHm7VPpXjhfd!2*O)_l_SMjxy5JID;5x^@veW+5b%CQbd5EiSs~*fp+2
zm^VR6WZx1oWC&>@I-D@}paLedsC<04oUN5#!EYJjHTJ=n>Y9Z))V1k40!9t&QwU2v
ztGYKD9`#%2Qw;1yK*>7^ks9Y&pJvd-{Uu=X1Okf&+<ntISLmbqqqJ|*u4L=V(u<h3
zQMeq<v?!Vc1Csqr<H}|SH_?3;#!+J+dL!+NF8=<?W~lxtnQT7St$P)WZ*=X4=lkze
zV97WcjfmH<cz12^ojJ(Ep(P*B#KwcKeZhkv8ZA8t?&)_AzxJu<_IMvp@IBb39eNmC
z;k(YK2P^s}1rth-WiZ*bypvH^4~3sFftM!o5W5oubTB$L@{=PX=Zy5ldw3)`d^_r+
z&aH{7<8CW#@O^a;9QAygI=?SER@b1ScKz4{*P*S!QF>-?-=|5YM?5fItEL+deycbB
z9Jl(aeyY~3e(Ag4=DXzYVX$2X1bx$X6<^Q&U{mlrIHv}|-w!t?{MHzF!4`e2Kkvph
zvBBRLol|vnoQnPiqY!D|Z4LUadTy*ZkLvibRuy+l&_vK)<T(G~#)$pFa}*g14)SB?
zt|3xc+vJ>)5OI8N2s`oZ>-_T2N}#EzEp=Pht@Gb@Pb1f;dh$NX-s$V;(_M>>)tBIZ
z*PiNYf8<i!myKtOHr<%3(nDT;rvghi(@(UAe4IY#-O~inSVty=dr`Q@`_^@_U;jnl
z4>s{zRotIQUEkKG<Jl{1QJ~se^))^CjZmcJ+uDva7W;^ez;87Ix9iKl)uErh@@<nN
z(%dqJlonjK*ZXIjW~EobRj7vFgH}aMqbltLNKaxYT*g4zLZGuRf*%a9cE5We6EoV9
zYy|_@eC}2zC+F;!r+VyH=Lc<6u`2gkn3&4^^HHT6n@1R1*Vxz$nR+Has_Jd~da)5r
zMEK0bW+CXsy_7(!k+8URs{^ez5JEE~gqXxDD9IGm@dC#Sm_WOc@Cvk*=z6#JKqQQ}
zgq8LM=TPvtzt>*lkSP09|6G66r>J(Kzkk~HqmTFJir#j&@0<Iu`B*yaG$<V(z3)!P
z`EzXEcc<;qZTbBl^%>nJ-#;(qfg9cLHZWe>>h7Dc|LfQQ9+(VV_+Wi7_6ky<G*i7(
zYj{@D?7g#tmH~S~C~+BH_;HKF%XAel8yYfc<`-}|Phy~)c7exN!A!jgen`en?2GoJ
zpSL^8<?HQZF5!X#l{<UJ?Of-yGj0kB@ap@0R#lxxOd%c{>{3gjd%+E!h)rz*?-~{(
z>%vXk47{NadKe@p0(A@<4l#JxoF);RACS%}4x@WN?s~B(h`J^Zqj_`Zn7z{ycHHmV
zs<LeuF7^+A0N?{9L(jO4gKfHmJGvLwoYJA4yfSR#2-$YLKUvx7mbS8B|LebYz|SrW
z87ajWm*W`O<+vEf6|Zyk%Tok4s0hQhur)JN6V~0`1GX*A{l4CE1)&M;G+x0|C*HNB
z+)`4I!41~l?u`0zI6KB+qF9{A#scgkBRz!Z3AC@-$#LuI?!^F~Vf{Cn>_XE`D=sf1
zb`jpOt}aVT%fQ1sXs^F@3h!#6vnvkt_2BH7agG)(-)@P-K%{-5-yGytJ^JLKa{r>g
z`)&8*Q|;bs+neicKGwY$B4ZG}?@q`0zHi=lr|r>g`TZaD8Qmt|KQHBh3oO}j^tSEn
z?6hxx``h;P(@#VFlH=YwT6c>dy#yoUAO^|Y{0zr@kcl^`8vtY@tOa=(s}<y2QIUh!
zE6&c~)e$&S;J<VXr%ER!j=++fczYAAEEztme6Rrh)9V;FL!^?zrH$cS2Id?L-N!Bt
z0Z^Q?n~>;bk}RSKeA1<^d3y;r{Z+h?gCw4ip;|!DVFcGap12YiAKpQbw1UA}#4sc;
z;zb06%hQJzG=+im8lIv)w7}WvHQ)kVFP++fCvbFh%KkS5vZ!zllqcKY{H6=dX~j;0
zAya0UWIRg1e+Wumw+|3Lt!QjF>E9%8!F{CqN<xd9CD2)U8i6x`7h1C2by;@)+K{yj
zEE0^I#Ch>ZVJngMO7W2I{OX_W=Wm^`^z3{a?P<k@-%oDBu$5Q9(9qgsC3QP(rvIw_
z__Y%lt5>b%Qj=Xncy+d2xB0k`)mD@QGZvwB?6ueI)i;h?Zc&MibzirW=Wf{ELkDbj
zq{n{rpZ-r9TF$h*<Q4nLzr10)4m<+BW6*y5<G;5S@K+7nw!3@eUrwKwNfPVl!!rT_
zp5OuY$1e?%L5M&8_~Up{aJN!|cMfVY2FXGM)+~TOv2vy?$Ju{9-ea|O1y18q-7<Kg
zuA@aw;TA5yOIXRIyw-(40Zf;GM{O&bOLYMNM<^ELeOX6334E%vi05euVNef-rUVVH
z7hFv`M({LTCHV-j6rc|IY3nT4rW`81_2!IiBX^?ClZ?wM9)Wv>Jbp=Fe-73qli6m*
z)(bLie;J-jmYRvU)TL<^;MJN%<tr}AvOQHvcA9*GD&qY5-@n^v33Idd>iH3?CRJG;
zu!Ir3h>w#qF%IIt>Xa0;n<bk@+o}TKDs=lO+G8RH+E6n5T@VMxL2I)e9N5*XW72(`
zCX<f_w!a>V8H8j$-Y7u@R8kRK<t5}}1gyIhKV!99>VZ4pnF3OgjF#Hl-DTTwYwu{R
zhc0Kustz2q)sbd<2O;|X$M)Jp_cZ{l!}ijPPm?So85)}u49APMXWxEXNl3SZr77|=
zUbec1dJ7NqS<y3l?5ocm1l+lb3xC>*i;FFckX>5TG8VcWEkQm<Z*`4ftUMee@W3@J
z2`s%~v$6nFKL5O1CipilyKu?R+bgFBEsK;+N?v-NFa)tKC~M0U8jEvnbNo2~okDl-
z1tT;9S{g`+3*`|`OYJG+aSF$^q9wfo<&50;rCEu<A!gCGMD-%qI~-WJz(FcO#R5YF
zNO-xk<5~KhK)V@dn{Zm|xc>#*%VAl!jv#0S15*n4RBq@bpcOd?lmT4tg@6LHU6{}s
z5C{0OSpK9T<d*RUEr8EZ-xcm7+L%nXkAUk@+A5{Uss!YyEH^ViE@tSvj?V{&C71mg
zc%d?cnz+H{SZ*pcy$cax)~~S~{?%m&_h53(0}-tZLkqN_7(of<H8imvw91*aOe?6|
z0!XyTjvl{Y)A3n^ax9VQdJH@j;Uj2|zdYV->!A|c-ni9TuJ_n#R*h9xm0L?|k0Vgb
zEhw~pY-uxb@M-0jBiuohU+rKY5Qdx*XnTO7Wae8aGnX)vDQJR*SUToH9!ScttTMuY
zO7OHsUQoK(u@W1B-*N;VIxnH{JM%&?QsiEiu+l4;x$KsWiXjuiThu1bXvtpZn9>1}
zqT}SkPXyT40myC^a=k5N7gkHcUI_&uSF!5oSP4<hAGwKzUlC@CY7WyPr?Kif4;tT9
zPj(ocJ1}VLczq9)20k{F2R3h<N9PpYM{aC+(^wcgjF8$^3C<sBM8DluZTH`RchEQ0
z7WPO*u5eGG6I>7b87Cd$TIG)fTQcBTSJ){k9cgOngR(s*0b10x_Vskz0JO_Hc5JuB
z@DTR7KC7$Wf`CaOJr|lMV=^~4?_wu|w$vZu^wc(kq4R4=FIV{@f-n)SPUEDQHeG@4
zNeChBQj`U~x$9qzt-tK(?{_`mV|LuFjk|6CnC9HQzf=D-=KgMdTpO|dpKJtvrvmGf
zbvbs_=XnGqB%C;9#{0=FjW~L$OBncV8-gKnm_9bjMgtKc9T9XJ%|)8^)%1eVS=FY#
zJFF6o`R%S<Ro--*e!3EAU*9+PFQ^b~zx9&wTVtR1YXpK<_5Et?w`fN<Tn6~y#@~LR
z!cnF%M0@Q+ODg?VaStP(69jlFK=!vO8xLO8L88`(-X8tkYfw_}I?>d|e;bGc=Ws}4
z+P_oLb@*%9ygz!om<?3?r>`4b$8WpWPfv}a`Wx5CcijtVO+x(tHbt6g^f6NeqeqXU
zZ%ciauy7i!&KKF?HQZodpgH<&N<6C~IpX(kd`4||bySJs_D6l{dUY-dDD6|xcTfA`
zw{Jc!s2MpoKm4)klkcCj-k=0prnbk*XEy>5+=it~(ZmRDxE`RurlrS1_j-&tvsX^_
zlQKOK19_?~V>9~onSPQ)%(xh_LR|eFKm*>wAl!%hei%$sEr#@dLUqJ%zC`?>!a5{~
z6nCf4jFAbvdl>V@^)4zBF`g;}Wdg{=P1=<mOBgO0#KlH1NLvz*qUFG-MJ!t%@n_3G
z8Kwzd4LMJp!f6!Np$RX+s%x6WQQ#Ip2slv+%W)7;s|2Ac>Wk`#@*|z_6<x3BXOv>z
z(KzE>RNphOgNc=(`R0V(7+$g(G6Y?^HfkLNI~HKeQfP^mwqh8V=9meL^U54eM;sNi
z!c1l{+DpjIL_#-wSd4`4*-JwNe#1Er&9Sy?OcdIb%u=(P=>Kj4jtdFi)e=^jm|5F7
zN|i|jktX>#Ho!FE=~lqO2^u!T@#i{bt(Mru>o<Tj+?cTjjQA5=tHN^>lPC<G##2ad
zr8iSplVdJbp9v)r<%%K}hOv>IB91T-)`CpNNMn8y>KkFX(pgd}E4R4DG<XBUQ+Ae^
zPQ{R_Epb|j{Z*7Ql2A^TaJ|<%!Y+nR0rHAzbk79K-_ut{T}<PG#)|9kp+8!_YRN3h
z8NF44Feq%B^kEVzpEiPv^N4!rr*G$4Cy8IgF3L004R$cZm^vz-&u-3ssat;FUP3Rd
z6up1->Q!rOY=j>i7IhvH39m_*2^1V$!X(7m<^qVt%<$TIT=7`e#cc2ahWI0U%dIw#
zhlZfg*G~6h^j@(V(rr!g@DK1Wjt4grBX}`Fszb>q@Y?MnhOY&BjxYhDrKFN+Ll`_l
z5{t3s0lQ2<u;@DSNWRbkOT;qtF@N@z8rUSJ$WoJJFT=3#K2U}s1nOyE6&=77ws5}v
z1f5P{d>1ZDS!nju=YiV-2Q|KGPi@Ju5C;6#t_iz_u&EW@4e$%ukzPJr3FAVNeQ<HW
z-szjOry2`v7-2ENTx$@e4Z`6D{Vplaa}TJD?e78&=o$)RQ%bhOTe9r}2J<ek4Iw6L
zEs%*tc(6X`nX^Zs!07=F5l67Eyq}p&t`xrSYsj%)sA0t8G$Pgkpd4ke5WEK;QWl9i
z{`~nWH)-F$GK5gcv~0ND-y;22Ig~a6>82nE6?i?f6lXga%P}TuTX+=~00lV%a-;Ne
z-s$-`dyDHDWQ_I^NPG><TS0Z9y}T<ABmc1d^kUeh82i1Qxz+`9!B0*NSxtEcUd441
zg>diCU^)=oRg7;MFq3z$g%MP%P|d(s&pjFg#xlqKDFrTa9`CE14BJXbI6_~Z-H~f&
zuZ`fvBt{bqaam?Oq=U-M$UPZZa0y8Ya8C3PFg-Pk;7=K~?os&A5TIFjK%udR$zlyo
z{sgP5f@&yX*-qb_#(qbtIJ~oBtiTXy-My25iox7L*OC}VEGU9%5dnRc@%{Y+WsFIx
zdztxVryHEupHDpk4}3`|fu+*h+iQ<L`l$O1t|Hi;g4gZOU#PN~@hJyOP^{akSO}mi
zSwx7b=uIdta+PM>?Y&Tq{JUqX$nG*>i>Yb$_5B5Qs4)+yK?d1$=Ir@{74~d>$X;(5
zM{uPA{9LtOylG#5yv(2&vH>Q?%TH8TW%{bUMcS@!JW*-Yc<&@Ig{^YI#%KaPO!PA(
zz)0l521&uhpLY*sn0~E_9RX152uoFFHe6N-*3G0C26?Z2g$RZg1mWJsB73$W9o{8f
z3qiOj3uWS0;tgF?kZs?3tk{mBVdNEN*$<v9b#HiGXFNviKZ0JS4lJ4Crr$b0WSLAL
zflt5tRHb#m!tm>MjOO&=Dx6kz6<KJgNw#N4vAwV}4^JvuBV#6QOkT(DJzry`2!p?V
zd%%hi3~Oi~!oW(4d0V_<Xd)A^8e|fHynyF+ifa&6j(EDheWcX7`p50k@UqndF_9P2
zsf+Y;Cjz?^SjcK9&3-_7vlhI$8E`qhgs?(QbKcJ~1a%k>smcW7P=qEu3)+4L4d&%X
zORb&^L*IX<5^_I;E1<Ne8uOh{EQNDx4}rqcY~v96xgdeR^;9|cEzSwOO2EcK+bfC2
zduTpah=}Oo_+swmIG^9*B~gfQIf?-K!>4NPAgSa|q78oau}a%Ys<$CtJQu**DY0x5
z9_S({ezL&E4G&GB;X{W5y-W_Q5TisXjIG4mcb~4X3NU>Kx8}GP=@UDO>;(ekMTF=|
z@Q4QAV}I|<2t05N%LhBx*4A1V$Y^17RHR+0Eb+E{r)kuou;(UO9t^HJ`Vg&58fyb@
z*ItO`-aOl5Cs`ILe_#X4w*T*F7eL6IAV8U>l-x5pA}C%x*#q7w<R10`Chj6jhivFU
z6!|`m$4r@Qr22<4(rkb6qWzruec?5G9A2U#=uZ;iZF=q8kn_kX&DJ;%_AEIO^I0~%
z-!fz~B)({53Di&0Q89g@@VI4(Nc*(Z)3Q)+d?ox6vq3y;q3WOVHBNA0VuDO%BBinf
z(sJf7xd=y@B<EVdWK{$jR-&Xd%alaCdOONe?B{4m&l6$)3;NndzQCOj%~c_EdXYN2
zDzjXKe-`S(8yEWQCc?f9$^6T+eNfRrH4=)qpB(Fgsw8Cp;|pch%<-j+BbP%OwsrfT
zU#?^61`dp|$t8bbG6JW;vA?3NMPM8CFqMW_ChK~)lNOAwyP|8UEXlSB1n_@8L7qkE
zc9PK=k@uFs7)&Fmv{ao3ZlNOWXcGIR9>hY<Ckh=grvAe*?iqqKi%v~*P2!+ZyDKay
z#U8pwUu8?0CJJIF!bX$>N(-k_j3BQ^u;qa>3-N*~K!8mFQ4vK<I+Qg>s^aa{cRT1q
zygj`=(_TH%1*kS;FY=N*1<g(a<FgQlR_a<Kl^p%Z&KJ|dic3YehBiAsxnlqLb`K<j
zX_la6Cz|1*`ZQi{s}9QI4h<e@_ciwC{*1sw;U)CTL|sxZ2=^vz4%xiPAxLyKA<+X*
zV)rQqP%0aW>`$Wa71Fx~K_QT(Fii#QN993p2_Os7jylBp$qTQMC5&iNJXgFJ6V#5h
zoIvRdY$&3DIZ3LnJV^1xE-DCtC~^DYSqdS5a$taz-AMM45W+~v{3MnLqBc>=uncT3
zqBapXl92RzX2r8DOqA?Jf=pnwV{33e*`YMa)gM6~B<y5|S)kn+CRq+PJK1Ms43`!o
zxz!0Z^DM$x+*b-gszlf*C6@%CFkNDm(3tA_#}StCP>5uqIf)iHgFdMZ(Xc4Bm$<5i
z(Fl@Q?qzTd;wKjafR2}aPE<VN^^$E*<K*rYy;mLaY)x}tM3tj`4#q$~1v`;oQS76r
zQ3OR%P_*`on#WPQP)D{l*@~tlu+WU$_^1zDhhjy=^MxGZ+M)iTjbz4$;lnNmUq0L2
zSaN(jq>S;5dm}<h=TZqq*nM@^A%)y8&ve^`hfC)C*<Al9*Y9#nB6W2h-Ghyy@4D{D
zXRw{IKroH-%w;jw>Z5xh@jU4KVtCz(h8rvQe*}J~0_%?SGCWzHPGsYuW1{MLk+9OF
zmAy+7T*Xf?=D6GLHu^to(*vZ*rAaU2xF(G&G%hAc_lrN8aC)GXS}ctHNCs(v;~k$A
z$LM&K=r(i=hupSZebYu&cg9=xZk&_vZhvryWUCWSktST*+NIypIMud<hC5H>7;Tdm
zE)}D<l9_6LO7Qxz(YgFNbgZK+;Zp)u`y_Dq8cbfkYniAz+AhK1>O}gWj%ZBWF&w1h
zbbhM3qCWcTRNZI{nBS*#Y^?O$F*;7|s|^+PS%S<95}gP66VR$qN7tb-QcSh#Y#vXw
zNACdF2Etc@Sog<|U9dkmmm5^-^S}G|q5DZ?jit`zk8^eT;rpkSw9R!ta_!OM>Bn8y
zr#{5W{U3qfsld|Bj@{V%e^X;E{_aOWLN6#ABF;%VFUIujM?~Kp>jiHG)sD@P$N;$y
z-Y37i5-qI!{S}ktHF&y})KqVOU)l$zU>FE6Im!<5E}3&)IoapvZGD|j@~I%}pw&Ac
zEiGV%C7|eC6rQuW1P@-YRov^>U?<gbs}sX*3*N*l{Jz&RiMwC)%qm^js3Op_k3Jkj
zb}#Ub&1DpWzm}*9_34gq<ExI>0yfvz#E=I}P=qmg0zRS$DlrOFWKxF3yOgL~cUz4G
z#;7N%KhZm9=N7DorMEBZT3U0CA>b#Xg1(ocXX7k}en>X%SQJGu4h%yMk-HW3@$GYc
z_KPzEHZU1DdhS+t^LBYjbqfP6E6}tyzEfFz_JXc>EpTHWUpBr|@78`2N8t1j!>-CL
zLFj&d>Ha)$qx;=PST{m9tZ!h_{suOK@8J2`q79U>(MkK!n|<~_{o!`Y;rH>2gK%P}
z+3t!2$G))6gMJF{oxC(N6EoI^(JF*tAqj8Z7WlC*TpzO(=w+TDtITDf09e9ZT%iJ{
z(+Dk@<somz7F0?+UVUDo9f##1Cnv+6-I;I0!(sb>PmWjx$qPov2=jY83#}@bj51(@
zYTy~)j@@Jx%+v?qq%IRMoe39x9Y*6fptKpm6~9Xi3s~a1foklAN4*1gcoMcFW%`kq
zD;bAD^8kipVj^EZJzzV@%<}@7Xqv8%*j0XiWq+|%aqiR5)s&FzAbD+t3@vkZfI#UK
zQgyup|NSJB^ToZTaC#GZgGY6Z(3oz)77)f+T*tC-R@mO2+f!tf7|-Ws7hJ4lSy`4P
z5R2H00l5H=;afK*?J(D;H1|yiY0PkLNnKl>WmzP+XyUx37{QNotyRU@_Bhar_O5Xt
z9;<c)PnlRW#(+FLv%ARgiT6RRbK=I7?ZWWBwy<bJVCD+2Q7Nm@3>kc`!FI6|FQ@Qv
zIT*T?5oVp>?kS)nBh*bGv&@O^IeUC-9{16J7mQjQLEF24Fr49@X2VBcNdkr@?qhvb
zuDcg!$g=Zuvfs$&b`2`xLwFd+N2iTkMfMbY^uKt&%Wwd)9EH9oH`66636GP(X>ie=
z+*9oG2fTiE!16Hg7l7Jo!v?ytkOUV$LE~gs%EIoI9*Nl;M))kmC0aeU#1{Hc0-yUL
z_k5PI>qKx>5@5eVF#C04PY-S@biw-r0FYOB=0<TYNcwcb{lAT8WSZx8f<S+)wAK$q
z=Lq*<n7J^4gC+}p`gxoHrSRc@*;geA|A8W*@SsXGz041P_(MB<_;7??M#fH3)njay
zw@3J@Vjh}wIH~@D>>vra^_8e05B$`7c<r9rTZXZjtS<9&_N&$@dz9=YCoc3`*9?5+
z;CaTGbh3P%hI>lMD>CEA*#^skIIx>}nA&*Di*cb4J5@8<TM`Kt7GV~6>joJ{NE6kL
zd%KQoFye+9MYGFhQWfChRsw<7F2Xql7u5|O^2fL5+O@VJ>zIqT9pss6$6J)mq)sQ0
zT396+k1il=x-smwlQl<5Nxv{#B+=;TnX)VJcWuK9HB5d)B@k%D`zFw4EBhMZ{T>~e
zB%#5gL-t>0OymWu0VpX_!)t)H9wWt=a_Ef#S2%^+{W|SVBMdQbSv*E$dH)jWLl&<8
zP9{PlF_lM8b=upcBpXC9To!c=6guKz&m@V1l2DvNAgy3%p9M#ijA3Y+bYkKGJBu@d
zQ;}#P$uhVfJqV~7AQIKNN%oTyeYVU*%!voEOZJv>pbM{^9s=f&YfA|5HiYMy4k&zh
zS<C~zY=`dW@rG<`>w<NI>_wrJRfH3XJ#Y#P0Ux=*y;{LhP(iW|{4aJC&aYyi9;u59
z_A1FP3V;`^tSlkqCTV-#u}Eye6D)hy0*(jerre76@;&fir%0oA2mxQjxDEn4Nk!<S
zVZ9gJp^0%k0~eZvTLN~$hQcI!kA8lY7sX&dsmIW;mU$sv1pC*8;7Q_z04au@ZV$r{
zGUJ>M0-g%1C0n9`+@*QmfLOAf`#g)MIg1xmKOWWXJP(ryrvLa>ua$6J9S~KX@1J!E
zOSHcjtV}0DypZvHr)kJ8l60aUul68)sK;AfOahAF#qJlD4{FvvJm-1f8kS!MCh@@U
z+O-Q0TL4v%a8Xe*g%Tddd6qMSFe|L{Hf!X;J4PbaQ69!Eyn`CREnT=VimN@*P5>$_
zz$Gngj*^j<p@keMMH>P{l>)eMl*Dm@7GxmA<`6Ks%JT*_O9G!S@lX!))=pVTvV+_6
zp>7(rF5ZrMm~{-#FDwLU(9Mvch9$}Vt@$=THf}8d5i4+yE4`Gw`AQ&>$wU@_5Q~FL
zo)C%$!-EHEFcB0t%d#}QeTzK2+Yy5J?U7pZE}z{bg5i;Ed5{i<F+j^(7>t9Gg34Q%
zfd{T@i0n0Ji_?Hdg@bw&t>NV2oDG3LOW+C4p^te??r!Dp<2ti)LskQa*dX3j^)Dah
zi>;N(c9w~&k`1!IzKD!Ixc%c9hiN=!LbZ!RCyDba0qhRm`i2**m>hmRLlf2s@U@8j
z>Q8=FJn)k^mo!#*1_egTQ@E95wxq3DADV(J?U}Tlibgd%N&-{HPg#HjX;41KEM5R|
zuIRYQd4zdC;7ca9OakCG-MO;4X4R9Toy`~vL7pUygMjP<Tk>%}Oygk%c921;DuKu-
zr9e2UNEiaP)_F;F%oAwBJHfB1l=G5n?Vm$i%>bttviAU^<`Lmg%*#lGiLfqOEmhAU
zFy&RgLEcNwjE5RQ3zTIE0PT%lpu}9meqI(z&$h@YAE?d1^QioZ$vguvZ2+whJyK<U
zrtPXqxAUZ2Q%OYNy@WQZ^l2*y_B|CT4pWwsodFFIFBi_gy(W!&Im<Jh2%i~rNC?1y
zdfGMdrT+84H7p4%skNe)dGW;;-KSS`^<XL;`cF@hqYXyUM3_AL;C9+WLQJ{b70x31
z7kQd4!^(L^D*yIEOQqf~NyD`jjYJ7iXHd)aPadjasAt|p^FT6MBPjV%9x9YCG>Sww
zIcIqzDlB9f`xG8`+=8%}g1Kr7uaJl}%`u2}5ON{B5nUksLo_tW*j1Js^dgnFs=RC0
zd0Pr>Si?D(BhGudKp!Ohl1PF&2nM5q$s!F?$ukGgti(cEsMK8uv;_pL!g2b6At)q8
zS%BYR*_i?WvzCe8hlZAn3rjetz@0_AEDzrRrJl`X6R}|$#}$AnOQ!>3Nm%5X`pL_e
zOve>Zr*I{Wo!$*fIj_t_@d&li*;T?@pvysU%^*~j7L8fQF`}tawq(B3mc}YYZSh?+
z&y_5PmB{m4@4OwT%VrGGuBanw8_C^yG&3!YQxH57oDy2`j8`eh`MFuLA(KO~3av<V
z(bq?p+;TOQ{>`DyDYH)qp)iN$xB#Eo7U*{_ULCO!VcrlvN(`d*<}vh*ViU@PK1k4#
z0Z{9Lajf!8yiTIfZD7)59+;=kf{+M6+XLb~opD@5n^zh*-ia=SmoklWgB)~1OU^Eq
z$_;3wLU#!(rd)%|;M1;x{i;CA9Y@PvMd)bk2e?1V@}rO)2gBi>CbBPsdm>g0X?&nx
z$RN(kj<j1kcN}A(=QNl5mj+gBj{Yh1NdMA-X(ZB@Ft|7oPKwX1SpDPXX`Xyp%LDZi
zdcmZ@mTIftnmkVH(pSE3(97`K!49Y+jEmmlG8lU|ywY<~B`_j$peCXW<gVSw34{ll
z;4Mesom&p*?e2}4I(XT;)u}I`BdCO83T5ywLuFUoBAeZtdZRw)a&w+f^<S_Gz5N1A
zS8z?v;H=I?impq~n6NxT&2xl<NjQK3_2i1za@b`tp-qQ5;(FzJ*EPF@Q`B>C4}J@3
zq3`;qIucHFgYUFY+v=CbfnBbS+E+UoBLPzbdLG7(ItjWC_g|pn`g`Pz^Xe}RiR)c$
zZ7yn8@qfBLy&QB-*}CP)(bx(n1!BS#y6DD0KXhMR|2a?sTzm9e!Q$$_+S5O^=RVbc
zzAJuG{c&TWoz&I&1>aEjbZvsL=#;uA!Po@##Wkiw1mTbttNyxir=E`0bK%mvBPbmv
z&b6!Yb25w?k-pG|>#I6M(O->?`lbFv7ai-*<Hnwwr1Pu4!uP4RzN_f3uFJRWUo6_E
zA(E!3T!LTr@<0WaAM6j`+F*~LEWTdsGd2RT5r~aI^a$K{8&>e93Eo)IC(?Hz#6uW{
zczHQ(NQ|BFXg_&qGSXB$7<BV-TFSVjrMJJpTs6I*^cGf;h7}P0>CF|q`LsQF0|#$z
z{r*s)5#@^WnQuR|{|+0X&+7%PmK<@|C{iMJs5>0yHu?Jc;hYe>iv8QxgA`Rqg5CdC
z+qyQr&81=KS_K}}SpGIe$y3F6s*KiRTvdL&yOrpEDq*OI|NQxM{jSsc{4_=V|Fms<
zn)Bc3*yuh*f4@^*ZM&B`cRTi8w{s4i=F490yTEeGJ3p$LbVw5|I?gr^*;S0f`|;N7
zs>rm~_F+3axNH@q9UsOUwTqPA4a5jeY7${oICC?MEl3k29>Z&#qXCHrm!oXYq8A><
z7LhD^Vr&L;(EFYz;5QLlOBx2*?by5&s~1KfreesIok>L=!gXg?2r>j?Ua%`269f^9
zX#kIuvO2S@kN}M5-13}>N+lB;#pTu!%P-4y_M~z8sQAMvFiWCkkwJKAj@Y?(`|R+Z
zGTTx>I0^z~ntJkP2^%DO6?H>Q4YIc^;pRV$hbxW{j}_trAI0@A8_YCqCSX_5cRiru
z2J0Y*cW|5p6WDI#eHwtZA~h|=zWzwD3vJ27h%TBMjrAIWU%d8QwvAv{QRqku*?5vN
z(`|QUy0y1YkODrHBp*|@oEowtKof-QS4w+P($H-u3|Ti?F7`E1Q!8crWm2Y>61!Lc
z{6O}+EZNdj?#C>;A8iD^xqpA7`xO2CZfCfcI(IuZw*8(*;Jyp2_uqftom_AQ{}NsT
z_wz0c07>aU2ZXAlI2vT$*ojQo)*<jcOuBY(C!@rW6~Jt81>qKt=S`FGP-Tb!U)<MB
zz!Ea)6a#IzNj%*o5wQRLS8H6P|C=x|Y{RJC-v);ihUTp~iT10DLyq;}PoJx_*@<cU
zyQ6)U#KbJZecuY5%t7D_SD|d#+E8HeV3*#zvfzyBdgYfwW3m;BkfT?|narsc2F_4N
zux!ipoLvHDu)8_~Xu*^Vxc<T6Qkx(KahQpJre)a9A(*leszZ3$s$n9Kp?`D+fyG0e
zgdqB(XKF2(^SzEidJ93xz2Qk*`SZXa((Ii}!w8a8`|n<=wRkW*|MI~AJnh6K;(dyb
zn_*n$FyLbNNAMm$-)Cp>HXdonvq89-*0FPpL#>mE2eXQ66qF#J7{lv$sJ7Re6Ic?E
z|M8XUcJM&C{mJfZ`={623?zpgW-NMe!>{o~%wXtM5{;b|A^J073A_YK5pv_xBe>cL
zXl^Kk5d&aUti(p(0gu3a5m<idu(-HrfBeUP><p02&CR@-10$h=S@X~|_SIzKVVSl^
zYQWQg?->9sy=O;({d)j*M*&IZ*B01!cBk2YIo)Ub;J~WF+cSq|lC-eEWLdRuK2d3{
zz!~1@n79Ao#cF&1!mz_N6@oQ-eurGearQXv^&<e^x;#b_2XIqp2smP_pR`-!@;0#)
zl7><XpmYK4<q%-PVh8}=z63miew1@gF-jysu+m051>mVrJnS(T6p}dhD4@hjT-F`z
ztHw(vo+@BWV1zR4aKi$gv?MDd{n!XztnWWtW`F;S8<tR<2?&*ZeZa3%;<*;~)dM|v
zW>>z&lh$j`)*{PTp0ig-+?oZu0KR|lJ$Us1JZ3;S^Kw<1{rq^p#X;rrIE(@c8Xf}?
z-a?AAI<&d#V7BVY&?;9KU4U~kLaBLh&A$0~IqA=)@H!>fA+$~vEhqQY=2#Zy@yied
z_TUMt6+s}lvjYh3bo#f1=2eKZ{-4jOKWV^2Rvky#J_L9TnTEn#GfqMS72yooeo6j-
zpWWE$zJwzXtd&0Hh5Kco1k}&|^<VAPpZ#n@SgGlBm=CIjog!gqiCccL>|m+){GJlq
z%R~x+O0ETKWeamqk_>`dfl>vqp<M3+ki<>11StlOB6<+<S?vL!`5Q7bpToWE)wof3
zr^c8?TK*-WX*>$nMM)@10cE1pkZvo}Dl;Lp!(F@$7rET^E3`L<ySf`%7-<y3{K&H<
z43-!eR<WJUC>#iUl%o5mz!wSGu4Ua$xVFT~Fo!S}gQ>iXVx&}-oKir3X<)Mc{wNeF
zMD5qXtE6?NU>Qnyim!`NAqOKNC%xdCQn-FC!5#w(mc>%GfH4w`qmcAr^J7!?Pry8q
z<+kVec+S1X`Em%(7H&t70|6v+u>)>lsV!Cpkrg(AF_K3zgl3ilioF$ZtSj{K<;Th)
zE=;krAoJ@XFkENbc8K<JSo)?Suw{djEl$LVBpP!05*r~O3<MMQKCU_9Bn#S%{f&*l
z0~&$P)JrJCtR8Z~37SZ+J_xw46#Pm8f<)U?Hr%)vlm!|TB}oZ*lSQy7%tcBd2-xk4
z+`XeLzfQ9pQ{vMjgxerr9Ck%E90@GB%>^?EV}D9yX9j{u#_K6G15xD42Bvtf9F~Xj
z880GiJS@SRBGhm7e-?oz7=X^JzDj6{Iz~1urMy>~FSRGQMc{51=M}DN;hWIpl=@3C
zahl+_+78_DGNfx5Bp{`w>bNOzN5TTgMx;K=?lz5<scbXCGD+0wUxIw*^j!#amx)H#
zjMh4-<5@0>h%bO91Q#Nxgfue`9T3n~I3^kW=Y+;qs0f`@+rC8iM`Nh=lwwR2H3!6V
zg>csxX{=%;HUbY~1n#@Q3UD<NM7NYTO-6|)VSXG|itk=4^UFzHthUJu=J}H}5oEHL
zm(NcOZ4(8I0*tfKGBojobrC9D^G28z)scX37#+U2<Jl(+3>WOMRT4m&fSRB>r`lJE
ze7NswPeMw4jNY$fh2hbs_UXIpsScp{Yx4Dit-)WlqyDLm#z=>3U~$;yyqp_t2IHvy
z`MyTCrSJY6z8y~$<GQ2@k@IP6UCJ$;T$|nbgYW-;duJ9L$91LYgS`qn3M;XLAPABm
zxQZ0DOBN+rZBMrycDtwB4!6hCG5usmOvFU^Wghy~kMlM$^Effxt#)_YV@tNRxJ6Uq
zBJMj#5c|FZ1r(~VW4`}pC0PVFSuIhLSvN>jR_4u{H*e)VdG0y?`HuvncMtB+tv^(8
z<4WaQ<LZw`;{Dz_A-RzpfzNmZK6Qch8TU8Y{1;>dB+PCX{|he-WMJa4xO{X1QbKNH
z@sDrZpZuO2fqRX>Z8hj#M?b}VdZgWd{v<xoxA$p_W2i_s;AX#!H_GR2kKpR>Bx+t-
z+B!z+n}mZzSw4z}Cd#s=pE$g0uuk5q3}76m${$pe<z*O~m@8awh?qvD857ItXae0x
z;(e%Y7fcLoePO#w<o>;i7&>R=0k}t*A^2J0Iu9!|P>l>F^x;AESLu5fsQtT&H`I6S
zPTZjc6EiH!?^h&fZvU5{@`RP@-`>v`e$N3sp#Jg1W7<xZ<OuvBBk-ws2|vySZ#|p0
zHoUXkSN^PHOjhaYowR>BLtG#R<jv4|h(+?;wGlftv||5_<ODM0T<s1!8r?TO9JD`s
zHqH9`pd~5D!%GG&3eo?~4Iz`A`#3Z)gYzl&ANCa6)t)H~?{J;t^~3Vw2{9&a2m|*z
z5Cc&#%dRHl^(CMYMZ_}3B2hcmHfhU@Qg|+f_^~Vm*#dMaP^&bTIA?*fzH|@_T~ES?
zH;xV2FoyqK1UP44bnhbwcR!hPuHo5SP9rVWV$7~guG!(He3wzCiA*tJ>|DDx7eawD
zZxaOaE`aAc*%h(Z4s5cCkr}f2EZV{5A{)kNo)XAnS-lKBa=<DH#ua13IT!=>5>HtR
zp6yuMgl!SS1Q@SQjQKUh2X+I&kPS^ZEkPav@YWxsPfF2s;wq4ZU;x^aOuN86i;AYD
zGHB;+OxSr|5Z`*K5lU#|I+HdC(?bO~wGd8^90IHrY^=D?Oz>d&eD5RmRS8gvAux6O
z2oewD;k*DHO(TgR6ni+10PF>Ba**sm^9bj2Ff;4|lP6kZ_XbN!FK<F5|2}X89(nMy
zLw56UDgnh0fA~W?bm-6>^fH$SaE*e`5{{{`5b}SFy#h5ZfpMyWmnj3gk3gCqLUppU
z8XzRm@&y>SGa-N%-A9PP<WBHhXQAeJf(#}TXh-Lv@YvZ<WPRO}c15%<fG^LY1%;W!
zP1QxV3f}2A7sdgrf`>xO31ftwB?x&8{L_xAY_zLYf>DX)Ck2>-StGzV(g;c}%UHGl
z{Z5Z{!M`<z7Fb@MYpsq_X3fq4v>ZUVy?18RHqq}bKqp3lSmYD%JTekS0Fz(>K6(Y*
z<`9TIkFoql4~!ICTWP-D__toi+rA%s)ZqA>V;E=yzK}%#u5e*h2(0&@AlY0=>aN}i
z`^Cix^5ZQ!dYNUkxJwWSuHz7B7q1q4Rn187UjqYKV*GZ%%QcJ8I7;AjH>?0-;N;fQ
z2vA;#*{`q6aJ;|*5Ceu_8VChb;QC^0dZE8T@F)St7(u@$u1#9!L=3Hxyn&2mEzpfc
z@MdQabbUBQA}|{W6nwrH#tqP@JqWZaU>Qe9m9_voLM3!JSJ}6l7n3VVf%U+Nnmp)@
zz$04{`U9?|fr`RI8X6j$o8LdCPo;d?2JclK==MHXKAFE@f#6rixQ^^{r50F1J2?fP
zgs@Mn1|cv_`3S6RGMF6NR$`l&oI%|4@@$DNC7%$MARf44vc&A%1f)Tl74<U_Ld<&$
z0UyXjJ1YY^5oW2m9T&eM{FOvP=NAAesHROZY;CIx0Vl|F#%?VBP_1OzR<wle^tlEW
zg3WA;hD9)pZ9oR<(8`poLLN2I=4g8>#CeV21;)`*1S3#U!U%Z_MX{1CA<eZ#&ytPc
zmUlA+51%v`dHWOyt|CujHvh%9RK({NpcZv7JIui-Q7KvyyqOXtb-+0wWUxbcR3k#O
zbFP>n7*s1p2n)HsA4tVEfT9`nL)TRZj@p*?Bf`D>K&sUuctdGmvS8472@F{&ze^+R
z6gwG@r_cfg3NB-;Ag3U?1Ds$8cQH?>)Br0vur=hO2Xgs6#FpUShpUkMlN^DMF#?ZV
z!;-*~?Wdq1-(Gv|HAgR_eqsS}whyqKcI3jCt)??!dGfnJyr;3GQJVNQ&W)lKiKI;v
zUlUdl`O;8CZ6YI?pb^CE!?|Sk9$HflOrtHrZJi&1+X{wC;knQ<N)a@<oQGUuSw==+
zI#tAE3rR?E9Zl9KteEe>w^a!+RkSmM)Dv7lG$1a%k$!994<mR4#aKs}MA6EE2m<mm
zTR+RFeN9Ey2`lGCh}kO1ATtC~T~sB=6z(C#ZlWP((~k+X7BQsG5oYubq=V1zE`=;G
zYVWp%;bIE9EINw}PBwx!8=;bp2J``>cJGsauO0y?z9%J&Ek!7b&PJK3^){+!SzSsv
z%?)f>^Nf))Hj6b=kc=V1eT0Y00dpqyi+O;piUK%webR<SYr+^U0ISe4Sd=je(a}pF
zllm2q%?(23l0q0!1p->*Kf#hysQh9y;wd!SpPhqi48c_or<S~u3NsRqln4y};q;hQ
z!HBU33>ib=Y+QUKOL7DrI0BE%ODG}b3Ic+EPH0KO_yR1hc-b^UK>|VUdgt!vM4_{V
z2~vQx+L3ErxG4#ZOazyp5^9It9)Ob#wWl^T8-&Yp?cwY9=xIEmsm28QREHc6OPg%V
z#qYPLYx$4@zy0_fxN8afq-)5o&#B&xv7z(*BG`aySKv}LLeahXH9T4r_22t1%$b0w
zI>*(I--C{8;;TQ3Rh&X_7bDC9(2EL7=ABRDYB+u`YDW!8AnD$9NYpO^xuWFq-Rp%*
zBgQte{8l>(cor3o#zwWYoY!}0U+y<ygVtTS1ufA%u(y|phH;qqnpyG*1gkL><U~-D
zLBfcJh+NpWrO0uF#hbY2huZggc+bNR+{sY6F??X9<iRi12s|=@_0Un_;Sw8?CS9^T
zULznO?8K6OdS5~%2pj*n9*xB9#8GlhsFi!vNeZsSk@|9PYt`_n7ClhjLv?4fLUn6d
zA?-j#T9Gs#6=_a-jY?SQEiMGMU;GF0Uf-Se;2(&8s=ofK&G@A@)VT9&Vqg7p60hm{
zcIPQ~eUql=-HCU|Ki`eTUA1rTkXEBxe^_y2a-WJm=u`-HF;)7-?}NT4Zr|&>f6gEJ
zx$*wQYWlD-*I216@;r$9T;nEfRDy<ocYG?e$Yam&j_W+XuC^14J6{b@{5tyW;tHmB
zAo=%8KLVe+4a<92yeCHww7wG$Vr^ii-X^$rD7tQ2@q%@9kJ-#R{OVA{{_gC!RYFB8
zO5O-d!VzpH+3=yy;5n0hY6*H4(csE(Et}PKynO@E%UlKbl!~pZh#0sPCW|mmVrB%l
z4Ry;DnVRt)a(O+tqW2AOzEW9fITHnI#V%*)R?-GcD|QiE(@H9oHC$T(+kIp>x!g~%
zGl9~oBcpf>qxB5oFtR%duO&ls7_Zp%!Lap{NT3u}NG*Fr6|7~fMnW_<85q|^ohwS*
zC~2h50nM0G_z?zg(dI~)NBJ(!sa3A6;Aesc$(A#XHxofbxJ?H2bdNI1%LWyNwL#m`
zO@Ds0*QHl4$H4CT$NmUjNv(Lbq|^OIGzJ<UIwgT2o0IxQ&%D%Av~wQ%8PO{Dk<FzV
z=Z4x~J&hsVqsx(C6a|p@$+XPY{Y{G1m5qy}dDl8qLE;bqvowU_mvPgwU1{DmR?`H$
z4*}tL=j^aet)^Hl3>(7P$?z|@$qcqioqwSNuO~FrLjLO(C;9#LEQr@$_aQr>7Nlpd
zjoKn{n0Xi@XRr<G{*;DHFUAWuryT`RCQD|8Z6&q$FTK3tS^bpD$&)8ti-IdiSUI7^
zWNRh%tQ`Y$6P_%UC=Xp0lOTnZ>$5!YZ+_5Yeb_ag!AQ;QwG(Y)1U;wO69h-kk!9ry
z(1bnYdU~-XMB1(iTR<2sG4U_sHb1yI-==ZXpMqQb&!4S>*gqSPXUu*%G-!KkbFB+%
znoc;{x58{7%9m=4#Jw1d^D)Bj!E2~gSpjHla)1n!mCAs$Y;T?%beN({0=gHY^LD0x
z-u8pP8b>%q34(6IJ9Zt+S9TG}49YU?S|50<StdSki0zvK)&)oT%ezYLQroco{Nl93
zS|O20iM3`sD+BaZ{VpVl!3+~~nQ$W~7Z|4kTL&X`ymQ`mL#uOPAYwZi1A$gY0AMa-
zWU9o&*f|<Q9HiPepRcp>j1~CgV;0Pd+OPU2ZI1o5cnd!w*!(-s*V|@1uJ4~7u(M=+
z`PQ@b)&lFoJ7)*%=J>Mnh*m(m@IIXQ<>0Y~5g4n;A90NZAQ*cWxP*-3!fmA?Ocn4w
zHSZ<lcoxrK2UxK8a7@$@@SUm`0ifTv0rqARHbnw7vd*m8^8})w0UD7L%Co%{Spa_{
zHpq*s3g^IS_ICk=C?!Q!K7!>WtPxG%?fS8S6_TE24Z(VnAn{3@09#>-=p}Q|B;)_J
zgEeHh0ec44Zu4rI^)4;i@jkNS00X%M|5pRvTrpDo=JcTLVEhhXFH4p${|J04OF{`H
z717Il_q*S9|Fx~+F98RK!nRH>!Dh4-6`iA?Tg3oI(SY)Jz{_#5_oF$D@rJJf<oS(#
zWf1>OS!#BU{l&owa=dw&QT8!Wc7cyl-nZ{PS7|r<ChgqFk~P2x(MO6Xl_&!1{jPZ=
z@)~Kf7OWbt)@gF&9R#en1pwsJKq7Y1mv_#MTPAPrRJ592__pM3p9D^^j-dL%%e5AR
z|LOzyq&A}ov>}v&OuBFEhr$K{Sq6+j!cCybS^$#Ye5T5(g6sCii4m)>^5|U7q9K*y
zL7XHR#v<U+3b?!$(HfK^un6y6H`m+;6r%-C-)p-|NKr)sRA2-Jzz9|m<}V=h>JTLF
z!b`PtTbaGGz0iJ+_pNnDxxLtw4SfuZ6`5J~70!*JeQe!QVy|v5vR`svPwy^w^fOB2
zutr-VMw~(mQBfMN@4wPuS%72Dlj^GyPo5TNdx1B6bziwXhmaZMB@x7P83FV;frs*y
zJ*5csEc?>~W$w3DJM9BedlljF^k#5a2p7@ngz$z6ZX%#-CHLEl#;n8>8mk@>KvctN
z_9B^oly~rbuy2Q-s<nNP3l0LbEkzj5^Ef>NY+^evf(s<#C@C$p?>td#Z-OJ+1_jaK
z=0XS@6%~+59>rp-M>sF@^2<S!oB+7mKw^s}UV3`D{PNnggS|)-^q0@-cd~prN8nS@
zu-vkb`xm{;FQAuE@wu@4W5Ya1HM~XrQVu5St&1ae<0iTDU>eQi?b<&H4KHr@T-@TK
zI+3v3UYBPpXgL3PJz{lLN~4Of<4xWT+@S!Jx*oztC|vRZM=CRPJrApvNL4jO_P3kJ
zpv(l>#u8LZl!LHq79fxe6nZL4ng!ZFz{DItm}GHXrOMLMr2-g3YDd`q;r9bp%;egI
z`Zclu04qvKL_t)Bj2$NHO)L1UG;-OkvlJ3r!xGp8QLUr_<jjR4W{@zTU4SFkI9`T;
zRB0tI-x(%#J)lx8y#XXE+th9XZ^)-FQ)qbUEMuyHAvj89`X!ftfdAuD%MNwF%F<{W
z0X{{0FYPL^tFWGm;q<j##mcTld(go!Nf{AGC8MlCZ7ijh(P&>qfR%}XfuiN@G=gM>
z7sO&*=q~^k{Xa+gY&%In3lTJf)2l9p*DQiS$yjw?#ViRYm*#<n<d9M=KtJOcEk3=a
zsDb?o0*)Q9Dl)++N}z}PilBw9AmCQT2Eq0czgLiZQ0ABtUQ7YTd6C~OK#szvYEtbe
z=V$QJ5VT|tSVU=n<tIXR9Bs4}jeB@}#{PA1-Cl%~On_oN7s{^V3QJUJY|H%iG6h6Y
zRYj60rqqg(T>I~@LefY^qne^kaFKal&G?z#`N_XusuB21y@b9EONQ1<*sv6SlYvGh
zAtSqr(j-lzP1J+<USJ}LMx_WE6&ac3fR{_34w6(fY!`<Y>_B6oHN*3y^jR?`gCGSo
z=jw^UVHO;b=!fN2_ucM-Ehs*5j_XJWCKj4@cBWa`X%GYgIx@dpDyt|HT-dJ?a9hHG
zO|o6cvQff854`@1qdpY@lZTN-ZhNsz^o&S5%W!;Inpm15<4WBHYp2*g!z_ixz2)e0
zXvAetQDgK)wk7?Sp;<O9CH&O=(<}&i4?D#b!8v&x_^PWDB(rp%vfoIvNvxwxGa8Qw
znw7E-$@U|d!C6rC0W_ekEN$t$qp1;)Q@AwUoOI?a+H>|j_6Nn;#R1)?U>AJ2zKa`v
z6okF-R%%yc5Q*F3WSe8yUFs{1aY}rwe8`5bA-IK*^WrI!5SIN-_aR%QenZhyN3ls^
z!gA@=JotszKRrWBj1#6jC$JS-rDtM6V~1@nNPpC8{SK{-At!KkP3=pTFXsq+>H<q~
zYbr_&<rfJCr;f9s2VE0Q`!t#K@aQ3wCMF}U@I1&huL2+1CyuBE_KRD}@i}o^`!%b4
z%#_;HKDDK`CG32Gspj-ub^U$%^o!$`qA}ze#iO5*;ao;q?;H<5q-|G&bJbt9sf+o2
zkYEt)O}0bGJ2^lbI+x`g{nCy5ee}+a-=;c9QJ1u-q4PYZ-VW{4HDrL+wi_?D)EoU(
zxBdDX$5fM5MN4nLj%a4o9SI~a?HGNGkC&=zELHSV`s1j+F?#O4^;`OFsY4RF8c&U%
z&b@UGZK$4pQ+L1Wt2!HR-(7?A5-D*sKHjt9wWa$O&{yN=KPNg*Mfaw5{r>7&iNCLo
z>wf%u_s&ZG{W6chr!KI*%!85advXMlBk;u@f!k`(XWUQshSpo!|DSL@f1h@Rp-(w~
zbJEVk9uQ{fr=m<UN;<)d+Lcw{4<|2=*qf(^tnXt`vUq3R`B!}ITDlAqz<a-vsD-&Y
z@$28O{{8l^v0UvQxtr!iVMa0G4mK1m>!$E#`ehztx-w1QuSkn{U;$1sNRYFN(tORu
z$93a5Vo4R$Ld9<~hG%me4c9NypapAqSP{%blsqaLN44kw&Ubw#9!uQjq+50Gw?DDY
z{kAzbS^hXi;7+5?XLwJ(+J3J~Bp%Dc#<BuE%DWx&_P5oZk^TI&5j#D!YX9{cUS$9A
zOCvU$nrV$Cuo1wUm4&hOGVbtg^+iyZ%-c0w{)cvy+Hxdj?Z6B6ko2Nm9zN_y@|fjf
zC%cOKeJv|UoM8}#hA7MmG27W#Z0V#pKZ(IzG%b4w>Wu-NI7!;_?7VDyQnW0@6pE=J
ziwr;w7}zg&%~)9(PJ^^HyEZuQjM2;Bi+18(zlq&$melIa#QC)k5EIGb?FI(o-CIjt
z%-%0P=rKBDJD{dn!brW2FiFFflf&|D9^=n-Jgv*%%xbZtjl+S}3&qY9hW<m%A-jno
zc@P-Lp_VeM0FEI}uq!Y?wA2?`WF}(Qx$h_OJ}watc?1t*c5b%)1%~l+SBC7^__}>}
zU$I?*WnpVwp^d`^A^Mx;@SL5VTC*R&T5o9>uTQs6+9_Tfd-14_(Dw-Ul0l-2-*v_8
z+xtohzlm5i-pvfg?8exl9iU(1V{-<A#KOc9woR|wkN>33Eej>E5`X8ro`ZyCV&anV
zj(Ul8?l_SA^0<t^BNJHufXbjQn}q0Pe)5x_*iV1@Q>&}1!(M{z3FA!wgSDuX5AUt8
zV%+d5dz(vvKuj^I0eYsAGy)vZS8;RCj!xkPjMzDFH^Q1<1>2+?f%Uk<-#pb1piz$c
z6bJ!l@PLtgq9)z0^n_t+z_?EuFQwp8D*NlhO!S5vd+*!;-ok)w2S#ub;OTAz)-(ag
zm!Wl;Sc=(ha8nB;$9Naa(iS|E%L|!y9eR};Ox!%MU1HiA#=}=#oNg!D$H**`VcQDR
z>=^01<W>8}<5SM~J-nD=-+!vYN`XquV_^RXVcS|=V7*YnbfZ-6B?HbBn5!Tj$;_-3
zyM#ux1pHwUdrfsH&HmRr-S#A@y;4_Vb{R-QMP7mR5TADv7>BY8Wy6`34d4BnNBb-v
z4AriN0_y~GrILeFq5_A)Z%++ccX-v#56ohuUMH9trilW8oUls>ih3DgH4MH$8IGpW
zdPgvPUmhmQ5aYBJ?Aipdjx&H?JEw3)q%2t%*Hz$o8||&|JFY-4^8~zDk4%MkcbEM0
z$36m&TwwVy2hq!vm6c%-)%5VZPVznpt8-&~$-dbFoM8#?5Q*{%F}6E;8Ta(3TN614
zg~)EmOSPWKD4rxJPD)88%JM3dpG&rvTpPyow3?gZXjY<l_%wM$ey5BTC3ujEU?Rwo
z7mQbbIWPv%(o7+o*1;z!`DkuHZgMcJc(-!!fUO{8WiJ-}%K;@H1ts1F_N$nr8uJJ-
zC2ZY;Mi@rDs4YoB5QIT&XSf6rd0a<UV|f{CA=MN8jEZP-uA>z_x3km=@t_IdS_U>t
z$b2Po-Gj!LmbOSPK<H%<;w|+dhyo3cDLBArYi$8ogJ2qz2=`d!62Y!Q6O(Vv2%0HC
zmU$e{MQcq@@s?+m#o5*yjv%Dctr<8*79u^!{Z2smrv$F0+=q%#<ptbuZWf6Dp{Vnw
zmH?9|VB8jYbA-8n-Ny>&SAmDKG<dMsR|?c5gvPgymg!T=Xo2Zr;$73pKgkjJ#3PXQ
zca%?j_9r;e7g+iE`S$wjuivJZVM1!jFv8Moj6{Eh__!6$(+!(vCK}NsAs)g%#jvp`
z@x&!Gple{7nxIc<LL(Z0Dn-;PMAbOBftHrJEUiXi^sFJ(moz)stVYmOoF+%#CxIIT
z(R>^%0Gmq+?N5WM{?{`Q-61fxaNY!<N72Vft_G+px|KGTbwYv{<8q&-U*yEXrSDPI
z#0VpjJ6^m~@1Ti|fZu8a-7mpW1fqP9YgQuUkOwvbW~qneYaUwR5?H7q`X=vP4(@md
zvEaTH5g<>QNFg-w<+2a*<>JjL`xOZDS^$|~KCK^(Od%^7zz+n6kc}=2oYPtijj(MR
z6nhZu6B--Fw-3a7Dd5ggu2)W9W(o6IU`bkzFd783s}yH6;lg7s3@=(ef^{Cd)goi4
z(5E@>LHQ!Z{y<cW!+FWxC}xH%`aA(DUHJ>eXI2gVEC?(@wkZ9+$&ws_f6oYf;k<;C
zyrt(cMr)F$BP9eC5b#j=E#Y@$z)eLJ6MjdNKyGchuVo|gieLs#p>bM?zgMnuz2#Aq
zoaUr+wV_Wz10-aG>lvMhfXs%8l<*J<4lQ4$`Dw1XKxk&2AP+iEE_lHe)E~hE)Mqy-
z`Ll@Nn#B%Pi4a}In9U*Ai29&=P+J0usx7ZwuRZlwZ3)`J+;_$GnKu3VRI0HXXk%;G
zx3&<2ss0Pjph2KZE~p&Y;`N*J9L#_=h3(R|U<h7Oe|2ZxIOtPf6~vsvMKvBes{X3~
zI_Ab-7oq4HYEOf4`?x#7{T_lD6cbzN;1lul-H9LbPkhKeX6<CP$6*A%Py#FYfc|km
zauRlu+MeL^?p50=?hv2;v3u=z6_bA+>k;r@wU2xd+*_MhwD%Bum)El^jYTvm(x#+Q
zsAxH-rI9P_5`Zv%$lG_1ztUPBR44Je_c+}xz3#PN`~1nJZ3hU|2F2@be49#%^&ZwA
zo#oeAVmUVpivC{xPi)h>lK+>m8w~glDjG*IM@%Ka6dqJR@qYX<PaHR=8O9q(+`r1$
z`-yUOSfN24y4@32+9!eJ3#E<U-F|M=N5m)AQ{7~Fd`IAs+psj$dbqU|@&CJrUEeu2
z4J_g0wQ+m<5>fcIg+%<X*jq3sT!ytEgS6`<7<8|~<Pal~fx<;3++?qjky_Xv*_48C
z%ACQ*l@0VDmF3tvv2~($5!H!M*RsFJ7Nblun&aYa*HSbc+fa8utP9wcBp?+cBCM3s
z%PU*;I37j8(6v<R!KmIb60>o<bC}6U=kA3B&46^*63DwZ?gvbjZtNmK(5(L)b;=5T
zSES1f27S@Vh+bG+?Xy5AlqT%x#ZjBUMqY<~O4K@QT+`9JU?)=BLi<aYX_>2KXbk(8
zgj^SxGV!r1j!t$cog)evEo-%;?jD@7-(H!rS-gI|;T5vw1iVbq{%@f+84tB5I%<te
znEOd1&hr?_EP`<6=PCS#>-TD$6f{j(mBMZm3ZuSi89c{5dlWor&Okqc@t^gtqtQ|L
zl(U0@t&0&;qcK*|Z|Gi1Tvw?39x?`X;lL10^DKC-B>%e`rpNxzBVQ8w!YUe#T31(>
zZQi_@P!<A~nS9a@WcTRoCQHl6vi--WD&bXMgbQiKZuF3DE0}Aq?+V$)i^JB9k$pxW
zNHIR}fH#3Lnw_18i`-mNh&6b&RtSjgfYPQ8tWaQe**frq$>tJZ^6Vw3`eNs#Ex><O
znG4JUI6^}itO^*}!_bxN<G3c;7y@fM*rp%`r3+nn{=hT+%}dpG<Ycd%8C$a*u$ElJ
ztGSyxxv8u6dko=Ew3OTHt;N>WJz;Od7Er$_-%+o~kll{kKcA!-#aU3P^hND3Yy(%j
z;Nc>0{F$x!V5TN)7M$jx<^m&Ojh$(qB704ats~4bi}GNtD7W*Z^C}>CJOZ=B8Q2b{
zxX&lSKxv{6!`(iD2DOEN@(ct^8-hN+G|#?wXp^;dj@ps#MT3pU#_$sEDM`2A!G@59
z(SILVg+e$8FZa(me)%+nePcnI9l0L1uO8UsSQN@axx7G@?N_IU9qU3lP>2dVvF~?A
z>@dOE5ugs^aW41_e$T7GG;*Qe*+Ir2WWKeNJ5n#2+M*1C!12gpq5QLFYhkcRvH$ya
zr_H8k*+KeIU6f+)(XR@|DTYn)^w5HR``H?6C8>x1{5<wE_c0xSN4_L<+7yG`*VkvQ
zt*!1;W3Tu&2`eqEuE0I@)b>*CMoY?qnk8cM02^O!4Y_?{IhZ32%roS;Yhm`i*g{>j
zhGic3{{$t>1hgVU(@S=yW5$X|tT*hmpfvm1{!-h5V0oc2-;SLdMKGmWKFkdt_RiTW
zOv*YiQ$<AhKgZ->Kv2#BsrdFY)m(qp&QBmfU#x|25G{mWG}aW_Q)Hnj%MaLJGMUy%
z9`W-_(=Hd_FboSSH#SnQpy4SES(v(lPbkBUymE?>Bs(HPGln-W$6jnMMgv>1x37ZV
zA}v@L&mImpd!57-Dk8a)mNQKs(-FQkv?<cValS7?iumWxR#_iBTMVQ9)$>*G0D6!H
z2{e%))*$QzsvQ736@t<ysH72qM^ARm+S0-j^e$+-KuUCc3v3R<NYF;I@8qEtjsOge
zL1*(fuhiQLaE_0<Bi2j+_4{XrY&s*$zW-7kJVVR$4VVQQqJXwzcq6Z(VQvR7`zrJ}
z|8mBo%6WcsHvDAJ5TW&k$d~vMv_bMliqjuOjF-o}okdtxLpjrn7gWX5!`)``&to_O
zk6d8+UMYb^+uPe6XjJ3w2TKcCzMKxYk)_|r_^iEqcF5ko3}_UL)kHizMP9iACYhEN
z$`hy*SxUt<%LD$i4+r663fg8|@%v$SNaIak54l_&Z+Qhdi?Kl-u)WaAl#?MQ3xRkL
z%#xN&qL?WHtlBj~PCu4v$_69DR0a9@>cL07d2#?kIuC#;XhkoRTpB}wbBoXhUfx=O
zFoW*KQQVj{R4|dx4cajTfv8tRT_d)JB``|s+>Zv|v4+x?Jb3H0CAL#7!Ll+l?RltG
z1hd)%Bw;s1gx=ywtOaTId#HJo>Z}4`C7Y3!SAtd)3j@Vu-%Lub{8X4oPYl|W5VsWF
z|2zVsA9e+CRyo#DU>79_#SoB-Rd84@?I{OoZ&oZIDZp9@M$y8EbwMR0Eeo=|zdt%)
zLGJm9hFk}J{mTALmbb8AfB!+Bt*|`T{W2`bE?^m4x0M``qP{uWKI`alg4}ZkDrXkI
zrEHajFj$l`-ov9a<XfC_3>ekCJoZ9ggIa7`;$zCskt~n#2t4v7p~iZnTA%p6j}%b7
zknjn&Bp&z$Cbqor^Vn|$;1n2f8N-vG{YG{iu^23|bi2|s2N*TS0Vt)cVRmwfKeIV6
zi`k<&rw3m9Gng<D5Z|Ka%|}Skj4RUdWJ^(gK9MamSKKmkkmcbi+TT=~>ohCbhh$q3
z4YD8>UN#woD%yw>rkt<)lI9~&sVHdFZ%4gCe>n|drA*VAj=p$>YY8VMdKSkzKu6TB
zvNemwSlBD1x?A=sDqu_)GTj%umC~ya0inKY@_VcfXl%q^o(28C6&|m>V5FRlkL4mm
zX`2ABu3|S5eoAH}aY|L7MM+4F0LD$@nz>wa9LnlK`WRr_gPNo|n&=6Z>7{6|B1jy^
zh*t)DL!@~V@L4G*BM%lfIgfrvv@?QT2y6?<4EYSfok?J;!@4IvWy6y_D~v!@{}izx
z_=U8?5!%<1yO!V9{eJG3O8$8qN8oc2SpGoEYvmi>{e7Agn&|qJMj(M9VWLT_`m7%G
zjK{z0i~hy2O&nARj68RGuvIj9HHm$Xqs~=Po1z`jS6wTy$j;^0clG$LoO&|oE3H;K
z5>NdQhD!BR$8XnNkL&Z#?UO)r7%eL4Uj1dScaC>Y0<5aT>WANs`lW8&re5)`!!B23
z_;=Ulo9l1<o^=iFmt9Xh?&5|oVaX{0qI1<B6=_bon3jpl1d}_V2YOy(uY1r<DM^Tg
zhQ`y4JLjnlwe4fj{O|JUsULUl_k^}yD*SWzq85DbpZcMqWB!=y+BD;wBkHj?X5Lt+
zZNKOozxZS3f8Y42x~k)!^RW6G&wtQ&zwHOr*?8=eoU^fw$MyT?DzG*Vx}L$?kL1Rq
z$?uQ*2uPUf3AvZR@r8h^5+5jENNud|K*a7~T#%`Lyln|o?<w?zrFZD|zQiXqvE9V$
z>YUr{c;6ELCZ3nLKe_$z5%6BO53l!`*YF<+x!f;wOyZG)=^$=6>FA&4`;3Dls3W5o
zeve?oIYoGiY(Nhy3P;h>N@(`Pa`!_PKgt22?Z+{Kvub-l@#;;|zOwRsoFb|mWq`S(
za6M(rnWm2u!bsestix7%rISq0Kpw>S@JWgs8`9b~mQliyaJJr6MkXVGN*-LK_4+h2
zch_8l?;gwlh9bJ1k;(hh`Xp9Q{Ea+qoX{)moMS9i#bi4aZ^y|WD(ln#FWIAQL1o@1
z3CB^qtBN<3ma7+|j;VOto*?wEp-%;K4?|y+xKHQ!|Ftw9z=WY+-rslo({JzYWAZ)=
zTBCB0VR!qJf9^d3m~_7wCI0M?5(+#|Y|-9skJyiEi{R>Bv@Vh%^n&e)60`SoL!RC2
z1k(iF%PL$=zq>SUyU9Y+MIwmSx_s*(Ro5)pc3NRt=mgW018YGB6XXmyq9u&WFGH15
zS&&Yg+k$Pyt|JfRxt<8##v*65S9X>F$q_yv<IEG#)s!MY6{jeh)&jz@r2+aDjQg;u
zSQ*fSUxRDf1r^N>sGHA`EhZ%^U@a8^JB!fG0s`^ewvfyEqf9U5817G8pR!tPMdM?$
zHbBhebFF2T0-etP{B^I@5O+6*@Y{v4{Q%4m?LAX?D!FIcSjTI3yqhE%^z#F_z-FSw
zaNRFiIp?I|iR%G_rR81($<x9(3}C-1#2c7Js<10~E4Kp|XoA}wKpK4bF~@I?wT!hF
zkdd*d6*KY25YRQ1g$Cn}9lfkHY-X?SO0_Gz7)r6-$-u8LAeWL0%YI=|jx9{j+W+`q
z%>K&@H8waLwxZH}5>IeGaE?vz+8>8Wqa>7XFOWE5nsMs|TUC$!Oxn`I;*wp!E?B>b
zj5!2ghggydkEP}7W$txHQ;EwE)H6DdeGA(eFEH6K+ZpFeee=XS61qj*G>$z3olpag
z7Nt-V6eO4zbd=hwQ1rZYW)yDt5`NzZ!hX)u8Ov#Yk4EatYs$hErdYh9#CBFd6_m;n
zS$4BxY<F}AkMV+a@eDN4&yn$Ym%-=xT@{vtQ=<dM3Wbijbu9L_J~ANftl`-Mr^iD>
z@caUfouKW*v2)?(1T;TcHb$19mX<8brr%Ct`49gWV=8{3d-2GDqrTlru!bM~=ts7H
z|9&edAhi{5E8dU%#%VSN_NKlzWQ}<CQt&u65HHsapH)UM;JklBq=4GqTxMHq^6j7A
z>abI2913J^Bkh)`O>nZ=)xJ5q2Bs*6M^5Rd76Fb{;%yt}q3i+=w44g80N~^s?{?b-
zFhPSD`n#F9eFR$<LF*Eb_mNj;H}o-Uxbyokri)`?f_l>cC69A0ag-OqU;q>efjI31
z#QH+a{@>{_ylg=m7G!}W7Q$z}b8+0|SsdhnmRE5E594paP;Ev)R3OOOVQnY_<{;xX
zE(JS<P(I(u!~{oHK?b1;m?@55pWxa+5%8!vA>2J}BWQMAOx|j+UfJu*2+aiqYSfP7
zne2iV=Zdn_U<988BNfB2-OE^YGGWfab9IsNSz_E55n87a5Eb;fZ(y3F84DP>^DPE!
zp$(3&1_I36;Sak6Yr`Pg?T779^k5mUXMYL;aSQ|e1fJDnU31n8gyhO#%yz)DwTjnp
z5GtTLyno$8({`1yEyJ<Uh0vHq%j^Odw*^mXTi2v@0fVT+aDTmX%-*{?&2=L#CEI?e
zajug}?0DOho#>oFONP5&UQw`WmBcA3h&&h1uCfp9ApUb=V$S}p4Gn!@(VihC+$8-O
zjHK9Cgx_OKm8fJCuC6oWn_S?Tk)Tu_!y{KFtPLQnyv52P(9Rnv0|V*`8rckEcZ#v>
zLR*vq9ALbns8UtD5YAj0B0<WW?JUc(x6cgQ%?X4XSUYhXikp8nCR^dMeRyMvyao~Q
zXY&N9r`W!xLibxsI!G%^mX9|Ak9<k!|2R^!b8-mWjqlJ>#o1IvFN3>&C!Q@Ko;pZS
zdX~vl8KN!^s*1c??QmROyg6<)06ZHJJX?9Km-6uK-5dh+nU9M<&GthBqe&O$p{vFF
zRRg?XFS-6cWMb_H^TVU!JZ(E*Q>entzXPpI99+ma9>y&9=FLYddz#c&$`^QOE8t8f
zOq2>0c;yPYUL}++^5!X{j68mMcpS4~a(H@MsqH|ck-<8Erd14&R~P(K&jP2IV4|!c
zFst#F$p8(Q3gMYWcT&;dLTGGc)MNK1m=tzbk&OnA8P;sj^Z|SIK(&42P?e>lSqKV{
zO8XV%`L?Gf*9pBBT1y!RgfUDLTL8kA!MXKxONj%1ip60wyjz<=h(>`>2>Vr#A0$s>
zhGoJR_B1p%wU7x4Hc|{sME4a$BIM(-EeB$-gS2R~5C{tTu?7UB5Eo?ueW*l}Qu2pJ
zhzhrYd>3s`5$DT;SqhzvQgJaf<b2FQh?Fo!oR4?5ko(eq1xSauh6E`;mxE?(tICDA
zP_GEH7C^9LNPsHFYXR(B3&+awP!=%Oxs2`8J4(QDd0x^5Jd^S!3#P$}(JByPtILZ-
zFGNiWjZ(d(@mrPW!LC7{XSiTL+NEfB_SfgSbZiynIc|)GSt={}E4~)1Lg-5XHKTPu
z38+^Zw@Lx9m?sdr62JxAM=O0R=ANE}&ZrelYwwm~5~q+~k>69SAuWuNGA#Mey=x@-
z=VOn+BiFF}A4*!)*T4RCr(LPH+ArBP%93c5rBV?OqtY`C5kfMFE#(?ZzO6hAN|hy-
z{t1>E!x55n?kwR2Mw+Tw-q1`I35a=xx`6)~rCt`oqlAYU*BSIJWPJuIF##N*jt6uJ
zFPbQSdr|i{@m*BJ(<}+ANNF{K?LygSg5(EW=4n$1kZdN}j?hK~WMeKfQlTpmmKR$f
zwL6K8WOOkVUaTB!S}V@3B?GS<bAn~4FTz!cPihVu*#b)(EerApB^p2zP!iKT+Eg52
zF<ezEESVw*M@^OpOH*O3+%ko8WgAi^A3-k!X_y6!+Q!mZ8M5<PT18PoGJ;u-*;JuF
z5w2ARwkk+x6rv>IC5o9GK9y;j(I@*Ex*dWfFMZv9dwv{Le}H7AS&$))SRUHfW`wGa
zOGxWHK`oT^SxfeO>ds=H%VjCH83My!1I%4y2_HhJuW<bsOVv~a@+!-7Wxp<Das%S9
zfB9&br6*c6TG&Tusl_Zk>v&molS@*$427`^qtT`kURBO@v$-yE)Il`H5GYEire{LG
zPX^k!63*&(7PweT<bbp!lED5B62(P?v|er+e`OE~(Ow_qi=$D-229=n5R@VWuyAct
z2*`50t77bsJyTv(ja@E3;0ju;1eq9O7a0#x>FAnzSxxfn*3fUIH`8;dFelkJD`;zO
zZovVDg*PkF*h@Wh&%XOe{`nXq@P+acs=tcqi!fp3;+IQbu571-@rIJoc$o*qVWhNE
z4=$6;O*Xdm-FNjn3=eI10#fuRxZKg$+MrY}0Zks_;Sv@|nt?ktei7u0q^H{_1B1S+
zFAi2A;X|K%!-MQFYG3Ym)$`r%>VrFnzH3`rlx!u6N0i~;r>BuCUjo)q+c;Ya_rpDk
zTgJgTyjya0beGb2gjZ4*HIcR9!vVOAi*a;t1&;egx8*|)gaeZR)v{YcR7-GwDK2<}
z0+xMfLVP$}eZfEc`*7o`o8_PSqWaP<)vk*EYE0Pe#?YNd=LAvFNI4EI#!zFY7)ROA
z!~-T_Dw0Nxo6gbo)s_OR)t>HG`&CrOdoENd-X<4RUE#faqx!N0FyrmTE4rS!^VA2<
zq<H((S8ciL@<qR}>nGb@UsT)strd$ZJzusn<x$jq>0B)(RoAshb+519c^U~_!!N3%
zyq3}gU48#R^7El1@P!gs$sg~bzgMzCas(dh5zx!zb18bm$^frVzvv|-jYF|ydW-5%
z;&Hvj{n`<hZ(<Ks*{D^xog4o6R48KXq-j_57EGi;NxYUS_}9|5e@<eZ#BJ5_OX7LA
z_9fh>yKn!%eLfSa-%%rRzqAKkD{)_9Woa1qEB87k76@sue%aXW?IZUekB#T}ZQbwl
zeq*??zUsMq<-n~^HgvsP-~D6${={ScKGk{9c48Ze+llA+`!{|*tWLslPED3iFal{X
zB>&wfIPc$eiXJ|x`Tl?R(CRy_`t&mI68I}4xk6gjaNGaWG1A5p$Ci(cryoz&Z}GbI
z6GGC5CvJlHKE<wycPS0eop3%vc#D!G7GGYDhDq3f-Rnbwa22>LgRn3(<D|G(XpX{c
zloeUFA5jg9#zmQnFW}N2;2K>RZT<K~;d7LE+EJchgg=J&E(p6pfF+-BKA9|e<`kB~
zRJs5o#3GAP8P0W|Q@HleVVf%|ggA$Gw8A_O1Y#)#Z==G6;{Dc=NR%<M_vkuH*kNX|
zqX@|_!P3z?VLfEhslv0Sy25aY`dQ1~IqYkxz!y$}T`R^=ugu9Qx)9gb5-!YGoVrPv
z5srvOuBW!o<ADof6O+7>oh{6G=vo>Nr3K5>cyNE$@aXm6O&tMhkVD(@YAS?Eup-A$
zN?)|h6gEr!R)m0yC&U1*7sM>~Gg060#n}S2R91cA&?JC0t{GfMFqBMxOtIbh?5BE0
zZvAIN_kQcUf6U*Xc+B6YIuF`TY$I_y@f?5u#?Ob<NjT1_$?^$Cz_a>&f)hXKDZa20
zy-ZI}kJZ=Ld-O6)ZfP+RN{wYPn+AVWh9^hm5)=B`@RGf>&r=9b00%g<tI`>S<t=O{
z>&@9~V|EtSg*`ARjN!JPTuHTqH92+!cX)Gc0ba%oJ9=}5hnoZuaAh^*5kP!n1|IW(
zeROriR+xC(G3t*2Tetxf;I%`wwtG{iy?buV#V3ZrfwkefKLkdq13H+ilgswkha0Un
zxMIISc!m)YGr%#z2%a4ns!!3!-MHj0z)RoSoK8xxfStH7Y!`qDWFa5}nNxNQq1>=3
z&w7Ch)ZkobTY+JM`~1&`8yx=VIQ2zA^Ypd?8=_w#f!KprG>m8K3b2jsWD`>9=z>4&
zTG*PvmF1+Y*>OB!dz;AWgK#=}VTgnrqRt7zlb->{I<9!IU?O9`e0|(bbdymCdrJQ(
z>ChOv5(MAx&xLI+B@@qMj)R!AjV{{Po~ni3CCh$)Y0Oe{vK`&c8DIeW@T}g1`ezVl
z!(YE#@33a?oanO)czRp$Tu$P=SjUJzgEONXNW%=mZwqjUxv6=Jk)h}>p}jdv>ah>|
zq38h`a0$2nmQA^Mhi47DyUhVp7+6TLAHV`pieX&Zkpxb%Jf0)qNq|Rq?@pWIdct9~
zw6wTSUEZZLcTXa3ad=S<HkY6+pgBy<+56XKI1b>EN$Uy^Y&vgd6{WFXLZh0-3#JEp
z2rpwk;5;Ru7{PO+R9@0Bh5^uQfx+NU4^&!uENX9&Afg`7<sh^rHB}*dWoMDSabnn-
z8cXb}I|}f&DS2obG&jZ8#3Vn6aCntE?_L_Ujv3<fP|>7mSZ2Up+gnPqhb0?90}7zo
zbWSdkjw^tdXvLZv$!}O+z`7MauE`bq-dF0a3E=7o9?T*<Xd<*bfLp!>7e2OjaGmma
zX(_h?-`Bd8L7mWvpbWwo@vQ?T_WtEjSP}~Chc8q*H@(K-DzJ|~d8XE$s0jeDU2@|v
z#XXD2uLC^MUmdQuYB;}?bVAE#C27_2TGtjdU;z)@OF$X~8yMi8w$+8~Fw7Ib!aG>s
zSZZJ28M0}Iz)!V9+e5a!JV4IFQrIa9?X8c7@Qea~0Pj5p^8@M@LU4}z&$5r&XDn2b
z?}XYmfVJy*Nx$=4t<A&2p`3-U?kZRJ;s08-=XaG_KG?TRT>i7%{#SOF*g9>E;lT@{
zH4ffNn2W?pmdADk9=X8MGEhbIGS{zPPoS6a_Ah{JUxNFprictKuugVNuh|a{*V+!=
zzG4_v*$X!5y;FVmv!gxM7l%O1;8Kq=*;(C?AJDUfvAVEqyhd(W#ZobcC08E8Op|F2
zJWmst7%h9mk|2-Ob6~T&07(7@^twD_DrxI0b`wFVEIe|{i#4E$1gc7}RYN-}F$p)?
zuSV$Pfy-$Y6$;CoMX8KTMga+hwQ49zvtOL(2ZV|pjs1d00PAMi$~DMMC{SnysMiQE
z4Xw?>v@6-A9Dkb3TGkx^a5x7)*FT>cbu>A;zR3FChlb~52Ygnv8<WrjuqcdBBN>H$
zcBB`ff@}rA`sUdYkk=WOgGM>SvQ-INgLHEmeym}_U5bDk6yUv*x9v&jc?SBX>`jDw
zYJ5D_5qLd7OyrW6w^K=7YoNOk9!t;wR7dbkc}^4BhlaTc=s<v%%a*DFYecL4=MVcW
zBR|^_B+>;}LTI^q*-Rl6W8l0p7`GYvl8%6@1S6J5+ZD9q74LV6e@T8kW+U*qcnSSw
zp+DA%-$gIegMcVNAPE1m29`&($V%_!uLU$wMzLS%h28-6O13Luq=b9X<d98d8FfV{
z^l2XaOoWZ_PO^~*8gPw}oeaVywgapben-MeZF;txunnOpJqI~pb$*8H!#oBW;f^#_
z1Ug)V<Zh0MB^;C7)lmeFHbmc|uoWTY$5~oQcx57xBoqXsToGOjE-Kl=gmw=jFk~AN
z6|w9)ax@6yAh4!vccLB^_Dc4fEc!3#foxD>VVI_mWn5o2BuR%sz>hgxFMz;PDtVow
zb_Asu0%xXlUPwNhz)lsSFX_BER^qlkjhRebtMpwE1-(-Q8&KvS#c=jV*X;$svo(dF
z!nwBkq%>r*c1y6&qs=XnCai?e9YG4jvo(dSO0+aI!|#FZjCRxb6xK=U*o2+Zg>>(-
zafzitzvC>whhPXwrYoT-JD!I3S+r0I$>fsIpEZx?=jS4@{Gs;4bbPH#`#tZVhsBW~
z5q3wDSQCgx+)YlQXng#X^Zqd@G*R^5UvjeP+HzPQo^rLT$u8icXkr8q)MR4Ts8Kf&
z^j*Ni6pX?`POEK4al?M+p>!SK3@S2DUBA>RO<G^*sx@6pcrD)>sD8;Sr|bLpFsgX>
zMBg+C)eW7ivCw%`ckQZAIxK<a>aneMG#*;sX}>1E1eCTp%}sXs)Z96I*EKy)Dnfw1
z$`k7@$$9FVkD;QfyAQuVQh)>uP2D&i5^nq^@u745p&1v(qU*XBfa>^t){W_c6oEO_
z7x$Gvx)wjP`}D^3c7GDbRM%2pljZRqfzMT7J>El~y!+$`BuC&69f3#gCHy!Y^fif(
zQ}>h9krv=%**Bi=G&El1PV0+n1sm%;PT#*M<B?RZ9;c`7{`Z>Hu<k!L$s@@TNRGf4
pYy@PcNS5RXBu5}Q0$+*|`2R?yIKZ;jZ<GK4002ovPDHLkV1f$&6Tbie

literal 0
HcmV?d00001

diff --git a/Images/ipython_links_format.png b/Images/ipython_links_format.png
new file mode 100644
index 0000000000000000000000000000000000000000..d0627e57db9650161561257282f6ed59c5ec16d7
GIT binary patch
literal 150759
zcmeFZ^Lr%U);`?9#5N|jC$?={6WjL0wkCEmv2EK<CN?`ZzH|6|&hwn>djEm<m#*%r
z>W#J6+O=@s6|NvB4iAF^0{{TvB_%|Z008hmU%>!S5PuGBZRqd-fB-;JL{P;Y<U9+~
zT}5<x_%lQAgy>FB2)5I$klliK0|5#q4E{hF4gE^B7C=l$h=*M07KG%<0Nojk14RTr
zS1<X%?c@eLyfoD&T(#~?J6iqiWj2*`o6(w)mB!3W4bNF)1S<0b`Wpy&5BN_2>i2*D
zxM2Y);i<%<8UD}H|GGy4+#T=(7xXWG_}7(h{+K@ih=2}2@&q;0|7rHmUE3f4Td@Dy
zryc;52XF<d!c;!;UwefFw7~!0k^X7Y9R%tM&;zS5T0#8ZMg)Qn*k=1*^ZUnex4#hf
zfB_f;$m%2h>xlkLa{aGT`Tr;RZw2}PT1k2j-C^(N8zNTLMN`&LM1q(czmL^I?`RxW
zBn*rcqKVN_=*`VdN{t%AGWF^h3_7jvSKGamS`Fq9nh);RQ0IIOxbrCl$!G-Y&Gc=o
z<#9)Z6^LB{nrG8ep0b{m(CLsV6HZ)pLjSpJgw3cUw_t$U+S(a&7Pe(&N5?wN)u$)-
zuvjXJ8;`4P5PH3iFu%`tOjL52o`G=GqS*TS`clf)HQ@PTD2MB{e_>#1J1m4NRL!sH
z+1p2CM_#1_>?<Wzcds_Bq-kk?OBa}YLlm-PltiVQ1WQj{l*D-#m6F1f_veuRZ9~IE
zaYv8<k&?ITz2UHkh=i))Sc2hHeP60Yfs?r+MxPgFG-?%z^iE85et!N8%Mv%>CeqTE
zzM`TcA}%f*OnRN3#d38re#W?O7IUP(yZs2dKR?>{&E6>J1*0JqYlk+Gz#vQ_E#{|?
z%zO8C_S^>sC`{t69_)kt407N;ruQ6f9KlWPI11$R9Zv$xk+!S-*t$5qFX=IW9h4rA
zx!Cqz2mot-M=<&+IrmaQ%{{|w=Ko_m+rj^sM5x|sDT7W+jW0mp=lSE=GBAz$^%dAH
zelV8IJizO82qzYc9}NbHAYo8%Ha%2c-4(G^scja=>$=0o_d>!E*zG4MZ~f6F58-Ep
zAK4USc(6%>J<!HR9XV>(JhGjWj1S8ms__*tGS<PAsHW3_qfgHh6*1`T0U%`F)=2qZ
zlJ(~iC!9tEth5hVE>}foal7R!7KlN?!zb5$-cIw&A^=8A#(@VRQ}E-Xu~+x5^IK+z
z#d@lK=DBm?(D0E1tr?0hN9P@!jwc{ORigQ7gT$+8vc>a!4$mM;qe6jr|5pg26o2-J
z)frG8Uf%1SP=eiP_RTmYM1(1h@#={ta}Wk^o|e41v$V_g`0)0p5!=m>IBNpH2yh3&
z&CN`NN3y4nj_BuyuQsPwXbV2C+ceQwZ?HQfG%5KIG1=f3d_Me|2a}A1pJ4+@XbJbV
z)K(1XwTN~*y~?aSx6cJZ`b+dAb*Tugyg4Fk`s9OyV0`#XS_*1b=>aPmYwe`sH&d3n
z9K8SDy#>g7h5=)wbx&)#f?z%oh6Fz*tVMps{)}dqH%h#ItdizHd-29IZX|~fbioBc
zga+;1WDRR7ofht^=pgz9Fsl<6mb*_?piacjFhy24$L5gl?pBIJwc_}<BQvH|sjH_t
z!@}pyH+sz`sgYjtPIzn8>E+W`BR$qH<L^#^NgZ**;p6i}3_jz=7RnnpoWL_2c<TpQ
zSx)fkz(MyeUad@&mxmAT#&liXp9K^+sO2Y~2gGEPuJ^Z9Bp23^b0BAQT|#f=n<$nl
zktJ0;a-SPX-@O(2RdJ0QKS;6&prAOy*RD1+o83GIU)|k{wr@33SR2>5vqfgi?M-bF
z^e!WKtm{Z%82@{lstfwjS%OwqIMYgm%-(Fqyr+KZ`ubqwe7=H=PNOzZESr%e`0`q*
z-Mkx!$R`ks&wJI6$bbA^xiO*PgJOlKN5Y7{<U0>m^zKtB%7~3G-@f)}sfi8CmeZdY
zWc((&OhuCI(+x5kpVfU$`w$vJqL{UL^62a$R=!5YcyqvII-Z_V);k2I?r`v6bc)2^
zo7}GGp8o6I#P@y^wc%^yTp<F9C#0c7=qY)BPzjdAipx29igovo+a$K<W0OdXE0Jmp
zbShtXcMmPUES}#wL|o?qpRQ8O*>X^Ex@moz?v+;Xch-u~)T%E6{$Hy)BACA9v;~rJ
zPwkvXOB7nn9l*YN=4$Ubx)*56YRT<ZMVa34F;bzAyXg_SF57270FtVt!a-Sjl;L&L
zQvx|IZ#*{qncc1i-vW^+zMxG2{kMi11Y38%WV5eYN9H(R6R23RAQbs>MXm4QnsE^z
z)~1#?@vYq0#9^<>V3H;xCrnoh#8$IO)q28V2Uu2W5#6pQm@>`NqWEoc)wg1?nAiWZ
z(~RO!frZSU&QH5v-a0Ha=}l@WnX}<os8e6kpFBp!ruyIqeBrI*la`DBx9N{?g05aA
zk{u;E6lRJZ>ADU^px3YEC7L9wJ4pF=ck{E^ZNfQt`p=aKq-5~>@zLwHk!G0^zOFh(
z$0WvbFKkPC6HD!_LI6bcEEcfw7P_cS?cjQ=5)H6Cx-rJk#_gmT;f&RQT|u9fJP69D
zE6^-hHSbS!=bSE>`rWJGcsMD{VKn$kO3K%ha|PIM!J!h&L}We!f>cRDV33EI={N7Y
z)9)*ySvJb_#9Saw?7@bZ$rsJF7Yb2a*C{&Q`8d_Gs}bw*A$T^(M@t19-u3&GQ~I%e
zK?cziO=)PSJbtgs2G(4j!l|mBNBZtO`6Q5-z&42vTf?E1re6wmxpMS-WIRj|M!u_o
zxW<#;$8^bIgC!#o;(ghR{PE<u6n8Z@{OjGWt_fb_W$sm2x+#3I=To>o)9fgw(?9D6
zoUWH#y~>YnJyVWb+2!gUR-~fzV&R#+4yk$1)y$~1))XUVT2e!`+1@E!s#kC+5Lm^!
zq#En15hjjzV1|9!>k*+!CjRM?5!#`S6z9-+;HJ~9RqD2@F)Y=YNWc4-+HLwg>Yi`3
zV>%s8O@0Fq#<2fxaUhXOp$(VI;xeH=F~}+>;ggK6`{4VfS1eJyivt^3f1&R~1&DOj
z$z@TlF(2pjzBXkxVvL+_j|n!warp>FeTC^*EWpqLPZ0rGL%MdbMtM*^a_A+mW0H^=
z8t{PJ-a82?W|hV&JdsiZwd!U(ujt_NU`{AS)$jUMz4{If^Yk-^?9+A@+2^R+g4GZ1
zQUfem>M)dfGGxp135~{8r|@dM#rKnHXd0?dKgTT~BY3mt&QFe*k@E|fmU^=3qV&r%
zF%cCu<kaRM@N6n!FaXDDBqC3%Rp@w;YmW+5Xy=3`=7jqb{n$Cst!-K8yC}bVOZ$vc
z0EacFH=ZA)ZZ8;4Cf4)3E$wF=O+;K#K&^CchnVuwXG+!WR+pZ|ot-xN!7-E+<1eSQ
z3cM6s?%09`I3t3KqO0fM?*lc_tNze?cgfR#y6+e71_q|H8Ph6`pw{ype_u)v#%374
zT#OyP`$Qm~)=!DBe_w#kgi|EZzdrq?BEn6E<8e<KlhYmZ`bZovG5<IisV)$>B&#sz
zKD0napfXz9;%vx{kkt)2+eoj|g<u1>1}++HB1<JRj`RTJ51Yw|M?3YebJ5Nh-iw&-
zkQaCg?&q-ICnmO-26=oVLz-!!N>`h~^DA3^O;JfO{bDpa@J`C|WsD>)=A~hyX!{{t
zcm>ljW{u@Oo+{^sPAgq5@Qz#PC2Q_Py~UDHi@nlpB*YjYrz?04J|lOfAW@^vls`Km
zIk{S*S(Y0d;}PV}gk9e(|7{2JVP`wu$(?fSKplZj-!fL}%f4SQ6%-<8<Tq8u7hkF%
zW$n+wE}Hl|Ipue?%GlJkCD4PQmJCb>5+=CMs$9raTn?`*(%+H$;s;vjr6D6^JOib|
z6!tr6SlI~|B0GJ93iW~{DyRn&K~BZ*#vRAp3YE=h#8oO??UoD+6?!lR;%>^gEE~nd
z40ChI=B!f7rKx$c5jUYh;=)C)0nO@3lDzwd7*{T$mJ_V!zkR_C;^p&*4kt|x>*6jg
zYi%T%nyy);QV7!i6-4+~kO~wJFeGt1?uY%nOy=|V_fMPa)M<5mX-Q?!XTx<{Z?eJB
z0?kb=QU;M3yX16`R{qQ?N(V_~OM}DAmXwslsUw_udU`U?=JT;6d>kh5lko6<ST%$A
zxat3WZ&q~I{j*vAVXW?~Z!%A5E&t7pq~RdF@uhHo*?oX%vNN<6!sET|v_<cuw}2zC
zW~Iegw!OBz@QMvsJvY<l9%%o(v$kO_oALOWvd<NxUnwae6CXICml2~wKZ=;yFk2DT
z`opKB@t(x_&2rOMRtv`^QnqFE7V*?YyK7GfBHSjl>a1T>E3!FzoREY^sG4_y1t*Wr
z?)AF-*X1JizMQ^KuDsRNzAO&c;kUr3D}}@7-iZxzlO&g$^)gg)6D4J3)|_eRrK489
z73X&S*4qv}u?DN6b+?zM;&8d@P1Ge*b6hFS&{jW3iO-L*EzCKWQ-f%7I<fled9~vH
zPA|&<{IdqaR?&@yEmjDN<1WALS+V*Dttd5(l%l6$v}(D1|1aqRntC1=ujPkwrUqnN
zixb!?{l={{zAZAh5{2TP858oyHs#V?rD+CYz^N^pMN-N+XvCKdV~=em>G~&?Bt0f)
zQ#Z*fZlC2wRmYhPzvj;>71zYfgSrw?bBs<Kt<MwUE_(+vR)$EHTf|v!=3<wjI+q1U
ze%2zbp>GkGA}*=U9$BMI5p_PT@5O8cxSvCK@1kR&DY@oG3fyUm8zF4TuIjgyF_UMX
zdaM(4bW%A(+j**!r^sARHKX6sY6o=WR#TeDjBWO5D6CF!y7sJO2NlQR))L7)lntPv
zp>gK7a*GR{W@6VaPc0HzGal_0?<hHRoVXYv$NMyy{~nPGR8d^LMS7kr_e385GKvwY
zwPN?lFDP)hSYxfwZblFeMG#%C(v8o|q%}C%>gHc-w)>n28_)x_>wagu82TD{FrJEA
zSZ_Rva-zv+(?d8DgJ_ur{VuQuH#514O=W>YThw^c)FvBRHOB-_S8b35rqCOu=^l*o
ze2t??4@EKYyIH~uYb9O!@F3;WO)_GC=P<|t>@*|m8Gp8%ZPa4&YWZ@0iZ6Y<k`V8i
z<3|OVOe+q0L~i6ITIew2=!$s#;gBu%g)m`zEF=p{J5Zp6{9v}i6IznxNv5SCKL4w{
zj>Q$Y)uV1|N5tW6kAx&-QP6{4#p|cBf-n?BU$_s_v9>&}`C)a2)ywi&7jyIVwe`hB
zcTL~VR|^U>21Adupfux)v#e?mp(fh?fy*`P^7{|Cdb7_6GP=mU*{YM{YoBkEZ%ch8
zes6)o6ms7m%YcG-%IP?pKd8p#(`l_1m%D~B($P1YO^8BZ!UqWNY@dkeNz>3EFRew}
z5AB4kKbH*Os-J)}MAq8hG-*0aY192$*JVVMH>p<c3?!p8s(cuR-^j=Cq7Ia|;)AN+
zS+HD+FTrjWdw;9KUOyAs;7E1K7NyU|vLSjm({66b5DgracQ5UMYl4nETW73%(FeC!
zchOq$M6+X-${3K|tFdI)ghRSTW*wm+1Mg3N=&h3A41JrMEG|{9rT9dmT|tUUgls-N
zGuz!?bhpl%q7(#|H4j|=Absau+df+Sl)Ua<Tm(L`Wz~}DoDb(|TYVZ+bv@ZIK=`&J
z^{RBQAIi^Q7!PaEa=l<RZfZqz`#$3fi)u<vLw8RNF?7=ja~^5D-K#O2BhLwyd<HnU
z13i5>DIRmem=902GXi(?H}sem(WUKflI1ZO*NYRRzt4=-CT#0Fx#HMvyFY$A%qqzh
z>c5_K8=pLm&kl7q3-L%oGdEgKFt*vVO7a>Kzi0JcbU^WZ1m-rjj8GbEgg)>+)pt>(
z(?d7XXUwA*`!jm<rE*+sU;*LNBDv-tkDS9f{QeO$jZA_&z9Wf|qmNL-0?M2s>q&?+
z*$(H!upykLFkk`5kAEUroYOFi)5Y=<`O`n2N*|S4jdQQqTPx$BgtTwrd}joy1j(iW
zgA-oH&Mz|Ej!mm8pxf^`t|w-(CRU(60b=Zr1cS#WqC8fYnsk65RUzT@N=)VlBR@vo
zz^aM%;gZkOadbQ*jia&Er|AzUHL-E~Zf!Gk;v00@<d_GN85V-dMx^8G6P$VsXva)C
znDM|b8B=4MbID74XXCGXH%1T!#rS;hyb=s{V?x3fAcPfYL9pboFdm0~+v>Qiit@X%
zWEV5OrKuPaK+R!~lehiV<B)nGxH0@L4Q}ZThm@&wq3lJ>E%bs<#>`T0M<HgRM?5A^
z)Jd4}I!{<Yqv`AGVo?XK{PQ$n&cb+kL;W48mw*A?cS7U4I8U!EU(?U_cVvjX=4H7V
z-_&?fp&4Nth11q3Ph1hE+c-zBNWL6?`I8D<=Q{D?;|g*<3?S;sW4IdsOhsGM;4ye&
zwI%t|jBhvFhvaM5u~~tI>85~LokkojC>pKIqULF|ix!@wt#V7<6?8hQQNw{H%PrER
zLe9+0b`<HlUsaxJl#Z{@djdj2Fl3R37JqbR?&`1dW?w5{kr}D!ez06H6T26#o0b((
z-n6nUMN<QnA&Dk)9P42B<K3499j&*RD&5g7(c(M8NYYi%9++F1#!8uM*<mwp%+4dL
z@PlOM#wby1cexo_OK4e#g2$DRum@DdGc=hwt>QNkHAczZjY77P2DCbTe-4U&Y7{K^
zT*;~9EoDexa;NEJx<<`+kfmyUL_|bwjAt!10c!|K*Jpl@gr%?Zra$`VblX(g4)4yp
zRiFlQh_cH1lTD)&mFH`sTvM6{Etc8l_Y0b7vRG|Bn1w!t_$WoJ41>94+qcY8+wE`T
zeVK(QD9{7sHk+Ot$i^X8c|8*dw@h17>gzZSixN(_cl8SrVUsS0-YFXVeymI(K~XBN
zREP$AEwNk`x}g&?Hxex3CP?|vktHfgQruK8;)OQGl(NdF<E-WHzm9yo9zB1bOaDA=
zaD9tK<j()z74?dwAvKKLhk`l~;o~@Z(q6NSRSSxYtO$j#hZ<fR>u{z8^<bwPQ~mL~
zFq!q*?I5%h&W!qYvHz7BQaWLHdUFCx7&Wlw%rHI0)qnvmD3d3uKeRFDR^Eq57BsQ3
zcZVjHd5Cof6&0Uc6D4;`D9Yl}j8O6P;QC1rYbj2&FZ^un4rLMho-NGykcjH7jx9&7
z{@N0c0IXDOPi^m9Q%nETL;198X#0k!(wDioJ3L=XayjB@`>2{Mhh$wIdqR)>#gQeQ
z3SPpSCcoS^68)kXwA3Jf3WXr##Occ2U1|E}YLw=RDpenbyX~4K--HU7jm7;d`;AnW
zhV9Jq3NAVZ`MK;s(?=F2-}%0&%?%nZ`w4dm$RC~&|9oZf3nE?INn!mhg(T!e+oNu+
zc!bz+fkLI$Shm(N^|D>#7K!_CGEoU%7DFU~xhRy_&T4{ry{WL!W~ZDoaL0!DBZC_n
zix-frLn-o(lu-<y+J!iHLRHo#Tzh&aOf_~;w5MQk=QuD1JrMd`q!Ss+11lh&mG>G4
zyE7=Zswg$Qy$*g)TN#HK?<Fd&U7O%>sA~1N=vLjMX>hvXGxFTlS}~{<Vt0P<)x0`Z
zqr$s{9AbitHWDQ1(|wsu!!b>&uq!kf)${Zg=V>qGt_j}>nRzqsT}?;#1U1agSI_^x
zP33o4W1qX{Bm1$ljC>~WU)k^#F@d*N<3~8~kUweOfG>?1hOKk_CvVTU(izvR)+-8J
zP19?PwrP_}e^eg=4TD~X(guPu1uTp?UEkv%d4fE-^5!GBV>WAIdJsAA^RhEynR{7?
zkAR($dt?l>u+aR9MbHZ^JEuC8Y2>_9AH+^%mI}hKU$7P=Z{>}?JgW|s;Hx9%u2FfU
zv;>eAXZX6&Q6<+=xbK*wRXtCZ>{QY-w4b5dMNJf)DK>iu7ww>SB~A7SCS!$A0U@zk
z^tR-&4Sz75U>oUX$GNW;({E1EuVE+0-p&OvY3;{d9OlQ2#`Ha}6Qj!wcm+kIV|mO(
z2~En#ir6?i3SNx~2Z}RHEPuJxPnijL>(z?;HMU(cYi77b{-Wjl&GG%oz{s_L6iY4H
zwSg%CiGxU($&r+Ip3#v`b1;x~0^6^=<j`*CtCU83*}lJ2$K^AAzR=q$97+G&Tc$DN
zP#^^N9#@?odv3!<D+SmfSswE!vgG8&mX?f*yIFLW?k(`y2T~yua;^683(I}69+5Z*
z?DYfR`+lVMQ<x)F9$T6~x6UQx`_f%F*5c9~3Sr<xCGW~iaXz0_I9nhe=*J_u+N4J4
ziLh1PpMkpT+XJ6yxRRS~?ALH^&1|%%2h{B)5&GNEd)&lNC6cBlt)ubvTfUEW)thV4
zB3SX8N2rUliM;k3<@!3JEX%3H?Kk)>w0K`K8R|yvL=&X!_%8Rh2#j0#p6DW0A-WbQ
zCew4Q8!)+M9e*jAAD_LfOs%13!)ksR&KC+*#QJ7LQ}sX_$_y)>Ygb}P=_Vg-t9YxB
zw@%r?YDPofp@QAq<QFSv7cCZAWu{mz=K=TTNJJiO9$<Ngesz4T^>&`$2cnhL*y1MH
z;cCvYgvS@jqzHNCxtyvfF6$u+)iYtc`0dbGUb2MCBK69zQa?U|AsVlAEdN4VAoe2f
z=kLsO{T0T47OX}{{iC|2nA3V9sXu1L3Gy6G=Gz<0AQKVrxN?<>q%;~W(L>;IA=ZKv
z)^K~)xjT(g@Sgdk(|3dX;X|uPQn-$2-1K{{vKx0~_&sGfYe~A!e_LSsX=jESMtc}K
zS~kG}!cb9+j}Fhlwa3YKIcO?C_F4AJIZAsHoD{(+s8+%_gw$7C+o`}!<O|B%1C$+=
zJaveQW!5(a+lb;Eb^TBl%gNX+dWi;Rpuy@gWQL*WX*aLaVnUMU?F-NRt8ohkm)BO$
z1PQ!jZ70DLyDUx6x*FkB+h->VYo$G4N~w$VfhYc!&%bz2_uGoStbjbX1>_r*CHDvV
z(^#=m3+l?Q@*CdX+hX_!?>6;i_Ac`XSjlk}>(R^w4ST<u)4xnD3TI3wQaE5MOG^&y
z{RDS^!APngDoo&3F5&7dEgV)nJG8!6Zaxv5c2qAc%x#4R=_lWdltaJwYLKv)<?<qU
zG5<}|!<L>Ry{%J5_K~~#VP_YzJ@5erwb_oYd^nrie^P|CVM~R7o>w5Vu7G&ukqK`j
z<y^A?$}PEsM?mn3@(T105qnjWZ0+K!%g9?CbV?f3vys*x7;yic7s}O!4E~vFYB)X%
za4yvbtyOG}lgElLMF$h7-phkv5^%T|-lO-K39S$}Rj~@CrvtUOT^i<)ekIP_d<tHY
zD%eyBva~HYTp@6%Sl3p1XujMM&n)Bf^ld}H^!yFLn*$u}+SK~GeABwK3HJ_<%_|cM
zw4l1Rf0#0A0J0;vSMx4qXNFitd)KOK(06pB%l0+rKbvmvUF2*R;RGL}yQANlUW)I9
zJ0WwK*RV+3&c}CuD~$hX{43axfg#u_nP>(9x;@u><FbKB8Y-W>S+vb2MMdPJ|DPGS
z9_l{@&6<c2M(_bsYZCF8AT0NvQn&`tj~$1fZX||8rkS0cOwRn-(kFAyQ97XzCN&o;
zvTvPdOd-y{5D?YpWhbQ>y>bMG?^kzs(eM=PrYq>$GrLp0Jlm?>N(BS4{2gbUbWKow
zox*2^K3@p!-1-Ts`1DTT_R#UHeZUKF4+uk2#sBEzVbL}@EmZ}lgK_S8l7k8V0c!}G
z^VgMPm@y@8BysKtczpg$?4U0>F8<ZF&ml^D>c7V)1%2SPU9$YNVk56z8q@@YLb;Ff
zWy*!>r`BWmMe3J)A$CgGtmCtBn+jHM?ktzbTAm7|*xNR~1_;qXepi6+UtLrFeDlY1
z<5d`(pV=Y=8x$TbQ!OefnEx1Byp~mvL=JhnU8xg#h(k}#VRoZi6tEh#4?2CNpA<+{
z5%H`0^rKuz;SwlV7G^HUlIw=o=sLnlzpwfg2VV8{WqSKL4IixxjZR%mYS+-);trc-
z#GX@ZLGtb4i2iAz6xt0-Ys{ESe@(-7nxLD6BYC1{>nuvn#42xoJO1f%*-L|`xS#=g
zXX*Aa#}Ckx!bS#DP3_;M-xU}OInn=uF73eW&T0P6v1ws7!aeUvO}tWWb5hUpMjc2I
z&3Qqv1+B{-B1LSYJv*2Z|5dAsh}K-C4c^P2;%;ySr3f9&B{`!DypTSmO+{Yk_cM1F
z?jwYyUkRP$ayabutNQ)6j5yO!mUckjyU38(R5zy@95_T3U-a}DTCa@fv}sV2fZ?6D
z<W;C#@>L)cjHWA3tB2;AMxA8z9T20W&fi^=iegL(3^(MH2oC(o|DTEXADW;`B_9)F
z2LLZrBs^dFbVnp<kTTWskUw=3J}2alHI#_dPdi>u8J$S;HAVjNjWb;fjG*6%1I85y
zZE*7J1^PB^a9NqER}8=28!_<F!MH5tF%4gYaP77?NiNO}iR|23*1mb!aFLC71>csq
zRp5)H|D0_2x?z1>K#{_Yg*<zroE>cL=37J~_DV1!XG+MQ(H0_Jl-h1ibTLsudsOY-
zWf$oCNKlmVj!9$wGbOXw``!Axu(t#S<eil|y4|syO^aZlEb0gg7ulfW`<MmC+WpN1
zRB62c+u)2VbC9T*C)MEE&LhNI*}c8;9ghp~)(qFHZu9(By*Q*<!Nq4l(F_QDA^Q7C
zcsMr`ncdAPcf!_dKVN_E$emz1g;~nZ{RhynFE#S+ED@f>%-gYiPRQ$~r(gfux%8lx
zkydDj`}sFoWW_grVf_M^S8V0F^F7tFs-^-*ptLs!@Y_XawG;I6H=y+CTcTGdnGZYE
zyC-5owi&!9ZaKZhBR&#-Yz)IdZgZ-nLBSR<-GRy<&G0?8Yf3`pm|sXl^BqErK`lx8
z7K^4EC!{vVH-a<o1rJa`QI#a&6S1z?)+XB$r7!LiQN0NaDCMpylJ>w5%Ij-L1LI5<
zo3)=KWAZVx%AnplkS&!{^S*)L+Z0hF0d@t8eUULPS?Cw3;bib?r1SZB5fcTb9pd|z
zzM&K+eZaYypCmth)_DJ3SJKyGkk{rJ`SeL(AP_`)KV>}Uu*z8&H7-g)%kDzRL21Et
z6q6D|cY=-O)%sqwGE77SgBgfHywlEpHTFA^qxm}&gsi;4OeGeTpX_g<i*UjO-B?I2
zxKEs)?Eova${u{rVbA3&e|2nwc+iTD?A&PK1Gx?-(DIZhu|*iwLLXfEprTI(fdQ9y
ze1MWjoYF!}T``%3@_zR}Z~<4lNZTMkbX1muVbF!D^*RetB71)vt(t95EQTs^$?q*d
zd0nxLQ>jpEBH374{YlJUv_T~&o{#~E_u7&pq@E*Jug8tqEJkM$?!uPShL)?0ISMSP
zvM-bPB8kU(a1J7SMAZ)y)k!MweM+LNPCicY#<QX#BPO5A<_<li)aRxlMQ^PjrKdKc
z8rISfT5(Ea@il70KJ<PsRGwG+@p;kd$^k8$ogTD;HcQCy)XhiH7m^i6I;4kTo2r$i
zQ2758seN@ECX`e{;bC8kVR<aTYNo~$W=b-eexl-H3Y{5{e5*6lewta^xo?{Tx>F#%
zr|PgQ1ji(wDGVvM@2F=8pjq%q;mT#)bke6i7e|ispO(Wtp`ivpj_S|+o<`>t)e~OJ
z2UL>rOK+NVy@5{3xa`j*N_F3s1yfm3NKA)zd0+=ypw=Ukgyc8jRtjT?@5R5`PC`=B
zZ14`}^vPaj**f)ihq4D-Wu-PgyQ}sMzf$TlzW&zbe$@${xF&l?3B0oWk#bOz-KbEQ
z_i!v+EB8KbYm(ji%`wX*-7Ue?^<-qoZ&=EL&Rtf$3Q;UKOPL5RQ}AV8DH0gM$~Yoj
z1qmi!5Gr!LD0u)RIc@4u_`1B%-y*q4RyxYkUT#^k*qt?|?Bz@v3}>ahNSSQr-OwmI
zngMcJ=V50r?Gvgo#TkfyViZ~z_I$?NM5q0fKC#dYm98k@{1slRnADv6{I2n5I#S>n
zWU7%u4!;F-51mdd2FaHNMLuspJHv+X)1uGMZ$X>fwdW#^mM6g4vtb7!9*o8IZpayc
zW@_w5-=Ov*Qrj8g9ZH}l(C5e6<8r>mxPZ4%#_L9bbteo)!Y)Q(FQ3JB&nhgn3n;N&
z_CRb_hSabCxtP^wHHPgH!Cq<A3w&0@2S40`)*u>uQi>_u@gsIX^_KAd2GRtU2EqFl
zH_TDOeNj!bwoe$H%H>E#=(S%~<L6`Jfn0C2GE2gIEk?$tmH(CgKhqDSf4qYc(wa;R
z#6OsbrJsm+Tz9Pzb9E_API?KZ0W`_q&dvGM`yxi{oM)#Td5B>HD;p?&a4xqxaQe)w
z5e`<QZL+_c+RUiF3lE1ZaYOQPDL;zK!?Q2UQy3l&B~ERmKz_5GFLrI0|IJ2{#tGa@
zCAoUCc(HDCIJXhEJMF|#V<_%Uwc}!6a-Ged>6!GSm@_%BfBst;eC<1(w3!y)TveCB
zGJUSKU=l+auY<v3sdiYMy`!LpJ4=jWYjC-0Q_W%`&OTFExkek)$)xEoJ+0S3kNd9j
z2HO1qrGY$PC+Mre?9ZhaCd&~C9qSwNELHigUg<-_sUgDLM>CCXtF%oSbutEPOdoir
zGu;-;CO$YX;ljH=$x1jkwGP`SlFd%6j)N1aKbe7tlP_-%a)^??1tV722clVvj~4y4
z$~ldRZ#M7BWy>nR*IEG#^WYuF4O3a|D(y!6@RYQi9+){aa*+1dbMQCDET%Q-y3w<K
zI}2O-BnlGQd5JIxmbb`fKt!EqZXZm)JpDJ--SS2G8l8ORJ^ZH&{+4<VnGl!L<VfYv
zsU)X(ZkGj4<}zl@4sW2oz0WO!IStP*9Vfpu8kdE#dz{e8Jg-?j&Z+}tFj4g4ZAY9r
zRZ}4~IG++a0qtJpoi!PlzM)c}xzB?F-+)J~d)Wr+?c>&fd9Xz@ar)poXqRI#7h^2j
zlgp}grE2I^-9quN_N|uj^0lyp<j~p2PHbLh&R&eeGz)E|wx+9O*zew|$%3hpJ?u0H
zM8Rt_URK=@zBo~JECPDhMIOI+4WR4|soQ7~1)~j=ztt`%Im1$hqa<tZ7=-Sel$OHe
z&jQLyayA9-H(`;qpV5CKyx<j`v}&y;{94@6eTvZvt10E(Q8fd9n8u03u5J{P94nEz
zNR8+5WjpNBgKgAH3(8Y;nr$;wd`d2l-L~v6YT|e3v%J_2(gx^dZR)OF-DJ@>HawTV
zUG-+8{ye^g9i)NoR`P%1u>Bf<Fn`!bZ2d?iQ^BcD-K^hQyrLk2D{q-_nz6!?Lu+xW
z_4%1vMZ5K{@J@mv7sU*_nE&v-y+AxR-(hbkA^%S$j>G9NYEtS?9S+~Xz#ySxB?#!j
z4uU2=?jw>FK1K%feNes4LKL<L8U{w_PnnMKdk>&OqqZ-gkj;EE1ZiT`8ABI0d2qj3
zTO)Zosz+aY%HXhVUMYz+(M#g2Q+AMm_nrT19?4FU?RdkcLV6)={pWJ`ddzKe!M03&
z#V776OyyaMl34uZV-`lo14;6}=#k5y`o@l-33LzlyzBPDeW=o#C@NQh*q2Id+mHcS
zcb(REEaC3;h$I&HoB6eSU}&>YpydZS=_pR3UV)QA{0Q*Cf9|6Tf@vLbt7qT~<h?H*
zZqi`09c!q@N3Jt>%<rC~+eSx|$d_$`U23iwsmCh^p30LA%B&v>Bg<fZzQ6hU4?Xi~
z>pysdyPK@I*E-YHdOkbj9a=}!sFyL@99au`LvqBL{jbS`cSQmf=iPRGPg0g*@yA*Q
zrnoc&ijO&l%e^C(%Sq|+E4??Cs%v3KjU0o+y}H5@AK!r}4$q4>i{17_onVPLUWE><
zXp9c%R=Vb)3O0<hDjjR|gn{QGWg!_hT!3062CNLn#>ee~%ELUybHA`1rzK#~*yK`(
z@WQ^F?7@6u!IsFGC%OjP2@(Wq>MJ6YS^u&?X&?;Nbec|)hr#EM%9H)nDUG7PnrCE6
zOPz^rQ49|XxMI|KH)I;C_ZqtwplW&2qF=5z6Izsg9vF}i7ObkuzM7>WyIFqGmfBpL
zPvyX3MOicY?trM@fw6#EQm|am6j>2@z~*ARgtXvohs`y`>wf`Us870Z3N>l6;gt@t
zF!=2G11#jgXoZCDp!%}*or#U{3w;A;u{qc7MiAtN^i!CkHSuDjxL7thb2A~sk+6ja
z)<U#mW|9Uq;(=Mebk#e)gJMJMolqK#m*cq?+A&hZZM<}0;+MIi=a_#v=r-U{zPc+I
zRP2&A>rL>nC&N`wBI4qEyMvK0yIbvUh^Q2DNinym;xFOy_os`<NJzP<4oMkTSN0u`
zn_fzx_I*LnF-o-M2kwavpx^KT-(pC)TDLmpzgEky%C!&Ahd9(f=A+sWuavn`p%vHf
z5zIzC`^VW^b9;hWpsh|0YU0e)EIngBH`pm?@dK`Uqj>)yjo?+NOB*v{qSZ+POuNoh
z=wXbUypWrj(zgA$YCyWQUE$=9S9;_5fkMhSfM5EJSTL`ib3Fpo$#l@{<{@tj2cZkC
zvOkXBa1a4u4aVvr6HjhmyTjKXbMn>4rARYkxo$1LayVh0og9m-`tZ~!hD>!s-4@(G
zqK2~ciQrI%5sya@ZCML%EhQ^tI3wX=ZWbUcW8g?|lxpuP7S|vSb*>4>Ep4v_$5{P#
zJ4A5JYzYo*U|juC&7S90KK4ta12AWfw1Wcwqhiww8+pM4vr0ZPf}HykB0SxLQb-*B
zgQp38GrI}Zuzq;@@M2gWl`9{V4t=z8Dv%3zXlk&8P|EmjDSS58d)9+J#<;&8*2VPg
z*yvoS<Td|N#AqvOTCf(U0RxXL>oyPAHql}>9tt7$*6{4EaW<1^n=%B+q^x#UH?4=b
zG_NMn66P&q@9X)sE0_skgv!v;t^_~CRA>)wvo2H%i&fI&r=4<btVJBPBR{#)k-kPu
zR1pbh{d(F0XC$)=^)i^1=c71~Q{3K*hY=;qYw*nA$u?rDk$A>ZlXe96ATc{h@hc`l
z;7#GUNe44+xq*Agx3zl1(KFPU?O~+LH{AT)f0Z=A{dxESGmTd2^=Hi_BqWR`V`yMH
z1!B>C6`J*MZ*{MN6;^hfmPLFShQ=wM)6KjNM^ktWmWwcYdU`F62NBNBiwzd@3hk~o
zofS8S%gGcqTu%|3(tM%1z<4nBd$5Q+gi36FAKp2`OT*zO&YXr}apuQuJr&R<f^IUO
zD3WkZAc0tuNTZ`xqT`<Z-VtBJrYo?No5Pa~RMA25Ns?W<GU+va2|NP_fW*|Pt)6ED
zp?m#aS1?x_5=zhW<LRf99L58eeZd|GrqAXV)Wh>c2vPc<8i+uV`n?=P+aPk*h>UU2
zESyw|M6m63GI6A8x!yn4F6bSl8(kM{uGY=!O`Cc1EsYkrL$bS(<;gyg?ex{;o@&py
z@W|h$d^_-?7!pNB?AwlJqqD*2&5lu>Co%=1_cszKKc7wPFBWm38}-P)-qyZk9#>!_
zKAUFY=>4iXe#<~_K>A+oO^ZeZ1ov1R2u7vIZPKu?bWfSRB>P^`(x8{aQ(2W4%O6e@
ze0ug+O!>I_IaBMd_nZp!=X0VbbQu=5aFVmylOBR|x^Oati}0z#&(JSVvr^p-_-CDe
z3U!308)72CdZVpz6$GFhPn&ihSp;S>o|2p&zVVc^{OsPAD-|APr4@;Gm5_je=T|%L
zCn5rl!(vXD_ThH<d)2z>*<$%=&X~*g{0)}(sM>E2{G`?0fMKC}mm;_&gL79thAorr
zAzi)%VPtA@QL~G+MU6@osl4k_vIIh>*Uvd<yQqCuLQXa~G$WCPW~0d$`>q6`wbAG}
zDLg_M)EDm&MeU+BtdfP<_x=)fv~WwZNDzc+8MFSaC=xsUoMtc5i&1&A!qlN6?;R2$
zWOLAjI@Q}b8#BvRp;p6pq4G4e$wj)rRFn?Y1m}zSj8ILPkQZH;(Uqx^$I?Z6l*?=N
z&e+DVvltH@<CUW@2~$78QYl0we<NSZZ1|W;a|FLy+=<WCDtC)C_j6<s%(FQ#ARYZ}
zCOE~%*;dJ)O<yI2bg#1}|L2n@s<VwANcp2qUoGsTMJ}l((>#L^1rZCvm3Vg($S=Lk
zn>pz2fX$Z&g`QzLBQdU=r3RMmJrMYsvwhjoBj6{!fiaNF^nkO62?Uq`@=JZLPDU+g
zjV7mpvb?`Uu)ZJUK6@5U|G=M%1?+bxsAh%cE3ooLD0+$2K~@z!dOdb1DLe~4#h-(R
z!s^Q%d=HHeScT+4rTsM1@gq+4`?PlQ*xKK@5$E#T!Ruf>A7Py0|3(G>mEgfC-!u01
zeF`&9`oA(Fen9i^{|O-6eM2(;E4u?E^lxZr1btDRKTzZU!qS+Q{{ace&LJxKZx>~H
zcA&2SKl;^?G2{LX?ff6k$<Ll!)Ed{gncJhdFI46%&c~eG|Jg@eB=j$pG94XHdage(
zZ4dt+!iWG$?vJt84nVfcU)uS@2yuY2Kl&UjwOUGo=3oK|{R;TA55XiPB#_!V4(oSu
zG0D*5{wLD=n`o}(Vwt_08$0@d@87cr?Z^s>!)rU|XGVsGgb`$Xf6PJf&olG_B8b`W
z-}vIc*T7Z<e^dhM0;x+z!G8*vLUHVq|B#cD+h!P<P$3!ruOyfnnI5840G8Va-i$;q
zlPZGAe>MmMI@E24F6ce}Hwo9^f`m2zko4oMzkF|I>%sn)7yFk9_rMDD5YYidj`TS^
z4)wdb0QbHBNxs<^1hoc<gQQ7TH3V&q`ys3a82S1fsYKqZ{+|;5=c-MM_vFz5vgKK0
zCjZMC|B=oJ_*X}Oz{J})L1`|6n?hbxZ1sOKG5)D?j*uXsdZ2OKNRCwh#=!lR6vV;j
zuVVnREs+5zOThm=d`QR>Ow^Y2gr?$NO5rW+N?fdF#zKpbxmkmJ8p3<LIZ31)psBk+
zbydEBe*p@Eo6L<}(p)i!_la?=*NsJgxl%E|2wz${Dk0|*Qj$@x_?4Tlm`7+Polc@^
z78%d#1=skii!UNumXyE-bx$eeAY-nVE{AC)7TWq-VBs0Pd-JJH^TPp>O9f$q`zNUD
zCEQSJ4WvZz8m(p{-<q>!bi{MzB5kA5M!TlC3&Q6IX3Sx=vnZ9-ESiVeeWg1l`zI=R
zcPiW&^TP{~HJ>}k=3;xE7jHjX)}qNwaq1K~MR)VWHPQ$7@`Zq*0Jn3s?i?uS@JzoD
z2xr(|%z;zr2OWSZl9v^NLN0iFzwy=m?l}Gf_4yl0cxi1*4Ulqbm{hT>w8iP1v`XqR
zZE`^F=Fr*08Pg4aD1%aj`jUV@vcL`XI4s0HxJqDUsPxGc1|I5Xm<c<B8RmdqTTreI
zg&>(q3wY*Q6D%f>yZ3&RQHRGDEI-7tUYU$v%-$Tzc4%E5HT0K}ZV@kM^OvnDt&o28
zU4<eKs|E`V5I5lm!=2c9dqUz>sm0>%j8b_!lKowWfkMsjgpJ-i`Vbwais^wfuWvRx
zrkSlLfiM1pb?xA7ck`<&RKoMCKevgX6F#qJq&d8Xui&SDh6NtS*M2|$S8?Ny-uQnd
z`_2)aRUj%kt)utJkOP|Q4`u@@fcl%7+VWPAWVWEfa4e38m8w+Y^^zN=2&F_3a!vLN
zJMp=~f&4Js&v{YvUy_1SWfQ!~rcYTr=jSl$Zgcv_g*T#homDvQEzh8rRrb@8Hmc9F
z4a%ZlH=W^*a-79F-?U6r<*T=QjL@kc%3>{}+l(yj6#1!Q7Rr(u4cB=W@l?O)h^nqi
z{!K1(nnfNzqG=F`Cz+9qKJ1XB%I3m;z+7l8_r@w?7|n-}n7_Hg&UQ?4ST4V~CF0bN
zWu1GRi_Xf#I|nD9F*DLaZPC3UT0EZ_y-~a?4A@?%j*9tyP9|}3cSgG(*Ax38dWJQx
zUE+x#`t<$>u_#8chX@kz6)gHdiL^ukl5=q72Qi4(=Mz|XmRP<rXa7^Uo4nCM0&5F~
z_y;us%LzByhGptEw6Q#j`L?h=QWEE3>bFi{f=XQgdYKS<c>-7V9qL!X`o=a09jz#S
zMA2So$gBI*=5Ob>JHLp1i`F+R6bemAo^0Df3XU%G^qA?k!e;g)ia{a|{e4?noe)ws
zt78x$S^aU-W`Vl3Snc%Ul4NvR)wp`V4e;7t>2GIYxSM#N)KW^JByWVgK*`=3#I3ly
zh|UJ|d3K&b?Kb=N@-!l0OE)JB!VV#HkuQ)WnSHEX=reu;-@p&u#GXnkHJ*Wj5?V^@
z<)1eKcV+rwQiD3}DqGNfQ8t!$*5I@+6j<nBQrl~y;=hb6li7vs<E6F?uf{1;ip3&g
z#LLuG?|>*_tn8`_jv(^P_tL_u2xplaow;rG1pE{1i_h|+D|z=Cbq7g>ImPy>a5!v8
ztQ~d+JD+NK-$R!qxFj&Z<j;2d*<lPWx7cTLi)kM4=eoMTl)3a>3DOas<VdfW>kNEL
z?c+QXgD_^my_1OKiCL|G%#*kWh2r(mjX2RQCW1h?2(RP3;U&Y&{_74|!|$)d2-ueJ
z0tghubM+t!#cZo2LBfNENlE*A#RC?okNkf^DoRKdt(rt*RH{7uLV9}{#vxU+h-B9<
zixQ_`H+(-`o!Nhd+Zgj$>s~teDtk)Ya4oRd#){nRM&$AV%oS??I96E%D_No+GKkO?
zm`Ire&#179?@2NDyw(5OaTbfvsolS&ic4>1G;LMphR#A)+}pHwguK<j!DGSLlh(f+
zr&WbZ-Cn%ox+^>o!Ye{dX8`qaVFu1i6`9@tl4m~QL95x0&0pkhq*NZEaPd3B$;~TI
z#A5qqR9b<o%>Lm}S*2<>@=P3oaI^p{g6kkNTj>i_jS6x;y;4EH1N{@<sS9q99+^^&
z8#4TFT0}7ceu6i-EWgiUpDSNfKb@^s@QmfjK>b!p^QyM*O}br4DL;nU5e;W$a}5KV
z%#}JRBZexS!nnG;o>4S&(Zmi=Y$Z^~#^n+tQ0O4O>(Ia}E!3`<RQbNDCKyt7tB2*|
zmu6<_Ru_gIpNeH}1f4*}5{2J++34t_$*BtKWg&UkBOlte`)lF77FcL*kDRJ%e4H6}
zyc$T8k`C{#9ZuTQ;fC4bo%Qn`fbJSrksRAQ+<7>0-0<@Fi{dsLy$%Z8Rh|ZXT7NIt
zH&VwSX2y$!G(+55AbIv$`cifJI(LJ|UfQ*5Ge>eVEBsqNrth8EvVlM*Fj3B}#&Q&6
z;$1QtkslZs;Hn<O{6FNSp8`|JBj{iN&d_1!slKbvhj_e}N~i}b#UKcP)a-RIjQobp
z3eg%?obL?c*(|s(y#?j$gGo@<3)rx{P65b|bZnB9+2JI5992fNDn;bw*bOnu#V+>}
zADhBr3kA;!NcWfLX{h;R`K85n5!nU0PCQ8bWWifc)tv-tnL4Kti%i9G57u)HCn$9z
zeWCAbtY3+5KYOZJN1S!&TWB(Y*#mkvh{wyQjnZDim6qXxh*gF=dEa&XJScI#)5Pev
zM8*_lI$ZiUDKX45Q@`#kpliv^RHw|c#c>u^(`&QpcBXfR`%Y3Tm0+3AJ7WsP4kcuT
zq>>`z;stBCd||m{$1uSO?oX!Xqh0ZCx7JtBu-_oc$@jZ|QxfO?T+BjH9JZip0Q#39
z@!X@A1q!6H;CM?*7oFk3-mfcj1LE+<87~Lg-Ltqp?cGADPmtBBd1K^X7H8>9BznSq
zmgBYRNZMG+K+#p*kN9jHOh525TeJzn(Uyy(<h}33P?h-Luhpmiu!lVTSRmkUN`#ba
z!L=?qW5Aa2bDJN*Zf&;wSB&3mQTW#0@di%uwa+wYHBqWowFz=<JaD*Xk_LY^?_D3{
z2y)<U;@RLiC#u<$J~RBq=d4Q+sKNq_%%)KqZL%<vv9(!o>`LF!RbS{pBg3Z=Q<S^l
z9>qs>O*mKQvkp;D`p`aGlnGi|xB<6Ii$j)3mk9^t5KWzlY~oP-;CmgYlS=pHJuRXu
zl$CuH>f#_+J}q-|j-AP@1`wU#6U+&~_kxjK=frYk{mWz(f}#du^<qS@GmBRjcFmP$
z**hoxz>^;}61p2a$-5SZ3vm-<Wv`14z=G{r_Hpo4oCbN11Ufz<XfQsU(2=7H9FkK|
z;Yzh;2zgCVTw6=p5;LnRi%82A$a1k3v=Y%sDU^MF>FlT#Qi*k<M7x{?)?5k2p%r}>
zpn>yjtQd5~R#qpQtb<g$Dt^K2hh4aLjN^K^xzBr~0+ml-rL;Bfd%t?>*6<ucf7deC
z&O3Qgt(k{k34c@-qo%R~@+Y+(4|pvfM<9ka^%Ydsf3spZQOe^8S)`K!14VQQ-4s7+
z$$Hvq=7yIN@7J7jsw`fNPpGJ*krYnGy=f(%*TW~~P{c{PCG<($ZIsrls2i0eOlmF#
zYLe;>#U^41&~;f^F9teeV2~_Ew8ssmfP<A89*{;x{&e5XcVOKfMf{7OJ2DRLI0*P5
ztR`Rxnvf8+RFF>d%L;{MA^h#QUGQptR3wu-47VydE;id`1KFvzek;fHJTbvsP4y%*
zGCwL7LeKzNpulhqNftCef1ekS)OYe2Gm|^VGnVJ^1%3&CH5$(%HC}CRV;=s`%K`rJ
zE&%i-r=?@rQc;V}h?Jsk@mY@v;|3yS$DIX4hP`c7)(9`lZQX9hOoWfnN^OgRPD>xf
zqB`!I68hnkO6UDNePtE<60Py;h{TAjkI;$1Ms2WSFQQBQ{4B6<hK_D66vNv~!GRie
zhKLx}bdkOIhwDXlYX)m-4A>TyN^`%`j-2r@E0rKuYLVL!HUtFLZ%)Q6^BtTYv3aOx
z@W~>e`)&U`W%pdw=ZrWfvVL{0P2%<iZw?yb>5I2ZC)Q@9Sbe2s1(|$1_Hmxyw!P?m
zYO>31nxKkxxcLrsgRVvt<gKH!HdU#VcSl}^dy*?qoquo)WSqCznq9&2nJznSQLQBv
zsu_#Ir=BFy>COpaYOBXrCJ2_6hVIa5xs|6}{h!c?0m(n}gU$}s^4+>kcJ2W=|5*r?
zxu;K^!{(>Aew!`quK;$#4<)}lI|8WXtHhu8kNEq*Zzzr9VMISNoeVdw3uw$7$Y!OR
zb3i<1XzQGI9HF3SqkJHjzaJpWJ6Eb9VY<={Lc`DtL`X6hE7dl(70)m@s_xokwt8YN
zjwk;pQ7lnJX<R0t)^8-2t?_+EE;U-HGea-LnKUgvq=|%B0B&(TGPon2h0NK?*ShTx
zXG&Oy;keKUDHlQn&olkt7MMIF%PpQ9Iej8E*orn*Ia5cSGB&HO)c2w?;UZ9k*=tjf
zA`c}Pyh$EbxYXmnGgm>F!rxS?;m%t5UaAX`wBU+0s0q!)6<WHN4}awte4#;bhlxRc
zFE9*&z9X8oLxQWeO=(kyy;4q}d22ztU?-`&0qvPjBN>yFbid%aKe4r6$f0GsBh0IV
z+2Hp|r}O3t()8YiVnU7}2Dq@OwF5f7DgQ>NqdDr|@bTNrDL{7j7(gnm(V~H{DMu?I
zhDyy}mR{C6F>Jk{S>XU6DGC>NeOk~CxbiyFx_-7|J%>JudX&7bF!^z_$R#TjG=z@c
zk-5Kx{w)rQ8w4MCO!Mwj#h{oR{ri$ylgH6O0IZjW<O1^Nt*2V&5Y;^Bb-R|)Y@@EL
zc?Kr@Tn%lH<)|zLneOp6i^1P#Qk$QcQVkzoB3prHUhD$x?5>`AQ}+<N<y|DRbe0Hg
zcA62e+LTc;TfvuggB2AyVXDOj-22>JAh@aYp^qg5M1&NOfL}a%$!EDtG21A3=<3~Y
z7Ny*CGn%x%shJF((O#(6-3&eHt<0nZ(Pm_D_*<%433;<KOh1Z?9C$czKn&wTxV|QX
zMUp(l;lR_xKX?1{`qN8t>nB|(XkzALU1qa@$(6OKT}Jdy5*TS-Sx!JLVa&8U!gj3Y
z@ti31dbf9=2<`A)DO4QU|Kuv}O%1X0gR9UBxDRDByd=VNx6GN;!}Grc;gcvHwZFa3
z08dNJ@G{Wd&+Mi=Q)X!~UcPaEy>I&YhN)@&=JZt`-UpRo{<Xyy3|>WY&_#H|7g!MQ
zM@;9;=)5~32hR$?6n+1HY<**RCEe2YjwcgMY)mk*?aWMU+qUgYY}?7iwr$(CZGU^7
zqvt)}xvu`PvwNp|t?E^^s_v?~J5Nm6^ySEWde3fuJSwmp*7BT;zX2g{VDv~PM;>Q>
z>`5S`C_9hGV;w(eo1fgM!^gq(%KvXst0Q4n&Xn-lY*e~!?blSM<zCTuU|SmmDusOI
z*MxL_`SHukthIqy<f2%eFi8kjDb+YCf0++4uL_^oP;BFA)T=8dF20(f3>qfsC}PFx
z{j8az7)zW|GMMC8Qfb;@1#{tqA0xQN*Z?P5scOiAx-DRoFD}VADYdsN>Xz6PBwdJ%
zY_cyCtr)S&AxF9xeovIO2L~<kjQ=!liDzvm`BV1?LmA9BOefag#Jw{^P&Aj<wmuxe
z^Wvczw}U1ICSigFkymyRg(OC%lkjjWDPL@C@4Q<k8D%6~8)X7nA+F?uX=d-$O%`dB
zZ&7gZ?apEdyL!fgQ*3j%96<+)^-wMJ4<)fBr%fPibO*;E)*(DLOC<$uL(0XB>OE0`
z8Dw}VCnGR!2;LS>-)C_z7kOFkNn@-t`j{yFN1kS9m*{qp-xZ(nX6NHKM3$Y0pK~w1
zAt12j1v@1Q3YqJ~ZoP~}$d6urIg#mgD~oCOHJ>5cUzQZta7Vmwzd@v!Izp=r;d`bL
z%gsZ(Zm|>^W1|EDJDS>{=eMcDLqofRr!eCF3Pzyfo$Em6md^3sx_qq&9ZU0?<Jz?j
z3}WlfD#BESN@B9qojrxO+GaUiQ6ldA70q(<#2@q;D{=NH`{$|#aPRv~&+!f{xfMRw
zig?$bvBg}8HP(KajErk{7G!1@8?C?7)X1*k09%Fm;DJ0Z-Z<re@eJS^3Fa$T28~MO
zFl>@dq&hTayM9TaJRzRplW=<7)3>Dt!(KKlpOYxZ*(rhJz!su^NK;x=p6;o$R;V>T
z3hb7=g=ZUF$K5~neft}R6t(te@8xs+xq17KZwaiBq*051(WIwm<>%5nl(=2z=ai!U
z&EYct!}Y?#r;GRx$ZIeUd|KAHBVJwAk%r}OKdVmq<Cj(HuP66$v9bS8c+4fnyT?+1
z!s>W;JBG5K>{@?EJMUk`@P8wyA-MQrJ-A+X_!_F>WrNC@-{0Tgy|Ti2A#Eh_KYr~E
zy_oFXqXs}Bmj#!RkqHV4%5q3+jhY!h^R4+GBt1S{KllU~&+;S2$7SyLqj#lLRa0iK
zB7=jYsX$^(NR1r-=BEIWLIPIALiwT?{iT%D;ZXx1+(>YNXuSvm@KJ>Y4FUn|DXdba
zU)3;#UHnbNKrqmo=*#8hrFU#>57q_;C?O3PP*9Epo0o2YAX?F>p~MG>P52I6RS2W5
zzvF~adEmQn<uyz)l3{<@a}iK@b^w8W6zA)8V%{rNxAPZ1Y3={XEY#@;3f~0Ir{cad
z-hNdhoACb)2kA?!%5`r7Q9|TMFL+XC==V+?YJmRhs{ncICgtCr;=cv#V??@n2?ppd
zzVh~_NAxuOzd-9hgruDguo5s$M-=<p!}||rK6nRI8G;3+uay5A)8_C|ZvgLX7FD6t
zsgQuOP+7htOb{rS$Znwy(|;;$-sf+xKQyVRC$Mv$V&C<9zaO7Ta3pPjYX3jL@UA3~
z`$#)~y*Kg#$XBxp%-jM=Y=p(c7>S<T*MVU2<<-^qU?h^wy*-2K*b`#|5lKlwdHJ|o
z5pi*M9so;ky_1kIO-$R(-Q^`;_7DHxk*TSvOZ8C;Pn=j#ZVg~l@2ANH)UACl@0VLY
zd;9vX=u5BB`DvO%x^S(mtRA19L^f!f3J>Ac<UL7o>P;Cm=sbBD!o!IE?8YO|Y_Ig*
z>|IK)k7&I173xi(T3T9<FE3*0x2I&G80yYIUL9RsUFjxUgCn67+i&Aj!-O2K=(L)n
zjx-Er*&P_}>_CbbVCx*Io6!q1>9s<=zqdCaC?eTL%^C5L6Dcsw7z!v9tP4)3^GzUI
z&gJc`Uri08x`sxcRDFkE+IL7$r3y`fJkj{g?d@N2adEI)`CPN6%)O+{%&5SQ*FPLW
zqVW_4qH%BL#GMy@;aR6t{TG4hK6Za}U{@80d~4?%_aXonvUMDdhAOWb;cInIuuAf0
zR0`pmBf^OgR7%6cTXtC$KLJ1P#=%kur{p&G_@3$;itCs%p40e>YU)qpz@FEMNi^GE
zAEBe)XWP_2u9-Pk(uLJ_^`!O_meZ5=!XhFB+ufgjkLO|lq~S#kSYaeTo}dTsNFZVP
zxkH{&<_)`-gEaP`FFB)K3?p!SHxPu%asNs}EC~QpCSilaypMh2!G}pJCqf-b@rdc1
z>|R$=BJEDUtAh*>flNC!*yvrI8R=h}*auJhY8o2~kS)H$J`Wm5*ZZe!(w@+2o%MAi
zvVBTFTYKMiMNc~$T1Bzaxu;Da3zp0^k0xBsea&6cwD7qRAtk6utT_6bf3f?Fq$Csh
zbaRlHU<XXgm}t(sKc!a+k{R{vJ^W}_rG)FipZ{dT2GlB-XdtCmQlY|Cu3D0Kcl#R;
zxzrB+YlI?N)%$9>IzR5<9f6j_tV)90o_srp0JxEy#g_lp1sRTQ4(=|0hM{w4o^*Xm
ziqRA2AhaP2h)e;nk$oxVJ~WD~1KFh|;puH~<!)pd=;>vV08KQS>Mt9p(O<^(P;G?<
zh^`~eBT8yIyso#x3~FfCH03m8`&y&(h0jY)h+V4fx*q|gQW;4SD{E^S%kCqkeBZ!I
z<4I$OR8Y`ehMtC$LJ?0fj3cjsddD7J&}52ifcPRA#C&ji4GH3AzM8@+oIL+)1A0dN
zI)t_Z-zD#x3IfpLQz33@IiMX%FAV%3`#ruFj?F4Q{HdneR-b~FWqkOvQ5jYKbx>HC
z-=TX*IBd-{x0p%>vsZLtTBdtlLc-$dajI2&*H%xUOtrp<$m{#tOA?z6VG6UEA$2oK
zMzZ_M?HN%zhD@F59IVMko0}3AP7<pXp6PsP^4C`tF7v%hDiiF1Ay}xL#%&eYu%J9@
z-5rK17j;DrGo0T26oF7Q4;s6>(L>sK$WTYaP~;6bUEF%|#AGgsKJ?Z;awLHe6nfM&
z6W*yUnUta7hh)hhA}&@C;EJvz`L3eaK740FVcGq5vsgv_DVr6K0Mn8TbrNvaTr1EA
zYMI}Q>N&(#XUQFd)fV<Fnf+)qz+H<qiJONqHI0|imqMNGt<--3;?RnP%5nQM7AfRY
z$Sn1mVA`S|t_&(&<xi5dx%e%W>8iLuo1{@`Lkg|%y~m|?@Lkuaiuzrekzu7KON-zU
zhCf!<XH^uq?5Sqc9)+}r-fm~48a>}QEZ1Af9RU93JN@B#$l%@G-M{N<Vo9aOV<Ix^
zst5N_I9)@>_p9^<2!;tFFi|*%S*_M_D%$Q-ZmC9iPUaG-gH&v$K#*}e4r~Z%*?FH#
z$+@SVKDI+?t$~z3Ocu*1zdEdW-yd`Br+-nKe0mz)rL;#qDFBj_(d1w?dc8kELBZn;
z%CAF0CplLtKpDtS;nkZj{c>7&5#q3w#WXzPVZ;fWL9~HFz=PEZp343S<^FcJ&`&sV
zXCB@I?2Z2h#}kIh2<dP<>yqEfkoz&g`w^zm;<ywyQ7Ds3r$6{5wmNx8PLeOFY&?Y-
z#_j%eN-Y<KMjhkr<zXP}yKkk-jUfu9A}o+1EhblgNg>`I`UCp|yIk?mKa=+quvaO#
z?eFkty)TJ!&IK9krjJ9ws1$@<!ZR@84A*hjfJTb>;lT~R<(sb~yyw0j<Cgu}_wwzS
zr-p{Z9yy<S4I#Xk?zq9kpKu7F@}U!Nl+*6Z+JF>)Xb!MliM-L_kSzn!UJ!S+cj=};
zhBF=#fZ{J49@^g)6li_t=+AKv(w!T#Dc1`7dCJ+FLoys`x(G7iTADb&(ZltIX*@%?
zYXo=rXe4S43=ncEu|9mkFB*CESZXKGCf*(Ifbn7U+4H$Qv&$_9eKXaZ^s@9qs2)9x
z@vju-;veIp>}^rz`&GOT$Rq|gHPz_;RL#^we(tZ(2`n7B?1IAIjFsYv=E$Dld@(Dd
z`T;lfgwN?%YA>Eyny}b;3T!o;7K$^Q{w*@W{Ykrw>gHf#U^-7MkEqA*u1n3T&HsF@
zWkg_FOMrt^GA#;HxEE3{KP@WyCblAf*DrJR2|ZQ44OkAfSZ|T)NDK8D*`{w_0=OB?
zS{;9ja4OYksUO;9h^-Lj!--|FTxE+j8OoR+rX5jfF=R>ig+kZ{(vBr6r}22G9+RQb
zYOboX$T}OclOdzN7zg32bqnzGAD+mN-*7$5vjt(|+UVyUnm3rvi8hBIQ!(r?<aX3u
zlieufm)xR14AZGiqcFa2y9v{wU8G|8p_~F)*Swc%jP%x;9j5bRfXzPqJppjxH8syv
z6JgNbpET7Cgw#L@qw&<st$LD$XtPxijWnbwe!Q=Ie;`gxX$l7uN#k2YBb8M3&`T{X
z*KWGc*{vuAQ~7%3X|-7S-F0Kn0NWPlYi?`ej<C`gn0eN?SBeoq|LX=p+_ZvD%e=$o
zPF$aI$30z2gfT>uA>8&b!nrpkw6Xkc&5u3E9eniiJyo64fwJ1AwZ))`{fxfO2f^%|
zKK{MlF|E8g@^hNyY8lK{u0sU&s}H_3)o5JFA(?h`rKO>Z-nZ}>M0G1j)fcmot+*6W
zV$&7V=31Q4iU~1374+ffs2Pv_ho6$(e<e+TzHaBEmpz^grP*xd-atd^&&IS|_x0)^
zO2ZByEuNeE^HD((y`Escq2}K0ZV9ykA{cy54^djulU9>$o<0Nt<X1oBn%M2Zib&pf
zWmnI6-udC{hYc^Wnzo!75<}v?Q7fQtCEZ}D;gPV7e-%w6H2GTuSv;KsRm<Zpo=4n;
z=sE_tSca|l+LHEooz8Ir;BiAXH?@O(aD>eDzZnXv)ti%#`Gq+D@O-&H8z#PeNFGwc
z7qO88x<qa5Kcu3a;wcoQv-m*#XKU7mIhK&r?BDyQp-kk&LI_G3_>FA#N0TcJzHGX%
zVI<BxE+t&6#SDqN=5T*PqwRQQSa-jXFd?-UGa1pD$n8iAXew#CHu@&@l@nNnRdhpZ
zkIn1ZT6-$=`iSaFd(r+<uAfajOz=9oXHp(!%e8|)Ka9VSvq8kcn85^Fd1j4~@=!=j
zQV`M-bcQZZmXT@bJS5Z`+*FOwF`*SfS2S}Ty#KRbTi7n8X^jO+ExjanvCpgO>AP82
zG0AS7jl9$kdZ%Mh{#i^vXfP~->qU-Hm3G`Mreo=ROXR{>`NALFgl~gUxJez_)ZJvG
zx!S0=SXXGOi4H=Oqati-X2kuqG*Hp&6I!%CI~Qpun@Z7<XsiYnLeKSjvHPkHH-woe
zo&eYnTKS_eSto9pSweTeVt67h2O9C>43NO4OBBmu9G^F;^q`s@PQF244<JtTpYh>3
zem)Rs?%ULS9`8V5*K+tP>DK_LvdB^rC3d9M2*O3L19N+abKO4k9E(~KFbpn|Jr)+p
zFR2x!L1n#v58YFfA_!<@!t0xQpX)l5mS_nh*&3Ij`Z&>251#8UnXKjwurEo}L0j{O
z{bY%=@@okOe&<`BMcQ~9QEH|8nJ2&KP-CuXOFPCY{g>jEkFA3-^20Thqz+w#<5nKT
zF{_H+)f!I>?sD`H%y6P4U5ZzIQp-L$dN1v#(YnHiGs-Xb<hthkY+p}qcPSEPv|TRa
zjf={dfsNP!bqL;K+X|zv8;<f;a!8zt=1~4QvM&vnnj4T`eqv9dJ8F-sTVPKpwUexS
zI(?xqst4u&dY#2sj-D4XapOrgv*q?DfaTL9*o;Gu8>GSA#owMUzozm%)PH|E$ozt<
zy+sNM)l1<0s6)d+Fh5=OJFu=%m;6`KQDXX_mVNUc^JR6%qYl{xofWx+%KGe_EEnQ8
z!#N;13{2IxW?;pstdMe2Rw$iL3Q#N5P0)`UHayj;0T^u7SrX=@do6Acmbb@qFjAR3
z@#5Nh_5uS^_T6T4#r`}V&%)`%Q!+?lrFrDH4Fdg1v2^U{U<})GWva|^<Z^=5<6ja$
z=ckdk64I!z{KISxv?S$VK|jGj)7zPwsS=h`SL<pEQMrAovHGET)LS#@b+z3GIe?=o
zs)V`mxGu}ZE{-YGnrSv)+VzKx5?JrHbc;te6MaFg5&xvp+z=Ol^KE<NVZU4`60fgF
zuHZKbyl$2Z>}zlr-c`iKMu*nMQv(TgbRfIYYtQl{dsh*aE4=Bipfk72C=OOauI*m<
zRq`dwbgUO5RSDdT+0;juXWP}1RVZM$1~{zANg&wBK0%xzzZ`P6&GW|I;&meuB&_LY
zt7C4HRj#c#d$^fzquo{AXY{7pKYy>S`&OWt3z@dPC(+s96t8mnSyR-e4x^@AzCCU~
z)HN#QL9Ju6B2l~3>g+wpA!42Q#0bO!drr7wiqxwXB6nneFu|*TN*u;?w8zGKQsS49
z;KEy747^)-yIKdfE?b@Po4=m$_8#8S&o517^o@3~?Mt!gyb)9V5RaEZqI<SWgpBY%
z@|t59hQLwHW+;Q&^35(Qtig@Mo$>0tA6Y-7H)mTvUIU#PVBLJ_n83<QcpA9Of=K$A
z?o|OSv)sSbG2TP>|3G^SDr4RDDU3~GxzSR$h3EfZ4cwVynX~rTpb%GyrPorz0yKJU
z)1|514!jTrW~=yElc#3|>Hm(J>#~}CdN5vsx_IYxI{?8qsH(d%nn6bTK_55ic7gjn
zL>>hD*Jnlkwh)b}6DL$R2+9OAq1$Cd#4pjB;J=+dec|v+`2lK@dg?UZ;u1S%7;En8
z*1xfD!DwgrenWE(3_eGF5p-|&m=Wvk(duCs%(hAR@iYy|J|vpq`5II~gb`2pr<;g^
zWQhDjcSFi{+MLCxSDB6wnb<o}GE3QX@L_B$s*YZWP2Zd*q<TUwu`nJwi5m5UHA*w@
z@Oad{RIl?k-FqcpZ+mz#tT&LIt;MBqlwTo$<hAKK<E1?_7ZZY_nODx+TfxR?RiEWH
zUcjylc5xkc<yH?Mo;({dJl6?9kO+JjB)ste?66vryg_5SDF;^r_^hiIcNr9K&3Njl
z5;sCEK<Wnkz4KY4`4jiivQmo06!|SBhyf<P!?2oqsd#*a)}5W5Dj@rwdquD!W>ei>
zvQdVQr#$9DTfW0L*>J>Wu0M?q{T%ji3Hj$k4Qr0r6iVf3=Lr>eoZn)z*GDAEDR?<r
z%)Tl7P=NM)SaX6~GEJLu+Xs@LIP43QlTSPu4^K4<hGQ9k2hEUREoc042HoxdzEb6Q
zrm6=!%EHzl5e`5Yj?~7DP2&Q&?*2X?4~It~7cP^35KOKH_oG?-M`BLlK_BaeyB^Q*
z!W1riImLV)!}EDjwCE0tI*<sWKhjRg<!0JTY0s`XQRC%?P;Tv&xhPtuV<)0WrzfD_
zX`{7Zon>i=>86px@Zsm$s2NuS31RIHp!WHkxs)T$4;!mMd+*f4nDZQMmc2$m9l)Ra
zP#l5CP@>kbfm(Q${3+A5j-=BlMuR?PSQf0J6MG_3h?$+YaRy=N<)eG$V?FGWLuu!&
zKoFy*4T|;MU%rBp?&+Q~9CNN;?j2z(Rx|ck&RTy>_qVHKe@(1lPQdKcdy$ci>-0#{
z%>*LXoCh6Of)$^i0db5IenLSp86N+!f<vW+16Q-SDHQDpLvnCg$UsIbM47%~bD0tE
zsH+`!#J4bkmhRv^(?RM%{DUgGlaQdG&i%2}a4)ejx10SjUo4gMk;A$hH*E<t>IGT2
z_%y@jvF6((S978SLDk9Y0JF&=9fRLl_?A2#0If2OfXkCpP5g+bHQo-tu@VG=yeDR7
z`BrEq=8li=X<n{a3FYg-id`eon=<$hJUs9cO#FN|{e`A(==c@tL6s%T*fraNU!QW<
zgiY+Y&oQT$8Wj$<gb;6TIhDyX$dzubB1?pr_*&Y}5Tj6FLSK2I*+O!S^ri$0PDmV@
z_sCZlS;lts9*JA&S3NhLDk$?^C&c^h?vfizQO`3cp`|>Z&{&*2(9>tO@l_)&Iz{Q-
z!#%DLU#`6!%Qo$v@!VfWIC`DT4diFe+P(CYp)AC3Z(mHP$3tW;2J_kbIXzc`@T50_
zs$$Fg^Bd5%w7UmoZz57)MYj*RXbiDGKflqPY(;|$dxH<dfM5Y^F+_|WJe5@L0^^z;
z&9b-$ejAKk?<2V0EYoMU95lsqyUM#Wk(hAKK4yua{_5=HQ+HZ(=)bGzcsCvGG+Vzv
zTfu&~z9DU>KV$%Na9LQ=RFGyOZ0d)=;C8Lu@~oJ8np4oy0MaKK_4QRr43Ebd;o)X-
zW}b`|B+C!Yd3$4}YlY*RK1w$z54hq?jM=)5mZ%R@kMGR^e$yi|QM9;{9>#ovBS_we
z(?}hAuH7*8rjPg10Q&&aO<+c@)SBqk>H$ek`)vW~%o+500V+%*^t3ok#clmslWU|*
z^S^c)O*<s^wEPRRDH=TWO~Pive<djS_5q;>lhV`)OAB|+=Zo1^99rBHhw;Nlss^5v
z7I;5DKmDzu542j#HI6n{X3srEE!TanOyX{|#V<Y0D|8k|mg@Lv-hI#@&cDs5H;o+d
z67`GxusjPc*WVrphdqVP=NRTg)MEPxCd)#zU$G-w-!^TG8M|whM=^VYMx4FL3KUhu
zHW@2{C(U?Ll`LwnYrYE7)<#g$u*P}4)L<dCtp~WEBW<n}f~hX=>l!`M|2_@tD)b5;
z2#{jkoJ_`AJePsnNmW8+z105=?kMQXWWf7%EJGIUowP`h%Omk7l|rE$JhT^Na_PL;
z<c;-&qvqUbba6X8^?B*S?~MVMHU;6%xQKegVF25OtD%3}J&g+0WSt;4YmdSHV%Ila
zht#Ghchp_&$0mkvWkl;rLVRkL?iuC#Oo-^qic>Y^<UJ<{-2=|$<=xNjsVC>XjSYiA
z8#XzFponWc6vJr}_%3eCp1K?SE+Kr{K5SNS(L3~LLOiJE`)tEK*if;Vaot+BY&3Xi
zWP(i5cTDaQVm;l~olj^|DPzuiQ)7ZWHxuJ}=#%B_zS=2VX^{|bo@@&PW=I4eNMtAA
z!`)V?vJ=V+8YzsI<Kc73P20l9j=V<WIC}kRYb(q^s$Munh^@1R4VJjBoq<Sfi6NRx
z4V^kURE+e!IbaodEFah_95&0S@dyxd%4Uo$4m+&&hlIoNyhT+6n?9By@`dD=0}HAb
ziH!Ix^EGs&o&&BuAEy?sClG-(3~9-b#t+KWTc+akU8{7S+Q#=8K-Pu7WprTxA~UG5
z{$sZ&mbWAvuj!%;k$1lrU2Hpo;k5FX<$xIRV`|$u1*Pu;-$)VSV$5?ntC{EhzQO%C
z#xa(Uf9sZhylZo~6S2zVN&JW@W2wbu`m9;;7Lc5Xq;0|VnVQ?2jcnpKD6#)oSBVyF
zmv>7aXQ7zJW1mG|TdWsfHP?;sNUkl@QcSLn1vB0ZX~1UNz(!wfX0z_HgasUXa4G`9
z)-#%8-vpZP3`6WO<zDZ==V0uIo$YXoQ?nimbVTw5NUdP;M2f4!7l+%Pg=*x04gY87
zEDfmMm(lJtE%Z%wV|*rLqF-NCnP_t>1c^?)PCXWGVx-1*HYG#TfTq{3cqu2g6#Eeu
zMhTxkwJV$vZzZSF@k%fa4)51;ojki{pbkP?(7VaX9ZBX|ATw0yr}%g<H%8IDvr#$N
zKpqxzUU$`BUltoI{Hf76or1SFDj{3>M;Zp%N<htN9C)2}lgu!4c&%0(J~h-c!>u}w
z@atBi2BLcg_;dQwomY_`O8>-4tHiG0;*|@7aU@wKTqoO}-&C~iV?tKaF@G%(O63jU
zwP!_pKd=-3s0&dBpG6*^`9`jiQP9`H?Qob?Ta*H%E5s{E(eyng)$^i_4ZrA9ZvD9A
zbN-g!If539t0tA}xkIqi=1_9A#}<3j*f^tbz>wTOmHs&?@GoOZ@Ru=Va`NOl`JkFj
zSJ9xJTU(<)ay?WiQN*;oNSR1~#u?$vI8dyw8OIZrMfcJ`FLf(7!)%+syD*$qF%I|3
zQ1dYKR2o<vF6M$eE;!ko<NN`K+{~ho2!43lG5&sBZ{6{F9a<&W1Pm0)dodnxn)bda
z!LaR5Rbw4>h$Ye--IZ*0(X;P6wPq?cMHr0P)mVCYeTZ8~@6EUy?vIVoH&mMf=oXer
z9(>!uB1DY#6+kwCSPmlk*MjHk;qNY-SRgoi$4wL;Sne_!%yuy+>f>_`o+Bsb9W-9X
z)4zkO6xXNZx>O`JUoO;nTFeGq({I>#M#>^hN97VWygs-g;jr|bU)kmh=vkTe93H?1
zIMUWk?^HK*7*%%Fm6&_>K_(zQrZ${t6i=(OHhR{=-HT$hx?{Q%OqKeJ{p5XwUQMVT
zF9vKou#otnucb!x=1UA-j~Dp!vJx5}?#G><PY^0AAM9ZdCt)&1_I3M16qBq4$nM`?
z*;z!W!G6QRg&TQor;?P}v~aEPUUVpv--R7b^%@I}yeAS}t&ub?>N6d?Dt%X1;-%8`
z03WkV1$dEXZf2j=K1jG7Y&oY-X}HMRiaaIZdq|D`rWrRqbZ9NWBRHo4xhz}*TVQ%e
zwo7mik%}!J2*Mj9-qb>wAAQC$GjMEwt4rUae)dw5sYG{YK~^Bu41MN7uiTBLt>S4X
z<#;ePW)b+bJiz`YXi1ttV1KBJZMN|0SCR0nBmbg(s`U)R5;DJ1r$ov0xwgCd9l@Qj
z3jbTg;jh`96S|!VYZV4-X4T=*#$0kfJ%F>t6a2<JLNBugrQQL)mQL#6#aa_ysoE~T
zuE5|gFaAu9h`!7TCQx|Q9eox}(tQ&4`q#s2%%rB=&*yC)s(y<W;P>L!3uhO8^qR5+
zzd7-+n?6;HauS|Pp@B1<g`hpkB@m8v**zc#bOtLtI;+5%Mx6elU^%K7zO0R`^K=R`
z^B8Ek{k2e=d3myr)>tJ6hx2Uqjpv;jP3qn_azu29GX=f~Typdx01vkV257hA_>{<E
z{-{St8~_f$zUlCaA5>x<F@enjtm9#}-!l=Mwm5*t=SlPIq4#>0V3o3^EU8IqzSjc#
zVk(;_=v#FpUYdL>Gy=pO%|_c(AFl02%cwb4N_n{)K5P^k9W08+C5yN7PNEzPmBHdP
zJnrWDW56vA;8<5TtH~)wfZm3NVO;95X$2=B3HUw<F<{88{Y}aKCfQ}Qe7tp#`NWos
zBhg857nm$m7IFl-J97u7_H?M~2d}PWBVbHqIW^yVRr2?lM^c3jxFQx*Gd&U1F+s%l
zv~F7D67|~3$L&gbo|^hICpQC+QFFuum?-QmMNa5W=p&cnht8Vpnoh4zJb-tzr!mtk
zff@s%Z()ZQrn8gpM^4oA^iy=NCSgz-zjDR2)8OI^ahq^)z2rGFQun9=a$uc{t9Mjz
zmDJ}zM-Cphhv#z+LuI$imO~5a;#2Miq98-*U*<^Z)Xry37m(iT>D=EAG{q%Ha_`I-
zhK)D*n&6Mzpvu+Sp*!5`0UlZ{@R4C05l$Um$OxVl>;hnM`q35#pv#(pq~*N6CcGWt
z)V$aYQ%>7<7+9uGpa^JrCG-gdO=4osVr}6Z4iJhT;XY@_7-zRpUie!F+JGoC?AI*F
zh+4xXO6snT{Hs2$6bYGJ;EN_Ss(^I2I|;7Og$I#4&g>bSA8y3CftpSu=XAVYzU(EQ
zeYXlG%%QgHFa2)T4$IAtC-}+UV)!1x7X?y`42!R+y03alL!+(C9xJ@~8^?w<wp_~u
z%dV=X&Uf4x>6e&>!9TF?6W<I&vyrX!DDOBLK_l1PK{7?`-y<KW8SxZN*#MCn>nn@T
z6LNL$wZG{B#Uhh)M0PxVly;}AYOwM5te;~rkd?B>pEEDtUaT+>ip8`QHyU#nZ(mGd
zaSllIa6j=v3Gm)^WA>(V`7ny;0xCNU^OMY*QZN}jNT*S51Q%Xm-jqJD#dh1SFHO9m
zGTKltT5sVrJ#~F#N^Kib>7SPOFNN8_&m2geSXbXYF6mk|wyE(x8Asp0dp#p5V(0a?
zZ2Elsgt0RH2<ds%OcDZ%ekFf==qU{r`pdz=2dBCQW7NaScE2s+Ym>$)?mav;-uV`8
zS9&t1q}dk~O>{Zr#oGl#+YZnOJV~<u=03JzHYptjHdO8GaJpC(5m}Rl56f%JKe+(#
zeCDletreh@gK&O+nStxuXtFK4I4a2OkIMczy%4~8VADz?td`+=O|I8prSam}KkxhU
zaF%Eh$|JJjD&dtzvD4Hs(f2fXQ-~rB%^Ehe4#;we#A8o6aW2e3_9EA-SA&BC=7Ksq
z&R-Js7N#eJw0N;<Jk#Sn&zyCuorKQQomQdkT>Mo6`#A?A)oa|pKc+e)2{A*$;j^qZ
z&MqQYwY?=gkuB(Hvau=V^QzAQSdQLB`eni8p3vu5G@2YnGuK?cvmHLjZGb4aGcRro
zMk4_vVeu8$LRZG(65ms=SBsv{-Ok7FH+)+M<yBI=j#K5r8(~~6AOUkPuH|YVnr>@#
zmNriNZ6-6{dc3nXs5ORj@wt@{a$ocL=|}UNp9wpaYN=IV(hpGq++%X0BE2Ng)SK9w
zsSZ}IJISXYwDjZ*iZTa9_p+%fiLQl8@tz%N@y^V~M+uo&ZUSv(vz^5v8!j`Oc`mo=
zyb&2P$sB3+&@$-EmcHl|3UId4GmN$*Lm0)w_+O&3-Ho`YJCg_7iM6C?Hvo_NE~K*7
zBEbIk_4HDNbid^r!?J;*2$bkVvO5!RXjZu`Pg2=k%BZ9(59=B@B!0eS4W11fC&b2K
zC8`YsrC%<Qe>oi(Ag+L2I_+mLj)LDOR$9*Q5I<Eq9x~iC3>Es+7P`&&Z&#1)RK71u
zI1L|1a;8I^dbn7Xei*eOCh~#RIGn7*B|fy{gk!NXcs}kC52PCb@sReHi=NR6qwSUt
zk3h{f*R7!;DZ)bAek=h-w)FC$g=C~MDX*wz(SXugok5B6aaUJYgV!jzpT6@YAcyKC
zWwb3ExS0lt1Eq3$d`XNvu2PA9=F5%bTDD^xb{zZjcn8(R;K!v6{a+{bG=~rTeRI{B
zmk(``nO_SV0x33%>Swuz97`wWTrA(DE0Q=bT9F%8T|=|!G(r|S)m+2^U|BbK@7$%6
z&wn4!6&sBZR*c?lB$3PIYhOlnDOWoO(~m#5S<TeL1;As57>=bHyu8^Kl&N-<L~<Hv
z>|Mo@P#g_a#Ee!Q5V<^M%Q}8M3<J5j`Q!5mfsX7V%iS|3FiB7~mj}r?*P4<xlJtN(
zV#x6T&8P6R8|S|JMxj5qK_j_qXeD=vBEWs%Xi&@E(kl5t^6tH&U?_hcV^Xe;J8YEZ
z+C{s9&Q7G1P-FWVVRU4MXUW!a5+ML{_Ohi+J|W@_E}RvHG<Z%bk9j8fm6g-zZNu6|
z34*BMH}9bW`-DlEjitk2Z;_hFp1tt~NIItj%c_q;J%t_xC%ecGV|2mcJnC?4W){#d
z$5ioOl?=1ej7n-Tm&o6eQx3^SuDIm#!(e{uh_#rLWuP~2y!aw;B}I2_i4$!bjVKr-
zUytVgR2h(4QyizCgD&&M<TTusD%TTON<Wy!Q|hx{0Kr9IZZFL4J&S{aA9u*l|D7Xh
zjjHZ8Ru`Z?P-QcoT5+Pzc6gsiA-7S3Vz$4CU^!wJx8;1E1G;AM-Iu00_SVf5tGjx4
zg2?VHx8LP(Igiry?lgX%h3aZ_*Uf75{aZ6jxj}U57~Gpo*+qmW&P2`7_=<pV=dKlA
zy1)?w<(rdK8`J16m!)b#kv6#a`?xubBBdC5oHsko{!|l>CvRHtWeQM$IY?O`1cA{L
z5;`&h6jH<T@YS|%acXKfBPWsX=J7BCVNYBWWz@yD=!1(}lQi)L!;F#ek#<}bvxU(*
zfI*sAED|P3?-F@gTaCKDCrwRu<EKz`S|F@7@9>?i5TNkmP7Z~f6@I)6;btbF^;Rq1
z^$mq-Par?HH0OE!B+^6)UucPo77sLHCn}x)K$$7RVD#p6_`2N<Ts&f2=s}cGNe0mx
zC-PMP@+BG<bDb0fsZbc)7%-?)YbQr%OQw1lNnxsXT>^UqC`2S=VPVnIlaaMuGcmVm
zhk@AIl+^khQ^3X+$NwSr!%%W3OH5cC0WhE^Y4BKT{3+>GVan#=jddhR2v;iq7viQ<
zj`nLvaqUyY&5Mk@p9(E>6ramiJ}^FbqN8(!Qk-J4S|QXrYLYzI_&9YZ&7^I2bPA5Z
zm_sj-!O`=!8T@IiN2%D1RNl(FLHSpp#GhoWN3UkCMk|D*uO1q2MRuKXZo`9@2t9el
zk+@<2z^jk{nMpt-WE7>OfLLlagGS!;EL!-xMw+^)C?*AY)Bd9ox<OmcB`r0M=r%VQ
zbn?<wIWfq~ZTid}MDb%1{#eC<c%M|~t|gwGsZYDuep2$21DAt(^0konM)0c*Wx4FW
z8}MAr$c2!k{bLyLX>%c4m)Rs7;I%BVhGUe}5T6U-I@WBPS0Aa`#~)F|=hu@(Ztu#<
z4oiy+&0Q@xeQkLt|39~%FO!hU;0m6clqO2>W$!U`1vK6esht>7;DCPfax$HH5FQwy
z${onc%DTO>FM!`45(SQ=R9tif_eR61fcw?6@n^8hAH<B(N7e)|>6Ii#kzCCzJ(~S9
zFzjvZPpYP~vy;#yMlg!k;W$pF1F~#JzrKqrA-{gMR0SEhMUvUld^0!5>Uti{b?GRK
z%;&mMp1rR19B|4unaK|jj;|QvQUrg`3-)Dy-=D$=BYf8zW{4D^q8WH72ew-WbX`1N
z?R<iVXK9Ud_wblW{cWL7!2>$X$veu77lMTb?7Ys>E6#kKm2>B&Zi{(LCu`G_Q*6g)
zN=sOG3Zf?R3Hi}U_@SN9tYuBIMy$Yghm4Bq4=nTAQI?M$WloBE&-xSI+1S@>C-&DD
zLh<qQ`(2SA*ZtoAm^_&+(i@~Kr_CQ$xOX_2r)Ft7R#gQuRP5v)EVaS(jdc*(kp(|g
zG;Xx5u5-SP3kV8spDxt|(DSVf$k%@=?9SdPty!2WR-jZ1r_C$dbIOT>7w1V)SlvLt
z0$kGzQ~dxXmM#Pr%>&^>jffH7kMxv#VyOttk$C0>eM=h7aIqL>Fi@(kXM5Oab==om
zr#;Ob(um9#=SDMN*ozBr>N+hL>S|?KG1f%yn~Iac`lB-v$$fX{d;KkB{YeggtyckJ
z?RPgd#FufNSO9b!9w-w)pAqeOM=f?I^2)p#?Xdod7mVAKAu=Lfu@`tJEzH6n%Bv!9
zwEGySb>?yrPw+`XLQQ>lG;AaCO@=QxnW%kD<O8Q2F7|N>X*lu;o*^%9l;utnxa$$8
zE|Q?ce4xZpyrBCX4NsI~vVZ=PW&_6n>x7HwdqSu$TGU@C4OvAybF(Sf-aT7wn7*fp
z1NSnJ#bPtY4r%!y`7`vDC+<CF=qRABdAos!ynGOy*M&3zmmdX|kL(dhmYO7eH1OQN
z51_hdt#HhIlGYTAeIxLx76WET44h6EZT~ow|8-cW@)a=yCgJAJ==BBvcG5>yGK)AN
zHr|%l91p1C&|k=awJmU-0$_a5t#BwJf6WRwm46@7me6scVBQoTOn|r*rwCl4|L;y@
zAfS2E06qk7BxmG*#DKsV{O8D0M+V-)8wieU0a<wVu(&vd{(gCzPq5f5ut2sc)PkN<
za^Q%Hh@~kSK+Nww8F7L>)5do$lKpM;AV`ChK5q!;KW9uOZdAjb{@P=u|BR3?`#viE
z|48d!V(avU{m*FrHnL$}AdoZsfl(9qpV|I<+I$FrcCi2Py)@#dNw6%!FoqZ5zs=$w
z|0cZ!9XD(NpnNUd1>=*IMFPG;1Q8Q67>F{v&vp|2{bB+FSwMMQ-rZ3bAOErA0O&>m
z?-oF#f04bB?4H-fdfO*x=<n9nbX;7Ai!#kZVP*dod!aG7ySw`%c3BP_$BP(fUKD^E
zAi9;cTPFgn`(ujx0J4$p*OA=-72rSX=C9B4UqI6+XrRDwv9ZlYUlV=HD`EaVq<?%C
z;P-~~CInSWo6G<Gdor(-%|{OHzb_Q`9jI7wUElR$|49G84uXn5dj=)_K5!@euN4VQ
z=?Md`&cJ4D&hX#Y1deq#9ta$`ko3O-N3w(z6dLNwyi(x)p9k`nxTRx&mnRc>-V?v?
zf)JuJ?nd+@-Jhk$CcN*{J-LVZ#2N3TFy**QTiSX|bIKVP6$|VJGz>=QY2MPSI*?Y6
zuIt0{K%*as*wR(M$H_ft4;P4Z_e(mhK~a5?m3j-=pE1?eIKoT$aY9S^CkgrQ!>3vh
zIP_{3-Mq%OtAcIdn*&3G21S;{gL=FyxkEIa@COc6&~MhUX$cHR@Pi_#$f;*Sf~?m2
zGuEcq>(|hl@QoVEe~9Z#(%F_0t^_7L>MimVL7~-}M#itT=$X{GJ{o;b8hI57L?`sz
zf^h!SkRzc;rACJ*6TcyZ-H{XDyZm3G2JWDc-oPC+HBWZC(rt&e@yNyNaUx0U(8Jiq
z0<83{7_nJ#INP*?!2L-Ird44s;7ev}hbNwbS?545k10!8jBiwqyMABOQN>PYF)}+$
z5#uKhG%CRs7XOX$U5gE1Wss1}LFyict2L)8?<AhldFrnj8gs($7OsGd_^p%4<=;EJ
z1h-EB@f1dEw_FVr+d;F8{9e3Xe7j7#VF|cM2CD*m>gW=i6GRwI9C|E;nTvh*>bHlk
z!lzeh_j*st968(Zk%IqPe-Nb7yEnfetO(7>$b90&1}e?R8X7I`!~_*~T;C!tJ$=vS
zmUAvwCMGs#z_fJDVj##5LygLxFc2I~PwSP<_=(Sxtv{oix)6~O187@Sq3+VxOFbxw
zF5i{tKeZeNZ!JJ*7x=;_Be{GjVeuzg^5?IhkeF;x)BCM-K;0i2u(owpIEA&xPycb}
zVeYXb(TiUP2ru{c+UvPYV?zeBG}hUzBo-n#eN#eYIYV#xJ<gi7R3bN7{Zlwazew+#
zbn*FGOb|EY&64lZ;KinDX$Irgns;&BZ)^7)Eb7UAk)^D&3XZ-rPk*tPN$?H+Zu(f5
z@3E%gt9dTOdp5K5L2{AtI@wK}bN4#x^f;-_{Y0){vS>p`SiRIn1Xh=(@Y)Sg0?#w5
zbOZM<v&?~_i4OplmcL<UTSP+H+1c%}Eq?jpHNh`42&}zSbymn0UNt`ybkSO?GsDYs
zWMb-ZVIBvo&W&7;tMiK;I_ppfixa6nyPy5r*UjGo!&>P{=k?}uSg%I6c%zJ)pfA)r
zAoNo86|PumykKQAXm{?>;3sTd8kP~>;CUh3BT_#O!w-I^IspSICv_ea3D6lK&fKqD
z|F%;9&>S*4kWdL(A2S^Y5>f4w`7-0BYJ&up-~4C22wtuJLoZ%TC@-Xe;$3CjG(vC_
zVWkOvWk6yfTKS`U-2Mhjzc(-5;FliXs>=^gL8FT>clHdDUcL8I4F0sBF5iyDN_P>l
zTkA{E#k)06dk<Pn>Sm>GFb(fWUNk9iWjb?N?$LJYPl@$j0ms~Uw$$6rM{F-^{EG@2
z3o5teB(`w-4Vd9sCcIe|8>&I-j}VShPml=(UIT-H%^>wPiyXCZ5oeZR9GUImkas>k
zR@@=6Iex6l&G=_(5ZkK-$d&g^5SJKFbgE${fl2JN+rLL^SDLv)yE--yPA=psU)Lw9
znYtbK<Y8a#(H=3RO4&NxX(Y{6Lx^;ZKWQ{VX)GH#gB1Sa_1Yw=bZAE{%fN^Fn8B_(
zIX8-yKZ3O1V8^Z7ZqU7()CuA;^s(sdG4>GBOLO1<$`2*pwYm4u!y9@Fewrzd?}ok}
zl;KuDL>$?CQ{)?Zxv^2vUoeH%tB~qf5y0a&MSZ+A+y3P2)Mb~1S+~;o(SeAd;~eXX
z8GsS!s7zBna9Q*=dA028T&H(-OKRDnHcw{0a*3Ov`3PP=>_%VP_u_1PNPQ?zU9&~p
zrknB49fc2*cynh53;{tX)*bT{JXIX%`Rqea0;Kl?zYLB8Dm@g9U{|WphY}Ez-Uc5I
zi5N%AWk;c9I1o;~skVd+Dt4TexgauHZeY(XqEeUBo9g2#kw3L^qnY&rCWn{+#J#%W
z9l4c~rJ+ksP%ALK8l7NZk-^()_#@}=F>Z>zeNEN^QfQgpS;f|7KOQ^HN6*}NP2I0J
zRqIP&Xp2}(x7>T{TKki>8qObaU;?;8v!u{+CxBJ1Xf~SY4SE8JC((3ALSe347@=4Q
zKf8B$b$53?O>o8d7Srz#(e79R-V*XRSGFtljP-Ev-p~ctR_GZ4fs|sV7-;_YKj%8`
zIWPdz$7*)?kbZ4|v!-t&!5wrRCuf(*?Kr-@YG1EUs+8YEf8MS<e2Y^quJw2iQaP+P
z0{J0PE-12Di$OgfI+e38l{(dC6>yz?zu4w`Ytr@cyj2e77UpE|w3~Q!o{CGa6?at#
zskOD6&bog%-*FH*b=Xuz?<k)9xvE58)WxNN3cVNO&XKld^`7#JR~v?9(m%K1th6s#
zAX#a!#4X=J4br^L!Yjj^LdF`9jlIT@Uhz0!X3%(osxIUOt&&yVCbCi|$%%hfZFHPM
z->O|9#&`&6|9A^v^RjJ{VVgQ0{<@)Q<><Ae(brP5j4Q6%U^l4__9ml5u@tX3l_zoW
z_4fp0c}dyLXGt7q*g19HvBnj9wq<U59M1D*EThqu3^?hNns&bCP~I<Y7X(yvnI}`a
z-;aZ5DpgIt;}TIkn4ewFB2c$;1b<VkpkmyBmqdYo!j<f!BosJyl&p3J)Cd2ZiTB!Z
zG3J~Vmgsw`HGZ$>S5cL25O_BCDz+mrSt#i6oRoeC(^QM%-P>?QGh6WR9YFhl60+ZK
zJ0o>=zr?78zU+ZQu<DE2o4LBI=j@Zy!NS90Lv^}6jg)Rl{plJ?HKgP0_J-f+I(bet
z+|hEOxZRZgXi(%_eEKc}`-SLgbyJA0o-+Q!evs?yzdZrq2cI8=z^MDH4^{k}vx2~a
zCi2zK3bjhNkqKv`ac&I6`!A*oo|yRIP59i!s#UIIfkCDXfUDdWY>i5F<%ne~EeI`I
zr?(GA*K><j3ADvX%m;yVfnQY!8p|i&`u&AHPR*2}z?Q{3%QY4ZP^{T%o|MKko)TXZ
z7Oi{1)x+OkHWG?Hl$FgBtq+^p*>3xhdJ(KZF7#j4{ooXXTD<Idm61y?FOkvhmf{KK
zD8FbX)(<m46OgNqLV5h0R*SOzhGUdllI?gC=GOmnL;dj+uat(y&e>+aWep+wIOs@m
znQ+P~r^S%>ESek60j@eIb-SY!^o+>l&oVf?c#@>dB`V4YsSYSLO$~2~8T8x>IE&uk
zAEmUKmY(SXS6Wqv9;>etYJ&a?4$tr#n(P!a%WfOR)O7pJ2-a_t1(nu)Zb^-|6z-KM
z7z<}cT<qrj&kQj;C+h<h38_^%ZbUUU_qoofyZ4qPf);&=9bCbQlBFzM2ta%i&hmVf
zeqj$U8R9DBKW^uvg$D@WpIiWcl%P`s)Y268TyelXFV-7h)u|aZ)1etN&~XB|j0dtm
zE~vkFx$!;@_qOg`s=j)ZvVcxl^g-F3N6{!(UTm_Y6;WS-rWv}|+)o7Pwdf*7>vrX(
z=jgA<R&HMyD+_4dRbU69)xFy2QEvK>L&<QLvj~a-t$2P$uYEG^g$Dte5Ut{mH&KPD
zMw_`(V5yMg_4^*%^Y#c^;w^h~L<<hcwPEUWHC|ilolw>p367^Z1w{ut;#Wc%)0rQq
z>5s595ihiYZQMv>-{uGI3!ro)^6)2%g{XVWpHNruJn-xB<ZR-pK<L`3&}biL5j|ci
zI;?XN(Yc7~31901ZFuxk5TQD48DNd1dV7d$pkjVUR+%Ynnd_nwKh6F)o4=bpY!^c0
zc_%4#-eRzCHUhlr-s>IoEPNfRL0pb72R1`Hn?BhpN6hyQ?bsA*Eg%%Inu8%JZI34C
zeL7P>Ku?rpj`Mod^2hpmU~Rn4`YDqQb1o5=k2KgIN==lzu2=f~;+PrgL4<Bl8VcLr
zPG!Q|wbAsEDGlclMX@x-+oNP4%WwoaiOL5+U9b0OewApvV1&Ee>?~#L)*xAy5@sYh
z-xH`;zq!mp9rcqjEw%5jN|3)f;qG6wVZGh?C@lo4LqRqrIVa9Cf@OHu%o@GT-lr@w
z1iIVN>3Nrad2P6YA~Xd(W)x|jkZ>)%L}gDJHPfPVlg`&vTqR;YE#Py9px-YVy{gsS
z2N*cTAbzqwm4gFDWAGzjX_Ph(9Mq6}`KRl+G7kl`Q$;{_7<bXf&#xXg;X%;=H^b|e
zH<elseNbRF?oJLA;@s_Q<5Mcl*Q1%3O#Z_QV#H@!yP(V{S8cq6`{bZUCUmbzMBwXA
zd}F7?6R^@=>`TFs)D6%k*q$|g&fUEDB@mI>A1q2+jEc8xwDqINKB(JWVa8IGd@xJ+
zE(+jX5r{gBeGTp^YDZ%De7H%ZvkdaNm30L7Y`=L}w0Y8`%u?{At{=fo<h;G!J$hVs
zi+{%-b3}byW1!srtMm(~6T=`N{Qxp3-$9xvK6V12<zmO^{e=MtJ1-z6;NZd2<Uz8Y
z=o7cp`5F=`nPBaeQ7GGIZ%JF4a(1Uo5|yQt)%Y;b_f>^!+oJ^BaQS^aE&&BM9K|N_
z0nYx7OCbS*co+#ps)O(G(V#$DEt>e6i#W0+<00RD$5?`|74cDeIa-2~agWv?y`aI}
z`)JDJ^4j~<_iIkldYNocpu|6;EFbYdVWXcfIxr+4`4fX8fodh`!k7cdM*Ayi{vL=?
z{rTZ9KUUzxM7{Z@#QPHXg#HKW05k;zZhruvi2$j8ZjOJCRI^t9Y?Q08B%DB8>c3)4
zVEBm)#GKt&-2d0Py#QdCx@SnXg!s2n{cCp^%mAKTsv&2UDlOom*N>u|zj)}yU_Tcf
zU9Q8&zw0wA;ii8v(GpKNNG9p&a^bo3TE<pGckTI*vFbG#k`c93=DysT;YqsnPc&y&
zwk6-nfj}JK{(`7sYmue@-}kd7j#~S65aHsi*$+*ecGpR9^mEh%VYEh>PK8-dD^N0c
zy44J-vZZO!R}%0d$;l<g86H^;km0MPs1@?jFQ95kaM^l@#|w&aO=Yd#I#h&#<caco
zcSvJE+hj1$V12LY{l%&!^+Rvqo^w9gxXTKgyV4c^@>+vWVZf-*+LHUgr>Metr!T#m
zk~$nC?$<<yD9wF+(zOfmzohXm%_RY@IS6Nj*Vof@*vkeG7Ho<p>5PRCoz433B4v&d
zWY&AhAyI)g$XYJ>s=BU?uLG~>?lwKgu>K3~@{H4jkN|2qHj_7Xgi!>j_gFcHcv*TI
zeu@BA;qTItPa%g(<C)WOAImJ_CVD*+p?PDW-1H}=l;7$pxX=V<-4(_D@AH+8_~WQX
zKq<ALePg`oC`Z{-M|nN?AT58hb>F478g=KwtTVWEaOosv<g(Gc9)F#JIUqTypKzbN
z&DOuQBKur2_v?wV^>PkW;gb6*Wb}Op^m4ovMI0~rb46&zqiOs+!ssIz2rCO%z2}av
zCL?!L60ysS>>`6z>w7$@XAnSDp<ZWHnG&y+9>T{k032cTeHsRz`igFOI@=#^fK~a6
zdpc^zYUZy>hm0g-%fuR!a8jCJ2tpNDL=Ct6`~@3neXgfmD6F{zQ6jD=K;W~2WGz&C
zbo;0}Nd|oUE=Om;ils<@nTKq+kzB@gH~p4eg$D(pz&iT$eCzo70t(Tv!~m7^T^;c(
z8kuLP`=ytxk*-L&&}2K~e_?0x&VNvz8zlnWNGMHp>d#gN+IR2@+k1`cV9^XiRu}-?
zJ_F6Tb8g#?ImA$#UU&p^9^#q@6G(5b-a4;#vNu6olL3hqE*%9hZ^fj9^T&opG%js{
zP#VS3LozXtlqI89R`+vXN`CqUrtC<#mtM8az6pbT_Yx{hXKGB(%TBYMz2{$T9%aAF
zc!RF3emmf?&29XOm?*yL_B`oQ0=jtDPFGgYjq33(B3A=_%GrXTS4eyoFz*+hJN0i9
z0tl-vz6hSeB|pP4RJV@N-77t888l5TyJmgwSqRnj^GMR$KCHYbGtRfxPd&~uh8~NJ
zRCR*EzF4v>mqz`Sp#RtE{iAkdUqMZ<q@YC#770lwKi-MWTP5NaqEM)k$Qak+<!l@k
z373CUgI$^#_T@B*91>AColNPklBj68a3V&+MYX29%3f`c@{3m(^aPoeJ=T`Nm6eP3
z(^luNDXS3BJnTT_kq_i_ZhRefsR;C#v?^wGG-(ASFL~`iy_m6_?GbbFl0#><X0YJ4
zcY&?Yf*$DCuSvffxO8t@Ayv94f)cKeBrzbETl6h|)NIqx!y3Ep=}>wm@<DdIYB8u5
zkbk&#jFpK5J@a4^)v|-|1tbF&?@AIio+{Eq%5<56wBo$$hJQ{&S-EbT|D+_;lu~=1
zBt|8@dkVil5Odn!F(Z8QJY%tP&xQ>AMvk#S6CV9F+Uvh<YStq(o8%^1hrKA3EhyHU
z*Q(&$4{myOqka<qK}mqwK5<Sg*@R6?8{&1PN59)0Ob&zD{Qo2FEu-SvmbOvc-GjTk
z2X}`6!QI`R;O-hcI7x7KcPB`2w*+^0zD4$V&%5{j?zrRryJPU9yXjtQ&8j)e>#0F+
zRFc}Qk73~=^pQu1WIoLLrxRB_-sJ{`M-ap#kBhIAWQu0Z)>l<Vj(!^(X2@fjTb8$2
z;qesE`7TTu@fjUhuMy_nXTd4%QYByYxeJq{phea(bo55K#W9s0$U4x}y;+3zSOa3g
z%l6KLntl(0h=yrSQGVf?XPQi5oM2JRQ_sjV=-a@klj1$)W`!gZKtF}rcL~NJE%^?~
ziR0)QiCQ($+DLsY2-bZ=+5D=WCXX5yT6{1?3>D_nQ`{7U+On>(^Dl8%L=ADlQm?5r
znH1-HlWx#o%4&@aE5oH3$2uyoEvUxM$`3-Wu@+5tTs!@_pI?8P?pE7T^098wK@cz7
zn;f$Imv4?8=L)&z!%)UO<Y#3BWdEk1Y#j31m(wF?yLNp(aFd%C-efY%YhU=SuNfNK
zGDZ+>IpIak5!{d3q;3(|U3FiSCl!+%?d`o@_?P(|n2ooHsF1u2mQgP;lU=X0;>rs3
zTL<l!!&7W;Qby50cNt*iJ!1($&J%7V#<0mkY|IS4hBM?pEtiT^;b8o<g|n=N|5-VG
z(dz1;^0NGWW3O;n<FW<WS5Dh^Q;lFu?4ROQ2uxh4Yo;PH*Gf$j7bcipwwr@ryc<cX
zf0FJOj!iEIxUwRJeCstD_RCa(xGZ*Wmry$DJiANMSXJSVKI^{MlV0UXjmR~RnAz|w
z_hXI4wMyNkhu6B_7nkOrw#VIln6(=Y9-oVJO9@Fp&%s<H{Be-jSI^^_^pb`A>^qKP
zIA3pi(9v8RI>*}P4?i2>U^#Sy+lJm7jkK;c8P*ZCbZmq;u78qY7L?~?hv85-+qkSb
z?6UPO@+m|P6+8^GRhH}l2C)(zg^d^dY{Tr~cL<r;WLiVIL;|-B`-UrlaC9ZMXG<zN
z_rm4dy>=(S6{RjzMs?yf<jemGZSVb(s{Z|us6TB};o*G0F5T}3ea%43Z9f5;@n+1d
zW$sFG@LO*2AAh+pFn~USP?S|q#(HX>2~>+A=jl9wnp{g^na-M<3^r+X`hssSsK@?&
ziQp5-Jj(^O(nUlC1M2bdJ-!o8q2SnUSJN%|&rXPi!)XT0({Q8m{g%|kvn-n*g_Z|4
zALLdo{Eol#Wd@%d#4<?)#+j2FJE5~+3?aPE+~1f&`1B1`^+a<JFG8(Y%d=sCTvCX1
zLQ!OrvLJo3AlN$X)I!1?o0RinpUjzYSIYLBtv4#}!(`rq8M+8A#>G+k3Ers5^2kV6
zw&h980x|tDpWc?4ANI;DG00rx(|39*B+Re;+O`mQ5E54F{}ZD4@A`8gU^Q$KWfTR(
zvB3h$wHx!_y3*$At|f`+ixL7$3;52Ab0My$>z|RN%_mMDwMZ}5vm?0#&byq^(gpiA
z-<J{IIn-b;Nu9NW=Xm6GgG=M+P?3qc)Y-UGS)Z0WVu>aw+%%Gq+h+MlFfJ-F3#ULV
z6keigP?VC0TvCQ_W#z)P((}Wx=OggU83=xmD(#ic-W`+|R9Gr~XiKeo&n$>(VvBbA
zzeyCK*tf*X#%6bOBR<JF36QBu0L;YPAyn*k{}luf@pUknzpIR)h_^b(sZBN@Ua&<f
z#Af1{@E!l(Cfu89`IAi{_Ku|dx7qa955a~0fB9ji4va>%;f{7%Jud(-59z6c&kFZb
zAcf*z4;he81{il!)uul@Ne%jA-E+EssZw34(AuX*iAt<#^{xEdxK`A8OW-j6q#)ll
zx^v>=YN302e%>USK{U3*iEOxi32(Z((WtgP3By@d&9=@y5j`Jyec{a3UlKqU4U1tJ
zahW&or7{?)MegE0<$rfLt{J06Ka6^3yF~8gNwyB2R)1{M61<{-HW9peYARbb4<=@#
z6Wcle8q=A|G<aY;h##p{ZJk&5@o+xw>i6%Qd9v=I1b>Rkl?W1JDub~q*t_2zU<zYC
zv%dT;)OuP@2q(i1D1#sW%{OZEw^f*fXEgJ|-q~I_w54++;oThfwyYBcn!d;cpS}<K
zS{IGC-igXTC=l?Ag{{iy7|vU;fkrQz8;0b=M3{Mb3XX;);SNJ|U;T8U;mXA+%k`?C
z!*<~`i`6LfouM&=0dG`XDA&Lbrn12=wOGc75B(nXJ3K2tYX!`X#+lhn?9_KkonO79
zgw<F5_gjCzG=4Bp>=edf_vVJ`Z01{4to9oab7{YUy%Q7!w{EoST4{XPOkRF49P;v$
zB@qO`L=mogItSY@*SDKykyXKCjb~R*t2_1lTq=J<yPKHUVOIV_P65X@*BiBJfjqgA
zQMGLi7@&a3pxrRG<fx6`afB8qty^f^TPv!x%7j8QIB#$hz|&+eC+VTT6v;B{MY5TN
zdH1ZpIz3AC@*B1;qX~QziAn5wU83Mwy%)9Bi1emi5Jz`zJ?_rFviBA0e*3;u-AMbj
zyl8EkWaXw8s*S6TWNRBVM|Ro4d|G(n<_auWem_UgK9*`}uA9Webc`o>eVOE=+UU2e
zG{ttjj@Ijs_};HPcl}ho%M+21akGQAf-!yTl714qGX4~9RN05?i1r>aeMKXefZ8e4
zI6o;bGMh^YLSTcB```Vli1FX^o^LS910>*9s5XB4s_oD_L(m7{u-MX4#(1Etb_`oQ
zSNZvco$rMoST-)XSR|b*j$6$XBa-`u2brTQzCRB<A3Hfho;g|8sX&jX_OrhKgg0?D
zG4`d!(Ck5VX*gm!G^!)#@)^HEbhC%hw4PLL)Y};uca-WQ+)z^uWPN{B=W8@qMr^mv
zIvw+?IrMKqGCZ9T2aoapdsoImScKBY6=bXxLL4DB8oZo*=Js?Tx*0ZK{V=gLF+aFz
z?mB^8t~dK=Ohq~^yC?14R&=Xpt5jM-)aFBnH=&~$X@dgpuPP7PHMl$>)2tyTE>5Dh
z0%BA!PI}Ob*Z*G7et74zp^6k!I%V539-Wv(o34Zny^(Q&!^IHSz)|N;JvmYDGI9*)
z!+MD0s+v(>IL(5!z1}C)Pg}|o-|?GuhHApr*FJ%{D`W<SJnEckRoQQvdl>XU$Ys?|
zo0>*Yy$l?Frrdbh_3M+KZrNg`vVSY<`1_yG=9}N{z@0L#Wuq4SdIurFcZTFfaYypi
zN>z0+My0ie$R=5PtAp2Hv(c@uPH>KlloYJ><pIA}vJFD#8HEQ@a){?a&_Bp<0hM9%
zrVj_D+LCRY67TYSq0XGj=-_scyZF3Aa;>jM7d3RN(|2tbN1X#gm1;r_FUV_nD<qev
z>w71bk&gyW1H&Wg2c#4OzwJ(88zJo#FQTeV!OzS-=W1dR7k&=Yta4u#pz~ZSyUmm?
z3VP?avs%AwTUwqRoeFlTufHJJWJfB1QyR~n*J(h!A97&Q3TUU3_QKxSF&F4Ve*F>&
z{lXVt@7F+0@A*LDeg1r##<~ev;4_}yW5yqc-SI@I-|h(Av3jr+zM&I3|G%5|KZOV=
zU=@^Z@XAD^DH33^g+>mC#rq6+mk*~n8tP$d@bevhC~T#I;mZ=Yy|tF_Kk>5@7&5KE
ztrHL#BvQ|GL3`NUc(l2u(_^AwKe^kjL@M=4fK03f<WOezN%CS78yq?4%yNhhoP~Wi
z{87*=9@sjJtS<P-CQ*+U<lFf?NIhe<0)DrjX9$y|1=UV+j1cb@dk{kPgI01igz41-
zA<Ku{x2yg6>}vf6I?QyxXVG~17#pq3VMx0j^*Xi>DgL!4)le<tajMbITrSBbXVhFw
z)R`ni^nH{dF47+OktSkv2ks9ag6wZub4ToQ<G(z+q+D!qk~qNsrmg?lG(sfk1}I%3
zP+DlSrg#vFxW@AF&9<;FVZjjOLt39a?nx$@O0_EPjVPK3+kzB1m=B%ZA26v-UWeD#
zVBl1e`8$Uro0=dj&4_i_(dW1|jfj6--699(lp@$g{Ll>5R>hE@BtU-`=j~p=ywt_>
zUx67J0uls*1#boiqz#5b06U-=gcd%|nH3}{<fb;}f4QZ&_7;p-lT3Ri3#bwHX8{=*
z;I2;o-~azNFr`QgIO7;FMDYK2Sx3Ck;#*8_5r?YbPyfY#AL6eUaBsmhuuWsbf2*)J
z(*y5Ki{Wi28PoCe^Cx6vWEc|){L7sIzE-sG_BH5jA`Fb+w6rv*Np!b>e;ToG%Ax4Z
zj#V*<s$K(%?=tzlumH@yD+p?d6AbZxy9lNc{{582^c-onuJXh*G_X@sQviCDn21Q=
zp<LVF<28z;5L)$Ckox-TXbJLpZ*(yLszJNRyUG4375*?g$rwxr42bNc>i#PW4J?5e
z^fSN(N?5{wl~KIWC>Q_DjKfD}Y?b);g93M?1Zsr-Qx5reIRv~GdQZ8biu*Ws^lZkB
zf~kl5Wq|V^Arzr(*dsp&q6{_bj9n-CKdcOq28w(zDCOt=FOmRv`j!Ne3X=3Qz_9jT
zEd2A3%x^m-ja?J_|7qpQZ=st155E*DrgYfo0$s3aP+pbbjQs}_BO?I{iHoOP10pwx
z2TZLL6F`fgaXmH)`p4tsa=-BzKZzy?pqvT{kkO|Q5fM3Cw(m@iys1Kc<I#Wr6f@s8
zj>1wBuw2D0h@RfwsSck<G<#^g|9<7qi+*`vk8V*acmhAAkm)k2Hkis6=XN{Nh-e7v
z`3J{xo8HK<(nkXn#pjB_zSUd;BelXKBWb7H6uJLF8VoLgbCF-Te*;WOq7DEi)f01a
zV);FtOa8xKwGXDRuMcM#L_$)sw>RkZDS1Iik@;^|7K%XGVA??IR(RWw6v7)helOhs
zU>>u}o?@APXSO4Vj1da;=f9CQx9^Q&Mtd}Az%w_&BH*&a00>*0DIhv6F0gd!74#>R
zia;ga-*#6L4)EwQ2>g)1X~*CS0PqjJ?>56MmKzxC+OO4sRe6C>|LF?&4`%>M0C&j0
zqsavBzzqvP5f&&FNK|Q+bGsg3lmJ+PnOihbe|sVza{kt&MG=|vM%~EI{{2SXK-npv
zW!AR==qMO?_}+9j^T=A*H^%*qRTOVEP-vnbx`7Y-<%JMIaGwP!DEfg4j1C{(#Lu6j
zTHhu8y|xdakI36E{zEJAT;Ngej%p_yZjLnZxgEOBz^IEG6dC`qb_^=e$}J2D&A0n1
z{)DiaDGCS<hL|Z;pRgF&&GCy30EbR-sip1&Lko|H=<a;EW-2n5QJ?{k4^>N4SW+sl
zj+ePU&VBt#E|<->s4=po5tsdVcSlT3JrwRW1q^KCPW_>R$}~bAaEXm}cKOpMoSP_2
zSobhbeYxfAV(i|Bwwxd)R<<uOMzV_I2_MR(EK1<`5HN>o>o`-7|H7q^1=hsM>tX}!
zHz8kGm;IlRB`6#K@EXwS=5Gg!gMt#jexMNxaUo93!h-tN$f<4m{PZ*lOxC2AX=5}8
zNQ<P9$(`_xkOORwikW#5#-q4ZpYP>vyXom-wDuPR9hVK~##u4Wksuc2mmh&kvtW}Q
zn&G26+T}D0uH0x;;{$nW^)h?Z_k4UK-<^(1f&xXN(}y*huW@ehqa>a)g!iU+FwW?`
zWNL3HSf(Q74N=o>Cs2dcDuc6aMfyQDN_N}+;*uiDn^p-X9DO_T(qQdgw*vz)c&MnT
zpa7ty@l727GGSW7WaF|(p;&LFO0U<3^CqcdVmQ`%_ooZD&o+A}%|Gy+Lx_lqqT=I6
zk21C*y!iwTwJo*cZ)kqM*G5NWlZ?L@F7SAq5nsL4B8!G0y~|0}`veO>ca15lAN7-q
z=`|${lXa8ALt3lB2gjv)$(YKNASSS!H<3pcFAzq5m1)hXgl2@VAaAkkRwMi#&egY5
zReQG;M<9g-*=hQG;Bj+vQlwO`obm!xrFRF`L!S?ZlHp7iGxD&v%Uxh(ffCblqAnw6
zO01637D%_#YA$`m;8Jl^2th37yf4Kz<+i-6N2V$Mq#VlI^ZiY~3eivj3YCV4nDb4_
zoWU05UjaK0mk^`{UFiAY8eSj9nmj-U#>3020O-#Rt*Nkk0l+=`fNE$&picJLeZ?^>
z@6LKdCTsY1%+#z=v)1hH)bI7n$&Y;35<{UNU4hr9OO0By7{?a?$1PXC(|6zkq&A|Y
ziD}&lVcK_zdZY!L&-A-aay4zT_xX;Gk5Z+CDVufdV3Vx$oyj<7*@7q7TK<tW!SL3%
z$#QW9K38LP71*1JYHvGaRVF!^22xr^P}uy9Q$BAbzh`Hh?Xddck|o)lg+3D&EAowe
z9P&t3n$GsHiS6chhYq_{bNyx@)FT4bc7H5{fD370dtbb-IKl)dfds+hb`+k)>y8`w
z6Buz{WidsHK)}NuhUP5>CNA2gRIKjqt9rvx+*4`y+7i3I`qfpnLTAz8-kqM!pbLa-
zr=Me9#~7l0rN$Kg=43U>ZnfpTnW%^pT)rt*t7weDl_g#z#?;!g4?pU!U0*zJAHK-~
zRR$GAI25u#`IT!XgVnW<O~j#~SqlsfXy5c=Kr-6;x*UWNKug^v3=HNUVA|=2Ub;3b
z#TigC#zbyPMP9$eoAfuF)o!^a1e06mUDZu455fIMg9yQ5jA6$iyuHgsK%0E$=h|MO
zS^ItJ(>0U<X~2b$5F+%rG$J^nX~ZZ)<BAdAPXcCzY`*=>A18hczj-4|pR&l7w<?s5
zneC*kA=Gb1Gq}F+`qo%@3C)K#J!ZHy`H1l%?61KD7zrubL+qMh>Yu{b90{e;{o?SH
z;w^=`%d?9iJ`%u<`1U1sryef#-jz~c&{KZch;PwtgPz_f>5Mj*8+=3}mG>9L{t$f1
zmB|$a#i<VqofIb0Qf%N?jD*T-=D!RKptAi6gqq*DI|a}h80es?Pp<GnA7M*A7Amkv
zf5$<ieg}qoD3S_ep&Y_1ARw?DTGX<0ob2f<WQ<`03`Hd&A+Z1yY)TF8piny;yIa6>
zbHCzO0!-yWkyNxYi8G!oj?M;qwz?wy*>lH=-Qu5Nw8`x(b=snG&xW-g?=lBCls_o?
zKd!-jM~P8)*bqZdkS2e=tcDG}6_25PDZ)bwrp!|{*$)X)v&Bl>4rwCq(}|WG`(}dw
z{#_8iQMh7nU&1tYm7BR4cvPx~8#@f5NDQr}F=m?TfpSPMDhs#c38@hi1r8xnJrlf`
zkELYLQnwjrs$@hM3=!<KK|(I#CK>?3oQ{*3tbu?vwSG7UBi6f=tVBhAYl?p`?6CI>
z5?|06R0$hB7tY)De5dGR0b0g<Ih;LL{zWPFaKbc;vSenJG;7M|V3l*xpm>9sQcRWH
zp%*JU9><%&86s;BSN2kU3d!$`hUb)Tx=F<W5R)or*#4_j0g+Nd=3;mpR^&>B=!hYv
zfH=TJ1%K2DfGPS}DT^@ntLdt%Ep=6CR6;_+2!LO-SZ(3xczHOKlSK#gW3@pyn1zGz
zkL?km4UI-xNAr9pqn#ez1@DbEh%<Ph+7{IdtUUdLvx}QVsq=cJ(;Art*FC<T*i6ZJ
zwR!^a-yRjm-1w*=&DO|<=MQ8ssjHAKKUXQIAiUcdEY|l}qYqXgL|eIp92Cu%U|cU>
z?WXL?)}7%BNtrfPX2j2nuTa22n9Z++pITV?bQ^Gm!JgaC`+lrC=&pa?zx{#EVRj_o
zg5>-v5Z=Ev?6B1~Lm_?RfD#nS#lwZ%J=^|eOGa~N<4X*fT|$S4j9f&>A@XKezgM+*
z8)_NrrJaY2$_18!E3=spn+vu;g`0>XR>>7pk_Q|-g~isI@vbS*>5VYrmUtmstyE5n
zvs_6NdTSMY&|Tp!H|2Yj;F#99L!4swK45#7)#G9GXZ%eJQ?DvNu2C>E;#gRo#3Ax$
zT>^p3b^-t~PwX~@<9s7c0Vd(5>B5`k{^GAn{P=Xhrb+ia1B!!k15p%1B<4j9ktJ7+
zp8?aRNW0Mva-Y9da|))Y+8O|llxPeJ42(|qc7M0${uKam+FS4JthU_*8pl~|7OJkc
z06^Zmi%}-W%^t|qR`{>xa=g`aeI01YfbYNkc}meN<T67{pQERLK2`G=51M08ThO*U
zC%=ue{G%!#I4j*@#Q|sXEHKX6Kda`{TZ+SPh36~cK?nqHHvHoUF~j?v0*}Mpm{8W|
zKKU#*nvRmau*jpen(aV48x)#s@O6e&>#?O<3r*<ur8!PF8pXv#)o4sYi)A5qlA+8Q
zdB5K~#&?QfRD@{YVzP_;1}>duMU+0_sf%~ltqO*s3zofI)vq4pgiRfvN3$^KjX>`%
zb40l**<Km&X1aO{cuA$exU(@uM~STCA`OL;9txE1ZtobI+H_atBtTrJ)=9E)a$Y*X
zt#2iAYdWz9ZtP<vww`m1U4N<2zpOc;*}v96z8<}gp&E1*Z_h|?hfUfY2j68j9l*uM
zU<w`J2x2kx&^G4n6ssl=j&ScgI7ao5J>Yht#JY{PIi2H9uS@z|;~^WULKq%CaIMbX
zomJEKRpDBQ_LY+SkAK!B51h9q*vLh|ydA*~08A1*J&?}l8$6d~c2`|9Kvv5Lwd?o-
zmVANN8@`Kc%3695BMA<#8-2&!7JM&^0WOG(?Q1LnZ<XJxudz9(dyK`bcz3(A2VL~h
zDhi^QrV9t%@k;2}{MHn)8i+j+{HF#4@xzC(Dk-xGXm|T?!gA+ZFew>@e(X#4a3de2
z7%TPe0xnk(?ez2cbW09Kv4}_S)E$oVlL_rA?`5T#L|Z5#!eyFvSX}?J5<NuINB(9C
zBGt7{-8xAOANVDX;#j;&0Iu<2jO0BN1xnUCstMh$SCfI4s;05q1fNqX-E~w~@#dQZ
z3Y>$O$wpCvC;M(a`UMZ1%l?mftq>YVvvZcii5B}c@VR44l7<?MP-)Eh3|3J_Wf$yK
z#?~x+ZLf!F_(`0;40_lEU*?#j!Of=8Ow>d4)lUX9R;f%%CH(k!zLzqWMT{i(#RKIO
ztAsV?hgxZrvJ7g&V_vHi0UnA7sGz=U5|~Rr*tY7Kt$zyyk1B^V%8Q;=TJwgl9GqcK
zK-(@ir^)V3{z_wB4_Q|WDTwg!jy{;}EB_5*mAgP6KwkLVgu1y^J*D3E?uDZy2*;N8
z9TZ$G(Vs>_puI(s_>xo*0CfH1zb1Kt;ezU#zkq{-pU<l6n>xiYUS=6X)AE73-o`~n
z_5dtD2TtGiN$Y7S)GIs;OxNC2!8Eu*3%rawmBmsWO-0id9B@uSTiuRXBTF7%+dMB3
zGyx2t3D;7QkJ;n&rC{?VhOSC2$B|#}$5sm%<)!3g#K|<j_?qrM)a>vp1%eVv$mtlx
zs_xlb4DeG`i<`BBo2EQv(BbAaQqQXP|Ilw_yh`{WZ@Fv`!IaB*IIM;@cfw;hX2h#j
zCIO;4!=V?5%0?o;InRJmPk7nksp9TE_!aaf#ZDlW`J8id0n$CrD(?Gr35igHA^QPV
zWxzmv$yUQ=xzfiJ>AktI%XxgD59C=#96F)~sH(EHQKZA^KtmBagu$R_cYkcbSrSiS
z8M<tj%&)%OXufjdarTqiBC=WVlF)f!z?trZgp$#7z2sVAa4q5r#3g|G#vpuWylTke
zp)*t@>Jciv5<syvj6&Wj5l!?HZq=k%__vTg?}vTKqCQq2t8E<J(uQi(cW68w30}ir
zaLA2eG#+mdSpy>Cn}<8{+A;01{TQ<qff5ryaA16KR{>M3@LSLa#N$sj6yT$S3lRpe
z@B-H1J@MNJYf;T&^~!8icz<iD(ZMFQUwSrcZSS&pTv^R-wd<`yDs)@7maHrL@$B0S
z$0kdw4?7-DdV7%t>Df*^U)TKrKOs5`g57^-rP&#AhJ}Siqrv7KZlrp^$f&Hc>w+fD
zxU8q>Iu@kbeClod;$;gO{6?Isq=~MdOYH2|@U3>Wm0_lSZKd~J*rF|*%wV(cv41k4
zgk4t&@{+MF;JJx&n@e}jEk3nT#$0qNVVpL}Mr?H}63F*G>!(}j?nv4~afmOfs=KF4
zr4k_L$H%8-Y9@`OY}%|=;aWxHADfu_SK?5ADhd!ayG8FOGdO&y-REU0O6@k(eDvFT
zf}DZ55k7UxJ44SNmo`SeO85p||COQoT>8#@e)x(2j6HX|Le!kx!XT0Uu!shfptSkd
zyN$yjg)LPj{vm9yzCA}~(%$e5>JP@u5K?&T4qGYD=4YYw)7-v{Sm3-|eIN6m=0Drg
zCZnVO@sWjIU>snDUYe>g0OG%+%4PiV^70b;Gk*|3tSSJ2wN2a9ZG6QkbFI(g2zbqi
z6@yZI*%xv;4*9)q`T*ZPjzlyxSNYo!+0<^|htv@lV4C%UywEmCk$U$?GM(euhA`Qk
zzJtSf!hv%MZ|4NUs=C>Vc4TWaZI^5#VQQ}Lfsr1AHiZ&DNRJ7BJJfKg@}g{#);tT$
z40qniUAGZ=cp}G(m%=X_jUibgmF~DnU1*cy0NS>U6kDDh5yv_6;-(~vkit62`Dsa6
zmlv&$^ma=68bqOI++@=^fl{loQM!Y9grr4JqD-lne&NM$Ld}*69HE)<7SGaZQPmg)
zG8FTz)D{GY*3cvxDUkRI2?WT%%(iN@U)Me25m$c9q;t%C>`0h;TdJG1G!rO`x_L73
z$VLjI<M1^mdDc-Gnkk?btxwNBRflJua?f`Bu~{+VuQ%7^z5yP#&8X|$$u?_$MGlAF
z0;*8(*?_gJLJWq8%ibmU`ecj)3i!E>=lT2>Zjd5f6)9|B2R~RHx5UUqI>*mS^DCEc
ziVbDwcS=cwlJA}<IjAl8xmFy&1@1P$-#ll(*JrLWH9CoC9K)HM^%i(71@j_k_fjUd
zsRWI>mo$h7YZA78;j<eQ#95l+3$IP18tSf^{w(^4cstWCd?Cghnz#;Xd?1XGho1~7
zz6^Dse2IowO&7+!iYs$mM{%S5a=<WP(<+aEMbpFck|?*V?mSk)pO^(@LL=@*h?(5?
zy#7(=eD(~5OcquS-rw#Kn*pv33bjmmQ9lLA<kfeN71B>rE7uk9#!YTX<1fYbdqbik
zLWT2csxGFVVQQrVd<9_QSK(alL+zT2WKl9d#}2Z<@^S0CeVqITDO{73YaiC4@{%Qd
z4)pB%LtID`Kl<%oYLY~~c`QPj#JMM90Ms~h68SZVM!i)si>wyb(l}?LKOI;}rr^U~
zL5;fCz)=-0*bIDbRZ5cu&<_d@y|o=D$QcNiE#QL-Ai{^?&d9KO4riRxEzS0VX@XU$
ztxWrU_IJ6}r9!<F-v_D!5fL@=FQdG??S{2+tdu7LL}Zm!YpBPIS;3K&Iu0WaeY4_;
z=eNHa%}SAEII1rGoM7sz4?Lzhysnj%IIj!C_3Dx<@n1HP6_r15%K6M9c8LQ8RXj#(
zDz~bOh_1HErkI%O0cY}9A^pC59pSDBNIORN3=q*U8DCD<Fhhh-iKJ&nNA^ZT@=`Pi
z6EATh4G)a~$$udD9N>)#)RXEjXoLf%X&L$?Q86&fWB|=S1Rfi=_G_U+mPol){m7^J
zg%>y(A#+uZ`@1H$ZAJuoE5%6yc9((I!W>^#vuBP9f2f%;S((K8J+D~tttyDzLq^mc
z4l5tw&z&~<TxF6zM|@6%qm7w;MQieT*`SozgAYFaY(kd({k!<gikQ1;5RXR<_M8I{
zgP&FfQX`-H6$_wXqNsm>^UMzN&o|XV3>#eyYsTJC2+wUTuY>FsTG^n4=wi64cF_$h
zgRtEXMR#^~4wKUO=yu+(oEMEkCiYq6$<YqWby(7$B?;_3_|_uQj!HI}ODB>@C;66(
zem-f6?>82vO`S!6fvpI(eq@*UJZa!ftWAC}7f3Xs0QKr_9Y?Q?VwTX0_g9?pJ=)Nk
zPNp%-pDaW`jeM@zj|>T7_G>UjWDwWXx#y{LU~@T9=8nR#H|2f;-x~)3f>z@n6@}|R
zW=6JzyHnIWvgud!Z^cMDsqi`5$q#&t`Q#@)_^`L|i&Y2Zo6kT_E9zd)H{JQ4TqShc
z$TBZ%EU@9Ix322a%kSQKNzd(5b?N8t&v(-A`Vpys&Y92-+|bSYsh*d=Lo=Y>E=da`
z;I|h$zWsc&^Q2^tr0%+8BHPVvEZ=$g@pw>LQ<8{^g_Mt?&UY0TS9o170t%)K9Ss4I
zz~N4geQW!8q}iF^;K@PfFkF_^?98>ilQC}6iuUm_zeA@qU}I=sLz6b)m(EI)ny8$K
z4O%|aULyF=O;q<*`PLPi%Ej03pQH>1J~eqd(CIK%yncu|q_jm!qChKTQC&-aM&tF5
z_!HqJeuc<<`TfIBoc>QMH;`(v)BmnCrCn!XNX;~w%EU$bE%lo~RXMq$GQ?GDfi_|+
z{(CfBQL+!xNrLuKR8L~X3P~GozG#z-qjC<}v|~d$(g*mnrPX7JUWS)1(+ggFONtt7
zPC_YS8k4oE3=y%X5gMAW9hRq-gM<6KxZhrLvI@!1vX8KcyH9!Qo9Lg@FC*l(@RoNd
z3K!y&erZLxF&0=KXor*ZXs1{l@xBW>^M-zh(del(=}r(U)^>>mN~-c$&GwW@y@qxx
zw6yqLINWjOhWZvNU+1vFuaI=RS{(tkV(xdTQ>W{?W@c<GHZlmZ3wkA@u3KJlqK96c
zts<f?>tQGV6M{lcr^$-@i}AV>Gwv>huMi^a&OCda73a2Hka`AmfE_UG*b;HYOO>~G
z)L=FH<DK0U;rWV#t%*a*@L!Dw0cAk`VrV4T9oTfVAa5xI9Iw>Ek+|*ER(CwRY4h>S
z?q=tAUMA`w2uDUVq`4K3J7Z;p1oIcE`DP+9=7;l@T(PP8zRlDZzMfXuB{8Y0mxJ3}
zuKlU6gn<EB2c}9tFIDnH%|1-^wbqq|?9{*ul|dXYJ8eXdsqmV_f4s)nTcgFeHdzc9
ze|e?#`k50mURHNdcAxioDy{y&KkqAN0?b|3m-%s>1kZ0HokNJQaLoBvIee#jLQ1N3
zl?RI&4eRhiNk>p;iz`{&1p6&z?9jVLj4}eS8@rie9W6g{B3gpi$sMwdfBI&|M)%g(
z4x*ytF-3OxQHq9E4BF#sK{6bwiYq(Oc4)U?4}LIR%btj5AjE^`!WULqd98IP^jA{V
zBzi66l7h(#S>u($7LSCXjtn$9Z8ut;s5w~j($@8|FD*3??hj<&SLGpXcSjTXH1Rn8
z8p@@Rm~Ns>NzS%=5_|f?r}1Tt>~g$$H}gvbq3zbBxSMWOj;a=ziyc82dTXC?B{k|^
zXAV8+OC~x44Cg3Ts3849G(PNm2d#Cr8dJls&ZvX$bCAvTLlrk&N^E95j?-7;=|+;@
z(YYCY*NB;%75a>(2qq&0o0Q`ZG(rfnmFGbu`6Hv`Zgl!Qy45~RQ182dZ(vzwBni2q
z<`TEtEV80UkphVg>YTmA9+#^<C2wHjMtEEtR|O4V3@|UBvUq%J{LHY>d=OUpvckVz
z-niD|4+=in>Ta*zkMD^wr27p8wP!hELz>NjmpZf533IrVOnWGcw+V6XgqxnO-?PR@
z*9pOqWpm4U9X-yc4aP70TUY0rzP}Q<R^%3Og+sr$HmR?63;1&XZJVJ@5=_pj>9@zz
zjmYtBqeG>djP=%!qDMMuf!~ZuUWy^g52u@1aIA@Sy#Yn!JXxX1&+Z!O+BNGAH=Y`O
z6x`^i6Xkh$L%f-VkU7&%!^0j$Q>Pezekv=KoP1ASi{8y({GrPqXFf7~k0<Z$(;Ou}
zmZ6VD<xV(eNZ(p7&7Iwe+EevDF}-GWr-KGGdn^O4J&8pAkv?}@(4w7v)=~c1nIyd9
zTo<RAwfNc>d9+f)xk!GERyNS<ezdKnsnyBcwe=p}ayYFatl(WF%Q#nFG-jWEM#t`<
zZfevEZLSF6hCVNi_G{+v&()%1w{iLIkKEDx-}pUpbSP-?BMZZ;45A5sD!3sH^_h1*
zN8E~ODt{Q@S23cpP<~@7b-)FxBBap0hovP&E`)b52qwtNcy#xuTA4<`-kt?fdb&bE
z1B#S_!T=<;4za)l<UnS24+r>1Wsu!^s%(!Q4N&GY!sinruOpUdBEhxaR~m)v-2l0_
z(tkod*ZFDIA<Dk4Fhy0>aAd-0#_6!3c<x6u-KS_%OzL1Y*e-{rrQyh>G3SY`AlO0Q
zzT8?@J3I>*lV&GmG5Ox?TTIt|edU9ceAreeug+*KVV4(dmUPyfT??#V?#q`XAK7YG
zWN}o!HEVjk@b!2;OeW2xG?-okCeu65@@eO<n2%z4GT`-Z5Mf`ln(cjyVdJ4i*bZ|`
zvV}pf;w2#<dVZkl$ZS@!Ir`?MSrm&Vc<;q@q$>v6b<<?J*3^C6k4TI|^iqfA7m?ff
zEL2mioEzxRi+t9*;hC^Sn?S_~MUiO2val(dir94v->{{wr1R9_gbWreT2XeeEO4KS
zbQoP7#GABm?@vEOmrD#{|I`GB=SFFmdiNL>9wkPsIV9ti(uBpHiUZS_xhgusNoUyd
z!8){?|I0RyQqq(C042SipU?sosR{-^KI>pkLb7!d@vYth5rK9KGVcCK$6P6TtZb-z
ziY<b3f1&}p-m4p?(eL}CsXWJsNA17_Gip$Pr%C=n{<I+Y)K_A#)S!N(Lc&o>W5ApO
z+hD&FkX&h46)Mn-J7>r&u#T+E5D(r@{Fo3r_wMx%6AUjBvG@@_k2iTh^pA8!^~gL6
z%&GU&=H7a7HB3QB_v(V5oJkX^L{Jae<ac?zZB8~|BvCX0@7z(QVcfC+)R4}B7-WI3
zOIop{SIBpxgVkzH`arSVGMlYP7dA_KhY7QAbU~-_b=7>oXVn6p%|Y?Jt!X3|Od?nF
zhB9=*5d&x2K4d{GL8cXwY7nFiMVS<s4p56la$NA%0pqKP^2sJ&0!K3H3K8lhy2{(5
zNf4$LJ_td^6)Ozlcj=H4{uOlEx2~3$Q4;-XYsXkkXufNcH+$>fNPIurBB+Ni_XLo=
z8~Ra;LSk&AEQq$+f8=Y>=y7$huux$Zyv7WRyHNO%EOW8}^izX9_57EOU2$z7uw!?G
zt^Wk0gquk$j4C-A`4XY~g_P%KJtp|`Ep!x}#3R-<i|sxqeSA3vGNoy{?SodoM5L=I
zdM2IH{TI#>>yuJr6a}h;&F&@>G3=|upE%Pg$2}L<j|oZHQBq1sbun=R2Guq=v_1~j
zom*-|Uq=aGc^d8fT_WJ8N?;!yt#lMLDfJp;+YX``(YmJ4xPolx*Ii(2e=lyuf@LK6
zzlrHCHZaf&a3;Z+zuv!y6Z7*EI669-sa}T>`7%GIb>8oMp`7TH+OvtH3W$!*@7O&V
z5ZO#-0n>qPOHWT9i1eQyIgC49(H8(;7?!b3AXipzf|%JZp?}<@1INU|kE-43X;z3W
z%Vx94bk6S@R6Qt>(m|a_7U2H=bBUf_otucw3{^~*q62a&C)2nAe8cgYUkZanXYu>S
zz9p+ItZ2o|rJtOO@WzuU#TNgvYpO=Zr4u3aXhyY^&j2GeBljhb{84(-HKF#zXU{4E
zFGdvwh|yX@=wr4J*?Z)}8pKwCoOT1tAS}>t2<ub%K>;gNkC>mGH}39f$`Pj<soRQz
zjWpFGjxXQ;zOxYCavBv@$K?0`Y<=J;AOu7*U{dqF9SR`^sxI5trWj2vEnGBobYUWE
zIAaw!Kqk(L`%f5{qHn9Kh#R>)!LW$Cr}7<#2``Hoxy;LbyQG<firDwoEUwCYykEGc
ziC3FVlh-B<4{Oo0Xl!-y{CcLB;E16fxDePds=zn&^=D8{BqekZ9J|58_jNIvh-2&=
zXPW-&#J@Bu2ASf6{-ROHb?G10sl%9<o8vj}08@H?Hi6Vx&m%Et)tx2p@ALh|TR>X>
zInY9|U<Y7|U=eTUdjlE5B4b3z4CBcXk=c5V-gxc^*?Imw?O!MVuiM;Ff!2eZO*~fA
zs)6}_@Lw)tLFxjKUkz43bnew2`N<*Qgv4K8eY@gEh#9c1Lc2l*3E+MoVDaBOK^PB2
zvy{F7lP|h|4S!|<hE{zjwSG^b80=4QR3`P^R|!leR|1u~16?czHxudYXB3L^5F@d%
zALYTP?EYMPR@z^?s$8!fH^=(|7D(13*S>yoy{YuMM*`KpYmDn)W6O#_J7WMV0XZvw
zEUtaKq7j4&s3MD<mMoy8v=oS`s0PKJUjbB3ljU-6bmZKW57J@!h!E7j>=$eLr#>@W
zElUoDz-6nb@?su!^vtSMl`!*^YskNieVYWv`&b>ww*prcf}PO;Zc@~ID<SzEST;87
z!sE9}-Bv73O-&f2Il(Z!zT=PbTvjtfV_`i!kn1xuDz)EC+CN?6KW3|&^w`h=1y{f^
z`Mw%i=h$}OfG7XV<<hoi-T9_U-)PQ9m22OYUHdZ;_}~uZt@z?<ZweeJUXTD{dT&G9
zh%&Mi`oBQf2+!-47jBGyFfcIqh6&=P%{TYGulce-`aeAOM=M%<TYv3oT&x1{858P;
zjP-JZ?T^Sqpk&5!1r#V4`wB5ax<S9UEaSg5ShI9if0)_%qicTW`{g+SSiOAp4(~5l
z%C`lRgOyZNO1p~=<^%h_9(5yX1DGa=iiv5O1cCrpBn|5!(xrF2qtqC86`Ypxhem&v
zVg(0mn<5%_kwL)V6RJnkX!BsfV$d=fd&T3jLwK;aRtC#PsbT;*Qa#;T=eO+DO<!t>
zw5xUCV)~)}$Fz8(hZWS@Mc&;COTJx%1Kr?E5RPHG@B<g<N=dWVaRnB-!_9Q#g!Q?L
z%ijOcFv*_?RS_OUuw{`)6}ZF;B?>xv5MYda{rc6P7p%yBZ{6mq%mu7p{fS^Amg4%Z
zSO^XI0dA(`z3L2D?#>FlA6`W8##JNDp?C7zwg#*MERf`$!eroGQ4B;1y=LHSse13t
z<LPQEmjx=I1OF`de~!Q%>)UDh-CFAW5fG;o0RZ~_m-c0Jbo3wBz^K4{uiF#GQLFE0
z)CyhITYg7@_l)p&$(oRr(U3w0{vk7>%Pi(MtR`W>B=OoXBd?b~Yo?yQPNTFNQ#g4c
zh!^IHmFZT4R#Rn1#=0{6QqP+bzTYMEC;nwH(SWGYVFq8eq4>_7WD@M1_w-57WT!ko
z1_lNaD={+>Zwf=-T;KrCWI5zbg;r>w0#%;24#-c|8j}vg@JN_qN?tfkXGW?1jAr-*
zoN3UYPYn`MikoWj?}x+AQ>n2(!o$U<5JjOsxQTn)V3ZkOQoqp8W}aq#gj4&W8~McP
z;~+AcFdYX2@*gaK2bjd&5j+|uD)998LSiNmfyZA?Vq)v$0d@d%a*!5c0MRj;k^|m)
zr-L*;^SbQC8%MI4jWI8S@UQ}w#tbPKC~46*Dx#jY{?cj=8t8k^_d~<O!fI+rfnd%K
z4!vslsG5vi_eC&MbdKCaq&Co`%+xT#-Q56qAFT?m%46nbyn_X{n$1#FUy{o`S6b}V
z75mq3-$*F<2XWl6PwG(Udn}0)IqiBz$|HtGh6lQBy&R|uL7F9RjW*SZwszMWNu}H_
zia3qWx(rNCRwD#=D!QhQ+C-c{@1=O^z(;}Jx3XUl<fp4a(Q<H8q&m=W))y&pN|RE0
z28y{&gakp1w`Ae#1p2>UR0<vF39x^CGoBp~{QXWw6jYL-RR`n3osf&k;mB`dkR{C6
zHFA8~B*Se%ud2<Jvhqvh#ClF(yL9?2ujIQ(%D+ekG6N=*0|vZ)iWO2*!#bMvQ|JJ!
zFf(4EPVhW*My`OZ(pMXQPo3_@SM?K#Z(!<^=|=_kTjzdY@l3B#mNOW;YYy-VNr()w
z9AQUSCSM2iVZ}npTx&zR;sZs5*@>~Gp>MYxoS@)LuG<W2zlS3tmsF_=-2=mnoj@Ub
z=s_4hA@(&XJ)hkQ;3v|7Ios(m{*1-%OPHaD2oooH&xNe^RR=yo?3AUj7Y?~^il5ZN
z{5RQq3RLNQvG8aVSun2)!b>`P;k8m27s5%Xs1E%IdC8hdV&x>{MN94`dpxwm0mEN3
zySeu9A!kJ}&F?&=-g|>YSibKOL|~~#L_yDp^MO`Yk~B&XlU`k^6C?kw6>hCVL;~3?
z^+8%(LXUduU|!gNewS>T<rADIXBk*8lb|5>Fs$?t>TkuHsRAbi(dVYn@L0I8<n+KG
zSjqTwy5GHIsMzx$u03G=S_L39VK-zn4Cs&no8h*TAlIZp@yRKHGnqockfx(PBSJFD
zSgcO94i*!_?9p}+FjkPXpKZ#eO_wHH{>U6b(p<RziW@jc`iDm<3X1m*fCe6Ste}#~
z<wVOxP>KH%(Fgum#l(T!Od3tXEceS7zE;p19mfkADm=5_sXGAc#$GF8_@nQj5*^C@
zK8?kKHN-%cFRPJg(O6NoiHQO#w{tCP0&Qe?ynzyo2CInXJrX>u$j@<(G)`*)w?fDW
z;&;?`NRlX%zah?Thq@w(C<_cW(!&><5Ug)0Nr=yp%N7@*o?9A(EMBTHJ((1OMzZ&?
zRwtRQDCR2HqXK^LknwW7gKj(#ex`gzbu*yp&`XInEchTA6m6UKa%gijiY#H^E0E8d
z6TGQw#Bw>WX(8}S4q^W%LM!%j^y%EL@zJ$<zUYbJ6N@pfTgY{v=`Iq8lT$nQ=S2BS
z*dckr$dGmKh^z#bs>H+u5}9yH{!oLfF1IaT7U;{A)BJfzR`KE~yGf}`Unh4HqLo-0
zK?QN620kyNZ>aOX$)j;s(BCa%8I2^8S!hsUK7PJHF^fPW<{)tP)eqiFiJ8jN3+}zh
z`(?o{`Ik5@$pE~Tl8mB;LO^fCP<E-7%#~}CF;yx`ZFo94ImHmxB3105SE+Z=H3aGx
zrIqZ_(@0Gp;YNg2Z-iA=(Xb0;pW2Ak69m^P*hRG!(2%NNM_z*(wZvWp-CYmrCoEiH
z=(wI31fOb$-wHmxn_hD3{mpNb@UeRX<aZXYq?l=60wUeH*MP6skY@lj2w3m7!<O`F
zmFKw45%PX`HFkFNB)GEKfHFP>`8`)=dIn|bgp$Q{JyFY%l(<aq+TL6VXrTBV(<%3Y
z$^GXd5D~mQ^Ggf4)d#A?54Kd9$i~hhC|Wm%foD%Zf3KXl!U5=5x&U}s!H2H<0tKO+
zlln9sFW8h|B$Uzsu%iW6nCB%i<HHnPDPG)g`PSHcX%)1a6Iqcfe~)0SUnGS*^dJx*
z42WYNdkUz{occ`3$x1K>bl7pbAAwNx;$c;(64XwmMqK6&)pkA!frCpg=~HqOb{HD2
z@x;$&H1s_Far`-C)Cg<l->LU$(?#zdb_n{{__a}{Y6T^t4`8exjh<3cVn2r>i`DYv
zi&s4|yvkFnBdX8w{<ac=r+ZWL-oXI3ynjg?umkGhEE)uo;z5$l^>}GE?e%n}dAfE)
zO9r-y#DNbJq{d7S$D~4med(4kJXR@DDL!rz>IVw#&a7}vM}~c6btI9S!hzD)NKLns
zli9m-=ku(vw4y>YgJYsE@jgA$X2~szx*KLSj5WvitxYO2DIt94$8lCO5VkxnVs(4W
zduS;5q;x&+!m24)AhHG!#N*Vil~DX>6C02EPFdAZ&&9Pc{XFj*@fmh*s$F{xW^IF;
z_8fkh{KkT-{&uCMnmW{O``Q7s%#BUwPLVge7Bf^dkHOE|pC_bt2`;{v=w|`n@Xahr
zd_Z9=y@BZdefp3BFDWBv@!n!SON0GNST8|P6$2R|Kab`@tra+u>!^sb7&^k<BEDxc
zYmU`N5RAk;+^&_0qECB#9vD1aabhxSzB7o6K~M2JI18odSoGSVIiDNJzU#4XE!RuB
z*s&(w980>{Az{gXKrt0*;4{|bZZRu(%0iUTxs1i(IFYMEql*hpngyzkg)awUl&Hk$
z6))a7{!h$K1shZqL<QUO7&uh{xT5~TuW3tlmO;Rf?os$6G4biI5LPS!-kO-mMIWIF
z6!yYEx8!mrRA-;?O(OMqLOkJ0V=;&s!7ReF!9R$f&Y@ZA2k+0E5F}u%;tG>StEO_B
zLCC+;Rhe$sTWdt#!mrrCij8Fck@m8PW)Qasi4dO3B)q>zYCx5)(IGa!`wI8jrw-oZ
zc4$q+c)7smNUlt?1%qZW40YNFA|ni%SeRJZ4mlQs=w;Ht<(Lg5%Scp=>2uhOg7i@3
zGD0~n6`A+55xssB`aUpvv#din?dpdUJ$zZv*@G@gDCp2c$=dsbv~qic+@T?usS%j%
zo>d%^-B5o6cFTF}?F`psgP7rkYFGXg#*|em1&gTMDhC$}cd(ZFXv}`3_*FOL*id%V
z(&$bUS{4G*5YYgLqOrycg4?o|B)l~rv^v)}JuY-ix^dTBuG>2|^~b}al`sINs-^LH
z))cp?=q=p>MVdf0lxi7E`8SA6n1#}fWPTow{d=0<MyPAfnWX!L=D%=0f7+FxuSN*B
znuJiQ?#fw4CNA=2V~3sSSrJq-cK5{z%=%@$`^hv*-DDh6MR|yd?i}RCW<4N_x503k
z^WJ8aG2vQtl7|s|S$%vUgFj-S-i}c1Xf=)xRfa(LMU3;i><3>3EM;Y+O(~fMB}f$$
zddv!RjDQMVEfUL1S{qqp@zm^N6mnx)6`62l%I}a`5fok7<&VG+b>}{D41?X0815!P
z=uOAtNi`#glZ84W#AS};4gVig-0n<n_~YWGcDg8^Y_2<djCltd`L5wloi}~*_(Lrc
z;L1V!DEw(&hoHtX_zA4hE~d(KC0$VHLdVF$;wfdK7Vbh>Z%iQO?IXXlV9nXBQ92^`
zh*=WfO24F1O;9!e`aR<39A_|**IPqCye)unqfRi`bHDHR$Ao{82Mo@q$CtySWdC*4
z1)v@l4Gj$imBfB+P#JKFjz99#2FRf25`WB!Xm_-3W-he-CHFcQ>{-#VGxXjkx49%6
z4=s*voFF8Q@;T65Pm8v+7A>v(*+FNnwnJ<U+d2)77IcKyd=dY{LyFW3p4!H|4_-g!
zrUo36WPxv?@mP4RgTsi5+=~%5X|Sc)_a+W0Z}8X;Mwla0A{to_7E2WrDHFQks>@&U
z{8DdVKEvG1x<Ye@JrH;i`wqBiOdU^_dqP~0Tj(1ohTBx9(b>UWGe2dbrd_O7-d${8
zqjjN#+pWAKdu$BF#EFc}$}n2pWUsR)q!UH~pRBMoCN~+#m;Hj%j2%)T!<llx4W-mg
zIstnQMZk;Zq38e;*xQRblS}sL7rF64n|_rii#`eXq)wSa=+%C7Zg01S;Jv!{^ZqAK
ztbWLRN95>Z+_20}lTztwe3*ns(+CdXXU;4ozkWITyJPj4s7^2fd6ce$7UNlArN=*E
z{}~%Fw99W9>nc{9$OKt{KP8}G&=U;naruMmM}uE_dW~*!*<AbeoOaXi6m4(s9iH|Q
z@{Kkv&tF5Rmq6-^YUfJlxqM@S7V@gDjvc)q-y3waiOGw(N8JC{oacMbE$I)i<K&CJ
z@=xXBwBoUM%VA+7YJN2}nM63GV1;RxvU~`OV~D6|u7c|0bU3fbZ{XSpVixX)Q%X=Y
zbfcbIfgim)R@elj1n$w~1!SK_z)kU53>?8X_}({y6dOw;fUwDL{@@&2yK<^t5|`eu
z^514L-U%lc9S}=lFW_2(@8RR+)&6>*7#tchvZA3t5AjtlTf-D$xQ$xQd9=&h(RpK~
zly_Lsq+9<aq+Dt%l0Qf`!*j^nL}VqKMMy{L!s()KT~L3E<Lo_Lf(LX+??mb@6EEH1
z3_Ls{S0pa#V@#<8iaaQ{<h`Khupp)qy%asPnF=YUpW6yYztd)ZAK=En=}dPpE6|P8
z5B}r&uy^kQ6tZ~I-?^Wz5y+;{gT|TX_%!4wP3}Knfu2vLsGg32q56uSJ!#+V()Tm?
z3o}vZ_c5Cgur*5rz`SgIBq9d0iq{!VQMIN~{vpki)C~pQ>WjIl112ic@6I;^lP3;k
z4*xplVpXtIXz+k7EVy9U*#(-;^3GGe!%;4Pl=_S3Su5xdoo0RII>o!xO8R#wCUQIX
z<t^y_4p3??<X@_F3>*f8*hpe|VY;^_h)6-7Jp0dAhQ1M3dxw{xkLa)wr0np)hxlFg
z|ALMr-Q?cmjzttg{$pvsQf@q`&v&Np0S6iF%n&Sw;sO{vrUmqVSm=OeaM~X9vdtB%
zVuQlD-4wPwxSSL!R=JGR@VSpOeNwOvN&HQhxIOEsK7~WaT3J%IJ=Q_H%r9X39`>al
zYD4>yp7Z^4$sb~Zg3a0+?|@Ib02KkSGYVjA=Hq=6g0ImF3mVb595QOTVBHKLmJQ|7
zurqF8?2b1x_$>_+s)LnpKmN1nRiXiVhIT;Sng$4E!NX2M-vW~mQJrUq-==s&BNO(~
z6lQMCK{R72D1{vjLa?&3(xM1<#Rcro{E=6{UGGRhT|v)!zf^ow1ACD%Snu%3jZaEV
z4MRB}j){vK1S<QtE4f|PRE4@c`&T)GDrwR8F6x|tV3BdhZ)VVYk$(FQ9tLghxr3uh
zIJ+!;_RS~nhyGyM^shD4kg<9|?xw2}rU<O7(ymcK4fc*J;H@<T1R+x5)z1#r<EJ#$
zJr_onPYA4HZtlh?vD=DLU?fFLa+0J2)7tyP6t&Jfj@W=!6bEhDftuVvYXC7D=m%nv
zso44*b*rAdURqmOg5StgXIkq{@#`X^)LTEz-E+r)jrIcpL+Kw1i@!zcHYyl(TQKy!
z&mk4KHz9Pqj_V0$ie5-t-A@N(nc9PavCJDKr4=PFph>loPzGLmd0}$p>Uy62ZS1^U
zZ-ZXn5`*KK6yxv4%7OYTi}enEoW*uv>r;%VNDcrEAuOI3CYgRO_c(kW4z4c6J}Uk~
zA|hbfelMPqKL9<u3AE7E)F&T#RY=1oefDQ~`dE~EmX(+YCSVM7+SDBXAF|#$s;VyP
z9){}$Dd|)RL6DG=mJ}&zM7lw`yHh$vr5mJE8bP>(bR*Iomu~6!_Vsz5_x+9c`_C~1
z&e{9ySZl61=SoM9(JdnuB$5xubPNj%%e#wb)(QY6$Mr~QBu12a=B!4vtF?#PWI;?-
ze+<avwvBPn>j@4bgjuZybu@4qi|l_2q{ZZ~S<FJ6MG&jkpOFsai8y{XfI3<J<Md%?
z45gkC5z>kAIw>ol&T3{MEn+fu^_>9jWr8A-)k5=}{oDtw_qt9QK9}aRUdIEp(??%@
zjC)G}T_v){s4E)akXTZz6UK|em$w{BB2_{0pf#LZgQ@j=0=i;BCjT2ikdInSOHQ^3
z+&>zeQbjswSnbkK|5iR_J-uLlIuv&ca-tOWgO@<S!DVSOJy~TMV}HK@n-(eV`=C*6
zSCQmCf!)tJzM+fM9ERJuE1D8ALB&_Ufya3=J1Kb(VyQ3k;wz%S>;v`E%NTI=d@j}`
zU|xF-@LuH_)r7U)TS-dqcZS$l>k_^;&5J%)yLAYDp-tm1x7QsIRdZWwh8@NA=%;|;
zQ`5;N`pK{~e0?EjKDQ#Je{r-XB`y7e26Rk7FPO~viG@NZC!LuMrI%Hrg7@;HL;_6K
zV+Yd~^Jr&As4k{IhuV*rQQ*bYgqy6qJU89lKnjm5+Fjnq>yU63O{n6CxC{~_dQ&O1
zqRS-aRm?o?qI788vD@kb*SsTtOwPBV-5kesSIO<U--6z!C$>La_dnE@Ap*81h)=dR
zK4qE_700BOtYU%_?wO0G)=$EsZFU|1WAxPn>n|oZ&oaCm3UeCoZm-yldkIrr=iL*9
zyk9m0MHwT}FLRw&r|@)7o5Gdh2(A<u;1gM>nls?mn2(`G5Hq)hJxf=wx?h@8JOZVc
zQaZ0d!Styc62y~E?VE(xRt)hHy;C2_h7B5#oxXCtcWb>f|HBh~X#us<UUB5-0(R&3
zOheJnw)pfc!^iw_BJZw;9|RIJ%DV7fZKRrFoS*xq!gp7c$2Y-2lnIrl&BX9~dq}AJ
zb(2jv*@6X`(VW8f+MYno&u9MoTRq49@xzBxfX(~(_V(&*|9+{|<>{_xQ*h4BB^ojY
zQLVlWCTT6(zD@YxguBfIJhr%&@UqZt6+=kvoS}Qlx=OCGX?JwFjPiF@WrWGl;Gem;
z39khznEn?hoUAysdl@fuu}hT`ci?`KSF?Nzgrj%=0;dWDiYR^x3%Ym7v-S2Xkr4f{
zJh^C+_jlBpRnC(7pzo}DolTlMi+XTz4(AjFw^d>zV{$oEfNt@*l)HA1+$#)Q!rKNJ
zac%8Xyivw6p$z94##Xt6<Xt0Z0jc3f<xZZg;>SeD0&qutVHtsGcxv-;L(J0f=08st
zVFyPMJ!;3engm9E1Do1g4?sEiisn7kkSz5oU?iievNE#ayhNkw0Laj?K|^bkGry4>
z$rP1T>NvskO6wVJn))4(qka*xTSP+8;vMCW2a)fg)>|6`&+SECpSv4ZnjJ*n`46ZM
zj@-sS=Z&VGeWI1AFd2AQ9WSDc(EWkiz%YBiarc~8XE7Q{b~ZX<q@H2XeotGR|K?;&
z@ZtEcLgiOuTJdmssF%Cu509F{zAN*p{pp(GDQ}oc4(EiiJ`4yOANpW*i=XDFxg;BG
z6h%8yY=7p-C>6dvZ$Cd6RZA-#Z(88obV~5Ky)ZS}<FoihuN?Te+iwVY|Cwv0zE*06
z@Q(w~Ij_AxfhpgM0?1}qj~`2li#NP3ypYb+hNj*L@@0_NcIf^v6&l*@@I5Q(T1e^c
zmRc#x4rr8<j9CM{ti!^QFm5B`UaR4H=0DbTysfbInfM!?C_;QhMRY|t<6embiZ<tJ
z;J+^udox7_(<bmA!uKEe<efO+>TX}vu|JqvRVEf%$89sKp6#PSR^EJBl=Gl=<(dzk
z>iBqkil@AX1t}h_;<xjJ4xF9|60@T1vswz`o8L-9b3Hi1*ZN_i?Mg%LECLCoSF<XA
z==D_VA6vuM*W(M?Z}}+kJY=*T6P`dC3u%~4<C|o)tkk|dr5hqRrCv;_%&unF>8|xz
zZ{%>ix(*`IwZr=GJn^pck*#`m=C}8H?vjG0r>hrztS!7-4KMNh=N|Goe9aM>aW-L%
z?PSI~(ox}ZuOv@$N4;1BjFifb)X8ev^$g<uAA&xE%z}b**8I&*uVf9Fl|t1kb-pIu
zfhU+npUpbPu*xaJ-^!pJ*b)fi&klVW_c?YA_pK6lw+hn=r^S$z>94iyv2se}N<+%v
zKzFm)ook#B(DUj%Z#q1S9il8hcK`M76yLrUY1fModq-prv`?fj-lKsdG8m-luLGH?
z@CO!U&gMN?>mLf+EsAtmROWl@+O?u2^1HGKU91u)2NmyM4-Rn*2`Fci?V$@AcAybF
z(ED=#Bc-=d9L%c>+jV0Y8Ass2FDy|n`{-LFpl(1o^Wotz5Y~kK*`7$@HVa|{Y}{(I
z5s2%8HxJ8?leeyNj}yEAQTI^DZs#QT$5v6XEX6%oT-?qJH9hYd25IcoYu~#w-}Ngn
zU0eG5jGai#D3@pZwh&i)QwZuowm8~;D`-q+)X&-#N%rCH_N;{^q)o-QY>?yQlRj_I
z5<c_i{kn;4E~Gk{uM02B1us@Vv&`tz<peK)i>Cf~kbl6NcrC=8c-O^@{9u4*fmGgV
zXo4W;ZVcjgN1W9Cp|E7}Q{Km0N%VJ5klkzSrjpV0>4qs|uFtgMZCm;zIF$k71cQf{
z(R$sw?QQWn#liFV22}qcy-drC+-_Jhe(-xnjCAx^y>8YaVkVvfkXWj=+}7gMzB4ON
zqa5mS|LKY*->yuwMuyqjji)^HReRGf!yJZc-GiA|0}4U-;$rRwmw1jImH-8yE#vsW
zRJ44g-`f-K$<p^FPGg)H>2dM7Kr%b%%IO)s*KnjjmtD=qL>`U#Q@$Qwbo+tBl606O
z{?c6a6D{PIru>7}m`JDX<G$#NYxV11)S?CEAy#X&{THA3NRE9Rb!s0u@v9yh*DbHC
z$nhz;eKGq0{{DTnTXBEI<?m@vM)u_zz;^2$JepduEdOD$F3nP%nMvyhRjKN5?lOg!
z5ELgt&-c?Q%f27AtEiWChR=NZ){VzuZ3!BF#?xC_u0x&P)kK3u+AJIjSvZUwneM2C
zRAh)>O_ewkIC1E!wS2P)x2Q!#4X}MQGc2T7j3Vk9>lxy^iy_}x?mscU*Y~uQ(goaK
z?8KSW1u?n%GyMcbPlqXt8{`H?mi4X9jI`qE!7!~YcKe^2P+GW5w4=n=b!Sa}0YG8K
z{p{RTvn7yKF3iki=6wjXy@_UB+7}4<A9*|O1m?=NTrt*V>)6XSmE|J-n;Wv?SA@uA
z;a`%T<z1RMFPv6A*Y#&dBE|GOL?+eCG#yHhrjBH$V!iDQSo|}g=ekNFY=DJoFAO76
z)Ak2U2-4-ZicO&xEG$&HHd<1}sO&*JLo4DWI;)ir^m+)56VdY|m`Ei$O`bzFU$F%T
z)6v>Bp>@v$Z%$uyJ6<_9b>`lHWK2lmI;Bg*FPn;+QnQ<V;es9m*~q$+neJkycY9un
zIx)p(s*VSL#u)2CXYbPQ1Pi&92ffpIU;=B>5a9L(_NY}VB(t%Dl3JsIYkl!LtEO(%
zUadL%VkiH(|DY;1n@Fl-vY;n7@pE;3sE2DnrB1qaC|3ZxvR;EjE;_cEUU4tYz*f%y
z-yuo>D+6bF9YLM>*MJ~V`$MPQnu&*XSd6-}1AQ*vDhBQ%>Xv%WlobSP`WHi2B~V1{
zPYreSzh_#CYlNMhW!zmq^SSw>M$lgd+9T5z&Dym;pgl_Em=(M?XzaC=iadUfb%>G}
zHiWHgyjNu|a+d=w^7CVUf7nnxv};7fSuRJ9{MxToo(yUSTANB7zFA5{rn@Ln($Dim
z(iwMK-9w)B7}bKjtfO6R{G@Kgmwr*yBzIlcVa+*(qcdBd5{x=lBHcC}4`!xW1hnsc
zH=^^L`C-dk(2wPAN<&*(iEvh#m0g?1vy4$aW1$zP(P*`{H9YGb3Alq@7wz<WgdRh@
zD_C~9DXK&#ajvx2j<VXD>qP?ITNj3k0`@BE0ochT_>5eP$&ANX!_<C`Uot-#y9^~f
zyP{whdVMIihM#kj5Dx~MJ6;$yv9Uk2dxwfcu|q24YPVQM4tR}nR3cCMZx!9X#l7P)
zuzHCKVxk3NjMMAJp-D~`0VGcsuV+j97M4?+A&q3;1cKI!ZYQNtu3Uc<&6s*d-n66m
zgT&(@iXYEHot2NryT2p^ZjkOPin~Tm?^NRZrZ%N9a$qM*bJ%OOpDp;d^k?l&erx}-
zR1{E$Kd`#H=tH#op_o#e>+YZ(=Mb!BKz<nvV}VxFWxnudeMKZkWUOF`lx%tw^i#PH
zH{LiKQ|z#Vgtr+Q7hklZ@x1hItG+W~HiW{XyTpq<kp?ci6n<7;T}oJNWHlXfhhL#~
zu0x)bkAZaO&E3TAsI@_?=WBG!dg$8Ev3`9$r5g5*#q;>0>S|TMeee255uD;7z6Hd>
zVcx~imm7y)A?9yl@B38o`<KNSF-Bff1(Z4r?tOcZ<GhA!Ci#%Q&J^fL!SFkZ%&D#<
z+$%&n7jsdjFGJF&7#r`(zDr>xI#&>%vIW`FN#`7qfZRYb7l*htZKUIm@aXo22f&!r
z8H(b&=hbSOqKfCF#&@+-H4st3+OGsx<VT+jh&dh^ch5C7U0WQsVc(5556)xHie7gZ
z{cO)md;ia)jwjR|SK;Q8T^?_Dq37C33jU9enWYxrgd7ZbVRR6Fx_57tucAHI!}5#E
z)u1Gx5%^uRvSoVpqNB6J-1(T01U952{>GWt0f;So$j#rfsA5z{``v*DyX*^xeacH4
zQ@HtZxTBl`e%eC)12)n{Kx>3$M#-jdTeoG4M~Juv``lc*dp|6j9FvR<%S%e9)5Kz9
zFEA4SRBZq5Yg)3lq{|1kypq#!oFL!i;J0L70VqKTxvn$2^IY93_SU6cslq>)s!~`k
zA9is&`57{Nl!#4bKQ17^e}h!kxF&sKb6sbcDSi1%!s0QqOyJ!$1TVO>MXfyo=WV)Z
zHwDqU@^y0Yer<|~k8X39bAJ`f&6hgXXkkGv@^60(V^b2gEqG~<3ON6Pp%fuy=m1sV
zB5CQj9?OeL;?Wb`rLu2^1~A=$JFI*vXz8$vl1I79^?kw4maj1{(4X~+F}u_x?*pM)
z3G4hbS4S4xTfo{j50IORXD(5w^E*=4{8FdDTn=TFhN(i|6+)<}NF-hMJHrKimvdPq
zmTD|9h#K9=dL|R>ph<gct>N;ox5q^w605b$$f<<#gRhx~jwP4$YKfqwCBax^hR=%x
z?8)u++J#&xJ`_9N4*2{TS8Mevi@f4+_$9Tvw%)*~NnI{|#~f#-!|@l+sY&pgz8#<U
zR9mB|Q~Rld67M)pmV?USMxL|#JJ<=__74}_G<gqx`msJ2$Zxq`18+Z@2P3k;IKon0
zH5d8$GlC@Lum}SCV|@kKN0H(vrA(=MkOq`IYrMtpdFzb+TQrN;Rvx)(7c*~hNdxnA
zwDd<@ZH;<sPugrcQeSeaLrO+>9=WSu+&rpZ3%0ah*}V%1>vKWYxH`OnQ?5)ndm3*?
z3Ov?5Cb<2xd{@CGr|Rh#ZTNFvcsYudWKrExm+U*YrZaR0-Y-;X40pWZKkmI$JU87T
z+tWV&t!10U>`!K$;j7kHq!qhubp_6k`@YA&OEiqMi3H5M2!h1M@qWkuGZ7JB30q9u
z9BrAAX{7s)ZZKt}>z)>qpS?BxC<G@`Eyu3-6vcsY1iEjFWO!Z%4<&P9HX1wc^m7bx
z;(cP8^2#mBaOh>oyKT9<*?%#kQE!)p9@$-*{M~h5*TmiTISdvHq;x!I6YjUCd+3Ai
z9%azz`^XsVgN5g=aLYJ8xZaVH+FY<@%0A5Z?id@p+coy)3I3yi_InjubN3JX`HM#^
zJEllyh4A-V4xZ2JI-sExrm*>rdpsuky!~LT`RC|nhS5^=yVa+=U88!!U6(Yip#}Z0
zYMUCo3Y7I_(Qj!({*&-ra)#A0>uCD`aOj#qP=Q;>kQ)FlVv~|KWAS0>PUbfU$%1sz
zNtEkEXK>cy2aV*73WERe<wTx<YT2*KXIbYsZYtOrGdVgS#5Re!1j&EduRL>IIT?S@
zNyA_`^Hpwq=^GO3J6qISrt*sxo+*R~3-nxT2Y3n8MhDJvNV^Rp7|w(Y_jR(skdQ{E
zt-1>=2XWe-t?}XrHh{JHmzps+ylSCGrleD(a>NYVjdXlA8+5%^b#T<-jD~}ma8BBa
zCRB}2*{>U=t`vz)IeGtCi~xZtCttwNG1W{1FVh{}x_gbEftNw@yHyio{nlP)-cNS8
zDRm)+iwAqdB%#yCi<4~*<}|f^^)D;u^?Ywo7z-0v^=twiSV#erD$&}>{DHo0)pk*H
zzWYvyy+@vGQUGB4@;$G$>SNwh7eUzva{d8s-{<>f*1Dij_;(4>qHpZGzM<oK5z>Z9
zP<L=>;;|Yl8L4>jOIcy5cE*MS??c*E4(>HTTl|HNffWvgHOYqdom)l>h2X=(!^89S
z6x)M2*9CvN<!TIiW|jM%7<WTLS0(OQ2Cz0@ijzP(8cQR|=CCT&pTu$h9x4`ZnGGf_
zs@jJa&6itDoHnzhaw*)*FBpY}zTfEKJ-z&?fJP{FP}fNwvNX8uX|SSDzS#J{GxiZ#
zU}~h;SGdOH<&3G@s{j3i@@INKXG{4)(IdphD9G_rosfK45<kQ}`Cva2cx<P?Wmq!*
z4c&9w_2+?n&?}=Gs6lCZUX|aD4fUXLc!u)%x{dJ3&-{ad1)uK_HYIeqtZM|BmIZnC
zZ5^j8(r<?O=9{#Mg4Mi$^p+6TA19xTinVIrxFX$h3!P3Hx!qpCqqrb-EY;g2!j*z@
zfA33L#ZP{y$j^JeN~|j;E(sm~^3e0x%%uw#tl0;iKw@#x393k##Lr>CePJNx0whnu
zG*%9|8L!;jqUii$V`JAPzrUo0tF1vVMn#3~X4;lOp3CXKEWO>%UF8c;bM21+II45x
zkWd_zN>W+Nt;cb|@sRH=0jJ9T<hLAyq3%Dkg?>sqWVoBz5iD(u`_0?xWQD!#zfNn<
zse2QC0b_u~M2x}XhV$i!w3e*~`~ymep%6JPe$=h0j(=6cLik-%J-&U8t>{gswUT6Z
zF3yi@%6d;(R*jLN&fo>p#d1FzGh#i;fe*&Cr(=cPf)^PZ!c-I-3!Z47^SH&^eOxn>
zSSWub!$ShTSxfBO47(&P$J-D#UAV^CI98WffHte{_p|T?nVAQy%B6p90sLeD4i?!@
znE$<3;v$Eu&oiqi)x6p16#C+sqYMptKGAlP#~#|NC$#u`-OqZF8t$+sA{J4;ygPYQ
z8T~H@i~fxW5fW|Em+q`ZL90o&stmWo2w-OM+R%`io-jTRU}cuQN=7>K@2}Q{0c?|b
zAxPkBw)?KmMu$TJ<G`4<%)?P=ce=(=qZ$|&sedyJQeru{pQr@~YjWVr+U+r+m-!R@
zeewOu*cUH){EYOMF(nQ=i0pOqaka-clP$~a>5<%YRkI;}#dw6@7u;4JaOlopWjJp}
z41M!x%K>{YucUbQs@nim*D~WE%<Kg_7vS(3h`@0l&cFF2!@_<&7nOvUB<N23`t|Dp
zUIBOA**_`-$7G40hKTj33ZWGQ88og#jXDxB(y%i$_3qC^DpR*}UqeyJ?K|UK4O&Ni
z4QtTMl46<dE<XCW9etQMR2t;v?46G(yi57uLLwOaaZx?eiXbG&F~)FsoEev3=J8Fk
z@lp=CyW7FM&G#NnW>0mirN(N5M#gpl#=#4oxOmfQ(UlN?Rr4c-ASBnxC4P*${54x5
zqVcjoTeyI$_cwCJ28Le>sk|f5+p5wBW<juy#X6ojCxZG9*hEPGQr0uQppey=iXxzY
z->FZ?(|e4<t0wxD2ek{vKkE{0GM<i>Fi-kgrIycjUZBcZ>F)L>>Z4A2Et$y2*+RR*
zXt6u#=69RFozq7EQg_B~KKj&%yVukBnI_7tJ3Q@F4U~pgL}A1pZq;fc?q6Z=G35ZP
zcaO+X<@ihx`Of>6oU_pm5?ohkx^A^bXVAl{x7-z|TEE#gVnz`kxJlU@M3xdw#Y%;B
zI48N7+@}D15GbUkdGBZ3R^WSg>V<ut?$94Qxm$)0INR^;l4QSn^JCZEpeQX~-mhhW
zb0%MZK&0NwH&gT%_ll_x@My&Q+Ayg!ZSm4KqJC?TyP+7HMA)qKboez+k~)%hq3NTh
zJ?U>lwcxz8_)_gu>Nu6q6pM<A@(>fk2x|ekFf}r2p%{Y0Z-3PD>`?@t+RdaGui9Xe
zk>PSn<7n^9pOb>W^%-YGeu24fXTM-#tKdKFs;4PvoGD0`Je$A+aO=D#6-JeGR;_C@
z#uOwydGpdXQXIhEw0oENhJpDRCj;^w4Q@!p{&%(hyVvB(IA~!+YJS@zeHdcuK!Lm3
zWzgY#W&kK1nhCc4txrDbZ7h(_xYOHM^P|0bD=bxuz4PKox?1=Fn#$5bYZZ4YFH@>^
zRKi5k5$2J&w2MkM8!PDeMEz;Ip^_u8w4yRjt-5L18RJgYB_)Up^R7>TgLr$t`*v_*
z*Rd84|DoaXEN7Ul;Vj<R+tJ}VJ-u1`E^@P(vQoTTjd>Mem*x`_o<d0kIRo%g5j?(U
zq#j#W&`+N}$s&oAm9fVM&@DWHp^amS8JC~3$%})JAGM9G=Ks$i{XSMy;E!Qr5c|(F
z@bGeCvFM>f0&pLmy1vPIY*&<@U-JP8#h|SA*mv)3Gc>#!<7+A!eD9NnAjuj|DeA^i
zr=Pcze_!7aQ^~WVT7LgjYmntd{-j6dO&`6)4<d-GqmhVYBaU&}G=$A_ThAmWLUaEZ
zb-tO>Tl00TdwYzifjUB1e2;HZ!{iqv(TCLz;<aqp{UZ>deukat!HAYhmifp@MzNBU
zn!z$7{v1-hb=*Iv*#dq=kil7{V8J6cruAI3f!bkmYE^20s%5Z`nSPvxXvTydW%j?1
zc^z$hO6$*5f`r*xsk2s*)o!=!M=)UPVk2);D2*EJZ+*Y}4$Q^h3A6-Ay4`@dqjx9_
zT+&epToA2ow#Uyu65*UknVcmb)XLa^!&MhnNift1M&<QxSKy!6={nX=!&}%SK+o$|
zo1JGuJhU$8df7O&r3&lA9sA)d_mwNl%GT`Z#aTV865(p!oteEz1k344&$-xb!bV#V
zjUtF(T}6+<M+B8{P+B~D2E$obsPf%26w$lHWT{8m|8bT+W!R0Ax5uNr>XJCv)*^Ta
z_}J6=|0LAs%*ZrO5f!?cM_(g#7Kkqe@w;PG9BdXYW^KmiM)vyVoLw*0$Wj`)IrR|y
z!o>(cu3wCBRe`??gn{Bu0I2wMofC}aD}J`cI|nHMEXL7rxcA<D?8SM{edod7j(;a;
z0R)-`R~s09%pVg}I~&sT*TN?l3{ez=McW$6(*H8yzKD$DDVi5-sawc!C5pJYxjmpr
z!!CUREts{J2e+yikP_IfMDe_YKwj!i{PS0Z@iPzPXx;3q-1bh48j(NBhzEKqxJ~aK
znfVG7s?EPh#f0~|k9hyb=^{iFfwBcoQV>8=C=7IkM%1hbXkP+_MC5gMEKTR!hZ%A6
zzw*9weq@aH$jOXPp8`TdL#ajC%k43RQF{bEP6`?*)(zpIMfw{hZgxWD;kCQm1?r31
zoA{mXic75mwtq~|ivMma5o@rk{HC4ioJv6CLimIY6lJS=+e-@&^1XrrdPG!an&uqe
z=}s>y-SUnI8H0dG?zG6%yequ6^v@UIQQ-A^bZjg(G12T+sI#MkSM|z0Du6u>QvC@*
z8&|@wuf(?-cX2=3F-_;Msqki`X^Jfv&V2Eq_zvj$Uq~aJ4!j?lcywctf(V@ar^Ub}
zUbAVlsSb9J9qwecIRS74bi|?%AU%c;NrL;_en{|a5q!eVsv)6m`h39OEkd7!h`Z0!
z1W~C;)~==M&DYj6;oFJ(L4?%W3ZJfpjGXLtz@sM2=@tr<XpVU}nVBEda>ePC6-C$9
zAkXAb!yXkMS#fZ3{!;f>`ety}+F<doc2X29)!}Ea5CWAqi(t!e`QO^K+_?0o3qD%u
zjA{o#w=p4PkhP)4!QrJDVg-p1;Vl*kt94i(`)bjf91FlhijQU)Lc-tKV)VU(u(^-%
zETn(-<|N<p{Tpse!(M^WViS5GlpEGhDolqcnog%J>-U?l4sNgZQw+{PS<3duAL8}F
zG%c+>&_q5L%E(CdExj0ree$A)Fbum9m4N=XX?iHxdv(~_e|AQ+YEHp<iBafmYfJ6w
zA3e-_Fo=R4SII7f9!3U&bt_q=T6R5lcDMRjp%c`KBZo{uJx}L1p!+MTKzCI7k7`XZ
zwFN34T(x@-rVaJYl*Owdlpq;en)fE*gAm~%Z-xf^TRzu+B3<8Shze}FAE>?tc=mA2
zflC>9v6k|kod}n-pPaA6r0n(xl&X#O+Ft|PFgK9RTTTx%3Zx@!E)h%u$00$}ej29P
zkE)>EpjNF_t5J+#FmR^|ut$2xX{NuoF!{~saJ#%kb%wW^<_^?_D-HsKs~~=BX)XL2
zo};P_z3La;UqtLQrrd!QZ5yq+iC8Fx=$fH=1m?_8@lB-Cd7avFTKV`gg9oRRp0b@A
zr#bWAY{%{umB{V6<$FRB{nRQt`P9$|*5=9gU=L$enG85U<Lz1Cd#S?J9kQ=JzjCv%
zv>3~K;Icc7Ql@$#y){uz84q%;TH)-BuX)J|i(o0X16h}0ZP{|m0Ew;}G9XO^DWM%8
z)Hk1z?&2LIzJ)*i?R<M+%h1AM(O`{&C{`{963DZ?nqnN4413v7s9tA=+I(e$%DpEO
zNTf=sBQHdxrsF`T`70v8w)7%C@yCMcLBldNs`oY7M$A5q0Q|QvVd%x&#YP{DSHafW
zexq;J$p&npa|(Zvg_KXOredF0>K1%v8q>a1{Sl8KMx?buH81q!p_;QXXu}pOCu0g-
zseF!+;t%TTFM*lJK{rjzIUY*d*5OnAlx?+Bsig(t>)qN!HD?CO6rW2p9>D_&muxPU
zhBZ_$ErQ(MHyG(&#`rF|tExI`({H0_^_kLkOd}<=E>=_FIB>30x=q;MRX#2yqJz|n
z+z2QvQc_Ydgx3xzWD$U-CM=TH`$XWdq9<n24h81N9_0kq8tA`}AScKBze^u2FE3Zv
zE(j5POAheO5b_=_RL+0MCQ2S4j9W2~-^Z_}h3hC($?Rj*+LCSE_%I@_8D;;+-M!>=
zCqZ2g2Z62NUWHqR4<mI(0%gr`t!tqpLhWI`$67eXR08{Gwfiy>>S^jud|9jyC*mqq
zk>BE2wH7_#k5$2qTu-~ol&K<ppZ|1eF$?;>SJmdK&RSC53iD)hoah1duTt2!H}Aar
zDMrgp$&_St1pL&Q!kh2n^4jZSesdO#xj+-_Ir{qw$wPwI2krGoy-fD)4|wzeHFwPc
zg<euk^Bp)+4531o8#G3HcK~j|`AjGGLd~3Up!ALcux1Q%fn@Li8yREE=~0nXWssK^
zvVM8J1J`fe*tsTmjsh!C7&-5<RZju_0<K*ep;2f|R&N6`YXr=n%5Pr~soKu_H%sy?
zAI9W8$?=iI+8sr@f2$eb`Y<iH(WZr%nOnB=5#@lGO9Xc)rYx*c?<V+L-CR`F2cE?$
zNs6w#U>$qW88<84TQWMF9}kDLc_81GE;W^i&dQ3W7pq)=MK(e+2sR2QL^#L?wS6T-
z^xJWM(Pe4auGpI$%st|NRBns=(36DksS1da4Duy80;n;$`UZ@yzNIpo*@M%vSB8p~
z7KMzA4A__52Q@(+0q`@VFP#d$wPn%5G)&V}=IOveUVw{?Ap=n3VnrRTgSwbO3_k%4
zIa49#@4#?~>6g!;^i)q{tXkdum*X+?4h&RJA4Y9;=#TvVAcY|qdh+!<sVm&MzFLoC
z=PrjY{c0#b@pcA1bcXjqV=^{O*tU^y;2zF$-$Z)5LN=7tO^M^{Y7mvru$#80ysOpK
zI+*~QDfuUsM|l{2)L&!nNQBCrw|=n;-N_|$vbQxeU(duNJvY*VJif#8w1ukt+3)qF
z`4#_{A~yaSoa{u4*TAIhA;+rc{tps_ND%Nz7>A`t7`+a)s!Sdr5UlH@O$&rI0-Mwv
zFiF6D^hm2Zz{{wj0PK$Bqo#Ub!Eg=E(7ea@PL<Rh;y~RU%_gVzeGmR)0k>gjs-a%{
zQ<wp`6m?YWlM+6EHd1X{AIh+uUOu%1u}#ODXKB#W{Wr@^ploBgE+T9b-Q_+H!oIVw
zWC%(`q`x)W3orO!T9gcQ3IX-zaL*Dg58emWPL2dXFo^-)+OFbU3o_9262U*elqC*Y
z01tGaSLOZtuS%T=PAD1<&e_|c=@NYUH<>SdmCt!%dWdg<O4gPM>8-MIu`Dt!E^Z{O
z<VLE8nL>?<QqtJeZ?c|LEcktP<{Q>X!EZF#?H?4+I+l1HVUT|H3ry@)lOEfCr>5%z
z3@9}Y!<7q7<E{MDRZdCmG42fkFSM=k9`O|uXS{UQyj2uh4fDanXRx#T7%kVk*%d=n
ze(xZP8*n~?W3GP#USC|`eSosZq2{aWb78FQd-JE0NFRU=L}qLIjD&2gjXRapT8ZqU
z`{1Co!~m*696;q_7zAX77N#CDxom)g`tl$7Ty8!)Xy-I&gEoN-E!tAR;Ol1N-givG
z#GMWw;Mc}~95?d_)Ym3DGp3vO_N20IA~^=xd?uf_yG%CE!3ED|jRLuv_3LGNdV1<X
znH^Mlgn7exV?ugEV}x%>fia(}Hmgrt0vw==gbKnp)$Jki)|1vpxxqG%o-0qQhu2y9
z3VWTiA@us-9aFw-Nmt3`T}E?8<sB%9Kia2dMlP_*&JJn514Ij#KMy24k31o9cwelR
zGP;&2AC;up`j+>9Gl*$L!*oOWOtLQ)GeS--x3)JEFrbB<)n#6)$XbE@@st0oQ8+pi
zKS5#GW@zNb1139GdA#l92>IP8whOJ4_<92Vv1MHHkNzM1M8qUU;d(A?xG2F=AHV1L
zeXtwg|CS*b-S5-swrWeCVSc52EKx@DZxB^Px2XHFcq`qj<i~bd;~-ivr(_<L?nt%I
zjYrknftx;aIYjkeALRWNG$}2YTg9C`PJm}bo1MDgGGh%G5M_rECU=BiF48D5E(aOd
z*+)tal+T3^I|vYQan<yo55mj>VOC+K>;|%Ceh`xVNgHZd76x+MWBfx9NPQqi0is?B
zDIY*;e#P-x!UEpBL-<>p7Q?pMCNonAuqjr?0HsWq!}fCGZ7hr^{CG%s0jlFPE}^NJ
zjNU1=Q~}VNbMW1wgxyqC<MFV8<C&uoI;<g<xi8__h^<4Sy13$b%Vu6m<ck2K(Rbd^
zf_K>poOQP=9POo?Nx?XjHtD4kx}Fb!K}tb0vblmZOa^L|Rl=BpW6t;3HEo<CV!na0
zTs}QU=L$!EdrRYds87L2uMxC6(kBtSFG)q^O`LwAk5SvxQf|JW(4n>MFuYu?Y$x$S
z-72Yp(DUs*T=LkEHG*dbd!Go!P$L%U?+NB{u)XGnn7qP1Wxk?_5ja4-m1Pz=z5R2f
zbwHO_Iu|m#(|*1e7jk%7Ev%$COO?g=tkgP=N`EhE_3h+;q#$WO(bT*bXkH*_^V0eD
zzW4_<t?SODL6~hC?NyhS=jjfE!@xy^*>5>-Jl)i)feQprsw~XUJPh3|Y`Y&9`4@ii
zJ!Sdqn*@3q&+HUSu**EdEL}Rm=eFwA_Wm;-zhhu+O2XIc5)fbVU8{0*t5O!^?{Wx}
z_4s-cb>Lsg_;%qI!Qm<fbjrLPM63vYK74qeGzQIGylhIZ<7X?rvX-(9D2TY!uAEOA
z#Uxo8#@mv=bn1@X_oDty^t&)W-J&;)8U|<<326lVT!)R!4pC>)5c?#{Y*_{ext4d7
z{=k=A-SEQjHZ3fvrGe6L$sjtryK*(C_7XKmkilOXfAN@+n0ejyM}3WYc1g9^8;duQ
zm@)y?8rcOk?>%K?p2sjwjV+8HSAHtlq!hMJ<Ji;QzCK(j{btmuskU%fQDYIqi`*@o
z@k5#78_<dW`hGb5?f3a1T*Fk)E%y)1v_-3@;X-wNY2Y_JX5K=_VV>9W8ihn7VeU4(
z#=$q(`5;E)_tn|mShA<sWlvx2>5%ur_tWN>(zLKQzvCMy)kzkdLbzC!w)Qm*Z;pd4
zONLC!Ybsxyst?UMq&dS$`bq_EOs~!!937D@FsYVln;)Ki86xrB?ilD0aP_)9zv_E&
zS@)}tM>OTHuKO1iB0*m?saHm-C@Lv<WFRKnK3BUck4#m3n^z|G!c}XcnacmZ+|-yn
z|G=Qb<G#1R1*(wN$A=ijt=TF*-YQE|^+<-WiU`S2+V;)rAN+|lj*iOK<tAfzAig(X
zF7BJKEa&U3%Y>+-C0727W5uUw4UdklrN;_i^K+b_9sHTyhJPn1qs{P~5t{m$3B|AD
zZN<}I|IIW1`xJI8W$<w+KgcKu*3Hss7#Gi%s-Bhc(8UCNqN7zzL3@mYf^@OE_Flu>
zoI$nC|AT5<?0Y6bnm)9<rds&bOi3$Av*9j(vPmv&zIx&2rm9JFX(UU9L*<JSCnd$F
z&+>_Xe}0K_;l9l9(R^(>pot!Nl%r~Gzd?J&-2;>|AxaP_&#7$Lk4)K6sk#@HyR}n|
zbhZd~WF5NY)j#>GBnWmH%~7bCwmW508zIB84FYchA1NaO0hX$V16Rgej{#kslWx;k
z)s7Jsx_N}}*G1|3xYA?aDR*^K`1TOT$U{Nd$8~c#f|Wb(&t4fv3m|QDii@hQzf$&s
zzaL7m!8qZ6&OFkRqGiaeUIBw%RP{B3>aG6!^9Yvj-RB=>F3O^v-qfFr%E&oce@Fik
z|NiGxJx?e;tX^E;xSu1JXu?wWDVyi1Rp?Qaw6r9v7jdF%4{bu?)oOj_GcbsHT3kYW
zkotcQwVXw>|2DF`Kp6}(^7Xh!vpoF#`D5fKS)t)&5ijwzIRrXfoH0bC)S=Y06f#au
zX>A^(G9vo8qO=Dl6*q8xdcxSHEUbHeNa>Oigu0)3k(Wkw&Div~Ah^VMVWEnR)^+XU
zn21^X2c9AUrRmI1b_}q$1@FZiFse@$&N$gWQf%wFEd?2?X6^IcOeT@?WD0}{-OA=j
zL@`!9ytiLtGdI1%viRJMPwnf$+MDJ0FxitV&_IiaAn2?T|6<brEETSk&OO-$ik`A+
zgVu!mDOWulwa7R9g?0uoKDn+Kq`KFR^R{WN<Yd<|hjblp*Ulfvda6Q%#Hh;lRqD4-
z@d`-t0vd4URMWx{)F{N;)<K;}km}3?T3{O&+6;M+MS$wHg2JqxiE)`ON1K3JexGJs
z-6PAX%3Qv+-@fT|7>8J+&+IW|7ljqJ?6s)E2$0I=d2D9u#fZw<JU;F0;-z)<A$jAo
zBF|3wQ7U{Yc6ZE8e<S-x&>T`WZCBRKOiE_=@$BjI=lwf*6`<|J3uf}vnc?Fi8kp~E
ztoe;pQe4GZ$^N}ZSeGE*r1$S7Kr3B9y)i1q*!}eOV!j6DFSdnv`ONQ#DGm>nxs7!#
z`p*jhNvJA_B4^t7VlAOltf4y3+;H$cW>0QhO}YKmD>`%`v*`3|Q=o5%mb&JdcguOE
z^4R!kcZLiK5;Iib&GMaXIA4q_h#@<_HdY?9u(niAj`aHR6MKQ1xU<IZ>@&Y$e+rL+
zUZh~dl!|t+mVTL5-QY-3M+6el3(x~;15|#J%4nj)|3V;}(2ib4MWKHS2Z*V@qXOU*
z<M*Liqx2bMX%S&Xw=eqM6)5^@I-FLM!|!DjU#cbOtkXVwmU4Y$<tgDG#vmj#Uy`28
zN^#qj_K{_zNY2xk%g8qA89yy=xaxS*R5TO<y?DbWB6+nZ-)krt5%T6U-BQ!>AO{8p
zM&`*YIJaZ<&M>&Z)4>4n?P<(ThdMl4t!e&?DF0gmw2D566i=9=j_dt4zoJ_M*nsvk
z)^%hNNUlMfeX^}_#XI<9(>W?8p0sI9ndx*hztY#Qom4U@os9|9777*Vnd}!_=ula!
zl@THU!@ziS{55hwF=<{s-LRO)dLfnJ9sRSx+cm1*qP<~<XTj?p>f`r@v&f8&9;Z8D
zfb4@-`5?^zuB|)t<F(&6atim=Sn4PoN}*YLqk@ly&qF{Vo2a1-G%x<2H~tHuA4Gz(
z<Vob`u3XX)Uzi_qrpN^#93^PRh&~U}Pv-XgM9ivd6&@#v48tJ2Hr_WUz=VDFdEcV1
zh?_H1Q+xtWs`_4*SV`oxLTB~_<iQka98-dQ{J69iz;?s1nQth?nr*~&;qyCUe$+ch
z`AFu2HcVF1I;1I=GO4E?a0d@vXDGMrd?WyW3Z*_&yBIWK39$lg2%_#6p*_L+zlXs;
zuk9uevZ(Z`qk^Xc^MELAL?C4Ys%M?RbM&XOJl{|4%d?id-Lg=c4AHZ;AWXSo*io6^
zbmRSNxbU5QHGD6SAJqwntN>QWF@yY_@ocNOJlPsWN2ltzVDfzN0}k*wZ^ZdxI{yye
zZu>Px0QeGBDIs&Hawmne^vokocL>N&e2{k~ax|Q{p_7?nv$qxIIP0SL*%kdjmt;Zu
zvkisUsr&wZ%bm|YwusjM+M@iOb|J*e>(;kw7k(w<7}zU>+9llnDfcCFbi)wTc6Lpm
zhY20P?yVmI+MaRkV<(mP;;&_+hzr2ZvjwC9ze;uxg}C3oCnJ&rYg)EH#!Iyz1Dgw8
z$8xR%iC&Cc8xWD!(t3zrz+)Y%`iv{!Qao4tnl^y>#D0dyRL>XV=$(~ah<|0^G2k~~
zBScL<N%X8rIA&eccLIL=q?rPv^EUB>8H%4+0@(62Dc5?p%P%rN-NS<DPu{@4hw^{R
z=k6(h`B>*%F(SP$Pg*;|2<DPw#)LCW);Vh^In8hp=?{L+lI{jjPrNkgPxJ>Wh!s#1
zWl##%x@K4Ec8=R{^##CfqEEepka6!9{GY&;$$nf#Eopb(%w$*IzEwYju$L@ycPWID
zg`J0ysld(+sE49he=uc5L%4uxSp(%uC;ECv(L=E+TZ#2M000k{YVpiEjmiCtKu|y>
z-Q4QK>R`+(^=ny<*`goL{Ea}{G$;}Bnp`C70nqF`;&4(kqPO+boaaK;%&&KZ>pu?$
zMB3lZ;sYxGoa_H{PQr|Uy$L5#T(UsfmM{uKhvT0_(M!<#Nidxk<zboMO!EmqE+s&k
zEb|LdjF(-6otyxJo8i`yx5{Su3oq5WH8Msv&EHajbUs^X;A_JGIf?}%TK$y>LIcr}
zhS>rjAqlF%zP{HW#<g1xdzO4?N8}CgAt^aIE}!4;xa%>FBwO52nZJHi@ZSLdQ91j7
z@aEUg`nF@WPMdFYLKiIr%Q^hg8&AM|C9}Vsgg5U+rop~B&O3=`q9ENn9?1<jwvTfY
zE^EHDFhU>x`i9_Se__FjK=RZo#CnYS!*5gs>^1`3A39p=dnF!GD#f5A^`ao~35}+b
zw)<LKS-6=l3i@-cTC5NEakz)#90{=0dvJ8_o$r9cLrofzugxwF(U;Q@-_I9ITtfTT
zDJto6x)%H-<Sl1t2S5DSy-Dp7T4J4)tA*cf9H;)a5r8ZGYcqK+<WHqR?%jWuuhBq8
zA>>6m+mfzzo#wutM8WgB-rmT_BO@-(7_y|}&_ioNdA$5U5$y*elxgw<M&0^@seJm2
z{5IYaOUcEX+CQR0L+N$Lz2IBia$96#yCA{f@SHdfxt(<!Vt$)$&7S?T>$RnSgCqQ5
z5GoN8z*M-IE+c*R<#E>&>u)IHu&h$Pp^*mpUY=~KSLh>yiiZi5>qT~^Y|tC?Fk*bb
z6yvKeT?9*czzsMfK^a9sg?rqytl80L?=2uEv{HF{#pd;)Y!9XI3{`ijqpE4!qad0r
z<NyL6YLKtyQ@h8xyHad`j8Fosv7CJMy#f~(_0<N<by3iUe*|Gr@*fHv<i@o!QG$Zt
z-02W%j${V4J`=>Png}_S>Mr_l-^$$g%nKt>zOxv5?sb@yojJtp&ag2<Y1*0r2T}S~
z>2T*?xQQ#+PZQ}^qSoP=NIm}0%`U@iJdb@)dhxy!D?L+gZf+6@=*D}zmYE%ffx^gQ
zHYf7=I-D*#qzo<HY8zgLqbV~y8nE+D%2l#(eU$^Y5{gF?j<o@(BQK<>G_$@CEo4cF
z-(*8Ac#+jag@b&7<<k;R88MCmGm^#Oz*yGb%2<<_NCNv0Xzhyu2l3>L@UKVo59%Mn
z6pRUie3B2;xK)0-nY|c{Gm_)VKyp<B?I=p@uNE<g#sC8*>S(>PkHv8_`0cT2F3n#8
z?f;%WTnPwP+uo0nSO(+_TG%-@0xD&?sU1i2PB#w|O(0QtuI@z~Eie0D82Rtb5e9y(
z396a`_BKoqd2bJv87}K|mH89XNQQ0M_uacMSK%Jw=;z4Dmxdf{3`E9&2W*TM7!*`!
z>uZc;q4W1FzyabpKb-k@X`k>5dZ6{i?LZ2l`F=)XkFm9$1griH54M~%ECZcQ)vr;%
z<hEZNuImGdWY78^$0-iLH{%@}Mw9`Yr1in|V&W;aXVyv&pS>~Y{`kbCxQ~CYK1bA_
z^~XznYi;z$&ma$DZ+QLBT5SpyNB^Z{NyF%H%`!HW5W`!vhRi1UD-||OD=cN0gB8dN
z5YqwGFW<K|clk4S|0z)Zd*h!Y9)5m2H_^c4Z}AkSQUGKZ4}h~5=MM)~i#)(t%>u2r
zh~_WaIJ58m*A(oZcp+03;Xp@6&5S5%8Kbi$PNfTaA_P$Yb;JeQONotqCK!mQhe3t5
z*Cp%E-zE6}+}-pDOUhoPuL<Bk3LYb4T!V}Us&?H1wuaqI3!s24U+uR@0x-1Sea&Nd
z`v3XOzY(^ZD9E7%vc4K4pma85o5MQG36@zPDeCU;?*JUlXi#f<*m8$32w%9v;3*^e
zzt2BN1kV?c$VEXsAI%?hQ`5h_zUVw@0mL3&yG6gL<w*ZKptE2a{rda=H+Bg07fL2%
zxEc}e8xtggesp>AY2l##IvI%{XwmhAiK(w12?2?0o#}ClG9%n_|GOl?cP{z@9$iG3
z&kd?=NazTWDB5Mq-7P;NKJvQpJlXKCujh>fbU6S~C@TKnk-y^rs^S8Z#pyKsgZq%k
zb77#_S=k=C$OAh#(-#pa0a!f9&kxBLmg)F^H%d6-2q>7z`k(}9ew7LMVdon;F|ggc
zhg=qc0Ra#!QZ^(t9p^Y(Sw++T^U@Z7iIHt7d9PI=T(L+uSMY(95TG41TF*#M4hO1g
zj$Z()Hay*#`hKKGV4E8KKPwzDa+obbscdUgBmWNFEdkWT>kkhpd2AljU%T$jk^xm+
z8}Oq$Je{#EKG@f}p8o#-%odnB`p<~9BK8#p{QDqKL|PhyrG60tRFpGT$C5!XAbdTz
zfM?_YQcF5#7Gg#Gn}q#ySsze?RqlpY#$5`%4uc`qhV@Ks5(CFFB6f>XE!Es~@x494
z-O#oq^?(2T!kx|~yAZnYIFvqrBzIRcgzR{NdOdK7^j9vss7xTV_G3adIS1TEA_Do|
z2%iutZ8&d@T7xp?Md1~@>t*@qg63BgiJXN44!g_l&m(Ll4$PL;|GWnxxuQqjzey+A
zm23g%^I{J4c^?Q>2`bzU4LRC@4KBiMs{S5mz&IgkS~><UQ9qGa7(RHIsW^JRsE#hi
z{0}DEvWHqaJcop18fH$ZCN{%Yeld#J^m5By-1<)1(c{Ihh!SO}Uq~%9qu~gj;HG<Q
zy9U;7_(jXzI~cH3Skc1~juIZjD5`&Ljc^qF`(C~Tz-aHT`R=7Bt)e+(LeKX{*!fT}
zXh~`}=R9Gs+8qnZEoQkhzfr-w7C`+)FhMznH~CZezG-P2nOcUK>S1Z*kHi`y8g7}A
zXk-+3{ET}Q4lnFa%rGj&M{%$pfO@=A#rrTSnHLyG$TqVYAJd}0R6h@tn2>xgg>+Oo
z`gNCcl?Li8c1S{`dK5?fjE1amQ=_{E006|ad#{wlpQ9f+RI>e9d86Jegi$B#%PQew
z9grDdg+<9mgd=prC3Nvep~>wP&Z?;?6)hJz9}2N3`EF=jz^|e1e$;WOh67eusF<vi
z4s^}9N4*M9gBA6~yJ!jxvZQVtEb(`4XdM4hU+@oPAhf=vH~1gqH9?9Tj+^N#Tz6GP
zP<Pb`qGMc2N?X%SaK!qrNKw6pUb5Wq8!enI7;YOs%l^>ajQ(h1aX5=mOmgA_EyUU9
zw^D!dIfYP4HB#ZyE3`<n08pa8XIityB>K_pG@#?*B9%|Z33s!`mjEV8cMR`r?4>$Z
z;f}8wOtDFi!jR!Q(T`XjM|C<!qfZ!;eerN(qHDf-Xi_p2H0y=gI-t-|dNv#UgxM%k
z^wlry?t#e<;#a4c6Kgz%-RR7CM6%|BSi2q>;f*%ku;1VPyXEC!B|~~$rd7YGXoM+a
z2QC+<Q*7HRSey?gBFhh^LymDrsUcT&`>*wH%Ia4yQ`nx`Fr{Bkl#|{pEAv*#@jBqw
zEzS%zqk5!=L?~>xY5YSgL0mNn;24Cl+BSmudY29ZREHofn}(kwxc6@4QhAkveDC84
zDnYt3MB=dwU<HmNI7|mCpWa`@M~^e9<^24}!T#OyQQ&$Qu8%&RP(~w4zTgqQklPJI
z>hwz#;k$F1(WBS6xTKLg?Tu{Cqn79T=J>(0ZX~FjQteG|D3@1I80wdjcn+7nO%1Ma
z%2!rsXP#U_KVx)M>Yq(>se5Gdm3f_WHC?pbnAJKynNMkMRg$R2>3$<6;_#OKWo@Kn
zB;GCNcE~U5&{r!g?X<6F3|6l950-|Q>_&454t74@wY`~)N}%+4loD-1&1Y~werNu}
zr0VO#-j<9u4Y{`moyvEu5}cwbA9c>R65=uqkA~w_)N4$bw|Hzy+I*iF-jx4y3Y{|`
z?$}L0En)KS#Z_<i;**G>hyp$+V^|+<22%M5Ky9a7wn|eC>_=8yG^<&Z{HW0Ol8ihW
zerulJr8B`UHwv$EHgH$r@F!mlgtdK6HA3a*!^I=w4JMYH07ur>!$c_H9zOfpF8uIU
z{_%*>>~8&6TMqR%_bXP)iO|q-#7Ec1&$n?E6#{q2sv1P~HmWE|NdwpCJeQhc(_-t4
zxPapnE#0L%nvly}dLA!6tWp$@f>5)A3ki7h@imdmxRi8Fr_GF9?>w^KoHW=NE#)b5
zTf~5>7k@L+%Oj<S=6IL;*DnWS4u7|HP4(?kF?;lb$d@iqe-h=#$Cr0JJH5TFrvv=<
zoY_ns&XF~ZzjOXOpIZ<KV@uQSOLJO9dkAp*M?i2Dj&S)gDQ)e?bPDN_gK7L~O-iMO
z-~ydMTh)r)aF`wsy;~s=xR`%h!uv21*?WdsD{$u;hWoVS0$KQ)6I1K-DK!lnMpO#d
z>*|tKE##wcedA1@v!(j2>Ou9>F}Qjfp<iYuq>zMxU3iOz_nB4znnc#n6nrstPY#dN
z?D3w`$As`G%k||t6q#?r3_86$BJK55Q2v>DnX4y%9z79wNsh^FB^loFkY1M9;gics
zA4z%}f6F!P4NCGPGTvK05kkx?JiO3PK1Uq`e$7yWCs_qFW%rTs`>CGNJ$x)`<AbY-
z!+T$lCSmChYrg)^Jm%0xoTzf5m|do4U;iaBDnf2004{Lr(&gMNorw>{rWA?<nBEdV
zK=O#1?G`{%z5zNY&}xnsf#|?30*U!(6HZC6aPRfB@ltVE1l}(%`e{j2n%DhzrI}QZ
zFC8gVcd^^~NOVH0?mLMLm&tV#F9|3H9BQZvV)0#RYx~SaZ8)2~6uR_19fVYDIozx)
zjBW3|N3n8pl`agliv#?UAVUKt?(Exg-5-XT`qZP;v{Yzlp-xsOq}zM*^0yghb!qmh
zypzM$0_7Bgtn=5`wN=Lg`viJ7fiFy1>o2dU^-M+bV+riGyVIrK$cx6O5pYsxQ(c@s
z>OIDDyj1woQ1i1TPZoB7=DE6YLO-#URq(0xjegzP&(P@f(5WWU1)IxzNk28e7=P|-
zt&FMoW5_-IfYeXXR4rBW4}lLqe#mm0)6RttipH~g>W;BwLZp}4yzcmxz5-aKWw>1l
z<sx2{_e6haFD_E}8QipgyJ=*#uN4Wy%V}34bj0mbwS6t?f)|b-vl7Dk`^n3U^^}Zo
zZ><TV70MBwEhW1aSwD0(WI7l$83h>|GWne1o<aaRr!NFxlZm|c)PeXg?RkK>yg}9%
zg1aJqi~yP7eeQcY(3)tlp8tE~W2^*a$zjKHyQ2^JtG>B}X)R4yK7w6szCDKhzHY=j
zL>`3eq#tp;(MRmlGCym?nm41L&XcowufG?#n#Vb}ov?9p8%^U)IiO8%vZYHMH|}=c
z3Xo(7@C`yn(m*fJ!NnUP7xu1&#E9Ks<c#9U6UwqVPpD10Sh_}<8!`F6E!tu8n$G#+
zy@=^mccQbSw}KY+CZbJ%Ktk|@GtUXrPX)G1TE9!{^=DeU#HsR?YVG3{3U5@M)OWn1
zg&3>fopu(k(W9+KsF`XBKkU0>dEe8?R7~m?)PLG0ECl(yK#FDemg?+0WAfyNvci%w
zT4*i5!m6Eka_%Lze=q0bg`)?9?y_wg$OeSLA2A8^A&A-Z|NTZpg0G3`^=b7W%281w
z5`me3|8tCA<ACPS(b=i$O!pi)aG^zeFj!1Q@!StxJIL?c(b6Xj=Cq!mTOQ&T=9#aM
zLD$L$v9YmH+bqngbCQZ|#$w9Qub(OEz7xhoFGR6>fEEzZ=~q3;OG^G@ag8e9gI$XC
zc@C$?m!cv0sO$iT^K*}!`g$~Mn{>O{@!>Kb<YmL|%dU2uRu+rZue8)cv;-bW7%UXG
zXa~+g-$zkI`W4lZL(UV`R|y~KudPZOZN=&+VwDdM9#nrjpUu?w%O%_tW@kr9x)5LW
z$PD=0NCJV}EM`8*K>nVQ@-SwV(?MzYUM;rG0VcLz8Uu-^s&4@69g?n_%fpXpnCgu%
zVR|GRmxmU4l1GK)ZpUlJ8&~u2#PJ?v&0dp_Qdp@s5QAq-9c!!FZqUFs4c&0rUJ1o)
zr)+1pcQKN~in9Y1>Hd{;XltdKrNId#x#~|NaE@iXTq2ANdT676@{@pNiJ0UY=p-cb
zkFb&M69O|r<U<sPqt%}Az&pU2S_ZBN@0VJX-Z!ul)Q>>l9bj5((N3E0%;G-W(sVBi
zG)=*+{Qubc%7CiYZfj!G4Vwn(5^0t0?hxq?LAtxUq*Lh<rIGF~>6R`D>5|&8zr{Jn
z^S<|fmw)2ki?yEh#GGS}F(zibEYmIAqI-b<PqzSUP$;LElpRb(#n{^;d=ey3_|kOY
z7mT5(idEb7LwrUSv6*rv;jd*`*tnAjUHGpG?}hT$KFWz(Ss!f&vg00#@3k}L$1X2Z
zt+*FglQ9qAl1F-napE;G5fvJR2z41dVBy+^vNIPDfqq6TpCS;vcp$gKG3B7@R{f=U
zv>s{Xt^7+2xrL;*bki9AI)MEPW^@-W;$!&VD^-UHRs*JX3hDcI6F5atN`8LQZx-YI
z$15$R&7=Sn2}sJVwcb#&vI2AQr9Ip$cUPA2QVLDN^~&W^R!qYtrGpA^Z>5w7@_DMD
zy0u|JUWqj^P1WKNUw<OAEzY7XsQ6B&tGVBoBQ`6VAxyEEv>~Pg1MFW_qt3|ob*v=2
zNzc}9hYvFLH0HpB&n?0;LJ*Pn4-)XFcYsG3$q}Xi?5;{k@_?$HTCYFBV`=;;Ebu`n
zHf+Y!M>P!00B-YVWw+*1QtuJO5tX~);rFuTuCPYq!_EI%J+Sl>5ab!iryr1agIp$X
z3hVX^r$Dw0ShN$%$jW{pS?cN%tpPc>?FcOXwR+eC0WPTwT1hQ3gAp%rh8TkD>{!Q7
za-nzsT+QJEw`6yGXA~_&a|~qn4i3<*CgwxNv-k-CI!Yo%)w;2{dFTgH?x0ZUr`ylL
z^M9V}uWfN+3Z?4L!%awlB*5oD@@@j)lxnn2&JEew*$V(24Jb&wZ|^<%vyuKiECDFB
zB5*da6L1tH${8px>0lG0Y=GK6_Ls+<(N9Rm2u9il9vcV*k1T<w9RKVaPrLBbJkEQX
z$Du+z*{|^|QQ^dHD0mzw0Y5+^d;2(m0<3x926ztv_B~#8);`_;yo7)D@N|*yw4JB{
zskh4>a|*eWAUS#YA`ySUValSsJz0(}+hY@j;L$o^1MPp_Ywk~6;1z!NbrW!=cUS=d
z_OtA6hgvgqkZ7>~lMxh<|9h=GIE(*%e}8Xse;pYVz(jo6VbKk|C+=hspw$4-z%CHL
zI%Y_uC2wn<nx+T<v?-O+*}CHkg#Z0*9oDC5ylgSRWdS#@@I-|J)RhAOs9{xib$;Fr
zD9|DRADeHdhS8XY|M{DLjV?<L3^R<*BiA-Qdmtjt;NV~<P!@DJ+w9Z1!bg2F>4?h{
z5AXi*qiN2!{jV|oukVX|XLuSSD7q(t!a5rYat>6rKx&p55(jj;%JKb8EFdTdtzoMG
zz9xc3Wi&-C$b>+rBZyAlX~x;bsM$689jT)JaC2HLn_4;P@Aa4tFxP?cOWNEXOBcDI
zp>%_UqZ7kz+#71*2tm;ZSwoN?2SR~kK|`W5Ika-sn^Zb_7TK+i3L}63!IOCKb;$Y`
z3yOhG%xC46)ve%vhbfl|qy;;HEvFZb0>J>qNYR93JQm5Pu{*4H(i2yglpq5@;MX@d
zClh(_W}L*?zQ45z9zTV=cJBs#B!<nvjC8C<{&Y@V?CMmo=1x<~n^twVWPWoyUJzmF
zPO5jKE5hY%DM0?`uW_fsK=(VUeVW|qCyd|#WnTQ$N#x!=plQSwy(M`QOCcN^iN`=q
zMU|()EmwpK#Pw+*{=aW4L&MpC^G-AX?BmpgYun9V;A}A;^(teLntz99Zu@7h9xPbm
zr01Rj8qp|5uIClkBafvVAZ_r%4SLHk#g*iEzVN;<rFp#`DZ5kOcd=7lBLeS}=0<pq
z8<nnkI4jL#C~;C)5AO*N!Gi|@>?*=S?E}wp@RIS(US4CBGkL83h{Vk)lZWLmMh~r_
z1lN5mpZk5>%)vTQt%f`gpRt#XT6LJ)t*C#Vax^QTwMjXCcK~FE2-nT+4p=u$snzM&
z&YoRW=oj9UD&GJ)eZi-NDF6uM$WBkZV4eBtC&Z8~_r4WTGGIuCA~24Oib9427rvc<
z#AHS0^;W|)SADSPY@4?#B7*}3qtE(DN#nzvGS}MR*2o7Lx*k~3(S`;-Didxg7_hRE
zX@wzJ`-Pt@c(eP8*^@1uW4L8Ui5iPOcZ1dwFgFkw9rjK-3=4%_uik-m##ht%in;RZ
zS-TcZ(-LsJ)4^FeTR|rd3OO@acj;F*m%HSHGY#0e0*Zg0k0ldE7pC?UX>i(}92COT
zjy*D-BOC`nN1~sn^{>01DvwNWigN*ZtOf-jpX`oiOXOw$(DgJq=af5Bg>>EgZa8Cq
zlp1rwHRr!DTd3;`dL+mBc1TRdau6{ehAvU^4foel!P!{<D9^`jthPDwq<+P|WDaA-
zqpgrp<;Muz0$kBRU&oUe2bV))%tEF-&h0H5F}^T9qTCwC-oyEwO#**}*r6JSc6O4j
z&q^Yci4JB1#^4?N_4A^-2sYR5R!4E{e5UZAGIXIOciaZ_JuG+sr8%gGN9dm|>(Lml
z4CW@B0tRaw1cD9j3II?+S@xQ5->8;hDn6G1tu6qiS3nhYi8cfRarn;CkPGzpd1Zgb
z3+%Gx$s!!uFfm>bTY-szG)@?prd_x7g6mZ3eFa*xFMNz@aVGmnC@MoRZ57~011gn_
zOZNB44lYh1DRKx&ZD;~}?ISYUAh{ZcAh){?eGM4qJH&*9R4o6ZOipwd)FyHt!J|Bl
z{oMMRpe!*=<`0N+YGgzuZB;H)sR!|C<k3a;9jHVY?E9`0e7^%K3S+XLcCr09Dz@l<
zLU5d?{j?yw>rIm<3K_o}I<-*_Bfs0>6VzNm+z)se$?mk+i-7RTv_m8VPOk{B$;rs9
zR(G6%_pieg=u`t!<>nTe6dheFwwoQy?lIv<3r;CW`;0M@6}J9UGe<1FfJ`z{y81}v
zxzC{j4;`)7-I`uVM{Yek64IPs(6qVf=?`vbSTQ>aRQg!Lsk}r6SLjuVCH1QLM3j7r
z<X2H=RsN;!ep6(F`)!mPCX7LOfpGn_I^^P}?IQYIts{^<A;0B?b9{DbsCO<_*;Sv6
z0$V^qG&32mZyqr~{Qu{GYh;BZgI{m1a-^Z3s09H9cl6L8YA01-ujvBn3to!w03Cr}
z4W_F(1PTauNh$np@-9s_pdYK8%;_h(QPM3f+Q42t_-WJ`-G~|*sF`s}!Sm$}dEQ;D
zFCNe3ZI&T;sW0xZy;<o+i&XMPa>ES_sJcEG1=Opf;psQjT}!-&cpl!k^3So{!i6jA
zclLclVSLUiDVqU{PcuY0ohEKj9ISAsl_ajj4-sR+p7DMGv55mr4esGOY_#LXQ+=Yd
z&cLpn$-%Eq#s^vsV)JWj!PQ8b>p#+<(SvaC!?-kOtDo$*(gr~@8us<4(!J9b|AZo^
z+(4+*xrUw@ZVLAbWqW5wta4uSXgM1B0jMdwDxLK-(9j6ub>3MoN;CNCW+?ftB|io@
zm4E{^q@jV^cYl|CX_oQ~1<&#dWWy0s7D+w$qT}n}(PZ8LQAi7KSN)2EfRH<8tLqmY
zr#+CIZ`js`^e*+p5HSwCH06x47TQp*I&Z<+And8Di`4v*g^33ef)d0LhKa;JCW9|n
z4<<SO2gnF4`EXrJy<u1pP-@5%hJJv?Kv$>9XL5Rqvv+ucNeDIY_?h56A(a*bAFd?2
zF^e*;KoSMB_`@CLFzU6Cor;QoeKzdg6hp`|vYE;PZ=SraVdIZ4TCgftl(&DPl+G)h
z_ypzb(1f)s*Y0xQL~rzi1BxMHqN1<>d)sh!2vFv80PM=I7tNSa(a{`$FxE%5#l5|~
z9ca{+`t|B$%@7r4Ax|8MiD(^w4R{y*^<i#F@NPb#L<l$Rq*cvvbcDdEwDz_EjaX=?
zsh{#FMRxug7{+<AVl{E?rcZ2qucnUMW9z48pP$1OnwH0c<6qWeFH1sjnnq;KV9;y5
zE2%sDCiA=94Zq}h)7KzU3i}1C=eYfr^rz>oOA`OgEmou<JFl?4W-mnO2J*s5lgJB6
z<a<f{Ucq*hgGnT>K64Yyn5X*u`L7Auw^Qj-Un_$%!Jp)K0Xu$%sR`=M?-G)dvg2<H
zQ}I*Z?WARDMVaD+Xplv_KgH}oHa>BO;(#772aWLz9Ze62_|4|?X#H{LdT(;;>o<Zk
zxZ}nxObAd<P*dpuY7<``<Eg|a8te_t-o1NjiW?>cTF+m3`E;q}7?2sQh_w}qo>EX_
zarc%C?(i!YgSNr<^J{7o^lQGjy+vZLAFQ0HN=hwfG%GY2@hUQzS>Qemhs3i{BhN8D
zze;J6a~=82sm4B29ZNELoBU7214OAXu-`jQF-peXK837uaMb4=GNTQE{oq%-(A%dt
z<?ERBGc$AZ4ZtTNImEx(G$|p03IRLwm38Wj(jg?;$iKe6{sm+{WMKEt&i+?ur?5^>
zucC|KkJqza#1+`_sT1Ip#Q}0CYC*v^D@&@*`7}g!4i44D4P{x`gkNY$#>RJ0pxWIv
zJ^jXsbZ@qlaD)TqE8kD}KS$!9i`#ht$42A6KZvMPSW-k@sQMGPegECnL8+oBBaEKD
zeyKhl17La97emZN8RGBm?mjePs7Lq+1uP}P20@e%o~FN0lE3fqpNag78i*vDOHN<W
zpK@aZY~6A&q;f~)$B!RVtQ|kE01(R*@Vl+5uI>U{BPltNBG6%;ia|iAdJ0I-YEJD@
zV&XFX%Ax)&3>dr;$$$nlYbqc@Uq7Kb1!&mwyc0lK{uE$O%3VvConi^yz~C_`CBxl)
zAbExQSt%=V<Z&R5BJPPgA1C;m=>BeAHj$0<pPlwk*lQ#kzykB)J&<YKd_tCxmNo_C
zB(qX@e)rVO0gO9kFebbHSHEV}bgDR#$Gv|Jr|kXs#^7~*%u@8b<*0t?>s1%SpJDv>
z`2+8efy~I)?1m4oF%Sf>18A>UK_1rD)=VZH9^8Z*-`Uh4D}d|+yXn(>xh@hRA>jys
z2LrC8@p`Zl^f3QS^#2OZ1H*8Q&;r5|H@y^cBatKYAMM+3rG6a&90XB7`{V%FZ3E5s
z2!H}^NLE%>?X;nuUJQ^cY-W2OCqV8cll_f}DU;zg)Bk`l{~3XG9poywbsbAbefkLy
zC}iIsfVSQ$Q#a;F6(m6J`Xq6d**H0If&SAX4h{})1#u25YipB3q0gy({_F1kx?E=+
zwh>`~bzYxqh6o;Z$^JbBE-rP^jXba^c@YLegMxyrir`$For60%{P9MBn)pBa{-5it
zzkABgKAnBJ_z2~I1B5?7_UJr&1F*dltJqk9_CIg+36ZF~q5jZ&Lofk>M$6d$G42Oc
zz%>r?`fbxYU2y?Vmf(dAAZTry2}VA32b@7`0oPVoSlG91%`GkBW(a$Wb<9B7G8oWx
zphy)|{}Q73YZ{ZtiRh|DEFZowd-pT9L_K;3Dr+L<`JQ9gCCn?;Qo>R6S&dBI$CRw_
zL?_?3g1(+i!(Xz#{2IQZ*c&ck^TnIw%%HrRex>R@-|^gT=<ghqn;$kKa(EOoaDE*0
z$O`u~(}C7frD5JT8{w)32COLehJXfT*7YKg{hrVdD#i=Q*%*LwAmNj3BbaeQFMJJH
z43@;8-W=TvdB)2TwmdMH&GF=Ne_Cx(`K)E_#zf+{X-n(=LWis)GYmpa47YR*G&7^m
zHS`u{BoXe!*w1Tagy(OE!b-Lkn_96|TA32n-SRklVEGv|;1R0TP)_qux1O-akh-Js
zdGM}pfBY$fLL9TvCT27p!GalFPZpl)8{ORw?JmKzi+}s6uP!GU-^^rfxK1U`7hBxt
z7ri~FRYDe7G+6=RA|ju`J*8_cI}v(BR8){oc`D~HBN#Q^ka{VGCw0h7Em!+&3<#^A
zrj3qppcKT@=D`7oVRM;KAV4)2n;7QFRA}}H5I3IztQ`{|qklX90;{H`hO!1WliwY)
zRI{=lFuJ9+4tyf70S&=Z-`=J_T~8%T<UN^?=Wla}R|R@7{a9xQ1E$C-Md|4nKUCDl
z(YNJbw&mu2Tp>Pzb9*X#t(rk!*VZG+AUges`E0vX9>@B$XB+9AX?K-_#A7Sio&`dL
z5^6q3i*OJ+LpDfqd(<!EEVFz?%DPSPI<ODK<afyjrr*s~SA}Lr=d~hBxBFw*`}))_
zHs6N)a}<cY!Cij=qbQqnZvv-DMG11lS$XPWC*|Vel49SnaD^x4H;PL_@*L&X1ZkiK
zIXaL^JZ!ys;8}3vHQ)*%%LGc1dvvYe?j2BAFJ_1*i4?A*YrOu6kWOTRkueld-yZAn
zb9zN)Xp&}TE~FN10LPUVk1qpt)wU(qnpC=1zcE6L+1~}z2~j}`Osn5%-p18jdnTf^
zDM=O=CEAn%54Y$hVd4MvO?kReE;(fSvl11RCKl2-jLYE!CaVT&;eeh*^&-iS-7(5H
zb{KKFzxN1sFzAR6tuLk=4rhqX2(65F4@2NGaE7hK1ZJ6JyFdQa-B^83z3StR*g7+#
zHjhOPuXnbScJJ`I4Tgje0lnEK6Y{{low~P9gdJ!=4wqZZaoMMx>~zIb6Rmh^wS*$r
z?NgNHThNZ3rj*^=SL>t2@15se`K$`hx+X<b(pg1CS6r1uNzx#wzk|jH>^aE7oy)^Q
z%;ZU@!gpJs+YnnTWhLGkHErs!J%a!yO*v&K&Ju4m{<$8s>aHBpFB^E?N>*rYMlH-u
zxS#QZy%8~2v*Gu|fCM$CWL#_FG>;1>NWZy0a4+%CMj2R4o9mG!_r`6|mjjqypG0J3
zQQS^eSu&t;Px~zZ<>`OvPsz+A8=`IOH@B?QVWJO^!C^E(^*QS!)T%K<kqGE|4VLfh
z>nnPPCj^ALxfwX@EpQR;-<Si^^KS$RI7Do0Y)FJnJvrPS8WBF5ZpCXp4=&GQ5rFUE
z@G;W7@#fvw<A(NpGPISfR{Icilcd$|#uk&eF}McB7h1`RQSI>(%^U5EayJ;T_wol3
zLL;$+45H)_5X}tl@N~J4!Yw4QZdw*K#%4phYb{^+-LjNL$kE_I)D9t@@R$U=xo=7c
zm3(eE32Ya_E`6+Wup@K)SL3DvSR>f8`ZnWMdxmLbRfy2AZx1-h6g2aFp-b#9%BH}p
zCDKYW1U_vCxb%WtgbzMhdD!%aFWoIFMK1T|Fx%}a2KnmXN!@Lv)dF#fh^rSGok%%b
z`3r&tV1*xdh0QP$kn~q2%d|V-(`>~jdftYYQ?$j7&p(rFszc|vcQ;<4K#15Q3pi|B
z$~)ph+$h$9?9N_SJ&G}TUG0UGWyx3VC5=0>e*sPGtjD>^J5UNC;k0>Ee(LvsD35dG
zYeT0S8mKneV%4j~5F;FLvU%*dj$g8Vv21c}yl>5B>h1%J{<CSJ!H(tkE<eqn$FooF
z&m`Zqqi5%JSyCLYSt117+~Nwq>7H2|8&l%EA}oe(n!mqei~WI+d)DE!4F4CfONU)j
zY-o>yx5LXBm?|TZ{s;tZPq|24oohjw3XrM*vLTrY*lxIWfOjbF=hqH?snUp4SX}(t
z3;h@fX#xp=9)j@Mp1wj}P06NEWy8%ri=6&9<7}PH50Mikuth0cb{S{L*?th?kxVaA
zy)ES}k1#mF#ZYl#cQ}hdkYSoDB2xZ`J^an60&DK|{O=9|_saqTw-X(cKLjZ{$YLe@
z-l7X}+G`GOOjd9o`=6T&c$_F)0*)iuO)&zfGoRVmL?Z2lm+uz<+Sa&)i|b%#wYXyh
zZxx>ImW{)1$F9*>FxE!)`y_0Uz0&3)Q@lO}#v!F(gI!od=_M<nTU1hMP~8EHGLrS;
z19#{84?_3Vd&=V+S1c(I{>EwS7LTh5w_hZ4PqOs09vbDbo=a9MNc$#(M1N*y6nxs`
z#j2+VWFy>i%$<6|AR0IL87H%#nldyH+IEi+`@Y#|mF7Axx)Ofa`2wfhCV1n`$-p7f
z&iW+MiP&WJT9wtfti<s`fSEaxz}YsQhs*Js^hMdo2jL1a1q0&Y^K(^Ju+W_vxRd1J
z_+zLxmQaLtkG|uE%1b>y%z<<}*i$=UI4H@F@@~|;P$=^G<==5;?k$edr;VL!1rMq6
z_yA9CWzZcSJtY6;=B9Ia7*#Bt5VoVYTIMz_I~%k5zH-uXeSJM(WMsrEGQv+10+Yb8
zKE#mI%UszS1Qclr0|Nu6ta~GHZUABPf$caq5E@#xfiBIIPUg%h--Wx2TS=kT0A1<)
z#w)d;XGo`LSp5Nh8w=MzoCLlJr3r<fC%kx*-ysWM$tpOxa?hwu)Yrr7N4UB4Ta<yD
zyj?gui0n$E6|z7<+nY)y$ntE%_Pb2UE3V|8#Z~;rXMANkH=xuc<Q#l&;yqz>k|;Kd
zi7ylPvDL_DF7#>envE?b2?iP=&b!kaB#WeN{9&C#qKF6C1(UPxc&r*%@(<IPb;xF)
z;|ay&E6_JJBnSC8$H(PEBdUYqkIkrxB<AyICId%~ZRJx=FDYs6l6|(#7^wM<IfV>Q
zU(U=_y<m72>e(kFS1Vm@Djh>Xj%jhu>+p*Yq#Q;tZSfTy1(oGD^pux#y4F(Ii?KPh
zQM`gVv|2rI^}((yK&i`tmK)Y}cjJ`9R@6t0uhj_^d!m?d@wQpSuBK<Nx~J+_C)?m$
zqL}ESD^d(-WR?xQ(h}M=DTk)aMq2WLQ|kv`*HA(k3A&O)wRLO$0z~~L>z}hYfDQnV
zw5HZg=^cWRkzpSItAPRAhLy0xLz^M>wVodM?ij;DIIviI$=3FE2=Mcj2$mYY6Oazj
z6?Fj(DIC{)uM@BiaC!#@<mW)$E?N>8<KMq0-Qm2;qVT;;089D+oNu5ye~qDo<Nh5$
z$ekJDoBUx7bj;)Cg}h;HSAZ@}kNbkM5iCmnNM{yq>+AbH>>k%$57)ngKfER^UG*cU
zOa85Nyqqm09#rLjB859<%AOhGrfh-bv9Odsy61hie2Pt^$Y(~^FMdFRelVQ-n>xz(
zd_PO8^$J^RMUYUi^<{+K1Mlmh=gFY$IV@??yG2pBMjYB+Ql|FE5mY<3=VlD}`6YVN
zGPAYc?L|BZjBJey7?YUSiRSRFJ_#;BEM6ksHHj&1MF~x@KI>8zw(F1I^_pyNM*m$8
z{$W7Ui!9iL$(Zb>@}r5m#MDCI@v^|7u)5AOk_W#u{PPYRdOsOBdBO-Y`D-O3&H;Ch
z$UtCQNh*ox*0X*zThz0zd_}Fs0;C<Rt57|7y}=P_KjP6f4GH$#x`{DA6a>tdYE{Ao
zk;$!M{GwvMOh<R5A8J|EjMAeMY6<7$5MEMu!r;UE#(p{>wG`x(Z8NSI{kN+0F%<v{
zKc+HV_k(`XtsfjjdmlG_9=q?}o!65X7Cz%l15n-!tfK2B`;N9n&knzbuVYDxi9pQ=
zE5SgewGoIMfkN2n9y^&NZ}jOPY0l=)7W?3iV4U4SVL$6+IINi3zM>e9k_V~o(IJvF
zf89Xg^O!xcmVK|AeCISwt+Sma9{tzY6z<5OTqw{LX^`=F;8g_iDd*yMJV%%L=UNzE
zL=PiDd`v{cPIw7#RZx+*qfCvWy1Kqs%eZ2OrjD#)P-hFxa(r))qT>5BW4ZL1yp@r^
zD~=RmB&qsP@Y+AhRtFDJ^Basx=WqeiyWbZB7Q*cxus*~Yt3lb52gnD$RtkwHPQyrm
zn$~M#r<hO!7xhZ8f>Q*=e8e_z{9zbOYdDJ0nz($vq3UuWtzYeWQ@wo8FLZl~B;ai5
zPiv<yT@3N6J(x%0R6~_;ISGEudc_zc6gaNo7qw!$765;AXZpQC3rFsP95;+hSE!E(
ziFAZyiu|_*!|82vPL*4ZaI+Qe*^kYm!APQTcBEIVldXkxDBLicBKB`3qfkWM6?MoF
z^kcgBds!`aPcYxU#Sr?F{Y|_C@gN&bJ8T=Ii+rP`)I)rF850l`88`~rfXxRf1lVN$
z+OAG^LcPTrlqJUJkCjV)JsTmom)V@8*UBUqY`%QIj~Bv5m)thY64;Ku9$@2<VnJbP
za1QgjehgkX90-#nA-aXN)uBG?GLG*f@2*K9t+GBrsd=^+p7Dmhv*)V`n`nBY?hk%3
zLSG{5dHenfyvS3m(_qev7sG-y>mt0zDT0W(NrVW$nb}0ed{9v{7q@<^_N<pa?_E98
ztv0{6%ZyCw>KlTkf)C5L?@Sqg*`x*!9TVKJ8|hC~{8a6Hk9o-FLJ-xN=W%n!8{wc%
zbG0Cv400F$Ole;-C`TnIgo*N%U4d8_<cHaJvmbs^wLjn1Xk-(Pl+!^f*t1AUs}-CC
zRDC`hx^FquJez*@F;sWaoBHReU+$8T3&v$&%V*Lk%!i;oA88Mgsd&*dJk<PgqkFav
zmEW*}{Q~S1?pNqxm4?X6@~oz#H7N^@r(iQ%sr3|w6EV^cI4A>gJ#MGv$c%DQYC5bz
zNSI;Yx+Bg<Xvv1$x@~*-x~1NCKH7Frr!3PoUmn0Yd-NSVL=p%al7UXW_8s}@pXz`I
zw3G$Hm;`)#b4@Y^`2SRE1N}e#Mcc>-^*gIzn07qFy$+FAmXHVm6BaV<SX1NOoO7b?
zNW(RxLhj2M$#c|-s4kp=wlGe~)3Hs1FQ0#YnU9(gsIUXeY)#7dTD66f=;w1S)#f%-
zcsI@jHnxHlpO4Ign)-Q#k1tcMN4^#DnHNovl+X}=pi4^#>VaumM0IK-P|+4G!fsR5
z5gNA4|4bX8VfNj=(EDfYJ<G_3IXGHGYK!^h+=PtlsIO)#spYEqqEoWkM`5}?gb5W1
z@mGBcIBzjt3I~fJyvkUJrv7EcM4nS86_W=bq^UbJ#ZvS)FWotW@em+*)!LVcNiO=A
zMfpZyUOM3kJEa8V)3I$DW%AaYUDt+Pk+~b`LayLUNddSFvgbNEI>WkJ)vR4_7pNF5
zSv6h?^OggiFH+C2=92eCu*g?5)s_j31&lc@RlgQ2AZgUD*-zvpXQwG>PgVrAXboNA
zCp>@s{JC6I4(0Nw#A}!f`No(?)9*Gg^W3Nn03+Zjv`m!<(1+2Pn%->sgLO3`!g2DV
zENr{mzm?Vz<z<2^+c(#)$Q+UpsUVI}Lc~Y;`Rb$A>(8cNP0iml>^D!<2os~6&<1GM
zr@rA6w)T2e$Y=>yzxj6HccYkT=x{tpO*6m3Q=J!O_VwjgZ8Nb1DigdJM!D}-N%58S
z^+l8+<`WA|NmX_jO~WLw>zYgT2+Pa8s91N<*<WUVG}xhjQ(|nI{gMgY#>U#!s$%%x
zeVjybeb_Kp%Ds>LFL2;*JT3PMH@<Yn(tVe<u~cu}j9^js#L3hAZ_I516{QL;7hg!-
z0QX;>z{Dj;A`||4{kH>+Jkt5I^*=O#+&&zj5FPDpCSd$DasR>=*K?>TY20B_AD~Qk
z0<3>u;$><8ucxx<{l@SA57+w(=n~<g2SBEG5C;#d|MLSMqn-|<DBA0u|HVJ}n-N0%
z#0cT?;}LlE?_K-{;WUC%lnlU!QIt%)H~wGv*k2!-h<SQmVqvmul7FA~&$B$e`Qj(g
zwU~P7;lIZ3KUcTx2PKbs*v$*n0U0Y9psqp;7PVqd`)>S_geqys2*9nzByk3I+>2`9
z0vqXnMVVBvt&Yx**cPE)#x>brG<gtS^(|~>?<r?~TGAdHTJG?)+i{)@x3tL4X`*nf
zP+=@Q*m0C3Rh6LFvEJcJHO&~n_D$b4IW}y)xVycL&Y?NuDwsbga(677waS~`r}X;_
zU^?p?8?fBm+>MQm>JL8W<H~&}ao@giJprujPXB8RMn8&_s=)wAz6k)xCI*-g#Ef2!
ziq&Y}yh8iF<NPhxrow`tN>b2Kc?NHO<VvFBUDwKTI8Q?vO~t2^NWT%^XzQJ`(F<^Q
z8g~Dv?a0x{$-52x80&WWeon%R#{BF?`(vQxk&}(%s6cxY+eP!p)rjxkH~t|x8N6?y
z%ZSuF4tPqdGj8{uC<EF&Em)(++>IyV-+z$bcZ#`hnP7Se&ni0}o3-J0hXCB*zyAN9
zf7~!2*FHz+6o!o-F#G2{JJ2Afv((y2tSvMA`)VDW0q#WS=<TxS>_78e0R@@+Q){aL
z6M-~W7PWF|DE5Tq%fuxOv<%iijEhIary4LR<puVi=?MUuhY)@~zSp&U^QLfDxY!(e
zPsi+Ew^aq8t}yZ>J|q7;LoP8kJCcbl;+qYoS_im8xv6dnN@YOB1f0sfpy%hCuN*%w
zlmB~z|KrIfS^+FM*HaKH8{_}_Kp?3YFtxcs%<jYg8L$5gPOj(E^1SeRCP4as444rd
zcL)p;Y%7&ksr$$H_yC~j(QU-8-l004(bgsi2%K?R0dtr-E1-IBv`}RXbRYZbs+^}H
zQtRsKn$nMV1z5Ntb8|Q7xZZY;O6Dq9cg_V@-}v+}@hp^_4U+4#w@QnY2C3*&3@ok?
zR@n@A^89#zx;&G;M|?c{b#5^Fe@(;>PcU=;gtd__PGAE-Ho^GN%M9|&Qqr_I$&X7+
z#NB_Z8QR?~hD;}_t`2@OpaB~BYyox4?&T(Dsykan$<N%IW-f@-rZtS?9P2wqAI^fs
ze(C3HSafbU?>ENB!|O8R_hX@lVYID*L;dWA>%6wXsa-~8Ja)DR1RbPeD3o)S`F;Bs
znU7!V1WgUF{(HDZww~UCBZOpTaczy_%a<>MLqno=cC4IST-5-q3IRmXDIreA<dkmr
z`g(eWY2Us%2fH2F+Ssta8zFi>Z>S(qIZp%7=61((r1kV7N(|xucmZU-Y>0_LZw1t;
zQ|V76m<sJjaZWr``Qd~VO%qRI&O7BIjn46v_Qk6XK8{3(3|U|NG^^6LDM=xA*5yGh
z-)!p+KJYRz1U{~3sQX+WXn!HqRD-EKa}?RdzUFJezq-y==v2NpUEj9}#u0>ne2pMN
zjI!2|BNLQWhYQblKg%oJKKTF5oW=xz2;@Msuko8D?fb_5K2uvSU3tA24*@ES<M`4N
zDZm42-pN3g>;h<Vak;s<R<@y6oq(I%DUg_N0p4QR8nA}GSNQ$0<k7%yHA<O7HrHb5
z*N=XNm+ET{nnze}1=$-Ay*auw%mSzUs`7Y{wc!J6mVd32ij0I0;;^@>w3`2PUKo*#
zY3-(8q6s^ZY^pDBC^nRssT$JIE*x@fYzLIq2t4%C{z*gs)U*QWX@E&YFaE=aPwk7k
zT4uo8QjI4Y^&X&bLrWLHQ~3pkeO%{o!kb6iROaV#ZOmBPip@v^DWpP!X@a5qliS>^
z8fp1z0GHzrnUUvK4ixjcs*GH*dLx~;H)OR~E={X;A6v5=1S6tGsi(bP%i23NE>r#Y
zM0vUqjHjavvA{|jKNRL<V*~I|@qif@xIiBs9(K5KxYGCgjvq-5rL>n8;4g6Gztq@%
z&0cI+b&30c79GIGEs+Sfp7_&2(d$JrA?Eh?N|oXF0yS{CA`X?yOO>(%mI@YP9FD(C
zyi5TO)1STQKRcz=(?M8(^Lh>QB?$(f>n~bG6*EfgY9xu=mw9BCn3C!J7Td{2hGw`y
zzGRbaS-Y-^@*tmSEhhGQd-nE4FGQrV;teM&p&;43$ptmNuhKepI8pL04o(xD(?bL#
zYEF9cIFC>UHgf~{q|&)pxQ&?*PP3nBD(_HeBo5(~HJ1>DHP3ERqL_u@`w9zXN13EN
zn_%r?Njw@~wz6GF^nDvCSohAjEyC8IsBqIbb*c+K<iq_C#e@6)hvXphlo(_2a#s0<
znDzFb_~Oqw`)2`ubhW11Lvs)42j}tdIBtS~Dy7g<CpkbZ<63?Uun*6sm4!Egv4lFa
z+*g^*$xGgTiA6)``+4JW<Y*&IfQ5yM*?5_ObL!YoTCg+A_LlM0HqkT#a;dvvav99r
zadX6O#q>u}i8#UxgjCz;?v0BKMutEQFclB~4@(DmEPsf%OSsqW^JD~FECVSQHC9%-
z9U6KTLP=V^Xs10r5=6loHNJYtK(^Y2AbTDjJtpe6R=uOfTD@>vq^+uta|DX0m@?JJ
zR47A_&wi)#u!E8i8$#=5XZ_3j_-5qr6Q|Is)62O0gfk+hhF_ufiRjuhEDO?;(RkP0
z$`^?dORhkVECn<YX8Ue?$%qv3qY!vej@t`mh@|HzlkDH4@o&1-Q77~8dYWt9E_vGU
zgQL}>bCaKkl94Ypnk=#$an!ctfVsD<d>>{7jT5SqOw?OD?ifQfrLdWD2$t6<{GMU@
z&nBPzq0RiAPX-bwuK!9Di#8xpHZViL{(&Tc!vwRkvI14hEtOTQy|}mt&dMTx@~#E+
zi!R5@O**d<HXVTa5<s1h>hQzhZu|Vf0GzB2l6aX&tD&W#^kC2e*C{|ub)%Fk5-bdH
zgY~At<1kg{ziBr7q@t--JP<{0an@JNICHr`*s|PISgX}=g8MsLt}EL8x;vM(j^-{p
z>(*WK*(>GJ#g@Q3cC@8b?qu44=u4j~d}F3+haWi${`BP<_>-)x<uK86In>rsX05Nh
zVU9k|Q*?-;Cnc)}Tx*hxTG?G4VtIXB*H+VeakGKUO1Eu1*3mq7&y|#-83lbI9JHoH
zr!lr4^)_&!kI<2#eE~Og#RYnFk3`v7Y8Q>p0lje4PVd;pqJ2j=uagrC=43G8bc_X~
z(<mcs-nMvm%Q^c-u~0!yc*vp~{3$nb9?>pb`#%8$1d}nse;4f2QLV%9L`xmpeO~^x
zun@G=V4oISURG8}4mr@S2o8pS@^&CJ^ub})C@U$3!47Is;RJv)>1H91({)5FW9j#>
zpFdbC^EY#bAn{R+?B2m&qcFqJnX40Vv#aAypji_7tsGkl2sKFu@6sq-HIPMg?b1S`
z8{yh%CB|FpXuQI`yJC~k_GuSRcvEFsW(ea&2khDSLqfscxy1)TY)Ac-{?<OTjnpig
z1<)myCPNn#r#?tVT?(y<fOJb#XXIN9W~}q5;j^x&g2dCxJj&=`KF_U+yd#`FMJAFD
zoO&{2T_hi;{p2D&qFxZL-W^k}`kS%O{A%`ZLyM}@YGq1XhS_ekS4Uc&Viu8$PB&DK
zS{X<>UHScu@i~oOFZIkgO2HlCY$mP*Mf4n5Qi%VgGLn}cw79*S$vH%Yr0+9<(M1l&
zf&u{=VdDAeCZWI4<eQ$M<h8FlhGJh56SX4<Wp9+JHrAqc1U~L?8H*r8mJd~x2pB)V
zS7dh29$-SHZ`zV*-PSlAZu32OtDsKWxhaABh}9=QjDe@EECJ$6GBHtUOC=*vyp2bx
zRe<aFIb1qtPDFK0L`jMS+$lNngZQsSp|OFDuPutLMMXM{FI(iH;-VeXn(D#$zIzya
zL<*>ujCSAazy*2OyT6Zn=Z?IwHEn_n(?lD6M=V{=>!h{a$ovF};xOBqX?iaf!5y=^
z0B4_ppd;~>_CzPFrL?d9(u>k-#3UTLFQ2Qg;#|-hO=(*z@>^ySrVPH6jh0Hq`TPn0
za#cYjSMKGiG|pS3aw4FIC4erjQBlUuO~`-KEdce3`tadHvZ6?<J<xL3WB?%JC5pxr
zXp|PIhl$--TdGc`bv$PIoOUpTZEWy2i`s3ZtxgDyT=lLtIcT0z=J}HK!7)-zUuH4j
z77@Yk@M(Gz23cCY;}BYMki&h1A0+dcY~4tINt>+Db2JEDLp_4%vwQ1VKqye=>yzi$
z!$B8V^FR;8U6y+SxayA2zO(X9*17t2XLMS<SXHeSLcfbR4hJ!E-{FWItm2_0=Guyz
zuD)Ufp9s7p5?u67>}bbW2GBzhX~S00mPeWv1d+qkoY--CuDhF$`fsz)%p;?~C~*&^
zq|e&El_qm^Wz5a@4`e7n`PV;aKMa+N#ER97-T3Wl%jtfpv|`3|@uG><lj%#PIl~^W
z!W#XV#2{@Va}5(kVT7b9ZSC^edR+13gQvSI1D7}6CKuZ2{y4+eD;gz<iGK4!2kh<+
z7339vYot~&)Wos8XAXH~;8n1+k=e=XoAG5TA_ZY)bDd3`l$b2(O{*){I<%mzN+eU+
z8)1o{c$6$S{j*hZajlyUl9%oCsK{WWo^$*gw|gqU9+<#lJhOXJ(E4oClr6qR-Gznu
zbFog<q)O~oM`h2a6Thfmuca<|^)&(>425Dp8?O0c^kn3+D0P1%H0)+Z6BdmU2TJ`m
z!IxQv>$CF)eIHB6h|cq?J%3{M(xS=NNP+KsX7Kxsw)#^VlQT5;EoUPhBWfxrF*|Gw
zLo<I-6wUI@ro1!lYn48|^ZO?Gwr*)EQl-(t++22ed(r(C^`BrTFzhL@=;3+wT}LdQ
znT;)t8iFPa0W#nxDgoddL{qGs!+LvUU8jTGrH>2+tWjH!U4eAeXgHY(V9&=V!|cR{
za6TC{Bu01YyRVqH12J?GOeKxzbZW>TO0ZaNB6GZQ%gn*GzRiSQi2p;ZsUu@yfJc5D
zQ;#@RGR&*!(*!IA|4aVx5ZRz|Y(001O2dVUACEfYk^$`)NUYO#-0R0?q1sz~vrQK)
zCddjD_)06^DVK@rDVp?7RJs>C2SUMg58_s{IB>pc>{#eW+sx%`f(_`>W18bel$`uF
z3*NF;2bQH>;#>Ul#O|F@V1fb}j}x37CX{0&rOz~hdRQZpjQSGnJx9{Gg*=G1gH9%4
zwTR9im`}!jYZTJ{g8Lw6<~3m~_<_yu@qjrfO)HvF>}E}ggLswR9U(7tpJY1>>g|He
z#Qe)Wsie}deGH@nhHXMAD(Y)|=<@UcyunV-neEr1tDdCa!oF7V{{l^gihTVXkpKS6
zc6W-HhEiW*XDQ3Dw1I(WN>HbSt5eNI9$tqNJv^!tSA24MNNpL}9&;uB&W>u;jM}zE
z5j7Jp9&tv~pzL>1H$Pb4*Pp&ju9XGp;hKDBJhdd?{Knirk%mvjm9<DmN0ZcF9+7Th
zCocZWHDaX+j#NoHAG+MY0sYd{c)u!rx)~I@XDtw^Z~dBv+{*G5qNV(KeCo%b!en&t
z`%*;uK5?oVwIoZs{lQ8XK|GrD%c4O=<<$PM+*ejZ7{d}2<mNM}kA*TVP_g$3ivIUe
z!?lbogEX&s2W1L4BDDQfGk!`W<Rr=Z;(R(3NJhzF%fQ974cn}{wWpM@mGE?gJx4=J
zNJ|Oo-F|t;|5ho-+-qAE`r`*vBUt_ffxy>bU;l0dkHDQew^9Ys;ZKg~APD+ZkJFx=
z-8j0Y^rZaQ*bv(*?a<QIH3i&h)dJe@tRJq(y}iAGE^`?2sV@?iVM62sA~*nvha|w9
zeCi;BjEV|)(b_p+P%V2U?cbF9!c|4O`7}%^-E12ltwRV^xBJG<tDW(KDqf*f1oPj|
z^1rFZ6E6Hj(SCnL<|{k1MVYZ0Jrk~|AL8>#-VjtU2r^Jih1U-fSQmkF*OzQm^+*&s
zkd+H^IUkK1XQbwOIOjV+V-6Xk0;HoVRFw0F%IO=|`+F?f<d(7?Lh_HdK>DWfmbqB}
z-gJ^UapHLQ9SxJjSGzeL>=)s<rKXeny>j8=pWk4!w~AF$*C0sfk4bPa8QKh`n7}ZR
zhnS1$OM+@$KBJ?zEqcjRU{}|+OE{62$jj0!M(4m=Tbrkxb~xOs4!Pz^r}Jk#V1>{y
zfg9NaKLrNFz&Q`UCAsshN=>y^=joapLLlMn(gc?AneF$}jcaceh0XS^Z~O*6POojK
z=Qq9|Nv2;<ULeoyB{*8*@Aw)9AwZjuc)r0KvS>#p{V=a`J?qOdQ)H(f39--dC2iU;
z9*kR#mPZJTCzPUUTZn)@EFi=w=LCpIBgA;IupVoN(eaN{Nv8!vU(BxWha;`uTt}Zi
zWXmKccG~>>fdchArHaWnD&1jY^imTcTOj<9U-h0yxQbb?j9Cb21+K?&z*l+W+VW1H
zEmbth8<n};6N$OR4~2;hY0L-H89Iv;b|?X9{$*XZ<cWN%qw7M%frB-gi7c!uh-q_&
zDX#$uq7KHe(SzgI$^OyEqNNn2fp3P2U+X!vaM$yBfy}(KR(m?W&7SzR!64132}+c6
z&Bls9>W&aQ!V$T7#H}e!s?R-M{<%w4^>tVJ&^LBZTJz1wrQlnN%&u?tV-kz0%2lkY
z@Sn?Moa~=VE=25mK#rejK_7K*Zp9EUB`G$XN8h9iH+h#+DCb>*{oSXho||+vHZ{HC
zW3(z4y@C6Cf3Ed{bCriF_xn-qA4q-Y4><jv0qz{eKwnyHCRoxpA@BWVWROGRpl4eH
zMMeRAeSM>**B$-cAxyWuUB)UZn9TyXLH?_T)w=oxI(2W1=U9FALn)6eOY>{Ju8?TT
zu<M6d0{SP_TE2lZkEOumD1&K<7lryfi;cxLAgM#%Y^{?6Y$j?6GLWi-kzhh<Wd*e!
z7_!XdCn6|$n2DruHxU(se3s18sn_c%CPjrZC$(t&IR}N}Gan1?$dzQ4(OT5Hf6$pH
z>I+NOO@WV_@DbGg=vS_sYhN$)XHbgMLHWq92~C_2${1MX2w*O`?)jSn(?g-{N3Ex0
z!NJ-$m1!hvmLEVivX(JIRpd+cc3pFdpQ_GF`?OlPN%Xz112)!CTkFjj5J$WaDIV8K
zi3)@QLr)%%gZ*^}zl~9Gz9#k3Cw^RLCT@nIVB{JD+L{njaoZpYJsi?33wB}wqAmD*
zT(0;x@lx%#;l_eDLai^M&z3z0SPFBG72zC&8#TPRhh%dx_c%!0RbFGARH>%RX6S4}
zmm@0cvm`o*mDg87FF3T4!mtdX=oS~Do??<5BvA?K8bJ{iqT6>alAa8jKMIZlmK;x3
zXs~k=L_r+0cbL6fY^|}M>BF}gC^V^4%ZQa8&jktWmHVnJgu10GS3@_2+q*W&8L<5!
zgfAeQ?l%ga&nWy(>HFR4dw#PVBxG9W5;4go7aDdKQ$WKhl&u}Vi!0~nbdYJE5hmnY
z0?sU}z1@T-;b+@Ut=}M&Q>+v{yC+aA&p`3BIqwq_CnTgY&yzP0u|RG4NMk=gxJxM(
zr_Hf-7qi!V%U7tYes(VsukE@f%~+(yePC7u^|AT9P>Zi@-<bOvzcae6nBE5fm?{u9
zD(t^Z+=|cu?JhqS+eSMsDoV&5(1XK{hmTKAEd8{{-N{MJ$cPf)EV8ymXH6GB-!TdR
zsNQ`~<}`pSv?-o%-sR<GvI*$rEYF7{Y2-av+RoLfQHqXd%JFKuE4+nSW{>4x8KeSJ
zL5umEXGO|Nyobzg!xgWw5^`4ix)42!ZW*Z#0-d6GbMsb?i(hoR3pJ=k5E7s+#r4Zp
zhQ&~oA;~eu8|q6!WFm6IuJEh9kBxNCKCH-)XK=lWxHD1m48<3!$A!Ny{f6tR$9zCE
zX!dHnTTkOMMcEN7fpaC18CseW)&6|sHywhs><TL8WO$=LRkj-u6KQaEziV|}HyD-Y
zy<t;D>4%={bP;Rn-2AoOJ(~L}ma*88dqqb1ILVqhNjZPwrfo(}*JF0GzKv5=UKduo
z!Dbim4?FifWp@U>b`O-IOYP>1AjAeCa#-6bJBUG9ze<`(rhpTfXY#5#xi3le7k$;T
zncS@#-gN(QBUDz)048RF&#Z4$+Ko{2<vP~XsEW!8>~(9=9J&l#iMp5HE`aeca;mRQ
z?;5)t-jZxwbt~BTZTm_TfeW}EZ1MJ$Bu~3doZ#6LMg8hJEWt*VwUl^~>hn>Xm_+4#
zTdR(y6QylPQTqKo#?wy8+q+bcmmH{_n`q{XyqUB~{-nWV-u`jj>-7V<zlmdZkBLBp
zPABJ(Ynr|6WK0WJKJ+{EGAKm9w~)Ye6JqaZL5Q`NBpad0yOmvy$6n*iXvW7T;5Ps(
z&|-|G)NtKT-15xhCkO8K3fPj{g8sLG7e)e#>>h<l8G0k*GFm1!jw63D<}pY=lH8|t
zd~!DhLyQ`3@`rpd9xOTb;fbDamMcUA^;&2nkpT`8bpoUJCo6xMEb^cxBJREJ1to$#
zlZH0!X5TL<ob(ZX0Bj><_d%h6K;W5Re0Fv$K!R6~;yR5BEHO3Ww?>tdfrYpw!chi~
z>~WJfN)#6ugDtDv%&A_a9Xxka4RI(d)<5WYzA0aFDot3F-<G$J4>RLVoG}nhJsM44
zSutQ^_|@zz4p~jhP(+H@w@&?0QurOn#CB2cb-wq7PG9xQ&t3|@`+QZOduWi5M7GN$
z7_)X~zHKCu34Lz$NR3>$_x*65t*#DeO!E0wpOHyO%Qt2j?Rj?TVBslM7S#QC-C7Cb
z{c+M-hKM{Z<)M4nm?s9-r9)D&{V-n34YaS%*<xJ@=gvkz&&a;0c<*9c2!D<O>Nfo*
z49EY*<s>3h8q>u29Uo^9qKQKZck-mwot|p<z-($z#V_L%oS>x>bP3&>RVo}({bodQ
zx|+E7Dpr~fHz%R^vaeqqY4Y^#^Diqo5qyQxWxiMj1SesX?R}H*dHrSZZC7eVS~M2v
zm%&^W-rV3e@;Rue9YRuA*N^JW(r8iL06c4G_)o$Wj{DV$_%~iEblSv&vXhhyczzaN
z+E5UiK7!2c7v3m<3c7Jt`eVbxIf<eU;p4yPZBX_XpP5OKN68xJI=-IrByMe9juqWV
zSg1eqmiP^*xHb!7cFHHU66#XW=@FP#^aUvynBjxlGk3n^uNPU7e1a>tDl-;GHP72C
zLxz=C4CNFT&G97BE!oQs)Z$a~7hADPelrKssaX~Q_lB>$jk<y0{ea8N$|}|4uTaaf
z|5K$Zk;*;)<oTC)`WGE#GA1=a=g;qHX8<jmn-4I0_<}!qEzek``-T7kC5g&!j7TP7
zOOmop@!ph4D?c{m_aYzLnf`YjpZ7)7?arwQp0|ehA79Hc7i>k3vRI!R+yA2W;~l*{
z4i(#)l+~}g-x|X3D%WLyW?UPsu8CtP{k?97ZNAPrf?tPeQ8N_AUObg<M{KQh$gm?Q
z#mGoQCH-XHhtN=cG?R9WKm&JEzQWbq5dW%TOyBEu3M!6qk&!DtGoo>m$oF2shS~u#
z)J(>)tsz(At_CDkGaQ(*9iQ7wl-k#Dn@Mk#P=#^eMOtlI;jpm}gGS)UyYE6o^gW1w
z=dEzKE!$dJBt?Ep%a;|QWBwXTo+rl>Q}o?Ecd7F@8K71<kF(b@tX<obGuV-PGvC!w
zF$c;lval6lAx?c7{)#^T0jyUEYVzD4eE#Lpn?;Bl-Mo+SyR2BnDyq=4TIa_tzTqiH
zV#(0`g^MFBa7Qn`ZCeFsUtJA7xQoWCC%F~L-lv|#f+^k7^F3?qkyfa?U#x_-5f(_z
zHBW(ECB8dK3|>`L_szS+bxN~0*dhaC;vDZ>><d5BQ)?Te<<yfWWHsWIV$pTxe(ziT
zAiUmFx0IkPr`*(y5Ihh!5OaSlJup)a^kV%msL3l@{=J8-#eGY`3ngl5%w%#gc*1Qj
zjJ>1YA&xiJT;82J3GN|lxsfDo44=Ap3EnEyz=!NDfr%+4LPV4L%0v=f27z{O8RYrd
zL$5`V$}uX`pN)-;2|lig+3%06Zvq>xt|%|8c9V~r1$M*jseu?%$-3PgF0Bb1vU~6W
z6ZOY?Zr%K<7^hYwRi%46<+z|={VfxGtugUlxJG9!U5JuU`u;5)7@J*b(Y&V2REPlf
zEX4jxM0;j}^hVX-wz|Q-vVDc=EvwJQw6lES3JU}7*eL&Wsw;{23q9h@BW4dG%hs~x
zseU)6x=II@k`6YeS#?p#b5;%{hG+}wDfs4TyDUZ%CF=F{*{JOHBa5B4mvoEGYVl&u
zIC3Q1a>R^Lf%Wa2k-M`|>I{|LhlaJT9dbs}30dpBsQIrx2608XglS*u%|o7j7;GeK
zZGf;Fv{VWo<yJplShe07SW_AWwU+70C3u;?`t9I+F0wsoxqLOV;=%YEx83XDeOaCY
zwN6yVl>H9ZDOFMNq97SOllmaFi`W{bXcb-F^l%IOagHxDf$u%sW7`j;FkG5$>E&n<
zHg_JiU)nTB$I;YjUp9Un!u0%n@OZqqN*8t(;A?!#h>K4<a$kQExI7E?IdwJ~yX5Dv
zndiFsJvvbJQ3$KXIVGL_n)uS8UPWN4qJ1q_)}wkR0;=0e#`bJQ=8Kkj@<uN5W5K(v
zGp079lnW%eL0|de3YGq;c40am%B%1!G_SZ;yoQ{65E?}m0?sP!T7}?o3k`BtA$kV+
zt&ouC6$)hYt5{?gsvz`=vdE9_XK`#9vP{v&`aFXBcxKG*8We|uM_Ib0nJCiVC$xbT
zimEM3m3YLr5sDN>Gocxa^zK6!!*|1hVDASN1yS6V)mNkqZWtlH=fp#bF^^>;Y}2`|
z=UnQROs=$-Xxwy7-H3t7f#yS4(Wk6TiUyyip~E!acyPNrN=r*=A!yA1QB+l=z(B7W
zHB7iYeW}3rH#bHzC8`Sz_7oDoIyyKt%dj$#x7|`foI{nZGU0_~ViH=&pRVex_gpu|
z*Rp*S@58(|PD5R67AS&snHtsfB^Bv&$r-s@RS3YUBy9^bJ|Q=AZNaP|(8gLH)I+m?
zh;)k8{-O$2z(|B`c3w>armem<noU7bvZ`wu{K4?>QIF03VuAX`z-KYV#b@VW$K6r5
zpDZ(u2=QL;j1AxKrv13KKuYz}SyrntUE+ObiJxzGZLA4%CWE1mYDgkoW_EZJvSu`9
za-uRg0cYLEht>QI`&#*ffhvP;Gu1*ZTFLz_(C^IAIQEiFjdoSI9#8ut*aZ6HCFEI3
z5FM$q@mQf<9nuD~Lhf6f(=K_#7oo$`_rqV!=Jo`QF(0|$PJg=q^n807A3YN7U+KAS
z4q=r!!WrvtT$!bpZ@ayT)QUqIDw!j@1Ev;H+GIrhTIS0K>H0p3i3=S*27<XI19?x_
zf|h4ny61sbL%DVh8>2;F(#~OWzgW!|h1<`qYbma<Q>7F_hR0Ca3kA||ckYQYQWf7{
zfUDdbf|LU&%1V6=5=jF-@O#BHkwOoA&^VcX+BPLDhmq+Gg|8CYi4j|_VL?5Cp6vP(
zz<F$=&%ZGXZ9{jzvuQxx3*%*b(aA$nkm&^BZS?3KANYT4on=&A+tQ{tuEE_UxVyV0
zxFtBj-QC^Y-6245cPChI*Wf{dyF)L|y{Egs9{mRbgRnPqtyQyT)m!gF__YWk>+(GE
zz|j*=LqdrkLdx~<EHkkY%eMOsiQu<DzG4}wpa-Ym2VyDzx5S#A1TIP;Hz7$Eds1w2
z^h4~9sllOS<;h>vaa#n+s!>w9;YiZ>Qf`lKGLCFNq_$>bpZZ2u(R&@&`xJxe#^d*F
zPeO;VI}k0^xXgxUESA<>Yb-S3Do%bM`kauxzGk?-9ywl={f?;qSBX(756C0t-On{3
zV8b@W$X;?`fwX(32)d!PJ%V}Y?DsnQ5R-9Fu9q}~Ks9WJb92jWd7sm3Rlc?Txi--5
zj3|PAbiN^*ng4M3mx=}i<aaeMdi+EWYQMov241*-;9Ew62huC=&d)caECnxnft^VH
z!PH3eS@^QNoxz;$S^)C!?iZj_-cVwHh4|Q^S-e|cy1ps4<?4Isfc2+<u^cUd0Y5ci
z<N2&4;kyjp<K<eq^1en2{MW8~^KXc(S2Ut-s;u87uA3{AowZdf25$S|W?Bg!aPJCL
zefP*JD1%U#a=ex0S#ERX2Roq%{~|(DCo!^&v?{AQa5P4qyS7>sLto+SxniTC<fN}7
z9Oj;iIav@boVJU%d))#JqB-qgAQ`0lm>1xhxv42FW=Z8LJ*u{~4MD}!;CNojtW<(b
zxTQ;u>EU{P4Pl+8W?4<r%UP2x0RC~-xlAFTnwkjAKbcUv<Rgf$hbl-Gwp3M3^H*Mz
zqvWUdh$y1%oWc?zg0+_-p0KOGH*}BaZ?qN1Tj)komSj?tpK&cv4gF3ZJeO5gBOY0U
zIiA72=_W=d*QfijZ*2r;<ZvHY(~p+9JxSMU_a2i0eyL@^FKKu>7|4*7Ja!gz$+}5b
zM}VKJmBGYPb1b93BWp!Kx1^}WCWG5(i%z{KX`{f748N+ygZztoXJ3UQCOeaB1@5=-
zAMYOt145!;n<ppUUyj%#|9btaf5?*uw}Q~3!s=2d_Ge|i!r*%g4-d!TvdO|gsp)F-
zb^aX^wEbjedONj!sQ%bzwv;g9qk>Ip<Wi7zr*lZ*GCfK0XMraO#p%iATr#aW5ktUt
zHaFEiZszYiy+Nwk-G!T#02-4qBJ7omcz29i&`QS5K<lUR{UZ}94PK+&)NKRN-;Sl_
zm7US>iJ_q~UqKHIJFi=K(!Fjp^;Kmv9HeWx(UYRmCQ6;oKkse{&|bTjFrW&<c#V~Q
zQCIcG^xI=}^2um^L-Ec(#rfz>URh==bAd#uC@f*w2>$SK)3=rdek8q-0UqA0>zi$~
zsFjDH)u&$8x^~~>C%;xU7}oh*S)a%4NGIA>kXOSkj#C5lDB0<EBTOZf?w#EY89tk$
zxpe-HY~$o^eyoih@3rHL6?*B<l3yte)^n}Hekv&=+UtW?ru%7xRkjXFFMn;LFPx+%
z)y{xgC~H&wQFSYNko;E-{tYu*ReJqPG#%ty+e#$u_G7ZAlQvH%-6B129XH0^z>)#X
zT7?($Mw518%h%582$uruN{?hit*UMht-p~0HJYu#TPY*R1yY{z?j->tm0e?27Hcgt
z#Fg{>TfBSJf9K6Cl(f{jE*6_Cgzx`5F<6xjn3QO<)mhpqYHGr7G391v(scs<9jLgt
zG|ooMih@@N*yq3Ksz0H6OQ?R+-^NBo^^c2-vm`!-ZbESs@O|ai^}hLHUgfwq*6Lr#
zp|c1i@J10(cC5udy&6$_(&jI~!-ak&_>c{5l)Sw>;K@=JD)cCM-xD7+IA^u;Jw`VF
z_l0=c0e)6$BH^Cg$(QPfc6X>gX+01WIe%gtIovvvV)v|66IHhc%w;;%Peyl<bgXPK
zeiQ?J8>STmu8=Bgfpcc4x~Cw>g_djZv4?Z65!yV<QWyf7&g_^=;mB+^^H6?Dd?Vb*
z=F=k>V%GWU@-V?{8p8F*msFI*w9wVZ#{Az-p3);~5uH?twL9t^bFxL?US|9f3so1v
zDqe?37tTKmU7ZG=Ff@dCDUk2F+aY$KgYpb($}zHebE>R`&_ZX8UYS}A;`j#Wm2c<h
z`!Y5cx<t6WI5D5(OY~ObA%7~au092mO!fQMvEkV9aw3S5l5exYexD9{?L8pe`chxs
z&5wPBbmQ1pUjMlLFnKFu4~E4k6coFMB;ltvtCjowC#dDUddUz5qCv6xjj|r;b!F*A
z*_0=7uKMrHIuOf!`;q;J=K+fn^%0}mJv*{ZO)D#UEP8cC=h5|bA_e{n$aRmS$}&r?
zM`0<-h0w|G22<p8-}A+Ln!-3I)UW64vbgQRRf=WfA=`m=)SKxUE$E20?iPEoIA!j)
zPHA%z{ER{H_m=v|{_V>%vPgQFT4Vp;#`*0nO>X&$MbI3j?d_fkgMVK3GhJpf7fnCw
zCZz5XxOK8RN_DlivPta`eEj%v6Chx*io)Nyd`Dn=`bC8h-7iqA<eAA7lV2d-udDI%
z<;9zqQxuzp>)WB&ll+i_!0PwSbghu>k!F*~z;-Jy9?!%Fo>dzhJ$)SFP7^<U>TL6z
ziJc!T#4AKsKMV9m_8^vMSB~w8Czw)_V{Tu)+~3e4L1W@44-_TNSk-saZZ}sF!zgq?
zxESKYlj!q_FAw_i<5}eB95T^Il-u9=Lbk%2_YHbOI`{U64djocaqo2IU^*r4Hv#{p
z&F6eH7_DEPe`W`ksIZ`Hy_&a!DjIrlmVTfTwYub~At2wrY?Z@opAj?lV4w<CtazyV
zAjnfkrogD>ETH;ww-3RY`>*FQ7ubdlw%jEMx@l&Coi6424{c-wc5iU5?YM*qkIs8V
z_>n2Hp}=B~LQqHuqbfpTfJx-hlIMZ@!Q*rS4ih^JLQ`dcUHEjHvt{gZ;egT<;JQ%@
zkVy@FChJB;chM~)i<<*8_tIr;0zKgRHp8*+dAIL>d=q*X;Fe^&WQ2piGXP@e6E1dr
zj`lu8Smql3)aGv;zN#IPlJR0w#&O{-I+`UNa4U<w*g{P&zFy2)#zZf!WVwm!{iEyP
zN*2hSCu7+0G^KHGHRvJxt>FW{%lB}U!k*)uv!x0m`J+X&{ji0$kNIzS3z0(zz6AaX
z#nrvYSIv5n$NMFrUKhrpk8t(}i~*s8K*MmHrmeX6IU%0VPI|KV-eC{=!n_6@e?Un;
z1r~x^@^b;M?P5_?S}`ZHJ8y)T7?nML_#5b=b*f%2!mJ}v9F`gF+n9FX$Q5U1oj9}+
z87U%`Ww4Bt(<R+nSTd&$dja{Qt4$P~%A(#ae0UQ{JiOVwFhZd235BZqTgo<ZUNG#(
ztV*7X4xeo9=Re4S_6wT}_g;Iv^t$cXp%l{M4N+3$y*4#glX)luT4rJ?pJaju>*fQ_
zAPC<mM$$aVx*`jq8zeoxGMa>MfOWxqn_Lg0lXSjF(mA662QFhCEcL&i=Ar$W?Tg4q
zo&WbW6-K32faLzYLR{H>%=E44;(Vw9sC?ib(b_<%C`0~=05zWiBb|t<MC=xG!+;*>
zEvQfsBj!;#59OiM7IGSv`oAxs>IwOL@TbiCtugR_zU04O2}cVHLxDI9(i#*0{nr0@
zYaE1NK&|bXOZch!|GeM-^OK6?D2h~m|DXRp{{bw-v<tALsL|^MXFk3Lu*fl%RjtLp
zT&@7;AlB)U386QyC>)jo@fY<LJJNNZCu_hDVNiT_yWeuLYM>jy&!8pmjLE*?Fm>G*
zv?ga{ScvMcUcb8x0W9viR4`(I0);J`#XY@idVe-V<XN#)&*ysj6TtNv7TCO;*!t3?
z6k|sSGovVi2{S9su>uduf1j6sKKa9olTcB?y!+SzzyQSARbV=>pZ%*IdVuacFe4iY
zhfbLX80k}Bf#R0mc~dL^GldMS=k{cEymN+=U71W)158*_QnI!Vr)d>;kZ+22Fd9D;
zFv1u#vYjp%fCC3fJ{kxC^krmB5M)Hz9vvJwEr=RS6OTC`QqTOWL>@G$Fw=_kzbh#$
zLB?!8^RfS#P5df}Mn#q##`GmUJ)IlyHk$_Ab2=XOGAD}g9M?+}h{rNGMh$&wm|ITU
zj$~UbN8{Q}uQ|Kf5(a^_i39-hGeL$fz7wlA?<^4Y3wOywMP*1@2;eG0`n4rqxaPo|
zmzpR+V)*0v`87j{emH1zQTlHjNkCC=w#okS0KhbO{U1!hamSYHO*1ROlIMO(S2qDf
zX&ipHnjx2;0|V(#fHQ~gm7ABMO^)+|MjprnFj~anaVToGqFQ?c`V4YQivR&}k>6Jo
zx-%bE6$Cyn4HBNs8qef{!Q>o0Zzm4a496-4?x-(V{y{;I4S*H?ucmMKRsXri6gg12
z-OlsO-2i{X`D0;B8;%!xMUZeV1O+N+%EHeFs;c8s8WW_tby59SuKFvuA7EN<$0S!X
z%=+T)?harRKr6s0x}u<w;D20mAXLu5E;-YVk0jG#`8!VVz&LH*0R{+oRDnRk>Vse7
z@JD*5;$lukK>`?_eA!z>j1YDwRjh;rvG;#hWdNi$<u5JJ<+2P?|5-3$)bn7_KC4G&
z@wvulRB8lpz#EEUC09G8?Rt5J^xX#q03^eZIJP(+eN}-??*~X0TL3B#dT1*9fI4;~
z6yOr46}eySVMaRwWpUx)CEM;7=tmIAh3DnG+PCzpqN0yG+*8^uFZG^xHuc7Se=0TV
z2k()9sZK6=o<A7u)+=)icNa3JwIv0|0LK*=-ls<*<Td)0-~0sB+w}oQn;|x}L^cDj
z!|$ovz;EOb^%EvzQhZWq1Jvo|aPTY9Wf8)SXdpY3fKKd18jxL%+C12z3mtfb@Hr~%
ztt-_%1AxUu1JAN-X)LO#siEc2TE#PfJr|g<RSEkLq2-w4`|AE~8_Df;U-Ox?e$xAC
zfoBcSkeUTS7!_nY0gnx~K>rf}h;mZ81js<PK(K<v*0vLw?gU&sx8G4_i=7@ftopZr
z=W#Z(*Cz4Xy%gZJmBF`${Z38FaI|O!9CK`Obno`vUH}UUeZW~XZ|LQ4wq$3;vYO**
zEnU*~bv}9cS_RZI75oYbdd}$v=w^GXf<T%>uhZ7h)>rUS1`exA2L_^Q)6)Q{o8<mE
zgw%vuH?l7HCjigGf`#NL{}WMhKey-5SZX|a*`exLJpuyl4d!7CNaj<4{x175GdN+%
zoUMJvj&o7KHnkgvTmUE?_UqM00a)B6SX7+Hn7Jw)K0^*>$3598u7z~d9P_4{*E8QY
zwnGd?-te=bXs-3Jx7WLFvzelBRLU#>*=F{;jbowu8?wa(O<2ou-6-PhUpo5VfXzs>
zOFMu>E7pa_FDty=*yhsf0U1(=Yz+Zii-_p>al=@QC`Dlh07&)aK{2{X!K0!LR<#{d
zvIY{AG#7udA3B}#BhhT$d%E$@ztQDAq6dM4v!`|gc5Nb(4*(mDUAEmC`|c|7!RNGf
zf9&yU*)(_HU6d~L_Atqb0Q~{aXreNt3kbzE;GoR##lyqHy#ze3JEzRpRpjAk$5F8C
z6%GJvM~T}D_VR7I!564px$UK!A_i^%CcXvy6Ny4EhYBI2dsx8$*@7pr?1YEot-;R?
zKv2EI+ucs6W<OiJo?(*7l?p=!H0yVP<DZn%vYzzbYZT`?|A6vi`pF^p?^E_qmP4Wl
z37DH&u)oZesTiI`)7Q6{O_SMb{Bp|Odo|KDKUJ{3mEYUYV7`0n1{h2xS~KVh{9CfL
zF7n-Ah>_Tcdv5^#7$TUI;=$fhTmrrGAyJ_Qv8&|qFw%blJd?xnYe5)T&aWBI_Z_43
zD;DS(!in|N7%XhXCq+u7LN8e(<@TWd|6WfZbP<E`{%Qw6!BIRd)$72lN1D;m@ub@N
zOp(%4xA`pPnqB{ggSFQBNwS01*P=vuRxPA=wljcK8@5Kg7D7SXb_oI&I;oVJmw1g}
z*KYF_ujjy!Trvt2%<B}wZ8RK*PV@i_28fu(0y|RsB?eVqAr~!#zXb(|#IJW@p}wPl
zRVSH6T(ji=&Ltv*okcbQn=L5cuCRzw0E5_XL{rUB3YeR+|C#L&LixMi$#;(!44nfB
za<ajuZv#wgdcJ)I@GF9>UGUPEvdot=%7Ne@O(i!pkowu%+FNze`@J(S4?=;@v%vp<
zw^3mfy?cR>lfq`7*M&+g)9ryXAUeej$z+=XQ&D@%jh2geOmDC71!v%B*%&jXetZw`
zm<Kt{ygDl9?-0UweUM4HxVE;o(jBnD7(esB`wZ!{InlILQ$+|HlPR7+f+SWRN%H@)
zzcDevS%qi*r4@Gh>oS(bi+mVH?XmV{*cySwY~B@io0CD<CsqF-DJ>XNXi`-CY<9X(
zL{(`$j^|<lJKl?&A5YE$abUB_nQ+{hl#32iW?;ucvmOj*g%j*(Z#=mRNh&t!(=PVj
zC=)@g(vZD9zxHcMm8en4Sx*-6K55;m9pXuxwyLSVE~g7)ZuGYW<3jI9SgQ%E5Ek-w
zGDxZKrQ?DMvfo?{)*B>B`OKUKI=)@%sy^cI3EN}tk}_FSZR0mMug$CcyCd~*fwRMv
zs0dh89$IgFzMk{_MN@(^<SoDNJHg+}U6SXoI>Sp1Iu~E}f_3|)m3=xrLe|fyJgYGD
zP9!u}x@#E^SPicf3N3&XP=cgI^|zn`m5S5T)%+FRH6iMIrl5;YObRIeJf!}~M#)Zs
zRAYv7Qj5M<4JKblm9mLR7fCHt{{S_iqByln7)RhCn0O3&&8<8ji~d->0}Y?dQ%^6^
ztAf>=Fp4o8SN-*-Jgu&t*rxl49Rt}(MWi1c8Uw4v&ThcMiaRdKhcJYEK~=7IfRsLm
z7XiP}BTr`utzv`puB;9Of*R(jBd(VSoGQN?hIjA2l(<(pH1s<DLOV8|&|PVJ(SIq{
za!A>n+7O`2RayK<+B4~<ZPa)*sr3R~7IFQl_@nsnSIOVKIGPZW(NBOMoA9{rYLoRv
zYWFh~dv(c>_o;XD-VFv?QW6i-X9ep|!Wq@|>%!q%@B;BFS0^-`yc>q>Ab8OG(bfPQ
z#0yiDD8cKcsthR#_5K*aHr?h1?p}j!!l%=yYLda9wghS7IQ<#tt~9^CxchA4spbO)
z-!4dU2On?G{6T+OKP>4vfA)1}oTKt`0XuDi<QtAfqC!JAW9k}0q^MC80a+iaS~c$A
zYq&S2zlbEQK>_;r?)b2(hV>9q$}S?0+%*(SZ2Ez=YyE?-kz(<5U-2_**}0&tr9Mb>
z=NL}MzMw`1m$fq|ri?BP49A^|DU+V1(i_qFMt!d*dmiSm`yk|9PY`?(JE*&-Lm`n@
z0Xr8H-qW~Y)tAyCa2a3T_ARt-J8u#O49wlTz$k~O+J=_#(1pK23q^W??3W*?U-lb7
zstb7lAWu&$Hf!9~F<ZY@FB=+z@U?3Vf-f3207e$;wKVZ}a$%1LQ}v;2K36>dUx347
z?*?FN8WF@Bkwk_9|Gt|FQ-d_)tx5Wmsm6Q&H<?1s)U>>_{<X<&(feV#DXt5*1UeyX
z7_QflNx=sst@{38C2K#@eVs7WY&u{4%~mEWK92p++JjK0^BY@yR8r-+BFS<8G3M!t
z`f5+vZwN5iq0n?xn>$e2?8H4+OvgVdn!klCkWrVK!N*2OIt&qoW${7CES!oo3w{7N
z8IdoRsiRdPAFnMiqmwr}{c~B{w+2}R6yus8W`(l-G%}S@QPE>lUL*~VYi^fruCVV7
z3<<S8`*}W1@CDp-d&Nwu4QwaiGm4I$(;w>!ViLdn?ZZr1ubIINA&Bfu6WBsHwwc0t
z@_oU4>MKOqC`I>i{S$0PoERwA?GwsWOfPPc*5PSSbKrXl#a*a{j;ikN!xY+F9W9o*
zx4F~$fHTL4|Lvs)C0c8)f8j$NRb9eRn)Sq5Ei@n6w*AT>mD)93@y!=QNNOkDRw49q
zwjfUxFkud&!-zx8h7L)w!Yfq90L|}H!hS1n1CiMH<3i1#du82afyY1dmz>rsX$5Kk
zD7%T^zC4uZc6N4l8gQxuP_;wfZt;>Q82X(q@2+PRb~^9Y`yr6|6*2G>$KNdH$`vVi
z%fbE75LMh|3?OVGK3z*-#)PdA9BXqTBi#KQekIeOlMEtd`A(QYg+=1Mp)Zb!#hm$&
z&kSR}nqu%Q#fCEf$4Ps(1l5J{8HtUm6Gm;j5@-6jG~c7g8uqIslXv`j7-ct>W}|gf
zC!1hT(x1Zv-oI#wW@(`k8$KZvpTrYjLGa?BzAi~|$^6gFuLqr1QWescA^L?dWIWP8
z;b-39OKM=nDVs!Mi_U6|mOYHJlfczn@V+vvMxO5#PScVhTe97f8V-RG$gc*v?`-tU
z^to`ql_8+io8k)Q3PRcLN^bs@kB;j$gIhQiB>W7AyVT(I;j->X?<R)aEFmG1K?bXk
z<d%I7n=)VdktR8#8!oUmYg8t!(Up$#R3IpEP&>pCZRY$6TZQGYKD}jcm$`$>@Pj5&
zH*BhF#K7Mlxdx}r#05TyTGNcq;X`+I=Y2<06j#hHRXTuH?Por)WC!c~L^t(5W!Eed
zsY@g6T(+o{pLN(}s_JwJXrC3t=&oZy5A1kW*uQ)+{Ag*wjYa%y<GZAcMVRj=tAIow
zW2N3yA$LrO?FGmIdTsp*-_N$_h=+HRzOU%-cw;pgNWp~c>}WynM-mPWOhlPda1cz~
zko?F2kPe%HfJB(7<*ggwz;W+qIm(Y!xMKKAivx%KWLO`KGR{cxWrS{@1q}5?7S!5s
zGmZwq4A<D?&)?k+A1=4n+blRyHc<9+7hs~v5EOmSahGaO5$JWgzCeNqBZL(F)p8!m
zW)MHl*#xqDQ0QMtHV->ug;qm4iUCKpV7F0+!L61>feDie_x=zYCVDcMJ>59MZ)Mjg
z{&s4OqNJ$)d32vyeCTJMVfCImw<&28!C|E253O2$Dm<MhCzS6*44g&Nea!nO+Wx^0
za0h~Wh)&k=epw-KIF5RJT{`Wso-k43Lmk2DZ9Xs_&f`B<07hXfO^;-&V5>_KEVRMY
zWk(DWR~#m{8BrNoxiXFx!i~qb7)O`}aa|L<OOKZjrbSaS1pB(*`5-Cf**N0g@Fm&~
zFuW(ce#7AIOxT{jOzllc4A9|?({}}0_VQ1f$|kf0oUeG`hV<U!lZoB+A$3rq>FmhW
zf$Ow%;E$Tgm?GXeum_$P4rd~vNK%Vh3t+j;&@;+@V@Awjt4RFQz-T86kj4XeX=I>8
zK>2zeD&+Rd<h1jCI}$kPlDyYK8mwp%>u2C#o&L_jy*%9wRU1H!R1!vk?5@VS$R!to
zq$7l>Nr5^aT9hQU1_&eUmZSSCA!7TZeAe=_TMKIHF^5ox?c%hq#)rz*1X!@DPe#Ln
z*Uh>(t{sc0wWW=o*fA|DYB96?=7r5&J;|YB?J0&XJ|8n0WF>8ga80M9Fe&r&t+A1Y
z-KW!P2t15ZO?Md`23Yf0g_`x_r%P9hvA0cq0Bzj|J-Y?XAPG+KvlKYHKLmoDg2BTh
zvQoN|cvwF7@G+7*$wf7vP<=jze-T4#bBY`a!NjlS<3UVs^w@SKCu1?*gFd#w4E?Rt
zXs499GQ`3yScd7i=heO25vN4`6JI#9iKdQ2qJYJY-{I;zA6)5%6-kw#z=y={+<=S7
z;^D6`>g=a1h=%e<dpDi3Nq4M;UjWR~QNCj&H|`t%@+lgxAweqjSGFpM;o(F~7vnA~
zOtuwG83{5HUxn+ff;BCP0Nv~(Wg21XXvTE$kWsBBE8JmKz)I=&K!#N8MXv8F&--8`
zyV(@z>F#W(ST1wSWpa2pfnjxLXU7;R`e%+noxlvj3&2JmC{-$qf&yiu%^c=AYKc~<
zcf7M--460Y#{^xPG)DNq8^H5Gz-ahqW0tplywn&H2nrjq%``e1@K+S-<<y_w%rkUr
zVeR765~H&S_Mz>F(L1f6Wy^$MxqkZ+9dtJ`Gr_bkG7RJ_Ef^>ggUiTGFYWK{ax-Iu
zu+|CU4ZjX>tWw-sO=6fAF=rHT$jf0ew3C-yC1F8rXI43UPP;GtX>u^nj4=BXTUg{?
zKFF1yc)U-7+rGkBA2mL#d;`P&vlh!=WLHByvBP+7DE>N6h?G+#vBV-XBiEBCg6X))
zv}C`QiNfh7V?i>uuD*u!?PF$lW=*@ml33DeJCu8QY2r-+!s%yh6T+Z!pKoioyXxj|
zU=<qA2!d}uSd`)meLr;9M}MP6$JrU>z+ztR>Lfc5%DNcG+6Udcmu{4y*ZE|JKG3=3
zXRGrA)k+aDKi^q2lltS2P=D%~W%x@*=4x|0tz#c5RA$H?u=fIEFdapat~xr8N*FU4
zrV7W9i>>SC<$gfI+Tq-*z~e#=Yx@EmcN{@4Zs0&mRIiXl`c_a_*eh_`{c?-nWVIXs
zR3O)x*PSMsy`NlmKE>=)==T7Q(^IXErsP)KcWaKl)-dc_zJGuj#&hnIhS_sK_q_$M
zm;n-xkW~}p6s;f2k?Zl2KQ5Y!JCK3q?Y{wa4Gf?bN$c@dL0;UC6vR`7k;PorpC=M1
zKZdbMsHr_yCZRCnkD86*P?3ZI<_lX2>LC^#nxP4%o8g23RK~_TmqQc#!F8YL*Q*L?
zOeTby{C3@wWa*>ZuYbl%)L-;c-Pld!aSpFt<FCJ&LuKT6aPV^3yCKk5hj>yiGX5bO
z_`I%*Dp?^H%l3+x#v!}li5l)a)g*_JLZ2JNgfF$VTO6;>dxB1%D$hY)Ph<4-YZSZ|
zZ5aLRn2z&IGSuV)s#bV{U+Do(!_hjXkXIv))=y1CyR)x@E4}O)C&h6jKWJ8riF@zP
ze4+raGbJHYE)&^%yj263Jj2Dkupf&=*?q-^<Nn;&cncU?XbZL8s+?G7vB}mDCs5=o
ze5y#T-Jw<k7q#5$v}z(T+*eOfXA0|nNMe%%KYrumnTD>FB*dkW#JqHLr1moo@-)UQ
zB$&Rw7CkHC?u6mk+buzmtC$$PY@hjoaebK}F{_-%0CS&?eS=G`i&gdu!kggg`PU0T
zrZrzpfIVK3@L487h$jep*YI$#|K30W=@Q50TwNd@?aQBz(+?zU5bN40F)z9}*6_M2
zWZIwTVChyu2B0Ow9Vb}?@baV#g1lB^7BDGIhDH}phjw4wxwlIrA7n(82}2qxLv6?e
zUyMAVZ})1sq`^oP_wK0kG}%?)P;mg}3(!aN18UHS@4#PqFb&9b%Z#|Umor~3`#<in
zh(eWMCKFliqNRc%C`BZ_As>u<T(@6erHI@i6R8vi0oJ8;7IND@4^Zrt^UU%1OXHZu
zY8+vX&GCytv*CTnD;SW=&K;Y&`lkF-FZ&A7%j|r%(pY|oPWT}u$&5vWA4i!5MOs#t
z{Q=^}RG9R`=$!;c(whAcp=6v`egv1av^1-6?f9wD>K~tt4QJ^H>R(A1{-F2LIEW<J
z0E^Mr%e;p?w(@UU>^`Hux&;@W-$t$rimk#cboDs)9xpGEoM&_&Cd8i|6<Hb8S^-X(
z)??X<$3vM42US9>#4wK+pHMAHooLb@X9g>&XL|Ycto_>dVd0ou8ke^#uNSq$Y3l4p
z>%?25#Rk7j4?nfsNH$c8g9*1-L6pw7A!r6y|9lOaI9M0Y8o>Ron-*?DB4huPildLm
zmm9#z<%GdiSH@{6efo47P`el-96(0^#nMO-A*^DhjTzDuZAmIk53A{H6P0|lGu8RU
z`q24L)oIpQz9T(1kv{9!>DrmWAnv&nb{tdN^e`shdssSOJubue8Ea}roCT!Mt#X}R
zsXyp~L`3)v+)&`tS+G8f%ZgefcI<s+e|A2d9z5)h5UxSVE0PKFybPxns6~7@-I(eQ
z=l=Ksn=V5%h>fVi?9SDd+s}H#YEWB$tkUzIx)#30Opy+8awG5u0<xj+eK@hgt6a17
zI%v{4qOaR`Eovlq=t?}i_#y*L&Q8*y;XW(5QpkaUnEu#?YC^ljrr&avEgnxd0_ihX
zlwH1>p|+A8iqM&0Kqor}8@klt)(;H@_%JY(WH|ZRGZ4`<y|J)g)G0u^H-6I2pKcf$
z7;rb*WddrI=lkI{t$(o2_an`8gc)9emVlfN;6{F#mL2s90Od_)E{#SBD^W_7Vxn)(
zK0X5V*U%wW8OOu`=vQemfJF@H>yO59JzFe?z}}9VD|{&g#7Upg-Fr{E$N)lHb035S
zphOOW4H|(Fs!1pH>nl^i>gyvG8e^9!dB7Bk$rmXYj}KOrmBoNBvUb;|<_(K%C|xpJ
zw#*^-@=n{)UXb0UXX2d+uCTJ#!$OTt3ln8gDH~J2Zep4yT69EgP9g6LA2!p^xvKkK
zZSY8pO-K2vZ(yE7?)hSD?^aRWfQF1+>ER?>1PfR6ZRRatdx~4JEKKDvQ$L^(^^_I`
zxve!sXCIu7pH^~V5UMUvjF-d^mtpI})lVhEd~Ttr$VlO;C}eN)rH<(}UXmA@B%dn{
zPo2XT$Bxhm*XXm4t3;G|!)Ax3Ha7kZeDLwxg``?I@v!c91>%*diFAM-#odGH3Vv50
zR#a9s?E#;?nx_^apJne}@|TO>C#IE|2)EIOn6f~>Y_;RHzK39;5Qjgpcg@w`qDOs4
z1|ysv_L2j>F^Y=)5_J-&==jdow@}f~P;PV;4@<(wRUKQRU>$Wv$uM+sg{0Tbd^jX1
zd$3Z0#2d1|{ZyI0jlH)tlnB!#wti#x;A}ffFe^#9d3zyq;UAVNm$Ca9C4ZfZ>rQ+W
zqTTK{Q|N<fH0KA4or|sU_*OTFLD`Mq`3XW@<&@<;y`TbR!MBk6xJ~^SPMd76T{0u0
zqtynim3D`Vai3c1gi<a_;#>}EKFSYznHr2|=?oC`sW$|plb6=54&^I}*L<37Uam)0
zYn-JNXn!d)-Okt%_G4Fch{pNJ_dn(U$<9ADQ&n5YIq*E`m}*!mqi@bGSZX3v36W#A
zUvFz6^$8*;j0ry?Hg55Zi^`-~PD*5{o3;L~^&Yv@5X)ii)?TVzo_IYc-FGI%9i!K$
z6$_viN23%fciU(gW*Q!sM7?HB;bBG5uVcong{~vmvA7~)+B&Eos-~+ibUC(CSX<{f
zERhM~yp^L*s!1-uW=9wkwf~x~bDg$(b*bA1@37@3rmKhgD|ia$%C=H?g@`8OD}@A!
z8El+B#q!Q{dBTfjnl9Fs=w3eJa|e%DngKSaQK-Jv2BjHMZqOny7XrKNgPEd@|6$vK
z*|Hshffaf4lMTBK&I0630huD7lGU(-1B>&D<g;cq+^dHJ#7Bydor_Vv{E1`h`#Y1@
z;L*@@-^|Nz9v)ae>mOs}gLAc8=#UHfzU#S(Lbf$o1q`+QG#C}pYvC5lt~W!+`lOn%
zDqj|8rVq5PR|4Dj2zs3RSMycYeWG6UbB7_xSXw;M`PWF%+F9XIGgBg#L;qx=C5QQD
zEknPkknL(^#D2rJ9&qLBvwJC}RA+Hy(_Kmlb_{ju;TZSlqN*GyL2b0V^D92c$&Bzc
zJ??8V9J3h#RR*+<UcRdKo?0Yg{-=uyE(V+<{kM!9;&ICvRVkiCL+aWP{X%_IM_4YV
zkT;r*5WNu%zWmIiZCbv=F}L_m-XnyT(LwO#S4thvWk-@2kCwRA$G?N(dEv}|v+?Is
z7{GcIc=XLQxKLj{M#y@2K<`y6NYzs^OvwaQo^TQN`XM<hwHON(OXDmgdT<SXF=ZbJ
zI8(0W<A56<cm&Ah<lJQIzvTFSu$yH5c{@1xG?((2VC)>f_7ToI(a_%L8{^-w?pQEu
zJVPfw?51PxAjuE-TW3@hhOx}AbeCw%6{a#BbYFEMw+oqz^u^ZLy^$#g2R0opaA)$*
znfojU^3v~xeo*u^34NOa>z1MUc`WgSx=<>}NVtqwD}yxL)bvq%i4@5;!Z(nOcYZ)P
zJg2VF`J)M8RNsKWU;WiO2yXEQ=IoLMNCPf8HOo)FmVId!#Q&@$EI7~=h6NAP4Enc(
zkHQZbq<=+0gHS|PZz{Rg4hyqnt!GMqN~&cWq*M~_+I#EmrqOWi>SXQ*whUu8?|H<c
zHWs1VRtE*&r#*~rS^nu_)M4^E`rbxH&_=#2x_xCJPj3>y#ZrOg<|O!W`K<WI8p))f
zDyKF5a;)Pi*Lg}&_o(Nl$BZntYt2pf0ljHtL{whDDf{B2Olrk!R8I`m8+ww{lVjF@
zCG49Cpl3HHh$YfF+L@e1Hju3!<gT}*FFpzH(-`kR-#PYPN?wB`G@?_DT=>9DMgbkl
zX*Dp%AT>oIcFJKMEb{qqDaiwp=42p4|1(Zo*hZk2x$f%1%``g~)=8YdEhj-AD!J+p
z5k&hBe=SISu`>SKXZ3sC0`lj73+E(3C?t!Lq{12*A6MM<dk#f>Co>q_Jj{#lz2bF1
z+pdPaRZh<ti9y`<@M&%OLRjDbkm)?fD#lMr(@+n~O%C{rEe{uw_`_}034CUMT~;n`
z(UZurnjCQsr_;QOdnRAiC!=s0T|eQwSY!GDi;fM=2#3IJY|7hYgtb}3XeCdSQXwtn
zp_{ZEF-c7V@zgA;p#RG<(l(<()Sk7)lo+d+Lo(DRdb*Pt&3PtPZ{$~?8Hti&nAFJu
z>_sk`f6yKcPG*)t*4E%3qpi50pV`iNAVRIhUIwaxJf$tvj`kXq?xQu7=7_+w7f)6e
z&%#eU2WxTq@D>=!hZ1E<pUh4Fxp7M2fYU@CWMM^d7X?VBo)(+~66}R>F3!Jd*haaK
z5z#pv5=1WskCIxt)civ7+I~B%WTKO{0lr-Rjl_$Y{0bA^aqRa~Tq(KK=Nxo8ze4&#
z=BJJZ@BMJ@`AD>(Eaw3Ya{~{s7)4Dz2j*(#l^=(0Uqs(<X#(a3!n`qX(k^#10{lDZ
zQ89}4ckI?lJI*igp3{i^>7W~WmTS%LS;+B2Q;WC8m7-Y~8L`V<j{6i=uk4c>xMYp+
zWL&>*g>l3FZg2yP@Emba8`11n{RYjlTp-a?LJi%jp<D7?!EdcsgHluWcj|fx?Z2kd
zk7+l3s6XJRkkmA&sm(3}tU7Ibs`p;zPXD{TKt}mxUqJZe6&thXj15@D=Qt$)d%yTQ
zNcF&iVpz(Ic~eO;3T|T)bAN(RqC201@Qs2{^<RSHk+IKSq~vZ!Fowb;gbRY;jP(4t
zm5{|*h&zFK`EN3p62s2^>X38NRgsw!X5kwg3GPY_&t55GzFD6z62!%9|MGmrhBDH>
zM0`1=7z`no@3<mHld43=I+0X=F^Xz0B7bFR6ClD{gB>y0&ZUxnDXv@z_YM<{Z0jj#
zY_Iuzi~CU;_+BW|PNwyC;pXmy+g;Rx+eJfDOP8y_u1sq?P=~mL;EAK$+$<Iw9_Np;
zu0tk~_ULpPCneZrXFT~~FO&=#?)i78Rfl9p%#-LlF>CZdoK6Dtnn<y?>FT8bvCD?%
zMj7a+TCjp6e*eA^obe0|x$ZhacWWvJ%0Yp)>&Md;<iMnx3DRN!G>~LihV64Az3YMZ
z2FMa1`G~$@`I~8Kwh!Nqhm*_*QE_GjC1m&6YOP!aJ=i0MtfqI*FiPCBGpGtpPejKw
zy>SkwYYUXB$s?}$E0$ew>Tf?mo0}E|9rG8;2>diI+EU3_!A}1`ARzZc3!gl5K<{b=
zB8^T-Vf!QfLC@D4=1*CWP0<ee8k&?oz;I<Gg{)kV-MQE*MPcm{Exm3ta`|PSz4EG2
zgR}8=yLCEs9B0&_BJ#`8PZEU!JNl)>n1@W|T9J&IT63LibCf_G{5$;^aekboCP6%G
z=_0?Ir<$s~3P0Y9a=iU(S2x#C@HNgNxmspFuQN)yj35AovQs}PC<f3+YYYb9k5?K;
zfBKqMxD9^Vs5eFaJ6|3Om~x~wq8O$k6L3%ctlY2vFrdE!FkQ!4qz6?$|EFn9a*e78
z<`>IShrlstIx#lJo~R9|GZGRKe$Lh&djkz9Kz|eK2M@A%rbA%{xyWx#H-k2rzZOA@
zfJhi3**)+3qr~{{LL|c$__YaM;e4&BMf)IU{>n9NkEZxe-$uH3pU3X;i(<Sy3{{b*
zE4<QSNcln)G&aMVsHCn<-u0L|(uou?X9w2>`^Ce3LRFS3f}Q&NNe%fA<}XLYAdjRa
ztk$H<KRE>be=>C8)j3x&C?+YT)A~f0Y2#^Dab#Cpe)}0qlNMV$=u~7G8ZQ<FqJo@?
zKzY{jAia~7M)d|WRF9j##yCDXQj(Gx&L1{pX!%1BaRZ!>7Pf8{zYPIesCd8^F$xim
z;*g{&`0qkxB%nN)EaCzRYk@GxlG4)fz(BIeuRTW$c5Tm-#u%y_)&DG99_#zUAzx=l
zHiGjC{hAnzCr5ky<m2vMU;*fu-d7FO_*SXaTy?=s4SGejo1np@0G)4?{hAcn68`ag
zsf0!%+m+x7$qxr=`5rF>&*WY-6}qqLG_@U%_tMhBO%JH%ydST7BbmCw02C4l9o?we
zt2@BD!YU=m-_!$$N)jKq0ZGfa_j;b4pJ)Kk5;Fjt^Gm;!W6jLW$O<$tzrT(5&8U<B
zat<z!c#Vf8ft$xcv}iAP&rW-oXXIriy?7tUdKJA<H=9L1qr%dHOFYd5YFZ3qnV8IO
zt5|GWGn_d3FDf=Vrhszab!Gq-5l6_UL&*(L^kD1^M)z;^hmPZg2`h3ldt95v`3nJB
zDE)&;ph!$kvdIY!53x6v9(Z%K7>EU?Eq0MHsT5`h=qm8&0F9UG$)dE@yHf89K3F5@
zoXsbt*44u1xHVUbL*xD72Z3vE{#{?hW1bQ@D9Gk>yEX?q-yvNF7v_5=Q7x00GlPtR
z5-t&4b8%=dU;wStgJ6@sc<k=(l2B3>!}4hgT=D_nFnI!AC)juF&E9zC#l;XYz7Uwv
zIZzXe1O!h0Sc%u@HAW+7hEUATe^nDx7r-%S6^G9O^-jK2ERn>YqwxQvI>H0*)of7B
zo<a2P363yoh6M2b%2>d)mw;Z#D+;VO$W9Ro^x89b?T5lm3$jaA2kI4DF|un&=YVlK
zE`lE*oZ14&v&^dq7K>HUfQZlFU@Ctwz~S-w5GboNO#yAN9d5gBahrE>jopHVX}(H{
zd<@$EzR^0~%O8HjntK~2uH8*{H>JR;aUg^JhE}{9>|hr_QU{&)11p*Yt>?W52^j^-
zWBa88vJ)L}2%LacQBk>=mLkTL0h2`g1e9H{z=0x;02ZTm7{Dnr1gv@n%{P<Pbi-BT
zc?PUd&sB5QRUm2c!MY}61s_Yp;cv&-CRJS=8+_gcX}~_7H1z}c&~s=QA<QA(lQI6a
zzsfu{AWWD9+>a0q`n+SrlT`nBzE=mOwmM!S{KBBYioCT`i~E#l0rkQ;G5V`u90TZ&
zKlTfR*Uh}z{o_$Ji|eU2AXw~Ghd$3*MKF`-v4-gMyc+}>fPTQEndyXClQLvxmi}GD
zPfM>QIyi;lIy%1yR`m;{DoF8*l>92-!To-4Q~ZtsD-NnLKcVgD<UoA7+Ma_EPR9E^
zWkuglf>e<lQP0OEqU<339UhbgtAx|@3H+?{E{8|gah&5|skU(B@_r{a^Ai}*Muu*z
zSTo?Tm@6fd3Yu`Z)<!M;^!`%uW|lgC!htaO3tfjn>puZN7?nHZ{SP-DO)ZpKtP1;F
zR@eeG$0Yrb9yqIX!#{=V&8hR>X`Kkg&Q)A}LZB=+u12(^<VEp%xmmmbpci`cWnTu-
z{P4bJXeA!7pZ_hd=EQ>NP|z1PZm1s%%!g8Shde-Z1aPu@XT<<T?n{@@ihoArdni6#
zvYh<y`Gyw7Ul<ZJDgl~mw8UP<&(`*5m@O-#1IGZJXBUJEh@Ax&fO-rR_WlAks;mK^
zCLVI~e#&%mLMWT1n%=d2EHHQ|YLtHi1vM@>YAjsxG3vMuF09>_Ix0q(YA0pWfWbE<
z@8+9jL*qGo=`+#N9lg~ODG5O4bkAk&%lVNc7ogHHDJv_Z5EB!dDP<_2o=1Q-Y6U!R
zaRzgd?n@7wg?x70CkkKIR|^-xM_K&)!g~W@0$0Jdx3{ATIe5_;Fc`ifVi;9uH6v~K
zJXMBsPXXf5#MHZgQzPIE#{dHF|1slWukQwE72h|y<M174ca*y&U=2sjV7V_mG$DhO
zwOUF?e*I#-2*XWGV~HHiR4<{@E>r0|CLYSoN&lg-6)>ujTh8}N(d(~Cw_Q{Odk>WI
zxo%C<eTWX9qlAi;u9m;cV1J}4hw>BR=lCrI-T+c>vPyrD3~qz+tLJKMZ8iCQ1>i4s
z0OLs(QfQt@07ADYj<(cforwy4o}MVr3wPH2Cb-gMeMDdIE9(lsr~3&YZ{qBStvyZ^
z%i&)U>V=6<jJ$?jp%l^r;=Bz#iDznp!`0^M2a1m$-RJ`z0Oc3oT)7%DAdamYsW%)#
zlFjCwf4<i-z!?IAfW`@pIt^A7uJ^dHXt3QNx?bLl1tLAOj;M%3Cy&8T*w|$aB0@3N
ztX;s7RrbXRk;m)aj_o#wXuH{Qrsy-J_wH~!<tU)dI|s=1X&dCR`~g4~f#I2Coy%=s
zP)nO17j+2iSDgAZ?$?q|MxXUFDu-0M0L%v)76I}rwb_oko$P&Nbo9+N&dlsAi}eb7
zJ=WPqclSs=AR%MJ>QE5D9tsR#%{;9o>HGn#t?;W^k|+jPdW#MWGLaL5ZpTui{f=a+
z7mhZo66DK}M^L!SyG)b<0V)-iK$cKmU9^&cRV0OvQNCi?mqp5nL8p!V;GXZuUa0!O
z<M##>YMq-GcG*9d&zB2mGLpcapJkq<(3k1dox6;XO(&<PA0|lDJ}k(ju`dG1MeVwS
z4@KPLg|PP$erGEM5|P?<w#x%|GW@BTjgT9BUA}$X8F$kY0zL>8L5M)_B~v38@EMIP
zj$>3(Z_#Wp+X8H(5@pgj9Tn0@Q`Mo;x=W<Z41SdFre(RTm=_}-XTs*ny}3=~!dh$F
zcHX2+UIvReg`*Uy;~K~*ImuN7rQfQDFn3YrGAEf-`2xVY$ehUVa2j4yt#{PsRTVcv
z29Kl3^wq@E`oufMF7XX;vE?I0*cigMX~|d5?mNfQiHz&5c#?Nhv^f_r!GDlH$M@Wc
z=E4#fdNSZpzQ);bz70M@oxMjV^_k|WHRMx|;k!5se;CmJ?x{T82<s@@#-Nqvi|u=x
zVZFOiNVScZ_3+-moh{L_Y5jh{v+s5;fC_PnzF?a3YtA#mj|%dmaFlW>nea$CEa-hx
zYOq?KNelW#f@K~T;5_k4ML~fZ?g%jWqTpypU6e~3gD-+c@v#-|(a;s{Mz3;YznW10
zsz=h|q;u#(NQz(^|LlcL3-P8n;P1-@TrM**MZc&9)gmH6ql)iH5sj)IUu_<^8f!qy
zxaOZGClKUy7!QZy5qI}6AxrofzDtt|kJE@j`v41vG6<W6&}8%5qKV`A)U}&!MO718
z+kOBO=v9Y?(<)-}ehLkp+kORNc*{XSq%81^RW{fdB|+*NL_<Hf9aZ%MyvM^@nOSi<
z*=W|rx9jlpajuQDhx7~=i|)6})Na3=qfjKmh<62|A#Mi^IH|VVR<IjDBJ0iW1&E%U
zCwUwJuoTg^nrW_q-SsFbGE~v^rjsF#?ICQjSI@xjVo&wG?<dY`9&fu!Qjzbyzt~|h
z8b+B#ws<>)G3X6|Dkdg7=mExCqKmti`faeU$wf*M`pk9~v}`%{<xhmjWonqLxGoF`
zpQpih-Uo~BmSdhy+b1iH-co@^Z)<G|$^b}w7)-N<S)0U{fG%Z(J%;bfgIu#IhDczg
z0|0uy@i*CSAZcQr3o>w;S@+tvy*?gwV&>mrf^EPKZ|l-PqUHr9tf-|!M*U1y9*Vf<
zIWofkl)r?P&P@l29Le*2bZ%xEyl1^U%+Pp#wCh5rhP%BR;nqh&uZLHZFcjrE$ShG5
z1?-jZODU9aFVQ03q|l+qd46|Gj@!2VVlJ`A@XfvBn~U6obut~>*}<K_Ju_yT1Co`a
z_uGV!&fAlRGfG72<xErCy8DjfVgQF{fTHd^V?YWy1*wlv`Fn8ft|OeeDG_=ZxoPfx
z8K;o#j>I##NZl%MQN@u;wSr*Qbh7Z{2@PhQwh;Mxz^D)3$(d+AMoxJ(=+K`YkLRcq
zl=d2f&oz;*dh7+{G<)tWe6Dch8ua6upc*6=OW@z%=zr_hFlwSP=cetiuI^FG*0)aD
zUr02V{JCoQYFDxWD4r>!GhA)Y?t#^`8q^%SQW!R50GXkZt>S8vjh%D9K0~9+ba9dG
z+S!J*rj%kpt2NCNGQA31bWtGy(1-;<$0UIe@GhJt8r>Uz7h88!TTwAMWEnAl)I+Tx
z9H%pS&}$jH-Wnj)yL~!K=skdaGTcuSH2h7YG{LKMtZS+eT6#wMYMExEuzc!E1NMBl
zOlhNp;4Nl=6zZP*A_68eQnGIKb^IB)eF{_9)feWKRJ=gnJZ=oJPfQs8C?X{E2=yhC
zKUcEE*=^Qv<dpwzZzlY3yQqD-IGGuzCe{LB!(YzSx=+)Oy;<9$_{Ck4)t#?q-7Ix+
z@xlK2@|%nsnawEY;q~ipo1`Cqt_?}XglxKBTmTXNxKQKbH8Qj10{zaB-&5fauws{t
zl^w;87T3NnH){3BuYh~b)rz>qIiOB9xeY-87JCEe*+4&v=Iy-#s9yS%y$<f3D7Kzb
zxH%G){*!1Tj{{OfJoaJ3I#_kYJz1wk?OAhjLhc#0Kv-@8!O6_J<ldrI%Qy3ra{x$;
zKmG*J!IXDbdyQP4#fYI?e>-DyhEss9JjpcqrhEZeE;|2deF?;8;DZDlgQb8{f5?Yu
z4yf5Rmb;{l^}^zuj8}kij(h6e;O^D|B;3)?FbrPi_FK_rKc>Ej_IZ$@vZwL32ws^G
zZXfb=mcXh*lAVMTiD84eLRau>{jFy7J?yp0>~4Gh4pYyt?Y>poBLdIxxic6lnA`3E
z<WT1r#nyR{W>)mE1ahrG_~=hxTq2%t4nXoNEX)-i`6l%89Y#}u<N<tuG_FoMW9XZZ
zv5NfX$hqeT;Q<n1rZfF5?w4&kJLkzq<r3yup46?QRO0^j+~+XH6I<bu7Voszi}ri>
z-}>So7lwurst0<C-P2zC34S`^1&LH1kvZ@1dvQ|{?nem_%8}LA_&k8q3#j8mtPUFP
zEMzaWTk$L$FNc=8yx9MtC=|I1-m)+F78Z>nVgTmC_<)=H!i^8ZiZESqW~c3RJEC{q
z)R%==k@3pVmDLKXG3`8M`O_?lXblst&ii3A<l+7}vAXt0o}gyak3d-(MlycDS^rW;
z{&y4Xh8G7eo$G$SPkR@)>(~!76+USEJzzFTh(TP$2z=!OH{pKP3i{`P+$smP=ZRVI
zte92msUIWpWC!A>KravG-AA4($E}Y$YR7M;A}40X%s>uW>vmD>ws8m0xp2lTi5gEa
zkjsZGU{5cvy-#TLQ#-5%CRS|vd0$D@rLMusi?c<S<oc`_-i!P0?H1f{&L0E0iMq2~
zCMQ%dV$>Kg>LY|Rdt-pR8z5Q^D9$Fi**l&uM<XdBZvs#W_#bu?^YVJJfBU|@6u-PZ
zE_QchbUPkFF7oGu`EpeRp23MpdpHt&zIp*pj=1^(B@Sz6wZ3Z}JsxKrSMqne=v6$Y
z#}FG^K>PbT$Td+Oyc+QPN6&FwKj$Qx;P<G4b$jnatjEFSKXu_CvQkB%3DeY2ZHo_g
zUas@Uv<dB$Gw$7_p6U{P?@bD;^Rd-6`<+|6`1<!U)n*iKFy49j@pxLTl>qy6ejAcu
z6*@{v&7k-5JEs^xntKV@&VpCEo^^GaypbQ}U9W!bkov(bF#k_mKf%_tt=ZA{<vR7W
za&2Y}83RVa*l~KyH4eel<JS0RWP+l^ZT-iSnT`nIJXj2jJ>pnZ)ZaKS&`7=IZyJGt
zo}4>@BRdy^yA0g!;!H+1X~9xM-$%NZYlpS@(2rRE=9$)asXz5U-CvIim{DiU*C)Cd
zE~wdVF19V^n_hc#+#Q#PnQ?&#GY1SuOl^0Pmn7Y+K8oRlkdm<?uKEIN`|@OcY`ua&
zm`Hw+Xm;OJJvso<eAi!QezrZ}YLf4r+gqC529N%B$BW1LhR`->(@#G6hJf#gzZtcT
zHuSnrqux-@z$oV=zKXK<w%KItcC*!OXkNL1v+|t?OFlhJu1d3|_Jp(J;a2vi>q#(g
zX!B<MQ0`FI@4KA|IuA&Ajp4nWwLb4<Blh7j$Zb+VBR-YE5GC06LwGs|Qo0PQ%JaHo
z4#kxMDTslx<t&3}D_!39I9bj>5{V_0OZJ{;KWnF%2bfG?ymv&~XFo;Qf1Jo%SWnN5
z_fCsle;F`(4&s1a{P0Q^JM|bK9*N+JhVDZSa#OjzcRXw3^9D|Wd7FMdc%_{#XfN33
z-hzOzBD+~2=So9UyastaOqA62r)$Xi6HP^4Phb~d`g`Uh>hfw0oIl*D*6v&azrtxp
z&JshAliic!|M8zD#&P!FIrwPr=Kh>{B5RlRgoiA}Dewi7pG>hXxn_v$+QglFQwCz>
zv!Ak5IGP?M#PXF%QKnOnm^Cx55U&s*w|?sO!x8%CT$}qklOBrBY^X827D?<Pubk8}
zlN2tmdvH9M=>&9JF4a=Iotgq;>{`?>P5v4mUAh|SGzofM-y+VMuSi@m$h##)B?9CZ
zZl0OtsBTuP<uTK%l|ng3qLE$4<7EZu?(CnBW_3fZLFh@<4q1~Aqx($1HLxbl3r1=L
zx2JK{beu7zxA|lLx$Qf>Y!{u3J~{Gd2szYZhBf`vNa=oj{?0c#<0z4*j6!WvgccIC
z0v9$akG}Uz?sf6oB+GeKpv6TUX7?1Mw<mJaU}mw>?g#9sk9I98pJZcy!A2A5m!PQn
z6x)HRm>=rRtmrz#2mS{T-~Jb2Um2EF*L6#GN_R;&h;)|%Qc6m5)7>36-6hhEbc2+1
zgR~&sjda%yoQ==>eBXP1oa<cvL)dHYwdP!F%{j*ybJJc<NylmV1K|P}@x>{J-n|EF
zt<n+gs=MKLF4xVnl2t?bFCXn^k2+)r7TC%yAF0S6Z;eMfiV4!DEx=R0v)BY#+(@Zb
z?R`tK1DlP}f>?DosO9si0nZNuu!I?}LR>mt8$CY19lHL$q1}d*s<D7h8vJpLJaWY)
zhSGDM+Pm45q=8^$Ai~DW;kQc<#}8c}G!Y9bE@E|;$LBNN&{fj0N1+5{h7$Wn_xHay
zqtHlqKZn3>sd$s8#Hb*3##v-|nQM>n`|mHkRZf*Mjh8?{`6kaCSB=IojFbq%>==?8
z<t0+1hc_bIE2}W>kK$Ypw1`pM4i)Uzd5Bx8Kx=Z%)e<>)KCBzh_;BtycJ+FeE9*~g
z?U25{Id(0OC&P(}J3*W^&zFhjTI6&m8@!)xb0{FgM1kTXKZb$w^D%gA@9$~z@vJ~<
z0%+b)Zc-Ab3j~4UbjfMDa{A4?Hg)c{w8ql!c|}>GqP@D{2?)QGhy_8l|Dk^K^lP4+
z3j*mp&o>Rn;RuFaI_Psw6TN#Fye}Kp5D2Eouphkrg%aTPWAFW}ur2d&4nJ*M?XQdo
zoJhfW&!yPoJNHo`SA7C__Gv}?yminx1?_X<^)H1OO@~#|3y+BG1HJ6FYjyVF&F=2>
z_qmzM-d{Ia7WGB^_Qz_QPV;uak?bM^H~yw!jEql>l5@Zl$Ija_FNk8;2~Z${xp@Ap
z1B=|pH;>ylzz&_I#|-l|T1-)!oH`+$I?sT@5jD+d>phtYJEKagzso>LV9R=ezFXcI
z|Dy`WrUvJYevF3){dy@g3dhl6!5l;DR8q>!t<S8~mbXS2o6keORng8nv~d~7$9F+?
zXtGV_&wU`2N(js0Pr2^*Jt6sK2O(k7&;oLI>o}&H_P%d^%#_7Gei7-v%6`T)<hPis
z7sca0j~)?df~~j2>>8>dp?q7etZ*BeGn!1qFzD>giWO~WEulLdy%O0-(F9#3gS#=&
zP!UGzL8l$tgpqvYK|vw(OrRiF9GqyH$#ES5{)+CUe6Z^lDxp8dC?|{Ew3Q$ql?RqX
zKzZsj%t`g*A5Yku&s_P_%!VW-@AZ9p?b#}|ua<$1Z}!U)k*t12L5WECft1?@L=%7G
zV*2=F{>u%J5c9S{2xYy*p{V&0pvD~9IBhi`j7?^wFMlQI<MM1;t2+)rsJwz;`$&b=
z3Fi}TwsiVDxmJl*pb}aH97v-i!W1Zz#ELCe+*z{0e6>SWbE?5C-p$zZd9<biJ+W|#
z62K>x|5{2dlya6k2Mn0lsY&c-oe6(zdsdwV$hSFri0c_b=){F}QeDWv6#!t_5He@y
z%v#AZU0Sley!uy)X40wb2klj<KYZb^gkr$0@nA^I3GqM5@AQ8-v`)Ji{E<1^K31~*
zyes-_LFjOux&3j%Fgtf9B!D<1kP1g3nvaZl-g&p?_0^Bf9xD_Pa~JVY?l&g+`y>@y
zO&pFhn@39@Q?Iic&4iYBQ=S2ugDK0Q*O0xqH@$U=xYGE%SNT*{@}+q;>xUK_-D@qS
z0kLy;Pep)!j~D7cLKUwf`y2ww8dO>Y&v%SUD72`X>#!pS-&wpH7R9er^8QL{w5Mda
z_T{sil;a$Ng6Ae4srf8=QLG4F{af#t*p6hgmS`Dy<lIbl1>e@gi$<>te$3$1XX_IX
ziT^=d5SL&OnkjzWW59?|Z)6zd=9=l5@TpSyPs>o*>?aa(uMNG7X(9M^cxsWbFl=ZG
zVlLFXsA(sA3N9xM=*fX(mdXHYYn@Y`ekFbIq?>R+5xF1fY{n9NxS_}3&lsuk)#WI^
z_FEY8)_tQlL#Y5mkPvg;L)j*5)eR-~PFF)9Ml6|4H;OKGBz^g~uRe}8yu-E}i|DDM
zb}%NDH(MC|eHdk&aS0=l@8%06_1=p)_JVAPeEC8JTY5vF=lxPvS9A9MaRh}`o5VrT
z?=p?#z1{a=ID1KiOj1qwo5Vrt;Y*e8ixPCI?9pEcst^DX%@aXvrr(+wy5a6_Rkor5
z7uI<;|LF`#3y$T>`pG*+-iuS3XW)*Bx#T2`L$$K|?OkjW=-uPjO;jZPan+s={n<M@
zuYLEL-{nrvGkz_Yk?3wFEUBtGPuYp|79RFw)Gew8|1z-^UIU5S2+>n><$xfmu@pXX
zz0Ct-8mSG!?dSG(qz5^hR9PR>6sGtTnWlx}_5n6xqv6kHRxyc{6wY%sQ_8CaA;ngP
zc(W0mwAPPMy(vv^V)HfDBVNI1?qqk<Z`g*Ta>YQmVIWe&Ro|X^cpV?T>l%aHU3~D?
zpV^E-KXI@^YlCwti)eJrxUp#gY6hQJ;dH`=*gN~1KXMxHD5YH9g%aM}_PDPPNH(oq
zcJ2Ay-<i)ivp-#*FW#CwQ7dHek&i#S6IRG%gm3M6^3|)ndCRiIFr&g6_p2E8er7&c
zni*sByVLzfv0^<|Foc8|h87JTv=F=p8UH$gMYGt@-|x2C+d|<N3g7XeuWY<EBxP%5
zZ;Fq2Kh!CXbus*+4#Q|t_QY^B$%fCHLy{CeE1>*4k^5Htd(CDe5s{Qr<ekGI8Rw-*
z?bsyt?kE~W``0w`3zTC}H|4!bR=mbH?QL^Hn;q_FXGI)t!+KKveN+OfS<F!q3OqF)
zTdmTg?Dg4}2-bRyd46Gttbg_lqAw_Bp>g3fKX6X*$vWxL8EX@P9xo@@#Vv-k(X~gN
zD7Q67J6y;K2Dxd6BriNr^!T1t=Z(T=QglM~fXd&s`eZzQ87|C(2JEc4_2--p1HP`U
zhhL3$`kV9tpRvqy<>2v%?q8s?05RDwV&TuhYutez4KIKlTue7*ct!rs4WWPjbGl6U
zIY4pxyhRwCMkv6!zQQ*=7)SLQ$28j^$#~qSfHStrnM*H2^Deijgn-DBd6|}G<XT1h
zLw|ajBbPs}f;)}A%bk@xP}__~CLSHJ7?W1|feU9}yFQSKgC<<=2vdkm$O;#hJC)Eq
z&(_e_FhHKUKlVb<r`d0?`PLao{0zucbh|U}#48jpzcRSsAFkd7&m2^IXtg`Y1!V*v
zv%xUHxgXXHbT)X>*qJOqo_^hlOdMt>xX@qNWj9Jd*%IX5=<AC>YbIBWliTo*(F||7
zVhH$bP^X>{!V4wwwO<B{G2^9$a#<%<aR_;jY50J@E&g~hKp_~;b5`5X`8qm7NQOOU
zC#Ixoq^<kbJ}PIa8>*Ddn*n`_AF%B0rx;In5S<`t`i*j{ldhv(H|cLFD>wX3aGr<Z
zy>W^kwn(%oe8&074%C15OaQj0o)S8@a5d437fM)jOQ0)i$$JvsfOtSj&7?STyqIgD
z!-b&dO%k)!7B$}P3+vRAhrJ5oIBq&n|6W271DP5ycq+@@w2K{=*>ts;$Q^z^Ct!zT
zG$ul@X)D(!y5fnc1bGGabfM-f4mH0aBlUI%k!oM_qZ*r4bp>f;iG5PVxjf`Vnp`9>
zb~~`*WP1Fp;QL6c@ajzJsPM?)?)Pkt2cCw9a`Bi)hJ?$Q!|}p&gkOh5qYT&6r#JQ_
zuiI)rB8i#CuB^V#K;4TBL5Upb^ZZ5~ZpDqV+5D6PwNyfRHjS~fNgRB@6i#JVdrE}D
z!vK{-3$AB6y%aiez-XYt9+rpd^eo=qbyebe1faA5la*6{0-oH=Yc|672K-@QZKDm`
zMf^al{6o&4nKAvw=bS4sn_T>TYlYUwY;u+&I<H_KByu|eu@g&*8&6Qn(SvmPV|}Et
zOfc2~V>kt375W66bWYGq`7g*{O~%zufpO7j>b54gxv_=!W8sBtf0&eMzlgDI)8i&{
zw3bi7m##w7uM}?@dbso;gsN#jtpxfu^*GY@xY_G}tpM$3pBB2EE#D0u)+s8oSg(G2
z|7Ao<Dz<5Quns-SaQKH1XGw#NLSs};mFv8Bc_fHP^&XVt{J_c?y&2>O5cksrY_zV;
z59(w;lUKEEpC#>BvyacyS++6q6eXsQ(UgF1WZM5+s28ygbKNA}?QR4GqyU}eXIm*U
zni%8O@R8U$2@%3Lo!eA$_^PuHDd0(#VkEegVmQN1>^JBy#*2wE_V&ffSRTJ`r=eU@
zNK<ZMnTL^N`C!iutp*oVpCU;i;}8+C>9kZIT?wG|^{@~5`hAg}yDc`KO#Jc@L$;@V
zcYWyCjEakHW{bjpK5$Qs((8nlpG611vM3|k^js&g{B;nI^kV$1@lv&YZ=cj!Imr{I
znb`N^No+!&VH!dYN$3#-a5YkIZ$G6(>52EfiM#EdW;1(z_?o6jZFi;YU?UZA^jIs#
zGm%H|Qi5`bYBn>A>d%PUWdqo_>0;DMkIU>P7Ja$6gTXL+<9IA=vcT$iNr!fz!kXSG
zB0Ib)=%w>D9Ib=vnU+NSz~!kMmHmwsOgm-<B8$qc!ua_MgFiL>@bMgw7;i?%IR_J)
zqHOtKe?<VTC<$Lp8_g?<+=1!}J%9r4Stl$euDnyn+LBT(Z~bz?GR&G@VUbnHZGLU&
z4Bk?JMdtG)b`>u*KIl&~UG8=WdzzqIWw@yrRS4eB?az~}>&;lvw3@=I*ZSSh!)z@h
zn&ytojI&6fV_>#%&`1V(&>xP67P{_)DxCKR<9zcV$fz0P`_0r?EI>^bi{&+QGbz_J
zWKS9PL=hAw6Ggc0E4Vj1tq@nUiI^nHr3Hdm{86l6COe&A$tu(Xbyf_0njmp37qe_1
zx^?O{cmzrC8z$Us`*8V9t2%KSWNyN>uv)_w+>|HlnZCCUEo!sss$A3}KWN=AOdr8V
zXY+4@_fLyIr)IM^f9&#PL|fa4kf1_=O6fn_oq_9@Fy(vQ;#XOIwElXuEoiAlb{GZ)
z8o)moM-=!Eq{*M_Zl|0s19+NJc9`y@DYz94#G$wWPU~z$lvpY2r85&i2NG=~L#PQU
zE7K%YTBgS|HVQwttA?6pOUL~lhk=C}#Q`}6hKxT2pW=l7$i2Z{s;_tG<Tf*}?j5!>
zuI{H;9N*PX7y9QlLJx;^(M~!jX(c?nX@eoPo8E&S{)@lMtMU?NmCxxJ!V01M(b!qB
z4;!Fv5{f%>C^Ij1fa<U|OX|&jYs|V&$>8a?$sJhd>H+@v-q!8+y~#hb05kwnw(mWo
z2;@|9icNnV11NO*ZU%-coB`Q8*{TStN?LNSL(VD4qHRmXCwz`26nUcuP5kF9A?yl7
zEeck62<P*)2b1TXL7Z=97`U;v8KJ-huEL~QSoJ-2F&dnA<aefgY3*HPB<1_Pg8@Wy
zQ$<<2L`UwCh(}$YtKa?KA}liYZZFjEr@Z5<%ozI)XDZT50@3IWzKI1;sOU{SLlv}U
z44NN*;O5ic{#wA=y9r2y@D}>LsoMy32Nr=&2y1kr7~O8RGP5i4;rHBIKbxAPv*g%Y
z{)_4kTn>&y48J#cPK7PIZiVv~Viz3Qf;DI{g7q8a*~Cy>o7Ty!tBaFuEW7oMX71Ti
zG8y=o&h3LuoyYk2OG)C;P;CgJw`+mEi$=p&YE`3>M^FSpZ_mMX%b=%bq}z+^XHqPL
z1_d*sF(;$4Z!dTSF=@)qlLplx>@`=h<Nd<@dM{GXr2e}c>QqhvGt=(@J`dN1z@<P2
z>v)A(dtegGPJ`$W^X-|E-XUw#l~)^2^})J_Lf}FZ9<F}7e4MKj&|wfMLiBkB|Lp9o
zupY$D$_prA4SuBJHlLt0rv0-*ha%Br^92SI<z#=h_>ELZR!n?J*hc(q3(i*!^}ulk
zZSX4}#fe|w&3-2o?4|HUDu&lNSpGga+m#8_%0x(-&^{*96)p&2{_F^W7H=~t=4NV>
zBNo);AvunYYXf5mqjpu9vicSyyUdxWIC43R`vtHPJKK7Ga^O4hdMTMiQzKloTWWom
zGk*R3^5kDG$IE%_Fuq&7!z*rF?HNLQ>t7#WEyy}2{4-wfbHo*zAA^9R?~mQOSzK4;
zJf#h8h=-BC&5w&gC;8*6d8_neAy4^DpY;BG6H``h+o=qbc_(1-P4-L=bLh^q!p=Tn
z(W<*Kd+f~v-RkjX3axhbyqrD@<jz*bo$T2Lg)A=`KMzQO7qi9*ARzRjIP-}V29OmD
zs)6L&5vN&B!KI^>(sIo&rOB{5OJh}>HkszldT`xjpaL$RG1T@aDyygYO9Yv%+NspE
z(Z??e(1^iMAO?VjX-8;l>-6nc@Wm6E+mebQ48}e22EIbe@rB8g-9vA4+G8PQi0Rp<
z3HwZ$VDwD+FK0ys%(M17A05jqEW#Jr7_f2^!rG~uM67<7ex1CW5=eBJqz+nyn#)SS
z!Xy6fyA_YZ*^s;`ZlxT9NJD17EQyNs1hAuiO1w~|gT4K)5}FwjC*H5Tn`_B`elMcx
zE8c!L54j7L&3vjM{hZVRM8?Om53LVAyDkz&Ft*KC_}uGpxpXvRKDS8>9=lJ~RP>nq
z;xyJ5b2MH%(U5vth3m%JN^}xl!d<VaLM6%jc;_C-?)yRJLQD#NQlfpLl=Da2Zv}-!
zUE#de);`yJ6}m%=Ln*gIQXn&vbe0S$a}TkP&PpcflNX%*FX=&byQV{*0%`c0TmGR%
zr>8$AL?&Gw#pp3q&ez$fue@{zA6t9}uY1TJLA*m5pv8K-Q2OWl*W|u?hIQ^a*PBXy
zPhajnpZ*p#yK6tM5r#1S2UHs*0@JwU7-@KZx4fe+J%)2lqFBMoGYMMTgQ{!|S72e?
zA}k0Si{BSI0kn-ceyh4GMhf5Wuj3=w^>Q)H<;0SqAn!4+0Rdmt&=?PNs)g<Ig=v6N
z?(OM%59}|gFXCo;`eW%@oF8@=6;c7D#QZT~n6apctSAELekcbA)u_A1kfhlr0fp>3
z7zWdAgRj=+yU?ZVQlVHz6Ip<}A8=4g%OTlft_y%AC^wF6pMZ<De-Dm|e{TiUchVeb
z<Of3<<Z7GmduxJk0YyWl*mSqp$DGqn3YGbRR=l=?Y5kTrZI+Z+bbdNNJkh&qHmWD0
zG7F_fKf6CoRL?IBymjc7x`1|41Rd)d5fd5tPJ3Se+0W8({xEu)wd^(W<n0TxNd<rJ
z!|yh`U4_6KX^Wl|&x_K>K*J&$h&@A_&$OoM?_`?*yQX*2js}I_Wlj9)MWEu5uTe#B
z-cXiHb%jd2iVUS#VWO(GQt#Q|=n#+LrgwCxPttY*1Jtd@&bZ7g-*st%g{?fA(S<Cc
z8?>j3v|VNH@?>?=hwI~uJ97Jkh-9BuT<bVKJ)I@%g(l>Jl4O%act4R7#p&-+UY;2>
zGP#T!^Skc)(@mlz3>e4bL{<9d!8E?Z8f*h4G>WWr9D-CGp(IcC=F5dV36>tQg=@L*
z+SDYm*SxI#WRPUUQy{l7X9I6651TsKlw@^zaG!|hZL~E2<-rjSl}oQ4kL4#myE%^)
zE&<o$(lLWGyO@}ZCd_ngH*GlODckUr3BzjoNU`*y)|jQL@w@c+FLLrMfmTE@ZfbP4
zvuniRQQQOHTEnbZw87`;2NsJ4S_cJqY>QQ^m$lR-<A^u_f)R->ZCfn~6Z?aNcL1_h
zcbocC*cWmlRsEeAk;G%!$eT(0&!Pl1pw|1FgV?S>1cLX<TI}=%kk-w!wIgowSfJDs
zuUOdb)p)%3prilsyRHEaEPYMISomtH`r(pR@<pp=;U)nBY;zRW$<(D==Y%k~=?Gue
z`W#WC4WY%4MlO%l!ud_)g0OMuGuIDJGV+IZZ9lF~pAK*2HP}(lRkBM4^ncil)EF-d
zxI{MuCVOMTbT21${Q%t7e9D_Fc6(bQtS~5P==^FB+2ZNXMY691*i-U9NY`4doa(@b
zsuJ;n&__<=e!@COLA$AZ!TQl9>b=n#J&*e3=HR;TE^zr#zh=lrcFp0t^gVC}1eKYC
ze|;~=Th0CT9T?x^C4fMQYR*g);`wR;`g4bha90|+{UG0HV<5t{xbo)D+w7|Rjvw9y
zi}ypP@PV3HKzs?yn5?=A0IP;lYCV41Exr%$pf4@=oX7uL&|0C`aYJETn)1<2#z*{Y
zs<i_9@?+b@9NB*u7+^q&#EHA@&&SVWz=a>z`Vo2}ss(5lZGhCp-U0cQViGD-R*(1h
z1b{<)ksU-FaAYl@CPMRbxs%7F!+I&dksmh9-5+vJoR{pg#WyUfR8JY@QXiP$Kz;)y
zi^*uN>?HWmTJ_^9pkOkr-68TH=}Bb~Bu#m!A`K4|c=H<BQ4ZtzsmZKmY88=+3DtjC
z5t=Wk!W#GF(uN2S2jurQd)p_n%k#LGDzg`3!>2~(NN5uZ8_}bhPh<>zk}nIQ7s1pf
z(*3evdpu5YI7~m<DS7oHKmG&pJq_@$hg(yJvi)7xd-7!OP%R_t;EeLF$n_uH#!mz<
z7v^r7_hv#F8BA%=hMC8>mNx4vcM-k8e~T<+`R7fQ<(@YBLVd^EEy^EHF?mR>y)Ivn
zroj9FRG&TE7ceVNd}I?s-Z+5Abt5(dt_#uJpJCXVw-VOWifR9D#>(L@tNg0#MuUfl
zHAHm%=@gK9^_w5gP-NxxL3S@?o?eicYK`db<ANLyaKRoILEYRR)@Kg{Bv`M)9|t?r
zx1;hv`&|oSkp^u0^^lphssGiDr3L`XZH2ESdnOOFIT!aa{U!Vh<dZk_pYI8P%CVKi
zkIC%V5S_9TyRhre*u{E(g@2^E-fce;SF<lU^~b&b+-S=a#osnGh08kE`{M;4-Gry;
z4Qvl@)HH=8D_Ap9!U5Dq`Y;{={aCO6NX{ew)X$67yaN-TYh8o6LG=x!@ZN`OO`KD9
zWDK;Az3#*R>S3x8fB^|SL&)t}6M(rvpXvZ0Zh&~kI1+p@p$8zbaJu4kLe!`5`>3En
z=dU;AXd^?FPWh|8Qj&?JQYMrL8e!X9ctBg%K!D22%TtI_<>H#Yuk|fMN2+g7=8zCa
zg2!udJ>ok1%+1X`JmK{mn?MC>V`OAZ5%p_-Mhr*{gh6aa#8YD*NIM~2g$)_YFI;`p
zEk=V#5+Obv{g1e+4_syQ4lxeV+S9|yz)LgEod+e6yWU$w!nf8bhEI%e|3E+ifnF@S
zs1NnwMx&LV@{d#6t3^TgHmg6&{6Ob}bNsPFji7@n3mb=%jy6}WzH2=XQr2Uk1%b>>
zRdSbFh_VNP^)m~`^@5RM)$k@E1BP`v3iMB7xKjPpYvG+qZJe_~j<1ljr}ORMNdRa`
z6IbDjWFE^z3_Ugvz!JHmn1g@dGAvSbTnM@O9x(mz$<?IjA;@;<Cz9!!kSkv+R7ec~
zl-u%^vPE(@*23wt03x{1tB4P?Z~vMe7S;<x4XN<wa4$U7P_sQt?2KkeglvMaz(Hxa
zjRSDQaEbhK;p>t8c}Xj_$N|~)u_3mB&C7Ylc=6N9NkN@rr09u<<ri){jxqTFSb|Al
zg+<{q{MZ4pMgYOiyXxa-V)=u54HdfG43}v#I}QcPn@UY6`g2QV`@QAiati^V%!we0
zvJVL51yT4xuvfh=qOo7$`pH%O1HvZ;v=3Qv-=#<Xs|5g+#92aonq2-hiHdRlW5Mtd
z=0MsUzNLJayfjDlalX3NRV`WWIol_?C-B;uh$&E4OT>rvHQtM);pJ`oF1_az;V<qV
z<!3UYrAGF&wFP>CGc>IQpxUno6A<qXuKGQ<%_q#adAaGh{8pO-Bzn|N3LEqRBeZ1r
zD{?9m{L8lB7tLDiJhRUbtsht7!iKevNqt$~LB-yE2H}H1)t>8JnG(TlyNvvlTp-kH
zGoT#!OQ>|50j3XYf=7juPtI9<V0c{Qf#taEMsFlbm_$Rz0sciGmj)MT4wBtmC<>Jc
zD+gC*3V1OXOYFt;!-_ikJdq44Km3(Sd=a?yrp{3aR&sw+1BbNtqZrPBzg88A7hn_M
zINpv4p1etddh_^FlLRCRc~iomfx2ZEZL-dWyPVQLyhI<k(o86^g5})jUlyo%;6FkB
zT$aBr-qf@I-PdttbKFi=nt;A@F96&Z#TtMO@ODI$X6Hq7fRNJ&&YU&ppO{Qd{N?=^
zw$-7<)I6@fXGVx!iTSr9|I6nqrH65bx$Ps+Hvqu<gd7I`*ih9)#se{Zh!o!bfV3h8
zfc%I@&iyQmJ)sBKqnL{%<pyW&pI?k9P<sVxf5|rB2nm0DC}M=_yopMYEBp3ZQIyJx
z(=@$-kAJb#`aS|>!X<CufSXLF(YIGzwYZuK6dk%kyOU2$6}eP8oX_|qZ>_@wT>h*9
z%oRi$XAlTPC@%KW`Eg1~)DYN>_W&sI5Kg!5WZ_?rFu+nqh4R7vYkmx{FgkqyQm+7#
zGa>#dknrnn^|(-wt*fhhf#Exe0Fd5IhHBu1{7nLPl=%`Ep~97Zg<6})l>}tfWa<eC
zk6!4XpU~k5*(`zUv08r3g4}=3T2(465|YT>Y`>MmyT+M9K;0IYC)k-v-AE06A3Ojx
zDseDVA+Odh@}CI!AGO+aP%FS;{%^TsXlY7KB>NUPG8o74X8=hV8L`~qC;IButG}q_
zFeo7zS)jApf4CXBA^x6KDD~Kr*Z-qX{dd`eNMS&iL@@S%)~B?MAV5lSz#=3d2nCL<
zv<fKcaL>HIQ8*Gc!0W&w#kEQJhv@MV3cf_=dsGuu`l3s|4@dprJR7n{m<fuJ7&1Y7
z0IfkoPd`An|5t_>9QOk0q2hhLK>2@t09aW%2iT&T07dDDG+ByIfH?AwCjd#i1&lc2
z*QM0UDF9Y}`P<yL)C=VQ_us}z=QwQk=U{c>aIzetvbU}=zW+yq8|xKtuu{Gb5huz0
z=S=>OD7Ug2mVss`fNz^Oc522mINur~pSmpk`ypSW0j>a(7aTy|`0oh@GX+E4Kw=3w
zH-f7DzC-Z=0H(N<6wQMB{Ae}c&C&v27zSgSR{!|d|F~&DP$yvgQ9xRyL(}5@`}cG8
zR>=-1nk?e%f9(qi8;)A?8`%Qd|Bi)33?p0ZIca$~KFHP|)}#Y3_KDSbe*yjKRlb~B
zdRaAA^W9xoRD_I_=9)Av{x{>J1fDR9DcFjGIlg^1T+_q82(@(Vm10<dHx-c#j8keU
zk%6CQjh(b{1+L6v;p~1lePi|T5OaT>M?)jD>)0IUYe3+7^j<O_BQx&P4_m>XuDO&x
zyh&9H(OWT@6W?4(<)8xdNrfvgkS0*p01@KOn^ix%A1{K%IPeBz8T{tc#n^xtvp(66
z5}%hPvJ3*inV<5zrYrx)wq&vGsdfhqNfm{IrUM15O-Hq@m)a5cOF|tOHj&PC3P4)H
zsfvqj@S;ACu=t0A4A67kZ*~ifwnL)lGUL4<{ESr`2UrcZTA}mYetlWr%kwSnJl+X5
zNiVdb$1DwGbDObk6(08`P_%cJuQoInkaak}C*$8J@L4Q8LXxw8JGB!ySL=a}vAxyp
z(#j-U2L3C-^@4{4bo3)VMSbp_Ify#}ag7@HvyHrm|5!N#Wez@6E(gj2!oN#HS&TF-
z(b&~9sn#?Va9EMTu}(03B7|GyRuT!(F$|<Rk5BT1tK8$0+TNvPVZ(Na&jZqrVo5>V
z!?9t~6;1CzMMc!yycCsy>|I|!4b=}Ui_nkp>8ar99*aR+!pON|q%F`lDw_1PTO_Fg
zT1w9A;xu9210*i)T4jIx8mU1rb4PVk!_pvC_cw3;p}7eCWIKsvu<q&eVJ2dRTHn=x
z--%6?wXs%`Zp3AQn>3a*j=WKHzpX+P@??h!PQFpqYbMudV&GqR|B6?S1YVrUK3P&j
z@X=%2jc7+p#`dwXI95Y-#{9SsH_*=`HT9(J<Y;86*(twqTxn3#tEHXdYXNO>;tzk9
z`1^ioPZeF2kn^7cjQP`yqB1`#N*KhoXb(2N{^*s<lw3<T{GlM8llKiNxb_Dn7BP5l
z&Rxo(8Co|-olf?ritestm{1QH89^wLosV|K_uTS4ZEJTdV)F3Yxpy^3U?p{mYTwo`
z8)dIDO9g_Y2WSMAjw|>SF!N%8%r|+mjw3Rl6dX~h+hVl*!)0)M+)aHIjg;?{lG^YC
z4~)#37^MLY6k@kltpG7JjRC+N#-5Xhp`yBa-hh3&hc4mvN3ZDod3X~4Su+^Y%KDDb
z8&04cf{~Rg+O;>LdCMFT_jvUtu@GY*#a3kG@!HEe9=bsvVRR)(0d4O~P(j_UkySnB
zI{${72+R;e-wn5^fzA60w0NJe<AmTISqv$}c=p>r@+_mwvc)Xpn6XIGC9(B$B73&x
zv1s4g826^y5Sp2Hsp#uaWeP(1wWlu!#abxc7){4hLSa%{PIE9OGwV2v-nk{RK9CQe
z4`Zv=Vlg)quE!BlIGQRB{CQPUC3K7xpA}3a1EP8(MTgp8(|<WGBAwEKkN4Xbghm)M
zDS1!Lq#PYBzq8%~KdkZx`K!1O90rjPOwYX;^*133)ZLNZZu9~q)UU|?DIikMExvC8
z6-ZzWI#EkG)Hv>45*F5o&f+uKJTo~d$zc}zQ><G+Z9fu#)?<?J+By?I0+-=UK<>C3
zAm@b{$M7RyW4r2-3TXt^Pa}P|fqDC3xnVr^_ZSl4-^uT6$3}`@oR0aj{+;FVMs3G<
z=!ej2mxEaGR7RvvpYrn5uMw~akOmDpYT>}8me!ipxXg{WlWx=Ne3nb^dcDfxG>Brs
zun$9IPnzFu_G7nFv{}+P$*f8@ICsLr2}MgqEu9FA)vuYX=bMP-8)x!v!W>fEw=|t2
z63s46g2hiy_2s^b`@;P|Cj_r#=GB;A3vFq@D~rNo!RSkK?u_1%!yD>yctR!#*}tF;
zeva5jb&UVj#{T0c_w7)o7Zo=@oV=M&q#7P4A<r>G6ooL<(`Y28uo)Q)mvU8Eg}Aqj
z9jWi73gI$4-e-c*?to}%hM~{VT6Ioqql4W|;j)GXrljg4Y2n-Hk=54^M1y0n2xP8&
zbTJg+ido(x867ZvYQAh7VlKId@EkWJENqby&)CKjH1h}^W5Lc?zNcExLtp5J-HM+c
z)U~w20aB3=<g`b#-TO}bU2mE-T$I>nS_5XNg+Hkl8doTT2~)3F(z#)$cG>$2ZMMS{
z69svMA97D}a`&>^hmSJTq9r;tBJ=8*tILatC$Yo#xN}If^B3{s#_T=TWy)7mLYe%K
zHiG4W9svgWBc_5^rgKQ7TX0*cUPK3OByV0Bhh9k}Mh(q6>2;QUf$OEASrlVX^|}`G
zT)EK{@WW_u4wD5bpxnEjM5SzK_p;9i%zcXqE1nC|ng5uh?OUJ3o<@HMf)OX@WJngg
zJ}^OA<+Q>=xISW%zlRMY(GQKb8+T6HY*B)d-{}t^w_`zwC5q0A73t^YHlCno9MRGn
zAHe!K+)60qi{)ub_tW!1O6or1bjZk~(C^gzbze{pg9!YY%VOMT^*@lLN*zesM*{s1
zct0;3YglQ~vB`dM+IK=ZM_`Jp>b!Yd>5Z5^vpW<Ll&+GR7|v-NLM7ZCIED;e&V2i-
z`R+6Jk6f7dkNt-4s?bUzDX_^YL^2v+g5Q^%dRf*OEbJCv2IoHPZGyOH!Ab}zg71*s
zO=R`})lP0bk8QlfuV1Nnc?lx%S-SzP_cMSAx(0Mml9G|>7nd7Ivw2xFYJ<(F2Isd6
z#=4;Sy#`u}P4=QE(r29pC)oT~st)~R&;6_Hk3*Gi9xMEd<nrFXw>6)BAGiw^Vb$c;
z5IX;9CQK#=FPy`SxL?omtzW0UJ6ytZ^Q%Sb7_}S<M-pX<P~W)w%-;14QDlCSTnSC!
zx&`Uk_u(z%yICliBP!90TVCUU{mWs(!qKEtSR!{5d}6fP7K_N*&e1vs*p1DI4rS=w
zy8R_FrR&l1<WU+7Dy*j)`O0BKoB@%A9WU%SlgC!PET)cpVO3+=DvIAFC0s~1l)cIH
zfgr8Z4xdk?)KzGwN2dM?>FZ~i93XT3)@Kh4{MRy+a^!+*TVkCQab_27T%Gu^s^r7#
z3L#2#__y?o30fEy9|y)#_f{A&^kxb^qLj9VBgwLY9-s$mP^k12+1!;6E`~PIxh&R&
zM%Zl#yu6(y)F1K{K~xC?yB87dNKFpZ8p?y>!sKKRXtD!;cr+VKsb*YHg2>6tmzTQa
zC^<PN=dYdtl|Wmu&RLlAN&k2n*$v=!X&d*s^JTM|(1zRUDB!1=(1G)2n14g@QOUyd
zbX=7=vSH)vXSQCRDprdmIeQp`tM6tGS9r;rKLqW%Zlu)&zxIq7^YA8Fifh!{z^-IL
z>iPryk|-EtR}%cz0*w7)s~Q`uGqtPl)Dy8eU@GLqe6K+EEj~gl9sa)7TJ;FqzwtCX
zTsa-jP40Zm$&y${`rYK;(ooaFK0aJa1Xyn*H+rP+4J&<BA}3*B4dxZXW30{9)6(P2
z6D-V^FLF<`oeN5+efOHqS<3g?h((u%IfJQz%FOd^Dd>x^NM8r@Zv5<B!R26g^-9g)
zF!LAj27-8pv)X1ovK@BKfapaz<!T37(?RCj2`Zc+y$+V6WV8D|Ul&bkfe@)7vS%YH
zm%}JF3uK1XYNEvm&^VL_q*EgyvhN%}V55}DTI-3dVQ?q3WPd13;hp5(%!k1zTWl1Q
z{I1^@wJIt4k(XN_3njh?jq#qmB^!pXzqN<)cRQyT(a#*>Pj4mS=8FgrFvx<xZPX8D
zi7L4YbGTLGys)`B95#9p(6qF)xJq8f7P06qP}cbalP8nIgFJ`gKl5#MWjJX}tt*W`
zWkcv5Kjd2!js2?Cb8o7os!FRRMpn~xW7FNJNijoN_h&nW0kWbi$!=fMZd#A{nwl83
z75)hHugmFu!2<;wdqXG22PQ6xh}=w-@FxuqWy($GR!jHpkKAB~p_1E<mhAqZLE5I1
zrLt3%9F)a|4?V&ZY+X$kHa7;lLGz)&J3WZ`OS)1fjIYVZF+&N}e(`-I^T)a*okO4J
z-d)dx^f{WfAz&kOI?4*6E&p)swIqW0-W^*?1$ikSMGoZT`e2c$hhR+BkL5$RsrDNW
zuG6U8%{Qi8Kj8<R#(2MbB}3+lBrxzPNA{N`Na+D<7}N*tc3Abr>r8OfBTgtmX&1gV
z_iT^fozIcihs6u$7W>r~f0ds5;j$<E&5N6v!R0%F=|0z<Th2#pGPUT$@3<`5x57y`
z@1>t>RFm1;yJk;1ditPFy>K{oCKTKO8F8r<$7+iqZ}0EkY?h@=6S)ueQG01H9!gL$
znBzU=@Z6^>+7@0eWWPeAWU?=DgZjuKx~mEeX9!McJ}1fCc7y!DG@dk!)cS<Tk|nMn
zBY}|E<Jj1C-H7I){S*!k8;Y^7Cc+cQqz=8<@065eGg{yOO-BNKGGYNj9tDtHdp#y}
zp>TP!`YdLb_j?tI5Dc5)FMA9#eS?N^8lri%NDdbJz?kIAf%mA?{y}Cbd{D?21;3TE
z#5ayTa4p(NzlSq_tj|aZ_t$+^q+7e!E1JB!<3FRfhpVycC4P^XRim20#l7)-M{c{D
z&rj~q7%nFSYh~hH=XPh8l9(H*11oU_CGlZo9&dczW`b{znnh)0MqddzJoKc9+*q`}
z#Tm28N1*T{kJ(QRp<%lh=5l-rEb3qa6a^6!TMiou<6mDG#*FS8T~HWZh#GOD)%3`&
zZ+v|A2a>L6XmVdOVYY~7e|<nboz_({`e=@V@Cey`2RWuyxEp><A;%BK2+ah4uDnNm
zI_U*}5E-T@X6x&AD>6D|x0N*aW&$ARqTa$DFvMpO2Vs>OLXjqonG;N2IN#gKhAcsF
z7~U`tx3W<Eq@n{A4SHB@{*(s$b&1m$LpiyhPgd2f8<pkd#8mN|=L>>44W+7j7X)nU
z_g+xYspYJ!tdbvep>pHnk-lsnuPDDyroF=3#w#vOn!N$H+(xp{$@tEKEZSNTlvh0I
z&oqM~Q{_B%npNLoY#qSR^vZm4PE<gE8nc38E(OiE3V|rAHgSf-;)JjjKG9z>SH+b;
zCoNOn<jY;-7heQX$yj@UQhl2fwX|66hz*Fa&*i#Pkj%z%DrMAZKFv@H3G{o=QXQz$
zx}3HA+4!3I%*<Q{((0h7=P{BbhYrJfBoyhUWCP?Ee=-GuDRR$SC>W2n4$%3HyTc>p
zJkWEL=;9^s{OI1w&=GtS;qoA(s}e#>7C_wclhyL5FAocIB(JiMY5-$q3vF~p?{)X|
z#3v=~EQRkZo0Dfy7M;Yqaw@DYJ<)Igd7UNq&FCCafF#~*YvgdV>}Cw~+<B<Nssp`5
z6}Oyl-+BzkkltW|7iv3e$0^p}K0acT<Gd5txB(C7`-sNXnL6c^HneMcw0+7cEtR8o
z0Vj-d!!{}|RaV_KJPhrpf(erG`BNc0Cg8r4W^3MG=&0XZg}F{gyx8GESw6CNi#^Dh
z+^{#}n)tVS4f&aG`YBR1a8G9wBSyVkf6d<0N3KNjtG0ItRMS3Wb}FS;SNw1&Ap(<#
zE>FCk{gd|rd{aq&lsF}By~1oVbyp@26S>+t6hGW6X0Vh=0pCDl`6~uF+8>**8fnm9
zloGbqAMF~Jhv%xp3l9$u42ao?85$bq<m6DE0?m35&d<-a3k+4;bbl-t9C3YNHq^gp
zQ5bTC8r^f%Qt`V;T2%P3QUBI%rBzH(F~-RUcm%whygY2{@vUEDL&Honxra^KVh0?c
zXph?jG$_b?-j@u;Kp!mi?*#I49e${sRx-dj45kn_*!}7*YKq@=P6#eU3X;indd}xC
z*|_dabHr8jg8Aa>AcWwoBxz&>GxjvBe|8M17<+KAl0!MY%AZ}|8J7~}A}3R7!C-H@
zVI%*sgF7JBwew2H<rrs<GF=^S*Ed#Hsi`|Yw4ZjPT68&-cNK#9<n5>*|J%sw2}b9^
z2>RILihs#*CO1aUS`~%O>yWnf`cFC^ue@u5Oa{%aaBSGAfCqD@&XQNRK56QL_d^8%
z$vJ^FV}JIT9~SeL@bq~~DGSN+F|{D&_V@JDaWjhEW}iPM1two_XPKu|Dh1d?h1zN8
z^d6X!hQ~VV3VmXnJcoAzYfE!Ln?ew$i1H*X0w%W|Ptt_>Efkn!M%2GC>@^8^c^PU&
zcOmPO0Wj^u<}(k|E#<{RZ`Uh>f}=Yzv2KB&3>yzG7@+HsUhIw!c<g2(rtUeZ-$BIG
z<=?^pGy8@ad|z)jsvSfm>W=J=y=Vc&=4U~baNH{Xw$&>iJK|@pQ7hk-Kr65f-fsI;
zt+jKSxUXL;ZSzkMe!GJ3==q(~krJPX-<_gOkNLv-J(UyUkx=v%p|W?*U1e`#Yey<b
z?S3g*tMH<yl16w6y*UXQwIh!v0J)W&ioKSBT)InDx#PL3Y{X9za-M-mf6K1GD5tF!
zvw;`nthFBSvK+1HaxWM^o~^WXBn{v}QYgn=JsU9M!^2;Fvc0DVe27SK{ipJeQ#cUh
zQeDCCDt0h#fY|pfC4AXdO~`(&v5Qzz;LMB_&(l`zbT8WF25AsqM`GJyAOXi+P~;gg
z{5ezkw<!Jf!jBJ}rvC<~fa!N4kvffVj^*PGC$kUmu6hRoK)yu}T1tvX56Ia7X7!Aw
z3*aH<N^sWjVqOC2Hnpr&VTwfltE)FmmJu_?*P-m|b{bCzq0N7^)tZP-6h+9`E6b(G
zw`*~pb}|qhG;0rQN%6sR->JB6tHvqIa8-0Zfj0tRWU<I_g1`8>enWfO?~YsYqJA3P
zwPr@GK5o{h(ZXN-tv0*b7>~zQT(Nk^3N^BSbEi`H)|kfO7h|Z+YTN|#cvGP<q=x<@
zx=UW8ty4UXtH@UKdpilPjGER%b+T8B(3y=zwVyneTzT32ndbTLg9XRL^~oTi@=u$#
zy$@4`Imy<%2LCk+r$~EGC_fszjkK<20Tq$KO~UgK)S^bGHJ+cAFJWd2<x7~^XJH)~
zjWo#o^meTR@wmq<_<YPliCD4a$ujC;pPf@RrtHa86yNU_=E?gP?d`kLaW{_S*-s~$
z>L<1d)`Pe+RTTF_Wmyu9_{R4EE_?db3zKnYjn*@`Z$@@Pw2vWPuaeFWEDz`rZ-4B*
zW31~6r(_XW2)_sIv{%2=yPPf%;SpjY?ddlgBLC&J51J}7(sHmL(2_MSwN`#dz<jcl
zbva?xw4G7oWmmNl<nwRthXckY$o~-5ALNnw8u1CJm^rN=QF{60Xuq9#WA?I*o3p|+
zz~s~qHa<8C*TQ~3{HDWC_UwM^D+D=gOMy%epu=9oo$@(TxV(vRwh1{%o|R0FkK`pM
z>apwdTp8M>s<L9bzr_=}-}-p_K%c#SLJ%XiGZBx`Ra>gv%H_51+EUBgnWlA%N{`mP
z32=ics&kAG&BwY!ZT!&JFf4oqnFXonzfH&fAz9ptncvpDUM7|0l*k&TSYWTC64YlU
zq*za5F@HC&+ZmcLdSQQ)x`_B(yDDD9ZP)dHI<rAe0y)9RDEJ%I0UyI>fMkCv>bUie
z)a$TDG4sx{6VRI41lpDH8!Q;$0ok#fN9<}msH2mNhE#lyp6>Vzez8B?I4-oGIe~6%
z9KJ)@_RVOHf{9f<MO8xKUS_{Fc%}5-f1Tt(Ey_gpzEnkSsPKgQNKPD6=F5*(&p%~9
zPP@C7J?uXnW$W(Xicu?Y<3LW6qL&tG6RR84BHJ2`8WOUeKkyt;@`ea6x;j5bI-5bX
zz4EKz^@iLQk4EhCriC-V2Z!oZiy+tI=^%5}D{hj-R;ymCCWB9}cAieebuJGb=hG=)
zt#gLv>ZdK8tcL#fYX7O~vGc5@Fu&qDZwggUBG2*A+;XaitU(30wfwu5)39;V{;Chh
zn#Vuu^#$O)K;!_pxftB-y>$Dv-9(PK`_tX-wo7^epg}_dv@dgks_!nf1kBuv2FM&9
z9Yr<dUI)q08_cC9Z?42<_h+vrh}%kYnhN>Vkw!BUB}^duRC;0WM(+h*0e7$`cHOW(
zX%F|qbDZtDT#|&ikgPAyq4^*vJpx{Aw-vXybWi&o3`46?2TDZ22X4jG{8R{T`cx!P
zA-}0>5F2-%aa{_6Qyflc`8GvUo9+t6yGp_lcB91{v-2HxX*#-kpS>1pJbD<79V7z-
zSSFR;cYE9{hdaC%dy371LK^g{X*BHmuH>p&$tb?AakH+Ze}qNFg`8@3+4YqR`oEo=
z1G#r#AaDR+{%_ex+Zh3_0LX7V`2*yid_cJi(KNq&=4GBH;103<Vec>NuYP)APdQ+*
zJ-V^2H0{b=VpdizsVEKd;bA`B;COosX;QvMNzEVY-d$~rk&j(JfJ_Hdv0WkQ+;m2|
zpCsIyAxIb}aQgnjxxG$JC$?b-JJocC4DsBKn>jg{W)pG}g^JTdpl_b;33=$M9M1=>
z5KWG)9_YN5%Gq^dCiY%OJ&(esT#S(2S_C%038`>}pE3)b{(Pe#TvH?{{VS!WW-$CL
zU9H2WQ!)JfQ=pJ~XH<)Wp`QB>S}IFkrlh|&w7(1g-3Ml>NNJ$SVKXD5Qon=TZOH*q
zy;5iXn%S1;4d4*EuTBTKXShf?&EV3q{a5v)^IE+Vlhk^4(Pb69^Q>cVrnYYRDbtVC
z@Cb5PuuzmLC{)f#brn9s+LSb`>IddQs6P#3Jg(O^LfO3ChN%swbP@7Kr=W*&-Gni!
z`(&N;;BCZBOqL+{Mr(XIrTX<tMp}#pW-N<t@#gJ%S?6W(W`BVrhtGO<@Zis%5^{2n
zEgy)9s>5EA)*^QlNtv0f&ja1S6&2xFp(<3vb@F%SAEKG%KR2~VA6H>vwzsz#SXiW1
z+kI4k3?Tp;0b0Wer@XQFMZWbG?`5pcC)(-<m5C?T;c~JqxYpVck&=%D{Jw-ivH~<J
z;bb>^7)=^<I0>pB{U(ZK-y%jW^*r7S8FA}b_dn;Tef&Fv|H<T5BKP+8`VRoQXwd+Z
z8qvhW#Ql2S%;A2&Xc4qpS)&yJINGRGCpok^UA5`VfAYWo@jeDHAZ?)@n8NQw2LMK9
zu^Ru0=L?YL;KbzP8ytbH>wkyB(1roJ<k_pkF!*l|Z=*}@{QX~6g-$vsZNetJzq|Io
zzioet9TpYU8!t;fKXLk^zD8yJH}5Pak6n%IpG0l5{m<L~y9-Ri(J5s`0c|w-&|Xm4
zEZX1qa&`=(c>zP%MxLYdXB}UX1FYh2MyOgIW+}R@sfmz>ho@z_29Bnh9<J%3<LJ19
z{+qU<86XVB2c%=q0IW)GX(>uo`+e!fPHa*c_$y$1on4B+>tb9pcP`kyoHD>M6al4=
zI7rr$pP#?@sjZv~7~@b792SOzM##~b3YAk2@Zm84HJ#0NlDDWYF8~&P4QxkVNI$3l
zot^Kv;0fi2n<drqMJIldY|IR0uGxma7nmmCk2B;dG;3g9WNiSwB1nVU`?6?Y*rO!>
zBj^cRr3#*r|6Y=u3hXr+C?X;v^xxVot~Qc$f4>>LLkA2w`eE*n<nJv0yDH!#xZALC
z0NF|6e#!~u^!M$OsDWQ)m0tMC`Jd_fTWaBekyr!Ro*uBFKtE&S<E3VF2h_hSkoLv@
z1JoOO-8vuz`tM_3#t1;l06NTuMMvAj`2yWk(GCB9zYesg1%HA11K?U<Y!}S`UScMe
z;H+VCnvv6eUM&89pN=K<f@45WI6rqB$>6V}vi<u}Vooonu|sX;`tRBB9|LDY((%39
z2K4@{-#xz2VY~7FTG<-{=yWJ^wp!EK|DHf#^#VEpicBaFCxuJ{)sd=yE${#R_$V?#
z`~fmI9Z5?S73}}^1O693a8l+;|HttD{RjhII3{LhhXsqGuFW$(ZLk>Bi-p@DG?AFT
zY;c|JIKYSan3?1M*EheoydL)LPP&~vaLV<x5v>7AoN?LN9U86*^DiGz%?nJHft`73
z<$t*x@PIi?xETH&!U3R}gj`@ft$dOJAo=+3>Rs~!b1=6mHM#;8&#8Px((Z%YaX)XP
zsZ$fHwvyd&UBi^L{6Dk9XOFZ5G~|m;g4iuJVe5OJ^?|3L%1VIY5J`c@@U1DPs=0Sy
zAx-Bt7{Q7FO>*kjAIu&678V9dHC_MiS&DE!9nbce$Blk|Xk0r+%<7&+<$Y6J{$PvX
z=qSI2`X+2ar&Ir90Q@R@cYnYy8J99P*4~Zzx|;U#c=zOZ|JBgw`HT2EPt;QHmQQtX
zG!uDnw}hSLT6M%fw?v?Tra<Dly1IcQf9)3h_#SZ7Yx)m$^*G?Pa|!RK8!HEEea!Cs
zJcqp!f{pAc^_hb^E0qRIy5r`(PoN<LzR9!qe17MfgKUX@p7nThr&p+aH&LHY|2&iT
zu)h}O5+S13e}F3-zk_ScawP2T$#<CR)sJkej}i9!0H5S{`Fc#SoI(h@=3SOMQC7N2
z_D-dyE^xaL@#6Nq{!$j#anGvb2(dd`sC7JK^7~)`y^?+}7$tfk;NAB1InIY+`I79(
zwd+0Ke#rC21)X*Ca9#DwLgv&XecB!C%&uxDzY)G7V(ONRYXk|{K2PD@o@k(U>etbZ
ziv+SKStse&H~fJVGp+{A>fOKMFN<*d#I+_nzk`1JXaAYf3zHrS!c(t#-Oqe$J;RlF
zS6TyrwCsmhMZ|w&l;Ly1uqv*-v^Yk5w0&tiM<Xb>$Xfe%e1nb`uX+smg$9pvs)lFW
zb>OykJETN?Nr1VY9lI2xm;MxNHJ%zatNu0L{I|2hu?K^#mk)7F`>x@V?{>-U&9YyY
z1CCwyxh2I<x!E<*s-*~2`sJ|87V?42WYW;a<Fud}oUu+C2hjoJ*cJcFX!iB~)m1Qj
z-?k?I(fdpOJEqobPipe^8-Wb%Go(sB8=)-itv_#S6Uf=AUvY6j60>vOvXBn9T+0}l
zU$I??HWuOs_LIc~G-h=+YY4~M^m*JKg5^1{rtbJIqR!r-HQ?b9M|E%f-RjgSl|vc}
zg+lB!3f3Y@8rNczbt1}an}|CEN=eAQH*7$=$@vXj6nI>Ow!>^#JCcUtc%@~J(WU!0
zzjI0z-pVibsOdn%Dbti8Fj)>`pAPnRC%N3(N|RHQXw`>dn`^-UnULj@PM~}C;sKOf
zo)HY-1lI)6BqfLJ38rTBFzsoUTdbe`Q|2@w=iRGo5RrAh{o6D54ZA9u75xC(>_^n+
z<3w;r8z*8i`2sTdk1E7@kcv=n|HK`VufMUuK-pj{K=*j@ammR@KhcIbHqCBjz<B5*
z$IadQca@BN>lWk+kPGim_}fPJIE|E>MUBSS>3&kg5QfJ-hds}+nx@A|cKfG%dKx1y
zbjkb;j?2VI2QTW=yI;1qx8Y@zZRY%0t7=aJW*ZqLMQhCi?E_8EEx7cF(LDhc2d4+2
zqTS7sT>dPL6;^R3tEHKh@HoIWG;t0G^Lc0ex7xv3By-h3Z#g;AnJ3meh2HQEAwZWq
z1zz8V)f>3aNgtoh8ZIxnozBrbODV<^WIkXF&}!&_yD~SG>-iC%JuV%*zdNILZzOO&
zMZM>Ho})ry9qu7{ZbG#i*A8Y<5>Wh#9Kzj?>7Qb4-=1a!@;hhBx?L@rQ&kTvRt&TU
zm{^#bg6eCLN_ssb5G>rWXPDuOZu?&T<h4fnXlb2yJorjpc(KA~TyKNiWX4za=l)<y
zhLs!G3)&SExMX>I-;aFq7VsiYlD-*e{zA7t9iLjP!;d&F{D`UWSubE>E{5-L3jh~7
zNUo#bP)?Hs{LU7M4b{^R8Vx=GI(yWo2yMgMbpTn!??PZeB#DZR4Wpnykldl9q{M+7
z*8FZkGaN8MQZzW7=R(Yo9c_}&31aTfES?{P(1S^4*So1Q<J>n2+|M7Q`OZQ<b5OCu
z70^nDjkcoE53gm9;b2FPtc!M?Y#B~(OMi`F5n{oGeJ~X;+@%qKS;>y^uhVDKPiJ;J
z-G}`0U}dT3Ziu#98{f@{`l$2>@9VNp)^zuo0;V~=dULfjpX~<h|3~2eu=d_jO?BP7
zC`Cb}sVE526cm-B(uB}OMWsvcARxUHLXc2I0i{cm5{d|+hZedNK~N$Aq=a6C&_VzS
zp+n9_U*F&NopbIOcieIRW00};DtoRupEcW4$D?cTww~kL+8FHUZ9daQ-;K=mxsxGx
zW=o_Wt-@GVrCwxN3k~bT=(ZewU#`ulOjx@Wwbkt+*+;*yA5=>EPx+2t=5z0tV6!$f
zraj?k`>XjNiB*_<F|c<2#+(oNYH6nb$-PjQ7Tk7o&~6(29YSk?FVx!ZU}o9Lz2lX+
zFENokja$?IId(Url_<#aD?-6MW(xj3^e%TD-K|?b1alF{BGK;XQqQLYriy4eW&l!2
zhwAmT&yshja24zF5R8iNTL0^du=xu+&Ha8eV{93piy2MIJo>_FotN?X@+$_lzbg9U
zj4ElQFTFj>dSU34efW3UgbQ=eaBqb#27a?i@4Lg(Bm3(nE3Wi&AVU64O7^S=K^;3A
zd1q;7r}?QJbU@Jbxd2rL#k&{L?Pm|;<6^~6D9G5Jf5u@{4IFoADsv<okdZl3&;r!`
zF(xLGVA=3<>c5TtFfEl2(vxS}Xov^Mn~9oa35e<HX8AQTw)PVRv1qjj*ymr3Z8*Xz
z9I`&_dg`xf-{`cP%a*;d5`gGyF<6OvJL~0vDvfggjMEs5l``V!O^E;YvP^j|2Z%rH
zOL6Za{1#v3oVk$lQq*WB^@A!!YM1JWE0OjLPEg5XMlD4+T*++YX>oiFE&u(suJ_-5
z1Ednw76ZM@JzE02@>douKB>J=VrAp2P^LN%z09jnW%%n0K3|nHrr8X_`oku6TtJ#Z
z`mTKRN!89P!lGy69+;G0gIwCP<9huKrX0sw&(8C;!!~3;<7IT*<x>O@Lzr%0$la!|
ztwz)xt#u94j1T;gIUXBG8s3{Cl;8^{GuuNSMEQeagP2iG{0!XQr#0j}849~te*F}P
z$*L%(!rKKz?z966>Ro&+$4`|Srj`S6zm{3w&RL2~wV!@0`#;OPke_=VcJd)b=-1@U
zqPvzas!6R0ZEjrSYb-r|Gzx0h)%#H0v2b8FLr&arg?ZRdiTEP;=~bc@BRX{GOI}PC
zZ-&IQ0QZYGKyd1#b{n(w+!+8#&;T5cZu~Xj$EMfPg6;(A^wwZH?k<(gsR6wN%b6GC
z_Q2|>)(JK;yMXDYikjKx1y_Zirv7!;c=%vH&YF@9)J(`u=}9+oyeuv}843ttvwLxS
zI=w?x4r1s6YF~PJ%8ei7eR$*Al&`3-Rpr4K9{DKEL%F!}h*pLDrtFIF>97sN;Du*<
zM96f9<~#QZxnwJhaLmv4w3zk}FEm0il=&rjPWDfa(Uc<kT(~AWNZ__g64t=#(dTHM
z*81)U_f9#t=^ga7Z~V^T#C-#S7~R*eazA+8f6o%5rvAYHBU_^AJU48KM|-bJb!YQa
z@_Jlx)#+~+Lo2UsswmQIj7<4QfBrJn!n58ob%(kDT!9P{kI(hUGONGARyhUZqPJ-d
z{<?nKJ3KtNQ=qxllTH2%x1zn8?B_ZCrd#4q^?km5EGzEW7}VqA7r#(2lYQkE-?b8Y
zQ2Vv7^JP_yZM(Uy9#&_iK0*Y_FIP4Oh%9Whm>31?p%-QdnruMMY;^s`Io&V(UHX(&
zr5ZvnaI1wXU8l;w#6J0QFm&1d`<>3|&`YlNx;p^FWBTJ``2On46+n-!&{Az#xG5-#
zv#7HGF%NmSUk$$g8wIsTb(-bA-=6vt3F7`Y;bpPI{0vev_ZWbbm?_O2uA}WQLjJqU
zm9@?*JmuP}_YPMD4sA|MRZYNix$k>F>O5<g9^9>I{;L0I>k6w7rbFhIT5DOvYKLmY
z6)+-2WQz(n`1Hc-%-IP@o>j;#Fm{)Cv&>;jb!Vw0^H~cRIWzP)d8T75;H3+>{yYR$
ztM6CmYGU#1<kYii&*0IgLQt|sd#+Ebo*OdZYE9HEDtC8hC)z7^lKf04R`2AW%kh4Q
zqUjXf#aR7lvh21(@whRWH^CxfjJ!8N{<;5>;vJ|R?UpmOOBbP`_F$&k*<V<du$vb@
z{(8jFcqK8ohQ~`Gje0F#%wz3vdv?+ZbH8(tXJ(K&`Ey$On>&ajRvn&No^7>5L5*~&
z(_vhaWme4>R;k4ZQ@3VOj0Lsp7?nFDx0=~-SF`DYcb{l?8g9t!5(oX4rOx6L6RrBb
z!L<w%LQ?9!oZ9gpjy;sqzu@}%?(>EHP4PDW&E3b%j)w-m2i>>acP`Y-G`-0C*;N@6
z2R@THtshnqUOf42?iEsl`)tw8WOYhByR+t<o>Ns)!P+?wkNdqxbxi<1yjr#(>EHK;
zqibdTwf2E*mRn5bRO3R9D{E_=3F?8LZJR+fIwv07H$4~K;CwSLL!OGOtzvy{f%#|G
z)FG1{Hz|Gd=d<4OwYBxlOLwjOpPWkS{3ZfZSp0aG?Oa&w*Aom<IM&9h0uF>JOmZ?q
z$o88PwdMhO)_3Q?O2+8dR~K^h1kF!7fN>M|j(GOaTvEhUlsxsTW_EV{rxBJHwQsxA
znaeVF=cGbt21NK;;4Te!{Q*<D?Ce=@=88eV$3w-g5Ko0CtHobFX!tdn*t&&GOO+`b
z5a0iH9Sqx1^@UB3I+cE&;(>7_A9>JJh@R6<Y}O74^RKnp;JTQ=DGb>}YwIkitj*qP
zTT)%^aETZB(vsmZTw8ZO#HMl}XCKnpbv1Lg6>rfY024cNjpaS`&Y}D02*T3RSJE}<
zZmlUu_yU*gRl^I5p^w8lC3VLHAzLqEF!w$OHn;24Xm+HDj%XQ{vEtD;f9-5WKRV6$
z0IYT>FG30B&#Euvpd86UZpSUm)Izs^)~nuX>*e;Ss6js~7UE*kT>pF<n$T*X_nL#9
z<Yl_{>tXgyUKXx56CQ@iw;Q*I&E6+p40buWE1F%VE$IfMz3~8r$+$xamAx!2Mz0rP
z-fFQ%oZWB|xi>pwpe!b2c_I}~u}vv8^NM91NUajT8L{z?|1xTh^|vEpg`P4-sIMqf
zYKl!T343ZKDQG;gpyXj)<LE85B&UZb<YKy1%jz3B<&Bhi&azYzC-x>rnKX)T6&WYJ
z-}Ap5{$^xMx)j#UW6Bfp+Xu2tw_c;c++1vd&C%GBo@uRmP0w~kP>}w{4U9fwKT?D@
zWUeuB<bC2NsxKEL)?Yoy_S!y@^4C>x*GwEwvB)VN<K0|3AG6Voz75;D;#^d!?ERE2
zl3VUH*X%u8x<h?`1(^I0TjIA0@~IRCLd4~e7nesZLRX9`%N?yLsh-uSW@cX;xuxtg
zf^|f5i(K6RRqoQ41vt?)_I+DS<qjEFfADBU;uJeLP3z1E3*QU=uWMj-R*d<}=tEjw
zIeE?p_~d7M<6o}FPi-*Km2c!L+S!l@QC00+w+KZyZnTd+P(3TLc^mp&)f_JnPhW}E
z&1rV#H2EZd$t%g_epIo_qm_i{oqFNxOzID}ziREg`_WtC#BuM*UF(ib=bdMuXYI9C
z!*{^hB!zWYW^vZ^{#@H}IQ^PS(qncxsal)&my|`-rcCi?-;3IwE^R*AdwAiZsLd&E
z|8*s`_qVg-#*dZq#bDTFz?HOruV>?L&}LYf3bcbjh6L_)OH;NKp0p|W)FXf%&o!MC
zTo7BkEIRG=!-g(E(X~WIb@nQp`eE}9x6xak3Rob;^6rh~dYbgs4-8jokhzdK(=N>h
z&uw#Dw8w(-=uKNO<oE}Ox%AgE6>nRGfO3Hf!mP$J^rNeW_X~I(!2(vl=rGDOtMN&e
zv;Aqu-oCB1qI~}?+Z{s-0R<OVWX`~pLx|XLiQBCdciwSsxH<)=lpPpoDzpf+p>hV7
zTO%WMM<Qn5sfx4${jL3{#oHZ^!wP8vWi>S_WBwt<FOQc?`CC;nQE`3`Tdfst=2N=I
z8Gd(sJ5k9aTnO#vc>O!>K=vh^&rLMU(erb*hxw#SDI|l-HX^I<xj*K_E46$1DP}uS
z%k6t%&Tdf&XGgV|#GFz%SBr9*z#$2tyCtewK;7PAKZ4oQ)05-oQ(fIxKm}50?aALx
zqWzGTXma>|Tk6PP59+_pCM2Ev$)GV07Dd-Hf81(i3UtFKT8=(CRR3@Or5Ij!!0Wq~
zfCoyA+Uvo1pLc1$*R^ha{6NfAyWRiofq*M5Q5+#=4h}b-cc)$us;)hTP17pJ?kk=s
zjOgFb{%1<Thsu`YwI1Pb-#Xej10@FGF)?oZmH!|uz){xH)LYs3??(Ri_E{>N2@el%
zId)JA0i*zyE5qdghmH1M7Z2>}v3x!0-T3R(1bplM3-$Y-@T=~J_`R(kSp}DZKk-jD
z&2Nbo72M^cY@$=-nxcx$$PjV+RaT>So#i-;Gz_7Vq%2!w=ze)T``>bVt%cIa*Vi{^
zJ7}#^Tg+XHW!mN(vgi!-LJs2&bEN7F(AV5Sx9@_Wz9ngSFh4)v{vX6R-FfbK>8hBG
zzk$196)MhzrIyGL6hLA#x3Ca`B#YXe*h*A{LZz3Nm(Q@Xk63oWx+P8t_dY+xzy%tp
zo6Q6|_YVLyjo(rz!lR>I#lTf>0oii5Jl<96{R1PyANOV!*O}hOK|rOOdvCQZnsw*!
z$fsA->7LWs{L<2g=H?k<JJ=}PCdjD-WK^f1mgR&k1xk=Yh=*(Ab(LOBJ#yRQJQ#b9
z-|Lm*pN^AZ3YHs?kT{m%-&;Sea&EfW<S-fCD%U2TiJX~+8*`g3qv<GIPbIy3M>R=V
zWfv64zZr7d#MbueX?l8iBERY0RDq7bLf^ZxvfB&V6kUzxNR~XLAeFOc7Qs}gdAHl`
zShr`XCX0OS?*0-)Nxcr#&`34YD&!@lGyGh8^V`-_&<&Sf@!|S&nt&+oWhzb^kLk57
zqpBBYI&eUL_fDnpu0+AO`1skmzQ-Y~g#ZzG(AnNrS%C;&#;ez^fgtbLAr=<Sth4i@
zBEgrHL!}aDr!%9`<T_>wffL(9zaX#NSbaD9RDM-v9y+^KrZoXyoe&E+v8&qR9o0qu
zNC0(6&<o3s+q53(ywh1xG1TriZqI^t3^GMQB<9E=W8rN9O0IT%iv^OW1G=u!JV~%i
z$G|=jdg|Yo9IF1VrvYeWM%EAKx6|V(fgYiwMK5<)gMq>{E<6?q2~gd-S$P8x+Fn=S
zU@11pp@we%*?4>O38{<Lof#owdz{5fLFTY!_ud|rlh^xYSA7afO`gwesRv<$L7}8+
ziFWt(6G0j$^0|qk&ZV}e5|WZYZrjtdvM+vud{!3ozjSnfVorKk#6E~%y}ZLn*-%LW
zC;^V!M|S{qWWNAE@-yHtHt5ud10yqC0gizC8Mw1k;h!^5#Yn~JzZ8@q@gtZW#x(hI
zZTM*z3*}9_VS@ARg}lC|wpJGT#%r4g{n*wfbCt;c0&ej59wAL1U*p;l$u0$OI;5+J
zmNu+{(t(gtuR1$KPi7T++h@fPAo}1rrUzIDR1A<i)s@1qlYOXI8}{`zn|V0UMu!{d
z*+s=9vr=Yu!P@h9W>1f27MtPgRQ=D)geaNny?c@JPFW#-p(Aw*69IdibNE&zPU7Md
z3a)`W%x3FZW#*2Vgt#E4Nt6pH3&On4J}>bBO9$)P?3JyWVJcE5Zu3x1zPTkL;=?rg
zr5WIVhuiK7q`QB1rJrdz1j@%elGfPHpSJ{R^e{Jr0D6`+(W$o=1lJ=h4ygPSrvbu{
z$jC?rJ7sN+>nlskA!ZykCxEGrG9F(@?jnA0Jo|HoXirma^G1hMSawp%(Yg#+gJxyL
zIg8zXg(L?tO@|!j(U4m~k!eomRCbxLYA=Bdw;kW<VA}N0wK?PvX8A>i0ieh^t(YR2
z-0v}sVdSd2ytt~*)Offxc1-M`_4Mh>hShqsbO3pON+yP480vbioGz+GNwvkC+P62u
z&*`@^ZaGy&L;1;MZ3~LX^dLbI7Hdb9(P~~iCE9L+l`iLcTg`MrUTnhy^Sr1YlZH7U
zlhq^jTiSy1H0#*rqYD6&hrV*iP9DuFCJ=}^SOdf<jGm0{Et!dlujxf<Dbo3+mOjR(
z^G{AdWHf}v{fXU%D91!%QV`JiuN{h;LSfDEb5`ts?lP{xmz>VCvic+GKul3hk@iUF
zAuWkxh>n7kvdYvzl=PD5KA^m(Nc*d}lxcN>8WuX?QIjFr=b~&ozh*%NgeIjH(!GFi
z5|7j+szMT=QtiB&fx71(P<0E020Z;E(BFKfxDw+@LjqXd#gl}LUIGMf%KZiHN($TO
z0fr3P+V5n*Ws)3UV}LY2u0iYtO0O5jW$x|&R{hQbmcUNt{?&n5C+XbFa;bNxgOfX?
zEp@mc??n?UyH?%lC0pM*QlHo~cIbkj$3p^ZOw+PNqtd2iPt<<Qr1o|_RSPKOVzY*!
z8{uxJj*}7qQnl&gQ`7GP^N72!)#;wxLw~MCpoo;?roNdOW9iLf;(9GZ!|<hk#9fX>
z;;I61tj<O8`7xp}1)@4FC)-~BHH^kDDP-nBzS!F^xp8XPpch(PQ*~gl%f8{nCMZi2
zo1mG(?Fyg1Jv*P8Op%ET1b90<(84uAf>g&{8$+ph0(Pvv!)4e2S}7l&{btgT3H$;D
zP^_yR!bCleBahf+eo<zYn`1bVh!cFhxVOEw_LXGZ>-{I);t3E0);c%;*V@`!QGsgh
z$~l;}CLE0A2K#DvX>SEj2WQBva__`zxsSH0mVix;fKD<!MnA{^rvw{NZA`PaNZW9h
z?_rkL0>-e`{W1_OTR4zEnB^fW0<mrnpgWB9?gp;aD4n<u@L|m^=EQ`$Ua5_LM+aCX
zsl{U$q7+DXk>ZaG*{f5nwjPvzF3yxGk-$6I)|gSPQp!6Oc#u%@tsaSKQ=%tozbzmG
zNP-NPBpHC9UJdcBf^bTV^WucR>ygR?5Tspz9yyiP(Ht`s9W%HO3=G&O(KOrjWV{!%
zizZ4bh4lV02)uyso;lI};or}Ys<=KaFNN6?zv{H)XAE^oQ`Di}w={BZbg9Wd9Eye<
z0`1{+-B}b%Pib}Dy}QmxdC=rlr(o6`aG0(~1#szg0BUCGD^RKkH#m}xBk+tf#9pFK
z6+0enB^t0sE78&aX8+h<rIMU>ySh%uH@vh`Lx`_~5}$JdTre_>qjjgJ+gUWdCAY`w
z!A|i})3cQRIzgZeJh1C)>P=6zz~6xoc+06{`!)>zE7$*kRpa0mz>?naUevPFQDp6D
zAdi2&B`wVklvTF?+Adw45@LWlPBSVjD)p3Pta_X|5m#|+EgrQvUHoq#2-s;TmA`8y
zaxZ80TmUz~4TYJdyn6L2X1W<D{%t6^D8PV`-(Lxp<MAnS{_XhQJaKPMkH3a<^E|_!
zxb7T#wawpN0RKiD?N3qxY+4YSJwOq$ZV-vu=H}5Om3Fg(1{Pq+&Dp%5?Cfj}V|UUq
zhao^~Gh!rtCjGg`v2ZBr9B20F=&C*c&%m+e3FB~zwN8&4&Gn6qvJ2{zVcdTCMExM3
z=FstJNRI)PCrJ??zu~a?6Cpl0Lper09d^p7{vTvW`H$Q^r)u2qfgeqz?RzM<94kVg
z?0-cgHxSTFQd`5;kbiJ<nGBGEW(n~Y>i%~{ztv0?MQ}pGd3UWI1L_~YMgm00ZjS4L
z03?x#KMANfVJ$Ji&%o_z>wb$qMHQmb!R5aq@lsiIjX-ma<4R>MZS6iAnGZQR$GHkt
z0QZM_URv+*Q2&nu^9Ji^%3T1Yy-DZF6vcYl)Ipy15`a%W1AVADz6t#{T*Eqmtd}7^
zs`dOIvj(cMXWj~yx=)_cGBu4nW@fTNkdKQu0iIG}E&4yxegwo_M`w*tl7BJq?=^Fl
zov7WN0<KRh&J<Drc?!RP!0d+zk$W|+6Yd@CcARB|inqszXz#>5ljhU<)PHvR_oKhD
z;P?rk>fIe9y^sYYHPr&m9nNxayakd^Sb>UiR|)#xI%(@|kPB6<LG7P_`2R?I6({BD
zCK&*mS=H67{znRYd^xz&HSYi!wuc;N7)-#QI>r9?C;l^VFr03fn?gqH&YS=1uRbcr
zOP#oZqWQ0-{>84tDaYsddprMMrSV^fW|Zp~E<g|i9RHKk^!G_%bW6MAuOP+}{~!PR
zd&&HNHSZHG7&WzjkjQ^eAZy|{r0WBDQlj+VKm7B4pW|ea|L==dT*O@Yk69DoE!8e#
z09>Jp=?qciofc2F0w+K}MXj-D<<$>(Edc&R+Oen7Fm<o7AKrNqwh)8*)R?B_TB4It
z>S}aRWI$Pta82phHLyhY+V<U7dL7;+^ldMGzQ0?`FzSxwlQKhlB@ZS9lx1|F%vWVy
zO<Hl<Q^H<hgGqa<qT`Z-G>4XTf8P2%M*}>*8-kyS7R#L1zSOWWAz6^)vxVJ7s0U1q
zldRr{C83HyCl=_2u(it$>9#azaZJ_p#!FlET%EAe0?}n1pO1BDPsD#rkrvy=1zGSI
z)2=hYy2LM~RySi52q%+Ux7K?rUiRyi5|~PBmTB=0>3iP5WU9pjKj|MLOy-#yz!!az
zXR4Pp=#!j`b$lSXXxU}mPPj#zs|9JviMvJ(2TYdOFw3yEw5zG6cHiH>@wpp#3doZ>
zJa72t!Vi2oAhgJMNzxMFL}cWWAgtJ{gmLjPJ-TSXE+dmb)PfNd3y_}MbHRB9NCytA
z?8h^*r+zT8f}tnyqA{b{B3v%^@AqVN1%{hOKk{(q33qEwx#$pf{F@$v)JL?~iTUy_
zIvVUQ{SI<B5Y|vvE_KL$;et&pGsK9Yf!Kgw8*+Qn2`?q-$mQJ`Pe9f?C)Zn=sq56u
zXUq5NVx;iJgj`v}WKbcJki*ERV?orpdpyt@0P^w~Yt&IW3p(I&$%}b9z(7r1H*^q^
z5<F<@_ZxXDIu8+~XTxT!ZL0@`h+W<08(evP1#*l|up0&`V+Lb|3mW~hsd*mV%4T^5
z=^jHVO9{VD4dq5JJMG&mw+&zq1{kZ6k`r4JYOroih2d-cB>zJ5GP&jaNZTS{AZv5b
z-fD#r_iSO#l56H}l{rHL`l(5(1*uAQ5R#k?Qp?eK!I3A-co$dXGU)U~rQbeHgLwP9
zc%m3)&;G>(Sleu`5D7*wGB&t4aSvh}Tenh5L2CVNE;{%EU`g+LTr|-arQEaBDb9rc
zzR&))Zds&LW${0KR-@@<K(g2ASBvk&_yml@xw}yP?PWfZ7xfWb*E^K&-Q$GK&$LQ$
zf$a^zD6ua<rw0?2{=w*uC1Mu6MvfZkqW+8GkWiEmz%{aWQ|Cpb4n{Vi?@qDba*=wT
z6a0OLtTA(_va<4}!9<5tOrvqee2y7t`F0dokE;u%;mQ>{L1GjM@yH`)33GcJ=j|n&
zTmUCSvN8i*OjI)hlOBh58RD}{N#l<7aB@e8GVX)?Zr`0!(LL|3R4Y##f43&`cf>--
z?qF$vn>f5_)Xos!-na_7<2*&?D*mbly<u`F{4`@(%#2wMxtR!dH!d?nRo-35l-!;_
zkco0o<^j^N8Q}~?$N7ZEdM7yPN(&~=Cx1Alk15_Ksfs&6=b;J=(ithLZf!{WE5Tvm
z5v`V**8<kx(Sq=_6x=6XB7pA2aI|?lhJhG}S<%2DcEpxgxMd&3?-`exVElA65Vo|P
zDS4v8$)cbnl}no;XrBeER8g%<3EE-Q)F5@{>RJA-u<GmrZ?0IesIa8g#>h%e?$lCA
zy$&g&J=b+aPE&oz;ent5j{W=Vm9M?s8CG|dA@!r$uz3N99Ic;O8D|&C(iH!>41{W{
zU4E*G&Q_cCo4})rpjAw*9^FMvNe^J7x-!O)<t5Sw(RsR~PnPC2{q^;>Rk^srrmn>z
zSm@wMYz!d1T%iuCxP$_i&?Q|9@C}^?j_2Gi4Wmv^!uGt?`n{L?t2;)RI#6;xNBEQ_
zDDN$9@0(u4XNK^!f(*9qoZSh}u7T=NE!bXap3fi+EF<vKkc6Alz9ePus9j6fZ^#GO
zjov3Gs;JvA^vpg&Bf%b1Z=MZ~`;E5t$&T)3BOM%7WQuPhdX&-6xIsSARXRoWg|pY4
z$-RF0NU&wa=nrv-JjBm^mjk;oj?>BWKxN}<(L)tg@e8iad3Q2Le^d;x!n&#$_9C6?
zE41ZC`^x6ML}_urc`o86I=+`2_o<o$CchgWpCMMEYtA61E-k8|;%f6qt|q6rv=qZW
zE~>^>oU`YcyyRD^`;bd++f}A@$uUAQztcN31U=wy94!NP@J=Ym$ZP1&Z<FdDUrLR^
zi51({&VIkNGH&a@ov4i+ZMGgf3%zb9Coq>2?cj4{vbBeG8iaN00N1J#M6UV{up!qp
zLi7MQ^&7e=qFF2#Vl`yqjH#oFke&on&)aRcnNitRglVD%w~TyMYlw!7iepSuQn?J*
za-E9<yHGzo#$zzIDYRf#BPU(7M5e6}>0q2;QYu+|=yL=WUhj1ZxB8^NnL3BMZvbTK
z0+gX2va(v}#F&{iF4iHHm+M_bHPZZqMZ1Nq4UuNT@t~Xip6@NzSe;-KP>Ev6D%q~V
zJDD<OxJ^9^bgmk{aA|W1*t*hHkoJ6ZfAy$A+RYp}VI6#)WP!4b?8WM>yXFt(o+GSm
z^BZp%s6?-NC-xh_<k%9H>VA0ddArK^m0s6TNs-YQbF^?NjD%LHm86Y+WFo9v*5+cG
ztCjH&Gr^D4)gS2VC&|?5wE^2xxKrZ(7i*QM260^@2$i$s!f%C=FtWC*m}H*{LoY&*
z144~drPnIS&g|pLl}FGB1NQ~TCq0HS7P)lXfME5;9&+XN-+^1@)!|#qP|X3hp>VQX
z^ASAaiTvhhL1CdM4H@Wx6fJb)2Hnie3?}G4QvH)pV&y()0G}X_^LEX|*#`*RSyUz#
z84~Q-SMe4u+R00@`N%0oVXRq3SH5yx^&3e*;y*-2*DqHw2&)i_m~h^kD4`l8SYHlR
z6i{YL=%qpTf<%j}-yc8OBOWCN&|9iXbAhk-@9E@KPeApO<>5)$z(Aup{ho_XMy?k4
zVn2;*Se5^L7XJU^vpI639x{`MF>l^@L>S9Cvrv=-oG$*Y`i_OsI4sD=eRJ2HFlWCI
z3g>`bGmw#6Wr}GS8pyS-_bD7-S}pU*uj0%rfXxs3y2Hdvmo{(ES1U`yyHJgO4u=B7
zKJCuYW?~w{*A5JM(u_Qp3lA(kHzT2MZ2b1;&u6b*zrG9tjk52E6=N;dA+NDHT^EP&
zCT+5}ZuXZknQnr70>d^(?YQNDV=U(zmasHnpu#>|h}^JbC-ym*L!`SxBn@u~D}zxb
zjwYlk^8rX$0Ya3~WFIdOymEjgN2BIRJx={*@<?qBieWwqejwNBFI2VHqgv7JcL;mk
z<WoF|i1sN(wXLr9ItA<v*vf+j2csK&!o5Y62s)ZXtuU*^JH@*L(Op$S!YcSj`;I_X
z;O=`Bd`B$^_1J<mV8@Ljh(tgKxxo^pR)e`r%q0=1z|Kn-zCP%Y*j<9^up8aHi^~`7
zVAsd^MH}xQR5le~!nf{~o10Q<nV8_mDd$=;X>kLC)sFI?q6e^&H)*?4k~PLOxNzBi
zH+34+5ILq?wvK{QW&~{uVm=eG$PAV1=gKu`&4Dj;FjhgedNYc}Ecam>Q^S^w?VQNb
zbOyG$>0hHnafNEY0Zgf2(6CFd_q!dle7o3(Jx@ojh6y)dJ=*GXH1fP=)`mBbdh}i8
z1qzoXXG62U@W1K|rO%MBy+G8LdKveXc6${~+-OQpk20|`1R*-AEf<Q4|5gT4hb&le
z%no2T7V+6oy_ovq%on^ZjU3EFcBTZ;VsP~q7V;xx_qxU1z^;T#ZEF2qEmF*&LF*k$
z5V`ARlevNaQ6@Eo$i4u5*HZjhn5eF+AFL1wnp^a#%7!dVU>int%;p!AUaWeG$K*B4
zeplUJwvUenRkcd>k6Z(xX6{!9*5WneMlFX+kl-9m#O?rM9FEm~$L!P$Cs+F&;!$WP
zJ*3<A_G2K?Vao-mwCV{;{3j*;Vb%pY`%ZJ2naBnip_Sg-r6DKu^<<<I#EQAN5{tU0
z{RjFz$E~`t2}nO<`PqSfKSy?BwxJjRv`E2|ROLXz5q=C#e(v&l4(u8a4a@=ccix%h
z2&Air{k*{V;Qsv+_de}sA5!w%6uha$aw_gBPWu#nfx=zj9pxoe3hysT->`!i)=A)s
zxX12yN|82Y9oSv64~yfgHOD?n;X8thnKC3w3B9sv2A5V3G}_Eu44A5N2ziXCwno`;
z(RuB(@3Zp-qz}<a$ZGM-(GQw3oI!mB8KZf~EeU3lZr)O=0ouAzu7(GQC*9L;@x_6L
zk1pMpZzXIG>gMF-u?FmRpJGnFz_)Rzk62;1d#?tKEl87?Z55r*j@0Nm+4kLTp*i4b
zgzTn&Yy{}8Y8U&|AgSkI)$4=g4L4N`aHIS&Qda-(S-MO$8t1`=;JQy}?=?u-D_q0$
zhyzUwbur>K!W|QPsU6YRxrh$tHNv4On(+A?k+v1z0+$YGuR~a*ACQXuGy^Siv*>}Y
zzwFQ14=qoNo<wLFFDd&v%l^Cq@qP!FeOD?^khZE;`Yd}9QWFO99(<iv_6+B!+iv?a
zR=J)}x}s?nI0glT;rOkb!c%pENmedk2B4>M)c04X5C?Lu%>$~^4!at*IOSPKRe-5X
zUB?XaB4s|hD7q_Uz8ZClU}p|?jK|r-2PJ1^7dhZ$g2(d7n#Z$_5lvkK5QeTW8HN6F
z(g%$)#tf5^v@yt7jwzC|%fSF&>bF_2pkZwamx+QVQsP?uI1IQ@k4?UhCG=MpB5?&s
zXcd<+4bCwhx-{;<K<xEb!|vtF+j<XpAx@6<RZY}nZb61Wn00rS4JBGV_K>f!(0HeB
z(KM8@Tcr{G!QWnH89%RLWPZ7Np6$w&s~2wEcyo#kKJ6!FZjclMF2ZJC$rJPQUJIRD
z=MwxdP@VJzL}T+TyT16m9+>-nN9X06RRQ;c92(rn(fFBZS#o!Oz$aJVBU1e|RHws_
z$z;rJZ){=|Rk87JEkf|~t1TEhxX-;2rJ2@+<Rvk<d_T0zI>)LT0cBr!XW5O|OX!d0
zUOh05PEy)877$j&D{}ed``+e(FXyLMqc~9oej1?!=sm4B64GAltN689{K+CM-7mS{
z<&U$7-I`LOzNDc!MzejY<Thb?w%3PinbN9R@6FS5nCcL=4K7?!FrdMkY-l~L47n<H
zxVUAI+*P`PFSmhFRwoTgxR3O2#a|lB<xGi*76nknmfAvgqn-s$<N2<A;US=3?oGiL
zRB8fwdIG&_EXLF_HH>I#n&rWr;Ntq5Ze0*!f7#xE-Pphl2n5u&yy-7m>X~>4nZYy2
zyp^JY?)7(6adAwS2j^=d_FAnfM&UVJHR@4Ckv@YC6S`rZgCWHq3a(FvM&WC+%4*-y
zX-JkBxbIUpIiAP4b<}t7UN0;TOiHV+V(7+N2EH)j*!Bf--9=jF^#sf6vb6NqR(w|;
z5-X@Ms4Fb~W0J`BJ)qcYNOE53g^cr1m|XD9IT5p0E9r^XlO9umjccZ5Q&&X{TndG1
z4^uk{VlheCy8}$5m*`w3(vq%%)cCUng?uJsw-Rhqm4!>0i}&hU;HQr+{-dXZsy(=i
zS^s)XjMFJ(1V)vtI$vWZf~{1-mqtf`AP<1wqgmQG!<`VCgr^S|Fv@1ad{W<~;G=^e
zpxRel9h~xTLBxcBMGRo|h%a)FW-9!pMT$8t4T-4u_zOKGJyy2K7b=K(t7PwcLRM7q
zdNO=Wwd<yqKsRGV_&w+JakGf^EO)|{@2PUMyLZAYd&}Y$<CB7}yKy~v&|LBGno^@x
z_}eIDQ|}X>+pe6sz%HC~$dSN0&sS5+YW_xmpPyIsd&p>^C(zI6n>E$D9+sEk@1?xM
zqaJ=7ClwWyy8FZ;iwiB7I4`h7)DB-XI>-64x6J*%%EK%7R<LiPKKvAY^xQi<CT@^H
zW=C=`0H^ZMFJn_yYpOfibNP16hJJKpk#Hz;0uc#zjouQxHJaxo_QP{Rbh&m!G*e~*
z{?H7RS9FY5iw=Ppg`*B^SrnP1!o?`2PAFPyq8XQbuZhq1tl{d~%;H9#jVfxIXD$sH
zlWbdl-(z+wu|FCFgkXeKVa*H`214mkb?C0xkp`Yn#?|23>$h&AZ=O9BleFe~(NVBV
z%bcL{(SHp{Y2me7m9<H}pQG6ve6deVU7rrBeHCPjtN50qSxPQ@)D8*vYHC1Tz+Fe?
zQ+3W#8g22MV}CujDtEhHm08!1@61wx#cN%CeWnz)R|5AZ&&>EY=iu%tH;rw`%&o9r
zh7j?Ef;dRl3CvoM!1=!N^6=!TVy*IS;!i<A#e%7b{OF}ek?*YF2R?^d`zZmpGs*ML
z3dp<Lq;C~~qo#lCWhGruwL5Du)4wwHo<io@N^FD7%h5Z}^r~imma>9TEb10q;K4fF
z00f~O23*BF_EI&`S}7(5ZOi?vYr2K%sBBwDgetz(JJW&&^)u|jE4~l)XX-o+N_9Lr
zL4I?koF8FF#tCyS?eA7z8^R5))IVJ)0~)VgdKDbTpH=noeB$8c{DO&>IJCiKk;c0q
z-35TC-S+zR_|_l+PLHKecl_-Yyx)Bns`MrL&CR*p&uTm$yrXeZ*7Lky?t?3Vq-R2g
zjWl2MM9=amZ!gX<FmSwk9m&K!hcx>6@q*qH_*~sge!Iv;S#OH^FV4+^*M0Cd5-p=w
zRbwj)DSl1RTDj{Rw5#^GLI?u%pk>a;g=Xn7&Acik_G1R^UMn^)PaA{RC5o!0bS*W2
z2U$tu)Q)n9O;Y`_Wl`?Gi1mA_g7yeB5HE%fW-J$)5{oqJOjKsO%mMw$9jOzx#L$3q
z6d0By1vu|Jb-lIKC_Q~vJK((QM7Z9i**>|_)2fxpIeFG@k({Z~FE5O|@1c8{t=!ut
z*i4-`7{0X&)acR_M?@$-RFlvbF^(jitauh4bL!Q7z7h!Q-uB|FdPel^yDv^YnXBPd
zySDX|dFeGq8)b;H=eoko9-dA8L#xq4A_>{`pbp7|+c?x*zH>&o!taW9!0Sz{_;k^O
z$s|3)*iUNu)V{e*9*@@g=wWm4jbB#I&9t}&V_G4P+WCDF-ki)><6@DrL1^?OR@fa_
z%8_k;m=X*yjY*_Yy_AfI>gll)iY8L@`m}#<a>8msT<0NSCJ|Vs4?f%@(7xzM;L`5n
zIQ-^gdRT+<;}@x#F8u~C)U3HX95lIRUAdO;z~L~$dIK7=00j95O825&mWuZu+{*I;
zy}P1Z=74)%Y+8LfyKiv)iQR;WLF8Z&>t#DL>Agtyn9C`b4Mkso<{t^jUh$L)_13tT
z995BR5hlbQ8>8qr$no8zio(gi;G&$Xcw<`f%-GdB!+4>dy=#Mz7G>ot9*@WYk2)mh
zgub@EMF~#dVEmHzg<d;JiefgJ{pGs4^p2mOB&*8nwq(wh`qv}Z)j7jGRG9DOe^k$m
z3%M#dw`xO<CaD(-KA2pF4nH*ye{~f0%)NOsJQ>$Am#Z;W(zJ0I(&c-51=A)4?|L6*
z1>hxrN4%m8+1bPkWsy?Ou%Yc^d<|w>aB#i<Hmu|l&O0^FQ_fDUpO^(o&0KgD?IEX|
z*OZ5s9`sv+M0(EWnvUhxNB<)54D|VF&0!E8^H@KfObxWPq3vc$X8p3bI?fTeDU>eT
z2e|Of`sGIks69RR?w!sVbHk{~wuhICG;jJ}n!jw!{hj%%6Ng+nDL&Y{MX(4WtG)Ps
zl5Y2;NqM>W*^tl}#pYK}gwl8AlIHb4WF9<Z=vsgFYlZ!NgYSjUwpTSY9iAmHpwY@Y
zy&?PF-s(!-p`5RV^uE3NpmydPAH}y6SnR0NiF-wNtya^e4X?$}I16L$SB#Ic{nQ|S
z0$&xSEIVuBl+zbD@Q7h~NL&B>Kr7#iGrhaGzE>_MaiU5o;d2RwyN88wReMubc}`<X
z4P!mxpe|DS@~+fHOdt%OhB&k-#O799)nRlRDIoyD=afk)2sCy2<75#$+Z$c&b7aUE
zI*7$Y%9!;pI?=vsTJFVSm}KCQmDKGO<6M06**YmKam@4D4L5Tb2mAdK>X?^BOA5SE
z3m+dZj0r&HJm9f|HGDrl<-V^AxLTo{n?c7Oj!cm8152#?EWQ4SnTQ)LKmEF3DcnS`
zXEh0Xx%cups|p#&DT6a#ExEpU8d&(OG7acj9;Pxb@{%+))e>a9XY#E-;T{ntcs_;2
zC>gi<C%71e>HXMz`@?ZSFZ43Z($0G&;jM3=h@ahkD+iaWr-^tQWbke8Z<fXa?R%2E
zNL7CQM+?p0M=BLJ+)Urt;$e?2NwFHG$eqC_<IrR5v9Hsj8V(Z#`9$^167Nh1=S0iI
z<n_6AiGas8b|E#kfi)H*u@RnL$%w{$gsq@phXhZ59lDlj^tyoxEUIWtM46zK=?UC^
z_7eI+^X|Ys)xFRX#Z5^nFY4**lMuUu+^`xiY#P))9%`I(D35G>r=8Rv7DkWmy?x)$
zHY~u%_11Y_n{>ypM-%tvL>dgZ88;%LEihpMvRx_s31h1ppOP4r#r*g*k|qVN>gL4n
za7jAHPf@aGg}s=I;wfL0wvp<H<PMLmkaZrJ19MNE9bL!n+4T4MJDZ}^Xx5?EC-tv2
zt2k_2SIj5AXg%$VyW7vg9q}E*PM(^CDSp#~L#fCRu)#ul2<W<xAU*08zfEN9?LJGO
zSIbDpaO5ZJv<W$<Ot_bSq@3OJPO(Urp{*4uifin`BSCZ({I9N@zMfUAUt|1qVSF&V
z9&kr)?H7`=5f7)WsZTeOaS4+z?tX#<Zi9bKmay@=$t~9)RmS?RK-U-UC(PR}>CoVA
z<j5^@#I3d3)RkNh@b2y0Q^uDC^h+@gskp9@H7gDsT}fk&dnP>hWqxw*gi%v0C<?f<
zTvwz#trbLNXROFfLkQ{}Dxnqf>3!zlcRPpn76(#kR@ks{zR%aJ+7krsFfp>LFX>^5
z7HY=zc^B#Qix2x48g&;b^$W*{nAB?s%Y$mOz+FndmQq2MPcGlQrkh;{7G$bo=9PEg
zyt&z~D!;fIzd+%gEhrxzApI>S4!iTEt6{v&v)N6No8k5fPkYulb-J*?DLOiH-_PbB
zZRfv<QC{PfzpDK6+}Zx2bEMt{I&YMuDtuxwj)gq@(ePu}VSUIOk9DwvaZ%%)ES^sr
zTFtf2b+36PxZB=L#yd=etTVnklZ@w*`}Ize|KoFBBMq%k<Cxjr0BBlkm8Z>;=6iw9
zw5X{RR}4mqSRsz83>fmCFCnrbq2iN!Pw>6Ac8!TBKbJx%DxC(cAlbeALpF6PubSIz
zsA8`?%VDeBS0;N%R$m{%n!f9Dsj3*ILv(078G)7MKr-JOD8Q8m-!eg#^mjQ)M^w5D
zh(}-xO4_;%CpeHqee(sdTy?Rn1ZHEYF~B*^+nngwTQJ{0J<g;WbM31_eKC)n(L&>V
zzwM!Y*#<^de{y4_!s`<V`3rQ&KgtsqhF@wiHQaNuw?Cy!DP5l2j1O<T%zwRxW^)^V
zNr~ocB>$82gJ%loBAY(n5|=%H*uacHe{1wtdG!Leal4h<yMwW9f%wf!>fB76(o2@^
z<h3YQ@9Jt^Iel)<{J760ct&q7<?d2~{ae9~&5bKiK1B+77;Ayen>Xn@=1E84Ux9kO
zL>+BI5YztlN^9w1SXo`*MWwpaR(?uX4&Yp;qWP~F(J;TW8B;c%zfaDGb5xR5$QsUp
z`yaOh$JSh6qda@kSSnfy42pYZ3lP8aSrjKRK|#cp`wOHNfT*O!1?~6LiZ<RFy{HtO
z=K%EA>?@eB6nA+$U!Fx8>)Zh02c=}}QKHpm_0C`?`NIoOiN=jCVYohDld#+`U#pjp
z{^g#{&X!WIiYrd5@vJC!Cc$EreSOdUh=ZF16^G8+4>!=a7N4mfeR0-Al+oy<(S}nt
z^P7!`G;aU9;^KUW>-2T6uRba!gdh=Y@NdtC2g5d3WapNYY7em3zPEg|eD=$(5BIa!
zUZ>PuzmcF6(#pD$Ah5SKMo@WbK@SRhr}f_8RRtCFVQ}*WnYp<D@+u`isphfql?|_o
zI1%P6E3pK%o2lU`EOS48)^iQ(RT^OAe%^sREjR7(U&bRQu>l^HSsEPPj)x@~7@VdV
zIq)UHK$%pX6}?rH<1xgR<O&@$B{~E|d99LJ?Vxa5Va&#GeN|(CxFI(j(qY67ckJ~C
zxuLi2I*mOWLoDexC$aCBgNnJkoyKxKs^u_Hw6#aI#)L>jr6&iXb*PA#)Co{u*zI~3
zD$Po?e+>cY#Gi|skF)4a!}ld{h1`Q?Jx@11InehsCL43+-i9DqYsD9OE!^2FJ>3Q!
za`4l*p>L@7`u2<+ugXzVWol}#zx&-QBcjy;fkcll{c-TjH?>=fj&n8^rS_-Mw}_s|
zrY4Y<BxvsqvEw1nxzro@$hfs^OGTPf2cH)4OL188($tjw(AP5N7u)D*%ShRfkNFfh
zScufkyXUTUS$<^L{<^KMaODmA*PGvC*Q%@zKSb3ZeA&uebdu1OVU%=3XD%O+UyUh@
z?&pvT5wP@Mb}!_&0_f~s@YMz!tGnOwR;*}}magmL^N?9%#Zr@5cco?Yr+1N%>x$9`
z5t;rRdV!-p7L}qNrbda!v(6*SC|~#}DBYo&TfHX4Unc*DtsOj7CFIM}K-YbIoJH4X
z&lW-)^@2B)qgno=;9YeMn$2%hZ$`d$-@0$>AhOXvi~BLow8=y>M>iIK8?q_{b<dQw
zwHW2RW&3cBZqkfzqn(`o^n3nSmFMH(4Ckvpn3*=vSjQ%aP*ZQjmTMVinDHjFh2er>
zZ5t=ne=coMw7g-#wT6iH#wG?22MzF1)3&}ppU_ixCNaK~@0J)}Dl1!p0h7=EnMm~q
zyDKN(gKVippV8bAN@fo`=c#o$nL4KDNwA&bq#`d^AlH^x*lM$zo@Y3q+`}(5CM9Jb
zQB78u*(ySugs&~XGf|<Uw6Xd6ICZ56^vII>WNPW;bC<(sI;z+MOx6;HB$W)`ugBo0
zQU?(_`-Z82G+|;oj1H5g@=_%w?ajyZ)hnCb;E<sle6OESa~{5;W31835gf2WNTv*m
z)V+1?r%EsWQmj%l{~Vgm=cR0v0<YWLqu90AYV6_YuCEWw14KMU<KslAo!(gO<_7Y<
ztZf%kE4itp^pMJb!ff$PfQnj6@kqaRTyl_m^EfMi57VP}Akn#oFM>$Q%@fq&?bj~f
zsX6od<i>og)Qzzv<(PwwOcDIIIMtoD3a%U9-aLvV@rqQQl6LUCs--Ej93$~TiH7GT
z-O2n^5YT(BARMT8^0mGcIxBX^)s*RHn;%{4w>1?nTXN>ep~Zyouz!u|t{#vN1oX5v
z`C*jVrC2+gwp{GSboY;(T~`biyIck^3vhzGuh-vP$PVJJTI~Gk?MO`B^`=emqj39`
zie*|XDNe*vL*dxQB)64%XCr)>C*TdnZfr#yEftH$Bzg>4K}j}hNbbcri}5Ov2@#k?
zriO$D9`p5)O2xsCTi^O{=5+%QclX=%>ZwUUhCg!t9W9KJ;j58`VN(BG1^L9cBHEcd
zp6uo6>&(n5ng^SS=6mZRb=eoGk1j*wgJkPWG*0WxIZPUPR`3)BY<wgu6`%!b=ofS6
zQj>0gmp`>^XnIei<`JI68=M=hzSf1OK*<F(E7TJ(=w1{7;nWPWmtz%*`H}_~kpV0x
z-VToS-;<u&o2oWLaL8k}VuqxaKZ=Agqx+|E8}4$H>s{3In>oMs%H`clEznL=j%B`h
zY6L3J1d3-dtCmv7`Z5|%vbzzJR)y^FRkE0hYA$0$-dIKTmzRmydK6cZX1_%D=yQCv
zuOLzfZ4Dmrs}eCwPDHs3*uraK25on%8gYZ-aBOqhW|c0KN%18E)mMT%^<iIgjScj*
zotF8;LZZnFZ!^D*AuN{dFikbyVExP2sb|Q|g`fkNovrN^J@an%xP`ZiQ}7;v=AWwO
zhpu1WEd(t#24pVoNoX`%Z|>il6~)Q$liKU&E~yV)ptrD4cqyutwY#~mL|lSI9es-V
z>Y`FFxA#ObirIVMLtem{uca+>zY5kf?2{|uJVOECGH*m$iiw<t3>=aNfaGCvrtbb=
zcU{b~A5a{jWo8CMX`77Mdfv2926=tBRD1SVZyn2%;5v}V{*abpg;xvfN(-}IkcbJ`
zY%yhaUBSZOH6aQzw+AW!tYAG`6;Psr*z5C7TW<6+LYt(z#N?VZ4&?$y!D<P_Wnzx+
zwY2r7P{jAy($_n;N+Y40ZCjOOaPwZ)p~W_!Ne5wSv?H7M%K*x7zYXMmEKD3pK!XPB
zB^I0J$CGp7He$q(Uq%3VMI`ks_u7`|Ad*UnqfAj)(z2v9Q3{Xp)SntI7)1WUNZL2%
z0=n|S-Jk6W7+*Uhbde4{rjd0)twsuM2VShGbOAy<#Ja<oR_;0pUL*6TygirRd{qqM
ztIa+f8uuz84^FasXAV&Y)p;#s=ulJm(*#u}OKJW_ujk$01aa{@dXad)FntuCOp?Gb
zeIUFtVonu~(IB;qvI}91GCPbK=TUy(BX0p-4g`<fjdGF`v<QG^>8{BlW8XqtGie0v
zh@im(Nfc?h0)_qI3|;Z7k{#<g!X7GapM@_6*eESXXeiXLEmjbeQ|(sd3wFf~+2J*w
z81bk|@u(WoeLgO-VI!^v<UCSg+=x<2mH-CNAsLocB<1PQORfpwzavQ|W(f3bai>u-
zO1WQRa=8kWzN8<fAw0PugZG}V+yU0_Q84l-VXaB)I<En3WHm;eP$TcXg|F)k7^4}J
zpI~!o2D{=%G8QOWMph|HGk;sM>+Y;4R$A^GxX^ybNXZ42$GBv0k4rHx!<Y2+iT3V!
zON}(bMZ3k^f;kQts9=$f%p=Hr`JOoDWyX6ab4LBiI$;hZzHCoCCfBJkcdT)6#x&Wb
zCb1GYa|qQ+Ah@FmA)AmNLayzqxUu)Cwvj3V>8mrx0_^wGzTkEH)0MV)CXn8ayKty@
zch}NDorR+upt>LcX;n-IUL~ZY6ZjrKN?g&_4%Nj#@IlZ?M;$Vcl1(br*&mjCK>t<2
zIEkQuA`jlYm=H#rajM=qZ`pP-wb+3bL0lonlVd7i>SmicN+s!N4m<R`JyfW>Ze=Cx
zDBx2G-}Rc*bbsZDB<ZnWY6`ms+P~Jl#pp~gH7lc!r8Vt6KX-fDF^t8u3k!kw8}v*8
zSQ<T2q2VI8g>Dpfk^>>}D2qY3L%sYM(?Y%TA%@WBJ*HozikCB+&_`4@4t?9oY&;l`
z{64Vvp!@sOh(Ky7S|5`HcC?-7C0A<}#TzevB7-Qhw$swm@(SLF6uluJQm0ZnH<olm
zq?a3$JdE)bNrN$jP7o3boRVsDK_^@B$&|cb_0G0nI4j~8P+1MUZ9fu&_tRjj0yHcR
z%(Yi;YZtIkpH57&nBB}l8z&DUz>AGT1r8kEg;3$idGN?`xp+inh4JK1apcK}N?Ulv
zVUB@I!Rt%=Q(F(<j{Br7o<pW-ilEiA41ZJx<>_=VrHr8mH^o?^I*Iq;0?E=q!T6y9
ztGEVnF&H`DZ%G0%QxKlQ1Q9yjZnnac%VpknjisvCoYrcU1;W;eB6PJ*zq1L-eBWLk
zUPGwz6}BTvq8h8^g{{Gkha7JBK~auk^QfAZt(eI|D{he9t;$MYu`1vt#+m3txBbtH
z)HD8zi;Ll_JbSkvPq^+cUZAWFT+-cUt1N@5;zjAxYHZ=lDzhYOeURIJ1|}^Lhw`sx
zLKpo4xsgT#oY1MR_#aXxL$J|#mt~<G(r|q`PDlek;A>JN)|?A3H8%-!iJ$M2G8?KF
z${_F@mWp5U5)G{U0T`8T^pOJeb&#KZ8LX@BKxw~uelSdGt4rC0B+Q$tNlV$2+agGT
z$(@|8{)-G_gXDqEu-OA47v%EGsTJU$S!b`!EUj6I#b6Y!&vt*%Y@tNNPR>VhNe#Q9
zA67EP^oVf3<p(N#FdHhI+2O>^2!Lv%jvpWBHey_AT#>lS0+$f42Jj>mpigQJzisi2
zDL9j{wsPy(kN{YBo`{{DoyslBp+l8hCx<F9@xl|$;+cg0<3nb*RPAbl5Ruu2+6ADU
zJTU&LdEnvBEhm%aTp4^Bw5YRA-8g2*Y7D1#>5|)64zxrA?S{A8*JR?zz@XCyd-mEK
z3T!(>WQY!_LsmRn<Zpu710DY9p^|RV)jh`M3jw7bds}R<7q0@C7pEGU29=umFuBL(
zrr>U^@LzMopS3LjIR?s;q^t+by5dsIJ}cAf-oH5PXg7R#KYIA^B8fg9J~oFtHs+2f
ztqfBOsAHw%3_a%o?dV&ggYN^|vyTOgJy-2_sg!a`b4|+|+Y_JcGU2vMo#(ma2DWbu
zC|0j7Nd-^>)A_nwGJO9LklOuXKXSFEo_TpXh-S>r+oIgk*2Ut-;kTdOlXcF&^oM>F
z7k8MXV`PYdy#b}W;!*%&k{t9d!M?<yz#{`24)p;mZlAkAvvBaz!7vE;UK6RE>_XjL
z#_VW2ohV?7$Z~EO8p>Xk1$EKgPDhd`e`5^=*OfrnRcD|-bixy}^g#SPL=xf8I6X43
zP=Di(qvY>#I`)}7nx5;OWjDTTp!~-Q{Wi}3{MnMiHcioza(98_|MH+|5TLEn(*-{s
zTl4>VHXTaIM3D~lDvkT2+5h8L_l}cN6z(10>hAr$!TkH|@nB9AJ&n@XNB_R<$S?TO
zMJpJ!aU|`=78v>^sgWDQm&qdWfFCBVVUD=Qj>EkQPu%RwLMqFiW#c3E`OT4Zw~TAn
zPle76#=@vWPDQ>t%a$2lwJFYj)px3heE$It^nbN?UQtbL-5#e(kt!m+C{hHeA_M_L
z6|ew`5DO#}=^X?TdNuTb6cMBfDAEP#B=i6g0Tn_CA#{`)dP(4N#&<mTj(cD4<JnJp
z?3X>)o@>qdpT9NsTo+i*$z5c+Jj#cl*;sb&yX{qEBS{ax!^U*3yN%jCu%*-L{vDY|
zXS&zpEd?Eg`RI&`%%2f1eb5-2j?vchXG?hvpY3A#xqB|@5r82@VYW!qYDamb;G1&z
z&TECdB0*{q;>VBg;vcJ{6Pe~59!Qh)HDFK0cvqU)4EsA%W3LO#g&t`K5cd{cKbE-O
z8EligP_u3Pw3R-N>uXO}i=s-x$)xsGK=j_C8_n$NhsLQ<GmBGWpFi*z6pcKQt63Hq
zAtTFUBZsgL5t-!Uhm$`elEis<MCTqpRbg0YbUb+RiVt7UDWrZyAsoNw%S2<7^w2mo
z6~Nbf$oJ_4uyVv_HhAS?UR$FmTL3E3+c~A-=Kq)zHnd01Sb{w&NPsR-MZ(FS1?BG}
z?cXEa(NXY1rJdD!BL#?d2TxL+b{UK{la!(4?70jiRNiZLVcxTW0RcP^#^ZiO_7g9Y
zPV$wjYDCWGW<!{!+jkH8PEQzuk%64Inj#!FmUX6b>Njx{YYvD(z1^J{{r!_QZHk#3
z?I$N$AF4}y=}osK!;U~iG}p9Nmc7E_H_0CKq4Q4<cXc!1p$KWk=#&*a?!mTE1_?uh
zTy<>%DHqKW_3S!e3N&o%-A4<E*}><(ljL1lVqXJ0d_&&(BurgvynmKM{HTPM{MKPu
zsGHm$zg`3^(ROGuF&_>G?g%$o2M`8g^UCX3f1Lp0?<HuTp}^#P8AW~`jbb3X=w+eN
zJ#=U8I|S5-t$jw0G=LtG@FYKNTviRHuxJ;1VF$CtwIMp4PvSE(g9C}EQ>Oh8ABL@z
z&<c?(B7qv%J*lc>jr}}mX>Kd_>L_4n0?H(vt*)UZ#)wXPFQ?4mZz#Yks9}GPBT>0t
zgpGJb{R4%rD6M`R@%=9X*ahZyzG>@@hvQt}<23U`Dfet@qrHaZK2-XelY4h{vF{E%
zG=5bp_3rrUPaR=E=-#LxQ`Ed~R6e-2w)S#TMq;?9t%OYPo$uFB@$lgWqK_IGovm-a
z10nYMA!7?Pq=U&Dr{TbID39}}Qco<Y!=y>|`r3w*b33ue?BIn!$y=C&w4{!k-DmT+
zZ0Sn-NbF*gGV>W`lIR=My{pA^uKCuyQ_ST)PA!BB%6%*jS71F~2phuxj2=C)D%20M
z?H27F(0_e&Maap4@6xamcCEA6XQkRlf=h)<T61{k(Z$|0LGsiZz`vFuo7}!!mu{Nc
zd(`4JqAfMiMApYh=>(feTF-xq`&MlC@;HK?wZ0WqQI?7zkFrb#8(z~0^L~C7mX>@^
zJy!k%7LP^^hdj=WmR8D;lev|#`f}=WX{*{V8*QZz1kcWI@BW%>%|Bhuh?yRbRgdU%
z2nl3MnDosPccoDEmC=#-LN^I&iT3kd)siTslVz#3+)z#EjsDtQ(fE{p2ScGX6k(ai
zI^{JE(=xpE{jNQsCu>8;)`X_Wk;uLG#d5MAXnnXnXo)%TkMByeIa#QmshC8D!Z<E?
zKqsB9hs^Sn;hTgsjjqH>F3or=<0FfTI-zSb;#IQ=Y0H>Ty1w04ah_I^O710^{bf?d
zH)Uw`e9PLm(2%y+`u)5I6)z-8%6fdT)3JOqkm&Ck0^q<Mm`nMJW;%*Nwa!(~`4OcZ
z#FGw5Ovu`NYw%Ru=ta?L8&O!{UPZgTTZ@1<nyW9Hou?u2o1zP3B)%9QX0l(XPw^l?
zdG0MxEsjfhRma<sMy2S7=KwRL#}zBB>DfU09Ml>qo|~*BbSu&0K4q~*!$svO{~PIG
z01sNZB_3EY9)AZqT;(qQdnG)hj!={F^ld`x^HD2rZ4QOI_OpwV>+RWCaD17@BpvU!
z%Ko%xceES!0i$Y1(dMGn7vKus!>=>q<^$Z&wz`yM{(DbJCtrL3W6RnnUE>W$QhtQ=
zQW=fuQ4n!xJ1Z%KIMA@vb1%um;YQBE0taB;K+`La+S#yVMXF(@#&n~6<MBnAhoMxt
zzLa>XO~(zZI>g@zDQx?hu-Z^$niROb)2Qr@UZvWY%ve~fUgm=RKATL23hjalc=gGy
zT8;&X`qY?Sd*JvX_A)9{@3VYxht9l?6LGPI)oLJ_rSSs}G2!PlJggF*{MAz;3qV~X
zb}$Tepanx(1yr3f2`j%e^x#6*bWJ}ZJ(T)u<~$rmOb)t3P6T(~jx><>nvK&4ohwF_
zT||9ev4z^X<g=fX5gU7iZ9ja(q-<_@U3MkGsQG!anG&_hdFzKierx;f>6m-1Ey+u1
z*4dNYXl@kGh3`Bgk;3SRV-vyVClEI@sG_WEs9m`_aDWoJRcz_jM-L8KU_F@HE`8BP
zu*MygH_P9sW0nGK=*qb(TpE;Cg<X+i)w?evg<g$YnhQ7R)=w1B+^=4P+8%CyWVmSc
z>kG)zABmH((&UGb5s(>d<%s1s>3zaRWoATv=kuz)F$%`wuc3bX+Xwp)(xv9s8=e@8
zeM?6}{=M7BfKlbchicBZUn5!*foenmo;NcdgW9pZj^XW^Sjz2i4j_L-&f4LEcJCk5
zm8XVL03{b6i+rAoVwJm(raf;bEET+?6c-I9CJTdLy%c9Hm>JT+AtEw4ujAKPPZ?3i
z<Xfp0J{;?fw(@EU^gE<i+M5@@wY_$1{5U1KnAIgMPYcftVUlebj~d5fEW5uy+EL6Y
zbMB)xF_r)U0wX^#W(a>c%Y!h9ztnT5;hdPs-@J<;NNLn_6$jh!Oa7@8nN2=yIKdY|
z7xF<fm~8mz?wWHlXN;v;KArRvc=9Z5L%^sT$zPj;QTC8!{n+K2+7?>_&Qkmw{+G`9
zcU3v!?zFWt=*sQW2q8kj>_w<n(Lr;er@sHHW7$OAC4}oShyN{+-rQS4SbPAN+mpx9
z^;ox_>g1S)^=-)Olwp2LEy=`M!S18ZEMh-~T~V3Na~p(8*qCmn8TXoO|LGztIOV|<
z1LJJiDNuA78;&KaY;O)YFLl<qB3|a;e+c$eXS?S&I#MVHEx`}km1VUJ&AGXZ_~6Mx
zr&PyEu5HA(aRmYvUv~U)0n_^)YcCt4@+HCN06ChKIIkg9s;ekJ#TCtG!Sde=yP~Jz
zHqCys3wj~3K-Um1?+p^wrQg)7Xqw6v=6rLRL7_KkF|V(2curazHgt&(q`<GcJ*^jD
z_8d8AJ|$A)S{wMQML_Qr#3}cIFt(NYk=bV|5JYJc4Ww~x@&N8^FVX<yyD4pvVeG4d
zJZ^24SE)W<f_C0)c8zdwyd7W1s)&pALqH-JOvi`I;~q$NkVHXwb^~Hpd%bxpkc-|D
z&H>a2{_&{I*Kr;LyC7>Xf2N*pruPe{x8a!yS#k}lf`Nw^Z2pW;NE969CzO;G*$p`k
zz<HrfxpQ$*i}i-1H$|o^ZgNMo>oOX*aZ+sr*)|n?ak={6LSc}G<mv<U!#I`UCquuD
zS-Ft5@2;vtner1~u1<YneahW+Qg3GIXQ4hzZj0Mht#Sm3-)((f-3+C{<8QT)I&Q6&
z@o9ws1mOGYJ@O&EpZ3-|=W87|S$3}EI|w3A-7%Z3VQ&0DFZxMrOXM3clouFa9Ng%R
z;$?4?BxnRtKgvyobfs7?xa|ciD%Fa72|O3LX0d6Q86Vqdk|Yphmd%xt9>fd;7jN+H
zSQdUUkujqeR=BeN9;1FDE+j5dDZ<YrV)cl#f9XM5O2`^F>z0(5Nac09U9U;aH@KX<
z%Rtcj@!&ngH*wnq5fu^6R<GOueURtf=(cH*dVq^RGb?b134ePi*4B?ijXXPXTH;=e
z8?F!MX}}G1l-vTjcblC01M$7G+Pkx~8Ge05`Y}6sbtr0v@5uhx@Zoy8E?dj*v%fN0
zh}`FR7htr98ip0OEA!~LYN@(xZADZ*a9=Yp7`CTrOA&Kd*VhfUqG-n(D(>ar<%(vh
zm{{ZMwp$Z^V7dG4_0PF$g<s;{v3jz;1bkd`9&z%UvM?^~6iUs@%O`)W&gh-i$DC8$
z>*e^EFH*q~`6~Of1%pBCoJWk&gc6^b`I{UY<%u^g=7k2(04NAKNV|J~Y8(J7?W@Us
zYcijaRd=F|7tBwLm!(ppMmDqyC*y(#M{jwE=NwK6Rkg}S{>1UFuDEHPMynCrPn*Z;
z^kiU_&%^XU$cT1*s#kQ)L!-0WU0I21Tjvko^}Pig%zC*9Nwhuin-2S3K=s(RZz((~
z<9Qk-Y2NmLMX`^;O-&1LK5r)7_PJjXwwjGpuVU?$3|k^d)?)j~yGLy*F<AH%v+r4N
z=5}~t3xkBJXDyEi_8n^Dvv&;rA$*<&D)H<?P62`mv&Il+)zv?Fe%4TJUY0*uZ!PMJ
zFKi|(@TK8)4*&fmT88!$Ul;NZcOnK2*1FejPh2~NQYc@4_lhZqqa%Zjp*x?HDLd25
z1&+^3<ZtWYbhqv-MG#7Ey3eIQuha>Q2MM}Kh|g^CFmD-aoKlcPsP#vLhHbU2UuMml
zF)`O!3@7m2kYnfILKKd%%6+ZSFb%$2o48LFagg64Lao{PK#?&VQl7s)JH2q|Tu6gc
zw?_2l$e9S@xTzr~0=${3GXv7E;k5n2y?FBSyG*#b>ICp1_IR|N{%lp6QthP?KHaIQ
z{-<Oi1hV($h`vFEhzkzj2lUIDsFatGucqA{^%fogyIQw+Q5n%KVA)0i)RcVac{e1V
zh^%^~IyLNIkvxr#FVl%UF1Gn79okvFaL|)|o;cr`#U_cm2qf)YDP$V8$u_^dx%jHQ
z_pNzMi|CDoU&jpGJgb`CT!ecv+7HKHd`+dJQ!*l24~xcmE_B3fveetG-K)&2z{0~x
zYD_I>0#X^7(0Sv=!}(pCx${7@EX1{|U~(NDavZM@CvP4%4=V$rQ-`;qG4Yz#hL<`7
zo>v8Q+O1}#$9yhiV?ovk(AjPZ>*b8)rd)-8@>d|AZ0|ellmr37iwC?)NH&#u^{b4e
zWz~IaLjy@I^3-yA%Ennkp{oF}J@=kKxAPz}JZR<u&Z9H!VEuck$J8e{(TKH$KEwui
z*Lyn&dcd}~j*EKw_6Iu<7cT9L#f)%xHn@1HC^Xf*YdYe%XY-Am-$^IWqnq*UQMBlX
z8>_!`U93qZK7FnN6PuwS+^y;gL$5QUR(Et>blLWo3do`ZVY}~7mz8X(VkRgkn6wOZ
zG#|I@^vf_Yi3(r){;EDFh{k<&qr0GaL;F4^x7r{bA|>0Mu)W(zC%&>}@1K-PXkgZ;
zP*`^RgdX0z{312=y_TEM@?q6NOYILIbjjqPD5Ejq9p@RBP?@}mlluoF$VtlmoDxIT
zA%tB6#a^*QCX2t>GP>5wt<IhTK_4&VkUtkP!3f|hc;dW>Xu#I8^G+>P13U~@i~J!j
z99+dt2@8c}zT+0}H}JXd#Zb}og7UX7i3t;OXv2^97F~SsH3~Ij(Q<h|Pw3i{a6TDu
zq$u_6Rs4}Vbnne+^m-@gO3|D}nRzp3H)g$Kw)735LtB+N){ox&p)a*qTsOCC+RUEe
zL3`<&6dY?J^`^`(&+WJr8Z=;j-Ed#H=e>9yICY@$;(5D`ju7OD#7Rk|U>qaqNu!g1
zZ@}VAz#DMHXT{uF&(#gmBSkTmWbRhZcJ_|jjhxneEUP?sCK}jXAcy>Qh<KflBqKp(
zjyNtzq>AnPdUl6v2xY;DAZF<AqLHzX9qx*^nPEL^E5qzrvLN8~uBHA*!&TdqYNsn9
zn$HJpdw-9;8sV$EymrIShWJjQtK7G2($1*H!pi8(kFbn@VVAOy<DFL^v3T{X2wf+C
z#%D?`arQYuoYz3#I%a9TH}@(*GwmUQJ*He~?_t|_%T(DArATnX1asxguban7t*c^z
zE67R_w}1=5TL@IAw-GWrMieX3{S@nisdBLETXtn`n#vb+ISFB%)f*J({(c-BEd+*~
z%wUwwFuTHM9JuXJvaO!tU8OpDt@v;7)OM4KvQY>Llo43)8uYib=b*v|-f!~AF+Y+P
zN9Swus~X3J9cVp%AgvpaB^1-0ve4B;v(@Bpu-=wjEVp&<gSD>#eYU5gMAox}w}%E^
zR|Mi#Jxw#N?r*Zz@psof$f`C84x5HdhJRPd?K~xoVm+sDjPq&zljrVA4LxcWa<3Q<
zRre{+@c+f!aGW?PSMS9rmY$%kpJQ6(x%O;zBIKgV57}-k(qBCNF{kkD(TK6&Mmpr8
z@VS)p^16a`$`|xC+<sj(2e_a6)Ptek3q)7@%=xdXK0$O5&l3mSg9Vf!<}p{9=API{
zsXZ-vVyq6DkP;_vHFdn-x_YuxV2}YU<-g0uymdd+MlX>7os)m*qdYs*9^o|@$m}%#
zW3jIz(y>Oan|LWSBhm$CISshDt897NRA1$|B={2^yE!g9oiK^=V3!m(($oC~eJP+G
z&CFNdYs%1?;Z^d^a@w2glIe>Al<p{pfoAuP)BU~&!Kc36k;CFJEig~UTUdTewbddW
zuc55I$}Y%<N7!KN)e!!1oB%UBtb8`=fVANKFdod0t7Td7UF#Xncx1kpRynhW>6saN
zl>msni}P4<eWE5$*Qon_e};Y4l~?De(D4aCUO>PMTga1fL;iTasy|u?JwK9&KE5Fr
zP>nm1mtkNQsxzOFSjj|bD5X;63WjAwM;aM<k6rjD9B72041}*%=JIXXNN|TM-=>W*
zAV#NA(%94A%W11p8!|F9p5y2##B}}+-^xQJN7LZ}va>yxe9aVn!I=J_)8WrVh7gPK
zY9I0l#)@V^6DA_U4sG?^!iAX#T{_5jJt@VC`e_Gd%s|Qp>r9V&rFuMVD)|rMIOV8m
zB?w+;0JI^H&T{*Yruaiu+XSe5hf`IbVa)yljDMc~=hj@ZjgsPxuUgiFfB5I$3}gMm
z*)~Y?QC4};|7NXk7(FQ3iUbDj{?sjhSZiqWU$Um+#LoHum{3H;PvPN+t{MMd@0TFR
z{AG42YW&%Mf5!iq;!*M!37sIiDE%XZ|F*s1-WhLM|LtCv{_k-9_0GSO?ccTYZz%XT
fQ2+nsowidt=y2r>M~wR-#o1+`Yp7G9Wf}fo#@~UU

literal 0
HcmV?d00001

diff --git a/Images/ipython_links_overview.png b/Images/ipython_links_overview.png
new file mode 100644
index 0000000000000000000000000000000000000000..16cb19f54a96b61eebf711af8262ffa60fab2bed
GIT binary patch
literal 24323
zcmeFYRd8J~vo2^mX2%#q3^B#bF*7qWGcz;o*fBFRGcz+YGcz+Y%ufC@=boB*xi3@m
za(C6PTA$S2Qfp~Rs;^tYveF{(uvoAlARzE!qJr`uAfWTWKQ}ZOutZVy$2JHE?2ws&
zfUKB+0KTlfjj@@f5eSGVe0hS4qQWr7;9+~Bwkb@s^wE!F@F)~_5Qt;lpF}=>C<gdJ
zOY!|^s&a~gT>&&9WKHP23%;sVRb<SHQxIz4b57N|0{f3(5^J{{o$r^A_7`KMo6Qb~
z6KTl~G$3&Z6@oFKr65t{6A8kQ&-A<BP?DcWK|ry7gX?CE?Hh?LAtUQ;>2y9mf)e4i
z8T^dAueAR3;Eksp+5rU-%IY0{qtYfBA>AK{6|Dv3^((TjW7Hr#)Q>xsXXJ}}9AR?`
zkpu9`WbBm2a!*Hl7s=3qfWszIN<sYQ3_BUa(Wbx(<mX+M9>WNhGtNHi|7QP8c9>(C
zp4_ZD0VT^U^C6Np2cSz#EejgnFQ9NWr#+H%)C0&R5dCSjyqWy09UhCyK4f~v%%T<|
zgO3pOfWI3nnZXF8P)*|`^;E~m=14KlreZZtaZ~d?Qj;FvcX$^lR7=3^%`mP&&F}OQ
zTuwVOOQ4BR?87h_i{6YJ#_<D-@P$wVm78@BA&X)dck4N7GMhl|pGZC^yCV}qaO3Ck
zr->J$u$PX8q<Bb5_Mdq3(87IuRbTEo+A>S0y+TYf_F)x{jwD)DK^BSm<<?1hi(v>(
zepi-8*1u+HR>v>18!&Wvn%7Ch!EZ;>g$Mqd?rsqLp$lyZloSb?kk_#RAEVs`<cT_g
zDmFyke_4|GMIKx-BFV&J+_9?G1&sIOVvif7&5Q0i10Qm`l_dYmRs#qK>rrdN6!LEa
zQv|HM%M>#hkT61!6c1N=vk67cu(sZuN;S+WACjpr!d*B^AW&OioWBuM?4Y5crtv|k
zz&Y{3lX+nie9?5N$v}j38I}+zx^S!TaKJ>m?WVvde#33SHi26Ecw|A{`S4wVh50_}
z;`rio3i_Kyk>nvJ^D0Mu>GN&N0wMbv;)jw&M23bUKyM0BhfCoro(*e?rwI2BmL5RE
zyO>3DL@<XK&TEr3Ne8B&%U%YT+QXoSJ{BObZR!e|221O6yXEQnO%p+-S7b~03C#0L
z2h?LX?ba`Fd_zB<@A%BnaMuDo(Sk$}(S)T@{CQuCg-@c<^L{k{xg(&Bmidko5TK`T
zNr#)HEBQ4bP|a7(pL{@37eNo}S0bGY-FuSU7z-6z>7V+Z76Y~=S4#?K@&*u%fb#B)
z9_@jRUIhKkU0YVjiLZwta0A3!Gq$64{Y_d;keZYga4f&Edv~u>Tq)Z~xp5}@O?Kg~
zIIcym4zC-p|2##4)AMC+@j??3q4fDV`O)|2^|19=^&sh`74b6?w0!Fg_S~V@hq(r+
z_IHhC6~!Sfju{ip$TOA4GNUd|Q<Q0uJRxo;jvWp#WVz9-32F<_l;#%h5WgS=<P^zK
zk$cBIqhcC;+sDt3zlpj@5YJDM_bLjWvpgX&r@kk=hq=#1ks4MG&h1lCFIOy{D{|C0
zj;EJf7ECU0lVy`>mVEZRk=jqB5JXcz<3Xz!p@<icpQTu*FeqVE@|d@smz>w1?=4wW
z44l6*FE9r+_pJA<e>caNhctyA4>}OponDwln4+15JZ5ddOc#*J#h8wq;W*AcF+Y($
zsXl2wi9a!!fBg>K8`rI8NGJY%5c3f;)8uGEcM@rAi8a@I;2aZ=v5v`;5gf}O3m)r{
zk=ufv=?mj5Q==)f8TUeushautyn0E!#%axhgjGvJAhS%X>}%7*&mXFT@}%-6%@f|~
z2<gAHxYqHtf-j`kLtAL?zq@n0o4K#vO&o_G&EB=&3B9b|O`h_YlrSuheKlsgMRAzp
z9mHSaHLjY>n$=yK8lxMqP1pOgZSXS&d6cZ*G*vEjx~RJ2+Hx0dz-b^-GKhrgTlO$Q
ztaB`4fpLLd0k&kSWZ*dC7^|_Hv6FH8fue)WE%CwpfeW=QI#sA+s1|x7m7au&M7B}>
zz7*xsNWZvY{b<Q3mc_X-bwfqNMML&dm6g5aiRFtGW`kbCsb#9A&NyR6GONSr*hBK>
zS=u@M8N$;2Qt5KwQe~}8HDFiwxXYo)vFTLQ>8qpW`O?|b%J7KTR^95c4{!x)xP?4q
zKCGmvppj2>PefKRSR`7cShPk@N9(3A)^h4?d2T6p=Gc80Uhn5xeQ$cz!DYrh#tq~O
zFi9Rq8Dpoj=$f61J#C(>oeVvW->Y9Nom}loJ=HpYpBo$syj0j_=xfj|td6YGd0u%I
z_mX+`zH)ffI9@!7e1dw$cyWO&g6IQR1m}cd{3;1w28RQ0kD!h_hZP2o0Kb8<Ha@+7
z#af>>jkFjQPmst@o-LP+mMzmu)tjeBUAw(ZP}?|hk#0aQ!dXa;<LA)R7{C)j7pjVK
zeX6;(7StT?kSVkxQW0bsnIl9U!j5y#y~5H$)IvJIDU3dgUW&uP)4=>7kOt5+)6{tz
zdy2niL8(I#gc%Qh>Rakd>8o}Fy=|DpPP^pn)ctseFov_o(9|fZr!&}I78(g{3Y8|(
zi`|JqAobb~a42my7*5+O*^|q_vw^yV*bM9p=OMg8je`x4=Ts0^2vFcsXe@|;Zud9p
zNhIWv^FMM@w_VAoLp<I-hMFa%-=W8%he;MoF6=koalFp&cMo;Ia1kpbf{5uLzL033
z#f%RSx2VY~gHRh^cC70<j!GW&j=Dc|-B&#D)WmAN6Ra?=U~M!=-meKC4Y&cj_12D1
zo6y*3=+W(8L3^iMrhF{-RVJ=9*RZN%2i$L^WHALU#4Hq4zFVcQx6C+QE)1623@?uB
z#PVDTOd&c!h(pZyUclpGKfDD!%iS3q3J->Juy|AMVQACVP*PC7$d6HWe8j%X?DI%j
ziZ9Qp?a0@7Fj&{R-o8_BXduLwBpk#eSf;nv+hIFsY&W>9Vpw%5A1F4MK$!(vis-nh
zy4_0erl-1i7b7XJmEM(G7R?l|sNU;g02a^Jxm!;kk1ohpZrv(PFaLP)y<(M0m5x5_
zJ{+C_obexcUnk2lEiCHHL(DsHQSr_=P~DE?%Pp+V_0oHZoov&~toMfAel|wD;8|5P
z7H=+gnUb+XMhr)AeG(rhz77gRHm2*Q7x9?c*PNi9E9(86Ybvv=6I+^FOq*JBV&8Mr
z2o)JE*0BO`xejbe?yKF`5+8~l-b_lRO>*!oP?&kS5#0CiuI4mkgkED@YNvP<-`XGT
zm#TnOYFe5!W_F&q-H$OnS`=2+R<NwwHcu~Yui2fj9BxWGwKy_wIJPf)j-D$^ES6O`
zG`8F5H0=ToN3<r^u^$iCPFwICKKUNsE;^vzpn4G*a67!?ZcW=vq-Yx&uc}9OYe?B;
zN&t;d?b(z0mzJFXz~F3AA=j5S9}Ixu`?V3>C+5A{a@2?Hc~$RKa&%X;#^fOvo+p9Z
zuaBB7nR%X4?c`4O=fp{!AGz1Le=74UNjFeC=RG~H(q~&6s)MRYFBvu?0c3AQ&(~9=
z3xyApcA0?#iQe4+@=x>Y`hAU~#*N^yU>F#ck@PYMq&j~Ec@S3zBoK2jkU9;JsJMcY
z9Wg}Hk^~XVm({%+z_sEz6&|kzv}`TpZ_~LRb<m~Yhk~MMxzH;<Cp-`ic8af(UTWkK
z2(jSNsZ%p~mijx?>WDkkAWth?zkY2n{<^M*EZ@WfoqF*u!A<li#R2Vt8Z{nl%BXi^
zW=1>qc*W~~1(6;Jk7)uPaMR5cRUK8OB-ssYtZ4KMZS;+3T&-+@XI>BxPFHr|qm_}P
z9=@xUrL_aQD;MEE33lM~-(p%q{C^^j7F>j?QnL60HugsNOf-x%bcEcn`1tso_J+pn
z@`A$ur4B?~gr<&;w(PXDE-o%KE(|m__9nEy*x1-;>F8<c>8XJd)DCXej(V=t)($`a
zW90v~BWUDcU~gvYXl7%L|JSabzKxS37a`%_hW`8apMDy-n*FbqtR4Q#Eue$6e`{!e
z(a_QUw{4&*=igFxSu<B7OI1NLD<f+MU>n?w%xs+h<o{na|EtCSp{e%2G#P%e{!h*S
zQS)D#oV0%(_#Y1aXSDv60*8wmmXr3shn^djV;XZ81cVnvOps5}74$qEUQ=QIa}beE
z_ei#DDo&OG4P|Vfmdc%1GMvs^7KYf^XlHU{s*Z@^b(<$-4j&J!UUWvj8){?=iZBP}
zyL2+50E-bXToRqp$ZshZAGo*4c8@KD?_fPdPa7&$hZ%vFj~*L`7qzvQ>r-XT&6wSw
z;J-nTK)^^yb*=3qw~(MgK=DDq$-uHYZGZfGyg&q<+I;Q((FXRnf)}uj1p>wk0=<R@
z6nPBfaE17bet+~Zfq(-IHSqvNE)iX>VE-bJcYh0zzZPu)&{Y@xR$g9SPEJlrN@#pY
zkNQvp|17@z0%cA;-`dhOF)=YR>Q5_BYXp1-^fzW@bxlv(9Zh8)98^Ub%lp#<JIR~n
z&#R!IASD&9N#o+=WF&6;c;A?p*SE5=va!+84-@y#72YmrP=9_o6%`ieGgU8-$H#K<
ze3VPi`+zKvf1LvRfz;(2Y!thk{ra-Hv;=JiMIui21r+EVJN|EoZ=)o&0+08JW)LGl
z$3Q^wr<8!6Y7s-e`}`dzE&-6cMiW+FG@$rzUKN<%AI|aL@1VedvUgv7I!Zh9_^DCm
zLj39pfAKY>Q)R#Oh@?@O73NEAk`${Z)5?x0zJKbxMCHZ5K*fu@-!9Vm<!DnVZJrS%
zN!8TNv0W1p(oSMK@MW3TI=8Fyi87CIRCBc!L#0JE8r5JWw=pCdAi93}_U_M1dqkVf
z?foQt<8vx<Gv_Xz$M3Y)75>xgX0O*ix=n$dOpF@tE)LRN^b5wC<!ejdNgGcf>gJgr
zIsZLqqh*H4ayO-xyA$K>V;nIY_MO7fPl))vgN1Zh1F+JB;!t^|#a71Y37Ih2PgM8)
zYdIlW$v95nmYT}gYb3YMX20crvyHvn^WrRt5(8J-s8S#NI<RdOo-3Stq6ve@#%i~M
z>1v~NA0z4gX7-bFXqAm~egMwt@oc9rNhE^jUU5M|CNEq~lh8;qrmuEG)@<l_qpkbM
zTPW-BUPVc>-tuGo4<<!+lTh1``26EE3lBoso1?eU76war8}8R3Y0ugHl9lkm+WvYg
z0P#2$8&x=q`?Ns<q`?Z89-^3>%-hrtf<Jg4^{eq}%bw31`vsN*7QxG|pCf31wKwl&
zD}xWMgNWrxJeK-Hr!f1X7DY_7%{RS~?;ZL*joaw0V534`IrC~F03`>r&n%lBg*7cs
zo`T(^40(koWy|GQdHvWz2HYMM%uY}#?OtUDr3<*M<y_6pg#5aFNG6_V(Iy|Bg9D-t
zq^0{%7^SjZyMTk&3^9A&<Yze%4d%JB?Sy;|*UJ9JGeLGU)E4-!U{>+vA_rF2YHO}6
z0agv8f5_hmC@nt{0;_2HHb-7(u8$|)tt0yBMS7G8n0~;}uDGH%v#Wu7a=T8jo@+o8
zbl}#sVX>)6xfR)|D0101u2fk_{%#d^9KTZdQNw#vf(EyDLoj|ct#^$aQ9jRm7#LMb
z6Er6Tt&~y8mM)URF+MbLU{-P9w33#o{4xGHuh}KntkG8M**C*-eo5kTKYS{|6Q1Sl
z|LskciI|MgXy-24_(_qt_~oJW^z~b@*4vzHtJ7&3iMb{6i9qUT@6V$2WR;?Diqen$
z_T4|4DMD@|yO>X}fBNv6=bej+vwfKMmkFF1P2`pDeLkJ$RV5~Z_%dzm?5%w^y}OoE
zLpt`E!ad@w9PS@3i^sq52Uss(O*J8y^dz&NB=wEC5gEPst60hTg&4L)*9fmvhSp;P
zc1Kj|jj1Q>RQyEcVpuWRNZT2bxckxeJ#s^7O3P*4&;qKAyo~4(8`IQPP8y8K3khG>
zOf2@=?ETU#InPhn;B=OrvD!lnjf~TDuZ7h|({RI9UMd$k>ms0!<EiC@IAW@iC)(V?
z3u$=jPPd3KYGN{VQpovD-!==M7mlPAdZ&;lZLN-eF(qS5mycFsaTy+^>TqsZ7KX#|
z{E}32g$(<24KE!O6%3N;Y^gCGdV*>bUSDg<#bsj?woY&0J@pbC%A5>jUzI=N9H?|2
zUDfbOlqx*NN&FNRKPW@XczASe!PwAN-wBxEiQ%2R1TcLU9heYRa9}tUhQvbs!S!M^
z_jFtB@hoc0{NdKx=tX6ZaynwJ(J?lL%WdkYwCgW;Us0;OS@89i?>>{BSzq<d(rQYI
z&q)};ZG6Ff!r@FAt6P)HErYYK@y4aBDwv62mXhDoh#-1CCFpq-_7GwrOC|8V$6*C&
zY^6T0=jR!XT4&D}RZnbY!$`4jVJT*-s^mlv^5Tu6#cRD%4V`Nw0W9u-mM<aY2)1vj
z((5Q1nX$UpUTp45jWEt7s`n6jyVOn$gmXGFiC&o@feT)orJRNdXIwgJ#Ssjn8zj2T
zy<^2vY}X^FE#hnQs$IJodvkd)m~tf)GJ!TO4k0@h%4aN3MhM@68pdkof@t}r%zn8>
zTIkKb{3xq#pS;<t|Fen=`~AI1`#_a>qN4YMNxh!u8$=ndUTm?j^+a^#iu8AbVZy%5
zcH&C-o44CZvrZ}@PPyIq)D)(U=0~oLopAI^5h)I5L;EL(#a=K5mdt`LvDcXZ^w7%Q
zlOi#VUhKz9m0hpq6hXmY{Ht<U@uk>Ga47As-jtu^@bm2S%DG&FROvjZ*Cnu+9t!gA
zD-fydEas=QvoX%>Ifiyy3K&oJefF-l6(U@;&H4rtLFF8S)Hu{S*s7AWgbfq+e|?&N
z8^snfZAg{&EIuk*%W&o`+9`|;YosW_MZo_J<Cw+IH5xd!<ZR-aH!IpO-ewx=24^#@
zeHeCD096~j@sq?Ik-NHKuIu-EluJUbw~oHs+0=IvzYM(Pr0r}fB?1n28I%3rIoz}%
z;>3r<VcL(d@v}!;w%*6vrRF!Jc-NC36U%sC&5p(9tks01!<|<iL8*x~TZa)Pv{L}5
zJ0=mh1C6)l;=E(g@y+8=-8O>kJXe*g+6u*W!~Jm@sy+J7+;^x@(YsK9WckmS&XcA5
z0RKPMUbKRGZNZ^Uij8b&f8Isn7q?lE9-_R=S*9m^yYMWZ-<X@_8^+UEtVHb$6#e9D
z4-0!P@V88asjf?3%ukB&xO4dNs8IYDoe7hrF@>HvK3^Dd+Y>*g`j`_Vnby0ne2UN$
ze>O%DP^XKvnrJ)Y_c!)Lz2{^FP8^DwbiOS4E*?268DR3qk{zUpdCQQjekvUaE)xkp
zTQE$Y4Vh6MR6*M>VV4HIy$fk{#J(Sz4bJ>T+c%Z8=T4NM9s$1`oGAon-kHNledRYZ
zGkU|(e$(8XC7_l(+OpIvka6Z?IYrs4zhisP>PR9I8QR~&tJp1KNr-;Er5#Hq)Joj(
zCNf-Gtwa+!^TvPPQlx=;4o^7u=5v$qPr^1K+Mh&J%w3-CDQHy96VFY@U{^X%&`s_f
zH8NrSIspmhldvdwIiakLoftx)Fp8%Y%A>L)5Kn`TVRzZhF+JzpxFEY2wpPw0zGU92
z7p^m1te6g82q(SzevAIC`Ogu#EMjkZ6_b;gQEXuQv<!;EL1aoI;rraJ%{iIN>Gw|(
zlf!{gwYdT7<vxC^_JiS(GJZduk}^^)^6PvAk5<%vBwwq!j)KI4Xj8G~Mn!Cxrs4{8
zsqgyP$B$jmaA{e`W6_<3t{XM(mER8$+chq|SMj71DBXti`*YnP$}sx!p)BVdD_~r6
zd?~JH2K?0g@vh<RJPhAYW*P}ylaW&pnBf{12rA&0dAmn6Ujs+grP@ELmE$6#4#`y3
z4^}36Rj5do(?iO;ZAa$cYN4B#5f&rsvAGr%uw+_uz-+PEGQ1f~*x+6G1M-Y84!AT3
z_-O|9MH(iyt01M4{A8uePZZcivJr7$dAk%}H+yn##9bz6Jp$E=Q!Op0@G>QGohdd#
zoZ3p!&DrZ_!;OSDlV(Q(jfttti}OmHp^0^jTb`2_<Fnk3cfoAppk5>ba5Aqp7~GkT
zt_ZW>8}9It(ceQXTxm;wNN>&2(PLjsMYE1ntl?IBe*ILT^v|iz7Jg3;JR23LujR&K
zf027jpgT&s*^V~+Hca0OgB<T`xLOPCr}N2_Z~vC8d8(#dMmV^_?ohNGU1wPL#qVM?
z$7Fnl#pOF)yGp*LJQVjY)SpuI+XlDCR{aE}CpsHk?(e4!L6*W)>$bh#kS1k(s8j)V
zlov68Po_@YZq#6vo3KI|Ttp<dgF*JHXg1=MzH)uWeZvoEY~ljX!=8?|e7q2y)P65N
zdb4`MVV8U;F{FdJufyzMovAr5!4<5%vOh(7otokRXpG@@c;cPY<|u>%34>cFe5OIz
z7};@<Zx4m@vN<t>0?xJMn~ZAf`+~Rib8;j^COYYMT6=8AlkMNq)QI?M*u|~o2DU)J
zu)qc?#xnL4sO8So`E!QLvJw6i*an6<)Znn8XS+Ucb80bHaM{q?QR`%JQufgHzSR1v
zdu!JVO0TOb4=cX4t-RHiSU{tt6{EnEJoBfW_g0GdM#ft5-$PLRTJ%hQdBAr2;PkeP
zfh`U7GxbZ)Up<YqgcjB?LHmqT^J=X8lDf*Co%0n!r)*@hrDH(k&NNE0f7{P5E6qP&
zW46SIo<B~Fdr`&z?X1wz)g)hDww9^tY^&K_L&PGG4;tzYss5DFqX)6`&)PG^Nq`_9
z#YkKr^ZWj<5?c-JFN01+i2QgmRN@l~=&+>#SPY&^f_JIMeKt~0Y+2RxsL~i{w5DUZ
zR50%NWa|W2Gfg+u!y$#jlH1!7I3pr@+$wQp$})Z-6`Ngc(R}nY>$12tnmm}sMl8j2
zs?x8av<cmX!i+dFvF&;u`=kUAAJ&2G^`z!w%^{=}9I6L~V&sV7fd2ZIBlJ4kX<H8F
z(DLQ&*!fQDF~oH$m0GCPRf{{jD<o$9*2v7jlSWTh0);GPic*@4z0SwCUyUk8;5GK?
z5j+K&Zq9a~I*tl`7-gxCgK58eN>d!UuzK$K_mO*McPOUK*5WvgrP-_r_UO5FNKn?O
z%B|aI`uy!Zi;JhW0PUuKMsB|-O9;-a(u++WOyx*2Iu3uO6ja>TvJ{+Wp$WS7n~#AT
zv3GpkA%3aJ?O3t<vAGAAWJNwsz0|>a{>(ZSWQWA$mi)wg61DKEFN{Hkpa|CTc%_}j
z^St_s)@kq>JP*2mfWaXAQQvtstLnLd^U*!N{1)h#iLD{TTR@vAyl+amc^kJg<7avs
zhZqo75Z_rZ`C2on8gO~M{U<)p7E{6Vv{X>D0`B?fpuewMBSDs<qDXCjazxnkl%O}E
z=cpOM8`@?$CHHM~^1}H2${nf_<`w|pesgIYX_PPK9K6y^wMj(W#lQVc9XZDl7XXz2
zh_tX1&35+a#4$63bwtF$sSmb}xS<Z8QG9}5;YnO+U6sO4k5fed?#e*AL$a=aEj<>*
zVr!mcgh`EQR9m$(du#-x!rGFe31;h#gP<~SKJ>26i`43vToyR&e&Y7W;?J*g<)IqB
zES>0d#P{rn*<r#xi5jvw{gHE}bX)#HyQh^qtyJ#rNQaxlF#FP6qWY8SKJ#Vdh&j|H
zpnipqzOngb`;34_+LWbI9nH6~G$r?-Pv+jV|NCHw0>@O6LF93(Ib6;<hRIonXr`_5
z8IAxT*+6p-)3?3G)DFane5;p1mwzWDTCt>S{8UokTC|FlPC=51l`Y_tzF(k~<WFJy
zHYZ0u<CDL9N&jv4(Bu!h2CcXljPHE+`4RPQuf?Q?dvC>>7q@_q9*@WJQHKYef|Do%
z#Tf<K$lm2chi(fyuslZBb+YRT?Cfr(CM&BeC6{T%#hcS%$Hl!MPpR(NRPIHdq5UTB
z4a0R8p1XTl5U`?G06+^LX{&R813g<@^<q0)Qoe=06R^;*%9B^1mT%sNN2=kK)tC88
z=?M-dfr^8j)Hbm>N(wx97H<5>#PbPG11F-H4^UIAzC2V@XQ~zHk{<$zl5BUTF$44B
z1MWke?7*IgI*{9fUbP{W2si%fd-XVZ_Jj%92$f3I`Drr{7}1&L=KdlYKJ0GiY<8Xp
zgPTCyc^K*H#(oR;MamDhnk^`WAXwFsQs7Ri<b)kXt%j{U5gZ_1QeoX^<Lq4iB#$7*
zKkuiioGt;o`%y=3I9zQ3)ou)y?hV{J3!wz3b~dh89Sf=X2Lbe;Z#Iy2&6@9D-Q@vz
z_1$>Az|~Y8?CFq!9o=yHM5N+O=U1uYKFBI$3C*NKY?kQeI~==9FCWy`j8}3W<(Jji
zXQ=a2_)i3;@&{&f+_$RrG_$dEDIQ|j*&LXy%%sBnlB@9HZW`y>CPnSvc2y*TAH{-V
z=3wyF^F2p%c%kNS7v__)NShx`4P3tpGuA#I1UYrT{9{XOVCq&ru?`4d&&%}m|1u~(
z+#*2AWRe;N931?27YG5!3kNScRsjr<KFRzE^0IoE01gHQ#;f}qNEiLUhl8I52l70V
z$RO|M4iaFXprH74yg+^lA<93%3`iJF9Rk5aGqKNaAlLI9AILUA4e|Ff0=bzZ2OxM|
zi{a%3w)zzbNZNcc%+^c!OU$qVftR%ykP8F>hlB?5S)esjbW%X@f&&EHjYLRDKx*fU
zAIQ-qh?;2<2@rTifE?LmYM?+v13duc2f1_kO~okr?SI>yE;1i?v}x12YrMrNmHWgZ
z3>b5vn3oI)@sc2ZJ*y60^7z>FYR&kD=cSu-rt|yuW*55`XJo>sV}k@0l=NQ<4qnjL
z$85K-L?yUYqD67?#qt{w#Rvx*Xq^<nviL|lpZy^Zbk6;8(;a#IC3b|Uqbqj&LA_qS
z2riHq1cT1tJrj;hSYteSK0}&#b*38198K=lV1E2G@#6a>xQ>LIRo&O~mUff<u9?*L
za___;`#u>nU5k{Z9ubj@rIt0jI-s~SZW%_jR8cO%45#7@^TYKAWet2ou#3j*u3szJ
zUIf}#QCl-US>}gt+Ed{K&nG*?<ft&N&8^pWjig2pDkPlkUU!#GK3#jLo$k9=10KRA
z+o|D+z8U`D_GG1o|I94at&6R(WI>dXL44zJSp<fVa%Z_PKE>Zfcl!*wZAO?EM1|}W
zrOms?1DL7HDHS20s(;8tE>_mvde5~m?52&a)*e??H5w1v&ei9L$26OnR79VqwPnPo
zG@bBkXlmW+m9Y@4W1i^onBVmUe-q(YsgPlim29Rv?E*b<KR)rWck0ei-TN4x6A5d6
zADn+o`Cu*)#CT}K)w`XNF<bxL<Lt76XXS%K<u1LP!mvmww-gj=-Z%=2vGIYNUd`Dz
z{w14W!HQ&xh?_v<mc&gJ-8<=d>(HTm#j)uSR=&ea!p_;P)p<;0!jtAX=+&^*S%X8P
zxFrdrVfEy_d@@vUk~o0n0`c3yaI%5MczO}*m8{=5kiFYEX{a=nk4e3|e|z@vdHPh_
zJZxCf3-=TYj|{P-^(*rLjU~}dWI>dZc8z|Ti(!Lm8N*^z=-Z|B9_x^jIE6)c{KBpn
zxB&0MMoR0|_+arf)G0B$eOdEE<Zi_X>?ba(H^-B2kIMx@|IkxJsZ!ztO(=$NA@p8I
zO`dIKP}ih?SgA1ixAc)GTpda?q4VQTxntS4lm_(h_m$O(boglQ3<O$5ugVVjBe!?r
z`I#}E1s>Gnqu3V1vsm)Ct;C~d3ItOBiDlhCI=zTOPnMnd&6FSs^_U}amR~8|=Y;Qy
zM6QvujX!77+KWwFWV*4l7X@cl%0pp`l<~wmjC=V}hEqd|u}W%K)v}~?6yf$Y78BEk
z_<AcBKB~$sXAeRgLP&6aYf=qaIzVG`7@R_ps*dB%_XNekE|hb(TMJCI)I*<RSgPvy
zOx<7h$k<E{cnrFWJsi8qkVaz0avmHT+#cl7a2JhcM8zksM#|kQ&MyD)d>4-WWjJvR
ze>#(??G?+Lr>U9X1*Ve}j!`rER5)5wI|?rUgC@sxy={&Sj&|TfR<64#$Bu!Zy+)7i
zTwn5}Z6CrDlgV#HaZD_;F|E@}fc^aaPCMOYVz>5#li_tX(TWWxu+ZeIP?5MU{$u&U
z1TGVci8WXYJK(_O!NGgQS$&1D2D&=q8UF#57K@+~i3hcMzevvzdRY~IT8j5k5*E=0
z_NFQuAu<aeFLLTj3pEM>R~2`hbr*GZ;O$ZHQGnBx<#3H~kMg;ox@%P6WU7wWqZjTM
zm4yN!Za%*3?bF^PXixsedP5w`A{Q7+=9v8QK`&cfy_YWa<pLR)W>%I{a0D$^ZVnb&
z78Xj%Mx$y`z7^(m9Iu7T;r7!Cb>iWk>jqvmM_v{`RAXzn8Q$t^RRDgaKi!wb96C;d
z%rE+!q6A~3;-f=+M<*~+s%l_fA0jEu8@;_I!zZ-ft%_!Jqyfd~H;NAfgQR4Rvkmv$
ziXgb#9^W_M2O86C3p!NDq5*WP{=Lq3ZcNZDQ7@sm*><%&LGQ;Joy<1F!g&G~2~h<V
zt>OCGY1u9U8zWke0!Y;4%o)G7Jzu`*1!9zVn`V+Tx;!?yUK7^W>*0uFehy~(2A1>n
zF1UJm?YJg~Oe^<#7{IG%#C5ffygrjw>RG(GGKDQXgdFPAgbN)Gyr8|M+&s26ZD~^#
zwgyKeS|M~wGG#l=`c_&-poR<i<{G)oourIwH{HA)<b`;8_Xlj<HuXw(pdiK=OKjYH
z8!HG%15obWXm}?&%U__u5S&v9nEDpA+cG-Ul=6HgJ)Q#|5b?aJn3<iPuHJ3RI3g`#
zTpQ;@+J0}pns{odtCF$37|>h0zpYZZN8Jsw*nZAA@koFl3RaKWJvUa$Y$dGv2RF3=
zTtR4m9ltdya?$LHY0JF$(d;U$13-CBOv&>_zPafrfbQKGQrl6$$;hVTO^R_+42uo-
zL~kLm8JQa;lRbP}nFw3!i))hsvwhYl==XC|#MKHsbvtDIolJcmN35d2{R~O_3+qA3
zw5Ok63sH!ljz5~SnGAtJD&~8O?A6r=ONvVNX&6iC;5ssFxC90oY>YntHPYjdWr>p7
zcj5A24?})-S`K2IPT}jWvzi2-0SS9=#tREpsC;}b0+JYY<ATSYUArX?Zr`6Sq~{2O
z5S8CM7U<AQ@N|lo!hZ^r5pl9>^+C6WKlTu@YnN1BT!qf|Mg5Qxy2R+<NKH)V9sJE7
z`?a}_zA&iwXB*+Ozki!!Nzhs@QwN0IcN$oP&GBzn3Z|Oc(Syg`F{17y+0(7DDEdVN
zDajPBDfC*|Zdbd<Q;N_tyE{8{8kM5j65s<vl%w+cj1G4p(xSm|xnD`^n*G$z>S)Zg
zjR<^|8Ccqg*kcgN`Gk7V87)VxA$mKASUi_fwwB6v4`~WLZ_qmMcfYEycFJPzUWy=(
zk|@|A-{wi|H@u9lUP^~nZo;ThF3kxV0j^`{ck}^~uWaxN0@W%`iGPq`5B0$frqN*5
zDGt%}qPcQ&OHoCu8amLlx*dNWV(gB%e4{{v%`f98PgHCRfnYDwH2Ij2u5_q1pl~Wo
zG0AV^pWvjRU=I^7ZYx(-xz>L~{ESr8PV!e`6{6*}-`G`+FsS}eP8hDZjj;y|<b(-*
z;84%<C{yey(W`~$oV@|6SRqCCMkGe<^qLh}0!L7(;vy|J4d?7P!nOlDjM_NA?x5>>
znVi@tO)SP$g*g76t69T*8pltNx7ufGVc?=AW}*uuUu)9h?S!YgRK@zbRyu!^msw}w
zC4D==duo;j0)^l0et>56y?Hw{99BH;x6Z(}*z(hQNg2Prr;8+5B+7D#B}HJ=6n%7v
zY1tp`Uq32Bm>VAMRAz~~Qo84K+9=}M{4Izi({TgtwK3?*H80|x-1AYL2dJG1BCmVo
zi)*v4#a6FF3pN#xu;lp38j{zelSU2Jsz(NN-}_}@-NdF_-R9=Aw`NbUN#%wpuOLdu
zR;AjrW!+1*2DjwHKElH%ZUs*~uZpX}a=!Js){nIq+=z3-%X+|U+vjcM8rKZqx&2Z+
zi#|?S{FR%zO*~!gxTQBT<M$vaK){-K<DfnkiP^fS_x?^HJaEN}v8q{A&XM%e7XRTK
zC)T~y;%NIG@x%LS@hHrw(#zy^%rA+F;m|J7ff2orO^=~gKRSLB+hAa$WT+?JX;14i
zknyJrrN+2x0w(|XeM=;rpujhN^ihGcRpHEEom2u0g14^axB&TFh4yf#xJ6^(TyvSI
z-55n-2(7!&Z#eF=`A*bM(wc6Q`D6;KF{cTV@#s9MVQU#kKkRmFan(9o?#iLHjr|G&
z$9u}1f-D4>Wj7Osfg~H8v>F{^%{4kds5U|i+tS3x4)HWr0{Zv)S>NS!HIljV4j+Ya
zp3tb&b6YRzRqH!V88c3<0N5_8J=YbQQM0CR`u3#6W+FOINX_N0-n$Ylyh#}=aoGEX
zlNZlVPwM3Sw9+9OY?MRc6DQ&|-WY^+eiogA4Epoj!>wy}1JpGG&jo^zuTav9Jw0Ga
zcQ`xj2{&%4d1>k-`aG0Fi0@M*VKhoeCo2o@!!7E2OomkjFFwfHK`pFxP#t@IPk%VD
z=;L}-ziO3MA38e>MB3vmJmOt2qGT_1QU;qnyco@}n8T(e=Hw(k)7*;AExh^@kBMk{
z#N^fF1xpMNn;)M9Dh1iPFJ6+XXrV89s|WD&RfR7vM&`nr55!%YiR_7?%(@G^z&tnQ
zGIPRHoyvfF@N*>+Lk?bX-wZfh9v$(rGxkbnBt=3Fj{O+bM1Fp-5dnB%vqeH**YFWP
z)ielFtE5H4)#u`0h-gT3ac!;lZ0rvALuF{<kMPv7xdrP?(;p&7Z$i-Ae;596V@H<~
z??gY6a+5RSsvBNRnSYQD=7UaP{S0LYb^NiuXc<MLI$q3Y<H;|-^3vS3503%oYie9Q
zj?5QBm2jcY*jNB53k*t<ntE(AU?<!ARk1EdU>2TEog{M|zjl&P+(d9Ab6)H<^YQRR
z%beYXr|Ctc%W?;4^qY<hzacY=r#-Yg=;>_CAof>|?{M>DR;x$vwa)w@f_6a9_@X9)
zkBv~$4Sxtbe?(mVHERB*bibFA(N{Q}e?b_jnu^@4VQ&1Zg#SM(kcofgG`48(I%?f=
zBq*R3{l~juF_&B@LPpZJ1y&Bvg^8$cR=z+zGmRp<@<?H~h3g~;lU&>PQU#QU@wZ3T
zh>pgc$4Z0u9EFY)Ci(IK3FW)$!?!OcJZiULxeL&tLi+$UW@=`o{``f{grQC$*tD{?
z*oKJKv(Jdm+nCVURYgDV*50I`Ek_FbD>F>fw!C0rJOzoth7DwCv|WKkjLf6{pneJw
z?c<mq{iEvzk+A!t2J6YR&*-^U;tT8+Dd;gM#TJVtNHhGho88=NBCK^~>e%1>n!ay4
z4x(bC1=yeVXis)McXp8IaAuqwp(UqZPc!=B9|;l0gV!A5$B*Vh_a}}-x7IEsl{Njw
zIlYjig%^_<p({hrEI;8OtZb&L`*Kv++U|NSl+kK6T&5J9-TEw7*{ZO-8Bsyqwn#_v
zgR$bI?27R2+QI?;g0*y1!De?PB>rwS@{aLlEL){67p~su#VgNn1J15$y`Zg`K{NSQ
z-8WGGoN}173eKYJSAN^9d^P@O*N#+2Q-N#@?c#c-IVoh!RwhxjtQTJhOZ#}@?G<GV
zrw1+%vsnKKkIZ_uw2ac>p;9Q3I_Q2FUI-79cfDo`HIZUSOIzJd<9UB%Ws}cfEUc6F
z2DLXr3Ax-g#C+kcxzXJ_Ttn_{lXBoF>mY(QK9#bTob*ri>nqb7Ll&&tghDiLCeuc2
z!~06?TLG5Q`Lphahm`WuEdt9vXFMLnd7I}e{sP`|MP)6{VZi0Hfpz5w^h46re39qq
z@cuP@aNf<`rJA<F4Op+WnN3+$nQ->Yn8Ch&HP!1@F`Gfm;dy`EiP4UI2XBH4a-zj#
zS_$gasF3`dn(kGnb{*gTGiWI>;jvY6o~Z-bMj@gWeJ?Dsk{brL+<iEl{um#1a~73I
zBdxal?l-hgMsyu2MDN`F<zx(W9(%@trFMIBuOV!J?lpWji(>EGCPZ5gk`8}TgGD(~
zxR>tHmB?DNrc0roYiu|np)>3r_=mBzb_Va&VLytWI86pJ><zd+W<vqRgm#1Z$4b*I
zQL4cxreG<-{XE1Vspostl#8@#fh<L$*bFnZR#FiEhDV11d|Ys~);1>c{$3bwRAM4&
zwoOinq5jCL+Q{oXLt)h;CxH`Jv0);OQg&J?;mK+b#c}^E8~?D;FI@g1yCT<13lkpe
zYg}!*kjEtK3}Vw}w!oh@m%7XB*=b=0Zu-^ur@Y@gB+%+a`t7`E!HH@1ti?xThYfqi
z(j7d1Ms41Zw>0KGoJI8qDnEC0K8K{7n^wMCve=V3!BE2pv03meC6LRK#RIfhG3)o5
z<sv}A!0a>&1MyK=?WVUmvsIHC88}VT0uQq(=bSO|q&01bG#gk{sE`kKNU*UpIm=F6
zBff{o;!_l(jL__YamN<3<I9LnXqWLT9ZrN^*B3{nVZcT2>34isXAG#6il`~0qU)qd
zy1Dm_kJWMUH8gPeFFk5SI_G_f+%~C9%(;SivEuFx6ZKTrBp4fme&mecxrk3Ek1h(+
zE(+80DqPA>qpfvVeY1foj;jf^(>&?Gx!(jl_;prCP<VPQo6<{8(Dn>#o^&D=%c})5
zG~XS^^SkY9tke`7tf|s!ji8c_2<~6DoSsZKs|bWnh!)|Lk!{y3q*$LBvsW+hbqS}S
zx-kTNKIDp$KcN@?uEciCp>McLr#Uw`StMUcH5MOsdl^V<VA|q2lrgorjxY4|5Cz0q
z4FDz%>+*)<a)?eyaNjqxSrT+|W^?na=inK8Kf%P0%PMQGj(!P)ka|x{8)iBQ+p@4B
zXfrS;lB$HDXlTwd&jQfDOBThFhG}q3&r#!2V3x4iqN@rPQovU@yPTjWlYEXAJ%+GV
zq_^hKaVeUBx<Id{mybqa!xi=CZe)>wjxTOR&#3JB;^|~d_v8rSO@n!M(y1y82<$ns
zqis6%df94gE{;;ZTD$4`lMTyQpfp1Tl1zu@D~mhNx|G&E2pGWLKR-Vd&bSkHN0X~h
z1{6f;3|;K5NQjcNO?2(!qQ`Eal-1C&FKJ;Xw(7X9*{$V?VV?TVb6dphp2w=l4(q&h
z9qrk*<YzCoyqj&QbEiMdN91O-$zQljEF@KzoMApKcv+812rSA)cj!1Y;)Mopw!CN8
z^*ou`h#s-u*B>fB>o8Cif0fiC+rfivZfSAW<NYaDu3TGo#^yLs1^1<ONh#kHpL(1%
zErRFDak{91<AX#kp!rY&F>2@LhCtf-XR>k%G@8Bxl20w}(=7UGh}9KLrI?bI$W9t)
zppj}M3z7L-xH|f3z^wDG)DE6T$}Rz9LK3$|;%RB=b;3f7!QwMPxm40gnk=#<LaJ=>
z9P6RMX3Xf+lL|TNlS6oTxtE1{0c_=|w`hEb?lw6l4SFy@FUZ7n**WCgwJV(|5ve2K
zig<biy;z<MJ1RV!x-}lLEx6FO{_+q0)#apW+61w`og@jN4D26Et~NI`JX@#XQ)kyH
zrII!$sOQklk1n;_?3pU;V&x(%YPP3&Yt>DYNmy9eCe1YJIb4!CopcFTNFc3>|1HYU
z`jvu)_!kotKxsGE%w=}-L#QPF<3nMC`DKx?%MwV+lIU{S?}T*;ar=y}<2L;0`d(E0
z=L6vi4x|7PTvDoQ+Up?|xCIn@J^li;U%v-(GYdgO{bPGcfeCOA!$v+?f7w_N@RrI>
zbaZqnsi)RLTVU!O2$1kyhtciss~J99^m?zauP-mo_U?s%0uro1UUn@W1YMFUb<BEw
zaZ@f=c)L?GSC&i{V+<VD=&vd-C73EvwW9uCb<2~FM?iJ_i~F0`=SfKk#apdof!u$R
z_J04)07)HpwzaLQt*x!0e|lc#I<DMcNBvJOU{)!3OD5o`g!Bjz{9lPAkPDW?k_8S8
z2y_yV<gGkb{X_b<IgnV8BidNVxPNJW1wdxnf_-5N=C7AsL?ADtd7%F~_4^peKc_@6
zxdJI`5NJA}`}AhKn17{o*@2b|4B#{VPc#2#(Eg^1{{J%5x_D=FYR*!B|Mmk@T0Aa^
zQJzpumYuryp#%FjN8|q1E5>HT*w^|Pp$1GtCWuEl?HK==b~Xk1JE0xZyS!#~gu18W
zK^exbu9w1GIwfObQ<jtpodImSzgpP0*8LQW9H}?FJD!dBM;<AQKO(svjC+!Oc{+5&
zT-?vbX%$dc%)Y%;Gi<!wNcu126hxDW!d7`w!DS<ECZSi+`)qt^JdR`;vYCKZZPqY|
zA`^AHtsdl-kbI`<a#28UX>)iE!=$8jF?cM4NBqyX6ab!bIhx(E7ukEIU41&0T~2wl
z6CuGGl@`9y<bLDJ)cSC)$~)K`uQI<@wkG7nH^igA*-@TC$i3WhIW<5G6dDh<=S&c3
ze5zb-u@LDqEP0G6JxORTmXIk^s|H#0pjEBDpL&M;`Cf^u&KJI>vbLt#5<p*5psLnp
zFb-=|a@J|Q+hNa%g79xyl0o9-(#tz#jytRlqgi&+W}xTN7wgL|9e3Q{LmO6KR@BL_
zn*t=Zs5O6ieArYMd!*TD1eb8MC#Uv<Ppp#ku-RuSCI9omQ3shS5qmv_cAOs&t<~@4
zteG>8eO_5Gw%Z5-5bs4qv(PtN+7E9#c7Yc7V+#omTvm>_eXNNDs%h45yKzqfre0h=
z&>koDh%M0Db~+zAWx|!UKDZ&W!z+0P7m`d`X;!v7_ut>_>5=9MFBeT%XYQqA@kENQ
zvbP=g$^tX5g`uM2OUMu-@1-~2jNPrMvFO3~cRPF~(2n}2sjE?3%#llMWPkQuWPQGs
zC(i8RuBQ5;pS5B=1(Bfpb}63_e6IGOi2q&2IecF8I?F^ltd*fYqf@*&9WGzy?M_v4
zi31I@=NwOxC`5`k$2n32E2KZ_%(Tv9BS5$)PA~be|Bx)mImI?k2nwu;9H*>ux=iVi
z57LK6dU_N5+EjWqo*DpA1TL%~;1|(d@6$Rvm2rrj<WGjHq?w#}Ps59=A5ZoVV^>zh
z79XKJw-_NG!jsFhweo3iY!HAmKvKB<C9}kYU<C{RTIGV*PUW<wP>+e6GK1x6L~Tu7
zcl^NXXs3sjF|2xx0j2wA@9!YYe4W=*=PBg`t8za0zdKacD{${<F=eXBc7;!n`TQa7
z*?BgDfHM{2rDBI+tj3d*?TUwX?OWa^p;DP4qq7?>qa4Rv^tf$FLvt?L=@{H-02A+{
zWe>dxt8meEyjZ@nMq@RV8n`YCHP4uAJ`=I-8l<A`ZnlY)b}ZDI#IjY?!D)k|+gI_8
zO$9sbuCp#Qgx6s<Qk?zg<+r&XEF*9o8m?sNDrT;q=O|98Q=gm&0l&Qd(ElLj{5MD|
zC`Zk&Hh1d`o~>UkqF*S8D?jS==|?3W6JuE9UrN|WXjvU$0F$7RRO{7!8Y-d6XCv8{
zfcp7Oh?G`SETg_)b$82a<$)#W7YPK($nkNdSLLaJ_G0|h`SRCNiC8<)4U3t2ER785
zd+jR3LD{s9=m_!3SM^@%m(iq-1K}J<e?5@@OjA<=-GI=#nzckI`TQQ2234F0zJz!p
zMRBJ;;pJSdr_0e55$EQguf8y8e*?O-Fl##R=ZT_v^1!(S;v<dRWYY;aRdk&CxA#r~
z{+=*4>;02~k>IJuzu^4d=jiXp(D^ssd%4mx_7|-F1rIO0jzA-KI>4Ry-Nj1J_%E>f
z8~pX~!e<9gq!(IXy!WHEfsWxXU<QJYc5b8}z?c1=2#oiBYH1{40)ba55NvAW1t9=C
z^dSKZUw>9mk9`G#4k;k8sYDcn0-jGkiGks3z#_Ua@Z;p&BmsiPMW~!Fz=`vT0t{bw
zTEH><1vcM+VD<FN^j{DDk2}D(Q|6JZ{=at@#KD2#w(Zu4ssjQ7A|fI}AP|9xCD6<9
zaF|a9_y!@-eF_Q+Qc|#*H7`z1M&h<EPHn`*f`RAE_V)KKFqqnZms1==fAeP-m6l?2
zIG1@l{(pZRWa;;5rF`)aD2rJV9TFXcPe(;(Or88^mb}<2&!uTXaQw6H3cp(6UA9<C
zGLwm2G_poJLMl>b216xpPK%3(VSSr0Mf-XNg>t?XcY~*gRH3UQ1yapT)hWp!r&A%j
zz~9qqi(ppeX{CArjkuDQjfyPv{HNXMft&be>=K)tK{l40@qPJ2eVfEwm?O#PqcshZ
zQse4M9$7-DD^ueNFfI1yN-0sE@4if-lpEpcvPonsmp!x8T8V;|Spj?V!dr&-{1Bs9
z1}8fnd_=NgZ<*iU%QsvCS&Mg#a>OlFhxgE_f!^Y+iK72?iz0qD1HR!L(L51xeOmTZ
zdc0?CV;Yt%JV|#kqa3&R=l*IVs!3v_#i-}r*kMd$lS~Kc<$dB~JV_$QCFcPpH_NeC
z<Eg}=5>`j*SG!wY)1Z19u$-MP0c7UUSj5m$_}620(+shAB5LZdk87G`bf(20x9KQ%
z*AfmK2<lM>x3YdRZ)$w?-6|Gesk&i+UA2O_Tap&DNl}!Lxq0ZgH36@1+Z~Rh)~-D!
z>EF)#{7}UK6ys*Pc^xzw9M9Rk5S*ins?9@vEGijLv;Bnh>gXR%MmEz+uzYR65-aZ?
zgZNVcU7suFzc=XsOuJvH!Fd^~ogFX>tg*CA%cwGB@Q8}<9E9#z&KXEK-ZL{$F|M~w
ze6*lZM3h%Xs?}~IPNryuX!3m5QK|T;By4D;o`!G#6IXv5lg`n#`el2*R$1C6CX})Q
zb+=NPMSF;5cQK0t-t?$xTGPZGTCUDIJ1Npq7#{C^ndRF+y<ir)iB6dM&&fQZR0dkl
zo0P`D)oRh}ZXV?e;an0#rxqtK7ZUrF^P63F6_%0K`Q1ymOqd_+G434hd-2UG&W6lb
z7lq|`X}V0Qxd0CR$dpuuMf1_mgUK<GNG{a(=d;BO=Y=uf2)OK%kT#u2k#b5gAu?Bi
z-}ts|-IbWn%X-j4G`9094V^)2%N~y_XPQyl%`{*9Ig{@m7`S&?!{%pNeE&{R^>0TX
zmyr-)pdZu>v$$&1YndI#vpIrgRd|!II#+uP5$G)s?%g}^8NDCzNM{eW1UB~~LIUzU
zTzyPW5~wX1FDCq6d$QyA7EE%;<DElVT#g#L(-}Ek#P_{_*lc*hy|oKU1WnDuZaew~
z6noMma7XWQ>mfS7KWF#1WS0ZUz2iCZZh@ZwyVQ@ZaswW%;3b=C!eN_yDE=Ebhm5F@
zw6sQ%NZinmjkXfPTF*7)X+suiqc?qR!Giv?M=kA4&G*iskUjgoh8O>z5~s5ZM%3$8
zTU`t@E}vlTA$#Lza~2pMWa}|!X=yz1rR!4q=~5m9Uz^8M?}VzH_T8pug4|KJ$Um%E
z30Rp(vtTW14qs#W2jVN?O8H84Slj35pm<TKT#!kjIByqS5FPnX4T+OCKPP!`FRjl7
z42)?3{qt(dX${2hpAY1IO$Sh$``o+9Hnw-1b8Zef1s+dg9ZBr+9F-9m)pEd4)Ro#m
zi3D^zD*?%!Kg60I^(7;u;TAabS4FTVn*&>5n%J3#JOE-C*ZbL(<HW_*ZD-cGTq(PS
zKT5;%xdm%kUHkQDGKh8~CNPYNBJAZ!)b81^!oG9ODWrfl+q1Xhs}RTe>u=>>O6Ra5
z#Y8J?`+o>rO-kf3y+^D~c^e*nSoWv<Cjk!8TE^b%j39x!#p!G)J@~^lQ-d}{CiB}b
z$J1yN9h_NSe7O7kvO?cejE|j0<D*}Rzh52!*RPK3KM3rMswL!v@-C6XCrZE_)+(Nf
zU#YFOOEaPQcMJ@)U9*G$$JNPjnY8ac%Clk#$uD#IsYM11Iu6avF4`9>zc%u*uP<B0
z<UCi>bP9tbrdqYjxzyfv+JeU9I@7L`D!z#Xkp2`>Qr`)42`z#cfqpf2Kz$^;c$~7{
z&RMLtfez>u#N^{P8NBJqW%O{B+Fi$xBUWu2j;-M&e^uXN6o>_$Y#`bHX3nV-UqT=*
zUM_7z0m3I#A68r%CMAQ^7#wh2nw0hW{~v)p?UNzp|Ers;jEXXh(nEttgOr3c2r4KY
zGa%j4AkrbyA&oF74MU@}NJxs5)KDUz<j^G@64Ei$F#8Rjv%6={*`NDo=hxixy>ajN
ze&@aQ+*=ZQDos`4pEzdjnE8{bd}pAFJ}lv#%rl}^&tucsZdIGG3wG3>KL!=+D`r`6
ztJAeY=wuy<X8t#DOsyZj-qBLlll728Mh<^+*Nq5&%@O???wu<~wqI6qV|!<>gS>{`
zHR*e&^isn2@@dRXnKxm&IKrxYc?dnj7@#6c1g;^0b74O{zh)FDzVs`bW<l|MvnD`B
zrqv+Yrzm;ZVDOeljb+aR+5(%Nz?3$GG@Hbj<krq-xRMYU_9R*~S0iZn`48#^7;Rz$
zr@BPr=IH&i(f9@X8UtKutmQIuK^_qFl;}}mK8pE8aoty>ug3wDn>O;0%vsZH*|4_X
zPkz-0swN$t7@PxBNFJ%g^VglwGbVFBtg|!r?iQNsi!E^z>rvISPW%*+Ofp8o?)dns
z-91Q&!@sdkBa;*@letxC;@I{_VOSYjF@L@tL#xWJRc!82^|H9ryPJth!`m_$*J03(
zU2gox@>fEv3GLVJoEfmfBnjnn^FzLf#0N9FrM)vh;YMjGPwESDf8o=}iHBZj<xXtn
zY?;u=F`SFkchCA2sH}{b+tG#A5-X%R2k$L+t49>G54Eg^D^m&%Ywwc$SBh%qUy7<;
z=5gOQPd@?H({Wk@`_(|COPZ_G9sd<`l`^wHm1Lo$oH+65(8oDe>l^yrcM^;5TpiDD
zRaUN9tf9WgBQNxJ=fIP6;Y9Wj3kymFygR^q*QuV@b@#U6Iy`ML&*)P1kjQC{!PQ93
z?{lG*>@~D2GtgZlkHZ{j!|~YriCS1)AoS3QIZf_xtYqs7{%R|r^={^S6k-n=9P9~s
z#-ry%a((IJ1SwA{&7hclV>)YHP}TBqt-e*P*=r@j54<i?<y_%R;805jHi^rH(a2W|
zmCAFJ>He%N>|*h8SG(Lma=|Xo!Zf!c#XpnT8LD(~5{MQ^AIQ!3-ky_wls3+zm+Mz<
z%AaP-A|-6Eeh6>zjXn`_7dL0^YmV%zv)9zWb+*RlK(!njfDYoA`B1mJsF7~UD-(24
z*X|GBJn1RrOSV1G_IndXegBEy+uV5ay>2{SQloUHxGvhfX&AeU>c}Z`z$yil5Gm-N
zAbQ=t%y?*X3#^WM(SG0mV4TciTC8oA>h;cOb<0rro+L{H(sgU+_Px!)m4kTc#@}&`
z>>{m^c#TGZ*&+ip)DbMVunxvM!?4L47@;}+XzxWev#&kqMM_n<eMKjov+U)_{F9BE
za~d@g*M@o{*1hto!n(S`TN;Q}Kbl_Gw3HpyLWb~5<GeBdK;l71<OdeBy~8hR$!J?&
zH^yYTp`eW3;-sX%U|P8TTi4LxhynO`vLQc%IOT$N5`aZxpNA?WNI0#BX}vf)#c8q;
z<yE<~?$#NIr{T97@+5j84Ba-O2glQPj2=%)`};>D$?Wqp9Wsab=ROUni4Ek`-uDlU
z$>zFLKRJ%|cc`3YiSN%N!Ixsc{^G#$8mVQE3vSksNfvUkiGLF*tL%O6H~Y!orFfxZ
zq?9moGdwaDl1GRe0^&mOp&i*!g$Pl~@mIoqy*btmWn1&5#ndDm)n9AcCQ*_lY({rQ
zT0J8MjA!ctJ=LgauK%Pm81p-7ZeMW8=_@7Nae~n_SDYrE@O{r3*c1~)2stKc>(-MP
zJGayy3p%T$we4I!9|}AO4Dg`{@S@O=6xhed-33E0Ker)+cge8;(+slshVo@$8?q@F
zo~w(saO3g;aAw3p@Zpy1Ux2(y27J8hWJ5A5ps0E=R~>YW%!T`tO9FGzHrR8M=ETIr
zSMf9G<UgcUH$~g(N+4pcG>xLR-R;VX3TF)slF>W=g1C@&IxPMVMk47I{{qav);z-G
zu$nEnwEsnpO(@#gq6#T7v+Z<|!NJgqvB?z{9z8>SeTsKWEX>yaAvaa$78X<%HPfV|
zq~6}%1_t<Ga5FcGX-e7h^oIKYHU#RbQ@Sy%<A%i20<gmE=GY*NGkYlyurC88s0SY1
z_5~bKoNG!D2q1oQ0|b&tssZp&EZPAFk!k-u2m~U3`Xm7aqLjt-0sy3!0Z`B_T8kY5
zfbcju06ICO4Hp;$!bU~`L=ZNa3lOZP+sA;QuQ)*<zz=TF0*E~-UpHbP^d7>EIL+PL
z!KOUBlzb6__X0Dx7}x)LZ_nbO3!U~kKYs<0_jF*ul0{m(lK+D!u&@t%2>P1idZq1b
zPphe^NeGQ=f<#{%|EE(mXZjR#5`YA(SKJ}nTU*^QrZW3sR8W~t$qz~MmU}!PEWr8;
zfe_$+wXmAD@!n{?77-DVn}J4A-qh1WNeTbu0}w$dTNCKb`vt;|ser?67$qBgGj|9;
zg7477EbuTP%l}z^4^)W1PciPVtCHaU<~!TH6}<$G$*TP+A<-wrId-wqDZN*JDW!bG
zB3B_ZZzt=$@MBJilCNf4=V&{+8TpPGa}ea%$m7d;Z+Z&Rel_ya#B}1sX{Mrr#C5!=
z;-7POY4VbkW|?N14pq6fe+nMG%3MF7Le$mRnU!s~WIac2uKF!m_WeZXp=am!A2kmU
ze*bF(H;!s<+<L3nEE0z_lkFX-FaM^lK4#)U=J=aI_B1>{fQ*J85XC|jTe`B%iIMMG
z_H72mD`p!WZ(53sOPixy$Kkd%=ca9r(av&k{P?lZsFe5vkt2_8yJpWUwMVo?B9ktz
zuKb^Q#o)#wU!q3MR$3;bP7%x7<9Dw(dLxCX6pgR0O;+TPE}E<JD_F#UkjE6*(pdyN
z{Wi|iQEMao7XePwf7>X^w~z18adW<jv>0bQc7!WX$X$vUuPfJ<&d3m^cD*qPoFv>D
zEZcq)oi|q6YBpQ{Xw;)5{-n*R+&;iaVXsPiQ-ge+H?w@*f6;%tFwel#xqM;X2HMHf
zZ);nytZ7H9WxVep;NdZF^7MMJHc;}Xj&_(!N+vyr8udESHm|N{Q28HxV@{jlkJRht
zc*X)G)fcal*Lu7_t`Rk*C-ZUCic-_ZHp;ptim1{^{NN@nrXy=;;xla$;Dc)8Ms!{-
zY`-6%vK~l~<WnhFt>c)3Z#_O{B~Glf>-p4Q>NakBAvi1rp6Yk}J%<e0rZvi|WaO4o
zxNd*C@}lJW&o|?i=4hc^rWsR8$FzobslF5ZqG>klM;m|mslpoY#2fN!4r*KNKP>om
zNcpCnI&*cKOD-7sWplU8>A%k)(j6$4HM1*|`P8iw(HpqQ(^r!rbn^1lxpXbHAbf~L
z7@Vb=IWEQaPP9fch`~^LbXcA_u*mZAajihAF%jxE8E_~dEcK5O$Tl;U@x3V7mrg6|
z4R)*ZDFc<c@}F6!9;{YIhgT8eDJISn3apMl%$&2l6*~GvG}eqYZ`wb2f{m=t$FSzh
za;D)szuN+>YB16A^MZR4nb(3U@J8T0ubUv2XYbxG>06!5UUUq$7n;hx;w>DW&946T
zDDNP%pu1A%r_$@jaFv*C&pzc+cfyf2+85J9pMFf1@jlbsYdtU}S0$@$5Qj-6YDDZz
zdSUPN=9l>Agc#VEZk@6A=i3|EEy=uztMrr{*@g{3?gQ5VDm=u@)89_<teM38hqdmV
z`<p|Tqt4}Tk|wp5SW^)mO$3D}?ya>-T4jtXB<3wPrlI<Fu%W~%nT@@gb^54vIk@bA
zV2oz%*=vU*3yF~i<23i!1OW;i109JC@|g|sq*q@(xwWW8c&GFXe0rS@&E4-9e_u~W
zb8*+D3(gui>gLi|OzYhF{g#1yL`ZrqRT0U_A1HaybgPl{Q)XbX<m>WHBgFwPRn2im
zX~Lt*oEoWE`=#-eKth*R`ZZh<hy{dVV+pl4+V?Z8!^k$nyd%EYPeXNuZs6L-X@$Wb
ze5uM4=CicheXbi9^H38+{rufUO>5hX6godV_x$xXk7Kd?%ObX6=hDcD{cTU0lj*~Y
zg1_CI!y49NKS!cx^bBm%(G{b^(QW+?(G}?*>9Zw7`lxgmUI+-=7lpXTbr+eXkBho9
zQRawDu6kGIb}p=agx%wxV{U{=j3Ym5N{AgVGyeXNsGdVque!CG)`*jdzy<Dga4y7N
zxFGp)P<!F9Yk1dgN>RGZdc;G~%K?|MK%CGn2mkZ9?vrQhX0jR))&h2e&h{*?7DHib
z4gDS^@J$6nuVbO{jcYF|f<K&c36Gd7kYD*Co#ZHMI=ym`=B~+F>aKy5>A5r+1T!Wr
zl=G&?j4h6&iVxKtt@7>uRHcM{KpSOyLB8KDjn!?BH>{p_t7-SaEBWD7dVZgILjU`E
zrTj?^D)X*^u^y*MKfh@3nxL0hn$Awua2!?Y9ktCDaJSU8-Ksc0XiCG1$4L1Fgf^cy
ztf26Ckk&_|))!f=5JfINl}_MMw@G12f=LzQBtyvdf)D<<n?<L0=5OeP(;vFnl}Zjn
zN{l+0whmw&h9nYf!6M(UV$O~9ho=&Xeuj;uO$V!?n%q?Xl2!^&M!LGmbyA5Dw>!+}
zDYq_1Jntt}(q5JnY1oWV4m5{J6a~x6MCHD#J!{HeoFifORmR~Pj(j+dr~j<kv}Tqh
za!s9id6akM?&G+#i|zz)FhV(L1AmSRJXX^Gq}k5&iz{OM+mZT!A%GL=>hW5!s@NmK
z12ox%G;|k@dJTnypwj}LL(T)rg;+TL=ow#iJ6vTYE^%^{$9sPqns=Rt|8mmcKZndF
zh6|71dYqNcnQCWrqMW%-@YPg^H0hCUNr@+{1=_p9aHaMb+p$>Vu@ZYr>@(boOMY*A
zT>`!^dQH2pPMPBUK9u3IJU~nx=wzb>LTAdFJ}_Kp13Id>GTgekeSl0hLNz!*3f+E;
zXNys_Ao545pyNp%hJV?=V?MwTGwUMmzR7_r0)`la0uBjgD%1=U>n`){5W{Hyb{;H*
zdH&0fST_o~3@|wYXdcqtG{K<*#yL`xJON<=>-%HWdgU_S%FXWiz__?iP<+hBkV;I1
zTpw<67qf&#3Xq3?_5=Yu07ChgemgDEw6QnVT`Vw8I2xY;cmTp~0hp~fJP38iED`by
z7^gT4!NG9!5z~q1l+WxDDQ1cP8=>N=@KZTFM+Z&68+pFpQrR3lk>r%NE>nZ$JqB1n
z<^r<5pZs_gzfsqR`+|AmI^;Q6{z`lH4b>PX%`rrDMRj8M#uvrLc(G4(>u{g2Hz{#O
zpRIskBH~qsRHz);pI_u`Bj4Y;)dMvirh8Nj%*?7Zp%b5ZHw5`q49?id1`<Ag&9>6-
z*{&d8w=J|?W%W9dcQJEuF)&Dj<axrBr<XKL_NGQHp;^@mKq**Y=$$3dgqkv&CyLGV
z^uQ>ES6>Cs&zO5?>A@e(wPl=H%05f(--5v}@vu?R?O?6oE&_w0_=c<m0dgYP<y0DB
zSP5-`>MIRphX&M-FbaEisu90w-pWsB=c7H&eY#Q?aCE}h*jN&gprEDr!Lw)fEHN>}
zYH?>@l*5z?#GB3)Q!k75&)~;m)XlNmbo0+dLU>SCO?xwXfB9wHCXS730)v9s)#@^M
z7fweS&K-voxtVD9<CP?`v-iaP<F&(#NQdyG(=sC28!UcVO`0qJSR?9?iFZR3W{(D;
z-V-IdrK9-+EuKrF%Qp;ZY1Q$8kCc??gRrQm&s4|Dd<d)C>(|0Fza2?Jon>;$w6R@#
zblIJZH@b%x<mdNVcl+g{;Trixl^W!{X7oKp+4sMl$C`v$t<n>p22AzU^G>MWC#hcY
zx9bCE7{G|3*j=4HI7O9mykVEp;>8^!iXF3m*I6jCUmSEixg`BgyL)_ac29ZGyx-DG
zhsa05pPyJ|QRxz<UzW(3h#qV(=Cs_rB6R(&mQ(L}d~9ph(V-s|)v6L2`QEhlDG@Xt
zwe@g~e)Pk)TIYS>f`<gNYinz_uX&u*SZ%7IQ?PxmyT4Koo#(jBgv>qFxx&fHo-Pbz
zmNV1r6kd*D+_A?y&tq|emOuSDa|>MNUNk-v9rhF7w4tQ+OaJ$D+*Po9%0vp#&0%D*
z_CEK)k@Rvps&cM9)B0}0N^Mu@hugc=^6%KRwz>=_sg!VUF5Co&>nh7?glL$633U$d
zM1j%s;^@ad;SWq|fs(tz=m!+lzfbaF<T}YVs2VFI9%<PcK8QDZkkHq6i<ns9>X_W}
z%0A-35~-$(E=6fm{sfW}_p@#yj*P9bE}7Bl%j|sS;*<*EK9~OdGx2im5(7T!TZK9m
z2EF(B=GW$JAFBRpoVcTpVD@s(g23XmyNkIS*>+zKG-KV9fm;}tnkz?2np_SR@dtH(
zV9wN?Pm-H3-rk?4Q-lxg@|}~SAncf{LJi|6kh5gBiHHaaZq?MXbxzUrfS=#V{m|fj
zO^e-msgBpCApe8ATS-72jxebv5zw|5#SJjTdoUIJC3<D!C75td_T}FM*@pQ8B6Bdu
zaW+*NZ%VIiQmuv>E!U?6WRB8^n$^7|Q}{>i-}IrbSy9m{p<%=k)e5F<%I`i+C@@{C
z<>{pmkvW+xiM<;0@$-r5h}w?x{1)KZMQOP^xX&7B_)sfMY~?cnd#%m6sIeTq!F-K_
z<l7yl9mBg^iK<!6+#!<Yk!(I)-OQ0ksxP(~;n!cUH5p+xKFcamdRdM3(ye54X<W{d
zww(<5bj`B!2%#(>#b}{}EsY+2zjN$M*a{9yKz5SRX?EF_*V9t2f3!d!TyS8`S>sL;
z+{?PPpLy}MVkyCU&}hP_+9U1U<Glj|>btM!TnSr5^XEsXwwNH4w3V=cWphoZIsM5t
zo9I^HFRuq$i!8Be{J(1({^nN^c72)A`6Bj1OO6?OM8*Du9sb=~XFhA@*&!{`GtfiU
zYu~lcZdm26PuqUOzRDPEI;fc^2(jJT%KVJs`J239czj^ulWa*xq8IG^_o6-}`)2S6
zTea4^b`h8c{8;}o%d}O60KXO(=M>|&I~Zl(9AuKB>zX1tWNLtNVH8XzB)1W*NRAsb
zu-iY2r9#dTGr&efnaHv1cDwHGy;Qm3kOTmU_qp7%odbG=mzE~ttnnI>%c{p=<vz9J
zS6^GZ<|gr&rXnawn<8>x(+SQbkEa1Bt7BX2pp$FH=C~yK{Nhvx{*E2w;?#mI&a6LO
zjI-;4iQ;plgXf@KeJ<B-wU1MJ*LtmMd7NP}MiH@4Y@A=Yf<tk1v9K{Kv0+r|JK~}n
zl?r5<Q9B^gC%`cQ%qIdG<KcI_(HjLzO6X-CEWpp7k&z+shy=p~2#fV2H>M$7(KR(K
zfUNuW?VGT$u=?|V*fkKA^*<N;o40weQXB*4`@RELXUqQ$J-7GpYwE?t#n#phdTxd2
zzoX}_{oVq<?Ck7Rnrk%+fgb5i*BE-98y-U-v_xKDG(tfM!GvK5gnJvnzcDO^P~E8D
zUl=glZDBENY=B=nKse!cEbbfDlY!8MF#Rhx;5q~JF)Lscgy3l2C~+<fv`%Pj5h$z%
zK2``|et-zFfWIJIbwz-%Dej=aX!Q0Fz$U;V6}x$hA7TOGm-busn_aO0>jUSm8#XRx
zX?lQ2&cj!_*%b#c3Gl^$)CaJhg%Ch+JtcwN?1~Q<A0%$=nCu{=-3A~u!Wr(~i1`0d
apewL=DhjS1^}rPb{8SV)<-f>Tg#HHvxi7&0

literal 0
HcmV?d00001

diff --git a/Images/ipython_links_remedy.png b/Images/ipython_links_remedy.png
new file mode 100644
index 0000000000000000000000000000000000000000..4c200e207262adc5dbf29f3bff269326a2818055
GIT binary patch
literal 10566
zcmZvBb9iOX((aBmu`{u4+qP}n_DnpnCQc@{Inl(nZQIVb=XcIM-?`7d>yN#Abyrna
zRX<(*zP%z8<t5-@abN)e0KAl>s4@TmQUh%3LxTc;$I%yf007t_D-jVzDG?DuMP~<d
zD_b)FKoY((#Y0tP7<2HjBURrLCSLvs?-)D|#Tx)|Y)C>H^c}^7FnlSwA6-*PRkS;l
zK9aHpLvSHjv!;fUU3CgV3w+M4)=*^s5maXFmZ$6e^3nNXjAFCZ<!~Z1&4nJ2gis}#
z08$Q!qnbz&hkRz<MMX(_q5y#41cDppj_sRCEg>TtZ5ebuK7x=Ew40E`-dEdy`Uoa7
z4DEmb#BzIw-)Qy8M=16O5+&<F1izQqH?Zmu9~vheE3*nEJ&tg>MJfddDrE1JC-P6n
z`<Ey%0Kj2WY2+XRd4HUY;p$W4gb52S%a373D4FM-^`kmJQy%`Z%}Q(4oPbhfSNM>~
zoC{z~&8P?;-Y=r|v}QPxbu|i5N+JFB+xBMivwnCiF7J@-89SFwj1oRZ)Cc}<tZW7|
zj9N33m%>*YGmj_TJdc*sJl#vn|42)IeBb3=q*yBjzc<^w3azlqPjor+$SQ?CMzs&q
zVk~|$b{O|NXiPAK7KqZUcO+#T%eYt1QH#|Ca{olyLB$=V7=o9uKnQ)Z7`3x}JS6qQ
z&$N(<Hy=Ix$5-v;o}(?REQTw@pXNcF;_<Phs~X4>2~1vH6t|eB;8b@NnUwu&wpI<o
z3cI00m!}0?q&&h-<lO||fsA*9;1AvCOCS_T(8PkSO@x>o9)Ksh6xzf{<&b4r_7`Pv
z*_fXeHsh`}^&X&t9~XQ4fObE|=WIgA?cd~uU|UT90O!%~rYYn=6H5e~g3EL(7{Cu=
zK)R17v(<zu?~nH0U)5UJQ$gfYVB+1lO8}@XP~JeqbSG$NsA)nFT5w)M@H9c#lwfp2
zI!b_;A<Gg1bvJ$u0WPS-AEznsi9on5*cK4`AfH^QyC9(}&>z8%hPc6myrLnNapVPv
zX@cr;V12>uxd6&9k>62rNh#4$M3^lh8t|!urSo7d2~^?UL9;^X1s8MakBH_FqXivu
zCmBIi47n@dGJ05yFvdbfwk<tDGGQ5lZnu0rQFReCdL_2RpFn-VI-wr_Fl;e_6PkVx
z!XjjchPxK&i5De>h$k+O6E65tDt;1=QGnO_?T&~cUI7a?G}Or0mJ$D_q3oB?Fs)#%
z5UK%HLj)rnrc_1`#`m8}V;r>T<=+~6+Dy2XJZ-7nshR*fp_PBKd-Ml3dJ&8_cO5w)
zC%zm;!VQpZ%{Y!a^|$D?K<d&|!EpqB?cKdj_oQj3;K!Zpx7dZd;<=W%I=pVa{`M3L
z&McI(B?wJQiqiMp?K^XiQ4d#-T@R8`W{EH>Q5$M+gzpZsG0ZifHpDZLQxcb=G+|6K
zyTDQz$BM2rQ&pi&_Jpj1EO9v0l;cLXF1$TdSDs(IQ~H7;;8%$fEtP-LGa9xT>ONs%
z@=e@LigaPRvR_HWob3s@HQhb&J<NSRirlbzM1G%!cBN|RT#2jBaWb>gvS?amyCRoD
ztL*dl8@c^dYEg6*bOH3L5$a^=<XP%<YLhZnHJ^FMdD(g6`QEZc)v)<1>mq9qYu`rS
z#&>JXc}Pp>@$dtQ-RXr{gem%2$Yahn>?{$5e9Y<C8J^?(6YCTCliHKkljIYN`ByCH
z-lRW9ri{{9gV>MQITl9~hLcEROPu-s1LxQTtPO0wtl&5yIPf@+to%02Y+$UjY|WPJ
zR{RS+mRi>1^V(&NI;V9DGIniEVeAUO6<=E(NbocVl_``hS||Lo5VDx`_|^&aA}-|D
zquLnmvAp@ct-M$7CXS<zX74)g#9mhKCQk({%2<}izL;~}qPWZn4iYX2n%7L`&Kj;w
zjWG^5W*L3kHX%ts9;NKJ%uvdhE~)Ljw%tV^a2tq~4JW5X%^OBYbWcPqGB0u}`YM|t
z8#c~5#%b<l?q=R`pz7jqOLj1S;6dkzK^x^7rH9c>Yb0YKlV?`AFGsU9(l2e=I9fJ}
zV{>j!*HqPX(UiATW9MvpV*6r;-DK2sYMWteFwUBt#_2LT_K>!DmU+&6hOl(MRK6Ux
zR9){-8?bA5-0f20+HxxC_Qh5AeCh0IWq3qtt6}vx2v~udZXplZ53A{`=#^8wQ;{`H
z7D*SW7ws{!(Em`I>$&x|J-1c5^XxthulMt<zPG#@;Irc&<A?EuTBMDmjBzvCbk9yD
zp0-ZbPevUl?=>!#Pp)=noa&vw&kYWRU8?M|^feh4*T&WuJg+=U`zbv8U%5Q$950^4
zK0!TWzIZ^EK=gsDg7d<#evyT*fWw7%M$pEe!}$S^0Kb8<Ha@+7#o3rSjkFk-Oq422
zm8X=4o~O`D+go5nSHHbXRNp*tk!8Xx!COp)``x9dIaDBqF-jBV`c!vqExa|^B}Z&U
zqAJ`r_Lmr4BscCo{|ZMNX&c1=uQ<jmMma8zKok3eNM?Ypm9D|t*i-T~2TB8qD9m`o
zQ{PfwdS9&<$ZgZ)*UU@aF2j#^2y-}ROkJIlMn;qEWwDW{mMD2rqr{yA1PZ_HP?z#n
zli|$0vOT420tcu|h|RFBXaV9Yv?SP2WnL9&l~5HvmFA)t=#CJxo>XE1rH~^xZO4`D
z2E^m-W2jjQ<{f4nW|%aowBmm29oOr^e(xw3Ob@9FQiz03vJ06e2JGZeX`8y-3J9(7
zW!Hx8<G8d@|G4`@&wbScUtOHvccN9+Rh-QxY5R52qoFsTxBmJuS`#`OO+AMFE9mbu
z%QTOb!Rlny);e|#+yVDn>A7rS3keHF)$ev$>uob`mkWcH4#SJ%28jY!B2$QN5YiAc
z!58rOUmxDWpOx-R4#fwfc{u!O_AvDs>S(BGUX;gZIzJNM74`+>Y^9gyw04y1d|2%3
zJ#XLXHgph@%Tf-K5p1(M8lAqn=xjH6tYX@AsUN5|SwLBZ*-99AX?oqt?`CCq_?IH7
zua)0b+Lp|eu4vvHVFoOouk-&teLT9LTDkSAw!Hl2C-jO_E>}MKu={X$7T`|!Aox02
zkz-@iU>#}QiH}Bb#)IZ{q+DrZcW#u`OXlX7Rbjt3^hVMg^Fm-()m*x{*lkJ44H+{W
z!}m#coccN_65E_*m{lTR<y?1ycCKnfGS^b!)F8Dqx0pG#=El9}suLwKT54byz~?!z
zCA+V6Ur%-@d3ZA^mpREJut06)=S6hiBe?pjDLd*K^HM+Er}WnOXun(ov|88JqB*DQ
z#Or>H?a`*Vy1t5I-LZ9gX?xA-gyV2i-mT4*eZ#e5*?06@U1qVO%B8u(!JuU~;BZ85
zV*Tsm!P;pXfy<}R<J(0i)EiVUA`5<}f6}dGyM-J>Q}b2r$Zj14w?bJ!^HWFOWZ|W4
zS3tnvY)LU6SbGp=fa&|S8RIARz1DKvhvIon?^RlScf8KzAs>M+kr&fP-Il_<K)HTe
z7x#1Oqyb+3b^f>N!fJ{Qw61wypR26d-%YjQwG@{uo3R0uZza#yQxpru50g$gVFRiD
ze*&mJt*;yRb&i@hBE}+MU^GUuDj<*=LR6Fio-RlLYfwOg4j?Y6=wwF<(XuQ>0{dlk
z?<U|{^_-SK&<0wu9x~8!u16bWDdM51WLhceO2~}>z{5@bMb=M?Dh44DJU(M;roh&C
zhfW)DhYs+x!pFq4!OC>q2wAyF05bLBUxuIRQ;rML4K->$*pl7o#m<g??DI;{{|b;F
ziB4z%g4|3iRZUk-IazKK2RnKrQwL)+dQUq?Am#-Ccs;p+O*=DJBSKF*TYDF7Pd?&*
zB)Ea?zugSPg#U=R+VBx;$|(|xI5?XTveC2BGZOQ|5)u;fI-8nvD~pQ%TOIhuM{Mcp
z>d4K&;Njsx@4-Uv;B3Lb#KpzMz{t$N%uENApmXuEcQx{)vv(o+my!QtN7T&4#M#Qx
z)yl!1@ULAXV+S`^K4Rj(6aDA;m!D>yR{uMbz01GD0tU$NcZ7k7o{`}{wt=d=e|xzV
ztvt<aHASuL%<Nr&bMUjWF!TN+|No5q?~MPYsrA2_T&(}C_+JD6R^(;)>;Hen^RHz6
z(+kWNKP)f9e=^SxyYaJW0|3B{ml73H^#nQ33J^@3{}`O~uUn2I2<eI=l_vUgS|t^V
zF@>&s)MFDIxtu)5Kt{{~-+2%lrO}CkuFEPUQ|(GOXJrf?6sidl0-gkqrym+l*)vB4
zw(Mr&)8sdKYrDk`nXas{e)x8M#?{4tn7Md+b>Na+pA}}#h=l}B2mlWRAPI&eAOZgX
zpn~0!0C41sf4T!B0MNv1uz~<+00=5bk$C>`(UGNtgGTwhAOx_B@M;Qm%aOCHs%m0l
z!j^s2x~5@eMVo_zgMxyB8?RlAh7$<_RB(%kG<Wfohlgk1xivO6Hko{8Wo1QIR~NrW
zs5kDPDc?~QDbd!~*R!*;55$g^I9OwQx&HA4AKLKv^yH)thbo)0S9UoPMl=Y3<oN_+
zcszUTs=f=()&L5s1oZzb6#)I~UEc@mubMizAW~(ZAR5rYS294bCFY`jsVa&)lOh%=
zQVRuWR~R8Xx*l|F`;_Uc&n9GAL%F(dzL2{_K+Q`^!c5xe#DmFcw&a?WS>s*b7{tBY
z2oDvHBla(mjpdme0v+ag{f2$bx0nf@9VSPTBHbP9Y=uyr#*-LW5h6mvNpOEeA=P13
z3%KM|yN{6a<bXp1U8JcSfnPi-*yJY_G{-IWI=WE1!YGwK&2x)xORR+&tRtt?Jk_hX
z5#u;bDD!kXPo-k06si25Z%&&O<&g#IA#DsvB{vRJY3joscx0Wg-bCJ{F1(Lbbb?}p
zh7914Xrftl7p4<$rWZ#FkryFhk84a&*6rliw#!?b7ph*L^N3trUzQTH%Um%z*qsUZ
z7g{NdrUYP~V=AM~D%sCxB;*5#KEpS^`&jF_cyEThsmHDl_7n(28(jEsx$mmUcoCeO
z#!e}QW#gBuBE-la3>+Rx@z9RmqcQrUj1>L)C93Nzlix#Jyc@QdF}+%UfUsue6mRHS
zNBCQM&FGyE9L@v@1Qd}2WYr%!&-%H_`SMhNboWmeccGz;A*Gm5eClg+0K0k4%$DXB
z_qIfBVmoA!$k2+$cLR0rI?vuP+oh8WqXlh|Qrp3YiK4GRj-x^|<A07JMHUhGa~C(V
zjyQYYJX>_gs-@{xy_#YSwu3v}kIG&{>8NS;t%0>nU@lL8!e$@Jr4X9lr<KlBKJT?&
z+Fp8m9`t&UOd>lqTBdUD`{{-tGp<@`=AT5c5tRquIa{r!iNDFotq<2lv1J_<VQ_iz
zO+4n7l}y_INLe7fC^3qyel@IEti&%VFzYI)Oiqo%8PSPjH9FrhnvfNKd2TL$$W=nk
zT&p(NPzhcW>G(`L*_COU_}I<(BQU@fP5LRo%Vlwq7Ax(Vf?wPlUVEmffB6YDQPR5a
zvaHjk&DnwRZshtBI_0Ip_ai^|B}7p|=YD(F>T3oZa-_FI6Xy<9)F$415*cw)-B2;d
z^n=)5*v0zjdPfPTp$U^VH<`yZYY^t-nRQxvFe2eq9BOW<D#m0=IKpsHKK5lMy3w!?
z52@93l9l}rd~lrWx<^oaKl6a3OH}w|8}%GK?WYN%&imMWAK0$~^JOGq+zbXMvNG(x
z<d4^vPAI3&6XJcAp<>q<3Sv1`s`_G<%0+{nW+KT6>-HZsbaI5GV@U+s3o7&6sBW2*
znOOTtH)#%2;irTAT#3Gc*XmV{$pXSRi7XV&bFDsSk^yU|S(^z{>N6*91@^%Vm(~w;
zBYP;~N6weesN9{>R-+DLoi^sU5%Xwvw9)G((}IH1OF?3DND($>A*u5IK1Md368`6;
zox__wJsPYe$j|gxT~s66CzEcq!#J!wf^Bv`wcp;ymd`aw!BS!kPUhQ~YEdiGoQM~k
zSIVklA5#t{v)%1iO*{zs>kblPyI>~=K5}Gq%`M+>2@thRrWf)9HjCr7lCq4hgNw&L
zcNKRP)h1=(ZZO&H(phI%tAZGQ$rF88eYWj<SAp(eWoNM;ApXd2F4zqIA{yBJO(D0l
zGkfwdBequ?ssH(-NBgV>Mj#eb&aLT#<=hfggEhOeLO7;EraVA$;|}v#dD1_*tQlK)
zg*#}|P4I$cT2p=fzHC%1>7vsCf{bS5t3zueYoQNX1y-aVN8yaKamhTfL@G!oy5DT7
zLC4b~GLN(02V?qicohMUvx_e012~)8vV;XWkDuq>bLxQQe0UzW+KX-y`3uXocCpDu
zJ=>@NYA34by`rCa+T(IlQVogtwo0VMi$2N$oCzJ{{SPs3+p!;m_Atb1&7?01nCx6Q
zGDFg^IXVM!JnVkikBuF0)C;jNomc!HR-{U)daSos^==ZBT~nsADIFEut)_Dp0xwYa
zC29&R?*Y_Nq70o*DeZ#&5gE+|*Iff*5|{C`X%5Pkqv>w_@xj!SBL%GasWxNn?_#Qv
zsb=7DDTAqck)8`Pq<w+iy&}2QgxWIr2A;GIecN|C$+t~h@?SGr-CsaI7!&uTMp27X
z&54?>=9#h=SEZSf?R%`<Z8DNp%Fu*58w_zjkyWhlJ<x>ZR7I&a4lrsuW0RBE=TA1M
za@6S8PYYojLb79`TWm)h2^x33Ow)1lC%PtRnX9L~Lj*}`Mo-ZgJ1Npw{2yDLz-Mtq
zs}<H%iVjsO>%4o!<!w{pWUa#|C0@3dZ7ndlpcXtf1uu4g(C2jy3SJi5I`FEC=qrC@
zmF3C%|DrxT-#yi-xn%cWEQ^ygpr@eG*HuhuZDU_y;Nqj_w7YisLo$x%p5-c|wk`9=
z?ZBGzN=(%tb<aQbdIFnQ0I3Sji^iF`#OM<J8|rUD!z!@3Ky3OCl@z?px{|7gsgyUY
z?d4?rUX>=Xz83-MH%DT)XaR33;-9p|w0K#_2i>MhZuTy@($iyPPy)p&9)|O|WvJTD
z=-ok~h`c$Arzan_b}>9#_Pir59Jt>D%48&9ZSN0S<yhzoeDl-y$EVy)c=QKP$UR-4
z*@O-q<FrdHZhl%bVX&#~s-b;vjQaBZEwg>yt)Q7onw;y7g`PZWZbT=R5vCF)&$;#>
ztCXBbS>IY>sHE5x>g+0Ae_D_Z8&QmWsjnZ;;p4_X!e%8N^Wd2{Sn$Ad=z#8<yuLTD
zi;^0Syr0?5hu^Hhg<spC(?d1~&uKEYP2^~EhZ0VTTBN=oImdRP)<7{Xom!efe#y?^
zSk5i=>+kRrglh_e3a>|Hu9P0Ro{{Ior{>2uy5BHNgjYyNM}F=OY#1?@eT(iCZpo9Y
zm(`jA6rW!+&ja<^;F2k{3wBn%jWYK*N{gseX6hOqGhk_xa#;82sm*I<gkN@DmQ-F4
zb~y~~erSbv{^0!lBP8}s^xA0dnV}Y>v`)-Q5Erw$)5tVngJ`*`sP?SqHK6jtA|!kF
zXp6ItQv7MipBnm8ilb86rYg~mW-m3eF4pH2qw+&}^45oXMK786w(rpFoNne|a+4;V
zH{V;$X|e}0kq_2NI;K&&u&!>hD~F3b;Ez79XteB#Hc5)wXYi!M+ta(6lbnrS6#jL4
z3WJ2(Wvcz#Te|E98K0A}{&&dVA={b<rgbyYbK0v5!DNXo$|_Nc`^kxPExmeX$C~iS
zux?-dD@!deYbkQj7Ppz-A$LZaHQ`g`sY;|&?K1d^HQ;?{7+dk{!L3p^T3a5U3;YBm
z&iE8Q0)$G;9<R>?WFsEg9<g7$!tc>F;n!IXBEKdg&_S@Bg}_m`i+8_Z?*MvJ7Z1M&
z;ER07nP`~#&WsQ2RkK;H_cUpanvLsVglE{`QPuuV{yu<{gHGMXAKv;@EzE54GcFpI
z%~Oxeaq?%@Z9t=ev2Up5LhvP)5_<skvucbb^{dge0r&L?Bzg<nf~=>_@KC&GvY`Iw
zhW4?9X7W(CUL32{`Ocs>{!bJ+vCU2_8(k~o@AOwOGFYP?ztcN%nOkQxI}fpt2>BR)
ze5*pYduDPkx1v7B=l(D_HtEQUR$v@X0I{lnc=3PTlO5SQvzoDc@!Ly~k41hv=Gh(u
zt&aE8b*o45aH8f(+cVHiZf<MwAR4vY(=h1!ZTYnf5CrvIaML2ZOBiXG>0}O%8a?s6
zr~Z!T!v0A*Bt`IxMe0ZU98aS(<-9CD6vLpQ3&KQmXN_@1>}Fi92{WGO!m)Q_osJwM
zCog+(H5M{XVfw^2Ja1~pynt{e@nY~#Kb<bMgGau^dHc_&mj=0jx<*#k63pvIJd6fN
zaLB}NKDZr2cSu_h5Fk$e$N+zV-RbbQ_#B@WT>T5{gM@$(nX`oR4;BzPBQk>^^+7$K
z2yFskHkK+VVfeR5)E_|2D?-%VU5zCEq4%&(u)l^bLIElKkiAd+`#ST-Pa)&Y#*UES
zKrCN_2&(yli|6ouw!mr0?eE9uZ^ZHx@!*8mO$jt9sCiLyWOV$M4fZnM5ZUr+{T>ew
z@BIASx^bVK1*4x3^sfm|NoYq-Tn!Bk3kwVO)fY^1)^9;;K9Hb)2>}4Qo0??}?B9F@
zNwfw1AO1iN^wg0C00MMO76|^g)&CLw2^~n-QwabEiZCJrgud&5{G}|0#DHMv=6|ep
zivo#`Jp@oI@c$+{|9`_jap3*w@N6#t9N<7-9)t~Z$MLLfY}VJ-GBY!eT(2<B_d^4n
zKtX)S1Dij4GKhB0yL^3pE7;rf8XFs1TUwS_@UVD*n(mSShlp?vfxEjqRstTp*U$qe
zPAE`dOiN%Ow0?Sedg>(osAfN@beMuD|1OFEu$Mb)!a6fIR9%n|7U=9d3P7k~(*}_Q
zsMZ|`q(10K;h@0Kx`4r-Kd$r+0drkLY^Z|-gKa{e`kt6n`*W1v<e9m1jSS(q@Gh7$
ztofGqBzw|C0Cli}&X}+MdufC9o<}&!U6#{Xb2Cf$W*uE)f_rO-zt*)*6G#)#+|wnp
z;fk%cU&zJ}#o{0ILJb)QI3iQ0Vn-DOcv)iO3OcH4dc}D(4HND-+|gq;nN&vjT;i8o
zIN3N+^lH<fi<Y2#wA3431w^sQH|Q^~2<NDo{K<0-Cd&=x0&UJA6Ripd9TZ5;FAHq6
z@k6G=3`&ni%-1@su9tccaKO~8v4N}x4{awvhJE?tK*{!{RyD@}DV?YPs@MDOM&J&k
z(w$^4e00_^c>_lR6_4E%TV~spxrHtt!h3B$N+DoHAr%eB{)|z(j`!wHNeVJ8HQOSM
zSF^SaR~jA+UP(pK2}dqr;K@0ge&v!LidnM*lm|HJ7kZE=tLkZ25g3<K`wD}GyI3ds
zbBE!v(WKF^!)czxG!7*0%yG`Bsm?FzUZ`X);O9r~3$H#%+~ZG<U*V;&w7gvFLKB<<
zm3td^E^^e?b4(L%@}{xA3c-_AXa@DN=!Pi<xO*e)F3{@8F$@__-VYul)H<ED>W1}W
ze_4wTg*A5~s*-u#_YpUP$Zrqc(AoEev2h}mS&OM^n4vv*gnju?ote`O>Rpq3_)xa4
zfqglUbhjn-ae+LwD+7y4{E1g2fB(wl8|goy?59*qPNel!KKX>*<J)T`6gDtEUP*9U
z&+t@*Surivy|8q#f(<r;!scoblAqx%LOdzrHiJ^drTO9dU&Y|=m<fX)TtM`Qg-p>;
zJYO)mnItxCxZRMyc9eg8#jUMJ;oBD(*-=<sb|8%#__q6HT#Hrdi;AjP`4BN@c=iXV
z%=c)*y_2xAI;uZ(MFwd?v9L^59_B5>jw$UtfzHrsn3f5`I9gP3DK+U8GLU3^?r7yB
zp7QuspOmIq!}Io@>Dd~RX>9n&Jr^7HLG(r|f)^X%Rx5b3lseMdEcY-_z&(u556aHl
z*FB$}Df^w*O~)NOE~m3XoFzLQ%%we|7q*f@7}-jcQFuBzJvnc#A}xPYajk<H>A6Fw
z9WO*TzcGj0#7ZQ*I446AIdMw4H%;bE%b~*P<7>5K$#Gf|&H(OW)w_Y6H}<{RwQZYd
zM3ovNB^#?Ln_KFfYk-Q$3;r{Se;)-zo1wsr_MQJaRBPKfMBgmm?C$j_D>^0&vkT>k
z+BOT+&jK9C6EWuzfqoOABTMkzbj)ix?0LJqkFRIVMTy7B#?r*s%iTh93IXwIk)zOV
zx6fcON8RRxn7~T?nkZNuxORfCFSL;qo&Q5E<Vg~4r%oW=hWsW@xd--!0>V`TON}Pb
zk4)pYR|-DdnlUEEac(lpaDMo`Y-!aQ(jS$XHGRT5@FQRJ4o%V4xcvrK<Xy*#ZDidq
zt1w3AzvO>FVF>GKNEtat&qDkloy~j?cfh%WMR+!KG^V^BqZcpjV|+I=_RZ0E!Ifk8
zp{SFUm<cv0u*WUTKs`KqGj@EmOidoLfKDbOOf~o^q`7FxHzQs2_;v1XoT29C3L5PG
zIm}S3&_F&81RP)Y?`oCOL>y7RSvdUs7CThaB*-yvMC5(4pp3tn$?4oa?c)kZfz2n{
zJM!+Wai&7gCi`cQ^F8I>TUTUxC>+kh|IMMdXMRP&ri~+E;VLt|h;%AoNTs1kTqSQR
zX_&3mtnH`kI~KlityFV)N0NW1#L~*dv_cMtdxAKhe}S5e<n)<kUWR~pAC-7UhjM9K
z=xp2``1+IY)s2E@f94mFG(LS(4(xL}1#w8|4r{DJnh6SjS>wZvJQc1$xgk*C#TGw_
zp$HPe?E;JeYi{1lF(|JYJG6+~M3OC|*NZ?`<Y7%{x1=+Wd(kub7dWwo4l|sJ6Wuu{
zAymsxYF5DSsURYiY9}ODEUBss;h&z)ni_SLdgEtHGmuTHTsw~-94vTfA`AV{gZUv&
zhJ#non-EEQ2QBe%pt@ij5VqKZmBC-6B0~m=s8FYV@L7Wihg%eDQVDC6rB3XIiptRb
z9Y=3Y_ysqhU9p#c<-2dMRJ1tpfGdcP3$c$n7MJp*>v{|Qfc3~^GHJ~N8k5hYOCi{J
zx{C?bW*qa=0NmLe>&k?KU2gC5-)linN0_Cz@**{iDcv0L;z%pXCBqAm(#8U7ONWHP
zj03nf>jKdd9T}BvH`5+%t+IsU42+p_(xs5OV$no-vnTcO0n!=lF&%};e0BL>4>lCL
z5i|-p(}H8jJGk2V%v_@nKV~jv^Rgw0(Hhco_fVuph9NRb__PCb#iG({RU&K@^Rn*1
ztUjlSi=doJLcPy4<wr+}f1{;H`L+p5ARR^4QeM;~nBUPj1&gmHTJO#pi|+LxU;N<U
z6=@KHO$h7;lM5_THbSmPXyiIeK<*oJAHd{YhkQ$_p)mBYy_c|DckWI3va&+EQ=9Gj
zJ%AFW*Vh|tI_b}yW`5d*(a4eF)YUBCuc%P7@GTtt-WM|rXf`=1wh5QoI6YnPPjb;{
zv1pqurFyq|X-PR4rbL(GdcDHn(8Enx39w1sm2tYBzxR^vQvbdSKwNJkOivAT`3pR{
zMWleNb|sTpAju?5=pT?~F9HC+W)CF%`u75p2zY@hu!tiLJodZofPiKL63G;ZzzhZc
z9`%RXy^_GQxdk2w+g1U=wm^etVcmX|;i7Kx;@PH(O@;WPDi1C)-I75-%+NvraXr^`
zf0@#VFu+CxIXP$O3S|pKM2-QdHALYfMKcAG0rc2!BqR_YG6*|4`$|Dbn0A-L$OQ+g
zTdswq5`^KTQQJGgWKvR2dl3P~jhQRiL2>(Leeo%dNPYVe`(U$*qlM^&o08YnX#w=*
zB~>Mqbd^ZWcdA6BWUD06MJ*qtQ01it6(ustsnEo!qOd7@jU_sf%B3zs)>5N^Mc%SK
z6u>kIcK;AG5K=v?gpv7;tTsEP=*5bUo+2Y}!16KH?Cql}C<^Lrrl4iE{83Un%tT}#
zAvU(X@kay>4sO+8ST>d#v#?N2?0f`Gx(-2Y6e+<WXTQ<WPtE(n$!Yu=?XfHbB=&Gi
z5AhZ8=ERT42w4lIok~PlQc+S;Q&Up9v^JQPVnBfR50jNbVM`Gq3nv%1rMozUa3%#9
z#Krip9%rSDl-(Oi8?U^QWj@av$q7Kwivxi(aEH{`fcgcr6|bFNHaQEMkCXq&$Hkxa
z{v~+wG2qLuLKou-BY12Urp{{vzL1o9^!rLxOQ%-Fh%_K0x7=Lo-90GaIHOF&#Tk6f
zF1YT}Jk$Bspw%@%&ig0D#~)T1z1KDkNtd1J^t~;Rcp4A5HTO`Bg$l;k=sfMZzJ3F=
z#&WAc+!*Q69TYK`SP2U_n*Fe+r^jh&shOw`D@jUMI$QkTr<w~k^hJ=iX*e}@L*T$P
z`$ONoAM0<zBYM`)%xHvG-d%e2W->Z`3iokHkIlYOcpJgH3}ytFWp$N3oDW=TF?Co?
zGI-K%q=8(r9-VZvr;O<zDO|9xex=v@qq5G=!+vL;AwQJ$dkjp-b-RReKnBeu+m`$A
znGAEU$V&|ES9d;?_xx0nmEDNApCqt+0-)o-ZDrF6qmL~0baYfiL{vn~?3ImHdU`#x
zWe90~Qt9t6>O)jHYA>GQJ@#vGLyblJlF+-pLtqO2?sHBqlgU$bjVEyKuX=7)6R9&m
zNO>sHG?d2P$OvN<gA~i)4Rb|wBGhKyAyM3#De1nc!Io0K^&5Uk|7HEfGw#rglwK=A
zmAFy=I3?vQMyC#(AxK9}W0YEXH>HmZFX{fw#zqbB>n}<|0&*teX-`Tb+vgM(>X?kE
zD5Vr}ZYQM4!|_5%wDC#GvXo)dgoJgL-h_BY|6fsOuff8k@zQA4dyJ|#w+zlBX*8c6
zdZ`kMXu?d_1WD4Sny}@O?h>snt?kJkJky5UZTUAlCiBXrEq8{$0!O3Al$_s;cNCvr
z&{t*pG!Z7V-stHLLX_<5`>hKs%O=9Hn>&%vafe<^9q9ed&^|-@A~ANqN$3>WduYwg
zRAW|6qD0Irpl^G?r9<^!yV2B({kn(a*mfTmdB+?cUjB8~U^y*i1JA|)c?gVd?GT(6
zuXbZ1r8wlN2-`%73PK3K1<Pe(p@^^QdKy{iwDd=J%UI~APc+$bXC2aDy)r^q+q04a
zZ~6ES6gY6<2L6{QGdrG>7=$zejL@7*B_XU-{nnKyVGNVtaT1G%P~AKSQAMKV7z0&Z
z`!^ZhzAtiiE4|UN2#Au5>L~Q<F1~Cz`D-1#mVR7muG?kW^gLqOQgy-`Ib}oXy+Z7A
z@nVqBxQ_xm@9o#D0#6Zxv{6oc-@t~)t@3+v>h7GwZXupRn4jiV60>tR_KdaQjsLC~
zCHM<{8KH2cC@%g+xLS0xo0+pGF5Glqs~$C-F<0B#b!fSCi14T}*Ud!>C`_y(^}HX>
zU}+>h-HtOJS4syCQnQ^3nELa58W!g5qU*_GGk^`a!?AQh{V>&Rp?9{}VHSYz0MJK@
zpw8f>N9>*VWtSuLzqM<Ng8~rzltB^-`S;LN1Qu+5N)6Z-7-02wkO!7^4v8=D03e^v
zMan(BZ{A>j$#uo~I|?k~5<upR^(8bJ+1tXRSPkXxmo6ar;{%evn3x3v+@jHa=Qk71
zD^!A)QADu6`&SY$iig2R>z6McU1uzOdU0{_&&RpJP1>fBZ-1Gi;MO)t_qL<1o?cc~
z*0Sv^K5K_b0rr>B4d_gu55Th`4N$?ndOIIh`}af<0)S70LWu%hsj&e4L@e7I>!%b)
R1_1ycDKUA`Dq+Lm{{bAM!_WW#

literal 0
HcmV?d00001

diff --git a/tutorials/table_of_contents_ipython.ipynb b/tutorials/table_of_contents_ipython.ipynb
index 6390393..2b1f066 100644
--- a/tutorials/table_of_contents_ipython.ipynb
+++ b/tutorials/table_of_contents_ipython.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:19987eb5b732fc336836589fd9ab146141494c84bb74435e60e69fab53bf0781"
+  "signature": "sha256:41219a44026c84eeb3ccb56875545bab3944aeb5cd6d39c51e32ee01fb0cb20d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -56,7 +56,18 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "For example, [click this link](#bottom) to jump to the bottom of the page."
+      "![example table](../Images/ipython_links_ex.png)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "For example, [click this link](#bottom) to jump to the bottom of the page.\n",
+      "<br>\n",
+      "<br>"
      ]
     },
     {
@@ -75,6 +86,13 @@
       "2. an internal hyperlink to the destination"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![two components](../Images/ipython_links_overview.png)"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -140,7 +158,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### One more piece of advice"
+      "# One more piece of advice"
      ]
     },
     {
@@ -194,7 +212,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "which is certainly not what we want (note that it looks normal in the IPython notebook, but not in the converted HTML version).\n",
+      "which is certainly not what we want (note that it looks normal in the IPython notebook, but not in the converted HTML version). So my favorite remedy would be to put the `id`-anchor tag into a separate cell just above the section, ideally with some line breaks for nicer visuals.\n",
+      "\n",
+      "![img of format problem](../Images/ipython_links_format.png)\n",
+      "\n",
+      "### Remedy: id-anchor tag in a separate cell\n",
+      "\n",
+      "![img of format problem](../Images/ipython_links_remedy.png)\n",
+      "\n",
       "<br>\n",
       "<br>\n",
       "<br>\n",

From 5dc5e7d8f030334543aa7348b8708401193df5b4 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 19 May 2014 12:49:26 -0400
Subject: [PATCH 054/248] hard links to the images

---
 tutorials/table_of_contents_ipython.ipynb | 17 +++++------------
 1 file changed, 5 insertions(+), 12 deletions(-)

diff --git a/tutorials/table_of_contents_ipython.ipynb b/tutorials/table_of_contents_ipython.ipynb
index 2b1f066..8ed93af 100644
--- a/tutorials/table_of_contents_ipython.ipynb
+++ b/tutorials/table_of_contents_ipython.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:41219a44026c84eeb3ccb56875545bab3944aeb5cd6d39c51e32ee01fb0cb20d"
+  "signature": "sha256:77e6b7710d7e7ab3d4491978f649959a8b99769df6967b7cb9feab83a67c15eb"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -56,7 +56,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "![example table](../Images/ipython_links_ex.png)"
+      "![example table](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/ipython_links_ex.png)"
      ]
     },
     {
@@ -90,14 +90,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "![two components](../Images/ipython_links_overview.png)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
+      "![two components](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/ipython_links_overview.png)"
      ]
     },
     {
@@ -214,11 +207,11 @@
      "source": [
       "which is certainly not what we want (note that it looks normal in the IPython notebook, but not in the converted HTML version). So my favorite remedy would be to put the `id`-anchor tag into a separate cell just above the section, ideally with some line breaks for nicer visuals.\n",
       "\n",
-      "![img of format problem](../Images/ipython_links_format.png)\n",
+      "![img of format problem](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/ipython_links_format.png)\n",
       "\n",
       "### Remedy: id-anchor tag in a separate cell\n",
       "\n",
-      "![img of format problem](../Images/ipython_links_remedy.png)\n",
+      "![img of format problem](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/ipython_links_remedy.png)\n",
       "\n",
       "<br>\n",
       "<br>\n",

From d98d0303f0d1798e091ee62a0a2ae0b31072623c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 19 May 2014 15:01:02 -0400
Subject: [PATCH 055/248] using header cells

---
 Images/ipython_table_header.png           | Bin 0 -> 38557 bytes
 tutorials/table_of_contents_ipython.ipynb |  36 ++++++++++++++++++++--
 2 files changed, 34 insertions(+), 2 deletions(-)
 create mode 100644 Images/ipython_table_header.png

diff --git a/Images/ipython_table_header.png b/Images/ipython_table_header.png
new file mode 100644
index 0000000000000000000000000000000000000000..897ba78e74cf20181004182900330a0ac8b19335
GIT binary patch
literal 38557
zcmZU)W0W94lQ!D6ZQC}dd#3H4wr$(CZQHhO+qUigdfwf;yXV~cQI(Y$sEo{tih43K
zR90F9777ar00011OjKAN008j%r`!$!^mFd__nig+fEqCs5|R}Y5+abbvo<odFa!V)
zg{@3-R#X^8A3p9()-r*Nl|I2e1&Kj&0{}hMAtvz+K+-1&SxFp3RgqH^?g^p`qi8`B
zSn^Y;si9y~oB>q@S#Ye?5juPZlGwQC?*6=fw!4}j-)^-(p2|qIrvpfUs}ha_EC-08
zoJ#rw_R6%6jFkF94giSd1EP~NacC&Ef{3WQquu@d3`m0Cp-&wBSZ(#~E|5q+vIhw8
zJEwp2gGP&NjQnsYUbG%iAfUvmfkmC@STEsJo<%U>d5qIBOwL<CCTp)eo^LkRt3-w#
z00b(TS_;&MC*o`ZM~ez8_?N({^aOgSoKfz@AhO*n#c`fRW@@X-6u2y#%$G>Uf;U5Q
zdPT_SVG)&!8U2Z*gRZw+5{ZAi#ohFG{pdtY?lJ2tW)AId3fL%Nci4xCvN`l%DwPZ#
zat}51T<$cZTpA9eG*?xx6IJQSL;FvmV$~$P{w$*^l)`RL;nj>2(<Hhm#Q}8ViP-Jv
zQJesvC_hkDK)HFhFp3!FN!PxU7Sk!j!Ku`viU*3{aIU}j1L+cfQ`t$!f>AxCqy|oX
zxNG7)zpJhGo$Q!q(%-<R82NJiiH#;%S4I?xV|48%zehI!p?s*wpcve+Fm3oHvmZ2a
zeO}N_!u`vZtOp;&hv8uu<f#XB1&|y8f=Ixji2%LR8Q_IBi6%ZwK5$i%?M)s;GAhN`
zeA1z&-Wf>X>*|0Hpu>~lHH!djx1FpIc&7;ffa9dSX$H|p-vkb;;5y9|5+H&IAkE!{
z$#hDQC!(W2uUZvz#+Pgc_)ibc3IO;H5RVUhnk@tb_$&b+4G0ebNU8u-k{_xLEd{`D
z9p)7{svf)=d>kN=UfUUvDIe$^s1`sgU-umF2VcP(pa{Qb9UMOb9^pWf7_tKRQ~{+J
z-~qpm8~_TKumGeS5(-o#Atn>h20SW1@mweqd`0L_pv)jTf#n>!6T$`fNCE4dX$Bw#
z9j*%K^gd=?w22_0T@x3;3@Ccv`yCG#WDPjwevzF&FF+o^UEt5X^gE0o1O@@V7zAt(
z(6>T;vBD&vu|(xDzY1VV|D46572vk|KM>N#%3$CG1?lQpFyN)=NWugKtNN)1QVuEV
z!0BQ!CNnrQe5S}vu+yNH`#1Kr>2t2QSWr1pHUX#yRrY4}X$@`l!|84B+i-wQ!5oJ{
z50UQ7*^Ju`wrIA1X;4=|v-@E8@871mP<N2?;Y<%2??c~k--_HE-!|X+zeIyD31;sI
zK#-6i4Fos_F!ky7arRmEA?Rk5{9++&L+%gt*kjUzyalKYbcyE>#UU?^n-I+^Fp<YH
zr7g`+lxdSZBkd%O9}O~KztgA-=?K!0=KIqnensw`S0YD4>6P$`f@z3+NKlw~7ju^+
zUYI8DSrWQnaYklF`$+T%`IwI+HL4VvKcK8usaU#D;-G$-$RxKaoLbo-%PG?;`5JI1
zb(l;gjH-ajk6JZGl_;J#Pqj&<U&iv+ebHu7a#3%wzie4Cc=5)p$PCcTqtT=B(+qtP
z%miXG<Va+Hc4;1NhHf70l%owZQ%EKseKvZI`!xT|>`eNs_N?_R@yvMf9Rs32p;y;{
zK^$Wk^BFVS_+(0F8ew9EBj0Q25)+@Lfz^Wr1S=2=7VDXX&zy-Bm}Q=|*@VrMZ>i5j
z)ogN6t*lY~ylzRtvaKnYO{QJ;z2%7*S7lh9T;8~K$}1BtlTnj*lRz`{N_sQAjs6kC
zjnB>0ZT(^DH1cHrq4VMQ+xo-wIlplk^XdeQ5$8RU{er+S!HR%U&2-MZ&c@6H!;np;
zuK%t+aU9|}#h^*LT>5NDZP%^EKI)L;P_$$S84YsoC|tZ#JbaN+k!=ySWV&SVB+CSc
zk*krTQRk7Oz4blm(c+OatqmGYxI?%mS~HEVgt0`fVd0?^^~%_wxIyE1**KQ@r4emY
zRnt{d?n;fNoyD2On<ZwGZqvC%x`p;6OI9j}{rJRF>h?v(CDR4m%HvA;YVb;Ry>+ek
zzRqcneThTMxu_$IgU02`#mn00nAlFk`l;`a2Wq+pJ7znsrmLcpPj*X2RMuZ6S*BXH
zLd!($r83fV>~DK*t90Vte;VB!<X!)4dDq5c!#l+b<_$7VokW`8VleNSpNT(jovxn_
zKTSMnTrQtp?@2$`y!>1k9tply*k>MS(kZTuuF-y7dlmPTdG)%ne^x(TK8t<<e?@<D
z1}gy_08s?tfn<S^gsp(afwhBE!&|_LfQ5tILfV*|J-p#)%$P-3j!7g;{zaK9my4P!
z(@)c1pi5i7yGmH!Jav_+&m_WAOo<a<-`5<(AH@)^f^>VXv9S@-nrNT>drhP&#3DNH
zH*FXf&LiI%dmBj``4G<^w0X309B%$5wkM$sZw*ro?T?9<#9MZx1|(s~$<UX9m4UQ@
zT35jPrfKYqYo2bMuTM}TXghQb^^!&g{oU2yW8p2~(j>a^dvS2&p1VQz<*oXo83$zt
za#{G+;Mbtr!QGMkL^mi2P(kuM3gQYu3cL!<MNtr)frfp_MEr7rCyr`1YgrBOr@N=%
z^W;o>Ojt~isbZ<cgJydUw}pdl;r8gxVihEyab2WW5>51&i9zD#bvYHFs*|e@4LzqZ
zspDQTkH;>DibozASnUtORc2Kj&HAZ_b&=yicR=@ET2ZP~>RU~HI)iJdpVX_=&y{{k
zq}68XmJM9qk2`5OtielhOGVY6mYJJvbB@<b!<E*f%ahvi{5L`~@Q$G3pmTm#uz1){
zA0e-D5BkS{h9kMzy{He+wdm`psi@!NC#bu=;y+~$`K2txR~J<G<m=p-t?FIwKWVqr
z;S$S|juPQ4GCLb>vF+7&o1E9tExVPD6q}5}O@l2&v|UwP@1^%M)1AFa5tKH{A1W<M
z=1SL89(B>ZmoGQ@+RvX)t|-^;U8_y5{XGTWvC5^&$Dj6}jxW5O2%ZGqrz^6}%^S?Z
z%)0PU@GrPgTu<aH%`Go=Gy6#$Z89sY4n{tRo1@<FEvuSKx0icND7e6)Mx%JYNl%mC
zhlQe>Gj%dc_)YEV&QLBDb%_^RDr_6XRu+~sW;PtT4jk0OMaE0DExmbNhIS+mRUhj~
zk42B~rlm5bx%ro<Og&u*ANvH>^O~~4Z_%%{(%eh$?M@EMm4T`?ER378yU$!7Cs?1&
zi>vFa*f(ujXIFMNY|q$_x1}B19N4xTI#)f$FO?*gE2`|9JFT@__PvkCG^aMPpN}@q
z+wkqb1)o2zy1+lc`{9}Ky1WwZO*)LF=$o2vYRC5L$hl<7yqjM-bEgZhExNtEhv!R*
zd4W58(Y*~mZw(o~F&|Y|W4>fBYx-|eV|!xNr;mB@JqTSHzv^~m7WvDyQoFfclc%+D
z^KbM0s|%~iw@|tlJ=||H=i8fVLu$#dnYW|8DLzVGZ)eDtil3%!vxA3{y?VVVzs+tN
z57keaw?ZdEAt9B=GAlq48UhvM0bJ}60L*{@8q@(|5{l0D#NbWJl0-1y)(`HyZxt_T
z@CD2vWb46vOcwgo09Qhvib`hX!fyl}@d3EGs9+>LRVkz3;z455XXXkl^!8}g;P+?&
zUe<US8Mjy%ZyUiXxA6gI-n`22lHJR30DHj4jfPvY8eQ4gP*2_8@dw`lq{kxTT7H7u
zOjAV_2NfwvE`4iDI$Z;6JwrMdOPimV7XX0Ah3ltiY3QI!;9_ZEWzXfpOY|QKuAlP1
zYI-7q|ByJC^Af2@$r1=z+ZhtD(y`Dn5b;3~5D@U#85nWN3;+3V_MbamA`=G(8!mc!
zXJ=<RXJ$HUJ7aoAPEJmG1}1tYCfXkgT6<S32VEChD|_Pq67v7a5jM2fw==bIFtxTK
z_$OCa&)U&}mx$<}qW?Mm>p2ZwO#fGtmHmHr>!*YC|61r7=@{t$NA?dZ&%atOSyLB7
z3l(8gOG7LBA02!woUA<mq5uE2{IACU!>RheoQxdo|CjUswEQ<G5B<Lm{GSf}m$m*=
z`(rLXC?5L%F+CrY^BS@*0Du61n6RLt3*dz&xB!X>y4Fq!P+m^nP?R3<U>NEJs=k=y
z1#6OU<dAUwDb~An1mlp3-t~}HjXy4Q9*|ytScspGkB!Fem$lK;(-RgBYsyB5yS33)
zhwHu3Q~K)aDrWd@f3UimnvtQQElzN`Fp->`v94~eA{C;Ep&@C24;XxhXr3&E4Ja@N
zVqY9_fGZ-9AEOZ2IFPR-R-fRA0h778IY=KNC$Y_Ml#U5TX$`_i!!f;B?g6M>f}ZGd
zRd3IC;sFaZ&-#?~LYwy;D<&uPT`z-X)T)cEiII_&7Tf$cFw*93f%H(K`ktL$e@hDs
z9giHMGtk$#H3P+1O5Ldu3&48Nux?Nc8VF?szwqd2dhM=`76>B&boG1)lZC->bo#Lb
zO5pME%mRozHe=l;pH-mi7*GH-zb2p%W*}ezo;rOV3WkGVS<(5q0;tf;{QOMJ5IbE_
z0uD1k$6N(OPzCB%*?|eO|1kQ2Vt6o14W_^$2<Etmrl6}~Hb1U=vB6ChN+dfu)!bsX
z-F&IX4+vjpnyg;I&$~z=z0fiBewBdQ7S@<C<#X2izy3!M7!AM(f)6kVj(et&4=cLh
zX!*Mm2G8=N&A|LZ#KH<UdfUdw+Pvu&G&0GEcJ~32)XsZzeN8QAnZGU<@r22jb+wcI
z>jc_JC#b`aCy)UGN3s6>M)*R3DAYRFQI4|B$Jx~*73Ugf(z8YD&cooSzhS5;PBKob
zjfM!9)>C%-XPY0HJc-7;si+VL8xE7<ZBlpZoMgho##wa-<$3bBv>5B&H!?~&gof&3
z&IG~4{NL};Hg@zm3qWLA09gckrkb9=08t<Lul@J$9M!dvo5HUxjVg7epu?mS_f`DN
z9uK+W%cShhPPj81tq=QwnU+S$rd;FZYk}O{1%%y>7*n=@!u{Oq{FdBDYeDn5jfcr;
z&ZiWWF5k~xctoM!cJdmyeRXKeY2V}Kn;Jazpm!F4jCIT^;2{UzMT2WoH+g?f`{$Ya
zTRUaGPUKe8%#ZB%Zs(-6@zPvN_GqHG?K}h}oTKhFybiVsuh3(7UPpW!GnE4$tmPzn
z7r9z(EQhzFCa=$TQ;zex%pq4Lp|d0h91XHeU6;#dE2>w40xbZ+HfWFm2Lu7eQmK7D
z`V|_E{uD$fMWx%lXz>iY{-v>;JaBi;=uK=Ib-@kqi8ITnI=T9DG&zq4ZohHM*!A=z
zE&U{oTdXO9#73(C(AEh}wD4EZNKK&rXBxb=%Q_n2PNGpDK?9%!cwh+hj6>`~T_CvJ
z{ZX{$4A?iqiyUC#ypH?jEUD!>Ho1uB6^NCX{aa%XNr4t;BqAb^>!$L7Rcv2nF?+v@
z|4F4V&UoZ7RHn}l&FL>NU+DKTH(+m|n_z4>&}3$2Ca%4mLXqTbwU685*=d2jot>St
zbG1UTbhp=M5{>HJ*&_9);!#Xqv%pNFF)!bCWH&K79-8M>>)35i>bvA(ID7Un)vk9E
zri*VKbV+Z=Yi*EzXI1!T3*jU96|DQu&xwBj(~~K$KXl64#;`XF`j*jNn8>JUtuFT;
zNv_-8fvBr2DKfiV%WkcA8rTf(Aw3K&)VCIvluZ+*<m~D}G19r>^Po2eHS8?>#I4o_
zuEk!eLW&<t&du3b0KSZAZ|9tzo?35rEkXu2B-3kewb~m`XYn!{O{8$zAF*$H5O2W<
z1b1_|^j{Bahr2;a;aHI3JC<6?7M91_BF;jFoNbTR2k?H^n8ws&fQ=o78N8j)B$k28
z4a!QC#VHPzQz;)fN2qFqtHij~ax3@1J^5r`!)$bMI4s+Fe0N2bG05Q-e_-;OWAG8a
zk-pumWAT<*f1&K+8eM7JhO&pWIzRDMZ5H2+$?bV?dvsclZu%4*W{T!)H`KiHh69Zc
zV14AXltVmyDU{^r)d2LcQ&LhgG9oUUFIRbfe?Hd;6^bW%y&j@_aLbk{Qfk~Lnz0R?
z&KJXjz32#F#>L0y=jGw?xByri86Cm%e;}ZvKM&)3pOAQ`6MDa@-BSds5O^it35!P2
z@lo<VS{9|i-l$EnUd!0&I%`+1y2nF4lxURMX4>iPMs(radaTcK=eRdiY3-PJ@f^es
zjMaH6d>zeBZ{p}R`9y(&fGiW*z@OP14W6r*-xXmaE0K`TTMKI9T&5rtpKUgm9hk48
z=bga|<MO)W%Z}czmVn?#LZymPAwrfzmUF0td^;nTfM&Y+HdD;th|>BrmX}it+55bJ
z<9q1j4WiSi$krQ4G2Tz6v-7`S)I+1w5!n2Ertm!<VzaZeQ+C%bTT;8TQG*jGExWC4
zwQWNf-R5&cHjA&r^|>2M?)Fob_F{PEvEhQSh3|QwCm46v9bPXM2^Xn2QfGNY)Kf8a
z0C)UW;3lYt;A}0tOhWU+(|s9{UKg}wXb9B#W0*(ElAp9OaujeYqQZbCq5G3yu~;~4
z_k`<>CW(lM2)rlLSh-B5m>j8e=SyAx`WgvGDQB-8D&lm9oIT|Sa7QEAOX_h6nJkQl
zfI~j=j<jdy*)Csq(WKle&X$-@6=JCP_y*>LU~psDK*+5HNRuedm=33V=1np2FEOeR
zUsI*!|6N&Mm&xRE90)<ona*0o3WByQlB5hS6-tBBLJoD3D&L7_{BET0$eGy~-5Oe2
zq9Wwf@6WyY9lxGhBKfsi^+zW%dG*trmLRc6*V}}9pIe#orc5rk$A`Vyx!q?nLI*<6
z!$4r45l{di-0I-hlFEm^3mt_vL;921z@Y>lYQI5lKJj|cait~{Gp#6Hi4uyr+Fel<
zWoa6VY2ONiBOS_ULcr^Rm{|NJj~6=$k26K7tf8l*wy##l>)uy=0nbScr2=1=r~UaB
zwT56i5uj1;`(FhP*gKL!(Q7_eT-)!(d*#w1m6f$L>rC0}-TQ-(cgWgXGE#%q{u=l!
z(m?)@XxG%TvB?LTtyp1RkI3$T0|$Rl*I*!2(^}`g{(Sv;$$3soPj;HeGp9)k_q8-D
zl|)>f48nE~+0%+G%w7|JNS~cDn!2Y{&y`;skX6_Rv=0JBl}2Wq*_d;EI=bv%v3k4S
zd^mCCsKlFES-1y1V#ryWU7r+E(kB~FORiIGvPgQ}Pa9*NPZz4kQ<>%qCAKFsgqaP#
zzP>6Hm<R|pCbLAD6u8%Snal8*;X^I@?;k$mhbQWexNt&SFr|6P+LN&0#0v9<*zkX*
zzF5$aZ+luC->o&Fya|0;G9&*|XNP4!n3hxvB*{CTeE5UQ#N+X}Bjd)WBnd91@_2j|
z^`~5Nxka8G^VUdcW(v5>GD&N?*0D02T_Hbkd4$_j8{*G24fW7H;#@?sVRK`f6EI!a
zpWVg7V2+|!<*A4E0zLw*xnN+2613Yd>o?&<MtG#;jP1Sq%TGCJsw?86$!?bh(8lL`
zCmK;T<A-y3&~dQ|0voYz*}t|(WLD9ZpIxArWoz&s>_w(zD`_fH)M<#X(SG%Ho4KNU
zX7$0vZoRil?@xE4X|%CUE+=$cjFfvGTU-U;VA@T9qLSP&q`}KAQ*_vXOpIz<{izlu
z3sI6l1`;?xsUGZMA``pGHuWQr;IgEO@cWGfYd{h9`zpcLL^PHs9HxktmP3@pki;x(
z6;_m(Mo)x9&X!{mTul(<R|r8Zj!Ql2Z<wMZ?{>?CX=U`YEH}hG^Kr~h+CeH5b*z~Q
zhkl|5i}7koz@@naYUQh&-F_8KVwg+S2W{_9$LA}}iiTMm?A)z_+Q)>lgyxsLNkx`m
zhKEsn$0v<?dLD|0+j)-au`ANZgB;Y3_k&GR_3BVH-q6Icgkm~=UhKDzbr}zCwsI=z
z99b&#if$q}z{msn)+-yWToPKL;nBk}eC15|F0_2)teAvaEAN3*Y8mg;wXMy@0BM^0
zB8kI%BhaBC^BUCMY2+<B6=zZ^F59&mqqjwnlZ<w@gOx%NMIiXYoLiNe%azP{<*bjp
z-|c7DiF$W~%jl4$+8^{Mr{jvY1Scmc2b|uTT%L!JZxZh98WoQnF3d(pclUOIbCZK#
z-U@4fX|3fiPY$xYs|S30>{*Tb(WbgTJj4N_YDUrI6HWf|h!dr7G}wU2pybz0LTz9v
z=4EuSEd6=NUmOvsBQf2jKrUPf6w5J-i3-f}F)FPKNZR>Ze*+>_rKHUt737mxKoV!d
z56W^})7l=k!-<%m*q!tynRAuMV(k$rQ+OwZOVm&>L()NEYxkJxPa%gCstL;Qw>EdU
zv2)#|%lTSMX^YKrEd)A?m)Gm#h1qzjLak9e;z)%zbk^<1)3-aE#(CNez+Gvv^D1bq
zZq`bpHshoY!puny24|XCb7BSBz7+|@Zbw*AMzM-!x81!Y(=H<fnm+GdyEj_M&JNC!
zQL-Y>)b9!ym<3%7^V4&zt-rX%su@nLt(m=lXqn-&AH4V5rPkH8&;RNibwd{c?%#4f
zr(>qDR;%9dsd_Z)P@Wr`yIU<!z!kX$3FdRZBQjj8?ltc^*VEU}xuse6!i8>oZ+sq@
zY5ApE93hFxH>cY8uA~<9(X$HPSf1#$t!-|Jl<?s={uk`<FBM1R#o^(h+|ZQL-QMDQ
zj|9APb{l?n0;BEjDQtZkux=od??}uT|F`yxJ=Ecs6qV;pFY>sI?ewO1TEpYW<+-!A
zCRFsN)!^&z_|HXWUyeq=x4p>~%<(D81l2>0%Ve)bvG34ffixg^d6LamRAldCJP>{;
z=uSL3`ctjuKbUn?^n7n_CI|g3D<Gwr_Mp#aQXihwCTQn`TdDEIo1D&EBur@g(9LDA
zpC6v4T*OpGgIV0(jCZy`!>fNSSBmnjVRPvoV}B#`<{@PX8?hd4|L$!X=y;I<2<jnc
zh}S_CqSA03K{B`jK^#PqwaCkrM2gBY;87$3feN9FDt<+T11bdqmkY3A{4g|s2|<Ub
z(6b+72~|omsFAWi$TvCa(5U4AJL<80fT5yER4PWaBgaJ6<?N&(z+$c>{N-cBCz8fp
zi*GxFttzK==K~m4Ku|b278ViVft#$-$9Q-{SKx|d`&wkliJpt(%6L69U8_H=RIN3M
zGIUy0WFED6jxlb%-eh^ZH<)5;0O@?S5xRIT6b#3DC`3{r`g&OoS9DBaqqh-^UXR~U
z-FP~3sZSU!@?<5uMcYn@frd`E=7KsZv9ezA(q!KV^<gr*I@=`v+Sa9oIy}W0l=#SB
z-TLs{x8>B-;9q1Z2pus(+p8);(+C8b*$@iNp2cV}NHg4=-9Ta)#x>#YB16G#JW1+t
z8QOcjl>-jO?>{!8=<$|2opbeZ5g8tD*$vltuIPx({R{%;K>fURvQ(GWg5C1Cv$(Yb
zK9XyMn#^D1jCf=t&b78!b){C_hJC~vr%(vZ7DSV_AYE~){cg=jfxpr?WsJ{?l((UQ
zleSF%y<Z0HV$<qC{WQ}G4ruB44)3c<K(`T+`pWHVCkaKHxkdRdjwp}HL)fWa%-DoP
zLg%p<GE~lCwM4q;{0Kh*<KnHw0E<S~IYoA5o$so*y3z7rL>Pti^^T8o@wdOHtm`G_
z5mzl@=FOpQdG7d~+%yQ-Oi(X`uwn18XcXA!s+IVXJE4ySlSpBMuo47jVwM7k`ykqy
z&n)yJS&-8;Huy_fhls?N0Vt%xS;s{(0D<v_klBumQ7;x(Y&vdUvV@=vcDG25MX3^_
znvkdj=^@xmh7N)VnYn}G3=}vt$3%vxgxEva6lgU;fC5#7j>O(WQ-=H{MUqfl3c-j>
zG;*N<$E+{}V(1qWSu3&=Ra%Il8Pf0ip$N?RccEy{_L0th+vZ{Lm|dFlS`$&mE&$_O
zH9WX%xZffo*XzyJBQb=V?as#9F;Caq-TK2(Mdi_}PqX<V`{{F)>djE4Kh6v!tuh7?
z2s$HXJ{5K+r*S~MRMC<bE7cP%n4VA6lN$JEcpE_}(Bl-#Rgh#LZ}jAeZh($%p?fU(
zZPDc|ENRJMEh@H&_)ph;Hgn#m<8Jf2s8#!lyo=Ykb{)-I9L=;qeXZpUj;+l)xwg<S
z22;8*Rup~g2wjiS7Sk5V3G5j!K(#)gc3tW?ZH@>#tVl115E2`3(moe@rQ|>Y^v<+B
zx<g+rDqe@YzlNB>4qZH6_tWXVF%pD0hsm0OnkRCi|7vv_RjAZDgnI|R%u7%txKFCo
zp39q@lxjkMh%v%_SkF7&QQK+xql0Kc@nW}FF+jq2ZR?-m!YnvyT*!ePe#IwewK-7Q
zY|<ck@7z~0%bfqfa)Ee$<SPP)FBbR$oP?A!Hz^`Dfi|-?c>WMPOf*EfZPKK$*an7%
z%pU4cVk5&vn<_s@(7Lx2h9AWgax2WC=%0mHioZ*hhMH*~^a%#yd#@g1PAC-hikRx}
zYc#E~k}<L~@q!cwaVRW^ZbVQc9&9XOf9_lnT>}(wi-MGi5)XwP5Ge(0lS7n}L(oaS
z2vw0>$oxY0A&mshm>M0Z(x(AZYxt8Ol$rR>S)azYlP12N{JBPQuF#>AXw({>HU;eI
z>FFy(0{YoS@bK_3G_1EeFrP#?Z|3a!gJAW-Q-9?f_haD1(?($Og768Otv6Lpv5Oal
zjN!spp2|->UB532^!Xd-X?KrW!44HN;JpX@z2{nWG*wLu%z*VQLQVvy#{=y<fdk=d
z++gHdN|tSE045_s_#$C@luF}Mw23@#RWFy5&*-q+7oTvy$6MX?;had(jv@gKkgQG%
z;s;Ygmvv-&DCNR+-rpK;#w>`xw^jq47ys2FAn=OjaCT~{akh_6`Ku*`VppJO0q}79
z!wWC}4l7aoi!ncb$|EYJTht^}dD*w#OEtKo%n_TxYNNo{l4^L9_}=16qIm-M+1EP%
z&n16KlVP|KmQ?;?il#D~)cM(ZiPZ9Ly1dYEOvqpAX!D3aoTwt`QvD?x-b<x9r15L-
zZ%(*hnIFA$^gK`#>R5S?bwb1`a$zm;&?oUXFNeVlZbfi9qr}*Ugj#zHj)KMXE<5vy
z!Z&cLhL)-*NW}o;8HtHh2InAsDPq?Mc!xge9y~53qCly3VaX_{u|7tkjdIf|I{v@0
zs3?9BQ-C3H6quG6oQM=46byLp>F%kVzI>Q4_}fX%p5&yG3Sb=!8H*0Y0qjSiMdxvh
zM!mR1stD3DmWoXjUrB@zj7hO9b41-^ZWe02zTrn=%{M`*lzA|?9lYFrZo{&W8_wB3
zo-b>+J6(Q#zF%Iz>_p+UD*<c;K3%NdzdfG2poc*`z_y@0^=MS|_E<UImhv`1tvW^E
zXN2j4zOkjQ9^l|sQYSEeR5fatib&@BlYbEIP87G_zpz@WyOh;)AiquQWH+KlXH{<-
zoER$)j<{=9FzZgJs1X;qee6jYk)6izmbc73Q4g3vOjCV`F(b}~{xV5;bFW(a`>H}H
zPGDA=Un4FLfr9L^ihzYWoU>6cUb<cE_=ld_=%v`il<Shr(5}d5`GmGwixkZ3j8sa(
z4GY)n>SQTf34dnUa=BqEZM~;<HUQPY$7}xx*>Y-O7=9Eb+#Zwe!wte-&Kjp2u?SfC
ziqw?Z{B=%?mzcJ3ey<Bw1EN9RgS1DeMYNm{futubZ3(HD2Y@_GPfOz^jo2B|=$FRZ
zB!4m$@J9&(kNF=w%%xSd=f+IU_(G9u!mqEYY_B!cm*s~eC(We-1_t3M>tEg{YX%E(
zIyl^)7ZYn<QlfEk{4|_sQ>${IS{m!Dd7+h&jg2zHw!Y5s9Dj$pPJjOSiDj~X;D&70
z)4KaD^%>LH_BMx9Y)AKHk^CeJEv8v8$O>+&)~7VoU?LW3jC0@;ldb}fSO}`)#oM?L
zsZi1d-1s8x9omXsyEye~X#>fqt!OPy&`(eHqe;eup%NUo{s~fvq=ELaBB^w}L_$Vq
zE!?fZJaUz0tDmdh7%1`yzSoPcR^TC*X6qbjTT?}lVbr@n9?a#{;e`otP62^Lu54i;
z^^T*x69Uj9WV1YJ+`K`GM-R192mgXG1FtZENk`j+ZFuj41D@5u9d0Cz7K&DL7ow$4
z{7Vr2rhc6|jj&|0oCs-=37#H9{eX)`qjsbj`GK}5NfFkGW6vuQ;bn0mIwKW0Z$~nF
znLtJd#w-%u+r*h`eG&dl+S!tvjm=}2+F$KTRfPz7Kbc@s&*OYuDR&f6Za?6C?h|JS
zFuX<<%GQ^65D;_rQb9K!D3b?t7EInUTq{wi3l@&_bml<po6D^={Kr+qXEFaUG&GrM
z3m%Lbni}I9Ny!+B>u9r;_tOX1W#tzhzNQlEYMD=YKMRHYqlnK`=#-YvV>)T&XO$IB
zo4FCht4e1Wwfm28xG|CUa%Y_N<6_)h?sB~Jr4Nay^HDLBX`u2(yt$owR`lg1nAgV5
zcP;p-P7bxkv2#Fby6=T{r=v>+^S<c;ZPyRo$~U>;Z|AC|=fl=y@TMsq1P1bhrAqMi
zU^u+?#^J}%DGLXourNKO0MZyC+hAWVZ5sjFTm)Dk?^S^KAr%j`EhS03Mojtm3l$K=
z-!U09aU`DvbO_rGdA>(wi$EZ!gOM+H<MhxTa0NdgXi7uxxJ<`uhx0X_(VRrW$W@?$
z?>dOWmdF&pWHOagRs_~&yKiEEQUL`v4V*5Yx==hMP=2MG5wJPhedv)&N&moJB4ryo
zA+&!$@ABZ1MQMT-P^g+pQJbueZ(qdhk|@;xRAIc~-2?<mz>0VTp9a+YUTSIwrm9w-
z<I|ahi|<5-{jye>T5f$P^dC^j-$EJbn{}7ZAS2*1S~Hc2&W_V*bkogCr%#<uSCmBU
zaTu);e)aW{OHfknagPiT#~ntMFL^<@B1dH<^|lTTv9^nw_3kXSO@y@uqJ>U+Dsz_Y
zW_bJErhnBQFehEXx4yg_J}Y>clf7F{<POmVca&8VQyc898?QFE;zcXX7yB56IuNQ`
zYuk&HrsRKXId(Z(jvJe!TR^Kkx^2qRz=Uu#0&&w;eHB&LGnFqr-OM0qrDp6+-NNFq
zTYl%|qSDf)f1*B`gddL~KPNk;3`lK$Pw1_z$H<0kdK3KOY*=f>Wbfv?G@ra2Hi}SY
zleWIFSTz|j_CL1j%4Vyroz(>s)QBj+Y?JQ*oiegjO)^-7eoe;B!)AwO1dy6kd9jM`
zuqq0f3bsQ7>-KkHpW#{WJ7z?ZVc8#w6yj&J-r@<t5OjuM&|JD#HpQ^6YgL|C2<4=1
z<!-jtdNmk{?~dN?C+BfmYIF3xw8!)g+TyaU&2V5<JM=#O4X*yRg};%%b+Wa!ry*Ie
zIuXieUzE}PEU{uXgc1ek0az=p$qxyQ$c5ql807}xEPIVXlc34ZnZ!^h;Hw%;POJGO
z7_64KnOO^fQk_;z53BWP$!>9x-~aRjc@c%32tXZ52qBZR?$th=YEBl!t91gCR}H*3
zEqAy>a3h3Je8WxREaDs}%U7WlZNLX4n?BwO&`%CbJa&KcVCo;6Mk6o^hb!0}^78C-
zvs~}o;}MTg%*O48L%24q)IS4!DA3@T!@kdsd9Nqx#v3a1cUHYBkG<W)qMn^-?Tu21
zv@>@^08mdPN^G;>EcBvE03(_lr!*S&Vj*G27wm|y+1Qnq&CW^Jz@6TZCaSyI9@t90
z74>%b+{q$9Ta%!6e6$=wo;qdGirD8@Z+T^SvA9{@baj3OUk(1{9eSNK-5KIdveoKb
z(RGjqJ<V$1`t`CK!qT=Aq!xw|9yM=@g2^(!F_IWnC03I~9OsA=3!HAGI^!5^)<5?J
zV0R*ltMA3tECW<~Fzkac!obxqJ9@2s3hK8%Np+-d^YJ?zjaHI=e@F=Nv-tX&+mjw`
zIyFl?+uj5oCXu58bKCwcD|744Qi2-#;vFI=xmsbBTH6z{<h2Ea={dLx6xhr&NF%T%
z>EQY%S<$F!j>$-$-f=(vMcG2-lp+L`4bw}Pr@dvlz`@1^a2!Hoywp_5iBLnbLvn{)
z1;ogf=7b)cJ(H=$v^y%x-+dwQ*;HeVeSCUvumWzK|IqW{0o7DJj6GHnxYUgx6V20M
zFSt97;Zpbc8Mlk$Gr>CYmeAWN1sx`dZ?X(gI@12((*(w2AAAxb^cY_?uCE#H+velp
z6x&6}Jf)Fh{cQ3r)~EZZnkRaxL^19!!NnyZzleZWLmb9aiLG>1gUO0_qTN^A>phwp
z+}qH)yjZuivKTSD+hfYcvC9HBjh+Mt?8fxK9etZyzz)+64fB%SMApD^GI+rB+d(rm
zP4c;WpUN(6OF^-NIFyfv{kc?KC5Wvlq%nsww>fn98NQ@-jl*Y~rm=@Qo8|7c<LfkP
z`7{X3ol&M1N9yj3J&6pK=%2}hL`s`Pj;~-n#!&PZ4Cc-w58XTh<UfVpl6<kl+>hpR
zah#A#;P(s1Vg(Pco!|CKO&In%+K*eE?m~h+ZKkxb^b?iuw>fij2Qqu|cM4rt*NJAA
z3W!Ke_}%`)Y@TwkW{JNEz&eJW9DB7WRQ6!;^v!><Fu{f_3p15jIkHnz_x;4h)Jt{_
zoOX92{0jW&=Js2z6Y%8bf0uYFlxg-ymSTi?r5V8Tch8(t_B6?{m#BsZg83Off{<%>
z;k6jE+*mgn9Ctkn#qW@z_21cUtRL`x)pj1zt5;ze(7pBCxNct1(rjsGuY%{BMx}4%
zIkXe%4|i=(jpk#g-|;bQxsSYY6AiLCm-wNep<Q;2H(|*1xmRNEmDjSJ!<jv2vjNNP
zb~jO&fAWFAnJTZjBg{;jFF6xw#VYzXRNg<nh28{OzNHai!5^YHoUF^v*@%9D%$w>H
z&*UFN5X`Db6b4E?(%E;yeYHG1bliUVFk?n4%&@x|Qn#peM6R;r6NBtfhT$j2!O+yg
z6$x%W<79dq;b&n!IG$%d7c{DQ4`gC8TU#3x-CYLqcWH(|a(VR5l<}kU?Rs<f%QMGK
zRj0Bcd#W6-jkM}!@3JRP@pIQqx+{^U!#b$5dXKlrU9PGt@EWoX5_%$BR#3deLF*df
zA%4gM-{?JrSHtP*{Vs`sXp|z1KuzkvKe`tgS}5v3B2koOWkx-C1$-Ri<cj)|$>*H#
zfpsKBf>f%8USbS1S@aR8X)hOw3`=VtObB@6>33TYA<#O<ZGVyFZ>qQci`GI<p~jC!
z`0j^Sa}za5jcp)6knSty5pNnH2r=w@V`*~jCv$Jhk%3^v<!jFwC;uW?%dvPHLq4%y
zkk!tCgFW1zo<_#+8EK3#tk<6kY%JQ#NK1BM23c*qD6fJI2Q-d`3`<+-`a$&ihU<6a
zSk{WA4i}(uM1Zp`Ry)Z2?vH=LgIE~#YV3vE`Bs^JQ9ZTpbQ4yu-w{IZa$oir+^<!O
z`6(MIiDGV*#8?<ktJxeh(DNoX_jE^9J?k2e&MfnabfqDwU}$5Zq);E?8W3Uw2ZkiO
zgXm_$`4Ta%vwX#MsPHmeeR-!9W1(bOC+1RuC8`!eM}#1Z3(SutiEftyo57N1SnA?X
z0m{@;)NQKOs+!cl5^Rb7{Nl`ZMYjRn*=VhZL6{Ix{YLOavhJen*Kzd{OdSq~SLU4Y
zN};2I_g77BywItWf)$c9v^jb9t}@$n3je}#r9rFHK07T`$8vEQYy<7zVQYYk`AS5v
zv&J<YhU3Rv>%V>(6u#)x!1G(FCBz(FqoF(Cg3CDqm~tsxA$uKdGZhCGBr=W)<T;;M
z_cK#|!zM^Tzel0aIfeox7sL?9-h}duLbJBNmfGlYcShKiBdF5O6RDNc*sY^FIyyUl
zR@t6XmCBVaZ}*3Hdez4^x<CidxH6RFK6#U+>fJHMNjW%LG~1o<y+0Vcpjn6fac!5c
zx)Z18so3zsG+N)m>Q=be1-?J>#locnx1x|yR@z&l8OnW}7~Ga>+hvgC{O!paEG2?t
zfR0~ljG4X2qdR??egun0Of*EK+vBt&5iOosw=Y%hQjxHW>Qq<7)&t#|oFAAl8+)t|
zIlju(^hAL9^KNHKtb}&c@Oqsh@~s*0T&63(p2jPskRiooK)62aizB^fweL4^0-BWk
zLWy$4Vz?(jJ80opuS}Vh`@loOtbF0N7)SK0<cKysDna{2g&;!Dn<iy%p|tI}OX7W7
zX5X=%(QrbtKJ3y<;{EhMTmY&<zv+g%)r!3KR;t|FAD$T|^65gd8;Me+b<>LPB#RJ-
z0{$y4DM*B&RYSfECACbxfV6XL$Hpf4#1aTvdQ4XIM)OZp`!-e0PHfdvE&bmhMq(0D
z+<pBmNR<Fy#6)okZVRAXf~%he<)1~@d<-EQm~Y6A8#vQN60b(jG>9;{0SOEiNs#su
zAsEW)#7ba3>nYL6n3_$m#aR}1!c;EER1KL)+1X5A#6N|=f^t-)eQTqk5nYR`r-h_#
zG0I>hsPxfyHxXvGH+O@`zl6^e%w+)~NPu9u8Y@#qM;c&<Xh_VrW`%`2bd=+$Q~d}n
zK#pojHv9JG3MGDK$B<|OCykiTzP1qNL~{;MG#+o5rtHoQDf!gB!4Dv>Z<|HFgv+Gd
zY_+-8XpXKJRW#!I1N8<#BJ227cYm#+eD&>PZq;XDOb%iKzIb7*pT)1fcxbcfucpFH
zQa4+@WR=%{o+m^|uxn?ud_bVW5WMS3n(NELnO>&<)ie28tQHh<Uo4lK1@$#Oy;B~=
zgf&k@mGLCN0pIjwY#GgiRaTfc=n=M9oGWQ^H#K(uorIL{9^q=e6)NKr4@iNM-<o4T
zSQk}H&mZa<i!`$YT<v-bcfS{T96WswkERV{MiyjO(8mWpAH>=(E{H?Tbr+m}-F$yj
z^%sp^zc!lg=52|04)U*qeFjTTXbdQETT_V>A<falVzDyO9+<cHiD!4(B6Z8oQRg#@
zsr3x61p-(TKsOebY!$Ka#bCyC9{tDgG^cMO+xEAs*CKl+tkol_w0uItlr0a^usmwR
zmNk<mg2s?Q4dL%vLN`qApFGlTPSz}|ki;NdmxnUQBl;5Hycz8vQ!^Ce9hkJ*pUn)?
z;B%%>fXs`Om`}Hqsj?Tl<G_ZYad5&BY058#uOa=Fmzh7SKY3uHJz0^!-JJz0s%04X
z0td2Il^<ekzidIIeRWcq=~0Vjg%r*B{tQSy{G#d=Cow+D&A07RN68cWMHD4jHa`_a
z#PhOvnrg7ChGG&WbJH01n^{IwG=<x_DrdMj+pEpv)vi}vb|C+Fw2!UEP!qRl@*EWz
z+25KE7YE0tj)Dy)h2NVmig9k#YISMGv-epwr$azmaA8zneZ3@#n=H_lAl8ZDx?jcX
zH6m(08LZPI)3;OPhTNz>#%asgvL-!|LpJzoe(GMs1coh7TzdQ?5>5g%$ofy?jG(>n
z<5RVvv3aM<K59i)2i7Z+H&C~LjtjvIknbhb*IU;*yLX1raf3_!5(o>{iE@9ME;#BB
z>dy79BqX6(^Tp&ev*~pK_Z2{aK(Xd0<`wnOE@jJcf<M$?S5rTf_L<VsaLC=~pvQlr
za(*A}_zr-JhihFqRu#i(|EkJE?Gczc*mbQY*+o7#TOZTW2qvO=P7p6EP=L@EJ;lg0
zJyG{a;Y$w0XAH)!mLsIUKqxAe4xWk#P-Mo6+Lu2>%-W6SU*zzsf$&CN#B4>-6x7VM
z;bHzr9|`6phge{k+JFX*oHUq3-bBi{$6lVV>m=cbn{oyG+vi}wv=q$rVKwlBz;CKk
zmXFZdGVdpg!?;7vvB7@n2{S{qH$yU!>Tgd-BKg_{=lcyNG}|}wee2w{tk1qm*S|a>
z50(JVq$<t!FjoxCxz#E?&PrWqzNcAZX5N9#$y~uprk&<R^_ISXh^x!SD_M~IM|8L4
z3PrQ?3@vx*?P0oVc)hq<M|3vAQl;L(YOzE3wMC{TAQ@SBb4{<*wcTq4=cqs)H;j9*
zdI}XX^|{`vKZo*(Cl~cSln5BFO*d5C&kz0*yVJG9Xfv>KPfbV`Nb5CDnBRXA&s<~n
zAf^UK;Xo@?Yf+S5XfxLU+gosWuyJwYNs`ME{95mNH_r_3H_4v#O;1#<hHp#m+4Z#s
zx&wovo|aa4YQn7(>1fO5HBSR7SU~VZj3OQ`m)hR4=ADIz_irFk)!NF#HRIb3-x{km
zi`#ZN3z-VVYua?SXUqoVptunv@fHH4_O7{Xoh7@2xM05Zs`UfcBFFZn4{=k0a;TL@
zgu~^<$W(;`$$NgrGp-L_mCVv778Emtf$AnRlz?;?7;1=%EFnV$0rF%7qMJ*WE<rW&
z=A(y)yFLFbz`&^QUkc>cIGQXm=PRqv5y<RlhwFG!+9azzLo!|$!pxgBheDXNt4*#F
zqYf5Q^tTOBO)%hJbb751<D<!MY)50d!c`z6=o8f*fM2J#9}>Wllx=AupXKII0rkZ=
zYoKMV5s^LNdPTpLh3dEe>6$=eQ1@x}!76~oCV5w>WJzn(Q|mQ~{wE}bR;Q_l+YpdB
zty^KyP-*EOIn)Bs0Z7IHOC@^;C{;H_VCrlKMWyA`_`kLr)0s%$6n=zOU4Cm%!pUI6
zk{(2CD@dX_qGdIg@3<jb=O%B`vEjZx_nod0k|8ormm_3rXgeTjN^*1a@M)#&@o1=D
zOREUCvBq?I>5sqcT{GB+xtD$^jMA+9$1Ir8GmFTa+BrDgV?@<vLv~v0^w(x;as5jm
z*823`vtBa=;*ZX-_fzFOb}6$>yRdu~<mXzc7M>-2K|Kckz|u)?81H*pp?K#s$@XE(
zBFUah#b3sVnCj<+nWjqp+DwL*oBN(r+;-3OUzeMOm(=O$ud}`f$(7-}bFG?3ffLs0
z2IGlBB>h_ci3#Vmobi4zuwl&LY5&aBy#)kgeWxH9MXfnruGFYD@cIIu%CmB|Ti*P_
z_I>ws-Fd3AHtsDy6aTQ1<BA(pAw|Slc=eVyZ(LrDERV;#x$ZrT`}h<>5&}a94Ag=r
zlq_fDK`7-=uuGYdEYferQr<>OCiMlA&fJ-3eDU6--?Dvej0Rt=T2){f8u!Q@+1BkI
z_1W1jY$89<XNy~k_GRC!P0r12BrJKxYI?oP`o;1vZ&=J!!nb|x>Lc3n0{ycqu)q_w
zNuPjLAea!EC<jW6<8mOdvy+C#OO2;=mnHS0y@+qW$)t2jIK^Xf=p}AsL=??(43)FY
za)8EAoQU~9bEE?sO(zCOQ}9OHZw~(n5b-^Tf{w20Z@H3B;|D4w<JVvLwE93lg^mcA
z56AjB_wsNSyT)su)o*T$xpkphTa9x%3GNO&h0lj0PPy(#6WwQ>`M7xS&Ztk0wc>;M
zaOnQb{&Ew~?c_etk2bp`eb8*{XONGrW#D(lvPJu&zW!*<mK_2G)dK!Cteeos31Yse
zuyS<NO!6kg@RC{Mp&&ld1cvQtbEB6Nf5P0Q<|fCVB-YY}{6NdYXn4AF_ZjQ;R`q*r
zJd;~Nk#6ZzyhWO~Zx<c$Y)ZNeqLXQ{(XHc)J!vlXH<uObck4#GHJ*;n<(fH4?ld00
zmXdoxtC0!R%a60R2wwKb%VFq0EM)4J2mQHDc)V^2v9Z2VYA0Z{2Y$TFBr1MZc`ATo
zx3)Y@e;FAQQz!q;=Pgud1{PyweQo$%^D_@2PGw){Yx%z)wO1#V_{&Ko6wf$N*Q=d)
z<WeQuV<KJ*YPB!^p&Bo#@1|W<h~T;dH~P^9+R1$0J!UsLGMd7Ya;>Z{j}Om>YtPsT
zF7>66Gphi#$)F9YWQx9v^XQ{5WsNMu*V{zGvcKML9wuz8ruPqd8T98TjtBM&GrPDx
zJ`a;ZGMv4ZMQArUs?Id7)>mLmv$ghH#XPOaBya6o+g<DJ*8GFyf@_HP8?J+#``DHg
znyoZW8Y!qOfsx>}9qnnK{03rUlE+vM+8oV}#OlZ1L7%F%I@`XMg#aEjT0WNbwj^|<
zP5)(I4~_x}`15d2q~k|>C8FT}BwC<2S}+b*a=N`&I#Mbdt`J?QneWRseQz*Sl|?rG
zK7=<~|L`Q++gi-=>dxF?dHD$BhOfT*dJiY8tj(@m>17C$46nQFQbR@9a+<3GF*gqI
z@!gME)YCO0zMa}?OBb<(bMGg$i<UpogB)dd^5Q(+!9G>Lh<(qZ-poX&m*Sq<5l7|%
z718Ie(VU^pI0YMI`@5!_UAn2PWw6EV8uQ)-%*oIQG)((o<7DIHL~+cDk~!TNTq?R!
zWayZL*yN`mX1FJLFJL?mLV}Pi)gX<6okc85;D9lq_0P$=IMWi~r5g+7`?U7K3V~5x
z%I8Kv<7&4%;?=L1pGAekr)PZK^<R5fb%>hnTp=~S^|y^fu)IE~*mcg<@qaWqs(h)Z
zGYPp@f0Q2xcDrKxrh{eflbib|C*FQn2!l^WPo&EhZ@nWPr@65`i^Mcm8vDqc6l6}5
zLAeuAPgUT2NL=7Ezr|b<27-o96Vcg*YA#|g+;nqL`1lo#2e1EG*y@^Zz8jh<U(%oX
z5`NQQax$LF#uqPuf<*Uzy<nbpW8>!Jr=Kl|3Dl&!NusKD_dSgK8(9i$zZC4lz1>(+
zp0R5EZ(P5BLzgfj8G>(=6>y}yv7*bCR)lMQY$DeWe@rHl6`OIoI^s-Psyhu;i@^?u
z@m*S1WCZ(fcZ?KG{mDq*j(Y5WuHqTwtS_~nNay#w`N<4xKlt`FtY<uypIn^vCX%L5
zQ?VQgbB7$2H@$0xWqzwr^kB`cPcQy9q3f6&K#h)@*XwtRNO9Qn55?6KE(^1NJmILg
zI->MH4)R15Fv{Xx_pVS`^+ttDilwl+OrLKXpMGnP-iqkBry-Q0DFFD{ySs*BySD}K
zag!D|HlhFDTMP!8TN&FB!uD58M28{Ug_f!^F4-$ZK;J`C&QqA|A=sVI+G-FLJj$Lk
zPwS6<Ri`;+6okP?!BX*dWWHP$cJVm|O=i|xeX@IixS+o+05~xpH@`jXZzVEP&oMIU
zUDv#M^00f45p}JjG1c3n&U6dod#UG(IAU$-9!;TUyV%dUyP(-P?Ks>0-X_*J-N2H;
zwqQM2og=$4C~em7%uQYrX*9WJOdN2l<n%7dB1xQhDbYOqgHK5YTjgPEJ6B0xcN)EL
z!TE8f91|!xM4jX*pA^@5G~_Dq+oMkSsV!B1@+S<)5uhOegV47Y%;t6fLDkQHx&Kiv
zC=?@&ic0bCy%i$3d-)wvdL&ywT5LNawD+rSfy2!JQTkzqZ(R&I8+I^mj>w=tF`Ghh
z{`LNt`fphQpIeCbYFvA*s}~DHF{^sdKc8n;2LO<P?|P2YoS@LxF(`qf2@@3uc(BS3
zfH6&nFBS>SN=fEyEx3BYiD;2!!Z_MtMD`POOaQH%w)GfbB9i=jhlpAApe<djggNaQ
z>H*9_Th2aF@9ZO5f&PUuJN!C;KJ9G@f#7#}k}s^RdkDbe7Q(ZE#ffbjgFEce`u4G@
zP^}1c$>eaL#WAV0Ta<ICtZUCyh^~8<omEm)HoD+&ROuirr8@ah^kv$fg9l-CTe$0s
zwy35A(9j$2d6MT>QXBcwI7LwF6p2i!b7NaUneo(nv;y+4TqoDWVQrb*u8GJ6T~Sb6
zNNKY<Y|hM0A0nL|VH;Y0L_rXFH)6$Xo={DA;e88da|Rs|T()}`mm>6r8I$gfSntla
z<yU$WLrhC1os~q-SuV3}-p^q}7>j+Y)_B85b;-Wm{^JeHD{PN)BeBqOQ;qG;=6R-g
z9%wDK^^Q719Zgzi2q5u`&LLBiWyZLwL!C70aomDNk!TUWFd!I>?s5E-)MAI~DW4cC
zFmoKv6ha|JGsK|PzD9wl6omTw`tb%jmD&SdKuVct&n^<VDOH|JBU-VZVi0~`J)pXO
ze!P}DSn?*=F85XQ_KN_YngzB72Om%WKLGha2EUnoI8~dkfPGeyw0n2Y9J!8+)h;9K
z=&8H0mXa6O?p}TLPuNSXh{0c3(z%H6r|AW(S5cUn7AHCC$eiD|T)F$!vz4D;v+6Ms
zXVxyBe+j^D*zt{4nHm#*_gbpbFjIkrDpPOWjd-TEZN{VEg=-H-JaY7D=$_9k)EGj+
z9Ug+PjLxTEgUN|xwS1NhVIx_>(LO}u<-=G4v4_&?3rYX)a(Nw_7yc9;Z)Ypg-?ttx
zu17WX00#ZCLYXyZ4ymiGbSzc1wuUye85L#S=qm<IRSJcYX@Wg1uy=yBwJm2s)N|^~
zE3C4!apG(dtZ9*jpmj;#t{#^UT)mUrwIO@QlY0MRBInVoaXsIjbKZyD6h&^YY)ffH
zRvcTyeg#^OMPh1{>*c|%7dcz+iQf;bV3CEPV+X&6i{@-Ry#I33F|7?ff~ojMYlSCH
zTZzdn0VtHM5sgIBhKfz7kUZmQ@VvFBIj6=`zV7F$R6ti9rv^I1zhD+3bP^ITq>&n=
z19St?1rr1X$k?3E20!>CYLTodshbT;pckn>me);Ky#4G>Aw8(*Pcws2oRyc86T`)i
z5gEZN|CDzc*QuID>x|eN+c)igNEYThw(K{gTSKf6Ep(@+7;=<_pV+zc>|Ic$t9x>(
z9PNeDgZ?B6dpy{*_QGq9yD;m!r$c9ssmU*3EpR<mW$HC`I`H6XL~O&>#11bbLuH&(
ziw0#LpIWsorp}bXzBbC&!P_>Tx|@htcb^_Z`}(Gy-@M|2_4qG8s$j*%hHPGOLO%4%
z5zsL8e&DZrQby760#{T|U`Ft&wJ)pHN;rEq4snjv`;Q&!<zf|kBXGxV^hYG9>H7nE
zH6fb|>DRa{HhB3VdCwNy#+_$t)Tt01RRpHyWKY9U*{uh?UoK0|zA{nTd-m_{z3-s~
z2IuyAc6K7AC_+auPGal8#hzchhKKUffO4XO@=?hCEk|yUhltXi1BQHD+wsYUg}X|3
zXp<gtB;vLAr^8CF+xghLj@9|g_po5GWc4;5_G%1Xf{<DcBQ6qx)8%O}Xhvu;<;%lo
zNPmz$pCLmsg71XVb!sMkeJSU=4(PhY#WG9%AX7s0s?(H2dE!y2fQB!^FNY=B(a&ua
zcCNMSSF=lfnW#`IR4SrBlu;#x|F&Yc%%k1NPBjz44j$OOLghT8TeZRhOHvYD#W)3L
zSKFt4%DMGB&pz{L*S}M3yX*TlUgkJg+L)NaWxQJZKHd}Zv|OjYer0V6(TXaETE6AE
z6IXAiw<>SXW!$<H%~kI1VUzVl8lU#aO{z*i^V_dy6D#)``hi>8?ag}*|0*x>P5bKH
zvB29gy~|hT?uFjP2NL0rQ#@)rMPI%sv+Y>MvJka}g_ue4t4xlII2+fv`@lZ$DS|ib
z+_~j!t&bd6?7mpux9`Lj<)Tk*KC*vxnW{hdT1DyCxW9!o@!5rq&p409?V8ssTjuS)
zA~1E#y?dx+kN0e{1VB+KE4KQ0QX7>*o_0dd&foU;V2zd^R?_A{il39qV+GKL8W(YB
z$;E3j&UNUNex((13gz6ZkWK3XA9{D{?^}%v`FrQKrJKfl*(yVpb}cZ_x{_Ddc8zVb
zZohr|_`;R40fUFxzPi5s$eDBZYt^&O)X_wkCPyt6G>YpRg=DUa(+HyjcJZ^FpcNIt
zs|+qCk@+d07tS=lzTyQM0{vML7{RXM0dIlkR&c_A=1;F2-?Qfcbw|$LO@M55rSRE2
z3OSj^xqke<ZyD!O-tFG^w&g;vMHie3h9zk$cHqc?vk9_{+gE@keMfa_P_bgI_a{xM
zqyB7+DePFjenaeyZ(F%#W6zR>YI~Jc{nmB3#7ohbp-+ttN#wj+)g#ZRa2D2<vDeQg
za&`Ll@~%{>Ox-pgw=OS>IQzI{eT=(8Z$Ia_xJWWE=I%#<etqLE=i%p-$8Mj9$(_UT
z1x~&r`?ac9v2p{ymabf4TDk=RpS0w}xAGFceJ70{Inu+HGg#y7PHjAJ`lx=L>Uw$?
zjj6K~nC^G6=Z(u3HcP3H<H604oga~raFIGR)aZ-Xl58=X^?J{qtFSP28DZxha^*X9
zZC=yGrDoTD?Ktk?^%ye0Zd)VZyRjd&^r}p!iEMjK8s}ZPVvR;!n&DK3Cke`gy9`H!
zGEgWHd?pam8~zslS4H2qKiD(D!hg9aKv#%n;LZAdQGE2?iPjxgrm0k+i<{Wb#qf!D
z4B+TML9MSgYCw{<FvYHM9oI%?qlH>@asAEXO{z9e;TKD#f^~hzeBZme1ItX6C6~$R
z%SNFQk&YQH^~J`;^E3)Qk4aJ0E5xT-J;l&A<QdVyX<X%+<z)1kTd8!c=*m4LgD=)>
zRJh4yD%{PgOog&g!5nrdRjaux7j^A!LPg0<8CSkpO(&t&GSkU%R#AI?|5;<N@=Cdr
zPixoE8__Sltm8QEp#!ZqZhi2wWpXs<G^9=`A+07w0a9xFAQy#<t5w@wP9Ma`hsqY#
zwxwH)@1MMHU+~)BgOO)x&$a_Uu41spQ=0<MUA;U?$Z*gM`q3gWwSyBrGkNtUQ{^st
z6nquBcWaoWuOF#oT|2XP7HWgyR~XZSIU8pxpPmQ+v{Rk#Q--yTKlaCppuO9xl^@u+
zEGJ8d!L5~cV&TsxgcLRw1Q6=^)|O|=FttKkt+k_drZNjHmz=X^!&%6s1n&^Vk8*{?
z`5@9?-{%rB@-e|tL`K%?c@j?k`V}@rgmZ86<5rGL!Rb0$3<R=NAapIMNDjMv_41Jw
z<5!+qK33h?=}IYfe$sGg(_sXzc|`LfE}2T2k(p`eeUhMuHCn9qm-7d@(U6z7FFvvI
zzzzxOdKCBym{2LQGBc62+F6#Z?8Kda^osLk3kEV0UvoAd>R8cl5!hX2TeYPJcks&T
z6Wm>{&WBa3RP6a0S~!%c!f{u+Pn^}3E{>BW+zYuUcXv=ZG;YS7KNWa{v#rywmWQnq
zw>2=(hI4IE%~t#fD=J3ekm#`KMoeny>9KDtYLA=M(^i)H<j%#-TZ7gguF~JpK#e<b
z7@Enla8iK?SPR?K<!UwHjvhRH@sVeTYHE#>ubjS-<kYc3xl)GgjQS{22WvKH)x=6F
zE#GliyF0Uk4z9UeX?pW=Wh&cpFY6Ed=|gX#jWX%_g>XkV3!JSfvD8{;qtn^gl_KjJ
zwV_;?z~IYe3f5bTD#qFdH!F2%_m+P7>16Dg<rT1T&7VV>RltUZm$%RQWWeljj;rrC
z@z-^8{VU^TMCiP!V}9Ix(|ge8O=I?OrQ0KM#`7yTWFFmJ&JUXVq?gyDQD3j(IKOol
zw-0IN1|WELar5UBMjQ<1hRj+~FXn>#XTJ}wuO93vk}e+!PIYPI$L;0ScsGCYI6xG7
zgF+{$S;>1_9Y?}x5G5-6wBop1Yd4*G7#IKa#<3+E4+K9=$2?{sIx<e-5+2;Te?N+M
z;o<k6%RC#na(8y`47s0@k`S`@uj^oA+N%{-;NHr7cI*E0bWHmfs>|Z)`ucKl5fO1*
z>sGatG%unB$7MNJX>7|KS-Jk&!{>3&qxSr{_Q-)dWGLoZud%J{;WL?M{R#@J3JsiO
z4<Dx0@vCoDxTfUcg0E$wAa55!!dh`xw(eN7;qbNRDVbJwB@kk3Yi+p3DZfIUyl70#
zxi#tKVH<aL{rbRb&l8f8UOo(5y&TgB-u^xkBX*{P43rv8K`3h>HU(rN%Y2JERLZP6
zjviT_lLh{<<GI|{vt{|r(5<`9N4<CzbM4^zokxzuO9>!|R3Qoq?UZ;pRhEOh8ocq#
z9F5V1cs|hJk281X!qk?G>Hq*h07*naRH@+eLBw6S5WIICc1#3g@-IC-C&#wB5D~F!
zi1pCsUC$(N>_DR7he94io$t#n>-yVCg+<()a?O9d+2wpx)Gr-nf1SaGSY+BfB{uL#
zOtM0ewtvfvQ5ULQx_NWcj5j03ZcXMa6wmfm_Zwj~Xm4cX+48%_f4ypRid;d4>P$Ji
zYUAe2`Rc2$KlI{6Lo<Vl7neX$9g)_=yrTZGxag^?2S8KbVZ{|&Kfn{EnosE;zkKJ3
zRgova>{_S$==K#j{$-DT>VgR&(~)Nnh^Oam8V>mG!?E38uRIdCIWiErIAH=v1r@RU
zPOWg2CheTA95}FfYl-P!dWwdS1*4O;6bFw69$d%+Tkl3yY&g1f(#o2eQ{~iq^2qc*
z*8aU}8A)O5G4RXo7^tYSoP1hV4h>53tm2H}j7Nj==g-R;*DR&tgkimQ1_kF6YFRK8
z(aXLR`b?V|-3PW#+IsHqW#>qKP9Cj?bg9O1)eY2mGzM^vcIv0NA~JQBs@cddx!s!%
zgdAKI!Yty{y8qbjbsc3&&XAomSs86?34j%WqYaSdqmo%w9X+z$?6pA~c3%5-*oa=Q
zR~|UDA(TYgH10dGY5DXUB84oikkneDwkIE>(mi{KEM?(wQT>aqN_&!8B$beuoSM+H
zomOCgN_zLu)HB?shzX6V*f;uQ*63;VH=MlFs$*N>K%#mr8!8SxeDgGG7RMbvHov7i
zr>gb);d_^QTx6Y!tAzz?M}F5fP4{uMXf)*0A@jD}Py5{N_)45vv3hDx7YmEIfkzj<
z*Oln2tDzK7t}cg`YVV4Nd_ZN2t_=Ix9*Xzx-^V#drlcwW9t$fwJDV(-RK~C4W+E+Y
z?Cm%b$f*gHrNqY8noGx99Eq*HM3tE-H>qHGR*@>ETlY$NhD>Q~Yips%z~0QZ4)$2=
zixH7jmL=$EVTpJNR#eHQ(!yW$ZDnh3W65P@W@Iuvwz9FavC5KW;E+TsTU#4NM!JHN
zI5<c!Ps)}bQwEOdCZ_XjCzBFtYFSgENN8zoXA5nmaw{7fYfh1#sgOu)@q{kJtS(~p
zYMdh)Tgs$T)>w+r)E3q@wi0DV(pvz6q(mu4E4d5?C1p2YXLT7>5_?-KLE9V+W#X->
zE5|}&Z;v|&4U)ehEF?DeHWDsNo-S2b+uB(wNn1rDu##s=Whyk7tju(&oZjo4m94c!
zM%r7M5^IE&mJ$i%+>d_LuAMdzlSyp7Z1sZ#7Q6+upZse^a6|RBg|}9BX|SwU>;}6E
zjYbPxAo~4w?eFpB|DHeN!|yio;Q`B@|7cOM@yhclH(Szuv2HEv+;C0rl$)R4UDkPw
zbZ&6BD<ubIzPT~kk+XPvd#$tArqDO1v(|MU!~O5-1TO;NnUTFqcy|eX{izS;!FWdt
zkoJqWlf{O_DWY(7d@=fE%>d6d;yo0xu{l0(DfVM?l~S4^`lOM!s!FB~6o@Q^EJMMO
z?b?C;Mew|2STUj>ZVJwVC?d-sFOHZK^u(mFG=n~3tD_3@SDC3c6w3uY3vW%8GE<sC
z)y&Gs(AZ1EfT>i0xiD2EvPH%e42599R0<gYhbWsRs$2s#t`w)nqehXLT7{z)<QA6p
z_T*_RD?_T8cs68bwU+XXB4`OJ+GP_f^A0bX)A6pEI(cRWfnp0znT~!PP9;rG7rG4$
zMA8B4jFnTNIpDhw<}-lbJe!(3n_(i2lF3ynJi3aUbxBwFf?mn|92E+Q;^CFhj7qg`
zt>}jXiNazNaUjv1HT`iQk+lPPJBIV2nM==1WCSS(bzC?r6VE`p6$^{7Jy$a)dZYWW
zcoTP!<GS#X{0}WM-`fer*Jjc9kcq}qv{d%G<q^%Z7h1Fh)XJ^6C{|qb&@zs`=N(a#
zDQ;L<!=d^`ErJl^su+*Sg<1t7#6W%`xgc|NkWff)W?Ri`BFBC4>%L>(_O^;XUAuM9
zxt9|==^RMp*Fxgi1V=yn%~%(`Xn^Fwfw9xBa{(g^luHw2QLA-t?(;G8_BQ{bz3tku
zoi#D<ETvOTC<j9Fi01FC5zLC4Env1lF|>d)eG6KBPSl=3b^4Z_OEm1e@W_OwE}Zi$
zziB-yIsoVS1Buq(7y-7&b^2CWIZ9j-&FcY7sAwEnE#u_#>FtBkx&aM-=D7BQdvn}A
zH}ye68b-Q~ihpJihw9P5O}(&U5s=4u^J>+q6$|yvN|-J1pRqs@fQWd+hlz7+HpE_)
zfxsLKb1^Y7?c296OskXQhd8Zse+(W-qzTMX8EjCq;<#+g=+2mFm)_m!P-n&Ul%R%m
z+C9g##gpOm>C@tQb4;3J9x{((^CD<i#nXspt;`nqrz}teH=P*G8I8gk3@l6|XpSFJ
z=b{M_9SNifRI?fVwYE`g7}NF3WdmjgNaoJn<TLX~?ROc<SvmFVC3k4f=F4yxol&!y
zBg_^sTfl6AVrBt}d&isVx4A$6yrGzBS77So0+#{h=nZhSwY5!4ODj==e8*vSW($}t
zP&5|6-B;Kd=@0jeg%r|@(84a@zeB#kN<q;qO)MU(Ja%<+b4yH2eD&&;)<^9o3No{q
zEnv2Q*#c$@6oLhEF*awpW3C-zb4<XerltbZ$-YQtXSRUZ0{;pNut6V+0)oB&6yAlh
z7ooVmeEITUQPy|65OHyF7+aGsh9na93N+VW4+$f50J^ocb*WNXTkD|(yxSHsYj3u|
zf7=4$M|c6p8KGX%qW);-zip_0y!7SEm(SOl7C<>F8&?d^8NurQA1|0$0kZ|n7AO!4
z=%6`nb_V4TjwqG|#(->82d_XBNbY7KIa+|#0?f`v<!oRMnz`J~v_J~Pu_|OVvKOj3
zM^(%WW($}tP@FA*ds%SM6EwSmpxY{B;jRb4iokH7I2Y&Nr<;Jfs0Gk)AOx2?G>3JN
z9#xC04*l;loVi413;fqCAV9f*<FEh=6d*&m_w#NXy6e`W)ZnJ-Mc+7ksOE2VQiDHJ
z2joJwkoL}^#;r1c&-}JUE)uI*pI+EBVf;|C`}xeZKh79YJr8=QMHT)pu$P}B5h{fd
zxkhv7%Br*Ql{V*Kwt(3J|Cj}En;UZlu2U|~9@5KO{`w_5x2S^OtnFMCz4)91(jqrk
zX+8AI^{3`~D^?BbQsY8;Qt)RkIj8$4ISf{fsVfwXDQ-}xInZnYvjzUe7BGee2*=b6
z`6DNfWcLS7Y@06dNa)!^#G86=>9~%>bl$x-UXG;<ijZyJdsAzA`A-UZl7T>Z{I#Ek
zG(=d#AyXD_AtzlR@a?n9*P~N1?(FO{WWl;c<7o>2jhEx85bnwOO}(g;p+B!&IBw{s
z2%IF!D-d;L6z8{o?$Gx28g^d1a}#%F<?SSmZUS2|A*gl9kXqm67tOP&S96rv0%i;R
zrz}8c*(nY(FceSRXwx`*==`bRb3s9;gM!Xq2--)_9#W~2mIRQqht7qE?;LDB%(rWB
zyecDdRf~bMhaS9qpPoI``PbueRqW4Yo8s)Db5T)?JMd=@LHwKNM-D$rk}Fd8ZT@=X
zxk@47;hScp4EtnLl1e3ey0>bZp_0M-Z%3UkzjNI5m7CtkWP(Nw#>T|#>2F2-7Z2b|
z(QXw>3X#Q-AjVD4%u;9_`9K<4US?YUQw`KyShEGp7SOi<Kn`O=aX26bj*NGCf{xO~
z90IK(XAeyZXeR^^B0!Z+&mLNL>$8TH>>G~yY4mij^(XJN=+Ji8fm=Ph)#s!M^;-HU
z4n-%@vxg1@&L@ZO*5uC~!ZC;{4u@n})1#^Vu5OxvO|=@3vxhd{O`T$YbOp{HS~WEQ
z^Cxoxk1u?$Gn8b9SR-f45^gx^G)t8hw0uJ6=_ePR-BPg#PTSHdd~Q9eS1Gb`8A_~a
z5pT0n9MPO_4!JF$5q_OEC+2!bGh#*1zn#QI%MV>hl8dY&3e`SER*FKZh3v&5))XgL
z7z8s21#kl7An>1gvuT8}oZpqSAmQwxh&5drEw_-9lbg5^-sMK9Q}&L3YvocQ>&Dmi
zy*BY-0VCxJvDh8g*^$qsd^uMxk<hdkLEf31SFaj076*tR6);XFRW%&dp(N{HT*?RN
zDXB=fHr>71GR|x5#hAg(%H*iOTETzhmuUAWKC^s9xxF1u=hEm<ywDttAw4}kHZ~Uf
zz+j=PM$J4?c+czT=vbjb1qTP+lMIV8BYZB4k7nVdQq56Bc`A@55%kXnRy2IMrwT)|
zot<52>TGOmbks(6?}~kjvJ{0;3up^DiUk84<HQXpID7&sIDCcJvSmx59vMGL!u~W+
z&O@Ts0G@A<Bub%B=|r)!hfc<2va^SxFO#!}BLD37(f5t6#k^)`4|y%;&mOW=Cr{6O
z4QV)<Q6mj!4~e{>!Ej^mmCOlV<mkHOSe!l7ktTtUxU?Ii+?(y3{MXa@!^;yy<Ta;p
zg?JIN(P(ZwH-VXH7oRGNPnDN)Qo4wi-xMP>N3(eQ_AL$_b#ij@@bCZ-8!wtkG3XNT
z74p4%_iEOxi9N!^?XSo(0)pSUa|b1=T)A@oOT>x|L*X{NdGltYMvY7|gvm^WB$-tN
zP@s10+Q39{MZ81yDY8-&QZ1Mw7CeGO8v}x~D+u9RP-kanp$3JPMX-kAI0YSY5fF+e
z1iv~C^EAR#4U4p|LCzlfa_PRKID6<}P<_9ivoF2w46qKURIZd=!o8Do$L;3)T3Xj`
zjI)Q@&0POr(Z^Qz4~(B4hFKaSib`QJiEsp{Lp*Ty(Bv`m_cou>-e&!Ha*7;bps}FC
zx6j?ijSl&$x6SLgXEM2kjZ-D}!e7#&foH0?S{dl0>qMN0BE6Q1OOq;7GI08^){wV&
zq4|p!FYvi4hH*q`I!4efSZ@44aGXYsK{3SEs8K^`f{<Kf8R0pj087M5&+fMZF_Dfr
zAsKgXflC%D0@kQjt(v$Z3duh2bSVm{eod)dmUWI-oo2(l1as851Hd8ZL=j4J(IjJ$
z0-OR^$=O2|T#cV^?-^XTulqd0+GoMxNzF=eC1?9h?^&q?aNcY3jKSQzsq1eH2ixPi
zeXFg~oIM0^wxJmk(OwWmgww2@K|A|=8hKD!FQCC3_1QyZ9PANJHA=gBcsB^&ifgDB
z$rr!AF{RMqoMcZhjw-=4;q%x1A(3j!(qHCLDphh}?w_V9E@;jgP)0@u@E5dhdGg`M
zW-@>bCLcIb#2I1YsA|=!1zRFWD-5(EO@=x~<iq5KlcFNfiLxw&iYO%eAeVQy6i~h}
z>X%tQd$TZ}?5{%YvR)a?Bt&z|YA7{C5!_PKG_`%hpsXN44hZj0c4d7+18Vo)pC!{e
zd#HBzAR2<hDKJkmcg7zeBplDi0+}f9iN`|;)g<dC3*emUkIc#%2@lR5IuctiB`XWh
z`{eAQ%}Ji`;Te&!IgZ|+HJVRRbU(~&yb3z@(bj?(lstRTUp`7>WHE1XL35(8N+p-e
zMa@F2XTE5N<=Y$;Q<NEzJthrXJ|885Kqm7Yl1z&WYv@$OJ7%AvDMcaGuPI^~PmWhI
zqEzEE!5Jhl>eQfVltC{75Un%IstL*S*+U!0cKho0Vs!S9*lcQ3>REyYkk}k-%qJ{H
zXwEbe-|f7Ph4b_7W(3Wd^6Z+ud0xMMjVB)PNUSC9<*=kHJ4XeRJJXnCY=ivf%^Q6Y
z*i*AinKJs}ri2*F%apVRd2%d;!BqJSEWlC}lK!TMWjxtk>GK<Gg-LQA(6dAGd>S?9
z<WLn*mt%*K-PuD_1j{LBK{;#mPjVDLG&eGo1tdLs^hn24uzuC5RqgHVb;3>YYTmtP
z%bk#r0I0%i%xt-`i!?E#>_lkCK_HWvy=1((nmv1VNJvP<iWMOMFCoAA<{PX=&=#iU
z%TaRQetj`F;_2xrPWJryb3pv+)vN2*uP+Wak<PvpCJGU=<yZ<aGiWG}xj4Ld$0pA@
zQKoy1l>+bK%a$#hE11Ypf4mApZDykzZVIg9(qYI^Ech7@yT-nV#m;<W*K69|j3?KF
za{{nBne$!^a#D+Eyrw|1wn$N04Vf!Dlk(E13UzuYZBpN$tw`Q|&=y%6(>a?c{VX$H
z-3dB<vG}36k*BuBix)q9`0&Y-CqNSvrg`(`SeNqn@nbxc&6+hU4>+Q(LVe4S5G-H-
zJYBtd6<J`VBNk({YSn7kuwkGLComqGO-2~%;%*0y7*A~=1_GH(b~V7kae(sgzWeUk
zvu7<@w19oU^S}T757}jt1Uy8pR{PeK4GFlt$BY>xj@Y$p*M$ogauhC3BBoi6;t+#0
z>f*?eBcFWo36`81WDo*rAqd2nA{jh`1`Wa!Os7tre){PrK}c>1V|o4d+i&P9CQX{u
zuwg?JW#*{=k|j${o;;b2v`;_%bnxK8It)3A1wVMu@|Q1P9vmEuP7YoyN$B0XcfWr9
zjvhVg>gwv{<%PhJUnl7ZH+zfN0+v`juXf3IkNvjevZbZlJA>v}%J$i3pW)uXibIT{
zKwzjofByU_Q>ILtHZ2c;jz+1rhVCyK^yi;{j+KQh0<WZQQ->UX_~D1#0=lt`{`%{$
z&6_u4_=AD}s8OSO_UtLv1F1qoL(xlNNuM|zG@{@bp%cZt49ZKEEIDDq1l)w=Z3Imf
zA0L0>#0mC>tz5Zs4H`7iNuHx*@FyiDO`beiCn57PtqewG6KG1Rix)4>oH-K(xpCtL
zum)T8XD={&`-sy*CZHT4I2q9tO~h%%w6U}pd7&WzUfZ^9iz^m5iBlP*A&N<3SA8C1
z;Q}W~uh+bVK>a8oAs(}Em%$Whlg*kn!%>k?9u4)-p+k5Y7&2rC8#XyOl;Ce3<RVM}
zGXhsQW8p$hbodS-Y!HuuKjv+4rSbe2*+e<oc_C|FC=;le-&beN;#p4GY3bzjJEFhn
z)0OzZ?IE0<*SMJ<zcXl#x?nKGDg`$oFbnMx1KR})7EGT$ec7^QIgZ-Y6-s`yY18N-
z7?cCJw{PE$;W;`CB*B0Pi|xMp>MOCF9BE@2?bxvcgF)QnK|w(vpuCTd50eJWx_tSv
zudnaCdGmh!@kgC3AdtyqSDjdRF$|B4jKsZn_Uu^{fw5t|FmSL<n>Oz5WcD09$BrG-
zNlWBr+fV55V<A7&8QMa4ty;BQTynj#i{+>zS?mRrV~z+ljzB!h0B(N&{r7ADofd-J
ztvO28;W33@teBY2F)iu>1$qDd_eYK#iH=sBR)@}&{yJ#|uf9GxiUkAO8@l{ny?UX;
z#+V6r9A+BvV1b7`NM<twV3T8q5FQQ@%u`}8SEDq+YVfm!f^J3#K_Vl_l_kVAJ2~Qv
z#k~lx7?mdKAb7YJa(hShS5FGcVj}KwT+9AFD_EI)%CnIQDEMI3=8Ro2l0}vPrZsEU
z01e?qg9D)BCc)ieER{hmjWJ_B2Lo}I1o8p0Y~GoL96x@XEzB^OJXaZEXuNmtUd(~E
zYu65#h?xY)#BH-`)hdjMMvoqivH5@j0}N(_K;&gC>h$T;csfo=Nx`ErlK>n6;Nmg4
zZ{NOOP=}%!OP-BnodRO^06iaEojP^!c@!RKFmg90mg%e`A)CCI95Ix$v;cHGa|o+W
zjb)d;Wb~JqAYuaoK_!TS{u?)L6jo9i6fW?K34@$0P+&v5-MxGFM<0E(Wy_XqT9J^-
z6q$l>F_C>jW3gARTnP&cL$k!#1%m)|pHTABrArLTAzw(SP;kmD@~2SpR}gZAid{j7
z|G);XKEE={f+n{BL1Wp~s7$a?IfS1j#FY^0D+nToUA1+C!h{)Ds`<Y_f1$cx+`rFp
zr9CQ>cZcuN*c^>%>eQ)GQBlwvAdU%KU=wR$cu|SQ2U;j(j)XC0peoV=!Ex8I-U8!I
z<cnJoIE$CD5fKrl&w)aev5dZ@4zP=<Nj&VM+rab@mfK@$7*Dk;R;(C0bSS>N6F-r#
zLa`!eBN?|M27rtifCWJO_19k^99<y1Y>;a(d5)5qO!1XTL&AW70H_T>M_#!ap&PRg
zasiaUNP@ybz5w)z6DOiX21n>QmI8XB2ZC61+w7@}Nk-3#F)8NL@YJ$tlX_ziA=hk)
zY)YY%4)b`ZTy*4k{m`L92R85&^W><%nAsSuuTPF**`0-f5t=$<#uc43OpQi}mV;)`
z!hwurv<zo(%xi1;%~v+HoVa_ZWOI&_?^_rbOCem@qB~p>LV5VR*Puw6irwAhN_qHC
z`RObTB<O~VbrTQS#qz~GObIGR(2)>SH~nBFB?|vj`m2ZLoPs>FmUbYc+~SAk`rt*>
zqz)|!&+>R7sSY5R&iGj)W3&*PJz<Rb>C>l3h+NQBfI!Q}i`G7U`hecEXAj0HVDR?#
z&R)1U)>uZEw888I27|bZuz?9?C-JTc(;!%df_W0mCm<nm7mH#v3d>{~lMFVDsu(kX
z53fV93I$ye`k@{@dI+fmLde1t$pSM7fgimo-T}C|x#58TvqpGjEoRG})>nh^LvsPj
zG2{HlAAcacUcGwA<;<Bgh{g2U`t|Geg$r64lZ^EqnCrkC0^TAB7KY{w${`Saxv{kR
z*|Ct^@}oytxNsq+r<*oysv|Q;{dM^Dy@CXBln`i4Rj34hm=_F;83gV)%%_2Xq43Da
z9w{*XRih`F?ADo^9ygIdd33Ps$3P_nYRz9WC@pEV_B+Xu`I&#?g#`g&uFU}3arLvN
zeItKPe7SQ%+Qt*@KC+Sx`W5{LLv{nYK{QiXho?<X>mmMDC9uFLPPuKp{A_||9^Zie
zC<PV7cLvl~AQywW=}2ZgKy%a)q8yyCcnLcya-2=^LvtO~b-X)w?qp+UaX3a3VsAca
zJZbLVzaQgSOh{n6$+>gqqLu&h%P$zp#l*xwI2iJo1xt>~z%UT2K`=*;SDjtEc11?m
zrlxAusu%}h!lz1=D*4X{9Rt??pf^HxxYn&(H~%vAC5#(44y8aA-MV!{=b_J#hY<93
zcr?LN3qTfC%+MS)f@PP;0)Wn50OUb3FckeL`bz*hURp2-s0&75)(ctWL9FRX@bJ{V
zdv_Sk;Io(Matf9cs11Q=f#@;?$*5DbMmV53XoTa6u{j+d6OYbYP*-1@Hf6ZX$MTu`
zu|<SF>`^$ULzFC~N}*r_T+RVFDxq^GLdJ~HlTWhKb`q9Wp?VytxJWni67lFV)1)kr
zUa5RnAIUg>tLHc5+a7S;0<6b<%H8@wPTq_g)1QT#pqsvZM7pVqk)0gl`Xd!}Wj*S1
zr;i6+d|a<{jbb;`Vr<|5&C!hT2#g73wvh`f3?@L&ojb?&1IQ*VV-{bHF|)0#Fr$KL
zMD`*GG$x%{T8vz<EWub>v3!9x#tfNw!-=O?41dsnBqt|h)`cw($7;F1|Na}xF<ZB8
zEyxiF*n!D3CK;SU6qvDmrE%lNEIDpVL5{#9gqR{(U}gjpf+hb#7Jw>!$vUaTUSrHa
zb2f#I2MbhzkOZ&-w8f@lcnx4Mttq8|O$0y}B!d8y<NYvZKMYD1bL!Bh^J6fMxeII@
zgmJF$WFVGiivD7LeOizpjuHZmsfu9=3chXIw)XAY!}wr>Ln=Isf`G-C7gstqC$j0d
zm0UrC6Ujg7fM<Ty#^sulA&^B89+o7F5Hcy5{$^=2DZVQ)^Iq0!&IIe)r7Sq5D(t9y
z&M`7kGhn1`?}nCuDn(}2iA}OKw^WYRC9_6aS8?VPDOqo9EjTh8ZsAzgvWg_jrJ2ny
zJuU7fTKH6jR;sYWnLi&TinZ)vJ+`A&2`lc+GiAJ^W$H=U&*#_xoNLq4ic3+<_)C!v
zRqIPWsm(e6zrAmPi?Ue%pM_ln0~HldY2L_;PGD#UO-;eV$dv3N_0zwaS1fPGOzoKG
z*zsDaStqsAMYE%($FejpnVBcNkcp`zTY|SzQY=(dR$17E{eS0uc87PbAadE2nU4<7
z`^+=XJoBA*zwhk4@4jQU{LcHwlZ*IC&mY3E>pQwh6|5xgfRF0A9rb))xgG)#LEJV$
z#qv%uC=n=`e^`fJ;XA+Dwfv*v$Gwitt%#IHYn6l^Q&;>PLzLqdJf1nBLt!IIJqYfM
zF<runIc~9{E>20@E5i(q`~cHYqeiI-(Sv_c<gOu1HgRPdFkk>29z1vu&wk-^j^|h~
zTiml}51uQ)st#K3*RP*&MFJd>Ps$xiafo#fJkUaVJf4paXX$54W$j#@`&G_J%2l?m
zMa7(-!4U8v7e?i*PWJAMp~ppnSWj&O8Q+sY<?aRo21pc@2+2V}U7SS@giciMxuSy~
zaY!wSLm{a3m~-I%Exsy&$346rnMdz2ekv7BxD=dixKodA@m&xn8%{ag{J_!{?rhVX
z1}7@M5@GcWOOE6LcO$7$&X<fexmWkrD6Qd8(Wp;Yr!ZzXV%l<5waUSbh_tPYZvksF
zP5WNjpaM$((_rEEo;x`smlSJePYH-Hmu&dN6c(fF+B;xaX2JUA0sW%=j(=CQ^%qTI
zKV7fB+Es^2W=znvEH2rw${cX7?$Id5uCfxdCA7ZZpR}4fT6Rl4zxMtmpMGL$*)5>s
zBf2(Qin8z3PE7DO{9=AD)zZ7au8luCqQy4@gvH}uyU>VFd)?-KqaM-rh|pZlD9$YQ
z7{3CeJtK26CQ$4ju_=9?$BvOz&-qYA<UH9`=2ru9s;sU+w8Bodw%ottps{|NKK*L%
zMz3S@5=$v5*uu5`rJ~}JVsmlnYBH3{Sqgw5xvlHq3WsiuEo{tKzGjWn8lG{(it1u8
zrcRyO^WmP(H|3n2t2OQ#!cXC`bhGuVt$6SeYb@L|?r4}z-MV+{(zVN`jhil9yl_k1
zTb!#SDpEQZQM>CtTUT(+I+?|_;rQ|6E-jsvWqQ8-_S<Q>Np#TJ35r}QMHL6Zld<jW
z6*i?2n%$0dsg1|;p`^H^aaiNUOBRnAJqGJJMCCY(kQ^c`&!LFWr7^xu;+`jdQh<}^
zTER7!HbPm>_=UZ*;PSIjrdt3qV&n+i$ieLd+>^i~0J!F%7QSo^9XhmrgZc;_-NZjD
zo7wp5_w?edE7}h213qrc0-0sQdOoEG6<OH_0|i#1;lkusxh;xsh!I`1rFX#4hpQ~4
zv+nLX?BIr?O+PcuaZ|+00bL*TU-iG@)bC1`tSn&-SceC7t(s_BxASYoSjszvAL7xo
z)MDNApq_kRP->1w&+_s7!nMC^f{Th*5A^#>6HP>Sf2`xom}kmjY)hVI<2WrYFq|?J
z>?n_YB%o(|PO5bRHH|HnKRm>*toA6i&?rxEMLY~y`6|M5rG$SkQToqMERG4!c`6kY
z&bH3Y$=GURyU$AIg4#<3;Uu`z<0HBx7J40<i%u$CF3qs0H{+i(`0O}&RH;^xpv3Ak
z9!_vq*TJ3+Js4x&gzv(j)U<tRSf<BKvzU%CUAuH`9o|~pU3BTwrP5tP&yRk7#eY_;
z{&+Qht~q7$l#qHMWJy;GtZ{4n_}DwQ$jy{f!kuT_t@6hnx=O;V3LbEa&mlI{b*?Uy
z&P7xm_bm_=$yoI|@WX*}dUj}Cedw+&wGEgBQ(7#|BQMjw%w7o3U@I)0z4!im_%Z~l
zRd~w!VG}mx6x>CHuX>=ufrAEu6U*drYKyz_IKKE+0k<9kYt*ojb3EmN1s^5clZfvb
zzvG)7KpWqUBJPfOu*ZX~TDQWNJo12?-<v14LYz1qESA5|Dq1l=pfQ%PFBX6Ci=|kh
zG<b6e5_O@(+`JC6teRq6oySQK>8S~U!`d>P@-l)B&jc>BRWsbeE}ybkl60&8joW@o
zy2`YTd7ZpJz|UOB&bP2IhE*EkK)zX~xg_;0!=2g&3cVGUSakl(e6si_ngnK-7*LI`
z{wp|ZSa5yKErpgZ-Z6f`SllxKQ{r{Kn-za>iifH+S(;hnS6U+aCKD)##9?Z$hZDiM
zyjAkWK~SLJD%Ct)P3$%<=T+4H#Z`t}mF59;y^hUo{VffWB&<l_Q{kw!9Rm?iu*>xX
zn~Bq3E3i&_MFDU3w!61O`wsYO4CnaR!?~%5|A6JXlbeRZTZiKoD?a4hGiE)il?pWB
zOyZ^O3Wq`{Ewf69nWR>&TH)#Dabw3hnD9GY&efqIrE`%|A5Yf_Kh2sp`zYh1@_H84
zoCP;+IT+%bKCl8%xK=q7IUbhP3$;Baqz&pfc)ai9@GL-68<bae(+fP?3bz<de51T>
zS8jY_83cERJ$S>}OLGD{n{vQ{P_eplUx)iTb?k(?IPD4WqC(|WpkP1cff0W5MMYzr
zY%UZp{IYoNqyE2qZ$8UAN})S-0Q3lBMU9u@;PG&h$*%As|GWA1f<=F5LX7Oe-u@xK
zlsq}M&I2thE8i?!rDuI#tTVbTD^$)wi%x^364>9O<(zBbkJ;c4=KlPKDy}EWQ<hNi
zA|!1K|FGg>{?txWKmLYBi-z}t)1==^v+LY@rl|L9ZntB7@%VWE+nTfDliUdZ?GnY$
z8&nGUp<TJB1ACgcvc9!~&5I`+n{TqQwX8mAy^hV*!P&y+fAJYE`(v!GP?qejAyE(K
zF_Ji}u;8vCkT_BW+`=LGq};QI)~#EE&7Ey<@t`-}sEZWG%7?;sF;JH}$k*lBp|1L{
zJiUNDUdyAd<w3r#Uce7H3(14S@)Wu2h5G(&dHlep%=kgpNage<%*u2tn#O65&l>D;
zk{|&bsw;BkE+{`w6gK0l&q(|JYdYN3FMSuExw|DLqCW*}kSw=U>eA_UaZmBNk=jQF
z>GsSw75qgv`cdsK8;W;>tgn`wIL+F3^B?eM4J%{?|1+O^*8j<|x~zGo!VcP}!j*M%
zVsqtk1D9AvM<X;rf|UAW$)L9WbCUfRe8rMSa?(RzZ0qJFyY!a(?$b@|R<bHfGiH1M
z7K;w<we~`>R1q)5uLP|Ul|I-qeu1z)s$++PpkJF$guv^lu~OS8{%kC-98iXfq0+RA
zBA;P%Rd*^!v|?E~&OCXk^oHP&d{XYIZRJKT-iPJcMwBX!`B_rwo<$SRLV4P9uH`{<
zUA^$&=emk2u@_Yoe|g-JjdKo8S1UQ2yC4(G_|evDj6Fj+0ZD9Lg4>?fBJK>o;WU^0
z{X_Fx!?drx$$80e!ZdfC#d6X7^f2u|-{dQY=gwGIGyl=OO=LFpHS2OBir}gcUM#q0
zgOeVv7?xr9sta@_?C2LoOKuGq(=Fhw`<d}Fw3^HL+=kK@?-q5N9gr|IFhMcjySDIC
z1MeVuQO=}haCH@TIH>K_HtzIlpU#0{Xvjdaxf-Y{kp<S3<()oP@a99FJ@j4n!U3Er
z8sGA`B^wVpops8r))Un!^GCWZ>Cic4TKPwD&;J9Fb5>PywsMwn1)p+i*w>~<x0~*4
zreQ|Qu?q^NZa4M(+SK-T&DGyZ&M-~0+nM1v%N1to^>tC(+x@QnX8BF2h!!(nDOkxD
zmCfBZ^Vs11=;oqsn|T1u?)UE*S~C3q3h_#l+j|DA8Sa1dXN$O&u>6vVe>K)?r1=vb
zx4*!dv5fd_-zY@<>gQXmeNy02v$MLtYI}8C_IevSya%ONkqy^5CGTQ$+_J-&<P_wM
zD&W?^MYT1AC)O*yNAzJsk9^28;SV$%2Ew=Ssh(a`(fF3fE!jBdbg~IOX0<eLg@$hE
zISi}%E}uh@!hobx2+zul(9j`2t20ko&u!r+6Zb-u$@1x6IX}>R7D~mDYd_Qm<(G|`
z@JQXVRod*|S@UKZ^BL2Zr|@Nfw<6-d_`Btzt&V8drcnR2#%T-K;n2>f+K-yq)$?(8
ze$-7m*HxDa*^%Z%L5&EwhmPe@=NIkYNHAC(hFb>@)z%RDQt3UyDTss~QSQ*S8#z=Q
zSBYmtg@JG$S>M8^dU{bsqfj2VWaBvp2+FKhFewDiXm)c<W#O0@AE&96ePN_ZRL#)~
zH-I-+KCMlV3##W_n<{dBse}h$_L;gqVG_JcE|lC&TV58w5~cP2gwiur4^(26%!O6{
z%PZmpf+-%o`sJ5j@I*6ywC%JvV0m2$J3N4a=eXggb?eqpQWMqO5Pm_2$Gt17N7OZR
zfz~mRi_TVxP(_}_K+wJ%>Vfbfe5$P%RWu6aaSIzt;sk>r5{gJRTrBS$=}I{*r4yG3
z;P$ZAkJFk+*Zm7-mwqcnNhb<^yl~>g3FJ5am;oMA#(Ng=V?yM3xDAQotmcL=H^&=R
zk;?y6hG#r!@bG3R`1EXfy`}IBaPSkM%Jqz&iQ{F5ND!O{qAKw5>0VUP_?BmIVS{sq
z0sw|o1p+>dl;bHZ{Q4TN_{SOoon1meP1NT9tf!rrOIj_j_iU^k_CTwg_$WgIP!xP8
zzPX`ubep@Hh1}k#F4m2aKzDfP(sp0Droj{E5S>PJV%<nkIM0xXK1e2SzT1#Gev;Mw
zx)H3@o`Da!MSkNx;eotW!$7EeL2@PHQ*FJV^Rv?9C%2vlSJ>b)s7}hmv^>MZi}<36
zcSX>!Qjj7iDpwJ@D3L-XBET=0uYI`X{&sh?XdGNWI6$zu7p<EUci;+xkG=YYt}qPr
z@gujClPlkCNI*gO04SlUL;)XTg08ZBk~e~#a`H@aC62fVnDC)?h4Kak@<uUJUUj{w
zy7BcqxYRCkHI>uz=G0jfVZ0y<1W2&X8~O7VSS43%l_@0El6j>fz)^15IIwZBE-=7u
zzU3|J<_^8ZDMDw7gTBItI163Y_34Hx7b1aArDGyIL#WPys3Lr-rxyUKD&qG%xM-A;
z!;oR#AT9%tDKK?-AQk?AAwFdM`y76e$=45Df8^AzrCdP-G)+RZ!GRbF>#`V)nihjp
zL5Dt44gIX1NUN<O(VePmu_`=Qg--#6OQ9-StO}nlgNL$;+VbEQMII8x2?Lh_6QP5`
z!Rj<UmwfILs2f*e1Oj#Zg-I9-@hu7$S1+BL-*|_m2Z?}0;Cc~IQ#tCoJ9Oukf9)p<
zw-P>AxJbtI9(5<oyOI*aUzozviS=XeeEaSz-;S{<NL{X6)XiFOU)RFL#%Z0)Rn3)=
z`P?@>o334a?Sp#)T@`za4qw_zhXz53VY>O}=H4;{G7gD=*CQZ!0W&_xV&3N<d+rBu
zx+fcj<UvW|F8j3$$JoWYxyDm9*JY*)zf;|&u`~b6S_+@8tvzYaFKF6xCrjjeXjC8$
zPo68S3<E*F)FUF+!6chk8qm^_M8MY(07p<#v$@a+a;I)WU%~z=6}A4Q4s-$h1;4x@
z!_l`VkD2h^4i=TTV(GgBV{XG+SJKwb8rgqV24j)2<KF(yYjG`t%;$fYJ8|ScSN(YJ
zzzKiJUdI~7q3X5Y_8ls^<B@v12h91kZ@XWIPI;HH*cJOe9T?LX&a=N;HTL<zn=;to
zS?}KSTk36Nmkhke?wi2(3r4-1*q?1O6!ADJ*t2S@CdPtB3K2t)D=pQAzidb%;Oz)-
z9#awmwTX^@u|5lx`W9#fdR33@Ox=-UsB6JXN&^BkyOwiCX6(|Ni4iZ>o4zOGt(>i|
zBz1bE`5!-a`u&6Z`_Edi>HEjqm{&aaK*GNsy81@U>?R#&Morms<iE!6-;N*3SR!ZN
z%V)maV(c|J(y(FG-!>%9*_V-VY|Y?-BUbkL@r7GXZ)o|z;3wyOaU`+P@~0noDT6Ie
zeoGy;;inI~Olf%H;$J5;9IMuQr%d=e4vfVj=jPryUNS0)Kn)<ECJ37SL8BHB6AqCR
zp;S0T%!<b)@`u(-Q=-L~wfV)JY(>VH4lRS&h#4cMME&QR%&zg>KihOTv3Ddhp1-F{
zv}yB+OQ%a_Vhf>duXVYVvAdRRIl8y^cYI~UVhIJIDGp-~empfgn6)1?df?nu#|$sj
z+4gT1^~Taki47QgZQIxLnm%0GE+q5OiBTh#W)!t<kcC}cbYAq>wq2NX0OTRiC?KZ+
z!o?LyiydTK)Q~EPfJC77AwW!loRT052zct861K^G=%I&z4Qwiiki~MA#U8l&zi8Xd
z#*C$pL@(BuuJL_H21osAu+8t2xmW$`-&TBRYCz&jt1fZ4>B3pYqUzmZZNJ&AjoC$Y
zFoi7Yu{)GfqnWcj)*nnpONS8;)u$TuZ%tHu+2S{U^-APYHhpB!X?^O!o$O-90e#C3
z_cXA@W{btU{$h`K{AM1{{!_xzIg-|S6JhU)T1F!gkO)*40w6e%h`|Mq)6YTiTTpC1
zj&Lv&tawmLT{E_uv-hE^-v~ax{i7&0*lgCCPwe}lpn2Pjcb|HCdWS=2FW!4=gCid#
z-LqJutIOBH{cP2*74lafk}s>r41OqllX&p+w8C-iI7MGRw}~;l%G_pBct(#mozLvl
z1~Y!1p6%9d@qr7Y+lSJj7v>nF9zA+AZ{8fQAQe9Z1+7=fj|si7|1u<rfJC6O5U}x}
zLYg?Z{)9*zDnS!;$GzC_aN=7VE@*Uyle_Qk_TX2)7n?xViOv5M8g%~XH?v25#u^st
zB4Wm|jM%Bmf4f?klm5x5DF<0AzK)MXZq?37zN_04Y-jST>-Co}A6qzzFSC%nl821k
zXuE_J9ExSJY5A5h9U7_(yhudk6g&wIxpyxSIRq;YB?1xw??3<ySE+&2V-4Xid~W9Y
z0TEAx&E>wIocHCpj-jmntk@~Nn+AdBqh5G<5S#nr@<T&m_G`owE#4chj-mA3`B7>$
ze_AG_{pcf`jd%BHH;b_c2R_c&hQ@Ur-;%Aa<Fzuuj*E}W2>I=4mjzNO5s(PfHUw;Q
zRQ}h)N=uh6T`HT+)kvES1|!o31_z3{Cf8_#gRxFxG#DT#1*J-LWtVcn>6CZ26^<I2
zfV;28tWSa>-Z_+?g4?7%nlL)wefOPQY4OqFlVM8)yaIu$ByrI}&^4G_P&xEmLw6+u
ztyFHO6gAxmx&ZTkk{=0aH(=Vd!9fwvw14@f2%n{L(MRX8P(vonq#zNH2uK8O9t5<v
z#visVLd==7;^yg?eNW_FP<uS&kwicuAQA9Q1SFgL=D5kIB?1xwiGV!<lFjY$kVg^$
ziGW1FHxZC*?wjK#qm~Fr1SA6X2uL=!$3q@T1SA3y0pCPGvbk@Ln~Yi_AQ6xV*drj>
z+#U~kBoUAZR5b#|^f*o2enV+9$3GvdiCdyllopjKiMl_`zpu{hy=B_FGBEF6H0d#G
zihFkc4!pO;7n6M6+!sep#w-!IsSu#Ohbq=3+It9fY44#5Ik~jwgEtzET=#5bLzC%h
zk**2Y+!vWq&dn<{`m&NlKq7Es2&i8pPJ0h=e}<#~dM+Nna@4eYY-JkWe2Zk>_uuL5
zxJnZhH$LTb0au;>VaA|nXrc$b@W#j3>4Mwwy@yU-!4LPJ81(u_^Pg90#H~!r<z<YW
zPF?w!;^o=d@6CH|@T#9w-vo@mZOe!nHK=drn9dJB+^0{EDDQq7E_KL7F4^1#C{iU6
zkO)*M0<`zgo^N?dO-)(P_Z~unmlpQ9Yr@&Cse1jVgLH#CJ+kvWGycT)9vZTF--*oB
zC)uKh-}>6ja%P3a{PV|z)XdC<@kPsaGFFejY>Mwal$~cb^Sy_L?rxr@*ROi{%HR<z
zFEM6P_8!s=`XVzk_0~_Gd+EKESBkLfku@1l9o@+?x`gV2LV|D^S$XIWTZxbT^~LWK
zJ_fN2S0W%0a6kZWNg3Oxhn>>4_s~lHSnNI2VdTsaQzB{aq0cvG(B4ClUE7;BVecW!
zOvb+a`gPiS=*y#fA5EnfI^u#rFH(#>wEAVf_t4-m1Lv+hW|&BO4=tPg81^1|{p;=X
zZtJOT8W#Y@`hR`mU)>rSmXEu0aEEDKO&^5&AT`Rlxq4WoED?|hR0jgsd+1O>=^Gn=
zeE(tgt99?8L6J>@LPCN<u^$k-l6zI#pkeW$`8th87ZmpN+(WEY0PQ_g-}c%p+IvV9
zG~auujZ#W`4}svZ_t1SK<3Z&-HMouT9-<@U!S@WaSXTE84-0J2`uRn>qu9P|%;$YD
zNjCSvL6ad%1biO>lhcDEwD(X-uE|nRU^3-r?)mobecK-X_p?d=xHUZ|58VIr`~5)r
zwD*w4R{g5+f+H9ijtU$f_-vn%)R%Iw_mDcB*z)S!&yUzNMHwu^#BAPeTA%nU+1w|F
zNd_qqxcLzX3~z@$DPDMU{duO%Kd~zTO%$^0;(;Dp&zXxr)``uVGziSi+CD28%j<>O
zyZ(Z`hr0jm!)$}`?4h-^_mF3F+CK0&_8yx1dB&wnN8d=cTV_GUqVor*J@w3npK=U_
z><|AIj}55rYV3pSEWY_1j^}}sr4j*&fX^XNlY0-{{p`P29Zh~BvZ;a^7SQK3_c{EO
zfaTspzBz6(YKcG<AW*uJ?Pl*G?Dc&5^y!;(?;*1hTOQeV#i#;sE6*kEJ=CH_3)<ot
zIktF~yF6DOz|xaMKq62%2-MKtL)g2{y1`N9bffyc_~i0X^}{4vln6)!d=~+^_mJ<7
zos3)}AQ6xV)H(#@+`QHex$Lk+;KmWa(k-0e*Yh{-N_r?i0tCA|d&%bRkdb<cfJDGG
z0^k(lanI!9y0X;XFaqviFPqJ;UAvZ@osApWmoHzw;V`5FiGW1Fdl4X-Ku)Qoqyz$T
zq+%sO$j!~|+_^LKa$;U10}15OF5Yhw78d5`=VyJI1(Kg%8Ek&_>ecPrx1(Bz4jmdb
zYE&c9%SI&v5`pSLfJmHjVsOFZcs7v|CUyu$QhD`^h;u7I9s|^)M-S&G6w6@q!-o&I
zZ{NOU%a+bgrA8tk5vZLA2r3ss?Zlz_drcE_cd(c7%l@-x&q^w<{xOp+N(8(g0e75I
zcH~~S_fY8z(=~D9cb?2Iy9+gB;TiKX*qkl}H59pQRw5t~s1*n(dk<Bxr^?<#ye{8+
z=-~>jBAAa(42$_9?63Mv{i?3-#@#XTq))zdi<QCV6$DsTlL$xzZgvD{dA(veu+jRL
zR;9g%_;G%g*n4QjzB7u2Nxk>bx2LW_Y0BO2u=fy@wD(X!=34dMLo4?Ep*XRe+OtBv
z_s~jxKIxI4{om3>Z0?RXhWxqhp9jpoeC)vZ5K-oRf``+pjCFGlHE-6UqN1Wpmo8nr
zc+qIIzCrV5?WydSM8E+7;w~s5;Qpsb^gs)8+z35)?i`+J@%)wF!L9Ql?LB0uYrzgc
z0Rfs_eD9$+>^<~mV#JH}rtiskD`)E~N%!|^@yCyye&>4+E!gz^<890<o_iqSUk_b<
zBW8A!jx(dC>^bsZ<M(gJ4`nQoKjy*r9y0ct9Le_{+K@PBUq;5UHG>C^T$T9a3%8u!
z(5l<BPtN<|NMfVqPe1Td23wr`mWV1RE1ku*c%`r3BL1q}*fj?}f5y9c5u<bL*s=Wl
zeDQ`B{*sn5pJ@>tt|bDfJTo&B&+y=WG0>?cy_VgS2oM75nJB0n^euARBQ54Sb?V$W
zT-Z!vT;hXver$UWt;iUIy@%L{86&1d(cVL!Z8~hf_fW}9#<pyG&ARu{vG1Mt9y<8(
zRKEAnpwR>8t~zFTf%YC+Iw=twR=l?D>v>HdRyVD!)0FL8c4poe#}~EU^{;39-#51X
zwU66+_uinmo!Pv3bMe+Kkd=JvmU>LNjJv*|^3c#wN#%~v$YY6s4S^e+n8OYB9y)kU
zO#MsLdk+nc`cvI{_3Ac^TFO}d`D@sFs3d(sfS+GL-6j>@d&peKJnTKB(OhM0`n!`7
z!&^iSnelEETl38k@4XQU^xWYur=PYnRQZc9FZJu!w^PXjiGW1F4FaH*iY53n7nfEg
z_W=zWLvYx8DC6BB4toy;+!6*eKU;O$dkDVB2^TUr-%)}R0&?s<R7k4J=dhq+Emr>M
z0(ezyR;MI$1~0XFw<^8m4-eFRl5B2=4Zd2+S-Bk|@<1Zs1_3afo01BuYijSIHa-3y
zV>1_ie$Ze%zhn7AEW>xXGqi$zsN!m#G57A({wjt>x|aw@1iS-*n%sLx*W&TRpZ%lV
zQ}@l{SVS$|^A*<IeKD1>_t3g^>-zQU=ZhVySj_3^=@AhT6{{<2Nd$Zyf%4o1#ao-=
z;^NR#&-26^fWmCRCP3IbB2dgVxdwY!;KroUV1S?$lq$<?tKf9XJKG9JjZ6Sb8?!zM
zig@Qxeu^Vy^Sy`6*s(&iTBh3?F~At1-ISCRc^9-s@RyBB1bh_%S}+#TR~dtg79iB%
z-b3C_<!YGJV?o2@<<}u~@KTltNCc`C0dNG(1ktMC4k1))C^y#F&A0cE7zH5!A$b?{
z#!i#Uscp>GV~r=vP&wYb!HaSGt4YI_r1<6!q`d*a8jC^A4h}(xB0O4%B0=;HX7aG=
z5FpG6f3zY-gj$qrUN-VM=_%{iuivr-|5NkIHur2{PFuNfz24r9F>^!=eqJ~5XP=^T
z!^!Onm!`V;y{!3M%H}nb4tn)dyPgM1mbyUzzf!@E?A#Peb#)*B{4om7ElD<a#>7Em
zKAF01?tlUP`r&{4yQ#Sj#n*XgKK%ByxjWC;)e4N>mb<e%YgpI2e|}~cGh=7|w_w`1
zbO)vFFu2dZ_op8n>ZQ+l*`kzx%ZY$`4j2#+fLC8oMNAcCs!jw5cHoau5Mw7sN3yxV
z#GRn+I%e_iy$cxYKD-Z``PNs~*{CsVdO|`%(%6MdUQGx>ikZGLO-xktvQnlGi{a{^
zpkb4j>ht)LgE2QNHDyN@WBLO-Q`1sYQ&LjXv#qUa1M0C||9N}5)z#WkSInHVe&JYh
z6_hY`eL7uE%qa_p#qjo*=(E!o4pZtSOiuSWyEmLnnVv*Z#Uv#q#0*<&GdJtEFG;et
z5%a3Lbz}D0SFNoNn>;xwCSfgK5X#f1rEJ-@i?Jg+s3$4uN*8%=vbVn)6O)kq(u~g!
z|6%PN{ZYA!NgBI8Er+W;!f01r9@GQ^U<x7&5|HCGIdbHPAoQ9Ds#doN<bgj%L5v9C
zAX&0`dCm;u*0(3IIj_I=`ul8i@{Sy3HCWrQTT-W8o0m<Sl=9f>jqguxH9t9aYqrAL
zS!<g{_nLnuZr#qk->h47X7RX~rW5o=mYeo*e6N0s^^C2V6dxNO->X;roD>_M@-P-p
z+n2O%<07~k61$sPVYy3shYy&xZ^626()WKer`f&%om;Hbqms5or>BO*-MM+$q?opy
z*RW0t=8R>#e!W!SVWrx*{-s{?uk_!4^yuEL<Jsn2`i%2<=MhrY%H){GCWTD@Xm9%d
zjf>)D4vFo%FjXnZS=g=ZkeSQIF8FBO>P7jB7jM?@I$vO7##1w5drjL6lV$y=C%xvJ
zvR%r8TXY^jeqaZ-IVGz=aiV1A?CGI!wQtVqt$TNFe3C625F5TEEtk26v9zv~Y6t;v
zgt}#-4Dd*BaPa>9`_G&?Q&3QVHQef)oYva5fHTkr=0G0!3pTeKAfTf@c97-s<{Xf`
z)R?C6<J0p7-Ie!hP<YCMz1zk_bG=!g6cQ7+H+Kx4Ts3DUg+zDWdv;=U(Ar_4L)J__
zSupc9Zp2v5_L%U;qCQIh@R_?9Gb~LAd+)Km`^f_tAUCEbgmxatn+Sy4gpim7?kd!v
zUm6xOZusi`3wpIGC}29{k3+-bf1SK_|I9d4GH0y}iB9gn;G6fybS=ljNrt5{VdJ6}
z?Vpj*C@|Q3=DUqq%|<;Ftu^Y0g~qJu|NhB$o(L>3>4K{nM=JmT3C~GHK~&6JCbbx{
ztk2%uPa}R^8X7Zh(cZHoqLn3+ytIU-u?aiRPVC4J^-B|C-b>2eKf>iNEwYv-47~FC
z{)q}ufG}-g)7WXNf699zoR*XFmLxQt#1`dl8xiapM!84>jH^2l8_-QQ<dnp_x9A;;
zc>R{iWHJ~GSFT*aromXl6?)i1MuH#Yh#nfx4CA@9n)~l?^{UR+=}JifU`-qXawPmT
zonEa48LKOG>*5_Y^}yyjolfY<40$<7pgQ~Hm2I6&*?{?Ua&%}ig*WNrlcS=;ft=ZV
zg>~=RMqx8;vxG=?MKRzDTGJD(R1W#J$R`;)m{0RyZqblu&Z&D;ONHsQ%^qW{P*uKc
zK+H1LA^Yuj%!BzaDNL~HK5=+?s2uf!d(U4muGhGjHCA7JCa)gAqC?GQey?&(a`+l+
z|3p(~f5NhOpZg<h#2VUhTV9?quw1MA1?mnkc544#gxai$s2due)Q7#NH01_H5r!11
z839m<nq-M$kgF5sh9DF$3-}uRu5dWdh|r0|DObC?u8z2@h!`B42s7s*VObs<VO&!J
z;s<vV$PffaLqGy|LrEk~HX=vBkdaF^FLUhjHq2ezXTiqV@wZ`itYybu>^xxYzA00?
zhB2*zo)topb>Mgd*fAyDFtw|7(rU;~VXU6IojOjilPjxDP@PCtsD&#y<D~e0dM@Vd
zMx$8^CN&z&TK=OrB*nzev7&aD8MBVFtv{W;H^_jMnhOV4b?!Idv!`=MwBh#M=j_}z
zu_IpTj*|wh<{MdPFf-hBH`Y>4<+8};R-ZY?GtI3cn=8MuP(pKi0y*@s**zT?`}`V~
zUaX)P)4$uy8YXbRu3@ycDF4(H0)i}@1<(p5undTja$-!P734UC4G3MtFDO-LykDU1
zR8O30oB<tBwW6%ZC{=6J#zL0s=AK8&sNb@Dnx3_4T))X3k<rnS%^EdnhBx%gT$-MH
z>Eymc7um%h4rF1boOkj-mY$tF^kbem_>nQw*{(@Lr?1V*$<4`1o&HqpE*3eaZ=}+a
zHl!Kbwe9P)tgQ5uwUcr4GHIoO85LK%oLsTsHnIKjwP5_b6n##9UhcjvOZ$h0g-%#+
zX2$F+{qEi0F}C^p-D&A*Y5J4)Pd&QmmihRnlX^Y>=FbIzc;_#%?QQh~Szu%`mfWX}
zS+YGnFF*gp!Efg$cWWBfH{HmBBVva$*01fb^=bMnecICLN#QY_KmFPI*kQBiIHn)B
zDmCk*J{3hlK?!NNN*HqyOu9b(r(ZAXb21>{D96CU{?e-t)AxFG@^*c8ZqCUq3r7!G
z!-hZiV6e6C&M>;@fK*jK0$>U>DssUu5TF!?AR0>0s8;}-WzzUcZtIrozm?ky(FVB%
zl0qYJR$~sO7#A8NyRTHv=Spo~H#epyg?8q5`xslm*T)%Fy~p8E4oqfWTUGCG1C^pa
z`}39`>Km4<=oPcztSt)%_QO>`Ns;~6t$L&PUDovq<H;?9?&(K9Bm0m2=lChTBhJ`b
z>9aq7`JvucSBv)Nj))94o=lnaOs{2DU#!pA1=A)Dj}AS~7rV(n<=vGvXtl;s=1cYz
zJeBmHv+?bTJ^5VOoU?Z3_#yKtC@iiF|7gO*XS#+fC8<+~#4lF5##rR!Mf0bOh_g<w
z&Dl$mpPaNti7c|u^zjqM4eF}Z^QCr&mKW?jJ0?2Rn4R*E5xs1#*yN9PPJgB&R_&8Q
zW0Zzn!06yd9#%C1G<`-+Ni3Mq5(*9>(6$vqfD#(yNWu)V%?H0N2f3;vuYUi;q?Ow)
zd{Pqh4MHaw6c2%j9!eolDVa$&w;PVieCw9kn3s>01zkv}^UEKU7Y3<cD`XdiivvSm
zo=K+*xv^IQ;k8065VK*d*_iF$YQ$SK8}hKW9~2Z4So*djYb3mN*ByZ~1AGOSZ!sP2
z6jv-LB-E)9Yg_V9%_5*?VTGbOFAJK6AQJ9{k_<B<bXZaqHJjg*R9+QhWMgvvSFkzB
zMCCM9$I>|olnXY8rNi=ORRmdEeQE<kL&Z%$2N3vsg&b6#4}(K(w<0~;c`U_i>3De{
z{c$wax^KgMi92VG&c(3@SHeaLHH!dv0Ra#TlMKqhAu$V4He|2~#Yr~A<7yj}LMdF4
z@y(P|$1AHx@J0@SHpzn10X1ZzZ*`H7Nsw$_Sz{snNCa*+1i%quW=(v7oM=VeRbn48
zE2oJ$lw>13h$_w+QRHm``de)wiM9x804Nf897<wy%27mSq88$mY+h}HB^#0m_y7W6
z2Q}LgSs({}KraN*6KJsBf?TjU%p8(1mVyTafT2SYYz{q09EXC+1)B>2Hj>Rfz$=R+
z0<{zYP=n|>sF@By98iL_!6~AY*aSAj=7Ps@2pb#<ODU9zfWR37CBRDv19NBur$ZJ+
zkl`7*Wb-l+mIe}m+KT|O22nHUgMc7HM-dWa<izGg<<Jm`tMl56V7Yq^7-1`NB5@p&
zK-^6^DTOR-B%9lTjY}WSMZjdIl?M`m+J^wq3NbRtz<`JY0)0$yNE$jMHW#_TjGTx=
zDL5kl7==;+)h+`g5zeGl^Ehd!u4Hq8As0eILM~sv99kI<kl6W^2ND6_K>!3H_!qJ|
zaPWvIBGx8j(vwEy>WZS4TIuVu12~n{9k}(JoY|c0C7as~LDQy9Gcz-xs$ai8AhRoz
z2NHo=h5$H%SQvu3>-cCuLn|t3?j~c>z@84(6{TDz0t7X3A)pE!QcmEjHDn1h$>w(O
z;w@zn5fONU@afa1@gp0%GI<~os7(k6E>;V~)T9wPXdS>fm=RP$S(}incwYrC6IqiZ
zQWA+nLx*JGU?$nz5qc1M6D2to$YY5>Eki(XF$9t+7xXQZ4gzo@jasN>;9YljiKbCa
zEs!i&TL`pr;7}^r{JKGx6(j<+1Obo*kutG{=ryQB=phI*mkchHqPi4ZBA^CY$Rs%A
zICRjE!!p>M>bPuMDkTCEftwxyFn}|0b1t#zQHq2^KmkDjqLzhH3gtn7z$8l{Lm-)Q
zlsX@l!RELtc<R)tmMvSBCkSaH5s(Pnd<cLwh?)hDppSyVA(Ngog2dIvHy<X|(<8!A
zAn728Loo!_adH>j)vH&xZ{Hpk7KVF+jT$v_XD0O$0g1rPjewwO>ZUjpf`k6%Mx%Oq
zr{-(4C=P`{VTi*r*c{f_`Y$^>8_#&)LG0@3gKR}2AQA9R1RS{AImj{%oU2HUI|Kwx
zm(!qZHh1Sp>Lmgafm(}z2iEpbRBPd?K*v2$xw|6C<`uwMR*?uu1ndxy3~mQUWgobs
cZ-;jNKjcB$H*u#KZ~y=R07*qoM6N<$f*^QEasU7T

literal 0
HcmV?d00001

diff --git a/tutorials/table_of_contents_ipython.ipynb b/tutorials/table_of_contents_ipython.ipynb
index 8ed93af..639753b 100644
--- a/tutorials/table_of_contents_ipython.ipynb
+++ b/tutorials/table_of_contents_ipython.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:77e6b7710d7e7ab3d4491978f649959a8b99769df6967b7cb9feab83a67c15eb"
+  "signature": "sha256:ba20b7e6666952b09ad936f7b9cb32fc0e9ed680a00aeff02035be51895c8e45"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -209,7 +209,7 @@
       "\n",
       "![img of format problem](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/ipython_links_format.png)\n",
       "\n",
-      "### Remedy: id-anchor tag in a separate cell\n",
+      "### Solution 1: id-anchor tag in a separate cell\n",
       "\n",
       "![img of format problem](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/ipython_links_remedy.png)\n",
       "\n",
@@ -219,6 +219,38 @@
       "<hr>"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Solution 2: using header cells"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Alternatively, and I think this is an even better solution, is to use header cells.\n",
+      "<br>\n",
+      "<br>\n",
+      "![header cell](../Images/ipython_table_header.png)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To define the hyperlink anchor tag to this \"header cell\" is just the text content of the \"header cell\" connected by dashes. E.g.,\n",
+      "\n",
+      "`[link to another section](#Another-section)`\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},

From f361524fea8331bdeade20ba1661f21fe8dfdc0e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 20 May 2014 00:50:30 -0400
Subject: [PATCH 056/248] ipython links article upd.

---
 tutorials/table_of_contents_ipython.md | 125 +++++++++++++++++++++++++
 1 file changed, 125 insertions(+)
 create mode 100644 tutorials/table_of_contents_ipython.md

diff --git a/tutorials/table_of_contents_ipython.md b/tutorials/table_of_contents_ipython.md
new file mode 100644
index 0000000..9089e1e
--- /dev/null
+++ b/tutorials/table_of_contents_ipython.md
@@ -0,0 +1,125 @@
+[Sebastian Raschka](http://sebastianraschka.com)  
+last updated: 05/18/2014
+
+- [Link to this IPython Notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)  
+- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)
+
+
+<hr>
+I would be happy to hear your comments and suggestions.  
+Please feel free to drop me a note via
+[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).
+<hr>
+
+<a id='top'></a>
+
+# Creating a table of contents with internal links in IPython Notebooks and Markdown documents
+
+**Many people have asked me how I create the table of contents with internal links for my IPython Notebooks and Markdown documents on GitHub.   
+Well, no (IPython) magic is involved, it is just a little bit of HTML, but I thought it might be worthwhile to write this little how-to tutorial.**
+
+![example table](../Images/ipython_links_ex.png)
+
+<br>
+<br>
+For example, [click this link](#bottom) to jump to the bottom of the page.
+<br>
+<br>
+
+<br>
+<br>
+
+## The two components to create an internal link
+
+So, how does it work?  Basically, all you need are those two components:    
+1. the destination  
+2. an internal hyperlink to the destination  
+
+
+![two components](../Images/ipython_links_overview.png)
+
+<br>
+###1. The destination
+
+To define the destination (i.e., the section on the page or the cell you want to jump to), you just need to insert an empty HTML anchor tag and give it an **`id`**,   
+e.g., **`<a id='the_destination'></a>`**  
+
+This anchor tag will be invisible if you render it as Markdown in the IPython Notebook.  
+Note that it would also work if we use the **`name`** attribute instead of **`id`**, but since the **`name`** attribute is not supported by HTML5 anymore, I would suggest to just use the **`id`** attribute, which is also shorter to type.
+
+<br>
+###2. The internal hyperlink
+
+Now we have to create the hyperlink to the **`<a id='the_destination'></a>`** anchor tag that we just created.  
+We can either do this in ye goode olde HTML where we put a fragment identifier in form of a hash mark (`#`) in front of the name,   
+for example,  **`<a href='#the_destination'>Link to the destination'</a>`**
+
+Or alternatively, we can just use the slightly more convenient Markdown syntax:  
+**`[Link to the destination](#the_destination)`**
+
+**That's all!**
+
+<br>
+<br>
+
+## One more piece of advice
+
+Of course it would make sense to place the empty anchor tags for you table of contents just on top of each cell that contains a heading.  
+E.g.,   
+
+`<a id='section2'></a>`  
+`###Section 2`  
+`some text ...`  
+
+
+And I did this for a very long time ... until I figured out that it wouldn't render the Markdown properly if you convert the IPython Notebook into HTML (for example, for printing via the print preview option).  
+
+But instead of 
+
+
+###Section 2
+
+it would be rendered as
+
+
+`###Section 2`
+
+which is certainly not what we want (note that it looks normal in the IPython Notebook, but not in the converted HTML version). So my favorite remedy would be to put the `id`-anchor tag into a separate cell just above the section, ideally with some line breaks for nicer visuals.
+
+![img of format problem](../Images/ipython_links_format.png)
+
+<br>
+<br>
+
+### Solution 1: id-anchor tag in a separate cell
+
+![img of format problem](../Images/ipython_links_remedy.png)
+
+<br>
+<br>
+<br>
+<br>
+<br>
+
+
+### Solution 2: using header cells
+
+
+To define the hyperlink anchor tag to this "header cell" is just the text content of the "header cell" connected by dashes. E.g.,
+
+![header cell](../Images/ipython_table_header.png)
+
+`[link to another section](#Another-section)`
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+
+[[Click this link and jump to the top of the page](#top)]
+
+You can't see it, but this cell contains a  
+`<a id='bottom'></a>`  
+anchor tag just below this text.
+<a id='bottom'></a>

From 040b553a00608166b52c5869ff73d4b60941b85b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 17:17:45 -0400
Subject: [PATCH 057/248] shell script for prepending shebangs

---
 README.md                                |  4 +++-
 useful_scripts/prepend_python_shebang.sh | 14 ++++++++++++++
 2 files changed, 17 insertions(+), 1 deletion(-)
 create mode 100644 useful_scripts/prepend_python_shebang.sh

diff --git a/README.md b/README.md
index 26facab..775d2d4 100755
--- a/README.md
+++ b/README.md
@@ -38,6 +38,8 @@ Useful functions, tutorials, and other Python-related things
 - Happy Mother's Day [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)
 
 
-**// useful snippets**
+**// useful scripts snippets**
+
+- [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to all .py files in a current directory.
 
 - convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)
diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
new file mode 100644
index 0000000..cff7677
--- /dev/null
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -0,0 +1,14 @@
+# Sebastian Raschka 05/21/2014
+# Shell script that prepends a Python shebang 
+# `!#/usr/bin/python` to all
+# Python script files in the current directory
+# so that script files can be executed via
+# >> myscript.py 
+# instead of 
+# >> python myscript.py
+
+find ./ -maxdepth 1 -name "*.py" -exec sed -i.bak '1i\
+!#/usr/bin/python
+' {} \;
+
+find . -name "*.bak" -exec rm -rf {} \;
\ No newline at end of file

From a22919a1fa90f99aef6f9dfa2d4340b3314c05ec Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 17:26:28 -0400
Subject: [PATCH 058/248] added chmod command

---
 useful_scripts/prepend_python_shebang.sh | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
index cff7677..74c89c3 100644
--- a/useful_scripts/prepend_python_shebang.sh
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -7,8 +7,13 @@
 # instead of 
 # >> python myscript.py
 
+# prepends !#/usr/bin/python to all .py files
 find ./ -maxdepth 1 -name "*.py" -exec sed -i.bak '1i\
 !#/usr/bin/python
 ' {} \;
 
-find . -name "*.bak" -exec rm -rf {} \;
\ No newline at end of file
+# removes temporary files
+find . -name "*.bak" -exec rm -rf {} \;
+
+# makes Python scripts executable
+chmod ug+x *.py
\ No newline at end of file

From 80fb307f24b25e8f07b36eb1d2df47a40d4a44e9 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 17:36:24 -0400
Subject: [PATCH 059/248] fixed typo

---
 useful_scripts/prepend_python_shebang.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
index 74c89c3..8555f39 100644
--- a/useful_scripts/prepend_python_shebang.sh
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -9,7 +9,7 @@
 
 # prepends !#/usr/bin/python to all .py files
 find ./ -maxdepth 1 -name "*.py" -exec sed -i.bak '1i\
-!#/usr/bin/python
+#!/usr/bin/env/python
 ' {} \;
 
 # removes temporary files

From f71bcde1b3a1ec6378fd70fa0903401439a6ea55 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 17:44:05 -0400
Subject: [PATCH 060/248] autodetect python version

---
 useful_scripts/prepend_python_shebang.sh | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
index 8555f39..e57cc49 100644
--- a/useful_scripts/prepend_python_shebang.sh
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -8,8 +8,10 @@
 # >> python myscript.py
 
 # prepends !#/usr/bin/python to all .py files
+
+python_ver=$(which python)
 find ./ -maxdepth 1 -name "*.py" -exec sed -i.bak '1i\
-#!/usr/bin/env/python
+#!/usr/bin/env/python"$python_ver"
 ' {} \;
 
 # removes temporary files

From 759ef2dcf60daa4cf31d4542ad25808c1698fcf4 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 17:49:47 -0400
Subject: [PATCH 061/248] syntax fix

---
 useful_scripts/prepend_python_shebang.sh | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
index e57cc49..20f420d 100644
--- a/useful_scripts/prepend_python_shebang.sh
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -9,9 +9,8 @@
 
 # prepends !#/usr/bin/python to all .py files
 
-python_ver=$(which python)
 find ./ -maxdepth 1 -name "*.py" -exec sed -i.bak '1i\
-#!/usr/bin/env/python"$python_ver"
+#!'"$(which python)"'
 ' {} \;
 
 # removes temporary files

From 0ea8947d94b9ffe4b2c046b9a18245ec31a8ec27 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 17:50:18 -0400
Subject: [PATCH 062/248] syntax fix

---
 useful_scripts/prepend_python_shebang.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
index 20f420d..175c4c6 100644
--- a/useful_scripts/prepend_python_shebang.sh
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -1,6 +1,6 @@
 # Sebastian Raschka 05/21/2014
 # Shell script that prepends a Python shebang 
-# `!#/usr/bin/python` to all
+# e.g., `!#/usr/bin/python` to all
 # Python script files in the current directory
 # so that script files can be executed via
 # >> myscript.py 

From c0316494556cc921b30a0e9c36f3cd3e78b136ce Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 17:52:09 -0400
Subject: [PATCH 063/248] syntax fix

---
 useful_scripts/prepend_python_shebang.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
index 175c4c6..edabd69 100644
--- a/useful_scripts/prepend_python_shebang.sh
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -7,7 +7,7 @@
 # instead of 
 # >> python myscript.py
 
-# prepends !#/usr/bin/python to all .py files
+# prepends e.g., !#/usr/bin/python to all .py files
 
 find ./ -maxdepth 1 -name "*.py" -exec sed -i.bak '1i\
 #!'"$(which python)"'

From 8ca2852356c9b9725432e719669fccd1e33761e9 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 19:20:06 -0400
Subject: [PATCH 064/248] syntax fix

---
 useful_scripts/prepend_python_shebang.sh | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
index edabd69..3bbc937 100644
--- a/useful_scripts/prepend_python_shebang.sh
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -1,20 +1,20 @@
 # Sebastian Raschka 05/21/2014
 # Shell script that prepends a Python shebang 
-# e.g., `!#/usr/bin/python` to all
+# '#!/usr/bin/env python' to all
 # Python script files in the current directory
 # so that script files can be executed via
 # >> myscript.py 
 # instead of 
 # >> python myscript.py
 
-# prepends e.g., !#/usr/bin/python to all .py files
+# prepends '#!/usr/bin/env python' to all .py files
 
 find ./ -maxdepth 1 -name "*.py" -exec sed -i.bak '1i\
-#!'"$(which python)"'
+#!/usr/bin/env python
 ' {} \;
 
 # removes temporary files
 find . -name "*.bak" -exec rm -rf {} \;
 
 # makes Python scripts executable
-chmod ug+x *.py
\ No newline at end of file
+chmod ug+x *.py

From 6a03169858c70335eb8f20bb796b743e7640d825 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 21 May 2014 23:47:27 -0400
Subject: [PATCH 065/248] logo

---
 Images/logo.png | Bin 0 -> 62351 bytes
 Images/logo.pxm | Bin 0 -> 1132995 bytes
 README.md       |   7 ++++++-
 3 files changed, 6 insertions(+), 1 deletion(-)
 create mode 100644 Images/logo.png
 create mode 100644 Images/logo.pxm

diff --git a/Images/logo.png b/Images/logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..64d8efd6172e9a3790b40dc22b15c904a9fd147b
GIT binary patch
literal 62351
zcmafa19YU_vUY6SwkNi2+wKWFwrv{|8<S*WOl*5%bK;5d=R3IP-1FbN?z`68y}PQO
zT~$x--h4Y!Sy2iR4i63l1O!n=T3q$>edO~Vgn{~eMIGuA1_6N+0f>ny%ZQ1QC_6b=
z0&FcnK%^0K)7;h7R<XjTySTb$(8x$A@9?}}N#^*ZF(FhUWdfAJ!a}Ir#33ayP-!*I
z)qjFYg_D^{V890lnJ&ZLpePUZs)$P*Y{b~WUe<8jZf3m=>ujwawJfh%UHUG!gDAfS
zLG-z3g9fk{Xk%QQ_3ERcj}JV6;h=ybX@maUv9yGcdK3p+c<mC%%q*84YP`Q-3NY_j
zW9U()a$frIPbA|KqksZ|lUU^B_oKo-083G89K>b)fEc?=rDYBK!irS@TUbrDH&ADL
zUv@~}F`AK1wO@dzGlkp9o)^=hyqQJ0QxpSHpr1jxZB^t0ZPBGJlLd0{Kr=u8m?ICu
zf{|%7aYR*M8_6nxfqM~o0|o+<GQiX6gcSY6W1R=$QEIgx1raG@T8g@y0XR4wv3$O6
zRC_Z0LZ8yFuoK}SKYiDP5XXWYQ0I^)0ZOT3mua2&yN2NRZ=Iz>>!+U8Eh0a)H-Qna
z7maJG^XWX1m^_^=+w*GXE-a9H3f*kN#_m)NkS04Q;e(xOrArc!8%U6*&751JFk8c<
zfn5Y@_~`R<TW8RG8z%Z=u9y5Ehq0b8o`=lwix*A4Qf#3>av4lE3yFi$vQjzmi3tRx
z^l9_A2b7P!pFDuc(SC-U{GZs@p~o63$0@KOk=7ld{DImDOy+PQ{);>QIGohbxPG}d
z7}l;vAikkI%B?ccBMt%}V&vcy_Cj7@zGr$UU*JZ%JFROH>zw0Rz21+WiQ)C5?04iv
z$kYz+5#r6AC<_^Ii?%{+d%@5Wpk2Pfu?I4-fH4>&5(Kb0f^U+5gak;VfTw{oNrM4|
zAsR#kS3aBImlaUA;IED#3xQrU5Q;&Zj!^m#T0apT;SmC)gyF(^<yb&B0|78WS16c;
zB9#eP#mJeWODOodFrH!}%G?;>gJO{hC`rOkGgRd`bitJcV#)${<SuyaFb2WT-=Jqm
z*rDD)eM3+)z~)U5S3sHrR_pl)V5IuSx5Yg$bD*(*uI<RSVhJL(^kMJdUx6@$8ucZ^
z2#>&k&q`{;;9rZ2CTLTDqLSJqk`;qhNMt1-72~(XN+$yM!J)znOfqn=hH&@7bz*i*
zHBFXGWmy(8%rZ3Pu@5;dQP={Xf=Wymzn5xLR|~npa-wF(G7e}O#~bU_r+)`uF*)ZW
zKpOsuyG!8c*NUUhYR^#%(Sp1j(-CmikGMm1?a@iMiE$Z(-0OU0>O<NM=TG2Iv=vW0
z)I*&Bs|#ThgiM-7LSajxj)Dhs1oIYx+Q+UESgzzk7KY+Epl?e1J#Iy$Q-**_F&RT9
zi~?Jgf;O`$P+jILnE{mmRTpXUD6hGc6ON9sp;9eHK8X<}a*~j83Jn#8k}QFQ=&B-*
zY@4vBu%}pwDq^|)f_7y_d#<ihm$<L2FSArKTl#JK-e^s7RiaJmS-Ll+Qp$3=d%6)*
zKD804zwCSIYb%&auW+wgU{Sqhf#gn8pFpULL{?#{X@qH(X{2fJF2|q>WoJ@gQVW_=
zh44xJ9Ayb9p6Gmu=Ui)(LKE*Vi(f2+Qe4F^^T{WJcf`kCcd!<e7J3IX2LuOr2aRK@
zP<<@Mk8G;AB{9h{9Wn8@?1ydBd(&&2bpXSUPTJHbI(s^wRI*gdRM6D;N_h>3MU2JV
zO1ny>1(vT=UroNQetlVFs*0U1n|9Be&NX8H*nbT_t#6O?YJL9tr~g#oG#M`ouNw~+
zPXjNVeTV}$%RXx~>mciheYnY57ybu??t$*n4?~vJ%!>(iOUlM*o`Ra%-(`%mg2fwE
z0ySpkjoMa4ij7m~21!hMTp}&{Eo$zfN^;EF%tm!8mj!|<`8D~)JyJb-{$oF9NPZbd
zi)8nf7ep2GnB<uh?U<2QCRWYT73mi74RM<_1Ea15_w=ahBXebvWMW4Z)K(TY)s5Sy
zo2C(L4y~!!t2jD_3+vZ)bgCtnzVY<)kN6aMW<Sxx3q)&2=VEPO88Q53P-8q}Al9MP
zfz-BZI<0^1|22?hV_@bx9QJk3tZ}Mz<M2mK1>=@m&u?G8EQ9(DtPaJF>3gJmvKOkC
zFVF(v9^qX`4=XNiG#w$ni>PPEJFQdKF?-O|-qhdcWy-d4b*5;i5T}aZ5wNeY7Z`Dz
zW`R4c1Wt)e=1eh09*zYI8f&S0x@XT53d^xaZUc4$8!G~<Z>=x%$a2VuQqjXHWywc)
z)9{87>2+F(T6HZ+52lx2FUtgZ1tSGx1<||Rx~qJdA5GrC-rSxyZkMl)o|j*gAh;o_
zVUOX8AtWI*p#|YtVbLI6A^Lkkdpm-Nndgis^cS$eL|?_+Liu4i5DYM%opG2L*sH0|
zJzF(A{5;$t7)>R_#RfX0<45L3q@rW7<gnZ&N2LZO7Nry<yd}~l<kGbnbQ<?1V{~Ik
zN(Hcp@iDzA+?*;N<~HQQ_s)0W_AZ#%^wS#8e>wd)s2HEFO)%=!0n!oS6BBNsd_<!6
zRUF9I-Wj#IA3!W8cZ9zr9SzYR#a<gcTD&Ggq=z(w%!;QE7(|-M)XHGW?13qX{MdGF
zFuDocuSiKqDWYYon5~!ugfu-i&5Kf+MAg$9unACiG550<v52*g0TwSb&<llU6Xj`~
zbu2qF;@;)|NXJlQQwHn6H-2r{w34#OA5)n?J-pk`+P5Dk9K9WV@#C=MaCf!pc6$$d
z?d7<zL8=K|s?}d>Q*<}zZQuFReHL#=Vkf<NH7+?GYk|LIAGNul@=c|_+t|AC@^DDB
zO;ly4i*ZU1$#&2d)Asj<Wn<0PwRAGW**~V^yHUGJl<P@nTROg4Pa6~WA-}sy?n_?W
zx7}de*EdeuZ?!W`bqrT$YPSt)4km62Z(2IE&bu{D^nk|5fOzxxCf4$XF4?-TQj2A$
z#ixOI@mYsmr`|2UbAJu5E%$8M`OkyKf)7Fq5@8T@`j0#)+ti5I4VtX)RQFGx6`r>D
zSos_Ii~p9sPMnqsl`0wTi+5e*TUh2Q=St!1i%lB(z6Tt3c-#IX=8ax;qRdv*_6WgE
z%5%Lt{78J6hrqw;8Rc7fLj9J1-#numrCYGHz7*M9%n9(8_ue`Qn9n7gB4iZG{_wer
zxzEgM9J7*}PMpHb$>zy-_q!1t3cZew#17}xauIBA+Mmm<t*_l24@|E!yl!)<SnGOu
z)kv?hs^u}@^Ud|``W^NbyF}EHmu7VC2RQk0Y1eh)*!Y&VR}<F_>3j5S@WQ-#-|l-5
z_mPVa9u4(;!PM>jVYn`E;=B1a3tfS1C^X^4{bv1UcD`3g4W~qxS1DBUw)nVgS<!WO
z{%zTKx-r5kKCD%Y`6KnIPN^Sqx?-AMvPtq(szz!$<|$^w>-*i{)p$(${9b4e?R)b>
z>TFfMpZiOfmFeifmCfu~rjYDrc{k7V(!Jf0`SJd|JK(akENZi=Tj)OUt>DG%ZaDGe
zT<K9MH9uC!-3Rvd#)Hbv@^I@R<o1P?SI6&jP6_KMt>X*=0+0UJ4GNN#g98Eru?JAs
zcF|Ul=QVS%V=^&!FtuRvuyg#h1_9ys;QcJxS-6;xc-Yz6JM(%7kp0bq_p|($n3;^^
zZze7_0%Y0>$|PbAP8K8_Ozcc7WP)%cBqaP!=9avw;*$S>e|{1mvvzTD<Yi`dcXwxU
zXJc}3vSR+i!^6YO!ph9b%J^x)=<I3lV&cJQ?@a#pO8!}oxP`Ns6Ts00;9yVkSG^{t
z4z4Z&WMqFi`mgKnak>C3|K-Wv`5$S0CdmAkhWQH<3-kX3v+w}?FR;Hf|10d1qyI|l
zzd7?Tar`^zZ)E&EM*hDd;8nG7cCdB*D-{iUfQuk2|K9@pPw~Gp`#VbeUob(ge`5Y!
z_@4kJCxFE#nSc2a{PGVU|1SGad!7Hu!M_Xt4e%Ewyppyi&eqBR4^0av=f9k=d|_eX
zXZ{~m{|Oayuyb^<{|wJr%)#CgVCCxc_c;G$_et2_8jzX=@ZSjh2aSKV09(LcS^cB*
z-*Eqgep-C;^nX~e{ht<}IsH#{{9XFr!}~|}|95u$e`D#BI}2NFae$qL{oe@WWaIot
z4gab6SAKs-s+n2XTZoxBy0|)7sCznE2>z4!|4;FsNHGUn2PZX06Elmy==)n3|0eoR
z>%YaJ^S?&;Z<_x^N_@7E|7sYltpD`&@8W-heEy1f#hffmTpXO#9UN>0W$c|@Ost$t
z?D(1gQ}qwj-|7FiZsG5J&fmVO&rU}W?i2aX4oncPmqhHd?-K@*5f@SS06p(=53rB|
z9veJNO-xxHRBK>~qLB!Tl0=fA@Id2=H`vax`9a|$+&_SrC4FiBG8>@aLSmILDl&qD
z&hS%Q=37o<Q&lYb#hYJG<-GIdd2I*;a<-7+bdEQ#y4L6Asuk|6csc3t9&GXoSG#-$
z`4O$(vo-A~cZSMN*YDx=W(YG&-pl>=h9py(si?d4G9X_1XM&pH!Ovftd(PpB&^9cz
ziiMJ4AK*zjQ8<u%hjRRfrbuWul-{s4%^$+baFz00pX_wdT!e}U1-XU>kV4o`yA72Y
zNB!2nQ>>oIA5n1fHu>dC5Bp&O=`((jP)3Z1zo4lxo(Hdw*kv%~&xs)xOY+0AYlkt`
zLauyZf3NCTlAuXvc3FJ<9Vs^(|F>h<G5i4%OV$rZnl84V@)S9q;kR<8EbF4$ys+*M
zy~0APtoAs;A|-kkubqBZU~1QE&w9MZI~+nnMpv2B+$V}rPlHlCH8Bk7H;>bu5@Nj|
z?ZU}^21SjcJ?joXE|}BPp;~68GP6l&35Hs(q`$=)`BdP%kpgMN4n$s2mefOFAqn{_
zST=GUVOs#?gES>Luy4|!aHT1;ccf2IVL+UD{EemZ5)FN+#wc8v3ZzG(zS?i_$&6u2
z>((5Cp_u6>w>p>IS8x?@Az%p95%B<C5D5780Y;&HR3ZpaoB<r+-GE%xh$G=P3a{kk
z{Q?BzM^MFs#1oofV;-RMKwvT}gR^j+i39n`wkjx4Cw<2@ZJyeo1lNmJC)v3v8qUmc
zPnkVL`yCNb2l`HWJ@*U3!?Z9R5IY<k=o$!RJTZUs3oydL%(EC==WDD6;L}tT22=5z
z4N8${+s4~&DcN5#Ci#GiXQ$qhPxi~PWq78`No=`3O4mezPYXDtTjyX(EKPMB2l0NF
zl><HYy2Kb&<Oxx*P~lPiGQl>c2gauwfdI~u*cwAjD}^s2DLX$GlAJh4<zyYS5&JzF
zDD9w{an)B`|LZGzJx|p&sye>-%KospJcS{?I`>r(L_Yy=z{*Sm+W0G6F-;>Pwd_?A
zHQX1`S}+Pjzr!*V6tA}@6M6Y5^9?gocp6;S!_l=h1R8P-S9F8sQIy_%GYTA*)4LMz
zP?GOk*<?L^6!`_1CS2)7F#S?P$j~vWGaHxbbLB97bdSE66mN8qCy+&C8rq#D`hCba
zqq?1#bJIfCG`4U(z}zI|@nV&Nqxfp&(xgO@9@#7ea3SDHNlYolF766dZXu0#sjCRL
z^Vx=-Cd$}UR1;YPYRrUec3QV8TzeowfBE_PxvK_7Eo|sdAumD;j#O}GI!Hki6foU-
zSZ~-%Q%Kqpn=unyLca@zmEl_s-Q@LRt=BYU!o$I>+DfIWbay<pOo^W(1>urvH1gl`
zhE`#+@L&Kk{C~c;G@-(`c(R|&6p9}o19*9Q9o`jSgn}y2{@@r`lZKMN=!Vn@>0bnZ
ztrHvspciN*j&=5qOV3{xh_`|s{z3`OB4#-)E;OBr0RMFeO@}B<qfD%3AD{}O#KcSE
zb+^+H$8`Loc43^?tECI$IV#!TE~S7<y4jH~xfM@H1Ln!Ld*7feL#E|1sbvu=4qhN)
zJQ0>XO(>rAtNp!NrgRa~H+sroD!DUCg`08qH7P+0a7sc5*0^K6-}aIs)#8QFW&1Lb
zn8VAkK_@jgI*=me;+zmHy0{ex(Gc~!P8eUh4QNEwpn#=)`!rxgVD}NMTK-cCKMlBt
zAoxuxtrYP>!b{QoPVBYRLXh0Ck%gDkTJLP49@9_Y!n!J5m(FQcY~k>QHy^FY-Kezx
zINoL;x_RAC8s6>7a!u;=$@Eu}6ei@~y}!YFdFrm^B?gV~%Ms#`vV?l=<UrlRQPPBh
z@&s8socNT4*GG@?jI0xsVtGnSzw3<580zyXJ8hsyXzm>K&YB6TlS8&*4#19O4Nw$?
zYb{BUhq$I7V_L1zjNC{ZRH+nVkV|Eq?<2@r4QA`;%0{ZQ!-3?Jj|!Ji3#8_c1U^YC
zCd)~nDIX+-UwN1Fm(HbLm5C-WUFz!b@}8y)XYH9^X};M?iBQOr;p&QfiOiK_3&!cl
z9GbPx5he+{b7S2is#<Ow4zY$Dh(cqGrPY_EouId%$x<DO?Z>)MnrY0~8s&bH1(l7+
z<xV$^=)h~2#KG5nh4U;aAuH|)PR%PG*TtBJ4}a3#>??xtj-~hL3z+Vi5Dr!}PCGtC
z?N(2br(|}q&bq^N^b{-7oU%tE6>EqGr^1vURMw7w>W+7`#D)`U0GG8;Pu!+HoOiUR
z<vuw;&5M-5fbtQLq{=PTC?-o0)te#!`(hjtXnJL)YC+gQ^N2_!2`2@MLah<FyBg;<
zjGp|6DZJKz{I>4RL;%89oNbg5O@b~BS8p7ywCbsCf4{q+Rj_cbrrhXb4W${_l5;NH
zAYcD;4a4IMUPwI$){zRx)1Zvtf#Cve$VbDM=4tGGC3`V8UVsJ9-=GGTT&&R@C-_7O
zrfkt!<lf9jUjK)91X^Zf9d?7UH>Jm~5oi%rh10Ly0rZt;I2o=ykiRj0JY;0aS&T{@
zPY;~P8xeJpp+Z5Mgd7~A2};Q6VLac6nrj0oGk!X%ACbs8$uuJHOk`pUDZmr(ZdE6$
z4=(G~HhmeFL{-}T%0k&J@x{~R8%!m`>%L;i68tSeLnBBgRPHVN{`oqBk4lEMkgo8c
zExiOfiwDyr1uqsIcU=ONAB1S0I<hdoXq@Pzjh1l8D}k}D0;|_bCJR2a!@+k%j$|($
zVix;=AkqA>%ByMGroM8N423H%tlkvn+uMDyr^Dk+0~j0x&SB#rb^uU3sE7$dSSdd4
zYfva;QqsL3|7XJ+B$?vG4Z?6Rc%0XlLt69N#x@!{3umNtp$q=AcR>tkO@wQeU24R(
z;DxKCD6#0mx9bVV$>?dvI}kBy`lRGP_^5Z2MDO;qLV7TpOt%MhBlgCYn2+m=D`Ao$
zC~wPen53U95MebP--)gKK6YC#vKWCq$w={wd<>D(zRx+W=evm4E7XK|FU0k12nLW0
zhy~xsG2r`TMJ&*01Jt2JQz)eZ^h=zM7ne%X^M8iacjjcf1Z_#i42TS!atsFggN6-p
z+L3kzgAd(cm7u1fn6|l|c-GYe;HZ0ARUK8)mP%RTyOxsJHJY}5yBar};3?xR)nrFf
z&PS}OQVrd?O@UNb#hwiqyk!(|<@K5QQYoW_3LXC-3~fxGzzT(vOdf^hB@L;brrB)a
zf>z4(qLH|_n?OJ0LTQO{%5a&_a&~t9!q=gbWcKx|1Vg4i9$O>6+4#g^Mo#g3HvYH$
zv+YUtoz(p;7$ywp5;VWBk@SEixzpT6?NR~}+zNU#93Y(qc05(9dX+~~WYcbVQ9yc^
z-?M^TPb<7VYyXsQCyJZj{#pnjl>4|a!?#Hubd%jKJ<0s4u?GIvA*(g+9uCB;WB)77
z=v|%dI!5`<-+UR)75cw^1f6@jP(nU8Yb3~r!J_>5I&7HZvBftiCHSb&U>o^^%RZ8F
zoNBiqzFSV;Z}92+%SFfFw4W0XIYO*7`Reo?6$d(RZy`aXvZroN(C!t_4r>W-9m6-0
zEQAVkx-77PR8N9kJ|8#@+Boh}E1VMBlcEkVY5s%aB#`A(dD%n!UaG_yGC^<DTN;?>
zk>$1!Xrucpfc*;XO8w9N?a>NK;5Fs);HgHH*`O@GnOU{=_Z%0Lm6>E;$#lbssTJCU
zsV$1!0gR{8v~vDin480lRnyEk5?=^pNaH^;2g*Af4mn0NO>_OoMWbF6BU1oKN7)<x
z1Dd;S%35ri1af@xL5SoB&^~@PcXS;<-8;v=0etu5%pii*XxsoFw5l?L93-G9tW?J-
zyaP^D1uhpX2hJ=P^XsK6tOK;FER`)RP>mOap3&rrx)bi3X>1v0o_azpf{Ch1c|p;f
zPh4@D!?=i=R$3DddG(azrp$5{|M7ONsTb_iVMKbFffv|yqX8uFm`Tr*wfp9Yc&N7H
zJc{*vYy@av`>U0*w*BUG1+YxG8-~9;^v7m-FgK1j!NivhkwO$-RHTs~e=~1APu-++
z*=0Wfb?fQA4S4rmYUl=Y@LW(J>&cmYWKv`sRl1JAcg`UvYR|XW1(6DqVW&zTKXbi;
zq6B3>7}{`kffa&_hXa$V)_@q>&=IiyH02(YxzrOv3aRofL__=MMhPOi%FawR|KoP>
zc3rHTzz)M=@%8yE2It!nCc?d@;>wQZ+=Wk2Sn7NBl0!fpzUgREWb0jnoatrPeXPL4
zN~Q{^$E>wsP`@QsPf<ot4x$53q?GVpD^y3)pa5zLoWxkk-sa{{jZF0K)U?T!L1Lh*
zgRw!@hN^KE`+nemGIDVTT6*4sxex87Sf6Ji3#FH|(z{x&VDg$$8p@3ozQkN&6bI9i
zGI>l+vU6y}$UD@Boq>^DPoldmIJtE1jv)ejL(_+5hY6-eS|eGFWwP4lNDP|8T?={N
zpOtc=QMO(#D8COAxmb574IsGLT-aE|Hp)IV5<(UfoF}q`JG7c`a@zhvd#TkSw95CD
zIPbiTYTfwl79Y*jNfjq>5eIxj%-#)nbB_Q=)>j0r?lq>8uY^$r7gPJ$VQ4srqtO2H
z-RVy+`|DVSp{&_WzGb?nMj_Q}7@%xGO#gR0P8s09z(S>-caSaLAD0;xDZ8%{<8|gS
z%6VL^k2#mc+H#ot_*<^ovVnWp_n)6*+2fdzJ3VymA=X@ku&|A1%Yj{eNaOj}rqcrg
zfZS+fnIuU%Q*PY6k`PzkLuXHn#dztS()&ID7~esRpuhSk(@E3VriU9Av#*NN2+c96
zohqy=7eNZv6V*R?7lKLk@l?UDaE!=9R&ukG-_O71na($8-(jK~yfiC~16i8_=06ue
z8^W`&`&C?d`}d!=5w56y2cObZCv)B%_OckMZ5!B}1F|<dm{N+Ez&x3KXR`YhXIgS<
zD*CIXI*fk03i%~eXIY8wvI_t(=i9L=w{Hk*zuXciW0h{o);Zw{QSLH8kf5?NoDueb
zJ+27JV+s)vR3iQw=Rb}V??}9d=PZ(e8$YVQ7yWR)v;W$Y%s_1kC?{EkyASyVrl_h+
zAdiSQftlZRFOCwKZDH*KohFQ?^p<fe+awyFUpxRVubESy3Y=S211MvJ&W#=Ivz;&x
zs<eGQ`EjGZfq^s6#}=CKq@G0RrcJ1)9YRA*EWbw$Ij;RS6nmrD+Up|6q*>ZamT*!M
zh%o}|wMjc6=Xvg95*D4LYRulaRI`C0^t!`SC?TV1DOPKx{(XLiHi7(ZjnkUaEg>lp
zPQ{b8P9-wH2sf)1!kfPvwjBtm^<dz>L3v`RTu6Y4F_tJ=oPY0lCoRIEAfkkWSyiIZ
zu?VB`gq;HE0of6%fdI)Tg{xheGckXr)ELn8Qd?!_#&%0nC5GE9N-NecW5|1@S$o$;
zNBz<0JF1nN@RUpnFBFYY4AIy0)%w|*(-J2@XVdj~kwKYT^$NvlhY;7+#k*3*SU+8J
z6H~tqcCthUQaSXbZ{0OH83y4eq+XYAuv!&-H-=qrF&eXlcEOm|JJstUS4}P2dYWdR
zz3wbi%v?6_PXkX{oV`=J9TcS!9m`&23s#!DLz^NUFe(<IfwJFnW?53CM&5f0z^4PP
zVFj+5_^$h30!!7a^!Qy^TvXezVb?)L`d7(p<ESP}<4`k{Z(fxe$wbA)@R`Df*7O^)
zUTkO)RKl>}cAPS>W(R48XO%X6Td(L0wF^#l_07RSwmD!Y64cVvR^i74jOl)MA8}gX
zuZ)Hw?wRgH+^HkaI<=O$9&$1ScD+@q%A}*FD22~(T`U&%f5fSj0>00qcOMenK<LI^
z9*^==y6($T>7aKuTL6Fl`U>IBwDUS@UVuTyBS*WsM~0)xn^}6LjfxjjAJJGw-9z3U
zS}OB->KDUgncc$VHi2EDp#y29ODmOW&RRTXGhyQnJYfzbOf|{*!P#9KRMiR;GA7LF
zradG}zPo5&UB!FmaAh#!&TO%WJ}Q6-^!EBS%HGtUzpCR+fzR4VjZZ6(##jT}@|4DC
zL{#SgJNx=dik~e|@H(<((4<8OVa<=6Dla$w&h4pjpwMXE=4<7#YQ&%hl5uuaKoC#w
zfE8TU5Rk^2jZIHg*)~1M0#0fAjK8maUt02MeqOH8K)s1Be3AEc={s*Q6+EN9EU80E
z=qW=JN>(BJySr>qP(;hM^8)8mSkvjtr~|kEoy#B6u1dLlQbOLfxsLh)RAnibC|b$_
z6uMm0cQxiNINHRty8iTsyZNOloh&`&1gu1N1*GNGQSK&p;cY6McPTIcr3R8WJG>L-
zY9jb#S%3^gXSy?NgvhY$j2zD|{o)Pm5Ml!kCMgW5cy$uY;r5g(2c<8P?4#97B$Gz&
z731uewC5UYP!7T6IO#@y3i@uV8krt5A~qR6f8z<gU$=cOw6%%@s&yKb*5B*J%;nmh
zr@CpDYP(>Yku#%zkBbg5t!78IwB^6wt+!qexdKN>UxpX8jG(*oe-yXgCDCWn_z4{m
zK@+u70rZ8DQaXXGp0l3V!)-?f5oD90j^TuE%4x|F3{h}2>-b!@5Fb3iKOD<t35ta*
zpOYeN=PHhB>+eye8*$F@hrev<qT!i48Fud1ow=?q@UabCaGfV_)e7!6z2mueBZ3<@
z;r>Ku!S#TduW^-JG2w2m9jupVcI)C-aoZg*Tfm1_ASwDhKOK~XtWOQy-_k{wmUN77
zRuded_%fibX}_sbm5AVK$vhq;9ja!mdy^`YOQ%wAp7TSnLi@OjFO&qSAF$;>+=zX{
zNf7`35_OEF8m1Kvdt53zv}l=er-M6Lsv1RCE{OoyLkr^xVtsuGcpoH}8pO7H9z5#$
z@*~4a|BFgiHW|+iF0*ZLor{UCkJeuG8QRiE&qG)K5N#R1P?69}ruz_A&y!f6zq;|p
z<=W<Rl*gDF5#jhM&08<>bX1w(pLn(dSO1s7$6z6D%w9%kGcPY#Fn0if>ruGHGbOe^
z`R4t@Wo*xLRZH2nfzRy0b<R;Y|9sxl1oc4n)?Gt!6+M+hoV8WX^(6Q80Ck+yu$9nl
zt;bLBi_D@p*tz${{9NP<{|Nv0bR~u$j;jVCe|!Ej9i<opx6Q=Z%}n)?{I~PM){i$O
zg%xDKD_4e*>S*uvz3b?k_gkSJDx#Ma=>22sprGYPouUxY1q_MarVEa!CF??u3Yrr2
z+*edBJC)%xIRKo}4}}sw<l49lgcDV)Hyy_~JU71-SV9<bXH)e-YdE>n2ss^TeHs}{
z+AITZ8C71dloC}0sYz8d45`A(0V?hR^Bfb<TbIh94B2mB9;qOeo<Bmy4GuuMnL^tp
zC?R_c8140GB>~$Y?vrv+I(>^Hs!<2~ON$k%4OrBSlg#~DV9ng~XJ6qct%g$P+v%*^
z$uAupJ0E=WWe+@t*ocVB@}4tdgdVp!uFtpp0k6aUKJm{=L)XKEg!kR;@qXvdcVk?|
zv=l-Iy<FYb8}i=Ed`4nQ-B$ejy$`Wgf_t#n^Z9w$XI!>X*xlQi*YTH6*^f2J#90FR
zv+A+meV*nXSB8c1$F4`D@-URVe>n^)^3^E%F#C;el?wBnRz#%hJ9)0Z5yi_LthjFp
z?2;4vv5ooZcJBOPmU&F?dR4d{)9U8owA*Yju2gV!&Lwo$$Ng{{@-KB-KQnZfNc5w3
zTc6##LG5{4`!Le?JaC2g0d&$EVma7_qdHXO%Gp;2S1C^4<NcT|@1mc2t%=pka-BD4
z&rg5vE9n_kD=A?pURT#m?oBcKwd&h4AlA>GX}BU(lqb_8J9i?YDY$2lNf^!EIOR=d
z+EhNt340PoNe>s(10O09fX+tGqwd)tdib5JCWx|0!6TZMT%>kPYgMz}F}o#X(kFF!
z^tGItUe}_@Giql^9j|gIbljzX6Rx>We9jZ~5@n>${!IaY@9k2EvD@wWW!K)Fk8U#4
zEUuee;GLfJZ4c%GmYN`0^rPA;7p=}?Fk2<-a#+dlrjh%^Z@8@9$axa+x_!Baen*M_
znuw=i{b*p%4BT>tZ}X`jrswi4E;^ilKd>6i{?zNFaB<-6VT*e6ChcM9W$Pi#W1)yB
zL7*phmT&9N&g1)oKc1F8P=QH?&;98qv?9D#yGxz-hTQJ{mKV1x-aw)hLDu0?mC>7~
zWZlDXKe1nr$4u}=?%gPTZs~&F);*|Xefm&x{>{k#BvO@oM33ihJ~o5o`A~=>?a{uN
zR3(eCre>OXS@<5_5muX=aU85A{y3_A`T4PSUN7#`1^x10b&d<x^YR^8$d<EDYIa(8
zD{jpdu=^74l5s2t07jnQj*5L3h&xH~DZwcmPA;o4jZC~itaMgo6a^0_Kat8<vTx?v
zp#8Oy8K~>D5eh|WU$9b&FiO3iRnrFH)S}pkwCWX5E7Qvj_D@S1ut9npW#o>yx-p~=
zXgg(qM=gq&wY)HD8eH8^8lG=~02hh-z6YLb?yEnhi<PO@GvS+D^RGO&9YS$ubv=n@
zlyZ9mFY9Kc?|1V(qls}kyBy1Y1VWF!tJmrNPkk#6JzZ))`Omrb!i<hE+DV6}oY51w
zD*zYOjZ<b2kBuIE=tLsOf|z{YA$<1<?U$3B3pdTeH=ZV~1iih6E`KcB^HjD;)!XN0
z$I9&%AoBo8r+YqLG689$b!dSjv*&}%&%To7!^1Zg^rnkr8o<Y$iAG+o{!S^sSfkkV
z!G~R=60MGf_xW<h{et>MST}h5RZcsKa!%|^82g1byye>KL<$oHJ?#Zr?r|Vdj;C=Y
z#+cq9$q9uyw?*bFrbC&H*X)c&4@KXITZw4Emq{8(R7M&~P)WO6<i}8xXw*WwO^rfS
z$<1ovQm*834P$oFx)Q^^=*Mb(u1;Ot_=0i@yH+~^O<EcJR6Ko~Z&*f6ScC>S<e~H~
z>jH*(4;TSKb}_*I8ASSY6wLht0(_qL#}3OIca;Rh4C!|A;}T-W5A?Z@(;go^#eo*%
zvA_DMiPmtzeZPruZg2^%!>D}MuT1G-YOB%qAW1EZZ;Dccd8Ij`r{+xx1Z*2i#ZT%X
z4t}>itYa!udyMx1wO*bN5aP~S2EKMnlv2l7qi;;^JV653Q?>=`?MuO@%)QK;>fY+!
z@x#jzMonePZhsgufF5f<?+MbwRg)}LW5<-00lAR#6%bq&N|45oOb4}%%9TYmoSa*t
z*<}c*htmSwdW5oPo1?ySb++z1l`XFZ>@wH^IJIa?a9oVep>#*=#cM+{Aku*QN@+R`
zD&bZzy_gD>g1$ZA!WR>)c&I*(owS{>lA?`qBLQ{PH9V0%DNI{{gr4Tum*(Kn`X9rY
zR<C)We!r+xVL4hG^L8D1JY;IU5HnCO+RqfPGlm6ey6+Ag%zx=vEmkhRY<&RS`tmWu
zH{#5=8`*T08Ho8OOBPKscuN^c7zqXX$}`$^XRDqqAcR6gWoY5fDYUt|MVBQbG==7G
zM<l4!?e|=Gk_zMp&j#78{eBfJH1zyt1f!lFgr}af-^RQ&*yZSwH&&pXN3^kA{EBmk
zc8xwko;%}x@;SbG#$Cc4eZjIQK4#8<7?5Y%*lnaCqc<uUqOe@m|G@!^NsDg~o0?Zm
z)Cu9KLwHkD>*Io&TqpmtZ+tWXNk>Z=jv#e(HV8~r7(?~c0MJ~P6$_QTY$Y}DMZN^Z
z5cF~`5CEqvt7ftjN?UsTKsQM9n5GGgeJVj%Ts!3=R;NU#QxIa8c6YZZ^bONLHjW*?
zyLs%u^!XhLb9qtax#2w{KD+U<IXr;GS3Bkz+Oa!K>@Gj_vNzxrPN-?|I2-ThC_m5U
zU@1<!^TQQOm@mur7;Q=(S4nBGf)})t#vu%qf4rT%bU9vI&<(ud-qaddis5*(DKfb$
zcg<vY5m1ZU^gbEup#dyPN!-Tb_xS+^BZjWHCLGv+WEgiv7b2_YJY+dfzhFuwPzDt?
z3v$(#r4hAjs8}?{J?>xvVCr#qU!v~;8U%GS1jW$SGa^>1@B>8AR8>B=lr=pp_$@Nk
z;eSkMmN5thqT%$OH*rcKq1_<OnVZ77!d~{Z(bS)`fVf9#gs`^Bidh)L>yf^3<fMHo
zD`c!zR?l(dKu9-Un_;ZY5JdAhX^2@GdDhFQ>?hSR<)}ik6Q6);*@d@XCnhl5AD}PI
zt&$N0d+8_6>jLq71-i!)W}?%CI5V6P`HX;Gr%(&=&I>)yP7m4`y&!1@5RH>g_22R*
zzJlVOxDe2&JL7Y5f6udIOFUvp!UEvTen+vw>wKu=IV-RK-83F|Eljo9&Nlsu3J)n%
z7GpsGxf_afH@EdPO>CYe<SukSRqX}T<%0AKp)V0XPf505*oC)_F=`cVGA`uo#+t_8
z3MG8f%d(@-TPoFS`C~n`tLS^CT=iRuRoywExsYQMl?!LAv2kjU>{vL@*_Fcr!()V+
zqofcj(7Yx!<S_0cZdwN!R|^v@lU%A#lK`|bG1=+I1QJPVK+zi3Itu3jP05LEKgI%`
zvSlh+%p8StG55Ay$(7&@*I_vW2Z5&7lhg#(a>VtbHE#OCc#;Y@xOHly-=c9CpUKJG
z{g(fw$6e8fmd_1({-oiP-qx#P=lh=@m1dh#f;XT~>mj}+(5ITU`n?yAGE{>FEqo-G
zW&GEP-%X#!Ak`SjQIESGHU+wt-inCdY<(WQKXkGKcs!YT;hU`&m~~V$qLwJfs5^E`
z7%=#sXn!Es&Eap|+233FjYMv{UWYR05!LyN)fBg(dhGhzgeurBb@8m#^<k;oEzMQt
zpngP6A1XvWD!`69j;g@=*`VLH@HbzVE9mLPnVUoCp+6=1q^z^g8*l?N4J|lz=yIZW
z;F=FqEb$MX@&X1R<u$s_hN~TbAm~+9ol%KnBB`-#X2T<Q=;=}0ZS?w@rB$5+Nf~2m
z@l#j@vE|KmDi5~7UnHz87ii+;n?R!qSWO0C_AF#=W^UvvJX_~5n%p${Q5jTnt2&|Z
z5V|3A5|4`4wY?|I{Oc@qT0iG54lx}pxNaO{?Z5EP;<{c8wC^{9&0d8fk`;LcFBDhI
z_G%a|oo^efQ3%Hxu5R~An0*a=?#T1U$6vLKZUr!+V)>CaIJlCfWZTn%G~It^XHObj
z3y$|>?=rayer}bfKB!$J@1o~Fni?Wby#y2cIHctmWqEVr_xAXlupSbUcleQ0H%oOM
ze<wGBM>%xet&NH47^-5elK(Ey%-QjEDzPGS<9-%`ex4RT3J1s+PRrwB7~~0|-@RFG
zgFeYzs{1pe6y3yNl3z{JHtH)9khAA%f<A+@zGCOrDnmQVYAl;oQ|kvhyVnX<>r>9K
z2NJ3YgqsLY)J&~Zpm1(WmU{}ycnqEJqy$jk`yLktLed<oS(|6!K5hBMx|>x0OrtZt
zRuy61BJBFNVJa<Ug-cRKNQ;N)iPC8YZxi?BE`aGaC5GAY?)hx_yWV?-$F*D@I<Xb_
z)fjzU<OYwM)1~)TE@nQtNjEM-PyHi9@USY;<F}WAj@2@c!^S)ezRM<b*(S;-l9c*G
zXS2&E|5y2Yt&5a0w~bz`vZB}*2q^-;j>iG&4^R=@q2EY++<VnUp2Rl^FGGZv9vPHJ
z?~u@Kz0;_wDF=QJe56dB$N2np*KdxIE53<cD+8V2SHlF(7<cC*Xiar+qQc(?k-3ex
z9tVa$1R-x&#;#}bTXXJ+!(pFh<2xxmKbPn)N1a2@mOpA@x@I!u{J4>2^3=2b{Ftl>
zKPhU9BvZW&%xdDJmGl{+I)iJ1#-Y!@QB_Eut}Wv)Ur!@9M5cds%w*-E^ZpV$qx{C}
znILh5J5v@Js%x>F<!ohnwQ3hIEa(rii6=5deXt;}BmaWnQh`HO*uaq_oMHl_(Rx>Q
z?{?Cc!n%NB?W^00QGgg*!ooUkSaJcEdp%iP>4^TSN>tJS@TEmmw-iL1qcmt%KBDUh
z{5|M8yJW!-FWRz|1-?S?x`9zha8WRCHD@;i^_Bd&^(@mWFLUg3{sukVb>Ye&ZP!W1
z3^}q3yzp!-e%^-8Pl5aVyGW}BE0EY}Bv9XuIB3}F7R0>foWS``1voM5F2f(Es5o+<
zw|gLg_Hx<6yo~4gM3M{!p6}xl$fm;9mZW)?;ehDP_IciAv{MLOmk7q|ak!`G&dYz{
zlg^hQNn2f9!A?|z+lDr9?Uh-|`e^Nde*d*hHKBRVQZG|!Q#v0%lDucPqVQLqM%L55
zJGSg&r4zowH&$#|1ZI$C1bCnuRj4(^I98#eh{#-mi!kw7P)HYn3r=&B9ov`CMsl2m
zhRq+(>%kzBHI<BBZ<NR2<r<*sv(2>*kZRvXlr1%2>!Z}K8PB_DrD)}8MP?gUn1^*G
z=r-!rRTVBm$`hAc@k3h_Y&<JLf0{w^X$2jfIqTbtJEA1%Kuk;8NvzDw%cVJ;?>&3`
zF?uyP^m)I{?^O%9;P>WDe}^r&mQNMXa&C-aFqYG=ZMOD0InTIp6-Y_msgCdbv4cyy
zu6<rya{5&_h>F@trxkxgLCg5aLnYW#$|pf7MU0s@=b^+Iv$;r4pTd&oyo=-)O;6Dn
zi6Y<c@$YUMUU?Qk2vLupzpN9%_IyGfi<1?N6`MOH39VJOlb7yj2M!qu>ZX+Whh~#>
zG)|ayxYj+RwPd~l=@O#}Q|pdt{K>}hO=HgK(soSZ?9LyDOwU{hVNkYn9N_yw(o{o(
z*iAwrS076>ZmJ**EJ&Eb2}3>DiKTnL2&IoDX2r8uOs9?+=ftW#Ibrk}?L^Uob#y*K
z7#Edo%c4~K^cNAIvPl-OO)z8XGeBS(J!J`rVEbWhTOfHz4v$ooIUKNy(3E`XV5q*q
zxcEF6dHWnWUbA6=WWzFJt2jSQ+@CPD2C)VGp~`~l%w5{>SssLG*O7%iKn%Zu_cR0<
zMhZYGm(0qD^^Hy6VZKLkmGImEmQW@*%k1#p5Xmk<%$df=pS4zHlc~RVMGx@4=)Y!f
zzA9ZG*w<O1=GB0u8$1)Q7VJAYHz${3gDK$(!s_-Jbyleiy18|ek<nK6$A!`G<`$=@
zs;^90n%gHz!`2|lxIkNW^B$_Q+>lC9NY<p-c>r+Tz|wNb@el(R)2?y6rbME&*)T+q
zCMoN)R5;o_uvG@BoiGDC=_0Sin9^B(huRp<NkYM|x$V<P;M2;@@0aqUt?%Mphm9vL
zec7tNi|H`c<ov+dgX_y#MOnvFJBVi{;y#@UAMSZS>p;Jn5kn67%`s~4=xIrxnhVQi
zIY!a3sAwI}dx~^de>v(oLK^Ofv4I*O`5?lYx;@D3r{EKNB>et<X{3fOJW+8jzRpmj
zkKdDOLOq8!w|Dwf&3VFeLuYJub+Z*Wn#z9r5{7d!r!)Qw-b9HSTN4MCMr%EeAx&sh
z#cPj%a5BI1i`fwZy}8^9j_=3IW(v8?1v5_MIO`paM;sQ+^64VI(}|u}6;YoG^}9<m
z=Ae5M#{o@aC2eF=Y}^>NmlJQp2p69O@P}8E<E(D6hJYLsTpEH7_9dN(4&Ruw0=BBm
zf|)q2k*E0)q!JZooOv7ql-3tKlQJ?S3EKLZ1jI_TnE=>eAt_>*NmK|v{h;$VxMt}u
zOnbC#upiB1J2}CJB+eRex+BzTopeI)66nC4v&y8q^lAQ5M((Z}`-90N&45ax?2hJ#
zKxiTqXkr%>lpyBjYjjXYMN6)mT{yd&rfC5C5qbn^5D@)v2{EUA12k)JGrFp_EeySb
zc(3^P*`5e8hT`ogSI)!$qRldyGCk@xWyo1zMJzto!57p}r;YY(kCCsmCvut5woD$J
zPy&%vXml(1Iw!hqrV?<^f4DY(%B&M6A=5?iy_fqjJ?VaERB4*8$|>89_uRLiIY)ZY
zdXu-&rBk7jwC?9KXzkXMxUHsg<!vwGrh+MXH8dBq?y<|TAv?llpPs7ubfXlcl=J5L
z>_BFbDO$|L4Y<{!4mE3G5%65?+D>7)nO+!1mK=5XikN0qnH-#(52)1Aew|&!u#qRK
zf##l@fGC5uznxB`3Y{1#<|mhHm7}3-tx-L3(8A}y@ZhN9gsDIoOZE)FtQ#w4U0A$H
zpX0Dlp}kv1b)(m?@OF4db1P5Y+Zai7ahFy0!wq9&s_RqcX()`G&HeDiNq9w1Vd(Ch
zINHvo;2XXB!`J=o+Za7>j{GzC`GJ-`ZoJ39<q)*cW5wsr-#f+obKNFb*y`i@kr4Vr
z=aJcN>mKo7<gb<^q<aD>m0$0p9h-jwJ?4`0C!zLrk;DuMl>DFJ_gcOA>GIyrv4@|q
z^~D_obE!6?4eyq(OS!tkEmqwJ{9cIDZv*r5y$y4o*QSr8U&wdk->;@UyJkwRd^`og
zZvvqCc?91hrw2ctgz9!Y<BPaMUn2=5)C=irJCn>V9Acn1KWKq1W<;nTkMYE#y!swH
zgWH}uIgYPpyC+KCA~~-2)3}sAmzu0I`H<<I;Z{sokFP$4G>s0Rt9|@7Z0W$;JnUOz
za&GHMTgv?L?Hvf}b{_EL<ZgJ|&(V<ywXGa!>|WL07V(l2c`UR+&E#^=&%{aHfO~my
zf6{|_+B?>~b~lUPtL_H9G+FPbGt%7M&-Nk`5O++~_h0&C)U0t(JY?(x5{_`4RGMfr
z6V!b$c_AyVF&IU?I<ut+r4EElkSvChlt5Us6?oBzWsS`nD{#E<fg||u9J>GtZzAF%
z6;hNx?Q0}UX;7!P>bl)9tA+^Lx2#Y@DKTYp6$FuTKCxomI>5B>jBZRN@8{dS?uQP(
z2kLr#Z>%y)f9lPvu=HEoL8-m?k4NT}^hYSso*?K-qIC9rr?DdL?sqz`H_|HghtY#|
z;*b5RHfj}gAL`n!M_RreW}lrZ++3*Z17_cZbZ*;-HA1g^e#4LX{G2JG$sv!ovrSk<
zL!K53rtUwJm&02P&L1=3IT+0TPq(`jN}fc}RCScDf_WdETKj(ELT}?Xdv|PtC=Z>>
zU(T~_W7PbQkUg!x;=1^UmZ2j@9=*-dtMLecl-uwt_{&1`*Bi*<U}WuL`(zgGWo#!+
zH+Q_&uqnkP?PsQk|59%XgtN}^*tbsGJ8be~v*k-qq7kBGrW7h+SFk@dyeT8#SVeWa
z7LlHZv!@7ea}?Lm%UVhrOY@*W=IvcM0i(c8#7hlQ*r`w%iWCQn`o>A7o+Az#teV0J
zXI2sWh$;dpRA~+Cp*L3ZNCAjZ8T=LpqzQ+>XLWE6J7|x|mwG%Zqsg}kI%-INB;cA3
zR$|!UvD<Cx#{`*-rrmueAf*=O#Rh7+VG%UW7#Bq~GXC+r6Y7x6GLk%fKQP+F=y5)E
z7#4xvjI$CsjaeGx7kR94@MoUyc?_*{w(1GOWZbyu_>IT++rzlqTaImDkMnZ}yT#M-
zt6Arn0rBqR>#wFi0xf^^)6$F{`?kzJb{|`}gXCCyZo}9D+(~y39Xz=3qwrB~o}os^
z4ewt+Pcy>5w>TYzgii2-UiBj9VG%za>!}R!4Q2U090GU9Jtyu}#{|&TY&Cq|&bE3f
z^vA*aac$KN!-W@MjT31Y=8RrTq610Yn1_%H4Br22UnXeHK`rmI*ZG`2=J1Ct&;Rti
z>uHIJ=FGebWZxfb*-AMXWTlf``qd6weNb9odQRKQftNx+$2`Q*20^Ky$|`}&<Af0d
z<QUTqbJA_#Y)_7VhDp!uaPQL;a0NrU@@OC>Lk}bmkRC}1B~ypX!R?SEl@oP-M2GPR
zXSco8r1jnox-{<S)ab3$uB!GFPO|VKwnBrguf!;HgpZ~_8Pset%^6pr{XQIy&o%to
z9^1QiZW_QS<Ub#F{3SAPbwUoQJ<=9gt+2&Z=PpD3UVqIBX=YDf!ymHlp_mkUAzF85
zCBO;Cslk%De4(Vud-e8Do}ssQuGDl*|L4-2GY@nXx<~5329_(`*mKgFPxXSd58v}T
zZ#t8I2R43!&8W7%HMyHDe#jCiV10SR$E7na_v{xPUXb^Gr{>f@d!S|TTg6sKJGAj=
zqhW9ZIPb%ofGu#f8wTKoN>ytC&X?-v3BHGP41p7QOy}fl``vWR><-WNA9btSz#3Ts
z(K`)#XuYo5!h@!RZG<<%7XaI^=GqHVirmoXT^i%bTb)j<RGHZdFL72W0Lx!-Fv9M4
z27+aXxMF}`_DrI9reYbhv?oANnDxil<yAlm0cH;5GFZq?&~?jeMN)`9e>tnFWJj5?
z+68;p53C7cTwTo01ZbfgX$7YYQc~iyn{g?W6uk=?nJ$dDXW?(m$l62R-_=WbSgV9D
z!pl#dop18+41WPAv$o;Y`V4`pg`%7qER)@1LXorimieg<$ghG%fpC)3<Y>$KsEtQP
zxWUd$Q95y1$bL>LcRg-QoEL4|7&ZpaH8$BpxR+-<NB07BQ0{9&X#|}{@&s<e1A*RB
z1ohtXSMOzl$g-1FAt`IqMMuWRA!VA;(nW%`pO4;7$K45Bx;l)lpk?2{3gNfuHl7}@
zVLvEoKS(MHKA}ERXWVV2cvI3P{?UrefsorPAP8CT+&tS)CXTEdX5zVADp}{mWs(5A
zx-eTX8PS?|KXkr`0jJU9Cc7dPvN>6j%3GUv7^DD1^2np(M4UC#ic^RZ<HR$)2c2lG
zq|Obb@W{70R4YES8nb+tb2zVeuPZ)Gx8LdvK>i$2<nG#brcGBCJRDl46Ji2Cz|C&z
z-wOR-f=zd4e9cDvyv5HO#3rtp%#eK*C(0SJOddzM;m^!liei2rP7FTOo_u|3<~5BU
zdQtph0Oz5&%O<_T&CGX}wSI&K94p}8E6jn!ZR%1X!JPD&$j1<dY?F+_%DXdYa7MpY
zx_p-;@EN$cE)U=0u-`4Qy!72_F>C2^iyF@N!3dHyJG3q^ZM!b1_m+tf(?B)Wzish`
zjXS{7qbE8@z&cH1&ImrBT5D`w-*2cy@7b~7f4uE?Ham>D9NgafAnJmjniz7dTXxM}
zrSu+hw8HB*oUjBxB1<O#Ph^hlNP+6U6JqOOxc`L_K6sU4_Bnyz8`z(1iHA+0_Tb5+
zc1SE1PtErSd0^PO=DR178rI7+T0h&Q9;S_f!ZvWT;E%agV)na+H)d@rYt+1b65Mm}
z*-M_FCCWwjb+4Hgco0pC{Giop@~Q3e65*n@;!$nja?pEqTz}|}kALX8-0Lhb_a|!U
z;uU;Z^}jgfsx+h@SFt&nBiz;7dS;)_5@N00wqpz@@Dk1gf0-A03x2$C7ebdJeye}H
zq87rY&UJivd|-a}_Br6o7x%okYX5xV)^h#TB7N|%Q90!DoqhLAC~B11pXgZ!u(!;}
zJH)LGn(xjN;BfV=x>)^45=;^Dk|H@C<SMA8iaBlvAQaIH@I(B*o@RD|-z7zWKOQHQ
z7RS%>%vT+Oh{3Y1N{(AMG03`|t-Hck1NqzGtN84?tIKx+P&;hT-xi$>8dW-@*zWJj
zOg%FkfhY&vhGTxzjd4V7?XL=j@^r1_`j`F0UZ`nsRA~36iiX;?leixg$dAlAgdf+j
zx4TD2Mra=|{z5dh9bc*5mmU0Rh+b0mb}Pz=f_;4~Y&!vI)W7YA_c1-Z?saccxqo!9
z>+dj^r~t<tD6^8(^|^F~aZqHc<pPrgidzieX2tT{GkD%6R$lLw{vQBXK&QV&?^|D0
zTxl#!r9EjVY}~%X^=6o`i34X2budqXPv#ci2z5>chAI7N%32C@xoax+7##;!Af$19
zG6^gZ_kCClAAQeQcH-S)X3g$#6=#}uT)8?{t(eZw54gG|`U@|=A}+h=vRKKo>AoEM
z@L?)PgR8E(h8}z+%fCC)a5l~)bB^no4_uOFk+0_2Yp*?0k3IM0(}2Fgur;>4yY9Mc
zb3Wq8CA_DCHOH=D?y&RDJ7FTN!eqNK4>bDvA>XB?spYcsdFNdO9W$w1rit%{8*T*O
z#j$D2W>(AgiM{sNE&4aEkGcQ)dCYy~n=$;r=~U>usSGN_aOplV+zoR{3EhI=*xPSr
zNf$o5-;xXfPA4yPsU$LALFGMM!Hds(@R{KW6Bu*&+KUrbg&98n39;;nCuOHJL{T&?
z?Sy;}F*`Lms`Lrht3};Aayi+a-pjCy9Cdg3+qW*7-uyn-lbf@`lIHr_m%n;p9CF|e
zvF|=paTC@^ebfiK<}m+Q#dMFT_mL03c<6?dZy$$mr5p&3A9!53!*rZ|&UN%e7sWH5
zc^_=~EwOF`$Fx84?{UBT+#z=3fZiuR_SJaIV~&IQ`@lT%!4L(%Ewhy()XGANK84uR
zjFh94sl-_AWjF<ohbhTLFF6%c*RGF^U;WS6^yc@+^taClM~cDzw`1OemTzF>+Q1O&
zGNY=n6ccVaIr$8M4UyA_MI%TxW{*bgsYEn%4}}_R;i9>E@F7+}BlD3ywsMbEtGUb0
zrSG)=r1N;*vtq@gAIP(|2giU>g&Z`_EDy4gCqJDAU^+}f8lrfjh*d+HWREt%CLIJC
z#F=IZ^3Hf|X&bTJ&A}HB#ZBE2idsOR&V);lEXQ;&B~`)UEE&D><dW)MvOmB0%Gh-J
zf5sM`WtsTb)AIA9{n#lqH6acDK+8tT*a(jhIy<mfPKRa3g5dWYsX=cE$#3oukuG=j
zv*Qv6^F##?$O#l>0zMB`cvcJrxb%DsQ|kYur^J${KP;y2d~j@`4L3vUG?%$-pE4&^
z2Iz4pNhPsfO+kPoUIl52qdzx9>0BIHIO!8V{?Uc;)iZw18)c4-J$A!vWuEHmN&PgP
z$~4d0-c_jH$3OaKEJ`2<$`b-rD^)CcvN>gCKlO=o;zvKdD4ul6@w_--GS0i`23}5f
zEh{+3vSi3H?JL%Cd;%MsH^Q5@tvJ+(0V`7>Myn8IN(rYDc8Zb$ta1SdV|_1}lf31a
zcw4@ER&03Pdt>;ik1|%!)cYP9b2L`}1}a2&eo98Og0f2U2Y%haWO41)u+xvpl*@)2
z^BV@VyH-bUM}%QVzsUy;8|k^gKU64SlmZ?*gPY#3e?4y>*>DM8)1aJV?;Df9|G1dG
z&)s5T|Gi=k+EdxU;!D)2Vb;-NH3O`ob#AViLoM9WT6_z4VwXy_V6Bl!)6}rNWbzW+
z=Jjf3tGG?~YA%=WY3u>3V6#6yFJ?Y<dJJCm4|IoXb42d#c0kOtIu+~ac-F!P>%*Jo
z`^-6e%ZW!RhAxO)4WBo=oLqdi1Z&rL-pXq=?xAqKJu%F9Q)SQ@4f&SJ(2hxEwL{)z
zNzA~)`O7cB#6=eWzdwrAr#>|1_uD<^O8~-S_(=VnD|zvQKs!=Y-D}if;l%hHMJOjn
z6%`2F?$-cc2Qd5bmD4YX+aJ11?6db|DG16l&-t`eFwoDT*u77F{LhCqdbomBJ--vQ
z$tkP4grgKN1*x5$;^7>Y6*q4l<l8*<*>_d^*AFg_dDh~OKjC({YT}1*?6tkyXiZ5<
zX&gB^#qjQGfv_?)1;^eVc2e01mIkNSZjAMx{<qk|h8p|4F@4}+(TAyVBR#E^wddNE
zrCgXcxa?y3%$e-vZK%n^j*H=44vZPzecR{Y+ytgj&!t=#7xWbK45zNZOfu<=GcSnw
zYyK;h#lBm`>GgMy-ab1f<9q8kvW~F0&OfBr?C(SaPcdp-a8CAkb9cOFOg{JVG4<f%
zqmSuBt>LVbU3xLRu7vnzN(BWUzq%$i6}ol5i$$*;@Egx-hgN$wY22iFVo~<cL)z5O
zQuOFP=YDi<Z2sg|B3}7+sJ%Xx_TbY2JH>#Jc(|5%1_JCT-rtE)RHZ<=2A8uC66>=D
zj&*a6rbisjgnPf3<JCd)tO`#0npQ_{NF88W&ToLvJh;`r;PM!J;T&?Ell*m_*pFRf
zux2NejK;i<F2_91dv{$5J*>K2etvAa`$@6#6;F%)afj0>Q3v>H`=sfFpsOqjyrY^O
z!^qsK(L*K4IMRIqhl>}E%pM4Jq30`KxsVght7FaH$oWR(0va4)9gW&MfAg92oS4fH
zOe1?A*MxmRn?v@lyVWR=@ucsxbN6^?_Eek|8rGCcd)IIH<-7r58jTbx3j&gH6|Y%h
zndk`BMj_~O2b2lQe7Ee>RX4=CzkOc}-tcBVlduLMUj~zD92$F`g@MVv5PFBr%YPm$
z>c-5SPKc?K?;Vpoo83EbpO|80q`!K3tY9uMgsve1UEk>d7$t$7m`*Tw18ip*c=mc;
zAA0FEG57NeW6QUH6#Wl<E0$jJv(5nGb)pCDikX3M^8umxOV@)7#oqN`-gLr|j~`+B
zJ@emV^Zv)i<STzSmOSKy=rezCG@4+TwvK)m#Lcu6U`R-5$ont<;~q^@H2J2Z1b*rI
z5F>TPkyxhs`cj{Pm^=Ht*vLa#{Xc(o8eLO6?+b5t<vSM$^93wXgR{SiHr#{9jKMX$
z_~)_ZI#>ppnKj48kcFCMcRf5NIVwN3_pUL;OXplNAJTCuAI^jGYqU}TpL4+pJ|lfG
z;F~A2S6m;1i!X~!XZ|F5Z~AIXTyl2Oxn#|uIiKlWheBPkA!e`&ruIKHhTr~5tb5Q`
zV%a<XHdZkQ+=8XEnUSN;QL$u=;v=1G^cDl`kmZG)lauh~G@X3d3h~7`FRpvSLbvlO
zUl3=vqWroNmLoCL^-lZrD~35aM|*SRz_d{@ye!bG!j3Rb<ZC4G^)f&DF)s;VqIi$v
zZ_heAFF&Rs6!hAS7p^W9uPI439>mB{_ZM8cv$t|}Y8tzEo{9JkFaP^k_D}DPB?lcI
zgRA-U!g_~#>}gD$-ZGw|V@_bB^`8BEG4=3!u>x{%^!c0un*%^86^fukv0|F--o@hQ
zPepS?P>h`C$TaC<IAlE^WnrQ`clHG_!>dPofAd9Pev%LqJMS06m2A#k3r!p8WuSY&
zXXz$(q))uy=Zs(&c8_>eEPc+CWAZ-qu1o5S)WWMV7pS1PUyD;Hybu%_M3wRc?bX-B
zrcZq>W}o#&=5Obt+-oqG=;*G-MrOjTR1Gk3CR7f-{W8}rP|V-^LD7HUaWQqbBcji;
zY@b&lxv$|Yq)IVsew<AsgA1MzHqGNZX+?-$p|gXOGJ5AervqmA2-6HJWP|s9DF$Es
zbeVVi9UK!^am;7dkpbvE_)HvU(AHmfdBk78G<H1oA+eG9<vjDoe0w~rd}$6rQJZts
zyrV`C<I<#J?$Jywj}SSMI0yX7SI&<E4rBz`XF4{oXJm!)xd9^x9b4jo*51FK_9{#)
zg}*5Piqa`UQxfkk`?j!R=DNHJGQ;N3aVUuIZkhrnPxWe#qZBrd3YKEIFp4-LA(}Fd
z^jveN2XN)4cjXPS_D|mulOK9tEI;J%=$*gLssjtdFzF^PVbuo<b@2Poj_D^qBqood
z=UG87W}|dNt>=7F32ZTVtoXOi)*e;AipfpP8P3Nsg@eYV4FOZN`fRq%U3yi_{qQF-
z`<Z`>-n;%Wmywq&+dBq(tct;PeAE0UhH>)7&U79-@DZ2J^cw!@AIH*XJUS*1Ie>ED
zOU`E!Qs`fs^O%QqbVUy1J!byn2eIWZ-w{(^_zXMsm{R-jU6!l(5)*T*>?AO0`YYLA
zxZvy@d$z=ZG5oXNiY2@dZ|cZHFk|`144tM6xo+sqqXfCKq_iva$w&lB-r;9oG26`D
zaurYhV?^oAPSCI1rdbWn?Bs@iJM{nnKmbWZK~%53DK>uV`!Vyz55?5iz7R`y-4A$5
zyVki4f(>o4+tS!@0ef4oeo5@`%!kEB_T@C6m@zPl*!D<C&NZBmprl#}$^-M3G{>|y
z(%h=ayna^_zx?I%78GP=gC@EYb4@Ht?%;c${miRT5QxwDhoc1(UWw1qqgIjh*9+d-
zsL<mDWh>&xKe;Nluv~Zi2}h)YkPQW)PwqBhR4BNZJxLou6hOt+KU6a9zA1*(KD*v)
zU;Otm{f>9V(nF4j!MQis%4x?>?Y%r^F8*0;I_f^L^p#JK6({lnH#YJ7dTWm!A-`Rs
z@PkvKH(Vv#lR;Bb!4J-K0Q~i~0Bf2`Q72E!T!0%%11x7AvOZ>ic45qX=^HV8(c7@V
zujVMlUJ?83z=*SvWlw}(DcGH!nO)*7mz*10dWXf-M_w4qd@JKJzgOsY4K4ZC<S5_z
zYU<9j<h=PkpN!s1UYWgf|A5;gbep5Mo^cMC9xIfNeDfEcos;6Zdp#(opT=7pPq;IS
z4*N3w<WvCy=qQ|x1n1f)P}_F$uIl2W;*HP5X65piys0O^*)?QGW)|Cx-Z|SPPioL|
zmtGg^K7B^aJ?Bqyl`{4@gt;XrA&{3j=9^QyPsjR;&y6K-{mWSK#QVo4a3l8MZMd9o
zs1zy-BqQK{y?4<{7K)N19+Pk7d|W}cpdcz+jv%QZDwWTJpvGkqA*A;D$nQv)RS1aB
zIO{z!nKY5Cy`w}mx}wK~PUVmp$-I$r5R-Vlm$Mfqzr7P1N$dXcv#|u_m}cY{oO@%A
zM6@)v?y}f)(a&P=oae+&fApl7IB+kf<MbY^>*q)0HR(X8a7#fRsip|pAWO=I$^t2$
zfH3u6VSb6z6&}ehL4Z0}N>D)IgGuO;W9}GB?#!OgqfUys&wV{Mz2NmRanac^z54;2
zPr|&r0!6u+;}+V`2OJ(#7hWA(PJTwLd-0#f%4eM%eU6`q6EW^e%YqYqqtm;^{URrf
zeW>@W0fI5;7CmNpx%P$^y(N~N@wu4Z@6edXWEoz>Zn^bm5<Rge3*{GG7#o>W&E5N9
zvE<n&$Fh6hB_^0x*ysC-)cl0BAsNEIqm6%#9_6QCM`F`#09^9d2=bXN7bk$vk-}(k
zNQ1!8nsW!*-m`Cmy>^Zj&v-=49KL^SJo#^8*=1+O<eJ;X;F1k7MdvfPeoL&{>%h42
z_x}%*`#oaiiFb*Od{-qK9|6q`Lq;)-p&aFsCanny^isCnL5Hf8=bTeZ4_C=OvM;UH
zALYw=2u(ln+rNF%%Ut@)qXjvNs3bL#I0DqwkT?fnLk>dxc+6GTFmXnP*6g<{wjSE%
z8a{+rV9h`D%NfIkLvA~Ac-J;vifOgwi{FXar~gqbqcH=9BnvUurfA5`OcdkQFO5}y
za%%MU+9glZW)GC*h)XBqDFdIy$%cxrd-|O%3zslmYTo?W_DV<@NVc31%QPxGgs#cm
zc8kg5?-0FH9voYC-6!Tg`)OW<a((nUra~4b|A_95j265leQHlO08jf=tUvwih+`O0
z_SuUb9KIlg7MltS1<gsxt#QKDc<I_bsB9}%=WhDI=VR^>Pma|WoeMpT8kaF@xTH#k
z{yr;EUgiv!pBtO@xoh;^`jS}o;@^#_WA7M4Mr2JDq$ugKz3eE{=}%%)w3A!;1I|VC
zX4sg@C*SOBDp#ReoT1|Ci&JTcVUq40(+|B{Y&idh=zpILOBMu%H*)TarL%zt9yhLy
zji+50D;{-^nA>wz?yF2vIZG{5*^aH1=)L*7^7H1AlgT*$0-oL3d0FhRnt6?#NXjbp
z$BBX%Ww{gR6lw)|B+SjsDj3Z!s;<CT|AZs&qI)s2;#3I9CJrWvBmF81e|fx|6QP5P
z*ra;i+hZlW9DO#DY;CJF!AjM-OMV{nfAgwX`J5-ja5WDoxyudCYCLg|T=o>%RgK~n
zeA%m2$h;$0^hwuZS3O|Cb_^z8t+YWO%Y*}V_NQ)tXsmki(_`}6pNmbWJ}ovg{}|qI
zWyF3QhNKn)_7Ub#_(>KUR{Y@WF?Y8I$L5cHh1D(UK?CLbJw<Dt+*W7fDN>G<ZuAcM
zAkO+%zdQP;KAW$$?oVfOFe`s+YtO=SbNlm6^vf_)uV(S#uU{F<zy6k3@svj}@n+P(
z9O+?xBpJycedAYAH{u;>V?13%J4xB07}*K#DBl<!%UQS(MEretI?u9i?~Vt=4sUuv
zY`phFV&dX+VuJan^$bkJW&0lzE7t#4-1r_gG&d4S-Q}M$m*1V&qM?JgOl3}aZZckB
z<)_7s{7vrbB*-Wox(t*sHw5iIUdUpkz?+ZDN(yIs`a2COrc|2v%6TupPi;-o<87eF
zo4kOYg4fVEG1Cs5ShFhTFsp_ydO@uIy~oBZ%e=H-p5Eovs~UP8zPe>ui=#*&!ba>y
z4k3%C%9H6^;i;5T4l)v~-lXNommhoNCCxhRT~4)PLr))jRIGmU3#0$B_r!+X_@o-6
z-ryi!<3rEn8M;1;4U>Bv8Y^~S&*0>z#s(gUnqVcT*XL*s9fD>@Q<80}(F)({o+c%A
zvlE;D*?F<y8Lx;XuX=T?KpA@s!gE)0$PiBT_hfsO|BV-(6Pup=)R_Lx7h>ffpUUX6
zFG2?vMn%86l(Xxt%BY|ewe}n5;bPv7vVseIhy<>|jOQ!JYBSOSbYkzq$qa@CORDTs
zF<h`}w&Po#ADa%iM+`2#H2O@`#W{BwFOJyfz=$`$HMV^H$FY<-?+ooR$3<<yRULfF
zBjJ%OkCer-O&`g0=>W0tqS8ty!1p`9^PrcFOeseRSF1FoybNn*iOff84O=IktKf}(
zS6_V-Ce{EGYj=c&rL#1#N_<Nx8iC|Tu!?B%O9;EQgYW$;=AZfpv4kG4&lhu@3{LFL
z(&(i>i;br|F?M?C@5TJit5{890_I8^-H1cBrX;*dw^-=ANK><iE2v^f%OQ*nJ|3=o
z1xIV$jHB5TZ5a)O9L)gNH0iMyFv%-_`j5F+Y@XuG)n`xRhy+K?_uy^oOpINsp5qwI
zG-s~j!yn`G+8pLNn&neIH=xZ=%fO%F&4@l}+?GzqfB7(}aD4AavFU*?h-GJfHKq<e
zBH{vGE=OmQeA%CO1ala3BWB6)U2lpNFZi99IEYmuR+21JVaR!0SIpwlwo`7$m&N=9
zZhN2^J;rClR&d;nh3z1wWCL}RE|+5a%u6PYJ|s5%<HutejXviNKP%H7Z&|YO#@KM>
zCgvhXv*5t$A+n+2ZkOga46`H~XTj<BLVjp3<@<sK6N^O+&y<;|J1uNB%`D{D(WA7f
zG^&OtX|FUv5l(x}1-PbZHfmj-RY||ljwa-3Y4|ucE*?dUyx8$e*0R3+p)WhFXCse&
z1lG3uD|uGqy7f%LcaD`WJT>Oo+wl2<5=5w_EGW(1#@hI|T_+zpw@ssLH&5&F#xgbp
zi|(cu49y4wtrT^IhuQ!#e(I3@WA!Va9r1y;$GW+znYc5DI>0kqlyMUqVV7@;=|k@j
zOW*kBxaqlniP>`ni*@YI4_HdHi93ES@Gir)4`p!GA}O5~Jl=A~cVgqwzZWa6_+Bmw
z#Cd)%5neBYPY3YooeR&3bq{!SOn>W(vFd4$ir{(nA$tdQG~H54*3LYB^CevsgSXYS
z@x=6Yb)eFuGjB;(h1+kAB<AnZZL^lAVo7Q32uuf-ClILLfK$a&%=IST{nvaQ>#CSy
zNm{8L(3$oQI4ma5_+o7M=8ssVqvPh!M=$D84fsW8-sZu|W80Inc;(p6zVW!i-6)cB
zTpfF-8)j~o6>yY*VYWM0Au-*!N-y}9W*iqpwgSCdTj9Msd^(Ie39=!4+s76@jyZhO
zTbMW=6qBsWtL(j9cs60n714Y9OJe#?hp|#c&kKsOG4b$kV~Q^W-nQ#1q39R~=<<z4
z<=iE~ZAh=1cjLLsUwJY+6_8d1aGnRTmY?$QSo*!s#Z3=*cnWIoU>?e1MD~H2;m>bm
zweAkF{Dbd{wa<Qe%=09oizHY)w0~k-M1-Yep9mx1AdTOTnXmG6;farq)q#SsBs#os
zZRQ#7>hTbc?4N&5%&>a4`k!7FOYVM0*7(7}s9|SALzO%yH|416yVzwDW^C25Ql)J%
zeIp(d-`ZsX6?s!hj2Pt|<*8`*&ZrgH>rxhSu1Or9z>HN7y=Tll@f7O#^XRk7pXMmX
zVpxld4e$Frr-Ik=ASq9Zx_+&UA;Wm6X1jwY1>d`JX<Bz#wwG2Cc}L3B#aD*PXyQnl
z6nIAhO|IrI1j3NWzr<8QO&N@r83~h2k#A)is^>#ZC5}mN{?a#@m@~n|Okj702WJ>2
zSu!2m>w&TCVfW<Vnq?PT+iyl>B#rtDCgh76Av2B|sNq?dOty$$@ot%UzDz-<6_?!Q
zPO<8rUKyL8`v<Y*yt6PFFt<4DXAe0zmqtApC1Hi@rf0trJNrU-#)LEHOj{EI?b_Dx
zPx;==D}-iGdR(mX0N~zhqIVe^f27Ut#Yl7wM>(#!IEL?iN38g>XGXa4#|hPJ6WeBM
zP1w4qv2fNW+jHwo@^a)B@p?N7t{5a5v$XVuh^}=FbJZVSi$u;mQ9Hle%2@Wy$A#;g
z6PWT#I65@PvTuLigJap3J|DB3E%T*y{vlw6C-IMz!J{WtpOWS3?3G`yCc{ns&8K{4
zSV61^g?d~`T6|+vOY`2Ys=N#n;po2LCtjZTWS))FV^TZAagxESI1@zwH(1N@4z$0=
zN{hYR^#6WbOzp9IK4jw&6f^<6B#gPjJr*GmUMXCSj_st6MQx39Ea^6Riw`ordMi7W
zTyE5sRq^-QJ68S0AI0n&{+f-hbE3C1bGcQR52O#zyCJ4gl9iuh8Tb!h8-uH^W+Wj$
zCeXNZgUw5UnfZ_J#>{;mNAJlQu{}8Rb@@ivO4=Hpi@D-l&Zcor>PsJs<xhSX%c4BL
z0SS}P=A)x>$+l=RI$p*t`K$fMX%driGjfaC#CBLzh=q4o*G^&>cb(`Ikpyx-3)nos
z$UJx1oo^q#=l*`oqWnuVwNxDI29o*D^S~P$*0Y>C&L!Uk`79wX+M3L2Au0H#Ndb4m
z1e#1&eG7*8)RHb!`3rD7gUAh!T_RoxlRo}O{DQ-;=oll9tkCRF&*R9(Pxw+Lim;x!
z$Bt~WF)^Pz_Qc%DwTIQ(7VF|3ugYA&ogQJj%QD8x&U72n(wh^9{_zHHX}{=h#)hvL
zz8Lo?zh=M?yRwJL#fz(A<sV=oy!-EBBj?Eabo9N|EK)$oJW4Xj$$=#w|47{Q7yrP1
z%8fJ_Tv8!A=f;&b^~_m6kIg6iAtMKFpIxKRxh2#ohHF;G{3WbX@g(H*nH*ie|M74S
zO8sH(NcRWt5juii**kN;t!^9EF5)YA7Xgg(yh>V>3_XkCg)vX%tF~t$!p?zFV}8}L
zSn<$%hmRV}F&cREsYmBJ?6XY$)-xh5yFM3_N)d~ek$hT~BZ%^?1eQcfPYntV5TuRi
zf^<|IP=&|_*KekZD@|+hYF_n}q;|00E8U*n%#A1u$TS&219_0mQ?&zLx;Fc@@8{&a
z9y*3_Cg+|Bp5%PhklkEYFwEtD{-^-P<gGQcX}FObW!V+keiK0#sC;Esjb&<}xAJcN
zRFb-n;~ybV7_GBp%NBQ2J<qf1)JH|{1MiIWJbuc4D<_UQAPBzsbFYu-+Z`E8-~4y6
z?rk4L0^kNG65R006*4qxaOvf-@!7B8VW#t<x8^Vef>Xt+!5W^M=Tz+a+ubXsfA|ho
z#@O`ZTvGC-nW0It?E~HZB|k^>-g=)xHV$o|T@_z^8HZWyPkxk|X5Ml5YG0$}9#sIf
zE#<s*4*8k5)4|by#G~m<&u1j2mtoIj(#H~l&h@Md8GVs2@>s|G&B6%f{G-Wn)uEKd
zG_Oi)%3|Dti8TUfK+-39E!+@$SNlMi3M5Pw(C&$gv~t$28YL%R*3<{Hu3$3x{;z|b
zhi7=sA~=@8LnhhdP2b}V6w6LECWg;1RQrmLva`}+-deb;Jj-6}43EdP#@#yq;#@Zu
zYJ4(%o1(VPM6vela!6o@a96qJsnc?H-+Lc@cieRCMV!w<7kqkAgsea<IgpQb{^_5`
zhEIJtF}kVk1eUin#D-VBGghAQ88#h{Kp{4On-O+jcE356zvZyI#mZ0oMNHlC_GF-Q
z0iSyt)_%md^4qqyhoN82$;WXXZi{Q7Ew-zOj)q;y+ey#70OUbRz_J!*(4OMp<dO%U
zkfwM(zy*I;(M>+j@L*kk6RTTvL`qBYUEb%_b?g~!oMNUt+v$6^joM|_$|sebiB%d?
zz*0X7>mSdcKS@d?1)EWs%e1<{fZw26jlgq<%$U9CikQ0Qhtc1gXRg^0>hH)A>#Ht^
z;r$;Lap0b0kR>&5$^5AhG>B-ob{&I@rt!o^M~kCT8j&sPHryBLd>is?4Vq`(ka!Kp
z6_0&D^xpgTvFXb5c@++w2FGnYOEkP;3)a}07@Yj{*!+VZVKPkTa}^stbXxTO;jJ-w
z=#jbUD36A_X_o#x)@fFK_^+aW#GyRoLx*!K>S=wd6WktCx2Ami;l)ZK%s8$lV8m^s
z1RY!|St;7qZDSQ0wuLl6J!{s#(;<24xQ8;h8X7#6XtzE0Ri5Lyeq*i_qBA+C5KGll
zwv^wya$b(ORFLX`)5Jm<n;w`Kf6Les47a$Ezu@dWO7l|O6VBRH0VF~ah3Ec2mlsMV
zv?GD>nUBGF7njI;yxksv%`;Z=qJO`;#*)?S7qD44gDJK!dVsE0thVa4x(Rt#)3KYZ
zZR|DAuc92qhwKo>O8BL{j;?HY4H=17KJmdZc-w1Z<0Y6(`?#Km<M3dx>BS>RG4uS_
z6TUv5ndm*^4`cb>2eN_6#w;ju&UO>~NUUNl`;Rxo)SWmN29HM#U+8|(Z?$C;mR|(D
zZTey*k>?H9xZo4Nowh<$s+lqkZ$^xjp%AAa7hp;jvN6r$&BJ>?kmrOh<op;Ld~Cq>
zcHtqp@0}5G#dUcmO*s^K$?@WQ#Vf;JVpU=IwJgKkB`=N75o7^)0iY>MhlF@tWlmrE
z7vdDiE^iSqf)_?}BTWTgvva{^$;NzR1vELjq=FoANFK4KopaAbrSeeUcHLO7BuyhH
z{CK!%9;M%UUt`qP_-;Lh?LbmDmED&n%yH^q#i@^tEr0UDn7QDr7#_%3DYLLD)!+Nj
zSpL<|#>UtG3ookpmpZP_PHPXFJCCcbnuraXuZ*cry)mYbyEE1syUxz>T(Y{&PN8Va
zD7QJY&Aye}I-(0_o9Q2=FK#>0jM5g65|wZilt=G*0bTFzM;5PG`Rud7Jjqccb8foo
zIu?KIq8lFX*01DZyiJ<|EQ}y2za{sTR!ZcPh9IzqFG55nz({HImz`V%8LmQWeM<2r
zuf1au=Dl#`y+Wy>b39zNk(ZA7P9-<Y1{@iPs}YFUgQLxRq7a2c<;Xs$9T`X*{*Yds
zQ^qW2aFx3ND)hEd<H<MUuU@3;ax);mRCJk*G&5!Z*fFVk%T~k=&wpy%^!;;U<(EDk
z!#y#RSefdnB)cCHQ?GaxlX6DdT@E6C6Q$Dl`>u#hJlxcK?Q3G$!%u`A;DaZ2#Ioj>
zs<QcLcUQ~GjWM~4x%r#2L){j#Gzu!hHdshgNz$}x*jirWrGa)|z|&wzux8xy+k(7g
zZtCy@QbBx1NpK$$%lpNgT;OTIn)7L%7;FBLS8<o@vSA8g(q_)|r&?)kDBs2vEF*|R
zbB$&#vNDTUpi&S~mA@1uG2~cK0>y8_w;yq&<F4K9`hM_Q(BY4-Aeto{G(UV(#^%eS
zch5V<<es}^eN>KIB84vdwMEYQcI7PKPXNo>y8Hyr)Z6l79k<4HTk_u$>^NpSlp(MB
z>F=>eEPc&$^P!~)_~R>!a%uLO&Flvq%#ryuywm{G3qJPt;?O3_G5drk#tM&)c-Rp|
zvl-++Epto7|H8CizV2J|S~{$4xy5a@6>JmY&MGL_g3+%S<sDS{vT>ARYM<TnB=gYM
zvbv0o>6ObzmtDhLUVd~kDpy31cRP9Gw@PdHbA`6jYC2)LIapLCM~y;MKq9V@qzY-w
zE24oBT20G)V@4y!yXke;pT|six9p0Ge$3Hz_CGw<%vyFD1^e*QGTw-Rkst}_#`9K;
zzuV%ixKTL?l}sPyy0yNl<E@4M)#0q8hdih6dPGdT^G&gN<7K=sf#u6=WO#NBiouKd
zJU#3iReHSX#ogp(fAX~G@sZFWPbgv}|LTN{VJN=0_8d$21>S1vt%ScdCV98to`rPV
zwDCHP`6)yzW61m1w(wSzxP}gwlL?+J<1}B)@o9+(&+bVKSK;0IZkbJ{;c0d&$~v)~
z6=c3r4&pm4S;$n{BT?g*<rUI}A+Qm!K)+0Fin9=zcruo#+07n_z<gNA%NKY`tj7|s
zES*O|hP>)$0XK2mx^bK>)wu1&xAa?gue$y6cKEF;Wm~9Rvrn1g$UG}svk!Yr4A1Am
zsy$f$HNLBwJ><mh%Ts~+fA`v$LZR}gdUk5t;{8=)nt&`2aH|lOSyF_z3|3$(WlLLd
ztGHaP87Y_LFF3qy`l2am-#7H-<Gbx0{W%`kLxu*PSintQ&O>WFLXpamF(W)pen)qt
zvQvaM;#Am?rcSP>bv4RRTBE|$<+cS4=PNxIhAiG2#w#s7o7Kv%fya?VH7F&j#L=&U
z;M*()*R88|H$RQ;xg}XO*qt|$@CL;^PH{6h+H#@vR>PTd!~jb($x7W8S8cHv&uz*7
ztH4^S3w}PS*xzZ_Sn`}F<%Vg`xmgiLBLBQOWx#7f=l3}}mOb)*M9Hf*U-(rhxTU<?
zZ_As_(eb^Jy%?_eJ8C;pJCmxxEzY;N>`db)vrz3x-UU2)Fz@kVJk3{B`7|I#*6`x%
zc<n4Frn`=#l~;F5Dw#1nN>d?7;&K2P#TN`ChybM&3!?{szWi+ykNjTFgprMHltc{u
zxb*V1v4PipPAcSd`DtWHIsa9@o>oxZHLvUi2bW))wjwnEoSa=ncvZXwf{wV#o;bGt
z0gITojvfo&R^<@awu&}}@Ym|C9e0X=44)5N!ba)L6Q9CH;Lq|+gSnF~h3pk=*0_4v
zQ+b$-=Sg@O!7cIW{~pne-?zl`ZA&ZK#@nLd8H3#p$|js!D^ImK8IT)SJ^f6@<X*gZ
z4s^cskZpEaaaGL!m{%5Ix-;WNmXzr+MIctX#^a>M4OO<LDH2%Zw5A%uIseFwL=~i{
z%{vKZnc{TYTAtbXvlo9PF1h5ISiWpkoPE~i@t*g5oey1php!v)Tmeg!`F#%0P2f6p
zNq_wmUe5%1qE~I#C80<KMizMpTOp;S3kkDbm)XtHt`XF29XTrJ|D1AU`P3)RNO8|m
zgzuD0Kk9zDQ|to99E_UhwW@IuFI)>&wfI7=3Y{Y@{QU25)p;i4Z@IWdBf_@rV=L=A
zQM+YaU22od43g2XG8TTvWlvt_M}nureTUWrZ%Q0;^nc#hg~FpM&pd^!a2Ynu4=VBP
z$0<yGmc~>5jL$NfK9~^&8PAx^5Iw*y5Ue?1r+CmqkK|A)FJ$1P<WGNmVLbkn<N2WD
z>bQpY87-sdKj)nD;z~}TwlnyWv#4T5EV!ap_APc5vu!@$Wqh}pG|D5M!ev~m!zle1
z`-)M&81B~zk&T^8*>p)+M;hdg2d9}e<jflTr7^*)Kj-4w=s%Sgxb3?qib7|?sQ>FW
zuol-_Uf(u_tTq{M>5)Iv7Ae%?(zX|pf;3b><8w8GRh;P6(d#?X`l9h+@HC~9bfQs(
zOfEUWUy;ZP^}wz@_uPx(-19Ev3kIwNsu-E4WGTbMX*H=f3DC?dHBKb`-csImHJcxT
zTzkU+b8DVAK&*(fe{@A$cg==azLJlgu!^$xUVHNKAfK2k<Q-L6Trp{(Wbi_p03%)*
z!bzSt5;|Kp!^y--TfkL-`CC6CQ(U$|ItDZHR^^>Tb!Co{{@Z<0(5Ut?Vn@+FkL1pu
zaDP^4E{xt`I}^rq0B-)S9QWoO!}u^W==D61_}}P?Zz{>H=bP<4at6jD@Motm9tXtY
zOxuJ-;k(fKzByf|$dG5qW7$2PMC~E)$<q#P(OrS2G|KnxyYI$@ndUnI$X^?V>H^B9
zR&Y;$^`xaFNt_yn-c0g#gc~{AvKy-fo40I;^Ul45Z{QsULF?nlBi6*ud`@cyqITSI
zb@vG{gmhfElluCRFwKHtx7x6XPaRyAy&x`QnekSdaaOeGEB|<!>9@vpoVzukzjhdC
zK}d2Wp)5D*{7l)>7e9k{t#gL+CqIn!|Mk7te9QxwoIe27V1)6hz~s~aL_JDQetEWR
zhvw~!B+AS7oTqx7&-y{0$*a2sAC-l;StiO8Y(e*!z#}l3y~$18YffT<@9tf_!^+qJ
zleXr)sS%kkWtHsa5ly#~-YQ<CQC>a-zss&G;wewL59DHR(UAS#bUgOtd$M-Vjs+c<
zlb9{@oW#xEY@DcukAk}9bQ0d@TifY!a!*=!AMhHT+VCnI1=RfQZfx^czgrb=eQ@Qv
zrQgP|TY~wYNRvlZvpm2a%N}yi=>Pa@G0zFr@HvR%?;MkRU|zX$WyA58lggXvP=392
zXl-!oN|EOZ;nq&!8%2w=!EnzaM&(qh8_4wToMRh!Wa;V~3XH`c=eF}MVjRNb+e`(-
zl^ji!cBphJNk`#y4Djn+UQYb&*y(z(L+Gp=opX@TD&YYvsWCzXbpe_S&X&U?kG#FE
zX7f&}`HuJ&3*m86cV>vmydW-uNGlplEY~=}xYgqcIBD*oU_LE4>DxREujL^j-^-LM
zW5z*r*Rz<t+=7Q-W2SM<-+|rYP55y<w+Q^-kmxXOG5mC+uR)u4`pCm0j%37MsGn~k
z5w>c8*j3<v;P>W){^m)kT5X%&#oN|dx2{g`B2$!6WdQ+~52o_29X^1=H`=ebmUSy$
zcFqcGK83+q1m8RC*LD$m^2;Hx(5V!?i7e&S6)sn+n1BPRf;h8NIh>%EGL@2$+7zP*
zDJ4nt<ZTL5z)gV)Ihb)4AY6Gz!kp0yKb4F^@b>m(0TuMnU7z7meV?K?a1=>&x3b-<
ztg7@vLAT&(b+`qP-;gAGTrR&jMF--;Px%1UZwPfoMw63ULzHba-l!!r=Daz1S&Wsu
z9iaWT9mza%<GHig6y&W2xqaX>MYzc|%qiGJ%%Q2vqYNupqkDp8xa8ZszyLRbsR_E_
z1qBgL1IP@W@E5xAw1t?iB=NUOj}z12>TcbJyx(>v4rBtlo`=s_i9kVgeH0|m)nsV&
zQ}m3&%~!>ZGrgs3|30eM_S1jUVzVtAF1T`p&LRIdiAvkK6RcZ7R<)OTbS-Zes>BLx
z{X3Rs8#>S^F%#=I(y^Ww^L{smW!>B?B!0+uVTK%L#1<WiWQWuIh0gLvmvNbvu_GlB
z#`pyVsZ0Won_C4WU~}actFWYJVJ&s>z0!#TPJ45}q-gGL`w_viWv3wKAF|If<ZDEU
zG*N6%_{ZbM{h8qwer(9<VQvBFHzo;g$dTU|dj9VaqQGulr;$um8C$21q+x*uvbdxX
zS6`Qp0K|@bCXgdbPSUfh-H}gZw9Z7?9FyC^QDs(iOE<2&x;&<*?BYo?tO6>AR}tDw
zk}70`Bw-UJae!9(LKV8gS~yEeyiU|6mQBU<{(EF)dYD)~-!QRqpNRkb0iQv;9x}7~
zDI#L9)CJ<ZP)6PApHlW)4gMPgXd6^J{Kk;9T|!23vEAfFk>ho0`NjZp&bgiV4#aN5
z)IPiB%1U;5JYMT_MXqe+EM*^F6yBZi7-4QW7b`?5K_^`VYSF`Rjf|%2P>^n-+#NZw
zVM^#1_q;7b$tk&+=p_1urHCSA0j>aRu5A*Nr9KKWb%-BGVKmWhoPlZi@k~Bc-&Q3W
zqZSYq-l__}+w?>ryBW8D;%3r+bE8|Ge{(66vXPuf?=R!XM|&0?{I-3ze}~Jod^HPU
z<qa7f@&fa@3$HBcLXb*rY63sOJHUKRP;-r{5_;)vmnr6SRZK4<(4w@$k^3MC*h!Lv
z7P02h-|ihk<M>P0-C94_1uJc<->kRKE%1r7IDkhcN%tG&4kd2TxFFWQ_;N6JD+mCH
zuW;AzRw(~p@Hd15%E+yCBiPoaWNzM@MN*RBmVh!dnBi4FKe-^^isQH1{r>u3y)Oge
zAnk6vs&*V@%Bv}jNbk+x;wzl;o@u26O&4+wvN#i=g_<;E?JiPWODB3kWwr;>;M7zV
zL=$RX4%^{8!*8-LD=_OU-vk`;5{EjIpCH}OR*1)wZq{!edx2jj@696pe+f6kfswr2
z48Wr3tu>;xQM)e!?gFG`Hr70ExG^Ta&LaUlk3aCGAAWiGy6a+Q|6^jf`%WomV*3Bt
zI}ZTMtLpxr_okQao$bp~7Eq~zfV~l8i(P}=M8%HA7$qvkM5D&Pu|xqYqKRFxVH8nO
z5S1>ll<mtd%PzY+Gdq3ez5nNX&hNh8@4cCUT}4gI{$_sfcgwlwo_p>&x0f1CnGB|a
zJ-MfEip}i=Ozwg&EHz0Uj#}tFTmXkz4k@sE)?nNxOF#~rxjLv^r3U(ITFSlGa^=tg
z^+SwfNcB{I#dQbVgzEhucS%1vL+j&D{*7P7Mocy3Z1g{9|3h$KfB8KzTS8_gx=*B%
zSqkPq=&NqK+Kf|i{4RU$ujg_HD?frcqZu4`!5o|9Y{FCLlv#*WbO-HnE7%<bB}twl
zFkS%(z5jST$)3Kco#=M`2$XMq{~*E(zt<6NWhn((UOAm*6MHsu;voAP*fG~MYbm9|
zCF~d34I<cpPUz@KsRme`hWN>qrQkTh7-^c|{6E-1=}KvHW==wD(O+XWlfflkC0L;;
zniJXtm+XJ+Z;_^)mOQU>w;E-VFVZr;eoIIMr{a{gr-+$z6|qemP1?i>mVV1_A$Uy|
zQr|Wq1V*Bpo`Ml~*}{c(NOVYok%{o|6kq}lWGP^TaVQU>A8ZjU!Vn<!x1#o7_FR)*
z4E0tX%q%faRr{}pS+>Hu7-1aZ#FJFhNIi6TSV9uYm+150e+EBdSjMzkY8vhG5O=b)
z``}2%?^>8-DB_z8kKm?qPdI+JuV$zB_n9g$le|Qp=XvcEtN$^??UdXKpgP0*uaZw{
z47Er~E0&@q*&h9dB!=vYzK~~^Ynd~<^Mo)-*4+-YzS%HuSaJ^mJ4E3p9E8-N{nG%H
z(&XP{fo?ij;-C5(<@OG1fABG`^(D@()t~w&zN6$C4tE~oFl#MKA-Ic0Llgyq{tS$&
z3B)I5xo10Hjq&BY3$yGQBg7at>sy4w8UyHj6E1O3Z}Cx_dhg#y@)82JA9x$dPC@G2
ziy2R`siEAREJ^y-Z?vD%Ds0-Tw6PS5mE{~%Y;CK!F-x)p4x}ex%)X8GqIW1w@HB&y
z;`!^`%Pk5P9&!_7bHqi33D?}O4tXGnu!;ne(yjX*nw0kp)s+uj?qX2xryrv000uZR
zQeHp)m5txCH&YO_w^pR!xt~vw&ixEB0ZjC!1$G+6PG$iX?Q&1=M87;-gh^rJ8$4ss
zg5mEtZYP8`;u2nA6jvCf@il9SlWI^XoR**nE?^=K;fg+;6fMkxO}MDl8`2}6Kl(F8
zCQae&@*|<IY~)O}wU=9!+it5ITiZkvy*#wd>?x;mD$Z<gp?Isq`3CnI5Qa2{yys~a
z|KgFq%RklxTPR*qz&t<-R{ss#gkJ?K1Wi`Y8$9BQT@p!ho0o8G%flbbO+lMExSBy;
zPRFx(B>HFnZIyM~DE}afjE{^B3=`t}ogEToIk80{7h0%=Q8fQ#_Q}&Q=k_R}1ruAA
zlU+QV7%p%K7bRg9EiD5-Zk7|hJs#08&IK?!Fd)UF^j8?SEOWe%{B)z06r#dyRH*<{
zd=<6Dy+Dg^MHk6rY0zEY^3&0_(F{@?^@X2D_(%WA`ClJ(=3Xou<pje<uIB<B0dx7Q
z_JrtEP8BQF(;mQmkQ}_Hf@CVR3Rm|C5MDUOFvFOD&^I$q+DQ-J48nsXbNbf8=LX4f
zAyEaR$EDi9FOFgPWivYxn;v<hp9Q6JpvugsYa6BJL&mP>P&5uPkl<CQFZW+PL|b~D
z=m|d(!VxB#RAP^g>O6U2*9y*dUd8T-0A|>Fd)S@EGcYh{eSNCNL#PHh{B9V3#kG$^
zr3VJ|_JfmoPjL5S;f6Ropr?m5XFNl+opnd|-o1Od7mq@LZ39|reet)xYumQ(aJv-d
zhd;cGeK(s3fkLo%<`+NzrComc&uuS5n?Lx$kL`vVZnj;!cH1|<`5hb7;RNvL9tJkP
z{hjaIO*g?e($T}^px^oKCD?DX?c49NZ+!jR{$0CvEx4}mbl-OS?ZlBdoANZ-Me$V6
zz4Q0J_+P|2*?^AxlZ*<N=Z1dRR#ENUXX8J=+GV7Iyj0mfp~alaiMHeE({i|o^4BT!
zq@;8eaVmy}#|teLh(W!6Bcuuv7T5|rsv<|7h0aSD52(O7<+2EmKk-ROGdOq50D$t#
zPzdA4?yG6Q{^1fw*8~FXfgoWdTCt8@dX-h5!!-!A*iAQ1@<CG<cZ8bcHxe|7hdVj*
zC^>Jv?N+<#x3}A49&?(_ojcDqZrotE-+q^Mps2?kceHhNPP3m~{!81%F_woPvC6jG
zc^BcE?c@g^WeXQBwi~X$-fmudhdub@W9{~}>+D!|`dxO}uWikm!%>)i1bCMn#bus7
zJ%emT-f1fjU1T?2zs?qL-SV7yo$O-c!c&x^g?kwn9kQ6qJokeC5Kf9|w<U`gyV6lw
zAk<sfwAN{JW^~%OzV&@O_4HFXKx4Zt;?T6|xDB%gse?l0jypEm5l0?rciwro9es4w
zHgDN(hpt|2J9qA~wYS}F$DMEjTT3^Qmcwk@UEK56+G(3OK>QGPE_Qd%_QIlGSf}FP
z{<udT9Qf+vBr8QFb@hNg32CzpZ=@;prLS1iG;Rv+r2ypy;p&}RZFJd**1?UvlzF~w
z+4qbT(Ky)&e|&R!m&B>vuNHs;d41$&6+W^H;y#{){64_;(_4K^)eCLM(4;cXciOav
zO+nnN$I0g9c`LX|`O7x4d20~nBO~`u{3_wa2-Ob1yy6BIP@V8N%I5pQ{vNgzQ%i`;
zViG0OaVW4#d)<aQo$C7@O-7B}gCO2zJ$r}PpTE`C9JR`KRSx!-ZSBpQs1-)+fyW+U
z*IvEW+S@wpkV6*PO<WJw%8^E^4_j(OoJiirBF)=wzYRs`v*E#t?duz(hT27quoqla
zY75m?@PZO@aA3fCDLEG|T);Nxy&Om|#$9|Z*0W~_Tm#nEyOZ*xVm-Ti;g_0dY#(5N
zV>frEi_bH<XV~3$-D4e;u4<y(vx9w#6i}_r9n@;kS4sVp5EAid>c<72@PCX)*`*G*
z?oTPvuNwc&Z^<pbvpcMLNc|7(`xvs;=RB0NjF~#1)#0~j5k|A=jZf~Bn;K!f;P(<a
z_hCn&0>qol`pP-%0xlqxhp5k<f-yNw_~v-<B0O1XO^YZ=-^>Z;@B1Lv_|CF%nla5?
zoz}FIrphmGur}_xsQ7V^sy6p0mI=|;IrxfC?&_OAf04cM%`fICBQ>$Oj{C4xysI%O
zjU$v;Lw58rhgw%xH~Ux2et!9N2<SXp$^_>iGq*>xiD~)D#cV~MYcr;|(-f-OLrz`I
z_RVg#9(Pkhm+T3D`7oM8zqLz${2P1i>tASlcJH)l(>w56j56%ABah@R^--2=w^S*`
zsVz7TX~qn;+ICQ0(bO9l*llxIy1j_Ygw&jS%!#MlZ+>$FHN!GCs&T*#S4R&G^-x<4
z+F^&U;Ix@L?C`^v;nr`<mMui7?y<#-m)WeDGwj#Dx{mZ5jav_gZOpRWd+w%iU<n*s
zrB!-rGhCFSQZb$=NwU0$^EJD&PR&p$NE#eapGScjA36T+d4}{kLKRmkD8}~o+W7Z=
zq3hm#a&TNtDK)X=&jqDb&T1Q@pwlv}GTJeYW<^2lAo1fQ<-~@3%PY|uex9DJf8cts
zdGp2G-A+FMM(I`6qHC(cthQNfkcy5Sc9rcQ=->c>8*bc4Nj7XJpS*_BP&)#%q^T1~
zOo@3l1))Yzb*?`(DTcrMV;g_s^Q~ja;a1_!Yu(>xcdxgh$3DqAzVOf7fiZ_>7Ho1}
z+!w<Yp92=Sk{gxJ+KcSx0%y)WJ-caIwbAF(^#jWI6kgvbVfXCmvsvsDj|^w?u}X2@
zpd)&y=Bp##?~sB-2VLPhZO@*&ycx8Rvv-AC{T}sY1~{!`;llaurc%4NugAJOJJrl)
zU-bz5<o4DM?iSQ8w<b3K!11b|gB4YNEZS3MDlLg8>e$O|kXMhw$zQL8T7`l9bq~n^
zHQ^wN*2?Zs`eO8(rq5u4O^4U{^<;w6E8OYTz(B=zGr~BXc`RKCuDnfWNC8;)#Hd`N
zt<I~?5+iWQDaD073p!36p*vE>ullVGo$?pfwQ!YR0bOC_v$Su-`rBq(*A3@c`S2w+
zGCCaBY)C<bz!f`ihK|#eXF5TJvuaHp`E%)yZn8rUooP$hXf-&fONlrT1DG-Vbx)-F
ziTUtMMrR1M7?d7mkQ4{I3z*!MSpidnbb|=OAal(Fvp>0mOd_@Un8R#r&B<1}h09N7
zvJ8GN3^`;Kz145q_^mIsCQ4~#vBWjw*+jICTR%Denp43bB~=q-U;iFT>P{OO+Gjue
z*>5Ou53vV5=r|Z7A<{4@vB4m}6}8;%9o&Jz-P9JGCrtC==9_M@y__+8*kQ}<`fG2u
z(;oUD+VndS{2^PiWG)9pmTcRdd&qz$o5sE5S6_Xjop>T8IOoQ;vZM0XEU9_=Gad^+
zo4g}^!wt9EsSkM|;mY2r-$^s5mC>0wvu9XWcQ-Y`B4EpQ&u(4t$*}}8Ii{6bZqJ^5
z2<$F9gb~4>y`0(GKWHmyi|f?O?b|jnZ%}o`-ne0_&8GjO2`}|wcHFhwn}K)Vy~*a!
zU1*nl|Ht;sXFZvMLbXyaN`p`!6?d%TI46$sTunN!x%vis*uzir@Oy6TgJ&o`h2-kP
zj(`>o7AS}Azb6xG@*O#&xjT5pb)0ff4zn3*lzFn28SG5mM;c%EJZs|ET0fS`+f|xe
z1`Y%vFG(hn^fb~az>r1~Q8R~&e!V&fLmrgUAt`_>1S0B7;uBo@rVzl(i6=hAUmPc)
zO%TfDvYJ`Xd>rKjr<zaWHYJw7kI}ACnKyLVuP9QrcoF4Pm8vlOOo9#{;*MzDJoyik
zNVLyy;UdhR{^SbVjZi({K}Uj5%C6zls$KDmE9|qM{vu7I%N*grKo7!pw@sfp*Dko=
zE1Utg!Mdk)dFditBcp>Hg*4k%tXhhIx6q85YnwN-I}&O~XhIDQ_4x^un^~!>VcO-(
zSJ@2JMow(W?qNjg;g2|-gZZ}G=1p5{<;vyW3BQLvMQi&E?|7@!@8x38?|uI=yX?o;
z+5k-}jaFTC)y;PGHS6rxzrNM3yz)l-?M>_H+pH%o?WFJ5wvkl{*I#$5tzWm(F8j$<
z_TN9c+;;68vEF@yjGXPHpcu0G)Nad{FQ;%ArgqzDgPdOT#V>x_uD*7ywXro}4EGWj
zl@E^$p_rYvlF7Jc1{Y?~B;K+`gBMzTts<<R`Lz}UKKJ$jZa|U0)DI2_N8oXH^#`dv
zs$03z;G%ES|KPHmUixNQVuA*!IBR+2gUz~79h68sC5I(g9_3Utq+wn6_vnx>3Pwr2
z7sb-9j1JigsvpETc9q*+k}}$aRBEPM(U;`L7<|w`lM1(@z!uGcXYyYOx`s6=QmyT=
zCwgM5RA&|4EJX*jiSv!d-^+a&lzw7H8or}YyjLZeJhpK4Rl^Lg&YLsGuDJXfTS5nV
zBez{`-naz-l$D9hrOTGui6<Rrt5&Xp-%5>|U0inKBSszQ>o9$gW9;!~JjPb6SYgk8
z&NHl^rPk+v><hM(l3Z=z3R^%=eaK0?53vUx^8kDN;~zuE8YKkX-o1MmLA#04e=jBV
zApM+9O6MEt53wp0m?|0F*Vk*)ILTyuj7e1G15YP4-fEOkC3m}@mO-;<dYhea(vc`3
z9rob?@BELj{CjkiJ`Tq+ZGd;Zz5Td1+Yq(O{CTtNgcFbA%gHiXsXvO~tN*f#rc+<v
zpbfB<wY!U&mh>LUb#H(9gfmduRh(?|Oqyk87o7K>wrJ5juN}vAT7nBwQEr^v-}^_>
zrdmnI<EcN|a?KUoh`bU8(h+C$%D8%N^r4UDW-abh;ZGEJMR33|=d>%%TrrY6#pl^)
zKkuC?4MkAAcp`~zMt{^lX{OGt`qg19Hl(@hZe-|=yL)`RUQLTJCT@K5i6TOqlLljS
zd!zt&2a=>`eH(;fl*8y+uDOyEyXRSH?}(L`adYXeTWs`%W3Bbr!?}$~DG%M3Nl!{b
z*c63Fl&llJQfk3$-@e^eux@AR(q$aP-D(TCm!z8xxtdfnXU?I2aEQ&GqnlGXQop5>
zk)$DSa-4k118K5!T3ajopuMdw;Z@DUp`di~XU~~tJ-zC$OtTr_QF>K?kYH<TFWEr<
z03GjHb_kv4E-vnDBEAKTPR*X%O`LNHH=PdqG@1+JJ`k|C=N=yj=<aU!_!e<M-y${y
zH8F~%iNV=(x@{)Iz4PbKWQ2>G3XeFH_B59eF_P9oExeL6uHn|MeF(ie<qtXKWSUVl
z`&iI7%@6t;XHcOHn&v=nmn~SZ$OV7R(W`Col39!%QKNgGRLobOhQa7V7TDZ*GZ<;x
zLz8m`G^n{3dw3k6?uWGB=hK*r5kO@V9b+r)>9OJWf8JVdy3tB)Guewy-++_vNB8cs
z%GocoPHGS}n_c7-)bn_%NXd<?cG1LwWO)7hyJ^aH(n4sb=|(y{*VW7p+Vw>pJsTB=
zN93QXoY9`<ajJASsGum*8KD_x*0+x}FvEj`wrBw-vMVe|5<-QY_!OBSb40F&%@`fS
zrdczs`RR|gq3?Z@l2Wx3<>mmLDEtRL|2?Zd;o%(pKS$jsI$<ak&Aq@-#5*z8Mwd{a
zF*_V#)^U@Ss+vGU2*(UBy>ob#jKrWK2_7t6J`bg6qa>c?=M#@`4ALk~D-GA`D`gq4
zuwkd#?QQ-x)&~s_>)1CHGF4zol=gF7um|V}c678O*t77j5I-GFI_}G;xm1IxW|43Y
z_V<%<vt5t{=R=rei8lejtJ-E-x6T+V8|dtezo208egJgSm+4^Hg_Kt`9DCgH<lBH3
zWa^)EF{D09Emye*IBKRNj+_fzq`9MGCIubmoN=OxF2>UYsOZ|T{#HiR7BZ)Eh*E`e
zOwS6Wn9voCHQ~W@U{5X+wQw$&WgEJYne#7y!CDuuwoz{0s<e~;G!Zp9*zwR4X^xXd
z1{oj*x_*~Op;VChN9>yLxPoLL87MUgrGl0>3=A|gYkLJ%^|1Z*nGZuKy6wDAT<j+-
zE?hW6ba`gQq>7p&JRVyZC;lc1sK%9lw6hd<^J&MCXsXMNOqz9fa--6SH7`5dntyzW
z4PE<E>v;UBPP|Vz5=@y?AB}AFE{$<ZLDZDlMaQ<Qi&C(MPA|gr13IZsdD^29w3*%n
z8Xg{|<<~-OGK11-+-|w)4ocLeG%<F0bLEzsZ>J5v&5l3*82j%_eqqml;gh}g{OQkr
z?xi^0CR?}e4ivr2#Nc%1OV-<?9(#%n4~(Kfv+Tcr@DqCh_oeIn<KNzRo85KyZbq)?
z7(+`Fi`BMl-r@L`EnmvqORue7eJFXMQd~kj&P@AO+W#9Fx?SVL!y3Ll@uUZNZFt=^
zYvaT~O2q3JKv=qL5jE17PgrhdqV3evPGIh2kK_E+RoC0%Lzr}AsJa)oUDQGyoh4g)
z>#eqF{cbzylq2nX-~I{ddm1%hlU;S?Z*1kN6*fFNWDlU}s6mZrhBk6q_LLtfzbAu9
zZjCV58N#g&583E<e(Hxa_?6&VPBXvFMz`H=l~=snnh#%QqYS1fU++EVQ#|$GG-?~2
z1mS3;L-5{e5%Y^t=!7{6BG%b&p>w%-$s&8u@rT-Nw{52+ZDrDl2`nbOM(9v05;Z}_
z82YT?kk%9)k5+vbJ>H?q2%&_gffA+T3QDGTJRK2W*~Kij<%$4Ms<S@!txKs&m^?#B
ze25NOC$gkIPDR!Dcy-y_W*awcvx~p|V;I}wHNqi`616d@M^D3kam8=!ypMm?en^K_
z$~eS@KmGmVHgCZ^`@|={Ob2`?a~Ov)YkmYxllgY?DaX=DpO0X-Gn#Y=&5s2L;~wTK
z4zt5nue3$;7u!iEKFD@5a@0)CpqcokOP4}g@S~rJz!Oe5-gfQ2hnepkbeLB%61Ln%
z>CksE%e`|~uP0;MwmWS%FyhGC+qT#+!@<{He}}DSZ}z}Yg}l+ykyaFQJ@Id|Ypz?%
zFnqUNf8#p4p5-ZXX3nz3G||Qx0V>mppD|;;EnmUxGkLIU*H&u4ar^A&zQPFIIy>U<
z!|VYMJc=4@mYs6y3El*2VMc!eGw%!F!(H3&CN34AN`vxJ?_~U+%ni^<o|ye6S3xyS
zlU1iDj*-ss_kYZqI}WkRZsw;LTGsJClC-ubP+-zjY~r5FV${(}Py`Ke7v)ZV$fNh&
zXdU^U_e?(3Xh?Y@lb%d+!;*fhG_f?aOYa;-7-g&+(WLZkX&JW{zVvangJl?NZ{A3W
z(@JM>qpt^2RqUegKMB*+Uok}|N$3nCB-Gl@INiI%WhTa|wAsgZ)AnZpW7Ee!!6LO=
zNftCgZX|s_BPm25&A?`F*J~NZOh%I~{M?riMB4aFfQj{|(9d|u%bsSBe8edpliK^G
z@;FVOoh<b@4k52FW_}1Y=n3=(=G$qfKa3&P9=qtmFWQ`0b8Hxfx6)HzbL3HW<eHU~
zxQp#U$DZU9d!wXl*RH$lI?meNxod~@5A?CrWhP4bGZd^ue}Ls9)Ly%G?X(5+XHf9$
zr9-Z2R((FQ!gFejB_gw_Jq|}c+88?SC4YwKyD+E3EWaia@7%iG4-=O{X})7ME2mc<
zwt`xKJ4TpD)QF@eBCo#sdTNb(Tygtp(w%(r@$jOPe#T-t^AALs7uiD}`Y;NJ0sG9S
z{)?KXi#YaCNNT`UeJ>~Ne);irddP!T;x=iC2Lm=EKfDrEW)yS&G%M4rFEO;Mds9Xq
z{&;IU@fepC$wJ7(fs=9Po!h91x58WEn=~<jxwK`=_vsI*!;FT~p4McS#>~r<cw?hw
z?gF_AMvoy3hcTiygA!~bo%22R<R_j^Q)4$(_7H-##@o}LiAo^2i`on}QUFI`LUI>>
zH7Z3}<jW(<eJq`L?Y~<4=RRXCT!TEieULd3Ca~_j)kgmAZ0mg2t7wC2NRN&h=<1ik
zYk=Y2StSKg+HP5U3-Prw^SpvmxrbrcW`<`qF_*$aza9-Ul9u)B*Wur8^XBRhZ#ulq
z9DWH?10Tb=6d?QhhOM)!g@S?$g5cYN1q@$n=#fXYtk!7l*s-13cnQs!5r&GnpTZ|+
zTiwqK7vk&e>|kzVkIkDun>Auv>8s43hEjh;w5hMhi5@iJ%JdBp`X=}Q06+jqL_t)w
zRODvXlpRV<HEUKk&9Xt1szS}*;(d$Fo3}7)zT1vE`fy*KGROqv=xD#~q)E4Usn&cA
zv+k^!IA_p7?V}*zj4542)5IK*W~nQ5lF1<Dy%uG5u#aaC_qI3D?q5L77?YQYXG&@P
z69tl*0{8Z)4~ujFHrAxhF&A{gudzGU+rCG<+GgCjkyW~!rPfEfmNQSY@fI8T>_=?+
znNNYgx=Ne+L}H~VB;*vf{wLv*$pv37<OM>b?YQz%pRScF|9+HB6fR#f$NI@z;m}B;
zuGQ8kt48W3Q4nU*HB(BJ-NfP+Ev;62xulUB;-P=%OC)AYo5nc%23L%eX|HOXQLZ3_
zuklW}CMt-SG9JaEU(Z%zW^?cdzp#-ro@LXPAI^1JbbRTlmu5BD&<;+q_|<o<<)q_T
zN~CI0wN)(wl6>C^B9`e9WAo;L8gUY2y)aNp;VcyW8O@3I%smM!fY~ED1go&Z<9v4h
zskDw!9w{A??Hy;8z@)8)H^qaK;}EREg+eQw1V>LOvCPV5y@D;;$s=!sh?bN##j7}y
zJ<=Lsi3fhf$fCk1JYSVS9;Mt8ed!x%%mDD8>XCpd{JlM)AZl89nkkUf!0-Rag;sw3
zIo7`XNE_eGs_L1@BDHwmvNhIm`Nyn@$=z{QV|x=FJ~(?q@?|65h%@D9C<t-60!ap>
zAk3s+`dgn#U$(fQAkZv-O$$gtl<EnOO7=3%tP1PXRpn_0S7Y+cEn~FXRr)asq;YTo
z>PZxZZNGlC^j(b6B;Xi33zac#Cmm&_Cq9h@jJL5rL>GNB^hQFIwL@RIlo5+T3N}W^
z0ncKNe8fN5?<;cEk`h{VqE#a2nHaTOZK%73QJE~J#~VVd*wU+#k7gk_B}@`#@4Pa>
z7O+4%sDwex*N7Jm?xwJ!ArwjQTDlS?AGloM;61{cD~ITD>2)||rsJxG4WX2K&>UH;
z5<lX>U(tI!C<nEwE|dvHkTQ{unsSIp(G+w>dgK}LWB5HE6?4MHA`gT?1=slc?Kb}U
z&yweyakYm+7k-v$UJctm<|LkOx&pdFAFM)+qamKDX%X$|X@GO`Ges(bCGf}`w@soT
z(qVLy3|6M-$bCO{A8^$sx`$e&4#f_P^_XgC55(Xhvsu_O8B}4!sKoM$_Ln}@%UWxP
zc~tck?h9|8wam)z|DcWBcq^2tlp{0@t=2kU!nVJ(NGOufNhw72eTNk<0#@+#Pv8@d
zgimY@&PTZ5my|DRSztxG;}qU_B`67(lq{$co<v_V$5$|Fp2grt5f4H|;pI4-=%78~
zK^cTwtpfRJm4se@q$ql!&7X)jr!RBfkB=7$`vE6Dp?>5gaWT9+`2EWnxw^w_1#>Jk
zM@#G)v`szKxkp;tV^5<nm3%Tk0ShF4lZ&AEQ+Vot<QM5vJ4BDKQsJxA!Wtg^gx@3z
zA^=4kK*8j26N>oD?a6e1eVZS8tc^bT=^P|?hgIh?qD9+zoC!TmRSkdQs~io)P%yPX
znb|`cOMZ`EEJcX~?LQp{3O^NQv2{u|P8H~&yK?{$Mb1=HwlFa{wsw=*Yd?a5%;&5t
zEpx$V4ucET?N<8G3$5wM#T3%xD2Fsd{~@D)NM_b@HrMz}aHf!-dgppu5GuFH-0ve9
zlps}RMay$$TgywH>XSiLnseF@P-c?IR;;%2CqHE)EEDk<;E`9qveasl`-tNY;+@i(
zO5Hz5B!9k0VV~E6tYsPc=8vhrHd$pUCnPd!UY^!s&6{>m5FTNjTq>mbkY)xFRN{|O
z(Ww!dKcOzUeWIxdE#I(DbS=8n6l5a0aLE;CB192U@}0~r`Q6u7Nu*(C?WY}UBaeHM
zl{VgLHdk9Iskd0kO?_e`=Y5URWw$q*OLRtb`DJX>zc1{6NPj5@xpwe5a1J0~x&U@`
zE&a~7oogL4mvTMEfH&()bJ*C$WptJQ_k3$v&1wUdGiktQOe3xhg4ST2f{w}asOai=
z@*lMgWV?6t(P>O^Hp151i;<-e`}=im)!Lf5v#seBPmA3RtRJGTWuFMM&C8Fl=I?&p
zhS-Qi3)(lvs5J4`x~knj>GP+K|4*9u-zjqSB{XYpEV&u^;`iv7Q#(v!Uk%%@o2Sv=
z-M}tOR#>#N0d1USHLG^mM5mSm<85@Cl=lZ#Z`aFe$bIxg+{bGJOrDjIpG2n)S_BM5
zP?U{`vh;gXCix0Ruo;P16aEZ3|9FQ~08)X7xaJwXBDYG$MXo67d>9MSo_nT^Y+^mm
zJVwo^TdHhN)gbBEdp^ZT*EaMfMx@jSnSj?ZHvS0i;Pa>CfcI@(jx+$I`hep^f5T$L
zfBhiaA69b8|DbogReNy9vNeD5ENfl8oPi;Yu&K|Yo+pP8Qj17*BvHkuzA&%zDOxbc
zAcz*>h7L$BB_GJM7q(&7X*?m=c*R3Q=T9I<ic)l`U0K#`xU8sc{hPn?rSKZEsA%;f
z!+yrtc+0hyM|+hHn<h2A3@9jCmXL#=|6v>`ij@Y=S{Tx+?%r!dAOCVTHp$BIVJ7P4
zGD*F0t&MUWcIy+kG?o#!CPq&imErzN9?DWA4#Y_>BwvL`P<0Lht@;0Mij#+TL=D!2
z+)D%e{HPyF2hAe1o%}$vSG~r@?p$Xr3)mK`A?F=~)-q=qW98@A=ykVcwTO^TfjC8Q
z@cT#QfDa)z#3;#9^GH33H1{=l$<M9ji=VgF6-QV}gE1;3S(V_^s(*i`HM54S!WGYB
z8r}arp2=jl)~$!8nz<H7$duP0;g$p}|JGMm$z9Mza}6Vj%am&h@A`NO;T)q#j55MI
zU>dDpiGBM^o<c{Bj=GlJv&6j25{o95WH2ab!{>jAtK0VZ;SN)gR3A&D^4I$wwEbZ@
zph8g1Dz#!X0bSuB>d}{fnA~OMH2Z)`6oBK4I0$1SYp$MmrnNnsW;PkCN$SQ<`TkQn
zxiC(qFo9F>DU6zcZb=R)+WWF6xQ6f4u{HosZIuYCoylq!*uqfXv4=8A_zoN0xzWlC
zIo=*69N&)0EIrJc&bz=y=&&;@BA(~zsyE8#;PWTpKom$xntVV7C(W3b7}g#5!X?(e
z?<NLc4q>+-2UF9GZeqq?3vpUs@eFHX9adG%ZIzoSmg7mt_A5P80T<niX2nw!qpZkI
za;+Le=44Ma<aRtM25R;US$*|{BeB$AWGh(YJM|-kVk$53sNKmXWNUx+88&>vBdxmW
zR;$it2?`@!RmRENIS6Lx^&eq*!e-!U&#Hgr<qb?#oQ`Yi*be&qVL70jOiJNhV)94=
zsFm4+S6pk=x4+9;IjO|9(+Z&AD=+G1k?KY(z585iKlwo{eP<P~s+~+8oLV6*K2jXd
zYr$o(GG#JX5Iy&&U6O?cR^UD)lvWSOPZ3Cd<*5OI>}f=l-0$m~>2=_}FW>|9hiO%o
zpp;o+-h9rBy<};cK`|pupu=8T!M54;SK7dTevjjUV5XKKkS-q^k#aOL^FZT22>*L=
zK*}Nc)>1pQ<yEk0(QD=Ioi_5X7y3C@6_y!Q+1F8;sU;<wZLsMWYk$t;tiq~fO&k~V
zaLVkQ(xZ`l3BBI*h`?}>LezX+83GQvYtC7y@bCa`Z1fjcz!Xi6Cc_Y)9H!3s04)-B
zk>2shlWpV$FS4=qoI$&UU5BKsx^17es02FaT{e2f^^RE?Qex;%iy<S)2N>Z&@cWSi
zsfiLbNd-+QvoLe$Yd^BqOTKBPRcoxupvyRgS<|d`=Diqz`OK@WnFA^+tm|*==!dKZ
zc|~)G<AKSW<TYem;j*yqm?vNUSq&0Eb&)!ps+vZEa0u`mQwH1MaQ|4TMYR4#m+W-E
z>4l7Ltl?CPy;hyh8edM|(5WlFfc)I^ZTv3Q>}c^GRil3O9(?{Z9Pq+PwK%fk6MEG8
zEF7}Yt8X$p>uhVA!Jd-4I9*cBm_s^gCf{P!GhbkxPkb2b>m*==CGQX9NwPXwn4)Y4
zoZ_f|LagiEy#|>WpdP3m&1AM5zDUSqkO%4}%xcDId3MtSR$KML*V5G5X-#ub5Exjx
zn`44jFgJ4PcWmfCFQN8efRIk0Mm=(>4m8$-@b?b~B;@3Sl!J9t)jN0D&^ylaB|{}P
zU{wbx_;d*;C+6!+$d=bW*D5+U%xjMWr(h~|np!54grsl8DdkRKYBfmGzSx{YCs^(L
z>=AGT&^IB`SE<k54Awt_;jNEW;KC^!N=ix&Q0|UTLhd0WenC|j5-h9PL}}Cc#78g$
z_#zwGu$FUZI%C*y8|ObAawtc^ywyfIBA-?-wGN*eeKH?>{&*Zv<}3HrC04(KQ7cBH
z`ak;(YyI9gthACt%V}EC$uL{kZNuDGS^C(&S=$LmaqOp>R+?WASqYjcA|*4K{1nrH
zG$|ThFGfAi@Rwu#le>5T$0xk8mk0bVmxRgOqyw%XN%(~>l%a??M_Y7tSa(Q*j-_Ut
zI*bQNgt+Ctei+T7=dEkmnwT4@Qo^*q>3KepRON*83MEigO)V72m&CvQe5-D!sikeP
zgo~vO_Z7!M|ND~zF0@Fk#6hj4GNV?*Kf8v$;<?tw5k94@+?%2*eqpCoX<7|G?upj*
z%*Xg@U5#8yJSK?91c!{`n#|=XNCj>B7JM)kWYdj8K%A*?E_f?k)=5u_k$m+{ufU2=
zBp-~qJ|rTuNa92I6e+=L1URaxa2Mmr&1%DrXhci9-{_CD9Jj{G7ktFVc5k)j1@u7F
zPvP+EW-UYc>Gy5l1z)uiCl^Zn6>ro<IlhCYKO_eV`JN5ak8wD{$Q%AWj?`wgP*sNv
za(1!$il~aUz5NyJAe~3YJPx6gbkslf@=_;DH5r>2svbvftA!8UfS~Y`Wc#1v#KgBy
zgCqn2El<HYtb55l!<3nXEWC1e*$=R96*#ir8u6K3Y0xlXt7;K=syH>W)&7jf*wFJ|
zXk!~V@nne~jL$i)G_}~zUV6v7ZRkf=00kro5RSaTCfmX155s{FD&bZ>jyJ<=YWbm$
ze#zQz_^CCq8ryabQu~vk9LO<z*9I$p_<hWIaLyLZEk7#_zb2K4dGWh{q}GdIU6@9h
za@iJyhml0dz*`gcJRK@y9hnCLz>(DeC04W(5idD~b7~u^?IyPf??}>*oF;Kj-Uss4
zb$fv-h6q(PSJ`h};$XLqH=Jo4WW%ZO`&hrD6An=#R{VCcbYb+F@3ZmSHdBi*zD}(Z
z&q3ul5Z)gsY{){M@tl!kBNtz8<@f!YHLcP?9GpanTl1n$8>J69`oyPO*K;0^BH_xM
zM-y%SarzmtoAp3q&*Pjrlwfkfm+<;uCciGrf{=UWCiuE0Rv?dn(%^u@jz;^z)jH@v
zf4+yA2>I9P-g0wGAaI)->zy-v_^7kxZOVecdIu~J<OFIMH$Z*QGV%L0Zfb6E*g?y2
zM_TLW&$H3KJI&_P0};C2Y}I69v3>A*>;J%K%zAgDBrHZj)702H*x(8t-`~F#ghkv{
zVd{s!akSFd4Y%9KbN<fS%Iq({hkAoNu5i3;i9;7OYSr=fmvbKTT>9!PO(pM?xB6@B
zjMwCqFu81GW|uLSN>5VwW6uJmujnF#!=$f3M3W>#ZZ5|)krWOlsf8dRjer>vjbcG?
z)lYL*dy&SY*4XL<3?WuG8RRwM9Zwd&0QAT$nZv<<GcU(ebC|*q#EW@3xUg$Cp`P}q
zKHREjy@Jg(Ypr=HR};X9v8{d9vT}{JU2vZDe~JkyX5-`1N8zY(z{4N>`TaTIGDM-N
zvy;_MDpPA$Ih?QmpUz{B>K<!aILpdCj7}j_rG;%a#N8y#pJb8ZiQF&33Wz2x6CztY
zUk@~YY63V#Ix_!|7UD?onQXdMW(Uw$GOcg8c^@Q+o(G0f4XL^^aIy*Kpm7)(0;y6P
z;cKQ{+0v}j2)Vl<#X1>nQ+S5+6hi8?NF&llJ9LPdP9lQOiRi6wc!3SfK9RLro7vOO
zspQaEQGaA9^Ck@a4*cMX8r!Fcd9oB7blVROAVxsZxh7-Bkdbi?mec8&`_BKGwSD!!
zyr3H0#^Kq(RXO}^ls@CwD_?1y&p3m8WW!rle&@o}nBVtJ#1x|D0gKpz*1+fP*-wNI
zg$M;wZKKEgu(^k84bF6zg78YaU3TfscJakmu`801hjNGg@|U;SH@@)`&gZ2)si9Q<
z@+WY~UUZL06O`a1Oc5sedR5^wsWq1xyj;ACuBH~|inePq;nB?Lf~{YCv-MGN>so2Q
zsDlxa@d1wb*HY@g{6`zRnY|-SR(VY;L<gVyfdk~DN?hdzrIs$+)XezE_b#{QbKYrf
z%PF9?aGn+3&GQ&-<g`c*L$$6qJfD>j(-_TSm#7N5q6jrmRg|5gA{5OjND}lMVM460
zXyqTEz$xj=+-35>VNWpn(!@$_vjEbDSDh-?%t6%~w{EbV_YBw|$7|lfRmr=#ui=(k
zIFq+)n%#8s22MfOfpYwL?IPrQ(l^Wzv3pGX0W^qg^hyLbx?_jv)ZFqzkBOgFueCku
zL@R&b-8Q`C4znfGoQyK<-tyuFD9LTs|Mrhj?eF%*a=xN*64VEs4v+&bR9P4lM6=~%
z*E6s2S8uoWrUhm_$dcwYbU7)_^L-4dwEgg3tbEuqzxqN)n`$~_|DPgoQAWZYG4#h}
zO3i<jhW^zcjbPHN77>N4<Q6A~cR$Ro5Bdtf&x6qZb4wOj0px+;?c;*UMO^N(Z0RB!
z;O@F1PH~?xtIJj&zQp>tr$Oh=-g+x{*>U2xB2-lI@{tKd5@xRu<dr?b?;SCDcylgG
zSq(tAf(`&N)%&YsCATIr#XDIVrJ^0!^^&LA@N=JUl?}I8Y1uTdEXMEbX9Q)9wSMhO
z*7F}<qASIoXDxdGg%nF9dkLrr4?g?B0ST1`YiP#0@DT)NmUP<Yw!5tFjUQmXW0Tne
z_HuCNq_|vWLU3^BCTsfSN38wPrxKo8-vu%9H5ZNC6je(iM<T2|Z+rxwhp83DSsPq~
zOU1{9-%HmL*GO#Pj(Pe*>Mv0t$S>(Z;0ZUEL<wxXa>T-+z$0vES+Vp84oK!6ykFjE
z?M&<l_hA2+ee)}q*<pt+;xOb9j_c<}CJtDR$R|rgN<*HIBGLD5Rv5kYcwNDY0VxS|
zG9#xrTjyI}%8iGM&A6(dG>ai#7B5yeariP@I$Pd(t_^(SQdA3^gwctP!mbA8!Ds(C
z;JJYyxSUap`lY98x(?I8{pgpi>(U>1fn@YMB-W+n96rqo?9o@f+Pa?gSYNo9?Md(m
zBQG=l*UPiZWKPxOd6bl5a^F0(nt&dc6oDMqZCQtKDbTpiCeFZ;f=K4VEJY@i3%7$|
zMix~Y=qfLrdj5=OpG5Ggb+WjuOb7i*PdVO(2Ba7ZZ0B|BIWoV4rdG2Ja2<3i=|xDY
z?i9BeCBH;S2z_qLyagIhrsc^R-@uUpcmj%XXce!{3)aoKZQuWZ_5a0lxgubuRr)Cd
zn7OU&;so|NOKsnCUTULDF1D7_PV%Kds^gm)39gU$psm0G6)w&%vO|M8n!GJjgAaV}
z+g5td`>c(HqvIQUQ61bFdLP|*yA2)ta8_bdvrpqzEQWYhXoU<2vGP@~p7h1!c@nOT
zzw!d02p)FAdpP&ba0Re~O9tIaf!ofZgdH7ibSEetarAKEnb{-O+AwcaF^W?WD*?18
zNz4q6{?~={y3enbi&Q$ff?)1knlIY?f`sZ8CchxF7P$~-#6=wU`5OvU1e+|p+=}j^
zv7ls_RUbf&wLI=2^hf^1hPJMwsl{OrszRyxG$f}R(FdP@u8nWn#L%zq4X2bVqA8Mb
z&^##zG?}OVpPE?chYfS9ZuRA7Te}vFY6&~pRN+*_603BF$&t2?{hgJ#GqI|>8w$)$
z!ko9mCyj2RqXgN-p(qdaU<r4Eh$fnBAvd}M-7g}=DdbPT{AJ3Lnp`M)a!e7Z!Qr8t
zh%rVQ)Rb~XV3cBvPM2;|WwwU^4Da!$XkGcd8j~%v`#0>4GBbvM!6FC8;)d)e;VHt&
zoZB;ncX+w##P5=-Oxl$lFMFzuyyRsza>vb9S}~10fH6Bb_<{9Wog1#Up>xl((r)g%
z;F6po9!GZY=UzGByo71qDZlA98-DaZ(C^@0#1VKr9HUz0c^&j&Su1zpC#>xu$5V+b
zU&y83p)g9qE~3Jm%fP)78j`6{ylSRP){$55o2Yr{E{?+Sx*#NdP#7P@VyI1kF5K#<
zsLGL&L|ctnHk4k<phpLT%AR%X-A!FwN8km4JIU^^Y?(QWyx<}u3=97_z$=^@e{%+f
z_SgG+H_B<yY`<zXuER2J=d|v({*4Vh_>olkYuWD{yAW+tuQjhc(waW=8SDGVMV#uV
zgUU4gn=ugN-s6!%9el_>j{1ZTUIMHAjclk!E|+PZY`N3=|L)&d+H*VW;g~Jo1ux0x
z(#mNz%qrc=+uv#(tkTtTp`4pZP?hhKJxG&ypX9>jrV&mOmxoVEljJ0QQz+SUGzu4T
zuJKPvxo$7eRp+VJ2>4JGALcDOh!ugr#l#t;<V6*gl9@#)P>&otf5|rymcESy3r=qk
zohQ_zPJt#x<`E_c|2E=wN40U;I8{D$v2~yKHahLgIT&C&htW}6s9Cs?tHcgH%9`K#
zZX5ja4<dCer;i?FmN#|8)J6G!#uD6)d@WJxQU|bKgXA;+#hAbKzV&0)`Tg%$(+alE
zvV>g|w54TTHi~i#J>yx{_3CGFD;N{ETD?m!zjp7wxF?}d9QVS1$=bbmBUg?9ka}ed
zcUOrFzvMVzLm0y@w{Z6wL;&?32_U}Gwu$~qg4W*zqD<h0M;;z&?k6WtQ31Z@7KTZF
z!Q{ia8KmQsSuteF8(tAzX0Clu1l@w#a_lkI`rQvOiO7YRo$NGh(;-2i-p-ZG^H<o|
z3tw)dKe!S%1}0o<`O)yDmtZw)|0fMzM!^T~Q@{bWE1I-hlS$&ff4|7uFZ!&t9J<CT
zbjl?Gl?7~I+sv(F^G>wxcfW=sW4WH10~x9sKyf^wY)NLEsSSK~`I&;-9!>z6d<iUY
z<)L!i)O5&+>>)ZKk20q);U4yY6HJ7`)%&M~PI~T{fCbcOPDJ4&%(C*hO`psb-{P->
z%HM;fa6(ZulO>H!n+68vIP-AIoC2B}h}H&;(<Ep+;}o+CK4gPTB9=H?tD+3!-jUK6
zx5<{~+3*=>+sM^7`Z1<bdNtLG*lP&?j~SG!@rQDd52~0;jQaFl^j#~z>z}Qiq4VlS
zu1KTADRbC#nLDt#MV=)l=UDR*tGPXnnI@eyqh`8<mEZh<!(ISR&FKkwDo{C25~L<a
z@>2nq0fjrZ8nOs1d;aD%O}Hh+QqzmJcMhmPVNco@)W}^)(@*v)f@GE#PuRnp;Do#O
z)Ns^X+)xKn01qe}G9{_QRgYp)oBAUqT|GtyPW$U8+xR=)VmkA+w1P=2&{kRbTV)|#
zClg!4&pyYh>$XrrYT}3}>oRjv^#2DO#Xph>voy4;brVAu|IEsN`#S4l8A)XuSK_Ha
zV=YN_c8d+{-fT@@{gkynEIQ@mT4MqNDF@>xUhhkE{VD27#>sm5kz)oclV_L{Q*#e_
zsD~-ykbjaH1sA?tzJ*h=&g=aT05bR#PG6ttk3G4%xKIIvh|Y@#Qprs6yaTJwdlq3%
zQ|1;C1p(n1c!VoGA{{Q_k;!}V;7gc7bIPsiY}X$77b10c+3VdBGit%eno&(+(FPvx
zV5a?5XWGD1pJ!w1Zn829=e%XSeE{>3*0Jta*8dM5w(8w?F)Q3cniCeqq0XF0FVd9!
z|9rl+5<an3`Ay38tGgYCueipB{_6P*^>cSQ%SgmmN?7<e-3B+&QGf5dt?Mat%BfOH
z)ZD6v)jZRvmssaFfz`4s8ThIM)8kJ3kk=FQGT<k=D__DdLy+0!Rtyt316ETLA?vdH
zA74bi7S`P%y6jiuW||~~;StE|%`PJ0pTTAEd)g8)04SEsO`pJ7a45u)yDF~qB!ZK@
z_$|WK;&cEBuihLF*<vkNHHqj4%gv*<_}5q4zBQ+DeZpF6TExj3{Ectwv+}B=to18j
zu)g<PVAi(>fgpRmV;ZyV!rHKM!DIb%`VqhUfsLsTCLU->(dbRL+Tb(aY1651t24MZ
zTW9I25M4Rlh8aq)yyh%xKkKio!Zq0%_KNb&bJ9`P%9{xcQ=SoDo^6=xp_3dIKwO53
zd7a}BRJ<W86YVv8c;)6O<WmhJcU2PJ2PrnQqUIoB07T7F&@^|;6h)%tc8^8$g+2Qw
zyS~LoVf@KHML(}avYMm@R|6nGR*NJQ6A?v?R|}%jkM{3~_sXeB82)WKe5FnM{5v^1
zh~s6pvsZz85dE6B^*!7}vBp|H^by<lfeX3Bg?qDoWJ}nz?n{$mlM$WA_UB@PYhGun
z@YFofxK2F$>v!6;ZP!?N$x=>Xhc`f!sD(#Z_crjPXIR%;UdH6$4Bv0=O)F<cfnRl*
zR&WPSYBbf8f_c^_O942^N$m?=dITD7xt!+0pUE&l4&xPs6hg9I4__PR4aE>ZYF-IY
zW+9W{DG`y7Kw3nUMU?zSVO^q=zk6i96Wwy(%;Zg)C}F~d__K~(&?8sT6if-2dL_|4
zhPn||Ya-(&RYtZ>INF-N`w2$2Sl!EVttz|Rymr{oV{NOLBcTJ@cj0%K0-}^u&hS4@
zk*JvdtRM1Na{^M25);DO&S1Olw*GTIZtd6q)S8$-8fP<_<iQqo*%-C(s2yk1|Mhk3
zF<-<Ki3$<+3%ShXoT}xOc&Yr$3c4CZfw3-TH4ho^RNJTU6lF0vyIUq33X|eYutmGf
zbuUPA<&j^=y<ohP9-0$cr3gi21W>;tv`UV-h9y`B6gd&dsg1-4hn_@O*y6Y7mf$A9
zBG}=VAu1xs%tbuJR0rV`pBi4_RYyy2G<9izlBRYpg)D#mW7fZYJ!^)T^kOk)MF4jW
zaDn|1R(kpCY(R4)WTq6$w}aM4Q5WC;ev5LEye7S&9BgLl>$kptzQ8&z`kb{;a8xKj
zw930Ww}YFYdRe|c)7q~1Co8Wx4CUao8GOgf^!IpEe$?{Um0}_T8&}+>aN?)uZx*CJ
zuY=pk&A&DL67YL_6Kz5qZ%xLrD{RsSxl@CvgpN{IiXXrkablPUAdp9@vE`&Rm?b<Z
z5--ISx!g0Q!j?Y~dDtOLrGl3<z~(^2msylxl?W4R5&WGQg+Ojpz?As#6E4x0`VQ(a
zR<#d+xOHn$7pKM>YqmzXEVOAUZEO6?Yz(XJ8Mf}ZE3ERozp=p|{9Gyqhg5^q|9RKF
zMsOoN|C{Wjgmj2|e!w(K*V)@(Js-ZvnrL#i9C|b>a{GLhLTMIPsqXHzf$_Vo>$1;S
z>q9t&UE2+KQop5G=PL&?&-m7Ufl8h8Ot>d!Wfne=xIETyX1H8Il(&w6bhun8T)g2P
zvMU)8m|*I<6W9fpzkx67g9vHSGXX29au@Q5OsgLw62T-m{1WmE4j?+YV!6B&w(-f_
z9Bt9RXm+&xOcFLzl8Lw*fU%|wlaDKlDi)dpRrZi{a#`rmo8DkU>*%zv((o_Mi(ZC?
ztIgKcy2wV(c(x7y{2GKKme)vOsdzyUYEwhrNaFtryYe>j&{IWz>A=0x*r4tE^jEEw
zL%}<!IYt@8XoBCRcIGs?%QjHC+uFW$p|w2XB-p5qEcuMT_$yv#SyP|y$uaQ-;9M*e
zQcwEIFYpN_<#7&Qbc-;d-bznk5-!J)g_k*BxtnA+zOF3z2QTvGm2|=tr05aVi-vq-
zm9G5qS|df8e??IwCcd6br;^B(WH-C3Yz2GfCw~VNOgwp*%$_{zz68pc-UuHE6R>K>
zWL8rvi%hd#w6jZH-8StXUTkA8V`1aEo2|NP27@rb+%v=}j-5VgGw{%t*zh$B4Kqg~
zWM#ArQNo;5FUS8s-;|}_^Fn!>F^6<jq#W##*>@5BuUEavIvM`1ZskBe6(+5ubv`Gs
zYqIq-|6y%UdN_PiZ457(@&dGmugD0`r!1aOA;q4>9qz?EOy;<pHNgpCn3KP|cz)*T
z2)`s}`X;|{OR_FFz0ooPCfU8Bh^Y<1W;KX3sfZw%6;9^dUqs5S_H`M-EpxB=d191=
zcsxk5G=AmT08nSe5_l&BY;?%%qEC!TkCY^YS-+lN{8SmG@(nYy=U6vK&<s7|38tek
zn^(*rG-=jo&5|W3$qpNQ#yK{^?n#Bwxm@bgs5X*l{O@_hN9AR_HM+$v(!y~IHhA$*
zt^DHGTIb?bEIu4!c3iXI6bg%{*}$edtnzQ~v99Mn9`0~fJHxqJM$*I@9l=N@^n}cW
zIhdW9-IIuv7jnuv?33oL%Y-9Ss7W7hp(r0cuR}&^@|Vd>qD5&|q`5sa7n)X4In=|{
zX41nms+S4^Ao^6rsFf59O??j`$Q33b^#D#l5=?O*k4!0SBtG2q8&CMxY=o|V>isjn
zY~*56zXlB6ai_yA!B*+yZDv|aTFPHqy2QH9f2;L9;B*!<-okKZ7ve(4eJlGLmL6{1
z8?Lm$Gv8@dwrQ3)xkIa3r6i#GU-5`PAtVjtIv+>}CyWemq2}0ApKG1nhuAnL@>SUw
zH!lBGGi`)jfn#s|d+UDrGi|($0~jP2>^v0@b#8LXE5HknEOid<SIn!TS)Ojsf)(iv
zxR9p=7nnod<R9<}rv5vz3lTExeJ|3YlO3gSA@iXJ>RKQkfk=WLNJOMwDMWgM@T5dR
zjFSmWg!cs0%pw|hY4#Kyc7={7xDnx#Tk^s$D`nh8ojUK>HHf8G2|=Udsi6tnqNVnr
z9Wi69SyD<yXry~V+DEPVuoc#I;X4_fIf^?-m>FgwQ887vu&<qSwz_}wL+g9f2iO#}
z!)p@p!)p>R!Qh|b@u(W;t402&F@)fRAk`=k&%8ecA6V``c-e1k^e>)m(^?l;rLEP<
zeyJupu!^}3Rvrw${FOHC?B|<x!4qcCl|tpP8k;r#dfNSmeB+ztOD#@^luXF4=o)6p
zogU$>;grceGEV{Uq<oGr;U}})^RJXZG8_x`y?`tf?hZ>HDu61O)Z7XjfrlDI2ECy*
zf)BSq9(|F}wiHkZUGs!By`HR$P;lbGE0M|EM73PAuUoR8+%0IC+%GsfN$`ZF7_4AU
zwB$~aZ$XcQgi6KrZVYBN9lOT5e)&Gm-(Y3xUA<PF#}F_9DjV2F%Ha>4-};*MyzQfA
zTqCAs-&OiC{=f<)mGzHGWVroFeie7*r}8wFs*R+GL_z8(q>(GHwUI}?*rqXTTb(tX
z{ulE<<Xvgy46iwcp7vau_Kug^_?%gOC9?_?pHl+LWj#Zd@;Ve_A`c>;gTA0Khe>|^
zb-HuEM0=8x`fuP%=?uS+*Lpr+%lM&hYWav}{UzmyiXy^G4^R+kSU4mEc?X@KBtH2o
zLPy{X2|rg9Md5H6u@09dP_R;zEQ}HxiHI<DiO+C^fAY!U6~A6b*;oXH)O(=D@PR3#
zCqtY1Br3{0lW}j-Y$By&%gM)C^QE7+{^|}Z^>D5e3-NsXpP|kcDaoflW&7U#36@**
zu#K1b0y-j^NK|d25gjSiANL8q1aHLaOj|R4O-74&=u1eh{;dt4`bzp)y{vSeYoj}K
z+A&(r`YO$m4?f}y>;Ay&nHXH?n@D{$uZA~IC%F>Qc-nLK6nEr*6moJa=27IQJCYZK
zEd&p^f~?@5+`M%Ga9+qOVE79r-Xz=VHt0?8xM#ITssx0`CrVu(f{%rX!bXGvAZB|&
zkR?S)UmTnueZwt_v_Pa2tZG<IE&l4Ch%>o~_+U`IyZq%BlB`;be|03Xj&}-QY>owU
zit))T!O5Rcl4x>?x(X6rrg)Unw?FE^)_n0r)<1%huqt(Yx;9%8B}-D;4&zwU^FD6-
z-v1dZ^&?nXwF;kfFQS@lI$|^N0}mohZyxvWZxWAE6Sx@Peqcrrv|ZNMS3T-2?9<<3
zl_e{=!fqcodB6kC71J#F;F{BI+Q;5v<&}%sYE3`Q<vXQC-*`N4WSmkt@GCTpYw!_V
zp8OZy%st@pc;u|S5xh<xNCmHW?QYI@@kRDj@MGl3WjOpjI(N@%6XaWpB03ars!4nl
zOAbP+gBYUfiElD1BO-u^%QHjTmcv8>!Q>GX2^N7aFsUYSdrG+A1yOk9;55~H)H8OH
zaE{{M!U&+zUr|x|qi{NeK(oMJ3s8!7Jn50v^rcVQz}*|IJcA)no!Te`x`TCF)Fdtc
z@;;R0^Hv&QMKGf|+7B<im9j-VqA}-J5@<ohTThFqoBR=;l;OEjOb&bRuHvCqE01&V
z-@r58X487FHRE_E({QfpNv<Ur-Fh1*ARcAi|Me~_A9Xl#A>}{@%EYZ|GJ7JMl0Bt4
z`Nr4ttWc2E=b?B{I1d`(^{Xe~!j{6MZ{lqZm*Dk<-#*Bc_$0T?9Ye?~+*fU({L1nt
zzR6$Tc(WQr^Bz<`WSmaI(<UQtI;rWv0zVN}QRkK_YJ3a<sSM*!$43WSXPPQvbz~`|
zIK0B7S8jTeUEgGPgZnqQVO4>YU;{3**E;URQL9P3cnh%PpEY&raVVbjX5i4Hr6k_O
zk{_tFF$i_B&Zj@ds-OOtkN=lvaWZ)uBUlur6(-NxS082NbKh<KoCj4JVFMPWGK|s4
zhsP03EogLGPG8Q4qVn9k*-<*CFdfLRMw)yrnbudaY<+ygR_puQf3eQBoLS7W8Vy2-
zx~gt`+P0ql*9vR@<p-_lfy{%T9BN)_p{8p0jHTp@DDa>7CJ#AhkJr#%bBL#cZK%O>
zKxJ%(GdadL<z4WM@ZJ&$JW=@Mau#-Eki_IXR^cug7JnlS3BQ{~4?IEK*-9)Kk37(T
zjo@YHNFxNE6S*a5UP^ILOG!#VZK6aqb5M1h@IK<~Xkn#6lh!RMoDfQ>5F`zgiR_p?
z7c+u+iO^(MvgMYZFbBc)ZUPLK96GamcwDng$@>!tgT4w?gehXl{4#KFI=TF+KN3wY
z<*P5}=4ha<=Rd(p|3SxnEAs%0S?aA-zR)m+l60(O-PYfqV|}0eI@?R(k>Ax$lZ=`M
zaRN?e<ykTQEMos8TJG&zgg<bz7Hm@ZbFLXR$5swnIO{#u`HS!S;T1YmPf0J$Z>5RV
z!vfAO>%9C!*2<+eqiS-%V=0)Z6P#12pOhZuL6#44O;5@qw<{ph5m*8uQ1FYe3YXi%
zE0Yi08s$WEAh-_i0<2IW)X6Q`ldz{+JLFJ8pK4+%1u2bc5oj0_KG`M5?k0C1rPQvl
zk*GP~2X2^kRDH?%_l?@tJ9jW+iqeqbg9BCDxbYs_!6qXu`ma<8E5#y$Xum-wiORJ#
z5);G*Ba{F=a?78LNev8fw#lvFp2Fl#DQxm-{NkTwV3FYgrLcND2gAs(<Cb8f2wvLI
z5$}94dmY}xeeCNv)^sVSYZ0WZ8RqSS?5;yeUiC)n{Q?IVh^g>Olgm+;#A;&M{#1|T
zysR_8w(qc>H-6AMF5xoGm0YB`A=Xz_bz^W(KU)BHTI;2(uR8q%pBwT6;tF&JUxMGB
z7U%0E=_-Iknk5r?>OCLikd&j~8F^HXpe8FdILVt}B+!94$(Zc9Is5~LTHobVwFzfp
zkx$}_T1r(JTv-%W`5&;7V`>nFF>xG6393wuHgRZmvre2N@fTlwjh%bW5AB+3Hd-48
z#$9#g9rl`6e$#gB?4cd2L{s&0QHn3YKw(_Fp;rVIUkOJL$=6E~2;vDhecdXTnp>Ei
zT5sFOw#WphZw9MOQj<#YY80wL*ds(O0>_`?^+2w$gcpM1qqIq|q$px6CreIy&9iLu
zt#7trMt7PR0)>B8VR2(+=YVxD<8sOuzs3f?ekmjXPhX}*;pm&F1_8~V<na=c8=NY8
z?qS*be^~dI|BKV`IcQ<SUJ5-5x0&Q02fFnP@35wCecn1Ab&?B`^U)O`$WHlJ<CFU}
z@C;DpWz^^-*qge!5ZC363wSZV6;Aj9XOf9xUP*f7AIg#7QkcLc_z0tT^H)lf6i0=6
zQz_)u^U!Mzg(E*}a6vcyQAMbUlqN1?D%njp?y+0%*lSl@xrOFg$<BJi<L&CJHgL*f
z)n+ejwue4yk!`!P&qjvXSHV%F*IskGZQ8KS#Zq`ur$<$l5|a`ggeO6aMCFM|%pr(E
zCGLd92^X$-<(NM?Y=%=&O!g?V3QsX^l|+F%ee;;qyrALblVg>x862VcA{&0i-`Wtn
z=$a0lY0U(w4#Ipch?zEXxeY(_B{uZ!pStj=)30cK_*ba^*bjMIrbFJmr<coYF0j^5
z^K#r#<&HiSmnL90Ys)&S*3+}a+P;QzJo%9n2Bg&$o4k_}=s)F$>@~#@zv~}|YbXh7
zVF7iXOEL7;WFv3{T;NP$96pOX`G=54KIH<hW|L%1N)Sz`B<Sg@)e>HS6R#?an>cg&
z=9|`0xNEtQ{AgOyABkE<UpfvdAS+a?t{^nJ_w^3gtv9W;TW?%vJMP+Ty?rBgJEvfG
zb#+=VRq(<^Gwjiie~^9s%U5!09*2aHwgn5OafYvIA%sB$`dG8HPi;9VNf0j~ROBh4
zkl>t2c3CEx9Ym~@XXK4DH{8>&hbv^>q#btwdx-|FS|+DB6DEC?s%)m1LJ)cJ$4gsE
z+Ul&CHtp?avBT~qG`DUt<B&9ux|bT18R;1_m)Q7|&$Q7W{R$;v)Qd+LB{%7<;dI~o
z??>?{HcjG@9n|s+8uSlX?}sk3mjCzfenihWb2%DCXkwFC6DxE3w{eZd=RaW`PkETP
z!+i;kkL1B`a5(QXyt4V@{F6zEQh$t^sHj99DoWr80Eh?WCxeTRn5(b}O5T~pL1h*N
z$PvWIFZ|P2;X=42Z~2UQ;0ZT{lU@FjLFs43=5T!OoH-0<dxxB~s=4g`g2`kRS!-4Y
zU70^RI&2F#66eWJIoVohuU1)Z^Q&KNu-{(0)t>a^lWgmj9X2pDV%Occ-k$&B(@`2O
zfFIh6f=ss=GdgTwV3=wv5hKFY+(`)*BNz*Tv5H(;8+RLuJ#_dXM8l*B9&$1>pUe_`
zi_+YggnI^-!Qv+ey`Eum&fH|IJy`~1aB@z0lHv`}fJ>vAF4TnaOJ5c(u<7rAjSY<S
zTlFhnV)BrsDeL#rjA3!#JkBq+IkxYy&#=<ZF1FT((w>)~(J#>)iDMNWkH1FL{bE4W
zzyo5MnmR19%C_O&k9^Tu-t{lmeCQE2#%cg<FE&=@YK1^Q`(VnS{&(wq?qfNgM;&=-
zl?dZWw1zGCn#}2|=y+3_!dYo_mjnf>%$`2UuHbH#M^WfQh=m|<g>p!C5}d*%f!4S9
zMlBFw<UZ~-Llo5Gt_Wi`drmhBGTlb$Z&!4!Ant@f7FDq83w|j`L?l8v#)vJZ0uZxG
z9NT^32}|w4C#*(c)ZJt@xv9gBe!vo%R*ZU82S7Yxqg<{t%6UjNVqtJ70XS+35ey*N
zxKXu3bSR8SlAwp1{Pc(>DQz%Ad6t_)0UV65$uF~~a4O-vxDNJ4_<*VZbqtx47+%CD
zzs5&xSB~Q3ykc84-#Xs+2HVHvTKDDOwI-H^S2oc%+c{*maJEgmYt;H4_DZv>&bQVF
zp8!u(!;mh;!K?0)lrAY?V@$u3oiythp0e_fYtRPH|GKsO!#n)y2My)w)>%zzYK>Vx
zSD{rt{9c=W<`c>9R+^9yMIjXeo5e0dYfsS}j^U!;+^;UYe8f~e`tsK7?xr8fLIF<Z
zqzH~T`QN*8Naln)zA{H`5ekuN55+Hqso3!F2xm-7ZX-U`D&Apt8nf{DQK;cT4dwwZ
zFVWC90b%e+*ev9oJNg+FF3lMo(;40UM9%0H!@FZ6G|PNwI0Rc_sBpqn|B5-uU1mKA
zHrWZ|Zuk&BJvk!-KHLKrfDjC<$C*N85b^_`s$^Ft+1#xj&TTHOd7N^KbQI$(ghQam
zkZ6^{CHT_3Vwp|*)Z488)W^jsDa*O;O>uL=xpDtQx7}$&C%@4~Z(wz>I_sp**J^>%
zx9}zc3sLO9QH6#I6y<>gTQBtEq`ma}KKo6pyzcMW@WedFHimN5%~V0d2;9C6w^`-=
z@3QGH{VU%zM%xM{DW*B&pVCr$F{Xz|yct{Lv*0H3xyE)UtcX7l;W8nd3%I)~U-TxS
zkMJQQ30KgU@=Rfi3L@B|z0p5#Qz3Y`h$mpO8U(sIvsYD&sufBf&21<X)!CLtN^{h9
zqc#~I<E$uZ3>_O^VS-S74PPP*5-C$6CJ0gjL|iDG%=w!V6~v23wIlU)K7wnqrzqq#
zvjTWHee=f|CcFquw2>o2BU2?<4Ua1m@fN?6;8hbzv}#5fE%AkW<-=C8HtX%ycf`qD
z)Wad!ERz>hWgBanmL5iPYpo4D=O4Hu@s7C7Nc>dvnNSlcmdW2-@RI@*zn~EBohISK
zOw+ZQX2HN0zQ>;WH(1BwLz&y)-bR$eN0wG}+aUX0D(^VQy3cx!jdr$E8<G~%t>Nj~
zb1yYV%$IozYT$KrWUfw^XoDW7Ezya;jFJa_`R2Hy<`4qholEi$*$LPP6Ec=$PBK=M
zFUghU7p2!EpvM*c;b=ZdA(C9;Pr8$PC`X1%?wTY87eGNsR5Ji@Rz-z2fU0Znbo-o#
z0w{xo5Dhb33oWy%EC>*h`co&}@qriOLP?4V5R8}--x{M_NrXf6L}ulT&*Aa*r^Z+3
zfZ`HN;lV0;^s6?FpH9Np;iNeGS$ZcL{0lDaYnP(H2?P>8(7bFpVwH9N*FQ1Ic8ryn
zOf35{d3dK8>Se4BzU7zJ^NRP_`1;M(qLUlpV{l7+5$%(|h15)96a}IMUP;+#`k}Kn
z__a%|`ogoUW67c15zi8lA>|P@-pX#D!x=yOjW+#tXIiy0maWIhZXtNJ_FpkJ?}Sq=
zDGu+5;psK6V$b+lgqLf|3%<cW0R+#(OJNIeGLz@ws&CMvu#&H0JfR#Ca0xxKMj*XO
z$WwgD?gbh+qF)j6>T$t0)f~<~qVSn?$D=s)3a92lWbjUmGK)xMRtDlB1ZhG=wTUZ`
zzS2-%astzcL9o)ch?wwBd}5C9Dj^whd}R(M1~Ivkh#B~04CCWZz!uG7b}$A+Zcv^%
za{?^;g@(lJzzUGy2=7W#2YjCvkIGde;`d)PMzw0|u}4|guiwwvv@>mN`!1_4Xa^EL
zZssHq_L5A${1WRu>z}Q{#A~UA%Vj9IHOKP%JnGYoWp5KrxIu209ewtTt$p5d8|x!;
z_fh(hU***^{5pb>SHIG_&tZG<tZu8)L{#650V{uk6z6;cNm9hfOJ|-Ko$<1mFM&k7
zl|PSvGjq6Pei6O~ho1|*!zk}F86dpe^?1HwPjVA>DM`qYhtJG%b9p4)n#@+=tv-ip
z6Q9q*UE$^C;Q*7&$5Io_3hz1)@zcbTK|;@0z#f6@LXdxjRBj1z(ome8&;%cLg;Oo0
zhOfdYaYZEwh9|`c|BP{oL4*{}^eBGe(woACIcFTi<0N|u72jZJ<|dh>Ndb$1YXB?;
zlOQy{9(ST$aq;gk!Q_?`A7IVj|0oOn_F8$E)dw>f*id{LJ!1Z$>j&SlzIUB()q8f*
z_Sf>BVuS}~){wf_WR&o&G_6Ljy3U6G^3Ck&nCk;7%{|ocYR++a(AXU}*~sIcYMpO+
zDfc*XV4ki)W8kKizI%yUd?IxP*x*?s@0`aOfAV|~Y`l?YN^tNmMH%k0CFKbJ$a|SX
z3G|iBIh>o5yJStW6|ml#0WRQ^Y=<(a5Eov-hMX5<+!cm!UVBKHfQ58w5CIF3fQdv(
z;y@WvcMn93L?}X^fFa!}OjKJztk*Qcnc+`k(UItsz+hSso-xpsBxQu$;t`(A<Tx{n
ze`<cjhvx+UJY$3-;Sp%S)=(FbW|<rCd6)!3NEit3_|ll6@5Xssk3ZE~zImbT+q;#6
z4(R(#Z}rNmvTm=nFoWHG{&}|Vo#$D(7bRkv#QS-kUZwK@UMVK=X|7}ZMvetN<?mTV
zwZke4nc?3_zXMzq_H<M>-ev=jdV+QT$D7R-FQQ4vrZ9Y`N|Vx-^JJ1VPr}>aJ+KI_
zZ&!dHwfxBNMSOu<ap;LWcM~D9ya&JB1z%JSnWMQRWsm}>^&;4$3`t%id?-jLL|&K&
zj4M%AgM{2?vZ%b2{8NLZZUQ;ze8v%E?h%BifRlZLFhvGJCsND_Vuiz%#A^(>MM9O}
zNKCONX;hNLSiW9z$xcXcq(^ZF^Wz(4`N|%QRYvK}{ZeS0Gk4*Q2VwDccGkvLTSImi
zq6Cvb3FI{f__MFTb#i$#=~bz&)d%fQez=wY^OM$p_j)Tc8CUH@Bq`-b*7c$!M_SuQ
zKWe=n{!f<4BgnqLy+%w!gi~9UFD33=tgPE=`(Avmb=YrNBes$ev_TIv&S+I-)2%kN
zh6^;=(_yQY&@84_r%5;_f|c>br;Z2eb7Zd|0nPcI2TE?j<asm+T;xH-Rm_jn&m<&&
zL67{EE_bWtlkzd%kR>TZ<b9IO@Ru^kU9=`;(KnPNxkVgu3p_5v#Ob)Q_>);_qMPX$
zGeM6buS94ua5Sxww$cY-8N4S3#nNn~S6Lo`+!L{hz#uXuJgUUN6>dscFh)3&Ig|uD
zO`<enmS>Ml!T8)wrHUAod5K|SxH}{$B-)?Gu$ile*AI4A7AZ{j)=FN%MtD&@uFkp|
z^BJ9L2y{LD(N;eHL$>eE_14T{z%os$X4?FtTmjd{UXtd2{3qM@neVU;3VvyxDnscr
z*%=LymY@aH#0Vewt1>LBmARGeJFWL^-12nwPZ@GvW2G$vP{<@96Tamw8`)b=jrE0d
ztb8PjgL3F>D=k2bzE4sD@hnPX<-4cLl^-+TV4d9SpMb>;0AB|0#vD%JYA^*qxp`mJ
z`KP>A$`#MODMu3Mq#Tl~Xu>DK&*f9{CtQuM!sN0Uaz?yxUi1iFN2h8FHAZR)r-~zw
zoKUs37^lZvLf}hG!)m5mvZbN(m}pbSUENMO5T1l3?Nnr{wsc9Om{n1Z`$KjyB&rdy
z^avk?spAzBBM$lNjfuo?qm2z9*c6@nCx(k2nS(AdSN>2IfU>xok6bCEJdK*YF90}<
z0)oe5X!OhN5lnWEh;ROQ6`WhW&mo47wv?^=g-^78hEe;^dLzr?k3?wqSqt(oe&;^x
z<fN3|SD$VBn9=Qi_Tw}aA<-up;$cD<2DyudF@BoJKsp^(?dfBE)p^$Sr3<YIg{!dB
zQOaQq4vgQmlfi;H)_LXot@XIW$(y)(Irt#lAam4-cUgpa#7;osccMn$IyW&sdov6Z
z!UcTz%ACRnEigl*;O>PjL`mQSPG7xJ3cX<$T*#f=6fT<i`U)--1s<y<gTTiuy5K@N
z5L4Wwlv=4BiXr7tt49MTQV!J|TCA%RUFhe9A<ZIsAZd|?bQRDe5u)gzNApREl8{Qk
zarcOTkp?OP5)+Z<i41#vLY1JTWP~DIFleILvqssI!Q>409P!+axCE2lz$b#_l!q0K
z#Rnh$2+!-%DM3+@oxR})?fmW6L-?|8S88pst`{*={m=hsLmSpwmHYcNg+4wCU;9RF
z#_XkL&;45)Axazd001N5Nkl<Z{@JzG?1#2VIi!g7PYeWbmBSbX7jR7@gVulkSFQZ<
zk8{`JF(`+YtrKP@1yk=nY7TZh{^(=YdddUXDofK(Qm6;=6OZ%s7SbG0VwJ;XR{tpF
z3Y+6_pukrEbQtH8_zmuiXSKX=JaX3?I3*i`OE5_pLXMym|8NVOVGs8tKRIlYsd`yc
z6!8YGkVnaE!h<=<EwpGvJDON1fv4LYKtj5`2k(j@B@yBf{vaj@@Xk2_l_)8O>`7}y
zh;WAXDtw->L|7z9jEPscWp;c*AR-m7o5LNka-nrbW|8DfCBg!evD82ER^s%{;OYUw
zCVW#qS$gjkhQMmZXnBtY@y0tktoyakvGF&ZZ3F9B`^0@3n#{8vRwJ<Ht)1f%1|RVc
z%(gShNPk4zc9UYJFS!88;G~G7OKt;S`Jpwv`5&!w#bLg@MI%x=q2B20^bg);?O*?#
zwLkj7HbOAfDoRnj`knkyvJ)=@I){^=;`a(BeLYWed?|biTjWs^ZuzA=lsWP@<$F?s
z^i^LqI^<%BUdfo`B*{*$9LX)0&5%dgLx%K?+9iD@lPct~3kDfh9HLvbhm=EqKFljE
z@Xoe+BpFbxRuEuDVA8e}k+&tCfSi!zCxR4NF;T&&l$c;dBskm@Rxr*;|B7G{u8b(U
zi*(65U*$~pgjx29?(XMaN}ygW;z)2Mq42L@(_81^?E#uGbYOi(nPR5+jlK#s1jV`f
zO=@0NY5R|LbG+%BUT9-yzQ6`H+-$~-wk8~_J8AkXUC5G@wKnu>&fD6?Q9>;m+7hAl
zkLW3}y<qq!SA*x(*0Ep(>%Me}4YS~6ba{S<4YFOf^qG$`NbpcUWWg67dRi#`yhse%
zoz4usqH^9iM<>P^`4N1I{7byeX^il|WPA^M;0igAnQ$uLY6W7->xf@r0)M#69${rq
zej#Te6QK+#Oua(n@+cgVu_Rkzmt01?Zl@M;Ws#edLp&jz;`Jt$3mQTwLDU|G(5Cdo
z&8wk^G!!6+Q%!?RdqPsDDYRPtQ36EUPg<*pBcZwG2QyL_$_U4mGD5^dD&dsKAlU7|
zK~?4#c(M2saVYard?_Qbdkv+;<1NJ(BOEjt0!Q&T@Ch!#cmRrKzFwpXI+`#6BD@GJ
z0;ztxb{DF-Weet5*SUXdBdbrgF;>y)ehoyPj&;AEx7Er-;^4=>%E*?s?Xnim{{pj5
zDl%eKUBA@^U;aUca}^(*^Z^Z!KnF|i2f;c1wzpW<vmR%o>H?BCs>ys`ywjP{onbEW
zNhxT2RDuSB@R6Cvc@-xA4Y!0h<5A`#{OYF$kH|Y$4)F$e&&$ZqB;fH?K^d|onMi)A
z{}41wmJ*EQLc%Vb@kT2~{f#tQ8ZxCgCA%J0vWQ)A<YBQxr6*f{(5d)bK~ls{e8!iE
z5I`qJ$P_3Di-^K4Y8pj|UpBH83D7L2R!gQ)g&@%JfGavwL3}VGI#ERikux4|&?kZw
zE)+w`L~<g#GN$7>NybG&g(IH$>NmOh7tVOKT7#>h5~N#z@Hiu0xe<Sz@I@FoNl7#}
zB8CfQOiNN$Ew|2#-a!X?F<W(eIEal6OKRG3Q*0}H+bi#WkM*-uM5}Je8ZY#9SI4*}
z6?=NE@4cV3j@z%W%Cf_4gmZ&O;f3y6)S=r0FM5%6zy7&a>16w?6q3q9#~5X*>okb&
zoc5qK{VLftKD(620blXO{c4aoTrDpmYz@N0D6rm?PvY<7`KfqwrIBJJ;TC+@lahpN
zB|Q2nY`v05hC+^#QUsnThy&i`&}CbTNR>=rpf<_9^8&oFln0-rG43I*W`RUdC;>1O
z2nj(=&^^%zI*C+sBUdQF5^2_K3V3O(h!?@>O-drdbLJG4A(?}b$}lBGPbgYs1mQ)4
zax;5!bGX&p<rcW)u0H}hDz`hXa7y@!-++-nw0LKpuvw>m;uqQw)=`FZn@&F3TEFvt
z)<*BN<`xv84MhO)_^x3#o-Mc07rfj?e)U_nGILml_>G{qGQ>6Xh3{JP1sAaCiM=PA
z_Ys1doT!Dynd|5~`C&HuoWHU1TuwgJ5)xmwo+ZcAQO}q9bO<hZ;qJ}@*~8uW;uHo<
z4qJfn@By!UO<^M61efJ?EgsRMR4dQrS1%L6+i(w|j)F1Fl9!}FAy0XMtU^}d;HL@}
zR{+5aPVU5up9(_vgPt@!;Z%xt0GtKD2p4<O6@ZKa!U<Dgy_9;fSW%G$A!=&*Lc0_J
z2&6nU{|sre6O6X7h*3?VacdDP#Zg=-(TNeV2T@T1gjr#L4%(clkx<VF5sgq1gJ*)9
zfstQ&g4s%bytzF=#8>VPqc7>G*=upv!xwD{XYpI~msx6{*=^cH*6H`Qf6xZ+TF<h0
z)<r4mK6umB#_C@-0lxL)+;qhraaJGd8noe`UvHzYdb71II@HD(A}-U%DQkwlcaZ&{
z({0*E-(uB6m$JWsQ32IRDJ`C6#hyRm6=_sDede35ry0IQ+dJ|mc&4zDk0MVZj)(_u
z_lx|CFbI66RDyBA^oS#GB7ag|E1VQ0mk)uu0K2@TFiFURwj^Uwv#7O_!=*wzwRFOd
zBqAn~VaJ^n+(Ek}i1dkSPm9tDteVM6keBiV4kkoG9gsvyCOwfE0bZMA5h<arol*=U
z4h6{EGVPTjIE<gKB?ZY9Dk({V5u+f>;l;4<7v7*LXLc|t+@%bWIKg}AEi73WPpRi2
zDF!aRkybH6Zt;X$#1(?$i6@A@ISmQ^-o8cribAh9z2MadC4Bd@&alz5-(aIGKQ|5k
zLfbeeq?B0L*mmi+t@omDFguS*ZQW%9Tu&{<QJbr22w~u8q4JDo>+j!c&0qO{)^<Gm
z%tz?Eq?LyagryWlTE#0K2ul1<W+$=`wwMOdr#HDJFxf+>GMti`nsO*FBd;P~6OP<H
zXb8DT^n`K*zhme%E1;3^)N~423wJ3%3Y**&Z<wJ*Qx-0tlBI|%DMC_Sr9;x^&1??~
zPO`mO0Gi;_$VOa-!WW;imb;@qE^xl|A~a8oQ57BphzJotrV()&rSQo)>79zQ+9Nyh
zAR%QkQDMB13?o%a3Ri@SFfuz`^iIp7%^5Bn>U|0(UhO~(II#Juz$obg3nNVmDrk3z
zT2gf6Zf-2VMM%L!SnL=ge1<G5O(P_~3Iae1S#z&5kRI%?IN&u$GM&qLUjNtD_v>4%
zoep|w;c6@2#n3O;Ews&DWn*tS*M^SgY^s~rS?ez^HA8WlIHt$@CQG?Mlap1-?>*Pr
zpL9BX5oq+kL}NtmHvQlqKAArsxkxCcdcrRY<vf6Y1z`E-Fd2T80HieeCI1j!`6)~;
z%<`AO=J267;g$qE{|3C|P=##3=E|g6CkkK5Q*!r$J1g8&5jYI~-rC^fMY8zdbU69Y
z<W{GPF-51_EfO|W-^y**B*7!Vo=|Xv5THDAr?eL#O0dtHF!hf(BP)SYv)01rVRhoG
zONZcThtq6O^%Z_n0<srj2P?14RBf{IC_(bqLCeA+zu2~`d=EdTfl?yEDI<kPLZdkV
z4ar94MA%40#7%5UFHnj%+=K^f;SvA*!657t0RjV_*@Gc7C#OhG&>;qDA4${F`PTm4
zSJ~icm)LY>yT^6K*$DjTuTo<yw4o=yokC~G+M4LpGYc;<tkUl&qa40Emr2M<8~aO!
z5i;`0YbZ|(WF(qJQA&WmN*rJPH9m<a#lXp}_)TWzmrr6k{z#krl<$FqSQJ<7jT#`7
zMD9shlB`4@B5+8)qFI$UxuqO|UvY#v4FW+8@JTsp3M9b?mJjgYlU?yjM36!Lky~km
z?7|J@HQA)#4PxY|2q4;pUhn`bVGyLmI-Dn-Na!?rf5h$5<UkzXNLaCC2yIqkW28!U
zje05JPPFV05JSbW#wI#qS}q?<Ov)H=0WZJg9yBI<Mzf~^*i0DmjFNvm$xY$oReX_N
z#g)4!od3VEbBopMI_mhG^L^*)`WDBx*op02?8GT;lA4sKsuWZe(?%64P|+gw0SO5q
z9*RIbAORIm2%h@T2PBX{LcmkRMF=XCq)rgjNoX27c48-X;$u5?>KxlA$LH?D|2O|V
z-`<~N(QNxWYpq!`vu4ejS+~9S+T>X#%2%TONe?Kw3u}1+Z?kZmH9dAd@zaN+|DC^x
zoA137qdTW!41SA}S?iJhts7d%uV~=p2BHjNO76M1pvURQV&YGJLtDpMlrL#2D$n4k
zR4K#d-!4O=L-dfcH~K&WL-GxD(89st0<YCIkhBnGSVztWNAfr26n^Qe8@8haZH8UY
z?$y?1L!{l-WhdBB#m&#oRnFuMrQ)Lym8kQChUuE8P)O(+cxO#=XWO`wgfAC$a(AB<
zQj_6PinE~1Wu<yLAo(m6y`Gpb$zkhjm1%;WGs+<>hc0?oO|cq>asjk$S7}gFFh4%x
z#fSrppUTbY6rCL6+emA40X8zC6CZN~;aM@#nv3(i7U+_byyBgBj7-^;V;YWoB(oyS
z3sXg&WRg56yDq@s-RUw3xLsP3O6Q1KRH9E7NYP&CYE?T~lfUxwG55@`MgJfFE_%Cl
z03A={E@-5kRP!{`f>g=Q=~&X^>peMC?^8NAS7!$B;34ZJ-j#<r8)<}9pSHB&SetLz
z$wf<@swV+>1KeZ|B39BGk@E}BVZqAiVO;THN5Q^u0@D!(Z`gqh8{U}$pAHsWPo0vs
z(U;@03h6i<3Z988IhwYpbx4*i<Ia(W5H;{eSFS-YLIdzdYgr(1m#ZjbqC#J;wV|9u
z01<ofRFxt`P>hye(@K;eT2Ggfq4E-#e{A5eV=ZBY7d}ZliVQ$RHRVDY|ENHf35+@z
z7kuLExOvKGc$%lmDuZ{UP!81jM|4&U0vgYCBn?(w#iP=MUD|@1!aQ5fO)i-n{;+jp
zO#jwbVo?q<Ej@DIBab!J)Cm;m*0)c_n4Dw&bH5myzVw+qt&uiB4%#Qzk6D<oKU9#*
z8<x*V!!YCV7kD?%4xvU#GMAA(a_S49@l8{4_x|MB=xJj)7xt4*VdHjL#4&iv78VBG
zaTdo54*znBb_XjRh}jMN)%LaKFfVAtTRI|N%(I{@JIi8pVJQGuf2ff{-IJ0aqT(}L
zh6|AsOTf^vf%X@f4}@}<kumULraQR`S2)mt%1W?N9bM`#55Wr#^zIlhm(%%}2BV7L
z7%UxMPd&kCPg4aQ8o}q6A;lpW9Z-xN#fF{6tWyYT^uTCQmY+bTEEIu5fRC(Tz}E&3
zoG}uI5J)%<k;M-RLW{icCG=%j0my6Pi4K~P8VU!6m4yxEq;?o{V@dZ%CXO7Ag+KiL
zSo_mIi$0$t(01;vlugpB0Y|2Eu%GBBzWG%hpnaFVOCrZoFSe3Z);$s0TXD+Ewi+s{
zq^MAtiaJ%&OK%B8e}zW~V|1c0ZAxt?skw}2e2>iFD<6AMn=rcvr>0#nz*yaxIwTHG
zPJz-;;Rl|l<Ip;<Mi%&)7Q5rbPjJ!+a)eNs<g0k{Ys!TkP(LO(IZ~vY(BVk33F?-^
z0CKy1;VTEhrEDZbMIr$;v`fI%6)gbu+S_DrS&gLksK<2<<4RxWWQn16m^jd3S<1`P
z=`_S(dMF{k1j|b~%HyFuS$>A-ov)&zfIjENB9PGhd1ce(M<XTJx||5bvFn9{WSqzF
z<xw=&0468O-W=YNpEym<vrvu$6o5*Tmq{TP451Q+4ir(^e1Mx*(kj_rMQ=7qM`q$A
z;}*3^4ns+)u@DAUddu(D)ObvN`LnSo2kGgnR#LY{7jahp`u1_XNAgCjedU*9<C71^
zlIG#OrG;;#nuTLWp;*aJFu0t~5;Nnr+zu?;XE@tjP>A5oJVM5Vt`dN{@=98uxn>|F
z!^UeTP)aZxKQ?BZfpOF3{L(q(f{n9eelD<ODw&6)h%Zwog-WQ8SDL2>GM=;2d=|~O
zB#=Q)z-t$jsW>AI%LF!>yyh0XQy)Y}KALBAeNE1w7`sb65YHBF^jY|g#rK{$72o~0
z6Mo)D56WHs(L(&?cb<)7&z;jDbFzFXj&%nNZ;fh^MK8m8g4WQLh2jDEV<f<6DPt`b
z=9dI!2TR{0*9j@>_M}0+?x=~*zofxZL`$#aG`!#qOkwh7WT76Ko2!gb(NIY)!u(}I
zyb`8-Sy`@q#Cvs3?~x;Z&F2%VXjNC~X7CfUbif>`aMJn}oUmXORvtXHev+c3R+X;@
z95uI?JhU&?|KN9Z1Ms3AOWCH8;$yLP@5boqm`pS=`L&}FcWsqJ>BT4H$3?OX@#SC2
zL|GLlbXt){cFO`am%#p$`N+b~AbQ~^9pi=4EghmvEtXdnlLor{Wj;8p5BXL5xLw@l
zZg<+V&3D^!zpLQ1ugqRQ@@zNhaM`|YOG}Mq!Bg1U=(wp57;$KYgvib<9YLvVWmJ?V
zjxMTBgsQ9l;0Wz;x%dG;%En3L3N}IvEJ8bsvATrXM`L1qE%xr)6~~@EqmvVRF+Q;#
zFTZ#`4n1;TT+m(KD?hqN)1s-Eo4c;hB=EhE#aL8#)|U`8_$W8CBs79MPFfGpX-9@F
z+ZSJYCw}Vjhe8j!s+U|6Otgs)EjVsS1&Ke310+oRXmrYGa1h>G!EUWG5NSlXHWqMz
z1Ea1=IW9%?b+X_GAKZ*fm)?y-2lWWKdKOVCM>x&p7(4)f7+!c8Dm(cP0qvAs5D+-=
zM&ih_O4I|1BTpWVS;3}G{V-+};t0F+o7eMOn;$(83px#OO-~53+r>9X+RNRMt>QLl
zBd&XG@>HG*w?s`u02`;%$xViWAu_c_=PT-^Pw4&k!;)i6>m*NggbAMH!Jr(9N}V4i
z<a8$UYkEab4o}jI2GGhKNH;r8xtLZPc!3QMMig*?d32#e>FJ@)naj&@?(La);<1C0
zZ$WsP=I4|N88CiA(a7)>G-Y8^(&@aMk)c}kTi2OybhhOjsU^|&Qz>H9AKMh~&CJK~
z<0rGx_V3>s`}O$3xZW|yIhuO-<<*7QvE^=^20orgH%{rnxL1Ghc3i$RD;j!inS~lV
zjTHxxQi;>+I?fbHX({B|>{85J(u+MhCpJ$R6tJh)NU5|P3nvksoGIxQnumDu2nKwT
zzkA4Z$}HcGg)`VWkij^HVc*c2UO|?zu&^wJEyM+V6h~UiMoj#MRwCQz)A&i)>i7#@
zUQJ+35>VZC39OP6m{UHX@Q#PZysTgE+G5=Jy>I5D?em)6s`fF(hk0~>`1GsiV)gU|
z4f|?z=?gt_Q9g#*me-^O8<K$Z06^R{xoCw^PNfXboCZIQHw|_qe*E5zcxOgO8L4oo
zsUa!j2WG>BcOEV%s|3%}Bjcr02=3{Xj2>LlGI~#o6sHX|6-s#HNRFFsjpJW6+%GJw
zsdJl;ne%%1Q;#&IY8yvD2Bz$kL%jO~wxmsvM?bMMosajP>%}xGMLw5S#5_^T2Sw&$
z$F9-%{1+b1jIq%T>Mh=hbFW{Cb7!x_w4C9AeGkTW|KoTZd}wcM**qO%Q%mvHuYFqd
z9#-*67W)`^PYyya&snxTH8jH<??m_e+TWj?$T;sxfIl`N7+*h<_aj{5rxR%1lS4kn
zj-%i_7`%)_Mo}XOaZw3YBM()u6M!Sa)LYW1%JMB;Qh?yBY#G%i^uiHy(`6mBHmYu;
zukWPf?wVvw+#FG$vvI?#;%!L2u9T;VI!IVWE3~69AJ&765{mVN6eC&mMJx5(oO=xK
zF})+gVGAodQ6I5YeQoPn4$^A+#@LH<ClqhX4=j<zGAR<Nj7AI^Ji>-&8n(()*?ql2
z%$dZ@#FJin%^qJjJkyxv<jEex#@Lged*pH&!##TBS|gnRe2*Zx7;osAS|xriyfp$4
z(!;BKLJ6feGqn`Zs3sRsdD4*_X+_5^vF4DgDYRa&^_;3Hl1?Yt;LX~%r<cwtSFMop
z({?F(YC%=~_P2lA8W8|nvO&>Ox?gouMymtj`s2R;Fg-PyBge$#xTahdtgo*xxi?P}
zwc)DmJe8ui$3etcU!$3j6Wp8|i5E}49S<Mc9S8R4o7~E~s80`{J#{sn_|*NeeY<>C
z-kb+8Qvo$jI)d;RLE2FRNAV9?xr3kif?@X<Um0;ahTuRYO@_>?l@x!bS1b9QlY<BM
z#h!cbikk~d@!D%Mh!&3?-W%ik?i_}c{IE+oar%d%I9M_wKDZj=ZPJ+<5Dbt8kuEmV
z%M&Nw$^*e4`^5dyLoZIhelg~5%*W%8tMg)>t`=iHU01h3-O7U;M_5B7deLAwWVMb(
z{ff5YgxX7qYx=aqi!Yvv2Or!S`|jJSlm2Jp^x600-g~EG|K7XoAkqo=e!YS6%-hp1
zoPZy)1Fy7UI0z14r@(kIZr?b}1-8<@2g1Fx6@8XtJ&wP8F%In87Q1)%<ECzwo_JNK
z>&tH*IlNWv#z@2{AfA*<J<B;%mWm(9S5Aanb=BxWTK#%^At;%n`udJeK8vXhEUH+w
zGD6^?3o$jVj}+p=c@RJ^&vv!pN<mnhdN!suj0V7a9BZTX45qnuYeEMPEXT9Y=ncB-
zOL6a0PsF_5&3^jnmo<Is$NPeP`IjE0I6GEPbILW1BCu6M)z1q(MvxjYa2ytG7%p*~
zmJmbIkKQ5cfb-z&I$@+go$`%pmHfTukH?Ir4QI|?j<5gv=i>V82l40M`q%i)-+4;&
z*)cVVdQR2ZPKQIlSB&$L(uOX>!S$5_G`3f;E&(u%Mrs=G$HkfV<9}Z{uhC;YHf@@W
zjhnZ`^Dms#ns78GC&zTUzCO~Ufn-Em>b}~B6&pBpnI~@mWdIIcUL#cE*gk+4J-w~!
zrjhi8=TGR<dpF|r>9^t=-}r_2uNO|o)Bkif{^(mzsoiYc*hD!4JkmI2iP<|V1m>Ud
zosZ$04yS12T?ewm<P+w45;|{EYmM>Tu@f<Sbw?aMdL+)gJ{$k}%*hzn_cSM`ABY3@
zZ^_XYT`-d292$_)w(StDK2l>%ol8FSRafum(I@}75)Lsk`;9$N3m6$Tg}}@=3Ng4e
z7l+xKH70@m@KcY%P>i#jdhPu<`NP?G<Mid&q0bE8qh5MZ`}mI>-V<~4%W?9xi}8go
z9?{2p&d1@8?^3VDeH8CfbobXSA4OtRHdN;$Glq(cHaO$b>o*_(QUtWi+K71R_?dY9
z^rble))kE&=i*nt{<HDwE2lJlyeGD9*`y7G199TyS$+FtJht!HlpTS~=<-jdE`wsk
zoAO+yWI->WBUi;UtaK-ofsIe}<LsGBakoBHv~#!KRoC&MOjmD>EX5~3`A}SV=Sn>A
z;4aC6lf#!wJnCx7cAgEAEE?Wd8{UospTxtUGLAm!=vLytzyC`7+~<EL-g@(L+<*V}
zxbMK-v0Vq$9eeI2%>=Yg)iY#tm^Lu@*uYzdv4dbw7^uT$3YDXUM;5wv(|URY-oR;c
z8l=y2Ecaq+<AkQ=y;#(jy$(IRCr-b1DYos{68rXVQ|C4xd-rZl9c72D8-`-5GSNe$
z2GuX?-_&&g9po3}wh_c&RboVkE;Hv(Bc!J?CEIU#|AF}(r8T=741?K&Gu_aCY#=Y@
zf->A~cTdLjh7Iy??bk2#<E8J<Xs)=b1@ZM*)Tb3#bNtO;9g8O)+o`9FSPSUBi>6kq
zt9(bUhGjP(kG**MMkN^~498K3Gs+Owh+?>>VyQ4S5P5s{Y}U4}9`n$*2WPKekH7uv
z=VHgU&9SI8$nw%P)%aG-&d$ZsicV=)ec8!bXA4<y497aLm0IzR+whvWuv8d-^J+((
zf^<XbyAr$Z*&P4;FaMJr(x;E;TkINv&%Jpi&d8ZR`<Wx^)fuVP9U?>NS&?ddjFci<
zr=X0=9}sl%OMis$bvmhJS!3#zckkF0|M2(!9vifFy!O6c5guR8_k1p1xT+BSOnS5t
zZFZ_^3-{EtT^W6i3gp>2%8~3aEgnuW<U|sSDH99nk<kTBH$$D-m6*S=6mK5<aZGCc
z`1}iRQRz7PrH@OGqQ@&_kDj8D)z%Z;p)WzGW?sm9r)y{pGC;t>wdlznaa2a3<Va`>
zQVV7-_mpY~{6SNy+;Wxt-o<h{cb=puU@S73OOKE1X#RP9IroCzsGiU}5gXRj`}DN{
zoQ^A(<}@^Jj!o(vSYk1Vjg063w9bOOrc_>gFulSlR0dul&%`jCXSf{*csC8cWEs;m
z5?U>a=RR1H0lpgt4(y<Iy2rDqPte_z^L$J__BJ5Wfu@$of&!Yqgp^t7ZX9TPHB=57
z@Gy)9<HGsRpS!9CT#2n)C#AE=n7xKkEXKh@ce`Qfh_a3D)TkH&oZ7tBSqyU;JdCn%
zZ9veq#>7HakA-Qc=#AIk(@3gMV5@^zRQla3vudv$8f|r>Sa&JuQ2c<Y%eSMHp|{3Q
znRbBc_`s=ArSe)jJBspiB#^MWB`~LV@h@J~h$V9z8Ci^N+c(G6tBc~h8oTyt(<!%k
zRVF?n5<6?v&oI~9_U!tihYw$Oc8~^-2FJMSDnlb(@x)=>!BVAJf25<(yVH(B$Or1g
z27?~cqJBcVR=%g<-VVI+@rkS;yax+>wN7sHiia2H;?-Gqz>fy>dc&gxcq&z}qQODB
zPGUIa7}w=ZN+w>36)@K(?5c6`^(<eeD;mn+NBtd#wAEEh<5>ImcAPe+V&j_kW2m0L
zYx`SHLmij2L*uJ>l)(eNJ??bo*c#IC!Lx%fT3{^Tl~d5ORyuRl6SJ1o4NbKhBOC_H
z#V+NFwx9i#Cg^d3Hnd@5oUL;hrk$q-*J+%=c<sm`7&yvE=gI9};jj(Nx=2@k)*89x
znC$j3;~}-I7st|>RK4VpG9g##r;WqMG84P=HMB^-9mPB`VmFAHc~_|lOd2Yv9!<@t
zP=#W&s-Vm#4V4BUg9>1Te6$4x_oVc6Aoa``Vr>t@v&tg$s6a2n4IL_I$6@;9AI1rs
z9?uT3rXGi>6u1~Q<vDJ+joWo&hh+XH(?>fOGNEY8*22(7*9XYAj^J7GaujriOsS#H
zA8<R#J46@tNV_lN=Ju)>S-uZ36lx2rc5!HU!ORnxtSigsbSrC{Z#?cL@X0cLT8@$$
zTJK0DB%MGlo_RGI&=%zY!*L8(d~(#FP5HIBZ5+7pm|m%UY75xqhaR`l%Vj9H;l)~s
zB!8F==i*#4BuZwGRdq#f$$=n0;rf3a1llTqM#TuYs04;cffyR0%uw@0rP!7QN2u)A
zgUb!E?CHrDjM0W0Lh=+DE;ndIq2w*LuhOWjxO!BcX_mKpZ6uUO<tzhoARDq;7OzE+
zmBk}8<tP}O9flDGKGezeA}k{`48F+E<*Gy5;*+l&!7KwQr>&<<nb0{Ir;#$*{AqE!
zP{#`wxdfrXGoxkTHfSlGZm<RgwWCGlw;{A;I1b74550Bs+@k}tagk@7Z0Ce~f!#WV
zr&8b>x4ZCG9QzQD3>AkDxNO<tL_;?Y;i2Zpv#TUEwv>*LT9JaVA@T<@wRJ=0?q&JA
zkUPhJqJtD!@vru#K#ZkA3d9NM9vC|qmU9eI$aZgIW_og$N<dqUsNou3>2TsFXW*C5
zLnXZCs_@4#nQmaP9#XR*^gP+9?y}(^j4~L%LvQyYJ9u^uwyeQzjlFdcj2%21eHpuw
z$=jn88Yw414xFQ@9f-Op&p4(dMcO}T643my9=eb_$1yMnyZDZ~Jg6#7I2^ZubYaK_
zPve$RwQ2Hg{J_cqOhbL0R|X9m+7%k(Hiq6fK+B`E&H$HfUa^DL^i4cR4do*vd58&~
z?51GKY03fzI{$>3ya+9?>+Q5*#6QaQksSnP6lP;;3n?d|)zk1LblN^^0Ldl$QAaMr
z){t^biA<{^X(XyuI)wN#LNGVOaM?}CLlY<uFiOgw9TG!HCjpkmDx8!LzTjf~(DBH0
zyEXylQG&b_Onk~w$a9>!mZK=9I6t6;iyQ!eyLTqnx=F;umv{{<bM=QZDLVsV&994B
zd4)GXZZne3N%OLP3ZY=6(VH)D5~8p*d}Jv7Iu4EV!M4$nR-+}Z<E_Kk5#W7i$EbWl
z#~q4KIt4Hrv>ic4FQ>`%h?r8WFc6Br!bM&|$OFbmQ+SzY8$tHAUZg7R_(%Qwe++r&
U$+BExg#Z8m07*qoM6N<$g4!&^n*aa+

literal 0
HcmV?d00001

diff --git a/Images/logo.pxm b/Images/logo.pxm
new file mode 100644
index 0000000000000000000000000000000000000000..b6b42e39e92a153a9f884f8f55f8d5c8a753beb8
GIT binary patch
literal 1132995
zcmeFa2V4`&w>Z9=1VZm(qXY|xN^hYl9Z@L)iU<lJ1c*Qql7JwfK@r7*T|rUo{fZSE
z_JZw#y`kb26}=Wz6jT&`Gg}~f<=*f6etqx%KJUNiFlWx3HfLv-ow9R6#s>NX`i6Th
zWgrMcRU#GhIH}Ss5idUPIXxfs$j{H$4GIfPmvZ7#cs_itlrI!;#92swzFJV&cV(rZ
zu;93fJgyX(k&p7#76DFx3X_WYf<$y_K03%QBs?IMlgJAQ_7jRzIZ`i<gwGA+NjYA8
zsU(Ca_7<j!gaV#G8X!mz!Zd=HRX~UdN>7dBiGfyTP}mqwN;(e#5(nllTc!jkmKP7|
zO0<uVo1O|phVdu!Iz=uoR>^j>a~#Zav~|eE1Xy+@Ae1K&rliAGj}`zkJ7tutT)7Oz
zcrCi(^%kZG#S#&R%M0S9ey3hr66){83h?!Iv~hNHw6L|b!6cb=B?)+9l29Bkr#?Sl
zqvNu8ip3mwMSyQ`aOpAxlV{$QJRgpf<C!W<$9;|axnyO2KAN_A+S?u0ICqy$9m&b!
zi6!2U9lKT}|CZIbL%LLpF+yL+HDtpw6kcP^j@A%823#YSn3up4^8{R80oELwu9}1Q
zj^Koj9wa9pUMB{|-sLs!yiS3_cwS1!HQolhR+o+G>=_*l`Irym*9^n-nRaFF7~nn7
zp*q<XFJnMe{eNH}JMUlUOIGe%Gu;mRX&Q>@`mqm^tYMrCUVN}1B!weQ05@Hy%H_rS
z9oop+Kn^!J?5CdoPFw_En&c@G{l%)~#dJH^kq<A*Uji@c@?xhB75E)biV5YVrSru+
zNg!XqPfbt#8({?n1tBBo3_XPWv0c$vig=(1OzHp`Pu$>EeDJ^s8#R(I<*{H(IZ~D#
zqJ^L+;=y78I>3Q+N<k1hD1#?n#}BOkrymrtc-&uI^DCmD{tD6b);y`PPyLm~)PG{5
z>M0LU9-urxd4Tc&<pIhAlm{peP#&N>KzZOl@j#vygW2801kFpvrlwNhoJ0&91a?9S
z-l4vp;l8YJPp^@_tWG>@01l_8^W$0JzGK4&vjkvLf&eq%^b*T!WU$v@*65&sQKNn3
z>2XqXR&Wr@J2=R1WPo=#E7Uh+q^Gy9xt)SycYhNU;S1t<nUb^=FcyvBq)UZZIi^#8
zj7=x3n5RZ(b~iUi^TM!pkmFgL{QDB)yY_rvCvPLs5D^E=ok(NkT8SINk;dRvtbm|!
zUw_}w!7Mo8hjE$bpv>-OW@w(%_trqib|{1e+Rtr)RjdL3;7wTla}PSx6uSqSb>+eD
z42ZG$u85826+nlgX*R=drL5Vi+00zPv{GzEdV})3@k)&GD+FCkNO-49q{37<D;5A6
z0Oqs)duKwmV|;?WM+f=_g~x<}9^@Mq17=<Rr5Qc7n2=C#)nWpI{DN^N^^S=%Xg-()
zR0Q*~5L&t{FBi>MjIv|faL50=J*q)rA!0s035a6Twd4#gH0NKM_gw<!dX<PtT4Lr7
zpa$s&g$)ym`IChLDJLZ*%Nuk=Fq0RLH51GV|8+VSFbxFrkK9#o3bCXFv?`RJm?RD4
zh!gn&fLd7!Y7<t&nYEG<F!el=mmpuchN$V*p_h{q!U3!tICYEjFJDf>xb?skGEXe!
zbN@oJaXFm%EGdzrkhgsWtOGs^7mDQUTC;i_#subLy@XQGjee*DQ&?~=OuzG*M+5}<
z1V_Y#`g(`MYq-3)M|QTOz3pI@t)q4BU>1O_2eX{O)HFUfts62nG(82(8N#{pcyMX)
z`PRkD=#aN($k?zHK9?s65%V(myh*+SI8z^w(_q*1ug=Ei&VjSn;`6n=Iu&pv>0%z7
zFo(0<xQvZ!;JolUd`?)0)Ha4M;R8Jw$p)+u-C{<1M*4<^#f%9E3jhyXx%TO0XM+}D
zZL@WD9?Y_LbSBzn6cZCX(kBKkK=^oud&c+$hsK131c#3d@E;Z)1HH3&851^B1xyzb
z6Y&6v*%ZGXpJ!FYo~9CPUK!Mt{Oc3SO3B2$I>v^#P@M}rDNNv}V3YQsVtH{Fv-KLC
zCWV9NCvMg<Vo^Yu3n%~Kd~(5}N~{4~jv#|0feR6sQ27-I3?bJ+h=&4slP_LITeN=B
zhDF;K;hr|aJ?$SFJUS#S#&>LpXHZx`a8OKGIQVOaJxeejKsI1K6UReeZd|i@E#68-
zOn?vG_guZ~tQ~#4to<zPY;5c-?AgvX7S7)GP8Qy_UN&qt+nep_#I|sBaIm+tu(r0g
z^>eWH^09Sb+xppi6J5J^$XFj@8a7zWhc{hH09eWqOGk%}1X}a+6@tP5h%8-;K!Y5h
zb6u>gQiR|ROcF|@R-+}LcUwt#aU6-1&k<Pp@FdAnp~xyA&_Bl7*1_3wc!)oK7a4S_
zf>Ya`5+5*+%#{koS-7stxC--sSp_b(SLe++(ld;BDtA0&mk^FjOXq;U>2OVI<C<;j
z;KOWru@-Sd;1k^V9RqsIci7<}zJ&QY<UJDRI}+Ue@S*`JtCq3z6`~v*yLwEkfXfSC
zDZ+SC@O=Mnoh<-eiY!IJb+Bd9`I=q)b^F=+8nBcdqsR!%qj$j+;6wZ=^mXt8*TBKq
z!p_Ol+QQz;j&0%P<!EhTW9{wi>*VZY@8IYo-#_e~+1_>zc22&Yj;RWa$UL1c6aWk4
z*R+2A7wT^oxqk2jA1Ux=&|_S?&_+D3LP2LC+LfJc0Uo&)HjW*STt^3|+}!EjbVh2y
zz%Io2fv!s|e1xToU?2bWFAQKU2y2_%(&@jvGW7Qk{P@!Uy{ks6e(9=Fo`A;SPFLyT
zDvrb!Fh}wQ$zW~z*Iz(vLG$~y@Bip23;V<0+z4>Bw_r96Wh5^Caf$QC$MV>~5V@CJ
zw@jDJR8cB;t6*iOrU)c1neKf!@xnNs3xFZost*ezktVzM0jw-<p_s>Vu(Y?d?lVM%
z#bUXM;}cv$ef$V^P;&2+B$bN5!+p}ENtToBEQR7kD;sBLXDe%4D_dI&Kw%-t0`G*G
z76M6MTnH%#JoLF@z6idrv7lT|oG@ML-lq>#)bWGcDdi`0Y7$5+F{3TH!c;3*V`XD$
zZS^xYUM9Z_Hdxao8}Ly_v%XW7$r0g9m@{x2-Jk_7oomDJJ!*)p8)n##=+2hmTw^=g
zhB(<;*$`av=pR|Y-&^pqhP~ttA?f9DKhj73T!lPZjs?2K#V>^;Neba-5|6zh);8AG
zZdO07`jNGBJ>FX=NZ==?i*ZXk>44L)P87yVlXD{BO4tih_^@3B^c^3`3Mhbo|AE5p
zA1OeaekKFQes3Pp`hRYn8|Ee8J6_6IA3k`khRgm#z|Ipw!jCnb?ZY|4xI6*Rn<J8f
zmvZ=uIfQilUn+j&#I`hq!L29IeV7??s2`~@J0iQZP_E`jPG8VNIy#1}EvZW$Eyn`x
zBo}Y6!3eysg$so#LjnX6DJN0PNp-XOv5Mfr-7oiOz@IzVr2-G0Au50qjP8}R!7rwg
z3iz#~R*tB5*EpnBt}$10qWT(*SDM<IWonPrFKE_im}`tvAFe@DPuFw<q|^`P0m=iE
z2Ph9v9-urxd4Tc&<pIhAln4H&JYY-bqg~8U;ak$P1YBY*f|#Vzv8ShY#nR;_c<sN@
z1Ez*kIU+bGgil}-db9@8BfHWA=iLDV`8|W?-;j<0>rvx`LUP-=#sK>0uB7^dwfK(D
z0@T^`^sWfW6$UuO?EL<Ouv^WPKB_B5xlce;t?9h3$T}xT<QP<J=!yT#ApeDk@?iS-
zt~ma(+N|{VSLiPv$AGDGW<PphSMoci?iK&$+Wav6u3$lr=}O*T=gt{_(+|6xBBxu?
zxm~IF*^DgBnx3fK#r;9{g6}6Z;RgYz=3)f=r+z38P#&N>KzV@j0ObM71C$3S4^SSU
zJV1GX@&M(5|2YpNql$TSM2Yhs>@o%WdMrh74+Z$BA8fb-=aa}4EwRnNmT}lTmfZe-
zJ-Gj0{o?;zLn%#^2Ph9v9-urxd4Tc&<pIhAlm{peP#&N>@b7p)sGyKXPo(_^qsAW=
zj<E&i&fN~7!bx6P(vA%WDu{>y6s9WuVuj$FT<~dQ22T<$<_N$^knk|WEMl)h4jpJr
zXZ~W1olW#k;s_FX;o!3au*p7EL}>LqWU=*4O_gO54QL;){EM{@<48IUAT;t(plO83
zFV+OV_9Ya6{-vu7e(5N{{r~DJn-Pr@Y8%z-)Z8>3Rj;ZlX|7S%Q59$esccry_;>mu
z#Y1_3@&M%l$^(=KC=XB`pgcf%;6L&}EPNqGfAI^k+-_xc9#Am)S1SmR5c`0M*KV~q
zpe6WMYr!}65-L<D0u?d8T19}wJB2S2fukP+Ig(^Tl}Zv&74@rCd8P0K@lX*s2Z&Il
zoB|Yaf4QQc@8ehE0%fCqwX$IFT{$=hhiDM<zr}N5A|98Yz~{;xSD`2XjY<5~jp-b(
z{xBz?kOtJH{%Uo9HzUAE2O86VwZ_f{{(MG&&Iek@|7xv2n+c%B08Jx)^_+v;OaR>f
zuLO?!Q)yLEQSxE7Q|JFt=l@aX|54}vfio@>(VqmAq0awXUi=@OOogAGN1gx2<x%JV
zk>^8F=l}hOq0^}I|GtkCM;>DLUx^w;o&Sf$z#?7u^Ur0e^Z&qEhuE=!@PIVx{68$r
zq1-Wi_#t%E`F{y6)cJqJ*@be4`N4a}MgE{a>;I3=|054E9ijrq|H{l$h-S6sQq6dk
z2&E-T93@NUCxD`UC=XB`pgcf%fbsz4f&aB0u-B%ib`{2;%LA_L?X~FquHqPUE`NM?
z!WBbD$wreN$^2oPC^=yI;gBoO8+FlTA8yiYG`eQ_;jpWrZ*eD2_fThyE^zBY&v(aO
z{X7K1zZ8b&$6yGme~lraA`x&z;X;2g|8K(Zkda|Z@h~B3I1he06Npp+f@zoHF=JE{
zu>-rXc;ler9}lR9u`<-fSQr~(8M$ClF+{52f<_9t$uK^UJZ>02KAs1H_h2}EoH>y9
zU5FY9W)z46|DC<KR|^(_m;yin&KR7KAmK>?`SM~#JQ@uK6D{-t!OY`<FpNVr5%{Rn
z%7WP{YK-*7)?Hjea%}z61Id2jCDr`vB~{E05AgGYQG$plIU1d(m%+oxK*iq$gdk(p
zD1`|Vq?5qrWXvNP*lFO{x3(-So;UEADm?2P>}7`Mmjj}eD_1R3z=)OLNpNtFF;>#G
zwYIT$v378=wPD%XL4a+GRjA6HVUO{{U{0k(_#WUu@(lZeMIgo!P$zdDJx0C>&|-oV
zv!R>iTCCD}tU1A~`NOH|n1EHw@^WDGCm>sH$N~`m&~ZVCrIjmT{G&3!y%!etm4R?8
zP>|*i17m>P4FbjN$B95l28`&3qtyWtIxjB`H%9BngWL-i9fWM%yEt1O)4>2b2vg{Q
z9#=Fl+1o!Lm6ON=XQfMtuva665=oZal^fL&zh>piP2_!y890mwgKc5PZ7L;=Q^Bet
z`0>&tvWD=X5?I~!Oc5n<Fg$P%cMbkjP|P0g7eI7KETp|sqz@c9VnK7DQv8yu2f!Fh
ziCBlqfL&+h%BnT^9jr9=XAG(Y67SS20LSuT1s|S>unbrrS*wI$RsY4{O-eo?0rE}5
z4O7St3G&B#7Zy87b1dcqcmN*=zQq$`rh?0-tBNVpjQp{>7Q-t8;orFwmyY*UxECY^
zBcq4<dlf9&jQ4rP?0}GPZ(KL-I`V`*K)aiGzG-2X_^&sN4)G8IS3VK*EGHFk<>&uR
za5xnZa>&M-J05ERcDG|TX%Othq5c5(d>K8T5oO064<7~v1y-u`Q6^nH3gz}7WrHXO
zgISIsh?$cLooCnuL+9g4p4AD&<#DpjwN~!>{J%~{`9l}6^Z)465%8b-p*%o&;D6o&
z#glay{M`m!-0xi+Yl|Ri!$349Ax#TGQUy{mcrLQWMvh}Ks}UNaj3^>D2*Tk?M1f&`
z;jl>ozTPYeAo{*}&9@K~Vy;^Z3t_RoulvW6HdiE;g407o0Hqx`iIxlCd;q6Rl8PW6
zzRl~!B}16TfUp=C2;d$NPQ>BC7!3}4KsY{C5D#de&7%0!cnF^X@T`n<9)Rg10bBrM
zHUjttfcvGSr}6>(3BY=(;6)cf7-|qN<#Ce$Yz<&Fad@aVfI<8cCACC3I8F|h@-l&)
z2;waiWr2WvNmAATt~m?DVPdm}@g}A4q*99z4mX)2jt3!~Qbim=7J}eyhFpja^eqcG
z+0n+?(b2-z61czP=Wi=JsQw;+*A}N}8iidmRR3}(u9LSDCtQah?2o`V3p#OeB?xk0
z27>gs*NN-520=9Q5u~C<u08s&zsSX<;`6wc(4iea|47h5*)<@i&l2kE@EyyCm%vF+
zk+PsuxgaWJx|jt%1?2Kr7C(04f16RxEIB&{hw>6Y)Kvjc1qY|#-2hx}upbo*vBej3
zb%p;;HaQz0i1%v%=x%63^kOU#tr|Uq{`xY)&{aj~(W^lX+Hu`RDvv?nofl!;fcJX<
zgL>@ucfV-!L6IimC$b>gJ2afdO&1ei^)Vny0Z~CT5j~_QVubWX1|SxQE#ic@A|8k@
zG8_p;!jVX10>VX-kW@sBOhR&yJfr}bgDgOnA}f$GWCOAl*@^5y4j`4tN#q=I3AuvY
zL~4;o$W!Dc@)l`ET2T~bqUxwF+7mTK`=f(UTa=9sL4DDYXeb(q#-fR+08K}8(CO$L
zbP>7&U5jo-ccX{UljsHXDtZ_F1AT$MN59bMG&PzY&5&kFv!bzSp0p9PaN2lUA}x(J
znU+s0q7~ED(RR=(XeVixXg6t(XfJ6W=?Gnwu1`0iThQ5bZ+b9&96f<9rca^IrZ1zf
zqwl02rdQFg(;v}a(LXa38M+J>!-C<;7{(aQ;4;z}Qy6m@D;QfC6^t{C>x@4bZy9Y0
z>Iw!5gA`m80u;t7Bq~f&C{S3Yut{OR!dZoz3QrZ96%`fr70nc#6o)BBDo#|)R-CK2
zO0it=gyL1jdc}`SCbK7V5YwF*%;Yeo%mQW!a|iP{^BVIh^OKUQlChGllAqEzC4tg3
zrDaOnl#VG~Q)*CZRn}7Ouk4~6tQ@bLr95AGgYseJ8s(?Ttt#3oW-9I~;VKhV@>Ggd
zcB-6Jxv$css;b&Y)me3vDqmHmTCBQD^}OmIs-M+#)CQ_~t3|7&s}-qjRy(0~SFK51
zUENgOLw%gORJ}-jtNLm6hw7g+bTuqB0yN?^rfRIzsL;5k@kUcwv%jXN<^;{jn#(lz
zYSw7J(o)tk)$-QjXid>tsdY%}w$?{&U2PlfVC_`xx!T*bFK9p4Vd|Lb`0B*#<m;^0
zIjvK#OV{nI>#fVxovyn<_pI(yJw-h;y#T$5dUN!C)2r5d+fApNeYY{)GP|wpcC_0g
zeY(D>et>?8{(SvC`nUAIbT{no*`3$Du=~#L*Sfd#=-tDk2d~HM9_2l5_W071)zi0U
za?gc55A=M{i_vRfuTi}w^;+HQY_C_n^?JMZ=JuZ5dr$9s1~h|#2B8Mo2I~zj8+<f0
zGW0hT8I~BHHhg8IZ{%S#(P)X$F{2m8y2e9{`Nm6(j~oBV>c;Y9rLc-wXIO7d3`~ZZ
zq?@cYsWxftW7cPMpJ{!{`#k8Y(wE&gq3@Eur~1C_XWTEaUrxX6{qFTw?$7R@)PH&Z
zs{SpeW~LFQGffYeJ~!)O7GO5nY`fY00qO&W4oDlYZomz5MRT@!viU0WD+6f*9R?;1
zTsg325N(j-ApW3LgRTx%80<V)FnHbI+ZJjTo)#Gv+b#aE)VCaIInDBb<r}MhR^zM|
zT2)!KSvy##SZ}bdwb8K|ZZpm1u+4khfwo-Rm9{tR)b0H2rrI5}duKn$o@c+>{*HsL
zL!iS<hf@yU9Gx7+jyoM+IQ4Uibt-kb&DLXwu;;SRJ1aPQIZt)2bpGt(=puF5<I?DA
z;hOBa&GosPnH$e-gIm3OA9s%XTK7joSVLlmtQqoXsL4>y&~-x}d-U~)_t@mo;A!qT
z(Q}9A8!u}wvDbdD7H=2tT<;S;bRU18**?|2I=-WQSNJ~k>*JT`x83ipzk`3a|A}D=
z!$u5SIP7+SVE{K^Yrxy#PQ!DDpBbSxB5cHp5sya>94Q`oIFJ@NJaBQ~{h<Cqf}jJz
zNN_;#;@}4%W+7=IM@A`*3K_L>)U!~V(45e!Fx{}2ux(+@;a=hM!)r$m7@a=))ELb%
z(POrbX^HTOSQPPStku}uv6muyMe-vLj$@7+Gj7wkk5Rr+OQW7fJ46>m-x_Z=K6Ctq
z3B4wyOgI{&8N-R$8><i-5xXt+D<_z<j`J}tAg(m-E!Ug7ocl7~BYsKz3!Xb~5w9V^
zJz-J8^F;T=#fdMHh9)gbdd2tVui(F%IBa6s#FpgX<ju+LDG@2<sY<D_sfPqQf@HxN
zp^-3CcvUn=R3Lhk=90EF?X7r(c#{N`jF%jg>VcQ3%jpBsXQcm;F(hMU#-~YRChg7C
z&J<)`&Kj6inAI@ZZ}P@$Mm9J5RE|kbUd|u6p1EtMpi|<eoSNEqYW~!xGJn~YJe9oU
zyx*r;Pg^wY!}N&hhw}~dW%>0p{AX-0&?pcW+@9$=vuqY)7JpWCp<Q7~VcYEZ*%#+n
z&sjF->s;>Ki}P&e70+ugN+_zH?>K+;0>uS_1-BLsUASeD_M*v)>K6wtu2{laGJDB~
zr7=q{EVEx$wp?X-`tnD`Ba07~^eb6V@^wYhikmCFR+g6<lopn@tcqWCZMDbhon?k)
zbIQJ~;jg)~)_-lqI@5K_)+?^hSpRIpm<?4M*&DZR>a}Ukrf-`Cn;&fn-EwBD)7Guq
z47SbRPTQWo{l$*x9anzy`R(9Ni=AuB^~>k%LUyI^`g3>e?pu3C>^ZfUy?5un{`*Sz
z>+YXjfmUQyygiU~;L*XzgI5m?KXmG_>*4)JERSriG^t#9wA<17$5f8xA8S88`S`~Z
zq7$!9CZ4Q66?3Zg^w`rk&Wt)!eRjmz^XL4|ov!k%I)2{$eB}j~3x_VUFCMt$c&XyD
z!{z<IJN&-C+OfK##;NAu73V95uex15c5Udjlh=K&pSuxo<I>Hbn^$j*zIEqz^zBD?
z;_p1an{xNvy^MRUwX*vP_h&!Qda&%F!NYZR=5^(d93LJ1!{?97kHa3<)^qD$K9M~6
z`ZWKU`m<#X#tmDa+di*+;rHUopHY84eJOg``l{fy_UqC&1K#Xw^k}^NcI?}y@5Jxg
z-_LF8*|hnC{fAQ@Lq0xiPHk>&nf<Bfr!Ak^pR2w^d}(N%^p*Lw_?!8+!)+tl?zgA5
zw}Zd`s?=>x-R9J7{(t&5mnmFBVi67!hj7RB<AU(CV3jJBHx>tZlJP-df$36CTnY?R
z!&wi=;t}4MpfFe(8x$5CHxUE@$0Z~pNzp-Jol6Mo5I(~D4ujJrBPkIC-8v*0;ngAn
zM2LuxG%$vcAW|e9$pD`nO+qq}EW{a^jAVn~IY=(}Jq5{(lqRL8#tAt5l<^?eX*^u)
z@DwDb@S@2Q4DH%c6$S#|OQNJ=Fg}-HWMe@?u#X7CM~@EhiP6#`*#i0T81Z<%08CAb
zgeh2X-3egqk8jqBi3bgqA${PmNT#DKQ<ka9ddiS%z|uTq8ZtdHD9lnKh2KWVRARLH
zIry->oIIU8EgT)4{6L&{8#@cOqm!M5leee6oju#u$=ksm_r?sQAVy2G!^?P_^7Y6J
zWG2D`4#vyZkV2qnHZpe{P$R^AE?JArk*UeFZy`m<d{DUnS%@q`79&f**ee@ZjueB&
zrSjuLgrGqoLa=H8R+}OMyuIOt0}A;P&|<6-yI^BQ1j%SPs22Ab3Y)1e(^3Xd$!(d&
zH6$A7Sb<Ck)>a!v%`j0jOdYcg@P;6MI&stt)89@_kq!enHN!;BFi|s1)C?0f!$i$6
z{qLG#0)L#;s97dzmg&EDmPw{?0|^34Ng-f(u?n$ARwLoTTKd+OHn!G|Hg>iwYfC44
zw!Mvw1Iq@HY08+_kTHOAEiz6El_}yQs13-*D1UE!+zG~e>yS+dPac6&Z9%q9=on>T
zJ1y5ETaaxC4~#{zB0k32fy`Zp{D$n5>B{tf99-=NOIb@17i2$Dfe(=MWLir2h_D+z
zKsrbbkf<ei@ZhAD;ISbNwFFNs!Bb1{)Dk?k1Wzr&Q%msF5<Im8|6f~zmmeG*AqPjg
z)|R%mY#Tc;G-BJ^*|8mok<s@HLC293*jVK>at0r(bcbV=p)#<n(*qx?oco8d3V4H4
zr?w1JTZXYWd1^t2TF`+Vf!Z=mZ5gJv3{zW%sV&3QmSJkkFtug)`z^!r<CP2Kc%_@Q
zrL~QnwX>r=7_m4y*xB1Tb*$yUjfcM@)#LoV{fW(d>yR3RCs!VWA1`qo?6eC83qRM9
z8%Q`<v583HOL<<_h!$PO>}vnuZR8HvGk6!<KUj<0$Mz3ClsVxGHhq+3hB70th0mPa
z!uJQZanIh@*NN@z<!IsM=mj?J*?9U`c=|Y6TR7PG`q{F*y`BAhyzrHxdSvce<cZAq
z$5o;R<T<tl?<MjIA9=ChHo75gmqTSH_{i(cKa9MnQ5QAp`d=~X(!i|vr(beg*#7MW
zpMRTC&Nw+c{>`s0|4lYI8z4x1tVw;WNqwwIeXL1+tVw;WNqwwIeXRLE@v)}-$m<<B
z@-iT|(b+gT%k89dc3^i5z~E;^AK{j_W~4>dS7!R-VCD<biY+6xA?^4OreDXFxBl2*
z21TjC47KGAy!BFB-mqZ+HISjUy#3$U@&>xICUx=*b@B{#@(gwI40ZAhb@I&r+{rU?
zgBg?|H<*Fj-fY3Si?+_>-ZuwlaG1i+_P?P@s4^V5plYZ(z6xX3vHxuVK5)^b1}@aV
zg&MeEqZaUs8n{pcm;W0B7i!Q#4O*x{%YQs*0e=&;s3S9}BQvQZGymlyGvx;^I^@8`
zklg=f=j31qM=tmtI9mrta^!Ly)raT1q1{m<v_II~W|_>(l59eIfHSdsp}kQ9)KE51
zHb^#DW+AhbS#1IbWwTHdv=7=B?I*LA*~n~Vb~1aJgUm5TOAl_N<D^KW95D=E0!!(V
z7%d$fjURnSB6a1FB6v=mWoC?4Hyle!_<MX-jMh)2Wc4LqD*Fz7goFPIfSuET+M)J9
zXvawnK71}bxsD^wS`Vrmumc4<aW_#X;5RnvjJlw%s2l1Ijt(CRPA7x`P{7H9Sb!H0
zStwBgaG^Yrggkdp#+G^Nuug!Fp`2wNW3%n82ea(0bFqDWs2AARhkDCgWZHPy4-Hs{
z`lG{St}-{-&}--jpm8J`2z(tB1|voRxghoq8FWeJE*lc8ZK@F%hzvxKo=V7OaFQxW
zv`y{pnjn!mvZ<|I1K=SJf%wgB1Rarsawly&MvIjRSrCT&FhPEpLm4zI$K{f;JM<9r
zkQbu^Ig0V%wxFOM-2!C6M9^Y50K+!mxHiCp^TQmGqlYw_Bptw$w&3(Yha4@G)xl5j
zKzhtqu#U80E3OX(ImQpmm>fKZGSIESHjp}aKs~wz)I(ZW2k9X{K?`w^7V?sHSQ+CZ
za1e&Pge@cw<^z(Sqyc3F;)q<GfYrCPx2r?F-__$bb<l2W12m8hBtTE%5x}R9$-((C
z{T*_Mb`fRB2lbN)>ce#c+1qis2&92&JLq2^hj=W9@J|w?!|;$E${_gkf8>K@SWn2r
zYymKdgE}A&#Op&mNKiM11G(Cc&ZG}uM6OKMK|F+iR;RY34Xf**fwBQD)bUpy*cP0g
zl-)^#<Dm|DJ5+Z9Ilr~HcLNE5IkW}jB#y)*fUX-%1TBOi4$?sxFu^*Km&`lyxPDCD
z&NiGL=z?+}4b+Xmw!)l{Mb?oyMpsVQ0diPYCCVgB=H1Gn4v?^%&?CnSWs(WfkT8}L
zvb#Zg5{5QnJiy0<9H>igS9`k(kswT#^>(!ZJWLSRhT+LN9M|67o4}JaKa=w#j?_WQ
zBJ<vIHuT<2>IJ%Y<4FbPd)nJ|_W)Z!-Ww#CLp)ih0{D6pbQm8&2Wgc-(gg|fbnp|r
zu#QYn7Ac$54ReTtbtD~LZfn=w3*>^-i^!GtcH$r&(kt!5X&|giz*rr@2k}rIv=8R6
zOv=;U2igSdARo-39*|==LT8750*~o|b`X5M_T&7$fDAH0c@T#3m56$X(*ZD~$9OP&
z8$kzXf2hOxaT$;g<Hcm+`k-u57FkE~kT&U509_Sr?RXAkfU*uK_X0T!gK->LhIv;>
zUIwI*FJtz}$;0cQUfjm-XaOD8R*?5Rfa^0p2;{*8$~{32<-<CNhdIO<gB;R89JC$c
zAw9&yda_KyBp=j`!H|}aBhQb?0Qe3%f`-KRJk;K<4HC>T*-+QPHrzHy3-FlSpX3-1
z#Dk=b@!@jFc68K3Ik29TL6nVw9;g%J#pK|6F<MA}xHD-(7}9|J$3)VRG-Mgifev{d
zAVcdg#C76f`(YbMK1>dw8`i-x)JfvWyvLFDc9<~vL=Iu2N<a_Fkf##{ZG$+Fdcb;2
zW*f$j!DJm_7cLv;A@vv$GD+E_Jg5`WV6vfn0`3R&K>aX5og{2j30~Z_2^^p`s%*pR
zAU&jm{6<GRc}QL|$K?PXNJl0pACuMI-dA1@0h4u@e26=W>%?S1T_8c7m|jA6U&5~M
zXfR&fHj;<TAsv)Y>hEZSoP3hDBPZ#hZU94`zH;<N$G*3VtjBUt2jz5!Gz3h_h4my2
zl-D5#&|&mMJ&A{WWP<gO2ji2&W3+O*I^-P3WnyIrgVYx!jGoNp`1%qsmP1-tCV8M;
zpp4mYtgTZPlp`+-!jP84gB<FGykvW!4Td0Lwh%dlG5iVny0-Sduq=mze6UQ)z}wW;
zA+JLYArIC=p1#L%S!6xgE?5U)(l>ZLF3<1;&eMmqfykjv@;C_ph{J3o@MIfEI;g{p
zkR`7J(vgYehdHSWlLL5R-sdEc0g~Y<9EN3>U>U;`^?hJHNdt02f(GLw<UsiyG*He@
zWRQ9wKSqBN_=w;$lhXm~p?-+#NT+eQ!xm8PL*PgnOee%cz8~r_T1Zdu^f}#z$(E;u
zG+3F?D_@7n>%g~Rbf$!kL1)_AO^MV8<bwcy;8~DE9Dq%ME=)GaF&==2bP$K}5wws`
z9tU;evO4)p0WBs|jvvZ`av(ic59>gJHkh6xZ~(^2Kj0ueNeA<RfS#m-FswI$JTMV;
zCg<9|;~{knB=C?PlO@O3L4(y1{8fZLn4fQNw}d$XV|D--;w(X(DNL{q+5mHqFnb^j
z>LA{dsIxo|<imQBj>sVnlLzRqI><-hu(nnKKSNqf4v=vHz%UW|`vVx-g5%rTEn%5R
zm<=6uI31*iv?LA23u!=tIC=Sh!I6A~Ov?+M`bb?C7u(xyKr#mj!Z1OYEW;eulS!WD
zXSC2Jh==qfEH6WzUtTYSNqHDQkb~haf&5ZiyUk^g%Owj4gE}k+FwDsW=`a}b5-`b2
z(nDUX4#*~HJL(`0Sr6b2UU?jVA-(nQ?d|sRsk##f>nuRo0_H@*Y1`VZ2|7rF(fuTc
zwDNTrFXWTsmDde*VDbnWtX$LHZU>S*z}eLRy#x$%ScY{_9+qP`d3sPs(gPY?23{{u
zPvRjhF86yGkQSy6I>J6k>v#q5fMf>~EMLW8h=Z^-NEi*wVVR)E=s?*HCWs^IU>T!_
zIsx7Z<|GX$lQ5Y>T1+>ffwZK466bgg)PqFogJm*dy5#GiZn9nuc7`xWP>#GTjGx5A
z{AwG<FE6)4Kjf8@gXtl7G59*r0g^MMBVaOj24xb)^vlsg9x_21m>@ldBV={(l6Zm+
z!vh-3M!7nYAIon5c|>xQgGm|^X9wy?7}7)dXNjzn&q?_|#X-A%BCCV;W_!CENUk7}
zavUKnmtZ|v{;m$n1$88@Ll3|~9{EJ_f0xVgWBLg_P#&oh;vH|{`eBa2AZLT*3d=-2
z#6z4jksyqfp-cjX^dyYgAy+2rI%E=jundy}kz;led{74aHmD~OsT0c~4)TB;mdPB_
zlQzJ52$MQG_y~EJTu={r9H1PS2zpG0JPyDecFNHq8X)*A9gHVY5JMILUkr7^Gz<p8
zW_wnkARH@31Bg*Y0;U028-c-SVf<PpR7Z}c8v%n_uz=PnvpY%O6G08+=tJe;Fbsxz
zkIj@IOc>D!z~_JtpoRE{0-f;R|9|6YLvK(qwPDYrzHWkjf!ZWU#nh%^YEv<_shHYS
zOl>NrHWgEwimClyjH!)paSK65f#9q`VP1S`Do2EEQUjaF)}x_l7{dEW4G8*565+9o
zwxOff!O%s-sd#7vJQdH!nq`f^07>9bJ#-uz1@^jOT<g$rnAB|iB)!2bJkKSWCqQO@
zz-;N{0QgBX2SeMCXk2VO%8L#eJCc*d6NgE&Qh4jo1h6Ad9)rcQ9l^`u#e0gmN&F0+
z_$tarCyq<$;E|zVeAN>~(jA02fM7~Pk!s`yDn#d@YtfVF@90akg@)49Xe^p7&5ssF
zi>6JaO{LADEvIdy?Vw$x-KKq{Gw3?>{&Z)$FMSj}o-Uy;rmv>&p<kmvWhgLuF)SEX
z3>$_W!-3(%aAvqN+!;d|o(yk>55te)&j?_QU<5LP8KW4XjBv(iMg$|8F@X`wh+}XW
zJjP_kOvXIM3Pw5O9ODh+E#p0-U4fyXqM)Xrp`fMEQ^7<#Ra>Z?sXbkLuJ#J;wc1;>
zcWWQkuF}4&eOLRj_Mh5~ItDtXI`%rAI>9<oI`KMuogAInIwd+Qb=K&t(^;>xQD?Kx
zR-NrS`*e=!oX}~|d8<p)Ro2zf?X7F7Ypy#;*Fx7%H&U0QE78r-ou)few@`PE?p)o4
zx{Gy7jh`F;Y23=vW9hScuuNEeS^Zg-EEm>L)(BQOi_4nG60@>dGS)O!KC6IL#@fKz
z#M;8z%G$}=#X4l6V8OKLVPR=uZDDIM!Xm^X)FRwsw8c1!XbYb6Zs)zuXPoalKXiWN
z{KENz^Jf=^i>gZx7nVz3m;NqhF5xa?T%ugYyG(G2b%}F{cS&#&x}>=jyR3ED;IheO
zi_1xub1vsyF1lQDx#DuorPklf-`wBDKhi(Sf4qN!f3kn7ztBI;f42X8|AqdG{g?Qc
z_^<Te5cW3geK<Y5Pk6s@)9|6;KH+}h!@>i?gTh0?BV#wlZjL<|dnfi@?ETmmvF~Er
zI0%QvVQ>^VN*onVcTP`EZ;m0ym}A1}%jwT?<BZ}Ya;9>YbAIDgaE^1%bFOmkaq2m*
z;%IToar$wC;%wuF#`(ns#f8L$#zn+M#zn=&$EC)J;>2;%xZJpDartotakJtU#%+n)
z7Pn*8u~}8K?#=pR)`wZ`g-V6$g}n-m3rz|K7TOj%6%H?qD2yrO6bcG63Ns7y3X2LC
z7p^JXQ+S~86gEIYXkf+y{-DWtPW~=DQ?Mw1d9ij(h-av8P<YHp&q&|U7!ZOQL%Z7g
z`m+7(e4Q+uJ=wMv_I|!>3r{aEXL#(SwKq6_($3G?#=_CT!QRfo+S=OI&)%8sZRc#`
z;OFOq2~fx71&4$O1P6ryRDQlvP*@NMmCX|)`T5MCurZvJbRM!aA03#VkEWHO7@Jm1
zKv+yzc;rZ5!XOMwkMs>IC{PEcE2v*YG?!`)()dlo6@b(a<pIhAlm{peP#&N>KzV@j
z0ObM71C$3S5B#t8fKWjpkDf^Lva@#d@v`=_u(PqTv#@77+gLbz+dEl!+j`ls*=%pN
zrxRO#jOk$Q<zwr>27^s+Fsw}FB=Q1+{e<FFjue{!!e_61I8siiaFSP+lqU%gBnZLO
zj|w7U0EMYazgS@i{LE6E!IOlGIRc4@BjyRXS%l6UI?$NT{KXnOo9Lay5hU`$`Kdhc
zVQi|1(CT@}V(XilD$67q&^})I7i%BJk#rhBXyl_n(+HDatSN|-$|Dr8#JmI^^ehjK
z{~5?&Fp~jxV=M!}-~1#g1GA#gUu>3F3jV=zpfH{^&rF~MG;!&_S`#)C3;QzO!3k$D
zbd<PEpl{T#ZdtI1!{tlK&Kp0iG*yua9GHJl?-w^LOvK~z6Zl-%G_p~u5~-NSNtI@a
zc=379>G`M!m@WF*7uR6oNG&L=^J{BR29rv`aT9r5DKaA;<uizRrZA}(d~A+_$)!PR
zU^>Y&+&9L@Gu#u-ZRO{ycYezai~uqeflLT3T{i9TTm;Eiin4RGwsqu=j}xWvB~oka
z8(`B7+!(|_6c9x;5ltG~56mM=z-QoN`w21alE{7>VtP5epHwK)LC}IV<ywAVCyx>`
z5*dv|Au&h-G7(8dWJo?T3z>(MAf?C}WIeJO*^cZ&4j_k-W5`+LJaQSif?P#zA$7=O
z<QeiBd5e5NK7(yPbd-r|pxS6R)D$&C2cYKYKy(l~7_~qxQ7hCMwLxuBN7MuLM12uk
zbT}G>2BYEV7&H=%M#rPED0g%}L3&C`ot7!0jHn>0h#I1fXds%17NU*lAi9Vi(hbo^
zx+6WnWOy%d46^}Zh!`Qp2n#U*ljD7nen@}B6fpx^rp&>o<b%KsXn|NFR){r7wjkMq
z>2pT_IU-I78w9s;L0my{2fv4aT1SvPLD?H5U+~)x<8j9DklO_r5u?>RfuE8xk}u$K
z#AN)P5O@xNBt~QtqovIiiuk;E{9!Ll7sP{u1Pnpn5hn9~&SM*e*J4Wpg!EACJc1vk
zlavwscxh53w89(2R}_zvByr-wACr`HF;Fk&CniZlIMO6vK|E#_SX<x=1YqgmJ1Sm6
z3h0lZa}YOGG!#cF919EKpr?<=P4g9S;!=3=;6wyDNd#jQq$LQVa7!?&#^W$SHKrdo
zg(Cs3z?}E<=GjCOEC~q`p7d}1Y&!urj5L@~)?ufXoSmM6#1ybTqzxX?M3XzL#jXlw
zEnzu9t0i|0F%+RLL@41ypLJZ(E*dfpw+?y`D3-G&m}nZ&A%4zo3l_6dcnLpT#J}xB
zpx?<~nxFasYp1N2%uCiw3jqn0(X?n<@iHYXL`iFelFVD?3u=4G43xCSDam@{0iPhE
zmy*oqCK8AQfml)@;FcSOgo4{B3<(EkMR*HSMM42M#z8U~oE72kg`q&73<Hm9&~_a_
zkuH%6Q!(WjGFVIRuc3HZHo;o`0ovNZ-rCvP0UlIn%?2-C&UUsi{uMaAkPS~`w{}8g
zAavDrWDF7k3>k|=BIClrf}Iag9^mb*#gs8*ig>iB@yG;tCP+MZWCm-)ZHj8c!0jib
zX(33eKq>~Nvc^V^V=<|yQB>3@>=liQ)l0?dg&l#4)l0?drDF9`v3mcFSiRusp@NP7
zHKO49|6q-C0HA&-4^SSUJV1GX@&M%l$^(=KC=XB`pgcf%fbzh9%>#V(|Bh8`c<3{+
z#+mJC=Qx<<XzP$0uLe|&{?$wK0g^F%2_LK^5&9BUftKK&zj*OB7_5Nt1!R-J8P(Rg
zd=;Q5=2!2m2#|QE@I@l9h8xI{BohilM$j309oIE;(sI+`l?n(<{s4=8&m*z=4_b_^
zXplQ7kcmAI7a$tundqydlMWi*%st*aWo}wy#w&JV>IwCg53}tK_Dd`6U7NP3Y}MDt
z$=)*6XcldR{%Ef?4;NhVR0!~Y{b7PsdgDm`WcPvoM?+KE-_*UF^3b8F#4d5~{1vYm
z@1MBUwC*UmyEn0Bb>z#|+?ExQk~h7bZ7WwDn)=~+%9rriZ*?=7=jOgx$gC>LRn=TF
z_fn=PC*a9i<@&l)rx%+_>la>F{P5KJFjN1wzW(Lc%$382_np5qsPWv?q@EwH?aRvf
zGIwsw@1@;d*lqu6@%3<}t=p)`{@d(UZ`*lcRPWVWf4h`fvD3sYs!r?6#t*_Il^T18
z8Hr(E_V4rgvf0fkJ0o7kPgb?MTyotvJG+PdB*Cf5EYA6_pOsbheF`k1dlXoVTVfME
zckG^iwtJM;FSodqIcjd??mm?VbF$*+_BbNuJ=ti$jVOO}?w~`m?eH^~Uf%LO*PV0f
z%SX<lOy7|YcbVAo>dy(52p$@gRPaU=uTFe<Yi>_>>*Utv10iEPJ~kV!-NyCba%W-4
zZ~U-LS;fba{RceQZx>o2N!)U8|DYR<_q$CD+vcFT)SP!@(#(mYYVVeeT6JyLkvT5E
zFK-N-)s)?wKYY~gNx`Lp`>U2XOygCa+p|e;)64yHZ?7F5KGA*nGN<%6$`;wp1qoMo
zO%iU=y}6{dng99N75%Fj&99a1G+wD*_c?ov{^c@#?QcyU#m8TIjJ;8^BO)y}xHuyz
zctGuOtMEAqzg^#!zT)G!^%aTZ95e?vNCxhEGx%h4NkU28>9+9K!Lcj8#ZOglD)`pm
zDmIYuja7@T+UL$Rj~nx4#r)u<4<jms<;x}|h$;t3UpZSDOp1MY=~8pj(dq|Y4WSKt
zy+l*YHz)TDVLtxccxUV3J$9EzzAP+RoAqZ{eTCuLpmhGSO(Q0yXxdf0R$IDm;-%^k
zs}&U~n)B9WT)$-<mY(srneptU@8?^ooO>0iyN4{_H1b}B!`#<*bj}ItbS6C9<2+Jp
z@lC(jY4<K)TB$QZN7pe`rF_=4kG)UrTVkHPxY;QvgqC{qN=8g*l;VNprgisnjH(z_
zg|p(Swrm{H*J@w^d(&K<B8M|;1{+ySFepqLZgc6~oUwk<b2g+E6)!(K^j)PWM_MWU
zDs@ZGQF#3Ny1IJL;EisP5x3_Zb8yhEdf)F@#M$D;PcceaeJ=H0@??=gwR6^<ui?m{
zInmGVFROC4p5L_Lg80S3G~vlZ2ecYqc$L-$cRO-ss(;&tM>Ut~c9y>R9Q)1g?8l8}
z(KTx6+Ku$v5h6p!o%QEluUTO9KvU%QM+S3WfUEJr*R<5T2XlMJdDJaA;1Uy<d3ckh
z+bWl`ldUQb%BON8qWc6n3(&O7Y4dtCWcq*YU3~7;uwKSZ51BU0Ht97DiL<eB4jL3>
z7kEEI&3OAk!+?6%N7?mD=xh4TD18*oX|Sud$PyhZeD4<aJZIyRflqgPy_#H*GgeJJ
z;KP$_j|rA%c1DzVO);xGJ@}8xvX;DAZsoQ!-OGD?GTIsUR<CvL@;@>^^?q}0z*PFJ
z(=U<D$@-th|8`>ix)Fsd^fpBnB=&svGOYMq`&;|3<-QNY&7I~54m8Fl-IrKje&N>R
znaWpT(XH)wty)|UnjJQ-O}gr`pw2S$$dbx)ZEuZYSMWaH7I{tJmffw1FI#l2RlQQf
zfZl3RwbsF1pEqvEyxi~$?@qO^8P^tJdHjrO9`|>fo%#mC*0P#~=5yK(xPFOUdZL^8
z>-^GIm#@LruP57v9oOu$&HKjYUWbeyP0#$}a_X0Pvi8)#yA|%CeFvTyBt8@2QZe)5
zZ{tk2oW4HerBj<z<j4hdn&tVgdi%9cpk~UYqEpKPIqUA}wmIe2Z@Szr^1R0>6dk!-
z->kZi?;TY$z5Q&R%-LY5tzOtHi-OF>RcXow<prB;22a0Z8n<Iay^)P|<Fv1i8I_k^
zS*4fJlMfSBzG><(cNC=^FJ`*LxDClo_;5T<L#cY`f~T8}20W8hPtPhH)O03#n?aOX
z!y~WK?M>|CWtJJU+k+nEd>(4m-Z-u1!HY+euj%fLI+w21ruAmhz=uqM#KU#&>%GVU
z&Vd(h`?7Ym<Z<bGACL9PoswB6F*kdEOz<stDyvaq;CRqD{+-L&cLuL=Gluji&q1oy
ztGhpPTeQ}VTQ@y`b$j~Oi-8MTj^!BF+5bMhDaNGv==qA&lKO6OHJ7}nWK39H5p^i)
z&?*CsX1CZCmGd>PIQa}+cvADdPn9m)&nTKM(mlZuEqva0T9#E+V#1|lgn26aRX|1`
z&?_W1!G*zD$9O{8^}uOQPwe~Sqhl(4MN6aRyj_hU#TNHkj8vNXwtBZ7`^L$LHj5NG
zHjUdHd4qqfIhJ;PeEFo!qxWsJ{{8Ui$(q%L*?WYqg0~kho@TbEwDIC3I|I$HBEdbU
z7u@iTA0MYJYTExgU`>p6`)O6@TL$&dZ<!CV8d=-3x6jgOBjyL^bINHR*9RmrO3S`@
z9=9?|GM2i}{%H2m^Ura<GpF>OCC+;Ep=kUcOUEBMeZ%mGceig>;<kMi?inz8)hd(d
zWvO?2t*s8$wX2Zk_gpz+Q<~Sk*(KX5=;cZ|qCNNY+n5_)F0PB*ck<n-RGmG|E4+>$
zt=(7ae5YMD)*}U}nltQ6>(zH>jC#}u_L}pa9i+Y2;CRqk`g*!oFSFp=^d`%s7Q5W!
z<i*972K9z5!OvtppDfW67o{1A1sC6sMyvUWf97c}UlPx5=+#i~R(;p~kJ728cWfSB
zAUcMu<~ryYS9NzroJ+5{(|RIZKf0{x;KPQs<7KslwSfiqmeuyU(tO|O<U?lU%5H+1
zXVOY5m%4yTp-0$=9#`&nTRHi|kWtTbi>60L>lKxRTyB+pS~;`0^laj&SG}y#R`n3R
z%02fPEp-}stlX+G$0MkZ(;wOH<7@jL=+?K%J<TOIC*;m?|DtoP>spn?S%==c=DK@K
zVH&K_y3hKOS4pe7bVQ+PFtgOMZh4hk_?s~EnBCVOO|*R1>%6Ph+g@)9>^!HtsWi-f
zV|uvtcHWiDHFFY9?)m!uF0;9RQskUxQBS0@y8Rd0$7-aGW4bkd{PW}L!_$@bPfOin
zc#E$qkyvY1o|~LLttBMm^~x6a&z70%9_X<3GuIhv$2{qg7M)W%py85w-qXQNY3F`>
z;l%h7>TG=C(C&6L=)AbP@0m8QRgyKwwOZCo4cF|vk-Yk8y6fn~@cE1C)c6msxqbX?
zk9Yd;wg}Trms(9Pw#(}J+^FGyv9L|wJltk{)2B!FHxs`o4Vv*X_w1&k5X)EjS@)-G
zQn?plvd{hAqfF0$TB)nTfjYOd(F=x5tx{l(LDEjA<rhExq%-Pr+SikGbHg%OvF}$}
z)!tzz(>a-Qn48||1l^{;8@l+o(MUJ5itZARUCe2c8HI=X-R_=r-t%16i`_5I8eYlz
zJ*zsahVAq$O+(>GS=z(j>+h$O9tqBGYJV}A{_^<Zlb_#DozNC^w&AH%WcXb9_IkIS
z$>%=4i+|HFd39Cl$yECN32Lv+9|QLa1=CH_j+ZRFGhs%p)s>z6`bUn9Y>$zm((~SG
zHfQv!da|0?8lFy#x-)K1Hmm~=wF{5eoPWHYm7woZmbPPt-Mb-~k#|~6H?W5muP&XR
z9CG!u%%ES+?*p477HpsG5#w+2VP=mv+Xv=Hk5f8yz^wF$de(z)ZXf8L5pR|cQZmT9
zR^(9_weNgik#bJn)65C4C%oZ|__E`zw%Zx|3nRw)9@+GQ%Uv<Warl>~_Z!DdvR)gh
zp}1P;o@sq;$th`V9%IuxyYtyS?fEO7s?XG2vES0(z)gpLncah*u<HKg-<d@|pVVR_
zD|QyIuMyUqth~*eJV9jw+kcnvs`At6H=R{l9%v|EuAepIO3a~klBr_{zV*)0VmHgI
z9^Wd}tj>F|;N^=_??(o->!r0`Ia3lZ*yOc&{;A%^ykR({@8#!98kDC*96aWm@#oPa
z&G{v+uD*|_dKjBn2aaET>*~VK#-e$}bxup8GW-TUnaW<Xo4%rC<)Su;C|lAh$(Ht>
zx8?V11@mehmh2e6(wX&0c7|5PtnWcPqhFt?J4#LLW?MA+oaOURV|EO^VskakwlVKa
zXw~Qe&+;DK8M#BTN)YGVmT1s&@(?R8`^4gG`pHA|J*8e-qZiN2VN?feKONk#%dJ}C
z@kU~KxxTcdVd1-ayHd%j>8fcv9jhD8?outC75DP4>%*<s!(-~^$4%mGqM+<kA5G`K
z9Ak9bdskD>+fkyU)!q-72NVv>ce~m;_ui7)lG^c3Ny`-uzBaAC?QraLs<CM3l<qox
z)oj*lRq0pts#;xlyLHwKkLbbAa*MWR8sCmCuFj6B9J2mX%B-7bOEqU_)%iS~9&qYV
zUi7HTt)<*jN$K>Uo1Y&a4fZWozBup6Fjma73-433pRYU#`cU8-)5HTx!{4*7Fnv~=
z)!EH!EE(fztTRRCguJv*ik!D)SnG`Y?GeWl%kPVXALc*rWq#0b^~`o(=E!fB%@;QA
zo8@=N$olnXQ_kj7hh+S(6Dx*4YYIANJAuB;!9elz=<1iRa@nz~CJ5c)pA=4O@tkvC
zSn;-5_PRA4godjBlm^~>i^^K>xCy4e-1~9dfeB@igSPq38&duT`8Gv7{_2_+w~j`8
zzj^O_(QWW))#a}O*BI{}RlD)Y;l1VARa3lA9$GVXfzJK**}WeZe$f76VAf#sSz}~V
z?!}KiWvcIUKM8J_Oz{sXPuQk`d>kCpFz{(^H}Q!-_5S324ssr^Qa}Is=4THXYlPNC
z0imnrw;c$4S@_<f*7m;Btd_gXOO9quf~b9m?lgzqx8$!E?A6}Sxwr2RBaH(eE152q
zvicb&n|f6B6sgS~V5+_HJ-Yzi{kzsn*}(>OkI(LISJLjL9lnCTo>E|wTa9KdG8olO
z;%CD=(;oCh7TbQQzErU~>yg{@%A``)-`DH5WojrjPrH8d&AT8*!=?7D&#u~K^RJ(5
zaG(EjxJ`8V+?RF6>*{J=_nBWCm@?v2s_Ok`Yu-+_I(YQj6n@o$eJ^P(VFtUJ(tbl`
zbu0MvN9N;Ck258QANr?UdR)4z3ANd6)@XP2Gi%;~tj4~U^*J*hHe?E3<>>0Qq#aXx
z$`GHvP^uYJ^|{a7tnk&BrZ?SfIk9v3+R6YU3r1aiuboX+Sv}96HqxBkxPQQ;BQf)%
zgI1eW(XXHQ7QbNbsKA_)53j7@Ro<?R&8?K_2$PnMu)pA$^Fa5NHCy!#X8>A$v31q%
z#JuHsN4Gz{V&YtD@_Bry?a-)wn;#Y&Kl@ZX^wIudU&hU;xH|j#>)XleM%yP%eYw9f
z^5yRjruMts{Z(D*xAAYCb4+hs=v|*4Ww<h|=K)V+!`er!w+!zamkam04HZm3cEdDv
zG55@*`DJC-)_jii*C<dqxjVM!x0y0KTGheGrgz_7MGem|9N#MIZu{))?YhWwlaL&(
zSJUS%-MOrN_tb9VCa50?Kc4ZYQ^B9(BRAjKJLs0vmxcA;6t`_2?fN8h-R$i4C(~|>
zdeb{TI%|DwigNm-_d8c{uD#hg#M4bHAw4OI*4<uU_HOm88||W@t#x`Q$7*ldn|x;C
z;(dV!!bGmsA+?+4UB7kGNa?GFG)GkVsAV0u(Bzz!@Wi_+8}miy%&RQw4=SE<6@8^w
zoju<(lclx1@M_WB1(Q57H&qPzQf9(r8~12Xd9UcqC=EN2WqRn@k_GeJFKbI8R#cVV
ze$Q@qHg*}>tKo`U;-6E`9Xh6!eX=C3d)8)I)6@3Kud8O&oP6=9Nf14B@~Mw1(JhaB
z+8QR8&Yb*avZg>a>wdzC)C+f7b!NxBRJdn((BepH?|bgA>)QNNkjDz_DO$m@J8`od
zirn9|>1lIyZHAvIe)_4uqH2|6l`Bh8f3h(<VOqWUZU>i`>eh3=jq6r#!yYod{(#9Q
z-8z%ToVj-$vKr|PTA#exW#cv1)jxG!*yyR<aA|t*<&cNxtlm0&X}{k%bML~sEXRQT
z@27aH>||cmeWI92x4+Qyj-uYn-tjI)FJj$)o4;73u74ZNdFu0sTe4o+O?B#Y(~~(y
z%bxP>nDg^1o=@^>+WRyqb<OMg5fTl9TlW2$9uD1<`+G*vhm)t@TYuhsY{Z<>Wyc)O
zW$b&t{~3MezM7fd(<@%qD}2)ZHqm`v?TOdV_$wv*3=LX+>K&hhqtn^?uX<OHn3_;>
zU$gjG`WIdELyGPZtp#C{S7;-*J<YRJ>#g@O=IsUMFRV6{-KxuzXuNWIYf$<n=+L(2
za<A6W`-TQ<M6CZjFtB@b!&24gT@xeTSF*mSzkE8sdFXuCs@_{0wf`LQ#^3z@5Sb&_
zm~qbMYK3U1ckTKZ(Xtb0$>w?b63;1BX^nm<brWA^t{J~zh-Z~TRbeG_#qt^4xrSPk
z4_m4ganIRk%*t){n4&qoz8`zaG;VVK*g<ix0_%5utI^tPc)CoGa`~}qi%2rGWpDby
z*fgfJdf2d?%C6-yNl~NI9=hK(H(qH~Y3+M9b46I1M+@DheOi;)r20WtX=9U~<$=DX
z8W{^Gsjy?(z*t)OQ*oKbkOS_X@a@Koy|D3V+Bb(Asr6sVs$TbNOBkNko91eqdik~0
z{94oFH-nz!&Q;jk)UCP2^WnEOSH2kt<6dbO`Ck1z_wi`XRAZZJt(FsMS_Z~}bIXc0
zo~mXR9hvcvS#&eDQIax$z^&TPeJ(Q73^*4DopP@CdDz|Eiurk)R)ejm=+ZC=>rl7z
z3u9~SGAacF{97kmF4()hxl#G)#j1HLHnq<fvf@nd85etHDP4MZBr`Dh(zd6457H+%
zMNPQ6<te*wHSO#d#r0Mh6P=TyD$4_h?0Ner?a-BzSEQe|RG62AU6B4hA?t$4&eKM>
z8qWJ_7|+k0q^fDCDrvb;|Aa4jBKmMPEjQ!MyZW`GuYZ`g-@tiMR$a=b{lN!~i|kwO
z^cL?s&8bKlx`oGl!X8)D_ub*$lcvS2EYF=FD=YjmJ$mJ9vFXn86=RM*-1)kGoPpUI
zp;hztjb~n#yZ9M&pHSaxaGPE5rDqp3BdcxeR?Zw+f5W4_cZ24nWcM6)R^Uedm4?R^
zOI{z(vRj?qoV&WnscOT-x`!dz9<?{q3MZ5|-QB^x!>WrvE|Qk&)a<zFcyYhr^+NBp
zeyv`{CiY3L4)-kU){c2IM*X<|)CnaIBYxvXY^_;wEGqp-Sna(dQ4K-qg4T-XMU`c{
z13nnvdf6jf&^YW(-j(KrH+S^S8}FDl+-Sa0bn))a;rI0FXXQCoIXup3UsG#(hd!^?
z-spLS(I~YObVJQOY?E1rk1`iky({R!IMcVvtZIkaS=%ay`h(1(nP$0D*3@qo-Jg*&
zHTEIBELBkbn5|WNa`$S3SSG`5L2F*=pc7g7rCtIQ47iL8tj!mHWqfY)5cfPZ=NfqT
z)_mWVSn4#dX`+$X%2KKNlH1I_!I$syjcV#E>vWq7uAeP>pTWfcWTM6|!Jo|iX3<tE
zYukEENj|uGRLhC<-CDP!yjNwoPde%((AD0reQ%`6zOq^OB8?9C-s_*LcR%4l!I68?
znm7K@qEcDd$06;e^U?}?^nd@RN_ekF4dZxv-q?ma%%Z}O=ecdEfzkcX(cay9*WF+-
z+As*bel;pKT&n*nYw~RjYZF;s@P26XWWCq>mL}htON*Z!bK5;t^9i@G+&oy{TOb>x
zN3UZ=jM-3|*gI=a>XWOj?`zMXY1?%_syhxUce>E}R=Gvp@p<kC3y0VDbe~;(@#olv
zw+o-o{t$RbXgy)3yEwdPZ}71JiHmzzoDY1JAZz(!+=m(aFH98~x2Qj!nfks`q5P=+
z_}@5Xm0@KEp5J-Nd4qnbUetE#)r#M?1aORE*2PEdaPqHRvAt&K-GlQ?Q<vw?Y2ElS
z=Ucbp1O89f)}#hBY@J`bW$FC0_0kVX_nAfXAlH@<vD%%MKiyZMW~Jp^*NF17$Hr$F
zztJomG5<AZu*T|O<AhgD*WGr`x4LzHaoU7xr<!$wXHB1`+-oeEx~9$T-pLOS#_g;b
z6c%X`v1822Ew^0^W)8Qg`nE}Uy6H*IxZ~jhc5YS1&A|OL3=PGjj!tdbbjL9%_{1me
z3iYe1_1~ryw*N70U%-LuZu@u#k`GASw4UoSsuUhMt*ckm8etG&ka*%9^OekF2d!$C
z+F8dc=c*yP->lBQv#m2e@1R$8yGOF`+@nRO`!wbi)p`teI@4T9*MF&c*>#O1;8awj
zs?^99^t0Bo7Z=)39MYcs*_eHx*ZanjFUIVamiz~QNO!HTe1H${GR{`DNZ)3?QocPs
zE=2hL;1u*@^+C&Cw{N=by#45l;J#5@t(IWW0%zk5uP+_^23}Rq_2q9C9NAYt>)yVj
zR-rLv`=@4?)zjI6w^2H?%+BbGc7Qi7y~XRAzM)5JZ1iSrJrg0SOjmO}lT?53+wfjB
zcA>YYf7&tRDOx{;)-c!Y$x^p!&w{Tr8b3bC@u;?Mww*rTw?+5r?;fA;3dX3tpXB3Q
z>DSnrYq;pxX2qi|KDpC(To0>bWvx24IpMcj@w8(n6+eex|9?dNc{r5+`~QzWOQmdM
zNhk)%mYuSWnWQL0D%lB1lI+YdL$dFQEZLGm)@08ZyRi?65yKdRA<JMe7&G(pdVh}J
z@%{5Uu0OByI3M@(cE6vGt5Cw{$mHmp+hF)~vhW4J#qjjnC4Yrr&bF`2l(qWNuS@%o
z;|nEUUO&&@n+ie{@FmTB?xwHbdwWvksj6nX8m%ZO`J_JDF<W(0ZKW=QS*wAqlcC(1
zV8P4D{dy@PT$SkL6}38(wcN2LhmH^^gj|kwyAy_OmbaiEM(mOLCtmc^;`d4JIcihM
z4(Qe3e;Ai5OP{JoLtbpG$I{N$B(2`e7iR$)4(STKmPncL0ya{HSoCVzeZmC^Q1cVe
zKFZSJ)9-Vg9G=SWg@_i@ymZ<58n==NgaW*mn8bD7{dlR_CV4P<y!*9tql=a%O23fx
zG~)CKkDc_t)#fp%7}r!9>m}`z9+XGQrRK7BX#LJjX1z>$`GaK+qS#Lw+2{p&U!RgS
zjYceOaE)X#DLai4;D(Y2jI9csL3s#?*DENX(e2QQYobkWmJ1oD1O5hpQfB^ZLfl%0
z3-ypGMRWq92M5>!Gpqbsiu10th;s7@OUGWh#SwRLm#Ve-+~?l&?faT<c)AjaIX=fa
z*(dx^PIK-u`nh**;PJ>q6EyV;CEPg_m@i8>evgKoSr0x(k_bypaW)-DTkZyFTIa90
zM9B!~gb6s4I~#YlJ~ax=0)mW5W_7x;44Em@B4IHw#ga@6)oU}ou*?2*jdBQi{x(SU
zQWm&$MgFw(*RWCgJ57W2E4WtaI+1j8f5PPy;iLjX{IgwYE&g01ycoh`*L*}PP<|+l
zNeE4(RqFVP?Qe`(_Ck}-enCL!5iu^JSop{GddDM`?YgHM60{g9fAY*{<`{K$%y3FE
z#?{fMtEu`O_U&3Z&hUK2<0|@h-gUL=ZQVlKg2LsTIxs$l-k~F#okEY;`3du_f=wIb
z<W{e~-?yB@mSMF3aB1NdYj4hMa!2z5Qf2ti`4f%POuZ`8M<2uvH*ni#fO=mj2h!Wh
zASs@vnL4JKtjlDL#jw;TZ`NWJ`$#aIXzJxg5@`3rLii?MV$H3Za6$8x@b^J_=o~(n
z*va>P=OQt6D%@&%_!Qw9@ZM11aUh6wp@k0UJ2jtsBD!USrPo1eWcD+pYb4npCrSx-
z4#rd)iMxe5Lo)L=y*bhX%gyYckRpf8!wb+3-dET8OWw6EZE*a*dbt8FE=j=TY&UOS
zz@~r#A>Om6v5lar%z5p#V2-Mp-aR0YwLVS}Z@i+Wk17egcz;HqX?Up>fHM#$hu)e|
zuM27nb3Vl_kVrJ>-aHxFROY_cpj~v%DcK30lQZS)S20nM$9SvU_Te%pPJ9YFv+%{`
zno8Q#s^+VB-L@W;Y!x_ekQ0aLLg=E<F?cFZ4^(WKkJf)`gtb?ztNU0Y*K$3C+g_<B
zKa=zo;X8m1yy2_XtOI}nMP;Jx3wvi&gGFZ|8s*afx9S_%HSHpV-?_EUvnP7)9XvgX
zdrMUTv|;@6VU8b#i}f73P<~;eqlTKcp}&2%-Y)|N0#;gdzR>?r<$h~4&5w?anlka`
z;$~e*D_8tQ@kqMi(%T)wW<H|U!E0ppwo?1<YuI&~&1+mQd_zAZcvt|FQw}>)_R-la
zBu>i`{e0RkwO&H=ulc<tTa=Tn{2rR3L)}G;sy0+FotY}RgwR0gjIf%hDQfMj4FFnt
zJ=e#5wem+UP<L*!?~7uIxcLd5JpBxc{g&PjK>oj`IjfX|FwdPPt1t_<!Jxph^eg0h
z$gQ@Y5U*dtA23b@hmJk<I}p%&&-o~LpbFrO7YlBLv!-1WeCMK)Fa)0RS$uMlko`N^
zILakD&b4=2p;z1d#Ao0-e<ISk0g)?V20%gk_gV77MeHKn>tVNsg1G+kJ*{bYF<1-{
zz6tP0ojN&o_@<cc3{vj7MeUXEqNAEY@BGUAD*8!|Mx>X0C$E3`%xUaLKJG~NQC(N|
zGW13r7i6~OZTW!g;rTlUAc3xct0}D*s=&j>;CH{=N{gy$=YPGn!;1$O984r$-7INv
z3C(Me`&@adt?gx49eVT6ixKQy3^qgr@b+NdDitRBRYkH_@X%q7_7-0Zqt=qMo#!+j
z)nGO?w38CL?;$apXs8a_;_(V16-m{4k$^lw2dFGcI7R;&>BO!QH|w$ch;IJ*9}<Aq
z$)+rI>~V}d;W)mkUv;a^HZ&ygUNL2GO9vi~63rMBxYPg3i%J3!@769wLnTlXp^H}V
z{6RadXE`7RZ6KQ1=OoVhM_p5WT|@C79ZlXc7mg$ISGN^c6QAvN-24}Unb==mq0fna
z7R9(kn~}6I!_z^V*6Wkh5{&E_tZh8;6(axJ_!q<i5OPEnq;)Xr`-T2`q1aJIaYP@k
zaKA(r-?;+^xS3|2PwT{|s_C>pGtQX(B9vu61y^8+a`l5VIdB}lT!BV(*n<BN<;5%d
zu~qtI*u4Kn$oFFa;Om25d6(W4jY`jMQn;@i*snl&#G}unFFGgIFvE!-h))VxwX5>J
zvo3_eSl89TcQh3R0&d+S2G5-ew|mydId69=o9Ux#rHOeT@=J4Dtw_S%VUSPv;UVUb
zJ6Af1BJ<w1qq%{b<U$jjI+cf|Xfh*o2~TP~ayeRtW;k<|n4~2y0r+uZiVoBo*fz-r
z#=z_VZ2c_-YpMU^hC1G}{`LX%VWKuyHZ{BML3<oh2~Q$5oHr|>9tLPR+9Mk?)j8@;
zG}8|n51@TJ+=?d;@LTf(73@9KL3<00q;gv1{-lKG0e0pxn+u%9m?5*wu)Kp6=&B|g
zYVvlrmT2GM&owo7c(h>c_<=~CnMxk7@LET6yQbxThY{ejq_mX~*~O9Dec4iex<$AI
zFMeLy>szqPfPNQ^zIN#|4m?`Who;v6_ZrV4mD)HUv704G$8~GPz^<Tmzx)8<4<wLi
z@+kHaS<l3=2+tYTIrgtE>0C$Fd5}j#Mk8ATJN!f>X$97_nJ5o$7ZoHC_t5YWH7dtK
z7$=S>)0jjn!ORDHGeY%18N7Bu-uk#|>v4FDnh17q&;IDBDSz-FrIWfP?{>8E%3|{0
z&AG>#mzwN0ihn@7qK^~Yn(mzZ=~XG8Y<bG2<W-1a>2KPUK?)tV)259`k=l^^!L+N*
z|I@6t>DN3@>DXvk35=WpX#xd9#I`$quJ!&8@i?E%;#&~(wS|kCf#EKfxrv`gPaLLq
z&R^-6MBe@OPE`9BVq{@EO9%DrX_bNlz~X8BH!}Y1a7Qt%Xgea(rO)_QvhT>dA?CB_
zdw#-qn8rR1H%791VH2=Rxgi9n?T4Q-DbEjW4{?XHQ!H#XYPsh_(EUF!$l3P)`3<4P
zp!c1-x<zTKfI(#L9?;0R%^(|%ChKj#PuAG=Unh%<FNGcMZC;tVN%szZ6HYs|!Z$+H
z*3(tlhhQa%+L<v&E81*-{+`I!w={lgTQ(0aWq(2aOdzDMrJxlch~{@P$(*+~t8<+8
z2R71>RGusCHCMGozivlPedrM)9H@b!nx2y*@{wNYeIW(nKy8DanZGrBB)MI%j^mvg
z&AIBLCgJ<Uo7*~Htbi`sgTI%OX{k3vwUpk=^$9I<k$A;0{Ja#xCa{V;!0*la88SQ$
zBdOUbNxHS2T?+dlx7d;6t?Q-qMt`t15eCk5ml3Yk`}^57@QTrXO6XN1P>qGd0R#gx
zMP|En+?b=H!$#DwX?}=kq`*|;#iKLdC7MJXR!ATXAJe^TWDH$<)~D6gSL>KPI2b^U
z%YP}aOpmt+PN7j=Lh7XTb-FG**u7oflf83`;pqJxJfpZuy%RIuAei%~4@^$Sy!!Q|
zKnIorw~(YcL#yM+*bt&RTP#(*m|J>>TgSWI85qlAa52LBR%n7Y_>_tqIXsFU-mMeC
zvXdAwbV^TUoWZ&e`N8szxYZ9DrJmCs(b*IT{_J<=z0S^j_^zBLz8jaefIB1Y+nzP!
zT(!0twx~}s^q#vhQhuxIEXl!7mB~Vaxk$@&&yDg0rlg&mRr(Ky(nc=+Uzcz@4W#z<
zIr;u_nnpVjHdWH?X~u`fBDId*CecY8fCRKbB1fx%)aE{W>xed$V_dy%u2ny#*JbEN
z{z2>{!iwB;_U~M)gh6BsIx%79S5V!gsG9nl7_8`hd<<<?&RchcRCJp4IRos|maBx(
z=bIboN_*lKzty+6ooZNO5_^nJk0V%qOZ%nfYP4c+^GPqSIQdG5BiUN*kHL#bl$x7C
zsC||CRUl@p<<Ul|k!P9BrgVN;Dl@KRZ80pMo{$aNt1ooaM|=%?9PyF^lfB%l{P~*}
z2v9jpi27UUBvw}+t>-u0`Rt{cpmw7!M;^9k>n!D}gn&YyVC!@9=U)Zu6IDcBxG&zd
zn(Gl%30(5+0oC5F;U+&8wyV9a`ewGg@EZDn(?fz!L$>&hO@WFmTF!YoV>os7g-2&%
zqC#r6^W=V|bB?ds?Ze~^r%BTq<2mU8HW`7B11c6mJGxWl!s(B&*<Y559saCuB>sul
zhUpVgxAzBX(rW=yi04tynbqnsBBkad;pP8<=Ut)q(~Ru6u@Ng5c337^@&WHCdNnA$
zV&=&KcE4>}XG06q<dp+@PeJ@527m8*%yvXE-OqP+3E#eTBw|MW8#*57A8EN5)^kLe
zlA=oP(8}~^)^Y=ThicS~WU6vT0^0`PlQo?u7pg2+Hl{Xm5Z*Ja&U(Cj4KI8;eTw{Z
zj=k23l`SMq=Dy%Rz01;H7gBPVYf?x4v<8;0Mg~~pu0{(v&ol-2LVB8OJQfkvQi&1_
zSzyLj<X27i-s{nUHP?_$&ui{2Y?j_-ziSCPhhFVFhB;-4WdL|o8svKU*5VGkLw#?3
zV7GC8bMIyR;X7RF;``_<v;3q#uFcUnqTTa8Gcy^>y-ImN+qtFctnnAa^3M#05F-&I
z35)l==g#=bg4AKY0t3w{vebPezZ#LzIk)vN82rH$9emIcL0kl!1t|wUNx?y0bPM?u
z9G41eK2h0BljzW0cr)<l<CYloEtL&ZaQ*+-B<9RM<vD}u<=YUl_zIAek+2#90{L4(
zy|*4BN_c$r5r)l|*A~2?m$`!4<{~hE>077}gSDwaJWx?csV##T7h)g@j8mItdbfqt
z0jn%m!VVnrS)T~3!+WbN?>)lu0xd+k>7O%m0p1X!f0b)0-_|smMj~cdHmQphb(QCl
z<eIV_4jceC;tgrk|7`yo>|XM|&K_QU|JsIja6uo^Y_Zkrz4MtG=ZF8``WJO#CG=w@
zol9d?45nzYcP04Kb~XiC0T<hL=CDhl_H&j$gDoPPRx~dp7bxp-TgEl^`b=G<g3&wO
zG+ydjJndm2?!Uzt(1?T-1-x`c$1xvCnINwwZh;(fF@OFi*084H6x}MP0U}$+2Aezh
zn!vqAw7SjioYcn$JKRUn3xZ}luDFy<j4#1P>i;96eWzfe*a!csP<x{73VVnSn3PRa
zv!(U;g|pF?uO2)B5QEEiZOmN4nYH;FXhgW$QfAXv0%g@w-m>M3<41Bl%hS>gLKsx6
zTfM9yo+$(x-I$*L><TU2AFgQ!-plpI$;>J)BF?eqe&`j>`T;{^eTMGF7mMV?T6D{!
zt3L>0CcAlK1BjDV87zS?F_kecLE=|usT#G6_-_ZIHT<mCMo^_$#>bLeDcsO`Wj(l*
zshf|jO0K3UNE$^9SNaM(cqhin!MzwsT70`0!*~ZDTY8&h?I7#lUh=AChez@b>&0xA
zcRSWYp6;l%t3J0#2KpmsNSJ()hs_$x#03VcW`VKHZd)R6bHoqkQVn&fW;~B;zxB3v
zsq@7n{H(8R2tQKfSg8}dxF3NSB1@b0QacD)=n0@;Id(Tc#DF6iFQ@AN(vHeV)hF(-
z`hz!xbgtrb-aI)tTqz`E7V$bB_rwwiZypfST8*TxolNSAu7yn9FBJA%w-KEt;M=w6
zLe#|t?Q@$Efd&3bN2O85z3SUIiOgPNp%`9zq%~A%>0@D#t1Hyxxa^nq2hp&PqC~v{
zB#qQTJsivQTA3*+5XF%(H~*_*{SSMt$#e^}bdtt=7F-!5hUGouqSMsdt{d7l{$Y*V
zj`VH!CR<;X<M}FgK|EDe9et9&yn4*s(~Pcq8;5E*y}EbFF^phif<&>~HVzyfY|M#l
zYH94MEcz5g7gl#P_vZfFn+0iJVm$AQgW<v+$IkYQhDG!JH!|<up2J&L1(*3%a2!ol
zp8e{&o<LP=t6mp<6YvGq!Gw*dAzs-lr<e)cE|~c+(unuV_vz85EA~-`RZJx&Jtmg;
zlGZB!_}4H%xtox+zoGodk-$YC2A$vDl;$9dUH%0M;M0`aZS*Bye@JpK-`1`#y&tzJ
za{yF3%)NLrHov2fchmGAowSmJX>X(yhr69`?$sA6Qk=pVj+*2w(!mMCwF4}N3RYe4
zE8wU}qrK>@u;MXxmZwfMEzY$Q-uJp@cCYEVA1C|RspK_nYMKp&DJC(~7b8R832&Qx
zQtK00<b4cv{7k+uM2YmS`d5UUC@H(H2UAG#b9~-gAup|M_DMd>R2IR$Xz%Mi)Svbw
zKISp-lk%zpNYjBUYv)-U=ppEaX)}a$2DT#CDI#`c1URrEe}7BMWozPn&XCp^NUM|%
z3)VRhrH78z;~nzTckwVSdc>CTp)k4G40te@x^r8^Ym$@&{^|<VsD{nbj11O5$=IJN
z1pHzc;MGx$k8eF;$EP)BNoe}%`Wjy@gjN8%(JK0169z*)3P)hNk3JTS(m3W=TX#bw
zC;wi=dKjfmAoN1|T12T2M+3J|bMCh9ply!-5L>V9Ep6+l+4)GoOUg5Hf{OSaDt6&)
z4GWoNA+F;v5xwUdCQ2=zI*Bix?N-}DD9vXXa@<Hx8ZDxJt5GK-lF@-+G3=cNSxB7q
zy91z~@nv#&$X{6gnQmOxZHDq#;f@S`>YIr_c<H5`%+W(G_&<}}6Lgjz#L;wHWnnE?
zl$=7jeh4Dlitp;uPpAVm=t`qr|F*}ZR)^Cm%OToTS71h~eF4R;(W^Swz471A-1`Cf
z4E4z(aKURkrIThgN{7|8jw2czqxMUFK&qdj3yshjq`zA^XEe_ph@$^<zQVr`=|YV)
z4BkOGswKPK?|^jBJXfS$!#cKmw_4Jy7N;)CZrwtwc_&67Sy5k`Ub3DM9-R-zX(Tc4
zUi1i`@(@nzsF~i*ar5ID7s1EvX^Mfcb3i&~{%x@|z<=aah$E0B_k-nk!Kp=U;xX$;
zLNwgEFYN1FCK}c>%50y*CjDW)^@uIbVwo2CuT7I@zvb~MR&AlCM+?Rk%>8}Hehf;-
zXusL}HET5;Zsl7kfT#8Jb&QcV6@_?0#Tkvo&8%CYB7X1`aEBo}nG29C#Ip%hmr)G)
zdIt?%`MlLvN+6`F*s)Cd2<co0-SCG({hz3wa3vC@ch6xSH%$wULodQdjYnAHnA>gN
zmIAeczjN{LOg%sP%oqCYh_x!=_^AqR{4&{RSO#-2F%*BV!1`Fx2<rUw0rZQBXR)2D
ztNn_^(Nm2(`Bm=e)SJ<6<r9oM%<1L6+C2qAcJyRlZBnh;(apvv#<1U2O#KKa3mdf1
zGxYgGT4$m-<&qo61EGdz?t)zGnbD~U7lWRuRGcKmWfT=G;5NUm?>sW<-?3D4xJ0z9
z;^ccaG5zqWgTuEeYFVg&;qDb{6Pt3)=BFx;kyoXvEjjGlBu(zi&NeBS%v1=<7aM9{
zRU68P%$BWfkZllnI93SHdL&zxPHorv@gmcgvbp)E_3V=8ipk3<3BXFU@NMwpd&WlP
zxvYY<4>>md68fU1xq!njGo^>k-LqJa2+zb3cYWnERCH}ep33zly`4G5g+BOib0!7$
zXi%(e?mq3D-pFmdu^A$we&<VX(Js$`z|#Ga8I3--AEHct4D5xYbuZ&|^(m}na|*Y2
zTjR#b1nl6k*o?zmrY%2NI0Oj@lRK8m0{gZFF*W*wOYfR^fETO789YZ6HuPx$aKg8#
zTKe@Orlev_<D_}P9{tt+D!jC^K5(GxEV|U@PpR%?spo0WdhsatY2T%h*4xQhk5U)@
zmo0-UCyUHaole&1&bRK|j-9$wq54>3fb(pPe4Kkut)}5nj?4$aCU9@}b~zR;C}chq
znR!v)Y3QegQ{qB7u~PgTUqW2Jih7ApaHLouUspJE^MEUvHga|uMgZw;dIQBvc0FDV
z`f_NI+?NU>m$#mKK8~hb0!j9{jaoKuU}Z-_=HUnBLj2arLs{B#AoaU41@HVk^O+A7
z)l=A7p5@1U#DpVFt5~b9fQ^{uyO5((EtL5wAD>)3aXUW{4<K3C&j_UK?0?40$Sw|C
z*PV@A|GJ{fgDJTB{}+G)XqM#@;<i=m;9?LxXH$~0Pu=E;&{rDWEg~+4+Fi+Pna$D0
zOW0(Fu!)LlNP$$HU#{pnL!r(GTgnd^`v>p!2r7pJ7fF}BP*2OjDey>U5F~>L4-7Fn
zqMxWf9C_@*eNoZ9&b<g;Y|&g(H?`DJaRZTVJv;Pqb{6pAX#LmbFE5-yyC-XWi=#2L
z3UzbW@`}7aq|wq_NBs?PejgAKMZB#+W@*N6MIE(%hpIl>?wLCQZjcuh&KKtq-Ezp3
zgD4sgRw}oUnw<v2S5)eBCEj@2vIr~@MEgnB<Ts^I%oh+A?#ZPi9~U`|gsY#<BW@0;
z3{gz%eIGu$P9=YA=_6l`GKQz{;!h&tdB(lvzq+Z;vLug>GlY<IC{WTAy=RtrU@_zL
z(K$IQXtDmsJ=2?Bq|MQ<<gQ)2LlL6VcJ&(>vq!3LP6f%FDbX~?KTFFPFPM;VTdnbW
z7&_^H2X8U*4xj6epAVb#%l``mH{Tk!bL=I(+SP(*%#x|F5x?3DLPAri(_hgwEzE&q
zE6OFHB$U)w-b77uqxmRgf*atFD|A@XGu0|f5-nw_Am|rnzsqvAz_OZXRYDXcYgpub
zNjfQL%vs>@cIn0+EuKAYk<n`#g4b6>E$)XjvhuRF`xL%9i4Mz{a-RU%fkwGN&EoS5
zoax^*fm&dG%B_XJ{h%LrYElKY6!+R|;`6#68ae+<cqgc1!|i9tX5?I>%k5loN%OOK
zaRd>hg5So2@F=lC0H$#`L5n5d!yV(x23)aA3%b>E(zbsfSvoI>aI=AaSguw~72JNK
z@O6|q&+~Ak81P=zv;-#7w+&<cT6e-gnH+PLR$yhR?N(>WQ1n4$+Q)2Wf5cor!;mQg
z#c#Y#oOP}6!s|tJDm2al!IU_&Xh1GyZVOe5;S$xQ;=Fs#I&uXucijrfqlGmXa(GQ}
zHOkDZS)}S<cZG-0aI(h&retFxsri1UnwDfbjszA64aDdcVoG@kdd=dSef7?t9pC@V
zMDjEhq=lR(ixh0lLz@eqC88tJ{JEqv?+EIg1Q*inNK4tb6^V#*SNApik}MzkdcCSn
zH}sVpp^B>gTDfW5=Hv@<sQRVwGj#iUeZ;>-Fr@0RvwEE2KX2o84N+yxJa;ID*j7*o
z4?O~Hwy{#z45w%G*Qq5;$L_UL?7ux)*_Xl-KCGSV#qzadh2R?<*sD#7r%+D`A;-`_
zHSEn^l2KJadc{XTRT?`RUbHJ9bv-z)&v9)Dm~$N9pg*E)4dhX2lOcDnX}$w<9O*Xg
z6F+#|LS0(^0Mg(53Q0sp0D6#RaN7qX{F+?Ny;WWHpd&x;YrY^lBprlhNoi-dTaZOb
z50B`@4K^iGQ6gYrw7&OnZT-E!p-+-!c9!5Z_h=7`?ABLT%b%ig=bL!w*V;jnbL~N2
z&!8;R;HxLDDW%S{%5#Sv?%Ddy>)|}O%?v-EOONSVDDvtn;);$sG68Xt7ym{m7y@FJ
zi#}nGhJ&lA;U5s4<g&9fB$Un^39IFX?A>dJ*^KOAd-bQWE-_umI{bS#NWlR89T<z8
zlh=^l&n5BcQok9@$S`u<HZu1!$qO~tghOzkqvmAU0G^VikZs*?5(vA#0B)T1(b=sZ
zBTas$^lN>a69AW_gq()uJG3PGwd_aWIi&w#x>!oe5kKuU^BAE2vo2nf6o93eom%?k
zJ=A4{M(CA4YgP>_xIf>y{zIox-T+7~zZsgQ+$k~1DQgO&`Qi@Jlg%ZqES`8x#fQAo
zO`=)!q-_bqhPKn>v-qHesWaUb&=e!1ME6dW<SaK~0Q!RSqLto|utbyM@fWe;<V!By
z_j;_WE~IcLcm2<KouA@NUPS>-hNSn~-Y=hCF?+*pjp81W*sAbOh8Z<WWB53Ikr#KK
z<~QQ_Wd7&DayF;hts>p~N_NYi%b-tdO}#H56x!Y2R9_4bLtnvDmwf)a<!CN`i2CL%
ziG91;=G!0O(EByNqWj}jO*<~ofa2MMCqN}7JMxqNGwMU7whlL$gJgDE9CHsSl@b#A
zOp|F$imJu$Rc!&c9enl+j_)7zwP3ge;Qz)6sdFhYhK5|mo3u2{SR1wPErXCx+hiwa
zN~35MwW=e(X(O~wfg+ZLM4Z4$Ae0Wg1Y@>90@`R&EAW2$f6Ne>00_<Tsq_;|wz#>>
z-^Kx)-Vox^`4qMM{?a)x*zZ>;c8IO*)5l{`M?7=1qypq!E!yoPauY~0&kJ_c9?^GY
zuE6*1_h%g&99OHjr_|<*htQAPW^Bf{MH06cz>wZhpMnCTdcR((ucI=tD_Zsvhiy6t
zlj73~vW@p~O<cRzyXRZ&quMJWG(Jd2-0yF4*fa}ucU46dT}vi?v*Sm29e<kG*0SQm
z9{wHnAlB9h@}X;Kg3^ZlSy~LT6mj(clAAsrmop<cSPm^yWu(k)B=$c}HWwmpCNHAE
zMmra<EwN)hi3I~}OJWN!WDH1Qb$mYm%Zg^mIB&XWt`Y}Kz}b}NAj190NL;h-SxkaU
zfS;>yiCl@CylpcoOX+N=CC72q`{w1Tn!aNf!$l?5u6M*{_tueGd|zr)N=MIvDuK*z
zKJpcGa_Z#SeKv*tDnaQTRZV{^CuTV`f2Btvhg1>}5r$@S8dKMoUoy7|TG|>GM@hF(
z8pqX6M`vJ4h?knOv_}EnVKQlI+rnUQiklL}p1ly0L|P(rz#CW2o^Je7u<@MAp1-1n
z0o7)&jU8MYgeo!6^)GNr4mp$RAKXyFT!$o33*@~^mr9p~Y&fy+u#3|N0wHDNFUpfB
z|LT~#mgeXKMbP8sEiFXihU@F|#Hi($ju}AkrKiH?t>*R~y1rJVr;M~CI~1ha#Phoa
zsmXpO3F~I|yMV-$U0@*77}Z>!3BePkm}j~7MO>_Q=_FwtV2U_8kr+$G^j-)U`od(X
zW^q{wkpG~vjfA+jS%g@`Jb7ByYAE2}V%hI?%1HQ!s(`dzFYOG!*~*|vKL4$3s1kON
zsM~9s2EAh!4=jZ)sm_U({`N^?s+inIN^ELzC0mL}D=vgfCh|Q!{@Tq#^sA8(QmZ%^
zfD#TuLMC=j#$JA7{T&YXo+ZKR%wFgNoaq9T7h_m@4FxbJ>Tcs<V!`vrbz!$$cW^7$
z!rq(ZTK1_W0*bFOM_G9&?*~dg7UX9z%~ch*zej&1zXL5F_ttt4r=IONxPD-%h08eS
zoAmFv+4SPBQwLK3fr{!-M_|jzvWy?ddjsHotM}A==piqe_(^;oCViSb7=h1Y%a-du
zH1WTsAm{%b<S?RrxM#}$|2TI%l|3scOl&-5;3VdShuFa<EC>{VTmckwJd6vlivZz#
z28Rj4hqw^NV#O9+^jHi8R$aw(F@ZoWO&s%1!PZBmP}!q7!#OHrs~U1Z+X@Q83C(@m
z!~0FPbh--I3vY^om2qnK(RN%^3Qxpo;?EE>nK<f&{mW`bd#)eg@F(kQ5r&J1)^3+H
zqt2pEOn?(dGoksm{WX^%q8j~Cuvr04qAg#gUT3@{#u1ZyIX_EF4W!~>GcTmHRTsL}
zu6D$EhQ6Cq8fNbbweehBosQS+Wqkj|c36<1@g#<{w$?@Ayy^7J#YJ%H$(3`Z!<<?6
z1t6?zpguI*O|xD-Qtwq!#Pp|5?)q7AKby|?`whx<t&$vR6wVwC9S%z^yQO>ehpFt#
z&m}Pc!B+p!dDv+s#T?NBrLBdZXSbVKTrB0;Trw27l)f**&L~ydU4)wI^_NK!Tt6yR
zJ6x9Pqf1p-7yEu3SaSOP$#N-Ju1oH^qq)}IO%2zsf6A`(>8<#I-<)e*=?^?Q>yi{T
zBERQgqlI!bu7_0sYN4Tn4V*Q(e`{pZd0ZO3IF{V43#V)wT>Rjn`ppA^IYF}I(uybj
z0oDITHnvedMpa%Dn}1W&LxN;1H!~<N9S*sUahLKdPu=HQeTORHSdwBoCOlm-&nm+G
zZ3-$uuHVZ-SiO-je5o}mv^)Ttz6r53RkxJHROJ-?%v&-9MD9ym@4@vE5@FLL$F~;)
zU+DFY^<5nx#t=RF>)=Ge(C<hgzpJ$T13+^cL%n!g4|Lut=0k3rfcfc7y%V+T8`^(-
zxIJxmg6faI>B0B}Tx<^<hX&taPUk-eI)PV5#OhV}#z#I8Hi|<geg{~oKZ34a$y6I+
zsV1BXHLWk8bl#jiu=o4nV0WS*TK}UGb$*ytp2qgjOm}lhz9J>BHf+cBMr@~9L+m!`
zN83N<hp}Dp8V-QLWKk^j1AY|&!bZCo<KOJ6dDa>PaL0IJ*C<aMF9CH0F9Fjuc0}qt
zw`vm5j~!wgM^Y_+4y#Y3X;q7GZg>d;*!Qd{$-wcTl|Mly!dWU}hos&5QedOVR5=|a
zoPi1i$%oSGe=P1{)dc-?4e^EMv`5u@E-l;-g7L2pEnEIg-bO6HMx=Za(J49!TVRTY
zhsVHmtB74vM?b@oHNR)-`Si0v@jNnp+HpDsoJQ_q!m}%hzZIALxGa7%jv;Oflyqpy
zK!(+TkA-^qYh0T#+5vb^cvP!$ZoJU-4~450Wr>nC%aGs|c;%1W@rh7HnYnJf8J0sm
z;d5K;`Irpp{^oiAWY2c^p7Fxe&~T2kmcHw1Mt}<HfqAYUPR>H>W>SI*xY)<{r_sU-
zUPH_0J~w4EqqgH4PpbAq4?!?fq`-o=8Z8ql?UM`5@7_BYYM1n5!NdtQYs$tb<?=zn
zL!*baE?Q8B1mEbLn4R8d`JCl&)mYyKqn*L^-mP~#BkT$T5YXd>?~46~1I9Hew+{!F
zYJJ<>K4SCBD>><}t~=+iSw031x|Iz&Z%P2Pl`iMUOa?u5U(s<fumnCZCQXCPEQVsS
zdwqDb^ao~?gYMCg=|xTZ^Ub2HeO;=!{|uq&;4dFkd~l~mOZ=|_Ke#M*IGOV2=b&4C
zQj$%J$)edHW|rHxW8XW!1!4{L=4#$zcdGwCk0ZvTHQMzA2PL^876Vf_fEPD`Q)YNC
zZ>ay!=x9*88i7d4m&??V%VmD7j%GclTOiVcHa3V6WsnPc7`Knus@o2JD}QxlZuyhJ
zgipqbA6Xdp4~|Q~BEZYq$~avo|3J@1YLg0~zsBV@^FkRbvA5S{0P(K<<`RmCj*?#5
z!<-+GUW`ZdJWlI%1L?xvWlbfym6&LY(~YEKNX2Fdc9s_z(WIWwJJ~`Ja)X@@>0GK^
zdowT7L-{zf%r8e{pGFe`Y^0)Z{)u*~w{Y@*j1a6`9ZS04lI2wekmxDR=v6MOS3%xr
z(R!p>a<j1YW@5oihH|xn$sey@aqPTO3RzdOkuO6vq&l1*C|PIyxOge*`=CIK>~-Tz
zrT#3GuXXRxgxu}y(4u8o0sbSpP4=Toe8a;Uch6E>o#GQuYtip>KMNBouiSp1p&mV*
zezcL(GS=kcVUYH6u3q=|hu>vc;1)l_Ug2h;I`>}>fLWhiWj6*6l{H-RcQ%fJR;9;j
zZmC)2p7PqhV)B-6Jh5)_kGO2h;3%U*_-Y5jsoA&8x@>P~A^lqnAjVbtSrk%Szbe&j
zsYXAQe_(cS_fYQl>Nn>aa9H@9nInM6<}HSc4V8(d_@3?k^t<FvfjB*=25&TKcEfTA
zt0GjXi6G&b${-bgE30ctR@dC}ijaxuF%7Gk8r`kqj&le@+}V-=D(8r<|F!*qy(tYD
zuWx{A!a>bSDh059lIwKYMrnJN=(18Yxs^BEbcs>3=nbiA7OeeY*p-QCNbkbV-)Q>(
z#*i|Wejlr^2*<!hE32zmTewM8!1<V9-3mBy_=nME-S)wnQzbm083~xIF{!T3#8jo{
zDDWIyGHu$KQkh87cPdO4A-z0Y(@xoG`sgLhUS6LhX)~$lMGEn&S~!fyd$j$V(c3_e
z{V-uP<Zi@$bB+RgH18(_*X<@@ZznWZN|HKQV8td0aJL%aO$!lXeX!?Ens~-5=CkIx
zHY53v2l(*dZ^3nsNI~^*uc&|AZL*i*U4uP^h)Ii;O%Tj#PE^Z<Uhc*fHGy)j3QZ(L
z-u%kTOvjt+zsYBtXX8$K7v{zH%<2*BVaY}X=quHuAajMXg2^xUGtjXu#GDo8ei>%m
zeR_ib-{955xi5%3C#Qj9dY)5$RyEyyrctgp_URa|MsjEzb;I+o;PPMqX?+64cO}xr
zpd60bem<hula@Eb7x>Z|J(*bSY46kj#}j{%cu6JE>S@aZimWw~ue)6kfUS;ua=B+{
zr@ox;qOn`+uSXT<YcG1lHO?QfCl4xHa9798szjG;AAc0)<6j$&yi4hfjJlvC2SJ)t
zaa%LEiPJWn#-8_+g^J)TO6nT1*920F3}iKu&McMj><u{G0zT%t^8Eu&z3$i5Z=l<X
zk$SF6gsALV)xPXSQ*T8{^reMkV+T<ybqS7Khs%CY=IUeT{vF2n2X`?%spC{WZ-v`Y
zAK!nOZ_y|C7_oE_c9bY2vJEGX6`6MSaU3<X!A$b89#f;E#wcD|yTPFQVwj!n2u-Bw
zY(A;}c&&u(|3Z&yJv3LOqX{uAaki(DtRTe02zc1G)qU-7&m|=qfFkLRExkFM%-d-b
zuVE(B4p&-e1hcTqGzJ(x64FIQQSy|%|HO_OCLds!#~8w_4I|YLFR-~Wql0r)HOdFw
znGqJv$NF4Rv0-RDe<}Fn5%19&j!V#yw<IQ>hiKF(qw?nUeHkGQaH4{cs6iah&RxNq
z?#|{0X8F69dQGy0drcldoBdyDNR<KuRTPk*0Gljj&tRD`vH;Y&&8s)zf~zaxW45bP
zG>E&{!XhTlM!lx&I`Rwt9^duropLvCI=w<5p8v`cFTSZBE&HXeS*x&6eCT5Br%zX1
zzaYO!KKc3tlbvRs_DCb>&DY>LWa4G>mS=jUgSFmHFRkiioVFK6Wj<vnat_NX3}(g@
z3oFQ3zNqUgvncf3``4Fy)8lrFb&L9tZKI+S{iyF)m|f$|dhfz;PD_pd$%QA9GmX)%
zJ1MaXY86w>5>A$#1BrRFR+0`Dp*Cqh8*0^D@_dCWGDWjF4U+#``{>drV^;OJie<SG
zsC{wrm96cr_G^pe>D-@pfj_OAn-cVW5Va4qK$t+D%#zocESG-j!+g;-5`~YKM&7M%
z49r|f0ba=IK3C&Ffwuo68g^Z7?hUSNF$dS$%++qLZ2x!)tTBDfowMkivveV+juE`H
zAOGv<2Ym0-4}E;m<3Sw<?9ue{yOGu-&)<T@t9EM9Uro{L3Wx}uptGBPMHm47i`O9C
zw!JlF*mvn{OiJ`L=ao()kwL9s2=+H=Ekx<}nzGsW#ygcx5MMNtUUSQ#&O7XMXzYw{
z>-)86&8rj>%h<~s`jsFS!ydowx#sD%!=3oE!Jct*ZMO?*pP~cO>^xggZX}QDD3K$i
zuCFMH8mKilso@CF@rP?K1=G$fnJTa@x4zC?9^Uj9BICbMLlCt(3XT2b$<?gti^dj^
z$(7^RV?KW?{eH&KdD2_DO)FbA>NPrF9L}hZi3`jI?ADKpPnJ8p(JVVE+E^bA((+#4
z>r<?(-I(&&nT&jZ#-F$CF;&2qdjg;6YI^TlO(_MgA#CdvrhM+uq#Is2s;^j8{MmN=
z=F(#Gnp;HMK*V<@>4@{^(~5Ca^=~pOE-7^Pm6kPJy)aZM-Sh9o0rmcf$!hk5#nS3m
zc^1C<(|w8}v?Oh+#}n44WpkAlIn$+-x(BcMJ#MtwbgP#(G?`Wjj)m-0{GAc9mTk0C
zw6w4EZOvs^z(|)6BibK57W41RPw_nMzdIbCoczPNWg*ro-^Kc}pEK_7Pss$s-t7VT
zd*a1fBRrJYFrJ=HnDb_Nb#!xl>V~Xmd>+h(-<DGayPaueVj`ukW6D=sd!P`zntDj7
zwQ{VSa+JLOk^S@4Z?E^)IegDQX=sRMGq18^adpm^rVR-%N@YE@y<+5NVgbzg@W{qI
zTcsYUYLZGlrRBY_n=Q0BaN(Q1W_9x8s$t599p%KwiqT^Od+9|_GSW>ixa>`PYyF9h
zv)2l3av<(GC%#wuj~rg;t0Q<78=>dh>^v`}yL}u>aJEX>6~H74UpgXW`%3k;_&VCc
z6@TKC!wST}XY(7~V2u9<i5M}Rv^d%}Q>UpIp+WydG>Dudq9dhaIJrm&0y|;7c7%<b
z#==-f(H64oHwi5vP*oI_mAPv+#snXss4;EyF@Qb<o{Q=zDM%bSAW-wKFsNOCXhS0d
zG}!)#UWHuzxphhbLeeEg2DQ>=3HELUA~5O+baEp>Q#7R&Fq3Orn{^=BY5eWE$L(6A
zlfBN6t`IzJ&f9b#q&_9p55q}%=fHf6BGH!WDyS%G8zi^<@b(;Q@CJg+yw%Cd3*PDg
z6vJiU+~8e8U(9@$D3>Us&+rlW@?oDL<kH~^`VpA1oo;pqydP&K1>Z>{5VyXYMV&m0
z94?kX_zwTLF>b*BnGL&16foA^y4=9j=5$EH@oE7Ae7s76h=S$7R8p8C<0?UkarOAG
z<FV^OZQJqS)?wSpbcJc#>e(VW9y=c-C?W3W3NU`>200vb9+DE+I|T^u*gG>74}1@P
zXSVSkE?Ax%9I0))6V=g_wxfFQIV|du;=wew>(G%Ie6WL6;t;TdI?1;kCwWCy$18<V
za<22C_J<QXu}bd})D)2iAGVT=6_enU--Eo0E%J8$f0yj-LSan@@p7G#mUcp@@2d$_
zNL9PwC5zO-s$bf60UH5?`10xtnI1>+9#uTp7j}Ug^#tFh&0pS20oa)G9Xo(VWVu(9
zik(DN^7!g;lE&RCe(bEjUvH8IUieMxYKC`h6Jt3t=l9OE?VAf4xWJGmYn(q#gz#y`
z3t)-&327qi&n*13tzR#m+2p|DL*1J8lXbcRu6xDes@yBf_pF#U5AeYxdji+?202s%
z=Z|je^t|3iEebo&gklf`d>H)>v=sk#>)^8<fuI+mUoEcR$)wpax3>H+Q8A8CaS#al
z5XrT*)viYv3y+X!-EFCE46W8x_kOmb0f!z~;HA6p^sN{I105U{5;iM+yc^DxKF$ke
z4`oV2E`(8tVdulBDa?zZyGl&n&|@zM;3Qp2bOeTK9wW^Pyd9aLgt3l@E<Tsu=ukfC
zC;=nmF7Kre9lNsW!)%zfpo!X}jlH2i-wVluN-^kNYXF#Ta&Rb-5-p1N3p!w6LN7Cx
zMZ<U*M=@cdC`uz95JcZ|jM72T^1Nw{WVWDHDygxJzOfb2cC_YA%F|ZFH_PF6TglNm
z<X>v=gn-SXz@`VdF9B~_)~dD?L`47ha5!Ue8lB{-@lpsUubF?$XuUrZ0IO~AD^Ff)
zWGs8tDM8NT4;lFA78KQzP^Hr*ve`tpoo#wRn(x)wWW%l@JBGErf^b!sHho&r#%#sL
z>{g{$4<&S_(uO&y)0wm|onG$MB4PJ!DP3bO*tTge!?sJrn^H-*<P%X`-Fn#Iom5=i
zvfALSi|gC1Bm_^CukGbusUI0GbJOTPg6TJ{7hmc8wqP`I-=S@92G1GxO?xL#lz9zK
zmSzf~$|<x~QKlyDu&51yUDI^<eiRTl7Zf*revkR7d_wV0aaNm^=i=4+ngpe`t9DX~
z`~t5Sx2JQa=X>5YzzU~}XUYpX!{{H3JEJlkE~A3Cdk7w%1DX6i;nj`@Nm~zE8LmC?
zf&$%^b@HZiZ@6`wk4M)?e2*{Mju^W<NYsdj@bp}Dew6rtlvmIr=WLni<aqq(0O^}Y
zymFFyF<c*9`0RYOM&))zBDYRl;X-Wzdx2C#y!_nteQhosMZTiA2~p-^iSv4(aFAV4
zE;H5{47bt}`;xm=*k_oo1m*>w9}p>)YlABznJio3TcJy$_k2@*AAKqqLEve>^pXd?
z=hPN^gctj>ls>k#6uOR7-ZggsXHvgEXS`lwy!n>FqQ!W!bKJ6rVw!k=r2ezAFWi5J
z23>>v3Yz+?sUGFg9_6qEgPw$5XwS03p5N&#k}x!65<eXwcK*&CDW>R-Y1kF*$J1IJ
zNz2al&8++_E@36O91r|%1Y;v5RG3b7Df56F#E8a#T@2k1w?=K(eje+W$w4xidmos(
zHv;cKUksI=Y*BLN?@7Bw1+}i(6Drb!@;-LfL4&xIbX2qrwdOdr-E{Ru9@!ho=BDI6
zX#Kib_Q!22Z|({S_TZtH1r=XJJ_P=Q?Ag9~`BVMcPgtWGY_Zv3@4BDiJ-SMR<8;lz
z+iCybD9FXb`52RhlreFBzV%emLm94Y=(TO4gsAa?k!7x|f8RAlt&{u2?t;_O$0YS?
zIAhj#?j07Hn_L4+)jR@Th%X2;;HWodLr+6Wo~%E}RgLSJ7o?xfeWXH5AR)8}@SgoQ
zebP!|n{upQ?Y8)rqGEG*$`?EYqABTDa^?CI7URuUbr9=(1?L^dkFiTx0>$scc=1Ec
z%Nifc75^<<5s=NyPFTvzltF*9^Izp(81SxXTKrkwDLD`$ASd7u`j?;ZZ?KxfGR|1B
z%|?sz3g#}589(R(d(gD_s#Wnzk%O#;MO=|;SaybE=tMnXpfBLlCG4!ABA?>~$uV$v
zkMPeU9k0}U)$aSMT(!mA<S&gr$*N|?i^D41UUW{^gvJ~%f?uQ6rYbnej^NwVSl)dz
zctcxwA!DYKe_An_O{4$r_UvGFvpTM?Zf?-u->GS{SaH`HJ~wexQNAc`iN6Y&0OMp*
zTJJLa%8yC=I!iCFd!Z;ggx>VEHc875682#;jl0?XZg#NoBmx`!F4t)jqH=!Qzv#69
zq!MR=MJwu)3xc>u_+a~nL%^}x<tw(;%hJBUyK=d@Z8O@c7u#L$d$hJ|U(Ij1)O2Li
zr0Dyt>GoF6{BKY&dFdV2iu6ir?54IN^mo9*<>cljJ5mHiANIXz@zYx8-Gznn>IBlt
zjvSxjxPPR(&zJ@Fj^a4^5^{}gqHyrZPD?cY2EJ*btWU!Nj}liznG8zAMIasr5S)Ab
z52BH4YX=!k4+8{HN5g}4>jPW-55g2C$q8K=f6kqBgu8@y#)@2yk8%!vYbdfgq8<*S
zLd_38O^;bl<SUMPH7ND9%D6i^@-+R0)oInYQYN@C%%95>bMtWex$d!bcgH_u{dUYt
zs*lYE9W)$fu0rUF4=8x5lT>q?Qb7?o+BhV+4?WY;tU4A_iMC4X1v<aa;}kB5weD6#
z*Sy&*79?e{cI)fd-sV+{6M5hLI9I`pHwaQ8coOkfRk<7{;lS6z_aQ6)v!E!wa*&`J
zW-M;motwIxl4bn+P`>TG$D=-7A@}?Nu`pMeM!g}Q3-`IM;g?FHc8dI72#i#ol@TDq
z$REu9gQctXlaZUkp=`MUW!DM<%5nqP?savRfN3R#8d#$x2rl9t9^b|(k_-Dt2V_AW
z?^@3?kjExY%yyTze<4UfQ^v4v61)Um0M~}ws(jkMlDI_wcSwqw!+=DZNXg8NL8hn6
z@vw_T^a&liw#uaa_%}=44M8f=zv$-!AoL!L3lksn1VCP!(E)+pJb^y{J+(D7HAX;`
zNheCL(q2|?EmBx+lfzkV7o4JZQ7{lunDab^@Li9k>uvZ2`i)tyKCzGbooXiu`?X$7
znXs}ybUmRj6Vq?zC<wlq!D5)q#$u2(Fd*OnS5bYqseO+lboE;zbQabbnP{FS6=lv>
z9BIzS=WrP!c|40hb|lD=?75>+%9=yDU^yhn^%eBnPt`=|(nsYxYk&e($wYYtPNm|+
zJg{y{#!$dw=HS3pJ0<nBvNBaE?4Rc*mE{TPlUT!IjT@1xyZD6c;bEcLJJuc^UvoW9
zefe3b;2>A}#w!I2FhN#{{ky}7O}Oi6GpS&6@zW(Gu07+7$msN9*TG+7Kb`M>eNY##
zWk=>tuRV*s+Y~BXol;@Vx9rz?N1ofx*!oAHXO{FO_h6J&T`0mzEaUYQ_oaBrD;m*v
zo!xB|XX<3-|01>I1=0r_UYku#_v}=P%vv=g3<pNuRPa`Bu5)+<Zm=2GSJvC<JUT@F
z>+eDwMOrq#;k**KIb>D6_e^1ZF7K!GPn2Kv6!q!;wb$HNsGArcv7fG(@E@M!xzQTw
z@WUk9uc=?;XU}t{kj!UO{?FP5=uSB+_l&1L{Pe^Z_9dqePsfNDI<n_*0h8B3{x{Y^
z-kR&$aL{Yr&jIxn#I2ei2xj3A`e^E?&mwfHIABT&E7K_YTRR32Re56gd!m!0lUWSw
z=n+onjv^<_npf}IpBXM-VnM6znwKtIc^wog|60pmY=jJ|z+4oG*>k8-P#}iJZhShI
zk@vEh)G_#smfDrdg*YdPrD%MUe68uPeB(QixqxSof565wPG;!vwX&SLFuOD=smC*J
zcAD!4#;&a-a`p)I!+z~@vYuVlhIi6m^ef{5gAP)cRE>pqh4!Ps_*rd%+!U^wyQI_b
zDvm+*wc~cFn!Elh!tGN3B0rnH=Ev3kxL+|-@Ix<vV{$`R(5H2#RJ+j$lW(Qu)LJZ&
zmnR$CX4M+v2(=*FwuX3F&5jAFmo2JIZp^0PiVA<V`I8jui)Mp89jD~g|Is~~-n0}+
zRQ%R1pQxHVKfwRW?-ng2!1hrU4yDUmjV&9_{x%aiEzymYnPBA1kfKD7w+x-y=f#~;
z7y6ug*cUpl_OKVb;JCYaoa3|0HTBn3x{K}w(<gn(JSWQJlX<Yh_)q*~o3lQcZ^WxA
zKOJg5WZgf$(f#zX<aK^Oxx|zEHnXSf#AC;kXL?l-29F;{W<eBU{7!3{z1MgflA7z-
zD~lEMeF!8Kn-5sLsN1p3Pvw&ibUA=lq2qa%G*$d@ky%O|hkSkC7oU=>PqvSp4*Ha#
z^Y6EQPSU833{|*Y)p3_7Lm32l`A|@t3N<<mYabQApD$omA_?3|6w;LG^fTH4Y0V$@
z@zIZBQ15<X)abVz2|uahm`L!#9ak?HVDqsRn9-e9m;zm7uzp#a$FmssMx%QZIogD8
zi0#w2!$!QXx7T~ibscB_ze{>dwoyM)#!f9&E{EntoRBznvt&V$H%<FTQ`m1Q9F}WA
zL?S)^LP`n{xOqiYIk^jE%b*VObP*2{nR~TioNj}BnA9%EYJ5v>t+c;lb@TEkXa%?&
zZ-j^j#8H{EI(h|#8@X~!7n5yntr<QS!zfhRZu5zQNg*}^yzN#h4;>yGb@HVNQOtRd
zN+V08)J9y4pidv}=RCTc&p$ix{d%E)FqEqpbDpIb^F&LHBbHhrg;XvFAs;>kp8IHN
zaL)2+O!&mvCzH_~69K7&6J<~kx=kjq3&15ga+I}9D!kYvu9tVl{yI`N@eKSrP4TWL
z@Hp2&%{}3XGVUHJNcrAZ49T8Tg3?CI!|X~C&OMQ5**ov_q}?~zcKx|9w0Lr(?Bi~^
z6MELKReb$L_M@H}_jtE)Jr%AO*XM?x2+KbP$v=k6n-Q!<(r@}!80lF{zLMp)^|%;P
z7&w@5FK@w*-r!lN=hJLn_wZ!9=F(WYoZDcI&Hs<5caLZKfB(nRu*zYTigK6|>Qy=B
z%%&*yl5}{zLJkw9!kp&JHiu4fSf!kqC@OM3pXQX2IUgp&2%Ga_X4v8R-Ru4N-F|<$
z?Qq*49*^gBUHAKSzwVd0{r#%$R5!7oRSpI=q(gBw&c@*R35VKS1}`<`V*Td#mfouE
zJO5g4spGet<$H@{1>?cz8#;Xx_PpKY^(nr<cZOMk1F76N0Px%IKKNW!z1u%iLi_W!
z^RL=o_d`UlPlbk|#<L@?#f0Z0e(KDnb#hypadGKN@Ohj3LTfu-+SIvGC{}6XTT)A%
z-qb!>;YV{lvv1?Q8;;pA^8}wQRkB<tvfeLP7IVe~9@3%eD!zKAN3U&3$-<@fie$AD
zr)(#PFcdf?Ufi^E)s|CFeP%@nQ!0c7UZeC^VL72=8x*}(6(p9DPLMDH`T%L&GBwH|
zY~3lC6;E!f1DN$CO987dlf2{D-8_}DbqO@z8zVdVNrP~OGQOKmedKve7E{1G=y5mY
zafo#xXVSZ}*SjqcS#EaA%Zjd7;J{BOn|Tno{y^Zsm9f(dyJGbHV)UF*Fk#eVGM=y#
zUzOavIZowY?M__m*)6iKoN_C@#}RM9&A*rxTE5%h+2ie>VM^3du+IcpgP0ll@h;5e
z$^6Jg9hVQIBE35*nd;MgLQnB$)};XF07J<ig;*;E1;(KpOGAE_<Yybcn_$d`EL<}0
ze`wtlUAu5(c=E$;+#?I>zHZz?g969OHb1oH@70IDW<c)J;MTO<m%qdAE$OxW%?q^s
zxFuXspzP<?@t68-ljwPuRNF$9OycZT^=YC@CE<#XVlBg`$@%#CTSku>IycbZ))j&C
zaS$*AZ7Ugk`Vk#wkwWcCd5hM(NgAq{1@|eQ$~%bIIEdBSv{!@TU)dxe+RIgb=>N{>
zzIuu4aWEk|A%5g+yOqnAlSL<7*3SPxxM-A)`KHQP6{@%;=dDw29BL1AL7Pt*+%CoD
z__YMjKU@DPaqATI{Ow*DRX%oY3N)Tj&_H#<rnQI;UEaT6`PpgVS10$MJpJDEvY7J8
zXG#5+)jP&S1ti|DvU#uC>q)-J4}X=Nw@a!&-+gS&QSIX0y&r}5wdBc&3vYbap>kHV
z`<gg$ZZuM3Qp;Qss}QwTn?IS_h-GMpxoycob>?cO+oPoRdPoH9`*PhBeSfbsBEwdW
z<>9-nB3Fq)2mDi`B8P5yK9a-C*%Gn1!ZSL*lzj{OHPu<dN=GNV>5j+O+Bfd&--f>B
z_St^uxE`2cR2{lKt6<Z(nrn1V?52Lv{otk7OP$)}f51hy3IS+;HTsuPbxXR~9P{!`
zr#g`^e6XJzem9W6e@3ijYDOCsO<Qen#T9Icr&XhH236xqPwE{HPF=*}OcIAYt0x0T
zU^BTH^*^}*+r!{iA$gm&(mm9{TDQyF<S`ts?+C$ix2Xl(#)(+(wwqaHCO9~lFx2q~
zBr^j1skELjHS=qv!LI&!GBk@8?B=*y6?{E(t!QQw+|IzidbIJL2tRot*#hp#S!$b!
zW`uX!Tg=wZ1REJI+jZ>(!gf;J>36O75gqPusJy`xXb~}F5@j9FvJ0D1J7--O2;<GU
zR|ku`{ovW1Oa8aqChLAr%<9n;S&9bw6KWvZKOt1ZyXRHoXi*mlQC&4om!q)?9yld4
z`y_fTnLD3Dc!#C`d1;Q=<^=KVS?_ICICwwzfH@KAe=J?XyntZ-p=9h|J@xNv{9P+G
z^uvcfkIX!EO14}MafJPQq3-vF`@jdK!CIaU(IJfW<Ysca&VHKcHD;YKNxHe=HgVG5
zEUK29Kv?W>znBd5yqV$OO><xTUL9Annk0A!(HySUa)742?;bkKpa_;sGq62gxYN2i
z@=bjTme!}-B4>xVhI0G9vmrescmFu;pM9gEr1Fvk)Zf5t!Bfc>%tqe0&5)dgG=}H#
zR8rcDj8*a?Y(ZmK)a}rK+n3V7r<{wZmeZ8G4ss5=$le&<Jwtee!FUAoKw9`;OSewB
zLmWn@zkRta3nbR+G$r>E8x{%>U$@}sN34(0kZ(oXH;3^?O*W49ZRnYdp9PEtrJC|O
zL-%PNiHvkm(Xw(Dv?=i4p7<GCy(^gnwFx0zRF=EXq~p46Hh4nD0ZKsC@2F*X`**vN
zNB_XKe|%pzNN?-$s^b*-uPye>1Xc4^@<*(Efl=t>hcT`o;cl;_V*`T%%xV<+IGcTK
zEv>~~ye`N)$m9t|jFfMfRFO0zk}-Lh;4Mk~C0E=Co8r_37h40r>5G*$$EW@DUMNi+
z?0aed8?#Z5pUB+y_gS6{ve5(UiRkhD{ks{){M2nDA1d`LQ$VI&uEhG>^66a9wKq^4
z<2;)!D9;fk_!2X}rMJ;{GSX)`;c62MF?f>^8rKt^yj|pDS+cVRwb2uu*ww%J6SV`4
z;DiJAc4{p>>u^x_`sj4k?p6ohvx^nEoXGMw%Z8LG)cyboVH?g)hcwsG4WeZI!psE!
zoS-~*!%455OnTXc$XEmvIsGl7>T0?xZ!fzzfh9@qKs)}~_4!c{!iTJVE82CcnU!B}
zWYlPnn;`iK{*oIKzQAAMYhgj=z$;8Max=dmx*Oosk35=5`SUd*<V}if4C?jt&oPym
zB!l<5JaUj0sr=%P>T^W})r<AOzm9Upo5Q?bQ~SiNLsvOeL=U2_qE4gCtxb>M<hYyZ
z{kis`?i=X30}c1!X4+Z(>aQwg;9jx82F*x=D_(CAbYar<D_NQrM|ZA{e%>jFsth=m
z7W?pY$o_ot@dFPF3b$AGm67FC61!-L9wa1+yWQzuHko0K61hAaF%TZv><~#7JQ(_E
zkYfqItBNkXp}qe4QBpZa<#zhv+Xa#mk`KJfrn4oM3!>GVz8Eg#utp+Vgr0iI#GJ4t
zn-w7m(-X{Gr>mK9q1Pury;wtS)%|1Lvg_WHSswaN?D6y#5Ves#;a*QP$=iJ?4|kBS
z*ot#6_Ni~Yr)Et$>+F?sC^uxv?WNIAuK|TLpYIwp;kmj8ByeZIj}$fzb?ikmtLyJE
z)xR=;dS?cDFlDT*n%ZQjk@3VSc<81DG_@Cagg@h?9DHmdnXB%YtG|7)dZr?QqY3=v
zZUcd0$zfw=RaR;*0Zn>$;EX#(6o*-8`({H}IXY4_6D=3LTOl9#1!jn4xNo(9Z5Xdt
zSCI<a*%u{itR#xDgHo94P)^vC>=fG5usCHW17f^h^M}C6M|X#vH`ec=Yv()VXF#=<
zhouUvGhQ<;^q>qF3dlW!;qOW>44m%KJ5I6-=#XK$$eM4xUOnv0|Jmqb|Gf6Xka6;*
zG`5!Z8rp%V<k+ng+?`Q7g5b6-7wqI`IbCodd^F^~vl=-mo8s~GuFFL{^VNVuZ}M~C
z3buj!I>NxXxG+IHg)Egqmr7xyu6xoo5ll^AG18%*j8?f`;*Y8aX?HJPL&b@b3=WX4
z{H*+$ntmh+y;Xj^$h$XutJfP(GmxlxB+0#qMKy<iA&H&qJt|6qCeikY)wu0JMf--`
zYi@Wz`b9GS{un%=6|}q~L{Ik1I?Jr|%`B9lDpo6%N3%-hA<`onFGW#NK5AcloX$=W
z!#$OIyUu4MFNKqSJCOYT9j@#C9b$9Uq~4g{8-9+6I3bDM{q~0d{2gHRZuxpc(PBsx
z*|D1%^>!AypC#k9j|f8dQN1&c&Fq<ZwLN9EUe9RZ*|)Abqk`>EP2|swLgSlbs!ltl
zJE+Q?amatTxvd}kep_bhlhKaeG}|5o^rF`drd^Te33Kn>Hzqn~xOvsgSd~iJso-~h
z^=;5M1a{fwjSzmH@f6oy7KU}VyLo{rE9PKbNEk58s8cr%L`y7FkWq&zk<|_AxO_60
zdy`W(`Aaf=;@}i`qS^!m+gA}X&JQR(pU#V_6)zT7Jztx1dN+L<)0#1}Wxczi9Eq0M
z&g3`7P`uFv_hpxvZ7<bsaBK%BECQ|;LiC}TsG+WdG*uT^cDo)S^$h*GHR?J!<?dY4
z&JSd&$3uDMwL~;YsZqsNK6T&Rr#G1~+gWo@PibvcO#h>$S7wu915FaKR4Puz0-rkn
z41{x)QgVUdjm8eHwYgOZ?3Q-Bp^Kxz(zk*;->*CP{#Xdz8(>lf6Fk2OFcI)b94%H7
z?0~&p7Z_Oy`SN_&oc5YF8v0a1-Z$`D!LY4Q!jonY8zEcCt{D0TfntJLb97jB=yFzA
zUg-%M#GjOxp&t5;kDK0gT0DU`S4V$MOG3ZTNQ@No%Q&GnCaiv>_t65#;&)Ar$yOF#
zi}nD5eqyMavg4cr#;t2lhxGe5ryj=ZY|YMUzeK&C<y_jvQ|*9RAgpy(0dJhZgemwF
zILg+uTW={|HoR48L&f-_RQWgLEP!3o&bqxC{wH$e=8O+>7OgMIy$zXc3jM=d!*{S1
z*k3Gcyyw$=XE0$|a^0L~13Stqvm<~T(ZddbN-Ek1xHfhe+S_puh4e`qbJ=}kqSq*(
z3;cXe<dojye?3vc$Tgc#=vcz9qK}TTWFOG(Uef)#hEPfq=U;FZ_L3jLOwZ{Z7jn!?
zuBR=F_7HLh8;h|3%GH-JvF<B`8a(gB-|Qhy=Q9`^Gzlr=ToHZ5j`iGIL{<rgE`-OZ
zqtDJOXgu~zGtyO*jVyfc|6P(<(L8aKCUY`>9Y#A^yPZBcmE5tjmj=%}_TE<YH2K{L
zM{BjjI<*<}a7A}+)2(u~xVXoNxPS{%eWePzADy-yOn#QE*8E0kYuP`(uwmS9mIsp5
z7S3F;>AD5f|20k;r+5{3CCvMY0X7qu-F&ze8(n;T8+%ha*%exZ*Zgf1wOkpq>U^e3
zyO#eFF6V)(8dCG5M}e23YOzQ9acQT)&lbX5@JBeFr*@TisjxoeMr%Zfn)Hu4gcRn7
zLu|9|NrxlTktE(Lfk~ki<RZ2`e6N<rB)KU`i)0MaN?3Kc_DlNswPQ<>*}#6ulp^du
z5r8uIS*MG>azDt|)a0PQ>B4677m49l$^olqN{dc|#S4wPfQYmspH5u)e&BC`Q}pz-
zg!E&u@bYw&nEDN}c<EKGdoDcTrL(M~Un+Azzk`^q%s@m{VQ9}yqko|gNE?TiQTo@8
z(>I0=S27r?_9k3$feCNn?O}t84(;7`P6uL|$f5}s>wf?#U<ht6@qjOUOT;>aOnN04
zL|k7_ZPuSFip5=@4i86k0E05lTuAWwgnG&Yu>$M!DTvjoKCb@+lr3cA+9aJ-%{(Ml
zkSS^OX?Nkb(l?W~U%}^l2*G0yMkYXzLK@W9T*qVPWUeORsA`@^y=E41n|c~RA-wNX
zXD|*%C)gfLcq3$YqA&1T{ipaS<S~B&&t6apa`M&X!f$WJvSh!RMVYBLovx=oNcQ?B
zIWEJuVHXS&m-xEvya>MdXLckSFL%xEVCo(R@57;ukj~8gQ(*MeW67wUYhFny`7wr%
zMHo_Cbpz))|5IhMjKr|t==8nI2NuGWhZWtWD&sgSRF{m075L>|2gZ1bZr}ht%o&;W
zOotFZx8Kk#((B{P2`T?<&WU_;^y_^Yo$;k!-u0Zdxv+`fm<aH$lRYSz?C)cu<7<-8
zQs?Dtw1?7t+7pZHcS8~!$M}|Y4sV)9UZ`EeS74JgRL*SjE{e)jZ=%)`rD~jiMg=<n
ziE|MLQ=>pPH%ztuqW^39qB11AmT2qzv$fNFal=%uWC)~`cm;BvxcB*T8QW0N{CkO)
ztwn^d>5^FGi!@i>$76QMRX)v@>PU9;mylne++)A78g*V1ouIFoqqjZI1sogeb6foZ
z7$38QWYp8*+}T3{+tH0sU#<E;QwPNz)1Di02mcUK4HZ%HfC>jn^|)69^}#a)hM=x{
zaP|%Y$M&1>4CFm-F<w9Lb|q!M1Kss@Wp|9_x6CAkyN&8uAQC`i-P32MF4r#L!m@lk
z{miGLmw6bCG2i*F#k3}UMNX(CKv9x=7oZaqQb_+pn7k>$ILC$jck-E#yaw-Ztok%2
z=E=(n3rhZKK>HJ{_eRq(w&fJK)an^W9zFkW7YNey#OwsqXrt`h9fYAk$5MxwWGuYA
zO!$Nyw`qYn)FBLL7NT!Nve}ghJ7}7=>y%zQZxfaY*@S8JacAqSNjz(ncOP6ZDyQ!D
z*fg_Ah%%Y+hGIE}<(qXK8et(+IsL#FW#m*)hy<sw=p#mC_*^5S4$3d7GhDN$wx1Zg
zf@j)cRUepum$@>#rt!VmcS*wXG#d$+?%kU)hDQ(Szg>1UDeFt_YZLMY=G7B9>k7tO
z1<muujt<q+yS@F#_EP?qq70q(#J}kf`RLVMwXC)xZM9&gym@Z2xSc;XAIXj97lZRM
zUQxPdwa2_JN+0GnzR+x0i8y#%U?yc?u99lqKOZ@l@dizsUZ`vJg=^gzH!eY*??<<0
zgVb=O4xxJE{s}VZtC(}<8To^SXFr237l7{+>@_K=h3oGihK>7$YB3tq5vG%Z(RJ6o
z-%FKxdnNVtmNbc?ev>r@`I+}tgGTMTcy7hU-^b}I<?n#&-<Ue(Z9rhLyv8Jsa#HdO
za48vxoU+MbeyG@i5{0XFplpUEpBN0K#)j~l87aKt7pN3?#Y10K<Q0p1MlY$p7p5T3
zLBGQpFt6LJBG+R!P{%{vx=9q(V;72OnZbUQ#8TpC=A|vC(o^zgT^kggBtWh7CM?(`
z7}220nWcA74f@OgSG*RVST_XdcX*F}ODveQxnR8<&K?IEFp8^}7@$V)_TdyL<wS)+
zHg7=kDUR72gG@yAW&Unp4z7!vhN&HQ)aK+YMJ#|fMVuROzVHNA8?c}k3`IYA;B#%W
z;Q2bIb>Z<#ku3s@5j}wi?FJ3;#uQFNRMwWCA7dEByWTyOXwI;|;&pU7+=`(Mj$ECs
z$dpS-wPLv1QyO%z&y9&}b9#5G)6hIRi+D0_=21@ID^}jKG-2}9M$78kcmKeOzV-!+
z674?|Rh++9yhyTj<&-$Z2AH@uLi~jWq+aCUJTK@&a>e>qYpV5GgAe%CJG~4Wd_5<B
zgpNHSxt&`-=;6IHgBxT_c>iXDaZ)R-^BQ*@)E#TG>Ps9?FKxLP?S+CU_RuPdnkN(4
zZ<8i2FI9n8Ss@4!!Gpz(^J$tjzg4tOh9@}eL0$d>?+2@fOlPk*cN(IsNmo#V6T)S~
z4myt`xgupUkapl_X)_FbnsWvGN%yo@gC<#g`IW6}nu30pP<@9G<UZk~fg-D96P_Ko
z6q&pJ{%o4$HKd#I3;S$=dNtK~>*;J|rmsQ^=$+UzscY$=*OL1T6&J7(ZF`yq&FSY-
zi-4JX!O4=kQoqYPFyY~bf8+(Gj=d~;&@gY0V4}eG-ikUF!R@V?PVfals&e+LHQc!+
zG}k1FIHjs?MZeL;{oGyPcuZX(Gf1lV2LL<K#G8@2s;2L~v+?%a3*dr7s7o49yoe1e
zDGquROg-LR##zNI9bX?CuKYB#b%`r^cdB~62I20}@`U6)hpDh=#OWG!2so6AN7?a~
zix{xigsptYulW%nI>J`Kn*6-DMT8+Z2l+O2)R_Q$E@UsSl%Wsj=%5`|I0g{+x(3FP
z2FR($5fd|9$?|=qGbk|`rcxiN8pbd(x@K@Z^(p2uB^dh4QZ$|T-;IHie9;hng=-8c
zk7(AZ+vGzYmix0*hfjH}8+YtRaP$=`V|@>L*e8aK>=e=?6*L@WDB~w)?VGHn4Tp-O
zheUU&7pTCcP?+RCyjxRp&3tLtUd)2uLhHP~yu(Yg0g;x##1fOEV^f|l(PLSbp6sn?
z-3247ymIUqKb?behSQUnDSB6v{>bsHNfHA<GwHgyDDnd2JWk@YcGpeRkxf(Pu}<eQ
z{dmyjhZiNkfU}}Nfx*+$OtYLMTbqbb&~VnE5N>1Q%@;vX{*Dlmv<+3%!Hv`PvjYwl
zCT9beYT+{ertYPz5CW<XSJzlKPA@DUV$@J4w6DT!bAXvWN>|rZhXPDHKBn<)as=T-
z)e5keS$herhEEK1ecn_2*W};q!5?{+;iCe=4)UW<ngC_<5}MJ|;ZG+dEi&ZUne=%n
zVnUJx-2l5;vmG`;_pbkOp>NiNElzZ;-Iy|dxjt?}&Ul*ycd7y37y{gi<Ejq%#63v{
z1~4*=b3|^$mS`PRJb)l@`jgC2-$UH}%xl{G@TYM&=(Q%inKS)rx^p4SLS*ZTV73$E
zN&J<(NOnemwf5XmFjwmw>=c_U?%~{-GGE)5Doi`FfOgJHMVW5BjdWJgZG@2SGAkF+
zZe=NCm#m-=YTk)uv#7K~f^^NRA5GKDId(hI-^AkxYx%{O#IFe>1hIfVLtB@|m9{z$
zd4;x6d0XQ5RkH#4hhMPU4QenGA;$%Bi=c%4FB(+y7K)a%;r#2Y9X#K-mbwZD@N<z(
z<Qu*Quey<Eie6r?$$IHxROYDc>3yl(Wc((lq_M3Z-M;l*r51Xu-)6P?&8`+sBqR3<
z)S42yu0I>GsH*^GUM3yVeB3O)mwBCZ!PsZT;lUSXFBgP5M6xVPoPG2nSdsI*oB1r=
zHL{-;;bL`0zGMO#?m2w8q{BAI$Fx&Y=Cr^Fr9?r@U+&;fUd!m^Y~A~+-KC}p;JNLu
zUSQ+=aaH3tr7*8`!p?2{cY0W{$+;2KAI&p791p8u#ND$AZ&Ci+2Y1-yptVq=wNHi$
zV8g`$Ees-BE}n6A(O@Pt?009ZzVzIf``Uh0oB9he0Ix=>z>e6y@rqtDQDc=Q!%nS!
zPS$qngawj8MV)1|Qj>dy*H8^UT*M+4m11p}MWJDH17I73IrKKdShPxN{8j;wxj07s
zBEYfhCgWfGy`FE~pooqeOyzS1q^J8OWD9cD>T?WFSK-DO9#jM4s;_d)HjXeG#8HY1
zfe!O(7SW#UOn))vmG}woMPzmSgn9#njUuUsK+UrR_ks52l8f%8pM}kGg3I=&>Eft#
z>i=rdd+$<XI<1-hc~h#i`swgD2@*NSOoxR^yiC*Cc42@2A)K4#W#DGhf)xT{eN8<a
zWk=i7a{{9X$?sVKaoZV(J#=gdoc{ePA6;);4ocl0+V5YxUJp1ra#k60)W@w)(_tUV
zf(}>nr(bpfJ}?uF+UY(_7PUakp%jwbcF<u;xu_u3Ic%XVP^79lZnZQ**Mqa#hIu1!
zQr8wj=ZkS1q;QQNbRmM>B`tyymEty$Cg$Q*GP#fT`i;SwtO`!;bnaf}-~OgdE0T$#
zSeJR(K*oO|qx=ZTMk6Tlyc6%U`Xew&LFSjqUC1Z5X{@0lOBl%Qu=VygwLPM<e^>6H
zoY*JYZJ;^y3n)quS^P*1l4F5h8dKFl!*1@n)7V@}dB>6=+w$sK&{aReAg~8K4DyXR
z+-UvhP_w-$Ln^a)f737>TX)1scmfF6#p5aMf}7Ihx*xZWT{|!ns&#@RJF||iQSgy>
z-eRsfw5>-^Z}bi?NAK_??J<5^gpk;TYs)@UuRn}TR3zB^Q%-JY4jN7k3h<5Qs!{_a
zZHnkCik#AOS+a#^H||9y2Z~ob9oOq0y-R_RbJh3oT49&5PuCd8P-;J~(X;PYz7RVX
zKg3uB48%@JN31vbk#Mt|!*4Il#l11|e3@o3or3Udir^im9Y3y0bIR#8JIiMM4j*E{
zm?wU&!xA_htiS&>`ICa3;}3dt%G^FDLjie6eoAY16=MImf7P+PH-=R#wv=ycJ%sk~
zayAiN_J@vgbd4pyi*4Or($c<Uv(!r0soji4d`m)(-I5P(jTs@HQlfIR*!R>!=g$qX
zoNL#fn%jg<(5+iAzaEIQ;2l8T6woBL{4Hj=RE#1t=9D%znl`2&b;bTdsbkOsLd0C;
zEjHebaCq0L96o4)!b<3?wZR;t#8gg!nr~>Rn?sJEu98kZ?UhZGKRA{rA@?6HX$ilm
z-E#i<MFpAT9#fYow%MF#T@3SwwY6ToX5%!-pf6lx^mS2b^Po`V{bwLu;@*0>{?g_O
zb(x%FOW15cNay0nfY?>n2t&o$h%D(-Yht}bVbS3~Wm!1)a3QZfGeS{%ulTApFCK!=
z_JfY6@8uN>_Z{MS<92d)@au%O1iL#u5`hXDe6qQ9y7~Xo27Bgl#SEJn)RaG6!o@3X
z<9DbQQ`L9Tu|VWnK70QfaUG-fnC41aJ~>&%p=R8DxlR4)esdd<<E82>n`1Ii<qt3E
znZZ;L=P*C`Q^Ri0caI_sOW%5g#MMK2Cs`7^*G%z?D0m6u(jsPaNS9si4HEqBJhC++
z{Mr=wt!y2>cJD(8&lwC$_nX}#j1Z-AF3lkM)rt~GgfYbHB&}c`K6P$7Kwn60$!1<c
z!HZ|Z?}XxQr3+=1M`cQSZKKCayl%G@K6U=S+<O1)@Zhsbof6vX%36u#or9ag1Iie5
z+{B7?7H8spEP6%mSC@f|_WY)`V-mFhk^nGuthh~++gM&`KeMhxzT!ccdt2a{sv+|V
zM~Yv}YNHq@b_~CQK#r+DZ*m8oUst%Mk^(VxOMzwsF|~=W&TAz%llGR~cn7+?iXEgy
z&f6-c6m$tGJ??Sr7i!7q0L;O{T3LnGe0FNzL+~dRTWE9LC3&Hx1GbOTBwqoG5eXna
zFCX$wi=n`le~caheg;G;T<dxM)zCO<e1aD7uF<+x<Q?b*=>4>$GIBPe?%MTa;Dc*a
z9bTh&KY6c;>)<x^-H%G(LYL%cgfBpz!8QT@sT)m)Y80o@QGjnDv?k8hNsdAcuo*1<
zaGnxjKjDpw;59X4e$QdgC0TU?TMkE&O5=M_=HsyTDl;R&c^0)-b?ClYJxK2^MBpiE
zt7E#E4UiLS;I9bbtq(A^{gHn9-vS^3Ss^&%^KeEqqTKB|=tFh^Giu_AEE9wRk11Se
z#PN>x(l5V2mKN)k^-f4lLP$SZ&pD;IAD+lie8|K+`f^_4I<0X>eKh9XAiF(usQpBM
zbtVM!Z?8>`D%ZU~Ca*s?O?;i;xHYK<$Et|42=-xI@55{K=Y5xXSlsMJEqj3ZBe4HD
zCk6S~aqMc#R(iG-d91eu)Bl|J3iII$CzV$e!>fov-(O+>4s;#@MWVrO2acPKrWny~
zohploJ5}7wJ=Hyyq(gZT>^u$?fBW&_+0-T2gD0|L7x6tFio_NN)meio3-DYpl#5F<
zMV>L~-xV4?>K?~ZIi&OIzPVM7?qa0A^J3(vj@+Bs^ub`Tf-U)ydk$+L)J{;v3qC5U
z_gUNLUZ#__o;E1dGTs}U%}-|Ue<{6yiGuIr>)izlhl_5?PC`_6piP_U3s^(w%6)3<
z0-z8AB^jz2t0!GIv^ec-FoTg(->0v*X0wYhWa!EK#^$b1{vl{bRXF)#Xd>Tu-mDtn
zms8rB1?Pn338vJz>qb*^)qyJ45@hn)W9YRhX|x~~<DZ67e=oTD`QH*OwS9zKdkh!d
z)$Q*~w!o7)`Y>UCIJ1?z2PHx}(TJ1NO6*iEJ0N&VoMfQLAzeb<_VW^Tdz#LE+txpi
zrs<`6HKnta<0J&nptMz?x>cdYu!_e~Tf%tB)`w5yw&cH;A4_?7?$-&~H_T8`uoew%
z88yp4{t;}IEYHUY2rYlJCv3Ci<;LjMPhylfLhNdZ7)5QY>8^coDKDCrE5SKR8V03v
zUaSJma#?QXr?lF*2bs!;Qq4qTYTv&q90c{0w%>vjG7pahilUB^?8|IOk2DH;yfw%H
zrp*G5Fk5BQAiGlJi;lB^@VTAnW6Fd4foEqazh*{%4H+dA2>^ZOXRx1~^N^4bqylDN
z%#qV*;WxDc<8rrrVm|&Kc5ms7-sM5q(T?kRKHskM!G`wGbvsu$Z0b2<QiLkT&1BfO
z8Z@rWiu5riwYk=8SA$wYvZg>x3AC{T6Xt(%BPPlS()2dopy9RU+${n)js{T9ad9l&
zd2Feq2Vl8|14NiP5^+UJ^2n3f@%?TQTdwwH$Pdij5A=`{q>Jq5#__<X#415nT{Vtp
zUrD#FsDYk8Fj#s-rw(y~T}~AtB7)2UT3Nobyiz1q9c>T>J+emhZK;7053p;g$2g%N
zEux$|6Q<QI?pmMZ>ZB;B$bLOOcDc7JV&$~iNxHk~XkZBRVq>vKL$N=s<m!7{%d}fT
zg;(CW-|;bX?s0d?O#BD5oc1LC$a4O+zHON01djYX0JE&Uk=Qq)P+s}#zcH727btrS
zP^&j4*g&f>z{?#S!cE)J)Ow2>-*Mkxx<<R$yGQFjv0rW760712gHMbh>Yi*F>?)1H
z)5q|iS7k17t;+=P=~c)nH>*=xuWr;;G}q>5#J&#ss+M33qMZ#W3Z-Qp+d?m3ZzYJl
z`~2{)U+%q8JC?@;%}DJU>oGY!iU48{OLp33>$DgS%tsEV3<?>jKQr{dXpYRe`a$8E
zgk`|_rQ^1A0ra+#TZ)+1&*nsZB@VmMX2zB?Zuf|Gco)(g`N?t`AuanHK6b7Afj>o=
z4hUTW3-ci3T<^6g-kpxk9q6=eBG=l#TK9iYyg`gwF4iRNgV%%+)ThY)@aCPrZPm7+
z^l*)0?QU`KLwGU2dgyES&_v%Zl+OQ+sV`HzrzXoJG)hBLM`bw}N5VG3kMOTYBB#er
zCr#-Zkc)2qAI1)amc(B&ioU*PL3uu$6st*C!B{b3kxHCbXcZQ|&RU2U+a$Vav`dZA
zoA@X5$QZl@W(gJL_y-aen4dX9wa)iUli2~Q%OV?3Nu0gR^Yw^_nN!3njfHT*qicwt
z<9^pnk0j|5SSC+Hp_SRScyir9yP;W6spzF|*Nu<)5X{Nvx+>L66ruvku7CT^HK{Rh
z!d^LVq;RhEYOGGU$6MpvKuME1o0j5M;GP7F02OBwWOL)SlIRS!Zs_)_uPjly>R^^0
zpq*L1;yUyr@H0cFR6c18Q=i>KfgG*`epyGrD$e@le)s+bIM*p$!>gOzVxQAAJN))6
zO)SM|<Et$-8`OGGP+F6&XbqBtQAaxOX?RDXRw9fQen&QIPJRaExngq^K8+u=RsSN7
zl-#H9yw&{WN*AE$ex&lX?|~(oZx32>>Z&4jAcC|!Y)%xq1gS{Sl`oM4w+nsm7F614
zyAs2wk|xV<J3^Q@2|@)=QO&<9IbHtp2a{WTO#!JQ<h=yIDibLE4ESQd0FnIB(=d+}
zBC-AtZ+x0j1EqfVCmeQ6Ual7+?|JJ8+G3Y^`oL*Xv~Y;v=?7&%Y?~5zGuRqx?>P&8
zkooq^Euf8O3Y1&*-}Kb0I9ZAotQ85y3l3O#{A|D5Q$mF+#eW0#YRjn(O0?)#KPD0i
z?SJ{HmLScrTnn1rxBH2AWTF1et{)`@Y7SU4MTTn?=R{IYnijoL@8UQ|u}gXGFCuqd
zuop4QC;dwLIL@A=;$Hh-|Cp&*6FEiovz!suZRwd85oykuPu?vZU@4bO{Z}*vw&d9!
z17EQ>dGf2wKg_4)WPZlGk85_)Ehj>q3JKNdIi(T2U_{9Vhy5*8z=2N-omExX%m)99
z11728owhfUZJ4<bCsw>S?Y_yg9|MpCW8@=s3kzzqYSjy3Vf{>p`@mws{n~{gd{>`6
zilwu&eq~)A&#lP+3{>ODjV#0<Fw`-~T4sg?`C9XE^C(YGbv{p)YHKoU(D&6?J%rNf
z$7RJcLTz-|f0VxU0K7(SS{SI;8g_PL5nYdI2<>I!5xm|+yaQ628p+rTFaRE5=>Y^A
z%=(ni$FP#IOuZ2c`s#-wv>ye6eLvpr|716Nm9_CF`T5&!pC{KV*5A2>kwq4@U*a*L
ztU+>P(Qfo(lY-ENElNIv{{~t8oxlC7ugHm)RQ*r=N;M)NWbQdnVkKE?81#I{dG56-
zSXf*6kf5e`|EX4_Q}I=td+2n>b`es+HbEpU_N;2U<i3aQ5_V!V^_1?17gH$xOA)t%
z6?KFPrG~`?tn)e3?~#K-$>vt>hS>_XS<oBtxucOR+<gJ!W0m=vF8`&I6e>3CB>yJV
z6Fn+q3Hek6(!q!ePylCt(;j{Eu9a56zxh85Y7S`^Y$J>w1gfxOZSQ~{qpcM8<Ugk~
z4g`KTdCf5rts|fIDgXZiSFr>?L9nC<$tSuhu?u*Nx#05|IC}=;p1O1c1pF{{5j#=m
zAOu*F!^Qp(aOGTkLC(@^2e$6Vg09-1p>5?(-q5V<OAb`2gT7z0NVoErTLxF5IjuR)
zUpPXpf{pHj>0U)iURi$dDzah-!Wk>d(Vq%36qNcPlKd5wH-}sTr_rvi7{PZFIG=fk
zBYzEV!nQm<-4G+y40061pyoFZOsVc>d44;IwPb*_>={GsXRDZgPW!lG*N$`Al!fCt
z1A}oh|Kb=Qq@Bl^49v*CBbH9Qr6mlTfW_Q|Ld2or)Iz%vn$a3brw&Ia%qj0wqBDGV
zj{Z`2JAm2zL<M-<EM@(!kaRsdy@`+X?!!_}a0dh)X91i(%#;|A+s6P|Z|F3$Cxg1)
zEeU*&AWYP5@bA0$v;>{G0Q<yPI5v#cH<Lm!-fr0`&jTf}vK)g}Cto<r3mAy$$STU+
zQ|xdm?1`W&(r@Di4YaLUyes&H?NwEiuZFM*7`rjpWX$>p#*?5nz?)u*SdLtX(2$30
zrkx5^B!6h*y<=YK_{`xeOk<<K?_xZq5^)ceYxf&iwC%Tqcp55(kaTO0Te&4p(rRiY
zADX-uB(`i_4VZJI0CUt`j(@rfoyO*XqI3#95@c}uE7b0*eor^dCW{HthB^cECLo1f
zqfbj;+$Bezz`Hk^^Q=E<W`FfH4Hw#kr8;t;?<K#a@8jRm>H^%<Q#J@nt-zMC*90*3
z__gIu{hH4HiNg5p!cx4vw0bWVgn;j#kW3?pO_84&IBgStQP0KU@-zoSLuSGwmTPZ3
zpO5E!4!G6E?X@Vhy@%vP4D3KT9a?h?fst@fUO*kK!ks-*P+)S8%)yW|GHK#OV2s+W
z6aHiM<wU~Lma^G|x}bTYQ?bIlz-H@CLXw09^>QDt(G*(_*^53raW9vUL}w2cw`jtr
zPyDXIlaIa}IjFY4B0eDTP9glJ-k69yd}k-(+4j!WH1AR}JF*vSh(Aq@4cB?x5>j86
zAh=rQ3~fe>;VqZO5=?|no<xSX7^8rwB{L+mgw?|^5{#4l=6-${(S?Lu<5T>D2N@<h
z@s)Pzl%1z()_vs7^-;l;aahL<yThr=9G#GXb53uwa_=_sR|&`Y)dr(vYSUKbj<;DY
zcAE<h!e=_X1~^$ht5{6IZhlZhd1x~TnnDG#N>&3Y%8-mM<Chs`?K>y)%^SCPyJvWe
zrG;5_!BFJ}^+jvd03~SrYXDVa4BLW1!qqh)%^4bc?GA@fL^-Ww#jxoh3(k3xCR2iB
zvK98`d9Um%kgiGpjiY7vd`!0p8OICvce*jtd<DG11BvRl%*ztaa*8;StMSg*68ei^
zj7CN<1^KkdR3Ek^DJLDSwRDImav%T2CgzJ{7q6&ZZSp?lOTe(PJAygke63RJiIIA{
zp=D1c|4!`pZs#7+<(gX*dQrNe!oe|!_<Tr9uEFKJeqc{=vz{~iZnCn^;r`pE$!7;Z
zECq*s0L7Isu#Sab7ixgT+vt@O3kc*NxyGCSE36#CS5F6fpJBu{*+jATE3Ei6^Za3p
ze(*-YqJgWVm;m3ouiZp01WWZ$ChmTJ&64vX=#Em;bYemZaxV<?tJ!sxPf5j_uo8(#
zq?mxrUHuT4K8agRQxGtcqWw0C?A!><bGE>vu8QJMpz)mqz;w@8E3cI^dTfmz4Aq#*
zXMc}2OnyfCOVAtFJ;{vVYC~Ewjx<>Nzy)$s1k;w}q1o3Hq`|hlgMMYGm08;KE|1++
zHs%$-rt>zvDd@2u!Qdqn7ECIRwJ@P+j?|`m_f{ILwtdgM^e1`slt;Lgv|zwNsepp{
zlgq73{nH=~SYZe%-Rk3SG7n&+xQurc!Yy8lc2+7BzaRV@%XIo(cH-x+ne7!Si&v%6
zx66bV>!Nq4%T?OqT*!-Es>Fcaadl&O+0s{QY$zsNg=DbwWkQ`&0ykTl9U!l=m1)tb
zLbQEET!euV?M>|`0q1a*Z~!dKl<aIitUWmKOc&yrAd+=FH4c<EKnpw}P+Iu&aD{qo
zA+Q_;Dpz?bQ+n@hv#LMMU-Wz~XM^s<7FxSX%G~PqF<q9mWfw*ZlouzCe@cr1Zky#=
zeO`ZG*Hi~j85bLVKyxUEMF-SRNQ)0||3eo8K=K0R#uFyI9}tG7eg%5g{=m0BtesF0
zG5j|K?`w(ya|>cOC&rimuheQGOGL8QF4LtNAza`JT`K8Acj>cv@ld>v1obUoqOn7b
za)lpX_I^~!{;OlR_f<fH;SM#+qYWHg>(PrtMxcfQT>cO)UUjHGM3KBm<UR4YEPKN|
zz8aY%fXO{m;DM(41Quv_zdjAT#;Eij=o4n39AE-c8a^V}b}@$;$bXc>7WvH9q~&u;
zGh;ZBzg!g}r@(H~DNo-tLYBmZ%Maag8}<CAiI93*O%p8aaKWT6I`<}@W{4F+oF|{S
zlGuhBeZCShE7+h3JG9^AuN`*SPj8bo-;sF)4|lP5SZ8z-2Vz(HozE^#Ra-71oFrIF
zC43mdKJ?&FhpNkf$v+6{cMSWibr-JZJ(0iVtqNuv-DsWPZ<%w|<YE2lM%bimKjt1H
z<lkN6D*3S&{jBkR;GX%`jskibn?Y>HI6%|S;zCzk3%Uuc@5Ab=`N%Os3&vaWeTuZ(
zzLaw=Yu6SdBEV4w9Zd*9)Mzw+crtY#?Q{y>H1A!p^Hh|9_}O%&+%9*m^c7uVfT?!V
zqF^(iS|7wqz->fi3$ja>;d9WP>Oxlm6Ic{*Szg%v)a<D)q3Aj6L7j@_hJ(w+lKV!V
zmb{3rl;0%e2}tbD+7`p<S7+IYZ^LfvKuPUFs)AgA3HS@c=HCLOc#{8n{^i2?r@0Eq
zh438qC4)73nIP-WtDd(>UVl=H^Ko3jQcQe)0%LTe4fxh7c==xZ4t3%Wyhv6b$O!l@
z=sF6N4d5!1@sq2EJaa2-CLx|fLjmyJgZ+3JL^UqJvCWF@N`kzI+j$Fe+v|#XeC3|$
z&-hMAnebSLaQTZ>Ywdj#R1c>}15ujn+=LiOu&K{Tao+u5VTgTeU~R2uU~<Z`@<T$U
zX`z(WmvhQij5cP?QB3mufLczN)!ffwY;I_4V%1u(Bwgtiq29KNpTnJiIqjyA=g9sa
z^DpiQLcS8gI<YMY{|>KeKM4emecZj=;0-n7;h0Yl8|>m<0`=|`+~XdfpgQB{gc=zc
zGVEo1gixA7X?XrqR#b9h;cnYw6Oa4FkK|H)O=0BKS8u_s?>R00JjU)qpL+0*a~lly
z`|8yi*EPHgfV_;bo{eA$I7u;O+RKY3{oJ!pL$f&-NEc=5tZhkm6<xdTqof5L?74`@
z+Dzk;Uep#?z{;T)BLo#hgg_nhVj<E|a^JAhvQ*{Y;2RVAX-X<Ph*;1av9B>*LZIc(
zti=0E`ik!bUwN>sWn12IUve~v{&GO58{Lu(x>*aYfI>POTAzB&_#EOrtdp!E#t;Q3
z_LiuSNPE@-_^5Jm_6_dj2>X;GATmK(An$8)g*`ru!AOUb4>v-#u|FQ|359ld%&Q>R
z^eVX)J`#vtz=!LNZv{5iGb@2o0K1k-Co*P&2P8YVjD{_giKo4%NB>krm4(p_2SY=$
zYn^~<7D5ZZkXsgj^j&4j)6KiPo=f#YuDfxol<G*q_^Y&{r*xj@n@(|Nn<kto(wV`G
z7CZ1a+a5~!?Ivv}D&V5QG<-^Y$yp6Olj__a9_fKbt|e*xa9I{ty;SXDX~MO1c0fzh
z1wp;w9oiSh<bCg2e;Y8&(EUQUmeJt1u0OKD&Zhsm@}hM|7&7jb&V?z5t4%dCH#PLt
zc4<WEmE;+@$B3aHVu-B`E?1x_3Uk0P)C7?|0Y#&6>_HAI<$^}$eNnR-8l3PiZ%YcF
zf}nzDW5IZTKf}kV<htHsU@Pd`B*aiZGkpccndZ(@412>wJ4Cy8z%QLFQ(YAs2|C%C
zlLs+Zp_PQ!yxJHZ)c+DHr``Ci-;|67?zDlTd7#j2#KW|mq<!Fa&<_XUvcsKLsP{K6
z;KXiRG%4wQtXM8DCag6XZfyrmRTm(bmfbzWaVrr*0m_GT^88Kzt1%-hzxkVb$j-`+
z8m08swtNQN5Bl0-YNAFII$m5FsGZ<y1zs{{DG0h$>#)=f!q-_L7QEl!@6E%_ck^Z=
z!m`3IZ^3(kW<X!%4bx>^mDk4!9lgP|{y*WvVN9Sb#$sS6LX%MC3wO4g#`MhcY+npA
zFbDZT<HOrfel>mR-w{|rpTZ85zI}|Z=e;VT1jGiGFlq-xPpsKEbtk)uJ+aSb3$5ai
zhbX64%l!#$pz#u9=<WVR%t2lo|1$L~k-K7lD-=45&gMj-kFm?)Ahg^j%$Z=iD4<#2
zoOE=>NMGIBgR&8-6*ef2T0!4>EV~ds=hWL~N(VJslkRB*Nxl8ynVjC^?3J9tR2~(Y
z;kL{LTeY(MVFQ9*VAH#g{2;=U@+rYa3-qFgll`g`<<Ys{CJ6>8B#S>8Sdhj3ElsD3
zM~1i-jv&K>w-k12(JQq+4>n#c_R}rH<U*fp6~nT44fJ`ke<Xc}o9feX)P}}h%B@X^
zKs~0ezm+eDwl@B1$a-g4uoEktzpe|wVq#&;UtE*F9bPD7Wd-iytaVi^pKiZhw`{N0
zz^Zb8W#3jGG^I=^5-YaT8V1Iblu;uMED-d|*3O)2L1GSu;VaN4LRU-7_?*+d;|&HZ
zD^3Alb&IJ&ixgjPzIbdO?N<D8k<Z{X59cYN<7a5Dr?e7Rj%XhR%Q2ffowbkuEHy2w
z?-m+@Wx1SxbKe<V;$(UPzVD8e?|BV(2_h(oD10Ze(As%2=SAkh<sWr{)&gK$J&edc
zb~icl#3U`Ewut<x4!RH_C@Oaaf8vy{r-PPMa;g`!l(!S6;oH<Ku-uz*A47mWh5=@e
zQR@VNf%?^)At)TS@jvFL%g*{9Pg(=3p~&EAti^YSw_NY^&LlJrUX@nv7O(;DU2g`q
z#06|@(9J@|A3|ea3^Mu&NURq>P8X|?F1=vo2dAhYb#7NXQZ{l<j2nz?v0&4|`**Jm
z@giU&a?BxKO1*Xn!eosYD2pD2iEnz)B6IL&C=w1ujBtJR;6D=X$2zPLqV`)?t1C_b
zaOrm0?8qHeH>HJe=cK6P>91CFVt-_)o$UTqrAKL*@V*@1*$WPkU)(SmV2#TdLa**8
zH#mk(vYr}0swhTsw;LO!FL-^&JHBTw>-;*%pJDDgujx4Eh)s&<-&-5Nh1|fP_oMnJ
zxs65}D2~h=wse*I0}u4Y&FJe<J<e?JP+;7dr8;l45dk+5N@o|)o2EqsH%((|5f~?q
zdldB)FGl+k&Nb~qLozpgM=QzyLKJ+mcA~2%tLm1Psh!9?+y@Y^_}QZ!8k<0<JMZ@b
z-I-x=o_zJ5_*0Ni`?r6;!Uqv8eEThrH3~I71)|jsQl{VW`PbR4#bn(S^-rfw*)gD7
zQf5<LdW;@9yvIE<;G;)Tv+*$*oxD}9^U|Tp7=e&?uZMJHT-sHn<uE7O5>ladlPvVL
z#o5sdWojZI@;<F4Ylx2NNbbwkY(5K-i#Lcj{$y^^Vz(*X|J0ljS!-#%!Xo%1D{7vr
z0kc8!-{Wc2+5btX4mViKhUW{~<o@s{b<i4zXOB9xjwGF}$!H1C{9Vxstl@dyR2{ks
zK=GIycVGKeP!6m%dZBfI3D#oW04I|$4?~Q{Az$shFr36`E{0ABgbc&TQC-jUs(28x
zshii_<ce7GgX366%Kd^DVxQSUyi8xXc7*YnNgOk@C&7;d@gazTCe?cof2)j4WBPSy
zU^fNN1Ee-u3KZq__A~99_;H3n&$&YW%InKeKf4@dtM}#PaH5)jgUYCyOepMA!;%D$
zLHpxMNYkl|f8L#xU$?ltyWLzkiR$s5?c*;M3l3giZ~SxdfMuQjbj>VtIjsSZDla={
z<#KHmXy<e2^uA|{yHHe(55K6`d-lCV8;$os?mfMJqHepM+^9y#!wD|g&*dlk;v9F!
zVN><XL=<J&b)oY8^GCG|P5g~1ay!gFe6FmI5gqbzHGR{u6iy*WV!T8zo;=qsr)l#y
z33_cdG5{9CSJhWM{&*_JZmiZ?zs#QmxC=l)faw=h!vu7~SntzfglSI+W{+fN%HB`D
zFIols#Xp9M#IN>5AJc1mX<PP}NXw#Sm|n_kUSa;Ijr#?k!VdzrBTx5k{>R0L(ST)R
za@>%BTaB)&eQ?-8y=W_5(5b1jOc%7Shc*dHZOfbRiLX6Cie*S0-v*}oKNeSYhO>|Q
zS)xhL2cx|Z{u@wJlj`R=BusBukvF~zCJ4=q6Mzf%1MXKMSFwV5vhWj3`&%3;x7;-|
zD~#638zal|iQCZCJ%B5vSvQ9>@FHUC6I*%7mABbc;dsZ)C;*fEgY1wf&}d5pBZhb@
zQqdFYRn`844XzU>k9Tw|!7_Z&vKBGax5(HGE0Q%=F!dvJ?we5OAm(1$U9M_QVUMg4
z!?$Qsa}x*rVH@Y^4$fPb1Ujilg5F`Ohh%aJnWmSrZ1qLR*{bB`E$u=jD}UqlI!{vM
zxm@4+Lk@_Ob!0^+c2e~x-V?dx$0n<@JL(}%F-WLf+oMKr*GroocnUEr@Ru^D^E7|K
z4*L#a(`r0ogI-<k3VGp!V8V+CWe<DOxZ^OHSZ07DcNq*%!K<s8J!ncjSwwIgPDtl-
z!gd0RsIdPM2zM8ja_-CWuB!6TIy_04o<y<P4JOShBqPW_WT#j~6V}h0Jh4?+3Uw8r
zU6NvgNJmk5&(8m)S=@dj`l95%w;}Vvip=!AzpmZeTbqiyh|xn_0y{vDI~Y3WjHx>O
zr*g256B(_{UW^zPUw+9F{-XI*?qJT=bi@jzN=1`#zVbjJ!r3af$&NAJA=GmJ`s$~I
z%Ny`!VClZ)>Sxirv0uAJA1~?Ls{cA`DyTq=Dq$nMh;?YQ(4=%6sA?B_gf613F3x`&
zF08kI^Jt6fo_ryBus>AL_K5`N>2m+9Ru><8slJGn(|&2O`0pSMYvo_{|1qf$sts4o
zYP%_l^T8GKziX8Awwv^5pBlBIFnSJs7&klT$`AVe9?&F;(Suje{PDLO5l%+=$8mbY
z6RC>mKLn8hl?Yvz^Z{OT)xQax4{P*NjA@wT5zh;3`%nt<7w={Zkkj`c0$s#z-7aAa
z;%r1VksaKP8iXg1Ds&cdO8S`rj|A!Gb;?ZOc#XkO6nPf2`=7cy@WUGq7n(qD5hvJ<
z<Mucw5T+SqNqS7&ZI<(PS+fThiaq-NB!>|&un99jW&<-%G7#?a@~s~&ljq6o7Fbgg
zQ{3y-xYk3sB8*wPjo*wtJSmdoSaFh&*Cji()5~o*IYH)n8*<Ws&7Ho!;^io2E*c}v
zQD{63C*Z1^7`za)|BRvf%zd9G3uxTa@vS<2^mOIsv};&jAMfE|Bjccrfb3e`4Bh~S
z@{Etf>VezCpSWa27`U#zU%);*lKO#pPJ?U?z6kyi_waoGiZE)V)O4%#Ay~ZAtwpHI
zgDEQrBZi8f(hhz)aj4Se9jFX<9sCfhp>rL4qw=pa^8IygndNJCV2<1y-}j}y&CPY`
zTu6?JkpPk+-SuzvvEX9>w^azlE$$XN$w6cT%I@GU%;AOeIej;X04+-<A!y@05Qz~X
zxC2ZRORm~<(2+*#P1qmcSTeY#&>=8~9oZf3?^vxk;oZCR7N`Ym0E+kOP8Tp}pil{X
z)|~!B5KN=J8E@fBC-kxw{NOZpE>cL~s7p*<eQREEef-S%6Vn(^PCtJYvIWs!OtQZ^
zB|VlbIND9^iDHB5ShUni`R0Ru-pZ`&8W9uO?0JUBRYY2gx{1BftAWlfA5XQ8OckM}
z?x+Z6I%;iu=La`oWg~@0nPcIisQMTKDh)mQQe`ypx@~@%Ks{lF*!3&==z<o7o_k>2
z;n8#Rcg7R%CYu<iW(Q4JHBRc366>8>%Ku~Q&BLK?->~sCF_ki+Qdx$2qLoy3nUSOw
zr5=@KL{elOOO{!r5|t$h*;AGlQrTrj_H|^BVJu@tmKhAjnC0`{p6~k}$M28de{IK6
zNB8HxuIoIn^E_{jdS=40r+(FKC3NQ2(JDHD!1-wX!Fgg@bw08WvckK|nF!cgo<UeV
z9yz_&dIjGVs_j*qmoveg9IpvxC8cQ;*MKRv!S9nIlW`;bw|BmkFuRFX<-Z_e_iYDb
zqf=(Vb0N)En)5CKc$T8{{<(AQd>8Cb!R=n-Wcb@8hp0!q&1;g12hrc{q#k4?4>ELL
zJB2c;{J`=GcR(?w5H=mLBtA##6w5nrCv{HI4U672MeM#-e?7OL`<AQvlFqiG(l)Un
z<Yy>Us!=N}d~Mn5hdMueC9Yn~ZOuc`;WnTk4J8N6Rp)I@5S4H`$G<_NR!w=Xv|o@z
z-`uf|rVIa{C9jo0=~uv!-+YH>+(KL1vm-uXKWv?L>kMh|X&-wEqyFs^aWh<#`83i1
z^j`MU&%Q60MGXoX`yc~1I{pJ>YM`qOzA)i~i{;4K2d%=X<#QE}O@E)QE|x5I8Ps!E
zx(6x9-X^VE-6RyBF)2slwk#Tvg^8q8qx+~2v<p)$p}1rx)FgZDw%>iECpKfEjPf;7
zc6slMVrcJ=1LjwL&lcGCFMn|_c%i(+XRX~lqJkFY;#CRgE3{&y-1VJfoXYPN`)c%T
zDGL#8wSM|qTqe_Gc;NW5OR6~1ZQ&IO;tP~;U(;W53t2k1({`l~!qwj;{P3Q4LG_gt
zEYuZ*IOB&cSr<hCZ8}v+m=hbQ?S1~5O;FV$ij<3=E{txYupj9Kc|<RdtfolG8*zC8
zBb=AIo_p@r!SRowqJTO=$WfAnm+|BRxlh<aqWAgFzb&cKQq;Hy9Vx_w;p|&sKJRJ;
z_g)DK+EcZGSnAVz3e21QIz09z<9%6D;<23@>6(+FceQuhvk(#-rCpL6T-V=F>lBUT
z%J*(_leBcpFjS4XRVz9$CA-!Nz&g0wNHJ!d_9eRa;t18@YkriTLyQ2C{g-Ha;$gW|
z{~|dyxN&8kSpIxc%Tixi>14p)&T3Ht^-#z~X#637vKxe#7E4tBlX}>X1)rS%{+Rtw
zNJBPP404HpNTm<R&JyaBY<?M)>2;KLLvDrk`;+xu$Us>TjjMuxuK0S3tnWTg5|=wu
z@l2Zt{m~wv78njA9$<@&2lmZ&Y7YQz+P$kc^Dq9s)Y6{zoC}Y+ADrC6PS#8PrV!mV
z_$nP5$;`lx@L9^TXNRzLU9n^fPmtJW%5};QU6L+a|G<;csu{lid&Ky^J|5zXKNGP5
zANYT9j_Ok(Xpgr!`-6~rTNpmFScSL4E=OYq@rLEgtbdGTZ^WrqSKd1eS3>kK2;c!r
zRY;IeXkq_uz&C^(@8fG5A&a=0(S#7dz09ap0ZVzR@J!V;PB8*^09Dd-P|m%PBHxUa
zSfPEML#3l+d$%l-D5A-H!CRHe+eBzyA&p;m15OscYAgJV^FX#rPL?OCr;A3T7x;_w
zlMP1NB^ByV+dt^${e+D_T2x7lf+ASfjs&@H=vMf*U(|J+_Ypi;oMFeM6U+^W4cJ0o
z@@2oLoBwR4e=d_e_<Uc2IGo=9;8e^Jnj_&@H!m8xBKsN9R)}^zzXv|EOR^UFe6jmL
z^rrXzKnLj3tK+KZHKaSTTD^ivhh4R3@kW0Y>F>OU%l<b&5wWqG2h(^EK*mw@JOL>U
zltEjYlzzF-s+C5z8fn>nb>Qw8eN_E<Vw9-Gd82{kkoJ$7bIhd@Hbdj(rMk-Y;z7MX
zX9-nRwG?U6#RwMMeHgBfIbXmRSGReCz12(2_B*yf0;v?6AcLPllVN8?0VADpufUsT
z?W;2czHO;GpZ18O4L+E;ck?{Z_`4?XO6+jH*zBw2^9JIB`TXF}<sn~GaYU#8m6y0D
zRj|c<t8uJw?uXeg<!muwp)abQWiX^s87=alslgjzKcjJQA~&98R=;sD^=@=KoUuK|
zj<GB0DO9~<YyM|VwK~C(Bs#8@)~6qiNu9onmtVD$(;u?Twd?+x*-&GlUp%}zKsWgw
z-j>ypLMpeHe@G4v_@f2dxcKNRL(&H8vV#NYKEI|kUJB|mB}u(Tz$Q+$K%D2hv5f2B
zC)fa?E-5>LR&qyQ)K~YRgrI^diDn8WFbqFUStXFJ3kZt6FE!8F{B`;YUB1WOzxaW$
zYlKj+6>>Ua4;}su)#7RN;RPVk8mM3YN1`2!gh>pwo38nf0&M_S>FA3li_N1Hk8Rs+
zF>KN$R;@4Ai*SwLhlK{gdXX6*VE~l#&u+bC*|4j!c`qa6b5N)ket+~&HHjf?QLHs0
zlK{+J>9B0hqxsl9hNcj(*)EcX9(6*?M)a8Or@(CI%-i$_n#=2iN)Zol{3T=*#83MD
z&uct3UCZ4`GLx-R8^N0hEx2L-a>H&!ZF=Jw^Cs|?Xu8w=F9BgL3h?o%3IkhGluqw7
zkLe8Ac#S@3f|e`1oLu4X9+8WDVcTmK(+C4pvbZ1B!Bcpzt3x2JBYyJ4BC<g^iHRsD
zMc{r{-7Loh;tPa>A}PkY*DUxQvw;NZBrs<LVy}EvIX*eI=X+DDj8E&OedBM36P_7%
z^U1B-kN1S^_+Ik(g?W{lUsT**MzHL3Xqt7WpShV%me6{p;hTS3H}p9tE-`pzly_=%
zr7;cb^M(D$i4%fH5=4E3RLs88W{)M!R(A#O=E#`~U88ywev3fKjw&Z8c&Sr4p2{n-
zn^Q<@S#Wc5zw?|jJ01@$KCM*0yS@*El&p@}suasTAY5i`g~AAezLE)0-UqzS0G$--
zYr2jm{O!q8mgR;Wwy(4iCu!f{XO}nqIlyrKOLT0X0*d#Bk$?7PT}0d&Om$z;L>vL<
z2uHvCdUu`QhF$QKNn-US#hCJqP0;%3yMKvx<HXjI>W43u%BXSx8<c6RC<_q3#4=mN
z#Gz?l^3JuJyokD|@9>up{cxq`lhZG|MO6OtP}tpqkEcLouQFHFw~4S<`>(F6SDA-+
zj$)H1^Zo|cLmBM&wRJ-%3;u!sf#>hITlGfOr++Pu^5;;!;-Y}oq)*=<MTPXbpg|<`
ze5nk2QfRKB??T7R*``oEp{3ud@3tz0%-_S4YU^KSEnh7~_5(%qpwg`Ly#xoFSsxTx
z2vS{EHuBE`iI%})=9$HuZ2ljJSQxl^XDZ8h>O1CW`~1dwY5|M;t4a)9XJEMD#r{O>
z#a!(@vpk9Ws=4s2OtSUB0brOZ*5vMk?L&mxo>aTo`1?FRsEICv4SvSGiS#u}@+se@
ztGL3~oqs3{_<9#uVVtV&xJ^puR(n0X^9)paGiF;$QB|~y=x_Q&-(ulBr-a!@o-I^J
z>+p6zt^}PvB4_AQ4n{=+RHxz6I`HYKUN1AtZsn~ip7!eR3Pyf~cAlA*0KlVxKUK5f
zc@mkotI8TF?+Kmu2qtTWZ(_m09%I=9x%-zi6g)%{x%rb+m$~YV>%JQ|B92-3qLp1*
z(`B%m!G*0?klrh)C(-d@1NOwWwYv@^m(?G%^p*c?1v0MVsX{&dkxR?)9B*>2M)%tv
zsM36tgWVy;7`^*(Fp2JyuE~B;^HPd)E9FY-mtt?7Z~K9NBXuv;T|Wcde7iMRpmc+Y
z7(T`V^6Dr>#0`oh4AcSnV-w0^<}x08S1+VPVj>;F-#1`id{yC4%4U~C0>omK%+D>p
z#WMeQ5v>sNa;0iN-b1wtD_d1D8BCs!w$x8=$*XlvbVyRorc##Har#p26n`$aPGt^0
zhE6F9JpUdn9CpN|$$B8CeF#*IL_kY>meQ(5@ekk~Qdezq2jL)4Zzq%2>)xtuCck0u
zpN_MBD9XHegCJwk=v+hNSxB0HX}OAM<c!QBEh$c$5ta-QtH=9GE^?g=j=3Ww=<5^Z
zf{>FqbnghkK`QpV?f5HBzfA1Sk@+@s{U%;t?Sl=R@3kTBr=?7qC-ifg9-OF*!1ulP
z{EW!q;3N1C`MZJyG->EN<IPOPo%8r;BgPTKBYmqz3`P7I{AHx=U|S#gO3s&7g4;J{
zres&(7Tl!aWr=7=I7YbJIx<f}(&8~)wNTcbT!<&oe#343V70*DR_TDRVNOmX<Tk#*
zsI>T*d%?@#HfUIz42dQ%KD3#zDi4y}sg4xsZ?%S+n8vsAVbl8pY7J$u7hAP8xffc^
z9MZr3N&2>i{hJ0_8i(Fq_EQhj+se@SiiCTik8u?xZPl*qON6zSi~ZF!Jm%VRQ_BsO
zFW*oUX5FXNwE7svD8FuU>U&kFQZDS4d}f<i^vheDyR9>N$3W>nE|uu&+dWkSbJfvG
zxn=$z2cVP-jm#5)N$Uf=T%&X;CB{*~tJ&rkZ!Wo1oRu(w=0An0>)yHTT7y79Hru0s
z2mkX9E2a3Mf-2|FF9S2ZH9{;kWQQ(?Jm`D7ri2Nm<E3d}OtgK+ua<^dS}bm4hqeqj
zBR(T~0TZS8aQNvDr6tr5V)?mWG+$AsL!DUs%Ar+bz<l5h8M<d3>h6BUvYaLZ!q$&c
zql>o`y91&y(-(MpUU7Sz@T=*<&66$}+<1PlY!TO(a|5+nHi-vc0Y0vM{s3jRPDp;R
z_tRDTo*1XQd%m{04TlCEcyJ;)$uaxQB<{3&!p))D5FK{%4SCft$*Ac0B~4$W7wXXW
zeE;78FNb9EE!@7a!={tGQxXcM#{$w9UN7>h@>{wR(;07oe9EZnNmU;>Z6RNSpy||W
zkE1P-u%49%Q6-3WSb$yY;~YUXlFpqPKY=Pg{DrHYJx=)H$#44S=?h3$1C*3}qeO(G
z$4M1T;~oJe#Fc}ZW25s!_0$*{iE$23AC<Ybj}achUNt?4i=!~K632<qhZhLw1jmp3
zJxdknnto>ZKhv6c;E~n7PLu<?mGIdLJ-c40G5_bN5b^nW@A+v}t=G3A`B!W%La?c0
zHxj;d0RYg;1CbVr@Y)mWm>nqP;PbOYtKs7V;dxMK`#ygDjovB3cS&g6KVcmdHl(_O
zob=jX(Oc{CQb?7BYD{_f{UJ=5A;er&rx4AKs}wx?VQB5G$e1rgDWoi|`yA&D*=(Cu
z@4df3lKcq!C0wppsXWLX^t!)*qOxPQX5p%eW3&13%%G0?o#L0dS(m5qP0+icPW)iK
z@GYw$rF~IywV~GwOmW_*)vuGdnAH#m33T(5b=)w9o|0u0uR5FTO==l8;@z>=0yYYT
z5Bwy;etK5mCdR$<%Rd`wXHpv&SXmA=MWaw^ixVC9Li~~6eHjyG&Arw1C0`j&JU4hD
zc+tD{?FsCc^x6)$m#KSj5@t6Qg})l!RuXzYFQ%L?MHX#hYOUDY&G?fG>LDX24$160
z$@2;6Q*HH|b*o6E6JC_?s77h?GfeuDj%=ywcXm%5zykO1RsB-V<p9Vhs6SmnQVP?}
zmj;`TDcv(ascE(W>JpQUHNGp8XtkfQ`-h%#;`2N2`H$-^J?R9T+J+NJFC-QQ1gaE2
zG`8w5(PjpbKP@prm=KNl+6H#ZcZjtBPvqf@r(#P-c*qd{i@+^&3u&=AH=)}c&^aGr
zSFvA6-Hxuui<0)F3J`Oo+@WqCSN$Sf<2>;){0Q@j7#QWHe_wu;Gf5l`?wlY<n%fyM
z2+wQM`yt?J)OYBDN_!}0!rk@(64*r*Ow^lb7NEihA%B#CiW>9AhJ3uWyJhE%-g4yh
z`WqSzpKWYTJxF#3qkFla`$j;a5e1}cOfQhVNWwMoe#vdCg|%##t4Fu&lJ~%(24&nO
z@3|BkWE}nV0(cxXDDm5cr@7xL{o&-Qo(cR!C4SMG?*T-;lhY0~9Yibqm<~}P^uJ|*
zAxkdlF_d)0!Qak`c`G7G&7>01+TY1<bo^dcE7+NeXbk>NIGz;*W)!XaGTlXgyG`8g
z$@dPbnlK>)c6J+2D^53PX-ZITlo%{w7Pyo={2F<9{+6eqncCE8n%e?l7#h<vOmOa&
z7<aeh9DHNAsu|r>aKFZHu)c$b!84mI+6(SG8k`!BWVwgF%7!kFEqMk91F~l&9Y?_%
z^hEPVS5TTKF0}4Y#?~qvPbOEoI##XmHPN~b3uJwhYTGAtq9s#9>D<m>q3*W<s%lMF
zA(h(8^I(Mek;}<9GKwAz+*Z78nCVOQyMny{|Di7C*IX{%eH=ix`j>hR-DriFw;TVv
z&5MuQlGT(D8`tPxxfIvHQDS${0mWmAu-<VZ(TUg|N?51<D2^Bf7J&E+V2Fk}iMY?q
zUhvYT#49B9l|}G}i6VH=tq3FQ|4s1w*O1z8K5<-DEij!QTn7zJ{GP_Y1WV*3Y%6lQ
zdda7?9Hcj#qKhFKMFAb+@(luw3XB)}b1>C&w0@My8|Zxi1h?`0keqS+{r1)Y;iTFJ
z{_g~Gl{k=ayAU3Co5A=GfU(VOwM2-+tuZwd%PM**cQ?Mb-PE}ISa72X_I&FR&ZX8e
z86U4np5lplqiSCEEUt(hQRFPsdj!Y^x*gVz^N%WFuGNLXogAwk^hjoKIa=<eq#e4H
z(QVN|zIdFRt>^L>B}Ch<p{L<O;@}z7+k>QoqP}`d=pqFHpBammNJ)Oa@7u6#c-QDR
z-~}OBAZ;nN%bj&oq1c1vKb=rXwZ1|M@CpDCeVQ6jhyJ<Z*uGJ314CxFvwad~KSLp@
zk3n>>KaD*SBRs@-@R!K&@h6jKv?<joYMds~h9cry%Nkqh-j|(XyRFwvZ{;WF8h7ud
zNN$^c<M*Om!d<E{P^J5f>prc{p!>r2Z?0P9eOZ&_=P${9ZsFF7?S^lNg5Qan6i+$)
zwq36DSeyJ_Tazt}RIIrcd*=J6vgrYE$_1(^H6N$9kDym*cuB-X_g#>I53dng*7a-7
zDy;)*=Rc#iVA+3tHGR_UUDx^l?2FF}4$uD#rHE4zEB}@5|LuJ{Ch#0bz4a|!6N3!=
zOcrW6#qSR!Wm9;(Qlrv{M_au?k$cRxJ2rur*8mde&Z)VJ4U{Efx5p#EOO%)msQQ*6
z-Q@?U(QakV+)sST{_w<rF1#r!ixMi7e#>%!#Os&U$}m_$0e@So49E29@`k(HUiW!8
z{YS`MUNllhv^B^aKdF~~rs{18!!u&{&{I8BQtpl`Cvp{gU#S36mYOUIs6JZ4=;O}2
z5SFY16>=r*(8Hop1QYxUABg#?X!Kt;p1aH1PB}b-a1RQ8ek>5M!DN23Z;K(s?V}Ue
z)cGtP4;SLomJ}$w9Bz8pf&#{Tdo!i@L3=q9VZoo_Sq-~N(QFq7m5HqR2MEQ{&|yt8
za^*qI1Tc-0$cBeiILgircQVTkFtQUr5n)rIjIU)|?rO(OI9A0iU4~!68d=KaB8>Gw
zQ;k=LZ!ql87w^@WPGUX*<z1_gB9`~bA+?BgE4+HuP!<F)ttwb@<Fa96{}9th4vpYP
z&a9;0Vdm`~TQjbT;JUk*VUy1LgOVrxrT>fa8gsi4f-#bvGpZM=z7!hg@Sm{b!K(hm
zPsEM<Ux}YUjo#lb&!a6Pq@Z8`>_)lM8W%(X4zq+N;YepJUlIRz&Kvdhr}w2pt@o%J
z168bq{8U-tyEfs>#ToW27R6WVcga-Xj#n0R3LDoomPse>XcPXtdvRd+MWsscg;v#K
zYj2d0VtDwsuoZox@G<qIS<^La@m6fvrZV>`5RwH#rh*7pR&hbYYQ1@?0Nw~gdOI=&
zgXYMV0`~Y2a!@bWUap+@xq>kuSWTJ(&-OUdwP3spvyPY3_>bi1+?la9cAy1bbO~JZ
z5}Gg$@MQvyub?2@BLCD0z_G~v6ilq0om|z7Gr;E#!8OHwb(e$%wN+Nftsu873M@HB
z#UcB3BI+{mvZP|-6r=%sYV3HG@;&sA<b<JS@>Fa4nK#MP48ueSQ2CeP^tbDJV>$1l
zZeWPm$CMWXuut+*=f1qVTV$v4cuz^4u0xEgBT(-x$>!#C|EmZ<qJmeGU*88{F_4Jb
zbP23nDPP##tNGZ|zG#63Q0oBpOsFAiz7{^Q9&qSfOxe2u(j3Y3@SE$L8jx`UzNr?`
z4x{Y!h~eH1q~{<IYqU@%-DZrxaI<5A5JFSMiE_Bc_U5DQz;NL6R1=cqIzfU$X3Ebe
zkm5H{=NxHkA%31D*&twN+~AgC7D(mpKT@Z!tRiQ(+zV*>k2428nSKv%(ub0vH$&0b
zQ*T)ysd4oQuGVP06f9W#85f2Dbnn4kZ}uhu<z_?ZtkF4PRr~s5`^YoO7LP;jmm{6<
zfz*QKDs>RGR;zGC<#3&XbH%`eqnhD^%HRLI(P>DY#F;R>cK?!lw;Z~3fprub=S4s4
z&RUq}-w1Bg`ok&j5WkSN+(1a7IhCh;yKS;8w{l_jOxMJcXL<+~h3x}koH?*-POHJ%
znY&Y$g$7-L-BsC=%VLD7=a}a4r~C7}R1<(~_Y;4Jjpv;zf4I<<_D0yM-Cy53hblw>
z*x-aL6c!?i=iiQp8op5~Ajmbq+TyiavtAkQ+*kxMjjY!g@>*@gKNl}v?UkVC>!k<~
zd$V**J)Y}%<c9?!x{24|@036oS|aRU`1_~tZ^^7#+E`X@uOQP|pvdz^yY^VZeWTF%
zil1R=GC&LnW~v~=5zSHjD)L5-G5q}-tFCH&jJ`vl`9=|F1z<2H#0()^HSR}=-CcY;
z2Q-}ieA>4ei-+Kj1|Lz>Q)bnP98xct$qs$_zXz{5A6R8}evjJ=3arXX9`6#x<CLOO
zDSFcLV3=N5$g37EqTZ;r#7{g2UZs)p5#7r|&p|pNM-4Lgu?&W(F9lLkuef$WfnKJ5
z=RQOGP~xK@*0!;O8{H-Dp^Eo6t|8|FU(~?<*|_3EKZfYIP<MIYk&<(V7BxaLZ|(RT
zW#mZvTF?<doIN;_^2vAj`c_thZV2#J;m@O<__SK+a*;lSaD7<o6CZ*G@ICsXmQgXr
zDYV~)4K>t5D&w`(!<xvZ<}>u{n*+7fqbga|)U?*tdR}dL_Y<uLlROfViu2FUG`aEh
zpSDrG*c|p(ll&#z<evQ%Tw6+3w2L8AujziV1QPSr6Himu4Or?mU^fDJFIOB&u8qPn
z>A*0MX6N5NU4USI;Z0I+bKr{G|9IMnF|ySQ?WC4SjO8fdhv&v3IZ7cg%7v4)N3N+a
zI<+Sn{BmN%tw}Q5iuPa&YC|?)0nB&izn$5!kQlg>xrqK|1E-gwmB&LXb)emn=bXT6
z80+A^Kp7PZiaPpBHM9Zxu6`xU3+?&_)N-w9h4(mnF{OKUKg@6GETMjjsq|p#9oE-7
z<+hC#o!(VZm`0&LM1%4W=A+<+CqkopAD#zlVbWJ*f8cF2Q%uMv)|}<!8;!+AU*Z$~
zsJ(zjEmphLTn7Zvg}NPLex=%8l>izem1NT!_^;=N;{+7;=7;I8*K!kEs-5H7mmC5|
zah4ip(OlRYtz#@^(1>m7IK?0*XeA7pB49u3`u7Rf?&?wMT;9osmPSbZ!m!#ts3L?;
z`0<=vd^L(E2J}`EvqylyUwr_{g8#VqHTBIJ$2zFXf|t@hPQqO_EW&|j@~OxK^$zui
zltB?r=3M65SP-%?ePH4jrl&~zm~i)5G$zV?Z@47${elALL|<z;Kz>*Mm7r(q-Eth(
zRw3}~`PmNg&@dJ?9lu2p3M(N1R~&QP;FfHK1Io6~&p&wvN54{bH$hkZb>7bm`cwhP
zvg@~u+xPG1#$g|eo5$yD>2}LW>6jCCYvTTFXx02Jp{~!*8v;ASTig6v{QBYic;t^Z
zZNRe%+T+q*=d&FL5AsNW-%-CbamFAD{^*aDqq&x5-y-T5bfNY9<Q}l@SnCBftH^(#
zutpD%HzskA=KWKj`vqouV#6HCN1<cSX*;p$7n}`XHTutVoKxrrnE6}e)1Z4eP~f$?
zR&N5LY$ue(0>4cMym?99xPx6uyBZ|<3fciu2|%j`bQ%6#z&fdC(IcuWn7?bq;6zXq
zI7W?>+!i3*#ck#e?gs`BL=995p!i~`s;)hl#t_lGfO*`a(dhTZpE4x1xt|x;V6*^o
z{$ZO<)j0b720r3Mjv#tsi+Zqa%2Ku7pqjmx{nA|c2mx_P6fpPnGT5IhXp9oVZHsR=
z3yu7)ua$vLs2NxFL$PTbFFpIB&}Asv|6K47+<Jb9d&LiDuuE&t1Z=Zshc!{tcPLtP
zhxRTVOzX4JYfBNGI47aD_?;&WJRs3<7+!btn&a6`Jm)t_p8NHY?=QUwspZ$sY~U0{
z+<agZaH2VY%Sq(NeNDUcOTXvNWXP_C1_Q$i{lf#vepjd#6~~W9mKg!w+#+zG>m%iC
zYSF(J{i{X}lLWNK<{k7W8c6>KiH4)+Ew&(6ZZ5wGZeAUHnmYdN@&oQs-Mi=UL%u7Q
z=TQB#WZ1&=ssm;|f|-`wcUv9~1V%oln68ExH=RLqmnlMAX$c6MipZl**6WFr0ZI1h
z@ukCx9Wy5o7h5lDT2)s*p@+uS{asboYMX9|WjeO&C?2!Zk;>iX*LLpA*3o{Isq<@F
zk~5d5e95`a-9L-~+PbBdm-GR4)#^(@w++xn2-noPq)duf@;GrN-HF(tC6yaSwC`jR
zBj~#0-~&+ft&PpwS^_-Jpf{Abi{PbuCOzJD87D(Loq4}1%wPOptm~RJgUXbQ0?WrS
zi1p1OWIMZM1Q*)!%@-&i^gMsGPA{#y3>i!<_VlQL6MX_?FfF%7@!`E-*{Y#?vRC-7
zrQlM(_C2);JZA8!tBr9oKhzzo%3>~IC-<QV=YdBfdd4a*T6|fLpTQ4qpi)HgzKYWg
z_;nm|$hu<rK`Lcj(5j@F@P5(gSl5Nts@G5a)L%tA-hGsG>m9f7l+;}x`5D@8KKV1d
zM*sMxQw3Y6dOL#pA9L(okUr;@n);VV_c*|n2q8;41wq&K>sy$Y9P6Gw$Kraq`rs-z
zZ98n<#MXJNCy8jQ`ldxLQ{vf>;%NjKU=ZYd3}BTy6;Z}a@6#BcXp8kzU!7YP_(k){
zV}#Gq3BsDMWo7r(3h?9dpPXY~_MJWJwPp1Fm9D$9Bm!`mR_dcXW!Ni?W<K&$HM4PA
z)<Zf1#7s+v04HOjC_NxRv=+#GYpcBYM=mCe;TXZ&bmt@Q5JSW3=#mlK_lR_BNQM-e
zvs&H#eCdi#ZMkD=;qtno$(eF(Fu1@sC|bUidKhFUUmbLQ7MR_;ozMA|=l|&}*4PD&
z-++wZhF;_KGsIO);1T5!l_t|)=e=YNoooLz*#PyFpAtC~6mZ-xEJ)U7JvM9G4E`}(
zN5f-7PZRVcHtH+<zqQzin=;9|vSjY&<y9&OjXdzv;uWW7W=G3KZt|@(S%IhUHRP_t
zAS8Uyvj_Xn`PY1L$*irffjuML9e&T^$0wh3&n{u7)`NF1o4?(wh-r5p>;HGa&oYfE
zwg{teh@HY2Gc@U4`u-_=!+hywu3H*2wi59UGmQh;6^UC-+SyuI@(~%IkDdfif1?P6
ze`G!R-nw?7SgnBD%gdqMB&Nwvi(EO}<U{ux-yO0=0TzLr;NiHC+Xpatidmcb41<Be
zm&u(C8=o^0hOwD_9!I56&OuTGcY4M6Os?y=P93x^n*TGj&$S{)Q1{&a8V=|o+T7pQ
zj>Auk;*TxfYJPFCg1u`-!%on(MNlBge~6@uZ&^Z@m>EN|Lk0f?JEVru_a??UM!m|z
zNj>z-06kcDBoThLIGMvRvXuNR*}G}#sTXx+HFtj$?5dltc1+-UJ+l=7@$DVlAs~Qu
zc_Ds_Np$PCfromdDU$P4hv{6urT4^JEm~pG(F4J@ZHbU^_k(F2%dWgx^cS|i+8Ov_
zN=&ggTEP-gYn<VZzXxJ17tufAQX7$nfV%{B0fK=Q2T8BROzK(iU&7z2sO*v+rJTY4
zVd$`e`BTayPoduapmY$#^QoB1o`$4?!2OT7akmXuwXgkR%O1#y2Rd<kfzo_T__e`P
z8zGxG(5PP~faz^4@@3EMP*KU;g4@R$$Dwh&8AgvR{KYG$BJL#=Ul{}h$&Xoeu9Izw
zsDptA7|flO@DuSa{d78DR)X7L%E${`jsoH~CM?iNbPmLqR30Z?Xw>1TJ7dsNufhS@
z{m?zJM0km^bm8+h_sIbF|B0H^M8oU}KKC^#c-HNchG3Cp`>9%gmOU3ga8PwVJAh<B
z3P2(gwGNZ+x^VzDJ5Ff#<&o$FuOR~d#7pd|5?A7fN_$@6my?C3oLTNraD%~uTLl@K
ze7@XgG{cTNC#6e<{efo!F{$<G_V_jN*;!4Ei>=qOz0@57L_WwG?uHF2cv;!O$OG$o
zB{QCqS98@Oay9dM&$Jy>jCs2zWzC761w`i_5dAl@Q)(B0&Db+Gm_db@@-5wyz$#TV
zsBdlucQq`+yBR2(|Ke@5N;|$j{2<^mQMP7KH6&%^bH%mh1Jq_$w6{!F>IuI(KU4%?
zvHnXS=3L$bf3b$QG}HH07K2iZml(ml<GPOmICVaq@648DG%WbcP$qO8>G=CM8;-+!
z#iqwco~!4Z+Y^kJp1He0HbttH>~A1Uty7>5#;ZHcLf5sZ0xoQD3#Z~F^Wl9g>7=@%
zyUZ)DF$j2a5m2<#D#4<RXmlItovtCwnKq2>6lU}Yr;N5NyMLu5!`7N^4krQ_<)RWV
zJKl{blPFIgR<mj06G7xexTD>@u(zF0i}Ip@69Nc|ZvGr$i_{?_tHc1$M#DNxo}J^y
zPwM<@ct~Aojtu{|?~MGCmQC_u(rUah6ZNBd$+&725_+n+*act4dmwd-3hs|_kCc@N
z5MI9tA%i3a_s^}bv=yh|+4z+9;!ZOkRAM%SW91`Vfcx{J98jnhLdCa934?BepD%qC
z&L4}{u^IufO$4L6Yu<%Bj?MMt&nHoJAy2YlQ-lr$I2A|)u}EEwJ5JqU&q@5DTGK20
zKFgzzHd)$ihF}UE<=wvMj5o%8W!1XMOC77{zC6&+;GD5>0fGhTTo5thy4bJl;BX2)
z6`ZgpK!6C#!?5<qDHbGG(H|^kTKgvVuW8pMO-)r*&584YX@;5>_`h%I2_IF9`{kcK
z+F?nK6qoik{cz!C5c}%PqsSl5e?uS|H;LDJD!`w`8WKOrgVJz6hYKnU0`7u0-}Gqg
z;PxbJJ|B|=E9Zx<KAI=(g}+W1Ij|oSxL5bmu$T!SvzhYKZd%FLjtLz7sF8KxdC@fg
zB@^!TMU=ff2h|vIlu7snS^tTCBUv2gn$C;ki#13p1P=?9*?46x3<N`a?uTl=_(koW
zmTxah56*}6g|R^t>2d&2%=g*N_J$+3iuwZQI86dP3opHjvx27SeMs)KekbPVC1Ud?
zu2i+~DwJg_HnGK#>x4%&-&s+T*T@Rw!fd0zDOMr`o&&;MxOW9+25&lxj|o8K&IfJk
zDW^$?ms5nw^XOr?PZheG0gu!<J-=~#j^p+g<yu`cO$%@7hfmKo@@jp}yo--VaEoe%
z=s=aTf$uj??9XJTFP7x?LMyENvML(QE+k_1ce!72<*k0kJ9&>e=w|u^BT+n0=f{R(
z&yTo;K}b0MKsYNFrPV#%X5Fr*#!v5A@L0iYqn!%i^U_NzFsNV}5Jk^=x~=>I4>Pxm
z^C#ZwJYUr7WUQp8Y`caz^jij!{YVMC#?9OV$=1HTN<AOLuNw&o^dk4ec1Rk}7iz*c
z)D8BFC52rS!K=lpopeVCf$$fD>zg)#pDD)JW(vfrg^h>la$fKwZ`GvtV9^t0*$S3{
zC)h-91BV%6a|`{RprBRkRKnqc!<2CX5ptHw4ZNKaA`eVB^HOBvN8zP$jTVs#G0R|?
za!16x2DVlEAbxTfcLHP@eXNHT4+#kI!h3pO)jOUh&Z-S*%%Q|*@nlWLCK2;kpUOWR
zC8Lzs)Ud@A;(!p%)bIznM!)m|N_WPr(5&A4fM#7&Lr9A7=&v?UqnZWmr_6LoR^L4D
z7HPQ1|9e8%;MtdjgNpHpj7m$7$TV&o_X=wPSM{1={}B1G?1P{lS5?hdh!31vXNfG;
zxvJPykv_F!dl}6tYDz7){c5=QlKj&TkC_7U__}s8jOP_Xfo00JS|!UPS06-P*|IO~
z@`Z7`ZM7z4X=K<A8~Gf4=HXjrfy&VfXPpjcMCdI|!&YssO%n`Va*AKaK9~E$#Rqv@
zpS9z&NTdS3mAvSeAf}{5jzn5~HAC209q{x1&|dnP!Sw_CEPz}I7;ksAVz52#C4a_V
z${+6ZTnwHhR7p)w85V62Oc$FeZ4&iTwL5DDiZ@B=JN#OCKrr3V;P#>$QsM;PpRLe*
zJ58YR*x8i*l#Q;q9;@o(J?w1Dkb7Ik&d0)c8XwtLKXFCyyveY`NmUxP!@_BMz>~3W
z9YXyLI+vXE3+<)(O=2}6fis~Qm6^8dn>Dmp1hD}0gtCuJ+4;cDW11@TlM^NH{Lu`u
zv%>rOGslgNA9FEJRMT0(Os#zxz-+xJ{t1;xRTLl$a}JYQ#O}klJQPxZV%{I!wk;JY
z_X0bDBtE}mzt_T>uCb7GVV1ZE4R=iJbenM2UBR%2mnm?WfOoSqkI-X8G42<_iheZ|
zwCt~TarH4r=|Di%Rts~r2F~i$<*bQYh>uI8(l(37P8J729P}S%AMYHsK#+uL--k6&
zWBNJxr}?qUq-+t4m5Kn%m$QB4esiQ`96{&3s6n8<LVLL{ymT;n*)3%S<3d_4rOjqt
z6sd7xWnk(kgz^NT1K^E${OjN{WUj|a2A=9v$C8`M$kIkx+Fcj6MRN}LpzgH_(ca>Z
z_*$Cvu+v*Ejwg-Tyckn%<Y(;RW+BfOmX;l8m&*+Ky^H?p>4S5>X`7DDqm*;L&E(^8
zvrNKjR;n-z*`Jq=8Zw8bl12j!88B8aiQpyEYZt@#J6jikzgi}o_>SRSD7N44mEyjM
zuP_!kYENQTymPbW*CC;ME@{}P)S%VB%U_Vf#`cQ2)6?^{vf4u}-wX>hf6%y_jVQ&$
z`8_N1nEdk2_ES&@pAn7URfT*~sDRO9+T6D7C=jh$SFE-hJZPlq{qetSyxZ+{H{?k7
z@5xxkX0CQ!y=KkHP1AWgcE;+pLQHVS82)yquJz-w%-rImmh*DYnj$HuMexY!j6{SF
zxf0Tpx}mq>we@S4Mm?>AhKO@U%?8RM>n5>y?j(P&oYB-t29e&~FE;K^-V48mIi{E5
zpAyq`s^l<*srdD?#*JAb3*Y8PE>%3{k0!f$De$C5P}WO-opitiO%8=iKow(|;g1aV
zvl3R2wIcXnC-g(=o=qn1G)So8rBf}2x!iDBrmQ~Vp(YdVTct}#F%v{%5Bs)H;K>RE
zu2j&#GVOcOY|&tT?;mbpS4V8G^UAIY=ar4qkNKvcXLOg=LR2B05Kl!MXJbLxXsJM6
zo4`xT4LC{poo1P(HdSD9ZKRgAT;t}3pZ0sbx~EdZ2UV$oCH)vaEJ1XH34i(+jXiaY
zP;NiKI3QJb;NK?UGv|zc)$M#o=BIkT%v%q3OIE-AripOFX0@Xq_KOlGuDKY;^Q@=c
z5v<_lq#51klM_qkN*sHYXt#p{6w;ycozTZdUL={lXfPL`6o}b>E&ZeEQi;q*ZoDNm
zcPGnC9!oAo-6+&J>%DFJiOhrW4UXPc<6)uw`ML$Ah-UZ#StU*FHF(bXH1CP#6R_z7
z7nJ_3AJ*iDX6=JNwVl)ht>B+@?FphX{QVbIfAoZ(UItihqgEezlSc@8@K!#u(SW{J
zx&-#I?T|m3Dgm{Wh%tl(&w~|34c~oCMGbDl5dDtN$yi=;W&0_Fp+|yn5|`V}g0G?$
zjH)#wD*1J@_z?R{ELktKUr6M@okiAGvh98|__cdEy)={23VF;6;{}ugHI|O+GtA&E
z=qD9oeLm`sYDP?K1U*`PZ*vMYdZB+W7kf>vxL5d4HLN_Z{17+G$Yjpw+;sU=)tE7P
zJggG2-GtbG^>Q+@QMjGrxSf55L#42bglRUKCAYSBJ-0M!%rg9@6;tvt74aocQ(m&u
zknd6BT{PXHV$g1A7+fE;@S3|h(DveQyvS~JPh~>m?AtIZBKPpiO#L2(YYFlu-WeO~
zq8UW^o3ZPg1QyqP(3UBWD`AfuKab@rtHR+MqI)6-GagD^)^iXgp)JirM+h<c4s+<7
zdX9ya)J8+ebNN=oR!Si+Tt-gm{Mu(eEEaG5$qzj->#8UXxwVQd89Kzm#`Y2u8m+u`
zk}>oX%J2<=%A&qPH#xVSLklE0HQ_kd%ErM2;@hzK)CPyamGoZtEbIs2N;j)Pvwj0x
zwiMI7+`yD*4_ys0W=6Oi&<mJ1)L7AC?$Txds9E3lNY7pd8a%N=`%;Aw6im+zj1W@I
z5Z}!+ib=o6Xa3cIm15>l8gix-ekfH#WM1;cA4TF`QH<f|Mdh}4yfL?XG=j4y<+@~)
zC-j(^`CVK3cWD<L|B=lT+1J4>snVb#b_zQmno_SS$VPg1P_<EG9~~bQ9f*?4T)}Lb
zh;jX`d)Cq%jCJ9V^%99@?|a4ng{kjM&k1Y{!G#`r)RfP&HtpjKp`DBMa;vVL_WLCB
zF+AS#Xa3`{)7v7|dQN|CE)j1QJ|1(}7MY+K`Dp=dDcDhacVppzQ}I}dWe8IV<DIT2
zjF%-U80T9KTdH625Sx&<RQh<HBcpdgYGbo=hTtje(E^5y-3yLy;UL8-7SmKjx7AZF
z)AdKMKB~wmlW%0AJv={Z+>qKuu=X-D>`?rA6?Kd*L|ovkm->1*?LeTznEnSBv4TK)
zY$GJ~EuTxnOcIo#ij}Hbu?fOTBK1<cs+Zot;_-&VbEsi>ueiJ9KmnM)L^<QIBA(V9
zX+5_lERJ&pOs4`*hlN(mun^5^*p*rfr4_=Ke?k7=ZIEm?MJuR6Yl)QXX7&$@N!7o5
znn4KY*;$cZi2ygkegWJ~ZVQUtkO(4XhOH;@EL^vE&(@wDWk|nGxz-<@K4L$HWt8Ww
zZvI$9?W$wJKSCL$>mqmT^+(kuCARXPomWbqtIDP#J_eAc>I3LkX4<nJf)jeqcxM=&
zGc_M`A1YW-IdYEkw<6R~<!;B~{oD3(icV=Y(U+>GFHb`T`~F{nYG<n+YUs&Wy}H$>
zYFAH<tEHXBY45$!V;8VsHfo^bG$Wsw9`LA_wodjz_wSU-^(U*`SKnr6y`kA4BV>aX
zJ-1sdRN<_;mAzHkKBn;-cvD20{9(O}1=m>EtLNkXXhC$!&{g;rQ395H0aslJYqpr%
z5n8iONG^ogvl_+1prPR&VZn76RFgc1O4(dezK&WlhdQu;QX$kZ=>b0IXqq$X^S&R7
z_b`tjSRp_Ys=B@Xe3@7CN<OF<{@;{!%m|Jtc-0T#>oNbJy@(n;O<r<%!N!H9Zw2a1
zRs`g?$7sNVFkEny!ym=xLE>C&`HEkI@DcREIqdkm<0iWwRq~CkSa2Uw<Sc#%6`W6)
zWr0G^iJntres0{ytYfvABF3N)y_8n9t`27T0=%J|*}s>LlXiS%5+W0MGt%XeKg!bf
z!IrTc$NShT)SZ!R&1U#fQ=C->^>xmNS%PGz!m5Xgh4c}A<NX}x2I~!+wtKk1j^HOz
zSs(79p!`YfkL~%~iYLtvci&I{YAL+G1&l1_+VHy2z1wvnEnnn4?H2>Z>x%c)15xz{
zhQ|oCm^ymm2%$$;`8^NK6KmXUI!i?7t1u5h#D%Mu-{vex<yJ~G2hfdvr-z*LSX9`}
z6`u}7FXSz_?T2sBb9i``1`elBJkV$bLnkhv?lp^Xur-ZhJrWy#SU1^uCGCaR;=D4B
zLk0)tNuQL0-czBJV>|nYHaC>NCqw8oRk}BEunBewpCFt|>sTEy+cK<12cy3jE5Vcd
z2_&#PLUH`xp6!9M`Z?BH_iS%J;WexW*}^AJ`X=&eSpSjX;6FwcY!5sBQb}?c^zwT?
zi(uzPD%|&oJ9KNBWo_`gd}S$GXfcuXia&4nxD>PE@vBjYc8>GeT*$BE@SUAbIdU&f
zi_cNd3=0!!PxN=BaX9+f`evuLe@r&Hn)@B#KI-(7Ro0Td_qy%!#6%S~tZut;>$XVK
zK=7llyBt#-?Va}&bAL-Ddcv<6N*sk9m5uzX*(`R{bXe_m+`=^j<XJ4q#?fXULi$Bs
zPvMU0fM4@=p0_Yc3-+1g_trZ5;Z=pBrV}GWdpSn2L2GUOP-f80GyL&rntqglx|eTj
zVUNkBz1i_&HYSPVC)or;wFBF&LUlit9tOzGWt+jsu4rx~JEN5L8un4q@&ILoaGLjw
zx<M-~2hEdgbPG+uNZpG<`#3a)rOAfDf})Q2kh2-^I|An!BHc|-nR?3G@eQqMNf~G+
z7NioFU?x+<YKTtE3%E8-e7KdaC=D-X{5BhcJS2hML3~ZgEo$e}#JQ(N>={al`bmbD
z8z;_KiE@_Ed}-j*XUdj>%-0BR{A$Q3QVN9fskx`?!}XCV$kkTAb|R-yPrO}Y0~hPN
zwNxoJ{N-2pQz<Et8J{*Qgx_E?-DZoowI3j5v+xU~5qvD<CvVLQMDdWrYFtSXb0R9R
z8F)IrQ=_#}Mf}l__1v&}YP8G!vha6GdG?`GZ>Q3z(R$Rp^cuw?Ug*^E+$bwr9=97h
zqO?M*lsUXz=TI3^Q*C5kjUAG)vct*_qj8N!J52iY*tTHfgv6Emfl54kr3sbH%BB9?
zsR5|C1Ug8uhS4<Y19(&Ykt-m&;Pn^8Ld<O4^JHzU=67v2;mciDeN)&w%B2Qn;!^{{
zT49ID-?zQY49X4KlXaYLpZU<#MwuecNZ;CO^0`)=W0bqHxz;WQwz=;9wg#VF)6z_F
zJ@he)48~?U&&x+b?@lRG&S!JkhduE3sqa*u*s3<VJL9TH^ejQciltxjQJYm$Nzk8?
zl&F7vOk<1TNLivb7?NXBz6idA3=gT0;-(MlDUT3-h}BE&8dx_$T)_-TY+Q)l3vV@K
zG(8?6#zMbEemGiHFE8H!Wd3}GN$K~O8!cMSp%_o!n2F2z(g_JhF_zjWhbb2xcxB2Q
zuU2XMM)>u`(&OSCGgf>iL9Ah0R|j-q7Ek0GhxwDdj9xK?j!>805g*hN-{)usXKvea
z$6V%3K1~rOF>o{wtXhzwfa#T37H&T@J=Lekon~38qn)1w!<(fh4tQ{3&GKBOqqCf$
ze_3)A_UWIGK`Ua*Mv8&{WVvNqDTYB%3V4Q!G&@~*DB97+S%y8hlckpM>G8s8tR+70
zaV3I|pJPuFTyWfqKi)fHL;42C9^hiaf4qJ&-8aa=Q!vwhPQRaS{o|uT`S{o{AC$>K
zNnF(Ill+?EGtSH*bei?1K>PjI5XOH0=2EMy58RD#U41-3j7&f6z4IB#@+GVpLeJYb
zLTC}ZSy9?2lDX4oi|}HzL9=Ow;7oobU`Wo|!+aFV%mXc?HU>gr5Ao#F@C^ZE`?Lfn
z_&=ByfN{_hfqD*tQz{FnMgxc6R3Z=W5R=m=mQ?UhX)9W72^9ll2-)N?j;OC8uN_*4
zY5|k>bVehznAYJ>7AgCqMs#)4(EbWOq=jNqB^bz}wSUi5rq`6o1aimu*1rOmIc<SV
z0ame8sdYXaQvAUkE~Y3|45nI?Loa=~@|xO7Gz7Rm38ggman-?DywID(8gJo;a9DjU
z5km-oZ2^S}LK7>gpQLJ8@NT&t#VMIwkG%YeAq_y2oVuR+<TI%+mV1SAo%@6uy^>L?
zvM>yfJQZo0v1|WiiE`Az?CaV3%;&Mw6S3o)CWv25%Rb07TLlgug{<0#Z?RZ34TJ0p
zbEhP4k#5m%YKAaqwhuEE13LNceM9~&%cNu)sc@!+CdeM@$O<kNLFvB(SFKGaW`@vy
zIWqqZ6T5os;y-aW*L~jQXn#<&!(?WAWM0NY`K;1QFaE9kSFp3zKeJS$anotu`qyS?
zsYb(ZCJGZspV)<03)}TRmCX{jDs*sE^-j{SQu20yt)s>wtmZR45a61$k_jT(LyaQ2
zB<>=WSe_0XUZhHx1cZGAg;s2wy;2QzvY){O;$FMK9}qD|P?ju0_y&ltCSq1Ca*d+l
zj!;(WWsw^5Ra_JJMkE&WM<>3`n6(%WuH)ZM2U@dI%nZTD2=omj_y~Rhh4XKRG57hS
z+T+_(+xNSF0thp4d?ItSW+nqC168;=y!6|x)y~?b*UnNH?>hm6&0^WCgjG!RrnniU
z@NZ&)+$@Eq<%R7P$cZn901A5Z$)EJ2YMtN4YdO~%7FY?q^gfH8>z0ZVJ?@sNa`VDW
z$0wFYV);#cbRm3u@&*<>(z)*8me1dQYMjlN+eed)6xxI>oZHVRpv7cTUo&YSY4h_9
zrTWFG#6y~13-2E!yT&q~HJC*gXGQ6$4!Z2G5+R{jcMf!1KbpPckPBP&qe*-WKXmHO
zwnG=$J0fiYpW%9fGR8hyQg=i)vERU6VIASuJl0Pfp3ulWkA1sf>m+q9?H`v;kG*hu
z61iUCSny71s#w#XK=?l;apM!9bZG;<4+ikV@($wg+RKLk2rZc88+**qNB|zPyBhLm
z;&kH#(M;&q3F7Tbre%<@4tK>~Vxg#Bp`*nau*Gh64#5tX0i9YfOW>5je>@CuHbAUd
z22f}Bavxc!0Hhj)wg?`rnnmCj57)5jp|chImkQ~v!2PIKnl6ZDM^p=0Nr@|%B9>X;
z^C9q;?I}%9uxUPKnWpmEQ-gVk^O{hGp)y-=aZ@j+UQPWQ%ir;mV>u+g)^_x3g+D=b
z)yB+S>kIh@8N{*J49fcL6^FhH2Lxy->!W+jHcmBsdA#c1at{0IMhxR?gPCXYJhHha
z-V!y^JNKOi85yKq2w}W9@omY8ez1p}qG_geJO>1ptYZ8o^?z)S&wBIDr~Ieo&ySC%
z?rwwCTO1gV@E(`%AZ=Tnn4I>AVa{(C4)tN9ECrECsaf0Qo?i)R%hKHvNon&&TN<sK
z*kmbMH?boI)(p$+<ko4e<V#b$>E-=myAh|2Uc)*i&Q)C#Z7J)<emnqxktSuVMT{M-
zAiV&}8~l!aqCuj3eF6BWI}!mxU69dJZhurUa%^F@o^r0nF8D9VW7fZfk2!2G7xN0z
zG#_K*)<M%&3n*qMKb1dM^;850kr)&7fL~5-e-JG<EbiGX!v-{1)=khJzK2d3_|L~<
zj7W{`&(rs{?>;$&k6ODRA1IuCGb1?eCQ;v)yD>xB^#%9o1UoB85;(<O=Sab7mkZOR
zoMFvW$|e@mIf9+>hM!m2BvkU5t7;W0#M?D%PLyeK!z%e#Jg<dc%c0{kg2MS*Ijgzl
zn?8Pj!LhWH=<y~CuR+Fz#?m^^RK%jyA~iK5jWyZX{H7&GI=0QU?43*!^ZMR46G!8>
zC93LyH6HW)AK4jOO$N7T8rp1{n1A!@L_i5j@YQeL>EOHRFoeX@K*f#|?wSiRuBCIU
z(~k!0I-uYl#gcI5=JaH(uJTjAY4{aR`XB*XF8SjRU3B(Uw25rKa`B4ZkbSQG^)pKy
zS5n=#3{7vU3w6CxDdzaQwP=$-eS1Y@40n^G<3S_sGxJ3o>zuY3C_jjY1xZ~o`kg2p
z13QE`s3GPg(`4O@>Cw$taNG~?E_L7`?|JN+aEgJw`VR0E*ifMb8vbBzuoS@!5zink
zFLJjTT@4)WuuKRbegqa$yZAKY@Yj4=UG(C`bE!b+%7Y=`@Yj&D{hOe-m6w%(u>0XJ
zCyze}cEi_~%hjT9LhB4Y7CgkmI60RJIx;!1`$jam<0`2jU#!nW6!45s!+)`{;pPlM
zu4?^5S!z!;t5#_4vJJ1Pl+j>EFH%}umOsjD!jvUf!oSk1T$<i>tpXFvpWugt1{o`c
zGRnaRXUeI_wSV3(+>UTO&b>IkasEu9>4D{EuKeJVx?PC6p<Q(oj0};ArG4&WpNpoQ
zhCh_stb4+^S}c6Ve<w8i#IQgH9Ea}c>$osZoj~;J;x{f$a;L}s0ZQ<be3SjYiib0O
zwrAnO-V|+*AHQ(#RL8dqx%u&#LAx@`Z5~%z98kUXd@Sm~0x9uG_H{H{M@#$hu1|@e
z32mu$vlQghi5Z4yb4$E*Gg8{;!UaR^UGN8Uh8bCv9}HFDkKZ<mh1#AozlMJedxZ+M
z9nkm?3XV+z2wRBs2R=G_wme(jDVoGaK%LK#8pWIt*W@$O#Akp6%6x1DfY_LZk&{H)
zh(7s-wSbu|2K($5?U4ix@g}qUm$Fvzr9Kd!zYq5jT&|%x-IzXOD^bJh1IYpFVrcU<
z;0G$mg!Vh(#JCmuAZIMLeYbEjrKS~{bZLMDA)jXdL%LBUv$N$8NST=}$pK=EivqSe
z6;Yof((!ivR{~HCpW)8?!7O5L0%~H7@E*5DxS7ete)I!*B346y0=N8m$cHp)J}lfL
z&&<>>*mbOW+~ncqSpLDN1Np?U%SvN1*nz|5T6g$m$OG!u&nqojF%^XhZ}gh?$KUu`
z&Drt3EKjC~QGDSZ|H6T2PYwFE_!zZoCez@3xOO?ti#~&&kiGJMT*LqWVP#IlH&$A_
zU($@8@>U8=x3s+{g`MilDW#S1L&vVA9m(I3!L5Cv^~wFLUDS~%RjG3*NpI_d)84lV
z<yG?!)%h#;HpEE&!lM5VP3IoY^!xwuq*4i;DTndtprfTeIn7L|q;e>ga#|(H`LJw;
zZAy`kP$A^7bfUt@`8ek}PdT$0hQ(~d%&^1z_wIXLzw5I9T$k&z`~ALO_w)I9KJN_G
z1nmum7oOAJp0AOaj*yu;G<9_9q>V|u?DU9C>JwAymkx!Oz!Xbvn|GMv>K&<E(ZF*W
z{kd#2W5+Yu34*bFkb`$_VBn(E?%%@(D`4i^;J?cM)K1~|+DMg!vP@I;b}_HJ1DO9>
z1vsg!LO?kQ{QeDEU>_*7pOBMo@O!Epxgy_YRnHSw{=`|051NifR-QMeBF~qLUZBQU
z;r#VRcC*5h0f@0G*@36*IyJ%yBNN=nDgnCT{64mk$45HcCjiulECXmjMY$-Dm|P^w
zIOF;=De_x?d;6ti{moTX&vh?2<I9pBd*zUZB^vynQNmWqk<vQwT{$x-IRfr(voe9}
z|D#z8ECkV_TvL1C`Rte3W7&$bfyRe>Lo!5HuY0|uTNhl|j(n;AjP+I~%ht=|CE4x;
zzcbGvN!Klg(!0CK`hiymEzt0Ha+-v`VX@|Uj2;P(iYiX^k_OMKeH8C3(Cj>?8%>jG
zzn{V?{_B*%_)ThUA)V&{)dULv4=~HSa)j#Z+f0_)&sQ7wB$4BD`QZ4Qt#<R{(a4)A
zIeQaFykE?IO(wc%xLnZ8<Lhd`_PcP06<jy)9GIlnMq_Ekf_;Ukj)mk;47a`z<K6D!
z@O^@@g#gYmg45jxF}BlT0zK5NYKk)wfjgvU9dP*THD^ol;aJkr6mNrET3NsgOZ4H~
zJ3jI6L<||BnK?G|km6;XkMQ$`R}AG7C<y~C{wqYa*N#V8V!%^c25cYHLg&lLi>rx_
zSGx`gA7PX276Z280s~Cfo|;(^-%JKgM{dH|osxuM<(`MCYEG)#Xq*qZ=@-9VuF+L9
z$T9||Xq*@(dq_F}Um%+)6<0z+I`XhN1ivhT@$!Z9vvzgBn(@P=GF06~CHNykNb*20
z^`A)}&`I(>UPOHWT%86j@~1RX?{w1Zaz?zxU*CEUnIX&L^Y?d=KB07k%wXD^HO9Dz
z$C8$2(Wj&XU;3S>5+1jJ`Aig;YOGfVSxPpB(`3i+rsZLvzU%BqzJe0};+m&Kk4SFn
zl}3Vt9yscJqgw~=zKMA;t3QU<1%=-4bZaoM9&|Hmc0~LHx=rid*WKlihcyE#8mMUJ
ztA?iAfqhA@HH8O!tMc!AUwLbH@7#OT&bKuqjfL|kyBP`=CH|q+DpO6<uQ~14HGGaP
zhH*OA#r@Fm+5bTZBndZ(^NvzHw<WfETra2J&i2_l>(kLMLV}O@k*32tLP=w1dAQeQ
z;B)rF`yFiT+A8F;RDTz3wf^`@0Jkox6a0owMU8xutpF_R0W~qy;6o*HA}k7cBbNXH
z&Fr<rma`wkvYF5}RQzJX;@;hGB^8)v`RlYJatG~>`5ud#AWYt#8mf{7WneHia__P1
zlV^Zo8$STtL&rw7dzI2+wxB_H2KHfiovfw7z4`a&rW9YwHc2t3q{0<u9WZ}r6=HP=
z_@uP56O-2gsD+*YHM(-J0%{VgfojJWr3IcF*;)lflN&`l!3Pdk6B@DeO9=wBy1B|i
z(Iz44ndsasAq|=CN#phZ`qU6O7EIIK4}_SkKo~Nim`+sxSpi}?h#MiYv;g1QY!lZ<
zWzsSvZ(omD%7@S*@HXWRA`RNHFfnz@)c}py>^hn$wqxV#M78Xc*|b@$Z2Ys?9GmJ*
zv)F6XxN;NZ<p-C|cn3xg;s;c289;P*80$nN|M7=5wrr+bAOCu6bSFVppnCQ3-781C
zwAo40uK4Mw7|*L--$7IsdYON1OBHo!l6S7e_^?yfdU^faxFb`|?>-ox(0~~Jt`M^A
zJ`xD3<F0mCyt7lf6G2E{CX&Tpa0{MD&wz~TaXxEEJr3?cC|kNAn#I((Fhn!foVTg4
zp>VUv=e(yq!HGfl=27>rQJU0!x7H*@3LpG6%e+SZDOnC`bkQ(hLTnWEi%|W&LxJ?S
z{G}I|apQBglnprev;@SV6;OdhiG(-u*8|=33PP-As3i-t$UG9WS~%<rt?I5><M>A6
zG6=seqRoWd<(HIEe<)dvi@aI0n60MBKaCcB0`HcDRv(mzS;L3Y#eAiGUbacq>A7~_
z4~%FS-n*8~7!w9ssBO2{){-Ieo9^3%xItxR=suFXkH|O9AvUTv9hv9@ODZND8wT!r
z_Pcg$Pmw#n(@dVYrBRM;XVBho99M69JNKyB0?gpv{+_6X#^6;*w_<ka2V<Ew$obVT
zZ)M-6+7Byk?gY&uBv>8=dPS`>lW4;+cu!WOSgA<%w=y`TCjhtjCp5ur|F!G{3|y(C
zp4z8}0N7mqf8g{@zDh4&l*)mUcjeP6s|=QI>0G4iiWd8EYVx4u{VW*nZUUKq!S6d{
zQpT$Ms2r$cg9Du!cquHPJ}t$35Fz!&zH<-Ry7xU!lh1JVT^)aOSxd?9S3vf?K=cCa
zq>McZvzj-7Is_q6bR}>mZ#QsF4<MiA;J*`0aX0BVoi=irH<Qi<I<`nY=nA7HzNwW&
zKT2wKo(?wfG;dc;>`NE#2r(3Q%h6Y@i9nUdvZ9YJAuc=VtRUAy_9Uc154T32yokM%
z2KwD~KORW&5<#6L7)y7_kU1GiOv5-&%j&*DO4~|n5_9buYeYO>VZU*rx9x>Wm$=S;
z(xz#x-V)uB*`N!*uoZtI*bO0bOuC76_n__qJ(OrHK@BY?t7B-o9a)n4wZQxIueH%S
zm0wWt41u-^ByTa%tzMNhlJsL$Y^~7I=Jid(8?qAm&obirZ2;AMTcygXMX>0VebsqS
z$c-zC$*+TuW~YH44#Zksy}Avr;Z>ift#R`NVM$(2?wZ(l_Iq)PD5`aAL3Q^?lpx-2
zg~!F6H<U?$YBKg_8}EW?9)V7c$UNm8j$uV!uQ?!mBb(XG*DAk`eVg`8yk+uc_GH+q
zu2um6|6`cGoYt&nLzQilnUMo$(E!ElW*M0*8EjXebrvVg8t$zRP6&A4xDKE~!0{Xq
zSXRwU)L3uwJ0_m}NTHR|038(Tqk@f;f(omn-VlN!)W6e&00gMYTWit@D+oiOY^=ij
zzb-eLTt-(yD6(49Q&8wnM6<2~EO6SyPKTmKT>zKZB+8|@tsH_Jw>T*f@^2$)sCSG2
z>QbUWj2}7VFm<z(B2bd*_gK)C7P)eLWyH4ZwiDFTEt0a*HzFC=8hg;qy%m7yGq+LS
zIGZ~yknQu{kp}hV%P(lg>;Si$*T5mXUDTr`VFjhBW9iP3M}`6LH%;SLHfV2%YmmNg
zTyBoyp9^xP0aiyA4XH;}_0~c*zV_531<v*iBv`U{4!#SulbJG``u*+$Ze4lxTQl0i
zic3jP7tF2w!kdA4CrvEyZ3Fk5YC=h8;YqQtgGStn9n9KhCRybGs%1RVt^HV?*l5`9
z&{|*rumwbFQ(}4c33eqj=T#G}kK|iu0uV`6r$<Wc&r3YFks`sy)lWA|QJ)3)(TL#G
zQh&=ud(*vc7hay#ZA{;}Xwn3=$%(d@$Q03M6}E80#(BmkFpu)k=8nZ(E(J`TmgLHZ
zG<EOi0zGfn`h=HbVf8N9i7*;JUbbbU0-0@#{q)%J<)S4>VOg5ywZZP$@@4x!V|Bs9
zox=76Bwz6|@|08=c28`qDK)pU!^f#_pGP!@wSg^0wV>Gl5@=F<8QvlzV_tV_QL)8f
z6YY|B*mfF4J-<-Y%@K}*DUt0$GPe%c^xSY!C+cs~Jz8BHr&lY#W}Hz3jJqhtM*MIc
zefNyo%KJ}j%`4D&46DO~aIZNNH-)q5<+Z!L7A^dBHR}!i7P%Dc-|+<e(kX&kAlXwm
zFS}eCzC79<JVsz<4Ohq0gomxRatB<7pM$I~(7AO$1E^F2uC`x#BXY;P4X2nnoVfrV
zJ#G%&yc<JTIx=vK*%^r7e!E$ElVK{@ep6oyjp%cGgGJ8b8`o><Y&Jw*>*3%}Vi23T
zrOiy_6*Yue=q=htZYe+CxN-a|9cn<Nh?8H-g)!3K3b#-XWFSwoqP55ySjWK&9Xe~2
zHkW(aHojXbZv1GOdImgqw6Q<7XyI3r$b(Wjjbw>>yzsq=Gi%AY!`uqi_dlfO_mEo>
z&Mh_QUdxylM3*NTlglY)B>fNY^V!C0dSbB9=;bijPdOm%>)N$jy@#h$GdK((gf3%T
z=8CB%nQ$vnyBRXz3wvE;vAYYzL=ca@Gg4C0V+C_<uUy6&ctN$pBO{gCN8?uKzhw<u
zcOgiNEQr{#B+lcG>Hn`bGeL!nd%9AF-6Vkx&*<Kp-2CqOTh`f#2|JCZDwS2I=@9pv
zYnPM0u*X1~=fdM%*T6!$a#pM4v>#&rj^ces@bMhn9$B@nZP!PTA(=XG&qt-DRQ4F@
z+?Q1u&)?$gv@Mf10pAwCFP<<|ZmIiFejl{xz+=xOzeC2I-D;@m^2{F`jd-Aj_p2~$
zG48aSn&D3m&;@)fJLhN%>Of>N6PvEOhS*1&`7O2H2sC`F4|-k0wdWF&Dt*Vplq&$I
z`FUnk{FgEJMLjK2JPWISLi8&jH;XfYCVwme;Xm*lG+j|AStpDK63dCZXw)|%e~pIK
zCkEsK(Tq{8(+VT`I{LYom!gHIf0YAYNT8EsMg4Y09YdZej4!Auq)Dbbwz<k4(bg0;
zh~BgD+bbb9c}JP&U5UZ#zN>yPGLuC8HEFK}D&5q68hj;vW8eKuqfs^Cj}kg*_mBUs
zr~SCF^LV>f#Ox~jG)=I0iV(1ld|b0}?L1gS8kotF8QYf?27|N!36bEWYrFFbyYYD-
zowHuG<iee`iYB3Noy2~Yur#%GG2q%8v$-0I#t_d`Rh2>VZ=;xakU>$X(0c}9ES-%1
zDcC?Qa97uiWT8YUthD%Q<OXWCJ6)w3dEqRj!7!+sY5r?r-ucjXxV4)IyH;zC@E|Yo
zKYJ_ZIJjx26>+GxzjT{W)ARHeLe1jVN^!r#GdoZ0HtLu=K{uhj#{^jQs!l#bmUyf3
z7q1RgobnWW?{<?Aq$}njgDyYcHH4Bd6y;FqRajtK+Y#gWxxyDjB+o1x8e{X_x9P-k
z@VhbLd1pegqd@oYH~v3Svf~-24$%($x0F(=lA+`k_8`7{F`-<8`C#(d5dY+kLiRr3
z4Jg{qpu$q*jP4@*F5Xb;$TUFQ7qry|VGx?yM-WGln*%IRpr^8@R94Xfm1+Y*GB=%b
z7L2#FrJCy8a^5)Q5CCnICSgE8qru=FVPim*TE4R4A4N^z$7I;jMkXOe_G_0+(B?+P
zBgi*^3@#-o##kF&>i}XzbAi6o1o)azNgAm?PI$e71jYzS1_gi&$G@&!EmUCwZHi4d
zrn2vWvLr~8RXzaNJ+*)zc@X@3#3NtQm7ZArH!OXkV@T2zvfpPAYDgQCnTJ*wPvxHT
z*3}4y?t^&Z1R*}_AXnezh97yfjMyNcfDqZS1KY(=Z(%!?m)5yp*Ugeou(Mf>VF_gu
zv3)W&)gSWK2@ZokSPcIQ-AND|e3`zDq!_s8@A*m(1W^+z9y%X=4PL8(L40r`?RI!z
zKRT`b_RgI}lk2~LT9oy*X~Ks=^-KfC>w`)gSkVi{Z&r|R%}*zxicU<%&kZ2}n0I)0
z@Y7yF+x%3W$T)Jn&bd(!9WU*@zW$L<im3lYZfC%Rjv?sjS6*~uplqhu%jxNvKayl$
z@tE9@k!i@U?560SLVi&r@ZAtJY(FyhF#~V$SnjjN3d^()fqchM=CYx6jQTGALtCF>
zZbB*Jm{+@q-Dl^@ZgfNqqz@VCA|!O)GdFj5-OrCxHZv93q0PH-(q9_pf*Psyq4N@t
z=YxQ0dwj*v_CC?UO+l|N)1muByw7?1#MO2CU!zQ$KC4tG{ljYy=l#Oyx_LY8OO!yP
zvbIle>0P;(v2nMts>%b@Z?0Vg<;eux<@r^`<jLotudo6Miq#s}Jf)?=c}+Am{BZ;J
zVNv4<<gMyijpyL(WTE4o164_e<U_GTsG{jj8>TnCk|-@w*GHf~%y74G@vnJu5vPIh
zUWDHcEkG7~bq;)8()=f30ku|(WOaw3%t*-fKLmq4K7sxSVa{oQ?MY|?#KPPKL^Yuf
zSChA^B(R9oC<+Cn1k+LTO2{ND_|0DcE(XpwA9*Y*ygDL%Vr589b1R4VfyUhmV;8ge
zk#qnxzgdVTm&9i|@Q!i?XAaxPwE)4LX{vdEg#SqUv#aa`8c~i6YS#P*O%B4lwH@0H
z=gA#o9}|67`VP6DCKptFU44Du@xHS*>Km|-?5?e|X98hk0^?<qbB^nV*Z#0`zmo0z
zH%IpH67QuOM+pygI5BHj+mFYWRqEbt4pCGzcf{S#rOfRfMs6qwm9DKNG|I-8Ij>c#
zF(N;K_o$s*B0pvL*B(p?vf46wsWN@&nIM((A}}>{<RN5{+>QRjt1kPs-fP!pLyLpu
z?h9W9TT%xS$9$`iVQHUswzYl)K^GAPp}2aJy~qF04)cd>`R1ZoNwNbZ_>_aWqaPS>
z_n*T4d<M)&{`oI9((r}=zm0J{EAWB~#dgLRN1@u<vTz~lc3)i9n663S5=+BRrwkcQ
zknw^abNici!y~b^b#fyew~iMEe*a=fB=WZR8a<;@4I?&kJrdeI?n{!zw5#@3^yg)H
z(GTzbbiP4^I|rp5JrHxz-?4387-i9@z4j*ROa;UhyCVliVUDX|jFMB=h?j?f{tb^-
zamCXY(k&Uh*}x$hw6MQ-)#wLJ!EUglxZS&Xc+b8bc3R($^J&v~{P6o;$#V);42@9R
zJ{@bpzc3fpp`_}Zens&PyB&Kg<HqRvfRx4ei)t?CTSD5x+oC5g8)`Nxs$3R5JB#>t
zjL_C@3R5slrCBN}aSSGxcE@blG`;D05dQJ-IzA_?NM=2*ZUNI6n47C$_0MDCThf@$
zf-pQ=;S@uOhV9lAk~2VU7<MlV>>m9#U!}&ytcJbs_@RUQNOqc540-`mR!j!<gB@u>
zAilu~Tr}A1lN6jWKn4X!CAL8hu^h5uBIp3o+AQ?n_a8_{$#Cta2{U&X9&~GO@KmsQ
zpzeMwxrR&dRx~=bf+Y0<D!jVl&m5kHoUqb&O^qD`_)BboXgYKm(Y9j>gtD)2De$eX
z35)wd=MwZ{Wc#9RtgC9Wxu&*!(RIkTR8x3NriNMefMprDbMB=4xDe<Kic6qzx=XA7
z>%zYgu0`J#w%SY2i)a1gL4$I>0$89~y_|}5ZdjJ?pZJ$#Wt?Yc7~^;WuN9fE=(Ddx
z5ELgIC)`!dAW<vkC%(2wf}m{|=gzFCw+QdG+lROp7Pb;t@ctiRi;oW(yi5Kk@}6|3
zNqqm&XNFgb@EyrX&K><x*hcoZ{v8OZfK<qA>v0593vyP1JgT|w>B;7)SmTSVs8zq@
zLI?Kl0(jhyKglMdDX85EIbflO_=p_^kxVELo!!~t+OQ&@d*E2{9=%In0&XnuL#_=N
zO;EVmp@(hY9*W;9U-y>YNbd<K&uAXv!wNoL*f0Mvq%l98chz6cjhPs9;0~gpw2pb+
z!Fg?eKi|6^ZB>@!niBFbD56`qub<Utk=9izV6Z$?kxIhU;$a?%F7Y#+ZX~91!#Qrb
zciqnnoG6{?kY#f9QS>XR#X9c#X)^;M-ziHL8ltS};q_;&KiKGKnuCA?_QGqI_a30^
z^>k6WMJACIR+Z(rxh)QKW|JN%svzVyJCBB?<%qB49d<i&+ZJn#I9&D?M2@z*f%D-6
z;Ue?g1G&cQetXhh6l^uPHV>XKX#Rfc#j+D%gO0Z$P<d29ZkIDw|A5#-+A19Yvv11q
z-=q$pSLp=MK=(8J{}o7_cfN8*{{`;c+~34atY!ByW;K4wn_$=5n@~w?V6luh{LTKr
z?b7sHDj|Md=LW*iL*NO5Vm}hDSQIWC8VOcZzfV8RBt-QjiG8hR3H<XgKR<FWu*g-8
z6Wkj5n0D?6##wrEuZvLLo8Uh5MJ>Foq-|~N(AD#+a<k{>P3D6?8hr}h@ZI5q5qLa>
z*f4HUTpsX#^h;ja_Gu2Zo4q>Tz`PRxb!7QN%L#{LpK)JAVGhy8X_Vwj8SPJuh!ED$
z%$?VUTted{Hp5N@smtIivAYCSrVOja*^w2a{S7;&an4yK@DhGiK@&eBWIyS3%#;9k
z*lQtBKN5Ec`$lgY6_VON{Li2x@r@yJYnSjX>3GYVR+AEii;8tBH>#0arW1SCp4C(k
zff?%;p+ap$YyYl}@OswKkL6NI)f0PjKV$Md;B)~Mm-WZkJtM7oFoV)Zb6K;-z;m_#
z6Z?K(L8~D>qLgz(Mtwg|wr$}zwM^O9KeJJ`%`VIw{Yqf9kf@BOHgWN5SJRb*aqNDB
zZ>nn{_qQcA@MsPZ3y)NdZ|gcgbV&#2nFq29&kO!RsZI2H#n~EDPNQO=4ux7`%d`};
z6N`)nVbf~JxTGRRTA}-jcaNL2{dg4Nbysv^<1HlFC%5y?i!*CryHI%z!qIo_{f!Yu
zkx|@K&8vaupTSgtvxZ9Z^DwCFu-tcl)5qUrpNXNbW94L~TB~H=+GW0#ji^eSerz-P
zPBx<EUO>oJsdDp+&l`YL!@{TFdQ7Z2U?qJTO`8y87KRgBWTpt3q%DfcYq4tcdoh_X
zF1xQA*mPJbq-x7;%&G^ek7zoO_k>kZPnZ82PKs;o{A<h9&O96V63Y^h=FEBW-dQ+z
zMlG;j3}x_s7-mGT;W9yYIbFW%YAqGC)VSIJv+6&P02TW0${~t<8MvH*`8VKDczU#f
z<ifjao0AnTf{$?{ffsJl8=!P2nOM-c2%cyYL{HmJxT@UzdaIewmCJMvlGr$1mpm2o
z@gKqTh=wik7TM+Jov|_8$cW@LqkYF3+_-s%T_0ZAIqH*ibT05q&{ovT%zdf5`cuhn
zct+~aW@9U(ADu2=$SeY-V^R1>6pRW@HIy15R~UR8lHc#U5R_ps;W)fUUsPRnCQfb4
zIqE={k_)ypNW))lN$~89kpc6ct3kgq)1hPSvfPj&Y}E$U$EXuiecl(}nB8O&^l3c@
z9LQfn|Lh{R&<<LoB)qR0Bf_BfZ2Jcx8g{6-Z}tE?lu*X=HO2ePcnsd2I>^t@z4vsW
zb<96z1?g9AV}3x08jLWJnLVkPL?a*KU8iV8$!<aQla#hAzuZ^|*XvlGI>Xj~5p>2y
z@a`bjpfUNb2I1aU$_JGPZ^w9sjR%pw6@ZC%-y>nMf}T3&iFw)#SGh*{bB#$y{Pt;u
zC)SN1$MS@>(ckpl8J*{>;)UuBc&H@Z@B@taQld1fAa`?AcI{cB>bcb$tGhgmiNEvC
z<GrK)zQz<)G8A5^`(645QR&^JV%%LP6WzRlal=7X%_jC)VGGFOgBB}xl2B?Q$`K)b
zg=BS3{(<2Q0NdbHimHt?($)z)xYdCu5QWyQ{29#0?qi;r^j<W;rq8e0fl(#XRIQQ6
zkXrmXAge_(ZyUQ~5(hFpRMiz)D85TpGBI8?hCguWIqC}v+<QN|N&iH$jQrrq%yQuQ
z0(!DeZb(bSb*ZKU!PnB$jd_%_mLSX|su0mx-P6i6UuoLnYPz~W^!>#3yn58vqY0HV
zAi#zF?6`hv_f4N$?{1Yu)8bez$IFQz;1w$)h(!m@ewh9Ip=J|LmPF}w>+{|$K$xH9
z4y2hN4T-PWO=C~i6W+^nL(ahae@#;)x*hXn5yDPKwy?bGgs^&uU9!QidPc)7tI9=h
z^<<&N)y~5@7RmD!Lo0Tj&D9fCUV6)4o)7PSXC2fzU^(C-X|0j;x@v^m1$iw?CL&S#
zE9{7qu_1@cO2@-c`jxV8we`+$>(`f!)puq@)AqD#L%nj8#-7Vip_6+TZ~cKT<IIdT
z9y-`eZjV_dI!^oqZ#3=qJ!-BNc(a6T+$kV?2O2|H&mQ~u$5_IQQ2i-9?vG`44XvI0
z@*8{1=<D<vq<yKTZ1U>_&pBmw!F=k2y$+|!JMU_?yrR&|A$(a@p4PtDjg~7KVLmAu
z(mqItM8mm!wTbN#Qc;h8+`fqziVWQ@BIGI%5&^(-eWEQF?&$V0sk`G4jp_axwjS`&
zR|_g<g8l1NoRtr~vQMr(J+Y%8Qb1Wl?+O*QY7oBWE#WGac7}f?4gJx1spOOI?nx6q
zmR(BTTqqt5dPli5{WPT_wA#|Cpsw^}r@R>eO)%~T&@Y7+MC$u>h9IH+K<@b!#j>*?
z|1Nj?J;VlEbhz`!9`#e5?FF*GeasY<%4sO0Q-@U+6<0Jh{zx%@`OoUKDsW*J$j;ua
z)!`m2{o2S0TU1FqTo$uS4CTm850wrAih%(8?wM|*FW@Z+wQgx|0VnCh-*TbFqP?I<
z==7(heG(L7HtVojc(rUKh*-(8Jsi4Co&rxnLIdZIQ2#V6eIT?Y`;JP^6MYYG<<i>!
zVv3+%sC_uL^pm3DpyH4(vJzPR<thaiX;;x(Q72w&0f9k)`f3@(<=v4_MGm%)ZbjVs
zdb}KI++`=&cr(&8P#y#mzf+$DJ6~w!@Bb(3$6jzn@|s#i8yNRZdU)<`*qqesr9;!f
zuM~uh8gf}R9Nk~ci47A67x)_s-=I1#b(~f;>hzjFSXdL@xn}H*2G$j7#3j>5M{-a?
z1#|;Pfac@|qB$p}`<d-|JbPb#dcmj>y}FJ~9HJ|w2)Lfheld5+_aB`tt^tlp$ztHu
zB4Afp8&}6%I*9!U8R(d5)$LjkEthNjK+e11>rf5y?2f%<1LNV*F?#irs|TjC$3nP-
zed6l!1Z3&Ee<HJ+0WQDdyTD~$y#|`M-$*c*!hu>J0^;qa%6+>(c%>&3MgyYh-MJ38
zXtW=8;F`Y3`nDhN<oeIh8gCj|U!)YB1mU!Gy&_%5U5ViyGiS5Q(&lmhgd&~K{>{$(
zP>QzwOGw1s+3rZ%z*Y7eh`w7ERbd`NW(wXd1LfR6KCSvu`G6%RD^+~y29FXvx9<$Y
zrB1Do<@R17#bdeSLSYu=J$xzQE(!mLE()4kxD#+SE(YC`&OZN2tKSiaN_XmW4;*WY
z{ZW71|4&378@>rEE=LTNU<h#t4a%CLVC78noMD`|SyrloQ03o%41j=$*n($VF>cg%
z3lgVTh!+OWj93isFCAREl}cmpl)E-8-6`DFC}@-c6Uc45w!<{B8x!0voK;kblaFOZ
zL`kf%Z`4wVkk^h|tzl;~99-lk%a1SW9ziG@-ZTE+2&w?41VL=<o?s(L+q%j4gUkE?
z7!Lu~CAEUNa|9-RP^Pj>o-U}BScs6RC3S#Lo<H)tg_zftl@TNM4dY*!CUhvMh#+I`
z{;Ha%bW|pTob5nsw(49Za*0M<Mo!|FtWmGP#K>q8hU`@p8IM7Xh5o{~l}FP-Z>*>~
zK>C-%pvoS$+$P05o+Svah^oyY&(TgaD##I1z>s+Zr~?HNX>kE&t53lRVN??KGX)fM
zPS}Eb{;laL{6%S2h3t1*Q~0N@r?Z;ZYK0G~MsE;q{WZzDmDe@OO`OxH!Q8MK*d0)9
zya@i(tYaiSXJhBykLzL<Q#D4!;31ZAAkL4h`fnTaN61}aNQcqrJ@mrHM`WPsl4wY2
zc20zJdc;@}r!G&~CJpX`5PE7>ejQp~RU+TdBKSq&mM8V+&hQ8gCX$*%=-~z5!7Qsg
zH=47o9X*Kf8A}nZRl4@nj<cjx{m5-)83_mPUR=v!l%Q(z>+vgnealG39ar*d>m?E2
zS8r$vI8|954|@bHq-+Wf)!+!1GUUGo<CDj&420@bb?Lt8wk@rPG92pp&n6EI&S{jn
zhX1lE%e8Uddr|Rv46q=s>5sdwsGr-jbJ9{4e%phn8;qddA$i(sN0dv8y83ONWPD#B
zbFhy8dmwzvD6ncs!{9^kiMVh4x+Ja~D0eFid>;<d-YudF(;+*SbjLz7b$qU6&=siT
zxwMCckNFsC>UrWiPC#L`Pvrod>pU>RlZRo`$!o_7!T;D?716hdh$oLqnK@JF<(aGS
z<*NJ0wG~yZ<uqyL0H-jk%7=&tJki5jNW<E3A$=zPbH|rkU6Abtg0cbGFDgZiCaAXK
z;D5LJAyV9O9Nnz#Tv%vQ-B=USd|I@i&z>XHkcXu!BbMkPh9sk;a0rJtwl-!9tC`}A
z-Z<1mG`<7xdyOH-dEG<X67{L)w$GE&<Cxl0`q!Qh%OvhHUHvB_?RT>5)~Ta5()DUK
z8rhD@|5Hp4fH+ahH`CZ$3yIPLKz7Pq&1MxgW(enj;T#Y?_lwx7_|n&OF3gE;zNmN^
zrl}}vU^!0f=9+>qrHj{I>42wA4E^6D3*g<lL3>Z}$uf1oIzUtz9^Xox^b9Cd!DbBf
z{e--m0`+mxvwXl@UJMOH#3~78hjkY0${C4WeHy9@2oqGTL>P>a8(<RBl;y0a9qAp0
zSH~SU2~{0byL7pfAf=4ePjn?=c-CR-B6OshM<kesha0R1Qq{r7#?#R4D_ySeiZ{U+
z<Z<DX3FX930cM6bQhuKy#FRrC8Ju%>HzjiZ5vp=+ASI9R<gw6?Dcmfu@O)R0LV{2*
z5wHd~1-v%!WPP#*lDm_x5}Mw}jGF|Oh<13poq=<^mdei7@jN3%l7BftrHs<iybAX(
z62V7(jxvJKR|BLc&E@nckIX=vydVg40d8zwnKLPiEZik@@}Lysdt!&0h6Ef_QF_u{
zQTk|s3)(p)zvq}I`4gF2X5?xa<jwfrwIEE(AJwUe@nt+6$X8-8bbN>WE1`u#&T2_|
z5ifPQ&Ww>CS4!wK!7a8gf3ZIC(>HYWqUaX}SxS^p*~_K!NrQU^$nH*+q*6KJsgU9H
zz=V+T$s}=M1+_~MWX!<%)r9DHZ-aV0(KF28X)UQXO!v-|ltJEy{4)>BcVx>DU>1sf
zD!VC+;{h+LfL8gPdRbmu8IVlxMjo`rplV<pK$ej>$&_27BudBB<3<_#|Ld|n3r4Bg
zW&g#xJFGxl-t+mV8V1P1>YQu;Mt-DHg$aKo)0)1!7AwA5WDs3dxw-YIu%{>Ws1ltb
zb}p(+iC>{l;psM1@5@-K7#iGYp-LdiJi;(M(R~V@b5P|fce1q4bD0ULdCXX8I}c+P
zy;v?+I#FV+0ldX!ceXFr$RZ1vLeR#i-hiQnr8y0GzIQ|7_B3W}H%B{j{SQVRZ`63u
z@e7u-ZN9Sm=O>5U`M{y&&U~oz2(PM_`RfMEpQ;ajQSSziM%Fxn7_J=>Em_EO<cZ0?
zZUpxX2}`IvyvjOREDyzw7%`mO=#bn}yjf?2r7*+|b0-x2ouZN9mh$sKwB_AmlYnz^
z3+`1EnMCECZ78|dwukew3%9h}!G8R#I*^dma((jqFy`IH<`aY-54-M+f({APeMEC0
z3hXdg0Sixptzy(|u*yKRx(#*x`MEHyELb9_NE5?>R++*DO)l;)Jp@MI|Na68)Ron+
zR?`HoDdvI{6EsrNKLXxe0m^Q9XrSST4)%&P$v~5#1d{!gcgumqQkiGN>#FnKNyIV!
zS~XQw5@-s`Fo<Y7lR|EkY+6}3B#jg~vB14Qt_jcy=|Z%hTC0?E&~}zXc<RUOzdsPG
z;xVudvU9q1W>I{k(!1AyZP2UkSO2imr{hSaf5&6?9oTPfnqI3>%zlqj%aq4X+OLW=
zb~WF%#*jyCzuJ>5Y;#x%t$KbF8$2<8?xR67bTIsY`98i;fVw-?HAij?S9_MV=H^=0
zzR7jN#A>=$f;QCdm`fRmv2<2%p1SV33s9Z3d32N<fXMLw5^CEIhBt4?g$2tjnp~c}
z{%lq`yDMrkBS+`czZt~fs$;!wb=4XIcrUI_ZN1c}PzRgXBSTe-WW~}MFZaDV%EUhG
zv^H`xOYgjIOtWwP*f$c2flpr;!%UMNc&U!+_wWkcg4Hs(3z%n=FW33BHy)iDb8h_t
zt3ra~<K<L9SG1hM8+0>S`iaYyoQExRZ$#6i(VP{DbD@hp*~6Sy(-=F-ISKO^z|3W*
z+YMdk2AFg1_?j2nCsj4)=e$(!Pa=O+qklJf?M*Cbjztgi=4$@vR!e-^ImvM2+m#sf
zU`SNpym2>J^)uIK=A5roLa5_0ktKyDmAz7S+O6eChSwJFO{H}1>PGF*(Ye)q8mSUI
zu7XAE)+rl8{fanQGU()20MlBH#!sB`X`H14vtDn=r@yXT9GS>-Rakq4{Fj>PkUY?&
z{;SjtnUyg*57W@87_aEvxN{%``-w^5w-vq|25(t4IzDn~u9{b)_vqKe69>j7F;HEF
z#f^PAsd|Rs{_5QdL;0ARb?$&=ZqJR9W}sxi|Ew_iy8y8=m}QcfyNiFp+q=Z7_~x4U
zQ&rmYpIe+wc9kKH(my>EDp=j|QEgZ`RvhD5ed_jqYx%nSpupCtj%r`9*(Q@#Gb?!d
zvt6>CDK<+TH3I1Dez5|cH`QehIjZq*aLtgdOO=k|zwG|@j>KW9IP&B&0%7m#mNDzk
z`drEh=Qx+_QoMVQ!Yyryr3){@(7b^hNe0QSfc5wRRi{0ErLbpqr^bORN6?=?>t0eT
zZ?K2`?0c^ks4(Q8SR9^y`#CUz$B@Z%mv8FywX>e31JV6>MiYAO9CSD;@?^esT7I_)
zP@v2SajW)b0Rf*R{H<{?dWQzKjoB4cXdh?Dl$eD4hNXI)rpo2r<rRd+SUODwITEMI
z*8D*eY~K~?zIN{l*3)kp(68Wi22UY*Pu`LI<G@2}Se@V<v~+<RF`4Oqf={MQ1RrM<
z{rd54FuRYWy}~;{-)N>f=Fl$NhHFUDX<eM~b4zBmXHH(D62omD$Q@*CG8RsRO-}4*
zeFFoW#xsWYQsI#x9UjxrX%#*hJcW}}E@PxaJLKL5uSdyJ7)te+4QAk2FJjQU(zygb
zIa1IEN=Y|jX4E`n&nchG<1S`50QREzGT3s#@7i36{R95NKvm=33dEg7vM59SVfrS;
ztx}AjC!9k(OWjC%)}CSI$hODmh<U%YGx7-r2K#&>K8<?>IW#w1claQ3dIRh!M~(t-
z(d0BR2_14eXvWoAX&dEoB_rnPxjNvnuri=!du3?1X5}vi1>;LtzRR9y_uapcMmGq!
z{8-4IkYZTb3uyLB-G2YM4MOrS^uXsWXzJ&veq8S<wxLf#AS(Epii?s}x<|DnjY$Y4
z%SrWX2jFY38#wb4B@mwKe-iR|`a~r{fB*wM_*b$M!@_jdR9MLRwhfas647=Vp8$n=
zCcw%eHac1fwsJyQnvx%wBKjQwEol<tnYO~zF~PqJ<OnXM8tkxuBR_NztS!XUyM@E`
zgYtfnVB%C!pU#veQy+vj8(~bRH39(_pY<h5sO#mt7v+}NSl_-&shm7;+bhOKJX$(m
zsKqsr(4KOGTjqZL=pQa;=@df==uF&P<3{wAkt9x|taGW0F!aF6p1i&;mwO}XvvB8N
zTH`=ki3S2BhJ3y6MJjZCHFW@Ro(9J*5?JmHfEB?v403fu{dP#svPk4)H|U?6G_*ye
zQyi~^rJMYg-V!y6l!uh99UdcAMm?7Y<Y#l3-jAv^geYC(YCGhlkOfVx9>dOF#7s7g
zm!HEloMh7gW8OF>1G0*bdni4Jr1D26IvwgA-R{?T2+8xITGBr}!p|*0$PnuA>X{(N
zife`;e`tm!Biu^2k*K(g<)f;ii65=YUMy^73lI^6>)~PTL7>fHuSQ&DhXE<hp0qPY
zo!Y;`ST@Sz%n*V!u$S0dY8+O|XL<?+-|r_=dOuQu9Z>C6-kbJ@<#4w(@N`t$E|llu
zGT|fb*drq|!8x+>`u%dsq1g7l01Z!_6k5s;_>^D+9jctmcvyNS4p+i(Sg0D!cYh>%
zsf-8r1_P}z=mi9Ojz+BUL9R}m7fl|84$2Lm{3(PizEvkvcf$OM!7v|TV1E~npWW__
z@dN66jF(NbnS0!4B>}@L(&3ew_WO5T4zd@>LGXI!gOw4<PkNUpM<p(aQi7*g(R{yE
z^Tw1ppAWc=e1oLjXPAV%kxbZ;h7lwOcZ^9{>)Pt7rs5er*JmP47t6yZ8D7k!q+#jT
z-rV^+aYC%(-Eot$`XrK{FeGdAFqygPy3aezf;c~z*uG2di7>oTh7R1W+9URTGK>F2
zlU!O22ymE3P%&);Q>%meZ6L0dUG?4gN7YqDMb4>=xOOpB3xk;6ZFsL`H3RTp_fitZ
z;4_p0;szAJh!0Pv3JNCq>wv8wBMRa<Mh5pL2n`&tE;`qImSA24|E=L?J-gKVq1y*7
z_|OY@c-#O~RaO~co21IaGPX#0Zqi$<dMqmleo}OF5mC}pGvC$-T;XPwJL^z@O^QYB
z;+3FjYw5r*I&ZBP$Xlj9NhA^a=<qq7owFyQ@(*u0?gyzPHV!etnlJ=22}Sg2>45jO
z+(32R-#*h-G5cJcR<8|=K2Ad7Vw`Zc=9QDp>^Y2`xW`CQ<(?A}vIEQwO^>?Ua#~yC
zb0nGOWq|rar5H7JF>gG@B1C7PxD*A}$AP$D7<%01@Fd<!+yR-|Ec}#ym#f_7XuruU
zm6&9a_^V1JGxdJ4I+qSz5*jh5@OXqaa5YUSdmxsq?@}ysjHq;I9yVRYY+L1(rNwug
z92;c`Uj{))OZ+YcRKC}IP(gE2rD&BA#JGtu87_~o(qki9n7+z|h%|D4m$d#kdRU}9
zVm?wmi(iUJA1f#|V;{o%iYnBLe=yv7<SHG~0|&9yBILwF4th{!;&4G+P|~>*2QwBm
zG?q2Ol<R-xmVEa5>4K&`rpy&}8O_~PUi&!0Tl-o~AeVAJhrb()9I1IgH0Dmt=TJ*)
zih8?5DMM$0B{f0vkS2zCT_aU{1)I81Qd!74J-j&u^K79x`n=WWm@a>U3wP_{_>XCg
zFv3Q;k)6Y4|EW#z^qo^DZH4;%TS$|)F_d-Wta<vjVVwxxeo-ewC%;m(T<iwl8=J-x
zUN@6-V2xIT9kCKm&z?LdzxCx78ShpgjzOH_p$gsB1@TE(#t#1UT}WJ3pN2Kc={R-S
zAa7d3_7Kn`dhIBDj-tY#4$ugmige_b-Oz!C$dPMtuwBN@x_XoEVcg9O-Fmk^lM60W
zlLG|N5BiW$S9Ov|7&IpHG*0&#31+>4Jvh9Y(vkM1p&NNx3D)zc_nU+#aZZ))#8oLW
z(G8*LHC^%o?kQ4bPsiC1AIC*UA7wIStsSB_D5T;?x>g=8ZksocbN0|y7gwm43RU~>
z$9)+$;g`|vh6^f51)uD5w|V;%F^Wgra~bW27UZ&fzVlqSW9%=tvcDhT!yjn+QweTE
zJAyopBOW2qOICt9*|;IeNm#IOVD1mxxriymoi#39{B;0X++*$hfFWu-Eh4jWOGkL8
zLmp&PSSgPVfKo8Br0Hwk&=|M0pC1Whd{5MJyQTnMW_>N=kYjn*VN#AYziVr1)nV)*
z^B96#;3mb(H4m_ypNY3c%g5Y`W1g1Ye!)t;RBbtWeCk{aSf3@g0$IHYhD+qnW?-H=
z8n?WWRa`Q*tT~K9a5sRJs~VTpPfd#d1;A^swv2CnR#H1C<1YJZM1~JaxL}O}H>`}f
z_Ed1fL0d?U1%q9om6R7gO7J_=gmljg1+WA2gH|1&VixUi$lFUc9J~ULdu|0{a#-%B
zs^*xVCg~GU`WA3ye+FaCN6=*LXQHRgp<@I!R`E9xee7|Ed6rhgoSQQ8kv47#EcL?6
z4USfFd^n1}E{N3z-rO=P^10hT3lkc}%(*;WZYN#?28Xxc#Rx<x+B^2AWO<aLNb@C(
z0JXB-3^T=&LaM0PX|*@ZPrbr2)eBAsHi7UtZg2;>Q6>riiBjSa>Md5I{OeJr6+yE^
zg?-g<vuoxv(Tjk|DQ%#G*yenetKWF^FRMHzxbI1W8mEp2o;}RNcL0iqgjYkYK{GsX
z0viQf%<s6LaeZ1nz3^SRz1}8BT1|Z^vOu>3dA4&4D!=N!Ua<3`8pE>G=O;t#@eL=r
z8a-DOzjyd(<Vo~ENM9`7J4Kz$-p|he#3(IUOr8>w3?SZa=DZ@K#=;UUE;eV$AmpG_
zdBS^^09zG}kxPeu4WQ~pBX15kcz1OF+-afuFjc)|rNGDE?JVXt<{aV0GRzCDw{{vx
z<36WwO4|5cr(x*0L(5VL<7XU15`i0ciJq%uIC_y0fO_S^qe62}+D5n@|JCogJ$o|}
z4^n@1ofC~toUA<)#y-(;^bbCHvhB0?=N2t#1{+WyGegRVxmPd4U-RIrjcz8X-Xm(q
zunI-JKoFX<i%4Il^f(T*6~beGlv8>^+MzhKT-|9N?q|7>)efaDYrPFvOz}#eu4N?w
z<Dt-1oWet{oU@f#Wl@rQbI%#*I;IoWCun%5yvyP~?T``pqa<f%UzT+8R-C>(rw4N9
zA;d%UHa(xGsx_+C(^lQ$az&0rq7jQRq^rZi{tb~Ik$MKmtvCg9qgDxP+J<s9do=5_
z)qo?|L1!~bka++$&lZ$yVdpGTZ?H7Vx?%%kmjh!Lr$U!qFyBCBMe;5^U?mzoYjI{S
zht!eJ2UaQqxi!B$E~P492zj_B-UwsKfLo(K#IOIH=;__|pFq#ukjo+u^bJi`Oz9Q)
zq*&gWc^yp?_DZ#zPok6@Z@alL>|Kg|^NQ`A7s#&1&u6F>pIAUF5*M{vK)-kp?CeT(
zYb}4~U$?^O0c&tdGf6BzH5>1+$(7gaL?K;f9eBQf%?r=%2Y*{**Wninn^%z2VO(4=
z7zL!1Z899Q<ihkTgkuCc#7a!+d}Hn^jf6Ex<$%yl1N$EjAXabABZf!8kBt9mH3d76
za0H<?$#Kzm^*p{p=P_7EIFA5#VHcCIf}_U7%azDKg=jxSu|#)N_^vLT10$~$tD|U9
zCBUd?*aueh6OI{e>M{^{v6nhb*?(A55xSq|Vz0Z*b^U5hj$><ZJdtY?N#3REn38G-
zo;w9XsQTN{kSme1E`T@(zj1<14$Z%MWG$?5KJ#_R);8;osA2d{s;p9p$tTZn0DgLe
z^aE*@cN8>YSxIcpyxKa~FkVb7aW$H5eUptJ$C8&)Kz8kqP!N!NS`iE&(c?s!{|K3P
z|MQ?hvHehY{_$ebp;7Zxh#!P<Cx2f(8@-Pkyl;vB)jQ0XLcwf*vC5{)vU<S{nFk7@
z^AaybzT$jnXQN$b#*)hK;>sbSlot$RUFDutcP@!n4C;$z@GW7FN<k?}=lJ<;6L%{P
zVD60TdZ&<sb%y(xzO%T-hYxf{pPO&A_qJf>P)t@Kq}tPC65;DlXxc+_qw%u_9|M{m
zQ6W+Ci@^!dPnMZ*Ze=tqKV-fj*REW?h9jKy9kh@mKZ3C5zo!dQN;xcqv!|WY{eC9C
z@P0gvBvGTHsMB|s%fS$9gmgSuSfWreSbb`H2H@bEwa2hgpdXPI!+2p*x^5K{UOg!a
zYVFr1Wvc%|<%LMUq#F%ofbGGYPy4}V6f8}(6IXu?@^9Ewne&432{(Dv$XdD5b4KDZ
zR#q~F9oCR0xy4dqh!Uw#i}g75E0`FreG29F{<1F}<7-cmilA-Kso3S(Mp0FoFKuVW
zlnZS)uF{2-3YQk$&2s3(6GrVt%XfVv{7WGo!k>YIn3`gSO93TS->hmT-?n<-EXGI(
zQL~Vg*YT<{ewWCJ)!)4kVyArH$^ON0kMseDEz(Jw4V_(Dfd4e5>g5N3@Id!njvP|s
zU`4miAYOazdQj}6M1E{B!!<>{lyw8<1Bs(XRzDzOft{kdf!Vo_<o5#npx69Ej47Fy
zmLFQkQWcs|m3Pr4hjViZEx5RNHshy-=jgIf4O11>&ZaBbt%%w~;ciWfpVjAc1NG1|
z!K%@~uT6gpPrll+C1LI9IqZ%}u!~f){MHf1+Yqf4Nco^74g{FKR$nT2SWQE#2UOX>
z##|IR=eEP{O%uXo_x{auE@TDNb}RnzR|IoZzlc%H+88e0uUZ75>4laF{_yiTWwsLp
zCAcVuZgj+5zj8Sx6a57&%uZZ^NNTwRt^=5`QUl|C?ulyoy1?=G>=(EeU_Xki`db(B
ziobDK#}E(ROj7g8UusLST1JvrV$%7=zC&+9cy4lYC6PFRDx=6D(%2<kJGM}jQVxw%
z*(WA>f|26jC4bS{KDQy~O70`1NUS_J^HetX(Vu&4k@yt*Y(YOa+2AmU;(d$H6~3Ce
z%>=It9zO(x_$^-xdBUxl57i#H<xMDKG~heSrIISObyHes@=6L~4R_S;gPRnL;v`s>
zL%Ot8*s3C8Tc`^M@4&*pULg)k>W$Y~m5UB}6N>obxcffWG33us{e5yfn|XQuJk)&6
z-5>gcOM4ui@jHXsy&BUdSKoX34n07KRs%#dZRNqE<~s8t?iaL}%hlIl_!$k8hu4n#
zAEo(LRu;3Qz3SPyl@Odx<M|1lf<*Mc4|F&l<_S$UnCcGE%E&Jf@ARb7W42nIzv#H)
zW^q?RsetPL@Hn3uUy;+(@4l-mk{4daAr<Mqj(i;Qn9TQJ=Zi+rv>MVNB^KM-wbC2C
z%qE^J{gLP3tF**lnbR>Ca(cb$n(Q|JVhm>y(mK)k>d;-^EW8w@MQLu7+6B}e1U9j*
zRztPi%{NRNt@g*^@2ubeT{-pO>~w6GA=-ixTa&(Q+tt59RG}`2^DCIE-yzg5c;jI`
zYRNoetndZ@=9K?gbjGLxuq!l99UwhELAuv*iqZEg*O&2GyxVl&11k`&o|luZDp5;L
zo+knmvHCAK=M8CPItdPNa>eE<FRDXI5nmUWb_wtFDff()EI<R%U3W<%6l=$a+8RF%
zI8=eCCH<{iMBMVAX;mh}bj-i^Ow+bLr&+Cq7a?7ZGT)+$`fMSkE60Uii->y&T?>i#
z+a=)k`3$?LgQE)}6QNi|MjbPoOh~94eY3s4Y2U7u%5<pjz*ePewESF=W)Wiuj>QOB
z2U6U|FN!#;zIO*QQoT%bhvqkOpW&t2x$_{9^5BN#zcE5QJ89^1A8+tErhmgTAU7Bm
zCL4jS#msuqMm)Xe90Ypr@b`o!nN{Gql5uzc;(ff2siEd;+t%D7QdoeFp~2++#RAr&
zwP&q8U|)rPxg!>iuANVpa@TDKTaaXU#GaL)?W+)%XFPwPL57LH7qjIZSW)s)w%I!j
zQvuNVmnR6`aUNhp&LUlN01i6=4y`Xh+`-O70`8tIVqeq66PjR5>DH{APcQI0ut)(c
zIGU0s#R}({_iAQSGw{veWdK#7EhzL02#kvX7o&8bHHmS<)+Jp7L#$f91e+df@B-+<
z{K6VsEZt~nU*-K?P(Q1`Q%UDHz~gB@v|~%HqySY+OY$P6V+wW+Yq%2D5aj%5U`e@>
z(*@&XW}3}OTbZv6D|i{FwHC<kwmA*s-nqFVBxAT>xtlh$43^n_LV-PmN?j=BB~LX%
zsVaAXk;%~KBcRY@uyN=5%xf|r8P;*mUeO=Xc4+FR3r;J+1xiiu?B?Ua3ZeO$D`52x
zGDY;XN(mI?IwWZkxSChvcbMFyB7SGK17tJPq(ArPkf+^HT5(_su#kfc+LVuvw+tQe
z`g5XCPLwKPP82WGNn{_033nrGyjxi!74@CE#A#7yEGI9;Qq;+EO&sIV?O87i8XIni
zomlKXZGTVJL95@q)Ri}tH}|3>@Oh!^xbHhY-Y1>2f#R@6*kT?W8#eYWS1-xG3<{$S
z7}ld&?n{HqLLkye`AzfZ8G1uWgxd^+$)Hb?0Ns(tHmf}1twQ0I)2v2$-lD-bS2?fZ
zs(lcvbuctozF%*A#u(XJ$X$y1b!lf!_`^b0cPINi!AB)^9GmDLf5^_FN~i3>fA792
z>1=<ux)yZ*vFcLDi^Qv(DF076w$zY%wPp-_l&T>J(QDom8R+mbHn+TELG)Q$;=C}O
z0q1N&WN^7haOSV_?MYO7R`rK+$`}5;^V0_KrgJ_4i5v00?=u?$C-q)?|4_a^Pl2az
z^;3!N(BZi7E-C&^e}S7)C6@~WO|iHY7Np$odVgM0Ib*NT1o|WT)vCjZj!!0m--cRJ
zEMUk|jYotP%W53|Rq5O=b!Sk^n<|f*LVL0I^!wPkC4J_Fg=BrgTpM1RFFknpp#Rh0
zCRDw7QGMd9dIphCE;NXzg|zJfF?_Sj%e&$mpYJu>7OknMw~0c`X=fbb?F!ACEIjm_
z6Dm1S&j{2Y+`g+yrutO&T=6Vq24F5A+_>%DkB@szvAUT8-Ab}M=xKJ261pFVdO^D^
zl7VZ;D|a_j$y)B1v(yWou}{5&R95r*KaQ?Eo(cc|f2H`8JGnlN6*)?-WQtfNREkQX
z#2g_RLgv_2sZ@?7Lb;OYaLzT1%`r#0Lf8xwu`q_QnO(nqevilO-#z^CdcB{o=j-`=
zzWnmI)Bg@y=LdC}c;@xV*dg2bqSzR5{U()R(0{J_gQtw6&vZR4^SH00xwS{sM`uv7
zr<5~dEc8^*g{6vwoAn}N>VMH%vtlR3!v2R5o?Ro<%E*gC2mK`PUN#j-uIHNexh9G~
z$=`GL*gvwWgL_9hG^?_>ROf9l?w2rMO=IUuB`U1JYem*Tl>U5b=W)n6n7SYlwLY20
zbD4oO<Y-(ClWfo$P!aYBJuK&jB87(cxpP9Wc~w`K$ib*9i22G3Uz;HcpUc)6>o|I-
zpjyZ+<#YU&6;cO<gvVj)%?VMd(Q3e+rew7M?3KK`*i@}huUuT$=!B+=Z2*|N@vfd`
zjGMs2YU^bkYwKK7t=sU>uB`Ijsi}5$%M@dJJhbZu0qCkNv?lVyb8Tit0RWR6)=2?v
zmHoQI9Q96UbVqek&OlKVv++ZUIA?P(!z8huAj;c77p|BX6-5%@IDjY_@s%jCcU5x3
z_Md3OJ?tbJd+uHGadyHlkqwC`+)tDGRbH>=9h0#9(&99OW_&@^uiOb%5rn1`U=E}9
zA;HHx5MT2}c%f!FeCi&EvrhR>;-S?Q@sJett~*^xt6)<j8ZnCyT%rf(bd}3@oEH3!
zk)N%E4Jr@~^XHlDL&Tml*UAFa)@_2FoD6P<!&-%*7jiVQvAiqIjDKv=BZ1vld{5O*
zr2#?A!C)<P+}75!@B~RMr7`jh1J=x5iJ(I9t7guUw3Afo%W`*0VrpzJel5+C?np&+
z3nE_LVoSccb820QeTLu?G!T48UhWQkwaXlFGWU-7AE+A#JZ9MpY|jej#!e}JoZCSu
z<t4IT5r5Y!tUio8P8CtbHYRYkM5QdXriVT6tkWf8fMy%-_K{3bO%nsWUpcJrvJBEi
zZh4h|jJJRHZi3TlPAu^7>8RfX@rXy+`)4n0=h*JnFn(zg8nZ##wVlxcgV-TlaiYhj
z117Mn)FlNP9_q5+4K95}Wc&S9@8TZ-t&50+W3<t!hJNP|y3n6t)4KWXM+@SD&JD50
zn|JkW4#_KsT~oL!rv5|*?Pz}uJ#V0ePH|cP^5vfkX-Dnj-~Rhg;iuQrir4kOUk-e8
zG4S|By%Fx^<KLP5)u5HFfqYBz!fg<hBPi&L=2wWK2)I*BJgjo-G&}yLI;qIJzF`cT
z0JE5Ql%QTlJzG6qJ(Q4<Uo@xTm1F3jF@$Y**1CFMCD+Z5D!BQz(q&`Tuf{$gFe%Pa
zsei++zy8U(Hc^L&Qj;hv-y7PtrF7QnxzNhY=gKpqCtOwa+*!~8M8JEo_0L4NwaOu?
zgHT<~bcg0FZ6NS3H$K3(#^=Ynycb)Pl2AV>e5q*F`M=_don6ye|0?D#5RBEXPD+YT
z*FC`SqXp-1c1?(iY{&Qq&>#Wq`s8k*f+a4@K~h)<x{Mm-px5hy1v;`XhIxtFLa!tB
z35=`mJe9#y?2GVJRc-bo{>NG2Z{f$2(^;H-i%dzOm#!cQ1IlV(K-Z^6EAbFZqxYeN
zn~2xQct-%V!E22(H~|h4Ei7Klhj}&q47=6by6R!<oevsZ%@e#wFgAo$d*@tg6?7a(
z+1H97e?L5i{`&1mdIk`Fnkuwfnu}jZW+LwU=eN|t-=%1t?vv1r%C)f!FSNk~*4eb^
z4d&m<2}%d*@p(R=7Vs?6!~oFzW#_NdS17dy`6s6bNz7<LpS@(pNRw0G>>mP)#o^rq
zDRF9@JQ=oq+^yHzOnduPyNn|y{=>_DxE{o{#~iC|xz*lHb`o8G!E<hIXZcuVu{TfT
z#+(QeC})Qw)K7bS4p~MRgTfUnD4}~^UI}#O>6c`CMMozg=D|nl;`(nVNHO6|vxnkx
z*Ae^Gbv#E7L6r98j(>;#aNvw7K@6cG@+?D)?=_?hrGYVP3zuC@y0pp%KI5ag!HuZ3
zC?3I5eq0x+I~VjiRYy_6Hu+WXEjG=go$*t4RXWy`FBzPE-=ZLK?PsKVTp!R{Hzo_Q
znS7KhN<Joyjz8z%m6e`z_KfRm{Pnm06Y?z4cU2nW^HO0%3g7takI=~1o)hj-alYVC
zNynX#04s14dfBJdV7;IsNP3_2KX&Ic;~a&wPNtBN5j7=7ymO{+$PwH#44s#pw_Ffy
zp0qb4KZa)E;cHr<&3N074JIye(TjoWo$wm#Rdep*Ae5qFdn<$is2S<fbPdyy{d&9r
zRpY3d<#bEtvV^$y57eZiM$9LLoX;9KoA1i=+?Au#;+?gYrQ40iqDlQ&9kLhQbZIDC
zfu|Dt8u@hUrP<HTj<4xAvKW}&=UdahI%rbZoQpwql#5Xv15wi!hU<nb?XTf@_AApN
zHRCx~6K?3EZU!4JPlU#~<rsZyjTt3XH$|GwR18F%;aJl^c~Z=FdL%fz#rB_9(*Fpf
zyB|%nP1bw=k+_T%SL}X?wAqPMHQfM29GpFyr+T$dMNOWiBk?!l%sMz`r4?h8y?!uQ
z;*-fto|H1#D@mD48nae8R*PHc$6C|+YOG6I0%P!-uWUAcrBAEG+*^G>15LOhlGk#d
z4WMfG5R?Q7qdr)YZ1PM~Pn;=u$D|}m=2ZlU*zzPB)?_Xm(pTtQ*K)s-vw#7Ov0EY>
zd<Y@Mr9qeLmTCR<o|ywRAzFwY2N|{d-5T-Ph>h7Ky5Q8vN~P2SIdpic6dWUf9S$#_
zIs$E`J)^C#y0vN?!HDnqW8s92Z`27~s@0EF*ti)h0VOQFvOfXz2HTZSs+uS%kDYRm
z(eTsPh{)h?vQf2Q-ij7(V->d2rQ%%+qZw6w9xG>KnyUcEPF{?UaK&7?>3z_Jc>2jO
z-4S_-35b(T6MpPT|C>TG>Egf6N;sd!z52(t_E;Jdd~`uYi6pm&)l1|Pk&lJvVBy|1
zs#W$p{rQW1lw1Oi3^XkON413BbDDtXcqYfyszzNB;^(V-P5Lcxf)X3(cN&%ib0|}Y
zIY9ntYaL*|G?^)*n6rDJw;rtF4=cK&T-2CXWRg@*I*XkvOie`$+-$Jb6-t+>*s83m
zFA1j1CPCUl@9W#(@N@7)h#*~}{m})Q`K#dT3(Q*^)VJ%tyj7_BgsaeQ@WKBgM)PHW
z8$I#rk=qW|M`98XJBqr{&Pq@E?~4mjQpW?I)|Kz;T(_<KaH9d&`Kt3lvEP|(6AcFI
zL~5Q~=gWV<19wmZYL{qtZky`BXK2(RVRo#f@biIpCPv$pm$#%A{HKpCP$vIfI7<B(
zzdOpjMgkaRZf?TVBdd&c+$wH96;nr+GVc>S$Rk_guZ06nMi-^5Vy7czsd}pkoEx-j
zYH|oY*2SYLN3D%lx|<GgHTO-bSX>C!oKLLQvxn_h8ljfW(mwj_<hb$fs4P_#guCq(
z>P}{*F#*YruqwK<gNBZ+lXR8$_}CKHEd(t6C7y3;c4#>}`Q+Wi6k8y5AU^yt{|_jK
zu0YOu*QfI(rs3YWmgiBbUBOnsC@wTE0LR`-3@X1@Ry(eKv=+Q5{g)|jCH@@fv#>kY
zM5uz$wtZuJeJETTek3$FO{}Og1#h`C_qSku{2yV;+Wb(Z4&3G=!Tq^dCo)a!H{pc%
zam(6nvEQJTZq<X*s<b^k$~QMPfxmNh4=PQJajW{*>aAL$mPb?Hs*<=j^H&XvzZ_Q8
zD_F*|7zAz$f={7Vd$+_-d?@UoM?_3d)hv*=%H)pwr+RTG?$8J!hqoC^Un{MIhg78n
z{<6vq5FzrB7goFjz6csViEaRHfM@Wj4S-pcwjZjB>bBqC_61*oJ%^jtX5j+5E`#U(
zyeHSJloU&?jM+=kGsicl7<h_<jOx8^mQ9~Ae%lXrH5mQ3ajJre9Z*q$H9stzGO~GB
z8Ppt$w|S49)bhgrTz$tYL;QKdT=8<$2rk$mex?UwHb*$>^z}MJ$iU9&9Mgvlnp2-E
z5o=FeL>FZme@+YEC9OyaIf}T@iHBV7`_t%U1$F}8=d2&fWa;D-4ZU-&&zkt=yOUXX
zXR|U>OWUL!F@*usnd6#Vv+21r^u%fk{Mrou;6k))l(ixw-G|VFs%7tAR-7vjDIY<m
zm;<7==PiKGpwtCDLh5?U@>2h}RY7APU2Bw$hYuNBTVQ(}G%%gdw<dlg!e&x`qDw}1
zdh_>i_lfI4dpd1{NnZ5hP#5~Rjs>z~qzEy_fka%XpKZh*M5}3>;W290OneI%N(2;p
zpWv$$U=O7*Rzqn-k;7i&e)x-PZyEZTf9Z3c6Wy}boz&hPMvx1`Om@5v-BuU{=0+eK
z!|O&Iv&h9@QYflr6vEmS;e*M>TlE2o+znBat0AvUv)*5B7*=(f{Im&VpzXnMIN}}8
zGRi)yWHkRi<C##jJrU(gI|z&g>h@j-R~_wHcAhawVr+veoP>q~^zW$}$}3i#N7}}X
z!i>tOkVVJq_koP)8vTM3+{O77DC<bsR?YWF^qbIA5K%P1Uy6bH$YIJkp~4UNF~ti3
zPa@}{OEZrhNr}oZt6(mF)p1C3h+FvAYZ+=Vp<Nu%TN&s7tSRj3LQSylc#F~=E@y$L
zic2QKt_>tyvU@Csdo!?T_4JC|xViKBtxM|%{hv!}5}V5`fXy!N<~1-A14!^=ul1y<
zX}#JD^#K=)$-_H=o*tc6yRTd?;x_8m&EiY$b1GXOt<vCO*%AQ7<`3Vd7m_3!m4EBt
z{LYMB>|YS!!v~wa+xK>Ye%Gu|RnBziF6{2>lX|hnx!$CC^0<%a{W8cAdnp#!E#@yR
zZ!ciRUHK%LWsJ2B>Z=CaiD_vOwkI89XSf>0v|j{9(p9Y+y4!&8U-w|~3zxae$z<qM
zvYM^Has%*0DEPHUh~ay+2QA_sg=Y#+CN}wj4lww*Bffo(cR{<k7`!Kh-QqhTjYCX?
z?8wx>k#^gR_wPm!Q`m0hzB<Ab`%#LfY0r)(@~PJbd5~-#6-1^k3|7OkOzI>vE306{
z?idqK?dRTs<1UDyRj=bzEf3(J-h6o#SKp;t4)y1b^=wtg%IqLw+WXEseU@iv`YYf>
z?b}L9!`TkV+7P2&eG3#e6xFoy)_vuy*{n`)CZ&e6X8=YN{AEf??dRr{o!Hp(gzrZ;
zsRnSqljU?d$){DiceHoDIIx^VZyLE6uE{Zp1T3ZUUw89<s#gIvET--p5jgpY4nZzS
zn1T0mi(0bcc=zV|OS8PffS#@{AW%)fH==pCz<*E#Ypv&@6Qnw5B;h*K;s^`~6W)h7
z5EAZ!i3dC48E+9?-mMo+6<II*iFnAZE{PulKT2@9Vy6;qo(p%0OHh($gC6#a8u?}I
zpl|2lXz|hnuGK~Aa>^d2y}<4z+UoTzU$K)xekZ)zFx`Eiw^HUOgI-Cqdc>G;6_h0k
z3cA4^j1PnR`PmMe!S#C?6*X)$IRL=-N#oO1WLQ<xN1}qNw50qK7h(1|)2PMvgigKG
z8pKV%jX78h4y>O)$HQ9dY9<OE&;D<F5E^vyk0yVxhuVoMnjuuE%AXP*0ktB%O|Bw#
z4G}6ogci(WzX=Z6en2fbiBcu;zG$z=h_8b~F+V;WSC6{;Rwg}kJ+kun%#+`*m#jKu
z%`h+N&ij}Bse>!sC=I9FxqmY<bbbVk^RiydRU8;3q|I(k9r0!Z577TUR$3^(xNTA#
zYkPFf`}TvvigX)UE-7Zz#A`>CqczySEkEs21aZodVYr1CQQiFXWjsR)tO%`0SagDo
zrOV~YEc}19)F0+KL4r6f-Jc9M`l=ut0#wy^@hA2#JpT%bXhTVz)>7S;`)yc6O-3FN
zEWZ(RU1_}*EFq#gc!}96Kj%Oj>uLX0jro5CG=REJm-=hg`c%vx+)Cg2?WeKkz24p8
zlqu3n5@Nqj?ff{wozg*5P*dy=Ud*2zEL@1-t_^Ifwy?3$R!H?Sr>Xj%7i|8J<}%AT
z)A{>^Ui^s@G|+<kbK#81Y`Ce~tK7^5(C(487!2TGUzT7_W9b#&4;P+GQ(H%=p`Xlv
zCgA=!#uamKMPYv}B~U_uL$2PzMX-()h%e*^<zou?f4hO3*)bFhLaxr-1=Jtb%+|s&
z-yVT7n&Y6opT0vW8SeB#w-EZmJ9B!quxS_>)x83xrK2f%Y-$i;n><J-6`|`N^Cd}a
z<c=%MXUGrM{H0Z^-!r7L+%shE`5%vgb$;lN*#Ka$|6?0MVQa|&WwO-ppDTq^S&f1o
zaT5a*Vx5hAoKAHC6WnL_0B~tTg_9zXG?T;Yo#GyvzqVmGm+hy8w5HR{2w@~S(fCzH
z{bw$E4KS%vT&uV4?U`N!;0tD(G?$my6YsyZ)L*JsGjiqRot~n*yVL8KgCt-5EfdT&
zM+mmiCT6=yG;;syVIB9D&N=Ko4E&|`)&I?NE69~9Fhn=IGAMx_cdusCe>foM$(7Vu
zRb8mLCvxx?Lh)Inx)o6a7Tmd6LFB(E;)HJq)3#+ZINs!a$YQ%8!ok$JXag?1{>`#A
zA2j^t;a;2uLEZB}mu#ZhVbeKNjE{3AA|9MO!d$r)(!sq++4eVF$?&yaX*M^6hyKJd
zl;%vbT&4r-@y449$L_Vc_XhPQj=1+CaHOytxUIBD$>wpA$))b;!DUSW7j;g67mCGs
z`4!yq#Vx%pPr-X%NgE#PsW1gDVuJdRanHz~2fx;iIM8d?85L{zj#DimH?Y1&Qv(4q
z14XHJbo9=^nF!4#VoXz*aA*Ip&B+4g;Qiw<r0m*<gQzp%T>}~yG+D=Atm8HPksTbY
zzHL&K`Y(|oBuh{b8*U0cw~wBvDIKM}+e|qn_5%ce!sFkMbB`t6C|3a{%~KBe(w=Ec
zAQjIVU4fR;f0UMXKAOKCozC+rZW*}UHkx9qA|2I!Ooe-hM)=m1DtPpAEzO|+(e`i;
z%BxO%E(x6G;;>pYU;25ja(&7mNz7a{O#!0pip0%){<NMDkC(ps3Lyr_)H=W|<aQz#
zF$dWfYJ){&MOqTnM|I71F@KNL%I{N5`b$~?a~nMHB8^p+y;xcKGAD4LcDZD*dYN`i
z+W)Kw@x5Oh$rWDx1DcI^pq87d%v0u=Xo^4O45L0p+1f0Kgg%!2NHdlsS2SlSu<hW3
z>Op-6aQz!~E7{b41-M&0c7f&O-5><rAGr2Z0QIPxhF(DDL$`k?lCxd)CPdX3iL<A&
z6AHF#5pzNAByfpyO{r)t;cPA3rEZICPSLa+&`Z_~@{dfU3^{74Ey%!EgJF=Z+pxPj
zA@H)AM*C;L_@7tROSmpx3Vu%pD+aL_IeUPO2Fc84{A?-Ssd@3SXTJ2o1M_HcjHr0t
zV5`X{3!Nvph1oIRyUkp>PpA42HZ@RAwS&q5HDI7nR614Q=fdOC*?xrQdPSk*oXW;&
z_De1%th?!Ujm+@-yy~IlzA$`90e@K~h7??s6#B=Pf-sU@7}>BegN=U|T&h-j0H}Yg
zdN7JJzxmkFYTRPVc&VvvO)a`xltlIR!kor<<3~*gvvPF~A|0mx3VPz`uSDwuBe*wI
zCw9u6XVv^J!7F@;VQ0w43rS^J%8S>UR{tk7NtJVW%suxn24o9ZP<pBur?undcZPO`
zeW-l4UD-diHljPcriO~`$1}sD<Eho-Qs+Qr?eQGO&5P0()FUveRm6#u2ml3we*3+<
z<Iw=(i)Kp)N?2(SIY~*Yv>P%0n%~N^>vPF>FSP0s*}!Zd&`zaL*4L<8sL(|E4`Zx;
z)&A4%+fxcecCCv*bIklt#Dw@xud-?4@Ig!=vtXmDo6|F5<${-6hi)Km9|dISRwT`O
z>}yv2aU*g1)70Yi-%&%E4?o{aY)Yqm$S}YL+v9e|4v;JD)(V6m@^TOIAAwXodVS<H
z+a_K>id#SzR=Si|g#6?VZ&_(M9J|_GD<cQG!PuHAU1_e_M;Dbvl_NL}baCQo);L{J
zyneUDQ{jGzO-^C32<>>%5n|SG|II9eogwnZS4qEC_xUVyo_-r@WR0@@Afxlq8sOD(
z!sSc5g>Adj#721IFaLX^Ob_x@)YcvwtF9r7r)uDFe<>1D#<w9ceV#M%588Io2%_<b
z++*gC5M)`G3_cX#{MVOf%hhZX_d%ljk4-%OOupj!*;?4<6D+Vx<I8I#YKynW{j=aC
zOzYw})Ac*K55O7y$jys~jpO*Wi_i{f1PIZ+9N1sI{DP}X?>?DntzuLaE?Bx0PPMHK
z2V2(;9)f1qCu&TLk4IT^oxs)9Y2j;`e{F{cTGncy=|~Eg){m9*dWhKqSp7=1TDWJu
z+As(Cw9WWv0-p|xU)qbIE)V>bs~oS%Dw(gWF|&)z73!IIg$*hx$4)ZlXUka-U*84I
z4n%r}WWEvk{vgDtUS$R}pVWgixmX1*nX>%uS_OC&XS{HB!#_*(55P5;&>QdhqJBUc
z<e2=%i(>#!_%|8ds4txxf5#Jv@Q{B)AXn^Ku-veF_t{&-jpaUX6Z4$o<MbGU8F%f7
zXXqcyGD7o}DGI=U{E}hO^fyR??F*=C>}xv%N)wYD4Nl^pX&bmp|4J&d`{Zw~7$Gfr
zsm<BX(V8;cSBE4(bZHL@ViE4`<37wL#rk2|>klaw>xyat@bmRPRq`;q-zQJ0fO2l0
zR<VCikH6u_RrbtFx>O;@lz1+DoYEDeJ(0X#*epe~Ac`%T^w4~_&b~7ny#lOtZ%H}X
z+}@={=yV3SL#Vv#Ia9nW(=JKb$zKNrA;%dwx$#gk#XuHPeLMtn9P=USLxUVE3Mwz;
zkbGD|_4$Y%VUiMoCIl<pT3z`^VEE<*fVOfxr>31=nAnFD*wvs5yaVP^r)EPmWtZK`
z;TJVcO*NQ0GFSP<1sj<0kT=bmKhig<2A-WtztLr&z9U^=cgM-QTlP?_Sq9)!Wcr;f
zh2RV0(HVuZ(WDFmL`k||yvXks)%mdAlawp|EwSR_kw~pbTy}A5)YK77%TcOkFkJ6a
zcdq}j3zvS)=ZSO5AXUpg6~(BRhhcyn@GGHgrpX#-moCi~F};FpVTC|$6x>#yz_m`B
z*S6x37xSi~3${@yst*#$ACH=W^LV$t-VQ4EpOlAd&Lw0WeuXqUy3_yH@i$>r^|e{s
z8lW9CSNd6p6Q(L3VD9r=c-Z4$wHPmP26TY72VQi_*CTNjV-d1)ZSl9;V-ZpD5Zp>I
z_FQQ4U%XzRvXg0_1b1$gEan!(5683U!5z=hRWzbA+r$Gh{z5$beM@Uafep3$+HOIS
zX6$>uee)hsff2HNs!xdTuZ#*)>=5DiG%;WNCOC^{8HW<g2|t_=@^4X}0jVda*)BU3
zn;hKD$!^|Q*)nW7_ZI7CZX=8MOO5r1nZDAP<&Ej~j&0M53wUMoh5B%?IgYiqY)Jop
zi%I7c&o0<IHMj6yK1*6zBr_o!JPxaQm0z75xzWA{80Y%fCL259L6Nbk`TEn_yRltQ
z8lWps_esw*VmEqw8%!LNxI6(D@vDd~7zpbYcZTD;KkiI083Q88rDe%;_7!t^ojgZm
zGCk2RUsveR2~Vl^NA)f(<qBu;0TwcO+h~qOkv21+Yv)oGc$_z$;R_<31P?RcL%Da5
zhS(e<qZ6<-@)WBaWmv12ScaLzQ;yMI?;|@+#4S%cyRx{NlLnMCV1=cGSw(CC=^j0}
zlv2}kjiipvdO7v)G0$0TBY%^G{>>BQTv>tB#e(k!C5KXBCpdS6cCZ@gojO#|f}L6f
z3925qEen?O*}P^Q+BY<8M)CSDNH{gw0pJ=|G+o#G9PpK^f$5EumHQWZ@0rZ9XF!MS
zSB!7VvhFAh2BgT_s0jNv^xNbX*)yKgud0|Lc=#o|tA&3nktBgnT_1ZPRHx(eYrrZM
zZ7LrH&4#Ooln<vqLEAT@3ujv=@xJ|yd+nGjz2M2aSgKJ@%lYVCqf=6E9bfEuzJ6%d
zIwX_$2$t^Q(~i=x7QYWVe(3U<e=%)+nYFbSmXEfm+Yjsz+}2?N^buQ^10%ziJ~Q(_
zzBw^{OMfvQ6%L8cUF3FSZZyXfU^&$e-6dV}49^=rte*5hst}rj@PVhLoVuTs^4BZ0
zY&~xzop)@YFb}J;t~((&@z(L3dS3k9Dj8F+9>z6ad|y@NX=h}n)eo)3-%L&3c!XEh
ztnL1Sy~8mhK+>0|bCUx-#?fz<TxQ)NF3y!!;kZ;*(4XJ)w(hSeT5pdWSdjxh55*2g
zcc3T3I?yEF#>AC}o|z-=zzggx?y^vN`BrIA_Z>v?_j?)b+o_j9pcNb`#ya<A<aDD8
zZ^83oaGKChv*47N<PN7b!Bl&l@Ysg9Vzaba@eSVFbg0Z+B^0#Mero6RGF%pPza=lK
zE1uh75$LM?&dA_x<=6X6pb?z1JZnP={~G&Xum8DE^5V&tc!75eJ@6+s0Fi?9NnxgY
z<4j8rYV~um2wrNKcA_?!iNfJ1acJkGMX1lVT(O$G0CqY4JkOCQCvjNf#S5`6{G*t5
zq~T3c-fU}5Ck)$;oNc8P<2r3!@S7jIh4+Mr+hq^2+lxT2sFrQUgj&=t;Y-wMyj~m1
z@`9-<pP`*)@7%iv2*u8xI>13gy|YdDSz&B@xN?LjY}->?;0UG5DRAew>oy}8Le3T?
zOc`f3e#qZvH3$1s%iCZPYhy5_&ky~s4bZUzeV5E^36rbJ%+FUFl*>fki1!;k3XAOj
z7v>OudvIr6#@AVET6`I2Cb3=DTnDrbYM5sM?hSUJ;uA%WC54$o-1gjZ#_BP7N=f`z
zy`Mf!#dzLtHsPG-bj~3!#C=f|7B|RsHfM`oclPqPK|r61($bl;mE$kiM5N(;Vq9HH
zSG<-Mvor*wR2N!*-LrsVpN#$X)tJa{t_07tG-poLU!ozv%~|6YKm>{2Af!t15DjfH
zeRGt7DfR;`JlNqEu*i)Jlj?PGEa|n~54UM+h&ZsdcmwV8+{bBGIB{E%>pK94m{EXa
z2lBvlwV_Qw)F%1RH|l<L`@%?xJlzDU;(5&YR5r`W@6}EjF+LA(TR{)4fOu6b<rl?U
zPGsX{fU7U8)Y%pLa@JOlTVsSbVS}&xS<?!8pgRq9tU}i@a`(WoS{KA8k}db;b=;rX
z<J<{C1|=YCwHn0@baS9`Zr0V0vihA$=^^0hV6cGa%pDFoO0S_Ys$KHE?gLJRrsr8;
zeolj+MR`^qdxYJ@wgBMo?MNZjKlMu;29Xo`umGrj#MwV|Xoh-MavuCbcQ*ZAj(u(D
zP=$Tod)BS|JyGXicXS#DKnbW{{7J_p+cuMDKr^0C;*&up?aQ(#=qYiQQ~Ty&#kg<{
zd1IDzsO5cLxc+7-#`SrYXaQPUUs?&w0fLC);}$*L3`$6+A7TI3*qEBokZ|k@tY57v
zcfBpt?_B6Rwhl{ZGCe*!_tJ@&+GivVGX4BPv<RYP46zUs?*Hx^=S3ZjBvIbP_W9S0
zDc74eO{X}~_$M#QO{!2B3f=qXELcx5&CLaG5akssg8a?YP5iB#tVSZ%AO$)NpHFcM
z2$UMno{Fj@2MBKm1*KB`6eYjdMtjuBq}!++_57cs^iA6$ex|AxT$zbyd``8Vcd5TP
zExR86!*%IjBL2XS3k`B+cD9Px3=;ynqDa=MQF}~|dkxs){ON?p^Ha<3LVl{bOj>Nb
zzO5d8FD2zv^F)`XTY$cnXRy8|cFK7bEc!hq3O5o+R`cISr@I1sSY=q3JlDGgT%t1W
zPy2Q54;q$I44w|iLX;2Vw^A-~7sd_Y0K}Z(Th^%4kCh~qx1`P{!AEo`295gCW6R6m
zs<w_};T1U0r7CUfI3En%`K#cw$Hs-x2<}bNn(f<6LoE&H)s%dxD4?Wm06qvJ?*~{-
z;zAZbYqHHRe=!O^tpZ7>aEUA!rD_02eD(kklGu<dYybeg7-u|1ImwSZcK*V&>PGbz
zDzKH{+hOR_{=mu$(jJ(>%wB>~`Pce@Xrj#;!lJ&RzFm2ICDN!Y^V`vsxQ{A3@7u0l
zabMn@1<Rr2=8XbNn+F2vWe=zt{D+&OaezI)9er3LS%c&T7VV!{d!7nKxE=NPNd4ZZ
z(~pS<(H)1DttK8LPQTce6JZVF|9mz49<9knoLP8=N44#O!8h(M9})Bh@EEzbpy-;B
zJ3(JSNuR^APYp&-UDiwzhP@ZOX9A=pgl%`LZv8otZFF42?^sV|0ctvWKFVtyEXh^i
zCKb%tzJNudc5{n4a$57EB=Ns`n1VqsgEl1gLUKT(kpk0)i9>ipzEg->r+JNQ$%pgR
zH)sY6bMLy=fu=Ec2`7a8w+n*w7hVY^q&K&zuA;^j`U@8=Fsu)9O5)iN4hnLr8~f~S
zV;fJb0qEl|g>OzDU>@K|1(4*9$T<j-;xcSeUKl_Gp@KBHeBR<q+s$@V^ekvSr?lc!
zRYB>g4%B~haYlVLe!9~Gy=BxtfQ-8Y9{*(^F*&!)swe|`9;5N-A%UQs!FcN};#Ou9
z@h17++xY~Hc(oX$Nap9*%2WC|_M5y+&45&6Re8^|{1cm7{$&>$B<Tq)&k#PEl>{f#
zgV67xmGzC)W_@g{rK(7E#4n)r`bj!n8Ah%0v*vyZWs##w={Z>^C@%PL76tsyOBhr`
z3A2R#F{>efLRo#xz(E28826s+WZv(c7X<W*{yCm0VfI<Z9}pO9?b<z}vh@Dcx6jU&
zuyr>C^c8uesap{xIu0OKV;yFWTa>7w@=R(9_^r(?n@rj$dCzCJiNx-=l;Dm_Q!2@z
z!afI$nh^(0x8M%BEUz*1d2B}%&zp9a@4eGFhw}M-cEf4g2xYu&G5k<-T+Ux3k++|A
zuFql}t+rmKJCz<vW9BAt^OShyt2ECe7XcrH*1Xrs>M+RLJge$owlPD_+_?gFEcH>H
zbw{l4g6va9{jHf-vzf}2`k=}c`SGupe$RoNh<ikjVz+1K-eSK*?S(yK7Cmv2`9I@=
z&Um3u^9wxGGGW&HMM7`}PjdN4c>@0`P+0hs|4{1`)~Q*2wJth5sRjVY+rtO9r}s}|
zrhU)4zF_x*gP?5wLiAVW;FrxE1<3Jg%Yb*Mzf?PWea=!nOHVw_h|5oCyxNe880Me8
zleGp;UcV;$A{d5rkN-RPvjndG<2oV1C%ZK=<H$tn@6K&dm3hae*YmA6E7$mMfRflN
z`$Jx%Pu31NXkA-ek?1Qp!HUI<|GWZSXxMK}YoXB1X*VWx3%+3(@ZwQQop2i*XbBEy
zVXgbM;!R2UpTVZE-^D&K@5~fL@XadF$X)-E?-GSyr4FaoA?yP}URA(+zd@?$1L^5c
zrsCEP*|<-!9mw^i6(?}B-GN}?xpKrR@6za%UOM0Z61~N)^QnHum*&j)zBG?RD~D=b
z<#jX+Y}Lzx+Tk*NlA`2Uc%sb5e6sN1EW*@lrmYoaKtcuKGoJg6)NF^prdtf=a|q|G
z&6r!Qk7VNI>VThOFjq9{hm3d5W2RzZxPFiFrYA@L?G5=e^8P(B1_z1LjOv_G45^-d
zv>{4>lIyRO7uV*DH!9)oMEQtVyLhADpacBqP3mgz{P>8pDi4413|&R-HT2B(htfDP
ze``}-_w8y^Ns+W7{im#Sfk=vUOIvuVyXslUVO4p7zxTau{eG0KFln1%9Q64oa-TSK
zje5F3@ipt_Hrh-j8TYJvSX#BwC{lx$6?jGjqC~Y{J%MNh%ue&r6(WU!K$W2`!dKvJ
zlN^4o(8qQ!%kBaAF35viphnj>>(=&%Xmf{Fo#_@E0LCA3${sa=y0HKz*isrmr4v!<
zmRS>@-uS`r=Xu>Y<7s75c(3fBcx6WBT@o5d6~|CRuD|D>^SX;-+2toH24FcQo$;sc
z#kHu4L?El<cXZvp<o?RWvd5C7iu+ER^f6|mn(u*6H`Et*ZQKq?v9QS=HzCyZb9c-L
z)ca$*zPf21qtbut_Z#!G?&p}+wmP(gBtje-u_1vcT>?N||3iR*he*=~slKPYDw!l%
z`d*fj?502Y!0aXB{}R90YOZQ-Cx&30NU}?1jV%<&T>vI=iK2rjTQY_*szeJ*nA2U$
zdU6cjE7<+cEqyMgdlh~cJ!olR&q{A}d>3;+ppo44??u5qySKOSs#%Qzi<ArSUEn&5
zJ-0t^ei5P`UA7F2Jf?Ih*B}4qq$KNTTmPFYfl<u6xUB3e?+a-Lo(KxRoW)g#l9dR?
z*j{|&h%x>F4nY_9q$jU?(my>e2D1Ko0T-$y)JHv`9)kf55K+ke_IcH-^&ZGHvwxE@
z3Br3Lx&vb9-^;Pnee1_-v2g!Cb?4iH^oFCh8eaVoDCjQcoqxZo3D@>KP|;zz!=>Mv
zUva5*$}_9(Gv>pSrXJ*xZV_N9C#x<DR^JQvoY}4D`9g9g;SCE4HhkLXch7RV$@AEu
ziUg@V&5tvh5ql!eQUiz62KebmyUyTT|I~)<5w9L)#&p&iq?!lg(nBY+h)<gB#$wOX
znPFHbPtnmO$*B*KZIfrE#*(gSnQ?y;>L_~{r5LvK)%?X8r?`dzZnc?&rso+O$$a$>
z#~B<~?xDdwz@&W~g^1MC?{B^-89Xk#J2QC}IgtEj+^`c`b?Dfx<*VpfT$R=X$VhSO
z_lEi*L{2lLx}J3s;=Bs94w=2l-E@RbxEp`11_(ZkArWpx{P_VWj<$|qeOk&$Cq|9j
z2+zfym9FW8)E&oCG|9PJZ+&rQ7@Ix&qf2C}jnwZt4d1S$z3BJF8xfv|Y@H&~EPVza
zf_4H;3~qS2)rY-`U1wBgPi<9^qb*l3U~lq$Dql1r3flKuvy-pbQfU|YB9k+Db12d6
zWt)2F(|lO!wD?x!*y}%tZIAnVb2R*Gx-}2yyOLvABT@Ti$<<e4uktKKpP8B4D$Mhe
z5}$-IHQPC6+P(#`eyK|YJJ`wr&t6$A&wBtHL=Ar_%ZT+YpW0sC0*ncIYZMMSAXQJU
z{s#1XpDf8TJ4EE&@7Ot5rda*q+pzM@Uu2$F-3Df(&TW39F-^=6PS>?nqb8$8eBRDV
zG9U{Kk{4|V<X;Mh&02dNVKldc_fEgyCHk~J1fTKfR5SgZRk3Yy{GjxC>%WS7g(<2w
zT^vSOgzF#B!=i?8lptrlgd=A!asEHC)dm0q>f%NsnngQmqVQ}LAd;2D8{hLEqM;||
z^7jj(Br<uo{x+T$ENAjJA#q*k5<gUJcgOr1HDr05{B%+Q=r?naM)<t7ns0Kn?t5pk
zAE6c1OjD_c)MWV(o(jFEO8&e5#4Ws>fAJ;!S&u(CLIibMqFGWrY;)m+x8o6kC%L-!
zCeU49nl!T<f0EHi3g3$m5aH_3anF3!*NyzY>~4;Q%q*eArv5fGxB?;~OejGG9FKP?
z)?w*C0vai27hd|WIPSbT`5BWEX?-YXxn2V!xBP5*crF&BsI~kiMYem=eSA^t7vO8+
zu*);nzJ)Eo5nN7`$Gh=5eOcqW8a;ZIlx;(YG2gF+zo8Pyp488=bY;2Jqw8p6^fy;B
z@*ajMf)Dg`&ojtD92na}B%L<=6;a5`s0=Sebm@dasB>lQlQlWT=RzzNisoa};aZ3>
zOU4w<Gu3i6r0z+J(aoRJ_tiPM!~5pVrHJ~Ror1D!ujKB+b^P+;yN1e?RgC1-_dCMr
zN60~=6ceF$0`>@MBLPRxO*f?1`p$d11CS&jt+X)l#w+2syp}|^0(Oz(020}}I435W
zVpsM5Ha1rFzF=N!12v)EuBEF*Xmmy_?5v??gHCnh)W<^5W|`L5m{Kf3&bncu3tu`p
zprIoxj4!w0J>5t{(q3skmdv)U`ta^#KIzIacRjtQ>=_BP$)XL!X|L8(b#d;Ym1V2!
zp~06?f?G<bDy;*y%cTN#<}sr+4+T2)lGG#poo1f%i^j!FQ!2D2U9(U4Wuqf@t^Wrt
zCxga(JNOP8w-KVpx`#pM-MTLdWa+0kE^_UY_l|AE0w(pX&*Cr9Zk;q*XlX_Mn0t9{
z`aHepf#z(~r2nJX5@DMW@PnkkB2f?NQt?>KjgYbOeep`N3+L9>z)zQkv%wa?jh{!=
z5qBF?Koxx}{c1&*MB~t<m09L}x^-4P8GtO^nbZs%#QOk)zLH8W03F}GJNbDq{oqai
z)Wcm}+>=4`l#e!jg~sYS#pAUU101pOiA^=2T9>oZUv1xC?Yf_(i!saXb_o3yHArot
zxSeTfDeq@nezC1PyY)o`AVM$}ew}r*?G{H-hMfgcW4qNkf2sXQp>++zd+eS{p1UZ{
ztByEr2`^2t4lGUii|gR}3?~M@AZ6j>t4u?UwS>!GPmGSV;L&+QjjhNR2nd>Fs%xu4
zs<g}3V|9yaUE`Ro##qr!Vd+gF;m32fQMGwj85Y3%@GJkC)<>6_CwWHowegikWL;Le
zvik{cvuWR6JI}R`J9z^rqIiqRhd(@bFD=!o$i$G;r!J{H6(xivo_7WzHZu2EM;vNt
za*&tR|A@O@;ISw0vDa}8@sSH@V(%0sSch2iJpJj9LI985mrMTt(Fk|Za~mCT@E<{f
z_-|0g0%$W(O<uGgX+sX6{0I=i9P$N>2@+l4ox@!cO(|Im1WK_q*U0O=FDPG<t}=&A
z6Z+iC2n6Lv-6iO6%F6TDx!d7^f_UrT?F%%*cSCowc|1B;06X-Xpn}c3D6%40p9VDF
z)|0q_Uuytd_g+8U;eo!}EV-5Rr3aC>u$`{WRj7Q<{WybPC8IG~b&vT=%grt0qaNxc
zQHp0v`B#<YLnOHcyZ^+NWwhR=(2)C`kIRj;xSrQ2K8DLs$|Z8>Qt3D^u3+h9PUz;)
zEIOYA866FfYHxPl)OX%FP}-a*Xs-TTEq0cku4i2slFRu>jE4l9u#mRZd{e1t=dPj!
zmZ|!X-PT|}uJh3qV6%G)<@a9kq?KRm%k*{^NIgIhn`pCEyB&;y_uV9)#At=c);u@b
zn%_kR`Nftwb=%HKOHtb6uRp`FA6{4RB)d&$_!6$beDOCK3H28zyC;;ZrWabQrq#!a
z5|z|CE`ThvJoF6U&zFNoCJ!t9ST+A#j?({nHL~C!=+;3H27k%(>xJ8%e$&%YRR2H%
z2hoDqGW4W(1XW;MyB8?LLdx9&!%5Ws^j6e<Ayf53gGram-_Jx1O4?j5^N*A@JYZI5
z{h8Y0>EXI-L~pO1JDZJ~AHz$gD|tcpT%{PX9tAf!!w!7mQ3mYc{Z(Zynt_GEMz7mQ
zk3|~1F(D?d-@$s+c)YgtU#TBqJ+pnXkwn?*BYg(kx(nKQJ(SsCPx}di@fZLldiQO3
zgpI9Vg+T{`d_AA#QBXn4W_Rppn)+ZG5~5+2t^-7kkRN@4M*IDFZFF7@QHd{g&ZY#t
zQE@V<spDW*;!s!V@zuG`_#;#WwCnUG+K4neP33!5@DU?#sd4+QtoD<>8^Nlyv(_g3
zovVM<zrZU`OLxfA{3~`85ceVUw70<DPghv^h0mZ1#4YIWZYlZ(V5Qc2)eM%l6Oa!{
z@i^>q*<QNx3@fq-8TB|eT!&t|(Q@mu<0*D^YL!j7ABg;Kz7j$Y5$bPajs;y;0A85i
zWqre7+Hi72qy8mub?*~7j5}Nc)@4IFt`EC`uPwBvj3K(-PcH{3Vv>mR%)3FVz1aFm
zdNeu5o)b8jR9VsZ^}8{DLeHAr_cLql53<-+f-)ZI%1^D&63o^MHpKgw@}Vf7GiJbC
z&0^Xmq5Mwa8jmbD{%fhl9oMcQ|BVT_tWo9gQRe-L1@uiXK*2~zdI|$OX3V{mVzblC
zdjsid|1q^{@=i`~A9#_M_%N09{haDgrLKhC5|hC-zM#V>4ZgWxLYJB%7XwcHL(vfr
z?Ec?eto&}_q1i{lf+XpG!dp=yx;XY}!;SZ1zF9qwB2KTh%xc6+#!WhjG;p>7mu3iN
z{ww6Ts9a%~*GVde;SelB9BSL)UD7mA_OX7H!G@=X)Du}E%8bSCR_UN!P3DV4iB*6J
zH2)1rL|@QFUm!I9rD&Ad#Bul!Ob2&~AW1iKBS?aKXxn8_iRFalI<V~ewrD)dT>|x7
zQL7-LT<bG*c`6Ht=R(nH=~7I*ZFap^+~d=S(%j{@Bxr=gCY{Yn{wOj}rBN;|NP%;*
zya(~ms-16c<+dD4k!CN@nWnAaA<H>`LMT>PwgW39Y$KMs(*^W&r|NNkE<_MUWmcwR
zg!8(A!szHc#NUi_>aadp@{jxK#_BoZo?q^u|CU?fAZI0oZE;>T0GbXXp#IC}VYk5&
z*_Se@^lqV#pfxxDEpXvW9(4FoC`&L@%xXltQ^G4mJD|O0RNl|}rpqi#Ih@{r=Xmw_
za;sK>qP~y5uLTxUot|?;-*_4~$Z2a#iK?#4ndrQ~A41!oUe&1&wbfru+Rr^is9Z(2
zH=2eW+Q(hd_zDYEpsWe`HM8pVWm#uWCwZuLtLH?!@H5YmLm(&2`r<<RO9sthvqx$`
z-T|yFCnzQEvwM$iI5vXupPS$)Rv~dj@PpL;)7BZ*$h`zFf`3Xve4=Uabk`LcOpcx^
zXi^rc?q=BQ23xsM)aZ^a1^C3q#B9I_LEjOd8DrHPQ<O)l>>D`M(u0ibG!B>ZEM_|#
ztMwRcE9k}RVIqt2JHpU9ZAfdPGNseN_t@DPQ2twH!9ZcmHUpr@NvoD;ma=KfuY|Lh
z4UD<Z8wbE;H0LX%&ARq?_!m={{X8?%y3cqg7L#XNPjt|%n~H>)3*3NhOn1*7eZN<=
z5{95Oxp#*9@{@l2vCfNHST5w{^)O2ZBD^HI&(_<--%2i;^k&QMu%03JuQaLf19AfV
zms;egq@v|cn9{WH2_6P+_|S#iY-xdhMs`HG_AOMtZXp{4UO%e~b(!8`?fOJ@A%Dg@
zp6ZtHarZfec5x!QFVcQ^sFK1<A^V<)6+Pkq^9s6G_HK@HuvyCk%kCXilSyRe`D&-f
zG8BlvcGqU;z2KXgH@ZU~YhDCXt0`k+t2J-GS(_+I9cQr2cls@<_ie{kcyez^7ws%3
zdWtN$xrn|+7k(;@#<Ed}hNGDm`9#zgbD=KgY#v%f6b;ejS?a%voEg_`&%TQ4{7DPy
zIV<yxag+STe{4d<#JFBEKr(2_>zKPuWW<#so^OItY4AoldT-iV(ON<G|3Xv&Z#E^L
z3O}84^Ge>EnXM>6NRjK#iaDj_@oKrlOepDpHc$APbm+LxpEF(Q?I?q;|LKUg>s0Mc
z-c{OqY*E!mLoQb&D|$Rb(C@t6B8rMg^Y11{F&8FU2CgtyOeWeNp)PKt*IU;BOv8>x
z$&!Mi-DuIK+1;#Fe4~J5k|WgS9$M`e_EZ%dAP;g}=qemJQPF3K8WM{sKptVuy?fY=
zv@LOnzX3^!9Wdxe*7kzHH}2JJSiTTCkbBPB(e2j(b8&j%Pqu#iXRElkj8YRA7Or3?
zndklxPH^M$uL;hYxwIiSEg$i(-X&z~D~OWzBLS}_6ScX8CkJ~YBl4Zf*iE~cg`;)E
zt=s?ko%}WBFo0tXva2DaQ1yRVw~=$UD_F`=za?uog4aD3)9~LF#2ixu8mt2>LsBq)
z8TJKHm!jEo#DvY!<v!E}TAfU2>o2Je+jW~P2;SUhuoY>S=e`Y3YN))z8~<b#TbBYV
zX?F%-`fa0~U-CEgji7A1!1SQf=|q{Ec}nV+bB<zawK<08xqE>rJ5h-;qxj*gwJl}W
zaPKT2oq?UO&Oobampl6Q6paHD*pJOr>v}KIyunb`(IyA3Dv)Ih&On~Qo-#hbzN`F1
zHeRu<sSPdY#;p}^qB;`W6Kkd3z6xKnSl)v9>Sf22z=vE#<1w;;ENuEXh2P}dx1RfS
zPJNtU+NT+DuPEg&IjnI5wN}e@=3HT?zdWs#ZR9m_X#In2qRSP->0U(TGzjCV`g-|Y
zjAdU-Z%}j#&j|T01d-f>`$qruI{-Sw!O^h<H%<RkWIAtAkMG^6xt<9BNZd{($u@fp
z61#Vf_H+3!Z_RWXq*8|{Z?n%2T6PTmC>12vtyJ){o2L&&6pR;T1a0B}vaMim$zJRn
z#B!+&bRydbDA-;B&YF%PuEx1@KBIT>6`!+|g<HxxoA>JG-hy^Cn<+bna?iGO&hd%<
z(Gzml@#(v07k?y}ncy-O;>PN_ci6x9hnEgjNzxLVpiceMSQfF!&18se9Y6}?{#7gX
z%agz@3@*$$gOv#b*IPmsl}8QTSoeBM22J?zBU!u!qj+a;_YC-CQ*Gz%w~3uYn7*V=
z`S}UmoLM>+Yd?5ryxQgc0Z8iVJCY&fyWVIJ?$@dh0m!)W)W-GpwNlbmICd{Sy#Z+I
zH)CI~PVs3iN%m<=G1?IGYqj_m<>xu>CaIj;*hRmFnQ=pjHYf1nU$!SAYEnR9IVnr7
z`rP5Ak2YUA7O+bkrg!HfzvVCTa?)GpRbR7oc6Kdn5?SUfon)KEm<M%RjBEaU;xnS^
zBH#k(V8!=E9SXGFiu)(-8FbF39Ed>Nlkz@1;CX#S<1n?Mq2x#AO*dTTjnRwSq}IpG
zPmLQbQAh84)j-|$NMxW>1y(!Z=((A}V!o>BOR?jE8bgW8Ou#F%Guh&-*gMCbXlJz=
zh<Ii(2Z@IQ&$=IKrq&YMRk43XfuK-<x~?Fn*2nv~Fl?+oM4%YnA|@>Ci6H)OB;r4Q
zv-{uhG``@ZE_{K#<2<QT4VV>Hb>)bxQ=(I&?WkauomZz|284`(OAHWSicmtc=#Lkh
zdfFvF1m0Bz))yEadnm|hF{A3GYJ?U^bf8K*VY;l@g;o(tP^R2n2QYT#%PDn6F==uo
zNy2KfTqn=YTN|845VZn`AZaqOhj1;8E8mx;OsDbm$D&w@;@UJq)523B5wS=Dw<AB5
zmxg1bqhWd6jim`Up*h2lJ-e%>7j^SKLlZacpm9rYT4#XzS9DWJybFbaO8TF8L1_y;
z+a~d$6>;`4`!Kfc<>@Qq?J2X43oz9o+!$^9bEUdSHMsTBiaRQt2t|LZunda6_u0eU
zq)XB8CssB`Y2xBUK*z)#jXCr#<{ssaeC@G!-CgZ>ZB_QXDSs{`#lVdA3`i}lMOAOj
zS|4DjpfMxss<47<k)WW9N2W*S4EEC#K?l3)>g<u&=t_p9#N~=-FL~@Qwv`We<emW`
zHi^m{Px8{K>OmKRhM$o;Pt*f;$P(7DS!Zj`(-k1A=cvtieE5i8?!y;3hXvPvJOJO`
z$Xm6W56Sy|yqzr}%=rx(6;}<v=xM&V9Xf0DqTX_1N>xO**k-Y;C%#3&EVdgu<dRLW
z;ix*YR_tL@asTjp`u<4yi9V;gDZP@Jxb4&0*Q{23lrAQ3q)t~{=OtcX(waUy37GKm
znAu?@=Et6Old)ZQjBmywpT~juOj{4_RYQxhU>)c4acrRbyx77YW^*yzE{E~R8<qiT
zha-u9d!mTTJ|HwmXTGQvY4APil8fO3z_2aHgZ<fy%>2A@WSNv~^=ZfffbVs-!uQ#l
zLK6`@amtD$6;e}rLnUteT8-K_wlVdXr|VbVKg)~Q&UoFGuNih5deObw>zUn`zk>9D
zpVIFJ&7AsS#YzwvM0NvT?EKEO@#{QqdT@pIk9B+Wwe^#02lqi!U?-V%mW?EKZLF;V
zweUi-yTO3}a`KnZNt29?mwSPd5}QqdiYuKf^(7J5@~{a*@W=Si$n8J30!8v$)>L@y
z#GcRYWa1olQ8Q~tRjVNAwZ|h0%IpK&j^I6uJx|;nEXUd|U=}Q;4nUJFOH2-)CVj9v
zQz!GYMPl>bRs6jSG&xj%J9_pq=Vh+lje=PAQETDkX&gt(Oq62GTk$)yvuo_Q<7LEJ
zo}0x0D#>u(sje7wkY@>ooOB6}iWwzq1VjIT4uew$9n=8E5^$s6BIa=oe7E(4&<yaw
zTr@@=_xV4Pt~(y;$NzsyQJJMe#z}+7tZc3&mDNJ#iBQ>O9PTb8n`DL~A!TNr?Ub2y
zN%lDV%scM5b8ftU@4ml(+~eVadptbeuX~N>$^agS@iT9$C|->Y{&R^_Mx*yO=UcD*
zZrwy6`?RwL5Elk4YXQ?00l5@NgCkMOxmEOhXJ{)56#}%|f%gNIvCH0Prrv=^&8r+g
zK~dV6A{jqC57q4=RE;Z66L55XqE>5q0uSRXYha8RGk;|k{@vX>mO-a{(54m=x+#XW
z3~~vadW4UFFr2Y|?lX&h7y{T@Hh0oJydQpRUS!tlk<_>F#RX@nv{&P00<_8TA1GV%
z_mPk;?YDotpsyvVF$+2m7F%Rsqeh|I!C71<*5%7R{MZy#d_E51)7%P&8-*~)mK})u
zR*&&^a9%e|i&yzt0ON9bYRlOmS3T31$*pfOeaO9LO9$JgthN|OH96M@I8B1~(|Fa1
z2S_k4Bl{lrg63x#MuRcovv6^b<@FF&sCZ!x1o79KZ^rptb^qo=$AyGMBVDgz#5TzA
zEL^wR1k)zhCqq4cnWIHjKap&|AkHP$21U|B54seDH|vf4mZS=<YP(&#QCV@9s(Nl5
zh_$zDQZU%csB%<`rcN{N7j$S`+JDMCTu~mIlM-3FA>AVAb!6P-iHvCTBh?K5fKd)7
zXhTzvE~Ubade&>??SiZ+%!8b6_aYN7Z53VaXdNGXk#q+s7~dA5(k0n4hNuiye#x=e
zHeJ{wK_LdDoz)F9;hS#|_Fu(2By{=I=`Y@8s*9~5O4<uRak3KuTi%1*cBmrQ4%0>%
z7dvl4=%x08$ifecpp{@D#&KgE?O{TF8N(w+0c$@Xe(R{z$VL-7l6jPa+=OPm?q4>$
z2T!1ZAXMp^<dxJ+){;{d4smm7?`<h~`@1hXo<>&0mTE8f#y!zJGOr%pG?qy8`$@@L
z<DGc7?O=a%Nl*9>G;c?8$($_n@&gJ}s4Lfa<@}s(r`K=)z3Qn=KQ<R;tN>^jE@S(k
zbtut}oi45=W~^h&k1amCFClzVKv3;({YIfH=aQ1FkCAqHWx0xp<&=HccT*9o^P-D9
zzfv7c(w?Yo<ex`lW<LGq%+fe5>U47G^$dgme*3XGodoeBqFBwtU8g6XG+*4H*p5#i
zq9&Sc0&EOoN?qJ&x~eX_u`3SEo}j6R$p!|L`Tx}=hxqz=58M&G1#H=xsy->X1HBKj
zoK`Fpg9a~8hM2tHd>@=u&b;|l%b0zm<$wloFP7_>*j*_47R#6THZ0xe&S(!mFY%#!
zSdbg0qAP98f+YW4i~WUJsJUgo)D})oig7vP`qCy3V_GJ_k&JTGKEgCNb)m?f$pV(m
z{_amH4XP~^JIBzSE@WJq8;V-J4TW?Cwq&goTn2gC4Gz~QLZ0!>ze&7h_n}+*`~6r@
z_8RS&E+oLdRiS9Nn4{lua|`CNjbGVb*o5zS3`%Veg&H&ct6t~b7TX#b_sKe7;S#s2
zYNxWt)1fZOd}pU|Qe^85Q<@qE%sXFUs<~z0wfeye0T)A$9mbLZKx)4^Q~&bckLk`*
zdZ5L9OCm5|wWM(_TVtb3Yu3haqUj#fe3q#e*!_S2RnOkT7as);;!DRS8I7P=DFURo
z8)N`qP4ej=MEc-4^shMMILU`yY_jYZe}4}*i8u$`D~{UDUAD}@J31l?dc2F>Ud%YS
zyt2Gpu29WV>GY2ac)xvODD5MwE5UzaH4ig3cFx67zu2_!vA7Xa?Y;M940i?NQ+Hqp
z<8E8$W8$UtyTM;8@g{z)n{^v1qbg+%0VWgPXWIX(>1^Vt#s;$mm;Bmzi*WKOahHT>
z*Sh&0liba1d|`Q~F1U&1+7<^YA@5!ms8{USl<~Hb1%Xy+J%-51fwQjO($Cr+R<Fu@
z>2NtQDRJ`8cM{_sVF8)OEOaRQRV{Q+*;Zh@Jn1g(2&!omC6^;-cE+v8s{NPVHK$_L
zkNrbhj&YdYmZ=vI&2N7%@=cTxY>4ari#sp3wi{ZOGlONVCYI8j8*{L@-_EjIIq^$f
ztC=L1qIsVr%AUg_QmeiUYryk$J(7bHAMdf;Jk;e=zI4Cu$`9)^4)Ne!QMbI^z2%lK
z-S-&}zXK+w<J_L@)3B#9B(DgJR*LQC;MS+d_s&=LX$7v6E2S!MS1y8gV<p)0>whlE
zDb<r=;?j$s*RP50bOBWVS8n#iJEF)JAA9^(x?+s%3_YG-HUGdB+ARpuPk&?9RnJw3
zE}D>qx0e=7&2x>Fult^y@U3?~nNtvFf0xABt5Mx^rw0>DGPke31>31WQ+snrHO{*T
z^C<c;Sx1K_#_5f}CVfo(B5S(5jdFNB@L-q&)fb5p{UpEo@FQhT0<9PL$UKEN8qBDt
z^RH?&#zjwoob)Hj_F6(sqbc*(DnHtgbJF80WJK65(@FNNe!Xes-9o^ivl1#He{<r_
z$f4gF6_SV~k>1@B;eD>O(TR_RB~{AOCHy|eqHDPzPxAcF0+Oen+ls66;r|1aQNi)9
zI01SDAN!od-B=|VC2-T8$ZC(>f|*P+X73N{xVY{mT5W_@f$OwCvBpBQ|MZjU@yd72
z<bqwIe%dfd3RpBLE<Omn)WR0o>el^Oj^cR9)d?AbK!Dit>@a*PB;jY_!X*AOhtnSa
z1&~V6+Kp1Wy9hn0ae<5nMI46!J+QL>c(o2N{XbJ6>-JMEU&8=*G`3yjJJ`61U8wIC
z8Gv1Fx7T-2GtjVr?M9*kFU7FV6Ei_$a<(8?sakd$iZLvEEVTVf_c9b^dkNg(g>saY
z%?a3_H&Og6@NIn4=QwKiphG*VtsUip2GR-qY=?M4H|lW*Y5@6xaCjR+KbUCp+cjPB
zKK&nD=q&TUB^?x^*HXuP?|%nJoUsJz3=iX;S8Rj<6GRl+_sJQr+L{zfYcOA0I#nN}
zxwuxD`WoGTtl{qRdsiM)IpSKhMuGwsNoeJU-|u!6QkqMJ_`kN?sRy~KUG3ol+gf#s
z&vXd7^TCz7Cs++!yC;F&&p1x&v3cDbDzwvg5S6Z6BIv`tStWFOJg#=LO2i-(YJn5h
zWX1%X616_BwY+)egqLJmFw{$OL#nUqsSUm%6ycls5tN$|rX@(A6U?~d1~v2eXv3cM
z=KuwR>d8aYn8~tm+?@ZNAKr|6S)<2AHv}kT;&)a0+DPjM`}uh%dc|q_Rr<B%89N)(
zeZv;@u8oV^!{#kbLj&sdt)2X1uI;zq(SEWqzidgVBvnY&;9SyUFberslLO`%?}u(w
z$f^KRYpAS*HA@*{7Op27&v^ghe}IP|US!7U6ipca0`c$tJIy+p_u#p=d1RnTir6C8
z<9lYf4|oe%NWD$dO~Wlvgx7VO=^?t{)~-4EHKMxqmFV);Ilg`|WZ)@<eM#2`Y<pru
znsWw=IO+sJlKiwPP~p5JtK$7bq?F(Tz%%}2{+*eJ%PcvMLuKdi=i&NrsU!)#jDhf=
zOCFc_;Y}i!avmw(@Y>L#r)sE(IH-_qv^s(d{AM&d{5ljfH<8N_OGnHjXejx?*M<wb
z=W}k|{Tw{#)GM5C?{boM?OuPbcp8}-(sL2sq!U944z{plB%dy*{P^%g=hVR&j|+*D
zC9aI{@1}P#y(wZ2{9)~W9}+n3-1muMHSu5l<JFYvXS;vkkMwU)wry(pgXlCqWk$Ni
z!KF#7w{$~1uByuM`CL{gE_P>!u5GGoUUXB{z!#&ReT6ZXAiEhxx>Q|3Qm9EptUbX#
z7Qfzdpe&E2+P6JKH0%<dKdaLw%)fl#*!EiiXzN9#J9SVpV$Z~i9{=M$g`V%55W=<j
zeNdk;35WF5HcDK{thmARw52QcZ*An^^(BXw+-1MOX72W0FXg@vqrdzp;sC-{n7*`9
zxIIp6ybuiESs%nT;?p950G_){A;!VaYG?Sx9Nu_f#MR6ALCd16fX>B>(M&Z?ww3T6
zDZV3S<FwaFrhn!hKPT8G^~D6(=Qz79T?SA}7T_h%TNIQz0l8&+q=b7|C;^*RwWk=x
z0;TYEko%w#mAdYZPS{X}tjwH}V`>CwKk=p}**7ZAi|ER3YqRKgs7V3r?osM=rN$h#
zLiG-l<}JjeMT5j8Xd>{%Y0smPaMZqCIp_JF30-FS(hh$GC==Hb04mBvk5L$d)ZVOt
zM-FIL)rISp!NH0fwMF$aW~!?a^I@ZAnJlteir>VFud120(#>7}o>%TB``c=VsE?bu
z%i7}ow;)TW*pD?{bH3%D(?T17Zz(Yq=4Icva1#*iq{MHgc|d?I)y6g1s(VBIsV6f_
zhy5)cV@C-%xeu&<k7EDEr3&2&37^ASw8V?PTh>B;kEtqch==#}f4JvyP$rQ*Z0jf5
zITvW6N*0z4x}~kb4KpH29U*&j)yd1C4;16<HtbvQ>2<Z%Td^PIlM?RiY|b<01y|Z`
zi|&7rCr?uPBm(|!2ti$F8ZP*>$ICv>3EKIONMC2VW(BZ6@G4L&1GX(8HFLs2VQ*%3
zNDvbLoP<7516j}kDWzN6ZY}*Oie0Fxi&qhm#%HSgb=ej2)Jzvqw~6&4{=ch@F&eYr
z{3Vh0$KwKDcc3?E3lF_G4`0;llv3j2=Q+Y3*!Z2yixXQuD`7J2y6RNbC;^hag=9Ct
z2&(qYxSn&lZ2j1Q0-8oQ<HVuN!BrNckzl!*XNbHTRlTT<5Nskz(sr6^_30{Oq9cpK
zvW%NeFBd^iid?+0mWzG8P2jyM8`Q0p$E{gI)fPYHKB|L}tYe2<xgCpcV<E)pqRJ;y
zlVK`|uuEOR3ioqpDrLA&JhXxf1M81|!JVPwQj4_iF{R{62b+5Ysi9o8UKCOu)El+j
z?n|8Y;raOnt^U0qT=#m@fa{09E%5I4rxGa9o0Q#Lv#%<uKH=kT{uwsZg1{i4+f8+C
z%T0A(Y5CiU+Ne>^b@ao5Y4h&F9p}FC0QdL80<aV0hgF9t;q=cUTsL9YNumJ)3*p6%
z4qKP1ZLXg2(M!-mh9P#QPWj;<lbsJ&`t0i(6t#y8mqx)(aa`OW@W`OFu$Zak(ambQ
z=?}HMmEv<8U+9XB;&sKlbF)zUHy*ym!hcVJbiVVT^iWBhBiNPh9b}d@+-L$34Vpic
z+4Le;N78n`c#mYZEy$DJkE+!G!fWRKF#SIX+coeCgW!3A18Ou9ynuUwrW@NRDTZSL
zUH!KvM42iyo`8=F;A3m=SZ4tN#z%qUx8iUg2xq#2Tah2fz((k=3|8Ia&Pd>0G%!-K
z>leJ|QM8w!kqw(}>A+K}D}Ph9YD#`mTCZ-yTxl1~f+eX|pR_?|l;gz%PSBlq8$uV@
z6b<&8mFdNY;8prZ%@>S?Ur=pn*~?1a`9j}DQI>6SyH%_QV;Vx+W16)kT>hX~meTKb
zoMV=8$*Qz+Zp?B5c@BS;?(Ttidoo_a9sG4Yi}AkaBBk-SzEW-4+8eIX@vkkzs>CsQ
zYDf~8Z_r}(lK*STK$07E+lef<-Y&gU%jbryd{${Rpk$978(F`<ab~pcdsOAp!-2qu
znnFvX_uR2nOH?r{<_O65Dmy<(l!|}f-F2*|OJ!uZc2A0Kf&U_`)Q?2(DH`l4jI5Ep
zDjWD0BGJd`S-RMc4xKRIoWfn^I}M5K6cn@VntV&@wuQ7CCW^I)W2jCZX?B=!_6dsQ
z;CJ6`c@n!_fKcD_x~Hr8U7~9ctM8R%W{8Kex()VWeSi^)V<h*C*P=1w_dQd;ar4X#
zi3(Yt+24xdeEIlu);EKX>BYnKJxq2)J{cyO&+3j$=S?SR_^E!gKDl~k1Z}6gaH-1F
zV=ro)UN|~-PkKXTpX*8vH*5b{W0q}`OXq>(G{H7FGFRdFjIa6pwEx$0I>8F<G3n+<
zF23b|M{}=DL`)*s@Eq8q-Fy4Hi2ZX*)VyBNjdK2UBO52oqyN^|U#>I?$>AC4fo==h
zm&XqFd`)P6AVRKFS&Bl6Jo<{&3wNpN^|4bUrbHslSFQ-d`Rkh>SavSoPW<8{5=1?H
z3a)4+LUi$1E$I%aEbpc(%5UcwWJL%Iov6vJ7*@R+b-W{F>jKvI*1ANmLrG-zuIy5x
zW6=Xc8&|*G8Y90sINgujdQjP}e&b%=sC<<C?~8__TmN}YU8jqUaGj0+kJf>F^N_=?
zMdF%joq1^tVmbO*iIP|A4=z&+ci1p2VPF)Ht0^qZY;Jg#({HZl^>Wwt+hFS7zgmC>
zM-M&_B+*LC-vu9It~-ebU`e2_S5oC<$Q-sWz%EMlrm(;df%e2do2}EOyazK|Wo(os
z*EyW+-&i=@$QIyt-17+C^YHqKGH8d}%Ls2_4jW`0@JM~Z)q)@fJ6|7p*XTDO=3LqR
z=KGmGPHgV~?ppuu&|C}+Ac{2$`aghI;f)A8>=-fV--QU_u#td|-D7D-MC=$g*kgCn
z6S|QynXKQ7@*VTon*?k{oA<_W&ARKc%UHc&{U#HpnlHl)g&X=e<DQ_~?Uw<h3B);y
z9w-us8^bLic|k6|2>Tp+^eKT^i@^+c?UlJ4tANfU$0!|oPNSe0d_bdg%sb)z{*yC4
z#6i9zz#t*C<!T5^^W+4g<B)85EbViGnqyiE@`klw`?Z~+Pq^`*ckZBOr*XWjzGd3!
zvMItVLWGi7CNy-ao9O-l`;7f3)M>ozE#=sAm$8QAI}M{Qj<hCmy`S;=)LV(Po6s}&
zsN&D$@ZFqPamV1Bn1*eU5Y~yP!D!k7w)IUS<3;Apy)R{N@zJ_+1JYGSjOsbJG${TH
zaLej6<*|U;5^bLUB~JDa1y&fS<q|y;T(!0dJaT~OdVx7D%KBzeblIhawmk@>N*-k>
z#tCdVT+ae3Sz3gl=+%eHI+2VDqO2F_vs>=Wg`&RNspo}~f7mOp>B68MM)fUKWQFZ?
zw;sjzFH&7id9~8nXO*v`QojVIl4Uo3rrVJ(EO@0xV@W|%8?crK3MgBrHvVr{MlSRX
z1}z}M{VeR;c6MJ;Xding!U7Y{qVg$6Na)f($tX>G#98R`oP?j*N)bE=eQ&-?HOgJC
zHT<#iRXCdm?%4ef+n_a#@vFnqB7(TFIQe}Ys}!K(08_U!DaqrjRx73=3zY8!e;YU?
z<UZ{DOb>7Vu`l^qPkGHdc_|~pckvMVA1Qg6iK9YYcWDEW3ya#SYo)SeTg;R(D8Unx
zn-)<zK9%+ovaJ3LdWhoYI;gWM_tj04v#u=BFXJLC;e6k<=Fx?n>ZLwKrJyyvDiq*X
zfY$PgS=y2Rt5x=3=93Am87;|sf+KK4vf#VIkqv$A2S`0#&KXfp&y+GDu0Ilbu3M7I
zd1brTj#T%rnmRkg_tq++3c9n=j;dB{w|n4D7<;tr)%T8%d$1JwuiVVhm+mhvsYvRI
z!6X=DeP~Yq8=L;4VN!sv5XXl5<-5;Y)lV-fr*mSWcw~q-lzt0>N<km{(pHqT?=sZw
zS*zA*h4prn|8tO&!pRXm`2I@fNew<W=+5BJ6aqV5%L!eIlRat+rbz{H-bHL~7zo9H
zWOgR}(`EJo{cN^lFa`vAo)#ZwTYazZp3(AaiW3BkPJyoWSK3gHAwU2QoG?jWcY>8}
zF}s9wiS{T;{_ClK-YJQ!N^p1pU;co64@KFM^~^2UtrN7o>9iJ<kuRpDY`Tk4`GwT8
zula45VKxn~9d{l`l!rcYtWsP0YN@J)Vf+l(p`E~KOg>b&lwjWwdlNg|{kA<yVAL~~
z-+2Z<2wHM+NxRqNF!Kkq;p4$&@O^$lZF&XrxXbn5*mNrAewv-Lo#t4?)7uf#KU2e1
zllC+`<lY?+>|D=pzQ^EIwVH^1Uc2q#?orYBP|O*;Txgyg670a8@7d$T+{Me^B{Vz`
zZah0;a57>0#`wIhI3e!?B~VwUZ}jZ1Lgff91a-A`xFQ~qAY31LdhX)gzStTIgQW%4
zn1aZN@5?V*n9C~Th9hG1{esC2mLRw5{QFH+V!D#?TUx9MA7VQUMcs=J_R)ty|Ad8_
zkOW4PjIHb}ch0^Q6hL&ngF|w+fZ&bgv#0#Xv!&GO-i*!hUu5}%DgC7&C3$Hvsau(}
zvU!v<G~DiUdf}?aul-XcLY)C6IY7yv?$wGhc-A%Hx!0XRKj1s`T?}j+(zVDt<2YNl
z!;pr(^(VUhoJVvXqwm=*<#;0A=SZ8Zo>>!Vf9LZ=s374i<(4w^N1B3cE!1X|xUQ{Y
z()w;<3vD6z&P#Sn6!EtAiG;RsiRN(!NK<Ul<#xQdp2az6!FobIeK(nOTSD${^H-OL
z+0%Dqnv+T;brSLC?vK{ZSL7rdd&$I?odg?P2;uYdb91^Z_mMM4tuD2Nq7Sck$m1>w
z3bvn?4!uy(zjOBH4u3wsB#WcjW0uGnMSt#Vi*!OXpAV{hsG3c0cPhKeSg6_cV_S&2
z1q1!5lWFjr%nl#<5g7dl>{fG#@IkNE-x7w{>&%#R7%2ztyb1^{M>`o<f487tRFYD@
z*_p|+Zezc|S%T?B{eGb+haZr0;nS@t>q!;hG8{P;&(od$o(C{B_5EiJa%teGAgJrG
z3x;NNo>q8o&!aTW$HVax#r4l+QAHB*7hKywJDJY^%uA2`JDKht2fzwxPT#`Y^rpVv
z!hd!g0BtfhLDGftzb0qavjy|xc|bFw!2GvCaHW%7hVU&yb&m5F&=Y`j;1v4*y0V?-
zbBur0q#Ys5@L>=6NI9L!5yu_hwtkoaQnB?ftM;P`HG!pxzfB;Z+JOSQ+P|-Usc&gn
z9jGh4{$k&!)j&cAEmy4#nFx-xxuAnAyeNzgA~(SBVRq8UwQEI&#BJf(iDf6)sGD!W
zc#`5r#GIg`-~9H4)X9hjvntoxp3ELQVVCgo!h{_42QLRuu1P8)Cj$hGUyGWc6o|R6
zM+U_H$s%>i#t8e4?vdf^&-r`$$<IaY)Jav9=G5mVNwm1LU$}s0Vbf*G(BW*SZ8T^V
z5}3ap47wYPC9sD?mSZMwqxbk7C)Zf*_0{%)QsZmYDylm7@$yG96)qq{ODUr4dy~SH
z-mNL56jcCIw&=4R?1~RRK+k5H477oS*L_s=(II_4&fD$BqIgwEl3q9K{dG*HZ%y`-
z6H6GCn4iJMv0XXJj8#+y8u995v-6e$4EDBu>IIIUt^M7uD<ENj*+rinZL7JFFU{5g
z?OhoppX{bCrug?mqkKu-CW8BE6VkYGdsItBe>%$k99?FcHqcVWS4Jm1BsX?k6@l+(
zzAr1#b{!u1XaeUfQ%EM+rh8Cn_ndMXJ*%2ghW8@59He(7;N~Cw)5fKd23ZP=+%w;3
zhyT{Uhy7&SwkE;vYzUG0n7BrMdK)ErfOoZHYTj3MU#8BZ5y!nhZ9p5d@z3Z_R}7LO
zhFyuQKb6_TjlH!L5=?abk2>Tbg}uUc5}T&#j=a*;?h(&SlkZW%wD(q*(MsE!!AU`3
z;}{`yEbX;B^b)!<t3?59cHg5I+?g_ykqwL3%bd5Z7v=*Ir0M;E4+%$8sSejS+fX})
zc_ydlTfaLUap4VOdeo|azlYabO5BYZQG4q$`l_+W68<u2JPf4<N|(*X-~I?s^$Ga6
zwByLDp`63y;z^%_(jm;_$U{H;V@(`3pT&QSQH|y8G})$K96vVC+o#;w#983ivsFV)
zk~45!=B(;SDJ2)j9&oR`aH{kuhiV<n_Q5$$!~Y3IK@v0wXc%0PY(eewss9%){LfwH
zz27WH92c=jT=>rFtILrT@NSw58fBd4+;7|?!M#!`_<Pcmlo+A$F#C&pBtNhZcB3l9
z#}-iF_Qnxwo!XB&l6nhdP=fQNzUKUyvbYN9zMwZW0R%Pq$4H%U%Fl+^Cl(&No7CaP
z`B$}u2#V7r#eSq$Q$$tH?J3fMY!j)GCpSvUe`?B=)RL>mNESK^g#5j!oZ+DBB_vg6
zpIo#mGPDbDD#6kG)tYaUzzxwH<6$&r+lOubsW;_8Ej!7$*C*bt3cCEsEX(hF87CR-
z&?$bfmvO=#HJFwGt|Dh1PlO$G%~iHAmhp7>Ym;IG)Gcb<5r%&z%`c0Yni(Dl4q1?K
zc__y9!#AJz$;ViNFmBx;j9#+lIpbcqPq$0yO>E9^?Ry3q{zXbLh-$JMtCwU!o0Z2I
zi{+Dj>E{0h9n$L9cc!h`TW2kb$#gh4>ux-MB?=XC^~Qw8A?2c}^tiV=q>^seX*Vr7
z%sR2qufk?3Epqe6n+^YcvC(_Pyd8uv<ajuae^Nj?mgj+N&Wf@i^D7gII3yxn|CF@3
zD%4-r<Xw(NEgLJ3fl}Wj+K<mU;AitD8MpJx+%^&GT%zRTqonKez3x*^*->4c{aqPm
z<Hog~EQjLP0smcC?W5n(d0RKMx6p%#@9qVf#|iFMre8=CC)D4QU?tA7=YNw1E2!f?
z4~ZYc>af{;S3JXah^NX8@)`B>+d!lyl50KB^)W)ym2NAJ&vZUG6rgKnIYlEpcVlZs
z?`n+vu_)C%!@5$oj~>^@P33A@e~Z(OrPi1^V_SU!6{8{v+XLvi6Y<=nE0Hb>svGN+
zz`{tn_1<O5TmFqx%jxIi)AN`e8TeT7bkCoBT7*Rv23t*ZBZRAexw(w=)E;xg`o_it
zBW%s)F=u6O2qEOTD6TD{EP%;L_Lk3_n>0Q~2#5zFZQ$qXb+q1mM#o@&Z#q41uAa&!
zD!O`_+uNug>T={h&2vIjwT5d@Unyz8+QE%{gRIkc*2V)9+&JztWLn6)heq#a$EmJ$
z_ZN*GUw1L?3C^2=I%r!i8hkaF_lPvQ{Ufmbz_!jtgx_p#vt=?Wy*#vO^efZUO+()m
zQ)O)9jKyNcPs@~Fo-mcBx?oQ2x?!sCyiD;`g(3a2!E+^#a;s#W09L2=AXEWs8)BkZ
z`!VH1uok7U-;-GUnlJsn;a#4wm&WOC)3~E|`;O}IZKo%n6cGUXnV((VyS>vT9F$gF
z&)-SKX5*Ck8r=vn`-O|f)%Og97+FdvL0~#}Q{MjD<Y{?-&L+1Xj~H@HHB>G)XV`&f
zD$CE}vpA~?x<Vz#1}6Pr#B9J0+D;IlZP!6);jA4fNI60wY)(`_m_p<HKmOO}{=F+6
z^gWkAzH$ES+;-(=BA~J%t#b|pFfU%fO*wpzdWv^bVEWG=63t0+DR*0$1(jFE@{_4^
z*q4(Qw7i=n?4RWgncDgup{;C#CN5gRctX0jD%d|!iHy%G%(U;Y46v<&TVt`P=n3Dg
zobuJlsl^KT$WDP<dJPtq)|}Nmvyo<wnfT^#%ysd2<8$0Ms~@k&K_9!DaPAE*!W?Z-
zvNNC?Rkd_xnJMP&SwrjUv;`K)mO;Ll@naey%Yq%M^oCoNygNgAB<@{9*WV%S1it(i
zTvexV`da%5vSUm&vmH?na}i({J08TZ)L2Hg#TTV69aOFBHWR(pYnm#mFviKlCvfCK
z_?C&}b$9mLx98Oa?p`>rthV^m$iDT>0gg_)qNDL~j;@QclWS(9U;SS;e4z~n(`ZN+
zlrvQf{q4-bi~iXJ1XY8v;p0TAq1moa<6m*SUwh4n+~o)%T_DKuGO}z7y-!NF3T++Q
zH+&RPWsMuevvS8_?D}6E>zkF7Kf!J7PAlhbZ*;A@n|=`#)Fk9f<oYHpafR2}&2TIp
zZ}5|Z!^j5{gdUxKE)ilidxXdxh`XkWT6@^I5sIM3!^*31$c~MVM$5Znu{MU<5k5<+
zUunsMb-XOtF}8G<?+#p(C^^D=+chm0?=#QB9Yz;FVy7WEvrHxs-o~aUdmK-!vVT(^
zDiS}rEwa|1+<|^l*-rrKhHbqDpUGufuKEJi+Z3PWtqj$SEJ3DXrgNIl@=9>bYh#A?
z@jVF^JnHxtk~eZ&-$reL&TAu>l;Jk;*Rr1dtdUMW#%S~RA4BI1!AE>t>a$_~zb?FJ
z?5RJmAcVW#;mI3&cnT8sW0zWWo?I1jmr#+=t)_od9hUu!0W<KpN&09IA0WBVF^g)|
zrmR2FZT|iHCFo5Fx5k~$scY_<@toFe+;L=-y50{XN8g!dl=c`z9{$2Sq?q4A3*8>n
z7v_2{-VIdqz2Mp-C#~?`#Lg|o4oOQW?}s(bjiCKV98Ngm6)V$eHy2T@jc?~_FrRbB
zS)`Be)~tEHmzR9m_`-wJnoatbBe)0QdoQL$2VKOt+0tKciA8~_M}tC(lVl+obvyVO
z!QU)?!g%uXyIux9T#rpVEDvpbmBN64uu_om`Vz9bK960K1N<8FtTBmc;&?au5Uceb
z;62G$C!n^mJE#M+UIwg!!TW?H%GTEmu#~a+2?&!8gQg&9{-C&EbH<HdrZlSg9l+uu
z<j<ngegVsMfKG|eGTT3LpVdZvv2+94l_37@^hdzdY`k&{iZ$E!1d<p!U`|m0WENSl
z&nO#EPG*FR(tB8T4n9e4uCIm~{OWC!Qv9zra;eOKvdVp@(%(8;bdPk}E;?uexwJ~g
zdR3!4Xj^rsr=5>;pI=U!ZLvpGZ=SO?$!S_481Tq+9G+;7U)eYCpU~GW3UUdX%DqGP
zjd*};u3C8dYBGZH&3gG`GSaEBQu~FZ6ihU-n~M_b*wW___f|&j-RHWjXxgpgGeYhA
zAuB%NkG_hT)+g}SLth5r)A~+rU9$Dz>&;vW(dr{SL*hxpV{vJlHQ~+@7>y)LU-pud
zY#VOW_l;1pQ9DPYcJ{)`HAkKEimi0dL)KF8L+-QdivsajlgYxCL)F|HmGwzG*e4f%
zm+fFgUv1)v57;e(P5pFZ@dyPUNaNdmjR^$&vZ3)}**nII<~$k~nD?Y(>$;-uYuK`g
zn0xwq$`WGRy=}0`tfO_WHY>hmzQoRIUm2k`G&@1TC(Zm-QyGH&ElyLKtc>O+`a`9?
zDQ|XHEtG;4wfx5kY+HiDfQGU-C|Qrw&BGnp^9i%FzSJ4ju+n$O${K^4VRoYZ7=U|a
z*J!>vNkXTM_*nNQo`;He7|{`La5rGU1V(pWL@@$oq_Dy+QYQcYk6~b$+z)^$xdJZh
z#@+N|>_Xg60P~8KTBbTVG3~y-6s2q2XIW)}ESk1Our6j&K1{1RNclYRTh;i~Ax!y5
zM>-$(Fq=_vvoAHu^n3z8;>M2D%cr7#81|GVtz5Y|{?}<pTD)d8Np2^TYvZSn>O{~?
z^C+Rv;v3he;XeM$+0cm>IJMF7D{-f&)?g-Kk5lL2or(i4YiJo<&LuKdx@c|jzxMwg
zLm$wfApq~<o5KJ{Js01+gcRf#7_Ms$rW0j?=|m_zj*h=c!=~t7oSiOS3baJ>sQ`@6
zjGXtt;7mNmpZKgdwLUEzxB&%vCl1U9fQdtC05^>T6X6M^L8n0Qel816<M78yf#2l@
z+E|})5sf*Ka9vMmE9-7y$QmMwRS*1Xoc?ixF#qEgV>kHwWLZCx6y>JHoRa4Sitn<!
z;GJOBPr)f)Oi7U@J*O<838-hR(htzW>M(XPwBAw1J0P62v>gCpr}<n*P0Nz<reXSd
zOp}?>sh6S|@#B^{YAB$~3Cqf4xXp-6y8XE1*5mUSf5~50W)Cu>pUp4c)9Y(uWT)e%
z_(oU~x22u;3)soVXP{(_J=@uANTb{MMT3R#num&>hPZb+MATtgFIhCTAhO9-{kE=f
ztS?dwt0wljPnfS`>2VVzdaFfpgm9KEcpf4gS;98#T@3c(@#d&MsccyuR%<-y)~D6<
zxU7|V6vkF@yPFVz=V?N*|DIl&YcP9lv`2Z<14rFy1#Y)fJVUhMfSmfLBxZv05x9|#
z(zc#<5{Lk9Ppdk=s*!K;%w+vw6uU0@!DGcdYpID2pA)z@A*-}`Wo3DdnZND^b-)XB
zdBRRWMef3H<xq%K{1%p@OEZPDLG1;1vCB#RMe8AUvbrTaY<yzcDTiJ%9+&1(b68Ph
z#TfT=HXr&j0S|XIu_gEvhs<b&Q!ebZV1WXkyUbMFe4r$0RzrpEP*l~pRn6JBxY0%}
zD&`xL25nc2jZI*-6X#VpjjCyqUkrfQu;xo6^eQS;a{8SVIi}(wxh6!?C^{<mb^d#4
zt~pdS5Tp7A{sHinL5(_JUP4Z4Ssi2RYvpWV<92RsxMSkP-7q)kT!<d?^$bR*KbQdM
zUfU0K$?2!oFT9>M%ICcvy@2+46o-aZ=n_!Lx8{(44=h9*HMIP3Zpu;3?HR~?`V7Q8
zCrTbuCzq<yCb3Kp4FYEsK`wrr+bGaBd9w6HS<O=auxwJ5N`~&)kAO}gZk#jqXpe6W
zM4f4J+v+;);KM%$xS|f+tN;$MPEG;QjL^s?VD38fJqO!)4}Z#h3{_^~i0}M3Ql_wn
zCz!X)vi;MlCR+BG(QJ0tBX=`dPGMbrYkq9ou4fAqwg>Y9ul>IoVLSA024rDqk>&Aq
z4Az3<TD=}P+mU!DU+6t@5A-v8ND{4pngAQt+R1dhoI>-I!lg_H6L%9ZMl1Ifi4+3p
zq|#y0jFDeJ0+j+S<8NjBWJNMgb+NZv>}eddmMq2`;)Xip0bxBmt?=H!=pU!0O*ikB
zJCjoifQv_JknhCuvhCzomF;a`Cx8-Gih0qgkQwBS?jfk=aHk?oB^W-Wp+}DXWN44e
z?<=QRn@jK58B9yM8f*KJ&4a>}IG4dILE+PZEL{ey0hc{pq@qWo@hwd*p%)=KGcyTD
z@y6t?jg?o|md}0eSJ^cw=@o>Jc}4{*MF#tnL^2VQpJRm;9xAXdA0dS4>I;jJxW0K`
z?L68ZF?D!}PhGO5RkIwLVqOQU`RG>s5Lu+M&ka)vVr0s0d{~`)E&gOYJP@;`f%Zit
zKP39}cXJ){4I>}uLLby`W!+B<79h19Sas=B_w2jS;d5mBe5Y^@+%iQz!>832m6@Fb
zQOda$kX8nrcsCb(%G<oNt20EKZ>0+jPfBImPM+Gr!7k@7jP+JL|IP<~a=Rq6@qI2{
zPYJ?=rR?(vj9I1*Ie$C?a88iVe(cT-3vBPXs9G#Wtf)#E7)qg5ujeC2e+`H@Xf61!
zigncg2|_O{A|{O2-;jc)6>v#CS}!KM>iUBtT5fY27uOizrX6aV$)6BRQa`eUWl_le
zK7cZyfHs(3u+C(#n7&8tKQ@fih#50IWaeAM_`GoZcNRq*^q2~_CDNqHKSCmdeb2w;
z`1zha`t8EN=mlC>7C_hb-n8*<<(n+p&D6AX<zNYjHn^3N6HFO>1Sj}<xkM#haq^{O
zQjr_+d`||aO^|$p*^}k3uAz&lzX$Yno{)9cA`2Onzl24Xr@Ztxr;=XzB79#U(EaF!
zNq5_(j&=F$@mxamIW*>x<0kpb8LQz(dE}>#RgJKD`ozVcGD|+0V|-s#&}4LFivU;2
zJ+{!o%`h#yhdN4Z^NlxjVM*iA%>USF<y;ak3|l>08B4eLb+_N5F9VG)w(V)q&O^)$
zCP_h_!lo7U0mL3A|4+d4ida93Uj+!;g32tR>LA~3u<VvO)VK!)<vGyAxKRf|ASe}B
zPY~n+ttPf@$;1dD{r$I$8#65OZVM3^O8M_F(755<_<F#a1|5Ly;s5jjE0i!`4mpRy
zt(}=|c;kr;nP7%9ZUjIU7m#zo;Oi{%0;{}Y8gtdZ4sdgG!St8PdtWadLg+UuIy5sG
z=xM*+&n#8c{kVQ{lg#2pc<kVZCdDbmQZTYXtxTsr$F3Pl8glB*!D+gV?A@{2`w&LW
zaG~zkk$P)0n_HVQ$U6SeB?u8|^Nj^@FdWSBudQhS1|>YrQwje~#wK@jg=njg`zu26
zVzDx!?+lW)9Q5{Vl@akuN4yVA0HhkeACH!bM;1_j(FZ{_s9fFOXQhRk%o&IMvMB+a
zr6T@w{}@289`|6qLfgEi`eg?7JhSptLF}z47P)XL3J~AO(&n@$T-I5$?6l647m>GN
zCpuFzb?@Pi2SheTX6p^)JatJkEUs2-S&KvvDNb^|OSc0D3R`{;-Re60mlhf%;eKd;
z3eVMq8hoFXP|<90I@s(jFvxFKc(=<4n(*30#keQ+SQP_lM<x2Jj;A<AxDH%8HG|8j
zug94;;4+e@JTg8ZY-b53tJ(HJS$VIsCF^09q{%4CYqkZ66}lAXb5RcR@weTTi^Jm=
z2v5db$~5gD`$>F^c&g*DDm*9Ky!IvFmr3TaNt;7j7X~H!=LcL=wy|jpM8-1gs@oA7
z*7Gfp$ZoaH=)Gx4a)ac*dTH{_Kgoc+Wt@7?pl6+()&SODq)~S1_>pCW=26<NmHO<m
zU&=!p+43Ep${U2hU1U{xaPJ1%qqrt!VEh^7i;DDx?9s2z+G|01m+$l4txvi;pq8YX
zC;ct<jI?tyV3edrj$Ls}u0CJ()Nh}gT87uPqXD-alilB$#+72e)X$`<J%Pgwo2~ai
zq2$sV+-Ub<J$1We$_%8g`+ygRCm<Nh&1<lD{6UetUBKO$1KZF@VaeI6b82<Q>XN%T
zd-z*>_~c7|6%71ZAFv3{WVXRK*by8T0YNg<-hB>5d@iNkzwpMp_9o8*<A}*{!dx&N
zIx_dpb%7}Tll2pjqMC1@VokyrH`>{r*hYgoaJQggpS82MoV7Em&)eiCGn7pX_VB)G
z@*jYHxjnpK#4Fct4E%AhZYT?;m#Av#K#DiCOtI|B5mMN^CVorz2#NcTI;x|3a{11*
zxqK)U=?$G=x(znh=GL^C$t5@|4A){`Vr_0)$xq+fq0iQ5VooRb{-W*QrDYHsW4TxG
zxfi^$uBr*{w#U}|brou6es89Bg4S+|={m(K{z*MkrQ*%rCK}m2;+-GH250ifci<N0
zX-&Cp)TfzYz@XD}^HluxMT-M;Sd+vN4BbM2zWLyGz0%2tGqKl*Y8s!JkkiSzvgRU?
zu7x*$8GeOhAA`a$->MxB%lNFC8)~U^j<m|Xm|WfL+F1@Ma6(Z=(bRY55|sup<Xyh8
z=RRO%OUFYVko2vtw*uPP=k<>`zX=6aCl&*>^hYl_{Fs#@Trz>89witJ?FPj+%`>;1
zu1F8zZ;$Te^JV(KN@@0<d&U@prR&r@8ij?ygVf?<7S17)wPoP$2jX2M?K2lzYxaZJ
z35R#b+MOIw{HtSPpzs`ys3o>dH_L|kVt;q}?@CnRkewU-wSV`rUha_4W#r_YFB|7a
zdbCl_KH0AoKF|*TTHo>`b>))O1N$|*JqVL4Q(ngK({_drI>S&)OQ*mm@f)U*xLRWx
z=spF10+0CQZktfjpx}$1Lo{^WbDsa*Gr&w29!*$(@9PE0l40h|3D8&%ZtJR;wUge_
zP6^VVDJRy(zw^8<vx<7J@u`dD34IU1Gp~bNceJW%HzT8))M%>vt?$uzyUT=hsf+ru
z&w(jK&iLd^-O%Y4*R+20-%l?3>@FRm<B10n1^f8hAa=9b@N?HwcR9LP=r8|+mO-<q
zO&2%fH?ECnk<86J$XaEWYI?%as)pQgGx5@06}TP(Y;6B=^&J6X0`2VI>~L{*0<GNt
z0dnpC=|Q<JlcAmA)Qa50x}lkG3uiCWh3x{20*qS}yHVZY$JqW$DO5=|?I$<KKFqsS
z7x3YCxTF{({D}b}nk}N#Z7+daih~vg*Ts$|!2hi)WVTQRb_-l&n7C;ZB_gkbT_do%
z$K?%e&AHe0Qyuqt7UkX21n2}We$qkNmD~8k$=E{wjWr7PoF>T2%A1xZ(((f^_Xh~9
zu>JovmpRK5ZEh4HA{RKF<xrMh((0#$7d%`HA`%YQ)z|c@)})teOt|3I;QI->4?N5V
zW{RC<b-2*CAvW{NeVaF6a`Q+yS6^ceX6S>8mag*vTVx^Ab!71s$`6a&R%J}t^kDth
zKoi6&>$0x&zF27!gd(v^+qDm0(O5i9?WX%ttJf^P4^z@!a0}ngKAcGEV1z_&c4l;Y
zL`e07=SF%vm1vZx2sAYb%^hyL*&^Tt_27@dePwn4GS2+PJYVAD0(ui|>EZEdyUC50
zU&iQZY*w5>c<5Kbtt!j$nadH5Q+1%SEZ`&5#X4<DvPQH6h&&q(Xau6?_3J{le>Y3j
zT$6OajkVv8ev}qahU5m$srsgtjn_Z+OtghNr75GPQ-s96)3CI!ti_i$NyDoXQ+ce(
z;)N|uyO7=n9G_nPu$N3}Rsi55NmbUmsQ#8wvFJ09IB>yXUjUMyz{4s4=#QO7VU<>d
zFZXFzl}{F+#WF7-R3g!yx&xyPPiUgXg}CJ9Rydh*E%!<yx^XPtCnLCJv}Y%?Pe{e1
z=OH@tSkRFCDQZG(@8IalR5x81mq|D>KVWU5E&2x=Mr-vF`|Bn4+e_>Ze8-tTgrgJT
z`MDjTDK<X?iS5?^*J!qy?WLfXFOo+FRq_GHfIh{D+M}-PDiEOuEymjK)epPzzYyvc
zGp%!|?>RQ{wS16r&f!qY_G$Z5T*=N`hBw6z5(~ZWIi`%>igQ_M(|HdMpLE?1m=Eha
zbrbGqC~=oQ{CRi6oq0Udr{_T9Dp{Ob;L!u@V9lQRjaiDluMgRV-hj?Z_G|}RwK^BA
zZ-{7I2YbjteRf*Hfk*t-ThNDM96Ve+|0-tba-Bi@e+F?!BN?;Ti74+<FtXf;?T^!>
zXKVMcJchZ6Z(zT~z9$27j)#G;%bbzDbFT-54vn_a$tbYTAf=`f@Uyj^&{^OlTN;2Y
zBEZor5LRck$V#Y#D6yaEQn~UY3KRZB?L^l>-e>2_*D<0zp-_G)CTU;Ke9X~j38m#v
zf2#NkDB6tbIc8;9N~>mEo<jxT?T8VyQ;lGgE=`#XryQOOexOMdTKRZCz5~(0Y?<`+
zaC3F5+2wUAPWD1>OH{d>cCQL|{=Hmoi+fDgot73_(fxVglEXGu1c~e3ouVI{_Gloz
z@G!~Y--q|1$G$aX`PDRTri{AQqUbb<pc}Lk4w`ATMNE?VvKP_=U)U5D{EOheZtq&6
z8o@rKD`BdtdnsEOKI|E#t=y?B$}NQwkCx#8Co)Bc!%Kmejv0G~J}B$-;0%aQt0iiE
z@;ywO)t;4D-kw2kT1Gq9rX!ZQooa1s+fHX)7*($~Li!nwS|+5_n|G6{o30Bu|6F~f
zrL29sMJaJ~llX=hN{;m8^A~J()<7IAPeT2k@f@g=&{@Aau|yad>mMazAgtTCn+<nf
z8eputuIHTYd|UjNy<ay*<{6OdYX49%GgY`$-4ySz<3Q9`kVo3Sz)mFv*5tI{8<t<=
z;MRtW&I;n_`X*V<-UVt?M)(0;sfZbvUZNBw8$FtcH`uL0ocD;?;e9m06@VVuwn1mU
z{CoU`$mYS2mKN7r8?}an?fJC7lcqj?lUz{Gzp1+A%+F?o{8F4zGcn_JYNIAH*0K<{
z|D)kxb2!7kB#PlL!^{xjOvJ^W+HPE|01qdX!sMm@%40@3eyA24A?2a@ef|@~anP`Q
z_K0uL$Tk<)r~<!XKv2PmD$A_aO;YzO*`d_Ei2=FVxU}ZxH)kOC#OYhfL%5!f(n4ES
z(4U8y8s7%zuCd-0HUi(8yeG6_XQ^GngM?ql3r$@AV1^q`ukU1PZK&bR+<R3s<jvpJ
ze2%ZIn=w-puQ4?WfYm6b4U0YeXj5LPO}Re*#v<st|8-{hMS4z9Nb7+pK#xZtLNY$t
zzcqx(?|Cx#u%{4HQA~i7jJ@D}c>=Hr+#RNF?F+>!=aQzs542oH?T2cwl?|_N#(+<`
zL9h7)x;JeS{V`_ld7VXo<qu%dllbQ2DDNVwMDY}Zf<Ml@2vYST7)z}3x@mCX2-k(o
zp)}2MxJ<hGw>e(U5vA|u9Uo?_X?yCXK4<&4eeNh=l^0>bwZ&xEQ|Ir>_-p{cO$wX)
z%XkNt3D{HCPvr%DDHv8AsqF>=me(qF<J>S7wAl>VDy|%rp!Lk!Nj*jAb06vM-N=-6
zc%<^~Ht3b&sXLY6e3`_d(sB>pOgzU03<ai)F3z=^Ijtiz{O=cJ=kZP8gPVS3kDaHv
zJfi>UKp0*&F_y-*0e`Hk4$>SfwxCOLff9xbKeN(-xC@Cne0qzFwhKK;6;fFx=}&Cq
zQ)Nt+bOK2aPP<qzHAs&U4zh(W`R_K&8uUR7V9&BR@RrSryNY{_*sG1(pU>HnYVGb9
zcZE9j4s{ocFlg2`4u4l|mFVeZw<h)<l7{4=6^lqRPr2afI{j?n$L$X0Y>oJj%iN7y
z8?PGaU(kMC(@wZerOA;JR+`{(4}06>V{}N@{n&faPg@i2>88X-Ih>j(gXSM@XXJ29
zlrJLDznp8J0h*ig>hU^({XQZ@e>{g1mND=ur#BpK9og}&H_-{PrCaJxdc3d27FidZ
zL*+v^)eGa242<9Nuhj%Kk_F~lxQ%sQjrNsbXzV)))5V0rn!l%3;9e2g$EhoH`C(Y0
z!)gS?A>T`4q|gQYX<?Unwm2V{t#F*)_w+bcw8h+TF{LAbt#$AxEm~v@XxL|#7uP5B
zan$}|{0BH74=x`&d$G=fG`gy3Q?|Zf0K3+>8jZlr#vaq*8d;lhat-0LsU<fb{Yx2H
z@r~$FT8`od@^tKq$tR~wB)(RzHEr)se6d}!oNyZX6|q?aU*B{<;fX$$<z<^@7E`Wr
z>!!wPBY{D!rv0+L>vC=d+FSK`ef(Jz=-h6rcWdN<2B9Mf2Tp5z3bIQCZh##naVzPO
za%;$1$aYRBVDu48l0oRPPqMk-KpyTe-`+=0m5{tsOp~{Oo)-Aq^Z#}baRSm}BO%>>
z4+3-nLNj2h`?Aar*5?_aNY)&(&%@jkdSe4e_%Y-~U<518^wKKFAgg)}iOBqOtKbt>
zaOEX$KGbJN*@T;2t4+c7g0Z>Ii>Rzx=_OQx5FMWc9`EFVpq4L~Sro@EP29NIBLMQ2
z^5fl?+c~7&uJypf4myJR&P`a~!1%!2Z(Z3__A{VU{VFKy@lNT{+yAAuuKS>+yi&~`
z@e9xt(_N=vPcqto$i}`Wwy3#)p~@axWsh<eJx6u&oLnD)dvYX%vO7He$e#ne2s3d6
zFK$58k*P>-5j;!oA3OnPU%yRP21jSkhwQjh#6U^&Pwu4x4iCrA#OrI7$P|VIKABjm
znnA2S-Ip)trs_^>mXo3TBFSp$gVM}mylN1wv8YM@r=GJ|&Ece|OTFUlhHwex{Ipe(
z@P@h7_QSsY1%3{DV%ryOA^f)UL}{8#os@)1?(`R<qMFJpa+~{davKi7bLx<3gKZ6T
z<BGD%MAC8Y#ek^CkKN}(#kkpBs+Yvyj_gKkO3nacTdcM3uQmy~)6Ui7`K6GqS{t0h
z<kC~Coy&XUpvHBg{O^PwK^$Z8_|+~?ce6Z}vctWlwL*c;6z&yE+SDlgnP%YzHK8!?
zfF+T?!$U3UbW4T6@w1F!bMrAM>W}L$75EFc320edGRE87=GLnEPPU6;<N|kq;-72X
zyJ=+x*^ldx3rG={`4-XWZU}fQ&aDV_@l%o_^)0}+jvOWR-m)>#@U8Q1MY8p$J=7&<
z(&}MSF@OddQ}EC+tmi;aIj_-<RWb)rrI97*RBjvGx@@fDTIHpdVs*tAfew0<m-T&e
zU_>SE!flO<w50VxuD8zo2^>yr|9IWZ8&(cW@>irs#<IRmZVs2f*J3@Pe~@@$F1|_j
z4l?!0lTYI)b1kY^GBORcDfMpt18oDE6p!$YDRMsbnPRpUFcZ{gKg8N|wd;Ed(y+}z
z(-!GDs$%a0QW-g7g<}Bnyma90_;A1m*?*0U^-X1(v`9xZaF6WJ!8POw;S5`!N5J#C
zHLqtaQu%tlc)WQJu=R6+nLy%Z^3xV-y`XF87#2S*=AWGzvI1Lz<09(+D-^**&U`L*
z*Hgs;dzA7B<o&?)j0k{k-A_a@Hag`_Pk!zL)KMl9#%x+Xh^b_pyTBsRCo&E&7$?Bz
z`X?i_iNQOv?4k7U57T(qwe_XHRF$X(nd98oobY?O<8Eq7=0Z4Q-kwI^gl^IRr0dFu
zDHB^Ei4}*YW3(HIG$^F;R?hMAT=pK>Cw`2CEgq3T_PC<iIMuZvt;aLpdnre!dqvbC
zl<(mjUvMF~n)U;Fi}%3KyXmMC^sN_>O-QBBQ^Iod{%3nyE6(?r(4ajwpDvH=>(Yj{
zgX&`#7XSl?&aWFIm|#U}!Wd-p$hV-WdnHCB5$+dg?ZvY;Y7iiO+@)OOxe)esj=Xlt
zzBu!9mxE#71vM*$NCN>;!|V{B|H}W5rgM*H^8f#TQdVzem5Oqhs1zX`9KxnjDxp%y
zX_cIEJ|D)WR7hEpB!~5WQ!26KeA>ui&T`0MV{;gD+}PO64%hGM^S%B4uUn<pbzRTr
z<8gmHURa3TO3#|;jICRK*N5-4%z-7VHdZ&hkXlsjNsL*|t>Y#bKMX!X%@^&Rc%m52
zwyzru9Dm#-VRLR<;R2NM$rt12Nz|Z^#?x=cm$=>|dRdSZg#~Zdan+5BXZ4vSd=KSf
zzRlNM-`&^b3>B7+r!GhkAagO-kV0$5zvGxFyzl1nd~2(qHJ9ufWgfxM20F4+T63|E
z<x}q8Dj4!ac*>5){93IwCj^)c^-6flDqT=+UGW5@ACl~1dDr~s6Z^~FJ7YFF@wAtf
z{w@C$Ellj;H{COXpRs+)*l9+`HRshvtiLA7#Q~8D@ZBq>0ibJiFWH_p=r&!rot%(f
z5%NXRyTVeVqkOe=gx&6kimDqiNA$Vdr7e|2M!9F8Z^gB%kRuO!*5r`!1*2~$&L_p5
zL8eu>IAA+jg6(u6=A^F_BWLuX9IoaMe0Aa=>fx)Af5wD8cl4uh<NGwy6D5o4Maj0F
zcME<RYtq!Djsd|C(8}gP-YL}oQ2g;l>0ZRuh=01D)PP<G7*aw2HCzYBH+))!p6{`B
zS~}<`KAMT0z%*?NEDkVL+-aw|4jYdRs*Xn9%hCbDvjlTS(do3u0e=^97N@}0@a4_-
zSkW^<+Dp+yj4Di^ruE=E<2@rTgtID(xE%#v{Bh`tKd8fEqo}ysisk#_xg9`J=_QWb
z@K0xya8%d2Gj-otDv6Qk!2j5-Bj()>jcEYhbpS&0u8EjkOnv)0|6;)es$tw$9D;2X
zY-VQev<xCziLByA`s6yri`I_56NV_d@6IS?|9*~PAZGxs7n$8|o`$cy*8r5lH!u<W
zk39z#{JD9yH{H^+LYt_o-_*GEuI>uA(S6w01{nHQc)Eg>`Ixp#`1i&zULVnpK#KD1
z307v^W#Qu5pR}e*sj+K%_wZQ(3Bu^ZW|6x4&F)Hl9Tkk++yHKf|4D14>PfE>@QX`v
zr=%F;s5~XwN|jk@@>=DjOy6yMc<DU3_gafmBq@_eE(_+8fAxpH(m+^?UI!$)n3Vh$
zL!pNWd;NYb)MB3IZkv1>@dC-vfUSP0ws_za9nbi1*Jj>lqN-*Iz=X$}uq%aMv<)@J
zA1|I(PLK>zT;I$11Kz%xZ;d>7Y2(#90!?8n87CSn;{2WwSYb4>GS?4IGZ4bmNSI=a
zJI-<}gXzVh8U5=xm%FNLy|E?F|7P{M@BJRvY|LpEl%v*p#BD2f<kxR4!HdT~EBzcA
zD<z3naG7=Zw(=-v$;H&Hw+q{7pSx6DB`SGUK$*0Q2SJHsesIAkxjUuNyU@DlFyB`;
ztHe0a`qjJj?3DVo(|c2NvX8Bx>g(*U({Z2b7cO5ogzu>b>P0&7;M-WIcWFK1WTeA%
z%sQ@|*4H=d);1WH<}s#0JBRY|EgZSn^Ip`;svrwX%E?c@t|?1q{p&s4p^;|!6_1yX
z2X6K_;=SL<n-|<`i~eOgLMrc+9eq$@HRd~{<>HDIz580Lk0Si+e`i-wpy>Z|S|c}6
z60Qz7j$Mzms;Q@An6V<EwbjQXem53lMH`YGK!B;eDx;ORwZTH=LF|Ct9?7QrqptKb
z1}b7X8F?`$SIcqnrlBnrcrV)|vbGSiI=uD4qHdtZQOrwAD@X;lJMDmMQT)xI|6!d6
zcMI^c^W^viQ12!F{d#OiOX50fV51pTFpew({D6Q1$}ui4o6Y`JoGOzCVL~cloM^M^
zeWTE~<$1bu&H;!Oc}5=lN1<^k&82ExgGclTR|ilBfr3sxj8QcV%u6j3B}PFz=9d7x
z3}FH-DL`U)acr)(iGJD0bFM7~^rwT7r)s9Z)gAA#%USd@zivHbQf!iy90Ypn+V)?7
zKPY562QlNAZSs}6KP%F7uB4y9-C1%+qt(mWidb|8qi_sUzp`v(E_21?<XYW{M&RiX
zZ!*R}Wyb_2r6>DXiaEGiOsE}cPm(=StCA43<nauEwfCB`IMYQnu!syz{q|+wEy?DQ
z%5(MM?{Saiq0@#_OE>MV!(0^ua%&4}TYO>+mp0cd?voA&H=%;kNE>+p3|}v~vVGE~
z^70HOB7QdM{1M=aMO}JkXI89apLj>W!Hs`Ei;tf4z7W@inzIrtRoGH^>8<9OAv-ec
z7UyQqDxTy5Ke&Ie>U~F+SLck-)<oHlEZ*hETnP5p*Gkg+{y0K+kB_dP)ts{UqKcAM
zWd5qS^6F{fL43<dLgFs(oh<2FWU1pZ4gD}cwo+|Q`fWk5wO7T6jmFZXSIuG_vHu<o
zRqqqL5B{z6c2rQ^F4?2Vgfq>S0fY4D3H15A68I1g8@IM$>>U4T0x%u5%RPej$TLHq
z+Vy|Wg2dbhb$0LL<yzGHr@ZNZ#U767#@22UOZ7@F@q#waCYoj?ay+!TRMN*b<ISu4
zgCp}XPlnD`L@M?3U}sPaN`Vjcu2BduID61{&Ay}t*M6G3sTz2di#Qqc+{O_xv-N)T
zd(P@eY2Zs}oNT$*RUqY>CTi}SpdMB_{3gx4U=5RC2uJ>z36{j?X;%^)wC!`*ia-JQ
zp8q}LoiU0Z$Eg1G`07RAwqT<AuGQopUDL;)LkK<t$~KCUPg&a(XA#q8>$JUm+JAPn
zzm!KR4{?aE;MUsUV<4wjEs3@ir$2}-c%>KqNeF%Z;_tRWL^Jb!`7*Tl2Y}klEK~h2
zaq<gb7b9S-OJX@DL!9qAQ)!4+(F!0Yh;&IhUNhqB2mcwxgJKuDSsdQ{M0zJMB{Yf2
z>lR`4a;)9|eka&OaNp>d#&jwLX9*haiPd?UcUYM`BAlkFTEOWlDZUCqC;zgPOFSz6
z!Wr{mXE>LVwcV19n2e6V<va_)S5myL)`5y_w6bx<#TUfZozv0Dge0IV%ii0rd(0cN
zRxIUMusKUtTuJmIm6-FZ1extB;l@?4Vn_>$Yn(eQ!LL0X;}T#>z)fHJln~7{OB4nk
zQw5Z@Z(6<FH?j5nTMxwPW8R#mC>>WD!-aK5j9c))z9~#E^2t!zldw>#+~cKHC1Fsw
zbpcPd<<4wealuWQ%Av&tnx4SKP%`G0hvoND!_<(mHhp>4v&j2ixidwNgU4YGN9`J%
z_qbrcu2sTlJ3S`OQq40jU^VAAa`ej@_UDugq1VdN!YXn+<RJ&3hE2uu5zFx8OxJ&f
zO}>Iuxb(2tEP-}lo3%iq7wxUUG@N`t9kifs3%+UvnLaDuP4qAtBJuQ}N>=4t<veh}
z)U8wHN#YrI&KZd#H>{q3FaY$U@7-?1C(=w-)Nen6J6?ph{$Ntq;NxA#-qteO3!jL2
zDZ5wDDGnNK(ugeD1{_@yMMP(Qx6&u>ScQvh$?)9^$sXUC{oprZS7iG=Gw~UR$?(lZ
zBe%@Z4lfz~YKKR$=U_0-`ASY`#eS6>1{ypfMMEir(lgH_$r62jughbp%0JQq?;iZ_
zL?jkh4ZUNXt^szJDB#HtrMu64WTfvmGM^smY;)$aQ<IMpQzcJ<w*fiK%Z`3$?46J6
zXEbf<xnp<*@!E?`9Fg^sj%X?(hSZ1z>C?d)L$+-acDKU|gx&Lx73S~cbV%H3G0G6!
zq%uUT<B<}6%MWMJ%az+8CqRz_+%x`@S6O>Qm9tOYBeqkN%=4EdNLE5u@K1p3pcl@8
zW1(DGJ`GB@=DCS^Za0^S7j~RvU?dIhbxPk_Mu5x3|Gp21+8^T>ADwk=W_T7b2UGzo
z!08tvyaCr72OVWmR9Tpm{d_}oBQT1)dhx~w@s-!zdHPIqa54hMLZ<JOM>JfJ22kuP
z*(Nr39s2-Jh$#;H0CZ2{KrfIdIjY0LEYSU#m@Z4W^#dY5TF^UB7=pyW6Ut5}N8t-^
z8gfVQWt0Lubvh=*qhl=L)4G!qA6-`sl@}ZU8dq)sG&;Hs*mL@h`zg7B>Hcp^O8)p_
zbGyvCfZP0Yk%_gw`AoOT!_2U)#8l{H*mqv-1s8c7C#Po&Fq?UGkx#RKwX%mlb;FEm
z;`Cp%yk>1}$L{Ijo*ijlMG0%?zlZY{wBI(Y<BaZ=@AP{LIVIk<5U8<Xd(!;kGcS~H
z3BID=^|l13$M1Kj0B6w!XEWNTK*>znC2F|+=yA+o*_9ptu$IyP1ijj9s55ceDP?V7
z`zP;nd>C%ZxGlWTJICuiYO;?rzxb-cmh6l)a?d<`_@$OPCQXv;pS77cDKBqx1iHW(
zKc#*_*uAn$3<0kt%sSDP$})X{+3lL^6607n(r7);XPLYzkKm%Q0$uY{fzc1p{cqhT
z22!-wonLT{@Hio9yh}<^*SrVwSq9(x8K-Z{^n7q|OL7Oj6GHA7p}X=Cg^rGbKg?3v
z_f;lxiy(<t+*7&DqA$by<2dFcEAS=yd*G<dkn=}gG?HR?*I`GK{aF5rk=hY<+T#SF
zS7{RqoYju2BD9W9_p7N1{mmf{#rZ?Zf({`R7Q*NtZO~D0tI~VgyLhH)_oFecYB6T?
z$Az78L%x~hOy49pZ8)eihvLhf125BhK#kHgs=CHIxg>u(`Bko8dhv<(3PsLC;g34b
zII}>B1vDnIS|C1sdX=hEm-R=4CGATwEXsZ8T!z;(_P=SNW8CP_g7%QKUJdL4>F1~5
zhWEZPB-_zK)>m>CkeE+@HW6!J*10oYVAA5fTMSv>_Eb`4zho1@mYEgXx%gq>JSPm0
ziikmV92I;})Zo*Of)X!Q9%LGCWoydJh#^5Gx5VM3pKi?HI6ksL^x^@gmkDJB^CU(1
z8aig(V?c1qYUgj4-5$^0PstI!x<2gmu7w)-sq9Qh+@ny3uu$ijXsPYIYJIO(PNgut
z6}W=xmNcFhhu8bnvL87f1{{w9jg%Fl+nrTT(NFLrGQdNP5l68E(>aGLLzhfD{p%}{
zU!#y!wC7S}RvCGtT*{H=;-OHQr$dr59Dfd8_@&X~U`M;>=n&G=D)3Y*w&anYA0-SO
z(pg+7U+eKZgBq&gl8^M4Q%2iL<33tdm=DEd>c;EH%=!{TF^56N=8a$?$^f<`v2uah
z)AwfOJ}rO)ui+WFFWi>*>#eJ@;SsU4=<i)7C#$lJtrx~59uFO8W1S+|+%=7SeS?s<
zMmTYv?(ceuj%_xBn>c_^)wcF;5!vbl*z|you|{}y1j4L}!s4uJuWxKwfswcP_@8vW
zUSutD#Mg<|(#kzI&S#qZJJ{YbrQXI$Ku%~n)fntpF^=8>aO~ekPe+q0C$u774l*Y6
zH{mjLi#yz{yowDylz$F6IeOtlStB3n$k;H$B(5|tS$k(yv80`<T#yofXX1g$YDw!%
zcI!5*!oCe;a^K@>4>Z){8k02qG~4#;8KY5(n|Uc>m1A<ux;d>fKj4e+SY7$u9ydhP
z@FEYB-=z(A^wLk{N4{y?oog}W!~2w$|F~~kbTs@^A&n=Se0SubQ!(j1?wRg^9$dtA
zH=l}pKA)VUw=SJut`Ll-m3rojrjfSb^V)jeyECj^B;V6h*Fy?R49;<eoNF<wjkgE+
zX~h+zT?<kaG^qM&9%Xi<?4iN5Q&>AL(H+&V(eouI_(9376%r)S5IMi&<VTYm5@|QB
zP!+N<h*1g2!AO(9bbiFi2hzHx4T_&%ZZEO4syaWUzjPNM3ApmXmwGbuPFcF`^OavF
ztR93kEzzUczofS?^|N_fBe2lV?<Zci2^Kdx%14ON4oq+tv9<H>Sokm<UFc$q|Ax0Y
z;4*arZ*5!<q@0r?30I?`9Ow)P+JVWtEv4H)nnqikMr+JM-ms)E#2hw00SJl}w;~&y
z6Ody%v!vyi$C8uh2EsRsTwrhIM7NVe*#kC<?FxQB%=NzWbni1MwY^I7n(G4_pd`in
z{^#7|;0c!|YOS)Z)$>eUWfg){B}FNNG!arcM*PPK%IE<0fr3}64m&#l0m8X}`BT9q
z9PRJoUz-*~63H(d;At{ByIoDD8uKjHVck3OV8yrl_WTl!0zXT`K@rw}EhR3j|IX0x
zKDna+Dr+6t{97#7v(<Fb{|3;%_7*Dum!yM~Q9!SBh;mMAXj<s+n)>dQ<>f3z$l?97
zF`rgPOFxy_;pRpz^_Yi8w-|HvftQlX6`B8{;yeK9mhdk=Z+YNfQRg}nvW^>)u)f0E
z<yzFM9^Bm&g}=CwPpMgI+j+btR&dk5v9``A%E8yeKZzaNDUwoQr>U^B|4k5je@}=m
zd*7OFZt+%9?HTKG#wzWgnoCyg<EnE6!Nn4u$nQgmdxKXQd}jRw|JsX*wd(j?Q;j#@
zq?*oPM3c5Y@uxq#DVKXt$1~I`Mn?M&E|Qa=!%E0gjzxk5-NZWT&jLHDhi&9{$B?$V
z<n4z4g51xl?D2?E&lKWbp*2e&v|d;RWI7}Y%s#CE{qO&vC{^e_y=nHJu26RxrfHFf
zuA$E0sw}9ZphtA_`GZWDnsRuu5brO81r_*DDl!J<b64tgR&1PDiqnwJ!e5mynXx%J
zU@H$%I{ysilE+d>1);hfo^R~2q_<>~#5MP7seEBjM>>L9v2mW->LHDkHsD3#1o+2-
zqP>C0<hSc$kP7uUrb;_^Qx&0<(3&3CS`jauJo{7<WKO{OMKx>^<KV~gEYnibD!)Xn
z*bc~2VR?0JjEel6adCVnHH341tODi3eYg>Pqea5a?uBF@p@$;ro}=%)AP<~qF;&~Y
zZD2d{WHEV$7?LmQ_P<T(r*!2MdY9~5a|W-kv>c#(Xd%;M-*Pe};RQVKs?4C~{72K0
zM}#`8*tiYcvhM6DFs8GNXcge%IOFi3j}VSHeCicRfOGl>5X@J1g;~$UGM#>b>r47r
zRdi&-NNhF5=&1m<gN})764#I6p8~}&f{r0Cu2!Hs$hikHCB&&w>(|M#f_(zm97~+f
z0so-|E5xM5kulU5kW=&)d=_%rgjmi?z=R<l8t4HoPoobsiH*hTL&1zj(dIf55FPC?
zrk<He_6fE?v59&)X;D6@LjO635?XZs`I_bL&Uzl#J~<Vg=Us?n`_7{h7eY1s{VkUr
zBBLF?)ed;K+*fm{ud~rRk5^Y-`$?|@khvyo9}U;AuTG;QDf8ixgvPDm+#O8xF8(#s
zoYRV`!2kabGQUO#)Q*5}{$7mtZSj;r=XsK`9wqdZZv3cC)F3cSLM^`7n&~U{dO1d$
z56$Sjd6_07bYr$Pef=5c`72w8^OmmPCR$cVh>!z4rlkz1$KXI*7(c6vJ7UzOr+jVb
z+>*rCq{B0|iE(S(O3X7npRtlN?KvS(Z5GB3ISi?-V!o~3P3!x5;dWcWI*ys|@D7$H
zFuBL&>JN<6o(`X-Vhwk9;-=nX<A!0ul}C6MVg;|$7I~$IOse1dA4A?dq5O%{c_BM6
zJ<XC?cIwlb@c65+kx~<Gcg3HG8ex|?Jt~MO$__GZ7_D10n)>-$XZj<zs%M2BeJ+QN
zuCE57CdCQ~mDceV(f*ZYgwsx{Fa<5p^E9J(M5?lsWwrtx+i03CO4d(#TT9hmtlam6
z%8SjN`)!Ns>L?6-Ejz=C%RL?#L3m-<-I1f2`3$`W2j-%z*KC_M=g(Ee2XzYK683T|
znC8S?Fonw&JmU7V?AE%lr6UVVnuyslkI;*7yFacS3l6v64Gn*AH@p{<TF8BWPlJVP
z1e*GzfYsgsxh+yJB;beATa8q@A7kqGyfe5erCYzefBw`Mz-5L)ngT~dw}>IoNesLE
zxJ^F!4x4DM?;8LJ%7rWic0S&Zd>H7z1x`=uziO5d^`y}*y645_O@St$A`8MxKAGVN
z`4e38%)e+9xHT}u>a-4!qGE{Ct8n>=eV=iFo7a`>s8RvA5<C$kk~FZOd0nXG5>aeY
z5spj&?!Z`>GD~KR6BLwOsdAM9w>3iK-~HhN4tsPXV4LwSp0{_YWnmC;P*2nUPswY=
z0BMdW2staR-#Mhu9d_7vEgaLa_<>LH6}SCq8MQlo^vwLmXOZNR@R-^GJGC<G6>2=N
z8?t3mLqkq|wq(UC`L$s_tXQFF_z`b3A7ynQs*1h7q{W7(^_EUhM$QZ1#y@mm$1c&;
zBUNb!UKY3VHlw{ysv?z-rvTW!1O>NV42mu#NG==Rt+nzY=1IA$5gpl4@gZhK(;Z*O
zd|h$g(y!mAB$}Wf`KtCQ`ukQuQ&myDK|n{pR@^Q3V8oSUHCGy$Z#(iTiAzC5|JSVa
zy81d<{U@?%(lh)yUiUJ0H(y%&is15Kwn5W|=fw>2&B+__(-HfeFU&Q_V+OY7bJMTo
z%9L2{>4yo27qu$R&R1C{fA=V_N;QN(_!LA8>@3blA;sx^Uxq~qKt|VK7@@9D7Enw6
zu+gzx=a;FU!TK}u%;PpCR9c2?jyQ}!9=9&Kb+F7Exg`+M5;21MZpTxMsWS@Qu~<+P
zs$_GH76eza2twQ&1KlQ&niYgpHt{ZBpqn%vVi))R@;4(myl72NN;HlJ5iCRS668j~
zlK|ouSt_~rz9XgP4JT3S)mV91qM;RbM9MiBig4xT@NSbkZ7LSdz3cy4drfR!HTdb1
z;_G$fONdQ?>Bg2R^mZY3Wpl^Tq2VL_dLt-tK!MhsoRaJMuxTS^#kb<_!GZGOd2*Oz
ziY5p5Dr)>3ADK;^lG+SthSjT2?=iSX1vWtfms6ZfPEt3oI@SYB9gq~qD$bH96_h@-
zxT2S|rBgr~ItA3|CrMNXx79KCfO72kqg~AXy7iurg#QVr%Ltkk$S=_}l10;FJ-lbr
z&-;prlf^mGO!ZoAr`Osy0uZ~s-t&k@@&aejyBHJsfA%pk`V1j93dJMNpNlkMRJZr&
z1fNIrgubrX|4XD&3k9=KL{(l~0AlzUmLpbrmHi(@uXQEBXd~9g%X=*fsrZ`za|{Tv
z_VHu>cvZ-5k}4q$zOMr$_mwYq*6I02*W@rLx3%XF!iK&uBA7{MNEfI)aN>zjaSdO|
zK_7BcuXqtv^Pdk<#5WHy@$WrNXg}W)@t-q|TIAy+x3Qu@^q}TUa?!gYuN+sU9dEX$
zhc_>0f@8e#K~);;c(i^y4lsWbb=&wwSg0V1OgGR_c;$WF>JY>UU%OrdcyYVK6^k`H
zl3RP{eFq;p(`45=RsF9{$?*)4IrQk5@pCbGQPx6i*s~^?_M@T0wf#^^L6ONrjG)-c
zvCb%RP@8Z0E%k`h(1I^8OYB^4?yP$^x-(v3Cuub^!cEr!%bWP{%h_fjP?Kpi|BJx6
zSW;0(7@WxoH1ivs@(RjnH|^VsGjimuh9JtXq|yl|EpS~2)oBXm6li&Gc#9_PwcwKB
zwT(_CYeBtx4c+Yy_p_tsB9e83U)knkVG{YI&s;g*hVFaR(7%*NK?Wekyj>tpbWFWB
zl9#lV=ediwbfbxzdxV__Mk##2z1K|H@jAWZ6X?8D?gc@gWz&tsr-BAB)zevib}b6H
zKi}ZVGOe8|5_A?hRo>yBFKe&9?2I?aZZ8ka*5G|1{fDlger{t0)Zd0W{Q|a#OlPvn
zJBsQaWQ_4Xj#|sDa?BllL%rj)@C9i=8Rf%#5j{5amCFo5POLsF{Y1g^tNcBXxlT(1
z-P+zxAYiZ4v+DNc^yLGiM~xwj#IV?P+7w$;$ePde3j$E4-K$GonWO&_G}1RVS_;_+
z{_XveA&fU5pND*STs_zNxzPu-w1=c{XSy_A;WuEfGkkQE)xD@+kFi_E{72>aW$C=;
zNfZ-NFNFRdhrx_%FC%vxbC>)#QBjzFEW4rK*a))P)c_2(0)AC9`@;TQN*`v&Kx%b*
zPJ_UO0yw)YiinV9;xasdrMVkafC%?WjG+}4Vsm2hUdhoDj7A^;@o@?HP4b=Eqs+qW
zY_Jkyk?Ef~7SgppGx%q_M3oXx!d2g?ULGy`MbNyYN;Rh@v)Uw9h$~ui8AzNZZ0LT8
zwO?tZhhizRiXSVQ4dg^V&7h<Zl;JJ$?A;GAM$?#@w}8LGMPRDp5WBEg6|J8T9bRfi
zK-~K8B_FeTUi3{{x8NOg{5*B&JTp#VH-k*oAbIMd#<!PX#m`30N<%AIoyJuoILBK%
z@OurAK}wUT6Kd3bVn~d5$h$;+-bnV~(yw$|a+jS_skIo!5q~d*Gy!2s7A{lTgBOkJ
z{d(^X(|S{cSlT}<i(@8NWItg^3Tsr+HCDyIhnOX!pvj8zSr<3-cV$-@uovxw@`uUD
zYY-*Jz^KlkQ-BTp{L27xM2z`3w&^y*>ag}PSdNpwUC)=|aAMgXCV@1XcmNrY&W#g|
z{Gs^0oNJ89CJT0xjy!MkHZ)lfI(6_?vp3iB)`$@=SK-(ta|^nK<Zk}3XCm!!t2ZZr
z37Uzk6K{A#@Ov(LdZ__8wIQ^o3$X%hRDPk7hAf5Rv)1e9Sna}Zi0_)g{UrhqIxm!N
z_31r(q9Pb@mATgv(>5e&F7j7>1$m#f{3I~mXM(@5r~@Y-k3SWO#1`H-&|O>aJ-;FJ
zPfHhsQe|Er0V8}r8Xm;1BdHRgO8{;jbW+@QOoJO3<WY1|pV@jy3LGseJ_OC*t~ts<
zv^?cA2kAG~X(nuh;&SHQ9Vct00m-Z#`qI|Y{*6-~4Wtq{(*XD`73!dK4Y+b*Il*#h
zB$(j|TqDGlf-J*-J&?Cq49hwDc9C&c$tuI?L?~eNu)+zI?Hn3N1->jy*Mb5JX9W?C
zAj)|1?8v~V=oHA!N&gaT9_<m~t4t16xK=d_!dpcFrsrwLn3-Lc7kJIKV{+F1X9Y>n
zaB_2hnEeR-kx9X69V_IRB^#av<!Q1JV``n-f0@K-p2EzZ5Vib~=8hmmq4{+mFL(w!
zVoBn#@QO%Oj)}?AT+Tb*(p#Rdow!)Dh6SdBrBvD3Dud?ev$keAAox`S>TUv17r?To
zi3&~eQcuGCn<_Rz^RNr|Kc#7#0NQ~^CjDzd3w3n#YecD}#X-B<Bdb>-zT$_Y+JD@R
zl8#@-VHda_Dp!iN6}QR#0{T#sPNO=j7<DNFR8UD*PEh^+w53vOOuJ8pSf{winG9?B
z;ZD;qdzDq_z_J-Tp{;*z9laSka0^X;h%<Lsx^=KHHJU1LE}=yQ=XRMmGs91bFGs4g
zGVgJN>OOZ-uh$*j`FYO3#v#v@X?+dnC0n!Pbl&JZ+2)OM$y@!-4115Ep28o9X#X-3
zcBxY>yqO;`p)|HcupZ#_k>Z9AhxS9>DmMY#c4^+i+I>S+MllXpAt&~rixUKA69f^*
z9)Ld$e%}o<f*z>TtJ1WNvL_y>B4OHpXKR-9u|`nD^wlsY_YI$X32SsxZQ69;^z-6-
zdirO*YxG2V3tkF;CUol|Fp!a(->C<Hj2x*IXwjcl_Wa>B6@Zx8{#i?9Df`1IKuzjO
zCl-C^#0pU_Eu%EBaHwS95UNWvoNNNkFhLdypX=Vcjm8_2Pe6Pu*Xl#N*w%)z?V?S9
z!U?HuO%jcOyyIb^TJ*!KGwSRA?9c~qBoeZ=SN(ZwV;u?z=H6NE7sn%FoV7!HE^Cbe
z^gBVOEyIm<gu~EA;0w07tKQkNd%yTONL5!V?EtOENmDWJ(qoVX{cF`O$O-zzH9#AW
z4dS5^APs8n@b-l$1&piRWLcmr`p)$!gTKnr@oF7Z9R5{{h9m<9Js^B%GW%kKj+u!0
z;qX+jbDtpY2~hSBB=`^;5l?|g-1^}Dcr;9be;o`QJCq1uxgzUVSYo?D*MQ&_^QP1;
z$Q5xsFAxSUtlEXV;`_!j!|tnmux{WGszV`EWa3v2RtVHGG3?|q=OrfkN0Pt^4BGWU
znrMuKXh~ts$bt$xQ)RGZ$$k&tSvQ51{I->0e(kO1r}6NqH66`}U#8<2tMC~NTheWb
z%9y;T!?`%CpS3sq0kaZ6<{HMC6oEjanr(}GNcFqaDS8U8)pu69(_V)eTRV8J7LpM6
zX{f0)%7?kv_;op5iT%y*riWcO#T8el*%jG1T2^Wu5^gwn1X%b3SNCY_zbu`TUN2F0
z`{0(mog_lZMe!dWxQB>Bv-g$Di5zyf{2iQ`9Ne;gd#J-eOYVA~2}{{L!@b2gD&?r6
zM<p&F`w__+kv+FKJw*|rz&G=(=<oH(pOc^zbKrZ2-r{Ze@7JSPd6>*g&P|wUEmj^0
zYEmR`9$f2ytg$mBFXP_a52l<fv&}dV2EF8ixTT6b_@Z^_^0k+^gG5=rpKc0NPL{uM
zW!vA6=0_qB`d9sO9jy=2k`@mQem#Zn<=V&l`h4S^@^JMo@EusId=Nhp4|npG!&Ok)
zt^7)*j}FW$s|=Eyh{kerBP|o~6v0I7lgP)ZU>GcyrRLhwnKSIk>2~(Vi67ui!mki9
zrTw;NJ_kn6Nxm_@A-2N@!AJ2$dXbOiPe{GTLt20rN+&m-jBCacV+F8Q-+-)1`SFWJ
z;nhI)6JRX5MCe}vB3V<Pqn+_9U{mor4BRLY8)MJGcmSxH!~ge^j;;g!pN$kSU5Dk7
z04Z)<aZaK@VrgHZsF?OT9MnA~N(G>X*AT6lr@J1ZbGJHJCi-PdC*UG_v9JW@+2s3I
zGQLHj2+zf1XSq)M0oNVNwSewrgCX;rWnut9C0gf-C}tYHI@c_iWgV)e+A-w<5zMq#
zuPo6^{(j!4W=JD>KLc|ANunS|Jd@hPD79aWrxgE`jeYmdSTLN#3;;)Y99@^vyquw4
zlg)Cw1epV19{Q)I2do^k&Iog?B1MZMHX?r0!maJnmwGvNAd@K!i=ZJBq90V6p=J5I
zBm3=l5y4k>2{<zHCN5-_8{aGv{@kN{I_ebWja;nJu7|7@)A=VWAK6DsER6*~)8Y^k
z)4Jc%=$OCj37r<rbk?>a(Sf^;dXt+Btj4Y0r#Ky}-L_iiQ#S;~F8usy^KJ2SGh`_h
z_XhPy{Xp*XbN02Rx8U}N{G06zdjgpv`%_;kUJXh9=lZbb;uss@vJj4)4Zqvh{aW`;
z#n)Pt=hsnIm-*sPuYGzR|G18nqnry7jJ7eMd}wDx5iECbDkc(g*f#?HkP;lqntIt!
zCz%p-J%aLi72>o7?^>Nz>C(qOe{@T9$Q661EN+A&0}QLEfj_8;OYDcZH41o~yB9nY
zgsypJgtL27SUb0h<(keVL`SHo5@nXFC-yKIA@ebpsX#sH={pAh&C16bhrT`forC@?
z+l*n`f+b4b(r#ZG7`1Tiq{QR&(kLinsfdPkjZEFgx|>!^m@yl=6PQ)$!E=$ctuG<Y
z%f!8s^bBnE19Df7iED+3ijtu~bNWsr3q5T!sP>FX$rpbf+)?r+Zft`@TY=nf^Ck%f
zn5(zigZ^nIWA6lA<UDzZj$7D7jgu4^Ffc>yfHEa(9z#s$(@emsxEWt&Y#UI1n6U>u
z&8Ar=g#HFgn*s?!*aAojR0i|eaG;;X7DTvHf#LXN!~+ZyLq>9I3_yxzNkkGfVy?#B
zDkh#6=~x8!!QKFO$kJeLvJJ3(D6TlcAu{IQ0D<WAez7bM<ab_(bVmIR)(Dk20S=T+
z{7<_oRRK3ylq}D=Gc5>JIN@tyLwU$TO_zIz-g}Xlb!YfjI&9V6^dM_1kh`;@eREo3
zX?(M2gHKy8e_EdFyH~g2KoIfg@%Tl3>aZsdxb5bgox=~s>gwrCom-rn*IvR@52<0~
zwNr>M&%hL3tb?&6q!K7pLFE)xtlz2;Uq<X;ZMiCPo{nX2P8hZuM^A%O#`Ws`p(gKA
zd5A~cd=~3Ra|x9E%;St3Z_|p=#`BXpTE&PeunjLm?HOz>5|&qK4_+LHE^#J0d*f@0
z`k2BuOzFg9G0r3YT+5o?o|VbaER+lM8OEH{6&N*Ik`(sZA)l5H?HtT?$HM)&M#MWF
zNglRy@{dMs`h#7t$@<|=ks&7xkOLB@AqoBkJ?>Vv8^5X6UT@~HnT3n={h64-`zXr7
z!6j{_1?ecMJV2VTKw#u?W6;qKIK>~VsM)x+PYFHG&<wDXNYQ(gqIQUl1t*C?aFWp9
z`)L%a3R6yK#Y@)uRqh>yp5nz#iL9p&GD)|lGi$(V20f3_ZdVOSqxse9F4t$72l<`1
zkA5ycfZ1rUbvzS{FQRE=o+aFII}l^!5DGnc`ItB!Qr;n&mA<uzktnXW&5UW^T{HK=
zms?fSmGeiE@eiLi|JlLu$l#anP>9nJsEXKT5fckYh||OC9{{;b-RQ%@hlxV$#<kk5
z@hc$omG4#zoG%8n#(s{Fo(2M``bPi?Qyh-mFS!HArTMPYt~`h}K#FyQ*d-MLgK4){
z0DA3Uouf|X&nQ|JFg*s9dLi1GKZeZnZ4zJMfy$fuqi>^tOYqpvu(cyGViI^WX|R~V
zsBQ&pr@@a+Fjzsf^A6ZFL3{$-<Y~^bCdB#EgUOdU#X1UWih15+3uuahFQ_VK3tt)<
zy2Ny8XBw>~v`ZGz_VaUcJm}F*EU>9gnP(9^Igal9I$ext2s5~MP=N2&uDTZ=v*;Q%
zGtvW`qI^a9;Mtg&W>B=wf%1q1fuidFKbO*zsKz7gKOob}^GpnyN>_5)@yqqHH1S^x
ztBy0=L@a7Zi#UHR`BL5Np@rXimgJn(KcOQ<OU-H>o&fp&5V9N@$wOy`2i0^b$IJD$
zq(#WNex#>B+A*W)tMEvdfvxI1*O&_nrCJ<@OSJo|v9Pg2f(?^u{lgxe$V#Ljp`tl(
zJdhdmOLS#9>e#Hs<ShAWanBj^gPiKLp$<{bSrh<xmPjMZY3^1&eY$flwj+8LehYzw
z9o(`_3qPmE-`B$JY4?piTUv8IqX5M*Uc`!G3ko;z(C#|EJiNB7n<;=s7Rfx!&jAed
zUvGSVxFj3enU!u3hOn)Jj&kg!YQr~*vY!afe-~_x=bS#Miaem5;=eP!RnGicAli6W
zu``|5_-Pz-emfu;Zc-2Yl8D5DLt)tJNywEFl2nY$)6Z6#;rffkYf+nOco27djTg2n
zBkK3e%lx7w0?|@0Ms=<JZQOAwc`=F6#-qmuH3ZjTw=H~FtF`~?@llIA0S>yyE1bYa
zp#p`4QHfOGYjiqTE$MW86tcWo_ceH0K@5xfIDV7W9^LvQ>$rO417rD?Ezwt8WOKkv
zx!a1Z48hD`hn7eP#muuX;2%VN2LApXI$(8{s<C#h4CKV$!UkNiWwb|SF=B>q(hZc-
z7D0cc3YfD`as`zNut%%#t$Pm0U8R3I>qYGW9u@dF-+3mWolSRf`2{@kjWpqyssq8i
z0&nd(%x(eL#RXQ7fkc;}7J3r2;*d2tk>{}0U`K<o?J}`TiA*RNv(=muwZ@<G>0KRb
zukn6M3LXQI%<*&9D}Fpgp#_+~#cbyxZt4|v%;ZE3Q-#$rVKm9(MT9~No<9n|WMdc)
zM8asbzzSTA9Y1j)XHTS@8TyP9cvugy2U2$Nxt<?Sykt+NXDn~7m$2G`b|uz6A(T<W
zu6K`%AyKbnb(y+TG+o;>w2?7w=zs^<Y{XtYS3=y#3`0%-SEt7+mDEFBDCzNdE;tF_
z;hMP@q!B&}kAVrZBjHC4Z#8x9iNg2HL?A(7Z*lP)YAF=8h^jUZ)B8YE5YW#qA{g1C
zgZnmKt;5Ah7yF1WR#w4cDM1Nhu9&YwyZ^GQ34N2HAh`S;Ckk7ouJB?=H}z6n^Woo5
zD;aqjWxeK8-7P(TGzSsG35A32+Rw%~AwFq_GCTX<&ocsvoxoe>1bXhYZ$ao|Bjm>=
z^np>Rfq5Vf`?S>#6o8T7PoTMX7k}m656kgL;N<ZLqzYKeLD-y~A()8Xg%33TjUsQY
z2EGb&t+(A!_UnyV?>t%p6}R22nVqf)#j~TEM&sEvE1ki#&jJJoQI%u#vHb#B1fB*_
zqywh=s(C0<H~C&#TALbXYNsVPH~+euNLPnpr$0n?z)M<zcn1tuCHHU~!zZYKu~b!{
z@jPhH{jz!2dUw=0K>jf>&3FdJ8$bbxG;Oy|({x$WaI6k3F*)q9;Sz}fB>#dRb(wU>
zARQreS$EW@68!M%0Ho?#!Q&d_e$cUy6x+eM48f`PVVIX`)Fv;<=3<13rP`~cF;-v-
zkLZ0Gn2MgjXwDxDv0C^krk*1i0v6PCp}z{K@xoh4q9+3kH4b4DbkQ>ey1Jq>9XnkO
z)hn$aKesKJHtM7XEgY(uUA>kT?!>7kggP6si5$|(htL5*pVgPH_dKe|x<}L`8cr4y
zqcLL8w<p1=k9rou^%ru;g|!W1MHekv1pVHl{*2;IptO78=_UHk#>d9$?84a4#zW;W
znG|gD(^kCsKhqa-j_J6ZXhK8NZ!@v`u3d~YeKNFe7W7Z}4#O?*kRR$=f#=Chk)khf
z#Fw4}um@erB@{%j_mK9ekF*v3M`UXC+x!ZOk3TGZH#41Yy`Ey!A3-*qrT+FXxEBrS
zdEk>5X%iKKJ431M=TRK}j64xBWS@?kOj$#yK$Sh$NN73tF0P^633GzCmKRkmI!FCu
z)Fqx_om-rg4kKsT$k#m4i>h=YBgkxtl{(`<r<gpkxKf$W8s1JXElq0@6@9pQpD0++
z$h7?wF@;v@cj<ExG{OVnqk`3g!rC7L)ggq{5231xyEaClKNl<+Rm&Tf{$ZS9iYDr-
zOm>4sY}<;`WgpYF4QXDYk44t*kUt8l2gf3ix<POVt-p@~Y@d-h&RQ%5RnvQp8_!NE
zlLwEMlvN)iM&m~c<T#6@+6nV9;B)W(YtzcUaY~WOW>u|Vtkg{$&b=C{=dJ3S|Hy|)
zzYJptk;Gxwj?iGdG5L|^G^R<St}~q{!31MG_kIvmtoIs@s|D>zkiQ2S9mYD_bnD|!
z(HdgpWjF5gX|h4zzvgGV4}q|rt6@L1{0O$%F4}uRhDY?T0n+4zSaih%CKfmGXe%H)
z5xh(0uMlRR&Ld8=h=-vxIU&@#87L(#>PMS*$Nzny8vO5t3btUSLDjhxXBe2QBrqPg
zFpa682ILB8-{Rt5MrLO)B!OI<^*U?{Q7uqX8vcuO*XkTw!bBy5K`Yr}F#dWeq-;*G
zRPGYFDv8Z>CI;Z!tEAG9O+TkHE{~%tvl(lpNph9Q<3Q%{L(XSgN@)7x5C5Ai8;>aE
zg*#P)6Z~J=W8O^kC@Bb>EWF09h_JO%>tn{nXi(Uh*cHjRk|2!AyP-@^44Q1du*eIz
zGA*{x4cUQRKB7z5%LvYYEv;K|BKYM*3}jaQ-y`D|O|%3ozPF^qgLxA5XP*G9uas;j
zwFy?L=s^a^N|<(n+<7*_H+^k4xBSU7mZwpnPUZ#Nt__kNdAeU?ag62j!`$vOx45b5
zjr!VdR(z_v6DkYh!s-uHp6xsRo;r#~)zq<HIP0OVl?e{I3VVm-09QZkUdZ*(!Nd^b
zHvZ;OYtm=eho}u+5MTpoQN-GH8a#xF$8}M{yGlvzw>I=yhJ)S%B-rnmw|$050>1ZB
zN%wX^yI~_Ga%thzh5$(monb=6a5#iYj2O?lFbZ63;Q#NCCbYuLEreEJu}=y{Zfd&z
zB&ct_KiwXKH?2sWe?ATBJu-m=Wlew9)nLi+Q~R&S3y@XqE$l&M%18FHx@ISQIY%bb
zBs8hZ<dfBqK}?L7^d)EcqI`Csam>X4<MvDBE%SAwtkKl@>;qE@ci?&>J~6Dx^rd2i
zIQ5!i@sQ6xFmgtl|Nfo#lhy%5@sTmau-F>`riM+XVhB?u5|CwTu=j_X8t!-se$|^K
zupHu9?>9i*#viU3-xemj3uwf>0hU4lLGm^r?g8L>-QCIei@XY01n6q=qf5@NFzC60
zphjO#VANX$2O~T^BJ8S-LsXQzm*XjJK4<y1^D6<(+dtxyGwLoV4293EJ7;hZ-6aC#
zgb>7)3Xi-z%B^@@?DAplL_hg{CLNWiN6F(k&hk<~2(HE5S)kxp<6CuV4qV@lI!24p
znMTQI#R1M|j}Kdl5#)y8>?w?io+G|NWX;<WA!5;^{?}J0=9N;Wye+jK!OP{QOjv`b
z<<RlqHE4}<b5}y^pTHXipqJ1m_crq_cP+%Lmcw4O>c?Bq`*V-9#~p*Y_%w;3&(l{B
z(znGB1F5x_;9!ladRm{qYz!pdTio5PgrCD%0RtlT(H3<AN4un=wy0$$yJh`kP|XwB
zwrTqLwv<yWNtYyhEbr6Cq>GV%Z8`3`qu~rz-%w@hSLwRbVa$d{A-D6g2s3s%Z?|CE
zV*izK#Q>jG|CYdx(E4yRb#btOOBRAoo6sg|@x<7aOS)Ek<JCa><Y$3dw_@<gJL9+3
zS;=UYE`7gRJ5*P+=C&cuN?g)gczcoC-chjB2!p#@*M0OLT%M0AiMc_3!>y$5UUEFh
zIQRw{C93VV!vUW-yI${)G$1??JjV9}zji+!A~2h`lJ-Z|2aHE4?M%Ug(K9TVGf<D#
zS=NOaW35?gFla!{1nB0UOx_ww*vj)ZI!apz1YBMC0rg)XR5E!%rn0JJOzCyiF8dxm
z>d|jiL$<R7VAP|t=tB~4XO`gEx0Wm@qm-^_>G@vBWj@=hepPgSLx^3cMNWMPf$HwR
zR|A$%IR<R_3Ci@}Qe2~29Pb?Yfc^H;+D-H=x6nnb<NsZ*3@+tFm5>&vH*abL{op1;
z6=S+O`^_?9{do5iB1loa<{|F6jGKqCti<6_GZ@MnU<}8n=}+I}wE=|VJVB9))NFUz
zaXxGpwy|rIH8{m?#!Jqu)^q;0_f^8^V_;HYk_VE}q9%RFGJ<O&1lM}7!f&0H@Bp(J
zpxm&@08<KZfD=XS1P*S1%`wmrt){aQxyho5i1nPJg<E3MS!O8FcW3or*Sq$9>c%@Q
zu+soK;xr9(##}LiWAA$&5T`qV50+qa9Zbh#?>enB)!Sz!%a`kxmeI+n$*mbG57j5b
zy(^T+(DuP)1k)|Q6}UFN^V6|z|0ni{y~SW_4`DmKe<eyq6{D>6R<a(5(%mCK$lol-
z8fpmYUcp+`H0Y2vi9?ZF{minU6_XB)hklhr17%w_zV;zQmvZQ_{*_<bXXIM8bt+!9
zdh2wOud=W)>ERW_CVuz-F2ue+6ii<}KpBrMu`2rMtpmEfYZYVVH~X68kH?3cAn1_Q
zc}MH8qAkgilO{OQf=Q5$Vps`bSp;hp6;a&Mvu&1KPX$`{+hVJX15DeCW(6r+<ZS36
z^jDD#uTgd+TxF*_*1znTPGPBn_SlrjJ%{#?+S51{vuZRqw<@oBQhBFE_f-VoO1y*t
zEEpOpJ2!_DVtL78T=!7Y7@)#_45D`Kphl&)D+Q6WO(?^&HKi&yRkxDjE=1li``Xhe
z`Zx@PmGZbeV&_sVEa9D_L(Ls2ZbW7XPT?>E1K0L*tf;t4#3VBLjuj3gUs2zEHusz#
z)z^Tiw{njD9+Ctax1ZJt0s1$#m&*Fw;9niN>T({rE(#lS%UsIw&j6cx+YC;6AG9$f
zZg?i8121>lw*w!?W7$NvzlA{)ij|P@LlIYxgGouSv#35i05-F=TR>Zj1SnJ*$U{@r
zL1zLyhF+=ID#ib0!5}Y37G>U7DS=E`fVwN;A?-k`NC+g%awKRc5a?Rs-xy1?6D$H5
zh(kWe{o5l+6~6C#9U}C4_c<>R&Xt?%G3c>w6%B%c>;=EtwxSy0KaMf8KeRw<@=x#4
zZ0l?!A2-Ca;b8?5Lr;YG+jTnhI_NDU;sXa?@1&f-r=(31;0W@RQsdwoY~(9Z6D<T-
z2+y$TPu_(-k3I>U*|lc~(mlps@#aCl%wF(rUcNqcz`O?lqlZ&ZIM(<dL&VEHh8>;l
z3Z3N(a2oo1Z`g<5F&}24EO;lUh$opAqqOsMsO0X*&cqAKcV4urgEnT*#oZKhG0<`0
z?Fd5uw%AAvciQ}5Z2TB1?2FN-F-6_dok2hG(Orw>3gMJzI$wXBnqFFm2G{8-|6{R1
z?9PepIl>mF3lpOzZ~I@%G=!^3y>}`YnaS$)OqyYp8__?!+<67Ft7YC5r$H|$bLhi9
zTe<ZH@T7%g8K?*xkMOpeH#t_E{a{i#0M>5Zp*%TVQ0x0#FluIkw;azoV%3J8{1LO9
z6E#+fTb=t8IJ;(8%>Xy4Dq59#896JF<TEiB8rJnJa}f`GYtUOt6U^o0`yLT&Srn`^
z@i)~C!FdSeN>s#C{k1=`Jm(}GcU7GH@f19W1&24Ai4AP&oV8kD>q?~Q$jLpmPTOm>
z?cI|5?`|R{t;0g=x4ZDpBkzruXj!^j`=1sx<vaK^+@<&=x7Xd(0M}O1`n#v?O>|6C
z-!%FO`q!#t#?j9UBMF8VzCd!|6R)*C1P+U5MM~U(FvEK`NrgV&Dnfl<wPe+6E-d&K
zwuilAZdGN0k=$5dpOMO1{p!|8r*XB)?a5CHOq+_{wYnvEN%&G`X$Qp+M2hq?U@Tyv
zkcqLNU*~lI%lY7|wtG;C8B0^89%cP%2+;kg{GL$%5~3uN&^Qs)wE7D?-+u#IPXu!%
z0SNhH-1zY4z-~dK==nS+N$?DiAX%L=Ee0mKn#Bwz<k|x1Sq*H~$@q@rk~o~A=^q9-
zrt)D^wR;7)3Q-8)eYI!vI_`hMYD~9W>%#kfyJXU7%VNn%^eS+S_0c~dH7B(*hvvrh
z4}b4`#x*G$@zsHE5?UK0PCo{go60o2-`!tI@Oi_0m8hkMa>~@I9#4o_@A3AnDq3N?
z3-KLct9os~2NR~b(*tYu7?U?ZgWr5~v-Z1cpiP!54Hf}MPlG9}qW6F2IC|sXBa8lf
zWIJ?hW#$h4p2D!VYptAc3aEQJQ-e5JGu`gYjOFA9V1IMh-q-BOc`7-1yA60_T_t#I
zjFopVxW7)j-g2{7^gJ`IE^lx@@+Z|5n(2KDWs1#c1NoVklI)ywf9gnYsZmRU?;+}h
zmI%yC^@tnB+}aTc73nYC4Ik$%oLN!`uFme^9OU7-@qu4cB|nSqWBQAKST~6(Ns-n&
zPOtHr0e9S}ms|e8@ZTaI5@OIR^DTC<SJX{dts2GbL@ydno{KJ)<u_ePjCM0H%K^Ja
zNxc`4h-0;OIm-N~<CC0C`3l>?=8J8N4%uXVd61O2uW+y?+Uhk-*tGa9(&)R7sxSYG
zB-qBhj*M?KZkWKFj8ViLfnE=+zwC?j;#d{@7@`F=DbqYPcs0ITyN99%Y|oBk9NM?q
zD*LV)+yIr(b>vrh6*1(QxIQQ~1q_9oV%ve7Za&S1QZcf}uphH{W$FmHCB*|?$AKE_
zZm_8bk_3i2HbXkGkc7sx|H}d1Dn<xX!1y>Q-h()T=nGHj1Q%SOz|PRTi&PQVJc5}(
zTWp=12YcP6!Qs@JaY*QYaRxJ_<8T5ny0uLF!$!=kYjKHx)(Vw*;CA=7WQVn{e?GaQ
zu&k^UDD#RjPeMR+VTx0_h-&g;9L%$<$|J47=}Z4wcDw2oP+N9;_EChZkjNw0O+(f$
zH4!|lXMc{QI=tdf`Z?ToLkd2(Ywx@D@@rMLZZ^u%$K1MLY5w6TB25;o0UqjS25(Ad
z6B`nu-Ac{i`nyu}K}!__j-*gkB1ZRgz;{M7WUXZ##(BESgHmv0>gQq_)Nc?%dm~8=
z>(F)_4>TQ9m@*1oS|A+Ip`{uf!M(BbG70@M5tBSPW$%><2XYIaAyAf{r63(yu3u-%
zUoF`H-H9iRL!&YzR~R&-Z|uE(p#>hx7c@Hb*vH48>w4#ZN+7_#*UBkI3T`jOgl6De
z$p%$f+{T5dHAJ8(&29-Ij%W1BJ=N{5g*)P15iwt<IC@V~r@>5J83kzbajR0i+w%q$
zLWJEsC#n_cBzSY0uhF3?{yWmwsUQrFf8VNOeytd2xW_+-n{Jr9d?5n4t2-T%ukd0S
zG5<#Y%uOAi^6R|bOl0uzOt!<nsE=*#$qUz*=^WyK)Q=uO+aR$jq;DM*7Ej+U@BR?d
zy1&cOAF7=S%#HYgL4EN|F86{(NxPW7xj^BTiHhH*PY+^sl{-yAJ9CfmR<=x~nYQF7
z#a)nJKu#cFH0B4AKC3*$<Rh9k7hxzTj*puF8*JzzXh7u?An~yf8@gNIiV}i4{A;Wz
zO_Ajfwh{XA(s_tv*p;MO;BO@}@w_WW1)$j5qzPGID|C7bzo;bbRT(-b6(}42&<PBl
zIlJSaAP!FJNF9Yf$k87KnL)5m9qmZ5ag6VJWl~;7T|`-QqL<3!^mwZx4Pu1*zdIwo
zNm?->OVv!H+scQ*u0cteE0N7>)<*R|6T9FZ%M|pdZsnNn@3l8eVI@mcQiawUXZ9q_
zF%GW6#5f?A5#+5hjeyl*<Ui}L9w!(Fo-q>}?JYJja{(Sm@dR#cVk4=#@Rn8RxP3ac
zU%7CIo6&w$tD-H~<#z+xMrQrp|0C(j1DXE+e_G`uMWx8yt#T*FT+^viRw?8Rl}hgW
z*hZ0ZtrNMHR4RAnm}?^DY9nW^$=IA58)oPG_v-um#~;Sn`}KT1kH_Qjd_D(M%At<2
z$Y^44>I}c^mYVh6y1_C{tFOvx6{jLftOK*w=W4__%gTnNIj`A-#Kd(~luFi=HvGnH
z+f(yu=Z>q{Mky}`{h1XSe*&K0S{aK3>lfHjlyFN+qTcUw*6v517tht-p>@7_lcz_I
zBt(vt{DObj`R1iOl^c0(>VywH*TX+Ez`OlT+$b5Y`aBd)+&3o1pT0fnW7-^N)3NxJ
zKxdMQEVg$fv!aSivQiQUJ~QkV2QPG;IZn=1dRE`cD#sy3^h%x|`{Di0j9!Z<Ya~kK
zL_nY`Qku9pKjPm}WUE^_CFz>VejyTWp`1@^2tk=z-)-6z_+_yqi16CP{v+4q>OyaZ
zi6y+pa2Se(yY8+rCmu`KE^GMWOo_aNENp9HTGPQCNFwp?m7VOoNpYVv4I26+HAY%h
zChI-yD>^7}eMsp3#Nf$lR0B+)9DKz=qT_rK8<7lCnZ`6Kl|JIsL7#<oY>OI$@!qqY
zrWKpnp0{esEo@bvLBVbwHn6Wl!%o8`Lr{f5Wu*3=E0ByEBm=&(o}U)U1zJ>J>V;wj
zzQ0x%jFxWkDBfTsrQ=!8h@;wz1Qfp(+RnuVS{SDVMAI;h0`m5-OaW30{FbrHNl0?F
zTNII97e9iuXCQ@A<^7p>*PzalE`LV?qC@xwY7=f+Qze6!1E+p=2%801y4|#sB;K_b
zk{$KiUtG6#^?|19G)94HmJYq)NN`=!V}+uj_0UWD#<^NN(O-b{u$c60?-ml`d49h(
z@}Gx0X_h#ycr1FXo5G+w<5^Kbh;n{4uPB~Dq(2D|*OIgNt)}n(<NLlSUXQX5iC|W!
z*0-8T<h9{(BPlC`Xwk<PP#b@<bQ}w_j0m;T&^itOU3F`aA6vHDXom(vDsI8IOjz`=
z?}zWMS6)34wX>G_j#DtJu1D?>siqgbg72Em#j9*$;OjQXWnF1I*46m6JN`}3p{f2X
zFvKRbwPy0h!(yVh0w?!tURI6L#o~kH-x2%o9l2f=;j^n=biH3j137D$yR_3-YU+^s
zqyx%~1bfX71Woh{1>!1E+p6ZO6*}SF9Gl+A4*Kt~2lqQ(&*90`B?-($mMP-w8F=Hj
za0-5p?{%8qRsOR!ejs+he(G4%g4lSuQZ+8%rRk;>YG2u5%NyniVI~o8SCu7XCG)0-
z6r`Slrk>knrb%$uo#CVBcDF-~9O=9QnHrPKvdT)CKi+1AH8BWt38&B{^m|piwqxBt
zSeGD~LPfq1*$R5eYmgkO@SaSsL1FC$n8ztIEu%<P^OZkGYg;M74$}7C4PWTjIleR$
zeS;Vav40+O3jQ|gr{F*iOzT6>f4}ZMkh&|9Ss`5C3z{p<E@EFmPl=5p%SlXtE*lYO
z!Oa-Cq@w07vfRWbOlN6FsJb!a*a$SkFA(!rmry|}Y2iN>A{laIXwxs@^S-U+B#2SF
zL`#S6Dbq_KZz(RW1u}xXuGILtLyCnIT&P(<I<P$B(!?=2Ho-PE#<t2onF5r@8tK)z
zmD749SKv4_y}<efdwcmx%Lp=jUEjUs#s=sb=CewNQN<=h=PL<JXu}{WVNWHohJsG!
zPx*coxbT$!-ETX_6uM~<(Ja+8(RQwkBjKUzcffu`dGj#Eo#z-+d2LMFVoWWq8^sw_
zW_fLtGdW)J9a4UIeUo7I$~cQ1>h|N!R*h4<x}V1y1NL$Psi>KYtfPOOFR<xlBYwE$
zovtC1Q8d@1n4>m<YF^pN5hFq8HPNezB3lpVD?iDa@1Z46#V^v`-eIG&yQf?k>GmJf
zN?>EbUI(>|iWKBu9_OUD7jh84R`R(yeJOL}y8oG#kVA-8q=|YOCgAF?@H~Mit8HtQ
zMv)SZciN<4;R8STn<+ai!JcFlrVhI1`ZJD-Mv`Uc9hB#)ymTcrNL%C*<F07<9O?jL
z3^YC}(ju2yrL%mD(jf`F%RYCK++$H?e$B3$q0F-U0y+4lhIa3Ucf%o6L65q<t;3D_
zJkfyi$ZQ$!!c18}^cNe)Y?w7w_}wEzXRdesNO+`y3PxhkLGr;ppQegK{m?B*l!cM@
ze@}Qg9dRWvwE7SY({3lhCZxk23y=)GF37rNrIM1;7tSv$e7lZ3Nr%Tr#A>ays*JTm
zPXx^E5orRX)T-k?NKgDYBtwf1Fy>}Ip=0`23xwY>`v8e8f%=HYCG(&0-$2bB!ZSp{
z=4|n7Y*PT{=Lqxq=q9=?eUPh4j_id(`UZXqd>uiaNzbD|Ji17VPg(63Rk*Uv^C+B6
z?{rmZ5iCSyOMI8`W(_UsIsd7%ckODpGvVv`x#<3zU^1~iD557(G)#~=-nA9FR(T_{
zqy=?1vpDz{EZslq2m|A*pM#cf_1UnxT(KXA&PQX~+q_WHMbO8Qr?l8}(-;l5tlXAF
zMOn+U33bSwiI}G5iG|C9qQ}kbvIM77_ClWRX((S2Isq6e#qeAeuWXm1dwZd_kB3FG
z&~!9AP?w{;lC)%wqeUf%4oqG-nWEM^rp|l`;hkJdfikP)cK8K&>w<=(TB_UMtz6Pv
zi2h<#*p`eE2YAE%Mc!=BP`!qYz<Fg>whF$1jQF-8!_HVZXz>2heY$4du`@pMO9*so
zN;CK9^MV|+zuQQE>)XZdqjxK3_N(W>%eD-3YBf<%NdzQYK%ZLahNQ~hB{A{?uQgJl
zjFBNymXhsa^}ATnvIvdhEpZEo-QApu^xe{ld6o(8QAVU;>gm>*$B$TBQ?~PPw1Qvv
zyaK!%ScI+I1`4KnXWFmAo8jJW_`8W*9FGtQ*(iTDzCsW?Bi>=NztYg-?0xYL`Hp?g
zI@DNqrwGv<)8+q4--zAzvJ$T_46UlBki}0KK9k#!4n6$@-H_55lGd5W?3>425r4(}
zk4>0bepem?N>!pV3355Qdm4j%EFe#<o4G-Po;Yb_z>fRsK{5d*QykF!RlkS}B<$hj
zqgIS_IZsyj`$^EIHW22tce9^gWFYD8r6T|{Ecl7Xjl&H&GN-sWT9^UUQ%$B}>M0aI
zVF_i)y_J8`h$AUL3Kb_YCO2YCMOzkgnm?omyMK24W;d1}Y!=T#7{+fXiS*8F8~8<|
z!t-}dyu=j-3lFY-SBC~@pi?ul>KKyHGomfdlQO2OoAxq<Sgq+1<bl7KxOl1R;paD*
zGR^L||A>vbrA8(@D+syd-5s@kLL`(~EV<(Gl6}1^uy^d}@shXw(%mCbXQH%RrR+Be
zrac>0B-?fAu@M6w?~~-trl^gK9Mv%4Eldj32eqJ{!NldK_c05x{nP2XpLfx-ouBX#
zm*a{y1{E7b$RY@v@29N_*GUJhGf4>Ff)Jxw4?j(6o6Sf6r-!{g`f4cYh(kd%njV<Q
zsz*;|3c=!|jU;#YfN5NW%ajRw(AdeCy3U*v>TQ3D*sG+S5&H48NTQt2swn9oBTuPr
zC7&`<*v%oV4e~b+f1ESlE^CS1*}UeLRY^P>rn)y@QOa@_qX6X&j}&Xo-<>~YtGU=D
zUgN(|do1;msKo}sJuM<G%<|Jx-<dCGh=2Ss1sy+YA1L-w)$!>cPu~_YTB`7aozMOG
zRgv7Xa7dw3Qqxf9ok(pH8v%bRINf@^*X2zsq#=G7+D?L|?bWg;gua*1<Cu+OxhlC<
zTY;<9EhmDQMM~|0rvGOqY{E^|1uuqLCzQqCZpcX*_q61T+JvW%bL0XM`#3VQu+Qv0
zE?Y?0E`<nV&s57kZtrfzT&Dt3Wp*S=n!&$YXld=NFG~s1E3R4onhtsUyrOrDof75O
zmUIZWGf`vEWTc5Buk8>mO30ZBrA3_v0KC@adis9?a*N@@edasd*ZQ^Xe$+GKPKew$
zwJxuFdYnpG#`Gwe=`bnhVHN^Qv-qvBZU~|GdgaswJ+1TVKPxd^$(jcn%z^wI?YY92
zsdwVBOm#T)b^_ORv9=wWi)k)8%)3~lt+9GAv3Vf3RH)gKa((iY+qL}q*Guee97HPR
z@W4j{JA870uY=xTvc#cqvm|QL6osqh*C$wiONr9?HMfG|(`jsNJ%#FGMVT}Eu@w@A
zXJg-Yi#bOY)ht->Bs9|z-_}t?DkIfDGq9yw>S`;DvvCZ!8=_HagR^19P2PJ2I<ENA
z4G*jb6<L^lgNcM*sj`Hq`YX2y<SBtueZW5Ksgl3Uh8x5Rj~{=|tnslG9?hH3vIk45
z61yG+=Lo8gvbsd<{qrNqXIrYJ9irs5WU()rCp`9PkWzN4GWlj*r3*`?)wd4!(srH&
zIX3t0sjBc?>K@-tH|06{wha|MP|gjpk-)%%$FSP@!=IhgrV_xmI@!_=5FKLYIF{nQ
z4tu1=>>^A1`z}}&HD~DsEKQMN>R;szXQkY{9?gnmAKd#K>a|{7dYT9OEgd_86mFm<
zZ<Gy?a#ML1^(w%okp_E3_-~;2e7-K>Mmw<#9UZiXOQ|l@;8BiL`FFodhs>;?o~$-(
zcb>|y-FR4(wl5ItX;$0yLJzcdmk(qR+NOGQ_~ZRWBqp%oKt%39yHI7KEG`g3g4XZ>
z|Kt@ouB!&eQi@Gz5N3ffF9YyxKmkF&2z(Qla`X{>h*X1;_VN%_ZX0loz_SS}v%??)
zl&@q$GlmT}MRb(*g~-m8(*%g?PayG6N*(45k9Pk;RX=Y|pt^#tee-Ys&Aj|#Kk$7i
zV`0K~7c)Wv5N$UM-+%aGKJ$<U;mq)|l`giAUClPnf07WC&~FxML{!_OYjT65Jw)_&
z6KX>yaM3&G9zSaC`DbZ9*z1!g-LA#gzm{LasJtIu{>_aIRr>Yg-KsS{t@vA1^<^=X
zANS69fh#kF`t~>0`wZ>YO5MxEBUqX;xss5<KcXtfLX3q<wGO{I64lA~@s<+xXM~^J
zCF^1nYnk)*QCZUKrEkGW8@=nv6gB9hvW*&Tf8dKT%jvoZ4@N#GKt$y0*#x^1r<83x
z9_8n=2E$xcj5N;#ih{eGBS&i2F>4BTkn38>x6J^g?@Emt&F~gPVbG>D44OZEM}v<`
z8FlaUD-C<4A*(8r>jj-#TpBZR-94;=74dG}?G$cdn>R%<b*51uzpivpKTsoYpDig^
zaW(K*J+e^)o1vC0GW;PRQ-vkT<fqS|OP5tHb5HVIFTjo=TbnGI%!ZdGjlW>u%0CbH
z@g1iy2WByiTvc62<`EYMD!FJkNDl{$=>lk`g*jPkJE*QvF2z7~7d<!gDXP>p@H4EE
zQrN$|`;JI$G6d8Q!)NwEfeg#z^rRN$OpsrL{0zM>ga0q%4vQJYKTYGyJem?YSsN@8
zYJBQ98ajoX=>(G584NAzEP)w!k6!+ViGEC5>Byx>uo2~4MxSQM%;<ty?Z%2-Vi1K`
zqsv_0(V1o6JXi6i3%a_VB1xUy%tW7LsM!UfpfD5}WqCxz68;K9Z++Htjzi^pWySLZ
z_eh>;u=%;uV%ge{JA^B_&&KFosA^p4n$A@X^Mj9?tACWPz0p`TZkkxNZ=UU}lmzKE
zUHAELkjbPW&4@*~pmb6<B21HQJmKNY$&jA1!95_>21@EYBCM33eKIA?;&+!7{<<D+
zj4PN6wHO^hqw7=6`*njVJtCg#ewNh3YBmuOUGcZQX52OX0<Y39Zy+q~`h~obv`ozy
z<##|%Dp(S|4$1Ug44cL<DwUW8$#!7HFTLHcgD?_!=f1d>QMpv;n0<;`>LPpgFVE~g
zN<Uk!++@zwC<w7UNTS}gR5>_rtm<W73;a^r<3{(%%b?zn2ry=wpBmnD$|r3L4fVJ~
z>?|u<m#$}QkNBF>%kz&kiFjTR@m;trWbq-F?z`;vW7(<FHjj<ykFj~eMpVBI$C1T;
zb3rV^-yFyEaE*jjJ4VpK5-L$^+bUR<BNZRbA9(pTSM+Zh5Gj(OSFq#nL~`EZAtK90
zU=6w_tv%okAHW(J5qmT0FbxwrHY3E64uI+|9HJHpumash#P$_LhuBe9Pq+me4v>nC
zsq8pt0*~v8{0Xvct|~(NIe)7$;}PW93r+qomNV9F%vO6Bcn6R8JAzzPnv0Um#yl?s
zE2@0Iu?gN10`1HkHD0#%#VE*T49TcW9PlGR<tu#G;S=i%M>j@IGY(~8uI@UfW^hcz
zuD;YQ(5^l^6&)KKo6LPJ{>5k8mv5681<rH$XR$yeCB8%?Gm@d|fHW#$`$37}xVQYz
zN<;y&@+jZ;1oX|`%+_CJ*`R5vYEt(hR_IV(rwiHeeeWSv+o#bPkC1^d70`SAf_!fa
zW45Saw368z=b5%w@55`t^J)Jeqm{Xr7`Xl8JMVe;yt=d*I5z8|&&O4d<l!zs<V?_4
z_8~Ac>L`&Q@=^^5zsaACrU)gfxWA8e!gIxDUcRb)sl7==<m?g&x8iX$+!aw7q~}a=
zNyV(lXOTchOIvW&ofGD0;kFKc8RwT{`AT)6X7I9cl!!blGn4gfIx6{K%U5V-P?NSr
zJn>T7P5%HL4MxfaSLrRMhd1p<EY{r8;pn|s0sejz$c~X{DW}jjmG_slZih>7aja+(
zcStE!TxlAEzW9W;zSqesjr`{kQ&1)2c;iXM@nttJN6C=yTpk6xvD;yrP^1QuqJI{#
zPvOzH(zBEoUC@(o(p6*Z)Vge_yKKyV_A?3U-!&;NqAOnzal%}3JH)`~Sp+w&<z}nc
z6h=XO#|mP}YhQ_tNvRW(BqvIwW7>r?sN<Axc-#<LoJ|0oo^zahbL^5p`wD}3{a8AG
zhOYzFMCNh`%CD~vHZXq`Eo~=)q-vw6a{mNISTA?yCV~(X1n00tM2gmOeH{q`v!AAZ
z3%_z5MKgBMCMCr!S#2vmKJoI5jD?2*LjXmZ6~p6Z)!788_?(xp`({7?5C=Sd;7ykD
zRAD!2Q~O*`GD6>4d7(B;-Kdvi*ZbH#*6(kM#EqjzeC!|WJ|dg8scB;PrhToFmMo6A
zgH?O@iz+2$3%CIf4C$Yz|9b4>6ugJY=Dl1CcavJIBx{L2y5hH2Ewo2z7}aeKO!2n7
z^M!VECM4V}EzN`bM#Uk1!1SV#r`2^4mhkN=&&k{x-_R*x(emD5#NQi+=Iy`P7crhw
zv-&riet(p4DD|()yMagnf2M9@hb~So;PjN>Rob=UE9772ro6&66!fyfowoYamTso-
zN?axvRR?M>DTICyq1(fG?3=)^4inkCjNV3evUU2yOv2wbSN^@}AE@(gX+TA>{iS5&
zgS)PF;v1@XZrfO}N~=xmdL0_t4PZ5bp@#R_<Y2z(c*@c7zxgqt6q_gCWu!}^xLjP5
zh+d1$ky8|Z7vlO(#2SN^og^{&eN@4bKWjY0%rg9G?*`Q1@1glw$orQ!h)Fbbqd?mQ
z(NGF&5nx-P9mac<)p8*3w;pW}*dw=lPAUv?3~0b+qq0m-KrdnKkdxG9(wRwD8s@Lk
z+bGT1Xjb)UQGR_)1Zf00U48`=`cH4sqn^?+H1sTNh6M8$$z|=&E+~a&oDQ)Ne62}L
z&FuYYOy$JKjuNGD%rHP8L9=wNAZG;?FVs#Xh;>3dl<yv4aj20`F(Sgn07Eo_=B9s+
z728UeER5LpBbN!Tf(kjqPVJ`o^Ow4+>t;K?%=K`czZBo*;zp1wj|50O0S)zx?7COG
z>WRDjNL{=2hpNg4yZ#?!iEb2lhAcQ8MME!}4Gdtn_#w=hK2MwDp1%t_wp+I6apT04
zUs;u@?X$|CO|vbPie-?ixyND|M`etqAh(MYT%D_L^!@CU)$YM9$>dQ-NdXgT7qfci
zAEjGY_-mym^PNCRxfA>!+U1#3=TY~j?jH53Pqq&y%#6759ZZV)DP0c3f!3dYc&7#b
zWg^yhBFRn5=ljy(SKwyl!|g&1CY|CN4K3MnPs2at^q`7N-kB>GV}AcsaC+gAAZ{-G
zppDAJONkHy0;|TvDYLTwXAgWahfY_0#fJw`yn6ygS;+?rO_TH|F-`s!iM{~2@Z9%5
zi?RfBTElT}B)OIma`yreV(gE!&u@nU6r-RqWYjwad1h<Vab7kTHz(do)aA%Tn4f`L
zFfle&zxcS8O@`xOYRlh1TuEa_T|-EH<q#(Hdyu=MjPl;e=%oOV(#YsC|1&)>kLgx=
z>jbc)12oKOsqYW9zUwX$;zCEK1kCHa-!B^1(~$uCh6#|T1EN7T#ESY?$AidJ-wqIK
zp)K&fTlqM1J0obAK5*nI9ldWJ{WJj6NJ0(@U-1J)MD~4#+DLdlW|&i|HIK1#0^_0f
z6AamifqGRUbez^CAO~RD0UXTvPIS^%sWCPIDoTDi$HK#WHKZ~u%$r`ZuEr}G!w*p|
zPQM=8*(=-GtACLgD0P@%wc`DwnB=!2K6lhSCv^zOt25+IatV0mrxr6wtFp<1d*Vg;
z!RD+RwD0wqfyR$q=%dnyZz|8w#3ntwL&>VMNeiC-ZtoB0Y<b-L?X-QC5^zq$&Glmp
z;%t-uzp%Z(Dp~6=)NL9aus5>!;xa4z9sRp3gXBIwOqpU9CVrW@9(;PCAxJMGoo44B
zZUKLz^vwLO$B9>&J*F2cQlGG*za8pcs2ZIbRV#O2HF414^oDe%4@Td3`C9cA;WKkj
zhX%)g15+(@nW;I6iC;6WJA1o6bL`W=497{DhPx4u#Z+zYu!Tit*jsZ6Yh{N_rE5zn
zeCbVMT6PMi+8o6W5KFGuYPBc;yK!66x}kwSl-TS6o@nVp1)+cRIrLeiGmsdcqbdcG
z9xs?|3}2oEP&PAVpRH9w!LOGc)uF0F<lJrVNoKBZ)Hm2SYf1;J??mO?9r(f>dD~*7
zC_n3I_e`Z~PYcTUyj%wu&>=4~t4m*YikjTdwXfJX*nLj?fma?B+zp$K-upk##C7!$
zlr;owV-reY?Kfh>FZMz|uU2D~K;DzTm4T50n1I}9hbe%DUO~dt*1wYhG|avmzz2cq
ziiYuF%#4HmMOc>oBzg&em~#I@FB>6*PSYDn$t0UWC`f9m#|@Ii^1DLE2{KF!r)7yy
zw3TOF2i>bDoECZ3dy3RX48}6%&iD(3PXx%Hz_);c&4p;PyA;939;~|Su=m&+A2`4Y
zm%fd~TJNYcywwH5!{D}Pfoo-f`W@tSlR)pwQW<t}<iQ8Kul22SSGe<yG4&1fEfLP0
z(E3elxP?<1T1h(-)t;7yrT&+Q-QPU1WdCD;BD8m2TRkIkZ}seCuPLD{R|<1_)?B?`
z@@H(}C(7ee7l%^yK963TBVt6~#Qpy+@8G%Wb-c*Zj&}Lz_!_<fTc*a;mE04_mG1&0
zYlSw@33vvWUr=1!!QaDqP349-ogkWYLcIe>j+u@9M`yxGMqY?(ky%zt#Qm`&njl0T
zHN|fjBpRPbi8jwvjm<=F7BTO0`r+Gz;RqLwspSxvS-@^VwH*FuGSymf&AGXyVQCt&
zh?hUy{da=`s$$q{5<Ou-?&c6il{FdPMgp_ZMFMY)UIcnudSFP!oLcVERTkkh@sqH#
zQruFATw8U`9g&zDes!qR94^fnEtkebJY{yMULT%Xe`&#7{G8e|#dO%&z-Zt>0;IQ-
zp?7eL@MInGQ?BZBs3_0l4di{;bX>sXHb6OVt$;hZr^@UQ<1j-(8mu$5E)UqZUw{QX
z`x&Ke1q@{-xcX(T9rT}zpVt4ma<G>_ZN?R`F7hwpaV+%T5nPLUCSQUMAml*6rfKGJ
zwCM0{8O%Swo;R`I1-@sOzlfzr>2iATvLTE($b7aezgU)8KV_h?Mc@k~=q0m9`#+V7
zYN5|{p^=HI{IdOvh!sfj1_4+P6XlcaIyRtRmw(Ee;9XlW6IYw^IxUPG`AwJ8Y5G3D
zV$tC`uFuCM(_Hl}pJj%zsxk|~t@#sBJ_6#yZwX6Gig!y382_3fekzAvtJP4>|Ed9B
zbJ{X2JMXb%5lcO_Ps3+RTyx2-vbA&#*-%X>Y)FfluTksmEyJnN4T-(h4vmiGnX(ly
zD?2e&?#+X=ET@y6HaSSRF+%$-Z{@Sf6@C%V=6#L7ys$XLD9RhWv1%PQ@PcvP;_wMf
zuM(Npd5UzfCmchP(618IJ}_k+F2z9&V$}sxtCN)s<NUcd?8lHRG)Q`0>g}PbKHS(&
zzH3S8J6F19xKq|n#%(G5!1M|&<_d345?Rl6-Z1Qw&XEXrbup~mXvCDvReDP6=t8Xs
zhtH@E5BP7Zaf~ih7!O5?*^xTVw3PLo^HBb5UZ)@l&n8elma1g8c;B}S+#t#~xQUdh
zG(5KHxJUo_+f_5aO-<Lfjbf*~EgfaP4m&(==261M<Tb?ahEb+D(j8)(**n>?@T6xw
zhf59;x3m6nVHBPc6H_@7DBa)1|9)sB@XWH=nE4rBo{fsxGZE)Nc!G#CY0kt1=&*J1
zf0$1L!Gvd?Sc|E(HK5Z@f(%-Z6M31xjbmJm8#~2FEBs%b{;$l#{mV<d$5>{8h@zFh
zDr|#dXd~E^8eS3TOKHBtqquM>MU|ii;u6xK^5gijF-Bwx^<O?6^Y2~mjqqH|`<ZXd
zlox*Jk0xeC-0bnWxupCDt(Esm)`bvc5juKb#i~z+78zjij<|OcBR``3lB1n@<FTut
zUgk!yA4*bxwP|YQ;W72XTB*mcF?%_R9D<=#-%R6dmA{MY_d||tKUC<K+F@U`$8kBZ
zVuFt9x0I4Q@>Hf~M7olW_lu#3KCRwI@a1E#i+%{T<zI<^uLYhyydEGQ2OZ`vc{ksG
zY0O-cHN@~M>$l!whh_~G2b;}do=zF<$5OPWA%j9eQiz7uR=GsXDK~%p?Fqlyzz84v
zy6w%gn@{<ub@I0W3*a~ZH-h}Rg6Pkz9;F;)=81=>xTD&?cK;{vm-pibZ%hw9DSw=^
z5Y+$3r%0p!rOk1o)df??E0|%Z|6zO2Pu)wc=}s|TB<&VXz2pNoow2c@C-bX!c&;8!
z;z<MlXoizgJ5<EobM|?H+?17B3g}Tz;d>eHx2|xghT}rVWXMFEZ-sI+4u^<1No)cv
zR#fRrX;9iTdJzDMhqBlR;}Jg)L_9@uzy7gT8PLBHn&<-tm2)^RqL2)=hwR+I{r^B_
zi5x4F5{4-|YOYq2uVV06z)V#yIoZEWL<i-yzdK~rBHRMMX{UkH1QRa{;mKcb$Q6yO
z8;J@wz&zTyallXU_f`G;L}Cq&@KJe4cBr_*wOrowg%{0syltvMB^4`FUM4WQs#4ae
zmpm;E@I4%IKn9^P2CjS&AQ_CDK+U<U&_|J;9D|^b76Cw0DPb-19J@S<W72T&8%vkJ
zK}h@Uq=`z`y`u85Ual^EL4YJ3LYnO8gdTG8_-(NAOWq_F;Y~3_Ir09|gC7r%d{`@x
z)_3a*QPIXUeHJ<pwFe?=xGtt+S|U$-(~GgqeW&fCRF2sCo-X6<OJdjjHza$IhTaFJ
zaIGM6%T$Z&=UIUbtuTG@@nYHWxRr)@3S~NgJ8SZ~<?9>^Q>d1&(zAU4ik({LTs)P2
z2XNpq9d~3Wyj&SgcX+s2R2t-!3#$nG$Kg<J3fIM%HVcrg&`8D}e*qIL<FeZc_O<WL
zp)I^zKFq~|ZKvWRs$Q_v_%~@_yJ!BNjD`T9d@2`{reR<Km;)Jn5cQ^Ue@9%yid^aF
z12pu(gF+Dzx&aOcdpFrNTW&8$#@Le~STdcB+H69A@rB_A`uee8m{XI$SCK$n@V+E6
zvF?X9`6IfSXqFjBVo~N$J{}4(qlYY42P1MK#j0qHN}c9qN(y{+C=*BujsyrFuP68D
zFZfKM8@=Z~@>gD1cV6&r+RD^uVkI}Y(9vI*FE|Kw<`owt^ez4M7Fp<Vll>RDEd>KD
z;g5_qgnGATUz1JXtqp&ALs()*6GZm<S~)IOYBsKWgk!UkE%U!O&fGSz3nFwRZ-7)t
zLZ$km!4<;;^bOdlIC-^ee;JNP7pGsEwwz6MtNv1}ze-4=0tP3>J}NoBng0A8r?h&1
zQlH`#a1+6?PH=HL%*5`6;ppq0_f>Y6!@%dl<Gt<fz+8Ej<F!4K&vLFZXHOQ*eXPEU
zqGmJ8RFub~f1tRzw0K+B;pOv5FuLqS4`w*YySCvPr+@;1r$v%nziCeVuGHPJy_5S+
zfne;_2p50P-?B{T@lzN2IxodEfl1L#0&OxEr=*bs;SFxq=XZ$xp6Zp>$%4HC_*LgW
z&@-8aVF;h_cF*boznaY7J(H!a3sJ)X&rA|-Am@@kWIN`vHRlB#L_>XjW2i=Z62r@>
z(C=P4PQlev8=#QW0$()@>}Qtt*@+Px5Cct>fL2A-xyqz|Q5UZk;$s?af8)6I-zq}B
z^`BYrVQ7b<Hl-M@!{h!_$ye@4PU@8!@cBONz*~}9)SE&dm_RqS-`lCq+<(S@pNecA
zU@ly~e$v`)wGuOq?A-l@3{LMEznhZ~$(zQEiA~uwk9^dPwX$6wN^yGhN3CzK(EfG7
z9?0;7ciD4)CCd%USo8n|*;Rw<iqgLSs9z!2j#Ts$BaB{=mi}EbGl#W<5}oM`NP(Ek
zcDsH5`#X>08h5seCnlb9TQW!h2kFlASc!LAIcu6l**J~f_hN}493ifxy@R+0-+k7C
zZDNu)Hb7C*RpVJsBA<fC{lDylpSd?Dc&h$$kE*<fDtow;&fcS0n25b17WsZz_C`{;
zh0V7>i}2`?hTT3=|IgL#Gmt%CEp=H``4k;|Ovqdft&>ztl&XS@QMr`B(_oKPRwCnr
zU^wrz%OQ8F#S<=$ZDi~mrt~CBTOeo1v7CG!p&kAK^%$T9-}1bKM>%pne3W%K!H)qX
zPkzHC0;D&UA=vhdJ3&$;<4Ad>>@R_u^CO93Kv}#f&@L#_tcSL~;Oo)#KvFNn4(JGd
zZT2n-nd{R&zP{OCmQ0$uvzQS?{WF8{5%`XW>+TcfmtB&V8P<W84@E7G7TITZR2RKu
z5RI*GceWR}-9Dc1n)BS<e_uj#{;jeqkia@>j-`ZL%a1RsevoCjj)%)s#a1?>GUP6M
z#0$d*G6o$=I400mC%ux!qWu{LC)g*y`MfRQu7v&?NZC*y4`SFDxdzAZGW)wsRn(b#
zwLWU~Wy4Vsx+lX-T#8lwSe4*4P~`^=80i)|CELzN>S_1@2Cw;6X}V&|1RKa&w%f-4
zS711i8=)o-Y9Le#`W>(P8HDFT>!EL}H;XnF>NXJgFdYofIyEogec9MG>k^=ns3YU|
zT;cW`ZKqVataz(;p!DzH?2|ZJM~O7pEHv<R;TO8D<P%gk25&g+5d#pyrxm|JTe$G}
z$Py#82ilwsnW=)s$dXpsFmstzM0J|d#a3b`dDG;&z?25)72j(7A37MAhbK%s5NPOj
z{tX5)c2s*7V{F&NBD9=Es{$zc=HKi8q#HE?ETxQ#s|O09@PR}g<y-*9879O|D^U9S
z7tn;LmBfaF06(h)(OLR;9|t=y^Zixv0Rh-H>MQq~JCvB;PyL1o{V$XCgKQKFLuGK_
zaOm6D;^2+3X4ab$w>~W$Jib$w`8cupPuar}pvKTADJHgH4m0ii&%rG)t^<k%QuDsz
z28*2Ebn_k(1$td90C@4W)Vd=Y@pS9uz@{)yMTzbua9DgUXIYdVe)vxI)yKnfvg1f{
zk5C~vYRpSuYUo>}TU%mCyKe020SM8?3A?X@a{wMHcJq&TKgi$xsBga;3r0}foCe!j
zC_@F2hl#I`sl5&zDVI>kytuyAl)c`sJFBSGsJ<6SC6J$8f*6*ssDWVoXA6;rnRWeu
z@4bdI_$DfBDs0<T#U9#MICKf}Vw|zHQWKKtF^y8YR}pRuwaHzLf>W*M8%33mTOA*>
z5K9Vmt$fPNcIYK%*TfO-P9ak2Fcg{53dJg2Jg`jIBmM~wHrDrV(x6L1aid6Y0_*I`
z-d_GE3gxCZ!GPYe4x%o_1MX3{0lYYN1+sCuMn8*3&@raI#+b*X&!jRpx-%ZZ#r21F
zcrSvf0dwORTI7{Z?iRc&U>GS{G)!c8ooC?iW7F{-%;=*;(w8&FC8rEi2UdhV<?UVr
zaF-2uoF7)7X)lf8_flVc4~UwwYM`Cu;zFz5J=)JkxDA8Hcyb>|2;-XZf8G6q3w3}Z
z$AFdp=+j%9vTBR-CBEYQp?k=`6@FmNBuCJ<HIHG7_K&9PmWg|Q6TroTifN<XYq3Hy
zKPS^;$ZQoQ>SdT<B2?<xh=vOHx1}8%KAQ@bg;y)9ZTAg@BnkPYRbKX4Do46G7q4Ll
zMXUBe<9UjU`;U(cc!<w7^I7QqpdMpI^R}R!Z$1HqH%44p@YIF!IB;mc8^rc*xYU}}
z`{V}kI+bR%x%7cvha2?-9b=N=K;=;`1F}^|T37r0E7+@^R`16w!Wqt}At*qfLrdsE
z=%>xH76@pa0;CbNKfnN=nG7q#uLH|JO1N2E%4IwPm1CsAQCkn$Fqk^vFCL*BnFPS8
z`aV2j`zpc&XI$Ax!gq1@OkyGx=9Y4X<^HA?Okw(bYG~nY@0e{+NEhUUAsdO(el08k
zF&&T{bk&k|ARFBE6;6+SZB5-)eq<fA@L=wzS535ZeT@{5^aoSZoV2jPDKy-5LR<_L
z1N*L7F-nK-Tq<sQ9cVS0wcTfwlG8xtReMbJm9p;H<CTweSM7O_=}x?CA7vHmUNee~
zpP<}5o3ppDUV;AWG=ol0EPg+C&-(7@7qh{lmsZD1zShpgBi6}Y9L0X`jQ}eqFHSLa
z;N#E1lORXpm{wt3!jC<sWCx6vX8A6;(s=^YNA!5~SHSQl!~iZOc_1XXG-cGNEiY_u
zp&&VA*$lfO5qCvnHNj6K#eFHV%D!&zq)!N9`;gl9)mk=T9X^{C`9<68i%Db|;bmp6
zQi1@<&aeBz)hix-%(gxf$79`7u}BuPh4(1wun6}^LWg}Kwd!Z7zdMa9RpejZ2f~bl
zVear!{70-$mvSJYN$GMr)a(Vb9cwc^{Zr{#t6it7i)46mxb>izU3X$dSGhGD2-9r4
za#LTo@;C7)f#MI9_BeNNUAs0tn2UL0xiNcTJ4+m{<ZKN$!;hAH{pw^=@-O<a5sc7u
zXi4~j1&IDmkkK?!&D}xnX)=pR<@b#I31P&NKD5{YC`44mJdqBK(gjS1TXq9g3+NmK
z4g#K(l&Zyyg0V2d;(vM=pd|=*5$MkgeSJes0>Xu~?;Q7CLgtv`Q60{Ehg%=N8>hYG
zU!v>JV=@OLREN$6VHzs5?lR<(mGO*s?t;bqWx~-e?MQpu$gyjX%&PqAp!@He_NIyw
z9(zexKBfCWJp_oU{>$ncI?hX%-k0>FILDEQztvP4|Fo+r`SVLS=7y>1D$@fY?AvzX
zqE704zoxidVbgnW+UZ}O=N)0Kg%zL^cQFRTv#1A>-5c$nNIq`<`qzC)FQIuXw{*?S
zsc`I^EZ6xMxXbXj;L}v=EfP{x=+XD3ZCwuLi-mccluoaD8$m%_SMe)#GygQ8l(eHc
z5LYQtpzYx<11QJwYRQBpe*P4u(9$u|eathRFOddQ`Uy=;tvfRaz3TY7Vxb|vTSfe9
zs>}D+#dko~BC32L`d-+J&DMux$TJ3DE%Lfw&~e$*fj?rEFN-)j%GPrgZ8HzFEbuwt
z&_Gr2(~G~g2GsEXWEw`C{0hn^NOKB0Ji&AIvj)e<zD*~A<xlMUbcb|khCeu{v_?_h
z3sE_Tpq?SE4C=vo%%72*Kv3G75EEY{LH9`eoD15$=0ZDEiadM%Je`*ve_bS42)Lng
zr-yR<@u>kuka2bmdL|s8<drF<&^=L_k0EwJ(@t)p61yH=?uR?bQAfjs<cV|pG*r%%
zt?6H#M`@r_LJD0J=y$uA7)Dv`1@tbX)CHT9(w$!GRd{u(iTaUOrN3LhqQx3u6_5BN
zY5S|}o?L$&-Dg`qo)sVvQhMEy^3Nzzm1m#w4z{~~adK_;hZMDpL){9MQfxJHZbT_+
zt@3naAHDoB!Sw^xg8aYGmjLqhR9MoQ>J`+{kh0F=(XF*iVT}&6^H@I)h@yh8K$!*&
zkidLa?$eU$8yUxEZ-!zE?#TV66U<^S?P?(PKN=kSJzuNcxenUTB2-D811&n+X-8<9
zG~ECNe6rs^nR26m`CX6H@QzbU<g1RJ7v<~g<s3rpl3oUTz8qsPpSK$2Ku5$stwc;=
z0*vX=G4wWWBc{TcpY(pg@D1D*=y^z949-zqs0aUC4df^*OSDKSIsoPtf<>WfK6Nx>
zI!19cRE`1#I2gZNMKsltpWt!(?fJ<AUppK_D?Q{&rsnUAe^_bglbdgzr_P3Ly{}Q7
zWz869HlEm1f?Sc=?1ZcYI_OY~g_2;(F>J|CWy9ZPuIoekzG=-Z6&t>SZiK5y$Rc*Q
zl^srKF8Wt&4Z<DDvT&sl_p<V0{*|&}>QLSsd@br=rPswL{qa3fGm!-`()9&mY-_Nx
z;-^+>$$k!ks+4*=P5i*jk-FCOYl$_!4;q?~V&X&5jXqOQ#Hjzh!mk44GNesCkch{X
z9)oAVUsQb>Xla^Qylii(s%0C0zzRJc{s=^s?I9LiFuW_YOv<@ZZf%;#9`*fvS48II
z8bV%Fk7VeXTsss+Xqb^1dUnFF^P+dlF8Ss)2C};Jd5lRSGy9<7+qpQv8=fi`i&yS4
zJwpOCa)(%ZaBNF$2IQUR*#^lHICX>9=7r1;Paw1F$Gf28l&?zjy8FPk4<IHMBE1^|
zjPcpe{&!MCA4hOGh9LZrhtN}KFk9ePtcd6l%pg+JAoxFN=Kq8C{k*R5?F`fH+28a}
z)4NEYvpw8Io|HH)B~n(i#e12cQ9tl;gD9;>VN$eDBu8SO0eTExUCGOVPXu&u0THhp
zM3AaW$?5;kv`(qY$OD_nKd9HonF8%b)z?**fRrcs`eblnIpx@)w7(?B&5OSsZid3_
zQ==Ro=yrSWR>#_=SSB=MPoJi~G&A4Pa?&)79am-yj<3x^JV>Q=;Wfiw+N^^E(|E8^
zwD=@|buc~fBn;ztBy@o3*F@j*@x!X|$4{Nr0?LV0PDmw*XjtEAbomFxYnDy)=vwNV
zP9?tQM<rBJl_YGnu(D@@)^sY&5}K=kr_@(TXgVlXtty>J-~VIwMcC;%MegY3(?Vnz
zL={HYJ_GD}cRyVW$0hD?3S%^^y7uMTU~@U$OYU9VPUs`6j1{AIb?8Qm<4p9WD$}~%
z5Uc3W1EY2GH-)u%Mc!TJ(e9VU-zTQ0{m0&gPkaJIN@QK}Ad#io3n<$fs>u{p7QNqH
z(v3?^>*h>fB`=C>ns1YUc!0b~q{iIL$33BeUXc4R1L!67APYf9pA-=X&J2T-Btfi)
zaDcNIZz>mN03Z}FHPtvjhT0kc(=|ArB2PR*_dV2I18T-d7&P2AkOAFG!Q~2k?Z(5O
z>M->t!%vdv(-Wvi*wK;KnhhWKjjKtomn4mz;&`2?uEh=%De`}K*|9;A4CV3*PhG<-
z^IfHn-5|F@43}5f*N!nGhnSe7!%qd|cINZ99Bh1Z&fTc5#QkR&hC@PRaM;d*_!jtu
zR?Y~jItRW-H4<W@=8qeGub2W8=r2Sg1>Rdph`jXR^&NXL@hkihKln851z^Bb-5e5V
zJLg;GW$ybii}b@L<ljaUHg=5ARVSC8nDagn#`HszQ@paCd)#K<bsO^>Ts_R-OSzBw
zm9Ay{2kzyXQu74&+!fRgtRKo_Qas0SH{jC##UIuwc!N`ap5pQNAwuupcQY&mCB8i}
z2Mo6#MSh|H(yE$rt-nTZBuiJ51ZiboNz#tOZ<?EaWug#WxEeCA)F>tn5^I-zopzI2
zCj)<^57<kAB&H}&#yj6NPUyP}cuuwWh{swhLE{*!X^fWmIr@u8;d3bvqwJrBiGWfF
z?p(Fi{+|p4fYsP~qpS+bbd@xy3K)LF%tRVSfQe@j2F`UtFYfYpwefK5x@XYB1l4&N
z!LAKUXUhW_!H6$1O_iNHXTR_@(wP6Oi6X21HAWj#B+Dj|P94HL`lM{<W#_d(`7BV*
zE2r$>tIoY>5Yr1ouw#$a@^fH5jKR91G3eSVz@J_Z0~*Y7cOtxTqGv9yxve`1j?1lg
zvXxE_-1&^ZagF&No9-136&!3H9VL|?Up?u?5b?H;uNHiHj&{<V6Mt>S#{WGDdB|tK
z_?>u9V3vQZNC^@Iwl9@H=xvTti)`cd($_POV^5gR3b5Ed?QUfRPSx|4{U1|@bdEf@
zd;;J1QinAdXT!e<$+PlJ6_ViAfmcXSD;l`a{t~=Kfy1deM`-kM$M5zV@fCR1L2@Ej
zl^OZn8J_B5F2^EB+z<77G=!nR%hzEzZSIAmR-8O&;jQ$RDNMh#&TKSoFVIhL6LiF1
z^?+}AW`!SOghPC=vq)+7#)Pm$0|=S5@B`Fa(a=2?bG(8CAD3nAqehegggGU;!j*vl
zCzK9tWaMXk;8Kq9aQzXH9}1P!)!WOeYj>1fw;eA}xL^*((U<JVmJUTf89~y$E_rV?
z=pAqmJY^Qr1*_aXY6yK}TEqefXRa6v<ncW40d%3^zAxps9|~L}-61`?q{lzmbkIUO
z^kD0$Wbr?vTM9=cqC|=MRpUwPJ1@+{_G?`$-U}FlgrL!O&bwtkPNf67!x9zBbS#PZ
zo6AzH-wd3!Pbn0fDGN}4tVed;nW*}>S+Qy2H-KEaCvNRVrHra>4I?gAp#?u@bNy4+
z2dF*5{tE;V8qD8D+sdIYMc2I>HjXyXqQ+ItgSQ1*n?=8Q>2RaJlfJ2`{exNmp!AE)
zOw8wi27|27tBS=Lu20lb%d&n8gOWd?i)r3)A+pkeb4o-n#*GgW<>hS+u(IIFc^mFj
z5AyOSO^o?n(MXCf)^zhXPH1L7clYtMH}e<_aRc4bG?Z=xN?C!8m}pt|&F9x`=gRCj
zP9$RP=PvoF*7Q9PyIu|IQP>0CO@Lf@z(RokRrPIuSNlcprrK|;B|FvIW*L(6??Lso
zkTi#JKm5P0KM5hWH8@s)q}~J&%<v=zKra;=M4W(eOu%P)Li^%_Tn+-C3VIva+8u3^
zo_j$z4RFWDKid6dBYsIA?zOwjSIg$_zt2Z~dB2Ukxtbz*@L*Vdl~TtA^`(G&a2NHG
zhKgB*2ilrveQxW2UBh^5X#F@Vg1lzJ6eR0j_*B-m*Wm1X?BjR!-5%^7i7x!Ic_)jD
z(!U<%ge}+aW7G_GcgL*gZ)Mc{85xRos-B2s)=GvtVfKgE`r|^XRdF_%Oartp4?PuW
zl8H~Igoixi=no|BV4Jzbm+|)kP<E;)xTt#WwJ3kio?JkANRx*qV9Du`En<eUU0l$`
z<xQBa`4T*lzvK`+{M3m|7feQ@d{lFZEP`}><!&$pOVe-a>h1F$(Rp6c)A#|{KYPgp
z%<^lEgycN=W?#3JUq*S7-0Z)VZy*UKDxlCkNXCU2mE|ZdSe0&gO=q5@Ev*z<MXU{P
zDHhsN!}7c4KC;Vdrab78M>O*H`B~7>x9+#^1x2eMQ}?q9J=^vP2EA^uN;PR?kTu#x
z7u#IC|8I&7QGI^Ns6;YABll}T*!$t`*p#-*PUS}F8mmKxBT95+iP)WiCQbi5gW{L8
zMqn_*vAp$CZ!>*bdA&a(f?Rz~=0;r}l!&3(I)0-g$8LSRyEV?X^Cmn9zOx@2VD>j4
zT1)k=$}|ykSGBzIL$`7tojMlzwJat$P}ajj%Gm-1O>J_Jw64Din!V`JpcfO>U2)vN
z&FU1!q!e^?_7%x46ZA)jN{jmDF6Spf2zEwREVKoSD$_-kx$pn;;*MO6=(W0iJuHVr
z&;GL83TPKd2PbbD$i3}<1ax*v=LY|^P*lpWOyRC;$l>GG_Na~VRE+T^WwPF0C=gGW
z*sQTt;Cm??UJrFa@Jch^G1FtJoDmEnXRJzPZsvjdoY&%xIzLpPiLITj+o^1x&_D9t
zUJ;d!XvyY`J>p8rjz4ykosfh?mu|83Gv}uJO)E1MAC>*_{-p(RL^X`&cZxn|rBH!h
zj){ThC@e`homj?v!$|u@ydiK2W!1x7)6?1AbMBl|TNVj>?CR2ngGY}_?`9LU4XLJ}
zF{v=fySCP6w7i|)J{JzXWQIdFie(&vN+q_7JqB$qdoD^Nb8NXJ=7Reh<0wH12q|Z9
zZuUv%HznG%x%>NnKlYZS;@RuQ@LXF6Kv3iWxP4}NVqx!qv>*5WI2Cgx{*T)As$;3C
zzljlNPyth@F_`yOSD{a4&d|(LE-q)F%C+9-oeCI>ZZhk?RH$USYBX9Rdw9|l=(9~0
z9CnC|h~ae-e9GOQ6Yt&pb#BZDU(v8t{GZ_%{?BmYb%`=d6;DA_aYg~RJJ9nNWIy??
zut}iko1^dYu>I11**j2_&-%#grf~xq05=TiScod5$Kj;UhBq2?!pnCj`g8;=+;wo&
zs)@sG4~u!Gy&Gt;NQMA(f5Q8RZv_%E6eyHr&TA-eA?H@$^<7^%%%*#u*-vXk^W@Tx
zpVpPUdSwhbx?G_u`Q0eXx*!5A!d71EC0Ag_p+Ko~mNKw_=oqMN;qfwJ!E#5ruw4j(
zbBfj6u7U--g+6lXb$5v?!zHvzQ40t2q!}=;fu6rw<nyx<mXj`;3L4pA$^7C^1uY$Y
z!KTPdgV#W#ys26NUTB9jdzvWc^u)2aR)M2gFDY3<5VLcM0JioL=N{}YVX$QKr?*$u
zD185_zP=GuBdk`;U7_?F_8M2}<qw1^I@0rc=us<!QQ8+exzoW6gNOh7z^wjAhgwM3
z1Jm^V0@dYAxiAlE)E`J}{tSS9BZsnx+gaUDy{Nka5I^{CP>++&P;toLeWh7`kyo@m
z5OMSQh>dq}S2q+%n0mO2yZs58`dx!-398bs9t|uEbr;E*B0s)Ze#&aL)-bA(<!B9I
zH8uDfgD%2xU7jZNo~4b0{(P!3edX41KCaaMh=$am#5%2n=DEYNWbB4OBe&pWx8bIA
z>ejQJ{G?K-OyxC<K{HtSfbrSrM~yp}u3z?i<&YUN`0izF3n+Umd(8{Y9}XS*xVUt@
zpFmgXNw@);&uDX%H-CXX5h>;dv4D;H{}_ye!HDh*wO6p9aEo^aH<MvkNEeLM-ouK&
z6t=V^Y`(SdtGO*7mr%nbs1AF5DgE+ALA&e2E50Njr=&iMIT<K+Xc^(|qDHi80UZUv
z3M<KcR~-gNhqK=*+{vND8c*<Szp$h<%EYrS7}7k>oPndco!{1$O3-oILaLnK!fm^o
z*0cwLdw-4@u7)2J`feq6C;Se0K!uN7fFeWKX9Zk}VCzR5`jBYx8x-$s5;{?4sq$lD
z&bOsm&LvjO(7Q1rhf{t%7(}C4fiDK7Jcc1U+Xo3R4R&z;jEyBY8ULwxe3s)}&8)yT
z!*KzaF`y}?ykW;ZXUFL>|M4Stmk{1Ews){QBN2((;AS6{KO31PYnz{>XZ$q$+W2*J
z<Y7S1zMY9~hqeDmsB)fLuX<A+<py6Q$)9O8N<|yyZq-x!T&sLb__xmIOS^j~^h8np
zSQiw_ipgLzU&voyqhXeB%1KXHs38)G)LOE#v~O^fSQ%A?zV$~t^X}~fOIHWyCfsyQ
zlA783n@32qicJ-kx@5(qbpBHxb6dsI5$y)9>Rin(2g|<`?zgN9i5F9CrdF=uzG#(;
z&>7GT&@nAtF3j>c-hY)}v7fDcrfM}|#lD#{d;?}^7eBHmDe6S55P6_oECY(;gD-Fe
z#cWvdJ9g1EjNu9L4M#8?Xr!WMk7_<H_z*+XAYR%(1+_0KnE8i?>!&Im{$ZsHsRt|+
zBD?XM)?81-0N}9Uu3~Ap^S_>w%Hc|3byTj)sYwiWlPK-ef}Qto7U6?&#S^H`f)M!z
zNPE#W>gtO>^DsZN4Jip+dN$TGwf;%4skFA;^_yE!p=RW`!AAB{hnfm8F3mONOro5}
z0_{>#&(^_OZ8?gdg)shJh7#jyF5F_!du@LreWZJ+5tZQ&O+@<B%MIN<&m1%`(BG3^
zG|J84;EU~ZLhT&mhjUIrFNbHiIzzH80MKUYoqYt&l(|g>|6^Wc9e23-->YgBo-Ker
zlMD*Y<vqV2>nh?ADn$ZNDVbTm!m3Xe^~c(nbk)cj;B^(h7~c8VIwdgGN)>-Kd@r-b
zM`Dy|NxnyM>C6~RE}+>~t;})^x)IBGfd+z}IF3U5B;BlmR9nLL?=D(u-hE!s>jip0
zDod--@6!i9*GqT|=Ye~C<|H&X+rl6{DlCav!8bHo&0t>M7v~Uq@1$P&#UT>4v_I)T
z#A^$E70h&~@Ao<RFWs;FSla)8RK0mT)a@5Op2t&D2~QizR!>Q#jIys&sZ>H6$u>zs
zvXiln5-MAZeVZi7PWE*yY05U)cZ0!LXD}FJmiv2qKHuN(_51zPKmE~k-uJohbFOn;
z*SQ@Y&da}d9TFNL9Xi$JNCVpds_A9-7uJ>v%+Jq>+h^GqWPRRk+AQ;~#tEsfKEJ1r
zTUGIaN?3fV?w3Mk+_282<I~-<<3LBnB$l|Bi5m<+Kx+S`{EtJ>hayiJ&V5v&IfP5?
zx4wV8aIZ@_gXNJ0pyt-TV$KICcRc0yolTOi;S}aQp(gk1zDV}$^<i&vTO5k<m&ST`
zmj=QRlbboZ#u_pvz3y>WD*@aw3W`u%9KQjXhH4fA(LoM+iv1?HUrNvTeLNu<i3L-E
z{rB#N`Yk5+UoRW&yhU5VuHdVjj9-gwKGwGhq2os$BgAh*-i-7@A-lbr_@`8E+OHuA
zdGlXj+GfArwuBlHwEDka_U_h~RXHgu`Zh3a7ah?!_rc|=jO(|$v>8JVDUpE+_KM~0
z_O7$OOtee-s^P3I>AToc7ewj)SF@xi`g*h`N$?MaXUg*ugJae)tRrz~tk>5{wJomu
zrFvfG&xpTbIvo9KJ*(%o(OzDLJYpqm7`30^rCs2vX;K)s#gz+bm?&#n>*MuG_QIt)
zypbb_hVej$E7o$I$$d1ptctfk3Nh?@<>(V}QwpSlYDTN}q{B%OYmJ5T&5yNR2$?gz
z1QNX(c+8%UpFU&S_8o1xh)-_cwP#*lhzOE+f7(H;Bs7-yp6JqC!r`3Eta>%IvB5c$
z>Z>RdJz`5%CIltdEA9?&2r@~732Mr+>p=~7s&ljg<^-?h-+-f`fH!O;B)dXB#AMLA
zuNmMQ^tW=;8cIT5w$gGp({0AISam01k+DV-Ju6T9xU9c8PS-tDEpksaI^q0wD9cuF
zr&G?>SrN5DdsgwWqEDB1n>%jjV63f@TMqgItp97fyX{S}VBXZ&(4@}IB*-DW2jj|g
z6_fWmFR?MoC`|FMj+dw@N#CVDXmY!=BLY=LcT7_DP^(=D1oagERc(!>Q1pb2ZVI9D
z?G0K0+rn+5Cb-Z0BWb`dl@L<Dcx(-i^h@{X-o!1U4hR`cq*BhxN`*pNx{Gu15!63M
z*f9CQ_o(H;365MTWCIPt|F3~t-Z#mIyKpTIe@poNRc>n}(J9?m;AmN4m3)){wLv+O
zN6ra`F(q5Rzmw57a3BnSoL?Ge&^@K*54Oo46FzIj{PvaXHR-dWC7$?>1&vHa+ib(-
zgPZPzMrvEK1cM&A9SUXXyiEG<Xx7USbzgbb%Dn&{7{*6l$DM4ab(ggqNmFz4r;Am*
zY^O(ee}J8go(;h^o@1bjot$0he&rFI(_<5~kIx1Co`zVClA^*REAzk>7dx#eQod<q
zK4(sOBOA*V_MPNI|Byy89^6du6{E72LJEVpW!2x}M(@A4FEhXN==zl(;B6Af<nL$V
z%)<1ApfWTeNpG>#RpsA|B9|ksny^+UvJJd0Eh2$W6b(Y44)|#}Ix12H?l<8tpwps1
z+{ihZ{Tu|cX#;b+HPE+T1M?Lz$MPv5GiUC0jO*;3N@vBG`}cpcojmUHK3{@@Wd=4t
zr0dC;J&9>h{UNr6ZWbuM)`NNcd*4kJE{5F_=^y5>I;%JAY40Obs?eAEsbZI;u`g0K
z*^S44vtM$#OE`8S!yPZJ<W!&V-Sx4eVpXkwJI^e3MU9!_8c?a2L<@RH=O+MnPBT!@
zp_hXu!bIUM-%=G7lNm;c%g=r5H6o=oFG-ox7I~i|D@q?a%3Kv>%>Lvmjg{!tHPK5S
z_U<~^4t&*VM|(v~aHdqxUmY}TDuhnZH;&1s+zvvrwIyBc{Y2%nh1GD-GGLVPqnuSE
zVw_XBqB&S#{B+sn<TH?Z>zRO+3%GWUCf`cmX9;n)QC2led>U)+XIuv96JI5}7Uu4h
z@EAz_-oQ=2jsLA?%=K(*nokDSMb1=5R5j;*wFKl3V@03N7v_Bx5}keVl-K{MjZ!V6
zyK7@J9~<b@!3)S$q<UCwO;2ALxOe+)^pyMOtkcz>htYP{aCy#7mgJjFG{o4r!brp+
zA}BVG1E;o-PzStY%Ef;f@{qNzdH>D?<i#9Pq_o`FBxThBi!VSEtu1&)GazL4vGPfv
z%=qh!m*uI_C9IiwcG5SSyAaChQ67`VZ%92MstV#Zhv70%s(F9cy~uE;*T!OF<XHKB
z{U2B^JNDO<S8r1LkV!&8O>PI1#PO_Y3E%rWH`|j~LHj^TtCDiXk2rAhVt2ZsEJvju
z)%H#Gu@qauWff3%9)TINUxXtR+ClTsW6P+v+smnpgO>YAcexp^kW?>&`!A82-lrCn
zN9}CW%Q77zO^n(eo#P{i>9HBV?Az-dRyB`ORvdD<{-CzHy(%E*uimrDi$AKnqD*J5
z1dFz`ti*n<Fc+v~a=yAG{(PC#Na@kJ#o^6Zs@1qXC2!>_?pT?VRM^7c$b7MhS!a4>
zv;lW0zVuJtCmx7OaT(RPnhn+C_i}FT7{Z=BlcH0&`LKMZBn!KTUVS-^+|-y7NyDzl
zKfJFaX|(XSW6k%SBAFi74BkWlel}7cpaf|}+@8Zn=71+>O0?jK=)1dVTx<x1yT=Rn
z-&38yf=(#5qDxoR!o_Y;r3KyZ^*wU%;4i&zc+3Y_B)POhhYF=GU=$Ut?%COoX5x}=
zc$K85j~1nT_52MK){O+daWSs)stIO#Otp$cG5wRc3{`W5eBAWQEo~-bw!MasSIspA
zY9HIco-<eeBlI@N{!-NmEb_AJpN?{=nHkPQ4W=#~UKDZ=C56!5eEL~lcB9bu+`_%~
z8z}0u7YA>HtCzZu4mX{7n|(j`q6<H9e<Vj2y&;|CKPZBE#LUjaee%6cXFE9AyK))z
z$Km^>v4!0-cG0yg^MoTc9bT|H_&oTl?C*O8nLELIXiqZ`5L<9z1Vs~}>>Iv~kO81^
z7~1VjjM!RMOGuFaPRU?#xH`n$r9RNy<ad3P8-2*bw7X3$SlxHpt)zTu;r!!*XI~9w
zv`ywNQanwDv;{CqH2Vl!)zlR%<@?Pn>?aMmV<m#}(!%3h#yN|8#QJg>E>C(2MENWL
zvkF{9Zr|-ztAC!KAy=!0v2cXB!12$0@MEYBKm0}eaC8A{BK%_t%wSb>LzX&)P~2n3
z(noXCKULmiqI%MKDOeE|uJVp~QnA(Gx%tUQ!@g}#a)#;|xr$(3QurUJgL=J*{_MXc
zUb+psRPJw4LP#}|oA2nTDUhTtHIEGnI){S2k{oP?RbGP!RHPjs!?oWE%4_E(`gAW7
zzPZTcFO~NI1JEcFeQ<T)&a(+R9=ox3*o)j&KKF9F@d^QDX>jFWY0Q0<*pIwWoVcTM
z;zQ0E-%S~G<0%53SJ0oK(~mzUjOWfGU7B9%7A#GhF0*5D9os4V;<?5{gVzuX41Cp&
zWt3&gf2j%a*tLmHD&@@gLfu|&HncnD^r1uYaqupY21Ty%XS^4087~~#6RJJMv#zq&
zJG`l~<5kaAB6j2RNvW9Jj*@ELm2+kDUNr2;m)MQPDa0&V^8(L&9Pkq>UtHO3=6r{*
z#ag{3Tf<-xZ!$#w=ZXG`8#^=aHmjh8N8*^boX;K6>Es$o^ybt3t{c<aeGPKu?|de!
zbk9fpw(=j;o560fYsZY;Z<4D|#Q_l^#8uw$@`N+qZ4Zra8o*!j`*R<h(Ql<Ci65%k
zTO9vNG2K4A+J5Kp%|C3c65b6_9Anf9P86$eatv5ZzaX|#yIqs9dy7(8M6cr}?q^7r
zU>x%zRG4UUZ3MQ=kRWkD?9+7M=mjWGd^#!|&I9|{=rR(MR5gR00}PWZQWm*c4K3~`
zRUcjg#~N2q7ZF<pRJZaUSF9xnjbKHyq}EZRot&5t)~=|P-5e*l)-@XiRAsBFCP^-v
z@hapWA6*6u3FL*Y_PVuDA+jlcQ_+8Ic;8L@>K$KJq0X}F^>ap1vE#q_MV;r*dX7z~
z(p~(2dKo*9`kpbtAYy8(_a$zKBo3MsM2wadmUux-pG}k4Bq?dpY6(1C<$`?l0lL<)
zQ;cIQbstW@)OXnbp8>)WU_MWKbNKxx`Xb*vf6RR0G**awb$|QbpCVl<_>bDVwAQ)O
z7~Ie>eN~o*eHl4xaaho7kZ;oCuc6&9kp5qJ=@|=+No3J;EaNr$UG#`WuhdhPA=hBB
zrp7VPc5VMXW?Ni;u&T5W8A-FR$HlDF7*p3Of@Utqvu<K<a?Ag2`75$sTr%>a!w)i-
zF%4${XN1CbG`J`}SWT!eh)da-E&QR$0!kD_T&q*%5ZwuNtSUHI&p}A|T%5;7{p)s#
zZ~j_uXpVLbD4`oM9%SsOm4-!Cg5XoAV*+iOX*+4P>Z0=xBB|4?brxw{;G0KdE9Y`Q
z*ry7g_hNqIS8f5Wbp;?<b!wpwG{f15Hdlx8;0ECX(A1`UfLc_@y88G)v;ex8wXqGf
zl*K^T{40&l8npW`;ysI5O1cxvf~*<cf0yvMvkzWI`mW2D(^Gt01e0kg>a>POqfq~Z
ze?dB98Z4Dq`e<1P6->%8o}IpKHewukzvaeqm2m91Jeql!JK@9?z%e`TFO8)^StD_1
z{p%CY2+!y4<w>c7SU%H)<<u%tc$2d1kCe?|e;^tF_t$Kfc1MMI;p7<mr@h|X0`NwG
zus`0sm|Dd~R&x{cw3$g&MqcV-@Bo5a6)~)X5{J3oPDcLZREFNGro@x3csK-ZwGAz*
zm0dSZmTT|-!Auf6T~~!$BISoOmntA32!extUfG3}O(S(Oq5*i0t@w2E?O(3_3)5~!
z11)hUg%_+U{yu-FTe%>aF(haI2njh}KPK#(msliTpKPYZMQZ*bajM&@NH9tw1|bos
zuH`_nsiKt7RCKn7?n>L<bR#tJ_rqn?y+T9#E!QqV{-cF5m>+lJ;brGng?OCf<O^YD
zZjTMBPW^g3uw?|bY8F%fg4?G@+`p>nh1=xK(ld?}&n+KY_}6!dtJz;bV4)p##{PDJ
zZ`|cQ)6mn<r0mjV@jqJ}2#8?$rxoU1R=tqx3B3o-rtQ}>09|4}mf&L~MEX@tRS>pZ
zikS0G;AVC8WuMe4%0CZsli0T`R<3N~BJM8YSMb6zTULyoXPuOzV~-@}tKPaBSYvJ7
z-?W&xfpVNApVbh2z^~VT!)a*v&ufoWyk2fwzZp72TmwDi6|ug=rcSGlw7ZoTW%JE@
zCH4%zT=ey~vFBT<02~J7sV}U*AZ2Qh{Q6wjL<mW2eb(ha#{WP=`2W?mTypf8F*@kp
zsD#B8O*MdDodB6BDZ4_Q(s<CqJ~<MHY@SW{>i;b}HDVCaG>`13x&_oqrVPbKwpDc-
z7?1<#hsRwpSZ?iMvzP~O!#+04&3`!&)8}2Lb?xwcWc;bNIP6o=c2w?RzOONs9cu?M
zOZP84=ehM~E<d9|d-0@ph7axgSllaL10sto+ArvmlorzTs^jIO^rrU51sh2_Ll@r^
zcprr@bTA}bK?3~EF@FKA2#8DFhNC|_?m)iaMAqu=)<+}p^=fxLY@rrJX-s68nmP~b
z7YSp5Z-fY=R-DP}NWYwIqKppcn$IzalRo4W1>$)nrm<KqrT;JiSe)XkJ`GWcUp12t
zFYBLB{+Ra0k|kMIRSgG=ce(IG(@x=U!oQC_#h?)Ut4%D%BOWmE!n1xYS*Mq7Tv1h0
z?z8gK+pg4S*n!Y3%#5deDip}?HLCJ}j<QPLAWeB%q4c4f-dUyIP7a1U;w9wkFT-&@
zMzhy%(qy-<cPvk0x3A!RSR5yxUtm@9>DjSwKxEoC7&e%B!h%dRX5<odEW}QBc60-p
zJs4iA+(1w7vfikJ(+?&t1OMzqVD!*QZ(@A`X8#qalr9y0Ytt|>)bX>go@1;Dlkm%j
z6y+~q*GOOOunvR6mhp2}q&;fp6?)EN|G*I};?-Sx3{B;f&mt%|^9U~+#j)zUEpm+3
zORcz4Nxtc44LYcLQjKV}a?DyMP!p_JuAQ0*PMi;--A^M_d!*Qu`%R{5N!t1G5>(ZU
z%?_P~izPQy4+!C)W=?aj5%PFwO}iX)0-!)XF6VDa!j@paG?s_Nx-Fb{O(p|)bb)SU
zAB$AnT>#d$|5;PeS^aT>7U11V>6&>-OR@+v@Xz`?kv@ynyA4F+kcXQ=D^HKasnS2&
z8wF14oa^23%VT&Gzlu_QhzH4mo;qyWLA5Be8~Ri9r=ulMEXt5wz{bGQK#|fzCQkL!
zV89fVQI>Os!&uO4ZFHzY@;ys+%K)IB9hvhjzdl=W%J_kKkfBGk@z8KUEUZ&|d&U&i
z#@=q4<cxcDB&k1G3-8xQj*k9$wb(XFS6Zr`OHvh{<nU{hxn3x%6{Rs)BtNHe5G2{c
zc~x!T2JH3}z|WWGbZSQ}xLeTvD>VE;XZ1cvrq-S0<P+eqbk&%(bA+f-cz}k_|Ht05
zpf(j8l+gI`U4@E0c2WPKt?a{W#7~h#$e+=g5`WistgHpUn&d^g0MfAHs}2o8ej|=G
zqc0uHE9#A$5dMsvOW}wB{N_U$E%pl&Wjra;e`j_1U%yfp79WYb22eBXuT$@Pe?uIc
z8TZ3eowwuM2nT|QkU2S1H_>2QKat)9>*lT0qiv<$-W8=PBuu{)10jl!_9wM_CqY@6
zcz>(+swrvlhZM!=$VZ6`HZHO6ifZiv)Q%sT5lkG_EzuQwnf$6p5|+u^p~7LT2#0Kn
zxfFwr85z;ufUk>u!&VJck%YeRFSLd0TVTHihDLjqP|Ctk!{$mey-b~&9!-N{C};rN
ze!}x(;}z&C3Mq1sUVtilF1L%qef2YLr(cBDU20}6Rv=t&yznf=rP3;${$^_OAQzk8
z`-lECYh;s<9YrI9T!1}D3~Uc-4{h%`;hot3?_rK=x9Z3*(^UVM6?~f-O#?hwy;6bu
zx!DY~PJ${s%T};DGT>=Payj5uE9Y8WK)F&_VG*Xmb?m4<ue9T`RRF_N;*vh^Qfqcy
zlbu&w^kBXHEpFfZbeOY!=R1*8qibTWHFEW(3KnE}gVR51LIuatOX{>X(LD3fyqcOP
z^*?j^n(h8Qqkp_%0y;V!`nvHH4wPyJX0QdUOCBDF#K|g7JdYfuF6k>i^f|OhzAbQT
z#{1-W?HnOPgOzglq0Zg{#mKck{-Z(#qu(BiJbNs%FyZg6(HBXq!OdWa{gkV!&(J;^
z?H%xocKn=pd>}OQ%`IQu9ia?u%e9}*=LkjqFM>z!mU-*@+f0i1nforN9x=x4UIRm2
zyPrLZXHD?(mt(x8&fkfm(AU0-K}%g<iz~lauLeT`pDUFaB#LH4s}@ScW(bdwxM?D$
zvr*~~P>H-VMsdi?w;h|eCU_Ur-q81Lz9}p$?0{I}rHEq<TW*YEn}hLpze1aYtH4w{
z#N_sr_>FI<5!cTZ57{aYY1o*Mj~{nUZ42@}y8vmCv3<w>!;TDwg4SK!bUtFZ6s-^(
zLjwvSO=yc!^;R*gnlm9&fRV7p(i#Ifz(>znptmz*vAW-@vtOhF%5YRmhKWqHbNizD
zmVqaSskwqTo!CI*$vPdLl0SzH;%HaiY(gu|h1rGIQw394uaS@q|2kdK7c%WFgoQW$
z`ps^$?k324<lPwte-e(=R~~sW&wr#}=_B<dF!lA>n_|kdU^vpadENR3SNUFi4?WOw
z`RHCD8|XPE+jPIm$%Sg0rlu7Z;gVD){{k!z{;`^Rb>;WPPjUF78#9<n!<776j9F(X
zZ>>5!LB;A|)3#84o&Dz6<$}0`ouOS&Amx8DXY`Qlih8Q|sj&Y(*r?4b4wCjzT3~hW
zCi=EAC!h7HCH<QuX9t(M*^26ZSRb{!)t%e-+q<JL8FJeKe~8`kutyEk;*83aJg2{y
zn5kB615C|IQNE4#pW21%DEbRqfj<R`FM_7FO&z_Y4?RzDzYJe7(bX<>t|<_%f8#oX
zwdnw(bYbj^oVWhqqjo00p+lzYh1_hJ)~asM&cuC6gy|_D9qXpAQzlRR2)mSLKK)?T
z+mx>Db%l=dv*-s36nf{DP24QzO~gp9<-QGcKj)Pj>a*5+HR<OOuz21>@z)?UE~D&0
z2~A02liIk|Zq=NGN)Mm>Y2Q;bi*UOr4)x2S?yW=J8tqI&5_!-}@&YUZA`SCQ95YDL
zl^%JXhiWCqTJPnW8owNJI^AGJ<f&L&MfF@^Ycz$YHs_>B?g<rqQ=lR*cINr1+POnI
zd~er+BOH=kPJfzLaBu6RDV(@0aB}iR^YVZdoKL(7+O+Ivg~=-wVjiBGu(j$-wLGV|
zS11EI1#ll4*PJt(^t=S0XH99E{3EXPOn?@nuZsqux&E#_IV3)I4}s66{(<#1gGHCT
z47Ize7wm@)LY^3*nTMZ-oy|NOr<lppmx2zH<a|a9oqn-!f{FHN-TrDb>bJ;eNSk@T
zC5^cn*SE7GBAoX<qKWM^cc7ZlC@IkmhLf6C@x}sj&@YGxemMUJt%|mBW;NHimvB;`
zi^Au52JU{PD(?eD^e2?taa)R-%N$x>0q9snv~VDD()holPr+W|W{}Q!`=h1vNA$-^
zlItAq$BtL;Pe>x1QqJ|@HA4p-3DNumCVeftB@0AnrK7CId!#TkmPaxoxP64pjC}k}
zQ;{&}uCb?v@X$C(L_nzORZ~evK$ExSGB&A9c^bQJZ1p@S$-p+^iQ9#d1lB=&^rL)H
zn`e)fFBm7OBp44XocVg9VZ!~p@i1(&MH{d9OSdvU_tRZbd)=T$f@I0<`#WcdznY<0
zp+YPE0B4T$eJu3rUA~T&s_6LE?j6H&crpAF@yfIWZeOPlbZET{rD9y^U^3)j6WFwc
z%)803x67(itL%C^2hpRy1#JDd{OO$ArzYKhXlz+Xbt$!QD*V<CKcel2CejgFEv{HK
z0nalpK+ups?DhG|I9EB)prxTbtvy>sgCPyI6C=u`gklg6350&ZOp7SUD6cEwtQZN*
ztYF3Kvc$&B%kEo2<E>S^0L5K)%g%94-R~thOCB+pQEgLx{%*tJu;2%+iXF;kcOoP~
zQlG=wcfzL#MmxxsEchfql~Fq+0GtiAN#hBnHB4|-$U9N~m${U}`96(XR@za5#q)&!
zh64SBTB4kga>R+};wEOW0h=9vScsA^a@r$ISBKfNUJbu9BNJ3RwgEGMqT{bV)Dwa$
zRfgm!QFowmR>&_pd*eDuLZc4M_*zb4<?mL5v(?}BVSQh4?;|FQJp>N{%i}PV8b2Ua
zLFJQISkh1S)x2Xl{1_QO+gLVjI1>Y{ij8{jsfV10MuD5SPjGiR$`bN2xA-|{7?eNu
zuScO@#58tO>|7n#H_3f9>=d+BaqWVPCKX7hg$^i9HMBQOQpCCu4O<_FK1wKJU5adk
z1QV#=+Q-B(Y8_({tu!76*(p}DPKW07vsym;oZB6l{JS`on?KAxuzyv9=C^{XCk*GQ
zhC|~<NP3SVqwtOPu?O$;gB-S0!@BOPrdQD3=DJX*sSqJTNvMlIltm6Zc3QwrR*2@W
zC45N3^P8mCr=LOnipSj@GJSunOWNFgM6x;Hhu7I3ng;zJQth3~jTa(A;2~{dxBDXB
zY9hCcT;P9yWfse-ViiEz@gqLxMZ-in*5n-$AWrG!)ZWwj!zM!OF)b7_Y_PAJ_7qAP
zDA+Re?V$AefSePXXrKFLa17&ni5@)jyc{0O^AOc@l{kt}8wRHf^RniE4NYS#=NpwP
zUKbO$e!^+=+ey8GvdjCe-Y}(49M)4fSNNmfe!;w;Dfm%j4P#XF^yzCQ0i7)k>*hkn
z&n;(%Vn=9zkyL>n8F!@#{T*|h;jcxsnbyb2XK1)hw~8#+o)HKF{K<G{L&%%_JYqf!
z>frhb<w4r^SY3>{8ffGsaF9+tG|mL}nXoUa9Bs-CVdYL>pQn_g-9~WZTpe_~gwiWv
z%*I7$6rX8Bf1}vv`FB4-)-gcWLf2^`nW%DllM6}r>sgRyZgBvcUg*_%g`#N7pr+52
zoFmEppzZr7BoIi6$^EdI9``>>_vu$z>+@NY?NYq2yZ?+^bBV&`1bYK->-gR!jicfz
z#A3vX3=yw%;8#mMowY{<u7`H<tqf%GNL<r=D26`Kh)1Ou{8-lbBEMW+a#jQHh)e;}
z<f*GVE@f^c-Jg^|mLyF-kBVLEC{kcM6|OF4bf#2A+{Mvai(Cr_*^#atb)3rMn?8=~
z_{vwTCN}Zw8uzN9E%2Q2pFZ04B~!-e>DBOx3nm~m$ti)E!jdeW!j6spRlbMzqMUTW
zS%Mc~(ggHXE_O8=G4Ph>K;W3!w8f}km`}V<0!#9hr^-HoehJ`nYyhxp(2b1!4E!Ht
zcm)prDt2if&=4C2OOG4)G}zAgF|1JStD=w!bf6)dw$#HR=1Z5)O96yutqRrW_ZMhI
zYAD@)K9VIom*>_BYR^RFDcZ2?X#3WNF_uNZVgD#kYHh+gb{E2|4Sb%h2bD&%(jzk6
z;L&XMqf>;={dRepv(eTjjr2fw47XF_JU4cn6n-9#!X!Oj{IJ9c%hC9bbLB~~zg5Pt
z0x1T^$;@ju<6icr82@AeQVL+OQ6flXwYOEtEQ|A6l16ltsEtVx7=YNd-+P6}<;AS$
z=uF0Rx{-xZrjD|VbgtQtH4}Cu=%9}oaw1oXv|t(5CN#T*M*fNQ>I{2Yd-hJ6h+!s4
z(4h@X_+29Zi`iAqJ0U|wmfx&o1k4J{4S=t)v9o;i6KtnAuLB%uGPawi5?Yd&><2Bs
zcftLoO<1b`KQW5Fex(OX6Z;Ws;cZ+S!Z<fJYf+LKyMV3d;ZMQ(sFZ`fMq`$US5iMX
zk*Ogls&cw(&~BO7M8HXBaw6|>Db6Dw?b;Y0Txj+`hmk)l#r|-~*zIe2&6$pSXz?V!
z$m08Wv$g&&H)|^YaPxmp*ncct^@-2xIjW@tUm$K|?b#RBJ`VS?&AFb1`kJXG=!4e>
z`1P90+`Cg~c(2L`veyL-)U^-_a&~FuorA+Q40n;~6TBzWnL%bcU2dt~18OkbaprZK
z`a+DvyqBZT61(bb^-bsMVdJ~~!Rlx+PgKZCOFeI6aq!?DL3O`xQ{Bo>Z&a*q4t2KI
zNN4vcQ$lK;XBnzdiAEMH5jv|$VU;L${!t8i7}%nEGVG&ESx@34G@liI?bjP>t*jjL
zh+<K<|AtvQt~vG?xZPn&jwxia1>usK?{D9}1mYf@*AvvR78@sINzI=~kJ(PPvwzWZ
ze!+`@=nUvKb+d!pD#1k^Hqvki#Xzy$4uT@`&3Wwei0pFo71j{f3-gmBsKNfoL0SME
zl9vBjayeb~2ahw}KKxc+?9B$SdxG0naHpF0rkT&g13Q}elPpGAb9dLozg$AuWFtq{
z1jrj4&y$bbL{(7+maAIaCnaNM=+EeQ?@^w0r@E3aHTT|0d|WOz;7*Wdm)OU}Cw1#e
zm5ytG6QK3oQE?H_@@_W~rGc6~qasAK(j+<vreMp0Vs}NvirViUa;Ag^lA2r?fxp09
z0tXJ<Cc71bO7$aArxyGDu3`}9S5qw=g`?wDB)N#NyJg?cW8j9HMB(QCF|156nK3>4
zB~93_+4od-=~r=X-yNE;jUHQY*lio5{>22>(_NIS+;D9j<#~Z2&p-|5HgaQQg=4HW
zudOY_`c(iIv2o+xmtdSAp)cWB&)PRG;)z;Xg1z{1@(lLYL&kv%97V1UN0ByH%Xz;V
zs`>RBtxWf<Z102WXM4CIv|KJ*7?4E@+KN;JQn~r|fg|`ceYExXjN5k)q?{F2QVLQa
z&&rB_Aw1StEJF|tI>(va!7nM%lZhhdAOBkNSn^FTI%+`IC_9GHN)bTr7egxd9w;4e
z7besYT^KD+#pjDtPn1g*@HgLWE=R6>F?IZ~$#IJCerB>)lT~uQgM)mzB0r6t_N(V1
zbIUr;FV;4hCHs73UV1QzEw9VTs+7BVMTaA(bA2j+s+c*q?LGtF2`@7%`nNG`diJK|
z4^DoCfZVvf$`JwKT2d(J$RmnH+SI9_*%Yquu;4SzhoY7~rRJslnQWvv9$8Mhc8rQG
zmt0GkWfL3s>}-VDYAIMwx@YA9itI~bID(8&7F@<~^d^}rHI4n@Io)P<COrPgB8$E}
zxUMcGB%B!;Ar|QqD+G!#Vqc|?Xqk%9Mx)pHcCQ8dDL+W%Jde+{B)5XBWz-CYgUet+
zQO7kk4ea2pU*+iFJtDnADf#Y7llEgwN1tV14RH%u6${6lQ_lidkRl8%0te{}8eg;X
zwOU)Uoepqp!jfdoF%jJipQbrWfxa|TZD;%~#I<-4cpT=8TY0pZCN@(xHJi_>jZs6T
zm+xEL-k)-c&h~ubVV(^a<odcyz(1w_n20}A1BtU-&z0ZV8<N~fE@E*7C1>LX_>NZ#
z5E|E<Yd0ci>&`og)LMK8{7U`8R2`007LBv1ObQmIVzbL}?-%^vHG`dmfFQdn4y_My
z+GhE~7<QbK^1~yHJc^+DkEO&4Xdufi{$dL_qZs%l4wMgwy(`zS&r~?7qqYR}<Upc>
zOQC+vr4jTbcASZH>7TGIVx80XEydJ&C{N_KiikF-AMs(xbf`<ptcIpIF?8(j0LpA_
z4MyvOz{*#Co|UV_BKgO^-dQ?~F<amftXgcMFS}zZ#xNCyUkmu*n}#tLW8iNw`fR79
zL2m0F8n;*BLHrs1<RjvYz8h<-=kd|eEot97nlGo&ItBMw+rraQn$-m25aS$@UULb_
zF{pB_{e+$(XvxYgSwXF!H0nl<#EV2{tB(yXq3T|oi08Y)Ei=3UM4w6=WT1AoTXPkE
z*hYfw8S=w$1M5blT`PitN|8h`<ojg{B-f#~i>XXfd44F~8D9=uM#sjty%NM=E)H`B
z=doCJ8?CnzR4`Os7`$5%OE?Z%GNVoi-@vN*25)jAM;eXIet^$T`+JSWBB~^bek0(F
zzNrRjZ~{Y=-5@nDEzaZCk|j5hxY`TNMXa=lUf`8L+nEyirIDWA{*ykx2WF_TDTez!
z^-qV|1tOtq+&Yjv^)sPCX_z@N<_QnS{fG0nE#NM~<oEX0LpNpA5s@(hs-JESIn@4W
zzA#F$y|y7~Z8Hp<HDOWXVF>2!R+-Zp;^hYKCA>@$o?1)Oz=S^)JLdn7P4EkPlU*BF
zIsk8}xMih?p)YfY6X_scCj2dvBwY=L-U7deTEU4G85Lz-qIG3#kpeG?G2dm$t~fy_
z>0Y}$mb`D33#=7CaES9<wK=Q>1M$jmr~I*K;1#D{j$@H_VK?oIfL?CqoCc8JJ(sq=
zr^2u^YM}GCbgXR#NVIW@W9^;emK(fD;oQ+_1HZtYs#r@kYMDoD@EA6yt4g=Vf3WDD
zGU>L+U{kP7onAF>=ZEz>7YC2&z06=njBRBpdhX0G!TPPhslAj+kJ@1G0V5dh1hXwT
zfYKrrlOg|1Fz4fLr0D<dxQp>0!+daLt_}$a!*q%>J>7TJLsUNIdj6jG{)KmIONVlr
zo7*p|+D%Qwq|(A(f#syu*VRZWcH>bv6CJ+kZ<E5sIA7oJ*CH9D(<t%LC8@)n!p^8`
zKWm8b36t1>L<rtmAZECTWwi@p-v&UXQ@Yh7uCn*V$y-$HDv$iX9Xp_Zgm!DwFi4o+
zwtw^|OSFD!&v5(Dwh7;5bT-#!$SUzq0%*c<T!-t5NRI(INn8*fP{C|Dg$a1=&5!eA
zFyDcA?=CJuQ@6Isa*q9)vL_Ja=Imme>pHW)VySccrd_1#+WGu6%}bH8dPlieT~xBl
z1YFbu7~=uY^Q}h9!q2v>rU>PG4t>5UjD9j6BV#?pxqb2O&>B{3zui8KHcz<4rE}ct
zj?Fkz-peC+k9d<GQUr$^$BPot8u)>KmVB9{0dPd+2+`l6X85pZzWtqx$x&#Bsjpsx
zUHQ6I4j*$Jl*cuH{i4B#b){qeM%XWw7(23beFGg`3hKqgIkEfv<Zg^7{@Zmrm#qxa
zeE**iKG+YeX1L1j!5Be-NiKs7$NYAAQO)40$g$~on{l&Tres{zxU&o3^Ha}QLe=gS
zuHA1_;A>3i6@ON3GvrNrjfI9veH(x3IKzNF;6!Km^H(E#lu;_!5NDo-UvF5r5Sf2^
zSL?5#G{(K-SbEu6_qZ$RYO_Sb%o2%hF%T+*0QU2o$kJ+JJ!4|Y(OZ5ONF&BRDGw>M
zw6M~{gxLN3cQyuVI7{)n(c?rO91A$=Xu@7}%%l8G<I5?=i1uuE?D9zWe)f!^VE*)3
zP4wU_;-K2}FR#Pg=qB>io%heM*NFk4i`df|HQG^;OV1jc)mWsxCcPxNC0{k|t;a@*
zik)5ZwKX4S92@Hj@I9n~em*U=1R{{M1BzERuyrGY4K4BO6M2+!!M_&!`0iyaRGvPl
zbj{09sja!S`bJQ#8_mynaGh1upgincLp9G`xY^AJ@{ZajaBbuWho{$$FYqX_2Q%7R
zrJG*LvaD@a^?7kszi<VU8J(<Xv@Wd)wtK!(nH}Z*seJ@~WhyqpC&EYYZy;Z`dkveB
z{Pj1zMWGZ9g6IAOC!)shmG}JWWWERS_TvHC)2)KHq(c(NZOab}Y?P{|_W>Tq5OPgI
z1F$Ha%|ArNj;_;c;y|IFGrTkgS5fNgrEGX})HS|w<{d`D+XLMP*lLX?%c!&g*1lJ2
z&qS|s`@X~rktF=TTHpsO;3a1!K1%*%aTt*OgE2!R6G#`8f7p>~5aMv;AN+1w!bBU!
zjndS0p}@ASW6V-0a#8C9x*Sd52*N(+D2#6|89sW%klurt!-Bs;>Y*J1sSWH-%z-QB
zZ{PO&xRT9${`8}3tc(V^lKXYX8q@Kjb2W9^KPqUX>cNA!CA>v2OVUF+dlS9t@Xo?K
z=4l9M>$T<x&$h6?nIDBE8@f)c&c)z$(N(U^=*z|cpKu?6b~3jp3ncGlJ0&qmhuqS8
z*iQbLS6gwy>_{z!R?^+Cj8;V8M=AE10Z`3<#1WA{J(W8R%bfzTTMS_aDxVct4x|_?
z;fStl^VnSs-6^cZ)eCW6Bj_v}{41~T;Gp|wFr}Phg7~G<^%C?bKmFP?)rX1_81N}9
z9r|*|H&iXFXdRO<3NSmWw>y}7(MGsVtTw650fC~`PEnx{VKTOq_%o(>M~eOki;d4%
z7W$=q23xosQCFFQaM+2_O5O^by(pu45=2n6=Mzr~I{l6vYKZkzIu5H)Iqv;iU5Dlj
zmhgK&esLq3f6*ezmVQT`K31l1iGz#A!B~Cv|1&n=6bgF2d6uOQdbK<o!RSVRU0IAg
z%_f@F`39_z{I(76pZH_o?i_x6=^tmLiviEDl)3oB@4!0Vs)<8u2|8FGKMZCN|Kz`A
z=NC7D>6lrL0yhNDBOB7yA^0$aLoEH+4&Z8eaXu=AOE@RZLc>dW1BX-3F$C(LJpkQ)
zkC`Vy6Ddyhi}1k(&R;A7LEtv->V_n(FtWJZxBH@RpmU-3b90GW%HE0WCXx^cosc8?
z!}`{+&)cDBFGwFvJlpr&^kz?5<aK$WYZWmPcIxAH{2IwWIm9kT)?+quD}7;OEQ!(T
z7I`P^Q#&klM$|?d79l*$2s@19lRsZi`(!F$%H3D@O3f89#bj6~rSCu04K_a|4cF=n
z8sCoTwH?xnu_e}4RI)mD=%G*2cgUz`GuL&ZH`<fbdCFyeykQIOYk$8D@gR}QC>z60
zwZY{g`x_lz--SWi?{;=L+Ys=&sW4D=@MDL;&KO>pLvFwdwaPjV`(Co#OO{j$$2G?V
z7R>0ZQf&49<aRJYMM$*2f_MXeBQV*_`+JmU8oPuFCCS?-DimH*<IL=E0J*Mf)XJN_
z#ADvvMCS{kWfCDDIZnEv3{%f39~Zs38aqQ<L6?@c&L8_uRh4#h60>OAeAGNN9P;&l
z0@!lAOHC_hy`X3(2T@uUrJ7lg`@(1a^{jBSksGSH`$1?r#Ue&vF#pKv$UY`IEjlQ?
z&02~pJZ>%Rz<bOV83~rDI;1wCxw@L^nc4g4cA;TY;q~CV{pmK)%J+>=L2#RA(Bn=`
z-k&@py2E$3dDdIj)$Di)nl@j=Crx7Swg@Y*NIU772CeDC;A(W6puizEal_WaYZAL*
z+`!Y<4J;ImgQt#CA=U*8Cwvb((n`^Ujv9vVKF0Yw2v@#T{$QhtLu@?M$s&<DH*sh*
zVMK74-g&V)Oq(6Hze2Kj13eia6dS&<>8*$SD64*da4Lk}P-gqO?8oe5z(@N#k+L1g
zGXis;`DQyEXFHKi)>w{jWHk*o-M=bi2Wx$rMHEQYc>RAvW~ujU1I==#xec?#z7~pE
zpCU)a%M0CL3?Jh>A~Y<GkbZ#!aBcN827}x0iAoV9W2=$FWKo%JhH1Lmrl9U>uk>z*
zX&a-CGKDGs^k`f(>>q5OM;vkiyo~5E>_b`k(WIXoczEZx-CxFc4K3kU?s4x6sXk}`
z+W?<{tw2bNR2k0?<^+mOEBSGry46fzXyuw*!m@fjSkNGIDWeDpr3=Otz{~h$2=WUZ
z|FLTWhc*qdm;7I%2`nq<hk_SO98;nkOy%V8lU`IYuiE8Xxb--7O$jNdZK5W=<9YYZ
zaqn!P8M3RwdXfQm^248&jFic^FW|BiH}2B$r(wTON6l^9kJ$=NMk76Fy+JL7;eH|o
zc>5=9-Awa*M63pC?u-W75jjbkT{@qp=v)#J(ADHZbGuYA)iUr-N$;a>dNxAAjzMjj
zEBQU~1f&VP`~JPk%OaF@<yJ_;st~Zh6)D@&P4j6|qlHCpKK{TYxys0AA05Z`$kdhs
zz4P~7lwHu*DOeZ;|KfvO&nMYjWwmgyU0Ron4d~hqbc?q_g^%Le%$H4LGY7dBzk}_J
zz*oSf{eq;5eH+M99|F!b<={b*elhQ)h_j<r@{<SERg8;vVB~E{V;M6doQZ3)W;;S0
z1upj05aim(?%W{7DCO$hZ=||yI#%98ESjgrj(+?ae}ARc_nP)Djbycy(ynplB_B(1
zqL12sB32XC{tkv@Er<d;8V{iqWSQ^^%^efpak$(>XQzd5V-4|??dw|3MfD4tykCf6
z%R!ynr=Mxe&y{q1AHx=7;XgWce9bQ^HXEWr0i<sw=eybY^ZOQ;(tOSRf>ym`9Y8{q
z`Ouem!;<FAaIpdo*G7MQ^!<_KEg%SDDDK@r%drL2nK|mgrrdODL~zKU2uMZnFm$O5
z5kRhR+RCqTaM7L{g7Q&;r^ZjqrytP@E&-xU%yyu@(1(z*S?+|JX0oyp^6it>z0uWp
z%9vlV-nC)_=a*zD+jUeZ`o%4TarPy}S6UA(eD7|~ADx=3ne|;$${=&T?4;TmU8Tl0
z09%%@CU2>i`Fyui(P7RP&qAs<#@d!>Vr(y9YxnJ2;q@lH-F8v#Q+AbSHPE)9?d>#Q
z__{T20VIDOV7O6l?{P|VWHfM&d_;z6;Q8a?R1a~fWKgKrd<P<|RAlyz!%7yLLy|_{
zXv!@;*`^a!_+5anwolO;p%Nxqyf)wAVg0DuNVD>Ggi?Ry!9lz1S??K3yAS3$vxfZd
zfnjev@4W|1$B2LEC_*w)Kg+3}NB^&lH>};S)GoM*IOBO3694rNo_&Qj>|?p0K5qMM
z(Yo<z(|UX>2#$);(ciHPA@*!YcO6wn8<pGYWJ|Gdh3=aN5gx`UDpvCFEE~zCA}~Yv
zUbY}ub6#?;=ez_D%9}Wm&K^5$1Q{1TfZL#Kl#{RZv<S8wZ6-%+P$p_qKei82eds*|
z-#i4(<B$WBBqS7R$GCw0vmwbrP&csKE1y3sIdMRjS<IsV(x^z4Pw7Vu?vL52iKifo
zgM4Bs%JZTmy!L3~R&FN0p%^5GQ@R|+nf~^ef%m%^6%ilWL4fNA8*8Rax}9!%>}agO
zfajadXT`^(Ja7AWHD8B}HRLq5INlXl{i?!8rD-nX^=_09p6_34)$QyK>I_Tpp1P;}
z)W|x}H7AU~Gr~7fPM7Lh5I^U<&soRCX%`nUjm3m-6|DlVK$`-w0dP~6@c9#58;L24
z9t%LH1zdopkKPv(BS4`>fh?D>{>c!KRrpcu5gWLWU4Ju*3Lv;FlVvh03*2CzU+wM=
zJJSj0(;!VxTR(l(tJHlUz<O|2$L)+(-|-3e4`2YcWXoJyWIVpGiQ_0l^2*>;T~T8t
zrEj_?MA<uDC(Uk19#HNog>N9A!}#bFvu(41kr+n7|KH=L)idC(fMQJDy*Th*0EVbD
z0o-N?58im7s$^H|&f=^M+Jv~S%~oFGw%u!Pz9smY0SPjdT3_$_Phjg*NV+5ILvAGi
zd19_Db=tq>UyxYw*<!%Z=!9u}is2Q}Iw6$vFyn*Wdc+}>UFB)zWnbv1YwLJPr{o6E
znmz*}kB(tAcqQIXVtqrDG4U+i3;YXK5=#=c`fxzevayl_H>xchTaX^dmZQIe4x4Zm
zt_r<UCAqka64YLD-rg|Ks2KnZrI@4-=?O;meOcaX-sX8FTc!2{!o45NwKl#49RmL#
zimjifpR(@!RApaGH}Xm~e+qZpP<5=FH71^gg5ML*wP#pP^NyXMaQJGeuW)stz8~H^
z%PQjA9~SP~fbN%<-a|N5$+duT^s1v%w|=ChJ;wi!5Y_ydo8JU0ob2TU3$XL<Cp5_B
zIHFfqM+bX2)*u9qDX4B+seAjXQ1+=+o;Ft4#~WWlk@<flOo_@Np>x;S4@7E(hmjk&
zaI&A#)s@y*m%+Q|zF<o%jPxBr<391_zB7Ib774Xx%~|pkQ_CqI5ABmf>JVdt&*Swv
z@ur&e8m@Bom*XpV`C~v7U8sWTI_Y+jEtqj*9aU|g%tSvLF&yL$ln&@@tjqOtRd+xn
zaI3bk&nBUcBxw%wo&)MrNJ5@Guz!wvU4eG4`kO0BlgZ<yUl6r9>{tAFfI~P&RGyRO
zxHhQkt@7yANET0HN2-+KK~IniJ91-$VtR0M#DB_r(`$(O@-hc^5yY2El01&_6X-$i
z|Ha7SzZh*Ny8Va7X^)Y5fEXIWmo<-vDQY*=fXq{4k=pnxsM3Mx&JB&(ek&XM5Xq<n
z#yD0c{O2udkQ&~xgF$M*CWatZG!5ZsBr2^t_YZ^AZhByLLT1|jW`u&`I5u?=ZRmHB
zY}1p&<^RVSPXE%l2o?Tv$~G!*J!sAU7pU8ZvjwFaA#5C*#Xpv?iH1%35^CMy6+^3T
z*r}X_6L*4>M#k3_{6G3{<mhs%avonzRD8-22^^;@MPN$Syf>43fB07RBdXmuUWGWL
zKPTIakJhkLR}=lF<M3y18Pr<aE-SC#zj3`4fxL6$**2c4uCI#S9{KlZdNFgt>aBf?
zW!E84a{4^}OsQWv;IZrJa<#{3!E^L5g_MKz#ZQ)OWo5z9aV{do#$$(g8&MpM6puRQ
zvK6VPpy+&abKb(~=B^V{hS#l5+z4SS84NjUA(#Cke=2ZosEC_IAPPst7QdfR&#rPb
z|HMjp+Nn~da#tqtC9v)r^M4Pfx;u6hGzp5q=)<v#{ytc*n=c(&QoFzqtMh|R_gAEG
z`CAZPa8y6Y)GwhKOcU5d_8!C!*dIRhVMltRCb$Ie02a>>oVQH-**Ah}YGlE`3u1x>
z($PRKJMZZ-p0|r7*{3SdhgTe2#LCzo1G?R72bH@o3m8LBQ@8VpF*dWd!A*-t(Kn>o
zxmW#atr$upM#ob?=TCSr-*CaRVMsB$`rSnrVSY?`q2_k_^MTLzgp=-Oyb0={Ls;sV
zpVwhWej;M9L9>;f=n+mMF0+-k8X%<uOIieaUi+ssW(9dZ9abDJxcQU0OtghVS~PAq
zXR>xBqL^ecFjw=yZbI{V(092F$<f#)l#?=XLrGLbL#AjYilMcH8WkNaLcbpsIi)u_
zA}AA@(RYj5XIe4(PxHpYppc}q!M3g7`SF$4a6`!Dv@xwQZdsF(X&DK$mYC4-3<n>$
zstVp=G*37&9Tqee@P)P4xc~pq%1H0#9yYNZnG7q}y2C7-7isksNM7yh<sgTa?D@wx
z%P#qFkWU%HC7i&9XvN*6ZG{^`w#6GyB2=<39@f;YWbezQG5C6pb>#2mBDxt@1o|hi
z0SpH#E+g&F&!a1iS|d+;Jn2leO@o7*|LQg?_I-huUlSw&>#ASC3Jl-;*ygzalA;%}
zPObmH@U35pUm(x(uE$mrnQxAMNx+ShMDbt8yzbllv*OuEpI?93#jMPH=r8uaXVeyH
zFtVM)^;0yh<e)n()F-nEKc}Ysr#+3Px7*TUzs*B1jZj2#s4UR{Qv*)<jxyae)6fzU
z)oauJ>gs~>5Zl$;FgluJ^tCT{4^_G7$rWdw|2MK2IL}(bpPIf(#S$vO*&B=4v=5y8
zVl&B~Y(W%kAKk<sHq3W2CA&sXpEuAyLr|+z>hJw$X!@VhHm$n#Htq;eQF^`AhV#-w
zt0bMbfOOc6ay0WhDQ-z|Kpe#4XO~FgSsLv8tUC`%N)jw<1<kt5co9i<T}M??>g5D#
zg$aJd`eh@jad^r*%Jqs=+B{Vod_?q0^*=gPrCukW{fJpTXBU$D<Y}UwP_RmshqGqm
zFtVDiYbMxLPAg>L93gGrzoQ5}s5lm&IJlnJvYLpCon1^7Dm&{h96w)(?3~dMMV=!~
zj-Kw`9_++1_P$&5B)k1v<S=nM!|ej`zM3e}M*i`&?f%itg>`Q~PP-6d>1&<iEhRxn
zV(9>m)|C-!YVtVF(f@qyi2H`Ci?76x-cO3E*2wbgViRL_V-*;&x!udY+43%)yhLKx
zcuRWV%5m{FN8!ed^uVpbxcBQ5Q@xitE8)l(eKr5W=UkV}Xir^jFJVo}l&Z6NUB(rv
za7iMmvst^jZHD<E*W0oB+7S3;ti#E?y#KAOPZO(_zd1AS{&QQFO}C-l)y0*b!ENp5
z5f5kDV{caaZ=2;iY~TXiU`}M}#+)2t?*{5zNof0Gm@|P*<w(Yvj$;GBxP|u65{jY1
zP@CH}w2J?sRxJ5!9W^Ao;e(%V1!t0$tOb>4$^j?U5CoPiVpH=3X}TL|3%|6L1&bXF
zvVX=crTpL1@qv2T?<}x7gSn&wYh0uFCiH}Sp3Rtl03z7#J?NmJF*N0IES#T9l*$%M
z4tr99yvGW!>YXJI<TJl6hE&#6JVynA=zeE7*0XNqp~N%Vimd<RC&WJ%bjj!O%vGsP
z%8wO5=bas<^~WAW^ZbneSzS6UA4VS13qi*2p|i!va+)GIgjR0N23HKnC51sWrnV=(
zyy^voGP^X@`655PF}Zz2(APxGm^eaA&3i@r9{&4-f0a|=re9e@b>8_XPXTKSU6kBe
zE<s}sqR7UMLv6yd(wcOSjv!ryfq%We(d7xPjo@^fVm3b=pHq3zjw1+XGqClk7LbEC
z3I}p!l;PuJ`VUi5cA9Hb4$^%Pe^WvM`~-rn(jomyM+d+MmY)E@4!+_Vtf}hy#uv^n
z;FT)&LT<YtMFom}C%B9Z1ukLDoWiyFBOtedyNEvH+qFZw#XgMfH0^5FFWkw)x>=;+
z$bd?=2T4-Y9gO)WT5*$DHl<2Qv#KyDJx~@>jvNwY4F}T*zUIDm6(>wn@7%Ii=ru`6
zxz;z0gVXUPoW8w8X9B6X)I!HO#0rfOmupv;E>Xm#I@#+z<n)$~)9W(|v4jkds7VKI
z*~ex4PF##YuNy7!*3|@HeKw-@eSv-csktiVhg-E*@<Qn2*u5QC21>N-`}<OC$9Ea+
z_(3On(4bAM?+7bEAzh(l3&$~Xur2~T?R_!e1xqKX9K7%eW1(Hej|ddM1<4-zOZZ*W
zSX`ns<QiRty<&dEres7c1Ri7;xP+Cu%4Y-)P1{NlR_kGku=wOXax*|~MUGccry;6f
z%{ihdbwjf87w1fb)XwEW-!Pl+6>EkQL&3g?SL@ycobhLItcp%emC3Nje*~vUJC%oU
zU~Rck8{XtJ*ti2xjHx9HQs(nRD7FiGPLZg%8STu=GHJfQh9kT$r0~$q-$DD+bx}=_
zY-1;iSoJ+3Lezyf1RCBDO$pw=67phin)>$*`Vz`YP865$<7gYJ`7MK^qu9TI11yA+
zt0b53ipCjDoM*OEcO0V?3XQ#2PuYc>Eh=7o;!8j4{Z%)<>@A3=E#sTO#;7Pr+q}3L
zHa;>Ae@T9Q;}<(V>nU%)P_PWA|3%;Zo;XbIfLJEEHZ}}K5(WxVl8&{d4rfD<>qK?7
zBE)%65NQ8zQVfTjX#d;|^h8bM`i7(>#7a9WBp2n1S~+S%K#Wlvz#(whUe7H2!kakW
zQ*mi}C*SYjWb2~_o|JAryJy8vo%Y;CfS3Npw3Bcsc;lskIMGh$IuT{75yRiK5@Y8B
zG;L;i2e-*80{enhYt}9STdEGLHbyBRrgtBea1MfB8j{z3(m%i%XYU@7!9btuYQ7aE
zK`e3d|2=j}q;ZfiF=V{oS5%}|!VFEUQseVfUSeyA*P5v<mGnC2&+o*bFPndpj4v@e
zEdSvDF!ko)Q1<cLe{$cc#8mFcI!UFnC1o9^a+gYIN46wM_T6A+l&w-j30a4-RrYnP
zV{OV{WE=ZnFxD9iX2vYnbKT$X@An+fKMsdu{u(pa=lwp<*Li;GW?!9M-9%Rm*4qTM
zt``Wu;X>z}iY{Z(+LUOUfNytMo)16-DF%gI)C#F*bpN4XQj69{`re&nV-uIb^%5Xz
zvfS431ziM4GdQDAOiyv-4}#;<We{9&xjBb!5Kiy;Pmk}3uD>I)U=%Z!y9H0{30s@9
zPWE8eL;pY9b4=jV^&9Nm5OM1Vg2nMIY-}jjsoxvkOt=E2B|UupRj2C8bm#>xBN9;4
zDlz>Zn1l*;riZhH`h#hpU=R>~jbxU*K;ysNS~KqRC-!ns$6dewtLCxG#kc~vN;j$N
zVLL<v|E{GDt<4QduCLzRHVzB15fIqqmYRw@cHsF!d7ezDU{E?9Z;*-2Z%Z~6nS1Dl
z>ko(oqbQ7~6acAftG`K(8WnJzAO0LXii3J&fkP()xCRn^0hH%a{CrY!$kxs^aKbD9
zSIYKo@fp9|!Xphfa*10XfUCEa7Zk#w;s5h7>q^~$;#j)`$sU<k0W{%{z{?T*Fv?%#
zX#{7z<W>;?Z*3NSa8gK{+y+Wv&wz?KE6KYY2OA0>+%{)X4##RK@Vx?;X=B50F;a|s
z2B1J;8!HAb1}u;%5{Rgp$Lzp`S#HIQVz0Y_i5CKs{D*~dmss8Vw+=9hm6tdNc6ffQ
z*w=YwUD|fnA%|Lo>x+8nHtM|{w3}GxlRQ$N6e-wL)g$SGl}xqk54OW`I~Sft8+y0>
z<ZfWXt?xqvrfst%!i%uJxVZiMf-(mk`Z(kMav5ItzvCKX;ib{!K*)D_z2+@MY8%qY
zBt-o0nK2Enu|aj1-Wl|r6g|HWn=2K43XLu=#=0d>*^-ELJC`nW|4iee!K+>1bF^!J
zDiyR#g2>LJUWD@#oJ$#b>bKc>a>&M<4gBj(fk=zi$Atpmr9oCG1pLd5Bl9=r!&jO3
zzjxYg3(Y|79yTe|0={{m?>pjE0hAdkfmiM+j{q}#BVoeMw-22_0T)z!#=Y#g4nuXD
za6hA+kbMFdVBHAn>m?i}8d;|cix0ZuePL+{1Nfx`@*QizfdTA^;W)gKWcVEhj}t)4
zV|SxMd}!vXwnHHt@O)!*RB3MTHb6c)sh8RRlzUR68F+jI&?<T4I*g+U{|>hA8wqYo
zRWv@^P#Ewznw)cda6_EgB;F)X$Tu*WxRFT*#$roEjKp}hE%_&@;F(b+Maif3YyBIH
z)6I-4viJfsYGpFP>1ffLWEx{&XX4HJ+IZKq@q%NuKero1W`92<y_byHUadrbNf#B6
zJI1z0g2B)JYIdv69gj!sd7I>$9<1(@`kTqIn;W5bCMboeKP3UvPmBWy<-bld*M&iB
zX<z1tf4`%$Kn;J&NEo#1F<gsSz(c+f7)u4z+}GjY+X@_GLvet#{5m{OGj1+Cz=|>^
z5E7V-U18z<83If-RgCUZ9K$`yb?B^vi%PWqY}*IpfzU@(A~9g*1E`HA*FXJ+z}hJ>
zn?N~xBp@2>x(wZQ40UNokm=OU*Jz-+T^KEY!xpQMeUv30AJS@lq;JdeCVs&t&pu5}
zY&}?Za&h5!EK4PVwm#v|#6J{tsaaUbvU09|$ZgC_Wvjm_Duf;o%P{TIl3j56^3bn)
z>!!>it)v6kJ&yvJ#$cm^F2>IZE1Z1V4{3qc8LjKG^y9gE*5ecdhK({@n{_{(5>&`7
zzH@Dts_xK6TF-~3iyLz-Y9Br^{;8-yvx;$UA-n8#uPG>2@1o*btHt^*GOeN!%`nO-
z>N=tsjfvpM2`^X35q>rJDy>8~d!#br0;FA}s~Oxik5c!eGEHjp!o+g1)+z*d53x+#
zSWp4kUgoa5MyhcgWe9ZY;$wLz;H9WAB)i+Te`rd-TIxS$)qgRIu@4$&oT^q@{^D8g
z1@~9}LpM|gX*M0%jAxmgyW)4%XHaSaDYSqXrXq{K<2IMWq3Fm|deq$X09FzDTf;Bj
zXP#wVV8VC!Od38rT}SG|7hm{yKv+&_oSQx+ea0K|CgR7GDqVxF1?rf81dPavo<Cim
zf%lqmZ@Cngd9+g}@xp$}dDhNTK`UtP=r=3QyLNl*k^q^iZMY4%@Zu;h1O&l*|Noxk
zpa%_ex6D0TC+-6ne6Qe_JmP$Ru@0}Vc{#EbJ(o6})|_N-r4jUPbKqW|7h2nL%(JO?
zMvL<DYlbiwQqniFJ%__O2~4<`2s1_u%xzj@zhuS5g~0l_v-39k4;8-k^x2se2p{0r
zpWr|NEkUxQaX5QEbGnIekoFj+8`B~3)90TxpP`mUt0Y`&yfbxd2CpG2kbQR#YLW6Y
zM1l61p#2$Q)Dx1a{`u$jb<!>7oUXZ`LKPUX4;yPLB)Oz>E;Sv5Tg=B~^Mfp$GTUm)
z#EH{o4fk&8dK<~fE|(pkx?>krO{`bO0xf3Y#uRlzN2Ol1;dZzGW9i56m9$k~dsVFX
z5OW=n+$#l16BC-Tb33RrDoQq#2Di?G<D@J@7=zZdiSb`Ri4KHP&#REWU*S8<1`hnU
zYEJ^!D`RS$zbVbu?;Z1OxNjiv$}CnmW>y}1X~N+&c>*|07r9dl1n@}XUq?(HS!scx
zFy)h6%9pF4<*<d8-+;F@s%HS2@?m-`xEGfY3SC9AzWVVQIP-OQlS4%+xWsSWf%f_U
zN@8VZqxC3#bNwZ-Vw54mY~?PGGkfP>((1;!x23L+o1-AQ4N5DFeX<j)f*6nVkd4D)
z6&~}2S+G)W@wKI0MYmel4{VG5K@)$te_qEQ$PgpTP#$0BwQIgc(_se`Ro&*q?9Y><
zb;a4dE2jNF*s(83Wp<Bk+|w$59khyDoLUy58ohVEF3}{;wSR1!$HbmLx1y{me<h>_
zpg-$!v2L!nO7e5AV~`xBdx-pZOy@M56)#Lrx%x7q6J?d`sw(1Wm&PEN1hak9WyhRT
zO4X9eTzM>HsRgDb#y!@_*}y%*XPXopEx8vu^~im%_r#+Jw_*F&roRsY`fs@o_K6od
zcCTVWWva%8^hN^{PG%bkW83&3fF3D4dTs;Wd9(9+Q`%@&6Q!`1w9`Eh0lA^2a)4=|
zK}PJ6Q)XvP&%rUf=t%RmcmEV30Rx>P;PN<-brFcI5%MqbBY}rF97`t_nSmmBL?r`d
z^|^ga@G`~$4BF<HZ-ej~1TBzd;@8C0#jo2m|11zYhDmZxX-Ci!!I3t0z%OBeJ2=i-
zThZBo)7@|)$5CUtO(2i0jjimZ8HjF;@o5XSmhSbEjNi0x%dwfjBZbGoGTQ*SnUrQX
zZ+Qm(qjK*x+DAhusZDR(V+NO?*%P7=a|WAQ<8@BFo0uZZsnk_n_CBBr!5!kd=++mj
z-=gA1jQVLM7hK$KsXE>MsToIoEI-dQI!EZN{iwh{s;96`h+jSb)WE1muUxE}=vxTI
z3_gbQT=Z<a+aiEw{ehrz)#cK6&JxpBDWnUn2ZoiI(&g~#4x9!-Ti5gXq<!==oOp8p
z<>1<KsyIGr^(8L#oq=7bV)4$on!7t^ZR*%xUTksgG1X(0qn(#BX|ECk-fXCCz+)B<
z4gwyI&dzJ?UIWj$jBYX(qCTL)xL%QTH0fHSZ4g8B_N4M-Iq$zfyZ4n#PS#gY)B^of
zH79}22#@ij!*p~G-S)0(2K~nQSR#-c0&*cXD&QmqDu)n*>ojyF{s1`}EdM^;&JRar
zrM*aWTh|xx$o!J_hSIF;QMucl(-}D(eQGRKvITb<Z93@m^-*lw2jRO?uYp7OuR&SO
zKdAz#`K$KBPW}5=|Abb1);~z~OgZSW%d{}PMQ?%y6AP_lEp`J=x0`|mf2@OzZf2%V
z!=+gJe%8{B%wg?%-ckbU@oBu+x<oYYj&zUn)I;3<e(LgFMvt%Vfn`m3owGV18O<hb
z%g-1Y=LwHF{i<^`NGcw-T+9D!CC($-RU1<B1iu1zZ+kp>#pz`cDQrj)6>7^ua<#3a
z`RAS*VxU%W&LgKtPY`*g@=Ac-Lrfe}i0}@7I1fCkhDD|;fBmG{2-=%I_r&|1u5xvc
zK%Vb8cdKS=SfjL&?c?(N<vF2N7vp2?6%VH#{dX`8HLv!)WJZ;L@KJL4w%KxS@fZAI
zWHq|6e&{dqSSJjU|5g12Zt{h|#O7N8@|edgM;k@*y6;*K{b%8$spN9P8_;OUwN=3z
z*V?-{huJ-dOlIxl!7)=f?q%@%loKj)0vtu`C3vLQT*?K%FSdR~YN0vX_&f7=bsvBW
zTACL)XLw)oyKoe=!?MHx(=B=lnO_TP1+1^&dOPA(4jd9MRsYXLg$l?k;gU7$@Yj=o
z$2nHTc`-msr^~D0K!9KEMM=2jb&UoM`LIh-A)XJj6=W@~!!SD$d0w;v0pz;y<0cp8
zEAI45NBjE@IOpV(qjaucz;C@fl=A~Wm^~s0<Sb%#<j#nG{x`)}>+b;Pg{Qb7-7E*@
z)agAWKWWz78l?H<+MzJ#;#^XcWXbA7{7C)Uk@+(5^`pR(*P@zUnYs7I_X1}seC8_(
zHAwONa<TbZ?iv<JecB|(d#&GI`p%4M1zsQ1R{ah76j}W{q(!IuhvXO^O1-62RAvYF
zn$jJC5;_BYG#|s=;}Gr%TMf6wmq#yPMDPb_u8wV*<zh)Lb|u88p>m?cq!YZ)Thcw%
z4Ij63Eu!5M=B(9MD37tWO2-<`S2kDuHN%YF<x_a^wef1y@}wj^M4YAEtUISTY55#D
zqvB%q@0|5&$@b|76t5(kTFWDy@J_u3&)C}~Ce|j6*F8Q0gE0c2KzXJ1_sul?Ed!pe
z4a;!6;D)3oXK42v>NMGs31?o}dAW~zNOk#t|H)zU;0*GM@AX5$5VR*cznom;ga2)Z
ze_O#Y6$#wD7l)F8^`4t<0)n1_g19^j|6QyLI4;sv;&xu-LQTbmcvCwErvZ^$K1I20
zdx%=HDr#c)zYlNp!MIo_BIdJthR??Vn!yo}SlA!SeX!)nIv=+Do(mn_Rn3Mrxro2!
zUgSD#W_q)V6PGF$ugEjGGL6oxd*Bj)!qP^Xyz#I9wQn|1Yz~$=tpNiTRAJDqu#0Wz
zQ?_o<R$7vX80q()TSQry`iwmN^gG%k)cW>tnan0zLME6Z9OARaJzMns&y%zm;hEd+
z5|)mL>U&Q)&GG9J{^Wkr^X4oYwTuIh;^?tbRj$xaYmoxwv}cHMKjWCCJQHaDdkFkX
z5dP?oE5sP9IAqy)<jBd8rHr5evV_IrOBXD8?KgNM?#99N_a3*@GD?&wC}p{2C5`{4
z7iSa9GjDggb9j(uN&7}l$=kO91TE6ikTSNfQKPZ&kED9A65+SLBQl~q#qTG1c$@0g
zl$)jyVig1jUKwee-pf$+OD~Mj!s`U4D}-(Zh=t7FF=VzJd|$)Mq|OHG(Mi6(7khiE
zfYqRdTu1C_LZ{LY;pE8A{OayQwY7ne`Y(E2=l}IKY^M6T81r6;&6tP!5ZuII8Y%zT
z2k~ysOHY}Vu{}QqJSR-AjKP~+-vW0^Oww3@7R8S&Dv*+kF9+*ZmM5!*<o*ne6g)Jn
zSMN76APu@fuUkJ9INu}OHA_)IE-TeH`33-5UjVly@F{V)teVHr^2__P$9zs8vfK)M
zhNu!lnzdU)@xMVwiToW%sqrij(3)zBLCV6ae>+Y)obB*|16>vVDge%^$q)Ry6)9$R
zwjB24O9xoPlsHk-^`OM-!DnvKZPNQLZWn-ZU)TQ*V4MUsH~Cl&HaE^*R_D>ERi1F5
zbpaPz!9A;T;_VX7qUJ&q*zl_Wm5a}AsXTFo)Au{rXo<5__sUo9s6`DtPnq-;b_>+E
ze=qAc!Po+67N_~blFQNE%!K4OR3?=TTXX$AHglA(n=;Qt$^b~2c0dd35~oT;W60~y
zpGCl5@**@M`lnsHNEMYkC6@Mox-E>poe9LgS!Rc{n~n>Mq&Oq?5#_^p`$oCVl*JMP
zHs>%=;&Y5h8I0QEx&pUPtZ+~9)O3I`->M8E^X>eXqNG&3E?>1uLLQ{O^ufdgd=m{F
z;w=<ZkpBF(v^}I-1?*}x^1ZL?ne@A=K_*Urx;55+C|o6AsUvx*lE_`ISV=zzFZq7c
z;UCGRkC$tnYZweg#K9iLHSk`nscs9eBv2m%2lTefw7?<0B_^QtWb#suknm;=Q=@ka
z0xKv(a9R#oW4yPveEyvAqb;VQC*-%$8{p+f>Wzr_Q=}Bx>h91p)D2Pp$wA-%aTm=m
zqV~9Xw|2gbkzg|hx|Cz8agQ~@>OE84l^>`+0F;5vpqrhaHS74bw9QHOx?{bZ$T<Kn
z4-q91^krwzZn&{3k)Q_uso+BoPie;)-1<@DTk<=4Kffs~MTO7Fy1T^>bY0VFqC_)1
zE*oMKk=bY$CJbzy4vTle-{C{&rnO~DdbcS>y@y0J`g+%^SI?R<E+%dNM`s}`DkaAu
zA_rChZ+MylPs8_;TniYG0yiwH9|Cekj5&-3>%P8$w-8HTy64o@`nOER8fb6_bB;5c
z20_(K_6ZJjiZ0s-XaQicr6^qrOhWz+K=0xIqm5>Ed$a!ISZnxS)t)xDwzuttjqf#C
z^A2bYqJVBt#uEW6IYLliyMr!%&m)z%tkwVZj-cq-4t73@HPL33VI^t}jSdA=@qw$W
ze(sNsj-vCTDj#!dYS6(;dafjSzrkgL4v<64t;KfG)Ek(NJ`JK^Z|+|}Md;UFir&;K
zZM8?$4;xhtS9Yl9Gmt&I&3@ICSB`==?vQKeQA`jb1irfaLk>`cv}eSx+tWEecU~bw
zCTrERZaG22MM^C;TsqA%|8!fI*;XX0Rr$VeLcDnJcs;MQ?h!&##5&VCR*mFKa!k9G
z|89O{_)41P=xvwc!&O4m4B5#>N4=)n^MpKuN`mUQk}BOgD-JQ~G<yB`T$09pLF?7&
zl;fzjx~Rd#vxQa#BT2<?)y$5leZSrEvE!_rE16g=+Zk4GFDGIQ`}S+&E3BUNyK3uL
zs83Xe!+SNiX?MZ}{GW~1ld-K$`_<l0siiCV_>Pu$q?YBh(&5Ah&k6URF1}ezZ`*Au
zT(O#2{Hyge38O@*7R#yiF0AfSHP!M{lkX{;oBx#PRxGczJUfDr9B#bUv@*VObl$4&
zL?KfRno`l+gIGV`U3GzQ@rlE|)i4YE#@Pw}ir?9q(zz6M+WugX#n;ZY*QoQ!OLG>{
z%XKex7UR%HS1OV;%ACq{-I@C&mU_W>rky)0%&8lhN7CqTAqed9)AHxk9?8uht)jC#
z#AfyZ`^K^pPA$Kc_3V2P;rRd)btK>;0gEhF$FH9<t6@52Si;$tT<rZnD1^*8&uKV(
zzv1_6nTCg3L7I3U_disXpv+{JmfmtQeS2Zy4Z;5zKn$Sxkw%-oxOE#iu0ZxLxqVr7
zBXibWG6(+<<V!wS_ZOuX^p0WQ|M{CNxEG;-qqIf`=>_kR<s2s`=>{)5#Vw8hDCt{n
zgO|D0-)hS@`(w{#l#sh=&mPz`)dgo4*KJ8=xEbl43e78D3Bhmti5X=!Q=Z@Q4E*)A
z+)b5IMU^*1reX|Oa(03Jm)>V49_jfSFZjzH-7l%u+bjY-M{Y2!O)|NMH?Eg#)l)!4
zy6753Aewi4!glI@#wkf-@x_Xi#apjwt1WgV7spvMxASUgM_&7?Z#15xL~ok3)S%9=
zu5guAxb|Tt**(RiJYwO2SYAm?qT7IL@1C>4P=3o%n>6k^{4DE>vv+gmL;GzoziQx>
zm@iwBI1zLP7DFt)`HAvr@wR2r_|9ix(A2Rks$?s0pg0X2x5E{LMRBh~Z>0NEgjxc;
zGw%z6K7s}=+@ccprtW;uY1Ubu*VC&AZ}o9emaOB{Q@RV?4SK$P+I?*8@)v0HHVAi2
zKpu|`@B`X`asGdMC7J&+MwYHKxOOf8wj#tDd(0~t;U0g*>{wg8BlG5$jbfw0t)1mV
zOmLh3y9HPvMb$N`sbNeQf;!-$H7^KY`akYhmET+$D#+AsTCM5ypPioD@qYKEDND`l
zF6%XL$K?_t3W%x(P2VucQ;@tSFgvUEjctWx`kertq=(pKhT8e_1L`_D%V)kHX{lET
z;fvY%5{p7y#&M<UMwdr^ZnX+eXgsG@by>MPLsv<XBMs+O`h7IFV;E7$(fIO&jk@8P
zx!yAgoB90~gO^R9xozgU_VueQ)78*5MHx-!7s1*wF@~JUdi|-a2><aL%FBR!NWpgL
zd0rt=y}zpu(M|YQ+LV8IPL;epIIXl<<JU7t$Ln9J2Yk`|>0$ih>YuOoZ`&Lhck67>
z+nIAj4@?s>4$93x_KS5f+PPj5W^^szKIr|n?n2F|D+Rg96c+C#G8*$2=l2&TyDSRg
zl`y@W`6DLjzU6^qK@O{aYnST;O`#D^v(~uye2BcxLoTqRGa3pwES2JU4+!c0A2Iq=
zf8;r*7i<OdHyK=muh*91oiIa_ctI7aiKG!QSEciOyim?hN)&2P;sO`w4kR>Qd{J2#
zyjMKN_;&Qcq)Wqn+VM#9R4vOm7`qgszp*fdu9Y0B+;Wu8jG945LKfp(2R@W_JN3X!
z0bC)T!}?h2F!NV`3JJa4##VzEei7w5d^_ZX-#_NKBTRzj#{!59`=gr2b?4UevBhbg
z^>z}bZy&g`Ah_YHI+Iy$^adr}f6QZq{Tl7%jBZKU2Nfx3>)1YCkE)cykQzj#zBJ5^
z&VR0_1TL@YuMYGlx|#p3+$}49RQI-tTu{2CHA;)rlgJgNE5DA7GEI(L4&_S3xjpRI
z9UJq|T*Dy`FNb_bNArB%pXnqMMyz)-?1+|oAVY;$MLj5HGT4M7B@4r=kjC}Yyhof?
zA76zW5nJD8kD3>3|JXE1w5dCyH5_TQ*2>ghg1WpYPZSxK{h+NYLU*)^X6|m4|2$us
zPVFDMh?4uw<^{Z`1!eq=a0or7R(aLC?!+(XOPqMS^D#_9+!VUqatUCc8aokt0hA!W
zV>eYz!-T8qMu6(<TNRGa8DKJm*;p_h3r6eN4tTF*@C?jb?66#&Y*&A=X37-a2<Cxk
z=A5YR<3Rq~UqUwP@c&S9y^lT`Z$S6&ihP!y#W=A4=EE&~$wwYTG%eb)7C=7f`CEzj
zhzHTJu>=KKmQEr#^Lp%HGc0Zb6>G1-@RW3z9l)eDHCKUAqLN2D{?Y#3Tkl8dF||t-
zK8YRtJ;vGeIPKEPi0~<t`E_9&G}B5V6Nug{|FfI_f`kZRgFI0ZN7AM#V);Wc&ARq4
zP}c6pdAVd7kI*<B;EAiBX<2NhtFeKg#3UQoCP+)_U_;QQJx6;TBIxxN44V{GA3|zD
zknC`$y2CWdNfxH_)pm9V`M^F;z#9MRSAx5lpBehn9KUcpBY3h_f9s2AKx>rL0#CMt
zY=eETsZ(*D66W2M*!HOc=Xk>`aZ+0A(HMj8mw}#3+*xCiHfLg>i|w)MLvXKp<Np1z
z#`5D?CJIAAs7)oYhAnO6V<L=`r?(ic;y)cO)LCrxPtO}zvQukZFs8Et?^BDA(en6y
z?Q{JktGBIgx_a!w(^kvuhRjFz3p|1*9-wtth{XuVR;?P*@BY$0f|OJ$+I&drUfu$q
zWz?$)o`_x;xkGX%$2MXnw#QA;MuK3&oh20+O~(d0ddGFE+QY1qB)XMU`Nn|L!1SkP
z1T8gM5EhCoq8Hoy&B^poMiE3AWBDP2B|D!A>P9s&=r4aHlwXXupIJh0Rn>v3MWPcz
zj$}-#UBAX>wul%T%9CQ&AHgtzu0}moe-(FEa-8dcOs@azA#|0Wb<S?TfAS<s!Vn;3
zQ3dIjky*9U=p9M6Ram+*BIzRp2q&HNC<R1IY9A&KYtDD2L3<K*CdhD^@3CEgLNHa#
zFXsh+eu}bOmY-Seq^VQGzZb+RamgIIO1AwPM?f8-i{Q+4Cz)+V;-2hYQ;j|N8TM<X
zPakyc%ZlJ*GsK*k<6;ehYjy#1gN{a%7b(@DZFWJ6%@pXDjqhhg6Y5IEF;&e&*3UUZ
zLk6n_CJtz|0X2<MTwPZ6H7OSKU@`G^dWsBeU1f{=+oClf)5cO_BVv&`zx0l%(J#iL
zxR4!RX1I9#Bd!gM%NF;C*LOL&IJ*{`VO_XS9aNc}y>S6c;l!<^j*G2v>|g5#d(t(g
zJIgfvubW;vYGAYqm!ENTxsv%u;WgKzrONy#@dBNAY{vmQ9v8YhGvW^P&SRex7d+N1
zQQYtZv9s~6pB^Y57>qWV%t0B<88i&23YO365e8x#G+^9|A)j^n7{n4*#&^=Gvz18F
zw`y+l3zcNGZf!2qN@gEOaYMy%7?3mZdId-?cLLs3;#{nmi$V^_>QsaImH#mXd*Gn6
zjIIiVK9#0Qlw3FP%J2|aO3xrI{NSVTH9`Mb3V$y>hII$J7kr89<FxngnYpn>X@$KO
ztEeTO-}j<hH5=`nrBmpKP4Q%50eL1|+jDV5k+IEtf^0D|jI)*Uw*O?Z5~J8Ztl<oq
z8^j?_)-C>t30Qza`fn&_7QPIcEYe_Zv~A3~;f~B*V0|3$yXDYys|2G^M59O-O4*}L
zNqA0PCW;?m=z)pOZ!~f*IMt=Tf9}NcH1ZOnORG;2i~MfK8nc8LaSLy%tlAIsaSb&Z
z3KDu?HcEK*-4W5wHf9UGF~n_lxU93HJS;FE*FKbLwpvH@Om)Zn((_ShuRj<am>w0I
zw6JP%Z6VOlq?3z>`W0f>Ob368C@F?-LF^tBucznPC<I^so0w!{Tk7Vy{!#xpY|h9p
zDXm+>vlf-Lv$gb91dCROnPj>me)fdYznK=}%@bUtehj2PY0r7HRMBs~Gu)b;^~5JQ
z`7sb7-ls}5Ui&K(N(rIa?Ti?4myDSfwzS=T;}iw9fnD&{pv-1(gp5B6xwFLpvYgBf
zj2)hK?<W;Q47cu2xO?h#ZyXv|eC+edEj;c}rkoBrT6<PPjS)6A$qm?ah`T%e);SQF
z`Xz{=<`Lzx9@t$_pWE{?RNnRg&^AV@uf)Gy8Ga*i$>DTEn`!Xj>IP^im`Dsj0V)xo
z`lP<)&Nu-5X4>6NuXcNAYn_WkA;J~VJ1J}K8Pwd#RUu}*X-U0$@VV*B($|p_%U?~R
zcks&gp@Y?4tiO{K^pr7)E>D9MwmKxb2SvmhtaXM_&(aJeED$ww+J4js!5X1gJ)FBg
z6WX7bHD1i%eZR>s4bm_KTDUEw)@`*WJ7;s`T(^!}j^4P9LzrX(a=g;@Y(;|@o|NkF
zy`rSyiq3dwTwY?A`K_8z<N9ctE!xAonZ)$%+LzYqzf?D}O`ziY^vK||qmljOdCiUV
zS>8Sa+c@sw$1rgwAggE}aLN|DJ%u65%*Otn3TbB1+H@9L`WzjNJu1x`C*ZPrNwlug
z>zhN|zRCKY+l+1{;OoS_S@gq9=}!^Ck7vVfSf!8~-DaaUx#h$-+T!fJXKrDrO}dFV
z<H^v)RLUTj>@qX<ZtfL7c!fimK}|J*jtR!V<UIYP&nOjL3PMYvC@#nzpuoBk@9Ilu
z!JkcR5<f2R6?nGMMcfHBM5DWn+<Fwh<h$PZG-VQy$+4afXT&86DE1wrD5mq$MD8Tr
z)w@2gS6q@c_LkNNW{gz3r@|-HiZ&;4=3~hED=`Y^ER~dP^b4xXNu5Q%fG+7S?i9Wo
zIGsEUq0*7f0omP@1TTE<M9}EfP{y`I<4#X#s)tW(=)+ExOvSs<yO(S(%g&RBqFL8P
z;^c}T*WL|<YKM{2cWDH#QXNCQx?Hl=#T~Mm-jrw1(nQ_EiaR>ujG#X#(13S);*K8K
z**(n<w~UVK^-;#=IV$~()c<VLJsmhkRqj+fi@_kB8;#^IUNfv2eI&85{|ZVLL+N~)
z{>cLdY}e}p<wAACyqV6I!U0SBlou_zbZDkW|4+@5tlL~VU~0HAy6l)1DMr|C$#?P}
z%)0zVd|}kaYq0r;;9ExZggJQ<Vd9;-c=hTiHQZt8rKLkP?;ET=*C&cIt+#Gkf<H8>
zVUK1tPS-My8;ILVKoQiUnt<1}o$>1qd*Wd0!_-QaLTI$pGJIiv>Nd>|QAJ11oH09l
zy3gH|!sT=JPV9{Pb(S}@l*lu_pq@|j%hqe(Q@y*WJeNd`UL;r*tT$6KZB=W8kUZ63
zbM@$SKqSQ6%H6n_);b!J;X71YH?^Vi$%}vyu4tV4tbM{Aw{6QZ^*nl%=QWg$P!uoi
zQei;;bdc@6rq>^>bqIGmLPL1pyj5+zMztoH<IK8+RJV)%E6I+A)xXyQ>U)yygCZ0V
z)49D(hw`1}gB~SO<xc%W8rmX@4J2CH$?o_#Dy-A;Lh1dNWBAs7$?A~F>um2XIf8py
z^{NGHKO^ZM8&1TJo+{o3&s3cZ7n#V@oCg>dpR?rAJ6cFhpSSZ?axSu3tK$Nn#W@-F
zlyPTSIic+(M{1<Z-j84>qot&hF2&p{g_&czYPE(<%4Zv%ax~RF9C$!J4`hq1u?L&}
zXFX-s_q@XODR@^^AM>a>$afe7R!}3I+H$>P+EeH-hT4z3JJi*@mAsdIJvK)oo7~p*
zVzw>9L={6IpEocrb_WN!SBY2Y1ixvNtmU#4|7HNisqbIYHS5G2R${k6YKfl}9T)>+
zRQXcqj>7K<-VVFKqLmCK>GYfqi{nig;Jw8LzRZISHBagTymf7Rh7a;c6A)e2JD{i%
zc;8tD3-HJI@BDr+ii~~R&MJAs*jz3zKLQ4dDy9oC@!JOtyXCq5cG<|l&tH>8OtmD_
zYOM4RFc9p}V~+(i*NkjqJ?j6RM?KbdQH&6GhP|HXervd2W4QAgwO-M<RWFPxipdPS
z)=x{kwtV!{hXJuWr~2|4R;J3ZeAq@8dLeH90$Q82L-kQ!ASq{jX|pgo*<isL{dFhX
z9CKdWAzLAG^>%cV=93&0x~r@O>a%P7J?nIw?v54moPThz%S<LM6TIVeHQQCo<!=_T
zhHdz4Gk>(&61(<JXP{<ydksF?U{>bOiW60T2&e$kld>spi*9Ta<E<cOms{6j(m}wN
zSw+!+^onA`4yV5^%~FV`RR>dgeVnR#`zB^MqW;a3+@&QO&wZpl6)R!iO2TG~bUrmc
zaV_{N1FydhpWsS`fJ0{DMf|NG80QnpQBc#`6BlxP(jr(;E47(M5tuX37u8{LusOCw
zK#R1?w{}c``Geef)sp+xW*PI=GVK?=-y+2gU=3VisHClIE4MYxbI372Q*yE;7m_sf
zEN~@qd2{AtuYj@ECdknvy32P$$Iz&R#2eM>q&3cv3vPZ7x$Uh;24b7DLqJIFiJPfA
zEO}tvzgmnvy2Y9(5iP+zQX*I!*oyB$|LPHq2}KSptaYWLWDB(N<ej|LiC)3@?WHls
zfXnnw3dDM}b!)Pe8zCT16c8MO^}yb#zqwCRdbj)KK05ubfUW$r@g$#d$&{}qlui2!
zd&=Y}sWv61wQaysx7JNCi5#vq`_D6<Yl}SZ!dnI#!&KvR1xwq)X>049iD{FCTph|j
zhH3KXx<yY<zXQhL4kV4pG#6crbUOQ};Xc?jwm~Q6&xZv2>OM>()xJx)aMf}$EY`Ey
z?V}gxf}>~`O>TzWaG!mCS#;)#9JZ-g(J1ItCZXnU=PNe<PSXAbY?$zEiwTG94|Zx|
z8K>n#*?kL*l=q9QcfLiFo##4npg|G#DImTTC@fs589B~JLH)AI`Ud`;fO>@c7-a%?
zGev+Oo(C?FIn(Io;N7gh|92te><Dyfj0HO5wnV<yQ&|B^QQZXulW&ReAS^$?&5NRe
zgL=Q~4IAHg9*ku7Pw>NO_w|x8f7<L>Z&tbk|2ziTj{o5+Va2lRleY1jj;>DvfY?ci
z5&Z@g%5#>n${?|?Lt*UG($cc!bK94RCj&`W>(ZTEy)f^SXI@rYEcsl4pL?f#jB%={
z*kU-}*r>J|EZ6DO*a+J=w{6Bnj#BM>SF7|(&}hT8b_xA;C~aZ&(AQR`eWrA|*wRL@
z=P#|x#dA4VkEFg>e4LtCaj*M`%csN`efZAHuXx?bHzzFGq1ITR)@0UO%2C5>G3mh{
zCk2}ow~MJ-FbktD9)o|!NfmOM+#P551>n$G7kLSXrbo_rY$^oL-r>)>z{!!der<ZC
zX6dIW?5@s0+v)vG{7K3D`68=g8&I6U4+p{Rf(Y{8OPO6Hm0lwe-c8c`qmC@OB(E{f
zA7H&KKmCjBo{2B+M5QFF)RfJ+;MH<Irfw-O?Nfag>=p$@GTdsf_lnUDgyr+f&%t4=
zu+^fPixWNkH$fuH;lhwUONsm!D6^`!QW|~DZY5;TdU`QSIqb+}_fh|45B(82#IJm;
zh}1mdl=v%yUL7dGcT*bZeGf4#{GCMhF2V&?-kAmd6(%{$I7Y*dBql8j&=V3%lBR0d
z*^v|GEwyG9%RW7cmN5eJwG%TiZb7lzK(3U1o@fu=iYcWb8t@2Aga+?yJ%*}UG*bVz
zw3C2|z}x!Gjesi|T2nW>zgzt?38jtbcwEDVFjuE4qeP`#u@vZcv>~hE+3>n=k?tv#
z)GJ6$4+UtbAa?;#VdiACdYe)m{L+L}9`-@7Lr2f+$!{hrUDU*%)7^OeCLzMo9z`t5
zZi+iMJG>72X{w5?ADGWTZe842__03CCP*FmK~b+?&aDxN@jlffjzc{T&X3X-j|5>M
zv3}A@lS_M7O7{5dWO!xy@QeXLe{INMvtzUHAUcIc8iFe@Yt<W<upy(xhU=*Tua{91
zx*9Qy;*LUc2FBpGZr$m|Pg;rFYp0BT<PyVpo$9|47NR_wwby<?3w-3BxxKw^Pxm)v
zTi_M>Phv)v>VLmoql*-qpliZdN&|jJ8^%mih9)}o_kwusbojmy_e#)9Cc!c`g8be*
zF*T{0M?I7zeJ^rXua5wvH${x=FL^NuNJn#z!L#BE|Nn6LjpsG04^p8&xY*pq?VbNg
zVp=c?fIr(_7l8^cVV-Ip{~wM|r;E(y4m^0OmS9xIO>Tpfm2abO4*rn!q1b5%(M6I@
zrRF0>_cGpIu+o2V82P;XeH##85@%Gy&HjFxl<DE>Rs0gTqp*i1`dQ>r>Yfd6lE#n}
zSZ$`Fs{v&(zlP1|sgv3lAk_VS8D~yb7*ph7jJges!NsSY|E!xU5%01pDm8s2I}eOz
zB1R5%o*NU{y7MkUIr{v-lnnJb&jqox`dkATqsEa{zJ4*9EdZ!?RO4>PNjbf<4L~T9
z8uvY@#Hj9<Z3OFY^LpQKa|0mEkoi-#v-*N&|3;yswROnzwW&3M8p%{-A|OtHYCWUR
z>QwRHlCN1Vj8)%HdX9i327Su!Nk2JRkK9Ob{6b2a%A3CcfpZJZ;f#hganXP%@`Qgy
z^%x?#bn;yvBK4uw(M-UIH^1Cj^mZpKTM&o3YLDWs-X?6PsQK|df|k6L(+f?#aFbz+
z&*VdA|BRyX4yl%1rJ;Zf^4T<kIS~{iSW$17lQbxKDf1Y!vr_7q4V~r$st%;sg_0U&
z5-(~6A9ajQp8h9CQ$U@Cd9@p0s`-5P66_BPhDX4-YyM)cBJ}jwhNP{_+Jc3#Ln!uE
zs@xLc^LA@IB%oVN=bHmXejtdO5x|pnS$tbBaCXf(Yg<o>eY-gvuc!!nqqBK(hwbLI
z@H)X_u$D~8N>H!2MZ>wajXQsOry}H}6oOMY%zg9W4H_G9#GU1We$v3}MXgkZVu4mu
z4{WR17}Bo}A2EebFm`1iR|8~H6N;?oi4KqKO&!qr4j~9!(9~!z`Z1c><3!Rok>WAy
zZc}cdyV|X1WPLsgyo}P%X|koZURK65G`DUo_>uY-K{1D_4pPTs8Lx~-_0REs?9->t
zm<E*FiR=Idv>D=s$5Y+eJ&ASw(n;8+?G7pZdf-Tld=P(;bVYa4ef{Zfr0~f3$ZjG7
znbqG@e_JWf4B{GD+3epMsko?m%d}bdV9)<+h`{tEif@E^6Wrea;vW7?1!E#f(CKhG
zTGbdXvAIv{$D?e!9kZb8NIW**=wZ)#*9%#d{sqPXk(|u0q2x!h1ffN0)OpN#QhLb<
zP#|ji%|1<ah{Wv~sv-}KH70$Uc<xDlY;Wlxk2$%?Ih>X5Rl0$8-s~ySn~I)APuOhX
zuXYP`UK{~_Jg)(hD+)bQ0LLG_o%WppuZF8~Ml^=YF4SZHgmq~?XGwW+MOygpz}}OX
zWH5#F`Bh9URbY9)>NuNn2+xi1RV8I6t>IIu-#Ltd^_!H~HrDP5u3TpZm~=1&+r(P_
zzI*vmO6`?Y_mAGwCy!)nY#PZ9z7E2E2@HP>Qp$Hk_O=My@EC+W$c}jX=f-0$OyAYX
zplF5Ak4A%E^nG?B)W2P1X~peh{PRfvOhPU~g|vR0z>M9BVvw1?tHhioN+BCMoeu@n
zCj*0G3K#E6`1-YSF`J>9`WhA`XhH4ISz1g8=JvIbkO9lv)n)2NG`?Ls@gP9rJx@vU
zX%;#4NG7CJ&MAE&furd>g6vO-0QHrHXOB{G{6&{5b`<0Jzv3FVztd6Ewcs79Kf*4b
zMpvlW`-XX8<Yh6!CNkKJANgGm=wVB)hU^WM5Omi<FX@-by;E<3t|Br`<d0z5^yM@#
zSLIJpil+=DSWP=Gp2z-TPu)oP<YnIyDh+Aa_<n6f;GNh@!RFo>#l6=!ePH#t-m^?o
zIIq>Tj`~~KpCn<i@JYgAND&Xe&_KGC;ph{PYozh3AIWI?YLkh!uQA#V_Wlv~tgE!r
zQevsnh_|7V5jwFIG6Z{np{;6n>nG+jO5~YoHa#D^I3LQ8@{mH1xAR2k5Qe@*RcX2>
zN13vxMT4`whpa*FabB}@SgbZt5G8q)G;9U(nUmZr@ILprW!Ucx9Jy)d?!9C+F(Z<n
z`A|M8!3%w$+zI70@Z4F=>gdcKa-9C<9TU{bd!05GT(C9UpDhs6_{rC!?@NmFEhbNd
z#1uEJKQoxOF$R7s#qKvoc5wUiqe2=jqs2uXe}mC8Y)_{+Qt__FuNs*5&EkE8PUdi2
zw}(jzq)$hYV2s=rNl;$SB-@LU{xB++b+wgsU^Hd=m6vuT$WM&uRzi)@`qSXJH`=-F
zimgc-YhPXaufXb7qF-jy6THKAsBvl#dr#tUKq-HmKcJP3cWbh~WP98$ax?wt?*21C
z3XS~X92H+80gYa})x~m6S6LqbsSno!97xkUNQwwg8Of<Tz=tceX>!gVK-#sw@rf?1
z(ViMNwAB~xiDkNrw&ogo+bM>P?Wc&&S<fQ=Hk*H?Ew9VqC0^{be6YZpoA}UIM!0V!
zeG$xV6&(mbgCQIHAisw2MD!<@0aqSH=X;@T#YS&ub<sCkX}S7l&~~>ke_iKf$f})n
zs^xQ0t%~B#jeVKihcW#5;=<|+505NWM+n~)c`p9B^S|EfalRZ2^Yq#MnU`vryXohr
zP{J)j-AK?Kq4l_J<D~;N^jbDt8y5YDgjc2~d5ns;Q9sy}f{m*}=?8TDMBKNv<;0V<
z@|Rx&9TdLUSeZw$1W@7qpIdSSB*dth$VY_&Qps}k5*P{J%Ri!)L`OELudB5m$rkrc
ztTRf7u)d<w9X~68O&fcVfAu-s3aSrUsv5bwx`sa~<E^>1_YQg?Y94h`Ma}ThXs+2v
zdSX?o2_FhbaD;<VWld}R5O8F69Ghh|>vB(6><6zUajLwU=3?Jg|7WwYg|EVj{S>3S
zt5kkyI^>SvPJY|L&*Du7a10?IAxKu?K2Gi`_I&0>w9|3kH{qNH8awzWf+eaEU01(2
zf5cD4@^z4ntHE_?3wbex$co#LpW3H?6cE1}N_jLLCD~N}TPb@Yjyn8Y`P%4S?uFM0
zauNzHO$hq+MkycE=!SDr&^6{a%Nc{C=_=g$^t~cY7C*K3nP8$A0We_NBxErmI~$!~
zx&!IGnrx?#>Cg`-5^g_jQx#oS74^%s^eqgQ=4KnIpcl{E`Oxg@=3k1m`VT2Ke1=3i
zW#;N(a(`_^9B0bxbSQ-_xakpkQs&A%pzO_G($?CfZRhE>i%w~q3Ef=70g1zO$J8hx
z%rC(LF%@}mDBXY@qpvsA^7XZrdz|;IOth{u&En{EXH+D<h~STj;+KPQ=CO@!1W^_C
zPL;@g<EBeqm^p6f(qn4#s#KijV&7e1hpB0<{=M$2>Yhw(H`6WlmiLfZ|3qyed{fG6
zsu;$kXus`pLd^Tw-y+#p>VhxIMSVgjz!4~O+>r{ep5H$~8u{vYiykLwKkE#vUKz}e
zI_jcqFn-TujH?%!!w~N-aazKDEJ<4z1>R6|O9^)LC&9WWc}`!m$A5?kG!<GfS6`7t
zur=DpRQJa8yz*l*Ux*do3q4jG$?j_o-h^-9AAKzz0X@Xsl(w^#m2i&_o6SyrsL+3Y
zkZR5rcD>Ey7tf%B9lC(iEOt}NkX?a9;T>%8O}0ZuAiG5_1DU7yYhXc7ih-C$Z0g)}
zAR)vCwY$e5zz-crWo554*ix(40=z?mg^52HcrcCGV?af#Mb0f3+5Si}Qc#xX6Xr(g
z2BL$Z)JL(ve263<tP2H0$7ZA7V{=oA@4)}u!TzaQD)UJU@v6IyX3rgN6nwbg^exwz
zN7^{H#C@(zsxVnnzs{T$!_cIbMJXk;1UK1gGkXY6nX#W+<<t40z`dQS>L5CNeh=|H
zRAVpa%X_Mw<LA@;DbS=hV$^tA1@tij;?{XIDL>3nfq4EsVnDW0*Z;F1a9;YX6|TAS
zS*L*wDQOO;1iY#Kz3rn>eU*PA;5mS8tl7ofXy^lU`U30g@e_2<AeB2`%+fB5=J;f5
zemtmn`7liJub{mx6B83^xcd5c=MHGS+pYC0D*|$GipL;vdW`c_PxMB|ub}bd00#ya
zNiiWQ(NH9(t_LSnCtrGO5L3mLj*;FF`#**T;A^K8nywhGhn;mF3T=g~neYazi(wJi
zH94Cl+#IpUdu!=oWyk|=){Kj1vQ4>T#JHjxJ9(+LNyjQJmiJn;$HZtaMB#0ro?8v;
z(-}|YIX!y3=-jo`8?vZi@#L1I_hGxhV`E$%B@F+2R$>av3O{Fvu4UQ&7UleuUyX3d
zdCC6lM4Mjh44u7!IIh#z?Z=aVkelV%B!i94orAM`f=zJ05>S81DlA^p2A#on3w<1+
zh<gY3>#fUWN&Eb*^sqI#DeatCASV&Xkvgb-48~hGV$)>$;%>E}A;y4-n!S7DDRck&
zQ$7iMlC2furFcTTLsF>eeYg(Dl=Y=GqM<;&Av#D=KHU3FY|!k*oCI2nJfjU=tbPxq
zwgtF7L6IGaE^I*kQsy^P^8VQ<9$NM^i6}I;SN%7zsk(;uA4G1~y}lb&WIdNY!HNB+
zo{5dSX_@hB4*kx~?j4Y^U3JVGZ&L!#29#ICYn@QM3>}3k_+-+PRefkp_xIl4eb}z$
zujuw|5mxLlpxy%Bea`ddP`-PCsJuoC(5etl*#{8Cm|k<}E<Tse#&+siP6~m&iG>+s
zn@Tfif&HqIwcNRD8UGf&psh3klb$ueig){@Jk9|Z9a<&5647L21)z<&wc-ZHxTiC!
z!^qfj5mZH+zQ1Pd1CdW>1u=FnIW}u>26G_LmANS<l#ORM9h}LHYmmv(pvlZ+LH8)4
zXvmNI<f53By@2<z9bI1QkN6{Czi?#nuS1P{p?et`duEKuU)*qC?%4({!?l$J342zo
z7Zf(N7^Nr6Vx|;2V(dOPQoT(=dQ)<ufJhQLQ^H~DsApW?#jA)Jo1;43fAg}S5$5@%
zdHI0w;C&ILzu>Mj3X{gTOzjD(!bp5zIF#oUYx_eGu{EGsAGDL|FeK!su!kpuUl3_M
zd7$qD8P3X4Q6o$4hwf+8ezYHt+W0!L1|3<;^rN3Pe)<a9NA#P$IX-#Hk9hGHhIUy2
zN6Xxt#+06^m>MEl&#gL&&c0k*Uv@slf_*iM37R&P6Giisq3y=(T{S|V4ryO5ZhsG-
zQ8u%m-#sV9hFJb9$v$Z&6d8+OOAp&k|E;`uQa&ez7TIFZw;xB74_9JhH-#tfX%Ck+
z9@xZg`kb^6mtE4coH<Sj$GuMWuMK^9ZXcFr+h*8o2>2M|rZa<Vg@aAl<4p0XWWe^D
zu%aNocq;Rp4+&k2I5dftI#%-OchzprWphgOZ8~Moj`$j!L;1EBxMvtGTrQ%<ZF0Yx
z>ueN~Z5cPZ3Vc49?rrlEm_mEwm&=g<vMJr3EQ4np&sq0_!P7UeI<Mc)YFKzMwPuHz
zdK}!++>N2nVTnKL{nR4>{zaCscXW6&clT}v?x2)`h`M)5c(Y%zJ|I#22`Jm9!hi(0
zw<1tKJgJdP1a`B8XSwyW=yg5ibRbd9cASjneP&~8pjFbNF5jCHbSsK%pcVNFH){+8
zt|b$JPVEBu=};G7$A|?FZEF@P<4^b+@+?+wkeF{oCpjd8qE=;shxM%-<UtYJ7>?D7
z(86t-Jg>dS)F{eV8nUsx;>%ET_G|)hnbjMUP1Dm#@{<~_k*YtyD7};I@Mqt`qxqN9
zI!{7la|Q!m2mgrz@f0D!E#xs4j&}3&F21?$g8$4Fov5|aABQyR5;oT`b*+v0Hd}9u
zqyV;=B{s(9qKm`VE<>lPu9y}G>bK6uPl#N2IZc|{fScB9_~7qrVYDv|cU=Q9tsr~a
z&3tWnF0ZsTv{|b!))0f(GfbO8H&FGC$wzh^QJ<Y&(DRI!a?Mi3L8fDL*WyBF2o{kw
zCZiv)0flS3X-be;G4mt1!yDU-WAr1KnD^rWQC{oVx+}KRZ(*0?#YpSKi)(L~8-0l(
zS9v*D-xC@+(&|Sh4?!$9ruk8sF?E+HQ(MN<5>OJG-YoBU*JM0yW&?J{op(>?x`fYk
zOwf3ONUNgyBooW#Fs7HK15W%Bhr^VS;cL<oKcS4^Xot>oAFF?9@e-c(nKXS+DW8Tf
ziX%?B^P<`NB)qXwinnIw;I1^8aA@B(T?AhOve*IlZO0STbS|TE;x>L7`b<HCj`0X^
z23|DMx9;IF_%FuTB|DtlZHI;)-Vwlns*TzuwF!j8c&M9SSr|30TMZIf*~ZG%q<QMn
z>qO=E0I>^|zj!BOnDC*<*}WesrakfIj1GX#Ay85Fk~BG0%G0D^tJUesr2*+Jv%cvC
z=lQkAA_thjVT+9Nf9HX+J&{}Z9wC%2uVgcE4X&sGekv8t^#GC0z*I0m0FJW+@MPv(
z2iOb+=G1_F-wF35Q;wV|AEWPdm{*A*p@{k{WTf^-kr|}U#A$l|cIysgNXTU9PKJ*l
zh(Ox5ZjoY*EciUzx3u;V|91E=SW!3zz}N7ihjtu`j}5jPpOB!*y7&=quofQrPbW2U
z-Dh519YS_l$?7^q7hN|CFns0d2j4yI+jz7WzMw#!+r1Nio+drbM7|TWsyTj1#+@g*
zhur^E)IBPHMvIe8xvD%Z6^|k#ce@^QpHER6K1vIa=-HJY9yFC)<~*d{d+ilqMu;0(
zY5!|k2U&+jcIiidQ?ca8?2of-hA$^s+Z_7ve)C6Pgxp8SGn1^+GbXM6jc2TV=NaRl
zPvd7K4{Fe_i@I5T8BC53e5x6+`pBWa4>`Oyt=2ut*;M<N?!cYalX1WH!@F)cd8L=d
zN?MWjIp`;yJh790fqjq@gFR7zeXSSV{Ike<H7w|1>$ByO@)bkZk7A!;9eY5-#n;hH
z!!^?2-6LEtx&5R4%oB5{PlDve(T&TT4LQypqif@z%93+8u}tVhf@EiB$%bfy*aCcR
z7IkEgU?28ea9rr5-$`}wNgaL*H<PTgzSc!F3rNwut~HI5wfhZWeNyz9PI75~<KQi5
z?2G~mIggQt%f%RPAV8O_U8Qp@W6*e_`#zxWx2msUI#hDYwr+)|kXk9!cPZqx#6f%e
zNLVBb2LZk*cV&#9Zft9W-}~HPVlOHofmByP%uDma62{GKYx{_4Rj@8G=l6)uvWp1f
zuQkN`fita_CEHEel)vMp5KRw{Y5bZU3s=<;84x=-js6h2Bpar&(HPMA(%dWp$So&$
zBuxNB{?E~$4i3upB0z!yHx;;n)$Cbz`adL{Wmwby`}RRpP?V{N6NwQDDkUHtL-{Hq
z0xHraA|M^3VH*gDNQ;!jR7$10M|TWq7%+O&7(Leae)s=4b{u=KJ^Wnn&-Fg9*L9xF
z9?fi+L(d16?o$lQwK%m=m>;C-c+25N>6sUb_e+S$TfesWn@~Qr%jbsadI*o*)VsTp
zu@8RH)9HkjRrsP8(b|M1c!Fr{7N|B<e+s4%<Ua5KKgxI9+?h<;VAyW0*cax$&8Skh
z1I$&Rw_&-iW#?~t0LG1u=2vaoss#OiKw8+!a%8nWDuw9JHFw+!hD}atI~J_hFy`1)
zW-701HaTR(I@p?R{}b$>u{*+M41hgni)0Gos=4od3pEeRM}|{ZzWeMtode%>a)-ZZ
zpR#!;s;TvJ;BlIT)OkNq0qh>a3;P9j;CCjFjoRCA(Hpf@Y+meyu0hIQM{{j@-36b9
z8NAO{w^i{Ox+2fte{x*7qhwKR3NimHMkaO_HYdjCIXcR#h;-MBZyPAQ$0Z^<YG4?v
zZE4rP_)1DZq5R|zNA^N1JUfppJQVV9`91vliP)pac3?TZEzzX(f%BRZxWoTZa8t1{
z_$vJ9bJw4F(Wq33n5Y^Q@;9E`b$f$bHMA*-_EXm0toEO6u%$HbN2PFA*2^_AhJR;d
ziPq)s?hH%|LJ^Aefru#}QERvNzIGF&|5wDxQUSsYw?4F!G+Q0)iW}rAROL|Qw2?KM
zu~sd$&!|z?qpnrhAs0@feffg<Jkrgoiwj089Q{%TfV7tt_7dQ!n=Rp8b%8alL8Dju
zzgF#C3Mi8doEYZ2ip=+JzoRXBV$uIFH;Tqwe}M2{0}xchqC@wx%j!j6XrxV-edst6
zqp1V29sSLs(9THkLXiI%dZMV?VsA(gK!uP^U?T3rvrexDt(9Re@7~pF7b%|aDpZc+
zcNH`2Nek;o#_^E=RWcLKv%*sAwO{WWoRs^ji)ZRb9%xK=_Q}q&c`b4;g7J$1bCvqb
z%el7^&i%+o9%@_HeKPUMUH9%873curpgVls4T0;NWwnCNhSMDdX3;6#{7T6_s0LSL
zrHNL@ckFDI3VKdABa`K8c9SD9C{$c?#)ADNt#=Qtk#50p7hcLzZ@d_&tK7{@m-Uq1
z>|NEgA6rjrK6q0xf2hx}m>VY58xER3MtCl$)wE=ks%-kUl@hJPITw6cjEB#v+xqnZ
z$WO6w$)WrqAVDQf1+U+rjEnJ5?jf8!SAh<{*2^wmouj_J1{qc!In1P=<SfG*T4pJL
zUEnEZZI?=^-D~;1qj^K3nG>?{xr<l*UAdN!*9EUL|D58b+pQt3^gg<L2VIY!{O<r<
zwo-J|7L~LBexQ}o%)|<HJUL=%+!;pjMGwM#hW!`_q7!?f(zO_31&4|22B~+Zttum!
z(b@;md{Xh%NX6OUENO`JbW&2e%*na>BC7RGYjy%g`hR^)lNsCo4?iX<mqmzN1^SJ3
zn%1Y1Hmk*;oj-2>{A|Y3THL}A`-Xq)`J87O;~O8vuT22HVIuahTVfZ0u%6_H9(V9Z
zIUWjIOun3TXM!Wu<dn6o<+dl#DXy4x5;rVd?MuC9T~|rx?8x=%obeSUdC{p)Q$O{=
z;FX`-2{!U(yl$_&DkNsUyA6?!D^t`0XMjUyW-BLru6sN6HIJYdn>>@f$=|EIF~TCY
z1gpYVwMe{LBwymuFSxc&A&o^<tv3StoApKJ`^#oE<)4aY&BRW$xRu9o$q)ti6UYZN
zVb@iviI|V@L%WZ@H?MZhfSf;_(Bcw9kMtR2*&bXny#gX%gi}oK1m!FS`L2qs83a5@
z`Nr**z^D3ACJn^tjCfP*WJx}$_K#@R3q)#f`x+Hl1YqrY)4aZe8vhYCY~ft%M)`j+
zOFJ0X$D<|U+p2F9Am5~b;zxT+X^2`!Ebk|!*xnFoez{FV)t<Nqcv%26{mj{CYn7n7
z!e{r~{~OpR^x+bQ%B#3R<=W1!k1TC<NMLPZ*Gn()%fxjDZ3)#FO|G=-|F#bAN!|;1
zYR!VsP9)!+5LPt!sHh@uG56X*8x&-~DYCuxI-d8Sq0_W$TB%-ClBq}UjUp#jqKtv}
zcdnP1*=ELfs6VXwc{?u^l2IO;1Vf1Zgn4VviDXFBf}pP#7NjgEWhSg`t?y*@o=dQ*
zyImn*S=95XbcrKQhdaq=@`Jm&@3=^jnH}ytpj=AX_rN{##$U*EOo!F8oj*zRuyz=~
z4dw!;>%o`gY}T#&H97||SwQsa*tvxxH5Y$<s+Ck0;d$TJN%-u;TF--6t%d}NA9IgR
z;d!m41N2+W0S0vB<*M=lW;-~)gT7X4v-i!t`Ved<RZnXYTIFVUvccz=g8vle+vq(b
zF@;D(w$ma@zHEpw?i32VsT?X-iBiz+ThC6~yY$sJn}Rb5<n0%Q7MlqY@dnmHZuN#f
z!&oxZXLRh5^#!_)^3T&HIEiMrfY2l`E)N=uutimT9TOZt*D-8{(2VQ<Zef|Dks<VQ
z4n-+Cdz14kE1D=pjUqKoldK_t>JsP?)gYa-b(s6dfLytdqteyCswK%VQ&E>IzrsHV
zu9qE@_+{LVjMWnU+;?uyJ?u(ybSq0((Z2!a0HU?yev4lxeM1Kte_<l@ER>b0U+xJT
zCEj6}a3HYP%v3k8@R)j=Em$gqMtH~gV&bVs_vK+m3I@}kpk(#qD_#0w3AO|p+O3gV
zpas?2DV;c9wqM@EJ{RB2?0yZnioU=j2%*jK-((UUKR(%xM?>JMF3soDyZ#;yy)mE3
zcdz7g(EkG`Kc`ChvI^G5vr!`;$ioo@XdM90rFV9Tj`=hKwvwn*H&zXyWg^+B(Yov^
zFIzlqUH7|9Hh)2uyGeSgWvK5bIM*yBZw@6p`badg(sg%M4p!rq0fSoF_kL98<9k|X
z5hb;Hf#(eBU!(^sG@mr5>d(^@>Q~nV3gplWTL_c#Snn7pZFYpDiq}4z{QalJ>}y4r
z)qS(+^-^mG^!O)5d=25$)2L(ti<A-w{OXBb1|?TuC{ymm5#EdatJiC(iBonw3Pf&5
zyxNgyp=kl~iNo$*;4_N(gx>*Qh}%M*dO7NMqEU&FE>DX`=%_Hw?|#pE*HiPZ{Mcg5
zc%S+dhiUP1?TQLSN-YLMU=^dzINWG<EzH|iN&{85{T=^fODBR_s%}*~q5E|`ky`A|
zMX9d3)v4^w*r4lyM79$6I7co2G#>k}nKEhaYl2E(#~k%u^z*o<c;MFe6?$Q2Oj#Q`
zrM|Wn^5FgI&7l2i1<K%+Sw5PU9>UQ*RU8+Tz6hEDDME&E(V4MWGM7=IQ%<L8`Jvq(
zs??R#!XHO~bm<{Gjm%!w^oaWKZ?c<Oz8UfF(Pj0O#r_lbYYeG8kP9D->y|DB&oQrK
z-Z?MicrC>w=TBSm$lcJo@2Zb{)#_C1?wkSO^JwPEs2;d)Tx!ENTT^?GeBV5jfT;!a
ze=Bzr{uGp6p8wrD0mPL05$^+1>n3zdn*1)K=8dpU+D|_)Zx+>QIQ<5xH`C2P^ML-g
zh+B=S7g}`v9WSzh_QYMCLOTp7^pF=Y2r<_#Fm-(4p4s?)!$LREjeGWVVrfem(AvL`
z!6sU6gYCZJYb!<67Y<seHM*wtbK4h=Q-j+r4Z8Sv<Xj!QzNDTszfU-Ke7Pi2W#?DW
zcuImRWhqSl?eL4jiNATSiTuhx)IA@0e}gU*HXfTl=vnrPdv3j3J4_{;(~PYvMd_4p
zv11D7?E2?eb_RCJ{ple7Dl8(XFOY+8Hz2P#@kZT+eIfFA80$`T?M1VJIKC{vYyD~K
zg5Zv)lT{8<(ZjvoDk4OIq28Mrj#iEL@a<$VX4;>Dsf!E4B}_-Dsb^$-vi;7_=ee^^
z_|+Yqj4g+5VbhX&0-6fH4q64uPwWy2YREVs)Mwj=aVIFbW^07?I-a}$$9uSa<`1KL
zq7?VF8wn0QxJ2|<SIV1BSwoSNA;r?9LxC82gZh-55>{+8*yDaXGo-$?>q;GnZB9yH
zFM<84zqM881>gaAk@qD=H-M3Kz#qk0;kpv4S)+%s8z#OfV9vZqV=b#~YrYXpZes?1
ziEbM>L(c##y6q`46&TRj?1OtK@l}ch&dIw&q2#qSc;SRrE1ih~Xr;e99^$S8ovNRL
z*if*$6sae;&(P>aI0e&Bc>TpRG9^C{Idnh`M$CH&9|{f6xVwL5z#o?>c=;t&x&gU<
z?GrB)e4*-2HP9uZHcFPZWK70YUYw~Bk{-2MeAw7Y_uVEB5)q_`kP&^w@oeD`x@9l(
zW)F>q6hDeKX5Yw2Q1HG@y}@|b+6-1arlN`?kM5m-PvHD&@RA89^ehRLx$Y&@;!&pb
zkEbZG@W$jXWBvUq-YHxYz=DHA3!{~tl2>33+5GcY!pux}lMq;pZWt~etEi^af9V(_
zoc}Z1uwCK`4;^C3`rN!vW96?&JN<9!aL3_Ra`l+HJ7)MVj8?CU0AoJA;C06bTg*Nk
z%v?LU<>I{eQ|i{ka#4PG|CX5EDehO*8&IlUi~UY&m(I6+*#}Rh%vK@Gy*vS0dV397
z)>#!5)y}5J`MNH7bs8qXq#y1mK%rr|va4ATO{pLkvl?ae(@9Ru^2D}q;&<ZZG{v?_
zv8=sDn@W2VT)r+Yu?JCRgqub8-ZOB~?3RZf+L_mCui!eqa0O#rhhMVWJyGS>;vf?R
zQ7yCvzJKsK;9TaBpztY=Erz=Q=|x7d#Dg~-NbSPD+3K6vbZH}$iGtmrdf4D2@(+rG
zq&cdf-6SJr7aRMn*lZ#@aE;4(EGIH=cYbAw_hqo@Rt|frxT9`^cS?&vAOoxAeZ!_2
za-vCeRq`HrI{e*n&3U{mYpW5k$xXC@26O@}UIrRu#;SUXU>GJKHD^j&Y;Y>R721$>
zMLbLh4&zaAFFD+vaJ+ZYUWr1hl-?*)r;^aQrj<oc3iM7UwLm1RsX7}<va;4fS+ZBk
z+ll<4B15vCJY@JxEn1q!Emq(C2ZNh~|CkDsWMM_8-5V>8*jlrVSF`s)SB)#Q$!Amt
zMl@UJe^aAx70SEJDQ3vQLnWsCx<ay{J1KNptk3KDtRGb}`Jfhsfv1HQ(NIdcg#PoR
zn#U^aUXM?7GpZ}R5JpGMoSOupiUbR<Z8b{f@uLgNeg?lLO^Ja%^Vur4!xkv#wUi;(
z^%Vcj0Ty{1>MiG~g4$%D1NHZR+S}8W1?Z6UMuh;JS?wb*AsAL@^7D%i-@{J(V$7oa
zlm0J?mPUV929_0O#!xM9n^`Y;lSO?#dcGajShx6oZy*JMaZi%rn2X0BzT6FE$b#0A
zd-?&EVgdv)X6SgHM!-{#*_v#%do$Uu!TyZXzz{f-<Q{ew9Q<`Ar)8iF4TIhORgjzN
zmds)6Mh>8157YEciFvir_;a+LX}o(U+3ire*!F}BSNTw?Ry}b;>(3Bhvbsth{^SSa
zsM+wuhU|fpMLlSutrsLCTB#a~Q0CA`i_fjZgJm8fA|aLTnW8s(j^duKc%9;2U-o<s
z&|05zF_hlcvuMZA`iGfIL+ESs<U4!d_H&)g$S6AFCwjPCz~V|<zsj_L=>u8@BO<8@
zLf1&R;}8%c;J$cBhYeEnN$P7cNb7OQC3umsgPO)i<?c<{K`Z}<UAvS5aRj|zeZvDB
z;u?Rl=<BUzMblEbNWm91HXb8<&1hHy7t-ShsK97yo4Ez(zFRk_WgQamQ(Y5EqBEgd
zgu`L`P9JG}&7H|Gc1xyrZxB3$4SR&&)>|IJj&G1omXQQiJj!SJ{)UUU+{!!7siG>B
z*~97@D7nt{Ob~yKkUz6>D~|ijic8@{3Q0H1Nv*S|A)ePx?NfY}dQJ~X?T|G+ayX!7
z;|$4!BFsM8c`8Z1izQQ8ZnUV4DFGmwV|m+CQBsD*RvoCx-*581RO816T<v#=ja~(_
zZWfubsYod^<i}X^s_7os)O*~C^g`lt5KI1xYPT;fm`-ij?s=@zgUM&gi+3EIw5A&`
zrbya_<?BZ6EC~&N^*Iq6P)@ha6Em*Scb=?zvCnp5kzEffKE5e8iygk4u7-XpX}!*f
zY4OEXZF=%hO+%OO1oI|9+HbnY7-C*EyP6ly*JME+9I!54+5@^|#LiY%8ZQL&aAYO9
zTvup{k`E7F>K!iBLzH*V2Tg10tFOqb?kIn2t0G1hdHku6cO+f}DOhBu*h`$hTtQm)
z`zd(^xXqfzIo%Yb5LDpr=`1kJo%Ma0_ZqKJ8XkK3)6jLKw?{UL6CVD^)CSXKe-?!U
z+cZt3zvj9>jX?O1272QiHGAT!wWDv$He_+T6+{o#YfL)%Ip}G%OT8Ya4w{y?`|_zc
zp_B80+3<hoqe?}-xR)9Pmem^mF`1bxxNZJ)*b_U5k4mk<%?~J=-}wfru~WphYA4}x
z?i&`{pigSvYgVBgk4Sk{kQ>iJYZt1S!y?i4YCR$f`WUu&b!wI2Snbd{3z?#t*={#r
z*7w&?{E-i<${pWnB0F$qh`3vpcVRy}6?X=6R-(bD`uSyKncQ;RYLaqH@H0-RG^O;v
zzKX==-^Vf3>P_-9g%A}NV;)=Gf8Cm_F5^3<Mq5c<44sQm44X%}F1Mdc$%G6Nq|H+5
zCq7Ahcc1T$EicK@n*E_9>*{F#Ny50%G9lZZvx_<U9zKTZrM~=8!n{d8zeU)pWWeBS
zkquVQ=qb+Oc-<d_&9k`amt85?kK4?YVDrl>d9`rL!Vs5fw@L<X3;X;0<_t!>n%bzm
zcFbJ+w%PRfyXa0feL-a2dt5PxzqvC_QeLADfnxD)j5tLog0@u`BV*|8SGh&I<2=c+
zk+`23GDT0<c+tQ6J1p|Y4IEk~`vR^UodP20p^R}KD_xz&T5^tV4!KIhzNE7mT`crk
zLgfm#Hdq8$^wK|4mJbCOh+{5H?aV&dvl*m9x22sqv>Q9R+%<iT%w0VD!RS{n2e|`|
zkIOvwOa4Ht`todlF-c^(@pH`*<A9-;pw?hsnmo}OxJ@kUj(&?ytuNn^T7Fzg<)RFi
z6z`Q?f-xnY+(h407*CP9xIoQfqb^UJX^L0>E=JdE+PKFM;eD%ow~a<KI9)woR3XAt
zy!%(@<O=t|$L^wWGlSE23ulnU)m#7OIsRsvvX@zsZj#R_-|l>3+Ovysw@s0(PK02I
znUz<^>`&9nr?(Wb%siK!Wj&(7-LK~4e+I30OB?ToI%HR-;kXmk4N=^-;bC&lbK}{;
z8?VSeUJ9DmdF7!|BliY@whQc74{t8Q+Y*7LB;zd}pTMlyqjrXlg$FMGS1L1{6}+0D
zYtk^iaL>HkEPADTXV9B`Nk$;vquguaZ#`7ZfO=!4xc5UdDK_B3g&G@-70CKM^udYC
zmNZH^mfQB8)0B;#ai+gkV!vV0qtuccm9_<`mq}-ouAojB^Hski9e3oW*YImfU8wM{
zcn=o+!Pk6uE}eoX;oi%>qdNgcWxbKGNgf*+so2B+L`tpkxFm;gR(*$Tiwe?s;#j`f
z(r8{W=JzW%`t7)t$5%ZW;j>$0r8}pgqA$BQ6iWvAoK^gPK>y_>LCtF8nn}BjCyO9Z
zh16<+>9)LjeuKD#saM<}*N>sH$<J!#1Z@#~@XP62J|FO^fQ%J&&xBg`CdcMZP!eIc
zT+aL&^-{B&T3}#+K-gJN=kA`uiBfl-TK7W_?J29ilSN|(S3|Eah@rL_muWTfmu(2g
zMTaEu_}I!GerUT<krg9%wAIn}4M_bu#&#}Uaq-uh^F)O_3)%ej33F<fRq{5Yx^X37
z+CSIE%<>4{9GP14YLVDtSYwSFLIj~2$>Xu+lDIB0Yw~B5A+oZoehK_&8TiztNY}XW
zi+b7A*EfLl4z60^v5QFK-rf35BWIJsk~=!0ZK>RvSl$ae;W{-1GdmLdH{ce~=DNV|
z?eE?7CPj|9;1&(8h28|u(y(#Q<=mY=biSvTD)qOf4(l%$z52BqA+r|Tyi7uF$?fXs
zs|1LGdea^-F=oB$OuMCLPYZOd#6~`qoc*?zrvYlG4Hw;8<u`q&-%5*|TY1y=DMo&P
zJ}6)NXfBw~IY2nDkBB**J~{%;ClAmUp)s!IPtVeN>Q5}($LK*cyB4)Wk1w&jIk49e
zNx@q${sXXPWwIXmaU82Kj*XH``kES<<qB)vO*%^`y?E$cK<gyyN1RkUJ#O~9aF0uM
z+3s(o!<~2mP{?3H7DFLWbThxb(nN6=PM}d=9fCvEh(ZL>3Hx!>W{~2Oeuo_8XvyZm
z>^i^H`TemRBv4=lsZdBgC48NYDW&fD3T?Cq6TsMyih!owZzw?uRjRV+CG$kAoRdOi
z7b6c{s-Juy)MSvM4mjS2N9<AD&q9|c&O%kFRfPS=3$#?4t0l?EdG=2<7qKkYXLAAl
zSHZ<)KtWW|x-C20>aEKMw>MQ2ZvXnHjCa(IlL?-CGVT9KaRQ-2c0;5{C)}**{%rL;
ze;aA^wVV?vfLtUdfys?Ri|)mPw^(Y<9kDw<BkQHVBd9ZyXVdCC0zmS@CkEOxNp{tL
zr>cP2{{8CrNk4V7)zC^@P$|be{2kTxGoR?IS+I0$U57}w>;Z>6(_q%SA(xrI6UA<a
zDJe}32vlf~8twB+>!$32R&wF10pH(hB>$WRL9O=N`0xmjzc|4|{8W5%528J8b&ard
z?DI3G`CXXMrT3c_1~&G*{WbYGG$V*o_<+^8ou;LHmwoh1FUYq?)VkLlJt5ULmR_si
zJNI8u^J1f0m>sOPbl-K}2&xed-y34V3ql!lqKwWQs{kxtt%?v&cSdDbVRY&}XRfVr
z2rV*bbWvs~7UA=$e7Ue_#v~<kXC?m0^OW+_+&5^L2DFhRG^zb^xHn2{37;fh5x-`#
z6#v|e9jgByzSm=+-kg%>UmjoN5N}m;&pEYNZ}wejGv%+(&Pd<#5s1h2i=t2L`O8IC
z(Kl{nl+SO2YM#0L3u#VYIii-ESbTzHv8PDd8h(FReeRz$(s$)w58_&?`6lFi+DeK5
z@^pc*>7As<=Q=jbH%z}jL7!`g<#$Y9CJb8I1w;40Oq^t>?4I~+20IG?))OXO7v^q#
ziSOQ>y-GBC6_~(DWVD#}5@@si*C4VQ>bGd-qao$E3ZHG$3)Rt^&XXjIvs(kde%}nL
z)_4?jy1-iWs+d$0p!2s@VKtWG5!Rfcp5`4M$h)bDI#s?p%Xw(!sh^n}RUeXxp?HRo
z<lmpXus-}abhCw{-iCU0bCbH}4=T;iP5_cFZE-ASJA4yfq&*LTk9rq}G%-qeDo?|l
zaHuxUU$fP3^<d-riLfr%w#KNF5{(+#CHpkYpNwg`;TzXJ^)^_a+_fG0wS9L}0t?^M
z+>|^8IX^a`55Q`sFS$4FR?U+^=|)x0z8CAh0pH_0(GC30sNxk2fUdIJbVsU$fs~~#
z!`Z_NyXZGAG89G=I`e@SSc89ZI|HIAX+j?Oo?1Pl<pUp_1gVQ~^ZEsxC58+v!H22g
zH<x;b>Acu>UbDHYH8bm)TM&h`W*SAWamCsfjZ^S467ogLo)p3rH*ueW+`$1q<vW_d
zryyL+iSPDt4{jJ?j}|7rc0D@n&DM5Q8lAurE%{p+g{;bG`i2kH^Cqrl(_{bts~QTX
zs!ScC!mlW-6hpOQ%e6qkgV})xrrXlqb*q(AWZzR}x3cmf>*h%(g%2)S<fGcGzgi2d
zTYS*mNECFJi?1v2v5V)Rl*6Ssr8SFk_>)3cY0<0)7Lk1bn|;u{{5x7Oxm0DhB787w
zm?2<n_QXw>^(7Vg_r0DhaBxQ$^e75!^X{>Vwn;v<6jnXD<E%U4oXuJ<=$f0{v_E(F
z`}Dki#x7Oga!tw>1w%t*Dozrsma?VS=SB6*{`4E-lFWR9`NU~akq<6_T2UVHMFFgz
z!^%eTaubgWV;j7yonkXc%D~YJ;I)g`PxJ|3Z77}ZTTEHo11;(B4u2IlQ@XU{j8{<0
ze~#PW;5tZw>++SM@3l3x>_CW8JMsP`b#B%hw?riDtE@#ld|*A+p#+)Dm$ZC$F8W5b
z9?P4Wy2K_Z%&PnyO^r!k$a<W6G$d0SVAJkXE?dspml~Zq9m#dyl!Rn)19kHq{B-%l
z8O}`4wW!dC*||4aci!^BZ$SWydNKGL;zvlIoyqx07oA$U%|9nQ2B>K={zk=}3<d3V
z2bU)lk)ZYJsUlR`znZmH^-|EIlku+q)|6-)S8e&+ml^X7rFY+;dwm7+FD*REPlr^x
zzL*~lTS~Z1xmhQvKu&ncD=Yxzcyv=xs;+s>_<eboMT$E)%GS+T_3vJ4X>>&+zkhGf
zJ``7~DC4YqlU{66Thg^V`0a(zae|C6LM1+HkN6w$t#HwKz|!}OPh5ask?_K|@{al&
z$G%-Pu3s7xOYh-li&G6tU-iuTZ9CozA_SSf%D1vV6L1I989~kRV(R6&0yw^s-k~-F
z+^C%Ofm*RZ<kH8Ou-%t;H(5nKlVPvv>^$<2lu}MnK<5+Hal8IRvBdy?zdNl`#~WtM
z^_CZYucV$~c=DU#ubzS&VnsG{Y_^0d3GBQz!C<s25$5$guy;pcD$A=Cr+CN$<>GEs
zmY{YufsYvBY3=k8PX$ZAIXC*IbkN30@nn&!Z|8}h#s2T1%&8urz{WmEhHi`c#VEhU
zAT0bM_A4W|?A$5lwP%an8E3G@B78gIX7zr^MwphDpeX~TW_-TTLt`H&{Ojt0$Ey&?
zVl+R>YCM7-MQ9_`%sRSj)~)o@w6&cyJ-0gl8mqVg7E)}8N7U7oW+%X4q#YtP!~M!)
zum)ZuoN%F@_X?q^7)=xG*(Ia+j7oQn85B59D@%E0*h7PK5K>B9==wnb%h3D;H@53X
z0g(8?f>JA6&~@4fjdFD8#?tB*zVJibMCDbpp>t4utN9578=s-ZtGmf%RF|3l``5kT
z!Jk5EO#mLL+T_4ThNi2S0x!<@swrO@4nwq-kKFONPa3QLCw<att3Q2kS=ueAQ>53T
z(P1ID<CFgP_u5W5(Z6zn)!h1|kb9m+b>}^=+*oRtHVJyc|IggwpHF8P^3v|)UuuhC
zQ|>&BfZvIrl$y;y)h!N8vY#}{bn*=RP8PIq@tKz1_S;?F6os!QmubuHckKH|@_r2N
z<MiO(q}%UTA!Y5SgAm^i<@!9BN83$Q<$u05ypNs-fL&TnaqhUv2A|+;<Nm0e;nb-=
z@V{fouyXnZN<1+<44L^B&n2}>Zv9qlF+-5#RS)=EfFgK1&(3wDvbQOpDHW`2hZHdd
zXE%hLlMC1PhX-<v9xX#ErkuLOsK{u@gZMICJThB>^4gdUn9$>KhZ<Acf!3M&vrQ%l
zu=d{s_cHtS_SxkH?YK(;$s@N2J0&vHTMT39!Y3EWr({xdqpRcVUuyI@cB5Y#k;CQF
zI*G&!d4CP7?fT5;<OkLLWOIJGz~24B3qMd&A9gz0a5MZ~TQ>W*0G}+5lOI{TcQ55H
z(&s3%e)v_n&LM5=AbCeW9&8t@;(aX=z2^L>U(RXu@XbsQcZSeZ9WLGIbj8c)*E>5g
z%#WYYdbeVfL{^zg6xUr^Vrl%l%oOFG?-uLnDaCIl_({~Tlg7r~%J{;GG7im8U$%Gm
zmCw~ae!BO5wli>Ism8WvI_SRU7T9@qsR#tO!h9Oc+IBWhko6JI^whP?*Y$kTee~zZ
zrZ??CB)HpC>qWY~{vTI54FH%$gP`vXZ^>2tby6q-JJ4_Srx4-A3?<@LF?u?tdYhuc
zOb^$cd5Jjuh+UdbW@_p5E7nkHx+XB7BUlvQA*nm<#u83nOM9|uTd3P(aMEe1B7uqR
z$<5KLms~FT=wZdo8&BMh!JRpYut#27X}$`(n<c`Ck=zE%SkfDME+QK+B7^ZB18=$>
z1!Ir*&uK)ebW&lb6nqWiR$N#bPQSpIA5H_;@k{XN354X;XDbT(Ez=e>pcoC9A+gKa
zNnYmAI<-z?VWYD%%KN{Rt295W$$AQa|I`)>FCvD}|G{1-$Kf2_luopi(>*AHm7&6e
zw1aV91X+Xpb^)}~TKkzyguy+)KTghOl9_k;3U+FE9GwerB?x>iMZaLSH)=WM^_HVk
z;V|>WCl;YOy_bPPKU(_P)!5xa8j0wuq^`nq-cRd(`E9`eaal0i`m?`NFzdQi%{NW+
z*H2%PGV%!;soI>Z(eZPh{aY;e0lhFaQ=+E}$zX=wZum@l5FQ1m9B}Niz#r=`aWrcw
zvmqYVgVa{kNS{0=-O+BK6k=!hi|<>I!J7kSwG#7zF?_B(ez-lks~!<b87oC&ofp*C
zBdq-|(wGBz%(+PkN&&;nkg+pTcVO8zIje(!lc8;u_jSiSa@|<)W$UWmNnF%UWQ>I4
z&c_HNO;!J$uXkvJha&WEg2cyoPKU*ma9!nRoQ`2S1_cH_ElyK<WSu1;dB>%9sQ^d8
zOzCK#IqQDedDq#S5GEB4-nRxu#bvRqgaDJH)c?WPmP5#?Q{CQKTWS|iU_7slS|p%y
zbHK<S)SC7oj9;y2J|wh8M5$}+zx2gapPzh@mus!8<7(Y(pAx^P*2Uo5;I@*tw{K3%
z=Et4>>+I4QF2D>Jd0yiXfGy6wCVtYX)RM<)3m{$sdO;S<>KhgRR9pu3z7sEG<POCB
z!M{WsHwmu~*F3qfuU;ElA12je)oS+8-I4k<rp(IrY2&<m3i8}Yb8b?(gEck>$)mE`
zRO~njZwuajXtApm=Zij>{Wjifp*3N=W0r#*BB;Gv9JUNRDHrO}nZYhk?0}GupkRpT
z41u>%)F(o<;1Ap$dUi>=)Z>xUmo9|ju5W+O#U}fe6v-Q@sQrU(^%qc^SkK0jEGWm@
z0vd}e<=Vc)@qVvNZ+u|9%exA|Y-iesj5?cUY!qH-3v*p9;~LKjRA$$NVF%VNRk6yw
z$R99P>!m**Df%@sg6CIpP$_J(S_`x-5;tYwW2do;)ZgLLBtFppS%wsxUKu4bb!pb+
za{8+bJ@HQ=P}k1T$B&)QZLKA@Zg1UT><fr1Z|6o{MlD~eSrxXAp8TFH5zx9zgQxov
z^i3MmR%~CjG)wmoK|$#@*yz0$Hr5!A7Pd2V2=%zqr4c{nXq3}^&%AIrM(x~~{$B}f
z=7hrUBae~301c?;U0s2VfFtz~PK%47<k0^i#jN#L7*aNq9_OCsDtt^<2DuLR0!XgH
zQ<cdpk7?M=>zQ=Em>6;e=J5QQ*i&c)o}4*AmgiM;@$B&uqTpYKi)rbIFj)Z$VYigJ
z3Z-KIam~1^UauGV8zUb@Z(}(;Xz{a#ZdWkc2j<tTkF%Jt+d7i3A>04*TANt4B=PJ&
zr25!N${H=-fT8sA-7w#9>KtJ>*KIrdxK*3fZCXFGQ+dF;vtYebrc&ZCNT@>nj6hSr
z)RzPIz_lz}@b{L@vuXZ9!B-nemSC_Gyzbu2Erl<GL({+HJJW7D_I_K4wn$d@eC>S|
zGT}HiOzz|orT{j$=K!Z<F;=R;|25>^8v}KK{T#8IJ&#e<TQg`Z5oJ;_ND$X-8KF1s
zj&ac{?cl58%qOGgRJBl7MfSrfS0s(Ind`2)9el*rUD#bb<EE12vX2UkO5`jsS&_%N
zIzIn0B{pZ%A_y86JvIEgWz(*G5^AvU#^IR)A$&k>vzxDp9nBMXm3oPFjo<CF`Ho|{
zR@_X<iV{*Ur2JL4-k*XE(;e`Hd5z5Qb}Ot~)?2KJ&Gf5C9q4fO@pPxwbS^uCCFYn6
z=#DNm`Y=9?VzpI!;Ml1qk91{B$8IHvd$P9x3qp8^LBxW=&n)%Vq-t_r)N3mRPJfLD
zd72*ZD$IAs*KSHn-+({#io389d3w07)%Uge(KJ1S7Uum`YS=o53MV~b*_J3_0SbUU
z1P(Nm*`gKi>hxbSed~Tl!sF4xQZ}KJ)qj}F)B~k*dNbhHV4aW-3bBy*$&eAI^e(j4
zWqNvF9T;-lFs|a#UIO<lkZIqIv?i1`e58K&Saiu0Wl>n;gZ1XbhZ+oznmcpWn9SU&
zE`rs%-N;ktpJ^>mBWPI)x_$!~yHqs^^Ywmxke>Ro!OeC1uY1zvM!&ik4}DUiXZKQ0
zje$>eRhfF<;U7XToBz#z7StvucS|>z-p9VJV$v9`stq`J8rK=d(|<nwomQ^eHGb4x
z&eEha7{?+%8{V{|*ZN<NsMk0BCQ`n|aeY0`K)|-hLed1eaq0f$#<QE}a}Uj!ls*d3
z-B~J#A`cW4sMjGD!Z;B_MjP~mN=k1&nwA6?UUmbX6hzQHt<*>i%Wu=|(73N|Nj%n|
zA~9w52kXgrY|xHALDU2vdeQt`)-RYuJN=EHH^|$|D&a=t)^^t^C<JJ6(YT*3S;P9U
zHQPs!&awwyk2y<=ELPnE*Kv{y#DQQ{R3Vb#Wh$$2hR$x=8bDymyjQ}C9Uzl~h#=dR
z#b9N~)k)ZYAoX|7C%css%awz4mR0c9#)W8Q@dZ(adE>(#qD|49n^DUdym|41m2p(L
z{_@k~v0ne^eivak?-o-^{K4t}<g6gSqyZ}BOAuJ4$0bMkXHAwqBiFLOqR~y&t=7tK
z6nz20Tz>e^h}TIoZjLeX+Ru8T@)7thvV}~?RmETAtBg9FNa2Vw_}%>UNR0HKh1S%i
zoy7bjL~QVs5;lx(8N)&%i)n4JZgQ52Y;=rNhrpyp9iA-PUv?|7=_r1Gd`sOA)ck2!
zBQ-~1Bzfb8|Mdo@W|Xq#5iszxngW|bcq&kpr3n?ir&=fcV)UG~X3Ks|-8J9c8hgt|
z^k6KHq2y@GU!A%RwLpM509B6L2eo+I+~U*#UK3ej9=)U><Z^KRNWmxe=&h9CB}FNj
z$E-P80o@ewZp#@1lv2oou;{X|e)yxbUI~im<!t>f?X=DaKG|G`&2S9fuwDIg-5uyR
zuEPqB`|d>w_VIjEQX7u*Ng^&E_)8Wm4(ZEs4L$tl^#x(=A2DsBi+y!HmmMPkf~>3B
z4Kpi|^xm6(F>)1p4%!rZzi4o~Fd&zC4+LHX3W#n+rzT#>PEtPr_ye?ZE$XmpP^P6s
zV0(|0qd@#1Cu2NPqH9^zS)-Hs@@c0IF+qg#im~QSA?(#N)}O>GTGf`$1zhDg&Uh#*
znk-FKHz*(lpFb7C-?e_PS)cnnKR0Tx4D@<8m$Mb+@Wj2$inFJ&d2}q-E8pt%jqr0o
z;rVfc1x^viN3d*>(4$x20M^HW$LU)(i3+PvXYXk)L{xqa=&5DJtMRsN!%NTt?5j-r
zZBp9{GsZ~|6Wxbr-N0VCr4Bu)fL@&6g;ln@+s>gc-M;m#F{MK}*`fmkHm<D3>Du^w
zHO=!Xz_np^5{H$xc!!U#HQ_%QT%|?seucF&;+DAF2#s+8q0_W!pxLeud;pwT5k!Zk
zCf_f|*q1(72^OD>&k>`?)2WPv;F{;%Fy_tkNK|y$;{cOVyi}Q)h1=Rsm!%)YD#Ik}
zzU7gBhbQ3fkeqQs_g0Xa6{aj>BEB<gk!aILNFy-*_|*&PC3%{&4&TzE7X8;202+}+
z>MX&V^wn+T9kXXxOL=R9=)@oMu#Q*mqE!VMUM<OU>jl26i(s6ND`OaqQZhc0d!w-0
zONRw5`>7NeVk>Xtaon@Su}QYoUn&CkU6;A85QHHr2|8g0H`P$|5RxHD_X;Ul?8*N<
z`|W8w1dAxy&iLl2>e5UAD!uy1wxsen8yOqqeLe7+TZi1L>3jl0aEbJc5O8(5aZg>!
zpW|}}o=b$Vp}qy&@BUM4BfQKlu)7#X7#Aj496VMYnc~0ka^8Dh%pY7VCg9fn)H5uo
zeficA-_lp`mLz`U&vf)n(Wfb$YfHTn_&@0Ih{00;TbQR$HY7eXDy%F)(AKNyXB&UV
z+|%}>rp+%IF7uUy0oa5N0p<6}+7Re-hhz2vjctlhJelmbAx_z2tPWyVBOeNZ-#@-N
zm!v>D<N$kIq`36Yt~7OZprBZ<e?fJ3>Dp3+5HA7#5cmRTBsn$aGs3$qi!HTcNo{u?
zUZUz5w!_9!GNfAfV)F)L*UGQ=W8ISav;p(lQjK&(tjaR`fspv<@L_`AE{lR)Tqr7R
zKtgmu7`VHyua*-pkhzzA$_%+vPQT4-ME=J`JXBpxJr?%R?oTdj!f-z033#+yCk$r@
z0}3n}Zi0YLo#v&B8_r7+dzz`u?Scy<A|9Ilxser%-1s_d<AmL>GNL}EDBXcWbeB0`
zRmnJ{v8ffzMwbG%fz~`FLNYaav9+Nx4G*v9d*6VhC<aMb9s^+VuSi=``a3m^@NArN
z4-s*6C9r>r_6rgIqtPF=V5dNA(r|&-fn9f)p*=pLj9w<+1e?8%?R8a(qvF49rEaa>
zn^2p$KpJ2V<>|rn{SU%q;E(K{6GTjCVGn%~hUulVSqn%qfCM55!|3?9`gzE-Hoy$Q
zu70klSN-iPQT^#zTP^YDnU@7>r#r6h)^6V1ek5e!9$kkaL4#Z?SF5yXNfSVk=PCQk
z!5ORaOKJVucBd_0=u1Womln>^J1qO}Rm%0*Uv~bEERezN!c}){TZ2WZPuX1Ww$sPE
zPw^;*h4V=5WU;~*`g||<Y!5CQjw*Yd2bleM7g`8<Wi6oxoD6kQSm$~&Ts!>}IbXUZ
zHMh=6V5xU|v|(Iw{U_+s|E83Vgd?4rqm>P|)faO|_zm3>RV}SkL)2Z{BNvZadSNB9
zMUr%_IV$@xrd{x{1(0RwT)0C3vx1Oi=MuD}B)0>1tKrO!y{abQ7x3SHE}B|+$wQN0
zn|<nvh+D4$zK_!juymdv=r;FN0e~o^LGF}VJlfN5zI|yK`+R!tF#F+GBido6yoSjJ
z{QV~mKw!4we3k~X-T|b#1E2X4rgr`hJUHWu+h^dZnPwL&-cCIizviijKNeq&^4DhB
z{S4aWqI3WjZBzks+!=R06K-V7PHLO1wsmj{VV)ldUy<YuSBez`STF}LGj?eAwCEvV
zeObpu_-Yim|AHx}NI)0BGbwHl_sBmSx4RY>7~>j;4Btx-YXYg<vU#cCp=m;U0jm?0
zY!^Wd8m@A0ex&bb(wY1^`_!O9`)5-Bl;kp_#jt(}!_Sm@V;4FsFCgUGO=LEOcquEX
z9+vVK!<r%j+mahXt>-xTF=7@({ag3gDP!tMTnwAA(auAUC;Z`UYWso?a~;$seS`1y
z0I#;J$n*?{<&|u)MN_K;z!e68^AY^sPeQ|rxuS(v0HvczdQ5U`^aesEIdgRuKj@le
zR`eX$&G0FFA!`Gl>;zq<hD&-e;%v@MpXu87=%DIO?IWZykWmBc`r;KNT**a$0pT=u
z;{njzRU*|{CN%`_L)wfEC2hUrq5TiF%aoFmjHb`S&=?dwyd{dh8DK?I;2{{d(@*Ts
zPD_tzz#TY`9&dH^3@ZvjzTx+R2o|vNdx&1X!_v(F`v=N5b~Ist9-Z;SH=YfyN(|dW
zin|)@KgFL8Jrkvw6}T+}6aA0DbjFTB?;#t3eUUq;?e^~l0H6KIvh5#V6dj%8E67N6
z`Z>?U$jmB-IH}CRFVFHgu&zi}I2_$$tPKOE594uVNR}b_KM-4x&#=>fwIf>X%(CHG
z3;Gw27-qU|tlxIvFDQT!_*iNpfEHP|iD|~94u?iB#yM=TFH|CSC{=H^<$V4gb1TiB
zkKuMmN|ij6i@_diyQMLQGq=;P7=;YT07e|^Isgry%VqmR8y>=rozOq<%3FY?mxvL^
zVUTy1)MhK}Y|9Vi0|gg7mm;ztFGU(0Q}Hf~ZOI1GA%RabP?Qpx42&a>PMr<W9x!I)
z;_`ZEx8JG^u17!Xld|~<nZX9+Ah%6~hj>*ESV3nxhxnTe<Xt}ZJ_LqqtCv@cGZG>u
z@$)N=j}S4IHG_)il<GPIprkiHTR{g+%dAhV;+J@z%3Nr5ekL02JpK&a<IVu)YsK1C
z#$D#rY`+g8GAaX$Ax+#F%Py^*+UhOwN}o0JQG0R<;aaNG7Ft7DSeqoPQk8}?2gAvM
zVL=BMK1Uv`LQerrfW`X)z=b5=lHTaxLqt`3*cpc<HGF0dBcez%u9pcecsb<Zr@$pu
zz0KCk#tT>FGr#!dUTDdss!oA{^#?faYIuAxx^g?Xp;rYzQY+^7es)eGDpA@edEoqJ
zj-YIT`P5I%qMov!HR_JhJq#DeRR7OoV|T7_ECn>x6NToPW)kH`qv74})8n5}SNVL6
zSDzz)s}wnO+iR=la`6xF#}skz$~#p}tw<jN>-bWYVsj$`QM8GF<ce|3+HWe%5_BQ5
z%(F+0ZL^O(n|bcz9^LCI<(2%@nciy*L&5V2xV)b-N)pfDHkzM-p@_=4ogw4e89d##
z#d}DbS?&5MXav3wKDAm5F6lTDkD)HJqOFlUfN7SN1t-J%nN^17FL-tKe-F3P70Dxn
zv&I-->9cAy%{4f(=F#zK#wp0Q0Q``a>W6QM-fv;Y(p|3*%%#9PL?w^e>&}J9EaXS%
z)0|rO%DZbyMpTJy^><x>C7Ah<OEM{4FiDj79cd-3Wn#U#5GV9#;OlD_PKF2wcbWn_
z(L~E8JANX88P<z1#v=|!7QT*4A2N46@%&@yi;<FzIV^83wkY*|f@ZD~3w1Aemce<%
zs{pglq>&ZNcJZ_x-bQ?jO!2a4fs17Y30iGExznM!^l$?`2q~BVZlO&G3l6`bbQ7ZW
z#z6fw(fvq=pVk0URM0AN?R{LSJ8zYL;>7O1d8cY8S0D4L3NzjBMD+^KzlD1U_QMeP
z6<Ury)2^g9`!jZu=*FYDm+;iW=JBnOHo}8Pv|Qk#$cFLUnIj?R`;))3q~@=?%YBz8
zRK_kDm6LXti+1e({269%v8bBPFEPV4T~@zYT%+`1{RM{bORw<+$x-EkQ!Y{A2HwVb
zG#(6nC)VF73Ki5_Un^ZmvP&{9I+Ck6{5H&$&P|KOD*CtD-$RaGbPlWI3QUuh-vaXH
z<EtDe?FMgx#nSf{fv%Ap#}Edkv42kNV86##1II#ZHM>2Ra07d#{@~r^j$9&p@qZB)
zjT!@F3uo&R@yDtG|E|^EA>QJ%G#D4Xl>VUayZ7!^0C%-l1oU5hG(P%ut*eTdRrPm-
zZ4a!wD5!0rpWA6CYDn>}%}deG2pP>254mgzN_Gp7d4E(=Xl;4ds}%WE)A;mZU^1Z!
z?w3=x_;{LUNk2GxZ{e`ePqo9+|I=<B#}!f7nN*<`Nl>VqKrWzV2it4$++TpGL2J_d
z6x6v=hW3O3(`o|q825`dC0Va-KBBGU<7T&cm(z+o+GSKjj@?#^bG4J>xr@LW2W#jA
zLF8T5xuvgGtIuFWKrhfIJxD)(^aagB=lf!EZdS9bsg|1Wl2AsiVE<&k`>}G9Bst`X
zLFuoYVpjyLMz0}6>FdU;^@9pJ$6Ei)#}GLk94m)cmIluXCH^PGo@kbwYa6L%&6NJn
zIEmx^fZDFM7!eh{80r0g#rJQw^jt6Jp0Yo;20!NW*r<C!SD)UWxMvA!K^fHjliQ7l
z*qPB+kEARaa`;ENwh{(HDntQf9zjBs$VWdLZC#F1?Rjpio)o^tX;svLNc(UJx<QMt
z5(TC7W7clb0n<%q%9O(68yDeklSi9x-7PM%Uhw!#wXSeYqLseTuGXWHX2ytz=r92I
zUA#J_3~EyM`7*!s=&uu8?cL39ANua;L1pEUx7R8OdrK!|E&g&9JT*)0VBFxOM>M-E
z(%OFk)ooS}ZM;*+Uk1KJaYx6eF;=T@p8C5lGG^7o%8PI$;_Ejp0?a;szP{sN-s3Tj
zO;4-wb<k^a)btDseGEb^fJ$qP1PGx@ql+qARz4;9PJ#KWDDn8C`iP<_OyFx*ll?Ln
zrlm3u(lSr;drGh-NEm+{0gQ@Tx%!jNk=&OrL8op^fQGxD7>^QctsjBPtr8qOwK&ip
zzpZ|T>bUx>l*~^$>^%soUu+yM9dQ=~g!3^0*gQW`grrL9F7MX=Le)EDUw0VpxXK{w
z$K6KWVuCp4dx9o6eub4ou80Pop>@waIZ}6^SLo~3W<zlkQwvAXA}w)?4~JiqTo^l;
zllj(g=lQ%wImg*iGvAG!O!wkb9<p02dNFP%;%cpJfuYm*^p?-Y2Z(~((UCymC(A}t
z>?2Ka-)-Ni{ZpK>%}40Jrg}NI*+Da(&3AmCIBM#4sPeW3MTqj&^t#e!!x7SD{&zVJ
zLse^=_fmq5FfKg+G?}X(o$$vXn0CiYV@h1_#St}9$MElaA|`NvL&ts(xbv_#PUfS^
zTTV@fO3($WJ(fnLQlgUYX&Y5~8i-leZxQ#OYN$JX!NjG>8bg_>M^~}zJD}eBD>(9H
z5AdGJmipRknWpHrvPYSxeF@}cUc7g^%`=wcvotOy`mfAI*fh~&05bgC24wH@_1>3w
zXhPC;QC~CCxdUBdIS67Dr#MXuyHOf+Vr0)Ruyl=s!p0-C`2!{h+)3JGt>DY>+d1%=
z-Mi5rqk84(>y)j*1!~Gq|Jt2-tnHuN%RIN7n`Ku;EUCDBO;&LyhkQAgk>37bllGe2
zd?MszVL?QV9S~Ic{vO8plTUpwGqn1)TTX16`jFLqlaZE*M3n%c=5g08wf+4;T<UWW
zxv_P=4;auk*@H|tLJ!E)6z&ezkcwwbl2TloN;qzk^GXL6ciIJ){fB}G<?UV1e_LIy
zY^$^<T0NZ3H`b9V)4ENxs&vgjGg$|<ly&91`qY>4e;^&$8I|a{ny05MQ$ppRTRxu5
z<$USj->uUBQ0gPktib=>#*T?*A*#&%D_$B<vdE9ZkyjVAhSp0J#LRcX=gHCHjW|&)
zOVN7RV~`SIEZKpU>43EW+aOM38&wJ!mM^taeAU5I*lCbr<yO`kbH+IT>V4rn6=EUp
zCYE<|q||4>i$Q$Gf2IE^rlp-?WNacvFEfPk{^-$`Csb3sU<ZzvdVsEEFzuX2vD2mc
zprEyMY>`GNqd9v~^h5{MovFC)4F0PBtN=Ib$QMLlTn*{0g>Q-*B&?ceZ<JU%LnccP
z4>@iM#~GL}c;By}5g72vv|kqyWUSV~@1IS-7xNHmUXES+=wlBLkQv*hrn)p9e;QLa
zZbt(#uQYJ?_rUH!XD|jkjl)(MRgZ=j>o-Aq)0SW*SHQwo8woUTW%a$$JEFO@$k%X*
zI4tzrtUQ&AA~ota;&5&Qj6$_EdinF7@w9LqK_KvlUi6$Wks?#ute&`Ixtfjpa~|uG
z&Y_Kk*)sXhXA-UanVURlU)N6B2TF-I?<vc#3U085=!Jopx>K^E*`{3<B{%fuu7LJ`
zWmNds4G9HuW=tQtH`&PoC649#1_R8B-G3XaQ+n-7F^fTtt=7shx9rxo;5L~K%zm~m
z4gOIwCzzIozFW2UjiBoc<EFZ)ze7<c_CMQCdUNzmvZQX+7e|x&6-&_h-)@Y4(ap~}
zHA`<e&IEsTfByh{f3|*NQA+f57T9i)_}$5_AL;~9+xwABtZKslOwF%hb1*5B?p80f
zMAKGM>2`8k<JEaQwp68KXu1EQ4jx#ME@rHl`WVJ!j-F&8GnSEo*6?}MahH5ofJ-lT
zfS>^ZvYvU&uQHq+xOqp%0MWY8@wPLSQvx1ZOUX<w%Z`){u@-wJQ_=m1o~X3a`t2N+
z@xBI+ppZPW&T&(h`)&V`?iMm=Nj%GjT|V|SN4zX2@%-qS$j}_-<^BPBg5B$Gy`bZ)
z$KB~Xh>1TAQY{1|mxS>`M+Ck@_Ni2w1LzyI^Skek%M*1;2jbfL*!jz@-JdR7i7p22
z-EHTP`JuYan3|PdC$(CMy=D~qDJQ6h0CgQF=)OS^*F_N~G{8mk^!pP<!D~)1`@Qx$
zU8G(PgR?iPw_z!qDoW=PqyORhYu5MWP00t8DyeRbjIy4|IESfu3`(tDTOQBU|42Q`
zLEWFVKi>Ryj_5$U4aI*6A3A}}$PCkaIdPZ%A4%Wh$khM;|L*NwiXzFSTvj9rEh(2=
zR;h%dQYe=tw_Ike<!)9{$gOfMmsLn|zu(Pe$ZadvF^stka~*cE%lYl|`~C&zob8;)
z>+yU(ACJfL(ciG=7L(VC9XvYW<u`e|Y{dzK9IG;@KR~zOG+M#aRTU#qaL1eP9_Bj|
z6vO5a>O4e{ef_zfb`^Nvv1!8w4k(alp2Wxf5D0L|o;ab%Y+j4ShruK%_0^?O5n@Ji
zLDQ-;!8Nu%utOwlO_y^nxUNTYX>UY0I!j1^2yMuT*Z-e!pN#R|LnxR=&FEfT6+I!k
zvM#pva6uxJm$8{0_v<k49i|L2nduwAUr9r_3)gq;Bnma>6>L9<0B&qc5YjN)a+92(
z=M=Oq6#aXR&pr@xiY(?m#>g2jAE$X%<qi()cnKG7MGC$7ngG83XBKjbFD!_fI%`{v
zYXP=l{{i;7)%%z(4t{T0<oAYL_>#caD!8@U5Vj~cVZ-qD+dGC&)PCO)@`g-@S#0y_
zPI10!O5M_xe~vl_SP;fNR4h<F;$*;-Hdum%byNB<t2TKynjs7Md4+z=HGWg|+Vkog
zRC#8#d}>vfUd7Tkh>@?+zJnhuiK>$!cDygUI#rwf1D-3&k^zs&wUl6kZS3`vJFh9|
zB?@{`yC=2W!e&f0-_Satp!n{>GB#6aS%E+pO*Gu^x0wEF?Z1tR#{C4L41%Nd(I3U0
zUKTC-U$cVbtnDrIgGQbXBQE}eGyW8P_U9193Q*BNzNQw2ks|k&rtd<HojeyGVDB3e
zF1j0#vZhS?g|C;xOE%NU<+yfhSf^o?rq8S8)MwD`@IIPbf}@_Izj5F<lXc#Ncvte4
zV7wN=-|$k#bzBM^+`=I8ZyY#a(-fplJOAt}f-fEN==K-wE-zhQpKy=<4~nV@1L_{i
zjTxxB@5JPC;h$}}je8fa{7uc4nLd;8x4}f-)r~IgJ>Hc55I*tS>ynr2&X<d2^&%BN
z?iSrMDIkAqBK?}EK8sBPXZ#z#f0SYvX|UEnekz%$X@NL@P6mrELH%vcd|3Rr-#yCU
zpqwsVBv}n6DXr;Gnx<=-8L#K3=(VEIVLe@!x-{$-w{m2ay|tfwO)g;8VfJ>8d1q6>
z=kd|GU^nAKo7&`aG}rj#;z#XgYkYjS1Y(MnN6_?hO%6ZIrCi_(MwF&|j|_^|lgw<s
zxhtsOtIim{n>J}0Pe)88cdO|<UC<~P|N7BrV{S04`2}E>h)TFrYnHG%Tl9=A(hSx%
ze8W{X(L2ic=`PUn@<*L&W(~DpY9Td0CbKHCTG4-~#EZc8(4fD=Dgryz`Yo?7%@f{a
z`+Fe<y(@nb!N4ou9B-iH<6GzV#kV;1?~GT~+@J3yA=$vw$#(~B#slgaABC6O^0tI2
zq&o)w+kR(~z~x_!J}nrTOZu8w{NsO(ivE3X?HG0{?ZdO02}GWhx-EV;R?a(nf$*>q
zmRaNeOI!t=X?n1VJPEvd__t<_s9ynNt$fC3st*--9|%Dx*ZrLOo~ouAXq~)!C=~6r
zpk+By^V@aPXd%}<%&*72*xm&*ROB-JB|oD>^RuaS`E|7r#+b)WTq&*^W9Y6~|FTR*
z<Y;Mv_i!abREC{)HpC;9Uz%jJ#TwKJUhqjT^-5lkX>>)txnzB;nl{`;JMQ*MGeO-i
z?8g1?P0;$vonu;U{`m>>q<#A5JrZ=fm|`$7ocx4`rZA&M#BZ6nt&fDUej;pgeNnXU
z^%d^SbM|G*gS)pez7Lz9J<ByNdXiE1d&0h%Q`W)67<3VQgQ}(*MoMpZ)AXI+XqsCg
zi>>T)P=nPoyQtH*E2$4;QdE5rSz-wu=l*rU9jf;?!2VExYz%(rTo=L}I5WB~dD4j*
z_=YBPl1pt6am|vja?`lA&J8`g^R_a*Xh5`&nXOq4k=1#hl<gkla(gELJGliS<s@2~
z%V=e#dt*Mt)63L~2kznbID(_Mx)rF8CPkQo=$C_cM%_r6K>4UsNLYeqv~<mbT&vVI
z=Y-Emi37>ImUj$PQDKY-rsGvr>Wx41uYuPsK!)U^2q9c(ZZF{<?uir6A%{!&F{nr(
zL8RF0EuPdi4?k`*qVrE6=oR)!IJp{`I{AVGvnju}cX&)$TJv0oQ&54pJfq^p%>51Q
z!)0`8{wgcTds%n-$#X5eW88y!wvqxfuBu*M!+Sw~03Z+<cq7^5g`#7OU2ixMqZl@n
zQsktZ^$}(-2C6J>3LdBJb^@#lk(9IOKDB5AP4;q+o;}qGwHn{BUcro!so}fa97_wT
zfH^r-!cu&mSKM&$&Sp_gBe0VLrf%<(p7Xn8Mdj%wJ=XInFIFN{XkSM8)E%Xv|AW25
zEJuOgZzkwU1j+IEBIh|F_%MSzYp+VbjruYUnmxMsn}u?BIi1$DO?}zGlo1{-R)QVi
zQ(@*`<Fq<OpN05}ZdXgsyyD;HelW_};&Drjg6)6oEP6>7S*qPY{Kv1kWQy9eYqu)j
zB+L}`5ZDW!0VlTAQ)B)Be}uz8C(i9M8b>z_-McI~jehFhYQ-rppJP?ly35ZQg*<8y
zRMrTJJL%k`QY%^d6Nux8^bqHJS#rtacIt@At<eGCgs`57+grY)if@;`bR4Os=10+E
z`>hdJp=Ym67@<zOKZH>#aGv4|{ycgO{KNXIho~2LkUtI77<5e={A1ryPLvfSV}C+9
z<<<|EK*`6eJ?C9sWvF!OX(2a*6EIIoLLW@L>;JM!b$CK`fwi~f4~7B@r+Yvi^0%$+
zC%*F|>-W}l-*2KGoZL$r8BbTSu(L-Td3h;_$`h-qqhL@AjjnkU9V?SCmsE{%F`n?_
zhAC80UYnwENW97#WZLs{nOD^g??q$N2BSd-v2=l|+DChOFrBxMmMvTKQY-6D&_6zo
z4Y@V;Q4I^|ol6GaJVKJtuA&J~<d6970X?-ux8pNcW(02GY%f3{BpZ>|eB{+(o>bYb
zo@xMJxUIeLYS?RVBxpZ-Pr?2BaXcyY_sAxP=|kQlMFp-^lMJ<zjcH}d;STeZ^2?O<
z4M&zd-!7(ad=Ea}E&inEYi29p5V@?Fu14MyAz5@8NmvJ-_&vVJ)OI+Mpiup7x0=-+
zE<Sd1^Vg(E0X6L-V@}e_ZWNz6j9#r7QjRPnmpa?zJW#W*)xvjac0*pZ`=&anJ<NZP
zZ25SWddYLixaL#Yq#zf+kjw(}GAe?+Z5oVpJjqVpQ$Ndi^K2QPFFhI-FMI}mR7q)Y
zUstV>2-Kjq#6bqA^5&uRangF@y31@|U3TVqJYN4dF-&_XvhDNFipsnqb6|Y?fjWOF
zZv?sVgcNeNE#a85zkLBW(RW!#K5kbBtTVo>>+SAG8>+YuO0Hfg8XueZ`D}-vI>FcV
zoj0$!8n8$r2Om7<J}XE|IkR*!aH=w{%omJvGVt%zkOSAm)cgp6m>UvVrK#mu*I{W*
zUMyt!)VxVOk&wO%t@G3_5ehZ^aFc#yd4{=%^4~WoSCCn`Q-8K){T0)3cb#i>`HZB1
zFU2DAi%$POgydb=&M+4LtgVxL=5>Oshs|{E!G<j|W4uI=`s>S!W{;a}A9`Z64jaAU
zbF{c%JGMzpHeCeyIdrvTd$b7BUClwKd(QVNRP5OI>uBq!WSU$au*yP;eL20xcKKh@
z;y;Yqm{!FzPXxM0l>+g;0BR<~tja6pcZNQq<xp@<C?U+g3PK_b2vSVgwY04LfW1-+
z;T7`I3}+5qAn4U3Q(QA1{yQBO-#*s75D?_hXvFT@VtAnPRMp~%CLE2^AtiBX7h9*o
zupj2pvxh7`otxJ<$_O^InW}MJt@@VTIbK0%awQmVWk{(pzDcR^KRzsU>R%{&JWIyq
zpKT{*n}(L89eg<$a$|Khm0Sw@r0so1KmE{@>7)B=ME*27R<s@XDP#zNTG3AoeS=?{
z4tUMn8$sP8p%~!#=UrP92^gxr64+@h6T1G#Us5c+$Vx~3(Tmhcpg@Cc-#vO)E7E2)
zTIZ;zg@oeonxkaLkJJ<NxDF$-V@5uTfL$w3h{-Ik^B#^*Fr~cP3W9zOTQ&s3IS07H
zQwdR$pNiFAb0qh3IR7BNh13poA?Ni8cJ_KtdQN#0%fd@^9c4gpDS-`Fu%Y?!_h##A
zv(Z^;`?emO=194{c)`fmk441FF(Ub2<11;eP=6LpX3pB<RPr?Jy(FoGiu^4qsWNCw
zeo(4ws3Qh9m=YbL@KS#OdqlW#AFspVCqFTd2m-b}ET$CoX>l7f@9lRohbr$$hA=i&
zi=O}et`pR12!e!<wnW}i_1DB}O@6KJ)K@AToi?KSpI3cf_gsB&z=52ten3vn^2hkV
zz|S)^y-z#@72-;x<m5ltu>${l#WxQvP5!DDbvh9yi78sgMqIboh%a{zc?Wxdxzcpf
z2k#h%|Gi!Ia=ZX}2Vu0Lm+LiJwr|y7*TICo;(7QFi3ohD^qk$=mvB}?Pg-7Rtlqx+
zfDs-wt2nu0MKJbO#Z`P5^v(60dh?bM_V=j{qTjq~ULM*tP;Lr_T1>x?-sm;%?56DP
z{$!0gX)=*=Ik3kycP4`*aV&&?g<EjH=y7$I+kVW)HHk9;1NEL%G3w~=47SgPlAgF=
z4Nq*MDg1p+$U3@(Am@^K+$|nPXEdyrRZ$ELtQ2dUvQ3HJV60cKG}v#fEy&}Xlh1sP
z<n(H+_q*V0@_jO{=iD_Bi$4S==YNJ}I<Kn1DL+ZoaZL|Ozq%<JADzixHoX~TC9S#L
zyoGhlv$Ts5TP?ah|6V6-^GND})z{ij^h$~rO>;xz_prbA(~a&I-_%SuM1Lazh6=%A
z(|d>wF)5nfyh!l?zUi?|h3<gh*q6fe86SPhNuc`|N60k35@NhvYP3C_Jpy0ckBM1C
zW%ayBY$n_Kn9-3WO0_Q&9=vNa64_$*$U-lW;)=2DmUkNQYHbi=x*U2C<J5#QO2o_;
zP+z@l_14FJyad^Dc?#|Z&T#?9aJ{P^W|?mfgIACKY2LFM13ZUN(E~!Uzy-4K7-!<8
zHTo3@;kg?I6-<exlif#3`T7k&%BhA~OhpG^N$l6nk^nf5!8d%4K<k{^6!!-vX#P8W
z`a+gp4-5_F>kkHt9fsNpf8Y-5Q^N#p^e{ZIt$?cu`qL$&v`BB^{j<yGETCK0g!N{X
zh=Q=!Lchsx&6^AM@T&V#>W{BgH)nR_jnLTR;igJDSB6JHX9b^Ik%Y~<!Sw9V^fkUN
ziftYu&u6Y6t8dJx&dHbE=@J`f2{LZ(mVIXo#eg5b+5LO5`@#zElhPy2__1fGo3Z7s
zE?whMg4*ClfeoWl&$a*Yj?aw;h0W1O4YPSs|6kB2e6k$4*{f_ozt1zlhOAg(9NYA-
znyuc^Er3pkWMN8FgAOnM(LK3gFn-fXfGtybD4{=PM0RZeKQ-pO@Q0m;l_r5oH0>ip
zikG(yU98BMd1~FHhM26U$LV6>p{${iH1R<1weepib<Wf1E538gVvJ%ngq-f&w6Cnw
z%E5H?^Sa?KM9*{z;k5g*_!UFE4d}hGh`t61th$PM=R2Ea`1&y9vh$tbU&HXx5#yo<
zWBbHG&Y1T=f85lxzle_!(dj!5Hom<A!_@e|25kyoD*QG)-}Wl~54s#9>+FJj{N;=5
z>bryGf{YP+6GZ;a)$zK?65n2F8|!C(OA<8A&7apKsV}*Nituoa*E@?8Lw5Bo##8b=
z&Z3)`DF5NCW!-1b(7|(B>ObLmo)*%av=pip`;<{ct9;;yObaq_jXf;I-)nl1acV+)
zEfcYuqZu7{kN>zH1)pCCv@lj$YYCY@M3T`tKZVk;EO%`D*YZ{)QSonnY+Y;2zaDoV
zzKGuodFliLq9qj{->$E@*M-LeXF%fzg7@n70p3&1O-=l@hH5KV^5#9gee5oX?{qzm
z9JX;dWbU(v#%zmmld>fuc~f#Fu7+GzWl+s=XkRc2VLuk@ylDhEi+pM9+#<!WIIX3o
z#-KE;v(k(hHi4%yqP6<ZQrb5logp=93ef}lM5QB)^_?a!J(*lzDqaUUxx2*`|KRTP
zODj6se?44$JmXRU%o~r`QBa+%4kfpXljxVc5AjElth*t+<WV2q7r%2Jmk?BR@=;xe
z!>-(}@SNxwms)ks*uhMl$;VMHGLpLM+Ck-{GP#$_&q^AZS*wQ=lpu3%+**W9;Vp^u
z>d@$(`JYuQ1KnP_o~|+av)7k?g!w$glIk5*A>w)unctc!ic{)hHXwl~U&IcX<(ig1
z#-w=N=t)APt9ZxdCpK~bqJjANre9v@#M?*SM>)xze4S+W`fkq8uN3I!`{4DxK6ZEi
zD|@x~?p3hJi}|xMa_?__jCaic_sxl>#KWJiTni@LzOdvL+}pPcOjThoAs2f4&L8M@
z-A@h4A|vr*c`Hap0ORp`G4))NwTx3hW`YaQ9j6-%0r_;t4y&(vl~HQLr@S}{&r2N<
z?dv=v%491e==d_~J9h7C_t4}=tNoGKnVCoGwn3OH_-{p@J)W$!cz6)1M8Qpcy)xU8
zJI1-YCEe+bC6yo^BgKAo5Xz}HqPIOj%lrndm>SyqqwJ&gB$i0&?*qF7qPTHA$+kpa
zDnubX12_Zp+WZyMz_g(E2(>vQAK2B;hE-Srqx<A|<8C@WR4=)4Y%60T-_l}P=@YpX
z-1=$;^HV5iQe<H(3@wsvHBid0GS=-##Ws#~CH56bx%3#{9i^`J*Xu6JQ+0cEXuxUs
z%O4x_tzpkXHb8!1Q9%OjLMU<FQ`S88M#%3iZTIm%D8{`<D+kk+(?x+hcG#(`g2JVW
z9ds!)1vniA)SA|=KboXok7htw>r>H$af2z2lL7ZP^Ax|}NNN!jraOsD&3ryUZOZfr
z4*J}Glyj#zzdTw_Cy=*@ba3hSN`Fvg>8UQ3Wz^gJV%&C>XR7Fi=96GL-l$xD%;of7
z7fz!-ECB&Ty|GVNet#HpG?U?-91X`@9210=6YlO>1H!<aQS?Xv%PPo>-9xLYieDAo
z3)}-tja@T0kA+IIUYohADnel#ETU>v`|Hi1{mse=Dj!pSL$?q3q(jZv@h;;xrhLi=
z!sK9mif)Xm!IAvNBJOE^dlm(*CQ0ea0IroQBpo0nub5j&F;rKktEF`g*()L_LUyVd
zDm%}y^)taCREhO+D@8z!GX^iIl(BiRSZBWjT-r^$))^m0<q9T2;>Hxeca<)!V&~-y
z-&l2$XJQtWO&oQ|iS>8*k!+i_P>LDGVy;*fL*Gql$U=>zq2F?p?$oM=%7-E2?PkT9
zWOzH%pv65!(+~gJz}*I3iZ$APmHA@X(|--%{^6X-+HFD(u%HS;qt|!o8Brl`KJWKP
zpCxvNZE>56J?iY`>64;hI^y31tFS4o_caAcrWA$LqKcY5Nd2dIau2x0$Dti#(xzK+
z9oJ=y)1JmeO9GrCQGn3QOvJG5()ChR984hXNO0qyDRiF}|4{7%ARiFr-&Z{8v?lDI
z=d?_ev@FF!HeMxorm2WE-ZEsAS)s-Uz1>k&2?_eZCnzTfHv`KFc3%^9vdg>Bm!0^7
zC=5Zq;gN?&b2!6*NVo#Bjk-djX2GR~qf^Zw9*3g0zd0ranair0pwtPmuvh7|rc)o)
zDm+LVh}jj)EM_xg?znhgntQgmK29-sSxvp9Xp>z!wN<RV`o6cM@`4s=pfkAPzQ#^;
zq7?(a=A8IbM%kCDkgF2f83@(;z}T@0-SPJSG8KJ5bb-y`?%7-kL>Rg}mF^XT=9Kpe
z^Lfu2Xwjxch0UFKUN<l{MuF_M&>=6@xh0U@u@3(}Rmg0H%=$$avt~cO_-a7S)S6+(
zrlByP>K3UgCC)X4!dM9bpDPu?*tQ1C<M$wDAQ7M6nh8cvJu;B-yu0hI9@v+^GI`me
zs~}0$p3^Z!JpDnA`SIPVZKvlT1Y{)}<Qk#OACyUbNIXX6w=^U0OqA1!-J2isxRbMb
zli-89F=XD9V8dBx$smCa8a*Thhr=XPmEepYN=|%()C%ZTAHfDyrPvF`eS4MZ<h_71
z=d<?HUl-cNff{`LnR$EoB^{qD;hfA?gG<CI6;`f4-@=?$Y5(owTB-{U9e&)0)l$WX
zN6C;nY%dn{x$9UV`pCa^TfD7#&_otiLdz6Oe@(adP$JYOS(%Ws0Iy7T`S(fPW*BGU
ziywoak6bxBRsaI#!Hh&Q4}Y$XD-yDm6*8WRHyr#?;^+om3|#0BaX%D1zd`<QQk5&r
zabv@|6Z-zRd!}Rd;vwn(3np8zEwO!6GEmH(jPqYXbdBX%aU1)c>e{kOCZ0&nKsGmB
z*+jF)n285)$uGZP_b2zQ%OLyxMoA4~25rSn3;XO(pm8knu|qrM9#u|20PAWbTlYpu
z9RT(3FajR1k-zQ?@cJK&y?D(>%AQ{aSO6PKno5kNC@fpH2DGBS?H8}AaPqkCbXI_n
zQm_@(){y=D?_^0b@f-BS7KZR)le3x7C#LRrUskw{82?~I37xw-1p2de%;Ifss5G}r
zXUpdcVff{cgq#0-mAm$+3UD*nStuH!HV@!Je@_1JiEraaC`^oq7{tSrr-<=>%j;{I
z=cnS07W6_$&hVNLOwc%I9MSh+zGe%}v6thlDX%WN7%Qc}Om;*vCmE4liaW-?VpHG3
z^rXrEa%LJf;(TVY8`iAz3(vyUr}xKGWd-}EU!Uh5<v3gHQr7joV()y>Ccw_mPOV{#
z+Ku_c)+h(=QT7UY$=FJ7>t)0A7w=evt3#xoa^~1+F`r8`TsOx|mL%V9ygQ3pvnN;I
z8&b;({KVr|V@aSjZX{DYNAcrUpxF4dv0$CJ{~94|W;Hva3><pFr~C>5&pfj+;{d^{
z#3}J+q!$)>NrCgxpDRKkqhBqlI-7q8#mznO+>J=sVl!vFN|)Eamd?$%Q{s?U31if`
z6<hDlGV7#KoUj)i^Zo;$J&130ojd^K{JcMdO(nUFLQAMVRi@H?Rn`Q~>#i=NmOZiQ
z?pdClhaB=YXufs*_@kr@W#3bibslyf>+=#&Gco>u-E3JkP(2Ly;4B@>MlJzL86Mg4
zSL#1W=V*;O8}?pLwzm-1w_9RY_7@cm@NGc0+AA$WEBCY{U@}+<KnpxlgTG8V$C(AK
zq@r1wnPF?pRRbA5k>+hiQhj*S{oIRP9T)V^#D-l4mUJk9*G|ngoauv)Ta{p2X@Fwp
z?wd>=R)+W)KsGz>jzUQA<oL>H?7EOCsVKqlnOj^HBO40zG#9xjW@woN8TB79fxe-^
z>pS8(E749h!8=Aw*)2WevzVRs81(#ir_ao&fRy@)0$=QKv;S0`4x_YvB^$#l%%jfk
zLTA6^NP4zkUdLf^%^o~%o-uSbMpT+;LNNC5h!z6ZE}sqY+I(7LlmlA-kNV+94w#f<
z5EScI8~2<pM?Ies-n@^qg8s_mm>MqtaxO0XGtl0N^BKK>DWnZ9wXiorhFzkpn9`&&
z1-<@dTYx%NgT*<G3p=lGY1~q{o?+F5VeUO5gU`Phvf)`Fs8EL(B$wn+v&7Mv)nO|K
z+w#BrLI6mpSeV+-M}TFFqMKe0c#<49mEC8&^Ksaps$6gEho%Q@?`+jW*k;R0*=r^1
z!vL%YU9l{OI=KIh5TLTJ-e$f7GxZ|IkxPY;=FcLVH~Zn$)|QY-HvD~j^d?Ah<YCh(
z;3Vsyet{V{6<9N^-#5DDy*!?|3Ec*=d8r{93y=jM-KMP1*l)nhms||+KtA!scQEOo
zjF4i~G;f_*t+jjAxWGkqSS>T@$6&H=pU@@5&RGwv=Or49k}>8#6430uZbUKi=dJTy
zE0VITm%V)IdoV-54GtLT(PZbS(oi+!z9@?W#0ah=G!GaB#u!<yN7wHLw56Qzaki0A
zY+HL-blL-}0o+?RHG)zgJw-c@M^yOPQ}#j6Evv?g0@TS04HKPfW%y%w8XFh1o$sb$
zMCaF(Vy<IF;WGXoRh7w~lh+*J!eUOod8qs;#q?i+4;GZp`P7g^so0!pNN4yrTtv6d
z_Vq9$H35mSA!#2yI#Y&EBS-$Y%?NrY8*?3VZoEl<eQy&@#vve6p`<Zre2iSR)nP2K
zz<lH_zLWFee!o$DYyF4@WmNqPR{{WsiD28b91>Ixwr#GM6jq4ej8J`|w{SUoZRrPR
zEr8rH@l3q$vnQFgDQHjT&fDCxhHdE-N`j4?sSd&U=)rLB)_7dkWeV?P|08Ps1Qge=
z!%6d=<zVY^#*ADrX%vgj(n4Qwi3B$-olgIXl21rz^k}PC%kzXscyCB%5Vq)rhT5ZV
zA3iy;7S7Aj?rIZ_3f5l8)qiYwjdzB3Mn5G4=<nAhd6I!Ff(rd_qA@%Vm^MU=YcjZB
zlEc;sHc2FA7|$C+dxw1TF`uHeaKf0^2C6G=ns-nC27J&%pXTf;Z*EFfxEnwd@wk9V
z6q60yYFew40R}jKjEvz~Q|y}nT5m1OqccQ7VpctaEVVD>FqpFY{1Bnbm~X#)e-=<h
zMI(dR7pT46^A`6*uvM(Bjf_EQ@PO7G|NOM|C}pxsDvxm%-e5$_lNlqFG>>q~f|`np
z1J?VF`A62SJsP{dpj*qI3PH*D>!L%yL4;RKAqMEkxtM2!JkW{J`o-~r)fgyHkD?$k
z751Qz>q|BsHAeJOW`L`u>&j6=%hq|UG6FdX*gS?5Rpu1$?n@SJe%`5@S-~CCv!Qta
zE2?K98J=C4EPW164O+St##X03^En9|TM{bxSlkF=-obd;P9V~acDYCGmF+Ez)Mtm#
z943~ONV)2xzAm}+o1Gy^K|Sqm`o!mo%XfWZ@M4PVMvOUCaAGQcH$DN82av@dIt4q7
z&VOl46&K9YR6{u=f09cA$4;8BJB2829ayh9T&*d!xpBKGKPq+GTXF|Q2RBEs3$>#C
z>`I>>G>$66##H4v`M&4J#(;GUo;nC#_VTM?Dm&CIHW%jyM?k{okmFpd*MLZv+9sxY
z9%~+8?-H4W*ZzDWz4rH9O)@k+sYGO5+=7+BOm0S_N&pP&#>yut@}(Gfdh4rgPWlPo
z?}yUZGVXnu9_=?{H#R}Y0(ID@+P4hoGEkiaWb8>U2?+-d&qCe+?@4HnBB-}aflt78
zBp^5!qi0tG(42I~eEM>kPeH~_zyXMFsr1BmTJLZNZHhLHH*}{^3B09m!Ye-_LE9I2
zr1-!*6i<%4&HnmB%_y(i$tqFPUP;WA3!a7aBz0~}{P3Axu@%r3j#eaK27tISGq82D
zo10>Os#8}ZGJDUqnGiFJ-xej7bMv^vo#^ozoL3V3Azvb))lqal=2<q%H)~8B{OoAW
zL*nnu^yJj#@gD0|w22B`k=;iXzWz2;cEvPIzTUb?kcK#5%w0Rg5S*qKZcnU@Bai9y
z+>~Zn`G<#Gi3S!yKO2{3j3h^|I1ATQmC|rsM{elp93q?BufyR|0U4-j-h6RGBm8o6
zT(H7;lG9gkK6k=lzGkd9g=2SZ`NN_`*Cjn^&=JDX9ZA-4++_||FWO}TnzNTfIbJE%
zCK{nl?JR+Fo0!g2gA**^b5%L02bd2gk9wwuIc}w?TQlD{;%r}_LbXTdFZ)D_s~9$5
zI8R8N|IQNr!c&5xJ*{FlkCz@{b_T|aKD2bR^y++hSeo!EN(gd$$~lvI8hlusU}<QW
z*Od<&m$u?E9{I-&gv75nKUZfwANlf-@jcD=M^Bu#twzn*uv`*oo8K#5=Gwe?3%({4
zMn~F|Bu&~m$9VlRJm~&*FrTJu<gCYf$NAoz<d5upgSRUNY#5(+P3fnYdu-cUh9sIw
zUw(%6Vs5+#VptM{i2cfUZVA(ky69qB!YADBkDGb6GeXaaWvDmYngsEv=dep9{@Z0;
z<18ws=5uhonv?}f(R#NA;aD7fXEt39=^7FUde$}Pn6s{eTs!|RRR{5(kj<i9@eY^n
zreliG((TQ|ZmTr#mQ<UJoWtgW?XRJ)8>tgIk!-FW@w#K;rflrw6Agv+?QlU~-PFps
zgZ6Jw<ZlDq{8F)=yJpGrwXC8LI^xWDSHsodPsSQ%sWJt!)S4+$t?lw7*{wT9!Le6Q
zPX;=zMXMl?=X~z$9s!S<NC>Y(m~8+nlzktK_E^O30JiLKW}3?}^%q--R^w{aOsdN_
zy-45#O?kvbqO1BouARsHUp)es=~K<Jm_=2st#k!`!J#WTdP2*xogo@Y(o4i+a(#*z
zFUIGDk}@8-vW=mZUpd3uG>BeeBtef+-mD5f?ocbb{*<a&o7G<+0seTE`A#H++yJdK
zSkq2-RO|G4FjoFC`0!ZAf^>%{Fk?L*J}*hF8tRn^z`r6YrE){}qsb&Lb%yfcneYz)
z8dZ0(H>G5I6SNH|gIm!AT780TVR>}&CBfueF=;)97SM^dx@EP?6OfI*Jw|uf>tl6W
ztJkECPI118kIk4WxUK8&KI3fX8M|N`DI)1VX^^rPb(x`jfas=6_8L5E(-U7<3UYjr
zk6NAs2SYYoj2Q?{6mc`c+Oo`EkN`y0PFGLYdbo5QbcL0$i%mc++PUx{ozWdY6bqWq
zF&Se+13`<(0Y>rB|KkZKr@9K243qRqRNw5adN5_q&7;THJ~#6&O4b4#!h4`SA^C<m
z+;4BTikNAr#ms`jm!nlWPoES~wF2Q*o~87M6iChsTcGnzl6X~STQVeBIPwtjXXs;>
zNHIq^nRPSSNZZk^WO=Tuw<CbKhgqjNo&x`N=+$NR1$IR<9xWkbakN?y-Ij4|biyw(
z^94R&^$pp<9sGXplmB|YupHy9JfApG--5ep$`j7xE-S_FpUt=%@mBAgy3hHH7QbjY
zX(gXuT%!27rXj^Nqh4Cb23sDq+Fgi5Ye0)@(OF<z(_uW1l3<nx=WXXPm>yYX#*a6T
z;00y3_Q^IE(E0^W{AJ^e-4o+raMQdrhITnEiTW-)U-GdsmOvEmwAL0=9rzS-!=bk%
zukys?e!*smOz*q87CcR!8oMOu^uTBD=j4puGJfG(R&;pCYbeWC0r{!rdZYMep`odv
zC`qG&?tN=zHcRQwn5o@R!nsjxtsfDUId?cQWNvl;8T+&Tnj5*HN#yhq{RE_54UhYB
zJ<1PP7PUh`g-Ryul0xz<KOV{-1+E;=d3mFQiXiw5PW6WcE>lMx;pnhA2~hUh`m2Gr
z)GP8J>T}JtV`YjY2UVQDAq0W2)QCxNQz!!;?K&}1@!qAJ4mbkp>+Oe_lOCf04Z3vf
zs3H|6{_$O-ZIc>6J~bdz)z4HS|2wNG!PI;WxNglf&r`7F3&9xCgM62;{VaI-k@YX+
zbcOqu$v4-XT-t?)P60|O;hgFEX$S~o_h~%uCkM$#f|9s@4r|T+yjtb|d!xgjzn2cF
zm`8hU_`SE_<FQ&AWm(I>TwX`m<R<6^x;XTx2w!xsoa+s{XWkpoSl|_Kcn7_opFN4(
zEEg#O!U0*pf&=COrwelx>}W>QklKUBNE^wD9ch5wbSr_zf4TzZ>Rx@y1s~MA;jF!V
z(8?11x43Qz&S<v?bCYol`#c#+Fw!w`<C-PQWdP?IW-S{62973Aj=Z1wwVIWeRaO9S
zZ5y_G3Yy}$OuAG6WUI32XXWP~FEgAZ0n<tKbij4pv8<@EqncSE1$-3mE^z|#8mT^K
z#O!~<dZ5${w`yQo#ikya%fx;KJ=t9VqyCYcy@y>3s>pF-Cti?cTh3q5Tq<bQmF-T=
z4j?@N8NSHnt**O+MJ~z)B&^RXP6!7>4v!reJJ)&YYpJ@C6yZ(VK3{LQ+4p{2U4ltd
zizL#6<Vc8T!XMoQ{bN!pj%`K0n@Ct3YqxT1H*t{bXvQDyiWtWyJBG*gly*P94{poA
zZ(~IuxAdA1IypC0&|uL~kgP*&-Yo}^A_YBbfE&`6u-v@|mfS=dF0X_}U{qEm2vI+2
z*^^%wFh+X~ZvOV^AK}2X-g004EQAlPuG8ViZb>lrTWmpZHil*3{Pa>wp9mF$5O=$9
z6VXGe`f}zuS2IR4^-jktH&eEw0;fT&7|v@8#V_<mE&jhhC{scE@U#_&kpl#$2D=F5
zwM=S`af1KzW$S#4`Lv|!r7vU1yAJ6nuI_UNGDe!ybiq``v&V>j5dY$`VlJ&ZW_ADU
z<CAy`s(&mvBs6N3;_?(k>Tq_zs5qBl8vE1W%XHP=)Xm#OlAdo!)Wx}`<vS9pl%^}`
zeN2x^{qA*K7}qMM+E0G=${F-|@iA<cjsZI-xq3Z4Wo+YhsiPPA4e$gK0U-9}fIfvL
ztfykkxRJnB_go>}@t=;i;|Ahi+yWrxf=Px|5#<P=!XPbLu)xiAJNH+yzj;Bgd3P}j
zSZ%_@NtXDX4_PDHUp)%8HN0)cwtLLe^QrDWr!1O5J8wqvM(A-#Ug<xmS@za>vq|U7
z!Mn`<KkU#*c>}|O;HLh2`g3pJT?T;+3+ks9HZYC<0j(h#oKrw6s>k}=h}2ETfFWL8
z<?6)fS8jm{nL2N`GJ0EUoa}pvgNCPjyZGCCD`x=5@1wHx2wRRPOIpMFWcG7D3Ohr<
zsY`a9@Bc)8C}VC58Pd|-34L1W%AKxw4{iiW9ii%nCf@cbBuDymFPvEsAEh0Vkl`Qg
z(6NX>wU1VagQ7R2<V{GDo_&ajvcBTPeP`N~3=8y57Z6P|e!?7=%(-~g_aO3nS@5(o
z@u_BX*XNtPwTjO6XYt_XeIy6DO-$J-4fTO{s6wRW5O9KfaV82$%1ZQJ(mzby5^D`z
z+1efn1H`wTihL%2QdNCrZ&1%<%tncpWlg$60J)5|OGMMg4M(4@OV@CwvXqHhF($4q
zVpC(&u(_nvwL!ZK+<@^ekKMQ%vDZ{xodfivE%?+dGu8iHo`!&yiMuf)xsEgXmH3@8
ztH+UC$OlSbaU^9tN203?&z5iJmEE5IaEcL<c3u9Pj~(tU3o60U)5ZCh?WcxOvi8S#
zn+8U7>;z#cviSTzO>~S>+r8?86-GGV+AiZql;o!GNkF{AzZA2Dss32A*J9ghL;vT|
z1@d>>$5sRV(BQhU)?9&LvA6*C46>^by(+TRgbTdB`>+2Tq#Gp;Yk7VG*_IY}CcnBy
z?2$W}WuALy7&I4VYW(rCxCdlxafqSLP>*7x9QUFpK2Km|h~o3y(FQK_L9XmwuC5a$
z<awe%{BQi)`d5L^3s{vA6zUCrprsQWen)1#>8l6&^|pliC;z_G_CVbQA=J0xo*z0R
zB;?bMB}z@uPQOk;d9d&tB)T55KspKpHGnyC-KpgrJ!ps2`kK9Gm5ua=R|=qyJmDyd
zw{aTzNW9z+I?;(IcZ95l{z!etgunbqHN4H?Hk?$UT%EUWi(DOg<`Pg+1_Uf#;LwZ~
z$P-z47i1Zrsh0Ssa!&?S>c#Dnc3Vm{jqVXaD^U1>^aQ(v+6rgZ__jm-l(^PADaa9$
zeCm!=VVA#rIPsjcr_B3OAMP1ERhA{*(5J&OJO@J>a?DKIW*_PKT6C~8yJwT45}9B8
z0H89*Den0|tV^BPgefO*1LU01uBBl%dw6MO^e+d`J~O2Sxw)<n!)_xYB<d;H{V{(3
zxqR{;3#<dJWtQI3zAn32Zn@ibs}Py7m7j86T*<Py%iy)%xiw|=_^V|K@1G|f%0>-i
z9GPu9w`>^*xW<8)ZC3-lYN4K?dfVaAS=nXmldFu6cwF~oLfa99VV8SH*-G}IOxRkC
z_ore9ZB_zOqRU-s=1g0ZHZO0<usFsJY_k&MaU%k$kfMGx-i`?ro*228QvlRF_DJVe
zrRBB-8sjGwDNeTtQ%4a>QvuhVu0}JH{Ch&qz-S6X#_`mNx8sneo)swJb0&T4vfNI6
zZZ-4O*)pf)fa{YW3wXFvA5Z+?8I1Kn8m^uz&VN_Ge*q+?^|Q^GTkPgfBj<~H#Ab&=
zS%yt6vFR^7C)p+Wm->M>_0CtA)V&N7eE^2yhqz|0nL2bCy(oG1{h|}$AHb-X6Lyok
z4|Ji}?f#v(JAW!5*(idS{v8cHlciQxTK8ki0`GDeZ#mS;KBO+e>lU){{HVq5lznCm
zfd6SpW=tO8r~egZi(?iix{lmq=E0`?><Z$<-D=coE`SEx_RX8lr;>TYE4;`Q5^=O}
zi>=E%_NP=0Sbf0aTRNvV%Wi;pex@lOK;1QQkIBzCP5Bdi4(AYqb+O`6&-z>lU72yj
zpauEVzOKafUqDI}cpGZZi1XwA!#7@wqbe6o1}q*sFw1__8GJ_YuZi4{rR<)mj*;p@
z8pX%JMkHtxqRpaZs|@?zqLY;Vvu$<a0};Tvwi!rDMA`Kmi4_^HshvE5^9I<dOxi#D
zIWS?#HUe`1({D_?R#FA%I)tMTn#HT5srzTAK9pEwhzfP`j;KCWKpdJQs32hC)(#n_
zdZanTNS)#);!s=NK5D7B#7L%|Vu!y?1X6QNNM|b~^(jbcjlz;3Wxn;eq33Dx_{C&Y
z`12_kCEi71#sQ4;kK$#tUzlpBm8NgRve19%hn@G9K`8D9j4}o1sOMQ)S&|Z2M)A~Z
zg^BTH;$^EdZvm;Qw^F!W#t~*m$auPvB@Vp^xLlpPN{H;>sx}aSTf{fP72W1UsG}z%
zzN{Aq8}1B6w+8Kpv^kuMrQUJ46&S-go>ZME{v8~=)N<V5dC3LNcz%J;fJ4P+x^Aqx
zYq1V|D|vMz2spLNtAjq&+fiD4*pEJ+7Fbxa0h)-+N(|h${Lf!jk2&z8`)C>Yy4DGK
zy+^lo%`zC=cROdTAB=68C+5@DC3PN*A!!{ERp#Ega(?09p$BS3$<>#JbxeWmJnx4^
zfDCvT0QaJ~Skxz=@+BjjZ5I%|bF>pdbWFj~R#Zt>ofanaF9}Y!2SF>^-m5LG^VNi}
z!%lcQcVQJfXT~b&km6D~xevs$OKxT#0-J5^i629SE9?~SW#rl8QVY5{`QURpuY!iB
zPlfKN=kmdF9<0~<!3<|ZIe#s#kg+fz*%hIugF{Ftj*8!4rJOD9N1dQTH0%5g2S*N%
z_ML5u4zInXD!`&`&fVi>ka*R?G$7DQKPz-k_@820^Ue1G$H|o0_m!C0F;fQzAw_S|
z)Y{WCQCmu}A=e9$B^oiKb>efh%PB|+0`Aj!%B(&mFvTa5crz^Ys>DdLo^*#l<8j1R
zGXf)ludt@JY9ryBjc4#z5r)oV8-@F+pBJrv`$Y@4zFtw~J$k3t%&O-?HG*uK+gm<W
zQWPt?Ixc;PTnZx*#nm*DjHVKj?Gk`v5}jq6D`Q(+MG+;9qt0jAcFS*>@@M~tIP_zH
zX3z`$48dfoG2wR?x7_CxG^%^f+Z}#BWbRQ#qLqV-ixal(7-7uH9M(LvN5^24eg6%Q
zxX$manZceK|8;Y<e4Niy=w10G*YEy&blO@Lb3N3vF_L>`WmF)>IRB&~)B}&kKYbQL
zC!2VWkX&dwL~U<fBJPr$5B-bzXwHrtSO4^q{$~=L@}=r&omcv~YL~SXwW=!o>m0eA
zpirhud0SS|uQDp_`0C!GLB)f;{gM^^_Z|;M{sAO;x`T4TI{X@|Ezo_w%L=@bIY1rG
zWrFt2HN<QMG3}+mqsq`T2`x|ix^RZK`-FKki~cDfeqJZECOO~Nirq__Os4*hj0TR-
z?(vnt-zE6G)oYXFD|)5F<^lN?TJSI4WTS!(+CQH~KWKHqxRJ0@+)roQ;9sQ>XQ2=i
z$Bt;LZ)7R{N8o!u@Oy1Wck{D+;=>V{84qq?eod&@=5+_wixjY_fXB#xBg$|jCazq*
zZ1K!Y*7Kd6DohDgdvj&h=-ZTl+t7u&SlVY|{n9=J;07JpZ&YR+!d0jpmna#xL>UMx
z2jzITbU&O{6wTvpiA%SZLoZqKEdpB^^ujWOaLFX110|>cIWFlu#KD<~kQG%!%AAw4
zD;iW~xm%hrYm#A-5w%lmY2zT-p)=}oP<zy);aXu_iRxsXvGW=H%$Fz_&gi3bAt9MJ
zBj3gibxd@b4aHr4!=^OHBsYc06SO;s?$>3z+(-e@atNnFNxpe9KxopSTqoAz(7WO)
zR0yn#B@XSur*@MA0VF#o-+aR&4CX&)pE~|?QujCZZkH(r!WHg~qieB_v*nqWx`A5A
zzy)=@bKk}u_+w`%?^-tJbKTB)%vgq3C*};kQ`+MSGnf8)O*nJUyl|C(57lJS0314@
zW)u1pI}O1(j94e1OB^k#$uIppl2=@Eu^IXAtd?p#A?DWP*8$fTu<$L=PQ+k`SKoY}
z9WTh-Tkpf=R?o}uFY#jzd=peW;Bbs@9Y*_&_S31!9ha?$bd2B$dYLKosI*x27tnFI
z|N9=d*{RxVw^MsuobcZXZ?88QuB5W8W||@M_H;Er^c?x5NOopg^&Fu0BAGWYrrj)h
z(0c$Ft+QfwA7OLBw{&u0V^>)d{%?6|Yr2(1$&{5^F(@u@&wvi`WLuL@p<ldP^u`UA
zahqW2b#;h60t7ZSv@YCQIaAFGQsF{8J1;vA@eX%t<;cCCHF1dCn3iRQKj%VdWn_IZ
zz?#T#QQ1wA5bpX`8@6Gx0a};kJUP<F7LQJZ@lA@Uqnh$v`i~@J-Uy9>2%Sd$G7x76
zUB+8t<WqwN2;*e+m<)~ebRe~Eqn0^+pHX8}fz!wJ21mGcgbDhrL6LP%@-#oh5l_Xg
zZ(`J2KFV0tNl~7&a%^Sv%UL_T$x6&lRAdO)hLLfYN_RNfrdJG}uPM@;GW$PNp+cI3
zdgm<;16o^#r}*l>+vi-C`#4`9$~O&WTnc<91SMJN)k_OohO^qc-8I{(APK4p-<x@9
zO3`83C^i0SM8ZT+Q4@0FTEj~DP=aSMS8+#<1tG@6bpp{4_PyQzs<)uofe)G=j(=lW
z#a2Gz3WrB`Rm%WU1fZ))2`(hf*_0om1x;pE2USybALw&#T>i5r7ku@g-lgoMcU3yp
zo^oCm-nmA=ULL@Dx3I7Q#T*0vUaj0QFCYEsB3H`#;MYy#Ipk+$tsm&S$Itl2-6|ko
z9J|LUsTc_J)49!#G1PIrl6mNu-K<;jkmAJCc9B`5pW-=9sXM2LhhdfXgpRMiYPDD)
zd6ZP-d8h#~TGmiMBU@x9XD>#4--5O5?7e}EKN=t7lUJV<AAewT7>4RoX=?mB<=TQj
z+PohKw=<3CYA$S(nKwUcSyOnKlzL~7g6be;=2rm!-OwAo;^Wb7=X<E*(%+Fr-!mb+
zy9nX}uD7m(3XhILA}NPHLd%!eoRq|lI%i1I3_iVSf72UmApzeYpG~Iq(n?$VCh-}?
z`EXt1D7vvus8by4wk~RZ*nJ&Edm&dwyDWEqU~#K2*g!JW_Wt^6U4CY_151+I`y3?G
zmT^AcF))pwJMxUfCtWbu@-A3y-F%8Yv=9~>D05$2NppLhe;RTbdn4=-odJcxHTg8l
z%uSHUJ~bN;uJGX3CmGXOsdJFcqb{B^`FJ+G$S5A?@`xWk_C!|}|HxNv3uMkrU6Vnq
z8w!c=LoB~Mc(k<I`f=ZxfxRCsKl-lzf?1HCwuTvq0e%lQoXXyCuFVPe^oFW$u66Y{
z)0g7>rxj_i${xGtQ-5CX=mb8=Jao?UY-oZO;^a!CT_p22Mli;x>~Gd8arEvAeUd-9
zVOt?HdNx6)_gxh2na~gZKPeX6yb#)h)q_M^HhA<0{jxNB|HvlRz#L^!?0~v42R`aB
zV*frc9I&~lmSCY?Xy)HI5}Hv|o1i64gmtp|3oeHEycz3O)u<67mrtb82sBCa4XM3+
zxw};J=G;tRL$^|^^;PL~RG3`Ht16w~)W-&abA}De@8TGZuh59_G49PR|6+F0CHiYW
z1$N0-qVFvB4P@>18bNd=a`!l}si4GyuYcSi>mvEqmv7WpsZ+6qf}ddmJny!Q&nr3K
zurlhtJ|2)CHx(34Pp`F<zeTnu!b|C16hmS{nPR@-!fe&iP8Mo;e|L$z?$f(*3Kw%G
z@T}mXIE-rNDfF+#{gQty_ciDP-C5T;#o8DPCe55wMrK=esjiu<>S)=ntMg<YyW^^n
z7)FPGu|r*TmLc4%+n<r8<isKjC+)to`7OPbd2c*j58rjH$En?QaPCQQ?a<0<?v_tq
zF>N05xbYmk<jAh5)b5e)u0V*g<`{-pGEDtG>75r$g+!Db)A~Bl*&LRS;#)stY+Pp-
zpJhvRXhV&CoYi+iD1TD+dEA2o$qU(Uto*QFwxD=4#c<ne0sAvM4=slL0c_o?AfSV*
zR++o+FOo4Vu^(G6zR*v-*!Mthi+3oS{BESXy-!H*0V#TqUo4%zIQeVkXN@fp!}ry3
ztE2y-yxLPHu_0&SVSsS_P2*<po{3w++7KCAn4|vZZ3U0(1QQXn@GoN?-P{LgVYkXt
z+{a)D46f-O<_bo6AvCQHztXHK$P%9jCZQ9Ob=(ibYKA1IZ8zjLFu2Prs=55Ih#KO?
zRaWBT%)zJ1bI^Cud%M<}JH<3me~TOL8Ry<M&c%nyE5}k-n+0;cZ);ZR_Z?jgfY!*%
zKk_}hC|K<meC14~$?@bZs7)*CB>gPv#G<gCZP23rd|q$KC4<oIBkBqdiY}zmv*}Mu
zK}taIu{-P`&c9yzNoCxghE}wESJ2Stpw+B*o^L4$bRIaT>;F3V<T8IEtL5c+=UXHv
z@FGlPTg~}^(6U~9@*);{d-V5k!1YY12d`p(!j41Ti>A=J$)x{76gi;#9LE9NA7{KT
zqW(%J+nT8Zb<jaHj}g$$jFFQuK7B2hJ@gi$HsJeiIi*gU^KoM(C&n`wXTKHIj&QhI
zH9kwPf-z7A%SvNgFyk3N!SGHIFe*%UWc9_s25__Q)$k!rpo%k#pW`>Sgq@*cGXVdK
zd>4)V<PK+J-doDAW5ezV#&AZriw%)zloA}4-IN+CGI&q?H}ka-@X4d9e}GEU=sTvc
zgN`zmQx0%~M<&?2QZeKwiPFD(h;~1Pl2%*7YVZ7I+@GJtv&H?|&_ADuBX_c<>k6M3
z<LoR_<W1`SbxZI-d7mwgQPQw2whf<fH*a-o%8k-xi8HdN67)-DOYUH<bng6R^rpp<
z)<1&veUszD`d_gBT|P3WJ|{U)W4xhI3TjI&E$QGzaz1{#DMMD0HGbv)<G5S}>9WC7
z>{~{<cfZqzvd<<>pb6@c!Amg!&My|dt<}<srswJdJ7NGksJ=6W4x${pi$|~CuF)5n
zQnxW+x~c5D-cym3``cC^D_EHn)+j4$uo=5WC{V-R%*qK(Atp2y<W@(%--9y5Rtu}k
zJIRkgxw=2`oh!|4@gcxt&KB%x-*8XG&c>d3(=oAmN9yH{)i-<GTzV=?(twOZDhpEk
zn|}|7h)jtrJ@mBp_Z8!(v(OZ~k(#&FJ3GkvWp5ENq`_Fs{}3@W9>SKYh1adn?~nVt
zw#yX6LY%9|>ZoZ7m%mTkTy^r~!-6oX8pCpbtQz~#fwG+Y3PRkjwDyxiM??qk7Yl)4
z;AU&LYGGBL)5UZrO|%MBYjhd*x(cbVbF7ckWhf{k#{K!;a)ABDUxViyxST2MBv6HL
zz@wWd?y<r2OY<spv(G-g+kWm=cXSM9Bzqe{)6eFwE^i71EKAy|2f0*|2<T*xK`9kF
z?-*UC6xC>u18qtc^QsJwEG%8ma&#fwwrwub0tFl-X~AESq~_=2xvC_}!Kn8vE9ySz
z+&KRY=Q}?EP~p|=f^%X09;Dsfx)(c_>Y8vj8Rbe-(~zExHB;9bb=Ut#*Lw#wwMOs4
z2RVR(f{FqjO2mdp$0JBfR8&+{1Qb+CC;}>kDxriV2qGX&MT*i?L`0A(y@cK&Vj&5E
zgdTxF2$2+1_P0I1d%u~l-29bcGQ*yCuk}2uJZru6Lp^A(ufrMM^;92rqTKk+C0~aL
zsCa2BdaFp;E}OcqAO?S-GwTp`R#b@N+DzCdH)OHr;Mw-<sen9R7;qzL<hIV5z4>>c
zK?U^;gA<?TNvXNck&=20i4#unY{-=$?y<AO{hUCPm^jK!i;$`jaDGkHV$y=}2|lN8
ziNJ^uueP*vDds})h5E7sXk%NKPb~PYE43_YA1umQ8DQd=5cVUW?vYr8lx-mVXs2b%
zH>bDLtsc)DYu-|HI+=vwf&pjr@fTQZ(7nP|A7H@<srZ{}#OId28SE0F-71FSdHr3h
z2a<oI@VH~R)f3U?&K1?<^IB7g3?U8WK!%IFjSz9O_n%$a#m}%FR+KiL?yEXXJQ?74
zP4qtHBni^_{G>vVb9vhv50PbS2llQL9OhWw_HMfJjv~qi3|AnV*!eI&<gIkS=yAo5
zez2fAk_qiPsRN}~W7|AV{_=cc*m$8t<3fnVaZ1-=oEX0mc=}f4#t{8F2oA68uKCjq
zzRK$~M1?;mR!9Tt@Wb%>*R>Z{Hc+$BWudbNZr-K`r86wd{cbOkf#rL_9#Q2wBwV&(
zwn}i~Cb)$cI<#(ik>n&ee*-$Vn}#2!Dp7i|!|B_NJFZ1R5N|u5@Z>C7BVCjMi2{j=
z&2u8KoVMw#@8T1~s_qPUp0-qB?aB`N6=h{LB$dC=+<pT2CC9O}%%M<ljB>Cd^WP+K
zC&b-1E+>g{&#(j3`4FXbo#b6=7a9h`4`(3zzhtmS>~mVv)XN5<uITR%MH6?0zi1R&
zA}iKOn()24tB%s$%PER}@g6xMSc}bzgE7EOjqN=_jlm0%5%=qpT1yiY3mz)@BthPM
z3QatVS}CjbHm3Ts3#@c%t17Y>PjO0<6%@&DVILsqu9uLqfi8}FYT{L{-sTBogV(7)
zMDD_4t9f&oqy<9##PzmK^<KX@*f#jA=qyHySrE0atj`#BXnCtVFi`Pi2V6%$xFv)P
zj{kQQ3j)sa4D7e4O51qxq5pL?Mc_o;F?i011GH^^cMV6a<@+V6a(SR5yo4%ASLA)p
z6DyH>2bMqx_lKEn^@IMF8Pp}5S!7ly@)y~Gm@>TWQ`BYiUt}-WyWODvxj}DDLT&ho
zK3hXHL<3f=rAK48%|N?>Ju{#!fdri&(^VM<N`XLlral}wg14u5w&9qpkx|pu0a>-%
z?p;!dr+Rlc*aw!kuWwr-zg2F%vQ^|s!L9FGy@ZJq%^gIR*N;`Wco(DH3oZUVhbeKz
zSeDVye53oCOtgC&zwRtkZ=1+>7bB6s8#wXK$c=X$B9r?k5HSHXNB8t{P=lXbp=M0Q
zAbg1-zpu+1&YiqZ26kxqT~FL7+c-2Axes>^x1FnJ;N}7AeaQXVb!Y*;{MSpx$H9~j
z4|lNZ+<oa~H6K^U1_tXxpKln3h}>ZxV(0n$-}M|36!T{(4Sn6%Rkpq(b+_oR{}qB&
z#v$01ihIGBgm)93@uR9|xWl;SE4@x?m=bokakbtmZ--t<7NlM85d2iDm>^5(+H;%0
zfdFn@4SW`No$zH^_?U9r^qVej(C_VmPGC1=djca67<&j9MNU!QZsD5%UL^dFwInlp
zE>rax@Pz;0dq!vi!qNm_Cjpc^^k8kkp2hpEauRq%c?{=?#7gMXja^LLP16R`xF5bQ
zf*%vJEaNNVVmR)VSk;JjJLMC1Tk%p`$+o)If~>&@^Daa^G)`IaIwq^15nFqOLQ`5y
zri7)PV9y-PBJaRCy1KsE$eDdJ+0XS{-ZHcowkkc()aLV?4J4}M;w*+Uv_NW3npFEA
zY9{akJWC0+8AH(H1>n<-<Y)UMDH2!e7|KI_X8~{Cwuwl2LPzxI!!^A#8|szt(<M8$
z)izY@qu{>wt~L^(te5AIc>4{-jlILHzR3~m4;;_3`0K-1>uP}&0ONh_M#nY&m9tG=
zQ9%$?)tSZ#6*&<tMX39qvP-1|ijxW|w9AAnZbfyqIPm5|PA)L9x5cr{Xe~t3W+?NT
zoa_4y+qj1NRT~mf=Ug>x@TJE5zBz9VG#G@VyLK;(U8)e@E@EMtg#FZ6*fzGZW)DYM
zleTLAJoPXxBsp!48uAjo`r-(RZo-7u(xrF~oafw%Mj278z3nLWkN<BDuD*Gcw|3=R
z%P7`PqONgT!@JC&nVsg%z<|BFww3#&ND?2D$XpliN1u8u-YWVF80*E(Sl#x!14Jsf
zuG~iPF(Iq>U9uQf2C#i0KXiAdcNB0x&ojEC>GEkCh_S=U$}K_yIeNh=kKY}S0LAyU
z-|xAo+*}{H6$fEh3lv*oLD`!duB{VmqrimBw(Y`4#|XjD#X^zy_IPa#<o;$;$jLO5
zT&3p5%jD#Rjo%?`UkLSY-bltEK}xUMZo%re|I^}^D|npDWzh}j5ge*5v?w4P)vyt{
zPTd-GaG_}|_Ty^5a4>Yr8^9QcMknnB&M@Koggbj8C<@8w(;jU_6Iq`PMuaM}Z6OPT
z87nv5PFGG>Q|!8x1@)bKbjv-=*DQ|>6-3%4?b!D({_(0aqD#tymF{LLkc4i5ol=^l
z2^`nuG0wmdj@+8%rDeZKWk+H$v}sTcHRk6KOQN3_<V2mCfeKIob{hd3_r42H_;77j
zz|tqN!q!6*w+z-l55ikK*QuK1L_l<FEA}vp`gY<z-|iu>m;`t~=)i^?8V@@p@+^}=
zvtAmf3$_Ct*S||`tWHH-(SbTr=kPh$5L&y1V*>4dhT}f`X;Mm+%RFL3PoQ{7VC6EC
zZ+7DL(*B8uOHz|%0)){=Y|<*f$&1ayzaR;1UXD1IqIUW|H(sLl)jxbGe`KT4nj;^)
zCbJ|8S(tcsPHo4zn~uw6R4<BRNS__Cd%7BNjN~~u*FSe7LIplUMW-tea;^}rxCFl>
zHM5<3yClcS33=aI;I734Y#~<a-%XiIm@VVhROx8p841w}^{B|V&bGO%O0LcKlS@!7
zDap4RGxDkKLd!N_?5f0^PTk{f?4to(9AuLX9}wPMhrAWpxQ}9g?j6OldYo^GP;Ci7
zOlh@%^Wks4dbT!~Uc_Y-czNZlQ(>9fRjAK$Q>C7}Xg#4I=k9_9%T8?(70S826MypS
z^gF_J&98bTNhYs*z{^MkoJ2WQUsg9(&*&~3J&W53wsUCYxUl_{q;Q+r)|c?`w5I2{
ztf`|E&u)~ebA99Nq=y$T4Z5f8I`lk(H?iCU-vk9w_Q#BEt-`t>BNQxATDfK=QhB`M
zv>jA;p7DW})^eGw7t!jXX}$sdr;QbfB7ZP61Ejq1x;ue^a_wu*$=kfO4Ov^7raB}w
zhx<g!m>3{C0OQ1hbDio@&!2b1!A8*c%PBh<w`>~p2&p<0amNk?_4*V&YvK=wde+4<
zYqfkoms?usnERn}p|=UpiTLj86nJrMk9WCIGXN&IsyOwLO>OuoR9gMY6PglL=9_Ir
z*0LB=%$AY$d#n2IJwa<bgXXeNk3cb8Gr;8@e%L6-3J2q9MrmU{4tJxPAj`lLlAb_g
z$Y=>;IUv=A&Z~h)!`7fK(l7`HcJj5B1pm!6^n0(`xn;jKpFPg8F=0Em?-A5<5_YkB
zj^n&)y0P|zCmG|ND34=Is=bG116Z!Pf8}FXh$u#AI)jUUC$($4)X<x(X<Tz{g_B4t
zO8im7(>^^!fC$CBfyZ8;E3O0<lwRbuQBL5huJVVyZ@jva?Zw`-Ulu5v7b3l(r!ok+
zz^fNn<4Cn5HpR@2L{>ke1Dhyzkl}mlZIo!Dmdyk{)a9%hpxiU)iGz13YC63KvFUhs
zX!g#k(KgB~&cj{KmN>f=|A!h7C6y)%9+jW^uoqpJ@F!VDS+_Mu<R0SNt;WI<A{^gm
zzH|v}Bkl6cA}5^w;Q=E>ROQyo7}O`x1@8q}i*lZM(9>L*#e|1q@jLET+E(0Jp~?v7
z@-{4Pc+0HIJW*Wh7U}+5<&?y&Xvb`Y51?CLbV`L4ug}d8y72K-IoY3=Ixt6xauTRL
zD+^3&Ab(>Ywc6yIfOF>|@Fko{Jq~{T@)6pJwHw8%_B5-^PRZtzorC<VCc<qzL|!jW
zR(`3lB&+}a+pvPxdA??8wq^xzLw=u;@ta&B+`0#N;|VB^?={!Qz*lE|XfN&^X$Z3_
z$a=AXc?b~UcK6~_Z+cTj$P9~-u$k+ltf2N|jz`90fkXB7>kuzoHx}{L!e7hX(^J(Q
zH7UBEc)<KmrK7I4(!?nPnCu<Y+mnsYdQQHbQE_kO3iC<xyj-?&OoStIpFW8m>K$fr
z7ddQejZSUb75mB$pZ5xp;Kz3re>K^AkXX?m%xbgzuKX?%_B+AYOeS@*Xi#>-rz#JX
zPpXmW!8FA;ZhDL!naYXUWuydIUTv2sc)Xr518bJ2wY}&}i-*hoFLMm-1<$YWCOH@A
z#!PJ#DCy8}j{5*jx%9MWEb94%Pbm_TcQoBSm3)g1<c|>DjDk{0Bx*^W<>Sm-OVO8<
z4PLih1FRZ#nGiqYvkPtN6IsiDxx+GT*;?xb8?e>>;az|zOuGukbZN*KT!}Ms+lW|q
zZEp2I@l$~5{21U*;_uuTX-!n48{V%;aymd7ds+k8O$CvoDw=rn<jx>9uaML{M_(g%
z<nK|kdzT{lr%S0YM@?Uq%2zV2>_C_!A#4`=08gqOkdEI)!ZX1!uY$YW=oE+F6Vm*j
z21Bch*jFQEcIF#atbgY55A=SWJC<J1N%$&Kf$o^m>cS$6*E^;(1r7WMnt~{<a5}pD
z*T;ES>Kqr0E+~cNdl(*$vo5kse<?8jI$3`+Z=+!2g^cWr!P-=X6NU<ow4)Vc*i4>9
zKpTPHQ0PExAXHS(h?iy*WTa(O43O6$Xc+rgiSHclv!22J2%L+KCVu4~skVrsJfvN^
zh3VlRoUWYVm3ib~k$z^A;{2rI*Q(aT^{sY(v_a6n0tOLJ{bn2%a9cR%%qnXj&{IaZ
zGe!#VzrbnQMMTxadyAiV<D6E`D$DPeZ>H0|i|KT1>kj!YUOna>KO?1&-Gxa6p0CD(
zgFG$u^7m9l;2ZmK{>63Dn<6jd*kK(b+p9k1v#$tt4Cs?;V}TWFkNE{uAot%#725$)
z-v8Uz%(er2iE>j1$76ZbxaP=qtX$jnKgn4wYjW{PND?S{-_3inksDI5@jzM;&`f(q
z+ZW$_nH^nqJvr|c*^NEyTmx}&W2f3CeR_};I*7jq%?5`XdDx?yKt44VxEU{Y+>Qe3
zhhtMnwWAc1_no?}oqo6D=*b(69Pw^hb<*pyk!2lr5{Ba*FvY&~2n;{KBhvEQ$?iwY
z4%v3(`*&iiKdn>kcw&nj7JGlsQfQ!u;UX#`{4L4x>c=WHwW0fx)D5yeN%+R>#49YM
ztvz&L?YQFKBJ}|pdJM{yJO67gRD~+NkJVHJO`R{mC+L-qEDo`c#~%+lJ)JXcBZx_x
zLyWg7ME%q0_!yvHor!G*Blq={8Cn-ZO6oz;rw;75chmpu0mC#SpyMv+=1<GNX#(m~
zmZMmgq%f22W>xe5sPS;Gbqts(Lo;{D@}JSN_RW>vZ(&Gn1`(r|d`H%rsPyA~I`u(7
z-Nboz{K9VW-^OYgxH(i>C74#Yog)3UAT(kh1mx~sYpfWk*j$=(Amwqu(4;V`yu}97
zCmZT^Dv^K6UA{geSTqEl5qu9jq3x)<JUn%Rj9CDMZd<cbGK8CvFy0QjPRPGs6r%)w
zHE`!ikND)9^TN&Z+b^%y`x6dNJh*j)Z7D(4>%eqizst8{xe2z-4ihL@7Y|2QAER53
z!!l=Q$Zcy<#_xH~4RV5iSnc|0GGz5sng1W-L8pLms~W3-xGKiILTHmK*8;1K!^jzn
z1kM3UdU!y1e3N}klFI;<7ZY9|L90KT>s01uK0oQ`23y{8PyWc{5bCxr0<ly4#7STm
zG%eyphDNcrA%7%bSXR~6-!lwxjg;0TO6hghcWtxBaG;n$!ck_yyC~{IK=MSSlR;gq
zK~I2k+jmT0Dk(%}xG!e?3WNqDy}gtEyz<;pznQuhAqDd*`>OIlR&yp>65nOvu>*C;
zdEEEDSHemNe@gG@)d^3ZPsYtc|C+h9lJ&O8Up(b%`Oxq{cv6beDdlU`%on_GQI+d(
zGl;1;J78ns3ns-V_*9MRzu`sbwVEXQ%YDZH$BtL`hU2q@Os6KPHn|fOx1dg(`fE_F
z|C2?vbz^NVk)Hr-x>_skSk96;7r*ZsVfGcqrRO22#3~NOfi<Z-X2J2-`4(nU58m0_
zwHK{K$^QH_fbFMdga^C}&jQaiy=T^C^jNrm$li#;o0ZOX%M8Lp;1ERoz>R)|7fHO#
zT77nsuMv-!Oi~`s&-GxF(#q?F)XL8Dzbf{MAHyZh9pxKH{WbT{p09T-@HJTHfpy-I
zsuVk1>6zZ?i}+RUOOLfG`%D#{GjhvNvY|@_7>*WHK@3F6s6J?%&ZNUzk(}Ak^VM8o
z@WS8^#Yd`<5dw|!m`~sc91Th$83sAnRX5TL*W^T~;7pQSn-B7fs|;G!agbTkQsCD@
zjzHKGj9XxsQ$}$2VSd440`aaTf5<9<e-mQzp$9Cwc2Kb4al$3pniAhVHt3EuEokrS
zFS3uQ9u&pT?f)Q7`|`Qe^>mQ`?6^~Fdqm_oo2<^h{{o21U;xn|ClR*CH*(}XJ-%rc
zMwF6#b);=*gGxWKV!BS1_c+RBY$INxbABz~$XSO6-U_^3x2lM^$7j?{!(Onci<%o&
zRlE6b4v6gYt-f=>4$svWT9}hhG10^I7fqfTMQbLFls8S#Tcz}B(P2&j2VZ2(oE*dw
zqwmP=rd5rY8i^GJlD7NBpKVoN=pPf$@xvKskR97TR1ci3*|&03>KH>y6ub~Yf5s|V
z6H~6cJcKA2<)(RoycvuLX!R4xd#ZCs$!PKmZW_aP=T#SFs?Vz`3jG!CGZEY38<#SL
zP63xJ93cggICmIbqZ&>&IXeY?VXw2vhuI951F+~3f)QFuZqm0ZPq-?Ha)5f3&%PwT
z-mpNz1+<Cm<=@=#c+hS2%m8cdz<Pm;rocZ;8d&Wtww{^$5B1)=!S>MOW6Yk^7J^xY
z*$mFDJ)u}jG>1vz1;l)p7g;_#54t+?oam^78L;=FAIKBRJr;vW3!;>UBcz<iF&5GL
zMi%=OF6Z83Z&sAo*UP#17GBR+K5vn~VZ2~I7)4RgJ}mi&=drKuuK|bfR`<d*08+)e
z4^Esl3TLyt@dIkwFY;z{s7pEfM@C=2xLD;rB#jJo(xl<QGuk}i9{*)%7DftO9?uo?
zavZudmnS4T5+4g&{7(g4sklVtS}eS{Q3vsaN{&xZ#Cx!Zzt8Pm2D)RQG<fgmw<FtE
z)GPQ}e)f*-+0UvfT<$^mcxnEM%ZBD=HRnl)t9`5BMYbF}sR_n(<<{<;IV!lr#h35F
z)SJZt$eC|N>&zKWthlLtB}AHS*`eCAybX|`?Wf@e2d{DiR>23mi9oU7nPScJFbh<e
zzzWiSq3hbaPzMv>;=0;KWP4}b23i@=1Tu`>LjFNCV$%+eG^Vb)X^X(RBesP^FJzQ{
zM#F2s*286RS(&J?RT1^YU;fMsAY1P-4`$yS6q_<x)Y01-l&6aIE1KdZ4CZE(=(wGh
zp{l?2dxE$me3pgqTo7b|u-Eos8mHskE(?|&PJCvBm?hl_3msOOK<$~qgY$i}(ZR~K
zWK(5_17@)OBdiRrO<i7_c|QWZAx1=YKK@l2#!t=jfra2w)vhDr({A5~3C|^A#UFU@
z5I6=T-C03{;w3rH)K-dI;x=@SS1&HE01PrRIvHRDM%9gK+5?3OFoCCh{u~qNlZ7|+
z*tVjfQ6WJUFp!!CHQmSEW}poPIy124zYbbldndn~47^wTYFe5L+`ePh9bq?%yKJV<
zzoR&fD;nhAQ@QZ_h)P6yHR-@YSA<8`MVXONv*I3Eh>O1`yoPd~8+O{RQIDCQssB1;
zKRHpj)3a;R|E@{!er@73(j)w0UTf=vTH&-Hh0VRrEEtUYCGnmCZ=~<$B=49UALQwQ
zB!RXizRl{vj)`QXkkc@PV&<>Sd_84YXe|kCv!0i>!w8ucYV;X<W47h_?=xtKk+X+#
z<7!`>24D7|tWL?M=f0Kr^Ri{t;*{#|R|WI;n^z1B-V+1bqDHm^K@b+7S=TtUvSO4|
zH1YAaz2_~?a%=w22nMD=VS7qzsvsY<M6qbsCqVulo6tUN6hAD4o7Py+Jcs)r-r`7m
zeT7>QzCG~IF=`ZzasygAgGJWTU`hn~ra}}gStIofC@LUB0r~C3uSaw2^>VtHS6pp=
z_T4VUi(~mPo``cyxt8xy&{u*^w%Uq{z8NpP`8Kl-(^1k^Ns>hEt0MKbAX`<@^M=hc
za<jvs;Le+`Hv2?c_LTUc%2=|U2>`}yrFzQ?;4B2?{egkD$8Ip19*L7!Gu#$-wPuym
zTLg7bO!cIz4Y7k;=Pw_<r^+rw^wIvdSK5BE>~Bb=ZC%^g`PHVcl!!0d&9nDwE-q9v
zH;%hfkHVHiMirL#xmoz&wg3jeXMPoQ6bo*Om`?RT>=l^_bDT!8aaFj=3FI8^M9!#K
zIt$($Lo;%G278jhy()0rSr)$=xYyhs{awsZs=YeJ<8+>s%$TILGV?&jn7Pd{7*x;m
z!N7tyL#=-BlGON7>~W3DALmy3QR)*>-#FAtjbHmzE;pY_JQ;XuS?;9YsSMR$nK#&*
z={c(pi|@o=hrSyfTlp(AVA7h{i73hi{^P+oowsts3akP;*N(&}`Y!B<s%EumgY!G(
zh>r)FnCEK_%4G)>nN=9JaZLPPlcq(11@s1MBPVJ8S}5k1aI6Y7&=#Uiz6S}7-hrH+
zmgX<tD_vzO+D%a>7stQXk@vsPpxz>WhjRAKSu9>0mP*JKet>A}yZs+ve02niS71T>
zery9Qi19qez9|@J=!xc)hYtiiI6~BAsz&Hk!HWeN%)At7QcTgvQg}G{u&HX2&f3an
zaZ}h7W3$a}Z?kaxrbp9#lK7u6VK9dHH~n1B$W9<b_8lJnDP<=oZYo-Q28aG_JVA81
zzp7fafMoV~rz>WUWaZs_%kO|5HtXkHd^)~OhS%odm3q!ajZpr7pVyg%YEQ)FtPE~B
zqV;%09z6DtC>_nIo38UMDf_hga%E)Ap2VM@GbXlG!0&q^-yj?>YbaYZw1*@LpI`AI
zA12H4Z5dL)Y6*IH+j(U<dOfi6k=`1wTbIKy%&EF9ifwe=)P_<EW?hI0SdY<tf^5ON
z%<?nn{qAOlKnEtL3kx@&%P}fU07qW3h|8Kxs{A=Yyiq)`ra<e6B0&W46Wp{plM1kS
zA7r6z0_;`NAHw#8w0{8tV)6}!qCpUQ{dK>Y<e{(II<U!NYbRxImN|Y4QoGBQ(<v*d
z*264!U*V<!`XYND+_2G;F!j<p&inn8;{2l&{IwRmdZ(Q$(fpaQsk|RSz5|;l4Sk^m
zIZsM9aex2GkUs<;fpSx&$ytqJEvm*<5MF`deyW5|ewN)bZ`!<Gv0AxWQI~pEdn+VW
z>5H2uynZrO*dE->GpbqA>W=ofGO?|npDcBLG`QM*NXA3io3&A8%_&2j8;zcT^toF%
z@21zcRL(~d$;3Zo;Y_wBzAdVTCjvVsj|}u4?g+b=XO!c?gTk!5?i(nwcj(-`gORAY
zRqLIu@KT6R`-hGw8uAMVE|J#mGBPZ(guQhdIeC63jmYFj_ZV@)?k5z{N?(zP;)yKZ
z2#Ci3s7<3AUyL2SA}cuQxZ7W}GYi$;rU;wtJzy_3(_J*95Dqdq&ppfp?8%tUiuhbH
zrcgpcW`DSP=`?iD@Yu_0uwSrSTV8sUSq0e*me`CRaP|3}OFk11nA8`c%DH(T%#<sH
z>(|$*p1!+7-e?0ollTtbQGb1Ba3m>EZ8%GKF1%O}|G=U=B|&EM1@L8j4);opMg6SH
zt>6NwVFyaXv1eNBEo@r~--H)fKB~oWEF9<4JS3>CFDAk(WP$>`&ZFIGz~)@O{uI3h
z@UOPyXx3CUCvHX!V`p)#mp<#}oN9F~>3oYOu1HiV_anB=XnWb;@q8?I(?^Qo;$K`a
z;g)vwdFuxS{(!6Y-o6Z5<c-PEpjngb(Z;K6aG_QaxLFl8RbNL=oij&-6&bC4GwcL$
zCBqk0YA=diawaZZGh?~;O^hEN?NZA;QEX_iYUZ31g#Jc&=_1sqNfXedK`=j-@x$p(
zvvE`2A_~&=ySUoo{v3>iWB~Eq5Bb~ml_%u*cw^`^E>gL<&yFu(YOOxh7^dy)f&?+N
zyYOR@JsU;cCR<^TPU5uUDn>pAfZ0#s-}QeFJ&Fl=!V8%Bnkq0ZNdN~BSNpMYNwsRb
zQ5L#vRM~Nnd<{ZJ#9I0J%2|1r;;}^-*ed?7%?sl3-YAZp^ytc^r0cGQx~ac$5-HwE
z*S%X-PS>QVxw!LN-5b*+I-qpT9SYx=E`fsyQF3=5bxxS1Exdn=37kL0oGl+(1tlAG
zL*@VGlo>8<A!%fPTU9pt)E=z$0HW1hSd!<+A-+elyIk2X0I+ugc6OG1gGK44G+gpl
zy{%?T+Y_Q-s~WfK{%y>&Ubvr8<<<8xY-XsqtSTs0F=1xBh?nElylXRZcqIm5RWht=
zrbaIg9o$R?i1vNKqx}y-;;-Mqrqt`!Q=Q$7P#L7orcSgqw`e_N9c~k)MtfaWK)*EO
zTQgzHlgs?4LfC=*EcE8du~(9Ti3WrWucF4pt-<Jk?)SYwkCNLO?E7Kh2GT;x%~8;2
zZuV^P!*Nkg^FBfRZ0pt@zWi+7a^kG&jvl_s0c{Fz#P)w#U<|9tZm>SNr&z@Yios3<
z*A$3`>FYv8a3qhQ%(5s#PCGEfhmC}%lsGF!T@s9{k!lOSgv{Z?$KSr2U}L}8@h3IL
ziFtrsW7X|4%~4O?`+jgUp!O1oqtk6q{SNmnLGyqIQN*w1kMf59CmOmkin&m?&5Zs$
z&)5FZz-Sz}qz$98s&S>Q?J~jRDnY^nx9`{mV7dA{ilOcWUGxmc9jPa{G8|)e1O%I<
zj;tjEljB;+O)z3W_bC$dp)UfkP@Jf5o4Av96)dth2c!=&Hx6>&l@$!}D)M|{0-l0N
zNpZ2j7_&K{%VUh~y{(uQ+Jhwp0Fr;882%`%4|_L+t2Y$wWGJ;?ptxOW>D3DsJi(kG
zFF^}EOqbzU0lWOh1d9%gg1+qXil}=Ts^;M-JIE!rJb2w}Y$;j%x0t716y_93(ouaF
zkr3dkseDGMtiOKYJfnJ4R=JHh3bc{zYp$QaVIHW#iMEIZewNldPu5Sa-fDikCdUpD
zX{jWQtX%ED{!dW>eHsDEUg|ViZ@QzPb%n>5lonYZzeL<?ftP|sz{cK|WZ^S8z0X2d
zxO<_LTar7}uWoQnxRjng@v!T>Mr}Bt2I0I3<fidj9w!Oc#pXnaS|PzUX%%0ephtLe
zB2+GUp&c?~d5x>{UA~~Nk;F17{^OJPotSV?hmu`*GuawJOYhdMw65GP-F8|Z?=Xj3
zNCaYpJBN`F!4ZT4U;w?@2SQ!0Sg4*V*JjyQ5YG)T<Kt{w6yN+m!LG*)XYxc~TQ=RC
zWf$n`7~HHrCYyZat!lVy-pR^AdK2swn(I>fr!bJr%dJvH2nYpU#GP~*wxx#?`(T`b
z&D~{<n;#d}^X?;teKW~emB!)nv09(O$Lp#dbS1?8QS1fwPSMIuxua$!=wG|eb|gLu
zEB?S4+V@8*h`yKz+=(x`!jo%0IxvoSyOraWPQUXpwh)jN-ai5kx>3Sj(O<VJ5);HS
z{86pCHgEB8kM<*;DsbY`A!b3Ri}17VYNLJZs=Zyn!Al?M)968ZXVNPnD4oQ&ESjbn
zN9~rz#o<;G@ky*|O!a?fXf=&fpW1-W9^*2`bmn2bqW_GHnimJY57TykUO><LkoI{&
z)*i;)qZHascjjn7>H+MYq^SR_?N|^b<TO7RY@LgFq)+4GHoa{wALom(#=M&qyTt9y
z^jS^qXiKR&WVZYhQz+OE516?bS6nwMNe;`3G;_lnnr&L2V6TVQkd*gi<;&N_j@>1E
zdMdQuo^VI2wjy@xx3=ldwLJpImi+&S*?v_3R?QA6|L6_7wQKx65_%`!VtMD1m3X=Z
z+#9)>;?02hHLf=EC4o%&g<!wqUSaiP)gzWOO`_z5LdfgX)6#wU_B2J6lToKiug`^I
zA-wirP;gPQmWFyo{W1+b61BTCb8aPanK9e)zp;$TwHem%Ud{y^{0Fh5I(W8ClOvd*
zxmjfE@mF+2Lx$7rc^E~k&eG3kFT>_8W~Mb#v&Zb8CmfTrH(OXah5eA81Jup4h%!Q4
zi(KO1)4)uuwH@!3!M$Gx^P5NMnb>Sq{u`4z>WU+{JLOfY9>l9tUHrNcT|-&DSB5_u
z(&1)xO<g6&(#8F^e|TZaKVSR`k+%>5V7dox0i&PWcRD;B=|)$Nk^*jg_Wy2?2z=fl
zuWNbRX0>A^Zo!%bkzO169~e7l-Qx!uKU=dw)@<_qR+dopT;oe#Ca-+AkhzBaN}rb>
zzrEz2loG*9Z}tB?^NadSN3A72JGHU78wt+4h#$$(VW&?aCAHr`+*%pRjYI#Kc~E=U
zrSKzd@9+@e_Im-c*hguei3Kun+U1X4VY4dTyrZLx%OD{{_ORf*MJCnKLx-EzhE3bU
z1h<(jz=#!K95M}?h~6h21=j$-;^->T9oQT*DK~JSPkq-t`K36JyHY))lX#3dh<Wc~
znPU^E4p^oG=b5gLW`&vx42Q5MVgK%7gdFX(ZQ)yhEK$BEmyiLn#BQp@>F+?*j)U&L
zN-~|sr~=2Cjq1|dtE&eUh}n6*-GQ49rv3C0k5d(Z{2aLaO183R=(+YbOQ!I3>jMf!
z_>bSO(N9o?J5-C0bW3otf$`L{HtST?gNPQe#Or&HjZ6Xkl<oU8@;{y({6PTOY+yXD
z#oq==;aLCr;XcsvDIucjC(CNz??#?C4U4CLLh0G=Yre#Gbk@MIn#@UFF9sK?@nu@(
zBU1h56}J_&zSjHL^d@|PRbulvd=j8DL{Ea>^v7R5o^#`8jpZ$Lkf#SI@(<X&J_E7j
zMvv~n)ZOgB&J<gYVp3Fv&3yiWT?*0;r@-XUE!9IJ>ycZgTlP-B*lCq)DbeY5YE1Qp
zkqBj5)>luRE}H(}^Mq$JoUs7w#c;WD+pflv*5ZH<eX$GgK&NMVP~%M(VDDBy`rgZe
zfcAdP0C<xNM85BLbkRInq1Exg<<fBd<+?6j8iJ%yA-7%fJ9KeZ_}%9uM<|XWOZKVF
zkS*KGN@PiX(7tG5%ic0NR1G<>y#sqsw&Z+}TWkc*vub~cHN@~vULJcPl-*zB{mskq
zC#pa9wU<uHC?=}rj=gBkthWREU%!Z;WxXyR0}Ni%pwo<$lo`ld>{~{9==zJp2h9?v
zal9+l1%}xKA%%`hodgqr8VYXgG+fmOr}RyX1xk^Wt|4DyqCd0RA~b%7=VUQRHq8v;
z6oFX9aPq-6{ScTzl6Btaz2r$U6(`{iGxd=l*VEptKkJc(Ih8pc13FHrav91#e!GB<
zJ0R0JtYiY+k@|2pVK2ZtAx&6G1=RE^;q?NI&_V3(doc%64vc^mY`Tj66qEW(XCBuW
z=`*W3cpo!^Ub)1~e>{tpJ$Ld+7*69!)Q>-w`aHzNad4x6BfbaKsQUbJgwujO@c7F=
zH92elnlZVx$o3V2a!9+UhFf#x;ZaONsGQ|z<GUm|(Om9zwVuWG9RttIDmciv0#UbL
z%wT!Q9Y=*@6ho5nvY6-Pz??mq%fK~P&Y#4_@qG+1-Q@SuYzU*fI+xv39kJw;H!>PU
zT<kH$w)q{VQd)s+pcXzjPnG-rMm2uZsO4{R*@nqv{K${U>Vb<T)T_-?-kA7~7eijf
zE)zDYv&|eQZvgd>B-3=O0OD|4o1z_S;k2yNMpcl;{jiV2ZjVdTcHojcHbX@+<);H>
zhihtiavgU&d^v5GMJsi3)7D_%NQ+iib-t8d&XqgYRQKO>0vO^cuY{@-1NN<eUCFgj
z&!l>t%jsjkS2w+_AaxWrIpufrd6L{<qUtI*mRLAtoMK_Qeqb}2e~t1Y#od1?r~>W@
z@F;tcEZ7}ShgT!=X}Zr4a}@M&xu07OG1J1Yl<zC^r+rlJnCb>w6#CL&278y=vB$D}
zVVfA4+bccT>M_$a<nG_r_C^H<)pFjp1141ZYT+@zU*$ibde~%E(Wu@JKDgqvrm8Pd
zOn&8X^RT&IuDtLw2Rj$c-xyd~rS$EwDVn|B!dmXhs~#IRsk!U9DLa7eh<Y=f;<1+_
zwA8oO-!5H#x)MB;8(bSubfOrg(1K(*fO}te2e&q0{`0rzvlf3M;3t857#N#9ypLKP
z7pMNX-xyxjHY=^t?API|3)VgA8Jbjl$q8i`na=mg!44sY{ZAN`!9C1>%V_YQ6ZH}i
zt6AuKKJ5+}2FjD;fotnv%l*#9Jk`$xu(DEe0$Ti2oAff`7(7^x*jAYZW(|Y2g<$=o
zB6J;sQ2me%Olu9!$~Y<4zH|1cPy?R%9imPZF@6>JA4gHu_ZqF${;00Ham>*(qz6r;
z##HtZ7d%APe5VI3gTIbtv06vRn*Rl-w#(}Z(>_$Lh~9-8^&c4$$#Y_QyPdo|#(Ksp
zyIAnb^FA@>CK?ZI^WOIObjOJgW+JmjWw2=?!`15*fvRJV?ftMrO8Q*6pw@;q7WE0p
z3C1(90ftj#(FWXS`$^n7DEA7tv1CHy52*AsgVl?+<x3qAf3V6vk&{_#)JSAAovY0d
z!IJOPuJNvuL%O}gCVkn7pn#*n_<Fy?b?aYZe)R_jMjut0tqP=M>=Z&JCrd8!vmHP8
zTnZz({h0!Q0daJaM~@cyG5TGBOkH*C1IX$o#ssnms>L!&z$H_ROJ!khu<sM&oPcMU
z-}$z*KCVn#?7u!KL<X`(9@a_$cPe&X;v1u;@(BC^_E*{wrYpEaZZn#0NinvX4F_}s
zfjj9|{c35-hJB-E^*{1+GiKkdDgx7<Hr-J&Im$-vusxX4Vrf808gi>_y{2cvRnT|x
z5!jt&&EVWsc>z%mfqVsHP_sP<LjASK&oZLs`gR1V5#BV1GgHu+cm&?{k-Xw}1&mu>
z*FfxE`KbXvJ`1IsVnH!nYRgBi&HYk<d?9D$y&zsV!)u`vp7x5vgNsPE@eag(7suw2
zz`78pEiA8dN}!f?%;yVE{fvpcQ<wR#YFqxV(psTw{RH_Ta13RHa;XOOGdA@ZO<8qb
z{iHOcTju{la?4TF1*0-2u)dW(uop-ao6#ptA0D|q{>nm7Msl<%uL=q{29^_y`YHaO
zcOAYbSfkwb@U3bky0-zn&U*e8Jr6su(DP^ej*DWAJSV))w<6<I0j6{_Ygf&!8;jaK
z95E$E&hLYD6D^NZ>P{^@0FZY(6yK~nS=`|EUE}t9%*2|0V=v&4N49lncjzB&kZj!p
zTp@24#R|c-HbiieT;J74z~O8M-dyF$E&cB9xtT62&;Wn*2GM~%UUvXcr#y3n0Eeqa
znM4(^gd;rd=ZRfh+NbC{5Qc)}-bMD_tA#=Y>kq$+b%yIK&JEmbs<apotqbKci7IRp
zVCm)`?Jk(l*X$Da1*iTNki7(7`q)M9Z-g%FMfc-*{5z14KizGD)~!)gf5fG)AlKyV
zQS*#qN?3m`h-g$Gyu6a_iDVw$WRF%YR2SSZs(uu)*UlsdsQ?)|otIxX16yQ*J=Ni<
zYu8M#E)(2}K;>hCFmqxrsL*@PbUBI|YLH)x=jT(M3;DZ3#w2xmIMg$fw8f(W&!bUy
zsygusD;fRRj#KMY=PHLVfs~CGPp@GG6y3`g)-oZh_4>KL>;zUypfO1!+MVSk01Iu4
z10vM*$jh7#rJaipF=hMLvQT=9u!+w#U{vS!HxSwzee`Q>A^39-*xx|j#sHpVl`7K9
z(Gumj!U-Z^@5#XOJpgC;@#o~#aM(_k<=cpq$l|Az!Y_hOGeX`hdKDzjfP0uk))R^5
z&9i~74hEvwh#R18`u;Cdm|py)&nDUi2BmDBOx0If<m2c@k`C6GPmEU}|0I-sekNXN
z7FvgoJ~Bt%|CwVr9*a3TAi8siJZE&lXq)ZgJ3%7)*c3WXwJjjdWgS;-@g$qsz7CnQ
z%a)V;^nCCxB-7&K&SH;rB`rFu`_6oFc1G&b(a|fk{}HkzV003yAo_ce+Z$Um@-7Jx
zM}2D^?RXh0Mjmz^{A7e1^Lw>@1_B)5^E)y?>CTgXZH^;vu=gmhSl<NYthK2-*hS#x
z*!B(#(U%)=7MB7=&R5RtWoM*2lD{4I!uocH1-h0hrPaCq=HGI;-HEv%P;4vFkjC!C
z=nFg?rEwE^ov>zUjF}*u8!CwMd4ks8BJID`#OJ}=(Fu%gpp10>>Gd6SDaPNx<-PJF
z3cjngcqsBK!G^=8UYx~k`zr78L8IF)&3c`h?(0CFE8QJp3rGSM*TkfB>!qG4t%@yC
zwg4T0mAR~`x91jDTW-hms$o<;Fq=+b6mctxJx^Vb`F=xn|C0x+A5sPN4NZerkF8Uq
znABliZa}`sho}d(NWo3pw;lpfDhY!x6Tq>rJe})p@B^r`%H5r}Ghkj2-L{4vib3$p
zl!?akbVpB1J|S^+`0*3|H0;q-#}Nac<Sff7d!qU$)U6xVzYBI6`MG^cnehCi%6WLd
znQil4@S5wTSY=!Zk4UG#AqEtUyF=;kYaq{8hd-w9&zQb11pBz;|7dOXg%{396wADc
zUx%=bxN<oo=35`>d^M;k9adAqN0n_N+jFu=m3gH-K?Co<*3BAadVSx#r);(o!`qsE
zQlV%gES4VfDP_vD1U+v#8_oZkEjNL!<Rtx61asT({MEk%iZQ#c1zPr-6v`uXfpb%k
z7I}fZ!1!br)_(jmi(2TRnJq0Db^AXdpIgPq46Gsba11CqGLxQBiH!ki?m{N8lW8lZ
z1PXQ!fwi3mA(v6(G>mO?{Ub&_Ez<2l9UjqZ37FEPkU<F-dOo&a(c(s~qc*)8@L^cA
z(?6~VfJ5nL`XmoMdU0{<n@2~$t10$BsouKvsKVbadojyR^rliR|A@(2`tt2-NJr!*
z&R22Q>38v`&iu2~n)UMHk#DGgfq51R2cxpuLKf%iwl8S#VJ!0s8U?l18p6mmp$XCG
z6-~*Af~UY3k2tzmPt0V(?xd<bU8&ZOJt=ypV)8jKgOpc)F#gC|P05_o)6>p`v<UI+
z6Zsd*u#$|Bw|y@ODNlk+|HfCTIeqmnZoOB4-bi)LssB47-t_27*Dsh|s9U=*jcT!m
z9VEU0Kd|$&A}fxJ=h8`>_~z<e<r*V#L1{|?EB58Xqp)inmmA*f9~E-M!!@OTi(H3r
z*FbacqpH_TR?;5&l=&E(ZZ$iDuSt7Mc<}1Pn=V~g>1f+#tFW%NBWgsiT<Z$p**LQ>
ze7YP)RP?t8#5bO)&+D9pT1xY&J|Vv$Q}-`=qQ^v5Vd=oY%Fplz7N&54#u7GHd^ZR7
zd<eMU0EmNoo)O7x-`xPLTgb@#4m^2C+0#cBMXP-ihF@Ve06}Z<9I^fB#6?K2@D&2_
zFaL+7H>2_61g&611!B1H&Q{zgVpuFbEuQu@5XecU-ICf7`U9E+_5bp|>o67RT~lG!
zv?apvF@B!&npTCc&)l397mf+P19o<HqJ}qJyPDv=Ky*DY1_WCFj2bq@Nkqx1CX*Xv
z0X(=-VH4bayVOS@Uxu*yRCWlD4&VMgU76b>^yB)9RqX0x#)7$eZ8OZ`u8i>`Q)3!`
z<G6{&(}l-_`vRm6{em??5(nRVd|}v{d<ZsBJm$!Hvq5ryx#RldQfEh#f*rpfJ~iIT
zN!ISSoc(g+zhm3rQ#FzNVtJ8Nys0ygqs+VA3mofef!p;1|4P04qT<FY_#G`r0A^+T
z(WmVIEwxs?ZTK;d{&3gb9B54P7JAaUXlsMw@dVwUCXWyu5*%_lwB`ru8o0nz9Lal`
zV#M!P%Q1RPkf$k~+EQFd5L-;?qB8~V(6~nRM<J(=bO4uZAq$M`(CalWtZ_;HQpK>*
z_-p7y%H>XxXHJ1i<>+*Iy#9+uP4I)=u5a0Ob5WghFqbuSIUR^t^T?rY8RbpqQa_D&
z+vpndxl+x3qlvZs{m6NZ;~8rjZ!wv}NG_y8C@HwTYOB1T+K0)$*PwH5JfHbH5=E^&
z!7jBLuKWBlV-96V6S8cLo?aXMiQJlD-Sd`HqH^aw&2r&B^g7f`yXN6wV8+TZTFq(L
z&JD@g)Mw%m7jhlH0t$VCw6$}bp#|(3=FkRgM0m6pxX=r{H9EzC#oz~)v42wy3V>(t
zfM<RptIu-qkH9^(z$#H6!W1dE`87xaY1QWheK5IrrmuBWJWmn)w};0fZYLx&wy`-I
zMst{k9?ykmxcFPqG$Lb#GXqDJDVrF$k!QT{;P#$jRrgkIiRW$Gg8A&{Z(3{@Pd+%!
z=}-se5Z>Eg!xqEm)pA1Y^3Z~VBkXRLce5Fj$ECM&)rRdy#Y>?5n_>&-1Lg(KpFiK|
z`)dj>l*>1s1S<1Hw6^%ZVP`)XeNRj5(3)<IVtqDxDIk(QV*k-^d{=A#P}`S)-3uRH
z6P~S~+`v_}&fH#YU!g=-%)HX~QTgVsnAbG4CgkYTQd!o=uGQ0W<?C1mz{Nn=L&JDP
z^r;2ADlY2b;OUH{#%qnQ<TCKGLw>Ifofn!0d50c|tNZL+Q!;rP-<`-WFhAc9%v&5<
z!$NfCv4ODJQHjSq$kg`yMNx4b%nn@hCsgd@sYmGyEMnK9xn+*sKqb5Z`?(^4?WqgQ
z)jGP$0&^iPjI@ZdX@@@90TsvSzT4{mpQGLy)_Sr_Ajho}o4-*o=UcWv?c-BRl+;t}
zb-i&<&Oa4a-;;3nzSPyf64JB0joS)4ydhIt!aPMP>rr;F3g<7hcVde+*`>Y1vxx6<
z$xh|&rszpAvsEI}EZ}w#nwR8lZT!Y{A_?C^I}R~9GC|NC4u|WWk%Vl)#25DcG3tuH
zNK<LkCiDAhegt@`^*2I(;iOx#e9J{YVI#V*;z94rch9=A`6pgPMeR6#V({z9{L;c_
zxNr*J%^Yhf%t3d5ThISmm4_UCXTc`fe4da|-r==>V=uKN*;m-HO2ogdR?p$@0xf!$
zA$3gZhVJ5-|1oJj5FlCA&sLiP4pjhSLD4PoEh-|#0RGStb{%>KU&!jgs%h%sm$3hc
zE8%2SglXMEyy`XZW($)w?0oGk_7OQDwO}|%BD`$xnLdQ>gUJ>1FA=udk?`F-7j;2Y
zsyqi~QHQzX5O%g8fECJlYEO~9P?_TCc+%K2OwRD*B;6Ny&`ycG-<pxSR=L!sU|}d_
zGOv$)YDQs8&12Jp%v33Tgs;MEFF%=Fr~*E1t@@>vF`107sD+4#etSEg5-euJ&tl+V
z?a8!-n2Lcd!Md9`kb4|=hrwps!j3+Gn(VL1xT-WMxA1qqCzcLb%$W+lk~Cuak>dU0
z7wO-{!;@*~rVii?w%jmdjV6*Z4_k_ESnxH`%bxS?A1*9iRaNw_@lni1^w8)k^gf@r
zD(#$4kH`_Xc4q9@2Xr9|fDWhE!6i4ZyOyY2Z_Jj-DCJ$_(OY^20m|lDbJ!j0xZW92
z7W1RF$hiJJMaa0teJRj8&X((^{X{-6Z&J(C6Lle$#m84pNIf!-qh91_jROs`zdW9P
z+VRx$uSVI|A`A$^Mi2h}oHpmTwiZlC2d22?D;vKdGvVzG0MjTnRr#4o`GN43y|Jm(
zL#AtSqe&iPymYxnS0h2xsN`r64!oT4gj<=^19coxb}J!i;RSWJ%10@G?xcIUj8Jf?
zrWp$ANL$|wURA{#wLP_LS?jwP*pygSZ&-RwZy^+R#$Vzm`r*17g?2Nu#Ip&(R%VlQ
z2n14W+DnA*d(`903lld7Z*>O!`v4lL38d=l9g3Hl4SOfb0wLwnX-)`Wsq3`~o8RM}
z8sxmA0yV&i81*!xk#wd!*gge_N@JUQpI>_NX9Ceip?*s6Pb>ABTBLFf$eqX46bp&+
zp9Jxax_fikV7uuj(o9H1Hvbw%vxSVDPvBKj=gS?ZQIBfjU7^NWg&W<ia`--BTE$F=
zpgcZy(Be(7LE_+8)Q8Kdg~3iAobi&a`%`lt_HRBvL_m*wHbE@TQ4XZ3KTtN6b+MvY
z{=}M1`)cGFl7s;v_BEgG-K|@=(g7@X70%yy0Cq@1*PQ9tP@q&+)W?R63eiX6fs$3d
z9Kb?_1xpdTJSqmFd}!!e^g$*rT-j|Bs2&lUfgYahyJ!atr8?^=uZ5Fd170dSLt$a<
zu6N}5F*DeHt*}#PfFu=tRz!bN%Fc4QV37W}^qP%DwL<8R*#7->G=ro-8QPri%XrxS
zI||IcK|>y7Tcbnd_0l}YCIS@8iPoV+H{G{hhryL01tZ!!))W`F$eTh`^&8;J*gvSE
zCNY)P&OG+4qRf(IK<!)kScUZrp^1^N`LHXf$5EW3O(Ryn1EXfaywU!W<nhLT@E7Y=
zdE9@(_ZJicK{}YWR6@kuZ|CEEYxN|ndU9^M79GE~T-)P2GHANq*y`OZb(7;WweKaK
z5$_j9oznrqLEld~xa&=IiPH78Z5c@yuct0rdIhUwkk2ec8vT?An%{o5Eo**Z17^BY
z7=Qx7`4txb=ZwKWII07M&4>F$W5CbI2W?y%1CiBpQ6EEp7}(n80|gFV1P-o66Rk*#
z(SZfK*XAsLuBj|yjn9hupzcD*61L@vP;VNeXho4KP~MVONF!w~ifH?DvSrqfoR<1U
zO&R|9^SD!*86X&eHqZY;8?h2(?|h4<$%Q0D>`__&>t}sQi|nL6=b-vG?rln>f5rpy
zuY=3<4c2|S6ZJg0R^??}lL4V{k@mQ*J#dK8$A8csCCa*`VH%nHhdhJZ1?mw~Y2lcv
z@>Xg9Nw(iqhcHE^c(ciUwpndWq>2>G;7=s}%y{0*H<l|a$2DfK4UARn;nq|R&%-Q)
zv!^BB#4O@^+K9@x71pnEtYe&zEf)xFfRNR~Lg<icoaC*>u&0}%FY#|TMNxTK$>MKE
zRWnYU^|CkkFe!gbG5v$eNc`aZ$z%0#gPVT+{H?0$sZn|tgQsA;0`tR5m`DEdVX)u?
zKv;mkBS<3%R2Q*<rDsHaZSdcaJVduhimUdV35&uNbDMelq6-}Rfqr*B{JZ1_^m$nV
zP>!Y5Rx|*+)KGC>f^yIHwf+HlQ`*K1G*dicN`F=q&oUFQt5NIMekva6vNtWm-OqCF
zAKMn_Npv8pm{g-RA`*NPB6}0}BA0hf<zE^8l(-H}wPXejt#_7StZCMrEziXbS(1K>
ze}8v^JNgh@T)c3+R?>U#nqZ8P8%8sX=fZ9MiB-Yn{)S(=G*~KeJfUB>m335Wc<iYC
z5|+;Tbm|Dg*BIrVLUJkas|~Sv%uksO&G?D5-2C}8$uiUj!k3~Aap4!Sy<)e3eCaT4
zou$)i#uHvXjG6!pE&5aG_K4LLfdPX_%4bTMF!YvJQp!0q3v#Yq(EE0c5WLkw3e54)
zG0vmGo<qm1z<~N>>LB7xu!V9k?2Hw7Lx=Tq-zlGnOOpUG|6K9KI^izUFTWSPAwbyd
ze7e;%cC!PRzT?La0b)xBfcc<}rYKP##=Z3_!&7-bR*@`U@I;W-4{XKBRGiBSIu%7|
z08~dqPf7D2WXIZy<L#hFpiIfDg~r&s*#HyU1I$@(;_`&(m!8(^M6E}ubN?IoO|t#~
zzu{=sS=oM-zlO!frIsG37eB7PztwM!)i3A0B_x2*{(ALP-iC47t5(hwioP8#FDZL7
zM*ECf(3SRStdx1pY<{2VM;|37B64*7cKHqJWAm~a8}#SCd?)w1qQjp}Hc*Dmol!d{
z)BMFB;M9M{$+{N&_fm!~P6`e4v*f<p-qH;$*Q<F!aD-P4(ew3GDp|#hny&{<pFSME
zIy&d3`l?(pDpmiuCDKqMXeD21JnD^jbwymoOjOG)t6E}Cu_F;qoeGc{pRH+le=L|>
z!BEIh^&`k^qQ5Um@&7Nf-aH=aHS8bHQKpWqQdE{nDkp`c>`YNPofc_PvX-r4EMs5B
zC|ezwp%Su8Qqi%6%-F{gBim3i#$aeLGMjZ4vwVJ^p6B~|{`vW*f8}#u_jO(G?S2f#
zKPV=doNl(Eqt@)2-T$`&#MIg%#7U?ghVI?V-kQavWQcZH*lKLpnbd&0OZ?ZrC0tyS
z4Lnah4<7Cgq~*(Vx*DLVbEx3vIBAK==@xJu45{1(!|qyxW%L10c;_a7deUd$&kc`T
zON4)UxM|ca!U?JAPRPjroqsFZpW5WU^WFR}?UG0DfSK0uZ%U7*)Pz`mLkIALmg;46
z-RV^>QUvkM?*I7tqx~r52)&W@vRb2dB2_~^G%?p}t!X-{AG|QXbCZayqfQ0<Hy-Lq
zWUH9a2k$-9y(zxlJ63A`Z1@$*SkgRU{*1eqquc<!ATd02XfA8|)1-2+J`#O*xo%xF
zV4{;BFVLwi8?30#Re7D6eD@fX%%y8sO!0-G^>Z5oMM|1ACnh=|LM@B^k1VeoZO09>
zO_=Mkta+v$o9~cUrR$c`yhb`N{XQo^-Z1!7?&<t<iYWEy5dEdz1E8qD0sfPadZ{kp
zthP$Pv;iikPIds(m$m`LE}B2Vv0R94f@T8ekc6Zw_V_*q>a6tEeFd-!jwxLLVzmxR
z3~3tMC6aOI7tQS=y>)_kA-@konsi+dM=T)onP&uJn#a92F&<Srbhs%uwAnmXSUSwR
z`Dw7Xzc@0PtT1ie4&d;410G&H3W<d`#oqC|e)#i7bKTQoN5gYAMrA2tOvCOPaNL@b
zi#ooK+2Q|H^YgdmQa*aI+S%T8{0D<fkpOpbGBP-2tOKNcPg^8WpP&MaX7sg-lA56H
z%xl2K3m2jrJPTHXCorEbdAet{119@(w4(c2qyFmbeAAQEXFF#PB9>;W6I(~qs`%Gz
zN$P9iD<v|%3mKwhg+t|DDuH<^69pk>)2fQsB_ieVJ8}D~xP7g@4Vp|&Qp491N%ZJ3
zxzl9&zAB$l<+l;1U)lkA-N3N&^C4(WW(a0amjG*(+Jibj`+JOVr--=^#0D1XlLKG_
z|FsaYVg(`?_SDr4@Jj9p-}DQGI4y*RfVc<D7I0CZx$LYyd2g>Yq61mIuN5a(58Q=S
z_%lDPk6|WIWev(Q{0H9v6BPk%z6anu5eMnUlRs!k@tW_XYC3tqIqI+Yc7T^!{@6>A
z4-;s4s%zhk_(V!TS#np$gYyZzHfVofbLFUSOQn*O48bs~zfP%XbQ=;UG0wydgJt5_
zpb_ckJ4a;1;%y7hl7f)9;>ozn*Gv0GjboP!mL|<2(b2t<?TFkdwUykFUYX^MFli8Y
ziTBA&Vm<+O;q!LWy#CN~W1W(WTvrChztrH4KTqfY{fxi#AW0OypJ9vo`mS2U{^BU%
zgqGWdvQAPZ|I8rQ2t_7s8AWlL%T`;ycjGBrzBAdik-RaBk!*ZKJ99e1r$_Q<lqO?%
zx#{J4s?>E}ce3iOhZI~Z*3Oyj_aH{^*USF*++5++hMc6izqN0u62HEr?$9IGf(Jd@
ze-hRJEy8ba#EPK#DS>w69bvG+#628TB&9nE7%p455mt6CS&Tua(8BAb{HxBnK@0)7
z?JwQ-d5_mVEWxl*;%0%y_IRS-BXQ8(eo1}GfV@B*+*MU^atn!^q+k<^=|)w7Rt8X!
z3M5O$?jCKj2yl+9cS|LzZ{nLy2E{_!;^+6YS!=R6DAQ)THLkW|(uB7@*3Hp|9Nh4b
zmjZOA?YAxT1;LIxx2_TQVhHz!mh;nswG@k1-`!qob?p%$c4%y0ir0Ya#wnP3pWhq>
zoAx><ZY{L?@aWF~L%WE8#{m*1KZacc#_NtO=XZ|!v_?n-XGotjToMM=R$H%@C{nts
zQT^tl(Vgr~i?!AfqwoF^i5W5Q<;COZT>m-lxk3c{2F?Rv^~SYIb??Q;%6|m(C6M2N
zKN<$*k#e@GDIrEZ;S(iPBl#lNzvMT9`xuT@=S_+pvf{hQtn+Ts)_RpRDVjnZ4EUU@
z(l{Nc(PTP;QAg}Bzx^$3BLs%XXt*C=@jkay1^bL)hKuPH$eMh72btP4dh0Jus*hj6
zRXDV{XuJ?9v%ca~t7)Ama1~^)|Flfz7Ox~E)g<C&pnL{9{$8p0h-y~kicJywtdFnu
z4LM84hH}JfpT?_{!m~>IXSc2;#|8AB-~}Pc+bQGHg;Qra_6zYWNH~z_7a}#31lb`I
zE0RRR1@8Cn<Aj&>8zjn6sDQH*z(PC+72J@jqxG0BQ5_QggzW`n<E%4*VVz$f8sRMF
zIv+QkN6BB9MTJ1#0s8-D0Q%{?bJ~E5%ugv^MwBZ;mdOIl#m_615h4M&ZA5&=fveJ6
zz;w8J|18p$_+lvJ107AgYqT&n2*yFC3<<A+tF=Mc6-LKo<=>APir~7_kdnU|@A;Um
zVcM@SF?ARBFqL8*slHgZ-cxzl`zH4CUej4re~qhDZNuDbbc-`2C;EkbQD7Q*(CkCk
zRLuEgq$sb);l>?2?``Y9u9FOOYFu~ZzfI6(c2pt!<w2_}7cN{dkDHKW9Bl{xyG5j=
zDSI>gW2rsu9(6yNd^A-ap}Bk1!JLEf|0Mn?QwwXT>HYw9PJ5O_OgEq2`_cdK7MiSv
zI^AZ^3B;c@8POJA8i<h3$;+pr;var}a~VqYFVVc^cgyYST|nqn@1Zjt=MiU!TV`7f
zA)kPFg=WZ&sb{<Xf1mCS$NmORH&b@2PzI?DT|*xX;m2N(MluR;MMZ-W4Ed_db|JK-
zck)k;E>JAWESbL7C7^LPNw{a~e=pptL!8OIz;)=SDe#RF>qPD)AXbJJVkADrK?M+9
z<)W-r(q&v9iOLc-khkz1+)eyUaS+lnfjKPm3yFYDRnE1_wE{Eo>d&R*=TzW{lwqOz
zym?$o3_Z=*GyH#_$Tskn0!18TwgQ`XD;3p1qavFqFGzVUz=obB>RJi^La;*U?QA_W
zO_5$sXul<?xkpxF-bNjg64w=Sq9rD9*OCHZibWHL9nO2<k-Ti|s`4Q}{7#$)9~K7%
zv1$Ac55}%s7W`39$8WT~v&$G$PwVRLYh#|&O{c$YK|f=R6`tqcn|z_v<6i=6>0wn;
zW7yA3Z;n6H8J`Z6H3_-F*Eat<3U=jV)izn!*;gwTPz~JyqsRH`t`<cU0}EsoQdnr?
zVVs2}TFsm7jrmBjK6B-;BV~vE+`!*Yvm--37nJrNOX~H6_|){?zt$spqLI)ezZLu<
z9F1+W$hR55N3JOcrF&)YCu%))gC-x|-t=5r0%r811jrRrA$DA<mBbtd%Vq_30O+%j
z+m1a`BcvHCu5;p(*jcY3wg8=GM=O^7CK*i#7c`li#8WH;&`It&Ga7=W#wn?T5AQi$
zuSMCDxdyWZfd;06G*WVtYcLKxL8qEEh(WLl!$PBpabWo*4<=?7`u%V5{Lhz>DDPDx
zU;^%PjJ&pn2F7Mn?j+4&;sYbrll3Dus#|*Sg*}XW4RR3g`Do5CBon#TlJN*)^?j=-
zQ5ZA&!ol(_b<q6dIH$gDQYs<~j@A2`+hngJV%)nWOV)p%(h7||RTG6x4zFDL0RsNl
zl+rT5N42I7IQ+3><$dXpYu^kq5;k(-S8C7sNLT>vk*h0ifOXMh@w5B~_Uf{cv2NR3
z_gW`?V>+bY+}F!WS9Uo%^g3}`dcU%)cfBwqt^AlKImoZ@HXJLyoo3xK7NBe6Zx8L=
z8%L{=K&BsQl-&PI)+|1@)M~$;<<HXQy+^*TT)A)QnCrYXi!fL=i=u4AKW)Z*0Ri7C
zYyKDvmplyM+Nu{&P_#H`OWaebDM4x14C=KA{L7|G(nPc%gaBj!UP$dvNQpEIsVL~1
z%pA($ycEBcgWqhG%I%FlYWPJQ<m0ufEyZhnk_0)=qP)-fmvD^-WbVS2y0=d8ks1xy
z@9FGRfkzp9fjuaI4g=W>z?Cp7Z#&}k{CD7TiAf$>E>J9h=vb4${<V-lm^@Lr$98H?
z);Tp!rGH<C6vLMImIX6{T7%mRavc3it}J|AV<^pypCK7lhQBfDXF={Z?I(67Mc73_
z7aQZ0B_S)N?s-UWOpDLMw8<D`;y~=Mcq7<VEyMgGK|a)GtH$;Qbunhu?i=qnXxN$R
zyb%3FW5!Ia0eq8GM-)~NX0e~R5n`p_(Yd~@H{){ilpL`<4zQ^`YB=Wio%n<DY&f6V
z&Ro^{{NPYOt{%|lCul(c<B{UTRimRNXnFV}<8I|KmRBCTK#e@+pmkcyguc3z>zen-
zza`3v9*by68Ua!=486fAEaZw_!CCOY!cpcDWey0!U(uRJ1y~jX)<hdm!3`%MG0{hC
zh#<sC?Fq&IQ#Bz#8cAk`yB|F0@C42q!4(bJ9vn<aYyNONDEfh2v51BcKt-spy*)CN
z!}WUesEaZ|x9(v>Xv4r}euWr=WbcK5pV~g`(!t^7mj^>9&Mie9lhdSM2-C{Q%=e3P
zumKy*?^-F0ykI3dkO4=)Qk_!?nO+_}-4V5ZSJfm773>J!Q^h5`fxQ21&DRgsPV))R
zC8dx~$wER8Zb;SaWn$QAG^)@X_kA$$nvaJd4x>MLA6F)e#Y`wl@z5nzX)5>ZWB#zr
z_1^>d5v}<*H5nW=F-Dlm&IEp(y#amVo~CdQIU@3aWTf?Ucz{yDjmWnKaRGH6d0yRn
zENC)$DI5A^YejZ!zj-4x)X>|br>E_RBbD~WF&{eN|Gk@o-)n!0LV1Cw65=`o!uQ(9
zV~PGECvBz~FN1mV6ZCbd#*^Y29dvJAW(Ojnv|MHU$_*z~Tc4^`Jps0I`PJ+kpD<o7
z<y*Z0^h*`lfolfnuGpQkYlI&9NuFU9Aj5~fF*z+&IskmP7`gz^!xsw6;GfT(N)K-H
zt@lVANCvg687K&K6Vm45Ngx|#I;@egdkMyq`Tm<VBJs6(bG=(ZFxQ4GEfIYMI8ihR
z$X^ZvISVL<-ybIA9sM6~bkkNXOxGK){Du7e7Ro&r%}Ii(fQ5X6Bn?AoZ?i&SY&n1D
zJhVnpXx~he4K9~ot^Q-3HrdlTkPbe&qhIyF-KQ4)vHF`)i?bM$bjsm{q`l~h6*v}L
z;Gu?e{IrI>gL13;q;!Zc2u76qrJA(m05g`+>TiM^Cc$O%eN@h)WsS9v!P9&H28#JV
zmU+`MWp>>x3N4NNdihQkH3kB|rMB3f2ELAKUtD1QQAzkCJ&tM23p!4HEXuuX-!dw)
zdWqUmu67h^o*K9)NSxwDu9RvWEg16gE4&eituG#T3Q$^~ZV?#FEfHFddCAF%McM12
z1B4<m=6^m;Zsyw>2cLqWh_^L0+x23f_WJzv!Uva~u(~B2WS_O!Kl^9MW6^FdYE3m#
z?fDX{k@mM3<6qvK(Fu(4`@(^R`(dY|)iu!u_9ZVsSOD~q<A)<!Q+j|A$R+GcS({|i
z+n`=R;8vIj45R}3n+@B5!V3cI{3eovfcDnJ%riCzIHP(_N|ba;a63f~9D8MN=#p=n
zgyRo9>K7~X2`<-xzQ=>VY<rehc95(g(P{`~fl0mAnW-<-P3FXBp|l7tEFp9x;Bqnr
zFYRKj)E=Fn*s8x-#PyugHQO&TZq^JwSz!1^>JfQ*0=A7iC-!VSNZ!)C@fIOk@AReS
zExGafWM+_?Y5M;|Z)CP9+)zSDH?N|Ze;tLGlZxd?KiVY^m+HIaHc=;Sp#fV0v%#mN
zsD?aBR|$K)P8cWFvO+UkE}bj<=-$Vo-#-15lbU}WIYt%#eSvWK=j}6ArgiVWUqRVb
zOoqV|t&O0SPsK;MXQV%kqv5(pAUNGLis_l7n|;>DUL(s`=bR6=*W&jy?W28zw==7c
z6MuTn^y|&mP|Hl^tqr#M_aqpOX+JQ1oMW`K_Y7Ve@#e0S|Iq=grA8L03D61qe2EHu
z)Hysi8~*=DkQZx&OVH!-5R}?ifyd1(5q5kNUCKDiMx6~ky_K<<F!aak#cIrSxc&gH
z^!?XYo*rNtcEtl`lBjO{o}h?pX*TiCgvaEMyZ*7CFqY$Rb>5+wcm|NnBm^YDzR83N
zvC^swYk;oB;hJ^^ov0QR-G2ykrUMSbnOgRYN7E(+#)ex8@D?R$=PI-1=TrT*dP*D{
zh{Pl~(jEFiB!f1bVfH$5-=&6BTnE*kQ56(5ppiOg{eIl{8I#Anj+l@qNBMFtBdWX%
zcE&(MjRerzE5QS@w$S{?3UhH8p}Oo^s?Aj&jJ3a03hC!Q`F<Ps+9zHiQ7#y6PyEb?
z%=K&6{%mJm>)!SwJP~+c{V2YEHzRAkQ?!6e?7_x+-^<~)hfAgG_z@(7RcBrmgg@dH
zJ296N(r+>6dY@0X%FxQQP@-(LUu#tk@I&U1&;Ar$X%BsIKDi;R79EI>ba(X!q*z~<
zeUF1UTSOZ&Cyv(^9=$pxpQQ#eu-A?qBi0lluH0|x{rl~Sto!Q2xVzrJfSbaCq`7Df
z;ijVN^|uacF;-xW9wvYexFS87m-GUo=fP3c!$$DbVQixZaDT%+^|>AxmTa92Y85MU
z&H=H17<_2;uy`GJS!$pCf~<)HWF$61=u;7tx`{y^X%bZ{3n(!|TRoC%zX@NRJURZe
zQI0F?eg@k~Yr^3MM@>&r?uaF8rw94SYAuo%LBG?69@VZt#$Z~E2f$-LpEtpAyP3TW
zBuk4HXq5bz5|KJGz+NJ#r}1HLy-pL)8-%%T(+Z`a&jo@I>AIPP@@D)mjhG_~%|C>0
z$5xtMK`!$Q$Grcv)s+pfPj*JB%<G)kCc3MP<32QvoqbTN>_!XpYyCL&4>4l=^RTQ9
z*=sziRwXlNrjhVy8hI~Dsiq)<o#3kGyD?^y7wz5RcA}|Ux<P^jEXSDbM`W)D#;duL
zpRg0wdwfO*{<p)OH38(2>OH$=_pZV*9`+S#=AA|nuB%&|O?yt=?I*t<za7MD>+~hN
zIkL%-bb%ilJ|8}rf-vyb6vZ`+8e{#M-gLJN$);}D-Uirw@_@x#@*wt(KZ-vm!Fj8k
zP#z>UXif3J(z~h*(dMSk#|!wR#NMoXcg<fZW`){PoejQxb*g+wJ-$;~g0NKATYYME
zx`+R_IAn>S%~fd-lc-har&0LB;7IX+BE}4Yq6Q`^lc0$PcC?};T``1sk2uZKJk#&s
z_%|iDb$&ECd=sNxGUj^$aQPGJ`iRY3R1f%3_)lywCzMB1hItdB#740ZT?m(_aOSur
z>zv>XpMMWpL9`ivjI_tc?x4-&-YjDeGt|cDhR13cPE~a`FQ?-^v?v03r4nW7tye@q
z0GoyH)P$TR+MC?Q?zd<?L3H~=vsgG({r@W0&Jbj6!_jJqR)$bSn?k891h7;3ww|C>
z3rlL~R8W*AU+Rb$Or$=x5-&USPlZNT6hvGqxD1_4&1do(g2F6(4is_C3uCpxr=S$0
zU|Wf2K!}Mv_}cy31LN+xvG=u1mcK_QW4hCvhBm*XHrK3-1FGW(xmh6Z^IS0oyJw|U
zyB_z*Ps=LJrmOKdSrx<zb`X?yu7q3<%@IXV6|HT~&y7?UkrMq+Ih^(Fq5{o^vXt`>
z1$I13UJ2iCGw-^<-S5h%qWZSPV<w|xtE@!_YLwR9(8A)_7+<K_9<ee$R`MFUi3;RM
z4Au4gQ<wh~d9=2A;Ox34LE2y;J_PV01epL-1$!?b$Q?+u+X5s;w%5&~FrA$sRqT3&
z!=J8nQ~JI8P7fAvl2>4Fjlk`8Ieb&C5K4ZO2{eGS$37<fH}g4lGmzHIx5>L^@f{r}
zVZy~m#IfXT*N1$7<%dIaAT*ap$l5<S=eaHzeqfiV6~R>w7&T{5lIZS!_AQ+Bh{5;S
zl*BYu%6V2FThfBdRl1!7?3lt7(@!t#xv&C&*!W;^_P>K31&rjS=D!}-gLwYADD2ji
zA?oItTWf@WW;3c9{t)c#zl<)JIn_gcnF~IG?VDZb=<}Yg9&rA`p1zK9FD(mBWD)%M
z2xc2jTSpgj*K$UNE9<M7%j~*zbSA1pHe!6ZB4W;kCm7$zkE~JOdl}+m;6#6gY;V2{
z0vcb>+wi@*lj{_7LZTEkkH%QzvvlYSsno^+)k;$lldxE4t~)w@%zIZAwR_p1yfcLd
z8+|YrwprM1)rllo&Z?W|Iz#FsMirV<!+qW9^vR&A&or?XwyVxv&eqxmj-29TFT>oq
zYSL6V>X@`-#R#hBmt5Ibg>WvN7)<GnIiS3KCFvkIxO1`x07RVx<b>i6{z5SqAEtow
z`vEbDG1t+zaDU}gqYHusLXfVQK{Y_Ah+A$5kY>Xb_9I_NOKd#R=iUJWossz%?uM=1
zbi;=jmp=gNZ8v|Kn8RoNi}!e-)+ywOxB#w-7F*S_{x*%Za<uZZeYQS7+|#M<9_sUI
zOt3z#^22DnXzhChFIjCo9p6lgUt7geBe?|HKaBqByg=~C1xo**c1R}V$m&JF4o&&9
z#J^2u6V4I~&~`=ilTfSC7YkO~$17JC-oIVF#fa9PjqD130%&`gX>~I@yVol>%H{Y%
zt4R!<W0j^5Eer7?Y^$P*CiH4aQ$OILhqI{<>?5K~5SNdRB6Bes-2V8C8L8U%rsx8l
z`MafRp&E+ZR0SW8B^LRIFlvFw=#(+nYBY><n7?aXlS{kk{9C)U)7~aKq}43AS<o1t
zE0)zz=~-98g|@06T*5jMTLP{WS1nuZHt(v|>#rK(q#m;y+<{|kVR*F~eP4)+QsC2(
zz8WndhqXw@X5wgfnr2ylHu2+Y>jZBtr(d%erJ(ME?>EYeo#%8#hD6ciI`q$=RkBnX
zu0~%wrbm2Uny;d#E?<OjD%E(!&zu!w4(c&)N?#1LwBSBM3bT{b6F>p?PQsLWrYN&n
z3c+WJLiC&K0pv6t3i|?>6{5k7?jEfQ1e()0{9PKXkor9mOyFUMi)aabJU*<6HoW|&
z06QwvFMtklFN3=RKWZN>U8#rpO}{+7(oun*M|BO74{j=o!A@>Ywe;uhHG@dIMsITJ
zvYH|(y1<&lOoAGf=1Cn-Iqae-ga&12-5S%xieDDQ4o33|pc8(1OV*>W*tjNc191w`
z-W9<`!aj$mr{khgz;PfpNDI)3)37Hd4uw1ik<qY;UuL_k&dV2t?ne*Gf<qwV+r={s
z!{LTP<p#IvA@&mybV;0s2vJ_l4LfV0S{43vqXd=$MDsk=jS+Umpl;-K3y>0%7tM0d
zbNZI{Ou^Zn;RSu#1T{X<+ok;&5axTf#qZpCB9xGY=2ja!jC-M^4H0?Dq>1l|-1dmm
zag}&rrs)V1oS3ftoDG9L&M&@LZF3Qz-?oIxKjGilgzJO{nuw}Aa$6YibQ6Z6W^j{x
z9tYW2TJkC>e;<hPt;#o?7ifq~avr#<JZk9KSbjEa9kG}h9`P&xA&75V!hf06!re+~
zE31z9`Q{hM#1=tdCQx0WOo|>9c};X*-J2<&rQ7xP#IdKn=0!E2!qS6Yst|Geuzm9#
zsV-LqL2&f~FXaYt(De@B72yVucofwBS-<QAw8pACsaqtc_Gbgt$V@4*p?gu5$9=>-
z55yLI7*+i-N;x2)HFw8|>@Ehk9irmKU8bWM`5~_=?hx8dKAVia8dPoKLtvjG-9xh6
zM6E6Xc5W>d4<4?Vw5?CVc@y*L5f*XFJZ0n;Ei8-{fy0jtqD}Ieyq^}BEbkJP5QH`G
z2XI`LTLsP_4X8lnO2Fn3F`c7h#S63Asa2LdWWF67hKC}GR)4+k2))gSv6&6;vc>iZ
zpeK&kLq%uh#INA-aZ}buAN&7Y6@6095jO5_sH^N{wL(Mn#W9xar6waUa-(}r&mWj1
z8Shv!S|;`nU-kGPijLLh80(s&a^R`?c9G*D@QC4ZYu-RrZpAqIS(5DNbZl^e++5s0
z|NQf>r$Od3=3IiXg#5kQR;oB27(g4@*DXd)f7lnhIues-gPUCKnXL34y0S01QJ;v5
zO4$;pe{5cp+1y5s+1nQ`tvBg7!-rLd9{0C}oTvb8NL(Hjs`?uK(1_X<ttHU%v1Ub{
z;b|>A5+zGL;!WLFv+Ly>l~Zgy=R<66pm(M9Rvpx1B=vT?md2&ir*y9#PRphOCT-fY
zaR)r89&s$}1o&5*jrYz03i64-;^G0IY;X}3R#Ps8V2b-BO7+y?<xOCx)dAA@;Ff<v
zX^+Hx0}9i{0{TydwBjW~$qY()5H)2`zjUB!I)635zDvtx_)bvYYk#&bL7Y4pCsB-8
zrW(|y$MPvo7NHjtD*}RWCFF|g3o5#l5?FMJH6bReX==RN;%HF7=URuthaTdQ5;*d5
z7C2c0dKR-N^4MSxU>1}Lcr;+AO^WDe7R<h$Uqn$r!Z3_5BTW^zBj<M%1aaGB2!~j)
zb7ofaC4M`#jGJC{3`luewW}Z4)!(*NIQ@yGo82c}^u~4)hfU@WX=X6zR*gjQc2rvX
zeM!#o_>Gs21ty~U!%e(zF`^la^7x=!oXz4q=e;<7i>Jh$zi#TSRr2ngxax84A=^rA
zT857jm$+o3IJ<S57Zy^XfLFKdcb|B`6E=z11Mjn(V$Q0p{HM15BR*<BvvkdJ{!Wic
zRp(CcJj;6*f^yVP{73rm%m-@3Z1jEaxwv3r$$fdJX}x=2EFzOKcxB{)b4hpP^r=ZO
zm3xZjRhPoee!xttagwHzEyubU&Nj1lbAMNtivM|wxKbL>pWI6};~@X=shQyV@L~1t
z&og<Fo$jJ%<hDc2TAYm>par%<@L5;H?Vm?!+_DE^4Zwe6`MbnvQhVJ#JO?TvF(TWR
zLAW}on63O>ven&TEPf9k)<ODtyHl{|bmci6K!+teZ<V1t9l!c|k)2KIuL~s+h76i!
z;kamq0jtExMJ`)3*GZ_&3u>hdHcac|?gk~p9|R`V*3ld8#3JZ-EIEo*co>jI__QU@
zbWAR4rki6z{W0O`clk(NlTB0}yt1OYF1V{g;)W}GPq7K<gRmV82;XocKRUN9qaH&L
z=?m#>O%B{;Ccc@FPW(fw7MTIWYO2^0|1lB7hBfaPwEm~V3%4iCX4aNr-@1ya`Yt&L
zLgceS+UgyW%r_bu-P+E3eeTb<YU!AsF?TIjaK14LpR$i-=stuGZi{7)RJYiSCJW6%
zM&hfEerJqDo~YYw{<XZ{TQ?|CdFQ;(CW?8Znvi<81pA2?*cgGeUNMVPS@(fLdY%y@
z*F*jlT)ArJ2aU^Oei@SRenwBN)UCkW?a-zDe}im1yJ$5>(4bksn8M0;M5%gbk^P*j
ztoKB__n5U-$*__mtF`*V-6P%_G&N7K3acV0=HDZ!{l%7<&MiHM8nsrR(l^Ym><zz7
z`c%Z8BDY7t0{FNy;P9qZXn3w5VqbH}{IH1Sc8m2H3)_Ww=JlHLN)3^<xukEDWVzWR
zks$9Q<JgdKcJ6|~8i;fp%UdZzeA+XM^wzzqu0VNuB73f;cLBU2N>;lZo31{;{50HS
z7PVpns?yhBgFbFbWED<AScmF_$*EY;)QVi&DaZf*gG<ohdU_$^DA*W~1^Q)`tNy<X
z?%#hzPj1-lUD)I?>l$oW*;S5%ZP2_<OS4&`_drNKv<TGT&VtHS@RHe&{f#C{O`#n8
z{uLO{8MXzWYh=(2qS^;-_<vY(S)nbGpxqNeNS9j4f*K6m`a{zTP`}>ecA&@~Mi0Bl
zl3XT-?>kg__^6cR-@`VqUWvUYyhfcEeukvo%VYirADunI%LW*=xJCq~g88ua6#TN&
zB-snv$cxD{t{brd>Z+)It?QSYM3%HVs12`P|Ko&OGX@9}d;E$IJH0`B)mlWiU5NU6
z{u}V5*<m3^KWQ-0wfTdj>V8IXKx(Hx^PQ{R-HXK(S<LInjY3sRdbr|JOQ83`n(EEw
zW{008FXwBV^8DVNqA*849+;vKy<dU}_ifEy|MkoMlu+XZC`0<8+F{&aV?*9D8$R4(
zy;JmlA4)}1CD-5poxP(Jn<<JLI1Y3o-y#lzDw#Ta>p>q-oJRbQF)W$*Ey<t$Cl}u1
z7*J;Gd*hs!YmRo_c)t^Uum$jm_~8R;)0mleOUr74I%>BjxtM7#;(9AG9+a1T6A5A_
zB9*+m<q?%!XJR<T#ewEelRl#SI~hzU%y3z@a{8bqOS^c(;GkplbNLpKY3chASE_L=
zsoCM61mknUac_Zq78g7npiC^Ef02Lm1K0EyY64f@Kykv~wVdFZ_PwHJOC>NC+7L;R
zuE~3Rc0lrsBSu1qW2vy^Z7y~A4yRF5Ft{fswzY5-l{X<-xU~X{QOg9P3FTtLmAV~5
z=o?@ewB97JpNNqZvEdY?pGyq)TtFF)q(rxo+5j_U?-?AM2S)h^`e8#p=zG+SkB;1+
zq@-Ag2Kk)2qm_-l(%DXE^=^UY#eM%(LmT8SC1UnkiTceWzAJKTX(ZH?3H~JIzmz+%
z_h<gdGxB*Yp~K?3Xtn<}eK%Q)q!bP+iF(3K$En~Yr{(^6EFK?`E(x5|i;ZU(xS+XT
zB2H_`uE78rZ>=7HtMBR2P<vd}w*_Mi8j|?iH1Wwp(HUv?#D>t{6rEJp5xwi;QhoL&
z#P#r7^MTP-6sIRYP~ud|Znga|%!4xqN&*YRneqLlvi8vn@Wh^fI*g2!xUq$DfX?fS
z{qdfaipciQlf3tYiJ*(4Zz`uAk-8Ztn#}E!k-8dcdnS}nIwx$}L}Mx3y<!YI(G_;w
z4Q2rzmw!dj!MlYdSGALLl+ubK!|bkG#@?)4dKz4V-p{|W96eE^9HFvUMTi<>z4CW~
zZ&c5ju%8?rv2L=i8gtkI@)o;Rj$ZdT7nrHdm1S|4Y<|jMxqWpz2C+3{=_2P$tPG{D
zMKhX`I`OO~=PzQ~xw$h2HwtHMt5haFoHz#6gUEv*3@Sj1Z`C<X@}e1vqUYlKHX$>Z
z>a~)faQ~)(qg9`<9ggvW+3P%&B{~-9pE|jGHWpyxsf~+zF0}Exh9e`C+8^sYSFejs
zKymD_1iX}t8}}7l_5x;68Z=@^JYlM}<r5&&n(aNlshCD0y#*R%e_C3do4$LHlMo=A
zPu?^7$hpZ6J1KsoOxbJfM}bK?VdGBq^ylg7Y&ss*KWNTy6*Y0=Nvbpa%AlZ#F-Wtw
z$VBv|g!8#?D(0!=yQ!4I_`Ax03UZTTkMaE4K^#g{1r>(VocLjsaZUtvc;r5`N$AwX
zgWnp%eelKFH-9YdtJ1M$(3jf*>ncrK?sY7T3PdR^nHSLyseyIF?MtA)Ai}l7v}S*T
zu7fy-?z!`uPwW=>>4Z|W@UjiKxH5`0it4X6_+RZ2FPDxxf*$)ydFw7v9<lXPI`Z&d
zKxabLr=nZ72AF;6D1!5LBu30H>+|M+S+ZKWuPgNIQh2GPIR<DA(_EAkUD`h*)CQQ5
zzhz<36}H?LqG|)vu)^=aLR40;l`XJpVl$naOkRnOfd=)pn~WS{7@QJ~5M9_47Zl^&
z`aXJDUX5g*;kC_82ebNm!0Gvd*R7Xc9OT$MehF6TXYt?$eqbLB9(tv`TA6mu&tV|d
z&*Tl~-^LTfr6XzELO<WWp$d*GzdKJmT(9$Y=h=SDf$rVx@J7Ljk}sddKE`~}!++i*
zI$JH(<?Ddj>6x1D!?D$^!^BOMf$-WoPdmF}cKLswRSsYNtUM8t3NJ6Kn>)20Klz!#
z%nJx~qh|o^5>Q;tGy5%${DoREZPcJj{}f_1j+X#3@p4}UP%F-nIA!-l!B>lGO#k*C
zKy`%tlUs^shbu<V2$MnFhLwj+0<k<xowjjH)M4#`lPB4bDjOCb<~7B=hd-*dwhC&s
zP8XFeTbh`dQte1qUTUp#2SiJ;L7%~p2PCheDz5Vi7Wry&`D+ua`rO3;72Z)YKpmmY
zt-=DEW)3&a^hS=)Z^%(6AcRDM2+#m7O0nipFa-|I=JH({_haeIBy=t)bNcB$ftlVj
z7vi$D0MZ~p&7o|guWfCK?_nq@yJOPXfz1)ux;(LdT!Yzt#FH?52TKJXNpV);H^>g|
zQCoQ~G}|HkD)sa|m?_^)1^x(z@z2>RYkbb7Zv>1wu15@je(mobDdDwda+!cyeEca-
z5mYl{P8QlH`p*&IyF|j=883VwP;k&~#=Q}-Bg3>-#kz$KW2)mko{l^yx*}=Ijv2bj
zm`)@&Pi|3m!F96aBbCR`y$|onK_Aani!#xUHH%2LA?E+|n*XjXC0ccQb&xN0G5s`#
zy=u`FIOdeQ7a@KRC-t9)^{wh+WlGtx!*Q+(dXPVhRt~DgXx@!}zf!infBc?j^gdlW
z@{hTy-uZ)i-?#K_m?2sY<~?e57?S+E47$=XfR?fGhZm=f4gaVFnro6*p&e#z>7J8l
zIIsJ~l~2lXEH4`=MvC7>B2`p^&_rPt(B1-GASa?<iw(c?@c!9+k<=m;kjPOH8z2_(
zg8^<C%43W_K$=Yo&d7g$Qp`ZMtq{@`1?rKns4cL1(m#HM4-eTx;VNj@ltHX$H*Z>p
zfLC1VidHwJesQj4+hK*}`U#<xo$=%2-@JhhE)W=Gvl@^WwYD755fb$Iq9Nhie8p`a
zYIPO4HoXcD5jC5a11qh3sy?@I6jWxZNp<0DPP~{lELtP@G(jgZOEwr;P3T$T6$3Nk
z-&1P39MoB2kO6peJZ9`1Zg{_1@mn1C7X(pcbtK8%!PY=aOCzzr^%mFb3=UzfZ!<sc
z?2<R274?Pnin$LyU!B#{aTVihvho_LK_-e0&OYd&W;B3?A*6aZyGN#z2im-zNKEKs
z2+`2xaNZET=Ubq~Usl`AuRzzO9^%UyYO`L6TNkAtf%oskLD~Y3`=U;-d~!a&sIL48
zUNoZt`YbMIO(zDQZcdT3%|+1+y#F9qZSeRI_P6h4@cA*Pqng$U8uw^{JsRjU%l(h(
z!!mURcn)ez3(Mbe0zMR#UVk0)@YB)3GF&V-Su_(Gv0h#IuLd!4M(plm<fzK9^Equ)
zYV|eQ-Tt=wN3mW~Z@X=&)uDWD-g4{KtUk)sT!Y)A^1{<5;1Ox09S71HJSKowUZS*S
z+Rh5ew~>I0Gk;xF-zORObK&0@dw1jo`vWzwR{ydh?!O|6>J4&R@jmviak)rPcrfu$
z;3NYS*A7pf|A|tX>NM|Mfo&W_RoO7MZRZoBMUP+DIJ&T<_8xP0hVo_m{bdXve{X$4
zJj%aGDek*9CJDt>1MbIZS}ues)oDvat1<OF)U|0;i_*Bea^n8cJ1zlk_D%Yov<A;r
zr`WYFAKu7+KTJ@os??E@pYW2hYHoJ;XG&wC5_Nby0NTnqAFC`5hEQ*c8sX~s&yZ~!
z9?VzldiAEx3FUQQr>2fY1Y-H22s#6v33l*IY;Do8zmgE|x5u<JSKiKP@{xpnJj0FE
z=W-)}pRSE;ILMv#OW8#5Z7r9w0jvMEgf9e=NM8?$lp6wlgWFE`{P>jauYXCskDcgb
zVKU2}bwvJ;QzztGps3ao62RFx`_NC|I>?`k^o+im20rhl^n`skfBU52L+B-N-AQi`
zHGRxDIS5B{brzWw?a09`mS2FS7*c7x9mO{Sc4Dz0n^kL!_Q(Ayj)8A13=z~3+tzQG
zX;$4FjA%V#!wFh2_kAfQ%^+I?FA**q{`w^Ufjn-_R~TD?`&u^TScf^Ftu?}6H|133
zf(>2*$ybGrTU*?HvU;m*e~+rY*H3_~(R}MpILbSeJR_tv;%4BeB#yo6DU%ox?OHu;
z=Z1%WSw)-wuT=kOdw*PDwaVcFZ+zTZF|Md`RF(5_E&PTmK4SdE&EKuXp8Hl5GL_o=
z41N9<h#y&@?{grNpvc~O>ntkHnnt9Z5m*YFPRU)B>O$fSRtUP=T4555|7d?QU`-wK
zB$=9yb-X$p8}IR6-#@UX3qD{{)!OYI5LEXbepmC0&lq$yZy$VzmLYfX)7(<y97qel
zR(b+2YaE9k$`6M7rE-Hf{8Ivta!xn$lX?|_jl_?I-*RV@72Us6I6&t>LD3`zfZ=3T
zfWZhD5BMfocr<Z7EFV<UeuaMo6jMWqQDx*lrc(CRlKIB<o|_}^+$xQ1yh^xRW$}6z
z>Nsd2+W3T4MP&MWUEY`O;td*<yhOCmJ9%pSY2;G(eUZ2K@<>Qa#D-BnxrN6^`QgG;
zG(M?O{^hn2l7)s`m1DZi<!$~wSDk;mhh8T!j0f58fRKW3+JOAtIrA-Jx2~Fa;wnd3
z?YaYjakq*KmN)KLh6k8}%qoN^PZz&!Uar;?O@fhxiqVHMoF}vzJoXSLg5J}q0fQI9
zc^@%apLFLtPf;^33no_DwfMuxzto$NJC7&Q&GY5wV2+gMidoR)A*QhD$^RXI=WDR6
zuN2P)P5Zg#H)`IAa8xQ2<-aO`7KzM*VH<-7qG<P`w5y`4TM$((%U@LD#8^HsYBKg5
zoIMahS=QIdd&E^K{?c>OTe~ay>U=_+7w|;Py#=a?0mt8rKE=I<Un9vN=$gxAfZzR@
z2-K8O0A?G|6DO{aZ~u0l=TD{w(Is*&a<pQ41JE^Hmw;4K6lb9S8bdceN3w?FY0o6B
zdokqpZ4^LM#H9J55M7o5F2+_nl)n1ogB9LLs~#g;lfc(CRTXB8ioDJduj0Hewc0Pl
zE{m>&l`jgwg*5e`1vY6A-+7MVXYf+K;cDfsw3R1U>|sCQMgmbr?M;zv|MYQ^lF}F)
z=UGQ_i>w=KC>hy^%K7TGk&&0kiFDO@Bxx$YWo#dP(a;-LQ$Z3XW`2xAS1!D<8HBSu
z%-IW0P4{^=B2J`ZHW7l5IH4=<iVCabpGa;X_xEp`@!~bqmvlHdM|k&Uf~qsJl38Q}
z?`0D-NG3ED*;ELs8`YOAD!26dt5%~P=XQsn`VYBN*}o3?;*s@TgM~MK>)z@f6gSG8
z_^TKCGyKMEx}Q?*Up$S;3{ow6k&?`~NK_afaV&_K&s$`r_&;Qk3C7d}@R7{MBaHWg
zI%KZB0PTvpL%H~pO1U~ubTl`*cEe^{mIo*VxV1H_*mmw%-)z7fVrSM|l220x1Uv_4
z!{p38G@_wJsI53j1zepI|3y=-RhREY#ope`jg^t&ce;Om-`syC|KxBWk4M<&;&eU8
zaAcG^@t;NPYkcq#bamf6_{{MESaDf{sC{x46-KZH4?q||*PpVFPo=$J$qvq=Sh630
z<vaUV2#jcTA=H`00j0}^pvWNnQ{7=4DgD8NP>8GQ?*NO6Kf*W_>_>`5K$8gvH8}&6
z0u?>;C<~LY=7l}IDd^6FAAv0s^aSuCw+hN^iFZ?K0~x$pidvul<|_N`Fe@@kLXI3`
znVx(TKk-OxwJA#CfzIB>?9A6#UymtJpIsq5b<_ei=e}(+B&*g=1A=FjgcLL|`t}Mb
zCSr?K)p&;>_?2wF@+A;QUafiTWB=Q3S>+ha@tsh{Yy(x5nA{}RM~mYLp;}X0AhXQ+
zE59nGiBqQ4QE3zpR6FbKcXjxN;8l-k<EomEK{5{(347GMcZHx0@xEe2@O?IioOrcH
zDfimPstG;Hg1}C?wDtv<PE?6Et?U+dZup&wduc8t3<Eb(x*g3$|7>_52K=gbfC}6R
z%<MV{XM&LgvO*dnYWPFVqzw>R5B%_1C;0u8-a1P&bPGHvwGWyV{X&V6D=?gKw*+H_
zuj&PpJ2KfXF92T9rREQEu}UcBsvz%c+^0>V1kt@{W8Uto;6o?&Dja*>i%{`F>Me7k
zuUqUAL6JS#T6nQpV#q;-MOTW5JXZ<o!Z#qyb-SkFS)$|f>@wiiLi68mS1$zT4K`%H
zS+#yzQ?$J|``!}kSq5#c!}t|Hw`s}h2SE}YMRsSdn@5xfg-)pxDb4hsck*nM*4ak$
zUIG6&M$V7_**|hT{x$MgKTR80J$3hVH<(k9+b_1>-C+6PM$>>rJhlY@fAcl-S~$zU
z-Op)ywQQ5-Cj_c$xp6UnwVg{eX}k2lx2;YT6p_X-MRYR@dZ4630iooM)$%l_nL0~|
zNOI>K^Qbwkll9z~318fdzVK@)ywF*9Zv8Lw)}-U9IYu&5y1}RCcLe61iz#&OUf*`b
z^6li5Ya><4>yvQeUGx6+QjCV10tOl)2)9_a^_AGc$6fnIiiKmte+b_0b%F*QB@`Q)
zb3u#c>?s!GptB_C=Y@z>I9_p;;0I<L=tUllBuu-$%W3rx(qt2UGsIeLCL2)Vrf)LB
z7;s-pFqT5Jh>R{tkozpES#@jGWmC+Q?gA<S?A1e}O#m->3^Jh%igk>vqj!tpniH6E
znH3nvf#bKqlZ&ElpaW@u|8$I<H1bE0lzeIp*o`>-7DVlGhC^N^Nwdi^;S+yol9Gpn
zJM0-RKxI^@&KX9m$xCJ7)z)3|1%oY>o17(>z0cVFbmS<;BGS-m=?#0~q)S65>!^oO
zQ<QIohb(fj)SYyxF&{5Xp9%kF$JvN+X|)z$E%&>3_Rlf5L0^0=ZNkQc;R}%eyq^sB
zs~T{7AV8%^RecSpoBmTq#2uh+vI9e2i(gYgme2i#LW2jEak=NcHc-~(-^=G~Lo$jX
z5I7@&GU`+d2;WocZaIO~l9vgSEyXB-aSgjxQo<e3GxF?&-W=^|6brK{U#%bB+)X@E
zyutdsEQuJr@FRju%wsqB_);x%&LsXNn~^G7cAF`VRbGn)53uCPipA3!o2R|0nQvDv
ze9G@l?rpLy6(7`dIcEf#571?<YKVjYa0eeUtpSZfFSdi1W7+lw&;&Oey<ijeCM#HH
zgO)dqGDXhn;EE#Z0qZTG6TxQ{1{#VyW_>7PjDh_KFPrqcKV}#uoW|+^FJx`L@_H6o
zQX(PIUuGrdaPx-(Qfy@j-(*~`GI?xlRs;@X_1Muinn^nZZ-O6BapH+^<JTYtPb8q2
zb6AQIa}(DHc<3TZ40=TS>O7<J&Jsa^TQU8wr((=P&MweEZB>|dhVf)bc<^(<!i1$p
zSS!QU+;QvNC8J$)s2$B*`oj!=R_yw*^M{R5LjI~{P30hK?sgktqgFP8_wEFiV1&E@
zL%eiWZ${_#;s{Y9+Q~{{Li3zg3;H+~iQMCUvGN_&S98*jaiyY$01(f(>>I#)N%F!`
z@oMOXyM!HmDW7;37ZbqC@^Z#?v(%%-<;yOl1G5;KToi|Uo)gGDFa4Zjy_7={(Wu@f
zgz7)1$my51!6V&CYnSdK!w8rQ5#&{Pv*FSn=PyF7e*M;0lrYdyY{Iy#$a@rNN+}MV
zi5HZgKnoLBKWaY5=Z7+Kzg~J)<Mda_i{r4`qLsVvj_1)!pY^8nHpZ1s?9y}TDr2L<
z6rT?O|H4H&Yx(a725_4>_GX4=c|gnU>)Ag5TUyYv)IRlvXg3oTAR7kAK9rs176Z%t
z)zF_(qR|uA)cGR<TIi2cJS&j-7A5>br5yltE>0s|MZPyDLC4SLW;dBguiu~nhZw%;
z7A|=i1y*AyPevq7Whe@1>E@tYtZ|JHpi~MLVf_w^!lVg@h5B6<fm<-Y?hQxVhS_X`
z>ss)YAO&3ERi{}y^XtcYhg_r3(Q<Ul?cUrBP_(3(4bG)1e}jQ1KO%8?*30=6HZ-87
z7K}{)Y{YiDv$OKWFQzT#eD~w0j#G`GG^10Xv9TZD37oHWTY7_0%eS(49h=H7(Ik7_
zYJK0`x~^HPm{khs@Xg7Ik`{1PGk?_o^Nv6-sI1!hH>@tVvMskW<nW^mJMFxM1T!vW
zG=<DLubC0vUZR?d6r+7DC13u`GcW2B?XoQ#l4Kb7?DySDl!gS}St7iX{b>V=$xTaM
zw(Yj(kH4&TGg-~ii)D13;&}kc6YXlVb(1;BABn+)L|G-{o4H?4c?!(X2n+OzwYnbk
z{J*`rMJM#0_SV~$P8`hVmLYDIY8WK-24yjA=i+ywRpcv==YuLl^Q20?%QIjYz7<qA
zf!Zb$&iF4Wzx8)sjGvHpeW?tr$lUkbZ1iD%ant|UR0x~F%mI=t6_=}VRca3=SDldB
zFEw<EtMt}nNF~6G2+@C_YRig3F!T%UtJDJxBBdkuNisMTLg^$Q(vm}$E`TJBZa&b?
zjNQtUUA?0>L7U(nU54G3Ar%Ac4?s&~4=|>C3;<Pjn1!+J77<U-x**(ZVUUb=Yxmj+
z9uk60@_y5h@Rn*3v0+H)zSZZv_Dj=bPp4@2xi+lV{0Y+OE&2a0wGw<cwI(af=1<qy
z*g)qK1I|nevN1Vse4cM!^bJtgMh(1vQI7S+u_fZfiKv~m<W<vIBl1t>B_q5s27=UV
zSFsPVF`~-asUILN_gnl&nUaBs=VX1Ui_=(vRsc{2X;o4Pq}YF<{vJ*buO0nKcd$x3
zp!^J%<pNz<eZx)=4S2wL$uLt()WeF)P+HQ4?+xtP!A}yj&<+4S5B)jQNp~kMe3RLR
zTi$5(ibi4rW0`xyc|U@3D#nxhADGPg_3W!$tqFpO;zY)9RDjA_MEhTU$t%u(kwbr%
zXldL|J?!(+IF*K8d$^y+>)sJFy;S-38W&KEnkBqYQz7gKK{bd$8MbbS>C-o&9l~eR
zr(oiJ!3k`S5FnO*LAMd8DEp|zL7g%JLizH@%CM?9I4aCEI)4I8II6T~qyeDz^XXKi
z85kbnxq-z4U1A5N9;1YO$ch5!ysf9Xwngs;`7Er5^81ez{`Zbi)z~7%$_bm<1pmMN
z^I~z_P?l)709wBACp;ll04?leqmWWS30N&!zZ({}UCR<Q@BQXAvJZrc&oAW++OM3l
z@c3QGY8HU|f537i)5bAymu>X*t-iaL0|sp8&Akl@49hFFm!6g6?RBF-Jw-0Lm@OJ#
z;^o6<QB5S)f2L$gIe0yPEiid-37S91+U(7|kegrE11_%Gd08aa7vU>8MJ9IWy|cI@
z#k~Is539QsSWCp7qW*Y@gJ8chQJ<Qp`TItBYDjWF{C9|g{d_asd~ZSa#4C!H9W|Kg
zuXOf!(Cv~?pwp`Y)>ZCYOJ*@?C!6l8F1|jbX;b(`lJ5Mte>w(PB3OUg!JAG9vopJ=
z1!kXZJpARTZ{oM2CF*x;!G+aQMsd^euAztfR~Lwpb7S;RQ=oPv=Lbvqg0B&jIKy1|
z{gFS3pA@egdD?5*al!!<9Gft1uEB7U77&y^b!EXUgs*dS`keYK3Mvg=f(gDoE|;p!
zOIjzS{Im*}7Pk!2YF&VATrg1yE8ov-muL}I9k+u5z$P6SmaO$k7UA}=QnrWNXMQy7
zbmf-I&sR^+#)GqNIs&@<D)jVbGHIrD#MM&><+qGJE&|w#_iJ6Mnx>{gg}dYR?{3h#
zD{aKe%dlh}i-M*0O85Hx5<^5=Axm1~^<>HHKuo?O`oeaLE8!Ze^@CSDlC+dTm4)!V
z$d<8p86KxN?Y~;I+OoY@(bn)ga<1?1yMa3*TRIQSBILYlV-KuUza_G|(tGBrl*Z4M
z+#LJ7t<EIeN*vUG#xi8&(U9Ci?nSOVM~x3dE036+E+VfAzg4?Mj6SX7W%BVc1m&s=
zuEVJT=Q$_Rq9>y)j)+o!E=7Z`k*U7D_H)+~;7{Y-%uxo14*48h(m-TDtV%j^CZ^A~
zR?5#H^FzNFZ?0N*X)~|OC*2%Q<+qiMwQ}JB6LOH*a3uNX7Mi4pl9heq&MzZHPH4X2
zygyG{*UESyiba~!cf;TlLQe}U(l5+2qb+@oaWI@(XF51b(fe3l_*>N4zK++An;gk=
z<Ja=JGe|;w#n<VC!V*DY?DC}9;ltZcSg7}|gLL^{sx3jc_r8?P@(1Np#%R8by`=47
zsJ31#y|nzWUSBep{}mwnR=r2~^NYk`3VgosB2+s}#rD<?AxKkL=R=wlu6zz88}JG0
zur+Ny;eBw4u<i%%q-!sEgnCQ8bXGDQpQOY)?)uDM+9is=fNS-L(fUoG$D`pZFtE#S
zMWH4DEg1p?q|aqwyM^FL+U%fIYU5e4rH4?^0|r05;HhT<4}hI~LXV6D0J9)0M9Z*e
z;;NEK0J|47>na0)1{h33e5vJU$FYyf->FHks;mty1$GozCNc6j@&%C{9<ot+$_4o3
ztAPiXC!)7Kn*b2C5Q^()AkPZ1${~eOl1sF)j@dTRH?Wt_+QjB*)$Sc){FIikS8Qe|
z=`KGeOVXKZ!u^qePpZCa>dGc8!>99f&~5csC=ev*Q+cJ|Od<zY7-U^vX}xOG4lLby
z{vZvMOqd6RX(<XlmJSsrDizA}qYFELB@4*^N7T26Gu_93C)Mf}t6PN}R_RV9q?0*q
zl}g=;x>Y)0l}aUtk;BY3qNvy^Ic2O;>1fVpMh=t1RyhnC!_0EpFx%Ma`+WQTp67c0
z^GB}B)#v;9yx;HF>CMjA97QaPvLcYm%7#;g9uF%Aw~<GY@}#~FFxH^m=6wJ69QHHl
z7n?WuM~TC@v7;?X`Yk$po=szUhaE%yUMY^koN5K#1$o`B$kJv+uvf0*!i5XG7n6*T
zq(hO$j?|3|kb?s2Ui+%SG_EQk*immM?D0{N`(jS;4#S<DkB{ECN%o)v56K`80fupK
zNIGCR7kH$>`j{jKXQA^bAut*k7*c!Oa2aw1kw7$(N5G;9fhARxGL;6H_LlfV6<n&)
z_`hLU^M`AQxH>f0FVD$3_YMFH6${GpKY@CnHK9RC8WLH7qmbf1MOqMrt_nQoi){u5
zjm0!=<C+EJF*VB=6knJrsX1_<6TS6b;*I}zh;el3YcLDMW3^r2lS6&av}IXqXC5!W
zg2}kT%+t3C*IxGtgaJi+T?c*K>EVlH;t=MHiD`%=-6!~h2j#zSB};8RNQ=htRQhCd
zkj1!VW@uEbSMbdwmCCW@cg<_U!)d%{{1eqtPu*s=#%xxvO}ip;3hz7v7ZSQ7vXU|2
zo#h98Y^oR?OtU4)4??SnI{GD=%`=jBE4`W0#eaGIZ#<cf#b0M-Q%7r_^Hila87cyD
zzoNyW2IO9GVgH^eVy((B9gcKr*S)bq1bw~#y@T_y2F1T;MWYs_Df9hbSl#UXAc71i
z-LC5|^)VD=42vs-4wI$Wia}5|W^{8!;R`inpW3a0taT&O@+@MV7B{Cb{(~pjD;Hlo
zg8|={9pCx5!#DPyv9&zQFx$~3rQ^8~T&j{}a(MKgxV*!oR!6)|nILs&wEZ(s|N6OF
zcKrb8TC4!-zjIWr-}WQ-i9FP@C7wiYdM)>hy6nc2KbYVEutepiv#|0hpfaJ@wgWrO
z2S9HDXoNfhnzxfRn^l46VpK2U6Yd?(3^47dZ_Na7n}!LK&`!|hyZ*J$qu=^%4>_dY
z7rJN7!5qbfH4i|EyqzeH(RO&I+R^9*>_qxA(+7yH;aJ-thvC2A>!@q1*IslCJbEGR
zD&vv%2W(NcNuf<NCu#2GWX(d!fkPLp>=p($mz!?MB~8uxHG1L0k_ND_;7Xl74pjD*
z_ImXyf1OwZk?AhyDDtKNr;0cwBbpu+&?^Q5x8gyJd-pvPNo*W2Y#WZ`G_%!kRxI`N
zea$=BRwx&61azEx(HH7*9+Q32fPKGmlOX;9ercs=OY(N}{G<Tq`-<hgK-+<X2&OEb
z8!?d{kcqih<aO`nJ5T~2d$raX61+O}VySAj%GxPP2@CGLdU?|oVx?@@-X3Fg`eHUT
z1={pY_F-!C&a}eUd%o(Qb$uLB1){egrJB-tgkN@gF*|8!dHi7+nEIzW&H+YEk7R?#
zcFzDMeo%-0Z3i|_YZ1lg)v|i1&LXNK2*BRQ(Sm4Z;zgKIhYHHrCJPxN+^Yh!i>8|v
z{up_l0qZ9;i3*DSm10;gOsIyGQ^C%;*sX-}2pn^x7nOlN5DsFPkm;@QC%=nV@YyG0
zM&D{f7P9XL#_XQ=1}!G{+8N^YSlfMnFL*Nt(=>>hUyit@YA{v}a;`|@b35`dmmhRs
zoSsm%TI6=(3|xI&hEd_hjA`a9VhC3+h_Y9%)q(qigokmrkFOEyuBbAC;y8ag=pc%s
zaf69?VUrR&mHS$;ITwBT!Bo$snEJ&xVMqtMzY+J;xBodYC^L6uwlm)HBck~-Ilg5%
zM6i)Pp{dYd3oDw}DOc$nwR&qh*B@{r>t=}F%K@}sO-H0xMGA3x-e_g5b9|@8+~&g-
z^-*i_Nt<h_*J-l#Az8Fev-DGxAGZXq)O;o`k3RPGzId~Fn9w(SxN0cyYMwZ-FJ3e~
z7~ejBdC1^jK|{D8^qCS-_Rr~udWZRkt*@IC$}RMEG9Mq}zYUU^p22(yKA^WV_fc!$
zn?gs!MKGTNcNom4FuF&&tESK1ncMJumevyZ+iuRp?sPNYx>{X!07OthgFq?G@f<>y
zilg1zEvHl`3e&!d!qb6z$?vFH3MF9BNC{=O0V`SeQ-Normwu4eM3KPMsb4Eriol#K
zuYn-Iv=_=TYm}WCB^*&aRixZP>I$)o+}1>(hBb)mWBa@C{v`d-Ul5Bt*Ucdd?d^KB
zUW?8C=k9fYb1Q0BYx36i*Uw(b+J?RVnn&g_>)3m)^MwHs20IxtZlE;#$xHvSxD{O`
zSchC&wVkK<)Z!_c=}+8P_={mDwiA-s^%FAu+%t#9W<>hrcoA-7Im*`|KJR#8uq*Ik
zzkJ1+yjN58vf0@qKT9`%I}n(>WOxyrSe{wmbJey_dqe*cUjb~k8X43RHe?OCRp(i<
z=PH_g4fb5Gzf@$UY>Ljp%~@uGc|9H5&#<!=zsgj{WCOspzWUNb6)>tbL-Ou7fedxP
z?W0b!uNHAH1e6iVn@qAuc7BHB<cdU5p<li(=8Yc?v?$IaM&DI|qW8w|_n?<$7QOUF
zxb+9m?GNwrp=JET{ELXQ2b$3$-R&;o5-`)^=MjD>7(B9-TMaEytf~N05I{0L>Q5Ho
z88#RJ#Bh?m)@)LVA{V$XKj92OR=sQ<DODjfWQJg5o7LPJ&-MYU2}Osklms$C=3tu5
zkwsLY_EQ>4O+Roj5Zf4zNmT3EUa3V4VZclJ1K)`sMZELSo%S11L(Yel_A#Uzus{jx
zhLQw;7}djudE^`3vESvt3R0gqDWO3+Rm9uiS&sexvwZf(sO7<)cWdUV4*!+}m!~bQ
zA+c`shrWVdG*JfX&D|StEu!L@rhe^}^|hO_9kJsCGmWvrVDd7iQ>`h@u?>SPa;&~N
z@<GGgn5n2~E_u(4a!*l>JKIl>FaJ<hwk7vWSU&Axh;+f`7qBneGXfLm-!5Asq?dw{
zio`Wp&PxTRlc;J$K=+b5-VnBZ)l$F-%!hC=8^)K@s@kTSPPGuJTkKt_rG50`{;ub)
zy#AR_PIHmQaRz?}gK4~+N}zrb{{H&4@6in<Z?_RA;uXfAx&wRnkNy+qVbkx1_bFW^
zaRw91u=(_mFLz(rTwhwN#Y&fP4rZ?z`|Aw8kdpqp;B<iA;YHN0idL9E^I>aI)Q7u?
zrXtgwC~#s0O+6cSvXVz%$_i@D!`Xi#gG@gwex4cHIky3mSbZ!zq+AV_Jo+$xP<GjR
zlt7j}1FtNzutU*Ntg4^Ir?vt411czZiNJVj=~1W{Y(4w%nB#_<)Pq-b5^%3oZ&Ytp
z?^VY~qx-pW03`SWuz&qLts{%T0p0sLU{<i%Jv?z8C?<Ph{@^rX{b6caL(SHw8K8qI
z%@THn;`U*$=<b2b#jR6Rp|GH6o3JFqd4BOiZSqsLw;4>=h!j_fh=iR8fad9w(RE<F
zbSMqP@NVn1e<-0TIe08OwvV{s#pVT1sUX-jy9#E+WF;JW7!vR%&7~L74lF5q_vfIp
zJx)Z_{sf}!O$y&U23ta>n?d%1n6pDs7W9g$D%M@xyFMuXN?7;u?gMQs%(!$?p1qnN
zZl4MD)G6novwZEYwrEQXjOGPVC#~rlG>QAO5z<XcD$~{eJ!24u&ceYc1tuuG`NZd{
zzMgpUcGyaubNQeneS>I{9xj!jj;D)<#fX{o8LY13hF1S?bBAQf+kq$lcr4~j_np3&
z3Qb8udus!2-G5-$-|O!Y5Bn32m^kGQhgy!>-ZNPwDPYP8ZL{nvEN<jy1$WCrvQo54
z&K=wgU>!)n`cA1c>rWgmzVdc*pui6f-CDb$cuWJi%$e`osEGd4#xU7ShCvc*n^FjR
zxlTadLaZ9qKWc|-TYDh803N;vApkb<2>$gz9)`^?t$1zCBTTYEVa^DlkrcZNJj$t+
z#+MJ%y$|%B|LVgUE?9l<?vsI^v#6C>E^&g_4Ewc&fUVo1!LL1cY`}PLo|h-KB`q6W
zK-mBGI~;F~T*%S{1Fsi#y1r7p?ns|O_w`LBOZ#7-KhRAZ`e1v}XDPDbAP1cRs|D^;
z#j}&yJoVN(ys?O9(5VTX%I~9xu@^f(O--V#FPAjk^?(E$-hi@_t`I{^uBAvSA(4)V
z!-Hnf)l|?wXRykS@+|^+Ujb*1;>axfQJlgl0CWaPw;;nL8DC=*DliEs+{-iYo$j5l
z#cv_DYYI2dk3Zh7Wfbv>wCZ}*5Po5cuwiZApv~=jG?~KH{17e#RK&gfaWyU58+5NR
zUisJQ4gPYKH41q4L1Zo>f)YD5M{hZ1Hiw{4R)dy)&UMBb#*Ja0*BsY3)v#4NeG*!Y
zUr(Pni(h}3znT5-o*Oq;r+ltBopJK4H~maT_SS{EHHJgD<!tqmn%EYn-&?^v={Y@#
z>TkKWmoo?qz4~Wnvz$BW2CpAf#6Zgdi-$Jf0h4&GLJ^4BNe!r=X95Dr`4eY@5Xm&2
zb^}9y0!o=~bBi#rOwJ<@0wFWTE9qOB*<d?6Q?sb*Tag}UvfH*1h?Y{MW+&vZSwK@6
z2vLeQEO4u~0KCc{pi0UX%qYOqdO$R=TDLzhL=toc)Zv3(><rG%Xm1h5;lHP{*D8{l
z6&PBM3SA<(b$v?_&gW~Nh;jfc>*j~`8~RPXJ!5e`vQZ;Y>#ZikNUt#SqNPa7<U<q`
z15-jJxKfS88<(#^C&ZAcc^|tef8VKsz^Lvot82gSH~k%*vfW*J3CbabM&{`nV@$Gr
z*vk|V_kn`ye~owlbK~s~)nUiS66D{dSVI_twsG|EyHEw#%zQ5hbpU>Q-OCLI*OTwY
zM%MP(Te0uFa~2i!Ij5J2v=_bS@GlHMKqD0<-fgVCRuL+6o=u%3w_Im@T^+gn*0Hau
z0lkh5fqU44#=-X|^~%906HpP7=xH>Ecs4^jsel=@y7R;S=BtjMHgGyCT<H%cKz!Ce
zYf}4CO)o@(2MMOG3DZ7LlUm#plt&S!wAbL$>)!|GY2Ef=b->_tRy}au<6I@+2mx}O
z!6;eK8;f1d;H#)X`P`+L4Eg_n0(Rb@NftI23PQ2dPn1UCNHFE;_nSqKi$M%E!t4H+
zEKPQ+UMemsvW&o}C%^;@;v_r)qq%$%fA_XEZ&vF)vt^h@OzFikcDxo_8d~uk1fYNj
zyqL_qhE2R+)3}UU!6sHfFX@Z3Z5<aI&3;C0=?3FCXi;5LkDs(nckw2lc6bTHjdb-n
zq_SXI{ca|7cUwi)MK{2djC4q0O<o7dGJD)TQWpATPR%4xC^NZ|O6F%sZ|&QdbY#tz
zm8(wZrf6<|b^PhF<I&nrEZ9i6(htk>v+B*9@)B7}3%v~$DD1_Ck^L)@6Avxhyl!{m
z)3{*9uf!j>Zbk+(Z11^600KNM($#}S4w2tuUU@oRo2Rf7bbSn|tduJTWqGpC@`*Po
zvpa-+wg1saACKeuQ-cGxsOeGbQZy$V87HoJep-Mn{@Yga)hE%qxn+kcf%oArd3vgq
z8`pe)gnZNFjrJdQjSn9@Ho#3hVFo5H2p!k5YOy!G)6jDZl3=@L|BvhxHMH@*oo%yu
z)Ty9Hu*Yjp7`UZ71Z=vQ$Xzcmq3uB|P;HuJCW+}(pCc1t?bZz!me)F;yw$C`lSdDs
zpSyPAKU7bhbrq4PxP>0HX6$cnn}mmJJC7a4paxV?CMjs|>9~+W@jYXKc$=gERZY!O
z#0)?y7g04*NV$u+67_q6fCR+j1lf1W6oD%oA|$Ftu1IO}-oPShn%a5VjTk>U93`R5
zBkRDSl(paJ2TQ{pH5!gt4X0<9SV)ofo7lLz&`omtX+p>3LE-XgJy8zFHwB21?NF3^
zY0vz$$ovK5;aG0(fR4OHm;i9B^7m`MGf80E%VFi(24#DBQr0rSZ!$XPi$<lJfvhUb
z=p;c<lob>;z8vdCoykaS%Za-{VU}bi%4S}QZd9QQmfO9@-?`X`!aIz7vauw&6E9KF
z1_V=Y-6|L5AoW6C1fmu>j5B%~8bsqQ$3*)m<}M1&%B*+_TdKFAsPfKLPubT5vto_*
zqfTD;{r++Z*r`tA)kRwQMMruK8j5Mw&HBdkua6=BgS8c)B;YHxs5<{!|C#<FvJXC`
z>3;9bw+$6sueC7w*8#NrvtHx8Y1wwf54vgj`MW=W-{PT&0veytr7EXFCjT~H*hPPq
zOrK6N&<(#1ZrjElXg*cewjX?6!+y%v8_H$;FuB~Dc=6HYyP`d|lNxHeILfPA5kZ8`
zd9D%l*K?zG!$tanjXZuc#t%-`tXiam+%Y3RsmJugNo6_Z2VW-xeF}Y9M**q=)@#2+
zFQyXcyX+usBNHODiLsNt*vdt&vH8Eo(;H$GxT;`UDlqK*O)V%ujYWQ>DCiNEu4!r;
zY}jQ$@$JjSit1#bV#RS>fwyLRfiGsen&rP*A1KlsA=Ghors7N0X4SA7RE(-!Emg;Z
zxRur2{xz&x@xUC-2J{4G8^!=J?2YBx2Mito8o1Yb92!SdiQcWsW|lUfZ^HLHOj<&)
zG!AHNQLS?lS85p}_r7l^z%_gLhj_zs1<Y}MV74{^sSK<d88K+{X@15IQI>WsaY=OJ
zx;uocp#Nr3jXHVJPun6W`c`@l7*E1U6WR_2zT@QrpTag_G0&2@n?#(~{#P8FX%tQz
zXdmKFf``c)I<xxYEevI(ZYav?T~Ap=;3KQX*rS&Yl%Wa^bHqm)Me49dj2SgC1XTh0
zk}BRvKatXEy{k#QgGAY1i<hi7QN=$LO;A&xRiU)+@uElLx{zz~tM<HkpYx4XRL?ai
z<)y}=&!%JFRjGHd3E>Mc*+xs5v^Tk64Yvr;{wl#FdXUbM+hiq{lWQjJhRYF`$l-}0
ziL=2$c@xV8v`R(&>mX#13AHnTp;v{n0Ss?XLxa-gp|DV(%6$lV3cyB<KQH$X-lNaG
zR)=e`a6|YMU>G$MTA}YcR)FF#)%0GfiZqEwJ9t6`pMfZKpKl08PU&$0GWX{4qZh!4
zgBg`%<IoyFcL8Phsx-qtT>Gi@l{#Q5T4Fskj~rGt;x$FiGqpERHh$5e0G56u5WJ(z
z0IsKs7B7df9+vwTH~4=quQ4cVvXdf%GF?Xr(?(Bge`pDPgy945bUf47{>-)0;d_YS
z8S6amOj_0yHd14PbA?*}67jsVSp?x&f<5fX!Zeq&-j^1DKABZ+!R)rJ16C7rojF$E
z-kTUP5QH3yg`c4O13O7M?jPiwZH4ban~ylbIBUxW4S(ix%P!ciu=&SBTMi5T%YD!g
ze0Pt<tE>jxRLJ29IdYBO2BBram4XYeSWoVGN)w3?KmpTf%w+bi$=$wz3=ha8YMBz<
zY0au=1oyF4<g)CdFXgBh4EP>o+GU5@G|iuA<2}|&ZKfkC%uHB^wzKq@lU9hzBi4}>
zBeyF-!&Bqz8lZ?}Yq?<*FK4ITVt0c6*081#oaWaBjt*V&bEMjb?30)l8`2#FmmM-k
zo}V8fFlfz-eQ<)!Z5x40SwE~72%=R2!<$aakcz*Cc86`u7ZfYqbnbCmQ)c6o6YHt?
zbJYp~K95f*YXfSN+{PCzo}@u13FQVdOcD4+TMNz8+WILR9iTxHR4aFD1LpDefD7a<
zk8lyYj{F4P2z=Uj1-BZmqMp~B*Wb;_!97#F?|18R=?fpijjOc4uPH*@T&kAcYXH(a
ztFi!o=q9hA%sl}iV-LJE*T3`cS0%C~^`O3;&JjIqP=y!6@%s!Cou_X%6`0Hl5(mC|
z%wgBLiyi8QL?$iMZqTzPSG*BKK+imk;ZrS$NkGZ@=d7pGQ>^(jBVkQIYXQCzHsjg$
z7?@`IEzV`5=a7OWL>a2hc2HL;2*tZ^yW75a6Ay5YF|>-Vm(3-EzN3HM0k^SpXV@<U
zJ8xs@4ng2KyUijc1j*M8kF{<nJ<td98cVKtCYKzaA5Wk9cyujgL7Y&QRkX|1KGJE>
z?AF{czuYp*4PCGEJAL<qNvx#0aTR!-5fv2Ts*1t1#n%@C`xkx`@Z}L=!}E>6LUMxH
z)~t|Ge4^9nenYvcYiGrI|8rAa+#lc?f&ODH@Cy~F9&__wzzhP1Fb>iOOjnrv)nXe;
zMn!XZe8!oYAr6E=k|KgE{o}=XXzx58$1B%s8fv!)8}yY;o>gdKSM_?BtXt4RXy&Pg
z!3@X2Sj}Mq>3a!S8*2`jfGaIb35CGG7eP7KVh*9;b_C~zscksDRzy|@FyrJfYxk89
z4a_D0tMD+Lg``g_ZUKHoja=ZIP6B{gtzWqa%NK2u@gHh`RcS38Pz(SN1(!+e0ZO5z
z&%(-28I;+J-9Vzi5*eT9=5-uqsL>%RW=a0Cz4Gzerh?t1%=I!`Iz+U^`#JQN#(Jyu
zQeI!^S{8TXEYap`qPusgtG9=+!*->$iOjrUP%wXeAW$i6UrW~fUBG*8-Wrv^cUyvK
zX|rLjMG~NXmSyd7h)`hk)KB$G^C<Y%_@tDIUn!~b+8*#NURd=OOt%6wo8tBe&4JDk
z4irhI!*f6n^veV*-2RbIV!g(TRw%>H4+EbGZYFN9W({+yJK-jAJAJA9wId%D1%tD5
z-;(c5*(@eFUMk%$h^W0a(Y%la4InwE>)r8fTv{E%6eb4NHtPYGD+4ptE0k_+eXh^4
zb6F1ks!eG;LOF~yPEaAk)GQZQA7h^KmP}LdD-16BCib7(3lC@A_E2bYs6s-hEH<nJ
zS$C+;d+EZzc2DY1Q+q=Jaj)pOQZ&}8nj+Y&GP;`peE+#b=9i>u<xlv~vXsy%Lez@D
zxA>OuL-_EV6}KQYhlH$Zoj8BW4u~>aDZ&QR*p0^W%c2iT(#9)28p{!@+n?|jUtQjR
z5DZ-WD6>`zHV*M37E?RM3CNI+s*&kA+C{&f_kd%`4PS?v2f$~~(`ve-*Uj%h_9?4f
zY8gkTEVf>}By&2Rb&8{|n=aH1=zLhpu!T0`u5B%6#we~O_bAROcMDlHhhbFrYwjAr
zoY=KjY&R@f&F!<7q|;pJ3--%Me0+AJIx+KcNjv?%-X7<+UH`<<fPWji(-=>RhyzdQ
zQnMY>y|AIPwnn^foT4mSqkolj%|lFnjFBK$5i{3hP9J{oT3KL2n=DSd``COi-%bsJ
zrVcqg0^UrZ+AVF6ju8BcPklb}{c7xDd;S;W#agv+{<!s6v`=;V_sKVLsIpgH;ZBo7
z7*BS=6|OK5GH;QmdHJKjNId&eQa%`#UHu5-U90R!2=^}8$B5v!rsErlzbM_H9cx~;
zRRQN!V|@zc2XI-D$hJA7Lyo~Zn}{ubLhZ)t8384K8(y5YVx$M#C9+B3o|^hDmP_3Q
zY}Dy5ZLB(Re=z-<=9yhQ#e6#Gl2+BtR(-#}(R+*;on}i|9Ut<a)mzd2kHK3`TcHQ<
z#)ssg5eFE3)3?B{1WTz068#A8g?RW%%WGBzxd#VSK^vKikT9fDfWWL(hqr(B&QQyK
zKR=2NZve`r4eD_5Qj6q}f#cJsbZ*Ktt6*Jz1OlnEBik7DH^GeAusDGP5y%TD3g2Fd
z8z<c7I%z-U;$lm4#tC^ErDCN0l}=#hswh)a=@vnWL~1Gq)Uw{OQ`XMv@ag@c#4@i)
z4CtITR+1vG*jnWm0sGPHPdq6kATujpX3$?T0Oi;myRR_mmAxz;q*gP_39`q$`v!?e
zYn~N+)$(SpqfV}GifD2giOQIKJw~{ct<DXdseMr3Mbb1N>4_2|<`)xa3r}ihQ;X8*
zi6<s~J_T!t=V4}PnTSW?=&+_oJQuPV^5n-l)nnk9d4h_6ss6k3ZlY)Kii#`pXMVg^
zn>?bM%D=JmIdWpje}{}^ZM=ElVhw?6^t3ux^_0|luD@r)7mxbhXOy(a;o1v1G+X2R
zOW}#u-ie7lKr1Mn2<?(&_<rnK+?&^uld#H@(9NP*(W5G)QbX>Og>KZKK`yLp*L$Oa
zbGR}ZA2ZLtpY7Lo1!;w=CY%0u=YAL9mUm6lwI#BX)N7IptJ;d6#bu88v7Fm`>b-l$
zXY8Bat|trVa4WXiFMb@#2JJ8v$3vSL(K35gi7cD1l21x&BgToEnQL=Mmina-0;bTf
zQLOBBpR$NpJ+x11$f1rC+|H_H42r5R;@)hG8bn%$2y;|#C8-}Kgp_mjmNPtn#i>rW
zGNqOA8gMa}+#&P`Ay)-ms8QmTB<rD6V3iU&Lwm2;4@~HQrF>b*uSy{F6DVJeP6fOM
z27&B}@4%}X%}W1nv_iA2BcN9m9Tp&S>4I>Okxe-06!w&|Rw2HH_WZ!p@6sarHVjK3
z19^HcAnOFWwlJM^Aszd;24nS44c*~0$26|s>KN|06~|<Drf4l}F{sh-eFw?F>Y|>z
z4)-0LcG=pCE{;#L+hh%6nKqj_=w;X}^PwF?T)CSrUQ&T_2+_=z&!yN|UWB2lEk_`e
z#m24RzTXK#*Y=)eJeKM{dgwW;@I55bU+~$zN5t~|2zzedR_T*Cn|!zub+n?i>{1@$
zkfQ~+(~p?>J4Af1I-HfW7N%sedBv>uHtc;j$Ebs<ORA~~`a4QmZMM#Z7?E|)6U|`D
zm6Q7>qkXIQUF(0FGD~zxqx;hSo=vp-hlSnsH2gnmgLBBB+cOEG;uSB8N12@qfa`=e
z%vL?BVMgGRC(l;o=#i5nhn@L^=`rjI#;PJ2I28W^MsZoqxJk7yQUj|cjBnfLNAQ%z
z$c7HeS|6FD`Mm>F?aXyRvOO;FBKkundjaM{TY%}TShxV*`U4#8c$c(Hi`>lp<|>g^
zup^nO_5tUnxeTR|+RR2G11~QMa#A}VW84y6GVQN|&X*NIH_5;yFxMyrqKBU~<iHg`
z(Kk<Pi=0EY9LUFA#+<}Q>_7NH0qashInI$yDrPOKQPfF+uAo2IMm*XuTz<gF8}^tj
zxqq$J^Wy@_R$!Jkxt|!X89V?H&!9<Hnq%~YTs>UG>r|Ql<MPXxVQ$ndXEH_^)ifiS
zE6X~E*e+ONYqnaBdZfY_uk>+_KoO18u&ax7Fb@<pZkhQOjHvFy2td)(Tvn@R9q!V2
zSQQZLK6_xoq?~&iaJeKh_wCxQF1sv}X(2p%415wP%YIEK%2)je)R*xct|%YtM=PwM
zf+*WwQ?V&tP-a7~NES5Rw(9$UsJ0ieQzemdKk3D4=)VN3(`@rvy8do8JtAL43R#h{
zcWge*KcNjp+ou+wjySwax{fyxnm-p4MxsWR0G+1b3S(yeUAR$f)$qbV<DhSK?f1}6
z9Pn-)tr>Fg6=kxi<n3fAniilIc>0m!lR`r6Tc%?%_@}OF52~s}jBLhFZ@z*opbl9|
zk=P_hdI>E|ElW@Z-bQ~gTrLcE=i}>oMP;Mj<oNg}`>Be$z6rJLU90MYN-|0~y$B0E
z!dkLu+-}h!`htS|s@CcCspGD-0B0*$7gh5F(5b#ci$L}Ps};$92dhanOfOBN5ki_L
zh>hhb1I4cZ$8%y@F8{%_3K|)Hu*``U*8hmG3lIl85fJ)Ddi}!@UE1^#?^(e~xu?fE
zg)VJH&3Wa6qmL^RHFTfYLaeBoXQxih4?=s6wpW{tu<+;*vh-7JdRSu*{M~`nwsI03
zACbi4ht_qM)PV?<sUHDTyJ_$P|56%y=%a3FV;|jugaY1K%zhOe+tvG#yf5F%_-Tb-
zi`VFWlwNy!(8tN)&|?_WS-(33AGJ-h+lKL|I|_@4uLxJzx6i-z=79%T@Pw^6J~DI#
zm!=B$8yRs=ZJd8J>C?ZUPXAJCi(4x2?^SeB(FboyB)`(ydPUyGdWCmU-r&m6Z{Ct=
zRd7oTJoHy;`DoX>*`G+RA<4p0U?3gWPqw~2_KwjH+n&k~HCb=KE$(pghQ{&&tl;?Y
zH(5YQ8!RixWMaLbf^Zj@9K*v8*~^N+Ao?q^*m-0{ykn#H{hXnlSHi)m@{SN<0buzn
zm2Qj_&}icVeCF&La03C$_=Z2d8YPfNAbh?N3K_L~ru;I5ojFl!I7RsJ6F4#eT+;bK
zE6D<e8^Bsz43d;JZZ(t|ZVb=_mf^`xAn%is#Ltl+^K^sRw?|5EayBHf@0d-f!EFhe
zvJ~&vuwTBD`ClF>l(&NJ{*Lo;H#6c!Hc|FA2`V?|x+p7MXAugRKs_AYrJ+Z+EO1tM
zVXmEOv<W0eaQC;36sK8vcyhDF<$G%Pb^$MRcY<*>q*lEM6Y)WJt)c2oJvry=LbrA;
z&G3D>_?~Hh`<&gFF=;1qYP*k9*NzbUL4VXN4L3(~T*Z97kR?5}d~!@KHw1U_B~sS?
zk{q0_ec(+!OL_0@dMZh=!?I^~;Bd`YEs|uJu+WK2ifA)TZ7hD|ckN}A=&Ugt?G2G2
zNdg@S?N_{(16|{zX0T_Oxd?ESwBQ}NYdeXYb$X|~*2C1KwX%*LQI}}C^MJ)lq5Yc(
zhy0>Bg#0#5{<ZWB6@8dq7G<HVG$dBk3@r?-;*gxF(5yLUT>-^AC}3}>Omptl0F-P#
z38!ropm<6S5xk^PuWEqM5M(|fx~4JvT44RvAOkFjW{ILzxk3V~S_C#n;omdCtg*C8
zbp3y4D!9s{<9M61CCGe*M&84s3Tc|e!Pd(LP_!qznfxY1<s|{LRZ1+*cz2M=CN(Sg
zpuatvX`7g>)x7_pk~$teWk`Ya&LhWu+@wV!u{5WT8Uc?5HhIZ_m-{03r4r`&2=Et9
z5f*?LwOQo?th^(J?PFZl7q$<4o2}|_H@B9%=|FI*D?y33Mtb)d5kqQ5nA(+|Sa_M$
z8IB;k3T_-yv+I;}<(;L$7W`EV2vdxH;TA!7c5M#=9_ZV+x6QP6Atk}j^0Kk7an%WR
za}C?YBwuq|y8?A6U=wM#-fBs-?h919R;u{H2aU*+5&dzF=>BK+JPt3lnFjpGD;EzZ
z<a*r3zOaHLt#PiTcVMy_E*ePTcqjt{18iz<`5J}Lti9)d5Q{9r?_a8r2N_O)n2{Ad
zl+10z9WZ&RH|s%p6hVJr*qz2|9#6hU-X`LCYRk=W#c-3EYWSwM%Q&x5>#Wp}sPJ#)
zS=I(XX1SM1xwYlX+RgY$R%xS5_|tl{IiW(cOSR5V7D9X||2T=CV4B~rP={@B>)ylA
zG<n-t82}%YKITb-B5)k49uP7(zFu&ka}LR`jjO)UQ1J^+?Ln)T3;3?ZbCKG?v>NCX
ze)n?_F>wi6mtO{!!Lr+Om`)tvn5quH+Xe_<AP05=4X?}dz?T76qz7brc`-NV7AZo-
zG-d&@4wwsBpwz~TruMD43vs9emVqnYU@}d#cD}9NCPi%J?=^ttG*aXu{VdxX!G+G!
z!tcpwzIW6RV-@v`=G`960pX=v8SH<`2TEh50iwM^y&=Tjzd2UedK`Wq+;ty1YNO7z
z!Q(T6DNGjp2e4upD<;S|sKO*pRG$%aFz>sMW$kmP8K-GIi-xA`i{JLwuMI?m-zLu%
zcC_khW*CySR)jcV?y2i^pJ;AK*BUE{lFs04E!#PJl2cHwj=_G_>c-lI9H_Gc+<auY
zaP1raZ<8%C@4)3p^=~4lIlY4b2y7nJnJ)Lc41SsO1PVaEuv4JyU|A1#nwugBUXAut
z&uqb1$7zB?#ivCyZ7>)_SSAgp8p7)85#B9+pk=0AdpE|DW)d#IeDj6TcHz~IlRS3W
z(e_^tf0^mEOmAqNP5nDoUNSJjwv~S7TL8&gd(L;DZ_RCy(!7H=Om^8`0Q~p^e+l?u
ziDQ*ePGB($*XuEb|1pQOC4$?zUSUTMJE;PPUjgF<;Bu$aoUP!EmD$y)z#0xN!KBT@
z$*%Z#1@3%ayttd75BL)`1cFh*c=X(f<`*jLLRLowxBzr?MLq6OU%;(J?yo(teL8S=
zg4O<md73fypK(jUsVEa#iPa8ra-d!HsuoRq&{OnK8<}4&8Iyhk^NsI~XGgb1;ax5R
zZIQ1yKm4z))E0c^g+W64i`|0Q{_!<XeZa97^LoGv+oUo%wilL&=G+>I*l#4Q_8x*$
zE^vvj)gE^UO~A>oD?aEaX)Z0u;2cUyDyQquejDH;>IrgR1+RLC3$N=$ySC#~%(c0=
zJpqm4<P;~OVJ<lcjyY=k;KsL#xN3CN8}b%NyR{EysCc&OT65zm^z3}f>@ZZ6<hx-!
z+#EGa7>%lHk8Ra!Ut4sEnI=iLFFr|iRA=gJuZ{u-pspW8KmUx{Y*iM*e9oxi)AU}h
z*JrMI&ktXCYQyV<8;voxhNiW1UlU~2{-I>aM7Op7$W4g{gWe>H^R)cVipBMGsYtc1
zHBbHIv(C*Vw~TyqSIpwC2wAtwh`G&#>}y(xaC?ENmu0En<p=WFIlStWUFk{4_0<<l
z(j~}eliOmg3yhUn13?ZZ7LB!b4BdyJN~45xV87LK-!qrYnnUD}FVHc+;JF(GgJE!I
zO<S@jLuDK>ihxZECG~z)-1;tc&^pCq6*R5huP!?o{1WN`VXCg<HhSb@0lnFNP%#*B
zYxuqq&;$fr+R93H9w-jF2tuI$06EfdM^;p{XS;W*(9Ep?m^ol%>FA08p)thQ8-IFf
zM}L*uTcF!$4UnS~kt165g~nR~d~S{oWO2cvVHzsc!nfn^EW&OfBPoR>HgkE=X7D4W
zx#-N$%t`}@W@Y~12AuaE0J|p>Fep34D`o4*>(=1o)Zx3~>V0U=Mx81zDqILXI%pPe
zIFIyq;zu_rcFOj4`_9zyAGd|oDUDnIiDGfSt_^owJBz|wjgWOW(04fdw?!VZ)q9E_
zc&_)pRReR%+xj2Ey2k)I@M#SEkkHxqX~<t0u*m+klOq#ttY&9*L^$~WN}5xAgbd+b
z!p>+WUN9#cVz`(!AD-m@?^jU!`G8J2tXO_`xqq!gDn@<5hL>bwOHJE|kV(sDOA}Da
z2PLy|*4Ll2eLZl@H%72BMddz(|H^{HNO)In6YyBG(1A|qR7vUc4Pnh0^e<1!P}O@7
zs-}e8)QMhy-xA~g6;S~fdltgpi!E3MUUJx5%}=6{qBo88FaqE=QoYxR;bWl_1K4s)
z8y5SXHbBG}UjTcFI!%bLl(Ob%pBUi6@gUl3wd`?(0z25*1A_W3{V;WW$T=ZIx<>&k
zL6TJO+g5R?nSk{Qhei!QpSrMvwOra0?i1jh=6SWVHW;3}3Y?Em6U@|U0*x?myDQ`y
zB9;^1pEd{%*xwW!AU@tt9o}>_t+CTsDH^x9+zGT+?`~M+O3&NhiD!Enmz@D`H^kqe
zhN(1Fz;K)bO(V#$Cp0=dK=N;;2kE-E{(a5~M(Rv;iP3z2TZ+wNt@GY3ifXeNWNn)N
zu;;<Km8{=`>Y&Cz)tDSUeNM}#d2LmS<_Aq-GIQDm@G*Kcxm0Y2;;%FLt~Z)w$AMW2
zp7q!zZa#IRuiOINT;MnI$tA7H47kMD@zaO6XYBn|_Jqy_2WB4KC)Bb+{l1<L&cGvY
zjaaLn<8%v|znb|T=LW1Oq5(ZE$V~31v0Ox*gO#}SC?^utNTm@xs)ytD+YMe?@4oF5
z_Y=2I@Z2FhO^zC;p1P1GwrN$6_v&K^<>M1TF6|!96Epa0VbsjjBnu_qCBZi5rkR%>
z{3!ZJ+L=8Rl@0v!GweUTt*L7RBkR#6k9eZ`!-x+&ho3qEvUiY4gJSh!9v}B@F<+-`
z0-kHfJb-o$7;y*?{SP84MRaxm4>QG0+1G%EtHNg0GQ4KF&Jcbt|IoBjq?pUSoM!1t
zYQ_4U1#yr4DkkEl>@!Op1ovc=fRh-cp?itFoN^q{9VfgAHMznk)aopQ%!h6YJ1EkX
zLKTm3J#!!r^;ZQI`a$EoT2>lftQ1{UQ7@^>j#~CM0do#@rOKeZ<ze=2ve;JORk!cX
z-_A8d>OL)@Abuxt0XqQ?lPoO=+Il_U))^zqVGJ(gn*VdrZQH@z1<wygyFp}a8}&v1
z(h%n$st!Y-W;m{;&D55ld(drSyv9PNqbPzOTdf!E%_EFvXa*<MsakBhG-l}zklSR>
znFM3B6#aR`EAZkv7S3^`?_B>1|H<cfE(aY{4~}5|fo|Y7sQ$FsgpEH0C&!c5-{C$L
zNDfIK6q@K9r|@lsHrS9xFOsv85ntL|L8m4f#EVvs1?MtYQ=~;Z>cE~b+T2E&J-XU^
zvMA9ogtUgihAa4N-kiHfBaE<h<aJ+@O$b6ma#4ZGX6H^f&IVU|ycKs3K#N^Js#16i
z*EJBk;RWpOE^*A&i}qx{$ofU|j}@@40ctih38bTTz?nU0%Ygr=sy@{#A^wUe_9yrn
zKfCw$Rp)}TeJW;ltfCJPK8KcwX(CJSeT_iCZ*#S*qeAO3NHN@m8IBN&Aby0?Fvav3
zcx;V2z0-DU@-+(`JxO;#T@R`MAC?u5a9T!-)c&D(fIFf+V>xXlH6E?~A~ladVSzNj
zd6q`#nW51;0sXc#t@G+Maea%bPEF5Yw`$y*MF!dL3-~;AfYUf_ROohA2>FU|k5zYR
z33Ic8fV`0HBP+Q-SJ4Xsu2&mrbWOZUG20pM_d0^SeNabEIi2YJQs_H>iDI_GKJCkz
zySn=iG6Wbru2I5GTF8)DCc2~Ql~>zm%gpHLiqbSO*!?<Hyjf07@rxiy^c7924~D_n
zn|zwg?%1-{WGh~VqC0N5jEu2I$&>${it!%?Vl_7~HM5g7Q#_tOhtl<VO(&%tZzq^{
zCPRNi5@m(1ZHjsFq=mf8Ss;8|?u?L92v#HazqehJ$DOi1g-Z$v`N)JuQa&o(=+}qT
z>D2CUjMZdpyo~-mxW&L(_y|-l!1P=%8v)~7(W5F#mx%LUshLlFX)Jc}dW~DLksIUX
zS?VM6(6o-(Yd^wACsz<(pE3%EZjk!fldUG;bP%HMS#xZhz{nX=UZUHU8-G<=Ob9(>
zNGxUilESazn1<~4J}xK+2{Nc&F=Y!f$|2*wBF^F^g&sz_fr@bo5n{i<wc+DW0Y&k{
z*DYk7;lp>!4Rz93^CKZkWs1u&U~h(yEbzt&KR_G3qZEmU==|nhmLVA@8bSWJYe~q|
zrCz{Ob3O|EIR-b3Yry}(wE7oeqX<^5fUpEK2T!t^vD8Scoa=7x0V1NLz`iwZfneJ@
zB;E4@6wL>ahNO2msgvbdhE<f?lFYy4=cwhh(gs!4BVIj-WlqHLE}Bh+nqK_?Wc;11
z+cdg)H27<>?E||_zbjJD1p06-NUGMokGtjgyj8=IYbK(Zfe8!q9$WJ8;#N53*Wdg&
zi;H?gkvd~>+tnU?C_ix?neEXQQo9PXMNEJ0H}wF%g}ZlpCJQ}b{e-8<%6n|++X@^=
z<#t@y$Ma?+FDFCwx^jAM|BNJnt(fPF+Ew#cWj_xktX+T!<IR6k^rX6w`3!XeUGGTL
zXbyIyu+yX2AUUl0y0OQp+=sb{<{JB7vi{TZ5As)Xp8;Sl%BwzvVNBkG_9xHw*DfTX
z28_zMuvzkmhP5f_=ZdhBYNlYZWFqR)d9xZfde$l?8hMLHAZASmaUAt4`I>{Do$gL8
z7ku6(MYxB9x`BPWLe%>h)aHsZ;|@POh+xV<Zi2d4r>Yx-jN^kJW&05}`QDaOR4Z69
zmzT2+8~rBWV%28x-@wwG4|rK|<&~T^ljafw@DrfD14}a$VaobLMl4W_^S!j6Ms5+T
z>jzJ`5lGkCrgDow+)`ToEOlaB-7ag;m{f<;3w6{sBO8Za0X32&y(V|_^G#0vO3}wN
ze+qKdL>&7qI&*)qz%D@0Jm|{_OjLMK%ee>8K)^`)?n_2#cEjmCtLXG+_ccpTXU~Yw
zn;0xzd`C<(cQq##af80y`;5cRMP!$^M@*6ZTdkj%s+GEHJ-iFI+^l|qxhmZ-$C-KV
zLq<kfUP1g7eDeF=FD{PvFAW_e?zP6Z<3`@Q_5Ll$%HOHxLrUrXvhVg~FRaIpViixg
zx9wLrx>Xz_*`kJ?{!cyTMI$~UF3{1}erDH<$3(zF;6IVsoiY7@{6gbVUk1q?RcQJC
z6AE2BI9bRX#@xu39>G~p;v;Ew;yF9xr?p=~3j@z{_nt#wn*Gw~CBR*O&K%BIYoa$%
z@x6mv<jyX%I^U_c-l|`Y;zy51UXf{fiw9S7n6uI=XpSgO{9y9|>3#9JC&vbus|5r`
zW;3Q6E(-gGVGlyeQ3Jpf9(-V(z;GU;F-Gg{DI2i#5<InLakghV0pU{s=d6x*>I3qq
ze@KY(bQM(A&>eK33U$A8)g3|&D0$uZs#c3~@=y7_&(&Ulu{#++Z<tcFLgfe!b|M`)
z>Z3*>azeIDUHf~C&{1*4<T=pcP1}Z-e=2LbY-uz>aBBg|k)E?OiyMf#Rv;LW{t^ha
zgFfZ0D*GB%?zOS(GA}HE!$_)O7R%ipC_KzwPX6ChE(cGU0?J-`R$Uvtbwc1y{J^XJ
zd7u3JEauo^j)PU3-i{T?E*HXQO@yB>=hb^vI<s>n8y*WreV`pl3pvPhdA9yH7(AO;
z^w3Vi?z|TjM;1*7pf5sZkaNesTPwsbpCy}tg`Kgonu&XR^JMP^JvT2yn7l(9&D32h
zE;VP4hx;Oa9?)aDXa6ic)TRo?P-hk=?2jq?tefHU<*;q@EW1m`V7Xa)82@so+3)Bv
zdjx}K$STvk@>_WF>?g^D&t;#N^1pEwimu^j0<Y^RO-a*h(0Zc(!=|l3Ul!bs$1dch
zTgvQJ-R2`GmArOg;*{MA&y5(jwG(qL{Gc}J^sg}O-xtn!fAYU>a_810Aq;_BDk`Z*
z>n1Sq!U$~sOxDfex`UGy=-S;3^M*qv8^3LyL5}r=dhcgc{cu3iycI=?LuP<so|H9@
zw0EYe<J&;d2+>Cd>a-hzcPT}mDDsalf%Wn7_t`9!Nd6x9-V{XJT!O?=R90TDuD*bx
z*x`>dD1Wupd}Nna%;dui`-cj8eVRnY8V?`NR0e=d!zd>%MKjn9{)fko2I{}ApGsb)
zw{FU6sQSbebz{{bg)DoctN>U<-5u0t`d`pPhTfQ^nR$Hqq6}250UD;kU?RsIRwp6t
z2l7k0w?!WqIpAdY1~=OqU<7AxJ3C!SWLuSIK_RL)I^yexKO<Mk_IKtH3fq5hRn@vg
ztB1_Q6@*%2qJK_|e!q6lch1kfkGWq3#}<#G+2Z=Q9uwAFcaxX7wBg09wNMAxECj$1
zs?n`);32<C)AOpI3FeLKqoT3mYIJbRs>DOa-<4LXjLWZ5LSCSrw>!?!8q(cwrp<NN
zrsL;(%7dC)7lz`)=nYm!25B!Qwa{m)UwFo5J`bEQan=sm-f#0&@bHEw@4vi|?;#(S
zQJ5l--+*m-r+q-4-M>XP{YqHtIi1Ynq_IVvkTmTn3-P9p6srlApX4XFA}nf53_ef@
z<a^^P`1_On+4M{ANCpz?RQ?>Udp4Du<CdzXduCJ5Ao)hxjPF@?csKI<TAb7`t(mG+
zzF$x~1tX0&1(RM?4?58wG%YrI`(w`mezvfg%KHmlbB@TKA<=wDP`i;1ePH+&KG%^6
zhu-^!X)3<Ff^p`(H?u`By6-CUv%nDv5u6G_>}S+WZ*orabX+%KcLgxV(8^&$%bUSj
z3|R$=k!C=rUHp@wZScSHmPR~reCW3^Ku=1}2sUcQS8P}cV2=~QMxlC3UIeRZq<(o5
zpuT!F!-YS&uug451>1(Pb~l;dB%vtYTXvOH#2Sw9SBVPf!M6C8NVVr>pi?v~1NA90
z|I1v1-|kgF5pYiPt{f&?j@Dk&8$X15sko3&(*UYaz!Oj-PzCbW`$2j5khd!EDa;M<
zgQ!Bb!Y%dbNzX=@;rHSDHX1qG4@(kqat_<Z)A`s@+$%EZ^y0UP9S-G9vaKK-U1l3y
z7`bo63ApC&c>v>2w!7*qXZwxf&5R$4&LFoNF>4M>22TUA|Cw!-U044MnRqa2Rvk3q
za)AE8ch?hOKE7&Xv+a<Pis8ULj#)0_Wp4sCOj28_&KuS9g^~hA%pgmaxX9H9E|eSJ
zr0l)(r1)xGQi850fmwdbB>AJ&O)SflTX2Ma*zM!qDE`9)zro5ajEIg2un|dx&(4fQ
z7(J;V-Jt#LkV_fHJE=%?m0dux8MqakmaM}z&NV7<1c8}13k2%GI&Qi3wN$$S2aFkR
zx6gypwcN|^raiqKA@rPWM7Nr6h~LY>FJMLvR#h_C|5xuZVw~BQMb)uAwNFKTB;E$6
z{J~=W)RNhMM9?1f%+mZnOeaWOhXO@ue`sr|K8IO?Oy9wL{bzLd&HgS23r)bq-GB6}
z>KWu6LMYHs{|jsrDh!0afM56k<bU<RFa@dzL?RoUj84;d=2E1}8{E9`Yo2CUJk5Fn
z4gn$cQO-kzu!B+v_-jR)k%(JYXb1d29U>?&1`dFqh^0Dhf8AZkW${mcP4FL7s#XWT
z(%e|av<qkmU^QG7?F9*zAnx>X`f^^m^(Cd_bWoz<Dq>DUedX8cU*?S=i(cN)H%1MT
zZ?PZ)c{(%UkC0_Pa&+(w(|?yNK#@YZY$%`wgr3aE_tH5Y(3k0RoE)<6#!?pU1&d+h
zRIh4riQpbQiG0(w7G)FQMuy}4$ne`I$l?E@lzo(=*<6O>$V~Ba`>0d9Ure6;*EpXT
z{NJ~b8Y&Zm+a9p;A6S|U1YhH0*%`MbHGlPQ!P$s9zVfKmH);1JwmLjqv$r7d@j@j>
z)E}Pry&~ClBkkhu(kE5lCCfr<jxZ}+DL1M?VcC7bH{3J4UFcG#tZDv{Cc5>fIj_dU
zKY^*vlFMHvJg|2G+7|gTnYrr=Tkoh^6k|4*m34)UZ9*3s5{p2FAtsAT6d$xFyG7R9
zw(hHeR$^dq-}~ZQ#sc;>37TscDvEtB)~Z>c3_vG9^#Q647zOY7+oBe)p!9nNj?^j&
zNO5aj1+e4RbF}c<y(V?Q=t+>%1_?8n0{rXcdK(&9=78emaar<{Zk5#}ehZWPxlRBo
zigQhH#nomPm}&@olUf#DbhG46v=Y%A?WK7gTW$hw0jEGBqJc-LK=+;K3b!M2iu9QZ
z8jSi&#OAP;0##{1OgX{b77z`1cp3y&4t<-aK_rAbau~p(XFyL2A@7V=OOW}RmD<|B
z`hYtBu5#m8wk)U@G_1IGr%lYCQ~AR=@$s?xLkmlYhB-~#&Q<hEZ?uuI4pmhBOSjd6
zZ-Nd!i22(#cbt%DYr(>bN29goy(!u5$(f6)1ZZ_2j*OproF3Jv!_U|QTkTUD;fX$!
z2erhE9c(%DO4&Kr_`J;3HZq*N?<%I6FKHVgsQ3pRa*tF7Igc)i>JH46$4xrDKtga#
zQs|4EJ5SvwBNp|2l)HoMW6#t4p0W<Lyi5J))yaZy;Pqb+PBtutB~~%G=Y`X=F32it
z25x$%gn9}yy-vK-+RQ#XUh5a(B$@^iU&Q)gN8{Kmvf%EQB#;i$rKnFfcJ>cpsw7q2
zfL^QPURSX5@;u0HhwoG6+YYiD(YN77u=oaasfW_GlxED(JVkbX*ns)y+NL^K(Jllr
zQnGoH_yaoIA5wwY?FRkz9c(1YV%B$6e-;?At7PJ@p}t$A7PTxpKT7E(Ys#!Ht%HK_
z%lSpF3MU>3vvs!z+WJu)Ku~@qh!K2H^#KpdDRqCOr_i=&8M}K)ClKcstz<-MKb<}?
zioXJ;fPTEXM{iS?AXXZ>>5&woSPn+&@e#UH$SZ`w9lf;aJJGVhORyr=*GJ(s=hWfq
z2eXZH>&FSCfZh!x{~<D?5*R0VFT(sciL^aSR+T+ts3BQ`xG?*#ywcSHf`MBVC~et}
zP7g_ycMwYWkQdF1<2Bchi2Oz(Smwl>ni29Z-N$?vH0=m8B`{(9k+&x1*76FM`P&4^
zG}p|E6oL0@^av!DOC1fb^;>K4iqocAjQ;Z^MmgzhY`2N!xs4iQy#E{9k+b5Eie$IN
zB*EF$gtqrRtYk}G?X4S8N-K%td<wbz;No__lzx2P<*V!!j?}JY9(>;EIh+djFlseF
z#3vE`yN*;ykSr0DF7;;%Os{i2D4uWCHg5hn>B9uJNkRm`{7SST7w>%U#(7_x<Fi!)
zQ&Q)1@3e<U5uPJH@Zl6u)5>?SPtcLamjdN+IO`i6B3$)h8a<w2KH@`IKl@{;Eev-m
z=wu6LFFE(PLrZOPo9qr&Ry^WrHWz5Hg5Dee&m*owOzLxW1Totfo3nxtcbwZ)(GKVN
zWv+1NjRK|}qv(`f1<aOXYfKir)&sy6vr{G1D~GY7%BArjkuK?n4ei_Qj(4Z3tp5*H
z?;X^1`h|VVqOvXmx~Qm>U_n#_L`6akR&<r7tEiNys3<*&bdrFmh)ANMf`SxLL0Uwl
zm(YWhL=*@m5Fms|Ndg3tK+5;I-QWApGtUge9}HzCxw*gRT<3FL2c{zzMQKfcpj1}_
zGTHFbI<s~tFo(2;!JjUaStAfbe4h(kwg0B^QIZ~jOiTt^dgDWNe)CS<JBVxN$hTC!
zd)5^y%uB>WV*NfSS#3421OoMsx5f-`PI6F>M8ZW0R}o?4BDHOzPgh}zaxbO#s~<s~
zxl2qISRy}B1X*7ZkSSoPU%)=kIw>@`1iLFFqwOCJdZ?H6Sor@O5e}<Fq@&Vv!Re^?
z2*mU5vsP%GO}*0)64ZbqX=vF+JfOkL*2a%5{kV&G*8ZaEMZn)RR6F*;SH(l(9}P~v
z-Dq#c;$Bd|omtPXx#f4(+&kLM-}P(~%^t8Fth!@UYT+H#J0C+Yr}5v;|9+a0dc5XZ
zC)I<S`$>HpT##S(*+A?^8sn)QXF3lXZoXRE@Z8u%>>OD8@4K7LGU2YppeXVxG`K0W
zwtrzh`jP%Hn^9I}#nH00X&3Et9%K(l`d!Cyj>jWMWIki}w)kA1U!T$)Ap8{Y-6fEG
zbkZ?AznWlkGQC*z8pNbasp>(grgwte&;xJ8zo18<BdZn<E*334eyf}pp6;nU_)x-j
zyT1iwh&IA?=W;ZgUU~$&Y40I$0Yj0)Vsxo+-t98v=hew7fu|%3HJs0q4ybb2yQsiw
zqBDbg>fv-vG+iQ$h5FCT>;azXwGN60*=7X;>2=XRptSJ4GQJ_*O<mM!ED!l9t(bH|
zzC1IEM>fm6L11aQA(Uc$N#e5%GxCBO=K_BNQ35)!1!+H}H2@caA(ZD0$^lc`&(M(}
zC`aKy(l)_m7JhN7t|VGEx-~=eQuJ1&C+U$?Tn4g3ntN6FX2=y7yp_bviHj*K$~H>X
zEZ!=?2e#^#4}~-gdqry3NHi8MU_WaxvMop9SGCMkHxiKcT2OfN+*g-Y{l<5FwU<mH
zi*$-Ve(ZC2qCdgOdNq2fBeAYhYF+Nav_)mZW*&uxqMCqP!X>qAJ2iZ16FbAt<V4sb
z$w{`<b9|eWXva=Xe1VxC{-f<kYK((XTScbY4i^*X(p;U}Ny64L)8W|x6?9!~`P?hC
z!{362&o$*4Sn7V`v&w!ucxC_KJyAyrve<P(#fo5nv;UJV{5j=n{hXH+`1S(cWAJ#o
zXNRc5etj82XD$F2;+kq6hB|8<?s_)uvkS#eW}bA8Fqn(f>rX$6I=VbG7w#tlx~u2D
zUX~8(;I0DV83%VAXcXZaZ#QwAD*DDOIW^dT4SvN>RNU)f=#uU=yk!d4EXyc{%>7kj
zS=aO*nJdMx!u3;?rr;r<cgWEe+Dzd_s(aUPPOz%)q`D4v@v0H%!xe)STB*!T56Sd6
zJ{a+90@oqBZd~tt@|>hG+CNc!$QX+F){fK(s9l~~AoES7{5;IUr6neP7SZC)cAvzR
z_xYM%W_vxMkNFBga&tC@tM;F;G$iF?-tnTAssL^?1I~e<z7JZV)CB!d;Nsu?hF!z)
zVNj8AG&aJkBwYa%h{ILa+~>$;ZtIiM@kgv`eNQmJpMF1qC-zGDCQC>QT?oGf<JS8%
zavumi8HEq`-`lF&&M2+t)~X9_j=`3%Eh90iyD16B9)K-*T8a^1_~9acf;npzNbWe&
z26oYeyBD7B&KgD@aGG$t7V%9vHT%V48MewZx;HEnF}BB0jDQi#7ZyICTG|gcb%W8_
zc;m~I-08pLp}1{wyUusx@A9DpFZt$)*x~dSZ$^f#D0il=rLbmPj9fI+HK0J3EfEuG
z?|h9pXA8aycM1`PG&>I;&3I_;QtuYvI8%M)z@^A?T{dxDkUd4;(2?E{&uU`KLRz7@
z13N>O)~AKa?qJ5=pZ1B7phqwr{m*6DY}|rgHIe<nv5Wc3kuq&^?e$2Fn@f61@c74S
zF9vy;lQJgv+RmSB589<H<hS{a+6)@7@ov*87|ETg*_sany(jK1U(N1on)lExyxWMn
z`xRao(h!zGz}V@h#txwN%Q>l+W2OCZoKn~?Jav%D9+9d7EPmmSYoeHsk|SnNqy2vq
z-Bn4i<0NsnxN56q{F^{n^r|eHCr^)`D@QKFc=QEw|9sKq;vb!Xhul<V0)z;OyB(xh
z1DSyXAO|cIIsS@gxEDLhM(w+*xTgXW+r7e7u5YC}Lx!o{!oP>2h3b;EzbUMK@{CUo
zeZ?H21y2eSxU#+os6>62!nQC4n1?Yv5wiZ`Y*ZfRzJzd*CoLl6kP4(&wuKdJ`hqI=
zV&FK32H@q~kSrd4m-Y7?(&7bKkp`nI1+Kc;*o?3vs~MHAPKepjj4IWbg>zDNrFy1u
zUK`YUsW_!Ip|I@=-|WJPb}iAe3N7{CFDKf<5PdpxV(BA<tXPJcykqctqCZdeGr8FS
zu2B=?wH)C~Oi#!M4fIfexyw3wquLql=LhIbd}mJbfm(#a>vBUXJot#T!IAVRVCWq)
z&#gyabi?B>s>Jo3+snZMzwP+K-W_!PUP@G0nq1$4%{OVe9XqJJRv0boFRNTMCV>!s
z<Vo1OSDh7WwLH6+uW~z$`ZMK+4919vzftlht58!0hnwSK`A*DHaZadK8l^(rs<)o6
zOU&xdcCbL>tI-T$HB&X6buf|g387tK;9wc-ES<&|>G{`Kj;&gna4THAK)z<_l51SB
zDNo3AL=%#7dnuAopGu#_N_FX5oGKxraa3%s<v~7O7Ruv{NWUA5zXg+jQ<Tm0aclis
zg&W50Ib9A<W0VA_Pm*XsDFDjQ)^dtDo=cVF5hZ^N)BOs)BuITx%xm<Ua4xZuUbaep
zUPLylVQHgi)f!{HO9EH9Xk$e;-Q$u|j#N$LsbizH&prP)eGR5qaBcrsRuI5tgTO5k
z#b;G<qn0dB*g@)xTQf+C(ZL*rFOGd?lTnRg*l&d)W}95}P{4{~8-QPWyAu*ao1no>
zVFBx;0qqmMYC%eOt4#cn_Bqy7hNUrw*ZR}^Ikv7af`?RXITvTH#pBy+=|4%*9BZy0
z3GH4(X?}K8OJ|K9Q|r`n`gL4odKw4K&fF&VnztrEFS8fk&<rmQzagbA-qV_`_1Yny
z740xGKcNc0^ENUd<*DLc&eN3E)8dh@apfaA>|zD>Ug8A+c8`LLml_lMX-9KkY${aw
za*l)xvx{WG#-rL2-P9U32mAX35-h0Mem&@kPIg!X^b^;0fYD`+dtzb{U91v5sJjq1
zqtR%azKBVr%W@5W+?tGV^BK%$y;%ZG(6XI`RlqSy_=&9CLyO6PIY|Ud>)|@s+%@eA
z^enZK<wTn^h(_7G+}hZ6Gg+uyHJ>(!>75SAQ^V9K4Ib^pzrH@|?FZUOp;{{xWu7YH
zh!2f3Gsp!lTB3)Ia~OCvF41AGI!<tm1m_>xhA3%V%2%hxQD0J%s9D@!K@5#ldbFG4
zZ~zDQomWjB<t=s<2$c@950%@4)Bqi=+mE0c8FvzYaE=`I;bDuGK{+$%r3A}e3Y)}t
zG}7l0sb~A389%s|kr%0$18m7K<AB~3b9Mmox?c%J^VcM)=5X_YBbn;9zeDLyvkP*1
z;p$cZ<GzQEC@J0W#XbW3o|-5hX#lyp3&<OfNr@6gGnp|ZYVl2k;vi&UuIu->lpTC4
ze35M7KkY9W+T086{eRwl%IlbB)TcPs*zF?b;?XKA&?-v@bXqH$Kdq#dRPSpn8IOuQ
z*`7l-@iMHqN}YJ*3VUpo^h^59U~FMWNV`nf^e=n+lxnDM#dIk9099mwp>E>uFw-mG
z=n<U{-Z0u?gLqll++>K7e>=k}!+ByD!DRGzBlg*re6)_Hca-8y^6qrQpYsP+1D5=$
zJloIXBRN`7@O$#?-zPbnm#($*t8<2sBAO`ABY54)JBFRX^W*Z2FKy&hN1i!r?OV2N
z`$=L#S^jp<<_Ij)u{{0IX(&<Pd~iHD{$}bK)be|G)KvSXCJD1_S1s!NYvyYo;%nv|
zx&LyN+`&&nu-p}rBYB+<eN(^;_K=f_Z)g+KI)NS24_P-!VvM~-vtSoht>!xOAl1Gl
ztUb&Z-Wp3K2Bq2!I+JA2QtW?1skCV6a3?g-BhN?#?PTEdQL3cTZw6N=GnMht(g2<l
zB>Cc&3m{KxGczCXu6wBg!1uOzYq?Mf{?-Q{1%{FGfD$dVdp*ZZaXg|PpkOB7n0bDm
zp(QQb->^LYqz)T58MU0UG?>ZzT~L08suBHVPbF8rYB@piRdiCES8w6tkm|`!)Z{eH
zfR3Lf`-qu4QVus~0fc)wJsbLD-KQ{gvCi+&;Z^<t$0o5_n{Q9IaPe>k=}8Ug!SYpP
zk6COZwwA!UKR0hdFa?#=Idw`)*U@~PHB`hy3^cz?oGPJ2$4@^~%N-baN*5zba7I79
znylC)jnd<hqS{ADNnI~o&~t|Mbw^0cU3O;T%h*;>V@`!fDc(vrx`7ijpSCUbWsyOx
zCqDJC2|DeISz0OFM(mn077d=1Knz$VxEV-#-Yi)?ueGcv6OxatmXQ#@3yI3;lp}dH
zu<O4pQz>;=Y`WWqxeDWx)r$?VraQ*&+S!gH$#FO*X#%cpt}9Lp@riVHG^myzv>G;P
z9Az+3d3|}*m6h-5(iAaVX;mv-eB`yLL}i|44ly4)FDNaWb7<(!*ddZBO|V-=<nvaQ
zdhQfsg0$M}O{&$YDmAu+cgCu3faN#T2fyEUL<%rb)Re>SV#pt%5=KDs0CVUiaqVlq
z<A~Az^Exp<L7xejUQX{S<*{M?v`1M;ArKL(kfAoW+EV<JEwkD9asV`gfB!HwLL2m>
z`FODq=!vv`zI-Y(R&^Hfsvg3MM=z>8r5*hVCXq_;5e*Pd3`UhVj5bfi6f)I!0*Yw5
zQ=Fc$x;6DXLt`_n&~oIFPn3|{&eTe(maEM5ePN^0<pFA7)1(pSVd!S)Csh0v>e3l7
z&lp?}x=U_BaQ~VzFV^7zhK9u`?&g(YuUuT8%VL*DAXAC5hQ+qm3Q*ld$q9yEB;0@U
zZHKP4!wtD}Dsn5^!iFyR0fnaV?}XWmg+BP=yRJ%iDQc;`^=5|}wq1KYOuTdxHIetK
zy?@zrd42c(;G3O9iAwb?w~AhRSnjxy77?}2`kc~+4!<*otd-A1o6RG2cY$f<dHsR}
z<&Va3Oj*bvtKMt-Cf^H2Kl#+~8*KczpTuJsMl)!c!h7Crz^=qy;!#AmTrc1oWocc;
z@*if~htcwTaL$F4ZjI8s&b)%@&2g;lsr;tKWmeN%m2%j=3(IG9%r3}2h+%>IE_mNh
zr0^5&us%r&9_1OkZa|99HSG&p$1AWuJ2u?d&W>8(>qUeZsOBJZh=C+#Dzksxu^ff@
z9PUGp7Q8mIvOXA0bab>Dr{Op=l+orjRMU^Rx3T6McuLgtpyZIoFnXV?ZfR@l=s4!H
zq@L9#8pr*-i>Q-5wB)Oas?I!lF9aBaRt^uJ<Q{Z^aY2A6{7?d6Pu$=-j$6aWJ&?(!
zdEkI-Nn8Ea8=t-*{ps&VLOO6z^|sUgv<ZL|adzRm$c_MS0gyKhIA!Zqx(~6V3Q!{_
zl!i6><P>=uR)0QCw+D9<XbomfK!G=HhKy`Pi@-S$SRgMGlI>&uD;1J8-w0ROgK|>u
zsu8NJBJ*yVT1&aTW|_=_r(w?^O?_pk<<|a*y?_9MwZiX1!42#%->j7fCfof6TjK5h
z3^Nos4RO$0DyXxSN2i}@W<<8kJoDO9tG0F}tAJ5;mB+IV^U$ULJ-ky%`XGH^bGA+~
z{G$USS(IdPMD>(M!;%|aaegz8vf0FxxF=R+Jk0S)$SCd!wn)c3ux<TOetP&qOZn}V
zYH=fz38Hfil^B=j_h2?mn05{b$3i6kXgduymynyGoQ=Fs!}KpEE$bE%Ec)g6Q{zj;
z7Pnp(#3Yi2H_V-P5jB4zUDBoy2e%v~ysyz97E4ULV05bB5%f|r-OW(zw)+eG5}AEU
zIS|BZtcHWT9*dih-()+}q#jdp@$i^+)xxFDMSZesE-U+q`amrJ#|W>?Re}5h9@{Xx
z#|S<!>=aeS32aGlO7dN@^Kv>SO>j+{(-HjCM@itHd7evj^Pj<wFFhA2PvTMztIQ!7
zYHq*BpT$6lQ2A}nZTXK@x&=DFA8k<W!4QAO?9n;0@#w#k_zPTq1yD)B;ShfI@Bx>H
zB_u(pSv{32<#V>`aumSkL>RN3e<~NShV`5hYj_4tF<-(oTPXWYuz=yX7#2$Ynx{}&
zrQfPx3Z~q{xTTb_F%VBd+;{mEq%Y-<E~YFX4CNF^_)Nuh{QfZ+f4%h&)!VSI>H`E+
z#66%@B6Q?R*b8`V)&CuZw}A}vOIl@b;QqpYTG-C}lbIkXP1(?|=a0oJIz73oN{30q
z?Kf|lFCx$ibmIdW%H|0eg>>e&!zUt_9zMbgc^}YqHz$D=?HNa)eMBWNglg|BB~~KY
zxGNO7or9;%Z5p!vl1aKg<?X&CD?v`@T<^h)O?FTyl3`|Wy%uNe+4bbk%V|ph?!IJ}
zbQ*m1vz0yYvHdMV{kv8e4>MXAT2-|j2f99);d*}6t;?zMTs@PZ9}X2BO(=AgdakEV
zl~#!@AvUV^sVQoixSI;Lf8=<a#~|1@r^oz~42g*4UumJ$#$yjU*JGbZc$u>4a{qLO
zqwLjIb5_@r$paJ&RgllU;o&_Yjajg%XYB8$39Nf3qgVrha3jJ7{4j9lv4XWZQO`!C
z*Wbv^epJHXu&tc-;I9~P831u=NbMggY^Ti8BT92KBcR;JW(F4mvRzzPsj|W^Rt>;Z
zyXVPC7v*Nzzd0s+_mAI$E4;C&eSn#7K8bS^_^1^p^~(ZFUqRp4ffm1dAYFt(VBJRu
zg*S)%r8LvJ1+w}4MZb_Cg*gP^6UE4P*cCHsd`+;;!1p6VtuLL+Q|<@?oy0sjdyd>b
zgFhnx%C5Z3KT)0zE{H%jK-QO;gOKjvqk+HtvI&D5<n(@pDhaGSK~to_l6T#&_&G#5
z+b&y){f_;6Iah9>MOZ~pZ)vy3y8qwP@fhoWXq^Dlp^<dnH(*Hll6fW@zf_Cf)INT0
zDRnWAW;k4IITQdm{+|cDqm)IvV{i8>x0yJSo`@HicGV)kY1V>EK4H?1cg|rTOZt(r
z=BED_Kq*xGGmog>yuXmLIdF@?!}`i?(~jrUtXRK-P(7NGF$iQNjTNnG^ON1fB#wtP
zEBJt-^8Oe;JngAcLQ>BsLylz~i6Yi-&J>V;Aap6G*ryk^EjM3ZH1O^b^qz=hr>y5g
zuk}#Wx%RQ{(^cJ6r5_T4OBKFkAlO4-sl(4AzQC<m&=AFr;n~T5irOnHdf)zTfFt@}
zmCwFtp0mS$G1dunpxkg>F!R=24$igr8N4`?*dC|Vet|r?bsX)0T(Ues9SMD`3`UVM
z+&L+&;)6patRhzHed?ie&<>?u!FF0z7Z`J7)rVD0;<?^<AY4XEju>-f+D?*0AY(^C
zqx;J{LKbdAjX^7*#^_mc#JKk)-o@jxRLu)$%Smjk7}+bI!N0_Ri2wF&_TXnp57_ro
z&ZPY3)_wn~9ymp?wU-rF;$WtBrO_t03l(*ESFVa%q-e*gW$Pn-YBOp=l~9{HM!;BA
zgXEB?NL*W2)ekM<bEJH~Bo7^p3vx<z6tF`0Ey3C$oo|9a*x<mh@cwFF)iP?n`!fiP
z!nNFlH^nGRt;xkYo>Ge}OMAia7&4C~|C2|mY4Flgw-|+A*9wRdPrwgdk-EPf3DH^`
zTthh261-V)?^3{i{L9E)3mz37$Nz1|V8gZ%D+r9e6wr;NBAY7giH48G<4C(|nLQRI
z^y%H}=;xy9Da~c1WOSQ9hMWg=X>9YTo-fY_Gsew^y|AxdVn)iE_fYa58yH{S3W<u>
zI~rX~8{Dhbb9gLte79APdD@a;PS9+1hvG4tgVjdq2D4l7`SUkV5cH?9wE&br-W5L+
z2POIzD;>#0e3u_-hZp`c?yRLYdcNaj^}l|%agcmQ!#F>oJnUAJBeH0_5&RD3Y^S7>
z>p)|L>ia~XZ5c)+BFTG{r@o-JtcW>s-e5fW5w0E^B2ELoGuT0bo2EwRrLE8$lTY9y
zJeIdpPBxhk`d_V`Ns@rE8x*G)bZM8C-48*=&*`lM_s-DElsP_dXYo#AtSNF2T|{@0
zpq}wK3`CLa;^bK@vZ+BMR<~322fMz>a)1F`ljHXpNETL4CDuH!B{^X}`XTg9!b>_f
zf(=X9(<=EgyfX(xOa+rIZ3$@0w4<H>P-CId1Mmb{3oljznvk5N+MPxfV~@*O75#Hu
zbIcDY*=~u?b}R=?ca?vypkN7%*LjP#pvJC;;&d&lP3$0WeJ+Ek8ciP-IlBT-wQ6FU
z+9aj|a23*lAePMyn8x2*S`pi^W~hAAW{YKDW&rbt=_z@Lzbh1xGl9S9kvt6SXr;jU
zV3{$v9rLWJrnHe;O_2OiPP}^$w^C@ss=dPqiHKg**04CTHjgE~`N&u)hMcWV_ypvy
zYA|6K!*jQEgseh)RHidNjU(9qvMs%}>QetM9|0C#`2=Qt5*g=_I{)Kk8^-dUdFH2p
zr_(<aSD7aW-n631-(uX)wZ~i*eUFHoxIqm-pdHgJ7boH;T9{bFv3~Y?!3%zCS+@de
z1=UBNU(g-Qin1~$P%5HMN-|5d+UB>J9)Bj+NB3_wzQQQ9i<c<OzNvkR&6y`Rqphd>
z;)`(~?G{%R)|8*-i3I%1?^&6qCfrBzWUE5A=~0a4ZTk04C}(aIx1iXY6%6-0+BBI6
z$6eW?;fUM_la_R%_@3*POI<h<-jYpR1JRbu2})4SBwjmEYs(q}$oiFP8WLzbnv3x8
zRu%ghFvY}ppwp)A3$)-+4=T;Kp{6v|GX+$VzpK^8C745$<j9Z=M1L$H+1Ox&IL&G$
z7@ze+wJu9A`Mvf&pBZy;y3FgNTdO>zNXi$sL529yr3JF_WY`>H>4l6mZ^Y!#<t`y)
z@_HfsGlYGY02MtEW$>a36@HVJ<01w6+t1pQc&7w~Y{fRofcc1mT)z)i5s)kE&Q~~h
z#?L%MXbOJb7GRbDI?Fv{9B-BuL|ymaNstC`@s|}lg7^QzKb6}>I9H)85BPfyNlC6M
z-?`OslKYa=X1~1tcOLKnC&*M6)>;s*jf8@m#rkdFS#3iCtLAydnWyaoqi$YT7LTxB
z*3Vtj<xEx`5S{37mWO%YY08!#26Ykg(NE&2M}3<Y+6tW}xbh9(78Xv7Y{ATX997My
z;%V%A)fDM1k~SqsZ*XkJ#s5h=jT=uIS~vG(eqYHcX~1wAH++V8BBHE$uOIy%Kjs(Q
z5uI`K+m?c7H$Bp9T9+Jd-PYdU#c2sejR^f#4^IE8%l-n(sR4UQ2I9yFpBq<7ZT$kG
z8T_S$^~?%`u_ThaI*83X<rk8`AR%ZWSJMQ`eMUjH17;)l_rczOg}%*OQ;F=&zuq^3
zp6gnK=akqUH9VKcqT75n^vCPIbHoxWUMFXtcRz}?mDy5mijg(FbXCeTkHC(phpgfH
zkv^+)T7uZdiub2~N>UR;XC7j-;CxeXLVkP_ie`HK;sjp=Uq4kbSq<j3<JcvbjZ?H{
zvWJvp!y7CF3dY%NfqVwRe2pNBnP<lF0oXigz(p`f-Cda14As7nXWs<5ULHa+ZEm-5
zo&?|-Vy!;@f5WrJ10z@xa{(~|mN8gn0F(x4aW!dc#&16NGaGnffMjy8#BrO<@iUnl
z6*6BQe8DOXaz|fYW;VWrhRUvhEcuA5vdYjv)*C6)rem=Vjo8h-ngjCM&i+&wqJop>
zPrUVB^%qWoXFT*-RN{oZ3mpO-4R00Yp+n!&8?D^X3I61L&*|GJE!diILr&SQ@k)zk
z=swylZ*^aIp=?7{RsfS)?Z{vZeNYv5A&8+r&GS5$-zT(%q^g}7&LSL9&G*)Yrxi(h
zuDzazRXmg08P08BH-K!sCm4?!o8ho#Mmf)QeR1y#!8(*BPyL?aSfCrJLpC~pWgVM)
z*LoyO8(Y{7iM76>Yzq#y5cR*Z{mb2nuRs_|`(wpdHntJh-k$K*FaDn^-UWX%C^e#?
z0nYm6XCj*&LfcLSb@LxXU9TL^!CECXpRnhrbrx(!g60qdHiA8GlRYVe0-HEszukvp
zX^5H<m>1Ir-+PkWCD+$;1a~Yq@%eO<E?W<Yvv*W%3#3JQ*a)^W!;Od=D3Z}Y)jLPi
z6eQ__u0VL%7LGNG(Wi?3B=Ao32mhi7PSEeJR;7*?Lavj+IV2Da1xgu^H?h-Dq*%c`
z8+HZ^!OYD0r`gu<b@GrTtvnvStTi2cgHtm!9u0b$cA8Z8t+J>=wgT1&&D?>zT0;Nn
zpXrVaJl@F5w@<azE0Y%mI@Hn_)NQJwmY>Qx01RdbF97f+Mf-D@VW2)#H9vNSb##e&
zEdJFE_s=}M8VD+J>V$49ihmEz6sJt<{8@G1sZbraOx<77f^974f6ZcJPhz{uNn4i%
zrvmeHJefX}%tv04b|iQ&VI^e_FnN|Vm3VZ!E`SD}?Vso|o-I^h*FW-lNz_SJpHqUp
zQcSDbdrPb2bVVM5zp*21x?C6J7v(vmX3+N~POD7RA0@0RcB5@MHsR4gNmN!F)cmRM
z`xrw+ZlTxu1VzqGucwiS;7lE5*DaW{t$4-%$lD`T26*>5`;s&oGMJ{?PVHCt33FNR
z#9>}nxWy_vyqQBzgQpf4+>iTQ*2Zxk8%jKRuO9Ic8|J3T<<>^Y)w>)ly?xGh6*-oA
zhPhQ_+o8x-@-82k+D1-{>;iH(*M@>!Y+XJ`_;)b9+y-O|w*Ag*q?kcRxVQpofZcnE
zv&eO;<wm<;V2~2S+T}%3+8pm{;8~qL1b*Xab(d-cY_ZnE8`om4lFFZg>zBrulw3wz
zaV|8L2Mj@}>6tdXGe#l5M<LxZ;~Lw$l@nQSB^5x{`tL~Wg~tyOD-uv!8LKHMF40Ff
zFaw{n#5_?5eS0~BN4~Hd#o_fuSwa!pE3JJ1#(N;aR?eF+p7@vo!Yde2_y0}WlSD%r
zFpX+jc;sqvwM-3&SuzqkYDd1paE7c77rlUp239+7*can#ABBd$M{HHv`(*(=kIT2q
z_`XZbu~(3F`702?2C?w8n&_cjjFZ1F2$zS#ixfZfZs-wRG0(9Vg*U}sVDdIn)_T&X
z?|c5djf$MvNErke7Hp3^a-zs1P}SFY3jZYidG;2(d$P@D6CcketfQTj4t+rwCD(4>
z<#<W+Jh5;6&s}H-b$<<1!!;SIK|F+#jw%-T-2WCreb9a8@7^9C;JQ3OH{@(`c>7sT
zdo~G5x1TFX*@64)pxSDEboC1*c8FV5y|qKUXzr3-d!X$zx-x7843%l^%)>22f^S6D
z)h2G0rnmBkn!KSA+$6p`WijGw0;aT0rW04~wT&0j_N?melY4v<Jn~N22m7C;ccx|a
zk$CfuDeX^|qh!XC<8#e-?7qwC<EBmg3h&F2tcQ=X^~jizlP@I7eEJ;iu(j&AIq6K^
zk}S|xr)pFN#zP-Q1FJzzK>-s*EIift4V=_1qgC97r?%IH6%sJlEG-fwW4IttXB^&v
zLYy*Q$7K+wv9V%gqipusE6I^gXvReN<`$PYeT{4#;=jy%pQNR&QNuPwNoL0INW8pa
zL4V>xJye^_(<eQLK49W#t@X2r)U{^Q6ZlEoAODTn_bCo%uWt3KV?JZ<0e+nQszdYS
z{ut4!Taws3g<jICThMrIuF$n+^`8g(0Tz6l$-*r$<RM*psQTAl%`t{R4E;KR7hC^#
zQ6y`A=QeOx0&zEMi_&UQv<1&##HM>Cs5W(F>luzi0n^{fpqR*t!;-#bJ`RwljRrCR
zZNk~{;U&xBXyH%K3EOAwxm({{`g(@&-p*TPhO#t3d97|{$5_0N9t!}E&W}cq_rw+u
zCttGyEvWb;(FL9zmguH5eq?^9;5h4Xe#qY<vyG7i%<4~iX@~q>QqkR|a8Y^5Ty01`
zA>t+8oS$amK7W7^Oa3rjQYTxzci|2BScO#=C)9o3s>EJruFgbB>O3Yqy!Dp`&c?gj
z;mYh4?#kPqVUE5P%$VZsHsa|)Rzspo^OMuAJ-5s`M{wD~Hn_28t$DVy#gKmu`FwlG
zMQ*tVl-FAiwIpG5bJjbUM|uIbwJD0NCt#%<+>!Q!FnWclH$c#fDDO?Wl0L9kP`!|6
zKfU=83sx8xf(U4+om@rv-~g}H-q%F1AdqoGYbcAOfrYoviELPI-6|ZDKwKWns*lh`
zkTe9!UFLE1U~RaRSTO-cCbQ38&;LCeX$)89J?AN*w@$fs4y4QY4i!D`vxo$<+NDmE
zYbR8@4}abN=5Nz_T09xQT=5Dhg+lTx$(jgF(PA@rX&Hn8SlVyLy?lN+!d0$RsIVOI
z+uRdXG*JnXUemD?ukY3^hk-?o4Wd=RHl+O&`v;he%v1%79<Z*gYZ;iU9W0^oR{$!@
zAn6127JqV%Y`I9TESo1wxcFseumBP0s$e;ic%dqnRSH<+JbLaY_bThMMp*_yqFfF$
zErO0%g0K{KfN9Nn1pap1{{+uEb382&&LeD~EIxZ~USKeO^*e@RTjPKnP%i?G_^0v9
zd8t`Jn|RD5%u2M8)Y|ccbP9oVoj_P#&rWp+b2cYxg*1#t7(nNT^L7UzCLT>xSUC+R
zVb<L8xG{7ftp(lkGJB6%8itVz(7!WdRHYqhu6c|czf7Yz@&}f}VEc}CAlJW8{GtUZ
zS_jD9hQk7%n&9rf5DItf9K6>`zZsFNdFR={Uzle<Yw`S$vX)heNzG5z3|^lvubH<j
zp~ZC<gn3NS!wO*z`VMKjEuuUb@1m>D6#lCpiBjV9lhF5;{u7CsdoFHBYBzQ4z*acz
z=dYy;50C|0OGIp0#C1LFxxvyvGr=|$p<ow5v(cFt%?*sS+^iKdJsVL^vK%cRR^h}2
zKWTwd8`E6U+9|84(QkPvCwO;tOvf))!JbCEC8|`sNmZe;0I8wJ*O<YnJr3qk-0+5w
z4+{ruJ}IHK8i4xunngsYQe!0hRm0<fa~EVV%0n_`j?{le2nJtpfeRc&_#J@<r2C%b
z^$!M?hjWc(j{jve*aXw8=g1GVKc825I{lB>UHnmd7Gw3r8jT2432cRbu2uuA4%)pK
z92wNH66CC>@I0^B8;X_@W$m>ed*1{hWNKel1477kI26RAm3^Eit29G8mng}z6Ysx9
zt1O+ePc9u5UZ08LPDO>nzN;!I<)0tmX2O(8B_%nFrx8jbBserdKYb!+!|k>3pe!xt
z@VBT^MxTwrnFeyVZ<v!y4i!5`OXhXymz*S5*FLdOe2Tf~{dQqbedT!Ts&M{N=Xz$u
zDOGW_J&;0@Ci?omwLR#b`=Ox5T5fs1<I|kIjTTw+6lD2~krVWf_KnXcFX;yCrRf}A
zmwlG;+ex-v&NlXs=MQVoHEd#^tZ009Rdw*3dWS?~+JqK6faLj5!p>E64tPXdC2%Jz
zFg4aUo^(Bvkd<dxFt5Io%d3bBt`p#*DK;aT@UVx+MEEkq@ZE{FWRpACw3Nq<&U{M;
z${y1Bh>5;k(}Q2H75dRVl}nJlG?vy>%<@_hIqksufPK7>WfNdBkAKyqk5Hd$8ao&8
z9%<MI;PFe6OM|7}%(P1-E=2Fau+GF`Q3=^saw?FqMOE0Gd9^%rT9m*No(d%FFWj+V
zH3y{coYhY)o`RFMaB6}_Vn71gTdxGcesL;d-V~14L`P(!kf+Snaizu-?1^F_W%190
z27&5YdK{x?kaN`^RmeP{i@6#vMp0!BfE#;c{9EQfUD6E%d+|8ojQAcnPH3{RwH_(l
z`y$FH8p8f#^e}Ps)}lEw(9B8|+(oMYe|ICB2#b=}`kU5Oul_I*RpX)DVY5O411=}<
zA?Z?m<i!G5*jQ9Zei7upf%6tOp&$=oR+^%i%NGFDBtXddzXvp1G7S5n+n)Xbwwn18
z7>$b?36992M7x5ioy|8-Z4ui7Nb_bjP0GD@Vp)!f$2LMzYY?Sob#5jI0QKhljW5zs
znG4%$97HS&qV>(!=(VPGFT>p`(j&iSm;YPnS{OI}X2cv_7r^*?E;`*^n*)l(kM@0O
zBBQI3Goe%4+wjf&@fcR{9<#logd}Be^ToR7`eEsd|2T9mstsy%d}^t<__L*5!O?9i
zS;P-+_ET$9C__1n$@7mLup{v!Y!lF*ga({bqfy7EqI#GwejSu{3X@GvDh+q<9Q+Z#
za>8tE`SA&fXfpIVzC*h&iSA})r9Y;I_+niM54Z`Qh|53HirsTNbGhf}xPpP;-m(+R
zFw>e>B9!!Kjd3>))ly>;)PP%5nK&`9=_m2~^y$3+5|1IN0Bye!lg7en4F>u%(Iu?A
z<iFfNLg9eO1Tuh|b#J*#RuueI@ll<6xn(JzAn>pR=rEkNm2(Za1*y|PU{`L+!XM7$
z3HooNh#7Bip4=u2+#n_Ka8W-;4(o_YK$0F~nneH<jxS?(Nbc_-2!Qk`MreQ^Xq350
z8V7;!S*~4z_|K>oIm4?0&!&7Z#;aAg=ve_+-_J7OH85j@Mfm|?_sb+c^f2dgsdv;^
zX^+8U$o&!YcD=~>5*P(Y*m%)JFrnE5d3peq|A@ZQS!jAJ>f<DS!ilI@R!w?WQazP!
z&&ak%_V7W)@<E+&tYwY{CnRk8{RFZhZjiW>`j~HS*t{?>B)DG>33E4BUpt4WnZQon
zACH)4E2nJtuQVL<8m_Pc4Z`DO6I85}P&Dks+Yp<mym-ZiQ$dX?8tHgB?l_;*t7NIm
zX@Qyfdr{+qr`{)ka1O)AOc}QZCE3yT5^W<*j98Uaskd1BTpc5st95Hx!l93*s)^LQ
zjf<(@!re#s(+{nG>2KsK@ktt*<uKL0GQ7*a?<}c1x(Ie$`VD617T)_M`S6{JNIy)a
zW)PVU=DmaOYjlPYr%H#=9SvDSYG2fT^~FlU&szHGe%w!MKXbTQ&ubqMvGi*=^jDY+
zx0t!~Qer|rBfQZ3;4WFFSoBArtvPJiY^z9-lHvgO*WRn;0+?D~t+I&bu=+#O*q~G&
zi_z4ZskA9LzofCyC06Sn|Dd#09OS{&|B0qpB2&^-bI+Xh1|u`!rC^@?d!y#t@HVCT
z6X0!jp50jIK7)&*kC@LI>Y!kAeidEX==>c-o4rQB#!3R{;9f|=@C9+0%r+=(pFB1K
zG(Coa2N)e`w*q{6<)ASRha+VafQOGC{9d&BlKIskXu$kx&FWV3s71jF@<*~Zl!pWt
z$RFMxfZG)tcZRP0)W^3^1TGLfvEb6CfLBn>=pkvqH4Dj*f!W;!GFXzT^l1rJUQjNx
zZHF@AL4Bh1UPaACaivHtQD)a3wo+sg0jqXGdMtMB;?Y}VxSjCQuZ^f2g6FpX&W)on
z{G{f5VtU9uE0c3SM$cM2|BlgG1MJbLBOa{qD2Tlb+L$xr&Td(cVmm$5Q8N4W{95#p
zR9wg7<nTTRb6wB+aR;4t#(ps@$hPI)9Qpo0T&q@Y<Yg%l<>|dga{Zxn?rYrogM_}#
z8urLdWLc*DYqJV9AHoScyuYPqgRh2vgN%PhMyec+A+Lvi3R$?+_T=`;tK`X*fS8xB
z$H^R{{qbu2FhmX9+0w~1nNF(BCdW?u!QR5=iX4xpn)w|`UH;K&g1wISNOY)bhTz0#
zA8P_Eo0YU<JW4(I<`9j0SmxL#`zyk8U^D_yDA!v;O#pq4qqd1-psjOH-4d?|wZMvp
zo&zrIchPTe!dMZZ=>vy|sq_(>RKYn#T0|4-qb9*M_Ag4e16(zR6-0vTccX@Hk6Ha(
z!`Z{Kx=5)g)UF{&?g@;aCVuM#<`hvV1{Vvup`Wtj;ptL^*r;H6EZ$ZYj`YTh|LQ|p
z@s!~Xm4wvn0>~W~##o*?Xb1wqY~mfiK{l=xOpUbAU2o%;U4h>k07gKt&0yo;9-5FO
zzKYkz79vJ4Z@r_OND2p3hp14-WE8NwgGrAB7K$A07>*sz2Hly;en@GaY?ovlXXYMw
ztsN30N`Sh$RYr`5hA4}5Do~bnx!?DTw-0J-JNXp=eH9Q5T(MJv;-Cz~z)QTfy~Vu2
z78!mgyyXx|3*;E|fj9#|2~0j4iQK>U1>zr%_2KpoceM*bGp29F3@cR|;U1m8vnA|b
zk7nz?%57qnt`t@W0>4hK%6!QXy6!-4JOv~NE|>Gne4?+s+8z55dWI<RS1yP*PJ{as
zYdv+z0WHoTr3XY(A*9sW(Bn%O9oDUZ!c_wk576|xwx#C>?`Mrf-Hcc)`|S3DTzsX4
zdGMb=ThN$9G(HQH+dV4FQkT1C41Lisdqx+d9m_a}?W}6|kFfy#EPkSA5;itvWwXbx
zG!*7UX;~#$MwXlzI554o)Ww?qWh|!qFVT4QsZ>FDZY_V|^xzm+4I3#YbK5LDnZaG8
z(!5LeB__089HUwCDC+e9=3VU@?8Wz@J2gPgnU3;o4}^PY|0Q;P!n&=mm2USKb0i*T
zIPE0C;3gjmHo)=G0#~!v1Ztvv>iGmg;U^%snLceyk=qr<E+E1{)(NcyEVn4|z(z@&
zL;m4My@C3H!?92zgJx=!Qr*0Q$N?AHSDh_3?4NQk)${cAd@_0UsQn7Xk1JadJJ<dF
z$LT+}?Y!z1_)@R`i(X!3j#-{r-kH48+`J2PVb4G4r1+`aR48mXf?OP8cd)A1U`s4)
z7{oMmbiD0<Z7E8Vq<K*zn4+V<m`$k1kI(VU>a}k_lrS-Q7yFkqTA{;Akf!^kb<#V@
zRo%1pyy$uI;;Q<JKx1z_u_7Mq&byH(TMP=U9f!#^wAMia`dqkN<GHH1ywPhF6z>z2
zuOv_ER5<T><T`X7v0tvlVioB=dEl@N8%{;ng;wRRZe<LdRyw=H45GWH`&)Z2G3Pmx
z?X(&EjN}5Tdc)X}IfW+WBE`pgy0y<$lNW58Z#};YLf9h?-|g7hWwJ`kYu={*Kqkv{
zja>g6CH%?2JvZl+7st6yhr2(TZYm*FybHE;d7-}bOpXbh!I8DbcQ=nmy~M@uSS1p;
zZIONx+!{asq<B`6hG-1?S04EZL6dIGI{6waY?S2FBRtjy*%tadR(!bi>cK*yW5P<v
zbKhr4I_Grfhdjm_?wj<|b}C>2<Xf}3VU&E#8QKR8oi8@rHZIjN*RN-jGAj{f+gsB?
ze=Yc9i`w74gGnc*r_1D<w?px@uH%0MsGvXkia&<Cc7@CJj&g~Yc3XO1ymYOX?@a91
z^!Ss*F7X>9yS_OttC=jhP4)VdeEE-ZZL;4x`g^JI$G4U+vfn`1+7Ms)yP<A&n52`O
zVt9o=7ryV0*hX1-sZvoJZja?fZCurT<}G=|Njb%2YbwoC1igbkxo`BgE6yyf*p?$B
zJ*YyWZPsKm&S|tcW>o9F7@Rt~WTHK>blzqa+f^58li7xCeL+s-DUFBgiWD85&Xezo
zFmR6X?=_?uuO!iypriMYr2JilG=wnv;Z$p7sERj{;#VkPzG!GT1R31r#Czj+3_<rW
z=ESO4J(*+L!{Sl@C+t%d=~deDSU^5~BgbwsCP-=NYO24W#!J_R6^Y%FKhhJ?w#eAC
za<c~dWsrZEuJsfHFsJv5t0Bz4O5KOgETw00UE3ioFp81qj^JS`a{ACKjhFXjr8l7V
zdgxWNoD%ldz#~+zL)j2=-6DbQKB;?NWj`A~g0hn;6y=D|Zim#J&!)>luZE?6H%UHD
z@qd0+uH>zbKEZwNWceZi(_=G*MIZ4CIi_oNC^<1ZYwmvi%TO3Co)<XYoP*zhcxPNb
z5ovoQ-Jl%~+tZ<pxwo{tLv%i9wl0BvOQ!T1E1%HFA8bx@q*;#hIOcO!g&WZp3z^O?
zanyvd!|3`zqYTakHm!X~l<X5zc}5e09J-N5hh~!(BSm?_IS;n5Zs=B6ilJLp^x|GK
z$Oz{;!8AZ$2(gk9`6JVtYO{<jH5SRmdfocdQGQo6!reW)!limgQ#D{FgXiuX{$)~3
zV$8n_s<=%GvVn4fZll`pA)(T+)Y7@+s(Wu>{+ziph9u9u{yRkAwN!7p-^5w<cYB9Y
zA8TxfaMYhRXpCQ~H0&U@oJCPrdcJ}SHy8iwp=}N$Uw%vs_gyxm+<>>RBN+iJyw6V^
z<97Ey_o>?3ZNzyzAlooJyi$aDVeou+_Kjm<r*{XeVVC&cm=>|!_Bz?DLKmE>gza`z
ztk8TxI})aIlotZ>BK~RtPHph@_f^Oo^JcjT?OQiLzG`H@8}r@(GEg%l^}VG2D|gTn
z^0^6<w%UPPBGL7yls04x0z;UdyzB5StzAES@MwU~l<QEFq?U~kLqpOmSDG}nwK2G?
zxEAxh>34`GefOwEP?pqkCm2Jedq-t~z<KWaQsu$c*W-g{<Yos~lhWFR_Lge<a}{_c
zB|JQ=ig&EnQj9F>6v+URU|FLsHJ*J3GJA}Pf)&Ts-k3w!nY>TF_pp0KoO?B?jt_uY
z7H!prRDr%oQ9JhZ-I-m)J5u9B#a#E+lgmEcHfu_b^}}7bS;N_xIoc#O^r@KB0)&o|
z8QR-BvU`Ps<KraDaR>izF0?9xAC#?&$S__m*p8azv)M}gw7&orW&^*P{n0KVtm~J0
z6+^I{)0K!wd@n&JyL1Znv^SQpTPbp@k|v?Onky+D`Gw|D#pKm+zOwmGruH<~%Da1K
zoqC_S!Mvkz?ILCQ6mi==Ws(BV@)P^?4-X9C&n3Cv@#(Hhebfv}fWb`J&THn=n@Gs&
zebSS&n|BK?_17|eJB~wd=STkn?1<YS>(nn%jkG$exAGG+yc%2QNetWb$<+>HLFeK#
zgA*jCp7|1xGBc;yo9*{NHUm=~tnQJ&R+5GM7`?6CVDf7ul0nKLcyry_EAt0n5!I`e
zUO=d6iud?~b=pwBljS*>cQ*Bv+u()FU~`w-v=gmLX*<9Y^7+&5yN#pgFZ|8^dbYcV
ze@4ENeV%>!*#d&?<urVe8?b25WiD?MyqR`nVdoIuxPGZ47$Keq7S9y!dxje0!tAEH
zYnufChI3Q;vW%?1mGT7gi<1WASZ?5%Sry6>-1voT>VpVyRpCAkz*a(My5(Phr?__C
zh^jb)JaGgsQw;U&STX!_RJN4A&1d_=!f(V&=>aijg~dlw=z}AjnhlKmG~WSV&%a9h
ze%<?Jb#lUFE&TvW={+p;1zzM8%>QCiYnV1v(f*Oo&@QE|WICv~MMf+gPT_Uz-BWCS
zlYKazd-?LUM9*90kh79od0E>gF4IdB+iyq*k^0>A(ttGnL7_Is-&-qWSeb{fC4W*l
zCs?IpU=#&k-=r2~+x)UlAw~(@24FL3-sugr5_5?~27&DRYxBpC#&`3x9}Pyoo5~u!
z=yyLu`-jYT_D!XerCztkUnLP<@x#X2C9DL!P<QuG>YAN8i7w@ig=@?YQd3IeFnjOQ
z#ieUVg6fI=Nhgaq^aQ<IvgMSazMI6wYVp-T+llCU*)JDQk3&;Y<BrQc<6#VR+Qu@m
zE_Jg%i~c25J|;{<R@F>ztXi7dD`|vvl!n<bv8@OgO|Y18_%w2>Km)A}pWln^nHh9q
zEwJ}U&1;W!n{^l-uZ=pmx}4867bhZMwUA}C-+k=rYsmGa?)*F~GpgzZ<;bK7(M!-L
z#GbZX@4;_Zwp!OhFq6#KM-)h^M6R4ou|xfv+oy*)<nu<BG=4<>ksL?K{C>5wzi4_!
zSzuwPP8s)27qY{%C1M>tw!k%y&VAF{ETkc@{S&0S{D?+;x)>E5!CPdWzNVu=s5-R0
zOx-H@94|?`L~}7SnB(hQm8?Q2_RUeDZWOv(w5cCm%CdP8dw>3glTQ1?DO~r-AD`M~
ztrh(Ywbp(#B2!9DR%?;$h0cpyTRim>B|T<LWzPFiT;^dm;SV>;_HI(@C*HwSkp@+U
zA;11LyhEhP-%n@GylgM{AVH?9;o)NDeft#Oj#mZ38<E2>mi5)9%EiN0h+LU7zEQTi
zWyI?{vxbx&Q>N#G?-m7;l{Oh#Gy=J@e7_aNXB>|m(v-|Y)Tj$!y?Gn7lBY2hRS(Vk
zegD`B9Ifbt%2wzdZe%|(K?0*6)R}ey5_^uqM+sB>BWE?d_<i>+OU!TU>lj`=HIDi<
zw?^pJ?BP0I7BA$MsBFAh1O=7p$#n#2Aub}>>Jlj}09E^$=_$6(5U*%nyY7tim6Pw0
zHs}$8c&u4(w@2df+K^oqs$Vi$yW<F^-%-gb+B7uc85*&EAi6^il&JVGnZajsWE;Gd
zXq#$~7T$Et`1#YG&S{+|XR}t$?^q_hc8w9t_s;B(xEpmjU3IYFOkR?e89D(;`@{?B
zc&prH`tJ@-7RjZ;l#R=vlw=q$eO@dd&%llylW^s@?1JxtWg}WKPMvDsBym%YQRdC{
zc~;G8m9)K(M3WtK){lxVPaJ0|OD&h@M>aWxN>u|i2N2HEQM_}_oAA4VcSQz5>|C7Z
zMf4lYWXEH|ar)n>@Sz!!zxld}(0ZB0Axwx5x^3W4P`Y4q%@44^&5d+y<OSq`5@8@u
zbB|NlH@Gq3=+@e%Uw5W|nRJBZ4!PFcp?f%AL&!!m_XjKyl7=K!I+xSiJhNw3kqZ_F
z4HQfUHh24X#}2$LesK7iPd6%MO-a^dj?C{*XIG>qdrhX)8P;|zL;B))*gnm+7_`kZ
z9xg-r27Ibsn+Zx0*Covt^yo_DawxI_*#;fxSX9Dz0wwD?NWc6B<wz#0$)EJ}_J~r(
zoJ4s)A^+;Gi4nY}@^vVF8Oz2u4i&H9+`9|De;i*w6o`2AJFp9+DvUFaDQ~*MTos?s
zeAi6<Tl4e#2`gJEQFLBq-NNfg8`D!Qnfo_g=KP^LP~G*gN%a(Lf6nUGOCxcE!F}V}
zrW^%_U`P=F0GyGi1x4CpSwQfe@t(xJ?J<xul)@XtMoXC2nLPN_S_#um;^qa4-~eP_
z?7~nic4`D1-YA*Qv9nRvkE0Z&1l^CeCaX_FJ9Zs0$xYt)Sa5f0TB#TH;y6X~7rf&k
z!7D8NR9O0>L51Cf!MKMR!rhwQl=}mYnZdJenWE171LB7nk?7Lh%#T@8*Yle3_1Rk9
z+8?O@gw#dA4pr5j`2fnPAR4iUht-dn4q9OJ*P08C!!T+K(kcH_IyWQtd)`y5U)19L
zEZbzcjo#0>lE+P{x#MnB_=SjcxgMhgYU8i_ll;5QCa0b3c^r#ed0$OJ?V;toMeB2)
z&)&b;1=+QGOSe~&=+%vbC&sZ1FNAYqf)Ab{68|)r9vvZJAW?Fk@Ogg@>K1dkA--4k
zyr%b41%<lQqZ46gy~{CF>!j%&s<U3p(IFaZeM7Pu{_MW7$EhjH(E`=ja$oH5vk{z|
zV$u1;W5Rj^kFR{ZB+A;7@>Y<6uZ4jciE?u@CTJ{lO8mD~$l+P{>7(3^ihEpF4R5=|
z9IoG=-mLRYT=M*VCwYz<dz%yPpLbTT;%<;h)oN2{gf0!F>@976kzL#?AF~2}JI1PU
z-00Q--kMOw8}sCGH+(q^^YcM1Qlx&0umlRel7bs#V;-d<@H`{#7ROLoP6v^WA4l+6
zhXab9t${o{0zg|KdLFCbu}jq0e@@QfW#gE>a?xiAY#{_vE{9Yued*9~zj$y{vgnnf
zFl6=Y>yhaO_y+Ii-McQ+DMQfefVh@>R+pXDnP0tj4XzgW70>)OSSk?>UrP)=KB%BH
zZdF%Qi=9O*xApt~jDLJgCGcSY-Rtch9v-e6RM#I0TSSn7Li;kQzmtb|37NbVFT-;x
zp(1`mMaq*=p{%m|wE5L)C{b?bcd6)IpF;D?v9sVjDq8C#%9S3wUtODcU3ON!QdUP`
zIR5HW%dvixS#W!|chZ5(;C*oe=bS7<3%^Tl9pgq8?k1%gh9l6v&!6JI)uj0-Zd0?=
z-%(DO(xF8>Af*%-m`H$)aq$lFTFtP!`;PYs95*fAKOyjH0wzBYvHTr9htR<|Y!XZh
z%RdiTAi>i6VYge31QS=}gzB$!_c8ONaOCO5t`U5wuKc0mM=PUMMApqnJ8br>7Kun{
zoZXs+k1cz-4)4%sN0|Cj4!ciZvN5&}8j#hoW>*#O5p>iFdSZeXCAs;o?Tx)9uPK)k
zJ#{b?tF-MvD|C%zZa0<N0TF74YNI)>`>!PxmTKsU#4Nex!?4GM$}>7&d<LtpGG)_r
zU&g=1TwaRXnetWy0w~So6GDl_0TL4Jq46EnySe*h_k)4gm<Iz*zB$?(y)l?={b?9v
z;BY!Rx?1hU!Kq^vN-vV0?+&@SeG>OHUh)hH(x`_o=xiGQmCxf;xQE%WaRi21;Qh1o
zrKgf3?!0V_JZgEPYGb5vu1s%8ZL^w5Klb(}IejX!YT}{hc4%;&B)y8cj!Zo7?Aj=M
zI6+z?w}bMk)dCML!FYC%q1<lBc;WxB^zGqH_y7M%QVE@;a#)AER6;7}!$jromQyN~
z!_<mm&c|V!sC05D2{|mOC^<L7*hoxHtDJW*40GDXu$kH3zjxoy@4Ecq;=26ddhL07
zoH%0`E?Vd_PfE;lyVtu8;#qz)qOoY>9+QzRF^KbLc0Uxdt5!``U_iK3gf<K7+Neg1
zPO!j5q$PzF$tXqiDw`kY%6p$^8?;^vbekXkX%@K>@uXW$9j7&;@M)tlk`j=8<>sBV
z+^0^Sx6^Ltm7Gvu-{{sUE*DKWoUgUO<G=U6PN&*NV}6x#)kVx4$<<o`m{#uSTX!ob
zB`wUV8`#it;f$8ouG7yBjQw}CKW@*4u?k)J@tb|&ChwA;*Zm4U95(kL`6proV@<9%
zb_KUw>>p>dH;T9f_CDOebWsUKWg;E%(C<H!Xm?0{>5hb*=OrJx7shYeQqH#h%!`@!
z(Fxz8d|}{<1P^PtZxm7X&B5ITcLVomp`IH*?iErz1AbcYC+tL;!kr^e>hnxKWkJgQ
zp4;?GJX8cmSX9&Flryx|_iV<@Q6MAli(zTWI*NETHO2;dE52=Ezko0ZF92igYOqiW
zoOl{PN+34VY`!Uv$M}qhvt~s*Fm5n|Y)j^x8w}M_>MyAb_Y3~fz|Az%z_E9Z(q(fT
z;1<@gNcX;NIxK^ikkwg~Nf|HJl3k{WFV#tLD-*g784)kvf)}1A?(G&X?m51&{7%z8
zCmm~kTYfF_3FvR@r(2|RZ<DWUx16uqS&o<ClBtthwt_FE$C;-0S}WwNiS1Fy=80ki
zLUBM?@)MQX1-!QXS)3XMb<*58;WL47(sb-zwk-uI+=4`*+#fox_KdJYWC}U_JA62^
zM+qxa8oAxqU5QO}7B>S&RCf1+>3G?O)D^{S&R!w3HFoR87eadx)@*>~*FRXmy|uDu
z1oK7m2u-UIfb7SU0+Ki{hPxGj@AVv1l@9|7AlD77O!kk&#VTP7_w7eDCNO}Y#_bh=
z{slRoyXu=pDqqU(-YBo$Q1Yuakm<cM5>d6edx}A5u8l)L8Ue3(0!YA4%l+Kl$-ig5
zNh)8(04D2zk|nmK`dV4B&XRu;pdVl>mFGjjUuw$#*l_g1c-*r?Bax48U(%O<*0KuH
zLgmNn223wh=EE;d?b&5|q35M|vxd-`WBqEZM)Cep-@;c#8Xb?RKSGnJoJ&WQ$Jf_>
z)ZI6OIcTgoY^%$<h8*OjF~66P9_zm__Ie5>*U&Wr-|7tC5iDXgs)sqxO5hzC76W<Z
z$vQ!`_dM@)OP+Vj{E&>f(Uo6ysQVhcP^<mEcq&vOB3N~+LGE*HPPJY=!qGkiSfqRq
z*l<fW;h>aeEtDhyQcko+tD5EMfj174;mU=mBt5o%ha~2_CSM5?9ZRc%<dp06jEHum
zK)YoJ!`l$jn^k3)yI2pD2%VB;6yFohkGi#W2P^7nfgQzcN2~Od?RNb({-#K13bgg{
zx#XjurMfB0Qc27?JBu26E@*MQLVcL!Ehpb8P+tz8XJySQBraT$(h9~tkOz)U9xeL}
zOh=U{<75=ibJ~aAM>rR0bUyYhG&EH0O)gCPD&9Qtbk!+ud%KM_^0?Z#@dV+Y5a@(m
zzWINiav@*&C49Z3dW8ehHrBiPQgdt=C=dU+a~u?ldB82>f!f8NKoDsJQz6zb1QQ+0
znk4Z+%irGGxqK>S=+y|u6^3aLGdHT?(P?7X1F@$+IK#zJA&ccBx8l;Avt3AKt!v(2
zBzO5FGt^%CEQLw9=ez`!l2#7!4gr4)9mH!7e9j58)u0WhHurl?k^4an@vrnqj47re
z$pim8kG{?1-K4B~Ew1qbLhoC?nF8l$E)9X0!?5V!x=`tF3=L`L=7Mk2go|;pdxnU<
z|FkP)vK36v@~Q^D{l^r<bm-`<Fme6GE~;*oF)3ZGKrApCH)qFj6P%W2eBQX}z%_I4
zc~Y4Cm7E8+m3O6uo9yqMz$8t}Z`64Hj&(+DXf05g6JKDs{+`Xg=hWh`-n@^YrL=*t
zCFzipXOVJ9r`y@K`&=7-;c?-oO_({8D6E<5XErS4Bpb#t4XExxAx8IAVSowe+2H%1
z*}q&ZNSey<Bk6%Z8$c&^TpZgsRd`<M-(d^?7l-n7n+<OMoX#m=er{3g=U99cv{0h6
z4LhL1cjCe_o0mLz>2~*NA(%QP2>*KidSt8eIrye{mT307&JXVP*5?n&1|7qrj!A(r
zOl5FK$_VW^C2-Cn)2lPO@;XivB-Q`T_SOBM8h}7jO0#gK;cU)=+4>Zg`-Vv=%lt~~
zKgTqi15-Y^V=qW;&36<y&xJ#QZ_ClZq9DsHVyL&)nUS)nS!A{|P`&u88hLA|Bv^Aj
zCo!4gK8o5GPkkAB9>vN3HEZP$Rz5{Xt-fLM+A(qL19jIlo^PY%4>-i_97lhV9OK2e
z`o#0kq!JRupsi1U>IxQYuJKD~SiRl^+M$(-HQRL3Fg>(4v+SWh=_eHu;L;)4FMv+|
zuoE#wY_~umsg{eW>2XmrDqKscP~npf_zi3j!cXWb?KXemCOkERmRZxY<CN(F1R6~C
ztulD02C?KB`N_qVH8LE0bX}_>ak6w7NNA+Sv{cv`ErbTi-_gws&c+YEj$fZW!i@Z2
zHuA5K<Xj<}yzKv@IzKQccz;i<xO4HYLL{g9F5&rlyXXHQoG(7@I;N*6RjqE|C45f5
z+a!~bv744Ou(Vl<ACv@gr47|lOljkKCT7mf2^!Q6<lL2Buh_muIe>n6`@i6g&!FJ<
ztTf}R^eruCq^=pVA)XJD(=SaK+r4LP56khk0eh;fml0_*%g}lk+5e*M2a>1%HE>t`
zgDj@r$QM!`^Q}>-k;7XuvGMLXzk#+$E{Hb$i0wSA<#^*M+j=H0Cjp9iGz17GhXnZo
zy%;4_i>YC7q=M+omzLgMx17|#3G~Ib(L!qs<1Sl639!{E53TCyukG`T=$n`jOt?ve
zoVBB1s#M3LU@+aU96i#}?7yP>6*Gt7ZaXHF#c0L@3pp4)otQ+tjNn092=tA~oEkdR
zNt|2TJ4c3^-$PN;Z<4R+-SmkxXw?k1Fml(8!ZMZNzbyJqtH(twY0SY8DB7i5{e9FW
zsiw(&4REPx7+$PIEx`4LTv(lkuA)FiAlGGIa-z$%=(T_68`^hr(V9MVAGFWvr|{Ux
zXUSrBXS0yfqB4tJ(HcemU2;_T*#^CEXaKJTq>TTei0m$B{2kdcegd!YSH*U#kmbBP
z9K9E!Ck*9y;P5P3i7Wq;3aezgf_thAOSMQ6?0=eG_6De>Z2)LQiwVr)n+wv&u0+c_
zp*y5TTZJ9WU9AHlxy*bndIiFjtAXH14~nlnnhpOzr{wCaV9j#Y9C4%=jT}8uWQyL-
z{Ebr^<ijwrjlW}0O6V@~UxO*`xbK<^G_-aG?H#Vze`#`k%$?u2)xHH6Sd7gAn<Ty2
zhC@%5XXh>oTS$NqHe?H3xK9|ZQ{%9(t2q*~tZ*o`{;*-RYre&_u5lB-=OM=okz=+{
z#f{zRT3X3uisMlHNw>LBNNtNHP~kX8hDtY!I|qojg!Wm8os#7j@>$N;yhDZ8sGVQ7
zxH)^?9)K}ub$H&r4#BRW98`LfWcO8$0#S7mu_S4?{B&f`8U_Bq<W{EgzoBQ>>&ren
z)S}`9zt(w7>A~QxAAXfe|6cO3UVvreOCiR&OA+fbWv>)5;>K$H?{D>O1_#0nhiHNw
zDdkt?<9EUi6|fVfl5$nF91AB46(!dTi&?alw;^=lz$5A9$S)@gpRw<%zX;w-@HEI~
zE3GgB8}1F@V+>wo&5a=OcBj=--(CvOOh}0BKnk|`#<P*S0(MRHHG9u%4wIPCZ%|Bc
zoqN!)IudD+h@ll5-o3Dtb(q*)BQtD;JsG-w>~nrMyCe7sir5_sI@L$NGN+JimONr(
z%x(>V*S`^`yIQpYZ^;EbHSTH6r+zZrXa?f<@hYrjMUjv%S!{KW)p!f36zDiSG;yPz
z8O5(33C1;vrWQXcX_T7v9IQLszfy9Rk-G07TO8g{(@tFZDKQLsGPF_oXt&YF{?66y
zz;Bb?>N|<an($7s>nL8`$W*f`pH-J%B)NCOm;I264_k^o();+x@6d}&In>#IvZ%;>
zcPG_$ZEK|MQN6qI?5aLVT=WxuzSHoo{L)uVKwHy0G>m`hXrOkAc3Gl2uT4@UmT_3u
zB+02;MV(F(IWZhVB_9W*tu<hM3@sAtZ`^IHnp~U5)`R)7vCKcw%%B8&kA^>=5W3$A
zjRGod7yGFr_HOZF8LoVS>v^dyR<B4fpv;qvB=u&({^khoPXi>u!}aCr&~by&<-eAH
z^;j_sFhC00s8<E^?RT4GgBu@cFCy*@A6KjJF0tUw4hANfO+Q{9t#8biVlW^R#VuQ#
zN<FMfqFK(D3J2wNk=XFL-XZkjuLMDuw0$Iwf*9HXqQ2Rqcv-T2xg%<_D#Cg3P5-J#
z#`Q!yJ$!$LM?h|ju!6Z2vU@Tm?|N%(+=L<Fg{AOdNAXdd@^W2kbR^gNdBj{8nHH#^
zEu_AZP!iEz0>42fkvTMAJhCMcDuU=189URDCdaMp_>q0vv0~E?tq&lYSDiMs=U-d0
zgQ4-KKffzBn+D7tawDYv1mXlA^^21Jv*o}{r#ZZ#8pxC+<UF##vdxYi{~BmX3AT3X
zZkDmp=&DI>U`Bhk$_yQOk$57i3Gv50vXO-DDy^N1WA_-9q_Kyz8M1p-t#y?=*|mc9
zPYan|l#cP?lge(F{s3_RE8L%A=F8BH?ujOnzDpP6$%xj2Ro_($!jCG6|DZ-+#}q?9
zIqU_o77RA#{FR;;Pt@P=nvf4xAoGJ6F@KkB`B*)w9{FqUEzBDc`i_*W;?^mh&?=*K
zF_f=o>@K?z18^F7kE^>1OvK*}7``nELD;tt6zRvViYL5<!LK{vD2q=|8oEo8uV*|z
zKBs`Mzb7l7RidX)L-it3$SMYt+icYpMx&mf6&B27<$Hs7Zr4c5`W4J@>P4T~jSIEr
zxS2*<YT&TbQ^@>@ch#mBt0O#-?<`-tT8WN4<zF^@J@BXL|3-n!h%oTB<K(H-JBk^P
zS{1*Nx-z3+Gw6$eU6};L7=*HPy%nrj^HLDx$Bj&21E6Lrp-6apBS}6ATM(1q*<bH=
zyy>rUgk<AxdCZe;6%SC`bj%QqGixL0&MH+A(^swj6d&5%qM-CIgYr`RU<Mrm?URE1
zN<Gm)B-cD4aT2Opv41p<ixKL;Ze!fXQX~SY3O9j^ku16n6{vykrF_K57jre|@}Z5Q
zRmGcC?T-V7?i{*Ka)xuBX;X*fhsBnJ<uq&sY;D{}Fyv-=OSRgVNn9|3;YWM&aGM`x
zBVL;^PgdKU&$3|C0!L26=O$B7blv(9YjKk@+AF~}x!ktZ5{@doR9-<d9Li*HF^6Li
zdA!1*^a;bz!1Lo6i_*$svv8c8TQ&8zkR-aR(j-&s3ER`b62F~|55l5S02%%5^DFxu
zR&w7BI-fXuyj9)g-I((RU@@viMy1(WU$-bGlv|uE|2q@y^-CY*pIi1SeK-W{^tF9w
z9N+^EZJ)u5$Sz;ZBWh38d7dkzT`lSGcv0(!i9@YUBOInF+Nt!!2&uF+eBYwDrsBp+
zQuX}dDlPI`h7>2;zX;w4KTiKXnVjHPf1d3sQ)@DV^-GjLqNyb*o2*}(5;=6$Vva_E
zlmh{kf(aRXQ@Uu5%9NT2ts)<Xn9Yey+lP8ZZC$adlS%!n9pZzrQ2F6t5?-xq+9hnp
zo~g&RLt_TAz5S}QBm-r|3F2_lh~g}L>D&{ou_s80Zg?3y&u8ph#d>Fk9{SQ(5ijXp
zEHcbt{W4o1r9G^JWd`yS2y=PjWVd0#RH#thC{T6nI-hBgjyCVFBfi%w_|zF{z?J`9
zk#UY3YFLR=xQVX5<H!8-d-hck2XQn%_fuCyB@%K}emtK4jDN|r-7c%Bm}$fSUTZyA
z-E49uO)OKZ`a0$8UoL}97bUcgt~`!*0_BSvd`iXZKM<IgM38Rw2>OMv5xi=T?%S<j
z0||$&MlV3?m0JU80dNI8b&t=5rrp^M_a5Nt-lco=v2SKiC>fv`ts|I2bU^clOK7eU
zo%eJQ1hQw)FGPw`eElb;D-Nt*T-OX>IopD=YNqT1%C<+-J2?6E9|ilRY|TMT%c)sB
z;<MuQ3i?38+mZHP%H~G{A1MX{YL(u&q!55GD<$@SyLfu`cS+(h&!}-y?}j%D5<NH^
zGuqOR^N<(w2)u{^#<tZaEWP&73%R{K=JL~E)*KwW`r0FE^ic#L5z^|uuXiKj8h5L3
zDu?c#H)P}n_DdT;gN3M(y)hmBHx@Jr3txsKt|oR@BWOx)^`!PUbKz66-Vn3USZt-<
z5OYZ!s@cew#ZlxsL0P`-x*zGcOG@{7%|qu`u9_>fMDNg0z9_w-bhKA@Q!V77;o-Bv
z^#1W@)NkN>z4X3%Pj6nHPBh8gN$LL91C9n8LiXW}cOo6;2Q^Oe3-?(!bUrOr=|nAN
z>eHn0Lr<K&VbULq{i67Wew@ge(v*j>;BB;4`ot4PwRN3%UVpN^hkiW6mOW|v%Vz+0
z7>MbN_6%#Yq?gd86If;HTMutDy3AgPQvjJEmty=vyc{0FW0`FLLdiuOj5&r1XvK?p
z^MdWGn1y4ciTcZvH*XNgbIE3%0aKpd7P@Gi-n7q$Z5KBqJEKknOR?QL@2b^rw}H?0
zhsw&SvE&t&{-RhPc1C}-!g50JW2d(^!yt<pIN%J}2e-q?1CNXkL|m4VdHLmewuja-
ze~zO~AOCGk?<RZ_sD@72ZxkCGiWr?LxQ=7@s&Wz1LPMzPYsc5HQ~5cs9Z(WtWBs0S
zs|nkBJ@C58#k3Q~y(ZuxRZhd*<LL3#58|^yppKxUpZ8pNnBh1Om9XSj4<0qA<h`Y;
z-SX;Juiq;$R0Yc~;cl@#?TXjp8{O*NIyVRV20Z;14NH}!!rsUBx6C=~K;}eB@a}v8
zp6c;@mHDg<n91fHz9BBA09RyVe~n_oHAhg-Rj-pK>`Iug&I_h04;v!-j;qaJjS$lZ
z)o{0xEM8)L5(aef+x-^jJ};Yl-m+1(@WBYvus3@?*>lPQ#h>hN*8TW|Xv2@XU~!(g
z1nONLiiF~6K`VaGUZefUeR=4#=ADR(Pc7CuT_SqAAUK6=1AIURzQS>-wmU#PmchYr
zYsr)Tdf#r`{b*Qm4{twI(KA^CbVaDT&jduFD8-yFf?UM?v-vuUsM`9<_BXYFg{$*@
zg;)$_5tSr+S^f?w(NC5K-PS5=^JIHUdKi!Zeno4f?upn%i;r@A4<EZU16AD?roKg^
z%9<P!@dbV=#_Nm6)BLXcCwy7p^{VqRwHLfYi(22Q_w0#h-TfYVbN@<S^QjOI98*}I
zxOko-uAJXT89ETix=c2A{bPhSbw6~cN^2p%xyw@cdke^XSnU6JqZek&om7`N5e{g@
zC>6eubuKw_&=}Unc8Gc*T-X;Xe{yzk{7JYkj%nG8N|BH;L~rftIxutAGeLe;`mLGh
z&_{IO?#?5d*gm&3C(6#Mng7alLJe-y6bq4wcv+@^dow0EZ|mX<UToJ1if~$1Ih)bj
zfw6H9`_;fHC&v+T$=gx290n^+-1~CDY=+EvtC4gs8cZS_u+?ud-j>I9?@hLs;D3m0
zL9AS9kQ|dil@(^GtZtc`ryNE<Rh?WZIC7Dr{Jth-fwiAcI@ENdW8WyoSN>e}MABLR
zn>%puA@GpExntu*IKh*xg%s%|7B!SXCX}2!FTSUzC9Te&qp(h%4_}K-#+{u!L0TDj
zGz~d@6ef#QAIG#WUIeAYkj@r~I^9v45dwX}(CZ1e1ld5{%knuiLvNa)S_9G!bZ)xy
zRThMQjJhlWhvVhAm@dv2OBCX4(-E~Qs>OYOqGCJqjx0lN0+ZlcR(W<aptg+wtJa4Y
ztApeZ0!6|ln)s}OS~>QhEO8Buv8?ZIlB{<>edp!h^Zz=S<td6r;LlUpH76*oBK}YA
zqXPaLYE2)~&@!;r-aXxCXMah>*)**oSK``LKdzDx$HOpNzq)(F6$}Sc_x8-aQG?#*
zVT6YTu6Txbfdt6=3G+#d<W#(LGaM=^aYM}0ZwNr2^hQ&hUm#@ePglUq?~YP#Z%gS+
zc)CQ`Af!HAi_LbsacdDpvOhlTf4ca%KhA74+#}|?Scq<v&d;wSgpU792p#-a&Dj5w
z{SwELrh(4q32WzknCv;fZiUX6g^wpKAJ+WaCu8Bc%j|{5mW5s5lsvg#RsNq(XZ3Y?
zJJpp<4ZFwZ$O)S?Xg-%&n0M9IAE+4{T7qdkdwTmf;}V9PXwjukG0f|)efIq~Uw0`=
zQ{gE2J~GHc%be%X0_FwCc#=<<1*mAH*s=~3TVT`;XxDuOZ7gqWy)<q)ir3Mf-=daR
z;5Yf`8Y6|a@4b7MU+KKNy@jomyqH7it2F^4JL|p~do#>2m?Ckh)1h`6;@Y?<`>lPJ
znP(Q;do|uqge7d`X7HhaZl{Ew3^XuSEEgqK7}U`As^#<E%9eKo3u^6a!sZUHSS!hI
z^_1tKN91j!%|X_A3R3n4p29hn=@YHnMJ1%kc5h8b`{bL2i^@0H&jnPiXjf`OWLAmJ
z(jc;j!TV=;Qh$8GN_`>wF;zb}*Yt>WCkvywsKh3{ISj%9KByv%Z9{L2cHw-c|DLfu
z3kCYFy}JwB#?em&?wlCZQP`#lmHc`aRRdA6;WSsi{ds#@QZRJp^!QY2vBpGTEu=PA
z-LbR~a2!Dot)hJDcp%|n97Lu9x8OH}0v*UjA^a<EP{VBQb60kfAM6FKj<K;5=0%EK
z(ii`RaKB+{X9L6{J@hR$9<g^7Q#e^tUg0-D+om4awJ|0Z4)%e`X!KTLXb^N0_`Zt^
z1h!qu+;EEQ-&2M#$3!5{*2Q#bID}a4QJ?><(!mMF0Jq!^MJ*YN1x(2xF<30*WaBYp
zP%Ur7A>f$-(3N4~9Dyk*MUevmX(6-%wh~##S%j7Ewtq`yO6Rn=-#QRsO*hpnBR`=g
z3m|x*$C55#xfq7KpWRP;p+R6MD)_IJ<_bEKO9Vga(jx}Og9XJ>W$MnItwSdxYiYy<
zMZcc9B#%&zJm=+axIb0EG5m>_JZ$)A7(me#l6DaC$yfU)$L!{KXsLpjTZX=i5oR-~
zyvJMdzh~D@$6_l|d_=?+V34P<hW-QW2mQ|IM<4HU`63hl_Y(ZfW&4`mXxy{VRIv<s
z!_=OOw&W{@VeUtMS7e_f3$uDTU}SWfC3Nd9GRMirIYz5jsb9YAs#}b%6M^aUI`CYZ
z&E{|BY6mj<<d7%F>)Lqh={~jM*E8{>21+QnUR2(l<TIr`ttMAQhrJ_*{RBfPoGQ%>
z;ysR=Cg1%97`mC#HXX0L3g^lLhkT680$kTAO?6xW3YaOOcw>MBPXkYNLlq}^OZFbZ
zAz5Ljj{DtfW7v+&-?z=?l%mcU%jISK24eSeRZC-3Q&Z}jvFa(gxq+Gf{7hunyCF6-
zwlSK>KF_4s#<QFWhcx$;*>yTWKS5QE?hyZdv)$GCM4y%Uf?2%fSciq6#_|^Vx$<Ba
z>OT$!D5PFn;XSsH67t&`c2eLT|D|Xskk@ftol(^GWYNN;{;uTQ5wxk0l41}E{S|xz
zV#@9L%oDliqyBpzQEVAiLT!EgF2XoX_<$OiFKDoQt!E|r=PBRe$SDt|%~8rv@xzeY
z9w0mT`ixFH;a#lynqGHLI}O|x(S?+<H)4}0na&n~zWIvK4qWA7e<Yc+DSq`oC7Gn_
z(!yHl_rYtVkJ!b};c9b{j!-yr1&_fyqS!GJv-m)~UhK48|JNHXv&dxwL*&qo>`J8H
z6rfRlJ89WXr4|y}FoKbV18~d9*W$ZHl2LT+DugRE1Lvc0jB+>D$8klh{Pr|ODu4l9
zHydUd{U7e1_M?8SEsLzO3N1XpIN49XAKoMv(RI~zhyz;-nJ1ND*b!=2d~fU5jw&*^
z>7{r^HZ%ZZk+cEQvVV4^ce*|G$~`z8$fH)b<PA44hHg6|Zo*m5@YL6gN7Yf{-7zZ_
z4+JmO0=K}exoS%f!BrCNe(`0Ju1>|Y7(8xh&=P>~Kt}%s%3D7cUDW!u3-5zR;HC-h
zD-O=VyGL_??;ZKOS8{lMzHvVus-J`_o=nzusy`-;tJ(GJ>dP>zmfE&Hpj+T>mCq7i
z@)bjhy5bO0`ENFgj|93;sZFaRIA1@CoUc&rzqa$6$5GZDRom6|Hfxeg*tn_Sxrio7
z@;SUY`IL~bYc!mDja;x;P;+EGTXxK=Q@NpZa84+^LQ9NMy+Ki~_u{;5;3BCOrJFTX
z!4B!J13V8!T>+ZKQQa^eNEfqck|_Wt>XaF}#`hqc8{W#bex<bO+O<@(ckF;}`Ej~s
z2)<R;7S*@{8QxwC8^@S;#{-Tt(!KMf<uw4d9#L?Kc8nF`U$sd+M}OSSqcLxfx4;#m
zjpy=)13lcXh|wwTdOzaH8{p)pvH~atCnT?Z8bOa^oWHJ1NdO*Q=NtQ>JL~GjDziPG
z+oWPVIYF$_z^$;Js}rYdLg(~7{pFwRh4yQIDG=p8&3+Axq8lZH66s}$BPVcw!ryE1
zy9n?#Yg-}z(U{P9>3#u|&c9|lZft@*8uVKE=gaZ-58?@k`5uPpEp&!4d&>m;FV33^
zfPA{Y6!?VNr&O4(gb31Ez?TO`e4K0r(YD$}8TOh!Qvp;0`ixV<1%vVD`y*RVs2L-l
zSy;wiHwMZE3>y50A1@28c;+d3q(NpdDW$YMbmh#*tYC=G3YKtVHB-43BB`xmS}1Xa
z<gK8o3IWWTc(-xWTjf+CGU~v<22X^nsvhVj%HJ!Vq+sr2MS@69KaBw+EqaJ^7C%DP
zei20y$Pga0bVw{EtyM_|7?HF<dT_SU{o7dSQXyil^Rwao<t;I{$Bg|j(-Zvy6r>y?
zk)-&0OK)bESM2Y6boyIRRgy0CE-NyiVl!>a-1PhPYE^{BPh!@Av{srb6p-`l<Hr(a
zf0Gf0@GmG@vbGQfUZ;^QF>`fXixMA#(ycX~y09KOan+FYJr^DLsH^nLi#0S)Cfjv_
zi>Dmen3poSVMDiq7b4XwFK8tCccZz!_Db&AdXr*vO;@uY2ZHyG^&Ie&t59E4HYbXb
zWS=2T_TLF<w1!&Nlu%^Sg3PyQbX*MkYUANAOL;)eK!BLb<s%{YX;JnbMY)&W8>;n6
ziC3pWE4NP+wC>E&+nh(%>1Lbg)A@U%bY*)7=Fhc`y<%r&BiU!G?74BumR=F?l#>3B
zeKL`T-OcX<SOK=^2<y^o-40ExB7rJnAY{~p85%>FO#E(g+bA6BC8YaT*LETXwo_)g
z8cU<;xt9AvjL~(>P_xkWubPp`3mcCCixXMFuuF`{DE~%)!Zdm(T(SFszj!8j-}T-8
z3blqoxs&{vnGu(lcIQuG;8|7o%ZAE*IxSJ~N4ySMO*LD_>=Q*Mx$DVJ^^?O)O}!P*
zII)suo9N?@|7<a+*&2s`Vfq8>?=!zr>)e-~Epy9hQa0YZ*w|;OZ>p-<eT*BF|4GZd
z`%o-$3q~DU#?o~SQ#ab(;Aq(EF!7Aq@c0$K;mmk!)+WvC^7R*x2cGh6cT8l2Mf|t}
z+GFDXX~$SuUIKagnDr(qfP&H{sqr*=eEejh+Y5d*-#C?)&bAp&92Dn;>Up6@klff%
z4SI?`q$l??7wy83WCj|W3>%qDCM;++A~8|SsKna2Dd8ji7yg5FeR-|23h(_!LqHj;
zQ9$)(HN(LrB<05QH##NcK37_&K{01<Yh&s2ZczgH+9b`&C$2d&g-rkZ=@RQ3VOLR*
z5qAqAE=<^i1iAkZpWJDTuro&`FHM#YUDM;qD0joWgknHB2#5)r4w+@oirc=QHw?Rx
zXF<GkqsDW7WI}r`OU}-}_ti&3#BzvQI=&MfN%82fL@M}&4t)f>g-Bb?0QpMNqbKdt
zz4!Z1DF>O(_>C{KZ-)h@F@V;ZDn;f^uV%Uo_WBv7=02m=3nyUrPDkz<{JLDs!2+C&
z{^~B3i0O|RL#RU+s8xiBhp00@EmHlOM=_AO;pRF5(6u0ZJ4xx%4aY_T*;gL{>2hVt
zQhbK!9qh&J5z!CaS@R2fRKI54iY>pr88}q^N5e@fA7V9jr9z=e(g@GFF5f~-mLCy1
z9g*Kv3-Y>M^Wit!cr!LqTeSlN9S^C6t?wiB)k28>_{N;&EV(_h)KYUU^))|+_xq=_
z!PfJifezJRUG*S5d+2I*CznK?d+gBC&-w29)g`nQo>!@y6FFFl2sUl!94kxX9R?AO
z_jTJGe)Wg0_3@wxh}bKg!5sKn-o7Y4pfK>XSv2pm%8!@d$SWwFuBD?ME)wGh4nJ);
zvL4vEa4L@+x+WQ3fTo;f8g>@!bf@BNE6X&dTv}v=>Lxp?P1xdeLS(wSA#F_TJ8oR1
zhbrxiCJN<dt<6_>3BQEQu@4^o<JZ+86}-{7TY#<pwx+;mQ;|PDvTJz;1{fV)XeSkK
z?3;VDLA_Ls4<UW6mMb<lT#LN;xy&Xj#si)Vkrp19G7dNqjYSmYwyWS`6webglsKVB
z#-o<=>-WDl`s0kgC@-X4vt>Anbe{4dsmec-vgmroSaxr!8%sQXvlenE1R8?p8=F|9
zflcHXQ2b>JS3XKk^3eDpU5^0{^}UjD^o+0t00VLOBpq&Sw@Y_hSGFvCpbYQBs3-!K
zYi4}2iP0Y|rorsY=)ZSG5e!iAo7?X5j=*fZ%SCs*8A4qILV2=CHBD9bZiWL6>aNVT
zlU8iy=2)0bU>=ATo5e++1~cA%?7{rrp*FHLOw*%gFt5!`btc{B+ShMdv0%Ng`SVz1
zWi?stt`R*b!DE$c_tkDz`>UfcufIB6jqt8dPS6FBw5{r81|Bl>FSpgQugaFr*a;wq
z9w*kqxsb&ds8u^yWp+T_&j3kp;hY_I<-_tcwTtS}PbDrk@Mu#XI}m>aC5EJ;-pRS+
zm04U&hj6aij}VRz@TU;~mB%O}?dL#OI8d;0kDx&esjzZCx%+4~NH33z>R7BvliiUL
zzj8@PTININjkSi)E|kHoM;@3){Q21;6J8I_={n<4;h>7+0Nd`p;B!ThN9W@lJvHA#
z=JDR{fkeKG%}cVumdJ)mmnHEvdD4~v=6raQ%>4UGmUl~8mJY*9Kmbhc4}d>Yeq>oc
zRkw9v#eP$*`4?83#2S{DmpK1+s&*5m6X~I&bd#-6JeM}Wt0H5ctmfHCs_f{t(@eNU
zxFtq3J++4Fb{!WNx*vIjk!M7fnIwSx$nECRjz@K{AZX<ACu_V99H=51TQi^8*z(*#
zv-)zk%e?WB`+dPT0uxW-SB9fqoio$q@CBBaV1cufwzAGiHJ^rEt9Uw5?_1aH)c&Ew
zRvk(3aLWug1ysaNh5w0%98(7AeIQu=ym!r*U0$?W#Jpf&F}B||tbXEIGOP8y@Z$Sw
z`Tx~z_m}M4vvlbfe}{wk4!Ma9PQ<o{d^^+ee<ljUihPciZJw}r*(I@(7OrjKTV9{Y
z8O7vPrIUWLtpX_H=+I!OEe<3NIg8W8_M^LbcA-!a7Q}9t-S;}l*M*iIY#R_-x7bTy
z&L^q)zYsI;M3zi)F^m|GyFIQY-(LFlZvZJn>x41AptisTWcPwqdDJJEqYRVnlkT5J
z$1NTPxXBqz6TfTopj40VDeTNuQ%^;YB&zCE+rBxWsI=1d>h9>xe^O4kZwK81i^{|e
z+8H-VYZHdI`%c9Kt!yK-SaA75B4wDZ8FfC}Q1DbQwrSNt-A8ud>o+N~V{SkhdOPD=
z?H{60%p5wo7TCK-+~#8mcl8YZhzo_y^|mr9eDnws8neO&Wih!T$g7Mg>>8|Vp5*FA
z0NN1%?Jg=Fz=7nH&asALVDY`WZ!Iw5BQI14^#VG8xD~HbtFiM7vIjop+%&MVRVu1^
zh%m6)PfSxPT9MaKivuLEU@NEWSLgg4tvD}jV)~qVbwTnj0&J?Jt3_QPjq<bm#aegx
zJpHNT=y?#*!hSJtao4VS#U6rpAE>&k)`gZ}$sB4#dOxo3)@<+6Yjz-omPe{pa<B2X
z_Rwl)(Z>>+#MZef^c3KHnM~{hX+wm)(hbUUhlirrz=n^1HY%UiF5*)xW{B)s;_H1M
z37d8QC6aF}Kvy3eN?8b}{5x*kB!2j&!DgM1WlkKypxs!QLfKm<9+9_%o_-@1=!WPq
z1(idY7rmJhDww!V+%X&eyCSH;hhAnA+UloX(UA?QuGUHUC_p6v=fzM{=ovoAkbNHh
z+*A~}Ww@Y#d`>*6=f<m65%ge;5vSk0^|zS9#*BYZ5t}{^z3X^cK45U+J^btc$}S{7
z!WJyNayX>oS${uM-{(Cq2l~jmA#nw<@62B&hx~VBCX^HgxDXEkXHno5d1b;iRXm*o
z4LMbN1Nk<mVKCGfX`VWXo@SxaaHJt_Yz+)_3e)or`I`ip5eDrR+0{Wn?wi;IJi>&K
zE=dP;1f=avz(Ej_ROU4Ut9ouKbY<a}36Q^=wWge*AT`qF|F63K1olV02O#r6y|UzI
z?;f68c_TL$%G`LuBS6BCfkX$~8jnNH&Mn;a`;L5SV~E%^@F?cq@{U=O?^AJp8GA)P
z!WNK5WQ39r$s$JPhPaQ55#KPtQ#m=orD0!jS(6Q7s{N`VT6`_06UzQwdzv$>UoeMH
zf%*!~4Zg41?+b*#DJ#$7_Olf5k7{=nuAoQiIw-qUhyHk&l{gNx0p9+@I|M_ZZO#zr
zk9oW!7W3vIS~8?!05*}7MS<(bvG_eQEsDV#*7BSXeIZueW<gKRw;XAcIa{&yhw=DL
z5bu56U2S7PhUrcjZ`7EzPIguGb*;=79K9G!k1*JUOS{<H?QK&giyLC^z=><$yDt|W
z%mLUY=`<&?*#vU=jHI#en9+}>>IUy>uZAHKFjj*qhi9>8T${t=O)|8{g@eHRdd-Q5
zRcR4$4iz_!8j&6Ypi-k%KsU!RYIyrBeXM-BZv3#AucJqMH|lNX83MBH^(ewlNANFr
zC^Sd^Q9WVX;&$qL=!3TK^=lySgmmw{|GBLrXujnGurvnWoFsOw%$IP{X>4a9EwDka
ztlUAzv$B+3#Y}m$C|uEr191LGI`xeG3YNKEc;JUz$J%lUNSl$Hv{>=@Eg7@vpJ*_f
z(}G$eTxvZ>w7m&yk(=0$PC4HB{q#`&Zg*t-p5&Rvi(w~n75;BFPH`d}>J{9v@prRv
zGJE@SB@$1CE%(Gx=RoXg-GqET-5D}|aN=Xw6|$41OP(xW(<IfssJ_U2^IvdWE=1=a
zcJxKi;JXeyd$iBCFV=wCnjPGX`}Jk3v1eJRy^CkNlmWYBp<L<W1MzRRHo&SUUE<0Y
z@E3PV2Rc5DVz{wcYrI>Lpt%hD{YElwNIJzWln#Xnn@6p6^aRkhDa`4EdRwJrzctJ&
zsV(%R(i3r0b4P}}{G6@gFORUsSM}>yP9qiR+K7>8Vc%MxVYyd{;>`>nNB`^aLm7G%
zTCRHdCMn~wzk6U$EbWCGt~Jj+Ef8M4i`sUz*uOp5!|<tpb#Ow`2|B`&6A>8En$G!F
z!F^nyfPaYkF2FinzD-$)$h)#n1mo#4zRsqvz?$|-jSfv<#E6Kyz4D!)bIlXEmdCfx
zSEN0cJ$^)`;MX?!UVRzUYjJQ}@?}%SU@MItYjUiquPR73N=v#zc>QRs#=b)Zmu(G}
z&?VFS2(f#91S$V$Mnm5@uBr%P&{V|aeHg{0-_j-8T#H?G+LK|Y=zWnmM8QO~l{UQp
zZfb~XsoAY+pf<@?tYv7VtiqDSXO9488Y5`(r^)US>4<c&*&<GaA}tMtsG0O@{0fDJ
zD!3Gk(NC|XEL)7=ziQ1w)F0C>&AV?AYSim(?vTEJTCJd~$hY*Oy-5r`!DwPgz!#3K
zZk$ttX;&$GI;)^%!@RmJnTh`9N-K)Ls6sexQme#1?VjelZ95%`BrV%^mSmguq9vH`
z^>OH!31r}P_O-H`LLsjLBH<Jp!f%ad6OzTeb<j68M0FI9Q?{*J&iJvoPXN_`vbSiE
zlg{&GeM4UrF@8R#;@q7HS&BHq7zd;QzrU19*f3yn6`k-wEF*FfZ3iR7W4E+dg@6I0
zcXP`4m2~CTq*-tjmAnuAe(*9^o~s;n;nM<fXRxxo&<?`uSVo0T3m@gr4#)%9XCebY
z&cXtzegu<3mKrrSN#=>f?q=?d6^NwFJzEy+^Yf=`VL^tG|KIJ3{6CGCM~~0LiTWe;
z-GDrhCi`7hYQpafBHVjvF|`EW+W!6SEyijof7z|#`^J(dlgDC%CPE02eIUkdN<l>=
zDSA}3<6FGdQb|W*%L)fd@WHdFY{ZFgK3gKGRoM#u-)Ca8>%tT5=@pEI=t9Lv`8$`|
zrp6e8uzLSO&auj1?sL|&OxadpA{2jTK#B0l&UH5Ayj%9P6x@jCNqm%{g#aqxoLDOx
zi|ZN^$L^993m-?l+uep-_hZw7HGLZt*Dd#gwH4TD{BCT=uHyaj|0Ju>tm+fil*15l
zYmQTy|7m4_<cr|kb7b@moZ`?sij}{E&Wg@V5$eA}<A4Fs8o=AsH!%NOo#~@IRbM>C
zpdBP~BRM4pmt+&f5D}V0xLV^$6f-@|X1bR@F)zS3fE%a4wQI8MlsFMWyxl;1eLZ8b
zU{R)Eam9kk3eRbt04ay^n>0s~x8*GkY!rsdZ*tg1AAJ~{>$NIpw!$i9Ln<_R3?G<e
zPC?MQtEww+?z2U3?zSA=6>OPZ(5}K(_>Ps?%lGz{E%LT+PuaW~5NABE$A-#yVVQ~L
z^wBk|ZXu0dF9#7f;gbiiLZXI+BJI|w6m5oh9$&=FKuR5MnlUpvg{ryA+eDV)Jc&Z@
z<Jt@R%Xv>TwAYqBZZ_N+sAl@%Q6+yq)%XNNGU=!@eCa767*$QS2BCq#yEvYs@O$ej
zHn{`84`(vuL`YM_`GK|@r*p5ws2$?%^evpV_HS^q{P%smv+7SYtjqbp)MAVHHED(q
z<-g+}Ox1qGKYEn3{%_3pAE51bcr9#-7YdRvVoxU4ZDX&2_%=vBDxH4gDFpqJSr+3U
zFph3kU*k<1_+6F`^hr`g*ahh1YV*t0vL(03VyaX%7n9Ki%p1@ZQxtSnV>I&QjbVJp
zj;pely5*Z=WOyCoL`yH=>r~zT=hmbk(noEHG=#t$kVx-#%q^rBTf>5YwLZc8O_B$p
zePgMczheO1E9Hd8?UcabU3=;y{QrhM4<c;vh^pUehPAQ*xCJ>kMA+H<q~n8gv_Ba<
zYQUk$m1=rJ&xI-m$Q!v5+4!6A3~{M~mohd8n6fagpVk8F#LUjhh6+HXa4qnS7#3vZ
zOX6aFSRXf3zx}9YwEh9;Y<t^YDxZ_^WpG2wiIKP;m1AxoRcM9MB4d&lhWH2;*qIs!
zTmLvGwU>&29R~gg>RFf}XU`bB`B=v;t#LGExf6${Ec&Kb@p?DY0$-5kbE4aYl%LJq
z^@WHOsOw6+*~qCX$gs5DBm72sc<so&B3XT?*LVasYCKFx7{PSC_lt5CKV4IH5cy>S
zHlgT|buaatT;`CY$7V%DNdyfBDX-`$pp3^_O={lq5Q=s34yJroEM>3wf$x#RLNx(7
z^Q$EH!8bCu*TrW0?j3G1*E0sxnM|l?;p}YBs@yF7E7eqtCVUsFgw_Wo8M;GX$Zrw%
zuo{I>){jFVmJEAUbgeTmQMV)+d4f|F7#mKqe>))-e=Op<&RtnSv(_l(L}GGI!GEMP
zi`R)P3=XW8$yB$q1nu4nJ_kkh)MzKoe{GS6)W&n}kOINibv1Q~)O8<pq>;qAZ*IrF
ztKS07jpOi;&_5E8f9!(Bc9e)=A$ibR2*}R_^<s|TIwS{#veSUWjFj2IPk*i7=z$Lj
zN<`TeX6hhtq%%)rt;{YZPk<N>QJd^0n^S6FAVmY@E_2DTu`7_!Glc|58xUlY18()8
zJ>1`|{*{5%2=GA2YB12+FO9@v46&5jL<uVQdKq-JjybFJFKB_S5UNtwtmIisef;&N
zf3s^z#Vx;nDqJKIOg&yUY@EgiH(0or|K5lY_>a-1rtP^ui4B7RI~j{!-wm00MEgW8
z{Z@01^Fjm!r*&`zt4I3c!UnB;R0e;%+L7&id1A#f9LUxbGhgtJXW!5?d&i4ea}2df
z7<)9h9p@XjjSLc_^*`^qN{?St-o=VvNxY<#X&RxPd^q`-27EflPJRPUCH!o*wb~El
zQMzi>J14^7d$fHC)Zbwt{+PaGtl#Uz?%fj&7tRv#E3wcJBHLw>s;~locccw@&3Na)
zrHR!qFBFEO8|P|Oyzv;;VS=DtmCjbW=|h>&*gr14=U%8@?q5~l(Cd1g6~~p6#zy5r
zA3JID$u$QC@(443B!ZGMqHhm7+kc2RJEqxaK-^PM$wa+g{xa`IR1g|qlSsYbs7+Z_
zh}@_iKX4zNHorpIGlLD^9$Tt3Nb1b^Y9hUUZBm@+?;hKUpd6^m;-Z1o9$&FjqNIO%
zErFQgxPk09E8d&vXP-L|nBOWqXo^2c=#(f?3LHB9f^6!s3pN_48-&H-azEXe-$dz0
zSO4*IDJ7~poiabn^`aojwp~U)ippR?je9iYm-AU%_W~g}e?O$Y1Uq`)p5ALm@^j^I
z6&HIoeGZBC;dt)^kcidLy0X}p|J0iIsAJsdJS)-hr~JLC%(n3Mm~pGgNN@Dk{kDH#
z7rL*2ju%%aZj8L$3Zf6@X=&-$2~0bqojo2Qf*>I?A*3ZL^T+%5PGUIy>>r7hglOE}
zV<Hfbp&e`FKdzM_LucRq1o(6zT|Ck>SQP|qY@R1MWrWV3NyW>+qZ2w>xc@7d$Ryvm
zQOuaI@i3AJ=1{f+yU(%L4fAG`L95+cdDfAQXSwJ`<I&OXO+x78v%NI|Ah$Eo5S(N7
zjE%(g$`tNG;C_NKT7$ptJ*$i%<utksh=0u?a%t}OdyE496EVYtr27?Mic7BZq(kOB
z$s|~!{A9aR=h4UWq&jsc{h!Fqbr*7Ot3L5N+o?5b+;$C|CLaQQ6LD+1&A`A+!F$U}
zoE;A|p8ogm%DtAwhhi?F(}h1}r+u`cn#kQOo!~D)x|&K;-%lc|hX#n>D5V7cVUU(+
zPg&?|k}-j7O=)HaS<#i{x3bp7W~ra7Z*5XoU-K}*Y$Q%2TeDgjhZ;i9=c*#!nvDoD
zVo|XTPo5qZTCRT7?(!h>SbE|$OVq<CulaL1lmVmjb8)>E55$=6w#CZr0zVBWTWu)%
zV2<N4?EB$rcF_>msB`y3wxkEz@K<T!mhpy*CMugfTwER`7si7{=J{r6Uc8iX4;JaG
z$qDq0pTkU|=6c7X_AK^rUVZsB09`s(6@HhP!o1^Q-a3x{T1>vv$YV)6uWF7E%R*68
zHD&`-l1EgP{D^;Cdjkx1B`03G<LA?-#Ez#%I1#YP{BwqtUA!0K-{j)h!>`G4^%s)u
z%EY6XQNts}Vx;HQJo`GxfY8bFjrgNbe7ua3v}lfmypn=)>o6g_)3yZ795e}1{$((H
zS&BCZA^E7U{k2v3dks=k2c03ZPM2hfVHsQt1fT!Wu$M9YGF{u6qnz>IDh#HT27DG2
z)<H(lsb3{BpgoV6u{0e)e$lDaHh4<}!EQJ2fu>PDZETA@nWA?ck)V1W4%NI)q_0+X
zD=D=Z*sZoWDX5Qu@mhOdlDZ$)$XZU))<0gUM*6ZQPih%TjXLofw7^TZzF92HjGEzT
zX<Xz^Jg0mwEpn(=gMeu7_Ao>aIJnJq#Dq`nntYZ;Y?4rfL*L%Fw*kj*1B)oL0z}zP
z5k!oMxO%=<S)KfMfw58F?)Xfn<>numr?xFeg1=;5-FiCk<Cxl$$d};#l67br^A5gw
zCZzF6^Jln$nrS~M6kX?g8gz?tHVv_IGCT5&B`D1=LiwNY<$*(@nvY%k^u7ePByRN5
z7x|kt*G{6ougG**H*kJ@2DSKpSd;hox|F2s`w?Oo=?+Sl7N|jq>8Yz#bDC}1ep!NE
zIa^}-PV5ntZ0bC&B!f9|-2rwo^np7q0xM}|YF3!obv<J!*Enc!3pv+A_%(WP&&)O#
z1KUgEG07y&qDVO-{y?1h3?+TuV`hJii^i{v_O<ShS}7YdmEZbzgukopZ-uVS+s}4g
z$L^-M;T4KG?MD)m7V7q~n)WDd3inJzqw8}~WMb0oI0^E*$1jXpjakUBm-v(I_v<ME
z8Lx*_p;zxVU&!(=4Ji{2zUPv04C@l3(O4ODTGUohk@LY5aEYx4;kbwOjzy9-6`U)0
z4T8rRo_;{>{l^y*dX{5a)S%kFncjci@GqAfr0tTxYvNx5<n!7(uoZ(eF^VE+jE%h5
zFRf|KraKO-{8~nxUWMJ{@IIGRl+_T5n6BV!ED$7$n_RdU)@@*xUJ^4;I?$B+9(~3A
z*;`QkpsaIKlg=BJdzmmXj@c{}H}_71jIeL_AA<oB`+<%#kV}7L#H-OGn8uuwofjEK
z5skJ*9(A&o-%R5dPS{){h+CBY)tC=vZbT@rHe+u>O!iCV5zuP*U|#nV4iSYiV_|x#
zFDMyzfxK1{r@zX^cDC1)Pufgm`mUArf(Ap;bC@O>6!Wx^*F5Qr{GH_eEl%^$CvZf?
zPW4~#Z7VwKU)UbJa8-6gOXY3%qtXMeV5+<{N8RZI6?f(ttGX<|C>MNALW)Ye8vEJn
zo{3Ab($@qCTzl{0cJ<Wy+1fX6_esgi4@^{3CvN3g0npZmA(kW6s$EAMPm8lDSFe`|
zuiRjNN@UQc7R{z>v1^Vn*m&GaKqFjOp!AS+9=**sZbaDZcVn{bmhMQmj>Ap%WEnx0
zi^ED;+ST_;hLQ&)RgCR5N91ttPhRNrh~sVVzVX5@?xmJ!k$VQWrYwcbL+MipFSB3R
zcEN4LGIz$Mx|tE!vbzc+X(^7d&kkWq6G4+ap=l(cpRG|H(Zyha;mJs^6M;cct=`Do
z4}iz062%x+pIS3TpUVvQ7u7AtVjeM8LCMvr?}!9mBZCNi{^Xc&x8VM<aG%Hq(4Peg
z@%=T&RG-0^b`Kw4GT9G;2N8!<doLJlz$G4wr5$!QJKoFfPo#$AM|d8E^xArGZFMiB
zceJ}5%myjp<)xOe{e|)L6nF3#LnXF%jNc2v@gr8vL777>>{G305GX@9lJR;2=$6GE
zmIr|$P&BrAF?Hl9sDspVBk64b+z4PeU-&Z#@9BucOY!Js(8QM^0sj%oLFHz_6@y@O
zd_71t-(T1)k)8~v?v)BjE`=Kb**+QGTg%g+3OqC(<f@LFhQua=IvV)g&p5f~bIy^M
zOV4)3K71h~vu2P(s<~~S+5C8MU1A*|6K>Tl`x>b553-txOE46d1R7OC2HF({Iu|*n
zf0}?ym=8GC{}Tz#9FgLGF}4A-gLO&vv~v9kjLUO6yTcCQE6OX!ERd!vKnn|>Yi|;x
z_UlfzDzzckH_5P!$AlaH0X-c4Vl~zkUk2q`&TQjZlh*~gTNz(dS3f!Zz_j{Z08k9X
zT4?}TPHr#HaV{G6>fSxnSNhRe9gk5w?_c6%maFYM`-tC2d(>}p&S791=o&MNITkh7
zF)h4X>M==IAns2L`EIx{aLRT`!0u|b@Drpv@^F5-W?rDAaQkHFW)zAmbF2{DgyCl!
zd4;}l3#UAHdfMrv4)FKonej5i{g$Jr64%%m^V<poSt>-tweQRElEnq6d#dUF*>j4i
zylu$KU8eG}HyUASb%0@$A<FEQhV|UwX5pirrs52LLopAQy&T}Sqm)AbyjK%*s`Gbq
zEQ;e+nUbu=viL?CUoh5u2pT3Mz1})EgUY?AKk)A!#OdsaKC$OFH|XX2vRGq(H56F|
zVn7S;#JH>wwM|fG=8Vi!Y7)wmqhRYx0aYDJa&j8t^@se4@ZYHs2?O3X{+Df>)viAu
z6!tQ;dz0g-bayAQqAxQIe3f#w=a7eYPD=}8)BhR&HVC}tSs}LpF6a#t_FjmOA-+ez
z+!i)S1LuF^8=9z3Mq6s(J3#qZs`vpKHu)_3RS;=umS9hBFl#jNG3C7ge|QMl{!(ml
z<NCdQgg5{+=>!Fjdmn-hqZ%Sul{X0Jbk9R)?$<zu69J2<Ho#aFv~$#-l;z2a-2Wd<
zUmgwh{{Np;Q@Kn<RF<jSqJ-4V&XA;g%hrOjOc8GOA;va~CCXZtlB}&1nUHnN49Su;
z*%`*Zo3YIdv%P<>KHu~EtK}T$)OdQmUeCu0iforTH$G&K<EdHa8vc_ww1s-JYYtS<
zo&bX}2|i^HLME(v3zU>B@}Gk8T|ygb{pETz53xe74@{Kz76ZqkmUhf?Q{aD!urmp?
z(Bbm2;U0&>0rj|-Up&oLs0z!9P)4;+RHDjhe@?+F)xDdquAR%|9o_>)?NhO^fhA0;
z-V%NI*lgmuveQS=$ca13j6k`^?*Nf#)buQ=nCnTvhr*;lz`(nqiZej>;S4_4!l3Hc
zQ?F2?9$yvacjUD`iH3~rY}Q}uib*_HioTOzBL1{#(`5SN<oNobyxYOGUi|x|>iF^&
z;M{7_{Th5esNxlF#7gEH-1!aeE~;@+j%*=})%t44%ny{9O_r}<$kg1kMOojc3fuSC
zKlQcQc%_#^5*6-iEQk@b$Q`3lD5G4%uFZ&7!b=`bwVYs;Jvl7=Aue9WvgGSf#R)8B
zxJ}66G%a)0p7%{U#$t2i_6W**R);Q>`gv<9nDsBM2&CDzLC{;nVAlY40d>?7q-S_6
z_IOaSj2LVm$GEiJ)f#o4MLCXCySn_Cn9QtSXcoD}QEZ&T8Z94w(`4P|ezECq&Hs~p
zHQ1W#Ss_BP2g-~SjhCHt*T@fhl0GH2?<^b64KjGLX5=_EfdfGHiRn|ujN@sih^IRD
zOdC&|9^>3l*g0)eg&0+mZmc?-O8hZ$wtrpwXOkIDsXw-cIhTp#`<9S^;c#9+se>Zj
zFOA7n!Wd=-{|G3GE>H^3SQGU7C_%E59S8YDPpyo~hv*fmP~{D-XY4v7d<}$<oe^hn
ze)5~CQ}}d{)Cg{Nh99S(6`CT{NJBhDhD<;X-`8Ra|4F1o0;BG<uNHcl@+#yt?94~B
z(H3HTyT8O(y{D}`6xhYdlhFPPpXi$bJ_gYp<v=1;u5_XHtWtundt>C65woRH=|d!0
zXxL+9IPi-^T1C10Dw6KINiSvKQ?ABGLdCv$JE4MqHc_|pceaX{p#0=Oc1lC4^EEK(
zBC9Wf)b7lz>KOieiL<N}Cl}~#Go$JM;Ann)LXr{)?q=y64di*pmT+JNs?E_uNq>6S
zgSVf}fwF*kATV`yvDO>0OO+W_OPwt_2K~4UUd9AZ4-w8t%61$A))UwlOdTW8Viuz}
z1DejXHGHdHTIKCz5KJ0hV%HQ;^sS4k+mU9>t}E>G)2dyzi$T5%7vZA;!9rZ`KVPxC
zwSFraH{4<HX8n=+@CEsxVP9NWi;Z-(=;o}y+VRPYE4%&AEVhNCx!Hq!bs8iVLs^?p
zWLU+vYc6Y$W+h59!fxG3vr*p)LLQl6i>;Hs^CHpIw&nbgj6toV$xWPk;UBy-9br=J
zk-KIbOcAS}^{sy?1ra{TsNzI_R`>TsN;TYBMbj-QYWz`U9rX7@o%P-ivvVd}pCChk
z$GUm}|J_Bc8L*J<CQaC)eev`8%fMmGxjtpZ5iR>1_`3W~zJZJfZ&A^flLDPWo;Vi!
zto45e!yW!7{sb_L(}|`Shs9>W3+<NH7N8~#nAFdHNWBKsc7Tu;<r7vF{{DR-Q<Oj|
zUbG>7KybN{A;a%9a1Ay#_LBxr8+b+6ZyB9_`U*A4v3eIIJ2l#a-zou5?MZVXhDbsH
zSf|dBaSx+inZs8ngPBd?34DH~CI~^|K9@cM0*N<7TcM*M<WBS)vVB{Y7}!pmXwXZ^
z8Dr4v{gl}mes+_7*F9Ls-6qi-)D7Y*lrG$o^Yglw2#F^Pd^d``i=na#iL`P2qIV&)
z;uL9Qc^VIb@t!jRVF~<XETTw!qWI}UBaenS`H9DOa-HL~C*=UV=@NM`<$Ip<t=a7d
zn&KWbXe+ago1(8^jN~mNC&jhd{=XTNbSu)+CpTp$h~3z-^Q_mJDt~_>U;Q$>S$vZ;
zVAo@zB|ZJ9LTsAk)r>FM9}b_wXUE^zT(dF>u$&;R;kpjl2O#vN`SXO?39{J;Iln8o
zV<x;d@s?YCwkB_*eY$Uc0{cwVe>8_=yrRq5zB_3wSrJvMXeV8TXhlmb;M-K%7tuyV
z2Y*q1Xn6K}P#pm{JQC7rY!9xaadVEL{!f9rgGxvLGuHFj%z0cdkWS!5D3TQ*Y0CEQ
z?UQ`g5@dBXfTMEe0%<+TgXd?<VPnW{@~>{jdbjIj_VDNG8zzS)(UeTjn!puoJ<d6&
z7+#Otz)GvSpsFRgiysAnQ~Ww<-V)d1eGp{eCQB0pQD!t|R->qXjAOHh$Ckd!RA{3f
zlHnLY2iuzQ*By$cCa{r{xyPEfqKC2?-;}GH?gDd|ksEYD^2cniMbr@bwI<aN$af4Y
z&i^?=9Alkdm-KpgQ6eC<KYC5!tmbtc>yQJ@SyNWi-Cm4}KN*}`xQ>S%2%(t|v6s16
zg_x84E;y+JGCTywQg~V8cze&^??lqx-|Mj}NTm%<u7xgFVSYf3>Q4Wf&@vR9%KuEr
zd!HS{>te=38vS5i;q@XZ3;<gDQN4ss=OUEWLTg1L4DXI6cWyJZ1c0EGi}9RTBgEA(
zZ0Hw^uNcr=M?@|t90K7pyH^Asq4D}5P3?2T@!qhZ7yd>?@6E)|4#M?=nl$adx{v|`
zAW!_@v}S`K`N;Bj`4gHUn7=CxX;Z%(yL7Tn9MmlPGswufR^a=oss686;7_fT{-Zw{
z*D(pxuDJWUNh)y0RAN<nWm8n{Z17#Vytu(Je3J7Ksl{3yVkWN^o$F%qr@U2D>Qm_b
z+sX6tN$&E)Rk`f#Jum*WwpaX1{h_W$(>ddew2RBRH{>dw{oTWee9`;JN#)=5lz*$N
z^wd-F(L(0ui#gqPeuSZ)h%;XcHOKKIoP1G+fkOMiC{Mp^$JHm@uhoOhiwDP^x#f@^
zPy@+x1k(3=m?x5bf3MeFs1rL*j~~PJ#P0Tg64a)Wcq4jRpSj`?ul2c}^l3@acDalW
zB@c2Blz#TL;>F(`)dv0Dr0-62^+LsfEUSv|N-ufo8sAPKxaUte6o_&H(i*x|_+d7c
zQ33I=iR)fbEt3@S@UWls){x|9_FF<-hMJ8VyNT3h*vH$s7`j?O!6x%S2i_MlmQ+f9
z^r8yBy`SH(A6=s&fQB^nrYVOU#dcWUS8LXsB{M9xsz(Ms_b^DD)M@5QCw7F)e5_YZ
zbbp+8EjU5rL*B0BkQ((MYQWzM=eq$5C0-^9_>}(=9h}~>RV6(n9}RT^^f2?%vR39Y
zA9oy^K(un&zHo5rjK7Rdg2M+Lr*DECEm67r{AwVyf3fA#QEHq0p1u7TOmFJGe?W6r
z(wZi?tz(BIKn$)v=X}4|7&ccYyL&eDFq0G50g_#yZD6(L+@kvWXS+L`yu}g4$k)W;
ztPYJEZfvCfsDp+u3-?oKyj`UAotU^zo=eg}+TNyb5VwkFurDVT^a$U_bY#oRnK$!}
ze|Qg8Aj(wE3sK@Eix0L<<`erhKrW8q{f+wK1E)ZP`cW6-DS{wP-4gV>Dg8kgtOnZ?
z<eqvzQfU<#%#K`c4Qhl4x<{}1LH2VGJ%>An97EI1f1rHdVo^V!N<)VgB)y`u8ZyWL
z;g17XxH)ou#&WhGBSwyDpf7PSZJhI@>End-<7tN?_;%gP1Q*y8VLs4EKJ4#@nIBRX
z`GiRJ(I-Pv#{feW`^!4)MdI)9@AEsCJ=MGme}V-j>Zku}eV`Eh|MNNZFfjsh*WcOi
zTwk`bzhihp7d^$E9&Y2Zv`Aa8oEOonSNp2API031Uo;IwSjl=E2R&hi<3;<q2S%Vu
z@s9m8Q9d@fL;g$aBjS)b`SfxJFKNgF)3KZhpIelg$zKiJ8*tdhj1i^sJV$$B^oBe)
zc2%rzt1~?ns_Kr>{j7($P&O99Sh=NC@Of$OUf2)E<PfKqi#k1*v9ik#QEn76B{>JI
zCriB9Z)x>xH~aIx2;P-Z8FsAt)x`cMf@Eyn79q<%o!RNZp}62$A)etnG*4Ul`oETe
zmyXjR%mOu$4_XI%PP|G)Uqu&9N-GtoWNP<^WKPjGQZoD}Ky7U1zTts^M$JPB8f4q9
zKQC$RescWNwqwWk{Jp!^TIS8;(+}c4kU0Xfr+^b)`jwq+ptz?lphLftIe6Zay+meL
zP?%W!dfA0RVwbz+fU|ozsXiM&NauJXn_eu1=bMyS8PIL#u%BKBIf^$#cqkWb9Be4_
z*e7;F3b|;CzOC#dV;xSs47x|B)1{^rp8<VxPQ;p=G86%5M0MjW$?Ke`JD^qJq<abp
z4UE@S@_;PlA^?6CL9^uPPhu}J%%~|eb$(~oC7|IH8#`Nh4VY8VQ+k{5hwD`I<gx{Z
zs^W$`Jefxg=FhJn`)tQ-X7%Gd4erl&h7RDyg|Sgt^JDk@|Ed*K^NFnjik2X0+w4`U
zb|W)l_7mJn*d)|g+SBZrC-E46(NaPgY|_7A%CH^J|4w~o5@H>61ZGq93ugDwh*7G)
zJnhPEE=luR%kK)JSwDfunOg`BEPeEgei1XvvyTLMx5)h@VmEV;Hhkai4=dC-(V5>e
zU~c+H%qG1)#Bs-h+h^9%Pm{UT@UT{qE$ELPCrlpO?u%8;6>Be%kes<wZmYa=r-e#u
z!Mo$Ku*PVTQz|TaEygN)5PZhmIENx)Gj9$*fjcsIC_5}BY&Q>vWLmH%12UG^^J?5u
z;6vlWpUz7n3Dlh${lrE0@Z)1)I+v#_!(#PJ$B>hcT)0$Egx-QVFHy38&@`>GvoL`A
zc=g|S;gu1kB*U``TIE4**eIc1qv0eC`F$n*mypV0gzxKbGT;RE)m5(hEPGX$fBxJ>
zZ`9t><xIX;i%diLuwzl7?Tv^pqm2^5osAOyow5Vco|>1%eOA}pso8CH=-Rj_9w(vu
zJTZCkjiFId@5?$NM{rmrNM?zXB;bD6TKWokeH~mM;qjsEqha2qITa{aQN)7t;ZL)?
zy=2S%WwY5<a``b6|Ddd|4JYa|PATkdXb-h^*wwIjJZ`x+e;PL>HT_a*`q(j0F!o~Y
zkTUhzVk*aIYhS98aaD@LKNcq{BK=hZz8r_?k*W|=KzSe$;@IPl<O%_iBDf9!@ozvD
z`HSpyXuaWYKgp229oIc+wxdy^j*}LDO&95XgD%esF@tZDo1B0ac>On2v=`C?bSu(d
zrv8WD`C8tTx3-*`Qbqmx@5y@T&{;1Mk0og5j*3E}I=OuK3bGc;9e~-jiuA&Er^-&7
z-HvQ|;ok^dhh<iKeKc6Qb|f&9NAC)nt=SsKSCF8Q;*wUVGFD88nSE6Vf9lye`m+$K
z6S-xka81tdg$eeJlL#w87gid)5t^_2@j_h#&Zzf+l_RW7B!8OVaf79sJLIhsMi&W-
zyUnLa$3;D_v@=3~)lv@Xk6JT|^WUx9xumjTh&`cy4}SNL;J11R9-xOdR6k>yoGlMA
zjun@5E$zgVATsZ<tCGD-pTKHl)HHmQ1((g!IpYWPb;pBhIj3~hXvT9&a$33VCnbjN
zrNnrbcIH$!;6g$Q$X|X;IMr$A^8aGE)BeW4M0ieD@{W0?3or<`x&EpB{i2L6Sh!`*
z(e=NolF#?=Mz~?GMd=O1%eEI$2S=Fe_RHVvQN^oK6=u*<K5g8YLaz-U{lHh3)0dU$
zsL5LVdC$e=5ACoi-&oFi|B;f`lX5FBupYEUHdPoqXwu1pM4e<_Xt@QL>!h(%8E>8N
z9<M_w#Gky~W#ggNa>^#F?<$moZbgN3#^F!JrL#)TB{$`2J6_TtA1MQB9Q*63ctYk^
zA^;fwvDi61*dxeo6Eu%hA_PDn_L29D_lQQ+X4oh5jJpIzj3xuMZx3kRag12eAJDF7
zD6w?D)P2}B`AbZn;1q|t?hn0XGM8fAAMvs{cl(&J(2rKX-}+s6nrMv$sKtDbqLU2t
zB=q5%vQDDG2AksoP7^O5zHf!vBcfx;x5N@=t2(9shwpvT2jRBLdP<<NsrQr%OT<HM
z%=?5;hV!^p;JdJJkO7ibaNf*(5XD=`=6Ko3vWx>w0^b)R{7weOhwcg)d8-h)@L|ED
z7-1DCdvmr|B6?`gPoEvjd){C}^cH`JynX>=v=`rf3VUJA4x3TQyUDkl(zw3QlCz^>
z%|jV})1*^$I+Oflt&egs+cqw2*ZSNyy&?XV_lwTrRyjX1vJppNI0wAQ$O__G@On1G
zeQP5al8n8Z4b8?$q)RhWhr9Y^HaV*`j)(bjP4k$Rz9_j#%Clvak{^_{jK!rAm3Et&
zY35>fQ|P|NkX3~K{4J!?%yysE&c-3zB<#OH>vS7`-(vEzXOidk4cG);RyW8^;m~4m
z%{5x9EyYEXrB;gET5!`_ecY}YWHf`@l%Fhb8-AO48zbq;?2n<352sdvEiT7wgMQG3
zqR|hB)~ScJ@*7TydzU<kEh-1?=v@j~?&VYF91A=Y=!EL~3S^Oco$!48uA;<+JK!*4
z%6JrnMLL`LTDN_=oX$C=#@;pI5jRn4#b@mCk6637AN*wH?VgsmPUc0dWm+lRblNd}
zwF>c*_=X<`{zkn@n|r^xeXQz^05`S5lV7V^)2njg@kOxsk08VQG-LKl|5Y^2HjKQ3
z@#j&?XT07coCd7lQ9d(9&j$m^-fc>hw+FHrz95{PGLA>$l>=0`!j4xL&hweG=g&c>
z*O4I^HI~W#`kCqiUF^{Eb9#hu7k(=6v~ZUAnKMB)wUij}R?EeYdPoGPn;bSx*GWZ8
z=cDiDX09&xi1^;F9&l52be1$rDzP?GaK#v^L!MuTZ437R>@W1T!|;4TUkvm}lw0QR
zYqYTAlKg7*MQp_3B%l?qO?RRmm9t`Xew-m#<H-sI76nPR_L6^isCXbFgLZax)R228
z2_8+ZOXjef*G|9fV@~7aqQ0mDr9yw})7e`losuo*!^Eb4V9gu0uKS5&kaB0af}kp1
zRe*-lKBar+v(-r!o%W1~4ngNjK%f0J^u)~fOP6THBheVBs()OvO{_;T``I{UR=A%L
z8^01DH>)#PIr;AO#?JELse7tHzxLZNFQNuLFztWYH*l{b5q&!MEqbJ9%OByW<&Nx6
zde#Y9T=y-j3D-7*JASo3APr>GfkCk6bHB*2cP)OsW$xBWzNP?bQR`7u&%A`8yOOb0
z@nIouK549R<M+tq5)PA8l_SX!G>pf7QGZxk?6d<ehs>=%2_Ny1ggbfSxiI3;L#l*2
z$+yUl=`cB4uPf0vQplWg3Q$EHf^P6=H>A!ln4&)&84o`dQYaZ0%er{Y+}c4pZrH%O
zRgkO2$m72Q9B^kUJ$enHtbW?^10!veF3MEwJk6ZIJ&QL`iiNr?C!>Lkb@J7^S(4>Q
zG7fpyVV@SEM{txY*vrVnW)Ci!E@G#Zz&|vqo1h;{LDFU~a*p<KKM1v!pP4QF7whis
zX-E^`*iXZ9c?h3JzjMmmJ6_&HHe>(L5?!W8lE%^WPOJn`4?Fa5mOa9QK%C6%w$3KV
zXtMTc$lX5J?lokhsOaz3mP?4EKjCvZWpe;^o_<|oef#5*vRRAN%|=sL8{fCs{E$Y=
z<!Z=0hgUPQ)bD)+B=59BFS|oXJo?71#q+QYPih7f(J#|0Ka;cu?f8=Y?@87*mIdqQ
zYp3ljslTKhK7(Tn(Q}euxHj?X?&q90*hi3Y9c1Kv^C!-DIRxXugeyzgUY82HE|s(J
zz74vR4%@z3PE@8aPqk<vq*C3(Oe#?lTbY6vRsCv<;@_Z^XErlg8WpW*Xkt|2D*arB
zX8jUVIHSt@p5H$*Op~?qeX6(1P>GsfnF~p5gc;+d41>L>S&a%Bd_#7u!qEL3Lxwmn
z!<X`aV{o56VZ)KYysJcBVO%Q4+zY$f_N1V)Cw4#3T%6k_9Q0fDy9nth2y_<jj8y3A
zc_kw3tJ{~JRk_i($_GEX^fi$UE(4=W8s{<2JAGZexg|TtjKeAUu=JLH3WG}%I0>5$
zX-G8d8*#;yHj7h|!+R;hUX~_Hq12SPS?1f;lCy3%k`UmIEh)~6M}AUcz<bm<(s44Z
zHv=~lJw}zJ;ESfK(o)Gcq$)hYaHVRcnnkZA<o63gk8nPhcZxA!ZOj|ETa=yC<LTL;
z8gJeZzae-=oyHj@saGR@pH}Yy821R6`=0-<6<@McQvY~i7QQ0u0GqAD`sMlvwN7%V
zv2I|R{2@K9RccO0%kLMw{23OKe<q-8q(6K|6)2IKPfMDN)la61I+<VzPq$Dk7O)=A
zK7l^Mzf2G=G)7z%z}L>b(~lRz4np?FAz9#<C>s9iDz4cM8h&4cKd1^ZU|{5{pkl3~
z0Z_hdZuP~H{r*M^GJ)PUPE4afYnJ2`m_Pk_daFoPtau<L??T;2@YKPgjmlig7X7(1
z_}oK(O67#s^ME+u5LHQ;8E*Boo3(Ro>m-MAfpL99<iFN*_@S^J)%fqUzhIW&5OkgM
zsoW4R4O7tMQSr<RdeZ~_UQ1)kjKhPQ&RR{i0va=N1<vIYW-AxbZFns=y9~Wty@ar<
zOYi~WNFzLa*f50V#w$Q#M-)O&4yU}rS}5Z)Cl7K}1esG;5H>5zen^E$cVvfq%eeMp
z#C(|RXXcHoZ5GIprBsaq?+N4KnzoX#uxp2$ow8F^#?VJvS)K=6bDT2TS|_uqqL_vw
z&#Xo7@Pp&c6^1;N6u8x3S*P678l7Pz@|HK&*SQ3s9I8Eu%qeRhtXlTvh%S5|y@-CJ
zrL6gU|ESX1z>5O`o1qcTs>%{F*0iF_3piIE@7eiO6X}qoCr#TyTY%EDjO)#g7emBT
znFRgaQ>ME)qW*4?Yp0dzr=kDi-9s%-YMCtHa>Ffr(s&Ux;vD`UfFvH<XU_2gm@3t%
zbm%=Jw%$P#<FolkSk{<UWm1iq5HqrTqaM1PsF1?T(US}iD&T4+@RhxmBM@$FEWwW&
zu*Ry~q$dOU3Me9DH9{6t?^nibUYke+HP#t%l-Up_Cbv)9Oj-Bt=PJTR#C2(HjcL(T
z83!Fat}W*s{L27b85H@fxN)e5<E4i-#}&P%_L~j$=yYDrQ59;jrf-2)HyEPW$&G`a
zUPEz(@UwkUmC#3vpIVu^M+0EniDuj3?<f%qxeM<Wl;25Q!G3Tm$c|c|3~kR(B*^XS
z`LJVI{EZ+)@BSHWdH4tNhmEghf2|r7zgCcPkDjpJ-%yxsb*Aug<(T1`@j$o!8!A(r
zYyY#Day)HOY|yh(hBW-F^>F9#eLb)E3qyV7LBkW2ChkpkhW+elS&?D}o;@LrsXgpQ
z-;7cXTC*k&UgGRxh_%|Ot#2K=df&VGW!)fFX+@l-C<=c~>`bo8_@V`R2gVcl*@D04
zqT+bp<w3K9P@P_uo}5Ac+D@*g=d34EO8UH1cTd*&D;cIxpfCFb1zEzw{L=f2di+uy
z4fg2F^m6~*-lQdpkGt^ul9t7Q$7GC8=_<p!jrz{(#!bq0V&j(IMYh*>nN`ZTpcEa}
zxAiNwmEXDt=3P$2K1gv4OBwuTE*wo9)Uhf;XNNOqZ`9#OWx}@J=VvXc_Aofo1qKoa
zZ%<Qf>s;*OCY;F1*?SvC&Plz!@oSP#C03&TWKpE14<7Ao7No}ZXdRovPMw`TZ<XDx
zfH%VL*7?uC>C^K1H=)`^PppayMPk&<sZr3}Wy*0$c2DG$z|Jzv815aYggq}Rb7uG&
zGX-eW7Dzv&<y~`%<lW+{V-D$v=hNYS2)V~(;dO}AEBIT8>1#7vUW#=at|~qEznxGa
z_-99;ACXClA9o2pjEmv@81~a<%{RzV<-iMTfq_p#A~tl>Ay)~zRw#5y^kJM3mI`xy
zi!*95A0wo?cL9<NFlhde?6?pkE1c~x>=iJ}T?@R1{^3w>so&!Jf?i@Q!2!>Xag$IE
z6m5}vT78MUec^E1J4T3slqu}y$a{r#<h3r5UifYgncVX2{zgOp4ZW}$z6$z3rFw!y
zpn_>aST*vfvgUhb94lI%cm#+#9@m9Mu6-Ld(kXD_u4$Q;&}f*`#2Xt_whid0v%|E$
zRH5ckztkB#qEt>ZTq<a;Y!)5ceKP#MNg-06PHazNRGgn#m+z-GVBcwgG`)AN<|V?5
z7-^&?ZG8VZ*wWWMjFgmEvW$QLEfYtJZ+F@SQTK3;7Y$2ePH^AM#kS}#)X(B-YYZE0
zrtxho7qz2J^E?+~+SXFt`i;^~tn-Z}rv3-IntGlMMz^J0b+L8*Tt|z}iFHHvcKwbK
zb^T!?jK|kNq+p!~R6@UvWoZV@UFJp6aot5Vqh6y!OW48K4F8?nM=pj&bs`Xkjhsl%
zRDVB#8`C&riY7WqSsJgF|Isk(0akn~$`xzF{id87h6koj=g;71@LM6ta=XiD$vSvb
zj!F@0`6Jzu0bAfGa$-4E;B10z@WydnOk1#}AsCo4y=EU_8gDP-Tj;LvU49vHP<y)M
zXcTN5-*3^%_r1@+%a9<AS#`dz%?!TsIcS)^0U?BG)j3?jw%B?=4LEJ(K<$U0K&!86
zf))t1*#w&GyS~u7b29{CNW8?AkarSx<?4S~QBuzf3O|ZkgrJ{|P*--!V!USXH)i!E
z`#(?MRjT>weNj_DC*<z?FN1S{6W+17J8;(<iD}jzAPKm=nbWVcfCwyZ6L~v26KEG$
zJxB0><+4RQ&VQ+p+gFK79tzblNZuJWVZOgX`SoV6WI_ZXoOq=Hif-zcYM7wBsrjO1
z%?JhQFAUji(tQ1~Jj{d~*d^r#Fu_-w)P_C087E2KZUv9NdXY3By9<6d{3+!PlHYP!
zZ@yVo^MO^V`H@;}pIF{efrWN1E!r$xQLz!9wV$Chjtf2>U4K7Y`vvafF$UqS)*zKX
zNn+1A;GvV?zj1<{KVyjIp2?)gK4US6+BZ!~(@LSXJM=OJUEC?zX6dZ`lK*Dw)iw&>
zbC}lURMsV0-lTx1k;L)w6n!(-t5t43Syc}c8uXgPzl)N?1+Nn7c-*pKJN)Q(W0La-
zOUAIY0~hj<MBlX@G(SBY0l7|-evIT`|1}a25Sjo(=741m-n^CD#)(sA^j_5X%M=af
zW(Ld+HIxgMd)Sox^s)V;vsIs7f%Ogelcwrcke!JwDJ>aOKj*F<%ZZ$|yE2komOhSq
z$^*yDDG(Dpi4&Q`W%HOvA(G_7MV?9}3Qgna!hl2$B&AiOikTohyHO9VKm)g4dyl<$
z?KTc|^763idG{Eo;U$rW;Cs1gQYARN)hKv0PekD7D8%?`zseK`v}<5UR#<Kr=t{(4
z`;*af8~S`pVgwYZb9Vwy4ZT|tR&U%Tq9P!ay4waCC5qm#Nxl6W&lKhRHb%;LEfs@o
z@o}^J!r6YWp<lh=4Fygg`ez3CK*4xb{$A|BfEn@O`Ldr>dNRPvo_mQL*(351Zw)6~
z#DK?m_c_>ogWa4n*W{maiRIh)zB|53mdVfk5a~S`=5NGj3WAKRt`)@aEt`2Q12V$2
zH*&>V)`r8jPjh${GY|I(@KalkFUG0Kfznx@%PD1pBk#KL9oem9A)46Ba26Y}EXsCf
z^QdJ%EDXI0%#T!_m-D;XHs8uXMr7=s;Ttc>Pa;d*L}ued{<UD&w4vr;a{biqlj32+
zh6WF!ve*ccS4XRxIyG$zbvf-SR=LiV?1lbho+X@<P!JU2#Fd9zi_Y=B`4*P`h^5?<
z+*N88P?{*a<T|5ZhICpYPYTU$Pqdjr@w|<YkQn=QEg^3YBeD`f!Tmbs3)GTT7Z~en
zd=GVdOviKi#PafbY-dU60inA&&L;`V1kpxH)iajhX7ge1^q9q(YABt^RBxEPc|mI3
z<8@{Fo6S2h&i)rXsu0S^cgpx}I<8<rtNEZ0n?fFy=>us654r#d3B)zPpC=&5NldM;
z82(<+;17N2=Qt|%%n085Ns8ZzWQJldAZcE`;fY8*OxJtH_x+A;dIUNCwLZQ=&1q|v
zTXElV!XwcfJ>XY7|GS0>5ImKKlgdeut08PtzUVjP<SLYzBEIfF6Ie?F5ajn;(}Ydb
z0+5Yc0IG)&Qe}1dC~QZ|fhmG@pTz><hLgj~ExJZi3^c;+=`Ox33($avrZmK6`N6AR
zpHRbg@c(#-QKUC3E?>;Q_A6qggXI`zvM4;78wvxHW7zS=;ySW!QN;>x9C!rwE~$S3
zR7OS&?fY5uuS>3`(!a_3mb-@^sKTP}xYYd|-Xi4M?TEYWs@&LnvA9IKU__LKte7~v
z?`>G?tL<_(DEW`rvGaS@Tvby!Y_aU7)g&W&cLgkgxur4l#x+NghYijR>(}K_?J~wX
zvAEBJ7y8q&tLF=rzeiOJt(pW4ZVH!7(zX!MQQNS$HveJZBk;&jzfyUv{8KzKVzR36
z!J$E7dn4Q<p4Tay*^{wk=3YKPd(JpDTv~;0%c{@VmUF#ojhuviTE_xZL(jQg*PzoJ
z8~Zuu;QE`TM68c}5Hx*E@7qT+_VBc@Df@}uq@ldEQ>yD`wq9wP{8(fyGwJZpm&~16
zBTN}6V@&Na4?Er?9>l(l6Hm+dJeEztNfes6B_kDB_y}%nI;>igs~n(9CI>qncwXHr
zT5VyP?@2aZ)_9~@qh;mr(bRG@uhK?6?)gRZ1ygi|(&Op4Mc1cdsWN~w$Zrzlssrid
zpGuKy_ZM(SIRy#4lOXo4)s#~(h3=d;0tz8fEM7xlE}CcjnhEECKKD7CZDrIp!(S=9
z2>su?+#IYPDx!QQL7+~ny@k)rgBv=#cQ)7T1*cKgGUE}1RcD9I-(iZaX@c`jiCO>_
zs@Z7Xs+Rr?@qND<7uaYB>(S?635K<AM6XLHwZ!=U(*Dx&^xPw57c-%fk6mRg0~81~
z$}I~q1LFrFl6Nu6o<p@qb?}**pjJAjO+@gjR4ZX&2TO6Gd8q6*IpVv>t<IofnJ;sM
z5c19ijeMU;Fu7wal($HaA$Nh`@z&qSSk`8E7k3+{3@FiT_cv5!Tk#A3QNSFG-H9e?
z0gX$N*mUag79{Hp-{#VmAO7|FQ~D#$9vquKZmqwjGmyN)_co~&TT3SJ>0^|VyNQFh
zY%{Ra{poMZe>6&j_cy}5h?Q3=5tYdVS~9&`MjNy&fBd(6f1zo^1HSfp?@OxYDAu(w
ztn*!zQw5zr*BbO=+O4zbFZ-%`ekRS9A3hbDsr0VNu-U4qQeufp?r6=V^dBUqo1;0r
zZsd@ck^=jcx!i<WQ}{m^&Ax!|cj(IJ)lKm43g;F@xx!!P+`TsKFi5V=yt>>ZUay)i
zkr6&7%ic=%F3-sv^s%JQxy~~xYJMz!+6oy9S#D3~Oi%CG;GoGW3>c1aD*Q$0PtUyz
zHYlqwsnpr*H+I+IaVF0zr<7w@(hXOv<W%1T4@<Ht5miPZBFzU+`6JiFl<B*t-+F-2
z@yvBl0(2b%W(vOl&ti>3s@i~;;3`jWog>;8`Y$<_EBsUiF2WxIwOI|$fsnYGoyid+
zG{ApgeuDxF+N1}K2`kh9w~kMn^xF)4Tk-FFmRcwLCc@$*GKGk?g!5IK$^Qq8>q>+c
z@zooX*ZZeftJlKM2O8<$<m?gwd<yZL%*r#fbFhuhHh0UWh={e<h1&pa_sKy8OIV-9
z1k}n(Q=anZ8bN1-oT+YfyH6lQ3S4asYAt!sV6Rbo)1e4d7(bbtTfJWhV}S9I5W;#?
z7t$Bx1J5oTOLo!{fdC->k!dvI?t7?gkCmSFut?dd+$@&c89P8YPezu*<6!T5ijlci
z;s02lxoTZlAk)y}p)3&}qaV{BUt>8}Y2~!PMK3JUUqKncK<HELQ^KxaJ<%U<#t(77
zFb$3AJN^@@SJ03Z@KGA^=zF-vVw($r0}EMh@tQ9kgFg=X;zP@_9u8zMj+&1vZd@H#
zKFl~FJl}K<Owaxm<Hqx`^|dw_V|H=t4AFd!DKUW0GuOSFf}QVPT#oZ>m*(<5KWLM?
zWw^5;hoh8v)x4%qD<kcK)ABT#ty)ULyRzZ<@(~ODaI(2v%}DFxu#zr(|NhCUrDd%~
zDq`I5O1i7-W1g|o3n8|x;338hyL%n_CM)ng`3Dg9%%1lhn5TOeyT5RCtt{{M%GwHK
z-cLqSJC+Mgf3!&Fi+$-^+?ic*GM0Ixdcj5~)Zn#dmu-*QqoL*OT)XRuE!k>G6Z@3i
zSi2e;q#8QUN%?x#y`uc{hq^JCFIbDDvo9%UhWDP)l&L)3Fn9u-Uk!qzS?e+(TAlT#
z;D}0gC@P~&Vf+o61urFShybGeZD>&=o@2z5ngw^YI02lx2sN}4P&ml&!6SbYk@=j+
zY5;t40k3+Cj$zIp;XKbXp7E&|#`x!?(1EoDH?O_oFt<i!U<oxcV}ucY=Z_9F$FMdI
z(m4VvDpN{m0iq;LnX`eli}g@;MVA@EyGbte<p}?KN|0c}HK_XlCzM0YK(z=uiOV;^
z=~WN)JzPgVg&Lr}`q%N9q<qVyg#l3~dOoJw@~p7w$h&kWr025u6~Tw>*5B2gqXe+p
zd6?vu?<|u$OS25-nG4`Oea6c<cDa>Sm&w?OI7?Hh?&t|8U#oCc6OG&Zs9{kxmN%1T
ziFq~0Q!$}$viXh7=Q134I<zyKd5NA(a^hz@(EAwKvsF~GPNV2NXOfmK+gx0T{L-Dn
z^!e$<ru|b|iuYj;-T;MWqNvj1s313iHMsvRCVV1u#_-y$cCln=K<gym*YC!L{kZA|
zv>KjWpIIguQ$F-AN^6LTzegVN^X#+j8xwxBdsvtrh=1)6QM|afDml=YJ~q09=vj<N
zdcK8diq=a>Q<}btSa^kL&kpWbqC)ku?5?+c-<<|rc%>Z`1~f~?HJt{oICnc}wkm1M
zbvRX8Sa=(?jAte<t$!QU$az&cDqN<Q4@-)X`Q`Q-sLveF;h%$Y{TaNr&z|U6482<?
z7Al(nm%znfgO5$TOYBfbt2FkGva7Gz!?rz)0-zU(Gl{!JHbq-2``DO(<<L1$m7~}f
z<qS=5*a9KWs$Fn25NS1<AlHJ%{{gMb>BZ|`rLbsMB~HqysJ99Z71RsjI6*hEEk}Is
z-0MJfLBDkHYsmj$np3~N3$~#m-tZd2sWd?);s_&3bR2Ix>L0L4UwQV{tEKI5P+h|9
z!AvS}+I@rU+#`5_>(nr28IuH!yB@W?$`dZEQkk@%cr0ef`#Ifq9r-ithsZqayI>${
zAE#hUAzt)imk0^;y$fO5M_s4$mz10SNng8iI4r&1Rf6};0s3~4cWaYw9n^L&cF;W8
zz_nj3^v8!WLcBDu4BPcVyW)(!^kRf>(4oa-tVq@&*ax+#Fp*VgJ2><d?~3d2cAGk`
z>o!4hPm5p?=?sF=NFq0*-?i8KuAxd`cJbJX(MpgpZ=Z+O*SA%S(AAd&(NBVp=6i=!
zF7Z@1b8_4BzU=b)`(Vkyvk{g1z9Z_Fis4@Y={lGEk2U|%OBv<Iy%r`4{RP&(9b+M`
z%5c`f={m4-4q<3pksio+M$>fsV91zm*7(_0da2#p)zW}A++xoy&DAk;O&`CvJeWcX
zYv+V|dVd)SScWQP+(svGlR~ZkLJ0a2x%AeY$?PcgH93Z*W;I<eXzkhX5;c6wJR>xZ
zIxEZglf*B6-ypvaz7gQ%sll+mN_QKZ-7~ng7REUo8_-`&sm)d{R*xg?|Gvoe{8ZCN
z3e()I8q0O?ZN&6z1vV-S%La{qV4xVp1Zqdfk0q3FRh&2Vy-ahJfZ?A%PwZ^{6Pyxn
zPpLZu7eZ}h`Zu`fj6<l7_&N0oFMj6Q<e{*}1jJgkfIB75MpDa0+}eK7d{t-V`v20S
zZ)UH{tddSQ9k{7ry4hA{rT1wU@iVj4ibs=eUTv|e+5ezsJk09a;+_U$yYyU_+<}jk
zcP{g@UoPFWpM9<ERNi5`W%Ltu6sW)FK_7C%7OurN0&m$7wPI)%vOiV*Ir83z*&AHj
zX5y5+m9Q7go?&O#)WRWBE7)tyoM6)-T&$4*2MoOnqcAP%#(yJIeyBYR^*Z74{;Kr{
zpG5@b8+zmNUCi0BKhv~js&Z;~GR|1tW{Pa07&>bT`pPV-%LwH25={kVBgvG~^UNGq
zByQ}rAF}WE7!DbP15pY$Fho#nprR*83OkPoaPE|bVZ`vQjuuX_v;RAEE3;v%9G%T~
ze4uwt(1S7XR%mKw4_HWUBJR4%4Lm8SgK9FBiSc{GzK!8iRi_;u)_Ts8?_!WpraY$S
zS<uGo*n!lhRjO;yWPIyVOv5JqIH^m(beq|(W`9`NR7SnPK`ult>;hvh+k0$5Y7E~~
z`bM;bonIl5Urj7GA}KGz3IlWYm6=!#OL>s|Yn=g(w(GHnM85WM*v+MnyEHx?iSy32
zQznAj9rb!ZMJi<)|D6$||C>Y7Io0BVH4P~tK1Fbp5@4bVk9c2@N7vUB!uyt%asr2l
zIh8|=@L^}k*zziHo-U((_g3YGf0)3xxr3^2x5XoV3VgV2bYW*fLnF(##4;g#SjbPV
z7$7QW+HZUcd#-no*~!YYOyu>H_<*A=_?4bTs%-YmQZZ!Q1-<d2z1N*b&QK`wjN9#u
zw=~;^tb(rA!sk?GbLN(OX<pi^tkt*6>>oc0yw>V`2Os0~N(>%?!^u{5dfSMASWlKX
z(--&!ag?*-nv*uL*Z?K*s*Bw=qoW4Q+}{ei>C^{s!)IhAPH(|X0PBr^e#}D1g`OmC
z+|bM@!OmK|LH2d9qEu(-#Z}8xu+X}lF0yHGSt|j3so(va^_2U7$mE|cL8@i+nmHM$
zw@|8_f<C5_Td2Zw-ZPg1@D-LSzA5#u+XTN1vg>&-RK++^KxHupR+P7$a3vA`4nHbN
zcN|OLRSo~!CNl?pi%<)lI|i;;8pO5bGi$=hM1FD@!)>zAQOJ?xsQqRcg6%6a^fI4z
z|5*2<Y+P-63qQ+*O)=;65`-pB>T`xN`&N+cJI}C1)(%I@t(<{s-b>iI$k|C;8T<vi
za=TSz8jq=h5_M0nQ)Lm@Z$-BUrtw;;^^kNGq*`H56Y*^n5meJloNvz%oMZ^xW02@V
z(!zfWHt*s4YJ}GCdguTwbDs4)v_SWdPs5)UpL3b{=06SpRoX->U-r(|;>2CGmbH-o
zG8fVCvbNKxZC{+?;V`f&ZSLAH8S-!nWAJ)YMGAi-PN;NbO`ED^CA3x;wpC8HF$oFA
z?-x#<?fWrsFRFeNz0|d~aiEl$<x`Altvu#iU5ZZL*GL$-_f>O1a6%Mnm4o?qIT$Bt
zT)u`sCU987eiLG7oFM*~H~Yz?<`4=O5N}Xk>-EDPB=Lvjya;O_DyIo_gC4zolsAq)
zLJzu`1iYu3o1Uc>nhNrX21M^4jEvUckhYA6fRwn5pI=vL@7Q`W2nGJyl>vSjHR}YJ
zVCrS4Mq6(Br>TuX76Zp1XlNGd5O6g}$`vY#l!#`wwiI@s{KK;(CE3v(&vGB07{xBV
zZO1y;AqCU|=+w!L=!pcYgukC?+bG3hdyif|{i07)qe{h)ndBT6H-V3%!JB1U6pz<*
z2AE#}8p1P2f$!i`b<W%He13koPWQU|(@KQSi*PZ2<Zl@mdQDfkGmp_!sXiI`kvIMa
z&GvX8k}db*H?XU{$7g#bA~Tr-Ebx_yAg`$oYX^VErE4f&j)Ai9RNjFAAr1N_GV*yW
zfk!KoqcPi7h+)g5?)6nvW&()Y{@MdFAwsf#Cqhsk%1u;l(R(NH4d}7l`>MN}MOJ}g
zN5!SiKs&dka*oj7k>Sf}fh2Ua`OF27T-tIxxXUN}L4+gp>)Q(QWu)i?pMf7II3aFo
z<tz0ZzB{@tP<f$e$jkg}s?O0%@`2Z3J9;1VqI*NiM9Rc+v+@yV1C6X?MavBGn?#I(
zjb5TM+c|RVP%00x<O;j?bn?mLiY?A5IhnXRQXNgs46XnESf@ys=yWmm(b~TkQe!zX
zmEyem)yMXqA9x21dELqwyvlG1kbWhYg%-Xhlk&_DvC9)>dnPPOTID+&zd&l77iyd+
zuhAnb29=$aoN-P4w6bq5yUH8s$%ujb0R4AWg5w7%EXcdeY^DlB@h)$ae~P_#D6FB*
z8PY`}c0&bi=ndw&|B@u1Tq^)sNg6r-nc?9rkI4RZmUWX0eU~v*uWKn-Cu#3<q&fdm
z^{ndN#*Ja;rT~N{L@VrWfHvE-v$!Uu{%_!asf($@`nHvxj6OHv>-_mKGMV+QFkNSB
z?lXIqKH!GYzgxa!zOFepJCQ{BG)Nqe{R#E3xslFBWaW<C4KiB}WaZKKQZC1oW7s=9
z*F!<}(&nimu?C#6#lQ`Y3Y+(gN^mGwVwlkpSe&Z>_p@EFhDQ091I$sC<C2rsBbei;
zR0D?<$7RGAm;_*qJoMjJ0Dxnm%BlaKK4&fC1JI(Pri7e&o?k71gEtR?--a=UfLB%{
z4+t>O$l9%6>hdz<ge0fDR*26EK`61Ph43%D(0yB|P~WE$;8#R6q%)3B;uGfxsajTS
z|Js7tj#5%iSmR}dkZAcwkil9sN(DoGbalBA7)~4Ci+RQI=u&-=;L98^806zBf|L3s
z1Bol-8}c{FI~E?Ki%N51tVdvFu(JI_@m!HdL(hj~^ToF>|EIavqcoCzrbTX_l&p~u
zHZ4>NRe+zQ00`Vt*@nHVVbVi6$@cBBPBE;h&I8nIeV*}7&OQ0v$r}$EbR#7i4fJSK
zRd(uIUj_;aiN(78a+5`FsB$#9A|nEt=g~!Gz_;6gK|_BFq<LQ!{}<g~C$eQQS<BGf
zK#O|?xckHOSG@IHdpz_e!`v*@f~cz;hsC1UVLzwfD_xM^c|lR<Cf$>KZ3dq%W|re(
z$Y|46=ed=-A7{B{Rhb2o?FZ+IxSs|uXuT42xc3k5iLH?iTT2We#+Rv$lfJd?f&$)L
zn>ds#@1)6iBH?Pb1=@AZ9zsDG+V8qfBC6{sTQJ`tb4-RRZp5n$m9h(oU~konaC)Gd
zmUOr>)xdO_B7J9gfAAlFlP#5nJy8=GR{W?5jfRfwxwv{#X%89BQQ_uHVE-{-z_4g!
zUx+d=<3;%AtR1V`?-rW<hd$;%lOOLt0>(g1=TLJ~FU|MW+Zu@}ZjWGK@-RoGV@rjL
zy72qP`-Mh<%o^V*y)~-XNUR!l{_-N7N|j#}Vgy*uBd8%1&Y>a%pFkA~DT5bLW3He?
zjPj-XuLE_G#?OGGLQTAURZc#GFsSXfAu<C@aPI-yU$8(pfhE*B`~$*aL`0xTT@bvy
z)dnSL(~@*I6@vuz$%D_=Wrh<Oc&#&b|Mshg9OG`wJHuXFmIzh^6cgY}My9Nki~@H+
zD!RFS(N_J0st-K|y{5h9xs>ae*9x$k8QaPZ$~|SjE#KjR^1$Sl?`CP={TkrQB%_TQ
zQvE|2Vmlhj&q+bi&Tc3xFN<R_Dn*=2CfSPYlP!+Cz0>0Y6Gpf^!)WUWtk3Buvys^s
z_;4sry7c*QJ0r)N{TV;_03lnZ));BV@=T}-Plxu%on=vz21=87ySr^4ua6rH(&`e>
z!Xqvt85Y`pv4Y0GGnSQRzYd8l(gK1`O3Z;$K2CbYt+83k1Rf!rT~R1Zn>IKtF{dE6
z&gW~pOfG)z?t551^pASVE#}LSVA3E0JAF`3_&8A${cjLGbyZk(nCWJLaH%*Q`%-NF
z{%D$Ui2S5scp;Q6a8zzB7S;3m_S8mJYg1ltd?k_1B6^kCe|V)j;8p%Gum6Vy$AIeA
z_6(#dB0xf>-q4MnM$i2e+vr0R%Gt_&JyFhg;wG}MB&#^}MP8sdu3|92{i)%IHgtUr
zqLZe@UWZ=?Ij|vnQVXU)KK-<o)#wd{Kb5{+>r4iB76Cd%j9gQ7@oQiym~s?jRq{l2
zD08?a$;@hC)c|*%Z)HwOH8@P@J%Ya&vLH(C+#RM$XI;oPSoPhRYov`v*>6zNVZ-C#
z1;tay-$dNxGe^Ugz{9%|5$?-17{{*}?nVXlFk$|>A-_qc`O0kDOT_e~ooPC`jS)I1
z?QVhkPDoVFN@1W~#N9SW>@iU4SV=So5(-wR?m5N#*Ql`yr7ASYC%6u12AP$jl*f>2
zDX^YVXO_h3M+p_d<Xj)Z!rnk7yXV9`LHVfPS{x(C8Z9$c>i>T_ed2kDdB}b}*wv4#
z1&qKr{;A&GF42Q3;Fv1of?$t0^RlCqTP7M-$~gAH1`jL#$rr%6_U2fpjI(9HK0H<3
zT45Afi5$l_YFF~i3)p|me_<`Za&a%O>GB#JcEePO3hU`q&m6gm!IR>H_a@&a7bvnr
z_E6FIXTyGSIXXqi_2D}u#m;yMiI*(V?f9I;M@e%P-6s)W+bJ9AszD9CZt6a-sqKOo
z9iL3e*}XU)-KMxE)<L`hS2zGm?`&E0S>r^h2InH(1H#-Bc#W)c>aib!>}%SWD6#Fr
zCCl=`CuTmyk`h70MJOAey}VWF>poLvVY5qO`Wb#^a|!pyfZ~1-BQXH?yGFH<MFxje
z()$gmbsw&sw_kFtd#TNNWxrTd?a|+17hh7Hv`W`z3LXzxqpV4z;vCe7<vD%g6$LFR
zGb#H8)8aFxt7@jJ#R{8vdvry@LtmK&v{kswcdY-mDniY+7RgAt?X2qm;cd2q^b=-V
z7F(8E``rFh91;9tDYWDr<-})Z%#H!Tw`I14S&7=DUrpyZGH)(&u8vq@@tZ&%RJq$C
zGGj~`Jhn6<^b-5R#&ose00B8DU{;4Q_CS-P)yT@(2oM0fw}ML{(GO!r?jik|k<2O{
zc9yLMrR0G&68--Je4%s%%%DQH;=W7WCMr!dC`ari7kG~hmiK^|_}uWnWcInM=^l0c
zBUR^L6rdZ6Xi(Bah$I1lTInJTMDh`eF~T<K;iP+Azx<5YyHcT%Odd)Vb~EcgsF=7f
zFc}*&1&mC+y$=m`TM<9fKP~n$4)R}C9Nt3QT!i(!dip<f1LP6p8^o_5n_URb7vm;k
zfJD_L?hCyq8x)%+KYz}=YNfv{9yge)T~w)1Ox-0OGP<wDB-e#(j3*JTvPV&0D)uB-
zc5)pVw}j13XE*l?A~tu1HGW>9t75yI1$9~!N|%KWyX+-3w);`*lqYUlXLYXWHoevX
z$q0-Pje&8|%VLRQ^0V2O@PmNHUixl=svzQgQbur8Dt(cm$1vDLRi575@=Fq-^Fr7M
z4LL&=)J6&Vy5SOYyZwq0#;E2ccF%mk+drH_`<I?=xj`G4M0VG>toMXSd(cg)yr$2b
znylUc5=AWs+xp*4hjd&gTvMO_-S3`sZhca5S2>T)qf4OS?3|`ePkDT^4R6TTw_K&j
zpEH~=TLmpIY%G3hbtDgVY7NgO8%QGCgtmQ7@hh3v{h=EU1Bv=R%1O%Q;H2yKPYzkN
z6K!bpnA7=g*FZZ5ID(0aiI8WH1tAJnPWxJ9FNW-Fz7VPZXLS|gDM>%)z0fRMbeh~8
zk<Dq2J#V1Zw}^XMjd+^zJnwnM^QPzhKUT+attEUQuOOie0)&3zS4QAdC$#q#MV5}K
z0pSlBMiE`3u8%ya=KBUWKr4A|kXxnYi0ouR9tp^+adVl5%WAyx6fSCrspO}<^?zJw
z-iZE*q(z<a@ICX3Q8){M<-5sRLQ@GwT#ybN4B!qmcD;BwLhdXd?q|eTr%Ueg^C=RM
z2yQ6ttb*E$$72z0X5mvuLE>-FmOk~xx^%-Rp`Wr@bG{Rdq!{cWRKHaZ$@M|oc-xF=
zN&}0D>?c!(T_BjdL@!gXzwn8{n<~o{vJ;FMu!-6M-H^YP_2?X|FhSI0RJuoV!l_?k
zd0GPo?%X%nJwp!3i;>4TMrGc0P*|7niKs;}V&7xLsWl_+_FhD@zT_A$A$xX*^fAi&
zed?aC57~<~cv;EO6ZJc6*Npdwhe&*Uv?euYcR1AI88*wE#pT5fh#pGzqdzgQq7^%n
zy|oWu?=tRw&MF_myBUrhz|`{GG6bv48fPVW;ip7}9w}_7OAKg6GuES_@6_WDiS}ln
z-E`7^8l`v{bxdNa^YDdc=T6BPa-!@{H_2Up4gH9vI@327PKK>$l%~-&?C1OcEB==q
z<J9}ju#b_SxvqvmAkDBZD77zvkZm*jf>MScP-`+mf<Bop?f~=TlsmJh4wQCNMm!Mm
zRedMrztkg1cm+e0R;P;^B0$c*e$S*1<B?paVMTXN6+l>WQeEa0SuT>hDtPOtV)|B@
z?Xrwa#qe<`*E48MM;@8HV$=9ZzVH3md?rTqW-|Mr2O><ejP5f<fjvL8Eky2T=N*@m
zpqt7#p^Fn|eJalLBG3|rF09`GJ|hn+Ihp5%$k<JI3oO#%STmwUDOdkSCNmFx+PGu8
z{~>*e4d18zna${I9?)a8@90eY$P2Vz%$U7ZLmXhHRU>#cw3#H^8Qc+0udu`ax3&l*
zAJNqVsVV_+Ag>F#n-egFBh-V0uWUt5eZ)D4^#5pj_jsoJ|9`w)E31UM5alq{6}l>t
zoDU<HTvz2#DwV^E5OSF1Y#S*`F-0MV(TNhv*_@`Fb685|Fy=ftY#4?eUcYDW@9*=M
zKXP-swcBHl=i~9XpG*au+dQhzFw|YA##IG8{{V#XM0aQ5B9$|&h0}wQQ?s)9*-6y)
zxd3CQ-z%xUSa`O`W;8tX?T8vf>KP7ZfXYDvm^2DJvM+zPQag=V_^##R8_(Y=?!Gi#
zrAHt8%7D%&2H&10lvWFfbvKKg!73rXH^d+Z%{_8%?@-Fy$HOr6PRI?ZVsK+#f-R$z
z6mcJHx~Lq}D_FDXKZ3Chwe|Esv-~n8e=C8eZ0OKgp_I3Q%D32`**{yhEdUlj&ukp7
z?h@tt$S(6K^t{wlq$Y!UtlrmmFYTBNJ>(FP|6K45E)nK;<a>tkJ61hOoyd`9WCc*u
zJQ4U^m2K1Y6hsBZvHb4-h$W1_)^z>x5G$wk_<_p+)?ek4`zIS+DvK>%-uVX#xx@J8
zp)g?+!wo7V%m7diFESCuAhbkb$M`wB?mWhS!3c+A#QZAl69;ZVg36(Vu?--ZasC)_
z1F)+6JAzsvdGd%o1ZJF9L?YdL^<;?a2nl+^FHsxoIPM`{@lrT5AIxW{RrEK-jGSBx
z7{j`g)DH1$*#uF#HsG`qS<O{?q@jI1WD|Kd+X$bZDlMshy8yQ&O&%hGeC+w<J0pkr
zuAXclvnOn>?g%hPy`x=_`1u+aJ=i>Q#<xO&!Z$xG7Da!c0$l?fZF_KD`%gVl_5!0I
zE_+j8rnOHdPnNn3lO<0MWdeL2Xicaxze)aKNacU)|CrJQ_YW{LwuUpLE&_CR3<q%6
zvkc&4#27jhgh~SD)pC;~m1^v`q2JzIq2E9pqeIF%vRj6Ypg6Vzsv5G2AS<*TQJeH%
zuFe(^nQ+=q<~<i%$*W5!wZ81X0<z!3-zoo#R9U2$OSaV|($dox#Q#NlrCd8HxBCmU
zjJa{+W0p@8+-2kZ2n6JE>*k2T@@-H}_!zdE@mmSGxhe@^7pdOADM_H8(mn%r<cH*P
zP>ZcU@Mh{*vvMW7jI&Bf!Fjsdy-7K`N5ki$;`g1_I{e^j4EzeO0VtW!E8G4a>3ck0
zH6HQwEIvOu*0^>4$k%B8{;4J0Jw7~Q;Q<uH4s3N}Cg0&mufk5Mc5XW0EBm}==AV?4
z&sC&32c!>hM12anYYknxH?O|N5O+?KG3du1At?x6Yn7O?LBxA3z<E_lS~<;NbB)*e
zJb`(K_aAtFNN><9PiH{-af!}n26?ZP`?0iosy^9JG2f6atz^wk`566cC9om2R<ZV2
z!0}B5`rW&s!j4Sd_?+QF2bJ=(MRLe{0`bH17wI!<Xz?oxOIziX(lT*=+0x20q=N5O
zGbm5$#0I#yw+c_2ng%CiEj3<oV_LG+{uJWzxJBc_{~paXZft06s+MBeA2c}#Z+L`4
zd5_;@H^Q4+ZpWCBh_^nyRIsbL`uc9YN4d^RcS9b2&H>-DN=u+qTSBX&qJWIl1vaIi
zf_VDa8^n8+WLg>1qidx+rVRE3pqAe(zgxatMPgl^8aN5+=Q5T$p2jJEJiINyw5<-5
z(Q|&p<cPyv)VIk0HLIgzc=icPUSr4>o<T$`p9z=hcc-IMH<-n(gE}EhUsTOetSXxW
z)0d5;4@*J(r81-W&Y@~Py16HC(ocn){Qvm>a9Sg{`1ksZ6Wk@$dGM^Gl2=zyYsD>4
z_X-VguX%<outq4S3D#Ip7io`Zs-sAz(!yHUk?#N{#gI;(Vr&Cg+wle{bAuhT&OA+q
z<A?o2F&EfTPqu{^sPJ~YfS=rZ42n64c%$*bZ#=5k;ivOY_gS4)#~_oS25(`Mw}rO@
zppPsh1grHbZ!mY=&hxH$rG}6J3W4U=fooLYV03>xa+?t1TnaoR(WVVH*j8S$M(Qs(
zdv_JmtpV`G>Q#Zgh)*HQ1yBD*X(Z6c=;gy#YT(_DZo%xZfx3HYgvo?7Dg6p`skOX1
z=PBY^)rAHg+q6e&=1D`e*e#J>q?z0L9-kX(n@k(!wQSrPfQw>~<wMwlVc0ck#xLdm
zkw3A_MUu%XRm|ru#Px}@GAI_MlZ3p2Aw#!L{#(7ScYwpoM_=<%Q`-0d4n}?>v}+s2
z8?45;m2_=GhP$xJh%}8Fzqhh2Ffx<B{2$V*Oq%HIl_!j->B4_za*jh!j_>8|9n)?J
z%CXg@>VE9X<w%d$a;ra)FPJbL^R4?{eea%&TF<GvAQf+QB;N5iJ?m9A4g>=_03GBk
zzA3aBms@L=Xji{`@=q1AIxQ+tijx0=Z>fy^eL6!kXmsoW$^~`w--K9xn9XJUVfyJ=
z96((c>l?5U#Z0NHJb!-%QS|N=f9x-S^0Nf(5YUB}G9Y{yB$S1Wc7jmtqD9oSz?~i9
z4;q@_Zyt(49w$`tU-Pdv_b7%bjAHba+Lr!z``<=@hfCzrnO#u0Ji<oF4hy4&t8h`H
zF0J~blv3piED{i{PreqMlCzsLSD$O>P=aDQNca|Z8u!vV^CswvsC7V(ElSrz0nzDh
zC?dB5`n_Oab_hXP8HuRo;Ih^lO^LfZ3mRBRVb>aBemW`)C$?FRF`G63E^l_zQFcz!
z7w?*tx2y0_b_#pFSveJ&55;$5UNu2xT4#0>%(UJcro;Q@qT`=i%AoCK;(W}{+g|u6
zw_nZX5kIbXN7!xu*OAwx2CYKS(MFfxi0_+-$T+KoxIgjt9SxobA=qaX8i|I&!Lq>*
z3m&xETWb@fm$<-rL!)1#A;ZHX3H8NVG1tFLE#}4_GDw%+I7G$Hz!j#7;=~i<>s)8-
zeIA#Wx`~|{SVGjfQE^|d7;*j}(0nQIK$|L}G^>rW_Gq5#7r^wn!APaAo@~&hR1SVs
z4JoedVn;CMX`*!{{!9;f=zcDzIaI?Z$c^@5-8c$3n~9lfjz$d}nmc_;!?*><yPum7
zJo~U~jYp{bshmE?;UyBz@aBM@c8HUcB5&xnkv6Y!Lr@B5&Ryqi``PH7Z^N;k3~pd_
zT+g?5I7}2jjd{H{9(5$%-cp7>n>(`P(mX2D>@`XVRe5o=B4!*Ily8~Zk!yU~=KZ9c
zRal(jKN=TZp7Ejs<tXRf>X%I$gg4%?ECO9Cwa|-<|0K5s{;f_M$N##{EHM#%fsEtw
zzzkN?{S@p5-^=UAK+r3AI2#TA27K@Be$T4GHI~w`1xL`AEV7^?*>MPWQHU?73L8N3
z77wiaD$vBD*1LT}`n`UXm>N*hog^kP@A<!xO<`+_!mgEwY%+CQUG@l&HobZY+NqhT
z-faz)sS7Kpe}z)&ZVylLO6cLc1^$~qYzK<tlwISL0L}6+D8Aonrn}A1^zjm(-*AMm
z8V!w~Mj$$cG|KxXSYDX(hpDLE*8B_#6g}%V8^xOd#D_XP$$Z^yWu<_%yOvi0o=;qm
znaY_qShW1Bw1YDKQCFLwSMk<A2M8b?$K?%xL)y3>dXiXg{tq3H4XiskiJB+;X*1QN
zH5w5jr92=N;A?d*^!v+-ckAi+lbiXVz0JQr?2!lM4dk_wOLrh@U0)BwS|x89S03gX
zY_>267a=#_t)JMbCb_TukG1^UmG~>QE7eUn-vm<%{&K*7kc0P&v8J}(X}{9lUVCR4
zH+vQOCS7%$ba+!V<2b))_-mxNq7ju*)Bl>=CE6gNE0c*M^Q`alE-zP^OO!M~>{S{K
zwi9W)jYam&h|jawL)7LLiLfD4eCHKD7sc?<W@ueQ*Ok20KI%x$P!K47;RqD<Hp7Z-
z-2_kg8xte>sM<4N^5^YcHa$Q)H&&05T@KEC)xRYkak{l2w<`J7`lWxCJ}S>*P_Bip
zWIHFZaUFHMbjPD#@c{9Kmv0Jlo;fH^?WZ7tu;90EKr+885EPQyGldR@)Qm$<IPn#*
z2TI*U_Xy=V&-ufn`5aqz$nyx`W-$xWn=a4s8}(EeQh_{pSUkg42$q3n%p~5^Bx!@G
z$*v)HK#2x@Mb%szBYt;h7GPVh4CVctA_VFgWHtOoH6Od>ut0MOxBkXA(}T__?!GA1
z<X&K)GG&&&WvWsCgycHb3K#VpbHmDA$?da?NPpCw0x3<1LS~{7RglR%eJ6N440&NS
zt|INvm)Qha#L8Uu2J=gr_*D#G0#8Pr_&H~P9P!4~n{a!k5Z)5{sZv}(&eJ(irn6^(
zHk3Q~SAN!qg`MR-IdugKG2_wOA!%ichidVfr;8C~%q{9Rm-9@aa~F<TUtk=6C7<=G
z%H_GS+RFV%^26obNu>CBFUd)cPp)w-ssSg@lYr(L)B?;l>D4zNl+$DETaLf=oFwnd
z-k~QM^CumPe}vKIf4%j9IH+0=(tciiF+s`C3L0tqY~))dt(;b4H*KNRBXtJF{Fhf7
z@HgQ}y;=5za$B|QW(N>-R}hs~U*fn@F6FJ+A&AIffs=s2aMngIEK^*@NU34>6^60D
z&bdkaYvEb?V=Sm~zTMpeW%6Ubx2M4OJItR#EpG}VBJW@TI!z&OdX8deuuJJReSdh0
zT5*xTpo!9Dr`i|g-D*hD6?X#5U6g+1)JB+Hi;^|QPom42zLWL3_Q3R=YVi`O^L@xP
zRGw6R_L^9^+_&QJ9_9`M<fTlh<=~80X-Tj2b4U@Vi^XJ!U+o05J6v5IB99?B69iv7
z#<pfR*Rmwuj^?1;D$Nu*vyDH#<3VDAS3OWM{Ss;9IK35EY7|W2rNT9iVX59+A+R9e
z7F&@Swp`)WIddv7sN$R(=6KT}V3$Vj6i(vort$g@BmcqQE06&m`D(PMe3&Sty_`NN
z3OTO_xe+`81#ajlz^xny9+NAe_Nn{-KhnA41pWYQ)f5=+uE9GWayy^GJw)Ip43Zrh
z_G!OzIwko8?rqZMC%L}P#=<ml;fZ`^GKPZ|a6asl8hVQrgqs=xHLtT)h<Pe}Gl&{I
zCf@x=2-qKA=mX_~`)~KYpr_&g3EyOY7`aHioI0TJsS}2wE}{nE3yni(br6}tL7(B4
zdq2hO5pNPcbbU|~FUJ7c!3OEV(rYzD3V%oQ$Jv!`n5=DwoubW!%)RlPpAT7+8@bBh
zx*oQ@&DL^JIoEA9hu+KlAEF+OYOeCPnkKAlG>7k2=8_Z`Pj&Aub2TRL2gsi!E8B5z
znvp_%-fQR0ih(St3}Fw)uS8ms(A*+9xX<rCw2f`yE>dfe(o*QrjF9we9#I!`*2T0+
zus>-DnWeVMl>>Nu(SYE&M*+jgjdL1h)pz=qavCmC<7?vKb><k}jG!P{J4oK)Mt$s}
zQ}as_(8kv!j6^xUEx1rMzQIY|uC&!2k-neZG}dk(r&%bb>mbm~fImW%R8WRdl~A^c
zND@yo#eTVN#A4Ol=`qxF)NH(5$j(}@9t(O-`#H;E&eG4aS?-Ai%Ze=%On=C}Zm*$>
z)MiEsn5k;(b`FN^^HQAI(JbFg@>UOCJvDN{p9ByDEjgLEM_sRG@S6JEe)#!NC)==*
zux=A#W{&ZJ6sxu)X80r3=w`@R?}+UYe@jX31i(=@VE`4)c*<%58ih4r&bv2&3P5t|
zE69>WVl7{1hdhWtyVsMj0}Kk-G~~HIt2FCjWB4fk)I_~IJB|nA3cm=!h@=PzMTdw6
zEk$Xj!+2d2uIe<;e4V)va-aX1xJY$83Av#>HqsO&(2c{Bfu|FHix><wRs_9}8Cfda
zb}e&E@t@hKNU;lwh@uKj6+4#{CHL)Mv(&&Zdu41kxnVd=i{Ig*H)5SPJB@3W4HOp*
zY9d97HN8U4MIN<dbYmN4cnQA=E3I!B2I>}RE+RAEL6VpFuG_K>I}_o7mS2#-I@IfR
znz;B!3t{hn^e!~a8LTRuVwI0ajhy-Ct>y{;bKZ^(kvj(&ZXoeh;MjMg$MnIVM!pGr
z!T4>TX^;MmX1Ga_dP|h6l)CcU-33<_F5+<iauiQkYE|4r+5@3KeKlttEs-FdYTiu_
zy|IV(AGj?KrnbTP@hySo26dH-hR#HGw2kp5P5<9BB2%{g01w}3@<{7zn)nR6<W|PO
zb9+X5eOZsH^703-k9W8FC^MeN*1*St$3oe`WB=|JrG(v#<g|W~6Y}RdgX8u4-4Ond
zg%>nxdT~Dv-@W{L;6*S`54_`XeRt;rP(i3=Y@?TgA-C99&_^^ENHSK}4cMz=q}<$c
zt?bHI1Gu%UJES>(yjw<AK$Ay?{(Te4C0!JKrlZ4|mW$SmnZO?t_@@#~p^rAxu}`q2
z8sVwdi*B7>#zEsd`D4T(kRvg`Z?B}6RnsXyF0A#ME5nNrqj=*M>@y}kde+H*W{$bT
z)FLN%Dqr{$`2AfDO?(}?CH_#3TLbNRmHj=}OSW%(j9=*LTy}NesU;(0|6shxwOHlM
zQ4fp5i4g)xsTIy6Ue3B^3i5l9GIrT%`Hi$s<Y%me0pbf^aq6&L{lSn&>De7A6H$1n
zO;ieE>Ng9tmlHSlG{ihC4Zm;N3&s!m=HF<n5vWhe%#@rAor>5b^m0PdBbaZ^NTf!b
zJ0%b3E2sd>73ah>39oVv_<wdYbFo0l)x)c#l<xyl$xqoaf|X)WnX{?y^=ZQTkFpwg
z-__B}Rm>1ZB|g+qOo&epako*mg&R1<3nc&N9W=9H*9GWENm&+WGV|<LP@CFQQITw*
z;fKyCo#<=PcbClHi25Zo2eo~qW&y*a#w<~9juGT&={^o7$(y7Jwi@CX7iH)CD4csy
z*FR?_N<hl3wlcZ(o4fv7=d%tlkz;cp4g^_#q+H8x$lmdd^E+WwPB7z#a&*(S+_VD^
z4*}S%Ro*qhHOcPI*#p6QT2C^M#z6#G={d{ditWYM)LrNnhovr=hrRiG@{kqf{k`QY
zHWt~mHQh<N$pLI}eGS@nX0_uc`p?oX{ZtH6zBEk2gN1fodth6)H)66l*6n_Tl*T^<
zmyfFp^f4Q{t6OYv)zSClU8F$rm(&2%B8Q0KbqJ*V8D7bf;Xms)el7$zUJB9Wy(^!8
zO7Hh@{Mgu6GlwZ$RA1ZC3FdG!Cr!Ythp_b5{s9G~2n)AT$OPvNq+q_qVmvBj{}>m@
zII8=rA;h=S3`G^q^Imc9C$69GAs*2WVGt=#MB1bHLwudVby1=22$F~dOL*j@A)g1`
zeuDG8I(N?G26<mm-t1OkvN8L3=r9ldm6}rPe0k7WDPMwq8lz%~*?M=EjL(N$`OD&A
zB$YFJ4+QUQwzsR@Hhw}~L%QI>c%@yWC&LPZR@aOQjAclD;WuD+QKpNHd0P()@n0QJ
zBs)zTSZ`Rf9Td6SD998FdY#0&ZYN4`nm4${ZVgp(3jxkVzYWUc|HpRU!gI?8wtQS6
z1Ef1p(3pDUsK)!>d-w`r427byvi(FMzk1)VfrQMpP$0r#I}oAv;rKQV7$WDG{#7Gp
zR65rZOp=;Oke6cB40waDDk8+5pTqdy{<{0pyA&<E>D?lh6POI<+=wl81St$uGw#3}
zYPMyGcpzf5)*$%5brRYq1XX2BT>}+~H`{#Dp_pXa&<ooqZo`5HW6hMghR$6uFR7o3
zpRS`+7HoWzbdTK5dyn{F0*8j)XB8}nkN*M5{dq`6`=8%Z)cxa$<4JZ;&K|%AQBnWy
zTP2Ibng>tHly=zd(tKrXCo%QxLsIgeDS3Z~6_{CMvqm&is09aXhfrlh)$i8iQOyol
zYd(eN4PZ4hgrmDk?OVwe$&v$p(wmh-p*+*I`}-z{V>ax6tC(%()`><p^ks3k+2`38
zs?Z-5FlQuY%8KD#RjDoO=Kcf!SrryTYiMRM!ZV}LQ?aW2_@+>|np!Nio4daFUY=jB
zs-~dU0{v0XN`o}|E|u}i7jBpQEcDR)sKZ|x@Ll`$pTeDG7^naK=Y(raq)0F4aJR&l
zB>}5Pv@fboe8XTgB=QVoJn)jJo1veCnWrzLTqsx|L!`p7w2!=p!mPUasJUx29galp
zNc}$xul^2;_)u19Xg5@dXolH+RDZR8F?Rf-NlF6m>qb=QwSS5)VfH?xnjKSBr2;bu
znNs_QzwF3>dxD_c&wM5L<Ezo63fu9l=|%iOhtKC}j?t`>T^QS8DL|oPou?o#1)<u6
zQoNY7%BeQt-xLP&Im;d-hd7ATIMQPTn(2^lp+BJA0Y|iZ)GelsX=FvjB>qzd@Z8Co
z6SXSSwAhc&8H|OoubXTji&Xukmkp!A(`*UBg~u0PHq6D0NPz`bagtzn*DML{fs1+o
zI7-SX9qE9g@+}$eveIN9+%a!8F+z>og@tN3cp%~6#OJXFrq~4F!@KB|3Sqh~0~1Jk
zm{M-`mQ~eNRQ~k;8rY>evDvU@c%5j1Q1;+DSbpE?o{4}DW)B{OlHcRLdzkymuM;!|
zgR>;J$0PTHi>0)3h)j%3vRU!pZ}qr;%&nR=k9e#5rRHAMOhL?=ZydIquv#Pt$dzS7
zn=jU?Y_CV!LIc62Q=FG3b#3#G&}LEAN!P*zh4pvV@@?|!Hcc-RB|{7tMa4~!{JFQx
zV_4LUtmC-2(0<@H#J=D-uRWnRP$~Ll;#f5zBsbTQ+fBX>`{5A0qnOme@}o5b%sMHC
z_ehdg$bhc?=&Zw~bM<J4m#2SuP=jXAfmT(hdozmfjbhMn8gZh!nF(>a&oW0L=0X&F
z*%;-3voYoN<BzkcoPpL@{|b~qIHl{8IPgrbHdByUDwL~<Kr+RZ=8lGE)jb+KZIYh_
z9#AiTJEz~?XM5xSD_7^5UYN$mXPsV>l?jOmL^Wev!;kaM(ZfJ{T7I~^Q=O@fh+clS
z+}mmF|FrQU&+X&mNCZ%V9Z*MInFGz|@+^K5ts+MU0f*RSqold#ydX4?sn$f9QZs>0
zY<bVBP<B}A6jN%_I0~MHr8@_Y$fg2J;8F<WW_?`adBo)&s2<zl%XSmZD95Ib-44_}
zK!X22(uw{IC5OJd7O{vwvQAPwF`5Z-<?K3O<$9;=JiuKoMSChCWNyFH;<|J?g!_w2
z?GTo0L@o~iHKrKakawhJW+U$lq;Ub6gAOUe%}Y%LnM-c}pxt|5_D|*fpnt{)y*lKO
z4>3Rcw!yiu_p}^C8=R)0$~V)fwS&Dcyt@F@x;5;^KxjZZ&<?_OtI6m^>EGio$uo_9
zKZz>MZ)KW~CqY_q*R;rns~hO}@qm5t6GxWoP0Z^Z62gt`PE8tM2)V{oR0E7+J9w0h
z^LbgyU)C+V{&B~2effOWaw?PIibPUIR3#&6sx{06jOPAdrcHGtXV&}^ainx)zUXA;
zqw+tWWOz&zQpDGM9s{ENd_Qc+_scimwSi>a0Zq+?vTbbNrR>6i(3LYNa{2Luih<yP
z@FA~{$u~w6SueV>xLAK4GpI~{4(G7Wabb7_Lv9{~mA75N%Qqc`Cd`s0QT@6Chm=J!
zQWxUbA6v)p!e3II+j)&Ugp5_ETFf2j3W~{_zi;6Jc9WY0!;zH(_Z$RHAz|0F_Aanq
z&&j*YE^05DwEE<->XG@~JTZ#1POoZ2jH|hh4C+H}zr7TAt}gQIJ7wbYUX9}<i?6Di
ziZ}bKSChu;A7&=Kb~0y&#w(5|*+!MeP0FusL@)25>;Tlcoq-%UxB^Q|8BmZ0pECGU
zVI?#8P~bQ}!#l)z7SrCmpZlC|UdL`VL^Cv3QC+-(Dv~sK5j*NEu%h3Gloh*XhX6Un
z>CS!5fFPi_A;|`e06d!z2hf7Zxx(P?vU!mops}FYxN$@!!_`xN85MSImJq0afC>ke
z9^Wy!R9Kzz;%2xeVCF$vpmY2!wj|xUal*G@Sm{Z-^fnl6KKD1My3VvF8Y}WdCZYXy
zH6Tvm-XfK?09J`}i>Oc!m+2_UZXzExN61%L5icmpXOI4`X*EFr99=b-t$_R+Yp=|P
z>mly;>gKqBQOsiStzAR{VBuZ$-QU~&bM1j^jNSZ}W|`bHc)Vr2?UDGK%cy)BEVuYi
zRK#g5%KrFYmMP+EG6v{9Q)*Mi-lrxh7{URme)K4atB4yK+;7^ZB?z-T=QuBg<HfL}
zn4w)Zrje!AGnLw#y@^<2hoHNTru3-3t}`9D`Wu<|$@Qlb6($}mwBSNoH<;N0{XT2W
zw8Br0oYE>QgiW*XdtYT)b*k>bi|)Nc{q5C>(~MHX3BeZsx3p(LM>_<@$aK#&GPLp{
zD{}Ho#-v7TeN2^iu&H5X_ab$M%Vjkg1Qyw>faDiV)5ioFw`GMuPeiR;<}=>UEj&Bd
z<-%~SSTPAfp&T+(if?C02UuSwn{F4iQs#GfTUquJ@`A5wdP6_$80r>UdCH6R?nXW}
zNOhaN7ZjT}?vEdb2+{=EWMF1xzT~7nVll|c6Kr|UH~FPbj$+tuAlm*r@q=q`*CAYv
zlw-5PlANyk7FwCG<tXcKHvzkRwTk&1B*bhHg<#mY96a@F8u<qx0M}Ci7NNQ<LqU$7
z)Cat#s9FSKb3iGi3UZzn(aF6~iKq-Pq<bNonlK)}rhWK+t)ysS(<g*Wg{s!B8!c=W
zv1T~;UF1SZKu}0KqJ$`F*Z_Hcpr+_CQ9uX%s<t~^<tXZ3rKFPBUYKU+M^K})<T%KA
zZ4$txkF#-KG>)w!L8|(RbtHsXM8Q^H|MpU|BY!Z+S$lBAP{rpoJ0PFt4y-$)a&&Wb
z|M;nB=WFdNb(@%@yECw%1aDRzW_N5$^Y+hB!hWeMcm6vdirV}`mK?91-3~jjY9@lV
z2^p50>23GM@9vA|Sf<Er#z&I(e|7lkSqI4DuK^A49#3k`#xKy;tE4N7?L4O4rS<Ns
z;T1M%X<4`Qs2{_fxn8HYaGzK^K2p)xQe%Z7-f&99m+Vp$r+=YbAI>Pj>ud_nY|@@c
z5WhlVYE4S5yTWNL%GF;*BL9%VVcVC-JxrZT2V$L-Dk#3GzU<o-2@A(D*_r+^d}ZbB
z>&S@rAD6L_9?u8%G4=T6j}-2Vk5&%bI|_{*g*bhLfwBl8szd&$ODyI74C98KgeDk7
zh2=zR{h1(s?^3~nCo80X;&GkvNLOFSsreP|`OQN?5|#tS^*Q0&4f_;UDp$V<I#m4i
zEWwp%;e@5H!rk*Tp?jJios>b(KIs1`+xqpFH3{7;d{{1talq(5xO!Zs^})EYx+rG(
zkEEj)ffiK?2ojX+Oz)*ns)tbTcIpKJN%L216;fSlZ4zG@o(N9BvlQJRRG=0+B-tWS
z)gigkJS?+T5{Ro!7X`l?L}YoN94{uELGHVbZgAU@*OQ6bkendt?-ud69U5H%PTQe~
z6{>%YKe(C1WlfLXQn5;|+!3b4cIbl4Xg`=c>NIBRE+WngZDoX-hvvkaz!kTK2=|;b
zU!>kOd8l||0iFWSM)9?P9ozh3jARLM*8-a`FOj^-Y^Ffq<^iZ2ptK}IyY6k1MO-qu
zm+xTEYmfwg7Xg0(;xf|1t${|>o_U$Kf!vOGB81ghf3x{Vz?r-cg#k*^`@-l!6&WQv
z5uzu%&bc6=G&On9R9!X7^+5wT#+hNxToOnbn;!9yJy6sxys!AF8ggGGl#P3vsCzhA
zEV-y_UV5F)YFqtkH*(R%KD!__)|d!ZnOz)B(LOhtEP?Z4t^ZuQ5VmB#ruNQXoEp9)
zTUt=0%U>oJR_<u5=wF9bNl!GaZ2JnSVAT^JpeYu`#8Y$L>2ePtFT4sa)(x?-l*{RV
zGuE-dNBI@!rKT5RsIKTQUB12*1H=AqZ2Y%|L-kXmA0*f}R$84y-wtqu(+avbX9{%N
znf^|h7T8X`T|{xjr2NRE2`$YeYzp-DmWF=#jnV{@MIHW3+VeFORM_rO!!M?4PkmNw
zk`PrL**sc`m0sC!_i^a=iJ;9%kq_Mcs5j%^VV9w9xu9y^o%%{w{Wcl6=iYTv*SKVX
zd8<6Tz&*6~e1kOpHa`@p-fb99J-u{I9WUM-as#z}+>{%gA|0i;hw=s7Yd5xX4pG&a
zQOmnK&ogYp&V}uQ4pl}2(^@$2<}2D-E4664$=|}OX+FU6CU`Cf0Fi6z1b%XD(R2~m
zGsJZ$Qjk6-A1FwE!YF=4Zww^CDE~3bZ@jQ5+u*z?pY(f$)VE8AvT#ESYeGiumpn#c
zJg{oc&_*Of?KHCy)ybfr0uGZ*Q5D#JlUpb0-4&Y5l$(pxCZl&N&qQ+8yvE2!eqCMU
zDVak1LI8~L^=$|Ih@N7SN$xe2bc9;?hM1Ilgav{<%0g|X4X=MY#-qX;A&s>XVe@?6
z{IgbJxT)xk^9hy~|E<yJHd~qm;+DTz5&tr6O2lH?YfeV>%GoJh%zA}R4;SlU?gBKQ
z=rE;iv&ZZR>6I@NxA|CapS#CdttJa$=Dz<dh|M7093KO}Q>JaTP;`fMJE!Y(Go<1_
z;m!H>BNI?Wt@$u>Eh}}^%Zpbn8GpvR$74<_|GO&%{%*Hgjq7^|RaeRrM(60e{If)T
zxZISj!h?6gFG&_8u?(iZgnPAdy-h~pn|So+i1~f%ms8!e#Eva~ylkyhQQb4YAy)c$
zxYX2i%{}A9WLKo3K2!U!F|qiEN644GJX1x@45QT5(zl|+izh-FKX>Uy&WQ07h<g2+
znkO79+V8BcMWW46aS2LF&uNtIy1$Fw*Zj@vbFS~Y)AHH_EnN065`F8^#f;i0v{Kd$
zwzBVK4&}oF%_60!9WAjF46zdG<C=q{M&eT25(Xa9-`?t1LR}}%E%aD=XmS>@KHc3h
zby$!injE1bYTa1S69_N2&gEW&upbua$9msw#t#PVX^uWAV-#*-i^Dgaa#d0nd#7&m
zx8Lcei>P<~U!kG+ntT0DpWSpYHOr;1)3OtJ_W~P|MJ7Ix`;Hz&8^2&luB9h9J!{Zn
zvtY!@&XveFrl@6Ebwc#=euE-CxB3<Sk-R?)u57$ubX-*wYE8OPm_cFPVpEtvLyCzK
z81uko_!JA^7QGxb-drCiwR7Vro3m?qUxU9qH--QC+K;>;gH#u%CF)}aFuGt?=2`Ff
zeS4mCKi#c5i3?R8{p~K9CqisS<kDNAS&UgX64<}jz(4F#CyrkYFqGVtQn<P0^+)b<
z99KE))Drc;7=bk<3IXsx3*^@l^hXct=kQG<!0Xxz7R-O^{(KL=2m#^%ezq5gvyWkc
zQ_^jzvd7*4zG#Ped6ww`>+L~gtU=8ts^}&BZ{<r^GawUeR?c0&6kYZFb(N|N=OQHC
z^lxs}Kvv7y+CsVJ6>oLq8}*PC*(nXX>>&wS*pcO$@Y{B5;g%@k{EjE@&rq+kql+c`
zhgBCj=@Tt=y)SA#*Lnj>34EmW+M{BHJDEl|bfr#Uj|!x!jXO-fAsdS`=jFRo%^~J?
z0=t;);ug11wuJC6Awz848ZN$6XG4E+KlR1vS$SUC4Vq_PegIVVob&RyllvN-AbOW|
zxARQKiFIFv$E{2C?8s0gOB%bGS;PfKclvV=@d?G*hfN=A`;7)0ZM6BWS)uV=>|ko-
z0E!Rf8)81Iyu8lf{d4xZ6vRpYi}Z1!S<t7`?4VV<_xv61qP*a;ynK_q{9y+R3=oHN
z=i+UDUU*_JkdA+xleVMj+galv86W$H&0}F<Pi!tI#(4wdhX&!Isrg9MS&jB1SU_NV
z`WgVUGws3@RC}J8-&>|;;KI3U0GUU)v+4F!<OXtvm(2fJ+WLFER|?$IU=93~(CrK|
zKODpgjhg7&%@X32dWNAd!UpGfJisW%ZdgLe1&_*@w*NEYHq-!CSwvG{VPwaF)#|lD
zKyr|&eIoccR4__XP=}ijM1c?5ASd<sl!7qYrB4N6%(*G>_gH6|0~@%_IdFqTiO#Px
zH*8H_8jL2>#O5X1f;{~j`jLiYX#hs#qb(6naH@40XX@^LRkU;a90>BU-tghSsgyLa
zk@^n?D!^KjV3L3QN6}Hq?IY8&a5D@{M%%3TpWU7h4NhbJRll+=Y?u9=a~I~}&6Wos
z>3Gt;(1QLU1sP_Cou9e|<qcUq@9EsxB}VGS_JW}ANvtWUzn5z-GB{W5iwC`Cz@>+S
zWr_<T5(}5;*M|Kt#Hv=gm%?WBolnGsewm0@0k+Q5f3#fR4Q1C|zAAT&*<mccer=+O
zjfY=9$)L7KO!JK9*Bj|WC{bZ$^kI*Vk&+qCgd#?6Ez+fjAtTvRe^m36?dpaH5?S|I
zRdUErI_}iqjL4|ev^CndxIRYsu9Vlfumw3)7sU}83xuswzjk?Mp2%-(;rW#Auy6r9
zcadz(^ckpg1BXl1bI-0F946=b*7)A`FR^y<;15EVXdlMi?H`{tA>SK}8|)Y{^VPHT
zt=k(fyPCAh`^o0IaqOFQnrmd5EiNpXF3O)Jps$fg<C-fo_%J8Lv}lVKNzqJ0MQ{6*
zcpxHd>sJPmPchm02vk-|sae<^Jsvj`*xP{8lRV!2Tk+QhrR4cgNVQVc6p}p<<^bIv
zni0bX_HfrT|6<TSJ8TJg+PcLpb3FlAa^<{&oM&7dR#1^2UP8jSnl!<&Z5-+UlhS~z
z#yaVn56I>YB_u&G*3S3PTU?YKa7?y@I@<cS=1Hcl!M{U)Lq_uraNtv7S;_*C{Y|-Q
zNVxpVYC-OQ7Ps`QI+z<_rq<6a9e$B?sWRUas1;U=UjTxyCE~ovVo|NL8Ch}n?U)he
z2jE9zj&31e=le9=A8LZ-%ERK7a?%by)XQ0aotE;aNX_`g9=aLEMqP4e^V$4qLPOOe
ze=7Rxk%*nx=WQW1Pa{bAlRr((>P0yhFm)#{LLEd|Au(db?~|_M$amKch2v~8ro+U8
zupHr7s`D9MIkc6B%8V~&_Qc5h`<4{L_f5DTdtR^GP+4MHHIuB6#-XrKrKY#OY$^S(
zaiqf5+R2@j=~Hpzbu(Ub&L|CQMa|e)^{SkzF?3~44G9(TtkQz{b%<2W-IDRDmaYdl
zO~wDn@U;w%s2LdatTeg(DV?0$bm>QgD^Xp2`_6kRJMSH|-u>%Jc<~K~H-~bfT!~uy
z73$toAuLs!c9QuCZDC>najMxq!JfRKll*!bXC!1KeRzSiY}c~V_qcovS3j^+7k)h4
zdEURfML3Up@?Qx<vD0!=XGsBx&<Qh191e1h++3tu@{~DEAJs~Vsjc+V!pYaJ8!bAY
z{ePP96HPf?bKlpND)6X)p%2d)-1tj_!SDbmR5O5dd_v?b*B!TcL-H(c1W|?L>t*^{
zW)fO9u9A4sSh=>FtP+f03~SRI2m=Qh<cn5j<=YnPC-EOlPB@>KrjKG!KaFZgt-=q&
zcNxRP4SZ37JFmuOF{8fMjqW+CV^@<qgTKLhEwTti5&4f|tC8%RyqFUHb0SZ@204n7
zg?ug!L;5Xpq!OrH35RQj0zP?*JUQ?d;i3UwX@=r02&)?ty_+XAa8D0Fp;;}W)NlL-
z*kk3iZr`klh0kkCP7R&Hll|Ev6~PT<T}!AQx0VI1yMrnJis&ES3OLg<m{t=IqdM`m
zhHWECZP_vf<Z4S4KuyxcB;+LY!=l|{7g0tkzFmuTaOgE)8*bw|H%o4xayuO_d399~
z^=c-n8M}3_(zo;Wg!|r-|Du4>=MGI@MIZe{dwZs}W#-Lbvw&^`UPJoTH#1S)KfG^o
z;iX~Gqfd&&Svcn^zf)W3*|8Vb%!q_jTe}}$e0MQU{9~sN50W}jfjByKxj{>%)630C
zTpyv``iCbkS-ddes^H>3RM8$aW!^1C3Rk@6U3;wlU%Y}C#}n^VrOurTyLz3JrT4A-
zdF{vMms>?apZwfV$J|0&=QqwOLdc`zeM5)#3M^=q*GE=gF#H=Gmv)KDRmaz?4&wQx
zI+)%O+jebc?}T<s<q>T6yE#`#wIJE;9dqMfVwcaBIx&-wg9bN(cZrj;FjU<_m;b1o
zo;7mDMD~L{=O(K=wIf|)1h%G~c+bqRxnd;_a5<vc%lXBP246?A#fKjAU2s*XRLowp
zjd171tRDmRa*E7o{$(zTm3=@eizpF9-K-_oQY&?NZpIBkuzcCkO_<K_<zUvvrbig*
zhsueKo_jgx)FXTby;prPOsxJ-qR^Ki#fkZRj63MmD4gzW_pb6b&>6O4`6%!TwALJ&
zh2dYd10;z2u>(=bCrlrZM*m*hYpm)vx1`*{{t-0azh6X;SYNEVJK6sMc!M8?6bz1O
zC~BQDxH<sEk9f`p1*|nhuWlmyp$6hF)@az{OV|dGi7*l;l$wtk5gT2&*agG<@|K*z
zG${YjhK3IDs7G6eQv_t567MiHhhr+WLq%1N+2}=7w?quoqkLNqz@%Qb>&S22^Ms_9
zY^*od45{FB*DO)I0{$Jz&|^5vg5Vv;9+-*b_HqN2P#YDTGU8+CjqDw_;QHDPVXm1v
zv5KyuN7!})_e}S8nE5`dk!UVWAK9UAKc~vK8L>lb4WybnS<QaYd7nE<&}s8=bqlE5
zMjsoznoeJWGVVN4W4;)wJ4}2EuJ|j7nVKP}VKGHSN#N@j%MDWsdQJBJ9qm^vJFo4*
zcvVU){W_HWBql>>qEA8xe)PzgLi^lf#sY((R@b^=O|Ds?f3}b<#%}TNSkUyGtc)Eh
zyN=A1p5_rkb=WDa<8&;hsLtJICEQ?yM|s$tdgmb#xp{u`IK{&86BKrCxvlYfAbYO~
zdDK9v7wax+_1R)V%|7(mz0ghh;6z;KhLH!;b6hR2R^FaNYcU|D@G+6iXK~YtNMX}p
zS71ZPcSBQ0<AAvehG}SKNX*bJivVP(UKR7_1HQiw15&V<#;p=JK;lV*!E~l}w|N9o
zUo(r;va!>WH?dw@);XqUnl=0`mKS}JwEsr_j$W}^qTyII?EQ=UBAH>@2*Uo-W2e-3
zcB{eh{F}WeX{#}t3}X0>VCp!gzNplaR|*XX<;l4&d2S|m)35vGb1n~S@=&#!6tw9Y
zpq#e|_fG(EOiHV8-e^bIS-A~#Wxd05f&X2nhrE3omtL~ecs@pkSX4t@yvWlQUd#D-
zAVxaakU@S4g_*3^i$QL?J{e24VdXx<7NToM4z4wnKudg82_?iaLzST+$?faRq$A00
zw5*CvG;g0apxkZLc(gp{F@Qc15Wwf7BBw!}DT8_%l>YX6PMi4VmjQ6iK5S!@aT5Lp
z;usqQ{egeBziYNx;MI#ckaI^xEvhpBZ&pWD+C@Pamd5MTFLqC*vlNewe>^r`Y{v2#
zKE;4OjP1YB67<&8>Gl45LUN!9`DsG#0g(ezzkw<l#vGcHZs&jRM^EEvegjEaa@W~B
zfe~k>t|;-jf4l6I`BA-p4A1cDAMEkWt}r$2+cXI4|8VL>OPWIKUBz#abpjjZ7mB;-
zgMV4wdL({wZ!5@d3sCq`+!UwV{6jx=C074Vs@}w*!c;3>$H8fnLe_P;rA`lnfpdEr
zt^SD=Vri{CHPlzFNv%5WNdMqn2(HvXYGi)oGcD?P6*^q0UD!7zii;b^<w7`<!THU~
z^{<|!1dRPunr56Sy4XF7fyISGn)d})hwd$*S`skRvKi$Ie-8!n1B`B~XXo8u?4A4K
z3%nt4h5OlLHf=PhWlrI1&8lfYy}1Mjep0J7%qyN*m{r>CJmDn9(&^_nj392+4|Dv+
z2DcQ+^(LNG$}KXJ1*Ic9OniAcgMVS}{D9C~f%p1db$vLAJnWz2trfH{0F~omU-Nz}
zw%X+`b$lbs=Lvs6c$O7n0|;;p!hYVvkbYs#Oa9o2s$HWxYnk^IA57fThUSt5{=~X9
z4UibVgD0(j*;f=|!*;W~i~7{K&nP>)poK(NU^4rm&^stOnsbW?5sU)gLd9<)y7`cb
zuqCVV(PG%m#pyc=eRs_S<oT#{$olVfW&&toDbQ;5L{XI>d*ArlDK!vtu*opcQxRE3
z<kMlRxL5O0Yi5J5htw9Gx6MaA=fdv|hDreDuhjmzs3=HlZ(F2UT9Ihy^Ljutct_wW
z0Zn4y7Pf<pz5*yd&nN#49MZhC;|OVI*wij<S*T;IR;=6Ng=J>O4d@X1bC{z7$HPyh
z%BDBg$J)2b=BGHXFpxm+j6E$8>^q`74}<mxLdo>QoJJVNw5U`@vsM_qI24e1C~37*
z(=9V8EasfcqqIoRrgk(zHF1E?S$<zWV0zV{ACXa%Ae_36&>2dLP#z@!MoX=%@5>TS
zu(M*HAgdoO>Z|^*>9aMxc~4EPq8fg!S>A9hD|Ej6p}YS-##Q>`tUQ6X$d}_e(U#SR
zV5NQD(2sk};*W(Jx8MU$zmSC{)~2g~<yx7GWLU2<x!F@Bd|%~pqe&n)nZDd5J7_9s
z3E0e{SZ`)dM%((1?55QUZ8k?5{3RiOV82tK!H_H%({+|hplDW+_VLb5;P2FqgyL{X
z2L=Ss3I{Gqu%6Y?lAOD>E&9v{_zebLZQpl(y~6tV-@cDSS5Jo(k+8KIyc);ysOl|6
z(m<O~{#OXt9S4M}k4yNl@`8E%=yit`Q~9@p+bwxmr6A`ACHw&>zpI^Zrjs$~o6qVE
zR<CA0*C##YUp8tb34tZy8h{oCtOBGXcHUxN#8Syh2wuS~0XV`XKv^i)TgyiT97PKF
z4=<hoAuJvN;QNN`UofLxo}kSoKyj_3#k+`I*a4n*3|)L^9c^Q%`p+Be*7$M#oJ!eI
zP(Kx3d5@%jaNbAJBqY}LMU{LTqn7UDj%UbP6VCt%bHZ<fnbl)23fb2Ng?|-B(G(&0
z;8q!>Z@!4X7tXF5iTM`IM2&7lWfGrp;eSxA+x+c`S88Hk!B0wdJ2i|)njxhxE(oGV
z@g5*6@xOnCmxRwbcVagbI>)B;HMC33_DlUG-X><}k{0nm`|ts#+p%x@k9QfkLvYV)
zdyIEqWFJ9v3|zextzwnnyn8R%F*QI^VGX8+*y>)zt?isJsP}VHn{8apdoq{uo?L&S
zqvI08?nGZ{-RRlXXY`H1PinP<Hkfq1q$7Fa!GL?;)ZEk~FJ!*)ub$L{e)4il4~VAH
zR-8@mvYQOs>{k3JJj4GiEA%GPM^{;m`DNRbmn`&UAgY5>p!EHYz2ZP1PO*;mW2uH&
zxBmz7NtzIRlK(*T3!2TjT`uQnjT4KZ15PLnZq?d(!9|UM$t?Y{BUGc3E0yH<zT+kQ
zx7hkq%-vR;Dpk|4MQl&*#AfJd@x!Tb+m6+2FYVga$)+K@LFJ%PTJaTz<jyMLsjV)a
zdn)I6J9z~s*>MS<akU!W3LH0$z&)&_()SOwyYy+#c(8%g38!P@g8PFE8iTAjZ4|kw
ztl4&l6>>V=rUHIVsuvzX^7&)#V&U%I1TO<+#~Yb2r;vu#w$&Y+f@!=Z=V%R5r)Tx)
z^=kqXW_9<%d5KsbOTuUW%|;#U^cyDcRj_@>XEs<t{^Ce`b&?Y?C?w}|q_z>F-%_3r
z-eLaY-iY&o^@^EN>j_R(w^n9mf-O0n&>BN~GeJ=~^+}bf;gc@Ko!|RO`c|38sorW<
zD`D3bQMFH&4Ay?$6Bf>6>x)<)mRPGTyjCb)vW>fi=GZjiDX7kU39QRtKlE?Z;~It}
zP696Rwh6GdZZ$<jMt*zqXd9%UkZMz-9o`OwcUK(>@66mg>|tXoa=@73*qI7q`l41}
zIO1*wY;bp|;y1~e3M-?#HCv5Tsw947fA+1NiHa?5D1m?RowZ`yKY6cB{(|eq1<Z13
zYTo)eIa2V}xjcLp55o)j1byyQ41D!W=^p2q9meb?1V7nExz%=z5EArG_We5Pf~w&m
zFaaiD>H4I{GD;^LD~q0S-swVc`s3Kq@JmhQ1qb>!J}mhO$@`0zWanY!pHA`~LXV9p
z27Fx7Hk#$#8vSu?D`Pp|>!L$l?Ty9X@i!>_h^f1XwpPh+&sLu_yC=R_NH%$>b$U1v
zS(1&d3NUWjW2B|QbIRn?A1);ytY4>izUs2k%o_^OYuSOZZc$h=Q#Qh$Q#`>%z4+zS
z8+f)wsIN8}ZQYm0R5p0?GXt}tOlv~oOEgr6m5T59Hl0n>!$(-WWPu~zTHT#r9ga(^
zvf;^vhK1L~RZ7&<D&*xxVUY%d_<62j?!$!)UV3aOvVY?V_TxP6Ru1>m!#b(Vyx5OA
zw=}Qa`CU=BD41?-L>r)?k>>u;`N9g0_WE!j6v=)3jQ_qRYA$hZ_lT8qU{(o#47W;Q
z=0D>{&v{rdh#0D9xehF_JwO-aqpvf(S~rhhUEr4UVMRCP313i#)-Nx)&6DTxH^|Yk
zo6HBVE30MLt;T@k7J%Y22bQ%MWK)Ri{tf$cox6EZKs*wD!$R>>6;eK+p#(7qHSl9F
z4gb^+?;4U0Fsr}}!1g^>I^}hGzJ4OH_=m12B%&Fr9>&-<!y;z1D<O*Y361j#UJv2l
z*$lB^)!{Q8EPxE8D(wc(<|lYHI$CM>dYAqu$b0~1viB;+%nn@%9Zo?!zq7kroVaZe
zimH8|BPfQVwZHbmvfR5X4}uTs#MJ8m7eiD6w|p1hC9@~UqMlqZiF3n9yqBQooh<Sy
zzeyi*af+)<jYr&33oa&Xv;7+P!aw}zi^BNOP)rjCzj`rp+(NUh&_&KC1`8XHY!^;?
zW^yz^D589}Dze}gVSp1m8WlIYnQKjNy;)N)LoybWuMdPAOcXR||I(M3-cdrUN>KEh
ztIAcf3os~5ww(===eyx=uJtpzm%ci_T9wpn0p5<4^126Ie;r*$C29}+l{Z*&KwmkQ
zlGx6zxYPOK3uky8+wa1V1ZtqQsbGO0hVWj3S2`Gg@;%o5pwQrgbOiEL3;zi=;iGmp
z^<ejZDn>^QKJ0S;m1#YK2$LKVM%CJ=*R%%^NFf!e!<rjt7rA+S?=EiyxXSX5ayV~Q
zDW(I$^|}ZoODnj!f)xT|5QTNUUrP8CeBTuQNu%B41w78PE57~%-C@eEW-gFAi8~mE
zX5)46U<Wj9*wDy6dH4`75sYpJ5-zmc&mRvqbpv4v#EpRYx#OE?^zVl<;K++LR11Hw
zLpa#ZTl`c44Gc7BIGp+4i`lV`i5FFxNZy9vd}NAH${6`=Xm@{HckqL<gelN}ZlR1t
zl|zX}ZP0b#hqLUpd47s8%*p)w|0aL-F9wK>$MG%?#txnBfh`csBDBj;>NLcC-dEUT
zR@|5#cE-&eaZ6IHmldVD8nPO}CvjB@JFxdUw*>yxh1}D5`2BQr_hFU1uQDdzt~oTN
zZ(nQh79qL^53Zvdl>&UT`1cj=&4Gc;<#J<r(RQ7+x-U8ut$z53=KK+kg1>)!E4@zo
z9GY0=e!YoTzZR%i%-8Pmh|`;NRm}KYMM|1b+S=Zw>*?}>Vf&Y7BVE39A~P~$($oL2
zl~LJ1^}L|lh^id7u<o&mT2ny2#1j%HB9~CfCc$FFJa?|3_S>)U;LP@$U3%WbbIV_;
zR-A%^%3%k4_a59!Mr%St@|ogKi&4Lglx=<6r$y0~1+5Ezx>PDvDW!Pb&iGL1tS2L)
z0s}_xMqyxtig>1Y`ArU(v7e&NUOe@>m4RW)9#F`g!Ntzst-pt*Q<;v9e6w^D`+bt#
z*hPka$q2jEi3%8F$~?D3gAs==Vbsg5MfOiN281`tjLEt@crCH|--NAn>l*Y@h{-(u
z3AIYnL10@x$VU8R&|1UoAn6Af>LekU`h`<HhW|aG^I$Gt`q92zaKek?1Nl@|oK)dD
z=`)n`5@_OEx;^3wV!;5<yapG$zl3Wb{<MSORF?*@82%JUJ%f82^+SJ-_<b4rE_t;J
zN+THD__7`ktt#pmR-dPsgnxxJGIT9OdT(Q))h1|#;wcKTb$SHi3|cQgz<tgZZ}Z<e
z2<6mj%0mUyQDx{#_sWGm-mEfSPfi8e7ab60qatj_b>O-rXMjKAU8FVh=W_iy<g^jv
z)T&RKhH+)(GuXZJTRgY5B8FflA^nouL4l4Gm$vKI0KV`OILl=Z>$TGEHak!6JuHpT
zdYw}(@E;+f@&u&R-C?ioMK|4@_#>Leas>yf66t4}wY(4U9xk}QHSw$Im$#+9ZXLVY
zpRh;V<;lztY8;D1?`xeGRU04YYOl?Whwd=gM_=#}Txm0J7pI`vcm9yNKhOBC@Y)*7
zrr{^<L?$uir>`>aUR#F*>-&sF)swlIZb`z=zAK7WW!InMwb_~4hdjE8S6ecvl_T-)
z{)Gu@lckXwUII;Dv;j$=d0aZ2dYwke|FOhe1rw2OXwhIm#GuRmWqG!=C4;d49^c(9
z-=~(=t2ct^K4jq%%{}UVEfHI^{n|t3;gqM{=O&DGx}9o}GIhk-krv^HFj9Um?B{(j
zO7s8xaJcB`PX<TV++;k>a#p&0=R95@!u1Hj#glhVl(#=Ee;ogWRFzm=bkS`AFALcp
zZxQx0eK@Z@BI?nDd3i5?FNdbgaZYs@)gMFh$8?i$ul1SWb&4YQqru`CimZ<TW4*>g
zP;<mMVNK^#qipfSum7D*mxKvIsd&lTtfLe3U0}3_-xEspE8l?JjKYRsL(=^fcUXzi
z-i9iq)~FqohoPZj%K_+}mEH#0p=V*OCK<x`nVIsA8t6R%o@#a^_)Bp71cdxS*sx!8
z@ExiDkEAOPWcvUANu_eE@+st4rRZ?WeT@2)4x$s8qg>^lx!IN!$=T(cqm&~d_gouc
zb8ZREJq*Jz_cpWb_x^l;|M_o!c)ee*=ksv}NSE~jFf33%&AhN9@tYz7_~T8OgTSOy
z^-}234l;YciSeG$yPaY!>du2QzjZ$}LL@sby4$>q4$Ia2AQD*fC`zP;bDT|)1O;$`
z6XF>-6~=P7Dq1QWZsI*8@39{oD)Hk7W^B9JMrt(Vp`YQ>dmVW>+Y_)SoL<;~LpSwJ
z%2Kc^zj(|`n%}xrK@lmMd!0`6e%qP}lY<b~2)s!zpaWs?2F=^2YZtqOonUl3pA9h&
zJ%{Y{eL0E3IMNl0rwfg8;usO!c1e_3+b`=ga-)sva+C^%6kOBw(o|VaOO5-kvL0Y9
zs_I=|&SvLyFdCXxp@V*$&{_;aXCE^aKgk<9a&j7RfLYwjM?<aw|NF|GVK+3&s+aRZ
zMEPE)?bH&mcInlM*8ym{9Gc(%Kp2=#wD)ilpgJBHM+e6}3^+G(IS@iz78>|>``umn
zqEB1(6tvzjoBJTDsQth7atWE-2zoGDv^KB<pi)*r5Ds|7oxc$QI6aq8gJYm2mG*LA
zI$1(mrSgy4f7j;Umir5I-x*maK+067`GOE1EuqVekEmp?#XuA9m<mCKM$y+K;Z|+8
z@XGP!7ZE2|lu`p1pho)5U0}pDuah@$kY<Y8yZ4w+q9W!N#(-{nlZMF#Q!ODE87ZJh
zEU0<9?n$%vU+*++(JEhtu!@1xy2(}&kYKEe2|-HEJ@`#(MgZXHl5jq%p7he_=hBN6
zezWl&+Freu;A?)-lPFgqrSMY}vn-2k2-Q$?MwSwsg(9|{naMv5>3pz$99?@2-F<B-
za#u`Tm+V=v<{aq?{jm6}x>N;fKmkE12}VxgH}9;gzS_7t8p}$kGUH3Z2tVHVA{W@Y
zK@8MB94M72nvm1EzqVd(gR5%^#^Sd`IAEqLW!cnXBEB_vrV&|8ot%@df@Tt#{x>Rr
zyCNMEVMY++q~3_u-i6&Lp?UrqZZjkdtKB>E!2tV@<IX@OY|yEWLXl7L{l`wbP5k9X
zom#;=%E>wIkB`DT2#vqiFzi0^HJ+ZAs*jKln4#s<Up<AqE4^aJi1wHuu;f_vMZN6i
zP{CQlivUJRdaKJ^Jh>r%H35Enh_TnD6fVioBzm1{?S%VbKWq^ZizZjJTDCqfAxta;
z8p}Ta=SZ6%FdBxre~cXm4T+3jbzEKCZNv(#-h-Jp_mZw=l^LnOTyG&ulTiiEgx{WK
z&Jq$(_<<OL;f~d8q${(s%mmn>q@e{b$AtLw5zk`&);rD=YT4{>z=cNAB(fQen|V@~
z6PZ==CX4t#;stS(@B2>kfcucZGBTQM7txzP4;*a1lQjQ>f%~XBr)jc6Fbs$xprb}h
zfGz0)Kl3P{jlW+&ugv-<^=+x<$}K=C`Gf`FNap&G+>t9<Li=jZaUAVAF9B~bT;f@G
z72R9LN{w+kG<Roz{Doxv{{7q-y{gSifR^EUj&o@V=d<i@MD+;IX(xC@LS&KIzOnDh
zIr)9*PlRR+?*^Hi4?CszpiS>u^l@0tahTXin)6ZG`L7Yj+k2F!<F#3iz*RH3ohWpY
zToN{g{T2B0Wa;O?zTcd|Xd|bF;je^3K~6X4<*xf|wDQ`R-BC-Q2!oW8Q5l)5<t7eV
zD|2u7`fj!|*#0<}4U*V26L1D@(g+fIRU($g&iRc<1Bduk4))apo!k32_L0vCnI}bV
zEFjbQ3w#W2vRv%<`im2u$Aq-{;fq0Y;uRDoL=15e$>*d#ZQ1x-bE4dfni6t_v7ctw
z;#wz5(1Kutvi>fFVYBMIFF{GG%cF6lrdlUQYcQy*Fo+GFf5=glihL*?IG%)ELfQj#
z*h0e>KjcRkF&T30kNy*;AkF&SOt=E6S;NJ(opXwG|0hRam@ZS=%|+8D(J?cK+{Y&N
zdm1QbO~(ssa1v1X_fYU8FowX4X)~zr&Yk>QE2+DdTIJfz4WLK<2h_c&Zoq-qciTdH
z5WvH;t-Q<T{#%li6M;t!$ivo+EiW75HuKy`MW>hx`eFZO@!Xe?OMoYoDE)qU1A*HG
z?eg)YCm}1FyI~f{tZPdsE3)J%+KVTLo*O&8_TrRVw2Vro2|r^gmDbI=u^lh59XIB+
zBmND@R@17t{6o{f);-2o$qI50_yBOo5i6-cAcAIsKtT{JxJET?8{lN5tPhNwTqX_7
zy-*98XuOq~pCGtA&J!wv9K=fZIljO;|Iy8La{M`kIg%(kXBfGX)N|-7a2@?g8c%JZ
zz0EI^pzYJ(fs9qo$_kxvyLdGMX+=ByGbyt1Nsb^#u1jvdaj$>8xh7HXsu-}-**<r>
zbjfxamAw8iU3kVv3r&tlQbnd(iWNo#Pq@?G6nUbK)Rq(vykBnLDpG-p7f+4sAfF@+
zIV>Hmj?8PyF|1@~Z32<uska%=<Df4n&%;koqv4K;mU25#nL6Ofa{562qi5$=E12Oo
zriuf_Zww0r+D@b-49<#mxQ?SPv6q$LIfj@+IBq`2LzWZTPwilfJ*#{C<L%!PzxF_Y
zBg6w&pY=3H5h8t`k+5m->o&QJto@Pd9daq#h97hwb+&2(xlr-fbt1JPr?Gx7nX><R
zW>XAi{88(2lggNHjReyUh!hs=!dge0zj*p+z=WQqe1HKL`(}R8#NiVR3lP9?<M*j1
zK^2z!(Z#@v^s%X#39CqBk6g&vXgm-Oo{Z?>2F(cu?kN<W)E5BoNN3^i$^(*#rsmF|
zm*oc&H=Mlo0)*SeiGqwry<FXbO8OW2*Kv~8$BZt3c8+KXl#ooi&;aJF+*^Jm3bgrJ
z(LbxNSR3Osu}_$VD=4j|B$)C~>JZ|EGooR0>0v8hUsU?o^7@7W^^VdqVBD>i(3NzC
zb+-GPX$E0D{{I5g+6{@OX$;~8LQAfNaPeaEOrJ*k9&KJ~lHki)O_t{52Bv-nm3o$+
z`R+gRF0hviOxlZ@v@tc^Q_H8>;D2O}yyt(~-ajhPviws;s8Pk2Y~?Itbr#U$SaRjJ
z#7%Qx;EOAu8h)g#+LqZ?w};rlmHuQb_0UceVQ)Iqu2sMkxU2%QzeaSXU+9p%Vf)qG
z|N0vD<E{o#o7!{fUXBpZbwRYHm1)+QpTogdaTS8v`|wAax4xBQO11tW-=fy<l>N%K
zl0I%Zd}j$Z+I@fekas}=<-I{?Om@iaQAF#3rkXdMOtT1Po5b``lY_alZ_j>148Ryy
z9jEPsW)VSI3Ebnmpa8v0a9Z!to>IY7+k$SE<3`}~r6l#{O%xEse#9B2ENS3ES^Lgf
z^`TR7iD4=`Q0}81gz-$FFQF@VGi+E)7Z`$8o6fLuxt60cv`fG0^;c;uBz5~Se!1Rw
z%&{i5g88YOAr%4Y5S*;2+>4fXMH_qqI!o>xhh<CA?2ppUg!GgxA*{BI`H3-_!_G)2
z#>N^_Ys@m;k|$X0B+cxmAF#R&OgbJ3PCX<73o8xqCv`xW8-)DO;|E>Y3RToTxr1&5
z;-1<OSQCVAA%N=S18Wy&N_P9mPk{8*jT;C0-XhTlmW(h~2%hy>c20ya)6+WO=UDIC
zA<Fm90QMJrozMF;`hALU+0a*Xv;Hk>>m&yZ$l-h?DhK)IE0GiUU!zUb-gKc8+%m#X
z=}p^No=)g@<IzKX`}5~3YXleJz{yzHec>S-+fadM8I(El7JzGd`*_XEePSKKpS}pz
zTL^kL0`m^2S}&IikRV`*r$PZaN(_)nSS}~%e~%dP#bu^-omHmbdwJq<tfRhfE^x27
zf86+9F84+-#mLX+6SQ3*F1ejJ=b=mTY~A{dtk{_j>$ieZPR7yQ5f~oU5g3o<+1;Es
zMrDt*;bQMgIJc~QLNDpY$Z;NmQS#I44!#5$ABc(cCy=Sh3YmY{|GQa!Bt-RxepwEG
z*&QOwyh-;nisG0e(>Wb{$@}I>Mf_f(Fpz(y=d`UWO?@LvZoeCDD1`!y0~p5LI!eZG
z_aEd2*C=!#Vy<M{s43=EljR-E%X6i*p$LmAVopEqaBZuG4`FKx`NyUvks{6P&lqBg
zWLvlCIiaOpFFGT;&YlWyu6xkN-cQt`q5pfW&+1-{l(87jp#5k9rDamR4pfXxM&xTV
z^G;y2U(okg3qmcM0~HeVX0~D;Kgi_NCEo4VCAJ2NMIRbd<py~qdVZz5RU@#I^|wXy
zPu%~|5?H^4EXo%qhc<!=`7+?QoT&)>1Ob!N1kzx3<9Qc(r<cwKLFP)QBAR_M!=BAV
zL0A4LBFV^u<=FQP1Kq~eU{Zgk9xrb&W!D3XIGAUhD!9(S_I~5|q?SNgA1g3woOIbm
z#;=%TI)|F{UiJn>$Z>tJFll~6z)Q44&XYcV{hcOxxCvq|f>$Iuv%Gig(Oa<_;r^o+
z^-p1x>%CKCkEFFu)`<ErLQe5gv4TYqlXd}9L=2*(-TBH3W1*F(_|^vBzkVjB;Gi5g
za|(A%>|iI~gzY3W6JZr(LW|XmW~6UJCL0PXFU)@Q3yRcK{>-Q)8c+={yL_t@M;E!G
zaTk&{ycF`X$gXIHtE6~9N#XS^y(qfJz-h+`Ij9i*msa)3(pLV(i`ezDy{aQ=veMm*
zZ>tT>Jvnw&a|tuH%$_*iWl-^ShkZ_B-eW%p2(t3(ilQ%d4Asfp!;G2^428}`G|&Fp
z+QpV+mfeK!1Kpx!boxMS5<~p%CyGuP{>hw98+VK)p`O~t*PVO10J-XYF+3}&+b(Aa
zoyVMFen{Rp#}~x&s)$7EI;9kP6Q?o+;%DG_(5Eb+g`4~NHzikvcIjR(XN(!AtJw9R
z5wy5w?B^pz%Ydy&C9$Pw3SX55^=ZWfK!S;EIE3ydJ_iPxRIbr+hTz~H66oRz<D1)n
z<i>pq7~;toDUc9YIFFQ}nf6vN0g*sqARaeMuRjPN#%}AzRu+MXz^0OJZs9PH(%Mrt
zf&s4();{azlyd;WZ4deU%0_k43)yx6(!q20@LQs~cx1a3GHibGB#&^6*kvTlEbitn
zdFMCY0T4X^6&94sJ4o^jvA~xd4_UqjtV&){@$_no=z`^G@BN}%5I~c3i$`t@4FKyy
zb3Qft{;GP*x4*ZF1FGODn<YG<D$vNyb%-gaXzqm%yb9u%xo@OU_Q+UD+<zdU%x~_K
zq3QObq>r-6)GL0?UxexzeITi*0>^yp1zm-1L@HKfWfb5o@I1XaaDH5ckiy4v#N*BU
z_ku6|-<WF<FZf?_wHBb+eVK_Q;?Ib#nfU9nb2{M}J=#PVwNZza^8}Jizusyk+C@K#
zxvq79+!8(gc+?6?;6DsOuX0~9JJc&>gyR%pA|2w=l)r(Y{bB6CmySC7#Id?ijYkMe
zyQF9RUJqsdY8Wtt)uNfF^~Pe>)f;D!$)z+uqj5_=Fz^nsb5Ho<V;-0OaUHkB#&Bfc
z6Qx)|{<yp#8(A}y)AInF()85v;YJ+cZ;3_XTSm!Hqrz1=0^8w{7c|ud`vflFecLdJ
z^MlOUrvis4_b)TVy(sm54L?L{H<)ju!*a@jcF~BVv@1v5V>VuSo%W31D0l<^j-?vB
z^;;RJN8);Ef$B5LL+s`{wACXjGTM39&)As(=Uug55jxIrMw?b2fT9Yo1wk5t%E<xs
z-26!TSm3V}o%3a}CJ6yeF1u$3tp@LTeE*F9JBR#_N*!Gh3c_{!S|?aHS!zLoA&H=N
z&I`nA)?Tj$Qm`FUkaN%(wU9Q51~f%72Wr)~<z(jHA8lRSf;1fG5qJ~$9mk`Fmca2z
z3dA#uMIkX$>`;qIawmP;Q}zD1KpD`v_Ol6?SO`G}yy2(=euD|l#XH~}M{*GW*)+JU
z$Cm)9mDCM_K!316b<&Q^k+HvH+s|UE%tpR_&H@Cy*wG<D02)U5Q3`c)C&R_L2l-I}
z=0A6&5X7&>X;;A(+y_8-Di1o!b2`s+I|!UD#ygjr=bZP|IfG^z&U;Z`<cd`CP22hY
zY4jzfeYCF^3{L6##_6Vh5WJE->Vna1ir=8DxcfCPubY$`iOuWRHDSca(R3F7Auz89
z<M=5^!8?rOpejw6N}B6BuHE-~PpJNv+q5^W%*V;d{?v_XnE{tLw8ewQJ3NJ)?p0Ui
zODl?IRf=63ODM2Mxqb^{$ITfT&-Z>Ohy}>|E^ZJ?8;h8R!<b`lu9X3cRY?^km|btt
z!gG-EY0s}t=X_g!XI~Z~dpnO={dC!&{Hd(-OGW~}eEek8sQp!#0ZK=WuT6Je@!g^#
zmyiy4J*!uZduT$o(_R8Ji=(vnMRUj*=usMYh~t6DspuyHi;as4TbSs4D$gRcr|c0h
zst8ztp_(uB_Zk!P=8>fk0FnuJ3@xQ3UyFOsJPznPaur_E&#n+)KpH+(7nJ+=CB2MZ
zPS@lt9}5LsOM=yaZSy-j=)a@F)&1Mb1B68XZR^+gmCT1@UrpM%ubS{f+|^TwTTNfh
zNZvpy{*;|jmqXz76<Z7?)V~b=+Nfu!|N9d{|NnDPyc`W=_L)WnDtVFY!?^T|bD&0G
zh(DwPSa%_XJ$VB`TF*ymtC=m<G+wPBFW7z#W$)8Z`%Ws0+hsgWyNoCXR;-=f=Rz#7
znhMZm^%h7%08-($_XBFHK0XlB(c_xyEg^g4kMC%qDnQ30qIp*#7qh_%S>o5Zy$Fyl
zDPJ}j`M6g=KxiK`7|T@WL9TUw7dyWS@&7vgBAF(tw@*giOUQ-NrY%=!cq#nUMrIZ&
ze*gPeK>U_mv=Cl>$~miP^@dn#RzKkXBf)6;y7Yu562?c8{2_YXC-HbuH2v9CzXTtV
z?HBywp^fjY$iCpGMTU#zqV)c9oTbk@{yCkIuO!QfpGde8(&#Dv>QTj<O&gg?=53gX
zI8jI*^?@+;vWIee-haJ*-^T7t=wnBiNea|*9{boIiHpkc{>X<p>gCEsg+|9LppwSe
zpLBWh-Ff`nH=(3%uJ*r5_NutiALNVtaQ`d^h;wM{()zlEe|@j#E1<@l$eA+6P!``D
z_uMy{KOZ|9J8Ei=L1_I`JHGsyard_kO>jqvJCtpW=VISfwg8AI$TT}ydGJ)6xfVl^
z_ITacZ{^V3_TZ|qmD>m#Ky-;?Cszv06*41#00fpNV7#JUnh^RfFd*1S!aTBqym)-Y
z;&aU;q6ZQ@F5q#uk9(1dyq)s3T*S{lk0|gTe{LoI0RW2%9@_>0!!y|yAS&qkOZC9)
z{GhUEd;pf4e}6QN3b>d3A_|#IV8Tw~3<JOW&PnMoxbs31$CdY9Pjra8N9a57w%rZ7
z1fY#%Nq%L&)c*sDRK6;erhyhuhMBDDJx?os#^0!TIiXV}ZEQG_)XewrpXzwm_hfT^
zA1(U1fwrKK`KT@-Cz9)%I>rxk2u47EkV>R;PUlc&?5JOgK5Vizz<%|Hj$-F2LWarz
zKT?Yw^zmG1eD4w7$(IW&{G&boXguh2wfo%wfD!#8F)uMFlS~DoWdPkrZ3+V#&6Y3$
zYXvp`s|??(coE1VK^hGG2Spp?Y63D|qFlur=YN40P~&h7z@F9&^F;uGCLpeRu>N7|
zW%ss;yNZ+-U(>-Xh}5}}$$3wQ|J%hjWX56Ke!4gw4Se;DtO-gE{dYhzWuTy)Ua>Q?
z6%D`BXe~FNF=7L4peS#@0_2u|wMTw1F9pA68ew69bC3KL=6;`(7*pbZ(CkH?o*tpg
z^Ol<l)YV7y>ECmi)Mf?xhcZ_`FEyIS@EKhNNlcHvp2I`kRFS00b9HC<B;UQ@Gl^c<
z(~0A>I^8WZOTyLL@+pl+TYNdCLlt*M11$_M<k;0^BL+fpoXjoh?@7blTot}&Gb;x|
zz)uGa*o^tq0;Z%2<lvF{@J#>+sw16%K~%4t*i)ChZW=l@=A>t*Yx%a@_RMUOPZ^0L
zB4zz*x7fFbL<>Ca9;b`SB21zI8sp<a^?wW|R9;di*T?GWDOt)Tz@JxrJ``MdeVAKl
ze^q)CN&YVOOY3{Wzzt+KIk3O_AE+Y?lDnNkVx)Rhd1z@#7gB;C9^rl5d&@ZwHkc7?
z+43(;*Z4Ak)z@p5d$}Ri045-Y*l#Cw08E?G4yb9>{zU?%so!)7nhvh~*6JlxkZKA(
z(QV29+9p(s*Tpn4pvjaJU!UcLt-M<4)_blmB6o86fLNcty3(j`0w+uJ3d4Exr>=7$
z&oPzsyS{R3g4EA7NRSbyB{((2CsuBvBs_-i^2j$=gjke?JhQf8o#&iI{{k5G%lP@4
z<sppAbk%O9q%>l7xS^_NoA2<M{D(W2naA<2U8rnS9<j_mBH@=kf9S(>#i4bh78@tp
zg<3O0Q7z%zI;8*Ma$CUt(a7$Blw%%_MuWt#?@gwv)ux#D8=`#*uTfD4b{j758b8@d
z@Gv#7^tSh&Ksc>7>ZwPpeZly&G)mU^na11HpY^f%Ro)W!bn|_?)NKhxp|3gPxyCvX
z`<0TUP3ZG6t{|nv{VzGeeF6!U0%_tmd~~l4#aMNoxGN{@;;2q$-#Ogw{3^@Y1pBb!
zPBLQB(7a<~)#R#=zGd8yV5AcAJnf0;^>DqoU4^`+v()|Fb$XawMDn?60L{K<dE%g>
zduL!7fqUZ6B8Ya-?(682&S+6vi$AU@wpm?xO{dN<{8x>_;DMe8zI9`JFuq55xYI12
z>wB|ZzKFv<DE`CDzpE~feQpq}l<YV`O2F}dd;<2z`Vl^c$u+C;Sr4mvS3AttAPJ&D
zHnkTL4owG+DH@Qw-SixcgRK%-pB3K8tR`<l=+vKXcGJHVJil1PrBdU-DD?wL@oAhd
z5#I2&d+db@3(r6F@UT3nj|Fc;l<|4p_;3iK-f<-ksydO<s;-vM0w_wp_RTE&hv#xz
zpKiT2+rYZaLsqU<n>wO#VfH-_`A_S99^N4TNUbd5myV{Xkf;?YaRH-aI)7b4CnGvi
zXJt;?p*5AjRx4`3AoR`Xo{Lv&LbG$=W$X5~^p$(s83t8i9hUCjPy32@*=b0iyMGUH
zn<ncg^-X)7a?0ojA8?dwyV7zn4%N>K$9xML8T#r#A7_JOALl^f_AuMr(G<~mtNMM$
zuuj?M$&teYkID8PYAeC)@!<B^;4!%-M!J-BPvfPqo-{e@@v@Uf8^n421PUVSXzHDu
z6ZZLsLcswiwvC~={jZ8*<9ssFNb4_AV=Yj>Zsk5+eh)i01nF+LPc<?qe#>+v>;O%3
z+t^7d-Wg#KNYCE&tsM_en(%K*WKn)cK1b%13+@FJ%;8yLg0=d#M;k@%;N~)Pxr@c(
zP%<!FQ2Cb9zacHl`PxE146)hLs}CGV`?*499Dyal6nZB=#L&mU+XOd*l%Yln@$NDh
zQ18?`RG+ss>+LnI*UdQZbUHH${|b3qCUL(}bo+vK=)KhGF+1Ns+x5mJ?mb(LNAsr9
zpXH~&*!d&~&!C#B$3O73JmpeBC;u6rjbk~wvmF~c#q$vBxY#~w&CTO{x^Ce0bzB^r
z=`53cj&ghzx7lRMBLpAXO=6tTJw?%a7A_<H=MqF^=I}dg>YGiYYpYGKgV5OSgZQfF
z)Ckf-*){{#Oh3^fNHctI=vTPw+tFCp$@j6TI(7ZIUY8g~cT~dMQMzVnO)|sa6gSzp
zGgxEi*-myouL~+F^P6^Xv+j%H!ITf{Jci+9_-FXbu^a5ulEP=L4Nr6!Kk<*DM%m`q
zy$n^+D_|wwLPl~)AB~a`r;>L`Gdy>peYE16V&^-kXJ!JWG@1@{MMP~xa?6$?XTNbt
z-4Eg(;NFA$8=UM<X4+KiE7$(Q;OieSpq%EdpPw;qSHFqr9OL7Ra<!L^^C_ole{qlb
z!?w#+KDaBVYAT27O@Buo<!|{~;-?H6-zSnf06GxWP9LtbU&Z}>$?;Dg1%2FzM68*N
zGn3tPzsUS-_gs4iGL*paXBRQ<ciWt0Vk%(HO5;@*6Zl40AMca=Zs?ucxb5IbNEyDF
z#@PNXIp)*i?H}$GkLFn|sER`WFeT}?5yz7Horm~x%0Epe!{F8>38D3dW>|uLqwfTL
zk>gux5!!5%UHK&*-kuC>RDN>3NagM#v-NX+0G1!$%8_KsGl#$$nk2Kvq}i-rbqh-S
zD+X!P{ww~82U1-8tGP`?|J5{bGCQ7gSI|7;($}42yzm1}D8%NTHPq22Y%B?JH=eC-
ziAZzm0Wm9?rXvNP006mu72+WW>{e=HqzSWxCHYmzV!_P`#lkXR>n{b2+!T25W<5Re
zEyo_Ih~wyM)j1|L=#mLluf{<UKJGs`0I8C=4oLe~jpugCl@d8q$WMr5|KmVFJlDkM
zejAX51{BMidamJ!{@=I#&w{CUH*qs|{>qd7QM;;4<2(RpWYPq<t&?7OCX@Q<LZaUK
zN!663YI+%2^95P^>^R=Wfd-S$<F20W^SObl%btj<Y0ZgVcZmL4j)TWM&lCY4l_kW-
zRmO-UXqJIb(mXS3zy@dZDTdlauz?}0PdQ!C;VcAq!#IYMbqQ=}T}r0n`^qbK%RcRv
zagt5zy}bhDNVMSFXdT#<s($_?YDwJ9;S?uQwF{Q%WPD0&-uX(J>I*^t#Ao>T>?S9f
z)vg8&p(S{*w&G7l)3D*bl&ZdB1}RZ^f$uc;f^7UzZ?f4oT~I9Jlip<VFx+`nzH&A+
z$zxo;2sM_=uo4WE#vKhFf4P5QS4}>U({YWJW9Yk9A$j4tlD!-mf<1Fx#r|%(ct}6h
z7TgpH^pw_?cI>QrKmJpzXqc`y`L#ep$VKn2?F*q_E_pwkP(PW6NFgfl=FuF|_8|)U
z%G?X3OSD)4S8*xVx13kSMB#m|lUMl>T5Eo>wz1@U*?ZZK-NbIY`b%y1J%~c+Yc=!t
z5o@0X&LL;NG0zRw1jw$lvPlvfX)3CHv)}5W(W9x~-R^RrX0;OJnP6d+pGF}ND(+Q6
z7;-!5WT~)>n*4^fgW`r@YH4g*rPB0AMrnNBc7)1uL2rbZc3Y$cxszH^-V|UPLd*eF
zWx0k)bx2P4)*o(0NkUZH>0WX-|8Lu8149W#<O+KpqBPxSJ%h@ug5tDOydA)oC=*|o
zOUQUSnSQG~Sb$M-ng#b!@^NRJ3vtYlKRqvOiz-Cjbfjt^RC@_WHjO4VeooZ(#YC>L
zt8$nLI#0{7;yo#;y(p>$!rE@Mkd0X2xCzjXBwxEw{L$7W72adWx;2LcVU?Kow~bc)
zQzBi?tPqYd;6D0hkzQHsrzw?6S-1%1T%+hrB`iqLb_J?PRqWz)AT=GVq@5ZU1-D+-
zw?H9_Z|H|^FA7)B;R%{d>4wXhsckan#*)qv&3Z3p_p^7{=^W8414t&Pv_5stbT&{e
zA?~2ZS*%&_KK$J@yK5Fri;+SX1Gm42YclC2JHi9A6PneHaJS7gZTX!Ez6n%6^j1Ko
zMoGXA!ZK{(AL1c&q+me$M_Kud8=UahjUZ;jjfohJI<99DDRC$z{^2(S)*pLj*`MJo
zC%rK$vN7Hh+8TWhz~aQ+-*ZBNG-1tc+%%6e0^E7NuK&%6OW}B?CbDs4QY-6knIlx~
zUq%s7Ztv~g9?Io>V-AFN>=`!`(>*iF?*_i{?N2%K5=&}^natq7m_FE?q<6zRvLa|j
z!#^ZI%HW=hbCkOnRwb08H<B+g`HGdp6XK5w@TCpUt2DX>K+ggy9>o}(6_4QF?H<n$
zOxIOT5V)eh?S4xe{4wZC_~-FSR5R?V?kU`HGdn?<<Qg(<C0sm05yolbKdU4^yJ>*c
zW;REH+`SD$b4QGp!9eizez;@V2c~`TIk@71PQIi;fvT$*%f`R?=zbv?WxjM2KlUv@
zv<MkaeS7?&Q8LmOWPV<vL+D26J9i-|4Qid&om`{UM2U)Wll2&xivU_vdumpyPGM2s
z_!V@<z`v{O*Lx*bnIjT223odu%HdB|=)Q{$O@?KFhr?!d?)$M~=hHtP+ZRb)D?C@-
z!gf5dsD5GrRpbrqKlSEKKq3j%H5zJ_)XK+^F^RsQpqSt&qgjM0(z=z~=rH;4xVFzN
zIO9Y~qRqO;zf5B~6_ONYEaqFMFNUYQXL!Hinx;n5JC`$lScr8Xon+M6PVkh>elF-4
zR%P3B$E|AONs|vsbEGPnzq}oS4&7eFz)jm;lFyv*1s19S`e>sbtAyg}e>YLUp$4|B
zUDgm^>+HNrV>(O*JrKn*SpMFA;E^B?>Zfh&@f}kH3marWH9m;NUUuAz2yT&Hq^w`X
zucWJ1_>Jp_j(Bzo(VBSIy98-hr7GHn_YyPDbh7uQc2A^6l+7a^k8o_>N4b32P<Q?2
z7o>05`L}{Oo=#Is_hMlk){L!EJ}h(sRD1Y3WEphP1o4xvj<jm!{P{D%3|Th@T6}@I
z>nEp0Effd>z<@E^Q^<jTbFZJoTfS!=TJ*pp?C2#Pz9(buD$fN=>sp%CO0Dbn34<Pt
zuN2iI7QSAn;}q3wgdY*lmq<&2?or&6DAf#@B0D+eeE<~Qy?wunyN4#OL?VrY3|D}!
zWiEzU-p2V)C~yC}WE9XDm+k`YHr*4>2qhS#@i6YY?^4;xu+oz#nuopC5Z`W0Xo)bV
z7q>bZfSM=ku+Jy%&^}+KCWZAjk^@RT*p%V%hW2(uxEZLaHYjQf(i)x5ZHi~A9~gS1
zGIVz|Mh{gW3ch$ikwMR=HGH+qnGfpK>hol6!4~F^_D$u(`Wz;Lc^KHIa&qzYq__5V
zRd2IqP)3`OEZ>vLlz#UD&eV%$m|+AlUT~Td&sGpZ8|vxh5(E(D2i3$nBu_m^6uz)~
z=b+jNz<Vx?FJPBWuPqvTbWecIrQN*@s)0Vb?HsL3I}^;GJ3Z8$QcZ)>^ac~9cu$(@
zTLEDSf(dN=*EjWmzCV)LRynuD3XRY6qxvF*?&H4tYN$qG7uR;8mq6M*!Y;TMOz{wu
zTIjF$UQcj)^;(h9AC0<B-_MqK)(FbeRXxHYdJZX?YgahF`r(o-qSA3p$YsU+@x@wL
zi_k0z<AjhqNL)YTf*oFbksN-iW2(z|zW$!zpm>t$PshVHIg9Ujl<MLeFw`T_O`B%k
z6VlNLob5%`=SrBK@OG-S4)*ps!3KA&@!=D8eY`7Oub@JuyGhQz_S_s!PTl)XTM@-k
zI)<_SRAwyV;T-A=9H(5Kb0c*&n4@F+!SD#f;2_v5!=AK)yCC+g<}TAwyu@xD;@kGQ
z#38{_mis_N^0+z^{F(HSSl3tPd0VNE))3H|VY8Mv7SVmuTQKw<R{3NcBnj|TwuFm%
zOWw;2;*{>?=EmEXex3|&H|`29zZME{ivTk+i#dj0&(#TRY}Ly>%GVVaNmr*;>)cC}
zq}@Im4o<dU$=yEH+8l~MMw6%8R8S-s@DuM7deToTW@R37y^wyZW*Rx8v|`57kLjGb
z>}cj^2)4B!qukE_uuNpHv(ZI|N<m6R5~W}zJ%&w&^VuOv90!ILA`*ZW8)Wp_;Z-Pv
z!Pt-AeNmrkCHnQm6<CfcC%lQ28u~E4$jVM?Bk%8(?@ixZdU_X;w5Gm5d=Dn%`(l-f
zqap0^PD0E|EhUS%^#o8?#q)pSB>tr3G=n4oZ36NOhNQF1(DKIuCMI6rqe*0sr}OZm
z32RfiwzX1+X{4^WSU}6~mQfaEKdbM^>6rC2z#{AiIj^5yP91#xpHMf{nhE;Jl;Y&9
z!S_ti&Xoy=IL3o{cj=bWr%<0NxlGGD2_V$s{l5C%C$OHQX82U>`X}8oOU4rSo41;w
zEVBjU;BJqXAk7Y?ws5Im!QAu4>wQV>>SnUC{Ggt4*t8Drc<Lfszq4R?rAN?3X6<FU
z{1-;y-SW)cBsPjgxu%o&$Nhq@YBM%svhohKX=mv_PiW1kz%{^lHTmLDwe?)lALMg_
zZtke|@4o`T(QVDO-FVLz&2G4Xc!GpBO!i?O%Y~ExYB3YZi=Z|E-`|L3phsrMFjDsv
z_wv?YBV(`FK^elQo!T24hVkpo!zOnI(!|eqh=1@`Ajwn+<sUapM!y8uBsJ4gQ<y{U
zX>m~i5xnHQY9gZsB+SVCmYX-%CN3)xD1|QJT1$~uy@K}ET~_XX#@nLGUG%ZNgBdfZ
zdi3_gZ?L2v`BLO24Xig^18$19DlA|Sju{d+d{#02+b_EZYFtD_aK-StCvg9!8HE3j
zSEcS=hw*&54?-%GWB%`JpSHvu-MMv=AougKn9tTN|2n~0f0hi7pi%OHe~#f<qb=r>
zu}p0j06F2xd>}6n&ALFrL1*-{@uv@Eu%jDm#W`MYiYjN3#aGMLvTW*ooR#d0X|%wF
zE#n%P_g;{TQZjSoNMjS__A|lRpu13iuwi-Q#BDtDL~&Nd*QWY$pV91E`$tj#L2gkY
zIa6Uf*c7JSjvCxBjS|C$^+*ulhPg}4`vr7nbQH4oV;tvDwn$yeuE~g{B&Y0(ED(#h
z*yEoVx#8`W9eNF&Fxj{d&<HwqKBlQip%Vx1D^L;mI??B)IU3_^hX6<pIVl6M{FO>u
z0qnw{q=zV=4on9^0TI`jf^+{RH8}qu1s*L-eZdr{z8nfz7PgIZ58RJ~VO7?f<bokb
zQYjp(ZH~iU8eN-PTIGT+sOMPRnXt~}n6`8FZE!`z_Oiee?!1dM=fq5|s-EK=|7;oJ
zyO;dHr3^Nuv|(@)0xi3-uEe(V=DFjFK>#j{t1QLVPlrr$OkpdzKs_g$Z>VN~m1loP
zCpkwNh)SJA88cq$PBw$CZRGcUDVlK9Y%v~z*9XLIJ6~^mE+0bx$AA+0WBkV$9W$u$
z>q&1^<fGg9yVDZo{fP#P{EU45>qp9u@ny)3(PyftB^ccT_BSvpua#w&w~RRoo1MQ~
z9=Dss#n}r%r6tBLUtE^<LG>p9;SvOxAz&|I02j3QpqxUny$e*ou3ivOBAOQhN@hKk
zuNi_bM|Fn;?t?Kj?0hL1#%<hRlq8a-qq<W}fmWIz-1~k>nxKwtsyd-%uPYM?<B<&K
z#mu6dx0fN-2r>pp1T^4+K{941-eh*fpzYR|{1QWy0QYR}F6@Nqxx9VD`>PZ7zuX`V
zKkFjI=vu-6+Sd3KqmnO$7wrBTYA}Oq9&FwS?^2UE_-D+-3bAMVxD;8sl{$?I0qR82
zR)J4a95e%y=?ZmE4v~!*wC~slh~op$xn7+0?VomyWVPCo&n(_(br1JByS{g!7J|V>
zw2lNA_{kik*-dHSzTxY>NZ?MwHU{1>A`sWAhG&N_mnFfq-3bU64#IXXV8|YDNeD5@
z93_9`Tv>^JtN~sH0O~(=-ymVD>zEo;+mid$QQNYc6Ku@b8)xIRqz(qRg2XHz^#3y$
za?v5J>F=4wg5~xc@2eMT<M2xThU7+s&!`XGaWD55rtCp0V0Jr-A;O^0{IY2T&)Cdy
z8zzJ9-jC_QP8xjloQcwmL_)JE?r7EIZO99a^LjZub()9Kc86Tk;iBl!Ntt~#=PT)T
z3C!5_@+J&8)X>z{&|xgUyLQ27JOjR=NqVr3_7bX)!=L963J=7eC>5Rz6BsyhL+e`g
zxw;$=3oQl^HgcT6?V3nk%wBZr<4Y>u47~SM9dfsrGpm*MSj4`-;&ylctem~VowwPw
zl+R(=%baid4cLY`X~W$AsUc~nI*~C?F9kuI8p{9+nx8SA3wR9CB(-fJG`;Np3aSB6
zO?MXPU;f2^>wur#X{P<RgV+d-5m{MmIKNmNON2+|kkH1f8T-lzw0s{5BV+zI^(||)
z92|=>nE5^A93$&Y$hUAVIHCLnUgiLM#K$g3Cil;TV>GB}*=W(&(h&umF*W`f!jC=^
z5wpZ>VYSrWNz5CSInlexH>epojoeDbwVhkPj()d1euo$QI;ElNS^93-?G@DW3=)u#
z@W_vN1gHsq53Ko)xT3=U0#Eh>H6~(WfN^`bY!PrG8u>@4!H@Wvj9d~+8!5Z7Y`fW;
zS{2$r$u*m6JC}!V%TaAmIxvoK6v^1~8&NcLY8k_1AS<0r{aZZuBjS*FU%Wcu7vVLa
zrefNoP}1k-`!``6aEQ|Hx=D01*SuX|f2d#tsY>uAywf>ycO_Z&$i-m=bZE5FjG;C0
z#{FKwI3Ta=oX~?42AK))hBrpPLqXeEjcY<P{`J{p39^i)qn2kRK>hn?&5F#@|MBM6
zg&C7osQLhF2`GDI4DKzn-Vy$~1*YzB9DX*+J?c=LP_`U+Y46@}votlztmS&$ME>C8
zTJ7v3mi{WUTg6Y$Ilm~LHPfB`{>JRlzX40Tk7a6T&afN()h&9NKwFxi#@`qUEaD(e
zBEso|H4u}6>aVw+{+Unj-}!6t_qrz=Oo(d!T#sxkB@xzg(BN8%MEOj02Y$OsG<4;Y
zw9evs<{HM~_Q^`<#vOe{(K5nl>nMGEl2q~;BYQcehx^xhWOln*vW|1j`**?H-+U2Z
z^x!690WNrFU1`hIhrq!Y+mXQ$3t(6)7t|K7nC<lwkg#w>>h=+fUR8AKxkaczwzu>-
z&2<M|X0-vzu$|i+>dDmH3I*#@*NhtgNB4+Rp;ReYet+P(&nKOk(h7dSRai2Id~V#Z
zXf4VVA27^4aB|LU3TgZ|sy^LA5uh21s_Ve;jWS?c2R3v)6)RbzsU%#IrjXd%?)>7!
zZobMx3&wRL@*kqRAB4yN;#B<kx8dA9OmV>c#?V~$OWrF(voril{)fh$LZU~@KFAcp
zQ&l?oL8<($704<5&hn8ieww8_GpfHzZdGZQ<Q%xIrb3RE1^xHYG)`m-8OyPfrY*yW
zkPbn<+T?c@wHdiqAmFb9+qR0>%m^s`)z8_wQx;baakxZ7>lOGiSoWheu<c=YLU~M0
z?D*rV_^er!tjfx<tl6+ew;aWc<$j;G-xwP?R*5g-QqXVi<ICPBD3FEBzKyq+rU!EP
zhQ=70L+2r!|1^2}-gucXZRTBOa9ya^rmV4Hkk5q1GJiQ>_7iNOBz|xrmlWs)^>?0o
z;qBXSfp>8vcAHvRZj!64rg)_OPTI#2bAQun@La-nve5JkG~^HA_2xe^;(KL+|7~bo
zFYN)|8`dr0++65OOmFB4{Q^8cWh23Bm}CKvkxW4_U5<dD($%e!jNFJn=K0MCb(e`&
z^`*XRu&t#S=gGZWd9Tr`VGdBmXjN$P<gQvWUQohaPWK-7BU`HHkPG%eyj}SjS)un<
zDB6(9{b5BV#}>gR-^rSS?C(9t-FQDQfTi~WJCDlSCl06J57Nr^Cg>TzV*ic~)j0mK
zs3-42Yp^d=?X#E4ii9>g>a%$leAd{o;xPpXz#Ospwo`t+I`1%?JQ=~7T(rTY?0l`?
zoRd~xnh5_wNT5EQ`mRI0TXL!51~ck)y-4Zn=(Ue$92^aXhvFEFon72(0(wGp$@vF}
zQhOl8QH<*Kd^}(4i~&hb<&AHNlZfB)b-ll%>!r0%(|3J&6nVUPJgeih@bnwmiH|@F
zmT&u%vZN67Temb-?HTqlIVZ`@VkP$KV<Tzr4D3<wnOEsTrE%K5y0q@cTuKIO-jvkF
z;8$t5mBib0VFBYOvQ``3cznIjwNar26;a|KOglHQyb!2!#Fxp8-}2B5Z1z3g`X_Kl
zWv@qrR$*1^BF28Fp0K-zpQozyoBC_FdM9yk&lrF59Xi0C8<@Q_HntZ&0Hzh^z(Tj_
zW%yr%#nVXFzfwkFZIZKVf?cVbviEG8u}_?`Px+;bOoh9hbKgfX_1rLaPl5l4h=_rM
z&@n`_ia&KXqx1FthL-NNwL80<`lJZD&D`?fgmng;qCbiE2Qvic*a91Py~N*vQIGe^
z1G!uGN{+@uzEVm_Q!IyOPHqHRh+wE7hdi4FiW(uX)&J;eJOHg>i)X3BFga$yNV+Wj
zoh;4Nd)5Y&*z$F58x1pi6m^X<>YIe@FKA0g?6k!g@4RCLl1pK;0fTF6RO-D!X1Bt^
zy2~IFXjyDf&Jr)}_g-(b^SML4(jy*4tW$(u4)R(*oZ^~Ra3J33p`?*vA9CjBQrbgh
zSh@yUaKE*Oj+dW4=rDohUz+N(4iuy@?Stct*KsP3gD0B39KA*gE?PbvE15?%he3N2
zttEmCo7rzsOMF)&CUq@d_6RcgVGhfgZwyOm{D&y_gRGP5h#qy^q>C?a0hJ&me}|J1
ziEzk<!>B{QPS^q;5(Io0sdLIt$tfj3f`lQISir<1q&Tf{dMc89PLCqw+Yj%PoaJ(N
zbl}cH-)*KhxiUQBN>WRZ{)#5SjDSUasjM*~{Dq#6kt-y<LUfm8XvJZp4iLWO>AHnH
zfcp2(W4x9U(g8`hvExdr@y2e*k!FsrsyQ?E163oLv-Lap!2AWi=6Hd5oSd%H)9W7L
zsl~EaRAKG1!R?>E)Q()*>|7|=7O@r{D?<_YRC7OjFmXN{GHW0eD;cGbnA8Uv2Rol2
z#?t5f8dnkO0WI>2mb-3aV;JPrZrf_xOG)<pFUiwBHb2j|*qn+#hQ8z{qGWrLn$jiE
zo{;4I+PPse#r4vkM#t9j(=uQY@tlZ@vhs$r3+|fPq1yQXc+vwuo)77U{ql#v3=H{K
z^o<jo2Uo(@7z;zyXM%C(8BsXgKg@#_$>*d(DYu8m<~h7~$_(=QL`1BRzYLJ&tXF!+
z*6X5=^(h2Sb!rWgww1wSBv@BFFi{&@X(h8ie!bsWdn1!ve<h(vsYvT7@HKqPST9^0
zf*PF=Ni$Yfk-2QOWkD<$JpnWDv!6s@m)Pb#p#=){*B^O&^DTC@-QcAQSA4Ar2jqoN
zjg)G0FGhv&66#?~ncs!qfpbO5t`1paAUU$D6OQieIb%tmcuROhwNKBn2t7YtnG<qZ
zY;@tZ?icoB_C)?>W|J-$Nxm)@VpE(ti?BSequ4gpU?dk)9ddJm87A}B>tVfbX1*u&
zcfK$FbFX7!Qgx_ObbMSIQv=T59NDT*DWfRWbnvSN=r%PsjrTnk>elYyZyI-xek(o4
z(5gE%lXKPkHl?h_buMV+4kmkbU7bC2EOJBn?kIliyUP8Be&?BPe~!z-w!QaF$7YVr
zbkN42CCg#<9Hq#H^oOgWyn$pT@0`bwGV1gydF)#~tpe>bI|Uk4fzbXOgTO@7`vv*s
zCP>FOP(L>{w}VUcbN5B0T4iu&T<%>d6>>R|?0lj7qvy4b#&lG;I?P%OYxd|-N*C!>
z#XwyKu{9SRIHw=O9hce7Y8qK<B8Fg@1<rfnP;sbYq0~U)-6Xeam!9{8KhQkj&v+Zw
zv*`os5xifL+P=sH=ZKgtPN{g*EJAJOot}TQ+0*1PP+@qoj8$NA{lS3_0DdoCH6FAe
z-}Sgyy>&Pg?4qRu$2s)pw*#vwzWKt3FC~U96z!O{qU90%!w+~fMyL1}(!k9=_?hag
zlR0ckyIMANkI&7C;57@KYu1tS&F$-rXJkGcmgv+m7YdD8xIDcxf8I=eiJ<f$aie1j
ztuXhL4NWCxeIPHDcl*G4Uqtp5@wNJVUL=FhnZ2h0d2*bS-8;<;5u)|3ff;=<LoftC
zi1<UF!`MR^Xa}P_>LB-vchXAVH4??PY8i1YK*sPavHnXidZN~M{SlE9PjFBY+GP=p
zPZ((V${Ex?lalD*vl7JR43@#E!qgsj5Ez|^;IU#L?`sHCC7{@TqUR+`xg%L-BC#K^
zysawUd|py)(9Q~etp#oOlY{B&bIh^4#6IU7-L$hECwH|hu6uLHuWnx5<6r0(PQVGJ
zhDG>mA2tnfC|+8^K0sEbi9L7NXwc`F$JM4y`!7P~*)pk?>F+UDyw%hZVVl|N@>eN0
zOY5nLm_?#NUXluP@DJ1o?lzs#z({PFPIb?b;`7oi34>waJVuU`j-jsRfpMUK9zsWM
zeG~d1Bo@ZvwI!R$_59v>GOZ8e8rFT@U5(zq;k>T{WTyPM5SyXywwAZpn)KaQH5Qhn
zeAm(On2r%IZC90=Pe@tZxmPzyjkQa4k9RCL#6#YxPsJXOy?H68BUD3yW{wE^7IJHN
zv&!(dVct^MBdHV^VT1G#)8Cj3W5%K4(oy`(iZ$OZJg&V-m20$elo=b&|NPA<ln-(E
zGn+acnaY0`l018fe}g_1nW`xlF+1CKRS$&Qx%Jw%j0cx?D^{3%0iVb{(3pRYm^LW3
z*dfCnm;Xhk9a?EUCigHQBadMF<z&4H89Wd^U#9&B_B%JNAZ)cz2L9P&@Tu|#pqJwH
zIi*79*sChX6726~f*&s5AwPsj2&dfMSDN9&hy47J)IL%g7}?;rB%tKbaJu~9U-w$o
zsi8P6Y3H?z+Yde1a|-szODR9zVmzO;fl<}^iNo%$G860Ib&NGNc@AYsB-eL#!(OGB
zEv=gaj77FT{DusciPrU}6#&)=huFNJFs+fQkZUR|l5XRmG_388DxL-@S6Z<<%Ma&f
z*?gthDNv}0zpL{0%y4HuPBokx-V_i%RR3V~m=b&9O8AZCE2D3>Uv50)<D!+jmuoTT
zGNg&)>am#H^!;V8>B9fk%owCLS|GZ(B_r<{?gvPDIeTjblML**;*2~HF*S3X%c@|F
zvzMPXm(+eMZx;FpCcgP7<$qn{>}-6>BwBsuA0U2skJLj``hDaf|K8!0iWOhg4)!E_
zUwTM`lipQZy{p5ATnaZndSz|*^k48Sm`QGNDX)}5lU-`zyD5g`PS(e*r(=KG)qOgr
zB`*~oD$;&a{@AifQn~Q7e7oP^2}$kXpr?fxpKYbVQ)?YRFg`^2*wdBZ*cPCiglx3y
zCxTpUIUFSya_YbY3MGPJU@_Yh;dy)`=GPIL{z0yPVd|m5J06Afg-^c%w{HzXF;_N^
z3MwWh0d&F}%?5j!aMXM=YeCPw?UPPIqJvJ-?M)2KJU~|G))w+XfSiFwAMD4NcaJEn
zZ(jO6sJT7w`ol@d`>ne7gV;N-fRNs?!h=4}x+ziY2tNU5m;EE%Ybq)9!1>>&LwidN
z826<3&W644-Dc+(&4DtaOwTt!{Lp`w%|_TNe-Ehz*4!9VW9@KMcE|p>d*?$OkhU+b
z7{UF@+Lgflqljizv(~LG-;^PjrPtWv*b_PIMHC?w7k(>(S=HaqIyEs=H;)2xuQ;XY
z{BnLKqJ06Xb9ECt6T+gDo5^N{PE47#g^t(%%jvt?UzdktC-{Ed5(4|*2*6AqM!3mO
z|2le=dJM>GjsYdfK3+Z|<GKE=EJEuJk9T3M_x_z&-v~zdTGoB!DWs%g);g6n2SUnF
zcvF_ssD0fZPyrt3h(!|BF4l2=U3GdC!grlEz_9|S8*n=okraF|YNMfziR8qvCK$XU
z1-m>;DHQAeuF39d5-JwVTTor2LUQCFyzT|q;(~j7k-N+8?hp7eoFQk7HLFL%`;zn&
zjS*vOYoGs?P~CCtK=d$gmz9Ub?S38)T`Lfi!28bPQ1e5ldFOd!3tw1p_xXQNM+@xz
zVQ4Y?dc4$h&+UeG+ti?>%KE@0cs=tCT!nrV)%3$1J-V^}Y_!t4s;SW$tw_X8cRdrb
z-cH>9k{40uj4P&|>03C-j+h>qALBo+wuCLez?@ZAs~3|wd%#Sm4q4kmzQ#PgzC@4i
ztkqP(IbgFer^7@^^G5y;zK{z0j+dS^Qs|SwkEhKCQ!QXy7CHnio$+JjF?1h)&W`%n
z^|GGP<li;*$(l9w-|;y|q7=>JPm3!Ol5c)+o}an!)I%goLn}I#UT`OlUSN?Volokh
zGrB%+;Qa1gE#;{EVL!w`R`5efW&8xf@-=K4AHI?x&1h1EeZWXn@oG3LNp@SZK_9c}
z@prPQ4-nfwz$m_8Y%Iv|A1e4qr}uP9OpX7g^N_ePZO=!d_|!DD@SvzP8{zwNy`x>b
z>;$((PEXZ-;YAnxZhQcOccm$*T?+cQeqkfE?XrE2v9IYE*6FT!dfrwgIH9vL6+eH|
zl8}Scgd`fx*F3B2o_SW;U#06pj)l#yh)*YHDd69Qo5yK4j7thF8|r3g=|LVdv`&dp
zsB^kr<j&Z?26uk*d*8GMO(Ch&IKG~`4`!<<?JI#+ZT=tujdi*c?Q!0P;pt=Uko?Zx
zSJ!K!bLWQ$nf`PgYGetdvtY6Yy5*AwoFWoO6*0>%%Ni0=KZC};V_|eb5MFsv@tV&G
zog&LV(%bm^V3}Z@LPuhw0sf!6Is121#&Sh4woLFho^cRfR{btt$B9*_yMV%iy;bXV
zqb<`uR{YX4b^E$>#N}q!$B)YQr}focroT#heT2B&>r!z2$M%|=9`0Vs1@f^jOfh^X
zQ7ThC%nMrde@uOQJk$UC{<{=OD2mEqNu?qxIkOR^Qgn1al^k+rPQy0kREd#tW~8(8
zan8q><K|GC^I{lnHjIrOfBSs@`aK@|=k>?-*dMR^d0o%@zV7QXY}xxEZN$P6*8RHS
zdMa}91@>w8qqg^TnNB*_+%--_|5&-|>N?<YHK+7kO+WR+{J>@s%PGA&|D6h1qvdCS
z;{@rVI~@6ePhvJ*#gId9yt3c(Tf)0<a#q7aD}E~21MmL>mj}CT^QS=lr%C2_8K=^z
zkTmZPD<fB6;ZGBHOxa@6?FQ}81sy-E6^AuLC)$u|pUnEmwd=Age#Qn`au#bf7gXT=
zh0eSct8Z`Gzi06DR7%RqYe0p|KN&C=t(^}9&N&!^Oz5?OZx8Nl;w|6hhfiw(d8_<)
zk6^r-op}xT2^IbhdLtjnRcnL(`>|1mbkF-hPkscKBJ8YME%<;qLD5DepKqM=q{KJU
z?fh8X`DTJu^tJ)vcJ$IT!7^Hpq^g2+oF62xtQRZ@91rofh_TxnOBbVW%!SMZq354h
zkqV-Ej<H(w2Ue#B#P#3@-C_h1-a$bkB%xH`q{(PH{lDq+3x5|3GMMsl$ECT1^*N}m
zpox=B1j*gY)xK9DD@(7A$X+UsqI|^AUlGI72VpxCZsRlUPWJWh==!1Wz-)%4+s}8>
zS3NIJ%c|MP3#3$;=t+TnQqK!gfwV$m|1tK~Tj{p-9sxar8))mVRB3#m=fk<%FXSsp
z+oo<bDB~Vhe5OKs0_FIN?9;`PiX{B`+wj5fg|+K+XoNT2<HV(0EyQx9C9L^#d^zgY
z;Jb_|kKww~Q;Gg*BP@Ap&Bu&m_EjLms_jjQ8}^+NAE)hCBUPO|6#`4|y>bixWvORC
zYj_mz6gH(7Hua$Lr~$3kBB5<CYrt<ev?giN9Y0am;onTPc4c1E2xHDNzV}m4L>-`}
zk8lg4*@}X!jwtI1RmMZmv=6pbl~RQU9%Iaa3GZ4N$*l~Y@iT-`sKYaY-vqcq@)t0Q
zCL6WNQ)A+|%s=`QKB(65O~EL`l>h~1O*D5X+P8npsI)o8m<#8>ll<MgM{B~fT}ZCS
zIg-+}PQ)buzNs_(&I5eUumOkJYwd7Kcuc+O3JVA@yoH4XQfk^W9PvbID?x-P#HPs#
z{HbO=U1mMoxsngqUeatTZ`EsZ@llmqI@Ubt9P-Z+|0wb(a&#N(#SI$0gF<<II^@Fw
zRDsipq1>7mT#2SuNV?Su3;vI_u~5t^O}UG$BD@s&B_cTnRw!h+Zk=SzBJuz2E=f{d
zAp8~_Yds8J@xOmbLmiv?Wz87X|6coOzTRi1t;7T{5soZ{Q;K<y5`c9m)?vyQpp8K7
zGsz8=TSU?7$%-!;5#_WtI<*R!5k+lqptRBztG;cXc8?8yS$aw{4{*UE_@Z8V4OzYU
zY}h!ra2eWHP6;EO{9O8I`B;$R`&`VxzQ&*L${a;1RR2cLb)K(DN?pA$P0tIkQ!0`E
zy_b;j=U~zHO#Lx@rpyn9@7a<Y4)c-DCEk^Fb5HJz-`&2SDcx2OI2@OQcM88}U%?vM
zz43i^HRuPviQ=yHFL*ye*cpGA1NyTLeY>N*Gg6dzlhg(sZ*y5b7)b~a7=~3Ru#nJ-
zcY^t{z`KJ22U0Uh^Gt)|Y;qSLIjx<-rjQmuK*pOL1f1{1!J|P(kh?2rqTul=a61jr
z^9{7ozeCw@xkdD$$hQ5>fsKOKV|KphrGPbQpBUpm9Plfb3tei-pi{cm$I&ge>U~!<
z<k_2zSg0j+TrJGHnY&g2SAZr+CTn7R<jhgPGk!J1ZB9OG)nG`l*Wo|3vZ8BGNbXHe
zdBoq8-QB{Eb2^B8a>GuSCkP;T5j5nPlRFEZKsgr~6?tZ(?uMqz>k0*C&kl{)e%$4l
zN;J=6<B!Jky!9Rpv`zcg$6ul@JZ}!u#`3jvJBzSUCv91}p3LV(MiYOPML&|@4_6=`
z&o2f<t6Bmt5Df^NFb=j~a3s;D3CT353h^iu83T9&fRUm$$bkHlAnu$b@f&F80mqE9
zrSh#;vW}D1mY&BNNA_SXxsU}Ch<$^zKx%*I!dtLe(Al87aI|4`{zR1I`uTGDn!y_d
ze1pNZg)h4GS1Z#W9rPw@LHEt0nV>y#<59Fzhg(JC&U=5iIkWG((#w^sXOYKRPf!-Q
zZKPIO4OpZ2sm+0RYfQc~vYZ?~>|Hlj?ty7U67sV3V!22iJ~xASi6Yy|#ThUp80mAJ
z!11UF#A#albl)5t5$y_ag_FB)0wxmysfe;Gj7}hL@sZEnN@#A370|~6spaP}oTH6e
zo=Ef=E&WpGH8*Bo=jB&s|6UvSHPm4QV~-La{p!R%JfY%?FDkC<20n`#Y4kxIp2(!U
z<qnk3M%kkj6({hN(Gt{lN1LY=Vs1hubc-ASHFlwtFuH+0uV3N->`Dm=R^mUQD(tlb
z?B+8v?9SLKko>a-@e|rJ2@-%kH>WOBRFjTOB+4k8BWjMgZe?v~2j6W;J|P!SZ0x1i
z*_({?a@&&`w2^)Z*7aOYq~RU%a?e8Vz1p}pZ>Xeb|FK_Z<Us13=YCmL_ou)2eU|k+
z_nO|M)am!n(xo0LU)8%XeC2GT(&y<9A3`j)ZrvHyz1aUq(<I~UH+Q=``yP(hklnp+
zVy?W8${KRCFgUnsCs?H#!t9yM$WC9nh3)U9?`GVrrW>bxiTz=BA|ox`BfD`mBkfq^
zy_;b#9V~nGb8{ZJ3RG<w?;m)r?(SyfeSGmkFFqsZiPxj?s=HWJsiWaC$qMOl;gOh{
zo-*NKb?_xk4FDVf*AyXqqqnYSZxmcOv3g1zxd%R&w)EG>^m$z0FR*{B*-f$uLAs2c
z@c#N(-~8Iee?BU{7UlQir)@%hTdJts6Gu6Le2IzE?57hd6FL&i^uwb2pf9d>eYm`_
z532r~dHtO^-wiGuv{FN9ezx-ZinhLbXK(Ml$|K4|Up3Z_ts2oBfohm{^ILrVKwUcP
z$k0I#;hNYJUFe|?6H2BELnn`Dp2v0G{TJ)|FE;O)^AV5C;CI<Kvx0`23XD=Aea2(0
z4p!t>j?c9(su+Z>dMg-&S4rgS8+<4kn6&nrvKz$tmEKSto64#4+HXJQ`57YSa}am~
zrr8a8!O9*C@i5Tj75Cnwm~IBS`D~4%aVy;j$9sWwd$g`^zp9JXS}fBc)WiAYOY2{`
zbO2wNUkz{D(5T>Xy8nD?=Pd=4->mXzWsur+ZBYk&CR8=r|3&P6v5~JRT>imp-QpxM
z(4+zsso*TU))NWA{JHzb3OD9i5UKCzVCVBBynp<AkCoxhYa{l)0{CJODtfC;WbH>H
zpoSpC8~PJc(Uxvbg^wuil_aoJi7%U}h(j$eXTsS|PpPeImS`i?+>x~AfJUPsCmn)y
zqQU&FMRfpbE+lC-LT+ZYNQ9D%0M#o7xy{U+2c#U(ljmAU^wC72Z?NOo*9A;veA2#r
zEdL9ge*kXD1WUYbzc;7&8=pM95#gV1A~aJqEn_M&`3)LnpE%n6H|Pqk0icMGf9m%)
zD*suc{kP6&#T40b0+2j1SKhb~Y>>`NgvN!>LD2TUwyKs_O!H`s+gVo_mIy^^R=5r)
zInxC;iRs}ThVlx?{*}vsXfMfbl5YE8e;_y0Ibu?u7&<JyuvWaItHUR#5a$Qo%x&?v
zH?#vc&yY34O1I}B@F2I@yjP#upG6b1-@7^)qki4SNpEegq#O3IzMj)#eLEZVIqpsW
zt^JSu?px=WH;H5V0}l-kR~JE)5@Z)67N`1NKu!^^9DJv)UVBB+&El<^f8?7UtXkdZ
zjd4FUZ1#Ee`m`F4$%e@qKeY^^>P598wO^m#PNwYRyu16ey#B(IV=15YYPvuFer)it
z^Y<?JlK5whJkJw~c1g0jV<y6O)gx(jA!+W=wux$^;7pCWu471puVs$Q=ULAhk5$;u
zX9*v~mDhIFx#+&dRqQS4J_>G>!0w(G9@i<Yl0uA$ldt<adb$@zk4IK^Ui`HzZr$i!
zJRIix)ASFnd+5jP!^FmEl)ZRXvpB+Jm|>4LMC=-%Ae7pZmH!PTRXVOXY}U;$l{=+;
ziDEX9GANToMU9E7ZXh~%sWX<k@qV^hA!!2)65BXg9YRxYfn{ErMv<~?sa03^x=;vF
zL#5v5MgCQ@2BEHeRBukK$UpZobdHAP)WuTz6BorU5=(!dVg>hKOc}6NZqTTn^%v++
z$5eG_WrFHi9VY8v{5UjxJ+Gv~ucuAOHhgE+)Ma|ii0!){h9S5JS+Ai99iDcfuE?@(
zEM9R(qm8eDo5t_tSNQh8qH%oJRzR2jBm%j{b)nYzNX$c8(Eqp*Sg1?l-za(%#|Wl~
z^1ZynnTwPxUX$8i_r@!_DnM{$o@uUd{;h}*p4Od?%re@`75<J)?b$Abp@HTyv>Dhw
zF~jAxDStY1t@>Ynoprx_N3{oDJ2%<UuE8zW4d|+VUS;{PhAHPU-aqWJ&j--4y4+IV
z!Gz0SfLTw6u6mrTxu>TC=AJaakV(eHfzys&wZO}N{qJbSZr1}d>-3K)*PjO{RqFz>
zDaJQ7D{f=1R{wHu91KYn&DtJ{zIrS)rc$v|`!Me9VQ|aIhwlAg(=*mK3E76tGgmI$
z&A#_jttRfRUB-Rj;JKKcu@l;g?{f8K7yk2^J&t(r9SXZCBRg^6cklf7kKJN)-}Bzz
zltChOTI0%(J?+4DQFe*9eLd=XKD*l@KOdEQ$1N{N|27d*8)A<9{M@|a!W*3IR>8HN
z(QXIE9l=7(s4{TRy9!g(Mo?(sqml6gq!Q&kMEUqi+S8Tmukbs;j!CsIt=`}anUW!|
z9D)o>dpeGjjPoAFgD-+x5a5mx?n+d!0Shosyu~w5ERobl@=I1bOl~f|j22f&rJ{FT
zB(UtO9Z>ZyAA<<-_2yW%eesbZ?DYBicCAj&fH&N87(UYk>mP<4hgZ5dg`^o|{MeWT
zWAxDFjoa32Rfa{5D`*va4_JqEj?6aNV7A-al$OX$pc?7R4l_QGdjb=&JRgwW+BFg^
zlgm2IKFT(5BvJK=Z#EcvW@zKTBG@4h<?}9Mrn*HTNZkU!g!+}voOy`;X_6@CQ1srM
zGgg&|`6%S)GYxusA?>ctvNPvG-#2SFKqKF8=x?NC_v6y=$(~<r%_)Q77|S|L_6gis
zo#7@|mp0Xvj9l^HQgc}O_W7Z%tJo&lAmG5>Xct%L+k>6g$YQK}8uc*EgT5W5FH*AX
zZs1U#C!P&>*dfQHE_VxC+t|9OMSc7*ad@lq<&!1thU%__m-98MzFvQP<qo0ctQB72
zg(XXVgn)m&GbSh)A%0Ix?9WpPEJ)6FaVwS`Mm5CK{W$L-8I@i|_jaORx-D$RYby>$
zylQ#vQa>V<3J_J5nKoPOH&c_gQA2&!$gr2nxKNT|Hz2Ee$$k5(MbA~y!ROCMKh#AG
zPd&+~^^ZU6mfZsT^5rz)p-%J>dw)mw7kwSI_ew4)48FVMb4l7c^Nl;McRQm7qA>9{
z1JZ|YDI?Ebg_DI3+spXQ9@4f|+JsEi-z%h6A3x#T;->k%Zq=aPR&=xdImz0vyiN*+
z$Rh;{7a$u;WC9y!2f~AR#?^y&!V{OHMyd}DUXHr{xG-#ZuG{CeWH&jX<i5|Yez?Nv
ze@>I-3jY1{u@dNd&JBLvF))*M@DbP*H4LgmKM-0DY=k4DhL#M~Ao-u+tK~}Np8e$i
zWT5?YPJ8F3YuwrBxWS$l>#Y$W82?VOYKFh2zNf+_6kpoKKTyF8?BF*z9zxqkA4u8w
z<IIsq`tactx&|M_wC4?R5gktE??@SnoGB;M>cx_ZN}j~uP>~s|%u|PrDJN&0K_8pU
z1)9cul>Ck)PKbxYuk-lSy4|StnKnK?13HZDWO4>tzeUaR`0?nN_Cf3r-*hDk^_$i0
ziDqjs%4q9S(@(F!6z3FqXXj8RgXh(@=BFMQRVw_1-gKdcS=0wPw;Lo{(DWrS6=o61
z&@&T>2S{E&JD4z!8bT3G$~n=cDysR3tKGm7)z8+y#*Q6kJBqoa4aHmMHikD3PgMQ!
znBU!;ZT9W0x6c$AN?2ISZJlxT_`6+rpz*3u_QO~BD=u%dt^}HN%{-O4k{Rq{s`ur~
zW|GCy$xJ&rd_xQIs)%}4W02N?rPK7Fiy4nL48fZF8?#@zU);PFx^~c_^XmS=GmDqN
z;Rj~|4hClr_S;%AG6vQ}`{<(11y;6g2h>9CK#G=q?(z&K@xEklv0|nAuS>?sw%2qX
zYsx%sK6y0~_AzL#Dvf@fe*X~Cf$EJJ`S(!nmHoBP7i;AmwZyazh8`TWRTFvgDdl6P
z{oUOVx%>L#!XKXM`F!0rZ6(J>;ys1EPkTSDSlz|RZKn<vjMGb1yxlc^-yLhcOlT@9
z2-{wHk9<07s9(ejJ2RP$JGPDTyCd~4=xd#6nLjeyp9-ree8l>_9@-X1Z>|l#D}mkk
z1mWKD0=B1`?|UHw!p$QI@!*V9D`Km=yyLG}H;-4~r6a@0>$<(nE73o0`@WNVq%7}7
zq;FloBy|zn%y{UtvsMJaF#n`@oOvW-zkom8AW27x2MK~~6iv3A0Soj^qm>^m%*0&&
z{y_=11q$+B?lh!*=PUthf*dD4vNV{bMp8uCwLX%~q<&BXNxgkl#%HWCcUv)Gd!js_
z%UU2zNzO-eCM7EknXkmkUNl?c{PsB-NC0(CPM<2GMo)9*hWmwdtt3(3roYBa<1bas
ziqZfxz5{2#V591LuB-BwkF4$Y>m8PiJxdY^k?1?Ca9Md=uP|5nvZ}!~^AK%P+@AH0
z^4(qB3HIgugJKH)Z3B+Aej{+!A+mv+zH==1I3v0Gx|&5PVMr?J$17&%F3U=_I<2O}
z*EQ%za+Z@{t7lQblVVzti+IWTn&T8t@i=~RhmS5wil(d+Vv^jmp!Ix+vrA<XWeZ)f
zV#A|)djs1n_5c2gPs$VO)o}JAI?BWa(#@2WXABGHf~6R$vxX1)j_TN)y4pLe)ZqOk
z*J{B1NX7-9)O#g1&F_Mlsok(kq|bLu6M|qr4?RCgdWSLY4Qd;EC+;&V|J5<rxnxG1
z+$lWR4FvPf?G&KYX5OLmq{Zz1E#K-H8_vBWyOrT@`DC#&*EDB$=xiRsq6wLsIq0A-
zvUN!heu_5T&#t%el^kZ?ZvtEO?P>k8ydTVz=pa@r7|gU5#WlMeg;`7JTG<X4nfjL>
znDcoHzBM#mgO{Pq_$6)7Z?G9BDzrRWa}o9~$@*HJSc%*umaX{D^@t-d&s83GXExs&
zy8fT{63oVc)U6i*b@x(q#5oNs+48Sm!(SuG8@4QN=QlAP<3ATPEM=}Ibz4McfU^VK
z>@?QZ)pzYdR7k}9OAS;18#JN8`9P!_J5a)%H)mLiydhYmQY778CO2X-;9P+2f>OAV
zMrL$!GwygwKg4Mnz?zbgXH{J42y>AT`TS^;yQ~mr^{gLlAsHz8?YHOYXoZZVs?U#n
zAd4)cdiNbcf_x}U;`-Vd{h~1NBKi8-eR==P&a<Q!VHmU^Y@@Yp$(bTu#Qz<;{9==c
ztbi!8ZcJvB1sYQlivos?UGf{j0)_3xSky;XK!ARmM!YKK`|Q8+HPUtq1)o<D4^EKr
z6JJxw=v46RV88XzIc@s)Rbba<e&3rZKYt+5<DTf8kMQ~HVR58-b9I5gG3pn%G1rv-
z*MLdS$ZOXV+eJe*SEKzUNZWIHtfAX#*_i|PFiU9pC*{FET0*HPDfX=G)l8Mq-Il!N
zhr7dHatBXXgaafOISpqm^4N`yd8;vE0t0+7Ym|E(%!*&87qi99@11Z?wvRnKt8wj{
z=1c&?SMH(R{69(b`3S4nJs-amu_2G+mYYJUiD$`jq^>m`p}`ZHg5QGVCw;YJt|~Rj
z1hwpQ&cf<BU#C;ThOu?^jdm%R@#?aB_MburFx3)}h`ihvWn*KdHeo5D+3y#YeaMIG
z&)j<{Vp(U||D7m(&Ng%FIXU$Z-px;>MZ-3IBM5gzuV*U7Dp2B<?3vB|*E3HI|2=kk
ztOXNC$BU*|q<vjaw=(Y%f+)KD$9i9=sTZ*6hzO)uA-c<#I`OMlMB8riWW;wK=W7@N
z#JRIx>s(QnK|Zs!4Z23mI!-<$!YRBL#csGE7e(u~DjOBKN9jH{yw=)#Z>>9Ht(4o*
ziBk{zUYGKw4&_i^CBuD2so`D0`Gw@w0j`YYFjoU;b!69odN03#`WK$GTAG*9IZN$1
zl@z=i>LoEq0i1i-PuWlBli!7vn3nLGWk%TD1il~wZbtsEAeswsU|5>Ei%bL?Bh>9C
ztor-R4G!tK%I>&&>bi;`{2@2A3qKFBWHHw;ZolD?Y1^s;s3Lh0HB1$+$BR6HUwz<s
z2vt%$F?L7JSsN`Z^Kq)<#0qG|)5!B<gAYjs_q!peL&^P%_Sf33o7gz%3_;u@C2s^u
zOtASgk@as^+aGgXPSf*cJPmM{FC@LJ5h&5e1up+bH)e_}VT7_3fR6S54Fm41z%hG_
z94#Rudmw}$kE}t>t~{}hL!bGgq?o?j9+$X?%Wh5>DUZ@bI_NL;!rhgZiZ_6HB2tTW
z02Y8Gsg}rm^YHR`qWpb&$Is;<#tdR;GCs1_ulY%F+o&W&a_nF<7ENtC_1z-m<^CNb
z%>M31^?~^?Fq3MgFefD20sd|!Cb^)+o_`ZD{0wKw@xS1GjIZwVgAO|Uv&8&Ulf2os
z!O6`SnlS6GQyw!Kq4q1XAknY(5ZQYT@6t@H8(f!fO`*0%F!wkJyAW-k4Le6W8)6B>
ztW!aj`;qqBfJmi!ojeak7<>t?haLig!2ltkxh{xEis2j3Bv%Y+#vu|F&C)_7M@F}H
zVf{mZx2Jf|u3{%^-IQRELqF13fV{<A{XSDA$M$cYX>-^=S0PUuMuRtME^Y;2CVy4!
zRIg26hf1;|$J68}?4g%0>I^CYqHr@netgS?*pegE+~LyM=bEYJTkWs>3`&nfp}p2E
zFs&Nzq$R&u#ZaCH3A*D%g7#^Vpo#;aPT$9t`ZRhVbK5vWYDrBqll^z#Dc=z@;PZ(c
zaUP$&f#I$|T@STbX(>%bW6mtXLqPYR9NPrG%F#ToODm>B))j5y=469G5rCCg)=~^J
zeg-Q@@~j;RsxA*~rxvRGbFavIQJ%3`Af`YEm%3ReY!6v#V#qTZ(poxNf2N*4w{<Y2
ze#r52GHL$2IMUi$JytgS`>{^Nbj;RWa>nJkNENFfdQ<IfV{Rfz%PNmzSSa+bWd_kF
zy(nqsY=Tau?WQr0Gy+JVuVP2^kg3ewG}9ZeQbN^6y8p#uNc=InxC!k@oWQ(GVqxMe
zO;jq_B;21+c?3h7dMUBqTHbxrKB_?OxWR<e^ib81mD_dvtJ>rGLlA{bc~f7V$ai%o
z^rq~)_Zy&98qUJM^<-V~_x(VG`tyt>^-iJe4*S^z$MGTSFt~}x8Grf9pF}(wqT&PF
zR_0FRDCeMG)y&6u)vR}<Kp?oYn%KJ40=>NM^H^5VEF<e$XJqn;7ebXTW6GL;gt6td
ziW3_Do;Z8tA51DU?^lz1{9vACrIAvMRYx7!+5O&SNJhT*GOdDZ%Fwbe`!(G&c9BoO
z2fiE8JJm*#rxgGEIS*fHk0@n})F2Sy%w*Gc7hOW{dc);vW9Ckp$k?9vO|uilGfMty
zBZFs_(l_5WR2e_dsvdq>`iOc2<EFuvhKYROi|!&*MGh`k8K#c#&CW>XHTr#v1vg6#
zZ_tbtvR~>ibhwdXzMGaB<Y?AM;w?+M2>Hoa$2wT-FrlH}yCeGdm!J8D3Gsq>@CUIG
zz7aC#r^T$yF8%D~YX)>j=C;G~hi4n+e%AjDpiD@>OY8K$4@%(fcLW{%#IgtcoD?@v
zJhdoF04V85VGxEZPAVrAzd7v^_!9>kCgf{TXC1Vs#}uajMn;tillgRw_ORdbtf}Rg
zcoE4d&TN!#PmyW87gK^Ajka_+NN{PY!&!GJS<Amt?HbRF=~)M*NK0|ygN*of5Yw;A
zZvc`}Y{E}kj`6k{y&tvsaP9X?hWcWZnZK;Kz@+6ChLp(mT54uC1MT19W&H&8q!UmL
znNU3DgR}4ddCa<+A(q?rmZVS-4sALPHIU`j?VHvGK#>L3bwNqP&TV7Mr@Mcqf;BVM
z>t7%>>Q{Fhdb}j<a&_!`d&RA5-J4@ES;-8ker}f27EqADj7{F%U5^3;VG@TP!`?sd
zaD0Nf7}bBK(9zmbIucOhFlX-AJIFR<DvNIdPcvg_qwKgAL?1VT@Mw;HaRo*N`k$e|
z#!Dq<5a=i^kFhJ?MmnSXxBD=mbWUh~s%z`b<Z2V;n|7dvZsUk9<A!IdDH~rr?8&8u
zJ~<iQ$gk%dl7@cxzpGgL6@-KB+$SH??2Fuzb9I_OI*U#TQ~Y1}>7h2}{7w}$t8m7+
zPi{pz&fjG5<mnY?994y%PTx(Vi$a{wEZT_2&xKxFiRNh-5$<m}cPY3f(4hxZh<dSc
z^vCUTVt`-R6~8>Wrv37WIawe~+-5P7bqGl61+|8DyASR}Fj2YarwT+peE{jrypO}Z
zf|a?F2l6;A)SeN>U+o2kH`TBGls~(RzY=32x!78utXJ5(hYU9EsxqF_$s&&D=L;n;
zTv&^lwuHRX<V5W$-}|LT>svL9TmlcLuFH>R4+p^9;1OJnBE3uV;YNZs0_l?!S(s0>
zEo?>x-uNM|cB!7~c5||(qUD*aOHe$QlG$EcQr?8)aaeNNr_T;A%)gZvk?(`qKXNmz
zlz(979;8J#g-*so4a?6h#>mr2ke5{5|GpDnI)`00{gk{vxP4qf`lfKdQ1+wiyEp#P
zjb}~OS6<4Cg`Bd9WlEph1S#>Q{oCamh+TU)in=v2JoBB2SZ4TUv+D)J0O10XZXs3d
z#*ChMA8RrD3&V2UgE52TE!5vmq_(QHf0|nMpML88{dSMxax=VQ*eImKqw~a_a6i;h
z-{N!m%@b}`AU|yT=Fir37MZan2d}Xb?zN>(Jt=qI1b+QlJU?~NHbB%T!1TTmBF^yC
zNxrA<Q&T-kviM6Lnz#ubEx7p!)J~|bYu%WOV$69W8+Ye^J|IF2BRZX%c9cqX|E0>M
zv-eU_fn<-QZrDWg1ht}lH$}^TvbXok-8%R6H^<hS|G03`kV2pwG~kCM5KZiNEI8j#
z_R7oPW;YG)t+8EHZ~j|R!M9k^xB2@2O3(S)A_Nvu?ti?&MU+u9Q88(wrxG%}d`bJt
zrNNWX&1^33EQY{nsawHyfBi62dtKK~iBWZDqkSFN+|$gkpZlkU6Y<|jBso_`DMpMa
zMpJ^C`BiAjn@pE#jI^}x45v{E*-aK(fjQLMsZZUMY4eon7XX|;;aDGgB^phH4m{}c
zJv(Ht#C)VK?GZY`evM^+pl`ON8Co6Kj?sEqY56*#0YH0thcjos0{hhn@Xi5)QkXXa
zL34v;HYNqz16a*D8zc{}jT)KSGZ$4SiGFfUWl9+y-3g28|H2ptV^J;z;YG7iXeXSX
zV~c@CNoZu#MjLNfvuY9QB>l}c_l@-0uyz@x*MM3H)&pTzJts8aEN~R3pWuKvQe1Ya
zbuVy4?b6Z#e!A+2@-66``64t9sJwE;WJL$I2uUmC%ZqK+6q}If@YUUfPfTeJXin~O
zhu*1H1if#)=?HLHzX-57W88&rrk|{W&B<#|%qd>^J@-;&GjywyDU5DHM~ef%w-H}O
znux2mgci@str{U~A~;aNc$m_RbKG>w^pyi#jT|BUy*zUXfY|cx=R|IW!jvMq(BWmq
z<O$qqN%rYRJ6r-uwS%vlFsrGGepW3oRIqGoo&HqEWsD83seH@#2k|;3XSr|T;eWKW
zU1#>mJ(h0@2;`qwlIbS))Oa+)#!<dsLpxvts_@8RSWsuYza@~z)BCF<%NyeH5b&wE
z7yGrVONWFp>ZnfoasykK&Pxgc`@CZ=K1GHxml&+3X%am+hRAPfSDrTlaG8gfjg%Dn
zWInNlkAwr17E=$*nP)Kz9~WF9MBL3f&wPFL!SgPm?A#%%PloRGSKUPE*6i6$&=Ce`
z5iEdFpEy9|DeRt4(?@mz7p7(GH|0%Ic7yt>V(pse->&O8D?e;2!#(uGRHA^K5m@-_
zb{JO+Kj~)+jHU@O?8as4Bf8HK^=`MweDN0GlVDCY91FtqL<K8OIBYc0F+iPbIvum&
z)5**^p9&c6cQw8=xWc7DHdIDp<=^>#7lX89wsEs`#qa6>vC?gogN<mu*~-(B%9Qc9
z7L=uS-XH%)qGhwd6=co-ccy0nyV(7ii?8U+D~t~RuSrQ~6RECK?r-?*6+Xg{^F7L_
zktrB~AvD^&_iGONE-~_{p%!g%23=*ZIS30F2p@}(d+RuisPEn=WZVY?8!Z@OC8s}$
zOn*<E{*%Z_aa)mSm>KP5bzw-c+zire6WAa1dn$M<b8BXA6n(pW+Rw&?L{$#lb+?)D
z)0y=}UJH2oqpyvUOddsqI~>_`V|^XYxDwvh%6{r*kVxNm+D)U8bCrFGQB%>%4f9EE
zG77`^61f*G>TykMn=!d@0rp<&Ahwv$l!q>7y2p@!s)P?wzaRk3Ba`3r4$Yx5vwudP
z?Ysh7+BAEvMW1>}@ko(g(tnCe|GVMbb<*T?BofPAgkGOBX9b_=<i4J%_$GqgF6M8{
zDQ<xZbO;h!HOFQuF4ZGB6*nk{n|B6O*WNv{s!6}EX(cRM5Smg)m5n`_=bb;MS+*PZ
z1}77mL*j()Np23UatLS~9##$?lZ1PH`-ItKs_W8K{O59Dfw5NR$-x3o-E_cV2q4Yo
zfc|R$>un)()aG>_VkC{zxA8Bw{4oFq7$+te|6irLy`)`;xLf~#&!a;00cieQ&wGhJ
zw(vN4CCE{Ss(Ym+1@w*Isv}H3NZwjIP793?cd^J^Y+7>TH!U)D{;MeFLyrSIlh?s4
zfJ@1m>5jfs|DGlInNR#(jDXjrbxTA<97^TM2Djh!$;h4e%FkWpAl5R8c<4NgYj~2m
z;%)`ve=>?~3R;DbrXe6;kAKrYd%2Igf1Z6Ua&bkTuW$G*yqMV#1?bHfuAZ>dTr0Lz
zSo`F{8Jh*s0^qRWgr}{bV75~^fW!EdbV=9`BF|dx7`U2bVl5uSPySip=5`T+Eocmt
zzdS?OQz8CT=Dh3&>A1;x!~5BLv#t!C+yy+4xq#G_i5OB`aW4j}>9Em4PQ9mnmR`oO
z31&ROVf#w`_?Gc$k)tQ2KZtDdx}M{3e%7KQh5V}C5w326O<OqXo(pQgK8)g;=@|Sb
zm;bZUHD^hw#yNte*UbA#_8s_rC^!D(gCJMrlK{fgC%Ha;K<_!j7JOg-?Fj&MssPGi
z)<cIQr=6N-t>?i_Y-<Ixwb`CVb;e)z{npYgSEzfdIIHk}naoe3xv6Dfsh9kP0~Kj!
zgUk5RyU{e5L&6%nL)vO0pE>90w$M8{we96I;n~c=%QiyB?__A8dR(AOe~mD@SgXlT
z@wlCC#M&mo@mb^U{`zOb_Hx?#80u%CHjawJUYZMRT@~fG648<oNBu@@3(*q*Cp(&A
z?yOQ<(0*YGV{W>3^ShJY1n$3S*TeOr#Y!YP*brkrA?pM;bNOlJl06K2g%;EHt%tYo
zmd_?be}+4=b3bMb)%rfCt>FpJQL~ZF+jnkNE|le#J*QiTI|HzP?x~j5GUxbx>byS{
zWzgyR`r~>i7+J=l$-Oui@6BS^t2(xiZsN-|SpbCUbH7R77jn2}H?(@@f8YTKU?UIf
z#1!VZ!M1=!j%NlemcI$v{d7E=*2~F44|sOXoEr$bDsh>Ry=|ryQ>lKqK+|jyW<nEd
zA{ZYwB?xT+$ELv`5)>w2w)<x_Ldsp*1dAcNMf7C|=CNFZOD-ikT>VMFZ(K?cacxxA
zC6K=B96gU+D^0hor(1!>^(9X3Wx8D|@b<f30wLi1zmtr<otYqFjXX*DkPLcHdpqZ?
zRumK3Otq1nM8J2H4df&y>ca!bY44v68>*};Gt(`0iahtFv5yGi9^(6U<2dEOMeA)7
zg1+Maky*Imdd~bgu1EuzvGS7RC@;1c+P$S0DW54Xw1^d;heY2ML;X`uX~bwp4a4O@
zB%M#PMP{_1uadfOL9QrRV|l0Ng!7by+4P$}{w+b^@?FDdz?xZu&-DX}27GC+N3ehT
zu3qw~JoJX=n7Z<?{&Nr8Y0BX|z*jjFgXP9UhKM>-kEZQ{9ACHhUnkb^&OV;`!9Sfs
zhqZ7*G=Z7W!A}8!@_}9^Re~<D_)Wk`JKN_>BSsc?VTMleZg9@!8Tk!RPT=Z&JezkG
zl4gzca_anjh@Ky9eM7Ui<AoR%KLhb1h>I1KRW&8a%*8NvD21RXFZNj|7?hgb5gn-J
zcpM|{F8ZMDu!lqu|4MAuZ3$Y$<us9t<1?^J_i_C~kFs8NxGP_MpM7C5X6TWJs%T^(
zN6_cp&&47*h@&wrYtw20Xl>Q4tb~(Nn;rJ5`&-bLD<&`XkOwOBQb;Q?<0i{3dW-=J
zjmamO&fe+ZnlcZzj4Jn8eBH`e57n4E?>N0{8~%~aY$7K@Fs}PEzT~1N*5ABg{00<+
zg41cX?fvyH#lM>mGCIiXjWQkFWxqtRPvxWR$EN#{%0K%<@3#v7B#z#nMa0d_=bl2m
zNpPf0_d4+pZW+yfa0@{OOg;1DQ#=_aH6HbW!xtTD!ZZCQ>bjldy1{KzoiAQkC$FLD
zTC<@|ww|Wa-zKCHpcioVG{N_Zq3fScA?p^eD(J-5{QT`f7dz)Cbq4j0p|0)2ysL|l
z5Rjtm@<U>bKCmP-^wS8#=YY4!^|jt2#-b8l**kGt2~8KYeVj?EH~90A6R+^WfqBt5
znK^iPlAKS)tK!*ctWg+!4K*G2>x`;P+mQ9z)C*b*vu$=inh!3PRC@tS{&-q!U<7+-
zqxr<r69%jpvBuw>)S#sICi|&x2A!xb@t&00`m;R&GP6GAFcT!w0G(^v&S=lZkeu*I
zcY`tJe<KGp7G|t=g;%17fJ>)F^<ckfP{Z~F0i?R)4l0tDAh3bw0;&Vgw;QG?Cdx}X
zP0J00dbGfYtOOJodXg!3k)~Y0?CIJT@X$H)2v`%}uwCG{Sj60)!W?5S!VKpIuYd|v
zt!$O`JjMjbt?$0nNdFh*9#|m$dve2V26~3tPWCsTeHf=0?zoUUO=s%bE~T4>Had9!
z3^i9+(~bV_6xs7J?B8vb#+=Eba1F9i*oRief<-T6vCxpA&5Hc?`8Mv_Q_53~^+KJ)
zkT?J&p)hg8oYTiX(0WerIT`Xf;S^tAah;Ff9iyX#xD`Eri>Mr6<SEkO#TGSz+8r4A
zSEQ;+qM#YgM`G#lFCxJgP(+`VsAzFGlEmJC6lkoJpY5d4VM<g-<G(l*N+GUZdNj<i
zYtKA9R6dnzzCJ;VF$AQ}$r`F$UWxgvyppVM(j*X;yc7xUXH0{Xr&)`eEeUEq9}WIA
zSq)afZcDFC4Ql9Mz|cRT_;`lHh<Q^nf2+qw#46W6)??`EoLE%!#x?frJ)Nmt&w>}s
zndqABoTHwms$cCu6p~|9_cj$~B+#d}HETbQxc7zLz6X5K&&w`8xjbv_xabT}A(ek5
zU;0*-c%H#cNGHWLVaMpc9T9Y{)bQcWAi&4Tv$_&10I|o*Z=Nqco#4l1=D!joS?^nl
z`K5PCcWU?Lmf2~(vb^jE{?ZuVcuyfaNG%l|r)O@qA7<&%Ei$-a@Z`H*i;ZE$**v@E
zp7&p6r@;uLr+630myc!Kt~+51Q0>xsXRjnH!>#W|^u<RRiQ6{&5*O=~{V}4r%fItX
zMAO%_8wiagXICA^sQg(N*^hyXqD}799&&h_Q~w_o<w^tEm_s<(7N9(rgpoZ@(v|1E
zQUCPr=gV%!B%DU;J{WoVNy*0GK_7nw=7>VNagSou1nNo7kI?QmQnitR?`~$)8a;oy
z^a(wKs%X3>+%t7ucjgn9{nsy#8BkFtH$o_H-?&Kz81xCQ(9h@_23lTn`3Z;WG+_*|
z#U=HsMuYIl7SqI1DT!N>j_;U`Z927!?4*Ey@F{ilt9qjc&4HC=5x-x6=9u15+e%nz
zaie;RMTp5_)GH$WWHG6Z>0UQo7C{PAeU<KRgRQqHZ=0g_0KrqG+5T<IjfIbxieVxx
zsm#!Orqp8rQ?6$Nnbl>}YA>v_TPIhP!)Yin^@a}hwneGt-3$i`*DA;MrhB}5h3!G6
zDdDEfVwqC_#0S^J9B`7&X}=>V!>A!O4_jSq$74hw^{Go1yL+^pCg>+Uz(_?RT@}aK
z2WsrkqvifV%Yl7tW8WL`rWloLRCA_saJzB~mInLo_SwPYB^}rZK(nWoNDWiBm_*y#
zq<4<w_ZdE|V-bGt2|<?a<&afe=!{o09b>TOy8|G544TBwh6*S&POvDx)Ph+PK2(Y1
zb7aE?5U}UrJgTsd)|uX^ZhB6=pC1VsUO;b00e62*^(H6C{@w;f?#(xmm^V*RT7jAH
zOR($%P?eJ*1qrAB|3al{`X(;N46fHH0C!sdVsjVZ+vC4X$+t$hJBH|Eq6(?H8v8<S
zd|FegZH*)BdBd?9;de<K2)Wc?m>L*8NsIYpWE8kyM%I4%X&a!^DcpN@qN87W-Xp!t
zrpqiTJ1<Tj`5dZp)`3)dh<wcmuYb(L?rvnYlL+5{y*0je@i&iJO!SR-@MasM8o0H0
zR&4W<M{h_{qMKvbpO4f!gwa-EXgJTt%PpyTEyI9T`N$GDDc~h%<%}*7o_S)c=ay>n
zgf>}-E3W%y5cGXbJn;lfHgU%4M4{uUA<^ad>R5^IhN1?if)lcpS7PGaUcMsj*H3+V
zyYK4zSH99eTrUPA^s>(2N~@k1wuGEA&Hi>~OzU(Ud(Kdpxa{zs;A-kOcl<$I<aM}O
z>8x)}9^S9+DkuME@^`u1`_n!syIt85Uur^f{ejT0&;EGlB62!?plBs9FA&QOV~Xq8
z?lyhYcGKX^;p()nX^j>@7MNjJg>d`Gd&#+QU+rjD?Ur(>p-$q_9SD)3u-1d;`v<hK
z3tCy3__YEypi<TaGW?MK^#o1fVO`X>lA<k;x}MA}TTh6SC|YV2&Y6?+sk_fDkNwx0
z=or&?B;{CBAM<7Jk>XB#V8O9Zq@4y~KKcEcfv>lBf>XrYekw5CLh`8sh48|HL9L~>
zvL}R?ATPhjtuXxdrn79GoX2i~6Ry<3rdgQnx3NNML7NTpX8oxhwu>DXJPZ8ZtlHHF
zvMrg;Tc9qi34s_~E&|XwT1IlaG5ygiD)B+o`u*G^-d5WWh6zHx$c0>80Lfo8MbNfH
zp1%QKJpMO?lDpJB3)U$|{eCe{sa7qoO)Q8UI7k|<6DR;}w^@I*L$;1LliJu6B}%&c
zDaq9UNFMqGS6@!xy|8kb-K|<NA&mvPS-py)wG-6a7Y;_TixIwNcdD1l?bdoZ2RB%+
zLi_TxO4Q)Wn<6J}5Tr+25SM9YBkb2R2(vBV0t8u3D`Z~D{5U@3yM!WeZ$cneE77lr
zPyvUIBd}k$Xb2qUW;7`9b)W+ItUSHMfGVp$H?6NT-RYy!?EdOqOE(Yg=A)O5JMH(j
z4tcrPznQIW!2UgcUkqD`ixsgO{3Y7En?G%p{Y*hhx(vT)MxH_;odT&4sx3_uVdU>;
zE`CEC;4>T<h7xobNf<tF3)Oy-HrT0`=kN|h5#AOY4tcLzVt4Yf2>D)wp~h>&A4Pqw
zDY6`XXxkQN!<b(@-p@T?WQBaPuGfVe-_<3ErSaqO`|IC~L=txj3)#C|?5<Azy~BPw
zU4D0JlXH_3$x&4EMfhn-OaVJyceBa0Yd6+8fR#NP!E6m%3)Ptjwc%sb8!5rfN7=~s
zR+fEWz7u44J4{o4zKl9srV#uYYW;Q_nd7D73{KA9|E+m%#aa>+p`-_qNw@KW9z0m4
z!@A%8_93|--(7iLAdZE8j9N2g&iYhMUeFHlC=kDAQJXst2LV@~hr@e{R#fuBTj8N!
zwsW2QW2>wTT0TkfY*sk|hgpojKQHQ79WqK~!_2acv!vs)=LP6lf7nA@Q-uQWJDt3L
zfWJ@S@)5gpB4>h)uS-6cId@ge=)=bWE0L>O(wBW=6>ssC4JE*8inmtaCnBtzX$H_i
z57nv4Igfdxn@>Ttw=>Hb{`3q6trs~Va>-%Zyll!=Ioc{GjN-e-W}p<ItSL?A94%$Z
z#?RjEY_pDC|8kwSYnN_#z(6ytWm+mtP=2`jT^GgLVAh%QH^j56qT`3{P;&~VYF*^H
z9cFIWv^Z*t4bZM?tgExGlPyRj5F3<8+2;gbvt=Q{#)bCsqJJ2XX_7mI8huJ_7lN~B
zVv)x;;2>E(^BI;8>)mLdH}##HQ>b5WmnToVx3VNF4QMTcIrVF<at!zjpQ^P8fbJZ=
z%q|lXwnl?_62IN8&dIN<Pi3xkXunmE4e&&U7oGKdKK*_wUt4ne=SdoR1JtV1S~1`t
zWn@*`W_f0+K*FE=U$D_9ZSb`T-lw1zjO;LbG14TWK>bGt@9`CmMmS!Z<e6SwO{pHN
zs5D2yrZ$%!+x0AmYKIRB%UzCmP2WvagmgW$@UY*IdH#!0HbE<?o2m0r6PnDpf|~NE
z;C2njXjH1tHcg7-T>@;J!s5B~wQw~BNZ$sqiExb~H>TB1*NOEK&^Vk0Nh}hO#=N1A
zM(k@vG&DMFG{$dE(V*8)XwZleF>=y{bkXOjEr?%x8(rR!po<4+^ch5G(wzBe1=@DA
z%Lrd^hdW6cIgrd>vbEZA!Pj2%$;o-{SN*8WlykoM*meqYp8<jhhq7(azb_O;MRd&D
zqV(YMTv`_$6V4Q4b4?{3rsu10SFX(2Z>_!{@wdKV_|DMnCqAR?NCROtez9`j2~OVy
z4%M80I2foDu%X=kCQknUFUdE?$TV~qcLkz<3W}IhX^@C0I5oem9EXqS6RLCGkJrQ2
z91ZCx6!D(k6`&BOB1NdHbrIIJi(LQTSVfR5|Lx3lMMYCD>@`%p^bUJe66OUu;}r$!
zc;7&ShrFNtRj>Q>)pkF&`ss-aVcj2Xrn0{k{Bx{g|B8^y$|RM?|8sdvVckU!9g=&;
z(<<`HaT03RCB4(6U>pLf=F*A2wzpHn^R9K4w=a1Z>@KFSC8Or*pEmBU6l<1gv{ZpO
z8Ztcem5!%NMQuZRpH^e4jG-=eg}$d|te!jTitHOW`YG>(q{wFxDaffeA<zS(wuNxR
z%aElziJSL#Z)If<SS?+6fIFBkcPj-iv<lO#_m5@!)lM3O+1(6HHew>+li474WByip
z4dpi&Q}YO`kGIt4-Zj{!fY)kkuwciiK|cmh1TY2uenH1MC|=GjtIjX>i9kLslzIdK
z+WBr2%~$p>$Xsm>k4w=Zw(5DH*{{}H=)1O?FhJ7ZL<{YJV3Mj4eFs2EnWoiWY?CQ3
z@{!ZSht!$>!OVto5TQ18jYZ#=#deL4>%~gyKRpcT)wv-N@@ZbzW&H{lfq(!eDKEy?
z^2F3bc&G>s(o%2zL`}DSolgLhz>En^KCw6xuu-4i%hsnm*I$ZcRLc1`bP6kBm%I7P
z)Xw^umL68JydOx96m5aMQ2C@eB7ii)4pw}jND{vaSP|GLG>F$i|8@8ssD;LUS1>ar
zMzRY0)TFjri9x{8kt*P65^?v$Cto?n><H@MXyU=Yf<UkFHI$9h(sIE32BBKPVOB5N
zDT*1ycC+Hm>#2Uv@rjqi+0ECN`MX?g&mb}UsyX5--Gv_G`hyx5>dz$uE0Ou3DMq_1
z#0+P>>16IGw*fG~t~u-H==LL`H2U<AS^<9O(4&I1Kn&!(NbH<!2na)x3bK^#*mdL(
zp=OQ4hnR@nBUG9u?IS;}1<^)I{ND|fcLmhWJd(Vt^^1J~!3-X%xD=fa_6mt%riHzj
z#2pE_XA#cB126s2|8~|6mDWw+MbYYN;L$(x;j9z5B0(1@;p9Znz)^A*@DI>*mipoS
zGe0-siTD+)O7S^D_+S}M{VOswNp2}cfQVgU4-jxyK8)?B!^59b!tA=75%iACPST}=
zr9i<zllC`e(#C-Ia#}|laT|NBe$c;=5V8eCk~v1!42fKa%R0)!_{319IEh;V759-2
zb{q%cl`HCJF)u$t2aU?}R2Jc9Hg!+M@cZ}Z@$gS65k3TXrMUdH8CD3{XKgCHF2#st
z%P!!SdQvWv=Tva|OvRNH5wr)%zP<LS+{gD`c<10B`BA+mCTv>e)pbS_mSmgA`7Dp+
ziNXaba(1Sh=}1NIvBgHcADF}>M;6(|VNlUY?MKTm`w*{ycFvy)3i?~#Gg_V3)8bJT
zHa71mv^DC0r1~Y)e4iR)b)*$Cf--5q3Z<|$MY2TA^h_3C&Sah$kX=qTOy!Y!_lj2E
z9@!JCxFVmrBcB>bu$jE|Av5m8zv`psT?FjhY}438<Y*r1Wt`Jt9$HPK1AL=zdm%Zu
z%$F#mYFF2LP)4JQ6Q873;ZZPb-!wFk5$&%js~$8saLi@m-3<#hX**_Dy4B>LOvh)B
z#OQwN0;w6gp8R{#zj?!2(2e1{>)D#OpwFY%SAy~^Qt?vQ1pbp^*oNXpizM%pIN5I~
zZeP^Ds&AC?W*SN_;+`lhpqJ`|*#qJ1RPy8VQt7h;{8!)ob&f*zq#2l*!RBKnq{JZu
zs6Dekl}4BiTWo=;3$r6AqoP*xf$W_3*stZ{uN8W=1Z&Fox^D$Ltwwwbz_0IDfBgYq
zHFX4EzZZx)O7>j;wJ&P1GjXj9&YxZ`ckoedp9y&4B@NK_5gh6q8*MN{-l0yKhl;A8
z8^WxcoX_mkutp-!*b_oW!rr{9|4|P%ePHlYvLNbIds3XdhEzHCS?fVP=hRC2kuBK=
z3!UKqCc`~HRWyKE85NbP0Zsk>4XYk<Nkf+$+kSfxcTsZ?yuU#H$|g<I>=YRr5x{^w
z(P?_A;$yhd>k_3^+3W2PFA>Q8{R{?(_oBUnN1y?qV_%2P*HHfWI5}U=loD*tJdgN-
zaRTNrk7S0<ke)F1=mFB`M-<50Q7#kQ)K)>6E|ofZ+|>EaK0Qpnz8D?n28$C6LD~K6
z*Zhb8f%7s1K)O-P9OXywAE&VO{=&-Up@KwL@0?J}d!gW_L$|EzIt4@b?r;K;Lz@}5
zL}g7V;oN3yb(Zno*S#z~)pNILo|%r3+4;d^w;6+UbpeJqPggcnG5(2ZOq~#*4u>{w
zeW_IPAGBt)`#N|K0y`y82mNm{YSt#GJ(U&h(L%OD3BSLwO*90^&ny5bu0+v<53mZ_
zn8nB=<w-1#6*1=NU-&DY4MF$RsJ6JY2!T1Hu>$|!<RPY$e0}O}0vjSRar(+AP^jfx
z9BB{Z&Yk*BJqS*Et)9GihMb=9f_wW;)AsG#HJ$72Z99jcB8Ou5TpUJs_jxmG!J;%A
zRUo7J^u1iST)xfos#STuWlh_PN^FfLg#d_lmE}@M{+M<UkX^d{@6VuOBVEyeG=!Yk
zFUy<BVNiXPbv;9FufUR8L~c=rMqqNJG%C$RQr`N==@3cu+rV>|0a?-mhdK?FhB698
z6oa4#Hg8LOuQFC&iiwk05wv~CapZ`<KcCEV&I~<XR(*;)xQq1aizqA<Y1(CnuhrJx
z+kvcE=O*a?V((oncL^BR=@vr8QMdc`x;%<*e!vUHa3&uYg7$l3-+de`dr{*Nwt7Q$
znq1N9v8$(-0X#cd5V<AV%zEqNJM_ye_a*;}R7F{wLohE>FHbtM``lpZkE(vb*4MjZ
zgT9LL|Bt4(3~RINnudWuu|kjng#y8fyA=&y+zQ3r9g4eqi&vmXkQONJ?iO5HytsuD
zAh-m3b3OO>afCk{<WF+$wb#s=nZ-+Ei2MyPuZM^FWpv(YlV&Gfe{T&cQIA6j84Wf6
zg>+Pft0I{mQhao+c?l&Dij77-4+g2?vvCrPhm7bu+%u`L;BOwsWZW>ss{Kdj=A}xY
zgar_MAD=`iIlssAuu^PixAeZdb^N=alZyaFn49X=hL3Uc-|v7pm!)ILuR7W&AbYCW
zeyHDFThS3*E|Z%~1MWK>3S9=EXk9P;s=6;hSAXjs%wr!?tiz*IR<E&ZLc`~yrUS(>
zDq(2LgrN-&$ykk~g-zR9?HDdmm-NjaV@;-ji2nV!g|!9rkhldKg~&&!sAx&$`bO1u
zX3|4zfC2bv8SZ=D*j<0Ue)G-N0>p*&a8~SGRWH67)@k+Z?<d=_O7A(b>?XI)_F`-5
zvkG^+*d&P1YB7(Y#UJ+33a`Icz7xCpi(fX9{9Y~X1e|l(K8StOaUA~QZG*j<lJ(*O
zZ2GrAVjJNx&O^z1of*OO{ThXBpVnNXy_!kv__Z}iZQoc{k?!83aA!*!x`8V7FdHoN
zk{Usf2Z#Ad^}&MzAi)9V-_%k*slhRq3-=n;rkcp}-#ZnzSEWBL5L`Ac+<cgC^|EjO
zmsox{o)P8(%L)7*^63*b+Vw~EJSWpFl8U+MO_YTo>-uKne$_sg{5#J{qm+a-r0X)-
z^g8?FD&Hm~2jv?0w=&n(3lbZIX01Q%JP+Lol|NSW)-aOmtX2+3+15?q^8eE=Y}4<D
z-RDi6d)+;~5i=)xP=iOol?at?3?@r1%MiQ&9nC^f)LAWlx}~XUg@wo-qb|<@MEF^^
zrgZ%;>ae`{kW{u~+CN9>8Pk(q6>~dm$3~lOOQQ39Y=e(;#g(f#?sp@ap<8=DcSOrv
zieM10s?}tw*%m(#{0-Yu-p$zhQ6b#Drn}kEI>hUDCCin0o^*&9w(IryAI)ae#2z{+
zv8#K>cI6)W)HgOS&NpI}sG9Rc5P3~7kbX_L_h@WBUBchs{ir{Aj*ilHME?t0r118?
z?Ws5k%IA-nMo(Br$EDGNMnAxwkp$%5r?B3|Cx_)Y0AHL*Z%L%mwd2ANQ_F2j{HO%f
zDWNFZc&hYfpYnFQ%<ZTSy-cA}kMp;j&4q7W>kGN4rB)nb;PcBkF3x$Vv<*}zzS_L%
zcD?Fbvg!jhy+U@cHx<TwtRSf}XbDD5U?s-a9Dct@(O>4gv0<?pCavza^Luzy4m$Z)
zav17jJ}Gry(*5)&9q*&7@LIh$!i^*Lj(A=(5L8V=U$s^U_M()({9anDDBJjCkY+E4
z2}6NJoZ+BCdUGW6zB{p`qJQ-s)=6B%_SR7=%)Ew$Hei-301~bU3Roc_(Q%n9=K5;z
z-gW8$uAY5+i)Y+m{f(;!EQ5hup=n-s)C8XfU5j7$KjZ~&VtrH{s|oN(RA@g5LoEJV
zi$av&wq1tlAII6FKY6*CVoe8rif)3|79N3i3_?<BOy}byrlvT7$p{56FvHY&|J^N4
zo=(Bz$b(?=s@J;9i@K}Z5=i4K6r^$M*knG`iP^IYd+K~E{JML3XpdrXI}G`;kxFop
znZDa<lY6S`p~=+m+SGUTsJZ>zsjB-}to`b{bHxnHgbR+zfGx0&!SU#O>QnD+F$3hL
z<1FE(Q!`+XpWt@y8f;t8k%DRpy|zQ7QI}9C)Au{R{sd3S!w^g#y7aqRvw`8;@UZce
zW7s`#QjsC)Zssn2{qBE+tW+Lc@p@Z=$Mm<rY9?s4I>4wn$|u7*Ea!T=_zqEDKLvgz
zbgp<+1Uiu@2QB_8$#eQf=LJEYn;+(t<ki-PA_anglWUF_tathEIoglQ%1@D?wg3we
zcW!4cME1!|{gjbni$Zo<f5jj9+29)pb#ID^EQ*B*^#<QUR^+Fx8DyZu-i-9X#AhJ~
z^0NP@P1uf^WI=3S(xIFsnkQkXJ}7L+S(d>RpP@x#Re~3ML)|QM<In354<NaPZ18RF
z<=x@7LQ37KYrIBvo%!548kI7`o038(8w}UNivP@dJ3|tha<Y<rbB~wr*NvO%#;UsJ
zCzkqJM{ujxEsVR6O~yx%qnS{a*UOST9X7CsJ%*sm?V))#t`X#}tr1KHaEQWX4PG0G
z!4Fsz_p`Jz?TEw>2)#f}m~iDHQXHGJK2+hN2k(`xo^7WJM}_#lj`s2KoIpdxN^C#+
zzp$u(;lpUVvM2t$+Id8_pU$P%FX=6@F)gu~hIxr608!BBDB5B8PG<R|vpn_i`o?7+
zYMb_x*Ev63^=>#d@FE5GOz8L}QRvXEjf&Pc15MNb_`IXdy1$f<nsn#ktzBX9Y4AD2
zeMJj-Xp7sIq;ZvId$WK+x5%f}fa`}Vy$ns)*#^gBBdf{FbhG*%Doe=u<2SR~=v%K!
zobCll`rf<;*el=Ni8p^I{8o}(?@~jiTM}OH>?{Qb$F|;GZ+W>iI=1^75nFKW@>$%1
z5R&?Yx^Zy1%avX#gj^$J7??~EFj7l5g)2U-ov)g!v`4dup!PT^t-Ts8536mThUWSI
zQ-FgY7GD;JetLY;{$syfIR$qAHz%ZL*gmm$6oCrxwU<m2*L*q+2)ZdyJRH=2vI#kr
z4Abj!j++qmpB6ztz|DK`yk?exoK6G#6RFBL<TO&!tRaMv*o{K^=qGrQ5H6QY5nV-*
zP$UYXLEub3?T0X7hoo;`9gev{T(_TPN7z<tGp;`HySJP&x1TLLADN@JuP*r5(#5^)
z({~HGR}RANO4XG_VJy(o+zsj7s%!I-dsRyxch`YRJG?9CC^to_PZ(nD$nov(4<5bc
z&zH_z#HjIk*C!r5!>eVVud$p~VB$S8OJBbJOxxvd+j(MJh{*SA1*&t#oc1rQ8DD1P
zmnJyvi#gUG)>sN#*NN9)Nw*5(YaECSRNv~~@y%g{$2&<82Wg(7n+N-Le4nx%`a{99
ztx-e0u?ZDlCBi(!-Tv>bIF=BCX;$@&(SH?Kdq1c^u@r@p`c-u!WW;YT1P*x?V~?Q8
zb+j3zhdfDZjq7Hit+~&x#dj51mX_>uZJRB!g=xj6!PzhE)k*gOC!$mM(f2aLx0sz#
z(+@igp*vhM`@$EwbQk%^eEnRd|7j6Ah98s!pU$5kY4?cw#c7{j6UCX0wyUBe4~F|{
zF!pmP!W)nyHuhl-?_isTauc`CW`swNa49@^yw%c(>9t|T4ihZg73G+*_xw(vuF~j#
z2+>eeuO0X#sM@n#>;I6t!ue*M7$&_Xx&166Glv@fKGS{mT)<VytZ8u9#+U@T^y3iY
zRrVKW>%`)};gOE>6vqp%9@4Krd0i)S?1jj&qx!KLmbxUu#q1EO=WGO`8`Gnn7f+sV
zpc$we&nt>ot_AqZgnslDEIj-UawaWG_n?U>nM#OK_rSt?c8jp<GelJ~e`WN(I5ftV
z<o)eWS3Q^kAc2KrCM)iQM=wO3w0&~S7H+@&fsZQqsjz_hNsq2DefJHFi=N>73N(+E
z$Zq3?ka9A2L-+Uot6N-uSA5XsO0@_7YL}oP__SJG<DydV{br4br{I6v2KP36#(Z6x
zfBLTr-TZ~PuQ{D-jg2;+cN80OL@B-q8Xq_(`|D@)Iv-mv@Uf&#RktW5_CPWXTV9Uv
zR6T7h$6-ew8u{e+&jNoloWu91Nl3i+IOMo(V=#`e?R0MG?t1$iRC93D?%JEj<2v&V
zyYiWtBGkAhthsWv5jtPQRq?lT;p^|Q>plWt>|XF@PKrrfFb|irFQWD~pDNEFVc|@)
z@bW@^^X?`M7T0S};sivge>kXmy>8?0xk&_q<vqa#eBkc$)9)d@r?<iPocIrXfHnOZ
zuee=H2%G(?QTYbUpnjCR%FJC=*tPcGaN<2dK|r?GhR18T@G>W_bw4wQS>ycZ*D%ZT
zlaP_Rsmd2s`khF84)gkDyi6AOf&pIW8{Dur#FPWUBSxL`{Cf0ZCw{FdDxdX35sVT;
zW5GIWAIuG=@A{78DxL>|rcN`?iP!2A)}Xg{tJIHX|EXx@5sbtBqM*&j&geB5E62zE
z(8)W77RZwr5T%W~CPuyKk`YCrbcE@l0?;QJSC{+mj3Vbfy1ypqtagMRQ{O3PRQR2}
z_^AHUT@ISmI73R}B6#4TW|DK6&AZlSB~|&ye-@P$I4{6AvYZ5RuH1&UIg7VhiUu-;
zvkVMDjTP@JKHNLpS!}($d-({3o?+035utSSuzc8v+>OETC4<aD0;XgsO7g>khce!)
zPC;1r!$U8^<mKQICqb3fbVg8_2K9S~AkmJagE(jeYK<Gilh?wtpy}IaFDVepQW0mO
z<u%};mU_)#^B1+_&QT7eMleA-@jx?4x(ZCWE3sWfo2FP>5EyIMkAI`F#>f&4R}DDn
zxu3r5<-@`f=B=JaQg~FoYy%+~k+1Jc03lx<^g={}rQ9yyFMGNso#h(VOC;z*fi|H;
zdkI>T!|1pVJm~<j4o>T;)?h%#*9lu%&(yoFNJ{0;S&@bZARyE7Iyq`iIN6K--?io_
zN{3S93}NEtYFgvu08=%FKm4a&RW|9w?GY1n9kcW6+8F0DmZ_XTD>Lp8PJ^-GPV%Yy
z6N-n_JWn1LEe@DU8C{;vX)4P&UVM4*vf$}g6JFlk4=U8m0D<(lX6k*XK{RxZ6(CP^
zJCq>L?adht1$Ltg1zq(W5t33t@RJsZJ7Z63OP5}Gw#FD}n*2Vx$?`Xi-nd3S;UHjY
z7Z0a}M?|>IM-4QRElIBumo)|NyP&?x{xp(*ol{S4@}%3Z#UaQTi!in;0F3dEiBviM
z^~fUjoD|mewm&2pRSgzDU1nF<aIFyC`H1er7~E+LffOe8qR#V%+^id(t5k15YT&x)
zyvzt{%PNPkjDY_0yLY&C!w3|ssE}#jQKioc7hHpKdfyEWD;{&4FX9U5+>UIskG{9#
zyl(!|9&I?@YBHkL6VNHrO|fYLB(xFRY?X?JhU35^oFY4Tg7ZKglZqW}@1PG7i0%Kx
zFCWG=F;}Y@r+sK87E-Pcf1(IITtvx+(@*_3hN|y1|EgzM<!(lpZiW<E%SIMS=D8=?
ziuc%Z_~+$g`zF2uN<BAa*-E|)^nr)~!+|GM=BOc?r5~Cno|Gq0>g+R}_jhX<+Dq5}
zAtiVG2Xg`iI9L3<7m+Ke)Uxk@s6^8#vBzIPQv{M)YYnD&Ou^t?ol2xQNjmH-w{l7U
zco&o3iGPKCMEU`gV7+r8bp_hG2iBO^LWr+KFJ+Q@%gtjYr{V3PKwZ!`00|OwjhY9b
zuvPS3`VTNRQ*W&_4e4GeC(ZQ4dxca#)PFJK*%tHbP6NKwUIx%XF_ah46JTMX3<0Qz
z&MO`kQN|W3atv+Tj-%a=+a~jrmMRMh)g|p0`W<(G0y1-GbEflFh0DQ$y4$ZXI%PCI
za1LvCIqw^-KSSQF3mHKOBL1p4+zDYeespNLHN;sqVw&ZSSw14p4=L}wD?liCg7eJj
zr_SYIUim=0<8o9**#uKx!*CE3Y%tQC^O%KGl3jEGh8zlM(LOcpE}Y$zGbPq?94|G)
zVi(47i9+g6D5gl13dL7>-r!u9mz8aY{^zq{Jd>Kkn!}xcK9`bzeVcMU?-#;Ta82BO
zhkE^@trITOsIx1J#hah+-{vf>KarX@o3oZG`&+gh-I(hG#*w7kM`2s(ofopGDAr^W
z+yHgCv^OEf)3^8LfscP>pY-Ks0zbyiw@&-wlB|WENNsjZ72$p}6DOLX)+$X(OTD9^
zeZs#5(Q1NM+#dc9?Ecyg&%-{;!M%EYS(y(&N~Ug~RzXJ5agiQ0*aMHb$o6&g2f+bA
z%5Elaie)NNB9#?{<(0vzslWuRK!_0^w`*c?FQlE025nwTlZ*f!Pzt?jM!U{5Y!Xqr
zO}=smZUanR?3i=u!nS{MLh~sHC&d0f#y%1#Kcuxxp3Dx2K*8<&iBuN8>pvrm+xNjV
z=?FRFTMb<BolOq*SYt?1$8VBpoD%atGB`4-nJ9lC4a@_f+*JQjm7+2WI^EBsz~zss
zv(+d;m3BA9Oot|`PW;*1Te;7wvTG&Oq*UiR$9#aSn8}CKB!--9+Hsv-4(&buC}lx}
zbNt=q?(cGf0a1|5k;j^%flm0rC)8Mc9hI9Bx*vr4qC^YChT#$huM9a7oJyh^KMT!G
z?YJL@dZN99pk$rVseU*C%1k?g(8!K|e<T0T7`RT3>`un2>_#=O*F~=K4G;G)OcGZ=
zBuG1($c0D#NYQmPWf_&V%!nebU0^PV83`@xh)`@BDTV`Uru~S6Y*cC8Z}*gb2L;R^
zP#^JU07*CYnSTwn7Hdj8e`1zGrnQ*+>-~sH^n`D5-qLnI&fc+VCh&iqMnQ}i#sIp9
z6xQ{avvti*>&P-5+KVo_GbyPv)X@kD)dfYhh?RY(3UB<jpp7^8uQb2v9E=~Td;Ty?
zmP{sE016mArBg&gV!=-d;czYZwLz)-9o~b)cLXuYK{HEE<52F@oTg<=afm;YlOpd0
zmO4l|C^=|8fFAxiSP%USSb3xSRwR{j6!_n9;C05SR}_{1tSR66njrvW`HUYwYEALr
z94w2lzT*EV3Xu}kL*PRw<h5Suvh*Fp=7{HC%u&o!&E;|j1Z)P%>y{{ajHi?irD+{y
zA2<~_F=|hE*Tj;{>wpM+q~<=%`4k{|Q0Hq>dWSf4hf<cC1h;j(md+k2TyTEdeR1y>
z@i=8I0~d&Ik|A>Cka;4N!M-TEqF?l#{Tjbap`Fd>#<|Qm3kDW7Ra~j>hojgLKk4Ko
z>=f*JsRZN@#+)J}Es%NtuMhR~8}ec6KKVx$22nc8V<J1EdZpGM6(rpKskWBn+!FM(
zB96CbyacDxg%1I{p)adxZ)Ob8EeW*M>8Z`NCWyVIlCaa2pmDfxQ=&Opsj_b}Irt=V
z#`$8@1aqhHHOEm!0g<W3)I8o<r6)O!IKxqoC52Cn_hE0P;V}Q1tJ8j0s+Fqcwap_C
z){$n-!wrbkw0ZMnxoFv*>A<94rfI%2HmWpi7Cj_BBq2n#v^(bXmi9vzOA8OS_mjsJ
ze8T|M3$d}t%XOsORKtndhLWxay<ykHHYPU)Jaw9aJJeE}*I>o>I{`VkQ*iq6569~Q
z>tC+R&<SUwyQF>k5TTFTouSv(N7^)2>I`Mt-co(YL8IdBPI?%AZyS`V^t+NS2aTsx
zpO^~6Csg^upF4>4hnVEkAG0(Qd8Q&$yQv9UXZ-$4+xQ9V=C}w$sb{7f9I|g_d>J2|
zqY|3hmm@%pWKyVNFV5%`)xmAB@GN7`q_8v5Bm0>XTpqO_vllFz1HiZf6+W<BaTRna
zG6c}wacN5t2GLz3FldKG^FHH`$HyjPktAa$X9_@fNwhWC+HtDiGosLUM={r7#Bje7
z?QqCmtiOdwoU$ZsW@5!R0NxxLwiCcvo=9J@<9nglHjBweH28a>`O1w`%fe;F1M#;N
zVpO>EYaU!zTDjz9>++YKW)h020)m(U!wJ$Fx;$rWoV$kW6vtk(QIFN65%Yd>)4tE$
z7jSAqJbqpEBBG+ho|(L|&OR?_V%-mtb<K3mcFifBC<o2<!UaNSoBJ?xQcA<>s>{~#
z+RUf#W}gzyWKfELERqZGF7Sg?KmUFRsbtqkBTeLz5%eycUt~W-eu?G+{1hBohDA90
z3^g0M%`p@=P{eV@vkbAUq%V`4S(%#SnB|=l?4yDPqNtb3&hMqj{qXZ&z9elRxKqm-
z#+-#z#Z<+dd}DTL(_I>X2xV-3Lv)K$wkn1Un^Hw~?6?1aC5#N&okCXmmu{I`rhz~~
z?mne-6{m@%iL2nMxano=8my=an>N5S-okwP)Z&s1rCNC|Mt7Hsq`zajW3e=Uzn<hT
zk(obrN0Z9Rh{9HseIZJ_i70;ZEdEyho>u9z{rH<cltGvepc##=Q2_-6gwSet;qOXm
zo8m+T79i;b$ys%S{Kpf7Mhd8~bi4nv_Mc{6a+$z`OPP_l3zKMN^|C%9&z&29bEn9S
z0Mm=`Qh)9OzSmM`Ne;uNUCuk^(2tLkeo6CxYqy%xn)ERla@#3z8(+mOe+HVCaLjVU
zH(K-7uj7}4Xr@uQhmoZq(T>NTV8vPB5>Ep17mgT6JB(!%z4w!aSt{yj_GH};A%@=J
z@N=VPRl7etGlo$&x?Zd5XJ}-Adgki*he8wmNm5wwKsojqnB>Ucefmx}4kuUBXH;sB
zWhb=4C)!8za(ZerFktZxrQ>){aMiy|?JZT<#;yAlgEHKb#Y-^6cQBH8ezBG@yD<G<
z_l3ZFScfLg*Mh*+M<nupwH>H=J61cdh;SQeHvOa<-!u;LI+V*k>Mx+r6nFYgUh&Mb
z?1ald#15mVDAa8NVmioRO2_szPc99suWk=bc|=f8!y?3($6NC=>6w}exAqAQ$Q~Z)
zi#kk5$0auTiL&<NVDhA*`R7s*pWYwi?ed|>eY-baIlO;k&df?_iRKb`OX<s4LL;ps
zy`>9`$f`_f%bXcZo-j)F@aDDV|9H3(<co3Ymca7^33Xvby|uKW{o0P<PePcjQ%^_x
zPm<(2O9P^`Ge{}C<>Jc%-%YKc(*j>If<`$}$a1AtdsWl|HbcSVmbBEnZYwFq1HURD
z#-%dx#NEmVv+AikZSLW_XixbQBSE`Qo^dB8WC4&@@c!%QNs0?_Ux!x~Os-Ywb5=B^
z%IES+l$6c08zp=D^93;;2n)d!>Oq855LZy3rWeF>pn_BId}*>HWtAQ7d>x^i;Wg5%
zPwXWv#c9%Q44m2HQulIB?s5SL=XVy=o?tG6$u_R1pppT|c8FwlXwvdOQUlKl5&uJU
zVe1Yd)C868B#zl9t#r#(vVXJXJ!QlGZd&(V4L0p5x~7&Uc|u~27E+x5R3(9E@tF>Q
z(RWJG53!}74`C{_N^O{v_=le}NRJ_**h~k12$Qg%4Phvo=jTlGS=!=CYTe3y7X1MR
zGK~rpRNy17`2mvNCI+x)u~0zTuR?m-;ZteaSM#Tqwqll(;{G|<9^5>081vZk39)T-
zP#P1OGpBT?&kzr;^8!AZzVFb$msLsmJ>@^s4t{L}oR)Qptrhe2I-K~|qJ%p&$75Jj
zecDjG2mdaHu!Lv{m_eFOi21%(j>C%BAEYzgvan*mXdb9qvNS|q*7XqQz!BmE^^tyM
zZwxF16AzttB6Cur@91c;bEjJKq#=4w%m>$_=(`w}dN_7C_0elApM|0+<x#fow4$Wz
zG?YS+l_G&ZgH_SAII@6lKV;Op?~|UHK>GtUFlr5Y?^i<}j$BD8ZSv@l5kd(v3{oQ3
z6lH~6${Ll$OxEKM@^I$l*Zm>D?x$Zvcys3SHicANy4?yV5>l<wM50lt`tq_G??Al^
zBYs^x|3`2=g%n+S3ATXYa(}tvo2s?MMV2%3mov12bnbM5n4HQ98%!ImJr|z{QvgWq
zCx=piC!}0XPp?G^3B{OmpG#RPnd@F!m_uZt|JQf{N1rEQk=Ws#$FRD`d&Uo=D~q(F
z)qlFH9Th1&WbE|<;L`B!M+_h$$JdGi>9q1lpEC2$%rB)cBW@&5a46&}wU18aD7?Y@
zbL?~6bNus-9!ci~VyySvO2Ap`LZpg85H7q%&Sxpk$v<Qoo!S!vDB3@h8bvK|K~0Ca
z?IsJTdR*r<0zz@1_KXE6p)yUsgE+R9_(^{VZeszjnYhl~dC^q>ro))iLg?&Of4+Mu
zq;wrs<~IDJs}yHpk!vN7|KXCM+jviDR>t_cDETw+aTI%;cg{EYR|rOG_&G}EO-r)J
zr0qsx=g5R$9JSc3>quekr}2=2g_%t*z(S%?8LM)Q(KSs(b5wzla;b13Wg!(Ijsfi|
z#Op_|2Lq5~&Nc4ryE*;dwsJ8w-Ry_b(2ub<5R=}N4u<&P9fLGPu=)Q$%iUXe3BAKB
zdD*{QB+HYLN8yk95I~vat!1|Ls~1k+23jG&M#rtI4-$PLJ#k1}=*W^^JQ6jLMnROj
zA}uLfh%Jcua7s3$GZ}yyc~PfkZSKbL#|#6$NHTog2YX_+?$+V6j%B*a0GW<y-_o8*
z^`1(5Mq>vqsjm3bX@T*>LOB+&B&T!N0lGkHbRbh1o4gP6^Me$pG*By@<-<F5#)=OW
zew)j%g5{S3h^Ww=u(ICJKA~4NU-9Ssq$FZlC`2$?qky*xa%s)M);sFxJE+D0NxeB0
zKrMK&(xHUf+Y~~*9E#pj8C642T1hSQ`Rb>zKjwekw9G7OI}-3FBt>L_^hBcjocO9T
z|D5=8N@-HDFaX73vI@j|%Cp*k7AF)j4}k{Y=2n9d*36HH<4IeENp`%V0DCf-=?Fj)
zI<X?(OAL(xI32P2ZxU+EgaDk9<TJt#)u#5j%Rb2aZ^z_qHI_5W@miONhY)zT_rmyH
z`qvUMP-d$%dDLx(Z#6~W8n!R`eu&mn6~3uebNm`MND_SYd**J#qk93Yy5Edp%6i4P
zm~B;^n5&BYvD4(=!)ga`Kd9zO@d9&(?d?Tsc_Dm-S;YL|3<e8F{GpW~7mx@QkeDxg
z>P<&2)_gslTaPmdi-++|Hb45=gi0A;+k9K$@FVCT-{-E&E(EAtO8c&MN}Wp65HSdp
z0U{}*0965WlwVrs_&E{g4JM4R{6M-#7Gv7X1x!Zg*XPZ<b(srf0+No`(FznlfQL9W
z;?ZvC?L)`^<auT^8JDbmjVu;q{A6k<UR~MQj$rk=(e@JmJ6-z+2<(ZU<W-nr-DlKS
zz(#g&!8_Qxrda$XnEk0Af|H1)ocl#B-e!_{__9wfy|!Y`og%gub@ZAXj}qT7LxZUv
ztJ{v<_DEt)Zm(EdG!J%u%fpWd(B{)GJC~WtA7+}l4Tp#1pGRb2+t8)P?J*iUy14~F
zL|wS`uT^1DUtvLzbAWh1cUs^C(4%|iX`iF?cp*r=>tk5T8fnl>p2-=w#`&LFZOYL<
zJku`dgEwG?(QY54uK;az_2@$GPEFyi*K<iQtNKfD(0a{0L}wAhO1C<21@=wEO1^*>
zBI@(J%smu)I@5}<&3vWv+NS{#-4q1DZa5J7>k)Tsu<J_5)%}ruZzrs~yVLIjU40J>
z!*bV%F7u6K7{kXN7yfG0=bZY%yc>MNJAC-@c{FzKendV1<MA6I4p&$!qEGTAq!&F8
zIti--gi2NSt;1FN0KK$}@CfPkMJWrG*E8?0^j=GP^kdNn{hs8!sj#q}z>ZjJ`orN2
zHV7d02ysXX&Z{9Y^CY{-hHt?(SIO@Qj>|XTYe@*_{_7Bely!DKz$Ac|W9wcmA<OIW
zZO)v=c%9c#PSiQDd=mTkJtN|Xdu2U003<%))(-0nLC*|;T>CF{q&y$xOgw*=3-ss;
z>_k}Ze4oS?Gq;1Np@L8y1>QGxq*Mquk9gFrm{hM}2ZC}vuK%ImMIPtN)oF%`N_S5S
zc^pEbt|Qj4mrT8v&^q79f|gS(+!I&vytx)Tt={Md#=SuR#6fEWYmORPGc<PlG(j`A
zH=*1c!IxtY$g2+bpp(Ul&Du&4?0d7CacZI*whzbbldK}`8z%VYgqH!*8-_SEM4oYY
zhZHUD$LkR+t&l<hp13Iu(Qm+|RHi6hgTyb|eMDRjMVhk3TlCxazi4kZ=HSQSHgo}{
zfWG|*Afx7`6un<e5YI|f#0+5bZ|tLfFJL4rOcS#VJJK{=zI#!q5D-Z-m+)9O#cs@v
z<IViA)ZP$|pSX(heR&K#ntS#A41yZ(bJFA%PgmOtU9@%Xzi*DH-h6wr_9FP{YJ<<H
zZ@VM*obR>W=<WASHD4>cEK`rKy`q{{SF15;2f_Y%>lsa6Uld?bi?-KPi<gOAEGU+p
zoF@R%+W&x4=P+`&Q?`aJaQ&*&{r-V)xFUI61A%(tofqykR#KdwIUir&%szy37k)eZ
zMM%TkFC<lieWXzPrWZVS*V9ENzEo8;y(@5C^z;FBE-qpykwdiJnO>N%a`VUgm6Ohu
zzU}%Ls6h_as%9poM=bl~caf+b7qM!xYJ2hZt$TQwq6PvxN_?>Oy^pj9(4G+Iux>x1
z^?U+d0H2#X5S&W3_lz=~5XVL})$osJ-1E_?)aS#@lbRD8^ke?QCy$Wowb09h>I*48
z?Azz6)998{m(u(<*>s3*d0}`&fI4qo`_`rO^ivPFQX+OFH_nwR|EY97WZs16uvMjv
zZZRdIQ}lOGQ$BuV=W}B)j#v+gvzf@5Rb*aW^fd<$v0Fpju{P{&M=WL6apwsw^3jb1
zI=bBy4Gl@lq=ebW3M}AG0bn{g+dn3n&&@vYAoB$0b2R;{5U@qX+ce3f+{zuF$3<5U
zSPW<B+CnbR^tH_eI4b}$i7kMDPhwBp4y?TJB)jqlRRoy(NFmNOI(cdoE&hF9KNRf-
z-fGxmfu?O|92tEfrkV1J2O$<=7^`@`m5coriu!d><0*YqgZ@}ze)7P+&kMtu)bGB(
zZQTrN#e&-N(83be^q1ga8=R3|2P?8|AQ2drZ#X=ByRiqb&N7F-BxDfCOtkpk_v?8R
zlW$gM?8^D#^+EU^`jUm7F80n6aXr>JwZQ)QH=aM}Bh2+q6MzrW{J<y1ClTzu^D&>E
zyR>g&bmJ1!hcOaQ$!f6|J<`}}!02JGN3i}HMeJ};K43U(8oia}$J2Z7-qY{-rn&|7
zht<FEWxexgSg&lX{GRz9d|K;|nE`OT@#zb#B)Ui@yU2VUtfAiabR=KhCj6?aDYkmE
zJKDLj%kN{uHN<-JL)PZ036ouKt3hAZf#CXIuOwj3kqVNw1)sq8=N9<A2iW3zQDGHP
zA`QDM;PV1|JbeHwBR*D4U#Aa~bOOhzM}kOqv}F*0RZWC(+Zv1A>(m12y!6Tuk7|l>
z+tR7qtX=6QfasKv)c)f_2e7FFct}$vH?43%a*>xlA|%za6GB2G+6D>1^Q~Cywvf`_
zKRG<z3${X}JeDvjUE)6l0Fh&@k>_(fVWCwwU1nIxOV3j-pTp!j+z!u+&en<)Y3z9H
z`8LEws(YS`|M_y!J9I}Pt|eKs2K0qScUq(N^Lk62x2~{gl0fESQo<x9pks*G*>xa2
z{xB9j%Xrdh9<R;3N7vGRz>7>B)cQvAJ<4FpRIgxe(c5ng5{HzOsaZB9qU?5@uoWwE
zmd$WGp(WSk3^*<&DDyVw8XiVwO4Jf+Q5~NPxX^+j{=g%z3<j&j>D#8=s7GiUbzXT_
zKHLdD-WmoY<P~AC3H6p6*o`QXB^v+J4duP@1|8YzkM_}L3!Tj!-=x+$T8SUSsy<eG
z;hIU^Aabw3$gW5;mYXc7<-oQ-1~f1{iwK69Qv}v0u+z+eGVOW)GX!Ve()#CpZs`0~
zqZ87NkyE{;A=^0*J+9l%nQC3=ne+<tJEG-T>J9h-`x1~$nfk^A{;K*KJq(uj>Gy4q
zglgZzy)m~m;rAG@9M_sApXS5@Cd_$Bl{|ZFPcX|hG-G#Vds9Lx!7Fs~p=9BH`4Xd(
zhw}LPx4>1hDN|?pjYgQzNhgyS;h|(LX+#(19qHlwe0Rk|+nV|#kJ@ef{b#ECFH}E0
zC9Qc@Z3IKB#r+<!3?dtYv{V6@FVOz~A5Rez;Zz$;wEuozqW-Au<7n-y=I-vyqwC<~
zYUO6>=)&#oXlJLcj>e9LX`LR<^xF=4gF%2DEo-UOVk1lgjF-347Lk6&i550wl~zN9
z19+7xUzE?fVkAX`HiAa%z^Nz65j9dU6Y!Z|oFxugLK?4I3`YMtP4~y&8JUDzs9<&0
zdY@;jnJLFCO-G#`!1wbv{Kay|0f9uyLO2@B;ZT-(;vbI7vQBWLbK_|KMOI8#KhG}}
zS3QU5<X7aas?NSayH*R4G3F&r;seap0j=*VZ<HJSXaYNCi?STPg2ypCN7omikI%a9
ztM|8-<5lko)BwGZ^F+cFTkOcerXGT47!>Up-4`$aIpChwp^sk9UC$Rke_UL+co2ay
zp-;!0x^z51#rW96A{=u>2E&@r>c)gkX(`NN*aH60UcEVC%)`CA4K^|pzrpikCeW#=
zlOnvUs^d76%R58mBgF>ipLiC+yC&NU(FGkinb6wp&trGnlyqPG&9Afo;7U}rn|%`T
z*m{nhrzGwY7NNv<hgVGZDtW09XIB11YzBj9Tf9~U|9JuS*$!NRpUD8$1-<jV5I>3f
z90Qts&OwBxSYL%Ei$;k&WhAA4+^MKu7L05;+TQvbj=jhXL!+ba1c|3HwNo-;!$*Lv
z?1F0Njau1eVVYz7{E_)}HE8o_gzDs<GzmU0NXWYm@~91a|D;s;Gl%z}T!1sqKcI6o
z$^+7!BVf@}QFk)dpC#k6R{3*LkA=lLJDwb-Kdc^DSbF3V#W>+48Jh@dP+)Bs)!j*q
zt7x!dap-7Nd1YXv7oxPj#EnN|#Gr>{DpGMIn6fa{cU|>cqe$9`r&D?j3tFcbwSPa~
z{QX}Dual0%WSz@*y;jM??>o~qBjwLW>7AYBB8$^iVoNMW=YED&xC}9T=R3NHE!)D6
zM6t;#lXz|A?DxF&T(flujruGN-rPF49Bv#}z`SdY9f#2LkmXCO)>!*i_RK$T$MTYL
zHx@&~;(w)Ela%paP8LMe{Cb-|VV``gVCgL8$=}|T&I%xv;kU`0(hoGt6%nf7$S58A
zXG*s*{yFgGke#)@30<_RBEMh|s08S$F<#rdS~`7B+{OgE$V?Y@YRKh%$AfRn)uH}M
zda1RNZNFF;dTxe({#yd5q!+Ypld-!m_~u=c4*z@7)^twkC%U^pU~dOPl51Pi^|LXl
z>KOIwIY#UYyf&y-l5CWSr+(zwW++Xw$lY*5)H8{i4Am9nsc*KIW_q9i`RI)2h>#K&
z_2<=L!Bnh2`4g5%0Hrj#=ub-2X7x;GnuK1)qnzWiKk_?Td@r@&HKl3a@YeG!`9o4&
z4N?b<D)nxQ6(^qm>>2Ee{8G58!0xE?%fYB3w{Orb@nJ<~*eroewknQ&bGYw|O;aL3
zPwb#qt3e3)>2q5N+t4VUcvPLN;(;?4#~1Tvvf!T!)bjKnMOEJ5gkj<Y9Y)R2z^g?D
zd@Xg*lC-OYiRR!`)-E5CwFkcl4IW#43$jf9ak0`l%dBot;A*$~5MCf`vVca}<H&3p
z&sR@1xP1=fnxJkZ!}&V7!%v_7+wrTi=evMqiJ)+1-i8s;mxfstx%QmWRio*Qv_GC#
zID1yMDgCmf%j%UkKBAQn)Z;g1<ow9@dpd5^>dgKJX%eelX@Q7UxX!1<f;Va!Y~#NV
zT;|g4+%|A;%K3@Bl~il8JSoo$FN?3Vd;fXF5cIWTwq(C6c&Ugrf(p_F>u9XZ^D3hq
zkPD2utC4W~#Nq<e<$PUcEj2)A5lXp#w9<3iz0q(g$6gvT6q``Fx_XT7cLF?D{+@D#
zvbeeMCrmd?SYW)vxx55UQ(cX{y7!A~Rbf^P&tYBG7dyHrZ<FmxB>S!w9P@lhG&)CW
zLl&g2fOYdDD)Q3^Q-UBqMlneZPVn!H0?=&7(#)^rRNV$OMSMeSG^R<1_?rjc>awuc
z@tFbUb%sGU8#9%tu>BmJ%xof2b=er?e1U{TP~%xm?tE<Jm*0PCd7TI|aKOd$%lU>5
zqm%|;T*(Rt2<myasFtz;=Ia%5b@KtnORTKc^VD?F<UUw><z%GW?8}KqPJHDe+9vt7
z-x^Oo5WkgW16KaAG$Yp#T`~UDWgP--)#sL|RXF$f!G4!KL})(!gOnNfu<9%3<(IK2
z^55l#7W|(GQ@)L;Y3`cB{9GbdOAWbG7ccj$>H=>^ie-l>&pEld7z}F|xov)O_<wmO
ze7`yr!RZ!r$32ngESTl?JFDrL9TprbYq4)7os!3eeXe$sK}#=*s;J`ZE4<t>bDD)s
z1ya3vq5Ny3Ed7sHxMo04OPggp+_FU?=2>vBS7hR8Rx{I=6slLFiP00;2_Mc^3i7-I
z=a~aZE5?5OdqbLMTbCeB*O-}~bLMZ6BDx_5OTTW;+Lkj^XsC=m{-E<~)Q+{Xbz)~*
zc&d0$O4`#yuxN0=CU_@+W|b-ePx(k0%9icy^wyQh2tViaF^K2^SAN=*c1x0T6qjMn
zedx%-28YNsqtcRfHjS!1N-GwxVG$ILtwFj{>PDQyG1rpi*wZLoL7`?ZdZqW0wRFVt
zu(&#5)M@bZ>sE@X_g`wrg*crDN9>wRLyh$gR@p>URIT_c;*9}T?8+UDg|<yGP8wy-
zJBqGfakc+^z4Lx!T2jmK*f~$NoL3U>PQ}?^**x@Q|Ke4##pSG6YYs4yM;4n}ga^0f
z@I%nJ6-%Uwd<xI?U{u);#ZZytC+m8K(^Za}BSTFR{tA){a+LKwA!&K{wW#+fn*dvj
zO<B9Ym9nMM5iLWOSHFHu>=x6D{;ZiUcY=bdaUDwZfSJ63BX$GH6%HRBa`Ju&kE{#d
zQd@^^h3;{2a^i$u(^sS!<1nMdf3fOn=c2vJ@iyqXfG>f>#(5g8?CK0PXvZmn58v7L
za(Z<tlO3GYM(3KTZMG;RbF(`mE<WeeVu17Y$SWi&Hl1yYbKoz&zSU~jSnUmr_JLjk
zLCe~$EqKdm1euq@8Wrdet5S<EVxK0(Z%6ZZXtgdB4LyZi>(O4U??8O$2VaZ!{rRpE
zeaHAI-fMU35AOC(uFZH@ee29thO5|!aSSTG#)U2eYfp*wA;g>(Yu{{H=5cnT%son%
z|HEy47beFW?C{J3Ec!0jgMW2SXUr8e$)8|tf8)D8A#N6xqtKky<`6e0&UiKPfpr89
z@%v?s@c5eHw+nVHD)Ol&KR@d`ZN*^K@nJ}zDX1j;kYDVlT|7})Wlc5GVi&3&Z7wIN
zVW7u4K$`I?nQOUhYWcT}WnkBYZ@S5wYf$(1$?kDnv0=aH#IO4|B)vVvG*sM@;wGJG
zW#SwuR`iddcaoP4KHswyTG8KTotlNar-`S=oT+T|4<oi+b$~mGd~f_khgRYN{DQ|V
z`uhm$7uEYg^<X=@9mW1xNi~MAs%0O4DZ!O-x(AtghE94gJQGgfz|r`9yIGlG!LTh0
zKgY9)Si-9QK-(Cw+fIw<uVNJijC&Pn<PjC!4%o*6M+oEHhdzF{!|D6+9^3Kc=pUsA
ztaNEA@oWlA>jP??gRJ?5L#K(fXY%#reW6ua166?4XRJ5j2ySuAR8_{`_pyYs@<c{c
zvR6FP5<BBL7?JAvm|&-Prg${8a7^&jfTHESRB4NDt;?HyObOXiA&nh0o-&b=TN*|i
zbQ>4p*Hp}atKz-0pYk`${q5uvzT>i)MgwXJfIKz(<y;FKDBvw9;xXVf4)X&ha{Ko&
ztdJgC<*-hzk`YJPI_d=vzx<SLc}cp8IyxmgkfMY^Qsu^rSl4Hm*73(#WHC~B7Z>fs
zbAF6BW~el}PlzR{h813b;o-2zwc}RqWT1{*;H6u`r+s4WS!T_9@}PC&Wmi$cOMJ_@
zkk}VDSP@mSXn<oDWt$jX+A&?D8#QsJpA?2U*vxYDm%We%+nHgLq@vaH97o`V8E+V$
z7qw{xW*>&^79l;biZ<0Mm6PFzkg`elhz=ejwO!3tPnma$X0mfF8jN>(r>4y}iZh>~
zuIM<Dufk%Bx9MO=8~<ZRzkoaO*zsp>UN)`VH;y>$9&z}wnR{wjK;9$~lAaW|2p}PA
zJ7UN24<liPn1Dym{$yT0CpUYFyk{K_BipR&M{3LDtq6M&_T&)|-g8h%>bs&q76)#K
zb)L}n?B(7Lnv078aVNQs#l>@3{y!U>`8a59hsa8oT=~@5y&6U?eo&DXnw`@7LR&DV
z=kZ~UY0P({Vx@+?vx<|^W%n&TS2~GG4K&8Rs**zY5^iRUF{#wIw!7c+fO`Q<qeF>y
zB`p5s>{L^%MKtn<m9UjS90rweOtd(8v?y~qViJu?c3BJoDIkW3<rN6ycs3tHbudCj
zx#qO6%kBI|vos6rbn%lh`MVYG5LY?~(IB+*LkKwFQLD?|Kdmt4IXv`Rrr$20;w`v%
zmaYeHX9GtN{R;H?CL8UUh~=&FaM2qebHUH=^mqn;E81T=-T(A>6g}|2#$Jxez^fUs
z(_{F?l^7rOk@x8jh7OMR^1D6;j)-U5t-8~)O{y+yCwB92mHI}{)$@)?l`SJOgT-x!
zdM-tdcWZ$c7)$%T>dt`$ytan#F5d4?H^r#T4`-&mFI_ooXF98TB4lCHBIX@jHOv5+
zY`x}doH4Z);8!?<k<#<C#R(KkSn_6ec$5eL2{Uog=!Zn{ssvdk8S(JpA|tHZ#ILBB
z`EWT3yPIBFjQzGsqkr?2L8q#CzrNN?CxVdiwMaq?fgbhYd9hn9uBd{znTENj&n@9s
z-5%ziuL7Q1Ub+mgzw2LEjRKi8F&s|gi}N{~ByejOXHLr8-?Bwb9<wz?o(1O*6pJ#5
z3nYA%cu%ghfsscxh_9B<sMJa`P{_2KO$z)ckfeiY-)hf(94uD_D$3BdmXVdGLyP2Q
zEII`F(2g)<-qh#al7W>3H>#bE&%b~rw!P%F?^Sois+IHU-H!*#HvBR9=pywLEif<D
zR=h}bCE}i`@trxuD0tL5iCOV0{^(-dwqoqJ*4o~R{O!xI^+^z;lm$bnqQ620<CC1L
zrka(KAo|Um^-H&dJf?^<kiCYA=Du|)k_}pSkI_wod7M*6=0KeOI{*3mcMO--)7o_S
z%Gzxj^cULm$ae$uAbt|aXK^`&RNXVr(UL~lXS28EVfwBG?7QIVci&7LAbMQc9O*4a
zN&#W`mh2NB4Dp3{JjX(RchR3nq3_suk(LRcO)gEl850b<^M&h>xq?U78CB-cZ4^u}
z)0bl6x8Pi4az7a>F3hXPOl0XcMD#gI7fj}sV(HLL4Q%=BB9j_77&6#d1d?Cd8}xbE
z3JL_pm2afXT{vxpvhvD*jRFa@B*Ka~*#wNu4!jkF$=%+*_Z3MXbz|Bb>u?aL>vglX
z_GQ?PC5U6FwetFc|1y`rmycrlukb0hEiu^}4VI+$XYCnr{08qWZ}tx=tHK(o_+qzR
zU-~2yGk6R?7xs03>6`0Oze%n}MYnfH{_bTEAV7M1I$-;wn%B+M1Rk3AkB4t~T1tA%
zMa{D51Wa?k(xE0Yv%sJ;W`}o$KR%U>5K2exj33X4Nuu96Cdv5wy#;Nbpna<ww4YHN
z5}c0nl^>*wmwoXgkxjw^BUJDn>M-h<jw#(Iro~hu_1sn4rjq{Kk{{EyTU6D<R^ZW+
zTklewW_+qJgA@o5`C+u;0@Lc@bT&{$T4765`Rg)hfZCw2v|R76HXz`+yeg@;kxEpG
zvLM5iY~L(HnYiKa1;>H#G}D5}d&3fWwfV{MrObC3>B?lJF+P?CKHhc)&-C7Z_*p}}
z+-RW8gIs7KEY!v9LpnSeITwF}W3c+^A*5prL|Sk>S6ZkeKfSFb`(-SG&ut(U@9h5a
zd8&b4gwaG_A2Kb1<N>qNOHbX>P$)-ww{DHsM=$1Y>`Q0UmyVTgq_O);NrcLF?^n9F
zNjR&taho{sMiQQL3qAkbwRI@y!Pcq#FT!fCSm0%UjdR6!uR{74Y$+e`Ymf8yxmj(s
zVAjWuDQ_WFxSp1F-cbUPFoW21=HDqqTU_p#eU6wt0?)s+?Aj6C+I&?^=Kj=woF+a4
z`s!7BFPN)KS|nbqmc+&ye2Mbm;5pRAB6f8nFgz-rsi9#uQ!8TT*gzcnDPPP&wHuEa
z{_L?}l?i>$6c&2*V&W5{VOzX46H_=I+AJeu!Tpa98IHYod5RL1n)2Si9;C09K^H$v
zBa<}96FL1@{UXD`ePvUOB2wx2FSoi>N2CIO$3l@xfrAXivZ0_K&a8NHkSH3~U4J7_
zS<z=eCuuy+`i<b3*b8l2dU++WpfW~L_MrJqL)Jnirdd}8XqQ6b^1^(=NOtsW<{$PQ
z)&)*Mih5UzXF?Rdq76)}!dWga8{oIWWB#GxgPry!%Ko3{vovl_k01T4zcE99D-aKS
z44f$;sb_Eg7rJc2dyLuaS%fwQ!utKd``$PdPFKH$_JkG=I?=+UdBTkDEAWs8+&&OQ
z4S=To8yI--qNTQK4AiK&K&c9a{H!Lq4}g!*g0y(=baCDURqJ!*91;%7PbwB0l)A$7
z0$){ke^b(s%gE)v-J4~L-or$T2roFLGCBC7U-{PROWS2=lzFYe;a6j!yctU69}^j<
z(c9*SR?U)T)i0qTfR6cU-|R+I@-4c>HXR-H5S+c*;#6m`0r`s?4V7t!U&Z`Qr*yS!
zgELR;r+38oX;$#%!9RjAi<&P%H+ZCBQyapf6Y^%i>W21xUImB7@dfpcpAqMpgG5{A
z-SaoV!XFC~R|ijCt?tAsSB(0I-6sQC!i-lBPM1x5|AY<y+eY$LVaEx;(Y*2>y2Bv5
zD+i{a>V>TrPbC_4yg#L#(gPR6vEhAT;iWnZXb`poFF-iU4Pi7mocQ9SG-r?z8d~Z&
zQ88Vrmo!=r-3z8qefuA-exYgJV1%I);Im=ne;^U*6vk=rL}w^DMJCpKSgzPaLld+n
z2F;`sML^;XEh2{VsZb7uf8!K{40wfDFX>LL4nzBi8hC%P(7ot?QSgrK5gTmrDwkLK
z3|aU~#<7ojrEmDVxD>04i{~Kd*z+oK40a)(06oH@Iv@NqE*7Wk5Xg)+&RIbfEf@bn
zBvR3904UfPe|tyz8+N}y1UX$HGtedRWG<8O#-N}p)7^>vRdY!4o{FL39YX{ppTH$!
zij0b+cRIZy;SIeyDj18MOAMEaSF?x(b4r(=7+afF0Uay9=Bz3@+=8KWN}XhsHWf{=
zF31y7{ZlE9gMEwcXXWtf{nwuObor#d#6E(M*YO;0i+(j9T`f=TJE@^y;U3+gr2#Ep
zl9f{xUbT{eFm8D1x4YHk-Ir~+;-#Z|#G$W!Rh$#;)R$%O>Dg4kkj)n7)%@3vKks6z
z-4C?M^B4I8GFVgzW*}APkI&oxq4E+coY{ZRH^Ggrx*B-g0oA1zz`xMiE}qFrSclr~
z5=dV=dH(8+y8wHy=oLnDdwY`E)&lpZS8SN|Ku)@=7DDmsI%BT+;g4?ThnV}~ck%@N
zveix@mLJ5+^X>$`^gJ7tbo0aR6t7f$Bc+Wfm$<0yK=933CM;;9^P~VWMy_}!2ZqiL
zr|8DIs7Oy6_*0q|I|S}6&_Y3<PoE^cbZyVV`bQop-=DD^0@Us%olM^kqG%;knLpy$
z>Osm!I;ug9>_64LtNMNdnI79HTw3RprDYQN=EE^9WONw{wT^b8iwDRpKWn!dYU;*7
z<Vi-C4CR&KFFtbVYBbNWfdh-#oPKKGe<5|^imJhRN~J$`AEzSZ9oF_t`+zLXk!=h5
z=z5Tmv7?e0{)nEGia|JZmOdO=t&rlK%;14hN^GQMq~%JS7R9_mIsK>$xMGx4*vM4g
z%%kUcnOx2v{4{c)tIy;SQz;jQ@!pUOR9lfpBy?O8PR+&eODl{%lEKEvQky}QI)*Xb
zY$G>nTbIR*_xnqHx|d6wyu<tGXoWAzbfz(AqeTrFZ3lWX&-v|28`DemIIw0o^Bg&~
zKN2^+)Qabiu_O?TOq&;07=`ou@4n0$3-BH?aOto)bWBx^xtgO?Hh5iWm{HH>U}J-E
zFQoL~pz<t^RhvUQ$z_vO<=@xSh2NOpu-dGftW-3}v(YjY65YQhB%7$HYP*mQ1(5l+
zER|$Ym?2JqYB1I*+;NvQtfj+hrQ)%SwBJD!YevLp6<vfn0ihG3iftoj(KCs-k~a6V
zVx;m_dfQuGM?bD;^*v9YbH<4bk*#)*zB~W;Kq%_bx~TB}#O81Pq?A$t!|ymZOM<j3
z_o~}Dn$bu%Vt}u>T^iAw{U9~d4^#4rdYK(~M90RhjRlyu@om^T{JG_>rHY&5vvFT7
zj41~_g!uJl+2u|3J8jE>1bY?Q$cuyJx9sI#UBL8@=_41<-ypP|GH?9jQj1pPa3?I;
z2bE+jR1~!iuuh<g9fh<J5q6*Z>bf0dWGDp|v`*86e{4Ud{Xd4Tf}yDfioR{Y#;5_K
zYlG39(u~pF3J6LI2uK-7IC^w<jZTpk38fpQ1w^GpN(BTyMAVPp@ZP)c+;dNuHkjTL
z;FMSFl6ho^<P(bFkWpDod|8PC0T06W(`1RWvmq8QnhilTk^9QKx&Wwp3It>|CBowA
zbRX;ps@~AMn{noD5zD&CjNZp#N@+lzcfL0c_@<B77n%(%>fXrMpJ58dB|&8dFgQ9@
zo#hkMKJk$0p4lB(BxP)TkToI`tX7ivP|3i(4x_01HrY!w&0Wy4X=pc^-G2q+NgB!J
z;TD{cr}7eG6g+F5tc699ydv26t~|WiGm+l%DzU|B!p1}FmS(L>1E=Zqi2gYF2SfL$
zxH?%6d)c^F%iL&&+6f^R`8>-JM;2{IVu+GxjfeI~wbKjZrKl8EivPHOX!q(Lwu)9t
zf66|bYP8IjSn<DUk2#qXm}b#z^Z!X<%X{Yq!>=XllZ^*OKUaf^f~r?-EKbE;?3IPv
zjFqWz(;6}TB39Sw%CL*8R|HWRF6kW*W;{Y8_G}(cI$(*#AcJeE$8vw`tIgs`0YZ_p
z;6v;Sf~c9Eb_^%qAO|V3rD*~BsO@>xN4-Mw*ALD&HcQQ^z#gst-Ii6kU!;GpG-=|s
zIh&T95=1$Q$!)?iDLz+jqP98pl1%8H!Gk%KF|XQ&m~sMZ(P9Rqb4|s{U#WBa)!cp*
zjmll+O!$YFIl5<Wi;QBst3&8N_S`O5u})4-+cNzciImJf4=_E-C|@bEdHjcKGx?R9
zMa0o9nq&EG*8}=gW0PQgLu(%wqche*>xM5<A0+oZIW@K=?i)&fm!7C%L24o1AT?jC
zX#NUbrwY`__G8rRl$hK2l&o8%I&o)@j-i`0TBE!ms9xVLb#Q=qwn$%OiKL#_E%<QH
znUqmC6`9j~W2<V+(-H8D05e!>f&duJ9rzPqypa2<NtRTVNK3jI$gU}Cb7l#U2Bq3w
zRa46T;?mj$oS6o+d@xPcY@tG9q=)V|>!K_Sq6$;`|Iotfin%&R&QXE81gv8FH@k0?
zD!MlTA4!s)_ivgcLmgbqlx8&+HG$Ib3)L~>>Zct!<#wEHmd(^d9!EiTqS;RBpTSdr
zsSMEj%t~JTD;?^|b1M6`RUP{7$)pS}_l@Kcy&L@m2dSaabd1{*_-;j(dH9R_KKU$u
zH8%Ll@F#rC1+A~lN_jB+5iW6Te3fZA*#^AaT~qNal`O}(Ne16v*rZg}Q|A^4S5d;M
z1(M0*g`4MZ#AB%P=6bUS`l^CC;(2WH1mb(^vAOl%D|w_@YHbo=VVy?}0$yCL{%8D|
zoD}`{Wh9Qdl-aWTi_ynl5T*lE;f`3sD;WbVN<q>a;K{h7e?{cBoWG5kx<}e|>ltjQ
zDf39vyR{K8FkW~^!XX)9r-<;EPqqFh6UQYR&YN<4BWdytvPYChnkkKtT4G`MVnRCQ
z)yG=D&viB*smb3JtJ5jD$10tKY0gX~dHf}}s4YGObLEXPjBS+R$BQ>l<hqiD6CP#k
zuD*<0u4k=gpG#DTTTgbJ7Q)3R3$?yv-YdeM3P}bR^|){%&O{Z486oM^d$%+%3!nG6
z3;tRt$Y`nyT4yJ&wSsHmLQ6wqe`*~X=ntZY;wCr|{|Vn$fj6U*5=GD_6fCoXihkbY
z7o#XzGqhAMm!$_KExDqCc<hFFMzePje(+Vkk_0|Ap%k6o3QvlAklw8$<3u-P<3+z<
z)?p^7!L3oYzL~PWoMT!@^Lv$`G*U1jKgi9OJs5(5By`Lk^wg8$A#JbL*pmlN6wOFw
z19<rYB6Vl|hUV0jefzmdq*Dq^y6Je4wh<X-<n;LmjLb`@Ql+>$vb3XrJVX#TT)uIS
z8|}q3COO}kp2*`onQGpZJ-eTNd#CrnwAX3A2pQMJ9gasq=sZ-!e|f8#p$+ouvu17M
zn?L}XB@w-waZ$%)!<%EylJaX$v?-R3HX$Q_rKG8`Jq}HCKu|9*?pbh=`h`edF+;X&
zq|`AauJWQRP|^`Bsb#{W6RA>kccr72>w-?0JM#%~>=i(_R1b6RF}QhB3A=y4B3O`C
zC|s?tJol)nrCvz7kP>TpnBQ8(AF?JrM286wkjkU00{(kD#Z(n3Pf&lra@@n5_5|x>
zQd^Txz7eW!Q=IKA-FlHtvFcw{CW{DS%!Efu?F&dBu=ABR;2d5k&r-0JPb=;fqcYzS
zp1vG-^@#aZVF8KQz)|7=<}-&P4F>s<K8;fJm|MdaTvXWJGZWc;lv|>yQnSyAuntYn
zLo{7L)R+P2Scn*pb*e7}&$|8EJsHpXEoU%Hpunun|2Dh(CUtlU#i5>#>5TUA4hLNp
zO|4nt(3NKGXo|>vMv!n?qLf^jAeW|LQYd{rEn7;6n}y_%dG1UycV6LKm9Tk-c5r1%
z&s|P-r@6Y68MF8BBGI9=PxJb<a-qnaftLP@4-8j#hQHw`>p(@s6F~#th1z!QAw&6r
z49l{HOiDA$$SOg>GIP^)7Hb0C?lb<C_(B3m70!|tY0*?mOUe9|E)B<0x8ATjP877k
z%if)|aiP*ULPu41zzAvhcd`TW+#3rtM^Wt&s-pCyHLR!Pdd!Be?k+a(S$Z%JuDGKl
zA&dQ9h*CM8Gfc@Vo0Q$z*?xU9(|#uFnFfg{2BdaX06ej5V0vr@t|ux^@q@+r^SfZE
zk`r!{m1;_6e<{4D#Nr@zxo!y$dhHOJp5!BLyC<1Ok|K4h;gvR_mcIP-Eg|BkVar1H
zII7RlwxKh#sA<Vy1uCmplor0*J;#RZ2{*7XhJ_3$-@dJs=T{0Vqz-(YoC+dB?j^Fo
z^<mKllH7Q1w`%DKSrjN+T`5B|hpUu0bfU@1-K)r4p6g7F>VeD%9?H;}y_MOh9OUK|
zVKZS3%%!vC<vugck83X6G+wVZk?r93V&pweXI^~OYWYhY!pce>&yx68>I_)JoWB)$
zEKRITr*Ed8<d^<4!f0PpC~)|ECHD1XYa3}-ez!`(Q`%efLN*`Yw%Yu_@xpP7tqDey
zrV}y~9vY2Q3CSL}4e}I9@!(aCj4tc=*!2*^*6Y{JQ0V+uvr;y?^j!gFQo#Huz2Fot
z@-lWKsP{<(bexSE0Y=VO6L`s$;B6`equo!&>4YN}S{>zszRvBQ&1;<x*?EiQEsS)2
zLH8afCR))9D^o@PDMVY)^59b%+^K}8DIThFly;}EC<(4lz7)B0L;8zm=W*@PTh=Nv
zYFPzsdu=u42e+J4G6iNor@?ErG^xx>Xd>F!K2bP$ljep>b=nq;JZ+_+yf>glGJ{Ls
zpzy5<<P}6LQ2T}&6n*9)e-OJI+W4*E1F38p9BfE&G`_OTL;xui)b#vX`uNJ6!D)ht
z!HU{^4TC;Tw;trt9GiYv%kazIkR%f?Y`6u1jWWPs9Cv$b!awPM+e|~H4^EWH(v16G
z%A&xSN;+kVyqoaNlv+mU$qtk4Hqp<^#o>~+)=MMc;5B_If#+kJl)<L$LG0x=VJ52a
zlYVAM(yZ%a&H=*AF{Qz@wIrgDCz^#2bP%`tP7bn?TET&#T}rviLRp7)&;HXBG$(9<
zi@Q!>>UK4r^8L40jnHGyMP3^oaHZ<cNLaSN4!YO^sU!=Hl8*qHo}|;Px^25_+qLK%
z*B`ecWakItXmU$>RDDHJ35Yb*(9gz(GGDb#iz;kP)0cq(Gr!z(b?O}y?pFYd%ol#L
zh}(Zgr^<y}#uKC@xs6VKwvzCOOqVQ9@v2+GTXo}R>T->D!ZaKc_mE80p%d;`fASMZ
z2VJ|?*hGvtKz+1DQ-9{*ny{AI*4q(_%y}LS=qFO@VbjT+t<OO-utwIjeChaSxFUI0
z|M)6R0d%<!Z|Jf;p~G|bxu{`P+%L-}O`ZC_V6o+(*vh<Yd?Q{Sl5P#f$FTv@Jzd_L
zu77b1e;yEO9>;nFr5vlKAs~3&csvnaQMs|y2aZD~RiH}h&lE)G1QO2z#dlu3tGOx~
zFcU288#(FxOf1#iY3_=lp+B`?cbMxardQm5DeovVDnE}=qG)Q`vLID^Dfo56#h?zg
z)Y<r<mP&!+J6Q*D*gM3+(IbR@AZMel*s<{om2^Hw7=n{S#Cl0~Jaj?(5r>cV?$ocP
zZ&Pmd5_{;Mf99>a&5RTTrxh{g^J|3nM$@DtF`RaZPtvV}p<NQ2rT}0{Mo)MG#Xm_m
zg&?x_+XXYaR9Yw)O1plstjfwFnpIa&U5$Q!#inClXgzM0ud{B6$;e-B=RU*pMqURm
zn-%NEW7_cW111VYZDl?zFr$X{OhwSyN;fG-@TesJzt}(Dm6+HW+@cZ9@=FtIOvQ@Z
z8t*j2=|%l@gspQ#oDODtNM1R2BYi(`K367xluM))uV6x<euBDJjk9up)||VcYkx0?
zmfw17H)I`p$FTl^!?PY*8yjwt^k!Q6JX+|7otZg<nsdA9Ms;}kqNf&3R(LaF7KYs9
zjdut~-*!+}50g!@1<2Vjy8VNX)w&N(mGa@YhGC~cEah=zw_K+OELu#y`!%(hNcb%g
z;yGDB6t?WYjXucF2WNYzN5;ow+%|W8GgE-h%aI5WzvWm>WT!0YS%Sx!=LSB8M-Sl?
z;vF=BJtRfnr?7`F?mko**xkw$p%8kML}0P}ns@nZk0ql#xq^kf5~rHjSNe)F@b6=f
z7)Nf|F-aF}q}aD&*K@sUMt1*vkHkFISBkvv=svmGC{!H<Cs=l~RW@^ZcQhubtl-WC
zHJ>5{L~YVwV1NLSy5Ef61xR=Ak|7e1Gm&@)QeX@Kt#gJS0bpI;aQR<&sV#niI)z=J
z4CX?Hy$=9Zq*&|OzK|w-BuKm*4+ux}@d5w^e%yCjBO-a+jh03~C-QIRxuAficEWqI
zmn-&^3&Q4`X5Ny6<~}F=Aw@~FzfdgiH-bji#oiC)l?ql$)kH}Q(>JL$qsWz>i7M4*
zG*8SoZzdMb-k=X7_P-03S*c-A;o#e}uwF^=Np$pU;reppZkwK{`}SuuCEc+kHe@RG
z%eS|inZG^5&l_`Ue^j2Y_i(B(G#@gOE<2PHV_VQN_-+_i+PM|Hw+r1tU-JC{Z1thJ
zL1_09L@ESOGM}zld=EcMZWf0(q;v)r;kgXpgukU84|z^%5>btpY38ogH`9Z%@G^>N
zabodtxrsmPn{9D4f8XE%l4lhKKUcwSmDYH%hj}PGaCaR6IlKG|04U#7ito%9QFv4;
zQJMEIS&Z7IWQ`%W%%9{4XNgp{Z2$rSO2ucGL4PPB)Ok#5)^tfFLAt1PSfb!x(h~hr
zixo>4xki%F){BcH&@cY%A$3{-J8E_Sq~c7h`3Am=0G3_f?G6c*@COf9nRXhH|1%>j
zyEn@(C|WUuzHR`7p1`0M!762dDX%dvEKY^Dj}7v-`o<{U9WZ5<GA4;|C|ZZk?|85#
z0q(W}n)L`EQSEN#(+d4q$0&pSpC2nR<U%04Q_H^c>n)E4i_a&MiHrY68_X+N+#LWI
zNw>bsx)4d2C~K6Z_fZXlx%zVH-j2y`5x4z&!lqK&Sjppp(cH@?k80U3?Z--w@AZU2
zx$kuGq>O=w6c7}XK-na#0{9KJ^vrTE+^K*>b^L@DQhx751>Ebgr9o2lVvTu5(^xu`
zj`znQV^tr4JAb%(*21$2*QhMFikoTBVj7}}y3eYX+8_(YzLSaM8Auk9nd!ez2W3%j
z)R>5@An$TH>Zh=C=@_Do?91sjpSS(V-?Sic)sdmBh3S=;FJRYg*?$^WGIP4yZ0yNu
zMI$;v*UT=Lq=W!r69Oi&W=KO{y>w@n6)kgQ;=KHu`|(fSqiX5~V0cm%eH(zRx#2z@
zMD)6brnS4(d2V_{0E9JV1*jr@+W-jhvMP*g5)ufoYhSimARmI>2D0M;J4Ui~(5ELw
zM9_#|0UmH-ig5tiB%lreCwbMqE^H(kB~Ljit_M((@Yb-RwkC+IWzPf-;C?{0#U-p$
zw9bmV;N6(hu(ApA=~nnW{ajtCK<g8-XEk*<Va>ck^ghXT7pz&Vd=rYcv%U3RWF2iX
z%6~%JbTcM1`{f(0G^WXMRuHEY;mT)9xsNP3QRyB24SHjDLUTk@9ueX;UUrb2y5^G1
zl)0w(qyja^e3@4w64b0QP8PrWON<o0-@@<Gb?4`fPbP$BdFS06XZL1iyw$0yl#S22
zl{{HV;Hgo?*Q~p5jnj<hIv;eH%)4cEwa@a=kDk!5en@!RQg>C&wd;2oD*dzZPRYH-
zOnNpEZ`B<3F&XVF#Tsbs_qK52{dTHva3xqHYL?qWq1$u7sP$y8%eGgv^Qm{}r+gAI
z2sf3vZ+TZdXy-}o9u5Hk>cBK@UgoRtj{nx-Z#w|W8^LL_7UfR?`>V<CE9D4QVZ!5e
z+U<P2&IS8di5jP3gYhyAp{Sb7Ot85PmV^wr=Ng14z0`&-|K=Q~+^z1o0;c*&x$1kK
zvEjkD=z=q3$2!B*r5dyn3Z$`^t4jburi&U>@r7K)=a;do>Wm`*JaZPyyy|}+oD)1#
zF_3f=B`{V$;7)e>tPln0#rFM*@LkVfml+61#E=6~gte@(GN;&WI?<|-4oqeQ2rEzF
zs$&_3Yhk{b<HsQ}9You)xUEfb%<M$s5k$BA<Y@f^R)C~SJ*|Z`#nk4j#y1uH^s^J4
zwNonDqqeVH{%;G6l*n{N&Nw)c#$hx+tpmr?NQo}6hiicZWzCBpIZUJiJidSQ_0l+a
zw3l#2WiElE_{3sz#?-V@aL!mb6~`&7g<k3(L*uj?L`!}3FRZt+_cVW&@O;<aFjp2N
zq$V3+oV>3~<g;3>q>2{EjNb)r_)Isy&rU^xDf+I=zwU#$Sg__aJk@CeA4jsK8!N}X
zz~=hAKlt-Wu^%GPx+j)RGqhKCZCHF!U(TK>Ezt#8(t4X#&qbNd0OiPsoE7@mPPq#_
zK!{LDG1&#EH`bIFb$@XJ0A+ZS&_93(&<>!?@SwgMBYgmQzdRZbWYViQm~fdIg7DT0
zBEwI{0Pyzhgfm|#NC2QzkCFmFGm1JA0J1F8RK1LjLu1(n0K5=%vlps7>wr`rGZ;AW
zDjxm0fu0&8<VahqO*?*-(cDXt)6m@Fun<wxVbB+hzxZIG;TPqq9;R>vpRiPt6M(FT
zrjbsa(hvSB*jamlldCW=po+pBB~7LZz&@=>ugO>b%9VRW)^D%q?CI4%oN;q#Az9DI
zXL3BR2D0V&u2ir?%W1J)i7(<_E4rDya%)A-<H2EE<Extzg!Gu5r6M|48Omto;@f6U
z@4tQSrQ}>kYS@b;_kRT5PXRQ3M-Fd;OoiuzZdA*Nd+Y95W@P3mSMz<u*+sL<ynOBU
za?)JKn6@rbt0=AVDcg;8`r5XkbA#eJTM)p)&AwIiJKjuN_@=1@DHh-+vFg%{lW`Q~
z`7W1v=neq#FtL~sR}QIUbpp@IjmDt0mIeWhWCc;+Aoq8=laIa5iYwq(1H9^_cO5_W
zbtCaK$PK^hwjA)$;`cXrQ2<`22EdrN%?SPo%;@N`op%fSEQ7zl6`TwOrur;p5K$bk
ze5%VodJ~<`8}if<?1S3B<SIQ0SFQ+`3Fgh7lBIj3MxpyB(R_16eaTXrr^lL~3RUrP
zXkQ)riLSOHpM4%oMKb|Dx#>o0(@&rr<)Z!MYU3VJB6p>3V)jA&t#$n}*iH?`Qy6yK
z_9Xq_;?Xp%Xe4|J3|NTlv9H(~*Y^OqHJi9NTGGxI;&%G`R#rTMEJ=ssqC^^olMSLx
zb-q+6M1g;dtAq-E`2JpzL<3zKjw?3yc*8k8nN-{x_^!cE&ZtEj+e#9rdWvk$m=czk
z=AyQs!vCV(S;DjJ{V5(<_{=k)SdgmO%Ion8GU;O#!@Tp)^rw6R-_~dDN|vu%@5$Lq
z@?2u$?J9L|!f#dC_zBtT8|5kICN&Qle{N+B<MLmTKCUggL0clr6C3z-(Ck;V)Xt6Q
z_gweTubaY7PdqXM*=I1?ZDHvTa2Xi^1p*{&i}%3yyJGDn5n(Wg6?TraBZZ8QbKI2+
z9BlValXU0EsJ4`&v*hrwmDK(#EuFb-kb_KZO6k>2f{G6p+*cdssCFw>_X_|@l!!(E
zRO(}>(8$xaOz62VscAm|zPg*}08oSalmU>PC`l7=M>k@~5`uM4JObD?h)w{IiFCz@
z-Gg@j=7hZD*}~I4nduQhTV^I8NzaWsoA0$53AVnt0+Lz%b2FmHyA-Y{@Bz=X?=79h
z{JoD;D`QvK6$y{@<s~`o#(A~hhslhe{1cPE`vUG^K<J#>nX8rRitz1=RVC<Ac9m~-
zM9xFVH2wJ|Jnp9+#{VXz80(JEb@u6qa`u<6y6JR}vv69-Cn394WcANhel*{JPXgui
zsAUVu88AdwjKj+9)CrZz^K!2zy<2Y6LOc@@@e&S#>gBdgV$7R@NxU!bP(AX2jSW^=
z%Irw#e_7@71kY=j0^1Ar^++`Ol*<XhT3W@{2Y^wis&y<0N=|CaiEE6HvwxSP58yyA
zG@!7MbtwR#1M#=Tl@|i;M*z2kbeUnS&j<1&I@y7;yCVY8;d31P-+#KwD{LWm26CH(
zq+~R3rcr;FMCaBw%>@PSOz)%YpC`_Y`(obVP-Slqz7NRsUq)**wnN3x;#dC$%~!z0
zJ9Vaiz$5svnD1L|Vg$tI_>Ln_vW%OQ#yAB<(h0BA$YW>u91`WY>$M$6)`VG>dG3!E
z6l<qS+!?@%^0_bZPjCx^E75<$%Js1m;FySz6thlOTp!o$)SZgIN!2o;X)~X-Ciuoq
z0kbLe=nUHMP@WEDK=2jZq(s98P<SLr<eWzrAplSVWu4N)A@SwssWpg!v@6B36-H_&
zvx5;W=kNN>b%r7_N^s7T3Hi_8<V*b%756Uu-g=3}vVNgR+~4*r(lITpMdE6sj7a*r
z#&LQHBL|v1LYk}@@3@50JHAL|W>l1lIF0mymrA1O^xQ1fTKb}WoOI~Eh)J*}4l!%+
z>zhjbMje9X#GEEw;5Q~N-Xy<1Y48n(XR*(&=RM%-8*)pU8Y#qti^m!Flm8H@STEj(
znd6G(XxTyBwjeAcZyB{Hw<TtfT-L1cO&u?ZaBehR{0S-D@x0%SdryqPPesZcobYfo
z=!gq9SKF}7HF3xQ(r3Z~%o~Z!L;$=lN=sa$TCW2DA@HW<>j8JZujdTv=GT58X2+;H
zYz)jH0sxBPu3eyjb$~vX2@fzi1~L%=%Jl^bB0vVXLEzgUo%4q97y#tUBwgsxs&XTf
zMZJwf{S=Lp;l%|C;+h2d5rz(Y!;m_$KTXjT_m_=!_B*!KNSz$+zxeK;3+t$Arxva=
zf@nwid}@wz@4j{&C6&F4le=G$UL?MAKXS(S{sU;rwj;%aHwOHaTnH`shAV}dZ!rH(
z*)G8U$Tj@x-x#^jPi^czSawH;u{@p^<~ctk^R%oCglEKHCBXasAQXZJ_GkfsDS;Ui
zd0J#6_2g!1?$&USKd)FVm*)b**!o|y(^o-A<>fcoIW|6pZ7GUuA!@6=h9IVdqg7ra
z9SD~UVvxTHAd;{M5Vue;x6lnfX1=Hw<TS~P!uy<??oXZEInldSpaVpt{7>;@ku56>
z>DXA~j*`p1V?<jdSlwEXWcO%7K%8<ulKQh|h}Z0<n`|!1<u-j2CcJXn^9A+3msge`
z^Vk$~A5RKvJb0pvS?RlJkT|3HcM%?**CMz(DTc32Y38^G0z)E}+&rerD(vRRU7V#+
zpG8h;>%JI?YN~F+ZpCz>q4E)q-zBVkG$uG?wZ>|z{}#NZ#AFt88*;yod@kYz7`W^S
z)@DGS*9pM5<I8vcMx~m!q^7|>iahMeY~&T=Srcm9H@<lvw-j`*BlO;RxVv1m`y;{$
z5{X9LBz4eJL(Nf8;f1xF&~lu@xCiKzv4149q(2|v=pVY*U8Wzxxyq_UWGZ9;D2D7J
zs`{KU+@1NyBW6$l?E(n^v{0Zbt^o|$F)LU28Ddc$qhlxJc#}aqkU2cgyAKaTg#kc5
z9PpjklweRdQ3DZ}AB0fhKT)CpQotIF1wei5U<ZIs9XSljWhu?SR-C|oBw0v)5x93E
zF>*`BolebANTsUDgwNMZ*u`@pX1b|j(AO2Jo%vU$*MMJpVpE76)N`g@$406P#Xo`$
z_kbyD#3Ks1(n1z>S^p+@Lk`@YjQGDoMm3XF!fs4;SZcdyPjx(g4DpTXJBkwID|;zi
zqG|eotUkMtW_SDXa?6c(sIRzf7u9a(W6-IVZo^JVn@Z^Rl^2rG<!0D4x#V!9L+!-z
zU>#uZpW*-jRuGXD3qVl;e(QYTTgoG`BDgG~OO977_4j_J&AtOVQ7;HOo%vcOa8!S(
zL58+%&Qu?Qp!irlxMMKTmPODxX3VY~Joc9^N|8JH^`^VRR}89bNFwG~An@dw5cutg
zPo(e#Y9>7$7Y%kFcuFKSA^lItLPCE>KX;+p#$-E9&+tt}MKjJtEam+ZL(ae2zmlA6
zSxU26@c>8Zk|-*jJcp4>{TE^iuh0jU{65b}otWxEMHxZD$Cm)!ern?Kk3DPHZZ5en
z(~^5&UCETYe=OWqNNSIv@Y{`x<kSuJ!9LIOA*_^y?53k={HA8Y#WUd%tzqyp@{_bd
zdj#(&a~bAY8CmWCN@m{c26xpsw_Tg23^#Qd;Q=G=gjfLS#4(3#o!d=WAi^s`UT+pN
zS$qcoT@t)jfP*QC|KG&&R6h)J=98E&>C{=WmkPD1o1P4=OYS>*Ha0ow+u!~78l!r2
z*9@E3DB@m;=A$#l?>J15?AIJf)5C&?3WQqKL|p`Qh7o&;IOX?4{PpYiws}(+hVSXU
zAyNv`y-T(Wk7sU!gZo6P&eMl|-rR~mcRG|6%Lyv2Z<VTG5EPLyQH+hKMAz0|rj@4-
zfrXeiQtNbmBODR5*dyg{`KW<5;9|%-$mM-hcWI#T{f-`k2wpbB3-6}8mPBx4{~d;R
zAx!AUs(79k4gkEb2~K}WhF4zxmLCi*sotysuLBI|>uw-`sXts9Z%azN0|0%L4h;Zk
z-R61#m&zgId)POY0pJ1w%qu!5qOV`5XnEQVYDNHH*(kOQm!e0bMgbO20wb?;uIxJ5
zO1Wl=`sLL-ftKmgFh2X5Q!ULVeRq=4OfmTmgARrIo_`peQxpL$rH`>~tU>D|hp{xM
zBdT=(0!UdTnF|}$ZH_;4*3g0u`aK%acd7cSJp#MI^ofA``q)M^+#pz!Z1#<>kdxRr
zOndygRlL0*Pqzo|jN5HThCa@D2CY(uH$8D<r-ixM_Fxtq%Ukr`6Ph1k|Bh4&4t<(>
zWFmej#unbmwhl0sx-ngoZa{M@N(O?Lwel1&%35u4+v%oYD~%b{=B3Nt7t(DITO{R6
zv6@n5`z*KQ<pX_ZAJJ4c6~<rip7>+5ikt6-L^kdy@#5QQwuYgqlWETZ<(1KY&XGT~
ze3B&YDWmy`fW{+rGHEZcs0j_Pgf6}?^B%jWavwR|WJWB*+n;G8yt%)JO{<L3VLAXu
z)8_2Idd!=0xaQ-xZ3?sEj9mRp!q_BxHht1=<H2C7sEXFmPF&{vPThe`;ms^uDpc^h
zt<x=#lzPnARJdHcNoM((GL;ZAro7l5gkj1+-joZ_N#T}g_s1@ioWw?tI^v+tqpzdi
zhL_Tgs6MQ|t1o$9TH^C!L5N#K`#oFEGIv@uZO5T43;>`Gk8X>u@6^8b+-70(8|s^o
z$k=|XRB<3RUUTgHo2Wq0W7R2<@mN<(t8MvGR!m-N0S-*I5U|zcd(BwF0e?`8)N<i7
zXz8v}j>_glsBAKc^Mq!yS|ty>zx$eh6Sl7_u$mF=#_6~d;mA(g&#OjsBK1rp#}X1?
zA_3}Ga$#@-Cx`q1LFwK;L3*0fuWT`bL9aC9w``N6LpQzQ{<PuDba;TRw97J_l@3jk
z0B#c(&6m4Wnt+Q`eEf!6U3`8IWZ?Ah3HJ?42e3uB6QfB8<3>R9RJJfa9+r|whH|SH
ztrL}*30A)lHr)|Lp}=fpEa4e6AYEjsnLYr(ff6N2M1vBKDqb1VhXa5ms=k`*Q3`fK
zf!0|q!dVghcK}c!OaJv}!c4pVGhiLfLszKSGvt|FYp>BEy48XX{F*`bO1Dr;^G3oO
zo;8P*4<s}zAaJ^KpsOgalW=%C3%U&exiJR9sjzc5_wV<@hJc!&SA*Z<ce9dv&>D}A
z79NN9nS6LtB%ypT;7_yZq`5Wz-x3M{kk^j~=y#a$MUc^uf(9I##@HGDk;e@>A{<C=
z@{mUk^3W2nWKhb8zRhD6nJ{Ug>@kf0*WZ^Wd<5MJiP|>LAdZM>v{@|?&Lmo5RwWIa
z+SGu>&!Qf@{BR0Dz#uH3KLhIg_yEr=d@ccG>+=8*bW<O=(lSPeCon#w(N8ydtmfy3
z=*<mLkP)g~cbSepDtx^<<%;JDd{vE;NYhP*u&||Tai1T!jL1Qg-jp7B`NE(w&(xfV
z_NJhVE23H@tz!B2I!NTc;LOu@$IHJ&SN~ZD$gjVbb+c%MDz7Q@*~Xin=Dy4*;7{lz
z`sl3;n@4)Ywx4Mc)d&@#@1gsTi=tbSDUn7B?J>dXHNc%0*6V;ShG{UYlnB4vXuJON
zLGgWnDs==NH096f01QYbpg{Y<9J`W#Nl`{Zn;~N2tP*{I2tB@`mj6;7==ShJZ1M;+
zICZ0G*e$=0P^@y-@wsnXuyUtrO8rpU3uc|<68HOu>NW8vM{_qGf;&r%L01j(aRLww
zs!^IbuH*-+G;PnW`X3aLpp+ORK{Y@jJYl5a9y#^h>~ah6Zn?pwH>>Y~d_MPkY?=2s
z3dq0F9Dk2x%)KHc4(maLf$E`Y+fVT%8*&zSP?HZc0P6HJX|edNx+fue#yVP7>q^h8
zoG~AER@QoYc2lR;>krf7ae~09)j3J9mf=m-4V9@5Q8vxx$s`Y^KK;NyT-HlQR_-^Y
z7qf2Lw9XxUHq2hLuv7StwAFIahodWA=ADwzI-mESeolG!`=M?o0UzI3t@`~8Iedc!
z?FJXGgM{>vc;F5b$u(ER2Sb6r2Rq;qu*hN2vfp7k;wVVK3S)F~wZct~{0TEFXZcsw
zyF;XY+CibDS|-3cphw4x0DRf`jsVRx=aUw2v4GxTsp9kfq{qDv>?gJME+gRAq_FFY
zn*GZzvQE*l!2(dT!*w747qmf6D=n%Mc*vn6q~cxc0B#b25A*>K*vZBK4_3D)fPAtB
zGXRfBb@Z`qaGiZ}7iFw<3I!4IfKYsd1Hc-KW~+Nv3;=xOkysF;CIc3f@5a&xV%MYY
z19-=JYFI8n&7<eab)`i1JfLIOGftsuwPU@qroP2-3*)g`Ty`;8rkWw{A;s9k4M<Qn
zu<iVs0$BLes{nGa{8Ipoui>bWge9b=OTumulF)=_731GN-7ESXR{Zq$SAkEichZq`
zs{U}BKoyr_kO0oBe6o99h_Y7ejb;3A+dYIJ&EW?2eC;1>Hi|d;m#@Idqu(2FR|O33
zdQPq&_*21{V1*l5^M$7RXzDQzV(FPg1Sr(q1pxrZrRvXJk7kENKVWz{Fyv-VY|yw#
z11oNiX}UA#nCL1AXQI^v@MTU;ypm)BbYCAsHWWk?U1aG6TngyRWo$gYDKfUS2!Z+(
zVYFp3CIE2C8RchVuH6)-8s$cUCD2?GfOc_sKs9!}jm%++IlKR&5Kmre?V(rSJaFjC
z(Z-sv&nPa<JZ4wUBcf7ql_#^A-EFwZ=W$}g*X%ElbT_|3`8ZDpC+958eUg|!iuRCv
zq$Rd=|E5j4o0Gwpreucg{z%Ba768IakYNFmzFY(zz`?MmVUjb^uFrGzq;fup<wc!&
z2<>Sm^Icx@Pa2!PwTUUemBB_h@Guu&EFL$T6gR2=_OMKfy0@DBj2Lk2ENs<|%|J@r
zri>3{n_DAJMuRmCRE;Wmh)a1|l-7jdih3J$M&_=YveCK#g`}H!AAlkeS<)UHv~^BP
zH?b1laPDFGC7!ibT)cl~&T`iHvi5_1F<g|3b!#02bj#6hCUsLC5A15Xhhw&yR!dL8
zI#eLPn^p1~c`QoSmbWcD+v8iAHc9!i9Kcn2p?vfj%D#QN0LzvfJ2mZ)qEW52=)QUr
zzH5g<J>)${2SYn`_!0mj%98?MbzlZi)(IbBg-F$;&~DDgqZuH?ATkKb6Z&4COSTq8
z9*$B3NK9h`lk*l}|K0qG4YfsoA6uo_;s#NIDtVKKwz1qzo!i^GaofdAyjiw)pZ}hW
zdT_O&Srhd$t01wX{BTszN{pnhJk10un38t8OP%maDZ(s#+b$ssWaqk1NY$*!(YsHI
zhD`SgGKi#C|A2E_T|~Ut6kZF$Yh(<6zcvk1*hw9Fxm4H_eEIYygf15+hkr*(#Pi=B
zlZe0TLBrJ<xSD!S*3Xj;6TU@udn5R_&5_D*f*)h+l2gHrrbdC`W*w%_7X|Ttua!o#
z;&SkK#Jwor<m)I9WoS6#8dfk$wgvnU05~Cs_*FLCzAEC$_Cuz;3e-+p>FdL%4U&O5
zBg^PqV;f0hUd$JL!<TZ}g~5v49At_9_c-+9`gL6ypH30B`1M&5wR{ik#HlX;>f`lU
zz=1-;SFT(*TTOx+`I$Hge%$;VT}+t;&-MRHS049O0cl*Cd0*f4dBYy|B&;@b)z8W>
zy-4Drt6gd0>v(aSpOu2IgRFJBZ!xj<Hr%@NB1c%sUQF7sV|s4|GuZi9p$`duOC$wm
z`~<}t1dYGGeRki1+TX+@@fAPWR^pIq*u2+)Iz{tP5Y`eP2XPLNU97WHM`>H(NS!Q(
zt;-&h2BulQ^g%sx^pwpAJi=K%^5Dh^3$)~kWjJ`h?IH!?^<FgQ%$4aANr0i5P?{Km
z8N81K&^bTgU1Z3>k|ly&uEOyWlvdEO%Ybwv42P$-IyRS<gz`coK30FP4@jYN{Inv}
ze`$&3m3qv3^JB$;{%tBWSpW9LdX1sm7H7Uts0lYj%|8qFISsSV=l?6?W;>&<!tX}s
zwGatSm$P&@=D@&v=WdAbvY7X2oOL4wcFv*rDCz5K?YmIA`dQ%j@a5~&GP0HnG`!T1
z`?_v{$q|>dIezf%SiWhpLitvRuk7~|T50l6Cj7L7J<XL&sP5=g9@p;%=8?b76wVEU
zUnUz&Pkm_?eN+67R2h}e*Lb+L5de>-Z^>ByXG~W8{lk1b6{z~uj~a~+OIeG{`(gQx
z0rTMKAA{TauTdGdr*XdmI~*6xQ<QE#e4!{6(o;I^$gvkgG?X~}h*=oQZUNpwr$CC6
zm+g>K*$CIF-#S%JrgDnUcv+KJ{XqQAj<S&tYPiGNRzouA{i@?*+CNZNiO>k{oAm{f
zl_>_wwiLC`Te3)UHXa9O{eD1snZ(}`A}a;I;qXsRw>mRlGj^<wv!($$6WWdMOtM4|
z@WA`>?5q7UYi3!n7S?alT?MDucw}I20v_^&SMo|70n8MN%sMRNhe8Bxq1;%Zrs!N$
zn*417t#w}-#c@4LotnjJDgY!VL{}_>s8@J+CC1@Ae9#q*v?mZiPdN71D)dw<SpwX|
zu9LDG65I4n3fSklScT6F4mqk&OF#hm$oMoGqL)rXgc_j+3Zz#?nQ9<c=Gk_2N%EEe
zA}sEAT<k6wUbcpe;}e}(ORjld8NQbEo4ge=O_a}K(Ld?trWdE<=QOQq^XDz;)}VNG
z-9(i^g{|XB9nPlxqK(fTqWi5lIo8O{`$9uQsmr%a1FoI^S4r9LwTN#?!2FAe&5^oI
z;%!Qm?h)mABy3L^KDbJke~Vihu5@5Bs=)nbyn$-=g#fLx#1;)p2uAn>i8;WH$b|RS
zVanE`U&GG_3lqkXi8Rg(!f@g^{Z<P?hh!IkbSjfrEc0#D;C-QN2w$=bhu3qjlI|a6
zf1n#{u6c8xSsj^<J78<w6mPZg<=V7k5vgcigL6lobVy52NCoEIR#Fl8(DYM70Pz`K
z;UaDRps75dZKzV+um=0ong1Hnk6Y`viM3ze!4f;wYkk{4+^v!H)h{q?jWot(oc3#$
zlK-}?9`H8^?2;{eE&%I&)6<V&XPO*O%_@6JB|lGVnqdI4?5JC9`bixR=ELTBcE(Go
zTYr6N*gLI3MS1a>Me~6^I~ne1br7aA$P8!*02+#QnEZN}iyS>NT%hxN-D<MFJRmQl
zFoI2_v9+Pz;Lx#6%R@!r4N!sttI)m!nwiBCTXE=e2!WXdkQ{qu=^?a#OvMaR-~ShT
z@@n9E<4&c}vc_dDyD1{Ahx6XojP2tE`W5e2CSP?AQ!1AouVuYD9h*2q+k(*G9dePg
zQp{uV0Ju20%BbE|k}kLp7DDO!Csjk8K%0Pc5s&l#+Vqr^S|8Fo-=nMau1t%(hy8K)
z)&kezld+RuqtDr9=Rc?@ht6)zi-!a&t7mZrTo8LaD&3Fe**=>)^qTws)s3rQ0v0LJ
zp+dr7a|3e8V9BnZ;k}0edc4R9uWj?GOnS-{{!Hpgb76_!4X=?|MY=jXJ-H-?d2fzu
z^qQNYFkdoyUg0m4-3>GIA={V_>=TtD@)#w_GtGA86?5gZiRU^4Yju_d*ml7zbESTY
zhNigty8sDDGk`Z=J@Ji-!J;y4f5D|u9D1OFq*a++UHJ{}z$WX-XSk;+ldG%>2~D*P
zQ!&K8-N?~!-jcsU+$bT?u+mjF@1D$Yo)PjD_Uv}2i$KnLIlm*9SW`Y{(|{Q)IX=Uo
z*lao9fJvQDQrTQ-<n$P6*PAooo6Msp9T7c=`oU1EY}B@PBiB#WPzHwlj7Q2bO>LyH
zZw_V-zd?RcV-FGEKsM&4Gt~1_)<nnmD`T%|MX<sw$qN?s4>aaJDb6$iX#b_OEv4@w
zEYvvei;t1+Mp5dL#1RZ%L;2hkcMV=lI^FPqZd7?WkPFQ?3TxW_2lI`eA@7W{j)KlZ
zlffTv@<1i6U(ML;HaY=>Ub=;8G}}Y++4xKHysumY&?J5B5FA`a|F-Lrt|YRjC;y~L
zcx1H4h22$d+MCkMn<;JTq1Cp3L6nC<_sU%GlI}i*k>~mB-UxTEgqcj*m@Mnu9zSf6
zJjDp3^GBjb=m)h!z3jHHZ1<#!{(l8gm8hVr*|T?I96h?H=@S|aD(2J+isEa5F>fNy
zSwjWkP2rj8)F}DmdZ3OuHOMeCi}n<_9*O0hN|0V9`22DQxz_x`TFhxtc@|cJqN}Z*
z*<QUmt1?IivTjeov~ZhaAT2vBFQ9Bq=q}0gMQ6!7ANAi9irHUFp)wJl>VIYlt4J?8
z=sT)lNNp#kpG-ze8F<Ss*)6Nty{X*Lnf3vM&wf`|y2)+^O_}Fc^7Ktd+KVT4J17f&
zA|qQov4S18PTr)cNq?DXWi!<|F*WE%?d>;-m`ujJWGZ?@?zg%mh{+Qfl^#A&^AB_6
zwXdCzOgmb@{Rz=P`QBPpeAx%mFa_nJzQ+9tCnQD0B}KmGh<eS_JK^tOYTLoMDaB*G
zn&ulukz2&^o%scRQXC?eG5wY;UNz6~C$+jB?CAleFBG4aA7zT`0$gM43!{hzk#nPf
zZ@(?214kunm4!ZDKJ+#LoD(27>f#Ku??#b7TS9MQ@<p}#JISL$l;$WbSoaxMlxb<N
zT)|(2NrGLeUUr|g6QkLL51@3D(~|bFc0RX95}jR<9%hStYxeYWnAElUaZXXxwT|MW
zJ=d6FwOGN3%eO=QgQW7^<ed4wJHr#g3>^Aj=6cS(>Zz8Uelw7}`Q|74FP1awOo>6Q
zAI7=^P-eea1+rL)YY6B6wUjJYJ`myV|FtF%syGxWAQjOGfSZKDHqEj97)~Wl3xdua
z0pO0xcKk<W*3hqd(`r&6CfX1VB@XPo=yzQw<IkE6b+VeJHViy$fuDZrytI+B%(ze&
z5mm<oVdWf`;>#T8ZdZ}a+I2=vKK##tI(3~jcro-p!(;^i0`*IhvGuo}jh8&1Ier-I
z<Vnwa$qu`{IAq|p>|1BM+;3aJE)g9*KH5mSPZ@l7F)u@q)tX^HitXuRw+tu2i%*jQ
zeRZ7IfhDZ(8&+z(|H}~TaooC7rRR93(t@zQAY>>Jh1ah0eQ+ahA?gW^T=nT&$gRPT
z5#(12<|Em{)WV;-8Oox*cJ_Vcv8vptm#|ITV=}@b!9GOfG=h}=wrSE#G`*-0CqCah
zN*8C-p}I1rA2p&55THUQ&sQn!72UygKda()L!V!aaR%*u<-VliX=&oEfL+XF*wW2-
zGkq)m(fzz<_U4LR^x#Yu!xzObx^yHy`QQguy`%Z3+!o@^iORNFZ<|QP*Y3a7{nk1!
z_4iAmzpHouga3dsQS=dbqvznGueX4G@*mw#zP@62cW%M$gE!(S)4r;v>MMrJIP8;k
zdXen{U<ZDP007ku6lyo_LzW~oX+~+3Lz<Kz5&*P0Zh`0G4*j;OWsV<F)1q@}yrBS3
zs!GNT++n*t#aa3J0rkmKgGu}MZ40Fn_S&t{F0BVA2Eq1wNe1iR&OJo`O!V{_t3vo0
ztlBYcZBY%LLT2|Z2OX01v43v;v~_csYR&Y1(r(ALWM#E3^TxAcc!Lq<z#8rl*0_57
zFyNu%R0Bu$c9Va0pJ2a#Quz92Ba6_c!)E=joNtefd^Z9nBGhM|8#QUOjJ>$g7#qcU
zEYuXqD&^}nBIxxV`o18->-Q&(QEIoDv=4-w#P?Ki>=>S|Gsz)}b<0hzrM>RNfUaiH
zZY(iAuN+8)7m<D9K)I6LT=!vA|1M2^8@92n+Yj)@!9`Qzwv$wBc2hxT%8E?NQs5Hz
zv_`_{2g{uY(X4k_bY@0w8ZpS+-g~Hn47Ka{g|nysa_5-xRZhdlcRy}XT?7XBnt+tE
znB1a|8>LBp7?Ay-32)@QY5x7La<y$~3P+HzwAy6vk4cV?yf0P$ikg3sN_wAm68(N>
zl;USXR-0olS*q9dx)gpMfvkDT7BEMY7|hqziNdSm0eOhK_;L5kAel_R^>=>fgvghu
zPnP)>@3`mJo%{`te-@nkcb|Ij=llP6<0nm~$Csl8LyCQxcKy2wed0n3+7+^i<Y@k`
zyGA<dYHIBk2K&<Nc}VtvN9F6YnqOq-*Dc%E?~qL%QvIF{6D!ePM+a{VuM60)B}4`|
z_&+7@XgrmFo#W_+j%oFDNa`}ty-)6r)Nsr$wPiK1S~Rm0WU;+1F7>d5Z>shDa5F}I
z)^1)KaTdq*P`LC=9(ZUxcIJ@&&=<(;u8GTQ@*3M1OsjGHz;regb2iVw0^hUbUHPx{
zm-mY@OcPL%n!Ip5R%}GS3D?*l#*7I|sef|w8b8^*^g92f4x~Z}kW=7a7y!_$^Zca-
zMFSgHK>C6N009o~`jEzgWHGq1S@$VJ2qU1obt1oAY{ChQRiup(;amb4daej}8MnJa
z1^TY@<2}jtf<!&OpK*zv26Az4KA!{){^e&$*1AUruUK-FeQvdrbvZ2i4_rRCEt8=2
zC`h-_Y`68*;p&D&9d_?OHmkCgYee)TQ0d$2-;p`*8m*otZf1G4eHQLCG#n&%9a68%
zwWFqyHJ&ncUolvG3w5t{Jv#kH`RPaglC4jtinjjz^HuLh9d_SWExJCaz3c<5Md$#J
zb~Xnv2xtKwD;2YbCbzREs{+*g5CC*|9^&|4fsp`kora00t=ZlM4j*Y=U(k;rKfkm?
zA>nu^MR?pM38--_*!HN!y^Z9BG}Sf;(jo!=O(v7%#03>MPSEacMF?9^5Wg+(Le!2Z
zS$_;t#+T()Bqa(|U}2Wp&xgKgST5jF5BjOarrKPQE#m_u%j#(rYRtoFQ<hVs4=4uF
z+J}Y$&m`f2MFKKuQFpmt&iOkEYUx}xRZf_(r-@0M_vF!|<}m}qjdO`{vV%t=u~22v
zORtER^XWh2_1!IJVWSd(;~Z~)io{gt&6h#Jve2JS&#+0X$Z##B=V{p7T;V%O^<CvZ
zsuC>_sQU2pt8USEv_qKB!}pG$f(tb&EXHV)Ct?8YwudIbR(1RupdAV#OULUtc?FA4
zHNm^hvE=_rFFw!kmgqiE=hI8df>^&(H<}?85!-drd|$cu+cOcL*piq6=*-DSRJb33
zT!@bq+pZ%A1jr(Y?<+Uc7YPz%;asr?U9bp;3qT{)RTps0({=|8=*4O<P+~p%+dQ<t
zFJ*VBz-4wA;OWU6uOFY^lEmF={xrK%%73+!-w<~{?}g|fO@ZiS{5uMXeLC-3&pV!G
zKbO7t=Pd{bifE`1`PD60<Yp8~`9pY$#IExmOZ?`FFcn=~W4Sl@Wc+|tSYWiJ=@i;t
z$9Bhh<(usXb?29fBlo<<lo|)XKUO;idYeW+!vOP@2-BmT;Is6^-Bev5gzMU`c(N0~
z6B1Vvy9y6mcdycEUv-eD{O7ZjpBhqf!!2a>cJP+m0uV~W{GpPa8~zK`;|p3Ta}b~;
z1;}Pwya3QxppZhW-{$UZm~5RI9^$Yp<vY6ZqfeA$elyGu5otE@e#lQ5pe}Ew{!f*2
z%l-28SK5I;_&*pmsmBFp?><}WwnL|hM3zh5p=Dgr;K#?_W((Mirw_(z8C4Eu>ZPr@
z^*PKBeb)gJd@3RtQP*S=pZW~J53`}X{D5<Bp)MeT>pz@4w;S5HRSYpBHRHZ_<NNeg
z7o(fayYy*OpUi&(^xCSibJM{|AfEF7Ok*wL4dMh&SL>d57Nq?&j*s!-N1dKl%<XH$
z!%J1lm|roQuR#(d<uK(@T?TWkFHAjLjjK-omhxn}Yu2_1XwEAzI)m>fd$MTi%yA(q
z=<rs$Rfx=r<>dFX<)#=t-Z5s?Oh-e1GySLZWo;u37OeL)+}r6!z%zC6@&6J$+O-ig
zFw(4}L?jA$K(*2LJPIGh6ZzcfrQ_e83jcG|;8_{YIf!j~ZqfFL*PG*v3?S$sT_>lO
z(jVSY!_vBVxpF+Am9_0<bh@yqpmIsOXZXYISPqBrs9?gA^o}n7RU+z=M@RtZ+}l(v
zJd%WF31IlvT(a-X^CUb#ZJ83F#Sb~`O2=i|d>9Y$m~ww3J94u4f&PJLeR^yz8GeN&
zknX;vd|zt|->WIl0d!+@i7bRvKQ2x@hHtufj#VNyVVYjLim@R3rHS^(V6{i2S0htG
zk9FZ1no?9X;aLjB5jEhMe9^eRu7_17?CJk84P3YYpdIBU$sZzGdn<MG9p~zO@?-$O
z2=3DdpfzXKqsde?k}YypyA{ZYF@TD;>^K&+&B5Rvl%jDFgl%F3Ukd>s*RnWpsyS>O
zU;?jS8(}P2lr&Zj=NVeQtzDmL%NFkTIX|W(SXA1eMR-X_H$Sgi8ptx{0@Qx*#K<H@
zoM>quOLqplfPAm%@*bv$U?+u9Q-Hagfj|$MP`G~)h5+L_XtWZITtQDMK_P<IcMZA-
z7!VWfTbZ&XLDk<ReoeyAh9C6of12G0(J?xC{dgYojsBZzALv4jfA>Gs0^4<}q7PO7
z06;2H#V8`<nj`_pkD_*(APd)raz!#cAV`YM1t1Qd6j<OUyk7tSA-Ew8*KS|N4p(71
zA29y*0O!`)_v8e^b<Ec&%Q|o;VIvU`&ix2L+8(8#02OFfgjt*tBfA8K1RF*RS@#7X
zM<G;LKo#1z41kW*qG@WV0OsD{4_$-tXdJE3H;Dww{%l#vFM(<aeE)NF7Jf~>ZyUZh
z28=Oabi?TGkal#Zbm-_tN<=^%ozfsujua_r0Z~UvH-d^vw}^;}h<@Mw-hbhV&vQTL
zbsXpA#_|T<j;UjWVgP?ac68LTYiqpFMLiBuE;rH*7~C@Z>=dFy4=?!uNI-1^0}2++
zvGG_U7A<Dm7EVPkb&Ynnk^$Ywdl{}3G|YRT^fNPe@wN)8Tyi-7UE?*RNmh=TmcISI
zF#581@nZ3XU|r&~TdOJA-`=yE{uUEv^mDa14KdJH8mZpw8p;}T^5Gr3Oaq8qTU9V-
zzBOAYtKvR6?)C6#SwLOHg|I<g6Q?#6K4D9_Fc(fCBe3i(2J=ZT*o+Cr%5d>BQ83si
zu<ci-Miv4@L@u2zsRW<DOeYmjy_n9U><wd0UadEpBa9D(x5}QRY|z?Ns{~KkBvLTl
zfqP|;)rGvWvSRl5;?UloV6Mj^yZeHcUh@|UxBU(=TJ#sskrvB*o6RF-P?R4|XppP-
zGK*YrkYHHpvwWXwY@#b8;3@c+MeyY^6_tj8hdl(xxaB>@%oh7p0<R#{u`Ke`w)bUj
z#ZTTo*LspTnQM=V<z6+~8M=oA{w>72g|V#eUvAy%NX)i)sWaxZ5H8DTx~ow$bHo2!
z8}9A3o%vYSN+LnWp^{%GS$mN)-Z+Pwk|l&%oC7_?QV5`pHt8|H_jb}#6_^y?q|gE)
zF)2(+Kl-I?`3yUu+TaqaBt#Yrq(@lBe<iIrQp(NKLG_|v{8N1UlE6mN-kprv&;jK5
zGd`NIb`8<%2W+uH5<}<UPobJidPO84uB@WM0WO}6QGLlz0T%6UktVS!^zrYJm=VhH
z4ldo4`|yF6yQ2W?QCA?p8y_e<S?zZnVrj5-UPD39OaKYBt3aaqMFGT$k;iA^2zzl5
zr@a<Ij`dROXQ9CYGPDP&e)D1rQZf1H?}(oBdMRr~HGrtluiMV{>oHNjy?&CgF;w`S
z5Dvg6s?9c{;WmzAgJheidS*;|#T;*&$h*PRw;hpd(^00>j3+t(j;IpA8WON%lrggb
z)I>r~c?}Ik7I*KUQ9MxM?7x0JcQO9eKVAlkwQ@KXpfq7r4bv}$dJ`UG8+x2M@;PP<
zd1Yjxb(*xYJR(vh8Wj+NrbO?^_ujHD%(^x~fGUh(YAa*TpYcIbz9;HIg4VR~)(7|p
zdiTnVVI8BCfSjhQpuRL~4EbOx^X;Cb%kIIax~<}%4C66QW*@g~&%Te<=O()H7Nhwh
zyXV=`N7b!5)|MmhJTyKg;BeXopSD6RHn;#54y~H}OaScbuQ+^t*6?I!T4dKZR75;;
zMTVmG_Fgboc{0RTF~*b8Y4THrj6acddjvf<N$L)){lMmv?bO_S<LP4tVr8-@I=9y2
zeo#>u7C-_2OT#^e5jIyMox`KJ2e=?l?70)*k1GB-ticlmHie;&sDmm+NAz5DiA#Xw
zk7$Z~YiDcRUdd!`N#<rBxU-ANDg{DAeYNQ-kDJ9rG5)){BB#A_vZYVw7X!&@MSZJJ
z_=9a~7Y_#&By#IT6LN#vq^PSr>c&N%e&v!fatozFDIT{4AT4fEAEg@a_4ud3F^Q0a
zi!)0$2^ymmqHh49rp5#6wG(Rp43>&G0NztoKxZ|}*{1uEht510K`54ow8HE!NP^=c
z0A!2H1btt}4Lu<m?}}mIaRw+h>fNZn!xC_QH8rUV3M?Svp>#({T*<{AUK^d&qcNJ3
z95XryboGylV%ElY$m&R|wPK1^UIrcvZGaZkn3}miiUk7@xOyrKgr<c|bnZSPK~_?L
zjDB5%76XugK<N)SiCYg)bI>OfBq;e;$?r%SjyWK&HL$!;lVg_HL|V)U5`Plu+F@54
z!Y3TSgW-iD4G0zd#IFQ=A$V4bVS~@&ULXj?LQoy{^bi?Af9L?cgr3>uC*ck)t!WBe
z@q#7?E$#>1#9~7LkK|sZqM$Jq7EpoNJ%|>A^d>ZTPe!ZGB>NkU=3|7E34mBIqOjDM
zE%ip6@^@0&I0K77G)c!aBZLir{}GTR@mRxeLzbrVi)QL9@S#sK>jr@)@vRyv5^q20
z^RNZtR;en@skE+4<zuLdGeg3Up7JYHk}}(9S+^#<&soixPvZBSUZkC4K+T$45|`ca
z8$RXD-e<NR4Opz+1)YLPOEBXvV?s4((;p%%o@*IU{)9Ju<_TT+0Z*>F8swMiOxFur
zAurns4qDI9LY^Vpem=D5KlFAnPcEBch{<VgTGkS&C{$d-2DVYKe6@DafZSD2>%DB{
zYAE%5bv&F$ufREvoIkE!j*JXlOC`tyWxxYgD?fToC7MZXAvbb+UX*U8JP$7eXjby<
z|0zJ`1(M_yf>dwR(MlE?$V{&sNq!R>S`t?{IuQ+*UNkh`S`rz*BO2lEaZq#|)bT9{
zBPglygf641kT^<fSEZZr?n;%Omh>AiBQ{vc<f?p?_Vo=TG5#6e*_D4vBL6B+7=Spq
zG}m0Vw;X9PZ9h9XwwDrLK8Ox_A@hg(Xk@8cGHdgeQPmb2BMCjEW{h<p#f6ijJ=%8L
zCOt-#gc8`+o8J)b!9fdvyybLVJ<Pc;OHd@iN)wd;mX@-cmZw$|p>+mKh6H9};>%EB
zh(LE!;<rh{@{gEZRAi|CYs82kn%`h+5n;z%koBvUlN;@fqA5gSK!bjf3<(A?>pNW5
z^w4k?wd6ONvvMTi{0i}LO0HX6()lc!39MMiZ5l_;{!e!r_O6Up@JduFPQJM{!Q1AF
zV?OnQJT$&FOLc3Zi(?{<itt$hf@Ol-R%R*>9+s^5=3puN#e{ZReD@58H-h0cBVE;K
z(V+1;Z1_+%gkRanqx4n&GuN|DrTpIZmID(a5!firrdcB&D@xa1OGQ{M92&i^Ly0Rw
zNIhS)`r19?>c%8(-{{>*{}e3A=$g;YrR1mb{NT`se~+~EPZ2iz_-)wkiQD_`YLiyG
zw9cU1(V28ZbtYKvyS*AF+62Mp&VrkFlYD+yHr--ij7-}{EL&y><ePFbI)7HsXZ&II
zc0LLHt!1e?%c48QGE&mahD$rhXsdA|ol!0C4&q+JP&n;(Cx~7l<fT(Ax6=sV*qXk5
zYc_a(%6a%D51CBXlfc_6tuDvElHXX0#Rp!09w!!2zU3A1h3@E4n_8t}kyiwqn5#hL
z4^aK2g0A_y6^isvs-cR!=+yg#czEo5lKakxhRjz1H_<%3$DS<Y;^P9Me6U04O|QyV
zp7iu$X%ga@Cw^|}o`n|2xxYL`uVED*h?5z6y)EGsEqzj0<rVSNFQTEqBSUO{=j0kq
znO|bK?6A*4SwZ52sA66^G)Ja?{LHpr*6(is4(T92NymfUgEX=YX43P>GeXhf4Z2Km
z7vdL23i-Sm*a&iqko&RH0Qtha%ZdvBQ30YvpTq*H*1~dfCHceY*c2$ypOCXWp~ic+
z289(xUy=zNBkV%oK%!yFjl9dw<Sl&w;>h?c0D@<S%^E1V-eW$BK;Y`AybF~t%sEbF
z6D9}`H}5gf6LHU1lxNgjvAdcoISl?}G42bRziKtyeV{nu>Ge1k;v&GPNdReKM-t~#
zP_*5ye`K0M@uFcr0+pvlgZb6Ce%#J#rBH<ZyEus;H2=3A%uK%vjrf*MA0xdc)<RT%
zi`cHlUlPR0&Q#GyRAD9auWv0&gh7J;<)4!JEhj&1O)dU;WA;is4iZ1RZT!}b6BS5K
zB<=FDbO{6f7dj5A4mBvRE9qEzO?i9bn}sU@kc}4ehbfloERjiRN_8eIcpZkMw<UYo
zh#OX|+N>Y8U$f^gt?biVx(20uNwpCYd8BV=;~rEgxGYng!T(33BriiEEjQ#gy=mDX
z%kDD|y=66S_T}l7oHwl7Ak8CIIq-$;FicmHUw^JwL!ll7p3@p$FO>O52jMRqsOD%^
zbQz1#e{eHzJKMqjgI;LSMy4THta_^Qp?IU&H`v$P?5!8Iya)9qKZ!HnhQjzpFxIo=
z;iDMh=0?Guls&);Nj#I>f9lpHEq|x{%Z7AylCkfOLb=z{%fgWB=6A&g1|<c<WHM*J
z$UcyWFsa?KNPFw-$B9~z0*@pO%YUnhkJ5@BT=h3QtC#&(@ttx$qV=)hkJ+B#vbYyM
zI};1vX~te?&g(nN**YH#p%h3yIjDvOfL=hOOcpH;fDakg;q3*QDE@I$gvc(qDq71a
z+DCC}E@=u=2JLEGDGR6Vngw8;@}>*A(&7@s0S%vK=(qnLTaR5=C_K1<h1<Nar@$7z
zsPJIoxkASGLUcvCqgQXF7EO-78?qp=Y&%1a-Jo`yiKYmKTt{^Kr#Bk4smXPpX(CW_
z)J;#?We0sIF`a?9OgJG1z*F|ze%BMC#&ISvg6;<<YwZ)UXUjD;jxo}=sg(ha>`<+P
zy=T+)>^UzL3Tdb<Ui@2wI`*144EY$g-rb?0yyPUZgFB)==T&iE=yc!Fzb5gH7z?6L
zf8d9Z=z1lR1noUZX3a1S>;+9f7l1TWJr`ni3rQLv?Zp8m9xVO!E~PNX)EaVVc=H~8
z0^^H{1i2ywpd3m}C@{Ka6$vc2U$p+q5I@oxDh~zB5nKWHI4eHIx|E|aned}uoiqod
z6d})?*7RCGGuE{%hW#ceQ1mFhb*BxnULwvYe?Cxp=J435qWjy>w}DiBxt}{yuP^C2
zt{-MD>6~<T+QKH%6r57#>hf&(Ekj=0*EMGkfp_PRf34bC6UQ_)<b!|qj8iPrOFmdq
z=;Zg%4t|>dR8TG`b4u$<_xal;;2<XDP;f^!c<fb~jB_S5hewAmpBMZES3iGUgK^PP
zBx<c^SJzv=|1W810|ywci!p$abxQ&)fP^#40K3K_pL>-rdz6nzQO1zc#ZOiM;WI&H
z@dBgmHVcw3=NAA5btOPqVNZap1Cjs@REq(y*jY5m38@`a^YM2SU?T%iJGZ$vdq@g_
zRMXnro<1%qhXfqqwn)HrOMz0HzwD^#h;CH&n$SYS$KLPo=gxf0mjOThvqp?}X(xuL
zDMf~NYGnV!NM5-+vwcigWMZscx;WF;p4w<nnx8qqe`@?w>qlkA+Ns5)UMNM>vOn*<
z#J~HDuQHA#KXjM9E{WX?jZ_J`LFe5MjUZ6liB$tl^g|4_K3`Sr(&0oip8W79lI4Hd
zmb(ZR=<pKhH(EqSL|Jkq2w*2di&ptHDZrsH@%wzSx(`N=mV^K)_b$W(1hi(4m;XNZ
zXueWMnYY6#K=7u_0#+~A>YoB%qZ&sWWDrOM6qf?*7>5XeE4iW@M3pC(x>Lx(v*}2Q
zSZ%#1@jPm8TlmFFnH>kQu%^S2u18t{P{a>cpNUo?0G#N2`q;>NH!*P|5Q7YI-w%+Q
z>_3Ic*%+{>fSl|<p5}S7{<DJHj}(z)jy0eo!NEef%y1+{JKExA>%Ywm#Y44Gm6%z1
zN*r{hO9%(G&qu$0b`I`v>EnTU-gY3reqOH0*;*XFPO#<?W48)*yy0J_4;^c;Hc|){
z3!EPc0>Do}Lh?w!NxWWLJr1411sHWLg)dX$P$0VeO?u-(x2#8L2!{}Vxfm4=lJCi;
zIMkNZLN|J>id=)Bbg~GLojaWQ^YckFzQsdCiC<~<DFZ3hO*;GCZMD%wS`?s5z@Gyt
z%7GNXoxI*cRQ<6nAgpxPg<qfk+q$#MjSz%jub5O6k}avi2RJbe%@Rlal%!e-TH*s1
z#lEAJA8Ol~Y&lX7jH%uy&P0uLq^jFGGQ=r1PY<>_ISDq^AJ*AEYWvbud;58ys_k6e
zyU0JOsGI)Oo5_c8Kjg2kY>mHaUH8<~CJ(1+V3T5{_XUset6Y06pNnq_b)YZb&b$nL
z|7my5eYNoWo6r~T^>H7{+yowfvt-sKypRg{_krhb$-~8?(l?q-JZeTB=jaK%D4_K)
z)B@}y--ZAkEQc%$cT;=yZcT#`1)Bh8Xk(rrRumN*_T#<sD9>J|Qff<<OKzh4gVVlM
zNs0;1^V;eA$3xGiTBD_PS0CroC0SxXB1%BGmSJ5<XmI};q%q(ZW6n7QPV0gm#$^}E
zwz*&{S{2e_#Y#?N+)WLWu824<(}t=2H|l`1OOoDuwW;W8qQ+;LUGob=k2HeIoht3b
zd?=~PqcLk`_e)YvqMQHZW=gCJ*;#$AYLIq=*_CKhv$1n1J)*DZFisT@-zp~m6keG#
zagXZVZw!4atMxZJRnO|LTmXln;n`6~d|P{+A28iNszl^CO(;U3L<dirjJ*3EO<Z$j
zYA{S-zm60K*-#_*0o0n4S^%OZl<0KCW8z_*79yCYm8}a%R-$S0um;xG*oiRY1vyyb
zB7ht^@pt95K6P65lg|JYrJ0NdT!U*j4dQ3-aE%r&PQ%H-OorbB%dY?BV2D$T$m+;5
zt@xEYRADe}M*g%k>UVA7h~QYzQ4GlNOYCSO>4|Cs<iW>WaLjhfHy)BfT#(~wz1~$4
zF<SU|w6CbHVMC2%t$V87wK}64bu9r@v5|mIO9ul$Q0Z7{qoq{~6z9pSp+CrO{XM}p
zZ9TL$HReFY_lOJ(m5S@e>%%pXoZ~qM>V*S;`4Axw64zc4J*y_8C#{dUVYxg*O1Fra
zpqfeSmYPT3Y>bB&)|inXmAE$uAo4<F4p!><{2yiJ&f=lLW^f$nWb(77i6hM*HX&#w
z2iC>cw#~B^;Q<2_XUo&^cBD$P@V?oQ5Ns_8zzkCB0iFRGJ|!mB(P-Fa5|Sx1W3LtH
z!N#Yn^iap+(V=k#04cPPw?RD+1Cge|)#%YdaLY|QAgJlxCq*rf<r(Mbn72FXJ-fVB
zftzJPrplkXYya(v&A)s8cDuRzLzI^@g7#+9c0_MP`z=n=<(th0d)<X^?nv4!`p!HO
z?;+>-bW>rrx0&tp70Q<}QmVOOQ**0VYVlqAKTKX`YV52gbzM#6yB-*zFs{_F8&uZ$
z&;2V0fLEY|x%_7eH;hbk4^`SejXA{lBvG)E1dOZ%|Cd3v&nB{c`ISI&&>h+Gsi`B6
zZA|>?eQP@QMe>XPD3a9Wu!!rWT3Q;(-?X4u8%nq)R0IP?S+<qT5eP*XK5ZgS`x4rP
zBGXJaVJEy{h%bz4vrBg!OH(>}@w)CqvGFZ3z3S>Y_BVauo64$|NrEh1jr+xNUjEa8
zc4~SizO3IPLU|sP=&;$(u=2P*OJtL1-PBUxrnZ5l*ZdP5p2Yo|&MdgLD`M`ukV94Z
zZMt8tQgS-!o%Zy%y>eHd)ABn%m~LqH8;CbvLs)VEurZ9?91pr$8DM+uqjYVkNqA<G
z7Y*dZ7Ldf;e5Nvcv$KZ|S&<u%T5;u>mHbXF-3buiDwP^Oe*=g&j4Yc-$0kg?Zx-(r
zbc0d%ugDv@H5w7&c2bZHxUE!8P*GhZg&>eBb`4NyiVAl=g+noh!Bn((38Ewm{mQd)
zU}pfBBSCT45I4zwyZY|*H{Q_oc(S!s792S5ve>bZoEOlt8Ae`1N~XBUiuhis;UGKZ
zw@!=Y2Vy0B24fr(MUXR%q7`b$-jP+Bp$0Q_FQjHso3YHjVJ2br(KVhFnZ(}6-rqmI
z0KnZz6ky~=@W4m(Nj`11AZ%`Bi7mea4gqYR9_T&k4PvF%kgjL6YiVWD`dVhqE2Mpi
zO`}y4R!PRbZzNAgxU0=ul#O2w2QVf`$bpRSTV4Q)x26EVIMrA2IWDw?%l&N)r{9r9
z&Ug)V|9jJvR;Ib#tB&`@BU5j`zAiGX@e;nyRo1}h{cal3mzMdmMP_3DbD`yOH%srA
z96Mx)%Ez^yV=yd`;c{LMIq3T@VyETP<@#|L&CCA=53fkgoS9K=sYnbD-E%74dDZaa
zs^N<!?GDZ;QK^a!sewOi4d*4rTb#ZhC@TN!eOD{x1Zh6}-n1VCC|X*Sv&s?YrOH#U
z|NKm^c$i?X!`gC_?Drn5$cSO|9{G7_YVa?LM+*ulh>XmfjMi53;ScH;EXu`PN}G=q
zTAowne5JI{P*7FiiRLI9-AmuJqTu7<9Ze)(xz9C<khK6XO2ITI;p#Pux)nIi^#Klk
z@6ibfM+>AXc7l>6JH6gcvqVNt1<k7>CZSZo%S)d=p)ol6av)wqyO*INHGp@)q(h5d
zdqA?1@^v4H9%f~^wL^o4Pa$PdxAOay4lO<^hvyydWge8O%QNUc2+O2w#|(&>^NDFz
z_Pg}t3T74^4n@gR_}kKdu%o`XBUI&3HR0dUDjq9M4}k;H6#IlxAf8RzMfpzsHwBw}
zCneB?(N`w0XfgABej0}tvjA7xW602Mq@f-!<nENymgV{iLAp8e>O9E|e3T*+Yu>HY
ze;0znd1%(6=|ms*qn_2`068i|Gn*f3=$|wjNcOvox2li?;gk>?Pd=19z^C)*Lsk#M
zUzs;rXy|s2fkBT`VNY~XH=Bhp1}1FjoZnvB7n+iSohWv-ED&}k?J0ryFhd+Ya#fP^
zW#n-7qz{2}BH=m!LUFO0>WvSm{G1X}uHt}VyssvmXv|2!-JTXb7}gX=GM1oLb(M7)
zo~;mXajI-J$QL8V;AsE++#I$q9WN~xzYwCVlV7wV4(%o$lgwaiIs(1{J(?NuN^)u0
z=6x5bJ!^^TJaVyhY>;3;8flmCA|2O*C9jLB_NYs7!~z^V3vU1mcaKjqRnAebJu1r$
zwjxFA0U!*;@m}$XWOg3cJp3ii)7W&LN^pyBR{bS)_+3(_Rh9$Be&@AD$&_Tq-Qxab
zjy#^`-<?y9q*vz;@O?B>y`MQkmYJ-6m1K}KX{}4e9})t%ID{uvR-w`kjc}P>NhBqP
zPnvM2Wh(w>aR)*s<YV#XNongUX8q&Uk||Es$r6)ucs^aZ2?JLEBM!ziK6(#F=|}$i
zK^wih`NpTzddmI<hGus{b7aN<BvVJtxP9U3nEm_q*#fyXA&P}>1Bo<L9*Pv}_flg$
z$=B|ck5Xu@VB*iOF~Og}&Mn3*>Jc6CO!m+jaZzq|X(UTKfRD?`Tu_Ut=^WnCp~MVr
zY2@h$=^%|NmKcx<qU~qamhyeGtA<vV^;Q>j559;Gb4pUv8*u;Onx7l)$bM<2tDr@%
zQnHnjV&pY^SA=)fLaV`|gWr)akXF8rXW=iE&S+LT-<0lvitc+ggZV}!5?r}~F3Bh$
z!K=iFP0*0^FSPBx6ebfO&f!ql=NRrdXjGM9N2^afD;x#Djl>`9poQ|vZ(TS;A<J*C
z>XJ{ie^w{@DfH87NpM$sf^}in63tcP7+fr9@OlaEm5V(lP4&ChIL{<Y(pVp+#_Gy3
zWN3<fnovTzvUtXeq9kL=Lgla`F?B5s{8~?2;+RS@!)gH>UqS)5B&EL|w<4(+%bU$n
z3U8P%^z}}Xc1yCxj}r^ATp<RUe^cRxu>2YltfCnIlirCbfPvvOZJ^ePi7ifrWuX<h
zi#9CQbb#xT<2zx_2*L3Go)_IBw@lP;aG;M2Uh!MVowCIN2h!B4#!^o)g<Inr7LDSs
zCN2q$Vsl3C0t&wclV&@xDRDv8IMh1oX#828;>dBa!KA=E(E^YN%L%lP2p$AIEuEVZ
z$H&DiT?zFZPg5^VM7%~EX;klhsCYUR^H=|453?^DhVq1+?hD8J{W8&2j(2R5+k_7#
z`!fumRkgo#8vml6s#p#9@XYZ{V(n-;X63{0Ny3jw&Z8Z|z{cvezXU_VOydt}hJ)3M
zgA&(|&;IO{vA!ysdz0GxTaqQNc=5D(i8)0^p+Hz?mUh>nlY}N1^k*00^Q2SG8z^#6
z1CNeVSnkzNvN>&~UQ0=uoual6x9@6Wd(HBQGW#_(d_aqxo$}I(L4uBZQIXB}U;E{M
zDQkD<Z~#1DjsGJgi+e%}EUU%Dw59K(t0Yrch)3;j_#=gSD!$5Sx%ofpLjnr(WkPc-
zpRDH2c`7+1dfBZK42Ei{D0<Nyhgw4n+8L|b>7CTWqCBn$u?5}Z$8mgcffL@fSwN-c
znr<~*j9&PHakSr(_f6`!L=VMMAWfVy*Nc)Z^C1^e$t<Dbhxx?r+wzG~wQN=Eu>{sK
z-1Y6703wJpx=n@;B)V)+A6Wo`lc}~+`oG%{QdxbFVv&vvO#!jm4@cwrUjDJe0mfMW
z#az+HR3AuEvww48e92P9lv0C5Vrz*{N;ZpVTw8Dwi|dN&W4Q5rJW56pmt0;GZPA^U
zI~A-<?>#!0H++&&GgM=zll7j7*YxG>F&X0ovEgNWDlU@z#i3_f_^bmq(Vo_u3GOwe
zB;0@wC)Z50aXDUzd$3Y2Pe+t6ruwt6*|8|v*t6?KZ`t8W0c73Rae!VdAnTp8LuSuP
zVUToe5$gorn=IJx-7D@?o?0?+W3Ka>$UEL#6_?UPGh;_5#(MKfh8vGQ-704BBEveX
zwLV(h9!)VSQi%;#DGh#sQe8|v`XGM8Nf|~etcQaRPgz7Jr+L<VIC2{6oR+j`%{Z2h
zKJis}X=YZ)&Htr;6RaK{z4O~>=KJkT+fbap^Ra)Cqikq8VV&LNaHjt7sxzc%{-h<g
ze^vg{kMD=M*zeUoa;c{A2g(!Iaql@DciFy`y~>U~WI%mH{(JU*$0G4vnI|QO)*(f9
z2W!+5O@o^b?McSNFy^vt_&*3cJ8j|4YtEtmLi-juj-RU!x>G#AtTi2XwkclQd&L%E
zLAk>-5EMo!Zp|BR_-xgn_0n<>mwTh)=yOlbT$K=+k~^6_Acb4zolxLnIPleSDN3#B
z3|FL9AfL<0019d)D*cY*>O#p?xw=B{ExyVQRW}&M>gSH<8UN6t{WUp*4eGVW%`{CK
zbk}NX7H$a_zooV2VqAC8UY}5MeRsJqtd-JCzaFG{{3(cnY2v2+qpyXUUmMYR9}UQ*
zLpSWXV$UyW6ot1965PrRVJ=42Eh>Fa?f0<9H^V=t$d!<TVCK?ATC8tUW#n6gs3(LL
zUf@s&>`7zXV@Elvk)!#7x$o^K3RD|O?QNAhbKoT|2`K<NDjpZ4CgAF2xKWkp<Df1h
z+_Ny>H|vr(Yne658wV}d@cB6sp86ypt%u!5SurrhDx+>iar89L^Pau_3Q4hHu~DmW
z^=db@Jvj*RPG$+YqA9rY{?vCGTNvp<rsrm=)kefscWf13E!FJ!0Q<7E<m$RB6zV;{
zM8`@Nb7YNsw1T#zfxT#d*6CY=C|ldTAAPRgH280<N?0-y;_9WO@A5^k6984>7tl@D
z2zlatBDV+3_ZX}5T}oNs>p(0ilcLk<6m#{3Oe`aT=o&EPaf)Y=+3OM&T51=m%~UuT
zZVbwC3JczkKjz##bDl16Y7E^!IG#GL>o7UNyc#n9I6O7<VWucfhC6UZeL6Vth0730
z*ukGw#V4ja&kmYKocmGf9kkPVzgB-%a40tH*UVw84pYluOgH?)5{`-k&WnD3m)828
zT6}$){rktKi;<CEfQVIsw6=d;u#&wo%pi8wpb($kh!yAf9L-)5+1|hFTl!Mz$m>f$
zc)=WIH&slIm!9=^J3&$0e&BIQr3c@D`nL?5pDCETH=5@Mwl8sHi(l+56Gg5fDkdT^
z_pp2<Sf#>wHbHb5KX+@r*o5}r&LNMBsB&&!$DqdAf1TK$OhIYvYl#}vw%b9T!b1%N
z*9ob05A~y;hn>Rjhv4t5x=t#aw8xbmd^No2Y*%tUGX7e)@A>y?i^inYO{wV09qY9)
za_JMu9(ORAuyvHrUe8v7i%~pYCD=Z4&u!rBC8cmw^h0~GxWRE^5m1sLf2crB;r`8s
zaGNVDe<5E5>2x#fm&y|J@z6-21|h0X`|7U@xQV5Zq=U)K#_w3hUvLqhx+RmuB8Oe5
z%LC&PaYt)XrE4l88M!mdzXCIIgZ-CjX5yZz+6h5Yo`@UWkmyM?dyK_hUBdt+G;z5#
zUmlj^(G(m&bucuec&G$(hHgJ$4GS6H^5_R^SQy2BtocUV#68yhjGP{AtT^Z$%s2?|
zbs@&6XW$>+$c?~;h6v_ja8zD{MC1G{lBne>iz6xRfMSX12{?+p22?+OMUkRNZ1P5g
z@FIU<9x{wLrUo72k03Jzj@$gn$HH_if{|O+ED&d*1<dKSc<2Lzme&qjD~N6s_;dF)
zK-^Nn5Rp)Zr!)H5fQ+QeW0Nsh7)*Mr<LLK)*prAooE!;^SUv%6GK_$Aa=RO{_r1;c
zpomou38=_P2nbKnZs01z`PHuPLDh>qG7^Z3f~*oS70dxN%$bFJ0U3Thx#{N9Qm1)@
z6Yi`Ir-OW9nudS*uca2glJHR-z&^c7nb@kpw50d_$9t%fRR2o4_}*8|n|;#t{n=_{
z=lI|wI{ZL3Qk{NCdXf#VX>6hj(=sV_g<eT(Sj!zuzI6NV>IvNkwl}k(^93#0RX<pV
z+5+Sl4ZY7KV1Ru8a=<8OJW*^!MC~SQw3Q!s!9B-uhn^73P?%Q}xCe*EtwE65d25hY
zH#yJ{QR5upA`sHCaC)!)O2g3%w-y2EnEQCjq@7k{{=&*Aw<=(=Txh0vk|5O5X7&2n
zp{ThjPJU9%-1<=65`i;wj_k6rDwc#;i*g#vR56mQb=jr#{JbxRDH5jvB<Gh_fJ`Rs
zQGSgGG112O{t$h9=z96K8czu)F+{Wddwiw7Cg`<BgSyIbT|pwf(}(>ZhK|qX-iv>%
zVcg!nv$*u&hl&1eg>A?};K%ulMP|-xi05|G^mo^8O;b79Dd|yiezaSati&=WvCA#~
zviW2FY@AWu{_XNh#e9#TFA9-bU%%MJZ7bcf%Xei--}h@_Rlgdk`J3ABU%g4|WEdwJ
zL~&CR+00Om<I6e{Fy;62RZfEaX%cmOFt}5$?-T%Sdk@RV>o{sXh$<(54v9=+Sm(?l
znp?N)Wp*M+(MsxTZww&kI1U_n*Ytw?9~KY~zW;txo8rdO1=zjT`|lass|q6G8pe`b
z8PPzGHvVj2+KV*_57?8TN$|?u8VZbFEAM&tTv_kG!B*aH-Kd9vtfxL;um7YTa6SpG
zGTp7G<u}MC5+GNQSjcHi`ER>mLK{{E0ciIufHHMN6z>FY#=_2nswX1jp8SSFd2rKI
zH|HWj!^(hCA?F%_eBt`5#a4rIv0ODeDFxbsi&XyAY*=CmqaBx8a`u_;C~~22=skeK
zHOYyk%7`4%|Ifch)~4j1H0#k3yPGiL`((5419b5D4ALg!if{t}TPs1W0huIBJ4kM*
zGzd6<%eILPcu>`qwEZ-gf&QMe8SAjpw$8R{d}%m!&Z5d2>=)+F+EX|OmVZs24A%nM
z&1Lq~^;>>1UO^`E5c{)DKJxHHM8(k=E9?5uifWwUD(9l?y!Qn7WMEO*IkMzh@R;8Q
zut<-sIM*B<7ja&t%M|KUQJoY<?icyN`6yYs`@Srn#$j$R=Xy;{1*%^^<?pj_?=>yh
zN0{=B`HRnnB6EdyTnx^}-=GE*3&}s-7=OwtRh7_Fkl-LYF)y3$iLi;8!XbrtP(lEF
zWPiG4Oc9GRe{;Sg{><P%Va<QPvrqNi4^s8e_Ir1$d-JX4WTKzj+KwRsNyO2YTfIFe
zjz#mwygT+|`n5WfHkC|v9;e=ofLMOj!b!|qH5wE1O@@`K9o7uUAI_cUXY{TJC!N9a
z85OH6-!)m|C@k}BKD{n*JhKfD32S>dGD3N}QDHRa&{FW#<Y_z|knePo@$)vz$ai%;
zwk*CTf+Vbz#z!qNMz$Bb*t|0lthdp;)y@bVeAG@}yJXd5tJ2t;zku-ypKfd$8M?Z3
z%eS$y$=N`q<rrDA38;`nek_J&{d_Ydcs2tQ-?hZnZtrgz@{B#gCuYu)PoWAQ-nmxG
zh{TSu@@Wn*8eF5NR1qb?_$sAqdI_0+BLI@s=&ZCN;^qem2tS8fa{dJ`%%CV$&n^7w
zc&_0PJT|c6{Hj98xyF>RC$h=e0zqr9hpo3UA^;8&92Su7^)XD-=GpG(YMUOuPz(CK
zCVxBW+jZ4O%dLv-zUA`&#cbT>wmokcaE1g78G1uWKf7gzBjs*X%#(L#@)hF-*B6do
z2B2aG9_|2&l&O0|tEGAEw}n46{1ee^Dd`rbV9$8;oZUTeEIvEjo|4uh!}>Qz+{twW
z20UjPTcnH)7I8f&`>8y_GDJ$~W^-4nVNyipmqbSbQv3S&bqmpiO+DYBboG?lK6RaV
z|C|StXZkW~>&W%skmOQp1zP)iECwy%(@&RGg@U9$)jQoJK-M$fSm>7rm$AJ`2y9l^
zFckUCk^Uqh!=M_W_Lgm#cR@=jnvTf?{We<6^MZe7+g7b+F(hv5R5(XOBiSUMQe}s9
zK1)>PYSm(_3SWKpyumh`-<XGujq}r`?*h&yfgX=?`H!=YZM}FC)Z%JI1uWaU^!ND5
zh3C%7+P~Lu?ZRU2S+pU%M;~JZwsIi=#cHO-lK5b*&klw!L`_WIGE;hmSrGY=SA98w
z5}@<yy4P|wQvCJtL9|-(U#a@{oQZEkBf(j5O*zpK64MiNNJ-d~7N4wa(1VyWTGn0@
zU?_MvD@cmjrnf)jccYiFYOeq^((y<4azlQ0A<E8s0}}NXa}AzPJAU<eVHVPS>vR9X
zlJjFEQ?<2dT;l#S#7;-+W1I0YtAjwb+KG$f?;5f5y^Piqwi=I46j_~mU%J|k_f|hk
z`{20y_sSvphbmbzm#aNb%x+Vd$G)EaaJP2}`>LG~yIh;x<9}`!VtR#f#kMMwt-+2u
z(6gy?rC@;C?MjypN5F%eJ^&3i7d(G3wthF}6;rDw4BXF1&j~~Jqxr+7OnnzO<T44a
z^GqA7E`T@#Akk3zT76w~Ap0Ioe>Y%u+p{E&43)f5ie&w2fChPbHvyMTg9lfVzO$?)
zM~8m7CBdHUG@|fTSRIjtiuhEz7BiG%aHqfPoTg+qGdl4X`r%%}jd>Q1$Fy{Wo28kw
z>yv+^O~SmD<O@~XR9H201#j{WB%hv6L+@Ac-#=2qnP}h!$a;B)<ngwoIO2EZEvL8N
zPro`@EKx}pb>K<sS<`}v@^ceAAf;v#j>x1ZgW&iyaY!U{Y}$QuNj`BHZ$&6Ggg9@>
zfUl)Pl7+{$TE;UPp9i!ZG<HcMdnY!^CoMRvzM4%|m8*!)rU$%DAN3jb8J%`Dk|PvR
z;EC0bTj%>{GdJh+hWZjt@K+P^^>mQeX7QTULv!^f+B*E1lNS%ZXyrVe&uTf<`|7o9
zsyLr@wm`v~Gj{#mP%&{?0gd7H`JuOZB1ZF~uNEbO^jifBe3h~SqYM(6>4cL}TN=6&
zydSBO8B%Gf=48FLLKiIlWj)1y=2v`A!DQ-@uvIDk0muUgH7ZuVgy)r5tT^3hgkx<V
ze}i*f@bOC3DR-5lCi&!h2}If6#%CIn^1-4#LEZ*kM@ug=ccb1bx^Fl{2b|3&ZIZ4(
zZou1A^*SGmIs#F9+&N8F6iY=4E0KESF+tVfA>-;yW@gtt>VpsR#n&%fyy@&rKGoN{
za@Lsagmk-_Aza3rpPX{@q&?$BB<<C#G#@j$kZSE74;^f~?bko&o7d;3{?zPsja|#i
zeXv*8+{8VouimWg#i{c*#C!)C<~TG+BiDAHW`6eYB`M#<GhTj<^=bc+OvLoh(Kzvz
zky=0l^#`9$8rYJ?j{BgFiCDn8UK)!{gA;oHWK}xv`+bLnlHwg<YyT_wWUP%-o!*gk
zkKpP*>YR|I0)V+chUIH@P_j7M4apxH$3m`3#*+fTPnu+`ZQ~lrng$86xQC}w3D3|K
zJY8>I520!PBh|(E?1d-^xa7@EoEg77y-TG<#_#|0@5H}<LZiIT_U=j@-+gxGVSX;1
zbqP=qV9tLZDW2LwExs;H=ax%_c`MSWc44U4jt4M|9{K!Z678dJ6)^x1X%f9C&68KT
zX}NnA0x7l$SRXQ6K7bUn)-i+WIpy^P79ka%wQ!u~&Wi^Tifhv;>uRauEzetgt~7Ql
z1s93Z;{&uyAviV_O*4kkE+qOWO1pbFxFe%Vk)fX#fI^D+MP*d!qV#k@j>ZCv<5OVp
z$`m7Y`(rhWUo>NOv@JUr)a=scZ$YCw4A!>A{02{jPnRt%>Ek|7!=f<^!C5(17+|!F
zp|FR65n6G!nAX~*jv}42Dn>)u6^XYwU#{nqSS=V9S$u;TG|ig?ZkmJ>mL<}S7hmg~
z**@ITX4csz*7$wc=%#B;5n6-!54<C#)ho(x4v_Ic>rzouYSSgR>colDtxHm!Y#iv_
zwwyP3kA+=n4&%s#9|$_u1o2+?aI{q5*xS|@5HQ-$b>ps*hhl^`(LZS_^Z+D{CFF!y
zkjb5<B-L!d+y9e4-o#R$`r(1O-ugfOOu@a(@y|9HwmYQ4&A0e6n|EpZ9p)W15W(I?
z8MU%ff}yIMlCO3ODjTNTb{9ThV|>bw=xd@2aBp<rJD%ZbyDxO?aVzMvOI4Bc$i2@g
z8#asu!Y$Ls{gbP0B`S@pe6rP2xplMw^(X#JClj34J=fy)Y$3I)%#cU`aw*V*a0zb;
zSP+pwf@1)&{*z%-&wu>P(``i#dLccufzpKXto=igsH+bo{H(M`VunQ)#M!<tsTWFX
zX~I#seHq*@(XUf#j7JH(;o!T5&rHXkte01Kt>}b`09cJ;5b#;JqWs<YZ^Pw3x}AUS
zMTCk8#joFW<E-ytP=b(rL2Nn2K=D8a^s`$AU0f~|b`i&O%r(mvOh86s@f5q2OgtXU
zVFnXAK`fd$M!gL3Jw<N0*TU2LJpT5bgm7|q9k%{P_QK8JfjD9ssaoO2M0;NqtJUf6
z<XtJ7(*DzLU4?$KIcYL0EFadXoy5$ir!4+P6IG*W&8j*0OpAeks&QSbZc4L5PTPHv
zOZ%PjVy8dvxQ_I)B}1(87f!0hm01yY%O>#Gt`75!-B9=!YhZK~>PK{rcr^udzU2>7
z$8)l(--g-mq8BY040-iKxPq7W_3|6<QRpl$4&M8<P9tAb_$4Cu%7`?D)x)vK+hSTD
zsmP;7XqhY#?r~<5m1d%0BFq)CB|LrK)<x;5M)=oPr6!#Ba*Ad>rnws#44@ZR`7S@_
zF2q#IS=zARe`fCe;J7|!Zykic-W~OYnvZ?koUSG;&S;oJ5aD)+4{i<+S~8Ks^KUXG
z+Ek>lc&{+VTC8GuxCaC^W6#0xlpPKwkO?#DROb<mw5gSId?I51gU{s@_Svj<?^dtx
z@QG+#^n=i70qWaM;-Su8zGUiAI498xNe(-Y_-0!^=ND=cR(Q_Nax=RbuhF@g9rQRS
zXw$`L@l%a@Zg(<YV9|b1MXvO8gH^VV%|)baX^Yu!tmLJiE!?k*`XoM6D1s^K%Yg*?
zfVwV%&c{=Qs_-iQCk=K5Fx~U}`TaI(GD|-kLQcn~Sse*7DFBhVoaLmNP#LGvLE0wp
zRkQY&GvOh!W^$DDw(Fj27kFf!YfE3{Yu{yrfLI(g26R8#_o+*~GuY?HOHI4Z!@Rad
zE$6`-<{Kb&z|lX$%`_2p?adV&^?&OR0j4DIP%O;%TzDZ{1oIZhbHw%Ym%r@=pvf%(
zn|Oy!5;rm=VVn{Jh>$vWp{1OAnHnB2X?iH(yC}UnxMKGL<(kMg6`n|?&{rs#w?9@{
zwnqMPxHbC#L;n%uS{5-|g4z6q;G?*mFNIj-J<D##N<Y{7m8G49m@#G0(;1Go*1Tfz
zr-{l(IU%LFTiAyxQ@2To+SI1`i?N>Fr(z|l7$sV}c}27?Cu?{n_qQj})wtXjh__ch
zwk%$}Z@SYaadg$t1^4_%OUIy2G)3F;SLJhex-)-^56T@O23O~)^B6`Hv=&UJ8Qx#n
z?r0W}jYXo06hHL$0*E-hOj>08--PFbt%Dnm=hvMCYK4AKM#<l88X!+flqx;d<kPjW
z@=(vHJ-nf7P<wvwn%`#1bj`JaB<m&Nn(1WcVMn{fpvh)&Xc|9_<sKmR{_3wVMz%R3
zRE+8|+`27s^AtRO7NtSgEzxAobqPC14%tTW&XK!<-5V~qrW|gi^MyL_C0(^YUg9?!
zIAZ<Y7cwK^AisCM;yT?k82#9pF1*h8uJhq*p(M_{Rg0rUEe-abAe(P_%0Kcve;F41
zzCpNK;7_jdmQ1RGi}q_FHAgB^bAtm1{Mi4?71#Q@K2v~pCMD#G^Q;X!I4fr{y?{hI
zfKVcX9%ViiN_Q`A!^Mqwd>YARc8y?(GW$Xnh3kb&6RF1;H#7InBDG;(ihKOtbidYz
z$#X|Tq0cMdTkx-qB9n6IT@|NW%ry?H9ve`Do<EOwUsu1!5sGOl<U#@lWD}LkD%k`o
z^M5E6ZYA-+09d3lQLS1o`s;iSAk}OU;bh=C1r<vGh98PS->ztK^ZL5%Bb7hNB;~H4
zsg3HvY$QXJ80kq6T8YynrL+}nis&tBhcu;K|H7|7o~k^&wJ8yyD4#l85K*PC{p;3B
z2Xlk?QbWw3R&gJsQdP4`{SMokrRKdyLsKQz#&>?=n||@#ji_kK@upgSt}F7{$V)jU
zN3(f_X8qx}5&5*`CJW!iHaUBWHjlWJ;19;~X8H4bKun#PHH=OVmB@D<x1S*!V0(9W
zv(=Qe-BLUD>r#jOb?tq<cQBT0)BC2grzU@NoHV>mEImX+B2cm9@sX-s>yq>a5hhRk
zZ1aCJOfBlVUWQ12CWXaVj_1xt{ke~W^b`dSioM<<iK-Cf=~?=Gv})`5qQ)WZdGh(L
zHLth=WA*OA*DY8dZ|$)??v%#IJFU_?o%S4x=91HC6kk6N;k}dL1UdvvUvKXc)F^qo
zw$7GX)_%0|cg|yf<b5{^&Qbc*e1P+yjvx5V-!T64><;}%bc4g`^{AyzJ`UepWyayo
zk9?vH4>_E_JPt_s_H3kQE<Zm*RNK+VD~%i<UEuXdKD{#TYXa#SV2IV5#v7~Noj5z^
zON5FOMSuiX1S(#wGT9Zmc58wFSTqwu3?yHz-1ilpWZ=;X!xNBT=hes6Y3^<a8n8g(
z&<H%3^^ye|+)@?*Nd^Vl8?-F>W+HU=hY7%77P58{iK7(A3x<UExN5EA$d8)&ArArX
z*AvipT?EKEOc-AKaS86e`rMWht`SWG#zBaE2Np>R1GM|FS+EtNIpThEd`Xm-nw$hs
zd#j2AqO)F0o3>?UrwM}`A#0taftlbVK>g6T5kOx9?iDuc-po8BG>)v@vIzW&Q4?b*
zYNm(8$rMc!NO>RJ$N{!>iru-aRFBlk%`oH*?>^pzSh=?B7toW6-6J*-wnReemi%xO
z)f0oUgw18`7J2&wSx&L?G+kZ{^kV=XkjMAqNw!ZffG4jUOTsASHZeur&4%7mYXEmJ
zdApxJ@!i|PCh^hQLrSMzi0v2$qdaNGLv!*}1h$X)+cJ&-$wx1HtU6gWjHm)WY%PqW
z%DjL=%RU8gc^7Ly1tb`((q)@UtvkE0h-eb&pmQiuBQN!4AFivu#r)LF#D#M-<N&?R
zv)7l|F-HC;|B==y|6SHLgWM>>Xi8K(`i&IZ{A{1kfMivl9I}MT_!&b?+LW9eiOeKL
z{xf-#xj``@3Axn2TxRn}J<8IumyZPX42HJ($HGcewN2V53uG;_D8=}McTXdWtj-jq
z`-P9S7Qf9{9R{5O>>AKQc@RKQ$<7Hd&V$KvK{$_p|BIfzwtAbDyFphCJeJi;dNp~9
zJeX6{>5;rL;}b9Z+H@~16RcRKT|DfZzF78eE6n%zaT?99I}v#5LmKq1EB{T<`!7+P
zVJ>#2N)_*Yb1v=)qw%6NI1<8{1&)k0&J&P^SZm`zHu`S+?rG*-4Pkp&d8`7=kKjD7
zH5Z5rz566xukO}JmE+H;$H7jiQ7+lr%3gZaTMjDM|2Xtyd=~Izen6;F+N;^V@=-DK
zR=8u)Kbs04WnO`TJ$UWIV&^-TFRHvkY+n0p7k9ST@7pqU2J9eq+FLfql3p<L-ue5y
zHQ<lev(6*AftK^qH!|~pyyB!0aCo!M1luobWq!LV^^QxQ1_$n;$?J0MoTf2o(gy&m
z1RR)ji+~tn(7o&U%cIgbi+p~c|DmlVT?sya-3OoVc&BdMA*x;1XgT#QKXg@Q#czGc
zAOUqh@`ju=n!bEowIO<FU_~K|co;Cr|L`#5B3L#3dUXyAb3k<I@jK&Phu`~>o8S+Q
zql4rei=K!ga_btR>X)0F#U`LbZQ@H`)ESv(fzw24m}Jdr-@~rR!gu>p(j(|$mO%~=
z_`Ef*FRZ-~5+|L625?un_>-a0@_8J92Ji!fs|7y}=)o&^Hc?eOEl4~Wn6?Q6#KhnY
zcu!QAWYpL1NsTBQ2>HPZbH5L9$M4+)P?)At?14Jv=F0@BR^xID#GM)ALG|gg$FF_p
z?$OL$ULA!%bo7DFEC#8GfsmoM&<UlNd<j0DR27RFDVA&ke8Lr5l|NXy$NVIWNkz33
zhpO8dRL8StF2sbD+0w7|W?543bT`X4T3)ky$or+(SU7y8Y#~R7H%(Kri2ov|MyE7e
zTEt-3dUP#cB+fK#Tg8CEThMYf`~KJ$j9_?=#I=n8!?b-JBNA^}LGi2v3S1@4I}eL%
zvMxe-tYw8y6C(5Gwm@3GW{Re4ebUKCoAG`JF`WPn#({_VsY69H%VMe&xdh%}L2Dyc
zwoC&;5j~{eOIERHuHdPjP(z5<!|#F<j-rEikcrRa3v+Z>wf6L$sy}NNlwK0HeCGZ{
zzUf&u<~7OOTNZ8LfrDHZgJA#^t@I(Q|DWZXRR<oAdhoRE-_=6Yf1!<mnazcz^LAk@
zfn>dSIopJ3+mQQS$VU#=UOv;?e11<&avs~zp_kac^Ld6t8^uIX3#L);y)quPSVp8;
z<DJazFxHK={*0J+x?|)S{<nOYI9HftoAr|@=&}=l4L7I;wuB=3Ud?;xWu@$KlGFi`
zmPkgY4|i{#Dy8+(!=MCbCCc@?iwXE#BJm;wTO<CDXVY!#vup(o4B0>aeJ*0Kb`lYs
zk%l)&15k`U`P%=Cb_sydG`~I!J5l`6JLvtJzSUe_Q5_;FkSI{l6T;;{FJ?jPxNM|b
z0|J#KY#LO7Q!P`iFp!NRUWOD}vwXGW5IVq9?ZB<QpEz6690WX+kg@sV0Ho1!U|C3j
zHL#!H)QvsSAR`c?XFPq7E32u<w$9T6t7l<1>P7C8CbSk>&B#$P02~JNFl!xMr<{vv
zM<^d*7U<l*BI$Haby6ATXSoJ9hfxCzMmh!%&y|5tD8Zfr2X;6Hg#lvSSr8Cy`hALz
zuI>SWRM|j&q*{%3K*<b1ieyI$7Mt_8`bkdiQ4v53<F*OejkepGJ_#+-<bB`C?7M^#
zj$FW#5vk6>V{>PIkE?s6kT+Y_aljs_=~hNg1_pj5FNUSU+*B&?AQ^Rv*3q85^Fr>?
ztycQ6oUz)|YEtR!oQqJ$Q~W~%+Zm~%#-r>p8>8_@G;AKtx6Il%o=k<0Kl~QzAdp9H
zRqH^U0(4ZpO}pYU1{E!-QESL$mwqpFr(G<f+V(Bg$c<r}biRB?%R*l<FB9KZNupUA
zB%;7xt8-qSYh@aIZ7eElVH9E`9EVD)4tg-x-H@<A?&zfzKHXFOBroHn1x@;0B7gpB
zw&bV*+hpp@*x&5JzfirAURuS*@#o8OUXC%Txm9MykM8h^SRGA2vAAbzf<wVvJnFMz
z9ksYfruebel?Bo`j)DC|*;*WxA1+DMcz^z*yYbUZSwxOSu^4|{z}CexmBvXwPG{dU
z*T!$pi^fH%Vh$M0n}2wYy{o@()k4ElWFc6vNJXy~XcaYBWp;hmDf#A6T4<ZKOToCx
z#F#hT4dsKVm*|FK(VnW(u;>0f9wnRix<-2*HHDP|)F>qY&XlnxUzqf<N4D{ew_Y?l
zrUecGD8P^dh>ry9Zx(r^e00|Bw_r8ne#?_|1b`$+Z?Igw`F|Xpg+tTd-^K3&7(KdS
z^rX9$(cOqhNef7qNIAMYq(r(wx>33WMMMNdrAtIgMBuB>e$U^qyZ7hb^FHTwMC0t5
zC`|xVL>;GMTYV9ukZYWX_ju*lwNB9>IX*N<7!eOh*ZX}2Y{mq`t7h$xJa{0Nz_SFW
z|ED_xJk2`FSC8Ml;Gv8D7ET*(?$}T))6u;DpD5&SQ%zzEs#{bHDyj`@aS*>FGc6iP
z(31C#l<EoRfk50^2>`dCS=>117T#qkv#XB0k7yPWK#&yR`}J)|NFoBqPMc8hoe){h
z@1Ks%8=1lGkzsLvg-L6nJn9Gx74vBTL)7E4`qTjg5XTiu!EPfcg~<ysRGUg^d)DRR
zDw!29M3kC@y|jckkciTfD7$Izm2Y3UEpf73QlKi(@}=WYMv^0v$VzDVz^UR&O~#0=
ztj=cSyA5}5n5eHMB0-tE)jE^UYNRc2WTU#%PMNFVuGFw&m#H$3TNu?FtM0Opssb}<
zb`C;|r=vQ9RTg-v^Uj^$y(>5Fk8v4{x^mEdx6gD}>1bCWG)W@XHsM*yBSNQuB><-|
z<)!q6k3^(iLP)TAH2vI_&_bDn>Ct0|Owh^8Y1biTwMyfI?jcsb@*X-RUM#hDUvomf
z=;Qjn?1H|I8pfJuQ5pP1FR6l~Y4ab;LMqfT<r);;bj_ciO@*b%J}sGwh{4{iHAi5B
zJ}0b|$87{X6UhfMxyYK>u>*mWC$1XrYbT<b>R~zc_6>Cosdert3O%`e2Lt>GLGny3
z1xda2BkgFyR&-ChRzejYf3N)ak!RXOd^$$_ay?U#uQGJn85rY-<ogsX1|~RO_e32q
z){hh_R0xl%D?FNL2%c;xoR+9hdpSb`j&Y%b$229WaRuu{K}WPOYlZ?lR^^?9KQT>H
z>&0k+$U*c=M2sZicA>>^gYqA8kw5}h3qc?PaPU8Kmset(4TN7~5JqtCbm-6k0+aTt
zsOtG=(w8cnx!T^zMx*rY(&9I@B&Dd=^d%8z5?F9ou>^?m&*}Jy-M;=SGIWK3Tqj<W
zvwcQ(EWi>RC|l&9EynQMy0w@r22?Uph7T$Y-A)knIQ~*RN<<zD%wk-u=wOCfmqUCD
zrcCk+J^ln)Ycvo;9A3d72B-tT1gVK4@$YL_RaZ7#E*s^EQs5m<k{3Vclk3A^;ry}w
z0_|cPYUoPk%P%n)Q;8rE))T@uPM!XBGIkSv0GHC2?VMeZ86SX1Cd<9qP&eE#Q<<Wv
zaPY5?X;RJp(NT$1^H-(TF?w%x+sLpz_X>~lKYD2eb8ekS9j%;PW%g?GcAfJ6Qt!R0
z)aZ5Y>lqW@mlm^7ZLseA{6Kx)q08P$)lNHe!Ky0=$E?dIay2gVJ&N7%DUR2>We%`R
zQ3#n8l14^J&8KmD)nmQEbQ}g1^UqZuJ;_4H6=y!xI?2LPZpy-5DQGwJPH#@5Vrmon
zaudrm|Fmhy*4Hun<R`}GlY|kU_8Hevg0l3eZiRt7+gcHv2iojqJmD?xcMHn-Jy~*C
zC2>OE5c<LLUYByY(89Wg(%$(BCGmWIqVkD|C)x_5eYeu&w`~?Io5>rs<PNhm_-f==
zv-{*Yr&hDIO@{j+hdQ78>O-mIHdM?q=oq6+>bEUtY7YfCUQdUqaFNt0MA!EhiXjsJ
zV4+ue=Gw_JfbPw&C=szVqh%c~Y5g8UYV&Pvh&>6C6uU&6Bcfb<ECRh0!c<Ia``08{
z5CieIa1mAH@$INdHCzIO6?5{xG`I}F4|*8WNPUlMI*oEC3N>SZGD!1|?;x5{U3n?w
z-i8J2P$Ubdw<)~nBer32@PnB6{&aM@R_mp7lsBf?55d|2Nn9di7x9jxWPh<~H2OxS
z1Oe+`>xp$IgQkloA$;SM|FBo+vQEBE{GZucA_~*5FCopUB}?Qckg!$~wRXoqMmKus
z0I`>rTp8al)-$|UUIP0@<N}~UXeb)MUDTtF0S-q(gPtI$1RV)7?d_LoA`(%Cx3NGE
zK4*f3&Ne5{nc7CBZk<DF=&-;s*3feJ<IKp=CK0h(%F<mt&9_nXV#*<(U!`Zinf;7~
zEn;0}@lYCAz!j;<K9^;XI9f@oAX_)~nP^LOu7zHWrQuUpcE^IO&Gw<H-Joi=8TS(z
zqp_p-zUa~Nfd@K4JPLQDwZ1QR{Nf#BGL|ruY}YoDf2b;Uw<GgrXhd%A7xTA~`yE-n
z+#i`LbX<%hw8uLRa?wLl&4a3EG4b9|tb{EbV48@ZR-<&IH!hP7{ArrYtd{0R)&{HN
zm-F0$)epj?J>*J^q_dW=SS(ML2Hho;lpqRVP$)PWh7VE1ASY#rZV|*ObJ(Io8Ht%8
z<E5?Wb_Ez-vWlJIr<*c)hCJ=fmJ;U0yM#HT>nBr1d3bn5Q%;5x-+x^I!swATSkJaH
zuoGcG;YIe4YZ44SNZS)XB&1ALQli{5hx~;lDh@U;ZMR0qW{kTS=9?BcJuXsa&u~@M
zmnJEidDwL=nfXH|>Ftj$otSHwQ}SdPg{!^}VMg~V&*RS>E5h;FGk$*r4j+k%ztNZI
z($lZj@%!+|6GceEVd)j^Ce|mob$ZtSS;w>Y&<l2de8aPKx%<v4fbRk3qsN*cP=OE2
z>?y5C{u}{=G<sEkWMNFXjm3pF_aKpj=2$AiN<kyr9&m^CXYp_1+?<|4b6OlqA6tTJ
zC#gBaE#iW#=mGhyt<oN3g8)F+q$6PwC6HWa1{~>$D;YqR0$FC*|D);PdM~94VCYF9
zW!~kZ^{(VmVV3qO%h0<yPR&4rVrvOF=Ho|Qz3Ql~Qz5}ZjzgIMB}4>32b<A=&G#@D
zfD$wrpon%AhNnjJ7w^5udW0o_4B7%e<{fhYS8*4}Thj(Iq-1%qw-Nzine8ZGC5YxJ
zRrC8SpqAmKxhF>kAV;ArZRUimVmb%(P++SGob%gSr1Wxd7Z(e<w)~j@le8<=UC3If
z9B2GGE-RU|kq$T$pp+(3ujAX)fA*01>PMLpBF4qY)^KgfIaz>M?@jfFqywO^=%$Zl
zHwO<H#=Z!IunLyB=hmleefGdbJ2?Q=*4cqr^2n%2czyhA4v@hpoSINq;ILIh$}S+7
z^<nhlN;%?3hIzMyf8f)xls1zjX_tB8K%ITctGBXkABfVl@>#O7wmQ`Xa}?ha^(1`g
z@f0m|IHI+0bFTGxSonI`k2rrv{0n2$ms(b(0@=k2?P!<FrWX}#ley2H%UA?D<tnZO
z21er0QhJTDz2kmm25v%GMZ0LJsb;nOuSVSu(fj|U?G}&)(0cBx|MWbp+o^X3gKGej
zZX(<3$y5TjrUxj7<u1h}F}D2j)$u#w*kya_zaCGLa$d3hxiWlOnh!-rpi-u%2Sja&
zxC$pUKl|qi30P|OOC-$e^+-=K`oG8OXoN9yKT=U&#XbtL>HM+=&_p<e^H_mu5Q4R3
z01*3o<Dj4w@VnTZLq6-^(%!$wekZs24FuC=iXQ&zoEFi#_x!q<BreFBcAZ1o5S~#8
z({`Qer4DTB`P=6w2~7wk515n5f4zwEnsKDG=S43Kwu!mreIJPQS{*7_lJV1HM%c%Z
zEHIm!HDs*vnxqG9W=Sh6=|>jIJv9CPJgf8Zy)$10-sICNB}O&?{K%AA!qTEn?`X_#
zGK{DK5~KKdb&&~%+8N(eEV!*vCmoDCK9F=VJPeS+9~=d+x&INv1o7(Q@j#Ah<wQMs
z%*kPtRK*eGBXWs}(*?krbLlbQD^q#=F!N>+E~~|)xrx3%fI|1rN7BS*94}687zSaz
z0ZVzeFBTFcv_SuvDJm3@u@661zx>AR)_qihqd`Fs>@Xa@B}&BV|8LxA3A)Ifk9f1%
ztIV*-`zh>Il32*PU&H%a9vUg6GRX)w-fCAR9mr1v?^{I~-lWAOT3Tk*;$dni-(WH8
zX}2`8Tuz$0C8W{D`!`G~p30;V&C*yfPX8YFN~<+5Gt4{6cC4s)DT0D?BiP}XAOI`z
zC&2=4_)Alo$P#@3WC*g+T3LFXx8?Me-^e(ax=m_1a8i+c@Kkkj$Blb9HV6=4j~Tg#
zE;^HO%E*M=BfiZ#iP-ez2ahfo#M}~zhl2S2|3M+aN%64IKE}@|k>z4*-#kZIY|O2x
zr8Du<Ej9axDcVh<<pRqLDq0O5q=gdN8}5Y;G8Mm;Ge6<=j$2Ya;M9Bdy7plI9s>uz
zr5RsV=PrRIzx+M+6qfqVm4i2k6Gpv8o>!8-YIcnBHtrvPJtzNUyju7pRan5bNn@Y+
z9c83_StP)4KcSXp_AcQ2Ni)?FU#^||fy4Q`f5(dr{iiGfVcvN84qcVKpR|aT19Bp%
zvwr^qDfejvD=rAqyB*z0r^01^!ncv-KnVxke}a$BqTel;dhq5CTWvQ{#JxI};*pgQ
zTp;8QK?R&>?w^TQ@e^@xq+)#7zGVsNi`SG70-iU(rA>k%*1nN{*-qjlxW7^k-U|p3
zSuTl^j?I#XQT}76S&0#PMPAtVk)R}yzh9*0lf1ies-2^t1_302Ucrv|C%2w`j<cS_
zq%|+~{&3;k;dhp(_Qwo_1n~h@<y9shD6?x$SdD_^K|&eCsRs|e`59L?eMZgy$Ev0}
zMz6HTQ@NS`nsvD{K%qaqLEmS?uXI!vfx`WULC@2)+7tA4%Q#dGaiZFiTx@o?qTpYR
zw^w>u!oyq;ERPMxRC1IW^@`EN;3azVh@;<_LDT9WD0hp2H*LYoQct@?&{10Wet!2K
zg5$Hr_lI4zt#c7%3ae}<vf;A#qQ~}?8t3vq3D<Gwy*ew}Q7jbt(^z&bo!i3>rY?7}
zx3TZYh5XR<kBD5gzZwc-@ZOB_>znm<v@HKEXXBhFG270{%py8ip7w4eAN5qEym;Jb
z{Md0{YA*&yn{r(FQY*$c_vS5Lv0K2qI4K-pHI)$I_KCFa9#8P}qssJ`LiJpo!TQsM
z#NM(+)oJa@+z%S6nH`Kna!jR~SNJt#HX8p<pB0CGc(Psoa@M0H?YqHL!NJYWTf*1-
zj)RaN&pR@NgjUO;@4YlG#!;W}iqn2LfAUlYp0j^jR;B)P?Y2}fU7zKVuW!&nmN!M?
z?f<b(miXc2<^Oma@`VD%tcI}O6$^(#)>Hos_~7wqlj~o9%fJec%hl0Bc?!X`B{n^2
zk6X<fz81Oc_+^odqaqx!aU-5;TgxO3uk@r}B7f|?gN~ARJ{%^VjcKuq!cJZg*}c|_
zd$9k8qnSr<Q~yWmPZFNrFQ{l8lKHk@sj_Rx98m1*#=E!DO{{)g%hp?;lOwB<Bd{~!
z9mNO5r}<k7!*W~%sEtaEnhO@6{c6F{BBv$1P;FA?g7t5m#Zl1;y8U$P;q<lufr%#8
z$`p$&f&n2NJ1o7D+5ggc({~hVPDfw<gL&F>Zlck+9XV&{PRzxox5C?z<PLuGm^FNm
ztiPVB{Wjk0S0gvfQ~zz$&5$njh1<pY?xfy~?v;r9CQpgiPSG{zU#Tdx7crwoY|)<M
zWBe%pMWpSLK7Lx_X`{2uI^WFZNOFyOUj4!R=GH~CzKEFijf-BOr*rxbA@zpi_)p&7
zNRMul@Yv#_k{yO2@TkbZhal^1-oqnm7nLG(ee^y)wnr3#e?przx8z7>zK7g#p1ekm
zD?B{m`-0397{5G>4O+HEfc?PE=q4Y#P5_aj(r*B~Jf27GmbG*3&LSpWCG2};UI@=N
z{AvH4JA2cA-2kA9kiqqz32v`k@Y??243tnY53iA%c&Ps|^-9Vc!=rJ`3A@Fh(=RyV
zezB*4pW0cy241~n3=u(`vOCj4sDljzLL=YmM85i^dKBmd9dhRKD?1<f=_&d%d_q7!
zOvCoqVW>>YZw8pyC~2ffdQe10NhBryMRo8iw8Sr=<3BGA$A30Yh-P1Es{fjcmJ;CO
z`iYn#ID>gL{W*-oueta)u3b2~7ne3ZwLUY=LGm9uae6Y7%wUamf~Tg^e#9{L+Fnc{
zz6=4viMA9N>?(8)606D=absU$9je~23p0-5+mZS*-+n^MgJa@u65ZuL9bHpn-b(8b
z@NOYC_(wGxEuH?7)KOp&S<BDGFXa9cu?Zs{w{XH2a`F_!KV3U)^55VmAQxF}n7i3}
ztLofF%PZtS)<U^)+PZ@rAQhz)ZXpok+uP0(L<-Hf%%7ubZr10tC_E+DnvAI4>cQ%9
zZuxv-o-+!iEAEpJDj(E-#TRNmH2K<J-SvK^41Lp|X!f)n{`fM-qvUPR$?p>i%AEHl
zk<*XxRUJgX$;En7l`4=KPoMqFjhXNkR$k)DsKOFY8@;a^s4f=vn}BWlKB(`E<WON4
z-Wq|jzOqj}Nuhl$`W8^kpJLI~qN`jS{bBqK5Q=?B6fN6NRrdO?IR8J#N&lFE_mTZg
zKZ(>uvWL<<>k(R7zEu#x$vB|$cg)@X(kpqw^QX#>G=b9yHD3`PF=@_&f*!Ou9$ZlV
z$HD4!8zH6szw^iVr&e-NjXa!PJT8&{!A7Su%l?a6_;2skU$q7k{}4fhO<=_p7(@XG
z9I0w29l@-#-R(M2WrJgZ3Bt<;{S$up-*2zgE7ub|_vf6;K=`lgErjVe&I&CY)_W33
z^Nkr8e=%t7Jgo7y*jill-8VG;!cG0pNF|wjgN$|aH@t?*D1DNh?<3LaEWrn8vv|dZ
z9|N5qSqs?*aq)5~8vU1sm)_!vcRQrG>-G1u@SC<X$ltSx9l5{cSKT{$cK4%qvx)rO
zdNoSF`|zB&dvD2l60e$!g837}=c<VSv5SvM1Pw%5#_k_Y!;HJW*?qgeY)!BH&r<I@
zUEgg^qKh<k;LvA3YYu4IzT=(F8J8O=Osrb@P;UbzB6-Hvj2VGpcmRCem{^VfBV$?o
zE380p$sXA(oE{Q-%I-pIv<S6j=8w2MgZ2wRIl_EGc`oD}sA+w9G%eFBRieT<K5X3g
zz0K81u1200l+7AJ68_trPm1cJ>dU&Qa>J+14;~uhaZ=LH>qh@>z5P(a-XSn3)~7{%
z+?Auo$+h*a+N7P{LI{z2wkfQF`RC3Jrkcmtr9dL{Kh;=@krgtDBG#d!zrJAx-tL?d
zO}X2e3^-T-$Rr-sWpOmo+)p#cHW01l{g2}bkxAH|H>Eg1^E{XE=i+XXl4zjOcF_|y
ze#yVM?Au-|AaR_P1M!_XUTYm*rW2cz<|f!)gG6^y4awbFXQUWShHkaLKkB~r$dY%l
zk%KC$u79%_ZX!VWrbO(pdhGMf^|DK=2X;Il!+%4XBmcz2zG?5a9N*<5Kh4b?<2I4f
z;5K_tD%4A~)oyd5{|8GcGRaU$KZ>02M+Cx>y8FSu-$*ncuNx+rnGlnuhCdQpmaO_B
z<=N{Fi(Vt01iK{UYd1dnwK6I$fP7by;sKXhA90+aJLK~F(T!r3p*{Z#e;;Ot@lVzf
z3IQ)27)Atwc>UDSj+M&#KXYvZFDw?`9ZNL((MD2-M~6LF5RwTbv8>-^wiSSDW|CDT
z;|P+5cHXoRi~yh@mF@hv*WvJ>3ayM()gi|@wb;q|`GvWtneEf+i>Lf4QKzIy<j)9l
z6G!F=*g8`f?4?Ha(o~HxmR@<|z5)+U36yhVucYpCW{Po~yKCef#Ris5)=H-B==suC
zs~>ynaxCh{*p`ijX?DKm8BV2GoCLHp#+~my?vnb&>!sf%WLJqMLY0x6UDDbG&DmXY
zn4Q~jA!s4CR2YE3#v<Hj*!27bX^__*b+TKd)~a$cKa#(cA<<iA>Rb167-VE0Yakxq
z((97W(vtOKcrL<F_9=%YtL9$xFH3g}BV<s*dmX$R0!VgFcSJn;PnDTiA+wSv?L?a=
zHr~!|_c6y@FOUDXRJsD>yC<m+1-dfCOm9gL9dG_}Y527uNoFn5rV{$8i%v&w$Anze
zht@qq^ghy4%`eULu3czsw7d8N{JUya$DhTYeIz`JbooHC+mZOF+N2zRLq@d(3?j?0
z7vvFqfPgmrnc-<9tD2})jnj&M)tySE|GbZG5_vKoq`wJ~-ldAPW<=rYh!y3{o*Tc6
zW1=W{oO<l9d_M*n5cy&5LQ3oQ_f|B0iA?W^0<}^D?AD#pX6R)QneBeudNBPUpo4hC
zmoP)rIwYaWY*;c}z>L))d~bMO*FF+Nep8KSe{lIQ$)eU~W#-4C%^z<`wObje?vH9|
z_ZhvF#S9{^%q%8;CO(c0<GfsNztH0p&gKol$jqtp829B}q{hlSr7i4Sd5UVv(h}Tf
zWcE+K*^tbnopA4b=OIyEboy5Lyh)|4nHmE~u;UioJ@DLff|6kEh-i8xtM_|FViK+e
zdouhnN;<dfyKpw7g1xwXRhBN_s`FMDMgoe<Ix%hG16wQY8l8vb>CY6k3t#K?c~0^5
z`HS@pX1>qwOK(^u9fCb0Q6?_LFvBF;kxB?a6=XYks2e!uJFaOG(`4d0lgRE(yME{1
zT1Kuqz1_Q`4ZS}#a#Sy5@&AbiyqNO}sn-~1WPj0Nx|yo5ow+WtBGM{($7A*3eNP{X
zzSl?#HdEDmMdLLsqPI)CItV&a*ZcD;`_pLkV=Ah&(alFrNL|he6U1AL%hdyF45TX9
zjj6PcdR>7k+Lk>jSSN)qFc|AQ{$EWVS)#~|N3i-@>tER+1!}V8XnEOAn3=4Kt--^B
z{+Bjfc@0l^?S<Xx#j2fR8jLs$JX*?QgQcw-FGB$&q$&7-cHtEi(4x<b66^;D-=`_z
zb2ks8gX&41injjz;(k0YGHlA8^cfYEH^kZfTHRRqp_B=Cwn1xo=BT_yw3N%^u!v=6
zg?#eo4An86J+RJ8ozK)^pY#7wUNpqP%9zJ&<UT)nw0N#SI~?D`-JLJkz4!@j-5+z1
zKz)mnX?+lYaDuf4T;^&Tk0E?hfaZ?{rlgoqkX11sam^H=7hyqD>J7jpc?9l_+{(lU
zaibg5hi{=&ZHd-ieyb_<?ra&lqnH1DfAFgT(bs$13Uq#NiQO^uke*&7FNK8?u>Bmf
zU7qoryW{zj6++o>V^SB6$N9C>ZzL>W1s`ue$q^6z`XXFXIM^P!^66WNDB&A@_OP6Y
z$~KcwV)jyg`nFrQLmAa5D0suh?;e;4N`?=Z>qos2Sr$f~yOrl7TS#=Z0?BPJC{wFv
zhva<Wx?E3YxFNsaew?hlJIIsM^;D<W{{FAX;{xaB_&@S69Bl(_r!Z3}1>E%ZJk4ca
zkgGBWdt#qB(*S2nGUnoH37T~HKZUjtPB#?R6yZ%fpbvR|*8vbGlyQX>m~jrfhf|F(
z<r2isD6*SRk+xDfFNpu~rMsfGZljh}>wL;hiL@6mvnLN>mwCylD4g1~-60VE$H*;8
zRcKNo;B)qqubvdiVkU%4|48#G9&y_e3<k@y_cKk=qkydlHTV|=*pczn6bHXTu#|+b
zWAKdz+%7(92Gm6G=!q==NK#d}vy?C*`MSmT1)nJc6C|X0DGYGWAD{q75D0&_MJcOu
z5Dyqh@z(IJ{xb%6&><AT*{}bUAd1_kyT3f8;CNUDfhtW9t1N(JZ$4&eA|}9#95U)Y
zv=ITJci2$yVLKuqo<9o+-t6N-Xm;Oaip+!=QYIWu8JM$sN%<QbPu3Wl9N6*S>`Kr#
zT-g2hB7N~Yz-lssrAJ+WRK45<5W*w*?#{A2)Jym}adzDk9)6E6!?xKLvUKPOi18w^
z1mE=4xT1X=<z%G1n9Rc;J2DI5?>@!L{bR>cqxf)yVBJi?@I`E4&)pZh*Tr}63wQ7S
z8sLAlvo<K4xpMyE=h@!|{#3C$jYA?j)8So0n>!G}W?izs{ptJ*bsfm>UnrEk?WqU`
znv)UlN6Ag`XjBbn$ZBstyb(t7rRR~rqDsKUZAvVp+fyp~kAf-|*|T*1!khhawp(Bx
z8D5f@NeOvDS%;h-{z*c1Gw$Q}H#%z!>@s5YN9wX?Z&`ho&?0N^%>urv>rxfsszTp+
z#6L9Tyt_r|nD%bEe*p;kZbYeI{dJnayEY5WQZ1?{SK;zUxXdh>N~r{(tK3fpbZR}U
zIFZTnRoIKUo(t%(PwQjG-qsC;^De4m{0+JG048nygrp?R?$(<Nh<<kAOBLfl$4$+-
zfXblF`yIauC6>)Ps%7546{NDe@8`jqJDZ);g<o;O80EY2TWvgp?W{a?=8Fd@fgkrX
zdlkp8T>%&44`!bH3#}r|INi#xa;@rRKie>{ZNgdQXkzSS05Y0l{XFc2shBlZG>GP1
zI9K~~p`Up?L=zzu3k6RJqm_^f0B%RG1huBTW{IuIwCxrmU4xk4#UUAK!9y(YWsXHb
zB=jcNO3;J=|D@js;4S;xVIgz=JMn$^sbR(0)?pck*XkcSJY41Zfk(cxU$>%{`py)t
zZV~$Jl^_#Y8nET6IrHdi`i4C&5+*>G^of~f+AeJwgmTBr>-0z`oa&6ewpviX7!0i@
zq#sCIrQ8_ta)?22-8{qpBX;yn5ji8G;RMv`y$oQ==>ke4%HF<JonM8eEd^CuxL#9@
z9<|?JxfIn|ruxlV`1D|0^g)c)weZQc#0wnJAGTw3!iy@5U4n2-9`{OQ_uVl3UefMw
zRedU%t|A0szoay7;B?RqvL%kyHiTa$LR4AEm0c{qPsKKNNofK!&K6^hp}1hx{ILIt
zN5{!|XyR|-r<HF;BmooX?FdA|eG_F{L!w9|U>+$c1kgW*SctC>jR+EwAbQNJX4ODh
zgD~^*P)BAWx=TfG*?s3DsQ#5YMInaA!_V$L)KQL4O)+8lsEQ&JQQY;+crHoDj2m6q
zMPKI67g0ZzDTMAVEgZG7swfbU6<@|rcWV>Xuv6q|<9fct9AxF*7~Jx!>hpcd3sKLa
zH=5jY+Jh*Vx4vW=)%r~q(dXloF=th#y_GKskT}U$%too$Q(iizXQFN*k!0&%aobcB
z92^32(XC~~A7;+sH7g<vKt>`}qy^LSx=|#SR`LIY`T2~*WH>%^F~2K_E>kPu8hyQO
ze_`wM)<|#T0q=6pmqLM7mu?9~2y?}@zRvN6Ue;Q`d}yw<!BwE1n6B4@`X(92(ls$U
zk`_AIGo^_RQq(<>BNjrQpvh9Y{NLDTE~Yn+J-@6}apC_mWRy_c;}JEgusO%T>mJv&
zK;=HUB#;j?d%xrU%38^MGJoAtLG5!WYvR9~_5{Uy)bOj;9tj!LWWz<P6cTVm#c!IW
z)KG!V)Qa{jwuDiYp9<lMQLBIVlm4mgO`d-cU;RFfi%S~XJrw;o+(j26AXoKEUEL+0
zVJcc9B5SdoJs{PLZk64bIFd){V^hUOaE9{1eZmMsf!XXWiP#Yuqa-8x+Cg8cyN*HO
zK)EiUjN3<jbQ+5z+%y)>&sz#e3G%8$w|&A3()(`THoCOe$Nyj018rkbO{WhGntEMn
zAD8lk5TaaWH$D<R;euAOq^wJfQ*sk!+rvRJ&MtU!>4ZwwZ<&gN9}fRZiq284h`n&i
zdQydpsbDlGtiD$Lu*YO_AZo6CR}7MM3yucf3KAFW=6O_UR~mb~s6Hs6y&cqFQ&9A&
zawPCW9uyT7<dur=jELChg_KCVOr;nV9R%c%HBmD8_+Rd%3Xo=_)~vPQ8yA;R2bp&w
znX13j!d6xK?0%o-8(w=P7V-VQBSlX2S`rpnZ71;ft(>r}1!8tmVZ)lb*41IR<{s_L
zT0v95Xjhk~q2A30tjA8G;UjfTf^+^EvA}3Gz5#`Y;#{+;SMU3;9i7gI|Mr=@k!$9>
z$#tm=uGsGqdgpfjl+zDdDO()6!=)Xx=Vmc;-qR~v9e8Lhuvv1YeIY%efMpG!z?`j^
zIZ^Y()jFKNsh<5}vZCzM-a$~+ZsUHiAYEo7d#tsazuUT#2xZ7ouZ0Di1EPFKEiFbq
z6N#v_^3%98A`|QV4uRYIhh@H6eStJFbQD)p{)^M|y?OmNi<ufPP}@bx$i%@1w20!O
zfS`LOa+^i&VfgndSgmT7N+2TW*{HmLmdMiy;!;-=Y&?>$50ysVCt{b7jxX-z`HqU;
zqCiAlqdC1m{+4dq1N*<{omTb&6KcVo_0FHa1RiluF_~|ATAX*bQYfhPeq`m`a%niL
z>r@WS=XAiwo4f7_waQYnwT|?7dym_T)%0uFlZ}Ag{fZ;c`j7Iu&c1VEbT6cmc0Y(q
zUMC7g?|<Y>)gT?R6y`?$>?ZmrZFR!Jhn9IdvVNJxADrZ^imXGP8>I9od-HLk9Qk;%
zskQDfLrOppIiwM@zm*?Qklpl*Me_4Ln~&kvWXJFOqF-4W=tS#8EuHOEoTz(Zfpbe{
z+Wc7^Gjx)>cS9>(*+I-Whm);IwTWV0L32m<B$Uwp%AbU8_u9Db+*fx|?SX6CbZKm6
zxLE_sc~O?adUEMN3qSHji6Z^RGrlSoxv)K4mG^=9WbjOMc{=H5Bk9cVms_cS(t<q=
zU&v1P-ig_E@92AGcUg3z`L(I{rG#rbPurZB;WSyFR2zME!}QB?SZr^peD$Vu+*N%0
zx=@ym;j@s^^hcDEhPC2+g)c8pY6>5<QThz=|I3JsLJr4CJXvZVrLLtA7QO6D?~cN>
zX{DJMcKDE{S;Tf^e3kexp%hI^%4EuGw5xPbgr4GUW2R+`FQItPD!B$EkC*U}hIKw%
zNp8-G>vt10!w1VB6P#F*^vKa~iM)97M4Vsyc{F=HB3f!1Beg;-D`uEY{H%+^lUj_V
zCsLX^Sb#bN&M~|sQ_kEKvc-9|oGq$T>O#cFF;-f~6tiSe=Zz4^+b6h;6zKVvU=COI
zC1N`fA{trETsO}S<`*=+E$gzGO?g|*;!C5eTzS#}r^S|VvI?&LNEQ*z(JCg(_eIvv
zx5sd_RpCKTqKJ6I?cUxy38spi`+jIJ-<J8UDqk|?I)^%AxG6tZShg@beRSBnRxSOo
zm)fTH{XB~=miEx2U1U5DE?@N|Jp)9L4!YwHuHe;J#GJU2<^9A?lHe!-(0FvDCJ?E4
z8M9<vu!M8kSYUx`Y~Bjgg%^qhux|Ne5>6H3c|pw6DovNzbQb`7jUNQ?n;(&uc;HWZ
zVF4jF4*>uz#5EDePKt18g)&-F+dC$frc&oq#qV{;VqIbr3}V*o0gNDy9>8m~3Fop1
z*B0R-DzLT_WF7(=Pk;p?Fa%$=NNr;M$9VKp9^Eo6iA*ebG^GjfXK<Y}bRZtLbf%5K
z0Il{SDD!iws6K-87q8$pW%2i7f~)}Wh}}dm9q$t?{ft;+h16_A=9sV`kifSF4`ULO
z2k_Rc8@xkiE&&{&1WU&ykeMQIMZPUE2+T<0We`C;E}LhM&q<1(?uy?Z|NjMsj|u#j
za3sO(Ia-KaVkAym83Z_lJw$V#U_Dmox-lX9UFM)E9_Bs(fpP9jz3ar07%s>$4vt9;
z@dgB%0R%Yv&7ft>62NP=#3wn8)8Yn8WMB|LxdtNh_@IWkohi{kDUV_WEg^j*we6zD
zAWjK)#=i^4fd#ePLsGB-aTfQ%)>VEUMIDM99FxB3W_ZV*5FyU+^7A|{dJ|h4^FJ7q
ze^zYI`5W^k;6gxjNgLtV2f@-u7y>33VM=#*-dx;R)K$va>4YvEEw@(mULwOv3WGWw
z-}3KrJC;3}9_-UpvzM)zdQ=_8oq_yNYGst!vVvvWu6`rkE{cb4;1%*`7XPeUVM-}c
ziX3da+cF%0E}|GR;TY28L4S5juUTz}^A_#HhgP{Ie1sCeD6wvF%%3c`3>T<`@G6CJ
zw?~;Gl|v}A(O4x`*r0r!Qe8@9mGZ*0@+N)U)|2OT{)HdDlpxaXEWOEG_4%-on3f5X
zYO_UY{mt^77a;o5Q!v1>g3Qvw(2A|lmFMAd)6@dy>Y^?!*iB-q2clYT3b->|L)2g>
zn|*?-C9%P4oy1}Z5^H%8s4&1zh^5cL$_DYfjtj1EjU^>?-#DsTtn|$<36^uxCaclO
zEea2}l_zDYjmndah4CA!%6z8~R|+h@3O3i?mX(=OYk1s?C!RZaCNvd?5#(<?$V<+|
z3f=h7IB|bkmc8vz*!G@4#0dAQQd7VGSY6I5UiB$y4IGO*Y$)R@Ae4W-Lsg-U!<|Ni
zvEX^%%Rb?V!4Wf2%J8f-3Lprb<HZ7_7?dWGU7e~V84;uvoo;}I*TI_sLHZMd0$HBv
zn|O>Q%v=}%B|YY6Ag728_G5q^4iv88=~~g=i7;=>^7YgQmvJyhs!2s6GXGymNjpJv
za6DEcdJn)2)!<_-m9uRn@wjRNQ2-QS`z0V}?smoN`9otszY0ZhKwAuA4klQaqN0&X
zvnYYcN#6fCKhp<ei@p$o&!mGUc(<#eSl*SqeuDVtbu?7??W?d7*=ULLm_b|GOy4|L
zs#O#*7HGq3lmOm3REgn3NmNCFW1M*)VvZ4#$Abkv1W4b+=Ilw2X-GN)#OX6MUL1h`
zvDH?)v+~b|;N7}5IEbG~OKY}E{B2iaOt6)UUi6x&RlMqIi9Z1*g+7EMfn<yUXKV51
zg$fqMa90xt)vV2>A~-MBTuf#W7#c~!%A8N6M|4=ce*mb$F}T|;&Rs^zgzTTAVXvMJ
zw2KlTj4>~@xN2u-P&_!^;9BJQtWa40H=W~W;oK!xsFPgsgLGy8))^nxbH6e6HTCEF
z=2IKFuSC%uN*EMarG9Mkg}=Qwh&41oNiI&pT3RUyCkm??D<|CgC=oBgbZZR5GRjm|
zP^e5MV|xGHE#+tJl(7rt%ES7qO8GQHzm(WOUB>%-&9VB}W6zWd0n1IhXHNu>qIWae
zMOja0;=X@*-gvK;em1dz$y7(A@T+;s+!-FyD5~|8I+KQO0dddAM)a1HU=nlLqbCy(
zNi5?`OYopMtbMUeMno@|+S)bO#L8^U!)$|T@lriUh9Ld9={-p?YMD%+U?@&BM_KDg
z6&7&sIlC-PxOAgU?)Nmo0DHAeaaBT@^1cFTb+>0<A5ExGkK3uK-U@I05pf6pNX_l4
z$1j&81)?T*iuv<^RKi!B3Qy?j=L=jkHQJeMD7^;XX6Xy(gl{b6jxgqy9ovVp`4yeL
zb0|-U_Z+}k9AJ!jcMFgJx+G8ly=0Urt3t8>Km&22fOl&rd@!{sX@c$v^&<^p@V)W{
z#sLt9p3mcHn$FBqcBnpklTP)Pu#T}A*yg2hZ;hq?W!V1sN>Z&LW#D^sjSicL6wJKP
zR>z7Yhje|(6MxU{j%^yfNPpz9&@Ae1j8)vh^>)`#?RQ3c$CLW+m>!us^^wQIk%hKy
z<tvy@`d3n<k=`OWUqMXKh3%X7IG9%y&R&SN$2SMKS^D0&ny3kq7;h4zbO!b2iL{N*
z9A7rFMMGn*;|v2RO^zioR1p{o98g@F-bF#@2q`dzhc(YW=7wr=-qALys5sQaWi!`U
zB1;xy2bX9~$V846VJ9VcA|`lPK(7Wrbao!SpE*FUjGM(7d$+ZlhvLGAq|w=N%^{Ry
z>o9@YX%*&tmlDJ!LVIB>(>9c#Z=0wg5{lzk3f5y4$p~@q2pkI51?N%Jf!7!n-ufz1
zPY$uE0Khj6<^RCY$3T67?B*xr65s`yQ=g9s2@HxBU<Q}djrpZ0=W^oK(es|UgvOBZ
zp>PLE#72xLZ!X`s;px7oblm07=O7#9%4muwF7!cagec7X4R<@mTy1PNx=P(1&QcFq
zP#jJ|3n`m~Qf}^OJ0#<^C?}a>uIhfMKQ9BfE)<d16Ra_^l5}zzJ_jRi&y+<k!EKz|
zMIP3vAA54XO(@sN;jF{sbI%p8mSJYo#_ZKzpHqbS2SUD$+TQ0E=9x{_PFs>Ynt99v
zw!0g`YLBP{#p%lbd(5BZ|M6f^t^bbnoshAl6golIzWAS|fgWG(8{zF)ej@c5ozdIn
z>n&rylWtj&xg9`@AHNf4Kh7O`aR)0X8;8T+a%5YViryLL5MC<JS>iAdf_S*Nb~bM3
zDlR-67iLQch^ueMCn<SD=+=8`_-t>oQKij!>91G~GVPN|hvGlUv8&!9mRsu4c*&g^
zg%N3u3_6+4MY!XQXI>RKDFA!KEV4$Rm}wdJe0&`Za9>`12MF#ntu?3t4=QvW+>4Ia
z;={<m!d}NyIwrEGLY7z|6;;tS$^ejI_eik#v<b!i-ntbthM!5DE{&ErjtBeoQu<Py
zPDum_lhIDGhOXLcBCxzHtSvs#r31dqJqb+R@rGk%DXV4oUbMNoK)gJxaW`11lzxpZ
z5<d0$<7@01X>jo-&Q=e@C;&({zWO78PXT~=Ov<Fbm_5DZrXu7O7PShs<$<mNjX9fd
zYsA>@y(5v~6o#5NIi>ICg5pxzaOO`v6nCV}H=Oj3SQvoL0enLzEvW$_pcKv9Mf`6=
z6<(f$6oKB%Gb&L<if|g>&#z*k@oePC!cRw6_aSr4{WJtxZq`ZpH3fNoadI_ab{J>h
zi!Ay}%JMuWWP||w#J#B_9)MU?7A!44^%|fggafv@OTRyV0Suy40vlS#4f8VNH9$`S
z2mg!F5NzV=LeSX>0M@fQhjWMyqX-VSz|Y4D7-F@ub3lv_#p0hmiN^_}5w~nHK@jx{
zV?eyUnIlvH!tT7Vg?M)x0$m>D8bqW>9R&5W+QM3viGW$X*~b!rOkH4DW3&bxvTem_
zr7VL0MXSL(?%=cZ`>zID*0h6TP`{%NJtSvz>(616G-pZHj_5M~5=^5o)A>CArb@>l
z`RReLtzfw9b=}-g2}_TvU>&k44|`iy+CH7Er=#3YNRO&Bp2s-_eLi|=%-ffM{dU>^
zNkxS!Zckb+)L-$%%+3!<n=i2%pVEGXv|9@9y!>_f?mED$Z|1yBFY6-V^^XnToQjuL
zBvW@9qo#keA3<5av^0m%7xkr$GJYB%|1M9!L2AXZrz9Mz@KpNYfoUQHLbN7NF$n4R
zWOB<;e_b(Qc_)-BIpg9f*OXQKH?}&z0|=JjO?<|SMh@MbL7{8gtYP761tV&QHaCb2
zA)oF#7EtZH=fYBIi0TqA3@>7}s4V7ZshD?VUg~^EUE`vP)c&BKb^czY+wX8+$?${Y
zKUKplW%Y12w@V%(*c#Z9qryOfZRi(<yci;Z&m~}ccu8&qauE3I5tJ31W^BLokUvHJ
z=Jm4-1ssHhC9@1+5_17RkkdxVg_XCbbln#x-dw;xDc>jUNS*#9al1HU@K;BGMOUW^
zi{)ptuv&S=M(A(8`%TQ`PWLZJyX<xEJ-TIS{*2}|@7%ql)s@AZhVv~ODwc~wNnxwc
zPo(%ii=t=T3x~fCFWyXUf4)bYde-wyU|~tvm+HsU%}+4q%(91D=82%T$MkLv3bYE4
z)+E#T<HqcwWYQWqkS7@gGGs7xs(N-Q215%Nccxka;V|yrr9Od&ghOa<S>4M*fppFU
z?En0-1@!1BTJCUjft19J{vuu#%$yK__Xw#jXsrKK)g$unPs-=fiUl|;Zt--)S`hR9
zT(@3Sj<rO(j~I8l0S}=A0(cefvLIthD>Ix8P|C=ZrbjFELjMzW4FPf4-q&mwEuBDV
z(rF_O%_jfw?+>N1C`4>Rr1PppZXHeLTAA&V^nAvsuM{_-G7)ub!0hTM3=G-=wG78>
z`<@ChQwe}jrX;eW<7O<bR!DKr79_Tm)i=qN&^d6($BVsTigi`G3KIn+{i*L)?S|%L
ziBLF@f9b1IJ-c6s2qdnA=nfig3t+Y`By`n8oT_f(WD#@96dDaHY{zIkR639yk^>Ml
zr=Y`HXqq8dnCyN{&xzx)i<B`;E&b$udt6PKqs$N`8bvv5r9z3T=+T`%<Jt}lPuo@w
z({`(5j>_|sD9JO94SL5|`y=|<@D1)JVM#m)LdfkF05BwiVt^e9J1!iGHFRxae!>y&
zOFe8-MU!IH^m;qE*qpG%**(mP64soby2;`&-l)iLVwj&6{LLmdI)U+Q#^!^?zOK7@
z*5oql>#Sb<%<57+<}7Vb$LSkh0@Ibr?d*p*_ln&v{G*UbbA}<|q_)_&0k_Fq_*Utn
zYzp+YE|&^V+)dZ*y;%Du*%z!8C#U(mEuhq|IDvvmT)BGSK<K<7u~H7NAU61s-sl_Y
zUC|qbXeFR0+ab22x+3k8eU3g0gka^e$c9!mIaS?FV<^QnTz;{&%F;;BQ{uBFpm#Fc
zv(r*m^zV*`9>2(<eUiM;C_S8P9QVY&Q9pS1K~;`Yy|`2x%~glKYp_1Q6L$DRIXoiC
z)r)aqvGFV42hZt!yH>o|qudPv{xx%APO!ne(A`fLg#;uJUoH$FgN%h4J<GU&5Lzt8
zI$Tz3iko&~I9w&C-@BuUY@XjM+ckJcBdbnBDD!OF%ld#di*Z=L{oW?TD4->j;Sq!?
zxiLuUP3&L+cNmwWa^k(yzlMUnYyM_g#Lc@<|7vmL2s+=f?1j0H4Hnm|S?<r24>tPd
zziaW{y49Z@yK$F^M@FS)c|@A#@F{<m$P+TYRe4HJJH9F3;mJaZmd@18^50hXaE;6<
zhm|?4iw`8T*?&2g`UAuXtJSo<3fn=9WX<U-HA_S>)v!Cjl_|3nCy=uK@CR_)RPz62
zEunQL6*vqIiun-%_GGa8q}L%LUKG!};y&R;5CK>zWAjA}^bMclYAnV=l@R>hC|0!z
z+e1e6=P2^F$4u7f0xD(PRnYq_E2k|{LPlk8bo&2tr=7H)HUfB?NdSTQ_ax~XUQ9f-
zLqv@v-Wo6`hEn3UI+G2<pMRC##R68UQD;?M5(t(b9{uMjAq2(USr%qvPhsI*GD!bG
zsZ7Eu8WS)beK)ZM_oOK?CD!N<;ctf4K1kXNn;EcsVFXaHctE~>BfqwDV1gon;+B>y
zGW1l?uh(wUwp+|mD8L^RNc2W1dpP%^l4)#jfDQwAqffJMYt#pb_K1-r8F@rTV1*|)
z=YrD!fB|zH7qMNc(+ACsM;J6TjUl4)NfwRqae-GRho+V8l{wS+7%^lgX8Cvu;nm=p
zI=BlvxV7z!o$&@YnH`>alR>6MW$)4*Nlc_CQ$#l7TB@m?>DM#~Au#(GTU>>6_Otd}
z$tg=e_@}$u@c}}l!M7>b-KVuIhV=65im98v>6CB!R?dFa?Z}reF0}W{`Y2M6be+iN
zAS=h!UZtd7Yfe6Yp-3K4RF!y(ck0YICe5R(>-69u{q<up@(rhUfoT^V9?6`|dHQSw
z3qFcVT1xjFU4DE(=-a6O*k?D`iuNg!%NWAl-!&ZVw*>~wer=8>^M%O_xSzfKv%ee?
zI<FXp{gKtUSTRx(AiNpmnKB4&sc}QN-Z#W?ZZc};nS2*7KjG3>@&4kr|Iy-0U&mSa
zef~Mo5WegLM;+?99y)v9tk~P86_vrI`?Es#CRnWRsZqY9xmc`87kj>38UC_CdO&nq
z)-b|W__a#9-vIQ`dfg5ylypf3sf#P1l<Vw|FQ{+e|8uXB#&*b+h5uJhYkMmbYai`{
z{+5sBZencjE^`ytTNu9oX8P#o)xdY=QzSRV@ax$F`akpcQc14gFxv8|>s6j_rqo&P
zhvpq&{?lgUCDd%*L{@9tYop5)xmI_-C$>&eDpr&UdpiD8=gS-k{iS7PDKu?P3RQX&
z0-IC6Fj%nG57l~0inq@?y9Rpdi=EWXo!%;|uIb|e)oC1mY7oTChVoB3a&Z(D+ZTsG
z@R4@Z0Njv|I<KlAVgVo+498ysxYlBR_BGy$aechlRV;|w%VdTO`oS1+cg0UbzH}UQ
z@ZtHPnNxFn<q5k;dcG0_z<w=9d)o_P?P^BJPPrd8gplG3tV59;f8CTBMJhrOs7L|~
znDZdQ+J|!S`=>w-Jxg$uB4uXwVdiq?nAs~jh&RrL^37KJy&7_wmt)$!&Io1}wKgBZ
zPAg>J%W_0i%pLnN`BVUv2#|}+xB(ZP9S8tL1U-2PSbPf~_(?4q*DzrhPalEfrXi4V
z-SgGMq^S8mhydjf9oa_vgx$*fS=794tRqg>`=5Pkltcr~#(v6Di3}bVW>UXOttw<m
z@fWR5Uu1V1(sQ5O=@s0d=R?&IRipjaJqN0~Ux9w&!72cepo&0-@z|bMt|Vd+F1T7w
zkHq+7^Dpj?*?4boyg7k~Zsco#RcR|$J4nWnlPF_v)~-QxIvwhcjIs4Xr{W9#;0LgV
z?+M&^wW)daJUXyw+qbN|MB*?tvam;WvLpXNW8ocXQs+Vzk}$&yF<&ut)@_HY?o_#)
z(YFN-i@S3ut8zy<g9^Dy<=`j_kk*TtTHCGIDVU-?5?d-3S{88_FDhT>i`qMwYSMj+
zEZ*-e;Nm(IB`elQF4$sJCB!+N%L+z$MQ1R@QAdY_dzh$|qv$GC6ZL}%6+z<DAB1n!
zxEPD$hJ$foUa<3X8h&hmIGzbAg^8Ft#+H{rO*y=UxlHRz|DMLuY8}OD9|^U&Fm{#c
zQ?F~1!)&BJwExyg?;yuf7mW>xJqTkKg|!!MrcZzKEi``^<a%jpV4VJ+0Tuuu80i%m
z*fQQ0`=b>2r-a^=Tm9D1l`tP=tli(M&B=1d%Z9CYki?zJwzra*-tDTZmfU+aB0T@?
z@Z31nW$-4N*Yms-QNJ<Rkl@b!J+`K^Pi?J!Td1A^w>@o+`Fl@`QD@q8W8|4ss+W+K
z+4+$yNNdh?5X8KZYDy;-it$!}xoRA^?5Ip$6T`WpBsyXATG}YdR7XwU9r5ip<?pC!
zeOV}>{dF{ny@b;(o;x$mzJR!tAl63j;V?ed1+Q$6X1obDC<lw(c{&!G3EyFJjwUG?
zf6uN??NKm0GyOO^d-g3-bfcGle<P(PFO}W=e|oR2n+It%Rf9i3{vm%4MvM?F?@0+O
z<BW;tNo)@QM3;Je%7`b%$R|6@I9K$eaWRViQ~VnJU4g;s3!@)ZdOpsNcO!#*JjHPB
zl=VyGm~*GyVX^ocP{znmTEK|kOo+caQcjjt&du$7=T0SSKUv=6H5`)CnbteeXwUIh
z#wV6Wj!8{g(dq_a-VI7zb0DUIu*F`|=L%4?*HJ<N#WLL~5zLpI9c6<zU#As+3_t;4
zY{3^uAxBFcpeZ1=h1|zndir`cKT6eexg%u9N-<1j=2K`=mTy+MZe`-QR)+PPc}2*H
zU=s{5^PhjPkCLoYNN-T$Uy#B$h;=!n{-cilUIEvw48eEf64UN>a5a^lqs}v_wrr{q
zcPhh-Ys*DknQy-0^0dip-O%THqpLkFSElb6UnL#Ws<7=+@HSMA*g5@e<~lKlqELr>
z79AIrX5LXr-mg+Gv+rDRvgb!N-wBV^gvvH99*dT5deNpIp6Q9I*r@K$|JcNEdwkLU
z#4g^&E^%}w#$oow&pT?%`B(s-Izn4`+xXFbfyPKWhyAPisiXXgMp-U_Q+w|`BD%ne
zimuHyZ6q!guTS09rdDsQe7aKY=6nzN2DkCE<4<h#nltyZRCdGOkb8H&^EWj4fVbUj
zc(zwddR6Nr^oK-%o;|1p#SDpjWy(>+=9Kp6oXlX%MoY)H5^t;J*^sg+rs`bcCR0U4
z!UUgM;O6D1IMI*W#As^+7weavMW#~iV+;S)XQ>AIa7m}Bp1+s3XXaHA(A5u7>0H#?
z0p$+2qt;K1xhy<X8^p(>^J|SWrdq4)l`HAJ7?kz-?Q=zKNeSbgK=<|H4H4qwGIx1I
ziYfyj!`!#(d3LAV>;n7X{H~r$BGT=PJqw=+9#t3-46n|lvjd2M#BW2ce1j1#F6A@(
z3A2DkS&m%XZ=otI$y5AtJJ^YWjVtdDGSO>~gTLm?r1_W0@fAS+Lvr#WAN?`yLe00t
z256X1L2T(HUGabIjn(AgfS19o(+R(zfT$=yAd!HCS#aBvOQ3?-z1IAqo_GjOh6Us$
zSNt5l!!WGsMjm=7mfo#|u%y5@VuKZZmXqV}(#UT#!eUbK$^Ok>e4N^@<WJMUVvQxX
zo+Pv(<q=)Hd$&}VGI@_Ep4{5y*Mmin8vn!A?z3$<18UiHsnNsEr5I3XC-H`p0Kgbg
z!$iyXiYDhi_Ix0vobJL4sKh36n2};XG79J+T84S=KFrJeKZ?%6uc^0<!_UUp#;DN^
zqZ>z$5J!&?DG^ZljZTqLX-AG`q<{#cLqJNT)KP+fNQsC=3y2C>*zevy;hgh1=YGz8
zU-$QVUvRF2YlzMb*Yk*<bz7}qqE#^?uM0%B<gs1C)~#snO}`(jCKy?77d>)%jy>;`
z*4tJ8&xqgzYp#GLO09jy9j{CCI~mO?-P;XSlr&6k_(v8DBuB=P&uzwX-_V#kUvCQ6
zR|z9}PpbM08icrc^xD|W3R^D4rZ=%;)l4MRW%M0f->buSYF>wMv8(ejc`vZGs>^Gh
zliH7d*T#1$B%Hg<X6J*pbnuOst4((s)9c4|zusBamyT+DPTOWn_BmjOaSz!vlb~Yt
z52QYE6PS9ohouY+e~({_waz-)&b%M^v8T+~Wc_2R>b>;HvGgdZ{jU+j-=&6IBSfu@
zc~8SVW5zNX!}@h1<o<+(G`vje(ZF?kBTaJ?q0c9pNZ<#G$}b+>OcI6!bQnTyZ&S1&
zC`+5bFhYQ%l(&7`^Zq~Aez#k0L2)X-wR5i*;{G7648B}Qpg_I2Elg5o+6MOiNm&0&
z*|;Au2bg6|cC+;!JK=U}tv8D-YwzRR!Yr;xh-MuD$j+hR)^K)+_|TsD(6Q)ftk@i0
z)9CdjuACJK8_~^@q0JJpI#H=|arqVh!N?#W(F3f?q)Hp5vMyK?xy7ll>}nf`v?VBy
zm0IN$rvgZI*7MkWNLopDdDXaJmQAXzUOEmWT043d-o;T%?hMp2ab%(+`mr%K5)p8W
z7h~$^eeSPW2|uzXU!15myOv_9BF@Mc^bU0}^YE+2)heh(>=|Q8j8EW50nLe#Xbpe_
zRHAyi_BrLOA?2<q+FFa;_^>_cHHBax>4&rb{hb*TkN9E63BgE)Je>_!@?x2q12zBw
z54oqI+INPx)PeVf3rfI^o%EyF_m`C9IVK_y5lICs8G-H-!U=N%cBMIrcV$8qIS-yH
z@SO-luE@>R&(9Uk^|gw8-Bc~Td-g^DOdn0m&-*_rtJvJ#@=nHE3?jFDQ*QO}@X8Kf
zdndQpObxeJGJiq}OOWC;(!QkhC6D2pfQMj|hXEULJ6ZFgG!?9J5V<Dg)|c|z(ofC~
zKY&rKkaX?*qv1+t^=XYWm-XyD?+9Ir9qO^ru`VN>t^VGCoPG8RC$+Y|;%g$cwr4>C
z&#LW44Ue%kXSeZ?>YB4-GjM6hQHJn^(psg@!o922CZ{pzai3JFG&5jOD<vEWh`fGO
zD8%|I@W_z*T`aYVk@#KcSIx6W<iPg_25rPju=z>5AOAP>E8X;}m)Z~f%Kb)NvyY=X
zwPin^S@2!H-*o2`P?Nu<@v){hxc%eLjn+R+p`${$;?IX~Inug3DpYQT>9HOx*p^c`
z?pS%N{imzxJ50Y$ZLrB^w_`RS#e;%s!ylNM#HvHS-1K)d2%xQx4Co}<dZyLriWvqk
z(((r`=qQYt+-Py>R(|B|d;dVj(0=mwl0k!BCJDB8n!Dq=)J*-EX`Sn38eD6Gj~^VH
z%CM<_?T1{wTt(swXdJordO#}oJ2vz>)X*xTf1NIhtOg(f(AVi%*w`5%v{gL6KD`f0
zHe8#Xh&5tHrqhKP&aXuN{!1|QQ1#nXJ0+L*sA_Jx#m0q)GEvWk3~J_(Ew{vUL=L)M
z6H2vk&;6m~X3Cjt=BKg3D#Ij7efo~e(%MbWl01fY^efo?4vo4`wRs)_IeH%n#Dec-
zcg%kRr^;t0xSBUX#0rgVDw1*gD>$WeO?zM+_V2n$<zo3oGcoZ;#$k4rPErtwMr*PB
z3;P*-%r{N(B%%_Mxd8$l!eSy7)wtj!u_}Bl8p{bF&;crvD9OnjVknLd0dVvN2{HhH
z96J=2(1p>JV)_jkpW8igz<%wHb|IYtYpKjs->#moyfeNE*x;`fiYzG*ug*(_59zUn
zX<od5G2P=d3GCq5l*wyHja5N?$8nW+NNlM8H`^>p47Q8(^aTPZ-P19^_76@!Yn|sE
z!vPisFu8qQj380cpw86y`?``mvuHUW3RM$JWH11Ogf=H45<Zl^xmI>ZNb38^RV)jN
z1gnxGkQn?{3`XW)rkrs15?8>x)n`lpe{Vd&Z)PDCkkA$M_@9WXeGDElDnTq!CNVYz
zxYfy*07Hr=6&SE575K$JvgiyIRwZJPj3l~FSp>lQ+Jd!-61?AyOmx`j5jnI2U215-
z9L`2FB2@P&tIlgzK-Xo+0{C3_<vLaZ5^#^H(u1KAsntDVK|Bcg9v|rny5^bmh%T=P
zI7&z&E~}p6twRF7S9F(4bnB`!UodmxDh+Vg$-4mvFs_ajLla}g;Hwa~b2KVN>SbjJ
zy%5|Fe%~PsSk)7qi_ej$vRUmnt7F%x^kBUq=@(aj(c#e3{Gx~DA~+lCpuMq~$*?A;
zt;R0V$1*zx#Ieq&R`VH(ImR7KV&hMQqrC7;OHnGmVN0=o@7>=-V|^`FrrYH57VnZ|
zbIO3oiu~`$ionucxY+Y9wQrptN96JbdM&rhWVz#<>%=IbUy>A5){&59pt+d<2}Qz?
zT8IOgmzC6kM1Z@rtd1hB4n#nqGdLtuLJl7Dvahg>d!e#zP%3`FTCi|`D`SZWhV8n>
z^DAeI9jEm(!m5%LM5?#HP^UBbgw-#mvaX!mQ~F>y8!xmpZLeM#_trf9T-KN4mK$}Y
z9e1zQy|egnrv7o1+l@>7*KWEZK1Gs$y)F3~*Q|N;qvE`O^201=Hije`*u>?LAcL2M
zsGxxdice=hoPWU`nyVybGFH=2eafaLNQdEfC;RZ=DG}5i?=y5R{CkVMDgRZuF>Fmy
zHP&vz;zV%UPC7?^fb}hI312TABDY#UVU@sesWCYn-HCc~(dv`9Pd5WFwmFp>j)K6_
zVrw`t!z7(n-yH%B_;H`g)_JR|5Svy7^I`<O^w!R_4ig2OFkLa`^Egx@%?iiN4KXE;
zO4W(%tQx)a7fmrKEg$c+ui4mCmdm468XUXMSVY81d$lxatw-1wL@7j!W(#sdZwjaP
zos-~r(Pd$Tj$%rV(D#Zw;-M=r=lT<)XXg65oPXliMxIIe{ovRA#%Y*da|KXYlKZJ(
z8HxU=l(O@H&mM+D=mZHn2w%%S98Dt=-fLxJH`%W^8PM%BN(c#Np?fWmnQ2u_7OmQ_
zhY(qsl}YlE`t+b_ae8|2z0GCQo0NIJ6No*wicOI|d}AY?B+elwQ_`2fr~oBV(8jcj
z3Z%O|!wEnTgLhXT0kvQfLox<$vP1%6W;B;Mb#vm!7DVJF=?k~7p_h<liA;oaleg*`
z)I9bcSol*4;AxlzjKoeM_7{In?UXw+Lv-m$3L;q?q43fI$<wUMNZk6)g-rU;1M26F
z4ZkF2+SenMoR(l^*!#!9=*`zG!K*T;*rYLn4{(BrJ4wS5Z<!=g<`v9k{iUUA7e6`K
z7in7$PXpL&*?`FIgzhtMCh<_Zvjp$Qs_AvFrki*g*?PW%CI4Q7`J01<;pj7&SMZNN
zOI11QrrY@*n%gAu9~G94GaLG;o;tKqa+X|cq(!H(xMwMmpeu&52cu%+ZuSmwC7AXn
z^O7a>`dN0@X>aS|?Row?AF2gV?x(#(w~D>8ExPF4Id>AM;99C)IuB#Iq&)if-g6}O
zg6HDhwg|VXKoHM9tTo;IZWAyPnm8Bt6&bIsKCf24BMB1`Q-TT`?N+mBsyO>(t<hKR
zjtx*ZMmyy-NriY2$4=fcL}Gsv$6ubm#w%)j=18LU2^xgoxz!^sfg@BnDke(X#t%qp
znYi2jqYI~Ift)W#Q}&qZw*GsbPIxkb*9I_*<pJoTi|YXpVJyF%LqHkOcNN5pBf=%5
z17kCt|A{tY!RBrUlvp(|9-cf#(r$rxeY2LxbuO!@(K`FqLg!LpD(eF)=O3ctvSLr3
z^?Ci`G<fIy3nWXy%BY+Xct*q{_!j_HwK8c$Nka^bU1lntu`#n0T0ecA6ejUnp#1iY
ztg*!0$AQe!LQJwYbv^lwaxsT1oLxNF(R`(A9U{Ex%cE&6B>6yX8dr0zQvEB}DY>IS
zjn8p2;~0Y{ZMn$Iw>H;!-3(-7&x%&xahF^DzB0a2{$28UOs?klMy=<)A}$!UbLhkV
z8bP1RoY2dcmvLlq^xwBTfCoyo5Xd{$MZQ9)dhXecd=`9Sz9-(k$&|-m?{h9Mp*waq
zX#Av}D<K6}$M((DDZlou`1v6f@!ImT{gLM64u6=~b{e>4tOQ7+@8A7m8NC^X3$z>8
z0zYnRwzri)qF$6v(2&rQgx_yM>BWlSRcxNAfqh<~vBtsb4P8c|I$EO;fXw2U8YfS{
zv4GF$8wAjVC#`p3AXT})q9yKWmJQ2xvR)BKAKMv@+9cW9@e<Br?<&t9C-NTf^oC8L
zhb)ZiDdtUrtMwtaFUqqiUi+WAN~hXyhU!a>4o>#V1m_~x5Q!fuZ}bp8?RRGlEDKMW
zP{cQvm{q%l7x3}fb5keG`tOS-5krIwr!)ZeK#9Z>U4ucK#_J7(OGWvfyH9i<|H%K+
zT0y9An4|vTQan375HtAwGQoS3N#&|kJn3u#LlcI@L~;jy%kVFw9G(%v+zmpw#M&WX
zR|}w7IU6C3l;?E+9-A}csSV#I)i<)vX_OFhAN7%RZ(5Js3ew{ui(pU=X*DI_U(>QO
z`)qNcJCgzuC<s<J>tZ?5MNq_A(dF;en4QF9ECUJ9sn8UErktvbJPI>tWFpap44sb>
zon@dx`~bI%4F4-4$Si=4K~&|z8v{~j0srAI(92xB{*1}8j|A<{7nkHTl%~5eIm4c6
zmptY!*;7u_RIYw0Rx45ZgBR~-MNhJUI!h;VqzEl1P-7LztD}foWfDv!b%H)R<4k~z
zlDZt&SKEh}^qZS!3x3X}=g$|ansD2hDW5;Asv>f~I?<LXSGM=E!hRTv0k9H_WB~@S
z%a^7gBJY>Lkr!Qxjvy1d$Z}y_{34K9LBoJimB_BKfZd|cKUI2x%H#*~`p0z6PbBEU
z9l@hJI)Uj%UyJv?L!yuQo9=Ly>>-F(nJHok$MS`9Hr$7{GAp$lk+zJ$oCGk5Na3{Q
zcc$;L(mT<DN@it#NK!_!(vU?SQYYyqXL5)+TK*@0RVE05m$#e4qEr%Rl@p^Pt0S|!
zyU{8fq2%^aIyox#MUAr&P5OyN%yq~IdIRJfyQDu8^X0S{%)o5c{oZt$<uRtJyF=&?
zC_?Nl+iPWVKn4?EW*bk&USY<Ebn!>Q7q-Z=A-y3GgnKnfE}DY^L$tS(j9r<_8dw~N
z(qa7R$K&ZSM<Sk&A6k7qW#w;`Z*^ZsFl)J#EB(3F(7Z)skM2%fcPWP^5g^(TIcYHP
zs~ZqF^zd+q1Qs|>u;JN?*LdaH;f*VTrRa9@k%S?5f}Ci}`uwVE%DtoOQt2yHe-csW
z@sfO*Sk}*|t{QKYRR@v_lO-6b{wq>TV85rmQ8QzvBK@N%L#)>0xsJiPoZZjtOouai
zLMk5f+6B?lR+N#_50#-M=`35?VUd-XLLR0Mx(TIS>03Y!hC}0$>G11uaOHtRie5^V
zy=R)zY4)ACoV_c(Ew7uPjwELJ`^%4denpg6tT&W?tt8a;=@n*hjn>Q!pg3QqZ9EoN
z;K|##DNY2{1NAy(LTR|GWZO<S$+tJ;a_VtcCo(C4CJMp)gVc1Zcj@H)s!|Ds(?QrY
zoIB&e<ujxZsvJzCDU?jRN5|>78^sg(`Gg6m96iCtUX)3rPEr)KMpZmz_CL<4ewL`G
zLL;GR^PB0JaDzY0`iX{x{3*6AygK2NwgpqI19OTYC4(a)w!=0$872<-mLoc^BoF#B
zC<p0B8`ey{Ht5$2yZr@u-8z{R0m6+Ii(}U1Bk9hr+?YyU07d+QBW}u*rJ4}5V0Hd@
z_`nu<)j##6Tx!)nz<*8f07f1vBhQ_!%!p(sz%mXPtRfzs<WJ2y50g)LA)9zwGw+2}
zo!8P?uddMz9gnHUrRVSmb!S$`$m>_gKgGMC*R%MKvulS*SR`u+g7mo?vC0eKpH9$$
zk??GHu~4w#AXl9Uh&xMtWfkr3S@otG=GOHNrr7<y=cc`8B%b9msxYP{;Sc^MBmgwp
zc_<IYToQ-&ET<jh-|9#vnb_DlY$XD*_K8r0kZN1^Q6aw+6i-hhQ02m)Koe^2MKu{4
z)*IrJ>gNv_Z^G&D=){E-IRM2<LS4Kvb;QJ1A&OPv#;$`*!}pD&9~kE^l~~h<GQse*
zf94x3WK0<DN`AFYNusSZl!=qVq-kUuiAbn^q+Ir*mp;h!g;VM1vm2MG3{=M}Gk;@r
z?PS6JnL=(mWqtb*)q5E+2Y`QRtXYjAL|avn&9=FWJytcpQ5G8EQZn?*c=<kpFd(+>
zQ-J*s02HW|rt4MVi7jPcbjv1pc!n<WZ2A?$eyR+AP?!!<B|+~%4^K#Z=4}Z<dWD|w
z4}nDT7hAL-g^*1&r<4SbM5;QIF`ts{JCf~EVy|MNAD|<lT;fq|X;;WDjr65qxX$<F
z<1@)&ZyWul5w=$ZNfL>2$^iJoDS@Ndh4!P7gkubZ4q^8SyJ1)w9TT#6N~v=G@LNAm
zAbm){Ci%dW1XLNA(h;*Uy&(%0{8dFUvz@HgmHfAn`_&Ih5)dd)@@iP7eoJr2b@+-8
zc{yMn#7<HZ5izXfApyj3Y<)jHyR7)ZYVDH^=@A`K7D~%ai6~Ly1t&MDrK{4YK!29*
z_gX{!i<`HQT&>kF5bANNg(cICXh14Yfe240kT`Sjd<k?C-kDR51bH4ZL<&XU{n?c<
z$bvd+BiW%Re2|CBtEl3qD9l@n#{N4!>Du+4q82T9?|qf5=Or7Kz7kVa<API_uuqk-
zyJ99wh74H+8Fn)<u^elMqUh$Ch3L)EXG*t2Ms5np4j%SM9Wt@3&-j1$#^jxmV*ogI
z62K*;BE)w8;RqI4mY!)kB0|c6kLTV9*v2(Mqi`#i)7kV&Ps42XcMHl7=vX|%NRdPK
z$tvj@hX7ud2L^d1RJ|joQHwGX`um%b7b&#!saF}3cU(yzygo7;iWY~g(gW=D$4T3Z
z6UQjq9g^bv7ZcuH{Khaj2SC?8OJfLCjA$ZHLI9SbQ7>^Q1met1g2XU0@y<7DCy(?D
z#Prsj@9!n%KX^PZ`M%WQHhhWle&B<VDKbF|b$X$se0I#hXSIMS%>Mbh;r-Kw4^44n
zX662WP~C!7QafKHr*dVZ-qnRK=Fn;dyp36M8k_tmh!@&Sxc5S(Yibh-@O4iz&3E`X
z8tyKOXupKu?|VypJ;nd-=~{|Nfh3Bw*wA#dD|4_guS@)hQG5CyOIm9iwjBRPCdf?s
zf^_55L}P6=>$(&vjZ@o^Q#q|oh`9usmKF;PiHOM@$^i?yk<q#|GQpn$u-pmg1PE-p
zocNqAOqjV$Fl(P3MPP6m?8=RVk{qQOZO`VmD<#r2n|?4sDEt(pvvcwVG$GE!_2sSh
ztXoydcipELcigy<21y5|0C=VvTze(H!{fJ@ty{*-TJho!bw#kuRl&vBL{o84>-KV>
z;?Av>Dly@X?37pbI5}^)t>2)}>4#YACvmS#*1nhYkF)8Za-Wzo<Aan&xAVt&-13gC
zCJrIU`a&VbX?zR^#SG@|0ZSt!Z#~P58J+p^cA@qF0??9-Jn*<h?t?v^THlL7ekv{g
z7YhR}R(}+Wy=1oa`q9rXZ;ih3eqD2uC}ZKA4q1le)TSl(0vN&MDEn(>&%vSnIq8Bp
zNV|}mcsyE;70mIKH06m=H&XFlZ~|ahHh|#(<AK{i9MdNboVgYDY$Jeuqqpd>tC?8D
zupcX7&pTaU@OK&U%YT*@Ei#zvsRv=!_&|F`2cDhC)PDrT!0L^R{{TLQLV=N&$4K(;
z6{9)%H6A{$xjcKm3N_4W)!qBmZ8Srr`c|dVRK2W!&o5*YF^Dlx;8EK`+eog3C<{Ih
z(awN$jw0FTgQ_V&;WBANe8e9^E=7<4_ZAI+*_Y&1Si|B&Ee^wm<sK%be-D~|<PyKB
zd3s+Ff3M{!q!u8!z!0hZ&Ejj$>59q8Yjew|E-T$rad@)wO60=(8RG~qhd{6XrKWhc
z%~sgGtPf9deOqo$6*ktQFGIc0q%N_%@$OMiO8EUW<u(rQ$cv%$h9ptk%}#$5d2;bY
zPG;7u7)jtIM&vNrW<AP=Rz!a*lO}ZqW`cEg`_q$(XLqOp%wqmQDCD}EgL=tVGy@yX
z7w+$tYU|0p6PP;enMw)xk1CSzT9#q<RKC7cmUAq*ccL22kdF3&8$pvXMkx?>KwM^^
z=rDwAgJ=xNxf8UrATttK@4N2ou;9?zH4^G@Il#E=Hot>l389Gp@t=KAw4hy|wlZ~6
z!F14g<cp?W$kdqsMZF=<M|+@kkLOf?I4+LAyyhLGw7pO@&S=5$fm3A7=HJOhbcSP(
zsQQ<8X_&v+Yzjrrp_7#B7PbIE@_cR3ZIT8hBL@KCS{9x`-P`Din0s0PBkG17Y5>;}
z1DDOu??jU;t`76~;IG{H^3SOz*VQS`ue!9=82P~qc|S0SKmSiB{fsFPL;tqoiHKDt
z7H~Z^kpz$>x&Y2f|MnBw&FFLf5h6Vv4+G+p60kHh3i}rfiO~hcXiiQbUt~#wgtP}`
zS$MI(AWnF4IV*8d!A>L&#6A=QfFI=`pL2?)=C@hj2(F)zrsDk|Z$3r<F8Mk+h*Gf~
z5N<oq{PVSNF9^0J|N6To%tkiG)45W6x{5iM2Emq`QHYuGns?TNkVXNTAbtLUqbPgZ
z!0QjOtxF$@W}TE*@fa-Fzp6uHs-4f7F197n!$h6Z{}lCEhQF{qi2inhFXI5Y2>?Ng
zb6xxfmY)R;r1Pf$W9N&wsn-|rNGXm*I?G!A?sD5B@)Nrc=9n;BIY`A*ZxY&1g93>h
zbSH`Cn%}Do>sO)S*pk{Cdn;tqp60>z>nzU`l5UNME(7>lZ#fW3U87BssB-j{;+BpA
zKzWo@7m2ph^?<kQ#napY8utho$tEeVGT?iGYjJBDkspqSe2W`X324hvp6V)NB*F7)
zeF01y`ExvF2gHgYrXByaefq7(o>{$94>(M0zX2#FO}I!p^LFsOVng2c6%wO>nG*>X
zB}$)WFww#q{Jr}7+CD6+u_V%fBTq67!e-1y1A!JKM8}uEq~<W!d(D>7{5fLg>-;gQ
z%7rr5Z#GDN@w-Wc?0J_>i1&upAl&KqX}sfGs+i_7s)^nxhbGi1_qRQ*VuH5!wlQhy
zH@=WP{O7r_cbDFSS01FybZePXKEGl=nGo_S*Ysi7JyhcA>sJ~1`(uMW`5&V6@BGn!
zx!hy49j~Y`rV*E*67?5tjt4<>3??!%Jpn=@BtuuDaf~|eR^f#eG{OBiSv;2kRLp|N
ze#tXk^;<$GRpJe_vh!>$y<7R_q*phS=JxfTcY9RGd8g!8{rXd#@3r3X?w`GS@hUvN
zf6=P{b;Yacj~TD`ZirN0cXVyN-=uY9lUu#}6S~`|i1=uCW94*zU3K7z(>JR2Prn(A
zuOk9B7z__y;W+S+2f;U~5PL_?G{fJ|<e#k=+KC0+$}jfrnLB3a>?!!=r#zX`Fyd!3
z-*s0-$Y%X%5hL!i|AOO}KA#4AwdUKlH_qNEy-%P4MoA!148A-5wY`3y$O*r9vSIB9
zdu8M*aOIUrayrAKl$L_Y8JVpG5L$pU6c7xeCV}k%@GLP$10zanwy7N|k<tbjh^q>K
zu_`QwmEox$3*=$Lm<r(5;bU_jJYDG?p^66se8bjhCu=EBitWOjrz39h*oTo91+J=C
z>Ac`R+TL6JBCcN>#wLi=y6-4anM}#y;o}qDzYrm#zgc*DKzamOm1yphi5(a)ZTE9;
zQMR>p9#e*vEKVlL`b<H5<vW|N7METHDR@Y(sipP;^%wt$Eq&cNxe(DlF9aiAQw)Kf
ze1=Afc`q{vY70l>QGe3wck1;e@N*Z^&nI!<<6FE-`iqtH6tK%#0>=?O>uNiB_9|v7
z{PEN0bd&UyQMln}s0%jpPhF(y3XK*Fw#%&aoZI|vY35Tbk$CvYnm?p`pkz+tmz5;y
z3*}+qEY>&xGYPt$5U~V+%Ts4BmgwAYPJJK62jSVv4CrvIe@4KbZ)-5mkdKES-^NSO
za`r|p$9^_Bc>*v*1od*zwEx30fp^`^5L=nwU@e9rW%p$V;Y*(*=BJqDeq=7k_VvsX
zE~>r8Y{8fb*+M{HWqkh{iM|M*M_3kOgI&)4v7k$@ZP0?j!2}9G$Yy=j+0JJ%4tsaX
z_xOjHY@U%&J1_J#4FxgDNmP-eUd@)Uw&MC7olDn>A@Dm!^hDC^k^vkZa6(Puv(fD)
z$h0^pRQUyD)P?h%Rr`S`>Q#k_I-Y?PT93D}tJOVnFerM>Uir0HVhP%RW6pk4HuW{z
zK!)RW^ODa_TP^L2hTU}LP5@eD&6?1!A36j<Lvq$2=;%$SIy;1`30s=&;%0`~@@r$y
zh)b+TuHkst2N|wW`Yc`_G4c9Dt>2Y<t(+z8lf+-=jc4nMt6rJI*PF#W4V`igOEgH}
zr;x%dG#7PU;3?)`C84E{WXi}r?Xv5=^+Kzyb9->f%E+aY|CLfTLe^{H`~aul>}kuu
zs70k@KjnV25fjJzd_m7CoEMI6_F0>;F64MODQG_~=q^s<{}YqWbjVJVJBP<AbYM)x
z8B7z|CQAM=JhP5XkPTVzx!~Z<k}API_FdY>g~b`LUQ*p&JoVVC(IreOzH8jb4Ev?R
zujMD<9%IkE!iY!n_xm>{$i}(nqu-9>R_9J={#?k6P1j)Ts}{{VL)jg;mGAY5Ys;y&
zQ&pF6iTUdtV9SC$KYJNX10mYqVe+r!kxJ5!FOG9M8)W^k4{YTIeQF)ToJ-akrv*A&
z%QJ7i6dwo)E^jcLILUlx=u9`KLEsaQZ3`zOF{#I@?$tLUKRynqG~T11Os|C+%p#E|
zt7%TzSRiTQ-+>fo;U|H8QJY&yku_g0=Du3Zps7j<j+_=ka=<DPU`;3)&zScON*Fpg
zg&_XlL{6n2nS}aD(CZ0!1=A7gB`yqb%N)uAjK{+-BktR2CRTt#G{i6S0g5M1JGMLE
zS&Foa<c{YJwi`X4Q}|}kmm{ODU5?1lJ*e*z6k5DteFu^<oA%|-?R!ir13hIS%|AvS
zKz%pgD<^z*te2Bqu_&3V6zy3I7gznJ@yzcl--ZTY4=uGLA=hn4n3y^$%HXj_MPj>4
zuLUCLEGjiSCvVQCrBTz$BUbC8y?JuW>%SKd#;xy**d&I!D6@XNyVAV>>3Ykz|1iWw
zz5i?n`^K009f2h6>Qv+HeD|-K<BiP58?R7BvuNbhG7DptK9u3UD@eeKN|HR9>d7G)
zyGpkTTFqN87H8BKyASy2itvP1qa7{}kGehx_E&jOaY5Ave^F>VzYY32Rky}Y<(<a2
zQNsE8=gcPq`(#WxU9E=xKzhC^=CzkUOAk63L8iFH2;J3sy-0mnX<PHB=XVb5!r*zc
zV4hCXyYDW3F*tWFPsgSGk87~G^tsln^D<vUaoHKFhpq%0xu|BlsY~MrOU9m?-lju4
zYLA*&g#Bk3*cq{+TUWtGx73q)iU+m-vi|L{Hgb@?IKqB4%Opbjjl+7a%XGm08Lh+H
zlix4f2aFBVImaIp*86fnZYhtxp;^A0BJU4(N@t^7B-#d<6U){<LZl4D@VeqeB;&9O
z111$-T>|&`D=tA#6+)f2Y!*w5@I`7UZ|P?THY%jXAU75rQi^?zbeK#i`E9Wvq&+8f
za6EANo7sc#`iaL9o0I72&r{12k0!#|{rso@2FY|EXT{IT&>V8JPO|e)%9?#q>fYIi
zur<CaRSl1laE1Wh1N;i>5VG98_iO_^q`FghECh^gM=2&2v24ylabgYfEEuS&OtW)K
zzQ3IgyVCZ>ply{QM;^=<XJm*@E_OZ5<4T&w4%<Z1*s9J}q&Nv~WwaTKh*W`>0TYAV
zYG(*ZRAb1Ad#D0HFdXCbAkXAy)B|=>Zw%l*Z%vR-RiV^W2(L|A8D{a1xr!R@RIYMD
zq)JquJP?GG^@0f&>L>t{EVM*<5=+A>25QC0Mt#La_X$IS`2pZIB;cr`l3O7<^xj3a
z$<<YU%!i}oG=&dF_5ZQ1r_OMzhKRnUV7|_vu3Wy(mjRI?m`Y`TTb;kKYGk=LGM$rq
zVXj_9e!K9Y&1BiS?Zv~ZcXx^d>ok5OL$8j-tFVPUf>K2k1Vz`<EFsIJ^>#sDC`fo@
zL=*z>4-6+#Q;l%n4&=Ve!;dN6%<^|&2PVHPuva?+*@kWZQ(It2v+jSte1WOW^Grxy
zP&;Mo(c18BrkTWiB}a0HH^VRoRt25jfFGdi!iEn1z;PCML5qZAXtjYD&021iA2FH;
zAw@r{$&&OZ9UKM#Kdgh{mx&DmrNi3MJXueUke=odKK4k~K6>n^KZoqMk^fo6Ax`^q
z0!}>z7oLu*>wWByjXNQ&lD{sE+?LvU^T^lhl6`7!oxy+5Qf6tGm^7>$4+Nj2p8a4g
zYM9?kw|q!Dg0)$vRkY}s$p^XLyIWZWyIP-T3ZIe{?v7C@IZ13!HgH@lr{>oOzc8Gz
ze~+unW9D?$W>W<r2GlwOM<e^OYWoMfETKb3V4MQ2mjX0D?5Z3SEnb=$a$@Z_G)tvp
z=%YeziPkDcj2`g0z0-?9wS2;kqH}d+!v4UO*$5j@zv3xE>JNDQ`1CE7`bQ7i|FE&i
z{Hp7(-><Y_6a0gIPpC0wqx^nq`<{)ZwoSVFk$j7+;l5~Xw@B5p*<|!Kjm=IRh5pbZ
zlCY?0NZ?M|lds*;NbMweP}H}>z8m3N(x_W^^usm&K(idfr=#_3rUx((o<k3}abGD;
zCkLZj2HAfYgRj0$)8E<P1rlb->ka&78uBD0X6?9$1bD@r7l$w$8y-kNg445KFh7}_
z^C2U_=jgL!BLuX%8S~lt8V5xh!vZ#}`PQyR4Z+ygMCrOIhWev3_;mm<&}tX)>Ku0B
z9@n}D_Yj>@twoW74;ioC8s<e(vtAG_VN;wCJZ~5|Yfo82itOfldwcElt~48?p|1=4
zEdDLQ>(oc?HBO)XV&p*06B*?{DpQlGk4`|x^z#c9!U+x7txVl1YTBoCtj~Y&83WOz
z3A1E-v7y4op;x?8E%%;Si>*!ftnw?ba_LU2oPPiFkDA+poD)UuiBgD!^j)R9bu-_&
zQEA-p2kHD*0s_XhP?Nzgl&xOszB3AU_-9z0?VInDUcmB={g#}+Hq#YuMVk!K39RTa
zOfr*Ko`kc*VBo3zNabR{6e3AdQEVcetokFj?W%1z7&==x04q!}?VdlQCeGDz>SK2y
z<2%VF)UiWB=sl6p@8V(FQWtJN3A^kNvdHEyJ)_cuOF1|ilkpLIWXCF(b8#P6H3kzN
zT9-^H&P-V3YkeS=(3nMsw5)=~5;jj!1&aV@5p%h8{z1V~4YNOj!Om-UqB82lxMGVF
z`3hSaZ}SCSGE6JUe?(8a8xpQKyRYV=b)dClbBmCHDr8S{m<dy;wjQrK@^SxIWYPv5
z?uTL$hiFPnm`F)dfL+y3C|OQE{G#K=Gx&Cf$8h5j{ATblcEf6Q6n%$x|E{Gv<+%Nj
z<zRztS&TI!_~|3cdSLB0$}`*crNDVJ-!JWx?7m6cn_rBO+5UwZWI4L$IYc~!4@ML=
z92Jo+)>*6GVm5*%66y9`4!ONd@ox=VYF=XrXBtd?kmbT5f5Sev1#nldwRf;+@}P$I
zc@iAib23sG_;_2<QaL$Sj_~Bu;{4oFAKZVJm<LO82WQmXkR;2CPz^#1Ed`{GIMt9y
zV)6^(H}=Di^_KBqxG^|Q1EiRUZW&pA?vucXp7w%!80PcWxuP+DImTBRB<L`Lggr(W
z>fwA^B8POThquayj|4ak6<vv#gIf4CplDpw2!-~86t$;&7FOeKYfIcIQPFSgP1bn+
zWhJhDKk`Y*m79AmV85d$t%4}AH;*1`y)nNuD!~-#f!~qV_-tb!nO|U5)A>$i5g5K{
zSOr3ijv@&`W{_8WhLjS>L=}~{Cp|%HH2L#*@5#21?Qv~AG>>vqEB2t1vVXz!YP|2%
z0P9aioKyWptW}H!PfVH%?8<R{>mlwde|;DH)C?E(r&?H@MqmSr^Hf=4UH4AsTdsB7
zQ;GI^4+=xWwivKJK~$ita=~gOfj;JAwiMe<lq9hwvGjhpt0$o38*(DSPW@O#CLBd*
z6nk+aY^AO_x~aLp^%QSr^U?OC_ln-R#BhPTXL5ZbA>aI~Hn<b|IEOy+ZLEt_r3p%u
z2#q*`glUn6VA1s(e8U8v*;?<Neeta5_xo<zrka(X{wupTq@@cD=L%8e{9JW=L;II_
zwRQ(C1Yh5lTFF=Dr!9BU*WUYR$jCQH4DO>+Z4I7?ZW?!s0^yTYUtm%ZP$Jn0FULd-
z6DQfot^I`91}AZe=_)}Be1%8Hw7lmG^6B-;w8<*9ucN2%a9oe@InOF6KL1!tCF`aI
zb|vum%)ptP$?b;&I0uW{4eTDf9`AGq4(TaX@xlp@G!1t)JzT*sFUJ~&o3&B&oD;XZ
zX#qfkJwY+?gUWZk(m0nK)stuT|BB~XB%Qcod9Na<K0FgZGM#%<v2OR5^4v;ZxgafN
zb+SGWTl(JBEiwEXmXhjBLwHFWobs?18tPXScjZYY@>gZ?e=Ubed}Ai|GI>kEr)gsD
z`&O1;R58ws)mnm(`o^JR0r$afnt}UvyA*x<ML&cd%4<oZu}I3XP$!V|z0mc2C|;ri
zv%>~E=5r4-gCz4Ok~yr8LI-i;scHJTv2SNfv!6w$C+G5B+Mk<$_w>W&hrZTQSK*7g
z)mOv3sKN$w3(>n4c;~c|xBKlX7^ZV|nr<@tJ;)V>&itH%W5C(gD;}N;+2k3DCm43e
z(wHW$hTPrCZt3g^H`*VW$vGLrn5g*vCO3~V!=m~@Q&fx&;iWu7*nW5bB-*tzdF4#C
zwGl<|q<gOUZQjeBcTV5rx;CF%7soyLkfId9X&baC<Gpnar6C(PbPRbH0%(>8cs2yR
z+d)f&d#I{QplL^{vt5^%V#s>R8;0k#(z0CL=wS!9xNf^~tQqjov+wg<c|ynDIHcO?
zrCjwK-UlrnKzC0HQr|W7bL&!L<~m2}rc=f=u`O2>d2k^Bx#LJUvTZMR{)nfHbzgDC
zj@=Z^*OE96v0K#^k16mC56|9i6Q;09%$ztIdVA6JrrLl-16Jy76z8ySi>l_tRgYgu
zOO5+tbnVQ%65}+D0beCX<V50$1iXgiFN^h6XiMT-tpk}4DZiLq?yMcFh6e<v1$8B%
zHv?L*dRjLvr?Unfs|Gkn3$gz!&!T@nD#*C};Au?nIOTAu7x+QTPBz*^j$IR(rm=!W
z_U+$79a20nbIuOB9`5H1^U;iVx0nZ8pyw~k(9?+=F5Hh&KKu*K#PvjSUxs|u^$le#
zn+Pv^cJR4KZtgcW>5(Z19`GB+t0!PMH6I8M%{&P&ksxtmh<j|*hl@5|08)Q8MVJH3
zmb>>0V*nb$cs^lX<eXDXQU%_)0ugTTfAMwlBiJfIJU9Tujsad8Qb730V=8nlcm!ab
zZK+s!`r5`rJP~mFXfRv{VxPY605}66iOr*7ZBB4#qBLizSA~lp1XR-mYF)$iie?(P
znqF0;C{btXBg&Lj@=ld4>@GyLtQ-BSSlBJpjW~+4Xgssa<3DEBDp-8#-Tbu!l+B;(
z<20%OjdK=24K`NM{Jc(IGDreX+8H`b?f|_P%5>Yy928+77G`Pug~7Y<Vhhz`Gx1#B
z?NhXH5>J1J*^?=;h0~{v72TGeZxIz+J}I{sJ-a;+v~}Izu;JbJ;<)E*w>&D&)_iuA
zxsg7)^8Bu_?$UeZD~*=jzki;Mf8n_^zSo1=^#6y;9pAjF%#!FYao`e`f31E3{A5b<
zG$PVBu(avr!8TF2&OOAeD3KK7Q49h)enQpF=O;h5Jvs07O6^IY+@=i0Gi-b05N6C_
z5-MfIWpZfZz+)Qfb)H8@H3I*<`jdonOiP36YEfxwJBYrKiUUysKF#=$Tl6OrEqVa?
z-K#O1uXgVdO7cGi-6kVSyykl~{i30au>Mu856d9FHM_gFMx_mUhQ+$+_FbG_p5%Rg
zZMFikjy2_H#Jd4hAhGc2q12ltoWJ0ZHqIe)+_WK_0k^8Z>nTy%Z8h&yDz!Uo_C+2+
zLS<Eu7CBjv;*9`>N|bQoOP0rTmy6yO;XIPdR1$1mdJJ<no3#(a->WOyN->)bTKgSe
znS(O=r1eeskGe*Igwgl=TA8ktCyyOQWKZt@A<J5EZP<A`FYydFpVJaF4$}Xw8(c1S
zqi!KWjQqOP6Re&kYG#{YKxnx+5;zoYR(pg49<Bv%5_gx%rmO4@TS*I*rS$T$)7FTR
zi_^ws;w%3Q11BEi4Sdsy)4WbqcNB~=qhvMg+X}iYmO>yd=>rl}nVD}P5E2dY>PoH8
zTK6>)qI4&(&H5NNk$|yp;f7JEAZCdK*@<#VAi6R=XxK9dEE{(QF{0lS)nEx2XTY68
z>;=pWD48ks3#%n^tRx2NstrAh=&A=`oQtNz<{`qBmDBSEvGl1EOV(SRbw4O*nfr0y
zC~C63&*w!pv~4^1ocUr+w(gMc4jU_5ebbHe6)#RtiN?RxVm9S%ZL(rMbK%;a;$+)K
z6b&%UVb~-hMM;pAi{j&XCg*F+((g-klMIh@kF+?=eoE!P$Gw#m?sxGy!=^A@rmZ&k
zU`M7S;oEM_>)4I&Glia{V-l>Vlch@0|6PYdokOs*?U9sWa%{8WvsfplJOPK4#ZLnE
zt&Gg-A1<W+;T7@Pf@4;~6QpjP(3i{!er-Q`fuK~OJN{+#sotm409MKsc5J|8``IAr
zH)8?hHv)8EkYE%*sO9K%XL$?_AW7A>P^+~(eSmx*LPQ3<b}vfx?=2JeP-=6UPq2Ly
zGD+)NKC`eQE7$l)+w6F?Tk=9^P6n@r8Mnceyz+3}Nxzro3s%Ssg`{k$yzoaQGrs0?
ziXt^LfJL?Ti)qP_6oXvQ4fp94yecJ(Jo~j%T7xiEMe}4l;xY5H6DyEnyjFMeW&g7R
zf#SKjYrYy)w+pv#zCN<mG~4D7@FKc77LF);mB;f)^!$1nSYP6of^Xrj&$b*=h~0P3
z5`4k=*K^pVuz>JMR#F+vPmP`qZM<vjXMvnvd2>#ha1a0Pm5YT2kCHbbI=^ZMnl<}}
zaW##ezSGM&)ozkDz6>Kl4j{~qq+)662W1P}8vD-2l<f=er*&rir$#t*%t`~=#%S%i
z(a(KSPUf3$6C(ZXUrSkExYMy`(w?iTCdK{aYm<WOK=Dm|rJ$hrg(KxN=4(!XhCu_c
zA_OA<p_+&x)R3!;#X7-yWSOE}+ta^z^ueQh^frg9lWQ?5^kRKmM~jcvW5&of_K)8f
z7P&(C)^69>N*&x>`WElFIMpokb?iBpr^flmIw~1-XKQ65VUAZ-*Cl254Etya1aR>h
z?mNfimI^<m*)pAi1&{FtJR;b8dP_7YcNP(4V6;O7o2JEZZ)oI+<h<XIJ8ho%>DCvh
zVZN~`e%CF&e`^p3zmwew?=!t;Vzb(()0<PcTcB6IZ&A7x_uR>2!M>H({>OnlpW7Qw
z6X`1$IYy_}otrZLrBSF_ZM9Y8btM||WLe}h;Y-{OnLZgO%xnQfvhb?dn6mcL;}2)h
zj#~rX0vFqwcBL5!ciT9R!mP55O1w(4CQOD8tn6+^__UN1uY|*&(fPrTE@{Af8?s)4
z45!Lh13cOqDBo0}NmsjQr2(21Z^okluP(d4@2KFX8U`8X`~qh%-p3<myquA{_F004
zJPsowV_Rr!53JgceUmmPNRAR0v&H$MGL}2I)3;4;3p7icPL#7({q#D`B#;@%XyafX
zVsIcLEseae+}S@<Dyo}roaJ$2=v{n?&(@CI*j*u&&|}yc+FyoCUI+%f%XKIbFHM~0
z%5PVAWxlON;z}JN@#WVnC1|N{{HPS|<4rp4FQ8!-(vSbDnOehkExtrX_x+!Q_NzQz
zIfwH^d;?j#hP#<k8@XZDjg6E4`BO4dE2ZwMT2Ons7)ffIWqWRObNqN0g9l#xRjbOi
z)MJ683(y?*R~O06))3iNf8NY%_crV*+in+D$VA>3Oux9Ie|h$qpADnpZE->Q`seX_
z7X<6;8(5#$zMd$r0HaJ>#>QU%0y?YDrH%h&H|WiZ_fY)}S;71s8>KS7u)daUkr1Pf
z=i=dLF|*(-zNE)^<DAmakpc5xb7{_yICj-Nf>Phr9TJyf`%J}RS>%tfAR{~Bo3HI;
z>^@AiWe55$t3NN^Td8oq)^U?tT0O#x?oQ6Qz&Q|HT2L<A^$pQy?Lj&D#P7@K$UZBC
z{!=pScPf3dodOL}HDaa$G3;E<cR3%ICquO-D2`9_{U92A0k4y0E8vIKW(_w?`C4R=
z`!Z%)k)DU*|5=sXx`UJ`C{}pswip-E%GloP_`PhHxw`O2ce??3P7H!qs7ak&5hsDS
zLL(6XmEb6CG376{s~W2`%Kx5cZpZL7X(m|WEY&9NRPBj*zZe=&Qgrw8_6|KPTk=&y
zJ9M=bP1F?C`%D)YIw)8_U8ti&*qHp%#_DwWa`r&8+tCB*Q?7uUp<eaA&<=Y!&_UW>
zOqsM~-1#p#{Sk0!An!at>@cw7e*%S<4<-(WAkVW=7sXx5+9wBPy!b_-{OYT1hM{vP
z-JF^qUr@%bX(PRc@+Op>g|_3n=`?8ec?UUHcUBrCYqp0ozl>#LGP5I^gpTZt^x~9@
zVB~ie+#?9JM->wpi+4)V^P%-C=j!J}*b-UQYx<Z3O|u84S(fK0eV;N`zc%6eYk%c1
z5ufP$6_F<&>w9t`b{O>=5rCKtLVHPaOlSg(G%rqav}D3C>od^GLm!zC0SG#yBR(mO
z?d@Y0Q^n@X%zJCnFP9KE7M{f{AIs!wBe-{4$WBHyEi&Etn(1;Z+2|j6_KJ2|93oT%
z(ReKl4ahaMB>ouDB>4v;IN=(V6RXp0)O)3yvUTa+ydFo$$8JU?pILhIGnlT5x{uc=
z8nXWQejhg_-K>){>Czx+${cnl*Ey=sFOGDKqR;9fI|3%hl8_CX1Of%ro)KQ9=!kn)
z5(n^M&gq5?MNI;kf<yya6PsEn*T`7?3Rz2-V!@Ip8NOn{<yqKEuhPa`gCbR(y%S+<
zcWE;+x*eG5H0c=ewB0<x*Z>lU;F!HW{Gy(5!lM!IokyQ&fd~NL>T`f;wJ__TBmd>A
z65`$Z0=>s+pL)|jqoS1xT?6T6zoMNE^9YUh_BvhKtA=Q5`@r=Y{+?oFyop?_4*mw0
zIu~LUu3AVaAtK8|7)TT(b339u1O{qobZB677C9}<mVUnE&W0FJ7}J?NbIS`-#lM$B
zsHbP!j`__LTCI)MG?Q!3lVdNEk0X(EXjrO_ev|JU$C2?@)A83TNb!h*TJpXRb9+p!
zGt4w{Ks@R`4%3^qmN!rki?Xchr{(YpVp$LW!&S3$?>9Aed}ei3oDm9U%gD)`eQ>%l
zm)wqGr@<2@c+>cm6A77++h@o$MCQhQ<0-v*HQvGetdkmrH4ee*9>f!{Pb}3po06V!
z$!%hf*fp3UF*{HB-2K?2b;OugS{d;7RYUo7i<*SqD(aRtG2h7<iWV$^HRCjsoQ)<4
zbZnHqJ8-t#`IeA{Eb|Y){MXD{tJjB6#+{`(@_G3;I*Xb}g}E^KjIOVWf!a8a6Sog;
z;!y(v5&T0L4<Amo-bx(IQ2(L+`>f9FlGbG3!!cQV*KO3mXC`MRQ2i{4f>RD10l+Qg
zfWW>A7mU2lyirhCL&>q`Hp&3V)q2BAG9o7x7noVnn^7_z{cVmyavv1yXB$}w)m$c0
znSs-Yi5w%zM8eJn`m8l~bH%9Kdx4c|#m<j6a8OnZ6yd*vpt-=YLn#>70&KvXEz+`2
zotMZUdeDXH>dh$WBq?^^P#_jd%Rh|W;hqfTu3CR=yPQ8e#eP0Gf4LfnS0iRWfv_YJ
zj!mfXdV)0*3C-1vV*rQ!BrK~CKLnJYF$G1WkP=b5YQzCdzXd{K(Yd30i88r`$M1|s
zz9`MCl-^kpw_wzr$W%lBEbMA%G))#RiSIW`Mr4MOtuK<l)L}pv!nP2;_mjMQdD0n*
z2;BkvF{K-IcS3U0RnuGi*_hRo+9uK5WsZW0Us#8cw*vN96V&T>UYM)FUhJLI9s1F3
z8EUV064f3zvfO3Wm_?$m-tA68f#8W%1t>Bba)GJOQx_QargNAScnD^yVVSbM(|>PT
zuWk~kjW~%c>Po7G$i4G%y<DfMd92Vp{>3S|<s3_=w&SMsn_2Q7`U(^ixyvuTP%}P1
z+f~$ILXN(TIQHh)iAsqia%o^m$nX=teUXg}i=;C7UvR~=11?uC*|W4_BVS%|uiUzU
z!YgjCOf63JsoM^;w=gMNlFG!odV#pSDnC>w4FY<R>5QSsSvJz+NeIV75<i>y4it32
zNND8f0bC;SFMkI(=LZwS9OV~G3q<eWI4l|rPo^I-=Un*I_t`He!_WYo>1-vP?i^)0
zaZQ(S9T8@Zm<YQ`h#{lH(`JJa;_C2^05Q12+#W4l!r&sX#n<eBb^h6;X=D`|gcH%1
z`?Al|#qJn(N0Q@<hGq%2GzRf1&7H$0hY(dw$%ok2kf>_VfWJ@$Njf#oGRq*moXy=G
z!#Sxk=nyR16lY}Jnb97{ddU^pdw`YdY?A!fl$cw}44c)-y{+H;P@~wRp0)mu=xxmR
z%JY?{Oxc?}$D4H0nQY@a7uJhN^mGryNkWL-SzdlZ!5rzj>14q9)i3D<Cxz3mmOT?G
zzRJ3uB6jMpQaaH&B@%ziUE$UwLS*XaeAK)Ow+QOKy#3c@6%~TK{Gxr}^9ANV7gH^6
z$ynBV9_b@dt<Jj2{-cbG)eD!jNpU!;_{=b2s)c{LO2DQKV#B$0YJo>l<&uegW7~WS
zUtL1wqK<w+=C?j%|Fc>nOdE<=^{R09Pmi~&*-v1@F`2n~T^BP~>&&p$4#>h6$?Xmm
zy;o9j7b<Ko-}%js>Vapa9}?SN=NxS<fkp56vH?@GV&QJBkogQ;Gb-;Blb{Hba~vQ%
zL8LJj?w}|HIK+hklnAjOLm<dMBrMw?0f78bkbFibCZVB~R5NUJ5xFJ#(VwbtC6{`O
zlk$3s^+Hi^Yx`IBwp8NX?k5WUy;XIUvgl~z@;CWh?-7aSh^@cK_FytcJ^b+p1ThG{
zN<rxUBl*QL@xu_el}_`+m>fTpR#^<_?rVQO#V+A`KiZ`ra_`>9g6%M8)_Dac>DL$2
zKG!VlrLV?;v+p6w4IoV>f&Lz)Gy}jfNVrZSd}eo^#Ke?UN=cmOP|HNlQ6ZZC8C(VO
z8s^!(`!i3pxIA7~aq%8SNLj#9)D<g%|C+G7cfx;A6U(w4_7w#~5gk_WrVofT7sgdO
zA?F6iXT!h#B?bIIL!M5w=NR-;(+$vi#U;0N)Edz1zO`L)1DY2zebA+|Y)w%!4u9^T
zD-c7<s@P}}E{i;YP+esm-$5oAT?Yhi3Kj|htin_l2|-yU6Z3N}y%n}Oa%VfJ;$dyO
zUX)QrnM8NWOV+!Oy~JGu^&)#vQZuYC+0S2qh9s%Kf}CdNAN()*{FB*2gnQj1jym${
zB{HNs)s;Y|28Lo_h=AL`Il8<AX4W=>?Y&;>f^1s*T&tEYcQ=k}JNl!;Knbb)4t+UM
z@aB~FT<PD@EZk4T|0p^Sf2zMfj(_gG+<Wb7Z}++`uDxY<?Y&15y7m@Q3F+E<Z%NnQ
zTcW6IZ&?*mUn?O=s3hrkfB(XLJkGi2^LoGD&u1M&$v9&y94obs9Y;kGo?~I%bIo7h
zQ$IDL*AeCO#IQe|d#kES``mJy8a^lXe*S~r{5M6a)VFh~?{5q8Z&R7xexAAc2`u6P
z$$UUxpCQIx?x`VKxn;_{<@1%?wJkD8<u=G_1mV*;11;viA%S(#c47AGsv>ug+YA~h
zZT9g5T$!RyV{A0;`l{d^sUKw9SiWS6nR&{rg?oe@FRRwV*mbeuf|Pf*g~V(Ekw#Qq
zo?>8-a(*cOly3NpvUvR=7wW+qDHI_6d@x}vSI)P%T>p!$P)p!VIz{{)5Ah$w4~QQp
zH#pUTe5if)in1Iv(qaCzdlxBw4j4H_c-QC76tlj%fJBkqc+m?O(U$UTBzyXy7LZrw
zQde`uuvV0D`vd-_fQF)jR(AXlIT(dc%pUH(O?<(^EDcR2JS1d~0eDv7BMNgZ!3HHz
zH0u_Cpv^K`W$<tk1=7brY5e_#kK-OYQCq1-=ydt|NDotd@eODc0Dqo75cGR@T4F9)
zakG=xMY#S{Z}l;~eQGN9J|^g4qN7vd`W^C<2`0Wu;P4MdMM1;-xpr?!wYHSl106xB
zvU3ny>Fw0&+-6X+fb`d^VVyY}?#yE~^0K$X6&K6FWoO>Y<E{nS#0lED2!&uILVFZc
z#58s!>~{o>QR1ZnXdm%d5}}ufjBN#-ji=?lw2$AI4Xd>!eKahX`J1`&;E|iF>UC8u
zW-VLC>R%}qnv~O<haEHuLX6xj27dDv5mNKJ@jWi^IX@08MPjs&xBYw&>V&{8$Goi+
zK6%SYc);t0WBP(LOC}_Pv53AzU7Y49xvE4B-`R@F12j`%v*ZRJ%_9yNh_x2LvcZ8Y
z;F2W?2cVAd00lJ!2O#RmKqF`j8jnMOF8?LJdh%_TCyMVMFtlY^w+=u-poyvu4i2a?
zD(4l!vS`}O1C=*KlBHTQQv{->w!V|#)Hj*St-U(y4WJ&d2E&c-yKl^t<?7y6neUH?
z%30;SBIT^wI352@?kj7%a$_902vlAVu-gS~Px%0RGldkU3x@!h?==Y!{KEr&3xpJX
z2sFnK4Y!o-%QS*b%HCsIi)YbB6iwDbGU0h8s@i1R)AKCI+d$wh<;H;P1_odj&z5vK
zB7nXdH<*Q4pYc2~%*lDB44A|{{v|v%C(bz|UlDPmd|9^7Q14MMs?9v)rKyxq;!3T>
zR#d8c;FQSmqU<Z=y?d?M(LXl*pSS&bVK#6|Mn77xFc2X2Z?(w6_?Nc;N=tiVkwhzt
zNPz}O=^sHV(Ah&m>z7Sn$UMgq5YC*oJJb&LhuRlVP+dN%suvx7+dyUQSydlAxKJ-1
zU^E`vEFpV~e{|ot12i#l8udij)FkJLFq3L7zY&vszQmN3cq+F@hOAH&G8qu1p#aEh
z)=cZ5FsGv2=tx6y1Cyg0&_99$NE{cg8;zHp&KLD@Fp47F{(`9%?noOzE7DojOUN($
z#T;Q}wLO1Yo&Hde8EYubs%`Q?>yZiQCYQ<ch6{wzDx!GOLw!2K%cw5>6U4(uUMS!m
zb9~9^^&&igzFRg;mRN|;=j=QdrVG)_(6^~5`yrD3yy4&tHTov~_<X;A>}Yq2P^9?o
zoH$I<1*HUl2Id?AkjuDzavW||h&INch(V*=#}8B6XjJ8A-Skv&k~m)9p{$7u)$Kl5
zBHsmtOG?e^0q>~8dnQEMpWjCJDOlmls+$!!Mkc)@_|i@0SM3W4_Katd6<%*B!iR96
z^>DP8=5ji>KDVnkHy5bFulzGNoPrPbf+<`DFFWMesFZGKh4qtEO}(Lb_#&`Bxq3i$
zI%%D@5ZT34+wtDV=X|c3%p?Vy*S1OB14S2E-ls;33h`0y7AeB(2xvbUg4{05bJ8?K
zwmX1|vSm_mlI=>{$=W~7xl<>k;lk;MtCmYYoScWJ#nhVRXKuW9!fp5WEO<Y(xP}D>
zX~R|`J;;GA{$}fFbjNU+3X$bO6`<NWN^bf5P$&NQG&Ai`G%NAqD|P2Uwix!Sf|WwU
zYR%-+*{)kQf<0&(z{@^~hVco(c{MTyFp{SO69FiA-I$zTitw-JF)&Jt2ayjb$lU~n
zJ7!B7=MofZ8rjQXp9@aWGO;y0sA91bH_Y#Jw4L_O#ZopICYOri0@7FNPTGbHJNX<(
zWC}IhlCBw{uNx*9d}CQ``%SLATA1kxwAa|F7hETgD1is*<%4rj++qrZUJS78QclBu
z^-TdyD{SeU@hkgdtELM}bUk}*t(lM_mM^kTW_zeX%HeZs@%h*I=6J|ceWK+h2q+;J
zm$_&`3ctb9;!lbYu)uFN$(h}+=nSSdu`Snoo)W3(ja>QF;w4sD&^p$}U`zGd5F7sA
zDq$X$U)3!7{-V2F5a#anIQ^qbq>ZJd`C6q&>>*wX&!!n`;Dhj1hXWwHcq1kbfuzUJ
zvVQIxF$|(~46>Rk?pWx}+q4zTF5JqN@f6o##8_k0c`?I%RXNYKt@n~mFcbxarm|)p
z{`Uo|SsIFLIpt#~Tu?kd+xbNeuGU`5O(6Lt0749A1^_3T{0uS>GZ#&e&@`3#Y370j
zvL{Y=nQ|6BTo+vvuFtviI&ZXKS6$Rfwj|POuxX`ev(b8wrGN}7nPivyY7l6bA#;o?
zhmTW=ic&Fh2A_qJu#f;E;HxV$DnzL|#J0sjJ|-V$2?iGu34O6S-_L(CHiAt&K(pEJ
z{Og|h{HwC)J(jIVYM|%j<Xacf$RuF?0p4<yhHJ=J`xM$}mGhfq#fYY$w_ZJGwLu<Q
zr8+z$%KSV(f2VBnaRHY}!P87ThGbh&rZD#nZhW5Io%n?W+SIiV<+t7Y{&6ch_7-%c
zmK3ak0A<m)N%QgsKSv{Sv$tQ5*?&j7J+8?7-m?Jfne`bO_e^(%UYTlx@tImsWX3Fm
z879iGW9S?8I)&8p14UI|d~TJvs!h<#t;*%4bRj*;#wWOs1S^*d--Th}a0@#4`q5zA
z8UTROAY_X|KEaOSDRpO#h?q$gmzAC41k0~GMK@_(N)-~0c6f)TLO!<JzU-XH#m9CZ
zDibiXy4#Jxo_-Y1GQShA@NqP=$M4=un*%_J*sn5JwoImyG%M=JC(5<LRQ-3J#24S!
z6{_iYRxO+M4#_I*&)zbsJSyWjf&F8+wMIb*xqytf!6}G$BEWyBA)4_I0aO@GwzLY=
zpokfyV?x1P^2m)w?D;T8z291X)<EJ-n_)0@aovgig=w_DzWaT>=FJ+<UB+C$gKNwV
zhQa<Uf2$&rE_OdPc-g<5!TSsPdOwC(`U}WdhYTaB2>KY6sW5;$I4FvCv{Y4(UWIo{
zZx49X(wDd*2y7h;^MY|28MsTpXYW@4^Jj<BfY15gE6#E%65J;0$A$EqYq@dY8?cPK
z0N{=>z=?8!%XKJdE*=1B&l`^S?ER{$qOcU|jHQDh%%M?6S?BLcLzePKpVQ6B-Js=9
zedYO98>@U7jHK$*ih__I=2KPn4=o{bpxd+nG;0n;&JB44qCBX(#Pw=|{&{Ve0_6*e
zWF0)edB+?`!O-Youok4Cu>V~!SV?>&E4FmPlxJY^AC8^Nagla;rP@|9S9tqtL~U&S
zEhBioVIObx2TMaiIp<)NbEhVcHgX)v@<?YKVD}jZX!b>Q0jn9?KcB8KXVE`pao;tm
z89?PPJ)dZ0rEnQz9;m;fx}l=|#OU&h*?UvHT=5T?_|&-ErGT_SzF->bs>jc>$CvaH
zmUlA;)vVcN0%g%+nlhNuV;yVWJii;>O7EU6b1DSa+V$&2@g;%K${1H#-Cs^_b8tpH
z5B;?|I8_)x0jWL)G@-#!-<Go_LZZ69+^twyMSWfIy^*wVQ?p~IuIiI*_ww6UpD#-I
zGlsfJpY49HKY04IZGPFWQjl%cy|SsM(IC`VPp|f)K|&bX<)g&L8Fw`S>C%*f2RQhP
z0CISTJ_kUv(gbQG==7)7vRz6_j%&9Bnxxa5GZ3q!Ad%96){_%*6gSn;9z}<b$&0ud
zh(bvdF$Pi82UwS%pwDMWz&;v<q5;xmPzA7Y7N_wPVF|Fg(V$1w>6~!rq%hYdtvZdx
zGn%XyG;=Q(;PNLUJsxhN64-5zPvBz{T%Nx(cli{!HpLrcA4a0(8W?ugJjH-#HQdx>
zM_-DH`p}5dD`)y1Nj-OA2#K#s>Gmb9$EbL2U+C*MU}_r#_>VytJ%A`bi{gTz@B+9<
zsQ+C`cNT!C2zC_$OP36KyrLLt6A^+h?9;ny+q7t3-kFkN9l<5&E9R$5(SVZ?UxuqJ
z!3AwLd8r1f+oK8eOp7U-WF}!IxWM6?>y)ciAcB&^z)T=5Cx!YY;I0SwbxFDmOAZ`x
zidFGNdxs5dbMiM3sJqQJ3D*P=K*a)}8>$M9i8ICP1n^{RnaDurF0c-l=sKZ62j5IC
zKBFVjdL0}Gkhw)v!xxmWONE*J988ie<U(ljCDx4_NFvs_Ns11>lw%B0mbpnI`H-_k
zDZ%Zc|Csu!$jePLzEYT{ItBhwa$mkdjL8sUYm=6CeRex~czau@I7X3I;M_O6t|ZVi
zUq?$P*cp@DB~<6T(ZZq!84;wi+RaESqZHLQ4yoC$Gms=r>U2NxnJtqn-S_z{kx?vh
z_(IY*%E;`Aq}Ar$@Kda42YCA}J#LfPY>k~i4giI-Pl|&Yq-}@KkqH!)4N3eSg3ttZ
zn8@6XxPqn``kI!P)!NH8qA+=VUqz;mmx2}|!ROy*UU6tmPKb_<xtau2$Rb_NRa`EC
zMT`u9u)tApyG!#9U|Grky&KLPC;(i(>7^wukeMLq=?Z+J_~xPEsxL_Sx@!k+pum4d
z)H3={i!GtrA5)pAyiTj*vIEu<2XNj&C|N3Ej3CbU9FNO(xd3?`B#MQ$cy#~#^g>OL
zCk5f_X72q*ch;?jD=#i|_mjckg7Y15eWVBWZ>`p)M_cGuF8qm6PJ?gNiA}#uhH~4i
zvgPT@?iKcd6UdM^@8%9!34t7_;l7>5fxz-bSL@-Kcs=7l8~eUHo^utE_~;82@1z7>
zu!5MLt*MFn*ye={N^Q<D3UgO>($j9*VWhGI`7H^kS>+?4AChA)ytgy11HMTQ-M>VB
z%X!Rr`6#q>Ki=Y*jQcv`mD$oabCwfxT}=)#(S!X#dCx;h&voI<OO+5ao{b&;jVR6?
z0;{IK`BVkJ%R5UgObDzk$6(N5%I^BkzCL5hJd|5UPM4#)Y&iB(BLwcTDc>}vglTe>
zB5~=soUlTuDF-P)>vy4AHk7-x0A6L5{F^6nfM|zZ(Rvm_cFQ%ravslR+MMOmJ2m0n
z)gd=EEwh%k_z{e7jRht36pFv;T9E<r+Vf$Vlv395@~+0pTz-=OZn;JdRvI=eH5;qc
z#_|nff_czFd^v2~M@M|+87Tv+Y}FY6sAQDD$Ga^LB0~_FLok+yIZYQUF=3g(RY`d&
zBGuuN-j>{T*99t-6n)ds-X#EN2`qjHMpePS&eGv3umnZ4<|(l0&^UPiYCiph7Q5m0
z<xjMCoQbKhB#$6WP%MK97t$|={VbR+p1_tVws80Jg8S;RZ}+BREe5<^c`6Y%EkR%d
z>Aa;q)B~8Zq%l~gD#@!gQG>0oM77UM_Cvbj3qFU0CrQQ&&qisxDv_&)h}0e9<4Kbg
zn=-1)YIbU($h)9#SLJ4VgHx#YWfz{zj>*S%e3!!!vpHUq#H3LGJ^tg@2DJSNs|<nd
za1k}&dQ-8h)Yzxi(ak|$)IwC}+{#1b#_{y`EJz5m-rZ^?D|TOh6Wm^av@LT#hX7Fk
zOS(P?Z4bzXkeD3Tsd*r9o&j0bEKyci8botl1y7I6vlA8yz5dL`!T{^q9qMYNqBLXE
zG5<$(l;`#4YvZx~%&DFvqSXu_PnEk35R#xHr~I0iAfa7gTW=-VuJX;#5ra9|uXJwT
zy_Kmg8KF`L+vi5X>4G+*Lo9-bP!TO=qnG40Ej`IE(9N3AKa@uJ=GWhh44uPsCI$D?
z#N8HMN72Y1^b*1x!cvZ!8*kAr9VssR9Qj;)(fNF!OCGBdK&Zx9N9%gRoD4W6;zT~C
z)=@<JbC|u>?X;x7p;e3WQfm35WmR=8X%~C|$F$}t<@6kwSdK<9Q585K*EMEEmd5<Q
zY1a0A9LkRE`jxr-FN+f#3NA3&ow-L*RJM0`O{`DM<yadB&e0O$MjY{N*t2)0u{E@T
zTu7%wv=ZMB&1-w<Z1ONmY4L-89G)C6PK=Eb`jnMgTr1f!8cl?a4+Z&uc~;0r57JYJ
z?}Bo7D@pgS;Q>k=mbG;(8qf7G%drsLo`$y9x$o`>x711sw_qL+lwcB;`NNgnmB-p1
z`@Q~#?TDd}#tj8h=(N6bfS~mq1M44p&39IuptBFe-p9#;`@h5Q7T3)WMxC0u3Thki
zZ4$VSj*$GJoVqdWpbYbYrwjlOQn;*0j13dufcCuO3{QD5?wMF;{zM80{2_}8NK?G4
z!Ivh)vyKiRic&A2A-G9uEqMrv1Lfw46c;9J(kG%5kxhNM-(N)DAJcMo@~&*#%=XPs
z3@l(Hn<%V(Wg|D4!nVRQI(4URQBY{qe8l}61b}(}=s_L`nAOEWYoR#6T`~O2-JO^n
z^Bg<O;T^s_#80-a&0QCv4PUl`e{z9!o>IVG-0+txR7gs_$M59ADA7j8+tr89BqaOW
z-pRDzVrbX4Hebhny{1ObuEnmKaj1zs%=lU&hY=Q}Qe*B8(2A3g=n=l=ld<qlJ7+QG
zn*ZJ(r*3N_D=E~{C&i4-8+Wr^aQBb>=n#N%h)VE*c2ii-z%Fw%9so_UJVk*v-d@Ke
z75CLdV-q2RGRW^g?!_}<-lZu^rNm1}C+P`7<@(IaxA=CBZq;$YNkP|vN(~&8a$OJY
z^~)A=`Ev5D`kfNDraC!v8v4g4j-T?K`jd&vcK!l0-O~N3Fm+tBj%p-j%&D{-+FDKO
zKcm;Ij+;(p^vqBEB)GKCkk6JEV-E!lGBrUb78~ml8-GESt{ejuN+ux!npIjj7T%bT
z0W|^!>6b}W#V|^&jJK&*s$AS>{Ze6#H=BXx%_;xdF#`N&(XR3^8fp;fWi|kZdC`-0
zu|P`Mn&@EnZ_4Sl*E2lRF3Ea^UzQ?ZoD9$B0KvQ{&5$3_rz{{}NtX&K6qJvZ<zX&q
z{kf9<-kTc{As28xL(0B+c_GiIP$BnB<H|U!=ds>8#)-rP6$S&O2o?YVCEw~Oi)g_t
zr%4r2?VGW}T)7a*2P+75zv(}9p9dYWR{7tFG9lJhwnM*--m7ymu{D~_1CclW87j0Z
zpvzMex;v=@6~Y|qzWDig%)JE%xgAx^rP)Q|A^PLxyz2ljzjN4Fv?Y;+XUPGS;3;CP
z7f8wc=sT0Z-VevR^305iYp}{p*Fw+5GhPzUG!9Kiths`3)iB_S-Z%vby@w2j{Wsqu
z8RV;yT<dN1V!q&!PzIye)aypbqEK>Uy~@t6V)uqkg+@QqmRUrD!1+##+BQ=P!#cpD
z;aY4}fIKb{hlU2XpL_!A>@46>=j$3i&kM^$wnN-*MSCOaXC$o-9^ben)$u0StdzK7
z9Q=zwOg^X<z1GH{ahqwe<JLjoFTXac?SRc?FhN`sw?ltLiygB=Hpq7_a$8&&N#3(U
zyX)T<uy@2{9LXLXeG>?lJtOX@eE)T)d*b!c4c|D|x!R|r%1e53f>Sc@iUjz-kL~Qr
z@UM|Ikt{AjWF!~!?G$o;B%SdS({w0)JIDcJj9IF`>%^^a&woDyU!`}*S82tE;+w|N
zLhEQjq*6F7L;t1_M4$DFgPNG&xV*3oHN_LbnJ%mXPHl<0b_NrvWM%Ee&6V`U2jLNS
zE#ii-x^;VE*)5vb)-NU5zYxkkuTG_Fi~r;N?}Z~pWg4cc%U30)F6C*}<n;;w3?cyZ
zzluPYn|A=DNp}%w{V5*cONWw&+((fBsI`{d89jdh07QVL#NrY#B9Jp^v<t<<DSXbm
zi<f>1rKX;@i%So0s+Z2<WbbXagaTjye{1Zt{wJuo4>u<blqvyK`4h5Q#awU{jV6#~
zXmd%a{nX)W!TUQeH-dW|4-0QR{rLTU|BafH-%D%2--ni;=E%Ao;8*6W_h!<N3B8UK
zNqZ8Y9nMA@IZh&~inp@MR7_V}{Z(Xp618X|d|On9@wafv1Sxk-?c7B!YF38nzUq1S
zFA(Vi@%p1PStfu|-%Y$769z!2AwLmo2rhVskI=1OKhLNj4_~>Ri7LTqgdd)NeJYW!
z;h|c#ar2MFheibCq4Cd?348EDy_iGXKZOSF2rw!$_u@i5wV|q~&fJlSD43pu_$bu6
zy7pT=ImCHgxCycpr#a;i%k&I!!BY4naUmm$P#=-7iuC^DyNIkmPy~3!f`IUXQ7jOW
zS6yM?-%hGO%?I>16+vxnR<5MBCN!=<yx9>~gz>(x%YByPYfUFq+eVT57`KaR%s}q~
zkQC4o0fwnvrYIz|fE@~uSpZPivqysduAwu5{u9}A&H6F#le>IalPZ6OYPzeLTXyd2
z9np7|7OH*uFIoCEnXkylf?(DxKcu5<i2xgA91570L!dx|L8aup$&RT$d!Y&`ChMi8
zu7AR={v`2ya$S6#>Kn_Fl&!%}iyzz>XV#r7feu+y#}4y#^`-L=6Xrovc}GU~p`f--
z-Q~rPGU{{xIb8`)D0FUCyUr&6t4XrLkv;z;6w3}aJI1aP#rHE}lNAAW-HAAWz4|vc
z17z?A-0_5fevPfl`al)jZGEydKPlb+3v`nTfaQN`w7JGQ8MH%rM?|^B@xqbz&wjOf
zW<Q9mkdifnj5(mMyLC*aaM%^$L38?fKkoP4w*9=CDKpZ!X_G_rTFr`DJBl&Y8crXa
ze1)CtbpBBFd1wd)0fGQ3s_3uI;>iXm3;JsO|5N3ezqBRw_vIgjzVe9-EmgD`)$Y=?
zNq8;W@VdgMvz>h~=<WQ3y!eY#V>=M@fV7Iwm_+LV-4xFB)|Dwir$GN_L-iNc1*eV?
z9xPT}nC?ecGOhoE$@|F$P-;+xKr@$(B%$FBu#NG1Z9<w=`GpBM;C``Cg+XWpO5`v*
z8b{j|HTg9*aBEkFMe6Al`GT+#E9u(f>W8`CQyo9$OFs$TwSr}FgE3kBv94Y0S}sCR
zQe7Mn13jPqt#&W|kWRBl8HPVaN+gMQszPTEXy%o!8%7ScAN&3>theLU9!{W@Auf%;
z+c2^60ol<jAe@VdkS@=U>R(*=*S{e#tunLNoSbK`zrVO*4!qZuuSsF_d19E<1un2W
z1x;W9NPHR$a^!6oy)g?Ar<t>GTK2O!LzX+mj9n1gP5AF>ky|Tqj|J3{on(_x2~-6f
zNk*ms6SvmqgM4n*QkTa9AW;M2Q?hXW%j=42otC=pC(7~;Mel$5e_POh-su?WA){>>
zqWh;>r|zEn(4stT!1zQJ>PgN91u(*sqXT`0s2m5ylUTx=_|Ih$dH~+R(i@DL;j-j=
zi?6jRO5iqy#n|9yb)QdN)j9^gGgP1M#1X%9$sgesT&yh!Km@p=gL0_Op?CwsU3B6*
zo*aQp!5|)(K>k6Io9`?c>92|$&qgrpDHrPSt54d}cAHR_XCC(&*7Z7x)rl@w?DPB2
zQR<`L1W|)<$=4I`mjDd0$-RGI+qmhe$|%BJZkF!zcyZl2iBnWwjM~4M)$Xac16TQ^
zMp|lcAV1X$qI47EZK<80yu#<+nj*yLnk31AhLAmMTGd9uVnSceV|y>Zw+(IKkF3~Z
z)*XW?1VAX!b?vrbW+LF2nwRkZh*kg)brN6&fN6(^ZYLkBN(eCaeUrvqg@Vr3yPkdT
z1Z17vcg9BS<=jPQ4DyJq9VJJAzlGJFhbgf$b7vGZ{ym!0?LHdrq7<57qYB#E&MXRN
zI-ap<yzAa<p{8FDX(v!B-Ga+6iz9#m`F#o!dG`R{3n5t;!6;A+1&Wgy6Sna^UxWI}
z#Ta;v-MbWW${3Y5vu0Ob^u)pGmNXluFR59C`gf&!KX#m89w%E_2A0qE-lnq?6p)pM
z9<p-pI_9G(_G`7v3dvu1x?@Gp4BWO|(-Gt^`Q!y8MLJ;cBg67gWzp?6zRbdt;5+4d
zPmyAs!Wc7!vImLA=V$9alQmLv<dmSq5t=DGo`bZsZX?&7DF_^WTAc9(u?)NA<c$rJ
ztlke=h@w8jCHVua`rZ_$tF54_dUSa>Umn9MF2~KHPS!7YI_Xn;(}nz%F{j6@T1NnV
zyQ5@^#eepI(jS72ziAc?Xxej6^wE~Tk+@%vrl>CKoLB_4(7=Bf64O?8_cjDj7K0G3
zQOB-(>VcK#$%d%xD+CrTz`3wStfBIe!gnE-oNQt2vDUzdXr}DT#Qge;@|z}|FteXl
zT0)=TH?{kjQ{%U#N?zb6)9TDSK@?Z*OJ@6gF{_LpI?LA)*;$%x$_Cg5c06!5Rj~?E
zM&2CW`rcmFk&!=BFNdireIQEB<hzn$EfkQ!SfG!KfOC2vMG1EZxx;kPIDkTPR|Y}y
zGRQbK1wQ3H$n4O&T5G!c;ypi$$hLM-Yxqr#TgukE!dtrLKX@HP-R$QMO>t~*I6s*+
zn_9eS(|_ZG1QDbe3BV+$kA!fw==U_f9{2FEc27(QuTPWzXbJ=NU#$_5WW6fkYO^7&
z7Ee)NW0W?-e%rI{u2h?pjs4*tmYc$MbkVcA3$w4cNcFtUAG$9;78C}Ye>u8_T}f4a
z{)of|Apxo|(aqr;j+ZUlc#2!)7h~r0aK+vAECV;az|B9N3I(4QTjJljU%!*v&ZZzD
zZ7lV<xD+>!$(h+PR{4NY*_x)PgJ9eHlu5uIVlys<CO9)>tP)@T<}cLW%$#X);38y~
zuwoH<?mgyB{ZXb40O2NC>9e%bhyeU?`B}LC!5clD$3;xRzZm;A!up@xa=sdO_DW$d
z^e#90=XHDKNLf9S<js}@|92sN57#f4-?y@ab#dQMuc;fZo<B}Pxg~|&k56(Y0AUHZ
zL<071k`i2S!Li)pBMXyiD;7k1cC1!%PAy%^xS@^?*6p{irz#1rra)k=AJG`xZ&<#c
z>+mTi3`qbeVs^1`97{$Wkh`#vOLaMb|CbHAn*4sOd0$1rVM5{P*{kg7tC6CuZtm*W
zbsOrzJoojecEO?p>}t_*EckhXKnj)jgZduNgoz`Hb=z7C=LC1&L|m9qSf2E1RBMhh
zQ?YLSz$Z@cq3*0Fbew-#L26pqHJ06<uyKf_5_uCCfQ{2K<Edj9l?mSHTyo4?$3}jj
z0byU0Fb2t-!2{g?hOO+hI=*#VyeJ*E(Y1e}aMoY7X*%M|@ce~aRjCb2-Qy;BdE(r!
z^xn?QMSI1f$~;siLFF|74=T@!r1ZX7FY5tc4_0r3B@VO?0Uc?~RVBcAt*k=|4#KfY
zGhj(uT=YFzU?M%KFtJh~F$j{x0gEA$vwOPOB#C4P3SkQ%J7$5(|2_aj!j7`j8>G^}
zVxr)f$DNz6d|3MLYsgx9$i|dlD{PeA@;c??TeA(H*PAFbJbBELCGuUrx5FntjIy4&
ztoJM!y5|&+H)a&8Nc>ZoggYr(eVhUO_oTRm%g0Y8Yb2Y|mu)ajP((YP<VYb}`Qpa2
zLYJt*ijH*UoX48-s+tAt&#P6fI&h4o+2ybEZuF}zzEBNsrP3OuiutE9$PVO^)R=lW
zk{lCI0Ob*$gG5Qd4a!Gn8q`&Da*ii!cC;uPQiYr~`FE*(nO9ByQ~En(a`lwK=ab$=
zC=+R`Ln*35b)^84$AHL!`MAK6tSF1?<MNb%o-j59h-=j;D_b-bRlpr#U+ya+kjo*o
z{Z6f@CYcz{Bb^_;Km`yKz-@bAX|CiO->9+Q8Hy`9ls&Lhr`f9KsACng6efWS`<6d{
zY1Vd8C&I0HUulNFn))JU^H#1BN=eG0ne_;U*oacit7699ut9$4$(6(&JTz=9-oTN{
z-m^vtZh*Oyyw9$;I-B^rMc-0{US6|H`e*AOLB(j3LW<>L@502MpRgioz&;Ml@fpaH
zi>ccK?Xqj|#EJCY&V27rD^o3!n##}dAI-&qWj^QZ%61X$kf8J{7MizIzU(Zh*N+}R
zs+I1;mmkEhDJLo!m9#+;tlS7$e}>eoQgDCsm}&&QhZu^lcB=dsbdT4tk?fTXZ8}dZ
zvoEY&E=;V0Wz126dA0x!Q84GVXcB9(HyxpLP0->^#`&X`y~)(R@AH*jx`ZCx2e_71
zBiZ8RZnYa7!;a(of;ORc>I7mr?js^zMhpM4X+N>gJGXkGogf{VOuCoM5l&g30>c?m
zih3n{xkB*%Q^(3dx12A$s+v6LIKqe%X0c^htxt~DqOu52>AIVo6`hEpA{YQ5tcsF7
z3`|6d@4`}i;IO8Lv{YFH{;Rw^<09n-smjJG1AiF4B<7TR)}e=s_sm(|XTQ^C?2W%`
z$p2pCk4`=X5qp$b4|&|XdldkJ6L|{b@pcq_#rg2?<ob33OX=v@!?I}Kw{5jmzx_p-
zhU-3rkIzJiWjRjXXccw2t;!fHMlmv_{hULzLp-wiI;6Wa&7}e||ALNsGWm1ya2K6K
zVNQ-ukp&G8TxBYzcdF>-RQt8Q>l#YAV++JRT95>lPiTtD5bx?)KWb&lYTSMcV-z!~
z6?Z%xyd@qDyUIOIb-#V3E(TC00^)g0L0j<+UND4i{~RTh6r!;(^UQVjm0VQI4`_OD
z`CAm2%aR}g<mE2crDE?9!nX)Yyo*F?h=EuV3JD6!&zK|Su3}_mA0@}7IM`qEY~PsO
zM3|KqKke66R6C+cfBmYt(FxT%8~3GUO*Sui$@V+Ixcq?={wV3fH91?t0v?qx9+U?-
zmz~|Cj15X$_Df{`3hT)S3?gSb8*-0MrVLiH8EIc?#_BG1HNS~NUZ=H|kI5x~T2w2O
zE$#vKl_1w_php%wL>*(Xn|KDdSmWo|?P?vbqE!~kLKSFA=UvU8|KJP^b_q1^{duur
zBTV(HSH3avi$Z~YLBdg}_^!QPR&YFUEthbM4WO<wuG7E%>~W_Yo&W5|-ajc!DYB_r
zkH3eev0Tku2P5$XTtFCg{Sk26m_XpxIiifY-cR5_$?t+d=uc{a;MAj6j_r3C=UWzk
zEI5VG*`%JjMuDg{mRwW5E!kdOa=YpH<F1X9cupgh#zsgc?UeaKe9R_<n=$}^N7xc?
z(AM3K>um%OKF$VIiN6P7dwYme@N78ye?Nj2C7_k%9Bj!yB$Yz52$5Qp9+0hg0)jW%
zVEBpE9eNqsD`yY-qyG~*(aDOzVmbd>&BK`A7qZId8jRqKM2wBN8+x7YZ75sJ_~o%Q
zKAJFo97`sie|l&bW*Z+6opD-X`1Htw%ZOvocWE`+5Jg;WpJ%|leP>-_KXHqxq-8Qc
zSCyhxOyt9q+zYjX)`q|N#_}D-Q*&PYYo3d(#zN%2+QQ1v9-*x%wsFunt+!9-#ngnx
zRdndm8_E>RyCo-6&qnXQFbkgcDODI+oQ#U$ryVcN{gHW~!?FHfW;C|>-0@0L40NI?
z(Ly^Oh@&KVkoOI|$K&M7i3TsOZpal=4V353bEb2%aGh_YV(&av<BdUl2HLI&697;I
zFKm}0{d};If8%X0TS5H{ORmNcr0?p8-hS}TX7vK`!;)|F8$5j@ji*Rz)-WAy!k|5g
zDXGzk?ggS_fF~^Kxj|LMSr{xU4w@7I$K%_GcPUGKV7meDv#5kulwfZTSyIefdU>-5
z<#f$C&NFQm-nB{%RkNBN$S?Qlvcd%G7)#+)U{VVK*8;~OfIdsufwb(=n$;K4YLy8a
zzWT4$r+j9dHq}xF#@9ZyE#DlLx0{eoc)Sj)v!Dzb%R%JAuy<d+Iea_yK#%h?Rf>tO
zoNdR-rMR!D-5*`;p>bhF4q;n<*fR7j`D$`|Oh^*6BY<PTPyo)H(UZm(ol8}YedH#4
zz}?RXvXJ!z`EJr}_Vil4ebZ`l<Lb6UN14P2@U_Eh04>!7Zv9E}fsi$0=i9SyFD<sp
zgBo5j3Q<Yl@2PY0L4?GS>}cUW%@$N&4j!^0APf}(bRRvh5c04FbJ+!Mnb`N!`;n&O
z2{Z;lwRMy+KK+C<C1Jt?kbQe%pOq%tLsoh%=StrWYt6u9TDw1tRq(mx$C}st3bGY#
zd()FA=<^z=lWZb$sLF0!n<|4~HR`2<B`oYH{fnuiT&OA)s^@+@w<qPr$E8uTD`${x
zL77<IG$Wume$I*Qq*awIe)(q{M*95}Zt+`!Kl`O$Q&p2{d`44qp64s0P;+g%I7Xn3
z=5t;TM?nO~3+h!)KrQ$^^BJVrwBP1qH1e~NYUuc`^fOid$7Th!=}y%0Ko;H8!}MfE
zs%th{UuzO?R3;`F2khR9r}iMU-C>K9%*7RGvY<Q4HL<RZWEI+@ta91|cn>NUjM)N~
z6=T+8K(V~ApXG3T37`Zr;jBh5z<ibdH5b$17z|%a#(wM9y7%(0Jmk08e-E;vn(pm1
z+CUumec%Rx#`Oi@4VQ|cp(WVgIH5+}oUDg!^EGMrU8FsGVM%s4!u|N6j|7X*@+??&
z0kkfvb$&B&w*35OR!Mb7Vv17rvrBh0vq0OvQ;Od-jl`!UR0I(HVq>G9fKxQ%07c)n
zB+#V%TwZSE%<<N`MBLZ<Hi*0TiF0ZG@219&idvHn=@0mm02*l*c-XzT=tkJaclf4v
zzm<6}Xo{iK-K;#;AzIXq|IUq5jq#A9m8{3L%h8DiT!}{rfH(!V=S?CB>$ibG;R^rJ
z2+%0bJER-F6#;SiJ2$K)e0vx3?HSnjQ2e~#_*%f*Tdg8Cbova15}Sk8Bi|ahH96M}
zNH30Fn;ast5S3J@@MMcf%KC4KSp7Hl(1aJa0GDgb@1Qy59oYewVNGnc3{ti05UMW>
zzYH=TaYQH9fs?WK3Ae2Xb22euX%M_ntp#Pp#wU!%b>T~#rzfWo=byj~cO6@PR-TiP
zV5UiS@sN7+V<O=yfq~4P{tJs9z-M9A;2?&is~gg10;j-56fPRy&H;?NX#hMKdPg9;
zCSzSbX|I!2)E@x2nN7xKLoY!i>vMS8>z9|Qx`J6}{(6a230kOZQA`Ksg;V)|>s(c%
zjW&+}0*)g-<f(JBZOxJxT}rcj>D!t&DNjS{@lOeX3gZuA_Cd)vBz~@bJ$@6}|KG+h
z<r;9qMbz|QdC)Gf8)62q8z-PZQLo5|Q`CI|)j!23FXAdn8X5wral$d;#WilrM6a}l
zImIpf>RkOQ)x-7S*PWLymw#O`eG`Hq7hTce&ko>V=;A9R4iGrToZpnk(YtL|aecV!
z`NUeeiJA;ze}gmM&HsF@y?+00Ny^U4J51X#0K>J@nKh^~4j0J|0Hgz1p>l#jbU*e*
z-kTP)@{4D3UwVKC{Q)WHXdvZLT|=FOHF<*S>!|l$v|Y`XbtK!5hC#OlW45L{-(;{k
zIzN4zODEe1{p6W>-W}$?X|eF4Clj4k{lg;9eF2Txklp&m6j9$_D3jye2mE7(>i|-4
zFAw0Ov<w0G5~xZ6i2h^`MLBH$C`oxHrMpEyq@-?flull<z4mx=5Tx7@o<y$yP*_EY
z5;?%1EOLj+$Q?dwXYM#6fKnzC2kDWNbsVg)hB|b}qC)LHjcAQ5#G1o$Vrn^2G6VCO
zanwjY(dk|Om#E*Ot*-HmiVO|j5BrkD<pDn7=g@0~>!4W^R>S(5AOJ`m2iH+GOzG5y
z=tu}lv35yZ8yefPSRTqMB|koM`tpU#ChJ$By*P_B9&4@foP8L`m5&BY)9?U3A<|h=
z+%IY4;o(Gg`yd&$@BZMX8|%__{%iB{w=)!1pmMww#gO=%H{Z*28Mjt=A9~pTzEQBe
zaVWp1+&{_GS2F3fFaD~XnvM%B?lo{khNQ0cJYcJ_2ZFN>l2Cy>>j2AxIr|F4152tT
z_}e-TkF}@WB?qefNa;Cf=@YEv>Lb2rB_#k-WwAB`<2L~A11ierV1+ML&2$y3n0yzx
zNsqV$N9mYi+y1?p2SVji0jwhBj#JaWL)N~DR$*6;dCqRDJ=3c&Q$8Zsr?c#bWiY~=
zqdEE2!IA}n-dv8c&u=-_JVKoeQj^=<4QVl93}0wcPQG0&W>(veW)zipZ5{qmI@R^%
zkfEOn>QjJUhFPc7*Ut?-Etg+z!T<WcZv83xFLQKFY+C&}RSanvP^H)Tg7E-=e)dhv
zM$VD#*rf7Mh1#VW_U}Khz7cqHl`@~Z+qSR8j=x)Kf@kf~ne<JIa7q2CUVYZQowymd
zB<{qxpd?HA+W58^0FaO9v;Md`9+B(xl^-kK-E%*;xWZxrSEB*GYo_1Z&xc2qX3qJ?
zkVv;_YAsL@1fY@SM~D72ug4)@X6JbJS7R6luCstwI5PI@(e|=vo~>|iIR@&DA79DU
zALF$j4%N*lQ`5AunaQaSm77@F#0ad$q2`(Gj`C77KZgS|-e9D@NfaIEW&B9pD4Cya
zM>FVpB@qjN0z{}Vb_;`<_wNV6ES0&Q#yMtKF)K~M!R-_Dw-!t~wNun$MK=f4xpu!n
z@#I-e+cFyiP7mg;Mg<(Bj_3{5<hREgl|4dqGeV>@zEd+`IR0GO8VoV&n7_)SeQv^X
z-w!!FFq#qkv4j%a4M5f{%u#T~L<9g@)E%B$2@Eu2Nz}j`J<S_=o4V5LWK{XLoU4{_
zw0QIvFl~cy%^_$&OcvDqh*OHAFjGw|hrIjs$GPl;ky)B7-G=@fxjMN@0z)D-;sef4
zSMj5WMj20&_;mE$8&~%JY6Kr#7k?rM=v4cBGim^p@bAV|pnUG|)1SSQcEyAA%bAei
zcJGXKE-Vg!^VCODjJl%CEBdZP9HvvZYo(cavs^Rsm0#wH2~i?xDuO_2NHWNbFO~}r
zA;?H8ENau+SoUKn>_Jsw(`hUsPi=K%nR{W1d!>J5G7}%~%E83jC)^{0?mE`0y>T*S
zu3$&HUw_NEpmE?-1hB^Rm>^uv3&$h$v2<0SmQ5<9$;}4WT&r1!#3w#$KgK%Y#e1B`
zgTK>ro<<qpKQ*=btzDBGu0JwJ*I&44PczAAA73@Eu}F8XtcmM^7j~HW$`SPDatJdC
za7xV;1BfUZ5OYT|YHPK0#y3*hBR+w-L(uIO?Y-C<&fA*a?<r>~lI_0~bqm-S=>QxQ
z%EqYT;!RJ`SotEdBF3B^gc894OuTOMRfyQhJBaTGuA}s&Ij6EZ8`RH~EIAs;!wr=h
z;yF~_ofb-Jx&sbUMv=rJjQ3G_w8}?fb$0w*MZ=FxgXy0&GC5``ZMST_xuVY*A;aTf
z(JF1{p*Md5+o?*3NDXP$KU>fd?--w<d*iJ>&rw;xYMW@HwW&p-^vsgUkzXP<nr!Vl
z)wFS`76efXP@(o7YlUdQzUXULZJ+i;3?+<9`sf<6-ctHX;Zt(MiSNGu!x%0diosno
zm1r9bLi(yfUN}RoMCeauG6KlVa>htEU8L621Ge`ua7B;hSZ=z+clI$%vHO5T*WI8_
z(NhmVrtQ<%vQ)-$N>nDa4V!fB1F&@UiBit!ErrCq;<r&Zs+Qi(Fz(;duj9lM)r79i
zv9W+b4AAD?hlJ_Kgi6*RGq3mDjlU?NLK}?lk~R!%)N?!Lon)^)g{pmTkxpsgyQv%c
zKFcA+Yh)!Iv;4l=g?Z+6v0RQ`^$Nb^P?}QL8aTgB;)D@2S6B7tirM@OEJLOFl!e2n
zo4$nB*E%F7Ao_&O$ZZ6MJXvQrf=ZoCA;{Ew3Bd3SS8-uiq4u6E1G@m<I!`h~k(T|>
z0I;p0ri=7fij(w|5RWTt-42}nUJ>uB!w$Vat=%a2Oy$1(sa5ig!<d79EJp-JJKLWO
zoiH+@?D?uA6w1OB6`WQ7Hi-}W#9}D)I6!c$E2;PiBY(J*j+tBphvttDQ|<ggE_PN7
zeS=PkAHw-gS(Ku7ha}}F<t`aLQ(p%*bZ&Z50{utytv~*ySpZV;XSu%b4ta(@gnDP~
zwbU;&H#buC##%lX8En|voc`6{TNwK}Bb3<7?Ai8ub(a>o)8|P?0(gO+f+%WE{_ZAW
z8%ZsW)A3pRNwbo{^Sz2RrMNDw5e}h&Q?2rS2mv#K+D*YTQIld6@Tjp-rm2~v<tm-t
zkpy+OBd54s@j!IB3EQ=_!4+@m^niS5mFIMzhF;xom_@ipOpEs5ky}QUdxN7_eAIRS
zb$?Gp$ib#rLY&%o98#c^yL<_~5yFWgQ`%FEpmhKs{?wg_G!9Km3Tk&vlN;HI#rXFG
zSoNX#f(8SJ8l3s6-}0g4@kA`OsV`ZFMUCd8gzhB8LW2Z%w)Fga{MUD|8Y>9TtwRfU
zU;BJ8UqLO!W6D6j8Bs1*Bi;=%9i*GxC7V`rC#<q6v0YNE*35I77qp2=ZE=@uUh}Ue
zDGcTDk*Jk5mlZ1I*Zr-i2^lJcESMG&V$`7E#a9vNNSe19g-!<V?_ARj)YIAdrdyCk
zIjMVSk~tSkm9)GSmr&iP$-R%@m(<BTSl(_Ia{K<gPG9el@Tku8X05lzlNvQrD%#04
z2!*3XLTZ5W(UIsKrf53=^pRzF7BsLMfa2hviJ+YPKv~n1C6M9o=IWO@tex#zd{D**
z58@+)-@+L&;HOa}tCygof+`N5U{?c(O%6KAYs*T+=r!kb(@~>Z*OcOsnWv7h+7V-q
zh-~-|uY{2yR{;i|JEY-b%}JhmNzCU!RE!-e#!>il_z5}9L?g7kQf^B;qJn9ROr_j`
zDT+urvX`@jCn<(E_PnrKi%3KD`PECd20oYFjI7->Fz#}hsu9!I>x0~l5;QBVx=_$w
z7Q$n|jq49ncVt}>2m;cC${H!Um^Yaue{Zgf?8qw>-4T^hXNS6yQcHaXqN9~I&voqW
z7L5#s(E=iKnnV^}(EcqK3~Rzq=EDTA=9>3rS5H7B%E|-me|qLcnA9d~(AO&T)`5x;
zJ{;E=NtA|Alz`u+LDPbXT>GN55)xVslFrD0&>=7n@LEl99bO+W$3LT^=l_~#P(Z!v
z4jg{;mw5xu_)Y_AKsIA!%&JpKd%>8ku;2ED_By&~&b^XKkXtHR;@$ikYk~tuTq2Tu
z(JHLlBz9&rXkgoWovp(}rNzh7YrCu-Bkvy{648)TVX~<_Ic*!e3TQicZ(?|fOu{H0
zY9l4k`i3e1hN4vpt4jT5aHkdf;=&w%zk6T8<-KGuwM}a(?p0z0_@V2Oxi%iT5yNFh
zVl2m@1|sJHA*8vQT4GDsZusK(UuujkH+7Cuti@GuF`M?>MEOUg-U1S(1WvT0MreTu
zJvZ0kP@E~#ma~Z2XR~@obDayP`BEhd%%r#6)!lw&iXAED?W_^PphTfn@&J#4?p5hn
zE>Gv>A39qyA8T2yoS6de)8ogCxDA8r9?jzOvh&c|3q3+J>S$lS(FGv7nTCG>F?N9j
zMR5`Uu*?3?G5&Of<F*GTOTJ!!dM#Sgo4CD?6ypLwBv3^NCGe4Ew27AT5=meOzcxV<
z=`RQOEF}+w59oV_2`US|iZq6{IdLAfYFKj{>=?B3bD;?IDRxR4t0gVuWp6F)e5|F1
zX_XLlxwt2#=rtAA+!!egmONeP3x%cacqzKKY8p%#9ldU0Uw9`;i#pppl+HPgF>Wb0
zME^?c_W=KsH#PS3>)vN`cs};FPcX#XwOP93!o~3I2uifdnn%|7;CvUFheL$bJEUo0
zFW_ADi_YZr!sYlv#-F#WT9<!zJ7>#T{5H^eZoO*jl?QY$3GpXzeX3#rVdOMxc*t?n
z*HI+sa-oGCEa^s9m*C|}QL0UCbJ!ymz1E6$r9$}vfGh%7=3mz}k3I*Q9?+#;=-{Ah
z9=xoJJqmZ9(vw|En?JSb-b&wI(YXEJRlCt^i@P;Xm9OfAZ`+#RrPrindB@g1xdX*%
zq<=hwp-4a5h4tv81#sD~C)WyU#z<i9^69Ud&g{BHtP^U;@=|Uh0ImxM#`2zg_eSe-
zygKMeLllI14-3t)p_8d72JfP@4~oG4&YzA`p0&`ezvN#JgCcf<te1b$x=$@^4jO#Y
zc%y1W;b#NaF=SvFDnG1e%^2I@X#!nvx^w{mHlSKf`wmCBhqqS3HKD2u;<TrMRkYRS
zJ*AA7ygFO}fEOT2AuvQLnRHq`YL&FZh>^S@bYN9{zWnP=rJ*-mI&SxN?DZ{2G)&&y
z^^_xwUbQp3Y7-Z08~mCl;d%;u{V7cZz0r1$N0LPvy<HJOIEne5G>?CbP}b?VN?@2W
zj-GE~nM-pN0Ch9G-30L${96_k^5a6;vth-bnhK^U(me`I3isM`)}=rRi`)C)eXUW_
z2(xJr;BC!+j&$`zN|YhvkhEFZW)ubWVCrSN-|nU#yTGY8Eqk$IS)nuv!gkbVInO`(
zB9Qm7cWxnF+`4&2+C`21PjpLB2@ArlxS(Gt9XT&gTmIaPkL#?p=vB@^Re%Xn?{=S^
z0RRX92o?ldMP}+)&Gn;Qe>4j?d4ynLFOODb0PDF1C)vniK{-0;c8;aH>U3#3t|6Rp
zW=@nj!}yM#3q@}bXlRT1+SZSs02QS+0BN651Dskoh?M(($zGQjq_o#T{BKR*D#?5u
zVEo6Hp@)^4<Bbt<DR6XIz+Oy#5zn0{FNkQ)aifX-50IJ2YrecJ#S6{v%3TJNKRs@C
z<7oPKfjbzRb=dRh5P(Jv$;tlb2>6}p*1FheN}{1k){`zU7Gk;vC-&nO*hxjQ02Pt8
zmkB!zoKr-T*o@$;e>qw=P(=L$E_V1s{ca+C^C&Y9__`t@aeL&C%)pP9kFPuy-+w!c
zjB<X#{`#xr=!1WjVz0hk{A+7`^zl0mAx`_VH8u9_sJb+_)lW1DfbPi<X}k+-O|+PR
z+Fi}_sIKww^M$S?&3_2gE&;g5S0@wWMSvx1*%M&pIpnkCv)LT&^}dm+Eu;Q?9G9FX
zNHJjMu%3qWUc(#=ns4Va7#eX25zB%enNbqPr9)WjAwhj{N&J;fhlIuu*O#i&uIrE6
zj^1+&3R%fBfCn@4j(hl%Qbr5AL#%=|k)lfA@TSDQKsN&HI@XF`D4SG;G^sLM7Dzm4
zcp@$yau8GNosVm4qn}jjs!X9XyC5)#adnSMw*ir0LU<NBk&LVrPlhY`GmE2c=felY
zL&QN<64nI|l?hyKo?0!Z;I{IGc#X61DV`J9tLf0at7gBb>A;TGZ`2_ISpk#(6ck)N
zZfX27eMRB;>&8}?b%)AfC9%=%EvV0K5#s$KWAQ`@wG3H0wl<c?YI`yx^FspYI--qe
zO5AX+f26}N4?q$bKLQ|*)Qm&CcOH5aD<Mn$z;J=`2g?h@!c{RIJedLnip7;@(Z)P*
zfKAoTnmja>w1>AM)8$|m9}awJqg?9LC8!1OdQ5n9cV<)#S-*9Ap7h1=&57BU?!WnA
z{x)AVt!GvIOkkh^@(O(#WElu4T`IP>V>-Nk8HSeNm2W0kAt{-afB0{9K5x47+uXS^
zVJgh=r$WL0%A;`VXvS0(XCnXZ!fAH@2bF8A{0-Esw`%$wY!cCbwZdLtCb@XJe>uIm
zzDzO^)hVeJ<v**8!S^WD(Ce%NPV7*wx|=!V&LZTAfms%S^r0!p1g5ry*IuMT0z_2i
zuLZ*;KZ;Y@;hAI+4_sefGKWqATstqd4~wY%-f*9zrFc5BQBVyy_75k-9iUsk#E^TB
zT$PWFupW}btHnW)K5DV*TmR$eJp8G8{6Bu~b-CBP!`}B^7uVjh<GQ$Jc4i@)gvg4n
zeeFFe<(k>qNz%14lTe{$mQW%2NYdBu_cz@8xaZv0`}KN07YtddKG}E@!5RjV1gIM?
zhz7t?a_e5wAuM9ZW{_e|xAO@9=@=u&u>2AGo2F5M7Aa%*@bn7>C7s3!^$WkBL>95z
z=kH)9mw$Kl++$xIc62eu3Uh~lj+-kp&_QA@+_e$Yo3Cf!7s&+RR+c`ak*(ZQx!1C*
z$g!5~Cs;#FSaGtdCo^-I$c+0pWa40S!ECua-4==rS_YAHA@tPyTu`?`f`tSFh%qdw
zDettzVtg}GG=vfTS~lGy(@8t+o1<brHk>pp;^iNWYN$9obl_by+UiV%ix{3a@Usz2
zFdzYX&HRA-SCQZ>;rpVLKQ=3E1~_+<r@62umg}<qwUWXpjwT%%pR16rz(=rRF}P@j
z#H5Ev1TD(|WAacLU;G%4^6@QaCF?wBzZ^CBy}SF@gCfKBxc>1y-C5fU=@~`gmQILR
zu6CfJV{f8+?ftFy<J+2HAo_-|Pe|8<x+MBR0fFJwybN{vREXO+!jeT}v<DDa)h59Q
zbY%g6Lx|M5DZ>Gq%qSQL+%%}GAW<~eegD#vDFA2>##iO58#HusQ#*et;Dz40%a;xF
ziUz}Be0ySOb9h~W*gL}fc3!~v(;l9;_@@EZkn0q`DejT!PR>9`5IO5f3VI)a%5Mmu
z0lV}zX%}ezq|jt$)o{`Uq{P=+1<r<XQKlc4Q076+rdht~g{zx_$evV-@!wkNTbtw8
zpW-a~dY)YLzxtu7BQ5FIt3uRg!oBMq=3@-E@*c$c)uiLWx(=-5Gbc4<6W|8Fcjwf*
z`O9P?CFXiOQ?AXWAOXL(XV#t~?$of|T<4x{)fd0=+7F|c73YcLGI(+284znUQ@f{-
zpmd>$UaI^aK-Wh|huBM}Fp`A40_l|Jvn^hlsoAA!#P)|?GGx_zoz$|Ko88$728TWd
zyD!UgAuOP#Fa|p!sp>HggeKlgpS~{<gGv|Fpa1(blandw3ch_u{8WC_;fIg`r6(};
zCnbam35P3X%cNkl*eg3*Ggo>5pcf<Jbu7_3OKvFK=gKwBOy?eH&lh2(w}n>Uk^(Ic
z8ByivcF5W(2@|NIDf+~wov)KarGYTVF3-s<jt>*?>>Q*=`s;QqYM&4<)8-=OXSslI
zfA2LqQ+I}K4Py+kb~Nk@1ph7;k}-B0x+0ckVqX-I`$cUdD}D+vXy)L;CXkF7o$(^A
zkU|6l>wqzp72}zdcray+sh^xMa%)vFw>9V3zj1S8F+jE{CPzwd_xU_)bIa4po{RUV
z7e5ONl>3W4RfyiK>;ye3zxq(mOaN{Ce1<mbC7s&kw1%dlf9ASVt4D!|vuYF_%Sxv&
z+qQXNL^H-e!K>2o$##-tW<pM6m?og;3a*&D1d@M0lez%X=<=L<5jqiTu^|~Ec1}{_
zO0N-g^Pe;mqehVZ)W)`}_w}oHUK(;uO7P`nx5(V{mK@c*WG>GN>XKKuxDBYEg>anq
zycVCT1kFrYvovcl?4b_?tp0OPB`SRHRpnQ3z8m9L`SJi@K_-Y?Wo_)fnEvU;aLd=L
zs<3a}XUM<p@%NJx%MsbtcQw-XJ}|M@`8_;?!?k`F6uLe$jy?R+_@MQ5R@7y}I?vnU
zYO2Krsb`)4k^k#`VD#Z-)w-$e&4;a~Q+dClayP`z%Z4UoPamia^DP6{QobG1RkfTL
zoA~Y2%p$<*dZ@sgP0v-;v<PFgBhvfoX~0@uGhV_Jk^nh}2&ROj{xMcxe=aC{P&N-g
zmD1zRmq7h(>{#Qv^=@G{{*=^}aNcO|^zsWp#{IxZdmkm-z6t_BJMDTYe7b;OygaAJ
zputAizt1G$D`qM?-I{CGx*2aq#FOlj4ynY<7F{MBX2}9`!oYL{cKdAR#&%|NNUYSl
z^`*ZC+PUa~-Zrj1O}k`_W!muBQTI44mh1O|X~m=k3tcf3b|-Ng3!p%T(wOIhB4j}S
zj2d#F>|v=vFWb&Sb782wnJ}n4ttfs4AdC^rho*5jC-lHBSeM5Ledi(?#iCn@LAyc3
z*rBbKH!FKASp##KESjp0-phLynYWz3@IFm$I!*r6#n|aoe;e^o6TBq>PXd}DpS?#`
zFEtJSO(uv#R@aDcM5>kXU}+=YCC+9sffXM1<+DQMa4?xg)POqd2@}?zB?XvB`)bJI
z@R}a|BA`{^$|WJ)uppWV!fpEOZUK2vEMz(>OlDJAW=aK5{7!AZo>WG<`d)KW(K)%0
zYO1wk)xMF^-O|#ao519l-F<ayeq&N=2ijvq1A@{^S<r(ISEd9e8903a$AAO$B-a9g
z?RWZcNR?T;f$UvTjjgu%FMGWmdBXwe(mMkJ(w4nHu>Ka!tIsoBzWD^nO@{4?le=V(
zZd8LGZC}D|mfrYcR)@7VAGF6;nSlc+0PvkHAU_&fE%~4!dNo_!<Bc@k{d|$$!GK=7
zK^F@Xme^1U(F+T%qlb7jiyIc}adkn&$mO*t)o%@}2>Or7!vk1()8;;pinml-QRHf~
z5DIEtGjNt+Uj3A$oa6~heExh*LfHngDvXCn69|IKXdgEY7Tr=c;0@DS517UeWp$U$
ztkZ<wpfvZM7_1@07evO!ky9~DRqqrggdX`b1i%;J9okAiKF^Hy=zp~&*Qg4IhF+!t
z{PAOX1G=0e-drqTU|>q{d87@EN!UO&<Qwt44gb07+Nz!BE?#be6Z!O0Z@{!!W(ban
zoU)(jIc(IAlL?ecaltZ?lUnf=n}$^VrGs@D@Qd_@Og0Q=woL{*>%N3?H%X44RHd;6
zV$)6Vy29VM0I5}rT(QMud2KPz>Z@B`usD^~1WhbK$iqVzu%siL$;h)mk?ZcA)dm5{
zl;_}oqR+L?^t)Hrjn*)V*YuS+h(%YQo0#|NzG4`);?S{TJC)U?W<5nFzfjB?u2z_T
zKOsF|{|~j+FYd!};6t=`Kf!-pe>3L~fM(&7Y{G`B35ubLrhK}Fmx6FSxWwVRBgBm@
z9sSAWv!eoNP#%Kb8KO!vcM1uSxx7}<z$-V;EH&d6ReSvjxmSuR*$o6ry&%)u3s#dx
zVq@*Yq?g%oUnC{3`hODeqWV>iJ#+(D02-XhQ($k4GhW^U3Uf^Nz%W;@C$?_QbYUop
z*G={;C(@hOd^=kD&=pBM)<T`Bmg2IfeIBbne9q#-XSh%UtehS$Xb;zDfg4T6G1&eP
z3yL+YH9)yUx$TJ&sN8Z~(J3Tgu-nga)0U!fQy+r0P7<5!{A)1aFlx~!r_nv#<Wc*G
zFLhR=`b``d4OsHTtd*|fa6ImfDsD%T%L2Ua{c5mjlAkBVeEkmAOD3pW80(P_PH}Is
z#bZ9vT`8vDDy75lun}13m$j6?s$$Sk#)dfjM%i*w!_y0zO3g16S%N9^M({8TIWGIO
zpDOUl5ieCQ@4&G8gBqqahKmgrMb(zZm?^=^K`v0dF-U8i4DjnsJWyN*3O<(`d=DII
z)y5rv5LIAEs-DO^LbvbWB%XUwEqkc)Y(PIjw-`zo^iA!N1z><KN2-^TJUs!Y)H?$^
zeA3$Qg^d!#-;n>(4k-3&#+z`Rg|ij%<75Q?CLZ*>&k2(Lsv1=|$MLh^P<_Kf`ORAa
zFAEdK79U>(x?&w9atR=N=9953hH(Hoo1t<L+`XiK;}foG*u46jZgt^>x;%ynfsA55
z;?;*<>u^I8VFO=s#({5!=Z8ppkmf7#$g0CoO0Ug<DctI{rk7dZpsG0YQp=z~OckGS
z6HMr96OY~_6}MxQ!V*7M{+Twxt!YbNB62c|iS+fpt#^%cOOpVZL4(QzduvOFJ{qbx
zq}PuVN)VK&y0a`YyC$Z@mBb&ae0tTwV`ER0Yg;}rWWM9o^i__Njn|A;Y$Gak4WMPA
zhed+cpecdx`K=GY3X>>>SO1Z`o0_M{4_aC+%?`IywVG7msdg7qRW4$FMcMtztP`JO
z7{=N2B)lwgwWss=C^gv4Z2QBj_QvSP9-cbwfMD&_bd9qzUSsASwA^vBESk#hWQYke
zdf~RS`?QWh!3LKL*XW$pb|H~X#8B9<uTeOn(|m$O{TA(&y+|vEu%0Sc79Kf*K0~0?
zq6PGcV%veg`My?gJ;uf)n;6?8*0J(}fW%d*JnM@{5c(x+#gUJN{*5BCbeExO_0He9
zGrq@lgtAlRp^d?hw_bHcim-gl!<QxWD@xoErP5KQ@X`Ec{vd{@`D<owjAbGdH_qZe
zL(mK|;{?#lJ#-V(Pe2(kztLkD(<DK1@|uiytW~ZbUZ;d}S?FJXjT%}8=;)FNx@Az0
zK@eW6VB}1PR!EkJy4&r4N2VkN`@L|OP`I*Li`i#)_kQLs%bD85{|F^dCl-UIzk1~{
zXT_;lqJwy&Vk}2{61G1_zd?OTS!HEMBz_%4>;<8-0%;tq-y#88C_P>AeIt5|kCUao
zJLmkPkUe=^5+_ySXX(YLXwjMyju5{IWE5ZiI}-+lbP<b<MFW`urK`_1%r3{Je~2UI
z3sbailcwWDvi-XSI;3%AGOI9V3{k5CfQVe1SfSM%F1iNKIt?dS+HG%(E{5qu0``?s
z-M`^70h*YP8vzN-@sO*bR6r>76~lurey~<`!)!T$-wMgg-=o%?(yN<2+%&0>xDXZ1
zgWfTm2(nrN(M+wnglhF>THPHu!zC3%_LUOp#4$%#*F1IF+v^y5y`HZKDDpJ3x$`G$
z@wlfTzt3ezMNx=bowL>7p=8BE9eJMH%zWAuDYx#I=X=kdsBA0LpyS9h?bLQU9K9*=
zg989Xau2qE;6jAKK64Kb>2Z;W3yR(uy-U{Wo<)7C-*t6*pRyzU2M&q-)(!uvNg$hD
zX-s=U{Hsf=6WmE|K-WhQPWh(7b6JMbkh(Pd^{1hk$4ZIA!e}ymo(00~4F>H}AOAOu
z0j)v5l$!VK{2Y07GA@Fz9?5mlMM)pu#`<i6y{?Qh+Ag;E`)~c6DLkkn2YF1y>}-SX
z^LOSV15SholK8;6LRE$zQ_4(4|Im9g2l;(ZeS9L~*=&^M5;FglfeKUCTZ|d)Vb~zk
z0qbUNU`E<a^dfkA4JzY#G9wEuru4ajcOAVL%%kaKu`EIl+J#nZXWf@k^k@*KJAurv
z0;y6khTOcJuhRSbTydH7KG#%FsQPg4Z$g3)8V3qlVxp!;)5e%QSm<Y#(Ste9loMZY
zM8|9%JFU%|hjN*Wquk1h##)cBTfr_V+tm#%SA57GF-b<1V!UFhQ?B<Sj<f3L_aj)^
zZLl83_4`^PwQ~1{?LF`D_8l4M9mOcz(H|&`j~`<*KLqJ@_=(iFRM93i!n6`ZxwtCb
zC7(Xr&%|Qpe~*N*Q1g|6C8iN?I<KqMn}pm6tW-f{sDyP>>AA#<l_bs71j*60qw6I*
z<0z949c^#$pErYO{mfgZ0xPeQF}AI1h&C+C7K9X{g4n8cDk&Qh$Y%=t#oEE<ml_l#
z3!?DEqrYiopXW*X-WC32lw$naRixg=zW=`Vtj&`js$DH_A3q4{k%RS6ciEoIbA73K
z`k=9&B?%{0=ahpdFID=v6jYs|v41^%cC@S_;bCgpsfE)@_rL-#ONsVpuT|r*1bLwW
zPhKxR_+Sgzy-(1q7{A@3Ty}0|>yfEQ+g5^FT>o-brIrv4BTvPzCSqAaPo)sk(sjCu
zsAM1GGK#)%3F*<O2=nO8OgBpWn3?<G@Q>2+@I~RYBaxq!sTfHyTZczC&)>y=$Ydgl
zHt~@bgx;hBpmi=xv`><QB4+>2$d~!jopK_(W~JaRpVm_!H|@k}5iUNwTR<%t$F`#Z
zpi{%mf8YN#C)}QnhVRmy!T?}^hA6d_<z-DJxLL{rOdO#2XXSbFESA9WO~Pflx~&%!
zAOc$m0Q~NQ0PI=Cg-rw#_8&kipP@vui6~P5F=Q)_jp+n<=B2B)w)Y=h)~H9wj410a
zFN_Q}Xb%RME{P5jE7EZTCv@PU)ezo3T{`(r{mM5-E{42ecL_dkrCxvk9sBI^E;qiD
zn=bi@dkDg`7R!J2xr?}ElU>C>_2*YAerv_qFb0ndD?L6b3yoLk`LJQXEc*C}K3$v4
zLDoE!rhgQ81?^NO`sOE~m0LeU_td_>M5|n7s)?Y;P&XjPK{0(#&(RBfw&RawgB*Z?
zBab}*Fo{waC3(>3rWld0&##u;L4%cM)AX<Z`0>UhEp@v8o76$I5~&b2BgS%<dfL+B
zd|GziXB`WGsJ?~(^XkzqAn-+R7~o8Hl&1=hrgJP{@Dte-#W4|DW_|sTM_iTMG3qbc
z(l+Q6b8Q~UKs|Fmlf)*X*)lX5(^(QRY>mbuy>*^<(k5VL_v6t&D3A<OluLnR1!}!f
zVlq^EKna_#TqS92O)L?|w!1u2#z5D-0Wi5v6@b$<CY{A@JZITkH5H%-w)~em%7X^W
zlA1Pa_|=4IB2ZD&S!-_9DJcW_<QGaPhHY_Emo8<jvS&9X-Veo41F%)z0f4bqp6lJv
zc$AHr7t$91k5&@zRw)r!5(ZFrn<h=edduETk=U0i?p^Z*VpCSs05(LGKUn}WcKbLR
z2nr3Yw#pmixG(dIPW7n{YMbBZ!<7GGv?9l^?<t)nb3kIpf8Xii-6BOl-1t-~`JbmD
zzN59LeR2@B>on;^tOiL^Ah9^3zhjB~4`kdf6wyF4wp=r}r=vy3UJSYGvOdvS!j4Z!
zzh0>8_@-Q&slinxzQwx?!Mg#$XXCumL#0ah!yL65C*0E{@tu21qtvE`7`eJ&6ndR(
z)Si2bTZ{yR=MI1H_PslE@9u><HkSe{Cd0`#VKJ>85?B7)mtoft$zLQzjvUx9c>_W#
z17JFQH2|!ZCL|?L6BxjK@KpJ_#hl{SzVAo$nKQ0we|E=Z6)m=te#Kr<O}MP>`X!#W
zfO<W%+%#up*4h-|xA?sC*ng%yr`Lb31<h{zNSMke9lLu1gTyY0ZzWEW0n9<QaPq5C
zKmf-=WaN8rUTC3P!ID`_xWS+_dV)e>y;5Ob-LTOI2kj;aF8i_pVEJ{fF&;muwHZj?
z>UVP<KYvUXW#ggJe%$2k$pml8t#XfXNM^y19tk&6s@g_B$;%Q@N6f4sN^=i^`2^|i
z!I?S<lvfgS7!%xAgh8PJY@Am*$<+~8hs(B@5;SZ>%y1+n;4UOsnwY*;ezg915S8y|
zn%2cHwvk^tmr!n2udOJqJT^Sm;BA&ym@Ha4ReGn>!CYcKGhSC3%DxbGVQ@V^qb(9g
z0~mpA*sbF5VGGpU0#;f*vTrc1ga4*|=2i0#xkt0NIIWHBHeTU!9^4^LhBg__dfp@}
zc?M8Q{}1sO&JfR~mqK|D0NNUr?X2h-YrvTJ;sLF?xNm$yO_~aX#Y0aZbYzF33X1q$
zge)Dmm7gZ1-caLWtMlE)>zWpA*`0@*JEaOoH&}Sa9@hLk=j!zBu0{8Ci_kx6r5pja
zN|nAMoO66s<2n`Qy|M;t$v?;iI^RS}81j{c%*Ue=&d|=#9QrudPwwMU)uBjky(2&7
z$KM>Paj|16%6b<aPrT8vUHlMD%X91my4(YRDiDG&d8uq01Ie;4<R{Mg|0S`TAoN4s
z4GcD46b?d6&!&Z{oPQ9?wTM4TY5w}9#9~_#^_C#6lz2a&RA%Vu#hhHT`(#QO^D+RT
z*G(blT7_I0_3RV$Rx}7u&~apz@9yPWt_#R?j#TP>a`8Ik<`?A+x9r~$n*QJ91dU4)
z;GmedU%qioHQ$UdM6Nm9af!cBMT~2qw4As44I^vp0{W#90B8v_PWq3da*r`*(Cyb4
z&k4?eRV3GH=Qf1|QykjtD)fuAX0p=nP>#Jw4lZPKaY$m3q`^5ub}pnnURLqDl#O3E
zQl+Nr7^Uuy67VfKlCf{x5Ro$?6oM;)_nsI_U@duB0G8V)<ymzvH@(7MbOI}irrhf{
zuD%F^gE}SPc6^s3E?=rt6bZDu<K)W0rC^B9PYa1xW3Q{d{WjB9;>bXeT^*XtLnv$7
zGh!ThuTzWq%xB{77x}QKlkmx%1P&PnpoPcOv*!-CKkA{;EK3b*2D=BdJpnn0=&4N(
z>b7!^9%rb2VA|_`ceenI<V0kV6*wenl<Cbw_GMDSYFP%+O>pFvzg2>Y`9o1t5s&+|
zmrPN*qQh~r9quGOd0qgZ7*|jMws`Xb4>!nnlY@Gq)j)_!s9Bm;YLJJx$F5D+?(o>g
zS4|$##Ij?Pt+!bUwP7{N<Go(122odSgae*rl0aM2GvLqseJAKE2QibVRSjr<D@Wxz
zz&4;NCAs_YCRJ;L>}pYS`|pe#?o|?pw_hkFt^4t0%@^dE9=v?_w=g~W2Dyp`-lofd
z+nwPYf;EVISxT4cq)fTr(f))R(+T^Jfs2E|iiVS-h|RRN%m#}|(tvq0`xQHvjh~g*
zIIXK@a{u1dA7~mU(=j~Wp-&;VG!1s-3olE^m1>0%msU{q48{=opC;3S^Zb__1Y^+P
zpR}<5q*@NyhI#%9sy~jp*~+={fxyaMW%^G^UaRl!N4oR1zl5ga?<8MANa$FcDcJKk
zwW^hkT6Ii(Pr3Bb0Gs>?g{*xaV8{0%6Ku&a&hG7X$A3s~wGn2K5Ws*X-Dyo)Gai4|
zR(2z%)OaHX$bb;tpNqL3DHmXz{FO4686C4*XKF}FAHxUX$XUu{42zHrlARja*!p{6
z5&QHEA<V$MpaIogKRcP*WASpaXUT{|#0qjR$pwfmgYQ89mANIwz-A&!#prmhFe-6<
z%Ar>*KH)SMWijH#&Q^!>#48XNrti$gu?>~pS2#JBHxOLCk|FhatB|sMzhE`)dE{$C
zUAX7@3~i+J9mJ%BGc-4sK4?htvc=fp*E^;OTjMbg4^in#4cJHUieFsvnPlA?U6BzG
zvTbpzF@4@h8tehm<a?F6cT4bhNz<HkSh6w#I0LN%x&RO?1(pW@2W{}~F|gT~I@f)F
z;5h%4sbr!u&iD#%TO>TOwB)Bnrw2)Ifu{G9^z3nbXF`~`NHM|GOnG5)5GN|AG<=&n
z*Q%kQr2c{HjP|9wQ>4JbMyi03BDx~ul7laG^_VC7b@98wRGAYS>+Xv`Hr3*LewR91
z**lTC`6HdqhOGGu_Vt<W5#!lDG7%=Kr>2rH6HlIDq9|Of9V)x?xaU5e(o`1+Rh)qs
zcc#`krQump2Z*V(PaJg}w<$^TjJ?Gkx=kGevaLzbpKIbXU;xokeY6Yp)IpT;^Qkc;
zp4GWvnE2LsvhftIp|fmGnPXToXR$9G=*rjdr`A1I=8McvXFx8R4c`=hJ|2_ZKEyqb
z=AOIH9X%%X<ZiCRR9?NMpiR8t=J2R{17R_oIfTN^{_dXQ=er$kSq{g{y_W0*la_aG
zY7T)=%d?x%`J}P41??q`R%RK3Z#HH0e7-(@@dc+}I`i!vCdy%<Lo!s-O~64DjbKj1
zH>XnQ*hsf!JqQ#@Gr6sscWJ3C>Vl$WrG1lQ=%3^2VFGngD_SvA=z##}yJ_J1O9V~x
zw$v*!VE0r@8*|%1cQa99@UO6DeJ*n=SNsb9Y%CVLWu(aZbo*1j$k#T;2mPi{b`)OZ
z%IEyw>f9NdB1-G|e|Ag*Rz<ADbo3Dftn&E@K}0SS@K~E}OcS!FN8jVl!OnkPkN>>0
z%1Ce$8FTzBUhRAVStniY+2ZUubzIs?S~agvW(clB!MG?|uwj3ip=y?)M_qB3jX^h<
zL-AtTMA?Y{?!Bdff~c^%^tWk=gJXDBrXC~&tu92v$DjFwEz`LX75B$VIV;L?^2~VR
zy&2cy@|&(@atIA736!#6#C{?Tq$8M5zKJX24CjCHn29OdieFHB!YRP_yxp0LTnTS$
zk?YxN4t1(PS|S2k0ayg(=oWp_P%6oUa{-*v1_ug$L-&ZCYNef;=QXsBETed)u4n6Z
z##;8~v6ZrsBTMMyB^!2?6UO4n&XGb-fIfOg#@0XVh<7hw6vZeGQtKlqFDjL6l+5!f
zS-lV_jZ9;A!+T`mZS;qJHx2YWP&Jk+@3}+CqcFA}WwwQ|1&0C7kK%hc0DvViwB^ja
z9G~CDf6Q8#=?HsV+#|zP>-dMq#alL1!niSB&^BZyEPzn<bMA3mgUfEF^ma*g@bfJ7
z&f$C14&tckFnmP&*;MpgQfZ%te~#ZBSm6gjo?g)<-#(t5!s>Nu#c`L}`h%Gc3Id!O
zxt{8Ffj(gdQpgYwi(n&BG4E8G>W%S>Vbp;OIy`lCM|G)|U<!ghfh_!_PDd6W1!D4o
z&(fDu%eWeZxS2zR(Bn@hd=c7=PsbAA?X~jEBTwg-y1+<8`cHBQGk90dMDrCdOU=XF
zzb4n8J%PMJ?{N#O-In9~R{_48S6GhTAMAVbNr!GN2V_3^1g;}WZXGz+j|w#J_+G`E
zGW45L?f$5>DpDtKvi}3LG=oX4k6GBQmYmB}W~rS>D=he}n>=%g0}mNj>&(!2haB={
z_o#%K{$|gVJ*RPFVfOI_o<5Ysi_8&mL*FN9Q|l!>$$(`H1rhiEW4^$|9wHfFOU0nb
zq)DsDFHLzOb9o15(P#|PgzfqwBl+ggSOgg0Bm?jyvlTLe^ARkDwJiY0Lyv#_#4)$D
zz|lAVz^^E@*KokH<Rox<^QmKdnBxpBo#ofu$cQ-qUvayU#;v}LUq17Cc8qmj@w7;l
z$XNP1k1@P~85){W4+oOCbE@UcD>(Y~GjAtD`$F5<=1F&tlAsHtN|8yaZfusjOc2H(
zvV&xT);5fXPOX(HA9hjhsP?&1c!M8T%{V`=Z$#E^3_ajLJ2FUSvQKp{BCGWb1osIy
z>1Z`;n0oYFXieg2?<nx7XXE*hIwtr|jU{nLIel3e6eXEm=rA>+tElbwSbJ1jD@;g5
z=Y1TQ`4`6n1O3Lo#CtN$wy0j&zdX6FcO8Ebo%h}vxph;M4RJ{UMrtp!(BRZXj+6=O
zRG5k=i7~~)-Xi#zs|(kZ5&!(Ik0#?Q*^oO=oqa)!BLIbXxik)J@CVuxmzNBiIs5pO
z>@~vgy;*&nmgyzL+aTC0gyq{uT$#M{b<VhSqs;~RkOY92WfSW=pth!RiStNI4^o=}
zH-8UlAnx>Y=|yuu8KZH|2S4<}%eUJMlUT{gk@SActpz3cq~;%7x*Y26j;GQ#DyB|;
z8`>WZb9_!H$qHr~&>{oci9HwEDm6J!S`EL77^z<z{jBUbOP$6tY;T7xHvXyon(}Gp
zd4G^$h6&BG$vU69Q*K#7mcP*fyO}he4@DR!;{jkHlJfgEW}exiZo5_UTruTIlcQ1j
zZfwp%4B+qua62c4h9r_CVLXh&yD?<7bSZgyMI>qEYPBLr6z*5rlH4yI#H<s<EO__3
zOVMztP)w$|;d?Rgm!oHsjVHbg3Fq_;)j`D%d$Y*EO$}W~4%qc1hZP{$Ulb1&nm6Rh
z6E62ZzqIJXrObwW4|*KF*t6V|8nDPo=GNL}OLAE`8~(=M*MKRIluWwagKwLcov^Cl
zIFm2ZySV{CD8$lSPc1O_=VeqMW$Cd;A!TPevzz}tn<@D}Sc6_JZwIsXu?@gP*Q!V%
z3E2RNoVj$_;isDt5ii-qwhg<DTi67jvz2^1zw8}7%HUw69GdZw$)i2MD3p~s?(y^V
zA;#O9fWlv1ylM>zNVdFO6O{Nu#!9eY+<76(N8)!bk-G$u4)qHsw;Co!+b0}hpDf2J
zF^;|i*TENY4VWvdiNkLe!SLOYy#2ktr>Ae)gsGk77**N6UD-7z8>r2!iE|9}D=Mw%
z#-rsY7>09<gG?8T#dD)svigEdUI&`&QyFwks8>F7WE!sD*D1Iyx6Y0daoFmTC9+#d
zx4!#pTC-8$ecm&Mc|DfW#$eLsr|mgyo;>5Sh<B};fjC-rXNP8FgqqDKz9p=G%p#<`
z&-lA>|4~hP4TWgeX7#bP?g5!^YHaMNYxOb&k_4S`W>5ow#(MrPQk(qa-HIkwX?U>i
zZ^}YPYH3d@jg6cO06oM+N)gz04Z2p#)wZ4Cn*r#RXJWog8?VE3-!X?=sVhG4p`~v(
zeZOEn@MS#dy*y3o^R0lOZePxS`~S)DFWt_|<Yf}uK$4;`3zyE(B9iQOphtcwC&=tE
z&?Da#w#bBc)P)XLmbN`3ubY|N<|R`4dK79DXXH(=8{h1V&Y5>3KH#N_sa;^3hYh~A
zi|+%^q)ppGeEqwA2uBhCcuOK0(`a#}D}SCaQd9|J(+zhe@mc47_b7RO$pNC;@3%Uu
z!RYGbVI;v%t+&XUJA4?)>u`lDBYz*C^ZbbfhrWpmP)~Y%|LHxyncB^XLqZqxM_Q))
zlKN@C+g=2ZLr+%6gS)W!q+J)p2Z7rrNr1u6a^pq!v=7wZ^MP&r`eR|xquR5S=M{{R
zE*6p6%FtdFaetWm+xoclge!VJeEbmqtuWKMTb7XOb+(FV>HByFvzR~w;7z&2_RCNc
z>j{@6mPW^N@41N2s$!qR`QwF^c=@k%i{In_h!lDU%k>PJZ}a4nUg$tMFm=3mtWxd#
zRmT}&8nf*g;cPmt_@kvpS}=p1>kqm6B=Na<@`4K^-X(e2SaKI!%%L+hRAUwm$%vpw
z-LMVlm1RAT&rIr`jS@9*m}RZX$;rO)xvm9Z{0BYqWS|02KqDy$nNp$w1gX3p;5hVA
zS`NG@+9t9e{2=vG=w<3JfU1}179XGg3;(1BR^`UyUj{?Ok`-F4a$B&>1uxUXGBfUs
zic2|MF?Wk<tS*thaqT_o@}s{mKMGU-Hh+K~yx|s@D@=9!1l_#k<PZvpw9-@a`AIE#
znfJ7Te7h9<$eS|9dcJq1;z3JzRXq$gfM5bI;Fy**5|)$5I|ki6yTW_4VxnO~)WZz<
zq!hOx+7W@D6!Y^MibD6{2mn1R*M%Ozz-MCTPs=c(%n2y@8O99|ITiv_2mpK`Q-|*~
z<&0fRT03B%OE^y>Lvy)s$|wMAG$f5qkSU0-`LS8%24Tvkt>!Hmh!vUE+W5^MCmYt7
zr5a-q$M?LuoLepiygnvs<Z-v!t#xI{SP1&oll|Y^sgWHwO&XN8;}7?L8{9M_ajv)K
zBkf;C|N1f@9maALPrhSeNf*3I0U*3DXn<^65(Q|wsHu~JVNNru$h&w_z$hIJtmWi$
z?CRcBB+^&V=%)E0Ak$AdOR9+mrc~!|CBCsgK(#rc<@?O%xW0}(abD$MCKgnmd<Q_w
zU+csAZvYyuvhv_&hdTw&E+OAOyUZ%w_ImZ1h1tEobWet=nU_wUV4*v(>RwaDLr-2v
zBMhjbRC0kZ<{IjZdRrP2%$=0pY=H1WOLazH0iw+)(wb@ZYPb=n`id;!sX-CV{iBI8
zl#tfs9}0f5^@-|6l|AzcFMPj1rhgUWPNpA^G$sSOEQw?ZYn_tnEj3sZV8X<aJdvkG
zsv%mXt5NFPJgu|q@cUe#Rx8Z{cA2qAhPVnhg`;9&diO{`V4Zn^Ss*nSpzF^<1M&Sc
z>z<8fO<D{?<ls<~!bYeA4nhVfW8xhm4t0PqdO@I|#@GEgl{|VN*{DYa9e_nLS9zL%
zR%X?Vs(@jSwKt8c9j<Doh1c994h2)1-p+YtTycC8Tj4ucKwsm(^rm;BU0v#zqvI%t
znO398wL1_%Xd^1|JfeCGU@Q}l1Q^PFHTP#bCJml)sZ+`(O)Pvxc5}|}?lS5NTg1O-
z!IOi(%bopOo|b&jX2+5)nzL_mst=WcMWg8PKxF>Nm8Y7<+xv>AO50w2+e4Y0!;h*d
zR}m!@67E~mLS%gukgU54Fu~WXVYrko1jEfrv^+p3eC&R*DeA{r5a>T?THNUv-T%0y
zHNd-}dE5R;are%R@RHUy8Jdt4Imz#ne!5cCAM3s=X`p{sz&{EBHOq`sk?kUnd46zx
zPE?HUm)}>-!nN5v9Ia}NB_7NCDwd^|bCd!hNdOt($QALtFS_}q4zj64;cr@Lk~$t~
zqD}ti1-T&`qyMC8MrR@QzwEaH-{oo7X#n#N9w1!MEOFjUyAP!@LVZnm;V$xRN%?gI
zQ)As%^)aXaz#4*|1fHHzb7dHYRm(7_!QHp-=SHS>#F0~0xbw)rZ33dPhMRVbm6iBU
zGsE<kEny$3iX_0Spl9;q09&<kdfrISps}BdcnzoJ8G+f}+|P5mW-!|%ca@@3qX2uX
zSXpxUwtMu%-*J4(8++I|9H^>0%HETg%o@9WM#5x;a}^S%6c%w+zmD@}nX+8>d@dTK
z8JFfA`ams(K+6C##hwBNleGc5MSTQ~VUA@FVdv=*caO?s3Y|Ar_HwklQ$1fb*@Snj
z*<gEQ*CuG{KN8!r*<SaHylu+&B4AdCXtcn#UTv0@_rh)dQM)RT<_^1DliO%mN-(-m
zdWczDu+DG;v>*<ZnnUCpyv0WZVFB?J9C2Q5Rv5-89qJuw3GjH3Oc~vFO+qs{acB*!
zegy?aUEazAn7~XVkZ_iyTyDs&L~?H&BH>J*lQ*SJN@y<;IT->H+EDL3Gs4#$s!_yt
z+FW|Gg2=uLTj2x{pDP9cwrZ`*Fpl_>2^`&Wj)dA+31aVo6l=fHOaZukU6iR}R$yZc
z`&-}S-8X5OH@X~aHyrCi$X9(s)3JtYhW2#trM-i+IgX9l^VCrh08eZ_0Mj{J$FdXp
z<tT?F@TlAVxul9w9s2&3Ma3=^6U$glGP5lA$*yQ(7(JrZVv`0iF<(Ep3FNC;-S0Ec
zTBm&0_*vFIZR`<Il>1jl_4(b8uf5cE+;)U3Nf9s}IB0!dPAj!g9ZqU=O$KekS9a{C
zl&3E}Sd_~BW$ipp9}<%jU8n;Mj>iY|hrGOZP-Ws|#OPAgac$&qvDVvaXI3^2zBMb3
zXC~IQ7n#ew;t}C+Di!|vpkqR3@iC+S`Am6sFwGZ$)``iEOqg(kH;n-R1=_)%-urr0
zYN|@@ZWeHQcZB*l=Z{t?)%ocygv5f%85PAQNnG06N;AuSW}gV!5%xInx)AdH=Epl%
zi#e0Wi-&%yy})(?Vz}sgpdB_nfkh;yGlfpqx(P#fj{4|u2Nfnp{IQi~Xl>wRk-&(l
z4wI`fyu~KQS*4!IThY~do<n8F!g{k<Vq~E|tnRuRW$bkiwTD<xN2NpBxb5nQ>{yy$
zW<=?g{}T{tuI=J2oP(ac6zzWhxppvr(#AH@I(esEJYP+-AXJF)I+}@s{zw2R=?WJ_
z9g_79?UIjP6ncOI;RD&qPB6OLDLlDIzxf82D}_dW<docf=tcGlpUkRn((qLWEnQ5S
zFmN9hAH2&mvugq+>e4H@v6!-V-G9~{GWsNyH&aDa;A-ip^;U8Jf_;XM<3UrM<&Ew|
zk@=glPCfMj;9n*tPoyN4wg`Dln*}tik}#1aF5_fVgRb2ISo0kNIu~*NNYA;hln!Ks
zgQ@pUm%%a+s(hyL8<Mv-Ug?oA&Ba%wPW8M(PB`8k^}p`=)VK0iujdm#C*Q!7BMI}I
zxXHZ3UD^%$$S-*tydYc2kKtq;sH=g`upQ3UbYa}YaMV5Z+QaZ~#cHEgdjPXM;i}DU
z$AxD2yNmxBe!HRCsZ}De)O4Di*Y&~B<UWJhyWVuglz|y0E3os7G(!*zJ9omm2LQSj
zDp?2XPdVzVZDn!|{mMdoC1OLkdiCWm_j`T(A5Ms0Xz$wYn*h<unJjwk0MsGEcxcBr
zt4c~N?YL_vy0qHxVyMB)KMwif;3#tFB@iK3=WNF*j|6lWVESeEIY^JF-x_1Th({~E
z7IsI5i@y!Aqd4$1`*;xg7ydFd9E{wW{jdDO*5-x031Ym_7FZ+xW*$;g|KMnx=q`yp
zD&w5ZG-H|#)S8lqoLhGvJul&4R9B2_f)aCQ>;r>#9AQ9G-*uqUfu7F6SqalBod?Ku
zcH%)x5SjtwGN4Tl>!bisG5DLpdxf6z-X$MU6XQ3s<wR&IW@5OC!*8Gk@G{^1<@G%@
zirK9YORuQxb!_hyHn6^24R!gIX|3+B58hQxHYW!lq^p}FW%Lc-cr^um@4np}95h3e
zZXo>@`wD4tc*Y7At@Y-QpZ!Muj6ws#EK9!9-Vq4jd!l&nAhmmmH>|$;sst_ImxUN5
za>2l%`@ji(09j~NR#;M1n%LfMX&aMn|B0?_ZOiN#n0$T>(!4u!-ejW1{b)ITmw6fy
z<pyx-IjmG12NTe(fuXwB$_hkooX3){>9>T;Oz9GK+Ao}F<n~MJ74y2w2eS5WkXW32
zexefa`2h}>U&&f$n8eA3ZUnjfNDGdq+uHC{onZ6*f}cgZiGd&Tz}<F9)7d;{gx`Vd
zP&baRLoHi$o!IZS9qDXa>l002zL7>9Nu-4&?tOTTgU7BY4{mc@7Y2-%3J<vOoK?rj
zTaPiP0#mslZ_6Aew!Ac+i&Dc*sRBCKOG0QdJXw+LS|31u&gg*#B`fO>I@skXYJAfo
zk_z!yraJu=v&xUw!5;oImU21PQhC`<O!}Byb*#lL%&a)}d5hhci9JHB*Vyygl`fvN
z(2yKADisiruokR9{MB3Lz<2|IhVT^>P;so|yZd}~c*x+A`S9k&$<`#F?%&xjp($p<
zx?141J_PMddR+^n$J&GNsqycT^=4qu^4*Nm#=M(kLd)-F7VH-RLn!*VZVBVzDUyA9
zc6|biDudBWCulBK+C!lsl>QJqqtE(Ay2Z~4BzEVL`DT_}I}S6p=|7_^m8&Jw!Qy11
zjjeNgMqfl<0EZZ^$;oP%ee=xs?9;8si;!W0emz1M*k8R_j)AkSk($sT1G?6;f5Up;
z*FiW_{@mHddgO*sl9|x+TcRsqRVo9F>gh6d!=9xjWaPU>_8xdNGW_f8jTAR?o9wIU
z?=+YunvAh6;z^nl-_I)b%=PJl?r#Jpj$J9U+N%Q5rgxeeaXP!t$EyzBw!YNJL&#3@
z-E7|0aRVV=p}U}OZe@FR!kCg9oeWs6qUMge0*MCmEZtIO!?rADzOdnfLy<MI7)=Vl
z=W&)@a>s{##RPFn<%3Lb3)hFriC((4>J%H)`aAoZUghqBA~uaki<l<kw`ivLzyedM
z%Ya&;-e&|c69n3#GFXQsb2zVZ6({7@Rf?&tX%VD4&xxxElGV;im5>S^HV-tTVYdFT
z+Fa7aX|fs4;AUAe!iwF&DW9!Ek)yJS@Qc3AsZBv%&e4$3)nfk31y-kD(_*>9LXty=
z;ba!)AyxDm%B-$d<j1rC_0X8c!i_0*Z95E?p!Th5VP8N9q26~}P2{s6@~a$rYibz6
zCBeZHz^#yBs1Jc&bs2aE1uek!gf3yKwa?tg!`(PKleDxuy)vgI0jH9m85W2WlLjcs
zfi2J=1z4_^pkm?qXXiXEu>OGpm7Z_#G(~(A0V}|8>{skAI?t%K8#F5zWGNu@m@UR?
z2<vY*ymKrZog27bw>kva^-e;TKXK|BR>}*e^$1mv?o^DS+(~ya6WYs2!;U8Ws%2Sa
zzQrVm{P)_6UOV=Tt8@q!x_Cew&Ba7c0HBss<|dyS7x}g0)r33kGqyKg{Pk=mK6zu`
zqW{2iu6o&nt%YU+0Da7~t#9#fR`eVmt{iEvs=%)=nFjY>yZ&ZERzba^T6UG(ZnJ_6
zQpT*5^2RIMeOc&IFwhOr2@U#%>+bK|Q#DF5c@l;U7z50FRydG&LtCdWxbvETD1ipR
z$$R>kNbZmh{Q>l26L;=HlH{mbaiUge5<{-BB=vh>a=>MXr-sMD#j|^WF?bk6KP)7&
zDuiJx95`SvFauTWTN%}cY&a0GO~9IJLH;wv`Y88`<@}2G^fUqhm?78303h6ltK5X!
zZ14gpCCk(53LBPmsVZzf?_l9#j#_2svNOkqi?pjnt5@{72B*%$tJky)>*I8%dP@v}
z;!qP|Mk?@}$^)u?8ZU`k$>$sYE?msxWs+?=g5RoCd}LOe*5D!v-m+RbECH>8Mp(dZ
zWQJux@a>L}p1&*l+^r#mi~gN!8Q;{yqu;Td6Si4ePr$0|6bb<-Ke7;P`G))QsrVVT
z(-->+ta{4UEG>@xy)hVGw+-uYj-GO5DOyXZM_jg>mb9CMs0BOhD>~&LY54r`;(UMc
z^|I7Lq>SC>{5Ovr(x&r`k0}GYn3Y5BmD20f)4KqgFTVd;t(?0#i}Tmn%!iHQ!D9dX
zdjx1$OPVAbt=E-0%vdu8lFTuK#-e*LUc~vJtKV|w7D?0U>RY24KHh8JQd$Q6naMq+
z6$^riXimwKsP96!wa?*3K`N}v_y^wYGcR5I@|P8OFe`U>yW6gI&v&P8vp3o%x0#|^
z+&1=9c-AeYZ`;OAIO{G31diFp?TIx8e;_>M4O-9S?*5PKRminfHr(3H%)0I1%B`iz
zvAHjwN5X%Ae)1)=JvH<8F@Xz}s3?ZeP_h-rmYyeYVi^eJ$%{TSZ+J+qvYGql*<vx-
zt3}L;jN!JHB4C$7H?}NtCPP{~B9$aev%>$TT?~6dFD+Z*{W4-u`uqBn3(iZi9vNj-
zU_RJ&_hNbFQD~mB+kul_b;9!Nso;5^&nn73Ay)_%tvK1iOku39aD_Jy4-4YQJW$af
zAR>vcw>PVP-{_SrT}8EG=G$5%$k2NxW)=zqjNJR1(z?vCYe+)E1w&7US1SJ*+7~Nt
z2uiOrU1zBl#%>8w1AG86_<kXvOQs)0fEXnK!_r<c>BGqM7`hZIZg_R8g8I_&$WiX=
zU0u1-QmInPr;Y4SKkkOxU5_eScVZ2j<W#$H&fz?sU|x9w%-ix~hTj8Q!Bhkf6aiLD
zI=A)tr(=yk=j}%Le>^p!A|7Z#K){d8DU^SS4c;dVh-FGB8`#NtHmI{?ovt~Ev#Q?n
znwf3(f0462cpG!5NyTXD_Aqz03&y_@SO(?$pK32kKgz%7If9LN<I3AqsQx0xUs>FR
zm$h&}Ng(@Dx0FxIBKdRMjDe}fXYo%4O&2*-J70dk*@ZN?ns%AjXldaLrI1a5lYi-B
zV)SnpTwBn-om6*0@v%+V{iMP3car_{4`(J}rd2Ir$=H{$-jqhqNyF_T6VNW-QnUc@
z5%D>J7el**V=^B)mBZ~+Ufj`rCv%HiQUS9BN+|{0UT0HrCtwTk468%U(Xj3F!PRr(
zbw=U2q!cF}$3H?f2~VOuYKBFdqxpfKy#`Y*69<v$)?7pBPR<iwoOy;G3M6W1fR^sY
zAF|8E39LwReEl9QRRlKbs?BUUSFQE3@PY^I^x1^VsIlZlL#sd;*Na9EZDQCbHeJ+J
zx($(sLLM4IR1I+2cevZMAGICkf%rpw$Vop`%QaZ}RYu}H<Lk1i5&l$2tfv#5o}yZb
ztM|%VB{<}s|26Qxw_wwnpypKmqD$ec=bLY}KfsEDv(LUW_srg4(xU;;6kMT~Gdc+T
zmqGXT`nz@dhpAYd*8_E{qhn~qwdEo*RY#T54<U<*><yc~OVpA1{I)^=6VvP`somz{
z2O9Qna#0+hWdqHaMt;48r$Jm%L-ATQFC1i&npYF*+8kT9<TNal6eopsx&{hgp>yj1
zLqEB|ps1@HqNr&zbKLXGwoJt8rhz?Rn1hwTwcN2+^l0tL05cg*J0uN646Folg9H+#
z?%p0=ldh4}81Bn8Kn8Ap<5*ICQi{74+GFo2mL)NdU1ob_ufq#!RYcFgJ1hjC5D?vy
z&u}?I+W-qb9cjPFbTV#9mk-%q2y&~Es6zOIZ(6PoH}S+f^Y#3;5f-TYHTjjp{}bCU
z$d%L&fzDE1QJ)0&okiLV%3|icuMML4x8Dec+S=-Aap?l!!KSU~%$bX}9i{fauV{Vp
zV{<n4&-UW4xK@d~lc|7Z6J1#VOO%3WA5YLitmGaHz+=c%oJ>$L^Dy8jXKYi;+P`L2
zbBq7?Pti#eahDG<ia*Qa4%QI=N@ZOSYUDlq8%Roflb@nk7HZ{s5n`7ypLNb_aLB!3
zw|1e!9F&CN?Y`x`&f1vt0REm$-Qmj+!BbEoxtu0XH_ys;D{}+@W+8KML5ePX>@1D`
zmq4fvb`VIv04pS*_ZL0W8Rbh%y%p06CmiK+rpxoAn)qB5V`pd}fSd`q$d=LA-UgHb
zb+)3^hi^0Q?EoMG_2+_g^FOv6J@m`R+F*0F;%DVEQl5MU!8z}6Ie!X~cV3tgbkPz3
z11C8dXf643$W9mA?Cu@Pd@2ri9=fs@v8f~Gn)v)mD`E$yDR-;HNkJ`F>;mZa?PW!k
zsT;|^+W<&HJQZ|wSPsa(4YUL=24w?a-8a566sW>{e8|j;Fu^~?GlQKxA6&Zs%x;Ib
zx<*xZ(zA8yF4HWWx7dEUc92=uq7i^#m^KaYIXGZ$>DK+FEHu=bQz5$~kW2kSR%7h;
zT;2r$512<{`55LH)VaOJ4}Ku~UUl+W>(Y(a`gzFHZ$v9)jW5G{Hm)2aR@Wc1jO-Qq
z@ENvsUSsQR8;%e%YCE`vCMj~HP^)wC;5cFV3|;TLWYDxG@(jJ|b%zGvW6|a}A%JlE
zK`khr#Cv>Tpa7wK8^K%8+>**R)tAvnGYQ;$<`c=Q01R4&F6JisGr+;)0Mm??Z0Ad5
zD`h)wLxXJ_Bg0wcLW?|u&ish8*V&ssJafukQmjN8^C-N=SHQqjq4S**QO0(Ewpl5+
z$YWr#9`wCKX#JHZAea<lS$WT=4y1rKT4(C1W@-P%YMK@reXms>`IUfnm)FTa(8&Wa
zY3^howmg4sz^0n<*t%Tvm)$GR4}KntX08%*C!6SpAC~TjGVw)MK}<}FYwNettQ%w=
z<Xar$@G6EyvW@#%iRdxZw~+UKb}u6@E!;DEX1cJn`;?d|<uD<i@$*Tcf}>K;NQXtE
z$@oU#q)p*SrVFF`v+q0D2`?GV2XOs1DP*YQm#t{VZYprBea>Jl8sKVSPFWof-PS{4
zbg?>L#DX$xw}{6g`rKclUax~jjcxe~fCmjM(M5&`AOv2WxElF@%6r@aWajVOU(cXB
zouo;wfs@?7`mLhq(15fxLUP6zb*X)u`GQTG&I=>2yiQ}G>-k()4SZE%#plCWe%QPT
ze;RRl0mFHh*wD)?PsfDW@QVb5k}#?OsCJ1;C%nn7ZBrf0jl3Ur;OvtBu_%3}-FEAp
zMWpV7o<R09^y7&t4iU{pCq-tzz3}(^#kXuScH)aihDA{4$kA|DgX3S?(C^=4_xPst
z(`&42!#paM!KqToPrC*6kDX0s9dY1!@3S9WH01HGNH2~N<a#HNfx(+^7oMuw@YTbD
ze*sBqG4a(X1_<DVUI`W^gZ8y^Dp_^Y7pM&${wC#nr(#>B|8(na{7b9Xl$rb}B%6@p
zSh5K)Hvu=;Ud;gXqz1_>fk;$XA&G>llC7@KZd6`!Ohe63Olv&bO5Ze%Bja#pHvyKt
z`(l(o@?JhSv^w-T7V}J{a=i8Km)82AZ`T)~KSz$M63nKuX`on?w!od9qpyqsLIA7i
zD3o?^W=FZelSOo<{7eCT=s8v1v~1^m-c6VbxTImWo=d|ycrP>S*)i9scct-MrpVI^
zj(E8;TLK!y;EI?Reft7eWVqAk)RT-thGg3XbXLri>DPl{X|zN8csC)buT5{Km4V(O
zVo!K>eEW1#yl6girBBZDD3P?~CZDqZ%Goyz3%a~J8R`74Cez}?tUl`iz#(EbGMcf%
z#mcDO`<ho2xGbNv_%SrSJm-#P`N8ygU5N)m-edn`=`7rueB-u%ZG&x$8XXd&8%8&b
z(J7_0(o!lQAnoYUjkI)wG>An>iy)|o=#Op{5ET*i+4H>b-*6wtbzj%{{d_-XbPR#t
zLc85btCAp?<&S}W>L|TtsR6m%<ZWubh^RV!|8{n3uECPwm;S?JDr<5fmHX56F2pyH
zyeykW7||xeenT6JtI^t4fI5gfPDnc@u}~n8&Kbbc<~#ic>FS$xPG9B_)zu73{Y!80
zJOz+#_oQ}O2nL{9n?~939tf_unO2}bZ|RwQ!zo`^J6DNlGR^Z_)pwn1G6Fs38-Pfz
zWkf2Q53r!ztx~Egu4n}=tKRQ{^XMQG^?1$Js|>G6h)LT>d1Z{XxIeVxpHk8HG~{{D
zA0yM!sV<}RTi_NYF3U8di2gm}^kyYr|0u^oH?y;sd$5TLT8Sb?Sy2c_l1Ii-0ZOf#
zBESIQFH_k(^|*%6>XcSNse8j<L|5H>^iUhFRBDbYG%&!xImsQPkTGTe;a^=y&~;=v
zLF$*hx!U9p_Z}8xp$0ev02tmlKN&u5Bp~xaMrZCtaqegrTifVW*Cmvh%qL3fH94^I
z`5v2jFC1phMqJu>5|(*|0wvUx^-UjgQDovgd#P4VlLFr@F=h)O2iT~`@pbPf#Dszw
z-G>&pY#*hYpKD@3;Q!V<#}?}5@|>k*9^qoG2ch7Fx94@gsIq|1UG?wZzs2#sNt=JF
z6Fpz^W|y2V)h}?%9{Y#QT7vn-j$qQ2V3P{Y`SUKCESLfg?cW~IdudeOm1Vr0(vZ^6
z1x>ZUqu3$Rtgv`JS9HaE6!#p=ov?~vuQ<eQU#he^d$hKEEY2(vytL3e>Qt82A<a9g
z!?f;axJwK95kO!(zz@;y{^2}$AD`ov%R$loP^h-QdvG_lQDRcqc;P6wu79PAucE}=
zlxlbdZYAzsnJx9QK7#)99uJLQ>eVE*iIr{7BzwAhtM|oUP3Nu7Z%#1gdTxJBt&Wsn
zFWY%4Q>!?2S|9Nsnjb&Qsbg$yoDy(n=Drb|q#QjNWSr(bH)Pigee4lRusv4?bkDVo
z;CcIf0>wI*ow_bYHDFN>)w1P#VE(5y>4*c$&tl1RA)s9Q^bKL@{mWBv?`XPloGBdE
zdZYeL#_f9d&z!a2D|wA3XDn`ptWpgQu0qHVyY(!gtQuPWtja%Lso>qd3%JX%zx%C{
z5}sJISr?q4)w@3>H8Qk;KJt7{SW2YcByho$=}mC*Vc}2Xdcu`Y|D4*?+%qaJ=jXnh
zx|flm9a{NDjFZg7{7vGsy<)lDsp6BC%!g6Eb$Ue@vVI&O=kyl#<@%N=k!Zop-C&#X
zScH|3k#YkRcXWO3ID_~PezzmADv}$WayRP2G1@=y@~Mn<|3Zc(LhvKG&AxwVE&|f8
zP6-?yR(89_5D@-%5e9%<$(pR$w|AmfSURIC_clc{hC(ZumM1;+@$clWKjNWfg?my3
z240CoGvVd6$9MXWcG^<Y(S1V&?7W|{j$ZQ1-lH<?(Q-)`@{MO(Wbc@qXLI-qvM30H
zAL7<8lP@_5Ang;{*RTUUh_h|P`kP#D(lCd_BWCf?|6|*C(16U#R_wJo4?lMCXLcQC
zJG)1Tg(faSl0p?~F(6Sy447A4GL-KsMsUzsV~cK-INt?1PY6x<VrUiUQY@hzP9aUn
zRAl`$ccm<_F}2aAMw#oW9ShCDC#lCBPqlbTWao9Bn2xUL<+^TjIb=xV6)!DiAw(At
zSvusGU}O_l2vC34-FrEfO&Y4Am23v1C6XK+(?rZSYLz%z1BBQFS)8*N0B~5llw?)`
zflnzFwU_nn?dYL*{bM!FQXk>rU-ciFG3<B8vN59QBK-S*qn-HsU2l`qw2MW@&~ys-
ziBKhfC?YftKBrEDhLPJka~(xeh@lE|V1>J%kr?Q+xdNnlWF9uESqbQ`#sHj**cmeJ
z)B`aA$Bqg%G@4AjUKUBV=~GMZWf#zF;^Z=+Y;9OdmG$Cis6{#79b|-(tB5BZHK%r7
zx(|UjNxzqYBx207DX{|@kg%2}1f?5h5)E*tXi5BP>$ztg;h+Xa&@wD~98jp`7R~0P
zL$z7oPg15<M{wGXrTeUDHC7qo-nVUH_)pB)i4K?gN&Zxsq;u<LKcODmk!DxwbmROE
z*%tNKt#O%)<Uo#;<;9dWBGT@J7Wj(p-hm<UV@f^<2CAI1*1?9&RQz>1pWYU?$`O!+
zaHbORr-FHjH=|{t5d#Dw8CCx_(zQm8hf%$FT>Ujv+QLG~MK^C^bX?p|x?)OA?LRZd
zwe0T8HHLPQgI}Lp64@rA$ailLKyX~4E`7gG)1F0U#W!l$cex0BV~huvK}J_;LQ=#d
zq%bDt{|KHGX8`e{9uoId$@(MIy?(jlY^-DN@+0nU#?@ErFIv+6*4Jc-{DJ6`!$htB
z_9&G;hT4q#e<eDfBsIjqSG6$x&;+y-iR?_u+MvK@>7VthhVD$T*=1zqbQT^1)@{Tv
z0-(iX*-heEvBX^z!TXkMaR^gnd6g4L&EB(EsV?^^RdBE(%Kk-3e@QV{YL^i;jD3x`
zC`O#M5Lvr0Cyl*FZOJkUh;vzeuj2>D$#Si{Q_}lJIIs9UwBp<%-)7U$fSRO$qS2F9
zEwM0T=z2zO7+3rycGQl<Df?8#l(8BYq5nhNzB~D@Q!VP`(qqaT3(4t9WSZ4seyb7j
zP!DOZIy@ZwNL8y}c7lG7m57C<fO~L%e*kXB?rL&``X~8PJ%25gto2}$hcY*N*!*ld
z^(ZO}9Yi=nBHK0M@Ey<w3=1HqaiOPi7ZYy1EOHu<XHuG-??uyX%9zJ)X7Xp?fbGhQ
zn^(Wz&ghX9*pC+U9?jE|p&<K$$ik=3MU&wyxKIcnMmI%a+!#+mdHf*VAc6uH4t7yo
zNOc%`Ee<2Xq^?ikqr#XE^QWR-e)hMG;ZWbay_9UE=yYrJo9>)8>ry4RP9>M^F9wMs
zw1xbw*5Q#lf2clb%EoFShy(HMw#{=Py&nt<i9Km|GAP_*xrn(Mbc)!)h|HyXP_Ij+
zp?DZ3HijBs5;oWm`|rL(P`zAwOP6-z$=D=YdLE$&W4=U0S3|`9#gQtILhn{JSSCov
zP8P)@b;Vf3h4Z2Jj3i3M<D4JNP$es;SDBy&zsuFAs84f@_N}iQb*`tqQC$&dEvwA2
zrWze`9-TTnkPKYC{9Qo?L{rL>LQl=O@1f)yDN?Ks@xD~k#9gHS?#$~weCjX@NdmK>
zOhsK^`r)BtV}7rTgTOCnS4uY=<J)v3^~RYh$<SqjFM#)#)tk!qZ#vU!+)zcBNa9%#
zI75*5S29}_F*N@&HX3N}Nfy&7vxUB``FORf`OWCZrBO${^pyJAWwG=h*W-WZO}wTh
zyJ_4l^=zV_8h}JQA%wY#7!n58{SOeLDC71XyfLBWZIQ>s6a9nJ0UTP0DZprB&uB%2
z6F>b{YBm%}%a*;Kfo>9;TuP~bS(`3<jIrIv=q;$_Gc(PplZ3MM@2LXyHbdmX$uhs^
zeTsFB-%m_h)gyJFAPnQm<3#0JO|SYN^DqjgBa<nq5~=*7pct0YP%Rz;B*vVE82)qm
zBRP`<_r(5-<(Bl1k0M#SE*4}<DRz;B^!C_QNe9l(BwIj^wHj3F<z4D!HI*zcew)b;
zcUG5qK6PC)TejNkkvpcy;UF~gg7h~KcMR8Bp|EYUrnho6%=|oNwDgYOqC}N(m7;8N
zMc_0Oyj*!YLny<qY+hNp;QdJ7;Ur<e@wm##Jc(8ox%g-p_3`9s`cDSyr*CJ(u1~^~
z+RKyXY!aypV52a?(p~v*Puk<wR)hSaip&y=@HCEX;#nYsX&>ww!WO7rX-N`f`mY(%
zy@LF>nwv}Nyyy31d`$g)jmdi(ZgrzAa`Z99f3@SZf90M8&mfhekmjNBUC^XL9sVO!
zQeYh3O^Px1J0)-LcAYUY%o9;T57tfMUb6xLR-`bjCZ%6l%c2zd?qfle_mxZ+f!lnK
z7Iu7>mY%WDbFuQgH}>vY5i(8Brp)iY%KpIjOfC@GxinDdWS2Bio|JW!b2Tg;{SjmZ
zN?`Ys_me%Kflq6WlIG@+{>cKNkp$#IQU!+tl5%~x1Jc0Ipk_(ryRDBO*IazQA(t!e
z^dWh>-^Dpjd9ceZSimj66A>DW!!)KeNf0qDsXbzv^B$=TlO(wM_0h5Uoq_An(O!8e
z3iBBEn}bHPhKp2mj=e_wwIiEhGq?e4k$6IhVJX#^g{TL^Udg?8Q)g{XT3b#p<9^uS
zc`ckc`e~$BGLre&-wMAT_+vfLEEpYj^A4xZ6+~A5{uhy_L@Weh7nfvf+TaxtUuZM8
z6edB~poY_=D<$LGh@MmT)0uXWm^%<9RAxqi7#hQTkw76~uM_OB<9S`E#h_!1!Q1{$
zN67Wt{~(QvAIqDdOjQ9NHDXkA4O3RV#Dwq0Jr+dlI>Wt4yH^#aQNF20Kl3ZUk^0@J
zvmmi=SP2AtoPP}A(@**`kJ^Y4>QG4Lv(O_JXh09xF#W~DLwnp<;zOS4G63@)GV|_%
z?!}vWFI;~Que^-%O5;Sxb$Hqo#v&%{Yms;322BA#AgCZ;`A+*4)|+lf<!iJ)^v+!;
zMoicS$2flgt9Y1&D}=OktOPQ(=v@>0POK|M9VvOf18I9qYwTq5+oy;(GlYWYZiS(4
z4OZmhu$&W#{QiPc>Tn)5b7t(rUh4`Y*aa`Mw4#cf8`?Xu(A>(Bz-IJ!%40-QZBT<3
z&k5Bn@1G|6!+_|0AWTLGXcC)<dZ+D#sK`h<ED^KtD8G*O3vH!wh7%kbzW40o&mxk>
zRFak=GAm#Nowvf5{GSve2?al4XBzJa*U5L?2*56(shyy5iWH8fR#x~@tWo0jqQ=ap
zElymz?09XOVM1n<I@pN5Fysd^B)<QOu##be7Zsv%u(O(8-{7PSrpweY*X|A7+Ve+&
zJ2BR0VAWuU6$aU5CLWm3u3s}d*Z<^xC*V((I%B|>D^uCO*~h*{>6QO^Iq7x$ytL!=
zCejR+ZY7Ry^Jx!#h^UA`EE$nI0|Zj%Q6WuIkpR#%rm}F){Nj5{-Z0=3+Jh!pB`e9Y
ziXFTlfN+*(v?LT8Go6T&d}Mov<^HdCeJ51Uzx^u~soW8$llnF4KfV9pciOE7K9yy0
zdfvoC@4w|Yxul;qj(jqI<E*+SrA3hlyz+OIJDthlGm}fcdqZ|VlZE$F_1lc0?6Psv
z5Nc<zFe5|Yx5u8-ThWIo3TDIafBPXHaSRDDHo%R*_0?`sI>#sv#<=;{@TL_cMM6ln
z_SBBJRG&)+De{&dd3jNbIlq6~aZnC66a`}}D7O^l6}C3ZXRXea#JsZ7)D3f%MhGhR
z5lVN&(W}%)HxoZv0}-ECbAgy60NGK_*^OpqB|r!mB!&zHqMj3QfFB7C@2V3hYafe)
zSDwA35XWB_m<nd%U>y^K;4G2^;45B8oI~yrpgiaX2)JiXU^y7WL9TV_0SLIi1i83?
zYF&Tb&PtQ^Ct--Y0w8JNe_H^!Vqfb+alPFZD5%tC|GUvX<fov-8Pf1CW*IQBrTf5P
z_e^kYI}MQa!YhEw>$@$G{9g68PHOq3Ju|dmWspfZz4vM6r1jS6KsWVwW1vE=H3x3;
zlQQ{n{4S2U-nej~NufmIokaJ_t-B6S>-1i@fAYVps=>ni4!aQ;=J@P}VuIhpt=@|-
z^){{V4nrcGp6%RiefEjq9Zne>zTp)Gyt+%CMW+pV6F9~<Nh{BjzND>r;cHjBtD6s+
zr(zzY@|-mg(xJ9JYCbaCe}xC>bAlfsb`viFu*ad=<|54BB+^_6O{ln4jC?;jd-}gU
zCVoyV{mp9RY-4mSc5I4mDbLnbGx-uY6r~t%vbLFy3@#g(ZpQG7zZP8(XqCi&Dttn;
z+(Uq!byo<R=PJ099`6sx**$+-@nC$4M+csy#D1BRc^Ug8)@WxH9#@JPP@_U`5CKK<
z+8GEZ2#w-u#wpdJK%+4=eYc@M{g8slWb)ahrE$?e<JY3n7mWinkn=HsSf?+7YGHvy
zYMeg^<=jyMmW-o0+tKCTPDXu|H{Sn!$^9)~>w1gm$n1cv-1lsEi=YVSftLNEI`2F~
zKOXZ(G_h~|q<;)d`ss9V=+w(hEf+vV6;N<8h%*tm7jBaV$qWL{z_aMp<V;{`1^A>y
zMF5AYlzX<Qh|1I%9670qrL@Iz^OuGlfoxC79zf7KXsPk!<svAloIpJ@P6@EWF;g|M
zJR(G13MlDijbp??BAp5=AdYsV2#Jc!a?hEj+D`1!c{fQBJbPTb?^?Ry$hb((SkVTn
z>X)$R-EVdg#vnt=36M<_Y<)InFb+|bDkkYO)jQrP?iei5+*%^fBU=H}DD1|4Gcu&T
zX%NKR(;CT9?|q`3Y%&oT8Fo{3eq*5Hxb(L8=a$IJQZK)iepP=p)@L-NgvsP5dm<`B
ze{D*}+wKp2>0^q0J(98Iiy@;kz(8Y|Byz<Y{W@0*ANa?kI&O^byFx9UWGGZCDI4D+
zV?^g(ixR-#XTpP3D^bYvhYB4nEvBX3b5Y!v-<-}{-+1@4uQMq4M77&7|Cf@?WldqT
z(wxyh4^s1$FCWWiS<dZc;H~Awg+Y42-2D;Z1QR!TDLKX1xZ(~k?>duCOrC*znP{PS
zT_}Wc?Fku<MAYKhG$H0q=t1xU@NGV-ViN4M3{OwooomTI0}l2>H>|Q^^o57pf6bvL
zqpA;oLI6Jn0CtpG-GEkWTZI4`nRspEX)9MOs$9ez4ZR<+>C9=)82NEeT`>^PY43XL
zs+|7W@G@ghUC-t9HOEx<4{h}Ddvv_(S?ILR5_wS@7HNB+Yx<ID4h|t6?Q(pM51<i5
z%O*O10#JfELsk`IEDq4Dtx8t$TD!A{{==*?!rF(b<i_RzP0WP0be4u;ylp1h8Th8V
zGyqsbAQ}KpSbqiYsF(k3?Uky0%N#O7i8LB=R+>5=A`L(Xb1fOH8zvZTROuj+AYf56
zmRDC!k?k!Ys9Z9RxkXd@RpNG9@BM;a+???>KkDR81;N=L4wiJ1=>uAdEcW&#Bq!zP
zm04!JA6{Bk(Q>n7Z<Mrt_+|x^+UQ;M)(R6hEMzxSBy6|k^|$YG#Di$Ymd=S~JLA0s
z={j?&wp;{jAXngR6@xNmkzlneh&2S)dZeAM`6}<_x!;m|NT@*7KzqI@eOx96+Frx_
zJI|F&Y-E&KaaE8{yT}@``r@IGUQR}Gf}kX_fEOov!{H#o-(LR3&zgadltzmoLhkUB
z{v~Ag>+~psP}GHd&>k}LINWN>{Eh<M6m#5(8ylZvC1$<1n>zV=DJriMxH5*hP#9-t
z70jYL+F?jrc68;_!-T;C!$HisOz=RE#?EQ(hR-X@+q0N~cOPh6X7n>!dz3}IN*D%#
zBV})U7%eN8Z~}((d}f5u3EaXXUUo`Sseyivq|4R5v@Tcwax>juu6HX|%5XFHLCLpb
zY^Wc5P3&uxQSSG>V<__9TB^<OjqTZwBmyaGGD3wMAn)&C8w8q3??dQgZAt%&O%3vH
z9W|r5i}2d3k*b40lpOa2KuruuSO>>Q_Y{i9n)iP*{i(tznsqfm-O(5O0@tKbAqR&b
zUDc;|8KD(+MTj3hSn^|a=GYB((97yRr$K`3Sa(^9UOSrP|G+bSZhC!VH(*cR+gJAe
z&Z{`wyAPCZeqDdMd%Ji7D7*Y4MH<@X^0p65R$OIgM4t7X3$Lz7u-RCAX0SN$N<SDd
znI0$q$J@At^sO$L|Mj22o|8oFiudNr9|-ZG*sHhma(wRDC+koiS5j#QXaSh5zo6Xv
z`U*J~6`wej`O~{)X1Fs6*b*O%0LaNX9$(%Jy+=?CUtE5AD=y6CXfM62>%SGHw;!}v
zYCbau`_XH**X#RL+yAR}d95>dMEy&Ef6Hb11$8K2e^myq#Rr&=YDis?s1?liO!Yy`
zj7n@{W{!DN0;EJntjY4Ra+oXr>{Jl>55VMOzZ6ezGR5na#^$^JQWCL71(Gk0psWyq
z%Z!+8B?ba$W7ZZ1oZ2-30CH7~cP-aO8PZPhr<xmIt`YHZ+G3Jnt`{FBCOcj|J+{5g
z!B_Eyy(*_?e0|E^oz3~#LBg%7WR!b)7W8>-cRe~{8=q2m!To{}N^s^fOITw^Lr(Pw
zAcsCy3D81zC7iG{*CV*?d<Plf03s<s-VYe7O05VGc79WwBwQLtMo84*i`P<Sb{(|=
z5L>YhEHji~lAGB}w|%4(hsI~yrSPG6;tgB}oEfV&UaU|IshL9tRlEL^^S5#gH?>kR
zTk&{Lp;}*ED`^@SnKk@N^OA4|la%|Yx%ldbnX7kh-6&C=n>qc|wMF-87L{rd&wk~;
zKn3BA^P#24%3p7Ra0!+@=7@=?9y*6CUDfMn_pX2blPPGc;(h%OYvca<zJIpsFwh!x
zTU(`d5l!T=WB~v=bb;B5_2T1Sa0{A-))1k~ahsc5@fc9?q-T|tepKnkFte0^Xp`=g
z)<ocJ`rO|mC-RM6t7cj_+12IIa8;up>-uvBltAL<K=kJ^Me#@E_)j9vREPTIp**V}
zE1Kj1Gpl#>r_{5IB>FCwQ(pp=C}o|(luV3(un12B1X>4kzMwX+roxmbVrZ<%FdMqW
z7=4VD*lc>6B(ZAqN$RH93yl>L>QuR$y4HJ1Yk&mia7=KORA2#wm;Qzi3`n|bX)w|(
zAG)lj=^I(!h&0-`W}DJ2z9IfZzjU{H?8(iVzFR4Rl6iXx{R)kY^#NCvD7Q6<Kf(}W
zS+2|hkP9zwxY@M3jt3QwfhB7U;Z4N_Ek2ea7WV~rN|~URx`B<aDurO^zr|qahDgzy
zHIp`q4#G}D61C&JHY;ep5&)eRDI&QwBpAe0sX?;ChqnbL5(J?b(rgih)-obZ(-~<C
z&@NO|@JM66Q%L4DvQ;BNyl1Ivd3M6w`owPe<&{d>x6f&JXYeckL`DUY3OK9IU+Rt0
zeJyzTu+&zvcUD{G+)Lxugi>Y8kK38uRk!=+q@IbTES2A$P|3ooTJpBvUivHbS_QQO
zk8AsDbXnLkUHEhPx{sOKrjgH$nXZh`4riJ~0^o>2F$lZTxdxJHUEFOR5U$3=Zf;h0
zmOv+najv}>mlFxdF4R*8>b#6vze;Nfcw(!#CnxYUwc`8BZx5e~FhH=vyAs%YSkrEt
z>7eogCH6>6L|@6tTuj<O{)1bNg=o7VI-LUY_rD4#=NOu!qJ_3*90fplTO^S@M?LXW
zqP-2{-^t@lNg3z-AJ!TeSh9~lAwi|I6>qVFV-({zF?x)D^ld|hWh`bB?a&9xzKKZ!
z(bI^6O&V)XJq`AlI`-;Kj-l5B&QudI;tWy-Fr19jkQ&2wxRtB*CbW$^LDHj|UoAk1
z2fn@3Rib`u$apIo?v<-aeU*L4$kKRQiT>2Vo6KS2AI`Js85vhA@o&DVMuPB(bp<>z
zUQf94%w-U;R>pH+0Br?H*IGbvhmhxO<jrd>evH!?WEHH$Givz(h&bs^`UQ8?g2|z9
z)tT~;L=VB~34q<DhAq3DDbEvvW0TPOVLm7fmBOPbnpk4eNQo`;p%+iBG9|u)JjJI}
zYPamwg4Dj=-uT(4;g3SRtYQ^NPFHKL7=@F{b-E!7Nab#?{<SWu^>Z1sO4>#Nlat$g
zD<p|2x#tB;AIM_@$rDdOL!yF=w@DZOLo0gaz!2qpUPT@MTJa(58-Aac3|3dvt@hnx
zbY<c;b$nXYMK&(FEwGXBL{2T4UoMBV4gIi~m0o<yhIerslCZ&h72s*Dt4_QyP!XO@
ziOOB_F(s0sl4`htVl`A}yBss2EVB7J-rPQnambpfM|ktT#do=f!J^8+Pc0XO`%!Ak
zbs2Tv$Cc5s)x<rSh412mQ549xGL>^9kQquBG830nl&FeVyNo*ZNvVO_!skCYxNVcx
zBLkA1k%y)QBT0E{oofpvy!aexeL`#qAuc2?-}LLZI<<$LyO^|O#fe+Hx+V|mrC|D~
zUO30B8C|W~=JvyScoI9`id!D&1U0-~&xn^vAhsVQ(0DMHbsY-wv}(>4@9*OGMfLU@
zDZHoG#cSy-8vcsM+G0+5abG4tRn8AjYZe|q4N`;L8Vvvy6suK&LuAwlKW=h&(7U;@
zp~h|O&ZbqMiP@$R34!CB`ArN=v>MolssNa!df^Cn_PV0QK@X04Zyg0!2tu|m(4Jl-
z<zewEVAk^a+}(0@_s37)Z%W&hu7@M88+VmlC)vq3x<rmmwaKn^YW#ARFbk0|dsQJr
zTYK5kvNG`M`RPA%c@iy4YyKG;>Jb!pWme@VLWsp&smAi&n`K1-7R%u7y*FJBcY7k-
z6U*~;ng!dc>$vYuO}!N|GK03U@ziYL)=hBiZ1FSTfVtV+`0ntk1<EHTzBqG#mSgB#
z8dHbxvZfOqCUwXOb}3+fs66Kn+PSIXI}vUcL>Eyg{P)Pi&E}CK?2+`yeWZI_T%n?q
zgpx|Xx}z>e-*w)?d`Yr$kkf6Gat#RpGaFv+ZUcU2$}G<H+JCo4qu;_YYXeYx4vdGY
z1tW?YBqm~@E~vM93Ni)@a4SmbxP<#DLMu;oBJH6R>alf@RXz{jJnA?yE`xT)7$bUt
zTLQ*yHk<6C2LoE8i--Q6;_EpcL7leEFO?OPy{bCd*V-889$C@UyDw5|StEUVn?z(5
zP;Ty4hDQ*1;-$4G9IwQgDkHRc;yZI&j0eS`W3CjqJQ5>IK`2GcBRPg9c-EG$;@T)d
zh2(H4BSggwf@jELg;)5%<2E_g#%>H!VOEdaJD$O3XvFX-$+2}%XAEo=5;z{n(p^y^
z(#u0YJ=Qu`zhB^^Xf*G=5w;N;_toz|fn;AiD`&zTzOflBMk-`+f72$gVrKV!;=$d;
z^U4veP=hO>>7YS~S|HvjtU5n@q`c<jfu=nyEL7AmqqS0nt+M8p!X3|UzG-MeQ1E_F
zWHqB?eV*8UBv%zb*V-jBp11Uw1<=gm)oMBJW{+$g-p#AxmD*U0%eo!UA*_W6bv_&(
zq@j`}a91#v#XdV|^~ENH=$yw@dk^cJKCWCYtSvNl3QRK$9#&q${jsStWN%X43^ky<
z)n5Ei#*68)c}5xkBK!Mrj@79~z|Kw~m!J@`sB<~hn6?hJoOYfjJBsH3GA@P0%Y%X_
zQjLqN+1?Lz<)#1H`<s=gu>^*^-e!V5;+j`dsrB%E591uliH1jAy^NJcS6^rdXpfre
z<`|o_?<Cm^k}JEl^HBe`-ME#tE|B+UeJgY5a&ptWc>HBt5G`(pUd8uo1Iz;^cr;tJ
z4+!YHmCr8iaxtu_lfFB9?fLeY4mae)4}&Mu(V(n&eA8v%QjpT6wZUt+RT}xiyRmRi
zmqlJJPiBgCHkUlM&A(7*VZPPUJWO<ppCfmGC@9$GzOy8V<EG<5qJ;ukVnRzOdhSfS
z2KDd1eSr_Sac-de<iei3nUTS|l?>EZ0GGuP$U6P20xlO`?4x&P>4%>>O>WtQm3!Kh
zciLB>1Rm%kx$kf4==3$8H#Xg5RE`Xmatr#<BVE}lWHnGS@g#k`*in{$UTWOPdv8=)
zR6$x_FQ_TfH{B*D=8fp~Dbr9a0AdopbyAGI!r$XXgi?a4V>)>jA+DuR=NZ)K7<JC3
zwxBNRJWWKXsG=5&Z!#0edpZ6jb58VXhDu?5sF8AUr(zVXbmwgZd#|MZ<M{mP;km<#
zuVeBf%8igLKCML)zEk!u4h@VF)>Ca^h+$mvzJZln8!t0A#VE(1;WKBM9@1TAH3K2+
z3M|5tJ_;%<d~+jLNupE3ba%IJaRv4_drhZzHfKH3b>!08>WWYi`HkDCnoEn<>DEh0
zhc5>m_-6b`TYs9V(cj!#a<gssSD$1nUfQGgLUO7vH%wqubUgMz-#+d*2vo3NJyf*U
z6~8fvG9NnUPNef4508YFa=ZM*XQoJ2pM}?#^CVS1pLW-bO%dOHy~8N=Oq__0Aq(k~
zxxwSg96x&4*(gQNp}1)XEzq@6K{&k;b~N8t9Hp0Bn@e3=Wze!uspP-5VjS{orJ^t7
zZC}KTy5sU;SB?#RMJ-GfAymH9TOsnc6Rc$TNu;to??VZpCi;Ylz|^^GLx*aSVSV>L
z4CmYPzpn1L@Va7R-B@fL^OtR{m((<eg^%8Vc5@m$gBZLO#v#2z4sQmGrBuLo24%EY
z1yR%2AQ8Qi&T8M121KNzP1b1_JI^JKlBE$Z4*tIQHc-~F%sKZ_Xr7aOMZ0uv^hyQm
z0|dAJH?k^G0~%#AdXd|<L6zY{E=x4p`%uV0%$Mlf_Nl?)vA!tA*|h4r`%PgRyj**u
zoHz%r3K?{QBZNSJhvu_93{IK8`R=T8iBD_EwIS@|NQZE$JB!2eK}HH^S=ybxw2^(Q
zL+NfwoyL!yy&g$7YUR??_B0bpF#vHvLr!*2YY=_^f|+^APh^c0K?w@bgW*BxEPy0u
zNF!9eMsCK6FvpLt&nuq6bpTXvi*y|#|HD}P9A{;lM}Wi00S}$+>9&Vlh311Uz|jw<
z1e(`_`pYDl&x;@~&MLN$vp>b7TSp>u<Uas31_}@hUvkh$G8|HeodBLVN#iJ)s#Ip+
z&E}$c6mp{1#fgJ89R{Z(49UqG?#GSdG}1W}`tE2G9laYe_wa)mJFShlE})V%g$8ze
z9{Dc=^X&W@|Fx$KyXT2BoTqapKkHgWU~5Qv%$iIBP&#zF40{>!p)~1o<C_%Cb0@zH
z&7Q@>p8;itZ}GA#IcFDK4{RR3|NO#ZAnKLk<Gt^{qyBL}c=z?!pMRQPjhM4|4~xsI
zd3>fB?}^=f%r(+!WLz;S;>~l>SxY>g2_YcDysi-tF$B`AKKWU&$c`PUp;>eZNf}Fk
z?4zNpfL{pV47hlc8VtDL6-Ec3wl5i=KJl1X=6o2qamZ09V3<n>i~?(##f(lyJNJjE
zTY98452xk+Vh>eG!>%m;te*-}V(f=1M8~{;CSST=kMoG1Gk+2j#-Jh_h2Hy+8kqbK
zO#mnw4dG2%Uj*|F%Q~xsWP8ypGyN506v4ouJpOeu<Pa)y!^@QvFg-%b6M#b@;2I>8
zOjnStK_x3NFw{$$bXmg32Tu+J?==cvnmbt9RqL;LkQci`awn_~C=w7wTMT&#()<<N
z*_K{qm>gek<dWBwOO7>2war~61yM>z8z1#_HGmAn<W?m!DH~KzEH39=`*{RI@um54
zWi;VX*Mn?No7|xSJD){UtDKqil8yU(l!gbB0ey-=Xo@3ribXz-qQ37AJPRD#{nU%a
zWr_D^-LwdIDkMOh?H&>^RFRcJ`@^IBepfQiI$xEe@}Bms8%Oir*!!*5MLq0r`N37Y
z5chs<9yEX<f}NB1y<Hy#USINUJM)@IQA#e)*?Y?nM_P8$rI|G+vNxcHAH?#tJq`W-
zSt_+r;ki^n;epJ6@e8TrZ-EpePcLqjRh@8*%f7r5xuYuIU?ol`gfo3IeSM&{n~OMX
z$#GwwM<`%7zGPg$HCWpjiOK?mcG38FuuM-;zYqNjB}`OZ*J~7aQp!}4qNNnUnp`)0
z75;a;>$6ed*4F3ai`$}!8Vh{K_JfiwhrchZY@o!WVoRp$9}g44x<3p5)OKjZ@EjFq
zx>!r%z$sdH4oRc+NjLd1t6Q33)dM;Hegg|u57?W|Oy>~NONtF0)6fMy(gBD^Is%FA
z@j~~RU2VKGA|IB%#P`*cz2$ur{V;7Qv)?}%q`abRHUPAA>nekwhOx?|K67YO*Z4w8
zi_H2}n=DPd0shf7%9*&fzC8w7mD5Y4P8S6${virVV`s6WmG*kDkbnLs4N+9z$jBN;
zmGYB6_4EZ+H_~~dnjnCl8)|3hoK7*IgCghfNFF^vzWd|`)vjL7cH|tD_+8b#H$Bf;
zgx5<;?}+RL73FvJ>p0C*aK{PX<iNCWy}@lcG>vvCSe)HAT}$Qv`t*m8CsG}aIV2Mp
zq($P^17mH~NQq7mQ@x{erYlWUj=g7DJ$=6W2V=9$u&=d6^@4(t?@Dq*ZkzrZj9$oQ
z!RZ;iWQ-lXfy(nPqpsa$O(dI3pQ0_5eH(E(>?pjl^p{cK6^Zd3_&lkHS!443kbutY
zU;YtlfSrwN9?ihj%RUyOQ|e$gN2*U595Jkuw#8zYMY0=qlZ6c?*-m^eGpm@(VjEJJ
zT&<5+*%-MisaOd>jt8biK{n`<NsxIq?9y#jAyTHQsk377pQypr%>A0h*S<8AVpzL1
ze@PNAsg{(J%IKP9q!UW7Q{`Y~aJ(6l*8^OcMrCbK%f<4{mYoKoE@nFXNxHj>ge6(R
z&FOW`v4z0F6+;f0p1q?n1tQ*7yC5*&*;u*mkFABr5E&VA%!DB|FTZu#4c=M(CxI<D
z%`4MNJXMVAL*ID&mEM;=t>^++)Dx>A`Oxnbp6RPV1wTIt)edIJL1BL{y7^w$4^gC3
z636R1b3%4lH5S^bnD9I}$Yo6WI3JzUWs@0G=F;z`jE!y+FAW~#8ZEp!m=3>v<=|<u
z^TUqdv=;)63SQ(Hs##TIe{!PmQGLDtu3v!suYwRwo62u>K4j7U%3mrbed0fn9T1KT
zlM8%SvIT!Iw@(YhxHcE>Bug7ujb=6bZajU^L7#iw=y;>t-8xO1fxg<Hn6ej}4vC-h
zC%wmFscMk4s%u}(k*!Nk-E>YG3Q|$b3|nnBKiFRct&So2l|<iIc#$u+_n0qLF$S<T
zUPZ&`j}3QC1SIr|4{Ih}+ps=zDz)e<qprS>#M1qE#zZbrvwk*Yb_?4+9#ihDE#&=Y
zBs8qpj(i_4`y$KRuy?eGf-IR|B%sSu{E2KOs~tfd%iSQOGV}PUd&Wv}Pd@fO;7>e7
z;>dw-g--^987mok%NvqOR-V^gOSaO{)=+KqW5V`U(oiPnpgThTXq|-s;8Zc#3b3v2
z+Nq^ibbIQ{a(%7L<=?rDq3FL}`VxJ8%o{8ByVy#aH&hP-zQ($jg(+ACJi1%(?Nf%T
zz*mRo4V_x2AL@USe|&nL-yVJLKh36M9yMIDRD{)neWT6{WLAvlybfH0><K_YJ(u{`
z$LmT67sqvmfUtCf^t1cNP<lx0s;)jSkQPIW!k98148B1h;RiBNm38KmZ-mN)DBZgl
zz38BrG@!lQakXi+hQ5R2E^A^mu8U1zX5gEgWXpe4m{PlUmQ$Tuyq8$+)J3P?wCTK2
zM#nB57&7;bztxugPiVKX@wH!H#wQb-?usoGczmQaBw8SdL0!tscqvnsQ<m#pA9m(c
z<4=587Cx$wMTPPEgr#RsRl-7w03WLl032_T%bPDYju+Z3dFL|LJU%8*+=bvv$zfQl
z_b!8Cj7Nf*Q)I!bLa|?*U-1O=^PJDgIz-xgmsCB|oc{Gd{tLq`#`Au+&)DPITxQuF
z?mooq?*}2bE<A#>`>K=w`&wOIuQ?hS^%<G)Px$QOR!@$-Sj|@xH%B17ZK*dHN{0hv
z3ApPd_6RX=+`sXQLS$Pyh@9d5-ecEpR(d*Q1q@~`{1j+H_m~J4L|p6*q6|BOPlHs+
zzVhqdzU8E@h;I*sqbTh;dDgi_=cyAgmNG~8=**Xk&SKi%D-zkT#f174NFX~IBh`Cp
zhSwqkhkTf#F9dKCK6izKE`@?mzokh=wJ@#lLr<Ydjhwwaei$*lEJGBt7@-_!b?9kP
zeb$}Lt_B+--!M)n83AIZHMvn*WVEd+H=lT`JjvxNEiC7Q(!)=95bYXyN?{3=r64PU
z(c1`}mGD&Nrn=E2;<X}ce{zyP0%*}gcLfw|)p3K-;PrNcS9`m!PeZP6gV<VT^DS)2
zSJ|bphdMSdO6_%<YI0(ZS#6Iw(0kl;3K~=ndQ>hN5#NTb9rz~xJ_mm^MVWQ){1E8A
zQYdz&3cJqo=zv}LVS#fI!oL~buSCXkl|1VS7&3j_P%Qf>tI>8;UHTgJq!}*seo{j#
z0jCnXM3U~P$G*<Y3mrmbgG%K|2{nO1<=-;d6XnODSi5Ulw#_R1EX;*rNk!C6`wxp#
z$cFl38-|2aC@v43b+-D)G`n8nspcIPt}c%4XYmspj<n57hVaov8pg|ZZHhK8hExyr
zP7)@Dzv+l`lxZ{I_}s>6i=Ry%L}?q%0DzC@8zNs(97$77iFl)-9o2K`35T4scpR>)
z|3!IBlf2i%BpI@#2_16Ll?04E6E2+53zoCzN@-Eq#&1aFXT%C~Bo>AN!%g5U3PpC2
z@o^=s(h#y8UWk3Ki}Tfh`N_H66c(N~QM2i$veV<3GHE_eV_;5CPN<uT%L-C`2#YuA
zo;PLvF%uW@?9`oh(}UZvr}EBen)QOFj+eF=vNQP{&0&o+U&&a{d)^<!MDUBV)dHm1
z%bW+b!)PWpp8CY>VfIPhOi1C`y)JBFQUw``<dWFMK0i{8eZ-VB7*Wiw&f!RqeO#vK
z|FW_<Zi?)ClUrMJz%^#)PC_VRK!$4~J{bUu1hKmQb+WVz4s?6r&~9wA_BGzn*h%`j
z3DFaVWWf|wMMGQS@T9>A2aQfcTA^0Fs(rX%QI#BDx1h1Uyw107hsUitg{8*Y?BX>b
zW`GQf=QnuFHfWPjZ9<T1D5mlnS<suB`Z~O{*Sz1w*nG9stA@aOH=Q+ttlfqH0z1G4
zB3qB3(}(0+ppHjsqakS<FNDw6i8Z~+ARNdn5dfyKA0FSJf<&XUV~FF&-)D?&2-dtC
zSy`raygDZCpndw<>a(U|_DP2~ze$<qqJ%cb3G+g8nE6chOXS5%yP@ZdRvckQixv)*
z#t+a@P>LEoAshrguba557%%n@&gxCq&}D9LpRtvU0{6?Z11jyZIa}Dn+Qdz;D*z-Y
zO4TFIOPJ0!98qMD94?sghWzQVqk(prfl01tBtN!9RLi%zbI9lNgn0t{!vc=fY}*bo
zUW$x0qPZG(dZPhFcLx1N>jU;jye^^UdDVRMIDAW$VXPd@MA|DE_qp>y%e`rv1>Gw_
zM$MDQ?@q^%rp)t^btJHl0BlC$Ow~O&1rxYYOvx|Zn$(g7vW%Pf)&eyLn-L@^Y7QXK
zTJilPSS2K-%i*bWA+>*ny6Svy)>zVH6wNA(A*z@nG#rwGCKO(V?WV_g(W2G^79K~w
ze5ssy@KgPGYVpnvZ5hL&dZT@DwDKiZ6Q?Hom#wrHF*B*Z7CT-w8Okr_Ej8elC09JS
zJKxrarJ!T}ip9+)Ze_<cj{qfahb#)tN!xl|i4n2=*{r6t_i>i1wMj1=5JXA^C5m{A
zEG1SNiqA}Hv;_k6)a_pezt^KJ@;=c0WOeyjBn_?d@K8Ym%0TphuFpV(Brmq+>W7AU
z9g-6`##~x&GziAb&?X!k(wWP4-#OMipTO>&Z<wcn--1k@O<FjLOi|sHkbT<qhK|qs
zm~S`MmcKb=x0etVZ;_u+wAf%9Q)%k@M%C}CCDfz|JMtf;ZhAuvK{$$N8<vL#@FT^Z
zLVAR4Gcf8Wku9Pqd->@cs>l%f)VwDKkrafG7@i_9XxUQ;iD{s(+553PH2omn7ENB;
zCc9_SieCOi>--n(LPMuV&+0fG{#>A)w#Pt|THm@zYO#&svX(@eN#C<Zqn97|lNQ*>
zL!TgUns!)N5;6z?_&iUGrP4=kzq>4Cr)gfbDYoIPVKy<sRQWg@=_&y&;y|oMI;)1R
zbbz-Ju`3@coJM~*k$Y_LKtE#oE_ZSxD#BGl%@dg3B0k@2W=+;N6du~-{@5^NBk;<`
z<R?#F1&Rti>~*Dw#M1Yyz5FnVPkE|p)!$q3bDpv%%T<=jSpQSmp3!0@{(K}^;?POr
zGazT>H`1N2u;Y*7aEfwu@m*a`NSS(ZFTd~`cWBe4^EJ}Br)ChA$YhncDpPAtpTzba
zxbPL&w(;5RjP;LXvX)YVbikX^N<jnGSV4msiGzs+EGZk8uU}ZLcADgrQ!N#+KGQH+
z;|jTP>+X%{P%I7oOwD3W8`Fc<PQ3temxONXAwB-@^zIe0fb}6V?<Qi&Bq1A3hSwpP
zzmrn`Ar~{BvR%xW3FKx777G_71*9TYFU82F!M<NZEMa1tD|jXvv*cf8Iy}e}aNqeJ
z^pQb$d3WqpzPD(*B8E$e09W2<Qzcq#yUi_AgnnXSXDbq?-zgRpTFEUkSlYxLUgI4$
zY-y5<HfTxJk;*W8_EAqxyVIk&w?(b$Q-*n-Yd=V|+)@JMr;4`DcHL8R41-M`ahp@M
z+y_nGr9AU;-+t+YZcz{;63u-Hz(39ar!?rZ&I->_>P!`-kYF>jZ$HlDv^Rb>%|*CN
znCm8lYF8XVV~H3PAZsPw<6D!n%4??Yy;IX0w0KQ_Cu8Ymve;)oug?V^>~qf+7QRqd
zg)9Du@-904Ao9gQ-Q7DLBP4>#La!40A13uTBZ%4wbhh!GnZQ4S?N>sMSK5uum5eQk
z+_!1d9hQRF_*f;jiY4_GuV{!rO$sC{*>)z2-;*k_V5~r%r)8np@^06okgshPN+Bh&
z+oc<c%5D|aL+$t2F1Qt{1buiDhvC&z5Ps!9o_Kq;PQnrqNl+*;>N`VG0Wj<W&;UwJ
zl{ms<zNDi<OAXN_wD-qb(*MoWt;QA}hVVil6D~4J);ZdY8ThTN@r$?GwD~&QbawHk
zKQ6M!AZyN0PE;+BvPr{1pSqJ~WVo*tus^>=nr3$b_ZNTkskFWGEnscU=pj$h+>+Tj
zeE%0j6Cg4K*yo!6fJ%l}J5VPsJu>GB`MHUMDL_oCd(S+X`52)NYlEyWd)dEGzyP>Y
zX$xp%8A%({p@L1Yw6}f*CV6k|gmQg;IH*Ox^R|!Q?EU?8`)AbiG?P#c4I!5F@y-9o
z*p<E74GQ_)`gYd%_5Ww=p8j`a>-p5SCT?Ws<IARpX9Zl>$M<ilg+C8coLu=4{?nOT
zq0686yng-DBA?K&i!m;tLJ*Y6-^5|T;tcrrxl@}u(|V-Ds7577w}NCqVYmY4b4J#2
zlfc9iS%8>Rfp_2DMM5<uVcVa$=cjl3>Y7up{%*y}xfb$M+}SRE{;<<8nmcI;AY*wj
zK$5cBAB1z=2nFVOIyp`ExFp#AMn0QsAm^{fs*3ij`!`*>B;V9^`hAu*?T(VE(8>C*
zlEgLZN%d}&PKtz|tZ-qiAG-(@Finju<q;hYO{u3rg%`{nps1<lr<NtO6sC<lvl`G1
zTnK0d1dY!lK%%d)u0pQbX5#==V^h;(uP*h^aY;HY#4?W2>cxC~3AZ!*I;?WSdoY-Q
za6U1T^+?Vc93Q_ZLmN^u384j3nrQB661C0~xttwLqEhilKuPN+x=I73W~X%(@^d9E
z5F2s|7Fx8X(MBtGiVmK-(Ef>uUn{)DnY7NDA&|UY*d~#>!8+Y{=|B1XfwK)`X4(w#
zuUDSmR(NfuNh`ja>Oi&G3A-0KV8{EaQ1oHIAK6MOG(}k@^&(9?$J>}W<?KO#v=bo^
z;V^)vU;j(+)u#2Y`S!`=?})64HhC;^iB&cQ0|XT)rM-ka1-lt<MsChu1Q*F$(r6Vc
z=zsZMp!r(uzM$V=7O8+JumOaZ=gi|T=TOF6go%k(F*J_9pK)Cz*e|9;4|jb!cy7i?
z`Hss>wgIA%cbH>`pU--U%3bun6IfEU_mTC-YtwL#%n<9B>PG+Vy>ebtGxShyc<=_x
zK{^wRGHqBAvET|bzo{Q=a;hiYdG$Lj@jsU^mZwJI-QU>UeVQ_@L%2vtAfDkfn~EcW
zRO#9z+8N|6pf|aQ(>6(2li1&5t{bG%G?@4aM*YI$vF-p&5NXH<nPTz}6KN;0*Nu1I
zKipC=G_ZoHny|&nFqB5vjS421w=v_FoG91gr4!9(**Wz5ZWobB*kpZt%yp&_@!m>8
zW2Uv@ftMAnnQEvC`q)CMASfbp?KX%zS|re~gQWL4>?2|1Nm^W<W32N4h(qc6J4=dx
zYx3$gwA^R$9OnyssU%>Ymf_FqO=t+C+&YyZJK6V6jn#0-suPqd*bcX3)aIchYpX8k
zffqSMV>19nj3S(Qc>OBZ#v2KWW3>%)6_mtYwqiB?f5==5Cez8?Bq4!WZMw-+JqhoZ
zLh_IK%am4_5FI9b`r!x3DJ5b%-DASRp;ew*LDB~l|8c!bJX@?vcUzD!G5M~xRcMzU
zCh&dh$vjG=obclR(v3o-U;f6YGNI4^X$Zv{ZutVYnpiUCBUA=~^(Vf1(}<EZI3?3E
z0C95>Oe``buSIU%n=%9Wh42WX$Zas+9!3XQq{N*8vRHGC`@ny7Yhx>UG$HVk+kwif
z3vJ?#3ca>fK1w85y3TsG*vkH<>D~)vmzMW@imb$s-U}QmJG>l|$dvwc4-K0W?6!e@
zHoPGQjcZ5{VwYV3<C7R}*9%Y57CAR_1p~XsDj9*-GQlB?4Fu&S<`lwboj+8E^1C<D
zXTOH0&0Rvr)Sf5!r={X3tZ`;N?0xV(cxteb+X??0ZhJyfX0dWZ>(B!Ks3_TB7O#H>
zyJ;o>LMtu@Q4JiCT-Mtex%7YW4W~&)lh@Kb@-ozKT{RF2Gxgh=d3?9?mx)TB*WuMm
zJz}E+E;uNQ|E}Xq=)RM=*~t{XO>{_OcD1a@uKycX(CU?M$@}Uax0TC3&UjFz(9b3>
z-=dQHBB1j*pB~1WU}z-AJ)GwPAnTl#M^MhjcyfY_SFT?S`nr)})1YXpw68Z4*C;M9
zrh*?pBsk<;I9S<(SwfvBnH0@qPhRIqAwT_5vAOk?&ehR0`B+cnh3!LTY2h&UW3ij0
z8Q9~_^IVsgn?}mtmAbhErk*GLCfsnx!f5g9>IuSq09LB=94(`a2nbZ44ZD-0Vx%ov
z^2Z$BYrq;x4=gH<$<eknNW!9aEUO%#$QGg<D<>K#U&Yr+zEBkWl!2Q3bE{x^)OvK!
zF~=pKi`mBnJ_pptJ6#xgW5zP)FJilcYD|;S-!Or17Y%A`ddBm*B|f-oyN7f2&c?0`
z)ZoGAwRe`1U#_nNGoIc2J=%94bMTS=sXWtnK?|~?j~cSynkEz8TK{ytJ<B$Jw|w|f
zL{OD?_ReEf5yzuPqrx9$lbwHAinjm^8#~bQWli$GEegnPXOPy?4LcFDL8?A<c9BGr
zoE`l{{1|%Z2+X7D^?;X_%{cOLYOZ+<t83*a?FjMS<eA$3o`h@BY$In$bnA|nQvz(=
z$E6c_$MkQg%j~A7&+4ftFMW$&+Php$#F6n37=ttK((s{h2Mn=2jG32=p!CevLVER7
zqrP5Vi+1}}@!c2~b79Q9Gk{A{HC<_%F`G2pHd9)EQ775xU8}BnFXw$Om*Fk<madq&
zm^<3E0bi@)&$0t%we{_tv<#^V3cRGNO<7pPM8nVheGT7Nen0v2z~epKysiD=nW_C(
zPjSsc-E?-}8wtsroe!RbFF1{y>$fB8hD-WxxH-L_?(Cr9(kM~;Z~%QSr|9!1&f>~z
z!Bv#rAa>%CGYCl%eg?*FcR{K@3(=$KKTKAlUVp7^SUN~wY_2;qS`{V(fR!B@+e;JU
zm>7fRyi(Poz1mkzQzA(pcy^s?`BQFc^!1m&{^{@ix&AQApqV=@Y;XS6*lfMqgYCdD
z9(Rk*2P){K*T7|Xif5k|LZZ_?F|9>9t^ME!R0}t8?^5e<$kGE|?6j*Tr~VHpq5a`1
zn%3(F=|l-jX_9Ph5S+f4PT5qVI#V#~?1>#a7>(aG8)%>TJ0~~;y@{lBUV8AEo^(TE
zBzrPHyTm=a^4e82AGw4*()&`gR6wm_X7=o>zgMr$*?l}}@AK6nUgx9#Vd~7^p?u#r
ze$Qr@vCNFIZ!=@azLPDDecx5KYV2zzDMa<ycOfK+v4#+$kZLRmA*7O2wvZ%SrBd(j
ze17}>19KcR$Mf8CU*~yVuk-oEH)jYwFZd7G3wIcWcUF3Dy>QsRYjo@D<s0^DH%}$s
zeBLII)_*jhLC(5|>(uG)7hMX<Hcl%876W^%!oox8;^|BNa^!l^tARY57yvsh<XbBK
z+{!a$>_bQmIZNpz?1yW-uA1baL@1NhW)9M4Ip$ac4hhE?mtz>jk6e89lzXf(T>H#p
z9sAzf*kLWjqQAM91h(P?Rg(k*T^8B)`Zu)ncs9(p0_V004e&SlCI9`Gu+Z2mDm*$O
zPM-}TuM3ikEkE(V>Q?1dO|C4*8~aIrICY18VO*4nBSRWo%r<rUEGE7l?qj1^A?V@v
zmDglR)BcN4;+&TMhuqA-`&*eUY>7(cKgJ!x!l3)(JzMDJ%U>N5&yG^F;DL(lCUQ-y
z?JWbgo2B2Z!&3bh@|Tw_On&pJG)ZMSMa4Tsp-&%Bd0d<U)ga;(LR&af9&xeR(K)J$
zuioEmrbrWnEqfD>kcqe+J6UpsAd7QZ?ysC&njiN^YH_v5@ABKNsF_r2)JB{{uV9va
z`V?E#E4UhnV~X9LCG@z4&1>*&1k1%QO2m&jEO)6n>guPzzEYcM7Po$wyv(*#h42#Q
zHWFr|J1L=tKjIt6S)z*DvDy=-=f0K+t@+NV#t2^1QD>EU?-*(*nY~y)hitklcy~dE
zR~408to!n4ZF+&Q(V!Al4y09q03b0RD@+68VA&ml3->TtIp|``jo<)EvGG~}FV(G9
z?Y?uUS=??_c^9l#4H?I7#l&G5>A@u2aIfGMRR43VkWhBhb>tPe&573Rjugqk%S$D2
zbEFl$6YaGvzCvo_5}sHQ{WSK$>FG_$hIrdM9>N(;O%8PuYhrAtEJVs5O69aFrCdDJ
z+FY#Yl8)i;l0tg<nW=7H;#>))=d^P+7E>3R|EXQ&GfD=8!V*}lfOipJobS=r`YED3
z&t#i6#~b;(@tXc{OJ^Ykv;3+`Q6;vhPgXPH)IsqcI|1xEuE!{}XXM^fDK+$AWiA_X
zZJn4UzYP=l*gju;;mSdV10s-woBI$%OJ!TA2TNEP1&t_mt%KL(HLCSAS0lC~&S|*(
zvTyrq{~e~jVrh>F8*Bc3crY=S*PiDUyLRmvEu&t~c-oU!(&81h5{70&hRa9dmqh#9
zExG1(2qS*UU6j+mTaiXe*`DOM4g`ZA06RP{4n|_2eHEz4F)2#ZO^c=jW}iuNOaH1t
z#4Ey=FyBSWM*}4F9<?Uy<%W&yp~JpJ+RpweKF?MmhUOh?*+2Gk(3lz$)_!f=;()E;
zr0b>;|CLj>j8f7LoEa#pIcsd|dgCrhk`UDtVHA^co@dwOhKqwlYp$sm5x46p;Nm0a
zpARM*t?DN11A651+BKyC#XGe9Te9a`gYB0|CF#qO<UIjDZ&7F=h$Jl%H#KbWdS!81
zN;y@s5_8^!&BFR<fM!G-3*~6K2B)oJpRHk`LOBNMR3@@mwc*&SIP<>3Tjzz7oRYhb
zAcB6B2WwRahq@SOW!J1FnN(h0l)SB3@ifRM#c@ZT=q)S}ye1B>WzkQ0E9!Z@LY2!`
zj(B_c8<Bmu=HX3;sTNBC3e7<#O`7CEHb}S9f~rmrL)pBxI}$E;FEm*Cr}?*8f32>y
zakoR(d~5_XO8G-LdDn7CF3h*qYEt^%%Uv@?lEf%=N;1Ac?GP<-EcpCmTC)#EATZ*(
zO4E^1%qPri?{iY$xFcWm9f(kh>OFTtbKCh?wy)C}dsAhTMITeOJOJaz%{$6YGWZH{
zppZDlFC?#3;(vjZ;wO45R7>9QuV4PcmHRrxUST1_CIIW@N@;-glxd$Dr=j&7_+1@%
z)T(x<D{`WrKYayE@m~x<EL4<9U&h7@BO1hM&wfAWatu$8arDy+Qdg?kPO-aCm(X!2
zVb3s8S|a#$_ie4#VnjhmPLNRFmX^9ki~Ou)`S_*ser__ilA*u*97V#SiE=O3|Fv3>
zNvxAg`eD<|$bJ*sxIHY@0YTg2%=L_p+x@(vR*2mXfBijN`{=(kd(HNgb5@xtEm?=o
zL>b*%x_8VNecj?K)rURO$u9Dkhx{>G<cU9=hYhLo*-@6Ry{95I$H@nyiZi04gYv4|
z_Z%W%2T%ghl<p`VK0EA9o{)*?=XdDv->^^47bEKhTFd0fL2?k*>q6Tt`$63|y}psp
z<Oe)sKhqPXA48jRRM=j?a{fu!{n;K?++0biJn$$<j78l3IeP1gzf*u6RPQ6>NqJpW
zJ_CB405f%zCW>q1l$i6Zl!c$JlrhT^P5-{3O<cQ`zflu)Fu%jJ<k!S4omaPfGFvY+
z)wtSqPCE-OUaT84(beiN{=A?B+mlNjmZSsWTtN}W49?L-@P$7-*jh9|KRqv_5OpBk
zTZL?PC8G*McA7R~@beT#!I*Ewdw#<zv{dd2J1i%cA~{zUpC(rB($>#reEV`v&89_-
zC$s%}7Md#}>=HW%+(wZ?K4qlY|I}pVUUuS==XJjemLZ<5^e-tkB0b%tj0>-xN?3pO
z?l$5Q3wk3R0XjGubH6=yOl!<D@65|e;0B^Har_Lm64|`!d?Ax!q?&$_4$oUVEp=y{
z{j8HoM)nLzRWq+KbWVxI;jty*%bfshoX~UB$Z*rB?wfTIAz`HB!}%~xWPjEG<k-+l
zHrzy!GysqxK-(sySX6bmeMotHNT}L%-!Fq!Hs2B)zF8-ztHs<5x^?rs;Pv5}erWsI
z;pm%@9X@@xW?XBc7avGQd326yX7H4*UQc?vdlDg|-?MQAc9vk+&GV@iPHqxiDm|=|
zWN}+Afy7g_Ae$cAyl3ZdC^2xf*GY`Qu0#^W@8DoeCi`B#Y31mR88dk*%3r%L+@mmO
z@oD(T*%}xAFW$XBuPwSkUD!7!;_VC~K|!MH(s^O%Q20TrDb?QVuSa5{XWqdJ<|fh|
z-(7VBU;Jk9)kQ~#Lm_vmH&uO$#vjIPc5+U(AF%JH%nT@eEiCu$-7!j|dzk<jByhWm
z&ybF<Dab!CXtp}tb3l4=xJ*p*yZNi%^lR_M_%}XaCr#UO94i(;`BGc?Z$8U@5$4Vi
zQ%!6b0}GyhDJAAtF-f-l<UTQ0P~jwDL=vYfsnbZfubhC{#GYWTw=kz>Pj7Cuam~X9
z22UN9dmSR!l0U6*TR*7dmiAxw<uiF)9d_6feVz>C)VbU8)kYOk>lf2ssN;M{hjdmC
zzU^InWb>b~@4vqm+IOFXis?DN^msEE{O?hu;~{mjdC#GDNg**Owk6jk>etMB*6{k-
z;)V*#kg(Y88JLP=(bAzCSn>A7UCH28vf(nQP!p%AoM5OBXkWFVe71@&2PJo8q^?<;
zEP?iWP1kHQ5kaUkpHb3V7dCxgjP;#c;V@`;E&g)5jr^;Rp!#Yte$^=YLX74GQL7gl
zPo8QHSQ;%p?LM(>8{s4xTElU5sr`#I-^w%Vr_YMN4xlrlDuV{|U2*$YhxD%xb(h){
z{uwr<K1iH3Xz?yQaq_uG*x{$Wdzxn_ZceBP-YxHwu5hkVg2l38Ucz&)S?ME0dA~JH
zSjEI}4R>Gvy@6^?^a08GHEN<bL(~o8soY8Bs+p8v$RAm)m6Ve<x#O^s9Y>f>NU4-m
z)CxV6w}0$R=KQULZ~h}LJTmzE-e}h%*73a9YoEARRce!#f>*r$W56$F;mk^ngoB0J
zNq^7SC0ssy@7U{#htn;m-W(Q4>L$$JO546-W#Ex5`MzMAzwd3$T#(~G@v1>lXbO+e
z;+tgr44E~qKcX|WNbNuTtMK>m-9VVPmIT8W$pi>PF0|JkE5$X%U45p$?Mi@N>PeB~
zu}5ASz{0D}%!LI`l_cjC6pc5$IgC9#|ABmaw|(2jmsRQwSYyPMCv;YTf#Wax6+pkb
zvWFr3OYc3IQ^fmE2bEw%Zy7X>?ryx&Pk~580#+fe_tQBbdeI%0ZX)&4t=kB#Rc!C~
zQpXgYrdT#6)B0=warB{EV?L8Zhs<tS-#P5X0Abp4z?3{iV}pOs21gX|EQgG5M>*Y`
z<PcOs<T#Yc+)&l6N^aDB+~a?6#`FgY+53Fq^`HLaXr5FkY?Gw-gEDm%5=q`A0lC4`
z5bRq=8^6MS=d-Ww+SS{b{X(nnXWw?fe*c%D&zy5WjO1woH2#2vOm^2_SP!`Rrxx~$
z9Y9+S>kwR`DhUFS49OoKU>Ppob}W*f^?llj{>#eP6{nbe|MO2m;`)pKpRrT^>fs1e
z$$0k$)MSbL+q`SJrhln&+$!)>r<E%_QL}bu2t05gDxwbMC{T8pn<G}QWj22|T$h?4
zIt!R?C3-(;!nAn^nQgl3ZuVb|8QeB+c{%mDe`_D}TiLPF^)nRkyyUD{D07$wKwM_1
z21gXz70P(mA@FRh3x%u*vcbi-rjn;nN(@>PMLl6~07%fM#T2SIjcehglm+59QhEZW
z)GW{XrQ*OiHSe3;;B11bkt+U|C9CtAiU;uN4lM{hu^A1JTlyASDO)Q};;S}YeNw9n
zRf&?Bmiw74V^ultD60$VMvh|@)Qqa6h{5Xz&JgJN%}QN4J@vBR^UL(DLYnK}PPk{6
z9=h|Jy}*G#ol)uGwDW?dlk!^)4Xa;!+}sc%H`dk|a;k?~_EmQ+RcTvPu$p$EEvncJ
z*H}^A{N000smWeEk>8|Pk3+Q4ImV&@`YgYFXX1P~fTz5QNYbtQEy$GohA64f8*B{#
zjFjT#b12#zz~}jFPl+bJEYV(kKqGdQm2)p3hh>H=_un!#Oz)AO3HwZhdd!@ztP8$a
zbLUR*x9#HzAJ1ezs0}$AD-_tYQPIfJwwC!&sn6oa1n<e*^LKa$?d69WPi+l+Yd90L
z-1KcwZ{6AG?t`6IDk0s7kER<kuPrY@DY?7x*OLQ5IL}fgEwqFQ_&gwsHFdA8AzkG9
zh^6nuQ9Xgmz(-;rehqC$0#tR3%N*wla462q2z=#XRyY0-Ez)NllNjK^INO%tdhDKB
zkfo$Xqq5D*h_8norh=c^G)S<2KhgavC*XScuY#K*mmDAc^&9xLX`U-Avivt8RQmqp
zk+65c)ph?GqrmWZ$SL<QRJGzW%n^x&=fT)4z%2M@V?O=Y^Eu-#KfK?KrT!|KHb2l0
za6gW1QB+pp=)601VM?NR!TcL>QP4ZKFdrtSF^7yXfNjg8{t&tCd^vc?YZr(f9=;~u
z@r3Zi?9{b6PoMd50Gnc?r2>T4zLpr&hT~#zNc-w{L9RyIoF0a{DYoOh=XWQ0+!n(W
zO>YlO&x`uxpUcjj+UY@z&A<B+9(vpLr`WmK>AW=`PlYUs*-~cKjLn7IJT2`9KQkn+
zCM3vB%<Y(0#Zm2jGY+RtsNM<V)@L8w!1v|@9xp9<&bhDRtWzv^QtO-$wkmUyS;zw9
z1~OHHol{&c#FMCBP4th!;<P0IZ2RzRF2c_?dP<-2;V!3STHd$8-6v>%`w!s{(yX>V
z*U_ZenZO-;)-(3%*#&?CWdyn0Z#*l^8!vlujS>YPK0ep|+alu&<@0I77do8^>|b^-
zKi4REQY3D}UuR6W4o~4b^4QLbk1Y--o=U0ZY<<2v6A@5UfVYrNc%%C{0o%&ew5Q))
z;q*PxUm?LL;F{SiRZjLmQgy3QRq|0oBsj%cglqY2BsPzfFy>^D3|<T574e8xHUp4%
zPr-UVUAdMz=-gLo5!`xCa*;JVRMG_Cj=?-}u?aASc**b$PU*0zs{=;jEb?4Z4t{rc
zV2FBU@ly2Nf*((g?JRxBP1ll<72A9hO^98`$2af3g){hHM<3hf$a}WX`0RUzKfdl$
zWi7@|!pZYRRU_1vJ0m%2ZM9wXs=)Bn-1i}iHvumuca*5jaBiVVoxY-MJMR(tQxloO
zV$_*96T#0njF2$Cok{zMuInT5yN6+`@)s&d8aXPyW9T6_Qm7&y_2?yyn~k;fbFRC#
zDaDnd!5%ieu^cSbPsiV6^_Fw<49|4@sUt>$M{3!#$vPXW>BDUl$GVjlOJo`~UW$=e
znxOof4;FZW=gOx;*P1E%$4`F3pL`yEL&52$u}a@h)q)AJCzZts%us&3H_5S(9Hq+I
zg-?Kp@R0|}wq(rYiOrmxm*|W%Auf$do8;#MMg_L3uKyjhNBsmUoSq}iKy1FqBuBN}
zkr}lYZtj~}oLZLMbX-RTdnlEjhdD$OQkNiYufZIp#ZqPD5o>435byc$O!$M_qr#oW
z>JNqU_zt#f$p*BKVk;t!W&}Uj{i2w5|9!gTrdzU5vxQMk;m4upO*3wIJf{FcB$n%J
zR~m1wV88j$(ULPOx((O+105z_McGhgdStW<+x*6=U#;i%H$0NkZ_iFC*1mYkK8r_d
zi|tXY!(CSoPu1*Ne44Z#4}0Kf^VE!O^F++KkTO}-kZ+Q9_sv&aZyv}2X0+LK*r3@h
z!<&xG#}1^v@8}$gtbJzO`||=)CrN~Nk2$vB2aVsz49~UVn1B-}J9q__{}uVpU-v+w
z+5~+iORLc(n&A|f)7^P5`>soAr|;#TC&7c*+qz}EKW7+M&5vLHly9ZGN9?xWRW!J}
z6-_ot^E@}ge-aJm_<p>>P8_+F5|&M=xMP&kD`$}2RVXq+G~ItTT9_PiK2xfrG6rxS
zMFEh3r{uI?TZiRLqcT`p4WjeFbyDDs=Rh$pkaPr9rzSi1dE!0))yiu(tepfRu+f+!
zJ~bwT%_z~~qKm0yeSprn!@nLHHT)5t7OvRm8I)DsT0^pzwnSb36v53Qx&+{Y8g_Ki
zblmS5moZDET1sr8SdoQy_SaUonCqeWr7hq73H0ryiAhupubQTd8P4Pn_XK&19;M;O
z6V@awuZveU?_PUROO@1|Kb7Kj{;(v&;qDjzM+%i6hb~WFLit|HfBf#<ud1d?4#n3F
z_tsyb#7ox|GcsWx1h8eQge&buAfx_YA*)ZftFCNPmBV>o%Sfp#cc-hers}3=UFE`K
z`Jc3zorm*HjbXp<Z($y7{>iPJWfzPtpj}R~5=)`e66XM*MWDkPwd#I~ImQ!=N|Y?#
zvTwd27saPc!??yTfZ*fMwB8*R7=kX7?R`9}f`%PBBdFF@^{cT|fxt=G5k9+h5F3zx
z@kf3U2gp0iDtM)I+I7Y`+zw{s?`24U?1P<KHL2w~MQ=$}vZ}KB+%1YlTPg-EO!j-_
zNePP#L>0#_)&Nmi`_PBqgG*up(^Y({E2qlD)3Pu_td&&TwZjKOT97`i5IFhJSm>e0
z{fAKrxYRnmqQZywBp&t_m;Ivk-U+O#%VFPPayi5US!{VuS!#1v_8<1Vw?jZCCY5v%
zh0j8VnXn>0pgDs0>-|AyUEY$}!FsjKi-M|~Nkv{BDPb5Wa2x?C%0;Q(VqO8YY{a?i
zkOCsr?WCCeD0bp(W=2kJ>tWg!RJp`uz64od&4?j8=q+?=qRdTsH`)-P9&4$a`WR!x
zc$%P@v+%<Bvp?<TC;kjW_CL<JA&+*#4}J|csl_slnEv*W#QYegY#m-QrlH{7F(Q#Y
zPv*x!car9vV4AV7bjH!N!2b50iB#ylMr8?q8gf7+uTbwz+1a?mh{qO`zQ@mfE$1gR
z^sfvO8?pP`!*Any;FdkSi`u&kd?t~1H7>=?J_Rxn?1)CrxtpcVWH?%8-^(<7OOQ%e
zD$Kuhl@x<@1!xN%JsbfkbC8=M6E_qt(D$Y;wOja4W+x%&xumKY<zerB3o%p`FL$O-
zLA}pXC#9E^_}NjpTN@yC(EGZ4?txi1ONhl~_$`UD&=KQZ3_-!DUpJ)%@6%lJQKbu}
z$`RO~QLVRr^}c(e!U8NKuRllTCOcy}^*J9@1WXCert(61&iD1sb(qp~o`%IejbEw!
z)7^iM(;DekLiOwwC1$xPVynKVt-e3lvBu>JM=vVnFk;zcf*|;Nq@fjC?Q_Ust#vaK
z`>s-KuV1U~k$qHIN|0u%pp=aRCSmOh1o-dO8)n~;u9Q_dIJ=^x{=v%RuG#bY2}Po#
zRd*S^{_*M!Ol1hhDzM{^r3$TlFi}>VRN8T4TVx;0M7C0^a#Iu82WQeo-f1$^{i>G^
zDXjzTzfzeFedt{lmZA`F>RK{NClwXT!K7Yl^}XZ5j<fVZl_H>>_~7|}?XHPAF|~dB
zd1Bk0O;I;3Z|JipmRjX1=EP`L&srYH`Qmu4c2bJZMP_sOE1$->Fl*ee-svqv^)St%
zcu3d}Vh=%6nF+%m(3>aNpfQ7@W%Rs@!&l3!^4MJJYm2RCe9=K@2vI#96E7$P0C&Qm
z8W%%2j^ZYyt~_*a@yX10o8mXUpU>JhJky~V*?hJJNDUk9ODTOtw&V@;mm&@w7c-T_
zR~Ff}=a8KvB?DB$g0DUjp-rR1$z!0ttK8ePTKoE}`Iy*+hUZojs@@C_<2Tp-jqNX&
zyH=@MH=82Y5^cUD&8|N+*)Vk<xphk4G)6;U;$f@&9hs%e4Tj`98HWab6Bv8z8MT+W
z+|a4R$1;)8$$RJOX@~d(VJL>59+-dxFI#cOlmx!LO4{Hey~)TpBpa2E4t7It^2aFz
zCR*sQtB~T(TV}FSxN#~6pGF#$-*%+lwb*Z%(yVu{tEagIYRUg~_I-#cs-mged9de{
zp+dQ%rk~88NF8mmUO2(7sG)_sB9ICbk+hgxt<GV8IoR?s<?|+o!+ZAJA^!6ZAY@|d
zkflzdB@Mc2VSyBhc4427Voo6oqgy!652U*d5#B#$o?Y?;?FXw*y&f!*M>+KhCRT7-
z7DC8b*Q*E8-GqcJox@<xo3P`amvW*NGXe{YMla2@8@reir(AI5`uGRJntkxC@x3BQ
zFh*?8RW;{aA~g_Lj;6otFEw>JqIOHF_>ci}n`=B=pPfCSNFfVoz-wnfr`{2-yur<m
z9*auxh{4yKIX2b5Ki&v^tub`6b0xRh@5s0k{Ie|!u~wiNr2Hrt;tF}GisWlQtr2_3
z!091TVMcp294{+&Of8|g;KG;<tJKM=RW?-U$l*5ykz=yT92sN{6I<PxOqokp^thmD
zvG}psBV70T>=9klAtZZ?8hd>Y_u@s2@0rI=2P!G$X8R|yRdpUSV&|t(>0Uos7y?7O
z1<H8{3AyM6UNqBL;+=j~N?T0N+r&Yy@<c{bUE|TYKhbkosAVQ$PL*ATg-nZw{3efp
z9I3Rt!p{3;Vz(cg))s#KP+7U(F}Wy6F?Tr^K=fO*_#QKrL3;W~wY`(}KrHp-L&Lgi
zF@=|pU-K>C)?g`&i&BBHxAl@BFFjyz)R806y69Yr&KMe;Nnu|HVgqs7yfB>^s6uu~
z*G&@+*V&9kcMh@5on~#0plK0#5FUm(>mQ?=9jmMH=eTHCtpEQQ<Y(CS|MRD_qC;4S
z<-RGM(+h-Gzblj8JDt4Yhju-0*vt;n>2Wf=>UTtrxkmKV0uE_EuF-+z1YM}~B)fG7
zS3am&2OLFFDQRiR&B670U%)2MjZ|)E6&I+fH)Lrj_12Od7xsz4!jYbxVsa=){m*>7
z&3CN&-Iv3Atl(*w%YZ-H0RUIjOS9uM014S@ScJV#DL$H96qH=3$<83c%uWR9%FZ5j
zuHa5iiE6~m3PGmt91nf8dNt>0I&aPGFm>Ae&Z8x=+wof}MFr#S=(tTZ)3_xn61nNd
z1tKZjWdnP)@B3q%d^p7nOm51@@#0^nIG{ZPv5BcVvm{pPdPWHU3KvQ_$edWT7R~e>
z6}jW{@xE0x^yB2@8@^f8`?MLT;5d9$X+--Hy3;;!)xJP5xf9CpjD2;S@TtlAW*Ym7
ziiifi7aHk9bSj|c#%p!`x!<ItBwjjt5fWrdiC%hjq%4UlJO1~d|MSX%?An}Msm}9l
z8}p+Njo&_<J~wLm@OI7^>8{S%x}}`7<!1x4&t@r|y`*&XYca_9$`bS{$%5{tV#)4$
z&TS5rF^&*pzdWE%DfOb{SiE$@bz#$TxaZQ*tf^RAIo{xA0#hYbhTA8ug_nEV?cUaO
z=UMl}#+f{`6MI*!WUh2xJ7!^VU4*=P=gx1r_ltb4)sDTxb`J4_w%wT4qtB+VTUfko
zpU##Fap!SKY+E+8HGGi6!;k4itGwXdhT^x@BabWgDhkFAwoAP~yT!kJXI2QA_Zgk4
zlu-C-0H>5thG0EUp63Dqx+5kdkQ#5*0@0A3Lgk158W}X7Ncx<GqB$pp{)bkoLojq_
z1SxP8Y8K<N;LXoB&ma2qXkaDq)c>A_*p8%Hcl}z?xIu9iBQ_BjcuGB3W^2_|NoT`(
zv5^4k>ap|GQg#wG-l!#Rj|BT1maO8PA>)S1@5qL_QXYFlKoYK=1Xq#laJclERo@%v
z@3Luw?6BLp>;r&G0=qH*QgDAg<;6g3R6|<(z)GA9is?}_9+b=oQ0_<s(_A-FW#fzF
z0IT(DkGoT)!l1dpndZ;tHvE0P<CESE_Ga<8RS5`4{aAn{GlG(z3ndvHLC@~X?g3bi
z5DWtF966(lXOdF`*-`<D-9b+!<d+OU<mj-za=d&JICxz@C))6VLDFs@)QpADc5%<r
zCI7LuMooj?6V^V9Px)Ias65?zWSrg%zkfH4T`;0RB_ct^C{^VI8|w#7!)9APNgZ5F
zyJ3*}*09j-@HPG?n4F?_tIIGN&&3YQb!`}IW{s=f_0{UKhEz$n+<)9fY1CI;C0a?^
zTRd8yx<0OUd_}5#6;wVN7JI<L4!QAKxu<n|{zQGQdpW7E9)A-3VN?fyydVC(-EMdT
z6`^ZmHZwNgJRjEon6MG}z~t^rJFPAL2lq}V$uDKEnX;QCsSC21I45ei1N9DXztk58
za7#-jmva7c-Avas=zjiLDm_ii83$vtzJO-ey~Ls|M4if9qUZ%KXY@0MK)u+pjW73q
za{A1${~E&9M#Z(B1q@R}#{D=ZA2v_JN~X@K)8pQE9u_hJ{vQkg`%R=Y3Le*6j;l>H
zlj2P`^`408KY@W|oB2%qLi)nNl_Br2)FlH>aN%Jo4IR_70eDi3HtCWZ^siBn__xSG
zplZxIix@hR|ITydJ_F4RPq{KysrS6Iw)HCbSM5waZ~i@u#dowT*@(3H_02s-f0^ze
zrSGc9>u5+g*tQRr&vq-?kE1><10)<USs!J_N^Hpz++6sNP!l_6o5B!T!^<s?LuhkR
z>bL`hMVNNvyC1l@GaXExX9KXG%`9dT9Re0|TqB*H5IUjr3-yX~|3XHsivosqAd_+s
zxdR&@pyN!y1FpGmJ;}yibO6x=m!U5X#W88UyWrwIk<)Y;A`&2tA~qQw(WqtJTh?10
zr%Du~w-Z?`UaevzsUjU=kllO2l3>yS_)z3PaDZc>;{FvCIssGF%pUjqa#c|JWN@+n
z9&q6OlmjF=$b&!;ODSUYyqp$ZsJ#>X=R};zfwQwu01cC4o`=i<(c<W4V1lQzc4nJ4
z*R<|dH6UrLqSC**0MV4g9U!1hHNYu}yAOCxhID}q8V3&XR7Mm>%bT<)I6yEN8*YfT
zAFKdSIyNqV%`(pqLTvss03eElfY8+{K-es<3Gi3fT>xx7&8GpTx3*lgY!YIJ$pxwI
zUX~$gW6%()^RyR0oppB|t6UwgV40{6&Tl65R<*N@ePnKwt!xuuke%1?N5DI;ib?Wv
z1v2SIuE6s;Y7-pg^XmZS1jHujD1%@mbE31#L5=i<QKtZ*np$Ab`!bDSiX#CTOuPce
zdMscYx(mBj^_d1Z;IwqW5g(Rz;c<2#=qqBwX99swTgiYc!L=X28MV@>RrP@gTy>Bl
zBin*MUaExBnP*(O^7gk>spPNMaTTkBt@z@=-zRQVeEJ<sDp0$KAmz%R{Y|(+TE*|z
zlx@S!d8OC`*)O{TqS=)q|5R|EtKUN~^x&MDZeOeU-;aS1`t|*Mo%<J$1(yG|mdP!H
zBv->(txF2>93&uo2)j0Iv?&q7nUqzwj<jtkuhqrD`vU{P<$m2fo3!A-n)=M(N5RU9
zb;WrN&7Wme_bwlx<xnRxDmLGVmT^RD+u<L{?*9l`a~E%(5wm+3$+#TsSQ1ie)Y)DB
z-dgtdufg<Q<^^*Y(=}vuBQ)7sZOrRO`N;^w8=(?Td_PDN@B<_Yhp6GN|3dpyA77s|
zDVyN5HYKIgD7q%-xxex>4YoK3Ac?f70SIt{69OzjBIP(kuu-Q2XEY~-B`Y=VGh6L`
zlO0b$jpBsf2Oo~4^7>eC?Zrc6rXC?M$G)%I5eos-vvT(c%d;C#oa(17zT7&q!Jhb7
zve?sOmJ1*nxK+XwBwn+#VZ1;d3{eEkw=B|dMG227!mn@C{wIs%`jwYgKP6ce5$G_W
z`>ZhEoHJUQde@2%m&D5EfjfNhqo<Q_r2xiGLCgWWv~q0#vyeqXKn8^?X->Z=b&<xI
zs8{_#D|zq(K#W8+{TMM`By)yH?%jf^P>}#okPy@><()T1n2;An=m$aIw}c7Hzar7O
zr5re&2R3I#AfRK5130+gA!lPdBecqb#2W@#i=pzHd)vD%zT?<6m_Dh2f^j`L2RBIg
zeh<K6m6P^>pd`EZHAKfiezP@4ri(aYF7W7kHBBrmu^0G+vG<5xWus3Cu!16(ESa9t
zIdF<Aj%lAQd<9vPFOSN}j-qV~^7Q<Y<HA=|*7|mcRkl45yEZM>Hy94UxX=W?`(Q3T
zH|3ehXKA`FBe~;^2mlg^IDdd(T;QUENbUimQqstGVM)XLJUzP>Oo)g6<zj9EF2HGk
z)bAoG=-pGLLCmDdpi<Av@Dtinc`8VmFPYmptf2JJYBDaZUo?T~0N{ge9F6OjL{&Ip
zS@Tj^bEwESl)Oto5TVav8yYTF1t4(=JPi?uN0r<bFRHlAA%Z)WOjiKlnP8s=V9S{j
zf`B-ZaOjfEr|1xP?J*A2#$)*w*IMK0OKyc9&Tz%GYUTzqhzvkYEH>kT&8g`yIIOwx
z{TV2g8XHCtFFXtod-HFS*<knhDoZRr^dFFg$J%9Bsc1Q~r{cYs34l-`XKGOo;3IC>
zDtDR0mp`=w3&tU1rYf7vqj+x>Gi4Y#@zkS)&SNU(>}@iFeY&Dn&uPY~hH3!G9)A;J
z@W#ynxB0nrJc|v8Ol9KXf<`h7&c5uHcRa|zECOpcH313*a2a#<(1^;WapY;X!FM+n
zJgvP|1_a|-#Y>F)51wu3jVQ!Q2CI$?8^tnBgo)83{g(xDiyUw4_$XbEexR)}P$qV~
z()<GQ#L@PjN8gN8xXGfDB1;wsh97cvjxRP?VAL$6lESZv1vF>lJiC9g%lvsEb!pt>
zC;Zi{q!&mQ4HK40mOxm3=L}c44Ez|M%Ln0C8>NpvJ<wF(C<qTA3aI3lyeWEnZZ-)2
z=Oq4^AJ_SLT+G)avk#l8$BkBxM@g>7UVD>~Xuc*+&1<U~x3B{?Y<H3ymyhzy{`Ua$
z4jQrRd6#$aNlVJk!Z|t!%N7_pLUR66@ZzJg@xjS`uhLyhk=5*PgB(oqg};|J;rx#m
z>fm(G)0Ad8ICEM8NiX4x-}L9|nLf;J{zYW&+aJ%|z|AQyU)0hmk!v{{hSz^kqaq;o
zXkOLd3dtN_x#LaMxqIWC-}ZTgP9n}5WbVD*eJgjsgc2FBwP%D5<2eYl<y#zx5<(V0
zdhmRpBDenBjj4v{^>cC06rPsEkcFZ5g}M&|zQss+i9Pr5OU%cIDkcpAfr@oMR_+{C
zW5EM^Hm;m3G=c(EZMSZm`qQ+7yq#20E{cdaU%YF!>!oVrjsZ^-!?FixQCx*LU8*<Z
zk5;m7XFTMkLlD@ql`f*8t#K}%TWy-7d%;|BB`k{TR7e7j?iROt=yy&DQMGwgC^*85
zy+sBfwUfD5UHAo#*W2u-VP&@&H?k%kL!YA$2^u3Nufi6WjOk(6N3L8$VTFV{bXyJ^
z7Y(+09GpupM#7gA&h0oJVU8~mFJGp5kq9tpR#KmHDa_^D7&mi`2csflx`&4madd!$
zEHoG$SUQt6#-azAT=XeUI>3#asaN25!1fc^f;fDi8_XV09E&&P$0xH93`yRy79w=O
zK`R<yQegdpL^5rHrhy2g^2`Gs8sH^s5UQ2L<1cbHb2`yU^aZOAqikf`Ss9A9J7P-A
zz6duUiC~;)hFUSyaEXLk7QcsDtsog6Z-QcI`HX{vK|(nd-#7Cg1KexQ19OD#)e?WN
ze@8M|3}FkR8HL5_>x4FVz^Xx=FMzSYjcovCLoBx&gtCkKV#TU^1*DB|<v8xaWF6Z%
z(O@kiOuuuE&IY^z$G#7VecZ5n>r*Om{zL`M!2_zn$a6nx=%1+}x*M4(Bz+)~4fy*Y
zv4s=d(B}RpcNi1!#|82;0MX0jNsl&f59<IDgrynXg=gz?tTGJ=co?U{yvqcQ3R`48
zG#my<su7gW5!3)95oPDuab$4-JIt<XD;#>Bcb<gbWJhhYUi@MXMv*Fnb9xb>9w&%z
zprFH*`APZBE0Z#dVWRUB;)W(f-+d(0>N+Upc*;Cuhl~mM@nOYfgxz6OX+k<LM0y}+
zbo<N!BfA%wtJ5{HV_y>sZr5yA)TZ^aO`3&;eEJZw2!Pk1w%2`b8GV>xsNk}T{<60T
zrGJEyLu%|wQ}ZXAwMlXAok>^~oud^(O8C2_FlOdp*phs}rhnsZ7*~J3q)dcEbBJ2h
zo72yfN2aC;|6&5nlGtj3?4}~6K18O(zU>c}u&v@fRXa2AMAp3dl;YsKUbb0y(X7ek
zca0-UiV^RWnq#@%VHM{-Sh}u%yCapw_UhU8<&@uZNGLI2?g}^C$1%+OX>MyZ&&1Y#
z=(H#$F<XAsecMht-a;mRP$j`aJ7LsdG5h}dPK?U6sHD@x#nshMiudQnT**zI)S=h%
zUPU4#Aj;^+)v$}eCt<vf$`uh8O|oZJ_Cgvpk~dpJ!aFP$Fyc&%%KREC)CE4T$w5N0
z8Cz+ZKn5um&^^u<_k7o#%@2gmU7nH)<eqBk*HvNXkBiU<In!1d;%^oAL9Cxam{-BE
zPz$ucX77$P+;l`mo|h3Be^x=PXU5BFRaUJi__O{unw%kIhmy1cyV<k1`vXu_263#?
z`BL|J1rO3T4^9}#bU@Nr^vBfFau+_75f^>hmAoJxKgrGf4ELfCRJKvf6V*L4GNc*|
zn-gvx$E^UNgV_|6YeT%YCEz&zo<QI7e4*1jJf9Rq&droL8n9+2d*F$J!ps<L;q*aH
z#vOjyeX>!TaCw6b*`NRHb#rA3jKS|<c!F;T$4MH#MxXKhX2T;j4H(>Hu*~hdxl`kT
z{9>?EaR#v>&7E5c@F!dtuWV%QVCRXr&(adGU9r!JJQE}?#yH*UfjeuziJr|?S+6V!
ziNjBl<tUuAfp9MZUi}RXZpPK_X~p2yFy2=rkBN>yzC_YMm`u_cQqDdzqLQ_^c}ze3
z0(yK93w1iP3vW{mx%%tLSw|(FmdsBCO0!;y=52+ANgn7Qcgtf~s+mCwL!!6sEJq+8
z*^bC^I4tA2ncoON+K-j~>8Te^!|+ju+2``m%QIq>i~MoEo^kBBO<_1wDc<UW4cUU|
z<pe-5TfqbW^;5=Pk3~M)^1_Z*xTFG5djzQ~Z?V+<W!oTbSHj4jcV0UebJA^7upFKx
z>=n#^w#gAWoaaysGl`Wji5TMB6_~%sG%1jHmd?DNjGzILh7y2PVKOQ(*;Ozf9*OKQ
zUQUJ#O^ZH1E#@6V8oA8IIKvNak>fdm&#LItdO)1AgwCF4qk1QBP|#`s=W-~{^nx{w
zOd95F)-XYEk8{7#a=u7qOEcyk9;=CrQi}~bmT@3-Is1BY&}hQp1Nxy0Hj@Ed+B@FB
ze^R0%{vDG|5Tz?h|H?zc30Jwo+1k>i>p$kD$y|>OUeTwV^Y*g*z%h~xH$-w)5RfL5
z5=_p_f$c9VllklwYFYkQ!vB*|qo2=;pEhrabBZ<+#KoL$RdD7%c;F)7Dt@~|0WfXe
zfb5w{v}~=tI(E91c=_GNz`Lx0FNz;k1Whh{J{g7X(^Ope5NMH<<sRswwJrnG%KEGI
zGWhcP@OBMdemW_&^<?tcQAkPnqr#B!7$xo*Y2jMY!cOU;a}_mPFYUCU?#>-O?MHfS
zH}o3s>aDKmtLKa;dit05DzX+E47C)zynRW+;uZcXUtY+c(w<ZQ1}V!)ne~%>D4O&C
z4B}=H_!@XQ*=~-V<L(KGyKAK2FR`PUtN8)Ssc`c38_}F2*?SY${2Q+NwuPbhUP3^y
zC+9v3l#45NTPU-k&TxSIOn6qJ@#_1goH*E$xN$9CoU2WFhlt5)q*AUy%FB>X_nUn+
z$xITQZ&yCAKnq^lK43&Fw{Wzq;0_8#CLx99OC5W(2=lH0eBhQV6`%j13(auJ8yJ}s
zHCXqH#Z4<S>oI0dxN>f;IhMp4>2dC&yy1Yl!yeC)42D*Fghh3w<v*GC7JvXzmA8aG
zkD#4q8_C8#yX?z+?~JWQ#}f_W0M9(1$ef5Uy$u^Qf*>g<F`BWU8({jMy4)e&wR570
z!VzIy%nz=Tr;omfw}OOW2}~~9lx<u6caobE3Fg|r87W!IGxvu_sZhCx4R^Ja8%*Q5
zcj5h$Jo8AmEXGP;Eem<5eRf+PhQocL%Ez~HgSjZlPiG7-Z!|4$F!*7T<>-q<7y1Q?
zT*V>Q`<26iNH6s04~wh%Y#d=@C1B)^={h=s({FI$Jw_Gv>bD`IPc9I(y{)lyM5MWj
zb<?^)ZlOS2gm0J4FvA`7?S!CLlJI;ee@h`JebBaiQcTt0hiU{TG!jlf!KM{16j#VJ
zv?7L-V&{6Q-^HRRf*Qxn)QR0sp7%C~MM_!PMbvNVN%J*$`}+k-QBMgAl^5mYpsS1s
z2Vr)^a{baC$hTaMuyOXFjE!V`g-~3PZ@Dl&Y@6pMYX{~isw&9eLetH@kb7>o?x)f5
zbz>3cG*^}t8+7{nm;0u17+j7tCm3=Dr`gsDO%PGysy8FbDZe5eTo~!>%-HSGZzjC~
zkFfjy;t5#B<Kc=-*%iOcsN{e*ao2ucKd?UZ%)ju${`qI?^U-GI*UYZS94s~eSwR-4
z(cH=Y__OJKuY&~KUwmF7xMzugBEsTUrRm(7w!_gI@Kw)ZYVXawH!~q{io`W<aVEFQ
z9D$qJDKfWUF7QJZbAQqTcA#<icqgNK^>xyUMX<K;r)vlm+vgdLDdV?z5?!hJ2f_h|
zAACn#B1$u%=#X0D;5)^4i5Ze+-fX8Rj_S(~lYGrJ0!~D?c0%Xo%}y=wOG^K4{P|e+
zUFz{usqEHT8!t3BxKdMA=dWE!6_~!V${i3@Hd<5>&s(_gvSd-cB2n<3?zMzx9S>xj
z+cTxFXU6^>m32)XGnMUG@-qCvrxYv(!{pJWMGHz{Tpx>ipXP->)GTrzq3C{Du#kn#
z!kT9}1H|Fb7lh$&1V><I>N)Le-VvjZM&3;Oi(Vb{zkaRt>ENkLIS!o%C`6ICF3lbX
zKCCNTr4}G@hU82^e<LjE{t*(L&AMMQfji=knNNiUFiw^r|4<3ATDd-QN$FK&1Rzyp
z$-fUC-PIE8$^1q>{F%q)J7k8;U=y6rE`2E7xfXPjGONpq2y1NkF0ovIWE2eu!%AxB
zBLq+pxH!xcME?z`dUdvA!d~NIh*}(J--8s64o&fpZQzYl>)iNO{%{8#eD*2I&KO$b
z-d{yCV*)6IgE{6rlFc%tVvc9x@_vfPBQ}gJUq11paxq!57zJ)z#Le8YTRg2nTjj3n
zfI)mGX&c;|#upqlgy(Ay&fUlU;Jypv#F@xcc%R0T1$m^niGIf2<dSPVX-g#S8{}o@
z15rAV0pe2!@3i)*^4I^-hdIYJ;B;xj#0Z%K;pYmCZWwc)g_nvMPAd2034ST!_0r+B
z8mJ(RaB<d0CWN*4DG<ow9wdE4FAxF5;c5rO(9)NLZV4tbUPp@+^Auqs%BLm)KFx>$
zYF!o4(T8X}$L@>(p#mV5{VWWo&qB|OzX)J}ULOUB7|ch5?d`Kv?|{CNg7u$3YO;=r
z__hBR5YtUn;G_O+g?h&UVt#iWuqDKi&>5_?K=0<abEu1*{(uLaK!bmdtpMC9eI)er
z`CZ_lcx=bKVfL|?3J_AbVA7*s2@I`hKt_-g(3P(<VYqzb3rg3kI-)$ffdJ?G6Wo7|
zBI$tDCl0|4smXxeEXZK<kZJ&y)s6rN5`>Fw?B9>A&;i4d3WQGy-r-6~GNbamCIwQg
zcA58XWHI4KWNraYHVPA_QF<6~nDmST5;qsn6@5X1Y1_6MGD7+8_gW+ZiIv|~03s<6
zu9P<iSCvC@qP$qhq5B}0p-M9VWt&|hT>UK?_C!-vmt*{<j#LkBGyMNby>p3t0s}2m
zk>X}yE{DbYw+JcZK^2$u^1k|5rOS{^AR47T{`Nnt>B=e_tRMY55muIyJGgSo_y)_y
zNXigLTjOEBFV$^kUgMG&-($Nxw1xbJm9Y~2L>&x=EyInTb!cVBS82ah%OG#>Us93F
z*MN7-!T#uut%>B42hZi3N)I=8-?JIa%RE9gY1Q|VDSIV)3}uwdndEG{mT1DLr4>;L
z{}WhQ5!1K3QBRiMiddEnnbcv9sD<j6JLgSSJ40&S`pNZ2ZLOWB&X*|T<#1JnVI@+T
zxopoBOXNVGLuf(7afeXD$evczd#>jP3+5v&2&f6{iOmM<EA|3eA9+tkCVi)P3A}ni
zlnD3&N5kqkgiZ39|7)aS!kk=j>&;wRWzRm1Ce!(sVezJs-E|4ZQJf*yUkRGoM!B?0
zW?m}4J<m7;p;cB*0f^2!4FRuT=EExOw+{8S$M})o=RAEV^^w8Z8VSZHW(8TwnvMBY
z2a905#r2l|(5<6&KT#jQ=BpD2JKGYvPPf?ZJUpYi`!}*4S8y_%_4wuo+}Pl#30kd)
zs-Qx^fe(FMs)Z$WG4)$1>{)|2BrN@s91PI)m129>Ij`CGZyzaFoZC4s5z)J++Hsmv
z-4k{kz^`5a2>QGAw3X_#6YdP}=29SL?luWno*E}$Bd4AQaAtpNtO@{`6kRW71hHFr
zE7M1a6>t<lezj2GR($HC>n}9m0MM{6as!xvXR3n$+q-uZfJC17cu~oI2yl35h&>K-
z<-C;xgex6(u&nK4bH)!>QY?=!zFBjC<;~6`MFA+`HDcW!C=lzOOMp)fI${i2Giey+
za^hBQ5=8ooa%hTp=CG=>(C;cVF`_H6h=;SWyJv<0LcA}QyL?K$JNI?79kT}@Tthf}
zA%m=1PFOCLlt>B&Fop)22K+h8D*$S)JCaP5Lec@YCvM|N?GXo_9e59}rCkud|190h
zLY@hhL|>>Np-MGAc(ZL<If;ZjyI_SZx<(;$k`r+NKiUCA7jIQ$kiA*9T9*KV{r1(R
z88+tPFH8^p006N(<vFt=er@5|XZu;JD7xszTGTDW3Qbl&O@2~QR53(Bo*c9*27oXA
zS#K8;ObI7g^kB_pjL`QAK+Wp|=CnXA`|ww)34-_29nBPI;FIWG6*%N#f;ZCE0Wi9v
zK<<*hW#=J&0C?lWEH>gxa-^%}Xqhyil8k?3e=3&O!MTzX${{FK#-8D71|8-cU)V@2
zB>|97Ok{^PrR^AI9O!d}>Krzdp*~mf=6n(Es#O_v+VNMeUJA?JxI2-?5=?mLu7}Wb
zJuk%!>(DEHuy~Cy1e7Sw-7~GM^kV-ijHVCR2B$0ILv4jfgF;4gfF&F8S+y2u)}|{o
zj@ruUF3;w@`K|58Wy$qm(HG>wO@mHtT_+<?(jAGHFKEseH4+bd7kA-;pPxT+%*2lA
zar2^C%kqfZL+x}0?8VbZy_~C&^>-$NsbL(POXQ#42Ct&_ffPIP>UoB8naVN`+$uNA
zJs$4tbj{wv%kPapBQ+l*ERofArHfA?6;EzAHg0T`RY^iX%s5HOJrQC2_T4cRxVn}t
zC&i>9pV+0`>az|j_j_DRph&y??BV}Nbge()E<<i~`X#05>^qD(b#!fMB6$UAU=9H-
zxHg05<-QAVh`d_}S&H$@J&`NtpW$bXI=S0!fk#Kr_5ED_B>la&W8G-u4ByiWU7x;r
zy0?Bm$y$494)w@fZ@lE?I`hJ(e)-AP9Yv#com3YsnWsg4!aga)=S4*RQ*K%3T{;bh
zn|`>ahv%sbujSsD)(Wo22uGMCH2lZCg)uQ-tx{*uVgzw)g9Uv35`&TAkbG$#L2mri
zPtJ>D1a9PuaWS5LUx2EU{&ZYuAi`%4R$g<?>y1u(^Dh|Zi9dSPj*Cvub=}14^%T9t
z!t>>fKg^I5C0r(lU2Q8jFY)L881oFnq)Cq*s+mlFF!A`BF|QkzTy;71O}w*7`{vu5
z?!Qi=8M{<S9~~!l8Hhixj*&*P-P#1|$ziO}_WogJ*bi~zzi?E*^zE8)$<}NYl%kwN
z^7=XWBGbtaBu~X$^{&$QZQ3-ZVGYQ<a0ECz2j=iEcwn>5v_Kwx{=ZiWkc!rY*tb+j
z22~2LaOuePgONnhtZsPybO#hz(tLg<2Y$+&v7@M~41q+2zI5Ch;oHT>v%^R9rS~2R
zsH!0oX@C#TuyL)*P2tb|)PsrIj|kM9DAZ6`WJX_>plaBp#i^J>%>)1oXe473s+<}c
zSkaKEnu%roVTZq`8fky}jGa&y=<hc0zQJwo={x}7;pVHCuJ#u6=<>J81bsemm?oyO
zh_pQVP&>9bMdCGXV$QDmc8r49vIBIISAnJ%`Hffo1aF;_cu7n)59LgM$?j-TNcCad
z#Ks{j+*h&04~NU0k_It20r;GqarUvjXtw^x=yP)znDMTIMUbPhr%uoDISNwy-Bo>T
z)y&nnZ+Fmdt#Z%tM3uth-7g%@7ysOb0LMRC>c*5m-H0tO{>HQvo%e&&?B9n^DLJG(
zeZO2Szi8E3O&P!FU$Xf&XZ%FcoL5apFk1)9i&D9x>tA7sQo=Cvqh^HkPTc8-iCJ}P
zl)n|Sbm4QmLwL^2iMx1s;qiS^?fnqt`=RW(xI8}mnL88CxCD##c^PKU8B)~SwrjSu
zturiuRvzZX&EfGzShbQH=CSU)dZj<4HjS5iqx{^o<c=!q1VKV&DUeN47yyjOnmxGu
z4H-p_GuIZ|xpE_KdhcJ?iQ#O%lJ~p~lM_+a%8ziB)9W}0XWoX&C)$FjeKBo#`1iK0
z+mI|qy&R98c|yFQ({|&gawT24{?FZ_TFo9_<>3Xqp-6!no)`kaO&FZ@_xuu}LbGdj
zwBp&)ZH|%Sx9;h+#9a$=>;p4}EtV}JJcLZF>CnN-;ciuVxt5IMf<jWlZQd>U-iDU4
zW<($Py2}ccq=Q2j%)FP2<i%A-f^yZ#a`?2Ox{RWstfT}fpe~L6a)7pmX3hr4C&-!W
zTsIFA(ipL6osD1}WCdw>^^h4phI7*NJ;a_ZnI6GqVw6sJaw9}1m$JiO$**iRe=rrG
zyOx2B9FE3@#HnEfxjERM<1W6@eET4mgCz2YWJD!dsuDH&BZqWHiXEr#E~yD5;;zXJ
zVxmBE6vGlWb<6Nn<q%utTK~1KCmOoTH%nsVFD^Hz=4&LL5DD(La;=m39|+a~g*yU(
zIzmN*6&V!UV#m1P`5Y(P+}eh2Sn-Cq@pk;hgH4PGq6Z(K+4T??dQRBG^G<H0Wgaub
zYDI%l+pZ0J5RDg;#qoy{xo$@G(B2Z({8Y4EQSp&tb*1S-XqLJw?<y7VAep*}c{u)m
z6rE`}lx+is@7c!~jIoV<Fc?dWeMuVoE~JvSu_U5GDkRO=cVp~Qja`wDM5)G-EeWZl
zZLA^nO6sN5`#s+;hd&Mu9>;TE_j#SyWv!3{1^7~Dxl(zpbHaM#f*$_$jI!J{1=uW#
z>4?mo=u#O<F`U5=I;MxsH1p%^9GMpdlu*Qxsiy(m-Qu0>j*$%MiQCmOdE)ZBZ|>tD
z&)OYckh@uvA#HpY`Qoa~@5jy$@0?Y<!mJpUChOpR3coq!`*lNSo|~nZTU#gx!}rcA
z$0XkfO9i^wzL02%5Ni?t(`_KzI38kHB_VC*!i|%Z>PV8r{=Z`eQ^hvW#bHK>5^<R>
zEVp4<)uG$Tt)l&@RYJ@CE!&vaZufb4Pf5=^RkN?)OJW~AeR|AIzk{Pqbhsr(l>2fo
z>z9O><Iq9boo<Q~#12}U(pbv_$t;zp^De2YswEWb#t_+Ces$=Mvl_uBNr7o-(eEJ7
zlDIM_Q#K<i^GX|e|F)k<&*9Ja@(!eW5ZW2|Bm+1w-Gs)tuTn1qib?*4hSB{eL^*4Y
z^G2W7%<U-W@ns*|tSX|ovqPK9xEs;-O*dw8j~&v}P6heg+xt1L|4A5y&A$93<0WR>
zSG@cXZ58+AUG>=3D{R=Y77Iqd+=-UF276oK;+CoB;z2JHbwss;8>N}u?-?X#%*!FM
z{_(W_ITicOeJG}FzIFuLDWsM2xYSmDAfjf-Ygjrt>`GS5m2<Lre)|ZMdZGZPZ5o&|
zR)i?^$E=+=;E0nv=<KMaL)1<-EQvp$KbyZpF8}@lXGj-Eih+6lWH#F;vMUncHJCMo
zuAhe1x3dOW$*l#P3Ay|sw@{xOl0@(S8ms2|Ds~ukYYNp*53zX-Gw7_RH4gMLq95K0
zdwJBeX3sOx?);+z5vC8|4>;1=MxE@5FoUP$3T{VR*L}YcrCrcdW;EOBsKqIzuc3!;
zSDQv+z1&o|+>Dh4w!d_~<|2Kvba2Z&3%^7NYjsgl6?Ancs*LtP6Pm6Y`-Bb+=#Qn}
zJd^rTXz$>3d8=O?{+F)xzB_(rAHUHycRJ7*)qY^PJtMiL$*(X!HmyV@gZ(X@SK%nS
zD_?A0_5n(^=<J)@mkPBS>ly-|h(9~@Hn96j3`*+k%hxOW2J}wvu~XC8Y<=(9LD<XF
zjoGDD?x*!$PETAQb4UKI%wuj3F1$;-{E#!izH9wTreAhj=1R}MMbn`kUITY;z1=4r
zEq{FPKm>;nCx^q<5c?iaAH3YXtC}g%NA&(Bf4uf?3s!QQU@Ld_c9o2N4Y^zO>X9#3
z#@q&Y^D<Zd^ohom+Kx}KPs2Wb`7(z4Gd1MX{S(bkQu4A>xpIqA7Ufd3s(PpN`0a!7
zQ=74SI}WG#79|ck<x?XR7)V<Wn2zI|cb2+SgYIo5kt=?pz;F_mzDvq9=CSlKI6M#V
ztPwsHET6GD8#}RVJ)&w{^WC$e5>$iOsr}u!KF(if&8^uR`<?x_b*1-PmFD*?f!&49
z&02`bK5xk#wH#G<&XGMY1zbLi`1Ypg@}`IOdDrgq&nXryvNt^bGEuI@iPefaG%s`}
z;G4w0AO3nTG)k{1l!%9kYUmoX(WDYPQuDFoX*HDNfgCD)KMVEp0eOE-XkJL+tC8ru
z{ocCCXT<M@JLKWbDEB%1h@9y2Nj@IN;a*n<1yebejaO?XV;6h1O+>q#)oW@B)_Xq;
zo(E-q%xJT0G7_PDFm(;fjxz~*sB67T)#n<Hk?7*{=uBzip5w~Tq|JgYq=n?ZA7wXl
z4lk4#hhG#WUhk}VkZ!YO-n*q?gmk%K^yc7c=~KN&-fG&`thnoSGV~WctdLc2cH>Bk
zsh(AUv4-!p?W=AxTK!0yeN-zOs;GZ^n5|v+7M>bvb5y`ms_O;j3lor^J~PvvEcs8+
zv|tyD(czzWPh*97pVD@aOdNbIvN6SuuW+g<l-NT1`7XM%#CMSkh}{_G;i<8flh1w1
z(pBXv0^ewyEUv*OyGVavj<J9bdc@jNte3=lJC}c{;`;bsX+jF~LvxqXb6>N|1zg@;
z@rPGp({5}gySi=xAOzj3T0G4z?sy%dk)WZp{qEr8vpcx=PA2ccW2q#UvZDzVejU5K
z_SJ5=Xl?wmY7iO@;b~t&i9FI}U(xB>+L@`r%ygaCjj6SXx+*7XqHX%r_>}DO!)Fqa
z>j!R~Y!9v<|K^`|$|-{|f`4Yg8LU5M>wf6L2~4*!GpL2_WT~;Wh>0xL6n!qgc7Hjn
zrYPi9xzK>a-oqrL+bsu$MyLNPzwy-Gx%&aNz%BCvr(2ac7?(O&gK_`!l{GOHrvtb-
zwV`!o+<cMmN2}21o_$UBHjc2mjA3Le^UI_7jVea-b28;~ge?K$#GGF+NIlz%)Vi6v
zwkNscceb8}*pBPPo~ng4aU3c<PsKw2*1}ALMFIN^)8hsVnB#qr2-lsMHFVbS8h$AC
zNWbn>HjSw?rgdFdN4nVmnGN61E3&j>_T7K}%U5pKe)8syd$$|d8M)56O3Vcwc!`v2
z+bi(oj;Ltt*z&_=?ee2nP8|r8-2J^*ao@ns)*@@YCxsvHE?v3*=;dF#{wDkWho=J5
zE(cz7o`2+8^1*$7T;y?~GX3n+^o6kfBCf~xxF0`y`PEik>0Z3*L4nBNIZWA^)Tu*M
z#s$P?8k2ddlZ*J!c6fcf_LZV=?-@;_wUJZ?D!U#D*H{73mWiZ_vSpJUf$z1^>wT%{
zwS<uqBEhpSZhPB*mq?~bcz=4iLpHy@0)q|5dU&I>ccgTL?b=jet;q^D>k7P^s#VBt
z*dRqd0S9($8pQCLqh7pmgKOQAa_F-=J(%nowIhM5Tn%ZS`D3G!!@SXiCPuxWll@2f
zA5`#uflVn`<XtF-&uve})R!^QWe*6vHNiS&<EZi3eOY4+Rgch=ifauUJPwHh_hqn{
zBrTseS;OMV1c-x^xQ>ftYhXFU@mbgijF1-b+XRz`-!<0qzP7*>0ElocuRI`FFU?Am
z!hv{SGU`6>lrKB<Hx0l_WMbDK_iAfp2SBlX<e#@XvuVGZm*8bUyhe-Idz<4O%zhD2
zyXl+Ue6-3V$d810zz|_B--eP(Xbm%M8HE>NXLxy`S^<|Z$?VBTG4`pSeUr+LDZ&z2
znh6O$s^!4~OztCy>%9?>U;F#If_)vkZ?@{|^wPo8kr$gyS@~UEZ$1o7p1yr?H1|?=
zdBxb`w9%v2L)T}mikD=aUTx;#-p3S(+O`s5XFg_c4HcAHz?7d?k<Z2mvZIq|rpL1Q
z0PaY+4|}?&%6mNeC~9i-FBZUfvJcv~aRjEu<S-K<n)U?1X6Q(Lc`U%8tofMVe^vot
z9{c9-`xgxcV^ZiE;$AN5*-rw-+1eTA;PUS4TukQBEQlJVi)ioxjg+1YXHn<Vmo{RV
zB*SLV#n1z28yFi%HiAeqIWB7bM?5%Zew6`8SS-LIt@!YocCrMPDR<fgl2Z3438&gL
zgE&iaiEF?*br8KLwa-7orz^1HKZE}H$b%NO2X>@nEVQ(Ao)0xC%^M0BPn#07D!()s
zkjFPzcLX73Kp2rg0uq77bWr?#3H-_4cWio-2isDc?BAqaRmh>i*^g`Z4%>G>9|vZM
z{AQ3&f|z@3FCFpu1_^%f<3lpK%<e><B~Nc@BTVFTzTYFCFz-;)zRKIbJcj7nt=^R_
zx;Gims&aoBUs80Kf}lq~D$vQSIy)Q(!Exs9dC#_f#h85ho|9GcO#1Iutw5}i04+CU
zd+FYnU&j!48dv=PQid|T2CC9ad%}zKeBc~f{cn8Mkf?1wiRqQ$qB=BgLtqc(2BQ>Q
zvMlbXKOKs++3`4EyW_usER~|4!y!>uG~Kk#{uP6AE)otPL&Wz2)Yka`Cd%oktp(X2
zZr~}gtygY1YOge9aO5!WnO9i(0psyt2-0y&581p_%TV^6&_`E10`gVqWB~d_>jBIu
z8rf}6lR5m?+)dkqqe?q`Z;wH0Rr4hTw^7Z`hE6J%R}&Wa3kORJF(_t1qzgtbs_U%j
zomXGF@;mbPb)I=wv@IA4FmHZ-RH$oN|2esjNknRM^48;yd@5bK;C`nWT)Z|^$~_@_
zb~!FU^Hwl0$x$%{XcqS@{G;;@FV}ucT!UDqLsp9J<tNnn7|$mHoFJbf)e>-CK9f|h
zB@jwi=qRt#n*eZn)*6b%IQ8P(@e>E90E~IOGQ2!!353ZEkbqC&!XS`KRk~IyzruT;
z0c2_`nt+U2Jnn*geTO44kq(lS(GAuUKpx{X{qy?IG&1I9GK}ukS62dgUMp`_3L*F~
z5AQP>W=jBPU6Shq^T{g@lHtOV7}6J<Ha{Y|g|ZY<EAC{lucRzPxw)MWARsNM31kcz
zxQjJ|<{dp8Tb4>Hi_3)8(kqjF5Jyu*d+uLZT<>=#22KG*OgA4G=OAk1!*6o6D#wRS
zD%}|+XaYD;gqohx#5t|e1Oal()Po78%mRdL7_T_j_^_hm(l3_dHxruo<Cs8@buP;S
z6C(xVn|&1WY%*ek8u3atK+@9)P!256jQ7uS7}*pD2LzQ20i0%^oTu*yf;O|1>)M^i
z>0636{2Dwg;qepzI3-)?y>d$kf(%uw`*Kw!qIXYK1Ra~>&*h5p-6q~p)RW$^Mft{P
zChz|_AOgU?L4R`J6&&RA?L}AK@ifPb`TGI@SF+9VraI6*o~fj?x+}wRMSjQT#eu1|
zMmmtp&Y`1~y)4WzJ53nqO(b9wt?Aa|FggWA;7BRp95)sB%*Tr8a}K(gzL!m&gfK4!
zfZ)XlH#HIp>E@VW?t)y?HLtWWAdMggK;2A<gr$dSOz=^5j+v09#{sDNHc38kN<SEe
zVQ$K+uv<_K-cyAcW+s_Sj#w5@JE$yRs1UjqpF2s}mri~zJ?b5d|8dmESN0B)3FHt(
z)3L4wM?z<-y)&T~7gj?hirXOpfH$t&SvhC?-Fs%b=FWi_6H#RiLNb9X#slsGGbG>;
zsY6DB1OS6LMkWwVpxN2ANbI<E*0xqyb3wm)U+!MbQ*M)$*xIN5<zz3Ycc4{epsszX
z!EnlfN&0Czx3*`bWmwq`$&#y&LSFBqoMBS`qp1HO$^O(n0W>uO=F^IbqBDlykliup
zcb2qIf#}zcXi92Rks@rf-ih|hF5L%Sey+?hb^NP}(+-U?B+M_%>x$kh>htS|pwh{k
z(H5#P6q-_!)yqgWF1LsR-DW%LwBmOKJ86;k#YUz3(kXoWc>Yf1gjrP`hkfv3mHEU|
z6Eb#bL+$TtIPN8QqX+lqncW=kvV&g*p0}iXEKnj{BOsuJawA(y`3b2hcWcz8I%7qz
z@N{|TeWLr&%r2AecwG|xhC~l-jk)imQPBfQ(-iB6Mb3T^#WJ(CPE9M<U!D$pVU%0*
zdZb%>-D$s@$0xP9ReQG8w0iPIkt5ZVc)4(NlfuH!!zZ@m75nx_HW}#|2E0S<AYi2{
z$k0$zSO#oh%JLc?o>|Qvuk3vvmbdcG4NX~Sl8bPcT$c8KgHV>H=D1D_W1Pp|RcGd%
z6PoKBs2sIGftn?~e#d8#z1Z8yYmYM(4}~b`#|8>|LMjg|-+QLfI3;ie{<@~cSud)j
zWv<*I)gnDhWiT_3Giv8V@uX3mW+lUo(Ss%EaTg~&nf7?sUi$Ia?l>B>2_Vu#-T@Gk
z7Yp)`WTYcN)BEoxJ#)3Pr|~qbf61osCS@EQ1t>aX-;r+RZFq-ob$60pvVSGonLIBS
z&;_u0!m7SLuS!_uM*h5N32S`O&eS$`t-j^+<ltKpWDuBDr!SpI^N0KkVk4qG#_V;)
z>Wzv|i38Qp<;N8CXxpaqfnk3ELCC6&X2Y$Y8{!a2bK=l2Dw=$JXM3j7JBySz=LK2f
zd~2&U%aepO!VQ{>fE6KUH^)HAp<`2}o4+&l^?ocKL<lQ=Rn1<Jl_N+D0!o`LHbNL+
zd_HR^8#)MBJAX{#mx?n$Xf~^=t1moWWnJ7O{hRH12fbDirPbY)6h})E7RdK@67k*T
z|Jf6I<Gc^6g@j*5t322u`n$SZ3D@a^cuqW}p!BMaMZ))Qs+o85P<b(%TER;{OlPw6
z(Cdud2WA~idN-EmR3~=(RZZ)tn4VZWP@;}YBQ*)$t+=z#UA(8EM^GWPQs~-YWu)3q
zc(pAMvHJRM?sGhrC9w=WwZ9z4wF2x(><Ut_uHDij0DtF8V69uan@Yf+j_p8=m9Mf)
z)d0dGXnn@~_i}{viQdULadxZZZz>JC+W|N|dyPfxd!1G$Fz9)$fL*&S(bET|pU17N
z*65n@gW5*jwI%7BWEPshTUSO$rS`_{QeBd^VlT~5iAv@Gh9vOj3z-%)&28Vg6v}a;
z=}fguR@+~zC;)Ea7#-FOs*aM6MAJ0{+4ur4M*!z7%W939(I-OobQm|1I}L$l(C=Bm
zu0%y8M;ey(l>S$wf&y))ARTZJ+9JpYT-`x$+j^%;>s|iVc<@Mw38LVeczO>`l}};m
z6aS$(E05xur%nL13q5uSHH(4Jjy@Gat80eC+S*|A<{(`2vF7QzL@NCz(=u@#So;DS
zCdCE`IiD3nKPafL%W)?Nl|;#zf*r68l~A^QkBOja8EtsOK%mKM&NT%Y)obl13PIwt
zqp)s43||fp3e*5`m#dC`z|<kzSGy7&Z+|24Y?SMIg?XN7UcCmS1yhxiCkH-LkJwV+
zbP6(1$3`VfF=$(?!g$~D$u@hsikgK1N>{3fYSt69KUCQ+Ig&>EY1-g#(BLMmqJK~6
zZ--6By@v9RuuBSklb%}pO`8JU5!k)Q4b4_99)vX2YWlin%Sz3edyXv~vV9}u8tzV6
zH0fLQc{;qOVa!A8)x(AlwaOl*iO?&l{=fF7dN|+RHDd-(H|w>dAJ0*E6w>Yq^bv{&
zh*+^uT2)mFlTHj0NwS73@#JR5r2-*~=}ONW-+qp5w8DbA^YuN0o!u*@O4I0X^$0&n
z;hsS>*WHAV%OMy$+b{LEAoXx3dxsegS!YNdzx1_}b@Z3?691;HepQ(rm70l9nK><`
z{78uZ5wl3%3H4Besaw{wIC6CmnFLdQrC_ykrkf#&SDvyz{c^4A^fFG3LK*XXEr$({
z@Qap8cqG%^_#Ppo(dr`2`gLRpw?~YWlvhnn&@{3=>})j}1PL_gk8G@jMMbsIAjrCJ
zQF(?<nyHQRJB0M#o>gO;&^ZD^l~_hXtW~3|SdlENEovr6QhOA|_y4tLJ(O&msdQ7O
zqT`oe2~=LO_9#0=K99K(hoBq5ml7#e_#-tRUVPI=((SJ?Is~(PV71Zd!NJ1Z6=6O=
zGlQc%IwmH5L%9CZY-48q6q^fiaf4|pcv)6^2sB}>=8fQ)&ytx~R0)dVbIq?q_Sw8d
zg^y%vydb>`szIWLiOLO*izm%*(19@j>p}ti8iOyy->|6o88SHGKjGy<{wYG<q^VXC
zX2b*nUhnKax@na|nn`seZwT3(hpBXk3zu_HOL%(U%+T<$tjUY8{SteYNEJC}bt_R&
zkj+~w3<}Eoc39buB$|wc*6rnZV6huMc6!Oh-g+PGgDqtDO@&zMAjz=Ne1Atg`3Z6<
z$$C%0&Xj1*EkIKay6E)SrA1$uyJ!KL1s!8?yxVwIpd>8tX%K;ErA6#nGIrCl$|tRI
zdH7&71Y=2m>X-Nj2s1tj@($YF7FG*RxX?W!SGhv`(Fm}TnL36+WD5L-CY-Kzlr=4%
zw9{h`4F&1N$Qf8}EP$0s*Ww5>;*0!wVdjt^KN|Bxm@pKkV6+Xuwn&q!GPPC4iu)d{
z2&eBSDp8ewjL`tL+|d42Wlt1;R(Ri64)wlb#imN{#7=hB<q92QQwqTfCk+3E#I`mq
zeDqhX{3nrM@>1cn-(iW=5!vpJpk-x$Iu;BD;~0(D6X;8+>Slh`G6t_bk{r)5%>?=9
zw-c*PXIf>x9&uNz#@N^Y|C-<g4#n92%R1@G@^D*Pfi#T6uh1r*tP~PbhFWSEE~Ax|
zL8@Rg2KsP}+Dn?k+)B-qd|S`Nv`2QPo*A*@6Q9EuhD6qvcTlkZ;3H|hubd(;NU3DX
zt!`4>q2uVNNKPD#Recdib|mc7`Mi1~(4)Q~LGjj2)0R@rdt}pzo7yvr&Q@QMDHCu%
zSvVJmU2Np5T-NTjG;@)V_T;x9g`!^zknZJ0Yia28xJg$L9A-^q&=E2bH*Fk@BhK8G
zW&N~G2%4L4?Ngzf?~S0$2U;d3%7=Yc)BE5%e@4ODL?Lf=Ow;$?6IsOzEX9UQ;XC_P
z=F6I^yG$v(2euF6ZzlMU{&W*8>m5mXG~YHb=u7+D<Hsjm;uv>*exb5pN6|h<Nfd~C
z9>eN=OF(H3QpVK~qU2TJ=EXO>nNm}+?ls@&(c>j+e_D>QA{wMX(#e5~uuTW^F88&m
zHr$e{o<D4Qm=a*x%v#;mJ%LDgh2XB@D{l&_E9?*u^o}IcGM*%>lG>A1l{<<fh(R4C
zLn1Y!o#SaOz0T?Op&rmf7qXo+(T><(CO+eSj?Tm5eToa#z9r%ntQKu5!X$^b<zQX%
zbRfiha~vzVll4<E`Ga;t*#k1z{Io9fh5rdQIh8=Y7;Z`%(M&1i?TCs*rII<Vg>jU2
z?Ii_9ljBO$DI(7s#pzKC{h+w4K<Gw(sW>XJ5iAJq;Zs)yy&+%3s=_D9y>oaerAVpU
zD`;sCB-sYO;-5LIlWVtitj{q!X$?U(yp(xBf;Fp<7A!Vn9uFInmlntKpC9Ghl_mLh
z)>I0UKg9ApP=?=5vEvnB3)Im{olbi;S?9{P>I<)5slctJl+Kg^Lxe{4_W4=B6F!<d
zzXs#H<pLq;cjp+kv&#NQRu|R%7EO3RMcm!0zSS?-@T`r5{0*=D8^8r%eLgXRQ-K#1
zaQ~KaT2e9$h~>M?07A%q*@`ajnGBL&8W3B&#W;+r8m!H?2Qa<@Mp72ms#EJBIr{8K
zy-_e74-)E$Fr`$hKG`p0qE;kA=ycj#PI<e<E;5j2mGLX$Qg}fAYX=~ZmSzoiGFj%c
zm53k;mYi~`?hyQ6ar*4=9t=+jrY~lt?Z=pX1u>WHNm4U$BQu6#SZZZKzDLSwh`_tR
zcqKHth-Idr=UU|!7e!~}TN(c7{j-Af)*xkn)A{xjgXK0Ahb3mb0jy0{tpLqDiFR~G
zCIXbRNQYIkH~*JN=#(f+rkZY#d{yQcnI*#e<4HAwwp5Q<(THQp2!_QT5$rqY$Ekev
z5la^4t_q)+7IzARSldkP{uGubF_)&SoF>TQF(VH9Qrvt2_9!chRG58AoljtiK63DB
z5*SwALZ0a#w5jT$q%~hEqh{V9L_p1%F`QgQzir`y6FC7}S<w@tqj+LnUTyAX+O=^1
z1USTk*Zfs@9xpY}hZgB$rr|h)_qA`fQ@UF^`9OSX9RO!!0c1q4T&-U9)mgv3XIMOF
za|gH1iW_>j=$FdcVyzTP_+P2;spli#@(8#K(&H(HtL&XpDw>stZrbL`&G3T#_(UpI
z!OCVoS6hKX0iVlvo#Q%No2&F5i9~FI7Rl+S?)IJisuiQJtcf_cN;u>1E7&i@K-}F0
zm1p;oV@--<?H)#F48(ZNOdfHHd41?*qCsq4jZCJYY4?wdRy$=`KQ4|xj#IITJK3iC
zdSw5Fu~rp>3QF&#$|INlIK>}e#j~5-YUDah)>Xc3-;V_MU06&?QU4!z+HjttDY7FY
zPq^=`s*1Her4s_c<a)EpJE)nIjf4_Cm8WkKqwUlKJ|?alOVZ*~qMcN0G?M=O+DoM9
zwT8qDpBq2YPDwi;H^aZIv@SNZtpFpPVXDr|4l1tc-~GesFomAMUq-8^ALrpIS?09v
z#Zfr*KPR}Tv8O;ny=K+S+wwPAyY>igzg#RI>xh>9bn<w%{#r+MIHgfj#qYdS)Z@DD
z2!S+LcdkNa8~Qa*4z?KA?r$OK7g*}YlY_V8f0?<4D!b==7cG>`ty`CZg`eCz@xhw9
zcwnjNycstkZAaaNb#hjDeTeqNr+l|DVgAap6+zU^rZcx@9`#pI1uw$eu%+%4BNqsq
zs=^U!Rw4(Ta9Ik$eUWHNn&!PZ+qVXTm&VA#oO_ke{W+4|C$-k1mVQn><5?orCMb3v
zjlYqOGWoGwfP#qXkkk07dNu3(*r527w0j1X$33q$UQr|FeG%tW;WKNw5<J)qYXU^0
z#xGa&Sp6~?OxT(6l|tVT(Q$<g3rO`^p$GHUYF8h|WwLf%6RnU+bzQfG?y9@hD3v;5
zeme26R<clgG>nvE4KbcZrm)GC3($n63KqZt{0V@1=zso$VgnGtGzF{YA>7J~4Tphq
zxlA}2nPH_2!cE?ik<J=$fPnElJLXUQv4VjmU8($f@bAT4JQ4LmtNk|)x^&YKdIkL;
zo~ghD;g<F|WCGQ5bKV;X7hsY?f0AMLwR`%6gTg_*2d#tl2B6?EzF^n&Y$K`sE^x`>
znLEaO8K1H<#r*x9YHL$qA}0h;UKKjWjsa$<?(5Hd@bjXDUru>|S@}4i`QQmK?b2#&
z5<I`!+!e5$pti#|Y&4jUcmOboLu+6Um3^nm@7cr(Ag1?n;pr9?l5hlF$i94j_KXoz
zLUWQ2D58RsM|UF2Jk5b%B4~#4v!1dzI29UX%3v~N$3kR;TA;IGpb~y306aq>CT3_o
zwPew(2$1?Uasc3)8){@EoaKlll40$Tt`ad#2bgqzJV1>7%uk1VKqn_U<$R7J8MgTn
zV7_f%s^d}d8R~<Z@Y~l1wy(W5%CoJ8QYmFRY=rtt=ggJbO>jUG6%K?Dc}%W)1|2?{
zt-`tJo>_K#Z-*BXeiaVT70stB6dn7{-&I{&rx({79#Jlyh^o<Y@%r>D>9W_x!p=Q=
zf1qqUy)utD?Dg_~W}mZ9{kQ9jdS@(#rZo|j0^3U#ND5(M9KSj`2F0`kc-=1>`G%GC
z#|J6Kwxy0PV%ic8o^9`%a_d)qWGp6X4wC75WOkb>z)WUjSQQ%W&)8>p^`-L*lXLDB
zM~Y9GMNgXS`^V)d?#HfZ>WL5VUvMkZ|6}AR&LwTi^3G$|PmC|b-xS&<p>#|Ccw6@D
z<P`F<^U_9(>rneCnT+{Didf9T34tD;>z_|4W!|c9*i#zR7FJg>p4O9i;8V7_4eC?p
zg8G%<G8)2d{O&`q_A2bno%O@9#=4>7zfjV3RU6Y9@+*<v*U&KL7_iT?BZFhbDF*xl
zqD+8=4-s#DEOliHn`u<<yrrT0BHQw8jPt#q%iY2CU#xQt|E+P%9*!|Zcu#xDNyg80
z-R}yLE#yyFjn;M#1GuF1;}?qKQ#hFN#`}ZL>%RA2)~K9|6X#yXtY}6fK|K)W1oT)c
zqLwx#Gha=%?&^Q>Xy1-u$XHd;{VTE_@hJA%K<iy<obZ9K<1yB6HDqgNx`41v1P;ij
zhUzEehAl-(t8NT!TyjEp-~R5F%m?S#P(o;9dIX|D#aVssu%}jat~z>!^(~7Q12>&K
z?UVv2?I-PtH`y?rVPm!aqv9b!P37$EvpErC-3epCVLhf0uXhx0<p>lJ^D#`Aho)T#
zkO`ne;4PB?#{#LI?L)nQAwVD;!c{Y25**XlJmND-u&Qf8<Rb@(v-(sGW39IDhoMSq
z5ixb-Smqp@gn)^m`R`{Txnm`$msBH@;GoacQv8i-w5I4*GVi`L9RZW4_`qxJ<noU;
z=!f2*Wm%gLJzlmbNe55^nJ^zMR2mSp3f8er3wjN}7x@%gcn;M|2}#ZXAmso6n~I1E
zagc6-1!D^TW%c;Gk{t!eV&S?>sHtx~36RVpA^-}AF|vn1x9bI#rmT=s^S|jZg%cnc
zucnlxpX}eYn;5+yS4Xew4adQpNK5iIjR05Ssn$Jc?$Lc%Yot;$+}0yQ6zaI=yum5;
zIh#0sVdU~&t^dmuuwtk+@6Q;#pdrKz6{>izCNjgnRbcIw*LbywNymoUgNITKD2;B}
zp^f+>iDK4lWzI1FvmGotrHr)3m|;vIqFFE(sfL_uX7&UFNCptfgdNqYp1LSff0ziT
zS?_gdGpQCx?$jt9G|lvskq@XzceU~XFoKF1i=>j|0aW|yuN87LT9eK;8`{T)TxWOf
zOl3|27}pq027A(fO$mfSlb6Pl>XNKSCKxObdS%>?0V7Adk9flP_J=e4oOb~5Rlb&H
zf+6|9nR>w~H_6*2-QhdzsX)9Z&p6d%?I??FfU--&TublE8R+=eA;d6)IVNK_!1!vP
z;Nx5zZi#_e2;?HXPc}5)l(<RKM{`@$%*yD#*x9s14ZqLT3DXY<Ht8q-IR^pMXz~>%
z)VhbH9*njadcC+M-$Q_<%s~1l(g1?^AAlv%T*_Ebyf2<mA5<hdee6i4(ao_7%SlHk
ze(v?zBj|T-Ukt71hnLIDSCb!;^W3MFiZ`h-lA`PCepBB>mD5^8h+Nov<}bxzGVJ4^
z%-mo@rxj_rk0UM9B@w-K9x)FmimnQus+Sb4E{DVUF67sry?Zrh7H-!_6-h|?#(M1D
zQDD*b(0f$)SlBIQ_0}a~iRmdnSG`n6W97a{7a#8%ehb>K%m*d{l&E19k7_Ko|44gn
zQN!*Q-)V1pCZqDvE4k%XCr%1ohRBSau()dwsb{Tl|24<mqH3qS^6J&9){wBje?wIF
zqJw?eCuqrqBKJIitPCyeOU>Q(aj&;;GtUM$+<j29e`D<XuiWqzz3UHC89&Y|i1a#N
zbUr?@vt;^wUcfVZJMuXt@xjf-DwV$70tq}Vd&qUu`^?tvohECre%n{*wd@1cH-&d4
zFP_Pcx&V5{V7*2*u$eaPytSS23f8!AkK^}Oiv>$BlxKTgd@OWZEwOhqf3i=rE9az_
zrgusHiCc;13;~R)3K82dzO_Y!=%AVASSfKf3kWG)_aErk(k8DeNb#E`*;HjD{Zhw0
z<)ntPOwAqD+2>aZPf$DV7dYKeLPzcFcRYgy3RPpxso))CK=Q`57%C@)pZu?h1c=s-
zt|L)Qpk9_D9iS&B*nKW2Ve^>a3vJ<q)+CP3^j=ojVC|hAGi5QHTmSGN5cV4}<AGE{
z^-F_I?omAKq`Hbjw3yQU5mA8p_fy~d?*cOxaoZ3L&s<X))cYBx0nZ+w<f62KHWJRA
zNK|}p3M>3;2y`8@0SvE%EX^T4G|waj;Gs1zU#;Xe3z>cLEg!x<B0$G_+-{huf@t`T
zMH&_+bzcb?WoqdIMWlN+7+Inzz;Z?g$;(doF`OE{?a?eT%ZF-7iOZAf`9{u4M6{Q(
zvX&F3^EUvH*hv;3#+ktw95ybs(=DmQQHvo6cqLW7aAI(C{tFlJ{EC99OC+(m-L9By
zhH65vdXeKRRYH5dptYnYnh8#%MDvozj$B{TOt>Fzww#c{3`q$GsAgZql{4_(-c-#C
z2x#J*LxRPA-a&eWi=n`Ha*8F?guIB<DlsN>h#DUSTtQu)04TFSwJ8B;{<~8~v6Ggm
z2eJ8TS_DxMY$*DsVzR4+s3`(Fb`et#Sv^A0tydFO;H*J8Rtd1`{LADE<@l2yn(qK`
zY$nbhb+I^weUb@CEUuVk33WFJL6;gP6Fjas+8|96nawy+W{l{t9Lm%?lgUU)>`f&v
z!5k&HbQQQG^g5@KVDlo+S~GPslj|Q|(QzCd+?f&{l){D#Sk%g3wAvJ~99Bu+q9u@(
zg?T8Z2mjVumPGfPY7U0jt)8|OXs<NP;Xu8C!^yo7Nqje~4-$A}GS>*X9f)4&Ccr~A
zL7xPRR3(09Bdfv*rkVFw!a)hnR8&jgBKewgqTJc^@{2Wtjajr7ufc}sLcLdarrM<^
z?8Fb9!fu`vb3B*0>73Sl7)wG+Gz4>;;+c9OdY!d$F+r4wyw)m(V#Y;rW(-L97nB&}
zbCC${j<iiQ?&<l|&u*=stM26$Tw`4xW?eS?EG+e|Q37G@+!Ak~bm5L3%fKlS{@EBa
zlgxyrlw35;*vg<fHQZ^~o4WOo^o=buXL;wv?)x&@g#lJ~Kt(Dc0#$kgT1YZH6fmOq
zwal!#)G)m5PTJLszozL`#h$vQYsH0}T=6%0-4Xkx1>y@r^fIt7l?0S&%Z@T<Rnn?_
z?y}^skbd8-2O8;ODEa~Q<xbfuO~tBXWoub7%h_&kLfbV~MwRxu_68MnF1Q=m8@U9_
zGvnJ!*i7lBvc2AEoAJ9kGBMED>!6QddBHt^L(g6SS{DjC-8doYX)8Jk>9OTivZD`n
zm453>O?sKiCc(gb64fMaO7Q{jyVUB4JoN1i9mMG!D9?K>yL?`*!l|$;>uOxz;8pBk
z&$p76>+(sNoYLQ%=wNh`)o$`(9*uOpL=AqjjhOzq;0-fxdX)OK0msrSn1Z<LjNE$s
zZV#k@beOrIlWb$7gXmmwb#6gOrh{k@nFlhhbc^aQ7tjwR`~AJSv;=S7F$k~|!=9=B
zvi&8EkNBD8owk_p3&KncGrWXp3Qx~`m{$1}fDQ8yC8pea@i4!UZx&2Z`8vYwn=7)Z
zOP5N&U&xo9yiaRkMj&)~Udd#S$Jke*KGaSXPKKy`felv3Ohd@yl@xuV{vrOp!DCKu
z9U%UTEO8q0foNSU06P+v;d%7T)cV*eLP|x)rBr^#!-M8bCmJ&xD?maG+>o;izAJ$H
zxz}%hcJM`F>}Zxh^(qPDZT*vg`%i-!mFg_s|KgeMaa)Iz?_ZG4*M(QL!ZMj9d~7p9
z02h_k;g_NkkwRji834}H?wk^RKKVnzs-Uz_${qUS;aw66kRN~P;qjDgFy1ShK9DyR
zcm2T{w>%`*>TxkS4*C?+Eq|NQm=dQ5fE|+N27qCrM;{9-IzAkgjz{B0PN#LM674cM
zwwE%7lMAM92qjq-S*&Ifg>SX&eC1M^opHed-jvPUgWTL=O(|+EQS&)6^4(PIIF&oC
zUBrA%w33^gg5x&vpl4gvv=({1MPo`Y5daI$XX(w93`U&{Kb{;66Nkdql5CK0D1!p2
zdrn~n<8Ulh-1h0J?sIJZC0z0&A%EekOBpZWLL9@fwC3Wgfe#5YhluR#D>DaD6E0_h
zn4ObLQYzOogo3M{c&E#}&xRz;f_VvsN0_Kn(uZz56?i?EFWeEAb4LjGe#rg7cU^^%
zoN^gL$&g!j_+4T&EVacomG7-ah)QzYVwRi{W+F%%rNoC=pb@Co^>cV@$_amU%ri5s
zKALUzgQWymWNVV+WjOPx?8x(sZ;zbP_p}IV4Zoj$|75G}bMUdd+a)6n;(?EbE1XFJ
zwq8$-T=cCvxnLJ8HT6xp_<I)B$?Ivs<kN;-aus>B#zy*YU1=FRDerDqrC!6VCo`%7
zWtPPEbN13t@TGlM+MFI&5X(!xU}dClWzg^9G%~wnEydqUct_~r^ukO+5S+YAuDnZ=
zh=U-Qf5mL~vr0L)2S;AqcpwndCA0PB-Gig;N<5F!?or!0Iom~Ji%(MD`XsVHmRGN4
zY$Bu>C$)VpAo>cK<Rf-mq)Ww8$%=!4>f!S8F{(MyF>x;rdr^uW3o0!D*96|#ktk=H
zx{2G-T{=0F$se+7cZ3@mO^cVlYg>5luHkZIaZht=4&_R%QI0qhoJm;=7h!rQCtgp-
z>SxSxK(euuf_7O&A@!hrm8gFD#o+<U+165?(Q>J&v#0pYoXHA}Sv~CvYHljyTqf2h
zIR$F^QiP`<EvP8f8xk$co3`A<tbftZ`&GuzK0wN{I-KTS4+i{vv(0y;J$WkA(z7W2
zD)m>A7#1qJJYe}qRFufTDz?(<GmD>%$uv|s##j&=`4p=Ps@tIgIs#A|Q~0MUw#`!}
z5wOZ>=DfXlI*Q27;2vx^T)mVj*z4amGP$BfiSQ|laeaOyFzpSEy-Xo`yaJ9$4!c^A
zo?Fcd9bdhAn3Y{8K#}GA8&tnfGtaGaSgkM1@qNzu`vosrheD`F(09MU3)=&lIuE;<
zz8KuH7%Dn802O($r$c?^P=+q>HC-}U1P0dXsoB{E1f`0`+wUTz^D_7L+P9^TRGv%7
z<3-~Ky>5lM!7@I>>SbV0h6`J|LMjD{P46C9rmZN&r`~1iF8ODcRApclVIKzl`u7T1
zaz#yxpS^T(Z0YMasF4aOH&F7*w)DF;m2GcyB5nKZ)09hs`zOmvUfHH)XAHJiKGhHm
znOmfkX9w&lkG_I|-$FYE<`EQOYl`{dLb5tuZSkJPg>OGD{_~GQ{<Zi2wZ0WFO>}(O
zsm>vDnQCRJ*pbv`%bljN$@z24wAdGM53W{^%6$1EJ!}86S4+7oO@^0|8jZ*&Ygw{!
zhz>oXqnPLDziAZ{_bzQYcinK#Og5DKUhw3PRQtC8!}Epclw&jfeC2KaPGigm6vL0g
zk94p)DnLo^<MiX!EFKuV>yr8#SJq(dn05aCQNoCJpI=~anY(0}O@M3Eu%DiNd2m&!
z*~rqZ*;D*@agSX6opNP__-kgj(!QS;ebcEovP#_7B>-n16U1Kir18l0g3@3%>P_<e
z^lZVq*|#psfgeNV{lAY7&wiy9O47u)7dFHMj9qZXjWZ@zP2#L~Giw!0F%oXgv9CD>
zSzF7bMk{q7?q*#p9j^<wh!SVQDF>*WQvV{o%=)3w1ez4Y6(ME1m_W+*>m`}=={DHa
z<KwRXB-l&c#foR{egD+nBYvIs$ZO`16EPH379*VTCv$o%BhxgG&1my}H6$Qew1O?p
zNRH!dZUmo9UX)%s=l14(k$-Wnx_jbu`3>4sr+S?e>Wkh2=KBqa^0UUU;R%Jr>I=I`
zTh~ug&Mv$q7_2V6xW<tl`dsYyxxhht#SxC3%$w?He)K81`gGcq#S2}z2y^-Qr`9&u
z*V<h=`P6TjUT!WxB<8u^K+Cgk(nCG$Kq1G0x={iihQ~L}gq<2646%IsLv6`W0$-Z<
zc{B8Ak?G5P$<ovp$7cSR37!;vk4Pj|M+wCHU3;7x8Td2%z!i@@4Kh^$zqU?(7&awW
zoxQU8QvBH?@hN1;CnK4(_>9yWu?lCm9+rupJMsIG%&2hlSKn_P{+Yl1gbSN9#+#xF
z65ic?_ni9SM24M5u%(5oUuN-MAH93cdT)vOqB~cf?gxSogD&p-?i#Rfq?~p;e9YyP
z`O!(8`-FT_aT6aD*v}qLef6s&_fw-^m1ss`)!DWiC>crmsKS-0lbLyM#K}jHcLm(z
zB#)n*&pIO_0^MafmuH`GdLr%@vd3lDclD&y&0|>7xx~C<OrKNo1SG|?_LnqD{tPuU
z@cA>PDbmI$@#<lt&9h7&E_2L24aNf~Edozu%tIXf1x)B{vx$U>Uhd=Rc_4#~WV0U6
z@vvQy>WX>xDxS-R8T;f4oNM&W;+k9pFS{ORNUio9k-PNhBORpKIs%<yH!kMw!V=h{
z&H<RYFAIXeQiUuvpQ9>~zWe&v($ygSzp0PwXiw$hcLj3xecIn?F`*S6VOkVHz&n4B
z?HW|Nw^VTYyYR!0jiQY^I*>3pLvxc<9*$yk`FxJxdT2|KVSp!6_nwUiOzl^^{Y-6-
z0t6Ax?D?}_S2i;hcCIaT{?C6wiO)|KvKfX{U|(6lMZ>@40B@~4#{c)9(l|5UT5-=l
zIsW(jQ0qYfNwwp1*)z%?oRAl~<-XZdEKTci<Lu$bG&7$%tKWgJ#{sD2dLY+3qXuy~
zHz1Ji*?QNdiqF5u>9o(*_F2tKpOsq`pFIc!3Inr|P&k$baJe>QfXMLI*T*P1UbMJf
z=`d{xV`{I<4O?nw#o&zv^l4MOh;nHI55wCsDMMa|28$X78+`k##?In$^%^Fc^0mLH
zv?X3|j##~TjSHJPr*Se34@}9X00-xW1BAQN<o$nkWoD&y)()9lcHJ4#_!+PHEELPW
zrk+uRWC0uQTdUQL#?%QHO$Utbiw80>FQAU3`(37tT*ofz;Yam~H%1ohT_nu)&tI*J
zhS^**QjWyJ$w|(*j?kzFZk}Xk@uf*Bgh{I3c(w;x<1i%-sAvr9_g_oB6@&n7f%E9f
zQ?tG)l@2YdH<%1%3KDD_7rt@T!M8T+8TsLi5$sm%&572}DpH0GuX)J+cfzH^XUkVu
zn~jM~=&l>qaac3k#ChA|qB`|tYewCtgD(!=q~1z4PGGPBiq#gN)~eu;_jRL!9xNa9
z#_;#i%Qr`4jO<lL%hp@Wg=?;5oB!kH-gx)s^55%~d;Py_DE>^S&#oH3s65pAGdV5Y
zO)EijmFZRGb4$e~Kqa!uG4f%-Sh~WwlAhq$ZuQ$E-}hjQMjv!MzkXKc7mHbT-*Tv>
zL-SVgb@hpOG>hq*w_je}jS=U6P%r@;1y?2-Kb~d^Tv59rzXq$k(QxPf>#Q%1<GMEt
z)KVFpF5*)I&MRHd*9t;IBb9=;{u#Im0j<xk)6P~VBj{rW;>@qmF;3xAZ(X|`S*K}Z
zOE0O3-7qw|owr<|wQQl1t?IBg#F1lOjk~;ie4{4(@v|QeccudewXiSlC)V*{Z}qNG
zGIQd^{Krwk^ZnK0dAf?#N%0kD;g_>!D>Rux&)9PomM)t=z_V>h*oHGDu|fpR$V$9*
zBF_DgSxh|g*6Dnruj_QgDW~wpBI4j-PWgSC7+QCoSWlb>PSjTLiQJ{!grdF9F`k;o
z*$wh3T?Ygz3QoB2KPXwBbYj2Y@1orGR4O(;j3LeGtgJn{V}py%y7-PZ;H7WScJ$cz
zmL?>aYp9YWTRL=`$@a!YFj0oX^eNdyIzzA_c_i_w({;tHa9BOmu)%n&LeJ<2;R*9k
zFR3E1{xC5(!`5eL%ghweaOE~xklSy>Y}jb=k+S)@jLtWPf#-fzNAum@K3%qWN?2En
z%JEmd@ipm7^3A-e)!6HKWEIR%oW1Cye;nv(VoBQj1?Q(NoLe%Z2HtBo^u?x#mx^gG
z3wWpy|L&w)U4to6<!KdYnZZGdpGM3D(yJ0Tmszut`RN1KOFP)tBki&^l;g*w-c1jQ
z@oAs&zY7QX4rP7^)f=miXLT0<H0_D>ZE9@kLeUjiID~lsr6hXzhkU0PH}!14N0_H3
zVl$t!jzi|1zNrHx_5U4Lc^1Ni1!*UD04$XB0$_@P36}5Dq+h;UA8GFq{zGA?{1Ra%
zUbmkHP@uMwwMIv0g>S|%L9__$lMm)x0Gzvz4fD_$#MEzS_97X04`7(Wfw{YpA0=@M
zpg>py#VB7=VL%_rRRGjWz#(=B$~n)^O#$zfe$xTO!?PzBh=51BBEzA%DNp%kash_^
z#SX=5f1<xe45EX#vH^#IP+qGE0D5S88-O(%VbP@CGV3lMK_)Os*@(ekkORoE5^)M`
z>5S8nrkiJHmj%8Zl<b*0%7mrq<@jFpWTxB#5DW~5bl<x8ysIXW`;zPLJRrx3!os28
zx-3i*et$K8R<rG8iN<3dq%6HMjO0}nNY3(o--yodx90B09jL8x^m^xzzkT~ewVQ_F
z7<{~JW!$et<5cnDWkmKwpWaCW5!rUN$Br*FLJ`}^j$Rgj)>=;8#&6%MMrYZQR5Son
z^pt23_g}~LFS=h2wmi7d?C9Y5xwALy^db6bAy^B-ccJfMZe)?plc`RKY7u$K^A)u6
z0P=I)2d6xbpHCq}dYCLJCU_@{(4fQV7G<WQzxI6F#On>Y8#FDeP2*>uE8fT&s(o^J
zC!4S!mthq2>TF6lLn-jaLSPWSrRG@Z8sMe(D&{FQ8l?;Zk`4N1u5;uV^wUDg{WoNk
z51i*8A~P2>C2S0b{_B~bYXC;s+R62tU-+s1J-Ej{9{*lPKH+*a5B6qXZ~IyOo)h_p
zGI6iJ4V%a}Ow^wi2209JAo32YLBBz1cI28rcl?$E-OA3|rp}B5L=vKiZYa|;ffJhX
zPG?2&gI&r@;#AM6T60|R%R#T>e!HYRnk*LDV{0;#A66|pn}~(4?$!{+){4(^z0-VP
z=@j{tzMh9-Wn0H>?2%<{1G^<!hGT2&20NZiJ=&-ecKFox;QAh)@g3pcUVpm9UXy>}
zZ8zw#p%A$3lc^L9#o&$40yk1E=;i?4aX_FrjdCc3+KvA<e#oZzym*VGa#H$WlUbbb
z%kIxbOkj>74+6BuuoNB9XBq_A5Zj3nc9d$f6DE=JQvYbCl{cn4y&R7oIw5;s<x|o6
zmz~#Qg8vQDcl++?<y%}i_9_1!7N|_PXL_cpAjXOx1tguTldpXJ97fzoibSk=8Njgj
zeYCn{21S*Yu)-v9B5ZI886N5LD)9%n_2+)S@o!7wjD)&+fBdOq8af|OO#dDm%$FKc
z+kd(+@5W-wTj`qc7ni9&6$LXOdg*Bs<V#a>x!Q-X$O}Vp?=Znnk)9kR-4@XwH>Bwa
z7R%=F7Bwj)Ry`JBtnHJ?dZ1DZIW?i{61kaS4!s`7z4nMfv1N@Tvn}9Bj_x)x6?`Dy
z{Z8Wq{s8vtWJQ;3uD=A!TapJb<nyEC(-`{xHio&MQlku1=zvv9cem%dUjw+d0&*-C
zKMMzBci1a=CrXH#HB2>JoaHQmZ&czn;`o(aJbfhizsOT;!O?(sU8oNav(}XEF0b@U
zBDpZMdoAF6{X_JmYTGXL8J_GSTEfqxTRkO_o`L0G5a3!Dkc|<-?L<FKVzQYa<V7L$
z2kr<4@Xy%pCRtbyw3I;{g2o4uuw)>_e<wl*ib`8p9{#ZZ_Awi;C{_w&)$b5Y65w$K
zv_=q4)?(d;Yh36g>k2>*KF~u&xmeim8h}Mh`7r^g#xaWi;TjjH(e^XsU<1_2P=<t`
zw<I3$=m2sb+Cg8<shTvCAwvc@#tkH}M>dZJeOgwujlRBMAMdX}|7pdOwWMHE>#AH7
z3zsJ+)<U$9UiG^}Hj>Is&HGh_Aw=qV_mun)1%@g+phgEw^z(jg%|!(VGSV%hOG{}=
zK7?ZUxmJa&jUae{Cs`g2Yw{WWidJh%JR(ec+n_QM##5-Sw0>z*tJOEvYU>4~$dRn#
zU-$({!jdKEeEe{dZQwM#jvzbJK0c#Oe12G%yrB8n3BO=y4grzXL@C(mmB>k19w1a;
zMR)*;{~{BAo<P9&JZbmwk8F5Ke(W|3$52(2TOow&LY<RD_nP|5HR-1eX+KUo(b|pd
zjI1}p@DAr%EsbXe%5s4eEXa%z1kssLXpDwl{RrqpuN=*awc})Qm^wjDsV7&-R@}-9
zfH6FOqJ<G$HPbJbf)<CCNBBU1UPR^vp$$x(&^Yq|-=4<EC86>ZO`kWl(Z3yrXGI=c
zAd4>LKZ-lmHXERPwqNZyoGqqw0>x5T<4Vfk?1woRD=(GQq^B4qz6)KAR!gQRb+2n<
zNfdxRSTTy!UPCC6glkN(r`Hs-JZzELQTbnI=6@x->!^Moj4LqQ#*EIUNB-KdQ1Oqo
z8Z}NGWX@yL=7V$QdyH%YY3GG~v`-(Nubf!i``0|J*~EYLR39y~B3PB$_i3TLIBg^-
zjaSl8p*YOoKR=(NuVedvQZpXF<cKgD^j{pqjqcPc05CP8#x+H&%|<^t(Lr+r!`wC@
zE*jw6h2--1Yr@@-Nht0)Z$<by+kRfoz{Y}&bkwCO6!J+hd!i_d*xb#Ox_MJV>8NP+
zEa-{X?&*m7r?sbsdtGHwckWTVu4ddBo!P3Fd;c-rH5>S^eXsrZpVototiG+?0gkdW
zlN|6$6gf+BGlm6U*gZ%rxqbTICTAW0dIu`*O5&<MTuz=ZQ_b!B%%=(qZ{DLgUa6Ay
zPGk(IuFZ+iDSQ7|Xhi=me0<pYUF4GMskNg0e%VJnid@6s*l)%MM5m-7@@$4%$!HY}
zErD~@5dOjJ+*JT;lDVZoApa$Bfe85*o)1v`qb+-6G=CgbONf-8d8c`t_vFQwy*JiX
zh%!oa4dmhvxH>NaZgE@{?#h@F((@Gr&Hpgdu*3HMqv+h@nfm`Y{@DdHx4F-K80LPz
zCgwhu7?n_I?w2HlB-PAqgl1GKH1|sqLg+S^+$*G_+jlMrQIZO&e&@Hp_Sg2<=j@!%
z`}2OkUeDL90WVA{uQw-{+mMV}3kI-!DT8`R^nw7GFcX#HUXO!V#n(7hXlfDUE8}Oi
z0+HZDuu0ju8cYv*4MIx>i}16beWjM6l?0UQWDWbH3<EcwLxSDo{L<oUQn+5e6Mjet
z<pk@NX@UNqHhi4CFbd$IB(;nNO9#<~Q|$q)M%tAkfSclD12m{UI=^ev!;}(cVTgZ)
z8KcKoOM<MI#<)O2kc1j;ODKRJ0!pC{e&9WI1s`~oPlc^T{V5axuOpg$rYs6len$5^
z;TPUi`jV^IBQ2aNhp-vf2#`vuFj5aN5!K|E*%t#~62k0l1skWc06=6%S!e-?zubEt
zs@ZHlv{})DxVHODByEybC=kh#UIj9F`Yq)vYedRbMQia0H3A>|aipz*MKIBtiu7$C
z(;86QUx~4D^TS7J1j88Emy5!kssKcBf0mV*eQ2Z_4k^OBpmpvj!Oj*S6pchBF-W2<
zAz)1QR|;UEm5A4Qzcr&+Sx4uHHocJ{$`XbFtMV1+C2Rs~%P-Q?Vk{^`q+kaGqvcx=
zgMAV{YIj9!{J}n=B-!S#;VD@Q$_GJQzrUS{Xn>xWT2X!#MJv-@$)-u#BM_UKPJ}pr
zS5|pAG-!U#N9jb5wqX^o00Dyv&bkosT~eQkY2apjYK)|&efPS75bn!1-?Jj62ceOY
z`*RpJJ&BUdh7@uxn$yUa(V?m6C4Lg!F*d?2<q<vGb+#NU3^M`T?rxMR0ednEN9X=J
zHj&%q(qkaf{0LGKw-BcUwl<KQeeo17LK|V9$|L{_g1B|4K1D|ZUfh)j4G%PWR7Fya
z6X&z!V~DiHAg`e~d=c|a@991JJHMk5d!FeYIrBkf|6u7*l#1@{!~I%U2g$hayUHAt
zG2B3UJy({!D&k-!*}{SuCDTyFwoVUHbrX%%)14~fj-*K|*dd4xx!ZF@bOQ~J`x6lG
z7xCq<Mp~4j%}=ARp&nmE?F1!Mr%cO&Cid)5-oiudl?Q{;I`rZGF|_lLx8W6dFhBCZ
zJAf$q>`hE=o^F_xw&SDL&1B2kK*e+;?Yag%+y5F+Z7Fr#5>uI+K2??ZeqH*9D_YEx
z20N#Q^8@Qc2l$yz6fy8)2hz!5I6%tgsx(YT^pHtqy@_a#86;mb-G3(h#15L(h_Y=^
zaG*F2Vjy9qH)TVx2laxj6-Ql!5{BV8RZpq}4DF%;54Q;9#QfM1?h$M5Ns&yY1DLz`
zJyXRRjrZMb)wC~8QoD7paEi4IQo)XMkjPabDTS=rd;Q;aFK7OWPwwr-Dh`Qgp?VW>
zDEXiog#bg@@^KM9e)yAV<FsFa5=u9+EIoFv2ZrB)`G}pM;pOeR<v1>%DOSSUtlF^=
zg|92ceN76s(@9^*nmAO>%xY@i)0@(f=Xfu6c5&W>beHRPYowtiQ_pJTc4<)G32fX!
z2RqmG`2T^Q=c9D<*4VbE{R|8?0kA;vJKo~&pr-*gEej9lXyHQ+`i%aR&-9W|q);V3
zNZ039EzLO9LiLYj0PX=lXpZx?Io4}*2OcMv`qmqQT-xSiW(9>cBe{>ms^M3#u=5oG
z6!F%2qtym!n+7R#3Q8)P7hG!5SdJ-glOY#Lq|9}&g7R5sU0DFh*W;gBDlI#sR8fNj
z*21`a9NP@BDDv|OJ{C?iWN!&kH|$wDhh6je1x8>QF<%0QAbj;{YCKOYmJi%T@^^OH
z6k%_hSO;oCV`CIGtHr=pzr69)e6cv}U^Jb`Lrog#@<3sny;JSQ_D0P&ZE>-WMLz|)
z2A5OVKnQ%*>15Qg76q@ByWKlyaVhjj`m`O`6gse~R()MKK+;Repy1m;r%gc^2MAIA
z3S^4G9NI)vjl|&Qv8*`*ZLAFJwvnQlXd2{{$bS*@N0>S%c!~;J^l3K62q&+|^>E~H
zu;T_RN)?S-H$uARrT^BjNXD6$`)aJi@xX~7sTk|R<_MZ2g>aiVVhYdp$(#Uh$ci;P
zYbAyqkeE#rdp{-k%Y={f=<5xk^RPe(O?&C`x(*n?h)J_EVoXD}+$F`OVe-Ngn&!tx
zUuT%Xf~Vmutw%Q9G86V9J(@SDCsv_l0ko=E^nFFLxYjF?R4t*^)nKqL*tS|1lqrCl
zn0(av$R<?$t2cxk7H2L9%dN?ebVfkR;2!5@C0a0K#vl)cDc2bUib>4Itpr3%)K?d<
z79X%p2e{n5gATv7<+-P}DAQ>u0JD>2>A-Vx<Q3m}hwNBT8u*aDgdmft`76>k<wnX9
ziu;d11U{4@Szhs@cWRw<Kr@gsc*>@5aVX84!^dt#JySWksfu=li(zN;#>%aC0T5ul
zXAqBYy&$XgUXeKVI{NR!XtNuiLv2dBUiSz+$P|70xBW?(J30yupw}@D4v9q|F4$5$
zIEcU4UD#vAb*L=<2Zuf4c&y{Zz;E!F2%J_K$c=MAe&yvf>{Yf0fakp?L5Z|*pfH(~
z#4W{DMy3>;-VN24I!|Ri|Fs!<n)iYYRfrJuP8G5YlLsI-a+^J{CN_u1V|m3GspSTw
z7(qG2#Uoz|A}vENCfwi8ibtK3|0k7dr|bE)`oj-e*F^X)nQD=xzrz7>#c%)ebLD4Q
z-Si|f>|KDaWFDL6#o8S3sGXRUd@+)=o6__3`P)sCGhJoQdcxCgp>j^lXoJ`je}8Jy
zhl470Or_t7pM&+VkIH-z5hbR-ut+j%Q<5Ss_v|q*k+-n<0I0K<#;IT`+zQAGNwQ#$
z)h+<A9$w20xXytoIf)V>pLZq|Z0r@Fz!uMxdd%+tIW@yCwitXk2$fo-LWXVVs_?*x
z6v#szFa+eY0wB+GYPkOD*$zOJ7ld5!BA$>7J}HpO<w+q~Qw_{+)@Fas&97&{2{2_o
zX5v>R{!00`BO5Qc-;O-nSn1K#e9^8wvhf|2(?GJAs~#Q-lt|T}DsJQ`gnG6@ti~1z
z0Czmo3&5uhv;SCYZOq5_4Mq%BnxgN@rz+s2`IAyd#x=|?(#<ASam1Xq4C;`VwsT`#
zva3_avyt@Gim~`nA1|Q;cYR*9x1ZE*LBp>vc#f_Rqht(w{C%L)+j;q{k!dCL|5Kj4
zAqhtbjtXmz6n7>KZ=I`Dt*<%p|MVt5Dy%pd3*eNSA!;skga(1o-kBw}cm`@H530ZF
zI^a5A_n#6q35Nu*x+p9aP{a`BbtC0ez&+C*ldpURV$tE9#Q;}8jDy<ELCAtHKNO3#
z7v;bhLDym_=~7gL*@(6(wOAg=eM<cR<h#~^-<1p&km5c=Cy5{<w*--&%%?#@q;5?a
zADq<;FrPKHL^CS{uWh6#stm^+1>91Z&^&&;Lp?mT_{hb_%rjf1`O5TFz;%Bd81naF
zK}<=EaDR;Ik8m8SpR;ZuP@60K!vb(!3yVv8=L4{rHsKHipv64d|0)ME*<(3i#E_o~
z97PbL?Ih?bSIAmkLi}tNj<91a?5WP`c?1C4N_uqir2E{SONbF65h!jJ;~1>o73qs*
z!)O7LIIu&s8H|`ZL*<%?IR;=c6fr7{lZ4Y<_MJ|YW5fDmRTr;SouG32-gtoytSCh(
z!Xg<ARe)p^2m^3Z;2RqteR45Xm(8y#5v<)X{}dXH&R&XLcc62SYA#5mXlro?r$q&v
zIC6w9&|j%PidTeo&<f5VqXD=r{@LCOEk2^j6;lb|>&LVLH1H0#d_p!;0Ktgk1K_Dt
zU5Zx@DIaQuxgKFp#9veH0NK<3$@Jv{-dHU)00|+9N1)SRFq#e2kWUHxsVrN-)!8ZW
ziQ|c8K=Dw=Wf8<+{*Hi5Ln^o=yo9yPVvhmg`(~J4u~+GOG=an9!{{*bZNAyW9~t1L
zSiz=K7Nn4;-ajOVV4VUm6V(ttM`n*KUT4ycSxARNo9dBn#VGBgm@|U2V*ti5g*kEl
z7%Oj{7cMmOoy^gLx#aK(UxnHOAYYgkTqZRR8GVPM?7?;?M#;=P%RmGQdWpAD<-=8y
zB@n2xpi42p(uw{fK5#d*E6V_YPAM3P!9#rshk2U8fD8RZUJJxdq23W6-h564P2#=H
z02ZZfPlcVs6s)tcIH;qD>s{%=;dB?w{jm4^o759@52@jv5OxCgMYJB)$XLswiqejv
z^kWW&rFYhRk0WI}pzsZH^b)TW;JC9ZHz$q|@(g}MWNnsZG}nyXE53eFKMohn8oDQv
z*thI#r>Oi$n6VzSLu^hJut&*R75s8{+I|7*ap$Wwa&0DC!(Clgu1j_wIF@TecU?Tt
zck7K`;B@joZ|5_IWDm3j=Sv3>+Hoy~99cvjio*AuCTQ4WrskiEwOX*u5t`kKaS8Zj
zsxrWj`EIn|S?WUTg2jlWCMM1eHk$CsYzce!Rf1q^uy<_ymsjZSq{GZuTv#y+J6{tB
zaNKG^Ps$*Sz6@g7+9EJ_KX05o_zyKDtxvb3|DWXSKoF*BS>G34+=DVh;1Qg&j9I$g
zfY-L{l1na?1Ff5;j-1`~ge=^gG!dBc;7a!!=t;n-{jY^>(*a*W)KcX;UKmiCf#CHh
z%=HQaa<%Y=QH4LRX1<{W=zW)SJWlJ1fl`%VPigY8i@9Fzdc)<qVw&ao|NnC3=4l~~
zDv|^4VSwGMWME#@pgw%!;1lv&rZKiq<Y9uM@?_Yh@w1{-R<`ETMT;vW5-5(q4<q#*
z-@@f>^}DPOZoca-%{^0g_vd*77lYTu!41bmaa#)b7(YJm5d%j(sNOFYwJLbECkPW9
zN(JatyZ<f|!5bM}7>oI{mB*Xmiu!KjDfT-dYL;u%G3ecI=;{~{!Hf(d{vy?sUc$5D
z`g(Cp0M@Jl;21k!qI>p>zU~Lm0YbX+U<wcCC*(BAfXZqy+6bA*S{+mi<R@ky?ALUF
zXc_@$Iv|avl;4M2<S=q+e(W=IpZ%q$sQM*hafBu|*p7lKgBXJtc+NN%xJ$ZF(CcpN
zi0yeiH}2a=DG?J}L4{f>+TPeQ0EoJTqU$6PsG}mS7wcq#m`aw}d)`574MyN5^y+y|
zntwdpdw^)q3sce}5va8Qfep-M0~tgV2f<0>`@CgQwWO?qPu;Rc=>Z=0?<Rrr_6Okw
z{m!nMu@O}zm#KQ(bsZ0c!|a>3vPHBa?KwC6l#fJ_0;H8s7T*z{8{W=`Jtw6p8=q<u
zC0>2O&K@?Gg0Q>h9voem9V@Ze9{Vd>5kJVyc}IqrAScR1T-G9ffoq)r8l61nn(k8h
zGF*o67E0^6uip&~F67zDav9AJjBTmzRG5*PZa4<^)VMVSW~K@ePsA$9FY~n||I9#@
zNZ^1SDi(1zRi{S!EeW7)T-2dL2E|?uN(YG@`Q<MZF2_XY)%BY?{t(zKjX#y2I;=7^
z{#yF$f@hA|mYV(2zI#KzG8qw~Y$xRLI0VH^s*W4@+(wS?Qvw89ObcYXE5Nd_;r<yD
zxD1?U1{tb!9IF*?DSPfH40#||lvAhWB*-`#M%H3|{%*Et2FL%VX7*n8Uc^X%iUGWh
znAnX9ENhN39?9NYr%H>|GSj=wO$?TVO8JNnbX;ya%M~*%4Qn+u-YzK9XY#Oe&}PeZ
z!!`5~!Mx5wIx8_NFj?y}9N5&{GGk#0F`2*5a?yP<uBQr{slns402M_rYe1W&-&y`_
z*HxZoW>^&tMGRL9idwadEb70t?q~JaGY9$`-Rc`k=u2!At?c6Un}yU3!`}Xjob&3l
zW(|s+`O~!>>-juehufPI(ot@x&J$vQyZ^gJHe3YP{Xt{tT?3iUtHg3dT@Y7~rehKT
z>o$lnf@eQNkhS=*Xaedo9RoLsftOo4<+YZ!^ceEP^zB=F^DPFunCwQs>taa*TC^)-
zw!?RadUKn*ii(%pxAJkb1)bQLuD1ngSbbGYZY|3_kMe@2Ya1BesUhFfL>{<3o}V}O
zCVT6HcJZ8e%Bj+%k6Nt7{G_EzBJ(1BR-(8s2AkeGaUU;Xq4A|o-e(cIGn1}ooJQWu
zJcGA#F@%EEGLu5v$4SV@6Htyy7uF_SDS)T$?4D&~+m)bf`xEjgL>(@iJ5nF9z8A}p
zPn{4?qgsefB&qdtn-HARgIIgV_hWpl(_p-V>nIjMY?KO-&{NyrMDTo<K(k;b@rQJB
zV}_+~wA{541wAcqrNyaFP=0LNYl}J-!|Ed+>&X}Ht^Ak43KnARO>e%KIln`Ve06K%
zil*LqgRVnp8%_JUfd+C`_H;bxybTz8;7cV4lo~1tbv=Aor=d!`(vr!1Vn3C{SJby}
zcb0j-Cnq@1bTi*k1CvRQ$ZQ6P={<$>nGoeUEq9uxr<A(#n?4lD{eOwDd-^;J1_hio
zYr)4=M;Rz=I06{L^)){BA&2>vvaCs23Y9yM>MPI9rhS?r9vPHZ%nc%FsTg5(B68d-
zFXvQfHq?tzCn7MqX-NQ?0Zr8;U~w`$L+7u}XWiNI&7cR!9YsKzvn*aqI2nI4T!}_#
z*@IaTf`80!{?J7q&EHl*acX!Go7sN;*-1VbG)tOj5ELm1FYm}n7NnXXc@eF=)912=
za&NRefHlit$hrui@B9FV+V&J+=Q8cC*$ba$R-k$2lJ(UyGf@;z+~INxK%n$peMD33
zop`n`KVv2GM69Q=_uMHKN!1%qo8Ieu*B--wQ_Yr3sFxq|W_H?SW)%C{4YzXRSJF~n
z+U?MgbjRk2)a&7UYItGk+4&^jnlzHJCjDI2^zXqn+0k)^)SDD!xD%Em1|yB%SbizC
zaXdZTR&@AW@sNWzT$k6Uwk2>bi*<(1fucz=uc@z7mjp`_A__*2)mA;khO>l{{4!dC
zh$)A;P@WEVIYLQ?x%w^Bhg`eC{tOFprf?eo_DGElbM?nGy2eFeR@wN`j2G`PfiZw1
zsO%Yys!ZoaP*E{wd2T!DbOOm%JLIMq0IIZ<H&D_sbqgSyU5Q)=5jOXf;$$D;+=Z8y
z<PT$tPb6F;Yr(tRD*}ph0z@-W$CzjX)p0u{al3SQ;CePMlxxft(FnGy;Vex!Ip?;o
z84vpf75H+cu5pbYRYy})F(QvT8Ce_2P||nzQWBKST_)vukS!`X6u)Q5(+_h8+KYIC
zkW?T9gB!!4z-1Rh>WHz&I*S+ikH5Kw)cioymYU1BTxaitzGj)Fzm4>95Yc#cjjatI
z*L+JCs**`>!Pj}LL)!6o+?0^aV&FBc3%DYhJ4e-z6!w?Q8SZ?jFMV}1)n}hY-&IEU
zMOyz;%nwxOFx~Gp`uH(==VMXP$xAF-jNSz<O#(PhLlI`Pf2duSGP+!#Ruo~T;c{6%
zpc|#XK7^B~Ckh}fVgL%qAfIz^n?UI?j_u>9e^zq3X3!`W>!ip2ucCjjDk0Z(Z{xXN
zxv5V4r7tgDAJyla=85|)fr7T$IC3h7YG9Mz?DA+GlPb!W-t5LAt<<v5PZlbpWwc2-
zGo%|@Lk;i5;|`02ygpXJ;WwCdhR>C2J#KRSK5Th8|GSU=T)L*gM<%BZV1IRwe9b~y
zPysnZ7BU<PUJ3Wkpj=HKa@#wt4rkz{7)J1NCQlddf%BrGJ{PGLgT>(q_n=#wZGkkn
z09tOP6YOij#hWNDs*6o}lEb5QZ>|HD=`k*<qR+C~NCJwFdY_NFKTVLuFySxYom<J*
z_hP=ARgY&6n;)ahjk*5#O^<LGK$s6IYoqiE)c<gRa#Ge70!56L<eWyVj%6+R9q2^+
zMC|9Y<XoJ4lehI4U}k4uz7#a5;)y|Kg9~by>qwE#tS#xc#+I!_1Xj~q%*Hl{w9udT
z-hPUCbl>!1Mak{);hd0}(Ye>x#;1{bHe*A_Gh1TPhuqUi&`%aH(W<)C>4xmy2&%G7
zWnfe1OjDg@sVsDXnIc?JJ!r!?fc^L)`Tf3$MU=Rn&YArVdKU%eFEA<TA6G;tAlC#+
zFAW}ppcvNzc^WJ|D?CY(<A@|XUul+>s8fXO3RF0QtLG2*KG!bI(>~Hgp7~KNi!i~T
zH|*?=-qMQAfmGdYIGf3@k9#v|B)r%XFSYUwbzP{EEC(BxsVn4L0XNwCO3>%P^#Gip
ze7LjlCNr%6v%Kj?*|#5Mb*LOZ#&VWVf-P`ho&EY4fC!S}EFqLT@Lyl&)kiY>z^y>q
zlFmDp9BfvSx-L*}GybqAqZucY+7-eRo_K$0e+KbTF(X>CuT+YSpemOIroRCUv^a*0
z+#siR%2-39&n<-;*9tPpA;!%?nHK-3L`2T3j%#xlSOisgs5)2@lnqbKhDSk#X&uTK
z5g3C$vrZR1-=srFwakTk4YR&TzMTejisJ@UZ?5C>=_H4AVG-a*;|2kx5|e&%*=(-*
z${{->?$4*iSHOpdK6E-OW*T;&lg81(L+{V<;wUGj*_h?31Aa+9mBTY?<u}!yc=P&b
z5<23+$GoWu%S@k8<CZF)=@XfwXRZ5=BC$|l6FOFTfeSB0GgEr3UI{yUw7@Jji|;J*
zSzNBPU~03>w<t-4h!u6zClw!4MC#)*DH?;8A~gDyYw2h`DuxGskY=X*xmlgY)Phm5
zm8V+ml<q&TV`R;xa21r}$k8hP>{9aCthyu-%)v)LGz*dxVBf!?1CsK8mNj<1L$Q8_
zEFWsaWF4gBW)Sz+(Or-whm8t(VM$mSDmOLOGjjQ7sw6y)lY<<0%Rquj^MAZ-Zh0#Z
zT0K2$XB+>O{6t_^|N6j(w)n!F^2T8&eo^x*c0SMgu{LU4+C0?F2`_?)<{a2@9q)W1
z)!7o?<Im!tC<up`S~@93oRCO8TtZt^2b4WWRpV6+_-c|?c{e>4lNa4AWBW^|w2V9i
z9i4x^`LIA6)ZDW-Z@uroZEb;WPDBgy()~%5qs>zD=+c<IX){)lUexqychQgPr)*#P
zqtZ0yrb+aySybljX3q1f+<i#bA<iP;me9d1&z|wE4`zNASq!M`4L>(4$^2ME&eBK2
zA8*hz2%c}S8nnjniP*^LW%(>r3@fTq4Pao1GBa2~-;S{Yb?W)B6}7%Z9&_6&^55J2
z^JzFe+8<?F|60XHvR;GD%eFTBEG~oMdmoY|q(3Lo*KU{FGE>!2xYFFX^wWwuXMGJ8
z5`L>k#Ra7LjM*oJQabaSeCcinP%hncJp>dy3`Eao73c7bd&`iL{PnG}M1DIEvjBn3
zmuA_zRNeVOkYCkJf9Nq!^JAVN0do#_+It;<(~9uSuv2ao7}e$*^uA56SPD-&chy~d
zAmZ#vPs440!`#Q{xH38^n)@4(3+L%4ms>ETL)-449x%PPl~P-!bd>*39u-bihI{$t
z0!nEcQ^pd#M0X71kC|<hhP-ewcj@b-76+ZW3;Qkgc=^}U=af5%r8I_myUXY7>B!6$
zOPC&&mx~3ZFX2G)BYD96HlPV$c=}k73{(|da3lg827rGICn}JVWm^5aK%z3$R&y+2
zVC2JoJ;c$*H^5R}NDpX92mtG2cUXX|AqY_QL_osJgCk86Z0_$+_l_Vs%mG7D#@Gvc
z7w&AQBpa&3K+s)Ye57ea;b6$5OcEYs+m#bx&1)&mJ{kauk0-$TvVh6q6YGeN-^1Gh
z+(yp_kky5s;Mm+RVxdF?82APQnMc5>?xR%hyS}w2FYzFf0|<-*AfWjbi~Gwa*rzH6
zV0y)V$F$?j^>4fuJNHKpo*EzMsh6B1jv8R&*ob{K<3QW{I0J@bI{ZJ1X=@9ft-y2x
ziM%IT9fB`8MKO!Nf3dt!MdyeCxmrJoIN3ogkl>oNaAZ+=lH-A_6T%z&kO+%8+7LWz
zZ~!2ZK9~TW+1;f7w*XnlSv@xzY_)zVxs(l?OB!I&A;kl~rr-0jtn2*@E$Xxxf4zne
zH%zZKdoh{HomvrQ1T4H`Wz`NC9fkx=&x|fopqL&mDr_unl*m&30td;|OM11-TTEH*
zou)Y1rf_)_3;=)ZSkrbl5MB620zyGc8~|tC%1u!~D;9^jWM$716behcxY`2r+%dY8
z7rEQ`<+DoX6fO$)FF^aTNE$>8Fe|9$*MDLIX+SORn6X7a@jQxvdljpMj|}~`5*isR
z*Zw-Q%QPHB5@JeNh>G6FKbz4^WPqj{AC1ETr{!vsfU8LV5KyGs;{M+ro^wBsuUAQb
zQT@u{+qwU)uKl^dkzAnv<`fGGq|IXY{q3?^M!|RSuf|-D-K;lJ&YBup=l%=SHV%7L
zZf$eU0-hETPsb;);9+^5Cyn=l>fT4T&U`J?1doBtx3I?%UUp%XwKr_15IIRCJ3%+h
zb=gXws&#qBRp~I$n#u8M^IbnLkUB59_T@JvNqY2xH8y&r>p$$1uWgUFF8<An`t{*;
z=DhEVxAhA3E_^)v@U1DRlRYA_uQ2`a-(-T?%tl_%4&u#~m9Kwm{wfQ-3S{_SH0VjG
z`mHgX+PI}S5PRkAUQ&M4o&5KpGH<VQ9=_cK+m`T<Hk_Go#SDI5F3g?eQTWjZ1W9rK
zAFJ9d_4j;RAb~DVMG&5;0<M|Fjnnm<DnbqiHfjX86rC}0Jo^tp5x_Sw;#`VQ-;F<T
z2tp^g*!(>20w_{=;eJmSz_tDW%Yw|Sv(g<oii8CBm`hzDODjS<k8S36J54_Rd{peU
ztt!w~U=xaeO?e=brds-3M0IuSdcB-N$WWU!(hUe*|7l-m*(GqfVNV~sK)H7{M67Qe
zR(?jodQAT<ZgcTrwjMl8i}<r-!*Y-fe(41#sixh_m+w<l=AxwJg3u`I=hu#Ba!uq4
z_558t8Wot1$vfYY@L5JVJ(jW-Cot%+`Vuv=DQ8!gK!qL9<cc$q>tEM19S0B%RK|R8
zs?)r}5|%Ao8At^GgcpyrmZp2)-MvKa(6f0$5JAd<@t3vWH0ug1+&`@-PT4a``{}eR
zZz?bOmVphP7bd5TuWHqGn!taY$K(ZPXF5--_LYk^;k|6#+AH5UmH6VUpR?SvC(M|~
zVyt;MThk&5nQt$>FieYbxN}Wn@e8kdq7(ubfDPz1r2--f>;fI2OK@K8KNf;1x}ron
zM}(idR{JQ3dG8qEqp-RhqMXZ|rOG9!#|1LpM2PsXj?%xAi-M^w!dE2#TWUtaw%;4T
zrY&CUQTP>;u0Az+&mZbqlv-%sdUH9>*}I2T7@QM6?CfTpwVkMqj}OSTNd{cacO^N%
zl85>QQojhRiyeH~RfKyaTP4DB^|>OVaYAbN%yEU!;tj%mXwyv2TPm&U02Sg*(xjHT
zYMYs!^*Vi&g5#=&T7|8)6i*)NS1sGRgCtCpKo4ZdeWfERv3-qSMeDlquwb(&?|8?N
z{*zjn5-e}EXi)c}C`Z`V--RoUi&tyb>UKl0Q^G#?sE0<ks(GK}U5o<UbuXn02O#CD
z78ldLF#nL?oXwTRchTCzovr7Z{OH8nPD@Cww6$ErDmd_M*;R>tZ!YqE%Nn~iDg&Jp
zc=Tnz4HV?kkyVH(?vMzzmzB<u6K2L&tqnQXY!WKlkk|G9ihZC;3JC`T8crYKlPjXq
zUv1uw;}R1Z^-X)4Ecjs&c~rOOUmtc~o1~?F)}hgxp{LY?ubOZkFFI?TRFk6#m2Y0i
zSJLI{NisF#K>{SFl=@A*UroJ+<Qx$PbZpZt@0O~*yIWMO?Z#lDzVaT0Mel9Oo9wCU
z6RGA_Phg%;d%?Ajmo(3RKhbu_2JXp!FrrQl#R{*+E2ZZtUgHLAv6n!}#j$xx4Y8CW
zJnn-YlGpK1vkaI1Hf7=IU}{#4AZh>&Wd28#N>%$G7#aLKw)IFdhW`Ni_cIt#|7*>@
z%bA)`K*qIf1)A#{^jz+K840@sMLvlEJ&k7Ugr?Txx8u)CB4G)`KF9}rYnLYBmP149
zH#LG7PG~s6f|tW{zuD&LXuVB>+IAes+7P{6?CnC7n^xvO7@N9|kLlxt>e(ECQo3cs
zryChV!4DX3;BOtDN4XUK(g?G-ee$sx{9pb!Ggx@iz}1QIT`esoMmz=!f5uTp;^+mR
zKSr({z9Aeby>jvix74F0hpjGt9>#uyH`d;k_BkTNSQT0h7k|A~L-J6MuJQFDtu{$Y
zcVFq9+>#5wKGZLmG;Ze$N(12;9;e_V4Edk>N5iHBPMBOQee>xEa3lPZU-+J(L=}HM
zLE<L}FEFng7(A)Za!>tk5%U646HH$`q--BE(W{~HmaupI)0Y{)LIJXCu11*bM=BH6
zyW^vNs=GrkZ-i1dd&oJxgp#*#xvG_v_u3>&-jVTH^Twf|_b)<LF3ui%Wlkckr?nL6
z&jm}k9dU6YEy>}@XfZ+gjqBxlEU6r_iKgcYuhwP2eQEmoL!vJv6jn#ZS}||#f7J9%
zWo_eP=Zp5cVa_Q5u?5WFoojNBqoP$lB)WuoHaZ-9C^<XEjLH)VSS{i|g@+SKtL+sp
z9Pj;u{d3cp|Mz$|z_mDFv?OY=DSAv=k5u9_M@tJedB>ki%gB_SlaQNLwTG>WmQy@a
zIh7T*U}PldS^T5f%}1e1;OWq#wWt^Y;#s!53dKMKtQ=&qZe(|Z;JFozZu7;e=(UZ-
z%#PkB$3Evgl=C{p9kp<r(SE7sI61|9)P=%57v-L=qWJUt9088^=UWYrfo&ccZg{Y0
z`918))iLUE$Ht*r)nNo@*w%eG*M4{rHKL0j$#Q$Qug+$*O?$QTof9S9mNLc@xp=2e
zdud#`A|0P*yC)5$S1}pbYx!j02qpN6Uey&QzrU8P89uFoiM(PI_Qi^YX7NZU<of3Z
zX*`+Vg|Xz7KJ7p}TgiZ2YM!OvQ`_HbUcbH}r@mIvbdz6J@1_Y*z6`zl^mHljwtoOX
z24$PzpPo-k-8%G*>CSDHn=K=)T9X@cUg`<AEN-33k;ewg!(tjL)lF3D+;?D`ultmd
z&px<E(GqK{i1;WlzX;e2?%rf!Jalq)aYh2xv*tio18s*jB;2vb9pp_=|7~ESM!h)M
zkMf2`Y6)MepYbcqWeY9Hw=62<@T-2iGu4k_L_7Rk;2rvP=af`Vk8FA}-czYB{6oCH
z!Lc}D#YTxwp4k$;>>C@P&&!gMA6H0Ve+tD7=aKKI5*xAyO3;ZV^@_M5_EVwswG%Ek
zh46)F6P-(Lk1i>-s+XjyX8>(-{>tY4IK85J=C64r>k9jZ=dwj)+Rk%!xlZy;5se@v
zlEEzlJdXtH(}4vPE9#C*q+o`g&Mj1HBEErA=_FA|7NZR)&42X{9Cf+191k=)ShTU;
z0j-jVQjJwohsUEwOCj?6@yxin!e>;<UG$3L-@`%!wUhB$P3O-Eb*X-r{o3&JRon^F
znxXzv`%Rz!1KOlVSu#9@x$Qib28J#Q%Nm#Aj#$@3S=OouSEIDD6`WKm^fcJJIx|yn
zRi*srqrjucCwH-7SUIAQFkt4el`x!1W#G<UK8P7eOt}5ksOC%GoZdeJ(*4P~C^v)6
z<x<qxUVPY*e)3f*x|Ry2j6=@fQ8PwYFDW(O<qJ{f>)FE(`_1p#9k71dAluQJAwTR^
zsNO6;TvJbUv-cfejAyKjeDeBXJ2IxzM00zfb+SS^<GjKop@@&@Pa_tjEh<dD+qm5u
zyF_V&v%l1|DfH4S+y=DU4jDG|#)ag!bC(zeY~F8rBAzNnUNd@85DKZ-dhZ^Rr8&M7
zBfX3gCf3<mKaIsgfoB`@ilIW%hE{2zt==?esrikM-2O|bV4qFqNABjUHt&K{S47MG
z&ne2@X35Jfkd-qs_bVgY`5Nu($80$0Zf?E6+GxCL2@|=G<d6bjuMlu5e3t(thnsgi
z9M*sY&esmAZd?x!Pl)I4G7!gp&+t*%>W%r2M~|qSw_u9|S4ZG!MX%KaZB?F2+Hw!O
zH#zG#)9yHq=U?<t`;n!cka4KJ|5!f%2ub0PIF}zu<C-B4f#%=vY$5TYe7lR5GMcSi
zA7d+G<Sv}FwsPdx$v#4$<ta5?6&**PX5ufGSmTpNMvd8l8jXDi-laQ!*R92FUoMsO
z=Kd5J*DogE&X@j#>levm8egC$sFnLlBQ8+LF_vbnmpSrC_=0LCu_Fx$E-FZWf4mod
zbl+rLc8BPp3{qd{T&A~ef9v9j=lHPd%ko|=MyS1#!m^)YW;A(oZgu1b)Q^8M$~+eF
z*e{_kO8ucuNB_SU**pTv8jJTMU5$1V^nl`&WBC`6+I0DHEe1|PQQ3w}@_@Nzd$gPQ
z;9YbaM?l+-N}7^_v6mc0$AG1QL6Ts^#<J8+SOGzyF#|KOI(6}V%>F=?+!o$Obt3nJ
zK1`*m_$}i1zx^fyC$2C~Y$_xe$l_yWY#DcKSC8A_V0>$XcH_hLx)pJ%c1aPNL^0BE
z1nJYJPf?cbVZEi1XVF^C-yL6<3*EJ_=9k~&$Wj<q;UDys-Wb^9N+D==Pw|esA1HAb
z9lI=);k_{LvtpD!@>2!z)0a+Gd#d$9_?TOswM3Jh#72Sd#{H0Q$yVA&-5tRzlQYIe
zUwydwgAz)*>HU?;-}F@VB8F87o@3L4{!dQ0$HB@BXI}~0$ScoQJfO%Q<o4I*Ry6nM
zs9l%Xp4QzHo;xaTrCdHMp9OfS(E#WPQO1?oJ{C`0a{2C=XFJ)W`;HvXI<zGmRX0y`
z3c+9M!hbS~S8+ZTi+t_zz-Uv_@A-E#%|ge-^Q9u(`5gj&uSR(jszXHn;#ZE6ZoL;8
zvXLq}pZ482B`f^#n4Zt+R?c_!CclredV=?uQFZIWue^iWjXsf@hj}E*6Jj_|j1Kdl
zH}ik>UTbyGTUN{dxQ*wvU*M%RJpZP+4ZaTl5ayn2i&$?nsrOJbRopP@+xCrUPYs)F
zJ)yKOu<fe)oY6V{{YKcg5h%;POOwpRrrpBv&0D(0U3>f__Fjk5X^zwXb=5~bLs_j(
zs4l?t`hj++)tl$kfnGiA?5%^u9T5jVDWw>g(_x!|;8UAhCE>-<X=Og)R|>zC;Zr#l
zN{%RMFrgeq$M2r&*&Ld{?rr-E!f=7Fo2>!rUG~jWyk}B*ym|*d#!o4mUb%p}z6V>g
zn98_OhJ8YesZMOZZOex79XiE#@3h_N<Y3ba#}1_l@aJnyxu5dxjxst_NnW7PpmE8&
z<6&EH{{J@~C;)@z;|9G-dP%uPTs(AJ)_SK~C?DUsUO)^t+!BJkCCY$jOW=<Z?eTx#
zB!BNHAz{P+O|Aoe^e%A!@9sDT2X@ats%>4ee}#o#wpN&kL816OF1ycu?;?q4fbvJj
zPBMU=v@}pIa3R9C6Q2MLap>4CCg8$)qQ;!I^cN<kfB5LhOF=7(ULH_1%XK%E6UiQ%
zE1pi`R>*rY|J@V)Zr^W#vhk0G!w$6F-!SsVI9b)@sA$8eiqr|wu2qntJjLOOOg#d4
zEy^JL*GSsHje`o`lB+6?J#P-F#z{YoJ1qIC$A98AB}xeh$>%l)4vX;f6^_mAK0f*>
zgnx4JuWzHH{GRW-=Pw+4kTR;1+mai3U|uC_`%7(2{t@(|vdeK}E=2)6e$3$Hf;L~D
z(9TX;AGv?8Kvmry(xC41G?A+}Ygha;%cGP;Mys@xd~UHu*Wr_n+RV;u{OTSNa7QTF
zD@}d)hW#*Z<N+$PEe9?jF%%JmnE=|Na~W*9?GSy~bxu^aQpm4V$-0p#JzAmvM&wp)
zB!ZZ2u#pZ=bABFo7F`{ysWaM^YrFf%mSEWk(Rb5d!`q4IVd<f4r9&F5Z=;x=>X_9G
zBjt!7tC@Y1w5S<e!)>j8qe7;TMSm+9w5lD)al>HbH^FS1yKTSGy~2i@U{LG9kNwZZ
zO0q>Nie$|#^~rxOhs<0#QFP6ysJ%3z?<XGp%mvj~Hq~v6-YGL)q9w(b5n-!N+F99-
zzMVWlnQ(<y8xdspRV9h5t*d`*Yn_Jk_}?iz6%%-<fVHTjR!J&LgQ2^&&Q)`;0!|7e
zQhy}cMc-gwD>Uxelj0r~Kan)nIjQ&0m%mz5YvJ1;Ug6gm^zjs{m{*>}ezlpOEI5wt
z2KeUI-@Q|CN&@bMw`w%wZhra<=8X@DY&@(xB~AM=@J(D8KF)AAE>cURgH&9um$6~%
z$@{2_o<ADQrj^3)AE>i6O89Jz?~Yu7ReO4Ac00YOaCq?byYnuC%955i-#J`(ty?Xc
z&8wm?!ZinV0%KsMb$kZn?PA9xKXf)7Z}^H?anlg&p&D2G+Hao1CqFiGw3?66+EJ(&
z@*iP|JF7mo)cWAt(j9(;1taf|l5!(jn6AYwRR_tImNg*6w{zQETmxG5ToPN=qUiCe
z!=LX;9>>iuDmyWROn6h-fcMh80lyexIaFVuV#$Bul`k{cO6}#$17(*tSC_x}F|>AD
z(+m+eyaKKI(`n3&=o;sS@>68gWK~)<kGL#<x~u-Ct#M+@m`kzK%J4%>QUA;R`k7wp
zvo6?=xs|*HKWX;P7Tufa-*fTieoKnCl~v+{y(-cV;JBo9ZHw%^*#9&ZR4j_%!Ea~P
zFXgDeL{^fK9f4ZbBzlKXQ4;wfMN2lMMT&8|ir6x?+#37M_q_dJvAtq<qF4Tk*j?;G
ziIN5>w$}NLy3c<y73eP4&?joF{i{Qj7N-(dt7oc%7SbVUu&warLc-QQwU5Yh!{fGw
zBkFEt2kI>qCmh7EW*T;aMODffyJbSXfrj$a%YsS-2y0qr3JG4r?DZbrkgzx<5X<zB
zVBFB3FXuXVW^Zs>e~Au-%1;C`LglP91eL^~2c09;N||07G`dr0*k`>;-8C?cfBFe=
zgvpdDY#nyVtycUK>g2P1m;0oe?7P0O#$8<_9I(1h0HROvm6u)>zhL6;nDE#%i1a4q
zz3DSLCa_$DbLN!uYV0xDSFq8MWD+$NbiuD*g<!H_aOZ7cuD*^HxV_Y(C6Jm=)OmBc
zXHwB9EP@hPUBnHeO)6ywpODO8A3)D79lh9lPB;AWD~qLp;P<&`LxFmsJ>k3x<OGXb
z`QOlrPa0FIG}Ls}Dd~dMBUb(4bq523{bX$x_j;WnDgc2oX&k!kXpsClkq3)4_6(9U
zc^Qdwre!gDW8p1}3Tz{$zJ!SjYx<A+C9N3)F;}8;a)fei-EcvYw)&dQ?t#l!JREQB
zKfadH651kY+gtey70K^d>sj{JvAg|H*aOXUKJE`5dxBFAv_I_p*=prCGtZ+Osc_L$
zhxFdH0O9t2qWg997X=OLnCO+Ke#5z0_y(L6)q)qVR&60&-{6BvTDgN5cX_S2qWW}8
zNG`3{zvqXQHEQX%=9AYz^57|%LL0f*izki?GUT1vjB}P0f_8c<KVrmC64$o?n^rZ+
znWu>v3^*(r@;&gcP4*3aG5yDb?;JWdxbo)JP*)GlBhRu0pa`HO-OM+FRwqC5>0~Iz
z)x0~wQ&+B%iJGc^xMK8bX>~rNg}1penKX0rhg!kU%ccd6@9gW(AGx)Wl(}fk3_Ydw
zy*BJU(XaQG@7RnNfnvlBOTOgg@WWNd*!;yok<I4!zL<vRD~)!VZC~CuMU9PD7*y^I
z67<Z!-^Sk-H_rmrkfBDJ0}^?92uB^MZ;J`lPak-1u2Z_W+WJ}7uHN;%B|VBKq#sG3
zR*+zDf-yAmx7i07g{ZR_HAkitK~Co8f;~bhXJM$O^rGTd!HGAAJt7r`6ys;yU%$D3
zGWx{(hxdMQ&%QgHzyIj(qg_Im=ZQHf8P*&8IXMURxv4!khxtY|CW2WZnk9h2s!T@-
zztx?z>DN@|90upK6(?*DYVgDTqk7hrJ~C*RObuLN&1#eif*FPyGH|u-chWG|KWXC!
zub16U@7?KmJD2IY_glif0kfLL-jdLB`b~>oMko~R$mvV5e{DqXe^(Nd?0&jow}04^
z9~L}$qHwrKFXWqGUreOEP23~@QH{VRubQ{6rx43yz70x;YbKped4#S$d!whtRJ`gj
z5Layede2nDwVN7gbz$hmh62JlOuX^T#FF0~57W5SU0Kyh<I4V$CC)o1!Z&~zBB2kC
zsokm*mFNENAmg)p_Rv(v$)%V}jPF;MZ<|XhI8u{fF}N7SR<KSv35TIbH#%)`JKKkk
zp84~PbxbzUu}+=cvvR82%4vA}nC@cAf2H65W=-rI+ZQ%>t7p^nfcVzHP<e6Kj{Qi1
z3%CSjJ+zPPMIO2$-%CBL=eZ_2xU@z1M0#~Q3hn4}vN|`|{@exok_*+|*US5175oUw
zDu3VzzoP`?!rH6s4rr~)K}R$*7FCBd`g1MQzW6+8Iuh*BSk@HzafNM=8))+-npxvr
zSiwIikoc+T{9w<f0zXak8NK{jZvG{=is&MO)|KLmZpYRASJW4ZGJTFW<wm);D(iHt
zeq`3@wCz{^lJRU{&6d2TdA^zly_z{@p!3E@hhr7J@GN&#+im^1%k}zC?(PGv3AX%S
z>!?pF!q=6TzEvkqc<>uu!i+CTd^?HkR_0g2d+mG5ufQcC70?P`V}b#=v#>Im-Vw9G
z^OuwevB9Po_(o(!@*3CHHTy5}NIwNY<6;9iXO6EIgCd3|&9#m{&*pN`ai{G@k>A4i
zUH^w^+9`;Y)a(w{^#1knf~03ERzCw9n~l9y_laz~?a``NHEGu0p!!Lyj?|dTM#=Y=
zNR}7>KT;t8@|np?_lyA`j>1g+Q&we;fMaS#u{1QOWs+wXbbD5KaE<^N2!kaRT!4J}
zE~p5M&cc#tNnYvEv|cs%-~+c@%dAzmqL@8M#({x;twr7Q75Yp&hI_61Da4n=LA@h`
zi~w#Iaqb-Hp{D%G@pubN(sF*OY>vr_J-RZgW@Vg#i*)6U=@`xN9$l{}U^L9E?~b-4
zosPLph>IJ)n7FpeF(mhXVGMm4`z64?o1n8b?!Mdb6(qVm5!$CBI9|8D(b10C7L4ok
zTgEyrl*laz%BP}4zp9FPyn)Xy92~)2{<vuplVXLxe#$O9h1q7s!&b4GYR%i>!!fj2
zYhsYI*6xwQI4*KdBB>X{?9+Jv?Nxt4sPn?lXaPCwi@k>POZPQK_8}X+SAxs(I>tA2
zjYNHo?;bV&mL(;eAQc#@s?sC3U^bOCqzJn!tGFN>_fEdYPqL3H@3Fv+pf^vh&!}I~
z)%~JY{WGZdfP96jJW&l$ae|Z%E!GHcFAY{Bd%aR?Z;*Xjl!eD)IIaGff%;`F(a4Y0
z{fqel$ePH-82i*#149v0B|V?8rgM0-5Z-1lK0ik-T-`9VTE!*PInHkdZ5CTU@FYw?
zwPuueae}aMK`pvR?UvPQOSJl?MMl&FfzO91()@J%k(JjC{>TCi%(LfBKLp|$bT{s(
z3-}6>H+)a>X|8|MQuok`KjriyPESw=D1K45*~PX9%Ca_es7lhXey!W2xOMnTTX|4B
z;5R72$k_6JzOHc6_Ke~Rat$)c{p7dx;$<aU&K*!tUAhv&$u$sMuB=GQ@hfbu_;4z~
z<COnJbt5HuvWJ*$*V?1EV%y{&!k)%r??gi+^{)hlZWL!5OO~(xB?5^a$es29qiBTd
zc_f?Fd<2Q_=NstE*mV%ux?6t$D3Ro#{5?)|7{>(DWC3|4Z>$1B#8ZGM9<DbR|MWmr
zJurAqqw?87MB%*E>LKG8uMqNH-^?&_hJtm(SL;@*B}@=^%yImbpdHg|@05h%mm56a
z^eQ7wR-yw(@|&XKqDJ9mcFt(4mkUbvER9S~F2WFP?@VyBj}~7ej6LK_`^5Wf?rd{E
zfJqCh*+S4!?ATA?3NKJCkrclykY16{c3coyZ9kQMtMwszgMBM*#CIw+sGc0{I&5ew
zuVKqCj}(+YMP<!eNql=CH@j>BNB;=dQvAqy=@5UZJ>7hr+7?Pu3~Z8POW>&(G&QFf
zx5_u?gJeT`%PoWO8vvalMPuJ>G5R%he(Sr3XW|oVrIKU+ic25nz@*08c7dqG_a}bN
zMyu3&w!})sVKjSHcj=G4a6slVQLaa5KMRP@X32V@@D-^F{eSo2-5mQu+*Ovoy6BvQ
z`>5EABXF{2)DP&VJN%Ry6)Fz@(KP53OYbYq-b>JlWQy>}40^uLXGun?e~(i3d}8)n
ztCwZgQ4Nz`0mvBdPx&c)tOo*FCJMUv@|eht19dmK76V`QoDkWF&C)&|Y^&+$eRDE=
z<mFr25sx57^XW)mk@hbq>f-g+OS+9WOte+kqGtFS!W^{bDbJPzOe6z36ar0Dqcv*A
zjUog-QKBU$1k=7dCE7T3IB0oYB`HfgF!KKf`uoE*iUU_1Q>oI>Lk;YT!fB8V7p@*<
zH0r*2&;JeAAHxt7p76*qUCb<a35hfLkM%7QaPtL?DNr$*%7Gf+0z;as(lwd?xb#y^
zKGIKg+(TKIh{N@bS9Z3tvpY+(w^;8Imv*kK>YBVrSkflVvBhoDgki$_S$LV*S32c8
zj%vm<M1Hik_g2i?ghiv~K-zW1rTw$JC>-T=fH*XU=PFn+x+@#hXC51z&K7;?<7i~h
z;!a6;5qy$#EGi&tooHtot0Wm;<rbmj=Fc+Nn`D}mbT5A?Qs#`!--|g~&o>U7^b&FW
zY#%s7jyw^(d9}s%bh~Zy<)gobR)2g-QIS$>Uq91&?%WUViPxj&BHPxk@SGR9cHV96
zJQWsjMYg^J7k7GNI1grMqJLqeg^CpqnmF#1(?{LBK=)<q_a_=5JPmXdp_FZ4Gj0~J
zC<1c>uqDy*V5lo3R|OUQw-MeCW}L5AsQgQ=Y5iUMKrvu7FfbYYH4?`D90~i2<m}0#
zHN?i2q*Zy_RHH|t+Qy12EANymc0H+l<4~3CTvhLobF$X+YvY<)H-Poa5rI5zQN-3q
zQjuv^+_o<#rpm1GpJ<s<*b{V4B7l9Fn|)!4U<<Be0B(d=i65@YEA<h1ND7%0EgJ6g
zA>a96dN^05>B)kZH%`y!E&3`l(|(+eoH%{g^Ypd}kjzM;4Ho`lnr)LJp9Y=T@I8`t
z$*3v!$(f+ks-e%upB%K+(1%`RpE+~o$B)#6ZLK~0>rt;M`(9i_K(}WqW&7H-*I%#O
zx?b?T6}ZL|<dH`;mm;aFHVRy_llG`Gk8bp6$4LB)rDZ-;`jcCMmG8AxD5p#7j$i^z
zWo@p=+;)sq*(I&f&-RNnSAAKZ%luuBwXs=?EMMe;kHe{Rd=*~kAZXhau9Molu6{^W
z-_YY1i9!0i`qsDV?aPN}XCB^v|M2ULYN2YW;0D?VRiRo!K3Gs;Hc85}O>m+c-ZK#X
z{Hz5pVNrc5*Igpjd&9uy0}L)9Pl-Yj0WyF)9P-EGya<cS3jNJq5+?oOU$%5a!}Z4$
zryA)mT%wJO%MR~PynL1EH(Ic*sd)ECV^s8~?8ixN=bALci{J%Cek=PI@<isYALBi!
z(&gjxW<1-`yGqr#*^A!zo9~?OxfaPmwLOx>e0gg7Fx0NR*0Ylbwl4=rh+n(6c3tw@
z-LCq;@%N60{+*r4aPqb~jTLxyrq}um{o>4Kz(Uzx-A3XGA6(B=c+ZmXB`tZFZ4(PG
zm-9{bgm=VZ!_!ivf1o5>iO;e@$st%w-Bw4AC89!<<;BIH1-q}=9vK{PM%JuCx!A;a
z{9}-%d*n3Ha`2H{?CZS=ug{%%U48A5N2S!cV=AH7vuB%RA%F_&PYo3%>DdbZx}&R0
zE_}II#yhR6T$JeQysuY64u{j==m_b%!HB)Q6+UpB6i@oOz7-vZgcJjhFF|*Np70EZ
zrA(iS6I43knP}0&w0M&E;`2aqueHSlbC;r$aC?yQaPVH<;LfKrCwzyl=-b%mD?PY;
zC*;y__rUuz{+}i9HK)`yo|U(H*Ww?1^y*W|l<)Q@N`74xb2s$n<(D13YdI-CQyF;l
zYhZO;ZidL_)Sk989d=mWXETqV3yi+neN+=KVWO$kSLIh1&n5&uP)q%3Y(xJK);AOT
zuv0y4<u*1_+3cYAi?E*^%gf)6*acpJeiTLf6i!~q(+x)H;C0g>ylMI$MQ7pG<lBbf
zccaFrkq#s_x?4eVbi?Qr2ht_oF}l0EJETEGq)S9f1w^DoQYjHZ{eAoXg}ukI=XuWi
zy3Z?Q)z|VX+{4U2TjY`QtFSY9g0l$XvzMP&DcU*gcM=`{u*8%wv&FG&%RP=<fV~U{
zMV|0uX%y+qA|H;bDb;Q!P{YWIx(MW$U6;7GTy|0HlogvutTS^}t(BtQ`=34)L=j>H
zuLS=dZpMcT*=h>CGx@y3r3GsYka?2jEih?Z+OkQUHSaY=yt^&+IlF4oswI|v;Gmf}
zIcPMn!C%74@zqSY*kivXzHsT<k#Ra($Jr_7xqm9NAK&DM>xWuvH(MKv%b8=XwGk9&
zFfcIz0APl3`VvZ_xKUe*smn8fjF12YSuw}~hOI@g&{%JF03STGmA=IY!~ZWze*5f^
zw$x+(s8LRsx}6+SIzjIM=6nWrm8uvy=$e*=vZz!qA6yom62?nJ%a<<=BS1I*g=0Cq
zUtAvG2}Vu;xdqZaCqN+kZhFqrI6&?vg9QTZVW21RqX>Tsf`7L_4uJLRyS&za2*RQ&
zh@9xGZdj6sL0yYQ5E<Gh0x14uTs!d)@J$kdcWdv`j%xH>f1u>t^U3|t`<CtB<B;;D
zK1Swy6p0hNUcVX3Ooa;He)G9yV$Ov|=x!T0|4l1@_Tux8fIrMEw0J=(mx0%oZGGTY
zm9TdRj5}`5*j%WOdKP>GDq8QOP!7I$T7>&)UX?rUl7sl1qwM~6sPkIo^9hK3s1kBK
zVu)ZlTHB<ELICX2BwGL}Kcv-+ae6e>44m7{jRNOY>J@+^kk5-(V!1T`I5J4RwS^=}
zXxO@3%VajUe8yo=(u@AAU?eBU`E14SG4G<_=d;{eEn#oA09Hs01{f3~YZ18`q^{Y+
z!)JCk{})<0GxD#cp?&?mG{;%ZGrCci3#yg}OV#ffn_)TEo+TVv__399(Cat@USlpP
z1Bhlb9Lu{Nrh^pVwcBV)FlN!>zG@C*8oiqJSn5s4LMO997PWzUU)RzAT8<!C7ItdM
zAh*}gEur|mZdFr$Wv^kPLmxC(wbaPKb#RrJ&Q-f*{`gby5E3?PFfrcP0154=YaGXc
zzeYqAJz>je*e72SZ;+CU6{dEi(<0xUclp&dz(N`bgn$8;BQf0iCR74CjbJB2U>OKQ
zcbd9b^W>UxMB96~{>I-oOTS-txtG+dm1`g5V3}|KJML+rLr|S-vBU34i({>Ho#wpB
zU+)v%+hz>FLWPpWp`*>|>o?<^YNBVMSp-rWdbGm;cYrRO-y(`Xg#?pVNv3SBBq5S@
z^V*$jhanidB})E(PRooUZ9piJ^@M!AY3FcU{Feh1k~%}y+<m+FG~2YxD@SD<DFh`4
z%PF|LWgb&ccwR6>3$gf<j#B~Plle9lt_mEN?eh95$@lG*mULb3MF*pEL+uaZc2k^J
zWg)wjdb}rKEU;zK*HuerDQBWs%kYzC!6-84l*5?EnB34H$Y?@e`@P;PK2wV%C<mr$
zxKmWc<o$%7wdpWNq)mu!ZAz&*w5O=i{QJF|PXe^OJ&bGf7GGr@y1`T~v{YLWkclyv
z5?o;_(VcnJ$rKA(<(NPNcz89vSU79kbmfKR8^$(X_CGFE(s}NhGQohTiJU@~9oS}1
zlnR|hmV|&HT+i>1EkU;-iDaH}1zU=}p1b8EN2(nH&SC|XG`2a0Ntq^mDuEqzwkwvB
z^UkNDKBGoSJb0?)iw*+bS;-4aAtHN7_l!VRAbyq_uA~l-dw$0aR#qA9jKcY8dbMDS
zzuW{ehwj&iKGSHXRXI+jr|LKYLEslSpFRMpxN!skQwZNxbiutH8_nAwHCM7}YX73I
zjN^|rcr=YCcGL2y-sJj-2A<^3cqnAuRe}Xmm+B&K<kXs*{1R&hr3p0iW@ZLc7c1vq
z=ANalfQ|ee^|=Ne$*agdP3b2+`q~&!j%TU05SkQrq)XJ+KpVo%`^?L*Nv%fV33MX+
zM@EI5EiWF6W{=6y*T>E8!`pSN>SR-X-YpmlX6M=IN($Yq4-<$1>G%Z3Bz}zQfzb?X
z-dQ|+*u*9Svlr8dbw(`UmjDvhC+L4G*qj1Zpjw)Lv-O+Ljz=CaipnM)UsHkzW&@+W
zc#6Xsf|aX+IVeedh$I1UdR78k2`z)TK_7DH3NowQ)*rpHY|iz6ZhSjp=1TjtC7wsQ
z=D;j~LS)#j(O<0S<FVAcLP2@AG#E^OTpBZ+L_G|IK{G=?s8y>0Pp^`gFa9(Sv$>ZU
zn}|w`cFOA)+PiU&dR1)!QYd{02;{~F00<^L;%0n1n-Qu*xYmr>Uo@|G>7q{nFS&Ae
zZWga8`;3jdtLEced({`8R$dB16N&Sk<E#hNbc7x5H~V!l0@Bo4qS-BP<u2ulrC-!P
zJWV|9`$6um6Q3#3;`Y7ssCHlXmi(X5-hpro{mJ}<We`{Q)hqTj6M6Ha7NW~<<$8_R
zdf&dFo|K+dtZ<mTZn{TB)<ga<2UV%h_F4s6IizGP8HJVdp*)<a(Eqj&s{jMb5u^Ul
zT;uygG8H4$;D@Z$2T4@@XdBJxySjc%<Z`lgkMn0Ib2~&xN8~jT>V+T0NJ={aIz}Hb
z^y-zUN}*-7q-^TJ)EnpX*@t%Id4I5cQy3s*98V9Y0EZiqUDMgaw-oXi<aqZVaHbck
z@=oGF7b{E)3vfyjc><t^@G4D*5{nWJX$~ii(C(;Ppt;dDmYrts>6QuZf8!o5S`r<D
zaD0M&B>Kt$-9gE@61$Ll?0r3Jwt*M7n`PYHC)zAxIZf2U(KF`LWBXW&Bvq(Qd$b^5
zeDpIJjmP&QuYnpmr^hk74+Pg#iw?iLxh+{$O(dlmDU<)UuKy9VnKTs~(0X9K_EqXf
zrOEn_;J`2TRSSKTYULDF|LJ}FjABUzbLcGzy}G505^9_rNwoA7kO}O0UML%ZKT?sC
z;uBJ5>CB&?{CpWlvQ($PMuQP(8U>87&+t!Od#X21E(e&T&6J|~o9izja5LOAA$Fge
zJ=#c-1y2E=phzRGXuZ<#gJ_viATM_@cMj&_Kc$1xd)2<-nM=VYjhao=knB5_LN@w#
zr$D=f?Al5BsU=|sw%}|CCBI_q&AV=r7$AL=Tbn}qX;Ui)@e58h@uG7MrGjATNMV`+
zB|cJ+LN{*)N^pGkx1|y%P&C}_P_aWcxborP>ru0ZpI;GLv-6!1lV$k+m3ZTY#iq4u
zW8`x|yUCB2TsFbJ5*-G#ANB4yPuU6!XR;!a5{s2RPo+20e(`ivhPZgo7ryKJ)%W5x
zk>Tenf%&Fida|FN<^6qEczydT=D%=?*~lOE3Bfzw#XNf7<L;;4`Um_e-{J}&hD+X8
zCDys8ikrbZC~cyyc*RdSX}}b|oC2ykoD@6y196||__=f7BXUsG3Btz#!s9tCycWNm
ziUg`s68wS2L~Q>9I2II*=1xEmy_~5c0o^^^ZQxy-O={*Hu%c1Wrd6<_Q*gUSZ!Re1
z`2%V0-ri6xBX*rYPnu~1M<iu4B?RM7n_z)}n0}+~f?i@CuPhg}?1Uu#$_VB~><i!y
z4pl-Xq2tXMBB5F_D9HjRhDb5_3eSoAnlsWt8%Y)qlh_GrA2ACQzeyvRevru{gHz=P
zz3EAj8SC6?uChwt-6^HolZAe*<5>{&!3buZW~|x7-ySn9e<(e6EUk1ZEhJ8FUdIAn
zL@@J2><ul?KGfzy4iQfRE2CL2>Vp#Wjp0eW*=!fvDf^>ZX=T9m@>`#GIE(9%F!gae
z>*w-qqZcbC<kDwZ+2_RyQ_Y5v?Irj$l$n)05iA=_WHX_B8Dqq}A0P-{YHTn*To(X>
zV<oM!A;HvzwB4w@R3FB|!`@=#bW08&abRS}?_p2@Rnau+Q2`Kzr~R^Gj8?=nRV<yi
zAx<33&rE`+ki|6;8mI0SuO6D9o<hS6yW&}4sP>A47AaB2aKvx@jXiKe45RQ@s|W*K
zGa(D{#|bH{RLScSsp!~lel?*1<<!6~EcbN0Cq#N0f;Ui&MTvmTqHy|scx5Wf8j>C!
zo~jV35gk0XS|hOfskP=2V~X9_u1Q{$X~~sj4vn_3E4q!IE9)i)$RMUhE=30|XJd8o
z*MOG_AKL=s8IK(DVI6|7(e|;7{7Nr%0}0JL`-jm|vM*hP=nWr!a;rD|G9hBu!)`1$
z5+QsNn<wqgt#qYQP%+U^-skdf;te2?eXJQe)6S1kdHQuC(oydBJ5{OJvE`{r%PUov
zSf)=*NlL$=nezNOK~RyAT(cks$%r`d?09wgn1ZA@Pfr*xCP6={68wW@cB{hI2@Z*;
zbcUx=&0)1Gqo&!ZAyVM;!Gz&(ED}P|-;xsI3e{d^&541w_^IdI=%xJDE4kHc;-+7d
zO{dJvp{XPDibyP|hTa;AqkD*0Ul9c=;UB#wkbZ=93S~+FcU_c_Ev1f(xV#MZOm#-)
z27ctN@B<#%M8V;>jemRvaiYtjRI@M{IH=RssX3CkrSMrH=z=P-@~zKwkL1rDtJc|S
z!r3);{gEAYLtzH<clS;@DM`E9lzTWyd$l~<i1zwR6XLY0KSIY}mYjm_jWaC`4#NqA
z#*2g==8PtZoOF8WO|VDJH~mym&(qF6uO}gR*gZDa{-rGWaxSnzd-ve^UEol2FgN1M
z#NFvzQMR{+1S_bcw>Ec6RBP`nqg1JJi|%*C3#-B-@O(%(D=cgp`Wo)~i)gi|qA)V1
zg)P2cE4hMz$XSw+9W*c#+T2B~Dm^2^J(}nlIy3qj1iHdvWm@5&sF&>V=l#&FS2Kx`
zS_5~iE7!S3zuxsYnWQ?Kl)9KSY;d+TlIQ%bU5Qq^e33r>XQlv%Z^XoJ8TT*v#ArXm
zCV0eSvhw@$lWy>-m@LT<G3ZqL1=2dA!%!0b%a)NaI!JDO29+oSLo_HZDuadZj7XDT
zTm0-}qGveW{TJvGM_4LD=G*&&wQ4dGOp9}Jv!{r`({z^KJ32mZ*N}=SBEnkY-Qb^`
z@iUV6pSk%T@VqI+61Vb09}AEW2ui1x8r)RNR@TVOgfAnikuipxcY7&#UUGXY3`i~J
z?Mmi9=(*Vp=3}o3n$JlDP1=P>mBIO;oX5f>LG6r-D;&~7+RKwXZbn8=<;FiWl&Y)P
zvQ%jQNufe@q4M(RJ>x%z4YOMD^TnqN_YnF#CrUu!&qT)YN&^`oiAo8~?77JYTdc79
zDts<1e7c0hq5zJ^>jXq$7!w*Ey#oMHiii#@n>ryMU>|4w>W~oT_@U?1<5wGx-+orm
zaZ`XC&i03iZ$+h4mnQZ9jUP*kJEkKlkd8@cBe-!C+r~G<ya$AP9_i>NAn?-H5o8Nt
zbc|g2h{$gG$YS*XtV<nMohJ&sfH#8^N>H%qY$b+$XiP0P9!2u3;*q-PA!V)>-=>ZY
zN}BzVGAB!0vyxh4V)^Wst#plF5>3BZnr~p&gC!_}krk8jQZ&D+TqolnHZJE++_WHl
zZ9qRm^&*X;R8)@k5wq)-5brhd-`WY#DRryFx_t|mC6`dlcoMt)`YrA9Zonu01tZdF
z9S+6%)ZS0AeJ_6Y*W15nPaUXFec5F^ICj=0Rm)%^3W4(wQYX^gH|ruw#KbQc5-iQd
zJf*=VJc~!;c4Oenq?8Gf+r@}4G|(AN7gW-!KX#fZ6Cx&kgBOkFD@Zw|hr~b+VrWhq
zs(RL5JY;^nsbN>9Y1gL3TDn<yb~+hpKN<ay%Ckpx#;-^+R1xRLNKl7tL5V6cm6Y0v
zZ|86#uEvpP4^?Ge7L~v-xA7U^q}2eB895HR#o%cH04pe_a+XgX%4ywy)~$?3$|(D{
zVS|Y#TyIMiNPCq@KG&?jv!S#TKiJALw#c{tIcF=xp6VtEQ_`Ix9I9pWF!_`Rvk=eV
zHc-Y%Q4v+F&@@23@+a55iuH1WDx9{g^I1K+$+lku%U1<MDc`K{c)iC`Hm9v~wQU2j
zl5*bv437^#i7%{B^9;T3$r<9<@#h@NRVA(+kO!x6bvv%E(V1dn`G<>TOp2s4o~N(L
zZnnq8o%yr{q889$v4J)u(;?Xfc;6_QR8lSZb3PT+a2#fKXRO8`gKQ#wMF3Cvlr10%
zPLEv6q)GjL&}=k^-QNq-bbhFHY~AbX(dQbtyZ6Q{ZP7^?qygcjGOBFLk=;#kdOQR1
ziayx|EaU|6IktAYXsr%j<*0X=dv$aSv7uv#oQQN62=L5!k{cd^hogWR3gMIUA|8^w
zb%HW@cZDtCePY)AaT%6>?yc8g%{V->uWoCE9vIvBH_)w;A;`DK9N(P6;)e(E_XD?P
z?I3o)#wVMg=5?6|w}+DvHiFpv?~&U^qrz2hUR}p5KTN47c_4u_ZCasxM!oBC<ojCL
z`1t@ezYE9o`6bpV1s|iF+n!FS{P>gPbDOe^Nn1R2x_{)Bgu=cDT@F30-+4x;ZXN_Z
zgBsiO&V1pE3?mWAtj_owTVS7(p@>F<37ek-F7hXaeZ%I@9!Jt<w!zSp2kA|vMe4cG
zReu20idE+mRVRwhTEqfW<r-9j$ZmD2VpVB~xN7%gBBWYQefDcFm3Z@UJg4_FF7F6#
z<;oeO$C|3vT#Vj9=;KkMLrmhO3KiTVUez#$|5^OBiR1z#=^z%S3-h}C-2-1!avowO
zn4`rMkR}9t;0}u%48b-ijoyGj36xOQqA$m;V&X1zH=iVvd!g>B?jEb$!ECcLtYWYe
z{VUkjq>g*uNB7}MnuoRSr9X%ka)1Kjo&^cM>upYi8p%O;X<!6(;dj|0rzZc1WC@Gr
zYDYiP*HxDebp{>}%T8RWd{xZd*2C|4{QGV(i21&L^W%Yq#2TYK<6EkGf{?ZwfbjRT
z3hQUR>C^?!nOmi)-uB74?xpDg?<+RX*zKJi!9RYm?QbIsM($G{_)ud*VhW6lI!Umt
z1^}Ct(pBxAdhKRs-IOj&T--l3%sySNsen5Od!z;QKZu&nipH<J-*T$QDB7wJK?S`d
z`MsUGB$0Q)rbFpKVj9pi<5Bf27gq`#lwKHAlIWd}<A+~_AN{EoP9XNMDC4L{p8SbF
z%K;#POzNSraEp`-&0nj;ao;5X0dgA1vh^Wf=BDLKee^xPr#zluUR<Xb+#h>Gtj~I%
zWSS35Pt)Xf^wnqEck#c~88+XTHb<m>NJ$G-eM$bOH}A&YhP1T$*1BKS>yvDS`LW3W
zB_X&~@}KHqMALzOj;5Sa=|2m4VC(un+jA&qM!oh+a-in-j#qn~*RJtLu4zoS-Nhd#
zZyZRLIeDD@kURgIUv{l}p$gwPqm`Jn{^dV;^P?c``uiPL!qc5}>26c?VDi5-Q{TCG
zzvGHS!`Y<YA$yj1geSooh`3VXjDIcDGS;4;I1Mn65lRQiOH{a@4=(2yQrcK%BELu1
zD3S@9rtL0}$CIoA4Zn*&!%qNkyRrXoT9G&-BqE5#4pZEOs{F-E7~Y|TFf!bO$B$Kd
z_Lorcuw`l3wb$SM;{@et)GqywnY0%x(s5W08sGPmF4J>dz9k@xRjSf=S_xL+%+;zh
zbY8iQRdt5vOt7A-YcCKmCIL^*9RKk00x{zpu-hAe*f@Ur$)dz1E>Y)GPK5!eku3np
z>&iuv=)ZSFnI?N)(_$Cn|58E10m%qFqI)1)6bg+u2O|W)P_{Eb{MI2pKB`?<pp5X}
zhyuUCwDFLsK<T>Y_3TfJXFP4%4;LTLU+5S$Y1Purg#U^i+PmQOv$jgXLnd6=FNP~C
zT-}CY!8Rh?z`$b(fJHAK3z8~TvnzkjpMCc-w_W|o^KX0d_4pD00<|oKM}~D=t4^fJ
zrP~v0`HPGIMlNF8_MR;$<LZNH1G7}V23yrzzE*G2b%fI;6r=>(Q9k5o@08m`W^|in
z@uN2!I{1bENK>mpDsRHebs!AOnK2sL0Gwbu<Nr94ya!mk!;e%I%=RtRbmrsDpFO<l
z9h+=pfQ+d$8HN{WHLkoCm*hGooUNX^8vvM3=r8p0u%H!n8Vtbi$y5Q59}Op+ctj!J
z+L|OOsqZ!lwM^T52P)J&+3|6}xtARaV0QQ`w+=X9tkW0p_UDJVzy|+x%6KOmde7yE
zi=0NlLRmA{%j%;sZu0Q){ohE;stHq~?dk{F2m;C06GQT~G;N1uezg;~B*o@&27pE6
z&si`)<i(7(RH5|#!St;im}hlkPz0fmgI7`>Xi(E6urw@?hQD{W>WngQI{!`^jl(r^
zsStPu&OgsP9QPnUmpQh!HAk>ayIB><Ti4bVrcxa^KHSYZ{}8qR@H&Ki|0}(T=Al%^
zL&at(XydP<m+$b#FJ#^+tz65#i~jz5`;8~?pD6F;MG{u)k{$S*;YO0dA&|PAIpV6w
zMKmg6z&Ja8F7qH!>+C`vuB^{FH29<=OQX}gQRWHpiOA-7R{1%5`?|?3e;7jDfIXF6
z!@#bTo27xqSiHqKW<KS6sAl}+Ys+fisOr3=%;ecm>p>GH&(@6+vA$WTD76OqDfu#G
z`Uh3Hs(6Z_FPkkd4r?TfgkvhvE8{JP9Zc)%=vT;%zvW0_2{{Vz5jjr+cOW(DR9m2>
z)8d7$YCEDZcpP-6{@3nvQ%8zp?ivfOa^>%nCu`S#zeH@`{4){V`Yh3PPb5`yskJdt
zj8KCmcb5=QTlAz=`~#4kLpe#dHkoGK8@Rv>Kl8F*%I%}Fo}0EP1AH%dn>V8AE*NEi
z22w+t240@_Ihu8DIH>;ooyc{IPeEgkha>++r>NT$xfZj0O3w-1Bsz*XaW*b>El;VR
zRU>_^*-m>rSxgl1R`>9W3|Gp-uCArv@ep}AE<e&dMMY%>al8kuJaq^*4u+i|Gnbl#
zzRWQ|J|!qqVP_ck0JZp-OJn0rmJ_Eo1EhRXqII9qXRNHhh}&JJ@;V)2DDXe~WVu|R
z*6@e01_op-@v3R?;DnLNWl9@TqRM(Xg)cZiY_MCF`XmO;ib@FZYl;<d3aS^6QQ1|Z
z^;wZ9vM_GjasTQ28Z0|2wph>-tMUy1RBJ!O-N_EusAz=ol@BZ9zIT~aV);>2B9on@
zW9qOwex-f_ZWuXbWX=*r3S&f6g_P2;2v~U@G09RTn6$#S?H;GXPyOC)pZor0#R_u_
zx`<Zsz@DsfMfU1#!00m+qc`XCuW!0ImuaREHzK=6IDWQ?P+p_BHztk??R^jZF!#th
z;em^E|FtQJh%tYg?h_WAW^xz_W<f2SMMhFFLf~Tf--?oYYn0fLzj;DMw^PS_yi}?T
z_l3`Hw#L2rV1?{5#+ps7veEHox-PSc@djNggf(yJyG<NL>@Fudr0?k;o(Y^sR(6VA
z47}TOU=t7yXIQ_P-c2-9Iv-3_Q{=|%4hCgR9|=$GImPdiM&-XPn^k*ZlJ<FBiv6NU
zwTVxjWiL}md0IP5!~ZYm27Z~RcbtALy>|X4tzE8x>_83|d!>urs&Qv`m6jtt-?DNy
zulx6;>ZdH;iDn0Kcr6<B9cNq<4Y;&ZcSy#-_2Sb=BIm5XMvPS47VDih1$FHy%UgsF
zL|7OaVI(%10llv{f(J$*V+*4OuTv9k`Fxx*E2<taSsC|piPJ?blF~+<IgpL4%0p}z
z*$obIpG43mDcdA^sAorc0SOpC3N#?A+|X!=V-KLruW*XYTVGX$Bzp7Nolnki0UxW8
z^uEEL{KQZuj2+}LW)IHk`NYtuR*P;b)P43?sL?p8S=pjy(q{R7OHxyd7$lQ=hY{6k
z96}}<Fkzve>y+9PA;QSr5P;1wC3!zmrZV~aafh3YOGn1^q;rbm)KR>=L#Nb~Ov#Oq
zDyT!5e3Be~j@Uhk?3z@td=VpSmnoCo$=znSR_wzrK44^WLr<sEX^;H0SCDPs36Pxo
zi!7nrH7aQEm_FGhS6=n?r%72I2S|AGOuU-S)2`m&F>I^Vnat(4cFnyT4omNVGd%SK
z$gxN4DF+h_IJsu}@<n2X&F>OO7HoU$$<w5lBbKHWAgtOy8=}Ql7F0X#pk`!ILJ~nL
zHadZ+e}}Y>w;H{=`zdStEy|jnj)t<tc$U$Q-Z+wNuZVef`w@@i*}c+lv{z=>X>t4d
zXQg_a)RMFUkX9xo1)`n28v6yGve8di3x%0#u})q$G(kl$Q12|t6VTl7e^A63?t-|a
z036UDLeJ|m8{V-V`s+V@A=ot}SenPu18C#7MK}nO<zEbmAM4ZcNRHDRSBZGO!n?$|
zu{erd0|Az&2Z30e<!{_3i)DAN8~)1&zV06+Uw9JbxcSG-sbkbO6Usn$bA*`!w$jN8
z+nqiv{f7MsRTVi>5qT^mn8`2Fsq<d&MXD0}j9(toq)EH)DjA2p8|lETj+QJ0A5QXh
zy&5#tH)Mlad%g~42LGp%^EkF`=0}NFiXyD_VV<?n`GRqbQBZo>25;;EzzjwkGf>t^
z%$-K127Gsub@U(XQv0%tZ(Hz>Y8o?DKw>a<!-0qE{Zi_;MPPb^(tF`5G{gmmDtoy^
zq0Vd&We*l$fdT}h2BGhPG8meFK;>hvXX`HtSVj2{gTK_a?~ft?)hqy6#fVF&AR&I0
zf9@gk{Mp0N-#30V{}Uqe$O5<hW#5ojSv}=BdJQ%bw%G!z@L9G@@O4*U4!8$q6rq48
zl8p*ToOJqHcGwzq1lB!f3q7umdpgr5wVw`ks;cJHcF>(p6dZn|lyc&-vM`a85_u!t
z_5pu>oyF$jJI>LacJxH|%w>mn{`>9oxQuwEI`oghnaQg8A5<NxZRgLKmnm4?rmx)j
z<d*D|FG)l%?9gL;QkRR|yIRWSr&x(&Z1+C<O0&s(3bXgmF&~IXGir=JJomMrWcU^3
z-}g-NhQhm_pY$ePYIVFtfGgA@ii>Eb|98zgi>)G){U8hSA?-vr)wV4F&@kC-A`a%;
zr+F>OdyPGNoyOqEcYkyh;mj!B*e&K*9P~iU5$`aAF?4Lzl7o;xafyLj?OdS6Z_Js;
z%!@x*C@**>F#-Bj<qHF8its0z*FUO7?kC8I3iL7y)v(yG(t4;;gUqBClM3f9&@L_k
zF5dR6ZRpYI09Khs)^!D~4|^Cv(3m3Wff)!j3uR@-qtdm+eVqv~3m)r!H6%i~EU&$4
zuC2zzv+Za);~3URQk^vmdrNW=I!Ai*X0FMe4=Sm<NQU7cYiK`R*!qs%|GsdN^8MOE
zW%X|NHgT$tz<W5Yy<b9~P~thw4+IFd$3f)<&*GEHyf_qmYL)RENv+T8t}p1}%qQ6F
z!1_+~s*#UI$e<_P<h_^XwFuff-DTAk`wLo07v)aJ<4otn9k+;CjlVv&bz^m8mbce^
z$m=PUlm{TMosmt9(V{Re3+54j4FvD*e#r;5StXdQzPh(19~~>t9O9|uw`v6Qlqvu^
zBRJd#qpnGJ$RSuK1cplJbY;*rgvIc=G<BD>VV`t%n>3zDNP9gYc+zP)TuF7Hpe`$-
zMfEZ04_u*a4|7emD|KICR;;yKI65IXLzL|dAwu3Fq8};>`cEoy16b^Z?9+ge-~$YR
z-?+b(wh^^Mj=oHhvPH79+cl-fa>YI78Vlft^%flzN$qC*a!N73LLVLm=bg>Iae2)P
zZsyf}&zrH~n6XFM*(cC`_9JWV$Hf%I{GWF3EIYf^F@rVOJH}rmq9t~n7pdwQG)28J
zJXCtTU3ODc%PiE?Kw>sN@)E+nD7UMJUik*1Nq<c9&YqSB9--X*;gU<Ni?{03_t&Ii
z?Yn5YF3Dd$vQiTjiEsa7;3iUz(z+MBHMXb7Tz$b}V+Th8Op#R7V^R$5il75Q{xc5h
zQ4JU?Ttpd;RK)iJD>EUekSHqRG%Ogn62~R*u!^FZ*%HEq+_PTX-M^%Jo|)46m>LI|
zMpf7xc}9R=$asU<cP#)uu$m+6-gd#B4I>Tf85~(>uzexVapnKiT7}(1U+lx{7zS(i
zQH#leSD*tng;o3tK>$#s*jhnul@R$vA(YIN5l|HJHih<;W=xNgYqU~JvC=_+vS=!|
z=a}+Ey2`~uQ$yA9sGiEZH$2CSyu$BQ6WT}`y2#FUsB?VCyZ!6tZ^B!CsuLDG&xuPC
zUJ!sJND0mf+YH^`w&L6WD9B%~DI&TUw~>)9zARpmMdy3LZj`w)kbW+$Gr6QzJ&-M!
zO5<}+hngWL=cD6A$wyXwN-;J)r7${myH#RWy2U5=sO6qI3F{lDnsNL7q7E^e31vKB
zW*%W5AK9T{{|;7ImjlQ}j>@U+_f>t(wRUJ_T>y*i>&Nq|U=+<MYKufkiWFOc{6&bs
zK+rrE<ue!iUOuLMeBK|0eC~;vD#e;*HjU-csBncgP89Kt&ZYISGb+5pSD+bCP`?KN
z;M|M?YLCZu49f4v3+fmpSm*idsf^u6t8F`(h8e`LRAi<!Ana$)?HfD<i?Dq=a%PUz
z{Uct}HQ=LR*1gWHX!J3ckkdOazO7jq-NT*FqhdnA(>@W#U1-xX+QClc)V6f$&ttpP
zp+@6JeoSpw-;(m-h@D#N>B;=b@@vfW2itT8+8;AQIzpO8O&R#S9s)8JA~YT%LbR&_
zj^qXId8NY2{A$&gPFnz_4U8t$04Ey&Kt^;Rnri`jZ?)NgXe<AnMosVJC{1E6@m<lk
zqP+ICG;q!%_hh}*0FyVWTE<s4jY-Dc!V2A@BDNMRPVCV&BKoWl`bu2zagI_!Qmje0
zNF6FOc0G9}9_8bGJle(*<ICsrM_c$GD`+3sVPrv>8V~c6R}3NiHV_yXnvD=SrA8~B
z!q2g8vD4`4_bXv3GokN6X5szJHI^)JVd(yvZ{wA7Embwu!ZQ*YfUn(BLHC|9BUPlP
z!S*ndev#^ao__x*|EM_7c4cAP8enfG8T*xJrGXhmRX=PigW#uRn*~sIGRXhZc*f-w
zX{4(!fSHRD;=%G~;oSHh_MRik{ZSDCs!}S2Y?)u6UHG>73&%Tk*6^rz^dmfvhFAp`
z<OM%M5d9?4Z^(AHs4Hb;YgR4C%yijUrPypOIURHwql|@!vFa0cE6EvZll%so-jsAk
zdOCtT=2298d1hQf6zq99U9RJLjz5&5-BVO+x}x{6;8kTx+Om7wfrUsT+Jl!gJL@~X
zucfL;e1DZ?z}+L)>~iDT;+38Y?f=XK$K5y;*4w(RQ2O(mE!p$(veFkl{ra8NgAf?^
zC;$sH{itDy#Q1|YVEz3dn^`8@3LS!~W6UJ=tT67OTu8Nrk_wen<P8wT$owJ+gh=}W
z&`<!Q2+UKm7C*yXDdhL1$D=a;?K&K41cYddWty{fSl;U>zTfeQtVAFrm$S0r`<(>4
zgR_%jCxji)l~W(7vOL0>2`d=B3Km6mj{eYf<Cg4zdWtAM{9`AqrUhmtU_~fw;v+bP
zuovREkatBK0t46i<<q0xG?-ZGQLm6`>np$=6!L*O#K~Fr8aA0f5@!B(pm5@uB^eO#
znI)B^RALW|QU&NY$g<jgknZWED6@LIa0u9`-Xht?-Q-7`0K>Bdc6T%N37guF>Ju)!
z{gja=b(h|^`Uc<Gq!`>iBy+}VqXvYA*Ttx1NHSSfqBHqwsL!&c)MA2lYT<^p{)G}<
zflA7?L+ABQ7F^M4h`zi|Zt4g1Ij^`(dR$nO8*o{$nK1^jTg__HXsxWXN2CawHw>Wf
zlo~}$kp^@hZIDl;VRe-n3pURZZ`h=-c~9?Zh~roz51H5oNr&4ChnG1jZ}Ch^kZ;Zh
zX*-{j@jf+g?fCO$o^f~H>YEECK1W}K+nAL^lNBH9A0>h<0NKqr@p}6E25n{BslxtC
zE~h_&6@To@_CHj(HDI(^$zF}`WwS|<V3O%8&eQRK2Tcom{M@NqEmO7?MiSI6af$Ln
z(qEjkzMTmeFz=R@gqpbY6a@_k|DJNad)a<BRJHbAwRmQ5(*FlYrtRtHSJ`O-_qRe=
z7cB0M@ruvu83+ab)(SkDdL4?NfiFG0K~fnr6-CLZ|3lfggt~H(aq;GXR*|G;f$d7D
zSPXD~ERteu-GENY45+ElAB^>?8LJD%XotOVBH^0oKGQ0S^H>kw2qEW|imqo(bbY2A
z`ZQHuuYlkG=D|~LC3qEb=W5ReS076bAL%x<?{%vDbE}zWY!7vQrmS?v0W>{JdX(!m
z3|Nj5vS4)aVujjv$e}<m@^>;Sb4@e)hZ5n(%=`KCWyOb^<ww*5b9wh%(1PZ5d4HlX
zr@|`8NGUoLz*xztUct|}y9LrAKia6<H=ATDSPuBi?Y*<i#wy7SP<^ZLB?!P!EElnC
z`8NF9|IGUlY;L+0W_|P6!n~X;<gecc8gY-$uAS1AHXlE51}(NvoboVXB_79sLvUG@
zUXtQ@7KQWGZJFekAK5+&t-TxNV6RDaXmehISU-a)#K2o6R9$5IJO6IFd+&9wb8s<2
zYS+adyjP0s9rRimsh8nNxDXqW-4T&_uvwt8`POi3+xQDl>6c(D-zw=k89n|`gJQyK
z3A*~NtG(RKf}@20CL<^&hd2_PytNhkRlj`0i-o2sDiPcF5Le+ks6l1Vuy}vb!+;Fz
z2nG<0f5S;#F$lch*T18&Sm6L1gusiKC_i7oyVk}}#{vc~@GbEFX3wG!8q)vd&hT`w
z?2+vNr=N&AVU)Tg9Y3gO8bF}&&i@ll)2g90x3}mpZ#6m2UH=(AV$)%El4tp#Y|**L
z;xzA*-_W+#3#;$>mOY#2L9eWT<bV2UD=*n(?CG^8o+k*3RQh@`P(dgM3VY2?k(hLv
z1q2|eD*!FyQ2=kK90dsQDo767h-9hD8eic@0_BUpX`&rX$0o`VXkpNY`$Skgt}gO|
z2F2v)5z0_y|7?@@Cg+|(;-i;r>6q=mmV(gjt!6tuIe`eRdfQ9i=CYS;ciTrF=LUOP
z$eXtX(_hQu(gCUfiS5=pz@j#?15l5jBeKiun@ghEzOBowbR>?tMRz7h*-db&U82`}
zgf#JQ<PrwC(-C^2a?gst`Y;2H0w@9o-j3{wG~O0*+a|122)b6y+1J00$2r)Xu`y}S
zDOp4F#EYqB&bD)m_d@D516%#}Z6T<_i65YJasr;@$8iJ3bnGG8;O_dnrilk47pO8o
zUySZlmNW&sBIOP3VxQ7c{372<{T4r4CZp+4sYRT3o3j~x$55y447$lyx@eNB7ZqM2
zpTIgDUgtF)B`d!N)IhlUk{EMkfcdL}Zv`_~XVb>exq)^LTlcb##!9PgBjekHTl5C-
z_V+p=b_?JZAXbO(2(SZNJbaahofY{@d=CaYUS<{j=WW&Z;GOSd3FD9ckNf<-1X@ev
z?*Dk{ZreL;D6Z|BUjCjB++2YozMUu{x-YJftr_I`6bmH&B*%CynN1Av9+_k-x|dSr
z)<QZh(y9+S-a6n&?*DOa#)Tj|u*&&OO9ur3@vJf$Q;#<Kakfa4jN$I4-C|3<uU3qH
zhO`&4j#Lc(=*?^Y(m`JJduR^N=uE{W<d*pL87UEX_)PmA;mBzVE1>96%$lx5^yh)$
zSc^{Rl+LN0mX!+exN*w^)$>>P7F>*;v7BYixwB09rW21$o$&vD)MvO?%cDNXUe2y5
zS<>*~GJvsR1(S91T6oTf|DE_-4F4yzn&A|i)cmQ=@R|M(7HX9Ds|jL-maAt0)f-v?
z;m@z%_~EqvIn7?2YtRU}<p&xD*^)|cHx(l|QOK)_g8g<NFhf%RpG^@ihkt=_=dY!B
z?PjlB^HReVuX<0qyP0Qq7<7`&TnWM53XLAcE&w2K;jH<Vcsfl6etH4uL=JZx(8l||
zFWI?@hB=Ta^gklPT(QK!;wTkGH^cKgo^Xu!aONr-l}sJ`E8$~((+MotaWM%Nu$d_q
zV336f6Y6per&4w@%WotXhueEmbj4h!$1n1+#jFN1;A%-C6IJm>g=Ju8I4j104oB^y
zCs|vB37DWu;e)p+R6d;=3{hSIZ&5wsPy0U<DQ>6fL8;5??wBi5+~iu3EoIr)9~myd
zs04a^L=9YCZPx+dR*vYcW;C7|=i^k~YNXiZe!d-|>2cwW?wjyUx_>L=dC0U`wBk~}
zSfl3IhLrDL%Gq7YPSzlQe8AplDERehk;Vdg<%Nc=>2{DPXjq(7N*@If0<Yl1fKf7t
zv6$N1)9<S>DL~C_P&YS{uKR#v#29A9ET$a<g+9nPO)#&2WLL@~lly$vQzPfM30DTq
zM7Q5r$Ks<lK=>v(Nd#A$t)5DQ0U+d*xOR@je&q|h=IR~wbh4H^DFXdoW>ZCDH7AG?
zuty63g3Qs65JZ@k48XSJGV^L&Lpk>e!zE&pY2`eM8cJn25Txh)Q@(+gu+m5>VbNlc
zv*681TCq231oFv16iY@%(P1Tw1x_fzLAhi$v+DL#B6b>H^Su+nw%X3U542;BSy{D#
z#dA@$u|<6ZS`t4A2q5*O;l`&T$FHWORo+`IrY2U7b+Jj{`Hnu&iOaGw4Bhja$gC^9
z*{i8u@lVhEUC(UEN7WEyp?CV7dJ4}Zm=d`u{nl+3B+Q8gFIbYL#smQ%Ij-?UJr<?8
zW6Ek5XFXdvIbsv-a%yKTeekZ_LD0#pXnQCJ0(iF5omp*^Q@s?^k(YR3r8VxuyTsq;
zMJ)39&DVh%x7by0H|tAngFz$W42nWSQAZu(8ovDoeBi_FBrE`9$a&(KFdyt$Ju_*_
z734hps~L~Gd4AD(<mm`UT#4RHlyTwL1XYrBLPHh)2G+>T>@C16?VyP*7kf=(y9uNt
zziZu3*0mE`OF@0$9&QDlN{yR^aEa{T={munBX9tySf^8$<ifMFez9~>7`%KT6MOmH
z*jg<sm;b%W8+u0p`@t9a-q%m+zkT+y_iopOgBG5|)ud3-Xp2qm8N(Fa0E)~gac=Za
z#4zP7YmMU6VHFo!^-s>BwVxIu-D8|%r!OhaAYdn97|*aUZ_Gm@rZFTDUdl*&h9}I&
z=#I`~QAly;2$|x@2JaEhEfY#LFl{N#>gs#=W?ILlVBMiZmPi`Ze4I}}-V*#`x~7d~
zoXZvZ;CV0GLC7Z&Ic|aeyFM-p4W!tjCwa&_vpIuZLy40w_qTK0-PfSyrLn#qrN@CE
z6WRKM#-B>Kvb=0JR@;2>B=Cpa(QI)m=lA8)0b!0)qhF);bv#8v*`iK#<V}AT!G#iA
z0{_(hJmSH~Fjp9aZSvnJx6ZF5ORYFi@LM_a(~(L%)#K+=lj=?2=Sj2?`W+`<0oarI
zh$CGV?(XeM;=CVQ@`nD{$&A<HB7etCOII9CcMrZ8dy0Uu4ek~d2;rDc6h6vm-LekP
z8q4tHBc*KP9wnv9w)V(ch=k)QQ+<#B&{{!{mtD|Q`zI&R&2T&~gxRdHJ4wV70L*^$
zp%{j(%;V)3y2^~6iC<U|a;x*&Yt3U|#T1-Q>Y%S|;K&m(*qIxY$HIr_>Fuynm3&z@
zY1M)<7W|4|ae>tC%KAEM%H^fPY`RCki`>?Rt5l`~rnl{vI(mIy{nT07N6KSpHSABX
zF>-W&hK23`eX<9YYc1qpQ{HtEZ*9r7`yQ5K_K8&D^U$jrM$T2~qLVq|Sj&OqBwKr4
zGp$cU>9bv#p0k7n+&sP}zarnj2h0S!Y>ERDpC_o`i#o*KSZ7Nb$3_wY2yhjB8Cm5c
zsp0tNcZrXL&--rE<QqdJM*|c{OofZH`fqw4!JbPKm%TdL&+5-?EKDxBs3Z4c&6J!4
zU?C|Lp|NP|24U3%Pr|rqA9~@-W>~Y>d4_E0J#k_LSzs>DY;fjTAi%o?a)K42<)XRs
zzyLilKtS%4kv3pPW5rQ2qBF*Q`I4<y$?oBpuf$l4<XC2T^(WCsNJ`FwZ!M@G+WxSZ
zRde2c^;lut(1ig|Dk#_NP8B|8>FDKN?;@6-AS=kl-d{-wOjfL9A!?ukrUBsK5%?@V
zs)8jEAV|H*O6VY$@YNbP$RPUkAbYl`>2ivCIAc7ipx;?lwvt!2D|LuuOR1x=D3q?}
zr6p2nD}C&<0QD2P`xX);U*#~waz1vwW&i<z#?S1I|8$HcIjJdZ9y)011Mdi0o|8&n
zKrTJmjxW`eehO)Y!;610je`b#gR*yTYc2n%bzL(pos_<q=QzG5Wd|`GUA}CX#Z?k>
znkmQdI>Z4_I8%tItsuh%$Nk>!!*e8Dcm$23<EWj>5p9^TYw8GJI!SRF342DC#nN~r
z4_RhN`-o%<>R=+9H?atldHyIpS&?HaT;R+HS%FJw7$%q*5L7V}1zBPh^|H2Rq243;
z^wbZx>iat|%7Kvz!=Stkm3Uk~E}A`Z@iMx-5_gD##NN|G{tfK7&qT^~42tuwr6_ej
zdQ&s-ru*fa@xjK+-Imo}0V?xR$q?d<@Yn-~7Y*}RoSG_-V3<cM%`;MC=2vOK$70)+
zSH}t}4a-tz(ZqlsHd+nv6dI@?iVg+?067rNAr5&@c@f&P`y{LDdE%q#M63KN?gFM`
zBb8;sS2?zCF1IGB=2h9BPKsl6E=^m-tyNv9RfC%4%w|<HC>ef(iKas`P-y%=ZYf2d
zA)(LY)+mXife9}(S&4ThnC@QtjhfNE8tP2Vi*>?voN@L_2tRn5n4PBcWQ_I6bNWxU
zg_|!>A1D)fO9qyN@Wh21TGS;IvEG2XjhT8VVO<rt)J`4M^pEm5fuki=P&tjlHAsQ+
z-;53{-7RZDb4tN{XA#lPRH;2uY&71U)vBJ!J!i}1bjo6z{OB^i5E7bE0K|E^r!1&(
zI)%qB*f*d<xie2`N{R|kaNEo7l*9@tLAR_vKzsrbxf5a3aT8$nh^|EsXdE1)q+##`
zArrl{qEeZyUK#pHEXPVN=RQjA|D~VBT9h0lc^>}qJ}fTq85RhRLB$jGTg55H61_aZ
z68|j7_Tuw3%-fqzn&o8h!V>cmi#neHs4rL;6Ua6eA1#px&;nc4-Rj4gTbaaB9a1~_
zOGWY73E@buG=9s<wC+_6na7j1GFpYn^LU%6ja_y!8#&OB@zr)gmV%m<cq%4L4g+O4
z6S$9mG(Yg^zIr%OMV-J_guuDiJE%6~LZ_@_fRb{Eea5h8Wj`J8h&wQD%np>OI+E~G
z&fNQNY6X;8wUUG`leoL#b!KNg=Sd3*8#h}gcgm`ngI1GjbtJn0)Gm6kh}1iAv=T58
zI16Bjn!E7{5P+nJSgezI3?>7Mh-Uv9C^DGIG<>N+K>NBSor`BvgMLoP0ySp7y{J`f
z?#+QZHZb4iqp8tKq4%P_%VRM5%)XFEG@KqkY!Z705S7GX0er#@R<cauM$87qK`tAm
zHL(_t`g#vllV2efquAov{|mnwq`LdT;0gpWo;qZ;SOk*HUH#ms$)>zA)6iJT^5^Rf
zg5eFC!J-i>(nlq!fgct1sfb^4W|yqjS7hbfOvfvJCR)&peT<4(FcpOdm`rD~pFs0x
zpF`TinhR|J9vNXILw4X&;jRMwcnJ&p5?|D3fu*W5BgGrd#&UB-9wcKW3yHQ$r&44o
zDP^Z*MAO+6g^xqii5oN&D%l38swK~>n=Mo>cU1lik%-&1tbDdEt?VJ)dLg=~8(1OW
z1c0{$@x0992N(!IozhL8Nu!X}PhnY->mLbf<~&O{*)Z$6zz2(m?C?la`vAc=f7Y73
zpHu(l$lOdVv|-FFR_D9PEG4Nu8xuT@ia*z#$&DrYT}&qszswP7x;3VLR!>?;jKxCe
zWAK&XPY70R0l<WyKxLt7ljv&><9Paj7{OX;?jj+gJ+?G|e6>gR;f!oa{}8K&*^8Wz
zPq3SVLfVVRL6TTRSjJXB!a{gV0dB{@4}bPP*3+*8f!~7vlb8g^y;DRFV1<|ED~b}>
zkA{f?@hUv&8_^_q7uoSQ%!6l<iS3b4?<7X$p)yNFg%sl*zmYwEwY@m?y(}_{q(Y7U
zj|s3`Wv^G%r@UmNUsGkpQyNnVRO;h}mAf*CESg_aAKDm)y@WP|l~kmE)(p;Cq{coF
zCzkb4PDpE^H0!AF#}*J2qJ^V^vhk9@L^p)QTlhAHDg)zbL&RJ8$coLB&3Q%1Jrx;V
zS<I%)7lt~w4^p!I8&*Z`=K?Z2ANkz{9$(=KKE61G5Op#LG!jCS))FLBc?vzmZq^$^
z!9yz!qp0M)R$nLaA=5{V#2FrhUjr~Mr}Did7=vs1puvG{a<hy{Ixl$6U%XY^lTw3E
zn4Ac`nk)fa5c4MK(Glz>ekw@#T&&q)_EuzAxymMwfdFG}GYkf#HfdR{w*Rq^@JHH$
zdn)SK9+B5j^h5{<6u0}*xS(5Y?J3Kj;xTB<E~6)kid7)}uBni~rnil;?e-9O@P{|@
zH0XSqky|*8CQB}PPWy@BRwU8qmiPpMfqs0jT{{*l0UnNy%sISwQM*k>ZYL{7v6&+^
z`Dm|f^2C4YByPI?=>xm|qns4YnMi}25hlyd4$8_t-a;xuVHb#*BJV;tp|CSIdLsTH
zti!p8W>1S}WoY?pSf*q?MOAk@+jAmqLo#6w&~OFb8?E>=I^dF_xHJxmDSi;{WovA`
z87$Qv?xu1p=djsbJ@4^;w{Np()_S{{>xy1i%iH9B-xH}1FoK`0X+aM$Xvg;OZ^IFe
z@6BqOt&!$>8-=e(6&2d_sii$j)C|5IIN)Pm&{O98=M?=j^B?s=J81D{k(EzP_j|%C
z^rTs7@FCe${dCRA@FsctbUP&TZ2i1mlUqgqv+Y755KD+9EW!fTu?sfaZTBm<@mS&E
zC23XqvIR$4e@nJAzLzyDny3!)9=)CCAjv&AKj?8OV?AnP+cYF0ub!gax_yX^jy;wp
zN|24|&p5hZmRmD?nKAx0wlgi&N~KGly=^c4)hD89QXmN&CCpGRDcd}^oHqf5d||Ap
zxnlOhiaN5o<ttoqDPBEz{ZSJ!VV1w-ED3$bXx~2zh^eARHQ}&l+)e>8j}-~jtr3ur
zV<Ssac8&Dbj+~rXg<hVT&WDhLP=LpgsDh?bog@bK020XRQ%C-`k(8pQ=(D}8l=yb?
z2Um}hHy+I!-(L)TGx2uBWAr$hv1@3jA2a!;`q!Vbk)8KFHs_FC<rhrJq4mW{2C5-p
zA&oeB>__{`k8!C3RtfT^<<e}=DU)Ys%vv1Dp^Pj4or#B-e&c}z_XEU^#Q4HBDOyMG
z6W7ZU4qm)H;b%89K%s??O}|lI{$S0;nPcClsGHKoh*x>V)7$ixo_E$=?gZ?R(oEIT
z>dWN1Fte#&g?}yb_jXzErsdj+gP8wU9PnR}!ceu*065<EGtsaPL1_d2kT8JprnQeA
zdoXU)yEmbnmlg!O{QA{?A<TZkA81ws5+1tV;77x_i2<loe*`L#dL_?8xVz9zUcpzs
z?=HUoF1yRH_fsod6-^Uu9fuaGX&itr*+~W_2#3TB8({(en0^zurynekfMQgiCDwDo
zT4y|5rGBma;Rs8WCpfX)7mL})?%O56#iA0Dv5}5mu;Q=P4A4X}_f4-CTsGuy5BvpB
zt?TXf|JwY0^W_<Bb<cAL=nv;t|NQzWTo1V1(tml}2SVmM@P{fIe!;_k3EaK$HCl3;
z+~N|E4V(V=A@(8cioNoVm?*e}`nleJp=WwmzYad=*UV5R>aGchI!&jg{^qH0Pe24{
zQYJLAp|Kz=0FYb|0@3nV7y<?%CW;sVgd5<5g#SgV*y=k;g0BCD0bt;)9YhstfW7yo
zmm6f(g)7R$I?j>Z;zdt!A?R29Lzq$*xb@Ayl1HGbMz{@UWywqRO5Yvk5Z(QIL|LPY
zeUKFE|4JzW+kAPXllIoO%kaokvWDreY2`fY^J6#vMV0XJVTzA;R{%lyC|2N`8E7j3
z4KOE7oB{kl!$!Y+JhoHZ0NUhH`cD5N=_~`9dgC^HHpUp;U1M~2hZ~(TQc4;L0Z~$N
z8x4{I5=xhJi-<T%5b01-X%HkuL`47Z-uL}_zMpfR-~D^;>w02C?1HoMHuLeblXnf%
zLQ?a<-2ryOzy-*7?LtWtcgrrt1&X9OKu+~JE2hk8BioN2JM?)fzL?q%3cssSbEon1
z_mGIYeS4vAzwTMx&srfQc|g1!d|mNp*?&rxD}tu0N%T`sGcm=dHM0Z~5vRM01_0W9
z{hpwaBAN%w8P^RwmrUwWYOH^9C{4`0Z%$7v;MD_->=?nY5S8MRq5&e_^?7V8zG(Wj
zNK26cdE<Vn!Y?(SO15UT(;xUVArtU?Pdn|(Ag_?#MIc;Q29n=aM+d>xj2V8foY50G
zXGj{&yr@h4NW0q3W}uI{iUm7oYX5&i%eD8KkkAf;5aZnKh1Z1*M(5|L#?4w^rb|C`
zq>9&*mj}g)bZGL5e0(a-jwlz{=(ZBUlZTbpdG9EcpiC@G%>=B-I+l9zWC1Tr))AU@
zaD&Z<dgTqN3JE;-7lB9tB-_v>=m8R3Sl5se2AS4pfnK7leoXg{mTK}PeIDO3j^a+P
zMZowfQDo)-@wLq}0n%)*A_+tD(dzCLim>tzCzklhrLjk!zI{1B#ITHfL<CCr9G%ZM
ztTkEP-gZ1~jGNT_HVthsdS<@()9A?>HH9Re9D&WR?goNrwGaL|7XkoZlz6TAZ+rNO
zg;2`pqZdNuPAxsKez}o#2$l;_;ff@|q5(?kL1PAXVi*hA3!Z(pVS|24qR&NYY)y^z
zhisFbjfaKDZ=1ifQw_KF$VFz3wY46~*Yq1&7k?ExSf&7#1V_8a<nPu3AvErrFn6{B
z=@Uo8ODlOPfRv0O#&+30EZPIfkes+#+jf<N>GT3Zfpt+Sj(ubqOaYF?7<ouo+e?8q
zl8(Gm<je|RNY>FF*Pett8Q!iMeNkD0YhYLXz&~y_rz&Jv%mgY}$kuMQ6PH<;{uulv
zXi%b@t9SgJiLAqlVsTB!E}B91^}p!b4>Wy>J<KYQJ&H-1R2y27I{a9(;`ucFnZ>ta
z)eL7Sizd!sybx@~+TKLY{bAzEc74a^E`!3_q!fjk(*Ryr$@V_H6``G@{<Ref2zb}9
zYT=4$;ow+u*fmB50s`1(Wnr0p^#l7G<iBCu@3phaH|Thnfj;KAj%QEl?wJ@ZQ9JSH
zzf{BZX2N{F2~FGHSL@CjKm@is@1Sw4=Z`*R)0(NA`EE%)73*MmN-Z$F|5H=uSZBiQ
zDSSx2CS!kBJt^rgWBJdCq#P(1y1!4jI!3s%hd6}Q{*5ypEmIi2wjZ5U2;pba$tWd5
zCOT8wA(4WJ01l3U-$W3Miw6@w5nx1K991`wtYC0gbcc|`nNQjo+4_m^47En5FBGvM
zmTz$=KZq7rntArAJlsN=h@H0*pgLm`(m!oxA>eMPy^s;4wZsyw9dXPF;#@|*s~;J6
zzIsC<;y5-}%!PbHjsXyyS|Ay+e%J-j9&(G#H^;l`VHfMK(zAS7GL`gQ!8dK0CysPK
z?oJW!Jl%!=EbezJ&5*!JR^NMCYv3$1u2a}9r*>C}!J!grE1T)eNBG;{B`@n8rbew2
zhW~Opy|N40z;nmMWfwFQlX@%9adS^5HG1mj!MkNC&63}{nBf!^DgAdA&K!mo@wr#|
z!XtpSnPKj2sRBSJQ~e2H)UL4f!+;Jn7}wc}kc1;Ua7z`5gqhKCE>QPh?-5+TwnRrg
z7|^KFh&jNiH5!kx)F;8h7+&BF=xAiPG<NlAL6;)jLo5le5i)zp($QiqO5{w;ZgROz
zpC+H%G&7Z7TUekCT544dA8qk`=-9S`7C&mpB3m()BzFEI?%w78>And^M{i^@*6z#D
zelwp|>Errr29WDnl+8p(`Z!tsyA{;C5Qq?1j~+RhN^@m}1R}9W4*ggmwB$yISf(n=
z!RX^}!rB@Eh93f>PKJNKyL0@{Y2|Os$zR%v;owWwcrqJH$X8c=n|FZWcjO5A;vvF(
zD}z=>6a!FjY0rbH_h2Za8$Q8(WV&vmnjUW#xnKMFOh!cMF8<Ldxa_$IP0Ku^F`fnV
zquiA~sds^7c3iLNUix&shyLwcMngH45_AfiZJQO<=#(c&`&Z7nYul+2@ytr@<+u*{
zTziaOn)E!0LqU=hEm*igf_Qor5Cu-E8O91VvTjPqr*F&z3i$7ap4%H;I_P)8dMq{9
zp3#AVJhrv{D9g-kY&t+9!E$Mmf$EhG%4&6Jwd&#*(%{srJ=w7DVd=9Jg4(Aw065>0
zEcV_U(wQ6#2jrlcLq*_eZ8RE7QP%?L$n6GL-GsA~%*c3=z%IuwLlkvx7~O#T1=Yrm
z;%sE79ZAD6n7)fUJ3pS5Sxir0TBvIIkDlkIyRELaSM!Rh$?K%}R=tsuP<hwp8s_1b
zUDP})@>=70XQF<(UUF-#;mU70un>0M{9#xnR81$W9{`LaX-a8sv8?jy*BZ*t545rs
z7o2Acxsr0NMp{eKGI6%I03ggY>xWD;l7uxn!OIHE3~MVq$4PPEm#C?064bu=pdjP<
z`9RCJWA&h;Z;G;!YF6tvjIRIgB^8tsjq}8$eh}7mCE>eqN^R8KUtyFezp}Tw0>Le}
zlhW}_IvBXI0^W*|M(XzcYS3ZeaA2m}4ep{L#JMbo_<&Qb{QJ3!^7M!(zpBY~rqj#O
z^2g4bWhJ0zy{QW0k|eUMK^5pRV*VFP4ZywCug|?>cWi&jU5E}UB($%oPJLi1xhDQy
zUc0naOZ`i;U7byYVe*-k2eTov6Ln^}^u~2Kvuo>cq>`x+2gmafed<>c7|sr&udEb+
z;8O@*GHX&Kpbf)Hjf^Pm0bqUo!~lS_AIaVO+@&k?Y*A%l{V}yBS35Q8I{;i#V@?}T
zb!>2e9wbJO1SsHm@+m`dd?5-+#s}vDHFnda^|tM&IE-d;H;%p>c%NK@>4?Alnrir^
zaSr0Iw3gH@M}X0!oe*yoe=)gKY?#~%a8NHt_z!TCRK`U&O~T`!|AKP4iCn>5BH0Lw
z`3;%}cqpsk7#MruCO9d1PfYxHTESIl8zSOJ{7*_V=Q(-PHXh&-?YZOZ1DUi7a)m+h
z)^KNvz|kICgG-rA%l&{eYN?;p0l%qRZr(|xh<?D7?8zv`AyDY=!$d;;8~>P3DY#sq
z^aD^<Y{#G|TyyiLZ&15u0E0?k(G#dd=+S5ngar=x2!g&8X-z}jKN0ajbezE?NE=yz
z2CjL25-=cXtdgyNF0w1sm1%SO9rl4xYvHRsla^+w6EdLY!uu8*3Z{q~geWzKi+Rw5
zcxL~oZ#$>xyAVZOWEe}McB?kD8GP=^Jg^xY$T6LulBvzfAAr`3NtlUZ41ILXJp{~j
z@4?|11$&XuD;R_b09?@dHdGuy65vX!WN9c$5vN%p)ot;Q<zF(^1rA7o$?0PI-0>s?
zdpVREL#gWy2X!n3VCWARGJ)Ho<QN9wofeQ%p*cXVlPv{I`-l_xoQhrm2Mr8h64@oV
z>j;iB%IYmR&|)tDa5s<a1DpOx47X`mwuwwA07hk$0&;Xr00N!j4E`N91c-dnZvkj1
zMSyphEGdTD>huZP?X0i49`TY-c?CWU{dZrPX<A2KMf_g9N#pFW>{0-glRERK<52tF
zPa_ZCeEQaTL%IF%zmAMYe)ebx4i1j-AoK<Sa=CxY+T6{*_U_bzqJO-YLK7W1;sKQz
zgLOb>;I#K$C4qKzQb_bIu<DWTG<jQ+vc5&%;hS0<G6oQPc@2{B^kN~Z8gLH3Oy3eQ
zrjOM(<ifSp9wt){(NgHp4rPg%&=1oZTQLr2`QBxIPy57!^?g=@ANvUHxW;zL$vkp}
zN333CyH{AYGyx&Fyc|=crv!x3RF1*z3rYc&$7&c^2||}e2Dv=yU+=#=deknGX)NKh
zoUBQ*502vkL}+3X`gxb7NJ{~zVB#o-4+>CMTxY4)o<b)8e-+(K1t~DE?yH7QM%vdo
zK@MA^l*?o@ytCi6j4f_km>O5k7yUAwU*MHGwQNGD{kAgR%HUS;T|?QAG{zfo-N_-n
zI>y11Gy(GReZpctvFr=Lpu-j(!(Ed@F)D6JF7e%Lyy(6uy5@TMxAik(Kb<?&3o1to
zrC<mSY5cKwtZY=vG!&s2&g#dT?#l3&udx9h^95}*3c|cJ+YLh#=}&4k>dn$vF%I<@
z6LDfd*)AN;ASUfGRG5=XmOJ{2U|mceBm9;#335&Ceg}Xqj&Kg8bAL$&KzRs>1lTzr
zKvAE#JhEqngUs(#mpa39!Ek3KfEI$tc4mGr1=uhRVE}OJvG-M+#_c2Lna)6V&96h0
zya`Kw%#}u6bLoseai3I!f|fd|DotPOOkAraTOE6_EZv%AWcGzmR&4*fb?p1@0fIz5
zFDh$C2?%E3uLa1t^oD*S-jEXSSoBlHu+1vSY}Gq_>AJyXaZQ_KlH-)MPXQ2-O;b`x
zPDDxPkd)fcft4flWa~GVY3|PN;s-DGuDLR*zFns=dRup`Nqh@GA?3PQ_BEacN6V0S
zf6t@SaRx#dh||%*T;7PK#&_fvhw8>Abz)gvBb!V?rgIqxfFYgF9B!bO2|;L$Q?;)n
z*zYcqkq=ka(^<;JP~`tH)8=^6XmR70Zg{OYnQKxPjI81TOU50%ek;mfmsDokVyT;E
zuGw|q@6y^}l~Twmx)^ZCu(@TH><?$7&(287qB1@fBf%0UFyW?sM6^bOb9l7*=Fn&U
z@u$g5GgqCTdD8q7zwy5q;iQ>{Aoa4UWeRY}TX#?+2rVx^vI@gK^x<ZP0gNCBl-krU
zn1fHemc-Bt7$h@9-HPh~12{F65fmNeMy(dhG}+iNO%<Jh97L=&U&dxMk!HXK0HgJx
zcp!t$oX|j!o(BLlC7-6Ral5n>zm5$+5BAa_JR(~TV*xu8_UaF*1jqHOneXx0!qx!@
zzBLyNh>>-I$Xtl?+X`GG%}73Vfl}8YfHS-v*a5(TntiI3Y<!vLi%u|tN}_tLt?20;
za(mwQ5y?D^iZEM^yXScdJ|Z#fq^HaJZsFnIlG%B-9$lu>r-fD9-GX}px29T!_y#SX
zr(<C`7<eb^Q?<yMXU8~lpF|l?7yNT6SYYjQVY7*w^O2c-&<C0`qVX$A5@5gxhu1^U
zjs7Y0*xm$a`SofUax?Eh3%SSAWILq(zPwX!buesOZ{^=)wfy$ZA<<;<!|vVZL$j3&
z<NV@HEE%XDc@?9me=ki}<GEdpv3f<tTm$*#=`&2i_ri8lkt|U!7CP?DU);!i8{f@R
z=&P8Qhsaj{RxG}As*J?KKxL79#;0FclBL+e+A_nhYI_c;<P?2J6kb~E$Rl0iNvr~@
zSt`22QjY4n0`5JedLLx!Z>7@KwcC29F;?5%YK@^gi`4wRl@$S=OUF&L-$!kg-9}O%
z4l32Kgsy*8Op;!s9|&%w-uTT_L%Sv(*Hx=Y6lHOT@1FJj>Vo>bk;s!F?Iibb6+Mpw
zoo%+`yOnplM!`JriVxM9%{1SZ%*oXNVLxu7d13&VWElbgZldjEvMg(XCtf6WS{$yF
zEdWoPR0d&*$O--w&0~Z==G>~v0U_GZ!x(^eKn(3x#+`t|s}bvUz<_mtdvLXqr2^&y
zfRI8%Gyv%P)g@*L%`?Q0p)Y@gCjs`0^&g6W@m3VEaCZuaMJ90aeqtd}&cknp6c48l
z2nmjp?~|MBy2lH2Xe)UQ%3*8&y4YL7<EH_XsOs{-BNp`bqx-XmKm!I60XK`5Gu4l$
zcjvp8_PB(@>RzBDxlO8m4U;#lqNcUl_kcPXs)|Ht5#tH;@0KgpQGIpdp?s3&6_L%)
zZ%Hj{f)yn^Pb-e3Zz4|4TfVQoXunMOKNh~9?RSirONT*-Li?aRyZzm}auzi*$~0Mj
z9o)WUAuB$31E2`~5th^f0Fh3Z-qjBsYm9B*-eh(M8gDTCL9sm<D{&K1!#ivP@Eazs
z>Fan5AP(`ABckJdYxzt5LPwDbtCi|pbD_G@*HyRqaL6El^2M8kXXM$!lgg#P<p(-5
zzid0H=Dl@s(0V<QIhHY$V*tp|a10i)QJAI30Kf(Z#q$wWGehCwoIc1Qg)!+vy1yL1
zH0+Rc#LRrD5ow1@ue<)FUus%+H;+LdhED!_@f;T8ZdR9(HkwBJ1k#uGxOQ{w0dV}t
z5{wjLCEU<f{5X>%^S+^n-lmo%7O4$DT5|5WmRh7d7h}aG0c0Bif{kuHU_gSA8N5)Q
z`&)qt{P_jNMyWU>&%P=K@Tc_6U^m3F@i8euLByv}%*!O6IMz63@ZtqomfZwih*-5!
zC@XkS0Q4dJn<{-%Y)JzCvZgwGa^mzed#&NW8HM;nHelL|&&6iGI<hmyn_k~(G4;X<
z5~A}`B7^QqeOR`T3vn9{^!L2SP&sLSl730i8E@TCXSW3wFcUOZnCJ>1Ipy~6CR7|j
z{MXR&1n>r!5(kG?B}i9;Grw~*3jVWefd{!=DWEPx&~O=lyX9#YsFVjZz#H1)yDUCr
zeA_a}h#gE4XX4o_UB%Wtn9m)Tu6&B){Yb$rFvQwm%!Q&*$6l`OcBO;wlHq9xUL=J6
z7n#KKmlb)+L~QmjWX#cx1);nAZIlaXd5Tn^2F7{dNl;BV$Q{0VEGB>8zUA+#cdVgz
ztht($qU~=+lX=|-Juq)2OWd*CX=O6kkM!M7D07oCy3b<Y6l7hsEbjh_JdufO<1}za
zQv=TdT2~ps&q{*Mxk)w|T(re_3%TMJm^3NG%Py;l`Mx6r>~~5n>o3bwrW^Sgj)<F?
zn-<e6F?j!VC0|N%k8^qAQ+en=^NPMQ?2RVX0g9++LlVMRW=ZIi(D4YUV{C}iykPir
zO&2Bld-ni5g^Y^`9sZ^q`-OA365?!_ORFhf1|=Mk+4}3;;A0e-o9v(_)^&gx6vTvE
zrGn1_03qdw7DxSyTt|RTAT;kQ*ZM<z`W>0vSU>=k{Wdcxu~<6Ur&~i8&y6DWTB|cp
z+Wty*%bm>sIc3l`9V#6IwvBK)88d>^^NRkO+60%Wgo61184uCE2A)juV=V%-sD?eJ
z69qUokOy!ME)q2~Q7w1*Re;Cx#Gbi40xh#WfE0%6y(HtI;o~ct19+FoG1&mXL<O`V
z0HhS?`mme>H}Ol%(_Szhj-XJwfp+mf_zL~OC>_Wu-Oeff11_ILM1F*?&I)L#iIW>r
zX-oKLIYdokXda2`PANO41!)r!QO2_9FC!}cX}lx^mRdzuoA70I+!_TE+C5>T+-Adt
zBLIn|I-EtQoV)q)ZM9m<PTLAk^c}uen$YgtrQQxHJyThnE1}trp*cfXdd(4^!SCp&
zsS=N<y4~LM_n$@QGJni2)$vM)eNr8)=5t--#UsE-<PT)qXV7=9*mVXMUApStwKfsU
z@~cKK<CQak@ArSGrS59zuc(_P@%Fc{gV0(@=NY`k$GjT-0;~KK+cQrVBeg%x==_n|
zB;%cM^>8`oDmP@rIx-U`J02NVGZfdlHetU^iCN-V&xEZ9xS^PaQ620qJ)Rp-Dhxn`
zl*<KGvJp_OAO@O#!X2VIHkWRdQm`bZmjX^uUCjQhFyz;2k<6ATY?Hg^63twe4nX1A
zO*mwmp#k;SO>6b8rXDd}h<U>I?%gqSI@}Dye}X!9Zl~s$pC>4_)5%#;g~^@kRyBoX
z|Kp=8l-Y?!5bOYGAzHUZZjcvCDF99rh3Pq%P+cob!;fR#6_R>dtEe8L>9|d^37;*n
z_b7OMnrwET;%5N&y2wC<PpkI_{`pJfLNedDQi;E~zz<&A-~!vu7q&l3!?pY3_lITc
z9nAf1%={SiJudhIA1RN9pb>POgvts?#$#hM3emus<qjgxx^q0)TGiK9(clP)vK;e?
z<EMBC!N^o|@SzsLNV+fhE>g{ND*6=ebp;R44rj0t>_?Gct&1A^uV6Y#xxz?g$Vi3U
zcbDc;b}m>|)Y3vhSxt6SE^yp>^v1$1-B*7B-T34VB*AVwMLf1gl$whikEfzXa|wtr
zY}}>?Z*x;SBQz}$MBi8Z@R;fDG&x2Vop6Wxr#_a1v!|eDYmPjF%#6f|R_edDs^yFy
zR4R2c02=do^lHVU<0Wq)x2qu+X!DX5sR3^B{4hOG?kMB(o$JiK2WOXD;{zYiRw&nD
z;O(&$fI8zEK-G6R2ZZ>wb2Mfpyw9!~+{l=N)tGpEXbJ8ZNFMjYy4T|9%nsRmZsG#L
z30jxk#q-x0R&<lk9IcJq=esm?c_Tt(o+ze>Djx`k%3zF!9b=uLd{r^R={*3?xnw2<
z0K%~jXVQ#^bE7iP;4J!8mq_q3(9*HTnDg$mUtvolh7qn-pUH)!6h<Po;9^vmK9$l+
z7{V9~)|Puo7uOc!q4&y7mX`cyMa-+eF;^gr5=4n4l||ustA$)d+$xgstNn=0-T<7-
zbCTP|n0Z8kP74Owz>Ad9wRE(~R(UO^<vg)aq^r6eGw1p$S11H^WDTbxwTuQiUx+m!
z1bD82%E?)MhVnCZbeFbHYUV5QGJEFxu7SOl3hJS?_g$a%?H-UM{TNZdH|3#Z9Fb^5
z-sBrMAdq(2hz?Rv(1}(pWdXGxlRI})mfLXEb};BSsg<2yQkRjUF5f;mM$6+{+ZLET
zIl5A}d<5Z%@soQYP3ad(TI0Z)(5YX>UgYh~irZp<X|2V=Q?2*^GF=&BmCzs=?aqw6
zuT{LMp53>=+G_mj*>SP5L6R|U_r<Z-8U$YR#W+npz+4t=(Qn5j91fIHtq<~B$<pw~
zk*HRQ9oJeb`G2LKS~FEnqd2Qp3i=zOXj{m9EW5|18~Q*%_iJc~ir-QZy=}e@iF;L|
zbyy~5MH9}2v*gBF$h&-^9S;X#dvj0Q)`!TBMTNcFRjLz*?Qw8!{Rx--_h??!L)0#O
z2Mpy;0GIxxf&;Me=}<E3)r%HBouM1T3{<yle*X#Z$R&k^q~aP?siITq(@9&?Y!Cee
zs0ahzi~7SHFCK>5g0Hx-VXTCgT`U|ZD-~2y;e&KoOk)B_Am%lOF@7Kl@NK)6$HH0?
zTu54Cyeow8H7Gsq005YH2=_Y6^MV_hwxR$(>#%r>#Hrq0anfLV{MLgOg{xFdenB#o
z;a1meE0zfNkG_tzGp=S>yZri;iXAYqT3Og{<|7US>R0S=w0J4uB#IVBc2LftxpYMs
zutrE?u$?_sw6ssdE91`C2$a}{x{=R2>F7J;sVHUW(b2Q2PH08}c~*pD0HKG55R@D}
zZ*1CB$y>A|`l&U%kv*t;SF%C*hX%3|vkFly9jru>eh6#u>2QJ3xM=a_sxg5)0}FyA
z<9nM#Si-2s6*t8_;!O)eABx2W`MoaI35}GQpekD`o$6*SV&Bjvy`F3p(m{wt*xjz$
zV^L`#vDV4s+K}_uIH31j1%W=<A(@QAI#k_z+G=67M_nd}d6nG0uK~2Vc9sBWvxXQ9
zqhbS>K`Fs-C|f1!UVmKTX>1ic)8=pOn%7!(2Ja)i-}m~x-#v~adfsIbOrCVDO1vJy
z_bKy0+UpX}YNE!4rBlZqGtbh1LEr6n9=+5Cx!G^^$x>?5(BXEFTs_Kz7)?*S6cx1{
zP+AMa8G{v|*$Xy-*YQred(F3vh)>4#w8N(y4x3VAT=TS8*uLy6A0{9AHH$i0^lrYk
z`^O&v-kFhgm6+#ar#8m$*Xg8C+4Hu!7$8~gQnk|g-%z$^RK8u&?z6crPd)LWn#dSH
zbqZYsMLYz4Q|IzV^M-UIOd^f8GqMi=_-bfssP(bMdazH<YV75#F-6_5I$J1|_t}sF
z&ribq2^OyJk-g3QO3LR?Q_<FU`wzrURMuuI{bPS((Gg!i|8lneeD|#tQHV}(R^(A5
zovThRvk(cku_9#M{bz-azRK3im4Kl8Za>X8D`|OD2y+eNMgqYU!EGq<+w(u>N?JRw
zr~SFN8~>ia?c5@T&}o8BpY$(+$_+u7-u6;B6}*>FSdgM8<Ma6AKsFw!^XRz!#R!-8
zZlB-ozkt^PJ=OPKZN@%h)<Tw{Zc?)+VqI-$-&Av9^_lhpxVCygqD=E(c<N7n^6(Gp
z@ic8{s#RvBjLS6GU=)lT5Qnpl<KRT#Hv3@~=}AwDN0a(%L@7*0Q+y96_k+Kz0k6pY
z>lTYVtPK<2Qm61c9Jz<iIDyU9Oivh?C;6xzMj_PS3IISiIcBR3QOEE`L>%>N4tt}9
zV-WA4P`*pY_mOTfN-yqHJ*quOFDpjVmGu4}f7Qu-9`7x!rlb6eV|}GQdCy;LI^UKO
zz>e>5rfBjQs}$~Uqbjq1#Xe;?Xu@L~*qp#>2AY<lq{%&s;>&}yJs+OmPdSqGjGXSf
z@_Y6s>+VOhj^DNB-f#a-{+Uh8uF0&k|M)lc4)Et-#<3Zb=&nLF1P4g2zz{I*764~~
zgDL)txdi}vHx!Vo=zbPII8i^v3*Z*3cmSaOn+Wu-1U+hhMMRYR4g*NRn23q6nqNzW
z&{Uih$rHPHm=y&`45tCCI}?oxQ2-F*VF2Kmx5PqF9l|ddz(hFHJ#=lHBPAqOvn-^@
zs%gpZmHja@d+y}7;5T<qn2Q#Ue@DG@I%VNtq9%<WbU9;rZY}X2D&H7!s)_{26-G2Y
z(4Jere`%yn2>>J!tH3W~Q9RZBYzx5ns22%1Gq7`0MB{hq?+cM@J?+|e`}6wyc^|m&
z77)DmLI7sMfdPBQGSHyefLqAUb&B0o8QiT>XC&2ZjAqxgm%>#^^`3bAAL&dH`8R7$
zZ^BQVQtv$5@P77AQpCUeol)6eB8lX{F`%3Jps(Z|4e#};8g=**<dyKZ%cyc72|>W5
zulFbkdnPXe48y`=hb3t^jtEiXgAX8~=ECrk_epWE|8z5lB)J4y&u4wviB)_*l$jL9
z@(CKwPM(!zyN)-Cel)rl-w3ckz@q0XBm9OZa&H97WYh{zs#6YxKqXRj#NwqX<_uM$
z`f%thJlT*TMM21669o5M`lA4bhsgjmajd!oVaZGqz{Fq1F$RnuHjBT4xk;6YwHAJr
zk)S+ZFrAyKlIt4%Nj7KJl579<ws=OPN~4vS@P9JRoj$+swCEeN_40J4xZJ9mlD-(1
zsaL)kZKwaE8E%qz=In|gk!HyNB`nPXpu}18Gu7`~6M{0M=FZIs(p9GXwlNV%hoC<t
zf$%btX?_US%J0oj9w383fqghx;qAIVJOaiIPq*S-!tSvrx?CmjCcB1Ji>A6>4Oka-
zH4!UkXi)YD3s(Ga28XxT^rIaxBra^`0N~wE4;nd;1Q{3);*%77&N<^sbIWm2ukLGd
z<a1*f_jdu8eDi(k3XwRj_*z2B_eN4Cp|~xCf5moS&S!&Dy;+wd(K@^DBM2|%zxcQ2
zXX-CFlHGdvmAY9~<bS#gmaMND#UilHn$yw0Is2yNw=)Kj01q_)-tSQy%91uT&pr^o
z3KRX&4PvP>h0vF`&Qz#&p`?PO@guF*pYqvuds8j=t-gN~GMEi|mcbJ>UM5*)z8tLl
zDrF9n??}zw$=haJ@E2SE_Q!;XfC{n5xLO$Nf4338#A;-DgyX@J)9I}+l$p*(sP`;G
zCK(aGM2~mVuk7~k-Jj+8t(HD9O3ak<hu-{SbS;c9eB6`QX&vKq_Q?7J*5Ov$zs8T-
zG|{8*AENih{Add!0Bs(VDxI7+cQh>l-d5&}GNc$DIHn!Q0f4M|H9SCWkSa6_c$Ps&
zeVId&F{?S?X-zKWo^Vs-V~k~&dcPN3z8p|sfa55V;4niBsd8y(C-v-8*3_&JNu8^H
z7B>dBCs}#2EcCPSWb`<dow8ebuD-2F-Nc%N$S4Z{#1qf}p$u)BV^*$6>GjuuChnL%
z6iZ~9h0G#;sPYQDxSopqYZ$u?=5PHDo0npssxbyZGDA>65}8kx&_q%8m?^Bx193)C
zF2^)rx564?Extdb5Jo9x6x6PM)B}QVEk=24s^)v_|M$!`<?9-`tG`e3JH=PF(o=G6
z0xu8b6@{N6rtml1m6b3ML?cNn0N`>P4Z(o)Q}pS?0^`J|n#_H%DZKSOUfUOC%H^hL
z>@DQP)r~5N`-2p=le7;uac}1C9nWT$7zks-)DYump9vu2Mt+m3(ARP!*AEc<-g4?X
zx;jq3?M$}*D!<WYu&sz#=iRzU0s&mmdDT6$2ZR)zAEAvD7UmhJv_<EKZLAcQJUdkZ
zkguy7;8nUW{UUoyQc`Nyg)cA%$ri}+Ux+$`1Apq{@@L)rHt<Sc^)?v_y1Eu4^Rgl@
zh=`%|pb!=he0{#Pt~;=?EJ{4P!X?y8EJ4Sw<~5-atIc}%{Rg`{q_O=DMxET$G!%G}
zOe8VE*#XlgfIq?zRn{Rd8#Jeg?RIpZT;|Q$MG)>eS=Dzw@sR9R8N9!<rkiG!nIPpl
zX=<_|0hk7?WTLVakiZ#@s~47hrnGCitW-$*Z&@DV0VfNhOt(_P%Ieic@Q14lppxY=
z7R><wEuLi6L$dL#+SdB6_BPtlikyc{S_Q4g7DsI}{kHCF9~(M-M<y&5ET*EXiLEVV
znOvOtL}iSIW+lQz9CI9KqN-ldTkGBYqrUX5Z84_w{JitJm?&IlWS940DU!CFgfCf8
zsfuC1vOHsEhi&!yi9kJ;b86`@4c(+v61W@b(gu}JXsL)W(5>vPgQC)n#w=3FdC~Hg
zM_>L-hdkCyR1b$e&93i_7}{@}k01K$Q`p)v){C7OQi3o@;b;&kg^el!yhs3Mp4BLx
z(UoEc@RW53&#*OJq*-{WKFF7sx7hctuC5hu7S{u}%!mGwHC1_IWl9IJdco!Blbnw%
zG)q7h1UdVlo*}a4BcHldD^b!sA=nDS$%Ctj6K9x#)ZophdpqWVhb}=G<6Vbc33n1P
zWJb;`g0Zu@T!TCih@jn5j-cYO^ts{(bW*=_cOz<ztP)TYV0*i@{_aqwt<|DufeMI^
z_qTbYSA@npytbR!3O7(ZHPyeia+nirTRtyn4e1G$?TQjkOhiOuPrj@?LT>Yy2kcFl
z&<opm>Ko0$)2L-L&b`zCXHpq#5-$c2Vjoz75Fxd3fM!k@vxq78SU?f2P{Mx`A9W=2
zRUE#?l34B>=yokC!hSf$U>3~s-r2_(a6Jy44ieNW$Ty|95Kxq#erBYp4Ei)6W}tYc
z!lUgr6gK(LSUAg?SEu4H^QX=Dxyj!OeC=u(73YFpXIt^b>92b+6mdeiZEluJJhJXR
z&))>Gw)dUPrmI$t|LcfCP6v=-t3^nvoywnE7wfFt!xcjSJTOu4`l3<#p15o#2~cMl
z!{3z|`eUYS*(WBx>FBM!Gg-B%`5$d$8ZAng(<xhC8f|r?9QSx0mUbiK_84(;!l-(q
zp%VLC+LFmjuQiKBOFfSiYpgeXHL*>!m;_h{+KQ9katod5IDv$UZa=ItQlucy&4e@I
zjAbdy0<fUef!pRsOoEo4%kL1WdGK-?x`UT&D;*FbMKxli0~F~?f_$6=D~fx`>c(N=
z&?l0{p-f!P+s_=ZPIh+dmHr1(O|pdjKnxHWw{BIjk;}9g$gv0`xoReL{=kP-=YDN0
z#R3Lcs$hZ1T&4dd4k#viLGl3-9Fm(AqlUY}fou**P9ZGdBMI-o!gitO;L7Ct#nrM^
zA{BlVL>txj$;jUni9}SfvlI0cbXK)GX^Fn*ap_g5lW(12ug@kMMCT5+xpD|YT0T@%
zZ_)KE(Mqt=VThPG2A**&CZ{3xsa6W5r;~VYZmHrThRP_I!7Ez(0JPTU&D?+*>9=-~
zI3a19Uet2Q+^9^}f4TM_h3dy-iiEn0B((n+mCT8Y^E7v`)0VKnFmEOyq!+|Xdg`Qv
zEd51$%ld2kr*B=MZv%tyT3|*Z{pOX`dSA%f{{L=ie%Bpn=r}7y4oPVs-)W$`bWevl
ziz-;Wk6(J>NNOXgx;>I<LWowF0VQwfZ@<LRBx(kBywnX9*ib6`FxfYd+~{-SHs|wG
zA~JL&fFVu^Qv!cXPsxnI;LK5FrDE=oZ1b=VvaH5d-uH95$hd3+v8M)VIf@91<TG9v
zs{#x@`lPg|S7k|{ySbdaP%(V?8LbJ;B925Vw24KBu%scGw&`X8C!#}1+4ItWA9G<J
zlLN2CwDL#~PbJ+bU{|2*#m8m%a_s9GzAX<7j0@{3{7f{5D@VEF{@sk)JmvgnlBH{U
z25&!>p{2lbinUi-EPn<LiCZ*lsd0`*9ZHNjJ*jgXQohovf&Te3LtWFCf=g{hY*9l6
zlr-zGAzIo*OqeQQp^cg#ZO{cs3Gc{DRc!;(mkZK!ud^;baCUqeTD{#SW?ft+tmY;r
zE4(Y}y~8dP_f$)4_(N6)_P;uaRa?KEj$yfarPr{#S9VrmvH?}^ih?eM%gb>$?%28X
z9=5l3di7qhd@4HZsY|*~wLY*_snsv4QTWT#uVh#T(wz<uQv5R27Ja`s$mP1k^8AUS
zcL1Rgl*AH6{8*0!jf%qqKxTlT5)sMzWb*7~4?;KJP=^Yk&?i*_uYLX`znKI-PEtzG
zAVn6tD3_G92ZIwQmqxL*?>{L|Wzj#%KTtw<cPf=$P#&%sm~fh{ahV-)BepZ?q@HU>
zuJGE1z(g0cUjHNK)nFZso(>Bo)qfm^|3u<!7_XX1;v8O<|4V!cSN4Nae{i#Ojy;|9
zTM8Qu1`Z}bbtm{3K(Z4Uydroy2{)LLNTAsI7|j~-vyJ&o*|{)VzIqnSo4lo~70@HO
zDM!7H`?%?DbRlC5>Nchw)ZA86iIYL#-|*ZR7ePh_vtFBo`&iMG8h&8UoKDR=Y{YF(
z^!%oOL(BL^Y-U1SvUM&vxqI|Y<tM~Il-BkFXUh$1EjFRlhnNV_PsD4XDm*M5taiUo
z)O+_f<kZyVM5fWBmt2&EZ!=$QWBAFohD*bf{=(s{i8u;p23uVWU`O2G`d+C#dyVp)
zz53*DSyKitH7o7OuwJ!bq#VPq_mzM6bFM~!!MeGf(#Q)NV=0=3N7QNdX?5So=q-}q
zVWj%eai_Md&PMS_!U{Ra&<4r_OD<SrtMvWzHX<Ck{<p(C3?>zpcNInGMq&W+IGB72
z4G$=(9Al>R-jAmO8`m}7UGkSqa^Umlzue0vRd#o)?Yu!nCIRgAj}36v%n=fBWMVxs
zHpwPjFDyoh1|UJ|$_5ZYFX%C)zh}dBg}U=HTr~-KzB%nXWuIv(k@VzpRv1etX0!oQ
zD+exQj+MC}*(a+)Xx_mTW01hp!mk~iWO76Wub&rHJ=!h#sSR)2otn^PxDX$-d8gbV
zM+WLQ<{PZ80LI~eX~Lmaf5+Ek9EaW*s5~F(i!yILOQZfnyI?$?1xOGjr6pp5hvaRT
zELf6?+S6A$T3i1hXbKl59A2Paw{$v5xf@z@x_(j?LT`z-8E*(%9dkD3=}>;}H3Yb<
z9#|KlCY3tg{PgR%_<7qFJ-pBf<TzpgB;hkpyni=A3ebhWED1YFIeng1dBS|x`*Zu^
z`~2%WbK9j=R~;Lci+<Y6C~_OlS;hw3`U_0S_>D+Zf^+Gx{veH!4Oq!0CRwPAgp{77
z)Q~42oLI_UlIAhJb8>-D3qY3L@gttegGYpx^NVwSa``hD{PEKI7#JZ8gsHL-q_qXq
zX(=U?12|!^w2uWL6>hR-6E&`1BwUXr&2D-)c@$EGqR7RllWfM49Yy(U0F6^D@w7*p
zzHU6M4C>`4VYZdHnA3C+hR!TtJqRm){pOjurXAZ#sse|hP-CJOl(ZBEu0`<Vs}K~f
zAPy%pn<Jwa8e38c>1V?RKD!mJd8$tw?usd{=b;&2zL_fPo9~<&R5$2JAfNAVxvSQf
zX&m%LyFWE(ajk5AuyRl(`4hZhVQsW#ZBgRx(bq5JOtj|DC^2Q)&WGLgPH>;0C-J*q
zQp*La%-O|F$40#qUbvYIVB1{ltu?X59>+qyTW@||w3EtA%2M98{W7V%_BGy@dneB+
z;Ba~3TIUo=baUu*6e3%sGP!>E&MS`#a*s3ca*E60eZ!C3nHB0w*yl^ixBp10+lW8z
zl4?OIGW=;o#qQUQ-SxV<c@;iF2|MG?K>{>UX|Vf=WEv0wPvS-QbWEh)VsWz2Y3(uX
zJ4;XzNV4poFe-nXWKEt@oFOG&KmiFQs`p6zXGF$0EVEJRN*j`5N!pUDClq47`LFdp
zSKED&Pe@wUc3G0q;a4rQdi|WWO4eQ_7|#)WNE~RKWc|ssVV80&jN<o9ZftmO>kHE4
zeahO_(yLr&I%or?P(g~5ltq;YhGTBQB2cB=xdN|@3ZBlCXS;uRLHsGtJCtX+$zxJ#
zb-v%yZ}Pc-ZI7Yzngx1+Sbx56(Q;KCuKi)Y$?4w0;@xToV~O?gD#v2!DGnE^8Y&Nx
zsC59ah*utGGGj^;YR;*nW?#+cbG0s`P)VcwM(skRYJHO4^K}nq+9v!Z$v~QM&6CdI
zz7BRpCugNC`szV}%-+!6gWQ0<<m>hC8+x+i1S-7R7tB`0)IMBqU;2vVD2(bdJ}Et+
z!{5}MDN96aY5${mX?(1yMDkR_mBTL7v^X<0FNgdc?Vtel>F`uJmG`&b;yG{VP>UTT
zU2IS#Z-RIyjxRhx={|>7g+hS`Z=ooRu~T7}n=vPtu+t!!jE8%RCzpl*5$QmHC{Bbm
zcFtp|B3<~C4ik@f*9+N<t+1K0|GJ9X18>O%n#!|G`l4R1vs2_3Yj{`WkC(x|>Uv$T
zpaJM~FaQL^UKx@m*)io)aRp#WRztJi4CIzyc6H~PU^z0HwA0=cq@y7zXfU>$5d)AA
zjY`32xGok8YIkpIM6w#hWgUo9WXOggiK@={dQFS1j$EOZ3HH^2PJTg>Kbr`Pq2arQ
z|E;|#?=ip+3@I)AH2d<xFzNY6gYyRs8#J-GU$dkvh?N<o23aztnQI(6m*GWXw_iV?
zblTw?S`daW2nYBtVozk>4zunkRo$=k4}XyK)l<&y4u$<m$D^-YH)WH5zpS0>?uMp1
z0jHdQi2frIZn2GThP2lZpZZUcu4uR%$BBJEcQ*TTMosV47<;vs1~1a`@O(0r_ho8|
zeiA<TSf$l39QMx6BRM;}ziA7AMN#ZW6MuT1sHBmFCWhi4ad<^Z-oKb53$M~IneK9>
zhp?gwRZ6`g$QHwaX;{#@49+io<q8Y*CO-YDTU<#K6jtV<>0SR=Do|1W&#jIK(@qN5
z@(lJNGjB@%@*zDnQ>G<r;#CfY-8O~hp`lF(1erlnY88K&$=k!c$-X&q=$9J2ONR<B
zzAu5AvgV58Av+BRg;AsR)xMV!I%#`l#S=3B9?f3AJbn|-xf!JxuIO<)$xrF>=z%T!
z=PWjoMPreMos2uXRCZd5jz1TsqzV-rt3FFkx#rISyKGCpB%F%A-g_Aa3&BWSW`>c(
z_=L1GFXSCm*v6^4j=0=<xbWSWJ!*wo@*(wqA=Y1K3bq|$(0{aVb{h8MCoNZhRo>Zq
zD)e8_#+Mm~6aUC008C5-5VB4iDF`tHxW%1O3K$Ep0Nhh!aPZHNVL<HD=sEzW_=CNL
z$z0UBAtnajDlHYfA)}>?@5i89DBhE(7Hi%MT>Ad+F-aL(h{lee1*B4B=5-owr5lT`
zGOV-o(cbk?t+qN`V7{4rVbS{eaL!)7I{aUWUE@NM9Ob`%T_)AjuYUaeO%#n0I8C&l
z2Cl7|F;qk0nE=g$9z5{u=6#@X-!_JNIj-lGG-%C}Ms=0U%>{%J&;FBo-EaZ+c0@?!
z0}25W7{CK{14Gcv7`rdqFy*-j_YCmQPw2eM{kR+Wc=cbRUe(+|aQIsEh-c@@acIQC
zVWXtU!)J>@j*n9>XEs4|65-BNC0|9KVx+s}23mk)3vmo!JNp;_d}^c0DbJxldkr<A
z6R2jyO;hi_@IO#>%{fyz;3d3Cjl%#E3S<OLs3fTn4I4z*nH@8(1eZ1b-6x5IMDx&8
zBCrB9PsdjzS{_~%R@2S~jSd)I+&ihIlj$cGdO#K-pkkg)$~%^=vOW6xQF)q}V%vFL
zkD8%cY1P|9$oMbg+3Owtj@h`eAH9d=`CUd9_YtR!iA$GnaR7!;cf##&0|ZvV|K_sN
zVjoLto{UGpz2T6F+uu{jw%Yc7XnveHy4X<cmCF8RHupnj(W{W}V4zkfO@!J8kk1Zj
zsG%)<YSTQ+RvU|{V`IGAVB|whEO7qluM3Z;j(eF+7oxVT_R_p7kq?t8Kwe&P_P-hI
z5+Q@FYURqT^cFE?m(xeW>h<qGxzAn|fSawX6BWPjRd*SDQLOi>tt3+X3cTYk{<$Oj
zMDG=D0;k+9TQ9RQ*)AA7a$D}`@tAaqD~LNyc}|~eyZs`JzAAz#l<Otg&jgvgCqe}^
zNU<rqw<5Uf^>%|dhLLk4(~#K`R1}z$Y72aWAQ7TrbQ;QN^h_0#O=0EtS?+6XIJEon
zy6JHFUw_L73ybYa%b8DNaUB+$6yT1RDVIRW%bE2q?#dZEfJ^($1(=h>3m_KsT;rSV
zrjlul>HSEN+Trsg6a2JSaM>T=TKcd{hQ$CJGNeyzW<`aaTR5;lYpj(^^Mu&<74-x6
z*b&J|5zNm$krp7To8De!cSB|Twa_L^;iZCMU{A<`h}m9d^Odajo89_Z&l0=IrG_^#
zj~O8gX|w-^mYa->sss6d)6YSp_?}+rp$P55FQG4xpxYMNNAlb(v-%QS%*OX@O{f=?
z3ZB$A3mWFi)x>oaI~!AQH;w}$RW|0fn{TWQrxQf8JE2Loippf)uN!j1TU<C;MDMFd
zp+3o>0J3*ZIvy_lHI_#XbqhS}AYWQdu~$0lJr0O@`zCX314@~1{Gu&CI22fVQkJ4^
z1cj3u5iOSFTEi~!Suwo9*L^m$<BpT-`<a#a&@GTt3V+qc3#CTZ$p$Hl_m;g4`6_Kj
zr1IQ5KVLJO;Z=~pA?-le92t*)63OdF!L`O$47eaWhVf*X!wCV2pI?m9RDSShU0h-S
zGRl5;C2DLX7DD5kGHuv`n&HSUmI*mtVQ9>q?Xxjr^$^>s?93YuID49HZZ|0n0|H>N
zd{)a&FsXA%w+o9Wa6lyv%L1whctoILd^9lg1*U|RHDrAwNrzH%wWU>g6{j^Pgasul
z#e%R9XNYEt)~$`vWelK6Y~~<=gIPIMvX0kOvjF^c1@Hny5Nf$h5;eJ6m{F^lmwcZx
zWy0^E#R`+oC{L?=qTwsK*U9q5e%8&yF;#;v#q0CR=d!(I(durx=e2Y56(7>X>Y!7G
zWQU(6U!MJ}C0XV8iaC~^TF}Mw!yUGTjm%SQ6K;(j6W{^@Ri@dwTuDq3b94`0w&+;Z
zEv}j@T#xvfSauKEC}}!iCB3BX(OGA^&%1<wtl#l()(l@_n&S6%dZNj|NZd%pG`7M^
zbCIqMi+jB(EaWHJab7ySxnSVn?f7%XE~i!~_G6rdkkP+UrcXjvEDm=K$orCLI!^BS
z&U|d0cdTmJUkqYmrBP}K!e&3p{B2@@_n-T%k;zl3IIa{!56yR?*hFY}s<<jc-G4Rw
z{E}hT{u!=aOx66y-|0535rE_vtc2%KCMAVaY{OV;nH|y#ALTu&x|k~-Ak>T2ob!J5
zT{*GuM4nXr^fk%MUICw4SJYFOx5s^@ZawuYhyP`M1`CAo$gS&^ZUTVDYFXC?fET|p
zGCX)P{AkO7Y^70lvUM!u>G&-Sk`#`jU@1AhU?o45i)u1^p0Q;(!G<OFf{4A%``Cgq
zKB%@Z{_r((Sma?Et$e6~2RHiLU41ukClo^pWVvlZmu_&pA|CsV@1sGEFtJ5N4&~vF
z-5uSh<^B;%5waGHIv%o?FR?l6!*f+6rX*1s(YWH$jDv(*b)v6BywFYa<%@?#1vHO^
z{wd{k(-)Y#!wfR@o9cd+HZ}^|Hu`*F6b&D@VC-!eMUpiQbg%X`yFAp%mYHo>yG8MN
z+aJ5rLJH#V;EvrdwV%EM$Wi`$%nAwb&DFA-CSu~M$<73-P$`*}4Canh=55jedpkm_
zEeP*r*T?dFY*AVFys2jV#GuK$N@WqLk@r773~S1KC4|gW3o0}h>*=1rXz8<%aA&PY
z6lbN#;ekDxMKLHvR?E76&`NgmX$+lCkda%5AV13^6FZfhspQu`Wp$mOrk}Eg0B8em
zXDJO=JV1(>0Rz+VAC!6~HNT35*-C2W8>Q8ACf^?E{I&8tusC?~_nx|Pe9KgU*GTSI
zo;F<PXLs@W?I&<%<f8`5*XNs;w^p1%ti0|36#<4aa`m3AWj%^@bW6ZsGkla>Ah46}
znUdLnQWx|BtE|bShBsLRFnId>mVM??8f~bq1F@W-Xb@!wjo^-tMX_rqlcS;jgh1^_
zDV5uoh8c3>i}vMAFM0xn4GeEfDt^eda~dyEEHDUV{Wd?6SJW=yX=3?i*2DL+5nOb9
zTw<q7@Kpa#j_6smYmr(=2eBDLaZHuL*Ozrkq~{%eR@DjVM4^x`4~(3r*}wt0L4RLW
zi8A}xS%s5DRXyl>@uhI?NTsFtt={J8r|jh(GtbZBH4N{JZod5dVVl^m)wprh-Qtz>
zd23#ApmZZ`Rd(BVanrn^-mvvhZMp6<=sjCg#;%|E^JSr~6*9wBfwo-Y%hg@zDsFWg
zEw<{z7t?>q13rDKl@j?<Mi#udR!U_9hs5-+d-nfh!YOfrnCN4D*|B4ZkZA6NNcr;H
z+{a|klkrOwZLyyJBub&rM{<CKa&GVe=Q^Jb5<%!tNw3e6l|vv^9kOxb)_Z)qZnGuB
z>*~|yw;N{RO$-f!4%Vm3d(($h(<jt4=Xul!o($v1<87$TOD!)=NwU1Vhf3T7a2GMQ
zXb>H}Gr$e<XB7%~wB^g4lw>sqjTG+l)6x*Wx9LcXo2hme$3THfOD>`>T+YTsyzbP%
zkR(2b#2u}p75!E}=ZlV<dd0v|vvpOb_QgG_3lR;YMzqj_G8We_Kis<_T+4qP+-H5#
zcY(e!X|_`%8hUu5);;DJQOQ;z-6Q_s1uJz<RbuyV!%Ne3!(XwNoV8ccHOKOX3Ebbv
zVsI2)X@G9Udn{G@%)>6@T9%n43Q3Y28N@Z~Mlt<^wwtj{l%NQ%9Czozc(ceSJ$;{|
zdjJ7b_($J$#m@-QJl>=iGK&l4Nh{%L2w1Nr{2y+p(|noz0+{*(J|`vcRTu#VoIt&{
zORSXN_5*rN55(vM(J415Qz9V%F6^2LNsMgIPIuhp7jkPRYDyL1KZJ;`FrTL=4@8R$
zaiC^G7GIhR7!2&6S`bSfzuts!CX;ca%y0Ei(jlZk*ip`H5;DCU0g|z-Dwz~qkGgnz
zq?Uw{zKDR^GLOr_qq21(LLUbhBi#d2qWcIQ7myq7&XxhUp>v!}wjZB_+M{4m1dpH=
zdL(vOj^?L4y@kBR9r@&YOO*{g&|Rg5u-JffpEA63|1wQxIG2kv3jl!oHxWc|bs-Y^
zwrGzTg{7xZ8(z!EI%qHs$_>EoY-|$g8G@&KpCZRe2Oyvkh##qe8e&UeN1oUqSmcCF
zc_wc~I>(?zb71Bv^;zu?v+h*`T-#ZZMr-BA)>;W~DzS033BfdW+P(|Z2P`<l-yZ_7
z*<@JL(rX+$-FGg~Nywy61ZO|mto;1S<)8WO&5--rb*^MGm9Cij8TN<x2_J`P4>8Pu
z8~mK^hO*20T*HnMEqO4*;}O>6W!zL>hL{$*<7fCMO`AtXdsXCIcsQYs&Wb<#t^f?j
zC;ol^-1jLT2Y*G}zz<g+K}G}}rsJs8@sMAn<Ad--fw1LR3SE%~gO#yvtH`5xF#j)g
z-tyJ?-Tz_gtRI?w-?#nR7%+Nt!)O@Y4P(^kMpEgL29Xj+j}8H8=?3Xm=@v;vK&3%Y
zq(nsY{p|Do>G>PBd-uKTI?nTGH<1yf?AJe#I(Lm>&$<6%LIpm&fK>ZN?Pko%Aw_G3
zVO>tB_xJ%bbe0+wksB*c(n3U*5jX8z_;N{jaU64iP9cD>cv+1GW|AcO4(tx$#lr|>
zmU*GdwBy*UTwu#dfoh)J-PB*DyPjIn##&h<2z$`+w~>$e!JfY>_7$dLafP!w!fne>
zX`*o@cl>OeXb{e};dtl1tow8%<i$&LMfRMB2`PY8km5M=;@AUYn8Y0x#kXK&{;%1$
z1SBd~7QjY8SGd-(L)9~PRnoP~teADlefp_kRT1CNpHEa=K1p9*8q6rF=JAJnFNDol
zI*PhP>XjH7&azl?I8&5Ho4sIfUFR6N23;YHk-hWLU(}^!*gF-~zulR3YTOd%s6n?H
zj!moIm22WfXc*x_7sZUj(i{eSoSdOcMmzD@JX*<9F)JB(o;})#i53zyuH?52OCo3A
z1wDgbizZ>f4*dU#{^Hpmp<Af}_UBau-s*mj*VE<@Y7ghtNI$!ZYoB1&AAQb6am0g~
z6>uYW(@yxCvlmKLg12(+ZK|*9^2tB=Vux5RWmm|Vd<Hdl$189RI9~Iqbw@dFQmNCh
za~RTl__NhS0##$Y#O@kN;garda_SO#f^{MqRvbN60LqXJ=N|wa;9<A{06~s(@nPyp
zM*pdlk!zaC!6&U=8W@<iu0N{sPeOG|LKwdPOp16u5b@#lVN*jS0iBvCEx?0N0pA9q
zNPD=*#nf)79i0jsrD!MRjYt1g`O;DwZfYevD7W6AGy>SO|4C>Y{sN=fws^!bbi@wH
z{_q_Q$A6F={;yTRT4YDfbFLaGPGR@{6F`tS@8&9F%N+^JP@NRMCnk>XMVTzQR4&@X
zWe~H}e_gy=y??A-TRx2wnpO+ZxTTz!jaOQ@#wuN#p)x{9y6phaI_vNP05YZ(-+IdF
zlv#0Ln&>;~6g(+fV2Y?^Kdy)84;mb}ymc&OESEs_NHlr4dIbmm-LH!?>X!Ks;P<^e
z&7HIP(tw|v+S3hKp0ywd{l$-E6YZYi#N1H$U2~zI>Y94`?27sdmVVfK%+xS%(LT~%
zaatLeoR-exZ$6t|SIeK>nbx@qzQ>>&JQ81EjQDLX|F$bPzA7X?uIOY*&^_dFpP1n<
z39WfJfMpAf31;$UVFN*Wwy){Si>PAIlGTdr`!(=fhju|<XA7`IEkc5mj(b;^Ek1?~
zV~pnj;K46QD@cjpUT`Ga*r>NkuOZLmjg`PHkI&A6f1!j%87fIQlJVDzf<G@td-Y(f
zWUWUC3(2^hTX){QM-zdp36|^}mh@UzR2-H_$L<Uar9Iy{4c(7NGY(3U^ZX=Bf21F4
zlwScAhHMy84hI_OR`BrF4$170F~?2eZ+LO}t^b-)_V$$=z9Mw`PESAlTc0zNGfvzh
zqNY)uNLl&pF;{}9rAR0+vo{<UUhgoY=scqq3vP(8REmyUh&XSbq5S1QT$vZUlutEg
z>K6#65hB4HsfHFt<wzNv@71-`vn^k;b=oLN8P^2h#s!tBUQN*w6rT8(+9BoW>ddBh
z+nJnh%TmwMwHCF4v$Xjc?s)Q>X`W})IPm4Cf{_BWWp@-l-ihT2cBU@kCq#g>!~NiP
z6!(fGjOu<N3Hk%QU;oQor&Z5+md|<_&u+1sWK9n`p2xfN1lT4hL}aIoS2_|g-6x!R
zf}hx-k>n8np*x4AEC#~PWyo;)MpmB=h)!UNBh06_QDHol&^(Yf#Aw87R2`$Gq>+zO
zv=yr22$1@T1nF>O0iE+I3`rPsbc7f$O+Tek56rH5-knta--|o;5f-iyn=ZY>*q*B%
zjNykTT9q>D-U)T&UBc;XIOI3lioWiaa#&X*x(f<$7-xEFwBJZ((3&Q8<c;78^W8N2
zF79ow0HX-PGIIcs!sTAz({gfBXaM&;*?|FjV$U=c1}~k(AS?ELFB}Fk>fKt*7#yG5
z2F@x5zY2&s_fv@6<iBdFopTwAW=<0dlNA({cL{hqJNB{hw)b~~yIOLsdt2de8a~(1
zOp=CB*61~sn-|HP1Z?i-ZVegi#1@6)vM)P+XvQ1OpQdZBu;}E^QPVxsu^jo=@QHgs
zrg=)cUG8-0dgz`hTT91Ex{h!L@qWN3&F-DuPJo}YNJp1$wF^nz6|2jhB$<8S>FX5M
z9mwBiLmcOlg%DH@R181QYe>PXuAd;x8Ex-)RnTiKxe+{dl-(=2_<LT_>!N|caG=9z
z*RY|-6s{Ilptb~Sq65n7SfByMJhBDJ4xQFQ>0Hk!n$`@ADes|Z{5Q*3TU}&^qVSv8
z$Q?#xhT=;oFT+khbQcFp*&Ysu&q}3Ak3=c%epZd>$3v#YypsLhGsdO!%sbw*m92`L
zK05z|gXR4YF;1+ZdP*$Wvr<p>kEg(`23$OSRIOK)?8UJIe8S)lsfI?}0Pv&|&>hJ8
zF%7DW9sj^Bh8P$OY~S&cgsi?|n4T40ObiFK`|+GY*c}_WtD@dDy%TsV;>f`AF|q1J
zW9Rhz2L9aUFO(EDd!t9R-Su(k*bGD30wP3gdyQoXpye>_*}-3Qx>UbC)8TtvueX>o
zcop3{bp4$rz<Y5yxl3z5clpQ2sx76bUhaD`@pj9yc%#`21=TBi=bpf%w7lYf<1ar@
z-OGZ5eBdBnp4kths1JW*fvDEC$XTJ+g1ZIf&ooaycHcT&q(o+C`8}y$KhxwUY<k!#
zc^li4yAdXs@%8Z7O6cK7!9^MS(<y#mYuJceK7hC12B+O6WpyMWEaPTA1VF5}Doy|p
ztvUpU;r;y&FJ@>9$u9>c{THpVgPRD+-~5Zib>ScRlLFUq02}g5$tp?=z^6#4!?em1
zLeOxemEr{ilrlUdmM>)vk}EUdq?G>)&eEwi{hG7>NTuAQ-uyV%rtALy--T~pVV<wS
z%m44iaE0hmjshtdgvRCT=TL{CNpBtm5bp8ZjXea|uu@x~s|hav_%#F|c=38nUv&5U
z9Vy38QsWduJAn5H0A+VSHGUU>R-WEz?0gf-293pB8TIa&<8zg6NNyAfnLhO>wR!kH
zJfHBXbM1&D3zIT){K)>)Hzi*}UBZ>bafKC|2hM%#r{)8l(=1+#%1JjI{~p+L6>uFt
ze)597XN2P7Z}5}<WdB4(VMv0q|5dqvynX@z7&!I+2vK<)*SHo0;OiLc4Qc=0yDL44
z{`a~MDp}XVAw~lH_Xa`z{;#B;B5$1L6xC*Bg$Y04R!}us6;nl$y%yhs4h&q7Imt%G
z*G<LH#Z37?@-omKh@C#=3-4SG2?@kj`n3L%px|VC+~8r|1>~Lhi^QE<qL?(~jdBi-
z3A4+00D0SG0{{$9gYOIqu=}DQpt2#3zw8{xiik|fxA?5w_1XF@4Ku+x!{`Q)h{4$I
zV*{a@72<u329srcv96(Xvp0NlS2JyvP3E(025*S*;tt{3`(DZX>8K|nli1?-SoTJN
zLrCd3r5nJqSFR6$CmFW@P<z<Tq}C8X3M{}FfT;KU?b3W5S0pRRKnhA;t7Ed=<8kzm
z{xuh&fHfcqSK~6#hUY-Wy12YF?%l6=OnWcRQO=S+!Lc`l9qS~Q$6NQ}jASC-@`o*-
zILGQ(-mfyf2J)vGypDqa>zMvt6g{u$J;-wX5M)v~*>ojA68caXGi17=|DwI_Hh4so
zZ$X9#17yx4007?44M(=JB#eb~@=%t4dj`ZWt|+#Z?*9Hfd|!ov>P0Z;OE+ekX<f_Z
zuJ5)A?Z3=Uw2rS&gLmHcsWgO~)oxtk{SH!8;+^~RI%70#P(@_UC8_5wn1o*Abon$H
zd4^^qjLY=zS3nt;0T#5}W00rnShepwwE($j_hvFNTaVK(u%O?0mQnDZcE8zE*8-Og
zQN+^HV0*Hj#Je<+m25;3x3K4bnH4RTkP<%K?BFMeUJ46MgfSKOs6;(g6(-fd#ux`$
z<8A$vr?37W_gg)71;JeY1gS}SSm7GQe*bvhjjf{4wKwNsJk8ZEHfBcXMW4x4*URxX
ze!ij<gh|yjc!(Jq%+i=u+mO*(lHXgUYVetSqSvwK)~qY9Aw1vb`z#lDIvdH3`w7TT
zKR<GWG%T==I9LGyo8n}BMl{}4T;q<Gt4X1$Fo6yyj0b{3a)bef=(kO>oFJmsuONsV
z0Rb$3Dc0B;u2o`K5m%ATNGfDk_v8`<{mSax`8b2}Kv}!@#(b^AD^Sc~y3W+wHjB7a
zsCk$pt;SgCqqQ3CE_^b>TuEoj5f!*EU(SEQWTM|_7bs-kf{_N4(N^bRy*Xk6paK9K
z+=*SF6d)djGP;pCK<{rLuelGQiIkiIgh)My!2ip}Qg%G4=IroN;P&d^Bw`+v#`<jT
z$8IsA(2(|ZZY=OEeb6t63OJjmXEV)8`t3~ULnlv2qfSA2t~L6bwj_~8C2{eInr|3k
zKMilZre2K;);~>QM9g6A_X;2NIAGv8|Fq8hz1HFHEhR2F!}~t;5jtu6%#WuoOa-(b
z{LpbwrebO|#luJM?Xx54LbBh3-E@NErBq~R4ftog_yC=2mBTFnfBg{v{`3cD3kOr5
z%RH?m1(5iFHqjNtq{b!%Wt#JoHh3=CuaE>0^4Vsqi6YL`t99PXq0SADO5!ZJc|?oh
zGQ+8;KsqqUqJp%oPFLS^Mp;BUL64T?2w<)(9wAaQ^cc5#1pxYw;o0fxg=0^j1<?t0
zxvBVig$?l|$bvP)ksec4SKu^b!uYeo(oqjfIMM^EC<QJfqwLl4W{g_p-)Hyf>gl07
z_+&c}##(vzHNPRUvnO2lCJ+7}P_+i6bG+Aqh!p{NgXSrj0D*QE5wQA`TAN5^O1Q~a
zHee(}+KKaBg$b1j*A<uwv<V@SBP0d^00?%#0EKaaCpal^9aS#eC){>^g~Dw)kh1wb
z5qtL<**wp3sSSO&>QBc3hraH6Zg~{`%NB#k-|vXaK9dQgL%(k)bt&3ul0M_%qm*T0
z=Cw2bp}hOknSM|}*}a_@w3P$kFAN@O+g83fE&eZqM(RYcp+7+A?5B_yeTv($NGW;w
zl;ZA)Mk(}pMZNG*O=4c=eUbZ?2<u}SUEM-S8g_+@tfOlBaU16x62D-N!Pg|OEWHYy
zC(QSVUoTv<Y1{_Ui!PNDF(sK`W1iam_^oa_9=Tw1tdIGb*JtA9ZBpavyyR6uyUEW|
zvn*2D9K`heYvpuY*%gB?AMJh5C#Zm}w_bz&X+g3q=%5YCC0T<<e7k}+ATk;m{;Hkt
z8RuvAL$D20<(&HA`I#_*4KX`p3!L%ZscgsOE81tXWIib&;(ooFy?(Z!_;|~6?aeA~
zOrQ<0FhQLf>PoBuB?h`l)pbCAQcP=i9nQ-s9D0#O_x=WXZA1)*$`Wp~@NOIz3{6Uf
z&6#M@RBp3$BG*T`I4g4=<HXWD<BXLwPMa&~lU88uO}#M}M*DLTwk&y6pYwNj%>a6=
z6Vci^C~l3I0A6l7GNlbi=99tTbS|_XgM}J+j5MEp#2lMIp3Z!f^q(`5+lnLKE-@uA
zbTtp?<>fxc70|r6-yvO=N7{k|c1urJYCauWjXWEmshFOMdYDA2b%#GV&r%lc7I_{r
z3-&EmuRExKlS4wki4r;h3X@j*!7<Itd57kzB`)Sxb!qfjv;^Vem3^A8*0Cb$X}K)e
ze@HHVSFXW<f)22ASV17gYYogzXS-chr}asV{)h3PHag=D>jR#5_WJPUBXu;s%$3+R
zo8({BsH9yK&`gS{?kV9r12VqHu=Iwq1<WUs30{AG3AwB{U17?vwUk!I!I*g?w~04S
z6mJU@6T^iN`?_aFmv3gun^d=UI$MihUkGHFe(Yw{F7ba-knfzZg>z?&k~%mOjBpYT
z%wiY@G8Yn?0{A)t9ux-2a)a!P0tJY6`m@krs8u+IKasGNyt)jEhZW9S6D-66^qH|3
z#D~0(hIGqy(Dndg<d=quwSi>`@+<*4bDj|w-jQIw6iP5fpc8g^kLc?f44OgBoD4l?
zs=NUbxAPON$q8Ha@ogvKq4)CY9<{>>b6_c)CYx}0ZxZ9T%2*wy=@yP`Thep?9N;lo
zDhoKDBd<;_X6a9%j!%+26SUx*>L`sQ^l+fBsMd52@ux$P$YiX*0QNgWqe-7#!kvQC
zLlq}m$m-A4bswkXE-LgM`_rh&NT#q%RnnKnVm3G)$w67PELOQd4*N22(kKzsIKlV{
znuV8gL<j`D1K)_oHl`DrP2pQvLKgiAzk0D+MW-B3*Q%{R^&GjvfUJQ9#jl@uD+QD(
znO~W(<`kw<Z8W}S%9xD)+7T@*ak5Fp|4+H{3sso7Wcdlz=yj8Km_VC^(6C^~%owJX
z41;$NcZnnrP>i4b`npkrcKv=*-8+DZT&1n)1+yvno+2?io$cArA(AhhBaO+n*JB-e
zswT$Mip_7@h6j@3hiP&d^mK^l0G-eVPXFX#@y_g{C@O|8Wk4q#M=c2^Jf$}(0qLEY
zsREnAOThSWaxph2`@yYxQZHLkEH7UoK`ka(qhUlu?JUvYJIt9P{S*jX#{q>A0*)ZU
z=>m-8gB+&W@i|+rlqYIRMunsQp@jJLxku)tMthY^G!?irp`~4!Kgfv?-QZa$ETqIC
z5^~Iu=x-SB0RUD3gy!!Uc9)2S(x%Qq{M(w8`jgBiUmNwXq`kd8;v$I-ctij`SSJEU
zn8m`PLB<qNDzWE4NuF3D{mlXm@>k-y<=E!S^u}9ejf3QsqH!?MI5ow1foToqmpB(S
zc~SfPm`QOAS!MKSZH4~$X!}5<Eyc#7V%9WscIfz@#x^@YWvXIMj`_Ba3vyeJGV3hy
z9rTl-QFLX|O8sF{N<l>rb2EhklsQ#j2{2ILD)+em5TIR3ncsIj!)@N#Uq$m$Sdh{1
zP-NqoOhKM3GU}qpD5$tGY!}(oTpx^gccFH1nBsU0po}+g*Z|n!FX$jR{=#mG&;#Ha
zD*i?tri%VYQVso3mMmh8Z2pcbh1-_tZ+fkYx~dl>$yYg*mpiJ1uk!VeRX+))z7?)X
z64b%gPygNk#IDlr%4hhKFcP*I#Bav@BPVXGxHCf_=uwQASHc~O?lEtwKe#-9h$iAE
zOV-yy_?|W_G4a~~uAnz>iVj5S5xXG4<j)g*6pN96`kIJS=^RZP)`qC7)hHgG)3jWD
zbZ<2}LErp{b)<H(GDVrP0;(j3frh6XMaIKzMX);YCO!$<#F#iatlaDI-#fWtRU<PF
zZ@t{cF6Ry8LWhl0!2C=v^A$u}0nik6PrE=`>x-9ru$1VJP}_Hs*gC_^I?X_Wd*Qwe
zKW%vb+=(@|8OD+n^}RBjC2hUZPcOEn;I<i`@h>j^p?LL6bD2gWrH{2eFh$qTi0P3;
z_(LrX79}L0^>BjF+@pjC$b?0;^Waey6r>0^Q>4txJFXZ9(8G9$)4l&_@n<dcZBKfN
zm_Z`?Us13*q?OsOr;UDGc>?2~RiK$Zo09lpqGmRkRz(};tYX1}x2A}WI6{!Ir#|6;
z!TO0RoO+-srs!9zEYDTXjn$}H*)A_~a`GGTrI%W3C!0l7-}drJGuC()Hfmbx;Qf1b
z(~|x7P4d`_Rn69NqNwJqM=9R^sM!@LCM**g6_0t4=b=TNiGxrVF!9QC9}#U?knE{6
z%e29Q^Vrv@?{j5X8m~*LqsK3;u%HN50IT%&2o&QOVet{gQP)*H>nB%Ws2C`p^z*4b
zr`&4V?q|)t&*ndhv90<Z75T8<l5&3v^Rz5zXi9G+zP}?QT05TR9l=7d{@I&(R9N?|
z(<VF#BM^xE#wC~;OSs5ue-cU9yODUqWS^<>vh`^Uz_1G5C%k1UPD13M_n^6)DN7y6
z&0)3D5k{Qr(_P^o{_c#+5IXo0)y()g6k7=vpXJvxFyDc-jy(se<JZjk6kBLCMqeva
z)4w7oXZ@Z`ii&tQ{SJI4I_Ny9gt5T40Ey>#6aL{M{QZGMV1jXMKMUpi-sO7ynlAO6
zN}}pF0Jsn!byIggTCbM2|F-&g6)u43Q|>V7P*Sws7twwwJVjj4n{{vU6RqhYr%4x!
z$x34UmOUD4J5>UIvAJEitBLh2HqDGms;FIh*72&Z$1*~vuCLSfQE&3>uen@vTj~B~
z30ThWV3)eKHw$wc9Y1RZK>{^o$(mOcU8uSXJS6M8PbLB+sbXbk(S#;mgliV@xQkCr
zD;_bH$RCy(_GyRl1q011R8`a$lVUkc`iubeTGuK<1D=>emx^SLMy|x3Nf-LFg3!@`
zJjWM!ibjw{E7xDvP9sC-sL#T$@81tTw0u-~s&jAdWG`H}T`m^A4w7pfz@04ESrq<>
z`%oGP@bDqjQLd_}POA#O*T<Aj{84rsyZ7gGhM^=e6HEg{MJtwLT9wSVKwt;v=QxE6
z?IBjIh;texsbkMrR?4`nzT|_cMK^v(J)v!jm#d(3kH_#HkQnaT8P+Ga;n6v?rD={C
zfA*Z%8a!_Op3p}3{1wZMqv_iC079`DIsx$=lycQ_W6{DHKp>vmKeH*`;_Yw;r8Aw5
zVeV4CX(@&Wy#mg6JpFcR1eMds)*1PCIn$l4hMhhO8}Ey0C+&3<T<o0uQ6-9%_{&7|
z(c7lz0I%clMLvK(q11q=+}-C=gZ5VG8d<Pv+e^R3vJ^BdE9NX44_FMkJV9W;EpL>y
znx1tq4j)1V@wqnBec1GqeS^&m*Ot|Wax7pq{O@ogh$hd0_a;WHPc*T0uakz=v!IVz
z9iBjXZOrTREcpoto!Bs_FF7pK#=1_qo5Dm5eCks#T8L<-*5XOZegm)~Lc0wbC>zKD
zS7L>VbyxtUu*y!APZwzOiUiO$ZioTziVQd1-N9$Fc*{((4K-E!u>F$W7$V>eR$4YC
zUXf!Vnm`P9bp=8!Kmgo1m4P^MC@r0IZ~EVMFJ&4%+4daS_P!Flt}YbApGnwDAv#4A
zTlOB169V(cVoZ>Ps~@mJ9Vr5jyoZK7MbwP)zaKV-+Aet<L$@3pj$4QcJYSd2zb9o@
zB-}O#z0XTQTzA>(ySsHzqd4NH_#fZ9Mq1;XOO4@K-XF3?*6WU0e(#0|Cu~`dS2K>c
zEb;BU@<!9JloT+kH)~0DCladVNh?YV|C~I>#dGs{yL($Lig(^iIem-tStYaadUjH4
zIGHDlCKtNuUd>t0S&z17H^xl{6Y$<P3S1)V&+bFYCIFd8!s9AWL0M1kJ*K);nYTf3
zwVrD}AcT{`pZA?=i4c6I`HjKAS5K3TE{S`u#C<%Dr&kd^tqmcD>!1Ay(O3Hw#o*P>
zrS~T2bT@sL6bLpN0)+*_wj$!n%rQV%JYE)n4t3ossG(-;1{EQ6bDo0*HNt38%<9s{
zxrG8m5|@Js^*b<f-1M!ac&*}C7!%96y-Igx!PT;%ngCVcgU;&&MheiG_OsvGIrBwK
zMT@cXo`+s`kNeL~oe-1vl*EW(8&Hg6LKYFkV>EHgg&>)eunmGyu>ETNATYxEEVK|C
zOhD56k*KR6#klA}T{Iz<tl5ef&qW<0CjvH(29osv!IK={pM@c{hSqC{aE;{@Fx;`F
zNG1P`wqTdYrw>z=f~lr|rrIJZwl5yP88^?AFd|qBs<(<FZ&ReZlvM~EbUY3ltTmWw
z%PVMoH$gEyOTn6YJOnrnOc0`j6WLng?Lj1uvN0eEir<jb-0(>y|A%sM4-K73G;SxP
zg87s6uWBSs2nMtZ_+JHm+Bw-%{#)uS^7-WaUTN)?t(){C<+915+(%X<NmymPRzqAl
zGyd=`{s$ZCHN2n0|0LBoTFmzhp5^MeAMM=|g<Eb+(fdd(oL)yQ9}=wQ_I(Zc@`v1x
zvj~I1IX&eV2*@CDC`aR77aR^5uffrpFQv;zpNisKKySM75j-7s2xQQY1OJDc$QkqJ
zRR1N+)2+%5)w{wM$i#eEuqOF?hvQ0J(`u2RZ`ru5?D75?0RIsIfJBdJnh5dAb*lq!
zXia_#VGVfUMBlT?A=$e8GC0m1Oi-!A<_NO_81z^^rE5;XdNqJ@WA-w|xE2Tm(5PdG
z)KF1ZY@4eMx%X}9-t!E%AnU<{$I34jJc4bMd7m59u6Q|1Vl^tvNYaP8em95kI+Ap0
zaASm!pkK(RJ43?SaL`ta9>A_o=MQjSN9w+K*JNbLA(*q~ygpZj27_nc1UkU@K?mT*
zK`^Ea@SeBm&Yxa@524<ofN0=`a4(-=Oi_;$6$R+WtbuB$pSeBn7UuF=tnn$*NUTB+
zvZW*EHbqvE#@R#^!sv(YWZhZB#BB=AP3XDWxgwini2zCe1RkP~cTc6$MpjA2VBaD?
zYM;2c;_+*L{ln6nc}GpF9cZ%*d6A6;2&0GLfJn-2D-c{lE?{X%wFRyVozwn(+EPb6
z@CXl;xP`~8Gjr$rN!X-6g3s`B(1&@7pEA>hsx^BgNr}jYbas%ei%=bYB=H#4z2Y=1
z%@u6Z1(>w_@h+*MQRYlDGXe|in4nqFQxyOMp-B}^Sx5vPS_72H_ilju^aOCgo<6P&
zfB=WHR8eh&o5bw^I(Fuy5zFaAU2VQ3w^5l37Rqa#N@Mn{gf=LpZ@eWNNwb-Ho4su3
z`>7o}4Xv&x*L-e2@c1*dWN&fdv6t3{;jf%8W}Kvr!O_F?8=#10*v#BBZ7Ah;M}S~@
zVwY07?be3TwBVuylX$f+&ZFm&jGK&6&oU#;xBrV+nF<=r+IIk5fJF$cc>hLMl*QDN
zbAch0;O5JkXuxmXu=vG_L^}Y?`Fv#NO}-7vZ20y6ci|H{Oa;5XJRqN?+H8eVUS%`8
zqk$k;2b9#V;56XiXwFQzJ+UzYh3|q>tDYq_^|C#;I1{UJ>t%&MoPnl-9RNZya3KU1
zb7MEIg6j{XYN^hwxc_l0C=gXt>$#*msy_GaUVx*46#vwiEi?7nz>^M>4`rd=8t;GU
z--V_=&Z;U>drbX~Xv@*8dnEfEwD=e0umoYOa-H#OTHY{Ih+p;m5p`4<XsHemP1zmn
z4&HG!vD6nzyqab~NYX45^H5jd*XzHY{#Ts5`x~iA#Qdu5KM;eLz6_e%IW>S+f10X7
zP;cst_7PkSo?T*w5Z5k^^<K3m5|3#$2y8x*(#e_0w`mBc7RX_$B=0};+XC_D0KE7^
zOC97DVfHDx!7pjgh=D^BafUdk^He52D!KVR4MX9hl{rC~BdJz}39Eq|7W4Z7p6enO
zW1V>`HX#oUMVRoHyekIE!}cU3yDps>rsjNbM?thwx-cb0%~6}{FYfg8>!V{tY^rlB
zSe?i2>BmIkcEk@cpeats769x5xMc^TDBbv<{`p=r?c%L5aXZSjwP}d5#|SjvfXw7O
z0w}0Fl!0e2z#JajxPEwM)K?cgq|zA16ke38<KumB3Zq(r&cGO@V&<~i^*@$K9>gsc
zn(5CgS+HPgsvI9zL#8O&+vT+X7w~6eqsVe;!Q`K@CI5rv)j_S3iKv`AASoI#RKsYL
zh{Aq8mLd~uvFW)F203u$Y<;FnfpZ0M-Sx;`&bTr>#JVK0nJ{}^7;M>cqYTrC2B~F|
zVYe))E{}GyYW=_4KWwj@c%R9opnGa^EL1tof6B4{?`M4xJY*`1X|R{XsLIUsLGbx;
zJ8{&JK-KR$fv<0u%?VVA0Wwg%^_5Eb(jNe5pv@Q-&y+o$OwVbElK9(M8RxX}Vwk)3
zhgVHsr?dDL1HdrKL^y%gA)wJ0f*+&kMzBuZy7hyx2Wy`zLLKVOFOnXFAdp6XaLRG7
zt+rWHp|uN3Rqh)A>|kL{SoWO)f2x<kYx4n-MHI*i^G~UUwB;E(SD6+Eb`tOemDKIU
z8m_=O8Z2iD*7my4uim6(#e8bH*`^~i-F6didtyg>^dZUHwYLYoq^*wbU5`Qf9ehhv
z=9KxoYbS|0t=Y%Ww=YcF@-b9u0OSk*c+-uiv&#(fF<ei@l9DnXH88<Sti`O8Hgyf2
zIef9Tg{Ul)9fOB(C32WS>eU-}Vs0pTG&L~n=rR}ly;Pafc>=B9!YoPcPUU20kaVsk
z5K2??ox9jPfo=MMI2ZQOJcoz?2KYt8iK8mAc7k2c*K{>4#Hlq0zYVd{jMqA>y-Qa2
zo}4u(M#3903wh)-{=uXgP&zQwlPVQt$2n6s@yc|iu<hZier~Cv&2<OQuLIUqp4W0q
zkJD1f@I|9tij=q!fnJL;Pv)_0D(;)Rwx=gABL(o;Ud{IZDl^awr&aKIb@Tr0Sn(AC
z`<qJL+8a-e`H*CL3m}hPki&o<G{*jvfN@o%y{Gh0BvNUo?o4+%fq-TD-SAIb(vnZo
zlo?`S==T0gf}=K03te#y1X|iv2VAX0>@?CG*je3_KD{pX(Cd4k=KYF>YR=3oBWtWT
zK5`47;8-(kZ1eM2-Fk*VvXoH;Q9hIqNaQY@Y889jl<dtK-UHD%-l*<Z^LAzL`vqj2
z4J1BF!RJnSPAuA-^0=r-ymk9+g#6}7oySegk*!<nOkS_sB54f2*fcE3D8cFmNAo%G
zPBmq$CB4;Lm<8~PH5(lmB(<)(*<B;QscS72&D=tf^jEpP(uE&4<Sm|lHx0FtEmmRq
zdVrS(Axv=R27`}ug)sg^Kb9UWx)CR6@H3^te#%RVKMgpwf7k!mOzGsZbFpSD!vMqL
z0%$XQ2k?6Jlgj{Qd^RO;HL{*!v@V@uw4-en-qxx_WRl|>v{%I$2}i*dk&b_WZ8$Sd
zWWf~E2PP<M&Vs?o4;MHrh3}npRaBY4gYL6%+ZQ)UXfaP-gO7gj*2*7NIh;%WWnD0D
zd;e<l;<VmqLY9eU?z3M^%4b%^rF)O>dpysmL@*f*1^*m=68X!v+b}QM%;&sMcJ5Z1
zw`P^8UfgSJ_THC)TcpjZL?!)Q7FqamTwydZ>aHQ2p*(r51=4oHjTkrI)YP~4XYo5y
zvhgX*c}Tjh=haS{hKCgg#jCVus^h(VZO%irJj4s03Z#G^B}NA)4gvHWp`X!l3UQx5
z?}n?ztbg&7%BY!($8S#%ZrXh4ww+F3b4rEdUUCIGE6?D4#9J??2K#r&t5a_66H|8t
z6YR)=8~2bcU#T1XvDAeeZViHcHyaDCK$Nzdbxj8G4%*+DnouM=XM*&@PO6pKKf35t
zpKTTz*C=L=nLx*H2m!E5j01|NUC_TFhTtD>a=4<-)mH2b>(nh991Z?o*W-H<B5o37
zrVxffwu~GEA)gb%12=Q>WJU@n`dh>>IjXnRj4>@3ey2MRpyxR*t#6qB&K9C>qd3OT
zeQ-Y<FY>|3jT#>O(s4>ZdWVVwr8In>ey@fFcR&C-UEQf#q9mHV&!fH!eAWLn1W;Dm
zrm+(>tXnXNEnj>b{)o9Eb%NmP_JvZb;OaBET!?Eeh65r6SC=EDhc?07;4fnN$Tsgf
z?YNzT?%p3&+Z6NKrc|Lu*)okc%^BIh$@o(**xbnEZfoyqc*horBj`l0??{(ig`js8
zdOR-0=xp5cCMTL=!muIc?z>WCRTIh3;1jiZRsvpHM@xOq$knDmqz+P$(_b(a@qRM$
zXzZZ589wA*!rd%|L;YGDxuwtOTE+)oW};teqAfJ7+Dr%VcqXC7S{qP14yY(mCckM~
zQ+&19^W$<>oO=)sP{3>wo3{xZ0Ib1rmpUL!Mfxn!-0b@q_tLg_okVXsv}FOC%gt}i
z^@`NErdLuzQqT7-wu>l`Kltn!(R?FO^^}!oX`i#a6hZ4Aw-u@t{&vo0UBYzKm95Qb
z&1$D#n0pIRzGtO&MW+_$vkj+-`NlNC3+8M`d~<Oj`XN8s)at(Tf|Gj;A(gY^_IzK8
zZHQbPx5Of!!2^zG9biNUA$g-ru@leOMTpH%b*dD+R)-x@r(2rGnXZkG1mh6%;*V52
z{;Hl{)wT1^LrE1!MM9kHB=AVBCggZHE>uDDY!BC|)l3tjmFSH0S@-4v!Be1mercrp
z1@><0rHe=6S)G%W;5qjJ5~|daEuclrzU81hBt$;f4IonqT*g2mxbz+r&`Qnx5qeBz
z#y$5!ybAv3(yD5@gzIHawNYNJ(9M$X4_#9!Nuz#gKU<!v;6+GIm>7iHne=wCkH=JO
zL3C(o#7WB}w6<B2H)AfyDNdI&o=<z8lq!;Cc%)Gx0jahpGg`sDOE+N<>?Q<zn*5IY
z^!>8m2Vkr;IuHerB~X#2K3I@jUuH9*T3d(ddoQ!SymS<i*jzgyk298qIyb~v=zoky
z=6BHWok)S}0x5vwPvya%aK*QdkyzoQkh1`|f5<*r6Z;I4_d@zh4_6)hX~~L7iE4#y
z?9Oh`R}w=(b53e&v9kL#d1<jO{n!oFgg4TLw2}~c>8UxHOBc@gD<n7nrH44>9FX%+
z0^rz{137g%&QYq#WDgIXA!*nRjuW(>5LdL;wUek(1j*`(rK!J0Y+pBlzPZKDLEu6-
zZ#l)#f+wbs5H$r_)Gr$4HQ_Yo33y+ABK9ENNF~vktUmR=PVzh2X(XMiTZpG_PO5jz
z1DPEwPod;ru$8-A7U8%h)k-Q>$77{o3EClm759O1*RWEyfWGCZNud<=)ubw$4p21e
zk|JYJDs#@ya`Tq>WJ_S=DJd_SXe(SOb;vVjo7~b6i2YB2HirxLO1G1sFJ<ImPO+5C
zt$(&82WqYK<x|S;OMeZw8WV5Bc0mI|Kl&YNJ)t}8+7fh=T^5xW&yor4TVihyeEo*C
zzee3UaZdF+Zw%jU`CR=piW`BO|ATAHN6n1rH@mf;*1VtGm9Gb9A@Z6#Kk@z;;g;s*
zxD?r1`XX$)w3(I}W2P-cD{J$ixNTnmoEb}We5$atZZ_o}p?B8-7Z4R*6)g|G$Ky-C
z9|^+w?&658Y8v`n^At=;tkP4U)txcVv~3<YU10lTE#BIsQ;>3LM#ShzT&mirtJ*p{
z+SYa7Topa-z!~#c(z}0=&<l;$>&b*%t5(GM!$HbP<3`)JH06P3j{LN4`NiPxp?F@x
zvm*VROTp6HIyAIYM8+;{`j3bAwRdvxx0C=tfxLSGz@!pXKtQ!BUkK!co5qmi3i}92
z-<N?$sdjO6bjf+nZoO?OLS}wHmmbOlzNz{=oj@Rq^6+#r<kyG5)!rIyf1`2qDpr%B
zeW}mV$&Cn=`5-LwCSKX=z?gTp^S=H!6fah({LHcZ(%10@;t>cg`z(r!ZXqLuFmTy2
zVpR`pzJG|s2}{$|KQ5Uc0s7l1)!TlT>bel!8qNU!W8<(u9^KAY{*WL$YA>B7uE4|m
z1(yeb+POTb_Rn34l5%O0#`vb9(*_j%e|wBqd_z`_+iBIa;pRLcggZGTR~Y`AqfyAR
zpI5K}S8#iE5SFZHIF3P$J@k2&=^01y35DPZLxQxDiSToCU(&W};c)qn;jJ3+J&hK|
zoN?r&9pE<+7e7UFy*o8S=u`BunsGc%Ti9P$N|hpWC<0{KC{f@lc$ss%XG8LoGKSkQ
zwU;D$UqzOjcYBSJ$fD{O&zqzUv&z-VDs|cFxr+zkhMcD_9y-yy$>5-!D4+n%GHu_`
z<ZH2R%X=b8SqL^kNK$K`DCgSi2RCmheRX3}?HtTn6zYg1i0+ii4?uWK1mJ(*vSX4R
zb7(&H-a&(^xYcs}J2h&@<i2RiZ}##Prt2LYDK_UX1xkn9?fnqen2D~MpK;SpD0a<%
z(<0fd<8kaD4Z1CWt(>2h8qBgc1w>yd#K8_IJe4Pq=$*iqUNSB^IRo(qj^k3SS4#3o
zNhSC?iPJg>JOPg6R!(LRr(XpZi1FE~Ab!jH_WR^4(W4Wlb8<?hI*UVHOyYlA(6996
zL8Qd52^I*Q=K1Qv+DqRtm+itJOWAIB@~;pdIKTdmY-SuC@P;le1x5}HkpfqTj_-`T
zI-|fGMti>hQJeU?@5*PF<${edQ2NKd4jO3^^>L}(@)MM!4@zbG%!$7XF@A^;Q#7Z~
z2s0!sT|mc$i`nE#L%QTeDm0Fq>%e7VmdI1`aN<tK5c;mr&kYPCM+;w!SGuP>q5S0*
zU^)}Bk)B6=$TeCF)5FUBH_^ZTo1%Uw{Iao?CH;oU!bXn&A^J-VwZ632khHc=4TRZ#
zBrK|sSAuRYA^N}Wogk0&5dG+5sts<|XcV2FKC5~KXrZBrK+98#Y2cAbc!iUTo32e$
zj;BDB1n3)Rbd7z;67PpTMcImQStx!5WF}^Mw#t8yBqMJ{f52^|8|^VQLQ@b~{?cB4
z`DND2gtnJ|Wl)}}rGLini@%1e9V<j%QLlYw-4;3<qG18eBMcKMsYJBL2ZzBSPvgy%
zy4MX3dpb>684^%Dj)$}5@4+L-U>v4A@{NR2opp;rA4x1*IVC%7QbFj;jr~(bpcAzx
z91~`QxzMyjxLw%h)hnj?^W{``?!J8Wt}p#Z(ccNRzuUeAz8Z={f8Ld#ucM#;XhD8_
zHROX?{Gcvyf&TM5nOk%nw8f^o@ty(>u$0dlIoi<HZgER-^U$U9EqL#BYefl)$j+oa
zmM}u}Xy<<%cs#Pgz!5u4tJ41AC5h=5bKNsr9(ik+NSlm?tZd%eNrZ*ivr!hzCX~xO
z0Xh_K&$=aU8L59Soa~;RrT2-Y!{G1OieBNl4;5*DB-SPDH)Wr7m6=1EKiF|pS#-Bs
zI)6_SvA?vQJgHH)VzCQ@q-{nJmuS~0m3)yzkB=8lzo(pRJp(FQB$|(105H3z983A;
znIjJjbU~vQBENcaqE%J1pTR-#$ROy%VV6c8wQh!r<aMQV96_^Xvayu~u?|U4480dF
z^M#B|f%Gh0MqDglyJUD=jzN#4Zr-%&#Fsu%zIl`S`>)!~e{nDHI`Y+16*w+v7wcGu
z8RdqUn4OYAS_v6BH3~ZTG2c>}75_`ps_ko#yY$df;Iw2NeJ?*W7dXT~2Gqfk{35Lf
zLT`U~)6gBIK;RUUe~thU7;t!b1`%N-OT-b-y{Re!v`MPlCRk$vdAbzf!Qxk%bRC*B
zod;X|Rs?+1*`qEOH=Pr%Fsv4b_}8lUej+%pUYkJijuA_o4j;wWF-uRC;*mYcGQ8Vd
z@>FxLRWBZq4zZ_GUb#tepPBw9pqeBjX#T~U=dIUXgnb&>*J%LQGbYyn7IG?plmKr5
zG=Jz1(0Yx{=^V<0kFhTx$%;6jsUT5+<Wnq1L@J8?<;IghpGU3s-}>QrAWSND1aOcy
z$C(~Bj5%n0#_`;>KlyNErgaMX^Z`E}x4)LdOBqb|$JhhxgawNUexaKle(oi2_9rEo
zYj>}&sYwWsgh7S=z<`}NKr((|4iMq(y%;jayCU*Pe^X~g@Ra|^@ea&`E($yz%H%|x
zDlT}jYza<n<P{7X%=(tf8z(E1_acZ@GDaJOP$$*K!3LD=Z@Fd3d0<ZHPbfV*duCor
z4L>4HlcfhNlMPPupN40d;FUujUg{wJ!;_jsnZq;4hf1$o?hZhdiOwIFtKLFA8v>nL
zs{r+TkrfnN`FZlGGgh;JVZlvISvwY(LlA*$5`e%a&eVBdu4@ox2=p2EFB6Voz@d~E
zV&#rQ#(&?KIgfHnK<&2vz73w$lQu()*wu~0(0F`kT1_(xi$|`u_s}Gfc73ZqtG!BP
zb^BSWxdmK9BP3ltV^P_zX8B%4TL9-1-NW0rpqWd%OJxqi-KO>WBk!txHK@~E>d=uO
z3SdVvi#-NAcmPlm6hA;fhH8hC#-OdBU#mr-1b?Lrsc?c{#1JI&-Qiz(%l84Nq_vjQ
zR34RE^&&Lo+mGB}DYc8~BF#?88aDL;`Pa<cTb|*x^3Go+dZMe>cGer}(BYB&l@cvZ
z?YzGg^>WdA!SPw0KljuN-O^OB-Mo{ZvtNUT1pzS%IA|`21rD;Z%swu-3#~b9FPE1H
ztAgcpXy5I1x-$-_mWq5;M9m0Yw79Ky^$I>Qll4gr#0d%Y8ejP>J?Y$%HRxzPly5EO
z{&d#-g&=)k@jvFtF%e0zgXLzDnM({_>yaS_f6><wgMS`%t1`Qe{LAi7lN$fVAnt^_
z>}#j^w4(1*-$V7cn|i4=undOWnzmwB+A8BexoM#-k&3ioS)0~*)&Jr|H^)^TIVW&`
zy7N2U&iwBgE9wmm6Lzc4<PX<7qmy{Ow?&P=9vZc5wmfw?T$k|jO2nxYpGP}H0-?VQ
zkt|W&u7!^b3v`9XpgRIpRijhurN+Ez-Y<jlROZS~K@M;n)|^95hxR9N5n={Qh-lzO
z;1Wc3V1Cr=X<Ej7m&9wLGV_{~WN>YS?*-fFO*G8$&$^pX0*6LuTr?vWz9Et{Dwp!0
zIPs{L5eWoC&ru}_*v|l9@C>7|WN6G-7A>kK%c|K~yl`9y*F86&=rHz~^dL9J$O%FG
z0X%i9dmIi};{MAk)#r#%gwLn#+8b|_<1EBs&XS9xH;9ViA&C=ohEu%j{`j*ZCP$aH
zbe#%qexlCutbNk@8EYnkjc_#={IV6=j}GRHk(GJb0fjyIsJ9;jtXy5w`t&-9-y1hd
zIqUbi>&ZwhnPoi@-HsDWOsJ4gq{PyuIW$m!8B~a4>!mdX==doAxh*d~aOZEgmU7!`
z(NR?9-nn~FxD&HYHjo-fL@q5abG^N*8&vuX*J$z~QQ0^)Mcr(3SDrpzdCGHHW>Wv0
zKEEFyGXzkOhXN#t8UX%D4K5i6q3?zqFYm>3I51-Q#yFqZc6#}XXl|Eu3Td(o76lkN
z9K#PL0=@<^N1SedF>?2hCk9c}c2VUnyZV^uYAV*fbA6!^CTKvbsd<koch~gi{gBem
zWHT2;02>UXOYZC(6Oi6I8(0Qp4|?e#9G<4(k;}(%qyU^(ZWsWeoFe|$!7Bi<@O}zy
zc?K9K%wd}?B*C66!|+<e;SyS?Dh&g`+L?bF*BUsG^T-juEDkGNepNVavfQLKH}_$E
z`2JK8NOjt@MoA;oz%GTYTS9lkHqiLt7fSW#GCN;166dVbD4BJJpy37EVfK^8vD7+i
zUR_9<_N_k|sRB*WusE_1qE-k(HV|covi6>C{R`-QtIKFtPzF*T5g{+q6On_cVKJsh
z2QkxQ7pUkx;vmzDup5i9e}=BQjX6dpOdA&tf4=TLAFA6R=zd-+{(#kTM3|+Nu0UOw
z$C}s4#~p84Ul?mKjp9vh>YBaqwt(@%-FvfoyG?ge0yJ9OQo^t_?H9!u4>AZ65KG`K
zlTQ>h7w5Dp%HZvjSuUqCtd7j37;6(aZx0+d{aT`XU}-z6Q+JQ^<^i<k*vGK*V$R@S
z^g&SG4)tuh{|b+O(c|P`mL!()akjX!3&oOSJPcC)T)tFRDjcshDCn^FhGhBR2~xpb
z!3*}JL9_GEL&ffDBLRGK-GtN6K0w(!`(0(5z)#I#T=nLwFcmsYQwCrEFuGNXV^f2=
zpPnzfSG7JZzqM;*`-W^9Gf>f&I4iXxjxfk7l^g`C=fH^n86<d0&Qn!L(;@dMY8N1+
zCQbc`Gl8j$#!av5{H)(^lMH5%-_0d?T9*WD;h=JGiTD~Ailih0j+GKy=TO6%SM1$Z
zuj-e~L7C#@g9+B2wcSAB0t>ZFuVqu#vJRigdc%7w4qKf-m(kAY`?<xo8R}O|8~hj$
zyMD1FnK7WC!Jp6J%;z2baHdzkib&`1C|V-e98*^C4*?Me*!yHaq?1y*2v{gk!bJ19
z6-LhCA0Ly6G#{dN0~)A0sPMeB(Y&3wxfl9%60uP}t<|oyO{qhaQqu-gyOI6~+jPTU
z{y#Dtmih@szvR&iGul2sibFUHgR{|t_(spiy%%@M<ua2WTBCVHDfdGb6$0`^g-?<y
zR}XR?1bmGabEET2u5WPR9oBtV730hI^9MY2x+t348KH7_FR?8Jc~8^AE)#|keLhut
z1I8IRMvwr511{APk8xNSVqp`eO2kx%V`y~w$U$n3S^O?udX%y1J8d5NUaAxs>J^K9
zRi)H|u~Rz|o~a^_rGaSPjLDO4!2i7koU?;*Zvn9(lJQ$}fEaHPBYI1HZTa7G5c@ft
zHT?-ylwLLEhWUK7c<Dxs3DMmABlbS~@}3m@W1Wb>EvHaY+MOUw??zcq(QCTNfQgNE
zIlCGXzUbHczQ%L+`J`XWShuc|zJH*T)9d-u+5wUr`7idHsNeb;=+C1xt4fNaGeVr1
zT$O9Fu8!yre%p^Pf1Y$)(2aNltRCibj5!|x2-}qdKc2(g({H@6p6h?E_-TGJ#$gmi
ziXNH2IUj#GBfdu*61{`-wZm}>zL6bL8UgJyf;INKBLF-|q}TtM)5r`HC;NSecwL#K
zh;DT>^Y||X=d8CH<ye|+=Gj==|EVM&S~mBbD%wO=X$e%}f*jUL0J|GwS!ExF@%E4-
z-E8T7h3_%?{WzJvOw7pqRpubTzihY8r5#Fzr@A8-2!Ncio$nLM1qn8Q)`*!nl_@)y
zvpx(WZcUl=>%Tpj=ABKD?6*sG#c`i2dzj(b`rcfZX$*zB=87O<eL6!DU)wkbk7i~m
z^Kw>4d`bP2dQI@0VT#6;6Pd}9)OS{kaj2x9p818#3u_(hD|(yh+$UuG?31ejR!gg{
z@&*ITtIk$0LeKyKC>jQcld3NOK`fI{z#=!UD>=06u%GlP|JzO@L6sJ=w-D*t%M!(A
z<D|O-lK{2bsO77mSyK%D*YFrVoh6A((UaTIDUO~Cj#zHfhN$yuff|NuhZ{5fbiRBx
zpp1`N!5CYIt~jpXAIe8mQ{YvF*fq#bN9r_ep*o)qH2gG7XwuRD`LtO`fn`ig_K(&p
z4Sml4V2i)^(+dPWIlod!t#&ZRn}!ShHGb*yjdxlsE2PM5%m7bR&Di@dCGtdy*{rqv
zz2pNGzG+&an{I4wWIKFI+O5wJ1CEQYc!^h>+~$6p3SyTm4|kn}SZi%C8e-v3(Ah|W
z=SJzK{`reVa}`fDSqjOf5BLuLffPG1Km;cJ>n1dMMy`Pq1E3f9;lpUT_f7Q|_R=qo
z%zWF8nek5eW$Km;%*<0-7WZ5GPgI*bY2Fp+9sPUJeDb?gtGLj@&vmy4ez@WAl#x8?
zv%0qX$8--2ko9x;Mc38Mp_<{vZCgc)0&yF^js*E2bAPE!<xwt1bH)$4a0znYj)RD~
z0DwmTi3^+|CXf__efA23zj85fja#gTk*H~5w;7ywR#*F3!_ir-%JE{YwhC+}Mi#WF
zc9HAnBTplYU$c&)F3F0h2OrKHvsKH!ZYBu~P9&ivD|HvEGY2=xoIOfw&@gU<ee|**
ziQ?77E1~6VPc{6J4trL8NL3L&7OX8tfLYM$7==XucsW62XMiBS!*g2~mIy>(XHcHN
zSJ9eJkBVHA`)4H{$BN(QpBg`Fqa)^jfZ|XWb5D&#JF{{${jLJ25ZNETy1Ug|#ARoK
zvkRgr<!YHI6|cER@*TT;6S~Q|z-)hsMqhCX@W)X~x&Y6KzVUM#h;U0f-~+wP0*+G#
z7*MbX0g)N~j&UO<>!RP(td#4k@Mhu4#jwk|j|4h0r3(+dFkMOJ-SjVDzT!Krp3;d_
z$Mn8o*p}y7kySkD+hc0pN9477?}}%*#}>>?C`}>}UOp=E<ugpqxO=>49+FYJ!l|7n
zE?(4$++z!&xp}APX;gC;!7}W>LnxLcFht{?hSOQ<AN?)dB16m8Hbi`&ej~KMC|%ks
zshU*K!==6zPa%dlLHe5z?%OHgUyu?GJ(~eEAI*#H0y*UN)Es1K#@YK@m*q;IOKN%-
zY5QSUN0B1UM!a2&ayZ-gD^<>kMXQ3ZZm7wf;F@RQq#5Ipzz|miJE55Sq~i)FWbyw|
zbl(3|{%;(=&*C`tF*A<iIQHIK$3A9u2puydJ0Wq7y>~(?jy*ysl636ZFq2dWAxYK;
zmA>cu58OZ9kN5q)uj~1Iz52NH<p7Xr1CX-zUW+!k#9}cqp-S<QMd-TtWehL(#xEW-
z`r<TZ=rdi?o(z?nP@i1}GwIs$0BOOkx<!cEraQNd5KBk(LMcv@{jCNa^8)=_oXL}>
zt>v8{{f1emE7Q6d+w{*XE(6Npf``Ti<Neu7Lq2uQ4C*heFW4@9A8%k-vr4m;09rCA
ztr-nXiL4PahQhHucB==44bzIB41u0E!So<vvYNEuq`EN+z+vV!pEI>=Uh{nA^2C&F
zZLRtZa+#pzKJb3do|w+n-8Crh?o9tALU01YDvW&|`v#JxQoSW1Qys*~66$c*pRG{W
zOD(Q?uwlg8odKW1RR3huV9f<M2YDGB0!AA#_^D4tiU0WY1o!@}f1npQmFdZ~5`+0z
zMKDERUFq{;dySy8AU4POoPi92>+GMx+-j`+D(dsMj&*|K+>c@fb%wB=<F_>amY34D
zG|fM!$hB`IUr3kFs+F9Grb)qB>R$-|p7`fMsYpl9NZC=#C#DzeJ^7rzwM99&L;KYw
zF90+sxPwRVWH=(qV6y>1lKD~_Phq^@lnV2ozMA4fC*(LEYXfzR0H7rUz30J;E~Z+P
zS_wcfbAkygqOv(dvb!v%@X-^J%L`)3^P3E*IbmAb-Pa-lKYe=fUlNO>SJY_f*YqUj
z5~SFW?@4*6h>jPqz(-}b;cN{$hOI*YU_VVYAg+IBWzv<6N`B~t)HM@tm8xR@=2;MJ
zI#qPhIPv5bF`*kEjY^8G%+~_wNP4^+!YqTB=?Km3-i!g=Rz-07WhpFs&$=k(KKp(!
zcqIb!?0KeyYNeZ5e~P@|zQ49i?B~e5u<)VXt=3%$(Mamcmzdep`2L|YQ)ReUKx)S2
z=_P|BkDgUb=Pn(Q1`Gh)4w+$mkyr=BT*t+c)g_@W!;^g_jK|*bjsAwUk|x76j$FMx
zH_2F*RqFe5Z>>d$f&K11gLFOlc#Qk$4~cuHI@18}VX>E)fXQ>Ok8q#&LRZ`_xewgU
zsiJ^lbK`=3$BTN=nT5US+IdVIj7kFPxFFP_d5Tcu`Y@-jcgf?ce4S@k%+3=Y^0H}O
zv=enL?oD-Acp4vz5IoMTt=x>3#C>bv*I2xNMafyY!%vaFIxhU%w~r%kv41rTZs>e!
zoK}Ws221%rYG_j#$Cs}B;atho&_x&%kUT5nPS+Lmuf;S96`{)!9)^G(BwnSy+lEO*
z<}FJ%1}y+x<fn*$Z~&Mxj;NwAP{?SC!eTsM9QHje<{MNLqn2GdW1L%g?R;w8WtxUZ
zShDF~ZV6rM1G*oawwb;v{XE(}{`-klT1HI4hA#5%<$M5u0`aj^092p91P&_z8Eljd
zFWrhHKNf&9z6*c>LRIB09;vxB>|zERvK2ePKsRbRAZuBSak)DPmu^OtSD}~|k73<)
zpe8l_-wj>3km<KeC!5UYiXmb>-ZWc~jqOoCcBb<0zESLTg`zTT>9n~ifp2!aH3lWy
z!sH@;F!gpf&N@DENV<;uwfingK4w3Abj#3V(2vhy<%9|YfN(q%;xGf@?57pwXWe12
zIZ80CS@ZR=@}7>8MPLU@b|NDBRvJ$hEPeY;Ke|PRHD?366+ozBFEeVeGIz#WM2iZZ
zA7@5L$1?!+&HmtLW|_P1Lji!<`9HeYP`Seck4O`}O<6KhvFc14{GWhSN5Zv1F^Yt^
zc+n&&yi_oTTeb$o+#oQf^!BwNe}8m_RGlg{vn?JMHO}(@X)ogUIrJ-DsLkcyYtFKt
z3yQ0e3d3#ZH`JOHV*T?8|D<IRFwQ@(xy1H>u7FLebg;bjW~4-yr+35zB>pl5X!Q(W
zuZRFZ(@1wyCwB_}6sG_~vxrb)T=218E7IL8*KX<~yTl1~7XW}(b#)iPKMEnl1$yde
zVYvhT)fr+@hy8|70T4hyHKMufw!emNazGl!<+z{eElZksfwtm-uHr=MghA7d%7uj_
zExWbUJF5)5y3~@I4J7gns?SXVheD+_1B3H1wxNXWhA@vw?5bYcwuYX(p7LAV{eB78
zuLP8(xiQf#Ygvl19S-0M%~sAqbUK0t48^ta7UXBQewjbE#&3ri(@iPqH>YuA7rRSl
zM;ByB`o<wAIys(LDy>^3&&&^OruSGAZLBppnQ{$&*}P|Zsa<g5kn!1S;)AYr_SW@2
ze&OJx!0xR|f`FZzQ%ETQ3^Ne_P^}I=D+))&6`mF_dZ$?nlX?8w+;R-~A=7+0O@J@y
z6Rvv4B49i}u_10urwm&}y%BVpIQn_z;d4($$T6Pv(UNJvmwt`IoxR9`XqjvtEe~#$
zRpc4@3}bX$91wZ8rT6ZtL``N4ZsetqBoBC52>^DP%#d8}p%S{$+{`r~;r_caiMPzt
z%WC4UC5VbVmU_Ub^2d+zHfZpAGST1QEn8^1QrjKtyR;Hj$B$)q4DBUW#gC)52b3_{
zVt3iCf+5xej5r~?@`0DA#46m6c=BQFg#KOBNdNf$N$JWy2k530NkD3ulh0<h0fSZh
zHtgobIW&~sb`)pZ6rk9&DN5#1TT2D-Dky4M9#I1<UBy`5s!JH8^K=HKm=?UNi=<O_
z{N>WEl;I>E6J=$p^{zRI>`E7=@20%&=FcV(CZ)^~A54G?_4V)@1hb?}bMz6bS2i?r
zkazpvt$AjvEK`$&xB)3KQ{Ph4Yw0F+nH>h~u?CUA>t+<%^pqgMj2mDj<eKdink6nQ
zfJK5DRV>;~o91?Go-P}(9e+F6RO8Uv^j6oZ8S+_|={6L3k!XA^&-L1XoOQ@`gyi3g
z%`F#y$!#|;yx8K25>|M5b7$ub(e~G1bGXR!H%~7P*&1z*c?1dSP7$&^nnB)W48#fj
zl~F!_mmK(^%2cz!4#tI<A$4eTwn<?i2Wv0W-}5xKzmQXJ&5KJglttXMCX-~`aa%BR
zRcE+R?)tH)UJ?WxGrXxHINN=B#8#kcEzvCCYt_7D1nOQn{7CYrS?lRlpF^NV+GMRn
z-G9?ni6Y?d^`wa+PS{}ED&qoC)5Y&trg(r1`vqn&ZZt6A>cxjNaE}3=KTokU9;<&X
zxM$O%ulI-HbLRj3{3?ME6xUmu`LF76XP-4qPs9E%C53jo&Ns?;h3Lp5PUc^)n-agg
z4ptfJzwfZOoO&jpYvQmnjm&=osu;LR;Q=gK)V+JvGt)C!HvjlqrER%bUhC;FVn=};
zP`HcE6d7uRo5b=nI{O#uKI~FWRSmV#{yw2g@g7q8?~E?>&NvV*g9UwVZl$Q*Oz||j
zZIN*2MxO`6;;oedY29ho6<vl3OD;J#lyD4)$}anvf3fI83B`I!o8Mpip5bd1n$V59
zOwhpL!4v>OMPUvQbYZQaTQVz+xs~5h7|8gMJ=$6&vtkbaDfPyk+DB}hQ5U&)9fkD$
zo~8I~zs*_KOErEJv`NggyKuehk4d9WUi(h(1)g688=3cha67c*7Y!5)FywiM@5g6x
zwXkurqYYvC6f`%K2rv-GcmzlJ@@hDI4_$Q^?mai%V={{InAECMnEy49H2Q*TD008V
zX!kPQii?E>f;zlQ`Q0fXB5bKv8#=5{jO{24i_g3%G%fJXMKIe^M@kFTx2@iviYaFS
z=@UQ%KA~iddpNpmgR`1`#Nx`{mFZWERJP1R$eqaip!|Y)P^b#+;AM&!*st)k>Xs!}
z0#^9x13y5Zqg%z)Uz;J_vtP&h?U?XWhS|Fa#n9W8k42O*@}lJZzn5oj&!PNEo$b=R
zd<U4bn=`auO9j~LTR#&|L9FE5dm(Z8D_w@xmF<gF#yXZ$cDbt<6$4%=(l!9vXY2C-
zKx=FjS6SW1q%8_Xk(|c4AMl-9E<ZoAD}0`?>k{qn(X99gX(vF&$q?c;gDrw?yGUSJ
zWX$)9c|WTYEL%~{S&Kr?^xRT5;kGZ{P$4qvi6+OBkiFWkF4`hZ-FCY|eSKH^Hjx!J
z%f&_2D=YQOeBp*J-@uW={?P~beAVZUgM9UL#W}UZawiO3irv566bgXG7lJ4&azq9M
zF<Fy>T3lrjyMhaHR><sH6RSB1=4COAV!dXoof9286(v-g4s3b+%r&&nKECaDzd@<c
z>k-pef0!)y#~O##@SRh>%O$F~iPt5Ue_T{~nOkDv5gczi#=S50im#9-;7O+DuY4y0
zcl@jdb0Ln;cZWmQ;flct|9W~(WlV1Jf>u4$B`scy8Yn7Mg4@BrGT+=+;^6`Xvv>;N
zY53mPG)W~c-s%{+Iif~RTv%L9B&DEp%8?+?KnmE4l(d=P?!zfqn7v}hRa%EK?14~p
zP6-|K2Q0M2SQwNOZyG>K@^6K7^9lCVuPCtb)Nog%-pOi8xb_)^184|75ONVS6gEnc
z(OeXZ?MH}nkBY~aap?w1=*DvE%FC4_@nwq*-jTsEHuB!*LLKBp{4=@#Dr)TMgt_Wu
zZLF|IOv>p$#hi8CO$VIcl`0pDcs$&0v*t82eP3MTX1(nC3%qx?gwUO@l5@08SnOV8
zfmteJeA8XaUIJouj0NL9GpW0Q?6Wc^8A}lu_}XSc##*lXJ;MXn9l_V{t#kTaqlj3|
z`zERo<CRbsHdH8<5z7ip^ezZ=B$~phdwBf5J%M*d)p57!@cVaow~E86UfjnEpYLp?
zc(pKQrn`A|sokLP8Fry6WH<;^4gk*uE8>?z)nqFPY@hchZ#_M7w$2Fnv1!<mxM|V6
z(cqDYN?b%yj7|w+49nGMN-I2}2b_8DU0P50%LjGetLwpi3Qx?svnx_*o3}VL{+;DU
zt7O?ciOT-TmiyO2=Qu|v)~}#o&xG?|4?gE5Oqb8_TF<YXt!c^LLr*Rzjh-vBuO_~6
zx|QbX)ZV-QjY~@@b{z=U52QG`7O|4~+=qFST8aia0sNVO;2wX~g678JFL0NumO{p9
zB)L6qQ~qr*XF<U#f^+4Du@#fY&c;iTa{cSzgQovjATKT$C@e0sfWTDfVL#`Y+O2>h
zu9avFL3UQX2M0l!kaD%%z9yc&IsmYQ!OED?e;6nLlz=}O$oyDNe}F?$F??>p7@l%~
z`Us>g0Ts{;$0gU_Xi}%>K+)8TR~g+}FBlMjQCNU)S9XVDsN}&ZVUr1@CgC2+z50-A
ztd3&9BA`E|AoTU7Dy?veXctXmy7@KX9|$x6XQ6??5&(AqZh)kvNzr#F04#_Lh?D_x
z>j@44{g-XHVpW_OZvx93^Vl=dB0F*C4qe?hamo8fm+-o}y#)Fu>_Yb2nnM6O%wh1r
zdW+RJe7->Jp{=LgB8Q&Rzv3CO_GL!ob!w7|&^ys1ra}O0-BiG}V{-DFq{dIAB^%lR
zk&^c)KtaVO0G<E(tou{;h)DX|hGoPbowuHk7p3yXnnF$>*;-x$tje1^SJRLw?3yQ_
z1M@_H0t7R}W}cG`ep%f|JsU$8_ld}lZh-4wehE6^Lvb;u4SltI6!d-#PXhz4Jf_@m
zG5H-I{xUpmj+#te`Xj+r77a)rpH&cpiA-***nhM75x@M7-@bibeeoAXChXuB7^F;_
z3y<e7??M4k2)+RT$jgN!FvSswCHh%GAr;2oBQo?Qgd)kx%w*IkwxPqQvNKho^}*nj
zVh*ds6f&`%ytm_AJ<J;J$F2Xm$(Gl6>h~RFc^ku3Oif;5a#<U_ogZ&`0O+r{SV+Gg
zZ^M3*t>k$AfS=R^y|bo_d8o~d#6(*qJ;n%GI45n4Xb(Pr-Bx)|uwf<~yIm!{0G<^n
zgv`cD6Y%V;M2Os^Hj$RKKL~(DxtPB+{;)VpY>_{XMg6o%eXn4q0Mo8fZ2zrgn!#SH
zW}<lCRcVeRD$tp=VcQPWY7TLDLe3h6=e+#(>#X01<De}4QXLw06+^)ul7Mm{t86d;
z;xXjFf$T18de*IJ0AQ9x0RTFsVnu{K32+4<#hVI5a9u0E&9`_Oh`jM055PkcG72yc
zoN`mY<ICFqd24C_4QUh&2fu)q{9t_9^5Lu#OemL+V4^A!Zd^ukz*TPcVosW-$Tkm9
z)28G-oYv@rQZR6@J9*Yp%nDj5jDHj5C1P1hPHWu`b;})xpLrFP=R}WZVNLq;YpYAv
zd{(xj6>rp4@_cS-ZlGtXZyrKwh4J{w$UJFBztB(P(BD=vZ$;N0ii?_?BrhyJT*w6n
zHj9!_HUMqCof{FT*exd3p1bo2itcQ*;Vkq~LxHITT8Y=D%DC*6Nt@It5X<vpJ{TWv
zPp+vSaHR_zSu~v3pq|u2b-rifbf#L}jIXh8)1N>45i6SUHS*jze<e^7^X%!uChO9U
zKoZCw;K#mfg_m8fW5YE}gx#EY9v${UUiF4T(LuF!yjZ;SO$_$A!bGmBKX?G&`U(+`
zNM<A<**~S6K=pq1Eq2<pMNM%3zGgHuRP2zSYb)Nb4NqIi4d36-|6mrs&d*boJZ?)U
zQIJgY#^^y^;WO;0#KG1-+%|o1TLu2Gv@BLhXgkeSn_EeBM|J~MtpQU3da*1ewhdj^
znK7*V7wbU^627LNZ7Up7TW1qk6~$V!BTuD#oLJP^h9QM&o(v=^;Ll1?#Q~2w`h7{v
zLsC|6`sQpC4!0h8y0qgEx0T;ru&GY6WpN+05)n7qz$$w^hHG?UMK)*}E8oOr`{~OQ
z*ug|100tyj1t6QnL?U<6SR>L=N||2ZYmp|c?gsP9FO%%&1OQ5;^6daJo~ZE5Q0)}}
z=F>|H2H3#CTCSu8X|8g<_DjpB%4lv{0F)`8d;+j|K+UK>W$LuS01REm2>>dzKpj9Q
zD8~x`xk%kK&S>xFi{+v$u;O>2j8D2;>5KGa3}@eiCai{Z^c_MeYH-Pc(|vBwGW)W|
zePy4et*4wZ@5ItH{TW&E@7E?9EsASKRFzccdlG^^+FDW%Ec%+7Rs+)?!U=X+7~T1z
zAf65$@o=Ygo3B|Gc|u`m97}&Ek{c+XGZiSH94e5Xn~e%RsfW^iv|Ks9F<iU?Kst=m
z(SS};qae6)`j(Z)<ZeZjU#tc8SZi?5!aPX<pLUYLL)zU<u`9;lPB@8x5`O~kQyM~9
z1+ha$$LsdK+pdE&%lx*n1W$RyT>0Wiw4YbN-Ga6vyN7upf1>bt|IqqXlgxd-at)2~
z1w!FBQHpnsGmsSqbd%+pp}oH)LJXv~lnaqU7T6K;1PXAzM#>##&%$5p(*HwSwhRVY
zJ-;e_ZQoYvBg$}OjZq*O7%uWJFc-AOC#=28d1fXa@lit$?Wce$b2Lh4b3rBq$sF`Y
zy5634K1sHE$YI>^@Dn^dxtGpa)U{&@KBw)M_yij_t8|ss!*kLy@q#!Z^->(06frjv
z8rNWVV3aa?svR^g%cFZ3l`?uFO%bC3;{YCgJXM-14r79$B?muLp#~}ziNb1eZye7u
zVa`LGEYQY+D*9Lc&jpuf=f|bAzl((|0QA<rg5z7Kd1HDhQn@s@DGX$R#-+y<A<LRI
z?th$W)E9~Nx2{lCBM?FUcDDSWCIv`i88?T?Hf;#$Qxo&bZ~D{c`6>fIGE3wn7le~R
z5GofSi&Lh5VY4(YLRm)<09%CeysFW55olS79sqTz8cq2CDN-uQ@45gmx4}({7%W$J
zLpzhg3qkN{(6U~vP7-?pfMzve;hdmsM<i$u;7(9fp`mQD&t|CzDiu5s&JMNFz5zW|
zT+}j%m{Kze_fJ)Vp6M>GYY4v<DYovX#hVsfUBW+#K$=>*mp)PXv`$m1QZvv!#jluv
zZ;a^i4?!&2+C0U-EB9je(8u3?Qd`r@@FH9>%E=1k)WsLWDtPQ}6#P-9UpPA_FIyQ0
zFrZ9>Avu*r#4K+=olA{H%$qz7{Qso2#ZPZHVP-*>yS@QKO|*$<e*j1gzDd1ztdh>5
zT(7v)eadG8Bzui5k}WZl^_L|PUf-HJuY7-StA#IAk<IP`7<El54sjZ(0c}qJ3E72G
zb=r#zSPpw=9eLDFV|<dnLSFl061yY8u{?kPKo%eyfJiU#oeSM?m0S`snYO6r%H$KO
zJ4?3DF|%lqq5(aSe9_LBP4nCMNAh0K$8{5I+RKFh(mLIK&&5xFP&s;?Fk4-D<Hw5e
zQ`67$f36-mW+21W9augK6(vrwwPFbN|9HV)ycQY?G*nZsii)_4nHGuZaMoYL0zlwR
z6<YLGDTMtR5%flb)~)J8JjHQ>!VWI-eZ<x;^$NJD78O>YC?d4k-`(|_CAaae?Uz)s
zZ8B2$?rotqR(l2~K3JV|!f)|X#b`J_t!=uW$W6mwbtkvW36|?l1niR>UF5s|R*<}T
z<Qm0TAlboeAUO^q5R|+X2gpIYx(uO_8q``hU<{tLcSW7W0LpDi1UN~lEQ-a32?bq%
zO?Ap?8o&)nOTA4ylaUg&#fXQLQ%^-P>QLP=Dbx+xWhP(DPny1&D5swg49Lr;Y35l{
zE1#%~Oh}hHj2nOj+b4DVL**3c_h9snwA_vW2#o~RP(U99dEtzS@b{6BRI@I-*!?J;
zm1oZm0#sWI>Ps3@Ln^u?vN_G-JJ&9$TGy*KlG4oW;I|`7Bg+{yL+Qeb9%{y@s?|}g
zvd*0=yykW?n;#7q3dKpxTATl5wrPZ-CQ~IIG4D&#i=|V-q_R>wB_c`k%Oo02U0O%H
z1mzA5w_i8QZTheR@^@u9^`jjBxyswccgMID^&_(Kb9j88Akep}rl@)yUpI?t`~nHo
z|BCjjJ4+!uQT=01h#0eNU4z~TgE@f}pRAppQ}Lo@v@D%TF-}(EL3;gzq5R{_;3#If
zSXiqk5x5EU3nN-x=h~x589hL7ce0Ef(_dI*>(*?b6E8&%(qp!B4!YrDw~bP75_756
z4SoEHXb!hLxR?a|_F)e7=6&)&Fs&(f>vQVaR!c%j`u^Y1u4R+W2igiFD6_>pI~yKn
zU0&zIJYEp%heqw?s#fr>HZd{ZT19*2n>OV^!v!VTuOy78BqpYZ?e<Quzkp7>2-^fL
zMdA2m!PnL&S`};UZKh{PvbaXq5%Pn$!FNb1I-C~Ql{^|yQyiXRtFAEN1A|+`VuoO1
zw$L;fk!4b9^cP)*{G<Fq2@Ec0?`dzpUjBVrox+zz2vfFP5{`(Y$EDWO-w<a=U})<`
za|1MD9yF-{Z1XCO)wkz*wLIn}0MeW;?KV+uiiBqeMFKRpvxvYAny-0KqDdn9{|U|2
zqjpL11Bq7p+?&Gj1OXzT38Bbo5@`xrLB;Wr*fXk92VoqIW1*((BpfA-YC^3Cff9J)
zf+Z9LBdSdzS`UD7dWHpcNZ5tq6OT7_S4Ed;vr6`voW4D1gEkZYar#4e)EOCFObs^c
z$j?79YK9DP7nY9RqkDki`D6CFeNMsaQr%RG!D?~(%p#IdtFkUgH|vNX-!<#7$NN3z
zef|LvdX~-5R(>R(CRT7i_lr$<4}%(6E$#EP!OaS~H`Kj0x*kE73f9?+OX)gF#$SKp
zYxH#V4h`Iwo5x3N^Fu1(y@qe=@Evqc=B@O-^J({yRCJ6$#Z-1MEM6;yNYLyS<078Y
zF@=n!OVrXHq~}CyGN;Sr;m_bJA1iL65sj}VC6jC!(&r}A8Luytbdl#0BjLm(w&10-
z_(%0P5^b9g?Vjr!N?4-WC7lyLkz)9Zyu}f@YR2Hk2YG%MPsJYjZp|eWz5Zm&V7Ecf
zedj`1J`isXv`WXjs3hPd;VjHbr&gJ^FO@QQD`&s35+@i5hZ(FrIextMluRAAc(X0x
z8OfEo)+*MrPT9@ng>*i%JJLKc!`dc73su<|PZ%9;kPL~h(qL0CpcL>%SbV2t?Z$k-
z95{u;bV_6=tBcc>ize-Q*<4;>dABFApC=7HxCWt?7jy(2@6axE0(fXYUI~J`D5Mr5
zQtSZpHD_m``mw;Q`a~lHo`GIx$ioup@x6sh1xWE;MlxGhe^Rj%lskui&b?Gos!=YD
zscePi%3j;(tO?;MQA(U6>LQb4Hf~`ZO92DE2Z*JLM1nz*mK-eA2>=uzVv5wyL_7;s
zY`g?rZK4r#rwNIGG`x&278gNR<j6DRus{H{Z290$T0(?U9pmK&bE3lk4>#B{0Hv5o
zP=vrrF^PaKS0pVl_#184?d<+25M2Z$T@|+A@|gWQG%pEo+Na(2HV#v?a!Rgq|74Od
zm;S*kE79WFXnkEK;v+c=Irfl8KiK;BZR-#0C4ZeuVgHG<+YiA`&=rrT33XTE#|$SD
zuFjpLE%Mt>)0_VIlQ-%3P??yiem`fkJ!>J>#5cMC?(v)@JkdQn<-{wwgUm*U<1YM2
zuTDVBwxqplsS-=?!$%T>p}CGiwQ95pW4BWFkR2y+*tXZX-P)PRH!Lf3d@pUV6`H#E
zT=gu+g}Y88hDr9lP>*bJd_Jwl<Lmk8*{h(gu^w8!u0(n#pVZ&|v|R_ZC+-*3LK0mt
zL|<<qE4bw>NBV^#PKFZ8cuj`fWFzM`xMG88YJ6>ICy)E5*A-{9w~guFNrJ_GC0PFj
z0Ji==34G7TJ1akE{t|S2UT?<m#Af*;y$Iwb%AAQmosA^C&X}w7P{Lc<g|W}xF*ujG
z=F+;UXzvqrCI-&v`EC79vtZeP?9TEB_NR4{5D6ZPHd4e=2yw5!;zKuzt>8NctMy&)
z9QN|3PjA*haJm2xObe@r5lqwQkBV3=B`<s=WZi#}$0CWb!3DQ|)TB52*kFG5*_DtU
z{?kpBkKsl6OJ1d!jPtlGBIFTSQ&MdRkhcWnAYAV}Xh3n$^6rbc&{vMICIFz#jZ(}-
zfljEAbYhYehH}6<ahX5_0#o1tmyQ4cxSrhAm6&Rrq}xwh_oPSTT8eK(Mk*sPO80h8
z^Bw#Gs7kht?l!p`pf0SHPShcH8j==VX$13VVB3H%O!VfC{RL~dG^~E-BVSUN^%hfI
zbow+T=!@iyl^cImmfWxYkRV8lA{KuRiL^}*!xn!0&DyM;PHbBSk1n$%eyL}Bo-N+O
zFH+wN=9Oz=UitQ5Mu`==0zdkdW?z}g5?vci_ymMQJza=6@2;<1cUgxk4(S=AB*sSL
zWgg=_Lg~}xV(z!bNG(Sb9lrqtiFh;sM5=&Chd_1)jIIRE+-f*}I!6xNRU_(gC$DKB
z>b~dn4%0@U-?HY5KD=fzJ%|yB{gi6zo$K_BwzVJj`yhp2>Joe>nSC30@ei%dh4!a$
zL1sUd(W2X5d}P(nND|CYTe{Nm09zaZ_<jR|MM+ypkdVK$C+s&CN!igNSxLMLD)s~)
z3zU9L9tJhEf9S28AzRO^J(unH%yB)GUC`&+io+!*kf_Cm2%XFoS+A!PbHx*b(8+{N
zrv!nJfFbEC{>TqyI(_PbExZfXWjNP|zY?%$CkuUFm``s&r9L+`M^L0INm^Z;eZ@|K
zqm_-0TM-Llt_ex^nk?Wi;J8hvp2Bm5ple#5$T%@+J>|ohK1WYQ@PoHA+!C2WKHd-)
zC(6OH^fL=rFI}SkYEt7&9X%V$ufmpnB;Je?yXb2og~1k+ByKjp51X(qr$e>y6wl81
z6S;A<5L%EK?1bQn3x4HvkuyLY4G4lxqexwRQ~*C^B|4S7#{Uiu+`shnxyiN<waxMB
zc6NR4&`!QD$!*1|<ZSD~=s%;-9GffGRXLv=`@bgp#EN@fpANtD;`AZ@^D9Qh>xQD&
zr!C}peg}RpX|SoQ`u^S@B}tqRc_H^URH_A@7t!lzI%fO8(75<1|Lc88&<i+Aw2AMP
z68KH1M-#y9D^g6yyiCe)9=h)};h?vGkDOuU7h0WuBSI)niKMNf(1GiO27FbY0RTuz
zw)uv8)>J98-HO3L+GW|`5aZAa*W|!krBY80c+-}~XI?~o7Gkyna^yjDwoD9G9xFh2
zqDvr+upV)@AxkKuU@x9_H1?$Lg+}l;-N_>by5-Sf%|*7KIn!+KO?*<6ZUN=uk2Yx%
zu7XH6t!d9!va+sn`pyc?3zpxbzjc}R;l;>an}7JkmftTv2KVupKUI?BZksXsij;qm
z_nw9oJ63+AI_iWCiKF}Dnz>*w#&P9GZ;hw-LlKNEW;T4V3#l7IvY*s0^%o(t9$ec$
z{Ni;;BPVaePJhyG#^Ey>^mmfgXD|MhW24%O?y<$WMxMHM2@xWlCGxK0HSdSN+W)=(
ze)k16nYY;SK;}!M_?P3rkq7l>?7#>d4<#CLXZZd}6`r@Qu#KH~Nw3_;F$(&}9aA+Z
z2^XHrpl*WH){98~ghnYiWEnV*dxZmXabm#lSRD%U9{m{&D3*Gx$iV>?TBzY8>s%Jt
zIRGXJp#k=+14#fDzsdp{{agmfas|kNk7Duxh;E1#+X+2A*nn&!r$m<d2QtN?Y1dUy
zpv++jYyUx?dS_tfIyI*tv>#|0ssbclx?LhP%}J`5-E12RXj$^Vd;4zbx_OtItZJk3
z;`_LMcd4!b>2J(w&sTrllZd^@5i;f-==j}B?@Lj)LCb08Mv5V4ukT?rqe`v)qh9B=
zkZV84UxX4aV(n9NFZ|^I^`Mq%w^xdRaPrJDplLD)(7sv=6>IjlF0C2;@-gP<jkNfy
z>pzYn6R%NI@qPjX3ZzazAIdel45W2E<z$ATgBFy_>3l!~yMd!LaqX{P{27zGUeLBB
zbzQM%uJ2upAiUq|KH|M39Zyv!)=I;mQmZlHVXR6rRo~%TbH^2O)f5A80os{75ZYH_
z`4>6>3eVGMp7~2Jwq;gx-n<L?lO}*;JG%wCw28#hQgc$&8Q`rhOyvZ{vp6DAqv8T~
zoSWv3*fPeLrhhkF96{Cer-zjqCmtxu276kly>5i=%%C&PUu*Rj9Pag7u@pip+u9G@
zo|2`pzm=@Q4VL@y&9A|&Hoh_Mo?|*Xj;Hm$d&^$B#D3F6VMfBXY;)M|$Z|<wRN1H!
z-!WvqJ)GK>V>5#Y$Z)iudu?Ish#;M$lW<lzfKkYV2H>Lw^#UB;?ov(scKy&4GM=pP
z4gC&j%zE+Kj#pmi_t^|Lhnt>P-DVhvZV-sDGy<+Z6FG(kd<PU>14K+|hnb?jx4Dq&
zpTt-U4$Y9+EmO)45|;s;M>QgR5uge)jOHD(@rVC101AspwHQGTPLeEl1Y!i37m%<C
zGy+;9pn#E}F6K;5V#p>kPMAuCe!e>bo53OoC!zAyQZDcD;Hz9)t1;yQ<0i*o^xPg}
zoVe*O-{|8pix(MflBMrj6mmD_`!AY(=jdv+b69Pa=B>1P-JEg%Y*^0h;BrsfP8_V?
zs0Yhb|4Zr-7u{Za!V8Dye2C7t<)1K<;nzKJvhbhmrY|NNkWhC=BPSjy&^&?e^Or}4
zq$D=hYPRA)epbKVLPog=qefLk!k;ZMHN@x4NA;R7nOfn)XRlH9Vn8IAVD2nShXXj3
z5UH)I`OWl-$88sbZAq67?EcYO>75%LjfcI8TRAzm-I#FEm*webvkw-7CICVUDFCRP
zI*&YL4SW}sk(Ubq`Eme#)}a>bAN_kD^lxGD=Z&5~4m+Y(V@M2CmF(<58_Xi8e}V$H
z{=YZa3q$EaQbX_c-+!RDmYW6VtXB1>MtfPLXQ``ki2YgG2FGk@8w|2x%V*5ZWxqv@
ziK;zfw_vxlso$62DJW~UbAKB;en+dWv-iau@>)8S(Q@Df6VK&Ta<y>`&kNJ#wN)_G
zoMN;po(YWQ*e7Mb`D<S5+sQZ27jKmQOid!JDRNC=-)5f?&s=e6h;yWO5^PdV#R00r
zd!GSty}z_~Ycs>IXSwh(R(9njgA4Vg>Jtrm&GvlPN|7B!6H~WU4{_NnGoIq#mupo$
z%Hm9UdNA2lX-Fiyy%`#2#L8XEuApFSy!l6U-!3z!;@IAu4IoiqMXW45P5>{n15+Ys
zVAV+fA?d%1-pbpSg#?|EeK|igHGoTQS%d<-;L>LSlrfPIq6<hMi6NG^D)8Own4i*L
zk7AMW9jAQiDFlVT?~1rz(M;0&Ukne-=0ASRI=(I}Q%HI(7iPX_6;^gfMs7}Jkc&6{
zbR~q9pLTj}kF+}Gb7$Bs*TCSr3CmrLI+I`Yt+sC4x$ZU=?6`u7Loc0?GdVJvA(>P6
zb0%%;V{T>xd^&Zgk;3vr9kQHWsHjrn2EM4gzSmfIdD~!;uF8JR<=<>63y691J9tox
zjs{1)$ypH)SVsn6UkYSu$}Qnd`e#IQe3={%!R}&zy=z<eDWUzKeb~87EVAP_g4B6y
z44K{$Ds_>AIKwAvV2X$@mrRBOV9$3c9EQE*<>#iAE#=c)9o~vU1z#Q)Y2E1;+=L*P
zf724G#b_N}08*<6W{mJ-zE<zn-Cu4Ash`n9{<-dC`$si?{rfZXM^RU&Z@$Fq{W7Y)
z5cyTUrlw)D|MSH6+bxC9EQe8w!yL$x+4P94y3c=xO=#I$F~zzH3(r}6&zpr}nR1Ic
z|7dZ9SFRUk^zHLF)(s`U;COsKPzzdGVPJ8<<}rcL!d}Ug1wpT+vVzqnQx4L5vH2;l
z!tp0gZ}rbA?kUn-4k0^^7EUk~{)xK>7i;dKks-QhOG)H0lFX5<v5VhVfJD1Ez$nnb
zX2n9No>6c>|5=>lFCR3HUiLwogsbr>|3g^c4I<wg-SH4-z`PD5@1d{MzQTerV^1d$
zq~H`FgbD0bn(-vv(r_=gi6H%tdFDUeH%~-Ugzer>_mdqe=&we0kY5itsyN=idqLdd
z(@V9sw@qdG{x`CwbXA(F+;1icDjmVPi%YXCJ|@!#S!W$hKOmMyTwo4nO`Qt(IQdZd
z(}P94&aS-(6LCL2KKIAr_sKFFD^toIT7Stp0|#gtJZ%GgCEj|Oc>DqX`1#x->gqjh
zo$!gmk5AqlDe*^dL{NZZ>47z7&~-Qu_(3AQJ2nGUV`h&4z@=Akt#Vq}xVo_M_k#~7
zC-1AAA3qXaj9!)a{5Nvbr#URS+E#e-Zs4_)%@2IR>_!fPds4&l+9(4!w4p=iz7R4f
zFbCV|d+o?z?(6zhEW@6_)Zz(FObb>^QB4XRWzo&!1c`@+a%hy1_C41>CX&ORZ>Efq
z-R63=+*Q7C$u4CLt)%hQ4TP5|vTbR63U!?tsI$8koBl@NC+j^{LOq9cy~o46bAH|x
z&8d~18ZOIFReEtcNZKk5r){x`_87dL-~J&@R^tdZYW0bLyh_T`A{H1MFw0Z*ZiP?M
zz%IQh)X3qvgy8RNBQp`5q8^@N)CZo+XOHAJ6fehX_EQ{bzxLy4f8UYiX?L_PLbzHy
zzllxCjng(?ent@(ktmD--t2*MABB9FET_$Q^m-=~aVi*{=<`D{bV{kc`HtrGP{7CT
zAK9iXh$85I=CdpTUTb=!EBC1Wd^(*5l(`|MPfWpx5Y`FQkTDfKvQqp*99E%fTq!Vz
zN4lwME_1qt1s}Yv_r9_B{X5I5)nO&Z;BfG)JYx>@2Khs9sC1*AlC)z7ytiifFKsP9
zZDV3xYxdd*lAJNQ_*G4}Isc_+hoX7i%WApn9uG|rI$p4S*J^5+5Q5#E$|$d~k^Ips
zJ^T{GRL|ZxrBFpOJp_-^3T_=ujQ(9$Xw+Yd@;_NFH(J5TFzj{!gsxT4IoLyE*^Fp3
zVzW9d)+XjvDGpZfBxbDI{0RKOma?`E(c6R9=rg}(qCu%j%A^nh-rPkLh(Uz(nbX(<
zJRFEwIYt|_bq)q0gLNWndM#+i<K%g+^jiVGQpzj)S4peM1Sk#BRJfe##9P5}9^c1j
z-ymWzfu-sjGKv%VDv3m~%4d5C?7LGMR=Z0tEtk_;&>5!D7C|DrzlN23%U;UI{<L0`
z7u#2OT<&JhE$inkumeGjeqi%$F3eNO{E2O2b{;b16=+g&#E%Z<a}Oj8Hg8dD={XNt
z1O(Ru9L)1i4ZLiAIy$cB9|!n4GY50Hi^woUs1N%)TsxCk%^npzziT)SKw{P3{3&9v
zzzAE8#lzin-8hy{&OA0^L?&g&rX^GI{|!JNujY%__ge=_scwZ!wk%v8*cZ|(jLz<l
ze0z@Bb^E<l{MW}W98)JQ+oiq?6@%nnhP<1QbW~jyg8*_O=oL_3B$u=ZS4~Y5fxA<r
zS%O7Ib8vK)c3*|jlx*SlfyGPyojn|XL>n9|duf|ca~wFMp!mbN#%Niu)=*(%QK4V2
zZI4*P692PSl`ea~xiw3fB~L}}p2~HtC^r8(D{|1N5Yu%fSscR;i4Ai-34n+&XRyg^
zQ+VS(d$O{@*5?C&mXIqB&-$i4D9GkjTdn}H$W<*B(>2l#O~O|~$1pr*(&4P~7*%J0
zDmMjha_i_RA<B~lm$@qs(szu<JFlg>_==Fe>Ae_@y;9fPF0aft%M^=@SX*7P8h(aN
znipsc<pV7Npany4IeTBE4(Nd0(_bKw;3-<_W{o;)-~WpJPibWQaef&Faz+Lx^Vf6>
z5?n-pi#*^_I=~-8VW08TO2i~PtaWVFsfg9j<C(^nvQFV*Z*?J37MtC;X@u^SUN7s?
z%d9N4F#P4O5!2MxlP9=~qh~f!Y=SmC127Bp=zG!G*+WCHy_BRGGyC3K<e?3}MX0E*
zIW7yPl69$Q@Td9DY0R(n3+9G0j%0@XHgiTBtKmMI5eWXjIwKdRcMj7Eo;&@Km`T;I
zd)5h*EQUfmwr<z`L;&Q%QGSeJ>C!^z+<<rmFj~Hy+$Zns<8{yfm=;Sm4O{p?xXDFW
z#GZJz4E|Dg8F}HxE?k;X?@dsuG{Z4@e^GEQM`Rp(u{!-BMFx_;&!%@Bj66pQ6@!up
z0$HlyUp&_v>;iMOn#p?aZBbHtR#ip|vi)^IjlHkwqUUp+XdcIF`Wq|zyUE&rSMdvf
ze-PZWdq8;S{Qkgg!PeQr;-1NYZ%@b<le!GHqjL*9rd_B?E`X*#;i1;GG(%yvFKnp>
zdRF|p)Ifs`HeHu!K^!O6nIE)zo7pWlAlV`%Tcc5?qEbm?y&rp+!FXh_oX4}Adw#KE
z4;uCvOxdR2M9~o=FcdDyRr4pF#}6|%67}?=nhl~L-5nVMCJqv@;V%Lb(U&pAqh&Ck
z-~tu1L$=rPlkl9Q+$f(wkXWQ(KQXx;W}B^H5nmd)z0sb&@LarrJ>F*2;{-)zz{?$|
z|DDw23>^Z@AA>TvW^|cuMt5~S!8q=!XzZHV50k|01-qJD>4x6fDvPNPH(5tKGVOlm
zX!2h_GP~!>e%Y%--?umW)VyQ$7yEA3XiY{cWk3OS$Yo|Z`={@xFxs5buAGGx=Q20=
zqbcltDa`~kt)MwfgSq5*n)p^|#|mR(3Lq_=`X~i&!-V4%h3ABA<1uCuyS8JS7XwlS
ze&$@le$ESxb*<LO%h9s{IdTy9#qYY(7n*MEwAs!r$T4?9^&v{D57<2p|AKqQ6OI#P
zdo|V)>l1Tr!%eHr?ii{VW$G;nF}zW@>r)nwQ`s%Ng<$}fOH2{h7~77yvZJ|3(bE(Z
zsP-?o;u!Gm72oI<f9fd3XzY5!_3G0V=sa`U0q*)qan;5gO>w>A%@rjTI%PS6qAgZ2
zen-=Rp9+94q-ZNQsAd+i$3o;iL^UYTt$<^xg=3bwr;193?1}@`(U?yOdNfCC3s-Z~
zT~dd!mp1v1?NIIt97+;w6>h>6ORCFAD(G)DGOrjBsbw3u40*ULgOkC&Gh6e)^ey2*
z`!s+S04OnGYeOj{<D1(WLnoTDZ|N<}HIsA;!RwI7jaRB6_r|j|M}BL%PcXP|Bl|1?
z&}VkVa&QS2vvPKx@Ru}|Z1>BA9;OwiN}Fp5F_#{VED1o3Ue_*Hpo-*-lrGY89b8XZ
zr=R9(anikOOZCI=2nB<^{t?FE53=oQ!~Zi+*?X93z1rRaD38uOy60!wd80hxvPE4(
zKkMwPj?KrrCbwE_`egYlnC%YlnHCnFR!-VSc9xhA{*9*DbPfM2xcT!^&}G}7d6y2&
zxPAW#ILi&_yW3`74HcIc2K6`#T5)(pO60o;>=Uj8#P&ds8}9uSMTT4K->x?Mt66t$
zxj;vpk&9DKal8>gZs`sPsD~N<xo&jgHF6~F7i*5epYx2j$xa(Vb2|mkJ>AdE;!D`C
zMYk}|XDm#(eH$?7RFi@APU>+UEz5v2nwW4)k4RBqP$bvMZEz6fI?sqb8su_t9*0r5
z?;~@T`6mcDqjaPVGmHHlKRvtcQ?>9seeV|Gn`HW&`=ix%lH>cUj_+0VHf+`Ao)z)J
z+0;G`%6E>Y#;Mw8Lm&bd;?tkW!XuE)UK7_!&g#`wBn&ky*kd0z_O;E86#Y;V92`|%
z>33dPMb6Zr1PvK+BK#HK+s4Z-U01994=NW_FNl4Q^O9?3jctK=h*!J=^!Nmp1E6c=
z%;-1jKr+qL;FDWW`=IIm1-^j91*ML`o0>V#`6ObC*CQluv2#YMZ*5AE-oMn{M!K6*
zt%*-U#r(Q!=p!`@1J?Q`9{AOKOXx941{V&ldHhF2(L(|~HZ3l=<VY@uqLrzg1q>J|
z1UecsYQfYtoIEPe9-oQv?P-fQ<`b$BJH5qKJaDt*FO3FzbVSwE;dMmo9UI?!ohJjR
z9Z7+ug-iQD(FnaA_{BuspDYD|2;WcUoe4sP3qQ}Kau;<mHiD7w|JuPkY}rp~Vef0I
zm*iS>g;evU*JW~Kt#YI5Yp|0S!PucwyJL3-r^x^gko}!<V8ko^Zk@cRr~k!aj+TMP
zb(KDMmsTzrDyW86G*sA&edtiZ>$}}_(KjIezPT63CZ{Pf2l4yTgdpD|_f+y`l1*nx
ztCMfV6XzKocSsnzKF!}+PZgv{O!*zi;s7q)&~lC*s!|1#-2&G%&oppXyOz24uj0W5
z8E@Z7`+d1O?$jwHmiafu?df8$C$sC(NuZotT~1f$f&NPT7q=?OkJ2S{YHw)=8K1o-
z(S1`3(i+QXAck44tDNk}Eq%|v(Oq}0Z0`NrJ^RqAzkv)<y^LrOJhlV`dm~mh*+7ob
zf<|1BVk(smvffcX#E4(sl~vZte3G;UPQP+HSh{a7sA67-tV9FY373BxT{(xB6G|!@
zc}@d%gvVMSgLp9iNC)>NfByc@?*$?Tt!5chZ+K`tHg#Bc?17zNU83#Xr-pP-kn=A5
zq>K3g55I~p=+2FCClwic$FGrvK)3h&PHdtyP9@2wCGoj_yaeiC4@J}hcVZbS&#=U1
z?bX|O&ZZPcOl~A!-p1Q6zrB?l-mZ;4>3?YS@{4~BO5K#Ds;_bCKlnW%Ls<6vfmIH0
z?@g15!q=7Z%Lma;G^$010r!dcHyg}~Z+G+&Eh>9&af9K%PZXHX#<<97`QksZjzLaB
zx5xvFS21x@zsK<X<~Gy;lqqGAk;F1wuSJEVk!0!+^x`KpK>zmu97}nWE5>`y3yvcS
zv@FYk{`^fPUbQ;8fBF|0H@GsjIVfW$k~!)XEbLKtiX~A)q|q!4Fq#Jd&bFj#62|uM
zwNIhRUrxr0B}B3ClEd@+W;N3iVdFPNvV<!w{BaU%o3!ptrq?>olr~<f-x(%?2oxd^
zqAE<Ge>%Yc^j@vP0TjD%7$=}ZX&c`I7|u&c{xc>M2h8SaeH#vGJ!*8GRW{;LqkNj8
zv^Cya!D#**Go}&W??-2|HBYnNUw>*-_9^Ug>-*hj6h@s|_s64eI?LQtDsw0wZMt4q
zTHY?`+c0?k5!LjzR%G+|lwao6_4~6M#}U)0Z;z(7K3zQOZMk*(%db2uqQ)JUY`x!c
z`Z(eRIQUMR&+}ob6klQvpw^=j0r0nq;{<_JQ~v`|INKztrl~PCMWEp;`+{84eO!vb
zI2^d6H&RYB`KY4H5&&1e8~#Eo=mCYci?Pr%jHK-2%S38d&Xb-{gL5XT=n(HtP1?dA
ziJ9z8KWD}ecoe=E|ENEwRycxF2Z6j_$1&o0)956ksP!5kAZ!u@&iD2>3wc0=s`8EZ
zrSOkLtu;!RI?Urm`UlKU0C2nL%5gTak@YOSlLAq=4VaH@uz*8S$0epcfnpM=t_utW
ziIoGX)h(v@DO2UJw=&vWJYJ_4=kD5He#vovwd68m(k+)Bj>`GX1?If-T&vwxk(|M7
z<&>+H$hpk?MNZp|J?DChTY*lD(@sIoDnCkGM8)zg0+-;fGai@c`3h62loI7bU2=!w
zg0NhVS5vC2X47I9FT;NLUG8Fh8z~%sV__mfd>0G=n$sRC-|FOGT7GRGQCj(|@TK_Z
zvpsO^$eb1U_OepaQ~N79$^(fXR0!1iMxmL7%D2<o4NKC9-{990$ZgZQZ>EdQ4c7vB
zTT7c7UNlWDz21FLtMW52zulgmAVn_&$z3*=Y3$x7AkDVVYvjx^`V4mA4?Jof<+i7m
z_q=V${~fq{U%K&m5s&Y`-JS2nwVgj^LR0+SFf*5T-q<a#4q^QI>Q(TYe|&GWvcHyp
z-7lMIEnCdaQTujq{PDrGqt-<WaV?iePFHTa-i<vz4ib?4=v@DLK2z?G9Iavb^t?{o
z{b0dugTjCd{#1s=X*IfYYU)%9C`#12Z?-jS@!q~0Y|ElLhVhCama73#7dwh0sBmEU
zi5yzDi`vxiMFToHpM`Dxr!}c@G4Qp?5|wP+$uH#+LID3t4vjo=TK_41JtAs*JqDX!
zU`5s8?jUr-0a`*Shsa4lHV%N=fs|eS7luUhLxH#M`AUONOR8;Ss#E2$(TRpKIRgfl
z%tuem^mpj{2|PS{X{q!pN+eLKxeb0$){5TMl}-6E$R_@YTBB}B!#9uU2kZ_?h%>3K
z;-}n2O+M+{E7uJj+j*F@6q2pY6J)v;o^Yn0T?wg==v6!H@YMM2vYYw`V~5|o7ynIU
zl16uw<0X16+`}c5Q;qf5Tga>QO{V0JNSPtKHm}0c$Y2ruu_CCc@meha@IgV#0B?eC
znumU5V7so}u#FI(P$J{q-1mB8!s6VRoeqH62ciG|Y*~pZ314|khq_SQz`?Mzsx)I{
zb>WxGwHqw|ibH4EN_D%$HpXho)Q9Jt&5Tv{=<4p<6t0%cIQgc9&lQqR<|!}Sn84mo
zxJWT_^vjGl0C;R(#JnNhX_~Q*3V-`7Wr|6+$lvf`5WhmZ2&1N9I9qEYPsx;;W&M{2
zZ`8-`J0ve}{;sSHA?zw$4vu!Uz-Zkt)p*$Dw!`-#Kl~fdy&?aIi19Tm?Wyn+BY4jb
ze~fi)ZS$j-ZO(V6TcO70W6NFOfS_T*M`}x>5dthHZG;CvU$>q~++1f6;E+?SE2?hV
zbTy`%)$*(kIV=tN!%E7fE}zyESkM7~1-t74*D{B?u)2<`nW=ThB13%n@fE4XzfITk
zFK~FtLsLusipn|(R}%rP(akfC0Vu#!f!Qaj%_kBqDHUSemU9Ml$`LB<1vNe&Sv3dD
zX^#YW1P?n?Ho?XT9dtUuay<RdQ|U=q5*QF8%;P~o&I1g`VNnygI-{QTSxGhTvuCc8
zSDm~%-Wy-(yJeqfPZ-|c!=9ox-dBIlnVt4BzWb9uPwHW2$lq{Dv(FQk88}J#ZbnwC
z#{6_hZo>8b{fVPaac>^3SM<Gq2vKI(p#6n{jP-jXP0~TNjr5TH4pn(haj7B4dgP>U
zI9rWYn-$&cjr_<ysifj@*+uq(cQ3s>Q#XCx_40f?%575$yQU{DsF6iZpFd|Wa$kJn
z_0TpxOi2QJW9*r=?|Q_|D}0$Y^z=pK8{0X`RcM&=CcxxAe22@O%2WOJwp^Bmv+678
zP%yV+8wQ7!^UjtwR{6*^RTFAH#Wo=amqK<$^4E(k!+3vi)Y3{su9e1o^_>${-!?x~
z=TT|)H+xv!>AU;l7Wb?Kq;Xb`vuavNA;zC|Voueg{C^akg<n%|8^+HDV`FqTjM3dl
zGrBvZ9o<Mv2#y}zNJ``AMnF_rTDn991f>NLe~73r?|<-o&U4Ol?&rR*>w9@KwHuW=
zR;XQ~+xQrXJ=0}RBsn`)sSKh;2&@2>Dq<sgu^g#EaPivDS8(1~6KWctWo{$KkGU?G
z4>iNwyovTODwB2aL@ykU_)D48<pF>IT7-8I8Ry6emXhHgkfkX3(bxNpz1b%jY)t|A
zXLyKoaiZin7L-{<*+7V%xKm6AW4VU`O{-0z_B(a#68}?66<+>%?X2OwLH*W<ID{(}
zOGzbeh_<=~8VZArF^trAzFsm%Y)s6)q;i@ofdubA|E{$^Z$y2ce43QII(B&ah$C?9
z@TlAU6KqNU?3+RAqSck&Sv!;HiFf*c-x`&Ok!^(vcMwEw?QL03U)n>8Jkx|gp(eIC
zLRx~M?6#~&$x!akvD*)ho!^M+U2T5rBQkLjw$L5iV#YUbFL0_cOBUxWD?I7HfCI@{
zYl$6Ha$gGz2-8I~HOI@|3lt@YmS;O$WEH(Go$o^|&MtQ6=@KNB-q^Kw+r5<!98sw<
z@7(s1{W9NC@;G)+lE~=sf|4GtUj+Q)K@XZ4JmJkL`D>$xW{`yFpzk&Rs%pcZo3v2o
z(PM#r2~9J&S`}|TcTHYFmz3-zs{!%3lyyd#mzth#cjr}Rq(yrDVt2Jc>5_4Znpypl
zZG|KO-yT#>uTGh7g?W2Sq==KXXt4W6CatJ~R^@J<98Sd>4cTl>^3Y+2;U{`L!~pn7
z2ZPkf%Dt#}ZK$tsLZyNfY6>}4nC)2uHinq}G6@UE_|V4U*fDqGEN#9PYXj&t82j=#
zYchzuAzb7ZHarZ56UGr^)-dekG)Lu`@3{!k5#)vLL+G&hEK>l8fK0ebhecvN@G`~N
zhCr`~e<>z6^b`M5er^_nE&epHA)I-X+Qes-<WImR^91a{d}orx!wAF3sWICz7}Y}(
zav)z9CkXsY=6I?)B`P-(J;qck7q(YMRZlC>AmU<Hm2;)xe>xz#SJHtJnG&lDl$2-u
zC(>};7Kh)Y)>pvs3LDu~u=s~V?pKX1@bYEn%PQTm4qZ_lXIduWn#x$3_d|7Jlo~Uh
zHL*%!M^4!JM2slD80_9aKr+GcL;^~eem*vNnigvtFjSGk5W_0+-3e}EJOc7e_6)Cz
zxM}MJASg5L+}jkYUp%&3rPKZ$ULnffA9)X}i#~sN_4`Mu_No&7JCd4YZL9KHnS|H2
z<i)?G65dNE96TV}hGNipOj$!1J|s@|jOB)<I9nLQWH<<VPAi$*VWz6Mq@z3)n6uF<
z9%P7{#OYG0mhR-H2i=PICn;~$l5+fGp<mH9tr(0UMcIL;mI=!qGfeGPHrat#x;i!B
z)!n7gd`EVApjeLBsIm?8Y*gq4Hmh<6Ceiz^>4F*(HULFyCkWCeP6Pk|j7Gahu>woX
zh)rP^V(kc#72jmcnaEe2?pj)aR;oAL^$g8NVz0S|wZ&tzVt^wm*;t3{onryJhOx(8
zvoYLhRma^vYgC{hee^$xlCH$|&+#56(kLqe5Pi~mF$NKZ)qC8KN|Hf1-{!NC3sTG$
zzgtYX^!ZE@8bYy+7t4EhDiGoo3S-IXLF6%jYAnA;BQgAl<uFKYZ@og$blTi1Klqm>
z`b&OwtI^EOSWvZwqx)QycA1W)CfJntvW;WZrNFjxtjl?*$~c*-7k=v~sQ%#TP5{R-
z@N`pa-W$TP``_4PoaS`LxLWUcU$o|Y&dV#pWHZ7#fgVwTb{aLZ@(xS9_~69bkO|Jv
z0bDYb;KpzcAtnFUq#JZW2@~y+t7rmAvX@%ctwVy0X$R8tInkw}aXI&HxbBF5m%L6V
z5#MwApRXwzbQLl>lA9({Gil>9wSQ!#dJxs3WnTL`B&<87;vDefM=<{XhL7c8*CQBy
z7#me~%I*Y_3K6B_1T$y4&Pl40ycM%EPpNtZ^InEB>b%s}kjK2fXnw6sH+N+Hzm)SW
z<&yilTlja-JoAm&j2Q@@daLA>`Cyb4jm?Ec-pw@lTh^o{olI%cM0o`cfB3{0`lGZ$
z2WC#91WSv84&H|x08p$Jt1&onFmPCRI9o*rL!X*75j_M7QvD^M+i}wraQe#34n+^j
z-j1LK!hkl&jEo2kK!B&dhyloeoV7IyR$1N!1o)w@-L(}}ee!mdG;XAF7hdeqgA9uy
z=%<7h>gdx)LLwG`utyB~r4=4!RJtSe*PA3o7mHoH*z4+*U21@k6&iB|AUHuy-{LSr
za5NLhI0hr}RBf<|#`$@+Xn{qIsZn*!yDk&sS%B&0yA7f9*n;i5?+<D9mNSE9tG?fG
zS`a5^9W6SyDF{_zBW0Uw9u`XBPcw~ZbY_@(_X%H-%Cq}x{)n-KP-|Vi(99<@kzX*0
z)w3;Y9yfVt7Vw+iLnaQZ34pD+3e6J4I^pLb$+CIHSqWw9PqN~cGryx791ottvdbq0
zO-N{ae)uK}@h%qo^LoTFZ2I%g#&&_j#REngy{{E#*OVkvbuM>t6K!1;Wka2x>W9LB
zLU=7&PQv(38|^?K3P4sA*m(&uZ$70l;+{&OD!uBSO75oY5U~fas89Jw+GF&Xb~_ry
zXjhDu2Wq-}2<u0FmfGugWY2bWUO&)zTi;Yeb_Z>3w^<ozpQ<|fH2s$&qU2NG{b{x6
zZIYORX1fYTIzg!~sj9{K=`V?sA8G&<lm6IjbR0Co2|8~^g$axwv@pE(OA3&$$SUe(
z9<aPnC;StLVqCjVT}0lCv)*(_mx)y04#Nx}F&J@h%vU^qj-+c6RlfD^{Y5EOu#<S6
z@x~EMl9xioR<9xhgGf(8gi(3ulA}Ts$Zd%Z!s-wZ+=u7+U8oZMsebjZB$fZzQ3W~7
zip%P9YsLUnJcby>TJAxNH&O<@W(MhBs#~ogGN7H)_?7mUb(+e-3^iJ!mN%U>Ht98E
z`8Dr;s&mNkY<etc+P04+kdE8g(BJ5bSaer=P0c%Zaz?)35O^)}hg|E6Nlj{hTiHOq
z;wuy1wuO*x`HLqaA70H1^}oE%rN$%-C){nZ##@t=1R1l`?cyyH7n8qI3JYd^6hzrO
zYT36@3M*eUxn_$>7>WOy!OD2!#vDv*st_7CJRzbVxI_ud-fCM&e*CR%WFcPndp(sp
zO(1BU`&Uz~{65f2jsdlnknAkA*(aRjPkMG`Z%3>C`j`wGPMe}5Ax3pxa8oRpO<d_Q
z|M^zR@3ow~%tQVG9(k*5p-qx*kskTWQ*^)M!`1SBFf(gjliTA5omMLv<)0pR@cEC_
zKLd4J{A)6Nra|1@poC9r(kD21LUzGU^4niDMu)N^J=H}~{W^3F{Ww9?4s$2&Y?sA^
z1tdKl$p-#V2}27(V@1QEgg!4V1!h;Jc~fZVONM?>s?N2bds@WQ!Sft2au|&S7i>81
zu!mz7bNJDQ+EPrBx=09s1?<2g;(N%TXj4cfL6S#2j(m{v>_Pb$&x!t)r;=ZzN%;K>
za!M6erCV~#80N-pDmH&v@OcdAsyJx^LUOZBU3{R(*GDayg^{M{0lA_jp19-*`Z532
zc#qrk+ufUmv?|tRS{K=rbnOlQ(hTa9E48lvKw9HUA#&@nKU1|YepAcjvk=)WqWOTq
zzPWt_pA-4dRn=|3L2n`DA7|y0MOSdaGDgI&DrqYakWGxslEwEON<8obAR|oDuJkM8
z>|+@M?Ad9wUfis7*xD}$O<&`v#zNVL?gRm*>-#BS^{*E1l|^TJ@dUnFh?wWEcXVqp
z664JKQ;LP7xYYK){>b@A?r^uKkaevY#eI%v@dwst#A=Ef#zRFnv|TI9@S~eIx&L)}
z4s_`KQtnUZOQK1+p25{2Ka6K*h)!Dw(@eP)m=BFh0GAz)I|mhj!>d12x0{D=gjssZ
zF0(BT<C-tM>-|E9n4k4BM?4Tz=O_6^F<XS`y=3lz)j?6=BOWGHn9%r1SqwHP2@3!!
z+7-#s8hAeMLJ7zo5*Zc=j((@Fzn*MKnM)-Pi=l_7ddT<9<HZ^?!zfHB*73>behdG2
z=JH5MgK|CODw+#b2@?%U@^Va&7J^pj<`zPrM!KB?uLwWH(*w_r9MG(LjKyAx(0LHd
z>r<MwdLZvo(tI8?3-9h_jwz%h^0;T61>6M<-`TfGWTFA_SOzO_TAyW~q<)2awDjI)
zP-?9o31wNy{WJT@kdMc$w0qZjO=o4*ev<#jG_7VM+uu|87&j<hzfIYP5c_TCoaHlU
z*1jX(HMVt9c$t*mR^2-Op0xF>qngxSwB?tra&C>JD92L&c=y~x^*iQ^r9T;H3jshx
zO;h(rla<<&{g!R=pVR@TVr^-C0VkXxBqASz!`VnP>Gx3IDD3kRCXAm2hJ1KnZ=k&x
zeDe!u=HB)R?}HL;RCY!*29Pr=XK|3)({Vm7;xqTKfe|*IHBAc5cxzBYg%l5Mjg{$-
zQ`A3RPzHFFKA`$ps+sL^QXZ5%YI=H0@{KmbLXU6i;e&MZz&Fy}8b48-aEokj!Y_G8
z5M1f$Uw$*w2KL3EH3ynN*1I_d`@oUC?~@L}qr%k}PbI<;2I+A0W2N5EBJIcom=y*H
zjGwp2#s>XB6MQ?`^uBdSH}B4`qfVzFqtb5iu)etSq!UjB)t*O#3MnxFX<Yj?h8+0_
z^8{n3kxF?{L$snt_nsyUqr8oRanh1N<CO==?~Ge`_|b1L018G4JB`G!rG)f?Ci2Yy
zupiV4p!^esL7E^$@d<$FH@imH!3_j>#Q@j&wWKZ~St8(MG*3MP1}DGS|JH{E?GoY*
z5g<{3_NrAGWs-P>%d1<w8s)<Z-X=+i;}dm^v1|?hd!lD5>?npABuT(ahlls9d{`mG
zuh9NhG8*u!C&NwwR)U(sB<U~hMd^51<%OcBcErP*<{q&5B@ADNHHKOqLIypqXlLD?
z>r_n{wXR3`IlI<Qq1pP=X`i|hPR?`NlANWu7k${12rav9SQ=k26}GdBZ@#_V4Zfhy
za5qbOzd6{~x_0_j@9Vdq$k)Q5UmW)U5N2l^H1j1LxL1-s4=&Qz7OCfVc?=NQP)9xI
zIR}4e=Dj1Q_qcbMe;@WHdOhC*{Z2pLnzmtRnn_{`Pr`i@XMDy!5iwCCc%g0%;_*(S
zqZ&<aV6NeA<DaB@OEE=6`!>7AupD=>>`nK!y(yPnv_mpJTK8^-MieiC+cf|nhoMdY
zWFu4y17Ml=%5m-2rR->A79QIa3cmEhFc1E8EStGB7HKkLCO_DI8yl<De)MH&{JYa{
z#mV~HGS$xS79>bRbMV}d2vye=vQ1o$jLrDR-l_U?N!6RN#UH7P220D+geK-!_NHTt
z-m})0o#xe|34&MRe;6!txcDc`Qzh&j)(Zrf2iZbWZonftW`>8eRAT`;<=&%K(L95U
zf*)R>S&CD67uZKbG0;9~O91MLvH<|k#c%*{x{0~7ga>bTiE;kcFuEHjgH_T*Vddxm
zWMG0S+*B|agEhHkf!yKX6o_$%P`XOA_`a0LY~GK<c?z2AjrCP2{`i(vi1ZNhE|T|Y
zWd|0V_Gzpr<<5<6A_suRhbF_yTZU;*wD|cyAlry)1#(|tBa2_Oz^w37S?a@pD2P#o
z@V!)z!ctZYbe$Df71901c*rIvtLWqT=2rIz$*yOeXMW{Bd#P|*J&V4kzgMr?WX<S?
z#D!Xc(etz@qtWieT$Q2KGbD4l^)Dmhm2XXQq-W;}@5}RK4S7_t@~f<uh>n5Js#l#t
z0*~_K{Jkr_OJ80Nt|^d?Tddxjd!&z5VtJ!M+Bc^h@vQG`zhsa1A%++auN8NorObVN
zZAN{LPaBG8<3JbI6Q<EWIY|0D5j&zi`Dd-pCMfakMzfJ})EO_krfJ5JdFY7g!uz_~
z<pu$5k3+7U{km47eqkM-SF!>V^wrz!P}idiyM}ltBb^1bIOkwAd4(_mh#VjVP3U7l
zk3|@I2k6L*p_tEYW^^DS^*wF8%<qC$Iwp%xbkh<WsC~&6ZBQ;AuMEVc*zpk!DMns%
z96TQJ&rJ>qye87wIOF48810l}AV-4rxFR~_o`sk`108?jQDe1SvN-?1%ibU&CXzkE
zwEY!L0rj7sX*m7ODeCTNX#Zl<7kHBe*_e6GqyG^x1k0M9rRd4SnCP7UW>Y5*P#ikp
z9t#7Qt7`1H*cTNxKIeJxK_c1f)>rP7VqVAL?&6d%U?Q2y%SC9412Uxst$=ZD(<9=z
zHd*2rAq0a(Z5UZ@5Lq^(FFy^0-HCc|%XB_!kNW#WGL*^26jB^0nXXo=q?>!8T>#s)
z%G@SiNu`A3s8dvuwLg|{na5IL00;@iYwe_weItn&_slWTa-HED&(R@w_6`^bU?4*Z
zkr!uidaWSluOWax0P>%pCd1x`#gh|?@ZH*#Z$|SZhTull@YoKSp9J!k{bdOIro+r4
zTKpxai2ixOm-_IMOah0pt`Q5v`X)yit$rJ2GpL?Ha+*XEYZW6E1>Nk>d{8UwU1<b^
z@%!B7u%^2Fl6&En8=y|{=a|pSZxsUVUZ#YoGY}~_r<`OucQp$CY%o7uJTG$`mVG$7
zS++FcHz<1vfEErh(58C;P(xiG8(+bG69cCp;}YGusMr;B0%$Th^jVZDs~0SYi{QSA
zlfU2Od{*@u1wW`K<r-h-KqWV9@_aj!QO}F6*|nC6Z3D5}_I718Pw556%gPDV#j0a)
zH244@lO!fxpm8uBKfHeg!XIB*@)QMdZA+M#8^gK$jU?WBCb}`s`sOZ&7kYe*o4dcM
zzL{&7HP6Sr_sFEIrVe>6>TAtuA<Kq5!OQ4o6`l3?k4(rV!n9BTtR?UZv3t9a?sYrx
zfZxrzEv0;5-9g!Lj1STFlU*$CI#tAdgm}X0w-E<A5Cb~meTqJ!M$V;VikpJ9-uDN#
zk9Fa~yHqpz1myITFwfUU<jh@q_~P%%7=TiBSmy4jSjj_n8HrH8mQ6joo@O>Wb$p0`
zdt55{&JmL&aZml^X&zxMb}@c95l*c?a*~&AexU492rB0kbWN`SWaB-aA>!~o>L|YJ
zqEs)Ve5K;vT?fCynurK{8$Y2eHUP4RLs=2zsbr!u5ID{aepgo{Y!|2iIQY!0Zoby)
zSv%imUXV<x)h>@=vmr!RBn1DRx25K%BfKa6Qfa3QE^eQ?mx^1cr_l<E?#vI=E=8oO
zIxU%$ZM>h&8<BB0%L_GydUsd7&->fF`l@59%;0fLJZbB|+xc(l?N!Z84+11}7b8Cm
z;pq~b{9;2Lv6z8w+QC_7+~8GA()q9GxI;L#c(iTO5qXr`N%wixO2bDBKirl=i4Bs-
z(~xjc$cNG6{SQ}d^F<WzWwPu&h(`43JHDPQ6IX}Gh>UODgKzycGy+6HfhsIBr2csh
zMAJ1Q2}9RlK;c70{7f+o7A9~y6+e;>@++^=vUGjMSI-sh?<{HfWNi-i<wUTyPtqw{
zyNrIDS%S*^zErB$)Xh(&cY4cHtugd|J$w<?aWowbLw6lv&Gg)D=jYZqn+<eTEB(jc
zQCg#{%2USSu*gXrY7MPHfTT)SF~0CQfH!DPl+&PSI3(kNJsH)VAulU&bxgLp3FRn|
zvBDbvSK!<Q(QE7099;J&QYR+`1ro|mtXULydfub@JT=lEGh)5JBr!pHy~Tw6oexV(
z^vnYHkn>5g8D*8}V<V=MmQ)!zA{VfZssOa&1^o}plRRM|Ih`$82_Ld=(u|D8ajLds
z1bkpqdHj!y8pUiPp?GLk0AM8sp4nGSgAo880#Oj_X6kl!)7p#|N3b^LvhhU^pR8#u
zA);>gXJec0x<N&T&y-cwRy2Q@<XeXzgHY}CqsF?wax+}e=L()jT@R|)H}JZA-+FC8
zRhyjO5V}+Qryt0X=P4TW1__P9)pSj(7FALn!mCqZ)${MCPGnBMd_2$d^&S8C)unYC
z0Jh1eh5qV;`k7*S?%>19yNqnld)eJQ5KZP$<6vROYe$Y}e1zP5En)=5PFZ?m#L$x`
z`09eGo9s;yX)Q+;hJI-+=0>W^FJLs3Ex{TYCtvk`#@0IRU#REB5woQgfGY$K5WA#U
z-T31gx=O$P#uhex?Ux#Lh>V(iB6UN<h6lQo#bfX<T*N6;>#wi~%Yc+OGyG?D=U=cL
z<`YLT?G>@G<@CnwG#~sog4B05*oA$|n7hjRA#kqGeJ(6ryD%pr?Dg%`W|(3#ePQ>T
zhyd$X(%oZ)0)0cCkrpMMT~!QtEK`%h5e0xw=*Yf+*|&F4#4;}QJnARHP6TPYS>@B}
zJXk*)U6_!bi?HjIQRN~ST!n`ouLdZIw`<tZl3<Yxt}obM31^-ohQCdD^QZJLcCo^0
zhCmH3Vl0<C#~)4TpyfWwq3+hn_pHcqfU|Ow!Yz~yWJm8(!^+1E>|PLShm{W}%ArVj
zoObUW)P-<rk!Mc4bdzM=^<vpZf}GTeQF+fWQ_FX>h(Z4RVUI<PudL5O_+y!DB#$6z
ziuiFJ5`LlYjm;l=g+AOffF-1D%`0uhK7pqcY{7E^pt|+CNA=C$Md4EtRKmEaqXv!*
z$%fcq-zvE!g^y&eaJaT&dVwMYBsJd~W~Sf3WB$Q8L@LRWXLhmGYb#jjrxJoRLg+6F
z?}zf8T|NoN{<W7|RoqaW+}CsQC+9RrHmuXNrnwF!@3tMS#VS*&PK`e0gk?Q(=S{j8
z{^AowC6@IY48M!(h}D*&Ug$9j5XTCSr{h=$U4qLp2f-NLKJ*@`OB-Ed6#xpO-J#b<
zup;%%^(QMd!C5ayz3Gmq<xCQJOiEv8r@eu2p0<o0AM7yHGFwRM#|0~;WaGG&&C?76
z&xqxzM<`$Eu44k`?@wQ^?8o-_D+~qxViF#{$mHP3n7iMVDoQM`W3r=@$tv#YOJc;L
zWSod_BY31453m=ugFaHms<Kx4GvE)idUCI*B8okK=6}|_4@Mu=kh=~ua5x?KVpBCn
zIT11Yp!i`D%$?<yoM^igfk93lJmLf&2uzj9AHESiQQ*xHcO>e#diRvaIhi-!Fyv&N
zLmeJh181(?W>sOOeyB;q9?IXrxSXQItHZtaIh+H$!{-`Y<M?-RacWof2&h4@Iw!Is
zkSi!8d^^V-v&3~5(nl@~AV{6nCHV*#wT=kB=Mw77KD06J_gnnK!=qwz8fj~?dX_a{
zH6JLx<ccprAueIwnDQL<j<g9r+NdTcSr?y`^krS>oxN_OO{eU=^woNv(1jqd9F?<8
zXYYsESt8MBncP76mCsOqX8E_&EoR1!GX+J^D$?x}N}dMg7)|P+VgRhm?v40l%7q@K
z=61YBj-Z7+dCX#c?w|K~>5iZ|#T0_3gfD-l#xkh6-kd;0yKBtjOb|L5{=D_CkLN})
zjcbf1%F26xUtO*n<M?&KW+-z))O1C^N*L{2-eF>^+|F<IZzFUWb%CL1>j^(Cry*tn
zOY?Rk4FO;HMav1r2dX^n*BL>|)91pHHea%<EEjq#^!{m)PHKrwNs;!87W~av_%d|x
zNt=f>^@LB$JX^hi$u<e3BUP{cNPHyDRC8%}apFu)E5s8+Fc%A(L<0<t9d}3?N_Jg#
z$1q0XI@Y)2yHhrE0;8LdhLrf<3W6y@o<Dg7GY~Ncm6YW871Fw>Bci(_v7!kEh5?Pj
zT7hv$ozZAyIKIA`OI^((_oD6e2;=l?6GH`g@Em*0j~x^~OAU+-BxfJ9Nx5UnU-?m0
zYhnh;2|`}an#x5dR6VG6<vF4Z8D<1r5W0Mps-X4B@h~_lmcL?;D3%tV@{Dbp7&-J^
z^zC(GXD{D<=K2@Xbs<&$mxQ7xk>X=6T@qCIA$_72&spO5B)Dv)b+hfEf>H|%cHQqK
zMNL1fEN)icC+LiW$%Nj^8r(dSN;sb+EPQI`Uo4e>E#o_bFrJmT^tGS*O?;kB=>LZJ
z^H7S=Xuari_)mV)9nQA-T>zv`&4Uw!_C`60rv%kiB=nXEbh!{P?pRvwer2wyt8_4-
zMC_<iqYnH6>fMgFkm;N=Etcy01OwPA!RcQ0Y?(zbGjZ<+RGk+rmzTahIbYM{V(Vh>
zp}E0n9i*s3)LFWUir=BOoe$p(hdH;LVQr2S5yM<-PKxHSh9Uzl;}vJ=>^y-51BdeJ
zzW<}JQ`(~wfn<-KjFSdewFN%uM<7M*mZTZR^w1kG{_^6nAAS!V{9*k5g~~x1wt;x0
z8uTGS)NUChUY226f#xv@VQQrtNB$h;YA&>b_lA=+pyK{s?*s>Y*s~aW6v&(Lfgvp8
zAOHqk=Y$VhBRPQ=p$3<Ig7<%+M-#1+e|k;?7oqW;Rkr0+P6+TDz}tUG=!&0KN%?^7
zY(1R})Q?l@A4oOLcNx`)R0(>mjhj2$$x{TD>2L!oyQr9JnxR;C5CvkJl{w~9c;z(*
zl3x{ijV4v7)OhBc`5I|aEYj-4O^_lt(pGwE&Uz)oQo+cNpuG#_)JJSj4FV8o6^|-s
z`J%A%)0pta4x!5tyZb^x;k&Dp{O1gx3|zzvTuj>DlO!3W2lkdpGO&m?sYy<ZXIb40
zk(3u&k~ucsCm6Y({Ehp)e+=w&1Q~YfV8+*}`&#bhJ;}qm2|{OEj%F;)q3!8YB7;*x
z<8nu-kBwoiaNQ4R`RI@2IiHLMm8_=Ic3!2+tSW;i$=Lty=%&(v_he7vNWVzhkW$GT
ziZ)rew0aTCfmE^*)Tn=rcu3ys_&!a^txWCyA62?U&W2cS--2@Az^g}Oxh;FKvYMQF
zc>p!M@4(RQ!b2Hg$=`274*djb{dqEKa}rmdOyBnW%zC9ihs`!&AoyGuP<Se-lOuh}
z+_HtG`s{D=+_MOmMn<UQl~KNjEunaQ;q-@b_9We!iS;R+rr~92D}k#!fy=KA%@?%K
zxsoSj&qkPobTpnNF_9Xzc8!jm<$oK}nCy3G=I1dGWQ%e5>$pnM`Vqw5@NAp(7Oup(
z_Bt?0w3ITehTy{#Z+K)jvFmOXuc`x!VFe83HiiCM$bL(}+2G~+BJBN*vz|!GebQ0R
zppqJYmA4KRX7jQL@Z9rV+L+vs7<y_|9=!-g4^8I!(Hjjt+3L}A&5iFa$wQ8<&yc<{
z95!<xe8gR70>RiOX<|Rywig7+h)e*ucCqX`*Av0JzDQ4kUia1?MQvi=+Nv=w+J^@I
z1_^H|Huf{u7yl&*igo2*+O3;N(MNVuNxsKZcJv(#`(i01J{-s)kC6y#dY>xMDe*q;
z6OsCNzaEBEnz@#KkEQ|;1j^CvGu3z<_*wDOr~P6Nxp5GF1L^iRXQqO8>ub(cy6nGe
zWOpu?;taV0xaJ#3*?wB8%sLpnDoP>rz=632!r_{Fmu|=59!WPCt~}ZH_&CGQit&jr
zrrn3ydHK|X7sJCxC~}My^kan|_i;AT{4#gjiOR1F>pwtyea9ZPt*-p1x~CmCvY|+y
zC!Z^ZY!XI)b7(z~ygyEQwy^Y7vy?9&jp#tOAywcT^JAO)=HV>*RBwyEYPJ$*(XO!A
z<xV&peq;S_;x=8Uw7|M?l+Cb_lSG`iyRd-sCmUf!1)B8FJ*-NM13jG;CA&6zWrzDV
z@^9<lX#aqpPJNutYtQeT6A&DGYL6-y$-cAo(n%j)@uafi%hb?c2q4LYBDHl2G97hX
zD?I&HErer!z<%Rt8aw;r{M+MEa(2f;#%+KlhIn4(mL62YUl$KVuwm!gmm$%`@nU!y
z&wjs|gsCzDC(l?c+}JC^AYS}Vv2?4uj38Q*un*CXk=~KXm916)0)U?1S?1$ZcE}6O
z{~sl(woCrstsw_Zs>V)$jG!tZX}TtR_PrmmI&3N9yNjNj34b{9v^mfW!KOwenNyXf
zym{OYB}-IYJ7kIe#@~TBd6rYuZ%iNs!+<FN<1aC0)keH|eNkTR60z?*yKSz7Q(>)R
zTs@J@(Q$V178O6&rDXrOx4l373D2iJwd+$keQF2MUC|p<d5}3FN>9oHu#ypy<AwBZ
z0lay<Z2$~7seO%J{%?ykpJNH&Mx|&l1Nb1tL9(}hv}jD0Ex&e_%$OQin6L==`;$Xu
zjdzB_^2zC-`MCG0fSnD7pn7HY;-+XWjV&4X?NJD`Lk&~?D(JF*;Ps>2mkUSNzm+oH
zD3L78DYJ!D;H=aE*^HWXz~N~kFW^8siUy#d=kJ}KU<N@y!JK|!<ck2BN#=>nd3DRF
z{5Pup>jA$#g~a|$1?}&z`~u<E1KARFH%caveH79QjT;Q(6n5y}>g{{l-!x4G@3TvI
zq-ox0`Ke_yYkqgC<eV^Q|9i+u8ksCm8=#PpFeM&5B&>sTiAdH8OdH5RUYftX$8IM1
zNovk4cudf6DV7&kKimF`RDLdtU=?`Ca1&kQK2J!efL6JAu8aT(!q^Ow^=&aw6+v?V
za2$Sw0d%Foe7YPo8nUP2@`63Ssmrb1-A)9!a-Dqi98duXAm~go=3D`T2eEfSP?)Yb
ze4x>(5##>HKLfYwa*)GT5IGK(*2S}1=KDcKT`NT}GzYM_=*R$U8tr!s*|)X4Ahsrp
zqWdiY@1KeawtOdeDr30WQUgdae8ESvTS)?VBkS`u&>d%Eh?IfaI{}HLrHts_pH7-!
zljRiN=&8=Jn({q8@!T92ozd}P8Hossg1g*Ag)}#bz~rb}?|%7W!O6|VCgER<yF<dB
zACt@ZivlNx4pJ8_1Y09)N&6akOYaB=4B{bmdz0Q)3CFl;;(FfGslMiWDNePDrzFxz
zrrxQseQMsD!$f_JjKzno`~nU385)}w+p(BIlmbA;G0izuf(EG?s|fC_*09ZjJ6(Mv
zIUAy_CsQ4&EydLywsTH=%;|4*`K2mb`(0v(eU^-AD<0#^WlcFEj-QT_x%e;a`q7!W
zI*8X~=<g-Qel*f%G$YOI)2p=4pQGP?8=;1SD~WF9P+S+0;K6fkW~i6}i`MMe#FpUc
zBl3^e?`8a!U#-40+_GSrdQeQY;ZgpgdYjYi;9rK>fVRhmPz}|`yzE$diTDKx)}Xpz
z3MZ8a!YI*lBy(B$#T6#q5I^gOTc(4!odKT`xbDZUMiDWe9=;Q;&^4;a8g8$c_Kb;Q
zbW_*n@x7$vXy#lexv>epX++C>oLKNrmHDDQ4j;QXA$7D$woWA{ns}i&W?c~geo+li
z0HiyTg4Eb!vu(i?)=(*$6~;^L=i$ogIm94+3N5ZF^7r{a69?gv5iHxrkov(bn&oYG
zcme{-FVT{0yrqaF37gHQ!A~HgzhV2r3(jsMc!2@FunYoWgzWl0>gT;y&L6@u_jy&|
za=o$mK2;;VXkALjRo+_aC?SgPAQI#M@DXSLME-*=A25Wvz#xrrZn%%K_Yu`6r+LQk
z%JoAri*spB&iwN^2FbM&{a3hb<m(H!U$P~h*%i5jQ`*+0hc4-5`2dx*tZR0hTSD2u
zoV3fzbbx~U<ymBxW})7D4o?wt;2VYX>T3oaIqYdsgZL#Jt@|GBQk$62lXz13d_*-P
zz4vv_im@Yakb<{sOPZXtqGqvr%RSc?9=Xtc%~Fm;GL-`z2C<&c5THWBP-C)Q<F(Rc
zL_LS|1x)bLSP@GD)gj#|7ZHyinB7T2RD~JKy*LTUUg~QMal?@7cGWm{sLPRuP$^V@
z0zQ3DmHoEZRju{0%c$93@!KN26`TRVv>^b{<Y@)ZKukhgIshnMv;^G#s*)H`eOpT+
zALnjmJh>QmunJnmfnfH3yhT0vUsV6tP8IA9jy*>b8+s^Rnu0#*W?8SUUQ2i$eW*+{
z9cC;&AWfc}&FeAF{x=flP~;e_+>^Y@P&bX$e2_oc=yT?0M``3+x4+cS6-7R3W-Yj#
zE~?hr4J*t?fdJVOQ@q=#%s6BDbl{8<nFWxUXx9#Aow8N{c-d;yUv%gRoWJ<K7b&q(
zAy)0y4z#Zp`C*29+Xhv|8zhTC0T8uqx7R}oEY!&vJ`Osuy(hV(eAHQ<7<Lm$H(oq8
z(4xYcObs0x2*6%w0zmIqR$WXwMo`lfx>KQstNN!oQF_q&!$jw8>6l0-MF#RO835>u
zLyQ419=#r7P{od{0*+e?-{aNxJ-~r^gR#uBHk=cu28X>LT5A4ju7?QQ9o+?zP4bjI
zrIVzzJ0_N>d&Jo+9^pPrf5BL~QT|k3P1bYnzAc4$Rlk7dJZ+sS=L!}7tYh~gqp;P1
zFU0r8%)oP#+0n#0rc%RIO-FFup2@FPI4|*e&i9+e5bjDJjTeR{KX>0HUHuUqeH=}F
zN$(uD9zlr){7Bx?e@<I4VbN@<Ft;IX_QZg2o9eqv`10gyx8}j^W}MQL&n?RAD3r8F
zPZe;oFbk&@%vo20GJGzS^z)U@p)bu#&RMS@f&X4G<(;ss5IZ$4{AnHb02G(u!eh7$
z6_gl3g9``cY8dQ;d&p;{ud(N7nwlr>5d<h0m$hNGqt?%eMT-9}U!%*K;v#>-RXk|V
z#JPO;Lp_Byi9(3`e8+l?>()jn!~M_=54To7c-n&n;Ae(@iDt9e!EvC#8&ion;+5Q>
zSiRuR2ewEp+>!dd<o*?oR>bKaU$GHs;QRcIYK9evzVe?Y2KJtL@J_+XO01kPF+Ewm
zL69Y$QI6#$eA7z8CxfR$3~=;rOX#1Ms77%DNOG1G6c~RyTx4Y36@lt{E89^83N&-6
zccC#EEnL@?qaX`&FM@q^9^OXs{_=^vy;IX}BGfN%44=J*YmyS<v}rcDc~!J1WrfRO
z5;RH;*vjAs8R0aH8AZK&;lL;YtU9Ox04SM6FfkPKl;ZpjHx9Toy@F9to1d4PsSpNH
z*Vt(uCz5KZO_wb*Sccb>U$VoQP!b)XB|3L$=zj=1l8U^z8BHH|PDS1W^|m<iGi|2#
zT(ouPTJfwGb^S(3Exb@ZR(UC)JVe-Bj87!hA3db<vPjRC3Ej?QJe2xdmN6!$UcxFB
zHKf9yL|9Z?kEdN9qn09%#^5>j2soFbjpENWNY6v>sB^TxND8x>(q)RsW8P)L=IL(;
z(%Y-c_>-!gHIu6Y7$P50R8dt7QwhoJqp;WaDsD1C(K73M*(m|aIa+-$BLpJL`+h&B
z=09icJBh21dn)=M;oKPjY#|N9K+Z_gc66ppRCTs?J`V4BN?c#=<-M5O-Xl<>6H%Gx
zSGG|ZfyO8I+ShaX$vQYq)D}NAJFWAG@u?ksq?Ch6c+E7QSg8M!&TVHH-4SkCWM<#|
zmkZ(=i#TkFg|gq`<ucW2wB_eYJQp_Ic+;?r%JI(dvFmSO;#cJ+jeJfqTt_(hUwa>}
z`<E`Y@tteaBX3X>LTHyr@72T`#Zd`M_o^${zG$H2qR$#a+JL*aH->-c<lvU_=|cf)
z7=RT5JN?GX62yv<V|r`Tb_j`8RA|5&>9Stx=U3FJqc54RM82!DGZ5sz_o7bSBtdyW
znYQpvf}rbmiO$?fLmhN6cF8GqnyE;s&qH@-4{3O2*UL=O-peFy0-9cK&`?MmP7Fp)
zNiPZlbnF=`cF36W$S_}sQgLIGx#J76QcVBY`|`&37>d-}Dnb0uUD_*e5+z0fY;1kc
zK@DX;ZaOx)C|3(RmD}aw&Tn1aP+3(>WyzTO`}zEQcYSZYb%B)W6P1)q73R07?RphB
zW@tA6r)ERA{AOrioJH&flXovecuz%k=gd*=P);`C$+T3_BC@)HPrG`ckOTuL;ceed
z(jQ980Qd)n!&W<a*D)j@_m{uhpDuC?x5Z`3>N1|cM*Y6(LI$G3jPS2z;T!*Q&8o&`
zWE%w?QU#63?36M(rZPQF=#C)w25Z$y&-((H5RH*9uIChdP<JzHf$Ike*DmTP8{&CF
zjQ3#DFOyMhbq0NQo>v^JI1C{CDmYT!Sa^_U=lMc^FS9VA{E7(C$Cg}4tYKT66O^9A
zQ9d*J0Op)iL2TPt+@TR{r>00fW<X2nBi>AelQSOFXuL;ZvNrBrGu}-%pXS?q#X=bf
z(ah#sB=*v@(d3MOIbKCAKsX?n0xqk--T4`E*P|MY!O*t!gf^G|b7Zv*^RV8NXjsjX
z{nH!%jPJT|8K7elDzf>vG1ozYv5qiDhZPH=84SbCM_@KwFcp%q#lP_cc%cUbuVnD5
zt#*?RqS668fZZN$l3B&ig^X)$u!@6j!iaaJYAWK8)-c$*zp6(>eC&VFJF(+kqs=3H
z**_|;@^yY}TZiguF?u(-lCWoyr_V8{dK)hT#~Nb=0Q1U)#sJ9kB<TP`8+6I^ihNqW
zMfZwZuHzvkc6&AN1#K=$&+ehAO|V{Arru_>^6g8#$!91$<-y0V^}1$f;E_YBar#|T
zC8GqAf?!7S*Yk3DMEg<Q)!K~aED|Y66s5Fjy!FeyJ+ta7R(QQ@hdDX<v8m@DX7-lX
zsO(T-RYZz@>l4-R$jL&<&3N48?7U(89#_^xSZOIw;l+!5J>gdRctevX=0kU>tB<Fb
z8R8F&@X}#w0_A!q{sOVhuRLq>&7xVBEy)n43xW=rcq4Qtieo;O#^{Ug^=eE79Jv06
z*B(RwJTRKDg%r*LS<n<&wRyNlMhLiM<FygLI?Lt@A)EJE!-qC5w(Z}SWX+m54akfS
z|Mr0a*-bnX>*w#p=Eph~`bzCJrTP$^8H>!G;~e#}@x8LOoQN6AaaUHVVj7cu4n5V+
za~-R#f9AISc#cQSHh)<9U|ZBOU37QRNSfY(R*XS=%@bBcEF{j7fG~3NSn067vcbnS
zxbtf4B6%_AD>Y%5PeqFA1sy{Jh?kVkN;dZRGWMJ^-EPNtlaoT=fw6XqIxayR?#^gS
zk>0II;t|%0ihu~vCZW_7*NKx%e{kf46?gdy5G~SVw-rR*P{Zqtk~yLN_-WgZ2F?Kx
zy$W7{UCabEOcY;9iUjA<$m2TwX3WFh)rU~OPfyC9Dz69onru3ec+fY5fw6}BvZ162
zrf>h~{yBDIfMMDzRZ`8oo+e5S9*de;?6NhMoT0;!QkD2IrbgshaU~ZU_hj2+cMkJ$
zkLPy^*i4H(iB_;f&C$hML@){x#)*NK{OMNL0N#O8e0O$QcTT+aDuyO6FYt#{cvNzL
zd(u@Q>GqTM{f6D}PQ8^h2B|XZeRg((x8=(+HYTEqa%=ZIc{aV%7ldkM`vlAFVMeZm
zHmT`PF@FTMZt$8#6Y*~Wz$T%T3zPScJOn6QKTeUsud%w?cV42#Th9nKZj_rHqW&*4
z+B%2upGHH=9)GX;6|OjfZnNl*o#>8n^)reTHjPq~=HTP84dE?Hcu{T5`{SCoG*k<R
zoG(gN`lFVocG>L3a?mf!2=_VMxmMZFg57mPYZ9c_4^|b#(o5L20azrK_fdR`aZ-eE
zdseUr4w6ElE26STCaWb`nhDQPm!4^sUim|JrI4O}r?CkqBWJTDC&DL}#ER0zAYx2$
zv$gX8bog9=Jcbs#f46?xvA+u1*;(^f<V6G+q!|g;{;gF|+_l~t8E+{0U4jS?n*6yu
zqdUUmLUen3sj5#)qxiw@-BA+(J9r!;xq&H^|Ja5X>(7c6B@kixYwPqPb%l6GaM0b2
z*pboKiZWy_9(1f4!hNJkRM$nAHZ(o1#KZh%O<p~9lnT|vc)tf(oE~jOq{y?yG*dI^
zNIc;blIl3lwl+Y?q)Mhg!|cPn)YT%`rOu?1$ZnPWSSkWLeDTWhdt?bv#pN--(>|HZ
zpjJ4KM=z@E%sZKe1Mt@kKATZ|_INV<n1WFA-C`AnzP6ZT<myB&P~XE4|MZz=yrjla
zr{Q#m&=Ftx@Z4Dxv+?M6S+6e6#{p;iyL+{8S%U`L!tv&^ip>Gq^uQ(|!{ohCMn01u
zv8lIA)r);}jdhF#snAPGc47@&^JWaw*=q8Z!BCFOe^m?uBLy}KIx>{;_WtH*x-X6<
z;(dL_hhtLX?qE{4GhdB4mF(#XO~%FZBYI74KX=xLV+qLPdlXxH4U14(dxwyVWKX!9
z<$m~oI`v^02cRUDyd;cv0{?#rTw6faBa%3Z=NUu!4SnLj2AS&4+VR5YqMdWvE|U<D
zyU#7Q4I!4LfQP67wh1%qVKg5zt8KzWE~$vAMoWPr7Zrl!tj$EUU*4cv{Pk?YXc15g
zDBJE>c^q=Lr@X^vY$A~eLClt1A9Ve_`TQISW)50Gg24d5n5sydSgc1J5LE~;*a=_B
zNy47Liy%1W?6|0-2oWnpANe~5dT(#E9mluez=edd5!SVV$3g+c1tWxph~xFenTUu}
z?!<LJ7Vk5JQ}nc>5}yycE#(=bRkp^CySsLY4bg8qpRLA{?M5c=-?(b>nZIv+g*i)v
zxp%fds!adbXn&J@o}D?jcDKdx1Z8QsY5Xtm(>;LG`E{!=zZH20{Xh@%%lTdXCiOi!
znXs9s9ZeK)9X0)*&1VN@BabqMy)?NsDE3TWe5bPh@tt&EzL^dx`=`<K8ooI%7uWeA
z5kMQwU41s!BxHIpXAHhrkL|6~E8B6a5Q1p$T?+nnrn;AOy?*dY>47`GWdy?^f}_Mu
zV5M)|N!HSF%T5YG;NaFh>K2dw23}vli%4eYf6?}tD)#;)$-IgEtlb}`3*E?za)zqp
zEYlFq-|prb|N4u_tLpr5t-Y58y|H=P^VlQ(EHAynEH8qeDfnULHRqNI|LG<T4K+B#
zT?jjuZ#U*{tQj+Mv4Hr@3jt{FZjwJdaDq54*%^=#iG5^%39AN>V-~NUmJa+VbM)<$
zS%uOE-(G7`uRnqy?AovqX0E%2VpU!aAFoEpk-FZRDV){ODV})8>BfX{_=$l3o&2W;
zXoIDr@Cp-*!YBkJ|B+w~+#~DA0=y-+XUe#L<?3<M4+i5~HW6BmzX;Rty-y)GBXSLA
z>%iju#Rp<l5Jm@r+%!ah@=n{qV<ZCvtH##4j>5+ycrsd<#oNTKMiw=)7YH-2q9c8x
zT-A1{&Z-ZjYwounQqMWFB)w&)>We3JB`DUc3*00jP{6C6c9LkI(X{#-J%tXC9pJaM
zr~m}Sc@v_U9G+V9(7-g1%>++04|1ojhHHRmW&R43s1NW2#WhH>`q>{SHMq0-ixQ3R
zURPr(C2pvG5JA#M%Ek0pEvuQcC+e5z`8_2ApUir+or>Z^{D6r;n_i3l)Qn=)tKYnw
z4@2@+e4QdoSU-saD`!27x1n|Ebvyo5k(<I_BQ*hKzd-<Jg#zTtaw`akKskVQlL-RI
zl5Y6@#EcDX+Y4ugEiQOwZqtY}-K@?k85E~{kjf}|3VuP#@c`%~rIf6b%fQp$mYu!y
z+LIRLs0Fc$%IA~HkvBTRRL&O}K<YC!K&-5&g7G%HXxBaHi0`dD<#^Vu_@{9(=VTV8
zHVl5IboL?%O;!6|5K_lvARDRE^4OJY%CuCEx7w6RmX1eUzg~Dc=SSWWml^(~_~c^R
ztO&Bv3kG^!L8Ju2q)L*fW3)E)5Y6L3_(9pEziw=b?w~LLX2SC}N?=MHouQg;%N#v>
zRz>i8I5}dV7-%Y5j%M8kozUWK11JR&LqrZM0%QIq8b~XKMYCV!31&2O#%~_5^6?!R
z(O^Iyb!bokYtS<cKp9h>bo+gs3%1CjsMUak#Y?9KT6@-E<8;#-ngSLHWRaLq0Izy}
z<|rOfV?NowRAN)dRsnD5Y3PV26PvyF;d<+RD)ZFbbHUTzzawFk%JF)Sj-N#7hMDIl
zIi_@rVVq$0{%uaZe!Q7OdX`)mWXfv|DV`#DPSGYj_VJ8M5x?7u9k}VUvZm|cw|VVH
zfrAsAUQ_**JA#R}V(pWv!kMnJ9T78vR*b_90{S;!vs{V{F|%I;SZkZ7Wp^W<)=vha
z)8rZg&tHk8UJUq^RDh%n$WP=c#|~#}8C$sX1Bq8nbdKI>c~310Cv|FbsLNDQ$mea%
z&fe00ls5IYz#9%Q-+;X!QHPGJlT%x64T(BGr|-ouDvtBcb-Tc0Th>n$s0Klp3x$L@
zV%RIg`oI4H1%Lc+P`d7syN@~>1Jedy5gRW7(T{xv`uT;L@yE{f@&z@T6evzLVIr^B
z)P<UmiIHb{_a}!;sLCOc%Fq%{wU7V3Wz~73;L96nfA2F-kI>bZ@}Hua<?p}9C08aY
zS^WH$^P5VqjqkLUGx{|mP(HTJftX91oxn5z&mf899{m9Xe{%N+XXAo3*+SS|i0c74
z(&Mf<-XMbk$Q2V5b{Wk02#xA-kJGSkDGJaw=98j8f@mm4f*A}3r0&{FD1r_c(GW$j
zLyBN}!xd6FSy0P(8$XKDbRhDsyOB_Npmit4xCgzv2uusci3Ag9FcV%WYnynpSOic2
zym;cye>w$_(ux?BT(*iWYV`}^`ayaL{MrRX);tU}2o7T>oT;NvXs}1@mFcD>JW-iM
z=~_w3bNReB;@Z&ET_?uH;ZNP?ex9?vaLJONGul4Yqq#sl+MS<(OYTBa-se)2HB8)i
zDj{9hR2yUBFPN^cPoQr8CE3+Y|H$sCw9A6!9>x=gJL<LcxBKF$;zi};CAmAb#Mrgs
z=^x%ED!FdQ&1CL=TI@)x%50y4g8=&nC>Tf-Ah?JD2(Tjn3g0O?uv~1vFCzc5N7+&d
zGL1wk#sF!#K1I!jB;^`={bKO0m`Sw}Nf%PO&!@|(Dx%f8DLeACE8WmVSL2<`CsVD7
ztapdQa%1LjxpUyezG%nTY2yApQBJ%G)f_|TJy0XR4*<x!ahO|jWVvp4Z&U4|9@Zo%
zC)NC(t6nvLS^t;S#r*x_$*bD0R}4Jv$J*s;OX`O8S1K;f`ipI#jH?TVwJzc0ZAQ5k
zqYZ8qy63t%A<1nMHv=V8$NY9V=$HbzDeF{lHlo<Q<1mM0qeR2wq=^wp0nuorig?UU
zQO1^|zL<4XRfVA;WmT=PyTj6PXmCm~eI5XS;VExzbv}a{>SRFWA4sPYDfo4#{GKj9
zKvj4E;+BCBAj+vmDjWm`(FAOrS2DQR-i8`#Z&Lp|ASPin+Yt-^miO$Tizy(2AJGD_
zBFe_~h0;cz`ZVTyX3!l<pn^~~Qy&gMaT4-qv;qOrgJP)N0eH$enHhBBsf)%3_+~&B
z9<82;rvA1W`=seaHlcP7iZRpns9iTa1?zZ9gj;>d?swA>n^*J&f_AyHbuyM>Rm;UB
zzP0*AAF~(Kc-=ZfB1i4w3#^ah1oi($O7%eG+yxA#_N^bv*rj#5+wyp8P&Dn)i0U$S
zFI1bc+UEt_WKG6U+RDCm%j&2P*2(tbz(+nYhC@_%#j5;*dBf=#nN{2zNtB=O(2(`>
z51E@;YK_@dQs{pzz{wN`7#(Gs27b72G6R);_=jZit#Hp$2X)?C+LEJS!^>kxsD?$`
zCt0f?KJ3e8@L*zP(jzy~M8eOsQk*}4-NqRED;}jsq;J?dLz_rk?L&0@(~q@&+mow5
zs=3L>Gv$N?{|9*-Z<8Bfxq0R4<78vd;gxqf&G6Fe$D^1YIFSwApY9WL>ZnXjeCr;u
zK@%UU!2By8nU_j=r^JjpkMGg0E8b`b{v-Mjo|))x$T7bCgt1T}rlfi~c45Ntntju!
zn5<K{a<<~g>hruVybX{`fOX#~ZbQgyV>@c<SClmz4%>5cdDs{76x|PgQFLy1g<^l0
zc>^TCJV2nf?vlvvfy`R+h=x1<I`%IO%|ru=__)@DDs!{D{*6K%`VVJq*9O)1w`=-J
z_nsU-VSF{LMgNjTnytbI{tUBq_lJQHIWo5b=M<}HLGQ$!x1U%b;H8&;UHnRIGdv#~
zr;Dk7aV%<_`TXu)8kM1V?v0~nooAW>_Y<~4ncEK0H(Ju}|D)(E+?smZIDWPfV{|tR
z7%;k#W^{LlNK1(bhzO#iHyGUrN=busi8#8Y5wU3L77^q3?)?wWb*^(g&wcLu{(Qbl
zR(V;0oznCDlIK=d<I2O89N?CRWC2qzGGESmEpz5w%ycir-&LU>d6~7Ua{BYvNcj7W
zSxwBFB7fq%y}#103#~O*q;up9jP5s_R4prS9X&^(VGz`=Pz*h<SNB>JohHLCzj?CY
zG)-ylS5%(m;y*~}#3fK=y7mAVj26h!W4;CxXd3_puz11=t1KFLb9+x=MiKyBHoH3{
zFGZfBRYn`GKtfLbY#h<#6Q|26m@`mr;mYACL!K~vm)^%X#)~>0{S8lgaCQyAh_wMo
z_qGb)+QbScPU@y2(9bVRNH?zuj-F_r5PKxgUnUH|ePJ*2@7*n&`Q}AkMFbV3*K|90
z<>4|d!}Blh@&y=@Z^F+Fq-1a;;s?oT174Q9z5Cx4-*`06P4!vQCgzyRq<0VL@u$!2
z$=(!o)LE%6kA|_nefs7O3^Z}oxuP;iTm9wqzO&fZL~Q-)gKKAB37MRkmDO&Qf816L
z9}dI9t;md-1WCXSc&6}_2`qErFQ2C0uH?&*XM4}7f*>_q<$$V<(JbJ0+z?GBjR)%P
ze*9O8uIY)(l}%D|j;;0)x&rei-oS{tYk!?K!SRw*0N35~)QH4VeSn6>BZ7jN=spVt
z=H<Qu079dLyX@X28h}mEfI-g<D2;yY1V|Y_e98?c3Wygwur5mF(c75MN9up(7z9XX
zi9Q#VgangOfPDaH%EtU61!#t&qn*3)+=vaC#$J(HYTn{{q<GPGroufVd-ZN$;m=Q1
z^T4-c8gfW6aJxmwFr#io>qVsYJcH+nkxX`vqS1`?5qNnao04j7?s%J}W4>CtQ){uU
z7-Wq|PcpN$zV!4G9ACWcYi83%>}C5Q<n1%Zfu$S^x0#xFezo^)6gQ>i3rjBzE%rI9
zP&OSVQZ6bXM}np=r>Cm|#j2&Z{hb0g$RBie{CInReDsWv2Z~mZ1!xc;S%7_HuM40j
z>ZbqzH0`z^*N>V%3*58HGtifKxk=VZdH%b%EzIM;#<_(Se?zCz_o(caLSsWR`p6H2
z_g>*9o@@zXB8Qd)^%snD9LXm2Gb$*!bPGIvZk!kRz8@@;6Ks<CDh#mbI4^)W+EH`L
z)vu$W?zC`Z0oY%?Y%hP%VxBI^hS;5N(~pGiH2K0<TcuKKl_mt7LZtYcDK-i0%qN%q
z;d@T8;x8sxZ9nw{8jI*Ym-(d4GS=Pj5|})Ev*7>s@H4$bj<=Y9(U;&~!AsLhWa1yR
z6%+-nv>#g285sR|>k+x|gG$h2^(Xxne1X@;9QL%-;bnAiQz(T`JU4ABnIJH_Dw$m}
zV$nzdhWM`l7VpCJsJcg*X{><ZpStGp>B-=<l3Eqs<TBwK??&#?1$;awVAuyujv=fs
zP?f_LNvsUW#Wq#$b6Zdd=u7sp5@Z!=kBa=#o1LN#i%LWnri%5IENyf^WlX#jP5^KP
z@Kc19qywNBA7ub=RwoIjAOpq#Zkqfo##0KAefn>1v#}KM_M(Z~dlezaTVHGQ{HiAp
zb<L!Nc{%md!^T4O!_F8@1TU%LtTo^A2p5<Pv*4<&KT%oZ6pE!VU1cVQACiRK0)+BE
z(i*Cm*$I6yuU}~^UsfdbpPPEuuw>k9Q@fpFSf)I@oiM`GN@<Z~U?$rBdDPSITWXx*
zgIXccrcJ+9r%aGe)BINb3>~RGnW{s@4z40cY2JgF_P%yJv(UWd)g3Si5#@T<Efi2d
zFG_)t3>6(P;H^{Ap<K@`{ge4MT0qNBLuq2AMheqYXf?!|u48!_tUrm8u0yr8{#0{M
zj;)mC|5cz!ul;+fCzH=}w^t!nRO_xre!vk+d9=AG>8kxp076!U2DvOw;3<)NZLiZx
z$cQisP{h_=e92`%;bd|&0X}{wpXK<;>A7(1X$-8TlkL+di#tJ~#&3(S;=UC5QQS>s
znC`wZk((OKx|Q8Nls-B`N=9KRpY5EXtOt8hZ1pU7<ZIyqqZGq;*6|1`jR6jWO)Gcb
zX-r`Kk&Z%cK(Udi#S&X}S5Ld0Kd4`2>R|zUVyTp$BUsm!_<^kZ64@(vLzjSFAWbt>
zjkshGfRt#yZsNT~YV}(-cK7K!o*N*UodGdqe_gomsYA1IDNZf)902WV>7{dmT>)~^
zpf_X#czyUX-6R=^0IG-QF~-9qvx7*~G7V6sW~x+gVI;#e5f<=0zcCi+6W$u&4A|0s
z0zv5mz)BP)f`toQhL71%;%<ia-$iWEX=eG=RUSoz+On*Mm1j(16OK=d%F7>{w(r`}
zCipAlk49R{s`b&9iPu1)wCxKElFv#08e(5Qwb72Mo2Rvts$pe$y%?2F8z!fBliSEB
zJ(X$6sas$7i`%ny1YcKZJN`&4<^wJ%3+H0&>Wk<(qi1%km9o$5=GEknLp#}nLf7l>
zV%#@5UHlpzaw)!&!EBt5=9l8?K3iKbP$;-;kT?RMH(7X~ke=TeL(PgCus)na;Z$va
zct3-Nq;7A&WO_lR0&Yy`Qr*;0{PaM?@1052k(7$Lk5+a?e!uegf}Vba3&dJF-P0^;
zi(dGW^qCkax~Aqis>JccSPuu)`=PZP3`bf(_d@vq;`}x)E2#RsQ*VXA)aU{Py`8)%
z_q?4g-S4*H_(Lc3L+d9~{+joq_Dbh?+0sxhJ7M}E%JIuoczkE3f|ndCx|Y;>$9pWk
zx!8HD;t_6XCo1?JjdDfdF|E$?pAzh?PVKe6nhh>dH7zHPYb5d_{fdjY!u&c8-^x+R
zEl$jjSjIU652rxXc15b%>2&~P%qVFVfRKh?=+!HfjxupPp&X!;&E>qBU^Pakm#3*5
zf!L{*E!Dx4?;#qi%+#eDDgbaq9b*=o61|dHBYlpfarv>bg0wrAU{5emjL8V}e4=mu
z%LwwBYLG^NGfR?~97IzFZ1nYFM0QL~qgEuCHp6cz*7rU7`}ymqrQS}%$6tz{N?zPP
z4L-TxNdAz%R;S}~S8~z3F#LXyw)5j>WlM+Y^A;&tB$$Jh=AATukl~oZw*-Xc&TTb5
zm~QwW=bIh=!Ti^>8)^JpxrdQ;<ZsDUxNdr$bS64gJ?E9WPqwa{>gAD`W1;__=BT7D
z$jQGOg+@V1@IWk$|6$<kWk6s^gN1X8lX5^^ztP&gF8_ihYb7n;$&MMD`ZUk}@wP<a
z^97MpCWRB5@i$ja%KV{LNy(gwBeyr0@Vqwu)_v1xec$tQ<eD+er{ogpGW3_R#}NJY
z{Ie*Gs=*Ub(Ue{ojL_Opc_KFUAjP=4O0sevSv=A8VP|czVfo|9fc(hp(x6Y()MZGL
zW<`o)mG^lC1o1IQrB+V^lx}=0-Bhxn@YB8>@u>Wl+NRa1^Uv<??y;%AgHT@uA3$mR
z!lup@8v8*(sVk?UwUY5<!adEzCFXD<4vPASY8q}gR~Fu;_A+zi7E;7BLB*X7>FFvc
z!gh)40LC-TMVy;8yTo`_Km)vBJep+l93+6?ineCfy3yjrD}Vp&t=Ds_^4v?rQ!9Q-
z6&&cehX+7Ypb-Rktq+C*K&Y><vPDP<mywvPW^8bedzX*knDVRY<CK*f{amTKjU&mT
zgOr`O;w_Je$4RSehXaQc4pHYGetm(xnR7PG658e`sdiK18Qy(vV^jE7Tp?yi(-V?u
z@;+0e!%J%3loK;HLbr5?1xqG8AgauE)|08%;}JlFDWAC5?&c7pajHIdZvMb3L6NSP
z;32{^I-{9wrkmm=KC-czv55y>2mREi`B_5<0hPVxwd5RF)p8d|CL`J(a(CBZ1M~?_
zXzyXE-jy^@9_l6qbMI1j<h@zz;{7a$fa9`-_D#d?OiBb#`dE>``;Z~;51E=OWjG-K
zDdc)OhXz=p<K{v3uC8!U+Minpf%d^Z|H}7bfkDoEE0PZ=c=ddVybr)l!S{3zz%Ycg
zIif7H9enN~KuQUE|617e-}~4?S(;l!ne`uobZcrBS8AnKj`BZ@N`{!{LFo1sgPsvJ
zB~>Ij(3}$*`l@7-g`Z9%C5~bl8wSZPNTrfmr7n1AV(e92q1?$$ETfg2ZiJ9#3~OQ~
zSaa)fRZ%SH>fY>fxD?ZZzNDz}l?X<J)a}$Q{S+Cq0+j9>z-byi#xCf}G$9H;gs@4$
z_ef1XN}Uh1p37W3HwJJR*;)huz^UOQVkl={y=~d*AOr0tZOug4oj{wxNfNAqv<@vJ
z)v9GHl}D<Vs!s!x$nKYgbta1vrD@rb)Uo&em;F=F<myt@G)^K~^yvh8!6F((n>{?^
z!A{JhU{P&Ob;53OSvYsXJfd==)8aJI*Z+#8CsR6Q57YRdn<E2fem2VVew}y5rQmFF
zD1lKMr^jsZg_T&=MNiH7zVk0)C}M}rS~lEqwB!n}@RNs3W*!QF#OXB~;Vle2$sXF-
z-kLnWd9|Jxh_wu-ULAX+(yfbA?$v)VTSCD05Z+I{>E;j{?tcxDweFs!K}tcIyB{{>
zB?F}c{9KSTJ78DB33o|-|CA*<fz=gYbhD<pe=`mv2M|d}?>C?XrY9TAsxfnnmR~C=
zA(C{L%d~2LwPa;<*Z#Kbd^{qCnB0NrURTt5S>G!PqVb+SRaxAR1+{nv!>p_`he<*|
zl2|WU(co5fSHcEenj1c;Okjd#zDVX^?dj<qWCRh@LuQPrQwyw)d~M$D@8Pvn8kB!T
zL8j`_?`a25!90DO&BChp8S*P$iWoJyNC6QrW08lJu5nGMfmB>YQG=#me%!GScFE-e
z4e!6&1fzl4d6D&1<@Fol%53o3O?>}9DBe?)2txh+Nz?v>9aL6Yc+n@xgmYGoiK>&?
zf74SkpQrUg-2>~1DOL;9y|lQGT6R!hHt%Z_@*10WpPR7GFk%W2(+yx4Xl@yzjj-4(
z(77+Cv3q^|+Hm;98lO$=D<toJRpc-?r#VinMJzdT$!oqJ2(QGY-G`so&)VVwZ*UKc
zWS{P&4|7dH^r~0P`FGCGesQ-oYXBrymzdnwQ3kTP07D@l`{IPb$j}!OM273Zh$fGn
zF_h67WxthrjYmLlick4GPVbxCTN1THRy3pmvDN`g$lsc_0%bmd<)QyHoBovm)UMYI
zmJu&YZ5ib#2!Ze-4dR9yFc4CFVIoj+!`@3$(W+MeZ;OSfB!9S}DI!S<RBK}w?DcrZ
zmap-zt+@d|qpR1F(PPqE)|G~WjEk!@8IT`|_KZ$;iK0q!Q4*r<(!7X=386*P!%|wW
z9nx(a>ZYISF*`D!Cf(XK`%(R6fZczSS9Cy<lV=wIt<jpDa%spiL2e9efezs%nB8hD
zQ}4`p7OCHe_=uTY{9TH>+3tD(chUk(szc=>OFFKQmoJzr#*}kS-UKd8iK?M1nSdb(
zp)0{g_Vx{N+1aw;7Jk7|Y0tf1J(Om$QSUrr9IHPJbH@cvy$ydQ^<dK2*347<Go~Ku
zC|1Mm`Ij-*3u`~DX}V}H3%Q#c)=ylBljUPm7!A<>6p-=mfi*p+eA7ioOV;{-KHIOf
zG$jI>Ygp{hcd&ehEJuO%J2{JD_r@)U1%aerByQHsfsc9E6vT|cr!MOxG`W{~AkhhT
zDIRSxo|K+6v9q9fHb8J|z~c1icnr(Tad6kY`0NFr+%35c?c(5O<BtzKG(!-Y_Bk}<
zB*_9+JE6CT*7H3({pSP0c{XNk|5y@A7&R=y?g@*uq`1a#p){vmSfVUW=0MWr;lCc)
z@}9-kRy}@HDqI^W{BcauY%=5Wb4cqklI)Y{qq#`3ZQ|fgyCc_6b|dePk}CJ!Nt`+b
zw<m>)nXZ_Whgx<!nEQSCIrA)n=nHJ{+401OrF4srDbG5;i<+mEIjWb>rg;Q?w$Rqr
zeUQ%PHcpE_-Ns(A@n%@c19+sAX}#l_<!(4qT6T0R_9TGE?U!sBHI3we!Rc7O$8Da9
z7Uli1lCAva@(q#oS&()vnQK5dh=P)EOQPYZdrCG&2wuT1N1~+s;Sydm*m0qOnteZW
zgwjFRi=*^=-tRAKJ50PXyo2{F>~*98gcnx)!gy}uCS)G>lkV6S{drsSheWEq)Tbo<
z?DTHX-9-(SdS6twZ;PnHP)8!SeEwh>sMVt1uiF@#|GW8Wtznd>?G18R!t%7h*!f({
zFT#8vep;8XX25#=PH<i}0p_w!rvK`ieGEo7LZoELAZC`_Vv%mJexti`R&8<NXzkTV
z{wY^3S1x<)uUO9Gw2yZFJ<{z(cjLH{StB3Y9Y~fl1dhgVR~7xszM^*h{jeX$(oVz(
zGNs{d(noAQ7jQ&|L+_C=A@Y?U{j36i`Vx_&TISbG+{RHHa}lZdJHl*Ic$KNya+?Hv
zS@m)K<@MfUIg6-9xl-aFfx!?|v-PWVfl3?AQ@Vcsdlq)#)zv4XA#7Zs<+j)Z_LA>(
zhbhmA#XgoZPbu^WVILNgt*VY?vFz(ih*Wb^3h=IZTy66Z>7jW!0?og9X2lucV$9Yu
z^pR=a-Wa9e@4C|O$qa7?hTpwg>UbiL^rMW~Ag9#(tgCUM?Dm<2>=c4@bVcJx?yec|
z1snLlEi}dRbr+}W;E3K8v2t946|3iI@qFsOGN%}mwHvVx*^3TK=^7Y5;)tCXMEXh5
z(|$9#SIf7aeCh-~CKK%^3sKETkj*V`9@Xd&olDNP3z&PY?i4UmcYT^Ws5$4IA^)ty
zTHNCLm(I#>l&n+xl<B+p2KUtrA2QeM+<HwamNU^YTTj=puvgr~fv(l=H0w~k=w1#e
zjILDN)khymlojKv?*Q&~o}H}P;_>-wtN6Q3DbeFe<tXS@v2l~j2kXf-6`#txsP`o2
zZ#spMZ#KYf3DoF+41`LiAN!8S<Q<w9)X}R^ImG8ol;1Q`CC4klDDc2?Ibh%AIc0LB
z!!BP-o6ODoU`Fdo4l&DK^%}p(ot7y}_d8;DX@fb8<voN%Pt{g;FYM?>_=>0p(;o~f
ziGI%U-|zLZThbzJkKJt@Gvgu-9>b0&RsUA{G){js_OI#9*KxI}+3>?@m&CmHrE3N}
zpQ+as;^caa-436kpov=j^;v}gK&7EI9P2e%tL(%uN0k<0s*xCVdPzAqgm|*!tp=G)
z>_6CC13<1TuxCmXYb7FRxYqg&;28k8_wei)&9l*Y#1VHioSR2{KVmypw9jff&C&~e
z-@E#?@V3TcRP@p#CS1g~y9#?r$3Hn<rg@LGx8K$r8k^L3@PVa(vGtNvmqPV*;9jc5
z^N+}H94G8O)ybXQZ+=Im{FSfFn6Y3ZF7{#i&ZSN@+VR=Z@O!F|LF;R#d$js+>Qllf
z;J;XnJ2R({D>?uS47w62G0Fn>71;4awYMpNX8>mjKm)oUitXp8d35pXH<U=j%^XFM
z^B-j_YMZ_wZseirOWn-M6&ey!?IN@9i+(65vEB<bI%`!G3DPdMtX+y|OV#78TJ~Ld
zPO<aGCfin!F<Iuv&2NAh+`k68{8s@;<>T<+V*OZvD0+D>Amx%c9`G&!jA3=Q&#b%v
zm3s`tiEU`lhlO6bWY1sPNi!PgM(XGkSs{OoGfKBtPA#;t`sO&!RW5D@+lbvZovC{f
za24@)C;4tigI{%ln7CPF*$cfL=l6orWERa<^(uXRg~M+vY;GJL%gem`@)C`Ye4m#0
z_OQYN#r`t%iZb`w;ol$Xmortr{}Yn<z48w)iGckRk(3^VXasrGfUBVHYV`uh2;Hcc
zSR@G;sDAL&4^4{vXAA%qnfC}f9;zsSbYm)B7UM6RrAet=av(ZWzWRdOcaW>><^KA=
z+-BMc%Tay+PE5rKXQzUb5Q46?Q=#_8<a{&wATE()*}@yyy=0mK`1k#ur*ejlY%%?<
zo}Wn6n)sf{o6?7TQ4&O|Z2g@wkeO8F7gEYluiCXVQ|=A<OY+jx;5NNFzfcTGtP<H0
zIjXDr+|ICBX23mGr}Z$n=v8Q0c=`O24zqDpR`$B`lFSji$-MPj8FQ16BbVYTn;gO0
zb#gfd`X92FsErqE;?8a_Elh*s?X8c*wC%5W)x8}@FZk_@Y)$gC;J%IzOwE}xnK~?6
zZx{HMw7;@^)@Pt2r|*DtXGo0I&w!brN%s11<(|Txj}W5+b&l_$ze}E?uh__#O2+T`
zo6H&7;0&vU*>V4&0uB@oCnpg!e^i9CeEx`uh{5JsU;zxr^|jB%H{renl1RrjUzFf?
zKmBLfxFdXN#o$Q;K%0<`y-&5UG~nE5(t}}csoJD|RwdDkj?*1nLfdv*0X^H295s1d
zvA+bweq^fwl0G?@`rPlT{5Rk0R?+9y4>I01ZrWB?{u21r-rQzcaL-t@_r6tkci88q
z<2Uoq@-#jjg#Oc*Sr$Gw3E%uQj`$pU7<<w4V}VFr<~4uQ4+&C-%=4&69y1!MPXFnI
z8C))2I_X_Hg7u-!LxrzOqz+@aw)Wi9X|~z^J=XE%aM~@OGM>oTV(TmWQDH@?$~6TQ
zK)v8L+`IaW9^GIVga_Iwh9T~@)Q1rP7=SXZTmgW<K-WMd+S(C>7NOu4g2oh(pk8v#
z0zeh2DZPxqFhs6%564SalXe}$7wAp)rm-5We+)}*0?m@vLkHxn2|38xw@3aOF+3E4
z<yfKZBumY!+n*D;4xCxt-SZY6eoMpCz3HI8?IslN-nh8GZ0@i6SgQkm-`0~_N=lcl
z#wWjLk;vGVtifg2E}CxnY%@8Y$A@;?j+9S@&cq@@zHwLBn8omMf`}S~lg>WhKuX?f
zB3Nx`x8*f6+d>f8JTvV^PfwXOuTq7^lMI_1UG0)w8XUBh&KlFs$l=2iAo)Hsfg4oX
zG7_)NnVc=qdt|Ph6<9(-DwN=8^J)BBKz#LaT@WdlH52frJY8iC^HV1eq6Nokt7AaM
z0Exl~YYC_n!-Rum`23ZMJsgysXILc5sE_e1sLh!r8Y7np_NNScP#EH%NIm%I{$2k5
z&V*4Vuit@cBkj%Xoh-eS&Tn%BrY6l;BX;ul_C2?KYdcsMH`YCW-ZB{iUJA%=-EF4~
zYxn_rL-s*=CL%I~!4K2i+Pm{50x+%bxic5Xw8sP5(a5gNnZMRBDh2JukfLS~d?XRn
zNebNh^#BzUi85q17k~^oQb+=%Xg&g1up2oRCu+(y@FL8Uj`)Rs=*bv@GV*?U40U-h
zm=6Pq9+AAJF606T{bIN)i+CKGAZ<PM<ijgcQI}9yqxe>hw<w+p2zQf}bf1O#6|z_1
zVONz$E;_T``5F_pM+>19%?HTBmTLKlT#bts3%^OdAKZ8OWbd0>QjLjY>U)@y(h|>v
zm&A=sTgsKT?QAWj9d|#{FkZ^uHx`i3%5-Ae%=U2cEa{M9r4C#q7fKO!5!6PSu=9yE
zU=HMh>)7?{N{UzGYZ-uA(rEg1I-Gqi8`G!I#o|fMWkF-L`PX!g>QG;5US5P2cKG@o
znkXx?0-2t!C>6eeNN~$gt>_mqy6d_TlzsJZ%rZ&I>^m%2IH9OXs*1+(Zhf;Lma4dY
z;H4sNKf#K!elK_6GZDUon{XSUT7TtA?*x_klxUOYu3ujzu8_#IbO9!1b+;&@Nu*7x
zr|f<G4L-Z|2Zahm&BT^x6+8IIGSC(Z2%LMft2wC1avF11+POV%p6b6e#dXH}1h?3F
zOi1RCdDVZyIxYL%!+>oRr7@q9`MJsq%oKDzR(C`CyhBboeooZZ9uUq%lp~M)IuW~Z
zn|GE;q5ey5UR}M>MG|g2P4$)~xtwsDl1>T7Q%2fdu>=Kx6Lf9(fwo5}gU7p1`!wT)
z_O)kvwo2_x6l=jpm#gI#kE%c}=7lx|6*kv15;z(FOF`N7;Yvhnz!Uy0v?rSXnIf*1
zg+2-IHen!DpA)EnM)I-fbuX6^0!vqZ2Nb!HoK=#uM=0etH`7qT><mM1M0b_y%Q#Du
z{X<WN#vZ=gkRWb<B2Rz%Cb0TcZn(#=#zE<g(}=BKwlnSWTwUaN=#dO`<H7CQja)(@
z;lw+wk603%Wlt+HhD`zp0aOhylS$(@_O-c^xUZ0qY5PpiJ~K;1L%U-r18n1s2$t*9
z56#zE$BHiVO26-5`JQf-`6CdFF$IqPp?uydxAWLtv~Y|;*s)x?{vPJg-i|_Qk*ZFE
z2E_R07?AwH*jNzRm|gm7&TG;+hB_Yrf+*AAwPO4!3t|i-Yqs;_B;n(il}%$l?kGLX
zv%&REV`F=G9L4wu9I%A(DnZb}B85o|piFb94I2K>EhThx*|xBv%BW~k&!ovBVA-L?
z=tG|Hf9lO2G?!cbo#piXw6_MOc#Ydhr4#X!X1D)*j7-K|GmwN@P#o@^-yaPsC@9!|
zwe`8_U9hsCM|anth7r~Nh@w}iW^i7|gT^rPqF+ZE$~PHZzE%`t7p$fFtd@*btc}I&
zVkpC~+Okac1!6VJ(Khsd_@<M#5JS{Qq7+v5OnpG;>@9U1Sg7PWahwqijiB`ji9MrD
zyf%w(>O_-KkzPmj8;)dU+(TuxD&lwRrKcz`bLFUbiY#h&M?Z@6N9goC!gLWF-WiJ#
z%bX>_r9UZfCr2wj>W-Fzn>S{O!ZZ5SZUybXn5!#?H8EMzWhJq5W|P$y$rs}!yNYh5
z!=PDPV%-N(Jc`Vf>n2q8IfHLf#e!MHFz03NE<G6<v~woq$d^OV2HrjOoC-a$2r=IM
z8N~degd0agjVcpOea^W=8rzm+406coNnW;@Lah{254?MSmBM|S!P+6WBx{(Biduu5
zbfr_VwK_HCTYmB@K5cFmOND${6NUY6O2+UCnfuHJ7OK8SS&Kvxt{qA>Pn5cnNrC&X
zR}3qva}(W;$%E~fL8ffM5_04%w0MxvnJuMNr1l19(wI@)V5j&NPpe-T!vi(k;AiL=
z6&X7he+VyHXp;`?n>*zL4Q@zZNh9|?hF%i^a8kP2Cz#(uWC6Zp3}`efm2S5aGzd}~
zQLNvThZdrwb&bLzA(+QZQm3Q<1{23OS848%5Cjlio5i63z@NR)|1BCGkyNYjDu_hi
zwG7R=3$P|8HAQn+owa^!;gEXV)bt^LKT6%9p{OiDbv>^t%jxYQ2}a?vMU+MvIVFAR
zN$GSe2gJ(G!$U42C%7QpQht`QDP3&+`ke<nEmj1%cSq2=?j74xn2|P&hy7ia8uHE0
z=Y!396T1_K!$_w;6B{Y*zjfdFIx-CAN=KYgwACjo=F2t5D*p;=vrJKl)ujxjG70!V
z{RB$wNZf+S7Bzs92Y~^7A}rjVgn-JMtF?y{ldOaS0BH#(?ezmiareQb%Xy89R{GuQ
z_HMbR(`e;C{JL-s5Mk)mr5)=U11g~ffJoX5CJf6UMLI;^JT4zAt5pwu*L70LaZN-1
zsAPTu)&GIv%wOu<0`EplV_{pa^@tH<)z)u=bXcz%N|zZ=C^V=Zsu^z5IO=j<7_!Vo
z=(i2Au@7U7c;?{>U6YNyyi5TLuY`BC%RXplZYWAwh(+I4pu5EotfRn&oMWUOZka{=
zPSG(`)2=tf9<hq`{9u^iv@nl*k!&Hh0aan3fx5rs+p;tsxFz@TN-^>Q6WzPU@HHm-
zkpZ^JQklPn{^d-FUp>)xbN3VsfJ<h)P8`Y<)AE-n-vG0lmd_G1%1;QW3uD3`$Tc%&
zRm7Xu5o660n!2JfD+G~S40AGX{^?Hzd@CRcnqGO9rMQ;(9G+dy1xON+g+HmJb;CTl
z!15fk$0ghfinl%o>#T>-`hd|QT%;=_)S9GJ=qM~pB8Jl}eeFqNS8n3UGvUWYd3cT3
zYa4^6yJ*0S7;K0RBE^dCQ}l0AXk4Lvt}*wVD0VxgVUU+TpIZik%HXfqChkm^Oi}jO
zFKYKyaXylgoNbLGQO6o%IUQg)7aKHqiY4oV)YXeOqe%~VI)!)GydJy}QK<GHr+lM;
zlJ$fI0I~|}vns<Mun(x`#;TJB%^zeSe|=0QO5kXA)*%;pzKWGq>5w%ZHuc}0HFPDD
z3o1Xt(aHL@GLj9_W~oI<C%IM+6Hy9Y+-a)KR}c7r9|xPw9;}K|QS)Zpnuik{OYlqx
z44#Wtc#L5QCW0Dakmkho$BC_O`Pc>8Co3bW7$f^3hRi!NYw_i8A7qzYrf<8F`Glj#
zpduKVm}Kr)ln|{el<<luOzg4Y&upNt?Y71`YXxea++<xUtXlGvw;7b0WPLVDEFLRR
zGXJ#5M?{HED`?6s{^-Xyc0C@Hphz^b@R@rwLnqz5WrUHkLAmIbfph;h4R59eL0Kyi
zSzWPyqRTKkm-&U`3zJ@Qc{%Qm#RHX`AqV56kMoiv6)>6h9%Y<I0Y-<Wylk&YMU7|A
z{nl8T;(%h_?E_AhR-Ls@6$2{Ll86ZwaNFq`Sq+(la5~Pw^{Z1=qi-n2*Rlap+O-}x
zc>AO*D>pCsvLq|-6B>lZ16ENA3Fm)_(hUlQ;G{`pdOQyr=1)egpP<QNaznar-CRuT
zrx10?EqYE_d#uf5{VUhLNc>@nW}gDss+v$aV=N$Sc*ZR^_Ix44n5rAx6|7=aM#cg`
zYYh`JJ~V0K#_BJ*=X(nY6Yc5<(&)beJ&Es2a?uzvx-N}4z}BG22LMdt!9E6On-n$T
zwi*2J3@SOuMhh8c;$iFy;)ewq%`5)9<i^BC7x1b-18QI5nrasLpkp6kK}ygOb_+br
zgUiGGIt6BGfBSx(C!Mwja$1g35MM7S9j`_<fnDHjdA=(*a4+>hBV}&POY6+hV3z8Y
zfwZFowfVPM^3Ssk6@tntoPM{9x>Il=*tW~GJk{LJ&${ZNjjfDhGFb*6yFr{;M&9=4
zx++rjyUiIjjOlCDiDS+r`st9R2DDE=q9F;>W|5V#Tplob3%;zyvxZa5C9{;JwNFda
zsMI%qtdD*QhB6acDse1wP&iYwYZRc;^!~Y+TpbriH)1v5*Vi-R&&A9;PKl!10Q}PJ
zb9*_xukBzUmZ0`6GuF3P(TCaa%vauJc$&5QJVw_lyCgOmug0XO9bW%u6x$e5#>@7}
z^3;t!pJ$b=0>;*b{XDOz`K@eYBtpb_UbTbOQ=IWTw$7<1?v5Tlnn~NSE4Q96N!?}7
z^Xr=$vmeeHJtP48Fz<y0xmyn<dFWtiIL_d5j;dDc)%LLGcSg^DGy8*ls>v13A=;dh
z3r5*AvoJKELxeITfRYK<K>%Ptl<C9#R0MhfG-NPY(&S}#W#V&bhQt3Osy2j_!@SLl
za^6TZpVoC5NmEX-WAE9&fj_H_+{K~@fKf=IN@=3`L+fR}PhAwi7%5?02JnqR>q7=4
zqiUx2_1VS9!$fTC0aDixT>2x@Fm5#@C4o>OBy^zb!K9_fP?pHVxflXMCm&i!tPhZ&
zJSz#9&R!YX2U;8X$ba!W?1$cSDJr3sI#!P}bz8=*O@6jtto9qBvP}PN13RRrQDi=(
z=z#LX))`OSlUI{+)Ct;j2k_NIV%(e|yIg8<L7RzJm@G$6N=zg&L{(h|TAihHb`QJl
zrTdPuplVB{*e@n^`n>}Ku=2;ZauvrJC%#6<I83?k?anYbNa=t&rDo>*96-5~M0eYe
zRY+7yw~D`1I_cdI7gZ9`8*~+gS2I9C;~Q7Wg$}C?k0b)jv4BN?I$3u_3qxhZn8R8@
z9Ys}-AA6}>rq6<@p)53#ixi)bm`}{&N*Wt3OpCe&Zi+$sb5U^2=WfqrPLObmC)9xF
zLciz_1}|_{7W0tJa9y~oe;J*5@xJR3(z#64&9o@@Fx1RvDgU7b;u_Ms7DYPFvsw51
zkV2=J*|HpHO^sqKE!=t&Q>XR8eYf78>Eihd#^FY*XY;W2cVE`SY`zSK95#+*ka_AZ
zSW4=ozGRYP4SB`MyZ?=HGesdem~&4|g`B&0Ei$n;*uFVL$x=CJr%A%g%|GLj%gG(;
z%33$rT2K6ra{R$i#N`|4HJVjqA>1OsIuHQ#4`U!byU+9x|G|H0=G$L=(Ol~6`W07V
z5SxJ;T-{|f34V9il73IRU1TW39gLOQ01)K~_Fg1w05RHu_@*o|^;BmqI(m%@ln;PU
zrNGdU4f_rp3$VBgK88NofP(8>0TQGeNfSWqYV-f_k@WDz*{tX;_UIA-ydn;^T?e8Q
z6Zfl1sxvn>nTQln%laM(`frI=Og9yv9{SH!!spgL*Sd#azPTYaW`_+PsGgA{R-O+S
zer!<vGyFj{-g*38d79J7*AS`GX=%1rp_TnD9B1KnyQT6!k8kjak8N7k%}+Fl)C}7G
zTqvqRuKLb%O`p27s&hCL=6aEhZe8*O&;Ka;XEX9b&DYzidm!<V)z=5J(etr_-<z3~
zgxdYh6ZOybB}<7o`x2cSoxODJe9Dr%-ZRN2rCLNiuO78I8<{t3>{}lDEBDDsvF<XI
zS`95!_&7f*Uf-24t?h_0LPG)teE_Ea0DyaTjYyj9KLG6>Gz*2208#JOPp(S<GDZOK
zn741!<zdR6a6z$rx^zYWBs3${L6;U(DFC<GN;p+K|BH7h23N>=3L{8YsA)A`_Z3}@
zjr$M3VQ(qgbXzaEI$}#Bx@K#U9|rd{-?+AFe_Jx*cj*l7xa~LZ_xy&N|Ba1XChYI=
z*&WPqL%1WsEX`fMfdYh|L~wz@AR&p)EevUHg4^q0@-MYEj$I@C(vi!;<J_%_zD#wD
zxf($=>l?w5-j$`Lwa1O=-+xGuS-krw`Nu`a@JDZ>-ocxDeGAyp(SU>JPwzVAl^r~K
ze9rPNu(NOFL8xNk*O+dmGs8}Ms@Gbb37At9pm=cc1E1_E^t$l+l`2K#MoTrpA4?>2
z9I4w_dML}XqKxG&dn<R8dD+q6FY=M1$H$}N4+I5b(#{4zefJ4q2d_c84~FOwPu1CB
z&s+T<s%oKqqrz;hM)#Z5(zpTvd0s593~bCWQ@RpuUXJB#uUZ37)l{#LJVQ^*90Sp4
z5XQBz1mxn7w{LJAg(hu24ks8g9huRPpdDcoQWgLbO|kw<?49Kb1tbOlwFPjLfKX;R
zc+f-$CxIqqB0+(c!#Lrf*-#FG@0|w&08L^TR{#bcsegP9fZ^G?S~LW2hbOB*5MYv3
zz6+4VkoFipZAJ;36VNqE%Q1UstC+xRtS2;E>a?7xOsFMB6W7{eQi@b>iHhYGt8VD@
zebu53P2$On8ukRjKwlr(C4B4&A&9N*=@!wdabS%r+)JqJRC}?fqw(YaJkddKd&l^5
z=XVZnll%V_FreU{2^0y4kxv-)OHK@Ox1(!*@bTL5UFrlYWpUaomev0FZI&yEWO?XU
zvuyF@PYJc(MH(u{>gootnV%-Ftf<`uyAPiLq}Z+SB@~8F1${Qzkm_CgLb^|^uy(p|
zm2H8ncubk?cWyw6{7_$gci;Q^dy#=_*za(9>x7r~6E!`&0kQju=o!e7*iOO`S5SJz
zw7v}s0Eh#V56^K?4Y!p0!CcjwYzfh>6v1GTS{Z?=6a8RUfCG=H7r~SOC@so0&?%Wr
zIv;$JuSutzqYN)~nYQA4<<-{tTQIJvlRas0>t2KKx6WUd<SetlzM6LI>IS>WvGyg)
z6qVmkyPaG90S_8_VC9-GR2^Ls#FU6+x63@gH?c$888I=%$p7?+ePZ<1&dSyc2Kj)*
z-#_^E9`C)N`<mvs-l<91yV0gi^LrXcq%sPuGGFXF!Uz9I8;DcV?tHyW1<j$F!iYGk
znSRVgQ}JpYP641onJgA&Fy3MrOe3#HiobF7jGjexDLADX?4y0qCq?=Cf4;SW{B>%m
z**jBx=ZZuOBpIf1FK&Q2Hh%gNAc!~H9zn!I3&bH3MF!X`S<0h$eYO%ZT7FRHPN)Zx
zh9^{L<Jth$Qb_?UjDwCVhcEfbEfmdk&cKU3y>)c}5JF`@pQ!8YF>!9<%c<5^bP}L=
z+w>_9G~i0!j|M2CyF_XGuS)UKP7?95&$6XOAt<1MQ40Z(Tlk5g0Ujct2q2LhW5xrY
z*<(Ecn3S$C3Sa`)Qrsf>g&_kW+I)q&ZDQa6k2X*WKoDLyJis5<^$Y-1$STWY3eh4(
zd}P%CT7OB@__1nWvhyV3dhOc4!~0G}Dem=1UuTl%pZnH~Fv%Z<<Ccc+9esnU(Q%gm
zN%gXo`;;TjA%?}Oi5iT8V?^*Ypj=SCjet!d^<`KVu3X7*t2(J6!alpea8|Ivp_JVc
ztIaj>(L31<+uEN0!P0%we+gVB9cDkiQ5sT88CH}VJU?NqZ6-4LFkfzY+QP_7t5#GB
zUa(rdG}-$amENdL+dQw7Nbr+}!~u}nEdWTT^OLp!HFb&JVJ({$|B*>AznU=0x`Y;c
zHMX2nm2~lOJE1vEtof}nvyCC{r?A#hFWuaqNNDCbLgz@d<TpwUH+Di_h6kw9Z#F>C
ze-*Ts<<tW&ER&@1VWaE1)XPOGXb!dKC>xb=&7Eca9zUV-YAPcX8y9#JM_wumPzMot
z>#vT7HtHuBSQ1_<02N{iNEWE$vME{kBWo)?o>O-V>|{ynlULO0GH^3ma_>|jfuWyc
zm&fhfrZVQ0ai@Jqzu{{|A?1e^6~lCeg@%%^8Os&{2#qH>0Pqu)gwbb&@qQ5b3_P2S
zDCR2<ypoU=R}`c=Uy1n-wx&2DsqKtK5TWkSXe`D{v3}>Ja3EgebQ)2ZVXUBV#C=r}
z7$xpH#7EvDdEdw{>-Cm+ppwQVJsLM0q|3~iX$CD({DUZ=AR^e_RUOR>=y{88#RA3v
zm`0Q27#~q?DLaCq<yIT5*~wu3>OrA#s<iW~#MS5kI9Uc#Jkt;80<2!#KN}ev^KJuJ
z!uYgZU*I550s#;Lp{+;-cp{!r5Bc*-&Be&=JhfvUKzZL6$qC5ds6n9C^bMWfiR+3e
zd&q|7>;+&R{1S`iCPXM;-Kx2dkoh$Ir)v7HWWXBXQ+{A5fdbwzyYD@YSrT9$#MqfY
zs(&{H7kP}c$rx>T8j%<DNhhEHQv5Y90CX__#9VU4jsav+9Kv|ui1Q6vj^s@td2~2S
z58rwf2!%8uGEaOy$l@^>t%=l52LL?(WJbAwde!s-qIImU;zwq?r_-xa8`d`d@&CLW
ziLG283f;~1ELwSN`Pl*YI1L`m0HM1cVTA+$uyscXe#t-zIN`Zx5gs;ce@%q8APj%C
zP^^yMoCMT}n~zPc@{Io&P)rO+)={-Rb`xurh)WS2XLb|i^W_uG=(gTZzYeensiFe~
z$dmu7OMWThuan-t<Q@A(BA(cL@q&*ojMx5miuEby*w{CwpILSmxf3wg&_FUJNj#tj
zU1tG6<|0%)acXCGMQAz0ls705#@M6AL2JQxQNP>&{^(;mf7CPSt~xQ%8__p%htFVI
z)`CTYxHf@F=0-iYkQJz`2|-(ls_G;bag<K!8_zAzqYd<JM4$`!=5x?g)3;8iOOR}d
zJxCr6<2t<tTRkGMj<2n_(3^5GJhEK+YusS>OzTSMO8E*R`sRIkn02fgA~yf6z<d8#
zQzbK(pMfhaf^DKT|Kvq6N=7T;y6C&cYS&u3*Lq!iuq&nee$8T`Cnt!d03v^6f^&V+
zJ{HLnebt5r7@~C<al8YBvJ>Sg7~riLv;6Wze6G4^gqj#)bS)8(s}kf{Wqkg21AaAT
z*n29iSxw?rPKEWUP>fye+qKlJkQO~8r9fsX5TXC@PMmMCO?tBR?Y5;%(=BOpl9=~X
zd2CZ2+EIyysfmDv!Dyk<EAILMuA^LnNrSdj(qzp!A9w*Hwavt?$$|dZi2mc=A{k3O
zca2rJ7gV7{egM;YL_INtq|wH1pzAFFxoXSN`*`pgK&wGVj{?~HdlnEFT0}UfXHVB4
z8xk^W*hIsN=uiVA6DMj<k_Hq8CiKx?#U)JTrnGsnuGaA{r2BWcbh9~2SAy3sGsYcr
zhOhN9r2kRBAf(fPxB37)gFQ?)5oF2mooTiIiOHH{cF8IgP%qejX}Jbli3ztqRtZEi
zENHMXp#e6HN;X0r125|VvSEOA{6HanG;$X^kO!<%Hl~uSg}U^ga=Yz5KqDY)08KP8
zQ)wov24QjGlp5HugR|3*tw;|iIB#S2%Y?hI`%;>@ej{{O?rhR^U5CO{l78r8;+E3m
zu2VmjgSuUj5=LP(z#3hs;}*<pra+GG`-GQ<r2XU8xC&mQy3q!oyvpq)?LZ}{{Noi>
zCt<2g9V#7g6s&FWFbxQx^7b0Q3RW5+eXm6)gSHdw&)f~f)1Xy;bfvWu`~tVcN&AjG
zZtAf&KfHsTGjEPJI%<-jSAIjDT8)0gLsGhNCu52duwt>==OlMWUJ>`^_xA#zI2I70
zlq)1dXA|b>5K{_uE$O3ELs6O@xmIP_`@xYC;hv|i_ck0Ssr{S|jo3`<uhJ1(X+dwl
zizv1>jnDEcktPIOSnBg7)VTMg8uz4UG<xv<M3RdFB60ly5o?1qhwFb#r8}%{jQ~K0
zPnK-c-v})|1_tEWqmOxbR{x6ff=~)}JQqlCNdlZIgJ+j}Xc2JDoL)1eTO)W{&OyAQ
z+=hYfgVt^V^^)bvtRZj7%<Gh^|GAqBDhId@XE>2*)gVQtMrB=}(D&=i?hE7Me6-%c
zvTf_lcn41h$wiHg(kJANH6VvwPv2XrYqC_}2Dm_9xm*GIbu|XUw6MgFM7Eb?&6eK^
z9a^oX6cA5#KS`YYv(llJ;;Hm7rX*JKtITMRmhp?ZpeBtW>6rSKag4e_gR7_s59OHY
zAg*v6YI?p$BsHf?V5lQ@^~|PnZ&3tJ`Y_G#wIv~ICy<&L{gRP#<58_7O^RB6V`iGl
zJa!bo&~HHMs!Nn<3Uy5)jY;Expcgm4*d@NEEYo~nZ=N)U)uXYt8%t&o<5ZUYEO}<0
z<i0D*^Czm*kfGn@Tj@vUQa0Kl7q#`T$n`6`%&+`fE-5iMzG1ZF8ZoOVv?b<n8QetY
zhNL)glUZ2ib7#@xz0B$Vr^n;S7)`isvRQR}!rB4#S3P3id->tk#3)Sw7^1+JSfgP^
z>&6UjVF2g}^C%NB-Z{HujAE?_09Zgemg@RXB=odp_`#qxfDXj!*#C;h6(ECW61pTX
zRMYg)v2Ljfy-77&X)PO}2?eZC5RN0)yBfGnBRB$#R?&&(U?>4t+!FEtW`D4D22|;E
zkf9QW_lSQ@LZkNj;IK9+Sz!QwGJ!dKeNTF7h->T#SM*Ns8UpNH4yV!`-sEtfNR)>C
zQ6VSU7LKc@x-F;6FXS&m)GF!ues!B6*8uXuIq-f!GM%SCOdR`Lj$ZnzCAcPVMJ$BP
zq4H_<@rwH+jvC=fNT8*wxUUMmqVhg@sBml`@na`29Rx1Lgt)ALEFRL=@@~crc_(<)
zk{p*w1Znm<CSU)&$k4HpQ<3ji>J$TYKG!*b51Qf5Vvi9UeVOHiCjXxdzFk1?>l-u!
z7j%(5UY@&u%Vc^Y3{qmhVGB^6nwVs8A}-i=TSj8w_eFg`!zA2mZf^aDAjUZ>-9}!E
z27@vaaT?*{<q^F$$7@O5Yo@WQ$%Akj0j)y=a9s91+)e#uz!S;zAsOw{&OD++!-?%a
zCk%{tmSD~s<A|OS@a!ueU9FV-54LF-!<ea+4*w&nkvOtEjO3D7d=^guV)>~R9=h`E
zRnaI*isDASR0{Y9gq9)v*6r^<F&O)^oUa<?E0wf`vxD7<yQ-Ggna>XRAV>uPgfy4^
zj&-ask8R8O5nW0bU%+CiZsj(no;Z1+1rxOQ@cMmScqUKuQASo61TG2y=hX0JYc!o*
z!k+V(5G?C!bgF`I`y#<T6~0m{q9H(6GVPLjB+V-?q=V+E9}ub^V2#c-c6-6PHPWTU
zb^C7H3791VEJ_SmAlAa56i5)934{k2O0_?M!dJ(&NdP|reocw5MXsNxfZ={#=-ck1
z+&t%eZu$%shb$WL@^}|wyf*;MQzjSc@C*SUS-N#&KIjul*JW}%$Sv22Ce9C7quX<<
zU|k<{<;hal*x?e$mLCbzbC)+5ev#pS?qQYWoM9&+3=Mpwb8m(eazR(sBY@p+y|>X}
z?=D|vJaAc-if$OBILG>jDC}VWi%z)qAhGtQ=YDh?B&B=p5I^;z*1oASh}Z9_CzW+c
z;Nl#Ns+QhHqD-cAHMRN!+kSe2o}-*l9+sH7KsrH3s5Y&p5-(TFus!rDB&ngQk`$ch
zS(n?F3+{P&Q>3;LnI@R(c<|@vtGq}1J^R=2(~DR+>SqrZ`d<I6d%)Zf%l1LXJFs~n
zVUE&TOr(=+@N-g(D0i%R2BL)r4T}y+`ZXD3=Aj?~x&mrkyR<voQo;n|t7wwmBX=%y
zdEHyvctqVZMM{5~e^70>pUf6`m{z$dCq|m?KWZ{Cp!JEVH@4{NXIe6a6*kntTzBL?
zdqH~sV`Yi(vAjEGQz*^xeUXjA%=jxlFT*Lsyop|fuhv0|vFRLpE14pt1x2TWy)rq1
zOBweAOS2K3=4%Zpcyn;0k-dS1Y7T|2%AC+m(tI-iAumK)AIJAX<KZ;b5Wkt0y~OvC
zSgrB+E@gdfDMp5GuYyKmi)d<kX_}-o#{<p@(E)-V`?SmtBLov44I0K)cAyU_;=^l3
z><bK$&(m|BD^jTdB{V#7Yl5zUq<~`>=&IrMhOWQYo7fH1JxhtlsQC8PI2KW1A`F$Y
zr=&$3a(+W_@KCya_C?$aqhP@3F^deGrowYJaWJSN31!&rj9B00)-B4?USx9fknb<|
zc2$GSo5Jo|?*VdfJO`*E#PH~pi|0E6tx1Hk1V9nYZq<Y$li~D{a3%$`1ba(kgd-Z?
z&|e^rHzbNeP@+~o%`NVg{;^9daCu*1?tZI4lm!`+3s{JUTCvHrWVktxz3z!SG>ej=
z<|@ft5^DKWLBbs*bVow7xO4`#vnp#OjkzEHr02P=NUU@}MW4fu6JUDqqp<ZI_dWq$
z>kPI}b)k8+qJ&=sxVM(i&PnEXSW4-{{RjKKYO#Wym5LI&#<FMT0W!#g%JUb6)8$#^
zL08neKVR28%BsEY4BoF3{~*NniRj1s&;ARxtqCFx?MCh%qI+}kA%7#WL@ypex2HNy
zrRHDBvhO!V{8N>HiV=_Ztjq+js5i4{>EtB`EyRCs?T)c4>`QytbTud2%N#He-YY`{
zB*)t$HzIF;U6e2h5FDP|NO0c0ofo5RAW6MTaplN!4pcM=lZH!v&hOCVZnbg%i`*6L
z?A(;;R_c=bt`;YoUUGWPETy+jLwP6bMPm1C!Mh_HS;LSN8qLSD`_W_0$52_h_USJq
zGBicsS_xXgra;7F=jtiHkynksNdGEM5kFf(cvdis+2ho*kAD(+5>V$}2SsvG4>AcB
z#C!_g|4Y)Ngd}J2`1BO8R>xBHvf2vQ$q%`qnKYtaaCG)fU?uui%-U>ugZ*xu#cBJ7
zvs}tUKyHY+o47YKXG%+nO(Ju|#F$2gJmni?CF;LHHcb8d4;eA{thJ=%l4eW9S;iR~
zt8j!Lu4wgdvv9~iQleN*H|p<~yga1O26*oOJcJWLzyM)E1`4{ld5S_yU;*NN0EFgS
z)4OI<_%Cg+q)Dh6AScgMt1=J&V=F|TWag8#Sg60v@DhEe$(N%A1_EigOOO4fQdKWR
zqpk8j{rV~VvLA0DWY0iC+6)FL1lT2kI-5p3b>38JD;>^kEKZ{fz-V!x84X|IK^<R%
z&{TK%;Dop+N`O(gJs*S{^#DY29ZCQWN>YG)dwvXXm!a;*Q|;k-$*E&Tyn>8GW0stA
z(CF+HksV6=Ts`4g%nxmdNbGx*jS%8#_iS3op{*lVFcIYY|Cl-ts3w9oTyF|3w9tF+
zy(8VwLzUh{rAwEpbV3Nyk*YKyRO!+?5_<25G?k{(R1j2}dh`GHKlj|^B$+ciyPNFE
z&d$E`eDCLXmSLR29BrS8=xT%0I=K=S_B(C?lGO75Qc)a!SV4M6R&-2Qu&~F~6$;>l
zwQ-Cs4njc&rk4O8f_aTl0Y|L^Liu!ErS?xa5ZB8)Fb5=yHr7}@tJ#7L?yDr)&87)^
zIOoR-OF(d^T>heN&qQlk0}e$!oH5l3v-yz>qo<-zE5_Yxkf$O<=X$&<X+fw=A+wt2
zU)uKzMz<$~TO-+fGS^+HJM~*zclnH2k#Qq9vN1jt8t{*J9vX`-W+DBub+85&KWOPN
zW<^BTHF&y3carZA9;>HKU&WS;Gfz?Q!@N*nBa*8o**lbmolVor&bB7X!PHHX$g~~a
zqGe$|v(l=j?z)#o`>aiOdxJI~nyxgE)fFxO43E7pU_}rr_Rg5kAxg>pYc`m_dQz>3
z^VL`HxSl&n5+IcWu$T(MlG~{g^%|`ZS=~M?S^(&K92cWnZRj8<dQYp!GD({Cvl(Ge
zo1BvU%S*Al)W_2$mY(c14l8m6Hb+&RsRs9^5-bte&YaZL)QOO90tt7Sppi+H#?pc{
z)-`sqevX}A2TKioyR$#p9$)t$>x9gGqh)Zgki_@KMjJA9$V)s<O5V$a*gWWFeN~#?
z^5Yk`-j2#A<CA<vU7jx0z(#x8D`!j2JXgz9t~OyVmtCO-lY%98tLZ-9R=0-<CD*E2
zRkv+s4{O%;^Is>oCA``0{%(2S+{*0`9NE86==yKe>H8g8^n-6KtM%<NODR!X^4$4&
z!<7*5YV|xZ`QxBO*8*hU<1MnAVu3}&AG~a_nR8s`5iS^B1Jc&Q;`gzNGXYkAV%skh
zztcUf=~1G7Y23!lUFVwM-5$-+Dhb^jB|5m?6iV5>*7*L`)f0H5D(fuqCnD<WgK<kV
zR4>!nO~Bf*cH*P+oT}n&I%EP4_{~9pAplT}01@N1+>n+>>LSIy9!Snyc(W4CsYQr`
z;)m>jSE_$IK$QN29R<*%aXR<_X;ugpcucWmFEbkQ7J$r$WxnV}Zf&sxz#c1vkwT3q
zV^65uu!zfQHH*9lCX;~c%`tx(x~SBk>po!mQZ^{VG@mZ<i<$^dgPhRZ)YWcp@3}aZ
z5Q}Hl`5gE_0L{vC95In-L-sMsY4u`l{z?I0g$TM1gHSNI>Cg?RK$2K5e)XCk;Dw@o
zM5s<Q-R8jHGA5kAop30gaSRWrWr1B5^T9is56N<=b~sn&n*$AA{Z#0uXQH!qDwla>
zYQ{5IK4LJjsn`@})xnPo(LWwCs99hYi@%n5$Lx^Hd&yI$7tEHn-f2^9l3L1VWt7xp
zA*5@hktJgjcqb`SnU{%8sPzJb95l+%osZ0PJ_#FJJ59?Hj9~@C?gLz&JWGX@8KvF>
zB&I)jW7kQmun^&o-Xozhu8z&;KBD_2bcx?TBrkL;>YC>;yPhdMGgCqiRaWpJeIyfU
z(#b%(SuR}EHi?Cxf4n5-#vEh7UIo}HoOno%3u%$7;#zY%Q94o#qp&{ax~ZNw|2Nfw
zGmili@w{^ChJUO*^KDo9cn&G23KgHrqTD?j;oWH`>9=3Z9`hlOWPIv8%IkIFQ&8nH
z1@|XZVzR)Uvt^+a$fK<urlIVG2{1XrwK0aO>Bn1V0t^@AJfc9F)|y0$LrilkJbP(t
zHoa>#Zx2zBNqA_pSsNPgwi1}?Uj4NZY1{g6e~-rPi~H(5YDZ6fVYpo9FO~vlKa5t*
z-4)L1S|fim`EQFX0t~)YgTpe%{7q#nLBCmyI)oBpq6_xy^coz6E53)!vM*fRj?XW<
zg%!P0lcr=b;BpLqWfO%e9S?Z^gRoNXCbw4-FV$(hdx#wQ$UNB=RI{Mfa~ymm>b9mp
z(H6$-s+!wZ{ro3wYKy%q-I$>=`daentllE{roVgE)B(bVbWy3CdchZhvLOZY`bAPT
zU8-knGruakG6sUhza~?$pq8m2&w^=F025eD70_6Wc2HyMGQO>Kazht<Kicfr&KaN0
z>1ASL<9w!0N__e$Z=%bK<Qb5W&PqzxQe$_jqxkD=c%;P5XItHnQ-RpyDC!n14FE^?
z`o-bWLzxGVd*ULu$^$M@q_TGL31<m=b!`BaJQ4_@R&@ZMq5NRT)h_!QL6;^JugT+g
zR5PjikIZyJf6@gE22R&9NEz~sJ<li-oos1HH4|8?YFnF~K4wZi8o8Uqoz}RL9hJpK
zvNLC8?pgXuZ8zgwSmziLEQ0Yl&G>O#G{wXM0$86FKQTrwWPy|9F6Jl)S5@QNGHmxP
z&{%2s;{@^Tmo9v4+sFFTx4yNl*b_ycsF5K3HB3t*7|_|R4(cQ}=j>T0(Y^q+x=Bqg
zK+p^T%(R;0AHvKMXml8Y=i49c6W?-xZm}z`(b_^DsXdONIcf0FV{vt}G6=y2K2@Bl
zZ08t~m?CMW2${MPIpTginw%cH;}6@s=GCGopGXH(bti;`?QISw_ykuzFfai(5fRkR
ztM1!tB0()wixUnkVV&b7T;%+5Lgqc~T}4DWL^Sa87u#Y^9@Gbh0SD#1^3y(9Z(lMR
z``LAX0>tykRr^kRnx~b6dO6~LxCo4_4E@A(^(5j>Z_hOrliIVMPI_zJ6mfqFIC#{g
z$nHGd_>5}@H@5BB{2$?V%tcvmXcKUiji8(^M=f1kiIy#gXJzcG@XlZHVHq>>xz)gc
zw(L)XwQ_BmGjwnAj7^HfKzc{rkV=owz+ah{=fVrR5o54{;6SSLu&(-k?#xMV#2>fM
zY5nufS90p_87^<}gv_eie1*k$Bwn5-GG4#g$H>fQR4@xRjc1?~AWh?qMI=PDTx73I
z`KUxh<rz<M6%_}loVX{YGG%PqVFL7AnwgFl<XZ`88JdEK)HZl75oZvd_#i-7BxBtO
z-Mz31;iSa0F(z;na0?GNpzyLDDR2Bv91C~!YZJVM{VZh&KqMKEfUqX8aRLCmleR;v
z6BMtqf&^&+KmvRrh?fmPop6#?_G-^{SQi<|&@OQpp3~4yP$bKrIOFAhNUiiFe^QEB
z=mlS*e4G2eT<9+Wo06jI$ZQu)0n>|iZr^ql-RG=E1-s6ClgTuL9y#M@Y0b6Nw(Nqn
z5-*oRv(d%;$izg{Bf@<=q-Yukx5j6!ewyx$0!UGv6X*m%Fg`^BoEwz;?zQ4+lIII@
zX5BO(vX#>z?!pbv(qYc;b17`Wk<I@h>TINAMFl{sY-CLEj8HBGX)CR%xT@3hS{0+J
zopi;z@UHT31;#vz#^}Ux7edKS(sUaX9+yEr!C%urCN%g7Oz^%S=LHov2p~Z=l<HT(
z;h<E;M?|#t1W5BZcYx5(jSw#(=8^#E0N92PjR3$1UNDHEO(c-X{iWb!)=P!0J{nBb
z?v-F!SwBlfM)<Fs!}=z&(ngn3x|CuO?-ELuDaGxCWTpk!MdLGvXD_h}@d^P|CRX?u
z0D=>TxRWcQ9Ro0e2dw~r9r!`>05DFT?95ME6;1(KCqX$9663>%3^6!MPQQS7UgHFO
znrc7Z6o3cR2mn<iK<PY((vSq|SHh_V_1!7v(4Sf^leu}vl1jV1m-l<F`|>h6`(e9A
zA~VDpg96}<!T?7Vk&F7C>*;H-3KCOGz1rKvq6#6L)ZZ813}u|}e$7}!%5^*bP?07~
zk-)^tfYF`+=Yj&l6_Qi1_?((H^59$mRK<d7x*b1Bo0t)umIsj7gb*cQ2(Wg9_KgHv
zF0%7sf+i$*Gz|p^qEm*zaIP5dT_OcQX*--~w=!#pps{2^A67TLxmI6-A!fnlTs+mk
z2boVLP~f@fIJz7gHm<6{buk;LwRmxa3p3;XP<63Udv@cA=xn{0Dd#DS$lhn@qU=DK
zHpNKTRsx6Y6M*yPWfu&9xAPrEQLTIBu8UVv+3S`H=Im&gtTA*<kL7^syC;9ult^cA
zMyF2x&ELv-(@gjj=WR%AjM=xt16V{r5DI!mlz_hr(1%KD#G>%?@{FlW-^v?bz)r(8
z=ASkDVF(mV=WHwpB=L4b6sSW=z?~3<x&Jhu739u<bWnwC$&>_8QcMd|B0=%jse)mT
z*bzHP&2j=FFBxd=%MEMuZSzq>&8U@D+lI~Q7HzcxrRmU8IXPCarn<#&heFPxg&t?i
zBvHOXqv57=P9_y?bJJ`zdxhSk1Vk&zcmNUi8OBeCfEXYQaeLYbfO45gFM{K3-Vymd
zUP5R>6_l6KnWjAeW*|xedqcb$u8A;$cADX#K+KjE0Pvs@0)6w8#``I$e!OT0W)$8U
zlvbX&@v`fsP|ktiZC=AggJ>bmA$c*fjOLK4Q;V^;DK=*pv{`<sj1LKe9H5d~(AX4|
z^iL3y_Ri`9O;LrI@;|XSl~_ChfFg#Sq$lEyiHV~nOmr{~#wfr(4tHPAj}MHGa)J91
zo&ZG1pga4@kWK{@J{)3|ULcbvO%J1Xd@7kmV!7C$#9KV+QnY$gArjV9wOrOsRwn7R
z+i#Y(VY6DaUr0F+uBHu0V2z-|>2ZG6c-{!aNkeX{mgN4BFvJ4|NI~EN03e1w;dp(p
zE^8X|VtkMQ=>Py)7^DFTpo{Ut%is(#A#~|}7*G|fh6!U#7(L2UuaTRV+$oNgak~$9
z^a`9%)cjDDjfDSveWqZhH}uLRc7pClr{P<e6zO99&GK#gDHR`LDgDEZ^h8Or6;?WE
zFM8vH$dA0XR1QfriDXVb=;2FY!sNyr%)C(?YMPLjn+O+?C<Uo9k&Ts<004e;>N3$F
zTXJnCCHhLJ6CkLvBMjmokdp?Ggc9brO93Ckd2ax~Yk*e}cX{*;CdNshgcd_QX?C0G
zIjifgie{ma%ny3~KH5b8+PJn2WpbmM%=d@>_aT=YD~vU33S;|)>D4^RFx}F7rZk!2
zHFrCcf#0Nvn3oE367UN?t}~Hp5G|_Yg=8)-2$2!PjC%?m9#4Gx-f>4LNw4v{V3W~`
ztdUlrI2|}xhDDnDq*^kQHxs#$(|A^4Q%H45#bXj0Uus|tU^qi6F;)6;biE{unov%;
zRRvm7Gkc;lO}IP^753BNr7RS#;i3>2FZm9#uMTklY~5*4fF=~KXEibc0JkxK5&&?c
z4WBX?e57cO#d=mCXO&3@D@WXJ>E}y3le$<!4K25w3YY1um~G9}Tsk%!-r5_;%{uVk
zv*Al>aT9}#$T@MuaOUjN)`>>#YQdBb&iK!}@W*pPP!-atg92nB2s{BH4{<GcD(FOL
z0zkG@FbDv2!3o+2APGv~bO1;o;YkmVuOlpHsfov;;{;8~6s!mw>_^7UT@+|Q3e*XB
z>oSC&tV=i5B{jf$TS0$G%idT%)j@>UOt&_A^)UUwxuV^ub*x*cmsvNNm>5W6#1JE(
zNPr;3#+>s`9B?NU!3e?u&~)S{ggJp|Ka^3^0?Qt=&oB=>Pe%S{7PAD0h`rE90UV&e
z<@j}D_W{C2FH)2Wq)`okX5YaYV@`CHs=}8L4X}jh>Zm>rv|NHT?<{GVM@j1TQpI*(
z+c7WpSm@b?Jfq<ImJL7bf_q+V%?nb8AR=i4@VuCdTgv*p8vbafLfTP)B>1mM*;eCP
zl_UVr#7I)}>m!H-E#r`m0H6<T*Nnv~L(&0=g3MZvVhJ*oC?CeAaTY(t?V+gGdRlYP
z_T3S=;)_2iu-N-t-z1f1CN(EE#5O*d?cZ-9FdVq*_1(Ys=i`U_H21Umm9&-|qki=6
z>=$<BNy3kfInR?W+G&t}MDSp&pFhc#b)3Tk6yQsE&JFRCe~98n>#X|f$H6ky;#b>=
z{6cvfkx-v-LY(*0Edbn4If~!{oqJ=pEDz^hH}`VTY@@Nft?^aik4|ZGGryR`B$^22
z_9j<|&7OZC=<t@E6B6A#s37xZ(i@+=G9%eAIm<TS-J!QNdftuaIBu6m7}1|5*U;cB
zK~v5F744;HkCtSTKyA^jza)tXZEakiWB6%=gj=X+=sNe6EIEh!ES=3gsjB{#rpIzh
z?qPj6lfDSZU*c_YL0tMXuN8<;5#OFxd6XbkVzGL&SPbOiHUvfoiWnU&<&8i5NjzSO
zLK2|drwO%R#qO(Ou>YA<<#Jbw-8+tvq%9#f!OY*o_s`IAi-!raDIucZ3${953}^LT
zfR3tG&sI_kT}Jf-T3WODWu3Nu*R~>$HZR<8yxj0I{PAR}<yBB#jKbSG!?$Xmx&mXQ
z8zuvFJsy8_$p|N+u}L(Qjz?JD&likO9{iM0S$e{$he-`5;IzO<(}7TA_~ZX{8HY*G
z(cTJ<*XO~6=t2DO-~-tf?-;RQcsv$kjk5&+18Ad(g&^fueEWGae4cgCD$|`RL4O-k
zpiMhLGp8%>emk;cgF#J5<eKj1-GgxI%kSK!R}G>6up9|<qke$20_=;sOziM}{Nx!1
zYk;vX2OJ8Y3OW#C0jP=QJ%A7WC<h`b3myz5hC@+iKXu;SBOo?0=R`p1twA`puTS+*
zfEHBV!j_wrK*1cd)bYIbI6>iNFCmk4F`@M1kVoZ6*U#qD)(S1FU$>6R=38cUlYcuF
z{4DBOCa=QFtsh8iJPBXQf-fXu-2n)r6!`lRL{g+q0Rv6fwQh7IWB@~Q70)|4&~RqX
zDs=otPMm|~38xm+#DHXE%0ikFWJ2f4DULqj|7ij*#}d?;G_sQbNeB5grA@wHzZ|<%
zi3#0(6gr{y_ps~R4;Tb17CUS-yztM|rttCEzem&hcZd3W-}ZV83a0)3Za-67g}RN9
zz+bm-An1P77C`#vk}MiPz%T5zzu{dA0ThvD-d{ksG>4P0*0u_eFCgUvb1@=;2-S}O
z0EbdBf?@&kU)Q}rtOj2tr5_wv_7O%A4wc|%GP`{M8a>QSV}dF`KedMx#P@Uf`17`T
z$e%wP=&|Ep$$afJ4?C>A=Z-}F^0_72a{TV8i~8-|x%udVmFJoqGwkHm=}joY_6%LV
z=r--EDVBf%aFRWHw2&YGRor0{`Mev{JHwTqbj)Mx(K{6U#Nhqnh)365j`Mrk^dk-L
z25P&N=|^vu4Ey_K-7~Hv?S1MHtXQwwkw-SUCTgWd|GTVc<sKKMz|+)|U*Llh01#Dy
z{zbd>DPF9~@^KImbfS=&t~Ghj$_8Ws5TFlJqtuiH7@q=H4iW6cPNUH%_Yg)xumdQ;
zu>c4BP@%#B4vd5jz@{?E7J#IQbpY9!GggD>jZomu8X*9J+~Rvh@`G%lkJHO4^|gBl
z7}R-uZI!4z8|J>z=zU)FO)oX8{jiN9(CUMMS^lng&*4#ykp8!<^kwnXA%D?vUpvo3
z?CZP6Td$2<KQ!yr@zxwjO?8vYpg3hLQj2vbd84qY(y&MlA4X~@|Cfm$kpQ3oKQFd$
z0F1LL8Ugd{PguZ|ju#Hl69SEZKg$;zKtH1b1$Z#)CrTToVF49NbOKs9n(J6P$u&=>
z3UJKTF`Jx05t*($1_<*ioJn@<L!?qpac}}zFVU2DgIOrZqgJoYy>EB2WN{tFFATo~
zH~&sK4ZZ)O{&;xiMH!KFho~1>H%I(9D?nDpc@BWE`Mk)?*rQd5U}Z*;KMG2Gg{)Su
zG6N`fJJ(}5zA}<Qxx6!^0lE>72!NV5j}U+kAb0^_VjR$hA5w+f%f*3AEV57lBata5
z%xziK-CR``MSnaLbpM#NrK*WKX`{NKE~>uT^TkwPrI^UBQim6wAFY*GFJP%}5EedF
zD4NgYbz&qE1QaMBtAJc;88IM9hHmZ+t#p;rutO1;nppyn^gU@5*d(8s`{7Q%16OcK
zH^4wJ>=ed+Uh+AV`?G((`>=Y54M3`~m0LkLA50r7SQV}_<0E_Lq0;e_BkRU`L)G<I
z$z$?=wa0f^T3U~jr4M`R$v7%LG~DN3Tn{T!`nlI(H&^@htJ>kjZcF{^n!eKeD{tOh
zZ)mKKhRw2IBBl+5t_o1@Nz~7<&kP=a!ZATvcMG2-@;L#7xOXfBjB;_!HdF)n)S<?h
z=6)<O2r$8`k&#zKTQ}URnk0e)t-|=;n-qZuUdq^#hzDp>K~=H=F(HzeN;qWB@9*8p
z@lE;`)*H66<B(CdcGV7)bEyzIJk^Y=-6q#LB_liWXhNJzHn*Dw3Gx^v{-$ki6*kpk
zAkwhk%EDvH<kb2=NysB&)O|D4DCxw|tGB&;s4;}6*-s<mv(ihNhHr}*MMg)cPN13$
zpy%Ac-<ukWaD7ltx*YQ;S4d&b9ioh}5m6r~l)=mcBrfv+i?e$e3JT^Y#zD<uE)e)$
z7&f3GCOSL~0(8A^FJno7eHTYE^)~?IB9fZAmmZHVu|qCJz_@#P$y642zAT%X8&wzA
zfgj3ns+(c?@QEfT)t0GlqDLA*aE%c~>aZ_qwXNUzhxfH!?1ktBPJd+ynqH<E7`Wqs
zm+5zLs{;9<`b~{r_;jNRY4XpCTH&87t|;M436Me$OLg*rRz(1KL49W8Mef+?O$udj
zp#_r`rCY#O*PN_NPww+j=?kxg2;-5Bm#G!@yu)58EY6NIiBghyT$>Y$u)Jp8kzCR}
z2;wL^FGcxLr020Rv1($10T3$>oN<9^xKNM8je<-)on2&TKD7J?y{)@cm27S5Rl1pc
zpTKGQeM<6RiG)}7wSA$lO@$Rq9@kwtasF{|%$4rI!XZYC&!`<Q@MtNm&M0=S40NJG
zJdf9kaAE-HM9l(ptXeBb3fkL8Cej;=j5zcGU|D1W9Dv0*)!4gi>heee5+fJm0Jz*j
z7ETRbMuOeXR-;$WFdAN7ErwJ|eVb#w7Ytd`Qv7oA${U6GsZ;jd3#y-ndCI6CkT0aV
zmY1dr>Dwf%4lw+<QH<vT(ll)x@MM!z9F$!hx}Qhy0NB8eez8IloB@!i0JqsM>#=Px
zw<n)YMk6~EF~G`NHesD8r8P<5a}DFt;xY95L7#_(N|h7mm76yfHss9TV(>ZWT?ZxY
z+?VuuWX^Q&3;LYhXFKdsaJ$;8(HkePk55*=v#96;8)Pgek#o=ab{Dq;h_AAJ_88Am
z0+V0yb7&mSH?OOxVT$oW9Mq)>Rw!aR@DQgfDGK1lG+IA@rp&1(iv%d7hSfFqBdMOt
zLN(H7nJ1BAgp68lDH#Kv!*>OqB3A3-HkAla1H>F<dwCgzh!qYIhcG?{K4(oMYJ<qx
zWm?6b_kuscMb^@jlnN{kT;AO_)_UUoX);z~;zVngZZ%oRQryA?m*XttT}0S#W1_d$
zlX@QcOxNV|?D)Iec7-RCGjU)}6W|u%Crc&>+YCw)iknk}ia(nN17^IS4jnrVUKIjh
z5DwR%sfAz@$PqEG;~?X~%{W>}`YAD&&4myF%W~K=(;WaXdjUW;BRb%M1c)twafpc`
ze(PcsZ}zMy!_^ONv><Uyc!p<z@VE9ZyY?aYKY}5vI@jMjC_w@)fH(@?2nEK~!`<tZ
z@6Q<(g)n2j73<x;Z`{p<2xA{!AqsRfIXJ4Hjat%sFH$znc%bH0G;*-{N9@&mCyy7C
z+tuNP;4J@XMIxULMjlpxqG|n3$^wh1K8%g(@1w?X@$^9+j9&?rmSBru0nJJ$TkKkj
z+$eS%0CTD*M$oH6ZPLl{{5Ytcu+of38eMq-U>$#F;YbclVZ>HRB|%eAznn5|pLhr^
z3Wm=NBJxwVVbeQ$=KaE^v5ruPbx}=kZRO<spJP_<`omAi-L&*~&pyj?($~bAcs$Zf
z4-6?$5$yG200+w2te4PpGb!9B{ShKgu<TEfTzSJqA(53BBbKKbF9ZV_vWHV`R`3%i
z0FWTxCS6)uzUNc57!BRh-_CG7ygQ(nj+6<#G|}co4f~UM+Hr!zxAC)K0@a{;t)<cN
zXbnc>q!tTT9UGAishb)MS0lvvfkG4^%dG}ST+!h0@+Fp8ONC-<5dcDn5f%UeuZDmh
zSL_Oh`V|+1ES6sbsqRg$69!iY!?*B3(jfsc6!5LtZGdr->sM6lYE-PO`1Y^*ogX9V
zGGwnBODgNa33Ge-$0J`ACXB6-3nY}(dA^0OfsJlM@BvTp$>epB;NzfCft8TD^iYAu
zgk#T<S2dFJqtPDI&jz<dKTTF`UvyUU^BnRb2Ig6JlBi2~X+M=R$K>(>tK@z`MBfk`
zG3Q^!F64*OW1YmQKor8&?7p0{<eYB+Ac7dGPgi2afY&k{t#}=hh_O!rpqq3ni)i4N
z<tV3oLdfg*b&g^F2h+ur<dUJHf#9;o;+450pT1L>a7r0jvpR!i4KZRP7ZOrcGBao?
zs`bO!W8)L}WB14U<$DdD)M?enYx@_n;@Ex6xfleC-8D1*yun5A8Q%pR8PvG&y8}D$
z7YKT^`Z@oOCjtjELGp5Ni{tsXx>HfnT?4L|J@!F0=7CDf0amKd#$UiN@~t%@PA5WP
zcMvZb7dI~l=xPuHLkvu^j_y!%G4ncBF_)H-qIzK-YEYCR0FtIYabb&DXPrP%;^qM$
zifH-+hx$cbv&OkjQZ#QdrL&!ww1OUTP?YMbNbASFSerCY!s58k`})jjzNm4t&&9l*
z*d*X(d5_p`tpp5S>_^F|z4~Q|EKzF*h&8jX8Wa^jV8habTrlWe1&OEW8XEG|&Is9E
zUk3Fcrcnio@8^UL?u1|ya(_d{fg~m&dg|8}WLwjuSb&tXhw<yYs1rpwH*d?18yna1
zS587;Xc4?6zQb*(iH8&)S*Yx|Q4MfY0ss`D@8<Xm?QG2x^M@xXjk9c1qj8sdcn0_u
z&pJta;jwzLcYgnMqAJbS5m_sT6EPUztW4I=kCCNON8%04DRo$hQAtEN0A2!Z9>6jU
z)bY|(W^Ea2mrklm%X5LMm=Dz`5#Olr_r+L2H%De_Qu__pY&>B+=MX`JeGI@~D#e^|
zXmS6Bs3d#B<)KSrUU}eoMHaUiH6dWxPZ2y?bHDMgrx_P{BehZ^vsadU$o7RPpD0pu
zoZn%Pj4wdKjgZ1&ABUg?Io+bI{Xl@V7TVgUsNI7&$h*0u`EvjA<!b*|DhZThwzo7a
z9lJHS_^El_nYl`t!Of&vA!J+2#Ct`YS@N5&JO`wMT%d>H{uEODo1ydL;k2&gbsm&o
zrYA=8$mzH!4SKx=nwY24!X>YDP0pAXz+>o(x6mVsc_B+bRh|0V%wK+GYI?tb7|Ob0
zxE=qR=x6eHQzIsHvhn<727+aKP?)n3_6kaZ#S-bb1G@X{j)xk2qKJV*_FY|JuKRK7
zg-rO98WB){z^kT>bf59uQZFk(3JhBb;{-^s#fX>-j)6@%BjNR63-1$E2o=0XAVZq4
zR#&|4KAlY$!NCLB5UJBCM{akIp_W^*5ubN<jHDUke^BThrKD88)Y0408>9gSJ!B5A
zl+U2g&eS=D=~s{;vVuE``=7Wp>mjoZ65O7m0n1aGR<f?LW)<z#U^TPd#mmHLqt8gh
zKr4&-A^_5-Z9*|Q7O4Bro^t|34k0awS+dMr7RTln+xm0_E-+j(3A~h;!j54efQ1vU
z+9>ckeqe{yIyd<7IAjtJHj*JGm>#v87{3u0o0PzT&ElT(r}T=}%#r!e%NU~-XUa>O
zk4xS$hXjHoWIhI%zms_QVSkC%cz!=E@uUA-td2{clsG12(A`5YD5}2CdO<ky`L3x>
zzBn~PRc0SKQ;EljWgRW()T15_823BN7IG5uIG8-g&yiuj!?y4=n6w>NIrHZT+7XwV
z+OMIUN&rZb##o0@9n+zqW)AxP@UZZW!yDK!A<5h!fTG~uneroR<{W(FJc}!QRrH!_
z&JDt$`Ybt`;6O5NH(IMd@E4;`(vIkYltP6StF}FLi4ZNQi0Rk-U8iLHrU{^aH%M>#
z5^s9Bd6t-20N9~0H8gW#rw2JOnqh@ez^<SfyZYYNp)u4(g#s9_;zSGzKQ~MBljqO%
zm?z`rO-bSA16H6TCL)fKVvd9jJsDjTKN*;<$kO54$no@0qHb<0G{<9a!4hBk1)VYt
z9d^)`9T{~LN*p|MNQN3GB}w>DB1r9q#_PHPs0d<CCx0$820|0MQ7dzS3y$PTS!^lE
zRuTXdl6<=>gkyIh=;j!_Wi2q1czln1ICK5Q7vxucDQ5z3az)0;iP$Nwk<Ro%o4Sg(
z+mj3e^S8=b21QbSS0N%jls|1I&<pH53})c1OpVE^w<JVE$PhH_Y)6=5cahdnhrt1$
zcXVUS=#@mNLB1Xmp#li-V-WRtdBBCxKo*8=csfwXG^|7mLeSv=wt-E{VZ2L*4m8AJ
zM&x)AiunR@=O`7ISOF)PcI(@^=DF;u1Pf%<93ReTW+`$?Ut~DOQaOf^DSqnF8Cr^>
z-YUM7a<z3+PWni_;;Fc+?@@lPAWTkPI-IOfv-<p=Yr8FSx9-`<BJ!7y>t5>vXG_;G
zZ9f|{ph0gMn+C+{pm&m$xo`7AA^<dTHUBO*T7h+txPzFU)p1JuH+scH4FGhY<<dmB
z5NF4AHgp7|W4yYTWDCNA5Nc<pMaIOH@?yuxhXBU!pahAtAV$T>A><+GV39#LU}iFE
za376NpofB*p}BpAR+YiN$cv!$EaH7@nt;X4Bdv;{hlh$feNq8OO_(pHhgV)h>dfM9
z_h>i*1!tqmOn%Kq+)1k{PF!F|zAXmNf0hfW#-}KCTCV0<<9YHR^RWuszz)Atv24sB
zOLHCAK`@RpR3w`%)LD(o_fmKO#i*D~go?>WXh5N?i~|Y07tDdaFj)sywP7tnVs65%
zM?yzYf))JPWfxC0Zgqb5Oy@pEPGu*4O~pquM$e_H-4X!3JBYqMNDP$*j#$^XTke;j
z7+M_IxK?~|W&}?8#0X3PiXNo7RF|Q{#x*a1%O%<O<^ih(f!!EOFwCx{%w*ry27^v&
zW#^3!IV!=*<ce;ZT^V7_a+;4*k`Rg6sD{(#9*akbnqtMGUTU@q(kpB^kHHZ(Sr#st
z;br9Pd+R2pNsKW`HUmXWiL})`IxUnA1Y1tfhr&@YZuFoSq28|i;}1;R*g(fjCKUwR
z>d?bXVW>Smt|c`M%L?Y3<QU!U%V)6;U$Yty54yHAt#eSeeg67V(uhfKL6vC1<Sd71
zKB^ZTCHaXgH(zYc8UCIz;W&}%-YpADjZvpRY<)@xrPJT^S7na%C6Y3$jrxcNPA9fg
zV(=q`fI9mLVJ_zV|3tjtcZxz-syDOTp@nj4XqP^3(ctFU&S!c&&pc2DMZ1WjSV24T
zgjl~igdu^Bn`s=HBR95am%!rTM|S>KluvPABtLXs6y9n4!lp#<UV9hYO)aiORU#NF
z=CskOch<g6xN`4A$v3j))>Q+h)?C>^h2&k+8!C<#u%RY8rTKT@?~9|1vcMf?86mHr
zQZ)|Ac2yK{A@7H3k7=MRaaWpu8=$tluZGmr>=PQ_L;!;^-`Fuc3XA+8F`Su(14oJ=
zX9X1G0M}IE$l&I$MgYFtE`v$W`JbTA5*8IT$?+y6eNM?GWe>PAY4;Czn;4}08;XOV
zuT@V!zdM5)DkE2DX8empf^PjQYk1A^m?)5)f4}Qj7jdLNMfWOlvyqPZ3mv<W=sqar
z3XxRA?F#1y{k&fx&Ar2HVy40W!;j1HnEe~2yS6Z}Mq}W>9g@d#s}ekzZKt79Cjc=o
zlUnUu{Cvyz_(D^x|4}H4;Pe;G(g$aWpCjLQ{Ai^s23U0V!e?E%YgrwfdnG6ITXrHu
zhjh%qLHrd^4hj~l-}hw$g*ACDkh;g)j>iJ?30u1jm$5#({Bu7Eo|`4CB@9XE4uM8*
z!iz6Til-Vw)X0<W35RlgX=5xsh7^X#UH^UM#!0ya`FVANm;bN`$mPvHKm+FhAVoQd
zfLH)9VPph;ge&4GJ9bxrfAIhahed(TucJ`x-$Oac2#Em*D8qW6;2Z!3_9Qws2adnS
z^p~UHWPNz=OQ2NRGx;xp{)>r{w~3<p9kvZ-B|j%O)rRght?`~6Zo=AJo2=^FlWCvN
zxWo;KF?ij}X9D-wf44j`hIQQTUw3>G!S|-i59d_(;>hyX8GrFavrVtpwM5|5knalz
zx~Infiu!}-kP)!ee{os^SO5+a{u?!jY9nUP1#Y6%I3IP?v68@^4i(+Gm9u!C7>Y&!
z4sdH~qF7Ytqg%abyYpheN%E%WjNG$p*Z+6&y9PG0*2_n~A3T`TOKACXo0;R~*xIj~
z{u3zpU#3g$>OX{u77c;;q*+v>x}T=o!hobV?b;j4!5oshBy`PMzo;YunetawDH`=t
zGHs|Pd_%mYGQEHdC_eE6FVs*eMQupT3EyGSY3d={DKRjkkFGfBacR^&Mz>Mv2wf*7
z3>sLt#7{`NsQnmhTpAG>uZ1>ozc#IXC$9dZ&Xq;^v@TFyG%LuIY39fIg6z!et7s{$
zK+HSkJMcQhnzp&@_=SPjbkFVirQsvZA(`eg2~pDx>Ueb=*gavC4Awa*A5dtqp<3TE
zq6PTfRDEAGXJxW{697H18l)`0v@=}1$8#4a*Y@Or^~t?(hueo0$aeAcx@uTSQnzuH
zRtq;FTEd4W*izJr4%WQ-D7CXsb?$wi-#hQ+aG-oL@D$Q7O7PBjL-_2-$zC+lrm>ZV
zxiiYn^Q|xll}RQa&x@3uea(p!e!wT26$zf-pjvtxeE0X@{utMy{;rwY^zc^C=G@Lq
z@|$CW13#_{bDFG%ynDOh;>S6AokKqN`SKgR)a-gc`S93XY{Db2lfHCu4@R|m&Qvo?
zgtC|xijRHz)id?(xjC(c5p-|JBg8P3T||r^hq8X^N7d~6wNF)l`WRe~pwcgBzO;T*
zF8cyTY&4wNSS!D^eBd}|KLXq#FAP;%F=!^}*Ur*1(s&do=CJ1cPPSug^6UY@(?zLv
z_dZ+m<9c%w@9dvrKNN!A^Zt~HmeV$}@OemREvp^eZ}B$yVC3wNa=*a>e%|Y@3Ekpb
z)puDxIXoBG3QsyOp3fQWuh^%0;gj<`kn;&rsc2C<TV_ZmyQHLa?R&=orTgxi@lc{K
z;$!sgWmI%U<ddi_R#5s01IO`O`Ohm$a|3zyNA*vnDg}GtT12;}gWfj(Tz_Rd6nI;A
z;f2sj)sAfox5^3fQs$TNpSi_yH3`*foIG!2-ZHZrzvaL3fY~tzh=NpFPOeOgB2h}9
zmgMpHxy+}*PL7EHlp*$qco}2X9ra{%c}{cjMZkK2m$*$6A)r3=d11AMHYUAwXdncP
zE@cTrKYQzl&wk64Q+D&%XXLNW@Q;gZr`Ms?_BGYCTKPH7!`3jzTVf0$$*lQ-X2?{T
z9!$;W4>eDOzPD|529A)w3~bFXBltFsnLRe^aSl&+GgO=qEOD9Y4O~nk>vr=OeD7?R
z=BcPAvS#?<18s7~Ba(9=qIaykf$V9oy>EJge*ECMd6`a`rYJ--d9_UI^Ws^NTdmFV
z4J~FqHBk7D5mS@Ab-`7tr0v}ShFwS{_j~a>2c@s&HPCB%e+p`Sxiw2a<H~5Q#TM+d
zj63h$DybuIep*Ay7)zph=NR#F`h!n<r``BgieBtjX_U!oJRKK?xR893yaW76ol>fz
zcvP!boqkEnFqS{o^?BwCViGbV3k_A-VE^a8_GY&R59Rn(%bV#A?|L-;^&luI89caF
z=GOZ9@~q;Yg;49Jv$kH8SyT5ET`X&je`(q`X;ob!ogKQu){GivL!MpuezItMX4MRl
z8gw>wnI|GFlXp$dm9VzLywQ2K{%FkZvwLmQ)4(NguZp$F&*i;_oa*yyD+A)=PpvNk
zA5{#D-MVk*ZFfK9yBNjoW#igHmI?cbDCa4LJevD0OCfwkRuZ;BQ|}8tHGQ`mGg6#$
z;uov8KU1qz6sSYx!_-2pK76$BsMhg0qw#G?nlZlo`~B@PaceL*u&CRd5&LPf+&jm~
z<T}f<LF*G5{+NAC$M(*bfX`(#hiRtO*9fb`=RGFbSs(1!!e4#e@AeA|`%v+T$=Cn5
zyUzEEX4hGydj+e*aoEL!$4IBK15>Bb9J6K`!Ff`UUt*I*KlaC;J7jOa-Ah5v)&+lh
zvU5!)asDH4ODQ(;`Ddc4bNTHC;TOptU4x?9t`w#U-QCS>z};zf?|NQsy4Uu;)E0J>
zkzpc#X3Bi0)+&czziPvQaY(jNUTj5gg7Ad5{86A>iJXB-euMwbuE}HZU!sML-;0hb
zvgMh+P1O8$a?5^K<2y2__Qlp_$^Oj?3S@<?$bkP_dGBX+p?`X_g?y65aw2B`NtJFm
zN`Eal%>C)>rAW|Kahf@_WOP_+&ooi_efPrW*{|}uHqtR$t^vkD7JuiGFYz`mFI$aX
zn+zxTn3h4s2JL3Qy*CPa<!(eNmA=T=mw*dj;I)yKEQ;}dpN#U?{^#MRg3}@yUONpQ
zG|PTY_5SWzNTWCJIwSn@!7e3SY(uW45`D@jci}vvUT=2TTrjeupH{lAR%N~rzu5J)
zIymjSTSM5cS#^&eoI=!BS=ZDVO8V*|vLJ);o$UKU?*@h1Zht!-*8`<Kt7e)uZ3-t%
zJ=TN1q$uwIMd|*DpDgSwj37MOlDbx;7Efh~&0I14P}Tj_<%3LLgfz>uz;Alk<2aOw
zR}YJ-h<E9Ec0t>>{+hd~Ql37uNzd(w3TMbgc*H#i=GvTGf@cWIL%|~BVSy5dV#>qU
zIsGXcL{wt46kXg}pRs+V40jTLp*oHKVuLB^9B1N`c#K*4dd$whc1AWHD}cC6rkuA_
zub<Qi9c;|kBoDIEz;0E3DWWxSY&9wa3z125vxEgS*#!8{9dW-}KUr=bGPRC0{3}Jy
zV^ylgcB?c~XGBGar_=Z9-i4g_Ce4q~k5A7|!t>vZ-ZqxI%lE{BCK`6U`DU<;PaH_9
zv78aK6mX%{*t=#om?Ib0cram;#dnk)T<yW6{GQ3mv{k;f*6Lj)L_nl;xkz#M&0>GI
zfn+^tZ8HSPmS59CD#DZXs*Bo#F;Eu~o8DgtmB~s~KGjt=Xi=D|Q|x&yW)LS9aKdZ1
zE(du{edSdaMBkFH(4z-aCi$h-ET@pwCvdf{hTeJO=-77G?T!A9_=fqQQfz_O-JYXe
zmOz*4f}mdM=su;dD$X|T%2fTzK2ofLubsSlo8j^@D;)#Qsv<73GAnXd2^xbZrNVz-
z*O04((W}IQ<V0>K*onc);8NUAUVtxzx<#sCf^AieS-I1%TKp-!dP=v0Nj5NQtn8ZQ
zG-YIZ^-CnxEivi?y>IG_I0}yGp;V$9M#A}B)EJ^1dw4U<tz|+%W*8(wTr3!hC|icn
z)LwNZF3CVt1u~mZEIcA2tXauo6<}NudQ_?Tji?YV{i2C|>7^_NA`|#kdXK!D*|CVm
zm6n!D%s^?t(7DH}j_rn-pWCr3R;^{%;?4R8(eN|M(|bDQlyEY_9%rrYyt{7>k-F;r
zJt<6D2ak9l1DrBnibJ1p?8d$-3r<I^lywXB!I*kIk6W{EX(oNkk^U*atUP%72)k=1
zr%XHOL{VjLsD_5>x(`YY%}cnXYSJpbE*KiBjkrbTuW7G%i}~r$jbUEx!r+mUoT#KE
z>BBzL2%^h!2}PeOAfA}_IkTZSx$qvj$nUm99?9cdWXxd_A?M6%dd#$mRn3G7nNr1>
ze{@fMwf34h3LmOX$=4(s8R*qewS;xnwGD`pi1e`5Oh8aPp3TW+Qe5wJn%kd;KIM~j
ze528<lcXl<{pj{t#i%H>*4sz%Qn$Q7nQeEXi`zy?<zbFf8^XfwHRv4I_4TEUmGB;^
zXxFLXw0}kKa-FjhyXXSl9^?2?Rd(`Ou>q@YAzf>ESg}R;K-0z8a7*zyn}*aiOF0)(
zCK`Ext5SI`srC33Z=?9&dWnofTNa(bk)5&g-3i+KJQ<n^W|lGU+=+<BNwU5s*tTY&
zxcJ7LMk!HV>+3$=?^+QT1)>6)VUg8CQynr78#U7PnO8K2aAe7w!w`h{O>8x4GCjAo
zFI$75x8e1)cA=g=^M;QK>}VXS1cy9jnQoe8ZSG8^5;p9hD>rHCc2xJe?)INm=&74J
z*rnmMq_m0Si)1z`?{7g*>8Q^>8=f>I%`D7#+t00ubaus&SR44ie_rQZSa8!tbN-C1
z^!5TTQ#omO#y5`b_t`cTrd^IYT9JwaYwZ@k(@yVc*;th}8J?%yY1s==uaz1wB{to0
z*4={}gHD$mFHM-eHT$SLy#|ey{CaI2#9ekYvOnjPS*uuZNvc2Aa^aSc4wNL&8$x4L
z$&qs|hm&#yda(Eer%xq+^yF%TjD5(Zc&51M%zL#7dR>Z_cR0w!h)DgqCkI}2$W{uT
zrcqkjbK!oy@}X=Jy#?ztH*pbp+jgh8w~R${uow9=Au*>7v&+B7p^Td|`HFac-eR?O
zrOj(%ifPx)7^?+aWx%RwvR#$VUdZBl=+tSnV=uG=3#pIZWOO%7s|HiQkDMxm6mC?I
zoV@5ynK29sk?uJ)LY!FJQ_iR>5M7*Pq=Q~wSrqkPdAHT13!anmNh-X5Ce}uqGxV1-
zwnTXMyHO;o^xoT1g9}~A<#6kq*^(8{J-VDf{LA(qmAT#Ebc)60KURJm2)n9}#}y(;
z2j&VnD{nq6Tl};rmznc`w!MEi0p)Ao>s#rwU5V9cII=H^xSS#$g|A=T*_zP{IG!h~
znWLGOFNJGVG~W4Yr=1+h9r8vCMqWK%*NA&DX_g?q)il`*8Ajq#WY)w-v;>0wN|DLy
z=RCYMoez6|o0#=Q6Kj^$0>PUcEPtx+=EU!J1O?Vq4z?B@lga4|m;3L`IjN++r;|iy
zhz0VKBWyQPr~3rSvmfyBG2gzH9SyiwSFsGUt&w*AyST0>%63b!WTw(^1lweiJ!rO?
zr}aTwO14c|d#a8O)@OQ5Vj7n+$YhHvc=Mq^L0Q@~H!g2TE3ZPis8R?rCic|4Pul$b
z{Fus``P-|HjhCCrM<29xjD;WCv8^%i{j*E%wR7&viQr#v0JSlHLmC-Po*v%;X(TOY
z%bBURNnX-yt=yu0`l|NR`tYqSHxfx-@*)41NF$r7)^#Dej=?tKIi+XfDKELSGVkKe
z09yc|`MHMJ&w6x~xh|^W?VQ!It_<~#CC4-=a?XNIUrNhh6O#hA7n4K7^^VwP`$V_O
z{Ym>ej`>fs9Q89hH(OJ^ADf0v2ilhDs>Niul$x$p-={plv2Co2WLc`ueJJ5mROC~F
zpE}!j(E{^LJ6fyCZ12rlJ}MX8XLaqS6YYs0N0jk#=#Qknl(A_M)1gyFd<~c3H|k$f
z9h;%kMaCzjX&6~vA?()_$}O|;Hp-=@(yY_dKh>m$-><EhaM4|?*_-l{TyC*$vumkw
z-UnpQIhF0|=Ij3LmLs=HKWy<Ld+8zDw0eTyNR@k>?|*aI`dawFlVvNCf<)%i1~a2w
z8hh3LY{@R+;0MBCxdFH4oQE_!Lq@}}4V>Q5TDxo8wozSG84U|}!K=6tOS2<Oi&NTq
zTl&9&l)bOW?ry;@yXXfw?*8J~gbE6s)^FF}>-eLaQ_wsAexk=MSWsSye;$Rxqv9X{
zfB*mh0{`a?^nc5s-~X-nPdyZXx*$N1|630b;OhzhSH`aZqQWo7TS(#CxPz$wSC8Ky
zi0*%7{8AA8|H=q_nc@GnM}k=YzxJyCEB|MkX8&Kk`G3p*`40FPhyU)07{51S0Ki@Y
zKo=g#2!D(J+`|Wn;1R$80|1D}<7i=S|7{7BKmd@P3IJN60>ICH0^slXU*~8DKn_Ik
zLe77mCI%2FKBVM-4_5cyyMMO{fd9Pc@pY!b9?o8dj~;nRm;|^!_Hcb79^~QXW@H2i
z0|Z;EDBeZ4vHvFM)^IfGbdhJHOwe*NRVcTj0D}MoKDL-%j3ar}g<28nK)@S|ay2!9
zQ>c;x<0VwN%Vy332|D~uisgeWQZENkclIp~d00ygKbsQ|%csiZPVt)i5fJ7n6sy;y
zPkgnavT8sTH|H9u(KuARJ3h0ubfOcfS@!xhm0elypS8)gP%Bp>#2^2W_s$LP)wN3l
zP;qiha14sC*LF4U9`+LO>KQ!k@c7`?R@AS17U-T*NNV>C@Q{qZp!juN+Ap%+seD83
z4^}U}3XxxId-Gxa*w06`odfs&!vUwjw9pkv&HUCitS;QA@1667fA`8b@GrkVhB3h~
zyn^uSpb#f}>-W(5ULUV}efQEV*P?y4*1uv&&c{%?nG~gh4$5JFIKH9fmx?DmTO)WJ
zxOPeuYiU9N*&~|K@8w_4EJ_q8%>&{TeX-zwedO0czwuu&<Ulfw5&B)r^zQgmDX*~6
zL1i47`}(kdAQe-UZa&3!@rg`7-))I>FsQ4h)+KFBSS0NCE#t|4VyXReRXWK85eA7W
zq)Ho;Hl>C91;^LYgcE%UB_?T2N-f$Z5#657Rp8-q8~qe1Yb&}e?Sx2G>Ps_iB-*{Z
z1~iqS{3g1VQonW8EOWOmYS=qG*tp}>c{u#dQthwQrHpR*Xoa9`bMrpr4u=L$m3>WI
zHOUT*{TM~-ItJyys%%$cS@FmjLA(U}@$T1M;*Yj(uWg0b&hxokY}(!@{MfMUv9;Wo
z%W?Kw5!Y&cZ>1NpUdr===h=hz)#n>e@cC9;!?-#MJ_&PoBnbsq{krQJ)k^cSJ=su*
zEhL+l;TKc<4r3Z$;!Z9aigxOpl}p`$$+M;LDu^TETSLOd3oA{o-n7=$F8-?g#6-i`
zCkq1?Zb_4>5Eg$XTd}DUn+$l~D!um2tzA}FJnFSDvzA4LOgDX5m8HScJC)*7VP!8o
z>|h^VY87D<_RUv4qCK5c`J4kXqEj8Emikt`xQeiD#&^C`c4v8BRYezLp7~e*%DDZ>
z=Nc8n1L6!cWP=^+zPP(B#eZkYddJamXOa5QBmO+6eR$LH4}nL<ug57C)VH~tr)R!>
zPHyvu`9&Kbmq|vNwn&cN^3CiO@GVTSgaBQACHp0}4_Q8Jq5`PlQFE+@Ur1j)lIfta
z%N&$HzT$)$F)K@87{<&$WW6x6et2DV!uj_Bn>8SdfF6d<cHf#2F+<0NuOO`lUD-T+
zyl@1w@EzC}Vj*S&ZAY0`tqs>^lRK^m;A>es)HlUeA!;P@5^nujZ(QiR@HD&u9!z#R
zd!-9%XdSCy6s*|@tkKtTe=jN{Z7^r|NYw=RUhr5gB0*FfJ=^=VvDKgI!NFLfFa<9+
zU6Ru~G94>_Io}38?>2hR4~9DQqyw0b^e>?1@kee_wF#M;r5PXnJ~zvZ(gd@-2%EQR
z<o(eYz~5=zL*qbtlAvQl&f<J*k4ceNmbmEQaDnioQqyZA*VuUWVu+lMc90XcVtg|q
z2AO0zPj6*W513rbi=_HbDCnsfFqNsul1vm!^C$gunQ#y<*DGuvoD)juvGNdYd&NXw
zAW;d@2(2G!GNj&lxK3)7BUasK_&AP=ETv7fc}JiuPhqYy=Dz4+gT2CN<Hx(UK?Pzj
z>y@&2gjn;6EoAIGZ|y`YB(lDH);K<*oMjfp;<cx$4O3$Jalp)S0Ynpwg453>)E0Q|
zJk*V1XB$fJ&9vy!LFUQ6%(!|wr_>Pl9VFey(*fQ}eCTsB;ec@Ri%m9@6~uI6*w0(N
zo*_ZHK@)FjX|}efSfEN-Z}0WJ8rIee4^;IS`Kv}GomvU?;`kDJ`En^}{AIqrBA><A
z<*_%jOJ3WcxV}r)w?B1tuhg-h=oXYM{hm;1R%Yuhrc1mqQ#V0N#XZZ>Hz{A#AP;zJ
z*VOj`N2g_yioT;wu?VBwqHn;_^!UqDjV=5%Wj-hw;G1_pr{C?M63I%EOcn~Zk_Vi3
zZs>{)JOxDDxB8srD*X39%NF4ET5fb!^5YV(5t|R4JFRyTPov(~O@(vqMnpXbkY2Y7
zw)U*|*HTTZRAaGOA9_CQFZiqG_3Hv})aS{Tj3nwUZi9q3F8aJ-q@O&rs)f8>D^j;L
zJA&R{uZ4e)<;i?9diB8e{{XH)QNJqAWZdl}5_(pV6rQ+B=~pUL1ffbvzI0pR=f4G#
zQC(?IDDKOMn@q5+nW=QDunISpQuhOu(>iaa*=v~%Nvh8Ym||xh5|?etcVK39G@K3x
z#h0pQ-{$LqrzG!hb>O(QGGJ%0BVlP+`dVMB{WQth^`C`hFP3Kx!b_d$$z`aTzQthk
ziJFmGkZOpMmpINK@o=uRbO1oyuRzs(O@?Qc%@)NFStyEHc2tEM=AxW-3M~aFm5n(a
zDOgPEc^f^JAh1VdJ7ONiraS6%{25y2LniU&bBeU~!3VbrObge_Y0?a3R4+NG+RJ;c
zT~D5fR<JBp8am4j@ah9oUFt?wE0n5|XafmSv~{;o=#lWDctEQtWjk{g-nJd3ltNW)
zOiPIze^t6Va~DI7eT5L=pwo2eF69MJs&s<A15-DmJBlNj2Dw+dH<?zoxwM3S=-gHv
zHf>_t-Ycr|ZslsnuMNXk`_)O?3x8|(O1-z1n&Dg<-3oXw2#~b=>l@g+XX7^Xw-N<6
zl$+0WcMaH`bpq|amGQz>F8NiMM{{1T4+b)k6pBm71+kt%eYuA*A{Wn@b6#yMZ+!A3
zcksqZshU|-wi%Z36PDp=^C@skEYIZo2HRHK(xskA!x&M*78{W`FYLy~Gjk$!DUC9w
zO9kg#>S(kwKP{U6E-hHXV&fuzin^NkY`EfQ7e2d-innvcc^$#@OpN4;mJwwTYO%O0
zBWskgDs?BbT(ylQNhy6uqjYH1ws!Fb={hu^rJc=c)8`e9Sy(FszKN_~3jrxEUDmL(
zF_rkgtTQH!HZaHm_nR2&X7nRg&JqcxWU9d`6RXe%ed<LzsIh#EY=;l-8F+ocseOyC
zn2UVyeD(5vhwjdW>QN&VUtsx9SIMEk2}Clg>$vS294+}NggSbcvM3jlZ60bAz=OU&
zl<Y*Mo_&P+Pg;567Kwy-;3N{P;;N-&{BvzEp~ocp#0HRW4*&(ST5Eb&sJy&|!mmZ>
zCFA(tCCZA34Bv}xiB7KHP1L##_~fnvxiD5+?h@Ut6uqtX;%nOC$kzs|z_g`~x$C|o
ztmeIF>jq_Tc5Y7yD8##{kjg>+kZvMbkV;_1@X7*F2nHB15UB=EZwoNAAx>!vaJ=Tu
zieqlbZ_d=g@W|+ihHYuv3@LcXOc20kZkKMFlOil5CW0^qZ0YWZ!tf5puKMfB7VWOM
z#H>cd>98;{0PPOkC`SlhDU@m{&}(L85ln*Yu8$B;R_<x@5-!5<4(#r+7ZDK65~95k
z@Z7})BN0jv-3WTFW41>kV*Q4&c5rSX1V&{?OnO56^`-7eh~6^DYAeIuIHdYIX5s)R
zG(>OycLiS+h>G*$%CZVZHPCL(Xw;!Y=!S)18DgqVB*bS!RA?jkw8x<a#kgDJ*eb$Y
zqyom82qyDoOueddAEcufBUr3NhN+Lb)FZ}NL!bafKz1c2Sxu~v=F%UdiY`aGvBD(U
zqu#LNSa(G(@=?A?4;nQrw9q81%I&x#Wu)-&WJ2+?52JENA^7#De6J;TsmNMHkMjV?
zNZ98dJql?GMuLDwp4tU=K@wC`0zDmK5+9B7`mGeF=&d1ZO$iP9x6+pa(we%-4+BU_
zV9-kn=%XlWVv!3=D$B0}C4Aq|odvK>1SrbhaAM<Q)d`6Hhei-xE1ceog$K(=3gysU
z#R%|AgyRy=BhEhvqBevOkcaS8<xq<XMj<YSM&<CoF=-zQvl%hzFAau43}&?yjE2U>
zynCz)X0X=h$ww06wKLALb`d-&hD3wYM8L4_?95{{F8nQwKxuIXn9kV|j=Jk?L_RSL
z#Bj?J!wof&4kAwSZ4o5!6Ka}o1i<dVo$2;$(LoTWATiGL`f)^Vlb~&G;BZr|H$x<u
z%lL*Pv~k0pE5$lBXRLBD>_`rNHG^&_Bm8x3e%I`L@$s7ZBd%B@?HA1kJgpe|LdzCM
z4kJ--Bw~(af*(LACeo5M8NyB|Y|J}^8Z2ZyaN`vY<lKUXs<Vi0umZe+AObfc#G)cj
zV1v9|gq}yIFj}pE-6A@DYJDZ|yiYVZd}1;>4{A>IBmfG$AH!fHkzVzN&qM$PAv8QT
zuT(7LxBzPv6@mLA1Ck)D-auqG9PLL1X}p1wntV#aio|5JQqr8yP(>qiKwtniO{&L*
zq+4_MKS}sB@A`d9WT@$)g(BcX%7Xe2z*VFEAg6Xb0+OQY=NwR$b8TeO2xwJ?(3{i=
z1B*u7rUfeSN`mONM>7pj?mGsKF1XM}R4{am614;o&b2U7yr?kW=#K^RyHiEEFDOY0
zHB{syeFYF`M3qTZ@9!GRtlG=2B2x8Ja`?J%l`Tt+FlP%d$gbruaOU$BG8LF46CGK#
zpk#9-W0NSyrY6B@(yWl$l(SaIECVx@yywD(Z;<9RE~0lVB4ncu!mfI1a3VkzyA9Ki
zHxqU~F*!N&ek}}A#4iUrsnt7`{w{2H6yifUF;x(j0IcxK3}(*9F0}8Hw_7n#a26w6
z_90*MzA{m8O(JmnC{jy9Wfmt^xkIxZEiEV_7bPVhMJ=pVg>M$^Nf%9|K*nGuD*}RN
zD;lOMZ0WNWLqj<*pB^ly0cvPWh&(X`)Gva*P?94W16ce>Fi=Qd+Jm?{%FIlnRUNGd
zPO4}hq4?NmSR-nrcxtOgaqQxwP5^)e0n$A}LH<!vTDqt;9|rCKqzEaZRZZ1&WmDcN
zw%|2@6oKnOKf(SyYc?xH(x9-Ua<7K`H0XSYbaRjFS9GQ#?O=IDCrrg$SdVWeh%HX%
zO5F<d04<h4a(INo;x*Q4r3}VP#g<jp`*GI~0*uNd)r8$pKPGa*vn|gmXMWQZs*UBM
z;z+Lgh`B2((1;R^1nuOG>x~EtTPw~ok!z5;Fkx5Mb5v5CT5bl6jt>SBr1=HKb<OuL
zGKL{`y!rCv`Bg$k5S~G@%wP44yOR2nk_s@g^H#G72~w2D>p4;j-&u8|fMOq6_z8gK
zV&^XZV^b{<0^a{=b2F2ZHp5EoRoLeOX6p68wAH6IQ4;PLE|jC}>NWUelYuh!9}$r|
zJ$O|)mF(<OI&NvXVAb%(Zr@&*O<#CVH#Q|YCb0K2vlOgu?{iA$xF=o}{e{!CpVN??
z!fePF&_7}{A7S)(KnHLk`X8hIB~B#FL-try%OvD50E7*TBzH`s9y=w3rUCkdAP71E
z^dCzCPYet?^W04@LVz$5Wq7|Z0yF^N1VGs2Ik?C+7Q8k<1^|l9jwAj6xgAa+_#a}%
z0RRt+3&tY@Piyu=j)GQ0#V|X73;?5`A9+ZEn6g6v3RrVuHzD#LBW01)+%TXL0J&Hn
zHaJ6yVNhZ+MuJ}Oui%XhEPl;oQxC~hsvgqi+#@!QH!5^eZDDq5rT`5<NfXX-CrxX@
zPWHCh0bmDi4OJK-IP=gZQbmJpAPJVE{s7<()C19!=TKE+WC36pIsxDv<OP%<BTgb(
zPeJw{gdip=^Npi%Bg2RQqg)@EPBl-8SRzk~nFs*HrT_!~oa1kjDCqT5@kd!)%%B21
zdJu)h*g16QJTvyLlwk67_5c+6WmN!i@x(iT3IO!nI#}Z#q%uLmG(2JQ9~lUxf>mzq
zyOjA9u=yDxbUq_kvRaX-DWzE*qC{cTyuG4!d?*L1D32*nR9ZDUwFeI?m$Kx<Lw+z9
zQ-UK>OQ(9WnR|`Xt1`bVTE5_pJ1|JHwzX!s?C9J#$p<x4uD91M@ZWsZaDgJ4k7m7m
z(1li)>f@2jpLST1kIz`nP-1fsk=O-+6`_FJEweZrlGUq;_#0ai6zC?ogd%x0Cvxul
zOu=bb$S~)Db<u<KS~@WAlcNeVxFLd=Qo>Vb6edev_3Aj)?(Z0aZX0u9m}?L_aKpHF
zhS-a@Q2cI=l2-eP6gDX914$Iya+#QWxv?WQ^S8StW__9wWI9KMvxQ?@nlc~^0M`R+
z=v{0URYdp2Wz^yz)?oAStU7=c0XlRahmVoNbB;q~$=6aoSrj8$cafk5JaPae`Vi44
zBq}ahZvt0$l&;jE3;^S99|7=DLsWQJ$jqP#r&#22Gxcl|1R+tBClAf4ZAvhWexVV3
z@_MW+q>`bOH9y;^g))e!53x#RlmJh<qgNV0Z^Suo$}fNdH2EAg*-5~10Mw%{Ah_{I
zLG(GfC_G{`It6o%M~|oFC&KM+SQ*Z7FsvW(>RA}Mi39IUq4v2%p3J3!J|*HG0rVaN
zdl^<iz_(Ybjfr*SQFc6yAaa=hVfRpA2mqoBoa1wA=rBXD6P^3kjpP4{gJEmxS4ei+
zg*i4P`OZ2Pz0)Id9{P9y8~Kfw6;N!+O%+=l^b>a)%T-k=Q`)4wm%CRrFMC~au=?y;
z_mQlXc~lb7tZ3oZbxe#%?W@q5;LNwr@Mo_m*#}zv*@x3oDCSnklHL;jyU?S1^?!eQ
z%73?(D4QEsg@;)iB4S%6W1B0pQy;S(-7)w&V_Q26m>aeyCARgpz*rJ5_AN9nlF+--
zB{jtGQ0#CX4_$7modQy{vzIpxi@I*tHduJ-J|BnPCo~(KVs77IY>i;^X@Pr<hX%C{
zOm1enONoLk={RX#Y<_D_{f3RrVY73*14x;glgu^oIoO|wf+qv9fgAh>jYJ*iIcbg@
zOJrQu&56HuBUc)n%AwgJXt?{(nE{YN_mF@Okiq+I_6MlBVUign02wKg)Em=TG&(p&
zcF$31nL;`q(lYtZBUZnDl(L^<@;s_}qD6}HubNCmz8|6XcE`L(sAX`h$^cRSAT!`V
z?bBN@mW_O8A%Y^>v6UheJ0e1=ePk~pv4WTqG4vj2{Q_b|qjN*?7&)Y#xlPe&Sg25*
zy8h&=)$iIkBNTH2#&bi96r%^1L&ANQm7xLL&_k)Ev>2qU_ohTRD4J?3T5fM1$wysF
zkyJgX8B8pChoV@i#9AYrx|Q)LG(ORbKu9T}7cyI1T>VP%ky%VAof6pN?SDl*aO$$r
zL_RQ*d|&E-;s4|y000C1{{n&lU{IJe4hsr~KcSH5lnxyJe?wxCIILPH7=cBj5$JRh
z4F8Biq>z~uT2C60#^f^zRE`@2mdRsKSp;@v3!TfOF**EFhdBUIVeuF+4qYaj&}q}y
zOkSTTi_mJ-Pyh;f3V&BBQ7JTvfjX4XEHv2UYA<A!RqN7f*Z>JfxLP2V>qYL-EWTgp
z6pRKB35CO9@fe%hO(tx;VzL<=mQM@<0YWmEe6@oV0ncGF*^D-OISBzjYkB+F2Q4np
z=rvI2MzckK*T#TP?XJr&e9>znS$!-!ZUEi!U|P-`D~r41YB`$S#`lxN>T$Z<?!+rq
zq}gUW-OkpVr4!A>{B^%A1J4rK`h8rzms{lm_S!!2u0O6_-QYdWGvNR-%zA+IzbixD
z<hCy3`tU#R!}$X+kYoh|LJ(U1zb42^5e_=6^LGleZ`?e8DTm}zt$=8QJd87q5=6@`
zs3G>5qDo>0umCQA0UUq;GH`*S2rI!Jpscc(e;<et5(9vs0rq~Lpa2R3N(agG)Htf+
zJs+SzAO?gVC&Be9fM7zl0xO5%`x`jSd%S+1sDK6muAm?Ygh?O)^nJ$2gv{e201^JM
zAM2}6AfQTPQ6>OUWcq!dG5`t&BB%V!i%iV&>V6+@1q1+|@3h{3N({IY9{@E_14y6X
z4FugF)l%~0pAI!5qsk7d1tuh5I+WQ`k@4(=selRnRUmSL3sNGpO2qp*2_%Yy$nfos
zq@Z>{17Ok=U<)QiF|th@rl<YNj33LY7J{L%;-_n&HQ)grr1nbatN;h)4SFN$VpinW
z;1vZ}DChmKqu@9?P=<hMA@_u?mE`kJSLh<Re;=x3XCP5%EX;k+*Wvj(fKq}k)F85B
zl)3<wQTvlez!K_vpA?`H1W~hcX&$~<3HuKv^d09AzSLx3spJv-Oz1TB-S)OEYHJO5
z-3g5uVIq~a-2%glK8UHV@Gi!!B@4E)j@=4=!pE=6F4CSTY<g1<rAyLuyQH!v@|SL#
zm87q4X`2L<Xj+m-sv&4L$h4}P{|KuuY18V7aNKI$x9mKQ%CT{HM(V5b3gz3e^U0@B
z!|n{r1Icy0hhy0@EMpzn_Wj3l;JW?N_XIysV<O}?(8GycJrI;51Nb%XkqbRg6!(00
zjdT9zdXCG{=s&(o?CZezgI(|YFaxK{Kho>q?|F`^Ul%|yZ_L0?e#5r%Xdj2P=Kg-)
z%lnZtP|?p3zker);l9JL(a{-ydGHAiycd3a-;squM}7htv&LweR2V()J>V6@IGoda
zi9;)8hY~0jbD9~!U<F}VoRY}HNBY4wP>iamQ&MIZ^GIt%2_B=7{G!NnPfg$jUy_J~
zcoo61h$kf<qaXr}8h8XqgmG{`mPVO?EL$Oru_*um5COna_8+NrnxmzD07=o%P$yw)
z8X}kg$LvC7j1;4-LXJZiBu6T1w11|PW<ppbMTqfLC?>ETfLUBGhl-qv9SIn}4oY2q
z#$gkYNI+YciN;h(6;~$+K)zU7Lx@Bvj2dEER-~diltq3_8e)h**U2VJYw8~nMbu4W
z@*;ywwo4D!Oow3^s80taM=xSNLYN6BF7c@QMw9G6(h7bcpbXrg89Z`IxwMRgo*XMU
z$qZwO(qd}50I#NG%E$bCF44e)95n9F2!I3ttquT}C|E(#8ec~8&2+W2aGsG_vxa01
zjxV+Ft_s@kjIC|eHe&E!9brZ%EwGw4hNjuuoZxim+@U%L1l|h~a&6Q~kTxPd<D4BD
zbA{Qq&x%A+X=>7N&FQj|w_i=1iB%u-E{!+`Fv2POuRSG=xtzL!PF;N%Ds;`uxI<n}
z%86`ql^GpUXHeH&?GtuYCEnHgz+#=P53DtovphP?bX>E*gU=}nJXeORt0Urha9!sY
z%FzUkF`t>$;qJqlJok|itvB`k_?&AE36;t+e0AjmzB9yv;5;fg5Fz><r|3&h-D^H}
zIPcjjKDM80^|JOT)t^KhSC9-HLC>bszql!CPz{{4ulV;M`#64|?RrA?>I_U6EMgA{
z=rak;CJ;&3^I+V_agW(U!L(m6?Gip9%hpIJDDsFT{A!3rfB{5!MtUL==UuXFppw%#
zX9&bZEKAm55aO8)A{i50=PrL;gPCZUIj>G>n9G+~wv-3jDo*UBHm_zkHkhO<pweWB
zHwi9Q(d>l3Q34tx5*pMWc|MiOro<xBGbo8dKcAIa?A`U|bMZ+zbjERWq=?UgN#d|a
zrUGdZ^;!VZp<7tYZD6Jpg2>dM0|2wAsjL(}0G52BU&LAs79(dh<5KlA5!mKP>yW*q
zVQ|7LU3k@`c6C@})c}On)n%yNbk*`vu?5vR%~;OP6EFt=%xO%xDfM$&4h(UaiU69T
z!8M5MOj|P1X|)$nU#FuTTf`d{q%__D)-gJTu=a#frq<tFqgE+R-jUKeUfbzCDLYi*
zwNXktb5auy(^dwZ>iR;EwDB9I?edGY9ac-~4P4eP29MJA+m~EzEG^|fUDFDlVQMt4
z)y@vA)V*9=OhHJhWeTdeg9lJ*)nV6;!mn1H!MJN4;oOy`yt}t8w@+n>f{-G*Tq@vo
zFEo?4FOc&-%LiL*5(%&e=>njGE{u1w4X!o+fV|i2?#}^Fwg;NV+Dk)l;VcG#yg2zH
zRprBXM*OlTe%D%MwZ@N&;$uZ$k#XK6fN0a}gEp=%+Jq+WtV|Vw)*p?SI3vximk4;4
zPGxh~iEp5MK4wMQ<eo+9-nu+GT;_`MuZarws=jMb(H=L>^RTZ6`cRz2CK1A@g&&Sv
zP?q{wwoGEhKQUHfx{h4)6ucGLZpp;ISJ`4ih)|&9%3zjo0ep~%{jOJD3a_mof;wqf
zOicGjy~WtnCpC!?ixTaT%J&o%vFD0o`!rQioq0@|^hvwl9*5m<|Mp9n+e_qZ>p(3V
zfI-^L8VbR|@#=lprQt%W>kaOZ^>%smc4iZ?s2?NcCMG1(n^q)wEpUPSo1|Z(rH5sa
znK7%ASdv`k$w!&Rt&OCNi6uZ1<p2p$SQ2siv~H)N*^Lr^Bhy4v?i}vKFIffmdKu{T
zQ^rw%2iH6}_dB^#2q{VyN->&3nw?6ADKd>e*|?jqvxyR?KD(Ew%QY!*uC@AzwsTiN
zgIcy}s=!IW7;{@eBV07=WGDf`smos~VyB6UuPM-xlsJ#7av`ufnl^#R8mWW}EFB@h
zy|(!woYB9*vkosnzBZ_jsB2l8nz^AmuC*A<!V_}Bsob|5D+&X(E@QGb5cV;vO1)6d
z32CRJWG%Q-`YqctsDiu{B3nJ{xsd5<C*e-9Skn^O;)-g?oUq%MiI+K(eK+fbjp66F
zJCHa#e>vlNILthcBD*)-KdadA4h%fGYZW=mm_&3#xPrAU3yL>$k*%`bsL}|f;@vqo
zml)~ft*QUT6i%K22C&<qy(5l}sH7i5ouEs6AIb+TdwP%o=0)sWrQr`8<CMh$?Jfcg
zIuZs9`Vm1nqnPlECs7uR`MZvD8;z?gJDLzBaFZ*u46c()7darl8pI<?7`#A_yBsn=
zyTCg_WRTGO3q(3I@)!s+`U{xck@<UyIj9ZNeiSI-rnz}G!dVDoQk5$-!>H4*kgggM
zQ;+BYqe^xV;~5D^i<5iaI@3p_;N+Hj@re0U6=7ADTz`N8N(nJQF_56TX#kW-lqSLg
zs3`uZQync3e!X#K2&4g!=zlMhfiRJI8w@ZZ<MpS)p1zTHGhsLp^Fa>qos%ed2>L$}
z`d>v-Nw2BKHk6Mvp_UxGLNi$}4^*t9xcd$St|Vk_ikXHWA#*|-Ly%)p32ZZ|khu;K
zH6(zBvg#*^k@u6zKoS8Vrt;%}3mv4~nmzmrKzVA2@`uZt8$oOip^Ox%E6|+u88o|7
zwW5f@8MVQ5&Kryb!82LF^IMSNqs;P@!Xyzv)F~;VhQM(Gll!BaLu0^bxvJw69Jtc8
z6KAQpOf*cEH>@QixZ%QTkHUGpPEprQz{x_SERDo7PKd$6#OfN*)Go05C{i2|VCOK2
zs1sWUofv(ukwwXf%ZO+K2cWwl8Ag+M1AsX+hru$V00cC76+3}QFI%HIcmx0`G`=Y_
zC!rRL0~(}pk3OOik|^mi>3XK}>=Hy|7CdDTOLjMU0gsy~(5W0I@%qENkPczsrr{7F
z*$$ry=E^Dm#6a4#Vgrro5jl*!4&@jv<I4?<NyHT##6azyyZbpLOu4v<t=$-{@e4&|
zBCJIhxMIjSu-+u1(~y}pylh28wGfudZc<7~xLb<3(Fv2X<-}8jPW!b|Iz=AU5=u=d
z(!~@Etqa8yLl{gjA6Yz-8I_ZvT$%`$ykZuWbc&^O?}(eHm>bW%>Vl4t))BDJOQ8V5
z+~*A%*^BHuEL2~^h_pOxAPNb1rvW4(Fp!hj?lhp45;L^GxPlEqMx)5o5+k@SRWr{^
z=a-3@O{?k2`s&AmNebHvJpxA)SO*wIRfqXw3fz>7xkJ3ADi$~blp?|a31+rpQ8KUq
z))<sPI&erMW<1+Gq>Avss_GD8q!<e4N*gu|xt=7T)Dn<(KH)dNO6jM9Jb)=Dk<mbh
z)5w)VE4}!ON}C!X^Ysz^cnF1g7HVsU)q4(2d=^45NkN<oq43mL^$06BjY(jUN;K3`
zfmoQ4rx}eCneB=h9-s{vNeHeMT-?EAXx43UPSCBD5tYAeV$&oQizC!EG<B$n%^d)}
zGWfYUtPDWq6rBm07HrD4EYO;$kczo?z;dgK7~!{5T$>Q6z?8UJq{X#Bq8xOtOm(3O
zz_TRr6B}r^iH)4wC9$a6M9M@O!GfMPgwPjx!b;+lLkt&*qK!7dkc&N3PD~`tduu?D
zxH4#~&4|Ix+iAf|a!!Q~%c<WzNQD-_ui2HyqPb(qVzAD%>(xz`02J;fs*X!zLzqe}
z02v^NK{S(~y0985)j$Ik!8_2AC=~hc3#mI8V2DkjD%X==Jb9j!f_Z=|000VU*fDuQ
z+d&;Ef4-qkNF%n)Q7XOy{~+;~6@z-jh{D7*9yiQ1rMg43I#J3MF-tsO3la3L@r5Qt
z{hq4e47<kC%t6tjK)KU`(u1i))1EDZD!eLIA^6mngnllaW*s&F)GdWs5(Ck-_pKVt
zKNbUzb26Y!AP}5X9`kyN<tjO92Di2GjwMIL#80`jK$hh+E+Z<<!X00uo>3s~IAtHL
zjlzi`nK1-Ks)&vWN=g*^<D`-+rm>y~E#MVZgH%%uH7(MPV(daetr$!?Gz&o|&;x)G
zV3YAm03ZXAwONtzFqX}OBGN7uu}_UcFryhcir~v#i4_fFKZroxKOl$_xm6SXD_HU^
zxh*COtKyPl0J(Ss-UO5q%2*UJMwpn(y0zqqbrnNftr-B(7b_(-SzaW$vekv;8WD9b
z#DrD3FdzXTJwvD$uzm{RF;)DQ)*48OtCFuEuAzB5Bt1IN5CBh&4kIBgv56&s00n?y
zRR@4~hmlHvi<gvvQZO1NmjQ&N@VbkFEs;f!7E3BhaIP{Cx&Sg?5`i}q@P9O&X`H;a
zwYi80DTyW_iqow8n8_jIAOb#Iz2@8#QATf=pn{mOl8G&j#ZbC{F_07KD48J?nK_i0
zoUfU2Jei4^rm>q?X`LD6Zq+;KjWvg?Aw)HKqm#Mzi1{?WgzeNO5m?x$NHC)ag<1;q
zg9)wwmX)N;+@V4<4oujK$ca8pZj}h->DkbXsI{&rGd>HNTe5Q%mpPx8nri9sS=g3^
z)m!$WIVEMRqeGc_h`JqV?v>SQWNFj6K6RIj=BG5o)v=wki-?n#fZp6R9kokKocgTV
zHMum{ijmBJ<o&y3H1CNeRN4a@X@J(wF0;#nsk`yZ=|-{)Ca|{!#V>>}ImO2@S<0V`
zFC!{`5oSZexW>+6uiPcdk2Ipf@jT-(zNPcPV#t0jEsqnJ)BrU$mGMCUKm-*~d=XZs
zfKUXo2!5~d)|2}o%1JvF@rhDVAW0^B5(s>fol?74iVbB(3Otxlsz^VT55y4zQ+@k4
zz6IZck2n!T?u-ybSm}<|GB<GY-ey^>oMlQ%!pIrpta<`OHU(d+ec!z%v-xhM_K~ik
z0$>8^<(-yqg1_1HR|rWw)OHB0?IyQdi_+~#xk2KPVWywr36PEo)XKJ?tqk7_is1<C
zZ#D$2yN`?;l#rZY#<r9V*>&NpNguF&i6VOk;WHJo%O2KyBN-IFT|OlpKUFP2s?|Zn
zxYmd9H|=ErhvfDm*t(PPM~=B}%jqza9QuGMG!s!a8SJK@`MD1{qv%t)FUW!Ct2zJ#
z`W7){&dw@g#Q>Qkc)YvGJ9K!6bFn*%-epE|HblZPO7m=KLb68=B(*THn|X`-8<(^7
z5byy!%#R^vwC|9uvROUWb=-<&+)z%3@{W8aNi3A2Fw6;63-LcsWr%W<x(Fu4)2kEl
zxPQEgofLzT<5G2w#Q@l8FvhFxmQ4l-K{=-Ioow)tWnG6AQ8HqkhnLRy?FENDaX%9=
zJCbBFaUD{ou|uE=IS#`&Fgq-q8~RZ(Mq<xbuv$lxf>mU>q#3OS2`!lpp*=8o)m5<K
zJ=LX)CFcmlEr}Foh?65|Un)ap!rHy8Ozx~fRMM!lD09PKOowMNUaJeR&L#00buMx{
zmiPDk{P;J2Pwu3MW&i4v)VVIBYFdtJzlrHjdUxQ$Ov_s-Zze(w#3;tQ&MTPO$ikUf
z!igp3Tb-yXpp0<!xR^MPF%b<X#4k46Y1{GE><Glj4#JB5#`F!z>|M(%1<X#ldp$u$
zB5!sM4C%&@yS#V;Q~7IU2HKZN8vr$Vllo!+5g_dDGA907@fzcbzIzgZosxziljwaF
zAO-+n29@zNhq!x@wbCO_#*oKuyzvC0V}aGi>nm~JQN%((+)a5{_iL`YL+0esoI#IS
zJ))s`Aak`!OOZrJ#9*BwLscu_rVS4JKe_R2toH=PJtmg#0B=N4xN=Qz?IA^q%5b^|
z{S`4>WI^#t1uesVE6h?ko1pydG%MvRExH0wp9>%4P(~MgjTq0IU(n>Zi(`#$j4q?3
z4W*QM9O2?{lBt+`W+8P@v0^CGVmWOSyUJ~|D3oR<m_8@siAjs5DyHj^jaDt)&7vaq
zFe4H&dk!-Q1MmO=0Y6}15C{SM0eyfU@Sp$~3JCx|BG7n95D5v3L*LK%e1ZG`he;%o
zFcc0=B!7UR@<-IJSO${-<j`mwh86&RzTq-BXaEHBo67+J02m4=0s>0pa(OhS6*Q1Z
zAr&bz{rfhV0H9Jy#Uf>6mChg3*l;dQ34efIwQ39e3t5uRCAPby4e?g2!eJBn+!ARD
zrhsh#AQR>W1ChdJ(3wl^^&Wo6-|_nx2A@2#XJvBugu1^)ngD=vXY2N2{-?jIbc#;H
zEmf#bFVa~ha!mq;!6~|!ej-l&l;SC%Y#csgl7_{kbeo)h4G)1qB{8U@_4&`H03Pp*
zP$Utkp3I~c8pN(`TDEzr*|;_SNmAI~B>bJ$Knc|W%ir%>vVNm36HfT8sHyy;ra%i6
zfvfO4KD(@MOFY&zZ38xt!V1g&yrxSEPS!Ka8a&KIks7*(D^XK75<kql%+NOrBUu)#
zkt;zQL(3d%6h}#9V$Q}%lzPs{uXG}oMh&DQ*~QXyD-lT2lEol3k{X>VN0QW05XRDM
zRV7Gg@%V(u(S)ZgM)KslE3Xpkrm?cIyrVD05j2E<LW?ZW%}c0M(0?~m6#qiY)HM+i
z&D2d3L8wgPoPyA_6h6}?Ok_buQ%PASNsW_LH&C<Glz{sm%pD%u(iI{~ejjKPPNvUD
zODcmg3~&uIA`Mamrm3Jf3<H1%$@+)2DIf{~yC?b@eIF~>4g^=g8jgCNj8F;!*iAaD
z0kKTIjC-zNAPT0m%K8}vfKxRn+^e7yH%DF1l2*h`Pl}?1PwYhZ6d;w<CW0)K&2N7o
zcU*P=Ee!SkuHa1SQm$7(3xk9#3KN8tp$kOJ%|ec~)W+hFUG&c3t-b4uB~tDAci*p`
zGlpIGo9%Vac<R@O=NJNOmtfc{?SNkk_EnT!Nltl<VEQxcsJK{bGnK5GPLrnQ8Kzm7
z<k-fmo?UqEtF6nq=5dm18k*OwP}=rYrBqs`t*PKi1wf2#3UdqqGDr{(0;9kR=6u=d
zG47JhTVmL=qNuw}`0P6BA_D<jTpEh3i<%V2fNdMphc&aBNscMt4Q$~b$_Nzk00+_d
zqF5?x!P3`Ecmmxa;2;Ks+3Agvqp68W`0}SXW(3r^;2nI8;|a0!eW9ruVu7U)k^gfa
zwL?+MG~R*s#Uemth<~+b@?(vnsq(GpfIKRMpr6Z~){1v1r)tXpK8kjSBAcSD#eSrn
z%o8U#OBNDLV@Nask7jyih@lQHWIgs2<P?$>Aypyj=&+gxW{U}9v{*`5qySO`MTlva
zD~P@lp@tSnO~a%l$H0?762j=o8BIuI?L8Q>Y(7?UDuV9R002|EkO>4SKgyuU5>`q=
z(J5dx$ISmR7BY(6s`xkW1r<OuSq8{*6nO`d8h}&Ufs?T*Bn~BcLepmoQ5(oI4&<D`
zQ(kBaVFG((o)wfP^6ps?EPdp%Je)&DLsx=kO9up~qeAFq$P;xl<&g>~v}U4{WG|Ak
zJtQA8izXcMc8G_W5hkN9jglEUg_2;Gk@UWn5;-SL2+BuDwMs~&nxIGWOob<g>nml<
zo=%cVN6ZHeEZ{Ugn9_1lr&Lf)rR0)KY2h9wiBU09G#OaQ?qMd`sC$<JiJK}skIKW=
zpGh35Q8RRhMwuxul-%f4N&ax8NL*P|l<Hlxy*MYc89o&fom2DjfFU?Cj~H3HD-ghy
zk)q~6j2nty2BsOC19AaOu>CJ_h=-4|s<VhN2LSJY?Gj2nT*tupkbnVU5aOBwKuh2r
zL(q2s01^U-dfXw#P7sy@R6)slq@Lv1<4Lt3pQ7mpprsX-kb<7P3KTh2R2_^kvchhU
zj6)(4Xs9l_q|Z!3c7sLGf;(e8!r0Sok4(Io5vkP18>{AJY<Z)#CZNdJy7ZC_sg%}=
z@~G>=3u}#`pfx1k#M#mFu(k@sSF*-#iA7dkHL(>mO1{@xdopD8`o%RnD%hKIQETVD
zp0Rb@y{!#bRVIeVT2*Y`UX`UYbwPJAa}f-j@lu7=y3<ucRK%GR3$^gR)m0mTY*d0(
zwARAo6NrFM<VvcyR^s2>;;mHf0_%GvHETy>y-a{$4FChsdOY#`JSjrbPv+5*zsLBa
zCSie?aoDaDlgLBYDHI=0u-H371ayg=f&foO+_0s~9*B7P9_<jv8yL?@020D^rYHgc
zavJfN!$?fm-QF#kjH`$+2mtO()U<=TOIJaZDi4+=H6m;P-nn!im;8knVjxHg`Cg!f
z7?mrmYEVE4qcYL_0stT$LCL|1xug09nZnwGP-HPwsSyB1XAg3pR6jB0+_sw2)~U-P
z4_l~W(xf6!8Ld8Ke%Sq6Frte_W;@j-jgehrXqG=~A#f<F`suf%MDhut7KCSRzr>kn
zTT9wRKL+fB)B>=ji19qo1tg}O^TK$^<HAFSpt2sc1uhAFL^F%f`W1zBmnTVhGWitg
z%_(A`*CjvzG^aO7q6lKGAs{;Ebb?5Nvq6Y)#6#|EqPj%XRHcNOv`lEbRw=%}a|J~s
z=8UXeM8iB$KFdUkXD8(OlbEG6vP6k;Qn~0^rZwt-klRSkMY@HwWy~0~vY%|IbWW7f
zw9S`SpCvz;WjmB5>)&%iV{h%`p{3&P;IdnK@B*DQlcdK+h>JAu6xwDsr0{dnah^@}
z>YyhP^B~khbMT`+#I{a()TE5B)asZ*ikB<pMp-iP;!am7_cScr+nMttTuMk^A3Y=*
zdseyvPbbvu=w^0*YQB!56=v8<wEpIy*Fvzz(KHs~*kJ$xqBKUh03Kt^dK1J%MwSpB
z1E7!q1CVkJ001xsAXtcimkRVWsU!ga8Soh-fUt$Av={&YXbj=100(&QyoTHffj}ex
z0(egdpa2wtushhh2m=7U0-GWFxk?Pq0I6^cguoqLQsB8RQuR3$)tEqx)zQIW=+6;Z
z)+$yPy}>5VVtLng&V=hTEn`dRl9hUd%nHJeI_*TfS^ApB4BKI6%tF4EX2FGw%K~Jj
z@cDgv64R?kzOxMG&U{7RZ>|;6*!D1k{U$&5o3(cOzf!<7R@Yw_?T@sTV*vkqlWV^%
z$GB^ZWJ{3!3vjxMl)0<|DgqGzXn?8ec>j;^vEmTj3!?!GiveQ__b<@`5DvGm{<#l|
zbz$@-!RQ`g+5i9~0$?EoB5WNY&>Ua@0>CcFW}=plel3VBMQomN2RtdP{3uLV{U+|O
z%2)x%R)>O0f+ALP1t@l?2DpOy(Z)J-XG-UXP<2OGb-)gG1&FWZ=K+FBcL#JI2Y6XZ
zPyoV?9_I$aB4{V0nxrq50Ht!6VgAMFI7p@1Rg8EvXih7E{G~5ir>Y`B%^ZqQiYQ0Q
z*a9>y0r)0@K7}TbQSKP?={EN+oGT_51O&pUD4tG&+Mr?(hvKrOCQL75sKz3WcLHcK
zLlpAOQjg7Gsb|=zg6`c<@d^S6F{0#6>|kD~{vTobTjiLDAPypd>>jNOB~Hk=XYM4T
z-peAuf@f&e!sc(H)d7L(9--oKWngy=DvT(`Cn-ok=L95TB@}5ePsoZ)BRq}JelNlP
zjzj<{!mQA*Oe;*JlY}O|AObByKx2#O0AqS=3|3r+Jir2=*N9By%ZfH=%N=lPGtIDM
zV=ncnAem4a*zR~tjo%{f9%`>@L}WCbWMdpjc9NnE7ORTggIL{<@Y4h>0P;}ZM3l@;
z=_9EgHjVt2=}QM9#Nh4*%t?hO<cN~#dQj3eCyo-C?u<kva+fZY)UCEgiLm5I{7%i$
zB?Khl@*GG_HXw4cZmsy961?WFWOFjj4+Eyx(!PVN@-@WF<<43)vgFKT=Pq*WAx>hR
zg$jEtpwlG`+XYyK?u6-LWa%>spaocP?sBnWSqWu?JOzXk0WuUo02|@J90cB+2%dKW
z)B->x?f}LNz@7jAE&~G`gh`BdB90y*;2=TF9D?2xfjAT;hHyhXmViP807L))kO?4o
z003A8?%*j0*h6PJv~1KtXQIZ?61^<yv+Gz@Lvr;5D6l08X2uxzs?6SLAob%iiY4YD
z#yY37zGkz`i}S`WgyJ_Ngo8vnLI-j^bI7zWO*~_`Br9zX2$DUs+C8R>vh(_nMO4Tx
zcqXRjE(?G_YnX{Fo+&g8{|GNYZ~Xx)PGbfXVFMbp<3T~|O+d6L#V=Z(sDg{5;!=WI
zIqcO%0)#`ZdMrpDQbJsriip!=41)Adse?2z;0Z?K)>1<>EraMKLRv4!Mm8$wLQ!Ww
zLi~;fDM%|oWQ#g%W@iXb-v}m5Zgcb{l(dVZAeA!VOX#4@@!rkitV<8PQICa9G@?j_
z9y?3uh)y`s$8I(&?t&yV=)(N^<7X1l*1a%sDg`ceVfjK++E&ZrQp13VAPBz8@Kz{-
zSt2VK3}X@kqVvXjT!qZ^!SWu=5H<{?As`OHr&fEW6v9!w$<Hi2f*No_<{njF1mGfk
z1kX+hm{_saHmsT`NgE?;VjtpYVuJ?dWf52H#XNCK4uY&SW!TmZTE?P?v_bwT;*u$9
z$TMSRaWU{@f}|g!=S@nPL$0P()0ieIl5h)aN)dPfC=?~fCM!jRoT#$DsWlx!^-R>t
zLTf^CLu$6f%rx#$E9J^^X9OsRP9`7_0f)d@!UV$=7CI}@T!Qd4;?)%~l87J#a$^=E
z2MWNT03OypZ%gl9O+h`PtUID?$Y;!X&ccz7Wg-rgN3h~3;~g!v=yc<9flV7M>xh}?
zes<sqW?~{B;!tnJN&zfB00Y2qqL^KUbcWK%Lo0xQ)AA<knF0ytR`EP|ViaU^?0+iZ
zNhotgV>DRElrt8?OJZ)Eu%yg}nnkHXGwI6HqO(Wnb4Ao;YlUqVmar%5Z$}hNB_a$u
zw!~C~=WF7hB@0@pr=@JFY9&aREC&B=R=A1-s4AjtH{@YS3{yH}MFN8?wBxoV19n79
zkuNs6M)uMy0)K93okLBvZU&k%w_f#v>N655FP3zb#AZE{)QQemH)1^6BA$`S%rm8m
zg5&)@jnb*Ecs(|Hyzw|r>9*rCt1hG>NAk)jPHt7VRFc&KP_6|g^ARcKD=?0LwfDI(
zr3)}Eu%7qGGt&=#3IlxO=OE+mC4wmI0&ErmU>4v29iiU@&eR5A00W>v1ONaS!G!Q(
zgcvTeOdy~NAd~<Af(SrVCxQNOuPh{mUM9ib9fD#M!9*8802{%+8$bYcFp3F4kP1Km
z9zo|DL3S5`V9_7|1-Nnm0Jscbb^rhv0p)rCLTNRk-tOYmSO>vl@mO1c7y-gl1czL_
z#E7Wop&^gPc#oX2kGngCH9I7_Kcc`YN8mQjrfrk0J8pQ61>hi3ya3ediR1b{moP)J
zyo^VHOv8RKheL4&5I?18h9`<l*zW?w%wGtFkQ2l#D1~q<f>{O-KjR!F<A@_T`dJj*
zw$`#XIKt?eFK{*dK<b=a**alaEku_BK7w~Gc*!k!I6y*1TyBUt`09n!cr^-`&jj>_
ztksp`kjogtAh}RE<tR9U4VEK`n0arO0#%i{Rg8I=kJ(L?nL&(Gs8)_!R_e=ArtM9H
zQH$@wYtPs;;%pwXZUKN0zS1{7$Z#ZJ4ghTWB)I>KxqRd0^f0%9MTWNd&!qQcN1tZm
z`ObJx#wkKWQ5`XbQcWE66U_T1*=+D|M#JR2QhKZAQgI@ha9{u&!O$Fl00Y200+71<
zViO3EjbW8aIRj@8r!@)7j4y|PC^r;^##?6iRSM@?02$CFE3;AuCsBfFQ-n06=Ffhu
z<^Wn^xmT3Q+3`Na{w&30MtAQK$pKzvQyGE=eVUGd2=snH0)eF@BxS#5>5`}-l3Qh|
zWXQV3Y;5<{q{bqEA9gtyLr!;4j-$n7De!!FDi{Eftv%1ITZSwrwm2WhD&u9{2Ck&d
z#!4xb4mhWjXcfA1!VDgv>@ROEc!KsLr2u_L;8Mlba%#dNC8A+Vm!NO9Nk~T+MO-mQ
znxd<JJteX!xyEzC=2^n_GFEcCdGj9iaF+*=DCL5?#N0JnVl8AE09H6yXQg8^Hk3x}
zsWnHP>K50~3;`pN1U8njk40vzn&PA>WwO1FS?6oiz*xlmF^md)icuS(^iFx*s0u?=
zTjC&8;&Q_qR^$YxxlEsKiNAUbx?)EyXGy?S{3++3m)BQ)8Bu)#Y$2i=F_~|{Vk(8h
z`X<A8sk#j40`8+qYAU<Mzk@J@qL4EX>sNdQl_E*R+tP1Y;xiJxz?tWj#yU=XKsuaL
zl!CnF8B@IDawY0DzZ?t4*g7Hu8IiPaajTLg<8;~tMA`&ln>?5*Je(xP7s;+s%A`BU
z923HaL&yAzHLfWrq`@y+`jNc3yj;I2f@C=YXdVIJ9#tSIr&1{4>^tc6!8{xy<q$~o
z2rRYGV&Xn=F!p>m!FoK_;<x67?qbi0I+r}oAVs7yC0-&0U_Q6r%rA#5!ky38$$niF
zQ*C%eVuKfz1R+9>A>!1&;`SdQ{OlkQ0SDn|3%(vAC=j6l5|eBwfKCL!J^%p144{?(
z02Tp`d2;m_00ER3;kW<+Mic>FXP{mMuGk8otO|BK9?rrRp=uVu02$$+8BkyXpfmsg
z>JLDxg+L!^WIFUMdM!c}E_~W=LHV_z_JKo?jV%=qu`<*7I3%MGt!pk&wA7#`3rz(7
zio!DUFL~Xn&}0sPJHhbmDrPBWlC*`7QUjv-rUOfZLij5TfG@<e8NU~64ry4<;FHc~
z=9**)yWs0G_k)W1FXNxSUq|e6tj3u62IERg5#{RcuR>Ki^EG6{Z;M9Mod)6Ox$oy@
zujqvn=jOH_1zo2-Vm1QiyaL{%!fCdJ)-Gq9kdw?Su8c<8$LEDL;E$_)kJw#kST<vd
zXh=BjqgcF09VyTiB$VWvx*F7N)?njw&!P4uA(k0or~m;{6#-%Z0CoppasU9F3J)Hp
zX<RW~=r3nj$KoEQdp))7D!!eNzWppX>;^k*Cs2$1Qsv!KBVai}_F5aT9(>{{3O|FT
zdH}3Yi3EKiZFkct-4bf*xXkNb!jT!wxh?J_NQ4zwvf0H$=MPk`9teT}aEhoc8nlQ=
zawtAKy#yrUj*vo;S3^JfNyl^tDmSt1MC3bAi}xdO6kzR%Q_Y>QqIvblJaVR!MCBea
z0%)N^000320000C9tr?|!eL++_zVF4e?s4n*YE%e2ZF_(KnJ_k?E(Qp->=XV2nGoN
z0RVu>T&4;FlgQt&n79N0{Di?^@^~ylYdU|wXAj^M;)gh#Pk>WdJL(w)e}Q39dCT$d
zIey1qkLSb{{Wzb&ra%A~3>7Gs06=v|`~HOdufJb)m^1Da0ea6Su&C5V2|Jy{rtp9e
z3=tRu18*@2#A*F>00IEeSgk@A3y9SrSzHhX?Sh|Kr#f1df+3oy=jza@SQ2phecm6B
zxzxk~C5wT;*q5`~vI_;;Vv!g2-$|#z+v-}W<gOI|*6QMYJQfo9g6(PZF*=T_k7Dii
zJ9_TyFC*fe^SV6Vw=<&`=Y0C{FE1Od+Vpq3p1&*D!M@M?T>?33vz&-Lj@n9^vo2Gr
z!8%ZL{Q@FwQ#8#&&0H|%An;41s=e;pr0W1D>-PMiE5t7dzlpR2`@2Y!*nLKz02~J~
zhwE;9A1Me71FDD7^1MNS01^bTF{<$Z#V^u`gse(y?*%!pW3ZF6k%}V_Cu%!Jjij>N
z#{EliB*^8w>oYi#pCAAXh&L>gdXu(ox&1h>Q_6O}%hT#@J<Svp#z~T6WC20ZG*tC8
z07`?d&mcen@_r{ka2~Xu2kGbl9%qU|34(y|0B)NI=^#dvU;}25h{7m*00{y(UpJ-F
z;x3b|CQ*)W00j|5Rsap-7$FE@=l~>%VgfvT9v}gxXdD0mNKzE?I1rZ(L$J6210d?2
zd#V6<91H*l!RoZ6=~R180f1{Ng{lAw%D^EriXMcg$MY(gEKxEN+kkGeWiU}Uo7FN)
z?t4T4%TbNzf~Rj%EV3up{Jf*eDH6pzBM8g10HW=*+?FSooaCCNh=R{RtZsT_)~VN`
z6EPtuCHn2Dc+MA_0GVq((Pnux|D5A^)5?S(**+xoXHOOpoGcH%2`#Bt_LqF;Xfr>n
z;JQvhspwk{BJJv$niqv=7-l1cX!~@~rRx|%54OrW{!O~#TIO<vfV=Pk8qxc*ai21$
z#}3!8`pd-<=_woT^&k5t@j)pP?zfC@Jcdqx-^cp`+^;BaKbku$e+9;AO;c`%HOckx
zxK6Shjs&nhZg`XPsXCP|A!oVnnwn;bwg3ZQ&^Q1Bz`!SA0PF|_BA<)ge2$uiv8UvM
zz=!||f?#??*0rg`kOy!Srvcf3>`RS(cVHWkozN*_2z|Kr5CpWHCt2VC0pNfcDx`J4
z7lIB-kn#~O1lk1>LZrRS^POY~F^j6QVun)EQgB6lp^!&9#~OLjAcj4qF=u$qz#Jg}
zEARmoLJtceOXw#>9I8Tg&;ZnXAvYuNf&elGy`3orXohtKlf;}<p!o)d<@A546vk-C
zxe_NtNUxAmph-#54lyMjESQ3LXw6aLFRrwR6emK<oRL*eB-DM6^6X)lYg>nD9grsC
z-vp9k`Ev%5zL63zSRmpBGe88q5;WYzogvR3gZ&l3L{AjpgFG^%9rdS>10hW^P$Ee%
zf0gpOM^}qmA_&x-N+XO|2kBG+;5-TufCySrA-e#jq?Q<$4oFv;D}f})&PaIra*YHE
zh)2MI9{7TP;lb;E$UvizM*NVUI0pdasC<u;Fqc3KWG2pNx1S{tfl-n!J%zl?%p_u!
zQ!({Lhpi4C^PKKZWaLWd!g9|fMsbuu`$dzG_a52LI;Y(JDk#o+q|~TbTI61y)7F6;
zqsKicT;81qteX;KVsjAG$cgjOG|<R~b&!H%q2$H8sWW7-fH72>g+K%Vf;5qwI&N>p
zi2zY^lA_Z+8>0taeN%N8O%z!SKGSxe##R2`i8+x?#8n@FVsJhO(wqRXiJuV>_}y9j
zK}u&`pVT!Hp;SFwtQCa&v+`zHOT=`3zz%2;fCfv4u=6T{Oh^ha061&0uPT9@GlgIP
z3jt)SB*j7Bmw*xhz<>Z4!whVI0ZJ)FfB*qNCGUi=nzF(WL<Xv?ev%Q~I>@+y55gqL
zfO1?K17K7D0F)E~D@Q0tu__Z{U|N6ytTo25#z?Q)k;G=+(OKkyp7!>!H)?+7nq|gq
z?R@q-#?*7t>nu7aprMv%cym-*{%4mpyEW#4ZrEE_Zfxe=(mTg^n`=#JP3gL~M!9w_
z(@besg^)Cx(R&-s*uB>=!QJu^e4Ewrk2iehwPyJ!A|qR3#5G9}Y!!lS<5*He9Rs!N
z>3*6;{k)fzt=wzDhSK9QcZvq~7IFx7ClO@A%^~q#oDp!Z`%i1`T0pbQ*w`_vNHNmz
zt4(va8gM*3PbN9m$@~~}VPg0vqzdW83=Upn!$dw#hUF4=eEQ8KCw_zIkbqDOK>%<J
zB|uT{J-{hp(n%TskEyM%D0+`zB%n?v9{-)<mqE)R1R|u=0~oLXJI85z8lzln002Y?
zz}p@o<H&grFaSIUdYm?BH1A?V6hRsBH~|18%FO~Gk_aFP0Y)a45~GG%X<3!ag-`+j
zA~d|sx>G3yT_U1&%svai^&X`_c@Gl5qLkqC9=b5!mGfpo_A0A3$;gYOY6UXRi8uhD
z5@`>j_FD-IJS6H=oECN9#U=FIb@g8+uZF<XqDDO`Q@TLmJPytpqw_4#Je~Dh)BpFz
zU)x}e?(QBvx?yxTNQ!iWged8I^yqGo?k;H^DJ>vUf&zk4N~#E`-+g}h{sX(V>w4}w
z=iKLU?)&<N&s5mgjA+80e~*AA<_FCA+o>QzH{gi(sdCkC`Lwe!NXBO|Rant^e)3l7
z0CxYj7(gQ7XALjR6I+;#_|A1izcBCyM-@W{N^pS9ERXT00k*n4%_<gCc`1prn4fD5
zEA?ERf!XZ4xnlAz)2h#%PM#_mx=xV+^QQFNq-l6^mh1wQql{xI$qYzM_0deIdG(h<
zj#nWw&rN2lq%-!9c;20KJ$*G(XjIvInow}SE&;^dnOuM0UTY}({^NtTtGue>Jowoo
z;Q8Rt?itMZyE$*ThH+Vda<}$bt-fL8y(?hAqJ&2L{bfZOC8ol=9`iOnwae;l?zw#4
z?yob;lgMC4_R!Nth=UIy60jm<M*yU*Mczp$a?qrCXu3fg<HG8mm+OzT+v_FEq1SUJ
z!7i8g9shD0C`>tasBp5=<1MFRJ~5AV7bUB>AOPSk0|c>FXl#fd1qTS(eb}%u*AT+V
z!hOyQx%AHzqM8H~aoQHRXn=%bEZS)X1+YW|)Y%zB*nAi?n_|qO8XqDbqAe9Yuo9*6
z1s`6I&#($<m&NhkP8ifA5TH`X`X~}kt6;V^klCXWD#o-lkfToD*0+^*o`~P%ksqx@
zEO#ri#n5^M8fsq?cDfp|`6W!njiC5G>h-&I`I2e$YBb}YXyf0aCSp|u4q^EP?rR~$
zEY+wQscO;pHZ_hhiZiAwQpW!NHikdU8^a^cK>P7&JNREmd1`t`qp18My_!-&s5`8_
z6u=PX^VPQ##<i_qCNlAec8iD(wvX#H5%3qPiY~)s7a3JBV&1qV(F!Uu38^NK6auwc
zIV7E(<^*t(EV@YS|0$6mH(Ad^cF7S?rJ{rlk&xx%tw-W7=_X>slK8gKYBu;}E3E}%
zC7tpq{#BwLFJXfANvM}4#%0N}*91|z$rxPC&(^HSA>pX}As4BE%44~PCj8<Y1~Yd0
z<*Jq?1pI=G;_ybUucWoubuhb{OUyx<^L64_38jAk9xV8Ow+r|tC?qaB8k9sJ*^7Rw
z8Sv%}d?Za*94&f2gbl6=1^14A)IMBo1v`k4ix4!Dj(@1;_0-WG<AGxjBts!3orUzC
zA4a@5WsDQc_q3`b%sSSKO9p&~3Ke>HGgM|_BuR*=k&Hs`rV9Ctfx+~ueHYppf&81P
z{zSqFfj>p<@mU2~Z-AFc%~>k9Vj25-u#_S_#*V?g(lm!{;_$_RM$x200MFqmp4XHl
z{w7y4kz{g1I^~MOahkkb*kD+&FyT<P048H>YKR?A-*zSKHhkz^k3!b7OopAI;@5Mr
zJ@U>|bCmN#5(~pR)pNG5G8ig&qm$-MqvlOT=3VArMJ!TbJ@NU&MCI;O$2tv?It)In
zE5&Zi-%+A4M5ubKb|ViFl|~^-iBDWR-`OPHDNe<(Wo@O+|5j0Yax!dtq+pW9Zf?m8
zkJNR%m+%Nql)(mxyQ6?dP{2ObKe=1rSM-fYbO8YDkfN7+6XcG?Dqi<v#&g&GnjOsY
z@VQbPIKe8;(EvNB5JP|gpzA((+5pau0WRA#N-aO8Kpy<t5A|7s-(f3Z<SZsc5>ts~
z`s5+?3Mc@EDz7EPTqh%d6o(~z{O*K%ue9kJ6eV*31+>@W5Jq_*p2`73fHFp8^6%Vr
zoC8g*J4E~I7#WWF*E8`IS;yMj3CgW-(RSF(L}<O&{}O#=u;V2O>X6ttNE0x|O)3w~
zIX_HXK4cUtN=z)w!EeOR_-R)E&FrYWq*Oq}TV7~exfn;CsiIbsJ=^>tUoT>rKB}1g
zW*L!L!>*!K6xE^bYQWdjvQns!JIaFIWHsAlH4j0Xr_?j#=$G<C-rEIRFk7lz77g%I
z5qq-|N4-?or|r*CV2u5%Cs$jdrkH4gt;hDbJC9I6@B{23WJeghkxm!TN`)MKcX^?f
zbW)@)-*Wzk08}&8$toF<R>JW~x8cLohC-$PMfQ9DGV}=1&=65WW`V<a2Blu}QYcK#
zhhTsq@pnoZCX(<tH>pBii!Zbo8H;xlsq3Fyc#bAMVyCKaszl%MS##s<6=;%UG>#kr
z_;_#ml0``pF(!`D$*I`)8OiK>ot8Y3p>7rPBFe+n0)99)+Z=j`olcEoZ!A%OEF?pQ
zWrt7>EJc}7m=@Ej%YmvnePF>@otWZIbSs}px3v0Oi3g{MTn9Q=)1+c<q{EiEW*a2B
zOHW0<l^Ha_`{Z0&A&HbS*k(4)<oG)|IWqfHY0Y_DNq(hVyDJfsl&)skr5(dxkeGzp
zXh4<0x(f2ha{JAAE5@7&h9hZ*d&^BSHZ<78T+)Wwr#Qmc299*==t}Uco+S7KIPH3|
zQve9V1nL98Fm}-1vDB9LMtg5jkL|ncRW?;WO6FmyNTV6Wo%hTZobYAqo~)1Zti0kA
z10@*JMvhdugpasedZqy%N5ps~5A7?XEBS-$J=Un2;P55dkGWU&Nzo&b=e0K{rXvvI
z>6f3JVux%(D&ZBS8EC*-4+XeCItOsAEaH#=m<5ai7^C_an9FnV(4h%oODQqjDdbA2
zI(PQwiM8tM^}mZWMZrnxVdFoF5Wjm3p5)c%yq+b>v}Ga7I(U{4@{Hg{JGO*;a%Vi&
zUptnvaCXqnA!G(7aGPkn!_|SbK-?{Ke<Kw<-Baxl{lT7;s!pEeCg5q2u$YRAFpJv(
zpfWD;$j|tT^ijEM(I~!Xa>i!aD+vdDAe0+#!;ri_E=y$}D&quwUm8d5EvC%~8kmbN
z;Ey)P_A=JEaU$4!X{a^;)DzIc6=DuWvqNxc{9j7fhnK-+WBj#7Fv+nfZ5(75AnQ(i
z0!14?Pc%LNm?&988dx7Tu%JeG91iU|8VD3B9NDX;EBdC{YuZ*?Ib<V7fhb+k`lX}T
zj*SkORWvTf7HSOO9zKeP1CVuvO)>&h$5~^LCgV*m3ZC2=?OPmd&hSp|gJVl?LzTIb
ze}^;IxVp5tGmX1E^~yGKew1`9QnOP((01FT=KZdPd;vSN%INpdFMt1&GbDG-HL)mW
zb>FUAIaYB09#aY>a5Tlm&s==C8gGG1->OEH%|u_Ykfw{HJh_vr=f8pO|CBW47*8VH
z)E)o;Vli+;qPi2q)e$H7<LY$hjO3@eO5uaUKYW=aMZc+53rcLNpbrY#KV-|=8=p|1
z-o814SM}Rzoywq>7_x)(6Gu4lg`EreJ(Hq6v|}g|ur+5YTK9a7An^Fr+#$C^Bawnk
zhE5WH5_2K^1HrWJ+G3;l1~Rq1t@zTo0UjqtL0=I%OceqmQ7uf__fSuLs^e;%qY}Q{
zpi+`$z#KZ^sGi)b{X*mBziw;=F>%LnP>(&w25%}ujyO>+%qi#D{UMuMZsVQkrD9Iz
z)M5XA2)8X`P48*f3bED(kEP9wd>wP+`1gH#leL2OeLvTl$<*9*t6sd*bFzT$nQv3&
z`WrB}K7!I&6ainVu=7V)9m(u}el!VqmxNspnP<Xa><8h56za(nQN9a(!VNM+Q%NHu
z4OCcndb#?Fh3T}K!DVoaivE~B`tp7K?YRdUje3R`k`^nY<}wb6MgZ6}vMB!vdi$5G
zJtyY%9mydT`a++EI)nH4l-@l3$_!8R9IU=@6A7>d9ub5LYW%EI{GJ~FSOIgz`yXY!
zsXv=)<i%;I0{164DThr0`={=;Pjzex(%ih3{G_&v>EUw3V(c07MIW^9`IaLtZ%*+g
z1yHbjyjfdgl<y*$A<h9|bUFX<B2AP(WpfmlF(#S>XigIx|H?zgaP^pq9G_x482zF+
zEo130FC`!%_4?sKsfha5ui8%>TG5L^c<`cF8Fk00T|#Xn4ovQKIUZS{A>Mjg7NV<f
zYDF6P%}I$ng@!vgy4A`12<uE#I3SC+9zmpShKt1Jb-O`5JSNX+qx_iwfD<ewhQbgh
z1_={HXQ#ABNI1MqWEaKq-`rOI@ju?h!HjV|06fGKJbXr!*DAPxPhclJUk1Q6wulqx
zjIyGRa?6j==1#Q25nF#NRo)$glHA9f;;^a3v;!_?xcH5S<Vxto--ZM-@d?K~KJ48@
z%QELt<s+$G#WZlA-9(1>%7ntf1#V^P#T>1kp((AvYlM4zi1z|QHinx9Rh*xNY*b?Z
z3wS`2wKq%1d*5q5v?Wddf}>?l6H>%~SDeDnETpE!o-|^e?S}g>EH%$GGyi=ot7d$p
zk{JHs9Q}x9M(EEZi)bcOx7dn*%~fAJ4<+LQ|79|0rFNtWeh~M)way-8P5J6|^WccC
zb8I^enC^juV4q4t=wGO|OFZ_slLc5Wih$1r3oaH_Duhz_|G19}9&UZ4xW+!$R8stw
zKI6CtFJ{x<dw$@2N=THd-fWPDy#M}hN#66npY0`YX?!KRy-Q?<B<)Xh52w8;3oFqd
zP@-IDvm#DzE&})$(*Y%kjMB*8An}Djw<W{{BCZzIJ@JjJ2#ly09H`lHqQ8GJP05@w
zY1M9D|2?}F1w2jg$L^@?;uhQscf$*Mt~wK?LF>%a<@&LGC_W5c0UN+X3I<r;{@MZk
z{R2S(0W$u$-+>h63Jgr&Ns%B0a5gD8CqVp5dxH`=CyKjciGTB%eC@tS;$NX5S5Uz@
zi3Lju0EqPG(xkHeH+r~2ms3KhS2k87Mn?5Yl(@u%m(44c#hbl!>`^DbkJBR_&^x}i
zuipaaRe@K|D>}_Z)K39>&PxQbjq;-e+qx?<BB@BB=dU@DY{Y>bj4gm`=X&zrY=Y-X
zZEOk!P=mZw0Qn64?#e*5dxhQXNReRpX{U1Fb@78HU!7F-ubUnoRD|U3^tOc){6UBU
zV-y}|W&seU%m_%o+u?mc^`}y~Y<a!p*qY=&{imB72dcFC<*$FB2(LHkxw99}C~oXt
zGJi|g9DMZH6*BWr#LH~zeo$A=aI?ab(JM~ua0>+H^=J48e%Y>0@NZ->Jb(y;NWTAr
zSy)jdsc$5rq-Z-d$GPu3&9d24yRiKYYTygAq#AiE$DjAch)aK#$Alt?x(eZWUnZ2&
z2=^}fvIrPZavKBu1krQ=E1G=@2z!yx$yHZBd#y(}a(LT>J4z-t)HY~Dsr0LgLT&WL
zyjk-p_5`n~M?&z;IR(J{#_lhYOzn~=1_k*ImPY|dauHUo-8}#VPsS{lbuj$=QFUfF
zA-_URkn%qX^|y%yweDr1$TS9LQuu6!B0mZ?MFlW@6&%ve;=%yoEMF_1PxH<*;*@}-
z^m3S>JAgeM&JIY*ARG0+4DTJF9G(aOR5(!%pdmkItSDog3qWGzMBH!DBoO2dz?ld-
z2N<9u6kHHt?J<d$;BsJ9g3JhPuSTv`fRzOs!pacJb;qaQnh!r<0f>al>%aBo2v{~?
zzH&xiZxQ>=@^g?lSG~pAo^4*O{!i1^FV;yGmi5z~Db5URphho74A9J<hy=LsqtY@6
zvRRvTxsrHv8wXNc>671QRWH4$&S;Yr(<_1ZM><R!T&c;+`xxyjHqL#3`WAQ>`?;l`
zp2SN#an4>wS~jHHJbR7&;1-lSG5yCr=geq+J;VR*E$!=8(yYq1)6~G!jIVNOLks&!
zB5czxb_<Jdg{kob<T<=Te@0fcHb&M~*?8G|7ftQ}!OU7#02>VSJs0h+NMh-D6RiG-
z&F_Vf#jN-JF|zW@^Jn?Se2F#@c@koz!fR&9Q0F7~vU0^@)3mVVaXX{dG}%JGVOv4l
zC!6!TNIuIoqxx58c`o$yhe_mWkavdD2x|ZokBHX>2voG;<l`6+N|am_F8B(=`=W7C
z6eSp&d8p!)=Yu~u6(bTyR@r&_$NqxE&o{R8HCmK_o?cp@%>qy9SHUCvjazy5RP!;I
zR06pGBQHVsIZGeBHgP1+{IEi@&Li(XClgsA6CQFY+J$5!KE5;x2%T62gm4Rn0ic^!
zi0lEuh}y%@@u68(F{Wob`^cpFT6i70j4>1R*icxuQEPNnIcep;3{iS&9`uViBi&Hs
z#S)vA<!B+fZn!3Ig<N*^gSb*6i);!DwrSYMsKu)(35w^2<B5c%Tv@8@%M?1)`|xdo
zF3VgEun}j>2zSOMufRgaQj^KK5@E&>GiN(M5MU^Tg8b7%w$p&6UYu-tKjgsxr`0-s
z^~UN!NKv6wNmW0q>3}uidF_N(;B*09yUplnSbkH$rksvP6qza;0AT1KNFbJM<UF0^
z@)D#qVvxamkXmG^@VER2KD;s0%(#*1=ZvholW(+NlZFp~=W!ye>Y|^->-U{(wl0*Y
z83z?NN*q-$3IKBq;I0%N_2X3t#sR>^3{mQDU?X@D*aeau|HL^!?+o{<={U`QK}Xtv
z1bA>T(bzs;4re-|lC<Mi1H-q<K8GU_@9=j71%Z(uvC5~VjdD(xDXXszXXBox*<1;n
zH7&EKyNl*_qeIK-$3el%FZGw{R~CLmUtVk0)_y4rM)PP9?WKuM%3=UfsQExvCIu}c
zPnU&+`W{BwUC+Jw#ID^Zb3{^0s?@g0v3-6bP4cI-r;lR0pHXC%tgY%5OI%z}rd7s(
zo`t)?yGrDI?XHIB+y@Lfb8*W=zSN-MuCLjPuJ48NA9(Qyz9;f~o0c4B<Uepsr2|+v
z$3s5~Oaj0SD`YO^PVQM<z{iKx-fNQyjZM4$r?|bow5vCtntn%~eF0%K=>IAof>3V|
zU+A7hJV60!F|#pmcxj*fG+?Yi9lWG7H<bseU+YGn?=_CCdy`MlW;#sG^;F2@1#4)B
zbJUKT8ZHi!P8U+bd_Kp9RPP2?kC)&9JM_j^yZb|j4arrZ6-k#S3c5IFXcJAIq`<%@
zE=Ll3`yW<v4s}g)@mDl28~JmlG9`6<3W@@Kvh~~ZMZctMbL{1?2dy`Wt26bIiQ}uH
z<2u=O?`NWdh`NH-NA&AB{FS<q0R5O;W%Qy=H`0pGXa}~EK3$c>bxf_{<uSy3VD!SJ
z`Gr>J;$}xmcCCe`v4z^{f7H_-b?Pz_Uvzzpc-w!c{HDP>=q->TvJz;1z5=c&NOvfA
z`$YuMW69m|-mGkE8vyvtvhOLJB|8A(pTWi%dZagJQ9zKMXx3{yPraCGP$GsDOt}-5
zZYu0fc{{>)%WeZC4=D#vk%Qfs!1NV2Cjfv4!UG~^89Xh$ZY7&J_&Zi@-rDFydh?Gz
z0a1}b9})ac;qGbqzUFjg&x#xFmq)SoxmRLJq}a+|sx0+k_QP4<F_EPEl*Etk_6z|!
zoZw4P4Ou~zZ^;?5?Lq67f$)QCzvBGfht#j^>{ESa6j%L*3G0Fh<9~fS(++tyu(kO&
zfAM5lyzeY(awzX?y=_c<S~ux#+b<#R?m3X!r_nkOW;ihT@o|TQw)c?_Bmn*Zs8GU}
zl(GG@>sveV`mexb$QI5&XwEdO;y+N@(xzA+3{N1DVBQMW`zhk7z_oatld6LBkuLPn
ze2DXks@+?)KR#*G<nnZCTc3|F!=P8EyX+xu4^E51U2_TpBxjiFn7iyMe^y_;XFaWn
z5BzSD**-$##&uRcJjr*n%qsu&5!#4X>*HIxCK=ba?Mcd{+mkkB5;L+rciCU}%F*IH
z9Io&A>GHf(1HX7PY=fnoWZvU*0S*L<yVr0u8{b!lJ0W9L4?8zsg&*^#D@H{T>gWH`
zoqkszSB|W90>G+}TN(a!Su~;ygZ1wS)FFNvZV;h9kGgjGzV7efa*`#uXurWzp8*e4
zOn%9#a>)xYfy!9`vIy?O^&3Hccv>JA8y;-rn&UtYFyH|=IQCVMOT>MA<phAs-z+4-
zHh%pl@E4PEs=<-VkS_?p)r)-x4v>&FgGk8TsIVc)@aQt)$KP37g(Y3>_C__WDqTKD
zyL_I{>-D-lO?%}f0JOaY+zubOPWpc#(ez(<+4jZsIr=Hq?CrsR-x{yrSJj9!&h>}W
zFLF-gUXmgYP7IHQ^Lhj_Is`Oh<2O5mn|oV3L(t9Myfx$`nKX4`C+<lvTNlqVDaqbs
zQ~0&MBPS9wK!uvb(Ih}c0-nD%+%XqcqZ7`0?Vk16n;^^ZxvO{dLnod=f}4s9hF9s-
z3}^U^0>&2zH<y4<EO14Jj(yJEEP9?Wm_z(!3J$a5Dm?%!bNk)EDorN5ST4z_S}a&P
z_I|X)u=~@^i)5fonZjHR#|2+=)R%-kUbxQUKu##ld|!9<=w)(Uk+^upUBkp7w5j*T
zE=DBnhlu?jXPfuxLOhRaIDKk3KD!wu5!|P&aW-&dT{_&7w*SSO`NOxckZ8o&Kz2(s
zbx(mVew;OV7XL$V|DS2KqS*6kv;cqf&k|UH)?qdrvbYG&(K0q<=N#p3Z#Rw0*`A#_
zD5tR90rI~^M0Dn5#Y&04Y5muX`C)+6cUN6;z6@{B67e;Is3t=)6mjss;TZUcM(yyp
z#^I_*uSH)LRbo>n9<fKeLd}rvzGc`Qqkx%VfI$I|A=GI1m}Le6av&F8#1|U5F6Y|h
zbn!9cJKn-wVFkZn1%2nj&<ek?65V5D(|FFZq@Y|*1cHw9;-i`XsL;q?4$cS;_Di9d
z?_k7M)4U<scRDPV3LYccBnnZCF%usPwDAA=`MV}-W4B@SLW0-zfoO(XQ?+Q?keHjl
zx4KX7Qf1dtBz5*}``2cdRu`^!X<K^++-In6EyQsSxA+AkZSwi4U!v-@RQl|GoL_a<
zHQeoOq?(Ca`!&H~M-)@$Lz;_p4Z<E;Lri45*GXE*6Y}QoX``Vt<g@}8j9Mu?rjM9Z
z;r^W;N0X!T0#*ld3@s$`wRtsPosk@NKVy+7lfV<#YW<@}%E+=?4gm2m)WBuMI(;02
z$--f<vjm4n_op2onotN%G|NLFao%_#L3M2K<nnrYdF;T5C98cr`2?=7AsX2EGv|9^
z&TV#<Y3gBokp#PRI?9Wh%D||Dp^M@NA6>@VH8b8>(e&~0z>-_I<_fVgANm(>Fw@UV
zu|K9&d}4KGMjJdvZG2fd^G7;-)Ykm(ZRYr9)Usa9Q37|WhT%Q1e=cHbo~CeNTg2G<
z@<JGo%H@*yO?FmZzWDh{tg>hZ179HOZI&a1lIBMj6DK9xYKEo=8$_&9R_st%S=<0m
zRa_`Hfu&1>`65TK=bCe`MH0UEM2PGw;h0SIKTyykTsBgYlTj&SGGO$DUi1N>=lPY+
z`4R!tq)h?<?X$%3SaZBsPWvuASp-i<MdNzQ#H<Ub+yj+PtQcNm%#87@FhDup95k!&
zon28oOOg%$QTtywu0xA&6s`$@+cm=Ft6Z1n8}jKf4S`T(vfV*I1<lGwpvz9L8~~Lw
zc_JbW+)*y*N-y$~!_2-Ta;FP&BkI<){R>ww3De_%_LtW(7c?dQOAiLk?T&x$f6tq|
zpuVEN@DINhJfZpUSa{D|jg*mOOw(^eP4a>C8}l8oGL?mSU?;myLb09?o~}uK$%-$F
zxo_Z=iZM(uex?7({t3&)B@ESvaol3AC^h)WtfhHbBoOn&yd4gIUSa8fk@JfqLy|Y;
zky*Nx1+{*~qPR$EqS>4?htBe}KDUj~CdZ{E*X5PXH2)9F!qB<2g!G)q$L{7m&JU<%
zAN-o;JR3)wHvcAd<-d&2%AZqMo4j4?o6kSsdG<PT?MNW_>CD(-dG$`_6``Qz1jp%p
z(KkW0Spium;b?HbN}VOiSPRIf$F%KxSXQF-_&0>BuzCy8&jXE^{BXN=B{Bn7G5%4q
z10qgH62L~Fx4^Gpu@gqU&u$PEli_H8#{7sQt%4>}QnQN8zi+hau((70hDLXY2kw=O
z=t={pew8_V^FY{u!|3`wEVo`#h~q&$2j6j;xLUgi#L9{0p&N}0`%h1Wr+yp;<syg_
zNzRqgkmquSrtmm7;Z?M{e{=Vk4!Jq9z1C4#$KmM2c(@xkJ(-$pbs=Kmn!Ob#Y#((T
z!}UgZAV`)byu-|o%H?R`PF=WIw^>9j@0VXcHC=~!=!!D)%YXivDMuz@gL)KQy-NO%
zas8qFx&axzUCLf*51CgQGFmmgIz_YiLLGxbO<w-7l|W&veHmtV3)>xo@<wd4v~({v
zU$r#^(|2>OS?JSR>qixFrad`aLjKLjblSi#_Avd0aD3Jq3JvsycOT`?m4<Zx_|NE7
z=#vwxL#-$Jfi4*|qrd+aB|LbxZ{hU&H$R2KP4Bnp9#@`b+cH>`#Sa~`z}CC`y_nvs
z$n-k%BZDeQ|9|V^&jT9osI@AK`2LeK|0{TAmGd>^SDIw#e6<x(4yT<chy7QMNbdn6
zR>@i(tHJUDm!j)0_X}p0YhNDrXxR5;3QE5>ya}jqXL`^sZ?|rMBV!il3c#)GMPwlH
zZdLJJing8f2}X<wX({PEub-(MfvZ1qO=MF>9g6FAvP&_sOQbp9pp`2bq8XmD>T)?V
z{Gc+}va&f*F@!vI2^N7=%Z&#+a(l^}(-lRFf!F6$qV37q=}1r@lAN{F+8WXa&(c)g
z!lP4K+zL1`gz1K66m++)CL$CIX#Qk-Nx7xme)8P#CkLGEVtHbXs~jJA`f~idEkbFt
zYCnjZ>trk}9EOUkTaymZ!$S(CHCOL)7n*!GJi_may-2j^XzE_%d(v444FzoEd8cY2
zJei7Tb2xW%!h3sZHj4V%;c;0HDwRtKas4j74q0#a2dw2&IrR3o1QCfoN&8o7-35JC
zJ6g#AT8t@n%%Rr|gCP@wn{k+Tx9CrZfS=-Z*pZ)_(?qruQ&~Qli*x<!zX?G|*<2a>
z+J_hKJwrFC%PjS3nukL~^<CW@ZiwK?9@j=Xie{wwM(-c|p#-j*GsDAb%+6|Wse`9u
zNvo!1=~E5=T!vRJvzv#h)QrC~_ue*_6PSBEm%V8jaqE#w`}H{=x5`<wmbz5Nk-aEo
z$Q}L6@@61MQD}M?`u2`2Ze3$q&V5Q>cg5%m{KMO@zA83%p$tSEr>Yn?Fz_JE-8IBh
z(&mjIrAqT0<mLWZ!t{_i{(!$`XGHvl6^FLjn3MrTb+3-5?Zj(2xlu%oOt9kKfo*}v
z;jRk+$fH<DKFE{wZ99K9-7~(Cv-(oOXx!6PuK1?YN7&4!yEy0gj(pEtQi3>BxbSt3
zyhdctQ1tG=6Ye2V6X@VU-p_1b+w`mj>koy>gSM~x3$62i$9zb(*p9r&S!x>^KI<8*
z+NO%s71+=yej8x2^YXg;^i9e)qGaY~wGs0Cj1BJ*iN<k7MP_};@m18T!R=z(zfZ8a
zOd~T)%?$rLN5SIMj^mx6DW<sDgID%UudEL+pnV^P|53IY6r`9fEWJunKFdO?O0NJ1
zn1sfNz|0jJB?<}%>-R@zTA>2}A}EB4a&E@%>x`Rz^JcyH`Io({{_9jOX``vYNf%=*
zQ`~rp^|Xa$)#E0M-Vf$X)78?y%fx!HfFYOt(d^bxTtt8MvkM-D#rwxMH*5EkJ%N)%
zZMDp}g1m=|kB_y7>NS~`A6EMD+5K+h+{+T^SQPWu$Tj?lfUEZ5uUMQcxiSD)nG$S6
z1_Hk*FeV34G@a|_y2iyLESnWY^CKzNhh-`rDhA+;M!17Ro*Cp{a6gJYQmmfG1M6g{
zuN<^DyDDk|xiXrgVzs3~Yfs(RwxdX*(JrEWpS{*4*$6k}4cp5J^Bz?V$U>A)jN1q8
zN)(4TBS!|d5B|P9?`B?R1K4ng?;rqx1lXuJ@Q7km%Z)v9xNvY2Cw2k4Cx+w%30jW+
z9$TiIw3#{_0r=v#e5`3|oTgSjUIrAIZ>q1XR??jL;tX7_(7s?#*-E3Dcl{CP5V3OO
z!>&eOB~rZ1=_*P3_cjfbpxLAMn-;ol7Ofkn%Y1QO9Ui^Lrw?XbUw(G&op1eBfC0bu
z7_@JvW1=CM;9<?sLqeTJ>tYkjNxz(f)=%BuxbB_i>8S%M>`p=C(m_2X01g<qp}o7c
zuK4fpDI;?(=wH)1D!A|4b4u^QuCvNJE0&)>FmnO50}Dt<KI+T(O^HsMUg5-{Z(q0}
zt$TKJZ_r)jwA5M<?P_R5<a1VM&$+K7J@;*Kkhe#NH(?H>`B2^;bv<XJTsLL&*T4s)
z$kKg+?AawTHjY00D@caSdFZdQU$ZDk(J0XB+faJY6%o?UPW!s-&GG2*gg<90Y{2hY
z3uof3^E_DkOzj$^c>ct%wZ~uJ2aZ<9=NLkjMf|_d8_-n9L^WM70S<uYxfn}24k7_S
zC<g|BY#ezXi{g!jf?l9y|2|ik>NJfUSwz7wGsK118~H5$j}=nh%jR7uI6qfg+m#p;
zBnj`oZf8GIg~gzJuL99!)BLZMIXnbSqsx&PKrDsxG&VXjHslllmsfuTv95HgMxwNZ
ztkzK7#Fnay)Ay^ftcMPGsvO!jrHa*rx5bWJ0@1A6@Z^VS`BwP1zHT*#!y<MXI<E0$
z8hWGDviwxs!o{0>lFj8C1TEb~6$Y1`E!(<^6@FaD{NbJ=gbYn#jr>mMui>;}Vf|Z#
zLR*b|Ne0imt7!LHs2!~Wzt=nx6u^Dn`2H{3&}Y2|M0I;~Rd{Je=pE4G{X$Kh%xX)H
zOIEzSF{()^94y=lK<W?04g?7$7R2WvE=z^q5M&Vd<na3Yi7>;=I;o)9N60P$vj&AD
zF(#>_;ajui^}fyTT}{KyIQAEljkUEuP7Ln0+f|F74tI@(olNw+3U4hOoPO&={>tzB
zLJQgW#n0=%Q+F=DOy`UDq<u=?F7JrWJD!n`UHG%oVgiF~C#F@Iy1M99{kJM$<7Ari
zl*(r-roR_5Z8K^5OS%c^&fBDx*!*YTJkt#jsq;S!c}UYm-}=pVY2#JNz5H1JWln<t
zKr!AUtoIIQJZ`ROe-W5BV<4w;IgI!OGZZgFZy3HCQ&T+p*=ifOK$9gOv%lbgJMHBm
zbQ+tfHB|z@ASQYiFftCRO41{!8UM(?hWASK#vE1Vjiu%pxmFtZPHkM8!>%LI(4}Qh
za!aEF+jekmH-69=+u2n5Yq+_Zk?I$FeK4(%jMwmbVvy<i{BJc)-3lv)qlbj4&TTTN
zk+RD*Zdd;*^+{#of(XM$g7(~P+WrqXnB;}$RM|xKR*;ojRt_C<@Ykjv$Ub`ga}kZ`
z73=&(Qb-gaI@gjo@eacD?oJ_H-~6aedgnc71R{gWAU`h8MWV1u%AM#~H7`EGUH>-1
z!<V{h>gS7(Ht#Hb0v&T0e#yNjbSd)Fv^SM@hpsN|m+~i|cBMG+1en$AeDMnLrDgs8
zJ7_qU_Wf&G@dg~?8uUclcj!ld?cNDRc!gH0<IY?H)(Wg+D$jTLVK1JRxZO}ov0oOH
zEtgv&eg9)#5Q}*!UfN-qSyL(g(x;(bUjDB-=TEy9^W<^Zg%`7wp3Me6(C-GxEmKf?
ziHk)H@)nEKg9{|y>Il^2yv#Sfb<}?xAfAr_Hq+>7lpXM|SnC!Yc}LPBv+wIh$Cz5u
zkKdrU6;*~W8^AQluVV@1Kr@sn$ZTIa;|c2p6r2!}zT9H_`FD2yCaPeH?oQgvAAlmq
z>kaIvNOl0iDEB#KK5TE#CrrxNBM1QevaIDqZRwXBzjh1F;*#m(I!8;)9-Z>BUr=H1
zy>0Djqx&4W7W!IbLfL6JuUqRI^Xs+6ELoS@+PoyQ*5ChnG&7%Fe8K~#3TLoCjkSJ+
z+v1+rc>hocxAM6dD3TWb#`CjKgG<L@^MuR>&F3fX>5>Pm)$;I(?B5b-pXgQnf*#Lw
z=20h$Wh1dX!iTML5qP^`b)t@Zj#NTPx6^~ttxhkoc#{$slwFDBVsi<Q*BqqHt=F%9
zanUBxA^oeqw>UA-ukFcXDCO}_cKIWjd5dpq%Zr~(ny&qA<9Z88r2Ic7NQ;sSel2<P
zw8UWAA@U>6Y~edsa#@{$4etFf%D8eKyPlX`3Zi|*SgL!&aq|Ovv!47`dC;RZJ!EO@
z>66yJ?~J{9<t#y|Z|K5_jJxs0$Q`G~-}(10OFzAEb<-|i=n0+OFTd9EKNi@%R)*Pd
z{DfD1(4Q`E+X4ME{G@<ejdz?^n51cYB=G$q)G3$i)<wrhRK!RiZ2R5M+M+3I65G#z
z^E-pRD$ABy-j36~TJiP)T#1N9s(u*0C#X&WfK&<)mgRO3C@-Hj@Y1s2&cYDeu#Sth
zp+J_^%MQMI1Rvt6-TKs=97&u1)^x6Ja@Y*YQWKPBrYt4cZv+pded=cjZH6US<t~Fr
z!oDO5m<n5e2m*h5(c6Z5X0KKYv-O!}<_mCW>7=`(mpU@-D~|KDaZ|9Y3a7I!R=N97
zf8gcTKe@Gp(`%Y~I>s5p?Oit&nl7=31E5IK16o2pcHzDF(fWRuBJ9i7T@}ao=Cs{v
zl5g5ggB-*KXUuD8$n;ZVg@l4103ZPcS~ezJ#;{ZHlStC)kBVCLKLG$sVUPZ{?rVBn
zJ)X=S_~8J3FmY2HGn*M`Z!lx)(>RhpJ~Xo*{izJkNZ!iOKIm%Itls>v_|3&oxeue7
zC=c$d5Z9V&tw-r^{n!q(?kHazweEFLU-&FUyUT1Uec4K5SN+p8XecPX>2J@bY1S8I
zgdg_%fP@LzQ#vc>Qyk`w|HCp)d7LKVogxL%xe&c5)1i6A>eS#8vvWbzuw5sBgG?md
zr+ap=DOw;-PHb>)n9-RoW_D%oH{Vt}=~FlAzm+>~qeX8(QJixBE}PdDL9WKIbgzNd
zkPj_~fuCgsby9n_0GwaHE)|WdygA4AT`7o!UJsQgwR!ItJXKBwlj|_h3N{Ot-s7Nx
zHcLL`mQHdtw!1&rC>O;Oo4n=80s;)@I7HZV^d%FnB%_b=?WFSM8^PnnDpmfTWl`9N
zvP#jr<}JeJ2q>KsLWqA^&g@BVpoCCjth60<i%e0U_~Xirv)5r_XxWC|EkbF=h(fY*
zJX8o?2Jk<>CX<n*C8x@DI2gr_X{L-ne~d>5;7sYqX#-G{2ec2sqYD^D1SQA>qk!jl
zJJBc!afv=cAOm~RK^4UYsM_&Fd+>m<tDg*fAnFFU+y?hHCE)}hq64G&WWiS4V4!fo
zEEh)S1ugeM0e<nPd_(|sloch`5Cp_V3M;_k1y~?20FNx_rN0v7PzwP3pgaDlD8Xn9
z4+`Lo4&g|F;Q`?#*cEzI-?~g6ATx8;cZeke)7$FT;uar5=>S}~8E%f51`@yn{PD0~
z35T8HbB>yeG{~4*y&lU|P@=Gs+zz}RjWe#jf%T?Jz|w~Z=OVU8apx{t+RpI~fa*OW
z!Q*K*JPfZYi&L#w7DE6Oz)%1Qa7cs#SV4yj6YFdm82YFJnnDCvC9_U;CxsM=rLsas
z=^UUoj|L7KRjt@ngDQEA*W%i3p=1CqcC0N2pf`^vV~r*Vt0<AUfO$X^oFWRqZgr_Z
zFznx%M!jAXLIwx0MtZh62$h`ytQgg|s$k<;m)SDVexF*OIgsT+a$bT+a+LGfoQZZA
zBx=kOpjH6w$TyA11wKI_PCCx7c?AZ<JYnrO{~;pMNvx-NU54dFM`mLP<V&*j!B0Cn
zsN166b@=ZL1%|(%>K4&kYU{jiw`v_Gh|*7<V>RJqF(1{Vh^HuC9+pF6sg6E2wfgef
z`VI+dyLNF{3jF3~ZbM0Ns$6k>wih#$o_!+d0;2(X@f|wK0~*Sf@L<W(3-tH0kS}F>
zG}Qw|e4oz^W@q_1W?mg;XX=V7GhV)yzwR#9?d~JUHTl&2`J5r^M!6bBMVI&*^9pvv
z3Ujtd9PK4okH<h91ctyPOuSM<P)y40OCJcP!rv%UlhWrGyZ$eu%3=0<0;HNprE$Z|
zUA$2XP=M}~I`LQ1=R)3t1;U-_?)DdpHdRFb8;PK5p?KX3?>7uk6^SEnVWJ%oWR<Ru
zibuK-?QllRh-U8uC)hnpmBWP%392rt#mX4`58ar{QM+m;A`*wSOGErcu)BCjp9F&t
zYbuHd>{&X>^(?i(As)b<Krlo#C%j?%ks3k^3gK4w_e-GIL#rNQ@otbYRTN!f3_@HP
zX^IQCGDCEOJZL5lU7^~R6H~5Sre6xt8wsdq@v)C432SlAjp7^toD3aY0Dvy~LVqiP
z$jE_10O!yUM;ie3F-Aoy6Arm+qSRw8IO4R;ryU~j#?FN8p1+2RMz;eHv<!quZfroI
zhT)fP(>W}cfy<a9S_qz6%Di-ZNphHnvu+S)^*{CzR|ZgxlO`@81+JrGG5R>zx3|a)
zN@3C8`97DqPf)(qQC;6DUQxP}(+ZjyNV077d?F8=^#6uf`iAiq?l3fuBM4t{AB2mB
z1<eZ9=;42*;+qnNOv-1!>ac5mHP%MrO;uMKr+`nUub9Z|$vAt}G~h`^FvzTTKb>W*
zug3N_4fE>-=S-1vo_<fXrS_(1uH!mx@!Y!>k*;MtWFF*P*@A1yfKA<!0yGATO&B23
zMa2^9@B)OV)_gAvGKyB(C3E*SjMvxmMPV-s3JYx}(ilXI)jwigw@R}(b0n1W>MOg2
zy4w=}ra3%E^&3IRl*n`>V>5JKBk@o%MM~DU)R!qQ$-r4A_iU;~09}-AQ4s(yYsz%9
zwV~^?8R#jtMPC0knth1TU2OR_x48W3Wdx{F1DB)ZUuEf{)E0APzo)?uB1vrhN>)=>
z(b$Fr_59XYY$2=6p`vn<Q&*tu<=7ta{kY_9BO^0vFD68=?i{bEWirgP>JY3d$qFm5
z#w~cnA0j5v9c0n?If{&Ie99absclw34dRzWBg}ARtaHxAAxlaSxL6@tJ2uMr-F3Fw
z4nMeFiU`IDMzEj&E8GzMhwWmY&;Tx*so6L4q#C3-wW_(KIv7`jI9JpBiGwT<^=7sR
z0u!Z{P_*CFYC^Qdl*`7jA4ATiw!3oi{GVx0DMA)CrnSu<g+wN7wVO#)^=l~5qm>}*
zk}J<ixQJ;;8nvV8*jKGK9(d`&UXWs)X<}dsD8LSP+}TlR5&$G2#^bSMDvrMN+9%!C
zO*O>|NeH&HT1cz2$`A>Jk_Z0HS^IdM9O7I(+%gbKHqtP-HK6PGd(PuHeNv_)Uix!?
zUoml1?fb-X+~~pk!D4ic^L=-X%1W)v@K6jvN6wr}#4CoaiH>ijV)(lmz{OC?Zt~4<
zwL7lu1_n6$zCtKhX5aIKyBs`+C#fMyF^B51a3~N0;K;91Mb-e+wbU^$dWVPkbnG@9
zTIZ{*=nvX--vd&+iZ%xX{N$RHd7{}refebL6JPqk1k_NCbqgzXD=n}7hn43%0p!&w
zLmvVHBDtL%=}1VzZ&@<aVi+A5rs-$vN`NH(L~D2dQ2$OP=E1{R`-kIy4!LO$ZF;MM
z+ytqJjXUBGgYkvD?G~p}r7J%QA)C`|pG;*`Y`bKt9LpC>byWqg83ym#6Hk8>3i`;P
zm|V2RMK-Yge%7SGh3f(E{(K|BAy|9wqDaO7XG?&n)_&Ps9aT>-W^9MsXM_T<Rwm@q
z?X_E&MA_V0^K(`d1f{h;2cThNn8)#l4$1|P;i4bW+Fb9?9pb4tiJ%QD7}ls8Mgl0#
zr#vW@0!@O#P`D~9@WqMu>e*k2_PE><fIt+$4=$Gk0I&FV-*Ih&*d-y&8{W^@>>4Xy
z>?{!7^DhDBfBGDgJQN>=#V(L$v1kC8B0|07Bt_1fKjn6ESWj?x3N@V6dEx<X7a+>;
zfnpx3Yir}g{e}PtU4Ap4tKNa?%+F7cW-1@e90a?FFX>r`w|z0%fFiAiI<jp^W<U2L
z=>HA6NqtU5uX|%^Nz|=1&$;{BW~(|~R84u(ek1glJN&-6*wXC-zpbg$&-{!@k6T=>
zrga~dHdv$aJ{smhh!l3e9wZ*P7B=Ah<~zW|x_-3pmvV5UT;0@BGthqCB|DMG$F!la
z%45%*Je8}){1W}b!>*yu7FtAb`hhA`A@^r5t+mgWk>JHQ?4&U#q>Imi3M0eC=d<+y
zPPy)f`PZ9{eCU^<R3TCPi+MJv#IG@9iN6gpm#i14m^?CXj=v-<Q7l#V`+E4Lp*Kg4
z6z<s<(u}G8E?NiLtx3KzfEvGh;xSBcc;_xPPbT>j-Ta<7`Ob6E?}f^lTu^(HxKcq=
z<eujzwd+8|i(YN%cyE(u`nIrg`;Y}Md?h@G;He$Qc&0Qkn$PFO3Oa)j^_m+h<7{4M
zb!n-W6sh2SZu%}`BMxqg69RzI@M!T^Xo2Ur2V)e5j|fHwLQw;FHs<B6C*N^*9$O~1
zAVHd=*fQi$4)_>otatzx#b;O`KKjMvWSp{Z-X=7|Bd!xYqigg+LQ(At8KfTY`sI_t
zYKsCM;9BXTpzn((GO(V%Xcn$$I+0-Ek0_iVEhKZ)M!|HdIjUO%%eX_IJ<i&}+UuNH
zzZ56GIT?sQBh-l0wSd8G%tM)D*S5O!X->GgGasj^)YfXOFZ_TnLDxSF`~B5sLagGm
z=yGeMhk`w3-TwRzr(b_xs!v*To2JPq7k+Nkz&!i5UKak@d)BogaAVSn!O)sSLX6mm
z1i&M8{YCbLl>%l&>2`oOP#Rh$lSV~KlX*s)5}1jG{eRAT41hw?ssAtMU2PW$PkF!u
zO3|o5^rbk#0g$p|d!Gvj9Zsq?kYTlwMarH3hJ&mjmO&i?D3?VcK{?|QfbyfZcCd*6
z5*GUi0L1?q-~X=(kg2*g2EebYjZyNqO{{GoB<VR7O#myfx^p4RGzF5(ux%wvc3nR3
zP2c=}Q9Q0hFOk3HMl1jRi^1dmZO7ibp+~q;7?oDqsh^xYprl8lYd(^9tXUJ&f$d*^
za&i~E`f_k($5STWjNYrfMaMeZMrCV0T`q;LK;ZMXzY2z4)+G#oKJOGOeRWmb6z}hU
z*Zc2*{Bo=qJuw@ID#>C0VoMm?e?}+?0!+fZ(h1vVpK}RfGr)<eDL1Ngf;7fJ7B`W|
zj39TS6AZA(Rj<a`=s&EV6Hj*lX`1AoMX4GqgWH?AxjEr;iU2OVwv|eNAPp_<2oi89
zBx}}>q1QC21n~AC?`SLP0yf<N@J^pBfZO5cphdwiAx9De2UIfDQc@BI8k|Vn=_l&}
z4p`+O5W$K!_aXs=@xYE2j%-Q**a3^hGUKVN0*vvyV?wi?w+sZ)SYtG2x*98hIL2Xg
z@`SycgOpD;m7mO|P_!VPJ^5Y7N-#b7VV^7tcj<}K!pbp`WUA9Tpqfz?lc26RC0M2U
zo=VX)=Y6mZ+wqd2fyIN0SyN&g4#`xaSWD{d|5Rok5Cs1Bpr;hi(ON7n|ABQ+F3E|_
z16*jlRd8^oQ~8f&Mi=Xz?J|2S8fFZ7>oBm%#d>x7Vc<rdVwJkp8ufMnJFPBhq?KiA
zS2*kH!4>MGrN%n?QQlut&zhtk>2_?r1FWcxzT6=D8mTMpXx1AJX7r~+DIqJzP|D`#
zWf-i>O(_g<tCR`gjN_qjRA`W5TDlZrjqaEJG4992=$=m=126w;wqmF;a`B*m9lUGF
z(cP|T3KRydPX~Dc{3lg<^-_ZGc$>rzJJsWPk&R)9dV1CJDs=*#>sr1U7|wFp5r1=x
z03XFVxCO~Tmno=wrQ6kME3dwZXi1`a*&Y$&|GwvgK;`(wX;9u|py21$*`C{vHE)CT
zy?*co-Z!<+^a+R4EnE+s!N;GHQ1VTx6mez-3l5c;uXf(Qw#&Kp<7xQJ(=^Ap8L-_b
zdGJ%1wLGmqbq%#QJ{!|RKlm2sm1v4;Jz$vq?uX)4C3lpAJ_?LTVo{>DGNp!LK;u8P
z%i#n$jJ5md7nJV~**`Z2r!4~bDhM?IvLDeao`#3QPR0-`ujH&+U=zQWW^cy<1!|Op
zE|F7mc$fj8DOq|i8CV7R4U4}|1a{mc+>PL?Hg`%{YLwR5JynGEXtKvPgUv8(IKa~f
za`Db~Kq{^wmU)uL0J3;0#sCSH<a^gn4`H(cU@FiIaE2*t2h5@FiZ$9powK;Fm=GKe
zup1j+v&3x3LyWx$bEf*KkN^brH0$1S3}ivJ1>ms7(^9rSBN@R8qnT89928H}%jp0T
z$2r8FiA>7AMOYJVSPyMw8>j9?MjK^FBRxT{kRJV`nMrxx1J(FE0c%e^Nob#yOW9+J
zxWL(-oKBK(a$ddR6j5&DQCDx={3eCib~pafFY?@laVg3A-2I{jrdN-~J5BjlDPGZj
zU@1yTMTtUL#dEiciu48^xAkLp7Q3X*dNQ9EG)QF%!ILkR13gKX{>~(5e_?ALE<i0Z
zKnm-V$hc6Tv?^|9LLG#M_<{lH&pf?Nh5ua4NVGGe__@`5O4Zw_&W8f|*NJD_xwukJ
zDJd`-ge?L*Pr8p@>KgwbG_mnj$w4G6M;p(;bN$$KM8xV-ObD`-S*s&o<x{{x376|N
z2Z=*PcvJ@l^Hxv&op|V>Qyxqk70<q-(-bUa&Kd(~f+cGX2M5;GOQuJkBuBhsa({k)
zQrsVMFq*|=^UJD9h0eRSWK?kT)<fWr1merVF7vYz2Okr>rta#KQh83hH0Fe`bj1#)
zA`jmq@nu;NpYNsDr?$Mlu>eSr8#~sJk0O-;pqvUTLnt_#3ec;mP}eU4h<NfpS!{>Q
z5H>&rcfCrz5~jjdLB^pz@RZz15Zm9bGc3PnX4@lERVODTBSj^Vt6<Nh7T0Otb;|-2
z<V;-Ss4NPLP3agx*D6E=>F%;-52ZF5*kLLdJ+vcW1L$JN9snThX@<YPcPa+p9lvP?
zAPVj^?h6u=8NFCNgaVAO?mt>@L5u=y2u&hNqYA`v%K^)zlPV&Mk{Wi1hc5{LP=Y<2
zV;3vr@qmvgbwoT#B6T3eNLEU<U_$qIrIn0DItX9|5UP0apa2IVa$|NJwm?#1J0^G>
z1vP`3w$H2oeW(Y3gDEC8B#_pwWj|Y>bU2?m=ufG&E3v?4YtFb-@LBTwXR|&LI?Hq>
z%^OaaD_$n87h3yJB8Z*KtXEzL_l>{`Pn4DJZt!f9v@rFn=kKlz4d-2Zlz0}xm&L<^
zKTW@p$?N{PGDm78ba87(cRbG;N2r-yO7Z4v(qM-vp-j8aZtH`;AreI+s;$%aGlh;&
zQpZ!ZE`Cx~YYZ6h9O_*ne(aKWL|W-f)Vq{Nt#SB`wOZuGJh`o^{_*eU-o){XmK0Lg
zQwBnE@^8YIXIYe1*#3fd0MU3H4&x+LF2x}VA4D}|=2F|c?Dw-K3!bVE+ri=2yicNr
zKHo_M$%YQ449YS*wmu0m_0n?cvh@1o+QiHAWZ~jK*3Zu0K%iL)diJbDcg)sjgwc}v
z93=TcMbGP7QQ$t!+u@iBn|^MuXVBQM8{(GE+r#OSS}Z+RA7~;3Jzsf8hWvUQ&G9sC
z_v0|_6o4g1lg7)lNp}DmFve)nGk}~j+TR?7eGuwgGiGDj9=pvlc<HEZ#;Ozmyl6*3
z*)D>+Qw<PBr5Bls_C_U!;UMerL1{9ipdCpW;_k$3jKz`WS{s-B`YY_3=?T$xd(J0y
zH&T^z%b)S55>Qk(0NDv2_pF9xWgaVP^#S-Jirhpd$rPqHt<<9q|7@wg#Zk1&;RTq8
zOw{*@YH~bJHfRt@1(i!)>Q=IR-m0lWQGpAM*BC}m0cV^SPh0lm%^$XJ$0MgA3~Gtl
z05A%U;;ZKXePQu9#NR0c^wxED4XJjSCU**Yrlo<S(W98^ylR{UU*m<UtmztTs1MY}
z=O$mr5tqnm{TpJ580n~q^Y)wWQU;>f^FPP|AvD9S7(dH$;xXPgOi4@9a>kSQI)gYZ
zn$G7*s~M@dE%~_T1rDCA8>tM%P)z}?Jl2+{?|I19&K-=as@F4eI5jn$+npp*bD?p8
zuGr>4WkI5)cC(SF0xOOfbbjz+*{Y)B#?9D<qPDnQgt(tpX(r?7iA|^4vCXe6;NWc6
zDTSooi28o1b(`!s_Is!&C)Man!9mk;9LQSVw^Pj_p!spv0oZL!N=E?=U?To+eiG#b
z+%Z5SSZN>ZC+H;O?`QY*tD9J>;s23z7Jf~(kK4XCYBYn<HM+ZdbazO1mz0PoI=Z_N
zk?w8~VRSbr2oKUAB_JXo>Tloo{U3IJc3tOnoX2@MZYD-#7KkA|;E2~$25ay|9;YX4
zP4xvUiHkIbdR@34>?MWxwl;$WpJ61`Rar7m2z%F#xi@ycNaX*HcP%pal3Kl*1v=E0
zGSucJ+l)`ufG;W1tVyTMM0ctpcQIiYO$YJ)6o;t9(}$BW3113qxoB<5?U;}&djHRr
zg*e-cDk7S920&mERUJhSf^<*{)qaTf=`jWWF_yiS$cWzzWRqZfL5XFH2zjWf-EE9B
z*Z=()XkVAT7{so<dmF)hivoTT2W4=hJzIjL-@*9By(ni21&54|?R2<vQlIt&A?k}P
z^jdDa9`8(z!oiiY{EU=|nHU8Spr!^m7BmTKNlSQpRQib!yW`s38khs<KU@er>eqEr
zBs#ZsVB<yg;cpiY?-Ee%yy1oy;MP!S?_iU-=nw(hMr_mAE<0F!p=Ps5MVVq)0laA6
z!MrX8R($(f{SVC9gk^*7yk<WO)i%h14MsA>T&DS_X0_Gm!(5M2!DQFf%Xx^8m1s@x
zdtDoe8Xr8tK)hLlPX;S1-%72S{&En!#n<^QHJd3uWM1SqiX%9Q7k^Q>+$j;G=7McX
zxUl^txxNGhgL+c1;?ZrCR`Mnf5;u%&G9y?GW=UMxFO{!7)ySYe>(s-q&gQh34+<iQ
z+P|~ciXXNIFG^M}dpC^H@jBiyUSJcz`+7^;SrvK7z-iTScZ`#0_BRed;4!Cg^MK8T
ztAHn-RZ1~P-;;%Up*NI~`gFcQ<wuO_LNnro+rcgCG{PqSElc&_TYvwzH0t%HL=z2*
zZPdQrq6sIWjx-$50lHFHvs+%BXy?8O_6hXlEUWs=$%=MS_(U(22KpeNb$Lcq!w{Q}
zh>Xi}iD3%rpsS#u<_Uq=$9-Uv#oKP2Dv>2CA!H2NmxzLlE54?};?_PsO9e$yF&U_o
z$PU2)6Ve&&AFKojfMkb&sA>Tl<s(>9Ns}}sxlI5JjnFE#j%*8J5KkB{`TdM?RVRJH
zv4mt^isp=60OE20kPd8+`5sXKi9%NgRd7%M-Ojw+zAeG_+^-#GQgY#f<|R7x9TJJo
z2h3i#cFIi=OmRGDBoOHMjcM+*(lYf=p&W6Y&vbN3U+=`FB8f7h`~^sV3Pj628S+!w
zy8>k=Y41-#5CA>X(ngeRjD0z02`4A)n`VTFn6a(h6_pb<ROS;8F7BdU7K`LAO_2|w
z$TgoM`XtClFZ{z~u|Y(Xt9sF|LXyNzJk3WWMm~!y8h8Aelt`qL$B<c#k2skKu6R)X
z?Nj##{@i7=PvS!O2WK_Gp6hHTt2I%Mo#sWEL%gmO0_@DexVP&yGj?3svVLn{-~ch=
zhNKu67;}e)(~vkuFdnzagALfQUU-$ND`3ldklhw_>PX%InpgCirF^7^;(RC5jFCc$
zd=LCtbHXMJP06z2H|%Vaf0S;7Iru_I2Zgza=vDZcE0r4EhVXcp>3|;x@=B%LLH68k
zFA<H#G>xVjS7XPpXpNOsjVd3itk~f8xEGYJQYNVkEczHG8){d#K`hEIU}JTY(g7Fs
zr@ulDYlmt4Z@pY=Pl+mp<_hxbIL{2vPA;Ztb#D;rk$RNH-{!hCN~!7hVg+D#>Ev`N
z+W;k#9}F@PS1V1<WJJ-*#vFA9f|JRXEKz$1%Y$|qG(JshJO6q5MfH0h0P3XG(dWGA
zZc#+rHB<igqQeT7+wQ~vL_ww~Vo<|k77(P;@s#~pAScf$=hJ{NG6oG|$6L2K2$~QU
z;HCx@X|(2{9K^|VYt@NvzZb6&HXdqkPZ2~ekt(vqoyAE>iX()J^*$fNgkx}u-D76b
zV^(jBi~piwsaxQb<3Jh$k1$cpp^ofUCkK>Me)*8k&DFSg#LFs(#C!da&J}qo3S-WR
zhDx{T*>&nWE*pyfcq2vfH0|!ESU$PCnVa}+dJ3BbgzA?#cdeCRoP(5@WM{@o&WqF`
z@6S1r3i%rOf=!t@b`aHm?oKYLhIE&+0>?~oGnXNrwF~FWL2tf%*%-5E@mU{=6@X&h
zXIRTkX@WtmN5ukr_C)_FZVyR}DyH~@Wbj{=MS6yY?N9)GBqZ7Lgp3qMb>bxW!YNz_
z?8uH~BIt&_ZN!Q{q6kBR0-s1~qsYh&EmY>DE1qjKh+4WsgEEyC_Dy`WxVr>aN;Hq$
zo?Qq9{|Y^Xy?k}@aFkL-_55|fue6}9f`e(OCgrH6x}qM88ezEjynR;Fgr!_G+y6-&
z9dt$7q*35AZQ*B12E#wPH$5*6@BSNKVBOmZL&d+wd~cdDf`ODYFx(NrhzXB5vcPvv
zmP*s!cT_=IVj*>3F_YjI_i2F4$&*C2cN{4|q8;0*7Ak&t_BDW_Cq>EYwjp8al01rM
zo#?Q-tXt=2xm~p6WV9pglMq|&?X-s(%ZFS{6;vy}<PiCeJ*D{zu3}K^U4-hLyfMgx
z{P{iqPDV1y8mozm6O2b*Vo8Y^ir;#sxqVG6h)IlkAnZ+(h{sKgI+GdSrU2PBD~oxu
znW+B8BSasp9Nb6l*6@CAet!ziI<TL|EpzM?an1YYr1R4$nZaVUe?{bM{@jaXmPfcI
z&L-s2YNMg&meo8MY59W1rA9H8<b5w8^d0T{Op#nS@0)zw5XTo%Qo2MdW~rIW`Tfxw
zPnqQ%daMXKE2P(5WNOT9B0hRq<>q2-<SOCkhJT?~q9O(^FQp>$d{x|(Dj6aw89R^P
z-FJQ(Cbb&;H=J`T=}#b$Ut1ok@?4ZcDxIC#(q`0+jWl<_|BI5qFJ>X1-ER?4fx%nG
z$`t~OO-`;Dw7a?r#SB(EMBln=Twpa?W*Lz(?@KHF?7^8z!ralG$62ev94xf5sL-or
zYG`3Qzg4m45p{t~7_|PFuB%M%{maIp7UrWDK_g-`wuc24b3<OUdts#y4jOo;Rmy>S
z4`k(Drc7n|3$OMT@m1%3HB{nM864G~J~5D+#AVq(Q#TKe%RbkoHItH_i5>LSa6W^T
zXh64tB+l_g<q%4yAlc$#JTe+h^f91ga|T4CQogJAl<u0mp9z&(Zx5<_7&<RFHS4_~
zi57Z7bHns-&sknXr#NJc<OQSH%gSYY%h^>&>|#IA#$i{oz@;%z&k0Lb=wq+nc=<A5
z^fbFI&<^(L*_&ad!nIH1x(7ok!!=&B#i+6L#H{&g@AT77NTWq39GSB7FEijHvMMAj
zjhKG;Kub51ln;Gmk5x-|zHv$okn}ARleTc(f=ebvbzf#A^Y?j%t%14m3+x=l{=bxI
z`Se-&gw9qUaLw4QXs3^8k3M$FKssHVEm#@Tx^Qh{`>7cvHh$p+4tk7U4J@P9^X)K1
z8oX)iWQKeWGvDT<1zV5F5k_+i$#VF>>3xDXh7QAr5jZ2!lfwu2ACyR)EV3N`iZgHO
zG0Pf<UJNqkLip~xoAN)7J+bk7-d1=-A3!-@=#!{&6||-&qqM||pE%*Oltv~z1GeXy
ze38YNVD?m&c8Xt#iTYtL*LWJ{Xi9@qTEIvds&YVd6)e#*Yf{X=*h4Q3y@f^8t&siF
zE4xkKC@XmS%%m`M^XpvJ<g1Ukb1y!Y`a>~nnExAoQ~4j;SYvVU9`4dR>!$IhEvWy)
zc-w4LKif=-TENXe=l9FG-<S7({2pQ;gfCSMB47f7nmfFtotp&kq2E!MaN=VUK#diB
zz$S_{2U8QqD(^x`09H~6AW3_Mc9k&@#;YI%e}s=>0y^xlmU|N+zb*qL1QRsU=pmMx
z8hhY}!Iib}R(~N*xfnPO6rTSE%lpljvj`BYk9wN?*}e2Y--!Ig^m+({^HbqiG|4tL
z@s!#XaIlYPETC{fa!EMnc@@%!0{NkqW_gHWmEqVM9&f^iRTzn5CjdB<a%Wf3zF7y@
zL1V%slY1I~0-Qob2q&<k2#sCxgi<;EdqN_V@6G*8dfvpGmlTCNvP+@h_sOX1`r|J=
zM*ps>uC6TtC9J`N`TIx7=UQHiZw4g$`nzOaPOM%_{gFu?Qs(~T-<!JT+xGT!_j#{9
zv2WMN-@OrjMqftb`JD%5f%U(?W%YjFoJ(Gc<b@AFLa{(4&4K%GWE*)}|H%s!e8rWC
z3t|B)l4LP_M>X})VhuHad;IzyMYew_pKe?l-m@r8O)$1tPYYtX>ZM6T>E8$tR<<N2
z(hWBVlLRqEqAryHz>f|c0eUcOiDg)`_W@OR_)Hxs!-*7=WCYSHCEtxlnN)>HGqf_w
zyg$VN#-$lOL-8ixWZ8=$I81<cF@|&v*-|D#$Nwjgx=@{lh1+OiLal^!_g;ztf=cdQ
zN1y-{e?7;yhcG1Oq__eSNMju>MM;7TKj7SfZ-HcceDOM*M=R@+lu<7EayS*{KmEW8
zk{Es@_=sX1@+=_@fVpJEu7667S8O!X`Ne1nORn(T$Ps1o`10f3q|Z8qX=^O2$j3Gx
zD~bnr+7_ozVZ2S3PfCwINktd}9hbVe{Rbm`5^Qsu{e#h-ih(-JQo{0$7m$}x6}c_(
z8XC4Ia?lafeDsHNVej0?=V?B>Rv|o2>62E>yQ4s@`k7O4SOp0tx)um!)fCEQic%z!
z0Wd<g1e<@kU(2T3gBUc(dhP&i(i+YYA#Tl9r4R;=3<xw91v&{WI?-^X!m<aYIMw$i
zye{O>+<mo>R!bTuO|aql;KEfK63TtTk-qE6gSXUs!ansd)N-CQwa~$r7)bE+?GY6K
zi&kPUMF-O8s3i|e?Ya#EzTgYcAHCRJ1&aB(P;JTh;&W{eDGvF5AIU6*k|Gy?bqd|;
zW_Ag_jm>hu3+C>>GW?GRQtTNpk)sb{ecX2lA~`-LnYpjYfINfMsNVqp>2KkF!GDw3
zq2C|<PyTF;SCEDFZ;VrZgnsS0|L5ntPR68MAJ4e_S21-8%!C;bn`$xeD%BQCJ_@d&
z&&{KHe2Y=go(d**rdFFc+b`XlNhrF;3l(VoF~pjt8msY5wPaH!!=yB8srCsIo^>c1
zFHkTq`2nccz-c;$T8-WRMyQCT16aJT86CL8_roNrNZmi?i4ZaKDxxFG%3&Bxk<H&i
zt=aUV>j@8y!^xH)@9KgwRcRm7ls&*zAz76T{E-2>>&9-Q1E+#AVED0Nkn>e@*(*5!
zZ#|%N0j~l{%@m<!XkL#+*ZIlw&_0p(x-o|ymY49=HiJH<=~;I)Wx;=0{{8`lu-kqL
z=dvvA@URwAlaA8;daHooCj!j!v$-3vT<wO4W=%tW$C6tIqYC+^9$k(#FFM<6YhbJ=
zVASzukxPd@SyhcpoT}DQZ>eE(8tXtT?j@&-PRo}zO2{nLMi1$bbylq4;Zy3nP-3QP
z0B8G=Hre14&9EPU0e8Jzzmvv6%%B70P%X;|bap7K2LLw=1rF9Qr6@Fh)JT2|`^tWx
z0uW?`dcG}DCQ9B)fmPlbho8RoBf<+>@&l-qIsf<c)=E>{XQ^%e7K%gvEuAS(M5|jC
zC)}_)8eR%T0qAW6wLBQoolwaSpoui_5hEoP*7#x=<-)d3y@#0puS1~xW^yaRP={C7
zzye=@KsUbU0B7ruLZdo}vgDfePD^_kIc|#o<l*|+mJWUdP608Z?IG<+-Arhd8Y5zw
z=#|~@_o{JH_Q4=UU|ad7b)kgNLR9OwA^xK(g%2+OkN0HWOH^$OK1AjkY1N+R#hr1s
zpBawhO5qMO>VB5XWE$T{eW*!QwYKn-XvzI~fA+N)JpNXyHV2se*;tTDF>LN>3gfck
z06_AFZ7(M5z0}jVim5&3jWE)>vP<@KxueONXllxON2i(;CAH|ap3>x-#2;*mLN%3r
zpAdhft|AQWG>fCYD<z<i=y&8xwDlu?jm$9!p`HeJT)NI;tYu1fSYBy}$7^@O@0A#i
z&Sd_S;geBU<2~uf^hzlE!O}?A?<>|%akkQjvFTLWhF9hTM`Wz_yf{>vY%L-Ibe6e4
zq%EfC($fPH=Jhfa<EH8xQ5C~P&N)V=Dlhj(*F;aplLS~)t*eyQSZDM`J_Eb1Uirk9
zsqFcrcWby^`TlkIPR40~aHr+`IPyEk%v{w`D-{oxeJ21462K8JWyIL_20)i`=yEg>
zT53l<EbG|;trt^WK;wXM#L%ld1>&~cK$ax`6vqrZ)B-?@VQu^00xtAMiV$ZdG);m<
zL<0ppBPw}slW7!U9KJ1AJ@fb?l~19h$V8jvW~#v0jmnv8I<+c=syneHNG-xoAML6U
zi)rr7#<YLAw!gq0bw0&Ex;Em;V(sNAh=2g62ig1bO&@fzO3<G*Fd5n!gRY|M8d%~h
ziUPPLqM5hvvUn0Ze$sL3I`I-Vg&bdh%9bI$WgPy}?9V;4wjq@Cf;=s>3-^y~?{8Zl
zJn}<v7mQ*CrEA9TqI-{@3oXp?=e@6Y!p*i8EkDH0OCD){%o)NiqI&DHx``bT7g&4k
zoS@uD%n%o>%3{fK6>xI?O?BQoJPl3(;AotR<?19Oe>W)W?%dLIy7vJpCknfKyAsVM
zHko>pbMZF0%o06ndz!<qT9O}`;lw;*@1a@Dzv%|{<!yyL0!jR&N0RoH$GOrUu>imd
z-+$%|cSkH5Ra$FFRs5P===WfHdvsg|<)`}aKYPixy_QfXxfg67xrBo9tjy;*?w&#O
z`Z*HsC4V1jM;)hCEX~c06Vtt-wz?L63l!SfI_VM2r}V$XA6WLH+n-D-mG|z?A`^H1
z^XjDZF{wqMjaJBIVCF_{M<cJ|d7^mPaR0<iKH@#KIUic_+0L~|XgmrD(Tln?1psjj
z2jl!ZHB5^wRGt7$oHk~&5r7upy^W{#L%Tf1kN}!Z5s$kwi*~n`qvFRvHb$AN;zQ6#
znILr#c3h@yLZ)2}v^O$O`t5LVyn#!MQ8t&u`+}Xp&h$hC5Lc7d+&NUpwbWG@*B^an
z7lZacBUlF~-$zmjC8TLZ6=w`_rbfV-yEGiJ;VuPnXbGP^5+FcM>Jf-Izbk0Y55nxG
zji^C3)kzxF87$N}#p2s5(@v*l?#?kF?-cIEQ?4*1%Ra@o_QQA1@RWymGm~;Xzp7Xe
zLN&G<$(nhUGkP6J7`29b{4p{M=8D&GO83f0772Qoem1m-@?{Q6dsP;%CrfiNHIUj!
zC$<$&0X?msi1rG`EHO(IDWHp>-Ve#}Z`8ys_t2Bw_oprRFI&_?(e36|q5>GsX?uxz
zT4c3M87hS|PYu&ABiTM0Nd=A_v?Vs;Ye+Iu#i#i=v6ogNiX`w7;7MY5`!5C8uTS{C
zQp-;9%l(9dcHhgH36q#COKyQlxxU`$wpw^;i}FA^MHXk+NBqT)Jh(`V6*=Qi6u1K9
zR6ph7@rawTn4qz8lR_CMmLG4Tx=s%UX!PYHcNB&%TLUTOe(wpFzi)aA&i&0&ajh*v
zP{dYW)|x59W&#r8`K3jxqV@8}K%3B%bG?Ynyu^vMhLvdBo+NvTygbHTWu1gpQrp1x
zqeg~#-?!?#>HDM^*$4pMP9QG{<IqMfBtWzPKq*R4s~}EK{P=(1DNS5(tow5$T0sh7
z$o30sq8gT%AObL6Vr_OM9Wy3lGA5wEZzRtD$k#P{^w*8_YR~75g}h>@en@+p-y2yh
zBQ7cm$&w8}DJ@81=V9e&vdl}4D5&XP==}0-2-Sw2=7Eg@K-IM17u9GUC{FT8wEuhE
zwYYG1N-$y&d(BS|2qD;ukV|V*o@z#@4AAk~BpbB>;C2=GOr2v*qGNaE@5FdaEh(wJ
z_dSo@Gf#*xQu(z8WHEL0H+34252>NUqo9YPfT;#dtCV};F*(rqA{ImKjhg2i1U6%^
zEK3}3FzWb;nTDX3jabUs`2#(9&!(+Zp~dJsss^&^CSDI!Lb`gE`nGp4)o=#s%dRHH
z$FUP*h9sZ3cwN8%z_AY;mks*xDp=j0P(2QuO(PMz==@=lEY%SQZ|x>W7CD>rD{~@t
zLZzs*aILbyZ{iL#f6h0Z)xkXJ*H;~{G16X!8*E4}Ls=71(VnJBwj?JAVE$e~0(i(0
z!yF8<RD;(5Z)$lQX6z{iTJOg4LoW09lRzZJ0I8llsTI}B9o9*&md*P9AKO~nJ;^S5
zc|X{v0vRT6YWrzK`b~PZS<ba-xLe9@rhcEdk_jcg738h8QaEvKofg-Q7T4zOm=?KZ
zSALjI@Lf(%|0HKkJGrjCz&r1Z#bIhg>lK(SsgL;x$-gs=HW@~v(h<~~5!F13k`~c#
zt@3}rz(NmVTrz~Wf5H7>8^3_w?I`PnzHe(T#|s}oF{fj4N++-~&27ico_)=~dx7)T
z9cAe`gp9*r&abI96rUKLGvP^+42b1o3nzhw-&cntDZ>o_w5-wmXDt2%0jL_lZ|)<6
zU;{4fXs#&6?_>@2Zb(JWY=1|<<W5rMUeaJ8VvSF)SQm2!I?DTpWaU-&nYw`xv=`i)
z5)H0B(-uMftryj@!p}~<F=IHVh0xd-nBqyBHmM5!P$PsAx`&f}S>+`gG)NfR_(;~k
z%u;XS)eF-|lPTCJE0MPRQcRX3^^RhrjHpPAu3@9KVU)Fa#7pf=M{UNRSjvMxsE1E?
zDiiTWM&nsWq{C9uv&L@4)NJ+~B$&X4cR9(IBy=T3@QpFBum0%1@a@|&=liC>JCXEE
zS?6Zv28oHJ&SuPB<Kt}O;oB^2K*J<u<-sJgKJ6n3U&K@aY|$_p(8J|fO|qcKno`vC
z_ctkgNmhf0vwfWfGRq6w8;~vTcW#i!|FwEx$DD1<mQ}{)MNx6>DD1O4)!uHbr8^x|
zs}*h0zn@AGW<J0oGf>{7Fy)<lN=fXq-PU2S%_H>5>g%*~s{*O*^5n4spT!!z7cMp)
z4Wd`#Q7)fXK4JFPKqGlv8fQcr+BItx-M5;5=ZbByjV8ZALYvWX7CP5SgBI^20fF{4
zov0a=h;9BzH05b-+HPO2F9~2Tct_tzE4R-lMXkj>i{!j>?gS<<`J&C8nJwaC)*@Z=
zpT(~=Jf+GmhCE9a|CmyP62LbZsX70OYcP^zK9=+vk)VNxte(Pw`^N~SIdy06Q;iY$
zS0nF;k!t|<g!hxTX~f7%JikJRkX47|MEaS#Xt+@{a@ErLgy)sYI838P68O^4VA<L;
zD(kmV@bSxeje*7CB5juC7q%3_F9z>MdkJ;34EGI;vlhuaM|RmrbSdHBC6{u0$^ROx
zSgJ-2Jq%{4`q)|xxz|TMzs99_^*#eO&7K(sxybz7vX1g}i=M0>1R37njh!};N;9g;
z8bbbP*|06EE6M6|Q)50`O>GXg@f)4MBFRu<OgmjmnG~D=!=a+P&XTd1-P)0%KX<5Q
zn<^qS@}0lxN|{+89ORBEsT~=^PAYp;T9SHUyf2%j{5p&m!K>$*ocdAQ2qe~J2?|72
zVwl?TLoN>sKmPe-@oK^sL*`MluN4+rk&F}BR$5kP!tYEd{`{L3Go2b{`=n*Hy1}tM
z&2C)t+AId!K0k_s+-vJ~wS83cr`N<gW;Jay-6FO)BEBT2wt_pXoeG=vJIb7u{WLoz
z$qqrcctHaLw?lDA>hQ8yw0JmXZ51sWhBQBmHE=B%k{}RIi?a{ISv2gLp#Og!!4N`|
zPaia#usK`9<EX(#5?Kr7g?^h+$0~b43|uZzv_)c(6*1=vvzD;0PVpIg?@A8@g$2_L
zoi_LPjys|bH(M8mIYB$M)!r!kUOL>t-4#(Bh+ixkb`vCvz^#sm;{*$R#gckQNTi<-
z!uaH-A7w^82XAzdH#xAWtl4_95e~D?T2t}0T41X?xFX${PUHDqZux*ZJl?-lVjG_^
zFl@AOxfgSzuRla}8xpn}Mwz&-{gQe(@T0_Y{S<VC`<rjI#|Ddd6Xvm`3UlQ(b<HL5
z$7(<Fs@Nn%*${rXs@QOQL3A`VPquD;lwx~y@n*?A2$yIZ<9G~+pvH{EkAqE&XY(;D
z8fI}3j3f?_dtwnUlPys*+YsZ&S0=mfNuT*hO<ikN=oGp?Vo#jQDdc1iVDW7w@Dq1Q
zX%yvWY11YahsECEApzcqo|0_H85Ts}#`gV+{TZ>r8$g;R)l@Un!_CgYX=UUklKif6
zu4`M`Y>J1?uHnyAn2F~GX+M+pGrp&*m2Fh=y+0Yn%>vv%@q^ec0#A2sp8aRFV}{at
zZtLYrq)^7se$(lG+hj}eLE-x1-CWzxIiWMb#xrH)nVC^{6B_gJs=8g{T+axfpbP(B
z&BiWTmL9h{J)S=qagU}8VIW#dY?N=psa|w7fX?A}*>`kvwwxqa6}7@JPG0@`uej5I
zzWu*=^UD2qB*GUqDHh27h}&b#t#1^1zaeQ-7}={)#ahwO>d%te0KftEpBYx6h(=3}
z7x*GyR7WAzul+<ZR4~5#_v6LfJ0PFmc1q7*_K!dSG(_%VTJe4BiN(YICB{E1nvX*<
ziQSW@m+D|u39K()p`+3tiT+x;x(4)Q^zlA?D7Lujz3k=<oeGU@7*l&$U+HJ)*6!cc
z1pPB4`7ra+HdMUa(LX21|JO&uoJ3W1+RZA8K>ER}@^I*=K)}IV0JE_5LiWtZch@#C
z0l^WZRa_*w+(-eQY;vhM%KF%oXNDlDl3znV=(jqnM2g-xBM99CJ)9b0FPpwfZrSV#
zM`)Z*z7Qzo3hJ^gzs)CW%$%l6yP@KZ7{(>XHH?Pn-8g-+qftzDE&b2s#tRZ-{lWv%
zS-I-8`*l?R>wtOA7`6${xhY+Vz4N=1i}qHX!YK^OuT3U@w)(%mcYii3e*461a<gul
z_{VfnTVRJ11OQ`UL@3%LNC0doZnR>F;Bgcw6fYXJ(~N?tiIF6X?JR1LJ>oEAXs|R(
zJXR8u#%S(m+($782!*%i>%xIYq<MjktpF~HjtIMG(Gka~6%J%*R&y>XzvvzURGCv@
zcPqVgv#uQgM#;KXi&H8zZf_er>-w%$mlftna0ZMzw(&J9L*wev;j6*tSD2*U=wKDc
zMV5g4@Wu<^G||!Vo^X;u!nD|YS^I2T*q7r9wI-cE0OVRR^+^N?stibqoR2y;IXXfB
zW#Sn72r%!)2Y}NxGD)Xhc5upe`@d~}A96Z-dS8+?6AEy_5(zZD+w}qV<>!AA|2+N$
zf+P&=@o(|De(1`zt?Osta{v4z+iv%Ya{T;p`d@Y6*geyHg7{DQ`44MZ%mU?*NQG|%
zL=Pn-+J{#P`ZU--nKisIz(|ra?rS#A8G7RP^Lx|yN`fX@e+CVl`S3>K=n%h)(`yN7
zdZ7WzWC(*O_%$nZ1cy=S>M<Fw-(TbhGrFMuom11lPajLmNCz)$GN4bNRV&p~?@fun
ztU(-O)g=``uP%%!O;olGK)~n@fO>`sOy@ZhoccPPv2_6x#6%j^m&I5g!5XHZej<Wh
z7SQX2nX%vf6=&FUNeoebY~mVjtRX9!Xuz2)pSNIgLZ)bvZGgqNOs*?os1~5A7qK9w
zDi5p#ky50T;Oso&0bo?~)cdwOz#y}n3cv#!A7i5jtU&j~Uy$4!ULXodkkx=$0_mGY
z*d^p&2{|`o5bNzVZZNz{&ZO(VpB?JKTU`YLdQ9;yD@k_G%TcNS{X7p!wEMLpp0DO!
zglod?9@^%+s%eRi)K+>elUIKQlPB9v+Oi}$+<b9+@{#gEDx#`;i{q8!<ZGQgTMdhU
zCMN4)uYPn$?i*_hYW4>7h;uzelpJXYUB6mXgs8OBkNa=`$B%9o(O^4x8Rr!bA|2TP
z?4Cwn_irVB)-X5_!Gxoce9%%J2^o?Guy`6rIvlnE%bcm;&7x*+9-{w*yuIG_BMIJe
zPq*>ni;s{+ohst-y5%2+B0+Gqb_SgA{gHC)!A4so2%<#vanaJ=pH7_<Ad3dmHGhZ-
zN0YrYuIc{ar<yErds0NkdE@Rg$-LjBQ`(wQi4kkX0GY}n-hTo564j}@n>k`2X@=qV
z%QH3VH7jllwxJJG9voxg8bt;41`|+S{2zY|hQzdA#%p!ZPqm!Yc#b!8+%acB!qW}^
zs6nK`<?WCYXv?W)yv3jQ0kC%T;&}v@7=hxGo4-XX#Ba_l@hZASu$E*FBZz%5hfJ(6
z9bi<i`9~t^qY@G`zOlZJoylcaF^@4;^3+9wBsehl-;uS3$a)-uI7Wy5rI$mak6&T*
z8<Cb~g#2N3kD*l>U(m`b$adjr$Y}$xgUENk+wT7i;yScdUR^rXUaUy0<p`c<H2TlJ
z?pBhg!k=oj8L)IZejP3r(}DnxTb9P@RUz5iP~b(N&PZt#(%6C2+Dz3^ByPl*GK5rv
zQj<Ow@gF+&Q90YYPFLhK;p$c8_EkRDUPx*aXDw5I)L~0pw^n=MiprP%;~+Ge(kjdh
z9J2Pgj08MEVM=hVInQM8H%ba)uj#Us{%{GDO%z-jyI31M&2THyo30{IuQc>w-D7o5
zM#p(zh=qjWrVO?p09S$d7di&%B@_cR!}hNw5Z#nuBp^Iy&?<Wsl>u3#Vn+d?#7f-$
z>1gy2WPpx6`6CTUFPKIo(TK>S0%VUA0~LavI`{KUf`c@<#AulL9*3zS9k;hg_q=`K
z)6|iol!bw55=sC9atSa+ab`;>_DRwj)iT&-KuH`Sng9roNu{Ls#V#Fy^$-%V?WhV*
zxdG?`GZv<>#OhMZk7GRZt_d`(@l!>0<I7NYJjx@HvvfD_Ef+Lkfj?gv;PH+8nowcF
zrB&c$yHJfFcUVl^4p(10M&NvQX5t-5ztx4x!!TdKmsHsVddd0L+;B;w`0FMJA;rpL
zc?QYvnyXJpOF!8Eu@7r&S-_&Q$cnUQ+}Bm5$oViJgN1!gyTa<0FlVXJL8wojo;mS)
zEQ6{&83kR?`BolG6hJ~3S^V;ULqF(BCktlCoAhFW7w`SgS)7xkRcZ|GiH}R80Rj`X
z;XfV~ci?j35o@D3c|e!4J|2LcB$aFVL;J93DlD7L#;37n2<Z9tguB^L1YlC(4z|y&
zAI*?h9;q#lSY}jaMAL@OctY|_6y8aelF?qotIf*uaJ;&*dq04Dq#>8L+1}VBM<543
zts+q>m+CjmEVGNCd7G@AF|L9GR~;9XIAW@`B78^nJ_i!9<v^w<Hkp(!MKuj$X$$KV
zF2%{tml3qTYV_50q(@e8sm^hfWMt6M7&dR=R;=&IwCu`$om`Z4->DYE8meaYw8tD~
zXKNORhY+JtZ-%m!mv2kpEJv3fgwmXOb0+k~2;h)zxEuhBza^j($}Ly4=Z!l1Yk?Kg
z_?*d_CuTT^aRl%KKxcBN4X%nck>QoD!fleCe!DgQ$%$g4sq?`t^ih=!XB+*q)Ba@d
z%EUr3w5C7T96_YJSSp$-U?=+a%m#JIfME}K5gKcN5PbLS6zte(+=J%H%PQ;QrM;OL
z6_y)yQw>MKJx}WY1;7I>BCDSu2RQUHxX(cHm}=XM#}ydGF2#wsT$C9V2Y5`XA3lDM
zFuvB%i}4{Qo#JF5fLc-e_E^fbkUz#L1dS3%4Twg2Fn?Ck4ty#x!`9X{obzM&tG~3m
zE`sDgx}Ol&GGbm9g<qsJX)Xp27dESK<tx#Y^yT1-^RImpDc4&smx2T=SlV*c=!@#i
z9!e9m^*etS1n<|``QmQ@&}Txf<j0eO91OG05=3MK%ncg1G?#%?^-ZMmALluea()Nv
zgk;Mx3SO9|qJXf}M-B3bx&X$qD?U74GXQ9Y8LGh3tA~r_Q8bsb8K#!p^3@MDtxNVo
zdkQWuq^H=_hwrE_Otf?8GhIIEPmG_k>K2~n=bds)WI8q2zsJ3jZ(3R~c4MB2K8VR0
zYMTD%w(n9Y_fDkK!{L3c@2u?I^dwo4i-P@sp6sp0r+_@pIl)u(UMI@v`;Oo!(it0W
z##+fs6>*YgzX^ao(K~#RP<O=yfd7Fb_Gm7e<f2maQk(U-)DhP{hua~9(kZ|XC5?eX
z>-5lw*z43;hBe0`nvz;t1b_{<_ZoJ>s4hiwS07L_1gH+|T3%{pNF@j6Iu72`hMX#O
zsya7@^}`^j0|go1YhpzT36*`&Xa<al=6{qD9LZOLfMHh?B9`>9QZ9!sV2VoRx_-Ek
zq~DWopPx*)*Lu5ePY{$gHlz?V5|Qqbq{#c0?=ly?o@CxrWp0f!`wAuPDiv*RVc$__
z-8ngqbtic}w|(rZX9`t-Ci)%@nH*mL@{O;Kt0}&@O&wmC>|aT$GV8Uv8<J~BllhoO
zOus%hwEQ+#MkeG>c?u$rvplS((@v?Na`t5`A3B*%=!Mv-@i+E3Jc688=pEIF;QKX+
zC^UuHHyL+s51d!jtL^-~9923Wyi310i}iQ^<%VijKO!qDCIbl);Vh-{uFQHNPzTPe
zsTA|PB&-LOb8O-z#8l2xB)Lzef9MOR#=n|<Dt{xOc2k4*6QdT!{MTaS;T9L~bjF#v
z$=50-#4vhJXePvZjP=et<M(9fG%0I#d|{SA!F_7ZBQI-#4k7zw!8E%9+jrDI;=+7V
z>qmW(OP!gAfG9_Sc@8z}Y&92d9p*`+a%cUiKlA;r1lx-9%WyW6-KHDfjFG=fV@%DL
z^*%A!+rHQUfQKlDoEu@PdxFpu83LeV8qp26CEp1EKmnB8CSNg_|3{>0eu4mg|G*PR
zjz#1kLk!P$BF!<VB84<^yGDqB*JmZj9)V*IeBrBf-}c5v*`oW3*ljwPC_PM-6?&$1
ztetjR6o6}=g@y?6eTjIJ6DpOBD$@#m7K;6y0RLhTUd)&xyc!R=#K+`JnR?AsT#09&
z0bp-06`Nxr(a(L9{KLW2Lw)aXSEvS4sEYxLK`(4wB0@y3UI`s>Wsm|lza%PD!0+bk
zk4WOZ+U+u+VG+C0-#PI@+-HuORK2%%`11QAA1OZP?kOZHou&h;_K=92bWTxGZ-?4T
zMWvhw8D|H(iYCWdC#h~x^b$6j_CDcv{>C4AoP}tgFpEr-qa2@ziSuor6Yji9I0$;C
z%%At|f{VJEW+!&8r_t<`^P#ytLDs*K-mn#bcF{J@(*eL%EU#?(6k?*V7h{87MS(u0
zU0dVCc&=7n|3vHJZa_&6Ep(i2rRGZd1i0HV?aHP=|1n|N!N+8iTl)uF{w75~*Bv6d
zFXdzA?Y=xy8nFj%Qgs-kYhmF^p~BMb`i`fIYgW9T&oJ46sIg!5p#`LlcLLRRlhN!(
zG?p{<lS}CwVhU{asJBLq_eO18lTkT3LvuCLTu-;Iv4#H`6~gr={u1DD{CqX1Gclnf
z!EGAqCM-WYjA0(}-K3n1H<HEj=?3qp9UqyPm6E6c^DXI)U?>NnWhtCt=YkOcFp8yw
z6!#evk0rFs0Z<?9UI3_=mgZ+CuY(?J(6J8QPxR~(?f@aF+~)!o_}X6J#zI2;ii8sa
zB^|#@wx#&3uG^pjaDdYPQH%})_IS)V#i7{603|2@=I1&KmJ@W@QfS++=i~fZvP~0s
z(Z2hgcn(5-@@4yEPvP=xx9q*5pSoq*SA;g9A}a}U&MyoqpcZU^!1q(UvfpXZBo7iI
zGh|VUog}{ym#a%*Gf~M9)piehNvWYqWA*`hD(3DRNg3OJx^s7OtgBT0mWnl59fk<I
zd8VHJoCeD0TP}Z+(xW_`rP;VcZAeVBvp~-*qD3^*r$|T21U^@7(8l=0IE_#_&PzDm
z*Mwu~2+b-h8Js-ffk{Am9qDzQ85x6q>+y0IL|brit{VuOTUG7p9X!!*$~Vw-f|Dkg
zvT8Jm8H`}n7z$_*)f;FlYQb<y(_M-IO$N)xec&4hYx?6aHFm~H2AgQBU{m>f@}Q~b
zoinC7*)bTLt05B}?Nj8|6T;~npQlYs-7LI+nB^YukbCjq=KRhG=RBl%OFSEv9qi2e
zS$9?=Ifl<{X3Dj-o$F^&;pgWfremx%v(I+LtXLd_Vs&VD`ssqDS&@0TvG(L3quPe!
ztrZX_AK-02*MI=XcI3&Of{2jE1XD32XZn$m0B)Iwzz+<oU@Uv&x&88UTn>;#8H>a%
zrKC8`dRqDV%wKx6w7?!6<yYB{{fpUSi5#~l+d)8)0GdG7WlnZsW^-{$bZOwO&vBBZ
zaOG>n2GG;e!B0u_Na`qd`R~RAVJe|z=(oEg6{pR_;I;29j$8WzKD)M;E3bCV`oeAm
z(hParG2w^7JMPmKo>)rzowvvB3X~>}or%cf;5N_c@T~C1H~d+<W~)>;cIuqQPo>DS
zUl3?5?&ZMfR1Axn6KPoZdo;sWSnaonn44%(Apg%ybgHt;0*>K(qx3w*D!HO++rY&y
z*}?W$orw-z{8#WUeeR4rRQ1{8d?eh|%E09674MgnC*EfBm^LeanV&`!a^5O%UNNVt
zh!)q_{iF@~2}gIc!7jkZRA<Yd0vH)u*{zB?e)872lSos?zr0<Axg%5eN5$WpUI*m-
z(Qh(MVk^^6EK4n#2~{u+D4uEBp9}CFEG2jTH5{J%pLx%!MK=pVn*VjzJ?~QAeCqcI
z@|uW%`nfNmoNL@-Oe1tmiPFU*BRip#y~zpN0PoNO+SfvQ>tgwBj0&Ag+5-^HFG7-<
zPE4ItaC9q19Q;tgS7oA$w8z>`+aT<KoK%GrpAp?f@H%?GLj>cPCkQbeGn6#dCVzJ;
zC6v^CD>)U0-6n~q$!o%OU>*1MW6n&)=v4a4=k2@Cme1WwiIdunk}-@J6nA-01i<2#
zW+zbc+D?k`y&IEQ$_FhTsw;C*ydx=RzUf$Rj(VjaYI1xJ#TUdLqV#D>gzjcP(qBG{
z+Hh^VjWJxWt*a*5T~W$ucj*is&H51e)Pmm2FfN+)eUJp+p=h$&)Z8BJaBoY1e4=18
z?V?i9a;9@#Kue*DHi0t6eJ-YlXM|y{!#|pf5hlDe?rl~uf+a7PA-x!v?m4^adR|y5
z_W3B$19c_B=%a?S`$>K)i+T|e$MTc)0mff}JckmDaWhF+^T9FeOj4uovH>~VXC@We
zsa>e4$SGEDm61qY!`u{@k>u#-f((q6G39Hkr`*%{SNT_8n5r{NGu)>sm)W0|g*iI+
zEQOVRLG<1BlqBWYFU7yn`TC|nW}38a+NiE#_35)i^Zd_e@B*{n^;feYpz>cIEWhYm
z&@kABXnX!luBLhQ`>mk3?0w<G>*wyEPCT5X*}F=IeVR_4Y6pVqK^o|w6kYh)GPgAd
z9ZSN2YQk59$S?ShwS1e`rL3e_fWG*hNTlNgKJtSTNS17&r)Bqo71T=`J_G=l#NaFn
zkUgc&zeWJmXnD*-a|X~{H7R2>75qtdO(C!!8@b$b*xhiHA+!g1QiO*s<Dq97N@=A3
ztZoCih%AT<_h(~6(f@Gf%O0qivfh~~w3V^6?S5Pr+82`1PZ9Sgd1+}RMDEt3Co6Nf
zB6O6TW9UK6=ANSHxkCHaY%le{1Eno$Pfloeo%lX0ld4`ohX}3v7VgvV$M~GB@O%s3
zbtg4wKwFQp4^H>7kb9I1UQ535hsN*^8y$y49^E<bsMc{mUi?P9-S3f-MNrh_`qu-r
zcL41pfbKU~B%)fzK1!4yYdqEN;vq(28Y+*O$g%~=ax0Dg92zeqvle#Y3V2eds274Y
zLJF;7n0?|tHLl91*QYdb{9`nm;72Iu-4fN`3gk#;w%vC<ZAA^3OCETeU7Ela0R7yH
z#{<e{N4LqdR&|X{C8s*%!c5iN1X4ZvtcnkQn-m>S*&a<=vV=V<TUe@Pg~=hL7XE<g
zW+`{VULAE2ET7A&&(2IZk!uy~P4i5;%vX}lPyLw^q(o*%exiAKUiP`FH%gdkm%(Ny
zo=|J$+aU=PGXSzA5BF(k=oG|9=^=}7w-ZVlA^-q^2}o^}ej#zduH`POWuBlOETSj|
z$&hu_4$LtP#!xwA#Q}$*%)p$R7(ik!%79Krl>mbUur7N9K%F$&ZEZ_2<su4Tw5+QM
zF{R5fH24AN_%mJWAkCOM=?pOBG;LjX2<bNlIE<@{G5&^&ml*7b;@p88n{5CX9vat+
z8gfQ5q^ft2wqOy)VgO)jNH8EytGx|EqD(C!7aI(U2^pX??ns=-rHW3$>ySY<(rO#f
zpO7e3Vq-R%4_G+-l&rWTUl^d?=|Di{ufI5CRFkegL3Hi^q$tZ4zqV^fbxD*h4|Yg<
zSp9aWPFuXC>Fab-eGe%6$yle@LYp@&Z!FH;Fl4Y$N*&gXDm>BkU#~ce34ig_|6M*o
z$~2BY$y|CpU}qzrt6ukcLGcfXPwexS^P^{S8;m#%_<Kvg{?QlF&YdHOHrhZR^;ii`
z8Ji+mz`_D;<p<(|%I*K&S8Y?EV@p3g^fJ2p1pr2*!O`RG;a~u;r{0P{s(%RHsXp4L
z>gd9Gl>7=HFsAOt^{)vTrP4z2&iQdJvA+)NFN#Wbc$Y^+l{6v2e~M9fbkS29-I!Cw
zjb}|*Wu!|&f?eLVTN{djU}~O{xS-Es+N790BI-t*AThwFm6lT?u)Oa;laP}az>7%R
z1sG#@ZUNXR6FToM&PE#}5OSoa0L~irQc0~O6ia{k+h4Hy`1Ru*V7&C7tWF24K|;T6
z|36`!AKv4#rqwbZtW8&^h%HLQ_W#+<1)hWWCy$l6?4AVPteUUpxa9J$2bT|cmISC~
z+sugYRUw$3gOz=mZ%00Qf5LScurfFHjCJiXlWp+a;vNlMXriS5`D!b-mHfrP@nDWi
zqr>qlp0CwhyDtRdb!VpD;|yK{loj6+04i7N9Dr_|YXPkd&|E{`FID*oF+}0Qga+Zw
zo>TxBp1=SAoOoc<uD|3(oSLDIhFLmF%`A|?W5AN4{I_!0QE#Ah+1tvUA%J%5egvRB
zycz>&D=)gAo@|4*o)^IZQ9|%GAWs~L1On({G>(I5kU$6n_E9krR@L_p*MIk(o=x6{
zjk#)OxFNB*5?qlunhVd6I1U>W^i>X<fTMXaD{cfF;DG*kJO-ec_Lu+LEJ6X$DEq$;
zvn@QKv1{MYyFeS4y6WK14mM4%=AY*jUi7Ky%m<Wq&vr#_Da2EzW8cpf@ZW37zsKP2
zng6|d9BNZ%RV}>0n14Ru7y0{JXrmnleVC73;6WoE7WL29dN{8aUZxN*${_fM_hrWG
zAG(IUgX~s-7geMG6!Iv1;9iT0vDR+}KJQ@Sag!nhOl}qY0B}C!#OOFAFI+;6Wh4aY
zih_l3Wi$fC7FcR7kMGNzCH>4x<up<+7v<gJ(zjZ0G(E%uDoC)uQji8{@jxjWv(rg4
zHubDosae^`GxBmn1I;~t^3<`#HIbh2$6xuo^8(;G_3gjlVt^}JaKF^VG8yKHzz<h(
ztuXm;W616IOW63fel_op2Fv3BE0$R+dBC7~IV)JBW8!`0mjH7wMej-rlC~U74Kw^*
z&9({$_D^qhGfwBUG~>!hU0{W2(;l8I)%jd5mMa<4_s>XsnCmDbK0h&<4HD@I>Z=$U
za*H2*(e({sU$MKJ-FTectVS?Iy+`1%L}b%~IrSIcWHet7VwZT80)Qpe@X_wW#BimV
z2o|xoA{7IQQY!{;H_wa6M&9ar9LTw~3-GYDddqW!UTy1~zCY-=DDcFPp8Gw*L-z5z
zfpNeE!-w05#--PiG}vJbn&B_1cxM@$ekd?%AIcxie3QpW@l4|-E?G%CQlLSvbIz>3
zzUfVSm*<2*{0Ipbq+lT4r^Kj{lc-qmgrI(%+!hncBlOR&M?5q9jN7In7G}0qG%uqG
zF&XGd!ii$m)NxfpIbDyNPnmDxl(=es$&@|5Zp-t2u^RvpxV%Q7H_F&Y)&gJPCUf)l
zXrPz_tdm@NIaTAm)5v7{o<NQG)mX3W488N3A?^R_v!7C$#VZ&fKuTEfQaYA^Byw<;
zQVltKf=O$hAh-9Qxl5Qr`gM(N$IJm~MWoEGb6iequhwMNewA}nkA;!1!I~8#*XPmU
zJkl3d0<_P4>rVc43A33^jZ~3O$Qs)wCOnymekbG`jmmCg_{O(7A2h`}{x0`LrKWq#
zMb&0ki3s~k{=Ht|JWc?o_=ZPha9?(;hQs0X<laseqFA_q&Vx{FVvD`2c&Pv1pl-?I
zJy63M#x8Z(wj_`7yJaj25+QNOzmj$<TMFrpUvn&7Rn@fRcy7x4++_S;BB#epl^}L@
z;jr_sZ#fdBcgGtaMWmDK%B9BA@X{S45mv&_4BnVX-YmsoKRwlxni3;wE5-{qd_O8(
z>-n47*i$MWVxttk{+ww!irCZxTT8Lo^D`Z3q(NG;aYW_a>(on<7<F^>4pqB&thzjA
z;SIYgKuunNH&LnY7&I+b+0o!aZDBk4b9Bt+e1j$B&1px7Ig~um{GdqdEJ?Q~hiFtj
zsLxtVHn#Um4&RmxEk#DFc~kJkUcHZYfck@mnkNa@`l=Z1IV7e%HrwcEmC8f?7)$gJ
z%fQ-nxM}ewq6wX|G@QQ->R27fI4rt8?{Vs2$C|KQ@;sS($wiT?w{NN0$!@#xwX1ye
zoQdbDudU|GUjC)iT$$@9>>5Q)IitI_pDQohW}gdp)rj|LH-WlV_vF8~VdIpT{k1y-
zi6+bwj|5gL#O}}mn3A!iV-N#8C=CE!v%;f`%?}j*7jwx@e^|Ib=tLCcCbjo^q#d7D
zUqpHSIEQO==I1@D7XBO&zvgjXte7NGNgZdA0(yQm^I2p4GuttB^_!<$9?P?8Z85=R
z6U*F(!B_?G$R4epq{GAB$hRko-%tMgApX4Qd}DU`Vz&MLpvjlm0^7X5jiH`;21(z6
zKEr-N!H}TU2CNdGGDf9TB_2S45Ao!aUq1VY^B(jV%du3D>hw5uH&te>Gd57m!CQo*
zvRv`Az+zRi6;*Na@4^JT>fEw(d{Wo3pI5KLa*@FdV9{#r_H4?y*2Ee<zy`n>v4$*X
z*9rc*tw1%@Fd;{#=3SA!$<3=Q_ftKesQw~y>$i7bGBz#zKtD`7RxG|Md|mhX!|~kG
znvy!0(&>@yG=$>xIsfm!D4CE9{Dh7qEO{+UN!(DWmOunXSVbn1ARpGq>_l;j-yIl_
zTAPys4N9PLx_cibr`+<qQU<|xJ>RMZIU})~y|B^$a>djU4!U%l^~hr)<OG`9@xcbe
z>YS;DlnFST(t3kfhy2!i={R~eIg`kWdri1S!mD~kD63|8>3@70^5&GT&XLy2sG+|2
zAW|wEnkdXFB6J0+-e{BFU>Dk8PT@M1#bD~a`ppfF`(VD;`aw`=kGH)Si;+%Vx=(8O
z0Z(QZEc1{z?9|1Lc1-^0?W0rXHqmL5GDron2oC~%Mk4LNGaLu)NO!SzrUkl74cP@i
zc5fa4QRcoJ&&<e74WLQ1V1P}p5}kgMaX3hATO)RSg?X$`>fx`D`IT2BlGn?m@qv@R
z5Rz%ZFZe})tZbw1TBvR#c%W>jVaJ_yc}*OBK#AkT2%Sd)#tkF@(2ZMG4HEfA-5Kc4
zvf<H>jAR7~bPn1BOq-=MzT+Qma>{%pGJO@6U-Ao2tL?o;5Kys+yzbb?>W1O7{*LdK
z=0X(WNcaaKYHj(wA%k?@sI&Mk&RK~dyhM}8DsSrUB+BOL&`I~mL9NH`#b|+(e~Bla
zN?Fv3q_07Di~?x4pQIZBi7KW6+8rVpnI??Ko29u)rc4tuWL-5?mY|goJhWNc@``gT
z{%Gi?p_)#3sINuYi(=|G#YfLKR4~Dk%o-W)YHh#E;W7qBe417Q$z&_-VeLjP<3{D?
z%jqvz+)e^TrA0Dj4K3F>aWdT_G&nUQifS}PAAYp!kO}t*Cyx}$&a7ytlnsf{qDEF6
zh^_V2Fl1pGZ$!?FR7FkbVWv^Ycu^zr*yA@iwPSrA6m^0AM9r^N2k%vvk|=fTh@yD7
z(-Cnhj$_h;y!LO^$_&T+&E!F%iQ$s{%%hq90`(EoV$y<gBgsv|I}?_}EW)DdERHbC
z1uU8G3Ybs$q-DileM{#4)bPStp$48U8KZt;JJx>67UZi@UDgPTo5XzCc=~U`@!<bh
zI?r%6-?$AwiIE_Py-DmnV(-{{m0DGet*Bi^(IEETv$fT#y_LqOT2++RXw@!?q6^jb
z&HsJhZ~2nrcyi>)bLalu*L9w){pY<b4wF`XFTSTtWcDaHB;}8hR{JDRZ0Yv$Z1<>;
zsgAiD@Mb*;KQs(VlzmPx4@W*-4L5LpU~m{Z@7ctRxJyw!z_JmRQ0}b}{7i$~k-(!R
z5o{yEsKej{#w#i7gg^63o*U?ic`;6YSmV@7frU8Xg4;q;G93$@FPM1Sjfg7KL%Tr!
zOfg28Pp@V8%{}82?PtX#wECb%t_8|GZDpxSLljS^BVw2?FUC{`(q<Gb$DG<t)Rc)j
zIUD%3rZRE_f6i#EQ`t_Lpfbh+la*oW**dyV^dDto{W7W5MJdSJM&wx0)r{2}BjY!j
zMpA-`>+lW_>1}oTUO5wIS|E!yO93!Rxq4l_W-26-jh;@cK4F-6WU_HUGk+ywUe=Y|
zNJiUB<!TC**`W<~o%m*(b8A28t|OO>kM0l3)Os=5lH01b<yGG-%x`&_@8I;o>N)`?
zkSm!OtUzoqC$%8KDz4qFzpIK8N>~O{j$L#V)pqXuuKIr69Q;?_=wX4;x0bthsUM=z
zxJR|xpoy7%eT(oGBFdJVM03&3bM3AZ3%2Irj!&0T<sYXifNGm9aRR|qHY(KOGb{yh
zOe@|XzFqp2sw9In&~hP;)bMxoJ|{(AoW)BHgV$mc0YeE2K?&vnfpQ9#1R#l_35CN%
znTiDZ7qPx}>{yPy_G4BzQ7Ktr0js9fE0<ezMYm)--nNdd-4<U&ln}O@(K_91rw<_w
z|Hk%lv+yQu3lS?0`O)RqmXv7x^vJYmzs#8QbC!VaR8_6(?$O|0hZIf($#DPi)Z(b<
zm<zvhsWN4(;i$~Sn9q3qwaIPv&30+gc5UN<j~ch7M{avPm9F?r)uji|rdq-hMpQEN
zX!6I*2GCS0r0vH9{L%E=?ss$x3TGTT^V{$gl{)Ar74t0XJYUlROFOMwH5~cKz{#A*
z6WX<{#QXlD*r}&W=kI0jTfdB5pZ=4Vje5}(@?Yr6Q%-GCmjrXu22X~K=49ZWp*oIw
zc4n4Gc1LgGW0I2bvMfmbb;{giZMD`n^<B`!)qW3$w|qp*q{yG4oCEXMPv%abdBJo&
zZy(L&5;MmM%vsaFo1vH!%NH~W8Rn&)htb--ri8sN;-_NAFOe~&l@-cX5ZI#gAG@74
zo`dlspYVjH!L3x9k|M&ou#6$#iph6dq?q>;-~kX(8vscKEK`<PW;*Lnt{J)+J|4K`
zD@Qjj)5g)Wrl#n?$Wcd8w)VJxJK)LM%R$Xu4^H6>%jBZzdQw4AeA`P^W6`>4iscVt
zGlEA$GgS(x2nFgBZRNaaPVMOr&$KtJj2%wDQFDA=*fm-rdT~2eXffT;@rh;G0@0Y(
z;LeYg4?5Z-4hx(nYiS%G%1`e@?2JAVjzoI_0`-<ggV#|I<ft&6CV0GV{=pFwPqTc0
z`naE=%-~AAg*!Eu1v1|o@{PNBPtqJ%)9>C1!`?x1sHyr_)=N-dXS~-XFVLm+sQkuV
zPo-8T5aTJ=EqMNEx$MqaGTREfi0iMaW#Tbm^_=V9d6zfI)hN187?<-QyupdKakx#A
ztQM=-jS-s_H3lcnhfcyG&EsJc@yx5n@hgUhgX<jpk2CE}GdJn$SJq-wciWxDix1Qa
z`<_Ve5yW(oeG=o`_WV5d%CNiVVI&05^-&-gD@_S~nFN@{0lEU#oRHp!>k67UgaH;H
zkY62H59+Z%M8eE%s89}l-@;=BS`ah*4&j;!c6&h5o>TS9@UTnm^V-?Lh4mMl#_e?I
z3XW!#FPl-@CnJ<;@>+-wV`9ov-(-#X#|PYXU2l&f^QJpPtc6iUJI@xT$7grsOct9A
zJMP`Q?JAvqe_n{@7njehRsO<9UE{^tspEWeX*tue&e>ff;q12)*0?!ihsE3Ln5K75
zxO5lvMo8K+C>PU{aS;jG0>7iN)EWDL?2UDAVA<)Y<&5`9RJ*r#$lL68Jh=0@zKGW_
z#4qRB{qg5V;|rfmZ~qkKwmM)8&j*#<DbfV{3wUgfPz&0S*y@!2`Il}{+VvNN<=M-f
zd8l5zLNjkP_?L7eDitqFYJ205Z7=1t>5r|IyXRiw1PQeEN#^MgJW-eK-~(Nc3<c~l
z@!bL@zC56%w%``fbs&<_;tDDGD_yf!{nQNM49q+IERVNFe(8%?73XRF%>C4}A-R_c
zbgwRfIUqq{F5c6Fq#PhT_9Y7tll!jm8e=j0;<;!)(z7PmhHy+B-z$A9ub-pea!QXN
ziaQd@hCjz$>YZ-P#b6bPLFy_n)3LSF9VG{eG{j2Ktci<H-wxl@0K<~cD_y<O&Y4(*
z)kn>Nlh;(-sr9Kk4u46sbA+Y6ikHat?#Wu-XE$p1Zn{@s(Qte5p3kh?leKcEONSHt
zkMmjZy=|b|30G)H%(<KWGulBgvXE|xvdZ&hhy*#XXQIxY_3Xg+ddm;K^wniU{L6>D
zQ6@jjm`@xy-5*D4Q3)QZCvzdcZSTszxbzyTd`-B2Y>ITK1+C+3!0ac{s)AocFj03L
zF1GD#8IYC?8jRHvF#m2Uv7H?J6Hhyu!2P4sGf{FvC3EH0zq`6@%>{I`m)@cA43kS~
z6SY|fz9k-9X%n5OP<PgYp~mx&$D53eXMHP9(@S=9z2^%3pykJ2VpWpR?sNE_7dRxg
zQ1K_HGwyUQhC2vL2(|ZGJoN_!%{4*hBI(_pekp1RvWWWTAy1;N5X{uN);fK#@XIB7
zE$E0hs8I<9ze}3wbK`ea-}3vBu}8t7v4I2+DmF7M{?)YZaeoRsqJEFS;eO{WKkB~z
z_UFtKEu+QvdOo}3_iQGOdZzCCnI^-=QadO=e7X_bD9}Lqeo1_N=gorh4@0*{WB0pF
z-72$ga#ziilrN#b2#0}Qms;#z07MGXdJ5#ZnF+Wur)=Fl1`AGVRuGTE-w<=$J7EyO
z<dG&U{WA;cdve=IANTWx&)nm)JH8W88^5k_5x?U7OK+QIkJ!%{a7;q_nYWkN&>yur
z{!fG90ALg6-gxKqyz!>duYD}=2@mCk(f<R60eB|84Dbh}!Ho#Dl7#+?({(~H8;cUr
zr0Ma@Gy8-vai(dOwb^O6LL^eBc_deet##OQbaxiQV7fY}60@g4^)uZrDsd))-&q|F
zQA9PvJURBcAx|yJt(>qzALZKgDvdpUg#YoHHhMJIkpIwikHe?e!K-U6Zed!X&(ZJZ
zt=lIr)nC|)FMc4VSv?K~n5N%}lg?Ta#y|8;_uq(k4()!kdhj<GXtohYeqFhJb;iP+
zs)GRu=4yol_-_x0ouy;|6^?mEyaGE6(5t4#gAI6Nfbc;_Jn41-9<UdEUk*qzlLH34
zbINL;9}Ipv4UKSA`u*om?75GeV`)f;)X{}Q<i|Pcp{j4EQDS9&sdV3eH<;PecQ#W0
z7XHp7fwKOKsvEvo8O|6``3JyD=?Do?;pInKDWa%<DsxS0spvTZ{H|8?h=ji5%_0lk
zSmu}x@t9es3tT5a`jYuoQmR6ZM&^zUgRVd8O-5ZoHl;ef8&*6>{e_q+K|>4U6mDIB
zxDhZ4TvSS(qEVudzz4uOs0;+p>0}HAzO6qJ3ksC{KOQHCXZfr&fjUC4!(%}F2AODZ
zc}P2k<$K)g$)ulu5gtclKv`XNVqBSEwskZ%>jxY0+w)HR8<XTDv;|e>l{;Rf?y7s>
zrjY_UFqx^@+f`lf7MLb>XcAua{$7f}on<d!_XHa+&4wYqrkaRMuBjMn3IOa`EQf_`
z{?f^Nd6zJ?#KfVN`}bocBe8ewSyMrf!|txK4ZiYEPqA>)-y+>XqK!?SS7~lYi`$QF
zY6=eDJ(oF9CLh2gXSddfrZD`?T|RnU*y`+QRg~mJ-t{L{yj%GBvWe|12d?Q<)2E;7
zVvW6GsY&FZ$TcP3{BO0QwbinFrq@BqkkmB)*L~P?veLUS{*WK)6<Ew9YkX0AH+9wi
z2Y`A9*sT7N!Qj%o^N@7xWd9U5_S)92`yt%>H@kPyAdM(nNAquQ#eRufxuEK=Mnp!$
zTW{76YMwW04-Sa6I-XIZ2dW)^y`26KYkmfAXcO-!iMY>>LCHKt>1MvrLQQ_;QoM2g
zVMf4crloUx&S?IQl+RgPGTqY5TlxXv^Ke?J#Rq?I9g-m&pQ+`>7bKDdf;)djnuV%`
zT_*<O^?~5WL9H{WTXcWGhS}A8Fu($8cc!b68!=2?)!`;&q%HGORt%v3nsifT-9+!E
zNuKHrE5ogE?i&BXrUGNfo5fushwI=beVFD%fUgalCIQ~H*p8?IFf&Ox1-ghAg%0do
zQ}-W;p-btsFaVdg&zt0zLnKTz<TzX%xXhUpOqmN4NbUU6cB6gL_CQHchrOWw-Ht)h
zEsjLj)ED`T?CplZ>Pe?=j|H7E<7{^OcPvn%%qU|MBN)<29G-)}5T5$5$jZM<#a|Fa
z#Qf%4v>R`{+!NB|*HsU_Ri)O!nK-#O+w-;V7MH!e@gG^)cmp%fR?)@oxKL~;6-feO
zmvOjSCT;>CK{wG(^r@`7UfQN+$KQ~%l>6iok&$b0Xr&)!|Ay6}MFKT(_2fOH-EKC$
zkKPg@t2zkDo)a6W(`GVVd*l6LC4<U7slv=F^0-gi^;Tj>f4YAq$U0m%+)qkl$Qp~k
zwxwtd5>Mh$WNAM5P!<1xS|&*)X)477_sQ?;CYUb)adP|1?BlmAtb97GXGH@Yp%Q!>
z%JjCM1?+7e2n7cB$B++{wvvY*>;S4=tt7bwYFtL#*^oK7_hal~P&FdD>Oxe9ru<iw
zSHSdZ#oYHFf2TZpbq?h1I*e{i(xMH!VdWECRCzyCcvEPSHK6=A6WPt9yEty^=t?sl
z#tb4e-Z4<03Ij-`<v1@<o8&C2ULb}VK+}K9=welOYM8!VZ3xZ2UwOPOL<RV}{&>k8
z@q)(%VfRD47SC--x-#ilZ!v<Gni#x8c3|W-ef?dEb%ncfNjuk~x>f38p1InaK6iex
z)+m3(Of;Yvumn#%iJgsi;8V=i*InS`ju@RXZitNIlPy=4_MmuHQk<jt!Frp+t7JO`
zDJR;8@VvPDjJ?`BwmLW+50K*L<Ro}{AEXfu%d!5jJ{!VXvo`o(`fM-PtuxDR>#I)&
z$KJrZumJ~G9`3}cY~?ZCpG5~=$#?E`7cgb5)$;$2+@&?-(+;`SUy`l)HJq#-a?vOt
zW3J(FuIoq|p2#9^ZM+r*1dh9Y>-j$Zw|3Yl#5Z8c6v8E3#{khbGYsJKqVr=<3VkWV
zu#&~Dr@XIys&P=pI485(Bq%){X8M$d-#8+U_Lywaay`V=SS`P`vEE=mPD$1EREJ39
z0|3}ksGbQU8)|07gk3?@JDj?Dvz*!~Wb!n<y2e(A)Lc1y22$O}TJ_-;wT6|&(%kA1
zh~2ij8p9{K$$sXH#Kdo|SSM{S$X{5Q+?P0V92_1LgOuODfz!LKR|kJWmvgulvDIr;
z(gjq`6o*(G%y!}@0xI>BPk6j(`^qQsTnPUP)7nVIZF&c4U>(e@Uak47%u03@XZGzc
z)g2hlM;X5?Qp~p*PU6WQV?qK-{gqjWUYWE;^4$`?L$bHsu~oez=?nYto49?P^aY68
z0f+G38F*Fo?k1ccEqAXvx$hQsLE`uJKy~EQI+3vSxc?#mEVAOZ&eb9N>l+`;k^>yY
zxQ{*_W<029!8i&%WM}Li<{P=>_<3Y<>nppYw)5`^(_8&_o{9zzoVyr5muJ0u);_8&
zCEq^7hAsL1=5d_|$EJstZ~gu6dTVEsTNJ-iCBAP6iwhII?t|@k2v`RN4aJz2gzkAt
zU@Gb}>r_0*PhOq=&Rn7BHl-ap8=!brGw^czr|We3!*P*`Fb;4C{iis(h6j~J?kUk2
z!hX9oAKt+H=)(pWq<mv#iCDOUzw`AAGT@IeWKE$u{Pl{zqT3tl&(KV(m&QxCAfI+Q
zOuy8Gbtbw`N7pbo><?yHlneiAV<6>PVx)T2z;4uh8uPq`p<O)}YPtxoi>%MW2&XO6
zftAWZG`j!n$@08yU9;`8$TzH*V+7xw#@F0qj%M9r%z59n5*L+1()Hs<ykMKh$CVr2
z&YP4;tMAa=_egnNMZo{~b6vfIvq%BdK5u3d{XJYKPi1*(oc)e>_^6hGe+<0A2l3(P
zJrVdEZL*<3VK1t+eoZVVsh_yfDh<-ipCfaa17)&+O8;|KMz7~NLkUHuoN`@aj2b`k
zl#2vKKWxlM$eu<QvLXQX=n0PhVu-b5000B<<gqer75(gJAbXRLQmAFsXeCsoS>tO5
zC^|{-jwA>IonjUsEtSO()Sax#6;Vu^>DjUQiWP)>!#<VAn79Up5h*8zr3LXG5zOfj
z|3>oDc2@Hqiq;n}$?*XM2sSrQX+BR`4x&8WQi;xFK6U1ZUL&o11Azw<V^kq?SOAV=
zluTyvenzJ8hOE36WnM!bD?vWjMq%DZF*gh|YN53dqLh43M<b&mrA&9GDVC)Vz#q_W
zRkCPg6Y{MLn~pii=WKJWd|Txt<qiZy0(2u4LU^vA-Lu`tIGy%53HklaCbn{b4cWml
zzsaf5zJ(?SYLp=u4VEkuuB=}W|8T;7v*CE^`Ia-=JUy!v3q|x(ip>Vr=uQA&qeVNC
z%n`!=r6d;8R8;S__RhCo!FUW#Zt98=i|=%PITfe!5jUn9+D4&Yp9C(rkZ^nRdkc=4
z<`*5ocOmHoV5RWXL|!~J++!|d$FVfkA&}PE&)4&N{daK)r(U)AAOTID{=EdHd2-%t
zWx5k6z70wrJD7<voH9Oca1!;c`>OR>%!A!%r2m*|f0Fls(7~v<_@H<LeOUcbMM9<e
z%u(o;WcctL2T#-~79aq_PCw(%+)mEkAP~_P=g!dcN@qTwSx=Wk%j)HCl~6uEHN7{Q
zf=8!T#4(3xpw`RF^{cfAwSB^&pYT-FPs7~kuRgyLXP>1qUbk%7c^0O4%EcaIe!1HU
z6NbD~&6kFqg!;_o*GjPQ1mMxM<jnbu+Ze<@CE~g#JT*Rlymyg?b$y2WOY-xl5&56Q
zkqli@iLbanEqkrU*^;r+pu=;awA`dOf7?A@tL^WtPF?qO_!d1IdLH!i7f~Og(F0V;
ztF>1EPFy2Ng@3f-LVt%zEcmE;eL`=W5WB~V>qYDlZEHGbZ_JSkZV*fc?Er|NX2}$b
zrlmoA2Y@{)%*=l|S|lO2F#<#Em~JjcROVA+-cjHIS|l$Ve9qzYjsp)+0&jE(U_WHA
zG@2WX;QY_kRe@yNNQZ38kN~C%r>E-WN<`%-05rr-#42u;WnfiguvlSeRd2Z1W@Oc?
zLJ+~DyL9Mp>O|91%V4N5)4I9$^Z-0nZkXOkhCYE<nVV)}m17WG4A7Su@<<p4HyVC#
zhkox??)+*dW2xgjOC$5!><5^Ac!mQ3P+)t#od{^sYzgjMD*O>_g8)Jx(!)T3lteif
zeLg2~+D(0L;F<u<;UolBTt*l`=CpW#JiuKN`JNO5P$K3{h98*HH_$s7SRyjmO<(Yg
zg7ccSRjO3^JHDgqy>?|tK*8KVbFj52i7>;+jUW@=aWGG#u0PL=&fUGpCa8FJJfAc1
z2t@e7o}E~A=lG;=5$e9ojgy|cO#!~0I%Ko{qxvUFuS9M<lL+iCYq}*$JLz;jK1#bO
zG?Qs1^jSiVN7!IQpLT!Z+%@#Z-uTI;tM<rbYJ5rdK1PoI$rdNq=cb2XK^S(7GKgAA
z0!}-FH{ar&NnfxS+bAE~(72ngR@HLH;}z<gDxZ(fCrG*a-G4H}+Vq%1)x*#`($+H#
z$?Nw}DVDCW<hY-H;lFg!f98z67fZhLI^Hg0qW*0#HyW(;E%|h#kc=P~(_v&p+S2gh
zubg?WMohV|P`@oYrD?~xgs7Md&X{simp8{M3~%Ho-u*fjOcv2}KRj(YPIQmw{*y-f
z`>wUL#9Q^_-uc(<YkXHfMpmzXskv93{QAxj@+}M)l}FF`q)lau)7fb8VS}D6D<3mx
zSV@JwM@Qeg!k$M;SE4`MD;@kf(|ro~Q}hTm5?OZsumVq8S^XhIs40NvION3^c*Vjh
zLBDeOKd3MkFblrsunJe<&V?gM^f2eth($@6L1R!KHP5zj@U-#w1<{_fM>dz{7XWgZ
z1Y1aHnR^!T{W3D(cjWucD9txfc`Q-CzCFUce$?|<g_#J80bzToP`_SVUZ^QxGokdI
z6y*dq?JS2@Kfzy24gXG+>#iJ~&tjfQiCCn@0<54XtW{U^o946_LVmQ4O!HgJeWcE>
zKNds-SR|N=Cm_~4mV_(=Tz@Z(sRezIyt};2-QXaiN29TT8ADY#JR{+gukGQUcf;ig
z?5iA*;!3Po98Xys#nXpoN(0g>9@6%KaFoujp7-N!+H{*_i~n-zzA5_3%YR|$)z1)<
zr><J{gE^`BWkRdt3CXXz_4toG+{ctAeZRS$^lJ%3RT6)w)L7uqih_ltcpf&NiQ+9=
z*exuT&ILr9M&4;$<{D(k=#P|JO6Y6s$%P!Uk>HlU$LS(M#;HaxWxx4+Y|oD&!HPZr
z{H5LJ_1PClZ0?Zf_`Uw?)UxK6fvrE5NqF}!gjhr&C)K{r{4#x!5or^E6;OOhr^KVe
zl}~EJw{{N_)$Ac^T}kU`LLhtJmyc=w_ujaDW#66OMKxU0Mc&?Z9QgNkXn9Xo)zJT6
zfxpY!-!*$K_X+EWcl>HW1cBxx-ZQK%_PJE#)KqZZafAYW!(LM<<VE3Cm-7Izr1-zy
zHR1Q|$Y0&71>!<-GuE=NpXM{%cK6?iS&8rbDY|ixi2vU}u*W0|_b%@JkuWlm*(o0Y
zuuz~`0zpvuxdH?y9mXCYM14UM04RjXDINfwb4l}xfI^unjGD1n2=_UGc0q;#A>rpH
z(MOz9R^n)8P%^LRh5}LU6l-*&{ncjkEoAf7Zi~{dmi~XO%8=Fp>NaIYVP&?qt1s=g
zcVm0+H5I>p#=0nZrY)}Y(W0`TS?P20fUUUlZ9<B%m5rpegtCo|UaTajoh?)%{ADC~
zh#GrwivT*+@FZja*ffSzBnj8nO6asq`}9jn`2&FS-3?I3LnR4}91%v6KnOC%0_+u&
zj0zDPF1n+5fRKu&Eo@0a`SK7xZ}A=!fu&$Qg#gX~;7(fnmmP^`AE@shXcpZJVJZnG
z)FjlKWqf;=97DG?g~-yrt9-1MP7l^pjosU|Mu;9w>1^!}GPxBHy9_fd^DunZ<~iIo
zGQhmm*6BKVI%|}pM<G^(oa(#d!i#<t_*pni?F*w!;B3?QN%!ISZk|4cmz~3ecKPiP
z?#ZUWz>wRneX6-Ar@SeO{OlzUSXll}Wq1x4po0+P=?J@oyLyL!Om4)CsK!nO#yAPJ
z@B!tsZmKtK$?I;hkJl0UCnIdHbLfCTU<GqMJTAa6!gq<443#8jAiOG@n{Z$ipij_7
z&fBU8lCUNWR|NEGyR(odV_h=m*d1vQ8IwDxOWtHB`8@p!$s`9k6Pcok&u38SYbX$<
zK7s|`m0SfPhd!Z6lG|tja$K4JoC4^#L=;Hm8^-9bIy-LoPDhE%l^Rjv7@y@z=adUa
z#qefO(e8SjXWt#cES5hDXC99?J{R6SQu?Z$=knF&!xIr6`IonY<gYo|#G(hMKKH21
zdK&`WFQt?s-6{!_$c=6`3bolEib<b4B>uefbA!elw+5-v+bf4J9#&ExeeZ`6eH!KZ
zqw+>M<xMFz6cx1|Lj7@V<T6+9=eL&6%U{u|J{B;XJs@c-nuh(4v8lTLHFY9b35EsX
z{aFCgv!MV03`7xv76$Yxm5|MgmUySae1NsKqH~#MEc6PX9Q7ZWDcHSC2gqU^bR)`y
zOXvw1HwD0&p?Y90k!AdD9Y8wVTDmpj{@piT)4W;?re(gP3fDH|UZX9XM;A&0G_DTA
z<1|^e)^sRVDrBPb=+@;>%fR`c7cJLdc2a+#L1HMFm{~iEXOV{tfH#SI71z$>QIAQn
zG0={k8QU;+pk_Q+$G-+`T&ksE7)c0XBm!q~0&B&9D+gLd^)?6*IV|ykk`<OTtJ1r-
z#=_bN0B2V7Dl`|9b0@}Z02pQ(Wt?ELR2D)%V*`+*jD<}eJ>=W~#XA+DL7}$`u|TN+
zv>XWMBAuD4j3Oi+-1)Ugo8;cnjQL*!lvVeLho{IfC0bL5eDpW-Tt^LmOfLl0M^$ft
zHx874{ke!&O^frHX*Xc_JG^-h#eM2z`_TQYyReGBW!u;i|4rfP3G5WGt?G>L{Cs(x
z)D#LnIjC7Sn#|cGKS!o>lX<BpRx+O;7L^LSBP#p*94*V0J;gC!4_I9o@-`Lzij^y^
z#%DCTtUgJ?d9LWT#(ufrx2bV6*nSpw5Do{m9TeSy#T+R+4m_!cKY7&UU7WsKePZ97
z_RO=yeH;J0-{~qU8Vg#u-&h65%U}V#BsrG2Ph4vQ4O)#a6HK~(R*|qS8T)~W)Vx}p
zT9~{ysLowG<A?E+n`yqxzRL&xb<V^wNU;U4^uCGhCR}$tU1DOFcOZJON=VCi92@qH
zeMFL?1N>Nub0lmxeEkrM5BJTw$|&=?*~DLem|SbefU_msII5n+*J7Sh7x}G;7x1==
z5+d_pO08Goq=@zG^9#4hju$e#<}dNf@K9T)7QQ`=Xh4qC!!(sn!EFzMA9W*{xva*S
zyUt~Hhk76?Iv%_vp3{8C`-T~ZcqAsX$tq}wr?eu8uNnJNtQDBH0y>bsM;WhG)qt7C
zq3-U^FVob?gi&ftEALcq6zrLh>icL*`d}8c>8fWx5G%yDVY0h;@KrgiSt^?m5^)41
zJW-uH094b*X!{b<iH`x$q7u{+Q>rnwz*L#L71#P-@2j40)H*ZlydgKfF$@5q)SznL
zbc<D0z0YKbR_GaL)lQUbHdsSnE5tQs`IgYRur6~wJ3w%bF$bC&)-8^*T0Hkj8=g}l
zP5jK^(wjk-b)=N2{};{+eV!S}s5Mc<#DnQ<%|<O4u?x;s)AUACR}dVy_$$lZFc`=7
z^H2ii4H7*E*m=vmgKCg-pa~a1h2A6^RuCpER<MKRP4UIL1oO*k*~}5rQXzHD?W0$j
z+();QU6Pr=J0J3L(7luuoszW`neG-~$*REhkF-H@?)6kLl5eq-%+W`Qi@3`9_qz9r
zggQJR<?C=u{fWv&3#G-eJ{_CuyM~u^HOC4sEyq2lU;VC?`TqQPQLu6tD#Dja-?b`b
zJ^R3!&(6>1M|-ZWqP6gLRMowEG2OZs$A=GtpTVwTtTjbL*NilpO$;k!oNwx`{&uaY
zHhJ{^osi?LgCfnFLNOe|_>DE9AQj4&jk1lbD=rlN{>|*y3SGj?u@sp^2nPy);M*CK
zE(&thnavVFAFxjg*P%(vi?c3{s4Db=YU)b>`)-rRCF)$WD<2;J4%diduA%xIoJ6np
zhzR;}Jai{)w}2bvotjI|EwPz&Tcp~!j93)Ab8<Xb=t08y_`SH9PYM%wrd`wAFp!$S
zuF`Pe;R>Y@_K+lEjR{ekrjg+GJL*+mV7E`7Anq3k9>4v2+<*&a7S|ebe%n^TYr`%Y
zWkr5A__@;pnbj_DD@|N<l|tOC(Hj3*aB@@2KBpYHey2y1@!jHJQd-AQ%ACTdF0TaG
znzl+T>3)Z`XQ?ZJ2{}%w&QYpxmsnV8h36^p8GHYpO;XY<@(26brw^1jsq6+_Y(KHQ
zUGAVlaf&qEjrcVk8>bCU1n9P6F5KywA=sm6_R+%N5|FhBO*>v!4|<cl!V>_`*_Fh%
zYC$n408wkmlY~+a*k)}8sMSQD=+s}5Opp>!8Z=wm79mlCeI~sZH3#yPW22;~_->k_
zbPz3Nk~+%-&iA6*l8dQ1mZ%M?4Y`9LGHdv#&Dh0XJGaWC!+zVYD6RDo_2w90QV;i!
zu{EOD031?^c)f`fJSZ@LVUiPRfCgFsiU52mhev#Xdb<y?A*n*LBFyRH{<HR<O}z)h
z{p(8Wg|6tF63o9y6e91uUK?q)%;|J3`~rD;&17F}%@|B}e6Qz7Dp_)fbOhSysgyl5
z<Ql?ZfQq_}i%=HW`Hb@XBI2!-o|;fPQ@l{5!e7UrTSohY_zvVal+UO09qPB=ly{KH
zNa5vkwqDkewG^Yx*PyhXDC-YL5<(vN5_MLFw;Wg9vRcK|UIzV~yhCNg`6UuO_D=lS
z@|Q62!^g2rHYdZ@O)k36BBi?5*7)o`2)(^gRUTZ19d$>Olyp>Lj1v=3%4BrUxN1!P
z_KWj)vt==GHP=+3EH5;YuQs}02W0%P=KIWci?UZ9mh)tK%#|!<cG!%2e`Gq{AoAXP
zxTM~@LW=$$QbIxlPY%C2y%o6M2B8$JVPB^oth9VJcNNq#V6P5Q3R2?*y1qgI#vAF5
z+Y%Ls4T90fK`gpLmmC*rJkRl^+HGr0euiIuV`H=|HuCni%#$nnq89;w0#p?LGH1~E
zAKm!2JsbZ9w~?A0r{xLgyS|*k3hH4Fn;gWq96a2qc*ji}tFS@h5z<<*V19PoxaSL9
zQ>=?*&&Z9I<PG<6tKVS$Nr^>yumbYXh=^3s0~Q2we<z;WucWC!D9lbague8g&^@7-
zsc$xpwH*{2CqVk?^o5hOX8+q?d#r=ejtUiccHT;YBEXIZ$6S%ff(0NOQRJf-N?^`L
zLy$HpRh5!;ekXjvKw$yrM-HzBWB5s0o7joXD6~No70E3ABan?oSnDhRe@p2yyL{{*
z^JRh1#U+fY==_T@@cqcsuhf!1i88O`hEu4|h~O@E|4qqEQuEIkWy+JG<>3IxhjCa3
zAh0>H6Hopt9$Cu)Q?T;(zzWG+EmJPgX;3*ksNDLP2=#b!<B7>lAl8hwT!1`F5`bSQ
zR3tKI0j5%Dnd1)ndqd*JuUO&{Rn1h@<TA<TKCLqmb!{WHqft1+zb9tGJd*UtCb@Id
zz`D#3agjkHGs(MVH&jLoTa1osCt}3(sD2TN_v`X-o`}Xkx)Vl(x1Va)&lJk@v<vWf
z_0|@<>EPX*hk#c2_DuV+mgy!1LP6BwDSzU&Y2}e=Z^O|mgqftg`&1k4EgpAHFVi-@
zKnw6x`BuAWL+x_2cs-Y}?NKwcAAj9R0`x2C_Y>dj9Rc9v<crW$?{Rzhady6&?@ta9
zpRdP;r)c0b113(P3-DaHHbRBKt06q0U6sO7U%>a{vBBlQH*X5DH!818>q06M=__=<
zC&~+se3y{0l5Z@$fD~4bL=v~0T$_bSeO(@yVV1~<L~T=`%yk9Ko^ySf0nAd#40<mm
z@);o?z!-#xhxI+bULhh*ZVbKPo2XiWaqc=GT9i!M$MdJ4gWTEAzY?P)Ezc#7Zpy#e
z82OAEi|76GHt~*&>9wflKfx^7R*;g`&~-w?%Wo7?>CmmOSLMFaeBi(>+H6c!lM-F8
z#6;*G_cnrikiVH;iq0Avw2X1PsF%9lOTb{ZR^>Bo6uEzJ?)UR;-w+G(N%mFSnF<Z9
zN}ne8=SDyUPZoE*-h#D^G~z9_pqcfj3GDPL$jsz4Eklhl1Qliv)(ri>xhMo3{Eo*q
zZ)2TucU@`ozp*d?(x%t2mjejZrqut+POU|hY3vK|4Y@Jig{nCgjXa%P2XZ1OUGzFk
z+fR4%qn=fm-eR=ARbu|@ThLKcQ{1uk#LhbXV?*G+p_Oy7jECXqyb*nuUX`8R_gMYl
zWVv|;V=G<La**+uF9EJ^IxI)$#V(r_#N;HwRE1$hma~qXI}p}+P!Pj^v%4^@Z8IIK
zC0!yj!(1<gPCq@F2d1%Rdb{nqaX%~#6+GtDHQ=}j+1nhga>)X<Tv;2(N5SFV-CI0$
zLbAcG^rZYF{7Tf#TvJ0=nSQFZSlsYmkwZ0S=dI^&PgF*RHuUrem=E3?8$(kUfNY(+
z&k*-i<vxV`uXmYj#W`2|5%+$>WV(yBvG5oEr;xJ=?zsQ~@dG}ddlJoW1h*$a3lRsi
z#Rs|+0{6cPkX{NIY97BM6Y@<y79N@SD#V+tc@i-1Oxq!pe4mq%@tL*C(Fx%MJ6G9L
zLO2EFluyk(we@U+_CKGv(IE^NqBH|{WRIUVY9rg<x+bzlG)wabD*06MiqtNv-d%Pd
z85*jZ@_VB+;~f4B45QyPq^7k}fm{thlYh2gVy)Xz-2))7!=Ie?hdCuaKW7B27pA^>
zsERznC6;!NyaQwYtNKSGe+E@}F^X(}5$rm665ku#c)jZvsjxa}d(lzWaleRbk7iVi
z&(9;75wl<NmLk4HH|4)&B2%uedr<OkE|nx^LccXCbSON}DN>q#MK)!l_^sz=meYN$
zWaOSd&hovA=GLp3VytW-MpQ1=eORD9y`|f_m|ui5fAN_N$Or6vS2GJtI6drPbJ!ok
z5G<*hOg5wnVe*+_u+;LrI~uY_qT4;-z?&h^0Bs&8?c9IbCs2B%CIf-diy5POKW8)+
zPJkmY52q3swfd*)3&Djdzc!=L<1oxY)K0=9`CpGZ@hkMdjmtk9@4v+Me2!+O*S%nQ
zRB&B9Z(<fJZCtPx6Wd~blgxZRfXO0^Y1EK@)0B~bKPc~|B1&dMb}|D1F-DhY8HFqq
ziz+xysA`FhPG(**Nz|Hka`C#ybc@4>2`YZR>#U_3qvqiv0-DlEdevOJ=kwE<$A*;T
z_GsN7-<ZQ~_rx3@jQfDy-n(RhalvML0r#9#zwTJ$Dh}~;?s6zF?K8eWB_w>$@qL~T
zBdbyoagTA=9mDH}-dRQ8BhT|Y&c6B1v!NcJsk6I5yhfC^iVQAc6e6^K!s2+8^$p*A
z4j1vQqvdIrMn2wr$u?=L-Xad?g)AW>cE5Aen)d$gcX!?KC?Y_DDM5;tz8y(Te-;xM
zQR#e66{s5hJL4&Zc)Ya(t=i<AN!Zw93C}dI{AY@zY2IIQrFwdK6Ruvyn4zXZ!g}<F
zjYc2)@E}FHP|{+OZ&BaB20kQM5WgG=c`sa2=K_pBhv+P1^-x@I?Ys88L_1}0@1ZF<
zue{&u4hWklC_MT3UiBUm61nub41Ay%tEFnAHu|kExQF|f_4NH2r@)}Wq)Nw_E8Ed`
zOUg6DP$juB{YeKn!q;%U-nKD<--vsr>8U3r$I~D?FpNuc-1X*U>NpM21KcxIZJ%aj
z7=J&ne42kj6JoU$aL#CyFP(O_;kwue1M7wPTJ6!gznT@emz=Ihy^itv+uItHL_8R&
zWfz%iq65XEH27(q)Bs?M3Q5!)i_1<DB`8)5Wzr~L^I*08{(ry~>(Pn_2t9n8(OD=O
zM}C2jM$*%uf8l(|+5vzH*nqPMwby&Mu`!t0s%zV7nJodT%*Fr%h55D_6VFEAw5Dt{
zf)@I{Agf<)c#*@(7L4Wxtsb;g5KhiE;J2*h*hJYXp69zitbSN4VJOe(Tkzl={i`oW
zNar5z(VpaneihFWNbhQBPh#(TT$d(IDno(lSE+g>!XPp~j=$4e)0p>decft%OeGAc
zg)}nP@@!s$EB8bA&BvJ->~-I9x#7FAR_+k_4!t_g3`(z-dX=Q#y=l0TQ}f5=5F$Y9
zmm_!6iE1oWQ6&4vKPO=_%uMUy{a=^}j{?~P5k2U=OAn{>5|<k||2`vU>zfF}Ew|?P
z=})0YE51kUVQIs^-A*nG^1V(Gk{uN&x|n9Zz7#Q1eg~rNv!%YY%H(FkV|HLi)lc00
zl}=+%?A_52Uw@QjSg)r?k>=%l&ccQtgm%$%&rAnHW{%AD<?h^-;yu58K~{m$COQb6
zT44Th+2{r1uqzE7<Tve6-cQy0wDyrFb9m^d2eAUgM#FDDvOSs5t9-8=t+*Ym3jM>X
z{IJ+4s6eRN=C<O}!@0-zIXZZN(K%$qhJGYdB&*x|oU+;&QLdP$s;m>v{?-8MR6%)P
zSm20z?+5^W4_=@i8W~&zzZ!~O=hUQJ9Vri{i9vi^5Y~LUlCtN!LY3r^n|Bo;zJ1)N
zOA2hC0swrdr9ouyas#0G6nde|af-lGHum?(jkq)x^h+t!V!|~ZPKQVY*>eS)U_?^+
z<KtaeKM&k(nrSn`@p47>f3J<@tw?ewLu6A_rSvLWfzS+YKWu?@azdU&>2%G2RFmu3
ztSUKk6@kmc>7by&O{)yqbG&Qm=+nLKulrlEV_jG3mR9FOuT;kvfHPsg>fZj}oIN+M
zsw#$qa?rtrs<E=Gl&#L;g^~HgfH(#?Y#_Xo2*XG~Sz`fRU_zm@G>gC<3P?%fv;u%^
zGRj&7op{@!yb#WbJeig7A@rSq7dfV>+}T!mdo<Tmbg0VgH)`JCb;+nt8Xr_gxc^FT
zBQJu9PldqZ92J{&I}TiW=u7UbpPGZK@AT{V2axf#TW`mURS#s#$wB2z_!c?f&!0;$
z<>l?mPZ+9i5irFqXCNMOh5C9$3X%KH^ROqi{^&{lhnJ(8gPwv`iYRDXuyUSHn?Tun
zCq@2d2g^V2&<)S;Wf$?e1%{1%65LNWL;!^H^Y!0oEW$j|@-GWHPK#?!P(_W8CVmVn
zC0ME?PaHE^5g_>RuNY-KdgmM}sw+g5H&u0}FKTK5iC5dX!4h;OEf#0A7s&ivGf2DQ
z#V`V*RShFNGED$r&Q+X%j+`e{VL_jW0aZL8@a+@;bpV9=iaF_$OoF<1pnf9NJZHPw
zC%gf1zX3Q_)9*pwk+=)FWxW~AvOX~}7(W77KNS1Qlb+xbu)I7gEk-`^#y?CFPGwg)
zF(J*&JnQw|>FtP&(ou-v>yG84uKw5Y+g$Ez2OWJdAGQ5|Kpa4vZO{r_MXI4G+_Gf=
zI;g3*KY~;7(m4~%)jt?z@;m?0#U_*S8)_1_ITu;ZnNsCQ#Om*%!asXvUw-^iPf@<^
z8lv{*9c<31hQ3iV`SSOz{PAxmB29_WTOf_rppQ`G2@LgK(!W}jM$3X-iWcu*P8y3%
z?01f;Ng&ucxb7aDMj{~<r#Mb;0Jtww4%!H>fH-a8I2sNv9&j3I?1YO!QlCVXQGl)^
z^w2a{(KTPPr(Tqug~MN9g=9u4UYLF9ju!pAOdQ>T1xP`S7&!T$jCVPdNCd-7MT|fT
z?-R!$0;fo#EfaZ3D}X{SUGwH=>;PCMlS`Z6_F=>Ur)ctLO=4hb6cXSL2USUkB(FWq
z%&~r{{UgqWc1+Mb9!Y@)#bAoyXS6!h2w+b!wJTR4lRW`B3lM{VAi4sbc|!oI34h?c
zdPYC%UQ}BMu#t|qnnsm9rA%?PLtm(|YwxavSlWj?YYVexEN^jm?|(+GLmdL$lXmpH
zKMUNJcQ6Y1__O92*2#zR%ft7XleF3yHpl<$D0xf;O>!PYpirq@!{SDnb7#H2>;Pa+
z52aBwwxq=ZO(IFQu4b+rjJ9eVcDpMEp&nt+oU^TqDU*TDma8P%8nIIS*&2D)MG%*f
zIcYl*@~%0GFJZ6w;WIR7Cv4)}eH4j>C@$kT=Z|oNqB$-#^2rUB^RNkzcgrzc;#iQh
z#OEL5IAdwBm}J$&!pLQpxioH#N8ox%ZW_aWvWZ3Wa40rn1nIO1CeiFi(d5gt<Y2l)
zq&^s*45x?*4u?bDq5-y5kkz#<4ruBkB9o)q%XlHsEDza|g@2zYUbNRDvUzd7C7v1f
zH|Tr8fn=ak`GI3`U^>^;01#KbUQn0&`#@N4@9H1zuW#3PCF%-m)Wa1r!d?U>5B+`7
zk69S3RQt`<rope1T0zqb+%^9)A8B^*{lx-vCAr-#q0Q=gP^KtAJolJJXbhn0z<knM
z@lvZg-pX{IXnYGUlODa~%-cG3ET<uss6IYqKG)YXR!oxj6oRl-f!$12%T?X)ks`m@
z+u7&pNxj^^EJpETw(K6OTClGQOY^!oIbF)e%L$e-*Euf{VE`J%i81|nFZ~U$aD^x@
zX;rb>{9Gp^Sk)k!u3Xos{I7{#=|WZ}xe+(buDU?!^}rZBvI_JQEqO^k`gUX6yxJ?k
zQ-6(Fh`s1dKZzzkY__P6O%T5FBl2?+Wkr$PHuaQ;YJ0_a@p2XNiIAI=ehg1}0pfdE
zkT#0ens-Pjv>=5HVltI&H?HY?_YU-j-m`gbg786c1%JUIjgClPOjS#;J@p&y<DuSM
zUB(j}I%9~?S#z+eDVBswTN^C9Fb$p1K363jmdd<u>T{DV>29q<S$;d~U#f*VfrfUc
z>4JQ8no_8;`Q%K`1`P~rVZv1|TAP8VFT>O36aFhhfDt%RvaydM0EI39oW%G%4sd7F
zVL!paLVv64#s;7I{9Dn7yatc0+NfexWt2rHgT~~(9Zfd~`k7Z-k;`hcBZ+1Gn+s#8
zY^Fg@qFO(YS<*j|fIb;`d?Y9HG=e(17l48Q_AFI(>A8<~Vl}-z1>h}*lo@C=+{X4s
zI7?GrAvzJhH}69Oby_lrXz3Qgd>IVmyqP>?Z4SC~L>cQ8!Vn?FDBx^6zyiSTrI+ui
zO%T8<Ucwj%ID1abqMwC4+MEvyG8fi<oU4t|hsGTKupQmF*R(~fu%@m3Pk4%>Rrh*Z
z{n8UN!DePm2Ybx06^Sx9v`0c$x~k?sE24pI902wqAQP2dnG=AE-M-Xq8#%95ElTQ{
z0iKZ&Rf}FQ7tP*%##@J_9y!*1Zhu+f=R8styuZuD`TxD?xfJ1I;V3+)J8gJRd-+-c
z1k}hp0s_A~Kqsz2d_GY?dybBw=f9BoT(UZI79%SY1PBO*F|wKhfIeLa0D7B=Wv#*M
z$m{4;Z^30&3p3)WAJ)zi!wV<=ntb$)e)<rabCnv80r}qi9KTymKZK#ZmTi;>d!ee*
zTbL^55D}J5I;!AqR@T`c-E#Jm`SU>uLJS<Oh%LrnFK7nox;$!SIL)#3Hx{>!6u3Mm
zrMaO}Ic1j~Q=?DGU?C%bIH=}$T6=6JXk?w@en;2hY`B2gKE0GxT0(LzA07z%<}`gA
z#&?mJY@y4Mq4L8Nvb(|B$L>vs>(;7`{8Z|xzdQ5YEfg}=HNjI30+269cJq{aHm^+S
z&Vn6WH*yQm)GTNklLzriOFZSwL`LO5({)W%%K&<C?%dgcFoxtfzrW-xcT_z2p#tA!
za_=nKgv_aFuk;iUjmu0202SyRdC4uy+^f8cD?>KsL5b7oKY5?m4fH$d1m>FRc>|&l
zhN7gn39F<B*M_>@D}LOm5xI??3aJH8XNqgSENUiYy{hdL(#Q#5sZXFN{zz=m$p-R}
zmCXFj-vn(W3v-u1lMpj1+|atI<d;<q0~exB)7kdV{i^x{sBqD;`sfWiG*KJX*~~1Y
z`~b)FOd#XzC>_gYRn<cOsH<>F@9Z!@q8y&js-F;)$UBU``MgA!mNU_xET-H}#OX;?
zSny2iUztl<wJ?@Fw}gd<NVewy5IOq!ARkqOCM<h7nE?6&3ji1_65v^Z^u?KA@j24t
zJjokq$dlBkOrA%Fz8cdOG!yB%s-G*Lzcq5Av3xAEu<w-^vvw9)=9U=U5X27GWVQo{
zz|-ZM@D@A}Or`PU4*dD%7Q4-xQ&$8(@!S|d(%Zuf4TWHTFv^gI_WgI71H83{1;#Rj
zPBh=*XY9L|f8bX@Mq9uVSdhsN#z?AX-irJETb+=8!+Mgu{rlo-gQ<90xbR=Wd=w*1
z3acm-Uvi0WgS{BI#($u~6I17<!kbiR3(lNl(K3wSH}La0EGUZwNgI921uh3jC0$6J
z=;9Sz;u8PM1V%@&q(b>)JSFkfjPPJ4^n^#;bLJ;SH!Gp|=XU#YK~h{4C4I}8v~$ae
zE}1+QlFtZ*;*QA_#1jf{iDIRg;w-SzaN|G)O9`k407CdT07&3q!rk@+O;5;0kOXsN
z!VOPIonL}v3|s#xq@pch?qT+5za%aCX<NN`)!@JiilVrh`u9kh1<gyl>N;`^USY7&
z6(lHRnaDGhv>(O5GMPTN^7u4L&b%EPrio=^ACx3dM?=~IQP>UgM6E?Ano>6u02+$0
z9q+M5Ub@7$uoP}3p_iqIX+A`pm~bqENFlQBMU{ER6I#7Q^C&J*PqSx-TAw1jrc)xj
zio|IYmTj|6MDRI{Xd(+EW$__2SXs+9qwDOtEKIEBo3$+KZRr6?gl8F*h#Wa;@5x4Y
zQrn+~`IAK9ZLa;8j!Rmy)g1(ns`M5(B}io=+@ZspXTp1v9iB|a;|^^dF?b6I5$h#R
zNPssVVc+Uu0aM~r9o7-JWO=I!kVJuq6t;4p@VQ-6<@2N$DET6hLsV$6oI$%L*=`;y
z=~NA69*yly>uSuA?U#Y|Zl+>hr`YpThHX+S31y%D&^?VKU%kbe`-sv%F<~`~iNyp^
zo-@4o#Rd>j#U^3}ALR@<1JWJ|8&O%kV5~x^fM+QnN%YtYEfM-KG0BWOfF*7C1G(`q
z1yWhs>mcDgFUFs@QHnVoYnxtV3}mroS^B)1SIE>Nfw2ld;i=7@dC(`4qHq_|C$n8+
zuiqeSJgb$+-L5C~EhXbxzR&0uohC1zw{>E0bG36Ed-O}wopWC4bUJ%0J})G+vWk-q
zq=o;R&K{ZMlFW{qGWk+SCwZZ~+$P{ZDJaF9sL{rD%K{SCo*;adfcG714I^F2g_Qeg
zw?0a+ZN^H5%NYey2u7qJh@cnXSBb5~t4_t4fBDY0=TY{2M|Lk%gz{pxlt8v*I<$G&
zc2h}Gl+4kBp;6=TD2~k+fWotc^Asvi;yx^-4MRB|3W;W>R$Zo+BHTHnD^g0$gQ2AK
zeX3F;nNnG1t?;jqX%L0Npy5G1UvW4r_u+i*6z%dHul|ddg0V4B?!3{jqz2ULWEgdx
zZ@lMPTvwI-puPC{94x>bjpfCHM5gBcMbEKE7Xl>ambf|qn4pph-+keem`6-RvFsh+
z>k9z%(fYKEqQs54q%`ahrowyi%YKBn6B>kzn%9{V9H1pV60{X@$1?CykomUh;ve%R
z7CVi4*f95NR<WB)_iUEZv@?I$FAX{@eRM1&xwAxKL8Iynzwe?U!#=z^Te!o;LW~e&
zp^dk7izocdmpNimG^sBLWK}p8uqMF}HckHp4(B}qYZFaSBJa&S9qJ`rP$od4U{!>r
zT-wC)N_vyNG>g{p&Y_ov=ArufP>LIP%l)<Dm4=KMZ8DbOQpv;=OuJ+SF|q+=zQWF?
z@St%yRDfm>vJt>&9+9BY1<CYFU~X9{jDgtk0L)>Ao(~D%T>`$HUT|ihqB*PYR7vL0
z!Zp00SmOO#f&9tJyzm=jC@J<>viV_FiW<&T|46LlAY`Di1t5m`N99_6B=8gKzAQhk
zgJX9E$m>AZ<%Yq#`a*CV2l58bs`FE!R)EJG1>fWj7-5Ic4w>g0%-8UXOxX@Mz7415
zn)X&<?&9S$rVHFH0c-cQ^Qj@Xj9|smw-gO*dt_O@wz{@X6sAf_UFeC9-8RQ1D`Ypo
zGNh$FY5Jr)k|7g|Mbb_A%@3tySXn*>To-@gO(F-Y)Py9TX)Egw8j?8^TE|kMCb$iq
zQs&?kL9lXW4nnfjAURHry&;G9W8%fO;avCp%9-pfncF;?(}=-omCnk}KH1S+8ZfVs
zCBCcCBn3rFjUBH-Y3Gz*QXuA2cuOF25m44MDEdCcFtB-|9k3-OYz(3`^RS6zDRU&c
zmlZi_q6r(3PaS5Ur_T}$N&$jOYewHOc$9pVj%F?*q2Jd^5UwBym75?1>}KiDSsBn#
zJs*~PE;&pZV*pPwIfMDs7`~K;Ioxyj%DUMV=3l*UGdf!T)JReA!K}@itc~t7W#%sM
z5g%oGUiG<4%`PBZj0y~tvnrY;(d=h6r=Q-!24N4!`<R!gSfhEb!O$t165#FgXbH8!
z3~JnCq-=HuwzhN$Zl$@@@?5J#;Xa!f#0u@R<QYO*=UWmQ+*t>1$x=w@S4kLV5s3a$
zN}gV7PORkrOo=(FxHo<}Rk@6|8&+W<>z1-wixFjDfd(~Ju9Z{CcInJbX7wf}Y!U-B
zb0y(V5|)Xjn2Foi;Dl)~y(a+@O}$nTja|lpY-tkF6!baFj~fZBs{bMB%;TZ_{`Y@o
zF=NKQWM{@c82i4&*mogGsBFoWEfh&(Y-7ouWT_cj_MIqcEXkITN-80QBwHoP`#YcC
z@9+8Nemv&f=RW6pUDx$2o|bWR<HR#*eINUx!l-whoIc7mqD`$_d9L(~1jL|yDcRe`
ziRaQ_)mnG|>ANd0_E_|ZcA3E~tcl<xY??JkE+WlBBf$q1-z5a0zf*i1i?cAIMMWX|
zRiuQq5y6*6xu~lYQEVTRQhnu&SQpLpF(EqX?%ULaVZF483}3Q_-vWRPWv#R3tWLr;
zhUoyS5<Z>+|ENO$f+8OIpgw^?=cNVz1>paS!Xc7DL{Mq>%l#2Ko*#rad>;ZtHA17{
z+5NjIc7;t+wK)oPS#PfN^_?@E5jhLVI?GP)2{x{M`I=17Ot@}91XhXF_BmP%`TC6s
z!>f=55eNm)6T%S3ZNbT_x)$IJ&V?c27z(gFp*jiG)o*d8e)rSXdYgU8>Ioi-L_`*Z
z2p|d-d3!<YmY4W@#FO<OJ*%pD$QP5`y`IPHcAHz7PQITR2*^uME8*JMvgGGPwLV_D
zDtPh%BO$cF{$}!2cuIC~3I~6Rs~=#Yq~q<1w_|c9L1Qz-fx9usQ;%#BC-2CnQ|^8u
zbe7qnifYI;THvuMaZUhVYJGctZc09z?=hQ`yVC4p$_U)olRtI+B-#%`G-m%GrZTP7
zU{?GZBL5gD@j{dmAqru>8}}B5<4<-!TCH0r3so0Gjn#K}wBt^$g!`!c$KNfRQXCh1
zsk*`Y@%h)@%KE)-E8k!1jW;u&`4jF6`($nO%v=3@V!F}=1B>az;hc4gAM$N-iUjTg
zOZPnQh5mM!iRxA3t0&)&xRPqd7TXqo&%2!*u{)mzZeg<{(q<K)ANDSEY+jgbPT6G_
zN((QY%t8&GudtbSFaGSS6rT=~!^$~6J2hE4C|k4HND`lY?)cAc4!eNeS^9nI3wFjT
z?0MO_Q{`GiX@L&61yr<eT<KlAm`qtqda8?93odZzMD^d1agl*GV~79;y%(9|`<v+`
zmK&GMB&wZ`jY{!Yqt`JYLIDrabF1UQi>|3=CK|L*Hhv92?Ij^y0DS*e?RX*W!%YCB
zNcO9&VS-6ymrM$hNGY*>Ex{6+dOyI*{otLgD9UR{K2pF;dqwj_&qnrn-fsBkuEFm*
zKG(YIzgBc=WCv@GT_kr^|88U%J(zsV@;BGPOGRe(Pr3nwWy3XHTmGETjlA5GE8aai
zfmg(`6CK#w8TI!YKW(L~1tNBTT!QW*R(Fv{W=xDxx!0j7x#2`>B+*>pRagE>&NI{A
z8+mCPOdPc<PaCG>`EhEHT3Igw>-lXF52e$Y*}Ifo(dX9b(bnX<%te>b`*GD#*QM_{
zY>%dw7B&&fvH>HDj0=+ZD-4i1h---uec@-{A3Y7YS2_T=KYR?f7{Ohirm9p~UA@Sq
zg(Y#^z$-I9L!}u=C5bd9OFn=|&)rPfzL|~{BZ;~r_`DEj+ZdhOzf~38x#PG#h^VVc
z7FcVAV#V|qJd-hVHybN8Chq+k7l{28|J_NjPiHg^fH>ZzIL+m_Qy-mQe7(tVC1B#|
ziOgQ-!&g~Hm`&C`>QH}Etc?-r-96=eUv#^iV{i8FjW`Z>g!KF4S0Bi*sf~BQ(1W76
z&j*Di7(V!M5M+7;N$w{o69B_s6wv>ieReq(B9{5gqshGe#LN#;r{DteN$NDXrdfhT
zj@^0BA4$K*XkMKGi0G$lA*o6yexhJ>Sq?Yu!@!<0h-o@F3WYcW2*6s5Ul8Yf=)9q9
z;p5otO?umq!+YfQ`Re2I!IQ2H^FLz}2gUCG<<{6ck>TpC$qp~#l7JX}qgcavDF0>A
z%MY1g+f4R1NPvQ@R-x^opbOMyGLVwvAVSMpBqJQ9NQr&+_AVl5WVHeK^LxeU1AEQ#
z=ieWLC6L@VSBM;=J;$Y|`_9u8zwOJloJ!gI!l5O%4`bi<y8fFiKZ*(NJR>vzEXeYo
z#KyvtN2jC<UPd1~D<BVT_VwAGNHp}U-AGKpOu8+v)(6VfG1NDO@4#s3L40Wg_;!_B
zs@r)IhuYgdieMnidy3suVQx3>L4)#SB%pyVd1e~WVoAV4)NFICqK_iWv9cKu9VNG-
zIOXB`jD``5k)uz#j0@1%!O-xy9??u4t`o-D3HSE~tYw!;cNnzx7=fMD{7@f0|Lwz<
zWT&N}axVOswt_{)JAH5!+WG1!U`J%V41|in0fVQr2N3~@^#e%l$pR7cBFAAol&*6U
zVT3tJ0I~&kl~sVi{;L<0;Hr?nrGoWc4@jH`AHB!~ZsswoElecJVj)EGL7{Z=0w7_M
z7JwR4f`5NOBe9x#6h|V0LJ1ZkWdG)yq#Pe}y8BL24y63!em-F>+o5lF?W#Dw>{~9r
z<w79yeDf<@&^tIC<vC09aADa`!pp@W(y3WitLdNdto%0?)5{@eP6f^*lAE={7n5t{
z1gpulOvm~U8+qqBV#`iBJ5?{Mwlgnj<M|I(y91I~Nv#d=dLSgNgAT0vI#nG4kU0;_
zMZQ8tAC|6LFvoUN&_)0>L1`HTlnDLQGfLuv5~t@hv07<YnP2rOdb8*B{E!>#?1^T8
zWH;UD-Rb$^Hvf}&lFw^*XPoZ|bBFdTvB4c#<lfyg%CbMZ9%4K+d0?tv4}{=EKu|#w
zfs5hgc!&~#l;|)ExDzdK3XE9F)!@b+)k8CAJ!28BIY@ej7T0x(6D`i@HA%;rOX+Nx
z=7`S%txRvZp0s8AGk@e4URhT?bgfaAvf>8d$bf?aS&I)To@Iw?n%8Vx2}$-Z@+U%?
zgQP+@!d~n(wuIYQaguIsBNzGpU{nu7*eZW!Idpx7vKygao%nJ$j9V;W0kC?GtI{rS
zr4wPtT$2nty^cwU^pA{cY#lv>$tF0$gp=cx3R8L1F98@Knl5YErg?4g8_NxHK|PH7
z9?n`BFT`?$M*a{}b3ieYiR^UCzlr@P#OJj;L^^IpL3-56%kx^|AR~zv;dIT0|CODY
zO$LzQyFL+*5LqTiv5BEI>7Q6ZpO3LDIW&MQoWCW$eKm8*O!oyDU|_W9j!R|GR{)Vy
z5oU0A_8osd<g(27@pcZ*#t%%YIHhsFR35KCyw)GC5T^<pEiLi$@17{>BD1%0y<6Qg
zXpqaO!8pTyeG?S+zo@|s7!PE(pT>wb^Wa7aWb>`LAIL>|0De*~8bzF9fT1gHfn19I
zhkK_SkR^E>mdS2*)}+fOg>X7AxsI{CSEoJ^ilw0=?4FP>o7*ou+7%kHm_l7g9!r)w
zDcr~|5;w8X`)}m*N7t<AV}g@<r)cyq(i*cBc39%5OjbsJa>~^PIe)vX?Y)J|#v2vs
zNW~LIYVqmc+#Z<8uu#*?Z5;TR>4o|c)s%Lg`$Qp_#iku<77;nkVIV1i{mrqTzcl4#
ztAFX}R;7f>9Se1(Q#gp;biP?3=e=FLf2mp<!rFADP3-*oT8(P5CXRRIvT`lsxBP8H
z0i!>2$Y`m0ci8cjq3kKYfuJBR<#BB*_C7vubw6%w8b^FHVDap8fB=z;=Ibvp-ixMm
zK8if~U{xrs*$$Nx0~ogZ9cXL9ur<6eEU*}24TrL&pBJ-;mx0i*ui2;vCP4hQ>6p6|
z{7$ES%CxgP6B!ZDnF~rDKa}rqR&5UWah}Mz6Jz};Q@LWhPwsK8&$Zx9WdwUK-^pdK
zyG1SYDZ(-LpNMz|8RlQmkTA-7dP%>Yvy+40z;s3BNG|X@m(t_up9;}jDt@x9t-owO
z6{^lX$`0MhAzA23umd3&rQvo}hMrmB2LNmLr**hm1HaufM5WjdeY;*MhxK@bPiAcl
zdcsMRxxk74{n<;k7bHdoWhL$ZjaHyPob~aS87~VSExD8X`OTh@bg8D&>CKCo-^52P
z4D%K0;|1&v`>Z90q8=&lmzWR3Rg<^B#w&?9(*_zL3zS(R#RiGylZH!hvq7%8z)Now
z;e5?p6**@jk?-vosqA<nBvDD!KPvi^^4F8FpSW}WVKg&K!bMJItry}1*2N|4oS&<9
zin1z^=FZd*8qR812#}OtLn&Z^A9G_TF61rCvywtLq!is%{ZW^Eo?EFyq%y&^{0uDH
zzpjG_-kvgiN}Cngxo;Rn3Uy}cD~&o&wtM=z@3Z};sas2jehm<4a^CpoUB75yloTRp
zcGpFk8CIeaVm~ctOpZ8DUDhY_EBR#P+y}7XXbu1_XY5o@y~y4Vp)bI4r3*}b_JD4b
zRHqyMxkz$f8+Cp&2eI_4<lkSu<tBc0gGT>xP5pfzP1(#x>3L=jDk{spL0t@e@_RPO
zfpdp@#{VY_z%RkD&1e?K3;Kw^wm|%`2{HbM4)Uu8#y0kQM_z?{wJyRn*Wu=h*TTh1
zT&*^x(Ro)*Sh9m}{{B!L)aPIIM^;pF$ya_yA#YFk{Hu|HvTjpi<b{8@fTd?^-G!O)
zXB-Va={{iS{>YeMCky(tM6eVmB)dKT1EQe^hE>Be2k~&uVq^+@+5BZYaHDd*k3C@Z
ze7o4dWBmzEtK3GYt;QbOty<!5nNw&AW&=sUS^h^&DmaHV-s7C4?o^P?Ei13C?CHx3
zmt79dxoJQ&mc8{({EjxuQSpy6jU=X%XM&4fNBI2DEc})j!Sd2ULt0EPz{2<lQggT6
zAlJ$o-*0o*^XS^LuNd_hH_%TQ<r5w3G$9EYCiZOmsMRe`VwMkmtT*M8A<&Vai?V|~
zQ0~>mV?31NV9NM+OXzV0x1n4OgKrle?k*a?z2N}_sqNDLD}c8EkR=f>7siELB%tRl
z=TOobV9aL;F;gp54=!;}me~jy_T-2vaK?vE8D{WN01T^iZXuI#fj*@XfqV&u6K%{r
ztl;AZ5F%EB#(m$P8IxEK78o6jG+Y;oP)x2ExikRr<%yR7KnN;PAEdBl1??}99qm!l
zWss7}7@L3cZPyOrdU1K_iRn>K*^sB?W-oMBUhWZ>851YdwnE+Gr)8U`<ociVuZDVw
z6=Y;?O$lvUs(DBY^vmtr7%^*!QA)?J`}9xV5St4Qb(MIvdzm_&X|Xu~+2)V01(13E
z#bz~TwSd0m)1uqrOZlsPyY>UCIImvm<)vF0y*>lX7b<r1{KiG)Vj7{<ItuT&5Eh}&
zZ46g_6}Ko+S?9Jme%&Dr%7%oTnAof<Q^Yb?J;;d5nDVu^8;x0(${-2hZOFhoQF)+9
zIb>VTcSu=kP#G)8`q1^NhglH?%dlW4j&+_`pzp~_SPJfpQvWL&7f4DRGBS&)plI3D
z+BK-GS{f-NW3ct)K8_@e;qqejrXVAapkbq6nVDO;JM^+Ul!$vp)H((LM$<pHa}eU|
zCP#XYn-CJDZ;y1O5{{=Up;$KPfNs@&Mbv>+M8OvHCvu-DOn2k45WF8RetJq~_pQ3)
zCrQU8!1-07YP+oUhq6t+qI?g%yI)|9hi)ViGS{b1Amqy(VWvd@I5E)O$u)85O|Nhy
zab)hGph~e!_>ne67MkJ?ZG*!qZz#34KcgQ2huVX^nO(d}z8Vw0Qs3s#6ZoDpSn7DT
z;TC@;j|jC>a&cF1Qwfml&oY!*Rk}#{2I+*r<%UA>n^~gEwiru=%2cONS%phu6;3P1
z-_h{@zCM(jzNzfH{j7ZZ_t$+7*ujAc+LHm4nH==gT$E}Y77gi=Rb$X?9DPvEN&lQ+
zrqzCt_%QY#wRZ^+g8eQ{3W;?Op!pRU9zq9#F&<J?eTR_p2=UFwKTHm;v;8quk(TOj
z&@>wL3Go{C>U`ln`CwgE&&sL1|Gwo$6Z^ogUmLlHO{6k~PkQBB`lx-Q3OVy1$%mut
zCU8WVl_eH-SlL9W3aLJN)nUyxWjT{<GFqsdhBr2K6zZGAv(2rl`kMQN9#fAkjCyhX
z>_%n!*Roy#OP>egjb;&Waj6)mW%oa2^L5;V*31266@+?rS#5lo_lQEl(y?9bJMN7s
z_Af)<jBOrQ2mvMg`(kr};zHiZ+|mo^tJbn7q54A9aZ&6pD^-zyao&QEfwCt#AvYlj
z5dd&Pj3^k$hRNUoNc0u%n-LY2;5785w--4)S99_Zb6wzpc!$VgbpWVGPo^;}c)OWT
zFZOfdCgsld{bAkSb<6(4HU+xH^skxppTxo?lD=`u4L{xGv16ThVv}>)`Zf2EIETY#
zec2xOSlfxQ$=A;8tLdv-R*IZ&q`k}t^_wLgO)Z?(2j8uyRd!swe&Q5@gXJ7zg3X#`
z)y;H_j*OLlVW}HmsUuo_2mFg|NY6X`eft7!vOp{X=lRWUal163Q)$TK!V(5>^nj^8
zkLR1iyL{zoW5TM#BVs4?tKKSxZSRxjO<r^9U*-9kyj$=pCdNS>GlVj=+#ie5^UTrL
z&WQRUPLD%Y?PtJCE0$E7y`T)-7^0FomKCnz0>wJrTX2w6vBFYBUQO#^Ye?88b_@k!
z9WW|36{nlG^lfV*HJoUGXs>Mzrxh4sXLbl*SYKqY1(yBP*E|(@_H5)`H|a;`qqx@f
ze|vo>z`!&wMkJfqBz#tj|D%f-T`M!eiJ^r8(j;FD^Hb}kAnj5MjKX5Ez;JGnh8D?Q
z4l1h}+TjfI`QZ(RYmXgW#0PH{RBXMiGPH@6H-<qwTYvYz4-Z>ai0Y2Y3s%@#8@c+&
z0j1MhONFX%a188gi#7fUEH}Vu)s<_Qc@?bEpjR9sv_<z-{h6WSHkbtqa}Ox|O*@Q8
zdPAZ6a5i9-jRE~Qk&UIZ@XN5IuAs(X(VD2udS>0hzDw|oZ|y#M;=O?;#ffj$IR-Wl
z@A{g&iZQ4~>yI+<sy)!F#aN!Ye|SKyXvTQc#i0=5A)3iDQ9jM?ZQZL5t4Q8dE3L5n
zsq`6E%z%wh-;7xPC&|-(TvVM{Z%!$%-cFNa*_@}R{05bs5>WIStZWYJgU5=fV?!58
z0bhwR6nnzZk<H`{48G_%bva4mLsE`Z{ZM=3n>dv-1WCDC-EDI(xwnAEenurDn{pzI
zOGLmQ&Q3hyZj2Q%a$%(9x-3kvc-(jL5Kt98?ljJPlz?^1jKLUgX`0gbiGVjg#(8lY
zBazM^x*^h^A!4{7O^ZcRZn=3hqv-ZYZ$~=Q@=SW6#^RQUu??C?VfC}ik)&~6)`#pc
zMwzkRG@6b|(=g#O&U?XAIudl@Au_fTk&klo(%|LMfaLu)#;X5FdCXe>?lh%{-&@((
zYf_#nH<4Sm9NJmrdRJ16on3Kc6mLSfuq&lCQ01Ds7F0vBx4CwK#0k0|W2u{8so!)V
zweg<Y0@5Ev;;U}<ZLb@tYM+P`dEfgrr?+u7%Tw${;QHCCtIPJx@IQ#J`j4&}JbKM#
zw(;~af8e#%-mJ_Jqkoup+p()l7h}r2Pt#5o@dqZP$0_tHBNWB5x&r;X#NzkEAzLb<
zzaUUPK*OHFBB1mSG|g3mh)P&OGf=FGBHPuCZLWHY-+f+*ar85-F%^?B^)p4C{0i0M
zNiE+NQD&-RSZ1{;pFI_|EH*H|UnE@TcDAF~t>eQ5g}W=Ah`qQMKa<fK_XU;1mn{Ol
zdVdy&=2ht%K}#Mi{s4;$>9>akRb?@rjedQb0>j&a)M0VanQRKGS~4st<e4a*>fhpK
z6K0H2L`m2Ejhd^BDjEqWc=FWfXXhiA;g$`rj5_~n%fJXBZ81w>O%WCExV4&$vq0yS
zqoo9m?hSFqQZ8r^0gNdeEGdVbZjq+`!bsFZ0rHH0xD?xgy_zH!2?s`I41%tixAT=G
zyfznOcHdC?$6b7)umghTAV(w{=seXQbqjJCD&PA-ynb^?C^EyTYxEq_e4`_^u<)-K
zTgIuP*2lV<XZTFKW^@D7L?s`~LYCF&l@{1$dcq3Mxn>@(LH?%TPvO}Ud!qTiK*C$7
zJX9`AmQqV+fM$(k!#m7*Urc;iE`Yty(dM~$Pmq}S^@1-34)5ds#0r!c^C<96>wzRh
zy{x<Bl`@+pxGxB%b6R=?jU3r1y9tZ$>BDzMO6F<WEGxFAr7^~7fg6(O>szJ|A+Ya>
z&Il_AiFmGw#&3uuxj;F~r2i=ZNG9KN364bp&dhz)g-#|vXJ*<YV}iuut8G=$EhxPQ
z0Th0rr+_Tc^d=4_3V_m7UKr~`7nEKRC7?EMp;(DMb)s3UuV;dixYjwLrPanA4R5*E
zUUSl32sf@n_NU5<*pqwIe}@X4m=ns{C%k@II9%d^|Ish|!ke}smKEfYej|73SU1F;
zKvz3_>y-gV8~Z}Z5?aJJMu_B#iN6<ZILb#Y@LQzZq;U&R2tkT2o&6S>2c@`r@jdct
zOUtpKIV^R5v^8{lYb`c3^!LNUmxkc;e*#7b!J3yly_Xt$p(C!(dFM;pb4go9939pZ
z7w(3Ja(jr!);;RgYr4cO3^}rzAw<%67=XSy?Q$RB*y=~aZ*W7+&!62!y=Fd+N%v7%
zzcp{18TiHp_{Z5K^i}9X^|(>xf6Wv_JPh>*HlC^OUDVvWeXaANPVC>$4RO)S-ktjX
zv%?2s-pf2CY91;{ESAYU7`Tu~W{hx4uCSL2fD{Q>{E<lud1z!NM2doZiUI(OA@M;J
z?Ssassr}NEp9Ktm<MSJ5^JSZQ{!S?z->ZM}{;$~g{nNa?n(HSJle#s23Qg7B$8tEI
zfjF+GhSGeZ@ta}PC5YEFoOvHcj9cjkXbdv}K(N7R4miLNJs2U|D98XNYXQjr#|1H<
z{>bJ!=7Xj5(mb%RV|xIeRRD`-sLj8;F;OBvoF^1=&-Nh~Y7O$wFmKeOE&Aw}A@1(T
z*ZrD*h`Sb5Q_|e)-;J_t>Ye=}0_2Y6aZUC(r)Y-YFh9~;K)~HhOYtmzU47TmcCON}
z+_K@!xsswj1_+>0kl%KFR>E~?EWiGe!Cs6$pw~l6fW`2W1T+IQ9|*B9&`=d~bn*;L
zZ;^=1@B51dqcpan43f`L#{z~DaO2+(t$QBz`6Vols;bpHA@6Dth?rWZ`wxDttNr@G
zMjH65QWx|e(tGpT(aoB(XwT(=n9ydINu=^BAbt*~u+1=|VI!X@HmRH0LQ;$SfL#Kj
z21Jn2bAYj*5DQpfe)LtokSlcqgG@h22hDrTwPGHASVn+MU6_CX@7S9vM99Qd5gJ6r
z>JI(&@w!I=u_7r%@JpW=05KHhVfU{i;72HtVDW&AM+gxC37g~r7-6mCV)`H9u>d4e
zMGpXT0;mQ^C~zH4$2{D?Z#@ZVwRztaqLaCG(ry-}qib8-Skf6VHeTM)4wbt9>PH7>
zqqJhH$opw-?j5s<(`mPi>?5uyTAo&`6xevUeggFAng4B%(>32~{8JW4YEFHU`Z(IW
z;-_OG9Gu!_Ort#Xb^*dI+2C?95%Sjc?)R{YD^_36EdXozNzQM?t5@X$Cz+u)&z?lf
ztNnI(or}>GPj_q2ny|p3PRltxm#uyMj^wm+;abNJ8^>$USL-ftr#*h)c=yh4qls@n
zpIxnw)Z2-v(>lLd?Cf;-FZp4O0wm@0&zRBtk0V-~{~j=ff}2%eY3NiUu~`g2siE}M
zM%@l2C3`xeSs-gTk$+|1w*gE_QTun`v*GKEkY|^NAy6sHU}%{;P2!*sDDJW7dR#kW
z*W94CYvAZn%=4l(=!wRw_A9#1?>etMiTd2qeIxm6+ZU@!*)E4i>c_nv0+ClVruYSp
zV9Ja#n};1AEobPZU5n!be95O(y!gp7;}IRwy#?T1_m<b)-t`ZxqYs%Cno_jvdNJLn
z{a^YPf6~%u?^xJi_>y>jYhKPisyO(^wclf!NsnbjkZDyxvd@yV^sM7X%e`l;9Jgf1
zauK~}EFa_3{c-LfTbQVVhckc!z)n6_<->=ix)4b5HOgBlIOK<jKhH*$_it&QIVW(5
z#q(2R%dt&Ci?0r1vZBzpdM8tjE$VsLLp_E=hp}(na@&R>cb!F?(`5|oUI;K5?hakF
zHXKqgwP@spZo9ic&L*rha!U2z-%ka9F_L^KL=R@87lpf8;!3`^2|yVkrX!S)GoJO3
zQ$J;qOVHRam_FQO>|+F6P#=>#$dNb8R6VeKD%YVJ@G&(n6*JlucYk_1lxMF}vAMm1
zZCd1N!Ji4q6udK<a&R_(aorKMwoS)<SIjuw(hGjRDPnjumGbAW=(4;qCxx;3y|{$r
zO4mQ4J+nVzZPv_iTc2^pMlU6Xg>fW9BZa-*qf(0r!XiAKRb6OBl)>hDQ=;U77@Xhp
zjhF`>UgprRS*exF38lxMsM)GSy6F<a0Zo}4@+6AZLsWf+v((;llRQEIY%->(k5Ak<
z%?Eb-{Fy4cR^$p0CImGGHNbi+4W%1E<fstpC%c`kjn=n(X<EH<+%+qZd#Yx!&fbc(
z*A0EBw|^qx#3<<E-Dk0vRY`R)!>&8EoQty2XRl3wzR5S#a>hR==8HvG0JTFgGDl5E
z*zy;1k9};T9$=-0E3MD=n-d7v+M8K*YiP_;xgS5Wtro?6jqlA3Q?4vL^Vrang@+=0
zDU0o0euIPrJSpT{7*B!Fj$lChg@~ZfkKc~>)H%3E+4k4hB5)rr+!5AAk5|2G9Uq=s
z$C1Jp_F9wJ9SU6zb-1b1#Zs2m@3AbZuw^tDaA)9h;YIqeBVeKKhK7DpqN*?b?!P76
zpBqR8L)_&mx%V9B{kN!>vdf+rFaJK_Fz30z8rhQ`9dhO8vo2o5qPxaRHwDRO4Yn(>
zyl6-J5nb_e_|Iv=*=+|TwGY5PE@two7$69fbj87og~viOMRv-C0N`vAK2ZBHg6nqV
zM-hhFOJtvpXuJ9Mg$zzTilglf+|;j1UK8Kb&hL8OE{1FBLFby!zldkuX>aI!E*$Lo
z>%zk`7F_1`RTf22KIQ_#9hN_=$c29%c+(gjEH1&>W)bj(>i|NDV*!8}N$VlSFHZ!V
z+Wrd?3*&&JPLNFllQLq%gA2X?JM>F9COE3a+!XX@>QuF8TGXs~-$g3vP!zsmsfEmK
zOrIYAOSJEP((iIvn6lR}Ag!W1z@vO;Y(#D15<c|~33Gq>C6gSZYzb@MtZtFVp_J7|
zbf)3>^uXFpb`QFeIL$6O*p&i+#G5vD_IPCT&O?BOV-|P#((65O=*k#OYbS6$?~K0_
zsAJBz&SI@?Bw4@*%)1V+k0^gSb$2C_v-X)7YB0}n#eIL+9vVV@FN^u09VNVa*C0df
zo6^_K>uj)CWd!F-Zm*0p{{xz@)9?(m5`eHDKcQSGc;7=0!RIiq%e*8aK?`C98&SHI
zTF-cY=cv$|Bj7{|{}TnZz}ud^0RENjwGJ6DP&&DY;0&7hNi_cSuk`b5j3_Erg9EZ1
zYFgOFnMy%vl?H(I<YwIp;wRm6`<_lk!FHS|QwIiBi5XiyN2jn`Zx6+T{9ZCs^p0;U
zKD=q_QtI#Av*X$n)jm`z+iuT#$X*&5ne}34K#C~zr;Vq2jVxy}a(g(YE2mw16!h?O
zZ+d8Z{?fV;3Rm%|c{yM6YlN+KQ|yCwjGJcT&s59fAT8@U?wGFcvC;`1Qcicj>qu>Q
zv|aAIZn?fGxx1rqI#P1~?&!X$wcFW&-gj1KD>@%I+Vh&h@Xs%8aX;6+ch{Xo_TJhL
zK7^S8Gvi;WdnRoI^g181%Ms+^CM2GM1Ar+1iNG!hoklau(*-WZd=Q(9hPw8e?aFR^
zZFtn$btZl4k)X^cM=_0ttjH(*m=}L;8La=i_2_pOui#-$Te3wt?h`%2&W?q3hsjlM
zUoYT`V4npD+Asi@%CJ$%7XD<9u##a&4xv!-I^@K9ASxT4&#Clma*id9f#UMLu$$Bj
znN8cVkoz)rGVDfO>a{-YScBJpp9mlgnm_8?GptVdU<5n3n|qW;dR#jC<PKi!E=rq;
zUst>EXVc$0GV$jfm;aE{FObc!R(9qb91=p|&ck9M)ui~kZ^XV*Wb>~~#%y2@QTNu!
z+p$kwyNk+m&2ssu6gYcdg}c-u-{^W1)xNe(mIrxc(5ds)L|4tk2;Ej)jz?=!v-ecJ
z726^#Ddg2Q>h1J70ntP&RcZ*0@t)4#`%mP`wQ~Y9w31R>u4Uk4NZIlLDu>asgKs!8
zE02>GCQy^<K%3Gy_L*t><L%jF{zHvM+RsceJ(Iuh)vKFDzUX1M+cKCgu-2}z9{gcF
zc*M11#>`jQz229l>&?u~pXC{Ig4~y-^&apau}*1b;4UY7gwm_u8T!T$6o0Y8C_1lP
z83*g#m4zrP(rvXEKXE8ln4{2Ilkb1nSRxQ5$q6I#Q;EPfan==puoO`Q%J4zT!GFl%
z*sSWv6wh%IBbJ|{kibZv33EgE#fVt={Uzy#(Ta3UFF~|Qlm-}v-Ps9nVi>=yN4f^8
z@X0rNhySIF+P~U^CZY>TnOAGncT@D%ZFPh&`S#^^QjNIB>mTbiaeKeP38XSbZx|jX
z=%8EjK2+rgELZQm>vv_hcH%DV*2F69)gFxY!eKm<q?EqF6n~bK1J9hvh@|zA6q;Z1
zd%qO1(?q}62LpQCMmoA69TgT?o&LE^dz!lN8M)zFx}#$d-om%@)<hTH>OGwWxo+6q
zsyA}WIOn=yeT$q*`zW@FueQTnlw?~IcT%rIsk=vD|BawTUH-TcOOByQ&IW4leO7&$
zuB@TLRrgdq=sm5}l$sM{92>FidwhB%2rI_#6av6Tq>+eW>P3*ce9?!}@BhoZF-M-=
zLZ@~cSk+k(zRSO!Fc>_3wg2BF1DNFKvJLohyX?I=M0bRp^u&dff*S(BpGWB?qQ)C}
z{gN2=R`Njvb9E&9EQ4e<jFqpLWisbP+6>tvHk0XMD(SCXaZ{RS{X|V~x}GIUt*7C&
zXJ5og!$e)B*e*60valv*Qg%Kcrub^H<>kHh+r=*p*R3dz7i~A+bi5JBug<!^8Co~T
zP_$Xb66hjNylodabt3dTKQ0|P49~zt8VCG@ILZ;TMIQK}A>V{8{r7rjKQoST<#T8@
zwA(f_P2L_HNu_?BU~d%9FdBTKo>sV$#w<Pf{)<YTs;Z5diG(p8@<9;I)t0A8t<zHZ
zpl&<hGIM03KH2+lGBua*$+{&?qi=u>qMl+AnKdg1r6`b<I35r!;l&n3#m3Av{NOr+
zbm6s>8^*?=|BBL`OypQ@?{A>&N1bAg+rs%3)Ev?R-BFa@p>iLywu@uBzeWpp^TQ1)
z2+Miozgcu687G;su%DUc#!z^PzF#M~c+l4~Qt%PhvM=@ZccNbsi5>gEPZFwy1;8*7
zYX<;Nyz3;4pic5juO$92pUt2I5Mf@oL!)zYDp+=-a&Ua{_vp_l$Hm75(VE65W+T6x
z?bL#t(vl?MG|0r*3_50nV%a;(I3+Vp#6g*5rCFI`(M2%DHs}O3U*2i9E}WoaDm?*v
zz|D*s-EVA6J2XmbpEzB0hPhR-{LR?8TYO1Pc#iea$=Vm-QRdV!R;joq`gQ7cNIRiV
z;BgouF8E0(U|1YOvY$)Z2FU8r5+Y-dA2g+K;ejw`QH2?bOFz|S>TL9Kf8i&)Xo?<r
z$<3OHSFZ)jKVRE&>ePo(N}nz3d5IaL)(>Z-xH<KT-N($0Snlg&J4SRCPiM<<tQ2#9
znlbt)PoYg~zmppw6@289zw$BPRxs}gauoZ6{Kf5QVZ}gte{Jhu<S;A)7ejP>1IpvT
zBrn910_mS10{-Dp0AWuA0QoRt=E-6%KmRdHeo(2rkZ{5!VV}Pl4Re!%=&_T>3y^lb
zW3V7`JT1F8sfW`w<Px&EO~(1B01}M|kfE0dl<4H;XkaK_1d-rRA1LkVDVQ^2Z2NpZ
zQFD6X0p8PD_}sJkJCm7x4uT&Sgli_LGS@#D4_^?LfB7eRL0{<O{<NoK`t-dxIrRy?
z$oIuVl6;b4r(Vy>Rq@#hw13Q)O~Z$ur^^+z{2KlU)~6f;sZ`YLUc1E|(KUdyxYco;
znV7c;84w1c;_>4unhpgI3!@+KQCzuod+u0vCN1+hU8-ZYODi!xFK>1-Ra403P3f45
z31id1vKgAyv~}v%;31dEm>ku_<h-2rbai)nk=kJ2<a1886?t=?3EQ^Co0<!uDeURc
zNqhp1?r>tuJ+MpMHgB?r7LbZch8c+Jm)OqbmRD0*>mAhXi;bfv-^c%Fkl@yqrYr5f
zVRqF`v@&?;Wk^x|mCR4CD^Ap!3lQ^eM;LH~<SjxS{ay3BXOs?>$@fY*{61^q^4Nb;
zQx+D8B6=O{_oQo}!0n4Wdo=wF2;4GL)`qy2bW+yUQ)X`pDC5<%x4w?Z5pkaYWtx$A
zqg@F~<n|)tpwHKaQ*xt8T`~!+p(&KN427<VNk54s7+|UD)H-r7)I8&*EEDZA>U|nz
zF(&R4^7v%~uI1!A?+4Y<H)V8^1X)7^gzC6{Hu<y`2vqb8v>G%9zBfD+HryNhI3X-2
zRMzZjnX>-32$Hi_U1EQwkFEM<a&{o|u)ECmjiiAtVs>OsHmpa#)p4k`m1Fv{)xAzq
zAD)|+S4u@gx8+mVTwTe#e3{wBDp8T{mGdIvCmrTxxTA{78ktzf?>OYNme(}KTh{G9
z5|Nm!kvE!(@3{}|R1WeO-0++l-fNARlE40NVDz)Y(M(YJu<)H0;Wzc4!u~`3DGykm
zTYSpVaqxp83j>@QjIu)H=9_%IDQC`Xaws8hZ$smsY(Ut>ARjhzn*nHx(^A+~=gUq<
zRoo63;&qLDJluV|LXxFw{zt+k#+8MSQy+>UH;hbwix^LPD1y7akZrERkS*c6@3U;@
zKQtAOE$+m39OmY8oc+Y}I<-d{)pethc8v%4@hDwqnDypi^j5?|JwlPlu1~<kLxr*V
z!T0LRmd9W4Vzo0eK@!$ujQc2PZC|SGm5rxGTM?h^u!=iTi(N}zgc`+_iRQt@kCjI~
zvAPm|{~5QECM{hHcb;5ak-I<N(v$o){Lcoer>B|0sPsv_+}j~aC;X+;+%w}BX|DhE
zdD;wAS8YUZOlW65{Kllh%iD@77e;+}!ruQWv~qg*M*oy|$pLpuHaCeVO2%Osv6J^a
zZeExrtDrLUG8_!7jGi$p+nxN)w_R^#Ydpv+oOR-nc+AmEshOP-uV|8m>m55EY1g4l
zhrR^er|%q@>Q1H{TbV5TpndE|-8gi2DpO4bH!Sij8T(U+(HuA&>AL(%m77KNDHcMp
zni7nt4MqMeVXSZ<;GS?b_p+?*qdcMNL`ugUd+`?gx7907YuOCf&%zE_M{%3hFAKl&
z%H?--%q4hca3<v~uW@a}p7nU&-MlB^w_h*wAVVy^C&I@d5IdEf$NwkdN=E2o4UTuN
z&o!F+vg(*{Il<%-@WoG>bg39Qko!M!)cy1H@ce^;p>JINf26JjMH-_a=>yqW8_;+@
z&g9XOW~+ww4bAd=*3V~!Kgbc6v(%<e=;+iQ?AbhXI3GMw-Y9)XQ6F`9H&vwdgnlVX
zxI6PNHZ`-AxkU#n6NJj^xn>_$sdX!9&4&m=7;-KFY?#n6JS8B=`on|-Zs#=TS0lmq
zCMS86+MwyTY>Jlcl$K&&|4_u9J734i&=>lR^@fZ5#rURos)b0c8e&HJSDUSD(iSCI
zvTK0-;O6svxgG+XWmkmkUYPYBFPr?0e)Ezbz0eMu!8yyPY7cN4NloFOrX0kwOJo<h
z+*FLIVrx`qtA==NL);pBP=2xPwvUMlCG<hHmFR`#Ql3@$2d@fhxW_)<xJ5pG7l{{w
zrnWU}c(c9!fwDgJve74WDlBlB?is||OCPZuLE-_00=QX)S(65q9B?L_h<|C%z9^sF
zXH{p6B`#6`Q4Q?TTP#5zdnn+*W@3$Z>KO?<@vxOs3oa7i5IiT)V?qCZ{R0r9BZUtk
z@Lx5Mp%Gv^zy8p7)OPcq7b@UorzxjWj-uFv?^scsh~mcsA_-=m+1pIVf-ISXP0G3}
zzy95#d4#&?N%69z40?sR=p-@mW~KHm%^WnmWhkHydQXkdU*LhY&NTh0=0lJ{+%wF%
zn|f(=6;xtCHhGP8_g~|NO!IIx$G_+s0W>!0LDbx@c$K0O^*tf*!%qW%ySK3Don;6{
z;IR|$EsJ@-&PSz)7)gP&AaQ;Rsj~*rIkaP?i)Ll%rZ1wY3xpG4hZP#PITNX3_&UGp
zRs!T`lFCS6KvHb~Zjqj)QGcUktwaxVo`yP>v$QU7RzDGCYQ6ss^JD22BZer(9{1?-
zHu?^RsMEy&LF3r(0GbwSeZ2Ak0xC=7W4;53DybamwJ~L&FK`+i_u%pZh#g~h*uP0J
zpF_uC(0%(c+Q_}m`qA2v0}}D_$1bSR0zmHr=`m-Btx6&;uJ?9->))ChAu8i2jdL)?
z&~H<q2B);EYb@Hf45l2>c=j}^Mf+@*yx)iEC!NS8tqHZ;t?)8z-RZ%<wQ2pj3f8S!
zAQpnAZlL0cKuRPT0K7CKncagOM~RKQ@%&6Uzp>~>;xh&V#*%+F6ZV{C+vQAd|M|A7
z8LtrZ&;dF(F&ieFpPT%Wx+9hAk{Zcs8j*N%4R7wdkVk9jPnpj(pE*cT<o);Y<wd`Y
z`p?y8H5ejJlhq^>#eN-9lT5#EhDVjQ3|Gj|rzs}v33M6Rj>9%}eykgf?~bj$ytkAt
z!?~+f>wxcdzGXFR`|qacA78#+&$Oi1r9_5!cY|{KWQZBHNC<QHeW(CkI*#&ItZwhT
zK&|yJMBK3oc*)aq(APB4hHNjYYU<FpPcEcTYnk>M@ou@&PPi+EM`!>IEzoejJdk~k
z$0QV>py3Nho|$j`b2GqV4aKLb6#nTxvF`a!gF?QpbtCn|{ob*?Pb_@B_>+pK>bK?|
z|3e0y)Vx3vZO~bLSaJEwXbO16z#AzamL?1B!>Ozu)_0Fti&lqxvnThvwO?_<?dAMh
z6bYb#V4)gqKMx{OG{TF%XZ1%_e(j%}p_0!}Ow?Mqp7`8~=A19{El6<AEa#A~j?GP*
zAxJuO5!LOD-nFp12NIw}c4ol21I3VA#_ybV*PjYUI~yOs%2e7*h@nh69`-0<C0V##
zAr}<R$e8P8(MW)5C1uxF6j0D-&^B78rarIlw8CA4{J9B(E+l%b>2;ABgUd3zC=qDA
z%lqNqBFl_1rfQA9QarY?#^cLu)LGp-g<B#@gzId^4ks)Rex-T;!x#J17h4VFMb!mC
zvI^%r7yPfOnJrYvobK;F1!uNjBhTnRXLjH^C#;{PE?GagKYzD&QhwR8S`f}%#pnvj
zP5JbWFIVkc!Jl;xcEVr7{@9a5cQz546$h|)M!uQUMVR^s$xQ-OWukHl5JMKMh3=)x
zFk()#3gwm``j7Y3ib~wJsY47_qN|VO80UoOD)42&RG8+AQp0<wr<z~kizTNs!})%G
z(6(_!T{KM{ZY$rv>AdsZz$~6hF<)3O5>z0p^-MnKck)Fm*Wuw#pYk1dPtH&Na)#B)
z14ypA-St6!GA<yK%U&}@*;to;g68!~E-p-esSe;3j;DW-h38l=SJ?b%U}$YWH4t|4
z$Fx1S7;CD<=nvJGEC~%~cSMC<9UPz2o(`As?XlH#-6GqS8t?nFBi}zE`KZN?&&C3R
z4I7{+Z~zGs00dUZb7qF}Y)-hS3lfUe=W!m@W(GcDI-w_b%Xf4Ar!ArJwf8S(cg0&&
z#gOw9`>ymcmU8ls`dh86(A|xjzfwwk0c>c5?R2)DId)C9d^ox?>@!cw+O9T$-zaK1
z*5_P+q^{FCC0kn)D~jR_O`E&B3q$k$RX8*xL;qGT*Be?GidlwV3hwa{m!ijcjVrwD
zWls+iM&5@;pE9iJhF0so!u^HW=R--AWTcUcKF(vGHybNKSE+wPa(_w!$T#i>IEMK3
zm~u%~IWEQ)BiBTsX-BgZ>-V*rc8(0>=Br8rMM5Ux&&{qjy=k1h_YHbs(>7I6py<`M
zd)oO;hWjEh$(6aQZ2d1@Gl>dGxNi?hDPGSN%b7Yb;QRj-0WE(#h6gQMZo-w8D0Nj%
zcQ~gn4vNaDnkbdJq&_RE=P7f-b0oTou#3Lrui#$od{g!9io(|OwoYD;nd}>mSH8c2
zX>tkQ{kU9u?~38uyT-o}7UeEif^VB?xW6pg{pJ`rSYuo1V)eNM9AG$XT$#+7;UKjx
z?5Y4(Y|h4rtLUMM>07hxcad=yf(n8j2c<ri%Ow_lyCgdCe%g0@;%mg;s`2Qxsaej7
zHm1PiLbH;m-*{YWUd9VV4T`?VJ|Zo%r;!}mB0t?}H%oc@Ax8#apJv7{6OzfFwU>G>
z0LWYA<VYbANe_t2ftOz%$=Or(`s1@-^e-;63g>Q=LNF5d{W|_W&v~grl~-MMsZ~G7
zxujJ_@XvNYj>ciKo#H^Xhye)D#t?1t09FxABM3s*hnf0`+z3k=fc&OFA$)#tYADN7
zc>M!o?r_Jz@=<?V<yI@Mfw3)_a<X5H*!WZ3u<D&N&Kyz_lO%>$O%cxNIl&WIIiLAn
zWN-O=Il9i~n5LcLh!zeixykxLLJz+N6G38OljT+2xWlZ}4Teipj^jI7M>kV0mx))>
z*umw$UVZ6Cra64fRZK&QO76?UIo;nrGg@y2J>P|>mvC=ux{l$pYUJ3G3Jp%=@!V~$
z86a(+a)C4}U=0CZHddd(^5q)l%w!JXCy!Bk2UcxF8?7OF^wh0^p&E61fMZ$9z2aM5
z<$aNe8=r6fI7arBxK^EVdM!e7uK0er;`Y+?fTzyWCbH=3kjH}MyI|>3|MqK{o&v3g
zcby%j0H$`OC_c&LMd7QX@`LcqJbPVlK4i?(XBPOOuv5X1g6fyE;{vGGD5FEQ+cQe2
z&93hstDpxJiL>uyv&HF!PRXHBORd?-)jyZsUJ#Q#mEfo8RS>_vFI$es_ykEWh)DMx
zCep=>1*rxi8k#l>{Oshg_$7dcdhtQlOaK<!FxrZ>`huLWf+$!yn=Gfm+*dO%ME6%H
zddn)xp93i$^~H9h5dxxL?A*)Vwq(SJjK|n~NnRgFZZ&+HZg?O}9>P%P(hawSaWoOn
zX<;%}*n$Ru%&*hnBDi{+1!j`svON5)s-%>|^09SNezw?sf(23c8;=|v?IRH<YJ`5h
zUD5T5i&NHYz!LAo7*}~|JVai9IBnOa8jCmFUKC4)6X_==6-JNXdQj10cm%T=5<&7Z
zVELW`^u56{!@$iP|7EN7xj*bBM&@LbawAO0Jygv`4N2%i<@`w2_20>&0?LJsBfA%G
zPY*2NFXdER%%LcRbq=n~Xl};2ZYo_2YgF`w35!V!GaNmIycPC|^Y)-e&CKAYWG;vX
z2Yea$(Zs78pwdR~*0u7>C5CI6b8NCNZ03(tMGI!!_v0A!tIG5j+Zy*)$f{o(S1fyp
zZx3j&d4*Vr3|bv$UJJVH@`FSF+!eK}$+Hm|n~~vW<3Z!wmp$2+{V-(b5Zb)6=Wd#(
zFOR2y1X0X7g)DFEe$A?&$$0YG<?TRu1rzaTR4!rl2TI~QVPbXb4N}KI#VqQ|S@DOy
z7p>501QztALX5H9t+LiGa$NT^bS8&(@B5Oy2|2sYuS+(a-DK8?4jdAL3o{O37Sjai
z2Hr)-IqJ3m+bAS^J6U{~*dK>6PRo*>!@ojc!%Ojw=<2ei`L9dcc-NeajS!=8!`pMO
z;j*&L_>V>vV96-~$}Tc7AzSGK9D?Pr*!p0#r+^1$Ck3GxpTGk6f)SQr1T$hlh&eYB
zc^W6p<ZuyraH&}?tG`e)RZ|94q?-E(Vr$?B{~#T(z7bXaL$7?h#s|hfM$D5>c6b;i
zY8-s?#YeTe+-mC+N&pC_O9(d&3ge^xA3<VGa0Y>4rQq6*WbKkMeM{?o`yzolKw`qq
z=RK4LPtC)FFE*m7sS4H+U5G)4ssQW6c@3k(OFzK9m9;;{)}~&fs^a_G^)sFiDdIi?
zMsN$`K`TAT555H<>AqhuMtcY`l%Y`#1+HTWt}aOt$pD{Nn;i4S8BjPAtzx>Qb|2zx
zw)cJf+v@vNR{tUdb=7(goThBm-q`vNA0)8HD`xH}=AIXSuk+$R)1A1=FYJeD88~m>
zAj88C+5e#!8arkfFNt;#z7!jH#`1er2vR2`uXM^tv=+|%<9l)7=FgM$oRaUoUum<H
zJI(-3SPIv=J7v}Wmv-bxW>@fau3i_ABbDW-UC;fF|C76A+JH}};5PS4gGht}ET1`+
z51NVp0?mbEb+GonSc0~;gHuQfYck+Y?l)@+1Sz5U6R^osx!hv5SFjaQ_F?cEFE-s8
zqtke{Q^*fesQb@lSx{|OI3*sHrC&czac(kVMX&C+8^MK>?Esn&YuGF}_EAXsaT}8w
zhWfFMdMy0?zV=oL7_(*&r-5v&DsDcKRGXjkM#WLf;LFfu2fU&K?oZi2-h>~vL#^Uv
z`F}`o^#E&AxXR{$y_xrYJ?j460)660M$mm=#0o|+IGo-x=`{nM`(Vx>R6}+Pjqw<{
zV3nDX^W`dP>1&2ot`2W(G{)7Qd;Pt@%x(F6wy$7(RAW5ac}y)JLM>-vIMO!A_=iw3
z@(P)KFi7nF+ZExVSWJJcaQv+KLK#|0S7bAoD<~Y#ar{(l)zf$=02X(zpNuhczf3(&
zn`SO*85)`DeeT@vVd%vJf$7>j6<5)Fg?K9Q?u?|oRb=>uvHB)S-}T6chEl6yS8kt-
z__;csVjKDULckI_{!5s&#~o?vRwx1RPy;x2$y4Th6^{A(FGbJz?MDoHm;|6y<xS<1
zo1{pGFCU&p?LLW(2$W=AQZgGqe!U>-vudC+Gb<sa&pR8w+E}^1sQT4Cc<^g@pgVXI
zTda!vmLzbUgb-#N$1M8NuggLZS6-uBLFvbk_|*>S_Z;h%>ch#;tPyz)20_f+_&uxb
zEchb__`3v2_WgE;pn6vZak#h<#srHYaLUGSLTa3#@Hhz8K%2k`U-y84D1;Xr)|qv}
zP6Z<Aq=;W+%zUfzkf`AF&Bk%)KNALBHFHii9o>;-lbV$z)Iyx|qg!n3pRc|5Ezk)u
zN;~!D?JKjw%3p<Ur`0nRNLa(6axGqh=c-^WUbwH#qi<QTZ%{^QL(+58ipDAuQTvUC
z#z=HEl3IgCczkO!0u$W7i;JsC&;9q^z3ZUzbd{v_(nj!g4J9!#Yw~<_GQacf1JT<8
zEhO0?c(}*{?krZ@(kK}X60rnJG6lCx;gf#jcd5VHxJ*EnjDng_6lD{yEWP8?)(iM5
z@i<k6egAz?cSF&imSUOy3oZl?^hvuf(hwLj?sZMT>zBbPIZP~fpeBuakkz!l!55y_
z`8<G-VPw4FwxH)wEOl89@Utzjo*G$mMRA{&BFReJDO?t~tT`C*eC=a4<sG-leU1Kz
zf^N>CP5a@{gMrEF!LD37a4e-nLQ-4<dMi2bqjNPDO7{?ZsDuzj;Lpj>1s4YX2p`Ft
zRs@k`^f9R{S-t$D(~6SXY6J9*k$48*7;ir|5yI;&q@VX0`qfWAyYx~<>G@%JoRgQ{
z%wq1ApZ%bKF~ju7!g;F|kLbikEn^b))LctnnZN=NhCFD5Yzf}`1>$GgC_Ezhbr)%z
zc4}H0S^iKaTW?YkF+n}y&D)P%TEA+b4knHNnV$)KUYArYp(cH$%zH5SVTo|9Mau-g
zA2#&_1&gMnSf+YlJVbEu>#obk-KHuy#P#2&t!Rn?o{5BkCCZEMH|pvu`l7~!9oqZG
ztp&!#UaTzSPJMJ>KGhxMa5W?Gf(>@iLFvMc`pahgVmzM&N6LmI!qI>#La~4c1^Z+~
zh~jTxPrt%u*oIq(hF4v`9opPvmMWzC$mXoh#i5i4P4jvI`DZhIk@!9FN3mDN$E40i
zU$gldr8y$LYc<yD_J0(ei$9b9`^WDM+srWMnPFy_^I1qWb3PvvQq3_$$)S+6Ic|=d
zQ_kj)bLCVaY2+Lc5*@xdbPy_}lFH||-=DC@{n-0`-`DkeJ)i75-XGHXr$jGY<t12c
zoxPnG>*4#=2ow@`wUlrXT6j|taPL%aH^_C5wAe1V*i)6F_U6BIqD!7fH2S7xUx?yt
z%XRQ!v^h92ZR>zp>XpI=?7qGRQ{d9^aC$GLT)WX|t>e>ybPJ9^PpHY5#u^RGi%}C5
zd<O&o0pl8Lr;YfgfE?<|_Rk{s#3ifbuJ9X>?cXR%uUbRhA%ZQQ@wW5KeT$%-@0BV|
zt^^i3g(%MLRshBZ@g#BcP%LO<Ei6$d73PO0qu+Pp3*hyt2A6$4*VvBgEYLFCLU|SM
zw)C1K=lVE|I<1Q@R%%TWe}AeSseR&D5HS1!ekA<Bz?j+l^cR=%B8j``zJF@=`d{y_
z6dCJQ8P2O3A|A8NIbQEbHDb|sCuHQ<wQ@hD>m31&Qn?IbWWsKw^Wozsx+#5Ik86D`
z%fIB;m)A>)U+?I9QM0F0do}O5oYVNB{&<?c--bHCZS@f)vwpFIj8_`?cAkQPDF=aB
z3}_8Ek4Vm%qisJaBU$f8GZ;<%cg5zywo8p(|MUB?Pu5s5!^i*URCe~{heWRrJuQ6j
zys#$;^<GaBKbzMJvR;avFwYNV?swO7(Xgf+7=D(Nr?|Lp0>O8{6$`_RID)8OZP^GF
zqXnw`=dq-}>bG379iHtMSXl1daJTFveSRxkX&J3p2v7E;F^ydV491d%WH(<64Z6!>
z5h~Q3iTJDMS;xe%(ndHG56kuYGerQ<Tf?H%9T8ZPE7sfI(QLJJ?Uh0PSnT_%uwhrg
z3xger&C|OW3fZ~#`)FB~M96RJriRlCp(j;e-Pt)l4S)Ef_u2SpjO+EhLn3-Y7812?
zDE^NPBC-MLfnv1X+kMbB=4{E5S-mXIcq!G@JD`U7k@8eAHp=T!e3xhJ|CX_)-$!c0
zJ8f{G1&3{JuX4@n^-TAy*Z_ttD#sz_eTe4<x_ARq8vu<~-%YT3Oz4m>R*=HA2jWSh
zyqn$zASfVZu!@Cp78Zjf^{+hVu(ki=PPZTm!&DD#(9E%b4r}*j1UT9Dy&EsL@;~02
z1FSqRvx7^<`PS8QUVRI3;-!?}s1OtJV{)v?b=~p#yj#aX^C5bk^Rr=p6G3c&PZ4;)
znIO&vn{sZM5{DWJK$Pm$`ISwPE!aQ3%e14;gh(*(X(|5!{pUq7n92*zBeTJx)(f_3
z?Vi|k%UH3W5>ja+o6bJ-5B1A4UG>_+EkpHdQ$KfkkKOO(YkL_k@1AwIaHQ;iPal+J
zT)azFUpgOSB6IxZ)%Mk`BNU`qQlB6oLJwNzTG_ecbz5i|pjhky;K#%^XME2dD9NLy
zFhPdFWis`&ie-XHVw$8GBQ}FR-21BP1lD(WTDQOs(E~o=rzYjNNRD>KX$07N#fSIL
zf3KWYM(Ds3nm_YV=-BmbJ|6RB@SU3{)(>DnRWO_Tx;7`Z9Rom{{CokZT+q2bEA96H
z2%5poWp@5x7euk``FlWt_8dF}0l2Xn@{`LD7EvWKTWgZPe2j6o33RZm>QG|>QXG%G
z(^u^xwGCBEAHzfB*y}2scqPL{K6OP0E|^Rp=)tk9cuuHg+Cf$rxGcYLyXy1GA7tl@
zvFG@Gp(ImfY;&G-s{Zo;1-RW#iUO1*lpNilXpK&()x4L1eu<~y3~#!P?+R)eBBtz9
z?>kQ%m2~45R;d8;PT#*~ZoRX;OM!Mp#8x&c&tAbc=v_%Hv%&&#Qfd3Zl`<<ZJZ6j?
zvRw&cvXR#{u|fy8&jn+iyAL|>yU#uSt{R$g4<&rD4yTX%qmP|H@)P9FZ+!|PE?Pv~
zRJK}6xB=X?+2Xc2&sOB=8^9W!NS8~3%R$x&mi$;&BRXjo;F_rKVur3cXf9St>b}eS
zev;A>p&@cBBtEFDA><&fwkb5ezNIDfV5Dfy@5|90JyY?E`A)u?U)-6t!Fi%|&4!vS
z03);roR<PX>*pk|x)g=De2VGjmT4slB*+6QDJ@@|U4DYY+KW`4Jzh|`dxK<>?>YL!
zy5p2nT}*=aZQCeet0Q%b0mp<jHbQrkffmqTp7HaAxCD@^CmaEGLOKdk0J5hrt6JP0
z-~uWntw2470Q5Ij3(5T9>^60da%t1cfvoux`*L`uLlwblSfzS2@O`mNKLxw6>9$OJ
zYSe!BU1HUDx9E>hTDVQ8(awCk&aFMhtY<(KUEIO~3l^*1wG@oJ=$YNlsopP}j1g8b
zY#=V%w-<oYo=Xvd>)ED*wWG?>pxUu4Am(d|#A=;taD_6`G9{&4x$fFClVlkW@pD81
z_?U87`ORImb3%E&%2wD0l<w?VTjCF3*yjor%mz_o<pCgCB}EGW4J>OMC0p}Iw+Yx~
za@icarIlCZk+-r6KA#3DIor8QaF%)a2_G<Q*Cy;~g9K?ssiIBp4c?RGT{@OCEO}a+
zSKt&PIz9JJKtbzZ1718j@6@i1t98DjGrcK5y7t~D?(ItPOlx^=Ea1R9yZi$h5);Ei
z25t^sbVGn{Xhp!D6z7HdO+?+id0E`9D|%F60_f2Bh+2@m7x6qhZc;C8|9@Eg*#pI_
z$&z5~d|B$*@ENH!;#^2wSsJ)mk$Lxs^_|O8PZC>^w%uxc>oDnv0;yTnzPI0u7md@-
zAeNW_VbRG2sds*lFGB!DL<;gs9&!4)qrLzYR&8^C&$+!mdfkAI-9tBL58esO*2wOh
z!$n!Y8n#HWkzk2jSA(HtEb3x2WNN-<imTMg*~Dh#)~Fb)z}}0kZt(G$X6&XDcCIGB
zP!e2wDlqFUabEr>r2z>ikA88Bno@Z7v{&D{RHZrCrtVoYUi3tx=Ya(gNVt_G&O@Ye
zK3ZyADGb-s06~YxTvUWQ3;!}k;57tfWV$;b`_|r0aseJq1#{*;*&8lecZ-W=@wO#z
zg;b3NpZ{&Cn>=WqxSOas5-q6^c_4N&hl$`BM=!*)U$8(xh(?21YV;VwA>FI-o~_U?
zcjv67zX<|O`~}rp8|j2iY%Q}pdtc>*6+%pAEiAHjpwDnD;*<1Fz_t0C5FcAnD51;P
zvc#Y=Q{Wo(o<!oeTk;3L?KU1;G^S12;Z<?4i3Z?&-Sym;u-c~sS7qUz@j(8^stbY9
zoUo;QatE9*kmIBV$VrqGYmke)h}?|9`L-V?O{bbmj0LZ^uKYEnT^5%gD!wV=XZb)&
z?B9mwcwD<0>ZJ9!?#Hl(`I_5#a1(O;`=fq)ft+cgRv8;4b;^WCki-r8*7LEsQ0?)#
z`!AqO`4v{s!KtbM^-wq7s(Yy`chH&Rvge}Shh$ycHky1ctt=CCipS?~hZJy}?d}f_
zTf9NVVgR6+kU>ESQ2pS%5uUi9O9EFZ0LD|;m<g=s0cykH7ldN?&Y2^xw+<`&6YiZN
zFJ3I2@iFif0^Q)$Qe|h#WL(vaAu4w}BCWQ_1F~fwiuFbYUwd?!S-m>#xsd9Qj=JLB
zTrEnOuD7cEHE|*@QVB5k$1w1b#@2-C<l6Tk{9Up7H-mpkt+5!n)_Bm;KIGh~;7ZqD
zU8B}XzfTf{(qG&QuwWKkm>@`LF;n9&JxCS;ED;I0o4a|Vb0RW@1u?*r_k1-hRX#aF
zQu6ZIE9^*SyV-9#Qjb(XKaIe1M^v88@MsF*#GJwoe-*s{_<I68@F6_Xxcp?W&HJoa
z=FbDuC}o)e_}U^fPvFFN;RlKVyq?UV0i=+dvS!>TS=nJmyEu_k&mRxr&MR=B4=x9N
z@RG8^0B@cO<Xw9q$=#kCOI00oPCT$W_F4Pq*|UCoYQv0Vyc_nTP@i6;Vq{R6(?;kN
zw?LG`xy0JNg!TV*=7cjauO3uNJ#sw9`@b2`P=)2p=bP01n4E!EGHY)`GF?LPf=D5{
zOOcFNq;~I8&(-6BZ~jA1xCce?a9nIBPccTFzO46s=ENW)|2S|&jm+D983px$FDHM!
zp#3)(rmppfbc9Sh$Tf9Tm)Pu^Ke0*tE7RhjX0i<E-(l>C;wAwB<`wON*mVsK!~to3
z^91Y;dOj|9?T?<p(yd&Tlb&`*;M?CNHy=x>=F+r>YwSK^+n=WoSZ1>M^$dk6ivZ9q
zP0diF&hv3{cwON8qL=N|JH`pVw;mNBD_}*9yCn>g3Ca#F@#k+<N49>HKIW+-%Es@u
zT(j8rn}Pf|g5Ak%H?7L&R7Gwn-q?)W+nLarTqrGG0@R&05S<=z9@4G?>A6B7MDeje
z>CUlQgg6XeByD~7(bGla-Td-dfBjfG4)qpJpvk=Y-9dTIKiftWjxJy~npWFaU{J?h
zcR4UliS?VPEz@jr9AL=l{D^{(0Kkxu<Y>o;7#KlZ%M*tCW05T+EY@+*q6Ywm;1j^(
zW>YjpJGjTpUcW_T*vG>n?36`<r^SUg2s|EXBHUhFt?n0G{Q54xu0qH94{_Rc@s-}9
z?o0O>00bM73G+#l>`e>l1w7+&3EaKJgrX9<P;ADXwSHrSh+-T}ll<5S<WOi6q%b>B
z+t?r<oNo@kdoT0lajHYf{;Q1c1itm_k{m!JYZE$yXz>Kvf_Fd^kuIY67y-~jCQ)3h
zDQ4Ta;O82jzX@=ylW=!)lEq-UoEM{U+Bns8CiThX!{U(YR<+B=$9-h>u-WA@sFW;u
zZ*i*-PgCXrNz`yWZ^y<9c9fW@+<Cf{Fjh=`UVYHPyzp6hU>l}&1<SaemZbu<4os_#
zf|x!U9JoYrjzti;(^<O|twb2Urs>If(PqwCJpTcsAbZJAXE5XLE3z>rKO6ZhH}+Zi
z(M0BQ1GI4&z{2tuueBjXxUAJ+1gSA1fBs@@<53B5!<$EbQ)-E2`EF~X*UrL-rFPc&
zLzrA+_*tX5D)jPL&!*BnN~2MU-Q@TCS(MgZk$<&Yb6AnyUi>rs+<oI<07|~X$JKZX
zDLm%vz!xV@5xPXPet0}$uE|CFA(EX*8<gF_SfFkwv_4nZtQdb~U+0;6G`|-qGN`h+
zMnqqJ)DcSa3wg8x>r?S(_|Zo%3^7i>nzlih`26e>{f-&>ZX(Goz!=Ebc>@mK%31tM
z0lWpdo<GDDny+Vxlnxr;KcdWD4oT|qe-|W_jp-*upa$t;TyOiL{}}M_>yvWCCPKwL
z|0kw`DPCN6!iyBViA!JXqxddp2D4eV(K(E_$gyz_Y?VV{yw8YEC3p)gdZ!ugKuzVL
z+i`;zV8=-R?ny<83sH}h<qRAxv0s`}^qQsxCmOiOQ-l=SX8Jms6tZ*_?y{*AWrg%^
z9t7&z5ND$9DP~UQ1EVnAv62Pa78HO7W87uc<Em(sT%PfDZ2pL>a?MzK5<SGj)HV=V
zAR|yvH2$}Ugq|2r$eoWts-u5Y`L)>8{D7;f3h$@bU?fZ>pB2liDA{NV?tF&_U#oDw
zN(ydc6$GS($UvNfDZtgV+AzLSUO);j6r7Mt;G(yInS7Vwgj@XE&DiZ5%x3wH*?=cZ
zft&ziYx)lcsNKqI_sPo7d3hhZeAiAf6~69%L4SawATVcxsq}Z0bpjZhlmwt9tIKd>
z!<10XEG9wEJ*I!~poorq9<fVN(343hv&c^MJd@@bo^EmaOsnZ<OdcS-Je^(eO0a_s
z-yTqt^QWnZ<}6{Lsa}w{UMTG}geQh)sSogs#xi(|y1&oC9_J;ENDP(CJMaw>s>Uxr
z<4@+dGj26<C_f&|dYbo4nluO<N2JdVLua@UsOzIMHvnorg(y%`45w!FQjpRw8v&4?
zilC5{k#bqqY2Kr~?Wofz{U5dClX(_>#g8Ym+skw2N!d29bO#u;_S262_z33;TkY~<
z`!|*I^iKU+y)sO=KKq$-iT%CipUNsWt+jg9^tlR~K_T7^gl9s9bFB93oz%8-01%mm
zJrDC1Y8bmlU*^`B5afFb6XN_|?BX7@y-@G_2a9ah*dEbu+7gkzoG*1!h;S3Slnz(k
z;Ejl-cn+kqC8hU;lh}CJqFhPvb%3rg%&U-oTS8)eUi~43x8{e~dd7lLV83CiEWdKk
z&}>~rAMexhsj#J^$yHNlDtiJJ@VeELUk={YoahbwRccpP=l-JZ&FiVL&7?>dkX|ZZ
zq&r3UB>nwrUU2qF)FWH#+FPtvgx;rz!e_`@BRCBqb@hKVy)THTD-YXd_%|P$aA_#%
z{X8!Et1kzw<nv3hbEOaX;FJ;Eptig8D$MuEITx2Zxi;5j2psgFLC))GXE87k3!Bl)
za9Vm^r4Kn4qEySwb$>mq(&_B==D+|V^=LQeF1=06ZP*Fi^7u)dthq79C>;wD!E!6O
z#V^6uD{X(HTekaz48dpjxsNk03N2paA)W<*C?2LP#q46Gn_bP=JN?%QdJ)M-kVyqu
zVhD$xd%ZiRA|9^K^)olLR*Bbbw}6c2A#*UUJkLbH<r9<wQ}GlxCkuhKWMH0BhM8tC
z0&M7sWs6oBfa1yo1;U=XEB^UiY$TGt^n6rV^R8rDsSn0RUB^_w>GdCx@jH%;lyB(r
z{KxGr^Qlixk2g41jFg_~Z)^SuD;WjE)W;Fn!nW%MG2d8J&KF2j@7uE{4E;{F#ili0
z6x51gF?iBheUz9}{TV#)C|*yoK#QhXQ{g+chG~^`2DjH-?=eNjo5t)4Bj|nkJs@Q=
zwtwR99g0mEv{{<f#><C=B>|GEWThln61BwCJj5yp)}1M54{vd)YdLx5IS()H-Ra@<
z4aZxdXX`z~ym~}l!X{+-GG6C>4=9uqZM3Zu5;+fTtVt_HQh;;X`%!PP-=R7uDTG+Q
zHtq*fk@UvC#r&vz{mZ;h9_6~!KDpiF?_Oy6=QRI@XV&}AqZar0$?;6XYi_Rufx&y}
z%ZP?pKI*d)inW9_QIv{Jq7+`Em|9cpEYum@pY?w*j~0xos?kisUx118==1j~dY)!G
zpu{hlE>=C&lazTDY{P_nq-%a`9sjt6^?|%n@>r@vbY2u#36FMZFR1ECMoGN!X_c$=
z?0zA+KgGn>r&oAGqctc%*iFKfG$7!`j4vckJ{^6M5)qd<#uGqyIKqB>`zWv2K{LX#
zo6+T%dxa62`9SmaTJ2`6UR+1(t&9uoRAH@DC=Dj^wyk1OB-vV-Mvx3+Cx*oKowC0E
z=HNz?mk~Vrsb=`&?&swTJNHn&zl<VD?hyqRt@BgpXG{U0?rE8W$>!y-4g25TSm|Z_
zle%)lyeUU;2D`N8?;ByCw-gA}oNS-6Y|ab@dKZC*$252iQ=|>=sQ*}7Xl&%`?BbQb
zr*J6po##*;Rn61rl_DRX<KiROxw4YA``k5mvG2F<9lge(A1iYc{zCuNUmS4imvHV#
z?@RdGfcoGMyfxoCR7z9qTYu<`+FoTUDeZ`_cIMJTllV<tR+qT7MuPpg9%p-KeScK3
zhCgWV(Dk=++2g+u{SW@bNOJ&RBuq<2eF>=m(S;PgeZt;&?PzNY`kyTkfC_J<2YbA?
zj($%B09F)dZkf)p^R5r^G4}FWI{T^L7Sss=Z|X*+g4{j+6*8-%3x%7jm2z!{v!ol~
z-SP5p7+_rokVP`d{Bx`%>Z}Hz9ceDI{hTLHWi$zf7A?+87FYh(G2n1pu_v2CYv3>c
z8uafC=E?*tKj@F}SLOQs>c%hWG_-5I$6Mj?#X0xM^7+qji7yP{vQ0sqUzpz>a%p_L
zt!;?^_;tWF0AvqFh`7&4Oru<w>QknnE*Wft)0rU@Wc<X}*aiEKI_yeko67Q5pV&Zn
z?HnsRX;#)dP+4s=1P!47MhH0%X9#W6u<^IM<AEn8>C2*Q1{{o)a*M@%80)GOFUmMu
z=;9S*QTj^YY+dN3$JhR2C-62gQ)yL3B9foNo*Ips0)67$U-`DLoMiaEwZ6hr%1U1j
z2}VKENgEVw95!F~s_y^xz5I`)F&!Nrmj8ID>y&e0ll2f!<{h*)vJBws{&%Hx0Q#{g
z{jam+bRzBSh$>m6B~}U)W_ubX2y`b=YE9VR`0LMk!oIr=ZOGE%3x7cP#(ww<@n1Tx
z47|7?FzhaF+KT$pbT52;;quG$ucl2d=Vc2oBiy}nV(-7L66+Ut9JrAaPSPHN0Wjb&
z3Bb;f5qJ<Uz+S%lYwb`RNTdMEC<f2ZzsbjIh)~@@#CRx0CJYh*dOvkxp#19$p#2{|
z@Tdw4;9W06sisvkT3lb_|0E3(qiFwRgrHT^t8M4E3<O~}rr9TBbq~4!+?Zn{u>{C~
zA7CYSmndrUKOr89%qn{Z@C#8lO<jd)xl0D-&XcF<TZ#SuvL3Ix8K>H&iVbn<C1Ypd
zf@xLrn^=UtlOXbGP?VTY<yD2UrXbxP6+9&Ar1A4VdSVqlN_d`z+&2pEKikkVck$iK
z8-oXN!bul!xicKs_?P$jHk|rNv8_Cym``hsfRhhzJX*c{OvC8kvDYU$#18ll3hx8Q
zb$2H9J{@cO8k}&#;l|CZd){*L>%rWqauEPziHL*?l&V<U@_<Yk=mswKH@pIO$Hc?2
z6r#;)+pQ<azM}%9jDy*yatw?VPb>521$a}akF?mZ%{n)@wP6_t@D6&uc_8zt4e%CE
zi3OlZuojsn;Bx0YUAAc*KBS~vLsgN@f{Q`e;VQY}xQJJZubx;7-<j=d%No|R)YY5Q
zHg$G?q2(|aX1iy5SvS7p2|-t;ib4^Nyk6C?2!N?%E#Nu<y0iTY)UZ(yDZQ!e$3c%S
zdH7HO?Y?LVP`yP5K5ZDjZ~WBqsvIaF^>&{4{C;~$HWL@rKoH)<<UX~Zt*h!rj3GeJ
zq3s75RV+h+LXge)SN>6KW3&Wkc915;8X#LIgO}qG;06}Dj>`oR8JQ^eX(JI#xo`6~
zyF+F2S(VDk)(0Y|oF+959()fietJq`?ZWj=i5yVU@}lv*<YkmtT++)iH||+VR<m}j
z-vn1^$#iV1X3C0vCu%g{<ZI6-6qRUB^O|4?oAw#ghb7BQM*z_kW7=ae$Lix+0U1SR
z7a%iI&B;P3xqQG?DtE&ZoM|E|)l~=A*?UBUybFU&kv{v@p&S1?!GHZ2FG)$yY(8y<
z7IwD;#5)E@a$y0aL<%P$jfWDnuAp8Mqs&N41fSsYPMd|%p&g%dk|R5BZpe-8`cx^m
zQ;v}KtE!e&a-VfgiZXIpwC16#$~}*iiPByI;DsWBmi^FhUUpRV`A~BRMaAWJeE=C3
z`CYz9uYdGvc=VI8z$k~+g~w{fkCzQMqKM!#QLEh7k2gFtyiV1y`OCWvph$e*K%VBs
zX{Hu;HaPAwwPenq9067#s*lnhfAgsriv7xrZ*cd#?xNk{uS`#qG=N!3*i@cS6Xqh;
z!yP0ZLdlkT%RQ%L!Z9woa;;5mife~Ko-1xPU+Ay_v3H=;LJ!E+37{v!5lPVHzb^I_
zcutQm(EBn3)B5Hb0>r^1q8>?dtA*wu0d6SuE69I|B!8Y2fNJs%PyX$XO*4FIv-904
z<Q7SFy^Cf$-gUfr`8HDVtOY<akY;0RDQ%%}B7hMXh@Awn(Ij@<HCeWtx%7wkzRPk@
z6-{EYLS&yQMJQ2T@Ys7|Eap6Vr&0GjYRcj>F0q_9U$s7E0v+{5&QTO&n3v}=o*Xoh
z_1mgIE9g|Ovb`C_8IVqHH^;28c&Xa+#-qLw(j9LCY<DCz$KO{VN$5O&pR8jes0@bL
z`(S)oX_0fYqm5VT4Q}N;`$kWY{t&TDC?i0XcU%}})Sv!59%nwsqF4X!Pp`!<tBOpi
zFzRHDoRh>=qT1hA*NJAhp%pC~-(phcW3O2a{O?4PDUK>^=)Z9AA>chfx4a+vC6Wt;
z{d`6N%ki)i`O;XrKd;Pq{ddge0$?#;k*F!TUG_QZoSo{MAN8q0egVEvpw7G;Fq|i!
zS^no@@ns<AO5Tao#wP$5&6Up;p}*?P9|Pc|fso_b^oho!PkFAWSR(#k?WBi430K1F
z!ZR|L<jmF~k4@LP-myU3fkT4atayLwzCM;6&5I<X_k}ut5Y~B?JHAhztY$t|_5Vt^
zQ*V56O!Ageo5FF;u8q>L%N10FP@|@YQP86I_Y|#=RI&4tArkqL)QCdyf?~_z&**<u
zKOM9!eu_cDQ;+F{o5ppA9@Vo`A7;K+Z#6s`Y_&22$fUdh_@7Uhrky?}!-WklTB$!`
z!`^h|+~FnEuUGTv!WS5NX~Tp4Lx}Nhih9i5*Z0Lj<}SOc@<d(`!iN#F<oIQk|K<bV
z^NxeeqPSMHu#C18ewuD%qU#G%_xZZwK>o(T#d8SXa{aXJ4~?c@VwXjErpzZNK+2Y}
z3C7d~6KkzeG#QA2uUzr6)IA1D?ur*W-(``-YoTJFF2vMP@Wcm%$_BjEQOfXsnI+0w
z=W*cpez4>^0_Rw+qVUXTEeu>_%?)0*F9&UFH4I$a0*K2fB-t5S_Gj0JEV;})D&AbK
z-Qn4x^9N(L7)p3`&KFN^Xd9`CK$^ZWEGo%-y?6@90D)7*m)fSmjp5<G-OnMJRx4wM
ztIxoXk{qs|v03*XMOgG=!)Y`;Er7oKRL*?6S5193=K%xsBjCO5RBk@jxShk9G%NnV
zCq#`(^HP`f54beczHQ;3mLuCbGiNjuUg)Ve5vNy*)a5een;7gi_$93pP?!(|$XQ{Q
z%`{RX0ss&y0ABc?mf$k>AA<X->G=guZSN~y&Z=I1p<C&k_$>iZkg2<E=_z}pe6;*`
z$%+ZisU%SZ&%c&_{8JHZ$ICeA``TC${*S}9$nk$r&rL5SLsbgkq)7xyjK86Mr8j>Q
z7^=?6`4-oir;@?}(dqn8BQBTkq>ja7gvyv+VrL(2on(--ETev2ua7^Q@*Ee&;vN3z
zSb6Worw1P9YR8kNhx$s5KRIq4J0|YxO4xUqvE{!F-=V-|&4Vld02c~CqO)R&FQ$%V
zxirPGl4fbMyAf66tEd`de~0sf!8_5n4df(Ot|RZISK<>!T8xT{@P2<l(O=dI#G8*>
zEq(1FVq!&iF6xW}4dNfT|DOfIoj!0vHnW?@OHt9?AZ}eX5R|QTRM*zYEd5p+mt&i?
zbt~`K@2B!ZJa?Rr^LUiL;8Y@T926YPT1!~Hm6oCYe%IW#E2o~LOT8NH>}>UA2Kzm8
zwdSRM!I#H_6MVa`@<-TrZ&&R+w?v<_6rCS4CvUtuoD!YAOGpY?F5~_=haXDDbAfCa
zhxGrQ)m$#&0>|@X<|Z?S<PDsr{|138f36}t6$64YC>->rjGrZ%b4*r8#;k0h<vIWW
zE&ZeqRK4y(gv+D_k9~mudNLCaWto2@DNX+^dwLA)ui-#%+f;p{5^!jfEZXXbKY5{C
z*>nrs0DxH;m&n3!Y!;Rsz&(fn=k}X7<&>+!pR~ObK&}Zme-dE8mCVNM9QDd^1usbm
z=?~7dB(hAkyEtdR?t2CGaUGN73yp}EC0(SZ!fpH}8i#gORwI4*%RQcPT&%dWYNWH8
zADNnl9s;|Gjn30HIdy?QuZH*>18w~^{Rpy111pDu0ndxeicsQNZFT{4l|$XjQ&ht;
z_z=@zR>5af{LtA<u!_&f!kaqlyy;a3T=QAKN1zGo<S!a=X`GVD>XFs*nl*G+1T0Lu
zadwKJ|BVp0_VKc<#^vxv2RtFEA@2l1lebAnUUl(1a(9k<eWnJ!W`1ZvIF7wFsXRSo
zN8Q8SLUw$K&~lm&whYEpFX9IxQJh}~3XNHYN%@Y^eV322k+Rko_4qM<6BFU7AGHOS
zl{5f5Gj@rJ$paT3!jvP9bn%T}j!amG^fIA{WeF$J4wn0Qac{VSf65ZYy_9p@FHGt*
z<#cENO#N1*&wCr)hLs!7SY9*Ve`@k-T&IxU%osNxyltWCsCPU^NA7c@-sMBB;TtMn
zkEzdo#HOzoeaPdb6dMb880YvD16;Vl{$8F}AJa^VEd7pwX*ZH6uejK<;|&^-<2v-a
z>hc@nE1}5IqRQ;An@;$xtqiA7U?b{y#WX&QXEl~<VI#xa?7hEiXn?t{_qdyrl+%Je
z9CAxw{tSZ6%Pz2L8L<Whi#KDfpKU};q&QjWh1M(9hLgUnPIOJH)#prZf9=@Oy?fNn
z=W}Q1rY6?$;)r7qoJgqCRh`$a-t&X?2iBm0kHYTBY#%c03>!`$SxSM4!c-3&wOE0L
zlfhGfNL#r}^9E}O7Z)w1i$OStUl`^_ZAX9dw*gqttB#I|cS$+26olo#GAMWzLgci*
zRd|UMEIY${__$`!`eTZykwQ?wGvXP|`GqyDDDa)anbuSGyU(LzQ~e}F%_0KaWx28y
z4~N$O3;bd|14)|U?vIecC5@Xf7ak~CSdF!&M$1H5e3FJ7*K3J&bg;2Y1AAm>%+rr=
zUo6le^!pDXwF(|^c$D(oQjSjSr#OR?0H0)Z7<OQT;S`D;Sd9TyL)ZYSx)cvep^@Rz
zYMuaR|21>qg&#|+C5_`!3`mKGe7;hu-m2@)yK28oaL8^mipj9B$9kZ`x7dH%U2@gS
znk2#$8Q{W}nX?hR*jOyv0GxyXR^talHf@oco`sp7liGIMsxmQYmq)m@G8AJsqFn2C
zN67$&y3p)91c3TQ2HchS6AxA<UCF{S{(W9}6_v(|_6mTm2tw`Y|6U5Ri8hV-9WJVx
z;i4QP8$+ucZX%mhprSZG=I#@056H4rtgsr84|M<~@)8FLaf@@35EBVEQP#`4oC$Q~
znl<9U^n!*yG|lw<RwZuVh19FGEGX&U1BW0UH!xnLi0kjoVQziEkV=*A?aizDyS2}T
z1@oK@5t98RDZ}S_uJ&}$ZSj8L{v($Pl0;h^nBRg!ZP3x|q#TGGE>G3}P>u*uO+7*!
zggcWsA+mZGqJXpWUuyu63`&w72#{cL5<DrAAGf8qB3<6^yWBb=QfvR+t*%o~KA=UJ
z8*h1dxjbqs1s59Gh%Z+D;Eb#O`V30uN{Iw5cMM>S`6$HzXcycRDznAWaH#-6ZQtcZ
z0r?fX_gw||y*ccH<3ev$+xNcJs+sJLQNC)b=FP9%Q7hz1C@c_Ic^OdVNP2&YvYZ=h
zBwu8NE#f7&a-6Q*yN4pa0+0O6RX>KfuW5Xf2WQr*^dyg*t4aJf?j)-7TF3v~Rl1|$
zz(w)3e<mWvn@wB(7q-ejsvgmJa3+^-{A{99`RlElhc8j!jU-{!3H!0OH(U9=+mk0K
z=0tzBJ85;1aBQ(aX1Ppjqy*@q@TEJT+BYp0iz?=KzM211xZmaEKi};L>=(73y;EV3
z-|r+Sbu_B4%kfs~=4u@MxviNFP*<rlH}AJjR!eKfM%Y<{)nqQPF!r9(AT`moR}zIt
z(8>!QmY{3PG&rm1!fHEl&EB>!??#<oVnd!{Qk$&aMIbp(hT$kf)`{RS-)=8}DNTgr
z8m^ct2T2j(8AIi|dD#yBCRXCaiFxvvthM>L?YJZ}9Lw^!;kl-5ldiys8gbIktEk6K
z7-8pfwLx|il_z)uO9TBc-}NlTJw%oAEFs^_ecjenC^T44*I(?w@+cONk{iMLGOqY8
zNKAXmuJkVg7@1ozU_JiKEzydk__P{B+=q{ce!9Z`j)b%+Qn4o$EFgOdg&VBO=l)LP
zIrw!IqLyTWtNsbB<;k$nUUJ*uWjS+lA|xn_w>=R6&iE1WxxuH*@_N;r-!SrPVwoRJ
zHBb-Mjs`#2kF#^_5-Z6wmSGtqoa~9)`}atsv7@Q5WNn>&D5KFl%;lv1Wyk2fLNLSh
z$!QLMVjxrAIY6!YelJ-@X3!8A*ptsl3=ALHc^P($fl!G#V%K>g-%8LxK0=pq{78F^
zY+9R-NFHH-Hb^K#W*HR@XrE$XAhdN5C1IdQ&d<4b>M8f2AVn22T#zD?ARc9HP$q3}
zkgU^8{{5J)B~=4eMvp3E6n6ayHr4Yv$q#5@S6@_*=^WPdcS5+S2F$xR**F%QLR}A%
zYdLNDUi`pQS}sA76d_eT^D~&`76?i6ld4l<1h%Gmo-%H=2C<TE>SlcbvjqN;3>24z
z0kb?=UH%d)GAOl}d)XKONN`q0FuM!p=MDmoV2KdmOMR;aPpx&2j8n#c_~+NN9ap&G
zwQQv5@}$U*9?Ri~TG2q&Q<twkIIk`|jlRmfszEEdwqW?g#PFiI*5EHqEGX_JiwAIt
zry^p9ot3@@o&>{ct`48YHJ1+N^70%Ud#3SxPW_QhCpbCs*y&b4;%%Zxc)^ATD?j3k
z#Fu>AAoF4|@DQ=WW@9r0_<VE2)H}l|#dOHcW+*_W3ggCH!u!-k06+~d>m#RZHu^?2
zIrk1IqhEI4JdjHy<>{<i#_Y!n@X%H>Jyd8t{dZ<g=MZ|eJnCLvo8pTMN4@SuC*K1$
z%p^57%d<uWL<l(6#4U~VmX))0GaVG8azYq5Osgm8n%t$fyrduytWJ%{`{TN~-DGBt
zO8ZYGPC>+Wxv2VJfGrOFvH)6cKx8i_${m8wNcCJ=yv?=YiLaL<(UiaUMjEMHeua;G
zZZhk=36@lve{`xw+UN3_!08fYP&&%s`#E*+(2k9=V2wk#?`tHel}-duMH{_8H)Kuw
zudrb<0TiIrwui{m6v%hO-!s-PsdjWbG4)k=;MoUW?k+|>$~#+(DNSKxDzAFP7qer>
zNN5?hu$SBil;S!Ra0dVlUf>FLat6yb=So6=RN*xrW+;4uwfugv-wUSg)J%;A4{yuL
zDxUDXpzZe0YYi2xivLA=m44;XaA<Z+@l5A*4hg49`_JM?5w1=;JD#qXB8~`>0VUAL
z5R76p7~%)KZD}jNh2gSSeLk#rh4nytT1+WpT;6?xbgyaiHCnUWa^O3e!p(Iu9*p&U
z2M9n|o>+F6Hxb}k{0X|icWO-wSh(NNB6#=Fg*EM#HmG#<@mG<(Un48mzWndy*Ti?z
zBV27)157Be;@|hk+*3F~WBo@RDvdRb=uqqm+tv3#LR%O^y>N>HmWqo8{Y?bqwogL0
zTzHZmGi=A@r)zDsl2Z1SRZX-^e^UK*lzwMlO4hgyb_k%p2c_CWg1&N7Uh&}&5a_Hd
zGYD7an~b_kzA4ld)IGE|u6vVFgd{M<GvpNsQP5Icl~2Z9E&UVKvDe=Hag^g0Z|F1Q
z1etj>gf~_1D9R1K?tl0#9_##vsw3hn^tgt~M(gR)r4n||NP+=?3RF>`doqZ_BsN>{
z*Izj1Fzv~*^P;`0Iy8M?)x}eHk4r55_QJZ=K8#0>Y8@!d;_h%0V^9L!h@$-`31y?V
zgw`=XW5M9*`n=D7zn%q(CY#*h3I>dSjgEx*#i4GfeE~?-{j<+(lW&=YpL@5cZ6*X*
zIG4+&0d)>W6qyIk;H8WC0C*sm0!SW0(kL}fOPhaS0N4V!^t*hF>j=a7b61%Ky5)G?
zQj<Z++0ZI-+owIzDYXrHO~dEt4)n*P`U2^zo4XQ=0-a0-<z2g4nCXA6T%$yRUtPRO
zQ-J>S3A@)Z^8ojwb7bJ8lGM=PGq&Maeb9B+^tA`k#l_%G!O=0CaP!9x|KzTlQUuAD
z&b*Q31;BOH%K$XfFO-`f3={b}D?1jcR@)W=Hw0uG4kqsEGdlIAN!zmt`A>qsn*iO6
zJu6}JwpFHxg}U8<n3)S{Y{p_I?1-d3Q#5EyUZ^xHQN9`M&tv(8gk|EImUa_ZR<D}@
z%%>!7|574o@th<Uw0>Sn_4n8n3P4@|O;vhT$EYQP;bk2Z+{Novxn<H)4;zKkwiVY0
z@6@Yw_UcSN_@=7|Zaly93qP6V4Cm(1P7gLJ*)hUgsipwHn=we@mV=BTjiKy4yO{|N
zqjA6K7<_`b)1UF=o>W!ys@j_xwrC8-<KDeXKBx3MvQ11gT_N9UXEsa{Ens;)r>4e4
z<Eg*|+R3%^uJ-#lmQ~b801erzw-;rBwu8$!-+OW6SnZ9+0KHW)t(8C7^Xp`rcSnhQ
z+ve)eP9i3u%8=gk7xZlAbigfKo|Feu&SRfb=_JRBuO^Aw{1i>502;D$#Nae(EOAVc
z3dTnAP{vwayN7HXHfJgqVkR%0IfYX6n=v=4za%+$IpAuso@C`^k@MRxf|G+8{VBH;
zPsOYHwQe8CUcVLLZ8BsM7dgvSrCAJ)mv2z3Opxek?>=?YvAs3oaSjXMmfcv{ORQY)
zJny2gtl(HE^Xbp0o9-vo&*@HYcgh#YPUQ16$Z=?=WmU>9zY=7C{5udj$4!LfA6(@|
zfLNffJWVj>&*jOj>L00Ef+^>`78vChlSX!sT)~g5xBkJbX==Zk<@mFVs2jo4*9A;|
z>x{3e9pa9>uQvJ;^5$!Bjr=D$Y(V`LEs|%ED!>sK2m##B4a}N|d^zb~o$^$5<gR}M
z(yM4}?xuK6(W4tA841Q}TrPU+#-)9Lj~OZF!D||mviAZXEE-@l$R9F^nG#MV*-wr)
zl0SGL3TA!mMFz@~Cim`0j#=8<|E^IBN<@kzYpk=b;Z;++{-pv%m`v6W84BRTM<twP
zhkll==M9z<w@vUcYI7Q@E6ZC$fL?}h1&#TZ77@f9g_>nk*8?j`&E$A$24(eK!b{m}
zkaWaEWLBdk)RHLB<ihK!9bf_ybQRaNTn8|ulvC<Ek-7jNEgSGA5ua4BLVt4SOaSba
zUcO$JJt<I$V1p7(>hQ463)1}%kdQC53${o8;U;<>@(|Pfk{A|PB9$TdVm<CuoIgjh
zS6r>Eku)=*So8(tYZ2{{Cb`vnN>zYtv6Rj|#dYjv)=0qJc{>gJ`Nt)7v5Czdwsw<D
zadV}nTf(@yJUh|`GfjCOiTg`T_xyn+?FZr)CIIN-oDWeewyyY<m(@hDOZI}Dt{;Bd
zn!#g$3=wLmPq<sNqG4Po7GU9T&mMQdDz5c$i(G!w002TTU<Lq@-%G-f;_;unoC*M_
z2*Wq+q;5!_#t`n2b?BZtPe`r3cZfqs*K=QjUtk{oY{E=UgY4+!a9JB<RXJ2hF4AVY
zjsYBXGz7$Jkss&-oXanGMVbuo3i`ppIoN*=?=uoTaQ%<abs|<y#6rCem3g<{#dwXa
z16Avpl9LSJwAhL~FoHcswDFN;#llCoSwb+R6ATaIFZwYmlK%T~U|{f`kd6T`u~KOo
z{^Rr%{9<}3sW-#mN@-`ke{k6MKV44XlD;c-Vd18?Lf})-nS%v~lgSToc|OTHAD2r3
z?QgPmqTIRpsD@GNU96ef1y0f(&%LuQWmeiE$T_ye$=nZN7N&1&@aJ-FFY2$Wftx*j
zT;=n$teI+Usz>}>t=b<S$8$oQ`{gDW;z#gCGzP-xA|{baW!YZl6s&lS73H_#+8(z~
zD*5>2HRt$CV!RJWGks_xKNK64myaryGNm`CW2aqk`X=Vqx34{ULVLTRWq-;$SoI)2
zf(z9Snm4YTv4eBA8c8<?6;qb!#{fvQkm7sO=0Npx7yhlP{aQ>K%Wsd(v{JBhqZSG)
zqvP-Vr-tVcX{+58b>k?-XKNmpz&~q{7>c$^d691P6ls;q*7Ky4_uhfj;%90&n3thK
zrK94dDdzROqS^n6l6<3N7^~7|gFm^X3W4^?rTU`UAPYInN|6T7`TR1{NniV+l4r20
z^NZcfYjTWCbeiUf@k=j8ryCjnC9j89<7pwCf<KbgrN#yfRmOgb)J!R63f-_6bvNn`
z5asd;-9F*!aOE6AJh^n(m6$%ecZnsGC^LHrg3tdtFdPxrjs=dJ643yVK;Xu4i<tff
zb4^k>X_}4=ctN3VuyN0`4@(tyy()&*{Z!^cQkJ(~GzFQ6o!K@HcicDAdvU7fj2Ph!
z6Oo|O5zxYKLWVd`<bORf<g2|BdF5Cr`IGcGF&F^Ns12`Xpm~E~xvmO=3x>b29bVt@
z)ra{4t6Rz#Oy@vcDyrf?u));UFd#x0w;D?JKCM=LbG7amU|$Lz?z;{q>Tm|Pp$Kyr
z8tlr=jrsX~ZR#D~Mt3kCYlM`)cf0P3h*RkPjjP{8*Sbw&pJjEscPaeQ?$Pi&oVE5m
zuu+5L3;dn|AT1%XHKRAP+XFNiz}JgJy?8uBxJh*`5`=~1A5*C5ch&i#cwu=oZ%euB
zOo)@CYH#{V?dnCgTtTO*_9?M}RWKa23krgPlg`XQ!6G*S5I35EwTAprsRFi#iWmQA
zxCTAfP~6uVoHFowaP2`{XV&a=OPRy}PTqxk`tS@KiYFi3mfbjd=ix8EAiCP?^7Gn7
zs|&CD(|j*@&1T_lG@!s33KiPqnr0Zvc><|eFU)=bqzQoL-RaZ1UdD_$KCyKN-j``S
z50{k4MIN9d(nPw1^w3|rmo~G6qhYbJfU6ReEz%z$#zj%5Au&|jK7ibs&gLnLClr(S
zWEp#E^wN%~>=q$!G17!i?@Sy0gEqWKD<4($IkD~YXPqV-3zm7?U#(IKjujD<eXmZc
z-jrbvfUWWEV`3ne?UXTx6f<njQ5@Q~O5-(ta90l2aZ4JbD(v4gX!=px^-X2SdL<<V
zi!~FPGom-^4_(mJ-QcI2dh26{<*sRVqV9CMDi<Hs8+xeKnQVk?hU-fm!Ix<(tXJuw
z9&o&xnR!wT#5}&4e!g7_PQ*qSR3=jUfPV>2Q$A0p-^jL+=&VMUys^FKxlmK9(<m8h
zh#qCUdZ0IUL&OVqtNn^%lP5*F8-|R{El%WN_)>r<?nO_5wy--4h>|4oe*D8~I(x)=
zj-dOet$UK9oPcxDr7tBRu<3P+cc2Ui#YUH^8w~=q!E}4Vb6Z@*4IspvMkvh~8@}tz
z$kGm^kw){?$aRe6A|rS$SFNJzo+9?<WB>0$m^`7^Y053>Hm3ZBbU_;<F@r2*zMgZI
zd$8IN60iMR+<2>y{@dtj;t2Ip5j$3H>_a+0g-|X6z*Pu1CY>AzDFy&vEkX|khD_*+
zw<KLgiR^|TY<0%!;?#E5C?fZev?gg>8=VX;2dMX!J9ufa`3w%>;z9bh2xT7t##EAQ
z>_uk);FZgf5}l`1-CC2lmW~JFBH6gt)iw#^lXacSetHV=dl~Y3`qUp4%hxlvxol@v
z8wh?zo^h!WJ`^AhV$K7gqJ<M;U`(1wTVm#RwU`t`fGZSjdukl_P(9g2)i^}3c584R
z`)FypVKTF2fT<KAK<Uk<&Y+GI%O3XxK*2&ZB;UiMGqhl?40Ip8$CDBF6R$@v@#}=e
zhtwv!O}4JIZtgIj>`uoC+qk|!?i~mX{Z_ZBJCPZBqHNB9eznv*MBw|sipkK{cvI*f
zg3|AJgtu*adl-}%29YY{<YxNXu0m7`DY5YajXX-IR0*v&yz$GqeA$}95qa+FVo0DB
z#FM`A3SK-2&wrUE5>IQq_NW_xBEA7Eh1`9CWI&0A6h9vE=SX8^8D*m;N<oYIQdoPr
zK0yc_sOpH@?=?&r^xqqj`tABpV~|b1)l_T7C#v;UTgF|bW1cwZ7g9{Q0`*hkD1gDW
z(jfaUvXZ-Wn?1~aUCa2-N92?$s%}-u0gaga<nIsC-rK=lkaoB}eb1b3;%=S+VU@ed
z`Pd8->ad-sbSLfS-!KqcR@HuKSRebEJZ@bR2luT`e$)#Jx_g-1om|6_ob*`~_h%j)
z@Hu}OZKMM_(BmzcvwdXVKs7?3$4ReP?5c7(Ca}{_+XlINkFcPsHOT9dm+ODM5vXc=
z1bI*ary&D`XD)MZ9HU_U(t0$t3~3mX(1xBK;AlNsEx*ZDJ<}!$5nMs1U5BwY#{W`)
z*v<5ENdOQ@T2z7|vDCa_(RKE~3J=A`+!XVZ*V|5*$Lsbh4$FR;f7scEm;dEkpgyd~
zsY%S~;jR}c(97oZct((NYiU`Dr$q6$66+d;JZrm5I6W@N5_t<z-Jd=inLbz#EeV4R
zbfwRnZSagqL!GA~!=RFvUs-<^2_AO0Evjmv^LpKsTL0S1e;$5Y@{#tuur&__L#1gY
zF_^C9ff{`~`i1dZGsTY$KhvO@f%%346gFq$uc&4XfSX+xtp#Xe@w9N?TIZj+HX(XV
zd`GJ>_Do?xu5U{Tzgke0ioOzc-g>UfX%#522+j<U`vujCr6Il2jFeL&&M;_Uko`?)
z@nc!T$ADomb7SLWJCA1Axz<vDaZ$%3i%*2&ADF7E%xO*4Vlza{A}NH{^hO?0q+lxw
zL=8q#063T+O^?Ma#vZEqU4<c`0zE}XUcL2r{++jpZTS2kGd#l1X7G{v<ZLt9X?4b?
z;tz}}@ouvC+^d&9Lx+#4%gdP2UevNYvqqjcLN6W>g&m}KV6ur|@yojJXr<`n9GW%>
z;r0~}dZc9i|EXvmf;T-j;AJpegLRYLwrSp}w8T7Ds*-dT^((ht(xx+x)+$(zD4W|a
zOYa0vdGcbmMZk^nVo1$atpv&vGyU~F=u>_XpC2QSR_JRI;-acm^mDLRC6y(=$S1W=
z6l#&OR0iJ~<S(tyGjTd^^<4j_Jx`-oK163o8_)F_IL_Fo%o`T-$)*4Pp*qL6^v{%b
ztg`JMTDEhJk#?l&uUcstO6UF&t(h~ai)KX#znV!a9Qzh)kyTsh>i*B8<IlxmjGGtg
znfXuGg(Qe2woSJ$$ovWMMUvRr90Xr10qiFwH^|vc+2$~Katztl!9xq{3=c6sw4IZ3
z7#Z>BL?$92u*F%Pm#^!mr|xj^LaUt+t`iab=5g1LTo#z_N)ePA;*E{r+m7X?xd9wz
zZ+CzMDpEL>XAs4ZUYdPSgKBtc*AD!wOrC4<S;iLMQQWyS(wIo0Zl>obQ2-Gx<3kEe
zj@pweiS}?h(=WYUeY18N<(~`d<P!$hwk<?Dp`j+g!V<~n-GPr+_#yIbVc~Fb;pEK_
zDvHsvv&?(itJ)0jMzK$|BEhX1Vnn5SPTh=4Jy~&s_pSL$5p0G%t%!E5kPsu@2tbsT
zq5c!)`ypvdE>PWq>OX<OJF~Z+H=Hcf<Gq%O_7b>j&^WpzADH~NE@EINfX48O!Oz@i
zU~%)s-biOZJ_{rQrsnBn-kfd-SoZ^kre$p>ZMVM3>&%T1_OK6bVBV=<Uf~+?F#Oy%
z{-#{p5w6e@{H@9No&7phoJECVuhNjGn;DmQ_<RBKW#RSDdDakxw#tfliR{lhA`F)G
zcNd|8^V7<KOJcLIfc<b0d&T?7NY87>Gb1S_ObQ@;gops+H<Ro*T_~Z``<oaNfufg|
zdrL9%An&<vv-;EjUUJ(F2@}E&CC8=ng~I+_Dm^nj@@H%Az-RJ5vi9ka&=P<2HtZi%
z49q^hqFtNDM?dBZ?dkUo1|WDIDAKOE5lAas%KMFHV}BRsT!syOlx0@b*qGGX@aBlj
z(fbD^g70HvPC%Er1}MV)5yHt$Q~lNw50p=(zkY||Js8Xv@rcRQsuQ@`)>#sb7Z*J-
zA0@OaxfYqKRdQ%GxMfEAq`YRLsO>K@WTHYd+D!M1$-wBXjfwfcG1TE3q444vW%3*O
zJ-(ke-aDA999kZ${!qzd<6mhj@;|$P5s#BW4*X(v^E*FJBAhZN5P_DHmi!LLmK%a@
zCLV#VBSB6dqD)3JeD2{-cB!38Z*cDNmZ4blNHN$raesv2(8|t<AaYYsRK<(y!P&=M
zEQ7nMef};)cG@6kTlBiaTE0S86dn}wh}vGuv*sZZ0Pk*qXZ-_Y%v>WDo~|8ALB`px
z|F}SRZJXgD@gi^Tup2&~)klY~tUP!pXH@6bM6)pJh!yuC3MI`b3R>bBMngwkD177!
zI`C4Q8IO{z_YwsjMi$mz`~2f`*{!L(t@jflg~cK9<dA!RLaO<`ZT7tuR{B4R&cd(B
z?~mfo#@I%W8a+0;8)?`^r<4c?N{a|ci3o_JQyQd17%9>ifT*KWN+bkz3{Xm>R7CLm
z+wVWv^V+@7bMN__^FB)=tVq7Yl%eTz$ghWW9&+=sP--tSb_2R-gAD>DeP|$z50#9y
zzI^4w&Sy+stz+l$cM#<^RrvAXZf;%IoA8sg7t>SKq=IzZ)|);Y&lU4jt86-W<im=4
zK=Dp;HcPewD+HlFt1}?6Wc3AiEhAXdZ51`sbFXe>&MPn^jk6?YOT`dHv-0L_qRKbR
zNvkBO@=MyTPOoj<$RJlmbe%*Lr-Xm9Bs^jN^7Bon3lgy8*e)O}`da$J?zZR2`|g@m
zou;CL&e_86VzaDtI&>LmnMcdW6W9p%9iS|hkRn{x36I;9fFsy-gLbYwxx7d|l1R9F
zqS)(Ck^Q@n@b`HO&oHRqOZrsvi(9Sp|Lp&9i>FP!{93C~#M2Sq3r^+@F+udkGs%l_
z`K_ef#ZJMan02<fYN$KH=Tnh?0^&6g5eUQXz+%G|4m1#P6y~Ryuc_H~fMsEM9iXEM
zHY}SQ7X%oAer$Fpzv>^Rm;o$+=C|BGyGH=r00q!T1TA(%hzwMM17jgVw-7Fd|7bvf
zAX)O+HuNlJgpjND4#`ZL`}Xl1_guh#+(~+C7r&h;p4&Zk@KE6n{r8liM!)je=_Os-
zi@?Y0mrm{_P2Fr>d}2M$ar!N!b)mUX^C#uEPtvFF#}#g%l|2XmueMDAoMqVv?4|=2
zbZ1A@Iq9v&4oWW<=xv?4+OgFSmo$@GZNC0lrcvWp{TH4l#=F00BcRa)xdq?k69`rI
z#SyHFw3WD^2>{VLrP_=O9zRgonhi1ik0TpLVa1As(ek7YiLm+hgvd%i99gv<KDJe3
z%^wzF`(y7HAX8-Jvp>fF>!Kb2tlk<XV*y*SM*xdj)dbM#B1%oB*{uN3`>_O;eU1-*
z%>X@uvb9Wjo?xaK`p3(&LIxgSMW~~gvH3jYtyQa1y-FINa_^S|wIw-uF0~ZBFQL|G
zq#5Ly?&FIDfP>{>#5aKHjsob}gNp#jKYRp0|G4LT?G>+f7z}<t@D96ckDNI}NuOY0
zD-E@D{QhU6;+3S&EH7WYp=9~qB(sX%OVy|5+YKTCXaPK%aN-F3hFrn^+LfU3>}*?p
zar7$jX|QZ=AR!vT*+>7k;>TYB5Glia8QhDo;G}K5HNXQV?MI6|>I-U=>Fra9if8Cg
z-$7>ePPJ>^@4sQMn<i`8h<&ReVS0`L{QYJDO{?62p~tuGPDEg!e-;zCpb|i6q2Xz6
zs4?07FpF?~+>^&}cP%ih#k>LekYJS!#0Y+)NG!LvIQ@zlA0I=2HB1bifCb02^2Btq
z8huJgXuiM*@I@yI2bc_)$fucLqj@WV5wQdiV9TncULiCP_kzp~j;TzaO-nP+ul@`G
z1U9phfi`|L>7BxNOS=zr5JN7KqAw#z##}(6t?Jv>ujnUet@&rKzYPWfroOm@>japS
z?UhO>4>c5IjQdPQ8f-}Xd82H{|DY=5Y04UIYPoQ5GTmb0SC>k)eV$@l^h-7u+fu3D
zz^LR2T`U<8;iFA6DMD9*79A46BxlykO){ta1+}fA(0>Edm&}9hrCp!cQ?d*6(}?+>
zr6``<JJ5E~h&jPbTrpAyY1sKfj^duutdH!RietIZwv#ZCu%W>mlp$Du&Y%0(_{alN
zemL`ShTA9`2+H+F12m<>=9*C#(74kvz_v71bT*H#8cHA-P_t$EUzIhQCS4~J1Wo*D
zgQd63cO!uBUGvOw1i(s`&VkS;Hty!f9Bs_y<4gIJ*AZB2PLla9`y^xA<+a}*#&=v*
zJC~J9)w}NDS17-lm62WAe{UllQXx-fugk8eeHgs>b7}raNB8z6HOooJ9f4&;hA=%E
za702XKq^+OvVDyJ;nv-A7d5%O--yTW-r^?T*+06mI3HzBSs8nJF<{}gmR|3Xs|g#t
zsFh4F`0IRUJGu%tau}0SKa*>dC^}N&^*0TK5rRy_E9CjPrGiB7uSfqP8Iv!5DGpYm
zg%mdFXBv;Wu}ZG0Xb?c#I>r?MvTvmcXyv|4K%d)e<WruAq1RKKMnnvEYq~@yP03y~
z_u&yF(!zet>MD1#4c$Kj4#)TmJfpq{xw`Y<Y~3QK4cJuxWhzqev?e5g#_J+IHB)bF
zj{)g#?bR7^mvG)J#xy?5$Zh+>fLIza^m3m>#C^>FvPlQf)Zgb^-KAq*<e<ZD0s!e5
zYYJAHX7ItV=ra!+;ZR~`s;g~7rxVzUK~019&xHzZLW9s1ar{J_4Mqc>NRg@W$V_<K
z-(^4Sp}#83=56?5jqMg=$Tt@e|M5b_v&M-uA7?97<ilLvq~ShIfoilb@64R+5F9{_
zOYAXIdcebd@0I=Bs*Ak|O<AFMXWHn=fv=I&V$^K~+k4>V5Fb60?oyxcHS(+n+dzKB
z8R!AklY~&9{!nXeH$a4jf}kU8!9irjhisMqsszKhLqrs<0Q_#c?W(^6^KE%SL!)ic
zdzk$CR5=8J8MuP@E9kj6`8+SE@$3FSLC?9&*E1W&hAFS974i=0g9H_$<%=keRwzrK
zAE+=N9yLrFR9NyVBx|s0w0%fWu4!XP-YGDD=k}sdKTy{kyAMiCq58!Qv0w!)46x6Q
zAlSm;SY{hDIW6O=>S8n8E&s2gAR#ei|D<qBa#L|(yPu&{jYnPUMBu>UUE^!nJ0jQ@
zayd>0(CxWOKD~Mk%kD$W_4LykJ@)#O-1nk3B;1Z5&4G)gN>8MP?*_^MZEv?aByD(L
z$|sz%z~Wu|ZpN?kG9UpquH4m=tbhZeX}ML(3H>~sFuehr7epd2h-n%Q7&k_}7bR2?
zfW52Y9LU&xfZhNdQ1Pwk9$|J6Zo5xYI%H{SmGAS8a?e<ZwUyj$JFK{(s20vt{-0Jd
zzQlf6$Wq>QPN|GOIw-Uw+=1?+uDHjps~!A~@M)6Pa5O7*SJGBIWAAalfY~^3xG6Ts
zCjDFUS78Um!*}NWREA6wYKKLgC5vyBU*Xpm=>;z0u|e1HzcV*F>HA*A{$3Be-5+Oz
zWm}C!i|UqGMG|y{gLwY2c0^jaU9c2S_$XkI>PnLenk3L>cd(48aNvV&6A($Psmk^=
zj2nwjpZY&T8r`#BEmlwJ+D$1l$z#oMFvdaoB=qjO+ByFHQ6RxM;yeBiw{Srn05rZL
zVz;A)5bpt+3ZpbTO_gE#GE`z@^OxVAk0)KqAE>BuIEhV~J9S@<f+*x>g*oex+J6GQ
z0(g+ZPz4r{rZKFB4%c+mtS3J3p1v^4eqFCgyjbf|p|SDeSM%y$$Jw5I@j-1~OfT<9
z@ypuUYI1A%279+RDA?9aEVRdzh^LYU_qVs|Uv@W6^!8O%b(ioej^JrGCjgoPbWPif
z8`#u5XPT839WS30cu6kCZ*dEGUG!;K9J{3#O07(Ije+QASNRRu_*g<G7yu4w8u*zL
zrjzh^zcT<ROdtRful+o;t{mvWO=)EeBqk{u`S+Ih%F7aFSYfgaHfG4B%*J>1r<adJ
z5||h90BycAzfhwK=Ru?TpToDxVQoCNA$;Q|M#`;l-f#R)YqK9X%{(_Jdwi;5NydY6
zC^mmk>=yMnlqm{|RY4;NL2pR_PrQ}sfVnMlt;zOE)WCC<z>j|vzj^sL)YI*~6)}D-
zaO)y|+0@?2OQY#EwLrKuP&9Jv&$jpFW`o^SO*2E2Nv}i1Gt*1fIpSR7p@+&HG6-<y
zxDN=1YeP};orl|;tO#!3(#VKto{{6{I`U%;JpmT1Vc_?RuL3?}IgzDvzR^~SuYq?d
zAt0K0T0Gh0Td$W;HbEsGx1K1f4p2Tmz4h7W7I~wNUt@Ig?5nf-(?<DeM)^UNpE9WE
zG;G*)hiI8-QihoKE~3u@+E+K}Ji)!y46(7$24FD*XcyWEGj@y$D_!)9o9j>APo&2-
zZGBec@gF1C#PqVIMCe~Ui#r~^-Va%y)2;IlZ-kDxbI9$s&ftwE0w664&AN!jEsHlX
z!~|jz3xnq}!}VSFM&f;o_GE)i0^*r`cBdPKamauA%0{&$iAa~CiAuprZJJ2CAwR_R
zz5S+?IPg&|emC#N^z1P2M{xe0rywIlXgNS8IY6c*!-rr|;^h&*G^3!?d!lLn;=-nt
z&T5aqXf+4O2LR1-f<mtL&MOlbr-$YGn4M$I;d@O=douZZb?wPQO1c9O!893w`Hgx7
zuNxMiCE*Xz$Bc2TJa_~ceyp6r#BAjM)^q#&rVG|k%ruVt3f3NwN=baze)Mhx|HSH-
z$OrxnO93fEft<Nf%V|SaN`di7n*>3_$iXnn*T&C`I3^cfb(bm5OaInL)f1A{U^r?p
zrlb$SLtDrXGC<-Hyp?a#SLa_)-GzRRXXz>v@n2=(k2%2VPbjAnw|p^`zrJtI>q@G)
zrW0g)_bqz&-Kd7m&0`w3%nA}S!?!p9aO-(@!QUcZ*TeRGwaHq`$2Z0U<qbqd1wjU;
zTc|4VuW@Dr{c1}O-7NgT!!>oi8lU{wM)DrXq<4s;c?^UZDfY^bG!`-+=97%~k`yv(
zhZ&5voj%zjTgryW!PA6%Y~|u~Q9DB@f>>qjF(maLxFQ#pO8-p=K@&fbT@xO*2Ntf;
z)fE;qpUT)H9><LxNhwUYAMlIoXPmS|>;DLrVyn-4F#Fp;<`=tj%8x+zjTWhXfeTPY
zHOudG*SlxE=k(gGTN2=JVQ<Ko!y5b>vh<l;gM}*=it;qz?Kea9;+;T!oU7r7N<Px=
zd(`?0dxpL5>)qb{Nr!z7ui(qtmMc*sK2<~u8Qg3Z77!p?Xu0_?yNw-?EObcRk$sth
zR18@4OHpHKAQ2_#x8Goa7|v$29oFotvv<uGrwP*TR}uTLaf41K!g<;hKB3ZToj<he
zA(Yl`gZnJ7!2u_v(9PsQ0T4A_kKV-J&2SVX_P7vpeG;y|ZH;j?R59{;7UmynAU-W|
zy@lpkz8s>G-kSU%Xl25`At^2%&njU~iDkIH#Rdn)t!M9j(>L?KZict8R8&`pyT$OV
zb(%tWmyC|bSHu(RrTSXgh%Z29j_2_UmUDE78g;!bIgta75d4V7!8qg023zw_u3*g>
z8x-z2TdNn_6^=>yK~2ffF*MOnn#jv}gUAn3;Y_9HqcH>ZZm-p88Y)7r2j3D?dTtIr
z4c!=e>$&^MbJC7j+vrWT2~9u<WJWv!PJ2l8E?uo3F)IiXdB4&Sq$H^SjW6eox*|!`
zV{$!}BJmu&oby*I&(<^e>+;O1fXye>_dbQtRaBaGEPH_9&3`_%939_+#K7u)d-V?j
z6GfWBg?DQ)pp`VrtSr@GniwSLj<d%NA(dZ%LX@;Qt(@sIWW`1#m0V<J$^Ikzg~W4p
z)A_BI$#@z+f_KA42lgyJ0`LwON@*6WoCgs<g2)6Yf$F25SfXFV0Gtv@N+Hq@-Jos{
zDY-o~p^`IyRW2bjZ;x|jc~lohrv=dq`XAPifXjUde$1Tqsy?UmEiE@G{(3xUt6eyo
zQ7!Rvv?1ZuG+f8e8|U4;7mrl;s+S-hswJ8!r*Km<7AQFf6hVkuqXNeUY7rX#!vZjB
z-;Cc^C85lszV{l2>QAdzo=C~)giGT&Q9ooLsrF`uGxK<JNf+6EH9AAJ1kMW%S)n)O
zRehtvAar2{TDMa+eFm|5hr2&y{2zq|1nh`mI>^_Fx|jAY{9(})UH$i$_(FwhM%#`P
zz+pLm?Qw$&(^n~)SdNVfHX+cy;I88%;j<#jig(o`=d`Dxc+_<`x9kUN6q#NIHwp3#
zguDc+VyS&o00b*#?#(0W?U&{;o7j8gzTe=nxvnUe8w#C#kHG#qR@GN$z73n~-9-+k
zOQP)`K<03<85jENA^{FZIx;{@zB*8;!XD&nkYRuKNpvJbj|OLXc(s@Cew>i#gqr>B
zP!$tH`tvw@aT?egS6npLbQN>ZH*@2^4DW(1o(qZlSI0dNp!W?M^(~z6@3M6eH6OW$
zM~qD*%3Iomi3Pp198ro7I%09Z#ZqrEh4~BHBnyR|9aS4WXl!G>UX<je#%ABeeqm8Q
z<lqYB_LVjv{3QXk3kH~$MFY^JR4<toga3;0H^B;XZWM)7sh8JM0Y-ht4jEPuNu383
zu|i~Z4<su7E6Ppx7FsBKt&@?^fB}SLa8&;IK=rR_*VvOhF9>jBij|g{W}NU*UA|OM
z_mIsD^ZXINkfvV2^ROl%ub)F+zja)?3O_q#+yC}y+0$2Rx@quK#b+v7FVhra=ImSu
zIM@07qhS_`Jrt}7zB8x}TmZRmRqL#-?5t)w#!`^=&jey4cueU98XUn7G=VTqgFYcw
zr4ECk6@{<3A_(-ctmY$Hjfbfsk}#Bo|55b32#U-hgZm7MGXtSrAh%N(+GF6P5DcJj
z7$%7?tl03A2+mV5(&q|YDrsA|6m}F4=7AU52u#5vP=+yl?vqmPdiJ<QE>Nac?0f<m
z;g?vn=@1>=G8p~T(#8|2J{2eG-6Q)kQ2)05JE7HwY1VGI$)Vm8S}gS#h^5D7Vt@ig
z>P=~3yR5;|4PNGIyfU$v_VB2eso<68O>>v8&IVV&$BI82iW}GMF4u;%sQ$M8dFt1C
zdiwkHmij9}x+t;yr%a;j@9#5zuhqCEKO`5lgOhrqj{Yg1)LJN3d2nNH&j!n&A;c!o
zOwYSI27u~WVo_SLZv?z|QTiT$`$-J}`Y$*|&}tEo;L!W=Nj;P-c+>dekI2eCHoJir
zYFDy#eDPwHUugj~N5Tqyf9GPMe^=t7pgv!rXOP~>HMZ^J=+VqOVxiHE5*l`<`cb>R
zj>xbu)whh&8m~5^Z7qhoUdIjl-HA|wSER=mY149eLj7$oOgIWIWOQgm7*=0Ztaerk
zIfqxP!S6O11X&IH-+#Mo3xHy{R*@sra**5_#^kdv*}XPk(K3aOl(5cwYmB4djB)=w
zQ6|k)Szh#{f@tTrA@RRVV8&Pz)NK=|baM5igf=Q;6S@06qZ?)B=PFO#k3~zKj%gy#
z8-%&ujx@0cB9=0LDKjlT4mb73+-7*tSkS(G)O_S*JLUp7R0CR<{V;*tlSsCOB2a9@
zcL4p|<Fom4+4{_Di0r!B!L8_~b?*>sFVG<wvj?u=#ZbLwa?vcRRDsVprt%N>7Jn@6
zP$?%_^j5#K?_$UA&d*(`Pbb%kmd12GwD1pTMO@*#Qpy<?aqRgi3f1-4m4~R`RjY6z
z<x^kUUcl0&WcyZ)F`qCAGO|*E`N}1jfOMVa_39qr-BWNJS%4}FfExJ02xMi-s(aKF
z3Cw>b0`!6xSRb}7*w^LJkzzY5q{OHPvGVml_N{f<E)-3vJj7J)JV@L$O6BxHzceI4
z;M*U3<Or{Z9$(KGHHXeCzB|Ugjw|>peS@}9!gtYj5U*j$7*a#1dfJTv)$%oaaA4d3
z5Sg2q15`OJMdB4S-QJ9svD<Nq>W0>Qb$RqYCFIeqOS#u<5F$mVVM3s7x7b6lM;W*x
z3~E?Gm&OPHH{TNg8Z18sKwgfj7lDj=RegQuz4%nLHu&5A9#ZrjwI~mAG31T0YuP%E
zORE^A>+#S9kNWjnaq357!P<Y2OVUVJXSdSCo143{dL_H|Ev9={OgcQ+LhASzDi4(t
zZ(qDFbvI5gazg@00iMuG0J!%n?%q?qM7Aai8i~gA%@Li2wukMW&pxbc+}JiMee8Tw
zMd<dX=tm)p`xw}00C20&BAYSJ#?YJ_B868rDo>5{wT()g9?gsEBUHh3N*!ANu|G2&
z$cSB+bTw6xOqyVhes%t)2bXkhynQZ1Lz~gxQ7yjWT5rCWrG-6^+b;NG6V39Qs(zdU
z1VJ*~?#~%bA|A`~^6x1JD5j5qQ1`M`gs13}SUY!<c(_5vl?NIkHvb_C2f22RJ<n)h
zfTh=wqc#0q;c{wN-gGzfmRhNm#LS}hzeK?r>w2~uyYm6upbu+^$QrhEfm-Y9`P~A@
zevwCul#T{bhbawUxk;5ff}Z(21_ICu1&;v7Zvo_Z7}f8@5Dfq=WDyBidJI6!+L~s=
zq?ZjyvMh!Pb+|TPUJoDpn)cGK`Z7AtMELY#W1{XM0e(T*9+U!fuoBRW^gy((pgKW<
z_rZk<;QJxh0<;0LPWVh^8Pse8K|=4*u-St$fI%yWT<%E2MR(6t7|s2)fk=~SA85C`
zj=GUkaLMbWvs!?{L_>gedax3JlUbQc+=+8a>>gE$Q%}id0|+-m{O{u}yW8t9-`R{x
zRLs)-Tl7b$57!@HVEb~A)#3`5;X#5&hTxA3FUru&Kq%Kx+id<L(KfHEnbOksQNkvN
z26>{UzAl<8vcY9V4TCu<wxAKk67_rC#(i=Czp*XiDmNK4=gI?5tq&vsrjro_Abs(^
zrYXJDxv0q;KS;ZyB(0$)r5}|j*sSI9TReC`jZRoD^tn+vV2%VsDgl5YofyX<1~ai%
zfL3DF5oy(1H#d_iS@3@EChN9N?-A&`3#ZJd@{6yC&<pAQlu(q}o?m#P$)Akhe1&WR
zWSoV_&*f%?VQMM3lPIxorPZFLK_ClffC{@*0wpKb((2y!3oN+&y?>2qF5@TSU7hjh
zKQf`w58fzP_nw6pqeSc0Xysc02kC8kUyXfPx6c6~zzEFo<%E3+0Muuw&H!IM<Ul4o
z2m3>u(9+HY`dg|+(@%2;?{a%9S~Sd={?Q4&GOV_nh4KlWNG>y6Y7Vct?({1>wZGl+
z4%fHmV^{d*_un{As<N`(;}AM_AkMt_ZmSvNJJxQW*?yU#6&WjDQy7e*CbUdBd~I&3
z(7-~Ofyd#cTXh}v{EihSFTWNzsj~I|niQx|Mn*%(WPm-rFOowJuI|QoPrHn`*-glx
z3e@eInE#wFl$~l9N{*bH)`$q`1hZXg=XXq~%p+>OsnN7L&Dwrsd1GPg37AK+`T?6k
z;p+9fUlLP1&_w_gLvR4t2D<1Ef$Kdx+b>ogyE_`CI-@>UbF00=RW0vqrii#+_C7Fp
zB5ym1Nip*%ZZh-8Ih6?p&(t*7&?ht$iiTZGE7nwLMXbNIU7SH`1RHIj+Y4dG*0xgL
zm1_wvO4xI)uda%K4cEmU_I}lY6%&ws764TqT_<6hsY(A4Hg+*@6QQrMNxs>f)%O2A
z;FY^Ce}0~?A7uM3)JYJ_8H3f|o2MIdiXIRFm#KA|FB~vm7;vGm=bVHkdwu}GGb&6o
zMl#kxANf;Bn?)%djGrPe-je+Awj8hCFyr?Y<KrL0<p$6IQba*3e}KgeqMuA9nvenJ
zBrNeRI7kPB&1T9$Fybb0%z}#K`^C?EaU@KK&*o-RdzWTY&J|pKN-fjqrnsc7L1lqa
z#ratA6&dgRj>6mz$o$}wryp?iC@4R`s7UOmjp%ldNBB~6wpy_25%ac6?QA70E7P4)
zhXhx8G!-8@avF!B71&c{_hkNJQF|#Fol@HIoSj6~en?jO(EVrlIJ6oD>z&FS$8YXY
zLF?kVe_s{fXg{&jX+9ZITG!?Ojt3{^3z5yuG1&~vSfx`M<QU0HZUD-pQZV`y@8RPf
zrKAo<4xtZ$3^^Hhl5<<^*43l3mI_}ZEum^0c)R8SqcNh>OU3^4%f;-rhTD)FO#g9#
zsFR4FWdN@liTiWrG>0k4L>}@jLIR@1!8%6<XtKko9JSo1DR!8LUIqpHYh$Jl#uSCO
zE)`jqkcrnqGz)S+CXFMrr=2%~jV+mq7rHE6HVW?gjZF3mOC6~%ML$e}ST7a#K?`Oy
z_2ueBHK~UsJXZZ!Q(w`a8HbdBr;PGq0Y)7XBx1tzj?ghU86)&Ta?WXAI+giV4(^90
zEQQg<O<Rb@4WKoU@WBGs$qK^!HV#hh*&-{s&yJj^mohFtvBfP`+O&M9osLSc+YYZ9
zkiUxau6d6H9P>#($F&GIMZhB-UOf`hD;FLDSTGwjXpf_W`oc(~@7v3X6YHb?(}`K-
zLn=S7$vuv|cc-y4@y#+z{15MM;aOpBjIggE*SZc=ST_ERGiutSIY4oXY2b(8jKu5g
zXUerDG<hp#W9b(31jpbr>}*(piI;%9;UOzt(O))$n_3?^alhPN%*Z|RlG;<q1gbAg
zPoyu#YhS0RKes0mo!>gP`RKODPp)_(Z2)>VVVr%j9ZMd?E9W@#5EOqIL&;mze?ae1
z&Y@zH1S4v!2g1^I8wJQHN<8qNv0|%or8u_>b~Aoq>4*c9_!v-TyLW*1DYSSKZ4dW)
zX+(uB_1#KmZ`4M|KJ(L(#W3)uq`90@+IDV@K(xv1Bhk_G8&<c+UK_t-cilOUZS~H*
zgpp?QAt>&z@8-sf^@oM>VXjiM`imTti66|K4dCI*WMDznD5qe*5mN*LzMaLe6x4Cj
z@j&|vTBJmwZ$8b&@wKgOu4j@F_kn>#TK5qpL`4MxZq4T620*05e%P^(+msFHZn3(q
z%-{a?$`EQ=p?n)YR)}kt&DRfZq$sU_E~XdQu>cq`wE!%rn3EyM%h_5&XQYB|Nt-)!
zvc`Fx(x@VnM4c-zJojQL`+8x(dYvQmirg*SAKcgR92Otlmp~d3R6#~k4U*hmCIFOB
zp9qk79N+)R5F2!h{WxD5!j`Ten@!8+FGn6z$avbU`q^Qz*xOAjF~;G1tNhM|L^AJR
z8N=6WeVjL5xo%3*)jlm{S$Xe8x>T{NS+#gEZ68oelxWZHZ`!7Y>3-TfU9@IOSeVhy
zz5Jj>S3(M$%!=E0fDxxr=zInMa6!{Zs==_;OA<5tq--!p<W2Y%ipZur@|<1hWyhle
zo^$>dQ^8kxnUZ=CqKy>iltJd--9?|%U6Y^Ww2Phd=Tno@oHzbM(c@1O6iN<%j8CzH
zc^m=ur@MNu^)6O(et>&!!SiSpqCxdhaD}`dkL~D%(oU#?S`yrtN0_&bod;X$g6@W6
z_NUdmL512ZiYC=0B0ZXL(xy$<ma2!gK%h%m(EXeO*^l@a;RHZ3eeq5D2`PgcLa^>9
zDpRAvT#Zp;<;y7yi1>`VuNXbx?b|3tQXT2l<1!jf#;Uej%5d6N*h^M-ddZlFTl6dy
zrLbB5JdY~QxQ8j$SD_IAbQ+sE0x#8Uc_@*<dDo#JDlk=@CWYKka2LTNd60(j>e{@=
zxR@&0nwOlb!|ldOpfYw$G@H=?=;&!4fi{CY-;FMRxQ*@8{MP5h-xp`ib9nL9QD?!p
zW6m>c)oQz%lFOZCwu4lLYASbbeKef}M9R|>ZoMUbk|<K22m``XtUYSgLlQO}vV(&1
z;9yu}QCB@JeeaayAI)Mo1zvg}u`bqGy3OOvQy=^RwqwifRskci0i9IzkW`!$dpsbi
z`6sdA<X!iTUOtA~Oxeq&OSaOibc0u1T0A4NdYkCMOc@`M>?3*2BNebw@8rBZsI|Yi
zqbV&KfQ|T=M1W1)03fm!fZx-Y(M;Q(Na^fm?4f_W^;5`62p$DV8>i6SkmvPH<~&UU
z6oZq4qaS3$4C<o^13Uz>821PO?i7VvBc%3W#?gW4wxQ^c7plj2>Yu!l?BLM~{%M%+
zsMU)aUM<sVcN*(^ZMNoyT5n+;u;sbZ%Tw0Eo6+{_ETBY74zU<NmR;J{nIXJCn?@Zb
zH@HH_l#)#@(mDdLluL>GR7+rFQ@ls^XfH$kq^@<sOSkDPw-in%@=GVpjEKBsGMMEg
z3@8pJaEr$_8pQ|6!L9!TfV%`UB0-%(hoDJba+~lwW{i4|iMz==pqGmOE&Xb{zIg7@
z@}PXMp?pMs2`DaYbw!}<5UYrJq&SmmnwT6Kt#@|=l>R5-)oBVEC@ck-_5X<xmR>8|
zFTxhUmA~imA5D6#((%)_XzuArq+p}rtt)h+(tK?++=@LM60zrKoM)Q>8)k(AuS7q!
z!}olb3&$YahJBxlVSXbdlRm&3U&hxC^fIDpD{V2&H|^-)#~d2pt8;|<v|}ac9SlS0
zm7cNGbYCG>d|<TjXN*YWU1Cl1@SSwbQM3NW@I)IEL7S4Ds7%Eb8F(`7+e7K!!%};a
zk8<dNR5-s@240VMmfirK^|F=7?M$p!AZO==6#6E@vYA9AXyYnJfFcFWMdpgldjMX#
zBP!T5Xo?}CdyrhhDav6TEc(1n*pR=~tc}HN&i?sXlc>ML9Ib)9r6H@yOa3u!I2F>X
z;S1N+g-vg%j|RfB6QN<r$?A5(2!Oh}^4xCQ&n@&nE7F(r%~;zq_`@w6dC<42SsyR*
zFwGT^_%GQPB0j=VW<npQE|49hz%fEKJGTg^Lzs4R%<ehCM|;Xh8TAV5Hm}@TJPkfX
zCfP{uzCowIUA>%Pk|JiE2&G=TeJvBMzbjAwP_=N?Qjg&;F)9C}jr9G-aw^GT(0S2A
zm0m-$j_x2|q_e(EhhE}yAvcI=Gs=)01(ca3?{kCb--B<A$TyIsC@p}~k2-8=_Klnz
zj$@4-!{PRk3avY8d5?Q^4CgGK&q)f~{dF04zUXxO;Y0h5n%Bj=Iwd`HGUk`cazt9N
zc`q||yWs6=S@xY7A0L*L=%7v_x)iP^IRem5T*CWCn1CIuYc#{syD9d|b4#AIV**R4
zN9W&&=Hsj?yI`2TL9#j<cQy)wP)o7qUL-^r)Ta>CKPQu;;oP)SJdW^HJd5&1$<ji>
zlJq36SiST2cQbRAXP+*29}DDm707I~@<_bt-;vQKU-aA)SUQp~PK-lfz*PHo5l?sb
zdCS(ecIu7v%AP0t9||zPGH`oSia%Rueh6rvm_7NWP`Zc3y-z~8W7B17GtQM}>GAQ1
zndj?*BhM>Tp$gM$)>=~2ivOm01(X)+K1(Aj6lSmH@#?+4jw_M^-=k+Z^iF)uKrrd&
zjN@Y^)}%OAh%*)yD^%2yv$88uHU=!c9)fMoD5kCY1rQ78$3O;RG!Z+)wUtdIsx<kQ
zF89J<RXMaHK2+fV5@DT3xB-*+fpm<`P%pors-GhLQ|;bIqvO=nw#K5c`2Otm<go|I
zxvg+4H-eiv{zL_CjZ{U%0MTa2A8)ImKmuzuO4CgZianjn4wY+{i)#uMi=HpN#@emc
zdqlZWGHhgI^C^4sh7rr=gLh?~IAaFA$Lgb)>l2VB-Wnz(Nt58jifdWuP*{i;Z1nQ<
zc#21j<7>W(%3de_?Zi=bkyJld08N3F#KStF54rEf@`nOsnK)BR$CB{p+!TG*%c<o5
zG8{h-r7Uqh!DP(^zM#Fy_nJ_G?m{nbvmF-}-e`u!sM0Qa5rD`<6W1>5Yt95FnUT`u
zcJBt0m!I+nKi_pR`h4k%mP5hfPtIuhVrg|^do3LOxw<|*xf7fb#E?#+1(mi?8&u2y
zRBS{d&~-7-?XVsfk-|MVf*zUv`=Gh#&%6CJJl~7HfIN?X_pGi|*sC}$C0tyibb55`
z`_f(6q<v(O`mxdA=gU|g3MKi1E74n`oY65JZW0a9`_fs1le?%G#$fgcfR@(-O0v-=
z(Zf<lr$@D?*2D!O3KZ<NbxfN-Bz7!IXQis&VMW{~j9!ejmP1+7fOl`hHkp8+H+6~(
zDe8~{b!ulSsW5c??U4gLowL>LAsLm=E_Yj;4u9uWuRfMKp0phQbb3s(`Fy>$S>bNt
z+Z%~-Of#P(co4Sd37D-6EdWKz@>;TM^(%g3o-ODFescsZ0ryM=Om36Yu5!TJWH<Vp
zX{YMjFOKs$yxzkpbK#=J{3F^-RduWbC9kf@&NvC-q~)|^;?p~+BMk{_BI1iVhTZ#D
z^{048CDjyrDK$h5<)$d6%kiq=_Xm8c>1I&Y2ilA1oVv+Gu`QeW2NfO#H&U$caYn6T
zD}rE--yu6UVQ@RzR;o`byu2Q^;1-|Bbox5@YM;4u`FTgHbyC_)4a-fD<j7E1D*~ve
zW;i6{Ffba=i^-etOmxh;ex_-LMTtY*$B%(&8rVyLtAQE@gPJ6Jl-#D+nnzy(Wu~G8
zMt}VaOVB^@pxCN38bz+7gGtBA_xU1gz07cXuT;1rEj3r{=12t@KyTX+rhy{oIU;$=
zN&hM)@sALQUCV17QyeDoY$@ji%(Od94Wo>hFjh;j(OAq<yRTzOiC#&n6XPNewxE^e
z;kq8g%JSF?DBDNYXDSY56238oj^P+?07{deoNZgU7?A|{G%4qBWp5ubf606ww0;nU
zI@Pm?AnrMkdnDO9-JK-}OWRIkOj}*Tr(v*ZmSF`@?5_}@UV;DS3J8%1L?zHYf1OE}
zRpc~!gxcVH_OX<Xp<7`+KQEc<1`_cD9Spg=X{=Hsu3RI2mCc}ioT=pYq~!Ca@^Qnm
z9n-4Kz>+3U1s46GRP1j7g@mtdjy_|^j61le2%5tBL}(@pG+QUZ@dtD|%w#;E@a9^I
zwsv-J>m$kQOulP6PM<S=O|hI%>}M_~H&$f>T);j6LTknCCxSZJE%k5hi#^cxqKNGd
zr?#dKbxuy&^<Q8A!VjA98(r~N9o^oA$h$yJhMrkn7%1>J+X(_(IVxidyWUp)Wj5GD
z(ELrAQ%sS+495=YdW-MKwO+iOBN%ezZ#CfWy80z?=d(X6KAO`%d@S<n@#h4@Y4{ZV
z{RV5<^Uo6;drV+!S6ZCaY?zBEoe0nLBl-;5pS|QmOzV8Wa~5eq5nd8?l>b$)ThPV>
zeCqwrT#RG$E~BhVe%jI_x?S3C^~4nZD;bMX8i?!cj^PmZ7ci1XmkA_!B$i;EssAo(
zO{IH2K%@V7{94YDz2+JLxJ|G=9LQ~^DN;<a<^oPK2&svwO~y=}Gi;sbk2Te71TIq|
znl_CY!gV0QFmld)=KXOux)x7<(A%J4*O>e3|3Y4{K5=#+WasX;O^n{v>$=oootgYJ
z4|dl(@rO<_!lVfSPWll>2-R&kz0Y4cee&KX*!R(j+4e-v!$R0(Z7T#0hyW*X0HELo
zFfzjcZ35zVN*D@2JYqx~nov-Dbp0eZ^{6ulaPRFZ8OuR;1{CyeglM?vdMlS$`n<n8
z@b$WI@dTEAB>FTER7AMe`g{B<>TgjyZw1U*$5h1geV@EI2|%33;0U&AYp3mCFvyL>
zt#$rK4A5Jj)gVK00hJa_%IRLCe+>XV?8R3Bo&PtORsvX>?GfM(MKn%f5s)(ECjhm5
zE85avfTl9*>GyILbhcirLyFeV|D-`HP2GP~-`wvg^1Zek1R!XH<0o3QmL7e*-A@UQ
zScx$fFBo|lRA<k84Yf0~ZK?74s_IeO@+)VB3!_|DXlK5Mbe~VwmEWpe3++Az82<fW
ziSc}6{a?o0MVk8oUSnexG_hzb5LJG?0-yyUFemq(qV0g>N5-u{c%G;=5Rp$EvC<2C
z>o$o|VVbpQsvxuetLkLwGbxsS65mr&v*YnK0kf;jijeqrI0o>MOA%ZM8cjyAWD66H
zY}9xurBhozJvzB%RN20#{ztS!_H%!Fzn7W;@d=APq2NsNc&N%iPxZU*oeN^`?Yksi
z93{L~Z<@xe+yS`(2^cyzz=tQ1Ky`p-kDk!bi_8EJ;OWy)kg@JfBDSZxHT{~T=z~5~
zkxnYpg@TE&F>f_{_A=r@Q4$8dD*Yoafc@!?*8ZpY^!EEsAAUXv=`^IPxr20++wDM#
z-+q*6w15=Q@-mtin*3}SGN#A7SZ_cT?~u7J)VB2$Dkp2L0=T<E0{LIbbpzOFC9%SP
zt=eFelYbRt5#Ns*B+wskHD6s|_t~XA$m(;X)7k9Hx=rXepygG>n`KFLNVcSuJuO&J
zk&SJ>SIPr*w|Y1S>qvmPt2ihbo9@J*4!{$6Ao*9ZHlhWx!G!8Sj*3TLZtZwvRi}R%
zV-c>%o)DMDlX8>e-26V$DvbLvH!nY7h&-$9+V;xUWYJ%4A#YWUxB8Ec_igM(=`OW=
zPaT?H9se7%)xEd;=D_j!@_7AYO|Flx<?3F34gBQO#q12A#x?<8J~9>g#vy%!NKFgg
z{Q?2L4M_CMtnjxu)dVjG&3~T<2Pcsw$D;46b^6_jQLBqU<UGbvhln(BBl*ec_jW{J
z!s8~8eu>ewp-&1bQC2?!RX1=QYFi<Fk2g&{dtF4dJu;HZ)2sm>J~z;$N#3T~z~rMp
zzL01Gfo6~UHouPUalXtUF5URU9$*?cXHuO&z+(&M!RPW7+(|45MiT%o3o!|kt=k9K
zaH}jd5ULOw^0jfHQsXM^U?~S@bLhf~u(%ayL2?23g7K0KNbF|Ee}gfm_f-Wp6?AJK
zPhZ8D`y@kFeySD4En%FUt!NqFm(xV*e%5y~N*G*J#iMjSTmMZX0kj0638!@6uP6N?
zz;c;%0j8wO+@~1M6g+Zqn8*P#TLcyKhxpIE2U#?(BxltGu-M>k-4_|DP-F*eCU8mo
zWc3b!P(zEIcB4zsMuLu&^cl9^15XEgkz7Xt6!0y(8#9WvlA6Wk9v*TH8NWq4nj`a^
zI^DL5cGwM8O4EbE7txZs;u`>CuP?wB=ZNRahNQ5ZrYO*Maka2Jn7!*()y=wXm$e2o
zaBNpz5|6ti#^Y7T6y1v1ZgObMBNRGnE<5ZnIaytRw|$7crnbF7(BAMLnD03#pI{CZ
z=6R=HJoesM>XWDHLUYH5#`O#a?B7vqkxo1z)P>qz-y9n#%H>9_o9JW8r9CZqx8<Ya
zKU2;&T%fg@wpecV;eEbxs_rD?q<nL4d<&%g>g)(qOsNsvcB2}Pl@tcWuMRO$_%$Ix
z71!eCJg8sy<O!2=73i~gLii0ehnX7*wG%zhz`i|V4J9J|8%|khMi0Oj5A_LCPmDt`
z#!9kaEv(OMsw-xMa-bGFetNbkz9v`L+4YzvUzd&9neF_HRS#!+)lUp|lTm%pHm^;2
z@ru<;%!3pl4jml<GrMuzZSf1<fM>x-D$spEP^i<T02HFI(z%DHg2W4QGkh78uWAi8
zHWSRl?ul&N79Sq{n;Xe4p88Hy5Ma#7vPQ;(tWi|$Ng?47#n!vU0)BvJ95q$wsCq?E
zr>kjqbc`;1nn93ni!9eTI4d?YWkFW}{>%!1Rsw-|EgpT7{a{EF3m`D6i`}hUF7@@=
zD2XSAoY#&*JOp!G<p-KSaj)~<Sy=T~fl*#&A&?$`N1Sq{wQ%-+8dFLHZ#>6FjLcUd
z%S-3s=B;OsZ0(X+-H|dwh5G!CK6jWJnKF`WnEBV%I*$&)x00LUxkXnZKDA4p4CS`-
zmI}q42R~8KODHQEjXeTSSX}n;dXQX_^UD{|)Dcb14`KesA!_Id2;fKDDspD^37%-t
z!D$i@iu5B3mAoFC=oy~9FE~10heC_=v!ESeSU{X{)iF82%_9k$b|kntmnYa~%wa{>
zDIfli!hWO2&Gw9Frj$qT4G%AVeCj+$U&&qPWOHG>{#>wH>B@NizY6c|MC;!edR{MF
zh*;?%MtaDZ$79oMq2`mFceo=5opYIJ^+)lJ&>J;8y@K;zJI?H1?*}Z+yezySR@U*Z
zaW8ed6&beWbW<~K>a?No4(eOETzN#(lLL1*^7-3ddkHx+w0>gi3u5}S#?~W5bp15A
zI6rJ@5ZQ@mUA=Sr79J~8?d0+DJ@kq+m-)gaZ1d80x@(QV3n1s%G!=*LTyNG9`khY$
z%gf->x1b_(`TvltKk!?*M3sP7(lQS+qY#O4mqV{>rdy~}Ca_Cc2zuPGFA_vepCzHI
z_50D5%C0Uiy7pmx@9xIvmT5_Q-mbj5`{uyEn6CTR;+0{(jpDY!s^^~NKR^Hdl(sjo
zGhf2*aPhBFb%5xkg^4nouia9LAy>Or(Q1*ZtU@bS1rV0rkPAS`-vGb~#p8aQK75)Z
zjyxt4H+-fp_qED38WfIm`&@Idu@LcGgx9ql(tR4?V*+_4ClCVlN%{GJm{!{~a#Wls
z7^VfUwJV){yTq1#M%+EV`-V9>)(~ix0{2kGG$Cy#!kNVJ&ueTt0o#I&Vn2)Jwd&7-
za|ZXn1W3jJfS`|+NHrD&<XiwKlwbt_A`C<M01#yDsreAzijDJ;_&RXS(h5Dn+?Ofl
zAQd<yPPMFf3^xCPFrbr|f9*j(kJcWJ$}J9Ou50-QL%#l?rR<i%GZQxbJH5?rQ#_ug
zZ40_Fo}0SdB+iR}YY?VHI8hOWU=%m5+PGJQ!&8Q*vRj|nel7M)(x6`9X1J_*2nN1~
z`VNSh%9XUXj#aIJ<uiL?w+RMPpaD27wig79<*e?}+z3Lg^;HU=Y|y^~CmOsTG17Qv
zWK~ZX^%@9HG%L-roezewLeI2KEfnMz3!41q*SH0?g5Z*2(9{S3B*peF2C$41{GJ3@
zR@4Qkl=Puw23D4yys)DtktN;Pf>HCS+LnV3Y(fTWc*Jt^g;UFq<xHP%e&7BiJ5g>}
z?NsA-GhpOPaQ0_2ix%VZ6{erO!R6nl3P1Pxbumu50JU`6{HWMXBE}b4GrvkBBe;iD
zoj{2u`dkBR-xu@M8sfHRmgfnW;!PhIGVg2kui=WXWVz)l>0JPGL-uWiZ~X98O{f5Y
z7Ns$VtknwqmG)fvngSRyz_`6HHe|+RWUw=_R@2(@9q%SIA>hU#;{FSUwFdxQCcimq
zWr=9)FR%~<$k~)Lri2Af6DDUz`yUJXY?|&2$W5C=CjaTMlqI%jCk`!0OzTUr5tEft
zMcAs_pGxfqTW8UCXUiE;T{2=;88Y}GU|3g#h~ix(q1_#j<o^>~x#QTV9K18!o^%Cw
za*LU~@pEba2Kk$DYI5sWGfRXgm{;Bz!k`igz2ql-j^G*+3T*_YNU#877;+pGj{=G7
zZuYxELh^?<aNHAnqC5Yd3?RB-y1a;L!Ab)p6~o+Xg(Lwq?q8*AR`B8mYHWuJMm>dw
zX@gJZu6acYx}Oxq?S5u-nfCIP852UU9@9u60jwV_eLl0>5Qc09?n8mr)SD=s)NWmc
z{<8hR84<4l)<LtO9qx5-*a#(n=CDTB96TI&cuVQAghgTCv~63}6m5JPt%8UDF|CHB
zqk0;f=DtF6i=(FduLG#I_SzyaWi9VzE<>gkW`9(SRnZ51=J+88uKX-%=`U$Jw(5SW
zz|1e|+o>Y_&tZ%WgisNgVcxHOrqXvAHULLe1qos~WJoC3_9UocPZ@JYOHgvD%NNXf
zA@SgXPCC@s`!%b4k%9>omd#OWnuB~`uQu&pDm_XKewuCRBb)p&v1AmOdVh(_A^SlI
zE)RQU5Kj_}asTk~S2+4X(l!WN;&IdypE`dhPRVY&pYRW@5)!hld1Me5YuLHiD)-hD
zH{BAq<)Sm*`4elE;3b()TXQRy#J&eAIk2`j)O21hF!|%bCffU#uk8)M+J|EuVKbAG
zG(ySU%6(69)EXgDY}Qp(F!DLkUnEbfTUOTA(oBzX1goON8=XOH%oxxyKyeXV!-DY9
z{M8ct6|<}~Aw0NeILxjAhgi{u$6a`?xv-ar!9|piw$C@`%iF#x!U%p6T85;_lr{&#
zzPRzUJOXzLih~%+P9(}5T(+8mT4t*Fx0CAa_X0eA9G&>H|4TZoPn<2f#g4ts{>?KZ
ztR-AkO7_|%WpZXR(LPgUzE9h^e-p1fAR;nmv3f)wJB<?h$T9nox7SpaEXLoi0CwLG
zbN_`Iz6q*4>pNoeFnS@I<t&y5bNf-ak->aF*EXnvmHB<z8#j&vQV8w!Fc!B(4B45+
zt_wtl<i;WXJkL@wE|n0@799(hKDew{wXT|pH}0K#){Ez)t_pWu>IjVnv?7gW-jvP1
zAgnL^!y{ZN`-fD!DrKDzZfjI4<ri*?64OSJRlsp<P?D_@c8@WyLn(+yn+)#Q-J8B(
zra3L^=erri{!{xqUc9A7cU~)eUI&{rr3b%pM>xyF+EBMKS&u7H>tU#wXBMsRBNUyj
z8O2&8TOs!cVkth@c`XuEjRipAJTFdISFO;!)xqn(ng(NH%*r!4YEc_$Am;TfV<R(Y
zB()i>@kUTRxEmlbVq@1KfD`~IF{2wWA{oK9*4R2dQzDy!9VNkrCgO*;`EwTRFvLD=
zDF3)YR)655KKRv7F0YY{xdh782Y7Gq=|hfbG&bIn06_D<SV_i-LICXZD)~VE>!IPt
zqBzbcZee-vS*tga7Nt_!sEqUs?p~4QygaU%JiF)8wu!kd#A_O|!>J8)JT-we4?Z~>
zMo8}!+bK4>1^-BYZYEok|Js{GPaszIekIb25D5s7l$`qodk-@W6zi(+_xEVyzkKu;
z(`O3TM(5Ohc&|rfC;ZM=nql^wj?@Dks{jZ)&?8wKiOHSq4A(7ims#>6%08{(vmC!1
z<<Pb!?Ru5DwBIvDJhLDdRhJ<{)Gk0OVg3LB+lmw<b7NzVUwZEo+)oTQOUxWOS?n2Z
zJMgzjGk7GuyyN+>=<63R=z%9sdO%lR_@7!$jdIPd((nc63yrm6%^CKIy4v3Av0z8f
zxRKBeGr=yU$kokY%p{I>`Uz%$1NjTUJo`qLh_8SP8c4jT4T^t;l#M6vUE08LeYp`t
zFuGLa;KooHx9rP#)eN*t!*B*dQ(KATm}!&%;3_28-p`XOzFDD%r&T>Z&~J2I&R;yk
z10~?WF9_1P<vzyw)E3m+`oK*}@U{a!?ya_8xGbc|wl&sO(xaMq*wNr=_+#5VG*j?q
zIEtj?0w~dulf=ob5r9iBbp<se3x=fRYg2?N6Pas-8j;DE%pA3)3znN^TQm#mOmKOg
zC(TBOH-D#Sc2y^K@76>fv}o>x^ZwG*Jl6jBAJm*A_RZ>-=5mqEnISm60DMxZMr<Ca
zmrd6N?s8*R+mCt=*`XGEm6v2I6xPvD)K9<L8%hP2ij0I8F-dPEFRy%fw-_;E8#bi?
zHVpz{pTVWenX%oX1YQw$vio<ahpdujNR-MY`k@n^xF+)i8hvd4kS)@rASke}v?^{C
zyY$iW=~fU*mYFJ#!Z|)dpRKab;5ckdFUn@x_1)w24CZWVUhItcsS13I51J2Xp5sno
z^LDxHMCTY$WA9i@y74oMAO6KAO7N#nnktU>jJ>y}x}|C166>KG>&u4^{?W_PJ}{gn
zk4de{<MryHSL?3oZW3*t%<VUrd@Aa4e2&Qm-yUT6&(%kSBnd)EvoZyGNE<f*vGd-Q
zUpdBY1y&`zNK*V1e%UVNri*rb4%4b@CO)UzKEJm7U>w`mIY1OKFt#wR0>RXT1^`Pf
z_k9qUhYQaZ-(!27W&<G{r9ipP$-gwVRAK%?xaDC^Nq=5-($rKq%K7Z5`5sh*|G%tr
zI_$pY_2{Qw*?+wH2$^5sykvqpH6w9-JqI+A{NV>tm|U+_XD$yaw@gY9oC(~$I|jQl
z(0B8y%X*?`+2n4mSzm;4JZnxoc6*&013H`_@(v8k(TzxfutW55D2T9-AQ_2Ca~hq9
zAwcK*`W6Mes2(tn!`0qe8d&*wd2sA8>R1JBDw&bBeH+p#2lh4dZ}QV`REI?3s(^#R
z5SiY6C)`Ki<vOH^8|;>L;s#*|?4deYO(AT4LG!X6Uzh|YHP5fer<U0zzHX;|Sma+2
z-i(^=ULCCt2!GAG_K2Z-^UAbwO>2C?ogLJkJzao?!5$w(LoKR05}ZvR=<fL%$I75#
z7C`AU8C$bA_?mLrbNxzRCa`)cTKVD8sxOZlz}qWQPZHsuRRFo~NsDLfb{;mTdC!*l
ziY52n5Ftv??pKZf`^dNg>I#JAHPC`Ug=p;C896;@iaMQ~rI$;lRwcNdrdks}Z`wLM
zF!1tO^U}U#%S8AbU-i}{*pL2!#pY&#)z6sx1<OuHwi>2OHU`qNsr!o7g?wMcuNT{h
zDul}#TQglV^G{pW`eXA!BUSH`;P-vE2i+?N5BgY>d*8iwjA;2EMQ0h%bk~OQvw)3J
zqZ>wd3P{T6MoL5;L_k1FN<ffsjM(Vz8XY37f})I0X%z&O5)n`-75m-$bsu-m|D5~W
z_jUa)PPMMgHQLmt#i?iCxt52wytH?Vo?jZ7798*!I-1$I6gHe(Bwidp!p<`iVla~U
z$E`oS=i!S{{Xv`h@Ytk|V3$tQCXX${JOS0TkfU7yc_0@@iJ_K&Y~Z=~EBK+0rF)g$
zNVQnHgMW8%m@F(EJ0mp!>+Q=T49Gta{cCeQXvj8olBT|!0w_E1!84Cy5ofNvlu@=2
z$rU4H@jV7WfXVf5mSTYNkSLP=7DEN;uXe@I@keT>3^nDw4pYk1B@BN&oTHb!cjdnB
zwXbGxV#{qBmPz)dRK8DdJaGE>P|DMNCAio5bK!pv-Y(uwu=#R%_4Gn-WVNeM*2ixs
z$3JBN=jsYOI{Q)ayvh}bhFJV7v-zSlw$1ci4X9V34HtRAp6z`s<IB__zz}s~Kz?%A
zAzQnC?fjM3{>Rd=^}%a%MZ8v-bWoieS<$~O*AR0wdh4K9E3x2Q0Vz(w8O^fr#>=fQ
zllO~aVEqTb_M1C8qT0<G6<J2oj^K?^eYorgTkmjwEMR!9G$)moBr-AZ4UfYK^NuLq
z_*uJ2N)e(y6?#4>NHaJq!;Ht7WKno`6V|*%{N!~J0Mk<ogQP)okZ61(hJ8$*@<W+0
z){OTq<rt?zs5DKLR0%fxirDOtFi0YtU$B*HFl~;2cWL<x)62FUpY|(QNIM;OPFu)K
zcTL|rZc~<@^<R27nd*Bsq<xas*)BV|y<KmG3PC6hQ6vux6O+)aN`WO9@zfkCT=2Y&
zaEMH<Y&gNs1N(*0_zsIRh6Eg;#0zlM?*hN<+C6U%9sty$1wR~crZgbdsgrw5oTc?l
zO(&7FRlAnTA8g5h83H=q$dz?|P7#Ytm}zv-Oh;l8SSd0H?q-ER46<&H?hZj%AKh|J
z?$I$-vD!sec){tc_kFn*W!r=X=eNw|@5HX8xCbkdEdI!SDgO{vi$MxezP+Tl2w;}8
zO{v@~COk}29CGNx+tjR^k;wyTf1!7`Z{<>Xxg>l3#=p0KpLKZ}DW;h=NwX>UePHHO
zO}t^2blc!VfWdAR2!=9hOTA`ZCHX{tI*-LkPY%~~{xpkf@<Xb2CVCe=d;HAzV2kdV
zE8VoCG)lPHKC;fU$=|ddXXy^DJhVvW8=n+n^u(NoL@`u#aNp|pm#`XbYj%2Ui4K-R
z!$wInP{$^dK;QPexR%}@SC&7`p(2)&Y#suxp?Nfbc^$n4Q1Q+iT7e5XG!kz(%#cZ#
z4{yyT9$Um;7}EfLwGq@+oPZTAoP%VKZ;4<}u9*C)3fIkFlNloIHut6A?i@FNYkQn`
zq}uupwKDR%OJMSCjLzkEXR%7Za(~2j8DD3V>dYIMtjre*bs4H%z(FFEaNsRVbqfGh
zjcQoHZ0~i&OX#|i#iRyWU~4TAr@(4pH&o=={QG6EH4<1nE>!gKPR<;fOSWRBv~AmI
zDT6v>e32W5;$h9gO2JkjXvKO;QKga>P45j42~d$z8Im$boB0R9(a{4WaKWX0iU$kA
zh*helNscRyThCJgzCZ^s^9Z@}&MOlQJ}qoH;b3QF=s`^V*!Vv&FU7Tr`BNQ>A1az#
zWO&iWG<6k;4<(2O;N+n$NLHZKnI`J2dQQ0=?=MgPX=}T^S6Q9RpciuWmzlb$B$6dR
zuao#}V)F&7sO0VZ<i6Brn^jV|tJ|?34~Lkb;kl?ONYK&Jn*tiG@1+}6nE0Y_0NImC
z;sm^Sj12{{uK5y0B~EQp&5=J|kf*dKo)(%|H00D{qi~`P;Pg!EV$5-`VGO!XUXUEu
zc%EZM<Lhr6@8QMb%U<h69kf))ZB`^)wvExEnBkeiO|kFck1^)Y3#7N33@j$RFb3rC
zj78o-N5}KjWHO*ploF(ChdPKWrfTm{>twm<TO{j>W;;6e(@m&(PCqD6+-%pEe6Hok
z=$l-e<n`RC=89F?4|La1lpVL7hQ!XskaAL&J&LX|@P!8H0Y25C+0M-0y5xSoZ?^qH
zluNVWon{B>WT&O=_fP<&6EO!yh#ct6O`Bfb`aRDDE;`-34VLq-WEMm>sM1E?uj%TC
z$s%ps?$=q|e{rYjTG5lcxd&G>b37?B?CB5yltIxtQ1@~K?C}^t@5aM{ZQSL@03_q1
z?nOFX3yJCBnMapHM_*cebwLv2Gl2>tPQf7?YoSb~horto`~`tqL~HLo{A!{lD&%)Y
z(#98g?h3J}&03)gImV=?Qa{LsYn&({mRCd#_n&c$9QT{Qj|uDuRy3fmF>tyXCC~K`
z6JHH!UOW~Y?g5bNp`jY{$A(zUnS<E-O2r5Mo)_7aasw;}WR&%OOpa^@c`c7ryKTc9
zthZtL!yLr+Z@Q4?m2M?03^&9aJh2WZ)@zMrQYfGF!`ru6uTm+|9I;(&f)i`h{BV$U
z3ByP!w!u>Xu~{3zNv6bm@J6$2z3xG3Bxp^n1fxAw>(YMvJKM~Q=W4~yy?v=B%$Y@3
zO;%%HcBH2LuCUMdLntE0@OxfJd&AUCNL8p(OwjouJ+Y5r9;eQ2cWFOe=zq%VaLRmh
z;eEgHROo<sanDaBheJFak*qp&y%D;FyA1%{2bv%EyHHx+JJnv7eLVh|p!Ua8x1KWl
z-~C7Jngt-)Vz0;6`H~1r>p0JR^Q>%`?+UW@{Up54?&rRm<m8uUhHoc`u{|}cx)F9I
z5(JgecUO1+rsq;KGh?YRwR-mENT1P>JO?Jg)%kY^{v_PNLuU1tXRh2&-nu=izv43Y
z;<=Js^_yka@5$`c2eZ};zxKPR&%jmnpzqDi=0(@7bh)<LpX2s<FV(kY?=%NLGa1>_
zOi51nZ*rlTg!^UL-n{wX5v{`}glrit@byQZBeUZr86n5^Y1}i%97n?!0~ea}-6x9O
z-!E~;<v)Dr7Ak)(-6C6&PAVB$<fPkro(#UR3{n#91#M-?fSupk8*xa5FJzl}X)0QB
zF1WPScE&9g+%SpB*Ztgw*p-I;>C@1sV7+uZ;E;Of=w=vy{bVl;B9Y$FScAR9OXKd$
zmGWq1_*F~b(_Oh&O+S3}{#5^QAbjI^p?zoe`lrCIRWo#}mP+DVE7f!wzW60PoggXJ
zpmq^*9UJtY;7+`t^H$(XysEm1;rh7l7~ZqHwj!W*ec_fgfW3idVp${NdZY}iA$|b(
zqo#*t)UiwgwVWq3S4&}nZ{lQQR1R(-F*gyMcq*1h2(-PXHJl(3h66Mu<0C3GE$M?(
z_af=?Kgu#M*FAPxPiw5v*e|GQ{2&V=fI>2XgvPZ~DkNDv4o@NDAu(t$LCcPW*T!9I
zi&|y=Ws&mBa$uOhQd(ELN>O*qNOZ`oE!8CV@r1@b!-084J>PO#d-2rVi)`dv#DpVG
zPY8(#M0n4~M;J15dUQHc5gLne*aX5HcyjNGgs!VfvxVHGW!`=vyZIy$6IC)E4t}x0
z&vc0xRzS{YLXQ#WD4{<cbT*#~TswFaLwlUgpQptuv6@Jj7M8E4NO6T03&uWWPAQDb
zId2`FeTKm!MRZ(wAjw3|?3DBEcOMPK(EW-vxObPWDWKRru}7m^BA4;uKz<d>6zT0j
zjWui0%;)y4Q$Lp~)p7sHW-Is74FU5MM(I?Rs*YZ+cJ;WbvgD55J6+9B-WnZ0)JV8N
zv>_9j*rAP-XK+5E>Je2<MelEY4P-geot*)-C#2gi-0Z?Vd5)d=n+CCEptnw@W9*!u
z68{f9u4GjS?hu}{>a_M4!{k6}HJ^G4?@XeATFtlI`%XR)-nWfRRVwPuo}%A;EtUH$
z>u~EaDLRc5p+%yCY!Gl+Iy#Q*;IKdz6Qj#nmLfp}nm7&bv9#(>k7aO}>`z1-g~4$6
z)WUa=+iDPbUpw-bS<w?w*5-lJ`;|7QlYOn`kmGhpd2-KP;c0@(ox&UiKp5CZQ1&(O
zFj>EyFzz7c>X}KVCX7=824k8cuWSE^Ak5cP?ADQ+RvDFw3YkPyi>4V|H-wPkxW+Fy
zq<Gu}<!qKBV%Q-Pc%as%>XVwbpguU?MfcmWwrK@v5>?S2(Sp24<qZ(tc8Sya)YIS|
zF`oCMc}1sLmyN58hbsLdy-{&~AGBZd2>RT*Y|1*OUjiUV^!JFg_+8PeaRDNR8Mlt7
zX^fZY7%J0hubUR?<~_Qo_=6>(E$6gx@Rm_3-8U9x5drA)N&~^cJl@&h34>Jgi_<M?
zCJDppYltC!g8l{oR+`qqPkgQuST5lrp0Sb26+$l2Y&kJO_?Uw&ReEpd?d>6^r3luv
z9VGVnUtjS|1j0C?z&c8<#)~FSG<n!ySOs}rZQ3CEd>zc^JgZfuP~ynz`0fqKhf}zo
z>$f;|i#U}S;?6LJ=OO?wgLfq7Rp$|Yq|&S`^KG7MU|B%kL=GQOi<~4%@me(f@*VD=
zM8COn_jz!K)b->%u=DxU*Bv<8M{DQXGRBj=cs-}5NjE3b-RPu1CgSO+Zzp#NZiaIU
z@jFy~c$dd_iK@{Q%?a0le&NN7BYA6#C#D2{WsMz{^9>c1cr>$AJrVUkWE<bK>rNEe
zkT&sa7lQo*iUWkFoQa(`k`4{)i;zi&q3s6J!%9r7_xenw8<{6)`zH<`3^N0XBm|LG
zkwgonEUMahhIIh;OA#w(G@b#>iZG?$HA>`I-no~pHM7L{xuvZ=o9VB18yel3OP56A
z>SS-zW`A2UIC(Yyv#l=X)zL4$v&61=UplyYLx)!;Z$A<fBCC~-1Hp;lvVp=4HZ>}O
z_s<7E>BpMqL{V4o5AEN7GT(mqMti7$)y6QbCtL`}jGNnsmKajxFIJtt4EIQ7%lHE8
zBU#8B;(f0!+cI-ydLkx*k?4?wlBGgA(tCUJVQ3INvpC6gBIk#}xtGuPRTzS13LzAL
z+{ROKt(9G~>Gj8HZk}>1T{u%u1dfj3j2{1la{0;PCGZe6`cCW^Vmub@3d0Fs5c27J
z_48+L!kIJuU?n8C&G+kU-}i2Mo0Nqs7cmdlL`N!J-|FRFq<udr3Rgx;6e3+Agz@5X
zh%L3QUDC4dQWkYmPe|?6x#aoJm#^ND2rZy_Qg{Wk$q|39%*_+wN<-R3;0O6|J@Qb0
z4&q@1haiG3Oi?O-tee;3L32`L3_mY=diXu&yy3n?w5ii4MZ#ARM2x8I_1jJK;*Uyl
zAH0_;sxD~gu3@$fwobFK1GOU)v|JuehNmvWbE%8wM58#jQQJ1PZl4nF?$PJ~djlW4
z(4f)ahB4mjuE8QDG5Skvnno>wxeJf%IjiKxWA72A@g<M%^+KrXOiHsRIH>HKj$HE8
zVfK52&C9V_7RY+#I9l%Ug&iIf?dMsCO_w|)xaT$}eq<rm<1%vk5sp6z?RNC8AqkC_
z7}`UhQS{;`)+@r7Q{RMJXt)VXGEJDIY8_i3qALjF924UV&_i(~MoMrw23J&sID{71
z1Oujh1GVjIVNE59!6}KMq_VoQmI7LhG0|Jt%Xx8^Cr>W7_q-ap<E(0&TC=j9WSXlS
zoOP6McFv%v7p=3{RZY4*g>y?H0_sL=DUlxGkx96<;gr1WJfW(1JYBnK;nM7asxzPD
zb8BD{*BGAydJKf%e>AS_6XPhDdn5qjGs9FJbqWqN0xuxul6Us>L;fMErKoe?6R<kc
za}es_1z>WAjY#D#Ii*s=G-Kd`^shn&2s~%k%g-52U+A~*{V%Q8F&!dA=%J-CK|--S
zBu8}^7dMSb3{L5FS|5?!x(G2BgPDWTgq!K9Lw5^OQr+#=%ubwNVnjZTiT+T!_&I~N
zB0cBNuFLr2wRv(=<17uC>iR0802xs4=m+av0>m4bAZkL;&l$Qpdp^7kIeDx}Sf%DO
zk0b+KV>m9(6Vb>$y~`d?Qbc07Ak1DskwKirN#DDnI3rhcD8ywUfo0p-rrWUMXR64z
zZd=vP9@#HADiIvOT;t@4>$889dxve<VI(p?BVyi^C$RMS%5F`<+phvWhQIt(2b~}M
zrN)}8*J<`Vu3wDNS!eZH*R6AXJ8n-+(-uyhy5_MMk2!cf`hwNFc;&azp5+an=u(3)
z>4(hf8@+7FLBh32w%yIn-!WCcaT{3)j0)|K%kVF>a5*+cvV$(n<DI;Xkb-j#_YVCD
zLbCAJR0xs-HH4jz%1`+9L7v-^SRfFf_l<2Szr1|5yha`eLUAVXI0ccu2wIql3=+f5
zuNDP7yY%MsmA(d{Z0PhFV^~)LQ4e)|zJ@DX>8IdvAMn3|^d`0~?s<<lPrgj`h;9pN
zpv^_b(Y=Y}8A@QMxho5`@<Ibdh!oYhM?TgtbDfre&c}I2k``F1P_LK&aUYe@F!R)A
zEug<}N$3nX^BvD2uIw^V{%Rxz$8P`|$3rX&%DwZ7Rs?aFk0>%A?9m$ZMo||9YO2{%
zrL9m&TorK)Z*qiY=tczo*WS=+J|V<p=8k?(tm9rG+np@fK77>Dj=the4cX-oany#X
z;T<N7)6VWNDO%ISdD8XuUqwR)THYQGTFr(HhNd0qA&|K)U60tt%PyKJ(;B@hq)M&N
z5l!UInzeOVlSn?5BFs7zXouib8i<&1#>Nne<A>;YCCSNzz$*7Fs-;Dp4_!~u^x)HS
zSc&D8be8xdvBE$TM)h{?_(N4B1_QZ%h`T;3cturfOHjt4)g@|PIsc<U1HD1{?)*FZ
z1-9SAgJ{-Xf&HXjs2YHfd1)lf;7!T-gY>Mn^hhm7X6{d2Tv0?^vf)gp&C{R#aF>0m
zbMMAIk|^+kXOsRFGp=H=Sy<k><%Q;V$z^)=>(PPh>AFv#_aiE%dxire8KzO8Qe$d>
z$0BTl&xD5v=P9AN*?h23lt8{$do{Upr>Z@bf4wf?ogRawzza=}7eU#(chb?<Yd{Em
z-0m5t4?}wtdS%(%ak8*wo!BuE$ZUD^{P#GnqB_zOAvG^Tlohc?!Eka&LAH9g$IHwM
z$X-uP*gq~?78yuq`$iV(YGaP;meMw+?20O_Sk|nO;XN+(RfHAon1xJ|ap?WQ!$*_9
zN21dGWY`9UNU(yM*c53S*YPYo!nucvcrBgNoMa8Zv^UqDpdXBUV50wqE2=q)=9>Fi
zVu5uuVp9>v`Q=n$mf(uaK7-@o_P8GXQpbJ4!xDz<CoYa<W!_QMq_F!$KtcBd-~-2+
zi;=>Ui>S?4qvz<JX1T)Hy%IJEoponr?}AgSv8#nrGYk%D*()uCd@mvyM>PmQhr}H~
zRgA&`kf%t8fZ!O{k#O(}7~)7k<m%7glqGY`g8ljVBMpbMCe!}pk}tCGvQw{pdGmak
z)eXDeZN+~&#ZwL48lvp!#<jLS=f3}*xolQ8*UVJZ^0HX*-a_32jqSZIg}aM8Y`wz{
zve$~8q`Q*;m|d~VnUlONx%yYuzH3fsVO{vGH7wS!((D_6yv(`;AhHb#0QI*JygLSL
zgR@-I7NQG1wTgel00d|AXzIsLkY$b|b4UHHla7q52~bD~^~I?TAqNj(yW%{tyPHSq
zvhOsxVi!YB{1W;F1Bw@Y|7bR-4g?qecjr8Rzwbb3rQefsVujxIEI8n21lAxHD*_O(
zU?N1KK%Ziq<9NjMIjwScFg6Xypv+-a>AsxnO|5ra7F(?r+7Rcgv=|Y3DMO>5xWr6l
z+Urb9Wb#FWMGSz!ypkK6T1xXZreVoE*A899R7G~9d(2-rM&>siy2LQNi5$a6`-&J-
zfYRiWO65&y#>wPb$CBK8>NNqPcqfvw6ketPV40(pF(}pNIK<w=tx1z@8z5$Uh|KTp
zc<oqeloNf>(`%b>jYn1<dRPqXmZAHmly(+un&@WYf}{SS<<McYF$18+y{E^EuDtlo
zhIx&*`}H(1D;SQXc_(JEi4#P%B7)QqI54*^)h$FyNrK`+<e*(?lh?HBQkrY;TC>ET
zRw-G<<7Y*`tbKd%I+cp}xXo7SI>xE#MVs$q_V-VM*9v8)J6BB~^xt|@c=OHeW;u})
zt><V9>wO7Jcg|Q*b|;q}fn)%J`NrLx2TMn2oB=fHXbC_FedAe@Bn1Yl>JJk;W0vC{
z1)J)oQw1Mn3V7FMYgnZ;9Yql9Y?t_4TNRk-t6y_UBF9}-Q4fHMR;=<OZ35sMOW|}W
z4w;a!2Z(_T9{>_wcs4b>*Gj(U%6HtO)HNyHc8=zkRhp85=J&e;U1f{WjmC3yIee~o
z6I8O`I-sJ5c5*VuOSp1=+ai|fD;o#%eeTde&ay-Vm)-0zaN@l!I`Jm?Ps3#JZH=Y-
zx3p^d$7Lj6n$?_yH<)94o$)@44U)Dp(cf5*bjjC`C-#!KZ=gXa+F(=6qhvYmI17w@
zqZ0B^LCLE4O~Nixod&+_6@LDqkvjjginT3;5$j48t|h%xh{D+){c;uh1ZTP^G{5?k
zUL#*bxkO){kppRciB9Z9uPJP}8l8>RNK}r^d||I;oXUw<|8oe#1<~O^E^{^OAAuoa
zG2^O7#299`cxZTOR?^lzaS)VCz^~G6{Z^lAX3_E11L!>gL*jJNkaEc-CMiL~=h4k<
zBH!t=oi)s8_b_eB{=e+Hb%-CS1RFv|A69MNCiqAS8GL2VELgoP5dYPQDxtN&b`G%X
zO`8;vSj~_te3mqgg<^vTrIkJf2GZJa8Huu8(^?zx05*@_Q~bl!jvG$CeBv_9Vk5@6
zP-Z+CFff_PvLJW(()47rhejB-q60HJT=e9>Vo4j@RH9?>j7(QLW8-nkFayvNu<iO#
zPUXO{2kOk-OYhfGD1qQBS~%m1jOpeX$@iu{>YJ(j973DHsVxs$*%lS+6XvCENt8}c
z)T!(Y<pzgvMJl-uds-a+@x(A?PAdMPW)OB1jMoY%o%e7vR;y&k0-B7DUH3B1Gdqu)
z91m_U#ZCG~TD1w%^W}UIw{*XOm8>NJ1(X57`L_V*W1>ioc;hAM5FaRe)|y8g2fo_5
z0`Hmm1M!gvz`Kbzf>Oq~$j2V)?RZPmCGTA;b*bn%);}_8_2ww1%Bn;fbY{&o1gL-F
z-8PwvpK}F(e3N!tffB~U2`9T(c}zSDeer;x#N)BjVx`vB`+Drk&g0O#a;jZ&#*Q`>
zY0I~6VM<>yC;#_G#ZN-6X^GWqmUiAoKsd)9FxXHmDp>?p3<#$c#4j0la6<Gy#hDl~
zUSlz{$qk}GJY^m(z2;{L3_`<kcn#`79|T0ONE}UeMp*9e3dmnQ%@->1xKAe+b|XcH
zcW4%Me_rt}<0OZU6ZSO*vZ<$f1S4Ovzq2<@dqY8uYk;zGkn2zjg2>;2+({EahtXx-
zWNh%@p?<d{qGh7iHkex)IfLhV27qT;hr+gCt)U6AS5Wf`_8IZstkKbT$goFf3cH!9
z-;^&#w(BcPP>4}7r{&ENlR9R*g?N{bmQGYzjeJ`$9(JK|6C>}rF@dke>2+_{jH&Yc
zL_1NkpIjafn|!O>=~R-SnLBxQMTaGAASX<u2Rf{~bk}$ePa!9Srb1%9jdLGCl#eb6
zeYcZiUnS+<V-&#JS#1!WmhihOUIObG^f<skzu}w&op3O@XeJ?lgY(IvX<UKGF8m6o
z|H+3-^|1%T^R1~RzU+M=z0$rTQ*1RxmO0Yt$^qF6R*J)m_|Kj+MC!Q!gXVH!>p&X=
zo;x39Eztv=>VVP~)Vu#Cl?4SOnDao*b@<JTwku^OF8p`p<95I5ApV-dU#v+_&xJ61
zP1G`l20-_RdWX`-4G)AU4>@L0g!9Jex(ZipXtyixg^K8ZBY`Sm<$gYeO?%%Ye}*10
z_R@&u+eyo9EmK#_pLAbp45RZ<;DZig)>$QGe2<u3sK?S)hgQ-QeQiq!YUOhYz3T7$
zV$<jF#kxc2TIA-)ou_PXC0ki-rneefzZZAw{XG^%@WV;5$tkv~3BPD7TKo}O+IQtV
zm}x`BRrLBgafS@|o8%pu7)D7eeMq_kEbf6bK&QeHAMaf{IgBaTgFgGu@MaeW_kHK|
zpvxbN9j!0+%{tR)MF^@K9gL9x!_nKQfGmK(?Cd2AN`?aonEUmgpGkI;lTH;CE~9!-
z&u5cH@)mM9PfCkJGd(&}((>7(H<~qROmR8l1t!R@T-m6T`10Gu1B&zl8>wFt|45At
z1h|OYDU;S=+P?hN(f-K{@)05EuOQ&eWX2WEjIpQ}rKh{5GA+n@3rDs@Moa)7I+gh3
z?dZAPHff#B<`a>>lEz<5>Oa)&=_9}w02|g!b|_IWiWIDLpttIYx2lQ_UB8zfe)Y`b
z9m-CZhd+}PhFegDlSfhRoOHMnL7Z~UrrKm(+6;jtKKa)`Z~X_{Ob(WzUMu4ye^4r?
z<a`q)-B$l!gC?w10PeZwY^?*&vY`?pmwJ~iLR~?+>P(%ZkblbGZ3a7^c<M>LTDML<
z0C+IROkx;r@leRd@}zm5z<bp)yhy}YhOT=>!NHm0r#oVU-_#N<6sMyP<4=KE5pWlw
zXv>6M<>)<wfa~PSrh#A_j!PPao-Tmsw)3_u2zbB++^x@s%CPTS*IH+03}Q7#(0Z^t
zlKV!&RcC9|C6G}CexL=#0=Vn;J6C|h;skNr!qpa2>!<>JS**dH%3d6mjY%kecPrSO
zO(^KTCG_gNwOp{1B<kSSjKa@gFNv88(is<|4wW7?HE%bC{wMMc#J$z|63&SOJP=hM
z94Gh2s9W6O)b1W>u2oCkA)~!h4F3B19(3`83)mFl%cdyOu^$r#QTK~*gC%)WdUi7k
zn$k;8Hv?=KdSa1Qy`mWhM{q5|PtD-yBlD&7s^8keFFzwWFPQLO7?9EtZ`G;#`6X>z
zMK<A%`I^Ttucfj5U516k{X%|5?!?W7V@WODSuv$qxunJdgYo*)776Ez8U|lvk`~;P
zy(_+^p@SNOl6Fs%;bo>u{z7eOr{f)BO`+0(0RVKONQdTv+vagz><MHGgK<#xSGc40
z%w6KF1@D)36NSjbGPRB)3{vI{#G0PvUQa0)O8e{1C6y%Ahy^=jP>ut9*fSO(HEx>M
zU=(UdOT{-VFDvtdnhr-cRS`z|rhmR8((;WT$1LnN4i#RYA|KXZ0E!eVh!d(uMe#K5
zn%3Y+BSu#YLNzAg9(eybdM=oIckyN~{1XjF$0b#N-%TMORTiJ~vSPiNjXz;32t7`L
z;O*$c*2tUKZ&X^68;3`b9UT%yX8IC99@CFYK;n84WNv=E^`zIRK+RueGgkZOas-M-
z;Nx9>52uH5-x?Au>=s%CMl|a7xfF2J)XgnWOzQ&@pe8v1NH;o;qW$CNMub8j(|pzF
zYsS8nUmC-#W!8tRK*IEX?Sf&onE!_if!ZYgky8P(1uS`&_68Z?p6Fo33gQh!v%a$E
z;Y(|o$$As|ZGPwNy=Aj68g&P=1?aMkNpc9Kv?{o7&Y+~{ub}3y@dp^SDY{!J7Ygk9
z$<jsuuO*SeGk%*i&JTEAh=!uzUyd&e(1AS>+CSVWj|FpcLSPxn2k`5Ir;d6K*u+@j
zs!8be1=#wMpervOt&>2rD?bSxG`8HRCz04Xyl;M&-&QYka7p|_Mz7-GqYoU`sf+xy
zshGggI1E6Lqi@LVJZ==h$A8nQxnL0w*WJpB8JfDpl4!#!rI9b8g@0x@i#ghLOx!5q
zic`*#L|u`%De8$3^F_$mS{=8-95WRiO`9$^CRtB|;$x>2I{%oQgl~aX*ES#GD2d!M
zSaZ|QY5wL?`g~H|(z5!U{wGM64|ErAT_9%3iwmV3Pp<pl)6*2Q65TkvqSPpeWAKJ@
zHV#>{XJ_#Vva;4=cnsy`QwL~_vs9{^{cK;~xfQQM-EISuI@@g$+;ry0vI^-gz>Y9f
zxZm`HoJf+hVA8gbSG`dB%bA;@&I&?)d2`q7`Cr)?^VoTm*j+I`adyfLDpn0NRy3EX
zd+N<j-1sC_ob%G4+KrOcZe}FWTcg)ddmvy0Z`*jMKH$yye;KH}YusOMpVt(zsR&>W
za<>vq*qwcurz=`4GHj4#Kd$>yntGV~@}>wshE<rv3r}_c>ke?rnGl!2>A%2Rp9XdN
zYez;1Zu^Qf4WH7-c=`VbXlSiW!Yl73^9|n9cE|E3Psm}^D2F8sR~rogy#hQxF<Md2
z#LiF^f}y)7cvclEl5T``5|Oe_U_Vh>@$UBZzZ?@GMP(|~Yw~`~c>KHfwx@}qfoxui
zSVzD{XsM}Qm>h!ts{SA;v{7-8B2zn*(Cv8vSz?KE?0{Htc>3}uY8riv4BwLaF{CWF
zd5~TmM~!Z7xJMS+*5f6MXk#R3$vG67EZrbGCP{%G$MCDJ;%VAP>x=<ybgy3a@5Jj>
zHmMm}!cWd;Jkw|$5e{P&*fjYxv}$J<j=+W%SRFip?>uq)`=wI^n>yUL{awxcru{6l
zR_coK0#@(yD~I+TuBy_@Dv4s3QcO0RPgT-IktgDYUy1n{gDT}>``@oTXf)0*7EM!{
z-@SM9i*u%BD2~`G37Ur?AJf{xhZ6=N67#_-qd^ATl3!;70w4K=r5FxrI9o_k0!^h&
z$$t$=*2nj61@Q|KQm<Jx3&tHvR;@-;h_G=QIY5M$6%7`b)o#(PE7V$&7#i^|M=%?=
z0*T12?<{)j{D50q@U;1n5hIup#CgHt3y@7y0Y$o5b{8d;D>p4ea`$|d=7ev2thynT
z?5&r6qwcNHqlO4s@uJ+Q;w$Cwd?B%$7^|iv<t^yNy4Ht>72m4Y|M+oUcROPf^!1K2
zW8XUEt{U_eCCJ|!miBsluPz%crk$~kzGjzD=}LXc2zM7|1v)gYxnti9IzRN-g+NtJ
z47CIVcXdcY052jCclhLf?2q82eRvrTxvxe-+D%FNY$|u@=ei_n@na=w)~RVgfG=ix
z<<lVr!_|a?U%IplEWR1S3zXagtCxLE3tknU2DLnnF+N{uOyG)yzPckDEmOT1;c?p~
zt^bC5WaE5TMBZ($?ktVV+eO!>|3b|=<MqbKG+D=c-UFXme~j|#Vpu6s{uzfmmV>@6
z{twTV!@NxSX#=odt#5282<SEQofA>U6d@Fa=t<pzI1}ie{xz9Xc?qQ6r?;V;Dk9=r
z_+Jt@;USt|QU?*r$E@X@gPYoFhIW{{38Y(z44<E<T}hnzXTKjSrf+8)UVMoH%~QId
z&(ZQM_!p5gA>&07xu-alt=Wh*$%LCpj4e{E=zh;~#kU~MD`#yf{tTu$(LZ*K1^09$
zF(=toyXKjZ>1r=;d{pxN=|>GWx)b&yqn#^twxaDp%ncI?yPVu5B<u1yL|G^SeEr=4
zwdQr>opAffrkhX^H*KD-ET1QRi*;J`Mn-NgO6!}bwFmx(xSn;j2p>sgl2q^<$k3CY
zw6CH)2SrhrS7n+st~At&&9=`>hn@eJa-S<fBC<%LI33;_*C;DGD*9=3Ewtn--G1%u
zk{hRL4;FIgkuf=aA!6~gdYs`Hz;m5jU_iA_aFXrS9Asn8o6L2Mz@{jytrgBGT&!j#
zdqLD-_MV@S3d?#*V{$Gn9Gp^=Dw$E24jd_$ImZ{gp0iR~OwE+C^`>mpmKJ2Mlb>t_
zbA&UJ76fq-@1_?k&1F(@bG_oN<!FD4Pi+=OJj{9&HFoP(1fRNUA)#0(J7O@uSueMV
z`=g9|$C8?csJ6kQ7#FGFaBFA3DD?=*9hg6kPFp-pdHeMjBRGFY8v6hRq*KorCkB84
zKU4v<ABZn4oujkuU%WW=(3vp4_vZs#n8JQen4PE4ugGs{@to8^&m6;iwwGY=TCd#Z
z>&s;k&9B$qYUB!+1lx$s$IHDaQM>TT7RgU#k03=~z=U;ovg(M^3J(0$+3!=L9q`HJ
zRQiHCXf)7_xP_tmNLY?a>cJgnVh!Y#Pl<t|mrBJJyvS$Sk5=m>4BW(-;~qVhQ|7ll
z`C2E=Oicb&U~niS@S+p7P+<OF54$e!418SZBK}9jJK44`g~MeT84+(ZBbzQ>A7g(T
zv}hzPcd=qZPO2qEh)l=-1<kK-#qGL@hOu%Q<DnaGMFG6s+80Z&_3|rzN?SzgrL2SA
zI`6vFJq(7SWf?+lx$IQ~0qoi`@Ly2>U!V0quMLYiQw*C*QoPqxy8NBXZY+JQTq@To
z`dE4V@BGQ$%>91tW4K4<q>-`PLF7ge_lI!iogx9Zn|@dY{@X9N2TS(agBse}`)h2%
zJtkGU=s8e(|J55`ZR^RH@?L}e1tg?)O^$9|Wgt|QkID-GCp2bT5Qi@?sy4mukvv~3
z+VS{Xr_S5P^3!X&qmxG`*A_N?luLA$x60jH`?n%~5{d%Qp^qAl8yqo=XJ`PYD6Iw2
zMHV#wXcP4}aFiJoHw+B8&)r^qe@byhfOg}pWAs_fAZI!T=6se1;Hw$!K>ORDc%ALT
zWf>Rczc~<qQLANO{Tn~DGxu6X_8$TQJmeJ|(cn7^0zmcM0?=mMfFCoijY@<j4{QeA
zQ(+L$?_PC#h0Oi=&X=gO8ESrM^NShr>UK8I)z5#B58glkB7TTjTY!lS8(3Zf*`_2{
z9HJWo{V`;Wb>ghqJ7G;22*|o)Vfs%b!Lt3;a@Yb-=%Y^oS%0i%Z=t-p>{y$_F0c%h
z&yy7+&Wj0DZ5{0%{yl%*unZp-`X?jfR`Jb`$F9FiIK*gQetsji_PHSsvh{hOH>mZU
z{0j2x?Y2{Ck{WQ5&RB=MhXAT%599wgD`!E&&&DnsVwd`LZ!)TEL*c&NWIv|4`aPps
z>+0b5q5WL)bXSTk@vcUzu>TnjNasQM8+SqIIj^EkEX5?4a}x)ec5<stF?2gXdTY5{
zC`A*sI5Bt3-2hOvwf`@yg$lFcWzZr7N)!uB5Xg)nTRMd39zQ>K{XJ7FbNq(t%#C0B
z>NB6WK4@L^OZar*;%Dd6WNTJYT7BzNLwc6~_Z^IFP7{SqZA0s%%`blV@iVzHN53^q
z(AaG^fpdoh0x~@)PLEQ8Rzy_A(YDn$;4Nj5(P&0OLE<ow)7VNT{67kVl(nT_piNfe
z8!52VA*uSo9&=0_#u0?$A2wo@QABLFSQTjGOo5!^<4{)~PoLhMQRQ2`ef4`HnmBZF
zbt}r{G=xg`8q#pz<ZWmWbhYQv=Ig#|w*shCU1=Y(B(_=q$?J9Ha3|bY$*ReV6);t!
z&BToDJI6d2HEjhv!(3_r>_qlaq?v>MF!3e6iXDObey``{)0a*c9x}VLmfsg|XHu+m
z0}22(gsxAZ75v4Dl(3}ZU2C-Je)*uWsMb{6aPF^shursX@<~l;C`4;}YRCr{e(_Xh
zenhnb;3uS2VNA~<WnJUXjn`=Xs!Shzo7KkpnzyX|en@fKqx;HN?>AYlUT+ta{e|DZ
z@Og^#)A09J{cmrp#n#<Z=g%}u&te!a99e7IJ$&>vW9W@$SU_&x=f2eV57KhD_@DpC
z;>91J><U^euDV&bHjsnfT2gquZ~n@QlexqCOl~Ze&y6J0H709D#%J3*D#{=|;W(x#
z1q3&N7Z)X|PNH{G`3a*xKZ9W<)t7JYk!F(h_{e3nIG`jlRE0!f8-KB1{+zO?C*o#7
zb^am8omvd4AZqfGQCob@h@*^fLZ%04(9s6vW-pzXqT3<}-86c&@5op04^X<^O$l=i
z065<2b7j>oJ&p%hCIsV<nLZW+gBV!H_E06FmUR)1@A>{#UbpBsC&Dc}&N4D>v5uhX
zH>3*QhCAHjG{K5W)3s#PAYD4#{BLDszqp_<$lPDk9c6oO?}Bk1gr8N5EhCLj&&1&M
z>&5G9R&>~pdXlV9>6Ip<n>~_o>I&mD7>Jz##+QMGRk&*XghO8hKp;D&z_u8`vzB=W
z{z%i)*}K$nF_v^<bZy(eg7?<ZaX8Aov+GEM=vIXv0xdG0En&Ck=XlcVSF}q)sfs&}
zg`CxHsoNPh>qi54SX`W!pTKS=7!0#hZ+iS!6CMbF3D@oVcQ_WD7eS<}KmVsrRU}ft
z>zfsa(%U)aV0@_u-D^wcQ(yY2LO}*kjmMYOV$xap!nzHY%Lh^WS63!)-Eh;Qa0{dJ
zj%dPXws-3v%Y;~eJiJZQyIOne?RR<>LC6;xpmjM-hF=c}k(Z?=3yHjBX}T_H<6Qdm
z3far&KN@w`V7-Bt2`2>}#Wz!?p8?(Vbfbt(0JGLY1_5}|krGsRImevYRNjn5P~<~%
z0LKTbf4Pf)B&<}B`MT>U@*IG<dtb=HIU|Tzm{}+Jy`{ktNeUlfrKF@e@B=}$^fc@0
zR0-C2N}=veOmSC1`0SM*%>A||(EHa9XD?WTbR>4qUcXVF#uh0z>J1?J`CmzcVl4+k
zXo1?YP6d+(?i|}8x2Kujl{UJbTE|WEkwg(NA}_VRqq86c$(4m;j>M`bYrMpNxq9Y6
zmU+6~muC5I?8#`A2=m<%^h5BrNAiwss#bOgwXveu(-<Dwt@9$ESQaQI62MyyV9~_@
zazS=Qt%{wwqJ`2zP?=Ew80{NMtdh#Haqs%8JXU+gD!2zRk@yB}gVVs<BNO0oUw)iA
zy?ISGoxEbK|Fo08;<?am#Su8^_AWt@Dl@&&OUF?3_|_vBkH)~d7WJsT8B%0P`(pQL
zuD_I(T<3=i?|phD>qN@frvuZ=%3jMWbBzl$U^cK|mTS{{ga46ha<&S4*rPDCkd#ue
z#9sJJ?>jYqUpMe3L@-nR58CKd*Q9(Sq<Ur5kXO&x^5S7cvG8;xK!qM#+we!R?~tlA
z5jNNUoT+^1c%$1RQCg0_-4S@3hn2V6kGX2tP9zIDWQ&&1mw1g%=%HcIU8hXj&RUMU
z@j5ZSQNCdrVh3FAS@yHi6hI*V`Y?KEUW(tufLf3#<b5fRM08t@Xy;`@PzT@SliM1(
z9Z?y_pHJTipLaQYTly->II<;ye!^Nra=ANm(N){6eTIj^=OHpF=u*3`48nFR4|jY+
zCSH8duyfET{OEsr(s5$xhpA$W`)!L=y3UVFpanpIYcLy$8r6eCq(p-l6Cz$bc~^QX
z>SR=X<b5Z{K*Z<$7v~qtKP8q_-*lE{uBy#c+Lbh~<y2k>R-@*xJ9;nlJP2r9LIbuf
z-h5(*viX=>DU?b|gnO@a{-q07yrkOU64c+CP{l4q(slvlBu_xfWDxU4pd+#k3M{s>
zdkY_gkJ4uZR|idBMFq4bt`zp2&m5~gj{`B%EV|2@$otFu@sVpH3H+1x2A2I7om+H5
z20cg|GNtNwsP?0+^h0dbCwqCrN=^a-XY%xtmtJY@zOf2vdP{>h*O2>gN}Y21l4^dn
zd{JIYj*wK|1N4na?M2LlR({QiDOI{;UH7x%hNMWvzD1iPwHeMkHxeZ-Ha3}SDDl@s
zJ%9VTGV%3;xSJ*agsfCLUm~%Wk|^C1PBSXWO9*Vl{i+PLWR0?#p5~h12ZE&ZxjMw2
z0%08?<PC}2Wq*j)k9SRgpj@-#Y6@Jx6`7MHm9+ay9drgK3ZI1cu12XheHp0b)txGn
zj-y8wMHg3FpsMTgIPW4>Tmd?UQNldY6(H0uwtK^pmTz%I4zlgbS4!`tyCEK=9M(+s
zc10PhrCep3YexZqi2MZr6Le0Lh&{(01R#1(3BQ%IuY~kxrwnxS>90m1nWObLFP#6c
zlAV|BvE7(XtzKa_2X8QO*fd_Ogf1_th4Vii^bz}`)f9on1{t2L9MbIq0H}d44*0;x
z1~`Dym@h0IicuhaX3}Nt-_S;9OAC>^>BcjNYEcZX(ZKE`Zk>bD#DG6BLy%D^HBh9W
zR7Y+qeNLc~^azPu{d8)$W1~~cji{B&TPiJxIGIMHsVN_S+`1qR2x$Gh4NsLoMJ?dq
z)_M}}YafF~4H^3V7e-4uMym>q!V_gZYf3|cUDwZ_TUEOtl-13p%fGTLaqnPUnVoOz
z=YZRJG52hiTOS!q4F}BkC5H4(?)`dVZh`}{{V+4K<K;x0alf<`4YGz-PrCogzh-3=
zlha_fF7biAHp3|=lSFBH<7PAIcZ*sKaTjK24d6XjG{)@|DEeS~Dx67n3n#u|@12y-
zQIT1%!o)wqq-dQVb8&<?<S-uA*ort~kZhvPRp_1=pV4(<ARXxD<r&s$mFJoNV&_H=
z!=hT8pG+<FoB!0S-TyP<idRgDzURAu#6}6W2UR*o!F%k{NZZ8mLPF5<Ds`mNSp%^-
zLo~X#@JSj=v=b8VEwFbLY3fMRej9g3j4;jxVO4<@og-o?K_v+wSTV)BTTpFx>VRzd
z^HAQ|*a{LW@BGdn+kA?+Im2~(`o}|tyd4q0T2|>24!4e6)6f=;c=~hOfVB}Pafg}H
z3qe_BZ#CE%06ihfJv4-=SLxH|cvEj6gFQ?;JPU9qf^kj-Il^oQt%Nx)hy(y<mJjD*
z!FEvI7AL)KqQ;fhdr<;f7CvoU9c|A0eYixU7c_mIbcBOILUyo{b4c7lC_Ev|Led85
z<4Q};otmIww(jjD$8;P%X6E+O5`J)wI|2PVSUYR+oU3Kyox-kxMHR=h3;OBf5$~-&
zE<8%r9p8HY%(g&TD4!`>p>cJGiCL|`PW0K-?`J0QNGTBxv@E_|Tlt>(gr=pAqU(fr
zvS~z3RyOU$_UdXG6uCAN*|*jBgTR;*U}0gAJWp@Nl}|V)iC8wkVj~i>+=z!^WXIu5
zi@$`)MOFL%F3dAePWtAZMP}_rgQ+60;Eu#9<B7>&{4e5%QnjFphJN*VgS7J&?>X|d
z7q+}u4W07x`euX<&ELCR`r|5Sa~H6rO`BBmvi@yA_D<W7sSa-d5Du5HA}V-Of4pS%
zA5C>+IdXt4`Kfc^Z+XTdD35i*Q|m{>VcL;xB?Ok1y^$GKB#*>OLNN*8Ns)s!tPowQ
zneO`P;6nZH-B+jE^?w5MlsB@^RbDEsts!*`w|rzc{$Y-%3{VXUE%qI6IVhnQMabpF
zG@Q~33Dj936cIDbQF{~}jCDj28oP_9v_*-ftXKY}D3;QCL&cuTrrR&67eV`mMTly5
zAl3{+))w8QyATOf{+>m(uB5&SLhKiJ>Kl~J{;-k1pHrT{s9S3%&qJ+VFb~GZL~cQj
z6yOmiJnLZI;x@9XgdVl_^5c}+gpWLFjn_}bI(JRW>6SrBBC_l*0NzPND}C5TQ$qZB
z{Kt04(m3X~N$Mt_I>(x!5Uuz3OT?{Q=d_IXvQ44M7y8UEOuQrf4#K}U$1g&ah`XGn
z2J2oC=2d%tSs|Ih74mZ5&ZbUNt=g|EYjfe$$TsYqd}E@3BnxdsNjr;AMR`ABZWdsJ
z5Rrbc)da$s8;v!L5a$R~3Pq-)Q->IZY9EuUPtqj+5vpoXjg0k32Ix-@%2hbzeHRqF
zFjpz|(WdL#8csDc^_Nzf$NXlc!Vh?+oHzKm%*-H@|8HFk?rN%&8oAOW+9RFtzIsuw
z7$hJBzv6(ylK0JoEBT-(Ot}YH--}Gf#HzupF0_1*<J{q}_@M={%Ct>On_EPX5tJx*
zl6PT(wK~!8<l)}=8rn2spJrJAmAiC=6}zyYnT`YVFV2>O7ro5OG{(!OY3_J~Va=+I
zD-YZUI>^<Gl|ODUET^QdLo16CsOQ1}xB)Aj$F=(o79k1At`}3pnIZ=jjvr)7EM$6?
z&JN(YLG<DSDl~2rF;XRlHjMW{Qj||wiN}N$diTvAeWi7yt+Mr1YUzW)V+&D6PwAf)
z-K8@bsrDYbTzhL(wktDXY7m>ucDBD7Lut7Gq1}3M&N`0AG_|^}@f=l8mux>*ZR|NJ
z@*1DSTE6(2eZrdEmh*KT=P9*kH)VG26t2CQ_1MQc<Dd8U#V<VBe5R?54C=m${)_Lr
z$uE7Bm~4+zfW7T$l!CBT;J&>fZRVJ!%cyNL$1T3u8tVG`ZeqQcDV39AxN(t)H~aXP
zSOj%bfKs2`*7H6OcjKhCzKO<0aUMiSyiXPBIYUQV%(j1{?-fh6u0j;SQKTq{dL1}a
z!Z#*BNf>WV8o71I^Xa0Q^2=*`dcAwUa-$!ziox<yrrb@VE`6)=m8)TuqgKyfspwWn
zQ-08};{arNs8d=IKxe{)np!gn6h-F8yVJdP$vT+z`C+1yBEy(`qt5qchWi2y^7a!_
zEJT5xgjK7t2GIY+a^OLEoWEGO)ahsrT={DbxGB7fK}+h4Dv^zKl>TOR^yYw>zng{U
z8ZCoQ^7}<=WY9Mt5tYLkB;a@h_P8Cid({D&>2B8a^r95?GTNCaIn=+c&r8jq--AZ#
zrNSAah#5Cgm!P0*Nn+XMqI~bORu$qNPYE7Fqoo&ij29beyBe*ZaDOKD7BoTVEP}`6
z>~K_F#*}AVXJp}?1fyk-e+P%>eSRX-Na~5Q&5)4CLIRKZM0z?o;Uk^1icq10=Y^M?
zwl_x>$};#yh9#Nvc0T);*JqZeCCYGTnS8QPyDy?%oJd<^3Vjxm#a+&3_9-8iHQ_d%
z&hqs`HY+M<hjru6#fQ;TpT$28o215nvyd+O<eeh)*a}$$r)fR6WfA~W(-T$mUEyt{
zX@_n|ie2#)rA^AX(Z{pDjUwWz5cm&ta~#{=Xjp_i5i0~Kh{N5QMw~&q_0r}tB;Gxf
zX6I+keOocysdMBt)Y3A2D^TrLGNZ4#rub?a%Zc58hl|5|9%bOll6P?Yav>n!>h6g}
ztk}p+NF9VQgt&7|IbB6^jpnU4BO+)iltZW=D~^PPQSR21^CL(5sdHddS9kQ9dYBpW
za)VKr{fC2xVl2-lST8$l`tKaX>Cd&Vusg^f3<6{XbcvLJkakUKy;rB^%B4WV#;`*4
zlS+4Zu*)cBd0|Cqv`bZ?a}(4daOl@2jIb}#IHXW(&t2d>OsKwcZGSR-$D-tGv(e2y
z@rnDlSmYHysyrp#6$;p2rJad86j1UuhDS-Tk)Egc-eGF_BQ$fox*s@y`u&x*z-?b2
z&tFPUQw6^?4rbaDoWq8n?%M^fURd1uH-72w1%{PFvA|SK&xJ?1I~Q*F-W1;XnT-m#
zR)LhoKRK+BZZ*}bpj#89_b9aXuyRY0kv~ibR?%D&)t1SS;M8M0f{Yj06z^gw6^n1a
zw|_kEDIf(R-1tQfzR;miM6Qpw=0)2s$cGp`w^jF(o4yTcWCj$T<+2JA%N<3G1%A`s
zvdbrQG_?rj!pw73=ar6~G=5wj{2KguAgMF@@|XcU;9O@-a2Zbtd@%?63NdVrC`u+o
zC}l6-r7?Fy6trv6RmvQe(x^529iAc9=MnLb0Vgjh2pNx)MPh{^6G~bzCT0@I<^67k
zf&6-_^ZV|WfrCs~+g1@`G|y<{>!9vm`@Pt>yML>+w_A+D)*tX42e9r!dlJORjsXdF
z?Kl&s`;;@&+`KAkiYa2@*bE5St$M%Bn)7^xREq$|uDBj`B!r^9lVHSASXvlx0?{R7
z5Wo3FSyBvyI>dkd`GY2o33qt+xoh(#k8O9DOb47p<c+-RG-Z8gE}b@fZFb!nvV9E4
zA>s!yAXbMLFg2c1>Mlr>0g!Lh{BgouICQ<^`xzQ?KE2=VXl!keQN9$nZFgsQw2eD^
z^jeNuc@PAGQ+ASkbw*7G;(;4FLIK5rf`BS4(!f@l9T>RHn<SXY@}-Z|*qk}Lv(e`a
zZYxEURa+ubz!p<cS`OX6;z6%lT;2S}n#nWU+1&Khl2mtr!0b8>^f+qa_%DhVL0meF
zDE>CWI6#789a4AGy`N&RyZSg+&LC9qxmbgPpw#HSFugu2FOFiBoll6n>sU^%5wT!Y
zEj}RdUX<)Z;LkOO0Ye4NG_kAi+IqIPXkF6g4`1^XQNQEN8kJP|q}>QU!VUjN)mer$
z_5W}DvyBBcYNW*INw+kkyAdgsl2$1ZL|}|=qyz*VodPN%f-yP-qy<GCjerOWd{I&V
z`#t<WJ&(?Hu5+%>dEf8XecuAY6{^61s5$`GCBqJRkUa|`wgHykbp~=ioh-fbRr&sJ
z_D5^3f5Epq^<N^Q-9EgOeD--i^7bViaj~qyt)DTMUi&`#`0eC&>}8#5`#W`{5A}`{
zhxQ891QLB?oB*m~_gRq-4ussPrT_%bCJvzPkF5m9R*q|IueWdRBN+veyK+wnqqcvm
z9{@$dO(@<3py^lAap%&vNFvnHT*Gs&9g71o4|Z4P(*;KvbQwojEeb_P@&en*eXQZt
z{}E)mPPQ}|=--}w^;Q_Q-1U~BNk*3S^u^(rU;o&DmZ9qVk97P;$5s?cfG$S0&UP7|
zH=xe!Pz-=r7{3f__NTV1inn@1N7)W1N8hApC##ydo<bxyQ#EM`AaYc6rgAtp9V(r+
zBOsFRHo74vrSMs$b4+SkfAmY~UCHzH?NtnI_72vrc4H_E;1|zy<3|#4Kp<s#7#Qc#
zuu3#OOqni-CD$unxZG#_{Z&oxq5Wx%-8-J5?asTB#Pa#`p*h&LAk1r7l8I1kdM#&{
zuIi_)6|TQxm;m+4ik;c?A`05h;eHjT5#s)9TC<xe?$5SOUdWz^%*i(d1l-q>pkg^r
z90oz5FmmIsJ}GG_y@~c_rgi>II|nn*mv63p=vsgA^Hb1knYvRsm_SC<(yhv*se)l<
znqk0LF5U)UZgqc}JFW!xL)#Wq&-WMhDOOdsHf_o=mkB^l8~}|sZY!OJ(0vQuU$6IC
z+A7<7=z&FH#`Na(EQa3lZ@+iOD$JJ7v5W4@T&5rR&YL4T@;#?W=<i3r*Kj$Zp;cn;
z)6|OX=*pCEx*NIPxgff5kQvbPZEvCCeoUTz6j$0@fU|7a6Gd9zGAysaA%3J#3<1=?
zmlY!7ylWt~L&}p?cZd9b%TWXbMt|S`_Hu8O|6jLK-~A88^A{d(vL0OLJ@fi|=naPl
zN-;O%uFGE7U*~4j+_^bP?ZAKwq#4|(w|$RGy4S>3IY*nV`>8y?l1m>hviEv6`v^_n
zrQL72Sa@(!MsKFgj}1am;|Mv3^dr>b!E}gnvKB#&E~XCtQ>O3!Gxq2&vD;JOuN`{>
z?%D-hl8L%(AbQ*GeAhu_jU^|nErNx^W!!nkAd|qdWlZ-aL9#XR8Tb0UPN4yJoCKEq
zd~H6b(!w<54(+9;yrQJs>92r(c8ksKZ_m3QNMFg_S+)y}169v&#Xb6YTlN?=XvT)Y
zW$Gr&-&E<F^^d{POl##U<UZ%xck+f?L@B74jVziKUP+8nD_s72;Um;oMsD~?Tis{L
zjDuJ?8cPPQP_#v=ZJ<fj(fgd#RMjDBOeL|}jR2vDQA~LFQZdkGOspQ@Rv`EULxBjw
zxAfq85c5I`FVWi~MU_rae@hTb%`!0H3cbcS97{;vt0-I278x5qz}y_O5shECUuPL&
z{rog?DInz5lfOh87e?K;3>t!P(TlHV^ge$AzXI^=Us>$awN|iCI4R%;EsLFzQG5=h
z@9RTJm0$jpJ18BywQpRB8yfgdv_gEF#GTxZdIoAYD?!ww^2&-{%C<nons=Om{x3p3
zAFAQJOs!}{lD*x%eX}1+ho-*M%XN8=WC9lD&lij^+DkiB1b<4hDIT)V4-vm->ajm+
z!vuy&-~3^-4jDD6A_Jl2y!!2v=JqdX79P4bNGVZDRh8^g?>Qt4`a!Ce7vKbsOLX+A
zOo;t1L#c!&P>cDCyVb*z&f1pMwmsPlv%2@-MZkqZuD8ZK4_P@-|I)lhyqa?XGZZ?(
zQmHy}2iKw}^v3amgVgV0Rq}${p-+sQ<qe|@&+dv-r4)%Sm%c>2QgZHn>|?G{6<SU^
zZ@y2$Q-AejSDg7p!kBJVbG6<L`Fq3X5%<dt28gct#oPAPsh<9mW#&op>{j*vGQ}~V
zB7sFzBy`s+Nv_V61T=>#&a<zOn+Xv%!FMDsU#T8TF(XRLaQ`+`><=n&6#h@!N>S~_
z*lGENwMHveX#;U))JisF79gFf;)TboGlBsk<<>kX#ANf@(5eo-pEn+}9df^y93c<3
zAlHFV0y97{_F!o6&H`0L%!SXl4|B~2A6Hkmy_MHbe6WXTJ@0=ty^-imQehJJ<^5Xz
zqt~v&z)A$Ssd7hfIa;;qXCR=X5O*klS$d19=(Qyb)E~K!W%2~*ro*rmbAFx7B~1BN
z2l8ZrL=wTMV;?ZZCutZCr&f=%5hlyNLnXI71bK;i{$Uf(-&xu2?>yOY$c}LkH*MZp
zJgm8AFHO8I``X*LvDIN>O?zP|%dPCWdB(_Np15t1GLWimh^&MQK-(_^$dWMbgozi{
z)}|HS?M};gq*gsb|9sw8#_Bn`9#;?!5%vVVHY%gbOA2@J^4`Y1^#V0K^BYd@O3pA=
zwQmoEhA0Ozvk&ft$-aMgaDJt2sQ5=3>FX6&LNJhCAf!JuN%XV*$6<wrr^wv?L^;3G
z^iXfDX0&T7_=qW>r4gGZY~i0S{gm(S-|Ls}7VQ;imqZEXEB4pSg2z=f297^_rBhF!
z0MD~MG(}fF*dN0**F2dkp)O3Ieap1oR$gCWCg2m?c81mR4jG&X7zM*Ys+l(OIUS&2
zkX3)j$_W~F0txHYAg|*BK~O;eHZ=|K%)#_3?E;PlvPo?x09jW7pv{h`c7B+}iqN3c
z!}sH?+3%PkXZrkOKHo>gv>nHWi|!thMZ%1Q-m{oOXhAyKl&C2`3HW|sN70+!M<Xd$
zmDFO2_*R)7l*KSSs2=>8oy(DvLNP{=022x<8A1U2wb&kmH;K!5b6<3*+5^l+{$g5=
z|G9UIElzPc$N%;rKzROd!tvY66Lq0AaddA)v$ingJ=HG32`Qp>o-)TvP%}$-r57L-
zd*O-DF{u03J|cOdG1XI|d<7?vFfGln6(W2n;CuT~Wwo%!8YIz;229XHaSZqZA%g0D
zy@la0u09yOIiw71?$C;KHlu^x+X_sxGR;@(7uFvCCnmR8$MN2Kw%L-<F4z2kVVO?u
zl{hZc0J<SibqK~KKvu6%d;~X@EvulR65NoA4&LolMFLtl*u6A8H`cnPpfNG;4P63J
zLT88BxaU38Fqt14N<&R>uVAHW6VNp`0f3g0y|r?+c4ItmxkV5)@EH{ZHE+z!$|$-d
z<Z0q@GP&m|>n&qo6)x28#<xsS1-Fr;%nk~HqRYiXK20Q}^&#H{&Z>hqD|_BjCV>VG
zd;CvLyPh971WBG}XEDZ5jZx^-tr4iZC?*(pB4SF*E0V?=xFuV|FXA~VvNB49DyRrX
zaEcXi#0M=^H1qWF<FHO}k_7;t(AdHMdAw`a#8DjrhRqNHW@mYWe<%Rd_v$m@6ytq#
zv4B(e8W#cZLSFIu=0QipQCK1cKh21L^|6``+gtz)%%pm)#vmsG*IqF&Y4+^t0f+-T
z@i7&rkZe}(oLU&9UoqJ7woP{|ivOkTsrhF0@$rEQ$-?SCqSa(DYqWf&K46v4NiP;$
ziwA(OLY2cJHhfe{6qhG%qOLLSCDm|PAM{J^ZO6w-%<n#efOY@qGC)IdFbeKkAjiB=
z3?QD484t)=N0?qe)w+H}yNL@{WRuSqRFH$nO}2y~Wqw`x1eiIJlWQ+Sw)f)8&P=~z
z6SP&-^}IorW~}rJ5}H(lM_fd`_^c-m*NXuCmF@{fA6FDWqM3jMg`do`vpfdvcZ0{H
znV>in_L2|*w>Ld1#Gu;vL{vSjUR$n2z4{)#-FwsAw(;@G2S3{H&%9E%%=De<)0A_{
zB>FPawh<IOt$KD<D-wC!0WbcTl>(rUCrkjL=u@j>m!}UEg@`LG`#XtAp;m-K+iC|S
zDOe|uPY8km0GGL-IOt|NKbck7Gmx2X0!N>IVM5G}-b`&}9W#7gd8+n;=*ISnmSL@s
zoTL!H;F=1q3`qef$xr}cX7Q}zMm<y1Yu{<ntWMDWrMFF<G)QH0cT0T8(_Ypkqh^@l
zwskGNJ7icaU*?5OnYx(NW$X7iuNOi>!QWuXQiI!))<*0QYFO`WE=Vz8`KF}1Xhi~0
zyvWY72x~mO9>ERfQwx!H<**h~MWm~;=9PII;wuWcJswj>5~fKRh&!eZF9HqlUK6F`
zU*7en#$Ad8(2~ne-Gz+S^mImZrA%Jb;$P9@kDZb-#F@;bm{<X_GV*ci3e=X)^x7Ds
zGY(2V8o4ZC+cv?5mW3`|nNY<WPoCJd>jqup_b}o}OJwu#lDhJ^ruoEfda~gA>}90f
zBqO+2^-_igum61m61pQp(Un#BPqyOM=v9VaY|xOEZ}fF9<t#R|^Z7NOaB48DHE=-O
zBWQvCv}+^MK)lw6Zcu9M7rBoJd;)k!)B(ijMo%x32iAXi%cbYoR!?s*^B;4YomdNp
zc|)l5kDx)i&g9+~s<gKi<F}y0W_%P+8U?r>6}j6E2kdLgHffsnh!75Xzm6>Weht61
zeERdhwCuYL%(~^;hIluQG@kTo{Fqd2JoO4x6-us;sMu4jxPNAigK_0Cq*^E-#6{KC
z%F}S|Q{RL%gQqYGqMKW50uAOP@Ei6lz)9*<d6v}!Aw*ExS^lOU$ikqcZRJpNhTdiM
zj-j79-KwEu@~nx?kFg6|d46UDfmH%OCIEnNB>px4*(lO201*y0<B+J>7r?c=YW5Xk
zVl+AG1=3!oSEet*b}yQ&t#a~gZY7wBygn1&_ND%p7t~Ia7G{1hCQhX5XLPaM3+n5Y
z!8R)@RupsV^Yc;Z+KM)wo?!q<<g!zuX73dX;#hmo)NFS+Sx$<1*6Ph){vSHNWUl>+
zACkJSctu3m;+3kF-EWWpvj>8O(*T)e$5YYBLjY{LQW)6+ez|=%T~#I$prKg7q!|RD
zd4R$iS(_f#Z?I2#K`(~quh^BS@Z|6ijx`el`d+YBNfV;2)KT=!%1>7Av=IfQarP`-
zAGyE%sZ##tbY>D4uq@8YLoUj7E>BxvcEjX-eODrrv{1fu5R-J}2e;OTGRl+6M{|nj
z7cb7=yu`kq7zl&VUc8L#$toslN8EF|de`3|pDyxij_ToqV|0krNtf^s?-e_r06PPJ
zGydvIZb-`%X;1<y#V6D#&~qF{rYo&{G2O8-Il!#*_F}Gala^Epbv<+b>f!6<_lX62
z3~Szi)SFbWSJ!qdOziWMQPJ`F=YR6wUFqBJo`-7(^kjRD+=VX&gh@OY4phjcr)ym^
z-5-(>0)Q$5P*6%7j0mXCXDYtDfdE&#$Y^096krHYC4ied$Grby2-E`~!}1RX>Gfvz
zoh@QRKwno=xHIY4;PJ!29gBmU{EhGGArWB3He3L0w-qTDi3Kq*KtO&t7C!e-XI+`Z
z=)>Yt;N)w)!!Y<3ncqXP{u#tN;9pGeA)`*wN!SWU_je{rMRD*d&HGfHmlCW6m{Wyw
zp?sLAY{CO7wIS1ZdRv>oRJISGFqoA{1fGHT`ugM1uk7#FYyVS=w`tMP*myq(0+cb=
ze^<}%yq>(UYadw0a|&J8^I2+g6E7cI9$j}qSsWXah;vtOUOa4}uY!V}XTJXxCX)jJ
zn`yxoWOO5h!19F#2Rs$%Cio{Q(9Ct^th`uvF0o)>$Ra`==!v9B>Z1_e0<1~QQIlci
z1c;p-CxGx8v)~~{b}Wrf5uCy#NrqyAS)&aVccZk7@XNQWII`fjFws>cEXN{=#G9Na
zJKBqQH{8&ky(30GGq}`j+MRP&^>O3IYqOo-x8pTNSMm32P+ZJ0f=KRJz<-6?5AY=}
zdIFenn8ZD7=o>Ey!GK0;kj6;A%a%x5?>-SonjmHPuukX()N(ywW`+?Nn1~rq<Vb?q
zXG-Hb>6@!p%+IqM+nGCjHI{IAD*532`KMRy?sXj9@O5!~`t<sRHwq7(-7Y*migR~T
zVNdaN>QK({b_^JIdalISg)i-*t$fi|^OUZOBq-K12Ka?<heNr2dU<adlk|&TRNvM8
z<ELW!xHs;C-N({bT_y@V<se=DJ<BwCL{5j)lWLFvA=&+XFz>*2MdFjhv~`lRYLR%l
zXJ?0OrbC_?W9q8vv3BR1oxeKS@3=nd=B=@-7_?u=d#LZccC6z4RnpFIDN)eRk0!PP
z$+DK7k+48*5%Z;&iWN`8q~|wfH!?h{`R~>?)Fs?-yUpgNU1Kx0&W3BMOjlHjKW|Ns
zfteVz-C*%4?NwQ;hq;y2<HhdKh2jo{uIZeI|21BS3PAm$X_oF(Tf1c&zYr<#$P>nE
zoJTz`lU_gn^1WN&EEg+H{n&fg`<m1GR(aCXsgT5u49~>J=gz!!=)Cc3EMMf=)txGi
z=aH*5OJ+CEM}2zUkhI;Up7Y&|SY03B3B76cB&L>N>U^9kOfr!IT!So+0jqcZr|@!d
zJkV#}FAn8Ph|$w*;{T$GhAH_@{hGk0%ey!h6CkVx2EAMEbYvg_cP}c#Q&<3u63uK|
zreJge=&2nk03sx!zYpjHEreNeSsg4E3#A5wTd18>TzV^P{JGVDF5YpG93+CxI?2y@
zbnFYH&RyMYd)3KSInEA?H`~75YCzKt@>bF3#FA<YXNUEWf*5|!ROi~+Pi6Kc^Hu|M
znw)EgR&2XYVw;-zj}YzG3)ItW1C)&$*u?;*@vfqTzXWSLO;JgL#Py)gm43qz{^G}N
zW)`)YCdvs|A*sYWtYU^H&$)#iVRX(Z^1k(=+NR@PN!QT>O}X|qHwa*jubRt@ZuUJ6
ze&-)w!g!_P^jAqM+d-8(S;-okE>@K+E)v!6S-2-r)<d02)ZbuwI)iWkme&E`>SI<N
z;IjYe=Q9-f#bSg{*{A*8JqD+ib6=jEb5?Nknx?(fxApB@+8ZH<?aB}dDnqkanT$Q~
z+hCt>eCfRC!CN&6(4gI$;5X$*!N%myhu6-q{0*QHxlK^JHfO3v?;XH0cXuAEhs4uK
zRAt2yz->|MPC%369^KL3aE|p3+$*feA~!PpnrmgPqiCt~IJCot$DCKKA+aFW_NTQK
zZxKlCkI4RR#SBGJ-=%#%m>dwHDUR2nFic?BB(#URi@`VYL;>qB6IPE`F6ff{uA;ST
zXQpVIzb?LWOz=}Ss;@pbd9cZTH?ftqqSsuR@|stqt^4gkMbz0)wVWSD;l^7KrIsXL
zoFiHhp}Gd3CMNs`v+7q+Nw>-1OhO_*AWIgT*VIn}gCgxrxjIZAI}Pd%FMv4(Q|Un|
zcv}JWDD{^~JoU?bL<Z1cD>`?emx@0KA`V;WP<R_l&q^KMwGW`{F;OExs&3#x82TB_
z<bz38$e7$2n0q*Y$@wraw9&d#STEhXn{3+jD7L(9&vl}gMX*|-GIeB5mf5LLcf}u(
zMgh0P+JJihWLx^kWSMxF-+kj>(XMAtK8!64R|w0Wv!z)y#Pu9j6%h06(w$-(;(zjH
z9ELj-G<aGS=EN+fUyf|{<uu_Q&+_E^;Su?P&+A20j?Y+b$(Kt3LW?sNcOTML&?uMJ
zV`((SCjn=I0bnHWFOnnI`LWbY#8R8uno*+krj}1r?-N#WC_$`mBa$il+7+Tg4n)sv
zg#ct=^j~xWt9jmPYzsN1y?6AE{H^P!g&GtO+L?T;5HYl#Qpx`G;F{u%td-%fRfYVy
zj-{UM)(Qy7Md)H0K!qyNo=lR1r-cbzw@b(jA!lzvr4OYW@&$kE4;y%27oEFb=XI(^
zww2^1<o-#R!crA>Q-H~KtG;5kB;{i2$T(hf&OYGNm#ix?zQ4-L6d1IIdJ1`Agr8X<
z&7DKctGpy|E5{C@ow9;w6Y{nKpphMmOJ%vr5Sw%7)3$QZh{%8$&**m7R;NU=2=i9s
zGgP><oo<UDuR>AL$W%W{`dSNJZ&%(>?91$g2{q<D`R|OK;dSu6@H(OOoYl_IqgGmA
z6x_u;^<Br*X@<Mn#sqbsNu`c9^!1dCQRah;X103#Ew*yGrcw~ws7(MRSZDNY2`A<P
zM%_+X0PbUmDC$K=R*scMg=D#R0OBMGe|9W{?R4tgvw<>C(m_U?e)hN600?#b)9cAQ
zb03aE$@K%lXx=FI%LC%fN_GTy2F<5ejO-WHzuO7753+nu@GQ=wgp_ut{<oVa3ZKEJ
zn)#BQ*kJC7>%_`8ygiS?kOOP?azn-*+hk`eF4xbbcwNF~Eo88IdY9|5b4WPdvi1CV
zbiSPtt?<Lt?&?-aYeu{8>~8_zuQ5*zE7_ObR0mQBd09}C^+j>eH^)zaG(JPPd)T=L
z1)Gs@S}VV;MY&~Ov+st9T_Aj;Z}6+JA*Kv77PQepiv?4IwGy}zKn0dIcc&)sui|!^
zXsOw&H3sr5W5tM1dpnw|+pxHd=}}a7Mj5Lxx!@6brAJqyea5)KKMA8~)qaxvv`Gtz
ziWGm(Mu6F;QlQA;QG%W{2f0(>w^O}`-qR{q#T~c1&u4HRMo4)MB%X~qk~cAgx*1?6
zEI&<iBDFv9RSp{~`0dZ<Kd;ivsnS(UAEI7YfurxNN|8TE-ny2&PwfxCsXIwzkc&V@
z5)<(TEX=}CoB#kuG*Pr=4RRcW)O%knvOQZB<UszH8OFe*PNJW_?p#(?+S!U{Sv?}j
zZGmvybJfL<HPJb$OAdD%K6Q+Cx5h7L6XMdG^3#3^tNG<WZDau~+U2O*iZ%d24$z%6
zqOq}1+YduhSwNO2@wwdTwqJByR~4tkv!}3dr}IgtJ~VzN1Rx5vV@$v*YPd5HKD<H(
zM<@(mNjBYs;S>_&nG!ulh@H~V$UIbJM;^YIQJodKFO@I}kOlEnt|BR(O`~B97yVsL
z@-HopQpC0)@?EQ@`-^IsbL-?I+W1acstiSYkkaM=9|lMh3wRh!s_Lv38&Zxao8Rcm
zeTFWrk3KDffdFYuSNtp-Mga#;k*k+$f(o@I)H8ND3Io?UMAA55J3W%)FWYN$A!)DL
z>qn~_N1y27AK__7?yAyWDGs#Eh)hZI3+<WohcT;($@vR#A(6LYWNp8sP$u~O5>gH?
z(>C3epp^5HAarsabf^iOM`GSdB)~sN8D=LOs3zbfP;yexv%~^-B1^PZ^-j<eD!RE;
zX*Sz5wl>kurayMbJp7_oB+9$z7IsvTz0$e+m9ERU#%gQ4Y)LgY-IrsOCN-G>lDw#d
zL@DHSA`n3u1XEc8R~&kH15YbknweEv(IngSfnK%i5iUQ4TIQ&V5wPK<8Lk5WP1=XE
zx@8nk1$DH|H#u02_2xIE%{MgFqJ&D$>dq8<pQJA*q(ny1)$_;iv=Ui{`c;idoe<>r
z7+NKx&f5+ciB^uDY5GU&8H5(vQ!ATr#m91;k`~IHRjKK_wgYFEvZJhPJvL?Y9&~<X
zQaxhHX8u+iywN@Drn(g;aYv?o_jx_H5yHevddn=a*p`V9no2UFao4B%)ga9>HQSTu
zM}lb@H*&U+AnX-<|8lYuppM>y`*5nPM1#Wf7PG~hP&cp<GgOz#z7^b1!5#<THV%o@
zA<><426`zK2PMd_(mTZt?Ms#N#y=iQFu}uy?O$@UY8z@E7B;8py4A6FIif<W>3`lF
zZ(dCSVFVBjS7S1j%6}PD@LY0}_OVR<>if15{7C*sxgHtQUS-qwDkIyfn33{q7h$ut
z*^Z!T_T75+BUkqQ4|DyKMKld_NAC0N9<R*tW}|YW1}zO`w5W^&lt~PF_@=r^1dV|=
za@al@)`oO=CQitw@A;)}o65_5FAwuF!#M>bsKbov){YFNE&fMz;#DN6y^uZ!fe?!|
zv`joAhnqe)swNfNa?V<MI*AgC3nrc9J$qQv&R3bgWy37^yBBI$SO!`wM7gt?W6>X8
zRLb9$^S_!}uXs-tfgpaG;2~$>Ix(jTsV1?sgyysozk8$D(nJ?FKP2r*c)ZI2q~08N
zf@Vp*4J^)Cr(HO^>hm^tQMLJ4-{J7ga~YMTU)&zD?5S~?O26NZ{UHD$C^H{3-l*Ru
zuFE8k?MrV7Yk(x^ws9&-`AVB+h?<PDv@BUr4SwIGUZk)UKe@I!5N7#d*GY|=q9z!n
zW~-OWBJQ}uw)jYbw&Gr%+nEe`ECT0&m^`K#w#)as@$9P%Y^6GWi<LgGIxn(N&jd!m
zH+JlgrpSddPo7V*xYL|#O8|N_C`0O<5OG)lJeG<94cWrpj}}yE(3lvu6tl*g(BHKe
z<>l%!--;y}#LX-bP2`LOF5>%p`SM=ADjYE2jvi;XEoNgJbWM|=Ei^-t?BU(eHq8RT
zEUP}jQLS>Dwt`{>kB8@#O6Twh(>F}u^!^!pmeLi+yqb&L_hIZeE)`a43D2b#W*It}
zufK{#QEKTZ?GM}ETl2iRq(nD`%lv{tCD9xDq<O5@b0xs*J=FFM-N*<;UpRs5mdr}z
znZ&#03!u9(GQFTP4_Ek5G%QtWHDyknP8F5KMGwP@QBmFmKbcfH+G5q%uCRPoO5S~#
z<s2Ga+QLfzC*J*)>?%A%SGi;rE$=~_NxAXDBwOjd(%iUVnp4+J)<0RiHQAWSCR#tg
zhg+dAe>ULmpGu*j+q#FkI%H*L(X8$|SCIB*CMbRKKARt^z&(PC3qW#BxKIF$vLYXG
z*i6U|z>(tK==bLtUA&L@3REob_)kvA@C-C@Y(-^p50qVPfh+(TH>ouG>Pg(Bt6WRT
zm)dgSi=EPlnqu;J;d9-rE=e~_KB6Btb6|3(eN(ovdlY3#GG~ca(ha-Z>AIP}fB$Y0
z$Gmq|&b|{o@*)A)!6nWnPX-oIVx*xw)M|3z^dP?N3`7n<sa4?S>fre<>?eu<)Dzm2
zsiH1q$~cd!Foi4R+?Gzlf;kf*q=qg&v++!N<Gz56X2K+u8fSypKGoECkCEF4w49#^
z;L;tXl+yEX_Y0repalHiO1Y_7o}Y^sSsb+X71!3BxSa=@YDHgp(!0|jDP6NAN=aCk
z$_iD7kN@S3TVF0Hnq&X1)vHjK^oRDT!^qsfs(^DTKbG^i)=Frq+5f`X?;{ky>urBh
zC_Hd-H|h+m?RvD=j>@>G^;kVaO=}rEWaMXfd%KIXbCdwWd8qBh!S?@4`n-5VXp79G
zphP`+UWUEYJN-^y!a2N{!qe^jrZ|H7vjkFw-sK_^l%ne?oYd~fvV)*3Ue{OhN%*Ho
zX3R=W$;SY6_tg!0rFj)6)BK$h4=?X`pV1WUz;B)9>8nLHUev_{0mcVnvxjf<J9%s1
zLtzbYLTn^z>m6NtNv}<A!^7CpfOc`2w<SDQDNeMRa#0X61^|vkyhf@VqxahIz5d0^
z^ogsPii{6y=3NT&m>{!CDw9z&otftt<RaG#-(H*)Br>+r_*@iHxx|N>$?9sMKQ-m!
zn&tGE`7~a%FJ;B}lD>bvR$(i_rzpVZ%TjSnPc}VaqxaR6Fjsbi^(LET_KJUo(>?+C
zB)cCaO<qhki%C9ELQ-NnT(tl;7ee5o#s~7c)KU$O_sM379qG`0*)eyH_+Ykt5TDX^
zD~&54`CfQ3mxo<(1k&va;VxCH;)0}dWI73EhMX*#+<A~%#p;z<bYp9nCLUSF2lu=1
z&<_f<?@0+dpnX2F7q!cGX<YjIu`u9tTMT*Mq!_4Fr^;PFXA&icjYwRv-%gteC_kBl
zp{i)cx8KkQMr#S{qvgJ<$fM$cm!`YW%hfcm!#i$=kL<4T{nq~G9>npfs^dX~fOmwi
zPqneYDz@ml8Gd&;I)jC?e5rC*SR{NOAcC*Xpe*2~N=cP-AU7gs3y$9HN)0{w;CE-6
z_qIcQoajPfi_L=+pF&7>3}HbTTMQH<6F`m*khq94=?h{I>hbh#BJE^g_oj4wBa6DU
zAEXuXpX2ISIohMyGjv%OJbUsjr|^MO$y0jz5<ygkHUDKrl-wnRUsb}7C<5W?1MbAM
z-i8TdXPq?$j;4!f>}Fj9enK^Z@I(XIiT&!1p+V$|v2d;J>^B%#?P=TfoC7vvUH;Sg
z4$9s8Xzvdr_k^)k$kZr0Iey4D)P9-%{?kW{NQUZxq9Ui4C_|e{xtDw?EcMnHNgu|I
zTTl0q92?E&Ic?fEsBJZM+=kJF{I1v2<}Ycn@tw}nX@ft?z`yjiI0jTKgZzYITg>gl
z+uRGF%Vb@4{hr{9o<Q6SDgEbkjXc>uG(|m5Slm}oKgXmy3!k}GcZDR(6XFSq-SqKT
z|DgC%r2e6`VQK`gb&SCPfpVB(bpG3h{|`*I`Q-3i5s<r0d!71GMZ$5e)cW$o1C<+^
zg<cLt)K1e?oeLum@=TvFW^M^XNB}}lfq*|L|GACQC1B(TI%H;55e`OVqMipHc|ym@
zL?n3HqN>q+?1=*_Ia}2xFjTgYh@F~9k)?RjP{l!mLC3}a7ZzyK;EmP;U`U{y%bQhM
zDh`4T*@vUiFd~_h$8$vnadZp+IebvV0PI*b5txdieF)n(sd0a(u%oU202EKY75L>B
zRiLe+$lZ9j4e5T>gCQ|zssM(&qrnQyD+mraxrg8mTl{<40q`VX2#v8z>)FoLc&WVz
zYw)eR_0s3r#ny$O2wUXuf96k?u6ca8Xw?xX_sJ4>4;N$ye>S)8jpy6t`)3jR)jt@^
zZo+U%75A*0dZ*Gscm)Oz^5Yjky-l+_dK_9}+@RmceDS5m0yH}e>wfiA?U=|%80LLJ
z3uyx^OfYo>U3_yrbI$W@|39>qEj;P#f>uFt@#-cXr;VgcC=0D#s+;N;eMa5Cxvi3Y
zs#I_(U!Cf}1V9L-I|&T$RDEBFYr>*vy4x6kFKjjhK0o~;oY~jd-6`F)K~#Cz1*aXJ
z?yrAg&{)>ZsBYBLX5Ws|(}t)&qFZ=rX|JcOIG+AQU(Lii%qFeULh!WS#Ja%kfh<!m
zSK2^;4KtkVJMu#unAsl3bQ4T4<E_Zi(pVh|mS%jMVaaiP8bx8Hfk<*6cM6%Tn~a*9
zNXykU5V%ld8uXo1Db-BkeVhQD#JnuM{>)P@0^I+SF#uR}Cvc}LWN%0p*@%0a#UNGi
z64j4S5;YBVsF!Y(U{q15r=V{u)v-)JD!f7gVXQSyfRS%*n_K8?Hk|`hVB4Yet?+k+
z?3t=!X~YI0RCf}tkTRUfgI`0f#3zEreS~6QckawHw1nlgTK{d+JA!r!Y`NIyieZJF
z_N9V(S4y(4lx&o=T)kJ}x58U(Ai<hCO9?0~44FhdvD>oVQh;lVzdZpljYf;3Kmguz
z3-Ba)1WoX6CJf&erkJNk3k3BXLxndN&^3G%J8cG(U@>4~5b8x>Xfg}U-b|Aj(Lf`k
zt58b>OKo=bpMTF(t<1HO#p%O6L?y1uTBQ<4U%XaIuX|(`Rw&B*{c_;blkfbiPd~Eq
z|IwF8)R@N-j7H->HF3LK21tJ%2Lh<*Z~y#0dj6Q860j&bw8c|#mUP|!q*j1%=6eJ9
zDmC$FJS9>Ig@Xo109aLSZ#*$GZ5O7;yxTyh_Cz6$?N66u;gN*gu_4P1yXU9JKLkou
zQ!Ys6=((Te62<~1+j@ZVp#4@w{*^OLr;kG#O5Y8Oh-L{DS47I=v}-iehdnyX{VZl*
z*L}JxDB*U+rbb>&uX$z087gXEk|OQBeLg<~afSBbxq4;y`}r$=t|EVk^+L^nEMFiH
zY@2wm4WUm^g-g(#^)V1<aA0v&2Z$gTa6r_eSQdl{>}IOIr8<t@*8ba?6M#6cW4!ZE
zVDpa>XB&;T>)BZki4y=nB4%<34LZ}G^^scBY_}dc?~>AGz%utZ$z(?+Hh9!j|8a9s
zMOgqr)`5tAw1yuj?gg-5JS_<zh60MzA5AtC*f~s$>;F@>WGQQl49`QF&?_YD3nfrx
zF4##h);*Ozm&|3C*)tw0_GpIHRLXqA8B;1%YtmVpx3To%t<c%VAj`#<=Qe4U#pM0(
zV>;+w*@m1iVspzVbY?%tYPaIo=$FEI<rXAA(;VwXzLhIK`i=k=cxf2?8L0+;Bv=i|
z*`;oRmF4kVSXe&;C)5)`M_7jGt}(#LhIutYAFQ*lNUxWMC!1JWM2ncj;Mu16IO^Et
zYaJ#^MNJGX-MY(Tvu~GlRl0nk4gHm8DdAt>sAAP{ARC7TFs8?P8X_kr>_WTLEH$ho
z0BpQ{{WCQ;K{QADl)ACQ%Xa;1Jt-fAK&#-o(vden63#$Hfdq_94KVe)h;=uZcoiA&
z+jH!@YZ?!@<IqEA>-t|p>%bpTZ=-(|bQ0o+FXFhTRy4=9w2<+1aD0D+uzQq%r05qR
zU1Oe@=Cg)+*AJv?Fu5DfW|tG?M@RIyEWL)Mq%GQ(RiC4huG8*nB&R$N=yR5I^7s{S
z{%k3~NpJcL>X(Vqx}(9wPN`v*{#eNIDaj=Nrsc50oF2W|nbtYv4LS)}PR|~6uE#~P
zn&*wu=7i73P;-@XH}!h&?1OPnse71PC7>Xlx=lK<8|o?oFwJYDa=lnl)ph<WZL#Hj
z(~rI6eui*f`#j&@5Lf1G*Ja3%f-YLphiu7EBS5v1dlB@y5E#TvO+;$#XC!Ubk?XtN
zyAF0Yf(t}7{MuKl`6LfwQQF$WC9-1POJ$Y<X%>&KA9Jxhvvx0<5R%Z}&&>&YtnCtf
zd}-zjlV+t1)Vv}@A!(q3pufqT4$%sSbDxwwG0;<Rw0`USO<SEe<bh>uRG#-cwS0*@
zi)|R+u+tYsZ#G@tCylOG6eI!#B*B%H#R#ldqjmOy%8QiY>WgHvhZXBw{sNv|MZvFJ
zbW7G5(gRkUjhl0FfNBe#<V}6Kk?_ePJlX#q|JRmHwRIQMt7bl4*P?y&kn&F&I_`vY
z4EE~Li1`|m4jo9le2L6}x%s=M@sM1!FGsn_@YxeTW>W2gCcC`6ZHY&T+|r`;&}6CV
zvmp(s4%)L6qmsFopa0uFSn{N^t0*6Gr0eB>yz@IOe|&8Dotk>^n-T`$hoe(7!BxRB
zxT5(*=u2H6WRBp51{pNJq8IxqzQWCD%eYjs@FQbuNmoQcmoppw>8!inJrqMFv%o`D
zf18Rf&V=REGfN~$&dB*@yySq%Zorv?A?NValnd%C#o>*11hlRVfGw{n<;r<cCs%sK
z>zWyZl?hc72;z?BWWi0Lz+9PZz0zNRIuN;j)<nkdUt)Nt7gq1bQBrAmUyz;IxH)?B
z`nfxQ*+!!-OO1!8+!cHIA<9qhX2nQRn##2dBh^>uZeH!UFzx?Q!s}*1hJWm%&2*{w
z?Bo~f{cW{cMAGHCJ9CL^eQk;d0bg;nb45%QG=&L&6$16!kz^`=(L=%&k=j^N-RaNj
zQ(!+(kF0jl)1!N7^g8qcK)}xZee3L|Gt(4r=GG7X=pepDax(KyTRMx!>01w_#HH2u
z#D15MYO!vku~<L#{k~fU$#fkiCIoZuXM$~?TjaX4p}yBNSE`medmCR^X<O0aIwg-O
z)w^1Q$?i0G*T7^Uk$tP{B3AUxu4vYKei8G??X_!WYkPL<{wOV41{eBWQS|9OAFI7I
z-q*1ErxUl1WFj&=#g4Inr(z61dKpA+L*pM`bVmu8I?TXObG^y#5`)%E{cmYqln4k3
z$o#nADWXvdM%iHSL?DA4-VwKmy^D-Ehq8&4DdtR(w`xq24#N`JKa5<y+rpTq*jYlj
zMzY-XiG(_D>~c`D`4p}`klePrG9Ge<?U5GSiro9QHyGM#)rmvVaXPV9eBFUv_(M(>
zu{g|E5fNI98w{`7>KTSb98)2uJsVyKrhrVx;gR=CcS#0vnhjgZBWh<0U+Xr4rdZ$M
zxL)*d`6RPvgT_A{hNaMLb<l3_3Q$-VjHg(|g+3h^o$e-{K177HPJT8}_>{HEXFblx
zwDQ*-AOls5D9K0?U=hSDxr2XXIAG$9Tv1XMRx#I0f#CE7dn^CQuS2RIn>XPS_eG)E
z>M7Nee*45%Q%Klj?asivaBqE@P@~*QrMYZrc(#CO(%^=bs`s8Cyjd5~{|pIh=l6NB
zuFJDdq}UtYKimH+Joum3HB{Z6w%XgoKKQ9#;!~Pk8ERVIMUPR2Wt6mTH7_23b&Rv%
zFViU>S<whzo8b|8J#$Z9J_1IX<VGV-ZWThHC;?b^EP<%1eckF%&*ZSu8s%xuBINmP
z$`z9+iJF1S&9KXdaVbCIpVPyC9(r$3A9T=a^I8vOc3o8diJD^b{fUqZilZ4yLZ)(+
zSCta(Dvgw|?<Zl*gH`TSaH13v@dyFfW8TVX9?DN~3Ktioo@(dQ@I2r)b4j%=SCp?#
z@^!I+k_76<rF8e;Ff<H*NWX$WC9eq(SS28bS6RkX$L(bwl6)We{CF&MI$p;wU4LZo
zQm6{|*U$_}h(!5e{!u;Fx?KMfiY0{>dd#?#@>n=RIrQ>)IzrKZzS;eGm@PiZTa!!6
zfAz$oKJ=r=bSJCla+^KPbRdoSi&dL6;lLxBogqZ-MVjg_2>I$&sU{u=T-V6`E=d7#
z-3|WqPIeC$mc0101ABU`-fE#aV$2zef#1K46lA_7$_5M?RCHbRm_^&g0*^c?GKRn5
zn`)Yy;!HV%UC7xwUNg@)=B74&z^p^IgVk&>kQ%Y>jYd7EP^06YM0;gpYk7q{v4SnH
zo&T)tH62-4ti%6U9|7fb_fKip)Y_jj$@^NM1NDz!woiHQ%^oSU@zmK)i-aF39eBSk
zlo6LT<zRFBQz_JM8)%r4FSt7SrWe60Ji$%g=mk>Td7aSZEuGb%BE3E5#!K&qc=I+2
zZFaw@Rc}Xa)xrrnst^W7C<3u9C7_Sc-u~M*(<^c{pQ#Z4Uee`_E#*(*OIO7=__vda
z6>EfkzrW-pftmd!?|3HR=iFjt^TPYydG~#%vGYnSe3P6^VefR;I~G>pdP-t*-t!}p
z&k<;4=4&kQ7Dj_<tI~mv6a`Bj`Wza&%kCJQckX2|rHi7B8+dmzy}|9Z!Iws}!GOek
zp{6v0&RJykr>;zpt7+3&ht${Rul$1#J%nmh6UH=5%>v?Yak~SE?#crars0i)J6(^K
z3YUaIt#{%3F0&D=JA(vdkH%f@k~^7p1wl{Fl?77LUICjkGLy64Jg%##m_D!%<CLR+
zWP7}LwPQ)ptIE^shu<3zzPIdQiBJ`pqB;J2z2o$p%G8xYm|c$MIb~5RF<<*9F8T_h
z4zeG37`BS+z6TKzXOiZ*@e5zDN<7Q6zs~z8zEw0&O!Z6iw%UGq^}3Ug9x1^WWcC~)
z@J7_oK2`)(JamNV4W?Uua6Xde4|O|It_xCrs@X5=%HFMOp?PY<m8jg~hL3i;bIRhu
zjWrR{Impu}0B7~5`{q_`q$$d0^WL1-e#3t9@ovoRiuN<Q4xcB@l$8J8yZQO7_x$Fx
z$#cw(p~4l9V|LF_y@gg5kM?NKJ2i-4mDegNfJ4LUdnDj6$Z%)#9(!!qa=_Oy?23so
zvAKepW3Z}9U=&ut$?b~>_VZOOu!=|Wd0hIB#L8#Z2^p-wuDg|E9B&leWjM~hkEc_u
z*v``3g}tqWQl19714cm7N`&iMU3MuNV0Ii;uGI}zIxaaLx_;7--l}Zl$w>#r_Dx%}
zCS;{A^kfQOu~Sx5><dhQUQUO)25;TSvZzqb`=`VzdfgHDU+FBz;ep4te@`PX0dQky
zo$%_sXd9^DMsNSh@Vmd8KV~wF@13o5GNn0HMAd@I_Xx7p%?cSNXekYr-+y;WmLbrt
z?p=7RU5a$6M1G;>QH$El5dU4c@*&q>*wd&ts;E0uE$H~)!*SJ{ZS&&D%kVtxB>E5R
zeaBDdD7FGUAD9IFm6&t$$!J#fyFWZQ_g??EW%a!7tYpe{=SE?}CW8W-*V{L)1V(uj
za#%(vmUE_vt1{C^X!ASj8=deG>9<ZgC0*Xd5eJ?cpM3L?VU>PVnDD0CaW(nD3WBX7
z06+#YJ$^ERBZ&mjix0h6Z_M8}xvAF~FznfZJqZ=;*-zEogGp>a-J4OB2u4c17U5j}
zTB2RDmMqa)cJ?3(YM35znYHs_y3;^iR}6;hD=W9tu~hD;+Qo!$+k(=#hFirVJieJv
zI5UsnX(jm&jC8U_qqBKhLo%3~y)<`V4numWI;`LMGci1L7;g7zCW$@X25rdlwnl1A
zv~R6?;&yv4UGGY4)RzJSH4#_gzY>rG=ZarQtQ`OnY~~9BcohX)yE^V^ZNFB*;!B@E
ztcm?(tl3?@|8gR$t;?TG$JSG13VV%iJ`lKD$&!f`-W9{K&u@h?MVlB|N2MmvUo8Ha
z6q&N48|_<Czt)l05MzGRfGxdd9vK%&f5Sm$)lPFILXuEr`TX4hO`h<To2w@5Ca-_a
zAE}(Lf}`FQGt-;!^FvnFS!3?kJh{K~^TA@jcyu?a!|@Lqkpbn>fG+UOoUo@Eobiyr
zJdET$rw&b@Ha_}Ljgm`SDKKL<*L&WR-19@P&eQNomjq-N3Y~|5JUp3|`FySbMHTOv
z?B_S8Cq<fc63@D0cBu5Dt}CkH#l?Yd(ivP=Z}55yz6iSaY{>d|!D3f|7*Cr@X+@f`
zh-&9zt?`<mNV_Fl>F`9gO5fjY;d;Im3V>vidPrysEDSRFfW+T--r_1MAPT|QUDc@8
zb>c7b$rRpmqe_MUYATp%tEf;wQ<wMtH~q>A@x7MMG8mcCixz(q^n+R<zx7?~q3^fR
zAMgNa*#$r*iWCbV3@qrpblY5k6^g80QU~hZm7<e`Gr>=zT;E>Ecowp;@Lg6YI-yhr
za(Q3IvPE;4KC`nsSID-cT!nYL#zWCcRBTdPycpWg-PIi9CCc6|YGwEW$8V&z=^e@M
zDwa}P<dVmIymx4}7ri=Mp4vI~EU&B@>k+|C06fbaY+l1>-l;=9zRKd{n(LsikG}MH
zt*T)F*5jxbiU8YA0*#;eDI5+Jr_?eWsuaWWEPw_tW+SFZ`@_2bP4+j<=mACKK;*!{
zi|Z?Y<%Y|y)VYMLYHO_?*b>WbjI{O)3;7Qnf92E6^wD}c^y<Qs=RQJjU*)Bs6}dun
zWMi=&;oSR`Oar;lp7%1~Owu0%*Z(rDCe`aapM|eZTGX{x!&Nl!JTe4k)EWa+SIE<2
zAF9A1P*VT<Kj^8L_M<58Kc8}VqCUL6r(_y`DwFlt;U(9HkNid-#a@<67hF`pr7pK=
zyI|AtnK*gf9cSJD2IEZZ=gx#NMOAzm?SnB{c9r}n%fA!*2swXv;k#@1bJw=zJ+`tK
zkMd%~YD}2tpPcSvHLqd1buJ$8^1cKg)m1bVR~h7uNn&FX?;3LE7M|oc+~scO;lb{+
z$X1_8n1J3IFAm<f?Z4RLqWI~mlgdGh8j0EPNqGRCNsHp)L{ehiSpdKeCd5S!Fz}CF
zX#4E0!V@z8KF;%!?w{8m|NLA2tFqL=@#pcN^ZJ<^A;`1eo+?pg2Mn4P9@lf3%*FPL
z3nT#&36Ie|J&gra#a<QC|21C135%H4)daLJK4U?+8#s-7O(Ll-vHH@*Ths>QUhw@>
z0fWmn`!u?{G?_1Y35&K=w=IH{NkWqK6#m7IQ{lH4z4RAb@ijka$75eXnqGfp-{GB}
zanCaP%BJBK!2I{Q3#s+p78f|Y1;xWE7HNQyP%c1)>+S*?$V)P35CH}Aj2qaUY*;5M
zUdwoc4IcWq^1<(o8)bj2!$q+6*@EbIvFXNm{j{El-&teod@AwRNw%s<?odc-NaUH1
z|6uvO#DmD_iA1LSnUx-#R?MGU0tMU78n-iU3zV<4e2@CQJEscVr3DB~ocS+6lVcSC
z?&d3xY1-6fVQpXWGD|dv_+0w2&6hIfZWXmdCrHG(=4pvc{)b%PIv)X2tjt6`xdY3w
zKq~5iMc}K1_Mi_%B$9tVIktA{-f%o&^H5|=0<WNOlZM<0nAj4bG@P(uW^`ts#2AaX
zST1w~Zu-5N1vlEV5+`LIy4Edg{(UtEGR7wIM$zJj!HlwHnD>WsmOm0#)vUs*cQmYi
z^#9PZ4qN-JW3{8#sxuj8P}%v`Y;9b>hv6Zr^}*MG4-&SILkFKUHSFG_!~GArY)W1p
zi)URYfZ&1}s>F8x5gwEE;A$57pKi;q>!w(Tu1(Ggn>$<3*F&WL*;nfug#f^>ND`2f
z*jJJ!d<qZ8vK-o;Xa>`5EGUc<_x6<~pH{xWW=x;FhFG8?`cW&2%=*l`UcAE!Hn$?<
z+Ji<J;$Dw^cj+2QtGe`T>RbmbfRq}rLsT(z3EkB2D;-MdvhsSB){T49{^f;IHOGOj
z)tlK*y*8Mt9CENm+Xru|-TH9dt~{`4z}jo+gkSvLmLq$0s2mMH86ZGoy+u>;i|++P
z3VvVL7!u4)ltfJg4-@o0*8(ZIcs}uZ;FG!fGS#PyIypqO2d<orGfWK&ZQo|?7WNy=
zn@>yvL)+N$MFK`7A~6)DZpMUT4rU|a^%P?KT+oFI0c~O>YI_PlGOpS#^*6|0(R{1%
zHtY{le=0mi+<YedkFovSjTlkbeE5#Bn(CCIH0SVf*<RBJ)017)qneR~GKjsBHVHDb
z71|unGsFdM<*w8Hax1|DOtcQcun$#d;ztL1oitiLNBU{Rxb;Lih(v<PJKGo-9^hqZ
z>w~*Q2*_ap=dJo_Ok%6A43XY6f*|hxr0K?>OJQ2Doeg`i^vdu{0v_<|kJ2VQo%Oij
z2QKdP50$X|^`4&cQ=~(6$o3*KDe7sGK_Xo|_MWbVU6h+Na-a3}=v0zt7(Ig;QXhFm
zWMoZEX-JGcP}or2<67}l3o3y;)^A`%@oM=PMo3^Cp0Eiu-t%V^EydIZ0c7zZKLF?H
zdU)QJ!u0&IN2CTfRo4o<vdwIRpDH6Z>Cp7K*9_E}nF?vtOTiqP7K31rZw6rao2Yb6
zoKOPZVDe6((Uf~h{oS9eJOp^O*Wtpm{8Np1W=v2ejbbh~opG3%k?8gH_-yr*0F!38
z)t;@)bKgsw*-|!l6^<{P|BRRO!Sog9b9h-q<&r*zkJzLeL2Ek;?tWaP+-%wKmG-Z!
z8kQsrGae4Na8!u$&|Vd&Zu7|^19knay8!U|WX<JjS;YSpL#_W~o=-gCGB*>a5#oGB
z6rz@Q((37NaX~p(xQPu=TK9DyFlchTU=Zora1B|#-l{a)&l2BEi!@|YhEkx5()(6)
zJ&_75q$NIKz&w2R$H^6jN1Q<ar%8$5p=14+ybHa<0nY}y-Q7_4(rhv>v&ju6A=(ag
zS(=aKjs^WI(iR%YM@xJ@1%qFpa$0ANB->{>2DY~syi%?dDs8n99prZz1cP*XjKa99
zbbsnxxzeCB6eTV6(a$_&q}o{Mmea^rHC53UO>YQj?R3hnA1qTR4MCK?Ph#WKAIm(`
zi$4FsV_sTbmor!QBvGhDTiEyKK1YF1Vv0LIRHR-7PTk1&l8v!=iyEd)R&u_YtT)b5
z`;GT=88Oe`w5hJ;rRY1M*UrDmX^5b>y{QPlRybqU9<cGzVBu@+%UDY`9|%6N=4P$K
zJHL0S39?ngSBD$q@~$vEXL)pJh6e*Y1aK|I9E&j8-;=MR3~R%LZl+NFO01dPVtyYK
zisva080dV4jnade4F5q=cU_Rlc}^6wI{Z3?%z>!B*>*#(WIc~W%6H6Pdft)@|MXy{
zm!mV}3`81JN>5E-Vq(xumdkjdWjD8>Gh#|dIGnyGBlOka{D4MM(KaXf=`&{umb&z?
zI84#|ylM$owuUL@b;GXe>()n?H6+A7J&ss)Zd?^@%(rWtmv~d~M1$S`PXEp;*9p6(
ztJnGUClm(5_G23|bcN5aWL{{Tx!$yw>NU6hpzo$i2j=CTGi<j7y?UbFA6zc1*X%(}
zBv<{m+9w8>@42uJ{vS(c{nym{_wloUjUL@Fx;vzV(G8<hq@_ekq}|BTBLozb(XEs;
zIJ%Ki0nyJuR76A+L`2kk-^b(r3(imHoa=hOpRec3&oni>zuC`HODzq=mdwOU@`P35
z`w%bC{?rq2I@gsmFm8Ul@0CM!7kA&@n&H?WRnqR^o2u^Uk~OQ|`(XD!O+C>sUq`bi
zfBrHt7+o?oYw_AO&)fNO|Juv9lPu0NMZsG-(<@GZ|J&!YyU?o-$Z^Pb$^adHPiU3d
zJwWIa3nO^x<}s&J{Mzc>M+siBo3^SkNZ<}gTvTTakQ>|SlfxrD;M91E_We4-TKsZp
zxYqilq+_1ULhY}}VP1jK29DB%U<!p1b@_D8JMe_0C(n1Hb?zg2_k5}?W|kn*E{ZIB
z@4qaF|E8El^kpGl&{s{7wpDWlB^A^~=UCnbG;fa7O$?2Mdl{m82SkcG$A<4CtbK4l
zufhYFdt2&=m;5aRrL-N&1nV+@WvtcEYMB+ko!Xffz~2+nSBnW}+!lw^y9Lv|wSes^
zfH-Ke4Ejb_)8Jj&f@miUAguPhN=9Gm&D8gtZC%CFD?-MxaD272;mm9TA7B{8fKR1C
z0bKN>Wmqsq-W(OQFY?`q*&Nyu?{c$JfU93CXqdB4sAA~%^*dh^|BasCWq)^>dGT44
zZc#pj@^H>$R;-tQt@kT+S;>D76&LM4{=q5t2#ACik~s!CGRuM(K|#PcJXX0K3&X_1
z5iaRSGA^k_#%DqBn9|XHX_{L&G+0p`lUL)AZOZmISdOuCIsZ5cz~^AXq}bG`Q#8^l
zanxCO2rhwvjyg--RFVchz%-UW3JOOE1U>A8aoFYo>K|dvR9GTU4oWx~H3k*eV}!)z
zlO7f5y3-wG=SuM8Vn_tF|1v&d*uNRn_rDehOsjuTEqb0Zuwj?m*s4U+uJ{Wl>DsEK
zDA3&8O7He`yedmGS%4GLr4`XUTr@ivq%qvANxLe9(;U$}A!;Emw4}x{niG#qc7}XD
z4L%sr8Jx+7%uYh|RQon6-!0<QAVbGM#=s&42A>gTn1;GXxg3dGPU=&;OUG?zFl#>H
z-Bp0e&C|Hs&<@aXJbWMydC(Y(2_V7<oFEPWAZbI;SP+5Hh#ZnE2SzZD`<i66i)Ojd
zgQ^m$BNGuilGmO|-!^uSHV(OCoDid${YTw)?|x2fW3!Y_%oCmL(`OTw3IeLnM%9h`
z{&gsr(Vu|fL%z6~tgL}s@UVm|QWX3+i3078BN<Y#1Z3bJ7N0C+6W$ds(C6V^)_gL0
zfLU)3Xq{ropf31Irgne_RG3Buu+O?U8VeBXV51zRv*K*O&sgGH#k+95=A8Wrsz?il
zTpCW@gDO2#+!z|r^PB*?ivxh}Sb0$=2dDs*PCF{2-h@>&yW6KXPR*Ubg2}EuZtlw>
zjO&IAluNNMmuYO+bA*9t4<wr79&@aesv0~i`>Z}8D5;75TEt_ZWZPzV!Ed11e_y>!
zh;Yi)s;jvoqe-H`K%S+tm}&`)Yw1rK%!dfIcXr!bTXMpN0{5Opg;suYSHTYP!4p^z
z-sz11ECxj-4xx;TF%mD%KPfE9c4!^V!U=F>&HTtUw<|r)ROL>9;h|%wDBzq509c?%
zH5@`fjg$oB2-CK%Yx(F4ZMpPFKt{qfr<G&s%8#twe_AJ;Tj%_;uDFodf*N-d8cx2-
zl7CCpIfC|gr$<$07}aKk4XJ<s&7*>I5tAMi>?spKCO9l5DMGE?DlJHX0`4}#5yxmi
z2X}o4Pw|$EDx|5YU*hKCj+Y!Jwr0$LMo~2EDEgFeKMYzfIR*>F<YWvksH%fH+%JnK
z7;R|>7&HP_Ax+|+Zp~TLWE%+6oC<?xcxi=OXoXt~0J3gE8lZh*xF93`eO>P}tOWZ?
zqVW;ifHlJ@|9pk?<4zt0Z%XsVQ1c;u^JUWfUR0Mak(v4MYQ^sSrh<(`o<)|JV9;<2
z?X*(VjD^;$POahMZMlVsMeyYk3>Py|@qD6tc}CiDIP1z#>bfCSZC=Y1)j?byF$E)G
zHFOHkAm6ClwV)lBl!yS@;1o7}O?-Q0E7OWL0n9SLWrc2M3CI*KD-~kePCsuZf0v;M
zbsRz*w9y6>+?b6wc(#$spuccMc#56nZ++^j^=+-^Z4pi~kxo~moGgA$m!J0(h4WVY
zTMsv06?A(Q<ov1*csVbd#gSO0qKcbvgd_aZ$?!B;R*KA^SoUY6a-?1;ZfNS|s$-h9
zw37mJTKBzGYfJ#-M_|qjKq_@g3mt%zU{m+KGCM_01Z}#z(NPFoF~HCzmO1wkY_)0?
zLXd=9QM0fO+n6}pTnP-c1xzM!k$T*8?5l5No2nnwU*I#b*3~ImwA~pRlgjLy*fC}4
zjs6sYNRBf1LGwvXrNAudlL7iU85?k`f$-Jb{ry3@(1mMV>sqKmRzrT)A)z#5x8iw&
zu%v|<rFFvj`p|QOR`blu6$c(G`(H}0@tqEg#?3j2#mkSm8#f+ljAy9Lz(|@6o_8o}
ztTCtr))2l&=s|Vq>oj%O9ie@_G5<ZXi)-mi$>@V*lfnTL90;Y6g2aONg=ld!7Bf>>
zEe1{tRuANd9Dk`;+n>MUz3q{>?~(h-<Kcm*Oti7fMzNugQz3=RgAnFPh=)SBN!aUg
zmiB8V5E!9^V#W{uBBca%LkvZo(a*wF&>}-yxKWhJe;}eetG~jLoqrrJGm`-FWSGh=
zfFlG!WivVZ2`IhMuAA3ru0d$*s|rbruYOc)#-D91iVVy_)n}g3yqB;g2)iQnQWeJF
zWq;w}EHo<#9G{D6o~qc`GP|}G;|6Wlz;RU(%o>DVb$6B5v>IL9U9TmPf)O%2ks7Z>
z^yx)(b`wzsQbyIR!Q+=Y28N+8Cr1ndN?xA>5W`Ve+L%G@vcaU4<W`Qm!_>r|=^pV%
zc{gX9PaLx$>9BCxRL=bjd3y>X`5GJZ0D$5;T1-vZpGIoY*3t{waAo2)h6!n~WpFm>
zHem7(*p~x`VIgR7I08|J%%R!ge0cAhcjw4k%P)?5QI1a!-iJE*B{}=$x#-N_a_S25
znAQ643!u{$Crj_f;*elzP?Xm<d|jv*5RZ|=+oq8K%5#fJJSF8HCjgUh^#wC`aS8Ew
z?s;JUc4J4EeD@=+<XloL20a3pAhCFIO@Uf65eq6~hA%_ln1l}^{2Ng(Mpxwe-AbkB
z3Tb>Qklt_`E2Lr^)eZ;XB~t~qVu8xCV%XSLNp1Sfovo4tM)ME3AGnK2i}M~icA0lS
zu4eDjJpm}%$IIpMtX&q;#JqX?_0STxBr*N9flu`^ZgoZ$j#tEir}k70dDos<EGcNw
zE6ROPvVW;;;OJnjbofB^;b5Mc)C##1HE#)B2B^idFs}hXMC!>JCM+@oM-H(j>}QKF
zS?tOj(q|nR;Y3vQj<F2K2}D#JfEoeN0whmp3PBo&Ae&aPzU4s0-l>n5-g~S1XOJ83
zA5DdXPKPAT_+@?e`*TOT`?Mm-qvdEWWIW1GB5obKTQwL5vw56xZj$DiM}c&uLf`(3
zpoF~)HnnV<+S0KO!P;F#T#HG0@rw%9{y8HCriY>jcWwb3%6t3d@b+F#AeC3pLrI0H
zN?GcVdFDW=QkthHb9QBGl-O12elJ9vm#`=X5Cz*^gNkO*_271rvgoB1w`N<FrFZt<
zDACmCn+G?3qQ}ro2y~_w*_F!#7ReX!-5c*IS^cBi1%rgK2{orn(BCxp^r2kSs^QaA
zox$&66-;&zLhb+=bD(0etvNY3YGmoC`hvcm|D-Rk&bB!4-|9I8)}%|pag9~pBF$|b
z@wOxTbni+`9}Qdgw=dT_IFhu8*xHZ$dGMT!ncR(SFg7(9Q>G08u%Hb#ID%qf!CQ2F
z<-N^fh(YAY+2moqVx*i>q>i$V=<M66dFGzQubZl90k!K4vgM^npS%QjVg^i_GGkvq
zjRSzm+b?gxGTK-6_=0Vq#tvVydD35h`n!$C<6wlZ<jY?viZO+YJUCMTqjP~7SP0c5
zpDr9<XsuSCaFQJ8ImjwNdD!?ScGc=A+o#NVB;_l8Dt2b<mQ3|6ql~W}nLBwu_xr1F
zl{ah!4A60>vrVexB37vuuY5f#%KgvOjz|(zEkEXz@eb~N9ri?%c4B-^b}*lEA#vyx
zL-=lIrci&X08nSK{W=4aPhs*KH#YDlY))E?>8-lF_sw@F!G`|lZ|QMh?zx+JywH^x
z9Dt133Ghgd0bpn=im;iq;~U)4(o|8>iY<(ppTxiOG(?DZ%&+qaD3<dB5M)^tW`@!T
zrzyV-O0N4aD{5gm=(IZ*_TOKjqHFJEsWl?w_9KoaPOPir<Z9w{YGt|n3V*Y|{qZ>l
zq#6e$#D#TU+>0kQR4Izc0j$zB$1CJ`9DXL46%hx8Si#z_J3B+aV130T=TFfkAG^Oh
zxA&;)*hw%FHa*+O%Dqj`NKAhe$WuGT6AKRGp}Og}=^`@pQ!ukDp3k1ASM!cJV~yv!
zU_xQw+I43$&-vSZBcK<AuTs}+M}E-#J&R_~V`qk(DKfE;4ygM|T}${x&zA#e>$ZM{
zlTTTl%l~;R6d;_!?BmrqYVN!F5_j?MUn}bxC@D|yNjiSr`7|Q+(=pVMKRUbjPV(te
z5IJOma)e%w_X@23xn%eAW4-;YyAKQUM-k;#{K+wj%Q3n}=jf@7>R*%*F|cbiS<B8i
z2n)a-^9*1hK{-G$06;OEC&==FKn7KwCaFejmmni=6#FTc6#ZQc1_L`ZqlEw(U<@Ac
z-EsiH=C%7|&AvETAER`B6Lk5nP-mC<Jj%6zt==wh{TS+4W!G#M7`0*O_|)yOQ}Bz6
z7iLXP?JglN;|=Ry1oybzeEB<V>V4$Ewa{1blOKM>k6gd?>XH&hkuqjer)FU|kZU^V
z7|Igxqgpp}#?JTYCo3KZ0y6G(%eY$xw+?6_=G5FIIsu4I6>u#K?2?Dm-nTD1l;bqN
zdx-Ls@cHpa0%rhta{6jZeun5*>{4ya0%2(dMo8F(qBG<!*7G;}(O;Y4#;KP3ZnA^C
z?!eBjN0z@4ztPXjox2RBZ|^_eeRk4NwZD|1yNm&-lF>K-;fp+K#2C-thVwUk`Uw-D
zR-E`F{MAltm;Vzp&smk&??T*2eueDa@#k(I^0m?LPD9J<%y3zPrk)f$P1(ZXypqe2
z(S*hW=v|(1N!q;1iKbKBbHP=`iYW<Ouy#kIqHletSy0>)CP`{R85#^S3{av;r7??n
zNCcrB89f1NXeHd`G6O(STeZ#EY(IE9Z`*uNyE^DW$>NCd0j2}UILynl$|<AL>@gl-
zi6BpH_pI-oj%txf;;NT%swRb!slxe+xU60|sngsSw}sL(?TO;Fsf__w5hd6cje@dp
zBEY9iW8$nwQ{`!J<?4EVmX5B>ca3*COF5QJVy!RlwS>HOeA6HL`sJU|ux%&a*@*2|
zS68B%4pm-+j<9upWs~lWTbk`)uPJ`gFUv#=nfT~uka&;$yS!7_y}@#mS1DyCuZmTY
z(|Fe`OL5AK(ZQlht1uy?s^8PwDk=>#18^erv)H&(k0yhibZ1(|GWc&CIB`2P*Bw7L
zqCPapz9Cn1{M+zJb3INczx*G70WC)(iDM=i6uS5*WcnCglw{?zt}Ow&k(}+T)EFBY
zML_uQ@k(wT{iRFgmn>gDF0miBmME82-}nHf)ij8}jABBff2-{cn6P#3%bUD&iZ!>T
z(9KpKrOXS3hi~_W^??*E)^vANEYm{XLO>d=2hR<f+wKjvy`1=|vp}6j!(44|@P)f_
zmP^UVe?QnGsYVQYKqgqPYDF;mi32n|A0GrgSBhu8S|b>E)oGYNXG`?e)b!3pq5w%~
zn5r6nU=@wM*GMav?g@yhWH&ndGYOE~h6)=yxt!nLc9~lnmig=zE4xbi_b_=0gXV*x
z%2>f9UpnlvHe8oO1UmQmW;7bzuT%ziBT?PBn|VNA`u^B&uIXL^)IPD0*;yOW2=Lat
zWv5a?Yfq{@Bfs1c^goQwpKppwNbc41jz<|67xD<-GQbsmI0!)jpTPc6v8jMP07A#m
z?nyIa+W~EiIxGLzm`y;*zxIB)inQ@wgYLOmh>w2z)#<l1-TH42^AE!e7f4?#*EFsg
zH^Rwi4H)TJyl%jeqDw%4hIbLE)C$x$13WLDuF}6+u{klIb?EGg+~2YYm$(tWrQGvf
zdfTQY%34lcLU_!oayvU;+QLt!*%-XbBWY%xDNJB9w%uj&xf@O8;VF~p?NCwEpe^f;
zt4A%d8!%JwL7qMzU^kk;MNOgcY}eXi=;UlK=G+*oUXJO@NiEzrh&L3DYf!k9PUir)
zli?N^KvK<XnO2@eMa{A|@dG~pGM@Q<7w5k1OShI0fg532`AC*F<9!9_EfFt7dmbIm
znNgG~b)z9=$aao9DIi#D5CxZaAqbDvMm;LFec)jB$xo(keWc8eBkp-kuA&sV4o#24
z+|uQ}h8#YRuGeoP&#qfq;FdH%kGvEr!t`^J0C?Uzy*m|wG({79{P)D#H?wBFaV)oO
ziguNAt4P0vgc@l9a>N}1eg*-hisRcAA-fweYtb!F%?O?klCKBNPmkNmU&+S{CGxuz
zuMmuxa;=Ni{y5fb#2b^s=+WYqcuzpr#D7uH#5YI(XyMmXgy5?zt$PJ^hHm(m^A7MV
zo{ng2O;^l8qVG9SoM*^07u9~x^s>dYY133~({;Flb?BZFh3LW!L4yB#hj3G-Ybaae
z@Hbwr{b7a53(0IB>V20UfNP!L6C^P6L+7aJ&Jd0MUU{DGiQ7uae9y;Z<7QBofd2?7
zKfn{9M6Ll4ye?HcdDV|2k@pk{g?QRUfzO;HW@O$kLaQmiTHA&KSFr5056V0K2{q^H
zO%r5e2kWYX9wSro$LNgmUth;e_7EEO>8zU)`pT7G^Y~QU&inS^>h#7xBx**VtK)~7
z-1%zx2Dq(|W)W3-%FE4AbwbNp+rn?)8$utu%{!q+u8iTiX)SPIW_jPD`KY+qp;|H6
zEclhTC+n+@-ku|y_D_&NJOYQD)7O(>;Xidg1Y8_->J>iQT6n7~yWitF<Utl{$S~W@
z-w584_3t2pXI!Mj0qO2k1a@x|kXQ$#&!j4vu-#%*VrhueQh`X)x2^GuET3dng9|&8
zu1^LA>BnzbEgWEOEt}G9zTM&ye%#EbE@5vhwv7wlXuGdJjNDFp79OhOV|fR?_;|GB
z3guqaZrI`-<Ct%QyFK3kw^<cdzAfpPk((iJGJ2nc|Bk0_T`V-Yi?Z;96--$q{+FnC
zK0WThm6Evw|7JC6*aQaEY@`4=95ycsbVq-Vyu7bM8&aIrmk_w4!3a74QK@|gV`R=K
zkYvpd;kp1R>|sU9jVxR+X<=$|gk9R(wTV^XV^lfvlrL@hLd<ag4S%g&NJg!~k&)b{
z{K@I~?RA<FD|6hNy!`258`?`x4t*1Vt^h8)J+sb31ZWwuCUkMS@T+s`y7*N^(2IUP
zekTf3K(Et;HeBpWhPQ$q$pA_Okj%QLX~{$lJ!0TPe%Nw}O(*7So<z=5RX*h(BCkX*
zUrgw9^cqj)it&n2vFSx?&rBajttES!z7LQ?bN?}JGNYDA+B5jdqRO|m82wmM)c0ur
zPoPL!twmn=+l>MyFy>16T<<xU761X{_?kQ=FKwz`HG+fy^MEoj-t8YxT~#=93<Ov9
zx6^4MIkR2`xFJ4EseFB(?-Hp!{K5|`#1#9>|4OM>>SAo!PcAtCC3ZXjiywUp*td|=
zo#F!DDDRpBOqtt)3=|df!9D7ASz>&p6Z~i826~!;l@t6O$8;F_KLy%5p43u@0PODT
z*LjVKHv@oM4fjYu`N}B(eI`Sqfezdf0=*CZWs0EG0E6cE<>rQ@8H94QF=4$if|3-1
zv>iB~R1O1{6p&Y})KogV%-t|=1y|XiHzDajE4y>s<JspQH>>VzXE?RJI*bl}X;m6L
z{Qm~x(#4ObasO{1uGl-$VQ9#1g8f>Q)rp(_eDIHmprrs2@zQjVfxyjw6r+gX4vM}x
z77B_d^q-%?HE2(HX=SYEqO!#sQSc9y8FSK6eBuhO<d^!BvI77F4vc|=zZ?Co%%qWQ
zZ8QgHC>1+8t+FsT4ybdGEzWR&Uy=hz#O%9lF(nfL6Wao~ycP^e)VGS~4?czivp1>H
ziS0<xFJ(+#gdx1;*jG;7g#MkF@DkciLaVB1jE-I@>wr3`vh&yBT86-KGWqyrlK#Kr
z*Kr#rXds(e_r|VP-e`ou!LIsD^D$}7%C01DX4U=Q)#uEUDh^9UmO!hTgDcdrEg()D
zF9<i=Es@Qni%zeXh}bF@4c3%xYoIq{>TJ0EgYbBR4eZAteOPniHPB3rs1_#zbF%pa
z&|d5#6v)b<pm~Ej9s>yJJ6e9uwI2~AGXJ_IeA}(xw>9GS-#uaLcM@LGG&suM#t1$7
z#D$Pu4G4etGB}DaKq2%7K-mu%lbiE4;xIt$Y4Y2B?~gjQYS%JLm!%1mS(Pzu;>G;m
zRmzGL_CK9MF+|W2TM7w~)uU^a4`U$^eXOPleeUEs4;Ddg;c4{xS6-=Ed3aJy$Xn+_
zfwffLu0ipL{_dOQp>KMRxbxokxAaWWrRd5RRfV`Q&>x?=u_hk(A%9u?tsFPGLXqO}
zobvD2uPuB>hu_ttTIPngr%nbUD~>i@zPq2jUk=OH;X6Fdd&TrrI!0)O@I#T;Akl!x
zqmunjEauz{fQ?4`1AxtD6xL<!Z1MG-@$c_s0$N{L257pHTc8M%lCfBV9)d58yofZR
zzi5>B!vAGWLUTu4;jD6IeSND~w(CnQZM5paFU082THOv&{53Ua7Kp!6fXo*L8#3TV
zR3{%CL{ka4S}6C9GPU#FChT8Q_85~@_7Cl6r;>jGsu%}(I;5TxRM(2?=WThyj@0dl
zb}HK`=TzY$#<W^%Dv24_7G8|BU&*Dw;T1QrPdDzray9U3y6Kw7Q~dDu5HU-_*gd#7
z_Av&KDD^(_pqZ@@n<>++P~ZwPOuYVVvWdYl`>|L-W6J&S>t9yB(<TY4*l16OJleQ_
z&O5%5LaW;eP~vVQJONsIM@6V{NB)&PRFc5{2Y;7jP19Mzkf3tOlEsB@P*ZKvboc=^
zumON|hhz|#3I-E-Mo{P}xFEGuYWkPEW9=W|_6UuaGYT<+brazH^-`XB*NWV<67>Pm
z4_ozQO4v@;-(s^OfxAOF>f`Ku<wuM7>5Jyu{#WlBs3!O1d@3r>nPgb}F?295)Szh<
zxLmo`p~=&A8&xzqy724E)#o%n9<J?xpV#!nHrQKbPsc?W`PIi>OM{4JcK7Db_Fb8F
z26zODbpe1w1qKH|kcAdZ5KHrGY9`NVj=X!-G$y9pYyf1L;sH^-6evT($Oje}dDsJ6
zW{x+cmn|IqLuvY}S*QN=E@PK+cxEu6auW2LC<6Wa&>f6XQB4s&Jv+-FoCZjW7!o)5
zkfcnwZQXb3hCcZu<~PU?v)KxKs-Ei$u5WD$q{U{lgTI_vNssrU*`;yb3j&>1PB)@Q
zTl*5)9S4SlZGg)OVQmY6I#|ay!SV6B&&*cC$w>-^;j$<YtMh`hr2k`?Cs$0RO*Dh^
z3i9Me=g8nx3ix&p$NBS9rbx3b#@IIdYIc@Y5H1F+Fu@T-j|P;-D^kEI_K|Y}dx6({
zJVS2x6;<?}&%GKK2TW!3Ol`G2;L`8-P*GHPOmj7{{U*%>2d&8HcaOK-|H&Mu7f#0p
z%im}YEw%gWF@FxE7lpqJ>G}q<$f3d>8&QoqOJ6K9cc}2Bu0}-$%=Y!Sxi^5A$2xjx
znwyq<_o*SH)3FQc>U*2t_-H4c1=e?Y9AiG7o@#9L?b~-Nne3{6sFEjFW-5tZr5>K<
z{tn1xkbgGQuE)UmCNl>d-5pjm|4j0L^eThJWV(zdUVj2njxpi_k5W?GFC>B>*4E5#
zv>TZ|Xbycxp#7y<n-t=?`B{URo72^-n?4LXXtaSOuDJrNVE~j=3YkD8C!^??8GS9P
zm{>S_UKIW(_Ve$JCDyR(Odf#fJ*R8ke_|B-&w3EHP^o*1nR*ksfzr65mXfqQmwM+t
z=gD|RZ(e+lO?&qyYoBvSzkiwEsHa}qILqOsL)Px&mB&Bc1vc_F3h!BfTo;sr^Kt^j
zROgfq)waWF--dg(T)o1y@~fW+P`Z#1@ikr(cGDc0OcTzoIm-Y=MywN%a=~2RVvjEK
zdnxSXb;bN?Ko@<WfbSm59js!W7Is4t0Lt`X`XLm7qYYA?RD_CZRUK#F?jd;4q0rp2
zY1y^Eer3x!TbCYjVOQinxIcdo=~9`6hN<1`@=f3AKZ|geX%pGLh&zRkYP#Lh=n``(
z@y-!h97vr*^ImDJxb%+Gs7|i=nAG_00U|PhJM&F^cM5%vB0zCM<Qn~g2d%uljuAgM
zSW7Icyi>}9T>l}Z3jOfZTi#3=e^l27=SU_yqQ}oekwhXw_?Tnat0&NU-|=lh8ro6l
zhU{Hqf0luq@Bhpl6CTI@`kQ}Z?HII`wLHKB1UzJ~LDA>)*Zl@9D`wXPmpce<V^d!7
zs#TtzF#VS{s5vxa_HRbDL2Qe3-{q#;J(au5FLjh$E>{EFGDn`J8$O!pqQi7bTbPS7
zn0zigyOAONVWyi~&VS2!Ftps`Uyh@P$(M4U_8+dCp92S~k00fk-ds}HTad(lHyYjW
z5jbVVMv*B6#vRAEJ4=S5BPK@e1AeE*b+|J9i+@uCq+N741xl!D<JfDP0#E}0G~(kJ
z0E9sOn?b%MjkP7NMz!a(YR`<ios9;(WMdwQu5Md3Yc2VZSyOm>F8t8oGu2mEgX09P
zY)K|B)lyO$<*8U`=L5?BsE-S1!usxhRiVk(dwS^mDJSZ45EYI7OcbQil-y{X%f;O+
z$TzCZMz$$n(d?9Zyp26)L3^`brJb6;NAJ3ufc5N*m_*W}MugW51U5kLtoVsJswgdl
z=LaNF79?B-g3d`z$jgYMO(!WcagGPe3(`JdyA1T&9Cdl^+qQhnx-ZM=BKU_0+wvk}
z#cbjX!iP@bJF68uJ8n$grPq5Bv!<u<bKk__xoR5D{`}wr_s+-*hHlNq)O3n0=UmU<
z<LO-xw8*Z7aHF?dZcJxEH|{y{qD5GVq32dOO7T<Z#%HZR!3NoukN)hv{VUVBHF+-l
zT=}=j>T8*Fk)S+XJ-wou?@EGwKk)(n^V+^-&U>^E08P+SqQIbTo;hXyu}k|YdZqT5
z{eZWBEe)h*<^-@LU{{_GsRcgFqeqS*iPf-VT5yK~d-H_A%`>sDS>u8qA$ro{Ld60Z
zbkrdfaKtF=%Rlk&_aq|jOW>z85X(1njKcAa)(wo*R@st|?@1bclvFI2I%MW-e@2t{
z_`v}qOZ}SE?Dy=6qw4@Y-x!h8Yd`@6XS$KhNjA&wkn;1Cg~Ro!gTvnY#UnCU#q{)m
zG><EDVJQOJ5Wzfv)CspdqDM_2iF^WH-+@Vcl9+26jA{B7N;>D@ZkrOT)Q9KRX1^Hp
zZtbz1?(AcMi287HyjxgMAu6c-Tqs|7?KsZ5d1)j+ZtYftQpSE;!)0ndmS)XSfw9_2
z@^4CV>MO=pm&gY0#ZWiDLf7ZLJn)!I!LbXQx!<|bndY8oTeFBSGa}#KX4&j++I;9P
zI5_cs#t(Mfdb0nT;_u(@fQINIxF%0zp3Bc?KuOZfLXh?i3E&|uAr@CTz1qoM34VP(
z?=&M%+_qE$r#LvYN>82-cf?1M28-%Z+X(}}I<+?~lqBDqEFS`$N7*pwti}sf_cS)M
zeLB7?9YIuFEi#TuG(N>dPFu+;K2rbQuCbnll6Jq9qb{CPrO(u2Y9T9~`N=eF1{Kw2
zqTEej^f5+fN_Y25pAKm#u4}<QtC6Vr3_{qjB|i1qz5+`=NIrgglFUS<=`MXijSeg&
zr>GV$jD)5LcKWB4@sS;|C;*InNiDNVP0T_P*#ZQP^L7u?=RLNKm#tWam2b+hLit(k
z(4Xxb)ON9KW?ibxzCY3gZN1Zth!A$KOM|s2yXMbVs-Io?8uRm3lt55}S_|=9##E;E
z9NAIDWsdkA49=ACO~2K|ZPTLJOpk*{Zx|AO3(0m)R$K_K@vpY=`aE*A0AFWVyV|0+
zS+Jj-uWD?>%CGd5KmYL7t>c~o7zBJCtaQ~Q&*HnFBR`orAW)k62Py2P2Z?`}MFPCY
zRYpN+4zKDMfxVdYAlEGLtzAhApK|49E2&B802JFjPcY0%7z|v{R?|`vEaWm;P4|z~
zZNpTklL|5e^wB{gN3rhmkBLz|Ij8YARAX=SBwRi1Tn*tjZ8eo@YLR5ix0*IX*{6HT
z;vYsoLb0XXIBPOlF|`N{wt&oAba-feb40;Vvh~$#qf6J201Gw&0Ch5AmEZ1skRu{*
z=u05RjP$w#)Jwdq1Blt9XGv1gi3ed`dqZ-V0BEKGJ#hd@#Kc!!8U%$oIgH74Xq<~W
z+s%8NIH-A)D%qEeBsrMd-!ga}>2Aa9jm4DOycbMHK^$IqIa&Zj=||%)Lbnw;IsA&8
z71EP_q^|WwC+QM-Kpq40aA%bU4~KhN!PJ*Bf!uf<t$-Zd^Fa;P=DE`14fxBaonRg2
zRb5%sJ<nxYa0%a)2>@iqs>}8U$te1&b%{z@&^I+Fzo&^kr$cW<CjYxwz(;YFgK$%%
z=#?LLZRQ=9DmhSaK0^=2Dm%{|Ck6n)gI%s(-*-Den~cW|`l#9aq^hxwEPn7TsIdj=
zlX+1ZAQAu-pjRaTaEyOohdlokUhl{B#=90mO<WzfmBZ%0iTB;O(dJ~T+LCdODH}s?
z?^ysC_)&_e-JIFrhnouezeA+x98)fwyw3Ss%;NPgw!h^8DOPq$Gv8l&ECpGajwhFz
z`^MgpAYbm1?1n0`Z5u$R9+0RjB=AiiNjbWuL?RV4Riw6Fc=p(0-o~G}gD);ec{+Xb
z+_Ch7;^sLwrPPkRQ(o-{Pk+ssdX)mve#JB)G~b^|fXQ{fE)ZlA@JSK!K|M|tmfk3Q
zoIT(nvgf=>Q+zURx0mnXoB~(Bf>W!r5ZZ|pJJaf$AQ+()tG=&PwfBu)FnPHGpc&RM
zHn7)sAp&!BzoYE!o@n+1?Y%9N4}9MGY6AYr{3?F|&@M>01dMzD^FcCDhaP{P<G=4A
z8xdvs^H4V<pY^Fq(7U0NhVOU7jU&GrM+v`+x=9H$VOxH|htQ>*8B3p0@o3GQcKWd`
z^fX3IFGJn%htqqT=#vaiyDW!(w3UL_ou4V(G;}WCy5bz}$DDL%vi7)Kc@W9)F!t4P
zMrjML;KY`Gb9!&jc<BHDEYn^Y<Ad5I^ezFf*6K|B5(_hK-`*lDI@Nu<nzVWIE|bpP
zXnRxPivMnZvaNkvxOe-}>TM1bHDC2x)=?Y)nq%6rklSgU@kGuR-u02RP|D33ly6IV
zoby!h?N{GN0y{6kV|ftb9!)YWH8E$n%!D7sKq*KRq4F4sIsxx;-tGD3Bm6tE_Z#m<
ztcP=A$#30m(WR4WCXe^%pf#3N3UQBJ1wb@Z==n_Zlvh4A@4eNFRg=NYD7U8&N9$YS
z9$$oIWrX9r&Z6r+y2FRu^22aGl4|+sPv({_1fKx6A8r4#D2<VJWK1iQKKokO!iH$L
z_cRa)^;Yba=|P0HwSJ)**E`Dnoo&-2pf+GB`DM6&u)A!gtvW-lZ?s7C<%6=JlXgny
z+@-?4JnK9E6=ibpr;$Y3Va$C8TJ8ezng)|DkvPsf!9Jf$Un^^8pn`Xad?x1rD2vQ~
z@_MncB%gEYJ~k3<1<7bUR?>2d?T|Mb?ibXy^sjcoegJAqcs=<7kS4UAwMgQoA?OlZ
zAa5+M7IsHaF!oD8&d*9ELm4Qma{ZdqWs`~qWQdD$S;z|JN*6`U`_VjJRm~N|4%REW
zQ$BW<XC0-_rF8Fsx^vKCq_tt?0|TWyNo)`muA*8o*0#DALoaP6xxNj&=VYz#t*Xy|
z7FVzm`?JK~dLG5|;5t_lokl7E+G#AEO?b7EXxPw{qB$z_<l3tztJZB@LYmFh<FFK*
zzV2BeIN{#dTfc?3G79-d<a$13w;lpDzaaU@yVj;l&*aF)MXARpY>5`sguGKt)$1LT
z%-ajQ@+x_=Gu>Vny9GwE#7J1xW0q+IX(>Flf(RV(`XZaV!X^!6gr0BLmX6-vcj&4q
z=(?2p@9K~7pe=z#b;Q=>h`-F%!{1|KGDG?mBEz)gS$K9#?$h7?&M!<6yqA^+<~`4K
z7a!U+xuT?BSJ>A#al~pJm1kVxZm961+9biUSx!ciwSwf9%IjZ-Z89(BPzs2?>i3T9
z75Vg$Pujw{FEw9+dGxQ`^!`sHe~<lv?}ZmhF6O<j9q*<{@lC@00JvO?97F!2wes7!
ze=Cb`9GSTNxg+0_!)Ym=`18h)T*jrMRx<DxK#}my%6yDeDLOm&Ml>Y_eWt-KUd`t(
zEi_@mg8>cs<v0jjVa$>TTwd@&p7DDELKjd_{%9o;!NLLlS3c^4P%nm40y$Ru_D+Xf
z$={$@B0Yg*07$5nA^p7Jpfj3mAVA)k<xE8VzV0Doz|BGl_N+1f6Ka{q)S#NJHbj|U
zQhZeJs6g>_&1#tjFBxOnb>RW9x(~@iRGQs{i|W0q(kp-RsK8(s4723b$&1S>Hx$F>
z(-UK@mHpPAi3Vdr30<Q+^b~2ukp2{)7jIhf;nu*?rz0)tqp$Ik)wfP~5|_MFhN|7Y
zem_pS7$5%-{aee8`|n`zf@S@$xU+kIT$RhvNLa=YQ0E{>1U!w*%hj^OQ0o0-u4(-@
z^@mb*WCvm{2hCG79$`#ZwQ&G!7x!><hJz9}x-1U{cQ*!JJnWU^ysW|87&rK-Y;4M8
zj)C>NdH_KGR(##`JLMBK<4>0>3Uk!!m4rp5xv!&07R5(hB+xD;c`A0yo{9q0RB2{7
zaGV6sGHxn?MsHuR*0R2LrS86FeU+Y6D*gS@$f*C+tdxqW-yypUK5AJjt^d@v?mGR4
zLMyYv^w3>vk~@vK(Tk+Uo1Y^HE1AKy6>3YVl$kcU2ujfG47YcVC>`??8-Qg`(*SvE
zkJ(NI_THluC>jtRMt(`Nw5qx(f?R3*_$KKBGv`hD(#ezhK9i_y7f-VI$DQ9?7*7^W
zx8A~tt^2llT=0aN&k--|2>B9!YhGpkP%u<s@y<6>vy3tfcbNV%0Cp1RzT&pc-euM!
z1$&JIW4cfBdKCyvWj*U*Pjzltlqo;7?ag_^=(erqFI!#D<X{idQt_k1`>L}U7h0*^
z4laDJPWwz}XI&JXW$7Y}c_V`oIDTG{k?L;2LIO=$myqpq32tI*$ST_a!4#7xITH(q
zT3@N9Ys8y|;(me-R*rZmkbs_YHk>1g9Rpit8L_0k;M>05^v?f<U~KZso-dJcdn>Ko
zW3zI%S}0FPFf_lxypo6VITIZr0g%x8*}%)Yoh(MqC(mYrwJMZVBJ-O=;bWZMtuH9K
zuidh2xux+;b**Bp&kEA9-p}yf|AR)6Pxan26Z0pkK1bs*-$qSz5+Or~>#+~|8(E@%
zT+NRp{nF0wI?i|&%6-{Kq}rosMm^${yj<`&``Q>AHEfyO(eCq|Fy4KW4b(OL-4*(-
zk@H(91~w-jTWlBu@K)P9kR^+1#sL6`9yL`H;U2h+B@cXNUhyymbeX>1MPd-o@26Ox
zrg4;_@7C!+NV2m~&4nDB6G6yJJKj?$=SNG>*QdzNSK5T%GOfEJIukNi^*z=ybBljM
zEtAn(**KF?R7A>fXb@m#cF%pEBA2{9Q9NF0EdAmcvcV&hrpv)LOHfM1Fv+(J4%ehL
z+^#uivCOC0%IGjv)i;(6EonK+Ft-O)y}()Wms2|5YGRTkcWZ7yz(FEku%%_~Tk93<
zdG%i51t}3{@D4`*^uJo7Yl=EKMJ=Lc!=ckDJT|PlS49n@9vi<OsAf-TG5%1H+(cvw
zG~Ou$JP`}rED|!sTNPpx+slQUk^IzB1ht!=Y=<&Mm_9^3e?ZMz)Js*P;`VOD%4QOE
zMfq%@s35K|^MxIQGFowJ4`32Z9HrT*$jKPh=idibSqy#sr?>tK{8a5f?oD)hKksh<
z08t>pGN?Xafx(8dVgNJBIU=k?Hld?bv4FwB2lF6py~n2H6Wd$$k_=&))d%M+KP`&`
z<h~JjA?-<CdUiMkBl&5yW$3%@%o<gBll1#FYIxVG2tx{1n^o1f=9YoAL0Q|~Y<H~u
zmV!rP=>KdNeRNYR)qHY$8u6dQ4d=QyF6;3>{7A#6hh37x?lIZ@O<l~b->o$l09fnU
zqqFE!>!zQPg(BU<chb*Vwm#OruyGXV9ZUA6nGS+I;_gW2h;nuQdmV~MGOrcZvB1*+
zSuqL9X^&7-@I?$*ya)gls{#O(O}%nracq?3hRamM9&)3|K^tfKNiN3!G{zbMTt&sX
zL%f1g0bq;Ogo6*9N(*U=`Nr)U8LdZE{i^hS`_Yo})`JzU@#dDUJE1@R8)8rY#vV}p
z`Bm(cmM}aazepHF)Cy=9&86#jr|1Y<sRl5JdS<`lp_iKvivlI_JnG(lB{N{tiS}6l
zMT7LCCXN=J6%4+3XIht&iZT~j^&Be;u&BO#CL&*AeyU*(XQNAp?8iy7K8Z!|=m|>Q
zhZQ8dhEt_hy81GHaqRR`v+|uPNaegi8a!bs^S$S`+MV9C;M%ZoQK2)npP1jc(Eg-}
zR)?w0R4PsD{E4YEe<43xYjvnLY~T$D5N;DR6ytl}-B$)VOp<vC!PCi<I5D{nduaWt
z^p;+$RVN#j4Sq*6pKe>*#T<-q^g6e1d<_J*dSXCJqG`7E++;)&SXfH5B5xdJ6RJN}
z2Zj0ReM6dIDhM-3P+`~)eqYZ>tp75V0T856rObzr7k11^MO<mh%0k#H+~JBqgo@6i
zdIxaj^Nu)ybJ`4>^uN7Lw9a*qRb_IOpcgp64E|VnN=8WdhD8xR!U6fNq&e35r+smx
z0UKexpY!@d5!2ebBWZ-jgF~mQHvj#wG#C>4;Yh>%Bq4OG&Gm@FQ%w%mVs*Hu#og=S
z!5ic+#!8`^Q4$xNz4SO^;@7J?P0oC85_H}d9I+2VvDZN~?JvRybPZ%Xa9qfQMKs}n
zyv#QKsOjqXL+C#=TR80SyP$?I7B`ikX&5$4j$q|(Rp-O$e#IK&l^-u!wk)m<_dOBb
z&tdR}Up)>jha+D?Ve*erqm7bCPg&QoAKJ5L>UFwE2EU>BCxmJ-R>#EOqcJ*EI|<T{
z?*-K+)9Y{)9d0R-nGXQ?Pl8><_2baqOzf|AKqcYlL#~u-V)+qya1huQ?nT>z!LV}C
zgz{%@msS3#XbebyQUyTlw&}4;8W=!@lCVu4>&Mam`V0Z2Sci?3Q)>X==z`p%lPC|U
zQJ(KcVV->f0Jongfaa2MmWEzovOYcLITsp;rvDqiNzUm96Kh$LeYhoamp}8Od3<vU
z$^aNLJVTy5jwMG901%E7ME(Y=vFH%6z3R6*T@pLPsv6D{x)iZ6GNTW865nZNsX<2p
z0EFknVgQglGYGX(5!1`^POceUDhvRWGa-~S0A)8fImR%$Hj4>pBg65a4ham2j*VJb
zag&Lv?*?Vqjr-@}FwKt{G0|Kc^kYIl$u;S~f^>BuS??Y;b_|eB%oOK<)<!H*o?}#;
za~w@ZZ{cL`YFFQk-f>u0U&+|~tPV(4tz}$)PaD@lE=v;V7SL8!(C)!%DWfy<25;bk
zG13LjfP8V=z5xZz$F!L1T4lF!$I#9yJ2iD5I57$K%QC;&N=5ulw)m4m@ke9tDEXJ~
zQuW1REd+rLLkJFw-If}_5i-_|a`!hfHfhw~dAGXqQlC6hwH1{U8opOu)9-7jiceFm
zE>!JX>6yRTvk$2L4r|J^1DGQ)Gz`5GNmR)XRV+9JMn$fq#Nvws=`2`<zO8f|NKMBl
zwdkhE7HsFe%WBt1RLf3Qv)Sz9>Qsws)}pyO5q4xEl%03+w6CzOZ}UBa?uVB9`|6yZ
z`nq$n5P$hbS8@(4)ZN}s52j8j7b09>c~~FX6Ng54^*~=aVY#Yx?rqnYdl7i45?9II
zh|Lhc1Lv@$Ag>|BEz-=Ou(BBqJQhHGrU~bM5|}dBm`0afStJqPk?B7hj;Uaxge|jT
zp{kIGcK}i>Ye-eey&`kqY593a8)5jKS6Y$V$OIK$3)-LEb(QvjP`7iXsyn4efnq`;
z$NV_W{QE@a_syRBhg`8cRo%n|;SqR&W|6YNq<pH#Avx<jE8lo`e!^fuIsZws_WZAT
zlZXOK=nCET;zPnZi|fA!)r*5j=QNy4+kRX@qsZd;j8^eFRljZNsxm_yByGk$tq-7R
zbfuWX!qgvPmT58MDMq^mG=@CKrl>)c3CC2kWNV4U^JL&-VK{&+fTsupD9LhqvIwut
zg`yDviwowUuBL2g5fRiFAF28pY8FqdVp&bv`So-Fpr`|302tSD+9U=5TT);Ee$elC
ztbMT=MV%CAvdR~r9HwnkgHo?aij2)cI22Og0EmOh?BYj3D=*9$pfIsV_g$yh_d+7!
zm@=|RSRxq*fHC69&KHf?UVgg?1~?&oCOCZ`u)b7CCxs3(Q&RQCXLN*OwaCvBTJ3m(
zrF~Mn9gQD)2|Ffas6`TAP%jV5kHt^IIp8Y<fNY`#li8bPI-&%*#v~j@E0zXWrwROl
zt#IXu=UkO%zyLDIyDVB&vM@gbvf$R<6QJ1lK*K%k@c>xG3`2oPVSIvR)#ZR6<A`4A
z9NyZEVcO%rxK33-S-uP1gATqr1{99>AJ8&yPqS#`F_#;fZ|mT6UFa?fGy2Ij+Db4r
zRj3*^T|P+rJf14B+hKo&E$pHl)hhVCL4r}48Gcgk@{P?ik<Bv!W9bi??*s7U+$0uj
z|FHnXKS0HRNJT$&Q7{r3W|pK97_&0a^qbHOoQm@2nHMq+@9k-g?X3+aU&*ngF<N;+
zGT{b=YZ`rHF^11hRVx>smeNQtHEmHCd%lp=Al#!m9c;}vvWVxkPt<DvVEm2lo;$WL
z7Nn*dt`_))VLdgk^{$EhQS&DBk$mQ66;vudpTSwT&^hKg{1;UJ5e79wHAmCpMlHUa
zq5rzf#70B5>8g6oU@LlRR@cHjXezJs08RuoAhBt9ZwzoVRn08FF#@SZ4O_ksNU5tI
zItpf<=Uw0*4Cod0L1=#&(ye=x`r2lM(G-$E;PPvjMs)}W7PAb)aX^K3XQ;P*m=P6|
zc278N*KMt*Thci*C*FI34KLdoQ5;V;BqdS>omuYp0-i8xDyIs3lEm_J?J!EfOBT*m
z!%q6Iy?HX9+KV+=4F6f3D(JE`?7rA=or>!<+HD)*^S?No8(9}yu=aQl`?op2e)Rz5
z%Zjd<?fv*}8tSX!H18hvzjMeXVXqCiV#Gm(Xu@>a+v~}4g6;9JmQL356&bhp1Kh_V
z&F?#;T3=%Tt4k$BaTy@<;t3I)iHGpFZsCs0JfLL&mof+9z8pCUrAl2cpNQX&gWV5k
z8+9rXzkCu?{_>nqw_NkYE40{4R!JF+hnCZGMqoH*uLb<&Iln7!l9-84)F@Y?3Wa8Q
zUuF)zgpYbD{xKNC8>fbuVgMG9fmHHK$rm<x3xQNrUd}ZJ8)RK>MjC5+U(j<CpLC5i
z%1~ttU`>Iz0KzD<Jj=_Hx7gbAN1~<a^nQ?A5i*3uo<V=7;MF0u(g2Yj16)lZO#v}*
zA;Vv)UCBxUG3hC%R`M<rlVSj%k`f?CJkw8^(bt=hftaWiw!?vBCJLgv1L9iR>qbx?
zkFL0IQ(_f)X$z58OhEzQzt_n15y_sy+UJyrF9yTM@fm$3X&cw{Ic}yehmm&-{%N|&
zBuR2#4?GlF#`!yAlWQ9*CgV&<Q?s=Wb)-UQro4#2%+T3122-{PeXvl#w9^1Y0HUU}
z?JD-bCh|=TZu+5C8jEZOJRAcEfDvP$Ms8j4TO)k{#8ZXV_ZO`vALyJ-D{KZzvP5#k
zz!Hv=&*cY0Kr4+mC<6j9fZ-?Qedy39ES%9-;d;sbvmDHAn89%>(E|DsJ%pD4mx&V5
zEFkJCT`YZu^Bo;e2MUyP<?4@*1SO`Yx2nqL?*%>(`Ml6p(u`{z-9GhRNN;^2vU0F<
zb%Im(MQzcGj)U#tcq7)w_iSMEflo_QhTY(awC4#q=bmB#5h)vHT4kXy6D+a&z%f&(
z+$EyYVbdvGS#!CJFvs(h5iOV`_p&b*QzZ*RDryD*;AH_g2jh*0>x_nH#TVBGCuhrM
zPtCfUapw;kcJrLx`Ddr)e+lgE$VxK8uS#kL;rx=FRZ_`9r44`GT+*dPbA>VP!Oe~#
z(UTbSjWyb*PE9NkM<N(HR|yw^P2S|@&HiuGTRn@{N;i!@o4A#_e9~~Sz^F%m;d}bX
z1=m&<(dSB6wI%N0ss`RNrF8Wv?Thr?O1bY^a$>5Wvy}<pS`1xrpp5qC>8N0Fg!zs0
zcuJa6OkdoSu`@%NxDb)IG<sfc>nnseOIhz8=A7RK(2E$jpo0kwq;7JA!~j31tZSTR
z`MzbSwd~%#fG3yf&MeNSRcs$UIT$n?Qh1;ne%b?$hi!(Au0m2tR%zk@&`6CLxds8V
z(9K{eh^}eGg7n?UWB`dh20&yReeEl%`@998j7QV=>2}W}&BO_Ls};%Kn)_I;iMN^M
zHnJa(bchg2;?4h6pw>M_7jQiZtB1jF>DRrs?lXXz=uv0?A|c~BH@wr4zT_KUYK7TS
zs6GsRyL<iX{SBVm2mpdpheYe?$7<9w@^&q0!YAl$@Q!JL|I*8Xh8dGFAKfv4G6bb~
z^_&|Ud4}JQ$}p&em?%Snz(80daL2#UIo8Hm7JR@0pebum*P_WR0LrH!ATFa?2@w9B
z(x@@n#{+ts1>B7y0JqupRUl4wSQAzd`RXno2}1&u%OR^*UuUgv)1u@daG8-`-RwBO
z^lL)T%XHa~OFrUgls72Lb+K@SYD!Eq%I$)YR8MK*D;8j$+Gl}*T$-N9<A{P_aRG9*
z6nI8#1<4fdujS{{7>EeK0HZ2zU?DHIr&GA^jrj<&vQUMAal|N?^6_(LKU>uG0gOfJ
z&orMHLT6cytos!lXAsY-7HcfmllfON)mNl&N-2I|Oc)tvBHR5nFqDSFcdh`8-84aT
zwSrW(=Kz#!<UxmJK<U6Z@n4=?*zrDf^2vk)U3q!3s>Yv8CH<=>9pb62E6+4)A_bli
z#sIXyB3B8%9xAVwYH<MA|6c71!-_L6#og`S2kII<^br6hhgY1DgszD+m?(k&7N?NZ
z&-j?Gp;PC>7&$|m4?VV|$Vi}kr33Xe@pZy6Imedlo2KTKpM2DTo};0=>`%|{=HQpJ
z;1G;>8!WAwY<NjNvI575eIkh^r$0h*F?oTwQsfmOcpz53B^fTrEk}Tmv{F3*a*mC|
z!V@%e=mAirCL<}=0;d3j2^6%@1`g;T>4_lEGJD`x61fDi9|59IH^hL5BwveSBY=7`
zIShc42de;itoQrN1>n$Jf}-T6O9pfa`8MtCCcwXjr5m2G&u0WkD<EYz_aeH+qs>a1
zjfOREP_@hTrsJSr&zoC)E=Q`tGD~0X40!}U`FZ*G$6caln8WA2&#;hzoligK+hI(9
zfpuwyOO+@v2)uj1oO#Yb_oxl<c%HS^aRkKFI{>h;d(i-_pLhMD5ugTxEw8U-rRm*P
zO|rjUl66e?PYvYTa$h57H|ihAnwLb72d(qK$mdArdjSbDZBP&Z8@xz@g0Y`|4?WVj
zbuH<4Cn1UUqwwEu?_j5edo34FpJmTS|B=bw{XdG%#h=Olf8+0MY;&IToVPh2b3W6Y
z&vHtVYI91GkR+sg!ko{^p)|*w5<-$R=VPLybedyGK9Z#4_jmvPfju6(ci->buj}=^
zu50Lr+PU8fBV<jkz2V+dm8<L!VlGY^LPivmAT|WjSb-tv3LuxIoc06b6A@ReH>(my
zZPYpbw7~ViS1>ACgV^$R?qh=JTU*?)!HE5;(quCbDw<T(k}I1Tf5LrBL3{m0lR|gK
zZZmigm3$93xl{05b>f@A)MVW=>jphF?#5B=M#qGKdk&uEKiJGMyGpuQauRN_6$FIi
zVSb)g+%s*hn#YDNlk@Kl$pSV+{U=$6m2DqC@AxqEEL?f#vBaolxVI<)(c^H94D>G<
z614U%Lp+me)7HD?d7VZ&+Pw*l@SVEbWH--<fih@&qR9lHtINeXQ<e?D!}g^I>rEdI
zbkCSvQdhJ48>I6jPhLIpU1pG;>%04o&k;lF6F>3et?oZBL739jI0#M53OcDEmIF}G
z`vMVmS_JSB9`*rJ7%BtETTy^G8qPDO#erNhNR+u_^fnh_w{Z0~9<}+(q015s&#%$T
z(H?#U@)WQDiAyHH_g#d9vp`sLgH6WlPC|_>wfGc3)o++UNPKb$hd-qVNYe|0{XUDh
zY4btImR^7$oQXOjTnmANd^Lu^m^Yw9MLLy?n0)}DsYRiU%KMwf*#tnq4qT6mLG(2H
z*HlQ3YE1*_Co=d1IlBj&tkTuUdLV6A`T<}`_)x&!9|2Q$qX+aA(+7%FBvV<SLNOfz
z_5z<FhRSV~@!6HBP7H0e!Ss~!$eW^CEnC2SRkImjk!+fglIkV(%0H=O4}O58l*vH|
zysZF)uDspL|4|7X5nc%aI25yQBUr46-}%U2DyCHey^vv^@QU5R^-XHY3vmK)<Ot&A
zq`4_6U1baI<FAL+E=@F{86a>3&-8^2;hD1`v?LMXfx$+9ic%W3?giTd8E_z^8j=Xs
z*x7}H#tK1~+Vha%)Y9YL>{+aw{F&8Zw+5vpGzlPd6$`idkQlEDLofWntSO@dSKr*i
z->b-a`lghZIE(`k+(<Hr<s(kPmsBZ5w6rEbuA{BYW)48y6uyOh%UkWS;#O{_0PkFJ
z#Z_&M;c+{BJNFmlw*BCMQyZs+y%qp>;j`41gRsb3v^3?CqEw?75FO5|AT98LWrP7t
zcptZW@Gy%hx`YGVv|N-0L5^)KP_a`Jk4!)9EoVdiR+E$%arF0$6a%<tAVU8=F{I%t
zTgxE9q*3m%LIF|;;OdXjgg~=!RjCi;Z;gP6*9=tN6$ml{jF^CiBf^xnTNaK9xDls<
z;)pYU=An8KB2`J@>PWpAh5@)23bZ-Cnk$ikB0J&Ib;0_CupuZ<fDHH$EJJ`C7LzBN
z7DEx77yM{7{6aag(15Q%gVp=^*~3|-*G@E}u}u7VuM?RJI4dNx4NZCSS3ausVOBwi
zLwYH@h%UA}vtXm)utuHN{EZWnUk)`MwjX#gpGV=JJ$DHE#h_y65ij2tckR{vCBgiS
zyTV%c#QRxR7Vbp`g!(Thg?`FkvlenmZ#Pi*e%A0-u-XwP<)VW%YfXaGMbr}5V5gj(
z;n4l_gIs9<Ug0KzWo$fG<sr?%oj&0IC(!c&RqbUETk&UmKT&EVd2AfX=o!dWzm#C9
z<Z{0<S(lF6AvoA)3sf(=HIMhG1Et<?3UjUg6Sadnjz)v9bs&5?PEj*Wi}0>(z|cdx
z=Tw7Zr9x9rt*7rXr_)bh(@bl@nZ~ou>n^q@I>yTH3V(J!-8v|Re0THE_XL@SHJ~{3
zhatpy)r}!+bt_1yc0+7Ry{VF|L6uqD8n@J+Chy`GhVvzhnX6ho`UoMCo}tI0`8Ppy
zS%hF}HLX|$wS7gv>qZdg&S|5=X!tNTI;zV2qk?*el5*cbA7$`z1ctEDDZSp%SLw#v
zLmejrGNX$Zru;sqHmrDpvWOh-hREKvw;Rb<-wA(_3@CwH#Y5HESwLi9DgXz`+0kM(
z@)qE`AA15lyU-6;D`*N9#5RrQcY&I(Q)~PTosqVBspYgpzIzodF7KF5a;$4N^Bg&f
zU4$#IEk2a-*h`ex{3T~)c!~#xU%uNQx%(M_T_wVPbeblz_|ES@ezFd{W_x|8Spbm-
zG}wprafp3u2;r<eOaSblF%$AN2kc{AVmdWs#f3n%6!?vdkvR5|ws(-ntk}42vz$+B
z(H@rXpn$;e{}05stE>^L*1+oOxC&WT8*&j(uVgQD8$+n)m#A=B%{CB4ex5{7G{3g=
zZ<>wozZ9<y1UwaL02A@$;?mruLaMnE+l~~D%1lo#GV0~UdU8?^Zzj*cCp4DI)nv`p
zx6rJ|fR1y~@&HF-LJZB1F8YWYaI~3pS$}l!B?O{3geeL`%m#qG-v}QCazi-4Zg%z_
zy-jl8wjv$PT;xPB^nce{_?|&OzK5Uy$*Xzi00`vaXDv(qE)bQqFzCZUif^+>n7-8H
za*57MJ`?Grshlt$&hP&;dm$j0M6-lu@P|0>(w&kw5z#MCoYs%xvt4yGw6yqhkb@P;
z%hSyU(j4&B1b*bH`n!B@jiQ)%wi4;E+ZBv0sjHuE=n%UNrkEONu_`$OS#Z)*JJY!z
zFITldR$`ZS6d?ou-(;YW0<MUWX>W|Cl0921JM?Y$0S4O5M$IHV<xMwy8u)E2csB7s
zEk%|66r{pVW^~IYfvXX%NL!h)24ERxz=X+bCW|M}tE$l(tn#v$0G?q{#mO`^fqs2}
z2p55CFL)24>ZjgO|H(ONJ`EU=K}`TOHkzs=KqCN<hOJVA(+bTjwq;OB{FV4B|5g>i
z;IyPjZD&1ZOlQ#?g(_Vu32tmWYziZ*QWPLyAtwL<6s`k+YOdCH6z@ATWSDg87%V^W
zCwVxsI(aC7ts&4(zF9}kM{%K?mjOhmAtLHUY-5ys%8esTZqvXb9a4vlroe+5h9~kx
z<7Ov+n-u?MbA&rl!2e%kV<~^qZpY#)ZIpoFM23npCBT13e=!zVHWxJ+0pc2+njrz_
z6)|?^7;+NR07=}CbK1^9Mw<=^(NQ0cMVfoaZD~7GR0d+DfyR2}K8LL&in+(*-+@fC
zhr`8>57ZScB{|si`473;)#=}b&}vrc{Q0`>18*g(<~vEO&Ooourn=6h7z@jNGiy1^
zWHl=c#R~d|<BGPnjJn@8*UIK$HsoWFS!8s;*48@YdS;Ow{|Y}_?7^@~l2u$!jdaiF
z_Pf=I!*Vr`xe1f}LcM3^9A(v9GN&dNryt36JR0PF)Y$WA$NM<HFuzLb_~eSrimvum
zfyaS+hid*Y<*{zVIGIh}0hB7gd(yx%XaBR{jK-pY0EL0(YNdgflB7R-107-u4-u4k
z{xlWwPa+87rW~2GQC43r*g%m>cSJT?H7ull5;Qky9wCLlSy-(e^g%oDqNpU^st&f#
zc$u}{2P(`Fn^&F5F%to*g8a(-?G{>%MUz*x4<sjR6?nPVu6iRA6&J!pl5+T%dsPt;
zrc^eZRS%YuQ2=p+#b{OBvN<VKRo!|<EzY<u^$2}fc-k#z@~D)vcMwC+kN`)bSfg52
zQR)eu0af4F*R+?SURYYseA1}W4CYbVCb1H44ceAa3&gPELY1~66u`xLgk-p}H{v(w
zYl0S<<+KyRtM(`s*@To}NX*0MKIfry5B4WrCY!UVl5Q_nd7}~|3r5N+qs3s)L4U?c
zh!+uXUY7zSXdP1W&JyiiT%Zfi@8M!TI!K+9va;vewFlrG&r}X4)cC)zoT}B@cxswh
z&@X(8rqp&l+==_Te?!_zTUhZATm#2LsQ*nEJxE=aW{>4!_N`K0@Px+(KkW;)J&mAE
z{3bAYUO4l#*kf1;)P5JI^>*RP@Yi3e;BLvG<q{DprH*Ri!T@}gfQJFt$d-957EdKC
z&bEqgDRF{2(qOgDY1b=3vGHRREFtOl`VJEQ`ivefw{A{y*#+h;4}b6Al(|CMz<EsU
z-9>NtI~sYLVeb8}r0f!fiEerew8J4;g2S9hG9e4eaA)^dDWs^P8hsS>zo-wXR=<#;
z?id{KIy<7U&RFFwYE4>t5ujG)?Od-?WvvLe7xi=U+u@~aDLl|Tk4+IOlVlcQD$);!
z-4=<t)WIhIR9w#mGJ|6}c!gXSPq=Xk>lU1{s<_}KY-=w;?U00*nv~+u@R2KCzN!@P
zZvj%GrC5-i;CE<FX3MXEHqkSgQg``{#-ahH(z4%La3MqvmWRPU-+GA|Do$pwQJ_+(
zjFuqU&?;nMyG#&AY>CT^cmS&xv(-saHk9B2wbLGd{Dlk=sS1&_HQfck_ay^Xt%I|-
zt>`f`glF8({;U&?0dQs)%t8=^vbe`soFoCAR5nMAYQ<utlA0rGOnNw$pDM)P=qS&~
z;~-Ir6<bGJmqS$ki7zz2pRT8p)sTxK++gtFIp5?=Yn5taRnlt}%O$NsBL8cG?`v5B
zmFlSbn&dXi$)HKeYL$a+hHCs~QJGc}<Is_(Q9h(UJ$JbkDqm$lKuF$EqW=H~Lwd<p
zPR22hth(KS+VftKh(_-9RZWd&{V}s(u?gc*gwGzBc3&WAO8tdEX?OddmEh`9ZJba;
zT_p^CR8~DM^;$21jnnM<SWi%Af`^j(?4m{FT37zo1!k#?`@Xe2ztCiY>fjUksw;#h
z-BgdMdet1;>h}a~8RFu=J<dwf9a9wnE%n732anu%xkt*AFCOlzsuc6cexB(qIC_=z
zqEB9>Po<|XR>9TBQu~>gS$oXm%)g2&>`H1JN>Q0|-LCzXZ&XC-$tnHH)qHfzACb@b
zQ|GL-nQZ2jU0xQOxKBCK3z4>DUa24?!lO;=EWu9h-Cg8x{KbQk6MLRn6m3+L%mICA
z;Cz{B$nVwq*NW+H&y)#10V;ou*E|-|z5p`*mpu3Nyq2Pt&rkYh??f`P-pN}E@k$B;
zA{G?A1#?0A`k9t;>)d86GtY{5-96J<^+?TZRZROUat7^bk%7OEVvc0x7>i9V+xEmW
zh2uvDB9KhAc0vJ%uHj!wbVR^)-0}$^+bCnPqf5)ezYr3%&=!Q?-1OOFPz;9d8<LG&
zT_f=Ki!2ky%NPJn2rH%ri#X))FW?1PKxn}&mSxK}a1#??FHS6zd_Ka(8kk2!3)KNs
zC;of&$Z#|oAU;Ud*W)VL1lAW36d7yg>dU+c<H{m2US*fA7BnF^9(=0O6p>g8<|dDf
zvHtL_{=!mHt&klC#8qi4dp`2sA*LZJ124<*haK|1VzDKi6#Fap-|retE=KRsyI~4X
z_KvpvCyoonBE5>YMI^n{xCqU;<Q+azF`5<5uJ%Ht|16t@`DKgPvcWL0WPidxLfFE!
zuHP!4Di1f<!^U!@kw5T+d@cY0N8_~yID{<sfhRY$gW`@ud@bAEoP>%COh$}`*RPl6
z0bxEs<lPYUQS0s$SQ<eC3gMU9*!Ktr+K8kE5UD(4n@wvZS_;urkxaY_j?J45xi@(J
zCMv9*0uX>N64|Fe(6i2X$79uFOp3-)u{VoP9nqdqwbI_3`VK6o6lqEV@y1e!sq<(3
z{>So1``$R7Y901MOO*m)m6c98DDje6`^x@#s|2g!_5_@d?)Q2(hD>kgKMs2Lvu%ds
z8cRpHxg3#mGU6?j*~q#5KR4BBmHRyh`WGHEz%7!9w8dfuO(%u{B>vz4LQ;V3@13Pa
zOZ$2Osyu^NU3j`ckpF-8*IEpIcv#wOb0v`TG5#E^N)L)k%+i?jqBRd;+8TcCNm9dt
z)NswQlkn*z&t+Zu0n5m_HKJ`m9Fi1L!zH9y5g1lb64v4GS)^zHVo_?zRe-7>@Pti=
zQ2K~dfL)VOTP+&B)F@75B=MYbrYInje7VS7oGgGyn6tQakv>$Qc~LR<8%YD7GJO>z
z;P-VpU;TW_`1!d<(P|YcZ1=KM^;O*M6^#s?ZD9zwrLcbyXT+55zidkuzf$hm8Yq#8
zO7w6{vR;gJukmO#NK>QkIZ$_f5PN7P3UWcxq^0BXY`v+!qxm!ayYivhWb8q!MVpKC
z_+<o%%SrNYiqyp?QfF%J-lWqmzB%W}Ftga`f}$;dtR&|DPW;bS>l;PU;8(W~upYb&
zMRF0FE1_jknyP<Ki3EZ=Afl4_YTvlmP+dOCOs-Z`Zdl3b?`Qd_lk)BM&LK_7msk7c
zK5WOjv-mg^Rllb^OJL&tnPHsnvOhlThc}f(P0GHdS8z?E*wdsHnrw`g*rXNR+ogC=
zykF`h7c{A4ZVB!Q#CEL85s~(#GZM2_7v~;i4#lK8KfGMcP|S)}=zoVw4M8wQI8HBe
zbt+JV#d46Ba@?^8tJ<|u2GIS^1h$3gggw8Kzn=pnu4p?RTcI3JZh2uDAx10`hco0V
zW6N<da%7h?1R&O5e6pDE3)Whbcz>7~M^yefKGr(f8FO)AQQA&_??XrrsYO+O)zIX7
zCqclNA&mU(fxwntkgG+?l_{pCdZV*Wi;LNV3GN{kOp>ljwS8XDKS6}b8>Q`BvMYP=
z1GB|cKCrGjrXL7}Vw5N$0rtX`UQZ5MUd9=t!k>D-7BNh+?pd+sC-Q{uMqFn8!=;u)
z*4GOn)ZP*UepA4&mJ>XWnE;b4EtIwzxp$6{fW`RkF1=8+8sTP6^Xj*<)eCV^9z5}L
zz1&|fe&`v2eGLb?jw=H0x&VOK*}nof<ppBFexfmi8C;lyLp<3aNmo&OFm%!o@M`CQ
zwP<Ca9Sqp29ISy&RX&7Jsy7Dlk3JIE$}DWbk*1dvw)x9E#)4QTM@@S#m^;%2p{<C%
z-lh^j4G>UoHg>^5CLaUVQUDSxtqvaT2AcRxz>ZAU1$b_B7{JE&v%UgzEj&I}h!X|a
z2B;L&_g{Zn4>DPpGj}$DXQK#&K9L3T9Y|UW_J84B!sWg-jr*QspxCiaE>(LQSd#dT
ze%zyf`yq+EauvdyZ~LL}?=l=LFE|Dyac#azMowfLhs?y8vyijkubHQ(bRjPGxOaf-
z2jD$r1uwdD_9YKN;0QdX8)q{B9!(=70)1dzF_l`v07(Qo3uJN->5~;2nN@Q@Ez`0d
z$faBANORCOxrSP0SNX4?MQhiS06T(g4>$(~_7PB~B{LDiT>3C5o?6-kBnk*XIU@-k
zY8)sC56=$y{J^6So4b$|1ZMQPVKa7f7{neuut!VVUp2~6jo?ISe|@VgHS+D~cKp4X
ztPNNPtTtABN&$glDGbC&q$CqmC}ffmXp1}oNG3IXx<jfjSkWGd*Q>@)TBCMZfFe5L
z1FUfHJo`4E0Id11QUEUs!Hbf-a;ueyB<q2*QGmnJhc0?Mm%&EAb^Dtd0wRZi0DN#P
z@DpX90ph8jnQ5c}0l!z^Z5Y>Pzoyn(RHUY=@Mz?_`~3flf|TO=W8S?^S-N!MV(sVn
z6F+J{Ciwk$|NF|xefBlhJ40TstCW44MHXb}^5rtM`Qg<|)azQmWBe%6^3iJh)yT`o
zAQ7vJ+DzH8R%v<<^Q2fh*ms6Nes7HW=;vDn?>lck4qu`GWa1r>y-QN3nk$sRs6qM`
zQq8Ug1Ja3;^;YJm_O*OuAUtQs90WKKrrsa$SHdnzJ9dQph<ci1=e_db^QRswBx}nc
zbv?!32l2o5@Yltwt*7RMM;~kdC%wkHr`WK{UQ1nzw@gn`0tfR@wMnlaf=~==VM{t&
zs`vUiM0Mrw?{mNH6n|)ceg8-Q>w^no1{{wK)jEeFZpi0+Newjm6w8E%)A(?|_aAH`
zk6pB*<=nX_lfQ88&+x*^kAGKEDlhekUR(NYUaYvVmU-~*;~)R@KWD!4Ruq9{Zaj$n
z%YtQYJ)p!tdF!wFCDxyd`ac)VzONp9we&mA4UuL#$Nll7;1G42KgE<Z1L&m|0OfPO
zo}W!FGh)n%<#tI(B;owj`RA8$c}oXJTYFBxfJfFc0umfD&mY@DD&gW$3|Dxhj^nX`
z@JK-gBp(hXQ(wBOCOY85eUB{#SHY(BN(Ql5<ashE17l;~%9Ie}yEs|<8uz^`mOl1V
zr#jsNLinnt^cTt`n`)%6T0f4(HVY#kbjo%%FVP|nu)FZ<-LG#h$mIM_x(<8g#x@)9
zou&Ha+@TBwr5<$T2n(n)@)xPi<u9}F*FtxMWb@ZKCo_sw<wLUg3lhwhm7a?ScvW)n
z>sq2T0|02nTjl$zvZn!ll8`F^WrFr40?1k@o-cCMXHpYjSbs;^pgdl#WAXqM8=m6^
z3~V<nkK|al7gw4q%FoS;quc-@!jSDKB!C3qJg@saKu3QoK^Woa%G>a6ZcdT{<7TZ>
z05ucOAmWJ0g2=Mbw=y~djHBYjVx42|p(sYg$5Ky8<+T~WwUFGx&ghi1Kh4Lr0KHFT
zeGp>;I}Xl$HmFeB2!r1MSc>i~wkd$bTcHUc#&egcGRy+vSKY$k^$~|Kg&m|fhh%+)
zPiDhUx2pVH8r^I;z&?=B^Z{I76EhpG*&!h>4$J<%L11<UfFUuvnFwCaUoAJQ?CsNS
zdk|Ss0$4H-L`GQq!pvQEZ8uUZfA1idp0kc(-Xve;j@9s758eUtmhNxX8~Ir#b#I8k
zrcfo6RN@4{rW8t*J2o};iTE(qA>9p5s-G3rVD<uKL2k_v+!#RV2EB;1R~mLnQU|*-
zE(B*zz=9QHh9+Fp6ommSiSdXt6Re9dZdh(3)YK*I{8)|Ih%Q*q;!6U$H=p+hNZ&Ry
z%h!Z(_Vb~pR^2f<J5>tFUpyp-vakrwr;21mvJ?|p<Qex5t5D8eXT_-IoO^Y^hBee^
zbtC^)$19vA-oVNtObG&}nDn*(q(){@TyGi;TI#Rhh;w^T2Ub7ziM#MX7d^)mCK6gq
zU=1<4o@~#dhCRf|A;-TQlNQhf9s|*TF-ducdBcLur0|AroCr&Rv0v|JlCd|ECMKL>
z;e3_bIwvSrT1SMN>UTrD4EC*$c`KJ92@Zj&t@EPO3_cyXzeUqdzO;7AeUX>kD3x!q
z*t_;l91M6J$}Hn&0)8aH`CjhQkOq5~kf`NqWh!|p)q<kV&NE@BEz&VJ9rrej#eU94
zaIKf%yZVa%)G^KXp?!K4_g`oJq<wx*YdzL0mnpt-wOfxha^$>_|CHJNuB+?dx`c?D
z)$brl0zklRm;lunl+l4RU{hlB4nB#wq5bMZ^}|r@&GctFuUxQ;CdS%l1y37r;2+TQ
z(56;Xd}0J`ktD0*Ji`%*2*F{hD~enFOGskG_kADy<f+r#<?*@Wf{*m$W8UD*;=aE8
z_2GWpkBD$2=vMTLxS4r3l2>^ogjFGklr4zLIa<+kTc1^OPxAclrdR`v?nqY~^Z0b5
z`bJ3a=OY(Y!^;{XA6|^=uKH{pYj$|8_hR2p&+}LDtw{dw7sh^`nE$$QIS2W;9)qMh
zJ^p-PkA2Vk*Uu@p|4q>tnkt@HQ<=<fZwU&xgV`EiPxK9i=O2&j#k04`{4axIE_S)4
zc5_wcM}B|}Xysi@GO^BCmL9mRfN+0y6q3Q^2dl>DdLH1=Pn}4RB8li%OX0bU&PvT<
z0dK2zyM@msa|%$T4(2rD{m<+LCDp}U^$Uu5;*vL$`RcAzc57e3zc11{>z_~Eo<8=C
z7|<S(VDG+a`o{jH_)BY^!~`;i`PjfNAS;4bs>is2A^I<Gjeo<NKD+FwFfTFlDbwQK
z{qhFUt3G<P8dJ*a{5R5fnZc>2gK2?SSe+r4u(-$rJd6Oe$_GS<D8lzbj+J;e)`_jv
z69Sr7fp#gxS&h3ar^F34cq3x*<ZVvShUAH56bY1J{G<UaE-}Tbsj2Aw@CL?dbb%Ya
zBu-Q1dfGlWG=GP#6m@$|2;oRT_^EMhU&^{CpII+PocMN)nkqhPVAg%XWF`phUd&C3
zg~)7$KAceX_qKsV=AvYNl6f=K!9HaUfEbO@D=_&TvS;HSH|4~B$60Qs&GA5s_p0%X
zcGf)FCrBhD9y1X{|9J;ZprSMPTO$so;B7lfIB=vfE@VR;Gc>g-U1P_xFh3O@aCBRR
zM3Bu@h}vTNQ>h7u4iIK&-4Yo;&*(;MnW+eKktgx~F4+;6(_(|rj&5)&D5lf_({V=8
zkQ>y<AO>C_@VIN#kMiTn^oWbxT3khBF3@{cR@|~tr8*Tlg?`A{P<6e~&wx)T`nH=g
zGEYf-4Vm^^ozWdZ$AtqRIA#c!jbjAfbhbBgWsAAdb<@>wUlF>Q00<-8uB)o}=U_V}
zNMWgqPa4%=J~CFpfz>H)PBRqJboa|6bnwqbV~k@{HT^|G&ZTZgaoJxM(UKHk@+yW%
zBi6*3dAtYg!_fUi_Eum12(gKTv$TjD;h9@Gv3Yevx}^d1tb10uI6NZht+3n2hD*Z}
z^Hu1wSC9{ydyIQB^vi7zt*o^(E!_b9?jb;J*C^f38k3}rWoK@WW7mhT3l++#)ijzu
z6xhG)d`t>ddRsEDgKeCG(iRsv0UOqn`m8=RBv?@g-K=$oznk7)=s|;#;;Jvjj$_zZ
zFLv$YsZ(=&kTDm71F95|5t}z&y3sS>B-Yq(E*nQiotBOqAX=k3b@E(^{c<4_IvFnH
zh6UmL8@&;2uA%)_avx1-XCI~e7jv9*HEPR$lQ+F+&DRi~i=PyObGX_*%3{aq8_uw`
zoD}f6MC+MU^slEAsGQzEr1G^Ar#$>*Xh{zOd+8MsMx0b{mMh=E3^;J(egn67UbtDv
ztr-*ItP>lb!U%)_H+=}WZYb(E#bK6S@{dQ$*|%Z3uVi3H`uKOT@t5Q%7a&J1#MpJP
zW{^FX#Sw7@KvVRgimpj(CBML8qs~JQ7@}uhiSVaok}o%l-IwtESKj>Z)_S}VDlbP^
zwY=%|1EYu2!dkpwDpOY5>FcObW@%1ZX0hR`wD|ws_Z%{sM@T;Rv~{>q{xz#gTi)!_
zn+MKoq}?EV(fcg7PsW$tNn|!uc&Li&v`W)*dfqmfifwckwip+-NmsXfO$f>SwXJwN
zYU+<9%8q%xJ^sjH+Jpg2?)7rRFf<tj9Dd&#-;$O;U_AJlpcZ7}Im|E_6&?1tUOjGl
zR!Ggh6$UiuGv38%G0BZ4ASA4giFzhZvUP}cI~ITcVTeNYr$<Ift__`r1Fk^p4kiyR
z4DlwdrMFP};FF}uu=85dAUA)w*v~cz_mB7q&BII2552si_e}Bi?_)RB?Q#$oU%zRS
zAiN$SFN$E>1l?L8+@!?Jq~Rq42sf3pa!FKdzWAsAA+>*cr`JoU=g49mot2j`ODl$9
ztLE+>1)2ARCoV|JZD$z{O}~)`7J8$^QW!YarZ^|R5J;Z?>xjI-N9Y7LX+Z0UgjbO`
z;dTD(tv6%;nY%4a=chXRbgCY`gQ-v8-f&bXm4e(*_XrD_g4=cW80f0Dxa2)sl>v{G
z`u@S^R*{B<1#39M{j#2pX2K*egvUauZpv!@Tq0)XE4RN5gzdCai{fE~pyS*i+{mEL
zBXnMfy=wK<ZF0IH8|2o;9p%Jj-!Z@cwL1?KJJDzJkX#<u$FDj&4ao!Gdc48=QL7dT
z_kNPV@{>v`cO1&#>Bh9i8F>cHS$Rs}Cjo?!^ogf4@3F5Phd0nC8ovo`#y_T{Vg~<5
z|9VF{;xgt<QC7g+Oq*d54!sJ^1p3VUA@>%DD1ce1&#kB5+x`WxQ<Hji+!0&HIV38o
zPrmon=N<muj2jj3naY3YeLc$RWA{|cHsh^nhA2`5vlE!M?jvG2dN6(;%{+4u5?$co
z1J9j2px62f0a;V_au;$ZN{4&e9q)9<W!JX2K3FhQqEGouXJ6*zy70RS-!&h|61S+~
zjx@^pj5ulZHIF~=`Pu|0$483L%bsPnW?d~vYcUgS@d-UduK_fUl4?B29if`k&)Z?x
zCiU&_Asu>Y`yi{<$z}4(Q#~ir?ytlB(6m#ls1>6#?7v*euK~4Ia3}Pu-XDbtKh1P*
z^cZ#t9=m-=>Z)8smuulbCG>USiy}*phUbwF{BQ`*p4Zv&zY^=^m+DAv_spBlAQPb6
zbxv3J2o{^o(4&G<LMQ0l0yykhLN|L+_kF$dAvHm$09xn$L7ztssbW88p0el=86ix2
z#2aU=zs7;xXG2yc1BPDGSSv#IJDr7Eb!o`-+bGo;(x;`H9gTUl@Yo|ez9t}pz@t&=
znfs}{z+-8=@^DV~?;?NVz;n``r=iqyr&3;s{5%BV8T&dg+vw$c6G?f~fX~s-mtN1^
zwDdyd;r-UURBM;vSg#Zt8FgFf<`wDL4Qh&0#o*o3E@>}nJ*B@KGwyUVIqYt7y5Hoo
ztBju4!={I=Qb%O2`5eygK3wbe@?qEECntKH#$GQ#hkq9wHYxu!PQzadr_{Hi3pVH_
zmIvk%Qn4Y}l`XN2l#dTLUlObNryI-jGkY2e4dM@Hl`qkUz5$$s>>3s|mjVGvj!1bP
zA5{1G(-Parz<br5LB7|E-IxFT>T4M$-As~xc+J+S<GQUS6VXZC&=4(<)o;!d`I&e9
zQj%HvjT)EiE_vlQMs%;)#G_43t_h#HsLM}H-@SpZr&^}7yLk(gSbmuU@KTl6I*`k7
zLIg{Hb66B+?57i@?Y3X`rep@bq-LUXMZ~IH?_5*Ue0jf7=q&F`=|?Ec{;L|-gNqFT
zd4ylZ+nsv9MyWp+c)$NtxG{NLGZ;-dB}?{B34iLaAO&snB9s8q00^#$F}LX%Sk~j8
z=OE>^ToM+-=9bU0e2VR}s`~*#M9caNmu_F{l2o1Y*|O&+JAXHfoG`AS-rCGxf6w*Y
zFqYBswFtU`7d3em6u)F-lkv{<iUV|Ivp^OvpYwF-?o(E$tGf@cxb@Kk5>FlOeze~8
zxsdoMtYk~t;fq%g<_C76KUROygua_wPDssvBYzG0^;E>k|C#NB2_Tvn>51PG`6086
zO4*5CA$sLS${5N8{qX4DJsKG`r;xvVv_0yU_}Gq}G{;9Y#u~6inY`w?ZOB!pVehJj
z@LBz_%m12@Jwd5^oq5*vi+gOZ+?uIvbn)uw2Zwf(6sO3*8R?qO(FV@Tm-3G#8hoeY
zjJ`%P!YO)xv~yTfU#$N{+KycR;JU}>FIwzYH9b_K?{WQD{_9$ln}xuGc}35ATL!cU
zy5T%#-zG&REJge{G|UO`aUZ2AW<l{^(47)L`wSw}quQeGlXE{##?hHuT5CKve0Wfo
zxFI+70{a~CXC0q>MrF?m8GoSmC|vNLy@4;x3U<ga{BS9>xFK-b^wY4~v;SrG9P)G(
z)~2`6Lge+<%%egM8?sI;gq(lA#ZkSMpY=$CwbrS1GrxOU*kYQ0+RNyzmy7e+^iR?<
z73-B-Uga$nCkfZhZ*&nCf7w;8Ykl1~aA>_P`qu1DXbSTCqr-=TZ<)+^visPGum6^y
zo4+_$d;V4R^{w+8wu2?Ew>_ro!_%*9r$$Nq`KbNOlD==tP9&!o9@7qqEwyJibbs{O
z<V6Kh@=;K~$Az=y{cDx^_df2kvLY@(54FLZi91VGGHvu8VM^}aSjg;MJ=f2xe4Y1i
z`_GAtTvkpk`J%-j($~hjo)=TjW>%Dr=<ZG~KG<*d3*Y8C{zt+K-0)dg<_kX5Ch&f9
z?fGNJ%&>dh{s-y5!|~k{70Rt5tkj&?Xp1%8e`BnuBNvYE*i-7VkB&N=&Fz*Pg)sA$
zS%D?*6=xvB?m+iB!#u<){ZmxP_NTu$2=W?4xULWYZjeO&_}?cM;vYza0GYsz1OL(=
zSGI1OI_hy<u>^b#0P#i^04CGN^B0Me9YqEYU=SRc0Tu<7N)UVh5&+2i$rQl<EgP?L
zAKNu|hj~otr>Aa=q4*OYpQHeari@*>^~wMCT@ppf*+8Z^Hi9$dG~I#fSyJ;V(m7+8
z$THFS`pq2aD3km8QC&G_-HSy%lOcinzsV4jl6L3BV^PGgnw#BUP8sx-4`0PHgkrv=
zC)w!ms~+!qOuh1QYkTzDh9i>Jucy^`_DE39vm?!-zD0JC2JLVBiyV9Bi$T`Q3(uXs
zJo%2NBfA-xW|#kof}Q;%=6xBpTn(&0Qv2_Y2z#anJ&dXF(_d#>8EO1F=N#I^gQNu<
zD4Xb&rdNJQF=IL7`jqT_#nbL$KWAheP@TgfWz~IazR4asU$aXLSM~T|n(MEBxB0G0
zch5m?0lB*N&_esg;ZTnUqSfwJLM>_=y(<5^t!gYG{iw|`YO*onSmNg)7MJ~V2akkG
zr3sH4K`5)8nl`mbk9Q9=TC#c{DUUzruGLh%{TmJ1pN72{a9opB85~@TM_+!dSdIh|
zib^03XZ^O7Qpy05>EL|3<Tbh0U!?9TyQSnS-L6-&_GHpbO1Vg9etnpbRZJ7Al&%ah
z1*oOgfg~)Ve68*lB>4$*=bQLBJMwsu`9#+f9gReV-9lkO=PsdJ26J6<JvBbhJVgFX
z#OOW=f}yj`I2r_^r05J-h{Zw(K+d^S7MG#zu516LiODIU`lfUEt)MsW@ei+vF&Pt+
zO<%tM=WKOqrzKlbkt4uXUE`Omu|t@egW#Y_;cFg4jh?TPHcbkWiwhdE{qmas1&iO%
zoPIbfzrd?Dn^osCROw*VSP3uvQ1$~B5GFMO2-Ug^!2br8abQJFK{4BSPv1^$Il9;>
zH&93Rb={<~WL4>#^D1T6lo_(gnFgn-_JA-u3!E#IcWgZ?1%TY;<Bg3I2&-N1rJybP
zx5`Ra%+5%s+SomzUL{BVDRyNB)DGX{Zm-lBG)>J^FdKm`(q<9=hp%)MDO8+_gJb7+
zkL9?`?!Gtm`_aDd>H56aq0r@#+G(ahSMT@+$0^g<o!3`@O7CX;fpkmK%Faw`q5fQV
zk!yF~@u26np<91%{8az1$}?3fBm|Ar$u7A4`2f!|$5BH*0b=q_N8Hrq-;gYUe$~NY
z?tYg;AquVjsOiVfIQ9bOp|EBlHvNhs1Ba+yEHnRj-u%?E{kUMA^$Ghb;h4G`_m~$o
z!=zcrm?n<P#fA1Ad~r&~Hr$y-NCNdlueuKU@4xcF7nzcuk6!w(R5Cwcr`#o8$+ney
zSJ>V!g5zhUR{GI}$NmxfPcj8bT(ho)bdS+7oi>F}M*kBNcoyR5l;}L#{NmQdyv<@H
z?m(K~Z3}I~J7W<DS-GPyE7x~o(&*ut{4RDm9mk{e^?0haC4c{5@Kk=8_|ZwdZlUMR
z18^6gbZ)VTT*{UecmHa<jl*XfU8?l8@`TJ&?(c3|dzapH;7WD9P*9mPZNs-Anq2dQ
zSlzy#nR$ty5ag9yx<l}NMpeEppD$<*p)Wp*dLolL&Ute!FMB@DO)FUM`{@0dNzsWR
ztCrgOHp^)9@%6v@wLNCl1${w9o-+vnyA>s$?(@j6|5AD0`c3cRA=77)Co2j+B*?#U
zGWYwlTk(WLx30a%@7S}SROK6OU5i+g_skYiCb-Dq?gfjQ?<GgBNsH`+cpj#mP+vcO
zQY}d75+maXsGIdODfG1hR@#!yFv7)bfu%DJ9->bYf~8~u96FWwy8kqJ5lW_<u3Peq
zQ#!8&yNV#YD?Gh#_q?b!E0}-gTwAJUY4gtJ$i@rbN$Od3W(ofhK;zBKyU`MD8;sfb
ze7t@=R(RmF5Z0kN<(o)7A)wDg@|UXaCQnjzf%N!$>hWUU3lgon_QQ4K!~X5eh29b4
z3u=-Kht12mbLIR&CC_<I-*J~LX{f&^ct%z!YrIS+IKouyx#_7V2tT~OXYd!!pV4=e
z_H>7CpB03w9rYcuy^=d#%01vRlAV3WJ=*~=<Wiw!XTN$)B~ywqvwE~vh3}2}NvuGI
z4eI+9rI2^_kk$Z(3>&b?Ga^`m6`D;6Q+&Vo<J<Sc=L>b04wZjToIQL0@%6v;B!!IM
zTyJ6tfj0AFZ=wJ<hraHL`$?V14;0&yXX3D_gtucK1x~+W$0q=!nvML?qcumzjal;P
zi#8|PoPjl?ak?Pu=uB0smTgTh?mVAp+V-z<2!v+4*8wmifn)-%<pPC-QgK}}W`6pp
zznkf3{u@aY6b=D5P8!PG^8Oy$1-Wfz*FckzWu-!4FDk($4MK`vT<#ekLpp95pkR!d
zfGC{_K`4;}xnahg)547e+eWa#E7Gn^FfBF+o5<W8v2Q5?v$pEld0LC6a+k#8YjDjs
z{p+qx4rO=G=$_fQiRQF$5dtIc>6OVC*-`IKH^}G=%}te&X~Z!*Pd=BuxoN)UMr1Q~
zle)@`_D^$=3Q<cM6I_)0o>ocFx0;H(-d5SQ25lwQgoq?D{9tB)h^oQ<udlG)jQ-$_
z42tzA`_3{zFmw->PpoW8ZrWp=57|J=l2@~NQ4?=2AAT+gxV2KX$Up~88Ku$G>wdem
z{Ux>i<S`0J^jdY7(v+JPEE7z$nC#Cno>{+oQ$X(TC$=Khtx&VJz?*hW9cQmx-F+kC
zVi#1<D{9J<^2`<QdvTeAOaBl9ln&XZJ^5Z@f9`vemO(~k&BCt~ej<_Y#?$i{^2WHB
z5*tWP_+E5YRr>0a1>VSM<G6jX85Z1V|0e;UuU~*y7w33+m&0g);KlgN6!75}dGJx|
zW^;q`k&bk(S!vbR4c`&h3k29qt2jvye~1=o3+Vq@wMHG9YQA&+M4s3~qmOX##jk<{
z_fov<cP8rfX)-rrOvcy5`1RfJojA<jrIqDcG45lVpj1<IaR2qc%TLpPn|}JO95fZO
zP6{nQc2xB-ksy><l0Ow=XP+!!z3<DNf;GY9a|6?525{;WugZMTZEcS_^ztTmMzip|
zJ-h|}1{<7kLh`_$k1<bx7?{QAh{+S$Y%?U{u1r!(U?``o&53Y-5pX-tYDLcAK+WmZ
z<VI4?QFPAHZS%&)?xP#6ju{=QNybf8H=0)3j|KHKrwlYNhWXXY*$<QM+}6Ldrr9DJ
zAv5T4qJHkiIgxa%<%MxG>^d*t0+dOWR)=~UuGoN8yua$7=aWKxpXEYr^#j*$Y}h9;
z&j_5J>5&bVqjK4Xe0DU;bPWy;-EHzY6&!bJH7Vpw@MCn%X|C<k$@6?}&Cz_xM>joe
z|NC%%B|^!*6_9x?1}X@&XPvZNVMJ6FLPMAdlb|C~18nL|cd;2Y;TdHOjhvvK>@(*2
zuYVaVmPxZr$5`5&L4ANc&9R6@<;b~kpBFqGXX+-y*OI*dHPLD2iD(-Xy&WEEZNS#x
zp+2Zl61u%My-ci}6okE%)F1(iJp2k8G)(}I!vP=wnmJi@v;nuW$h`68ZqtFq`c+K>
zrC$j(2~FY2<%^`L#W=vi5yDy|SU{NQ@qkstaY~4t;>~V9U%qydvq~r-9FD4;B381b
ztvR3O?nsR$3rA@K(8$TC0kDf~OYWwl(rx&isB%n_1*9#OV=X>N-A+a@B!kJOw;h!b
zNH-bvG=Rv#|2HH~tC7eFyIi#`{be42WGn=0uwm#e0)ip#yI5gD6yhfm{=e6LrH>sh
z*m@SGjZ~9I%t=v~IH=4+v?WrgKk5z}lA@qW5kX`L%qA(x!W@j+7g?e=9E8Bl8ku2<
z2U|%JDWaP62?8}tr1FBi#flmfie{bwz><w@F1xTpB)SUq!z@dcIHDaUTEzRi0MJLB
zoffP#6<7>(Ta>$qGgao$u%)r2mN{n^?#lGLRZ;X&^iT3<e2@&eF;)VG8Pm*5C{oxb
zR&GdNMT+*v;UOR)SOvfbUhoPaip7G^FzzW95WHk5GN|xsVb}*txC+|ITf|RhWPr0k
z;#hzaYLJAy*&#<DP=Q3s<&yeL4sh$Hwr8ErB5HJKYR`}v`eRk80shJMi+FVb>B5YS
zn`^jSSy!92DXsC4qeD@8iXK)i&DOVb&gbM<q9o04JK9e299$#NKg&pkE08(ujkk2y
zr;}mEmxM%1Qb!Kae1s!axOT4!Fy&PJjSa#iv#Au}1sHe#pid5mn;zjK7{##M^3)I-
znGt_+Ux!)+TWHve24KGojE2bN{$Lx3DzgK`T@!XigjiaHS=+pl{e}%~=@0-?(R+hD
zVLu%GQIO%cQPN3kXSq`wBD4^ZCu<*?;VbW;`jSMU!XH&6Ml9tBMrX=un37>%Z;Ka`
z{BC#iKLl(&x^x%-Y$yCvx*Q&0+dHTGV>T_CFVp*4c=UmSivO<TTdmdG5>{-Y)hc)E
zYZW{pPU#wRF$DaV*oCuDf-}^`d3TYBO+N<O+`-fv>yu_vX6V#mnr!}0HPoUrnDlaD
zRQBN^M~XBnNE@=|V|*Y$r#ujQ2t_3=5|lOcf_O^7y@ntY7N*VORZ5!hVD~#`yZoy&
zAxK0_ur8N6bBHbDu2pE=UCmtfphn+qzh*c8=7|}<vApA&QN#fW4)zZr&c5!NP!sPN
zY-r8bjPEWRU3UjQa^zL`J8`bh)Yzn#;%#efMhMi^tdN--?Lj+jOLgZn#V>j1v<|hH
zd@AYkf5?M&^H9k%IpfYV>D}47+{Fooieu^HrTEVW_=+dP=q@;P>-|WqlQZ|z@D8H_
zN43!i>Cr9G14EJvXu)CZSxa_BGPdxka9EIPu!5aM628{|tjj<QiMDfq0i-GL0V{O)
zEH4gB5g-p#n4j#YDNrw<Xk!Sh1pI5NkI+p$B2128>`wjg-CzHAx*?+6U;Ofbh$XDH
zv3TW?WvpGajBNWd|AYx*jY#Z~2-_65sMdAy82B#zg*nE}=u1}|OSNuviUt}Zf5@nJ
zPn)A^PkA3#K5dbzNg}>gBO~02;mZA+`S2kFC3!tN?NGU`veluo`9hm_$apyU{Ss0u
zw*^#@Bo>C1)V_MqrX+JGL=oq-$P7-xbzO?#7|Rs(i3HmRM5)nu(i5KowOBF^K}u$4
z_)anc(RW5l4df<n&nq+Jkm_tOXL$f21Ft#cK`{qih#m4}=WP+B>Iz{cmB<;`m9B4F
z@1!iAt6ftQTizAl+6|--kalO1EoCK`sa1&iPFkNN?;{O(CYu`ppofduO*r%d0Dm;u
z*Nb`3&BNP-u*XN_ID8m>x>H#m70c_U;*|}$7OT=EaiM<aD(bZ4$SYx*?}J6jSj%7v
zDMGg1qkQp?%vDtG_Y%Q=^RvK<-D!pc<Q-eAh_wh7L4<BC^ug96K?J@KSwpy3JQh1D
z;Ie^+0NhCIXpdLf7B^^FK0rNF;<_g8Lf{yFsKiP=cIp>@rKqpKJTkI}gK$~*y#07U
zu>u2obTA=h)!f=Y#mwob6SWy2GH);e_k-u=EQx~$lGaId`>#{!EiwKB$x;jNI<L1V
ziiTWq?t8U(+(<~0MBI`dbA3?_f+92ypnfTNEV!`$j$d+H3_Bf8x|@38pYFwP!X!4@
zkEqm34vuT{I+kPo6RyiuuIe))a%z|Ls&b_kmBbOZZa|y7%q@U`Jszr#6!S#*@+yWt
zv9W$j0KCQ-0Ms_Hx3bW4NP<eae~Se1o~~z+Ced85-yzz6Pk^SvzqlXU<EHvkma{hb
z;Z|h02Bgg3^t2B=mn5iE-ELVOMFC*oUYG>2b&~i_>e<-T)pi)Sy&XIEE^2;DcTeg<
znM7E-2<?;rIR<0Xg@TNu2%0D!Hg@t6`mbL*maVEJPpIUP84cz9e33H+W>53)U2XmW
z03Tt8@BD(f;Z}26VEGHmV>#Iv{=CNW^Y@c$k2tJfP_(-r;CQ+zd1PQhCP?WVnbR|~
zM1=r^BIP9dO?1d4NiA)pDw7DY5v%hBU8n^LYkxyM6bD^MoO)I6Q;LGVQcTTYvN&fX
z6Ty1wB~*7i2&;|QR_87|HyxqE;_ZJbEz^yT!6^q>Mr)~jq*@>H*YRtT&%IEr&hF;q
zUw6!Z-6VjxY0QkmxN&lVvS!%wfeQw%IZtD(J>=75^E8@phT)od>Wv$QV^e7tIKTQS
za*!Ty6kp)lzRmY4f*MdCs9=8ae!{9qnxcYG%$ZQr5!cg-+AE3_Q-viYZ?N&YdW|E`
zS<ZuJ5Wmk@pm;tXyEgboXOjEg(v(AuM@P%CvuGo}gf6EDf`KG!D7j}tU(Ns-kzYHn
z_*GX+e~Un?`OjjTzuzr*A$kGAOE2z$F+6eb!x{Pr<p2KSSQTUWZeWUB!v^Pls{%Eq
zj~7Fd=l2FjAuNokmdOU1TWJ@rCdZIAD;$5H6{(z9xy~mMANDTyHOJNk{lgYj#!JYr
zDr6v5S?;tjCArKwxYGkiQLycn$=IbBS>BaZm_e$m!PZIH)PJO#b%!k4yyiwk`Ao-t
z)W#(RYE}2!gr`(-=+D2TB&~X=G`K{OJa(3ZQVf_0+<y+pB>{TqTzbzFYM(?U{K&uK
zfwtkGC_c_<G&#}(8K|-_B5C`IkNFEn2E5QJpE~K8Rfqmzw=i0TSV;vhd6`$h{s5w0
z{Jg0kQT>3AzG$BaANd6kV8fDOzT{xTAf8C=mB^XH^>Z~<c1xV##zL88&?}y~85Gbv
zNILtNxM!KwcP0t_0VytA=_{fxKKmh@!%hfGP63rWGFHf!I;JAhud^uu+W2S=(vJQK
zrO*A6{8YH*DGh&a2*XH3e-PmV@5X`7L6WX`fBhuULs+OpaKTsze)hot7d*N#`#Tus
zY#5R%DJAoz5-)QtnE7#+3^2^N!$};0<7S8|>|3OOAWdKJ=LEho2S~pXPI{*D<BJ-^
z7iB!*Dr!x%6cDyJtYe`mNVEkAo1gGkBj_$#t_f2QIneBdXx0E>D7YZ`h@gB>PDQ>y
zOyuV10Rj*}$o_UVl$DjVNI-97GKo~Hf4pnr(S2BE)-hHw$1%6jfflhvFdYhnTiw8J
zqCODh2y6vtX^?&p;(Pt=l?)C5h~QHcZjJ;8W&UHLX4iIrHhz<dd=vT*5LO}Jqsh;Q
zI7kMDnJfE0VT}MP`QvE+D;pv+F>g1lvutYobze`eY=cO{w-Cnj&IFL*8>OI>O9o(0
zbZPp>nK_VYtq2f#%*0MVU@WDf33SD|0M6cuiSTNHAvcguAYdc}a@{}vw_n}Q+5#F!
zxHjkCr-690rUQS4)NWV-{&6+{tVx2v^n=b&yZ74sS$$cG(zYo9KTNA0q_#E#Ygrtb
z_qs_vxPPpLES#>BKC}!>D-MwXMMGk>N9wP12?*2{g;Q(A#-sb(`}6Er7}eZ~_`iUc
z9uNY%ziDZJ4NPduRmhy0PmZfd*HUp1A|M`c!d^B_t2<8q=4mpwH=GgB=_&I<#fEJ(
zsIj}W91WpOPXMviGqIXnOK>qCl2s2*m%$jOf5{392(m3;ffSuO+1L!RM;;+w8$KQS
zr9u_jFyZAZ3CV^|FZ|R5>|bO3>jF9JIi3&zQ;U-UisU=VX>u6S?e)D6_3Jpeb!(LN
zA&Z-|Ki7uY)a*N8PKHSA0MoEh$zMa2{_iRn1B#xyP02`5KLavIA%88aYp?}2o-aCu
z4|^Z(ym0a`DXYoF$GfxhoZk_>moXDEyMHfHygKERfHyqR9@XwrvmA!7JC21wug2p!
zu-l0RG9ITVVj=X}nE8R#>JD^;${6m7Z%E3s3dJXSA^DJlkYnBphLqKXxsJa@BUOS!
z#;DobN}By_HF-Pf=t>nitft+2v*Yje)DzNueOlz`9WND>(m#mEXCGO58Dnum=8fK^
zi&dS$HYeWic{|s2M2AF4nOCLVkOgcAU_w&yP^#3Oa6_GZYTt4w{YCmp^8~58GrKV0
z;+<>$?PLG^(zUy!EDCQZa`<2QaM)57eyLH>X<O`(_el#otdZWf`SMpnnzk~}O6K4-
zf93lD#m9YaDg91;QM=Ze?U-{ySdqt54!ioL`O77q48-wIpV?rd6APxK!T$#r6i4t@
zdj2+7Y}+;!`r5Df@TFgl_j|MS`zsm8&$Z6!*GA@_G;q~}lYfgIc}c3x0Iz$a9QvMh
z|3}f8heOr=arl{i#ya+$F~%;9HEYJc#*k!b8L|vXNV2q?vCWX3gwR;CC0i1zv1Cgn
zsc3tLQrao?s?_^Czkgk>nRCrG=b7_-p8IouCw5QcE=E!bC98SH)=3m^n}c-!=AS=(
zRj&T_Ks>GySG%e?!PLWQ))l6}bNFKWvr+tkD%2t$d7-DlukFR2Pto-SZ-qWp9Xc1%
ze)%cwYWxEwU!gRjV^9&c_t{MPJ}vjjiyG)PQ>V!n09ojhGYbT2sX#C|R$Fpuv*!Pe
zZypFAh5v$J!nYcUVpR5g&qLl5`O)Zag6Rj)!i9%~G8>SkkNY{&aW~RquX@*okj%)P
zKqhSs-l@!4;-~UJ_|NEqOHBxsZ#oEuUezURJbqZ-d5`Y2hV@FvojmHS+$-N6EOi-R
zsFx%efQV#}LU`IuO7;z3b8H=Sc;JiDZJufdHRmm^^ni5Tf<Ih<hml5rdoK$_x)#iN
zU`h^U284r~%pou+6Y|{y12W+8AXYR6Q<!BJjfA58Wg=lO9X`Vn7x)6rC?vWwQUoBd
z9Sa(fpy53dn45crnVk_or011Bn=A?!dBE6vsR;NcA~lPfy>qg34lp8y5C>T4V~X~L
zN4-ZE@7rxmQ!_VbDX(TzAwoiQ69jK(UMr;oV7(d(6j1w)LWpw1tcnNH<4M{NdO3tU
zJSx_g#0N>8+l9U!k|O~#99SVizbpYyZc^-Pr4_pxC7$Nt4k0)Os+>OQk-J|gb56fx
zT9qBda->UDEi?-YYbBPOflzR<!DRD)Y6Pf6$l#Pt#%i?$16Z=T4~1>R@R<#pdvf|+
z4BBFWcpAMG7^(RP%r2EqxdcenPHAV|J_8V@!>+uJ3sAY_`Oj7*1;%R%C<eTx=gHnB
zJyt+j{tn3SuO&;*J$i2`>4@GuQ6MpGFm9kPllCzf0-GwRHGrhK42x0L90g_;gM^Fd
z;yx+BjrvQ4!r6|Qk4bkU+Tp|drP8JzTjHX&Nh~MP{|}zNdWUyUFOBzHo0OHM2_k$X
zwJ@Z70QgF$U+p_QQCn%0wMR$9q)Y)BO7N(hA_u-Pxh7=5q0#_P=3OZkT>30Hd|XH^
zEL0f5BLlFYh}`8{Brxn#I6k?MA<=Xk)<wi-CC%#AO$NGlawS)q_X)BXK*V;^0)Pt=
zc1bt#BVWpPbpj;ARS9Z=6F1KN9chB9ex$EuESmOqaFGIYWhJiu<MK2Ruwt~Sluid|
zZuyXT(Av~TK2SkD&%rOr<g)c60Fva$PMb&JGlKI&cV#=1cMi5{eOJLok%YNG7el`W
z_nwJ&Np^Tm|9Sc5t(1a<K*=<&t3EgEcV1Vo8Gw`0!nE&sd|>6A{SwQ2fk@{i3Vwnl
z&cC|2M@_8C1OjbRI3Ee*=&bN|?l#PmxrEN4Bu|EG6J-=u1gLhKY;+zGj#iVzbOI?P
z*TEXH{c$e7bQ`7yx?vv*(8B$2Ib$E-DVq;W=d_+rZ;ehoK02%()4jj%<4c8cOvPTg
zp>xs5Frik&ZPS?#W#%MuQD#m-My2IMg}7W`e;m868I2P<fIz*@v37L4b?~Rv0n`_k
z=p*LJ-Ve&;vn#}sr5`qKbR2LrPPu%%8}+bx9m7bmB~U**unf<u@a2UErQ)Y#+w|T(
zPA=o!OTT{Zn&Vz!=aV;*B-;Yio~X2sr5c$=0VHP^L1>lMGe`0ZM7?AlR!zBDR)Bgs
zy%Bg>^~+9;?neWAL-%a+5!l%IrupfIgVEcT(fvCp{O1zI&C?fesE+x(971)(bf^v2
zPDKA>74&@1<!d!bj&z%6UGg9I|9NPrV2K?#tow0SmVkLIc(d<W^|8;H4+ahr5c}O)
zfn_mz;PU&HKUXSW<Ob|XHer1BLk*q~3iEB#4B7c?-*Fm*pUm&(1MEJM0&C0Z{CdYh
zsQjmw;ETMVQLT$Y2VefPs`^EeJ>pbvGjQ_VDnepu1}RMa5ljQ(xYt)hZRN!KcEyfY
zgg@o`6`kBMJjJ>2>|7LjM=$CI3^zGoxM>3NUBeXyR#t2EZ2A=l-cr){*2^{;(DW*s
zAEMV_W7ZECaBL@3ZKsMrvxNf8QvxY+*!a>$s6uQmEM3;1s2`b|TD$Yr%(bs|h4gl|
zhiY$sgyC6gjyF{%1t|o7jm@ounrj3DixDt=c-;-^>addd!~S_@NQW&z55^J<)0@pN
zCk0^!Y5`wVonIp`c?vh1gHvEbzysasLrFSeW`>25omey}!4b;;u@4^~gV+<e$QU*~
z0nbaTCFvsje^Qf`ASO<di3YTKY*K_4tdJ~bvCEUsrXOft9Rh2j1w<?qDnW87-qx9#
zO+A>o8H^|S(Pp*M3N;&%ba;;p;ty6BF_ah<>Gh`CoTl_On@a@Al95|k*N<W!dMcPZ
z<`OS()?^WobY2zw|A{!pS2i`iZf!S4#~I~sBD#5@g(M6;mtylz=S+I(7jkl=j!DE4
zS;Qd$b5Q|$z5p|cUEZEC>yhzJ25V88$=s_`l+Y}fjITSALm$^xJ$!3*yrjP!_H60m
zeKRd?+%7-JSwL>P{sgmm0Bc9gq*$Sdh6Z#w#Dzc;Z|N*_MIP}1gq)BhquV-9Of8YH
z<Wg8@*A*L<<+BIjBAM^tLfI67)_>=iOR6xpr)~DuCy4;CGyPj-oqto|SJrj8)<XB<
zW@c)R_(g4(RCZ@A?3xvU4r5zYu~3BR?6x~x`21E-eNzDHvS4XROm_-FQ%aTg)h-q1
zi0cEm&0*xKzM!jl<JJJ}D^oOxTEf#sa<~5<GA8QRPO7sWda~2g)6ERc+X5i41I5`+
zjTKSpkH^WbqP6VMQ~;a*@N^wy^KH;6pb~{H(?)Uo{V_>?03F~8M8Kk28fB%KB%D_$
z2#uMRz!>XV%vro;I@flfQ>&m*4e4<|7YuJdyc0F^6@EJ*Dr`+N0w4-oNyNx{+w(4D
zPg<iMnhHaUwzE~WDgUOEDG&(2w2O7$sgQC>VneykWN})X42o*(@j6kZf1>Op?t(uI
z>DKC&T46tlv_bCoW?}g6B5v}O$lAC-6g9WJfc^AHZqRoQCkGdsl<m{Xk=>ig+)qhL
z%pr;+^j47O`Uswq(*sXV`W|&^4EAYulfivu-V><!Ad4w=DZn#ZvR6R-ko2lgCS_S2
z|Et^bcM+bW;i?9a2Sl1r38u$MGLK;SaR7B#gd2<9>`b@%UQ7}%AR>q7E1Tsxb>dfR
zNlLz?|4UW#e9AfBzMoS~k&j#&lpDV`gjKX`@NIPG9ZK;XzA>PB&icVlf<$r6)DtCd
zdJPfL(w~i;>}2=v&g5Rfkb(?&*SdqBmpQO&zRnJsU$RmCPpbC)_6NZ`_?&D5D?kM;
zq+~IBPiVjnYSzkUpi!Mve4${jSmO@wzyZM*77F!#@1FYh)LOr5mD9ay{^9D4ciB(W
znk`CN<$BuXW^FC_X9m{Z4}#ots$DhxXDkgu25EVN-XAz~0)xrdD%<)h7Y|vsnd57(
zC?Kv`J{uYQFmhw;ABBN?gX`T2RVgxU6W4z(_)P;V!VT}tbS3&E`}cqHiw_rF`vNf0
zWz`usBI~P$?<(?VtX&@f?KvHT#md1a7g87TXP-)?zLYwCkrWmqF^>%R`GRIte^UaI
zG8@nFv6POx3K5e9;JOL*H`=fol0Dc+C$O2h(snU+tGU_h$yV3V<^(?#R6m~c(E8CO
z&ada}a&#^;sU-;@@CIqzY$W%Q7HN^NI?g2(shMBnilh9#t(9R0eHUNQ_Dns$dOWE9
ziybGI7ytYMy~vDID~=CqGz%rfO73^Og%JGRy6KhgLRN2ntlnCF{=HyxY(IALh}!J_
z%((zSN9Pq9za-v%>_11KV}LCix6r?P>4dbmsKlt3^lDUAdqfVeMxHdDbNi4sr|3F0
zH_uqR%`n1o{l^1aOIpc)1n?QpJBaxhh)!}w*AX+dya8D2JX=j#48!qH;ny58s2<&g
z0b~*pq1c9k<(;<hDaP@ZoULuke1ee|eypPIsgm5ZagOno^DZUCwHjRX(eY>L-OPIM
zSfY3}8#n)MhV*HYAe)t{l}V(J@?PQT8py*g;Ekl&QesZXi(IBTier(tT7+7&kr8ss
zo{`a#KOq)PFMyR0ZTMkri+s}fL{do*1K^m6xw8i{m;ZwgV}y3cF&4vd2aF+L_X?f>
z5`X-X39oT>=>SVY$)^~13O(BF&KJMtYPL5sV_D3lQOq_;#{Ueh5FtRlBh4wu;JBhM
z?9BAm!3z96KbtG%au72un~C*op_`|}1(s%cRyi!53+zSe8h0wNZu+zret&2Q|N3II
z6wL_K)Z(aX+mrTY#^0DXdxYfbB7^@x<YIhby}qo$cEkO!*%bAX3>erq24DPD>4Oiw
z%*^~UQ+D;Pox{<N)qPio2Z6#(iLO{mnhP7m;#%O(R_r&cT)rt*3?WgW{47_Kk*%$Y
z<rEsEiz%?U3QqH%kuf{e;<MPDIePkZK5sN_#EqTEfq;2B>uyPUds>n<;_El?z@1G!
zmOH<hY46#)26o>JgZtamJeTcL?YYdO*v*$%k&3dlm<aiV+L5>%s>HijIBLpZSFB<E
zazC2u0@n{%u_E1vlmU1lGV74x;#f?QwB0vg%81SSDV=%xGK5ms2+XVXCy))IA20!y
z9;!7k-QK?!b5;&a3aj%mz*s`$jtqL$^I(s%By)2gUJ&YEB&-$4nQq(Zb9;8yWp+gr
zUFcTzLQGyyMt~T{mi>fl@Rp-L1gjzi(#La&cL>BpoDVA<+987v%W`=MN2|!*hj(2S
z!SIjsF6~7wIrdN^t$9@|p7rrwqsk;lWlHSwYxvnW7%OHtZ4n9jUKf%NE7-jVnf7MW
z6C0BbTv<De?KuK3ecSc-UgY2e46e!Bae9`obd~B9A4o9HVwN;#3yAGm6swo=+<Yu`
z=bTjRm!{uk!n=+RfpZTEaX%X`3@4QSxgD)<(bhPCW1Le@3V-_AvMe@&OeFM(MOe<B
zRbG9lM!jf3KYdFNCrm||Dymc#1^9j&Q;IzSg<B!?^jIWI#(w)St<zBC2`lYeN-OdH
ze`c(H_gy>vcYQ*sLPh1=-Lxl`O*FB#CjpjwqcR5C+6E?Rdy8_Pv~bJrp7hN-_2k26
z<@LkzC4KVk>?Z~{EQ{JKFBw{1%^JMT@qb`FbUVlY!Q)$da*xGbcRQ7TEbqKEOTW5j
zeBD6u{EMr~pSS_X>-?|g5?>8h7P8N&23QS0!C$SsVO^z!sSa3rXl3!cr)?onV#iSW
zi>G1ttf!s@8Wb%O7WbW3JExG(VWrS2N+7M1(!8XdOn;%ZV;N;d5cBAzB=mJgQCVKt
zfcHTu+iZnepS7huHm<{aMz-*O{u5%RjCF;ux+4M4CN5R|O7nzJuF76<?DN#rjm^CE
z&s(%zr~g%VFEhlsBLxGy4sf72T^{h13J@!eU`?c+T{s>gtB0KSVv)qftuB}JQc8rb
zna&1fc2>Zwu*#r<2QRN#cKkrxRfW!)Xt@qN_k|HZ5YrfN$n%{(sL|$B4baD3df&a_
z)FXTEsA@@Rlvd-u-W`(V5NQ3F&+m;~>?OH=n5dS6!_O-DMZx%fG~GUfcP`5YivYv|
z>6-wkA+;2hzutx9oN`;3<-PRL1#D+Q+jksFZ|2>nHVxpx@J=9bwrUjDqOslCne;uX
znwgD!qr38PAhuxls~sip=!s@`1eoUOMKyDsz>sK@Ba2p^Gm>v^G$XY`HWPWP1<p$n
z3bNxPLR~pTo?}?q+{qeIEeEujqn3swlCPik=r_b$lmN9t!D16hi|pnSgLFAqK3byX
zpP3_$KcnOPX|R8#w#qgfL)+b2=!u>e%cT0gQyk1Ol;&^)F)7<>#R$=XPnJvYZAAp1
zlk*-t^w^z0!XM(kfBOijI^~bbpr9S;l^G-5?+yE+1Y%DKP>s`R|8*`OOy}>s-odOd
zhv0_$znfU>-*f;msHl~%Ux9Va7a9;kV}Qke{aNJClu=;l|A~b7_~R5nk3h3%?H>RM
zGlal}B8ED13j##EK!j8}OZ7^MSWPb6!pCrtEQc~6I8PJ3Hoti<6t3sn*YZH!9u-L0
zOSX1sfd#}kV>CqQfD^#j@zzi`p8f(99|8lA#0<B~+#!M0Snb`X?(lewgume%DVD08
zd{pgy#h7bBd`wmXP?-gUUuK7wegShuQ5rhXKoV}_XV8TF6#+li3gGG=tbrYhb9XRl
zO)qY)fNj!kRL~iy0VD^4Xo{0qGzO9r^798n2ettj_eZh7N=3+>W*MPdGz+{mA1QeF
zgZ=}{&(n<XzMqq-jNf*>e^{K6?En;F-M+dJ89)+K%ORuV6rD+MSN#nML@QwL5YOi<
z_qPKyiwcM4+>q6%$_M~#3L0F%(F>R8K*Je;al^Ahn|1>5V4cv5SA^Ww37QOXbY?9_
zJ_BKM>D^Xp7Z33{^&+7z3aAvX0u;paiLKU%Tz{Te!$2G~fw1c%F%aVQYk&)0NR>o%
zOrK;KOp?Q+<ik4wT@r%_iVpxNaJ&tU$yZz<R80b(==f8Hmo|pl+@^a!S$oY({yI+a
zHQBan_amuYd-lvvl7;>Jq%h|G^X%WAHKa%JiCj0r`sV2?K;1aDI<msle>@Cv03e;?
zJ_5wVQ}pR6-*ZQI!dgDr(<G+Sfz!A6ge?}h^y}0vAK%77?<pOr@pL6pPRuEMEnI^p
zqH@B84KB@!*Y1_diQB2k7r|egFYXX!@ppgxLtjexE8(ZPBg~f-cwAp5<7veE&{XAz
z#CRn=GN+OP$Dn9S&4{h;Y8BJ>Yc)OzkDAW7_I$eTu5#BRBJ$BHIpGYmbmLzeroUfd
z)x#9+k=U{w#<gj;roR2x`o3}?V(&wXn!f)Ib4;)9zmy}T2R=S}VV3~~30uZRLCO`-
zt!Egmn)ju@gsVQyk1BP3c;a{F^4o-$eYtL)lXKL|IJV;d8CBVG@H2n3>+QzlV`+`Q
zf3dF2D;)6}2=49>u0LG<=)s;#{du-ed;*0gGwm$21hq9H`a*z0QP+(5Rle8-*xxyl
zfVz0uf>?2p{dGOMURFeN_I~BEVC~#w@J1Sho5klUai6Y=71`H?t6-+e712$MoyQ)1
z|L(PW_~Fe1QeH9pw}1Z`bnu_8{~o!yXyz$aTR8kGzrQZz_HoQVXFUO+nK=@nvls=&
zwA_rW3qcj#&thnt$1M+oO#CU)#(raL>{Ec84-|w)O#r^U<uIgM0)(!~e{<OP_ItuD
z$HXECd{7FF%`8gOTUHv*R_9A=P&2h`9`hxUkW0?xvOdoXha;JhMf#VAGpJcqAgo$t
z4iM>bJRpxMJXtNndT;HTG$a$6hCSPJT9~x}l+))=z)w{sPE5E2Pboz(wn^dT3>n(4
zI(DXWM=CyI0A1=~uS=XL;jk~{{8#)uD?#6J6;5DRLm;4x;%~+X4|g%aw`c^-9Jw#j
zyYE8q#?D>l=32H3mbi?Yh)Et>3~!uCBq!a!;5(&pR9(G@N%T@W7I`?spyu-MYpW8M
zT1j#)ED3$-AhLM?rhvp)v*4@Gl%U3n&koV}1a)-zd4SRi6XF3~M`jDW98_D@E~i5l
zAqk>!1BFJO23XPrKql<Ufz#bI?N|X@4~+`b5DkaAEcrw92rYPA!X>l6>dSbWY!blo
z=HCe0_O3M|G212&!IOBX*qf71N7&D%07^C_mkTt|{nfQgLY-Ok$$|sC-zwWXsaeEn
zfY4Gy@xe~^8~=Y7x9B!wQ%#hL&~Mp?>-HUvO)gNcF~+8(Nrg1~0|c6sL7qXfKUQ65
zpqF-;XB)3mFZ4QW+PvXznGVN~XvLlsf{?0aTo(ch-M@2^dQh;qQzgJRMq+rm)Pn`-
zOBL5)7F{yVK@F1Chz^siH6T(ucTkqt2efh(b0R*y@r?UzP;K^8#lc&@GFBXMf8mo-
z2JOIe|GV-?e-He(eAv$kX9(FY*gleOCRD2pCI3GAy3mr%_zKU{*b{~*G|b!}nm&XE
zWbF8GDDO*{X3z)t6x7ZU$=}%6JM#EdgDfJ@x~)|SlUL%1<zKhntbQS<alG5%yTlnz
z!fS_&oC-~tnigRoG`P-4?CUuSku45`q#<;mI_8pE%5(f7yXrRMJUBo$o2t3VRW65I
z1T;s_E7QBo?IAkehGB+nK*gSgHc45w482ToAB=m0`YsK<GLJzM_4!9;Du;J!^oCi{
zIppUM1Edr<9kL>of4=L;R6)@ZVTUG#5q;)$d#`<cG<Q8oZt)|0pl~=<qWA<}6-`p0
ziIhASD0$!(vAty_0!UfZ2mi8ni`-A@=<RNP@X_1GdYi??;HT$XybP*yYdifjLZ2&-
z{GhQYlZcjw&yF>0A@w5-2V4y2Pe-Z+cAP*Dz>PnZZPYVk4<2LNV~N6YW_YZF(=O$Q
zpTjzaci-6IIahs5VJ_O_UCWE+(Z1jE%t7|iZHt9tePf=<8jFQH$DO7EEVaX;ubwY_
z9D1grb&s3Z$YrCc!dab}pa-9na`!wY&dtAa(A;I+-1fNprToo9(A|}emZq`(eWM2x
z;8?ely`x^7t>o6Co!O5$IJG%Fs45UQ;E%D|dX8EPYWS?EdCza@onTH}$?=KnGMajd
zJ(yP(3l-~MPqaV%ebQGydw1N9?^k~+d^^51<!iBke6e76C4okWrH&(?myrG(s~;{e
z2o}+Vz$a<sAErn=2^DOB^7&#C1Co;3UPe_1UcObf_ea`)zBRu;k#-p%V#g(l@qq+~
z88KFIIC66IAUE$rorNF%57(mlj72z*xR>|V8duB6i*qdu9dI9K&d5W+6~7@`fRhE{
zA%XpAbT4y=hEp$~3^}OhXpXHl0+eD%9vzrXh<yQbV*ou{wGZjPYa8qiMxxjDn|c4e
zhNh17$X2{|P4`u4{i9PIA9~+4#l>PpAi+-t8@bY685uehDXiovk1~2Afn?l77W!9>
zio3De6$E|$PuIOp5c1H$uE`qK&Y2@Zq;37(&nChL1R|v80N>=WoN|Pc-)0xD;i;$@
zMjqAA(Zu$hX)e7O&1<tASBxf$a({HF!kAi<5#Q?9xD$4)cspUznv{rxVyYN4bp4jb
z(wq`v4j|+Rt)8eu50NVy`_;tnKmwH+lcdpT&8B!pUid~`?C}u%UreiV?Z;gdYCem>
z;y)MW^8YUd6lgLCp{gL(+*owUH4V8|_@#1WIU{P0If9ISXYK(kpc~V;+Mh(!gxs8l
zVOr5Imo_I1?}ZuCr&uqQ-u+dZa+5}cN;j4xU0?^KBI1xdmLJ;%ssxaH`QWBoFx5Al
zv|~*k7GdIaMl?O<$wc@q+U4)(p6!6uazndF@bmdc0<AO*=%H{6oaOgQA!{PM_k;Xs
z?8--2>2zz>rEk<TBRcROyVDWcg{0wSWwivk;Icy1L@Ne3pXIM^FY>2z+(oDJ@H@TN
z4D<`udpBrWs9gsKPRP(I33WASVCAs1I!;Lt()OPX84&;t_;&FC{+tkZYh*?U1zD-5
zcWAnH1T0IFc=N)HaUR13t>>cnI$BAbVat=$@WB5Xv-loLm8Ah?)t3>$e@#aXuaf*Q
zaS~*x7yu(}>XvNINom^<=`@|kb<0S|)UbAjjJT+)%M<9%7bNuxI43DI`^v;m!u?(}
z5AGJUYzw`KK#!@Yt_3VTR<eD0d76LzRWuTiJ>U!GXIb&Ac*ETa4tg*j_6aPD&<!yU
zWo^<jl_+!4C6t=rLV2wjnfDeM4c!D#R$w4123!5?_(-l!`bvVEC@&7N>Bf4lj4JGJ
zu@`-RQiw4ov{vJ=RIo#>S580@9+=W&;ERp)K#YoLgiu3Thq7m))GwJDzitSpa1$Hj
zS4N~YY*&>-v}Be}me$IPMZS(3O~63Gc%B{FLnxw72+uFn2Mo+w?mg!Px8(y;602jZ
zIYS!T1_m_0ZO)EHPfw;iS@C%7i`-Cm2rd=2HI{sKU(@!G;=?e%^ExB^G>9~wV!W*9
zTDx(qc5a95h7?CxiABB4RaQhtCoUfd+V0_=(F!KYqIb!j6`TC}DQ0iDv?@|axrwhr
zlJ%frafWk9#%h(Lh&HpeijS`>^zajI57{>GCPw6WUhA_kYd7DTJrYH3E+U6CNH4a(
zRJpvMJh7k~ukljDH!Mr&Wm8Dwr?m&^if;6Qh_?bBJ!Q|w(;^GHzw9n$UgohRl_Q5o
zq@5a$+@q{u+gCK-qQfU&i8Oj2b!yj`^ZC5r%hN8Uz-8}okt1sZKJRpnzSBC|M2Q*~
zD8Z6r(51}q5mmN$uIC9W94X#eYnAHr-b-hdhkS=geCKTM-Sl!rL;gMXzPm_sLd=h+
z9i0u@m8yABzd3HEIh`pRtD3>fV~B7$02FQYcQT~qZN~x;tJPZVez*HoLrx}dpF1LY
z$90^4^bPqjL!eEJqwLV93_@EOP9okzxVLivkS!_2Ro_qTx^3j!(UGWs-rZMm<?Zia
zQ(0eJ`(IaW@70qUA&TLu-3sV^lEHRX^@``Y^XYAh8|!NtwojEBb7Yrt3n@(hCYn2W
zpAnz7&SPRrGEjQ@=a&sk1Whb($YT#Sm{+XY!n8i_uCSYlsuYY?e;=dyw$vy1uj{gO
zk3+K}YusrVOH@_<*)4LHpN7S2slw>ws{}cC-kjdc?vXkq52zr%l36#MU{cw(bMv-z
z7?4GTvfQ_Q^x>fqrLTj=TI?Q7Ya(Se(_*H_1_D-{G)GMIdqPjJdd<a)*GEE*GVnPh
z9d&4r76gzyoh+|$(&SAFq9mcN7Z;n=-Dc=2QKu<11}Ispg}y4mA24aB)i-_$I+X0I
z{S~k*>9;Z%O^G%!G!+K-5Ajdq$pgt6A+BfhOKbZSGW7cG6I_w6bIv|T7tdhC{gE`~
zuBi?(;-t)60zqcRb|gBePFVj?g3K^v@6Gv5azYC<VYvG+dLpVUV+mU`4TxyKJp)l(
z(CtF8iGe`S)BZsk-7e}BYhx>P&47CV^DqMm>2y7rt6GV+vW+Tj|JRV0f*TpY@gdK*
zC6uzdy`CBmk*9xe?^fJo0K`-l17I292kzf&*Ld#U?4a>|Ca3XvgS)ZDiE$Zt^U+qJ
z&MAMD?I07MfZ|%}a4k?j9uc&DiGT%LZUT;U()sbc#ajW*8GI`v1ncuKl6Ws**@VE5
z%Ihvf`OPaLMierhDm3ha-IKe>dKy2{u-xbaJSs}<$KfXlwFEb%3Ze$8lcPFMmYxda
z`D5n%Vf&`T(Vnc}8FiBSk=;n%#7#TZzv>^S!&Q3~Y_G+rb~_ILYZ4}c3n@d<jICAY
zl<c&GY4hix&mzm+IJqQuKE4<;-oKOv5F?60WM#zj?b15>Bz;Sg8+t|vOA9|mjxwFh
z(Zs|jGn15-_|~stuj(8Z0q$ZtOb@S<0CBfkM*y?e)D#jtI?}9Z7h*bs50BXG-OF@y
zt^s>RR>qZ+)jZ{c8|3*wF1UE}ezyl%=x2y*c6u}hc&E}F(1xuVbp{asmnioy0D&0j
zfU&&`Qr;nJJpRE3>F7P@%sz!i-z$wN`<|651Xp_QF{Y4wVIP}=N}jX8n;GQ^3;EZ2
zO2aymX=JHo8Y;9~we|<Ov{=h_6R008*FNO4ho*TS<$Bb_O)!dN`>`D6(dT|-^1H);
zkZ?mv<_D(A?dM+Ov3pG(L&h!s0^QGyhrZ4?KW_}`SVcZhsBkPD^y<CkVLTq1R^f5?
z?Jo64yCMd64bUsXW3xwao*_Y=9e^eItx*%#{p+pQLLjq2OFBfbW<w+4o~k#tb6I2U
z=<i*T*jqjW-Jk6r?aq2}{G1E<Qh$)wu5(9(w%(ld<+Qhlwq_*L4%K^_UK+Gdmb|1;
zsG(UGwX_vfK(+bq7Z>tCN&BxP%SY^ZNQHj2U(-wV&w<PL&hNe8I+S_H*WvfjxRigY
z@B6@)d#>h}j|Xbr-{Id8T^!nfh9sndO;i}AC=F9Cus%!aFCdp*{~<S#8%NV5s@|d$
zza2I5{}kmHSygc^o~*kH5ciPw(P-|NKuEX{vZi7A-L^{uk&pl&Qv-c>-?qEN%*;^D
z>~?K>J7N-<+0p1faPUU^jfs@~t<Ns16b9`T8h3l$t++H}Xd5p>u*du~Bv{qfL;J-H
zH@Zfe-7U*?QVSW{9lPKZ-KgMb$Z5dC8(a&t*idP7`lnm+@bK1x?_C)$Z%#%OI+%~#
zjv~3SQe^z@mNxqekXp9$X=sX<D>)|bbxUOf$f*-P7uv%`t=_KP(pZ)pW&Pcy)vft<
z@sd+W>PkP!V{D5YK7-iuiZ*n^9P0}IF~eFtb~z`fPWIZ~C<H^cI{0T|y$qU$JP;Y1
zhL9I){8D$AfuH+NGMl(El4=p!W6Vc;_-$-gqHDetv>W~%g+12g6kekBZ(^;qn4WVr
z<loHvW_*^(UizWw&{+{P0RAjwpu2yDh=Z_xFk`wv{QQ|Ka&z<-v*2OZpL@pNOw4sx
zO&>{W4)2qi)%cYj&MWqJ)K$FwYSnTl>37q|zR&uq9NmKaA3!6-K)s)6ix{N&V-kn(
zjO?@5Ix0SM8C9$%9y)q)D>6n<(3BkWI4wcCA`L0yqZ^(4qt0#X#;CR_lYB~KWSw<s
z;0^;=H|FTg_*(LZ6F$D!?l6{J`8cS&tX<>i3G|e!(SMOSylZu$dje}NCdRuqJNJYn
z-%e6?8@n=R(xGT>C=qNbp*DjSq?sK$+h8w3x=Vb3r2=i{QOtN=?N?3zANH@)693K=
z*nWET{v|w(0P8n|<DKrIaz`<b>_c->ql63Czeo<<sc-WUxVjPz&KrVGo2TNv!GhJV
z&x4vQf=X(RnLnTIt2vBJ^f@V(gwApypE2><2U3UkBag-2JlJ>uidgH~gKIm!jCoOm
zPpSVK`6Wb5r4yjCtklJ1_Q$sy+?}|qwYxHC>)=d+#rqA5a6gseYfd5Cm&1S>^BwD1
ziz=hE_MSxK1{iK8!HovTwJl%+ZRex5kpBT28l9`C1<*Nw21w`_6NS9ti(Q>UOq6=&
zttI+GLJ~qY&k{kRp?aMpfq*AgcG2S`IF013?_Cq|5;+aMB2G4RSL{0T@Ll24<95}%
z3U;p?k9ri<<=6grwNFqoM)B{Ny&vPXg%jKMmD>KpgHVq?#4Cxg`N(8yKA>^$eSPwK
z+2L0@M4j{Bv(rHD^z8c|F2>zzs@?YMy<7XPm?-Cuy#C>S`anx{M}L*OS-7dA93tYi
zk9l<E1=->$gTa*ix%!@k!`IdaG<F~KK)^4FyYp5Ei5P_#=A}LL4s`(`d=bx^#=lPA
z{1FG4sg>Lr*?9xN#ebRUH;%Nb^07N@pHFUS1pL{jFCluyy541k2H|oAfMlkt^e6CT
z(7*U<&fw0aMo4fN*PgpYYU=;sq2D{YIvv=gT7>w^Z=K!HYHUAjADCJ1nW`qE(jK{)
z4WDpr-ch>LL)<ORN4#{G20%Vm2?E&Do%e={CmXbOpGG=fdTpH38(T*`-FT!Eq~4^G
zLjYo#8VwMKXbpK;wHBsWeGfMs#C5}rY75nManp$g5*1lzCl>v+Ojpa!oHi!uC??rm
zx=X>?dn5j?7UWskaprQu;C{DS=eXxd0Mo+U53tEKObB7^0|e_<`v8LGFP}cRdZ(^r
zgeMSsTIDF}6`=w5^8*DUxlLYI%a-8fwZsM9mKb~g=t9_SVZre0>UcxvFiRR@_Lve6
zu{RDNHq+z*B6gqDrHNOy=6%rvLihSV{+1zfe++Q5l;21G2^sKo+i_aw(#q3KEmQm1
z<UyKliL2|56zK}x7I|t9HDAZ-btC@4YKp(Zl%WSel5$kx%c8?=JgU%jlMh9I)F1ae
z`1N~$v?oGqT=R2u5t$~Qo>ogE;Zc+?*EddeLT17tjajUk!w?R^gD=S6UmPHaPNJ^H
zbxUW1@^==5nyvasoi_N=FPJ+P@SghX>5-U}B5g4NgT$L(9tjjTvnP;ckc(+f9^h)k
zFX;=Ie#%;Xf2tdN#s8Q{`=?STG^xfp3D_1=7{DNb0o)|e{vn|~6m1=)$1P_Kji2zh
zVQ_YheTPmE-n0nTKQ;DA*LxR_`z>B#u)d1xT#x=#^kfldelGz+vlQJSB)wDv4$q@x
zJ@^L>E!WRqWUjeKQx0}ox7H^WFs|Ip;l90TxPPTrp>G)KbG5TOE>im#V9YK_-i^Bg
zKpN3~5(~HniXf}z#=~I+YG&0NNSau}=KX6WbhW<NNY9x)`J1BVCjk(*1Ze-qDzDC^
zk#@$jA9}Ddl*AR26y0RHvVmupt^DZDMvsacj1EbpdT3SH<l?_3jaJvi{brin&9Nr0
z&DhGgr}=`aU+s&w{ad)c`QXKd>CTn%Q5!NpGe15}MeX}(6(7!obEUPyp=da>zoNXC
zyaEF#Hd1{r8z_?LdhW)3F0NwaP#@BcugZ>><&^(<5}f5QW`<JID&tKER;z?ieJEl~
z1F57B%5FE(3ga8HMVGYInPa(i5jvk-*SY5$S_zJaXin8}Mo7KaC^Rn?fbs}IbhPx<
z^$nz_yK}toLVan;uR2OxnobnPzD$l_yzCuv!Hx29S9~Av9eb=Bp3oyhUfq9oIAwe0
zCQnv#4tN*{B#j>tmfg^ZAj>I+yIReZeRC@loqLqrJ$sLi<VFea{(O3T>xI_!L-u#4
zy4?*(&lGeh*>`N8J4A(|9(*mof;WgbFcyO~=q|HyEr~aCq%^Ddx5zIX(WOkT78X1f
zI<@O$oP%ScT2bv6p~?31Hj@Yh$pOZE=@{F>KmV$xS$%%&y3~sAzw9`7+kv7=afl;z
zY(piUYs!v1KGu$KtIz!?6lEL`>hMXu+wcTuVGK@$Qt`u{@mX4PmiGMLQ1Blq#rBr0
z@8xakK9h%By0o2kz4R4h{ctT4lT`twf)Fs2ON)~eSR&=I=w#dZ*3;CL&^qaE9ZqE0
z^Tx1Kk*e<Ey^R?Kg~y`^LGE#Ciyv;3NJKyOv}nFU5L9;V`@|u>dV(sCx~=`5QCRuo
zu9r*wCyr>7zc^jl?%im*Ap?8r(FKKQji)(*Sw;E^qr9CHOIdM5v*Y<)^C=}TDne2}
zRH1MQcXv5Z<-UeVdz$oO-pW6Nk?PcKruv6li!NJZ<;sT7>Ehi~!-GQOO-jEB$5n3o
z*IaG&<dxx<Ryf$!0?`QuXvKwmglNQs<Ffb#mm^-W8_q!KHd!huST<(Ic|V*qu_?vA
zT(1;h#b4jba7@_Oc&P(>L+i!swP5X~FbB82gq~Mb8PoXRDhu!bt3=M&S63)!nBKUd
z+ei8&JR)Hfci=h*ue|)eR>YGsd0gb&P7JMCIyRwy#8tO4ut=;uF{^X*kfwAHqgecQ
zmISSLPtHvBaoHp5BE&fp!36c}JL9D%x&0DCRug4T2*~0WX!~4>`*yNTr1{LXvG#yU
z^NB-YH*?HS3Z3_D_tO!x^S|b_l=w<^O<6E4|KOgS(c`Wh6^eqHZIz)-je(*$Iz?zi
z{6V2=5kFRgh(59rTdw`|<7twFOA+SqPM7tJBWwl1Yad)(<GX53p6%LLuo}y|U!lx=
z|7oN@tvtWnAAMBfNOr---{{=aK6lof3I9CzbH4xW22*?K<->=z?u!23l%r0up_jIU
zmz9=YHlKd-STOz;^CN%7>e_j!NclbH$?QUo`LlDXUv2IKT(U!Sa;HJDsr2fp6h0}E
zNFt`t3<_Qe5dS`A>+0!RcdBHZ&=Ip5du}!?@02ka&oU||ph1LMu-VXz+S8qo_T#<!
z{O*d%arT=EpOvtb_;1>n9lk0jRybYbT8p<$#Z*olQ6Ia0o(}fknM&Q>xWy}6d}TFb
z+f#GfU|W@u`-z$Mzs62S?|pH<`}Pk(^DY;eDc@`2EI!p=_uOfC_I=Ia6UA#gUMwt2
zRo!#hcTdl7@>r<;1f_9bfNiJlWQq6iPaDr{!+#G=CR01b^3JjZ9===;7#L}IR<eoy
zkiJ!@vIZc!JVsed3C-<QV9Z_Z$=34TCs%6duQSZ(n-l2JvKZQX-TA#~-%y7}-QGI;
z4xz|rjr2wV;*!{G9$QchrZX{IfFteBgm+7^CyG|lt;GHvt*#c8K|QLv>c<#*F5Q%o
z{p{H(HWQzN$0CyyIkKi{t9dLf5zYdpQ=Dm5v`sJx+1LD7U`A9yK47~fsz~9_^~%oc
z85Fy~NXyZ8J+7J+71zs_9(C?6h7d=}W6!8qZOea9Z?dI><8M{!h#4@}M6xqv<^pO`
z9Bii7&$=EEMPzZO&f2}FU2wUUZy{9Yf6DlSnND`12!xPbw340H?RKBD2BUS_SvyHR
z1dEqSzWRmz_4sKa2iLdT7y9*+JG*5!?Pk}E<Z3uZE6pFs`iPb7gU{7mcR8d#EtxHF
zDDjXi--5*P-C_sutxY>xjfCMzl(P35XMxQ0d11yBB(c$vtSOYN0mOfo1@12S+9Bea
zSX%zTVsfF&U4Zr~2_lrA<Gz(7WS=!39^&007-X7E{lTw?u%C&F%%boQ(sI>Y@M~*=
zR`jmi;GV+bUdu}9SHO9*Y^S8=HQ63+d%PJLr_{pEmfvRrRTv7U$WHxh!^ta;|CCop
zB>xN*_?T5iD#`0SQ!)8g-l9r$$i7lrqeX5BD4g!0{mPs79%0??eI<XTzfNkYul#Va
zYn3)<xKUah>vS@$a{$_rt-0gb>vOC5XQ}Db=wQe;K&d_3G418;v^M)TS*y_w3&v(8
z@zllK8jYu_)5u!6=sk8L-(^EqYY7&;aku0cPaKZ<?Nrk^*Tc|OsCMXn*yfsYS*Pk;
z&*JU0S9Vn$qvo7y7SY^FL!jH<E@6Bo=ehaWn+?1FlXS&7A%qAnDdn<BGW=5*e1JIK
z3)5o2mWu_df$FghjisnI#$QRsfA0Kr-bTDgYWtnlJlyb*4E)jg69EJevM#8}JQfRv
znDL}zRk=kcL=^g)HZ(13ZD%r62V0}>_1rj{Fnv~zJu|{Lrnd~~Bv{du5YKL>1&v=y
zofOg<z0_Vf)RQ4hoDwlSojbIkr5!eQ61w{8ftJzzoK=``dZg1MXp=2?xjQTG*@E?9
zSb345Nm>@(IxphUd2^&vj1`0%44;6wcgKDW`sb=DdMXtn#^MI+UZsx@%-`qb6uvSe
zl;l@^w5`ukYt>zAwft&h*w`A0vp)NS8X(YG`<-qeJbLUeCFpm~4T_|q+MKGCm>$rz
zNS!{Sp$pz}N}2#jHZ}{)q66%zYbJr5H5t#5O{FwgsuwMazZ$gt{mY(r_I7if*lZ@Q
zL7{j}5`xbQ7L1XTd1?MioCSh>(o-|=xNgM12$~!r(C+1u=MtuD>K88GaGk#{e3mt(
z)&E#JI@=)jqN-}8K|sxn9gE$ZD!G}k#~)yuqp-8r+10{?LLq4O0>VsEi3U@ByG6`C
zT(QKoxt}xkIh-3T(%fiDDgRHEdtGcc9m@#IiUkPaC<8U|a@Kw$+q#0ts5aT|t0&!M
zyEE{cr#Q3Gdtr_OmND2%Iv~F*4pRkdgK^C%GJMRF!QHc)2zj#sAyN!>;V1w{{1sGk
z7$VT8orwLJo^<?G<7!7(nQomee>)S(JS(^5C$O4~%bgW<fphN=h>6vo%<F8Y!Bg6c
zRu5-&MniC33CTIom!x{V_7=rFV&I{o08m{cMy$hXg=Cqx;t21m@agb)H~9q;60e+x
znk+uH*LkYGJT)$-G4DJ-1WOGCs1UOaXBJ1M_IQ+PUh3nOP|^OAY+fOonP|X`$Emh0
zLgEmJ&U(<2#Xo{GTfxB@C=Dw&DGiGZ01$!JgokRk2e<%OY@7w3{dOv7;BgSEYC84_
z@u8|=yZ@L=@gg;^-&|jM%GQPACBMx~Sas-5iABIhU3KlG!CKLsO}0BUbG7onf_J|Z
z<=+lQwYiXw4Bg%#Z*P9}L)7HJcut_|)z=+29#CZ>5A?O^3z6ViDPkQ|L8$IdkD^YG
z9IxCz5f`;t{!JbR3M+q`;b%{<<1({op;>T+iE;dvjw|B!FDaS?0wpasSks4q=94};
z_ve_@b8ZE!7wuYU>r7SGO63Qv=}%%z>{_yqr4Y3<ucV4WeL}QmoK)$N4vB|2V!Rsf
z@Se;I#6fG&4E6~RLz!6>A$79rne1Jg+lz7Fw1O<-bq*zJ`#!H83RUY-1x0VdZjXn@
z$<OoS6hXylyM1@AXsB3ob2;23X(GV(5!rY<H1|7pKLmw+cR}Tn?Mesw3@m08%l1W!
zNhXUCQ_R%$K^+cEYNa6*QKxA8)Qf>@RsG>TbaUczRE%eZbknDhd-Lzi!|j&MIz4|n
zdI=8S<M#^)Bn)|M2+n`kJT{T5_^&2+@&3lo1L$A54ZkmroGFWO(vpXA4tNPuo9Rl(
zQfV9O!V>!KC&GDXB1@Q_{Ki&%UFa*V_NoPYfi1`##d0DmFIX1amK89?5lYt~zDN%r
z2Qk^AN)|WW6qHKcTX?xR+Ho1A)#BlNY2p*kh<g<{*~L{tOc{Yr!OM8mh1jwO{S)0V
zKP%o~qtj?(cgZHmN#N|itsVY8)`#XI9p?6lTb|QwonJi*dht=IJW@U<imhwdBTxv~
z{Zm=xM<H-5gXf#Q7MNuuj*AP%_q@Qr+jhfMx_GtMGNmkkWZZ05oL>6sKS|i^LP2B_
zse)mN0(mUKorJYJ-u;^rV^)#1w!U$a6w|C*;gs=v<fweg3v*k2^zdU3H3G4{iuwDB
zZw8(k`au?8xl!`vDY#-?Ay!&8UKlJ8AfS_W{nL~F>t<y9two8;rwD%1w3dN>=jNt@
z{#5pe58EX{2KZru$w)J`v~3AbeD~&raz@Vm;a1(2{h7(;BpGm%a7n*2WBrOr+DNWC
zgtG3KDr*WTTU|RQZnrFsKcTLBGzTw%u-@{{-j3`1o#h{vl>}g4Z(F-hL(zX%@+YDp
zIackLg{}Zxd5Ywwb4IKy1V~6k39IRoR1!wsLab5$exp`Q)<S|iJrfiX&Xq~aP2#}u
z7$edhoNQdstWPFs5A1X~I@6pZVlCocxUa?I9Dd)B`DsMI0;@Xmaa#=BlgV`PgFtvz
z>|p>R;nD5)lp5eCqKsL01&}Tio#ID(>^F4)B5^-1dBGNK-DW>b>|O{iQBU6)TVE?U
z@=L-XKKtVd89uUAJ+F%klhEG6v{)sgVOj_OGyH}34_zBHR_#p3?a&4|6rvv>)>M3D
z380}^M9UIYi%8??rp<yFe;GLj>f_}<=JFcswPM=F6G7E%Ze=o4j!;;2>xy@<qW!k+
zC5~GDIn$y}{fHgb?Pe31EgKS=>YP&TpzUQ(iK8tZBE7bEe0`+4^QGXby@6XF|76jJ
z)vG4=M|dYWK21vXB-{simjriL9{c?4wMnxkzs!G?^KKv+lr)dy){Bw+ZUn~6XE9dx
zyuTcx=*x|Tgry=>CdvOZo>^HV03Wr&4G;)WkY+=#=I|Wq8-v>J@<R&u7xx)guI5)}
zNKaoAwC)XZ`vvT9@nv5tTiTwvtOfT-K!pVTNkK3ip!-c;7KWSfu}8OS=<-#Q%#GgN
z`|N7ikByDMW_7<Y8x!?7*a+L)dBOnbAZtyepx0ND-(^9OvDF<AQ2#zAu&~({ej~{t
z5eV!THrvH$$PE7Q-F4ZtHF?Tuc^@Sd;Z!=MsX3d+>(9%K4fZEGqG#bj9|>hdZ1lGI
zF0aaKcCmR4@#@`Aih`op5ITo+3|b68Kz8Y!d91~A>uIwOd-ViZbsMf)lhXD%`L_4^
z92)Fzm{Z=rreA&?OtccQvwY(+WHuPutoeFyLKDi@tU<Punudmc;e+mm$yw`Z?Dl#s
zy5cgq#VZRt&R_WvG`|#Oz57XX{=Vo&>8h4%<-IET3N@S~2eS)L;G%poR}Tg*?15`q
zM@r=Ju@wlacnMP@jY&@Iq##mv=SjthauITOO9^-wu{}`3bqSeG#Wnx_V)t>X5-&h9
zL&X}y4t_-SFf=Q)M0+FiO0VE6j)<p4+*OWPQF*&TV`9iQp*w6YwyqjDFdY^+0Et1R
z*{blh^fb_jEIZovRbR*dFlRupfqyR%g#%7}npqXBxB)lo04`_*eJuo1Y;Gh13O^A7
z_n#~v#gK&%xvOP9%LsFN;^KO_gXan4#ZmJ8)mI;}C}0i2rIh}iFnA^$tIF#}W0Ch&
zI!W8;0F_~*Rye(1C(}C5m7kE5xqwmR{B!il7Fu6{(8!NnIO(J*3C_F<r76%6KX;zl
z&-FUVL;Tn}2_fXR))eKz>09v(5?&T;p1e`d)xPuYdv(IVei~p>l>zz-P4vXo>V*D7
zKw1zJi@;`CLg+L)j80+Eymv|d`lhL6tbf(IQ%~DTxet0p6}<iAy}5O27qpH`9ey#s
zSD&U}#!@V_19-H4_WKm&_bs1O;7yPs)`Dg+efJ_fv+CrHOT(Wz2!1RXC}5`=fTAYi
z-g!m30W(mV2io=Eb<$KM4FEHdG(M2V3_Er)8CN5^HIkL}RwXg*wPwCTaATL*1?K!g
ziZZ)30YHUjiUfeGSizMTK5xO`>x?#KFvIRz*~J+ZJ5a3d&P-DXG;b5Caju;OvRUv1
zc=_?3m@^mW$`x_&Q=l^bs$Fc28iZ4wj8mUbeGC-F-@&Tluqh4@FkS$HC}CL-NfZ}Y
ziH=7Mul>j$tp$KUrj-HQ@M;V|lFlYh*I^&@#sHl6<75cq?c)dG#Kz(QCOh<X-TkQh
zh%3>OO)R&gcJ~l~*;N%@2@rN(=V-uRrrnNS{#W^$KiFNGXz(HlLdPlFZ%b^&N6I^u
zgCOZEouP7A5=c?WYA@QXx_C6o0_e53YV19CyZrx-fli<;5W)m197F?Y5kj|6${+K&
zKp9?o;SkcJK^X!32D~ctdcKk_ZC{;4_HaGkm$PQ0Lz_~STc+N^OEN<qz4PruFKt8T
z56_zD4E5G5ojgOm#Of>M6nq*F^8_*C1BqS&Ftm8df)W4uqE2^C`59Gw$ekg8%jPE=
zWdFj?&=`f9{eVGMZ70u`s?=Lw3Au9t$d5JMy*e+9Q@`q5U{(|S_7C>-#k*z%Em+H9
z>@LMe@Q%(kAkc4EymVg-f>YVlD&Lgd3&i(t0_%eGLO{&$JWGE+F>6vngd6_jKq6yt
zClCmMm1u$?_qzcUu!d@W^RY~U-Q9jjalHNQwZ&WQ+%x=f<KoEGb3txCiN~uWTiT;8
zZ~;^3ZOS2GI?k3e3^NGvBEzgb%6ET;YAyAfzT}L{9)jD$v>f8YBw4-o-c=TrvhdW6
zeq!5YIQo8F#RC(vqgud#;z@RJ;W&Y0p$lqHjhtfPIcU=)xF0N!0szHLR)Y&LQJj$7
zc3Sc%B_GA(7i~)pJ1KtIp@t7xUJ|opN9m8t-j;o(inbI>Gc3pV$hw<LxP=y`l8lWT
zo%{18*P6@JY~QEzXj^2=$6}&kmY170fr8#2S>YsaE5Q93cxYn+(2(2-jyS^ASQ!cL
z;KoP^Bw8d!bd7Y}`A(f?MxexG^MpJ_EI~8NEMt0ESWiu|o$UA{$njA^NX3WMGOx*O
z*;hK;CqbS#+D8=#)QHd1S;PqAJnmPER2SAK%295{6!MWC?q^Bv1h-Bybjwl29I0;<
zyrCR0d&rajN-<=nmgpDiGudMi;q94R{&UsYP4O%#a@R?tvQS4+v0R-jT6MvX7#m;N
zUg?mZu>uA@aGn<<yfg$9e4?~(>x?6~=Gq)@0fibZwdfedv(#b|;Az(@+w<<76=R&r
zXfBl?0dnISkQ(9nYSIK)5``8xv2x+ap0%nTL%oZfo;q9G25RdQGb<WskRoqmgJTx-
zx7V@S;ZC*^6`v1kuD`n2_@iUo?{nf|^V98_zj_P%AKNt^g9_CZGmGU!oZV{|bi@YY
zE4tApHJsPNYYSN69d-Ys=*$D5T-z}I%syrYW9<9P*v69VNn*xM5<*BdW6PFoNm9*N
z$G#@*V@a}wB$aAN)<USH<;(~Xl_*D!)A`=-pZR~@_j#Y^xv%@We;54u@LxH-3*&Nr
zONVciWkegY8_I9-ZJq~xG!5Fi!=~d-IMN{}$ABryD@+=$4M#*(W=W+ccHHp#oqq#$
z<-*#%gTb!hdFT7$4=!cIJV))h5}V2?GP;H9PXY>m?|v<YQo=Euw15a#c7!uvKFRHP
zIFx|DWN=MN!v8Eqaz656>WHz!{{L!hqyUEhuQQZZuIK2c?T1e@a27pYm2pi#TvzaK
zkv?=(J^0r&5&K@$NZbg!Tl^96qoVBs5aT@G1@K>RQ!!`?f*W(o{XZ@r#P;%`-0~RL
zGyQLyW#zhns7Kv*+h)_lv)C}gk9vUIxZ>NhL=JSHSCAU>6d?lwtma7Q<gpjx!r!MU
zKcGM?1IIMZ6_bo2fZ`86q-4yNvgzmrN1<0Fj^G|zW@2Xe(c3z^UDd-z(<GDoE^DS2
zqOJJ)rdD|4j!$U6<N<2B(^Y^_4<<lI25cB1&R8)oC2NI$<pD+8U<kJ0LVFe{kizFd
zUf{<7J{Eu@v`BKEThNO(3&delj2ZVCB|g}NaaH0#=E$?8&R<4>Np3kru>IMlg<Vzm
zeTA8~<e+0oVVPYurO=i78M76@gZJY0n$|*p9DZ+>gEtJ=PDh7JL9Dy%m#Q^I?x=Pq
z@c%BIsgtl!N=miYIZ{H5%KH{rvaH9!F`SkAZlVCh%kUp7AcZ<FX{kAdS^x0VF<DZ<
z1SqL@Kl^_H^AA<=!Jc-RSZf`j*wF#{w{~h0uis!+v+`ku0X;>G4?ZjXc8y-U`T)tV
z03zKxrMr3T(<K!TW!vfAxT8Kej*rs)PXR#e$H!d&KuCo=fC5#LY+!&Ayg1#rK4_g6
z@k({kH7cLe1yEs4CxRS1cN$=ABwAmIa#~ET2wbpu|95NzLoEBH#V?Ol1cJ<5;5)qW
z=o#}{V&_Sw5`jL72L;)f9KGE$7~W5t_uxw*u1#*0^)dTB>(02Ou}@~1t)__y1?CzK
z0>eHzC^UWTJ%HgTPsacpFgl4E=<D1or|C(Y5G)i>4*>l)ALyZu?F+l!y-)RX`=oKa
zvSeVhp~CP=5W%DP9=!<a>*;B<_SD*Euv>kfwj|D3#GM00q!27}u#Mr^@@(}YnjH9$
z43Q&y0@wjd7(I^=IRJB)`s^b^36PyFK!>6Ug4Z}cK=R#mnM$x4BO5gfP*<jv`OVTP
zK)x`6nA0NO;`9HZ<(b|**aOAhFC>GAWPY9fF}YwM14Gp3A<*S&w8HsN%di%TQ`;^)
zcl*NQ@}l!1W7iC6qaqDw%WYP)hLf%tq-y6(gKRRH@v)w>DJ$Nm#6U}CSSZ=ftJkQ2
z`k#%>17y~wR-k2Ec(8sC*(kX7XnL*6j3q1Wy+!5w{Q>}IkDMlp;lFjhYjC3nX~M+m
zCTU8$!@KUgvkjf&#j-;$ES^38!`x6i9`CY<3+ucPM7q#Q-=)@8!Pu(ce5n{Lg5vN*
zGNgzsq*(n1i;cWg*gTs(o|8?sgn!$W!`>C-bj9|*RKw4+&Yf2Rldi^IB8R`1=~>X1
zNWL^<ip7mQ!dn&%ZqOv^)zJ}D+($M2k({iq<)>c8X%6Dxn|Eco{EZDL&YJ*%o7vZ}
zY9^{SaU=#+lTt_)=ixya0G;@PP9sEsb8sz7y;c<w;LCEC;Q=X@OBHj7!0%fk*VqTu
zofJtfu|X!-PP2jnU_i<}XiQt%4uAnXZ}n_Cp}t#T2Q=P)%k@H-TOj(6PQK3Rr<xZc
zDz0aK+g7}nG@Rav-)h2rR>Vzrrv5xjNAKz}lpAxR4krr$QaP9htrp+z0jXLVtb1I8
zE~83~qkSpo+a%}|U*gTAR#G*oQ7|#mEXAe#zM4CLO*z?JLe(BdM$YR27cyPJ)EQ2F
zR<G)i0%~e$&2Be>y0&MusoOS5j`8xIs%lRd8gn0FW)zJVK3;B3{!|sG$m#rgtWL(x
z7r`|9KVNV~{|+lvRtR0a@^=7$=EtDuKp36LLwr+x`cA8=UL{o%%59em;RlcbiJ~K(
z?{1;dF{`B|ALOJXlH$qeQX&IcO3Z5alya&UZ|Izf&1o8*?vkzF73_m#I_U;cjc&Ao
zJ#Hp_a_T(rjgW{Jbc*tT^duYrJEXvH7ZQCuohK=@l;VTA+AS<UM+A9Er6)p07;DOW
zjBjj*<N~x!fYJ>e`MM-e#siPxF1w|^AV{jqM0+)PIW)K(dHU7l+iT_SX$`umql0G%
zlZmGG!UXCjK`wd%F_2fT#!Ud=r#OAZhp*0XFj-=|qX<4mRYVomj1db;Q4ez5_gsuT
zTf>=Fo-2C^E6=D*S0Down1IeVLu9&#+*Zf3vsGz6$GbZeqhDuCj>&ePfs^ZSd4H@&
zpQ8k<9Xun1dZ=9`q*|uPruvJ~=hxM)g?!TSrz1`G4_T?JNFJ>NFt)5P{Yd3;(?E)D
zn#ox=HwygLwyLu_qGu&<uS-#Ec5cg&gC~G=jQ6umnVJO27wpI6KQ(MOl(>4E36O3o
zl4E4-WI<3vox?M$sxxs9gQ91GO3`*-6e~L?wz-T7ibT{l{y#2TQ>9l4nR-V=$z!^Z
z{OD>f24-~UgSE8l&q$JhT7y!2`@OdB+imr=2Ky-4`bJelimWz2`b8FWf1+n%pkF3;
zgiM7y2;_xVY%3FFjmxFYtTgE~cNSE%0f|bIq>nl_T1rGeOb0P7D@-=O(N5)m5sUu=
znH@tA3XD&RIs&9c4UHkh5?LhUr*st4_t}Y|5R4d^ZXY3@@`nfHh1YB={iU-GIFLcb
zT;w5_AUI0>M0FW4i;HF-w-uU^W&&lS^S(6SR^gXnM^oTx4l3OXdO|ZvRKCevia7g^
zs6E!54^Ss@9>R%T-7$`~R^j{y_+hVa^)xMNtzO+0Rnl#OLLz>zz@D_Q2lXOfeXbYU
zoweC^B%6PqidE)(G@I9;CXzgPTx=XxMQVTztKJKIQGJIYtR_Z>W)u~7gi|6nZ=hT`
zb)2tz%ID*fY%&Jftdbg`_@?&%Kv|@PyztV<qy<ypiZT&BESX#vz42kR{f64>9l~$M
zl}WM@WQfOEu=Md6!5a%PhT7pfHG`=`Nq6ZqczTm!_^5FwI^WPkyF!G6y^nKQ+7_?3
z3%-N1&`xG;)Zs1e88sU|Wm@zQA>fP=V@u26osRI^8%C!eV_FJPqQY^o;u|P`3MjEL
zN?BaOEk3`E0WrApxIi~r%<K&<2Man}ul7%yN$YOL-<UKxbUYT`t?K;D$yOuEg!R*q
zk1l*F*TLBUC39(-5r(mbMvK1crSvn};l>e*();x*;8ebcyZ%bau3JL}pD@sP(}gM#
zd0n@#L46E40mB0#^z*8;2=O2%)pQ<J^q;gqqH1iVeqU<N#GRwV`uh2o<>h9ttX$5$
zxI?x_M8Tu(&fr%`HRNpFb6#wNVyoV@2Fu&gcCjKzt&ElEr4rToD=I933v*ml+Y%ab
z_`=bFQVEa(ABQl$RJXH7cZ5vZG7?yAjh@sL{g5ebwmX%m!kN_*21<V&AW_Jq^ulAP
z0FlJ`m^25tz~D2_+O+yTl_}x$^qX;!!}nTu0<Y`u_Gt}<ja0YPHYSc9EEg8*d6~1t
zo4cv?<8&@5^UkePw52oJ=0g1q@v_{^?G*~Qsk4@H=HGjkkK67^*9`-V_r7!>r?H_Z
zl-o-tE24B@tQi-HZ8~t1+wi{&NQ>Iz%UbsjY=`Tq6xpfJ^<4PoVze9p8bV&8Pxq}^
zbZ@ERN-SdYjo@#pOFo<6DLRXD3UZ_;;i$?7cN>O(S319KG!w~7)tsNBO2O=^nDR|o
z?f6`;8*Wq5CzMn(>(a=1XlTKc-9y%7cJstQP`xoy_27*%X_osaW&SJ=h=UD6`9k}Q
zrl~_q_*9q<eD!EIUkOtsM`3GJ3Y6#4Sq4rNk&}vQ)7A4=1NQhM1Ps%jX6?@-q26ZX
z5O;cak=*wvQQn)PrcYF+84?wzI;&!4tHsi*mE;&r+)*eIkYK5LFt(Bo1dk+QKlBil
z58h#PKOc&YvQsN~3+{E<{dJ0I*fsp0wcE_0VBTT1`FJ`&J^XZR*#`z>Oz8xWdF9i6
z`QpYvg~mh@M?0RK8@PSU1Nt0HN3<OVRI12gyhZ2fDFy=&^>kENU(AFMC%es)(VndK
z+J;YVpNo!`?SvFmh>NC-qIzVMNp-tO6I{gh|A(X!5q!&6d*8dWTm-?*d&mGU;Nu%R
z5Xi&qX@t5jEvkep3ENKinti}TKQNo@Ya?ee^V$Zs!B{BE&a5%BLqp`(y%cY%zmwbv
zC%`5&q_oI!QeW;v?j;Y267|viXinv{vQC|hFk05eUHK$YjJKSW8GQcLLpx!_YIm37
z-xB*=m$AIUKynq%!MrZS!LasTE*%hf1B5vz#8<B*XG`aB1P$|9HMA=H-O4e?&+?<(
zvo0kDRb<tm@oh|GiLg<D(@mzg++^NKAa~*$yx>flNy>=4?u@uj>zSVlNU}>*^t5lc
zCJE+pli7?DKv>PkKDu_)x9#uC{#1c^F^{F)pQ?_fIgry_sVH|X2q+7-*1eK^8a<K4
zSa??+`z2K;M`=_i*zU)?uD}=7++vQ<ByrTMPF(h4ZxMBzDmGUoy5&ggw<79{um`T;
z_{}(V8TU99DodRtIwGjCkYwx%<BP$lWEfc@2zDh67vj;|iy9pfVP_{eH^=#3GOip_
zN{g0Y5J|^mFtXS1ynFDiakBrH<IkTNeCjy@*Zfq2j=^jm=nj=JP>Y@!QKr0z1)eCk
zRvY_>FQe)z!WDXVi^B5q_~$BCpeDQTs%bj`759gpQx~pI82>jxuA6&MkpKM+QipAX
zaBxTPEmm!6!$*|9Ib7-ACiRgL7XE3v>WhKTtkG#`eV%TYvEK18*tUz_Ze9K7<|l~7
z{N(3T82hh%zicHq5h5b+)Z3ch^eax`V}2COC=`>is=2`?CAmdCuT83uDh_h8V{S?;
zqXGd@zRMi;Ai#LcM(fSXO7kDXqE~TKCv^6h^Y$xuxb0U8>QyPRQDN&u1FA@L7>xO=
zb-ANNxUplRjWw}Csa!k%>z~xtGDA6j(_nZW<W2<Ef+BMofWgykghg+*EW|}KZD-tD
z@SCDiIeflH3u2R@lHaW+(L#V2JZF0;-`^~d!H*;uKtkp#;HpXFV}6P}2T1UkLe@Sw
zxBNE%i$w-lvHiOF4yr{Lep-o0o!hIrT6c)`pLeE{IuE-{?Uu}&o-LZfvd!N|9^HTw
z0ASPIxvoZdOeSFdps{h=2pPR(zIm{XhDhEMVN9K##y<*akcCoiR{jqf><rk)gf&hk
zLKxIBZ<kv(0FkC5!2Bx(dXC%$xD!S#VB_>7ATqlN^?c!vq54fJ5217V(Gfms^;Z$t
ztGWNqWjSE3ENY`sN$0r&KV2yRHzdaZam;g7@%l1f5j!a09_{&K0jv!GY1|~FFk(_(
z&y`;Iw7N?!t>>?4a|6^m$aPvu$|rbw+#Y|LsJOf4b_lWfSYYCZMy*2+1beE<%JVb#
z#JSdqn+MiaPF{m?3<R=Qoxox;TWP`WT4mhGlZPtFY~(dr6fCZicxHa>j6}NIzbX0p
zPm2D)?5+c+PXojXc%W}`Ljk;vAcGh%5UWt-DR|9L_wE@M;SI~7H_0QOMq~HLIF)mq
z1q6Eu-q^dqzj}*Zy``UJ$K|D0=jgZ<8R8ZHBC#=z2|(6fcorzu3VQ&W!G(x%f=;7b
zrO%)#fh<l7pkO(j00ZOg891-vSW**zrJo57UGVrlc|w^9TJ#{;sU69gNhc!OY2$sR
zkM}}Ci1O#teG!*3`5@)GVFh>!Md5T7viHbg;f*Y&uhftrNowN}kd`2t5IFDKWNXR5
zj@ZisKHem{IeJV$!kxMP{Kvv6_1SEt>lf??lL0~~Rc=T~9ROrZTWM9AapR94A#6;>
z6(C#A1!~i||A0yAT9Jg+J|1lGI2)7S3t$&$fbfCPf;(^FG?S2e?Q>h-vC9r?>R!@N
z@s3L+gDPziI-nD8k0xQkyQ44Hv&9je>#Nsq?G&>k=Cu%7uQ^^60Og}rW@lyrAY;AR
zPEzOLetXd=?Vp!+hrXS=6>*Rauc)QX3<S(p?7PVYjuJKLMfmMK1MRwOFlL6|&D~Xe
zP5Fzw(OrPvnHS7Ponm^w!@XQNbIohl4cmqv^>R;pcrbMroo6iXOLAzDRG@WgH4n&z
z!fGM4RA>T3(=yWK(To@fL#xRIQJ2vRkUyD024t?g;*ydMVQ~4hvDW>SG}(|3iOdRn
z0no~xIpU_3ef!{*Er5{boPl3rAsOOBoy3LRwB3aB4Cn0U2{!k`w<b7(6PsvQ$8_f>
zEmw0a48}&m`teo+kaAni0~*<>WLT+M=Ea&QHoUXW=;NFdkj#ku5pX~Tz?}A#Gr_Na
z*)U%8EZZEbmL}TEhOuCLV2gG3RYS(GA7p!E28syI7PY!)quc@6whXfBsyBcIWdsw5
za`*-SW~XgetK=eLsQ=2rI7iAR?|NIH%mJVGvQX#1FP8$val0SG(}5DAC=4KZCEJ47
zF}InGQd3xKELhI2E}lP{K2`yhU{IF*brP~OBY<tXx0sS@fHns*sV^pwb&w{~X^s*A
z6uWmJue9y7V%jkZw~~0$2GFtg?{zQKC=K&P{2lF`PK)n%Ya1HFfZ1E>J#lVFvx<Ji
z&rJCf$Ux1GSz?NwHOD}Mq42PMMN9*X#M9t=BLfRc&o`0_ZH*v}2wF%c7>!#=tp2I3
z_&KT6g39q-d@v?I%2Rs&Y5>74=?5auQ5Sm%3FXslg=b1E=efsI!B}hX|Dbsz@&T-1
zMfq#S*aKFcVXirgK!MVR`2IJr_jww9ziv|B{%id*px5-X4BOpblrc)_jQ-q8EJ>AV
zDAsM!2+5lz*~Inkqak-=ieQB^*3!<A`hJA2_<JmKT|RgxRXB+kn+b#U-!cI&^3D~G
z_ci2u{iBv8lUe41ju<?Ki!R%klc3DNCwRXZs7}KSBZ3s~RJJDKYfxPYdw9epUyGJ;
zfST?|=dQ}Bd8b}i*+lv2{J3K)T{PuG-EB|vTelmW8mAgNI4?zPZ?H?sEc0r&a_nhD
zGsl!53$G+x4iE#Sc9k(UlK2J~HTA_iDnTyJqxX02N0YOzjB`QcGb`F%7VeF|WLRM(
zs&P)PYRm@7&=nq5TS$7RFzcRPTuD3M_ouKUZNM&@hG;jwa(<F@7@%7wltvdUxx1`v
zo|)G<^G!r#41YVIou0Ydq`Pk~9=E`l1F66wHvrU;`zxz;eJajX#6I4UMFt8v$fk>N
z9cgSZt!T@JCgdaZ0kFhq`&FZ~KH|XrI!|Oc_mTT^*{P(Zp1mvXePkKLG53GTJVmj6
zuU@<2ggSv{Od#D56Y)a1yE$}GaV*`<MRbsVvvxve%s+Y}@@lk6eXNrBre%jHNfN{(
z)5HVYt?ezXl~Y-p?A8YsUgn$e0^+zw&5jtYFY@N01LGcu*K;o}cB#?{*`X#2mJBSU
zb|&mXjT0mT+7q?3{%zB;XC5v6c%&?JSxD*%oM^qKZ!SHmu&_<E@?Gtr{d;#9P-mlT
zT^ECt)C$CK-9CDBFso$SVeUu-#(kWlct+gKeN2~-LgBsvK1eL+d#dnk!r<sjCuIN*
z<GWc0z`XK_;=ac|1!X&o!yHM46GjjvcLpGctn$;3D{^KM?yR`3Hju=QUYPzj$HQGB
z)xaiVo{N}vm`yE-rx>9G4@-OO9LrnxJbYuW;(f8$l6cA}yf-XYk_iIvCtYuQ`bLRz
ziU2~8RLqyyW|Hu}=vMdhowu_S!b_Zwy@^u_#Fmncb%+zoL`a?UY_#1aH5xRdZrazj
zE>krD^ybav5Mt5WRu?erxAoST-(x^~?2LGox+-Go93ZeKxUw-v_;K%!8JUjO*&40M
zOfTJg&2l52@9@IOeO;mh+^-KO+Ur&QU2O;q2eK9BO3{J6Y)C3Uzv=eF0A?`jw@vKj
z`$W5yt5V0jg8GVGwKF6i-1RN{eWQJ8)I~+<U?6*6=?@F73j?=hwoi(1sLTRXOrzsW
zk!O+wRpZcrJi@PTZ#K(ifB$`dJm-`dhq>)ixidknBCmlRqF;$EhAT0FMHX{*wudky
z%4#tQD;)#Q>|g@xdNa(coQwo1mm&^dm)h0rzMhk!+<fkA?w#hH-?Lv*=|5H6v<L9V
z@6+0z{yb9=+qL63E)oxbt4OI1o|B}d%OsZ8s3zjs?V|e@Nb`SCY$lQpq*-1!AZeC+
zHc^NKAONjSVg!jnmM}fxF+`XE&4*OjD>#rh0Pg_%^uwRVL9rG(PV0wz$C5ZhO1Bdg
zDeae;XV3xwQSyt9%dJrI<SNSn%t{JoS-_S%2K%GI@R#PK0L;8JABd8teCeU+u=#EJ
z>vO%6xSL)`fjjbeup?zbmtiIegJ|$I3alys(<IgPo-Zn*u##nK=Vdtway)<?PykS-
z(PzRJQ8}`3eK(_5Nw<esiIvF6EON#cJ+CtbXyb04f*N0*Q?B5^3<{6Y=ksvjii?11
zxYys36(=Ligv^i<K|1ECI7kc=Wdj_q`2<}GA{o^7Cuum?{6e^u*tH)DvQzzRrJFWl
z!}{sY10Uxay>qGU+lErBxOav>{>_S!6%G7+(^|Z)04<2-&Jc~Cn=ixFm0u2cq3310
zQ{(^;m6!sMmxt|o5E1w+BIRXc+e?WRd!4sL!!^~`m42O>=zMr}uzr<Z+?d{KpqeG4
zA@pTlmo?R@jSXHgfxL5v3Pp_|{2~{`hm9l~y+pq><i8A2s9szjMtD_ar3?>csaKoI
z>3Y4QtXeVN!^7)g^eeA<>Z0QF0C*v0;@aFYsoZE9;cRsN7jnJjUE6ZI;`NT4$;v>N
z+YP>z5#vqu{bW66Uyh*Pb<<2t#@^209$cY9%gXPl)jBoDQ#hl75Q9eJMY0YcSy&=}
zMT@c;6jVj#;e`5`!RmC428K#~<|tye5pQWHam5ht^inO!Hqz^^?oOjff3-b_t<uAG
z&yZ{h{v9*=_oycKg59ZmQf^XbcMqiu&9&@Ah&^%TiSZT|7+bq-l2@Rq#;Sg^z<$gO
zRO@@5t(@8mACpXjA}CX7A%W_jYBU&7NW*SDG8*)l>!!CWb@%Bo$PzEs6)E+xBS-=h
zV0lI8AUzn|Z9SC;6959gOT3{-p}QW!Jc{#rQ<yTh5l-pqB*er<N~r50r*E{LL}Tr-
z-4TgaHhwC3;o9|LV^!G<8}~6a7Ngf|M>^d$IEwNk*Cr~dQr+Tqjiz_cv}&4La&M*z
zM@&n&B8Pnpqs&+`No}RB_9Slwq)R6*>fWb9mC;v3wN*{nb6Ax*jNQ^orV@JJY4I({
ze3B+oFFIA_Sv{88US-zyNVPZScFFBMQk~{TJ=EU7{-=We`beXok1EJVBHjL3LJLWU
zPDGi&mzl|(`y(PY1z&ze3^G!N4Jp$BBnd=vR#bY_F~3B`*>R|S5tNYEvtGT%-?7(2
z$F)KYRH!wI-Ca+zBaAhv9(*hW0@1L&W}Mm{+`~k&osjtFaOZ^fKsgP!y+*7RRm~nb
z4p&bwEBZN3mXZ+4HbYb`tIuQ}oC%X>l!~%^r73Y=3#LtOsi<n6F$3=tGd7K@+&qGp
zY|>8o!2amku~a^Z1!`FzoY^MJSdSN8lkZ#qpgHgQ)tn$Lc>N-#Y9<rhsWCf&CiA|C
zH7LMNYyq+S-LICZ2utb+e@B6fO?Z{*m()6{Wgx=X_2(FX>67~YSZ8$L=^cNV7KrC0
zKe*WbKms&99WT@)HLc6bcmasf5#g?NyroKXX!>rm(U{ZvwhB3@14uFXel*1D_7Ixb
zYfPa_w(#T=i?+cmiKFY}ZZRT5<#!`Vl>f18M(=rRAwkbdOM~eyKp$jXlV5o)!H^AS
zr*0U(CVazA2NDeg9e8rrUcSwwU{%~E21b{&jc93r%c!z`QdqSLf#-dK+WZ0`0ng17
zdSb!l71T@%+gh58O)RVJZ)SCz0Q{I~M1x?@vP+t<T7|(5JpUeG&`2Bu0C@(s|MbQz
z5pKT6)7$?rJMllP+Nd(p4EG7*`~l{SnAQ}E?h|J}y>Gu)AQe~K%5PJTQ-L5%mG#G?
z|NIne+Xn!i_r$7hR2&v2!%ZY!;6r;na+O_P)lL%9e*SnV!doXw*Z#k&05vpVDjDCd
z6qU@y68Win02vZjbcf_Q74i-nl7n`qirn-Py<!>BGPf*WucwQ!FQwS42+PF0mrVe6
z-6@>z#ZSP{lK^;(uOWEMxHK>Zl+rVWKeyKP%r&7cE`$aqFVTdldcqi)LCZZr9U6pJ
z6m~#2B*bhvR#3wEzJfwcS_H3C9od%W1Ffj?b`5Sja?j9ByG(jaf}w4b^ZIDF4vAwF
z2=Bx4a#Hr73z!P98~WZ$->9BF0k(Y*iX<z^?EhV?x=bW{4^3pvM5=kz53#59AJx!_
zqKiZx3Ch?iFe9nhuKH-;OsM&ds(AxeQjaM!57a0>Z^I^|DBmQz{SAfoeoPr9uYUu~
zdC_G~M$ZZjP9aBK`8#oSw&EM^y+K6pZz@$PDond@wwY+B3$Dds`<%XYl91lJ4qjgA
zEqUS+ca?0!yRBBN6Uou3*RgYXqwCVITdLXQMeZN>GB?)MQ~S#<%}ts0ZeMqg(zdc5
zyYbJ}V&ykFh|m@)062c*3En1A{49)&r?9q+b+#Ln!rRuc4~BObq9n@k9)@jC_vpHt
z4<9hn4IsxGW>+a1M@HmS=^8JunV9tD+;JK8x9Ld5vnCG21&`>`M+kg@p>UQKn4HVh
zgwJ(YQ_ZC-eO^-zOZTsy($JUSD8tofY0;8)RqtGJS=-qJs;y;6<%WT9LuGAJ26jlK
z(u_2F1%lE*Wy@qF$$ciA@ssDx9>IT(Qy6gvV3SCGVKp%lY$9|%DU;5Ww4QrIH!4&8
zWMt$2Jo+ceu8hWOR~r3v`jV6yqfBQ^%3!6<S0hlO70&hj9tf5@LzcT#6tJZG4oVE0
zvpllW4J%ltpFx^e^;(=w+3{l~yy`}N(@tLH++`j@1vOcY;PpF`>#$S;T&v;u(|w0~
z+do$4S=Hz@`eBN7-Jbn(@&4k*^;0jZlWmQRYAK<QjkU`Z1c2C$*6AMY`hVk&cAtM3
zZ8!sC68A8slxKS(**KD`pGMY%tCK{?CD@twh)>c)h$j3xT|aNqW9iJ!uT44oP29hd
zEXQ2QznIf~-y@b2PY!O@_Oxdqm&`(pk5_K&(w%pwH{%|3Bf`!iflIe`s%GxTCl?k^
zkONT*bw%2k;Ib{LZZ~Is0IKjOZp+4hD%m8+ZLErPulHMx&3)ty&bW!~F}vhF{!}Tm
zhuDpi5G|JrZ9$RuBmUOsKdqr2s6SHmF@5u!TZ@+t{f~Gz{6(0}-|TN%a6<fVvAUF*
zmFT*i2=DBiR9om;GSx<fXf{W!WQFIHp>OtAxv1cyXq`K1+rD7g?`#GS$RZWF1nJMK
zZ}A&M7<|F^w{zfc=cLz{I%gv)xsl+)<ou;BWUqAP;Vf30;;#E$KW22`?aGvqU1Y`^
ztw2H)L6Os?2put$T?D}~@fP}ta&mx1f3oC^>ufLJVMluIio$M4iX`o!XJWZ3Q2NbF
zDxv(Qm8jso7!!u8DG()(|IRQe5pL>ua5u(W_V_;^UYa~8#&3|TtD|a*kbxq8eXreX
zs3A<6Pe<_%WkVJly9I^rmNQB=f<ig1AAhM(yrgZekQfx~EjZ_&QG%Etmer#8%?f6J
z@4f#FjIZRz=~F5^sVH;9`k8*-!ng`+c9(Ivg>ZaG79lAbUBJACbA_T`%BBC+3q-h~
zz<vV!fhKfb5HDW?fc27zp(na@8<4uK<tZ~4K@*-0^Cl91xMfX4aHsh&8LKe>xY!yr
zoTq0<kHgqC<@TA$aDp9Y$fC@5R#lzk>Y8s&{s4AX&d^Q1`?4r2PL_B%7DWqPotp`M
zj|}{G*<Za*&8-l2tEu^bUu=OIyAvP2pxiw*B%PEc(1fQ+KLI%h3P#-Ss<&?l&0=S#
zyHY9DYXNGns#B%hdt^UrSTEO|&A7MUThtB9=C*2<g=bZ`L9O8ObQoVHxgPZ`wN~;r
z903f{Pp^om7a`=5ukSqiK(*KGqUgwx{;f~p#St*tmk$y@U)-<@m|4S?_Yf5F%akf5
z@|XlUY8e@ZDTPdIa6lJ-vjkB#VE_QxwLjGV;C5;fSi4X&kYlIHG~Z&1N^-4*;+;<g
zI0UeSk==nh+Yq3{P@U}e`-$ajRkHcACBi=QNno+o0&ELbJh<@k(s{R<{~Ek*y>fcy
z@9128K<!o1tB70sTHe+tazAa31{c~c?fd&z+be%Khx)yLX|-nS2~dvicY^f@V}WvK
z5n}O|Z1N)*B-;G~Kvz1OhuFma9|S7AaB~2@U7^}qL!v&Q;B88_y`I!SSJegi2AAp`
zyMk?mDlK1|I&dA|3_is+-TLXTp^&SV?iCeVB<n_nee4UF^!;{8@L!c(pb@Q?s;$2W
zs=u!oz?T2Mpdz+lfbg!H28u7{$6!)<yKA+1FdY}@Ar&J4oL;015auI#jan4|Vf-D4
z)vDGprcYiStRM}r`Ti>RcOP{gN&!fr$$ow<<uY0nz5<ytE2lr6ZfC2#K69PP1SWZU
zx86_y3g%+R2EGb8syC-M5YAspRxZ+R#V7nlDY*O~t2wNSu(oaJX{CW{M0I=ngQYkP
zBi9La{*0;4)0CfTdtK*iEnckBVK@3Wr7m`1T{h0K>z%($SD=ePf~3YCOO^*YdrtB?
z9e2SP<X-0A(XO*IG|?WgTQ0DSAF4|M-~9ww4jqs+7)<)dZB&@K#X^sFVB-xwo@zHk
z#mv6)sQe}MF32@u>sip-#E{EJ^@^o7+xF&6-!@jojBi9L)J_49GL2+*O7EwsO>@Ws
zZUA-AX~>ICuJVDt?=?BR2u-HBRsx*g0?u5A^h^Q*@F4Bi#tg-|!?)C%Z-}S^Z^(7d
zTaPJ&B@`>QH6#B0nYC%dBh{kI_P}-Eid(!=zdP7f&{+3)doB+|C}y~q(PEXdXuw-q
zgAEMxY`y)l@B=?KlG*6Wx-d36wju=5yjpajYssB+jwhM8fhy5@gADDs;reu!kt>f2
zno8QA$0=pefsb@Z{Cvb^1->4E4f&9<R5GbtJROVyWekH4S+b6Al0)S~lOS0-1VVAH
z3P6RXhzsk_OW{JnTi41L+d%qc_G-`L9l4P=^Qti)+90-`qWWO}Q>T}gG6!3TNSMWY
zw*%+ruJMju;=v|Q*cMNFi6i&|AcxjoI7@cmwP@lXYw_r{^BHbm47Zq2&{+S?!PQJ<
z$HQ=sGaHR=c^NSC!vJ%R4+v=>2JFv?bpe>VUna~gRU{FJFg;L~PX#GN2HOC&<}N>^
z4g;iM7+pt(Rp+aYtRSv^%sH*;UJTDm2-s%NyLVQwqQnLT5<O6E0&=fncY<ED<61Cq
z@@*jY<U|dZl2p?T;D+aI&W$=RDI=~VvSG$j{=}mM0+!Iu#=5h?vMn7rV!QPLf*uBy
z$D*A64((?~`I3Pl(ixpXkaa)pj2#ijAsPrd@3N5s9#JioXqx%pZ^zI)QK!VbaY)*c
zBLm3E_5hLteujq#a&Px{NWf_}03?a^|M<51rTJUMpcH`T#B95=r~_y2P0g3M2AOhx
zltZKG=wKBw7~d447zROZCd*oA+(R%W15Gci?<FH(QAI$E!r#dT_b;(QLP2dZ0z}B*
zDt&2^2E;-K?2`(j9Sicns3P(P3TCBXRK_^7cZDZK2k`}oGrJrECDQB$WZ`^w0LR)u
z3s@#~Jh?3YpZs7uvu&bk3jjU!u7u1b!dn2Y+jpA{T`C<fOxC|qv8w#dSN0{}0(1FY
zNK_4F{DSB7U6JB=jlDN?uOVKqX88gcDjzx!c*bu!Md}LAuK^$h9}SpJBKA_)7<3ai
zR$rmoE2@fPuEho(I2h#LkKi9=Gh`FQDyC%A`r69RL^hpooI0=RcrfyOd~=pl{p-|P
zpGZSF)vScCH|?XZHcT8<|D0D?_%z01j0>d2ykfJ3zJH<r-31F0oVUMe@oZrFfqKq)
zH=?K2acAW-dPAf;B_(E-eCQp2UxBu<=dbwSK<4qqCs$|dLCNn&J3<1=WS%8@722Qk
z{q>kH<MQ!9p~mATS<>u*WERWkV!~~aYqQ#ytByq3_gG|q)hS%{+H_*YXR%EHr7hqb
z+zE&Xcc*hHSmhk%KPq-%Z^Xx^-tJ{6dUl-3won*x?Ei7+N$vE?0hG~_f#D(Z9frkM
zf{Gj^dqNv;T7G@A>3<D(@vWbuwt+Cv;*?xvkVSA_Zn3GaPKLep&8j(d==Dm&8~;*C
zd!CAWc@Ph5q{JD^MuTJY$64-+(PSFR5@;eXzdcPs5CiklW9<2ch88sgG5PEMTF7Z6
zn*Ur8+7;yBy&_0~L#r79X??-p+Nk>B4rAeA^%sJlHXcgPD*7I{=E{iZVSFEZjw{*>
zY#Z5YTK{01O~)nuOOa#4MxDh=8&|j+hy!`qmK2DP!Mk5$N>)BsI&T)4HQb=oh&+RH
z%-Ll(d;(tm=tkGLg2SNp;>*RT74eDNN0!DD?VQU1HEznP=!XiLTm(rF^Dk5%LF)G2
zCuI{%@Iw2e+JU(Osh-VC*$Tbw(yxLVuZrBeC-ccoi|}RP#vW0xAs^+~1W4S^18Apy
zNb^$t4)qWjTU~In)tbd1UOlT`N!50?LrOjQNN%8HP{x{^>)U^JRY^2PjhxVffZk1e
z#ANnt80O2!u{~OxClWfZJnDpdS@vJvBR$g|pwj*QD%{Bh3!nryU<C|263%Goq1d&5
z;B?Cu+DIc0io`Ekht;3Tk@MRMzeJoc{w`P9A>FS3>Ay)ootiVWE2j$sz13envP&XV
z=IZkawo~CoCx_bHt79E#Jc7b*mh3}ld;@dvZBW)?*5L~;m@u|3KT3kw;T*l={^*va
zzdE_UI_BS5sE39n|4u{dM@~;K78|PFiISwO?0yT=w@80MWMfj2C#J7zV_HsC#ZZ>R
zBD{|DO(vZ_>2)y9bpB+YU8PJnorathLGrGZS|1&~SkNlI{e?d4%a8RaA}pRF_D=^W
zQI!}tC1$jP3q&Ny8rhvv%sP#94%=vb<YI5s?Bx;}nV#_G*hjriS6iQV#yu3-sjsa1
z6T181RKJKyEu7;9Qfi7$<@kwfMyPRl&|F?jhJhyv3W-#L1XWQWJW>2sk{yOpUHW)&
z&<Y2D1~zrK&UaZRTm_uWO5V&Z6%%o8CRXIKgB3|!SfO7O4oc9eNTQNoJ`4k>7JqWZ
zksfn^fTC0Gf8bmpWwmNqO8RDdiq!?nVO_Q$s6u6Dr|NzSkc5>EdH~?Q8b!`Xt_xIG
zr@f8mkV!C7rZV&7ZvGS8o_|SqDn(*NvSa_kc1cx<aPe}FBs!BZEvd>u$`}0d<f^{l
z16{T^>eQ2eaVxXC<zD4HP*Rx6B##Z`uDtA`=jE<sGMsgBEf*}4oz*KdDXZ<`w6uCU
zioor{p8A<DcQo_V&5W)?(7+nP@B?_yq{ZVUZR2qhc01IAbt67mGzRxY9o4;q%6S^u
zD`GB!Q>cymRLX29>n>Kre#qe__nOCatt6%+eqaQw%!-Ryr?Bk!2+Y(uyLY0P->=%S
z#bs+wVo8N;a=Q%nj-z1u0q=B<L6R-D7e>cJ+qA3!7dCb+YXixUTf@eb-d%Xr?v#Uz
z_=0l=I~az2{*Ul^KAm+!{qP{XNl31<$-vY-GhSzNcaWlPJgK#VE$Mg}oXz8&!St-j
zx1v&GwBR&=Sg=kj76EcE*_PazqNMHhHP>KK6|=d0oo_@8rdkx8thp`u-NgobB+47m
z&N_Eml0Fse5h(~RYH9UosTOOQp!e+(cj#Q?F5Z<a_B5pBk?PJiF%yeB4`k#ND^(o2
zb6+x-|BGFLy`p=@XhR2HJuJeIOmA_1EGG*K`~MdWTe|ejs1M~bkePj2B<a*0-o2YK
z7eE+BnyC*T(}qGyU_n8k4kxjwS=_mD<;xm^Zh+Xh@QCl3;qEBQMC8kTFLjN=3eG!B
zY?`F~HVKMphzfe4Z94c+yf0}+bjbyGCB?LxDULjgrNc|Y`3$)?+s3$<?B8h16Bu6`
z<Wy#6jvt_(#WUfhYc3)Rm#&&TD4;&RJK2^0?(j1aQ-BXQ-FT8UB|6{)OHGz#hnd0!
zT<|*;f>IG%+msf#wktV(TdjHi|6PdOEl*wUvyE{$BdL4&R<rM&J=s&rxkq?00JU(W
zCM+<M7F4q!dVgTJeg#UaO6Ph=RvL}qv|9?6w(^(Rqk?*6+WWo`&D?}S+zndg_}0zj
zMX_5=cd8^Y=J7;<4=T)?!d8cE{9m`{5TvaKw^y#+DQMr*>@C?)_pM~3^LBeE*7m${
zB}@%}dV(ys|DQ=f|CN&)FLOgXS1ljwIo$o6-j2<{&#Gl?u4N@_ad--_k~;9umCL%t
zY>C^w%vKqRbGoy)TinNSmBl>|=Pr{f9`IUu@fJHh*sUA^`6V9zut)4YLgp?U+ntxw
znSE#LGA$C>YmkW~($i@G{vs5rpj?}F?beRif5Tzqevo5hn74VM505(}m{G*_i2J?9
z#hizNb}E^NGu|bo-AGg&6IHNVlU+H3JILkcB^OY3++E=wkh*|B<mFkG(=b?kQ;}vA
z7Y})j*+7c3=Myl8*M@6U+=#+Hz3GeV%6I=u!a_aE_k=Oe4^?uVLzG}o0k6BRjMhIb
zd*MF3pFW@99len`mlF;=vm=%qm6l*RpYZUzQPC`|QF=nlZD+7d<V`NKw+c#mar(lo
zd|KqUrn-1=BL9{{^1YE8x2Erh^ijX$#8lzF0+Z*<1>|BkT0Va0>i~C6e=0S<@dCzK
z4C(}Gg6MRT0sC12H+5K+<;pf_zKs1Wt?P<oe$jpDDwdp?Yw$7Ky#w=3I}Z?U8;Br)
zrYcSE%BL>(3Is#3Dd%Db_AMb`v6x$;-Vht9pEKKRS1K7%;k+kdirCR?Ps=TOAQMog
zU&kGKS%~=|z_cWvXF^npVWsh#=~zZu-8c}D<krkSis%`8^zQc0`WBo*i+Yo#ZoIS3
zlMj}yctV>&PJ6*Y{K4ObhKQV&vgIQ>?JdFghhMCd7P;HTeqJ;^Qq&TcwNp$XEE2%Z
zU;KIy00|gA2*#Dy+$AURzynJT&NNQ0Sqp-I0BXOr^0?udH@32%EF&s&l)jQ3gJqYj
zsqH)i^OaWH$injAyLE3#x@W(5{^7;4>WeJ_@BNIjNsvD=-)*mro1ZxZooSD_@0NeI
z6y;%}_qjZ(C}(=cZE)9Rm?i5Sy+I>7zq^S0<JBX*-&}T-@+~T={%D59bvFBCu{rXt
zd=5oWS~eWLOE_in_OQdJ=(3ld<v11FmFcV^6tN|RlWziAcBT=zu+^Zf4NT<<-2G03
zb4yK=$X%($iGp(S^dr)ApR*)NsyXB5?jXK`ASO%xT6aVVyWVGV_{uZ2JC!Eia^u6>
zy*+%d5~fijx;JcJ54Hq83@0@vy)dM6lcF=f_(7p}5(^^C<xlxbk`H%{zy0Prdoj$n
zJ;?7<lww%*k*8fixWF!mlqP47XdH0)$3FPfSYME1vwzq86qn#!{!BuIlo_dD7g;iM
zQU%->%{a{K%DcOnS?UmY;2*>h>IfbzK6&nb4WF$QGk5EZkLRPcT)F!t&sc9upW2ec
zRP1(`nM`U%MOK`#-JzIo;C(eT@u<V2ShJ^xOC(1zv^dLO8FkJ!w8WGy{R3~m<-dMi
zFfC$M$|$@Ua}mQ$BIf?d+Q|XJv#lIF*8JDyU<bc{_v2}u-o(sZf^*`&u^5TzdZ&x3
zMxhmvZEUfsSMsmL9lQ_RH}Jc5iFQm=t~UD1;ld^5sFI0@&*&Q6%+}X;_e;{B;Dx%3
z2(5}_SGilc5Bh56efTmV^~+*C{z_roPZ5EZ$4g(m9UC|&X_tbBPhjh&uhKYNb_Buv
z1d_jBuION<(`CWLKaKp7#*aQOTY21fdxM-4P51@*9P`TnUg(?nEa@bS@|8n(Moe*G
zXG{pcVvkk)rKkDi19Dz@XK}N_edDha&Pv4x|9y3cepf&D*rAqphw>`)_4nYOz4t+V
z5D)oSJuwUq)2vUTBc(rKchA0(cWDXvynQL>aQ>Si{xq&;&mgnm@blu2?*j|;+Ws=m
zX=&1vR_`#KyvN8k_-p^(&i~Mo+{KS|X~vy7zOgE|#~w-aKImO*_i(S(U6!_6$XHOz
zDGBQ^oL(cJm}Qf1T>T+|SyZfOSsmcyOOXC>FNMCC{J8tE><CiGx-{cAl0IU;`etEu
z`@E__Ikn+1In?cVsfvnB(4kz$Vb;3{$A|Vo@zIXFH~42hhc0~(r6s-~AGwrtEZz18
zsEEAGML$GDiMV%3ek#4VU5Cr(<;aWJjJ(PJnJ+G~E#LNEk(kqX_9@W%ht1b@Ap(T2
z4mXXQy<YX8)?}!4;??2A(v_viZMS9%dX^El?%6LoSYDDE%81<TX`DMPjv+VAvBR^x
zU+LdH`l|8GOYWDL{Lq~Y?|HW94@IuSLDIJ%?iu^=5Qq1F?On>u!Y8)R-uA?+@5wj%
z@Z_V*{*DXn;-2~Y_RCW|oL+#K{5fB2)fe5@1wR6DTckbZ(X6}I=FWQ_A1G}gFTFvn
zdv+Z^2@#W9Lrw-wyDc@`8K;cGyOz+wCo6*%^P`sb(tOmDP2|VB)S^7$nWa+$w`w`-
z*Yde{-<))79nbxzW$+Dm;pJ3;Li0)26A6pab?@EfMo;E@tm7nI*6fc3IZodz@yogt
zayI3pb+Er?sJKd@Lw_!=pMC1qq_T0-=ft3#vL+w#j3n8>&9Kr2$BW5{&i-R}{l8`C
zW>UD#4^G&3mlvgr#<DOLC2Vj2O?5;j?du4O@7};*o@M7q1SXVEiGC6(FFt(C_ZF~Z
zSET=kO-iPYodWm&u$dY(8U{%;=CVaD{j2MjInZ8M@$I9!hyD0q^k@9G$x@A1-#WfH
zhfL`|?i;io6j%AS{_4Yb(Z9Z{tg_K--}lan{+jz9FY!G!^Ltst$J&1me?otsY-tV=
z;|^o@n6CIAfg3yl!^hT!(~Exnd~nKxb%hYM)^eXJy}H|ve`0MO^D$O71in;{-myRJ
zLHBO%si?ox@(xsO)1zlvN(h`=@iN`H?}Q^i9?Sd~1FS5SikqrU-^tKKKN5BZGIl0O
zZlU05Kvf{8M3tA!VL;(}y`iRZxegSM&W23}p<rNk?C2I2rJZA4wXzX>_7MD>%^Z=e
z`^65j#PP*HudNf1*BNu$hX@ME0QRVkj{Z~uzBu0k;;%}T05P#kc$*KfDB`&%Ox{j5
z$yyIs@%JxO$Z$lfhxtZiIkfD_3w&NHX3BsFlCo_CV4N@kSH?4aX)#iGQMj$vBxX``
ze_-zWW#gsb>-N=`ovHr29d3Fzytbbyx$vh?^yVA8uNP0Q%`3NE<ILUu`%&g80>UA9
zzf)+rR)0?TseogLGmY<-I3#=4y}$>x6i&5w4ay`xi@E;obBfi+(L<xJPX}+kIQmo%
zoVDIT-`DqTKPKbxUs1#A-E~-<-l2!s_<`@WH0UFziAZ*38il>wJbN9-L=RoRxn=}o
z?_G=PXyXRyoa<chl$q*U&@IZiKeC56OJ9sUe6C%z1leMv-9u^vq7&n3bxpfWZZ<DO
zYGE45lDSGZKmSZ<9bBG5A|KKMyXC1xMm?EU&d4YF?fY?>>z4Jq>a&d6q-4_XMM%9x
zR4*8JZ+DRN+-WiFZ1VgKRlJ^GZ7aE|v`M+GcG1}WE9tt;lOaj<zsz(u5c-tkjac~3
zHIwz?iPAQMmHTa9Wfe^K1%FnXG(|pBR4Y5CuvFR9W^F#)ly}GG#ekGR$cx|efjQ-)
zIQ?^?L!&r&D*8;mM-`K>*RS%`{$rur_g<R1I)s&2K23HQ8~`e_vF;FquoW_x^fx3R
zRkK}kgjCF0@RVg;4!M12WRztNXfu&jb!@yid7si<;npiwO+~68TQ_KE0CsK;4LUb$
zkI--qnQysK9uf9%_qb?qMZfe!Q|W_jw9CI1gGeaYb#lwKIHYdk#zpdEq)AyFsdYIS
z5_S`mv-seWCYK2V3^B+i19}!+{+w;fc1gbs^e4a4KVFPGM_P*O^1fiEf8)}(WBZI~
zu~$Ow*!<b;^R$6V#eQ*_Flke~#n7{O(VXRcH_m)?U`R@*(wcB_v&Jl{K_mCz$r{QL
zV03piOLceySqTJ+T6Z32m(0V#_@_d(L7!CFz8e-fDKPf)OT`#ffiEy2AT~hKNj}WQ
zTY?X8h<kh9ucm(wo6{~F&ZTbenkQ+TUp%!}#w369MTGD9YRuQ>i=RIJX)gyw^;v_v
zoVut#dlWc7Z(j3R0f*(8umSrrI*>W0benjVa^lY(nYUq7)Geu^p7bzmC(2)U>a0?A
z!5cmeL3cB?mCXtU7;}jod@m#9YfYI{790tDIiQAqWk^jwcAJgN<vaB+UVF&x{2s0F
zv?8J9qJM^^v(u~;D)}$6mldU@sY&-dDbH#8`hn$^;s#oBglODbU-bEgt0v55Y=clg
z?3;3*bfIM6M95Cj**}GM5^DCJx?`FC>~q_yD5;q9%UDZTd868;*>B}PIvdQ~@hpoM
zjh$s^?}h{QpB3jL0;u636{V<a?J{r;f9;)J1G6m-`@b}LG&`p#n%G$N3%dc2WLf{F
znmX$*X}U6J&^xn(I=CvG=4^VpE&Z|eT(|o9^bmy3#k05G#$qm@IJk27X?+FZIe*VV
z&*GYatCxDRoM-D=RZUMxQgd=*>RZqB*L+t~_YWy83@GXy3~Z6WJq$sH+o;RJZ<UvM
z=I#HOLOOBT<n!}?{lZ@P*`CtBGxq_m`K(7DNc=2!WUi2H2ABZ$T>St|ObO96BWr-p
z9Adq(4mo5qsKHwruE7;MM_n5AYAMT_;05qKD%tD`aj1S}1!D;&hU*A_x&Kr(*l@O%
zTyg*6vcGs22N?5D=;)f|gl3l*^|=lE9NVYXd+e&)-=#jzxhDEC;|&eA%~MOHePN;^
zRVJZD<ClWmB1D}XjXT?3J1GY26g_pfgXEQ%FPaiM{My+ltSvc813}(<zth=h-A3_;
zX!MI0OFMK*-p^kC5QCgPOuT4vdpKD%w?l8}n$bIL$rdb3KmDzE@#=Df#2qpZ@(Q=E
z>@L7zli2c=m2Cwb!)KiD)g~d>`*W<CDKz5~=tJTw3f~UBdvx+UO4xOvp#8?r1??P8
z>pkIrO5u`|*JMux?{1%WzV_m;`o01eE2Fdaf5Ek>0@9%ypQlc_=gL;w>$O&uFkg=k
z{I*ZyCN{I+nryHxr)B^E=e^~D{XQBfVZmXOl-dc4j8v_mjqN04sI!D!ySaH1`)SH9
zMi``Ka?LZ1kVHl{9DYErp&S{-Y5JlWX$bo%zj@cX@{!>B)Yjx3%{B3ZewaOZKX$2C
zw;aX3z-DU}oZUt{ed=>G-NNvYLY-tp{-~ALP+v7|Bw{%DeCL10JB!|HNJ%+p5{~cX
zp#mC8Qfet_oiuHEiwjlhCArUaPq+H=s7wCslRA$)TDR@=yGyuAeWz7sXstY6-(w%T
zZLwwVi<iootMPc&l6rWijeT9Dg|PjL^MWnogKhnpeO=v8qV`rey1w0^1*_Q+Ir7hg
zRBa<K`2+DG#$Ls@s+(Aw6jiQY9V0B*`!B}E1qIGNw?+B7QuJMi_{@M8vvVG`D)pCt
zF^lJxpLG7Mz8U;GEAHEsXE)YA*1OeM{wB=^^sA>!AIJ;_;$HdyJ?N=i<>ao)i11-^
zf0*m&Iv#%bo<4H_e?*;STU2ehhSv-O4BcHr58WX-14DNT2+~TYBJIG?CEY09ARtHy
z@6atNAfQqrEg+yG=$^g5?0>M<hxM%Ex$o<|PNBymt0kF(b2TT{l<av>fTGlfa2nxG
zTnXiC_X8j{jK4%3V3#cX*w+HeT!S)vQ|9j<bQKJ=ElgJn|FBbVJFFpEv+#W-V$*v6
zf()>@V|uk|GdFLesN?P}Q`13Toly8GP~RX;=zbhlwTNE3$UArNZBq5eM$kno*@$On
zs_>_o*Ip;f;lDpJ!HtS{2mIWdkBVF`-!Is9MBqab6z}1!5Sau6vC-R1>ExhhTQPM%
zk}!H3^g#*pbB!0YU+Cp{v|pU#2f341zLmH()iqJ=zAy1v@sRBaJNKwwW0S>087jL6
z<-zxVJ`+=qO$@He`S33P*Y)R@mPUIs2f*al#|?v`s)A--4|zYW)aR8xoN~DRg0~n>
zjC=wRBC6;$Vmwk$HC0$mUPhTsCs22$zrNhM3==_ZXM+b)DhU~1jJkvanDBdmqYA{K
zhiH3{6pq4)fAErU4*AvG@=1A=X#^BIbsV~vx-@ni^NUPZD@O2(2kj~mxzzCLbP~1%
zzxJ*<a)FW`oDlSKh7)28pK%W`N*rCs4*`8oGOb>1eVvX<mJZ=5HmelG<+OEYxNKsq
zY;-A|<Gd=ttgXJ=1B0Js=2GSfN_!^Qk+JB-kyN~RNtlkXs>itXsQ%G=zcxI#kB?D^
z2F>gt9*fjCZZemQ#M0%2Q4{wVi&VAwDa3rGDC~ri<G@zIYdfE4^b_SiH+Q&yg=FVz
z4?dS@F<z_qmTLXEComMk6;E<?CmNn;nt$eZe<ENhEoN=U+$ICQ<}O<@9%?!qx__qc
zN?L<2;Y_cQ{mTC_{k*32>stNEVjkThx^3<7**KYzw}MXZ0FWg^DZpB$LWg7Dqu#Lr
z0Ely6UhHLMbI?ODT=FC+GtKrB#geVI4Lcg<gy16ba09S=Bx(>ysbn2D9|!V6O32U6
zrPMT<lVO|-#an7G>cm^^j8C4jx2<&S^Q0u9yn#IW(E-<^qfzen2{Hr$-{!E7>!%Mi
z;|`LR4-PjDb+->I>6_Dj9r5hH7K3u0jlO<Aeym5(g4cCC`uT)}{v<7^^>oR%ZZ9EO
zuxEFR%I_~Pq-$o_O93SdE2Zotmuyd8fMb%<hE$}SHaViMpYB|E#Jm&+NCh74vQOPH
zBdFaRuLRYo`N!yWTmn+nNGtcskG2UUzZ*U?GVT5rbJb9N4KWOH5)5exzW!DL-Z6j)
z&Gjy@J|dg@sS&3{k;IE8wd7g&<?(=_>g`uNJ`XOhid1O0)*8oGJYg7)p6&-gYt+$=
z98rEuFaYfMdGjRF=j*STG)w)FV@r)=jbSR#@TO7?R?=}lR(+oUKvkFSlaxaUopGBM
z9?;k7M_7nt)n|Vb6@#J5u&HI|$1UG73^{jdf42Ih=zQh%g!deXzI2JK6ll~+6jhd>
zFIIn;<Fd9OmZfOcp7iDmsYn?f<={*Bt^Y2AK*gE3U--OBCxMz2Ic1w=Igf}30(L?{
zm<ZudxGqBVsMOG2q{tSZtj)*E5FhL+CE9vop4@RUia%ilGy+~{1`>cZXNC!M0&`Vr
z9Z}+pmNVz1LgJo!%E}T(DlL!6+68l6tyZ)n-#cFW8?VCgqEAr#<iqV5{+223Ra(*X
zm(ib<vgwF04<7`UFu0FVJ4xz@X#@OefCM1ti&)D#(85i2oQxnTrE5!oq1kfDxVzYo
zuZ9`k^zDbAH1hXGJjK;K8LG_RRS3%;UBHlM8Zmw>WeWgknPPxP1ZQA{0idm2_C^|E
zXiq5BTV&QFMY8RFby<bOByQqtF3={hBghUziEpZrNi5?60JzFYFr<j!vOXXQp_^eP
z`0d?d!*y8Aux2t?LHfGhFpxAA#`8W*R*Jxjz^`F0hf6ett1&%d;I{EXyveW_06{4E
zUJ4sldo(wC>K-dKw_D9ZeNS?a_Ssu#dhcD>ct`UcAJLJvxt+93@qNoZMbS<q^SvD-
zv9GFib+z>+VNIX|*B!^XzE}|<-B|Ff=dO^|Im#si1}PefuL5>^jO+<tyM!de2Mvf6
z$7&3pd=Qs<qjoYRR+XSHdc9QDCR@oVu7<06kCAdbl|umR(O3>gLk9XMG@OqHIGb5D
z`K5U1IrwK4H2L(na}W*UBJmlrWN;L(1MVnf1K{Eg%p{1UU5CjXT)hQMoO8+<30_-0
zuLM$#Z)Ux|JuO|IU|bm0E&8B={Qgmk_FU1}o>pW<OVd#~_sZ`+p<nEME$NSbKaTV#
zT!~$?@OMmnGYI`=-uN}G_&*a|lP=Oqsqn8I(UU&+ZEW1jh_ezd&uX;`WH9s9eM8x4
zZk2th>go|3{hXb99|Wgi5mwL}m*5-e(umr5%=w5>tc!ZU)XA~at(3m2W>$8><F|X;
z{U3B@2?b)&)NnQ8`;Bf7y;#}Ow9|oRC>94$^b-w2@I~d*3BrS%_wSjPKB;f#om5so
z+Et*7k<qQ-lu3o9+kDHU-B|g}dA(&|QTKdu@_-NXfV{9Nr~3gG-xgABP-SKttzkRl
zlA)g_xZ}wyM8X@R_C$|__lVlp;?Sq#`?HSywx`$kI?Fs#1?;<Qev2+~3Aj2+lR3`R
zidh=3s@KBl1)P?}gXyKOmKlF8D~r)1q-#p&uPt0&KX(W+G3L7G>J?=`15Ay45!{u0
zkb2D&)h@UtNRO+qaP(s${KXz4Dxxms&ewHYpShT@6Y0W0Wb#INhPXfP5!)uSeAy`h
zRv%YfN$OAuL!=Q5vVw*YK-P`EJ}Y-?M1ZqKqTH2FMfS|7V*Gu(EGDTeYdISpe@9eD
zV)wmlYpv`8z3h)hIZS3breqnvEoJf?c)`~kvyV7D(_yuz5dg8wq&$E0CKqz=3#j?|
z)*LlR35E=3`oL3GlVHOu6FQrXYcJw*Am_+d)sx>dF2DG&D#_{mMBvDw93lA(P@_Q4
z37}I5!4PmM7tD9>^T&72vvnj*-1yG9-i+nrGKtUS@N6{F8^lo!iC0{Nt>~OQ^uVp$
zXtWdJ31N~O5w^f{0PjI%Ueq@Fn><gR2I&ofUG#p^ucyne4=*aY)fA)0kE_@D==M)6
zPU3_ZLsRBmoFAD5Tc^FwioNlSY5o}JJs4)?ZJ?%89cvUHbr-)ZdSw;eBOkXu>n!^9
z75h<BqK(tL@t~jat>XDL;BGN9u;J?Y9`NRd!_$Lp=8SeLf(a>3dtO|AUczpaU|2r4
z%^QiT*L_+=Y^=TzvQ5R%<kPbaW3R!{9iPF;xnRUg!c72#*d=j-;9+^{cnSgM@b_6c
z&5zogF^yqzYFn|!Tdp;VOm^)o>g^iwiUq&cZ}gRJ{M*CscN9J7Shs)6;+&ml{r0}(
zr)VdEAK6jScW&F3LsiX%9rCJ!5H_uSy0RESa!@$({Q#PkeODBYdLl}wF!=P@eUo1T
zIRwZ@5d{-E@KSJI`8p`-RF!iNgh>KnlhaF?$x8=e2xGifLA6Jt9M;7G_?su9JT2>;
z56}N8-+y3L?)&N)Zj+Mu>gQjp9zt};&19P(oSB-vhoP(#RCdE%{<ObYK_psctU_K;
z>gil1v+8}ikCp1(m_f%FW|J73XH~&^Rn1>wng3M%7}R-Y6gwUj#~hGt<62W#V}Bba
z!rXqZC%(1|eU&Shkp5b?<F0NsJucwaPlXnl&;|pW*V*vEq?*>GM+)3kC;)k?$C=II
z0I)@gHE~`u=u?6ncQ?OQG&d~NeHDAH=2&R<esAqk`%u$)2q+S}kQ>;?Hzvn}U5+3C
zLYO5C!Rn;R_e9u3-nC42UB5&u{$(pxwtaq##dFb4Zm~ph@ssKjoBEQS))I^RlC=ZZ
zqsEhidQqvlAE}XOCkT6P0CUvfvod;GA4x{eGWibDNQhWrWBRs#_kE4$vxe6m7cE+U
z;z0CJdB+PIB`$Ht_u!!<eDp7}Z3q<o2&T!!YjRGt^(R@g>6x|5LzbVBvM7Ehbs3j1
zEYnX|$RBGHcXVy}zF^sa-b88b?!qnmfh}(tHm_Xp(-3vTkjAqLYORXE*<nH|jOIs7
zQD<f0?-7}=Rq|RReW@{PcJ`t>qoI#S*Sr@cDRyk0j^%;I#)!rl1C&2&#n%MJ-@F*V
z$q{X6avUU@SooP3q*I@3RKIL6x$ilpg67%f7UWOM-X3ZyuO2{yQqTY^l5PdY!-`yW
zdJ^M7>xjD2q6LdS<m)n5Q;1T!Qjq209TH@N!Q`;?#Au8uqQMgj5ZqBQqxgdBk&mo*
z*HHa^(Owy@dag@zTn{lzuSm`&PL1=V*MEV|{;`~Y;ke-7zIfp4d#Ct;xVLgNT;Qt=
zpJ~&u01(LqVIA0jA#E8p0ag%<wiEzwEz9$MGTyZwExz(4P#@_1dpG33=RkH(r;)>n
zk}c<vNNL18?nv3(gdtSG(Gbuh;c(?4stOlLCvR_s=ssr$sU~x=^iV6g^PH@8Y_HDe
zcVL-Bd^EMdt>|-`cbuE70-FecQiUjLL(=mv)zC{?&%aC<Rt=03@b5VqW-Jb&CL+LK
zHxZ2b5`X_<hBtg<+HsISfUc0JJr-nRPaJoiuYF>u$cjPlw1u$x7y}^vJ@Jqy<!n+#
z<9n?IUP0dCFK6KaZnkal@)<J%(yQ{sL4Tj)AqX%;m%v#QKwDqw_rF~Hbj>0qsV<Qq
zV9HO?eODd)FKfPT`A^el&nMZcC+0`WpRa-k9QHb=dA2no6PL;nlstZrzmWr<XM+{}
zmw+d)s7ncV$SMJ%zE@~K@!}JCEUJPIY8!wrn>hFi&G=|-rL_ZSHv8y|rTC8u9|6iN
zV9|GiBZnLXZ13IRy>)@D{IX4(=Z~Ch0?3a`iD*EBEz9no@+c0jLhoLFdG_Dd`S0+*
zuk2sE%=-3u^l5PS;nFQPMLR+De<T}35Tv*{0|Bz-2?tb7Je!|bt)*QI0;@avk5s)*
z?IymO|H6n&E@6`Z#G8<8^}Nkrmo!A!0hHj5CbX6`LlYi^yGd)+U3T<NaoJN=N>FDW
zmKz@2QKE!{NdXSRL!Jl_Os$RL-T-u01h(xKiHU~kDQ&*fRwIATk-!q1Q<qt&lHcn`
z!~6#2IW2#sZZMp_+H=S%{o9x*1z&acwf=pn*8mA$-`t!sb5uP-9tdME#hoF9Q8)~o
z$O#T0($(u-K)6TWs8dP}^_uvvN$`Dw!Q&r;n6}4<zLw<@WRL{JzV|abUTD4OFn5xq
z3`k6eH}{8#jx#vBTA;Y_lJ9L{=&f}kwTy>=t|01pQ@UWD&ug*(<2u2lww5M#;_Y`W
z!g}2AkCfVor<XP;!O-qjagp2KYa|$u(gQ|_NSOF98oDQNx&~cbjX*uQX`TaU*eGWc
z;PPcd1I*zf<6IlD91g7V<HIM1wy~B!P%-D_mXhJ8RS25FFm$5hE}jw$?tx=s)0c%x
zw|+TcqOuEaA*No%DHwF7UCT<jLG#1H<<zOi7+LL-DGDz|`mr{z*WqOKIz(nooSyWV
ziOA#GY`kut-nZZ1`6@2|?Dy^cae#E}y=9{G6TSHRrbd#i+h#uT$pWV6n;N+hJq$E%
z!2kfPLF@(+2WCKXj;8|Wf_e0+XKovn_LvDbS4TBzrX`@npJW&c#^RC@P*84+a>}?o
zE&;V^z!$FM$(1o?I2j#qSTRn+uV|m0W>xZbob~#yZ}9_>_EVkjWZzA`P#0Z@n`>cj
z=Tl$Js772S-svo~_emKnw%_4ItWPEIEbz4l-*(tM&k6o($unV{A>-8y)fJB7w;K|=
zlt!d)4{?)AIM=j2)XQB2{L|*$)ad)oNF;P@zJ9GowneYf*X^N_6K<Q;SG%XJ%>=7#
z8*|!v?oSapKXU(I(W`OxV5PO*zNvH=Rk4Q6*Tr|J`id#Ddg-ugYs&B2M4yYBt7*%B
z55xk#&0Eu)s4^xk94h@nT^ESoMm(+9tz_Wh0U548i2W9)z+*wZG@;hXoq{L;%$pUl
zW#*tLM<aC0FzrjGVE_<;(@WZF0g^i5hVK$3zf#A~QrFvzZ8ON<(-Cl$t`VrLy&IT-
zJacKV4-Y8w&$o6Jac%F+qu#UA>kj_)1>>F9@&WlD_ydrV7-)&P`bp`ysSK5W&%6nQ
zCT!_R$4M%K)b~5J+ztU9$4u$U*2iyj6iGg>+h+M-ixJV`t!UDR?0|q!8deh@0JurE
zlnU@Si;-A=VGcAiBHfvjovh8k;Ohks=dflO*+E1}>?xm0g)<@VWoh}hnalo(crB8K
zqomoc*r`iMTy|!*>7=89z=%>U9d+gNO)yY{nJnKqc*!l*N{#LCIC^nRZ$(^BRJ1DC
zqFqLh;Ta@r%%V2j(h;+smj9@x7X60#{9nveC{5WfZ;SeMf;c|vn*0z;t!mReGR_25
z0JQVq{~A$cf+w`fZKu?(<4~^QJr~=3T*X!n!t8?CRu!ZyWEc~RyUxsk8`TdX?EEO!
zNC^^V<XBQQhgz`>8nnR|n^nXz>Vijs5e5;Ve*?mZLd_lB;Kyt<Pm|Ai8Ez)k8;hR;
zy86!m%pibF=LY~|nb<)bOy>>(*V{%&1q5qx*Q-CgHmJ=UxIP}=K=SGC7xhEE`K!sk
zykm$Z%}%s>o>;e-D;JgfQ0QZr@6+%745JTJib%q%Fk5!`e;27Mmu?Y)#)ayum^4i#
zrQx$i0B_oiBsWI!k>6hq-Gcn0yizR=e{v18+QbfS7j<eyAHEd{^DG!a<(M{=?#?AI
z#sMI}xY@xr^U2fw=z|@EKFGzy^5oEJJDpUrTZ`R$V*0@06}<s@WdY;Q{v39*HpgcY
zDFU|h2U9w)Di_$%qB#OBFKNitZ@H_jQVMO$?MNl?r0aW%OSwbXi=#WasEMQz0`A5$
zCatw3#FfIPk!`{<_XVz8-3GaQbf_RKn7In18$q3mN3oeVQ{lVp!%;_(dIgS-@(#<%
zPqsqYs;W8WMfXE1r~)Pbgta`uT2*|Ysba7A5JE?=n(AaXQT$L)(?CXZ!3`u@?`D!v
ziAe=@O2_zB19%(uq;mjR%j*1D%&$mAutv=0&L?li;14BE|FRL%F^X%qp@1Z-3V@|?
zL&7-}jhAS(<%RL~!U7ZyM&hOfgN#s%6tvc8`zpHYO;!T4e!R=InTl^dWS)FyqD(`<
z&nUK8HGh6F<b5K*47PkPicbxuZWt>>0C>P$nh77AOni<7w^5>y(N`6ZuJjNPkLhuW
zkZXQQH}v??YRlr;75UXP`yOK`7%lCup8tTGQ}Jo<@Yoyw_OCAAUWK!j8(x_@;}`GZ
zvQc9*^`6{&<r@z#=kum=Bx{_Mtx<z!UN3LM==j;YAB&(#d-D!Ln5Qi(zazL(P<qAq
zu4im6C5%i)U58xnQ^ypAp<*u&<06iQ1l--=+HWpJF&`dkmmSzK5w|~+Ss?uC=YG$u
z{ENBHBC|)w@~5(zhL>d`M=PeetEu%uS1n23Uf+i|9Mz{^w~O}gHL+;xqbj@WIoyFU
zA>?Kq8YrX30s%=>4&Mn{cAcHSvs7~NzT5fwnSaUQD2JLd8O{3e4~jH`26PEp%D=&P
zh?XIg#)UFrGO$hkdW!aso*7hfWx7dui=Sz=E0`LR<r^VblU9mBRlz?xy-N7lY$9wW
zF}9uLm70Cu;U89EGAif+^SmbC944OeX<fQjtVu#F+MaFmX<mTWE5C`9;8XFl4<ztH
zC13;K9Ak9!gl>id@H9gImlehWV9E|mun<(97QI!Q*?o{O;lW<VS<hY1uN_<DOx_nI
zs~q)9;fJ0g9uEgNP0=GsQM`8uUfpY&o<Vd8KkCaa>S~+6Rjd#b@Hgqj7D;jUvu7Ok
z;@DfOH%eSD-(ZIF)Q59^G}m)02})Bd1<=2Xu9ntqmX<GLeK1vDF^(#1q%Y?qnru#P
zYZ3;cmAN%!LR9<I-k`zleGd#9l2@`;dz3SzWTZhYN9!GR-Ho(ngH!V|83ZbL&qbn8
zqFu8RRGeJrG6@m_$$nrm7$(ljD?h<7)}0L>>)H$rPG$tfA7oH1Ql?#uF%)!_=qwT`
ziL%0L667Q9h0w}i+X&A<ndnepVjC?l&}l^g-j2kD<T?{tD5yG`S}<~4qh!dn5~iqE
zj73d_R1~+Q+A#^~fzjI8N#pIw<HIShJ(J|K$+NkFpdU|?9K!qk!V4%};##LLT{@|h
zKJnTq@wHQcq$^Q|cY^q8Z4w=K*BdzOut+-w>JNjahX5uz@eZW4U9^~5$pm>m5U$^7
zj)b@r50Jp^UVaLc@ueObl7HldN4b;D9ghFHDVAy(a_`C(hSuKLcRk=$)JM^p3A8<t
z>&0}dSM&_^iVrRJrFg0I-W0u~qD)Dld?y|ymEF&qJW?+-n#(<wyJ4&JV-oddir8F#
zIwa!V`XcJwR9YvtJ$b%?W|3q_Z^YbEdS`hg%vdAQyMxC@xx%dB*kqI%+a(NEA+e-P
zamc$_C3ad*;1aFFj@CHfy`_vI@i)zOevAgfW6=-u6AV~e@Z&yCpsgZO;ow~83UT^U
zjdZZan=ta@R?-WRl;E>B<+3f>gfxs1QJ%j6tmGgLpg^$k{{YyHTSQB<UO>;f{U!#X
zm1@PpG-|w5=JDen@aAx;2n+l<SAseJUhSQP@gJ*-t0@_X*nD(Ib>IPEXaT<U3?V{S
zvz9bIRF|6aO;&}8*idT*D_$bvynK>?2pyIv{|sG=4~4-%egFuMAwixA#5l_?k13!W
ztG@M3|5HHAxKBQJDn2(l&XrWl^+R9;#66%i1RZSCZ*Q}GE3YA{7fzNu-L39_Hr2#R
zJI+4T(5sFGdZ(4A_CKhfKcFWX7MS}au<JW4Jfws=8w$~^W6qd%s;u!Vok_2fZkVjk
znnN{eD;-h~?~h2Mj3@5s7)a+vlwvz*7uwgCg~FHevOhGmapt{Sd4I!Wu4Hde4w`BI
z*mtM@LAMFIwyvW6T`Rq#*=ZsZ09$Myzo*hPY{A5ty2bnH##-Unn1=*<IfP*#!0#e$
z^SR;XdTId<h5b}g@4$Aiz_`bc=n9<R7eeM22Go5**Yaeqf-zRYxKAF}i3O*`tR$Kf
z{g5;I;mE4Yn^PA`BYA2Z@q85@y-F9g${RgC1MP|>s=T<DjEHzc#gQQYfB=r8TeQ2y
zp>Zt%sgz;eOK)=Z=C~%*$(-x4>a63TUlTUNXy8HU-`i1eWjq)rT8Lg0T2EI1C)|$^
z@qSRbZUt93u4Ci7zj{oRO!R{3WTG$Al#^OZzTG0~x6W4)b=FP_FG*~QbdtVI3e8*7
zwUkAL95eHGQV^v#tiy^Y_cA^xBqQY(^ass0{87rPCU%Gf10+!rsnqV+hoqqC%-Xz*
z*F?Npf`1T8iT+DPTbyA!=GdzlZv7EE!;wIv55zqkF|k79K*J|f3w6~Get$+jS<b8t
zt%dTI6`2(K?NdV!xXxzD_b2|d{YO%u5-%Q<Aa5Nf4iEwc_&PIj^AqUcuzr(f=CJYj
zJ5Q*F7ZtFqd5fn-L#mc=7Qeb0KMt-zQvqE7fR!CI1}m7|maIQ&F5`qUo+a<EqIvZy
zew`Xi5%ZQ{?4SPp68q~*&hIZ}-B#-HtHxF3SJU@^a1}qVc*^gPoKJCQ=8dbQr2e1T
z8%~%jC`5-MK3={e?_SEO3rrC|PuRW(l@9@;gwT}iAO`@bRl~EwM2Q^bbT0y&a1kj0
zn%~Lm*3q4jt58zb5tWsib((dQrYTc3$5i{4NPS^ObxOU>rb$fkX+5cOlU~Z3DQ_vs
z-mXOi`vWK+$0`yJcLobdPMupm&^}(b&NiDQc$2EqrB=3Hp;Rw#GjT!2aMCw?tGj-w
zFX$eRn$lF-W+<~5ni)8r0S0!a=SQ@k(=bY7pvd;G<PB(z5%iEo_HsFwgAQ#`Uq|2W
zn@_gCqctYTXFgcSZ6=P(185mm!qteDtRJPuImCzvuGNBqT+_s?bgR-pZc1cK#%BUy
zcfjqbY|ODe^Bc{ZZxlPR22Zupx?v=&^^%LbEmYi&?n5u2PXJFAu=*`<!l8BY3>_l$
zhL?mo_M6Q7+84yj%FVJb3cMP3H{UFueslU&?$ROM+6Dg`^4KSI&f?E&s>4?Vq{^{h
zjfraGCo~D4lEu2&32*9I%HtE9q$2ZY2`3k7Lb<7oR~HnB9@3<0nG8_Pt<XF)Z*YLn
zi!(-1mGO_`gu&v3zX(5BfoY5KIEtf)>?%#shl=VPe8R!Xu@x)xD~{*xHbI<{%5{qD
zWs1VxQxAiQn)yiaC7nde6w#rXU2LF_;*zIA+vBNJ>A)ndic~kbu#Y$!XET$I{MvS$
zl5Xa(rF70b8BWy`o8B)?hpIQ5JIdD09ZzdIh};m-Hm|2k6zxMEe-zp?8ceNefJbE(
ziMYt37~G%5Xtw}JGwZu=_FiwNuil$>ENCo08A1l^M$9YPP!h3KZnaXRNE{Y+{nOO$
z`09X(H+cpC_HlkQ5VR?oxMx2W5<lwlPDFkUGQe3RXp(#m%@3i0hoHB^fPG=GHU>k<
zR~b41l_=4TOJlc+G3p^#s3!VGKk#<@T+Fs@N><sIFU(i=2evkr#In*?^ZI*GJ4urS
zW&PD0Q#L(zc}YAxH3@OA768;lvh7|bFoIIT{R+~9-cWiFGQWkC8o#%)CD5cIY|VTS
z{e~#&bg5hUZCgNh<<p0z&2ITo-<OyGDG@?DmdXSXFy{=0Q6NE{f*kfdOK_qv^k3fg
zI#bNK_wNgh!r~W`FCJfAEsU0K^SoWd4y?W9m2Lz1Ivh<a*w6~_GCR3@pmDX~Y|(f<
zV0WV!0CCv$e3JHS0un~wFC*US@Pc=_)C8|`Pe!VQfz73;UgBYC2<4Ga=exi1x!$_c
z5o+&7=s4w5J!b|qs7yWABRL<d9-YpR2-(0c=c$pyLgLT8EY-_CJ|jLItK^$-d1`vh
zH>FIW={5Dm_WC{i@2tV2Ij*)5-!dwqeq#46Wc*w!T7T109RvV@vgoiFBSJzqTLPxk
z_~0o(JNykdM^acnFD8v7eU(<C)|U^;=yA_l8=r;|5A>N5?I6pH8>qFGb{iA!k4Lhx
zh1zrNGpW3mTO|9UR=9c;&nVja{aMJ*n$Vw}Vc56rjyn`ZpXiH%-+dn;5g57>dxvJq
z3=;NOez6)$hzLoLH^BD;2$9n`a)S_(hkk6G6zV%h<320#`fLWCRneG$!b@0F=32K)
z20&bA;fcnfXyp%4Ep*J3a?!PzXqsE!b2tO>Hxex{1+=xU#On)>gl%J+lP32CZ}*OB
zWzVhDB1rwhQGKC~hvu6Abyw&`b6vnV9PGf21`tlzzjWe>BplI1`3Xd8qZP$Pl~fG8
zfhpHPZvx{LobUH~TCM)*?~*p``SDfLxn1uGWO7qq*Yi|q`U`5OZ(w6@VB*&4LQ97{
zURT3j&&yZ;INp${*mNme$Me8V?QWRSe1lfi+=A`?2~qpAD~9sOv~R!in<Q+w!yJS=
zZSntu#+~V&v_RvQ^3d*#nN+p|8Fu_ks?Z<qYvfEpv67jJ?g?LUrl?ThFbr^m)8H<V
z{6%Ph3TVMJ?dkCSi2Ui)aH4(om_ws#UfE!PKAb)D+1Ss2@xT7%{jQpXe&_p5S+lZ7
zb;?kRR>k7(On-?WQMk9J#JeWJe?EYAV02+7{rmhs0MOdGfnG)oFg2y)<q#JFfQtjr
zuqO?QP~ODVVsMcH&kY>Tv~HL1<O;`X(tmOP!ef6m1O)2rGW}*sMWZ(f^O+e>i7^1^
z;y@DW0!QL)X$u{)0-#y6R4LB~oY-j)<^ZQXJ)tU^KV9kldi^{2KL<bKaLDQNu)$BK
z=dA3q&l1OW0vM0EZ<OUGjd!eAM>+qoPk{l7`^^bY6kz}{WKbRiL@Vmre^11McL*ZX
znlqp-Cx7k@8cF%BS`eF&$0N4yh5RQ<K@`8k#p>26r%H*Dw-tiZic*k*f_>(-S<S+B
zveDtaU-;7m!s9f6JYNcRvdi;_w?DtU`0w*&|7`T_55<FB@SoBb=Hvq$M@!HzQ4YX@
zc)9~<iv*1?V2;8O<&<mu-v#FeRDDQD|MgKR=@I;AqArQWW`q&+{P*JKib;!f+HF!3
zu8>mFH4Y=_Y1t3UzLshZWsKzTOu&^C)N*czl*a2E0_-vKxGd&ZWH<o&rn3tGGqnWI
zITsa_so?c6Afm?Pc03ZL-H6XKQ**Sm4SSr=KF>Y?mj&>K(ubp=$WSO$T6<$Mmaldx
zi}Q8h5;bPreepNzV)lu$VRyQ|3H9QYh=JJ$Is*%f4>{g;_m}BXU9FdMn!F#Z(9Jxx
zTgf?zuwSLa)c23elJ5482MJr9kERTzwAVHCTw?AC=t9FPVW82V`j~TDTwqiu7yv&u
zABAujjDsV~#y%})H#H!CooQs*(0+Sc+s5KPK_OJd4Tu#6#IYfC!#VIv0dW};ZCF^$
zE39q#^K~Kp?ayn3AOG+Jiy|MD#M82UE!v){^K-TS{KK8SOM9v(CsknnaJKDyu!KPD
zeW2QWdnumbQir$a!_}ITa;0@N+0@|SaMPXiUYCzv>a(@xz{+o&<mAmqwV~_z$GtvB
z@Bj8arCBkxnf3Bk9x<lby+!&99f33{^!~g-rL$?_A$<N^Od{*mzd+TDD@91vjdztP
zwKLcchn3e+dh9hh`~s#ISHjQe`*KCXW?J9%rLEOCaNYqQz2p#YxDkw`h1GT4KfPX_
zi~~-E4xj}M+60qQ9{|w7eUgBR4EcyCpGSC&F^o*%;UVjZ-q$yQYoX`_$73j{6yV|^
zG~VA9E{%eTM}t~Y=JL=+#Z+=n0;t2rR%OYN?UDb@UfunB_5JqWCIo~wCI=&|$UvJY
zB0mVyNwsr3f{jZ>EU`GIaVPYh&cI{UonJFAF5X9K1>6U~w{8Hai)#Ww%Dht$KoPCf
zOIC^x#80PX(n3LmhflS~N)N@}hyqFKX<EB?GHBGKGEvtV-X;oXE?uRA8k<2YDM_Pn
zF90oIagcETNl_sioad|?huT^y*uMCdqbO!RU7{p;nZ=yF>m#W@!N&B+5&WJhBD8V;
zIWxDmdn&SCCa!Chg)hZBAxu}!MWebH9EY??98vtn8P?(4=${rnr103upb~l<6r1=-
zfh_&9GhTAd=&}wWS^T$u=jE3#ApyvM5pb#IitwDEjF<PhL1}@5OL`#2NtBMb%zrb^
zc#TbcZ-PpoF4U>W{U$(2=e=NoVND5S-o@juS2@boE=lqlDL`3N!#8@c)GaUI-XmVN
zs^mXOxy=?sX`0*u$ZNpbaiDKN6P8tl!St8Xg>emK?w%G!;(&(#CnuJL_l5w1NDb>p
zgai9o1_vO-NN6-job@(c|9y$LRbO2}6*&9+n?fC*?^L0Q-a)KHwfXx(GaGM%gOPhR
z<|me#KNOic8Yy6&^wK<%)K8&0efAoSn94ChB;nvk7QB@CL<vKH(Csaw13*L}sRnjB
zAHFBmB%2kTolK#$9W7}HP3QBlooB?8+gk;_l32r|fs;bfAH)g8-y%V7956I|o&xh3
zHCy<Y6XN8LD_u-?+6Z;A0L^G=FH9GxbGp#}KCH;!O*w8&dz39vETZY}k_w4<oCzw`
zmW}63ZP3mz5bu@V)i;N7n>MAIALZlG@Hw~?b<X&6=$sG19~9<!Yqh@B#e5g3GV@Ul
z=;)Huu@~jn+3z9Sx-qcP5@liiPcsk_Cy(NnutiPy2TFLF3K~h1--*~Pq6Y7I?$$i&
z6@3R?n-nVONfGNIvZ|w7>H+TuBq;d7oR6PVQ&O05fjIdRHT)>3p;EYpUpBC@8C7e7
zbp7VJY+NLZ+@Ynqz5_%_gKdKEP+Kj<DWC*G`!i3?LkaPenq%`kCd!f#3999Tp*0gN
z-6MiLOwRi6JC&H$$tUhbC{0=$3A)-g<y?$fPg(nl3aL{+5<dFP8E5->G=(A@cmGoy
zx~!FON_uO^_=SyU)A~c}Ft6V}4I85s{-Z%!Etex3Z=sDJo6+$m3?VNPsM4!6y8Mvw
z=|qCgSXxjHl=&45*KM(#hOUe$(2Y<xkNE>)X_)M#Z*NXn%YC`VBBaOdo%<%KdbV`N
zHf856uDLP)y;N57`<*{3=^_Mb(&~@BTy%Qg#{FT+|IJ-Kr=NY#@^bXS?aL?rDM1-O
zX<z6PX^5C@bZ#)<60bUy$<1Yo-C}&!awW5ZK06V4rhEVPVrZwobCTshD;b)8Mk875
zGV^I=z{F|&z-+Iu_CJZyyY7{vzGsC+*4DXDmfqF275`(HJLztT|F!DOgjvK;%CmH&
ztK}H%1udjih&toco0tfyBGo@#7qo4cR}dx|V=uX-v8s+n{MF_D2<LQ=phK_TC&B?S
zxy&MT37=_I#VvhhQwj_}D!`}3c0{i}_pSUPbUe0u(H8V@EN@=cN$g{0Tc7Lqkqq)G
zQxi`}9*w5;C~O(PE7o_M1H|J(gxN>EA~Pyi1F>8t9I*pohX%y<O!Yd&@(ZIyX4s=V
zfL2I-71olD;J|Z);I&#kM|ZHiO13eX9iC2RculW~mX?x5J$Q83X7>EiXy;Fbp>;ol
z*UVShfh+poY~M(7sP8QIcS!tx`Q|rdu2M+jBf*X<%MivpxqsFpOn1xM;$Qm@NU`MH
zwGq|UuzYMGS&ti@ndvFXpVXy>?xrt(9@Qszr?U$0tjlfKnSyfS)x6VSJtt>=dXrxs
zlj4*}{yMp*T-lqCEc-=CC)5OwUYuF@&};fM^NI>vQ4(MY`NXy_tt!FOyJ!JijG1gu
z&RJNFr<{{>PZkZZ3@ob+fT@SP7;#0R05dyb!ahHQYo~dSuMIY*Cg!?_s*JPO3z&D!
zwezA^-Z}+=t2s(-A-E7BcEb@j%`WwUp?Si}q1=X2cxkek<q2t1rG3-YToujAO>ZMl
zXUP2y$Xiax&(0~tuP8i~*1pGCVy+`kS7n<>+cEF?DoS}z+Rw_NERA~r2!UJsv23B}
zwcfD}kAkA7MJPW<<8g)hZUbNiZuPx7xFEM0E8p=7)ME}9tQM@;W5=4~B?(jTk^?{}
z{vZ_Ol>{`xi-$^McS1RmX;scr>2z4%s9*ksxTN8$q>+34JY5iI`MW$l_`>(eBVi+^
z*J<tN*>KlY{jy{B8KvJd2^3oZc{we>we5D2$(1Oc<s`1+47@8R?=+~OMV;NcoM!IO
zE7T`+O{M!DXm9B4`{+C;Cn^40SFP3D^~v3>(CBu9k`D_N!V7@qQ<8o`aeYar{c^Md
z8e{!z6akR!r8M;6k%A?SWtC0pE&Wt{jniz@5&d<RqRpFi>89M?+BR`;eat@^pX>Zh
zfB}@rQzlx!NzdZ72P!*ZYelKQcBNI1YuerDBCJ$~WE$xc)V)~ZbD3m=v8y=H5|suw
zpchHd+Td*{=#a-SDGudLI>Pgly*_`6a>LKG<z()+SlgZ050JPNzIY%saw?JctCQ+u
zCH^*#PdeBOJdd35giJLkyORsYfEObgU~`1iZObw{z5^gQoe%j30P5OG9GVKT*P|9y
zOFDH5=GS2qfkd*uaB2Yf7O~>Qn<>Gio5b5tyo7vAG^P4fKy6wbQ7geWk5`cSe)0MI
zNB(VTB6iFop3JHCEVE%uulpnjdgN0>U0*bFw2rW*#&lFXV53^%3@Cl8zR1q^gyVc)
zBICGI3NC+sDdzvHP_vKY1PpJ3tRH1>yk6bhCE=E&{hLkjcY*X@3_h3d59CfC!cPP_
zY{a$0!Ywq$?KuMyS_Gwm`&u}e{lvJ$c`h<tELtRynGf*ly77E)5Z+g!?IXq(cFZ6v
zNBl#3VQPI$zajD~0rLIez$P`P3O~q=$pHXGP4AXR@<{C4%x}YI(|uElCurK0PgS@l
z4^<pLCv%=bOjq&Hqr^^7VstbNtyEr~V#<3~>;4&N;3m5`oxJ#gXte!HLt^ygw^93@
z(G|#8xNF(_?fp?>55F;4jWfRKh4p}K{%tZfr`i}~8yk%*bxkPy)*~hW07vc0iPZwN
z$3U%gUE85EI3~|!jcaPyfQ&$~+^;ohldY6l8hJyAHZRA{5f4J4*hieDl)C(nII*OF
zvAqfYHN5;vlPKigW#n>J^_#`|Wg$7$`IkRUS{3JWm_`FxYG7^phLVd@tW9z04tH$g
z7Rs%kUA(KMX?C-+4vk)PstRD&O`E&;S|4~OBFK8WE+IL1HycYvT&C22-Ss$A%8Y3B
zmFRfY-h%(0Ngl;GMLH9zqZq$PEA)}w_8Bw#w|qoRTJ>A<C|?+FNm{72TRoAsy}#hB
zDU)4UQK&VUZU4p%i`wQKxCFKdY|4pr=`=$*e+h3L5FkTzkh|0aHk@VoEVQ|EcwT(^
za724;v{Z>6y`XF(PqNo4_n<T!L1|2(F9HPKhq00)dSK{Bkk+8ZfeSHxb3rH388U58
z$+N5g+FNOEW6LYwpx?f4?|gp(mL_k1j`$lN1Q?R90rS^OPC<2peo~IhF-XIgII|jl
zKghBFr2``em)b+OXl^X|3)#@dX!3aa{XFha4r6OFebc$vM!RWxolSrprU2FzCfzJg
z5|jr3(Xg^}FJUWLH7GDK%{_40H)ddxlBxlIY_o{7SGBiW)F?jDd5~Add=e2V)=SY8
z;+J#DwRLA3`2P1M$o71R>*>->#-=^Zj*4HZ65Vc7^ea`1V=1y~f!Z+D*OM==Zuh3|
zBQk04uAzU8cD=z%UT^7)G@8_Cg?kOQqJ%#8F2lvGm|RKS)zhh*jp;W@K7x*VQJDtd
zyn+}FYYpdn8lCMHZy&_ux*&M7_;Ty`A#NE%%^bpTWJM|GU>Y}qSR*T+vqF}2T9{)%
z{K3{V-N+~?>V5tuV^Fy)=Z-M%*T#|UAk%#2%5wJMavFvqFAgs)mz)%72NiJ5I}&vp
zz@b5|6o6orU=6e!KV7E5K4CKXHPYXGP8XwB@LmM}e$jRJ;x~xmJ|UR*sl<Kq+HDGd
zy*8=;?X>zLS&NE&_K0Et&JQYPagt?^X&yKpHDlZh9LpDTAjSM(mjYcx42pB+hjL~a
zA~^RSa_)jflz`1}0y+h<oL~TO!v{lwa?jvB@rWU&?5Ek>E0pqbYW=v%8=gzir*%xv
z%3|-=gnT4owx|lD!={NPhvjV*R-2m3IES0e*nXIgps3CO3q;O;wL3qGB&bZeI(Yt+
z&7OwhWn6z&YTxsTXGpRk7o(5JE{N!ejAJe&7gH%Bpy?~r8BMmjCC?`=FT`#1-C5)h
zCP3YJNz1rhFUshPOFQY7>3uv)i&(+1w6_M<R(AHmVsT>R@GDw(o^8dYijt~hKNR(u
z6O@q@!RDy!&UwrrnFikaPMdH-^t7k%vbi73Agdk+bKSc~8HaM*2Q?|NV{Kay(xfwz
zM5W;o`0S=JmvAAbv_jd*{L}8Y=SjcunnFJ|^?Yi&*{otBM8#j44xUKwGQ!nUICQpO
zI{u=$&Sc|MpqC3~=k^(><?-Z6P8xiczx~WSR*bJDMM-1hTV8xg(zke&Fo<W84Xcxa
zHYRiEf~CsB(4wH<p@NRr9Qr)K5Q@9S_UXOU)@Y&v0rq&V=o#ymN`d8C7UhpV)+ky0
zXT4Zw(|`=q)9R+{3tKtsk{Jf5#%wXBM4NfaoOt2qAB(qlO5|2oet%Rc*pTnuiVQhE
zkvp9ekj|VodL_@`*s88}GnvCkg;v}t_5JQNYzb%3aa@LVrxdt&<T&q~TUd%=s1?<h
z<xq@@JYE$sOm{qIA~C~(gW6ZI<Q$c2DxtDfaS-&$xw&Sp_QT4Q+EW2K>5wP*Z2<l>
zEb1Y{4_WE~D*&Kn$q@$P9|bqgS!**`n;Z*hlmfmCp5VFWMz^v-RegZBgYZA$O(^L~
z001~Ziae$RYAmFQd@4#_kafPW*_nFRH+4HOtu(?C*CW-VZ9cqvwjmQ=9-C44Lw*~|
zhN=ZXPnb#pmNI6Vx@(rG_W-~-`a4|5@x~o_Cuy_fLPglTU<>cyDlROREl*K#+yEb{
z0Hwu*PZh>Cf$%}%ffG|$t@OEsMl0Fr_2IPju)=la7op*d5KY~pz_t%H4GOcEE~6l^
z@3BRio<-RW71sZCuMG9jO#fc5>sfCVB-M21UMw;B1zXv$ndB%rzYsY4)ZP8<xj}1p
zL!1|p`YUq^hwAq|5B9oy@RHgD`YzfZ>3{N-XZ>4r5Us$5HTclCBv&3RRo;elZpO#l
z$ixUVR4fy^99>n0i<-rYo+^e6CGZw3vU0i!PWvrsSM=5GpK=dR%9Wj+4wSN-qo{Ln
z=DA=dHCeXGQIIW02F`OU>QnqlBQP3QyhrFm#c0sx!TB_n(Sfuaxq<h<7!SReCn(px
zZZc|FuAH7o^C_aNf_Zv2>r=dh&)WP8rLn`vy2Jja!<)5b?sARYudc55nkU&6=rrDK
zywwKJN)AGq%Z7p++3UCV8QRjoZnh$zGd{~_ADvz2=DrFAJ-+8m4j2H<V5mya8fVlc
zXt13PixNhwPy)*^^b`DypokeBSWTKTsyxDVb5DpPA~s*;<|LjWW|XFNi_f6*NB44A
z(9e~o6~%ZKyC>~ggEx169c)p>I$)yB{3QQ5@^lN&+Shh2y6aZE3EUCy4E^jS?$-Y0
zt&8=-7=Ev}5FsMQHk3C%1#uuEX4H!QU{fbbB?a&P>;6nlE<t2DNpj&Q<oS7P=dMTO
zi+R(lg%b$G<zIlQ#Rjiy(?#H|I1GSN5)gqg$Sulzb5U||ocgBGCMnx35Uq+M<^Nm)
z_&^lQurq}ycoDcae+U3f%{Y|=`wItvU5X$@!}-M~t-)`cJQhH>-;v$iMW8w7;KQH!
zp`h8iQlc}^AN)u()`2isGzMTMLW3|mK@cT6Y}94~C0ER!=Nbr@;4~}rU**yPWjd*)
zh)J3T-&}36Jib{k;(HEboXEe_{$>Bxab7-8ypgXzn2MKu@0H9xzGP;jf1y*g=zhgq
zV)@#-X~pxJg)Mj1y<^?`CriolX+-dld6@~^z=#ey`KY|hww+l9j0>?*)GU6sBq#*f
zXXR<?4uSC6To|E8thbn-A})(a>hyEsGnxZUJgp#cx#v{w`_E2~&M&fp7|hyN?^WIw
zf<C0$AP3|A$bwJ@fKoacP6>yUA`}pjZE0@!6r9ldv-#Ia>g1T#R%#JyX)5)5G_a#J
zD*7%ry~1f#;Q$WW>iH=#wmOqztIjS`TFW|)3+bm!T6MjJqt)@P$2iOJ_!AB^I5?jR
zfPlaX{Rpbd!dH@thb7as({W$=#cWX^&2gz6%GUYfM0Nq+CER9xDl`p)bFe$TQ=cy9
zn|NKzyp1uQ9ZIDbr8N_dE}beA?w`*dp^t-QPvyqQw_H>hE?$1(*V8mqR*7CraK7T^
zs5zqt$he6J0CpKHV48!{Re&hGu-O~>ki0<0v2gsAJ;5r~J3m3=+Gfj4P8K8gSf)b{
z)!7eYgf>>BgANaI@~BOK!<EPdV8ciH0N@(W9`Gx|q21?(y5*Iivgyq1M)cT^(kK;p
zGP>AQy1W!yXJrM1JivkuNs;K<Om;qJ`PwMR=Gt-@2bM>=uwJMsCaz2>47c`b=DOI~
zC0V3i&}dis=^__08d&l;WH0R1;~N4@nOi7da)sNCW>$srFOS?mqX`j+9sc*!j5V$B
zlGB?8;_Nu!iyPHNfg%kIu*pY*7R;1Nsp34)K*50oYQ8xA6<P-@6@4%HeSM<9>kWrj
zz#E0yq*q#hw()?>g0o1j8aLKLpd9W6fQN$LMd6p;)Dl!@^DI(+k#V(F=#YJFI4m7u
z9^o}Ap@fO+&Q(rc9<}gw<>id%mG7L8h)FGyqM|+Go28CAB+!jo4HMQg-R%>9SX=*s
zeo5i<&G7s2;nma`;z-D*idf2n9qn_a7pz7!TX(9kC45NSM{^f^tRt;~hL(l2GS7&B
z1@&DH(}*c0nQL}DaaC^5OEKL-u!t4$%(7GqY;wR_NW-g{h`INEDV=?W<KLE2s%Wdp
zTrgut_L5~mLzK_CS9-fMi9sW!l=%44;AKoFBZd^fJBX$j%#?`B;HTmWPA1)I0|4~7
z0vc9<G{enGy94>PCLHS?_mq^~E9P=O24{jcK+yc(5Crbxa<)VdrPL+F;p3$Pzv9g2
zjbLsCIvqc|`b`?T%*I%U<nl0hWRmhWCf-%rAyiy0=E)hEvRc}|IeA>NvohmaX)A}1
zKj_a-nmuAuDhC%DY?``rlT*EWF+6OvK4}6|_X~W<$qvuy>SDwK(6o%NB50yH{-zTc
z)u7VnhkxLbHG=>p3~IkNfyJpj`%6GKAae9Cpau~ZtvyYECvFBqT5+et7>(n9LDL}`
zG71`&NV{8V>E^0WNR-uSiu3DWvkBT7Jo=mEu<oGNb7v#|@bsmPt6=hT-lB`o7vw{e
zAusL;SGD)#t*kHE9<c6~U8Y}WWd&&%NBJ>szq)4>%N@Sp_<`}`<=K1ZQ%;Hz<bN9J
z2lm6%=9HEFwHB0BbY|CRK@Ud5yvrr`R0R4=6G;d6H?%*du{a#DZm{rawpdNLKlQ;O
zyH>N+CVDE?MY723HyP~kSXA?Zu2>!AYc*q`BQ<Q-T%1{MK6V6Qn@KTFHU{KIC|~Ce
zg$Lmh*2*jOIqnOQ*&JLK8VUZ)Vwb<o#tPd9N|AHyF|@%^wAEnHnYa%qUx*SGM68>=
zWv12Go1IA<nGywUZ8`6!3<eUpJ8Dgy(9XWmpq(je&*{v){_@~Yl%EgXP*)!Pj&1No
zx(RI|r>Q@qktAc>s-UKLF08tQkHDU=@QOpN)>bw{DHwrRVa<R?=tH(uC+`KQYtRpW
z@_hKkEM<(lesHWdX4RAl3fK-i1V{0@5Q5Pf+-3Q2i57VTTEd!RCLlA1c)Ee#u2*WY
zn|7AO7eTdjS$dX3X|(?GyWLB+;H-11Ik+Q|fBl_d{@Yjz4@UBbp0ni?x`|o$8$Xp0
z{}ez#is#3je$IPya`VY2np~dw9w>yA1oZ@}T;S`yDv_@?`e30JNN7-Rlk%4Kw8~5&
zt?K2dWB29c%O$~-IzhD}lc(WDjtL@Wn=__#7N!&yZt9l&u4xfcdmWNVtK~oLoEqV;
z5bL(r-bcv1!MGyzCg*Dt?Oi7Uk*MKn1o-pl_*Ve0QHNMET_ny9&4%|br%SDhW`o48
z#xV82^ynk@@kE2H9DD<+!8WBDZgTLZf>dmJHX{0$HVlC#ai|+EZ<0)e1&L9uyXX3!
zW~N2ut`p}U<|T+~uCEt8^Yy+P=z2!Kvk`X~!$`Yv;`DjVqDs2Hl<UG-SLum{#%=Ix
zV?c`FLkQJ5BZRc%8h@-(^<mB0a_x;oSZ5_a3Uda^1BZi{vhbF(w0}{wi7TO#+Sp6q
zQI7g8(I-%<6YxNxypqz3FDlwZh1fUT%oJk_k{gF~JINJkpDF)zvD&L!-TLvVBcWA<
z$U}BQTCw|Y@dAO^sQml*K4u=Ew)$TAwMaBaW294XoOaSMrkticFo|7M)Xa{CpRX7<
z!NUn2ZG{;AU+^f7M;@oPc@8aRW(TKeI7+~k_&5kaqh%&m!vXvpsQ@>1BFa*cGH%#)
zp+ITW?O&DwCsjc{=r8EGhW%??U&(?a63s5%0?si{06<8J1ogk_D#c?}7yH83o+7Vx
zYoyjb#LYZTn`vB3MsZ<W-){IAhq=CAbm+Bhe3oBy6UAJH1{@I5Y`+P@`b%fWZw4ZP
zAl?t@ahN9nafAvMcznuA2!B53YRb%V9S%xIH*~&pVSWyq*W(G?-=_qwpC7zLyc7E6
zzzq<jd}Wjn4kI7{^JDGWRZLi8zjQ48)-2@bn>%XsFQKgked@o@arPh%GxY5;G~(fZ
zk2^no{}ui}RGnp5lmFX??~NESx@(M4qen_9qq{q$L=Z$;K)})6As`@(4kZ-?#E~N1
zCGw+78bri;_J8@j-HYuwcJJ8zy|3%@InRjuO4W<UKYm9%zyERX$M1hQYA6v1mw_NF
zt*<Y4|JEzG($U>L+yV`+FGUirz4|m_7J)jm0(>|eLD%}Z>!2aCJRE=wfH?x}5m?RJ
zT2d)T0-e)x!6`yqBlVTz8Oq3uZ?zi7MmEmFkCB34fZ-`Y0DxB^N_zoe0JhGiN6>e0
zIn>m+|MC-;D3$YJr|$k7rbo}SZ6a-$&I^|GYKFd7%wV|TVo_i4>c?HfR+o?RW0Z&F
za)z^)ty1HS%#>eBO7Wi_@AN+zVOAM{PHUhhtV0UV4wY6kx5>vy&DJ~50#bH8pjXGu
ze^fZ9zd}@v=oN3{7ku;s?alTj2(5(P_-W*qdqlf+RRu{fxmvroc-6la7}t5BS<^w7
zC%hn?>LKPKukO{Qb757&=kXg;R#PXRRaqkVsHCRuQC;I5Y=Zhsn_fM|G)lxU_m`5L
zOy7T)kBfafkv&#|+Xt$j(Zow#2jDl)O7>gcJTA(l(WEaL^u_a?_UKiJUwO^H+kbT=
zOGK{m>6_lX^*5KIq~;uXrNp89dio4ZO{-VfaQqE5w%GBR7bz)zhEJ4&?Ss-s`!=!m
zwce6Z*LQ0F4QNuJvl))|vhwej`L9cZ27LU|`)m6NTiVY1lb3IfyH43AzJEA<xk|aB
zWkP3O(m$&BjL0YXB<Ns1epD^c0S*dw#XyviW*9)b&EI3MO$y98FRz4pouQ_-!etSv
zKwR3*DqK2+cNo5Sw=Z`>9Ek?qun^)G%6Fj&k`d8hz#VVh^fDsfnWPshZ0vbDNF#gK
zq3}zro}}8Pf>H7#&Tj@!^$Efo{(<)he%15(Uh0{dQNNk4{sVhvB2_G;I}0beV^B_p
zC96prsg$e;G^=R>kInlMS&)5Jp<1GRk=ysdmn3+<CpY3nt{jn-Cw+kkQHiRB?vyQc
z;Ph*Ti?>W5p8NZ9zO-*&l8?;6Z0pmQa%0Cdoot++@vsEp?R+LlP6mQRT6Kc+Cl=bx
zj(lRxVR`O;t+v*uPRW(6ceSb_mW+Lq_Id<+0^hwJNp5?p;eJZv%nRUvOK~*dOo49z
z$CP4SqmkYKz^BN514fF<*0J|%jb$ZT+*k8G%?M`C$c>IMuh(W1l6nlsagD%;mjEh^
zEFl_TC+}5^#ygCZF_|L(!0{RoKzdGcGuWuv-)EZhAV_<8S8i|m?cJ-7j9-r4zc4v*
z&|MA}+a>JHH>GsevyLfSC5V+td*5IEgHEF4;+AvQH3o3zxmymOWWxbZNsV<`)iVYF
zz$={E5^bb8^;ZzXT+7@_8B2j80v_EZlXurp&-y*)T=n5A4X;*)R`3Ndv=j=udIRZ4
zTEKg)xTqab*%Wg3f*yvSkD9s%<~dhdJWjZ}%RgjW@|rWOGL57A>Pq*OZd}E{cO^Up
z$mW2?FPu*9sMoJw*W546(r_$P0PaUc-;!^t<I}9PXHYXrL!L^8O@b%D9;HfnAjwyw
zaC`p|y83TK)eef4$Vve4FNrqa4qW@TdHUJF`?jBq=2S_PZ5Ot=Z5Re}`e|)xu{yhr
zsO5f3!tdHnhbI6*U!NGF6ok=XO&HB-QF-KmW0w<6JPk#bu=v;`j^WMk)PAaGPd*qO
zFGx0atJZmY<z6>J5ASaZrrU5w;eh{W#D~e#0ylwA!`Jx#9s=a~#l1{-0zfTxiUVR4
zpCA2(%Y&q}u1{hsUycjFG4D{B$r@s8(mByhRG%CVnG;abgagbJAj<%y6eB{wduItL
z0X;gbOm|M(PoGXi_9RGS^A}J4-v9FF`57??XGH~ew1bB3YLHf39+GA+^^B#x?Xwix
zflK-jDp5B5lJ75Ez<^|g(ZD7>b{GKBEZu#)bfapV+rnUC*C1*Qai%*f9wIPPHfpt#
z)L?JN=)Se^bUy_@pa&6Vc%Me+KY-)B@uhU;^0DPu&glLAg~bn=RLy0rF|UO0**Jgy
zJZ>(Ea!{|Spv(L3vGC}%k|d1vPVRb~OW#8SQa=7|e2DlGS$YI33XK5>ZZMRwi76S?
zj!L;s6w_43=B$C%*o&R>$T_F$j|>67POO(FMnOb)x_&d$v5WO!Qm4p`wTU);3|B_E
zSwr3aJE5ydn+kDX6`^x_q12MN)G8dkR+xzw@9P8&?}0{77JBm43F}zw=f+XN`yxbP
zKFcodC3s7sOvuJZ-2&&l&o`ZEzj<?GyA$)NfqUgvi|iIqEwcCkl!osM4#qY@s+L-T
zM9PE*+C}>-e)qnmb&tHSr~$J%T{%A7YL)3fg<7b36JrX9N#S2;<J>CnSu{(024sus
z1Cw0Jiv5R`>AwHOuQaFhf6V7|=J~DJkN%7o-?f`8^tb)#Pkp`}oiD?hBSpisu*_A!
z^j6+OUGb5%HZjNWm>twFCz14QDy@{~nL(?&i(qp3mx{;(7sS1%<^r#{w$>wTOKIJP
zr9UYADie076$H}<gNdJ}ki_BMC`SPjaGanq%Vm$_ub&e$Z2B?HR3tAY?YS%du)geG
z9W89V=XXKW&}OnZ{&I20^1{%A=%FPr-8vKsu$*nhtiLe2Vqu<YK2YAz4bdR~o63Ky
zRBkSHbMm3rr^;Qh0(Mu@Qfl+@>`El&5UEWT_|_wlwT}8*u&MpqFtLXJ6fC}Z>fgP#
zzggL--e%Gd?Q;6t+w_HYkja|JCCFf6oVes|(}x!Ocj;e3^Sp&0cmBE^ZNIke3fjp$
zzxf`;Co>R}M?mOYLn&0Y?t)xm)$X8oa^qDFO~t`spncSk;A&dTw;|k~E4|`a9Uye_
ze494;utI_B5ZHmicpC8074n070MLNfF=g!q<V3s63`KeB!<SEg^LeK1PDPTSVo7`w
zNt#kg&N4o}@u}Q0TTKftI3q#q3Vuq=%kXB;<>C7Qkip6uSbA~z+CQzb##3cXDvp&S
z#s(Oy$smkCRPhC>Oa$~}9Yx7_xX#u_g8;rZeJ~W39GnVAgSVpayE!;+lxSTrc=RGO
z^14r_9H_Qg*3nTCZkwQ@URGHc6M%)DvKI_e|06BEOlKXPMhQ)#&XO|e9}YzAP+O|z
zObTC5mI7CkMpIEiQ>S(cr;b4+(Ce7F9&So2v0!Ya-H#(aeBJ5BIpU^nZobd;w$GvA
zN$1={T5F{1FA|&NW7l7`%&%iVvl1rVPw*KN>skCp-1IuGnQBBs)MDXbUkjDs0<!JT
z=I&o1N!lkZN>n?r=KX)%tLBZj@kzRXN(^tM779+dz6s?xqh~xAfPsV=MuXUh<pIt8
zW73Su%|8SWY4tK?ZsYaDqZrNVld`Zpuxg*DXgfmu9&LRe3WgG|8{~kKEV#*LRZyOK
z3q4r3SSoH(iAbbGEYdF#*^-Jpi)4smRpxcncfHu5>dvT$d$_6LE_Cu1vMyA1o9Z+-
z2mo|EI_w(;jOzfXLH2X+iE5*j#%?!HoY&mZK9Bx|P&qSFlpHW%;XcrlQ(gk?z(7DA
z1ivl^2FRSD#N7BGI7~xTnvgh(vz$a1bgPP{<az^y8<PE_ISW0dqZ4DC(3qwh47zj~
zc=kj{vJrVX*jXn=TMb#OHjZaFRlCY`yV|>=7Bc}1Et$aZvnMW0*wyQ@Tbn3yX3^l#
z$<Qz3QkR(-H~LB}PjcA?FJ3>Ing>5hB&um7QDVzl&+u)#&3?n9J}BRe`@Qd$GCgen
zIIPKVkV7OHPGHZ`+A*HKAJ)9xlBui&?WG;}`P=-D6TACQrSH}D9HNC}u=d7F)db#q
z3{oEA=!4Sfq1hwNg)s<@(EyP0$)Gz_O8w03Xr|CWi+|ElwV%EdQ4W;dN}scGIc<Xn
zc|e$a4;&)}r6?f=g||3nVMyE&f0P4=1n6V60ur^_Qnju!wZ)OT{q}+cj{Fsi+Mfax
z_)W0Ult2^u=ja=($V5s865gA^S_-glkkDm~u$9g6_wG8tSpm1Ge&UFQb7?htweYG@
zwa>5*sxQ@@!$71wZlG%-w4(N#BRH-QvPq#!d0moNvmIRk-ZYak6}4{Y%u9#bz7?#x
zG;+*VqRUn_HHo-r_`-3tTVBgSUqe#)a5&7=p<>$ZO5FLXG>;_P`+0UC<EibPMn_Cl
zAXA^AtSf`;*s>f!i-jw<T%0Y7WQmA+N9=TiqjZT~?kj~TIt8&h_<5y5W!tS}A7AUx
z<40#=bYljWQ;nB%zbPU)i1s*C>N%3G{)nBzr2GDu{{2UD-D-NPWPS?szYvv*@>Q+k
zmndx8WUg!pepu-!R5J(fO=A5dNINyexsk$pF~Tum^OT3@h%~1~3>%;|_Jh0-gCbXU
z<$*a;%k-`h!jS-ghDuTp6V}MWd5uLGGcR7#C7!yN<vt7wdH62;;dP|DFp2(K(T}3<
zhO|qR#bNt`NJ%t_%;Ryx7_6uAT00tG=E`YuGGT(GF@f^ZdiOuYNCM4_iU}_Sb<^Gp
zc;~9^!*^VjT{>jq__(h1c(`W31`~v(J+c@uIW7y1(;-L*R;DD=Bnr+PTQhsh7t;TW
zwo$_CyG&)K)OjqO>32;<BHD{Ut=XqsLgFfRvsTNd_S(62y0flNc?BWUZlt;H&wahP
z)J-z`oTnigQgaqoZ^HuVxU9r{tsZsBxecF<c<B>R>X-GfZ8-4LeY9?WAwT|5{%tkO
z=B&+pmn}(&U3%3*^XjkcN;Z<0_oTqFk8!K53%{l3nl9IV&+depsr)4-U~%_w;Qj)e
zy;)$m4SoCXKKIw>f<}XB9Ws1Wj>EyZpj%1Rd{lK>O5_4e1<I%jW8aV+z>u+l`ds5P
z1f>lWW)!_#c?`KbpY2cV5I6+tIR;^)U^sSU*)7q!DsSl;ITlJH`dwTz;EcdagNMCf
z%uRXmr@K{n{7qznY-~bsT<Ag)uSMEmE6(yW6HmZv6K<%KA+WkF&zjK#D-&eU?qKYV
z!8f|eW3Vm+gn+Y3Ke5CIji|@Oo%c=Dq>hL8RH8CvGPvkqTG7Pr2#lo)lmpG8iAx|R
zETrgg1ON{{rz;Rx#S;-Gj|La^cgy1?v_diRLh=4b?<MaXhW$V!<tmt2iuhZiDqo0&
zztz!Sd~VM0+}u(|pTU-)UGM6S5#)s#>7upFrC&m&*Z7ZzWOuykEd3O7F^IsJ0?kb=
zvR40;BMg`#c*8zK-%S4erL9=u+{i@ZWFf*?<^KD|kj1tZ%SM~?uEOSzoOf_ymV@xm
zZ1yx0OXiE=(yM<MELtyK*wE~+v|e2_pRD8ndP~pP3Fcp6wfI~*9rrXuwvPg_@Qs9D
znoYNZkLP6Y-#=)D@tY%9WcTx!^y9qwXCs3i#Q(}bf6_EMU&(9()w?p0tHWM8WGGjV
z<EIDf8r`$RSXOYwp~JVUioD>&>d=tt&hYBnNMuKf80l5XwBENpmHnTLQvLTlqdHSv
z^*Bfc8S5Q6B2f%gT5KCL5;v8Yj$Iz7b88-9cRs!XFzny0cOau$)lq{{w2c~?3{|#C
zs5?gBc#z?k1|k3K=5&?3PL^R_2aq?o2tpvDY%T&!WklnqLJA3&;<Q^6-`WLotd$u2
zyK=0Ra|$xVM1=n`sa&MY=e9@YZX8feYCs$#1E1R7Axq+a>g@O<D!1JmF%Hv99aC72
zPdAQlGu1I-$x*$0#Y&YLD)&wiODTP)@Q0bkZ4|9s^N!2QSBgIx5?O{~JWmRxM}`ui
zoD&li0FK&tQU#>P;KVHHNG)!<&aJu5UC?sAldQU25=hbN;2sDg*eaR*!A>W0@B5F4
z{<GxP^;d<{CIqDlaMCV|)TfO;QvKvSF0}C8%~Yp!r`|p~ke)wR5^H`pC%3)`|Gnce
zQOTuUsbK01ns+@pg+<G0DfpZ~VHQ7F1%{z_=Q<+@vLJxBKEaEH;e~LV7!8{8sgRi<
z>N{kB@A(Md(7Wk)@K$qyYVOn18pX<yu-cLC*VTJq!zRrSx_hEk-0?A3HJk1dZ6xO`
z+u#h-qbURcnxm)X;VjDpVGyv<8F>pjvMqN`-?mIgGWq#*;KiqoTS|Qa{V>p}>;4QF
z$1VtuI7K6=fIc|x?gI}^&51wi4}xFKH^J8_t@s^x*s*s76%D@pXd&24Oy{Jxc|32q
znztC=YAdXtFQLE6(8?y#+8$>7OUHOU0AlnYFV~%#i;%K!VDO0#yqw-h@tv|<z~!#J
zYJW>)pIp0YwOmV!C6p^5x=P46x5M~lM}mIGG*gH3W3S~-u9wC4o{HN(6P-J)`hyMZ
zoc_ih(6L%}i!7aj!hf;vY;`+vbbpx^__f%vi3E_tdbV3$W9>cZAA0&XKC+xg$cZ{h
zMQ>8H-B0cQ_f?waftFA;+J`SB_(R$;3+?Jqf|c0}=Sr>9O&e%qT!qj}T<Z!cI`Pdo
zps$JCAjSDT)+W-dMp^&X^LPU5bOz#!0&tIr^opSDp#YGHGLQ-Gy+D}!)tJbBDNRT9
zaBW)DGM90=(^h#+vGFM4^HI<E(N*H8q@LJ}#+U9+vD>kYPY$*kQIcv>KzS#u!GiVm
z0w;6J%SgWi96L<SwtL6$xNbsql{lkz(w~?MC}^^%G2?p3jyl+a0_?^cw-CSxujHYG
z55aMML<lHoiQ1@$u<ftRV9~E$k-oOpKdn!8@c`6pwSV)HPG;#MIkWooagz%VCl}st
z8nLP6HXJW1A1q=5-^#h`6vr;bzDvOnEQ?I&&RV_pR$lJ9mu_mkS6uzYzBnM$1bYAo
zX*SF*W*6P(m)AM&%zDPk<Hxr`^7`v+cy8NV@k)bjwjGH#96%FNk`hIuaVRti2_f*j
z&v9~bUl@3ZdWEK9iXH%}%**lPO4WS;^t^YwB{)Eam>>(|3^1*7{1eXV<u$5}!Ez}U
z_wUfv9up_3ml@a6CN{7-sm3VPO0(uLzA%lNeKemhypqfpHw93wQ>ggg1<mnY=9_0(
z;pG@`qc0p~e}tcXbbS1R8FZFq0&pb1LjjB@Asd{5fY9G**#sAKpcVJ^Q=;K{c>WT{
zCWIpHBgW@t0HroT2%$0@C_{z<ge=L_;+O#77f}=d*axs&|7i!IpH*MpYFp*&mj0|;
z6e}3t^S|YQy)dku{}%X*qh#^P`>&@@f5SML(m(H9hWvrOvNV{dd_LiNjbtzB0ERCr
zJ|>ve{z=3iEKpEDWYmXbt@I(w>PpT|2M|c_<Ieb`SdZ%_3y8lsyA>8>Edt#=bnc56
zn5Ipp>M5lre!)p``G^2NFgv0pz|W4v6O%C=e5H;q0UZb$J=w+q$e~pe&oDb`;$t$q
z#<_%H5xPprz;o^TLsnLZ(8!1hg{UrV=URpC;Vg5DUK497YD5TOE^)Upm=b`+S8Q`Y
zRPgVb7=;9ssa0G57I2Xw3{_s8&EVRbO_H9d^<<2$)<`6_7z)2W4gD&r|BXCeOjke-
z=fiMdLfZh}*I|CoA1EJaK9Jr*|F0~`HPfQ*Ae8X+Las`Pb@kg=U;8D}a3{yA6BX~;
zc@sZGBDNfi3SMzPyq8hoq1X1a>T{r>^62N5(CdZoSy2tf+TYXm@Q;UO&hxb8T~7s{
zx-{Jmd{)invDO}3pJ=^OUjtqxt5w1=kdULueBxlhpLUS#$n7N#5Mq>y9|pSl+#dwI
zEfdQfiIR{c&&i&bAakpc#m^N%Fl6LkS^`IaM-cy5*3DDuc!r#K576)Es%h!)WXV#@
z>xX>^b$eOuaT`}&`zW-kzW&keQ0r^Y(B00x3HlGWQ4KnOTzBrBmZ;S)Y96}1C=J#j
zw#Ng)J9To<0P7?6=Guca&W;Im`%%LZktwe=f}8KX@7)I!Vi=)-&?t;MKu&r&OemG)
z&a6RR8kUmv!u67F^?F;lZJ`C<GyW<e;$3}z=}54<eI=kJWuZPu8=Ji*Xze<c-I~N7
z@MHYL#6S_}WzK;dL=uN*CHWr^Do-iOtO6DB=YMCm3<C#gG^dG^XO*tf(^I*g9#wvq
ze17*1b@*S~b->IVLDRl%t)A${@O0^eh3+2<wchLkGZj7Ni+>K^&h4lNQyTNwi6Y5K
z@1rFeTA7qzT5lA)XXe&uCV~*MwXxv^yqym$ewx{dA!C#+=3Z;*vBhaRg%<6scClM;
zIXuu|D|Tl!vs`6_4~Ipk&rE^XoKhvzSZFwDgFui`Zw^AAh$JVkJ|cJ*DnaiTRpwcP
zZke~FvFs?53G|SKwKAdV{7j+=0O43VI5ouOFfoKm%067qWo;tVs3>RxGnF*=89~Mr
z@syVGE52ia8~TK(#R~ksHOoR*J4Bq%b?#+$R*<3g-cjY(X$L$Xul3`@s~2eCR?1~Q
z1p}(Pg9E<PVjBP|@d!x(AmfN*Cl+8qtmrK(Bz*d#N3DEqy(t{zTO~czRw1LrP<jU1
zU5|*y7y!WV1F{Mz7Xc~pBW-I!M~cW9J!DL`!Pc9gjHCUopKHg%s_*n93xXz*cGX{}
zO@nzKly$2_otL+sn309=RvQ6VbfdGOjrPiSt2=(Ayxh27scf$?NNWa=iNNcOudDWt
ze)AvZR}f3<GFGb8Y`HS1j$CNqXq|s?&M`z&tB^5T{?Nlg$t`@OT|Qo^SepN)4~&(K
z>5a{M%~eK=$O!RJVR%i-IP#Zkl#f1}@hdqC?N%JAwx4L=Jk_Ed%D#Gm!=SvK%9+8{
zVURat%>4Q${iS$*Wy7s0e`mR2^L=fCujf7DM;;ojVVqHV7R;bWD1Dz(7zQ9gR1n`=
z8go-JkV$I{$R(%}Cop;BjLc~{GlR@400hJ2K))Y#Z@Dzo$tW_~(Fl#RxhDY){z}#S
zS8truIA0>-|Lt3!W%BN|en?1r&(}gH`XcEAt78l_EHAPz7Sd712nU{$1$PjBvW^G1
zH)6G(YE&1y?pf$VBggVA)Bq{1qJ)Io0y?!{5FEJ%K?PLKNQ)i(nSiva4FF-_7@WAM
ziO>h{Y|RumW1bYn>}H{fgVTE(12+jz7R;TtUMTX9gI>{{8yar@hZbkiDGkWo>>k)t
zL7L8xI?*VRpiWiixSX(txF0?IU4|sS<sw&45PDMa+RKulh@4^><4e??p_?~-SJ#M{
z9eq8naJNYM1xb@zUtNX?%j!YeQZ@B3-`dm<H6uf2xq@D*BY}E042o$gyp5#VTSRrE
z-C6TlzU24K@ADt#y)#fxw@M32(tGbq>lEE<lVp%0Z*e`rLxgW)N9N4z$VQs^`6-zO
z=pz_UQ28@uVIvqY>~V)pN(LZ5aP~%&fXB;f<7mj?z&n#S?73@*j+=*n>1?cg5no{b
znc4Jp0QZ6-O$JX2uy66uYlj^Gw218q(0n|kQJnh17Ej3@MOpIudxuYVKDGGv^=120
z?_0yWI$Wx$l)26bh0Ud-We>PZMliMbqpuJl>NM^LJ-~jI>13-ol1W2TVc((9XrWT)
zD&!AW6zg3y7X?SD`3UALi%QtXhim{!TduYFNGr!xOYt#?x$m6xKT!V6x6L+=1`%`0
zJcQ?sU#QcM>;zw-j#qZa1{-w*NueOMiV!7Qqo*ZT4ka?7OFKtf?VUg74A7iENuF<O
z7-~XGVh<)oQ+9}WevVWGvGqR_bQ7M;tzEFTDyV67(|JPSNC11l(%LPw5?k1^tTFzU
zTUPC8yS2`X(<WrC=5>nvkzhUd<WwViZRr?WC+#|VSgu-3fjqf-DzE)3HK6#o-}Y+U
zf49abGH<ikJQE-ktddD>T&^TPbM^qrSEtjWDV5Zj+W>+#V|+RRtg(OaQveR)TuwQ-
zT2{SR;}ZXhRtHF9nyPYp(2G$GxSNmxaKZ&ZRHb_3_#>eV96%755ECz-zYloEILzl?
zeNZC0^Y!14e@egcqXiI}9gG$Xknm?n+OMlb|4un{`iScg8nMkc0*HOQnbB%&xbQf*
z2LM3ghr0neOmhE&l&v@Ex4CuX3<U&Vs{Bu(w}ezRL4Z~yB!dCK>*>(y1h~k^1WH~j
z;solitXd!v>VZ-@vk$hF$b<aEM4=`ujH-4`Dw?7by(>;P++}j0U?(KbO@Z&wc3?L8
zNycDol}Y4PUAooHi6A@_yt%xNkglHb282w0P35!QD<`G^WMz7kz2q*h{;bzGb<-?6
zHXuc);3+83`abuvBhNAaE^}VjFO<SmG#uBUkbEb%k3k?iB%e;Y8(t`}^#}T`CxbbH
zCaPKl83M@wh>+%lo{Dt^0_iXBB{>%4%6=Bg?o&G}SNs;zeRJ@E4AV{$+Ydi%%W5um
zdXVN!mlEdA6GEFD=}LkaD;6x`)r!x_PoT!NN#hBo9ex1tEGEP3&6zE+KvH~J3I-L<
z8|K9cw9BPDBK7SjZN|X3WnQpRrIg0JW{!#XA)(L$NB+<3OCBwhg>P*{`b1(9neUAv
zDm<BTnqJkyvoVMnZp`bXLOQfmhBh=)n>|ZgI9pqxTxDDG#VBbNDx1_O0=hAURk6%1
z1!c(M`6dC<2|BT;tk`@P3;==1rqL^>U=V5_Lee+{OEk~jK8Mkx{llqlUNFP6NIW<x
zftB>Lu#_5N8oVuPpA-Nn38Z49Ofvz1y5RCn33Fxh&1x1&A`Fqr&&9{bwm_;iDF78@
z%91Ag_Hwv(p_3)7ulbg?p%1Mm!him=X-lP&tdVLa5~iji9wG;+e~7OIMdCOCfIl0L
zh-Ow%UT-QQ0kN2vr~4zw{NY{Ny)csDoZzN9lN<%DALE&A{n|hE%HPGh7q;{jz1ReE
z&S|M%DoOfUa;~(%X|W193~HPC?VW_VPLvZ0Qv9rdx`47oQ-r#%{Gh9)dW^MTjxf&i
z^FWcE<dyxH+0pB5H-i>74I1$wiOvtgH<O`j8vgpa{f+}K<2(FA6bX#)MmdwV2qrM!
zCr*SAZrVc5T!|}U<Y!P-(TJ3qzf6ZLWl<gSi0@tai&eoYf+WX;J_dW;!63z>G9oei
zq{AU2DLP*n5-oB=Z`-GHt6aB~=F&f_!;FtPjsL(+D7bj)cKVjrp&^;FK0u-`j@bx=
zk&}b$hpPL4;%g>|P<9zU7DW3Qabe_`XaCXZI*6>Kj@CL$lN1*#+mf0+5uPflUyn=7
zzcmSA#>hrOd?2Y)P_QFss*jYCJ1sChMTUtmg@dzmNTq%nG$xxDsU{7j(33X();(Al
z{S`6>71LvP72o&|f60<^P%7O+mF%BtewvJBGmVuQaP0p`9T=m_8J|6x%s!gdhd<Ys
znqwYQ9?2N%RZuPu?H*6OOs?hX7h>#^j}nkl)NiKNKl-oCQ`~@CY*6t!*Tc0mlwV3g
zIHhfOU}hXn(8-nPFyQ>6PT+BFgGo}I+DOBxx}8PBwiPLnYD4jpBCF35G|GcBjMeBj
z6OxjKVa}9~F@tQ{lQm~Urs|V9Zb@pwQ%Bup&MfT$RHL=hJdWK0e%}(90}|H5V&#S7
zt&E6@s30IR03F6W{FQX{i)^y7Mkf8ImRn0HDY5#2_)cy}J~)B;IS9Q142!($5Qt)q
z#H^F#&lgRoJ8;s7swIy=wi<J2=(WzD%~f)(yso$T&|owE+GZT_zVtN<sbsFU`lnh@
z8ho{}j5(Fr3Ag{05c4tq%#C;|BR*@WsE%d2Z^G;f!odM$p2~xkUe9{Q(=qVQZCywp
zK@>QA+Di_C!{eFo5nUPZA)1F80G#BM5SGOxQxsF-_b}irQc-Am{;ZRsVFNRpzV=;w
zWu)2Pte?^BtC60L)R^=Kb9%A4<7KJ>L)5hY*kkPlWAXAf@k!?_bDz+847=%J9G)*i
zC}L|q1yqJ8&wts^4tQjKJGDU0M?>UnVau0ubk9P!jni1v!mm_uC2t{Ggf#rxa_MF5
z+s6hJp8o~x8FZBAKK55+_$5exWT0P5>H(iPGKb%!w%?e>T4|;Z6D*c%<|AKP4%kh-
zy-|5vMW*Ly^&nOwh@wRJ)_&#b#0;n736!j*qf>h`@sOW|^s7}xO(yB|O4yQclr^r@
zGoIEw=6fatWl6L#3dtx~mI|g*$baQBkj8G>6TYOjB2~m9RUk`{iVK=1i2&u(;swI1
z`H_)l48y@mhAQI&=ej7bLu(zE^v4`FCA=$ZGA`bCTq0y$lJr)Na+A^+X%8q8<pwG{
zQ5eAco?|d!+bhVr?OO~Ho^zCp$perjA6fpmj3t&P8&GpZ^SJCKdeJ9@Fl3R0j!cEg
z$v+1FWCxgqz>4wJ)I=2hi$cqw#4pBB^FA!P;KOM6Ds47Z16t$jf{^~A<kwZRsRmr8
zw0&2Z{rJWPNKzhNGo}!0xjj<DLoBbANSE;p@^;5OgAXjd=MXjFkb7vFTaEMm;V?S3
z0NgS|6=mB}IMaM`>{R7Rce35H7Xm%hGN~s-eAYYgeRtw}*Wp~xx)vIYx{04H6dB*&
zeO{WyUaIyZso_}$D`nKpJ(A%Nl3l`e`;fYVCyp_m3>u{M*w<~v&&ka859oiB%}2f<
zirN6TOm^u^&gmDM-oXZGuY8Z=w%)N-FHb-G2vCQSqwh1j`^0p7#K^i4AC*CZLla5E
zUujcw-uxubju77Ew>BZvnkYaAbjBA|#N){EXfrU~4WK3S(Vh0)jQwzO>WiBgM*Upl
z`cuQt&&p(^xAM9?d%8U*K6oxKjoEKW*-(ni4D-T!T=xCqLsAKcEir(9>;qv;aKN1#
z!ezzdBsafhoi}?zcM>OT$ib#g!Jv1@m&rAekbFXlsbEa=2xf|3HJE^az%x>=PzhU?
z53(Y4tw?-3`O;tPtyP<7nr>qQnpQ-kJL44U`rD?;>A-Dgm*R`(>!o%+m%a~c#cLl8
zC9(9o?hfJ`@h6Ym+QuDhS?TswVvvgIg{Xw!nYA}T3&{5688-_ejb2F}bD1F3?XPTP
znS1$9^uK#3)V)(z=(>NflXB@a;g@P4?P;l|bAMoCB5b#nCiH;F$C5GY;eL4hGBW`>
zxr{iO$SuXuJC(~Kjn*LD(1PrM&Zn$g$3xHIK(^BH=3+@b(g~p@<V(wE0`V+!o1kLn
zSM5p(<1eDV_4!Qbss#{X2O-S*Z%YY?4|)LSLyTI!!ME7aug#>b46f0OLjbsb)aBm!
z4CLnv5s|+5e2Qr}Wz-oz3A5l13IH*-0m}B`_ZaTKuAJ4Cy|c=264&GZ4;?_I9Kcc6
zo+kh8IJq{r%{Bk2uscAM@8sodQ=Z`Egd>h$CV!>Pm$j)z8o^w|H~r;^Af<pCg##hn
z*Ru<>;=>}-Z#Y2i0B|CLzfJRve6kxuWUw(N=%v!KSUt?7<ghj)wG>O!KWdBH(J*k>
z`MP}Kh<(2;<^2qLlDC^?C$;PFt?VlTr*1wYK}pT{D0R@PKV~Y1?=nIE@QH(u1UFA|
z@WzwW<Ks&P<oB~0E`0|A?D&M!lih#(JnI&zH~W$uD~><TBqb7DP!m;{iQ-2-asNJU
zYr5YdT~s35bjD<XY<wmu&b|#{0Mm0~Vs>&>(sbIPimSzijP*r^wg`&zz=-qMD60q2
zQfV|o_w$(sq*xMijDSLocqi>3rbLOrd&Lz$rPmT-tI49&a<#%!nV-G%@rH<lVmABZ
z-R<L4Wbxd|m!k$0`&kquaUBCm;ukVU+m6(K?@CFvOIzCrEQ25&;pDhbj6E_EMWuz7
z5b*)PjvO}Z%`cxd4vz53CTuKywEK#g6m_+^;vWio@+s{3Fq+@{+Nb_OMu%xxDsOP$
zmCu7ibP!>O8PzXaw&CCg_vVq#2da9h)-!zVOC<~DK)EgynlL${GV=#7u+`(lbVwQ-
z0a0EP1+^ooK9TguXaEIdUa4LaDn0{#z9!`{6AZW)-b~t??m(_}eSa`pWF=xpf0yau
zF8^u3UA^mfPv;?CYjo5C+$|gE&+(@|kapiEbhSUk{8weOewfW$m;Fq@4<CT%O8?;i
z@;|o#p3wvFUkQWg03|dW=_=3nFG|@tefY@l4t<)cbH?xy%wX+_kgG9@eh3MQ(dY9I
zNm-$hP5~a8cH~nUQWdD}X0wE|irsl-RO2sik9cDEYMPt=*8guK8tC1d)zT*ulX)}9
zYeo`wQtVzmU6%bw==ho(V1@FZ5e#mg0s`|1OVk{fB(L?ovKoZ-NZra+zMXfj9%etT
zw7AIZe8VQj|7rJfVfEy>K;oqOMW~2IxpwW`eqe##TZ%t2vlze+Y2eJ?I%a)5tN(#D
zyQkguxNB^CL{2I~iIW-*hw=T~!@#KAU-C8p0erdCabjNZtKN@Tlu%5lq(ZR<@9+5E
zw{ZmNhJUR!Xa8IQ1d*Up(0+u{LmaFV;OZ~|z%B`$iE+yEd}NWsD`+etq4zeT0$?VN
zY&cFTi7tn>#%;bmS2U&{G`6#59GnhwVjeOs@?agB?)GK>WV{*z`!xMK5<YCgo`@L!
zjL^&OLCQOTWEw6rvOiBt<+Rk-(p)66K_r;<8P-87Hu+CB#-0LznH0c7wL}c&i$A}K
z#h}R<@$??9o9Kz!FN}#5&_bP7{u6}b%aT$-R9G~+6d+D0>i`JTqXqd$JMq|hLIAQ_
zbyH5paPMZ;GmDc_X0}r>k+asUst<t7v=q$gkHGp`bcLg$C50<}>A70M?C%epnkvRk
z<z8i#Em3e}EpVqx=e=7RTLLF)(}VS$c)V70vK1#XkDi$-clhAzGmd%q`iuaHBL9;W
znqYwct7)h}Ey8H9^o<RD%7CG~LGs<<z*rBgyM0+0PtfC%OQhRDr+JT{E$o$yKr*7R
zHE~xu4Jmu1S3X~Z@VxgS$@Y1UYvM)suf@mqIxA+7?^5lYIEJI2T#^mD?N~Ha=u!Ej
z5s1pbqk_MKbK0@{2_Ls)aZ2)^BG&;1USH-&5M}u7TS=XA^`st)nRxrU^e)}DJGP?p
zY|_*Im^sT9VkgGqbI*s#D^hfKEEZCwbCH4~Lt5jd`z^AdNo2v&5hszH_cyxOoC$9Z
zistLyejGrG#aUvB36KxB(YLNLSx@<n+6RxNK~U=@l1MlpD3CCR!wDdqWCar=)3W5C
zyp&R3u{EU63}1E>aD;IGTzg)&f9vq7_V9M?WAo83hyUK6{F*L)xlx*0P4;rYJgXGo
z<lor``@gp(Nz<QufAy>kBhmXE`_OnlF<0dXpzKu?rce0lw@x3<EJ3SnK*|q9UoF*x
z)3h)Gf)+c8ru>j2l{FG#ahC%rjQH&tnhnb-u+$w%O|RBFER1i|$`MQOk@u1k0^97*
zf$WM`<INrx>8R=kzA>+a22~LOjuu)qbR(l2TNrYI=bfn`y~WB*;%=27OJLL5cnGm}
zTyvT5r;L_7T?(0ckVi!pYe0O>cT@B>2R@QR+h?Q-fT@+^{Sq7(6G;pR;R$>d*iy@B
zJ}#6`DvgQ6VWQlwb{208YCEUJqPZ(ett>&DPuPG=kSV)j+DSYr9$E2)^Re0ijf4cQ
zn54}4-gwjNkt~NdBMn%DD>OmTb!`GVtB^q(g=!<FKVOsJmQeR9qtA;`9xZ)HjwSg<
zML#A;<erQV4HD+dbgtTV)mx^%eI&zzb7%9^mCR0M8K1LQwT0?yKAvYZsIaD;eqUBR
zBb`f6D^35o=~lQQACxXxC-<p<juzVnP?kA}Q8evvzyV^I4;e^@?#SQN?q#Pc-$AmP
z>fKK*PqeV;Y?l2ikU>Il<diV}cz>VFRZo=ATyQ_9uMmE*$(@t+rp=X%GpttobdELF
z=#W>vW*$~yR+wvY_(0z~CaH$bSy*f2oF2&H<7aPqK*cIIs&ClzSIvi1A95518_=>z
z@d4xphO4<C9z?wPX)0uXEH(p<U|w3jYECrK!|kx*j21=^PDq+F4!T#3aCKh)sSA@T
z0;qg+hz}gy$+q>7nh#LwfUSCNZt*XC+jSpHXp^*?_pE!rR*{16Bv~wKwr>A3%dwPQ
zim~%@xd40O7TIDTt$}z`ezZqLm_cf02cSu^O&g^bMD5IYmv8?Da5Q`FLn&O=C$ZpD
zp>mbwX`QX@0khst9PmoEkrZ18?htR$<_#Pb<$N9*&VMhF({yc`KX7E3+XSkL{@tua
zU-OD*Bi@7PF-_~tSIzM+Te%M+E3huY*7K{)T!+(JZI#5IS;Hm^CG{-y9iiZ=`RBRQ
ztQk2CMh{0CE{b+6{28DYtNbcLADda;m;bU#YW5Q>NqygDlee8!sA9ppb$F`c$0tcV
z46tL-hM50#BoFgB2%0BknFb6#ZM+}ir@p0|DL{RmE9Hf9WzqD0SHsWVL6uVgc=!=f
zzpNR;iG+m)CHnl+vdq1tC=2QV^l};(WAZWU`?+HV?3G*U^oo*}2M2G(6;{f#BK!j3
z&X#5%q>oG$$O3pRYOTc)20VJ99L81nnf?u(Kv-aM&JaO5;vuIiCOm!i8cYA{PI+Zh
zj{3k6`upceprt8-v)l{Fu6J4N?<Sz<+9$E!XKLj?lQcO4AZAVsUCYhr39qM*$0LeD
zEgCuB+1h&<r#8N`?G&8PjqZ~8`d&lk_#t1tuMg8;->3mm)I8{)?^dv!T`!VXwEk6q
zcbP_{cBKN%XcNwd&_E@|L?`oQZ)mwG;r#~I|4@FWAGN+8eV7Tz*S=P6JWV6tA)Jf+
zuZS&TfB)YEu}_8!b}&!RSdCxTs+fP4tZ1Y=lA9^i>0XF_1Q|ZA{O+I8Mv4<w3;xez
zBh;Ppd|kn&>9wb9I&I#Ofu~;2-H?ute(w~0Q*GP5)P;MyPFzDerZwHv*(zBL-TF~W
ztNnS8s6q7mD{7w-mjLjU8$JceGpLivMvxOnB~y2FQ1#tNTCcrGg*IW8&&`OZFKoEo
zq_ANB*Gq^Mq_&iq3V%8Q^(y;oqJmCqEC1q7im7{D|Ckl<%AT@Xu7`2Pp8e$?y!nrF
z+r1{XFu1pX-^}k-SrDc9sW|wOL-uPX*C{82)eROE1*QTSs{%fIFhNZ*s@Ch)NoCC~
zUc=|ZUk?qnGl}*T<i*M`Wc$U*tsr>T|6+ge;Rzf-4A;=tdE<|P%Ih3)QD^{Y%3!eK
zyGgaxh8V5!DR2EnaD&%7UiURB;k9V9wS;Lx7q#T^Luvgh+tj1>p<x@|E@Bl4Q{{bF
zycz@@KmrFg$cv~eSM~er0N(WmihRRCk}d;bt{A~g-5zelf^AJv<^i?v8Fq_Oqb`np
zdx%d0+t6chn>{3v?PfQ{XOK&_ABY{`+_x~oQp;_r>^%`QbWXvkOzmq(obha%Z^b03
z#JHW!;e<#2LKD*a9_2mA$v>$(JZbsn2o$FEfVk`KBrXl9>KwgU^)^%)Oq-_@c2gHU
zCL(Y?L7_Az_S-X2C);7C8b^OMZjGW(BCvcMWMrIWd<^bjhThQcy(7O5T1nH_mJnK&
zH9m*2C&}AcV=4>$2RZY#^qlLopi~=h`Cm?q1EHon6)zG1j9OwQf?RIy>+Cw8+fp~8
zGSg~)iqCQwFLGpl;h6ppMirOD?IwJBAp-47YNq6NHfwk|&r*r@kIUm!U)SbZd0q-I
z{O<=aOphc1$|~^N1aQ2TzJAVgs(t<KnK8YD!6vg?v8DQcH@^5)ap&!>NduTCNjTb!
zpcEj0uai6Tkqp8CfH6S%53dRtd^46pMMf17)tg~Vcs%!vTB^AIk*5AvLTX*2sk_7y
z8R|`@1kKURG<c^>)VkIuqSwD|n>>lXWka-f(@KH&XBdF*J?CKEo&W)r0a8e?jA?6;
zY?HNsx22v^>VpQOfY8XT?o)D~Vg9OsE55Z42bL&Aj_YSjhN>6?@23<|?<sa{`uI<F
z!9hZkr94T1cvKQMakP-8Np8l_UTkn3_>In36yNaxH<+B}2?I?ttneIh#~By@W~xa1
z4&CSBAG^0hzUUHhqh!4Z$v4}@mq96X80pIt4H^p{Rhh^}tia|QL!oZ7VRo4EHo~bw
zH*%ZSNh@3#!^$(kVI0~68M(U$){e@%GYS3~Ma$QBm&KSk$Aup5M=1uEP+I39i=6|(
z`^XS{gk%=R<(2%8RoP7%jPxl*=yG9FY|3+-D)ihcX8bByZ~hwZi!{p0>IBt56L{3B
zrp8+M`fK=zlVF=XOuZQZXoj}DhqEk;^^CA#2Vtgef$}ce*9xwmiK_3@q&EI0FF*!A
z&WZ=;0WtDoB3KL@A&N1eK(`?ro2vBRfkm!@g~&`@2@B6}xs39|d5e)Qa`Z8Y$T4ez
ztZagd7OR#*bt6^6nX3OXRHVEn^&kHf)8Ag>Hx|lpN7MGCQ3s_Z98nuZ+@+n`xLEKx
z+~rQt@X%oAi*3jRJ7f~)ijIzLohZuGX;@`jlIyNi8<_1hjpcZod1+gUi(2wOaC-py
z+m_zMSqh=pBbRU{#ATA5q4H(Y1$j2nIcmjbo77!nGXQr*&w-`lPL9dBAkH;U#sD7{
zA0saV3K2hLNSkR*-MieD21(-Y3{B)k8}(|;-Vs<Bs@=9U7R7xkqo1GB%wax8O-Mwq
zOOe`y5T&VsUAdgm@fa%pQ+oNb2$r!TT96LcMg`lPD-S@OzF%i`MepH8m}Dk(pUyoY
zK#8h9m}Af394)O=sztHhhj7F~qWHkmSfzM%%cvRmEf#t4M2a>cH%1pXi-&G`o^GEy
zlEodSYQl0~hfNvnCsf&RgRbNTQGoM1>x~Nq3N^#BqLH#Cx=1TTSr7{^D~H&G%oUj|
zDKtz)owH&yI%;?=*YqyWZ?4H}Q+0RPv3Fd51qT`)<VZ=f?i0Iy1l{DrE9?+{Ui#*@
zu|%;vu^OmHLsf}lggG0s@vrpijRgsP)`$`k1fVkUeF>(ShE!X=?Ub8y>-QO$^xa#@
zrW_ARg;Si_gg&VoUJM$UlPrCw0DY&<)POMOvewUYfv1Z$U;q9<-7NTRmYh4`L!_Bc
z^4bn+Aopx2_uE?TEG6Hu%ma00H3r8DFth7~^8+<CUu_%izyOaFQRKd#U^<1w2+CpC
zY9~O!JiUT{`a84fq%;PAzQu|me$Y&Okdc1l`T3)5;4<B}Jv+K*eq$ka4W4#Cu8?y>
zCxKsO1_C2hZKWADXSsKMGHpT_P7M2u3_O%|HR%z`)9}+Fd}4sU*WI}6oh}B!Nu6K!
zHZ6jrZjW1<KO@&#B|FNQ%pnS8s(i+$ybr3Bf;k!xpaDYoNgNu099@iPcxs(iQoKEW
zFg@*dQ=Vj1os?gnv{d_0>VVtXX!LCha>rBJdDc40LBx^B9V^IdS|9Uij&8$K-N}Us
z4P%Fhf{wGyzcuhbP7^}anR?BV3{>z8+C)KdYAr}^gnJY=2DXrf24Jy{#L5tN%8n$W
zDQdkPQEVP&`H5Ql69UPazk30_zRc8GmW(zz6PkBm$xk)frMrTz@S>KOb(6b9PZc`?
zj#XwZl2sr0%>5)@G!_=hf8PtWU$o(6pv+*P3o@uTdEqJQfiy?v8J-ZO$FV$39=OjG
z9qjGkWtorjCv$l*@)!z}>G!!pB%7Z|yckl-+ltE43Q%$0j*l(@qFlA-69k;m3WV8O
z`xdGyI<IzU$Ooik;vVI8jFb0|$wmHlh>?@_ejLHHEoNRDG5=b|OSD_`+w&N!9Lc5s
zqU9f!a~f#$oXQeByhzWTlBBoM)39Q-N&Iw|XKk(Zm9pTjO}X<ugzkMJEv#Fzlhct(
z**m|DBl>D23}nKD2Ed@1(o*PLP>cD-UzA(g(x#U1K^8x)Vs#2C(dgO`M;+v}8l*xE
zNw6bu9BKKMMdZu}LcEEoI5RN5H6XY)$Y4WH<R;x-Hro7XEd13tl9zcLYoR%XSZS`(
zxmUmRF?suUL+zVi(3SMa)d(D*hp*8o3o_W`uw!q4vfba`Z^W1QZq0e_sb-9mA(2|J
ztRWg{5=KeoKP8nKTo2vgPTQadI3f(bh=PK`OA(HhkSF%mLU&&e{Qk;QOu(uM{;8|S
zTPZu{_jM*0^Rn>kMZtTm*WkAQY|WNz)mxIQ37Dz%I@+Ifo9q}O7Y)lRAm|_xG(fVR
zlBP^X16<rCAU1z^T-Wg)fh{rjDGPIEd+N+q7>MjHX!L(yoBJb}yP$1n#S-=9osNNd
z-<{XgswdjxAck77RPwF+8$tN6mKk#%yf5aD&UvYdbMmHgNsF!;h7x2U3O}7gqG>^h
zeNLy}e;#y?R?7l$k2p6WWqp$O`W(ccfUzC7@Th%g>3bXHO_+@UEz_Ans6=rXmz=MZ
z49n+=P}9iyAqna`74dB+tX3NG_T|2xu{XR<Q6myaxDag=f=9+|+}mFt46E;E?yUkq
z+q7RKI6-NwY7;QX@%;p0E=Tgj#{m&bF4n<i<4<<pj@;w#XIz$M4Byzw=DH;qu>j+Z
zqbh{ZXJPoBRRZ^o;NF_54cn(SXp>fGu>8LT4wnV{hYQ7zG`hg2z9dfl?1;viSUUHT
zgujbvq5x<SA8P(zrJ<Ks0Y}a1G^7-{`lt@6ru=WvBK)4SLqIM}6l`QuO*`f+>KO9y
zi>ja7W}_}>!?lHn+%d-=(?*KF)#4=dZ3rVzYdadX4F#_1KK$tA+sH}%(ixlk{=0B;
zLb=_ST7hSyf)e?XzbbN=1Vk5Qji_JxGpTL0D)}jV>OO&dfP+3f`|@FDya)Zgr(-H&
zx}Un^sEg~*$9YaMQhr=Wpjd$~nH}waVB_A@$Rei1F#*}0w}v6qI001-lwk=Uw)h=b
zu#aaqK=}-|iDpx7b=>%mc)=h5Eeytq#;%KkS#!V$;?LwN(S7oXqgsFpad=-0htq;y
z?-GZ96U=E_Zd(?K#`|jA2ujhQO`^507IJU=-yuk1!}6#Rm!E%p8<7_qd7?g=`+ltG
z^?tfC#_ogcKM{Lh^3l{ul#;Oe>i8YvzSDE+OARMyCg6bqPXm2gFBm`J;$!ShQHWG5
z1HdfTlwi=eH<5oGQn4PkY|YLqqvbADMT?7+MZp4A<_}8DR;o<#^h2~7K9>kyf4>F?
zpqc>yz+<vb9boTJS9g}F!Z^qA`1gO4e>i9VSXJjTv#%nkRj_R@0wVr;(BF$Pli%cH
zorJ?Sfn;M0tbx%7p#L6@0Vem>LBB!a0A4W-(A<KP0Pf7;IX}VQNf^!xl(0AaPuj=w
z=IY<x)?y$G_xW;|?+FA9a5&a3+2!N^S15R`);rYM2|sW6!wJU-?qq)EZp>hBQGhm>
z?S3!d{@dlyY;}~WGnhB+@z(N4rt|Wzrw6dSl}G-#^J`GI7T=G0-l&6T?Moteo^jj`
zfd*|wP7l(^TE#xv?Jn4<edLH6cR>}{CI6-bLKHDL(9&59z(cdIWv09j5CJ`Cs>RDS
zF~(Ma^Tl%4&wrAi^=01vw1_DDm%Yy2(hvlpVc**c4I3HtXshi2>U@ouB0l_AIOuCG
zKl$uE$Cbf04I1jnbeQ|A^6&kpEw4@vywMmu)FQZe4+q#V#;yZeS$Im;Qy9b%2!G{a
zWcR<9CZwQfAdfemB7rQnRj5vi09bd&n-!<nI%aZWSxf-B?Vox8I|MXPjnU3GVIahS
zplVc(fE^PN9H1m@t3J4iuQZVRO$-nqJJ2{t<{H32<iQ*VLb1Z}lj1MfABv+{3%?{t
z2{s$`K3A-E>r0x<%ZZUkt&S*Oy6)XGY)IOYm>m5IQWnmxL-yY>ly}aoO!VJQm4BS&
z*3d!n?C3=$n&vDX1CiTKV<74P{BmMjn7xbE|5x}g6}U~D{#MtWfZvh=;3g^aN4N6Y
zj>JE(<lk_EyyHzkNTj14Kp%|;?#ZRNuO~2<V=&ZdKwj^4ybPSyAk8FAgxt@rU3xOW
z{*L)#kbQgLf58rSa`!?V+TWgr-M=&cBjSF$Wa&gB!xkz>VDIAIn<cWVVl`tNg#dkd
z-0~d2Nkrocz=*i~@Pr>1cM#{k?RBnc1NP00Z2Dut_Ku`oi-uR#w-ZTK-nI$h67d+;
znODjf6@Z{Tp)U<G)dUb%9MHvpKC=8DMQ8oj<omYqYXKXhMt6)H-Ad}{(JdkBNGX*P
z31jr=Zcv($k_w1QOAClf>1Tup2nvXZ7~f~lAFv<xy6^jSUFUHg$NL~6n`G*i9~*+C
zZrT^Er;L-@A3H>8w5I9OpN4!nn_nhB)+nRzP?ov6^0aNDO8ARSz)4qIOE)9y#N*2`
z{j;4nWT<F#KNh4uGnPP`^kjN4ta-Cu`o)gAbmi4FaWiVj+tc*y%6Qwt*UvMJO{1So
zzgl`|?s3q`aJkoJK-&*5*sV@4KX0-P`)z3@@$A2!$2BnrZ|eTGJ`lnM0=qm84Ns?q
z>vKOLsUE^l=2Y*yS-N0QPQZW#RtVT>zz6nkwby^R03qdH<vt|@@aG~rEL^)fX)ruL
zMzF3BuvXfe0^m&r8vzDL{2olvC#y6_6*NX;#l^^Z*@G@-L>>hJvBAO>kwhz+e6$4F
z=!)HPv}I;lh@S22qFqIFe9Gx07U|xzAhLg_L7e=s(%pEhq!W{{*^}2UeKr$<(vf)k
zql8UUXPU}?gJrSPwYdTWzKa_NP)yW&E@FU9mLd99$-qDT)gmz`x$4EnI`d07-cdV)
zfVEO(@9_V8;1VAFw7h6Ildhcr6WJHj2>=Y(E?tDDLW*ti;}y9X*{nt;CK^)Wo}C3b
ztlkzTJsOgp-=T^(4%%e~1s~6dUb3k%xne}lk7M876|4?QBSIFX-m25`=~wDyR8s5M
zlQwNkm)2R{wJCCx_=Lur#3>rQvu7j$!OgqjU9{44#C|H?iY&v@U&uU0oy$D~X=K}u
zFA14&+6;dnbF-Wd(((I+#ee9Q9PNEBS>zW#+ZEKZ^z~zjL8bD3Tf2<9!Qv;jTOtUE
z8jm#Xq}gwOl}_4wBGSl7Gw5QVQQ-mrsVv?{xO0GK)SymClW~pHR-U|?H(&Xg|1DbR
zYtSs~P^}Z$BeHztUSHmk^wl!|$9Fyt-f7|r9<aOI9p-uC!(i;fvboI_vOy{NMdgL`
zbajZQ$KQt;nPLD&x=Hm;_@(J~0_efjD2R`ITqZ;T>6C!3^aPA<4Qe=GO-K19MQhy#
zXAh!i`oQB!HYo9aFva6xKvir;xp1&E<krFc%r<q25dgT>_dEc2!S%7<fnFSmfIzLd
z<|A!PeeJ_DFsu27K51t4_gu=xwEx4@f3`r^tFMsFi<+OCU*Y`obSQ+irNJ*SJLl86
zm66FHt|%#4N8W2JKaCu5>i@eFS3dHFEj{u|<RIqr@m9Oif3i3*!Uw=@y0uFqhNrIq
zaEd}7pr=Ufmd{45$&Y9s>#p>L7J4qtt;84@C9bkO#~FG>qZ`m~ovxw80y%PX+z&Qw
z_G)DZt=9T95(7_!hPe}2+_g=Tf>iVCq^7TOpHlhOyRXiB=@eAJ(zWMLySdwE%}~eb
zYU`Oa0z<<URpeYfDxM_CGVAg~E{Vx4U0DmcmgHhvQ==^BN(hrDOSIP|7Tvyhh5_qz
z$a1gsH1fBT(({MIPrb(+>HYJPRkOCX{?oTR{&6+rgmZOk^)r`5q{_)M@q91bds`~i
zs^#JTxV2KReWJg2e(p($j46lV<=b3QVx?~=yIRK6Q7PvVmLNS)IVnrb+nVc*42^!F
zK}k28Yg-D8cqRxRyIKLCsHa`;k(v$ukV1ZP_CYYlU%=hMw0`=wDKBi*tbq(oqA)~p
zIkajS+pjTT?y3XGw)R+IE&F;XfMj0q#Yuby&{Qk>av)0+*aZL{nH?l60oSzxaXJE6
z3WLcj^nbc*u2Kp8fI*j}M*WS7MJs_}K=YPpthF!W0pNs|-vE#>>i4)U7{dBrzGN_+
z_A}eOW<QrFXrEOU#>m1>-K3uQ<W1KX-d7j2uWvcRishfhJ$f^m_Bl90>-)mzN0^~j
zS@D~0+-K7E{p|Z;1`i4|7Rv5--0l?3I8A3pK9n03*kxm}hiy;*Y~jmzzL>!ps|I=S
zO^A~>g5jGAZ2hU#(wXfJu_z;uyZsfB3rer8KqI0Ni>C-eJp<u#$<EqJ7c-6ojmIM{
zN`1QfwBS3{$K=z2u}&^hx@pdTwp$~w9pSXV-w<wmQ<zr8Xh)y_T_@b&N%`BiT{)wM
zdR!9M6>s2JQy=aAuU_m`K-L#udm&+g0A`#v+-ds~GR{@h>j2OPmeXH`D+MrTeKqLk
zrgQc&J$RU1`;J~SRrcy%oY5VNvYfBsS6#r%W3TIvI%NodKWdiv7{Zro&=VJPsBP?h
zJyFKk$aCb1d7NTi8Ez7FmWs%+fUH<TRjj0tkS^o{HQpRnU#)aPK5vkLRLG^$<%dkw
zv~}N2>9yfldv@f8YuD0ZM$%$g<28ZS?@T*OsKT=JKh!_<lU~a!4ONp8^YTgZ_j3e4
z+9Ye(m{y$alge->nj~K8BS*x<B@wRD^d84M*~b^rB0?78)$QO*4n$&E;@JI~6w##c
zK-z;as#XUzVT2OIW5NMUv65P71L5^gT)fX%Uet%=iN329V=WG)*40pS1*x-DkP{I(
zo7R{O-M&~j@s7HHjrTET{o<R}B*+ZY!88`PXz2CyZVIAUG*YyvK8Kg(nVJwCLy^tC
z!?2Nv+w7Vm&q8Y1eAXobv-v02c>~a7lfe2fQi>DdtWN2)zmlm+=y_sbO-Fm8J_bZ*
zrG2@_fKeCnkPw*Hpo9ES9FJi_Hwc^-BrbIksb(@{BwyxTVsK#MHYW;?ibYD0$|YZr
z0Ep4g7|Sp$x*%1)P9=ii>8HjF;>PUb$2=3pLKFLjol=Ec^47x(+3j=uY&%LE5-YzW
zaFH@TrV7*(qL4tsg=F{B<ZjrJ2v9opdx#C2V*g9r(jLg|Ea7z~o$bPWb(l$DGGc88
zRXCqW0!h|U#O0R6Q=Vv*px8nM5`{ZjW`x<(`f)2DN_oBb;!D(HWNc7w3&C<!s$RsW
zw(utR(?2Tc>zvfQE4gy<WhktIH3I8D!ywz6VgSrYon55oWcS#TwYQx~&-$HqjP_{w
zwksZv6-BSoktw1wbHc3Xf0^6`{Yx>pJ&!`+;>T+<Sa%g;?*F8Q@O+^!1~iND<aDNf
z2UHF@gHBIiS9M_c{Sz7QTsjSAa*IJ`enyH9=Y-G`f)6brbl6%u?(#m1x-|{bTx6Dk
zRP>Xv+?|%~eY~P=B{>Lzya|9qJk@SXpru=4O$v`f^!zVTJ*3$O69Z%$73C+0N-&v0
zFvcjo!%8CGLkxIDcXLCjLiDTvWD&rf@US3JqIJ>>yZuCRPOP;uHI0j~y+VU`m{F%8
z`XnYU-c5=`mPYpg9p<#g2F8tbpmIN#p*$~XskEJaOudKAqo`Mm$aRmqB$5|ncWTzR
zIv3!`r9Q&Nl7G>ZNB8vkG(Lm=8PCWUlh(OB#JW(Gtx#iOAQ6PZyX_i%oD(!pGg(CQ
zh`lvOIq4huNsBA|Z$wMUb<_T4%$Y+>#D*m9dvo9p@R=p@(h{krUfWlUiku`0!XPW2
zf8K8X;HMb1Cj<%Y5>Y{R^rR$zLVhnYOeNXpnJm<pE_9hK496{mw&ktiJKM;qnHg2d
z=k1+JrnIjy*}9AQtTIQ2(Dhv;QG%O&BIAXfYzwV8)aesloQT4ancVZwijz|#2}sg6
zx*p@?FJv>}V!qrNPC6Fk+*(rMJw7Upd@(AzygC|t&TbYHJrTrSscn<>RM*0CvUNCx
zo5B{4Pst18tG?VZ{KRHbdvf(<id1gN@$-`EKFN`L3>_|$<3z{XY7D>H78N@i(7lfc
zD_D1dd~`aRRFqgoLj=MxNKtBKb&O&K9A^-_osQVd1Z;On-GCH+H*qCziN7t0|K0me
z){=&j&X}?(j)c<cH_69urvrEz#1;i&LC<_;a@-Pr#4sbxKI4WW=`f2ZMq$MICltSn
zEmRk3y^lC3iYGa<5aGbhT9}j*Pj*hQ1}uRN=O~f-M2C2Sb+$UZ68nHuq@?e2%TgE{
z<Le8>iwRK_VSLDF90vp>1NN%K@}lT|o>mf7HV3d)otVx0AhRf&kPock65TQ}NrwrQ
zZA73HixnYt!_d@Mk2@z?kqv?a5F$4uq@5V?Y58<iS!0%c?18eH3QwuyTveg&G$(zC
zM}gOeR5?Dj+x(b-8zcTYb32Le@oghL;q$zBX0=8!Tnh5aKoWPjf}(puCP0LnD^c##
z;(UpKCNbNIXg$*r_*~Swl@6Kzo+F3Orl9#pD8BrcQdTl)J2Gi@4kTfTB;m{hB<!9J
zgkD}tY7>ZF&6sRZj^AQBqZ3t}K$6ySLmUeY7BSDG7iO3!C(LWK=rvB30_DA{J|D&e
zj9L`O>3FZzidLsJ=Vw^oFZ<*%MM=*@rXwmP;~O6*0-M5qngntX5g-%ux<_5F6-vcO
zGeBQK3Ok0n&3FQdEaF#MKbrP`1b%^uK8!5t%rLwZi6Y)O3&1QYt?7}*g3$XMgPsv#
zwz3UXi*W6w_V?faZS+jOoa%Md((F`=$SEos+0d`miF|){Zi=m(>9j196kpX}>p;9o
zO9`Y-772c*2;rbS`|_m1NJMK<Jhv@NxeT;gD|Y*0Y*sbUHBbEYYDrm@&W#SIn<t<|
zl)Ae>h$xs=D;^MTEmQAbdMjQ=9w9_(>F)707%UIuE)D$BICrHYL-fDt)7~`fokXXi
zSVg7SUKa%{ph4T{V;R6=&QtZWcy(KvML?73Jb21L91M&j7!9}YAzgy1HIPZxVF>&(
zv{fn=4<HgmA~+XIRG{8$qvPs_PFHFB{Wr@Mp<6eoPB9-vl~~g!zz2~*jq^)wW}M2$
z^e-Y5Lqsx-;iU5<94*6uC6S1{5c;hSNlWy_ewLMGIDT%u0ylD5om+Ldvd@Lrp=f;C
zETLJqkG^Za@^e=_s^Ix{b#eGL+X|``n}<7`_0*B(7jN8;4iFyj98Zg=;OMyBh>W~|
zJdrv7c8e+SX@#bmN`<ooGKGIo=~ZApq7|Noj39RO6HlphDJXQ0s<p9FaI`PR^8)mW
zav7q>c!ll5OW0xJ4KcZH5)*q*Cih0C_7)P?l%n$%3j=U}Uuss_>E<R66w`Tm1r(J$
z=LwwmZ+@)kMjM?P_vtbs+@03f;T4{$;L+Jq>geiYw6f0EUVcjN|DB<u&W^_;Ex(V5
zD@qJUNg|n`B;llTg_yu}h=_moh4fZjCt)qr;|B2?r>1)tHQ0aNtSgHB+y##(7e8LO
zkeZz5=#MvtuoMN%@-4h(Q2$9+gnY?c&){?i5h05Z4%Wd(B$is;pgctQB*v1p_^m}_
zuoZ9(;XEfAWYdveH<?rjB`T&Nrgu3W($|`Ekj)@FU20ec_)_akdP5=IeZsy-W#hjp
zZ<kdUi@dVRYD!emRky&D2F-`!wJA*niEW=^$(nHzrU`-mL?9P4{~}gcC0X%KFm<}~
zF^r=^fr+tAdv~FQ(y}C45dA56LYwYmwqCa}oM1&Ap=5*s)JNh2kd1k#q=p9Ya#LBw
ztMRj*+uY}2>QpC$Tr&D+7oAq(Y*ZamEml!qq&2j#+>s{L@`}8pbe22AWzk1AJ*-||
zax0co-rM4vx&55AT_E~bO;tW}KD;GUUeek1@qvTyzHq^)Yjxi3RLtrN3_IpDIJ^I@
zLp`8zx=JG4s+on?*~aq@m{svPRm$a0KRn5wNLWA-=vM5gMm0%fI8nVQ(FvK~&j#~R
zL{hMq{%ZA^k&xYgm@Z2(>saxx+u2Z>ipFtjk}iAy-T(RX!6|}x%5s~5soUMlK|VVt
zUFe=Bf2{Zkj~R<nz4~9Ga71OeZ&rUeeEB4C(KX@9QKH-bin0$XonXzyZmEML<O*l4
z`c{dlO5IiQq^Ev#g?FE|!dOY+#Fz_^{R^O5?aeGF*iN@A3P_`i?LBw!^4Y^k2j0m`
zhws1s!d`j6*VH#9czi42BA%VgQ$pufAK}(?NeY5PwM>YCaG#Ts9TpNQEd%2%_Cd^f
zMYr<KqSBMm25}A<&wu+a@t7r^E=Tbyd%ZQjll7*hA+9rD$3m7%Eyd958&AT*JB`<T
z7NO1-PbY(R0;T;b2=_c%=UN|LSl4h%l+Jtr$^ng!!&AZ~8ehA_1pb(A*_*GS+5MN}
zn{q%Q>O!D?oW@}^H!Gk3TGI8$cv|46PuynSFl0@`dQscO9~EBEyMKq}029M8izp0F
zbOJPr_VBYbNP7qLw-UO(oSFqP9h*(uzMGv+f~|QXvi=vZXn&lanx6k5YkoQ7Jx7uc
z5%g2Z`|RLfobH;iFp`xPfT4A3MTSd~T&YHGht}+VDjcNIYezirNDImA@lQ6Af5OD2
z3SQQPXp}=QtK}Nju||Y`4N@+csasJ0{_E^70R6;3*v`MRl8gUQfI5nw+951_YoZX+
zv<`G^lL@frVdDV$Aww7#Kxv|1{1<p;DB?vEZQs~F_T@~UJ;u@FX}7>0j5c;&;kG*4
z9(`bVwAbJv63F_?;a;t8bGr(Idqi)Xi|d7`*m{e~_p7q*(eUUO)#rPkOC&vAy?N*I
z^R1uvUQz<XT`x#-rM=n=j&OZ(o!XGKZTRD7c&U*WG|B&<x8&qW6nn|AmJ?6jKV#+l
z7Udb=@XS{~hUv~X%)*+z2LZG+eZ}nCe00}t@xSDT>tC42ZOOa;`V|6|YqQD;JpYbb
zN!e$yLO5mqJ47-X${p7al+UO5aEEc06G-gQ?Rc%hMQn@oG4;eczM3+6i`{dfJ8Qzc
zUfbvFnUEx_*!?{Gyfg=@3Wv(fHzqA4e0y$X9wDR<$bgPl+ulVLNbIVxlQ4(Q@9s!J
z4(^HosC6a=z)jdMiXe7&0U%0e-WM{iMDEFCsJcEJ3yiX@zxd4-eK@Xa%O)%>zwE^`
z&$8AUz(MEFqHMMu`OTtcTO3Ei;(1ZsA_p$nkjLM!@FB%=)v-kJhGOn+VXBN5A&AoT
zxm*Y!;%-zil`1~w;{i9#8X2fF!F>K-;l+WlvP5ejmF&I^Fr*j&G}DLF0rCofoIR+I
z*EM$m@NrptfM6L6u>!oAobhW|V3-w$1Z?rbd#=sYhIM$<Bjc$92viu1%2SM6WC6Rl
z1RNgi-`)+Beq^Jo#p+|u9WAski|m<`{@H#I(NK0M(qn45y0T?4b<5Vl=1oc2Q=Z8X
zX?0uF`I4R+W=zpK3NnNuDUQMQW`HOz2iy`Q5XM$R08xcTL@K1Mv5+aQcT8&mvY*uU
zyGrq9gJk#CdwYp$qUB&}S|@Umg`<=pGf#tzZUywIdd0LDS`s&F=KeM}5`HV!g$~wh
zaZ8Q+oa>5zzNQYd;oNbVg2kI30#*)hf0W(2^1dg%)Q!qCchII*v6cnI_D%6~)#+i)
zA$C}lCT*zIt3#)&)P`C`$Cnao^PNY+n19D_O&?aQ2>+gR%X{V!b8_Ed-;uPd&Ghpb
zV>B}8gbhXjstU<S?#6QgcPoBhl6YqufO{k*;WN5pRI2KC%LT%0tjNFC?}W~^8MHBp
z^L(JIngVCl5Q(K>+1ItblvB3A7nLa`t!v*5PYXp|k2fenC$=BDCTS{eH$`OV6*5D%
z^#KG3!H7U~m=q@^dKyKoUcK<<Fx%W~s#?!}@WV>7W8^{oq&QrL!^$?#y1_#K>229s
z_BkB~BjK3xbH8{lZ|D(GH<juVN%bj34CZk4<X1T839^$zD&rV151i;HVn1Dj^@XPg
zf<cE&R;aK&080X*IFDQ<7%3<pprQ0c4k*RBPlia_lvsEqV|vic%(AKN3}`Ol#Na(R
zWm++h2zf6lGV|&B<}~<a=th!90*F*%I92Hx!?y$0OQhg-A&>17qTbOwsa+**w)u%B
zG~kYd*}3brp9(wUiYJ`wbxXWhD)_=#4KcYejW=aQ(;Ii^nCLc2>)4YsF~=8bakSY+
zi&4zvWN6(SOsGj6Fq<MTWQVtL^@DH}4dvo!_^Nd5YBLCBig;~<ry6zBM;GOIugvYr
zU$35LfAyHnZv323Vl|l!^9{$@%@|A!my`r3uE8J}4JGLn`tyk~o|SC+eGsLU{ik|E
zt0Rm<zSe%Z%x*W{roq9o!NH~J=5^azB`+vrPYktzT`B&~UT5{w5H=iHcvp@HTp4D8
z2K?|CqEEHHWo_y#K`?rjV|!H&?}$V?sQxf+5aT#n$g>U6H4J^p!t$M9h7^EO+alwM
z$zwtlFjzT0n%K(f!?y?nhJzzmzY7P`n@s0hm1%1Xa{QSX8_ox6$O_mO1(1Gp#~VES
zAePvLBn=BE54*1vs%zt??6D#{+KC0&b={<RVw!h({p(<g=4%o_>N1D{kc=KU`X5Wu
z?7fU=${0|xgAm1{nRUOoOxUo+)SO_kpGT|RJTXeX^3vMUdY>u>V79xS{Zb2Zz<#}|
ze>}^!;CEf1MGP4@Djq-Ip!Y?#aX1Xee?t%Q>m)YDLhvp953`Ogm@YI!d~bY|ZWp<b
z_#s@%K*O_LGWo$fiwi>Y(_YMTC-#TaapGfdp}dtR#-^IR3~u2UX9w0IMy#7yd?~Q^
zL=^B<Mq!eIIVhffbv8xDN!90(mNNGUlKj49?`Z>_O5a&Z7U`G8)h-I~ELw(`e~bQA
zv9fjD=vvASowEJueZ`#ySF1mESD$*?q)=1x@lL%`qQp@AQlTO1T|sKQeFm$y$2kWj
z0l{M?{blupg3Dj5^=pZ+%C?U*N=}0GZer2#X+8iVRti`N@hrSV{jGKjyvDm4hkI#l
zCr0B-mleDNK9qGV{XyZ?ZYy$iv<8eb8po^cHCG?5xQqra`@7@XnForxiGkk(7MdqV
z#!8%#Npkr;{QV6+%Dqo!^c72n6Ekk-UI6Jb&}dE}pg8;&2UOs3OhsxZUm-;LT%m6~
zPjm_mFPVJkEautF4NAH0-|$WFx+6@dMkd#2L9R|B?1bg$ro%1%1&&W=?tfhkf`W33
z)t)?+O(SsN!^YBNGT3f&WZ3K{50tASsA!Yg10WLT5(@NxY2|s$UqEz{0Y#t|18*tP
z$cwWceX&_j4PFcHY^1Qb#+|eR9FBO750!X|VgPY^RBD|Q&0j`{z8gR@7)dcC8@Sno
z<C)k&tWgCuV$F$$-XHQ(a{Pn&hsYS%I4s<zBFv%YTeF43mvGQjfH_qEBOR>~KeR>7
zWih3QrC8NwM+EZT(Y`k`&=39Pa{?lqaHx@BRRD)Q-=j!P{{78eWVaQ%U21z<F#h5<
zyYaH+^I^~J<n6|POlU;E7*bGg{HKD*81o2LKb5z1&YAKK)^L4iq)|3L*2$|)eajM`
z@2Ii_aLh;OCpuu5uP<O)J^`&arh%|e0pkGnAwvVU^IiObJ{J9IzZ<}wSUe6c3W(yE
z`DD3PD7lStfz(2T&c2$9{Kwfr6eJdR%&MaSf?*_F*E|Zz3|~{?ySpWlA@pij@K}t&
zj(w{}SQy%;K88azirugmryI;{lA|9&PpL5uW$x)P53?{BvJ7X*TCk0<m^*L`rLwjx
zw2hhksZ|<FzS-OGYUgBwcngY#>H%nbevOaKYUH_Be8Hx<M949L*OG3vOX$@^hj2d~
zziU~&P4X3}o^;d`AWq;lzmJoG-^`Sh?3|}EWZdI;;M77}6?!BtoTCDSqvIfKzmAWH
zb-$bXHQrnne6O0Suemsqx-v0olUXLfJiiy`T{jszC1ZtcUp`uxpEmtzqwMQ@I_5wZ
z{IRU2U4-q@yPJ9LpY3IN{$A-dwfRQ_5JlTG)U8*TFcB)8APC?#+!uR3AMPb}e+kK;
zsW=b_D63Q|d1=?@V@gHEE*$;1cIn5_QH3MPVcex0FOu5bmdU%`TSrc27?o^tKluAG
zNyNMWL*Q+HiD5;Zd-QBi&KppC`91E}?R#@p_L$4wjd>d`^68!k{W?$dqf^@e2%Z4n
zY-#*Lx89aF96Zz6vojcQR4qFqJm`M&-qTdo*2>zsj??=}ar(<LMl;F`&qew=ty}Ww
zca<b+Yey8Is_*J4B0`Su%K1a%@Y}i%Z!U^eh2Q0_Ed+2u&O#20QMKed4rAm46jZFA
zVUFi=a^KiD%q_;DJJ%zZiy>B@yZJ7bt%)8pGi{*If(b~RJRRV0^%bs^n$XVgdNp^p
zK|DnzDenBGUM)@pGQ5^f${ycX`6=-F_J<(`F6R5H3%mDBrlqj7rLSx#bT_|~oqsx1
zqzrVg0{AZi5Qwix_+0Xeig|eCkS+A7$6;$<sm0z-M&UN;ujei?=hxYN_0JUALP4CV
zc4Qo(8z5oo4j2eKUnWtV28E^(>1X6GXhq&J32qBambxPK1Mls;(y4u`O-agLjEwFR
zwll4;90-?f<~}Non@|fJ>`?rWWRk1s)uS{-<Ij+{v}%|6mA~;#^`lpr-<^mDf^hL{
z^y}Uk7r^MtMt}yr5qX~!kId$dae3;9x{x@sy{mrPyIw$~gpl(5j{JMLorjX#Rcudb
zZ#VhH5=)q9R#y`DdS*Xw5`*-38qE;*?#o4IS}h84k-KW*?MD5Crep*ub11utU4_Gg
zM$;6%c;X?J8e;TWY@f(R2B;NTS=e|%zLBR3y^WysXie0>poD+%GjqjJCIg;YDY;?;
zOP`}21~aFtZ_SnJ3gllnV}B)3{VpbjUwRly^N!Q_HOZRW{39A$clB3xkOGouK(9ms
z2wW+Lr|fk`3ZQwlgBKcI_*@V--}6R{N8?+wlcBw*N0RTlYnye0K=x}ig7QNa=xBhz
zIQo9T!RQtk2eY=+1A%k^wl=_e-QctFx+941x+#!w19jySrrJI|+t$UxWXDjd#=a$=
z*RY+!jghl%UHw|+Wfbk(CM7y-sN1Z_a51I-b4g716(SW*79aGQua*L1Zg+utWJA5H
zBkPprSuIUbJ&lIQ5p_MhT`g=11DnGUA@6AIG6m=%!T=D4*+@9y2V+J)<;P>iRxIzN
z;y%b1acf_9W65}EIl<V`@Xx!6{(1bcg^eE448?mTCE=i9Oz-EEUqz?#J;f)_M`@g%
z2^?!-8`CUaLw}$}SVs^+JYGUUoTBco77~ZJ`*Q}IS-f7|nm_h~$%TE*?_E8OY-(qn
z)d&4a=sKhLBXu>>wv}1K*=-}12V>+T6FbJI>9SXd=A#K$JmEp%NGahl3iQ&I%i@xU
zAHB7A39m^tk($GTCHT|Q=GSqRg36fy_0E0jlf?~9gR=W(gXgb(Oy%~Gs85>e@gazB
zz9{I{<e4gx&<|9WDXh(*!??<l#0(QvLp0Q!1``^PZwOMa?p84WxCEKVJg%gMoRtrN
zRD2VKJ%$26+n81o_Lut4ye&#LO8<>sqomyNBSYV>Hdbt|#Ri%27H-GT?aY0sDjpS=
z8N4!twZniu7RBtQ({XWojWKNbqp4&{;9t2J=hvFoUH8krk4C(9-lv;CDw7;%Sx<Xa
zHeeHUbdlbH@3BEOgn3j`$7#EZ2Y}!is<30^vl_KAEr_dmT4fR3cy)58m#6gKTX*l&
z>*K8-Gd`C?^1&Wd>JJ>ANfk9SucL;AIid&SU_SZss^6~&whRhFGF?_;^3JB5#j*Q)
z@~`#sCD41@!wt@7d~FkT*Zq8^d?ceyI!*m=4Si7yJY>D>c-#5%C<n2XoIUCcVZaCl
zaf=$jzq^D@eZTBe8{(2Q{nWGji22JL*P6tr=ugzf!84Lxi^b$s&tx}+Y_Ty@vCux6
z?@5{Oc&$91&u+vYHDTBphH_vu;_qi<h?-3`W+S^I%8wn|V=P@V)Rtbr`{E1OeF_{7
zSY)DobO2=I{<rPNxqI^t1U3JVy%NpAxb1O_)RjcX;a=qr7WB8{Ub?m3=P_`)LHA=*
zuGIRC;jmFQfe&VBmumex+*7PjBIwtRlvda`1L1IsfnH5J;YXYV5V&B<sy#7=7&ZST
zsPfrF#~J#i_Ek~Z1r;|P`^G74vGBVsXj&*Xa}Vkd7!rhw@z8D)3<yPK5}Eo}*3?Fx
zD9u!@$9QNN-Tis>+sCN??tS}*82DM(BF$d^E06vnYw8Z^5%Z*<^RWg}NSwi)5e>8q
zW}7QL`27G7(I%Ipgo4hD1#0{^{^Oau@(mV}G5W;4g#D2g0o4H92|76U2LLz4PF-a(
z;ht2KTVnvLIfW_x({Z>@yPbFN-<#df_xo3Bi}$@mmPK`-vFD){#qE_Puw)My-f2xy
z1vWhnL%y&{q5b7$bj6~*9XZ&E^-LZ!e@GK%wNL4?*!oxwKJFiBO~5g}i@!d#nkOY|
zG^SEom}2BvyYaJ*BeQF!PcTd(Y2(nwL8G457J_1{WMhMbVpH#+(O8W1SNO14OlG=C
zaHGf{p|KNi)1_gC$us_+n4YYrMC<cG&Pe7d>-EeWti(gT<855uc1-^c#AU}ETpzz=
zIOQaAYkSq7268~&lVw+#6T>n+*)x|YNFR%BlY%}D9?B9^v(eLcmx9NtUT(F;9IRqn
zHvO=I?(v=?m8wFPfujV#<JNh$c8)`=lp9n2s$xOVJW_IY6(9YlGA@d~GN55Fc+sm9
zW6+*MLpUePs<AHe=X$yPXdfo}pi&qJ0&|$b9z8R4=xl1v_yeiFuVl3+)j~^VaC-4L
z^VDWv@bFMb>4#9zFQF>?)1(VN(B#Hfi`MS8V0a3{^z}?HfoED(U_hNiub+!i0$Q<t
zh&evj3{Q@wVStUE4kI82PyQyZx|-Bt7?Vv#!i8E+1OZyQA5KHiv+N%x;s9OREdft{
z>Dl8Y->vnA?#trgqLYI*{t!4?ewjv6N>R^12C9VYf+?EEZeM|=kyZSY+mL-~Un}oT
zYTHk0Co(>UTU+i-4|aS0d+GTPQ65Q}-gZv=iWYl|pg_-6@+U)WkZ}R7N|kqJA1YWZ
z?u@;X4J<dyp`%Ml1&|irFS))5Vy1PUn05aKdDZ%$QM6W1oAx)$I;Xhz9foXc$*%^(
zwUH8IU#+*Wo<6taE4{<lUEL+3`2x4s*Rf_*@KCpZ7eF7qgo~^V(~eiI=pbQfMop$o
zj?H*^w@QvpREdo(-Crt^tw}-SpmNhkpS`7--BU`7fxGaiR_f5+wx;ugb;~`!X0?$;
z^F+%ZL;r-x?t=JoZpcFbS!IQV3%M2YG0KNd%e$(qRuWm5M(|q_NOSK>KZVqrgC!h~
zM`cu@Vq=kY*EKbv{a~FSBy10>-eV#RV?Ye8I~r<86syCfK`s5)!!+mq8iqO>c3v?&
zb~BO;U*0LS@b14h!Yw4n+gwEu{4&djAhTlvN+IrCm8JrakLnVdZ{_8`>LbOf-z$EG
z_YGPq1morPwiO^pniSSb-7Q$`F_h9xr-w$(%|Hz{sqLT^qVwYl>A3d$*?P7a3ytip
zQqmi`G?H%6SxMN99<e|{yRj}Sw4>Dgpo$u@Bj7STZACNCL6)pQH}sPg8W+GJNXbaO
z;>DjcvLuJuwAv;))4NdF56)Y6KeIW@uS;6`G%CbO8wFJ<lAqDtm{`l9r)%$3V&hV(
z;x`iiDt6H2__ab+h2Je@IFNiIVbiQ~FmYW%fcF*LKg1H#(3&kGrb2;_`gD5FwFvF-
z2DW#L=J;()&L6%B25fiEw<VoF#=E#jtNYlfw{6?2HZ#iC?j9QLSx#$=mT1}k8)CWO
z$26dsZ6<6Sr4?_qaRN2qMEm6Q>D|OOCKb*|bc%TLSSGMaVh)7X7nWdugnSnTylP)o
z#8I<A?5J#^fo}f97edf>P`3>j2$w6)&OH`+MptBLBcUC0#~~qvqs5pH^N+AGFVHXO
z6unBT>`eoX8Li<Mz)2v$2|itHsQ!EM#wzSy{$V}-PoDdq3_x4dzl*;o%(tcuUA`*u
z!fnp!PfUWC?#Xk=Yi=T7U%TZwpvg&Hm>T!q_;j9;+V-|~j9U-$f1@Xz9DtAO)0*Sc
zkmtmR`iWP3h3GX2;zXQ?@^l_wAz-y4c75CUr9g(a#NJ!?nGVLm1iMh{Zw4TBYQyp<
zGnVazA^T~oqxTxpyWQk&s~RFqZpw~>DgNhOkHNyrZ_z8t-PNqmX%gCGcZeSxWY<JT
zZ@%SIY2lY(T>#X%d^Yssr*bTG0TbU+cKecfo{QuB38m?O5j6fv95?RG2;G4+@eUXX
zcl{E}k@b`aaq{`^{#W_5aSo+}z4Q(xrw(j#%1Z(llVa>234QhJUOq3>Ib25XsEG=w
zayAL|3j*-$e3CJDv@6H@)V~QVs1+WmDhhHh)VST>pOJ16TzNfJaPTe7wIRl<N3;VS
znb;@etZK(LXdO+9;iB-cf=#`GA?p#u&0&dl6*bvpQtg>uA+`S&t4pBjUHrNxw{U2F
z1ppN}0k2E1#B}g@)L<JaG2P)0JBlUsxCI-|IjI`lpxmthn2<^|t=O(uEdqQlF*Hwu
z(qB-bk;E1VrPZ<LoeX6~@MQG}WbIxW@_>)1ek}f(d;6BM{hKE7#Zh;ZSm^qKTykr4
z&Vq3+);wi)WDqUTkibRt>AD#m5U5uB!Qa!ul$h5#Y^#Ar0}0%Y;njVjaZ%p+0l!$t
zavo90!I?_k35Xsy1e8uvR2i+IfMpcJe55=44=3Kivi8T1+;yAV<xl%P4jSk1;G(dE
zN_+Mn*k4m^&ePUrS~N?m9S6o%Q;4!}Gx4|8JN)$$I~ue1=WJ8VJL+W=eWL9?B_oJb
zM@}44ypW}W|34A#|9l=w)wG<vQ{0)dUu|dC#P@0i@m%_jTB`|B;@V<&+*dYwb}r`L
zgWLCeMxnmwG2t0~6?w}HsSKi}oQ4snNXlaNr76|glxw`9?w^s4;pbz!767+?9D%7a
zrb%o#d4!sDv$ak)@R47N#ElqyS!7G-PD$8f3<|$3^u0fpK_E#zRb{r>{}MClG(bYV
zz-`w&XD~r<%$;|jA3%25bv-UbM{;l3^G7e;a@#ws8(k7PzD^~AFD_+%x$sHEC;)O0
zaWfSrN`drOBR;>N-x~(NkUt4mdFhI7KguaSO#RG5cIYIWo4)<hgt=(new$=AU(i#n
z+fSpy^?Sr^uNT*Bv1NDGF|B(T<xPB1u87;b>X^S+mhexO(}-&YDKKFEDteduaT8>-
zHWw!C>bqNcKu`|O$6$Wt;n1Kt#@NeGq$A<&ghWKeQOm$r2zB@`ze{Um&p>&I0f7}1
z04+TC6{fEIc-3gR&j(K1<9nj-z!qj;U)Zx0_G9Off!x5)r8_-4GOp)K|Hv*m&$ewT
zdC<srqc1-kvZ#7ppu`4|Y4yvc9+ZS|WTZePI*et0Q$!jrv9oAe$*X$$(0ki<!EoeJ
zr2f5v|1LdU?-q}!kttB`N@Aq1!L4u9c0F*tsX`2wtLye6-!64qe&E<uudF!HYG;_^
z9qsBm{5ZDy0&iii_q8|)uegkCxyrjERurs%gVDN7yvUUSSx%KVg+5M!5^9H}?x@*Y
zMb%C*KP5^HxZ~NEw>&;yODg^vxcx_9r<;$*PY~lmMO$#>rc&vE<wjegwEwe{H^S=J
zYa}AmJ>Mj#`$&CT#YCS7ukAsC!(l#Xm~h!B5ikmqt*V6~8x*0awFmC_9Arm!#Jd!g
z?954tpENS2zoVA_wyo%Nt<ZF;8s9%N`)DTqzcd)4NH6uMB(-MaIkxbUV%^r6`E?I>
zG-uKaA;|R<$Rf8}$t}0#avZ;Q`*};Aqqq;~K>T9@R9b;2EY;meNbf+<SkR>3PJ*CL
zPpQU$QiSK36kxSFEOq$JYo0UL&BVpw?lJT;<P%-&pz=vP&&p7o-4OHjM{iQwwKbKC
z=Il7+S2)hu_8BS$kNYDhKFISw?SRShvW;qAA8lWIg`h`JXkPWIjIoegt7yj3gKfW<
z>^7>$C_dT_gOu0I14sjJuYLNRztJHQ8T3W;e`c_yt?3PIb?WSj#m>Z!iwgg~O*x5w
z72F};ZNFEX_F%K~oXC&q{`T$4p*R+nndGCSL7msERIYx+>nx?l$G&dfCEslfo4P~h
zY}14z-!^%ipPOy6<K+GFTyis!uy1Plr={$s^yui#e`jm7Y$9<ycE@1togl44V~Cl|
zYe9Ur?M1JMl{einbn99K2CL&xO!QZ{@gJD*<oe;908Z5423)JsBKx)^TZ*Q9kD@Zn
zaJg@8<-y$Fi*Nr1ovJM`uWXi$b?9?sc*g4#{Ts6#F%mN~8WjcvZgv13yrevSf6TG|
zGp4<`A0aKJ31Dia!a%fV9UuU(MQog)02LMk)MYCz5ZM!(?c;azxm2=W;%w_yew_f=
zD3LWu|HvHNv&1nI%~^JZ=!h_{_E2JzaZLO`rnN!+A9#$ZI1Y_uMg6I~SkHREAmaT$
zF!<lQQ`HHN0LW#D_a#$;hmkL@?c2H1GSfvq7`s*QJD>A^NuwU$7WZgP9q+|_66*=L
z?|!5B`D8m+!AXXG(F01Yy6nj{UD}+Bk9qs1e;bpRHY<2PgbJDz#9kKu{xfF`4Focf
zL?|5y2S!!1s$9R{T{@e5;d5S?{=^0mKd`B_E52@$o$a$_o!0dF{fm<+ud@Ar?VNA@
z$j$TpTwguJ^CXy82DAGWzMM?rl46EeWKGYuD}WSgIB$@yc(@n$c>OI(-?|soI&>>e
zut>j^Uj_AelAVUG{mbS1NC4e#*8xmCn}A~^f<Fa(9#nnMm;^P4h|{NP7r*?Ped(W8
zmWT9+p1G_E-Gqf~ZQtOR(5B~l^G(m>&FZ6=2K5+o`$x^~R4@wD$_z{ACDNzr&(GX)
z-^|0AY=isFiJ{aQtlDi5h>jyumyVwm0ZrMfO5A=mU1-9z_?SMrSe(eGCZRCdOZX3?
z%;%a}f!F<tfDj;i?6E|kuE{WljpP(AkGcN<P!z!)28a>>XwXAfsP#impJ^)vq_|@h
z`3I+-OfY6?x`fp<`n#NrblkWc_UYNJ%Rgy`f?V&2Plj01|J5vd<3H#nW@SF_RI1Wk
zd_zPn>-+K7M>gBgs`A>PZXjS#Zwdr!=F+L*lS&fo(S$H+@rlBhm)<pB(xjhZo0OC9
zUrb^9cb}X@M8c{*yJrJI;+P<St`i~wMC9~gOaesI_2Hv(@epM7t94E&xIKBzMm{)r
zxkq09Y&&Pl(eB&_m%S_9uPhh@8x>XZN4sZsgZ!4?%oP9FeLdBoWzwnC91=O!uEmF2
zRle~%r2b>7nc^>u7U$l2_f2e=K#vGAW~*ltRvjgNbCBVSt|>#|oAoK9(XUk8+pQ{{
zG|>+;wWWXK=`L8n-udyy!<aMma;|*LG#EEPpNbtKJlMiAOL@C2bvD(I6D-=cdxJrC
zzbWF1kDc<v9ow%j-3f4vz{<`1`bT{gH@$cTmD;ZHSN0o3LXCH&^*Zsn!|A=_4tyJ*
zraCSQ(f?DqzGJ0w;aUd!gG-P1rMqI;7aZZ>^6S%AeGW4*|E+&M7eCgKOO@DP?K~F}
zZw;@@a#y5c*4WlZbQoElP(`)hl1(~KSwmd+tab+tcJJw6-Y$?P=l-bDG-YOZ4C)Y*
z$wK9wEXoIa++)~t@J)8QylfKQVJAS(47-}0cW!@NIoQzfhmXX<Cz!~><&>;21;f(;
zZVz^k)U13JZWs-RZBLYf)V`HO{1Wfvk{HlG)5LV8I-8J}_;KJ~GK(KTARD=a0bTkA
z8O}DoVkH}Nu7TAhioJIxL7e?K*S<Y7+rZQ$F_`bsz#<$ej7Ih^0tkA7eGIF7UG$hX
z=R1=wVn7=JEQH_YpYucg7P;XD^CzsRH)gS4&-F8O^3YCn70)mG9bn`}LiCmGROkJ-
z##^7NzELf?eA}$~s^m21sI_;jw_2)A5AVC#$h>2D=kfUNQo6z3)$td?`+wH?uhRy{
zAhiHg(WpB&K|h&3P3SYBO;$A0XL2YHbJO*|Cp)onNI8aw-rYav*zEta`2AM6rEH<x
zIPP##bUNbea|rsW=YCR7r|xl@w>V1g-$O42-Pa~`cIU$mKipQYo*SoBEQfzf3AnAc
z-6tO(N0kn(LDbkblo-sfOD~3%V=g+b`24Q(nxBP<R60$~(;Q9<`hTWL!R9r+FUo{f
z=ZYqmAx21KvA@fk=0;nZOV}rGn0G`q`}rZnY?MyU!Ed?PdLtslR1Sbm4)@gz#>08z
z#2y_?@Huu_jq`lWBCOeuKZXg-M_S>qw5>48J$Aixym29`1JD+Og&S}WcASTMvLpCy
zb@3}at>>lV@`O&_S(vy5M!b2Fw6y&?_@={TPr<p*uD>fnZ{}Vr3G7K$NX%@rVnn-d
z|EjKidtn%f{dvCX?rpTqRRtW_Vt=Pj!=$@xl-R=r=9a%n!nDga^Db51_)t?+OzNH4
zX#wT%^$a4)G|?9hfG|IlTFwfSIY)i$yaohbfOx(R`jIhudL$^%PJy<vmvss9ak69k
zY>Qmkjj68Q>q>r(r8i7>i=FF@Y9Y0w-D}UDd`G04E9bu2f*4QNQa-fXZ+1AdZg_sb
zdZT3Z=)A=*UaLFsHObY#k9IYq0WY0y2}6Xk5#}0O!d}GnsGE0Z?x*r;$}g3)9hc;W
z|H<Yxb*c|}R^2j@E8-`~Ugo=W=Nrq0oqdn9)V`j6;;+p<7qei&8;S<>E2f!#B%fV1
zJmA1aRNZ+2X^44=<*@!ITc37`*VI)Kar<Wt@BH^rzmPeHDo3%i!)CQ~-Ikh$ryndL
zef^6=7o}#Njo!}Fkz|OF8UE{DGPn6C;b416FVzV^-bq(uxdf5N5ay~j0h6yuKs>>t
zY3IdZ#+Pt|L+xyKgRisW%H~u8w`&GY-nhTq)EkFZUQq-H({KPOm)@B3s%$Z5<Gg!)
zc%l9a$?QwZNbg<8l%V@cqv7xQHcCSWJ)%;tX9W}q6)=4*m$UU+_7#)+)K;Ep^WJSF
zeBilzia*h&U4z~FhjBM4kLsYfoeyQU4@mncri}Di5~oPeI`885XUr&60R0H}#|*$Z
zr|X3ATJsUhv?ilDDtm;=d0aF87+rj|!cxD<ey`*$Hq%`5EbC3uXDe2>++J^!Zzas4
z9&aC~cGrcKXmFhK6g6+neH4E1k0;Voh~IO?)?w_Q!IGxQ+U=ElsPTt7vpUyQ&AvNY
z3Vmh|&)|XvGg;#v#i$yqXItM!$3&FZc^3aREGQ!16c_qczq>V6RjP7lpksgQ&S%|P
z)eolDHS#YCE^6L9%DOx>OL7Y<%e_E)>LMQAIl_v$d$fPQd8}5G&)M#{vqQC{>W%10
zl}@!knPnDnS2MHSXq^E}d~$rIb~_bt535C}sV!|Qt3nw(_8FARaau2h8af+QJfEGl
zR&MZQAH(vW4di%uR33WqoT1WNp^C}}T6W>*u?bA&fOaf3(FachiA_Lz69F9}XoTn#
zj|q<}FMI@8T#Hd+&z9UOCqti&t>n8~i5}#0pCO5+{fKZnbw$Qlr~~ylCSjSfB3XE)
zW{NRa^rT#KdS#ScOz1ka)sI#5wN>@a)%0D@>HEs3wQJH9-6<@p=9Es6-Pq3^uj|_p
z%17T#Z;Q^NrZ=&qL~A?^I)^qo8bo9Rtt!UDs7C1P-1eja#YJWf9pr4=<1dXoA~OKp
zu8+N|sd%5L6^kIFiDUp;l~IKd=(ES9qb#CFtm;5YWiO9NVF@nz^&sXpeUA@H@1xe5
zf;J>b!W@;^<e|)$DY4V4J>jYCF<58rGwPdqfrlMCDQ4uc#&CR#e`B`YV~Y3B4auZu
z^uz~D(lzQz15j=Ng;9es#!S)1{7&V=u;sM1vFhH93}=fH%FhnlJ?-IJ9Vr{w4ay6=
zRmS)Vnq}oeq*})=9G_#xs$Y4b0{$XLzf)$vlm2UGPv+Q{!x!t1R6F4>ZP?KVKM*?w
zEJ-{sllbA`kANPzOY{%RStC8<hx}<V;B!Q=`{VpgSo`R6cs{1A(licNu&GuU;rsiG
z<tBpLCZzoO$5s26<@-r-vLd;FLE)jE7I!jUp(6l6j7f;bC7c?;hiDUsw_1I|NV_ZD
ziVqO;6AEY;QOcffJ@&jERyhuz@ANefwU8Db2UI#a@HFYzJJcOKIF*gDj)}FFMa^Xc
zTUT_JU>+rf(><4!?z?<PVr3BY+3I-<?F9?-w-&DNEPMmHla7)~bV_G`FcxMeV@TPk
z0&e2Nm-AVrdu>I+cT`USZG}NX_eHp$OF{&W_`97Xbswqh!Xo-5<<IMU>n@T3P$YbK
z+!|kr2d$m}rBbdMcn`SQBgpi~QwC_E2nnkI#l=LkJMa|uE1)RaJLmH4uPQ^>>h&bF
z>b>jC#`?1rjPwWEh~u+T?M9Koqp4mwT9ZbN{N_s7SF(P}{BaV)|5J&0V)RVPDD%Vf
zoi<~=C>`^k=}rPnp^+xv719T_xOO;m!}~h?5^BNs(!czf)Vh!^!L;6RV}m%neZqyt
zEyd$3Gz=#Y^<}KQ@5QvJszDllDyM!bO=>gqrC8>LRM&C*-}yZg_Q{BG|5GMDYm=Na
z6LO46-(gB>nYsp^uJgfbK7p=S*NT8jyPl>A^--}nnB4-s+El&t;iuW8M$=9*7l(n`
zf<G4>N>=Q^kl}Bib<X%fdy#Osth##w5zwCM$1S@g^aod<BbfSWtm&er&RQ8*BBryh
z*=)>YmUp5k^IX<<hZiG>F8v5<Dhx2h3S&uFi8|`Ou0NwkCSOg;+k6e8FH7LtAgP(E
zPQ%|{qRJ-E11?S9P~TlgYin4NA5J%L@toDCXZ~Ox5Kf$ZklqT@eP%ffe9gi`;_%6d
z{a&=>3`E2T(Pv&NXg&3NvIcgAUW8)VV8X!4ffCN8>yJT%)2l1Op~NjBNxrq5tGSW3
zdCn%q6DUdN<Q`^>?B!mHniJM-?fT6pBcN}jLu=Tkv~+oxcd@{(;imS%=+X^3qrbV2
zevh|RNwhtie4$gE@0{NEDeJtxh>nO%hXrfeJ+^d3L91|K$X|}QKs*zvtJJA9y^H&K
z=e@Z`3OO(~gZtgw%9_jNb#0RwL25jLMg%{Vp&GK)owexl{b-!;Vvpek+mwBws&Ar~
zgYn%Z=s(a-6;p|jZ!ap;vdf?HwQ<EzWMPw*4bJ=*OU_85)q{$s&4(iIe5N(9d2ecr
zvKZj@Vzu`8(MMA?rqz%I4#PKNc}p(dSMKI;(JZq?=8OEve6>Ika2DZnV8y`-=*XcY
zDwe>V5rMUhFQKJoJeo4UutFZakeimEW2$t`<)k)_se+mxMenYJj=(I5+5L!e&O8D-
zmLN~U@{33n4E95w1plTRymqNdn|@%Xg}2f+;LW8E|GV}N5SSnYp7V@tDQH-&eof}^
zWNnuyYlfz4ev%mX>=Q)MXfEUmjVUe>d+D(?3Q_ow7{rh`!tLn#K~E;hL~n!XQ}k<w
z9sbAz&TM>z$rYej3xfNpcOZz1U2NzC5%B&O2v;P)nPhvb%Axeav0dFeyguz2$N@W!
ztUk|w>v?~Kf<z_i6|L=2&Np=X#A!YVpy=&Jmfmo&mEPE3U^E)8D;c4y3z>JZ={4#L
zd?8nH&NhU8_bYSe+;aHFa$bXRUSp|kQmM}a{s8l;dWfVAJvpR?Pptzll16S85}=!!
zJN2c_w6c|w7s%3DX~-7IQ^*|2=&bmFUrm*&PPZMPT%-}NFB>#reW;qwfls^W>Q<BZ
z<5edmBC*P<A)qL0e@d*AUv;<)V{b`UHj833qrZYvD^e5sc>T@L@dr!44@v=#o+vzF
zExeKyu=Qa%|HBGJZh;HkOTW9;g8m<&NJn>0nLY3z@$NbzFcHyjU49T2uXqIjq(Urh
z;%v75TqsKR%cJ1-q@cqV#T5<dYeHuGvchqg{#?W?UF#T4^Z_@!wNh(2UtS6AlMpyb
zK6C2y+0waN1c_W<%RJw!`Inx+w4QyGpue4?Z%<cMg^o63mNG{y;1qpg5=x&EfmJ*;
zt)%Wub8;U+{+JkeXKHU>u<+<Vnqg5-rs^(`@-{d_>+i*}b}KA3)PxYv<gPenfoTz;
zNFF2>@%n|xY@2&-!#wBz(RG$#P5l8IJ{z!&(cNq#M>o=r?gnY3qyz*+S{$QBHzFY2
z-6EonZUsaHM5P3zr1TF4zI)&A?{)3lu6;Xa*Y7;%dG1^I)YZWCbMF)35!H8TdA3rG
z$u1AyzVUeObHi=Be)9)u9arIi_q4#P8y|1U`gso>8r-wz@fv5Iu;(CJ;`{C8r|i1c
z=e|~}G9d+T-Ly{&+ot${27vL7N7T<}J*>WQmapV3&sY26tMAGeRw>Lfzmc~6k*4RJ
zh&Ndf&f+$wnb`Q96+!-#F}1tuTb5_=D}TZyvweWipN~u4H)V6UQ-8eH7rpbwY4V{A
z8P@il_H+*qqknk;f>U}&@9~bk(d5sdPbl=xn0HV6#FWWz_KIsSvZYUH@O!0qn<|0@
zIco|E`)vY24S~HJ=5((TllvuOC0)V?G5(5k(WL7(01%Q;Nd**!QkRpy!RH&VEfX@n
z7wMlas|zaf-$@QK8iHlHc336Nk3JI>z!njRL(dnqWMF~-9?cSq#$gSzpJjjOKq=KB
zKtA^bgCC)TGlh6bm(e8)ayeZUcH=iV2TJDRsE)~!Xfu+Q&B&rm65M_OGCpsxNr3w=
zhPmkOUtgL63(7RE=Qo(ETSu4}jq9B3p4k6}wxW_HnSi6)Bv2$4DHwZ+!*(1b6B3Ge
z<0xbb)FuB6H#Mkul6}j&U50C}4t*`e_Dn!3ogrq3=N-!JddN@z-$3w#zF+A&Ph|T3
zmbkq`u{ZJ0UCY!zvzfl0^52)ISo$*01bvqmkRIWf!5OM*-4HtyK^m^E+c0*c_#WdC
zJ2T4Qs5IoL{ln4N+f|ckUB#L0tRKhJ8_#%ExSY4Xn!>2x>{w)c%^JZw{17R%9{KoE
z*x{q9tg475iS|x4`I^1HjQPP@;wfG4**$sJTNW)u5_Asv^4hg1xydcU1LdZXGV6GK
z%c~kMnhfS|65r3K>@>u$n19I&g1@iabGyyAQ5ANS!CI<)bemq8x`PSYjDZx<CSeQ<
zB_AbJBmfhU<jq_@kNq}ZocIpBjd)<l+R?tE@M>yJnRsH!r43JZiNKaOV^2iDwzxP}
zGz`svUB<QVvy>;x-rdHkP92|>J&StO7nS*(d1#Z)>bi^&Uuyf0oQ<grHOq4HIZ0NX
z%*<8P)_fmH204EUxI}SAS*0uFu!|ZDPU+wLG@Aow^s_f&-7rgDWe#(sO+|+8=|#Wa
zqXJj|mz~tc7GWiMH;%Kgyx)GlB0QhE`v`$9I=#(*8B~AB!{H8P<~@fzV7nc`Pa<w(
z)zsMK;yKR#%*I__>_NfA7FWsR;j9Ov6@}kg{oc%lQoP+N?49cpIwJ3Ka0{SfEqkf3
z8N1GnBZBZR5ut&)+pKv(FYf)UX#?mc#nE42BuCfRfcourTg|IKzvBTcZ9VTb1W4B4
z$@Y#Gru~<q%PYlNVxTbZU3Yt(4Ffg<<r~}~>MG%P#8M4;=^OBHRfQ+Ub@X_?N-k-z
z27gYGY%iByg@q5Ty-I(X8L+S)yeg*QqhIviodF$^l7A!3P_So04@^T3!%`%~O@Ow~
zBCyO9jNt$^EE*7CwB=L$E&s?En-h_C76ywk?f?MJ&1W7{claah27XJDG!06xq{!Vy
zIL+P({zauzr%(21xX1oCzYR<Ks=r^+@@T4QN7GSr(b0cU+kbJ#=gOa%7tFICzApd1
zvLrx3*bfl1TW8h@1`^fU<^xGOzLI&$W2i%TeE%#20-#K<*>AtQQXdM<N2=cK17&F4
z(K~ag>GnJCjv>s%Rf9nQXs4JP*j$BERN4ZhX>LVuir5J>WtmdCyW<B0ei)qiTuM23
zd6e$|Wtrn(Z&A%6!9!9L7)U07eupeTc+DSwuYksF0xY$Gz&*M<001ej(*wv#E%1!R
zlHN+hB(wQGzO3H|hwPS-Al2(GKQ4q21iYUAM~B5q(gO%!jt>Ba^zKQf<2K>6IPY@$
zB!up!0vKyXQU0x*!0r-N_U450kT^tWPkz<uxL3w^#vr|BP36}2ut{jGAVW#4<u;;E
zgu&wAMzWita+P<}eoEiGcK8(HZP($(N=r>n@iSwwC^xwSrpot#JX6WXs@{h#33llu
zUJ6|-Tq7Cp@vBO|Dk$uQJ5U$}AiPx4Hk=zz0AdhOB#2v&R%q}rA22yDc~wuI3Q4UC
z0K;STOuzl5E1#F$y03kM>hZl#x#{gG)ydML?fi`?&5goKl`+La&Hcr>9z8GkNp1HR
z@Ewg2ngU604D_JoqXxx1pBfddtQ8wla)!go2;P8L%GL;LL~Bii%Rnq(xH;S@4Cnr!
zmlpdcNhU=#ZA@e`6_z15)r!j9n(4|~ypvLUyuNcJzdeO@n7RM5Dk?Sq!Hx}o3%AY8
zIN5UtsoUg?PEmCrRvF`A?&$U>=h)`O@M7C*KeWgI?sLYI{a09MbfZfV0O~bhbO8kX
zO3DGwu+sV$sbxU!uf-UNdM_iebJEq-=kA72(*MevmG_Dfg4b{otSrT@ExN9pB@%%`
zN)xl+EIclC(-#gmlc8Dqre{r0y^bvU_0Cw%vDo{AV6=Vt+u+810K-ZKtn-m!=eS8R
zg-l>`5q__!AJb9~{7j&8*An$1g@|ENt(8er{q#R^N*2uOx;S%ygwzKBjqxY&RI&5M
z1HkD{7#95(m69E%nWtx*|NO6h<1DhVfU`K+JQm<*j49dbbJJa?P<LO30>o*g224(3
zun?VP<_yE5iQ;4+3M)V3X5B{;?5Wz(X_fboijziub|91WLdS2V;+2iSRiKOwWQ)Zi
zQ-|(~t-0{*uTeG5j+#GM%ns6~x0q^=0i~*w!}V-H^p8k`vW6REllgpWc`1gBCp}dL
zlFlf^y+yg=2q^#<xCzAoI`~r{*H*|HmBv@_S|)G|9~|V`RG-l-fbL&j_^O|#q%f1e
z)^Ep_8hFH6QUabadRHt{WT{osdluY6uc=yW7SbluIjvwlEy|w`xm(l;pEro83EW<s
zGnNnUe7pD07vwFH)Xl1r6dTISg8}3^f4R<p6#S~0I>y`*lZ%5Cr0NrTyKb$2E^a<0
zD%X|GaZ_K6s*zY~*%U>F+gv+?^;xh_*NWN?^fC-D6~6yvVQZxJNSbW5wfJG4g@rwX
z`TP}_)QTHD=cO__FG5x&NYc!EOJaaMMxHm=^!b?tGX-+qDdT-CzFG}RxR97~<|)fl
z75v7dU(qG5$3P2z3Eg~{h6TRU5AJ~8VZ}(^;$_I(;Pfn)061G8o(qQK#=rXW8w6+Q
zGa6jicgOR*eW~<}mh@)H`89g6pKPUA&hzl=)2^kMIeS(r65GMX?6Dyu+W+L+@hHjE
zuRgK^JHKw4vrlXJVWO1Gb7Z%GDrN!@n%a>~sT=VS`0@O8ft$*<1*1=<LHgaB-A17w
za>d=7;@fDlU`9}HN&!FtL4pKG!OJGd@;zo$p8QSf_cYS*{;y*PE-60l|7kBieQB3!
z?33{>G_w=M`UU?l<X%U9)<hXd|GA*g2bFaBkzl<A_xrJuq?L}_p5I)f5K?bAE}bE7
zDMS?|we_rOxiM(KAS>}T=!7`4RnW!rB7vHEaKYA(1mT2dx_Vc>lDXWj?rZXU((H$M
zi3M(0P^rwwN{hh#m(QgyBjm)R6sD3)g^h1~)i=}HNA+M^J9C~YA_i-`Z~VM3Q~Tlm
zszdfH|I;w~(Z{Mo-M@U_UA&lslZBwggOvoB@6UzY)YQLo?4O5R>HX8{>q8P<qc^UT
z&wS>7;VV<kSiNjvECO^7r&Z~V{i^)>p?3c6;{e{XV&jz%D{0DubYEDHutH`C<;k_O
z=6a?z4_sKg58%L+*nXCJZ-aHY)VdqMk()ecU(pFew@Eft=LEQ182F2vB9pNgQgbgQ
z9Iqw;vbr8f;Z(JYtYGC(%VO51M_H%tp8zFO3%jlhuaucpR=IXIJwmriOnOtXf!t(-
zOF^>rHAK8#Gv%m{`00{DT88Ycs3!kOGWNJEfW}-+m;mfBRsJ}o(VzzFzB}!WH}_?Y
z;BKZI7C=UE6_vmq-VoEj0x;4-)Ipcgx+jvPE&x>`p;cXj^VUEpI8!i39NVMfX_CzU
z$Cv9A8wja+ZDEjmdu3Bk(j)1hj~C2d{3G^_3As~VapP*5RNSZS<iX&~_H`e{MB5b)
zciS*t{X=zr<zY^BcS5)uHj<_ctp@FTj@=Tt9@I~<MvRZ-$?u1c!Il<}JXl~`Yq`8D
zq?3GPNw3`39_I$f3D{m21$+Kwk(pZ2d&Bmipvc1f8N0`4(GcE`#y(?fCeH4~&ilT_
ze_1r|7XlROwyG?s9Tg1FQFcK}Iw$q7%z;X6PFBpALk8jzQZ<iWl`#7DGpB7nH|nQO
zvaheM=)I8r)x}3C!1dD6A39wN?E?27oMOg;hn}ry@x5EAqB&5lJKL*(`lT`jDYqA%
zLwyV&zF$N+ZdEw4;A^?Tzd)Kr5)*l=8iBT^eNLOL88x^-T*0|Xfq?s^KbhZ>>%I&T
zu@7*oyHG>1jsPG`C450_A-@y>Bg^4E+q8JWllvokH3B1F-qebwU*JvKr9|{LB&*<N
zb?~ev_*@&j(IiAOLuq^N&X6T34G(f0AB7}#iB_E}kO>uw%)W)-*i|(oBX@OW<PfJt
z5`Na-@sfW`$<4yfJ55<bSaA<8NLkVfmgQb&|Kz%FP5?;C`v%4;Z~OlF<c@6DSy~#7
z;iXu<IlYt7cK9E4{lSs+{vn@DYj<Yij!wpmcKx64{*4g<O^kQ?UkA)Hy9W<WoE$aF
zj+mS?$M0peu4f9R<(yiL_=b=0_h-!*LTJDM4dkSa+66-Dk2eDN8WG!#OCkb08q%^&
zzQ?c~X9<n3*^znL_&g0pTS}OdgwEtN-!xC}k4)#{eU(c6P9YwT;>2JP{gJ0W?Ly51
z!ij5&knq`tv01qI9bbh~(jC$7_|ms&SKw`K)55E<ZjerYXR6iPspzLc(nT5q(%*Z(
zg3BGLm>6j$9E@476MB#hV-fe>@i)5t&Bj!xb9YH>R|k8_cUtI38KUCN?%>aAH3Llc
zMil(oP2zUyQbG;xv9i~@aHFJ|G>2H!(*Qt}TZ7e?gr}tzUf~-ka5f^Qd+}Ffiz~oW
z;!R!-m|Dw?`b)|>3UmR)@o>BFeR5%kr7wU8z2rDJ4C#W}3$)l>YH#>JzxPwxQ120x
z(xi(sBIUECSp@}yi1a%N83q{{OL_Z4y+E=68?-tn#Lc+Xx!4WP?PbNK1LxB9FjM#K
zb7AIGUpA~k-{0K~`-Vg$h1PrQJs&SkkGHbr5%=a>$87vm66=<hq#gnfaIaIRZfug&
zz)3I@4*>}LRDcA7l5{8C7~({ilRDsr0?yfcG;80{V>7827Tzt|lhrVhrRZC(DVW<5
z0<TyqebYOv1}&Akzp<!|*06{#f2K=S(;jdVzjbWIz)Jm=bCQlt{G5$OV?-$J`)LWW
zJA2aX!0rjTciO^1*BvA5$#c5MOB|OTv%2R|X*IQJS7Kska!f=E7_{=H6Hj%{fFC@s
zxh1fIiQYwCDt|T~D*7`|>2FY)&P+-83Vd5ZRcUfluH75*jEKF~ba}?(wo}$dJY`Wz
zBv2|J0l&K5_~jO$7qs}L^?ui}ige=s2)o?W-xAE*{_p$b&0rVZVN*dvaYd^gUB>@R
zyVoD_KS0x*%_j`lknBGiSrOtzO7kowyD8i!zz|+|u3ZDxjb;Eek$|&e1GNsg?x-QD
zmH3~DGqV5YWfNU6`nQ=VMI!F!7UDmO_YcmB5R8;Yod64m;M#H!GD;G|O%3!S%WX*o
zOC_gCrHz&z*U5JpZg;$|6x7+2^emQkuaU{`a<G2pkpIGA0^%SOBXYhYH)Mfu0WWX*
zoA0H_%uR9LC+-)`!4)G+>Vf5`)-8Z9q^UO<^i#Y_A>{ZX{rxCOXQIOb3^iW6d>;H+
zdH}(@iO~3IhLHwi0TQ4--vyvp&q9`4kp!6W#{QsW9JHv`0oEa@2gQz+D*F^3)<LoY
z=X>@H%YS<BFjw*Y?S|`Efe%L8ZsMk-C`k{xjxfyL_7{*Ia#hnjCYBwcn{>E|j_?Xf
zg+g*};!pMD8#sIGG`^E*nwLUr=;=W`rx@@l797&iSt!7EH?Z`qPRm0=`aA@oLyn!e
zpygFBVylqhx(N0ppO^->pn|#d77M306aPRQ^uKNY-0>4OX5jyBZqi-ib^426w8Df}
zO}GW_vr}0n^z$uEsf4$99iPF{f~rwZ`%PS&G0y<s&OHBEmevt35f$~c%9?x187oIw
zB}Zyw4H6{1D>xEsN@Zvdn^&Ls#(naGkw=E6VF@+F+9Qmct~h?6zIGamWm^ZhP{NFx
z7aY`*Ak<D|U9S4Hl%wT^@I1IjCb4(xIKwk~T8B~q^s@uB3?iz9%83vfT8g?k0Kk}a
zc8XP9Ect50%oXyCtBT@rjuNi+;0n9CBZo^7+_FR*tMq!+QP`5(#QRD)rdoW@l>WqH
zgyHA<x@4zKILk)S16SE))FnHrFElBQC-%gE2WG5AI|L3tt_-b^)^g0?vH%z1Dp^0e
zxx6RiOIE)oAxK)?9SQ>osYrl~poCgym88X@Hib3oGLps3sb?(&wy59xQXcD*c#rr9
z9D)M`5$RnA>7ij6cU*4hEe{r?GTtfkiZeU@`A{LWdgyMAdhV|$Ax^A53a?L7eUHxy
zLuokgfKSN4w}oz<ERFi%0>j*z{CPKdOpI*7ug2TNIFFi9LYYiI4|x|o(iORRmVVqz
zH0I7LSkM^z!NP)I+$@5)P5#HA7ZP6d-P5a+yr4EuWW@_{vFdddz9{<cz7Ohtcl(>c
z-4J0=NC=_4A#&A#eoo_&kIv9<oMZN|tgyLzVTyPylXvB_2!5l+s@n6X@wZEbh2p9o
zgtL7Nw<Zs-wtb_O8qrkblx6&c$IZR1gNxZtdD=3qi(2`Cd>PSefeJ}b*J0KT<Ye#$
z*-VD?%_V7Dd0o_)2v#C-q+0h=i<mq=R&sKeQ#;2lKEwc|F0bGka0+H=l2LBe5f<|i
zjMij~rI~Ws)`c-Ea;9fG&OWn&J-5NWK(|jpL026QJxdSQYvKDNI8utNSZ*Q!EZgmz
z6*q*->Dj7Q!Rp_{Hm;S273;@TZWdMDmrN59Ajm+Y9{k`$SO4M>BWlg}0GuuNI&u`C
zhhQy2v8xpCQ%J#Z67WaItpTY@TXba#;4Lg5o>$Rgc1~6wlMrBq|D|{Np7|`nbK*4p
z5R}0mrkYS!T1R*D*GeY!d*-Ewy?i!4i?Ag{Qrq7B!-9&u<8N6rQ5p(|cFi~^S&=L^
z<-6se9O7pOl&fUrGCY6`(c-s{c0FD?8<RC=GtXhJ4PpJKwPK6R^ZxzTkW5mW`-W7I
z(R<{|WWfjcpTm)<VeHeqD*t;SH-Z@uQ}WWk7Z-e~3>Nsr1)8tY&VGS^0c~|L8VFi6
zrb3h6pX`Hur=he(Sg78Yr*4)Rourj1cDESW-$m>iUz`t1FJM=`hiTml5A%#0`w_l9
z8-1n4v%nBhwBbprDl#5#6`a*ddd!(MhuE;LRG(lFR{&SMyHOaZys0W)7uO=%FAzw3
zw;VD`RoL&nrnU+^3dpCd!An-UAlNUs*A>jyMJb?DfXg%#BM;j8X{^N<BDO3HiB|#r
zv#7MP82%Bv)_OzfLqo~GIWCepC#cV35l3M?QJsJG`24ooO%nm!Ek7L|e)DoY!-PVo
zs0uS=MK#A1*vJI{SyCsFt<;TV>kRuQt*j^eeCS*rw$MjSR(zPMxQ4770DuaWT=H|Q
zC$gMgL?9lq4wpt9x`8=CTE`CF;kKkRQVXfE5wE*NsfK-ZyFEgYAsgLWx2i9H4PNe2
zM1`$7xb_r#9KNu)5XnvL+RY&=mA>F*3vqh3qxkGSUEc9?_0hE;QQd%B-A~BYFr$0U
zvmZ1Cr=ZyIPx5197W)s(TTzFBj|C<kH>f<Wl;dP%<J8yWj0(v~PJ83fXCi;W!2J7F
z1a>Bj1flqF`?07G^S^@bjG3tIJI|J?k}D!!tP1DFmxPpk4SZcqw*1C|=kj}wc#i1R
z;eUe?^@!)a{3;Aa3r4?f9Fy`p_`~@}-8FlBT}Q0SZ94B9F#Zj#jykyUXumsX)KTxC
zP6DvW=Ir=c72vRVI#qiw=H^U&vWc`oMR({r!1#-g6J^Al<OALwLO|$%;S-a14FNDD
zqz_pYx%Op~7WK3scE|%$Y0J%dhFJF{O$uzoT~K10IviK^D=9Gvd*dXVfD2vStsokz
zQ#9?<xia^kx4nMmDmHw~==j^Ic3`n-53v<1^5&b78O3vrhY|}S9K>a=tHM_7HETtq
zM#X|k<S*n`eG6TI$6JWSNR><BdHp>Bf#f|Y6#_qMA2hm$_&=2d3GyBBI1ck)Xd977
zlv8z@!vzLU=3Qtlc>vI(n*`!l5DM}~LjY}`$S*Q-9JcKHo~$rGTe{9x)`vgz%BZXn
z4+}flQcb2reUF@E(HM2{C+eQ`E|j>tvbnln-?15;S<0lp@i4dT{-r3Etd7pES6PUK
zr;Il;uxt5dUB`}z(5=4bu5+p%^U3`0tA#4_Q;S0v=)naw5I&&p0$RUF4&_S6kTd2`
z5}CxWkmW2Bpg(_y)Hew%hrT@`IJNzt-w?GIex1w6{Kj6M9&(Vkm|4*!sA$$m4+S6@
z+Z<ZsG?Bj)R9Xc)R}cTAeVUQM|7_)`7t77Z8#Y)s(z6D84W}mA_}&{NCc#rS2gLF|
z%%Jqjtz5BeF7mom0PID$iDH#p0?=Vc%o(vJobZ3g?*0Uo{ZcSumKAc?jDG%W3svu~
zvnvqb8(*jBlN<R}BP*_qT!gkNzCcrQK$$}GTlV!<wZEt~Bmar7GPSm3ybTLqV4lf|
zP{95NW}O*f3Q}G2lw1EK8~X*xgejvO6A`Hjz!b7JxgX%FzOw(BUvmjiaa$<<9PZao
z5rgmqZ2WqJ@P1K^=ka%APaVVWLFUUEHr3(*7mhw)(klz-Dg*GW076|s8X&>#RpL@A
zmEdF*&=Up~NwGr8X9)T|T2d*7P3c|P?lbyty1{(z7FIoSrdHN+);71Sdmh}jy(O3L
zWq+$ZeyyUV%Ru^@lJ43RVNg7kHfCm4<w$}rC7d*7(yM>bYYI9jY^_>LS2Dr{bpc@5
z)^*6kj_>D}6R31y^hb?D-_qD;U2v+~sPQZ!hY;GXuR&5pTa8Ob^~0&aXf#D+Ss%Zg
zW97M}-x6kiV7cf9)2LBxt*^CT1;4(U1kA0+Q@FV9@RvfRmN0v0?>Kp>N}lX@h(N2b
z1!rQjbi+_p5ifbG9hdAZe8o|;qZ2>=^{t3(cQ;_L-7h{pbn!}JhQv9_nL+A0ru3+o
zgS-FTeRR9@RZ3HJJ!PlLYzt+^*n%9D#Y5<LRO-mCGJ%x)(`f7U?#qFwss=u#tZCO5
zJ%1Q_KL<&DWaBsv$g}_a-IOeTa||}{>KqH6X#C0TRlI(shE<>)vUuEd8B>|284G-9
z7ioQ_jt>2v5Zd=Ke-!PQqf@L&6c2y?>1!H&e3^eEEOYeXXT>z$4nDyx>*yZGxLvW4
z#6;bz?;ZH@(l^|Io@!T|!pdvPBR3-g-Ng(7x&Zg#SO+cX=E;(0_+o*e&yiTbm0EO6
zC7xE}jU53Ewnz*gQGF0@wIZz&ICEIa#mBk{-9RERjPW!`9w%LbAW1nu3u*6HSHBb`
z9@e4R(zFf}JfW8SK26Uy;q|r}#k^+A`7du2)3{#T#M41gS_;W@ZnC;~zrj*WudfA{
zWrA|NsvBR(u?_TF5<oKFLX3Zv2*5eRX1{34299`R9QalvyUMn>u{OMKP&)cwrGsVf
zPPyrawo}fP^P_KKT;SpuXQtTlPI^ZDh<J<{4x7I}KoQg_TO&y*sMR<$w<Q&o@WC@{
z`cSP{@FaU17iwt)pHFsIp_^;0N1h8WP)<unlfK+FsEySBO0F#{I=>^U=>3r$<c&_-
zF(owI6#S?x<agG0)~)kVD)p|Hh=IrRdhoHc`bLyQ&EFST=0^2O!avDl$#K<OCCzkY
zC+{&g<6iv?J$9+5UWb>HMho=5#&+BHGMMO}R<*}b&G%`H{WVi~+@zMb6%$H9xpQNy
zy_B;l$p-3jsBl3a!O4-Ueg7-h^SHU4;hEdq?jx6FlEB_4;T*l=k*aciK{6`FgHREE
z6sx`PaeX8@f{dS8l_lV0os*_c_83~lj!XC|h1SeDMp=d2G&eU=vCc%XTghiyoBXPL
zN{ZjrI;#(==#Y}V<K2ZNmJwij7m`@OBOL?W7$MX~^HTkZjXlgh5V8_GVFk5Wa=K5Z
zHt-=SdpI}Uet)oo<-#)_FI&s1VtInVNcAWQEC7uMe39i7^jT(1yckPEeeDu3pi|(!
zYb7VURI9XbnSIy)L0d2L*ouR)kH7zY+s3h?Mh5jO4h$*?Ra5c~i*AyH88`|gx&%-M
z!_h?f0Vl56>Gj4;G!ViP@Qw7@t4n{;(*2^G6Cl1E|DDR9s5FPvdA>n?b0evoHNnMC
zFrF98qG3)(iuJ}}mkNw68{x8ku}OND!NqtxW*aZbEbECaDLbtp{+vo)scH#}Cwwoh
za@OjYLTxSB?!0)itO?Uvw3yF0P#4W<qU*gCFtHUsQcISsIP(W$lFD{R=0}Q>A$g3$
zbn>woaL8@!)6N%PYzavD4KwHuX?by8Gz%n#m@*bb7%O{vE^l<xB`$ra#sh5bqJ}(r
zKI+8<cNB~_rYyA#)i{1I)@CmyDp+KxZ>Gov%2y0j?8DEfGQuiNh@u$F<YF38F$6(w
zmdsvF6<Qg^3?9)fvpfbEE8Ro!yri_lXJMQ&-9j*)C$w&@;XdCWGu@k|YD&?^$!cYM
z)=EWg@Z`oxZ6l3v8S?jV{}%Glfk^yJvC~nK!26GD##VEanIp5_i&I)W4V)lty6fKr
zl4E4mCm8?~K)e8U8OY5ZrVVzm^)8c-wk41*Y56Eolji5N;ggxeoqw_D2TWf+UmUBW
zRl>Ti0UMNTSU{2u+(8DVt(0*Dm$prGL$j;zNi+(4kC*$=yT$319`s6aJp4a|R%jXP
z$fSun)%`j|+rXZ{V%{eLU-}CcOi10JiRhQYs(u$tV8+j5HTHUF5a}a<b&f#de-Gtg
zfb(f5OM=rI*9FAmz7_Sc0x~d01pscw(VAJuU;+AhecEW6&Nm<|d!AyRR@M4~krrHu
zgAYbV<#H&I@>R7X?*)j#<%dIJGPy&+ivQ<u?!TX;OhW}dajq&e-DaMa8l%!xV(!tQ
zRum+1qudrQMU+iR9X2VltbZf0lnL4F8M>eB(8Jl5)OS;qjC<}s&3cm~qr3yLD~(qR
z|H|m?b58AE;r~JA#(B@pZtAEx&yoDy`@ui!-ZTVxZo)m!YmMJidB}S(+~iQ=s;KV)
zsu;PL{<fS})}J0#BLO=KhCMJ~E$Gtkfp6+MCu4S!T-0Z=KyhE_qUY)4vi9k~)pC)a
z2$Vp+7AYI=b;f}ckBFn3Wol0(vXR`05G+J~-(I0BF=;api!zY7Y|la+=1PO`=qR4$
z(4=x)0`@il+$U4o#oGENvHgQq`KQyaQZ^VXDgPz!TdE6!!>W{WedO8EyF?x<)u{w`
zctmb&6i|Nwcq95)$>J=DD-V`fS{yd0rp#3akS3)Y96~*ZWYN~~Rb^>f$qjqR4~N_s
zj<x9*zOBd$Yd@LG?OzvpUY16SOTt_dS3j-c0i@r2vDhFQEHtS>hD*_yLSZS#)mW(h
zi;{lA%lh=HCo4_ogE0NbjI1auW<Dtpnm0iL>SF=`cwC@F^VBRBAbVUNl>v}ok^MQy
z79qXl7na#ii=XkWXSdtQ4am?-Hj!hZNE`XwRfNqXM+|g$M{4>%)f$c@M|&n0Gf;|5
zswhv5edM@d<eKZwD#ajH*z8lYX<uZ>DrotZ?MZLPS(dEjLJ{`WOLefo0sl*PCD`3%
zT}ltGlyEB12QPuA($9<2w${=?IjSUIisKZ!CQ`dBH93l6X`4>b95S!&OQ#1NkvEL1
z)dThA>(Hz5S3|g0sp@qno17gl6w1GM8A>PPV`L4r<Ax+M@Bbjn(#vf6ue+NsHHhT(
z6F{HsWCC}q^c=_%f(;|{^rFeRMeH#$4j9^;o>(@6v^qArwJCEN0<Id<kDA)Aoyvyt
zpf_|+vUOykbZ}#~$~9`4OMC}EtlM6kqyQ1QDRPNRDhfzaSH%Li$ll#cfZ3Ow1ZHAM
zb5SpPT~`^eBo&b<RQrUR%Y=R(d1}lG^y48V2FDx3p0WNgB|N&aJ_@@|UIya@Rkg**
zih?xT04voS;a^0^$?)4Uc&Qr(BX>teLiGI<W-q?HAzgpNxRL6fP2EK+=^9eU^B(%{
zuCf9y$@UW3?f@ZWiJun+@b?r8Nz}~%SxfvNVO_}OA@oAGm)@Z9qoceQh*Q>hg8Z;h
zA?$G#4(yHK=o19PaIqye0D+v9Epl*BnxW~z1ojJSiSdgfvKNt022Yx%emU^(|KmER
z;8BJ%O&=L@!8uTwW8IUr7Ir$xc|2yNhV1-vITdwr7Gv*eirr^xHQ8Hcf^;^?RnBLU
zf3Vk8MbAI)6;$YBcj=GaLx5!#K#Pm<Sq7xM57_GyDhZFIy?dmaW9o{9CMbj1x~46<
zrt2!hX}hs9z@54Qyxx1MSM<@75hkx#*D{E?Tc;4m6G<6$a`A6NeW~gdjRh3$is)H|
zSM;f+oOu;4&!GEExIUq{&MatAc4j##*6fe|>7oI-(hK}9K5z@=m(59EVNjSrC;ZPi
z55k$&%qB}_$heKBGRAPc7KUx;*nNEWgjq+*cA9+8c+v*r@>o>SwyxOnsqLAW559Z#
zGz|$#n&(df!eHKJt>J+Q7)LAshw5KrT@7IdqDX~j;+nZ_AT?9r9IT22bh7cORPJOl
zt8!Ky6>D_TA4!w&NUXv)XgOseEjd_$LkN8Xe1aoJi~yMq%*QN4$|yI7<!RQ`6LLuQ
zA9ohTg5k>JC)LYu`0>m6$;&k<`ReREc1}+&KX69KQ|@K5Fji8N?^XnzV9|XQd7~!5
z=p^(57}^=)0%##EhxG0yQF#?a<|Iv|@(q>@4u0W@&_!^ZK5aVUls%(F4zk+?KBfh+
z)JFqS2E>xw<9bN!9&44PA_;)Qo-y_RZ3Cp<O>t(w@`!&mxk^vZFI?l&SD%#S@WT2I
zZ0;N5-1~k5eg=o9{mDnvA5%xnR;{|~>)Ot#RsN)W90?k0dCeW{*eENjwH>P37(QV+
z&h~?8t!2E$I@5gh_Shiv!rAT4VN&R+Q4%(s4?x6TX~z;cL0JG1OB^qCf~9N3vySG$
z<`^XZF~Le%{K5>RPZuV81k>L>Ihk<GCxQ;|K+N7}I3~X{+?Xi;LOnSppSyu}QYrz*
ztFb+JbxLVjtjsm>Ik#gy`RlG4#mPj=ZRgxAs!25~Vsm{|(@I<0=16DqLZ`y7yiJ-I
z2n?m?O>)0C&&q~d7cQ&eo^lP_$obY!b2cuntTP!~FbV&A(T{xp1m=?Oz@*eL&1OSr
zUoy(t5&!MpxuK8pX-<Ph=Ggi{i4I4sQG%-g*}B(g_<iQJ5lV&FqIobyvLd}x9c=P;
zu1vYecJIi`XA+X>v|32I<?(*}d{T!V&n5M&Bph7%3&4moVgQ>waXwHpRp-l;giNHp
zzR^~8TKuPEl`Xm^k1cEa<z9uY0>!OrB9h2?1I20>*s@CJnzd=E50U!lBS3F^KZ)3q
zw7shyu1`jHlGqnyvlmgeN5*vZBKrWys4%6yKvhkdin+2^XLXfDl9cO{_64Z{D5E61
znkq>i(3A{#T>ysW0_owfsa{xl?n1K}wD~Tc2gJ>9n{sOZ-nVv#-c0%3(8qTUA4@!?
zoU9%BLJJQ)cVaw~jmKE`&bIDM=<yQ7rffdTFd8>HJ{es%_qeZz`8uCJCsh>AI1!fg
z@Es1M9Xq?^^kCU3yKyqRF^gw|8Te;4GW&Ar_NPnSv&t|pzPQeFaozKedT88d>z$h<
zyA$_q(X?N?RvvWuI6EgCm7w#VCdhbDq^OnTk1u|H{VOJ;<$`upnYwUC^Pm&GCrtA4
z>M7N)#lXL>^LuH2TvKDp7z4rTvY+2mS>o@$bdhsdkNxsR-<qHUH9oG*zePzP%a%;}
zxPObR;<)*-RSQ#ky1~=?)hjD7mxsm`u_Z6cU{Xe6>$s%Y>sDMg)D{!pH-Z@a2etM9
z>=Y>iyPUF)O?gNZ!$%Z-6qu;CdQqq$MVx%(_Kk8tJ1ZU<)eT*y2I>J2tCR*o1V?O7
zUQZW4k4##RTGb=d?k-!lZ{Pkgx#i!j$w{e@Z2~)Pb#Glg>iu{$gdDS`#@pRzfV}dt
zFBNhF89(PGKA1r3d&u@YvA_e;72l*2uf*AyB)4on@VTVUhuh0(Mf?6Q7st%bdnL`8
zMpl%cDi<Uj)nkEB(a4<GSN7N}30zRj*XZv71Ck#uapN1ic`2PY*$#JXb9NLNcZAKE
zdhAXlfSRYHbegXv8Jow+V2(PTf$k!nC#6G@l_$=Lo;eEMPE)E&%K1aD9hVy{7JcRh
zb)A($7sdjLoWq?I5DQ%I`&bR)+cj!eS{Mpq0CM+R`-38Iqcd1)z|m}wAj8WMKXxgm
zz0hD7+9T0WYOhB6Rbpy=_)(Ba=G0Sp@9MOP$!T;2Qt#?j>DIPOOg6XqrY!krgWKok
ze2qoLw+zd635EKy6$@BOo!s=l2w0ZSw^gdWNB4Y<U1A!@Yi!DZE&tk@UeUUI-}0wV
zCb*6jSU*$m=(MrLG`o};GTqcB=8#OTPM!6;G_x}YNZf(+D1d$_zY<pmmVF@yffoBG
z+Fe1t^|!sF6PeiooEU=giH5=E?fXklE5~yYh~v2@Ud*rYG2^9Y%vim?B$tPj$Vahe
zv{-;54swM>i{Oa2PvQaA(yQpl&!I~~Wt&C_Ut_3Mt^eDw|B}N0%T6;#z*F)*W-=(?
zy?fwf<r%=U-bwyMNgpf_7C*+C^forL{y_sR^y`lYEqZu4PQ=Ok8#*DkGa<Ld7C@iY
zQ<Jht9@jsL&7ubY3KCEZ0PwT?u^N4*(oh(~;#NS*Yf9x#aHLbbC_hj>zUz5|<Tau+
z<c-gN9duWlw>;AV96pYCwpFA!`?0>IJSCfv9%k|Pj2*5h85;``kqvI=VrTn6zcAiL
z-W--%*#AlUy;r+_d>g0eBQEGQ*Xh9<1f>j~Yu=&=BDFy7Z9ToS-|no-Pep@P1HJB2
zRXwX44uEZTOEJGIw)z}W`#CzdL}AnU-zeuzxg&qM=ojiP;oHZoG55oT;##f}vg{_`
z>(CJXYGsZ1DI_8oiEOZm2DkS6HaajHEfo%h^@2thmXiI&T3=lgOtQ7y;MWswiV){U
zCP7mS1efpJF6PXaTzf``1zmzpx?Tw?>JQ<|Tqv^n6VcuP5JtMgnuK@VD5pr;Hib5{
z05@HwmJYK}68gW%;K#WlpbLtJYZ)*su@?sYeF<EH2xSugU4lMC!9*t4AS)nk$B&uj
z3um=rA&Gg7bis(yl8^vcjG00nx1C7rV0rL!$0@rTB_;yx3Vu5Qpkm`4hJAkKblqwT
zAFKX5@=s0ct$eMOYHqdIG~c>w-TyW8rE#L^o%dH;KLbIJW&kV2rfaRgJ%-qOyK7YI
zSFt^71gL?kDyc^=u}5w>1CTv*^5{kTm71o<O`geQF-ig?9fs8xY!|H~e|iS_%Q1}k
zCx5zb4kEE6BN#dWlCG`ChJHXolgM8nB+5?tHy;oS9vIXA`+DBq<H~*~xWl#0Of>Jn
zh(XNyo0Ssh^-I%dYTc&F&x<l+vbWFwi!6D_q(aj0!}KM`YlOpSTGf@ppKs#_%Ow@9
zf55nB0bM5t70F!_=SmZ@bYMZe;-AF6BiHqC5C(`rk`qfHoDd91z#yx+4;;lm-p}`_
zwVqw;zfS!_K`@pg9&#$)_VL%WuKqyeyux9Q9b)Xn8}MKw?YPdX^@p2n02U071^Lga
zI4Ajq>@DIHd^{2asvBmWix|-7@J*u7DfcC2&fPvpPQQg)${WhuB|J4q*2I-f<~klu
z*Z9D=0r+O&A;2A?2iP%tV2SCn2!V3AOHWyHqWa=Rd}(KAze!BFF~eiE=6%tfQY(p&
zCzg#&ZpHGZ`M5S4^!Ze-z0!lB#{K$y_s(xk4>s$Zlzh2jrA+s){%i>nc%meh^|#wQ
z(1EC5KT&9Ut$u9g)z2r~f<q8TR$`WCQIMye=mP*97rC(+jPOMXfL))xtf?{aq<S)a
zyST4M1Yz*XN3|D_#p(gkvVsJ*1nyV`S%4(Yr9Czov<iVNxBxWzqp1Wic*C^Q0oK)Z
zHukd)M(=S|_x)?S`_7YA|KeS?HxHifJ~@6^Hu(5Q@UBvep+wc<p0w6f^P>8GZ1LTd
zMeW=@wnBdZkQxaEfS{j=Sexg6Vi4BS_8sDE+VY8AFd6xpu1R-w%w*CfRKL{31xy2e
z=G`k$?$(EmjR(qshZSf+dJtB&2zzGrC(^OYiZaxc|DEsfWrMfy*GFZ@p8TZQ`hoqH
zcS5QfL2LvXk<BVkE(zzl@#AtJTgWMmAy?q<7uWYco_Ix8{_MPApXk$8rMP)yIuHhx
zo@3<uf><{H95&1Y#8D@K`rH8eY<>_EBI&|>Ojnshe1H5KE{Y@4$8DBN)vW3t>iS=I
z!ta}i%E%njMnkJ)xra3n&&L+~rf8i6vD%RC#|y<W`X~It1gl?9EuvY73rrEh*&~u%
zrAW`abT=>~KMCcrUO=k%L+twhy57Rf;X*p-ugL1WssM`-*>(B{B}R)|g;x8qkAw;#
zPv58M*^50<XwJ#&fNNHYo^m#7!)j;NhH6t1Vnn!Y`NFB)Z-$)42#)spurW>j5xFY{
z8?J;nehE^MmFk$O>~7o`U)_{#X!rq0l8(Dik^tqLv6-&hm~ls9z)I?k1<mk|D#mdP
zH*e7JQYPx#@q|H=(YdmT0W;YIf%tZC3B<%ZRT2bvivFZov>jqs7UL0%j-$bOs1f?;
zQ3Nan0O)YX>61W#!VK*DYRve@K45`vaV+8b>{f+@?zjR-20bdbGqO@_84zH+^76(i
zozer|zuR389toHU2?8yvv^n6DcX-w{3HLe6R7pTt+hqU%j6!+J>DAIN|5#vPOJY}+
zbc^#^>K+x(kb2c-wb@w+sY`&y46db1;0h$D2yg_lvf-YSkfcx!wlPmFtWMGU$6q|J
z+V(Yqsk;R);FR)q^}@K4&F`Q4&*e9UM*2*eKF}6}=+x!i^5qz=Cv&>ix?qgWobJTz
zR(w&ii8cPra2i&RxqdO?d%hxPt?o7Ysr*-I(3ibyxlE_S3Iag&`GhF>wADnDnq8S&
z?r9i{)t2h6Y3goDVUoC*%QSrdPNrTpSwHy4Em*GULJE#=XNIeveIT_UatXeLQVXUE
z3Az;Zc1xyP4~6`Idik++_g3<C)(dx9Ry#;rgv4adAuGTo(%kL~nyFGAd{*IbM+2kJ
zZNhHpMmcX3gCI1|t$z*VUFey}s)aI6$jPLrIH@JdztdLYZfyO7usXftJm&TyG}CF`
zrXw*>xwwPv9=XHI17Sg?{DQ~y=RN}av3-R4{pD%drmTYZL1KB1MisUIg7<QOUGKA-
zhXxt|H@jj%V*-453eI5y0A%m=rSmEI(f21ZBpV0wv+?H5yB5$KEE|2MqE#O!&&HB7
z0?=|g-mPV(*g1e)3w9Uj(w%|@P*PRhN8LKo3SYiyr1{O<3u_*DJbU+J{@Iw_wB0@N
zI}0!KGiSEFBv10t%o*_$l2DuY<%sUHOXW{})yx180PpZu8>c;<7+&w9V_^3&%a*!D
zmmp(S#S@qB?1t9y=oJ>p!i<+gczEY1iUC$yP^k{JdUz~OQXE521S7~B9|%0q<Pf(w
z6MT-35oRn{6~?`>zA@{;Y>;Ew@Mop2#>?>q)N@zv%0*3%ecUP?ckacdUXQnQ&%K(q
z;`$566mvH&Rz{x-&%Bc<wg#6rR9MN6x?`gJ_sVQi67=pbYg#`)e)((H$sz@v|F#6k
z5rJuSD8ICC{XD3uu^;!AVR-E)3S3o}5+a)3*tvu)ASC#c(f3EbD5yO6;>-k7NGLL0
za-JE|nuaM1u=a-;fP-P?(9=qKd5{%4Nt;%cQnHtM$UGB>w|b^WZ|I<F$fC#pl{Ex4
zD9gaT7j(yv@lL{$;Zm~eNYr>|i|TH#xv!9R@qC+v>}<o-m*z3_W2VRhg{72P3u|23
z$4_>Wd5ZEre8TptIR!F3BeYL+=nmqOl+cLHW}@`s4O*}r&Pi_umMBrmWqjY`+$tNl
zBQT|2;T+Rj%YE}d4K3TYmv7N2-rfA=q?lqM5`^K}1E7-!44M;@M?Fc!0`YkJX=C66
zBYv_q<(W^cL}un&sHDYh8~UFv(_d&dqi?65UaQ-O<Wo!h3}~<t7HeuG?*KAKoI!e5
zo_rXpvtK-sH+0QOm~d+V)rNrx0ifrqns3gOH3KcZ&=Z`h`MYSyRyETH;dft-uEa6}
z0tU*JJQqCF?!@8|QN7{92S>)=E$=7agpjRkAYEvsG1OW3kkOHKg*3ge)^H^wX90tl
zgX1EpBizzf`_*XZp$G8bDDuT@xtej2$ebBE&28DtYQ+m_l|Bj)G*L~@tMl($mGo%g
z-KiHYT7hP)HIChkPZ8u@kIX934J>LFeonrXqnon$iBDS7LkY6(2Z0jkM+}1Et&me^
zpKDJ1T0FwR!Mc*oUyi0pr|BDxy4S0gTd4YFM38JmwOx~Q_g$4PafNPSk1iA8tqEp<
z3~r-r<|%Pqx&y0zajkvvoh_}+?sQ1|@vX1*o|1#U%oPHCq8Jp|D3^C=w+8*(ozpVD
zP!<9a@V2Bg?EJ=4M%!Q_tR496lzO0P)4H{eW!&Hvb?YHrOog!z*(p%muFqmJ<auIy
zKwWNAIW~`HJf|!-)UZ9;XPn$ylitJ~*KTW9ocXj7kgf*bD`uDmL+_#{?qX>XWFsE6
z8O9opGs#v)!f3z5DUHMoYcbM~cU;cn8pXbD@A9L$d8>)~n8z4y7?OCzWuyS`NSAoX
zkd6<A5~&w`$76j810YO2+#6r59Q={4D^N109+k-D?{wlna}^M6UCXY$y?bl<jX;=4
zpW&=y!A>!s287!K%%E;TEDkcY{sL9J;z9x-*TW&*#kWHaNIDejQFG12-WMKQhiaau
z;9CeuqVIkg7@{OWW<hJXDUl^e7e6u{2x!ZeN5cc~gCN{36wrT+csFIi{v122+QFo+
zYwtqpbZqI0R`@<Zr5^}X_DIH$N~I9$NVZ>9RY;9LqR|d$IPncLu6^%*086F63AJzX
zfWq4gR)^PP=IUfq2BoX^o8Okpne}}lUI2u<l<LCtCsSJA*&Q`ZPInjiY8_+FNk6}H
zJ3HyV?vm7_hUuj;M+^mQh?~+TD{YC0Fp$=^jZB!fN^g18BQ0BTI6niASr%`3Lzu~?
zxH0{NS=_s&t!g*@c!9^57KTxJ$w41RBc(naI>wR5OeE2{m4-}dIeO+UckqAaI(YWB
z2v+fKuY!Vv-<E!$b-6zur{2x?m1mN*BkRDsifuoFU8<nCTbr~&?HkoIfgy92T7tA&
zh~3ex$<1K9nR+~!`Yfn>oNNRDHFHu1eoymbs=$`HE<|sfjgXtFcy0&ozXH;H6%CmU
z@dX(f*wt?e=R5I!mA4R7d~)UDg#=F6nIvePSOSAUlJU};GYBL+coE9s;aSwW4B&V&
zaLN2ICWRqC9%(xPtG}i{KxT5Jp}DlvzA1b>d_V(mAp+-0IcAEzFRE^6fn+2y9o?rx
zUCD2wz<k)|dD<L*DXZpl5-pT~(wRpAk2E1Gb_S@V1Z6rql@A(?sEQE)a00}GUs8IV
z+STmhyhll_PVby&RN`BsPQpKvq1V#WEmj8ET3zLy(t$I9o&wT$p)LM0Cm}Y0SewQU
z*~ZFMgF1W3tQzaAxw+$}z}gSnP(`Yg6;jsb_Dj9-19?73<m`J20!W_V+-k7b`Yw{c
z3oE7X^002RT`UEaEZ-rMPa_UX?<$5d?FejwvC?ehy2Ljipg0YTWS<c$8BGF!T1V$;
za6sZUH)y>sZV6GuYiynNjYLZDCk_)jeN;Ti$Py=z>#WsdY&=hh76(?3Rb9TP25xf=
z)y^K*>95NMX+c(%A%H6PJq-B0UoHSvH_Za*{xUwoVqBo>5arwPAOU7YszmX>MT71#
zk)&s_f^qY8?H~SToIV;DLx)rHZ{CT2G25Zwc;`AOZFs}~-9Ug1UDWi=o#(J)2cgnA
z@8IVxjP6o3i{AGmpNfjRmH=LE_WBu9H(XOJkgHdGuN0pR0AGHK{b3(3e)F=7)@$cM
z<tIR8VXB>EFnkgq<Gwcspw9j*V-bOcN9~}tmk|W6iB}7}r5Q72{}hbdDaipPf*Q0v
znhyYN!hDIy4zu1k()CsP)3S1<TPZ*)>d!GyPD>ewFeRp<Lx&W=Mh*1cP~s4}>eiC>
z{|X~Uv;CY{#^}=Su#IIm`*XBs@V@z_OSg|NDl>7^0l1(klrC)Rpk@PNW+~wJ6ad7d
zQP8W)uN5?IZny#ZroF971Cd=Zej2N(3qAiemD$RqdPqm9OCareLIo{Am9EiG$(=AE
z$wQ{Hu1CFG_Lk&LCf3Iuj>CGL{fT*z7kmuVQoxmBssiYhK=Q_szMpg?O6FOc=g+Pf
zI`SFlxU`w9f^5{7IMsfRXFqg#CCl_e3)f(s!*om4dcpMeE&O4Gt!=IahjWEo-rUvl
zd#>^U^LyHjA712JXWQGIlJ(esNU`pFHdqe&95x3AaC}%$pC2ng`b1cxshsZkN7-&W
z0D2w5JFYXbCp;J=+S^j4x2E?1_F+vP!l9vSy^xntI3^`<A)r4LncO}puTY^#?uO5?
z^u{4zGfih_={yS8I#0G0-4v!!5gKPRg2^1l44pztvgsZBa?}|@zTV~a!d^v<_D%42
z1>mxil&p?-6iRn>T7+H*#J4h7Xg=PhypA6iY&tN1tZjDyAFXLoF)#1@?Y=s4WL|q<
z+1VrF@XTsfb8YeX_mMfr@qtp#e@%UNisEa#NAqd;E-4p=D)SboV_JL~ezpS85!pK&
z7|?`K-kxHNHx_7m_HgPg-OsOB0;fS`S<=RSE0*>p^MF_CY<szj?(e?L(|}DnT@As0
z`DiS@;+X2@dSMZgs4*WBDj%eki~|n$s3^(;OXNW&-ZIOpXo~cL)aE872Kq{e?_aO3
z%i4~9|3kd}fv`2;Z@r$k%a&=x#HrC!DyI-#TN&l|V6h5B68$FEo?mDtG&pi<4`i+v
z>sNX|z|G(sF23{9j7M9iolffDzTqK2z0Mr3J10sC5KAxEPIVkw7D+IJly=3d&vkOk
zYo37_kd(IRY1;P^5394gqWzZGYwX0X>??m>Ts!>QlBIK;d;Q^x$?WRFA~E7$74$db
z44-qFRNk_}c^E4*yRzNHk!vdn-_V+)Ploy7D<0SuCv%aneK47f=<*<PsZ-fr>Lq^s
z4zlEf(l;j;fISZ543O48cFvUj`aI#{H4U4A3MFVXukt(EedSyQPFQOd5a?lzWywFa
z_A_J_9-Z`I5+8ATOuua^q1Wi`o<*L`8Jswk{Lb4mf$0f1vkdE`5XBS96sKnq(c^ZY
zNEameR^~#XWeWC;+kFC{aP{MdPrwyjC}SS)dPKe#E>|%2=*_KtfwI5S;LAB2VFlAp
z6=!D%%qA1Gp10pAAx{rr6#gHQ&cdz9_wB>a27>{kM)w#U(k<fXMi^Z>I#o&q1l;J7
zZlpuHQAE^{($WH=bccY5fXMgV?|A=$9Xqx?_kCUae9kjMwv|#fHR4Hv^d*Lsl@6f%
z7E#;=VVx|0F3A9Hi8)fU;>n)6FIX3)Mp_qIilBfqEkL;<cE4d!<i&X4TmuC-Q<@Vd
zRtbPpot|*XV88Q)85YKK7AhVf7L*(Zd(lYqV-P*uIPnc;Jjv~m+!73;iT@{z;G;+)
z$l3_XU2+HZI7l7=6sN$7m}GA;+FqL&flc%MtU859*Xk9$<Sb*P>ant=bU4tA)0(VG
zpUS6q%P1)@o@lQy?XCn!cCzTP012%5tw~ITo-s-^f;#i4a-}E1ok6#l4x+FYJJeh(
z^U}_k`xKNCdjv#AGW1HwFopY)kaa^NKv?$Ri31J&QD`*(>A>3=Cy{4Uocy^~g`<-$
ziJGatfBW;>77u*WlAu3+e(qslW$52b%uYJf{<i3x&F~Yk_w*Oj#X@LV#a8^|8ymDW
zC33^;*{9lvlFL=?FMc+FGXHd12|-BS9U}Ma)67+dQYj5ACvLcY)qyzWz4Qxu-z#{S
z{pN%L=#<Hg$T}5;;cF)DbS**TI*lV4oeXBHMA4-#?~&8Huo`|73b{UWc!r$!mwM~F
zxz5Kw4B)?07owH&_!yc-e=&~x&QeI522?^&rR?L0YaWg|N12^1k-qPmP4}GJv$kW6
z+&&r(`s<5kSpP*;fvSoES3gYlM5qZyc#SWD-WhMLy>!nEm#cb{Fz+c)!c`fsJyyLw
zXvv{MT`loGQAD3PPs{Q9r7z;mL1sQX`f@0$Qk|Ia+n4|p`gUgk9%io!?xT~KNxPu-
zd4M|g{KIaLw@g-FT9cq8op4VE`gKxM$B45!mdf2u(<hQTg$^B4#RVo*TOh@W8sS(K
z1U?a#f}t;7<@p%Ko96r|&8Q5`F4X+@OYmd+!w-LYf4_Je6Ecv#1H?>4a|%J7r&kC0
zG=-X3${c2r(H-C?rxy=?`@Hpb4DC$zw6TmX&_jlbsXr_s6Nw~{G-m-7RSc(|s<<zJ
zYJ#sW-FbFPmx|K|Ix8{5<eOM^j?zs0_B4c(ui3d=nqKwEn9JV7zb7{mY@gMXTw%-h
zcZpJ?LPIAXE`Hz;mRY8=$(1m8@gaLl<u~0-D^EaECk0cx&MRnz{bP^BlU0w5=j&X}
zkrmql<2qE=NE(S8*`88c3akVqTKnn`H#FXayTdwRguM~dK?<&Gw@&y<5ufEJFyfd-
z`BJiNc<|P7+pmyNaj|#La$FA6=GdzZgt+<6G-25W!ZNm$%J|`F_t-ZqEzangqI^6c
zEaP9QylC9U_%5ZMVW7l+O)Y8N|3A5=2IWsOJxZ?czUnH3>&r}w1aVs*&$%!CSF!{l
zE}CI0njjcN0`!A+<7@x8vr&|N`*N=J;z_p#(zte{so|KoAg)QuQ*BzjXQ)1yt5gYC
zff{+bDthAi>kg{JVP|-NbMr{4Nlu9{<CUNoO9xp#tAX}XL`qH-o$$&OJ}xxOh{G7b
zP*|Raou~F`;Oe#V!woC``!}&xw<D4E5E4#=*;26z`Qb+ZTMl)E5k=fca9;xz41l)q
zn`&}CL1RBRq>Q?AnWpoXvA-~hewfb?tf8i{7FHHzHnrybZhMp;ban@~cZX1LK@l2(
zdW?>|ugh9E!5x6Z@dLg4(p8^95%>y6Uh!TXb2WVNQP={J8|~}Sh~1kOH3S#H*u5ea
zQ>;X>l7~c<Lc?K1O@*M5B(ClwMYqAbpbIm{8LX4DbR%p>-5}Xh?NL&!B=Wwg*T(W>
z@E4=euA~B{L&gujv6@C5-2$<PCrOeGTQ?E+SoqNUy&3>t%U;sPflE@-;MwU&k8K@r
z(6IPH@BrViOy2MF%W*?U?A&&I;Fr@eNB5F35DrCT0DwD5<Q2SItY^35fg1S<3OI=(
ze?S-+9wsU{D%I>dhK&CvB@XySaheK=9nhF@Ea5ftXvpU8af-|PeUA_Xk9^?#!>Ll9
zSIF<oyGfujoNws--#zEgjXg+_SK{i7e@FuE?1ZT~=8$6wM?pKnDBw~nufSkD;tMin
zQ{t9*EeYAvg37LQ@viK9!DsO-d>bmCPn)hAfAhNz5$3iDscwEfE>Vd0^3TCt@y?q%
zodXJz|6spGULX7yPlK+l<sU2-7e-rXs**&$F;w?6(57Yb!Gt<TYsW%$a7&18Zx#v(
z`fkUn?lwIHJ%@9mksqD7i5Fw6H954;b<>S}Of2s7x?v}UGbU0P!D-}h4gm-!z7h;u
z6$AJo@Vs{^6hf+GL}L~F0T5_R&kJ{+@yOHFZK;cQUZQsA9DOR0A|x>}rX*dV<X=^+
zvnJChLMesJPCH$1Z3MLYxKI!%Whvs&kKfgVx(CLR#LV6er*o@eD;ZZ_otDJFxtuy0
zBA?N$p##@2rgc^P;F(CL#aQANu8IHTy=5wm1Cdx5uzt)DE5SwciqMH9D}Eur@`ely
zi#Al?!V-&m;_rfP{GG-NHs0L0E+8cXBF}6Rs;Lsu8xoV@VUy)k&{?f@bXP<B?SyRD
zX|MKCg^Wl)OX($R)YCiY?>e#j?4mTunjfvp<fcut`ZY^^(d^016Wv;Ls7iXg(~bpg
zgXobzo4Bk&ML5^VIwuC`F*zo2zJlX~X~H`JA2|ToksmRTsYcb}ZPphqs7!x;hlG}*
zh4tWU!lp7#ubuIy8aZ=G0*}<~TjDc0)UMKga=sBExu<?^2Mx};>7toZGNVnB_e@Ml
zZu7S&2*Q1a)xq*TnF1|gN>Y)Gl{p3cg`QF=a#9&Q0VGe7>fGEzh50vm;rA!o6NGyg
zvrnx>zf2@N{%SqdP`oV!qyF(;YEmph`j%hkw5OyP?5bI|Z8G{&t2A*p!9;(z07L`!
zbGYJ)x-mh**ojL4P`EDl8@Q@gE3S(}16aiqRQ6pOx~J0=rJ+3o;-d$)J;zgPJz@rn
zDl+6^1ZQPAlA{=|#=S;dEJoN#Q=ta$VH^>70gc39uI-OuNDD4BIWQP*`O@IQ-XqnA
zNkwC?Br>ub3>U>WO>C!Y-Oh3Da*-dW(M{(eWg^ZRW3u{0j>dDM%4&lrJ@O2tFb<DF
z09335!o$bmAl0<FVgx7T43IvKke26GJ3RoWOjYIZoDomV<sh1Fo<UIlU|Pq%Ev{ia
z0Gg&jQV|;tgAgGk&HyE<34u44UA+JzJAq9S#Zq$Phx!HrYbCRzS!NH}sIr>Q0?g!P
zS(pe)=&r7veVzVlf0O6_)ima98A<1{zPi9qisM7qXu-Kz{c0A6h9U9;@ac$vIaMKM
zivLKKCbep-e=GJb8INH#KMB?qQVcRO+u2&r5*3<zQ0JoTtt0Qko>KBhquPa3@bqNY
zUca6Gr=BTN_x=D5F|bW)=2yc#rOxvk^%==I)ME7cw+Wx9FZ#<6-`6!?W)S}qv+@XJ
zp6d(Oo!ENn_))R}4^?l*-3gDWBzcA)xR2Oa^&V30_j7+#QFW2ky?(PHe`}8*zU1+Q
z05_X73>NuE^hte`*V|}KQ#(&__wC<yw6)R6SC3zl`%vZH9@J4FddZ`iXI47J%?IOb
zlH-_=W1?ec<L0La03z-_9XvfJ-`QKoSe_`Y&vkan$c?C&BuO5ADUaeGU>j$`Y6J8a
za*v%0!wcH6Aqw0f#@tYr!bnkQ1YWEjJW0fTafyc-`>gR4GXl<A3p(U?q92-Wb5X3v
z`+0=<CM?@n#H1_Arn~LzT8e4+q}q#Yt@j{gkJPz8C-OMGgCy{Cx+$=Nx&Y7&JVuYR
zJCW6b$g_`3ez+=vX3607(zK=k39sL?m^{&3l2~Ffe~MDI)h)A<Q}FNv2!p*3FjHdN
z5O|Ey$d;T02QBY5mBfwlXXi2IB+Xov_-unN3NHVuu>d~XLZ*8jnc>M!n}zDyPgrIi
z&!l{Cv9=w5%kkCq;PI`(?1ARDzRyaRpZVf5CExiqd^eDBGJfW-^H{ogea^0Ag_Ar3
zN(}&jThO0U_{<K_3`9(tBV3>vepJsKGhBNpp6~P9DY-6;+M9^{3blbB%Fi)pFXl_R
z{?RFT@n`=<pTRC!wrS&oGzM97rd{-D<wIRu74Zw<x98q|PYL5D*;dLW*)Zd2{8!zh
zg6+jIiz65BrO3<V7v#)tiD?g?lmU(J!!mnH7uQN}k-QYKj8>Es$-9y={~VbbKlu$j
zVRK{lja-uuY9pslkmC@3G|fFTs0`3)i0Hj`7zoDz4BbJGaaxSqer)MmAnxPKIoKTy
zJb$=gCJ~3&z-fDLpy)63TTdUrS&Z{CjT0}Fgj<9-(-Y&DD0*O|*~I|Bs$L9li@>9i
zr)s=<if<kj`9eSSGW%A(0}K_(V_!F2D!zaJ;r&^>=H-+&i#iFKN<2J1vRY52^z^|-
zAV;k*Yst`KoC1TeA5+PzC){hw7;-LA)OJIda;cMprdWuU)U<7LfZwGjz1G+E7179k
zSBDjlvltPOLaNqDA$bG<!4M}$5TUI#_p9ZzgbMO2VoSl+wxhY+t6+q?%WJ5J-FCpV
zc_8W6?e(Do--1FLqR$#LhmdDa)g&ASdvzB@SO!m82)p%${-;zYC3VaxvCDeilx$8e
ztbhD0(Ly17Wri*Q6cxF}svZmyDv#PH!VoXwZZym;OruG*H|a5{^~Pt{#^E~s8TBQZ
z)Er81W`~&2b^*TKjNxaS)nETsW&f-eeNqGOatWoL`1+f-XKz@v=J!F0K;o~+&Q^iq
z0zn2ZixpZyJf)@Pxth{Q*9Mn3^1j#^idQrk9lRPTRa2JZC?@-PBKW$5q4MF?srnxH
z-|vR_d6$@br|#?y%zAfYA|EvKy8gA#P$4HO9)bYs56GQrboydj-mB=~T$zbF+0um=
z?&x`H_L)X>9QPr6$4U~WDM-{O)_cxPl8#jb2Gum00NyQ5Usui*!8R;0381Hk>ZV&E
z6p9`p@T}jNJNTpM@8mKgTNRWu9(;cr^xX-pZ<ZzPoeZ+=UjKN<-99<M%z-1sGJ}&}
zbm$FwJKGar@Wg^Sd*@k82)v3t_#-%HN&FMS-xiKlK*`ZpO+CJC`muvUj`XCc#N`0b
zn+<jv-!+e!iE@G~PL}%&w66m~J%U66uwSnBUCP0=g&<-v{wR|Bo0O2lrw@XH9=k=_
zs^}{byY$a^6m&g2QrWXq`N@v{Z^Njy$mC@J(VU%q_&&?~A)V!+W$H3^-ys!|XW15q
zNqq<G6(x%>702#D%}<qW-MWinDK^B~&fA>&x})rUBw_8N6+3T<vUtjy$DE%kJN=26
z5KD$g^v%3Ce!oz;zw@d2Lm9OkqC1jVm7|-)x+XXut5hAvbZrQB{vDh#WTk&`>SVQ_
z&-<y&Ec2f5V=d{&zERS*1+X9i{H8^CwZ&BYQ-%PMpF9>|uUToGi5XR4amwm=$ebSI
z_|?iV5#l;D2LV9osfa-ls@yawFn$)2s9q$^1hEqGznDIZuTCE$HCwXw8g13DYX~A8
zK;65PX|P75>AS+24+cDl)5hFrna+@!iivkiPu`jF>r<o+-EaI{1;B_22^j(^itNV;
zPJtjRzp8iy_}661lF|``=otV=^6T`e0CgyEE7hF_N+b*#U-P?yf*%tV{5VRH|2H6D
z(=#OeIQzQ#f~fj~8ulCN!VZ8k#-(uoFas!w?Y1uV3p6h);&9vn3fK34a)%FK?#*)!
zZxfJwZr=*#eg=J5etf~s$wXzDu;|M-|8C<;s$wy!-RAS*`H6hOgX3b-{6*q9KP;zE
zzZK}T(*+9}&(4(md%OGF&z5R)CWoUAK%B;hqwr@<WV`kbxC`O6hfb^}d_SuTv=>eR
zE)<>@2jK<)DoV#aVz$UZ8h!`@rLK}7xjn;9gvBeB5MxOfuHV+U2VIvm-8vvyYJU7H
z0;E`7=gY}+@+$^W31=l^Z)k|f<kr=xu|`(L5O8$-lB0!j9Xq{@e+2#5{r)R~r<;p&
z(~efKiJ;?|FYiwEP2L)kV6WMf^!ipJqbuTkC~c7DyeebRlCgBnh1UTBIROlSh*dKN
zSfW+k0ll<7;T&)g1@8Lju9w4ASpW8^-ld54X^}~hHycFti>OR_LWL=%xyl6HOH%1C
zuC$Tbckv9t^Om*ys+jsZNXXZDC?T5o`YB;qAb3(n;aVkKU*Uwx>SRn>I3ysW-et{x
z^0b!y-GS3$Vh;hjNl*Tgk*oP^9CF6w0Je;Mc@q#-ywHE9mb2s#Y_~9LW!a}e>x+E^
zhNB_erXw`=O<5MJWUQilCuerq?2%NFK(!K23SaFHtzMmRSKiEx$vIO2$7&UOj?br+
zm>-+D73r_mrqrKVHcr>U3Z1-8&O#fp60mox0=qv|fnxrQ<J*MotUs1CRn81c>g(s+
z`kL$q0Cu~oEU4cpxiqWMZ#^xk8g#r$n7P+`^aiM>PuxDVa4cZtdYO7b+)6O$sugwn
zXaL|1z72o~A5TniD#YVlP<=j4DLKBh74Yux*X>8|02)~3!M2s$v)L!1Znj^a{Coa#
z{_c^5@4~yCq^QC{Je=UXaBG(U*hzhW;M7f?w)-slLUVOfN9zPvfV^4^i`>t46P)u3
zyprg7?KWt-lpOp_p=7_duAm6RS{f9NxZgw@jt6w^zI3O#Pg{&iVaEYZI!{nYp^#DN
zd+MGWZQJe*u7s0gI!m?3Y0ZkRk4Do~WoG&@HS+gfgJHbHQ@u-zx{=OX)bFO#$<Tfg
zQM#@)O&2^AW^g_I3Ni^n`WB;-?(nExmfQKV&ac+sj`!l~rOS4_D7k~ZVut%~^Hsf|
zho?mu|Lj9V(qsXk6e$Q6&?kE$0?-6NO)-$N>h+bo@p-{!)qIRvr_hW%WnabJBYJ_e
zSqg?W)qB0fnA#SlmaJYs@kGT(DVM}eFWVx-M-AT8gX2}_HsX@u&@vql!R8IAanS=6
zT{_vGKIgp2m#!TA^p2%=q$@N4$XFmoy-oRx)f>h&lTe|kt54sgNX&Re=Ze$DCtf}$
z_pg)9*J-uWWIh<=@wQ8qtk5{L)(r@S*=M$_TMSnAisT#o%P`n|GeG<I4X;0YvXp{6
zB%OClp3qVmA8=PK$ym}qAlH!l6L|hr3Cbm?@R3F?ZI_xm$U25SsEZq~ga!8D;E+>_
zlDzrPo>YL-ksX@^*gH;D7^OXSpC4Sl`z`Xyp_mZSn#$>%27B;U+&RjWJiBYQL*${}
zNA`*Kj51|~lUkf$pfVOiVS|de21`1#U1L?ztJEm5C&^+@Jk^rIZ$BeTd_8PEYvDO-
zTbmiM^<Macl?_Ovh(WQl_vE-xYiOC%F~zzNWF<b@v?Yw^4U_R>oWwJLOn17X*@J>{
ztTyF@f^UUlTA26u!|(v-l&Yi^Dzne?!bFdh<)TiBX}VLv;CS986)@Ix6hRDVYBI?@
z*D;(uzT|TA0b>Ku#Y26xS~Vok;AGR5C5klFyo`%{P^CwQ3quA+hq88XpY*RL($SW}
z9)@0lbz<smv5E0j<<PU#l7pa-l%pTonNIZZtJc)!79Qc+wpZyPsvn-kbMnhNJS+RY
zWk?e8jB0z0riUdr0n;E(<_j|ekW|l3vZSj|FYxHN^GrU<vd>c2cblS4xP2En)pvI4
zecB!+I{_Hz(23MWTa=X_+DMm?M{2BdPQ~xhy|p+lo@NfT?Ud?+dd55PiNY&(X>cRf
zQKw`!=bB8MVIUT?n`YV8p0%m^g5h;l7{J<e*MQ+|r_?_WmfPz#vnt+01A?9#hd&kl
z@jF~<6x_w-t2L%Ya&)8pPgqQ7#x&&RQ+xjS47t2TzRSrKu=Tg7-~IF@_P@LA6bTA?
z<o5%fn0F4_NEyzvQMYRi^rt&dN9oY50Yr;0C>{DrB2!-9R=aj3fUcC8HvT<o;>{HH
zqQJzbn%;_!JufY-bmHI<8g_GJf9<G;)yw$<RA>Z)WZ^{NfHVL|NJEgsu%d+><BG0=
zwu*&FL*lzi4@Ilr|8kL~*s}aUxA(e$z0B&Z#s2sFhUE(V*YBTYeC?@$ICCx@1DO)n
zX2dXWj*~M%%_@MSbjdNJ`=b)9=~B+x(88<M7?{v{i-Hv3-Y+CgWU=k=)>oIkv9SyL
zj<~wDehtn&Sp(-8Ul8LxK1ZWUQQ-<oFOvHJAUd;tIEB>br@iYVWea=#?wV$g1k|g<
zv(JA*?e!KZGtWA7_FHGTRBnGR&$zYTc-8l7-}ilk`lgl6V2!~?`5ubzuiid8I?#Ua
ztNb7T$6ikE29~7{np^@c8ubijM&@sa<@nkuLVu3^1%N*0<yA3$3376e$&_j>7mEkN
zo@IJU_)Os>E*gop$+)*ecUC|C+0LNi>?2<A9<&zc<{iJC3U`{maw?0H+=tudd{u1b
zGJN@AbacSkCF8I8=VK;yPJZX_c)k9WuI$p^jB6`;8w>s!vK0x>AR1|#N*VWOg{nUX
z^>RFq);v_9dThsENi}-b8t2=~HNK&Lw}t)tjQ`dXw`_G~?dZ_I{<|^-9IEqe3VCfp
zoG4&fM$b*6JA*RrC0kVjmK&E&lb>+=Q47w$Vc|3%v#5x`k>a~ZpoENPKonq^qT@m`
zk{_eHok12>B=I6oaV7=8B_cXGkAVu^r!|V^%%{-gW)m(|hId4~oE?GKLDF`VFqkbr
zyom=&!vtK^1E?z+wLB<IXjxubeEO@H-Liu2==fmpOGoAcvZ&a#I{8=s6Gl++{3mW8
z)9WRhNk@h%)mC-CkihzK3I<2r#>J#qlQ36`SdG_XD!JzuZxJ(=b*Cs^P8R7G3(>@~
z#!VsIKLUuVs;PAOm9|@f04ZiJW`aS@hbp{-H<m^Kw1NW`$P_#0^RU;XD>Dg-v;{+*
zZSVcrclHZRYvX#$YAGu5h{C&Pb}u(D#TT6oUUkJ?PIxu?xL=U>x=8Ad?Axr!+Z!%o
z>(GX3DefuePu*j;gH$Nux!>2myNwhqwYkIMKXLntW#CsQJXIqm@L8(fKd@I%my)=$
z>F0Q>f(-n>p7|HDRg07+Lh3?h5;&ZCY$;FgNAWn@l5Zc7#syIq51PTK5B}e!*6AdJ
zwRxx6E9quF<bN-pW|p+2A8ci7In|_m&Zz=6;r**48TW%r;ssM6e|Aa`VUUEp3jqKK
zfO-2dHbG&352^qrXU(P<iv2j>D~gI!xpL+G_3{(zjsS56^zVM``uW|~KhkaWx=1$O
zvxe3(X%x*5eQtz4+ds`k3MJqCPt)ogX01Zt%H;PC$T1%TFT!bHocO9wHCES2J+d*)
zHKY?I(Z*g_z%SCrI5x+PWPXG+&Hzv?6S!o-$KmKt;yAjOQ-{KGuiwQ9xw0>DLSGq@
zc7(=T5fL(cF=>Oz02#d?Rd_6tJVh)Vg##scWa_+lw}#`8c%3Q37F)8Uc#$s?f@6#Q
z!w~X--}^0@-DYpVj#2E(_fC!D8T;h$m$!t|Nv{WE52&Ctk4bfK5YKm{2r~s{;0uLo
z4E`-PKo6StK2hQgRANfj(;wwmA^hq(E6p|eJ*Gb|Q?8j2Fs_QJqQd3?0A36>2nx^O
z1pw-zG65(JIM0MJp(Q1~Y*#(?Fn9kzT(Dao-8i6|rf&NLp4b$pj4?qhx6U{YtYi&9
zQzxC>OkVB1w}+RKGib@=-j=RswEd(t%nNt0DK&ViH9D$I_m-mq8FR6~T^BHL;Mw&L
zF-9E2fe1^y4x|_44oBW-H|cBR|0S&y<oO!Wvv%p{?z0JvOFW<G8tiW$l1Y0$A<-61
zw-GUz7#G`IoO!Az$u?U-f%xDx%TJ&739XY2OC9W;Zk|aR+|ALsZjvrg)kSAYUd)qm
z|03NT{HSM@U1P)71i?Cx0*J6O+c=8Il32UaSo*RCiTQW5al@EW=^7vY+uxX5CG~3m
z%>=ZJ`2P}IU#{#<Xt0VhdT2Ad@Krw^*7{A(gmzvZvvR8=-Xx>e!p5hd*Fd1DAi;TA
z;97!_{e!^u$5@@+^gGnARzf*x0uurO5}DrUW1T7=Vv@ke0050iB?Y9i40ocLy&fk@
zGA1}c$YVhT7m@tj=BR+q*Ry_73lRY8{7af=Q@-oKE*U`Bi#3f9VQ$6J&<IoHSBEDi
zN-|qZrNrm9s!a>YV!y_Hr!KB>M+kix4}SFW)G000)#yVZ?cR0+@%qAH$=%ft8gWcD
zgyzt@RZ_t^EMU|7JBU+<EXJyWaB9V;Obhq1y_HBC<?-ov_(-18b+3DRZW3&fD$3=-
zs5l);4XP$Rh6_Vs;qHi78ZqS1=YGBWfG&M(Q&pbC8^Dk}WAvYTYGK=~y2YyVWG9_T
zagIgAQxnzv`hQ-CO|~hRO9KO|QT=c_H)_er-Xx*9(Q|{FRZtwsJ|)vNu&PLB=dGjk
z7)>iC_MN+HG^$jXt>J_62Qtq%lY8iiXH%Qv%7V<|r{Wp418mi=DQdm6FW;bBOX?+M
zQpiR$czr*~3T05<wjcFtw0ZzJ)-+vbHvM<^POz5QtCW^Ktr_BUvsYP?A9Oxso@L$A
z;CD!oWR76(_Kj6PpuA<&q7d9-Zrt)POf79vxHx}Uu#8#w#mJa7Z@PkHspstavR-1=
zaDkhE)Q{O;vXcg{Kb&}ukkgLvsoeRHU+}OXem0+svaja5hYX)H6%|7goi<e&cf!ME
z+V90Ho-cBgDy)Y%MsmW<y{zy1oLc$>fAw)9$*qZGHu#@i2xtMUf>?csdTLUrn=YY>
z3zVv+@H4XX*86?#v6=x=42ZvM(vHdCFraA&_})peA`{+$v%=#5tG*oT4yY9okI6V0
z4<;VgS#c50j9yzk9H4sWxkNw=XO`rCr?VSvupiLgsCinJh>Z;cVribS`T&qvM?y7#
zcmxe7JXSC!>l3lfuZUrGsHQRwz5fKuTg_6d)V$dqP}RG?ZW|_o2W1F`0D@^TSV}$&
zIe3W|K#9^ngaZBKGT%};ODe33x3}O!svWxtKadXI6fO9~)IyEclr_8X?@HrL13_{H
z51C|~-gF)^4&0e`cA3;FWqALR>pQ#O_h98IcF(d8-0A6~4O3k9Xs$-~$L@&!?=6>z
zg4=1AmfE)CU7!Y1pD*DEBsd_r@mQ~nP@rQQh&is9R#H?>KZ3!h>7l!GC;M%S1bu_A
zX}!n?5a0dpKbI-KFNdD;BC8qmC7M_7r;eRJsMpsu{H;XpYu013{PKaWwJ(%Az|1OU
zc_+;5Ag@K^m5SFkRtG{q08Z}4%488Iq?T|PsU<c2<0HNq4rs(=>b0z<V@~`9Mw=Z9
zoL0`;S4pVfTj(Z<Xx@E9-#^E^t(fNErRr67;}HA9TViz_*-gVr+*DIz0+g+;yzga>
zOCB*yuUl?gP;Qx=;>j(H(X5Qx@g$%Eh|Bx2f)7chU1Q>21G-&bViyD_yA~WDn2q@f
z=wwuF*+Ly42$#@!%)G*7IjeE#!;7att{Qei;tduYPH+Ix&;y&6^m`L932?sg<jY91
zOx*MaFLS4Gx5K%<7yieA#pz^Wz)z!dd9M=L%vwTUkf{oQVC{dVm_#+zlN3MduzEwQ
z=bjQ!X$Xns6>1evO{Qb{MH`o7nlhh&4?4Vf0bCVh2{my52cFx(0*pAy$apX2w?v<7
zI(kyv-{vJ=tu1^xvC*ojMY%ynsI>fqN6HW23_q>&w8#qI7d}H|1#*-4#Qqjc672Qu
z=ac5+OJA?ZAPE(yg}!(F%(Kbi5zLVr&M)p7Ub68*Yw^_neW&qUG|!^YPR^HbLF5je
z8H8ofey2Chv8f!EX{!5DER~qbq-@7r!PNPQ`;$Wtdm(q6DrD#WGQ~HY8lAnUOSXjR
zGikOxh}I{&#N`UrRalCrke4UpTmD?Hy<lI>j`N{nh3uSQ-O+-f-7u-ZKF<>?7}dk1
zpzjR7qd1uVEhqf>q-oDW#Qj)WOxpi0#YEz#%H9!Jy;nlBe2D<>O@}vXsdby}smy~~
zCfC=GU99_ETHZNiLJ#zJ&#3*^kwh~3ZT}jYdSz0Xg#<(TQi#3;hQ>@(<oB4xaIY=;
zV1A~cA^3WdoX2n6gB_bKj{5#o4JnWG4L{tqW(7%~(9$Jd;V2@AX-sHUyL!lsp98)Z
zF)5VTdMJ3T3+&DjOG9g%LQ;%;UW4hPWRPWcn6Q+X`;c_{*nRn@`roCmh}0XM%V+r<
zVSkD-I8sc-#*J}|l^OX?NOWW`w(`(vcZ77|HKqF8m$}TNnH%yrVyW-nO(6YN#*4EO
zX-x<m;P4-529g5B2O2zs;c+09J;jd%*n<_7$(?xLQF690`tGVc(_xoS)<Ja+ZYen@
zc~3NV*6h#hO!%z)a@PK?F)3|01?Bv=^xwyH&+psuzZ;VIer!E|UwWvUZj(s9t>Xzz
zP8R+CgqgwQo<yOZ*3g|fc}ZL8i3^3WRD(d83#7jS0KziibO7QGgC=TuQ<2Y~&n~CY
z7gzZs0}&rP{=?ZXkKX?IC6xS50FxSjEi)g7gSyC%A(7J==7}Je^J-Ws?_;e8&lyr!
zB5XYk!&YWr2!8ApjPOfX<-POIc;}k*8W|qTUlG9@-~4=*h6GBq1P~Kmk;VrKx+aPw
zV9_Q3%Ku0~cUPP6L}x@~aou<f0Y$7yl{)0GLPo5nEu5~q?vu0Bpf8>^!q;?}sQBOQ
zSLzQ=S=cKz<?<(;r}bopUrM~1bZX1Bm!p*|m|i*Ya6zWqR9T`JtPdIAX%&5~n^Smx
zHRkokvhK~dsKN^0R%5a3AiDsf$EkO*7PU)zf9X>`aW^h_!|zyReL6J0gc)H$;f`Es
zfjLwRq@f@8PQIE>QN6YYID|JhII30vq8|$(qNPC$UjQt^<^$Z2?#Ba^!WH|e$MclM
zdgDQ!%m3PeN1QXHO@fR-XlySE=r)d`7N$k4(Uh~3ae#0uKbN}7WbeHIoF(l@iKt(s
z^*hTLICFiYzaIOKq7MXA)wU6&FB3{#E2=b715K(vgpB7W&pqKZ-bP0@^74b=!qBqU
zz6TLGu7Q9rZ3PWL%%&{}kQ8o@<b`peKo?;H0FO8Bb=)_aJ_MGABtisg=DAhuLASum
z*M$C<%H=&R_qTa_f)N6c1`<{oKyeBO7BKD;K&<L0M_|YLOu4yE!;N3b$F2TTC`GUr
z<BPV}#r{Z%DuqNh-O3P6{nSLv0WE>MP!dhFXpljX-kR|INuT$w>2E!+0;|wIvho!5
z8GCDS<1(yWz!Y3l`ktx56N&G$2}pgDA$@S$&5V(`n*!GtgM5<N{ez9N*}8`k?dDQm
z<B3J>cOVWmzY{kKn#dB@*43VB;`pr}Oa6E^+=EDvBmo7Y0r*BYEEcO>L;s?P$@Gpw
zE1rV#>FXg%qju?;;b);W<F_L2*}RELOj@)E$hT&y*rlo!5d8KN){9os0t4xeh%sJr
z;<?<Z`5GFKB()!Z4iZ5$+W7gDNFoO7KN40_K?c@*sct~wJ;wEU2cF)Ff|4w5+%soR
zMU4D-k#|cp()FK8F*z*aE!3zo@g5Kh2*#BW7L$dqQvjQ+4Z;wFOC`UPH}3D8T3)Yz
z1^;Bia!e{Cs<-G<Jy~PB&t5X3rQBX3iXJc7H5Lc{6Y0|1#mnnc%H%6hyk)K`%Tgv9
zp1Hl~`L52kfCnU=@K|=Dc4_VB-3i^d<~c1o7LLtp&-)A0)jY%2VjHuh!9bA=ZaA=b
zGY$)|IaeDK+`+or^o5h}{;_=+em@0lN`G`Il^j)nDfjWRjKfL(%bJj*@@|rL11?$e
zh*}vjU&Vu1SEz}=O`~Y5He;XrgcgZ(fw30oy~H3%t*UM%=|8A*o#}R`P2!a{ORy7_
zFpK3;9!xbymSkmvBVs&iDNTX(4M!0For75&fdJRL_}iwsy@$`R7-$|9u)_(B^~8b(
zd5KSGS=STilKsU%q^Hv2+bpJBrTnU|kYA*Y6VHqM<!NEGo}s+H{N+SxcO7g6jAC-0
zYxcQ=;Z6Hs(i|x9F=4FCu{S^gOkmzo`!p<AQWNCWVa52uIKMT($N?(NtYQ!IPxfsR
zZLfjwAmc?qqG=$=V80pKIrU+Ph(pDYi(q}cU$x<0r-pDed8>JNxOpN+M77@lep7ED
zWVl2=<5H#Be5go)1W{16U|eHG+=7V|!VdMVebKf$Lg3huQGx`!1qz@;x@$0;T+ag{
z;>srhecam*p)(`unGyd?a|TFsw%N!msk0uBxI}mN&%dW7{+B25C%C9E7tJlf*OkU^
zu?G$OFKJJ;f=k_dNH;FeC-z?zZ#lM+F*j1Ik2!^@3jG=0_Qz$69IJFze0OSk)5)1z
z8Ox*W#*&Kg<oPWkAf;0X5a1^c3i4Rc&R0C}h6$`Gj_t!tkqHNQ3X>^KOp_Fw=08oD
zP|i(<x0?))=@q7*mNJl%*b834{KrL`&7YZ??<tzrR)j>qlZvq@9;ry1RcoHTltwIl
zds&y+boCYdX780o2{$C;b&lgKEsNl_AW?unjjx{+7MO#B0|*6`srNwaUxmGcH0C|6
zwA@QE4~<Qi+;*e!xVErbV)o}bv}Z(E48XaPNKt@&1J?<(BzwG2h5e@Y5*8dF>^&Gl
z{VWfJRovnwYt}J(Mz5uNBI{a1Wv?QoQFbrN@7-6G9ecKlfQ2*z<OrX0)tt;x#%)<%
zo{cahJ$6>mu+n}72&6#(QqcrqHdu@^c`{E93((?<xeJOawlGe+k8L(kgK2_VQ~PUd
zJVLVCDH7yaF)M$i{&@ePQT^piy5tNHb&yF<@Y8~NU0{-3jNx!9qJ>=|<u4lZwo{*&
z8{YXeOt|V53qhWc`+XIg|L|o{mz$(HenO<<tV@?qr@VO~;8g`RyKa<Z$|*W3D+q2p
z!k*2?>jlb&2baHP;kd=L_DM3{$fBt3P3!4ly7cCP?a12G%`Lc}g|VXRs02vTRe1;i
z+87NcW%&CPsks_uKv;Eg1y-;5`_36*@{<2@Gx}DuFFE)mWY+Yh_U1A$fPF#<!;_)N
z6Q3_3edcDRNl$1+XEX+1?ODuf=v0<C@8;$%^$Bau<(o!DtNv`8h1?q~CVHUs*&NHN
zr2Fke@D75ErH9dIR$$LUVY@1>%$OAbrx;S*3V1F@?#)Y>iC$|B$#I^aYc25^W@IMu
zA3!3P$r<+_ZNoCnrs6F3a(%zIBRb|DE}85H`S^~Dhvlbb<IR$;Qe{gUh3(Q`(lbF<
z7^-!S_sg|TokC0?AImbGxFQ;?N!jLz0Sd`*fRs6pVW7>A>R+ddqm+!1cLjU$tW_Z;
zP5@{$Bt?1dXqch?ImNnYs32w3bSAku5xh&(DSA)3X<R6H+4+gms7L^Rv?uQ`Qp^Q7
z-|>Z5i1DlV6HAOq?z>z0Inb=;gzWhaw}*${KXv$wy4fF-kxC59oO}&CEFB>a7})Zy
zOlHgz8+htZuQo~u)F#QG02<E9K%&%BssE;wR1C{gqF-k@Ycv?>+>P3O`@8u-B^v_)
ztfU5kA|rrw_L9rh7@)>T5e*J1L)<Xpqs0If&X;tTL^MiT?~jDYGTdWH8o?OG^vziq
zdKCB{vOCdfe6zInFqd>^qp=R>ud6*Q^;p>1U?$8T);Nm3LJzMQX8y;yZCw$e;|6;z
zlp}^7jj|OIQXEkzApLIrbFG^UZ>`bBf%~D4N`Ve#d;yGdyXEruHG;~P7I?CRcUzne
zOOr*;93X*DN0)Mhu-Rk$4%D65j#Sg!%nt}5ndqCZM2Hyys)vX1NE$~ZIL8@`l2l{l
zBZUGQn3|GnPgkq}3G&UFPfK~5a}P>#EBfLv*3prUlw?L^%lpn6FRx*euCS=ceuyTI
z+}HY2uA~}r)GI1-=Stiordu7~CPJJlr`BQ+3y%3D!_j<SFv9$29X*d`wp&{Fvq_9s
zI=m7O4Ns>xs8+^yR$_E%$Xk35Y-&v4#^bQG<iu4`zau_bcoJvCiBu<X&a$>+mf?V|
zqbHh33Gq{qP;u*8m0~E@V*ovRiQACIlXGgQ83`r^yK8aBSE{UDJ+EAbs>39)B!qkk
zGD*x)eeMqT4M?M{iSHb;qD5e>Q$8vLfTxgBDk22$L!Lh6?&BAIXrMAUzE@7ZQjhOT
zj}ISERK;8;HlfzHKt3tXnYzz8c@*)bP$67zr{*~ed8B0DFM2{Smlp$oUVX%^z`L!y
zO5$0)_BbP5L1NCoao1g&R=`$9hZ_HVq|c*X#-9ft@uN1P#Nmj^Zbl3g(W@lgeFbc^
zaTfEo6;qKEo5>dA$x5k6i|->==skJofq~$Cq%on|l}+UQ@JRUbars~5@#V>Jo)O~m
zUh&n=P4P>?!3uX{7oAJdfT6US8)xu;z1NStKmL^(O;7Cn2YW$GobgX+CI$S|GHoKg
zabHww=O!-f=I0M>4}`@=&Z{z{dAR$O!N8E_Mh?kypu8;?s2pFhBE|!BstUBXUV;%M
z3t|_FXj`ZuuVE$019;E#bV^abn*y(f0;x0IlBZ0!<j@l(kS2oK1SpjD{CcPfS;MNU
zU27ZYi8hsbbl(8A0wlszd^891DRr;Ijb9&^aJuXm)@ThJ7X#`((h^Uf5s^X+4P3&7
z^+4C2Vm|lAonW`B$i^DHKIbv627HShv|mppSC@07iFVb?DiV%Ual1w98Atu_54G^L
z%JE)x#8XEJ&zcl-nn{H339nDt^-8!0?eMm+5e1xxjV}C6>r^q?OdMWyTRl9@KP_u{
zc<@dcRyQ>^Tf5R%hkR)h!AYc{(O|}j(_q+WV04JrU+p7YaX~|Eu~BD6J%K=}lHBOL
zwCfMUa*4?Cc?H5D)+@t>jt4-CAg~$;@U|-!r9kB%R`PlqS@kvMq>U9$*EG&XS-=@!
zp@|RF>4lFRvG?n!LMkh6c_pZu{k%v2>z?t2+L_Af`sAGrAH?bp`-C~RIxzzKOk_3d
zBbJ_{wvWr%RgpJOd+@-%OiWShhYruU1m(%CsU=qd*Zl*kMhul+_ZgQ9BHDWkROZ7~
z>}OZ34D)CI<J%%zgLYh#GADd)Uu4aB#7;$xo-=3+!1E&MH9E<d1H7G+pCp=QzT)4H
zO#PiI(ldI1?lgZW=Ib|n4H3(jn!-%|<1py^`}>oq(9C;?iFiW29S=@)cJ3PX>LM!E
z>gNTfUM$=D+bs`Q%*L_4>e9{%NV1hnN~rKl>5YsAHp;!P->DkBF6XAPA@1FPsb@$3
zA{W{MJs>ms559<T@MGeJ6@o)t0$_q#27)p)NxghzzuM+Yp~}9S3P-b)r6wdIIXct#
z`6lXgoV+E?%yg@39{7AvP;*l%+XJF<T)lx==cLzJ#wHPfYozC1aV1&tCaizHvK#T5
z*OfaxpDF^nRFh^>edtYK_tTU7Sj%{HZi@OW!#|s?GIm-$BS3q^P0M)5@rb0}Fy5?9
zL@n#k+8J=p_@nM7?L!Q?*GEe4t|*a^U|N=J?fdt(z~E8;R$ggCxyLx?Rvw$f_&K+{
zR>?SHk{02zDcc+}4|zJgE+$W0{ALIrD#(uaN>b~+N}zd~!x&w77<n#ksE2_eG4!Nq
zfnZF(KH>O81bV7-naw0wmKx(q-M7C?J&#{Ea^&&QN8U?aw;xyfYe^Ym`8gz3g-<^v
zAS$uDip!kHOcpmvgOlR^%?tp{98MabEIvhILX%C6bM;z78Uw75k%5bz=nl&FrHPH?
zMfM0Ha0kKSR)G2_7e??Lcl`gsZYu0Y#ENHbBdZ?afin;m9FQ{R@=bWFh@46$^+|ES
zRN>*lbDw8q_Gx(I9LzO!)iCq@tKW?=_uksVTyLfp?B>L6%vI2uCAU0OZQEY`^U*@5
zgJd$-Mks}|8f&s3u~TH9Y)z?&evc7XWaxFzbM&>5AyRTtRuGInsEPB-@zZi==R-;=
z>qEGRK-1WfeH@*^&H9{qI{H<LiiHJ=hQ<_%e4Q%2Gb`s$J^h3v%kC#lmw?0*avmCe
zb#FtO;6)O*+sGfY+2w}i6LVWrd6nb@L;@mQ)Jfw;op;`;%sVOC)ooMiseoOIIu%!0
z&tmabrRwe-RUdfyM^J5_3q4px>u;9-q4x|2G=2B(9J-#K^x^d0avS69Z)LM)@|wBd
zs?X@C+iq*nf|s+-B3ba^W?D}&tBw>rYlB9`m>3Ud^r@)!Lj-sH9NE|fy!^$jx>nD4
zN~+kg02j!ZAT?uOH7s61aJc0uImt$r(cb|ZoN8vd60yW&4A=1H-Nz6k`kkdB=ye)?
z7H1N3U>PM*uEYcm4q^qSMn#(Wu!5*cf?w3F7~DLd{v4Uc<$io9^c(v}c(pXi{9L;5
z>&X9v5gI%p$7gCA|DwJmsjGf7E@{xj4a7uLF_)BoESZ-<v6*XEHjmrz^3S{E0onti
zJYot<N-|oJhc)ym4ZoHA(l@yO6mp=LrBzoTfD`~2QDf#2S7v<yJwVmZvMT{{WH)a;
zSPOSaIzuaz<D;58-^nLm@Y&S*y`b3drdK&H;x>?jns;M`1Z{5p^03vqwrag^6`bi$
zDW0Ov<Cyj@ZEyIw%|Bg!>5GKq`M0>n|K2#SO*~aFy!d2CHY-u9pEy4Oy_kcfFw=3%
zOI7S_TF~F(3jz=KCljgUp|?57*CpB)>=NHORX9(vP(p6G@NBuI`foXGB;J|!E3Z%R
zQ_De<_@XC6^rmq}Cg}>l>PnEPI|vf{6!VM-*`kW1GN(D6HB?#kzfW6#b)vNZpLs#A
z^Sj!Pyc;KMDFal|ot$MFjH^E0SU9MPHlsWfa(LmWY<n;9HMRe*)NPLe*US8etJExS
z__Y@L*XDj)uHH3aP+h`3e0`g?dK<MAW7T0fq)Vp0HW_V6*($d?_$stoP|YUEH_oz~
zBjSZlIJb=RsZood-u^i26$vNdaw6`?{FoWw?&HwkI|u*4js<I_K*$=gd<`5V0UM7x
zp<ER|Kqv@%(uF&58>hwu!P`|<Y}bcUp$fha08TIQAb#~eJ+d}sn`fq*M@N&(Y^5Yt
zqA@;>=O*m!)uE=d_Le@`lWGdIGri+z7(Ze)=R?T~-KwMBY8cD2Jq}kqID9ymSFNS&
z8#&FvCmDm{_f$%_vuFkA(Nt2%&ZR+PwJzvqbN4BzGMI~S=o7!8MAacyLP)?=;QC4~
zEG%*jwwcBY^tD+&@+?w=a^a3O6v%#1oJQm;H?;k3W!d;Ubh|aPw%u_uPHNe1lSA~c
z?n>a*liOaa%+GJxy%krEcq{(>&FSB`3jgOf?AI-S63{2Q^UWJjjx2`l6qhwihSTdG
zbO}SacdCm@fm38F00d(xB`whDa1s5tPX5MB>L?`3H^e6*@Mlh0^6%}$z|R1kVX~uu
zGl=9Jqgmmtbr1iYEj6{bal!MDEJ-#heH1-#RKxLc_@tNz54S;+)wq>=>48ppx$iTS
z#35Gm*vOqZon1i(Isgz!1`w9~KBWFgFjiP8c+LeNi*yNeMXD$h9$yh+%nsqKNotI^
zNZ~&ZjMX?1C_%834+Wy*Hr@`>T>kc*We+70o=B{MFl7T-i>uq`h7wFT85e-jKAg+|
z85r$EkrcPtP`(RAD#}OV(zhiC^f>Mk7A|(_gH?vqhyT&MoT{<z@TF&<)1<pp5kf(D
z>oS-lxVo!Cs#jD*&3~|(7J{y0)c;vIikpny%RP`ck`zKVsJ%;T3^0(q9^~u+2q6yE
zm&Zi){DeLS3uQ6DGI#`l9{k)1gh7F@PY)2#)*D7J?$ZXAj5nNA2+%2qFIW*;<^X<q
zD$XEY6qI4Gi5}A5at1;`SUjn_APGQ5LsAUH1(p$UCLk~?qEl$1+TrbV_qHSx2e1(!
ztypIxEnT38lDyPEe=nb`SMm6gVd^C4!~LJx-+uoG!gXVgbL`}}Xyf`FPjbHf&eFIF
z!($1yblmm7Fm=57*8|F3aJV8sEpyrioZGkHfo#)0fGA7_3p}?1onJ(aI{PcOQT=?e
zY>4+0SpVZr3=Rhf06^+M@UB*iKLB&Nh7fh{v^3{r04$<u80Y30m>BgNU2KLm@9BdF
z*~eV7m+fy1pDnLDUmQ3c|A!Ki()-jfssZ$Us)G+-NYJ27Nt=XfPX9GdB>bajOsNwB
zOG-E@zbqfMvHQ|J@^|l2l&noI^X}t1i+$B5c672zZK*D+`YyZYSbtT`F1=c6r#@*#
zE$jtHZ!_IrSl_+cl~FZ6-J7wY)N3+CCpywILHCqBk+|w}AS(b}lPClr-R=eeoH!K>
zfbe>vVO*aH1TW4pEF4m^J#_J}==Etj<AhO!%2k_wsV|%6aU?0@Kue+rM+KVfk!?ys
ztJ3cgvmN4pbq!c>s*<@Nth}|F_B>45yB?au`}t7s#e4UDH0Z67k`fqOO(&CBxZN3V
z%NVT#m}YEt070DEEgiL;giI<K2n&qM#IFEPxuT?FIm?T-hee4&REu&60EU?nVA=BM
z0qC4p?Eq$1IxAwFG~e)HC3HuO0*3<T<;jbIK#m%$0=%@+l-%pVl3^?f1pxL*k;jq_
zo#GfZOE>}0N<3<>)VpTuOI=lKdHIY{*+y>1+e1eX;3s|5DMfEz{$W7B$L*fm{((#J
zoa9_^ccuFXrRm*8Wv0T-3AlNIIIXLHwfwu&U$6Iqjaqt8qjH@&+P8`G8&$`trapM*
zoqv6Uk_D@D98q@JqQ$%%Kbc5-X*z=S^mDJ<vD!1ifrV8pc`=;4-S+{`E!9c@6cZ6Z
zETy0C4t^Uay^$csz=<Qe+r<+~@+qjruV7I3lw=X%w!$P)fLBYSh*RD76pxE#wxcP=
z<gjlIC*;li5I*C#&*9(Azgt^N8-Dg`y0&5c;JM1zw@1%E{`uI$<vncqgh#(j4k)Sy
zC_Ol1cYwzQ77oH@?fjrub2rM>=Q#apTYbG4UXV<?pblGeP;ia)FF4Q2@Srsj(B~XN
zU8>}(r*jST+davkfy1Z)1`1U{TqM>Hg`e)@N(zX=%3c*uuu27J_wQF*4D06pnv{50
za;$FkG$IYn`iPQGkC{2Z=^H3G#)M&yyTd2Guum_pI^;iII-0Nr?$CI_Lv3EGru%DU
zb|OtT;#tt>)HpPs8ks$kd%m7}vJagQ3zx5uMOM5*CMUYjYSL+V>t&;BU&I)H`TVO+
zOXpjdn%k@wRqC91kGnoO7N7@XH2~t3LRt*yTk7P&oxv0;kPB~6vdM-4wWl;s?{#hd
z*`5!*Mrg<sKBJsDESsk{Tu-oDo5I%{Id(a9*ZcU|fT+&G8X0YWPjrPtHrRYNIFL(w
zz8s5Z#K+(-c&c@Xu~ocx^mLZlk_44Wac}}a^^Y7D&qPE6kaa8lU~TOKV5u3BctDR#
z6+w`c;e$X>9s{f>QNCv6qg)zh9`%F6L|nWa3dp5%d<DQJ^9Y+8Q*xXEKy-{FGqP?2
zR@iZ1j4?_1Bt5sA&@eVTc6CAb!Wcz(U--lU?HPIi++`&N@I&Ht-ysZU?dZ~iB;n1-
zT;aZ>c1^X2`>Mz8i6?K`q%2-j;c(Tz==U`kQ)z2(Jhf4)rR}ZIZ+Q`Bu{-3g?7~yC
zwVskO4KIN={4ur*qv{Qk@TRuC!~432VYianwaffEEzQ=aEhH3+8P7DvQibvmPWb03
zk^zJ?;tqr3##X|WD1<Z@41(5cYDL6Q@_g_Cv=;=d&?I|95%pRrEVRNlW_Pq#Ik;9>
zaMS?*dmzQ|8Aedbq!=LIl>k7x7uf^h@l*NTGW`8ZyV+oS#+rjx3juJa6~((}kA5$A
zF-6^71juu=i*I~Uf*_6-A9u<LfSspniJS>>TQjzo(6sbTnzkt#w7Su2WN)t`a`(j`
zrQR5#52}7i++%q};lB-5$B>SekaI8b5>3F-7&V4)NoulH0Eb%<AtvkH<eH#tX{EiX
zBde(9=^v<V$Sz}aOgF-|AHDxUf9uwxh5zH|tlye^|2BTF*hY?&W+O*8(u|ar7Ep0?
zih#%m%+aGmK-!UxZluJ~BLzf6K&3=LR76Bj)Mwx457>R|hwHfFJYVPe{?`QiBBt~5
z*V|1?%S5GDouaEjuUc&r9%04!`h6NQD?BoB5f}zhq*rEX^;L7fA&F<utgOQs#`C+p
zOQU@wg*SCA^Tr@@VsG4eMb;1VkDhILY~ft0Vr(sAD<e2Ei`l)163|u$OJvQ&Z}y&g
zLH}V`HfBfXVRyt8QBn$LyU{m`!esIJzW1VGdPO<mu2~w<5*Wo`S!iGbAvmV?X;1Ba
z3K}l;q2$HXz4Hw}-a0xwvHY|m+<3DgP~w&h!XuT9Q4lg80wrV~&=F>ND1agznwGa1
z$o~MP4Ya2K$S(>2_mE5$QeTDFF8s^1-OfL};X+h4sOS6z0>EzIXGZ|#C_Sd!TLS>m
zxaNd#nZgffBm7&y-YdvOgW6L$P$Ut3csnNFnz=3ic2}97tWj$y*hz<U6%DT^uKY2f
zuwQd1-qL&Zn9Y~70;Uq~^*XBJN^L`Mt6+c7Mc$f0e+BIUC*^alQ*hO$h9?zV6YunX
zS%uvBBQv=9tM3}hM4Rfo*Aag5-(r!~6NhsF1Fz=$Y%HOM(bc2-8HV@(i>~g{p-O8F
z(6Q2J7XXgk&jR#Mf>SIEL`?te;C@)dObxe@I|rfXurtJrfPEp6z42ZZsuo5IgQ!wL
z(5-dhD7$;z$hc+g!|f2$ah?q?X6|B#Jd>1U6?L7s9naF1&-b1_{VBLD!tegqn{OH!
zVN@B)JV+-AYX<ni6`Rql)0H^qBT7@XLqUhV;~BEZyWsH>w!h?2pZ=_9CAFB%-p>@i
z^+XUwz8~Xi2_G`OEQM>2jzP&wlcL`O#@7aF5j>1GG5HK#_cgl`i#A(YK8a^)Ei<#D
zo4~?7q!ApisM2i-N@2CqO;Mc27iGeT5+8=O9V%?dmuaBZYAB@C&7`y`i&NIfW>e2{
z7h(OR%=%rmpjSmilut}wNF>lME9_<ao=aPu`oOPl)G3(z&EG8PJR~kH9~F^E+y>)B
zz#%k{o|E0fA`YLM_~}kA`Yrt6Ua?Jz(9Wj{N@7*(LQc_dmU`|SPrHF`F{Km-eY>k2
zVFLi_MKwmCpw6Zf^c@L+JHDZ6$*CO!3XeCx>+Zk^e%s923Q5>3dMV$>sJHO0s3g=S
znBARonepIEY{`Kt#B*lYCJT-ZmCw|QC0(GE>n7g*3(l8{K?4v#i3N=W{K|9KRvEez
zfJ}f6<v}+P8Aow^y5`MKq9>4;2mm9{#Yg~{fEBiAiU3F*07ps+EYSfr)4A@xgC~^)
z9E=@toCHaGgqMu*(_}KYCIHUyndZF|1_l+7kjQu0u4RIuZR%+_cY(7C^$+J@HOb?N
zW-Nktu)e>gl2;n0+1e$n^*HqoiPw3sHl{{;v!oB)L!}JXuIFps6{h}f6#QPp+eTB-
z-ssHBm?@LTS-+0ELV3PXQU+h9bwiPB_X#4lNsw%+`L(3t+x*-~g(~t%(Z+C#&iGmH
z|4~zC9)P+(oV^d;ZG>2ROXbd@{Cd;u(xslnR8;o39`&OwGdd5^kw{pf<p_bp0-#w!
z5e1Jq!6LYs0a7%<?{nhjB2$Ic1pD)pKr5tvA#t|1G^mvg_dchrZvdJz<uy7LKGwt7
z0^bWvOJ*c;M>R!;_R|g|ss{}%h3--Te5m{y1m2uZpv5v3y2q!IO0qUNMJ$toii#j_
zbh3(AmZF6WImJx#^{Cs!0056Z<|m@`$fy{q7?E2J!>V5B64pTL*FFnI>R(gY)X-RY
zl<s$iY))3J)IrJ67xB}v2B0Y^^|)QW@!R!Ej7P1mLaizc<@-oWm*y87(2F4hhQy(P
zn$os5zCj){-6K@%*`faaahs%RMJ}}7L$ty{V8BzVVnS2QNL^{Gpu^5c-=?+Z)Cp^v
z$!ybJL`@regJZ3UhP$swbcqe-bAn81`jIV^unKQEMY@g`e>&{X`o{J~A3=Sj7;~Au
zl3yfBM%5lWTOffU=D^6VP}5$sT8P<3-Oyw;{eZ*t$#@cq_JIw%*(CyNG`wq*Sm2fp
z9#19j3$RwF+hoGILz>oX<3Zl>T~}bV*0L@cfdi#jb5jDI4q0-(ooEz?wj}_82~&6)
zXZF-t9a1zy(56|N@O;*vV%Ux3u249SR=kaU{1QNiz|#?y2?{?nY(Cd?YY{YO34m<O
zmL%<mpg3>B_y(Gq1<$r)3ff@~?};%Lj&-hp2mFOB>BJ({38_UyZhAt!8{uq|KI~mu
zZDx8oDZS8z-V(=*FU)a><5h{_Wo0P{Ct7gCOSKf&R)t=IRBOB>amJ-l{dP>-5oVl`
zylHnB7SbzVcP|yE$unlNj6X3mqF$6<aRz<4=z`|?A=!B63j^go%WMuoT3b5w4a@8a
zhoXZT_Fi5jSOj;o*YC1CB^-K61+X{ZxWq)#TR>Gw64$7h&8x92t$*rL;;}eMznD_A
z4`37?o-U;?I1+GKa3bSFH2cfhXd0dTyfBUjv28WGy_ET=7*WcWGobtFH3J3Arq4l<
zjj~~ixy;$JBG#Je|80`jkH{T1wR9=|`xeZ`6#(cGDB*eex1aIn@IFK5=GHI+sjT(?
zN_4{YV8!$Y4|VWQ*=w=^h0lq1i<1;s0ovKMBL&u}Qm45v2qf_U+E4B+J{m-Jw{WsQ
z5{!LpaAgoD(CE_Fpg*_&LWBOI%Axep*|;P?5m;diXMT~Q{Gz<!YE;*YFznkoq^iT8
zs|RB*eygi$Ne&o;%lFSIg(<ZTDOB}w4=^!^6e$a+id4|TpdMQDX4rvLHcV8SHZh67
zyPb;)OO)-UqXs1c74VyP(z62L_=u!@ODM&Pc598su@V5YII;ngAGp5A6wx&SC=?_P
ze6wMLTktxO>~vZOT}5n7-(43~+L_AAbd4wL8(h(S=ER2VE~U2`&1;^T$)w$-Z^120
z<9FQh)bHB!!qWZjjW2=ouTzkiBL-(Wz(4w&Vj$gY5F87@X0OI)0uZzc*Q_jf@7@JO
zCfw#G)2wlva1tHC?-e&1jN%~xdQgNads<ok>^=6b59taIm`IWB^;L<;Q#f~M6_%MW
z%f_F4gSI=%%z7p$cmg28l{L*+5>2Y(24%9O`9ae-lO9s;v@e090RZeL3JC$OMTo|n
zC|v3KY-M;2ghH*B8k>;mftn3T#OECIoT|%Th)V?obqM#JcdsRFi#l$77>=dNBruFu
zQzz?m{zz+V^mo6>?;fbWv<n#zzFg;h*ZcQK!SBXPy4QTT{I13};%;|Hi3cfeUw=#^
z6Vq0TGXLph7C95%O#;zdF}xaS^|kokZd+Ca3K;7uV?w<qayxK2--Pv?0ob54Gz^}m
z!mq82#-C<~vNG5YF+_H>n8TR*3QU#f&ECpn7C5p~b`vX0;ZznqG86cOOrTRJ`DNaS
zi%x87t~Ic-p5LExSaw|<BH1GZ?zQfY7Vj>M@@Cr-xo_2%>b~K&gOQgK7RUsAWS84z
zyO@2eoyy!;Vp6nU4n|7Ic#vUlPmWT6Ec7e-&IwQ6Dkid;gfN5g7(~9*>a%Ir2s$E<
zE{c1)rfa&|uMz3)RMniyfG(8vc-_l3oJ|+W=M$PK`{r5u(MJfso4G*dvv=?K4_?%X
zmJOd>WD_#HDe1=a!3O%Lq6R$6#%m+?MdTKpO(oB4ij-S8kBIf;yVXDM)m~nsS9Ys&
z9NPX*iL%Q)z~_Uz4KMIbAP3Uz0JMcc*9^c-!x35f^j%RqJG8gWm4y>d5bgk)X&y%t
z{K@d7{CkO?OcSYh47N)7MU(n;$t4f<=+CrTM}*s7eH9tMaO2KDgdhs?mvx(ty(r-~
zsjpac7E+473vW(=QLjieUxWcp>a*#CGO<$K6+>rh(FoM$EQ)Z#Nq}iSB1y$`zlY#d
zW18a$>Ms(pq364d<FNJsa6Mr;5sVX!J^&z*8hUZPUo8#^=n0GicVg?!Vlte?{t-*a
zeaTFG{DCh4u!}zjrOOYXqYa4hGl8rt#aJr}+5KhQc|lNsvZJHm(HmegHnznFo@^I4
zYZd!tALy#gW(;Mg&#cae^AtZ$E8OxfqE8z|@cNf%iWC)2R!W_ezW!A5wWsFUn|kk+
zir0p^rly0MVz+&c@vpmvUM~iwYzJ)LtgTBU1euPpjKsb$jw?zJy;|Ok;&{mPtiBgY
zNkQZ!zA+Hdced*8#|$)-C0WB~?qFOOn@I~L>D*})`}o2WnB6oGa<d8xg-4A5E<c!(
zULYm^T{`FZ%%)?Xb8&g?83)Fio%|w&7p$$3OF+~m5}*ibB=DOp8jzdJ1i%kjuap<a
z;i#pioqC7fmb|>Op**tb@r)N7GIc<^?+GWYdptj)Q?`Vz3PfgxG*u!1c-YT)l7~FO
z7Dh-+$czQ5&6tSy9!Cw(qH4M6WO~YE#?<5HvJafZOuy^<%kzBJC#yo(+XS2P#9RKr
zfeZ!(0k>~|LPu}n64UW_Bu9Iu{nBpcuO!blRD61*nDR=PN%H3+<>>CW^2Q;Z#^GD=
zS~u({W^oKxBupJ^*v~obDV?Z`W+0NE;<iU#l&r2zZ@oVcSH5%J?JipRD&%_c-_PZv
z*WsPlW7Wi-?b*1|R5AJNq=q4DwtZ^!<MrH}wG!(rL-DTkJbz!ZoJ#uJUr8v{SR3`q
zO!m#!5Mw7k7js0jq_%oy3i~Njj;EDj;BPzCWc<4qzgIOxMjs{7p`c8~9W^CiCpnR*
zh9c?=ks1nT>ZJ?u#fIF1+a%d+@Np!$HTpf^MrAfp>FE1>Ol<y33@L~sn8pR9#em+%
zhNy4h=mAn)qVOLApdO1iEVvs8j*W@MDwAJ_aozioR)&+wCxG?QB*=#oD4_SAgrWt-
zPZFM&0p|`BfJOfvfSzMx7<i7Cryz!1wg6KxMheT=W<Ng?H0Vx&e>#u>B-qj@@V+CU
z-BpwT9Hi_6;0H9Cir1?#%OV*7@Vt(?8CF&EH43lKlpbA8Si(7J#!C(Pk}oxIk*_P|
zIzM&&#L;CHvtrZkazm}xYT`vu|GSWLzj71dcOGoq=+tr&{Fy<;H%6XC7!>4AzP)zJ
zzwPtSVaD$UUZydH$!d7}XgTP($>?8Oz>lXr>AlyVt_BBbG0_6q&({1qHBij8`+u7P
z8Z&3lNi)r?`U5QeGULAh$PGY#>_~O*XF~5?0Oqj|EL^MuKo=Og0QS2-qnc_nQ4G@e
z|H!QAE`HUdYM6SGsNEO#rtWE8SYn0{;1o3^0ibk=W6JkwrRQtre$f6MJR=}<;q(Lm
z!GC`NG!UkU1g-j&Ue;=<dYeY&pyl@Yb_E9Y?Pd#Yx({Pt7~?V2Y?(BXai%G|0<ET%
z(kcyWBhs@ng#PipasbP~Qk|@v*DhYi#v5Km0p0Zh;H+5tUb>c10E|!oz(N@nl@;}g
zM?Vx)XyBlrEZb#0y$);X#v0v^Z+;QQu?Ce@TIoIH{8GH(mk2NsZ7GW+)B3l|NK3*A
z(V6r8aRjquF6u0!D581RQt=OZT!y2_m72DdJU#nk7#O)U-DdZ0F^n@&xJ~)h?blAJ
z_tM`gz)#OUy=ewJtvco14cK&MFmCf#P&@p@t6?3>&bm^Qb1&p-#P41mr*A*{K3)!V
zxav`t!bJXfE%f@Xq8m5t+bt*f(X&vzoR@9%>XZ|Bc;pEUX?p97{PiH3#s=Kl6!t5X
zB7m^wtN`?ly7Am@SkE0{b<O4bZm(Zf_Xv$`OYqb+_8nCyDsXl`aI=3k8Q(Rj?C3Jy
zVC&<8x*qpsx!yvAdq~#zkHh_5l1YcQ<b{E6+&~hM89d}Vm{u=@hIX_*M!WKHl4%v+
z@t)#H!gT<O0UKKbIcRx?!5x4vVI2>+=lduCmf0T=G#T1m0Z>Twhy>8^RR!9zz>B|x
zXmjz*-_rc(z7RD8EQtbOQf=@ZL9)A@UEUhV$sy0o<+MF>OML@?W7e!`mk|fuyN~5z
z_AH-JghDZ<X8*5wSm^BpL_dM&l)cv98U9}*LBt`>#MFbFfSdE^+iqsyeb?RmPg&qp
zv2O6_#RNm^N!NYGHk|LP&?on?tUsUIhb>I@n5p@m%~HBJZcO$u6L!||g;aEi<6LXZ
z-UR)?tLBh-4z}3QWt4&Pm0Js0A>z`E9lFAO`8eR{IvNOJ9^{oZK>>W_kxm3aSn~oA
z01fT35=8f6O|gI>QIVCWEd_xtBVMMbkaJ%j8eUjq1FZr7MZYqkqgliN$VU+btzUYX
z54}D-XZWNU42&n4VTkF%IpN!>i-ij}tCMijTv+ck-gr0DA2A00OqeaBB-3%(b7J35
zg6E@2jy|~i84q`2s2CkVTP&0`=r?bE@XKbi?>1^Kt`H8@1^}#2+DB&{dS$vO^8xnk
zVUv3cp?w+%L=#}Vk!G>{3}?cTz~M6wBCzo~`^5)nGy^)G1V9kyKu6o&IATNs=m*dV
zF(h?ETp*7$xw+W@E7i88Ws_yn&sGeJ681IvbR~r~fMh)1;2d37TKo52u>o7@HmF<K
zFLkBiIAOLtyU95aXq8h|d60VhHYuB<r-@fU=ympxyL3`SliJ<RotC6aWX^J1@T}9r
z75WM<lbnZ!e+)$aU@Mj166SlZS$I&+JVm<xQ_Rd~2U7s(RN6?=0NXNw=ZlkX^pQea
z&(AkKKg+$b_i{TEc1&$=VHm(gj>{p*qD2Vj2HEOI2QYf`1}2pc?X!T%keRjgd5z?%
zTGW!0#}j%Xp*6NzmZzfu1TBAn#2CYr)gWn<nNCBhuBVj^(#mY<L30SW+WLleM#zC6
zq@89DHj=^A%kT^J^e&2{=izl??42^$cpKV4c4z4@!=T{obvw!Um%IY?&CxEZmG{OJ
zwrT!+REF>s)Sy@sV_B{ZNI@nRaHebZJl^97VZ1f&bs1SEQ<9NbeQ|`GN%O;NP`CFh
z1%D}HGLoK-c_!FjGbh_5pr+nm`juf>bflKF0pUvCy3(|ORD1NU<FzyF{zuQBnv!qj
zD>bI|NI`sY3RkAwMKG}vS;K=1^btk#HF_Zvrm<3+aB8tug5BkF#Hvhii<GmKkc*db
zoT@Qu?n~L|#totJF1I0lJ6Rjz#~hN(9mDgzw^+)qKHmfVs;Im@#aT`m-ZE;=$ez;m
zT>1f2^>NUKOVGuNpaBpEA8J+;#+(mR#&ZBIStngWVnIfbvp*a!ztS%jt`Bn95Jz|~
zqI2(KW?fkf+_TGXnWm!ssbzOWgqORxyALxW-z^Z5cU>k9J_<rrJeastB;(t!a7&UH
zS#GL<$GsxmFIczb5fIVI+CAsLCIyWp?=-K6HOp3toG(}2Pe_bt5G48AM&nYMHf*lh
zlK@UIm>0Y><*#4yx&5)fwnuJ|V5vgkEkB2QIa5YIWy4)rKR@f}O3Q|<zFa|xYJ4a8
zM(nuc<h+}L<@rjio>p^&yq^8@+AQxTLuKGJDTH3CZ*j%2qORfcd^$()-o)vZBkjJX
zo1ja3;RBm0_@3vb47$E)d~QbqI!`K9wBR0$$N_bkR+Q;Dbhh{gff3{~n76v5`d9kS
zm!yZhwK-JcKke{t9mkG=OX`D`crTOYL#5gIVJ-q7arLL&?N?VO92YcqWaT_I^PKmV
z@9dGdA!XlAbTa0fZ7KY;UWWrcZ!8#SBi=V|X;!UYd(57`J(@;cvL!gp0zlun7#epT
zOJ4$^MA2E`0TmkN4S;1#lAeh3aQ@G_0BF-3p87%$NFqaCLE=Y8zo|GD(07M(F>7`J
z1nWyrq2Z#7N+WL(zjW8QA;KXH0$Mfd$QwqbrJ%sb8$3sV$S(KN`M&=gzXV{iwQKZ#
z{Fs){Yf*~d{p%4aMhLlnMtS}BjC1qy<CLo7{`{c%4Q-C=YZunl)gOIc7n)1#Z|72K
z`ULuSE8uT3{VF7Q=yTtS4fNl>nzI6P7Bue{OReOEewpFpt1PX16(?q8M+pJqe3%I%
zkTn%F&nX=Sc3+&YF>?2rxAR~-8*&(r@3~+^uc6?qkp}!V@JjdX5i(8&zS6wTc|g(w
zwrvU__0tRUA}vgGb{6f1sxJmDc=#Q^F88~F%+%c_sFRCJ29GnQ{sGO;7!D|LK|c8S
zTXaS@=!hh<IHOmwjk+7PLPmXpem>_0XdCqm#}q+uJ&uP2K!TdJKnK^ws;{G=4a2-b
z3EQ?a{!9~yIYwyX5XZ(gLQ>d6E6&7l!y|3;%Q)1ARw5#3P%cPPNWo)~3??94C(?kI
zj?Pqw#QZCGX)a63jJzfsoPCMu?@HP@(6s6@z2!u;Iww2xw)}9{JO12<h_}!O-057-
z(>v#lr>dscN`s|&cIn=i&%F1p)ua!?R2{GHZeKRuHi-{*uT{F5j@}#rr%m^iTQgP!
zc;Rg$YONWsi5_k20oBR<Tegc=*Uz@T*hw%uf1nru%pTs%ehGs;3Bh6V+xW`SaAVoK
zkGEWDhsI8f=%x>YV1T*1c)dxSEo=?gLj^Q?{rwel5)dL&D9>Mp)I`Voklc^+wmNge
z0(vq?7YHbj;TIuj^Ni<Fli$84hRT1zMnHe2Luy}Iq!CIood$%#!iYd4?sTZG;J~&p
zsFK&c764pf6cZv&xYd1zpBxmp*&_&A%_(~)$e7S7jgbS?z-w~12r@nH#~o<;ht#!4
zkbTYTc4CQ6$hCZ?jmX}?`&{n)w?DS_&1{Kdj`ji<w7`XTzE<yT6hBJd|0l!v3A|PE
zWb5~hnfpV!wzENhJVjR_z#n_f7xu2dZ(9C^pR8mosn`JLg*Qy66xC+g)iTZ;q3J7C
za7NirAg?U7@5JHQc2zol&EemyBZrzBPry(g0JM=)$IU;C<M0jOAMg^z3G(~JIC!+{
z$#N$R7Qv>!Tq*BcuPl8ZGb8%}EQ3QBu743qH26Xb8&Ej(31C=)DHuP%iKC$#F&L7d
z?uKZ(NLQMqc0XOSq<?unN^Ua~&RZeE@Nkpo;X_&-&a5H-`HWLwH$h1MNSo~^y9%G?
zs51b{<3sJUl{sm;=ke}dlJ$Jon5pDVU<QJxszx~O$lu0bPWF<B>O&70P)}h2OCJY*
zVJRpeL5Uc<GwH=Gco=Dgrq^84rN<@Y;#-Sob=vZbt?>Lb=ye6C2?)Dj%w!Lw>T^42
zsoO>YM_aNj5${^Excv;?+U7jYioJOK`$g|C<5!4FpU16}8TCd8t8<-7ti8I-%&XSo
zo4I@fFn>F7UiU^_T<BZ7g@5+8rEVXB)1_S3LR+hz9Infr?QEx4wRF{OIO``z8d^Jj
zAeln5>AglzRMLY0J8tj6N0@K>K9$2C52z9`d%u)4cjZc)t5Xa{vqarJ_GEZ3ZZu1N
z-MF|}>Fl=_uqT5y(;k@*(XI649>u@=rmdfSY|<D8n9d5_$hXErA7b`u_Lr{XpcazF
zAb=4}_7J>~d(AH=lIoFw1Hd@p6mJf#8X4~_?L7jsnyB{A7$?Eus|IL6(4ef*=zO6l
zA4;SdZQvy21Ry87y+kNn?tZr^=)T1EyBQ@);^8uAMip?@Wk|jX=)~{k{2v+Y9<@xF
zD16_0NJY3zP1)^%C;Yr5NImeo>Nvt>?7jNnp-RT|`OQ!buJ(q!o<CcO=Wl#GPZoSJ
zcl&SnA%#PCKo+jSwMf{czjAX)n)qHfuvxRxRXQg1`e2`+iYuS~V!=9&b5w;3lMop_
zOGI;D^mf!N|Me>=K?BJb@cljSr=2TC*HPv4LPG;SA1nbcZUHo#vil+33r5J=Fueya
z5(vYP&<?`{Ra+gt*IpPpIpuIEN!sL2mgVHwc*DH+k>}&p_bkmV|Nb7KIQb?2hKFN1
zS&bL){2&<)p9LPXD<D%sE8{xYyKy@$@}tz-h{@Xi&KV!Yl#3ksi=tW@F}#9Phfj>S
zX`mqnz#`APg3+{bB)oLurU1al9`H^ogO6@wQ1_rddywy%;!~R=J?pe*LEp%#O9H&y
z+{4aNs-bDT!IBsARnNU9sJ#*<wah9QMZWz!=yyv})LBd9`oUVV0Ke@s{$LhEW{hL5
zzJ2~R0R+z1ukxyDX?fNupUJtqb5#i)5+Yypg={As!c?lV&J6XJ1*(;hYo!bq1fgN{
z?E0G9c*vy<jMdPYmY0jeNkY{i%KcEr$2z(C+ki_0^<$)is=fyo2&~5Lt1Bt^7YoKT
zMX4-}-IUS|h-<I&BVM<B8m-vp=#k?v!XJn<m#)N5ckFnI`CtG}y-ovfW9FOQCdQ!(
zfP+oJ2#4_CZ7hWsRC@$?q{?_U3W0t|8hB@KPeM1@e&XpL3g3-vV2_A*5X!sgE#0RL
z^5g2f>I3h@?!D88nrPK6-VI#+BU0HfnrEVZ{&M18|BC_Dh?w&eFa4dKs5rGgZ`upc
z7zr>qi6(y`?DGVE{T%3d{rZr1@s!F&C-q+qdnv4M`13!YU?tN3M*vm&G0&f{n7=mV
zEk*|3zAb%HD#=E8R7OCdUaW&4hb!;aJAnZu9sRX)?%9T*Rc`{9kZ@|A$eFPB;9Xdx
zLWr=%$0>aBpp1Q?=HBWu=CqO)LB-ArtZ~?qyuxrYOlUHO6b>8Y1=mSb#aYX}Rw11i
zC`j9W@n5n+Q1Ne9cx_MprC~euO*##7SzN%ro#i`iNi_ge%@2-bH(U_Zf51YZ7f3P(
zi?(Zq-49Y!k}P)?hI-wstbZlyv@=vz+_L}LV8+g98xXCp!yJI1DuQSk;eo$&7a1X>
zg%2cvuF1_PUO>G3Gu*WJH}n$IRKQj9pEdc~v0W0iAL0!!MO~MI#cZWn|G-#_T<aJq
zh-pdcWG&;J5FG9bTB%Tun~@fEluzsO=We+n*yX&Qu2RwEPFyKUT3mSVSdyrow`zOp
zsQ!rP{4i{($TsPg(4B!^t?DvPpOnIIfV(Dn4E=^SE#2oNNGjLG7~RO3RJAlI>|G_?
zDle>TmH7M5Q{yK>^s?!ZpL8`Av7cVDZg=gyX$n-HHJcd=t|Mb9VBlCe8jUEdsx+Z6
z=ZR4+ZnR=(_Na%9;1x@2#FQ*bNz$l;Y2e?rI6Y=CqB#tI>ak<wU6VQZE^F&Zw{R!@
zC5C0YeHnAsA$6SpUy%4<h=e`!$s6LS^dzgr9B7bx+G2Tblq~e5H+{Kgz~Jj;j~`Qz
zy97@m`OZfffj_70gXB9a)NhmI6K({aEeyQ*R2-yQl2=OEw9-LF1^$A~6ugPyJ|CC#
z^TqM&Su@tz*D+=u`HeqA%z9ksV$Xbrp_-;n4}X~m9hF7kVQvPz0w?{v-`fQ+axLyB
z=eB0SAzRq3tvg3){@@p5=jP?8r4C!${|XBql^52w*QW3CSz-5p0(tm!Ie?CYc?mOK
zI*J5bF(iJ6MHxkjcj*c@l$=G^wBNINX3K}!b_hIM*$mH^c4v4Nq>zlL!tAE3SJe-!
zad>D8=?Cy2?h5!@OpKmuVrkB?x~sz3*O<_ft-?9<bx^5u{(@Pu4*{hgy$pJJMz>}I
z07{G#F*GN4eJ9SM;ANw7-DDBs;nsG>eQh<a!?#6N9VL_tS`I_+tm2$?WBL5~w7lCp
z$1)X*R$wh3w!%EJJ=S@q?f;u42*Bf461xSj_i$g@nZaz$J&z5YrtQ={-VcS;ZG8Vm
z@zy>QDQ!%4G`gy|*-L*zXoa|I<o3iO+!I|+kM5$4a)jb^$z9zkgTm#0CH=kd)Q(Jz
zrI(2XtVFA0KXLAQh==-2jPhNC#$xk~p`fD(X|HLc{5=ol@R84V%d2bj!UDoVTilXn
zI+x9PKMnuNkBgMbLJla`O6hS(7m(B3{~CU<yHx1`oX0nM)1SKF0%A><)iz$$PDMo@
zi?8J)aZ6}gNEj68{qzj9Nzu;;&P#e9Ej$ng-aR<JE&ZZ}UD@^bCg$?-qeY$>mE)Yf
z?&s5LBP#o>#gChcB5M3LZ_NZrykPk=XQ=t0_d(t2sv`THzwEIyjZ$&F*69Bgjv`?(
zfIG_1L5_zh(QpIx`h;GDGsSq_hQR2dA@IP2!g@l2{R4ybNOq>8c8GtO`X4UtW`h1g
zqxIf@odsaOv7qNw=^rbRo!fvG8*-OHtQml4T&2)dQU*wg_1eYPzYqP9(dJ^WG8ye?
z(5aqhgv)hTM<`hD1Io<EP7e;~P_izNa9jYC8HNXv>YswoA@KV6R0N#w-t$V50>`q&
zm9+6NwtUM8Ti!Mxpy}hM9RS=i<i+Ot_lX4zv?|gokoUsBW99l<L___i<I^mHP#+KD
zJQ)Tt4Y$AWBZLlG1PICJvmb6uRrfD^>d{<J2B19xYk=oRty#b`tOywqAwmDv;E4af
zl`n~owVUb%cm#_IuqU0=mLAmwfDRZypA`Jqnhj3!qY;=^C`>Qq02Tc_g+Ai=Y4gNh
zbR(7<JoOaeWz1&uxpz77DKXWGLzvaeZP^6dZW8m9oa(otJU8!GjF}r`5WKJ(L`<fJ
z(XTZN&xO$y8zI_bg@@9ODmRP?9Bq8hY|m0RgiWP2&==+_A4^z-#b&8gA1+()DkBS-
z&a2w1RL5Jhm2VYH&5NGOTYhgpE1FDO7*P|qt72lAkrP90fzY7kh`~Vut(_f&rpaN@
zado4<HRN(MaAB~&(SpHf@PSZB7jO^Dj{JM4cE<3r@wAE^lcCT-<$bEDSP~vL#-xOM
zRENCm7BeY%dmdz}G<nPKRh?$gc!lo)gI%4%f~-vU$M2{9RI}T4KGMjS$Gb8CT`;FB
zw>Dk-Ut3|JzaOvJU%n~3Yqbax>~w~hqOEnhau)pCjOYO{K3h!_K5g!j7Z(D+W0*6U
zpW{*~^x1EvDjXhH1bnwl7D`j*ZXZ%)u+De>#&88Rn9ZhCvdxpsdReb~;+dDu{7YMr
z?NhOgd}XRyD5rO4fV{`rX-a6Nn#Q2=V-uI&_wr@Sg*WPkd;Yd??JK3c7azMLKGK1?
z?e>~2us2MaMbzdc_bb^B{&;B#NrnZ8Ot(mUPS17Q<uU?YSm8~!5vgLoV(D))kpw_1
zuU48##PsDAD?8mBEJLMS^ON5bjQ4NwwZF_Wk`I_odwg;E3loimj-<p40hQ5o?n=;p
z$FV=oV4yu#YOy>?in=&W0sUP2QcHrl%@l4ey;`<P*!+Cw{$^-{n(Zh5hk7pxry!me
z*8k2MS!U4fklezcfyH>x;>BD7Xx}w|spTaBi2iknTe9lXM14+`=ere`+8_xdn~%kt
z)tC5ByY)so)*o*)Yt0tW<U!i27~d630C04o2zq%1E(rZ8UgfLU4%<A=I+X&pQkb)?
z@~JF*_bD*isztBdIeg_Yqn6q1AKs_!?yQBwhO%A};(Q!Ai_{(~>yUM=%;9$#IlNKC
z%G{btPW~U|gMKNtZZcIHO{`b#u9}XE={<40!Sk@<jrq>HHWnIOm|g38NpY>?$wD{%
z9LPI;PfCGZ{Wm7v;8eh~Aq|GEtQz1Jw)enW5o5@}L=x7?%ccaoalZ_&9Obw_+f*m!
zXPMjnpUuv)zJ##zNBfgzF3`avr4Jh~-<<WjRb|a9+<KvaX&+RT;dmgs9RrND{?N0W
z3l*yx{cdVIrZV<+vMTS({aklxx6zI?RhXH)DgOc>p~H@f)dXI-^BKe*^%r`{SWonb
zi;Yh&Lz^>)#;Cs>%4c1OG)FUkz*^`de^NvI$)^;>P}L8Wt5wxD0UzC+gQ4iR=joVz
z$#rr*4e=$aT&_NaFBS5YzEwVRRl7<aX1E@g_E^=ymFS9mTIH|UY!CIeGL6^FJNn!{
zY<`QCw?mz5S}wTYP;hb2KrjK>oK;AoCyMg$31vbH0s9kA8`7C{=4{;ngy1-q3;`hg
z{yCxQGH!lU-Zg+Oy=*J8dKaT<(#cuPVOeVpdLl#ObjbdhpU$T(pS)kZn4w&%iyKG*
zV*uWgwNVaJlnQjmG%e$G)m19S)oPf~H~szaL(!?DXi_7;c8tC~(^Pm3SLbV;r_VWr
zZCflaXr!@3|5lm|%`Wr4a>uHLLQW*5u+!9VhJox4a90Qz$5ETn@>OXZqY2F7)*5q5
zGjrn<YW5rD?IkiTw|-sHUSr(MQ@S3^i9hL~Vc~?ECm?;IC;$X0x}X76eIx*Gu071n
zGja_6#gkyfMb15>(;e3s5*-=cujq;mT;aT6?KJb*$h7ph{0>>zsa15u>so?Gj`M|G
z44W}OF2&RGvSrArQWdtfq=?ad*ndLFh`j2I8CDviwsDUvQA^9_Z)SXJqm{Wh;#rS1
z+d@C_VR<0ghx#X3c48*xr>>3g!_2H!ZAsWHfQUv2*(XQYNQsztuC@jNa>emxhkFF2
z5KtqT&GEwUb!m}gce$*-g5+=4WRt(~6%O<I)vDPYWw*}w*Lb5W?OiF;ROr3H?)tWy
zs{X7YVClH}C%kVqHC(2+RqFc2XM_#9Q?THf9^qM;jS1iIb!WY}th*1*$g{rHZA$E`
zqI4ZsevgpaNgPN!g8Sc5vIgZEk??e8$l=ER+dgB9q|)|6^|^>4!b`^GG&M8J7p`)f
zJ}<+T<|E?G|6_k;a^&`?!-BH1=crZvHJFsO>|=7iclCY5)BEe9?%gL4PjXs{7-xY;
z;yVpU(=2~9!SF;6K36GJt!DYqBk~UWtv5!-R~j-gYyJQ*(ojJ(Pd1!UrkjfqykJg#
zwSa6ewSO!M`E)mD@9&hGyNBF`lHM`mmF#H<uV6Jf3Pj)q02?zn03hiUYH`t~yl5Qy
zV}InZf#v;@Hi?5G-Gapc0_KVHadBf^Ij-#>1M<0~J^5ieLtyGLOzfP!tyCwdj=t+1
zEl2xb(wq|j&#XJQ2GBE)=Yf(U&m0qUoB(IJL#3OFa<Xy@%HRk8jhnkVepH&G!kTYd
z$M*>WDn%^K)^Hpk!l-W<4aNK62+z&jnpCD%w^eFi`>VA<&qJ~~tc>nxR1GXuiAVIW
zCSOk%7SY47pHZXYOFT4&mU?&2{Qe~A4k};t+28Q!bInvrj!3m?6BXOH=dAepVIuUk
z@a|GdmgluYivT8+FZpfOKNC9%wiH?MW$ou8Iyr&b>4sv8W$H>Vf9KkZvwoKo4FN=B
z6_imd%2;DgNPp6DPid4e!1j#e3c#{{sYuZ-DH{p@R?;?T*i4+`iRD0WUxJ0`5DNwh
z0-U=aPN#_Ir?ld;l(O?e-gVS@B`x~_d|BdIU2Kz%5+2p4!Qkx4kRoym1?0=wW}<F0
zPxAZzgnPb&4bNqJB7uq%SMp7pP3k4zCln-rZPfB(?ssBRuzX(JFMGJZfmK9W)SY{E
z*-BIWtI2J_WNKWEOGPiH*a-9}*Y5<oX{63ptfCc`CFwmt?lrOq=V>X_A`<mamY@E<
zEoEtC3}Wx@vsHNbpt-E$>92u)ODAMtG~<FzmSJo^sENtY?_pU^W1nmXIj%D^p1I5k
zNsb1vciTT@5>XxrL;#L9hbgqtkwX9gR;=(%yQZJ9tDeodMOMGE7u8(qxy}$$kQCLO
z=iHb_nM$NWISBs|YD|G<QPA2~h}0Pf#gLg5m4by-H)ki#*Rj0$z+(CsTX{)%pcwTX
zJ`xnK1dXRUztC4GL{*ANINudtx&gO|hACVDfEzG%*Pf97a`WjLDD}DcMo#yFbd7@4
zkbBJd4W?h&yvy+j^y_TIy`**9CtmV;UCHpSmx)~nRP$Ys0u6X&2A_y0Es24ahBI-G
z=-2K8>YMfJ6a0PW3TF?q>K|okJN6&uw;**#?jV>}b_F+g5L(<8()$!PjPAqFRCgZb
zIiCLC2mO_F+0t8jlgG~$6%;FkTkby3EypSGsI~NHP_7JAJQQRvG(cBKE6%HCx2imh
zJ+buAd3c0w8+g&$JD%dIn}shpM~#5Hr-6xBN<J%&Yz5t7NDX3aBNgSkD<V(+a@eU5
z%k6TLT=F)xTH2u^D;Q)qqvg6H4DSS);$;vurynxtz^%6PX7lq4Nfy%86<!$vfyD=k
zhR1Ig`qk#POX!=9XMt4;r9-My(FTKw`LoF^-JVzyC}Xa?CR~umG>6+#CEq=fnQ)Ie
z(<MGDnGlJ{l}tmms^pDlbm5!xT6+<aIgDMIjDf-Dm2VjFv|1-;6=F(f3+m2?8%%PT
z^2S{xM^z8I)E3%3vc<j`URyRgNE}IZHnxZ=ghbiK^Vk{^u)~eiz|Illb92Sk6@#xZ
zz3t~n)Vg)1q}?zr)5)w;`|g9sQmvb^vmSJ00FJfPz|rWa;QZz&&V0zc{nY4$c1n3U
zHmJbVg094FA&X%SzLLan06sUBmy5qkFf)alB9h4{@QxURf+QFl4N@QGuN<wMpv^vZ
zC-5+6kxQz=z=nzvvS!8lBZ91!9S+Z|5HZUs<0s-vcayt9sFZs!)62B-VCpQbK<E}d
zH;`CEK&qDjQr$^54VG_>`JY*E4bn617nHD~3k>0014r#WVVTVl051;+PXi5^z+@33
zN*XN8&dm+WA>2#j{-+*2nOrx@gocowdE_}SsY`cA4~<J!d_-(LoonGN`y+&*rc7#l
zd0kO3dAHvpuJgH7)WQVT8uFDjwXuItX7Z?`f`2V*icV2<(9$%X8hfngE?5!E<t#J9
z?xCW$0Yg3%<KRpaz7BT=*;kZ^KP>J|3mTi+pVq6>K1Zd&G_^7Lk}<y(q8aHcTP>vq
ztx~#~6xU|jLSmI;Y8jL@X#Bd^Rf5tmD5;O7%tZ=;AV_m2K02Xi1mX<P6mG*4Ish10
zn?12~0MUUAu6+w(RFeCfLa3NSSRA9FX(R4y&T(mHbeW?bU%MHVTx8-5x8^cYZ2IT!
zPPvmJ4gU(k67(x>N^z*k(iNLw!fiyeQ6SL#+Cfsq8B#oXR3H8SsEs%q6COUf7t`)i
zK>*g`p(qg41z>%;#;gxZeNhHDr)Kkr+&3k#>>MEfnT<Gp=!)ET)v~Pn;PJ}O(o;5m
zdg!|gw(*)?)%c)SRNwIp+s4=4N`-%%GTiGX0pWt_gr7G%7QQ~6dppJuKeG9`YqkG8
zdx87Z&F|Xjk1jRTzP<JPYFC0*_xpp$yDnYw8BeyeFK%A3VSKW(m7y2Xiha?k_V>(4
zyX)k_1jhs+ah?tQ1P3~JE9AR@`mdC#f7=V7`-ZhA0PbDk1i*joHi4Fv=m##^HRjZ0
z1SsXS1luL2i~x*ABhLb)4{K{{wrGv)c+d_^EYVL2MtiHZi<NIs3*(@a4UT;Ytoz9W
zeH@A|Utf~MASwA(N}5A2%ff|2hn;amaq6MW)OTs$?yp=@B+$fv7#Jo1;P-Y30zl%Y
zujn7sBN#yy66?P~!irjlZwUc|)WPmaQMaKn`8}_nPcNKN0P##aSK#6v=J-J+yqP&)
zC|ZFg_-&Ah01(rh83#cpqoA0N-+na%<6gRHi@OTUU~J;}3F%40s!8#ay(za*ZDMct
zIm?}wrF&YpU^RZr%(=1_%X0TRKg?&pmA#=6aW;1I%2U~~;Qo(4x&xa2yKMgB>gOLa
z&#TTG@4dcuWB8iS>ZPG^n`@8K;Sa{-M^sYmej232TrqunH1c*aPb5KTZ=X_qmF)dB
zR`~U?Y=WDlnx2K%l9<fSmWyVZzn5vFXWT~1)eoj+@`Izd1k4E4k|}F|0SZC_DOt11
z&VnPP_UM16VJ;qfJrGZ#0{4X8xc^gq-YjTXxSiAQQ~vzLaCfqZ9~)vvA~l)+%W!R=
zW>!ald23<EH~`KO9(QG%lb-p+48mC+-gw?*xrZyI{AwL8uOiCWJd~<U=ImE8x4+6;
z7j;KR(wKe+8DBB>i}>I%i2{f<xON|s^1REp;4vt5rkC<TcE6rC_YFG#s#u8rIp1Ra
zy}72<O2y&j<<Eg(A8#}9UhuRJ^DUidvr;}E5H<a*dZBar(es2zzKWq9>EJ(n-!~EO
z7r#&ayHjw~U3z0TxM#<==1h~EcKi<w_O-Z>b}9(wPMP0zcSj75RK-u}U7pJY{H^?1
z9@#{gx?i)Z_y%KXW;DwI(F^$@0Oqo71K8R$0Ex3K!_ljZ(V$_Gb(oO16mcB#pX#u8
zjYjUM>70J5j%kS>%A+=izg#0$6q`EWfzu*r@CoAM+M@C8SrmdJ(g@8IEkbwU;Rz5+
zq*>s0#3r-Ag~;)c44Kgo^T%hPblVwCe(LJCoJ!oalq;JTBG6qF)TzW~<!#Amob*Fd
zwe^~1cm^5(Czb~)#3><E?Y!<LS#Vz5T6>%kdiI!riMj>-Vp&`v_&do`Q-y>|kP(gm
zB@sTwY;G}gg&1(hlpz>E9C5U!1p~k(O2{8ayP+~VkInw^G`fDRo-@G+!|>{g$*xxm
zUD&P%XzoF?tg`T{@{WqyH|my}nloy_HykRr?pGh*(t9YaArWx-s_pQ1xkWyfIfs2W
z<+neD*_(9ohgb3JyKM^g_{mGzUp*{BGwNS0R^?y$S?ZvYCQi>qd6bGM-tTHwj*S>|
zE}zuHHbyre+w~)rl|^LJGYb~`pYa(gRE9g=&)68%gzG!fmpLf3ixX*GnT4qD9H0yi
zcZ{zEPhrlzWOUhyyJOqJ)!D4!`aK3So=H5WeYzEQyW7$jU(1s6>O}#&!=GtCki@O>
zHXitteun)^$;PPO@+Up3sWFeFU>wP^$U_HWZcvdEXpQGlG&D#`ftGk=AOP8{LuNs@
z)&N&6Q6)&GI!tJI1NEjO(hQK4G^x`VsN8*LnR<)%igsAx72^&g6?S+u#TVzCwqLOK
z?|9&IkXOR{<#<Nx)xFSz1sy)dllK$#S~rB>>GA&kk?sX6fd}sj1lGDOK6|b9Q#Z2z
zPN{$JAC>!^OrPJeJK~<myzhHd`C`?gaGZJLea7&?KkVh^cj@2BHmNdU>8&UQlSqJn
zBftn?tzpE4{M5(s)Jlz~R>PP171E7EbK36vo{Kv49DDYY%|H#`bnhYtvaXiF>E}k-
z;Q@dUtOW(&9t?jbucU+KX|0P-9&k+k4_m;h{{uGXU1>X<pLe<FP1d2o+p0i=8*Jcy
z>U(_+$z<p~b_)*|_?apY_erK^NHy0Kw=D=o+cFT%rIi(6e48WyXxaOm+<tCw_Q!Nn
zHk<DIHwsd<Z!L!_0gtlQi~43)%C8at?Zh)t5J?+go>hS003k+@QA#{2@f=VPD~9IC
z^EGVXOAc)|YFGN{RW#b+tM&02-1SRIMM<6S-x@8)fQUe`H>!CHJ0(XqE(ccYn7o)e
z(x`vpZg*+9oqP}Z*q83)eBtw!f}1QsKcny3bbmF?H{|zXgr(ZuzT8sK!BzD;jq_E6
zU&=(0a#@gerrk(?Rma((*X4p1p^}Bs^SRf2o#~4SKr`nMH$Wp#?33nnMG%3OXsXI{
z$5vd9rQ6lO8rjS2kb_q^28JqWWxt}u%TKbmj~8<jTJ~fC{rATGxwcAA7ySzGpKgvd
zG^}s+@m-tz+!oqR>+Sk3=ely^@{AJK>F@mCH>(XyO_-d_ASt$fBC}MMY@_FrCUU%Z
zPIo;8;!<7a?b5LZldez}&DR3w%1;}r%hZ~>_s_Ro4NR;0aJ?yieU-0Z)hg|Cp$YH7
zo_$MJdW~?T&bs2;dbyCVs<RLJMR)bEY+*P4OMm^OXh;t^-GUb)F0`fC%T2z{zFQxD
zc~4A@T!o9t-XF)|&|r3~p8?&$HU_*yqXBv(;yuXxND7iA#?Qq@r(1q|2W)gJ*!Ir(
zL|bpp`DY&Y5-s2C3ALTv9Z*$_9U}>WX|)aUv)PFT%_bPY-UL(Z<6*G`d8+Wu>!G}T
z>;wB>F0%$GfyY{Z{tc{;-)SFhHn=OXt@fWV%5XzaD1<R<4F!rZmx(^cqUp>xcx6C)
zfziux>%X|lVgS%P4*hUp5Sg!1>F0Q|N1bk3N`$1S=^L2BgP>A*OCkt7YF@!56bA(;
z2nV8inn*ZcfENUqG3)z~rcMaDbbbxWu$J${G6Aym0X9aRKJp%>IsFqE{H{=Yc>OB9
zcLxJw`XZ!pG1i@7m|7m2x>R+_5L@fOlAvvH^7z9-1#&rED&QGvn<Nsqloqgph<L*^
z{B~bz!@RYcp5LuuubQ6z-ND&%$V~Y*&U<<<!`y{U%7?@>SZY#TX)wzF`Pcg|U<gHX
ztxTJ>T=)4P1Bf~-?;{lmkEs$3bAYez#jonjmTv|)z4-=qk3HLxUG}J2HC}h%L?1~q
z6F)qmdpg$I_r<T7c}Ka?=VNI0wuI6LNb-(t=|{PvF8Ya&U%vB9e6ej@wVPH#;?iY(
z-K^XLxXRFblA=Q8c*l4!;9)5`$Sn?5N;C#^%gy72Gle0C9d&o$7L*o=_S@TDT42r3
zF%HVxn`rMEba2R*#=S3JIl34q9~%QQGH7$@-Cj-1eE+!cEhAUBilWdwtkH_=t6Tkc
zX!B3rkTFdAa43VphTN~@_AJ}AxYBa=MV_iyka%LS&LCEBj8{Pa9d5w+92yYBn4ha(
z<{OyFuYZ?ciT~v-_rdd*dij~AeD!H!XRh#l(d*h&VXC73zFzbR7|%buhJCbVz=WeY
zh52w}{5(uu#<NE%4livRaKm6yZYSR(hGJ{yt8f!>{>8B`g4vFY`s?8(RetDf5Q8IT
zz(o)eU(;`wEo71aF8u@=M8z5RMQ}ZnmjZyzpjq4M|7Dz*7-Xv&p4i3M*zFs2)_00C
zw(4I@_yLZSrgyTrIFH%q5OF_-204K6mGE&v`YKw5j5E|Dj08XjHgI0ERSH!v#G8kh
zm>hK2%!}K4rK{<d?4N6;53j<WX&Lt|VVPK%T$nz0_L;XZia%$y-y(erdYeFBp4Pau
zEpFx$ygYm2z9+o$jf*rFyjo4U+I+N(oO$<o7nc>Nn}cLan_2oMhbODcV`hCx0D~N`
zhSQ{Nmh5HPat2gGaC1lv(aVUVsVzjr_LtCL5|?lzZkwZOdTM>*#68_u-^5_w_5ru>
zpbBdNcZGWYLV{w$Y(p0DoB3C>Z<Sqp`}Y({PqS*wgTtF`pIR7oeja4yR~+3XfcU?V
zKor3?%&)e1u!8MYew~raR5kizg4B+IPMw3u6Y^80d{x#Gw6Lo?11lQd*l0bk));gD
z5Ze<bs*+ND-@@-;UMajTqGw*yGCTMnHW1Tlro*D5!@~b{$W!!(tb<LR7N?SWn78Ig
zhr{@g199se_{hWL@2{y~|DAyR1YD*2eB2tqKHx>`Bjf_zw~y|XTw{~12*;&}V&z>G
zJ{;;~Hi((cE<I5gYO2PpA6PJ&=8`TN;ba&orTL^H4VEr27<EZ(1G}^AGQy^?XW~=t
zZ4#s5=(wv87N+>U`tgwYzM-JehvKM~{sLq5`aydr4iLB%$&D$d=&NUe{_=+4KxI2v
zG?!i+n{zDZHBJxF7{LYKBZ(hplGIhA=Y+X&pnPSeRF$siz(Ibzi79ADn6_enOo1uI
z2r!D_;xLT>gNlZEQP^Zc_w13`!n@9v@)~Xx1N1N37>7svMLM+oVD?GddR8t0NqcYX
zr|x$)7vFrvlyy<3vk0dmlXk>d7cLeUj=+CEw4~S4qm$RGm0B?oe<NST+Htbza8GxR
zNndTz>w4vC!eF(zQHFzj$T!z(;ts3kF8Z+L>LU$93hh5=(WGehX!UidAx9Fp+gfe?
zL$NUHCNy3|AVJWC)yE??G_?8}lewp^l;^o}8BTp$Q`;w|>CVB{J^K`xpyBqm&r;_?
z5wG0r_B?d^XZW@w3&xYrmH}xIoQQs%+6#&J9Xi4veYjf|XxUhq|I`ZZ(+FI&L2uj@
zACeYg_^cLs+;WRYob}&DHjb~iz}rurMXH>2vGLrOv)qQ{Jv}rywMe^dr||8TX!m~v
z%lR7)9LKMi-`FW#5bvn$jpRtbCAaWHyC`4UEw8}e>Z;?5^56vR3+IOug&RQIRQVej
z3O2sL|50?7aZR^v82&GSZPe(7(cO&*qq{>&rKCYh1Vq_JjUL@Px{*>sM7qTZs34$#
zh@>JQqN2~c_v1cqyMOn6UFUfmp0-@ua=ev>tmNax!<udGJ-7bGM6_idxNUl5Sa2bN
zabZ#tCr!~&D8ALi>v5?=uC8=0@Gxv-pgq)wbg4Ip{6NdXwt@g5#Ey+QL2#$fP|U`6
z%|@app_ues?8H0hc)f}-0=|vM^*T<cnSrxxt8Ar5ka=S~9v)LnSbKg@J{N^GFZ3<Q
zyFJ4yo_V`XIN3m>V8=Fr+ZG~(7aA~-ey^RZ1#Cb`)e?NSv-n=6$=_ZAKvU!d4#d6J
zVS|}^xsALXmpU~q+J7i0o+~u&Pbr%vq+8^_;FT%*J;3gHV9ji?!C}fbdCey`uQH7n
z6V{C+TZ4}ZD?(?(B1S`5UxixFmRTPmeW=hrGOMX|wTnM)GgE0F7162Q3|eAfixm3s
zxlHH$s6xFZ@LSbK8_|m3cV!I1w`(xmFMm^`J86o<Ao==Cl~<UrsoWK!LwC3Z?5?%i
zxYP-*8U`2wB4qda_d5ZDcR1B6CcY!Tj<J9&Q3b3aAv<3tHtq$y-7PALc5#R<_57C0
z$5Heve%`a+(u=d`h~rv~_$*uO5P>#-jP*51#vNth`9fvH`))upZ<0iU=vTguv&kMk
zRIbPsjZh(BnHbAIobSHgZaY_H<nFx|)-328Gyk_G>wRzk;P>9KD`$VOj5^r2g^$=p
z?SC){M+@3(oI3RX(=2f6m#a@uq)Ldv3SKa0j`iD8JMn|g?CdD`jN>!L(Jy?B`9X!`
zWf1SMa<a}_*3Gn+m1}yL?C3Vs^ui10&sh?Nx}{s3w&4RNciqKDRDjTOsdN!O3{sYV
z4W$2`5^YY7P|%_K`GH7^DkR8e6%5sq5_Zx~Gg7r$nmFu_US)cwhP*mG5b%}rs*zmu
zD2KP8;>~a5B<NTFw`8PckND;mqmDTM+F%<lP^x^dL*(RRYSh3L{+_sXi^J4p&p1X(
z^J$&83*%FP3-iR=j(~tWZVYO=jo~FCBr4wyr}9z}-2s3+U2Wk9!YOb_kbpwM*!x>M
zKScE^Zo0N(-q<~}lFj7EMEts6+M4Jl{&M`hi)N*4qIR>Cy76k#Vrl#4ZG-7Bj-J~k
z{oa{Dl|F2tDxV{dFI3?hjcz$XMLds6uY`qBm$80&*unEKhu!DoXQ;i{qmsUg9?!ZV
zke;Up(FF$SrvX4JOdhNT>3<Cbv{fOe232i3#wd^O1@FYSx2TZo6R6<tbRVVxg`QiL
z+cq`%NgN5zcYjZPhIlP9?>@FKF<I)X8?}m2sx(u}j?uWYbY0uDMK!kmnz_Jx^nt`a
zmA`_d$}?z%MIxnk86O|ys->ru-`$G=7Wr7E%X=EHY&nOm@79E9jMnuW*t7*N)eiPX
zvn{`|XdCOXy*0e-N^AE-?0c`~v-ucI?wvI*aNqhXQJ&EW*g*#j=>NX8;teD9?zUbl
zW1%#^C(0^-{qLRtinqa-eX|Jwt+Ch=_^`2@;2oY+1Ypf+P;$>Ej|s2ezbZJBUC?r6
zqFp50Ni0h-5-#4uzYA_f0qAfFck&*70-=kf7}6?gXLi8huSFX?EUgLUs9k>;iAh*9
z++zI87%BPvxEb32DjP4Z9LlrP%ayTA28xMehgfLlz@Ybf7$k4ip%DIxC0KXjkV_-m
z#LI8<e&@;6JEwrBvDH?SED6Dv+}uOU2?n3=0Rd3*%>$T2Y$3_X9)18dL1Z?jSo0n}
znOTjv73V;qdFM-<{}(skHnIfw_p=j~&63d1wCd#*ahUIYwO9JerkYr$_Pc_m%N`yz
z&%R^QsxotT4Xnu9sA`g16M6M8Ov1hI&-u!+2lwkR?pL?ZxR+>8g8nkl4RpQKJ2vKC
zBl8%;F{=7HDoo|yD#OeoW?Z(|^ljdF3#$(Liu8bdT6|Pp1HaqUu-(!cRb9{@P81Lj
z*LcO1b-mVeRN~|MTKt#AciI2GG%v*;ep6~Ehh~saG7SY?iF9Wcpg<(*@NEFxG-7}-
z!%KIn#l2&|MVes)h}Jm=z%M*+Uyylbka&P^h#kdvQv?|K9O`uCt2a=9#t_BI={V@C
z=7{+&n)_^IR#)Sa@h!t5)8yg(h*-&g@su@-d#CS(V>2B)S6WylYIOUII$VN}HOyxs
z{JIvJ2q}N*j_x<w9k3m;%)Ce#aDS{8<Vg1}y<YpccQ#8_ydY)!WTm`KRJr1n?-_gF
zzZ4S;ey-|v{lSh91oG<9!ZGtDB?N5ssDsn~1;6YA%*|ZioxP27tl}z$%2e41?02GL
z`Go*5Uao{xvBJCuy}%zosG==+4yM7yV}M4elJv#JTQK1AD<u{I!~h%F)>Gs`Jy()D
zAf=X$M;g+g&;)jXFMGtj5=Dd|a8D)fMNTQTk15jXh0ef=o{KTdJd${2x<8=z<#)h?
zUKHq*mFtTw)Se9hJ+&<)SGb~jhn&CW)}@LeARzJY=ehMSHJ)Nsrm?>t)FI4#UNrBf
zN7vNJY;_liE8LsI52BpNWiD7Tm_3mQNGPNkBQfkKbR4+_7S4ZiUl}7t3DIhB{!b8v
zRt(vg{Z_87XMNbzyKaO0%wj0p9dXroEZ>#ZWTD$Yq|1)RL5Va||3ltLF<lNKy7-3k
zqou^mrx;7yvrf`-U9*AXsV@GOtD|IOYY<>E`G)_7gI^|(y@7(hp7%A$fC6jB%=Iva
zXHf;WU7QNdrj}RE-!v7wzNQ_VT2rog;peKn&}eNF^||3I-;gyB!9&~IE1fX}sL?`D
zAT`UJw3y2~l?}U;@k<ARxTGZ|ZlmCl*84N{PqM4HZZEg}ZH)5VODb%3qfj^1Txjfv
zJ#ID?qsZr{?Ve?nWKk&Y=AZKMOZ;rAFxBw|A+XfyI}?b&`I=D%HIJ=yu9*9+6qY>1
zSPYbtu`=%YD%EzqfHSKaAOP=0$^V6`?xz0!ZVF@cOWk<SwUfe^Iz^dO6{d1G@3Rzb
z{xkmfp1VD6Aq%a8pCe=vyKk?woifVyDNl~cbNwD^Jdx74#L1RJ#H%9Y)=g}?G*8VY
zyJ^hz$0L>QvDMkFTbRz>dFpU~DT=yl@_qRHRz0<V?}=k>J$(axuYjY@U&>ln@&DX0
zOU^%8c~Y97eq0-%88c=K0K3IS#v{c%!7w=>Yo6r`t&z=W|28o0Qo?rkgBN|7yy8kd
z7sudJ5bH__1y?vd`PoS2M5~K77fA5bXCG+<QxNY!Wf@IBxw=S+e5RFb{*mnD_Nga<
zaEFF<5q#GgNA2Q`0t1Z6+N^LbgM{sMI)-I*Qs^@PQf`|bZmvdU41nq*K>|JaA~z51
zYcIK%xmnHaQ{GR>`o$m=jrCu>44VlOy5J&{eyH@o<S9Iq1BnuW*h6f5w}pSJMa=z6
zJ>yE43Vv(2c!lc|w&a@W9iK)$D(?KbyRxrY%#22nRt~z%*%SPU#tu<+pZdpj7eJBy
zKf-OCVmG6jzF!l6@&amJ9-CFeA1d{kG+C4CMmUI4e8wU5^cteyxwF%-@+5YwQYt4m
zNd30>=UJ)RDrOZKkhzT0#WSbh3q>c$b)gL;K!x773S5bkt-TP@C#Bv)LGQ}xQXn!w
zP*s@|M*24c|FZMw0BGcLO}G%=#C#w=d@rkP@_gE9GahB0lvAJ*{c^@mLVvEisTk}y
z=Mtgn&-A&H<QciXW<lX6Jd@m$a;ob*n^x)1Z$OIW&_~Ft1_++Po}lx&N{4v@g8&#v
z=pktGkBxqCaZ=gI4UpODSa73e3e$uI{EG#H+}DsyaYcbeSVVPr-akQ>+z5TM)ExP4
z`)7ueoF3HAGQyQ*XdR5SZgQ6t-ZGePZSeK^vaU&pG;dun>0SOLDHPG<&_Qy1Lemsz
z9uf7Xk7-(P|C=$>3#-K92LsTSSa>s)fYnC4@N%MNrPDCEI24td;{;F<Cpm9NvGM3<
z1yWT-02^0XbEf3YP0c7#ciyb<>R%Oo^eNA(7E@fwzYm%!J|Qn<DWQNT0AT#E?f@$o
zx~Xs@+{#K(`1Zt;v>WNc9&PVbz97$PRvrF)FH8I(y2xzUnECoGN{<lr$|+QBJV*(Y
z5E>~ezuO*cx+!Ahen9SqUz?xpyQN=?lv_ZUdQh~*dmVyBJ67ozi&fBoxg2z9m2C*%
z5_s(W+oApfFFBVlHUC4>?FSc1ZY(~BJ;dI__%s>+fG69Ok8xIQ51+9huA26Mk;8;R
zvi-Mdwt~suEjUwnjpwp6zKciF*}x!If3Uzf)5&OpL3ly;OXfk#$@E^T=82N|wLY++
zcjI;WYnq+Yv$+gHjjja>U&UtUZp*d(_%L9E-qY0ym7%<ILP^kEV6b!imV2TBq@0zO
z<<%7BIuF(HE!?vgb$@Fasr&G|kL{d)Y_wbHn67A-&?9m4ggx7~^)Gp%VfGi5lBNmu
zeg>ArEaP_4`6WqNG0MkoA+t>-Pyb06oq&*KsIpvzu`%h7GH%QXzZwa3q?xt~sJPi9
z9RDy?S+Tp=vMfKpN5kUT75_-<UWY%<y2@tSw*o6{)z*hx%UbrD-td<k1-Eal{6K_L
z_%!VQuH<#0`=n4B5zFzX#BQ55BC>!j_8nC3h3XvdMsy5gC>>4p-GN}qotF{Wtpq$q
zIQ}vGhZx}=UG_p|ib?l!vepPxBP9mRmLNx-$r(z)2(P%vRJUwKQ!u=-!wCh~tXhxp
zaO&9c36TzcD4n6h%VDt6P{`N=7SRF(7<o}Q<be$W?KIL8;SFP3gEW{<UtIE~YGvL2
z%CsC3+-S3MxB_^SwG|$m+I61~tGA$6mWl0b<ew)(J^(jCShw^#EW+f{74S;2e<|6o
z>B{Sf3Y^sE_c{{gQ<ceMQQ)f(JO*=xEm$C(q##28GU>P+jq}q)p>Mq6Tf7l^Sv?@>
z9G3_<Jq*bm3p_N9XSrubk@_`6^x8}|-Q?}~jH?3$wl`_@r}Q-E?!Q%!>a+1Re&X5D
z`FN~2@S(N13WJcNkGXV{dHK|<hfh8uy73kV!VgUnGCS9LaypL;#)J)iKDhhkui`V=
z2QS_hX!9Br-|V;3*1cBvE4rw76?ZHH(WB@h03x{o2t;6N^gTiupUBkjv>4HDYC*d1
zSF@Jmhf>p0{^$w`L=}Fw?)3PZYhv+eg^(~hRzGT;Ivs&Qy)Sp_g!k2O9Ag{1B9;Bi
z%KFKXI}~+Uh{{ZcSXmTz@tkrR7SC12a`6D)AcAwsz%P@T($3J(902RyX#FmeJ)cYR
zmUzyS$`fUndp^@{0lwr^t=UF=W46c4kz*Ig9N<>9nd_mx-JZeUgKO*wuj)y^GrF%t
zW7^D9O55C2MbmYU`yf$up`&MGQgt;>hzYA7NUtWjra!tSc#za9pvb01#<_v}*%RUD
zZ@P>j8XJH#p)QLQPls?zGX~3idPY@@r8v!_Vz;0OJ4|j5!vIuNCGHrfb*iCwG@khQ
zwTo8!b4i8FN{~+u_&*vvD2p3GXnM$x2B<KQ%-$Ga>lHw*UBUh(AVu*=I?hsFHvCb+
zoaNYBG>>14lJj3!f;ZKIvUrZ{Kq!{VI2w<1rBnkbFRYTB$b5<*D*3M|9iq8%S<iH?
z<|>vB=hBFGTr|HHXT(+xm@`X@{VEfeOl`O%-<xy;MsuU@Ymm>yb_ygR4$Ds7A<^It
zpqxaq%#Nm-xsb~#k9SU_;Fnakdw*&)gmHhVc)D~l)mTfZR-r3#(Df)oZ(}g1aS)`R
zaoND!wM`4)hf17k>=aq<?aDXrX}xwK&R8lS9Ewjb20!t1#%Rql_h_rtYR&a4l$x`J
zMX<cT@xo5@g(Sax-hVF!2Ic!*4Lz)>J#H!3j|ern%_$z6op_7b`T$S}8cho<6_fcf
ziVdl47dZSLMPEZg7DrMge;|Q)mYui4r*E4%v|8J2SbvX8thaTJyo`5aZNtusRXyMu
z<Kr0PvIMY28kNdiw&ms<4O1;mn%nbnY@^*@RHpl<l|&nXj9j_56c{IQMGpY%oh#_Y
zr9?o0s2_#Flz~DOY`KXLuga{Bpv`1YjBweQa{6e9mGwkT;%<go6SrjKnDJ~6e5P8b
zN)M-`cc$Bn>nk77;a=M2Nz~IXH&*?N$cQvm%DA-dGt5v1BNr|Rhi2AiKBoSw?K!`M
za>QQ54AaXMRP4#n5*|*?`lUs*(b43lnLF^dkSH$v85+zfloFC>PBF!CgkRbvc0wg4
zx_EtCdG@-37#Uu*r^RwB@Fx6|&Jo_JcPZ`A%;;zg=^xN8h0}Dv4GF=Jv_L2p&^eX;
z#qg-?msNV`%u6P_fnRiE>IJ>UBBZ3J#gTQr0Hv7UvvxCMxd)Wd!BgsAc&F}5Zs4cL
zzukuWS#l7Jgm-3|5Iy3#p4@b1iMiw-?Mkvb#yB-^)jzu-E_XK;6Z$Tt7>Q{IfC~wn
z92QLA$0KQ<t5Ib37Ea+G6v8!!)<)H6y^sy1?YWvT30>00tPM4CreQ%(lFroO3M~`r
zt&RSSr>f&8Zo#A5rlaD_xrH;AwR2v4Z!f!w7e!}h`DV?>6r_fP_U&1)KC0Y3g9VH=
z)7Lxf{>{F>zYG%A?iw*0{f$Pr=?IF|-@&oFcnPoHp9>Vz9{rn{uB_-i3*J@2K<DPx
z=Ic$_i;KU>T0g9E!3_0>ru@Pe3;~($ZOj}UwAj0~e^9lJ(>W$V7H_~KUINzYFtIt#
z;pIw}vri+MKi_M9YPe@>8J_T7e)!eiQ2LliZfF`VR5-!wdLi{q%kQ64&SX;EWS<)2
z#0f<DrloYoa`>6j^igd(!bvB=&Z@Y*J@Iz~$Hb!nA@~t|HAvmTxX)TQMWXRt4>?;L
zabKT*l;=)0VuiToG}HKlY3zxwWI($O*IId`ubVCX)xVT$PbN^?7KjS`V&CeRuHv=8
zN9<E6A{6SGeyOi}>!)g{;d1wBNnt68uH#ddEqH*kJ=74=i?q=izwaX(S~X)J#_h~-
z1HIUSs#mccDUo+!B4vs~gE7u<Dt9ETfdyhM4MXx{E3N|-D#htTH7svQ4rh)93s*-%
zc`jP$_7ad?{wXoL7~m$QF**EaM^T(di3-Rwz6X~&Vt^H4)Eg9#4;X;SHfL{o1}4oa
z1_$UC!S$AkTmtQL=HBAVbiaETTkn)92DS|v98my95XAvl5Q{U_r)5MmG?zblVfhk1
zBJjKf@toKV4_nN?<%gPi=2+8obv$rT^0t7CKy&)1n_m1LKf<cBM7EfA6d+RaOdsnc
z?6%6E3pqr0I0zSd_q?;YrFA(}6Sh_JH)iVzmNosm*}k<CziYj&O=oMelcUPZah$m&
z%BlQ?*Mlo8n}Li5=>&qT)0-?{t~hbXJQ1A-FjLiw!@D*L85oL(hTT#Q|KxDf)0ba+
znZamgw<2_hF@L<m+WdpGp<PKss|$_eh(~`(x`S(Q)sy=}BXllL63j+}I90mtK3!9@
z>=twBEmWU!N=k4CqG6fdGEPz_@b8q3gp%gHh<+}=R}s#Yw7s84!^*`Tx8tyxIpY$-
z&*$$CrP=;vyyR&T5+*%xk$66;&pzEF4eC3yRAn~#B0cl=4)r>sYpvw@<b%7Y&-40u
zYrEhc9z*e){>v=YPaJ)?==zn4uJ}w%y`h7FF`D=`V!=e$?3?f2vi@5PusC%3RfKVr
z)G|O(?4HwLZLk5S+-JvFM;o-it1>58Dl*KNcL`WnnSSL}|KyX+vL~@j=2h+HM&Gpy
zyNR`>0)aokkOKWVN{j9fmIg6{F@L_CCkcKKk!yNQh<=cF0iGcJVQ}}PBp)Efwn?3n
z6w97{p|R@e<yo+e)<Rmo)i<)!aLuq2!=;SR!Al5%ZNhKnjr&(~jB(c+@F|5ED0k!p
zAb>(ca2vv8t@d<!%PZ&E=Q(V=^Xa)RMFaP;R~nH#$|8G<QJErqpCIkKCXVFCQE<ZA
z^WL-PUsr4>SG$CR_m4u^zkcv4u?&u2e^Dm#PTqBE##*k)>MI!yMf0xEd;E|#a$<0|
zOH1p6mPkjPmf6y<`YN;6^zDwbspUfS)`xGOE}s<IkFy>{zs!Fj8g;-LG@W97C-Z^s
z+z<^x%fQ>4ivD5l4OFs)+^@%D&tEzVqcbh&<>Bmcj0FXrE4gou7Gjg6SoNdqI}9FK
z{C#A}QO}X(tjxUeK0Hz9l3!W>>U(oRebyVJ%Z=_PqCf+nn{ik=_m!PoO;7!JsP<?T
zQLuRN^?>7PM?i?i(s_IM!wdw!nC7Y*E;4O;PNKI-I8l>2w$6?B-mSFHVTeW&^WK-r
zS7UT{Uya@)xkeW>GD-N&pLo#t?nLRGCc7PX>3mf6W1;u@D03>W&m*%&`heJ9S>5#*
z*IY1Bv4d+!7(Qt!pc+BZHsq(gPEs4DDt1qWw{UQT$*ebEsSxBuw^P!eyd-{ZKrQ64
z8YR_R$cHWj{9e_9_o|xTNe&PPaM)ElKAM_-q7Y9pP7d`NN6d&7g9zk^BoZPfiAM^6
zrm!d!R=Uua(|VaE-S0G=u2ZP4+EZKWW}t!OBu23t#&89OjH4w5QVD>dyAA*lorL$0
zbBf9N)sk5%gbeu9USOL!>vEPB`h4bH@WOAoNNYSQM!h%I$gBwL$H%`RGpPoE#33p3
zP?&6pOz9Tg#rw>@&l;!R?6b**&IY*!uaA_6Q2#KVChvozT1?}w&V<c+seH_oYWWrN
z)^y8MNk%LtoiBB8JNSO2{6op(yV<|Kdnjo3A-bPkt6gFa?evN|>a2(x#Myc-xU#QJ
zR%^j(E~nBqa$i`U-j;1x*Pom%y+-{>I^;#iw=4~dVNpgwWdcDCe4%VYpfSOlC+--+
z@8|&3nvZN5GpCc%{nkF^;Dz$(I+?F!WK(w3m$FRzd!f20*liPf3pmDitxn>ewtSd~
za^yxZ`x;ET{;_L#r_31)d#~0JW+d3d8|q>kKaB$UO{L*{Jg9a&Df|o`9)3%G)}3`!
zfcRb9SOSO79Q3A}(>{_oDVdmeul?M^qFm7MSIAdy_I{Y41&wq+O@GEl(CUM9^9Xv`
z@D#?354Z<@uY({CYjqF)1I=3BBI$RmNQx5T{`!A(GYok9rrB48=XRcCx6ge8Ry+Wv
zfZ%laNKQxu5=a;!Vni=t5C||%rlG8Uh5ovaF3(zKoK6OUC0H?lS<C_eALJtfl1CS0
zq?CZX`j|^FRnf~G!c_T!ph}e%h7ikrgA$T}0jze_XrL5W?JC7D7cdwQqg{7<s8~ft
zmSL7wxEOiq0j9zZ(HH&M2B;u74C4jU_g_?GF6I!`>c28b^~ff{niqli2aGhT1tl8^
z=OqGxa!$!~;#3ppN8Ri~jv{{-EPy*^1H9SF@q1`gS;Y4rJYbNY?A`I5<gGgx9)Dpm
z(9WPY25jj|09b$aSww`zJ-ElkTz~6~DSZ&qm;lC9z6L1vm2FFxgnvl>*)KhYSlrk=
ze$1Ys4nWMHPAs`l*ozlvzcM9k<$Qubd^fDR3O6)Yg`6EG`2PnTKEyzPksw9=GF`c6
zw6GEDZ5?DM@=Skxbk`-M0u?3zXVq`1xlW^CqXFHWkY`qC(2=tDn;PlRSLPk>NL1w<
zwP{Ib>9C$dj5ob@^QF)W>!*nqw6QPXA8)a5rq;2|w}Le?ma8<Fk#gH6tK^p|<&94u
z#semH`sKro(%!jEO^k^vg631+j|9!SK7*^wBJ$r|C51QZaEV3yWyQWaPCJa`ksuDq
zW}?ZxT<{P`#saxVZgddQ9EVsw|2T!AA-~k$@pX5uXilPyP9wAjzOJ33$pZRaoJdHj
zp>5V-Y!8Ik5`1G$)X||l>B{^w<P!haQ?YMoKZ}mQ_l0GRzWq`CEMaa<2+4?UiBO6F
z#dqG6<rBuXYeP6RP%1)u2s-a$y%ob+2!<`lv_8t{WFKraiAMe>a6Se+TYqwOaB{_Q
zQ9h*cns8ydNt%7ft?Oa~^y#0B3ntRc=&ETml+-QW<y1Kx^+a7KRr445B7F?9z59eK
zD>|M2C2;KYNkmjla=4k*di`{}S-WQ)C|+OpUM8>Mz=p1s-KYP<a6!O5x@x||GOgF$
z6ZNMRAP<@up}L1ZN@_ytCg4PXD~Z9AjM#(3Qg?1Job*|4aLqiZ3mMhE)1jd3UcY5*
znvw|6$ncgvsz3W|*Ay6cJ|R*+qyQtowfvYc!17yZluoDy76PPe7QG4Fpn*ohV=h4=
zkEvwpPA0zzZ^+A0bGQb(Qp{9-Wp#;8v3v=A2nGUH&Po6Pjp&itExCy6LD4Z!RG}Ep
zu)Jh=XjnxSgUfY*O5NNA15}b?0YLuCU$UWnon>p=!7@Qv@nCHK$qD}H47n^@<Cl=9
zT;mDr8kGICu>ye3z_Z9>u)#7#B09hfd~Ah64<QMn|Ey`fykh+1sZ^j1E4F8{`p9`M
zmFUnEX7M~@6H4yUE(roZ&OInqSiHffx1vhHLf5}fU{vh^F+VrSs7YOkJO*xAn6Wks
zxU8((X((JoXqqPp>rW(N9$Kam(i+Qg_3T*oh2QtZ@cNU6o(x?1W>YpcgR;i3DfHU@
zpk))oEdwUz8q%e|%%>mgWFv)Z{!AIfl-%kPa0hP2P^qHeFS}t%Hw4ijOC$9yF>r`>
z%0QUt;as!P5(~X2Kg7wK#k1wkfDg^$l5<c+X8XC!99x2l$ANL3!U*d;UVqLzqtahq
z#iy_Cp3rqWmw>q)pI(&EYN<K)(PKS8d%fz{O>gh4bOt8@3>j@@&hV-|^6ihZ^i_{Z
zk5lAiErnAF(oe`T7$u@KU3x`}*fQa%z|o<M95pYN45zRFvks)WRr!Q}P45=+?k(xd
z^X<0H=u9q;++4k0xvPSoV<}z{SG=X0=zQa~-%Yby&6;(zo<$?O;VUlQ^7WB_n@HO?
zR+=ab8pf7eJ5SoK<zlx{davy<pdm8Eqk)Z_icISWC%^Z%glK4m<aF^~cB5Z(q{V8O
znJ5P=yN5*+t-%E*p3!E@0B?raJy0mG1!zk0j>noj5~WK4Ln1&yw)YtnHh#U-fJO`P
zZ#foN7BK{iH|$;~W{+^=4#Viht$_IFL`qQlF9p*>F&v<vU@-3i=KdlCAS&Y>Dbz(l
z5KIB)`?)XGR-;7XQrhS#6Br(o)oMCb%HYc<mRj1<XL&J@w?L@{=!Eb9SJwg31DZ#@
zUI={{7cTES;$e<U!1;Bm#hoX}s_VO{XA?h-)Pki2?(7ZH%bW|aciEUT7$+Mgb5%wY
zv$hQO@C+Sd#eQokw{xyqn3!^s!P~jak?tl83I;~h-=y->TnOwk=o+=dQd=D+!xMS=
zEwTU7svS{dKNKWH8~(a1K7fo|kR71aYTD`Dxw&cnmY|3&5$G~rB;n#NgSd7$jDO$R
z@_pJvrkRJ+O{0C3a;@W@&9}H*BJXy5bur7=>g6s_E<TV-vp8XrXQPnR_$ggrBce9z
z1D!7`d~o}<p^-BD4Twk&QSyyDJAVcHuRVQRMW@;apdhqXV9a5ILr~W|!e5}~A_0^0
z?@29|2HSm+iqUt_-jUKH**TP`h_}zaPXc50cLF<qQ#UG>Npvpaph|`jasr%MZ#Xll
zx?8O*ICy^5xjbPxh*dOQrB(88r7K_Jwzz+u`9rTT!r3`3Db9M6Dfn4<b<n|Tud1NS
z>ZET(GZ^m=dt(xQR2-u<JfsP4uVuCBc5rCtyuwF9v=N~Xc;#S@FZmS#ymUtgx26tL
z&a(ElMBjkHYc#C=x5B6OBsvVr0l?eCBtm6I+yOXx1q>3t>?<${(U~4D0vep|_el*#
zs(Z~pcg-hqa247bZO%_58dn}i0{&=WlC1YYR-PO^0Pm&`Klj7Z)tZxE93u}r{Rgu*
ztJX0+=0LVzdcmoS6$z}p6XI0e<{&gO|A)zH*S=_9;HkTN>dIX!_tEz722D(D3P3z#
zeGxjsJcU%JaAZya?nY|gm}YBJeN=9X&wj!%!O+0PIr}Sl@je5$MZ>>9db@^k>QBrb
z$tK}ddfTa-jcGDzVe1N^19PeTAFGWYWFtywbTV!is+#%Jse34`3WnyIj!Q5qi*r9O
zY@gxu={Ru^Gxz2EM5k```k(F=Ju%U4a&lsMPxWu)Lalu-K*OJ+k1bCG6hsWg3#@@5
zZH|(o4<eBYShO_%-@@=ZKv!Tee2>oHv07$*%_$B#rzbMM#i&@V+eu;-pId$<p14%4
z+uL#g4v>3A@d6_4<|Pu~MOplcBvA#2x$*z&ZlwW$ayaq*O`Ha+j+47I3gZsFt8-!A
zX00c}DPX9im`I6jwiSPwRTXNik(s;|iYEGKY>H=;det=;cGkR1ET*-sL?%|Y=f6EJ
z10!_b)9kurj;$a|sK16ROFPq8)C42$(251q)`+}IOI{5Lqx~kZM*2cWI;r>Ml=;NG
zc{snW2mLC<nor@w2PR7}=h}u<Scm{GD3-(UlG_sv`s|~G)3{zIBlk%+ye>S|k|?(&
zAW6<-f1MDQm<reuaqxul0nF0F%V5z+C~<Hb-2zL8+U;CQkFqOy)kV)P?Z`dYWk}X&
zKa*%$3b!Qf_`@_pxj^+8fDo!QPI9o?;rp8Apz8z7Mxk`JWkI3rO*Kq9w!)w$ZVUhh
zZ3~)yhJ|SuKJF}>&P8Dm1;U&J@e}<Kp_yvte!U>s%@d$1fXAJYAI(nX`Ia1mfa9Q4
zm>c9utIVWKm#>6o!$`du>5L&GSV3VmS8Z_2GW=Dd-ea*~dL^SeTskcLr(eiWJ#kp_
z@M4ehXJ*T91s}6L%JY`qixBn^`pEGh-Jg$#ex4+XWf<xQZe-S+|M@m9M&C9f>o=)T
z1Pd<_CqW5T?h}U$!oD%0-`bo_*yrf$0uq-c07d{r;?ey_gC6=TF2^`5gCzgA%yaWb
z%1x*B9Y*d_o%H@^bs-@INX(W23XZeYtR(1}xzIyq9YeYash5TdM0l*nHV2qu6>hC2
zO3|NB(uU8Gya>-A{$$s~FSvNq(s=vQ=Qhjj0C$r8O*yv;0I1Mm6wVY7AELag2et^7
zncLyKf{b-UxVw6aTxop)BRq%tH$o$9-&n+z%$?Um6%i5Y)N*NWB#fx72Ys1z1oo{o
z4C86{4Ff#>Q=900DV-l+I7~}8eoi`IQOu@|SP?W#(X#t;Rq;q`lv2J87LoB(X%^$b
zSjns_cC!k~r;Fpz=>|aiENM@9bbQ!R0BHDaJeu)4k$Hd=YN-!KN6>YRQu4LxW9PTT
zt^bK{wWS)GOM0A`Y@UORB6$I=$RQc(p+XU$4K4r$&z#G6%^27iZ$TXoC+hWH33ANM
zpu+Z;@i2Z9+b@#bz%9={>80L|4l1w@P<bW``YOgB&Q3OU4y!X%UT`cigFW_zH(2=s
zmMo2X$6k`e?R8AR&?6y3qi_GbnL<A6mR^}u3S(xK*VzW))&Qap1%(<7x&eib9nd)k
z3;YAznPnz(MTUQv(eqklcF-$a)uz8@S7RfTXeOwjC$hcsqCj`<Wn4gk+R)UIy=|l}
ze&hYOsx)&N)5p1|Ei96cGkzz~49^J)hufxa*ZiD1xE3TX+QJ}V!u>P5QpkJZ&q9@n
zJ9y?!=!8|;xK+KRdADiP*mxp^EB&Zg%_ApqI;Gq>+mQwu_c7Wb07CzkaZ}FTGoUF2
zF7sw!2;vl}s3I|f`%*2(x20j>1>*4L0#eWBph1OsZ1U|qSE?A8Jy|~!<ccF?=03H}
zw`?d#9eEP6!4bw)jyhh8@?H}!G!sVnMz`R^($@kCf;yfo5q@BIA&fityC-g8ByM|t
zJ}gtyakN}u0YVc|_a^h2lKAs03f?v6YFL_GVyiE#HDKN_)QoB7?P5c<UCUO0#(T<^
zO2eD*Caxg4mkI=ppwo2+yFHHs(wYfVpwAmxAw>tmy4Djd-Kf_gGx6F69R^gS@H7LH
zy%6m!fz{1pRZ%obr<|M&AcwPAKP9qlR`7u4cmw*#mpmNnW!xF@xo#e)5#i%)OQhDy
zq;OJ%&<Xgvh<JPjxwA`e*g{eqX^2fIbfFgc+}Iiu8Q~U_@$_jfj7D<)IO>Zwobvz|
zug?2CcMwC9E|kK*u{Z9@Y<?{uQu5aNa10NT1HO!#=F*$VoM-?%F9m?U@?!UQ(s}`j
z+(R!;mk~idM_?sPj|0aLdJE7zFqHme%CFnN$C}8q!7jV`kAkQV09v%D$2OjCh12Qo
zh5Z|Xc#F8R0<&~{mB$XE<g1yo!_So4vWgCjR}v*ej64(UZ>Rd3W(qD}4e`4zZk#P9
zAagR6k8VNa7~l+{6)tb=Bo5hsycu`u(MjpViYj=X)3`cac)8tn`n_Bv#xk3vJTgw7
z9NeOg4{sI=qH-?w`^Ek&ojt`hIwwP4>9XL>^@6p(9ukZTTUT0g1%A@)jSu~6$QR!7
zkzfr|8wqgFnvhe>aAKlK`Qaw&RW0v+z%#cP95myr4}Q6VbP><Ysg_h`*seKGN02hS
znEDi3{-aSeebp}SFQV$bbxt~Vr8vFiqZqJYuy5*4%b9w(l|&M!)hgIZKuP<ncU^_N
z49qJu)WG|^I)9|J_#DAxiVv;Iw|fy-l66b5BhO#ofK*ytSgn;t=aY!8-lh&%6mveF
zSb2I7%7IyRd>gFtUdN#FHuS!hsee*kPlcLRRm|{1M24)Fy(_pl)~I}Je$CYXk9K|j
zKd(f0Y$+E8;MS31#_e!`dUP!=!VmQBEC0t{FU#8qiP%=zh*rr7e{f_;x7ls$+A=6M
z(!dL_N?=bYh-d)Npc@(*pr_Y$;?cMC?0SdMQBBfXO4bm+ocb)ZKDQu7{nxbGP;LXp
zXG|I53FXq6kKB}EDeB>78{-uW=fi&G6+H)jy78yX-?L1UNKuv3P1Zi(j$nL|RctOM
z{N9nSygQvvM3>$GC&KH_{oU&ff_Vz>382y?3os8Q<?XR`%UzxqvdDigmeur~S0DPk
z>CiW+`0>z)^LBdSw<H$xlh5}$#vBq>`OA*#Gkl)DxZ@?>>49g&|IibaD>HK0E=#%S
za)fk__Tpnmv@R^;-O7{VCj`Ptt*b)IxkI3x*xU<mw=J6PKMql2*+b($ZsEg+`>dMr
z(>y)J{Yoj3JSHLl%e(7DnZF*=nc<&hie_adSv{k_WN-PjnAoH}`39Y*DGYMG%Hr?7
ziDpLcJwET1Z@Q!Q>_gFxc}Zic;*Q8l@_NC(tYVaUU}1>@tEpF)QE}}TWxBNjfg5&5
z4EcLYuQvojCx71cWajfiDA6imDTWkJ6`_>Z)T8Fy#7Bpm%Sv`(l_lXN?S-on%<r5D
z@0uv`sv-hbD%2Bi1&i2(A_v|r4pv7UT#X4`QAh|Ry?$sKbER)O*m0AdyT-8Z_4Kb#
zGo&|NgwL}+tsO2AvsYN1y;WR8xnLgW>hDzgd<1Y_6cK{2O!bmNH;G(lL&!}{@!^U}
z3~zk>xp?K7$~3R=2A%uKcBA6&ANh|u*veW`5>fg3e4-2d2DuU6<r!w%Wj1bGZ)ngF
zCs62HwZ<wUoe)fj#OuJOyRPmLWz;h#%2{TPVc;J6Bh3j>$;9RT4X_NBNcCz1;5y9Z
z^Dq_KoM0~E1rL9aLDPg*H{DG}?FvbcJ?7WMapwy>yyi`L?KkPLvlq2NOF_vfyXDwR
zbcd$Cr^XOOFhrOqR7!xt&$bS8Si?_uQ}q))-s#F*;>|f_)x@yW*&4128rY9Nsx#iV
zlkRAqs@=SL{WR6;)@)~93YV3qL*F%J^At~_>FHz_*NtrDwDUu_K3CA7Ywr1SLHfOb
zQGXi!tdCt*S0$x*bINY(ojmx>m@&-d)w^5r#kQdCU+^w$Q|!$=KobW5(;0ty+smHI
zDDSC!mz&u>=MzRsJU*7QbiMy)`TD_KwYLcUUliFVWX1K~;0SM}BCp))`HL<C@WYo!
z1VPW~=VLGL7CJwgJXQaCT7c+Z%xCRLoQG8q0XU|JV&qK5(f#$gpIE|gLQ;#ETnxCy
zq9rO)OzyuD>r`k=iMOIDQSt2Ed8N`N6P-vG{1Q|0;3tfm>!?Kb>t%TL;+3+u4Y#6R
zOgJ}ISojAeH*4;vcl?rA)|{Lz>`cAvn2q{&!9D!(*W}0G(F=~9@X377NfAxq{?OR5
zXd`BU4Z5#Ez+RR6h5(eu-vt1La72<>vhLwK`&yNUf|VzHo*LQ(aIQoN{wIw*y9E*N
zNvK)wRvZ9efkzuupZ;EwGej5ykwyvn7r^jhP)N`Q69ong%^?Atf#T>S&=iuJ8Vmtp
z=t)qPi{JOjZFo7&=YQex!@}RynEzpQDy?_(U6d)Q(doeFZ_DO8NlbOR8$`CV{zmWv
zEqBW=15dQN01cKv5+MBa3jlfecnV2*9h~?Yi0IcB1O7d_(O_*XZFy6=Mte{3Pb4!P
z6%@m#h=TMN1E6#$2Ix?V2TSD;b%x3x+5#Z`Z#K~dGqO5hH{Zq83O*0?h)2|w>rX$1
z8LfQ1Tekj#{%#L&B<99T`80lXYg1*-;xAWb`<IBA^1s`x5sP2e#GZ5f7f6+a`w?}+
zii*uk{oQM|vcaF#@Q{<k_6GypH{s{l=wBa8Io*D!!DDm((`+kikJmJ%0iTH>Y1p3)
zA_W*gg9O}mgAQrdfMPUs^d?m6l^}#xi<FQ`{a7N=h<^0(BKYvx@u40Fivca%!2-aP
zJgqw*Lyg0Lq|A_W1R#nSumO!12>_k^(wRyNP3`py@=Tr)4?B-n5dU@gP=z7=7Ietk
z6k*M*SMB$@kIt0&bwfk<x`-HK%T|@ZOVd+z{$S?g0m}dc+f|}pCk`ajZ2&ytI|P3;
z0K|U?r7xJp{WY_RY?W8FI9>YpyjF}L0l;xi%K)G6`(9_3L~HUVqeXC`jIuE-@d?rN
zu$KG<e$(x=*E}+_wQW^+w0IDkmAc|+k>dL*?0&Y{vWh~Ah6`&G&lT&)t<<1p72$Eg
z73%TFw_d5dX<v2jrs#jae0t|)%=OlB(ttRdK*qM))E$ij=b|6K?rlU<C*5|ZeeyA3
zy;p?`@5=r_FwL{gQZ(I5H6Tw!T>SePs4Dns9-Z_lM5qSiyT1h%PwAx)BNFwBfrsC6
zKC}T8xVby|<ImXrxX^&G3<lso?on<CYSz+%{}%e=mJ<srvXSsO+2I%KCCjA<rp2**
zvthfmU^E~h4Qp2G7AESj%M@Z$&4;Gi+_|=sO~3r1Px7*3q8SDnyoCCO)VT~K+E!>_
zJ%G`|&RyQrm=I4X?@doEC*{}V2UQy!Kb;#hBp6c#PFGV*hm0AoXDdsaMkaZ!n8O)m
zsV)$OyzT-bQQ)a%E!or|03qIm0npSaw61g99Ievjpsd7GYwR@`XKvA*vaf%?dS=Hz
zJ4U~I_UhZlvFymXK~^&4<Zo7U@oA6Dm$CQ%WLIAzp0(HSSB+~s>&K6;ebN~9yLWR%
zPnVh!cnC$c5;|{b`zC>Sx1GVSBiV3bCV%p3M5*Yv`Ou-EmHfMg$s}EbzF!riK)w|v
z<E7C3U0dd=8s_5JzH0)zLgQIEOJ)!;Z`wL1Y9S#!zcc!fAI%`j#E2_m3X?IFuz!#j
z*$$LU<IFN+<np6~!aQ80EW}$iTatE0nHfwC4l0r*7c%_bB5=;*&+nAJfw{hKI>^{p
zw-T$xKV6#1cTH5cnEb5KmG4i>w?VO;I8=gkux)V|A_K2L$F1<#6uzKFGxDy@&Hge(
zxUQzD{-<qaVduE(L8sbG!*TyC*?OPzI&qfyL+>_X_9nGWl;nzKALChBVT-BVW3{R0
z)fx#mW}BR46Wq_2^E-17DIPq&_F9qEi=t%CCFt>~m%z18>{lYNXIk)9wJ%Bw<#y#g
z9TS~aDO+KoyJ1)B?eu5tj{oo_&OHEp^pgM^df`uN_Y`oP0F~S~l<vSy3}{SjZ3xcw
z9Yv|=$!wQOgotOfv|Z`bPvb2yjY{$l1C(ZL=R9zCvT3V1MEd<wzSHuexzn?b^$C~_
z2UxPPbYd=)5`zVU^w;7Fz_)dLzQ>B%g@l!UKd&$`G54X`E36+u<Z-^f#&3;|X(k&_
zD9NCu5qCSX5KeKDNsrgop@0QfNq4>FgY~WVzI<NB;T4Gkq1o$iYQoMTG)|vD-pq^?
z#A0t8$ry!B?F9hVtRiCwe@oWg(cJs4=~xP&e!qrk<&Ce;!G71PzCT<Ss0&NwYESwn
z|CRr72;^Jm_h^Zw=Uxnrf8<dA6z@-=JvsL0rutdjF{e^hU0p_H#|941FUqql6f2`C
zgZ)XzI^3WaM#J;EpN(U8<V=T_Azx9yu|#2A@dZ^#$LTYp?{1CWUKE6|dhrRVJ9M45
z>9=64;E6cXRQn7)Pn|0!!Ko|?0>`);ugCdr%RRyvx9DVT{KwZ3K<VQ2JY?b0-NNd<
zwW!})>m7B7Y@+mF!^rPP1&5uZdn2k_`m1<m1Ie3eAsWgp&Y5BQ5uKgA@e(L-BRJH8
zcBbb3w2iw&qG`LMt+O11{4Wy(3?`e4wdz||Oe1WM5z={e8XIvv<YAN9$-G+=^uu%3
zb5{a|q3h#9*}_ws$ZC$r$^zv-B{Nd-V~eO|(}52`K0^I3B|mD*shy39ly{Zc)#A9N
znQzyveeNq&sFjCb_ls1?d@A|P?CLt|Hg8F+T58g6QO(+qid0>>&)cB4TTC|An{d8_
zjW)pM+$sfjfHFS>XuqpTak78SB?9-X<&f>iNizHz#S2&529A7VbZZLi9|bz96&M}y
z4=2_<CfJ;SZAd>m&;Si3vQ!s$Fe3F?6Ur2;6_l45x)_tVgw%YjC7e)Uh~XZ|?%dL1
zj3^ZZqwUDs5&4Oha$bWts6<t$YieT-c41(!tlqwF4*u!N&(6ED1cc{gD5DXYMlb;d
zQ0l2U3t_wq^X!DcfK50$(XwtqV%<lqI}U{}(RN9{`s}7M@q?(#+tbnDB)+ho-NJXo
zFui+$PTiuO#oD#+y9%*if7<U61Q_q62mL}_9a1Sb{J37;^gvc|uh-)0<+++*w9FT8
zL)M~ZG}|`>aa+LfS~6_yOexT&K6aqj^6T24hrbE**R=}wPe-E+ZSAh^k#7>*OrbB4
z0xZ87JJ^$J$^pk-{2!G9QHiE<mgk5+6*5W#18rjcwi>GU_B^AlsgzbY>LfNYUKju9
zc`sM<T}INKx?f?d_hVj>d2AMY8OP_j7e$g2(Jz+|hlNmO1eijzukK^PX2xC}hsJ0=
zBJ-BX$HcP9e>DDmT@L+m;}Qx>SOQ0xa@?C(k4Vax{Mjx=AMMJ2T}`g+_vF|hnzetF
z%MQ?WP^e<g9i`Wi-3)<4h!D2)EBVtm6g?8U)jgF;ekqMyFOH<GGqYk>tf>p4%S<=z
zhrdwT{W#RkTrb3g(zD7mPQ{D8Nn^N~MqREM`n(>C5SM!(I=$I~3jx^Uazo=JHbSe=
z00qiTdD)SIR7$0r2sljwdJbu%9_ps1EaNY%fxhgXbM2@eonensR~*%zb0&MC<~>RV
z(p`*uate_RI3|bN)FCJ=Xowkxqy_@^GZ`MMc(WlOf$4wL@Nz|BLv`ecXKFD%H53A{
zlbVgOl*<tSa0j~UsD`>m$qoR-s9dK6Zk>^|p&jn;u&yNsHk`L`&a$}4Zb~~C?-xgQ
zoAR=AV$H1~#WHwi4R|!Ao;xIm6i*ZB!or|X!oJWhUQJzQ!&?@u6Qs-3x!E7sI6>Sq
z939Id9@E*?@}-*$qt*>q(=uo4*_#`9kB55GTli`muQ*>CsdGG=33+tT`dFMTd!q^^
zuxB&^VWsYP_!~*X@5&E(N))RqaZBKvef}($I-h|#>vWV*NhX^%$nvwMPGhe8KAG5L
zZ3}0qOqeubzOV7Dx3URF-8HBgx`PPgp>m4KaGiPFi^#Yzg*NCiste(5P1D-9u7aqf
zw!Tqg%F?WXtLozE(~{4<s$n|J8khI*k}FrMnez7i=*;Ny6&c!=u~!eY+D|VP1vCZ`
zj@9=xnO(U$^ggkvo6Isg<WF0@NI%VBu+?fZ(i(E2aK1)<^`vb)*Scsm^CY6XPRb9s
zJMNv-j`fIebC2mi9S@Y17r6Fff55!^nqu=mb5FRC65KqIo_bGj$j%R~gwPT~7C{u0
zoUuxaf4lvUCQ4nElv;*@SacGR=tV2`40avnHMKd&&|cuA?j0Scj}{q0^XNXYUz&7w
zpA0YG7ao`M@OzH1->U#^HU^Z?(x6~W{LRZ)<vy-Nybav%Msg^DN+MBcueev}sdAui
z^`}_+s{V(K+T3wQUHIm)R5RL@woHXyCb9byD1Zu2nc5qVZD&zMYD3Ui5S*W;^HJJn
z(?nc=;l(i1uh(45SU|j)v`PUm$mn;Q1AwFcMex{)6P0^xi~GHT;{2u-7sP94yxLna
zu>sv>;?uawiOVnAazpuzdU&<^;bRcoI0m>^lrqJEmCQ{*O8h_JhsOc|85rY2?t6~a
zuSL>J8`HpA)&l_<?o-WJ5gw(mR%IG#`1!o}3rlXD$>@e*Vb!^e*y6>?R)(*5-Di^~
zc`|{<1Jr36Wl!ya>MONZ=v$H}ack23kjcOvLQpn`5LOzBqZa`4>nFCE^%~jhR0eJG
zC0RckOOdq=;rC=R(O|i9I^Mi+WZ2c4qm;X3PcV9EV872oHxk{vI5{R3&#>?-wO27g
zPPI(43;_R~Ce@j208ilsuV3fpZSKy*rjgVU9JKHmyEhz~u}r8#(=<Rssflipxg)5Z
z2^%!CrBEHz%B|zxk_fJ@a-12X5^N2ho7&E|+j((xf9}0@qjeFlu?Wt?UKixtDJh5{
zkZqe4RiyMzzdu3;mxZpUPo3*bzx$cnzj~3bG3NT8U9*<9MMhEN;^h4t^ONi7a9!3_
zWptXWb`AG@y1F9wY+XQLheZUtK%hm0N&exdw2ThAC(-kX>o2!SN<0tkj#T>7B5TYy
zbf7GYwVErHr!L&vW2lOX+}Lu{rimQYClswRPXuB9lcg;tjn1!a!qnh@D5!=WV?Z@9
z<gMf>fQsFa0ya(Q9!$>ErIeatuKH5V+POLx(z{{W#g8z;96VstQT4zHm#3Y*;8f#S
zs*S`H0&T_x9mHJ<h7<$C<}uL|>K<*P#f5!8{7?{jy7y6NaTz_kGh^{;NlX+uwob|J
zj{)u^M%%-z{kfN+DeY~5^BPR<zKzEc-ozP$0>hRcIEE4d{tNhT^WL0&$>}j#;XOLj
zsi*4BSwob4(NVyX8?Ye{E+*H2C2(q3V4Tz#-9t64hIgt16pk|G8aVBu=`Jd0Er#Z8
z_B*SjCy2PEm}4V@TK3F_=NzC`9=WW<@Rx4c8y2P;r4U4G)ikY=#?*FeS)1Y5{oY!=
z?g_A1N1Qb0PkWj~I%hVb0PUpwWg*X(0ZO?2+PB{477jECn}cu%%{<nDRQ~+LsX88q
zT36yg=8_>wlkvMlDub*7`y1|jP23lSDw=733fT<v$3(!9#S>^il*ySS7VG4yMN`LO
zQv>m3#J?3nI_a1~$1(>}iEOdg+qY%Q9#=!3m(gB*5|hsp<=ls!98u>{Tvb}ID!vu_
z?!+bQGeP@h(afsCyRRm_#re-cwqT5CCn7Gj*r8Lt3zK0~&P8$;{B9VG-**2yGWJ+F
zadt&f(|rIWVnEOq8xh_QL1W{EfOPFA#Vcm&!Z(b(a{~<AmfkIR7Ab*W=F#iuQ`E)1
zvTK8V9{q3UfxnK7wn%ECw-B2<dVj+Zs*zb`@#E2(>*aLnW?y)k(L0fKje^hgb;W+&
z;4N8F&7l)AY4yFMfAe?jP`Gra#dt;zArl$>6y}nwkW2w{zKQlJMZ(=`Y;&x8^mC_1
z-~<w>SUxqTqMR$=Z<}SRro^vK7bq!#6;k=Hr%$n9VY3h(F#r$;9b<vSYCtU$k1lef
zcCsdm;~6EWc1yMzm;Xo6Sp`Jdtx@=!8M?b+h5_mBX6Wt?k(QDMX@~9x>F(}Eq+7aG
zB$bvH5&aM6eqX=)jkTVI2+f#2muE-rzIR?FBep_6Q5`FeMY4>?kSN9w*r5#q=$7hr
z6hY5v%A`@zc|9gmkC|kr2}VMmnd%SIH_mZ#1<9}{ogs$kY}6PBZ(wjN6vhqqF9WxZ
z3KJC}0W?LHL)~d(vb4^!>>rAutL1E7J34WzXt;DcKP`QvcZ4t~ylRV9+$eIth>^|8
zJZJl|Do8qQTd4=?B@uiG@?&ai+-d!?g7MSk=C)XfN;Oe#$Ml<WqfFzUwqk;Rdw8@~
zsJd@1SkBwptbKI*IU<^{uq7BkR)$QNzqzf2=o;r(l#=!h?9ZDcsJN^sV%W2ydAu>Q
z1;dra#-7(!&-kzRyoD4X^jIktnxVOiEmcaCe2y|ocq0xb%i8h3wMQ<SmD)HdL=5%k
z_ak4X58RA0ebdva`Px@9!nH3Q?G7SVN$@e$P3&SdrqjsO*Wf{z*@m_cn8>h1Bt~9G
z&5|9T5mFO8RCa4;Z#lzfhzEC83)PguJN_8a)iNZk`mgPK`IOCF;^x!%55ZV|=l`T_
zK?7*7?UU>^8p=%;Bkf=0wx^kHUVd@!b=n!lZkP4=Vegdv=6&K)#cMPl3Ub~mrl7ti
zK~sG0AE(5`F^$hW&gh=}Xu&NcU?%_I9SO8N2R%o_+U8n@9T|+$cZaC1>y1rzk&+zd
z4W~ldBMxUJ+8=PcO1{Qg1}tp%^!TBMp0gqyxLJ1qux56RW%#rEg$+lS$SaV?@=11#
zs(iBoSB$f+r7C^PjfRjGeZ(rZ7?p5gH)qmM2*WjAJ1AnqFb(k%O?DjJU<+wjLxAy-
z)kK(H22pfIr}Ab~rn?_AJ0kZ7DVA~wfXIk;P9c2Y2%~BRfA$7`)1z;o<Gf7TqjMap
zT+mdUxQTGyS)2ZKyZ`pUzgMneD;hY*o5Ecrt$bsYZ2xIzd&XLGbtk=E{QHX7+QE-i
zK?9cvo({tmru8DVet%jsOZ)BJR~gA%EPFzfCxY7dYc#*rM*XU*&JUsu5+jqA`zYa;
z7Zb&MA@s^2OM97eas5fKnTaXCmT2utH=$dg@(uu$BT^9G5TI&`h@ZLjSm++9sQ^9Q
zU*XDs4kK78uOji)%21B9ATe7E^>zH;P#Dq)KP)0i=ds>v6qET|^1trBPL)@)OSi={
z@iZFAS7?borhm39LXhEbO9NDdyd99!{F;<_|1$eBOPZMn{~P7&PpJdGCK(VaPZ@{r
zbPgUru+@uJqzgfIZamUiFa8!}O8;WtOw<2v;pzY1X5e@4V8d6Nvt9PXRsxi+HyTy*
zyXiNY7$3bfy|P%C2G4(xmf9~TB>wmP*WZd8%e}3p^D2=8RUDP-Db_{27M(9IFbMQ=
z9^OQ9MFNAqnc1l5*^M;D;flOvB&w(AJI@Y+9rQ~KFz^ofY7j#N0MJ8#l9VGxjlbYH
z**zWQVl+vd=k+Om_2<R^h~A=r0HB1#6TqODcHbSW2A&|&2mrxCMe|?iz$>>DfGQEA
z7=X5CCx{6>pXZPO3ymixZZL9pq1W4j(%{+Zjs!HgX!iX+tzuh3GNoDp$d@KI_G@pH
zkbz@T<3T)P!^pv(mq=WxkpB`5N)}_k8sPv9#yge-G{3jN1roA@3jj|D4}hlm;=w(F
z6Avxy7@_Q<8yZ$V(b3&dx&Hr@Uf<lmYrld&?zq3rrybxE9oh5rrztf2oHbf|!}FIe
z3k&1liHCFk1-jI`x$?bETFGy6ppA96KHiC6T|=Mu$K666yP`i%p4}Wlv6Ln*|9#*R
zCj=1c*k=tN=85P2NjB|&M3rCz<NX+FHe0$cwj{ziEpe|W#b@AA%>>gE?7PQGFrVq)
z4)f5ML8N#_Iq^T>+Y+Vs{xnAkL!9V5Wfq|8whJaQ)EZ-k!9XzO6c#^>fYPeALjvX>
z*WO{B(U*92aHZv?mVTXj)iWh^ecLbT<(GRWJoN#hxI&(LQe&DN9Q(o}mc%=2AJbiW
za67B{p}26pR;IBLy)<f3B<m`sbVpmlPSX4>EwKb#IZZwoTAU5A#4&`q1~)0_Xg?o7
zt6y-#RjwXuSrP1|k)c@Wwsi(4?kM2S7)hu~i_b}wvCeseo4Ri1honhkLsYyt<IadL
zNhW%^%+99wt@tjQ^=vX?W@{BKs^*4YCoDHz%vh~NYEVD<n0ocSdHZ$UTHI^%7ix9g
zOX0SaiL$l4^3Aeer*$E(C?pSdcGe5=cB~p^X9703q{z1SL_!1;d%I_HXl!!u6jJx9
zJ<CDJSfckWNT(&509Dmq8aG-bXit;$Ib6diTP=3Q9|?F?PD1N5l-`sKvb6_6<5$;T
zmy)nAV-D1tJ#33Ripjr2PMK`<7bACAx?@TP!Xy%u-TBams5HB9i)$AjgSid1<bxM$
zn~8aZI;LF{`y9#2Ba!i~G+qUh9!V}pv=W@>&OWEp$AEcM?43G>K16s<Y~*DSpHuDA
zSj(D5G2l1|x{)tLwCVdt?mPe_$>G*C#No>Bloo76n-5W4S*ITY_kJ^E(miae4aGhz
zlV_~^(*?c-?OWK??e53W%pKfl>p7oE4E$GmH4t*+{ZzvvUH>rH!Z3E?)J#-yJd{d4
z@Ho-*?eagV?Um7_B8EqVRtW|D+kgHRo13aJEh%vFlQo^0JQ)HhOc?sDHZ<>_%!250
znFBWJbUjRcehWfN{uf+Aj{;XorOa#lB2D-J(6V9xXoG9CU*ePjumTg~L9y&+97G%q
zm6v|C@_jn5y!Zhr=AfUktHqX;bZmuSy&cyZGLJ58wJQX5n0$YF9oR_Sg0nsnr`)>l
zh1S%E#i~enIZ5f9(V~wE5s|AjvbG2dD<(u>0jP-(PX%N8fdR%&ix}g-hKd{oKzVb?
zmRtz^Z1+3TViXWwf|u^n8&3gWZ02>oNXyu#R6c`WG}*G>#n}~nxE!{-$gf8dbX#nv
zF|Aw4on|uld~CQ4D7uY3&*RxK&H6>nhsjZ_Usj1CIV<I;nDnM?W@Vmd{_<Z;?Hh?_
zCaD^tO~Tt5+xf(aDP>_O@;NeaDCCe8KoV#b$OJHYO%|l=){9^}#FO9^TPN}WDAB6g
zpy`7G64`O%P1Szr8)UK=5iJwR)U{LOo7&9sy=WbpbU^+guvF)I2?_i{L6`%e!4*;r
zOD*&R5lpI*Cr~~C<n2U>rZy5!D}K=icEEu$&s1%!orBBEgcFfo3R{kjd7vLr_CSIr
z=}{I_DEIxMlfg$QSfL~#?lQlV7trE}_bB}%UgD%8FpO|rC@t%F0#g<*AehGg&|onO
zQ9G>~m@OP{q7dO3y}q%A4dfAVl9v*1*F`zx_L_gH{NQ;&wYG?i7jMfv>VME2FFbjH
zd|h+%ae3~pKAkv}X>Vk(Yb%17{pMZN@R}4g{2x_6eF*;L$WYg0@HbV-_NgSu(5ZQX
zr5LBI?+FncLkE>0aG|s5eZtVFi?gc)C(otk23(CBqgduB>{|b^@aTCk4rPzBbD~Cf
z@Jy3OrVyZ#fGSu*4Kv4b;gzeiS}3aWRV9!P4MXNqYCLP$CSF`}qu^LfuXW6`vag$_
zPT|JA2*cLV<b)D2i|SqJZjYL%`m%pUMTfd9!Q0rQKm6GzTNRd~i^sSvjg_haGtjlD
zJE(6-uOX=)1a}Od?Bb@dpS5Ew#g3e+YU(J=aF;)oW?WmCrAkCKmQR0s-$wURH6O~6
z*xvq<Xv^+E;YVal1zCnOHIH3QSnOq*xA0;*&UZ8(zcMld+jLp?PwClL?jgRdXeS<a
zt0jdL_5t$&cyAmua3<I&-?2q-+VqtCi<=~T%NQYIm8fjXtl(8mzApiQ2*2q*<9VTF
zzYXEzY={{1_YU5=13fpSOjI)II%0`+^`9sJbUy+$n|?A-3V;PEwxu7uB+m^ql*3y1
z!#=8@j#NB|nM}1|dm&1+g8Cp`!JZ3KZ4T0+ZxlrW7sRE50KSYV52^A`wJDb&{2#jS
z2o;b%2)vIA`><E|CYa(dirRv%5V@FG^i6r?K;)jG6bMv>0!m>g*eS)=kmCXYEW`9d
zyFo?44Tkqb(=GqROSUy44=`?$5!m#T&*X(eoFszG0V)5yAFLhUzuwZRrMaDxu!<+g
zfw#PR>@=cz-;s6Y-HO*HO)GSUt<8Nm$I$TN{Lp`V|NF-Kh|+T!lpOk&>KN`E)Oyyh
z=0#vYAlnz{)o7%09Q3P3)~wB2S&r+xif*qkei%U4K4Ub>WY~^h4diwQ>{${<CChM;
z%Jd?V!n<_DrNjysFaG;s-W?j-B1AJKhIC`s6WDQ>;%}}hREs_LpRI}d!86V{BBH8q
znbt@1LdbX&8&xIsg<ln~BHLKxDLICnE=pdF0;}p^ef6PbgYBAx;OOsiQt1S}{X(n>
z|J?S2gzBz}$9^l`a8|oErqUZOqF43k?CRX&N$Vt^=CRan(opyRRP1BCf7i7!Q#5qj
zW?`6hBmbq!kIEeXl0Jgwzq2{Bvqu|uy#MMi7BnzH+}E0#iq<Lcg-cqnW23lJe_EU$
z@WKyz^ys5tX;;d~4Zj7UKjW`Xm}PFsLZ5M@-${1;d}I=+D#Pf)Vg%J;sc<%Nt^+By
zf9#G(_6RiAL%(h2cGEp34r?+fZvL|*5oG<mE-1!803gd@%rF{Mq$2$w8GqEC+g^L>
zxeg|$A^TaWRH;1RI(`M8qqU|Xk#Pq=!=xZj2l`PD7zNbD16`Gh{d<~F{B<u{`BtLq
zQ4N#2HH#Y<kM4&bR5rkVP^b=2#F^Fx5HaXr8Wb#LE~ZFSjEqWfh8tVV;j!s)Gsiy1
zzyf;tp}n%`eu5aGa#$tfe$5zZl5fD34QXBfobxZTnSVjAznnm32a=h(E_#1VTpXca
z9?9Jv#|!<4Z=jH8%dYc0n5z!J+c@LmIh_<SnFK4$*dj(sFLc_&HG5BO<-a#h{?PeE
zby|Il-9pDZ7~!0v1|JUMd^RH}M@(8k2Z^GT9dL`EX96a$d|Gt;8Zfh^%D!`1tuGlb
z#qJU!U*11CcC8eneJxL+)WmUvv7yy;WU3|gCs{VZO$ns_C98tKXnxqtj%Y3tJSqR3
zA4m32vguH=L(->=WLTv>_j6{sF3FkY16ouSisJZT6wEDU)Q-jAyE*hSV3?EQ5B}hk
zy6ive!fpJ~sTWZh6>TpR9;US)?-S(zbBxJw2G4MdWqHodCa>u>9SGx$zH&+WSKmKB
z+jx6D{ORWeTcsHD&);ALa=;71!~@RNrpQhH!^;rh$4@bz4&{cRbJY=oTQqIGIQf3$
z!b5MpG-TB*QCJjCD!yNwz1cHV2yzXhq5W=67Mk|_={&Oo*+7*=02@>m9aoEn>GUG3
z5*Y9b;IYQpp8O2|v}e2Q#|RIiOIZOxb700!6vPtN9vzlt68)ga`m86T%!3`FtT6-8
zk=y`GfiZtfgnnqp;rX`qW`Wj9mTWJBa<|)){$7u3yoxW|k7JlSQT_P3G}7h4%eob|
zySD-Z=l~WF5`YYrV}woO0J-#o>JSGppvqsWR)A@5ly#h$lfh3x)`y>UJcvX-kk_+4
zLsO)V@w+oKnKM3FyOx5pxS~?6%;{qwQ~lALqoM=b{wbZcoOjpjpGvm2(zkCZl}7n=
z>d5qsgRff8b-oICYzQqbgprhXd2IP5eL2&tbZ}NuZQMrASO>>Hbn}i-r*5AM^r*T1
zP;V@ZW8_Lx%T(MWB)6^gyiD4Q9|wp~VQDzgR`3rt#&i*?mkzMhi#YWYi=bj~VR&bi
ztGFimKzDfnv5bI*+Z4|@i<a&xqubt1!bVaBR*QWNWA&a+ER$U;mky@L-56br!@fwx
ziUnp$Q&`Y10Kl@W#FNcP9?~Q!!AeoVNSV-v*N{x|Wzrp%vdjDv4D0+17iaTvc#rA9
zbv-;X*<`A_bI)DFnu>3hwr)0?Z-}$X7aK73)7IkW_8-rcoYEpT!Hs`o$9g<iQf`x3
zKYMt+Af}N4uL$+&(Lm;zhSmyv3`fSZE?uu~c=`D5MKaX*Y8ot(T-qakT75^ide7DE
z?Z~fEXQh50ZjTB?-Y<sUb68i?R}s(;y%pT8Cj^NB02j?22Iyc=OTHg|Zu;lMS8}`>
zSn?Kn(Jy*A0^h%uQd9>@8EIiCRWMCogdz?e6#&$T?O7HABzvI`i-%FRpT1aBFhSGx
zS5=k;ox}i8ac$@%!7dB^P;M;}P8W8eIvn!Rjw^PLS0d%fB!U#hTFh=#zXbJ}!-Hx$
z{12)+unw^7g+5|DcTfQ0S)oNgsQQ-vJ{?Q}wETk|bFh!OIWsV}35nW-Q<6>8k#XL>
zdM~Wf#_gS&(EJH!kg(IHy}?J1T1l?k@OD>uMf(bU6|A4%+a7crCP|}HDt)TBm64=g
zwWy+IriPxS&hCEvE70#^QG;DZ18=P-by9x4MuXi^BefnL^Z3zqjIOTo+^Dio=Z7C;
zO?9S3aVGX{_*b=4Dpp%$6mlDP@Y+-0AK@+?{^^D)7+rgmi)R>(Tg4Fi+RrqT<{%@i
zrSeh1CYf$eRCluOrIwXj9bUHEOY@3AW+M4phhYmjrXe4NaPHFhWIg?RHlyXd=YM3h
z`X_VdTjpIIV@wp5SgaS;x8qY}rASgfhwpX@AU1HNIiJIfPn8vZ6(e_=xo#EPYF#AX
znP3gas6p*t$g?MH&qlD1lY5!tdMMUI&Fp%_Lhv!C!;zcFI+xeLTKyweM*FAzQ%mN0
zzfSq-JL6U|j;XSE8_e-sUkRy>|B}nzrL4;yP!jkHqwpjevkgD#32)Iikg@NTL&>IT
zN_NWWc}OoV$v784q4VjM6j03wGp(oXk<L;^@1}jaP;K98c{FIvmoGvo97qq3Viw%u
zZu&uQ0fJh%Y>YG#4}{X`8kb>L+Fp08W?M=_H+4|V<c&W>j|vK*L;|3aZI-w|dG_z7
zOO<{YLT0b!B|0BUBO2kwctBWikadImusjq-?}4yrL~`N7hUo`gP{Fi_p?lzGdma6C
z@)#BD_nJ}omKQf+Ds#r6hehr$6Ys-n|19H!0Crzq4<9CBYCT@^b{qd+Y2Q=xgZ6*C
z?Q*6f4?9+62_ktHNS@-<o}wny=QlS`isGo&*esShLvhvRKl+M_-S=$eN+s7fG(aPu
zYba3cT}^;@m_QRn^?SD&AE+ESaw}SgVbKtbwigk`Zglg`Hl+?k_w$+*sf98C*>Hz^
z)s!s=l|P1;kJtOr*Dmo$b+f>;IW*Gr7{_t-lI`P3pKd#3ja4~>)t3_Ladeknwip{{
zESaRGwU5T0;wxWcWjiI8_md1M5+sLks}8&!W_ze_Hf-&HbmYS_i`jf^-*->`6YOJp
zSmltvFcqn>bUp0sF0>jI%<LF7tLW<#B7Qr+%ege)wX|Px*q5Y|{<8ZH!FSiAJ}@U@
z#?wg+5I`;SaN}{&Qz_BAvt+IMdRLd}``LL@s3`l-Zu+ydw-P+0N`<(0u+*rasS`SS
zDdRvH1)XXJ02;6_876cp1!TgO2BEnKd{?a;<H`iyH%IcD3^={7;7c5#rcZ*yJ+h;n
zbm!0SH^6X1P;q{RB@>>>b$Mk3bP&Fm2&Gw&_mR0zK@u1tvFNVZXhw?xkQHjlFcH0c
z(e9cmg$4A<;z0rHAtpld3$gHTqhVqsLzp+Ws_P5;BWA@6MkOAY)z{a<uIRw?TrPkE
zE2kgKB&KXNO80dR3xcvlIM|rrayL7)dwsw&P~iAoW{Sw~dNXH!-Ii)Lmt+795E7DL
zw|4bVN{UnH8c`;Ut-@AO%`Ks}`G@#5MU9u>XI*&0Zg{en;+FdHZE9#>@5`#j(+X27
z^~daPDsr{;AF6MU)xF0SKOkg3Xa$phUX965(==;rU$6X26O*ys(%TMD^}Hj$^@K6h
z%g`f<NK}o{8^}io)NVD_vO=xJ?VplFbmCMuEsk@qXY(kGKN~fj5jL~b|4nmgpg<UN
z+C@BkW=#;ZD*qc^qX-zkf**X}j&p}z`~8yF=Qa~;i`0J*-7+NZGnI^xH;#I+3o2}K
z^qfEMXq}1@95KTwHUHz-RX=Rf*AcH?qIWm`AJLy~)Yi=FzkM4^Pd)VAhd+BMM!t4$
zTD0D!`0bdRl>iUhbMt?D!wL-OB=YlmZ2IpKZlHXD%Y47dk9b)7hsC_>-8s06`KS-R
z!KwL6eO#;_3&Q9^f>!8<kd({$#2`~vnfKJP`yb`R*ks9yZ~U0uhb0H`l187~?z_#|
zQXV)DYia$K&9Wodkah<LF0%%)k3bom#ww-3hQ+;6ZRW#GgED^b0vozo$H4B@>1+on
zWa<G>JE4_O(Qb83F(X~+EmWviJmf!J^p3Q!+H?xlS1ukRe_Xc8Ra4RR;g)!ys14d3
zZW)9CPH{0kvK#|W1O6TX&AVi_#8R^hcg4g2mSr$`q=))*jv<lrvrxrcE-_QHV`!U7
zmfGCNib5~%oNJXg&2I%fjYV>^@&fWd-_}m%>Yii8%4V#Oa(NVRuaBdnJ}M4A14l`4
z|AT}hiY~JyV8Fk3GFB-LUXNrz*hnd%L)$-AthjN@1>;-ZKQF>3_jN>`grE?R8a$Q7
zyU~s|MVX1fU!O~XAY#_iv-<X*Fm}PzVQO}NCntBJW?4o~xhxc(Gh1W8QZH17iVD!;
z6;lA)ZzPalD;N?)mm-Y>28G+5et<((idK$ah>4PbJ|b?4A>T1M8s^!qOzScK+LRhD
ztCS_|v4Mc?5=+=K8x;gPg!j}RPz#rZ9~YYh%R?2SLE8MiLECMDGHZ7CIkWvAHTu?F
z?!>d{ywt$!=YvS}&kt+>TPB_*`u^2DpvOvp1d8P_ARw?90=w@PJD@MD=r<D0$>ML7
z+F(yXH2(VF#$lcRAdF}NdrzqL^Ts3B?CIr<a3T#pxcHNqz(F_n&B=EAB@Z|sp&f6A
zA@WAf2vwSZS`Ls)*5g<m<`uvu9!UwbL6fVOAO%KUB;=?@2os!X#?Dua$mLRR-_Q#3
zLNp^Jb!os)^ehqaaAiJqAChUl@<g`jv-$!KH4pjrLbY_1DmLi#Z3W*fvHY^2hL`P9
zKJfb1Fl&w_Uh|)r((Ww)yrJF;E#j&M{EK1;%`E}6U1d`MkSTTRQ|iEg&{r>AyaN(;
zZ;u=Cx2QD+*r{jlmg0E8aZr~>JXC0G0N+Vz^d-(EU9sNY+XzGoKumBE1CU3s5RhB~
zWW=Mv3rGq~X($d1aLR{)j#NnzpcOL;XPLcWkP4qXAf2Rg`A$3f_+KCL>`w73z4mqC
zu`q!(>|dEa!>;O!AHht|t#ma7fCBDL0)Wz^_o0xTQWW%8b7i$)K<iX7r2SK_YTs-U
z<L5*hkCmLn;ZI<K44W7xr8Et{doAvn9QD>YyB<BP1bdb>%as9-M%nao$KmMomrJ!C
zIdiRa-leZkWaSE3dbW^#b^dL1$sbs@J#wqIOzmVH61Y-JcscBca#(Z0UYRvibWN#D
zQ+Tt*XUf(ztJQy8gFAU@w~{LASQ~RHT>n&l?JS!29j`wZcPbpa5Qh`Cu&NXhR7<@+
zenQ!*vPo;LKUG~*OdWZSb8Xb*y$D^Ja=PhUNX``*+@GPj8-FcMPL{mqB4t0>>RE5Q
zFo&DIimRl_A+$0uC!De-M1-xsrT8n%e6#D*nZw;EVYu=ikwQpVRuA*$zwbT!8CS9-
z6@<^g-{SPztR**3tPr4Kl?BD|k_2_QCG&Z@7Pt)wCrG_jn?F32Ji@z;U%XLZ2}a3_
z$WPW(C*-T<1oy*G6t!J2rH?eo2O0V97fLat=M|VMSd>s>2yN80G5Jf5)ZY-vimR~P
z6PJ*b;m}9GlreQ!WFt%`2(L+_k6X!FmE*GY#08>b)?SQ`vZNS~Kx40D*%f?+wDDC?
zWk)f<ec|e(tBx}Am(5p1mT2ahDUUqQ=lQDLqc$bPEdbdu#4ZLzQ65?WfWr;XbHT>&
z_wGBJLyv~Lw16qcqL6CBT`^BIX}V)b{FNUFN_Zk&z?=I@<%<V0jRj@6TagnK!%hIx
zMOP-k2!b4)e3X9@SIYMx0S`7~URghT&kyK$ZxaFD2N}Jb)5NQl;QA%B^38y9B=bTs
z+sFzCGXYyv<?JUCbV{z|KqvuFz#s$wTDZG84AD%$Q^6!nTw;_klhB7$UKv{{K?S^4
zhZG_jKUEDIxZ*4YYxP_&5j%@H2w(+g+I=9#JV((VT}*1b4$xu=vw6#zTG%)muW<B|
z#IF5iM_DD8#^mQ9C)+#HMY1{tQxA6APjsoe--`|N*h8H&7CS`j$AgaBUhyZPWKyk3
zYC8}IRB8mTWh&7bImsEiIntFbGZPt5nWZsuDaIRsyI1pu4<#VwEda1XJ+kS*i2%*_
z5?~!;5c){S9}S4ejlRNw{e2~myV1!{Xc%{h!5m#Pg&E32Ly0{SJH9texvHD6r#7ZM
zFG|1RWf5JdlgBz<{CiU_;tr2`&98cR?%8~MrgTIMfQ;Fq^~$9+>yCmE>K7U|gMl&S
zX~NpJ0FVs=*Ts%ozMcm4+Z<i)w%kRz`|b>8rK6Q5kcTaZ!l4weP%R%(vz3$f%~3oY
zVC5q6%5Wm{$pWmjR>qzhQva`2ZTPNW2?ZlnsuE>4eydM2YIDE0EBulan#7@P?sG`K
zgJmK;gWqu^g@@n%Y?55cOT~3_qrdP(p2SGJdU3yTMI~^gn7E7PSuqHqNj;L97=eh-
z;#pR;Pa={`z|^fkVC;}7fNtFO?BT?~a}22cn%bSstp9FLPo!EU;pK?pXx4jSR*6rP
zn3FqLn3<&0bt}ccL9XWBAie?25oJ@DZbB%?otC^J)`#4N1Hj`IU?Z_y(%Iqh$2V%M
zGvdqOo=)N|G+>g;z*1!3+Q&|r6+$3|<X&uKyiH!lkVIPTK;xdCL)vdCV+aIL4lvL&
zug*qoNnCY3G;Olw9$r(lkm;cus=w-|TVWCSw`yNySc&;xtphGf5yyFfrRs|Y%Gh9+
z`ohFAs@ruco#L`_+8X|}GQyL}4;w2rNlRkJlRkeOQqB$wy5Flh)+kOe>)ivo5d&K)
zr*FUT-!-oc8+GT}@l^2tt6Vm%|MKHvXEU!bUu0T&vigtjS0C1)?q!Pn>G(KjChh1t
zJdlC%F^X?vRp>I+y<%&}D?GtxKs5~h4DU=LJRE4x;$tzNcSJ8#<onuq`01sUF`y+c
z2GBM5$>&ggj}`nkcv^>N_@l8Jc=6?%WIc*n8fTiakj!_=&&&&({VXEQR8tbn&EXcK
zVCl$&pe-JN^jxw=tTgqupw8lL0(QL^f{cqm_~P0>9RFPw!9D`JDX3Kdej*%#B6I39
z)i}#H@Sd8Pk-f{tGJw2y?VtUxzR(@5S;)ZG-V52I=s-8C_c4+^Q+`b^zVzu!ZBY>I
z{{AW2D8bKNMvMBr;(N{h^2;J+W9dbvq;NcUXBNJ~`Pl`(*!pJl-e1{>;!T5tlQKLt
z87A%PBEnm#777Rm4JEBUOatZo*}Qp?4d4<!>py@*V$B#k+#YZ7_-stA)B;)jVsHte
zicMs8d1cx4U1);!aS-e2H3i|Vv|3K6uFV!#bTO(p1qFxz049aN-E(0)G<3?L=oG$4
zz!!x;y>jpsnk4`<2fjxHwot?;n+7sr#PKwP$y0S&s0ovSOVZG{=nDfv%ZT{Ud{GAg
zy-zJ`1D|w~5Mt^H053u(LxGe_^w0P)j&?Yw(o}v|O^%DPfd}c!-|=IDhhyEc)V6C_
z6FXvgI{ZtA7dcBMI4D-Qb5)}|mOpn2a1m=4;n?z5msyoea}ue&rM`~t#7wJO$dH3Y
z5vq%HnmOjxISPex=Ug?65C>B~ue8*bSkelFbCs!XTw~ij<>7&~3<fMceKnqCLSBCI
zW^b7L#YdNNYFkEXdn$yVbh@UiVw@SV;(bJ5(-=CR*=I5o2&eKyiet3Hy0crlYg>>p
zm?8^G5njo(3@MqEY6`<Mc&Y!@HOI3TFGpY7Vu1|1_+w&riny=qG57{Kh$afP=VD4H
za4{jcb@}P4vjo;t<JVMpyqu(%`!ZM*g-bY`wbSu^9~ueP7%4B4oR;xoLNYdVA~6s}
zCijW451h@{JaM0khEDOl-x6PD$xvZUjEr*Y`}BDS*6$Bbj7)u)BJXSK=^I{9uIo<z
zV@kj9Z8D5#3bH(9O+uJ^NKCZbggs3dcROV^r^L3vou8D(kS%At^MRy<J|+pSiq>zo
z)-)W=!M@Rq^+J`GQx21U+J!^8&P38*Sk;N@Lpxi#NuA<Ix>W8!uX$4v#q_gxC5FHs
zyV^1;{2YZx^I0Jjb?N;c7MQ*O=F~e*hh@h)!<D?G(oA$FQ^KS7qGAMw<&XL(kXH=K
z&47Cr(w!yrs2u{Z#t>kWP6kDaNum4cM(%W@9rPlVpKt?<n5^6gu4-|4{o|J+vXleS
ze|zH6yohg(Bj*TI_xPO*yd^)r3wvPX?B<xme3#@9oPJxHT));)_q)C)O*QRFZd9mR
zLPQm%aW2nTQ2mI~GG{L1^@vs4+^w9DoV8Ftzg$@ij=rv31V0I*63ZFC&~S3x=vAzj
z!>F#Z=IHOdAaRzQfxIVC;U|SIIfLh_RsBV>wzo*cu}<hQFjcv%RbEm1C1^D0M%X4u
zSOZl={hu0*5k~t(w`E1ma&#O&QZ(i>)`NGn$U%6D7&`?95T1yFREeNSjP{pAvTouP
zk73l?#1#+5ZtVB)N4GrI3++yEh)zA{!!i-0qd$Og(IaG#2pA3{0D32j;F(nCqsEAR
zMqzcETL@!BDVAM{xcEvlhbxsJC>-#htjY4WgrX8$f{a<y2zL_{E@vq~`fz^YQX4R0
zFF@e0?_UC?+QkSEBK!b=fSz^LNAZ`~s8JsgK-E(mzEH(x(V&Ae4mR$4<w3HTze_Lh
zGZneKu_YeQ@%k*>ekG7zTM1`cSS^_gUVMu0o?*@kOx%Y?!~%$%yxBHxj=4j-NA~_Q
z%JdJ4d}8WzZsg1hE&|34k^A&n85W&<5}H3W1irccTH(C+^ul~6eMaCnPo_t`;^Ak0
zQTfC1<LO<X`M)pXnW#g*f-I!)TBZIwdw2}ddfoN)=YvdD2o@vd+FdyF+73EVsyzVw
zz+3{rc)B>1EX=>}>l)pi;Azz?q7OjD#5ETfI=Rdgf>cfrc&<zB@UT)=FBhP`7<7PA
zF;T2s1{r4Y4aWfO0PckHVdM7s2!JuLcwz@Frf7*n1_GO8Ux?~u?e$xisphk77-rwf
z!l;;JeSF7(V8H+*(QD=3hQglnA2DSf$N6GocwC5z2h|6IRCh!lKXo$i`Au~u#p!TL
z31IO`i{BD&9IAQ?lALHa1wB3JRDWKJ(B-E8k3r|B0a19bKwY1DiNI|-%KWu_?YTY?
zr?7PSEhhu(a)+Rdj$XzGImxxT>v^-K6b<VZeeZ-!StG#)sMRKEFN2=`bw)yjfo7x`
zL{#mQq{F%{J2R2Vkz|XTS;uKciw^0F3I};31<M0-80tsHb+VWHUi!YP6@f1`KJ{1)
z{rDqv&?}V4?P}nA7Z?4C<Zvq{6aiZDYO_f>k{m=L=>Q^>9CVa|BR{k{*4InlcV4dh
zXjqy}-+=69($q^{8wzX_MZ$A(i9%sXwdxysj>8<WymhrKy`68Oq6R*2iP2>7)DI+m
zX?En#jjq?in)?KxZ$4om!GrY`(5f@CY68{V+PXTiIn|K5<A6~VV;mY$40aU&Q>i89
zhT;o%-`>s`4@(_&otw)H-Yj$9cZW7d39iU04~^x<exxPU#<l>*pMYHfP7TKR{!fNa
zpSdZ688<yOD$}Q!X!ZI&-h2=@SnWyHr?}AmY8bB2vy%69%Zqm2@8Rt>^;p%zypi0&
z??M;tX6PvaS`c$Io|re`=yj*XGqs+~n|2VLo8i$6I~j9I5)x}sW~5$$R@{QMi!tQb
zYmvpEG|lA~>ac)=Iv6K~imaecuu6OGATMMbH&HY8a6m|U7l7dkHTAo80FH@-{B!|k
zYWb}^Le3c4q_9<v0PLsSPk>_<ny>``ZG=D%HUy(4u%uJVA<~5-Dc(_bU`1)ykOLaK
zVFicTH=4&&lGCJng@Q9g_;Kx$wa6<4mf{L>7yY*_q?+u9_{7;_7qP0%=e616ZR*3r
zhNo`=2;S=uW^B!xFpV?U8SD%h#0QzMqpT?267`owyo|Z>iD)C8?Xk(QFpT@;#G&6t
zxE^GkOr+w=&qUUx{UzFP=|#nqg5rQ3cqeUf%zsi8K44RODL!(?#9f~7p*(gfKEA}H
zVTmdgVS`7@C>YC&fQ=!6WFn1>aC|h0N%T|r>oYJ{vEQh|#FeQ|F&a&U!BZdkF0=iF
zLB2w1h;>Xjf=98XrUN}i{qxFR-Vfu>_EI)?W=#45*WFxlu<uT;H?uRt-$qpTdRi<~
z3=-?e)=b%YIW=n<_8Oe%Q62znbCtx&lKoCPEGQBif_&>LI*`XqC5wQegufA7&gN%(
zov%Raaj~+j!yIOt#GmgS)gRgGq<uC!lj}9Uoiq32n+BbNFkeFIN*`S0;&^k#?V|HN
zZCR^}21B#Yo#^LWqo}##TcfghjQi0oGs6sAi&)~qW`o)>6AM*VdB|=mKzOYlh1dWf
z)yi>}fMk@&(sLzk0a9<nrhliy8O|nB%f&2G+RpRYk^+bQcx0(+8DwID!tqO4q!GMw
zjLP0&$k1!Oyj~UNF@h>4unxyb9ShVUM=fQ6C{8)n`bCsW2=VTjbc*o<3c?rwrEHN+
z@~EQAQ{aUHfaJu}H0PMbVV9@Ch8k&voL2Q9p8;CYHmY61)q93DulCe8=`#)3lyVw}
zI<TKqn|-eb#>Qjny_y8#({-F~*lQg5HCsF~`3wcewyZ){N^b@xWEsZm!ODz8t%-HR
z==79W?M?Cy@7X&7#=XkCS=AHrL{M>+*C3HR-c7^n*LOSylUig?_6!>#x=Q|%v}OOW
z#~yn16mo<~iFP-2PdOtQ)z&$h+Giym!pxG7J;m%cfUJ6h_*tgu)K{3UGA@z9)+%M>
zF+r>H;rF<c)E9yB-C4LN_Drkc+4B~1(hQ_<|I`UtbAAn5E<?Jgg8YriFA>L1hC-RF
zvQavwN5*(7C{<X5QZQ;>Lv~b$M$*nL*-1_U9sp~Lk~qc-L5d@Ko1aVf8e+)uXNJB#
znrmg9TuM-oOEf7oO}~v(%d3OhUFAlf>aVC~hX%`vqSZG8HV>nlbM?XqsO~CpV)GrU
z>ZVC<8LggNT}Q{>2D;4TGIN5PPq1a*G}c)TZeM^i-jYsW(DjAFT~D3RUIV5E7Ja2D
zXucYtt=a*wK~VtOQ68zogB#v7okVmitann;P$rG!<x+mV%;6Z0c)YC=E6Ss-Vdyjx
zbh}ZXGXX@rl(bye&|1aP5k;X1E=}V5I_1bMUkn78qyQ2?m?dP$l99l2AP)hsKomG2
zDJw@$+9haETk^!T!%5xA56K6k`rnC1rRMc<f5t9yV_aZ)tTZ0p9q=;L8)ov9Hn{}G
zz9#!^=~Lx|FJ(MV^sbf0gdw!dvww&k+M_hN;eW}Qe837jv|P1`&s-zC$d*Q*CJo#T
z+zL5A*^N-~S>G*ZV#p-?HQ8(|G{itksgnJTN>xvLV10lg&o7#cc26OWhx?TZ-%BDz
zngMUJWBZZo1Fxho99l?q&{R5_)i_5f$TNRxx;32rlSF%8>)v#pQT^^{&`9<#*0E(a
zWWHt`)q%$S?Bi$rla-*eKd~CN5|lF%A{;v!gQUBjboq&bmEC0Cglb_(KsydxA(JO#
zD&spw(6Bcy#}*)4jCRa;DH?mR$?00vpt<py$opJ^JL=lEvYuNbt^AX*N3`;~64P4g
z#S(7rsz!emXVR(-0hf{jNTYStNgCz6yz`CcXYA{B{BDP`E-+{1rrbJ+eWP8q04=W3
z;e6o>PnbkX)JLAUlljumDG?_;N&bnQbqbd7-8(GnarJ6VI?f}S{Y&WcO&tMH5Al&c
zpUgv#;4lo<((ll{{GamhV|Sj+@-W-l;^R4j_%bd4iK33yu8cCMj`E&`ryM#iALJ^G
zuTaf^`nYq(b<CB9SU`IRwq0>GR=u!PAH+b%z<?kQaKJcdz_UE$8VuezsE%Mj;FXq!
zsl9eG7;Yf|>*1wZ^wx8oY7CLZ|G~tbCmW+BYpm5#_f15P0$83->(K^qNX}-#H|o{{
z`0p<cwa;e}mtf7&!!e38Oe%$dpO`pOnyXYgQG;re2C<q4+yo|5%rx~xS50*0_(Cc8
z43vwrw=5f}+yu#s<H1cd7fmX(r<eAG59g_WVfg&e!(zVU9P-1?yYQ5|d2PM=nBQ&=
z_k+MMeDAEY++X#@Y(J4Nhwj<t+lLhjAIej5!&Br^-!r%=>Q^9sUpdUy%zbDY&UY&O
zlqSVP5X{7}MXx}_-!wV5U-<fadFdsNO3m$&+cqmF1^_}35B>uHHn1{VknGZb;iaXh
zLiDzS!Dx!AVAW+gCGaKRa<ed~naQ`AXk&qzlQW!SX$W0+VCl8u7Ti*dt++Zx2E{2Z
zwppNt?-wfkVBOs_KdM=9-vDLAnTIn-s49?y`6XIy7bk&!t(`V6zAy^Io||sa$wHxo
zcCH_<`_}o&D!&wymy$rGw=*i&P$>>jilhS_!G^jKR^#~e@F1CT>b-F~#v{!|23!wA
z`Xd1&$NU5E$)Q3&{94_aMG*i9MH@Mw1P^=lJZ%g@?0Zx!X{G!Q-<ZISh)H#2%?-Iy
zQw0{G&hkOpL7aCmV@UDyR&f>!jDs1mqBQtSFT0$n7Ykve;zS!}l4a4GC_IprN+$Gf
zK(#@%X}nOIKH&`%XeHPjGi;Cx8@(ljwz%<eKxh}!%&&^%97WEn>reFYWyi+P4lB=e
zTF;%oByc_4jVjS#BwQrJKIrU>d%Ng69Ni??D6|VI+_1xv)fBy7Q4{u##wy5;m0FBM
z+~Tk+3nVM19x9Kf?bIFJ$%fObha#NU$xrKGx>&Nha9EQ&ev>a@32YHFF8cs4q$;H!
z)wB&P#39>9%Fy;gKvSgEcyn16Uj0<47%ad#UD#B?biBe`h7wc1GdH!Yx;KP@rUYil
zg}bfDlnL$aFabb2HtZ)Wy)<iD0UHXHI;JtK<lgQ(1!98b<)SGIQx1#ueF5KGnUW$W
zZdye%6hgEL&ZGqq4OS7I@>WUlG&mZa=&nvjY-lUHFT6r&-895C(eVi@$c)*!ONQLQ
zgnzDWD*EvA%ZurHnY1yAxHZ?Vrbym73$zI(5OYXoE{Rp=XuVlBcWuCp-?Shhsu}i!
z<0=B>gV3P|TgBn_@?zTZ=ZkmTGsJ-!czh0|0}Ty-8?F_#&W~{|hSgDNH9hr~Uhg!f
z;;}>$$!C(vF2K=CtVfc9D@4V^zaN9)Pz_ai0+56})r+AMV04fcDF7NC$0)Xc_HyAD
zFTn<xI3prt8~7sblV1%N7{_hMSmJ=d9j{jYP>}HzWd2l+sWTuwJIP?vUAj{4_$t%v
zB$ygYlbj>_tO|@&EEN!^;Yoxg^y_4pXX<lO#vi(<P%72Ie(SWKs|(7{%?^@8U3e_N
z(JvpnEuhtsm2YGlwmVd+mm2d#hv|&p;^;y3bDUE5JrfiZTaG5qWW0p=F7M~P-BKp>
z8^~|ZCehHn)B_>6XpHimAgAv%^R9U9Q}98M`pUxL?Fy?)WuAg;g4KUcFZ!g%rfkx5
zTy9B(SFU|-<d{ks=fe!2_B#U9R|@8Pm#kHpGTO{zKL^Hcd-6>)&VK=bPHf97y--ng
zBoi1-0Uwv5WPF2(jm{YC9^%Ay%%;fY`N4Q;)cwv4lIdgqrg*-#kc*jZr3#a4-+=9s
z9vLR&y)<KL{R%Q2YDz%<X4S`JnTQ@Y9?3)F(D)~GI@)BG5R{o#aX#9|^M+U9&e>RC
zJ$8s|nP&Y#6g{y)j>2<u?1fqU^2^%ly3Q`A%Rv6B^jOlFm@)9a-9P1NOK0sh!5Il-
z(6+723K2*_YRHu0xjgUA(aFAtGF*bx-or@_B2q3(qbm^djlsMYvS@@N=g4Dcs7)NL
zc-42rg}Sh#1*4()L>SJG4*9(U3eM02u-*yXpf3_Bg=aYm8?;ApP6C|O@cEHks2Im0
zNAQjtJ0^%7gNAcskBf1RoX1=8P4y1~J+2*ewyW3Y*v-6lhh!scr;WR=$$9T};_@iX
z&qcNSlco(??i60E8LU`k9g|Y6lPaqnjjIsc{C=OZ;xBuvr^ua|+b-?p_tj$^FFB`4
z@qYG(2sKYjGM|GfF&Wdli#gvmts~item3axZMN;^4?lZs*v7RvDgqk%XjQU7vqyzj
ze1X3*qkc4M)4)=E_t)U}mR%J1O=64;6Qaf0s<0<aBC7}^tMn<1PG*lakb&GRtJ$He
z%k0w4?zctN)Io(0jx#4H7<Ln!^#u&hXD5|!{=UI6{l{O|Ju~Zf$$S;+1FE5h#k$XY
z_-hkLc<V}LU*w5$@SSy4bZ~{LC<;Tw?rVJJJ6itfByT6%Y9)RX4WW<9Dn=rTLQy$r
zq$z8Hhh}BrB@1r6e(p?I{uuE)UdHx*OVY7Q?_z3)#mqn%=N-+y4Neac_1J|$gE(Qt
z8SD}20R6p;gRJ0CrqwegD7qbZzJkL5Q0R#*0uSoG77@+yVtPXvz}qWE+o|Tv(ETLB
zfnLOxYlrQegd)SZ<u9HA0nl1+_wYEw%-c>?LDgVdOTH|a5N>fZ+fJe4&=XjZ3TjEh
zu2L=EaebwP)N0hgVh+qm!0e~Sq}WvH*UIakh|X;PfY#$rLBcqDKX_^<tq_0eE*DKj
z{W)KNZ@@p}@&&&F7@ztH<dsBr^HwrPATLK>p`eRcnX<}#i(koqUIj-6im8-$COg)X
z>G>0O^;Si(XT3m6p&;rFja`3mlU|AeWxjj`eZHxRPML^0jWF69pK2xFTu5;McJVoZ
zaxV#xODU*eYF9l69i7!>@yzG&B4_wQRKkW^Iuk>-{YOk<BT0%eK!v$igSo%91}<G;
zswzw-)pK1C40Kicf&q{Ou?7Jc$+&33v#Z#qzy7nvz@VD%T|nB1N!p(Vs_uEyKF)xd
z>e}?6ZiX88FRpMSlN4hCQ-knk3%9?dbzTh7ep#j+jouAbD0<kP9a*RZTGR#NJlXyY
zs~u)r7Q*Q_D|^~b#xB0gTtym9(U03_)HrW*|JHq;u=wCvC+X{N`qZ+Q%les$^L=?w
zd|?;Cb2-K6LH$A;>L48|FTF!Y?dAe<+9W_xnNl<@XS@=1eXpCI5~EqrX%f2<&djct
z#-NxTtKym|=h(Gqu3GX(bZ8dX6M$G&V>r>_e$E8|d6v;gv4an)7kE+%hEI?Ktrf_I
ztirCWQ?uFP`fr2&+U3mGx%%o@b$wd{QoOR&D=GfbFBkKMY3GC`G#D@Ca)tKJ8RI8S
z0+mfha{K-|Qv@<%^w9ktH7m68*-RZ#XX_y6xEIFH+nmWYDHzJa_j*zos?jRFyU{ea
zIX++?7hi>i=mj3<PRhRWsX^?As7~6pL!UgJmwUyzIG`QqYZ)3Mc*Vr<k4coUhBE5R
zCR4L7Nw!arg1*T*hlZ&yCG*pnWmOJknBSP*PR;Zh3cMo?Fg92*_*l_9P-LBeUEGbY
zoBk>2^QU`z_P0lzKcAM29mVdY!xDbiavCk<CPRV=oH*@j1G1U#?75?Ad0S7>jJcL_
zcOyn*ci(uJ_WxI1ASE$u-2P2(iEqP{v|iAUm_2tYh*s9?FKZ(|`PJ9p)oe%Zziy8g
zlf1OfQM{Rt$&DZTh-0O@o=ip82Uz?HUj6m?LD)1N-1CH>!_qD}Oo+hPdH!nYpB1X;
zX^zH#Z%q~g_`CxKLZqQE^as!*07C9I{rd9&qK%rcDHh2O6Z@My#i(;kfu#aVQbQ0;
zMdYeM)72Psc-S9BAb^~`-Xrm&G*!YZ0-wMgj>CY3L`82?_YUC#q=2hI5k?XqaNwKH
z0*uIvwP^C&wLd`giYIo<zGaKi8<io={P39vrVjDVesxJfLxztTIW*_KkuUg%G;!FP
zca!>`tO`}EZHrNTAv$Z^`0LiiFZR*mu=#!O8_dHlXk?<2fCz2~T?i;@Uf}+3f|kd-
zxX*1lE|rxphQH4EiiSYRZGr8h9>M{8SAVBvwb#jWA0b84D?gbFlJOu$>nA%wAFw|r
zYlMsk_|u$zbA5TXXewr2g!=j0&5?M+?qhLR^q=-?skvc?=^=cxhu@MH8F-5wNsdp&
z1C4NDbZFHB1wb1uMkSW?yb?wa-EjvL2@fD~Z{dTi_en+Z7+?@Hf$V)}8S@d|H%5%Q
z-XVc{h)Y5bt0S9gV%@D9b{i2tHg)e54BW=OL+mm*z7z;zt!SOCE@4c3UX+$jp$rU(
zP2HdhYVfvUrGp27Ue09YlE-u6@)cFgFqRlJ5Kvuu689`TWkab<OPJ!Xh-VxJsb1IG
z@Ru#4a+RoQ)S75Arg{E5)#tl)O`&gpTGrDiHJ>n0tNX+;t}0mG{#oTtHCWeB(9TMJ
zY4wgHeZ}VuJ6nSuDLeI&V4|~)zVct)Y{NTZr({6LHm-kDNJE@+i$7s)vYKSIeZqh&
zGug|O?7VVpjqF3zET5r5k>B>)v1H$G9Gt&G%zoYd7i#JELd$!nTjAezy)y&BK*Nud
zfcR472Xs7!s&ZR^A}R&&B#77nP^i&)00<N#hAe@8V)OESQ9E&d3bXg{K95L}XFMBc
zEVeW%-LZNpWN<}bQJ)n%fTkP@6Tty!5S~<|NwSY(tQZ(a0+;}LHL3#8>+$fr$dqj@
ztEs0|89asEr@;m|k0Nt8Yli0|E`A-v_V47aU44XW;d%4}{A^!41wSd_Eq<gSNBQ2`
z$j>%;HP8E(@5<fF_Lm4x+3rMZ(3fb5%lgkM7;Q;;-T(1jifw&WetJB(_s+j*T9!@Z
zcKYwm?aR`M4DCm~HSV9$0e4dUKSMuFn%62}i++{1oBh3|*ZiK_<qzkXDB-+6`UkrB
z)z#LJ>z_SOA^V=ua@7K|fV>BX7Ri{0lOQ?LBjGjG%EZq<OTBy9>PwJsycaYRbov?T
zq->X}I5gQzWdfN-th}Zw?&Fo$M?;g&<s90YIGp-hmU>thVJSSp$(Jku_~9jf?4e}U
z0#LMq6%Jrb#w9OcsS)<jfG{XchuYB^v#>3Uu?>Xl6%Kk0Hoc8&3#{ai$can!HS`GS
z;I+DEJ|$0@apPMA^%VkL23sVt9-cf61|nq!p1+jqEVmj~t84WLY2uyZ1UdZ%x=P>+
z7BjAB+|}Eb8#o4G&aqw%-R)hBSU0NS|D>C3$6y;oUtSWagiZMV8(?hx8@gn#rE>q5
z=>;f`0gWfP#g{_9=*9_%Bcggw07zvoT-Xf)+VMz$fApE;0t7_?0EOfXCWr8`V=FP*
z&L^#eX|A!IZhpAsC(KxOWt5<xi4?$46~w?@K{G%SdX}%`j_WvxLi7KW+XxnShT_PE
z<OP!D=nYg3Bt|gdn5<Nu{QT>L4+Z>752oaf_CUl!KYshxSDZeF4ExY&;W)Zs>T_H{
zJW%<fQj|CBkhEm{%_Q_K)xj(qAUGfSNd(Teoo|+gY~0zGs@42~7uy@Gg_hmR?0t<F
z+pN;~7Yo7W>Bs8BQKdKsk$4@I{<Y>2nPK}V&YZ5VMXFnNr<shu7nD>)MCd;rIZWHz
ze2YJ3&BJ<<ShNCDJ2|d0D|anQgR~zKEPm&kl}9bPT2O;@`{5tukig50<6|En&zq5V
zFl+NPj4eARxu;4f__N{{K|&tQHIz-|X?xrMQFPXSO}z~mJ{tqJ(VfCb=>{obbc%p<
zj+7P={34;FM>9%F7~M!qiK9yt5ET$)bR!@t0_wZ>57<vTXZt+&bKlps?_?ggO`~B`
zgCbn>8Dkse=yq@O+%wvZp3I4SO9Tn6nL5%4Z?VB<Wu1|E^v$)nr$SzV2IK3=PJ6ly
zbeDq_4TrH#rjf?F*&B?XVEkf_TtC?6+~O^3w{jd`PFxvrW@iDpXVHy>jbLyM0n7JO
zTLl~>zz_K<D9ccvUpp`VQ0rkXSQD$aJ2hbH?D_O@%jwnJoz>)@eGPRsuccdJP{OpX
z$DJ@~#T!vhaH%)n%AXC+IZdw>X<%%?*Fs_qNi>`8DLF{U&4rHHY>a~1c$ydwjxmB>
zx@khUIgXx4)ufE-BsO`Dxnw=rx$OH#nDhDSMNX3F53uTA+CQXwDJu)q1V8j*=;x`p
z2nEU_RHEqaLe|>Fu``)e<GW0v<qLb_aZq4QN&n_ZC7eF!NRw)P)<XNGAdAu!<R0s9
zbA`WX4^^EzUtVWF$l%cKCU4U@rIO?O*!Nzs02OY|Hs*AnK9hAv>Zs^pPtXoo8BzX_
zTl?Z}sEI^rk=?4{kC;kzHphToyO?`MZJ#5WaEAw4-|i|<*WV5st>@}H34VOTc%eP)
zh4*Dhn@@djq@LPubG^K0Gr|FPUi#`;?YuED$`PzgFFoHMD-O+g_j=jgq}Mv>hqLJS
z@6D1+16X>Ylv0jAjpxEsavPQaK_X?@KtN!3YVftxr8jS#raFrWN0g-kp`+HthGuWS
z@|=EsLnNh*<~Pc5;!Gh{P-cfQTGX%9CNvHYn82_Q0Km|5?d5CIwRKQK8<cKp{X!d%
zQn2x<-*m_mw$#q9eXJ~I!h4;jBf6~j--G)wOEb@#Z)@@znC9=`<G#~0=(9F$8h*C9
z)%b+btCBR_@>}DXHMAjoy+<<gs^{T%vT-rK8|Gh!cUgILHjnO+Og!wc%|2Tb_R_Qu
zd)zx{XE7_hog9()yIGP}``MW~uW`yJ$J1eduTM=w>~6>DcOoQ`Tviz3|J<xEt#O>v
zr~3;sIJtO+r!&~E4EZ?rPlcNvANt)CkR$^0*w+pXre+sxkfMLNW6qn`k4*Gsd|V}#
za&=X9cTHRGF3uUWgWA{TA&iJWQ1mbWroCjx>~iSvZIUu;Lw<}c+5rrokKE7ez9)9z
z)6b>L3eoZHup5{IO}3Vv8kdNgLRv+QH^_T)w@7v?VV^02E>f;iQ_IqDv_0}gRLR&?
zn9zEkDs>gQLIXmTnfz29HB!q)b_3|d7kbw{oTbzzSK!bW&a6w#BXGFiph+rJIq*tP
z_JWC6rhU&$#ia@xoD{>WM5Nw#OnGa<YmiqrF>{p~zPlaxlR?(K!C<lC?|!s&+nBTL
zL|l7A)>xg_EEfo&=6xb>uzicyBLly~|Kv=jh5uIQV{&AC{UUQoCZ5jwZ-O_gK+Ny5
zCx5HNSc=il5zyScIbP~VunyBfMIk(-%P1!Fjqs#ux8jIwk9!tRDj-DyNA(mua!$mA
zLtFGS)_JJ9^?QhQlZkBgx-b|fVFXn|z8k@afQ(!ukzW;*vWAm)VbG?K`kn~hZaFmT
zk$~b800574?Ew0v>Z}+9E)5+@2Hy?Ssg+4wUnGG_G7MlaHO3IUCIApG@^?z1pj$Jb
z?wZ$3$Y-N1ngWlm0oo&pa~Z9MNJQ97&xCPr{sW1ugo?pKeV+dikD7H+nJ=@=IVc@6
z$8&Y_TDg|Gv3=<LI`h<e(YCV>R6VYlt+$oSq?G02CGXWXmp+sl{M~c)=H=|Srs!YU
zry|T{DlBK(xl=%I4^nvu{_@nV%G+mvJ&vlmSJg;DjuzK<?^PDbKqAFJ+4q57Z5j<#
zh++7kVbA^i>;?*ZW};_B`J^oSGz5VylFXE+S0!Un`M{+@jqp>s?cQtDHC!kvL`A>J
zLt6n|>&Vgg#~~C%PHc1R%Ez*!$T9-+rHH&D0PvQBt`E~({_TR|W$WU~G7dEmY3i-C
zrOl-`v6Va%+~oKZB3TXU^%R7n0RSqJLRmDxqb^097%?UkGE&t#N@ho}xuk067bOR2
z;5Fo`2&*#L8*<C9@)(TU!}X9)ya!EJOx$bmMiavpas#YGRLyDH#@@PlzfFE8k7TuT
zwy>#otq55(6lnQRYKw@u*S-(x>dUUxaJZZ94tp^Hf%ZBX*S%<VH`2<x>QCWnC^FRe
zZkGnn7)%i!SbCmXA)!!irD<eahdLhYV^xTd(j1Dq8~j^yBYL3pBpqGC!T2ph#wyS2
zBw-;kZKetC!2-oZB#bw=xH(a1xzJsFZz|p4I{1%K)^CQ;P9N5tI)0ju>75nDXV~A>
z!hU-d8j_%x7Bh3Z8$%8RPe4$*5Lgj5G_OH8Ife0vqU-6zNnSVf)r;UYE}kuuT4Rit
zQ7m48cK&8Xcw=+BDJ?FAJ;<`)JDO1n%%=KmR4NUS0>e?^32V<efLuTy0zdp#i3vAG
zlcLRN07X!JS7<`4Wi=*k9G{>%KaqS9L5@G-#wVeXkm>>1gobf8V%OPi5t<h%kg;PH
z3IGlP0*SY2GOEWAYAvBgkk?`W<_J*o1n7`xcDt&;rsUabo{2K*Sy8Lz=K!52OqsUJ
z-~Vo5W7D4p?|rMGHD4GJU#z8ov5z5!E*^b1{`)KtYRyS+ExN~)(UAp8oQ-&zBg?F_
zh{>0(${NqeD-f3{8)%!5rC8HaU7J$=LjRdG!mxtIIyG#e;6rOfn(0+bZ_jt*@uf+J
z4C}s1>pog4iZYd{j!E|E)DAgux%Np05<@mCp`<$#a~drEMNE+2lUqp|0wo&4`G)O3
zmna(3yS`*-=yrCUj63Gp=+EBsooAW)Xp}W41&Uf~=(GExmtk>iqxrOZZ~zZ|k~d=!
zw+oOFjN+6S$W9*9NkwFd1rFp?7rp&zDzcMiep7ADGts9jyX`bRFFcvJ>Lx<(cA?Rx
zoNqCT(KBgL(#qr$TIA#DuiFX}W|GkIAxUg&g{e&{8+7*Vr0-IzU)xVogion{m{AQ(
zbhJ2Hx${Xi^W}=yRF^k&C6=xtGQ{qVFa7M)O+09P;-89!Wujc`fO#6_4cPmx$-bG@
zl`J=N4)eRohhi#k-n)xT{vm&%Q!;3Zd!J3Cc}{K0e>#Z#)?q<T19F}{xVD)<q`Js_
z+FLbkYd3A#SdIL#e7d?i{TFfLr@Z);&E&AiG!2({c;e=HUk_-trX8-e-Gzyt4Y9~i
zgE`jup`|rZwWM=C6^hZX2>i1$-XymSho}ReQuPU>7d%Ld+D&|tS2s(NV*iM;`zXWv
zdBR!m+zXMpULNyVCiA^~8KIH}p@o{|BNy~DrQdQN%<xq53aOR|<*qleHAXQ8MSNGm
z6UfmD{4lgz6F`D8PQuBD2moyurX?p2Qv%qKW}JK9F0!l4c){9&RkNv2m=EI_zyioB
zhL6@zKXW)LETJZhf;LT8hfOjwjP4{NVS@kzNC9DPcYaGW25N;#PgqN3)3z)v-L-;B
zC?)<)n=yh+Frx#Z-Hms+di`5mZHzn|KX-L1`h83!$w<at8)Eg>L=#}CJ6T@S^b`8q
z@LQ^RZQUH{eM9l1Sr`v|9-3!|A;3w!rbpDw(bH&*;@I2MR+H<i8r?~F!fRc$R4#Bk
zYLla2<f01t6qcPv3RC%Fq|u4(h^M+r(at=RVD|QG8}npZ*zRig1V?y^FEAF?^DX+8
z@7bYD^LDnwJk@-Xv3KcTr%f_+8C+i54fP@^%b)LA({-j&j^{Nwr7K$WJU4#f6!&?>
z$qkwt<?WQ`Smma=@gJfhEnV2IW5ZsUt~@O!X=$s`;m=(2JKR2lc2(pbwqA4&+r1fk
z6@Uo2AYc>tE#r4@LoB>Wf2X0FL!p>2lf6RFf~}>q%LUDVnhAR$*PhOW_QZj(ozuUV
zy#D~DyPz5d0HFTA{d`vtwCSIW2>bZQpZ&2UG=u}%p2hm`?mO={0}VU3J;!|Hk2RkR
zbsBm~n#2@y(y7gUIBcib%epDTV>?HC@}5l1f=H{wwMx_^kng41mm6hd-KQ+j>r-yx
zV()hBdR`nMyO;WIwYc+cJI4)V>PpBGw3se2lh?`dD9xvQRcB6w5zZAw;)q3LST62*
zEFNti7S?sWGJKgHcafg^c~EJED`L_s*Rw$}{n&|N+iP={a%J)NgeIz;)|@v$XzKV6
z6^}Ua#zkSYIW!?<oQKoaB}Sdygsj81;!QrR(CZ3HxT58qO!xS$`h6gyWl~|Xc}69K
zMgziV3b@Pw<{e@#Cpr*wW=x08Cr@X!TKN+!G~rwi#(!Si6i=B7N?{fuai*GTtY#^5
zPne$<1Rz+G$LcjCtaC4CYjA||?hzhtU%Gq>C<IVqGrR4O%tY8gmgq`~0#Z$zC&}(Y
zU;Dk9MsR5xgr>7&pBovNgx5AcXwo+~(DP|(2`9~oTd3$`Gwu^3$Y4Jj1#_3^u@16X
z2D<g;le52~kUbkqNfNi2|JlYSD-Ts>*IF>B73V17c3pXc1Dq)`!?xq^hj5!|r~S7k
zW+1t2ugS{=AX_cfMT5E$bS8YPG0%={@8!r)mF2|0Eo5L^HTD1Me~#r)d*`yV9TWWA
zb1&o*@fY!YPRDD<{hL*M5{t)fg5Oj?ia%xS&pN8d7tcL)(JN^iAx<m1C6D$3*1Yy9
z+ct5o%Ll!SZY(4eb_FMA213bP0fP&_&!{#J+sIzVPq1)y?ca=P{x7cmKnNEWz!{{-
zHq&TUla?@3Lf&N-mroPhWj%&^P}0o~ZCh!zs4JdOd=Z$FBi+T-Cvv`)l*mR+JxhT~
z{=5md`I2JA9{3QgpNJ+%M+c8bp9o@hY0w4iX!{G4#djr*f6OCjXg#)FBf>Aq_|Tnv
z1u>G+S%#nZ6lnVp?>~ba3+KA=n|cdP9Q19@W&v^^MjE_FhufUAjOI9@MH>I1q_&Tn
zHe7hKzmesNTx<=bt<6aN4fy5vU~==CNytoKR^A`-A?jm9q%3pl&er{Yy=&!f3Dl)c
zKm5OWQGETp9!GWTb@(8%UUPQYv7lN}@-^(gd7297jn-uQPn$vKwug$lx3S!)FgVno
z{p=k}N;|+wVv)YZ{H2O|Rp^Xf+vKS{7j17n4f;SdZ}Z7xx}R!4z0#*oX+y}B*hm&`
z$EtC?ea?7l13L9{S%98#HBD1wR8dw7dR&a2E(~UxDjmAaDJhp=0Mbh!S_3s>s)&FC
zPx^{+fFdg+00LGC04wYqc>2Z}4@e={fyjO&n!I;k;(KU663DPY0&r3=K=bRW9|=i~
zMiQQAEuJDs23H?+^;r=ox@a^_!k11ZQldolECEMdeDM?z&J47XqGAP4q4Cyzrlcg#
z)cH=sKK%ao;29Z`jQ97M;@_xB`sx!v@uoM)1fJ$#t4@*(q+X8{ssX@YV8Hb(luNVd
zdUC0)TS!eyy(HQT*JJ#%MBoLm`6DpWQH3Wt#fK2K;M7SD8AEsG)bKy!p@<In*>}Tn
zbn8~6wI&D%mfrpT5>&gS8eav=sdnjTw)^b*BIR(P<n^D=F--fL4-hT?9=~5q=y2&T
zx$$$5Cg<DXGk-ocf{4G5$)|p5l|8V0pvw7-d?SZ5S%<!?-9ZP<)fh}Y_0iH&M`ML7
z3EosyoKF5BMDW>=l&|TeJl0)+b?`{#@{c@i-$V9Mx@6RN^P$$?d5OU2z>2<>jh`1f
zCd+J8dQ!1ip-J<iT9HYqh(@tVv!V`(so9Ed$+vf6yn%iO3UQ`#G@JlxLcrC43LF59
zsK<f2g3;u)^yX;t#6aRC#pNUwy?bz;jyKVgDQv6WV4l>R3{JWfmLy@GY&<N}A1*a$
z*^Ml}zptepi@*VNH0A72UNQ{ta2k-#!C8o_HD5FVd_<JAf#4Vo=>>fiZ<mGlA4T03
zt!^Y0Rx^j>%NsD?@%I?Lvz)si(|IRZALq5-0|t_2FP`#w51E$Kkar(Hcktz`NN)Ns
zyWI1uROXh-7uh&W_Zt&ANK`fLJJ5ZT-7@$FA~#(8zQXf1@qo2)30RWeg{oHkT9)9=
zxFweT)kMU^s1{k-p)S|&#)R+cF$C!=wGH!xzaBBR3-xq@xW#EK_e6vCeb1eIF724D
zC)ZJF9;}^gnQoVYM)7DrL*=NFY)#&l;;~kRm*vn&{Yb)5d=$*QvkV%!CmH@c-TTSP
zA8dDM{8p>F;zly}`pcl57q>4()w2eY%afi}joHpb)3_~C<D(|n7<DAg%m_y{UTr4n
zP4?%Lg(Y<)sEr3-1V4MwSL7XdwzL*A!PHDYHWLuWITa+pXWQe_Hh=Kl`UX#>Bp-lm
zqVo|z{%~pk&Ul_O4GFREDHvZH%G&f9Q@+;1M=&4E<e%WVd@ee+07UYkK(S5*1W>bY
zk>OHLN^2>tE;Sl#Q@{-XHjJrg6jM{dcGqnTjugai`n2z4n3ZCdD<bX!2>|R6%o9s@
zF9X}2irpjX16`>j^qByeP&M^pX>2kXT`9?BoQrIo{*K=#_RZk`c$8ga^%tQXGNX4W
zIJP5VL(TvsrH5Tzu*3a5dBzP4U>qT;yYu?3rp<j=F7`@auo24cWf;ekVi}*2=+9Rn
z6>Bx`mZE=0N+&in^}Y6e-SMDXRTwi?H;B6~*5Wk%2Mhap>r&iaq*P_BF!vqRn?}0L
z>DW1iqa^UIb(Q;JWo!a_-jcp^Vn}2~`RE%4myHlli*E)Md4rqFWisXkMico4LKA(J
zIYGtNllhLKu05Y}XW!XZ344eNShVMoSuPqT#))~*nJ8*qS_=mYz0NZwk*#dc0L<If
zE$LWq>@Ay8paFSpTm=BwZsyUQ`;RLP-aeE!WSjl#n;bDLW9aehMzBd`7Eca0k~S`#
zmR=KIV@!?%Aml)u21uV%aO=excJHe<k}l8ec)E-QqPoJ|!T4OxF>R{bMY3kTNb%sz
zV75D4q?YQ&tw)Ht=LYxn_Z-Eus76zSpX-Ty+K6&9ces=tUJf*e=sX@?EEDf2(YNB?
z^6y}hloVXDTMGBIeb<pL<1I2~Rj29|qFgH}Qf*<AOi@MaSUE0Iw<lZ}P;($(ZP_z7
z>%CE=mj=t8u<LO6JXCrAt^AA6K4+^{@{gM=SNd>EFYRp)JiKzA)O3)kX^#Ka6U8AQ
z%!&b@R#p7WERK0UhW=dKFI4q6*6cE|dt+8p9_dWa7M~YC+9Im-T~R(@O2I8xUGJLw
zhFe}EdGjR?C6tPYZ_$A^Ux_-@W(s+?ek^Ct_Naq!X?0|#z@!B^W-V)i<v;TAF8ocg
z8NUL=gTa}BWa<Vg+kBgUC+N>l6cTIzM%Qd&0rjoYm{~#<nMGR`>VjKK5r6<0IF_NB
zE=t8eJdd?^r!|=Y3<mJoq(o5+6al2Bbu0)VB0V~OSQ17-F%T+FL}ybtg>sS%kB3s9
zz9YUk46iokBQn=NIVR(uN_qqvB$}V2a6F$}?u$*7xEUHy8DvOJ&QA>I6?xq~sE?H{
z<r=$akI8tgG6~gX*4`9Hyyv$p#acAdV2}02(5K4iU}_d$FQbf03Keg5)j^@3foJZe
zMZbMiv>@V~rR~p*m=Z}wu3u))F#1hlm;dcXls`{D_eh;%&>59`MT9Fj=>F$J=QZA$
zW4atXSNmAO%U``ZQ*+C7Sb%#$yZJZfi>hF($-tCt;9}X2&^YSHP27`p&v=?TtCk)(
z_`Z&8{_$>j!OkwlFJHVB*N;ynlG6PCRJK*yUfXWk71E3-VxG5;D;dSk01gEX<b^wI
zeC_~M>CIbc#UXa07J<fkLuq;T+nreRkU`%mIc^>ZkB9F=y<A9veox>#R>k3TG`;qi
z4I?kANR=!aN<Wa#5AOUMlHa*~|K3BprMA8`69w8p?=NCT7WSTE$u4aQY45g{R=vMd
z)eN1(JGc6#ihTb*YD~Rt>Z)2QVwGksKz=3hk`dP}=@WS1%j9&YLhSJm7Eb-O*L1b7
zLB$_)<zDAjt1b4`zpOISb2<q3l=fHZ3#!|lYYgyr`}C-};ef$hJT>X%Pxgk#RT>{>
zuY?a@{OhqXJe%<&b=)at{BC2H?rf)d$GiTr@qoQ+$)3)s4*>*HgC+r>iy1r*Ko~*i
zYR<m|^lu23Tip}iJMw+-;IC45zT2C9me<iQjrhJS{fW6NF8;U^Ee6;Jy$Fb9dd=_3
zujA$_{SZkB>4uqXHTM>-`<waPaWZge@2%iq7PFg}6zmPL5@y?&GgCcoPkwMhX0y1y
zEviKbXDUSsy~LbzRYU|+C#3Gdf&yepsc8v^`~YJRjvfSdAWFly2S<mr*8$(B!h?S7
zpkfX@z|4bU!4d6sqnr;E!FW~T#2jMK$=>*tV-3&YT_%Par9?LafF~JVlOG_9Kte=o
z&efwX!DYu38^C2l5RDsH%C%2z*A!W+D!kFoPhuVy!y4mHuQME0G(=e&9gruL{eg;1
ze-EKHyq=i2EnlIlR{QSu=-ImjCxseij;OAOJcFF~>B*4+WZ*vZ2GRjRt?F|5ROGQ8
z#!GD&tfKK3S#FZVPoLb-oCZbxim{mV^7^2KoOCa^He^+dl{1*ul)uB3n$nqiM`0c-
zW&jOn(BI7P=Vq)~o-vG`t!Oe5S7B&D&eJ*VjT_IMx~m_<RkkJ-wOa2-&EL>i7#NxS
zN@R&|4Ud~iq-Qi#ZWZiRJH+`jZVANEZ8oyClx1x8|1fK?h0OT4p}4ist2bL<Gl5du
zO*lBa)Hhw<-E7%t=nZm_8w1A7Xy68jmk|$;tgRzZaC5$0ZiD%Iv_;5uu8357m@6{~
z4ZbDlCE}hUS9xd-F1mqd<zM>*`KCsu!AXn^Nzrlw0OR;Z=(GJgZdgq*idr5|DL%}}
zpYfgI_ydP_gFjs(hjhc?w-7=8iWg0NM#n9JzQp$H=;m<dfh*a@OSq@2#L&%g4wp`j
zmUt0@5V*|!<(a$FOT+=0zaZ)5v1qpI1Fj!NhBdyZU6b`AvW0K&EEM0x9tZUtw|+gA
zIN5@?yH9Y7rc*bJn*SJ${rQdchs3mG=Aer}(Z<O2Cvi71c)^KbhE+L2RO4`nmzCHD
z1wc<(U2*}yiwt1_0E#5*humvF32ufzgIIq|W$ROWdBgEG)pGam={i-E%N@AiKaRiu
z@C&CFwRsAsi+7da!(GmD5gPn8t!S7s4?P`}_qSW<@0oMC*3kA5j1!5^W%JwgZ_S1u
zJQ43ZuW-;UvTH@Npi+&?tf7AFASExaA9uP4j}SFa*<6V$3+tdF_j<g;phS@?Gv~ml
za@^#r6A2b1z!^0`QYL{wUDKe~0YHXiER6f6$m2}};FT^!{D=@WvxX}h8ejnhviQLf
zs7x557yzQ7NIw8n#xDMqIm(lT_|2{n`f+Jcd^7-5!EvzwfB@~$a}UPzf&I97_CW-I
z5;saCddPPW10WS3;L#zzn#TwHeKDO8XfTi+1#Mtf**-=L%2L+`e%2DKnjgIvBMjd)
zf(z|1NseX>XG@><2(Fv(wi6!55Ug5;W7-}`xAkpe8CgMS_UakdC}IU?oeKX3s#SLG
z8jK!)TtF<zYjO!=at%@`DE%3Xj*j0`{?%=9G&*cF)&>rT0#Yj!(wc{u{QA?ea!IFk
zv(TzVI@P%xuVk!})oEtxDQ+%cSr;?J?)TEVA~2Q;VOYgxjCK92|1(pkn6FHN9fV^?
z05^E%`f(XBAnkIv>FaSLY*s|q?2i3Nyb)%^ivE!!9oA8%A@@|@Cda^Ns~EZVo0Af^
zDi~_z+HSqBCfsyeX|zhVbRUfd*!*YM2+Oo1<~P9cz`$8VRyD1r3D>}dkSkhku!CbA
z%0)g1&=-%OG=Z^0(ArO7Jmo#vX`VPw;n8`N!O8y_Jm1jVxB@a_1p&27sR;!S>T<0Y
zm}JIZw~Z?{9>4HlwB4c(=Id8IEx-BWQ?2}p&3=_rI#}1d)eu|g-MVcy{O)x0md}UQ
zL+{B%c$dA%sB^`VlBSx^j|+_UUoMgw#sOm{6hMv+B}V|n%KFpyf9&08Ls|rz_J`YL
zUi(T+!{$2eGAEBsCY!gz1Ka2ibgJcqK1v4tJs#bYlK8phRWI?J`2;F)Cix)n@!Ko1
zxLfpu?a`A*4{kmZq7~foRTh*F`B@#Zx70vk=k)lkd^Fl$ON7IvnP?7WC{<%3ipL0b
zP#i!?@FI;?<Oa{V#0_tZdA7gzsutXlmrwCbR1nY2mC<uGY@@E((X`VI-LWdrl{i5e
znm*v&lr6pFb(y~b9#eEZe>H}nlTGF^CgUEe;Y%17#$ZaV-$;V~DSJLvi~SZE_gqC2
z-eXj&JfwVe0MWdwyV}9qDp}RP07)VZLnQd&kB0pOdw?-HmrPx*fnAw(BEv>(<$gTJ
zt!O#?1r6#ez*xlNa>;#9Skt4w)Zm1F(*hH9_G(Rz0oKk|TEIr^h7!0efi4PMD*(8X
zB~jV6pczZGkUVwl12_N(CI<omppF7TtP2Q%@4*EIal&|J5G|4tfu}aUp)?>xzsAfD
zFe}H6frcb3Vhs7b@>a{_6h#G$&*D`{hi_YavQgwuIW@*$6f;sXKRsmq+)&NVQjtK%
zo)ohy=HkE%BJSS{*rKY4gavBE<Oo{m6@eC{C3@Qn>ZjMhqbbc3GvPodlTiU7)O%;u
zOl8GIo9hpTyuH?#h0jUnFjjl1U>O)DNHRN~VN<$kH=5HbP<m{&5nvJN;IoNIy*vER
z3$y>-61IfZWPGj_&tIH3R`^sYNLQpZm@|RS_SSguJ*4>Oq+-ik2U|+vx2~;aP&SPm
zEte4z6hMLOApOk1z~4n&1O?=nZj1PfG(BFZx?QxRZ0}zo^W;gHf6Df8NQIq7`K83d
zmcR6p{??^l#gdjJX}-eyQYTV>`&yUq*};7GkIuStqGbij`;5uDnd~lj08~Mhzz)`E
z9zpSP>t~m0e7CPu1QJ?p*G@3*e??pxxHX0z5RY6a0L*jDOQ7K#lu;EZ){+VsA{o7b
zzO9e5pd~CI(9vXKQYCKG!m+ysK7PU<#vj@F@DV8MsAZu&)|GZ*3+C_35<t!8lWFXZ
zm2JBo$}&sVquW&_l?5h*p1>t#(KZ#ywmX$xwNJvIJ7Hlf#Yx(EA41EsPwyPUSIaeX
zb{r5}Q`J3Zu{BAVo}EH2sthg1WHVc&1b0%mIt8sZv0aor%iB9FqCr+Yhq@tAzRrTJ
zX9xU36<5wBELJBXGp_vEZfQ9;pBsMbahCq?fjD1G_-FPX`{lhc%y%YI0>pcR?WbO!
zt!pGe3@Tdse~HypJ@0!i+C4Y>SbHM;rsn48yx@=sk4q27=V*Em7bEK^{9`*g>T#Gm
zrMyH^YZa-Jy^URWuaj=pT|VD#l}Q7Z5i!I2nr$s3G^Kvh%<co1f&(+0>qWFYU@3qd
z<W@%K!b;mj+g~OmHo}rH^w`9P0=+F+d{t$0!H!6-pjPT7x=wEreRkv*uhy0M4&_8&
z>hhgXUeFv0g%&6ao;gNyfe5UEgX~X`#Qz<JrZdB5crdTW<OQ)#>buS!RkK1{MuYnu
zj~62eyX4l`e1>9AX!(L+-4$}V=JtI`AH~DvQp4U+MM6-8*o?vnSWmnuulEVBiyh+!
zX|2)#05F9Rh;%VLB?9*qP`DufYgUcqZcm~hyw=bSr3WD%;ut~EWF=&XTa}E!%@JsC
zTB7umId+b2+o$T5qW5gyTfF_TM8{<Lkd7h#c_3xaC(Ti2kUJ|5U;||gqP|iCZ@&U^
zU@b$@GG!rOzKjV5{^^h}(>#<&>d8e;Eq*87SVhg>33*OHM)0Q;>qi8SiWDc(LjDJN
zv!$8&4U-#s1Wdzw%p~IS?ew;o+gC_kt-=ytmrM8if~4#{ZF?q?pFN8qKDz`H{s;{T
zJ!tcY>M0ea>C~c{tjBv@f#pr~Zlt-!F*>S<EvJg}ZyFIg1i$SP3ky}1>pM6OiK2|x
zs*>HuQc?6P?WomL7JHQb?NVa0$b}mn{PmRMnmXd@I8u9g^z`+S)9O+2+R@jI<Kms;
zuMM^#?~FE@oW5B3MJsCYKc{u0zNa0<Xvmm9v@BcNcUNPZUSa$Hig^JtF8vtJr9yu&
zRH;kNR||8a@+|-==Dzurlps(~5aI^ESdgHZV|*##73b<q&xJIVz?(80gmM#9FF>c7
zZu~b-qeXjsw%H#xHwEhT_kQEf`py0Pkn7d&uS;%fzDL422SKdkvgCJ2{SVHXW&f4_
zE-5*7E|-5Z^xHMi^G%_}(4ps>>7#!&67g==C505TnswMxp(C3d*E}Ig*NAA~_;DYS
zm~-X<w>VPpBpNjh05Q=-67x_>5L?Qd)%Pa=84`Fl#&5289WMtwGu)C{G*0~^4l>?q
zSxo7X=StP#3#ygt3DGDSugQkF|CD7+H+Ij$obSKTPn#E^>-I2DH>;bkZM|Gx!U{K=
z-N(@~p1snp9=k77@BZWB&(mpNnX3?UtY@)x^@r*c_{yzngNop5L2jzVBF6@6Td@EG
zt=zez(^|)GY)pXKDlUU$diMgjRYXMqdrMRRjFS~;fbfX8GY&M%cYLRORiRgsFGc#E
zf&&i%I1PUgCvMr}!sfn3J=Pr~-+up5yUWKDjdu&P>8Grr1s-+3(zg&{1t>_tc<8Vl
zwHoP@Z>#T70PNrElplT;CF=^{rrmMKieS^1Mu;+HeS<-!n_F{!8*ET2r5vE=2e_&W
zgTVk!5%}*#H4gA95X1wK+!XnT;Ux%whE+K^4-S?hKfR2+IX%NgE{n&@5qDcsHZ*8r
zGWDKor;8F`7+!P**yBrEuNnxzr8*TJ5ToP+gh*Lru|cAOe2Ews&8wZM1wE`sAmCT1
z+eO@=yGQ|&l$wD6RV;MiB%E)g)WHWJ?GCUfs^QeA0TQnzNh8$Td}+1QFtukO9Pmz-
zMr`rtR}3#S3;-ii<&C9;YDfho{UGp+3ffAsFKPt1DoAlk#4btFAk*|kRf^ruHi0SS
z&|<L@<;Po}K53J?mY>v+68B7v&+S#bH{Stlf*Mr-XM*rs0P2cB17KV;e~HedIUoB7
z`G*izE3dNq{=)K`^{%`PBi}@=Uwv$gB3S+;^|sAO%}3cG1-Y^oyBvQ!TkHy;O|Qxo
zl-v5cr_15H>b=Yl+U(fYNL%`pdnfC2M;e1KYOYh*b1`o}%XQ&GJUMACCl%|py*Nyt
z^)g_TDZj_7%E(V(@v)=JVv?E>oPXBk7+~F2$|P=2U6wy4MaLv#1Qg6Ok9)j7ctd>H
z`SQz9fx0)lj(gG$v-b<EEc+fbb^d-6sLDc)J>jJLS9O%rC(`uBS2Gk`vO5(0l|V^y
zwxUj3hatUjGaG5ciUJKf<mo4_H2~h-zdrKYz<#Fln!9X2yH%YM2yi5{yaOPJRCYM`
z43ze1{r$a*uTg9=u1VagGZ01+JYb_ri3IIZgN3aS5!|UW<djOj#(p_U0a>S2Cxr)0
zE?b<u*5xM>N##E@#GW5c{5@9tx&5bz0wY73@vU9+)4eaXQn%bUOWPl1+vty95f4%j
zbOs|p{+7i91&{z<B1+`+n@sC?TQp!tp<zmVXWue&TJw$@o*IiwA<Ehx2{}=$1t@Jn
zR_Vs6DDSjOi&REzv22{#JI~&J3h|!G!+<RaFdO0ci*ANTFzR(mn8>VlFT({L3%pw?
z`4qaRuIOo(>`o?BnRWwdJ|99!7q7yPdGlO<jCQToKXFvGMm6RKO}V@2tx>6V?A(m`
zgzQ_sjq8*2L4BpoK!A0KtqGiA{$fD!CM2l=ukpBfXx-k|Uph^KuH^!ik*%5`xjA`5
z*wBi=mM=pMw$8NP04K%lDGyyg#cB{#3x@1i_p_n=bWk3WbtW!S+SwcW@?`JIl&mTu
z{*{M>&}WElg+KVj|H?&BKuh-29x@Hrlaa|{$sY#roImL+)P&=NUS?7S*vawYCaY`x
z!3b3{frkJ)>cb@ezL#MlFReoS%&Hd-P)Q&Xq=5_uL4*UGYhXV<QE6C(K--N)asrUh
z6yluK*IcfX6xjt(xP%I$0TcMwneSYJ7(4hW4j_XLvfuzF15<#FM9QKRuRWw>3RC;4
z%uT&Eqm2eFsMwR3JZ*4w$kkn6E4<pT`>0v_v1(~K&+J?fLFL0}K)X8E>RERvW6{8z
z{lymYE8u9QT)nb*+LnQ@No);&3%X^UU-_SuM?k;A&%2HVkB;l#zV3N7m>ra#EafBS
zgtw{6-mvwzYxZ0iv&;-!X2PloiWkcv!RDt^`OHqkMSHecVoQ}DVf8td*|w8k{2Gam
z+MUXQrO6go@gdl&pPvY?T>jcIc8sVud0l1t<|qkB-%_ORN&%RSf#Ta!d<eRtbaw|*
z{X`KiZQA#A(XJ27CK#pkA-!~3n1IB@pMnSkaR^Aega^HDm+oNU_<(ty-O9(uw+z=X
z+wC*2*UGN`b85Xl*aG`FDa1ncXH87l_2~OQ%!O{<G6@fr)YSRYCPzCR>whH82ccqf
zghrj9<Rt4%r5Nw*!t}}=*}Kr++|aEE9_zf$3b8=(js~2|^-cQJeB`rkIH{%Xyn%oB
zN@_?z*hjD6WshRP@jN|dmxgF3JIW@Nxgh~})xsEAw)AIISIl>PUIkV*2{tNObS??i
z>E$tc1^)a`-f~&A&}TGU8il46npx-6Vt1EAaze68&mS1&!V3Y&vQevC!*)kwq2vVV
ztHj=0@7nf)Mi+r{84GoDmo}}9*g$F=AVY?n*5aFGp_vPqFokh?yU%X&Wh<iq8yFJd
zN9{-mrPYXd&x>F~BuXQM@1Yks{M_;6r(jN^@Bpkoy@dT`&xnT%-P0NmM}e+Lb%ZKd
z=8$fbafB@Jj6DUBLQZmFB|lnQ#pGJys4P9K@G3)vcoY?h3rsY<vp+8pFaV%U$<;=M
z1|3L31A3<hKf!Jsy0WWgbyzx%9^0Z{rncCv=wMO#{{+3y1;1)nmaQd{BLb&OsKu#~
zb5E)$$K1Ooa`E(F8h2X9C7X1qGhuEaBO1lr*7veMj^{J#Ks)laF^f<~yPm2!JIjyO
zj;7?d53?A896eH+J;~qJzqFBM)qHJZe)lc&1O20-H`Fp@YVX+&4Q(vfN`eZhR#=-<
zI#Tv9zrLEkStd6V`0QQ&@L<N=GJna=D(=KPPR#a;b9$mRNh;4g$!yDtt77u>+bokc
zIXjk^LS}K)dIbX^zn|&&`hCuq<lCzcby*&KeXq9Q^WFcbeaWWMe5~q`&%IAeSJ>F$
zH!Ct)x;<>#0pySfn)qh$eS-qwkPMPbRs=R@y5Qma90%iFL;za)smPz;908A@EM*ya
zDUzSi6+MX-<F(f!R0m%fpMR^6xYr@R^on?C@Otr57Tc@%VTHT73a<h$>8g_%FXx?o
zL;Zywq1Yr)FH%{{(T4xVd%42+n*~t5&op?|e_$`@B9g}Yf$9QCok{1JQl-A8P)m|*
zocs*G=oaOZlbS+MZ!jgb@s~`Tr8RI1v_7dUESC`#D1S>KswG94sgk%aL7604wa@{l
zXQgV@6)6`QxRgst;o!@L4FP-9Fpe_Jh(Ljirn2pj%`;WoKZ$l~1DO(Zty6r${}9m3
z0nld_Y#3eguRL_PHUd)uZ$(}{NjoVmc2|V?rgCF_xm0Pjf}sP$MtGY|fsAPk5v{aK
zigp2_Ff14}OB9AN<s$6G8ubg?zUPD<nwi{8)SnD?-;}mbuwl_6HnJGZ8yJ44sZFW7
z8Q|LOMthN_qaw)1D-#Gw01|?zHKnr&r(1wBe&A#u_Mm%sFO$-PY7fZqDyAC`OTG9*
zj{YwZV;(1D2LKP^cOR;<O2;510DwNq><BRWanqnOUn+#$^f3SsZQDnRu4!>I4)G7Q
zNSmE#$HO@<SU@(M0KlEY3Xq{Z;w_TN{8Ew=S-^Qh;7|Z8B>;+s0cLa^r46**g5=|o
zaMaTjlH%gIBgPN=l1g=*izhT!!%S-)$%Xlpb8n;<d*DWWZBko#n`OBJsnry@xpL(a
zBrKxK){}Ii%KJarWT12tG?XOlR5M_-P!dr`$e@^<+GeC&C%~=ZD9+a>ZBfiJ+rtWa
zS}0eeQ<}t<+<SY2nAZ3-Mh#9~{B0`Jrp>Mu;|H^+bi*>%8<PhcKrfyjtcsuGM5Uir
z7Jk0H=ByL=x9H0UT9xtJ(za>!Z)w>-^<w)ltyjg!q<rZvbule^_hxE-O`4X>sgn%Z
zu#egiqejWgNYVUh;a;T0>f3O&#`2|i{F}{ftGCVCV82JC!ZZ>IPbbGSRWd*@6v>S!
zBW2+_%&eSFp&jKSW6ErJ<|PGUs7vg6t^-#ks+a|+&NsE1GMN$*7Do~ej;b>9&=#KA
z_$2bSAL_WPVxgo$oAx66AGip3x7e1<;jqm5YH8+bQ^DFytbTh8jBiR%#_W9NHMOky
z6!+Y9;Q=WJDj8bw8QWLVm)}J`9zG1br;b&lI3(e=7u3i<?KdZDqa#7kleVE--rDsi
zrr5(8$hZf8nsw0f9*Pa#VAte?YR=RQ_DItfN;J&N@<C*{`xG=@LkIV{%W%EswIE*P
z<Xp=&-=$0=36;VRt|4|1Wndy^DD_Slobu#JDu@rzki*PDbyCIJ8x<3SXFo_oxL;6?
zyj6t<DWxIP%%!gS?-?S`X?cSzD42BN&5fBFG@~<EO2Ya`UOF9XUWWj@W^hxpS6?SD
zw<`9Ks@*|sez}TZKvFh9T{EByDam!@P2zdM&vPFR;5CpD<X^PATr9a1@pPqv0I!aQ
zRJy<^T|B}?U*Cr;kC<l#CGc6@_3A`F;bOqi12X^^ih!c1&`aOQ5f5JwY)F%eqQ<#E
z$xm+O2PQy&$2HrNUI1i)_s7Rc0HODQDJkWTyST1dV3#UWNCLdplGTF1Of&;Bm!te2
zN1xCP%zUAidPW1ye`&gksR4KCKTV}=OJSU|rE|cXepEBd68i89R$fDOag+hA6lEeY
zQe107AH6ZnA$mz*UC$(D)>CVp@+dwo)*UY0xW6Qwtad#(68e;SxtLYvU^-(p;bMhi
zDY#lHVj#3DU7!5sqA6$1Gp2l>ypzI@XsnUejmo9wTm!FO$|?cVtHrD4q8wLyB-Spt
zg0t8E`Ny(F`oml?y}ROrP1j75ts=J5B@;1R(26{9mGM#Nx;m!V@HMHi$J-(go2CgP
zi^}ODY-N*<p+s{ftm6H<ivkN-Y)<+7OwB&FezeE$y=vK!!#S=*<R=WepVNC-V3X>I
z%NUCL^48%?;@e&53Rxlp#ne=Wkxy-->U^<I6Wf&I9s4f%p$|D*C9e(IxW(Gp47|DZ
z2gKokgXVT3)lXX1H_Uf_IAgvz=jAvz4%2pAIY(YPul$i1`R5$+Z`JX_`53f9YJj@k
zSuF_k7&EWPo-EEYFaHHlf*|L93W5YBs*{qiO+`2doPH4A+I>u-eCQ;NQzDjxkcDqz
zUAe_Phh{MS7bPrb(;|ozO^g0~3f<8Q5(oUw*B&aD!d;ipqZw!t{Y46Z48uNT$KWH;
zN<hNIH^wf!y|+cL<;qg!U+Q9@$#ORF<2;4OJii*1z5iTQM&(jt>4uYp>reQ`(_5D1
zqS}LN{bnN@X>hk(sO{CYgiU3xk&uI>;_WMO?PlyUg@RVx`D)qjG*ch+yivvP)4hhH
zT-;j9{O5Z1C;dh_Fv?pk`+t+evDzAW<zY%1F$Ri%uAGf7k_7b%wYF5pQz}K8zw!zo
zz>dqcW8!L3MYf-Yw{p`Sy318+X`o*udJsJCXpB_mZuQ$Nn=V<(eOvi!{ox|lBg)vb
zkV`$$UqHNaKI5xLXy@G@U`g>33U?MvQ}7O#1)-SrzRjN<UHzQ^)Q;1C_ft%`+)o`j
zty=a+OOGSI?)EAgf+))DN6?+yK>l=II2BXMn8^3c4x|O=7dosZEOZi|*VGF?%h1(t
zUs{)2rpF5~xQJ`vGxlay?(A7ey-5?tM#B@0bjNrylMC&*^g)@f2g$hCE0JHR8&Ga8
zWjY^w-?64-c-<;V)iFin8?`{H1=eX5BNLOT`VJ*jqBda|GQgBr{`ZY<4nzZHqzD@t
z+F<hV+tRYdnJBI(0viDEfT1_gfD0KtDz4cfUWS;O8As!UqlucCF8F7mK%$iu0Eo~^
z3p5Z&P8(F6mks2>=%KerY-AV+)GVk(s@->b0!c|7IutSwzdXqwrI2uRpJdJ;ILwbq
zXll1NiBd9^y&F8YZEZP<gft3VA5D?J@hJJSm-6+AcN(b;XI_!_3KCP*scw^(P`Eb^
zC9B!jr7PI?C=C+=A`;s4To=gDK9LDCVFiOBG!&c|{`;etrymVE*8R)cGfpZs*IUq#
z0h>sg>t;kvB>8yv=+g^|P>^8UqEl!JbU<dtpp&`m9xV5BtKzk!&Sj3xpQOmT6Bno9
zN16Unp99;*oHEupG@M$M>{raDlgw<AU?elb!-31{lyfO647ny7^SQRg>MU@#=k#y3
zwlf-sjvmnGd}Am?uCeqhx=3=Lex^JX`08}SW~&p4I_ReAZl!nS@0w{_q^w@jyuI54
zOLq}4u=tgCa)Rv_HIiGVLq=j*lc_ll`d=KfI}U2{ODONYXAMn}^NOa6PLZpi&*{B<
zJ`Zc_2@9s{7DczRebzkJV@bZLcFawA^o{T6<4*81-Sc$L^Qx@#kgE;<cm6vD!M}`x
zOH9xI8UG+RJAcV9{^wJW5@eNhos!2r*8#pZ`T$!d@Dl&5V+{k{vPcV1r+5)XLy@X*
zTE#lv-E4T3E5W}c{WdrA7&@i%h^a-I7;nUOrpa4!+O1CmFV08lLTFP@@7{a-?csSs
zQ1BbRZt_Rx0qntfy%z8qJhM`1wMG-U_MnGzO6!%B4}NZ7(CvlouRV4ecv5-`t2{gJ
z>>aEt5|r|tU3vzr5t;?<2EKiE?(a{sp|c)P;hT==Ph+#9)AH|{&2`shV-Rm)f&@8j
zl`*4|{SJ-`<3ZS#5x(Md7iJRYlO<0j2j_UHep!_uflJv|5#~ABWbIRNJ}lLM#Uit=
z^U39tN!d%uQq->(5AWAF=+#T4*Bkj7dT`!IyK^$cc(Z_BC_~L|CH-aUz)oV%HA=7g
z@>hfhF4y>9q$tTWT<t@ev2Hc%x;eATUe_~0n->}Qu#ODk<4<YKS|S%1v0nqugoY=S
za*jqhQq9a9Ufx&FSbyZr2YA!$Rw@(k-%@GG&=vv53s;NOmKM`S-dq`}KXdkPF7S@y
zBza}$KjlPLc_J*&*@HNzbR=C5z73VEVLQ0IH7+x}$(tz4q$z8-bpPJl1<11=24(r8
zz2JIY56F#!F!=C<T29O^K6JW~>L`X9L4lrl3@c*Qm<c9FhCJ7YKX>(3=pyHuHB7)g
zp`t~rI^9K(9Gzbg<8d@pp9q~|NE}9x53L;oAP_eJfRbJVk!mHxTPju(%4{wEvkWNQ
zS~wf1`~a(HmF7+}-fl^Z==YTX84KQ97~VG%$GzLO$gg1!{)>B4YF>*c{jHNa#N7s+
zVFjQNFq-D`vj6$NMl`bd*T$s+2n<Hjj2&##0}U(g87F`cQ&9I1KDVZP{qS!bG4jM*
zt^9c05`zQ?fk@zYqz3oPg$t^`&_4Tew7zyHEo5MfuVw5|!hy6{YvXYS$3E&G$dBjw
z*xw$H(0VPvc43R#QYCBV;zPG3#WTa5w>RhQ?(?PBl^NY!QM=FVk+^NJ(J!&8Phz_l
za`jrBo5Ol#{p|L&bIDNQAfS28(2Xa#ED)wSvwDV48#L_~5Fg6TtE>^esBiHKS7=0|
zXsM@4gju2B3WA(!2Di|q`+{s5eP(XmK{*cvPu=(bXn6^W^b6c$`(K}y?gkrwktJ_C
z0}bvTgznd+w-Ju@C+;2a_m5rE2^!L;R0>bZXdMBQnQuG9r!tN1(~1TzZPCb)ul=m+
z91o(6Eb(n^n9=gn2gh=^$<UJwp4PeXa(|eIrlJWl!;vM)?bF=y&`2;DK;CkQEuD02
zQ>se%9^6}+)Dy7&D)ZnE_`^rao^$=(>wji~9Rg;8CEqSjEtPVVy=^RlQZCP}GC<c^
z^GAbfV00Z^xlQcoo(8N`=%ZTwd*ge|(IuiC&|WiP%@4P^bG!gZ6lKe9So4XGIRqqf
zrkPxFAOw)u2(L@b^W0xMPbl8$r~$u~THm>8xwmx7RuH~|jCa1^q|dwC@u)X?^?md$
z`!$9m^M7(Sd=LCl3u-J+KmK@3m9Snv;vD^&R#(HpRngomc0B|FOSeeZe3h%7m+7tM
zx#;vL+KR2R(cj%fr1Kik+mErT*wwmv)>&Ub2a=<{RD9}vbA=Yn|G*0pIrppV90UZG
z8Mo}6Ofr*!j9b2j>3tYNMV64)Tht#?7{&K-`%pzuygh3Bjk|9-rDf-GF?ED-UR+Tl
zj(+<s4L4<eQ3>ySbfy`fs>l~q2y|%65j*}~*GU2##o494o4laY6^Dj%WtZ;m$Ixge
z9QbqadyB9sh<vYO1$V$EO-MmLr-xykv*wqOR!bkXSA4!#$Z)PbZqw@y(K3l<-8_z}
z=R%2o4cm|Q{`|LQ*v&Fr>R@nH&TzXVe7$G@8Wuf#yg%=>UcRS1XEe4*V?)*VuV?MT
zR4;3s=u-~Qre!5bIt%4Tc~P+O%c#DeJmTj2xZI0_g7CI(viG516v!^rD6nZ!B$Je?
zBuIl)&{I*o5E-`Rr@CHgk~3M&V)~qk4_&#ga%@d3gP>v5#2Udmm5KL_+q~I~_m2n<
zv4YpXHqmeq{iF$cF*WNk#&lR9a>7Zd*fJiQ?qd+~Ft{8+z7&#azv+w-6fM~u%*-*z
z>FY2iw0YcRq6nh5=Z|u2k4{Q1Z-#@Q#q?;cOTD#j4a_jEzLz)DCZXnBN3g@XSSWAB
zWtQ@8r5}-}Dk)U-<S<R7OupOIBx_D3H`n}~75~_$u&s)2wPBAd1u(ot4}F4&ZSUr3
zR|c8RMrg$Y*=Sx#p=8#74&$*S%(NhKhTtMs%Ee=`UB5zuWImw=xi6DJ7F7k5JueI%
zW(v&Nb2${;v4j`>Y$hjFwcU8}LL@0tiOiHZck|*Aylx$~jZ_ezYX;EJ3FM%zTEgk$
z=cCQ3y*QBYos54Xn-;wQp4}n2_ObN1DF8wOucn^d)7bLs%#=~|M`LKvNH$l#og!;I
zCy#*CMPq)OA>gh>j$(pGA#%y;inOmh+J0nZCjPi*(;^(yU{cXiX&!aIjk9Jluc7tw
zg;oBnWG%Hwt(9T+RNj!ZQsR&5EW4`r`Le=ImTb0-Wy_&V@^p5?uG|dZrhp7{1s=EI
zP;;J>fP~W^SqOp*z0ORAoS@|58xCfzjWE!^_-Wc;nu?Z3x5Jduc-$7AWYb-!@xTiC
z2(%<j&Gg_&ib^{)Yplo|ts40(i(?$-ssvs~Sa@Mx!j!A}$y<A^$luA3`Oh;4xqOQq
zhnkXFk7=c;vPYHPcG?xW)@X&ym{k`I(^M)W@n%|*G#O7@?0zth{$nldL@d8hVs8sc
zHrR>)AObf@C-1>6S1Ncl<4&3jLA$Dikz(1ZZGQU?u@Rd;79(P(uL(OdqZ5bzHxub4
z9b+o4o8LCOPDee<ju48lCcxTe5-a*2cHKIx>1H8808|<cxA3G{dY{i*NM<4^ki<Ix
z9!dcCz^_Zi<Hil)y`V?ZLA@TEgbba(q*`lVjvIf)iU+)?K$Qk#r7U2CA`}hyP!U@Z
za2|agqArgt1^5@B?=OdzdF_)tT%@(Q65QHW<Z8WX!Mz!}X0G2ANxf;2n&5s??CQ^M
za;ls3aK6yx!{#0A(+mUi_Pz3&dUgTZPpR4i`ndL&4DT3H>+HCMw4dM(X=@)zV5yN-
zhUouMbd~{4zHJo0H^vw}M)w%qjR=UNM|X-kdZaXnC}VUtBB0WeN{Nb!qdO(7KP4ie
zfS{<T@80ix8M~kRxpuDKImdwJnIP{*cqkF#DU-Ae_!2V1^05e~cqbWZ3OAJqLIDIJ
zT(j7k_oB)afwQK^biUOgLdfTga9~Ie2^d0hRkokfS>Fy5;l&dedes4KygG&m#9)$&
z(N1<WMy9Nf6AOO;<L)+@&rH-2r$)4PS^|z%#_$V{0C0Oq_y9<ul3_I9@9%vEhGqcd
zR2xnNJXBV$09-E&NXJ4te{o!)S-S$z3^PoJ>p97|(e-LRw+&NTIC!K3z_bb_(1Ka#
z#X=;WE9p-1$9m&9Se$-vIr<^n$A@<fca`4WifDW#)5zc3xF`21qO)=(Uv&mIMSGJ|
z@hq_N<+19!nem)Z4rN0tN_H;Hjll|>OF_Js@i=Wt$eaR@8I4JFDheXms0VbFURkwL
zMi5le=}H%8)qx*?_{mcL;3<)AiB!U9Q<39C>2z=cL|{cn{2l}?EcsFR$!v_wcp=d<
z{6Hlp$BBD$yAl3AC6=y!#Ff*pajtTB;a7p}-lwY#3z3~?T@n*go0AVCx;rf4*T3Jo
zJt5XSwQ)1_*09tG!JB>YMIlFuow3$-Vw1{xPUWwp=_5{o>gAU75DN;}%p+7aOiN0^
zxC-HMunK(u78&uyBNb?sDr}<u+~rz?@cKxuF2~Tg6lZ)mH=dGI)XImUOWsST$YXG{
zCp8h!Rn(1S?gg1@84afjt;@`&ovD3Na38MNSBPww9;c~2d(?EBW@l#~?)S4H{8@@j
zcWbWg@R(+EX?0rTL(FzXe}1Of_tLfP#407NK?<=NDnw!m!qubuEDCGkFX-nl`5#R~
zb=79NQr2<N3^3sYEfo<7MNFN}pvsFX&jEd|3^O@n5+{=6)UQ|Jz5SuTzw~B+Qbtaw
zG~M@ebT+cvTeb;o_!P6)bqha*9xk24_YfO)E5KJd!S6v=WgH75o4|;G8Gp<`$vVYr
z+!`uXV~&+V!-aPogv5q;E4vdQ;T)BX020X=p9~m~0?>3Vr~ss10DxjzA_;(YdSYhP
zDhji{`UJ}mF@_c)^*AeP5CWv61KDgDF%D@ls|jyb`yb}_cg~yh<a=yt<%S4^@Um<?
z6G&~G7oKp^8`ISLp7z;Z&|Q;OZ|=X1cUhree&b*V&LN*CcD|M<<Z~TGiVg`-^X((?
zLR#A^o!H%i0EEcdo9&*EZ$QLAsYGb7p_}+OjH^A0ST;0iLKyN_#)w!$rwXgm=3I*%
zcauW7VJZ9&kJ}3X3}INa%N{^X*N_Gs{sBG5Xmd>rNHbMqTN2YVw}R3>y8g(1`1)vS
z&EuP_(u!>RTu$Yg?pir_%M5G078?=px;>VxKzvD16UNI?0kQuxi>p%rT!AAwETQc{
z_n~>pFXi=gC7l?~2?9z7gW900?X#AzvVgA2)Q~Aa$>Isw2IEB(q9&?^lGHYllBC8z
zpJzmsnC=G6>?yrF%_i?XTny_7IcMyBkNL%(@v@0jtWZ_S3!Nj84Ne8OLT;vGws;%|
z9#B{`9x6FOf<oN16r@@Pv6@Zuf5xtrCTkB`BL}JAc!}V;Ho(TOGm`+pvOy@Ur#9=F
zg$R*J2rmE?7Kx=qBQV<lFvd_(tq8n0fEE>6TbR+^85LRkrKi-mFL;1f&9CR4<eS|g
zHOI#JrfZq*^IE@<B^Thq_rKQt-je$%-~CIf>UWZ>&+Fu8b~Zn!$C?}8xYhj9xHf+}
z{aWu66rb~M!CK5$Ou~h<<*6%>yn+3P27E#T6rlkH5xpohz^7>EB8*nA;MXJrqz;wB
zss>E=g&xVG7ygmnUBTu^m)EX_1LDl>R?Y*dDz{>CPs2g*l(Sb)hc5vj5m9kPpE2gh
z`kwks2|g#+jlR6wVhJINU9hbC`Iyzf*ZZe_`;(0#n+7l1xwmu}yc4ZLfp}}mDqV1m
zzSfbJ;;yUwk@ih@-L9JB*R!$53HWH}0RP&D1o{KreG>w8uA-+rW9w(b95h7QFh0f3
zLbprA)S;?RAwztmC;)?-Wh#~o(q<H8$wu@)#+tH-i5aN;mk|8tNI^TnNlw@GT#}rI
zTOCjQ044XE8`3o_v@WuCj|b8`0r>zZ)_*%5WF?$2K@4iRDeP{TN6=%es&V`{rh?((
zT)V}K1*{Mm4gmOzRV@pD0T4}7L8&}8WP{iN$U|O+9w_S>0Kb^Ys}vg&b%Knv+xiVe
zjIevTb%NTh&HIYgzxmC$Y#!Id`E2RWuBwJ?p!G`U#um>{GHGS0&CmYy-M3QJPt9aB
zywlo~!IA$O1+_O<MSHnp8)SX;I(PKuw5v+6o~hvnJ8IB3qlE%8o(#HG0vqo8o^Ig)
zqH_@dn$Ttd`hz{>1fgB5=a2x>O(ER@goy>5ZsUWf%+lNXuwRBKLlv9rU^<sjEO+rK
z>vt371T3YumvyiUqRU~}`|dKH`Dz8-KY9x%n<ZU~`sqGGr0|xP4J}4LCty&2Ep2{h
zicV`O>D;;8;v^R#0+-0@Q+o1*^+RE72NS}P0&dpv=sW~qh7r7_u4u7bonTn406?2+
z|40<dbe*!ghYawAl(SZ<P{>z`4S<7!qUxxl@ZkTE&7oGD(|X!#+5jjLR{=|}0CI8<
z8dRpK%d;bf_{Y?&F6<n|u^qKp(j69eU2^lblcsvIJYw?!?UyNs1rFO~vol#wX&>U2
zo&);px}b=>8AN1V=gw8U9krYVL&X$*>of0)BW58Wl1>rPR++HPE5zhYkK}y)2$|k)
zUh_V-UUL9(<zU@mKo2pnw9=y+0l+{_XDG%kg~=NiY*Yk3#kkujiHn*k?Ry4g1zlvr
z?u7d_E%qCI9yO#}46)B_W;0F_A32*Tuo+VO`L!z)Zr2>{k;_Irb4Gc}|CcwC+vuNX
z(3JXfByq+oy3z1YP~mNN>hChT@6;e4_-kU>*F3o#l=v}7UY!nD{2TrsE>^;L7B@D=
zRX~N+a!fTePljFgI1?msC_)9;g-j@nIOp1paOxg3v7V0D_5bi@q5^;rICT7$b*9}(
zXL8uTNbYDDRzFcxFrrx|M9dmtdLJ?9VMD`JttxqsO+9o$!L&$y&Hh5dFkXhh6rnFS
zkZWQzr#2Y8BNX}G0dk<+DKOn!dL)b&!5GR8MvJ3<>F)lLvj~iY#XF25gczXLLL7d{
zP^oW`eWP3hHz{d&b$txl>FyzRj1D)}n^^R}8)M=L9@}E>H<;BA@Yz1XJEs!X=P+SB
zk|O2iUg=gDHi89kB>JDSBga>)J}gRtnS9EiJ^&4~yQ+KH(oZE0ks)6<BHhcxBBZBL
zc_*Y=LWw-jgs!LLJkgjDxh&NIM4eSfA3)~#A@cz2Fx~D!;Pa@7bPy;)Cd5fXSnIJw
zl6(zIo%i<cgZF`iJm17ZV~0m*>qYPF9nr5JiY}?Y-uMrr*7TU=EC=cz<?vEqc(u3d
zwJ&1Q9yhXTCfc#{KAVAW!1_YHr+yl>rTnvOOT9wnRFl_>+k8TODMC!h(qG(b_r}-6
zP?b;sJmL9O0zs4)q8&Tu0sETMF*=+E9{|7<Bf1m-4XWesSf@*NPimyylDSy2+1!jh
zAn*7GV%c@<*qi@EV)iWIn+ko1@R}q2DkVa+tcys2O#OffXMFU=QZ#Mg{iV3ReCdAF
z>l|+-$7*Eh+K$NT6}VhJ10d7Conbszo#`QBSjEABeSSNELjv7J-}VrJPI<9@aM9Gc
z0?Ng)7E;u!#p-qi02wME2OuaG>=z}xfSma&SiBv(RyJ$ArlgQN$GL54yaQE3)#u*<
z*=}dnC41RRK?~b^4(7D|iI0;!8xlo*0(?Y%aTX=fAneo~bCNmH`6}g%G)cqZ0+m}r
z_;z|*CUX)O6M2UT52!1yLV^-T@xPee*d=vpEh_N;Pg@5A3>9@0YXo!`bKc<{pU$g@
zN)xt9%Kc!&?&c0aD?OHSjdasLYg9_r{o99Vs}8*A6Z8UxdQ^R$-{7P&a=S@t+oQfu
z<mb`x3aycm;&h?@k7Je7##y8E1%2PaH!o`qxJ4XPF2CXL&AmKqaEJHZsnN>a+i!}*
z^)HMxZJZB)vAIJz)W$W?P2>MzwB@GMgkVaELi(&r2i$Ar-&(XNpe>{M9&|uL)#r=4
zDVxB$g#fY}Km=Gy7kMjREOmBIP%abv3;-g~R9%iW@AXPQj@R{=`cNkFehk_jUZIXj
zP?imWIry;OeJ6NtCjMsetbw7w+6gbs9GC6N583m-`x9WmID=_Q2(E`p)}u3;IM@oq
z6m$32Xg|w3@n6UA87AoKtna;|(h>H`HA^+1f+v$@%9!MFI?Un94U@GC4_Fw?>)v8D
z+$-kl*e{7O&-WV~ptSqjgmSACo+N&^wUK8P3FAvtlXhqEuGvn}6A^%X&|N)a7NR^g
zQ+$-XRmx^vtC*R1B6gOqV9bY!-;*9(wg7a(yTNKXd`$R{b8Am|<MiStiy2q<V=L5Q
zR3iLB1DG`=@|6ZJ<WWe)7z1_yfO5KQLnsh?P<fn!VN-J=<o=H84fyi}x1!d<(%H5v
zba>*sIC%DA;qkBNxyT0pD+$6WpFSxe|68a&Lsaya$^OYZLR0!-Y0v(~kyo)2b{H;5
z3J@Z5@%}<XZ;{unN?6aRsj|sLJVGnkKqN=^zxG^!asoGB38DcPlg6JogE#v=I0!Py
zn#ji_b_xmTHUkj!(lX@e`%S<_zet3bR8WpQIamS0mZ=7ymBF%j1WhXNXmz=?e<FmJ
zzq1M}HP;0JYP>{1CS1V5R+6jTh#=ocfJo!fTch*O6zV`m`exF}qm#YXm?s8qd6i82
z>>WznA_>*R>xaL&>ky;dwB-Mt`?RpkgYnJ&51tmif7dY|zM>EO-rCn}0ya7WE653m
z1RNPjXuw8~itWLq9^3sXdJou4^eQ040lSe`ozDO1(`uF50XEu`XfWcX2<VdB4k&+}
zd*7#9!oJ9$0$`CaEU+u0Mgvaj)nB>P!VlJdjS>+*&!~m{dC;{LSs12NOxA=ca$r_E
zp0#6g_aeIxqb>_)jkUh({GheJJd0ud+aS_F9_>Fgny?Os3J269O74q~F@=^tAOrb~
zYA3)%MO+3KnK$YIOJc<wJh#yEryt-jD5qnh!lnYv9AO-wrvjm#Nkk&uP+*yl3Y#4#
z8lr1m<m*H)9A81;0#G|V5KM)P0BO2DETAD7BLv_wcnmI<ER(6<-t4o!qG?2U5t@uX
zyz9(AG@>78KP;hXMH-H{F)j3Dq}HCpVBy#^uFAR;LFzau4F9%-@G9VSI1(-M=vWLr
zhhN!Nt#*1txckiCW#N5--%{4eEm8+=U7i5Mw~?88wB3j_Ws~=>t?q3TnL7B>mVr5H
zFc%Q+Y;}7=g=?%?)kzg&D)EA${&iHdMxTEOmwz(eyNBEy0TKK@(SU<mClx4W=~B{(
zAiLa80|Y7{GA68K$cL!AWUzUjp#nH?QX$FklGXiyh=AdG)zH^2j4DZ&>-IJygj`%~
z9o>f)kFR?Ye$87w%PM`?{kBG8SBL>aFe8o;c`s?bQRYQcS9O;8O}BpG#Q1cm?DKFx
zu1a;-0>m5LEab<U1(%*x(lT>QTQUrN8mN3B#Wv-5qqr(r$e8xvK}CjA5Ui~{@I3>N
znLWFujFo_Q1g7rHJRp*TRG{$$N<kK&>D{9tQjxdJ0qXh?fax&tXk>c;fTTE!DKN25
zfuaLujLgh9awT;}g)$`$h4AA+0B<rG^eKNtFJlogMvvcNbM#Aq4|iP!5KpVZ17&z?
zaUAeF4a`^@=clBx+9>u!5oRDjKhl6vS5f&PS5hfdWNq?1qp-;NNgcz}J=YNNFsmR2
zoe|xw)o1N`jH8_x7=G3q3Bhf(GZiNvGPrS<kgV2v@=}9?GAR1FCwd3Pa)L638c}w2
z1kEbk#c|$H5j3L(5wfMVJoigxkkFE&xP1Wt21rn5>_BC{Qi((!OJ5COX4xJwAat;_
zWg^%fYkMbK4N)d_OWMQ<&EW)f)H+_X7O#ERM)R%x&LR(hw5!n|0H^M@8xhidj)wB-
z@v7T7lGsxt?jb6?TLoNUh!X$>I59I-1>*_uX#&E5FIjSxZqsBPwj{Q;^XfYaD=>Dx
z3cwY+nK|bvnY}>kk2F#>i-!8q8gmNjZFVrYr3mUFJbXwJMk%d;UH7SxX#WwE`QtnB
zf!7cY_mN9PP=cCX_GX0K5-PxW+p<dl)VdSpW12}C+!<%?-3Bm)bb2;PVZkq%6xA(;
zGWhv0*#TShOej-4mNK@AUU@zczB~L=<Z|xI$%)!H6UHz{XLHrQNqCErnA;1-@LvCF
z!{?@p?Gtrdq*9*Bvpt*(I{}Y=kmI&(0H0aD3aMAAoiU^Z+W<-83FahA0h*^Lk{;l4
z=E#}&=qV$_E0Ai|N{xo>dMfF0d{ZQ3Q)@J}k4sn`(9gyy?#!IV*WjXa5Ca-CI-0aX
zB0~R{ttOn5;Pz(Cd56D@Y*=UnE$W*!USkGvyb;cJCxLoH?d_#r>YZ#0L2vx7G?nJR
zwEvF#uW2XFe2U(+%U?0@jaQZ*d#RFt<j&j2rrJ-IFuh$$s$MVsiE}*bJ28f?aT<qb
zdGCaHopxtN=B!{44;Bl|#(WHin~JEd!VLH9cly*kF3Pvg0<E!vaeZG-nN8;bRJoyr
zZE*BJ_%#7}JE6q$BW$F8Q3#O9$c`WIP9)~Au7dFFlo3u!)&c+r0<*1jP*j>nLf7Y-
z((iYy?8~BD*v^(*l<iF!T{db>Bk#Ju#w{J*Qc&6-;#n76_2#`?u6(pS)m!;T_Fivc
z%}_byEeY{<#7?zD?mSs{9c?hK|561>nCw6KkjcVYp!`Kw==^yv+DR?ZFtI%0p}Z~t
zR~U>Fp197|9;BZ_{_PEr(7d-IzBrTXl$SDMv9*xZ%t;hBp#q4ZB7;|><Ct}c6-*F{
zUUDi(fb3}}lgO~k+$wsjh+$yEoNUaHASS{^N11>5VK~62mv}b;?ai<<a$RdKJU2%y
z7{Kr;(H`pF7jtXabcukzGQFR;D&kF4r;3pFjLWH-WN&x-Jnp4^S?^St>RsCXJ6p@M
z<Mpydj7%GM%1CHoD#tEYhj&Td?HrAn?Z!vm+sRXkQD0o!7%=eN|1?W6meGk9hP$mH
z6D^;a+a-1uw*N|dSovn<0u?xM0Z`3cf0S5aL$>6V*_<yz64{`4xfn9jlV&p@R}=x*
z=il6O$!L>u0Jc0>7y_TMUMER024(%CtV9nnd{@*(n<Q5|K+5AmqiP-@8rSq-B{`^q
zq%;_R?oiOCE;fh++#B-gPPb<Ss8&6yi9!+1_2N)5=Ldnos{LXj=C4W@f2P_h^I+0)
z-v3?-8)v-Yi>g{t9TM<ma4S(JLZCu=q<nga2mnNO6iqLRO*b?uJ>E(h3vh@bK8+Oo
z2Lnf&!wYzTOM`2WX5@+~xr;(D6ZR2`heZ&UoRV=s`Hv^T*)U0^RKy@U<6OApq4WDK
zlzxgsVA&V=)ezwiKTO(`7l*?8?t3sd5@ZEe>QtUJzNrbCpX_@UWDszhjAt-2nmHAI
zl2?AC;*?_P5QhP9F_o>Gn*93JSp{%T3N_7ja+KK6BvO7$ADlxSu-=c6R_j0$0VQH{
zQoXgd{2(5r(h{Enxr0gSdglvd+az^+{?!|_sX|b$Ln@rA8t+BAH&UgD(zM`jU2Uze
zsCJ9ryp@&O<Z+qFtKNS_>-tbK?1BuxEb5KyXJ6T)*Tv8e+AjoqfBjd?5-htyXwzNm
zZQSTnY<%c8omz4Wxs#XXjA3@l$l%Jze4WTmyTlnBMH3>NIAbEgbHpXFL$82M<TV4^
z$g1Gje(deEJf@QRwiJ9shJr2(ZFHwTnucQZ0D>emfR@O_H<*Gn;S_}sPO>4)crC>#
z;!dwIsTDxTeDO#CMwpN*Km!l*!D{o-438}f&kJb|RuLWYQk&JXq6e2j({@{`aT)At
zUXwK9_)<~&m9o^uwu40Z0s*lM3FSGLb0sSndZG4bz32ScHAixIw`X~5<q#z~L()MQ
z+L2Q0K_O?dks^kkA`=~ECyxZuNLm#&bo3Axi=PLiA_XA_oOcwIp#Iv#hX3;245&sl
zV6A`#5u8l%q9c&xnF|Sr_dq?Zv~x=H)6$fKlBZlH6i=_J`t7@?=cov}o!7PFv269r
z=cwwxv6fQP1c#+Z8zwrL6M=7X9w`B-KzfHG<>AtCB}7(lA-_?YnYQeTmSQvi%H43K
zOMy=ibLVWMQ_TWR^0}T=p-f6So?PlKzx3=}|B=blw?-INCI9fTz4|9_Z;ZntO*Xm_
zTsxSM_xDOB&$AfNfNN>XCa`)709=yVQ&U=RpwcYzH4ka8FS04jm6g^HDrJ%=NNg@4
zTOo`_*Enh9%J^@F{M0z<Xh6N{nruR(W>H2VCt)}vx>IDl&~{UBMj9?1xLD+<$BVD%
z-sKus*GWkk$xc>qr%$y^T>epmjG!cvB&C`NbJ6Ij-$WB~stOykBwdKy%nj#L3=6jF
zG@pwh-VCkvSi;<Dj}IGk*d<FoRFtF?<t^l^6OuzV#F4T9=)jcpNKa*;AzgHC3^YHZ
zU?oP>5`g;js>o9*QiDkXmy+>OOEt1Rjn*wS*9`XDNCH9eD=8<OzXz?5VbH45Q@=rr
zxI|rlRTZm2bpWWbwlhfH_`W0)^eB;qKC}5VV?2vNz)>G`^%ZS{_Ihz%^Rf2NW*by_
z)(atn@k4_NDTagWeDzyh(WwAENkgeJ*Dy;-Poq-*1h$!oYqC_Q=U?TrHI9j-s@qdb
z;HR(-NC`UP9gT!jtSy2LjHGeS#9wrJkRdZbv%PB*9iO{gD(w`MymCYxY@b91)Lhn9
zNM1-=ej-{o!3uz7#@yw3Wb-s&<SGZZX5ukD&-mmN{d7K08f^OXjoSAa2tH)}-0<e|
zZCQJJGYO6XzLrVR7ST&O8_c>|68S>4HW!5Z<~9uuNVXeYj0d%OehA*{KD3BQGo4Pl
z=!|^z$P{PABxqDJ-V2_VfKtaMvcU$iEges5yX)i4Fp5CujUXbF&Pf-BG$q2+2Jl?*
zseAy)l74#-uSda8gYJ;c5}$Ubc##ATl$oD$Lu%y}Br;MvHg4NFai}=UhM{=Y7uu*Z
zIH(0BHD=p3I2J|uQuVl<5p#ymrN)KXpRm<yFL=!U?9R~l!&EGdU!Pp5$aK~ZEXStQ
z7Q7(x@Q<ssue{|Rd#;qqBAn7zf+0sE)Qdq`dP>QI6msmn#_TdtKIYGiFDud!3X`CM
zkzN5ojAu+~v!ylGxJUOV1slP-%?K7Az>qpG88YZrrfJFqbJp9*#G70*uPUr!Q{FtL
z_(Hk1ec-_XY^?~vXca0CI!c8HqzIBU<4pwSi3{<|l6R>n*O8SdgHyu2CcgBMwv^M@
zs`}zo4S3n_r-*QG&KuHvMdj#~{;s_i-gUOh*C7?}o{qI&se|+iy(|JO{40N!Cjy?F
zWBc+XqC)J#8&lVViq$j>Zdjg}KS7wwP;H=dMWkOR@H-_CO&hXSJJal{Xz7I0-NXQd
zlA~LCr@}vZl$1DPo~RCtt$I?P#vq<SawVLo1L@<VIeU=jsaj<y?{4DQ<B|)r)20d^
z4zg}Hbaa%|Rtx=j+M1Efg-><@)MzU<bPP>HZc?zbg)@UNvJ^3Cnewwkbe}ifDT!sd
zKs)xx;+@|#YfM%)uc<-4VlrPA@qJ5-k<cA?#_h5+L<9}4Bz}Z{B3F*I_cI|_J-?m~
zCxVb#lZ3vWsagfl5g8JtCRBK;0!LlYD3X>XPTszBfouUApM)S$qTx}UWLjdSMhY(U
z4#FaF_5RC!2z9PAH4#80P^eJ`6TV&NK+CB!kJ(Nq?T@LOUKoQvSq+!%Sd4jE8t|UZ
z>^+rrS#81_63qbDmeSWFH4}r_mWDo6W|WMgs@Ld7IoKTP7u4$}vb{>S@=U+;t=>v0
zL7EQCFm0f3Sp`FQZCWn<WB`i$53oaMtH5OP-6VBeEN>wff)`TJn{aTEiqV+(*?^Dx
zT~lx>keH+`=}ejTD$cU%3&7;HesJd#dLGXIDTKv7(8-|dMqaJenwOnt|F(TCe_qfH
zc5Bzctw{TEPV1GBZI*_XVw1iXLUSy2dbBk(f3KtCJuIHzP{-z$LpL6dYNP~5W^h@;
zmK`WbHpyP2W9aNef*}l1gXVE6nMrbprs6EQi8!B=Ib)Jo&XNLx3$_s;;sI5KCaRtR
zjN2xaIL*qCb0x(%$y!u%>G4kNC4`uifeoU;eS~?Aobj%Ms&!e<4V?|n!G%n|$VYXu
zxo+?C44I4@?EKrVPsxQTdCq!&K;$&Y-bVNbYC63(OdF|m9#!g>EH~^`TSL+7KjQP+
z3NU@VCFQkeUR5eLxTPd^cl9Q(<lAb#H&|k<FzN}z>jwVl6YH8kM0FTHS9prAL=9K`
z1&z2`u3%IvW5%{hf@V87V5VE91tlG<<hGr(&on<vm}<qfVoa#JpCftiiBiiz(U^%`
zf%t8HBK6;StS`KFv|3U)p;dEhm6wu=%2Gu|An(v~rz*YoeNy@wllC%=V-%s2@P6;H
z>4k!?kC!U-yVg-mLhK)fLI_uaMusxP#k=V)|9M;K^>MAa^C0lu<@p0fzaIeC@|0EM
zQM|?5>hqJWpNT?I!N!*vH9m`v+&t9aNJA9UBBD}KA0%#@*YiOrOB~1wZk-^HC0@@@
zHHXbq-$zy^BCy%P-0|IxY}gK(wdu5q?5fu6e0)iYnI(BS<_@v7gz&eZJ}7z4JaNXX
zd)Ys^wx2{1z}2;=J_IpDmvoxA&>}|&RdmzKoeVQ6F7Ggx1K)p0`eZ2}kYySxrrZ<u
z<%!#BN?t#Umyj^2Lh3=AMWK}(Y5j(Q=m>l}D4pwEg6$%SAP%)>pu$PamoT{m@1|<z
zOJ9DKmg*1h$0aVCVY#kQe^7-T;3x}X@KYg~%MGs3NG%J|z_ul}n(E`|DP=D+KdmzF
z^)RGP7G37!2nh1eHd>xp-p#_~x!+sP@netY*;d5+6!PXXC1mA@rPWELN4N0vi$BZ-
z>C5T~*(LB)Le0Iy!c<x4XOj;Fc2p%g^&d5<xD-hD<VothFJdNyC}wY~bQxsWh*JoS
zJB&zbE_838(2a<vm#N!dQmgrD<AtF@xC9C17vT)otZpKn-K1OTKh)Z0Jm$3@F7~mL
zVQBy8ndX7^oBuTSN#{%4_3$h9G6LI1<$aD7K2xNhXCj~OIuL?l=1kVE-=$<KKCpM0
zSi_Ro>*7-HIHp$iIpDzc_KDuqw$$pCl-1m)+`^9uB}r&&t(j}^-zsT$g!f0c;!}Ab
zbTFcQbxMsPO~s9p8Jdn`#*Y$q)Ux_;E_dnH-I5tmlAX0(r9#s1h$=o*Jt2=X_yX1V
zAhDhS`L^-y@~t=K1%&H$yKGLy@W(e<cbq<OI~DNrEOs4p&94Y9BYPh=L}!w3NYg4!
zT`OZPUnuIiBr*H`eLx~j`AYX*JeP?`=+N}S%4omj!y%=qhkMMM+Toahdwshf##6Z-
ze9?WG#sx|LZ@Xg5LPJ*u|BpFed*OBz1-vChr9V%4KboBDHxeavd-zE5l;+<GMX`8c
z2*g>9@D$Qc3pmRx0eFIW8%#VGP+8Bs0AC_50y<T?V=$Tz1gB3Axai<OTadW|C~(jL
zB#ue};@YkUsCDznNCW^CGxz6WxZue!=*;%DZZ6t?dT}=ODl00KX}k6!SykihDaHRg
z)3DHOfA6jX#Kk|dQMU18Q2k7x{rKJDS)W=*`6n4gM`?kNSA)6&RLZF0YF1@`jGKqC
zBzIj_cxDzUE+7ANODXCFV=9!Yeu>Pyw4e6xN*j?_oX%0T8&_!+@%6!GH0|@ME9c}_
zl>b(o-TcfQ&~*xNZ_87<BZ~xDd@NN?yLgF%r<bVI$|kU&#EJu4|B0nP+28{2g3b})
zVLHKp9u5Wc_U&h&2d^mX<GWb}@aiD;-Z;xHj0mx^LJ^_c-BJ{B=SmW>LZc$?{wQfa
zu7?cyyVNLMTJcaS@RH1=f|*hR+o0L}WzHe%GFGmkyyrYaMl6X@?DDgF@#y{*SHFq=
z#(Hq`emYBl3l|#V>Uf(7*}N_Y03VI^@QbIf@B1TXR5aISB3X$vLSsKzMS3Zfb^Z0*
z07`7CziGM(Np>qGLbwd_(LfkW1UPmm|8U3$0L7MY0S3(JLjYeJQ-Oh&>!)!P8JiB;
znL$^+DrfeqF-K;n069I9*YD7ZGl{tDxM;PP;4r|R!RYD`Q5)GPakw9HNvbH(%l@JL
zLi)hsLPtP>U?a=lTaQ2es-YOxrNpe*sH+!)&ZD&uukVi>{_!C~0a1VmB~}Ph7T<e-
z4R&Y-Xxvu)08Qno3*h&0egoJ=jY9#CVcEM|^TQt}Zrb{)jse;#90}0qtU3VdUZMhk
z0Gq<t`;0U2x+hXL*|RER{CUNC_Ahg85ASN^a5{3EQdv7YCk3_1oLq9e@4%t-v0T|i
z^;SP&Vf}9H(i;ws`z<bE(1!Yx$e<dpe1VLb?Ldc1P5zFzeA{+aXaCrE{l-{2%Fh3(
zsuDa{g_Z<1*%d)$%wwC(3kH7rC-BIJY+t|kkTE1!vf@;mfe5MaWE)FKTL;{RZgqfB
zJ{2$}@`bzzx@!~f@`qP!PTaetMcAs|6^Ty8q@Y2eXiEArydHvgak_QiRb$ZBFn6)C
z*mj?3_%`9ng_~>24s4|9y=Q@XSC&3JXxSUor=X}e1eU7r`u1GR`K(;rm~v=$OUi5@
zHoI9L;^VxuJlOWZ({9zE(Q>DzHT|wIhvP)%WQ>}uLGCVDY4zW1p2N+g!f}qd2^YI=
zn1IJ@f9ZNM6*(r$(;)SxF6$Q51i;j()1>p=3n2iN#Z}D}NI9(1LlS`3BRr%u%*K_T
zOqG;s#z8^?R_LV;YJ8OtU7nrZkkbUuDv+c`r@tg2WIV`1hFP;+YlG3voLE4}g46(-
zXL6=M(J&!6z>qZJC{Bz<-qrc8J@@=Ks9m1tqt>v+^4u4!KD+VK`tPW-J4SFDMT?=C
z@g0xRkn?7EBJ82@^FJjGC4+Y(x2w@tH`6MNa`tLU^4Aw8VYdnGg~QyoYEyY_cjULz
zeq-IgxQK-0$oleFl~9Em74E)NGYFmoX^8`L<zZ6HUu=wC8zYS4pR0bOh3cZTS~Jn#
z1!*Wypd#hR54w^A4T2dDM}~8s0yue<BDv<HdS75+gI1MHc~fMAipLonx)fpPKu`}$
z5kS4NGuEP&h@ks|3>Seh7uYg$(L<zExDrq*EPcuyaN<g4Wf<``lJ0g<(hJxN_wv7V
z-@fHNif4c2={+&qA`y!kyKS5xD<_$yP^a?Zxx#v)Ow#qgobKCr02$M88(o8tfn*Xz
zUVtL?BfVfbjCsxa1Hsv-RdCTh7w8hZ%Ux-$VnjniWrvW!_;KNNX7+VzY}-7-jV&R`
zsu6jcEgphJ7poM%SXw;qM0s4SfmrNC-*(*_$L}4a&*Q#*ewkv#XwBQne&tuGu#LfY
zlaVHlb~~VY7hC<2iQmS;o=E$hqwk&PjCH8OTI(A=G(=N0IFp6{sR4paxlfS#l=1l!
z-$o5UR3gPFnRhf`h6Gzp3!oVlV0Ul5OoSvVN(dK*wj%DuJMuB~-r=F|4Ho@>+%4Um
zz+90Qf=D8zMOgCDxuDiKcAB~Q-8Tcyk%XzMM!%-6wc$G$89%BHrmkFjpR4f{Ik4(I
zu&BU#$>3DV9j{h)Goa~$Pq8W8<L4YL3U6)tpW+(j0#uI7oW%rl23C1a)T}>*A!SUp
z<|OkI&ksv{1B_?RL`bXZ+C*4;8Vjp^>v>0=5=FEAr;*vKKxrtzk`Yoajc&b?K(w&8
zq+(Lc=nbWuh&4W{`;<e#2Ayt|qh8G|Pil=V2G`AlHjZx=ojdsUwaH`v*Z8kE%sBZ!
zn`iwJ?`xdaKi;>$CD_Ix^yR`nQ}g{iyD1-MmZ~FCyYfDX@Tfblz26TkL?#-A-eG6s
z5;(*sYSnqIps{v7Q2MR<c?HGYQE2Ks{g>9146|FqBewuq(Uw9X#&CSQ0&VI>Pr=LM
zS`VOgWCa@04E<(iGEcDId0Xq+%sIV{D;JF>D7ErrC^3yH*0r9>rB-sIz}(x3ELA3G
zrr!XL>dk4bH-bE4F4hJkp(c0gv7&Htv8aase+9TzlgMS}nn#A2lH|Zo_RJ+TC4_cJ
zOkI+OafzgkY>~jjTCs`Al4wq7?F4*7D+SdU&GpC2E~kg^x7b)hb-ck*LJ5A$)sIP2
zhTdfU^1;16{xAdGaUP6o?IjE8&<CO)SCU*|?dKa>VYU0jvm}q7hJAg$f+GjeC{ZI`
z83pLY8Z6||pw$`%(D0ki4y+PkhbJF-`ZL&W)`;AZCSn<iMz!MGtxlgPeD{SI{kgSR
z8&Oo!80cnyU+FOQzv1o9kj>bFm5p2W51Z2RmdE*P0oB1xp?`G#CfrYhSEc>iMW}9!
z{il=dB~gT$ny`LuEN`+`ct~XV@gn8T79g|U%K5|uuwrVGnLkCCJA(1Mhp7aEQk}y@
zoiFK@OuwWBCoVMc9ScxQWhT>KSNMdz-d<gGzIyxM@s*+k$GCTYT&g_ejs}Y?I~bCv
z-nFp`*J~M-#|Z9Z>u%r#){es6OT?I7NUj~{V4?5znq{g6e(Dj=i6BaJ2?B0kLL168
zzKoTuLYGDBuV6+JKi+cFtveTZ&qa^qGY(>!kud?-SLRB+6JoF2pGG$;yqX^K8mj5}
z)YBV`_ijEoyS5oHJ=H!GMLhxfW(sJ6Z_`=@dyA3%o9U*S)dy1EgfO7|rv&Ua)g*`Z
z0%iK9*3MLNVpfTCT5d;zQo3$>I4~J~t}6L$3rV$~_&N=`+|MRqoG97Y7yQ*lW4&8&
za$b<GdkT|0)&LcZ|BJ^oS_>Q6O;_pZ{t|2va5`utG=HU&-nO3<cs;%7U`4`NYvULh
zev7xy%D-mYj6#iYyz)$9Z`#`YieBIJJlf_d(1YeC25($b<&@Kw+Yk6?$yL{nt8-se
z^6*ofRyn05NMC!XQB{ads<%BDSv1y_NIBMoeu&e*%(<Rl>OLo1)%i=bbT4FJZIo-i
zYHV$`M}byu{Zo3r2d<E%`F)VxW*U#71ZHE(NN~Veu&s3i1ufnpsL^T_2jS(?yae-(
z6s$iKDgKRbM=I8{6%C|+V)>4%r~8&@ID$!z{&btVA?S$o;YHhhu71Of<Pl)HNjxl9
zHgl11Z~Zh_FEi6GLT|uFbE<-wai$sPgxeKUdx_L|J4|_SLcd8o3}pAaEa}r+n_p@a
zbW>bdmD*?fl_Z;-{|}LaHjynW-$zX%9t_c++IXN#Z22{R3_N?j4*XWMb5=CFRbw%I
zka(69CM|Q|G+f`;d=wqp_h;fvR-rEt)4U`<1Pnim84_IRP6P5`(}BE-M-9}Z!cuoD
zhkb+#b(XUGj*J<D??I=G^)=dzUtWWkm79L`=Ku2A`$u!&Ll-*al&R*g5EP)`q0-)@
zcu*!k)S#MMdk$W*sO-jC;6rzi>S$9h1OKv>6sr!cbIoq{3l9HfV<EBLCXR|3Y|(e8
zNsK-)j3H^ghy223>JN#b-445qGB`DrE!CU<uz$+^qYO1Hz|I_+%UX)ED}Q4T!yQ?B
z8|GJY<|P`<_Smh$RcsK1ZFpzi3&WPcivw2-XWfe@O^8$%Id<E4=h=CV9Av98Dpiw8
z#y(s@RvRIw1=T{Bc1iT-5G<2Rakz!iyt5#aEjnCLFHO+U&K~_`pg>Y%{quP-l(<oN
z8aAI7tD?!pb{(ycU!PMc2r$L4*&KxvYCA=Z_b+1S#ElD^OxgH8nhQ!Ezxc-Hc2Q;c
zV*Y|T#W$|9Vm|lxsN)dM$SKGCTy30D8I|eKbkHFdh?vzm7rsVo%H^}CESK!wn=>kx
zCGp2*7lBOgJ!ouuz2}#f$I1@Ks`ScX4mTKt)Dk_(LlLT9-_odjwFvfAvH7N=N^h%h
zU$rBs|DfWJwOPQ4kNQ^W_astZ627MaC-QAWJ=DQxJ@3M!*AA-J)FSw`jaP9#4Hh<M
zzV^A(^SkQu9Ts*UVp)G&Vr14;eP$$b18W133<}3u`Cs~GxG*Y;5thVeYYOFGwH$%y
zBcjzFzlm2(y0R}S*m?2%!(4)$wf=l|0h=QRrHoIs6@b|q90sD({5Be-^&Wb;vzZ!K
z!<}hmF)9fdJ5KlTEHf0nQR5|!Pn9?uc~fW-M>V~x;&E;G6Kg8^OI^|?awa&o#M($2
zS7x!(MA9cUSNr2d$7}rN_D=}K8HBkqm%<I36Sg{g*l+u*QlUSzA6xuxKlCj~4S2*Y
zhx#cqJua(I=FiI4=au{WgjsID-lj;@UR78w^e=(6o{<09HgZYbD#dwmKHx!8z}z3z
z#m{Q;ZEQw)c8jNh43d7x#Qtx4z3ei6Q>6Bj8;B>Y8cEk(4C53cYYb0vXl6QWR5gPl
zs(wd_J4Zg8=_)?5e?<7`b;;4@hJ(k&lOCLdqmJ(->?H5iqUG4xj}S(Yx-5$$<+Jzf
z2Qjy=xjEc4v|dyUKX2$5RI8+E_*p-`7~<YV@56zTSF?6=EOL8NDX7Np%~^fpcK`Wm
zxFXGhTU3MTL)BQ0YiQ2yBDQ+PP6wmn8$$CbUQb$;ip#vipBffIbnR$vJ-p^~+<XL`
z)h&+q<nSz33%kS_cV+%{Jf}0s<g%~IitV>=@=Kof*;3L^m~iYB4j~tV<a@&%{?4&e
zNnKlE_4FS8m|!(Qoyhz=X#wTe`7^nL_#1gOK-H0!x*+hx-0EpC`NXQNL_XM&B~*0S
zy5pLQLPKDix#~GeN82r*+hCx`EA45XO%$JB?Ja-yG@Fw+t7O8A?R~%8$Bqu88WG~5
zb<P<%N@pkW7miLyPp?ZkT9dr4pwUE;IqzXgP6|z;uA9|UPc?q#RDE|e8VMKW%+$pY
zB__~Rg4w<6c?2SihC126GdGyQMp-y*1U>iEuFIFU#N_Jh=wk<FiKHn{o8>^d3+nex
zJ@Og~=dMX5Q0iTskEimaA1q+mO9#Zt^mB}b4@ZlVN41&^YiAMf<Yc`n-t3a;*u8JF
zWEe=WUScPGy83)#pZ$5QKTS~b@WFSD_urqp_2T{RNSkS|rIaoxe%}%8GtHWCQ=Yxa
zAoZ4O2<KI_FTre|_R9aTb^KP2SL)j@;bkpd1vk1^gQH!wMcn=kCmyMv>yf9wgWUM0
zX!YTO1<Tlan1%+ErN^WBmnTHNKe3!o7(Pb0|HVb!nEa6RTeJ0=<CEU)FEbYo<(vPl
z9t-Z2{rx2*et7l=qoCyQQ^;b@>TgfR+y7`^AX%45<QZLVDlN+arTabC_$Wk9PCE-T
zIZtmGN}hh9!>+5)d4Z}24Rhlh$_;QjI-hd*>v`|C3ez`()^}m-Lx)<1J3@0BxAJiz
zuq?RvQTSCzrH$zZrh7l!Qd;B4MNn3!y+4BKeA~O%s*|ZMFp_yk=5n=#F=}&FgD|lD
zNXoZ8eOl5urZY{7q0vXD@$-O2^|;i#ullvJS^sy7(3RX%!PEY#Q7U+>QQtaW`c2<z
zvW%zWQIQ_Yg}EX+TcZxy-wu$)B4wq=w%ZptUUy_QUel^AEK-_kn0!mfb)wVgnQS%7
zGVo9?^tfs7u>JD7UA6oLm$}E08qb}bPR2CeyfZB|4Zf6RpA!1)iP_G|<s}~6N9U-U
z`!_~xsUoDVGRy8G^ew37=Lf?WPZwp8H4{m%uWj_wXr8M0ZRp~%A;3`HRWD<yjm<Nq
z$5yYBWt_`FTG-m(_Oh%yswhIZFIV{QiDz3EK6%r|(>`}RH(OZoQ`>;*?d_;3n}0q(
zCRmEit>x0vJ|E8a6bg=PRvjC3dtQ`iL~7?q`t6wx^Y}g6V%pt0a+nO*R@92iFteFF
z{=OF26L#M0_Lt*S^WWM(UbQ~S-0-hfGp7y`new75cHnr7t7vY%%;KOjv#X~)yE80q
zY`+_GdGc~OV|R{iEM~zw++J#D(Rm`taxvoMwt3(}MCjoq+sT3bc}M;Qoq6dmMG!Yz
z4$+$F<v)IZhgBoIo~@kjr&Oib1v18seCMS9mZ%y9AG{D!UyMjKop`*_>l(BX`r*J;
z<&b=fGi4*=R$NooZ|}12*u6Mo`px$?9>RhdUIq6z>;GAX!~FMPU&vkMbEWY!@tNlf
z73)eOSIyY<2SktB7a>rG+28T4W}6+J(HZDa+*RC37ul1Q-fBQPiD9jx!!GErb-B2|
z`6Or8_nHM(C5TkOzN+fF(g%}vh#s6eVIZ+<ZWSixKJ!?ne`Ecfi~rwr5uN8x>dXI>
zy}p^rLMb19P{~MF+8J9Dsq|ow?}{DOTJYxJ>iKg5AFt@DNzaaIu-J#zy8aBxNq=T8
zIVg7g{%v*CSJmO7;qI$u`MY*QO2*t_QKDwN3wahbeNrc5^~;(aYL6s(a0vv@iOzC6
zRBYO?sL7G%n@=t|vbz^<Pi$WeOOj`H`+5Njg>WRSt30awrrDp47v{TuM_JC>?8&b%
zhuKpz$<b`SO;;oD8^4KR=16l69SDyZUFjYNr>a;UVmbVhw-JWnm0{g@S)runA&M@~
zI3B^Ac=@ic8kb?(A2@}D%V6!gCGqU!Joh(&3KKb4|I-MDvPqg0@tGxkDP-}xUJkuu
zm#CT=SQhIy>HzUMjv83NwtL=Cpwwq&gYUyyxiSuBojd|c=R35nK9;-FFr;YP9jjOE
z+nf2x;+pLCCz(d7+H5ggiE#AT`aAuoB1^Rr=~_N#KDU&dxyskW-6ghH(vzetjh7Zy
zu6*%IEX+qOSHJjtUbsoCsYuD)_W9iMhRNuWhCkH0bkw^`q+`9A5)(UggI4US?YM$1
zkIgv8;FUeS)mr*H*+pJnR|Vc4+0?ypH+v9l5)c~bI6C;9cQoeZ-u}gFb{`fL@63mI
zx3mT!?nMhYwGJ}<F}OSY5PE<78v7^4Zm)WX!Mq_ZK{g9^m-SwlZAKcEuq=w8pO#W3
zf}Hqrftn=qhWfpPAg%aa>1Pu=saGVpX=ZLx=^odiI>$6`xD^Bm7dpSXs>^io+TefK
zu0PKNbY)Q$ZH2YV(5-4hHF5a!x#S;5oUJ#)je-uJ$UZT->$3RIGPsL_g39!xxI-(Y
z^1M|RB(ILGuYcRyywPND()Na@xwgh6#>N7LwLcm--jo?>o{QpIG3MC0+dkRc1bcn{
zcILjRp|qoVY~R?hqj-UUc~N6z&&wv$g7+_@M(5PJJkMLabZE(XsPusUC3jenL;3CK
zCx5z2UfAqr>6NR8Je=OrUq1aWF~9%(S8aoOi;*q!=H4HHnY@uaT|tjKo?Uyl;96&7
zCTHJQ9z_l5lY&%*M{G{-L%$3f86>)~Zzr^7CGF5JPpj-2EzpF<_>MnmA}}SH9xLrt
zUwSw1W=E58F7Z>&_vWJd4J$I2EnL@?A@V_ikYqH|H?{g4bK}3XOW_ikZg#@{3h6)I
zteixxWj#|4Ug_`epqt;a#oLfMQlwPn9G*&5xQBQPiRgcgMn7XR^YhlBV2Ds31I$$u
zNpWRoq5p6JQDwyu;j5DwGTdhodFn!U)@rRPIDI2ecK^{|qE3o(WVqgcPtLUEy1%CB
zXY*0YAk;qoKuf#KGPR^AUk$aW=KgW2yKG7JvFnQ|=L<b|ujx-D%CGdhmN^IF@;Mrx
zQ27zEc(3_z5X)K?{(b0#MEo}|Luhq5&3)MsCH6;cHk|&3-{lKBC(hp&kyAnV%RbA_
zw>mwP!z#W{y?l^oL0+uix$UN(e(_sfZ~44SYS)e0c#Q*S+H>7}-_8#IuWrY0j4j*t
zuX0zJJ6^r~``Z2eIhxtCo$v3&=|c{~BA&XM#0|Xo)$sM~GR~NRJgWGmS0d(d<^tWV
zQ6+}q5tse6!zk9%OS>tXQVUkn!;+C-9wmA6e6T@1;BftVW%lJm@nMA%&rhb3TizE<
zb1&RKxApFa#@W|4w=K@*kJp-)R<fh7icuZijEoAMi2b-7H^kAU@)s7vm|0$XX8rI@
z)joV~H~2GsbiB9Umi*sWtw@sTm0feh#i5H}YCUP&=3fop7mFeFkD>EZ9};lTC4|;X
z<9?W|%5#so3`BSG_*msvz9TuZ+ZZPB5jZc_LB0E(upyC(WZSJtBwNY_BHdTUKU~cI
zJC(`tnSkEY3;m|Q^DXDdqU9x>zuj?w$iU#{`^inQAed~ztsw^NAYv!j((@xTUfbl}
z-;ei(9`s~=R%;ogYnO@1W(X3i`L!8@KHWJxYn&PGr_;*Z?r)wMX(|41RO7uObX^hr
zIAl<yPW~&buZ%oBoB1%J+xKcz>;^-{O)1hE^Uy{RIwso(#k}1nhgG+1ylwg6w@k@j
zqlsV^WF}Pm-%PXG0iA_nz~_CgF2)Q_rb<X!Ef>-fBFpytFf`Er_=z8Cb`w{%dyfb=
z=W0u#ZL2WOXroH(=hb&m-&-bGEba&WM9@#e#vgF|iIH0!sj_!c7z0w$j+e%dQztV6
zL`biKx~|E?=C{-D98?^(3IGJWce28$nHk~PSTW{#RImOrYrebn-dEVN-cFeiOVCwh
z6XxI%{Ub|y%Qt^EzXJ)5?~IF$;(GLqi=%$E3}5}QjSo-EhU9WrSjHYOm`aLGF|A%B
zuPf<CG8~*X@1m-<avre>g@{sCH)_3Ss>5c@?uki~D(mJatkvqV4;7imc9}P<dN!U;
z9IBm(R*jE6L+!#hK1b$DCLd2P{+6?A`?|~0kij;%#+rF_+rzFs^h3~`%9X>&ZMtjn
zO*d(C70mMjUQ4!d9+rXZdV}fjxANK}zU=Zd;;tsUKg)Z#s8~{V;IJDe$Yrje$%0Il
zdjd1RPHp;{H&;~2%Boy$N%3%r`<p3Wz(2|0C;^|JY6B{%C`79FC`LbV$&6u+Wke_+
zu2GO&o5Qif3iJEs%S#|gu&@Yy)B2_ys~%h{@3Skc-1-iseonqe?eNNQl~WzcSj_0H
z@XTTxiRZucJG7amq8(#4vPaVO<6^@);x?<pA0?WpyD87iMWcs>`J|Df8ZBK%$@~Yh
zgW;;v5i)%$AYsHYz+6iCXw2rVPExMZqc$z6k8FvhzioU_A^L5Px{9RUomtD=o}1~I
z^1P|^X8h=A_0iUb$l}d57mT8h+)TCRwc~%U0kOb{b81UHJWgS%D$dW_!`us+@<$CO
z&HWXdEX>S5dl%v#sb#L)o8413q7c*)hf`MMSKx&Q-BGgW*+;ZL4Zov0zPNbUVkW#T
z>2Y6R@`0LdW@SDrTu-US@2b+4j`~4@`9X*}Yi@7nm(<V8NA7Y4w$&I$*S@gE^Bs?j
zQXrc45}Bb^s^5AnQ9AS+IPqbQSR1W(gYt&D%omc_KEV>3CfWyegC%m`{Rp+NQ`NQV
zyMK|?!W1_2(+=X8t%`N0lS8#Q&f=PxH^eKb;OpDAe)BV5w2C_|rFA*=*o>r`Z6;6?
zomGq)HWR--UAx3k#6GdaCK(WdT)r{)&DK}oZyTMu(oqM)>fh;kj4$abE9`2u;SnpX
zCaV<Qbcs{(#?{&Su1WOH41}liu3{GpR2g2z_S)|7Ke*zcvT)9&jLRyYhxWy{bsTu9
z74tlh@WWtbw=bXhUOT$W?Ypt`nZX;Ezgz8F$_o-w4G*`);kDkad1mn7mB4r_4%dv-
z>w<)*+K_+N#y^pUTphBmQEh+Cj%01Ndu<jg9z-aee>{J7(Bb;}ldNudLAJF0XMb&s
zK)pDdmr|gk{Z$7aYsS8p&e?i0VaLrXtd0*Zi+}$RX$&7LN)sL3mnJbv?Cm0R?rrA#
z0-L^1_HTn08vnNqnk5lm48u}Ss}%na{}eUD&u=APrD%$h&PPXALvJ;s!>(uEN1DEN
zdt<9NRWobr+svoG6WzItuQBWqVlGo-oBpw2j>xQ6?;aYey_j9#=CgLag65@}?|Ku8
z=Sq3Rd*yvoR5i2pxau?I$fYTh8M2?JoPNl$&E~p+x}mWvQ>wYolO0#e{8KjGth%P>
zd}7V@&GqK`^0gB`yc8}C_?Bt4tzcI_pVzhM>XKJp+c(x0&x<zexm6w;RJ<(js#4+E
zy31SYvr=9uC;)HLWn=)(QZq0xG66SDGcrQig&+Yin}vb(VH+beM4W*YD9#3DGXdFx
z48Ya347@-!hZu#R;y?o#MWJk<ETb5dZ46|K!`1gO${?w?g|b0r1|f+DgV`WAd;{9U
z0KCZ_0vT#wFfgPy0dH#G%D_;s!@v*$Wba+dz)<)QcyIU_28JZyoLBiW28J>P28O<7
z1_pjJux^Ih4-5=XfhSn>t1~e11v4<E0hd&(0JjEAi)LW{tj@p^8o|JF<{tyAvm68K
z#f89`S}+XoW@Hj!5CORW0UUrH1!7)=2#~c9=;M_9<kH;Kyb}G)ytI65TK_#{WdLC@
z6s?YF+zf%GnaSBc`T5!U0Y#~0nW^P^C7Eey*vy!a0^EcG#1bfGR5P+8nGupvnwylD
zn3;pq6t6w33?S@|V#*m8Muwck%G9D_eJ5iBGZ!ZVH(g^xLt|YN3rj;?OJ@^vU1uXF
zLkkNFXA4Jj3tcl)Qxjud0|NsiH&X*A7b8;(BR3OgJ>Yig!~kzrHjvpYKxhRFuwoBE
GAP)cvP`7vh

literal 0
HcmV?d00001

diff --git a/README.md b/README.md
index 775d2d4..7b24ab9 100755
--- a/README.md
+++ b/README.md
@@ -1,9 +1,14 @@
 Python Tutorials and References
 ================
 
-Useful functions, tutorials, and other Python-related things
+![logo](./Images/logo.png)
 
 
+A collection of useful scripts, tutorials, and other Python-related things
+
+<br>
+<br>
+
 ###Links to view the IPython Notebooks and Markdown files
 
 <br>

From 69cb078345ee3741cc9ae4b8c8304700658dacca Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 22 May 2014 00:53:15 -0400
Subject: [PATCH 066/248] logo

---
 README.md | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 7b24ab9..11ea2b9 100755
--- a/README.md
+++ b/README.md
@@ -1,5 +1,4 @@
-Python Tutorials and References
-================
+
 
 ![logo](./Images/logo.png)
 
@@ -7,7 +6,7 @@ Python Tutorials and References
 A collection of useful scripts, tutorials, and other Python-related things
 
 <br>
-<br>
+
 
 ###Links to view the IPython Notebooks and Markdown files
 

From 33309eb7c6021144732614bac90477ef740beaf3 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 23 May 2014 00:51:01 -0400
Subject: [PATCH 067/248] small readme format update

---
 README.md | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 11ea2b9..b6f8190 100755
--- a/README.md
+++ b/README.md
@@ -8,15 +8,13 @@ A collection of useful scripts, tutorials, and other Python-related things
 <br>
 
 
-###Links to view the IPython Notebooks and Markdown files
-
 <br>
 <br>
 <br>
 
 
 
-**// Python tips and tutorials**  
+###// Python tips and tutorials
 
 - A collection of not so obvious Python stuff you should know! [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)  
 - Python's scope resolution for variable names and the LEGB rule [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
@@ -26,23 +24,27 @@ A collection of useful scripts, tutorials, and other Python-related things
 - Sorting CSV files using the Python csv module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
 
+<br>
+###// benchmarks
 
-**// benchmarks**
+*For more recent benchmarks, please also see my separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
 
 - Python benchmarks via `timeit` [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
 - Implementing the least squares fit method for linear regression and speeding it up via Cython [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
 - Benchmarks of different palindrome functions [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
 
 
+<br>
 
 
-**// other**
+###// other
 
 - Creating a table of contents with internal links in IPython Notebooks and Markdown documents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]   
 - Happy Mother's Day [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)
 
+<br>
 
-**// useful scripts snippets**
+###// useful scripts and snippets
 
 - [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to all .py files in a current directory.
 

From 4900267a0201e86ee85216f93b905e6670a659b9 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 23 May 2014 00:56:23 -0400
Subject: [PATCH 068/248] small readme format update

---
 README.md | 35 ++++++++++++++++++++++++-----------
 1 file changed, 24 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index b6f8190..710e62b 100755
--- a/README.md
+++ b/README.md
@@ -16,22 +16,34 @@ A collection of useful scripts, tutorials, and other Python-related things
 
 ###// Python tips and tutorials
 
-- A collection of not so obvious Python stuff you should know! [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)  
-- Python's scope resolution for variable names and the LEGB rule [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)
-- A thorough guide to SQLite database operations in Python [[Markdown]](./sqlite3_howto/README.md)
-- Unit testing in Python - Why we want to make it a habit [[Markdown]](./tutorials/unit_testing.md)
-- Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown]](./tutorials/installing_scientific_packages.md)
+- A collection of not so obvious Python stuff you should know! [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)]
+
+- Python's scope resolution for variable names and the LEGB rule [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)]
+
+
+
+- A thorough guide to SQLite database operations in Python [[Markdown](./sqlite3_howto/README.md)]  
+
+- Unit testing in Python - Why we want to make it a habit [[Markdown](./tutorials/unit_testing.md)]  
+
+- Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown](./tutorials/installing_scientific_packages.md)]
+
+
+
 - Sorting CSV files using the Python csv module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
 
 <br>
 ###// benchmarks
 
-*For more recent benchmarks, please also see my separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
+*For more recent benchmarks, please also see my separate   
+GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
 
-- Python benchmarks via `timeit` [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)
-- Implementing the least squares fit method for linear regression and speeding it up via Cython [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)  
-- Benchmarks of different palindrome functions [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)  
+- Python benchmarks via `timeit` [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)]  
+
+- Implementing the least squares fit method for linear regression and speeding it up via Cython [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)]   
+
+- Benchmarks of different palindrome functions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)]    
 
 
 <br>
@@ -40,7 +52,8 @@ A collection of useful scripts, tutorials, and other Python-related things
 ###// other
 
 - Creating a table of contents with internal links in IPython Notebooks and Markdown documents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]   
-- Happy Mother's Day [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)
+
+- Happy Mother's Day [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)]
 
 <br>
 
@@ -48,4 +61,4 @@ A collection of useful scripts, tutorials, and other Python-related things
 
 - [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to all .py files in a current directory.
 
-- convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb]](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)
+- convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)]

From e8db4ab294edbae5401cd1a8cc0f5e3e8b4c35b9 Mon Sep 17 00:00:00 2001
From: Chris Seymour <chris.j.seymour@hotmail.com>
Date: Sat, 24 May 2014 00:13:20 +0100
Subject: [PATCH 069/248] Added a shebang to the shebang adding script

---
 useful_scripts/prepend_python_shebang.sh | 1 +
 1 file changed, 1 insertion(+)

diff --git a/useful_scripts/prepend_python_shebang.sh b/useful_scripts/prepend_python_shebang.sh
index 3bbc937..686225f 100644
--- a/useful_scripts/prepend_python_shebang.sh
+++ b/useful_scripts/prepend_python_shebang.sh
@@ -1,3 +1,4 @@
+#!/usr/bin/env bash
 # Sebastian Raschka 05/21/2014
 # Shell script that prepends a Python shebang 
 # '#!/usr/bin/env python' to all

From 73979bb5e59e8f95b1533c95fb808b0854ca0e36 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 May 2014 12:02:03 -0400
Subject: [PATCH 070/248] unorderable types update and separate python 2.x vs.
 3.x section

---
 README.md                                     |   2 +
 not_so_obvious_python_stuff.ipynb             | 379 +++++++++++++++-
 .../key_differences_between_python_2_and_3.md | 411 ++++++++++++++++++
 3 files changed, 777 insertions(+), 15 deletions(-)
 create mode 100644 tutorials/key_differences_between_python_2_and_3.md

diff --git a/README.md b/README.md
index 710e62b..db00ca9 100755
--- a/README.md
+++ b/README.md
@@ -18,8 +18,10 @@ A collection of useful scripts, tutorials, and other Python-related things
 
 - A collection of not so obvious Python stuff you should know! [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)]
 
+
 - Python's scope resolution for variable names and the LEGB rule [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)]
 
+- Key differences between Python 2.x and Python 3.x [[Markdown](./tutorials/key_differences_python2_python3.md)] 
 
 
 - A thorough guide to SQLite database operations in Python [[Markdown](./sqlite3_howto/README.md)]  
diff --git a/not_so_obvious_python_stuff.ipynb b/not_so_obvious_python_stuff.ipynb
index 0a9632c..c36c0dc 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7a22f6c91e4aab51a325c721dd7674622d1acc5b4a3a038ff512c736d83bbe4a"
+  "signature": "sha256:b37510e2c48c9811f7baabeff25c48a186e0e5f154b5d341f4eda6701df705e3"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,7 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://sebastianraschka.com)  \n",
-      "last updated: 05/03/2014 ([Changelog](#changelog))\n",
+      "last updated: 05/24/2014 ([Changelog](#changelog))\n",
       "\n",
       "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
@@ -24,7 +24,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### All code was executed in Python 3.4"
+      "#### All code was executed in Python 3.4 (unless stated otherwise)"
      ]
     },
     {
@@ -59,32 +59,59 @@
      "source": [
       "# Sections\n",
       "- [The C3 class resolution algorithm for multiple class inheritance](#c3_class_res)\n",
+      "\n",
       "- [Assignment operators and lists - simple-add vs. add-AND operators](#pm_in_lists)\n",
+      "\n",
       "- [`True` and `False` in the datetime module](#datetime_module)\n",
+      "\n",
       "- [Python reuses objects for small integers - always use \"==\" for equality, \"is\" for identity](#python_small_int)\n",
+      "\n",
       "- [Shallow vs. deep copies if list contains other structures and objects](#shallow_vs_deep)\n",
+      "\n",
       "- [Picking `True` values from logical `and`s and `or`s](#false_true_expressions)\n",
+      "\n",
       "- [Don't use mutable objects as default arguments for functions!](#def_mutable_func)\n",
+      "\n",
       "- [Be aware of the consuming generator](#consuming_generator)\n",
+      "\n",
       "- [`bool` is a subclass of `int`](#bool_int)\n",
+      "\n",
       "- [About lambda-in-closures and-a-loop pitfall](#lambda_closure)\n",
+      "\n",
       "- [Python's LEGB scope resolution and the keywords `global` and `nonlocal`](#python_legb)\n",
+      "\n",
       "- [When mutable contents of immutable tuples aren't so mutable](#immutable_tuple)\n",
+      "\n",
       "- [List comprehensions are fast, but generators are faster!?](#list_generator)\n",
+      "\n",
       "- [Public vs. private class methods and name mangling](#private_class)\n",
+      "\n",
       "- [The consequences of modifying a list when looping through it](#looping_pitfall)\n",
+      "\n",
       "- [Dynamic binding and typos in variable names](#dynamic_binding)\n",
+      "\n",
       "- [List slicing using indexes that are \"out of range](#out_of_range_slicing)\n",
+      "\n",
       "- [Reusing global variable names and UnboundLocalErrors](#unboundlocalerror)\n",
+      "\n",
       "- [Creating copies of mutable objects](#copy_mutable)\n",
+      "\n",
       "- [Key differences between Python 2 and 3](#python_differences)\n",
+      "\n",
       "- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n",
+      "\n",
       "- [Abortive statements in `finally` blocks](#finally_blocks)\n",
+      "\n",
       "- [Assigning types to variables as values](#variable_types)\n",
+      "\n",
       "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n",
+      "\n",
       "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)\n",
+      "\n",
       "- [Metaclasses - What creates a new instance of a class?](#new_instance)\n",
+      "\n",
       "- [Else-clauses: \"conditional else\" and \"completion else\"](#else_clauses)\n",
+      "\n",
       "- [Interning of compile-time constants vs. run-time expressions](#string_interning)"
      ]
     },
@@ -2282,12 +2309,69 @@
       "(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='py23_overview'></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Overview - Key differences between Python 2 and 3"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "\n",
+      "- [Unicode](#unicode)\n",
+      "- [The print statement](#print)\n",
+      "- [Integer division](#integer_div)\n",
+      "- [xrange()](#xrange)\n",
+      "- [Raising exceptions](#raising_exceptions)\n",
+      "- [Handling exceptions](#handling_exceptions)\n",
+      "- [next() function and .next() method](#next_next)\n",
+      "- [Loop variables and leaking into the global scope](#loop_leak)\n",
+      "- [Comparing unorderable types](#compare_unorder)\n",
+      "\n",
+      "<br>\n",
+      "<br>\n",
+      "\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='unicode'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Unicode..."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
       "\n",
-      "### Unicode...\n",
       "####- Python 2: \n",
       "We have ASCII `str()` types, separate `unicode()`, but no `byte` type\n",
       "####- Python 3: \n",
@@ -2341,7 +2425,29 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### The print statement\n",
+      "<a id='print'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### The print statement"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
       "Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ..."
      ]
     },
@@ -2395,7 +2501,29 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### Integer division\n",
+      "<a id='integer_div'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Integer division"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
       "This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed.  \n",
       "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts)."
      ]
@@ -2432,7 +2560,30 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### `xrange()`  \n",
+      "<a id='xrange'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "###`xrange()` "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      " \n",
       "`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch!  \n",
       "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore."
      ]
@@ -2470,7 +2621,30 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### Raising exceptions\n",
+      "<a id='raising_exceptions'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Raising exceptions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "\n",
       "Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:"
      ]
@@ -2509,9 +2683,32 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### Handling exceptions\n",
+      "<a id='handling_exceptions'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Handling exceptions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "\n",
-      "Also the handling of excecptions has slightly changed in Python 3. Now, we have to use the `as` keyword!"
+      "Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the `as` keyword!"
      ]
     },
     {
@@ -2538,11 +2735,37 @@
      "metadata": {},
      "outputs": []
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "<a id='next_next'></a>\n",
+      "<br>\n",
+      "<br>"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### The `next()` function and `.next()` method\n",
+      "### The `next()` function and `.next()` method"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
       "\n",
       "Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!"
      ]
@@ -2575,7 +2798,23 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### In Python 3.x for-loop variables don't leak into the global namespace anymore"
+      "<a id='loop_leak'></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### In Python 3.x for-loop variables don't leak into the global namespace anymore"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
      ]
     },
     {
@@ -2591,6 +2830,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
+      "from platform import python_version\n",
+      "print('This code cell was executed in Python', python_version())\n",
+      "\n",
       "i = 1\n",
       "print([i for i in range(5)])\n",
       "print(i, '-> i in global')"
@@ -2602,21 +2844,124 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
+        "This code cell was executed in Python 3.3.5\n",
         "[0, 1, 2, 3, 4]\n",
         "1 -> i in global\n"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from platform import python_version\n",
+      "print 'This code cell was executed in Python', python_version()\n",
+      "\n",
+      "i = 1\n",
+      "print [i for i in range(5)]\n",
+      "print i, '-> i in global' "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "This code cell was executed in Python 2.7.6\n",
+        "[0, 1, 2, 3, 4]\n",
+        "4 -> i in global\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In Python 2.x this would print  \n",
-      "`4 -> i in global`"
+      "<a id='compare_unorder'></a>\n",
+      "<br>\n",
+      "<br>"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3.x prevents us from comparing unorderable types"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from platform import python_version\n",
+      "print 'This code cell was executed in Python', python_version()\n",
+      "\n",
+      "print [1, 2] > 'foo'\n",
+      "print (1, 2) > 'foo'\n",
+      "print [1, 2] > (1, 2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "This code cell was executed in Python 2.7.6\n",
+        "False\n",
+        "True\n",
+        "False\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from platform import python_version\n",
+      "print('This code cell was executed in Python', python_version())\n",
+      "\n",
+      "print([1, 2] > 'foo')\n",
+      "print((1, 2) > 'foo')\n",
+      "print([1, 2] > (1, 2))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "This code cell was executed in Python 3.3.5\n"
+       ]
+      },
+      {
+       "ename": "TypeError",
+       "evalue": "unorderable types: list() > str()",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-3-1d774c677f73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This code cell was executed in Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -3936,6 +4281,10 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "#### 05/24/2014\n",
+      "- new section: unorderable types in Python 2\n",
+      "- table of contents for the Python 2 vs. Python 3 topic\n",
+      "    \n",
       "#### 05/03/2014\n",
       "- new section: else clauses: conditional vs. completion\n",
       "- new section: Interning of compile-time constants vs. run-time expressions\n",
diff --git a/tutorials/key_differences_between_python_2_and_3.md b/tutorials/key_differences_between_python_2_and_3.md
new file mode 100644
index 0000000..d7a7d78
--- /dev/null
+++ b/tutorials/key_differences_between_python_2_and_3.md
@@ -0,0 +1,411 @@
+[Sebastian Raschka](http://sebastianraschka.com)  
+last updated: 05/24/2014
+
+<br>
+
+**This is a subsection of ["A collection of not-so-obvious Python stuff you should know!"](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)**
+
+<a id='sections'></a>
+
+<br>
+
+## Key differences between Python 2 and 3
+<br>
+
+There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,  
+- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)
+- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)
+- [http://python3porting.com/differences.html](http://python3porting.com/differences.html)
+- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html)  
+etc.
+
+But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!
+(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)
+
+<a id='py23_overview'></a>
+
+<br>
+
+### Overview - Key differences between Python 2 and 3
+
+
+
+
+- [Unicode](#unicode)
+- [The print statement](#print)
+- [Integer division](#integer_div)
+- [xrange()](#xrange)
+- [Raising exceptions](#raising_exceptions)
+- [Handling exceptions](#handling_exceptions)
+- [next() function and .next() method](#next_next)
+- [Loop variables and leaking into the global scope](#loop_leak)
+- [Comparing unorderable types](#compare_unorder)
+
+<br>
+<br>
+
+	
+<a id='unicode'></a>
+<br>
+<br>
+
+### Unicode...
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+
+####- Python 2: 
+We have ASCII `str()` types, separate `unicode()`, but no `byte` type
+####- Python 3: 
+Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s
+
+<br>
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">#############</span>
+<span style="color: #888888"># Python 2</span>
+<span style="color: #888888">#############</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(unicode(<span style="background-color: #fff0f0">&#39;is like a python3 str()&#39;</span>))
+<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;unicode&#39;</span><span style="color: #333333">&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(b<span style="background-color: #fff0f0">&#39;byte type does not exist&#39;</span>)
+<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;str&#39;</span><span style="color: #333333">&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="background-color: #fff0f0">&#39;they are really&#39;</span> <span style="color: #333333">+</span> b<span style="background-color: #fff0f0">&#39; the same&#39;</span>
+<span style="background-color: #fff0f0">&#39;they are really the same&#39;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(<span style="color: #007020">bytearray</span>(b<span style="background-color: #fff0f0">&#39;bytearray oddly does exist though&#39;</span>))
+<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;bytearray&#39;</span><span style="color: #333333">&gt;</span>
+
+<span style="color: #888888">#############</span>
+<span style="color: #888888"># Python 3</span>
+<span style="color: #888888">#############</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;strings are now utf-8 </span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\u03BC</span><span style="background-color: #fff0f0">nico</span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\u0394</span><span style="background-color: #fff0f0">é!&#39;</span>)
+strings are now utf<span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">8</span> μnicoΔé<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(b<span style="background-color: #fff0f0">&#39; and we have byte types for storing data&#39;</span>)
+<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">bytes</span><span style="background-color: #fff0f0">&#39;&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(<span style="color: #007020">bytearray</span>(b<span style="background-color: #fff0f0">&#39;but also bytearrays for those who prefer them over strings&#39;</span>))
+<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">bytearray</span><span style="background-color: #fff0f0">&#39;&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="background-color: #fff0f0">&#39;string&#39;</span> <span style="color: #333333">+</span> b<span style="background-color: #fff0f0">&#39;bytes for data&#39;</span>
+Traceback (most recent call last):s
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">TypeError</span>: Can<span style="background-color: #fff0f0">&#39;t convert &#39;</span><span style="color: #007020">bytes</span><span style="background-color: #fff0f0">&#39; object to str implicitly</span>
+</pre></div>
+
+
+<a id='print'></a>
+<br>
+<br>
+
+### The print statement
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ...
+
+<br>
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
+Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
+Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
+Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>
+    <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
+                        <span style="color: #333333">^</span>
+<span style="color: #FF0000; font-weight: bold">SyntaxError</span>: invalid syntax
+</pre></div>
+
+<br>
+
+And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=""` in Python 3:
+
+<br>
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&quot;line 1&quot;</span>, ; <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;same line&#39;</span>
+line <span style="color: #0000DD; font-weight: bold">1</span> same line
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&quot;line 1&quot;</span>, end<span style="color: #333333">=</span><span style="background-color: #fff0f0">&quot;&quot;</span>) ; <span style="color: #007020">print</span> (<span style="background-color: #fff0f0">&quot; same line&quot;</span>)
+line <span style="color: #0000DD; font-weight: bold">1</span> same line
+</pre></div>
+
+
+<a id='integer_div'></a>
+<br>
+<br>
+
+### Integer division
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+
+This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed.  
+So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts).
+
+<br>
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #0000DD; font-weight: bold">2</span>
+<span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #0000DD; font-weight: bold">2</span>
+<span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
+<span style="color: #6600EE; font-weight: bold">1.5</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
+<span style="color: #6600EE; font-weight: bold">1.0</span>
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #0000DD; font-weight: bold">2</span>
+<span style="color: #6600EE; font-weight: bold">1.5</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #0000DD; font-weight: bold">2</span>
+<span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
+<span style="color: #6600EE; font-weight: bold">1.5</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
+<span style="color: #6600EE; font-weight: bold">1.0</span>
+</pre></div>
+
+
+<a id='xrange'></a>
+<br>
+<br>
+
+###`xrange()` 
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+ 
+`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch!  
+In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore.
+
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;</span> python <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in range(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
+<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #0000DD; font-weight: bold">66</span> msec per loop
+
+    <span style="color: #333333">&gt;</span> python <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in xrange(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
+<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #6600EE; font-weight: bold">27.8</span> msec per loop
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;</span> python3 <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in range(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
+<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #6600EE; font-weight: bold">51.1</span> msec per loop
+
+<span style="color: #333333">&gt;</span> python3 <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in xrange(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py&quot;</span>, line <span style="color: #0000DD; font-weight: bold">292</span>, <span style="color: #000000; font-weight: bold">in</span> main
+    x <span style="color: #333333">=</span> t<span style="color: #333333">.</span>timeit(number)
+  File <span style="background-color: #fff0f0">&quot;/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py&quot;</span>, line <span style="color: #0000DD; font-weight: bold">178</span>, <span style="color: #000000; font-weight: bold">in</span> timeit
+    timing <span style="color: #333333">=</span> <span style="color: #007020">self</span><span style="color: #333333">.</span>inner(it, <span style="color: #007020">self</span><span style="color: #333333">.</span>timer)
+  File <span style="background-color: #fff0f0">&quot;&lt;timeit-src&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">6</span>, <span style="color: #000000; font-weight: bold">in</span> inner
+    <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> xrange(<span style="color: #0000DD; font-weight: bold">1000000</span>):
+<span style="color: #FF0000; font-weight: bold">NameError</span>: name <span style="background-color: #fff0f0">&#39;xrange&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined
+</pre></div>
+
+
+<a id='raising_exceptions'></a>
+<br>
+<br>
+
+### Raising exceptions
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+
+
+Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:
+
+<br>
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">IOError</span>: file error
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>(<span style="background-color: #fff0f0">&quot;file error&quot;</span>)
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">IOError</span>: file error
+
+    
+<span style="color: #888888"># Python 3    </span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>
+    <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
+                 <span style="color: #333333">^</span>
+<span style="color: #FF0000; font-weight: bold">SyntaxError</span>: invalid syntax
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>(<span style="background-color: #fff0f0">&quot;file error&quot;</span>)
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">OSError</span>: file error
+</pre></div>
+
+
+<a id='handling_exceptions'></a>
+<br>
+<br>
+
+### Handling exceptions
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+
+
+Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the `as` keyword!
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">try</span>:
+<span style="color: #333333">...</span>     blabla
+<span style="color: #333333">...</span> <span style="color: #008800; font-weight: bold">except</span> <span style="color: #FF0000; font-weight: bold">NameError</span>, err:
+<span style="color: #333333">...</span>     <span style="color: #007020">print</span> err, <span style="background-color: #fff0f0">&#39;--&gt; our error msg&#39;</span>
+<span style="color: #333333">...</span> 
+name <span style="background-color: #fff0f0">&#39;blabla&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined <span style="color: #333333">--&gt;</span> our error msg
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">try</span>:
+<span style="color: #333333">...</span>     blabla
+<span style="color: #333333">...</span> <span style="color: #008800; font-weight: bold">except</span> <span style="color: #FF0000; font-weight: bold">NameError</span> <span style="color: #008800; font-weight: bold">as</span> err:
+<span style="color: #333333">...</span>     <span style="color: #007020">print</span>(err, <span style="background-color: #fff0f0">&#39;--&gt; our error msg&#39;</span>)
+<span style="color: #333333">...</span> 
+name <span style="background-color: #fff0f0">&#39;blabla&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined <span style="color: #333333">--&gt;</span> our error msg
+</pre></div>
+
+
+<a id='next_next'></a>
+<br>
+<br>
+
+### The `next()` function and `.next()` method
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!
+
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> my_generator <span style="color: #333333">=</span> (letter <span style="color: #008800; font-weight: bold">for</span> letter <span style="color: #000000; font-weight: bold">in</span> <span style="background-color: #fff0f0">&#39;abcdefg&#39;</span>)
+<span style="color: #333333">&gt;&gt;&gt;</span> my_generator<span style="color: #333333">.</span>next()
+<span style="background-color: #fff0f0">&#39;a&#39;</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">next</span>(my_generator)
+<span style="background-color: #fff0f0">&#39;b&#39;</span>
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> my_generator <span style="color: #333333">=</span> (letter <span style="color: #008800; font-weight: bold">for</span> letter <span style="color: #000000; font-weight: bold">in</span> <span style="background-color: #fff0f0">&#39;abcdefg&#39;</span>)
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">next</span>(my_generator)
+<span style="background-color: #fff0f0">&#39;a&#39;</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> my_generator<span style="color: #333333">.</span>next()
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">AttributeError</span>: <span style="background-color: #fff0f0">&#39;generator&#39;</span> <span style="color: #007020">object</span> has no attribute <span style="background-color: #fff0f0">&#39;next&#39;</span>
+</pre></div>
+
+
+<a id='loop_leak'></a>
+<br>
+<br>
+
+### In Python 3.x for-loop variables don't leak into the global namespace anymore
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:
+
+"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope."
+
+<br>
+`[In:]`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
+<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
+
+i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #007020">print</span>([i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)])
+<span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span>)
+</pre></div>
+
+<br>
+`[Out:]`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 3.3.5
+[0, 1, 2, 3, 4]
+1 -&gt; i in global
+</pre></div>
+
+
+<br>
+<br>
+<br>
+`[In:]`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
+<span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version()
+
+i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #007020">print</span> [i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)]
+<span style="color: #007020">print</span> i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span> 
+</pre></div>
+
+<br>
+`[Out:]`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 2.7.6
+[0, 1, 2, 3, 4]
+4 -&gt; i in global
+</pre></div>
+
+
+<a id='compare_unorder'></a>
+<br>
+<br>
+
+#### Python 3.x prevents us from comparing unorderable types
+
+[[back to Python 2.x vs 3.x overview](#py23_overview)]
+
+<br>
+`[In:]`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
+<span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version()
+
+<span style="color: #007020">print</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
+<span style="color: #007020">print</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
+<span style="color: #007020">print</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>)
+</pre></div>
+
+<br>
+`[Out:]`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 2.7.6
+False
+True
+False
+</pre></div>
+
+<br>
+<br>
+<br>
+
+`[In:]`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
+<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
+
+<span style="color: #007020">print</span>([<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>)
+<span style="color: #007020">print</span>((<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>)
+<span style="color: #007020">print</span>([<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>))
+</pre></div>
+
+`[Out:]`
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed <span style="color: #000000; font-weight: bold">in</span> Python <span style="color: #6600EE; font-weight: bold">3.3</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">5</span>
+<span style="color: #333333">---------------------------------------------------------------------------</span>
+<span style="color: #FF0000; font-weight: bold">TypeError</span>                                 Traceback (most recent call last)
+<span style="color: #333333">&lt;</span>ipython<span style="color: #333333">-</span><span style="color: #007020">input</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">3</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">1</span>d774c677f73<span style="color: #333333">&gt;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>()
+      <span style="color: #0000DD; font-weight: bold">2</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
+      <span style="color: #0000DD; font-weight: bold">3</span> 
+<span style="color: #333333">----&gt;</span> <span style="color: #0000DD; font-weight: bold">4</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
+      <span style="color: #0000DD; font-weight: bold">5</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
+      <span style="color: #0000DD; font-weight: bold">6</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>)
+
+<span style="color: #FF0000; font-weight: bold">TypeError</span>: unorderable types: <span style="color: #007020">list</span>() <span style="color: #333333">&gt;</span> <span style="color: #007020">str</span>()
+</pre></div>

From f81049e1ac3881ea46ffc42b645efde9f6b511e9 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 May 2014 12:05:09 -0400
Subject: [PATCH 071/248] html version

---
 README.md                                     |   2 +-
 ...ey_differences_between_python_2_and_3.html | 632 ++++++++++++++++++
 2 files changed, 633 insertions(+), 1 deletion(-)
 create mode 100644 tutorials/key_differences_between_python_2_and_3.html

diff --git a/README.md b/README.md
index db00ca9..feebdc1 100755
--- a/README.md
+++ b/README.md
@@ -21,7 +21,7 @@ A collection of useful scripts, tutorials, and other Python-related things
 
 - Python's scope resolution for variable names and the LEGB rule [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)]
 
-- Key differences between Python 2.x and Python 3.x [[Markdown](./tutorials/key_differences_python2_python3.md)] 
+- Key differences between Python 2.x and Python 3.x [[HTML](http://htmlpreview.github.io/?https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.html)] 
 
 
 - A thorough guide to SQLite database operations in Python [[Markdown](./sqlite3_howto/README.md)]  
diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
new file mode 100644
index 0000000..431d7c9
--- /dev/null
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -0,0 +1,632 @@
+<!doctype html>
+<html>
+<head>
+<meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
+<style>
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+p,
+blockquote {
+    margin: 0;
+    padding: 0;
+}
+body {
+    font-family: "Helvetica Neue", Helvetica, "Hiragino Sans GB", Arial, sans-serif;
+    font-size: 13px;
+    line-height: 18px;
+    color: #737373;
+    background-color: white;
+    margin: 10px 13px 10px 13px;
+}
+table {
+	margin: 10px 0 15px 0;
+	border-collapse: collapse;
+}
+td,th {	
+	border: 1px solid #ddd;
+	padding: 3px 10px;
+}
+th {
+	padding: 5px 10px;	
+}
+
+a {
+    color: #0069d6;
+}
+a:hover {
+    color: #0050a3;
+    text-decoration: none;
+}
+a img {
+    border: none;
+}
+p {
+    margin-bottom: 9px;
+}
+h1,
+h2,
+h3,
+h4,
+h5,
+h6 {
+    color: #404040;
+    line-height: 36px;
+}
+h1 {
+    margin-bottom: 18px;
+    font-size: 30px;
+}
+h2 {
+    font-size: 24px;
+}
+h3 {
+    font-size: 18px;
+}
+h4 {
+    font-size: 16px;
+}
+h5 {
+    font-size: 14px;
+}
+h6 {
+    font-size: 13px;
+}
+hr {
+    margin: 0 0 19px;
+    border: 0;
+    border-bottom: 1px solid #ccc;
+}
+blockquote {
+    padding: 13px 13px 21px 15px;
+    margin-bottom: 18px;
+    font-family:georgia,serif;
+    font-style: italic;
+}
+blockquote:before {
+    content:"\201C";
+    font-size:40px;
+    margin-left:-10px;
+    font-family:georgia,serif;
+    color:#eee;
+}
+blockquote p {
+    font-size: 14px;
+    font-weight: 300;
+    line-height: 18px;
+    margin-bottom: 0;
+    font-style: italic;
+}
+code, pre {
+    font-family: Monaco, Andale Mono, Courier New, monospace;
+}
+code {
+    background-color: #fee9cc;
+    color: rgba(0, 0, 0, 0.75);
+    padding: 1px 3px;
+    font-size: 12px;
+    -webkit-border-radius: 3px;
+    -moz-border-radius: 3px;
+    border-radius: 3px;
+}
+pre {
+    display: block;
+    padding: 14px;
+    margin: 0 0 18px;
+    line-height: 16px;
+    font-size: 11px;
+    border: 1px solid #d9d9d9;
+    white-space: pre-wrap;
+    word-wrap: break-word;
+}
+pre code {
+    background-color: #fff;
+    color:#737373;
+    font-size: 11px;
+    padding: 0;
+}
+sup {
+    font-size: 0.83em;
+    vertical-align: super;
+    line-height: 0;
+}
+* {
+	-webkit-print-color-adjust: exact;
+}
+@media screen and (min-width: 914px) {
+    body {
+        width: 854px;
+        margin:10px auto;
+    }
+}
+@media print {
+	body,code,pre code,h1,h2,h3,h4,h5,h6 {
+		color: black;
+	}
+	table, pre {
+		page-break-inside: avoid;
+	}
+}
+</style>
+<title>Key differences between Python 2 and 3</title>
+
+</head>
+<body>
+<p><a href="http://sebastianraschka.com">Sebastian Raschka</a><br/>
+last updated: 05/24/2014</p>
+
+<p><br></p>
+
+<p><strong>This is a subsection of <a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1">"A collection of not-so-obvious Python stuff you should know!"</a></strong></p>
+
+<p><a id='sections'></a></p>
+
+<p><br></p>
+
+<h2>Key differences between Python 2 and 3</h2>
+
+<p><br></p>
+
+<p>There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,<br/>
+- <a href="https://wiki.python.org/moin/Python2orPython3">https://wiki.python.org/moin/Python2orPython3</a>
+- <a href="https://docs.python.org/3.0/whatsnew/3.0.html">https://docs.python.org/3.0/whatsnew/3.0.html</a>
+- <a href="http://python3porting.com/differences.html">http://python3porting.com/differences.html</a>
+- <a href="https://docs.python.org/3/howto/pyporting.html">https://docs.python.org/3/howto/pyporting.html</a><br/>
+etc.</p>
+
+<p>But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!
+(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)</p>
+
+<p><a id='py23_overview'></a></p>
+
+<p><br></p>
+
+<h3>Overview - Key differences between Python 2 and 3</h3>
+
+<ul>
+<li><a href="#unicode">Unicode</a></li>
+<li><a href="#print">The print statement</a></li>
+<li><a href="#integer_div">Integer division</a></li>
+<li><a href="#xrange">xrange()</a></li>
+<li><a href="#raising_exceptions">Raising exceptions</a></li>
+<li><a href="#handling_exceptions">Handling exceptions</a></li>
+<li><a href="#next_next">next() function and .next() method</a></li>
+<li><a href="#loop_leak">Loop variables and leaking into the global scope</a></li>
+<li><a href="#compare_unorder">Comparing unorderable types</a></li>
+</ul>
+
+
+<p><br>
+<br></p>
+
+<p><a id='unicode'></a>
+<br>
+<br></p>
+
+<h3>Unicode...</h3>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<h4>- Python 2:</h4>
+
+<p>We have ASCII <code>str()</code> types, separate <code>unicode()</code>, but no <code>byte</code> type</p>
+
+<h4>- Python 3:</h4>
+
+<p>Now, we finally have Unicode (utf-8) <code>str</code>ings, and 2 byte classes: <code>byte</code> and <code>bytearray</code>s</p>
+
+<p><br></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">#############</span>
+<span style="color: #888888"># Python 2</span>
+<span style="color: #888888">#############</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(unicode(<span style="background-color: #fff0f0">&#39;is like a python3 str()&#39;</span>))
+<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;unicode&#39;</span><span style="color: #333333">&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(b<span style="background-color: #fff0f0">&#39;byte type does not exist&#39;</span>)
+<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;str&#39;</span><span style="color: #333333">&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="background-color: #fff0f0">&#39;they are really&#39;</span> <span style="color: #333333">+</span> b<span style="background-color: #fff0f0">&#39; the same&#39;</span>
+<span style="background-color: #fff0f0">&#39;they are really the same&#39;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(<span style="color: #007020">bytearray</span>(b<span style="background-color: #fff0f0">&#39;bytearray oddly does exist though&#39;</span>))
+<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;bytearray&#39;</span><span style="color: #333333">&gt;</span>
+
+<span style="color: #888888">#############</span>
+<span style="color: #888888"># Python 3</span>
+<span style="color: #888888">#############</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;strings are now utf-8 </span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\u03BC</span><span style="background-color: #fff0f0">nico</span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\u0394</span><span style="background-color: #fff0f0">é!&#39;</span>)
+strings are now utf<span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">8</span> μnicoΔé<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(b<span style="background-color: #fff0f0">&#39; and we have byte types for storing data&#39;</span>)
+<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">bytes</span><span style="background-color: #fff0f0">&#39;&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(<span style="color: #007020">bytearray</span>(b<span style="background-color: #fff0f0">&#39;but also bytearrays for those who prefer them over strings&#39;</span>))
+<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">bytearray</span><span style="background-color: #fff0f0">&#39;&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="background-color: #fff0f0">&#39;string&#39;</span> <span style="color: #333333">+</span> b<span style="background-color: #fff0f0">&#39;bytes for data&#39;</span>
+Traceback (most recent call last):s
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">TypeError</span>: Can<span style="background-color: #fff0f0">&#39;t convert &#39;</span><span style="color: #007020">bytes</span><span style="background-color: #fff0f0">&#39; object to str implicitly</span>
+</pre></div>
+
+
+<p><a id='print'></a>
+<br>
+<br></p>
+
+<h3>The print statement</h3>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<p>Very trivial, but this change makes sense, Python 3 now only accepts <code>print</code>s with proper parentheses - just like the other function calls ...</p>
+
+<p><br></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
+Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
+Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
+Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>
+    <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
+                        <span style="color: #333333">^</span>
+<span style="color: #FF0000; font-weight: bold">SyntaxError</span>: invalid syntax
+</pre></div>
+
+
+<p><br></p>
+
+<p>And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a <code>end=""</code> in Python 3:</p>
+
+<p><br></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&quot;line 1&quot;</span>, ; <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;same line&#39;</span>
+line <span style="color: #0000DD; font-weight: bold">1</span> same line
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&quot;line 1&quot;</span>, end<span style="color: #333333">=</span><span style="background-color: #fff0f0">&quot;&quot;</span>) ; <span style="color: #007020">print</span> (<span style="background-color: #fff0f0">&quot; same line&quot;</span>)
+line <span style="color: #0000DD; font-weight: bold">1</span> same line
+</pre></div>
+
+
+<p><a id='integer_div'></a>
+<br>
+<br></p>
+
+<h3>Integer division</h3>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<p>This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed.<br/>
+So, I still tend to use a <code>float(3)/2</code> or <code>3/2.0</code> instead of a <code>3/2</code> in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can <code>from __future__ import division</code> in your Python 2 scripts).</p>
+
+<p><br></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #0000DD; font-weight: bold">2</span>
+<span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #0000DD; font-weight: bold">2</span>
+<span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
+<span style="color: #6600EE; font-weight: bold">1.5</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
+<span style="color: #6600EE; font-weight: bold">1.0</span>
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #0000DD; font-weight: bold">2</span>
+<span style="color: #6600EE; font-weight: bold">1.5</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #0000DD; font-weight: bold">2</span>
+<span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
+<span style="color: #6600EE; font-weight: bold">1.5</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
+<span style="color: #6600EE; font-weight: bold">1.0</span>
+</pre></div>
+
+
+<p><a id='xrange'></a>
+<br>
+<br></p>
+
+<h3><code>xrange()</code></h3>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<p><code>xrange()</code> was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than <code>range()</code> (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch!<br/>
+In Python 3, the <code>range()</code> was implemented like the <code>xrange()</code> function so that a dedicated <code>xrange()</code> function does not exist anymore.</p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;</span> python <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in range(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
+<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #0000DD; font-weight: bold">66</span> msec per loop
+
+    <span style="color: #333333">&gt;</span> python <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in xrange(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
+<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #6600EE; font-weight: bold">27.8</span> msec per loop
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;</span> python3 <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in range(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
+<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #6600EE; font-weight: bold">51.1</span> msec per loop
+
+<span style="color: #333333">&gt;</span> python3 <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in xrange(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py&quot;</span>, line <span style="color: #0000DD; font-weight: bold">292</span>, <span style="color: #000000; font-weight: bold">in</span> main
+    x <span style="color: #333333">=</span> t<span style="color: #333333">.</span>timeit(number)
+  File <span style="background-color: #fff0f0">&quot;/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py&quot;</span>, line <span style="color: #0000DD; font-weight: bold">178</span>, <span style="color: #000000; font-weight: bold">in</span> timeit
+    timing <span style="color: #333333">=</span> <span style="color: #007020">self</span><span style="color: #333333">.</span>inner(it, <span style="color: #007020">self</span><span style="color: #333333">.</span>timer)
+  File <span style="background-color: #fff0f0">&quot;&lt;timeit-src&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">6</span>, <span style="color: #000000; font-weight: bold">in</span> inner
+    <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> xrange(<span style="color: #0000DD; font-weight: bold">1000000</span>):
+<span style="color: #FF0000; font-weight: bold">NameError</span>: name <span style="background-color: #fff0f0">&#39;xrange&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined
+</pre></div>
+
+
+<p><a id='raising_exceptions'></a>
+<br>
+<br></p>
+
+<h3>Raising exceptions</h3>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<p>Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a <code>SyntaxError</code> in turn) if we don't enclose the exception argument in parentheses:</p>
+
+<p><br></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">IOError</span>: file error
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>(<span style="background-color: #fff0f0">&quot;file error&quot;</span>)
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">IOError</span>: file error
+
+    
+<span style="color: #888888"># Python 3    </span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>
+    <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
+                 <span style="color: #333333">^</span>
+<span style="color: #FF0000; font-weight: bold">SyntaxError</span>: invalid syntax
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>(<span style="background-color: #fff0f0">&quot;file error&quot;</span>)
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">OSError</span>: file error
+</pre></div>
+
+
+<p><a id='handling_exceptions'></a>
+<br>
+<br></p>
+
+<h3>Handling exceptions</h3>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<p>Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the <code>as</code> keyword!</p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">try</span>:
+<span style="color: #333333">...</span>     blabla
+<span style="color: #333333">...</span> <span style="color: #008800; font-weight: bold">except</span> <span style="color: #FF0000; font-weight: bold">NameError</span>, err:
+<span style="color: #333333">...</span>     <span style="color: #007020">print</span> err, <span style="background-color: #fff0f0">&#39;--&gt; our error msg&#39;</span>
+<span style="color: #333333">...</span> 
+name <span style="background-color: #fff0f0">&#39;blabla&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined <span style="color: #333333">--&gt;</span> our error msg
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">try</span>:
+<span style="color: #333333">...</span>     blabla
+<span style="color: #333333">...</span> <span style="color: #008800; font-weight: bold">except</span> <span style="color: #FF0000; font-weight: bold">NameError</span> <span style="color: #008800; font-weight: bold">as</span> err:
+<span style="color: #333333">...</span>     <span style="color: #007020">print</span>(err, <span style="background-color: #fff0f0">&#39;--&gt; our error msg&#39;</span>)
+<span style="color: #333333">...</span> 
+name <span style="background-color: #fff0f0">&#39;blabla&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined <span style="color: #333333">--&gt;</span> our error msg
+</pre></div>
+
+
+<p><a id='next_next'></a>
+<br>
+<br></p>
+
+<h3>The <code>next()</code> function and <code>.next()</code> method</h3>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<p>Where you can use both function and method in Python 2.7.5, the <code>next()</code> function is all that remain in Python 3!</p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> my_generator <span style="color: #333333">=</span> (letter <span style="color: #008800; font-weight: bold">for</span> letter <span style="color: #000000; font-weight: bold">in</span> <span style="background-color: #fff0f0">&#39;abcdefg&#39;</span>)
+<span style="color: #333333">&gt;&gt;&gt;</span> my_generator<span style="color: #333333">.</span>next()
+<span style="background-color: #fff0f0">&#39;a&#39;</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">next</span>(my_generator)
+<span style="background-color: #fff0f0">&#39;b&#39;</span>
+
+<span style="color: #888888"># Python 3</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> my_generator <span style="color: #333333">=</span> (letter <span style="color: #008800; font-weight: bold">for</span> letter <span style="color: #000000; font-weight: bold">in</span> <span style="background-color: #fff0f0">&#39;abcdefg&#39;</span>)
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">next</span>(my_generator)
+<span style="background-color: #fff0f0">&#39;a&#39;</span>
+<span style="color: #333333">&gt;&gt;&gt;</span> my_generator<span style="color: #333333">.</span>next()
+Traceback (most recent call last):
+  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
+<span style="color: #FF0000; font-weight: bold">AttributeError</span>: <span style="background-color: #fff0f0">&#39;generator&#39;</span> <span style="color: #007020">object</span> has no attribute <span style="background-color: #fff0f0">&#39;next&#39;</span>
+</pre></div>
+
+
+<p><a id='loop_leak'></a>
+<br>
+<br></p>
+
+<h3>In Python 3.x for-loop variables don't leak into the global namespace anymore</h3>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<p>This goes back to a change that was made in Python 3.x and is described in <a href="https://docs.python.org/3/whatsnew/3.0.html">What’s New In Python 3.0</a> as follows:</p>
+
+<p>"List comprehensions no longer support the syntactic form <code>[... for var in item1, item2, ...]</code>. Use <code>[... for var in (item1, item2, ...)]</code> instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a <code>list()</code> constructor, and in particular the loop control variables are no longer leaked into the surrounding scope."</p>
+
+<p><br>
+<code>[In:]</code></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
+<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
+
+i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #007020">print</span>([i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)])
+<span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span>)
+</pre></div>
+
+
+<p><br>
+<code>[Out:]</code></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 3.3.5
+[0, 1, 2, 3, 4]
+1 -&gt; i in global
+</pre></div>
+
+
+<p><br>
+<br>
+<br>
+<code>[In:]</code></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
+<span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version()
+
+i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
+<span style="color: #007020">print</span> [i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)]
+<span style="color: #007020">print</span> i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span> 
+</pre></div>
+
+
+<p><br>
+<code>[Out:]</code></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 2.7.6
+[0, 1, 2, 3, 4]
+4 -&gt; i in global
+</pre></div>
+
+
+<p><a id='compare_unorder'></a>
+<br>
+<br></p>
+
+<h4>Python 3.x prevents us from comparing unorderable types</h4>
+
+<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+
+<p><br>
+<code>[In:]</code></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
+<span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version()
+
+<span style="color: #007020">print</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
+<span style="color: #007020">print</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
+<span style="color: #007020">print</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>)
+</pre></div>
+
+
+<p><br>
+<code>[Out:]</code></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 2.7.6
+False
+True
+False
+</pre></div>
+
+
+<p><br>
+<br>
+<br></p>
+
+<p><code>[In:]</code></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
+<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
+
+<span style="color: #007020">print</span>([<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>)
+<span style="color: #007020">print</span>((<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>)
+<span style="color: #007020">print</span>([<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>))
+</pre></div>
+
+
+<p><code>[Out:]</code></p>
+
+<!-- HTML generated using hilite.me -->
+
+
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed <span style="color: #000000; font-weight: bold">in</span> Python <span style="color: #6600EE; font-weight: bold">3.3</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">5</span>
+<span style="color: #333333">---------------------------------------------------------------------------</span>
+<span style="color: #FF0000; font-weight: bold">TypeError</span>                                 Traceback (most recent call last)
+<span style="color: #333333">&lt;</span>ipython<span style="color: #333333">-</span><span style="color: #007020">input</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">3</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">1</span>d774c677f73<span style="color: #333333">&gt;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>()
+      <span style="color: #0000DD; font-weight: bold">2</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
+      <span style="color: #0000DD; font-weight: bold">3</span> 
+<span style="color: #333333">----&gt;</span> <span style="color: #0000DD; font-weight: bold">4</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
+      <span style="color: #0000DD; font-weight: bold">5</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
+      <span style="color: #0000DD; font-weight: bold">6</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>)
+
+<span style="color: #FF0000; font-weight: bold">TypeError</span>: unorderable types: <span style="color: #007020">list</span>() <span style="color: #333333">&gt;</span> <span style="color: #007020">str</span>()
+</pre></div>
+
+</body>
+</html>
\ No newline at end of file

From f1ee8fa1ec0d51386e3b1def9f168b76158b686d Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 May 2014 12:06:51 -0400
Subject: [PATCH 072/248] html version

---
 .../key_differences_between_python_2_and_3.html | 17 +++++++++++------
 .../key_differences_between_python_2_and_3.md   |  5 +++++
 2 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index 431d7c9..34cc83a 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -171,12 +171,17 @@ <h2>Key differences between Python 2 and 3</h2>
 
 <p><br></p>
 
-<p>There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,<br/>
-- <a href="https://wiki.python.org/moin/Python2orPython3">https://wiki.python.org/moin/Python2orPython3</a>
-- <a href="https://docs.python.org/3.0/whatsnew/3.0.html">https://docs.python.org/3.0/whatsnew/3.0.html</a>
-- <a href="http://python3porting.com/differences.html">http://python3porting.com/differences.html</a>
-- <a href="https://docs.python.org/3/howto/pyporting.html">https://docs.python.org/3/howto/pyporting.html</a><br/>
-etc.</p>
+<p>There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,</p>
+
+<ul>
+<li><p><a href="https://wiki.python.org/moin/Python2orPython3">https://wiki.python.org/moin/Python2orPython3</a></p></li>
+<li><p><a href="https://docs.python.org/3.0/whatsnew/3.0.html">https://docs.python.org/3.0/whatsnew/3.0.html</a></p></li>
+<li><p><a href="http://python3porting.com/differences.html">http://python3porting.com/differences.html</a></p></li>
+<li><p><a href="https://docs.python.org/3/howto/pyporting.html">https://docs.python.org/3/howto/pyporting.html</a></p></li>
+</ul>
+
+
+<p>etc.</p>
 
 <p>But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!
 (Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)</p>
diff --git a/tutorials/key_differences_between_python_2_and_3.md b/tutorials/key_differences_between_python_2_and_3.md
index d7a7d78..972e667 100644
--- a/tutorials/key_differences_between_python_2_and_3.md
+++ b/tutorials/key_differences_between_python_2_and_3.md
@@ -13,10 +13,15 @@ last updated: 05/24/2014
 <br>
 
 There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,  
+
 - [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)
+
 - [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)
+
 - [http://python3porting.com/differences.html](http://python3porting.com/differences.html)
+
 - [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html)  
+
 etc.
 
 But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!

From 5c4a2e1b3ebf2de9efef32bb4f4924e6ade0d703 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 May 2014 19:48:52 -0400
Subject: [PATCH 073/248] completely overworked key differences between python
 2 and 3

---
 README.md                                     |    2 +-
 ...ey_differences_between_python_2_and_3.html | 3774 ++++++++++++++---
 ...y_differences_between_python_2_and_3.ipynb | 1301 ++++++
 3 files changed, 4577 insertions(+), 500 deletions(-)
 create mode 100644 tutorials/key_differences_between_python_2_and_3.ipynb

diff --git a/README.md b/README.md
index feebdc1..672788f 100755
--- a/README.md
+++ b/README.md
@@ -21,7 +21,7 @@ A collection of useful scripts, tutorials, and other Python-related things
 
 - Python's scope resolution for variable names and the LEGB rule [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)]
 
-- Key differences between Python 2.x and Python 3.x [[HTML](http://htmlpreview.github.io/?https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.html)] 
+- Key differences between Python 2.x and Python 3.x [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1)]
 
 
 - A thorough guide to SQLite database operations in Python [[Markdown](./sqlite3_howto/README.md)]  
diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index 34cc83a..fca0c8f 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -1,637 +1,3413 @@
-<!doctype html>
+<!DOCTYPE html>
 <html>
 <head>
-<meta charset="utf-8">
-<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
-<style>
-h1,
-h2,
-h3,
-h4,
-h5,
-h6,
-p,
-blockquote {
-    margin: 0;
-    padding: 0;
-}
+
+<meta charset="utf-8" />
+<title>Notebook</title>
+
+<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
+
+<style type="text/css">
+    .clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0}
+.clearfix:after{clear:both}
+.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}
+.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
+article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}
+audio,canvas,video{display:inline-block;*display:inline;*zoom:1}
+audio:not([controls]){display:none}
+html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}
+a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
+a:hover,a:active{outline:0}
+sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}
+sup{top:-0.5em}
+sub{bottom:-0.25em}
+img{max-width:100%;width:auto\9;height:auto;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}
+#map_canvas img,.google-maps img{max-width:none}
+button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}
+button,input{*overflow:visible;line-height:normal}
+button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}
+button,html input[type="button"],input[type="reset"],input[type="submit"]{-webkit-appearance:button;cursor:pointer}
+label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer}
+input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}
+input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}
+textarea{overflow:auto;vertical-align:top}
+@media print{*{text-shadow:none !important;color:#000 !important;background:transparent !important;box-shadow:none !important} a,a:visited{text-decoration:underline} a[href]:after{content:" (" attr(href) ")"} abbr[title]:after{content:" (" attr(title) ")"} .ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""} pre,blockquote{border:1px solid #999;page-break-inside:avoid} thead{display:table-header-group} tr,img{page-break-inside:avoid} img{max-width:100% !important} @page {margin:.5cm}p,h2,h3{orphans:3;widows:3} h2,h3{page-break-after:avoid}}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;line-height:20px;color:#000;background-color:#fff}
+a{color:#08c;text-decoration:none}
+a:hover,a:focus{color:#005580;text-decoration:underline}
+.img-rounded{border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px rgba(0,0,0,0.1)}
+.img-circle{border-radius:500px;-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px}
+.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0}
+.row:after{clear:both}
+[class*="span"]{float:left;min-height:1px;margin-left:20px}
+.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}
+.span12{width:940px}
+.span11{width:860px}
+.span10{width:780px}
+.span9{width:700px}
+.span8{width:620px}
+.span7{width:540px}
+.span6{width:460px}
+.span5{width:380px}
+.span4{width:300px}
+.span3{width:220px}
+.span2{width:140px}
+.span1{width:60px}
+.offset12{margin-left:980px}
+.offset11{margin-left:900px}
+.offset10{margin-left:820px}
+.offset9{margin-left:740px}
+.offset8{margin-left:660px}
+.offset7{margin-left:580px}
+.offset6{margin-left:500px}
+.offset5{margin-left:420px}
+.offset4{margin-left:340px}
+.offset3{margin-left:260px}
+.offset2{margin-left:180px}
+.offset1{margin-left:100px}
+.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0}
+.row-fluid:after{clear:both}
+.row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.127659574468085%;*margin-left:2.074468085106383%}
+.row-fluid [class*="span"]:first-child{margin-left:0}
+.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%}
+.row-fluid .span12{width:100%;*width:99.94680851063829%}
+.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%}
+.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%}
+.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%}
+.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%}
+.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%}
+.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%}
+.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%}
+.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%}
+.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%}
+.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%}
+.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%}
+.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%}
+.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%}
+.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%}
+.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%}
+.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%}
+.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%}
+.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%}
+.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%}
+.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%}
+.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%}
+.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%}
+.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%}
+.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%}
+.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%}
+.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%}
+.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%}
+.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%}
+.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%}
+.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%}
+.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%}
+.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%}
+.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%}
+.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%}
+.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%}
+[class*="span"].hide,.row-fluid [class*="span"].hide{display:none}
+[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right}
+.container{margin-right:auto;margin-left:auto;*zoom:1}.container:before,.container:after{display:table;content:"";line-height:0}
+.container:after{clear:both}
+.container-fluid{padding-right:20px;padding-left:20px;*zoom:1}.container-fluid:before,.container-fluid:after{display:table;content:"";line-height:0}
+.container-fluid:after{clear:both}
+p{margin:0 0 10px}
+.lead{margin-bottom:20px;font-size:19.5px;font-weight:200;line-height:30px}
+small{font-size:85%}
+strong{font-weight:bold}
+em{font-style:italic}
+cite{font-style:normal}
+.muted{color:#999}
+a.muted:hover,a.muted:focus{color:#808080}
+.text-warning{color:#c09853}
+a.text-warning:hover,a.text-warning:focus{color:#a47e3c}
+.text-error{color:#b94a48}
+a.text-error:hover,a.text-error:focus{color:#953b39}
+.text-info{color:#3a87ad}
+a.text-info:hover,a.text-info:focus{color:#2d6987}
+.text-success{color:#468847}
+a.text-success:hover,a.text-success:focus{color:#356635}
+.text-left{text-align:left}
+.text-right{text-align:right}
+.text-center{text-align:center}
+h1,h2,h3,h4,h5,h6{margin:10px 0;font-family:inherit;font-weight:bold;line-height:20px;color:inherit;text-rendering:optimizelegibility}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#999}
+h1,h2,h3{line-height:40px}
+h1{font-size:35.75px}
+h2{font-size:29.25px}
+h3{font-size:22.75px}
+h4{font-size:16.25px}
+h5{font-size:13px}
+h6{font-size:11.049999999999999px}
+h1 small{font-size:22.75px}
+h2 small{font-size:16.25px}
+h3 small{font-size:13px}
+h4 small{font-size:13px}
+.page-header{padding-bottom:9px;margin:20px 0 30px;border-bottom:1px solid #eee}
+ul,ol{padding:0;margin:0 0 10px 25px}
+ul ul,ul ol,ol ol,ol ul{margin-bottom:0}
+li{line-height:20px}
+ul.unstyled,ol.unstyled{margin-left:0;list-style:none}
+ul.inline,ol.inline{margin-left:0;list-style:none}ul.inline>li,ol.inline>li{display:inline-block;*display:inline;*zoom:1;padding-left:5px;padding-right:5px}
+dl{margin-bottom:20px}
+dt,dd{line-height:20px}
+dt{font-weight:bold}
+dd{margin-left:10px}
+.dl-horizontal{*zoom:1}.dl-horizontal:before,.dl-horizontal:after{display:table;content:"";line-height:0}
+.dl-horizontal:after{clear:both}
+.dl-horizontal dt{float:left;width:160px;clear:left;text-align:right;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}
+.dl-horizontal dd{margin-left:180px}
+hr{margin:20px 0;border:0;border-top:1px solid #eee;border-bottom:1px solid #fff}
+abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #999}
+abbr.initialism{font-size:90%;text-transform:uppercase}
+blockquote{padding:0 0 0 15px;margin:0 0 20px;border-left:5px solid #eee}blockquote p{margin-bottom:0;font-size:16.25px;font-weight:300;line-height:1.25}
+blockquote small{display:block;line-height:20px;color:#999}blockquote small:before{content:'\2014 \00A0'}
+blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eee;border-left:0}blockquote.pull-right p,blockquote.pull-right small{text-align:right}
+blockquote.pull-right small:before{content:''}
+blockquote.pull-right small:after{content:'\00A0 \2014'}
+q:before,q:after,blockquote:before,blockquote:after{content:""}
+address{display:block;margin-bottom:20px;font-style:normal;line-height:20px}
+code,pre{padding:0 3px 2px;font-family:monospace;font-size:11px;color:#333;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+code{padding:2px 4px;color:#d14;background-color:#f7f7f9;border:1px solid #e1e1e8;white-space:nowrap}
+pre{display:block;padding:9.5px;margin:0 0 10px;font-size:12px;line-height:20px;word-break:break-all;word-wrap:break-word;white-space:pre;white-space:pre-wrap;background-color:#f5f5f5;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.15);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}pre.prettyprint{margin-bottom:20px}
+pre code{padding:0;color:inherit;white-space:pre;white-space:pre-wrap;background-color:transparent;border:0}
+.pre-scrollable{max-height:340px;overflow-y:scroll}
+form{margin:0 0 20px}
+fieldset{padding:0;margin:0;border:0}
+legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:19.5px;line-height:40px;color:#333;border:0;border-bottom:1px solid #e5e5e5}legend small{font-size:15px;color:#999}
+label,input,button,select,textarea{font-size:13px;font-weight:normal;line-height:20px}
+input,button,select,textarea{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif}
+label{display:block;margin-bottom:5px}
+select,textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{display:inline-block;height:20px;padding:4px 6px;margin-bottom:10px;font-size:13px;line-height:20px;color:#555;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;vertical-align:middle}
+input,textarea,.uneditable-input{width:206px}
+textarea{height:auto}
+textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{background-color:#fff;border:1px solid #ccc;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-webkit-transition:border linear .2s, box-shadow linear .2s;-moz-transition:border linear .2s, box-shadow linear .2s;-o-transition:border linear .2s, box-shadow linear .2s;transition:border linear .2s, box-shadow linear .2s}textarea:focus,input[type="text"]:focus,input[type="password"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="date"]:focus,input[type="month"]:focus,input[type="time"]:focus,input[type="week"]:focus,input[type="number"]:focus,input[type="email"]:focus,input[type="url"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="color"]:focus,.uneditable-input:focus{border-color:rgba(82,168,236,0.8);outline:0;outline:thin dotted \9;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6)}
+input[type="radio"],input[type="checkbox"]{margin:4px 0 0;*margin-top:0;margin-top:1px \9;line-height:normal}
+input[type="file"],input[type="image"],input[type="submit"],input[type="reset"],input[type="button"],input[type="radio"],input[type="checkbox"]{width:auto}
+select,input[type="file"]{height:30px;*margin-top:4px;line-height:30px}
+select{width:220px;border:1px solid #ccc;background-color:#fff}
+select[multiple],select[size]{height:auto}
+select:focus,input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
+.uneditable-input,.uneditable-textarea{color:#999;background-color:#fcfcfc;border-color:#ccc;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);cursor:not-allowed}
+.uneditable-input{overflow:hidden;white-space:nowrap}
+.uneditable-textarea{width:auto;height:auto}
+input:-moz-placeholder,textarea:-moz-placeholder{color:#999}
+input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999}
+input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999}
+.radio,.checkbox{min-height:20px;padding-left:20px}
+.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px}
+.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px}
+.radio.inline,.checkbox.inline{display:inline-block;padding-top:5px;margin-bottom:0;vertical-align:middle}
+.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px}
+.input-mini{width:60px}
+.input-small{width:90px}
+.input-medium{width:150px}
+.input-large{width:210px}
+.input-xlarge{width:270px}
+.input-xxlarge{width:530px}
+input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"]{float:none;margin-left:0}
+.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"]{display:inline-block}
+input,textarea,.uneditable-input{margin-left:0}
+.controls-row [class*="span"]+[class*="span"]{margin-left:20px}
+input.span12,textarea.span12,.uneditable-input.span12{width:926px}
+input.span11,textarea.span11,.uneditable-input.span11{width:846px}
+input.span10,textarea.span10,.uneditable-input.span10{width:766px}
+input.span9,textarea.span9,.uneditable-input.span9{width:686px}
+input.span8,textarea.span8,.uneditable-input.span8{width:606px}
+input.span7,textarea.span7,.uneditable-input.span7{width:526px}
+input.span6,textarea.span6,.uneditable-input.span6{width:446px}
+input.span5,textarea.span5,.uneditable-input.span5{width:366px}
+input.span4,textarea.span4,.uneditable-input.span4{width:286px}
+input.span3,textarea.span3,.uneditable-input.span3{width:206px}
+input.span2,textarea.span2,.uneditable-input.span2{width:126px}
+input.span1,textarea.span1,.uneditable-input.span1{width:46px}
+.controls-row{*zoom:1}.controls-row:before,.controls-row:after{display:table;content:"";line-height:0}
+.controls-row:after{clear:both}
+.controls-row [class*="span"],.row-fluid .controls-row [class*="span"]{float:left}
+.controls-row .checkbox[class*="span"],.controls-row .radio[class*="span"]{padding-top:5px}
+input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly]{cursor:not-allowed;background-color:#eee}
+input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly]{background-color:transparent}
+.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#c09853}
+.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#c09853}
+.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#c09853;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#a47e3c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e}
+.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#c09853;background-color:#fcf8e3;border-color:#c09853}
+.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#b94a48}
+.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#b94a48}
+.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#b94a48;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#953b39;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392}
+.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#b94a48;background-color:#f2dede;border-color:#b94a48}
+.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#468847}
+.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#468847}
+.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#468847;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#356635;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b}
+.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847}
+.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad}
+.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad}
+.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3}
+.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3a87ad;background-color:#d9edf7;border-color:#3a87ad}
+input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}
+.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;content:"";line-height:0}
+.form-actions:after{clear:both}
+.help-block,.help-inline{color:#262626}
+.help-block{display:block;margin-bottom:10px}
+.help-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle;padding-left:5px}
+.input-append,.input-prepend{display:inline-block;margin-bottom:10px;vertical-align:middle;font-size:0;white-space:nowrap}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input,.input-append .dropdown-menu,.input-prepend .dropdown-menu,.input-append .popover,.input-prepend .popover{font-size:13px}
+.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input{position:relative;margin-bottom:0;*margin-left:0;vertical-align:top;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append input:focus,.input-prepend input:focus,.input-append select:focus,.input-prepend select:focus,.input-append .uneditable-input:focus,.input-prepend .uneditable-input:focus{z-index:2}
+.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:13px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}
+.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546}
+.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}
+.input-prepend .add-on:first-child,.input-prepend .btn:first-child{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
+.input-append input,.input-append select,.input-append .uneditable-input{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}
+.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
+.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.input-prepend.input-append .btn-group:first-child{margin-left:0}
+input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
+.form-search .input-append .search-query,.form-search .input-prepend .search-query{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.form-search .input-append .search-query{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
+.form-search .input-append .btn{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
+.form-search .input-prepend .search-query{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
+.form-search .input-prepend .btn{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
+.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;*zoom:1;margin-bottom:0;vertical-align:middle}
+.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}
+.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}
+.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}
+.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}
+.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}
+.control-group{margin-bottom:10px}
+legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}
+.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0}
+.form-horizontal .control-group:after{clear:both}
+.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}
+.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}
+.form-horizontal .help-block{margin-bottom:0}
+.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px}
+.form-horizontal .form-actions{padding-left:180px}
+table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}
+.table{width:100%;margin-bottom:20px}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #ddd}
+.table th{font-weight:bold}
+.table thead th{vertical-align:bottom}
+.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}
+.table tbody+tbody{border-top:2px solid #ddd}
+.table .table{background-color:#fff}
+.table-condensed th,.table-condensed td{padding:4px 5px}
+.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}
+.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}
+.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
+.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
+.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
+.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
+.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;-moz-border-radius-bottomleft:0;border-bottom-left-radius:0}
+.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;-moz-border-radius-bottomright:0;border-bottom-right-radius:0}
+.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
+.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
+.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}
+.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}
+table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}
+.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}
+.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}
+.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}
+.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}
+.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}
+.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}
+.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}
+.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}
+.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}
+.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}
+.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}
+.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}
+.table tbody tr.success>td{background-color:#dff0d8}
+.table tbody tr.error>td{background-color:#f2dede}
+.table tbody tr.warning>td{background-color:#fcf8e3}
+.table tbody tr.info>td{background-color:#d9edf7}
+.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}
+.table-hover tbody tr.error:hover>td{background-color:#ebcccc}
+.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}
+.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}
+[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat;margin-top:1px}
+.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}
+.icon-glass{background-position:0 0}
+.icon-music{background-position:-24px 0}
+.icon-search{background-position:-48px 0}
+.icon-envelope{background-position:-72px 0}
+.icon-heart{background-position:-96px 0}
+.icon-star{background-position:-120px 0}
+.icon-star-empty{background-position:-144px 0}
+.icon-user{background-position:-168px 0}
+.icon-film{background-position:-192px 0}
+.icon-th-large{background-position:-216px 0}
+.icon-th{background-position:-240px 0}
+.icon-th-list{background-position:-264px 0}
+.icon-ok{background-position:-288px 0}
+.icon-remove{background-position:-312px 0}
+.icon-zoom-in{background-position:-336px 0}
+.icon-zoom-out{background-position:-360px 0}
+.icon-off{background-position:-384px 0}
+.icon-signal{background-position:-408px 0}
+.icon-cog{background-position:-432px 0}
+.icon-trash{background-position:-456px 0}
+.icon-home{background-position:0 -24px}
+.icon-file{background-position:-24px -24px}
+.icon-time{background-position:-48px -24px}
+.icon-road{background-position:-72px -24px}
+.icon-download-alt{background-position:-96px -24px}
+.icon-download{background-position:-120px -24px}
+.icon-upload{background-position:-144px -24px}
+.icon-inbox{background-position:-168px -24px}
+.icon-play-circle{background-position:-192px -24px}
+.icon-repeat{background-position:-216px -24px}
+.icon-refresh{background-position:-240px -24px}
+.icon-list-alt{background-position:-264px -24px}
+.icon-lock{background-position:-287px -24px}
+.icon-flag{background-position:-312px -24px}
+.icon-headphones{background-position:-336px -24px}
+.icon-volume-off{background-position:-360px -24px}
+.icon-volume-down{background-position:-384px -24px}
+.icon-volume-up{background-position:-408px -24px}
+.icon-qrcode{background-position:-432px -24px}
+.icon-barcode{background-position:-456px -24px}
+.icon-tag{background-position:0 -48px}
+.icon-tags{background-position:-25px -48px}
+.icon-book{background-position:-48px -48px}
+.icon-bookmark{background-position:-72px -48px}
+.icon-print{background-position:-96px -48px}
+.icon-camera{background-position:-120px -48px}
+.icon-font{background-position:-144px -48px}
+.icon-bold{background-position:-167px -48px}
+.icon-italic{background-position:-192px -48px}
+.icon-text-height{background-position:-216px -48px}
+.icon-text-width{background-position:-240px -48px}
+.icon-align-left{background-position:-264px -48px}
+.icon-align-center{background-position:-288px -48px}
+.icon-align-right{background-position:-312px -48px}
+.icon-align-justify{background-position:-336px -48px}
+.icon-list{background-position:-360px -48px}
+.icon-indent-left{background-position:-384px -48px}
+.icon-indent-right{background-position:-408px -48px}
+.icon-facetime-video{background-position:-432px -48px}
+.icon-picture{background-position:-456px -48px}
+.icon-pencil{background-position:0 -72px}
+.icon-map-marker{background-position:-24px -72px}
+.icon-adjust{background-position:-48px -72px}
+.icon-tint{background-position:-72px -72px}
+.icon-edit{background-position:-96px -72px}
+.icon-share{background-position:-120px -72px}
+.icon-check{background-position:-144px -72px}
+.icon-move{background-position:-168px -72px}
+.icon-step-backward{background-position:-192px -72px}
+.icon-fast-backward{background-position:-216px -72px}
+.icon-backward{background-position:-240px -72px}
+.icon-play{background-position:-264px -72px}
+.icon-pause{background-position:-288px -72px}
+.icon-stop{background-position:-312px -72px}
+.icon-forward{background-position:-336px -72px}
+.icon-fast-forward{background-position:-360px -72px}
+.icon-step-forward{background-position:-384px -72px}
+.icon-eject{background-position:-408px -72px}
+.icon-chevron-left{background-position:-432px -72px}
+.icon-chevron-right{background-position:-456px -72px}
+.icon-plus-sign{background-position:0 -96px}
+.icon-minus-sign{background-position:-24px -96px}
+.icon-remove-sign{background-position:-48px -96px}
+.icon-ok-sign{background-position:-72px -96px}
+.icon-question-sign{background-position:-96px -96px}
+.icon-info-sign{background-position:-120px -96px}
+.icon-screenshot{background-position:-144px -96px}
+.icon-remove-circle{background-position:-168px -96px}
+.icon-ok-circle{background-position:-192px -96px}
+.icon-ban-circle{background-position:-216px -96px}
+.icon-arrow-left{background-position:-240px -96px}
+.icon-arrow-right{background-position:-264px -96px}
+.icon-arrow-up{background-position:-289px -96px}
+.icon-arrow-down{background-position:-312px -96px}
+.icon-share-alt{background-position:-336px -96px}
+.icon-resize-full{background-position:-360px -96px}
+.icon-resize-small{background-position:-384px -96px}
+.icon-plus{background-position:-408px -96px}
+.icon-minus{background-position:-433px -96px}
+.icon-asterisk{background-position:-456px -96px}
+.icon-exclamation-sign{background-position:0 -120px}
+.icon-gift{background-position:-24px -120px}
+.icon-leaf{background-position:-48px -120px}
+.icon-fire{background-position:-72px -120px}
+.icon-eye-open{background-position:-96px -120px}
+.icon-eye-close{background-position:-120px -120px}
+.icon-warning-sign{background-position:-144px -120px}
+.icon-plane{background-position:-168px -120px}
+.icon-calendar{background-position:-192px -120px}
+.icon-random{background-position:-216px -120px;width:16px}
+.icon-comment{background-position:-240px -120px}
+.icon-magnet{background-position:-264px -120px}
+.icon-chevron-up{background-position:-288px -120px}
+.icon-chevron-down{background-position:-313px -119px}
+.icon-retweet{background-position:-336px -120px}
+.icon-shopping-cart{background-position:-360px -120px}
+.icon-folder-close{background-position:-384px -120px;width:16px}
+.icon-folder-open{background-position:-408px -120px;width:16px}
+.icon-resize-vertical{background-position:-432px -119px}
+.icon-resize-horizontal{background-position:-456px -118px}
+.icon-hdd{background-position:0 -144px}
+.icon-bullhorn{background-position:-24px -144px}
+.icon-bell{background-position:-48px -144px}
+.icon-certificate{background-position:-72px -144px}
+.icon-thumbs-up{background-position:-96px -144px}
+.icon-thumbs-down{background-position:-120px -144px}
+.icon-hand-right{background-position:-144px -144px}
+.icon-hand-left{background-position:-168px -144px}
+.icon-hand-up{background-position:-192px -144px}
+.icon-hand-down{background-position:-216px -144px}
+.icon-circle-arrow-right{background-position:-240px -144px}
+.icon-circle-arrow-left{background-position:-264px -144px}
+.icon-circle-arrow-up{background-position:-288px -144px}
+.icon-circle-arrow-down{background-position:-312px -144px}
+.icon-globe{background-position:-336px -144px}
+.icon-wrench{background-position:-360px -144px}
+.icon-tasks{background-position:-384px -144px}
+.icon-filter{background-position:-408px -144px}
+.icon-briefcase{background-position:-432px -144px}
+.icon-fullscreen{background-position:-456px -144px}
+.dropup,.dropdown{position:relative}
+.dropdown-toggle{*margin-bottom:-3px}
+.dropdown-toggle:active,.open .dropdown-toggle{outline:0}
+.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}
+.dropdown .caret{margin-top:8px;margin-left:2px}
+.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}
+.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
+.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}
+.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{text-decoration:none;color:#fff;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
+.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;outline:0;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
+.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}
+.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:default}
+.open{*z-index:1000}.open>.dropdown-menu{display:block}
+.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990}
+.pull-right>.dropdown-menu{right:0;left:auto}
+.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}
+.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}
+.dropdown-submenu{position:relative}
+.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;border-radius:0 6px 6px 6px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}
+.dropdown-submenu:hover>.dropdown-menu{display:block}
+.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;border-radius:5px 5px 5px 0;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}
+.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}
+.dropdown-submenu:hover>a:after{border-left-color:#fff}
+.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
+.dropdown .dropdown-menu .nav-header{padding-left:20px;padding-right:20px}
+.typeahead{z-index:1051;margin-top:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}
+.well-large{padding:24px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.well-small{padding:9px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}
+.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}
+.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}
+button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}
+.btn{display:inline-block;*display:inline;*zoom:1;padding:4px 12px;margin-bottom:0;font-size:13px;line-height:20px;text-align:center;vertical-align:middle;cursor:pointer;color:#333;text-shadow:0 1px 1px rgba(255,255,255,0.75);background-color:#f5f5f5;background-image:-moz-linear-gradient(top, #fff, #e6e6e6);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#e6e6e6));background-image:-webkit-linear-gradient(top, #fff, #e6e6e6);background-image:-o-linear-gradient(top, #fff, #e6e6e6);background-image:linear-gradient(to bottom, #fff, #e6e6e6);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e6e6e6;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);border:1px solid #ccc;*border:0;border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*margin-left:.3em;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}
+.btn:active,.btn.active{background-color:#ccc \9}
+.btn:first-child{*margin-left:0}
+.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}
+.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
+.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
+.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
+.btn-large{padding:11px 19px;font-size:16.25px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}
+.btn-small{padding:2px 10px;font-size:11.049999999999999px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}
+.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}
+.btn-mini{padding:0 6px;font-size:9.75px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.btn-block{display:block;width:100%;padding-left:0;padding-right:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
+.btn-block+.btn-block{margin-top:5px}
+input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}
+.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}
+.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;background-image:-moz-linear-gradient(top, #08c, #04c);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#04c));background-image:-webkit-linear-gradient(top, #08c, #04c);background-image:-o-linear-gradient(top, #08c, #04c);background-image:linear-gradient(to bottom, #08c, #04c);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#04c;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}
+.btn-primary:active,.btn-primary.active{background-color:#039 \9}
+.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#f89406;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}
+.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}
+.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;background-image:-moz-linear-gradient(top, #ee5f5b, #bd362f);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));background-image:-webkit-linear-gradient(top, #ee5f5b, #bd362f);background-image:-o-linear-gradient(top, #ee5f5b, #bd362f);background-image:linear-gradient(to bottom, #ee5f5b, #bd362f);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#bd362f;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}
+.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}
+.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;background-image:-moz-linear-gradient(top, #62c462, #51a351);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));background-image:-webkit-linear-gradient(top, #62c462, #51a351);background-image:-o-linear-gradient(top, #62c462, #51a351);background-image:linear-gradient(to bottom, #62c462, #51a351);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#51a351;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}
+.btn-success:active,.btn-success.active{background-color:#408140 \9}
+.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;background-image:-moz-linear-gradient(top, #5bc0de, #2f96b4);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));background-image:-webkit-linear-gradient(top, #5bc0de, #2f96b4);background-image:-o-linear-gradient(top, #5bc0de, #2f96b4);background-image:linear-gradient(to bottom, #5bc0de, #2f96b4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#2f96b4;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}
+.btn-info:active,.btn-info.active{background-color:#24748c \9}
+.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;background-image:-moz-linear-gradient(top, #444, #222);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#444), to(#222));background-image:-webkit-linear-gradient(top, #444, #222);background-image:-o-linear-gradient(top, #444, #222);background-image:linear-gradient(to bottom, #444, #222);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#222;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}
+.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}
+button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}
+button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}
+button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}
+button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}
+.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
+.btn-link{border-color:transparent;cursor:pointer;color:#08c;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}
+.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}
+.btn-group{position:relative;display:inline-block;*display:inline;*zoom:1;font-size:0;vertical-align:middle;white-space:nowrap;*margin-left:.3em}.btn-group:first-child{*margin-left:0}
+.btn-group+.btn-group{margin-left:5px}
+.btn-toolbar{font-size:0;margin-top:10px;margin-bottom:10px}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}
+.btn-group>.btn{position:relative;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.btn-group>.btn+.btn{margin-left:-1px}
+.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:13px}
+.btn-group>.btn-mini{font-size:9.75px}
+.btn-group>.btn-small{font-size:11.049999999999999px}
+.btn-group>.btn-large{font-size:16.25px}
+.btn-group>.btn:first-child{margin-left:0;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
+.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
+.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
+.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
+.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}
+.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}
+.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);*padding-top:5px;*padding-bottom:5px}
+.btn-group>.btn-mini+.dropdown-toggle{padding-left:5px;padding-right:5px;*padding-top:2px;*padding-bottom:2px}
+.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}
+.btn-group>.btn-large+.dropdown-toggle{padding-left:12px;padding-right:12px;*padding-top:7px;*padding-bottom:7px}
+.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
+.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}
+.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}
+.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}
+.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}
+.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}
+.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}
+.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}
+.btn .caret{margin-top:8px;margin-left:0}
+.btn-large .caret{margin-top:6px}
+.btn-large .caret{border-left-width:5px;border-right-width:5px;border-top-width:5px}
+.btn-mini .caret,.btn-small .caret{margin-top:8px}
+.dropup .btn-large .caret{border-bottom-width:5px}
+.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}
+.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}
+.btn-group-vertical>.btn{display:block;float:none;max-width:100%;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.btn-group-vertical>.btn+.btn{margin-left:0;margin-top:-1px}
+.btn-group-vertical>.btn:first-child{border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}
+.btn-group-vertical>.btn:last-child{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}
+.btn-group-vertical>.btn-large:first-child{border-radius:6px 6px 0 0;-webkit-border-radius:6px 6px 0 0;-moz-border-radius:6px 6px 0 0;border-radius:6px 6px 0 0}
+.btn-group-vertical>.btn-large:last-child{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
+.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.alert,.alert h4{color:#c09853}
+.alert h4{margin:0}
+.alert .close{position:relative;top:-2px;right:-21px;line-height:20px}
+.alert-success{background-color:#dff0d8;border-color:#d6e9c6;color:#468847}
+.alert-success h4{color:#468847}
+.alert-danger,.alert-error{background-color:#f2dede;border-color:#eed3d7;color:#b94a48}
+.alert-danger h4,.alert-error h4{color:#b94a48}
+.alert-info{background-color:#d9edf7;border-color:#bce8f1;color:#3a87ad}
+.alert-info h4{color:#3a87ad}
+.alert-block{padding-top:14px;padding-bottom:14px}
+.alert-block>p,.alert-block>ul{margin-bottom:0}
+.alert-block p+p{margin-top:5px}
+.nav{margin-left:0;margin-bottom:20px;list-style:none}
+.nav>li>a{display:block}
+.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eee}
+.nav>li>a>img{max-width:none}
+.nav>.pull-right{float:right}
+.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:20px;color:#999;text-shadow:0 1px 0 rgba(255,255,255,0.5);text-transform:uppercase}
+.nav li+.nav-header{margin-top:9px}
+.nav-list{padding-left:15px;padding-right:15px;margin-bottom:0}
+.nav-list>li>a,.nav-list .nav-header{margin-left:-15px;margin-right:-15px;text-shadow:0 1px 0 rgba(255,255,255,0.5)}
+.nav-list>li>a{padding:3px 15px}
+.nav-list>.active>a,.nav-list>.active>a:hover,.nav-list>.active>a:focus{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.2);background-color:#08c}
+.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{margin-right:2px}
+.nav-list .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
+.nav-tabs,.nav-pills{*zoom:1}.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;content:"";line-height:0}
+.nav-tabs:after,.nav-pills:after{clear:both}
+.nav-tabs>li,.nav-pills>li{float:left}
+.nav-tabs>li>a,.nav-pills>li>a{padding-right:12px;padding-left:12px;margin-right:2px;line-height:14px}
+.nav-tabs{border-bottom:1px solid #ddd}
+.nav-tabs>li{margin-bottom:-1px}
+.nav-tabs>li>a{padding-top:8px;padding-bottom:8px;line-height:20px;border:1px solid transparent;border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover,.nav-tabs>li>a:focus{border-color:#eee #eee #ddd}
+.nav-tabs>.active>a,.nav-tabs>.active>a:hover,.nav-tabs>.active>a:focus{color:#555;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent;cursor:default}
+.nav-pills>li>a{padding-top:8px;padding-bottom:8px;margin-top:2px;margin-bottom:2px;border-radius:5px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}
+.nav-pills>.active>a,.nav-pills>.active>a:hover,.nav-pills>.active>a:focus{color:#fff;background-color:#08c}
+.nav-stacked>li{float:none}
+.nav-stacked>li>a{margin-right:0}
+.nav-tabs.nav-stacked{border-bottom:0}
+.nav-tabs.nav-stacked>li>a{border:1px solid #ddd;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.nav-tabs.nav-stacked>li:first-child>a{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
+.nav-tabs.nav-stacked>li:last-child>a{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
+.nav-tabs.nav-stacked>li>a:hover,.nav-tabs.nav-stacked>li>a:focus{border-color:#ddd;z-index:2}
+.nav-pills.nav-stacked>li>a{margin-bottom:3px}
+.nav-pills.nav-stacked>li:last-child>a{margin-bottom:1px}
+.nav-tabs .dropdown-menu{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
+.nav-pills .dropdown-menu{border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.nav .dropdown-toggle .caret{border-top-color:#08c;border-bottom-color:#08c;margin-top:6px}
+.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#005580;border-bottom-color:#005580}
+.nav-tabs .dropdown-toggle .caret{margin-top:8px}
+.nav .active .dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
+.nav-tabs .active .dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}
+.nav>.dropdown.active>a:hover,.nav>.dropdown.active>a:focus{cursor:pointer}
+.nav-tabs .open .dropdown-toggle,.nav-pills .open .dropdown-toggle,.nav>li.dropdown.open.active>a:hover,.nav>li.dropdown.open.active>a:focus{color:#fff;background-color:#999;border-color:#999}
+.nav li.dropdown.open .caret,.nav li.dropdown.open.active .caret,.nav li.dropdown.open a:hover .caret,.nav li.dropdown.open a:focus .caret{border-top-color:#fff;border-bottom-color:#fff;opacity:1;filter:alpha(opacity=100)}
+.tabs-stacked .open>a:hover,.tabs-stacked .open>a:focus{border-color:#999}
+.tabbable{*zoom:1}.tabbable:before,.tabbable:after{display:table;content:"";line-height:0}
+.tabbable:after{clear:both}
+.tab-content{overflow:auto}
+.tabs-below>.nav-tabs,.tabs-right>.nav-tabs,.tabs-left>.nav-tabs{border-bottom:0}
+.tab-content>.tab-pane,.pill-content>.pill-pane{display:none}
+.tab-content>.active,.pill-content>.active{display:block}
+.tabs-below>.nav-tabs{border-top:1px solid #ddd}
+.tabs-below>.nav-tabs>li{margin-top:-1px;margin-bottom:0}
+.tabs-below>.nav-tabs>li>a{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-bottom-color:transparent;border-top-color:#ddd}
+.tabs-below>.nav-tabs>.active>a,.tabs-below>.nav-tabs>.active>a:hover,.tabs-below>.nav-tabs>.active>a:focus{border-color:transparent #ddd #ddd #ddd}
+.tabs-left>.nav-tabs>li,.tabs-right>.nav-tabs>li{float:none}
+.tabs-left>.nav-tabs>li>a,.tabs-right>.nav-tabs>li>a{min-width:74px;margin-right:0;margin-bottom:3px}
+.tabs-left>.nav-tabs{float:left;margin-right:19px;border-right:1px solid #ddd}
+.tabs-left>.nav-tabs>li>a{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
+.tabs-left>.nav-tabs>li>a:hover,.tabs-left>.nav-tabs>li>a:focus{border-color:#eee #ddd #eee #eee}
+.tabs-left>.nav-tabs .active>a,.tabs-left>.nav-tabs .active>a:hover,.tabs-left>.nav-tabs .active>a:focus{border-color:#ddd transparent #ddd #ddd;*border-right-color:#fff}
+.tabs-right>.nav-tabs{float:right;margin-left:19px;border-left:1px solid #ddd}
+.tabs-right>.nav-tabs>li>a{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
+.tabs-right>.nav-tabs>li>a:hover,.tabs-right>.nav-tabs>li>a:focus{border-color:#eee #eee #eee #ddd}
+.tabs-right>.nav-tabs .active>a,.tabs-right>.nav-tabs .active>a:hover,.tabs-right>.nav-tabs .active>a:focus{border-color:#ddd #ddd #ddd transparent;*border-left-color:#fff}
+.nav>.disabled>a{color:#999}
+.nav>.disabled>a:hover,.nav>.disabled>a:focus{text-decoration:none;background-color:transparent;cursor:default}
+.navbar{overflow:visible;margin-bottom:20px;*position:relative;*z-index:2}
+.navbar-inner{min-height:36px;padding-left:20px;padding-right:20px;background-color:#fafafa;background-image:-moz-linear-gradient(top, #fff, #f2f2f2);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#f2f2f2));background-image:-webkit-linear-gradient(top, #fff, #f2f2f2);background-image:-o-linear-gradient(top, #fff, #f2f2f2);background-image:linear-gradient(to bottom, #fff, #f2f2f2);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0);border:1px solid #d4d4d4;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 4px rgba(0,0,0,0.065);-moz-box-shadow:0 1px 4px rgba(0,0,0,0.065);box-shadow:0 1px 4px rgba(0,0,0,0.065);*zoom:1}.navbar-inner:before,.navbar-inner:after{display:table;content:"";line-height:0}
+.navbar-inner:after{clear:both}
+.navbar .container{width:auto}
+.nav-collapse.collapse{height:auto;overflow:visible}
+.navbar .brand{float:left;display:block;padding:8px 20px 8px;margin-left:-20px;font-size:20px;font-weight:200;color:#777;text-shadow:0 1px 0 #fff}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none}
+.navbar-text{margin-bottom:0;line-height:36px;color:#777}
+.navbar-link{color:#777}.navbar-link:hover,.navbar-link:focus{color:#333}
+.navbar .divider-vertical{height:36px;margin:0 9px;border-left:1px solid #f2f2f2;border-right:1px solid #fff}
+.navbar .btn,.navbar .btn-group{margin-top:3px}
+.navbar .btn-group .btn,.navbar .input-prepend .btn,.navbar .input-append .btn,.navbar .input-prepend .btn-group,.navbar .input-append .btn-group{margin-top:0}
+.navbar-form{margin-bottom:0;*zoom:1}.navbar-form:before,.navbar-form:after{display:table;content:"";line-height:0}
+.navbar-form:after{clear:both}
+.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:3px}
+.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}
+.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}
+.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0}
+.navbar-search{position:relative;float:left;margin-top:3px;margin-bottom:0}.navbar-search .search-query{margin-bottom:0;padding:4px 14px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
+.navbar-static-top{position:static;margin-bottom:0}.navbar-static-top .navbar-inner{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030;margin-bottom:0}
+.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{border-width:0 0 1px}
+.navbar-fixed-bottom .navbar-inner{border-width:1px 0 0}
+.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding-left:0;padding-right:0;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}
+.navbar-fixed-top{top:0}
+.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{-webkit-box-shadow:0 1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 1px 10px rgba(0,0,0,.1);box-shadow:0 1px 10px rgba(0,0,0,.1)}
+.navbar-fixed-bottom{bottom:0}.navbar-fixed-bottom .navbar-inner{-webkit-box-shadow:0 -1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,.1);box-shadow:0 -1px 10px rgba(0,0,0,.1)}
+.navbar .nav{position:relative;left:0;display:block;float:left;margin:0 10px 0 0}
+.navbar .nav.pull-right{float:right;margin-right:0}
+.navbar .nav>li{float:left}
+.navbar .nav>li>a{float:none;padding:8px 15px 8px;color:#777;text-decoration:none;text-shadow:0 1px 0 #fff}
+.navbar .nav .dropdown-toggle .caret{margin-top:8px}
+.navbar .nav>li>a:focus,.navbar .nav>li>a:hover{background-color:transparent;color:#333;text-decoration:none}
+.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#555;text-decoration:none;background-color:#e5e5e5;-webkit-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);-moz-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);box-shadow:inset 0 3px 8px rgba(0,0,0,0.125)}
+.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-left:5px;margin-right:5px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#ededed;background-image:-moz-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5));background-image:-webkit-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-o-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:linear-gradient(to bottom, #f2f2f2, #e5e5e5);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0);border-color:#e5e5e5 #e5e5e5 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e5e5e5;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075)}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#fff;background-color:#e5e5e5;*background-color:#d9d9d9}
+.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#ccc \9}
+.navbar .btn-navbar .icon-bar{display:block;width:18px;height:2px;background-color:#f5f5f5;-webkit-border-radius:1px;-moz-border-radius:1px;border-radius:1px;-webkit-box-shadow:0 1px 0 rgba(0,0,0,0.25);-moz-box-shadow:0 1px 0 rgba(0,0,0,0.25);box-shadow:0 1px 0 rgba(0,0,0,0.25)}
+.btn-navbar .icon-bar+.icon-bar{margin-top:3px}
+.navbar .nav>li>.dropdown-menu:before{content:'';display:inline-block;border-left:7px solid transparent;border-right:7px solid transparent;border-bottom:7px solid #ccc;border-bottom-color:rgba(0,0,0,0.2);position:absolute;top:-7px;left:9px}
+.navbar .nav>li>.dropdown-menu:after{content:'';display:inline-block;border-left:6px solid transparent;border-right:6px solid transparent;border-bottom:6px solid #fff;position:absolute;top:-6px;left:10px}
+.navbar-fixed-bottom .nav>li>.dropdown-menu:before{border-top:7px solid #ccc;border-top-color:rgba(0,0,0,0.2);border-bottom:0;bottom:-7px;top:auto}
+.navbar-fixed-bottom .nav>li>.dropdown-menu:after{border-top:6px solid #fff;border-bottom:0;bottom:-6px;top:auto}
+.navbar .nav li.dropdown>a:hover .caret,.navbar .nav li.dropdown>a:focus .caret{border-top-color:#333;border-bottom-color:#333}
+.navbar .nav li.dropdown.open>.dropdown-toggle,.navbar .nav li.dropdown.active>.dropdown-toggle,.navbar .nav li.dropdown.open.active>.dropdown-toggle{background-color:#e5e5e5;color:#555}
+.navbar .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#777;border-bottom-color:#777}
+.navbar .nav li.dropdown.open>.dropdown-toggle .caret,.navbar .nav li.dropdown.active>.dropdown-toggle .caret,.navbar .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}
+.navbar .pull-right>li>.dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right{left:auto;right:0}.navbar .pull-right>li>.dropdown-menu:before,.navbar .nav>li>.dropdown-menu.pull-right:before{left:auto;right:12px}
+.navbar .pull-right>li>.dropdown-menu:after,.navbar .nav>li>.dropdown-menu.pull-right:after{left:auto;right:13px}
+.navbar .pull-right>li>.dropdown-menu .dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right .dropdown-menu{left:auto;right:100%;margin-left:0;margin-right:-1px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
+.navbar-inverse .navbar-inner{background-color:#1b1b1b;background-image:-moz-linear-gradient(top, #222, #111);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#222), to(#111));background-image:-webkit-linear-gradient(top, #222, #111);background-image:-o-linear-gradient(top, #222, #111);background-image:linear-gradient(to bottom, #222, #111);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0);border-color:#252525}
+.navbar-inverse .brand,.navbar-inverse .nav>li>a{color:#999;text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.navbar-inverse .brand:hover,.navbar-inverse .nav>li>a:hover,.navbar-inverse .brand:focus,.navbar-inverse .nav>li>a:focus{color:#fff}
+.navbar-inverse .brand{color:#999}
+.navbar-inverse .navbar-text{color:#999}
+.navbar-inverse .nav>li>a:focus,.navbar-inverse .nav>li>a:hover{background-color:transparent;color:#fff}
+.navbar-inverse .nav .active>a,.navbar-inverse .nav .active>a:hover,.navbar-inverse .nav .active>a:focus{color:#fff;background-color:#111}
+.navbar-inverse .navbar-link{color:#999}.navbar-inverse .navbar-link:hover,.navbar-inverse .navbar-link:focus{color:#fff}
+.navbar-inverse .divider-vertical{border-left-color:#111;border-right-color:#222}
+.navbar-inverse .nav li.dropdown.open>.dropdown-toggle,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle{background-color:#111;color:#fff}
+.navbar-inverse .nav li.dropdown>a:hover .caret,.navbar-inverse .nav li.dropdown>a:focus .caret{border-top-color:#fff;border-bottom-color:#fff}
+.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#999;border-bottom-color:#999}
+.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
+.navbar-inverse .navbar-search .search-query{color:#fff;background-color:#515151;border-color:#111;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-webkit-transition:none;-moz-transition:none;-o-transition:none;transition:none}.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#ccc}
+.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#ccc}
+.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#ccc}
+.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#333;text-shadow:0 1px 0 #fff;background-color:#fff;border:0;-webkit-box-shadow:0 0 3px rgba(0,0,0,0.15);-moz-box-shadow:0 0 3px rgba(0,0,0,0.15);box-shadow:0 0 3px rgba(0,0,0,0.15);outline:0}
+.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e0e0e;background-image:-moz-linear-gradient(top, #151515, #040404);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404));background-image:-webkit-linear-gradient(top, #151515, #040404);background-image:-o-linear-gradient(top, #151515, #040404);background-image:linear-gradient(to bottom, #151515, #040404);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0);border-color:#040404 #040404 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#040404;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#040404;*background-color:#000}
+.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}
+.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;*zoom:1;text-shadow:0 1px 0 #fff}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}
+.breadcrumb>.active{color:#999}
+.pagination{margin:20px 0}
+.pagination ul{display:inline-block;*display:inline;*zoom:1;margin-left:0;margin-bottom:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}
+.pagination ul>li{display:inline}
+.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}
+.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}
+.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}
+.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;background-color:transparent;cursor:default}
+.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
+.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
+.pagination-centered{text-align:center}
+.pagination-right{text-align:right}
+.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:16.25px}
+.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
+.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
+.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-top-left-radius:3px;-moz-border-radius-topleft:3px;border-top-left-radius:3px;-webkit-border-bottom-left-radius:3px;-moz-border-radius-bottomleft:3px;border-bottom-left-radius:3px}
+.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;-moz-border-radius-topright:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;-moz-border-radius-bottomright:3px;border-bottom-right-radius:3px}
+.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.049999999999999px}
+.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:9.75px}
+.pager{margin:20px 0;list-style:none;text-align:center;*zoom:1}.pager:before,.pager:after{display:table;content:"";line-height:0}
+.pager:after{clear:both}
+.pager li{display:inline}
+.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
+.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}
+.pager .next>a,.pager .next>span{float:right}
+.pager .previous>a,.pager .previous>span{float:left}
+.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;background-color:#fff;cursor:default}
+.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}
+.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}
+.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box;outline:none}.modal.fade{-webkit-transition:opacity .3s linear, top .3s ease-out;-moz-transition:opacity .3s linear, top .3s ease-out;-o-transition:opacity .3s linear, top .3s ease-out;transition:opacity .3s linear, top .3s ease-out;top:-25%}
+.modal.fade.in{top:10%}
+.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}
+.modal-header h3{margin:0;line-height:30px}
+.modal-body{position:relative;overflow-y:auto;max-height:400px;padding:15px}
+.modal-form{margin-bottom:0}
+.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff;*zoom:1}.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0}
+.modal-footer:after{clear:both}
+.modal-footer .btn+.btn{margin-left:5px;margin-bottom:0}
+.modal-footer .btn-group .btn+.btn{margin-left:-1px}
+.modal-footer .btn-block+.btn-block{margin-left:0}
+.tooltip{position:absolute;z-index:1030;display:block;visibility:visible;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0)}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}
+.tooltip.top{margin-top:-3px;padding:5px 0}
+.tooltip.right{margin-left:3px;padding:0 5px}
+.tooltip.bottom{margin-top:3px;padding:5px 0}
+.tooltip.left{margin-left:-3px;padding:0 5px}
+.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}
+.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}
+.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}
+.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}
+.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}
+.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;background-color:#fff;-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);white-space:normal}.popover.top{margin-top:-10px}
+.popover.right{margin-left:10px}
+.popover.bottom{margin-top:10px}
+.popover.left{margin-left:-10px}
+.popover-title{margin:0;padding:8px 14px;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}
+.popover-content{padding:9px 14px}
+.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}
+.popover .arrow{border-width:11px}
+.popover .arrow:after{border-width:10px;content:""}
+.popover.top .arrow{left:50%;margin-left:-11px;border-bottom-width:0;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);bottom:-11px}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-bottom-width:0;border-top-color:#fff}
+.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-left-width:0;border-right-color:#999;border-right-color:rgba(0,0,0,0.25)}.popover.right .arrow:after{left:1px;bottom:-10px;border-left-width:0;border-right-color:#fff}
+.popover.bottom .arrow{left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);top:-11px}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-top-width:0;border-bottom-color:#fff}
+.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,0.25)}.popover.left .arrow:after{right:1px;border-right-width:0;border-left-color:#fff;bottom:-10px}
+.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0}
+.thumbnails:after{clear:both}
+.row-fluid .thumbnails{margin-left:0}
+.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}
+.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}
+a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}
+.thumbnail>img{display:block;max-width:100%;margin-left:auto;margin-right:auto}
+.thumbnail .caption{padding:9px;color:#555}
+.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}
+.media,.media .media{margin-top:15px}
+.media:first-child{margin-top:0}
+.media-object{display:block}
+.media-heading{margin:0 0 5px}
+.media>.pull-left{margin-right:10px}
+.media>.pull-right{margin-left:10px}
+.media-list{margin-left:0;list-style:none}
+.label,.badge{display:inline-block;padding:2px 4px;font-size:10.998px;font-weight:bold;line-height:14px;color:#fff;vertical-align:baseline;white-space:nowrap;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#999}
+.label{border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.badge{padding-left:9px;padding-right:9px;border-radius:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}
+.label:empty,.badge:empty{display:none}
+a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}
+.label-important,.badge-important{background-color:#b94a48}
+.label-important[href],.badge-important[href]{background-color:#953b39}
+.label-warning,.badge-warning{background-color:#f89406}
+.label-warning[href],.badge-warning[href]{background-color:#c67605}
+.label-success,.badge-success{background-color:#468847}
+.label-success[href],.badge-success[href]{background-color:#356635}
+.label-info,.badge-info{background-color:#3a87ad}
+.label-info[href],.badge-info[href]{background-color:#2d6987}
+.label-inverse,.badge-inverse{background-color:#333}
+.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}
+.btn .label,.btn .badge{position:relative;top:-1px}
+.btn-mini .label,.btn-mini .badge{top:0}
+@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0} to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.progress .bar{width:0;height:100%;color:#fff;float:left;font-size:12px;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top, #149bdf, #0480be);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));background-image:-webkit-linear-gradient(top, #149bdf, #0480be);background-image:-o-linear-gradient(top, #149bdf, #0480be);background-image:linear-gradient(to bottom, #149bdf, #0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}
+.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15)}
+.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}
+.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}
+.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0)}
+.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
+.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0)}
+.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
+.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0)}
+.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
+.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0)}
+.progress-warning.progress-striped .bar,.progress-striped .bar-warning{background-color:#fbb450;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
+.accordion{margin-bottom:20px}
+.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.accordion-heading{border-bottom:0}
+.accordion-heading .accordion-toggle{display:block;padding:8px 15px}
+.accordion-toggle{cursor:pointer}
+.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5}
+.carousel{position:relative;margin-bottom:20px;line-height:1}
+.carousel-inner{overflow:hidden;width:100%;position:relative}
+.carousel-inner>.item{display:none;position:relative;-webkit-transition:.6s ease-in-out left;-moz-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1}
+.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}
+.carousel-inner>.active{left:0}
+.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}
+.carousel-inner>.next{left:100%}
+.carousel-inner>.prev{left:-100%}
+.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}
+.carousel-inner>.active.left{left:-100%}
+.carousel-inner>.active.right{left:100%}
+.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#fff;text-align:center;background:#222;border:3px solid #fff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:.5;filter:alpha(opacity=50)}.carousel-control.right{left:auto;right:15px}
+.carousel-control:hover,.carousel-control:focus{color:#fff;text-decoration:none;opacity:.9;filter:alpha(opacity=90)}
+.carousel-indicators{position:absolute;top:15px;right:15px;z-index:5;margin:0;list-style:none}.carousel-indicators li{display:block;float:left;width:10px;height:10px;margin-left:5px;text-indent:-999px;background-color:#ccc;background-color:rgba(255,255,255,0.25);border-radius:5px}
+.carousel-indicators .active{background-color:#fff}
+.carousel-caption{position:absolute;left:0;right:0;bottom:0;padding:15px;background:#333;background:rgba(0,0,0,0.75)}
+.carousel-caption h4,.carousel-caption p{color:#fff;line-height:20px}
+.carousel-caption h4{margin:0 0 5px}
+.carousel-caption p{margin-bottom:0}
+.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:30px;color:inherit;background-color:#eee;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;color:inherit;letter-spacing:-1px}
+.hero-unit li{line-height:30px}
+.pull-right{float:right}
+.pull-left{float:left}
+.hide{display:none}
+.show{display:block}
+.invisible{visibility:hidden}
+.affix{position:fixed}
+@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}
+.visible-phone{display:none !important}
+.visible-tablet{display:none !important}
+.hidden-desktop{display:none !important}
+.visible-desktop{display:inherit !important}
+@media (min-width:768px) and (max-width:979px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-tablet{display:inherit !important} .hidden-tablet{display:none !important}}@media (max-width:767px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-phone{display:inherit !important} .hidden-phone{display:none !important}}.visible-print{display:none !important}
+@media print{.visible-print{display:inherit !important} .hidden-print{display:none !important}}@media (min-width:1200px){.row{margin-left:-30px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0} .row:after{clear:both} [class*="span"]{float:left;min-height:1px;margin-left:30px} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:1170px} .span12{width:1170px} .span11{width:1070px} .span10{width:970px} .span9{width:870px} .span8{width:770px} .span7{width:670px} .span6{width:570px} .span5{width:470px} .span4{width:370px} .span3{width:270px} .span2{width:170px} .span1{width:70px} .offset12{margin-left:1230px} .offset11{margin-left:1130px} .offset10{margin-left:1030px} .offset9{margin-left:930px} .offset8{margin-left:830px} .offset7{margin-left:730px} .offset6{margin-left:630px} .offset5{margin-left:530px} .offset4{margin-left:430px} .offset3{margin-left:330px} .offset2{margin-left:230px} .offset1{margin-left:130px} .row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0} .row-fluid:after{clear:both} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.564102564102564%;*margin-left:2.5109110747408616%} .row-fluid [class*="span"]:first-child{margin-left:0} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.564102564102564%} .row-fluid .span12{width:100%;*width:99.94680851063829%} .row-fluid .span11{width:91.45299145299145%;*width:91.39979996362975%} .row-fluid .span10{width:82.90598290598291%;*width:82.8527914166212%} .row-fluid .span9{width:74.35897435897436%;*width:74.30578286961266%} .row-fluid .span8{width:65.81196581196582%;*width:65.75877432260411%} .row-fluid .span7{width:57.26495726495726%;*width:57.21176577559556%} .row-fluid .span6{width:48.717948717948715%;*width:48.664757228587014%} .row-fluid .span5{width:40.17094017094017%;*width:40.11774868157847%} .row-fluid .span4{width:31.623931623931625%;*width:31.570740134569924%} .row-fluid .span3{width:23.076923076923077%;*width:23.023731587561375%} .row-fluid .span2{width:14.52991452991453%;*width:14.476723040552828%} .row-fluid .span1{width:5.982905982905983%;*width:5.929714493544281%} .row-fluid .offset12{margin-left:105.12820512820512%;*margin-left:105.02182214948171%} .row-fluid .offset12:first-child{margin-left:102.56410256410257%;*margin-left:102.45771958537915%} .row-fluid .offset11{margin-left:96.58119658119658%;*margin-left:96.47481360247316%} .row-fluid .offset11:first-child{margin-left:94.01709401709402%;*margin-left:93.91071103837061%} .row-fluid .offset10{margin-left:88.03418803418803%;*margin-left:87.92780505546462%} .row-fluid .offset10:first-child{margin-left:85.47008547008548%;*margin-left:85.36370249136206%} .row-fluid .offset9{margin-left:79.48717948717949%;*margin-left:79.38079650845607%} .row-fluid .offset9:first-child{margin-left:76.92307692307693%;*margin-left:76.81669394435352%} .row-fluid .offset8{margin-left:70.94017094017094%;*margin-left:70.83378796144753%} .row-fluid .offset8:first-child{margin-left:68.37606837606839%;*margin-left:68.26968539734497%} .row-fluid .offset7{margin-left:62.393162393162385%;*margin-left:62.28677941443899%} .row-fluid .offset7:first-child{margin-left:59.82905982905982%;*margin-left:59.72267685033642%} .row-fluid .offset6{margin-left:53.84615384615384%;*margin-left:53.739770867430444%} .row-fluid .offset6:first-child{margin-left:51.28205128205128%;*margin-left:51.175668303327875%} .row-fluid .offset5{margin-left:45.299145299145295%;*margin-left:45.1927623204219%} .row-fluid .offset5:first-child{margin-left:42.73504273504273%;*margin-left:42.62865975631933%} .row-fluid .offset4{margin-left:36.75213675213675%;*margin-left:36.645753773413354%} .row-fluid .offset4:first-child{margin-left:34.18803418803419%;*margin-left:34.081651209310785%} .row-fluid .offset3{margin-left:28.205128205128204%;*margin-left:28.0987452264048%} .row-fluid .offset3:first-child{margin-left:25.641025641025642%;*margin-left:25.53464266230224%} .row-fluid .offset2{margin-left:19.65811965811966%;*margin-left:19.551736679396257%} .row-fluid .offset2:first-child{margin-left:17.094017094017094%;*margin-left:16.98763411529369%} .row-fluid .offset1{margin-left:11.11111111111111%;*margin-left:11.004728132387708%} .row-fluid .offset1:first-child{margin-left:8.547008547008547%;*margin-left:8.440625568285142%} input,textarea,.uneditable-input{margin-left:0} .controls-row [class*="span"]+[class*="span"]{margin-left:30px} input.span12,textarea.span12,.uneditable-input.span12{width:1156px} input.span11,textarea.span11,.uneditable-input.span11{width:1056px} input.span10,textarea.span10,.uneditable-input.span10{width:956px} input.span9,textarea.span9,.uneditable-input.span9{width:856px} input.span8,textarea.span8,.uneditable-input.span8{width:756px} input.span7,textarea.span7,.uneditable-input.span7{width:656px} input.span6,textarea.span6,.uneditable-input.span6{width:556px} input.span5,textarea.span5,.uneditable-input.span5{width:456px} input.span4,textarea.span4,.uneditable-input.span4{width:356px} input.span3,textarea.span3,.uneditable-input.span3{width:256px} input.span2,textarea.span2,.uneditable-input.span2{width:156px} input.span1,textarea.span1,.uneditable-input.span1{width:56px} .thumbnails{margin-left:-30px} .thumbnails>li{margin-left:30px} .row-fluid .thumbnails{margin-left:0}}@media (min-width:768px) and (max-width:979px){.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0} .row:after{clear:both} [class*="span"]{float:left;min-height:1px;margin-left:20px} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:724px} .span12{width:724px} .span11{width:662px} .span10{width:600px} .span9{width:538px} .span8{width:476px} .span7{width:414px} .span6{width:352px} .span5{width:290px} .span4{width:228px} .span3{width:166px} .span2{width:104px} .span1{width:42px} .offset12{margin-left:764px} .offset11{margin-left:702px} .offset10{margin-left:640px} .offset9{margin-left:578px} .offset8{margin-left:516px} .offset7{margin-left:454px} .offset6{margin-left:392px} .offset5{margin-left:330px} .offset4{margin-left:268px} .offset3{margin-left:206px} .offset2{margin-left:144px} .offset1{margin-left:82px} .row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0} .row-fluid:after{clear:both} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.7624309392265194%;*margin-left:2.709239449864817%} .row-fluid [class*="span"]:first-child{margin-left:0} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.7624309392265194%} .row-fluid .span12{width:100%;*width:99.94680851063829%} .row-fluid .span11{width:91.43646408839778%;*width:91.38327259903608%} .row-fluid .span10{width:82.87292817679558%;*width:82.81973668743387%} .row-fluid .span9{width:74.30939226519337%;*width:74.25620077583166%} .row-fluid .span8{width:65.74585635359117%;*width:65.69266486422946%} .row-fluid .span7{width:57.18232044198895%;*width:57.12912895262725%} .row-fluid .span6{width:48.61878453038674%;*width:48.56559304102504%} .row-fluid .span5{width:40.05524861878453%;*width:40.00205712942283%} .row-fluid .span4{width:31.491712707182323%;*width:31.43852121782062%} .row-fluid .span3{width:22.92817679558011%;*width:22.87498530621841%} .row-fluid .span2{width:14.3646408839779%;*width:14.311449394616199%} .row-fluid .span1{width:5.801104972375691%;*width:5.747913483013988%} .row-fluid .offset12{margin-left:105.52486187845304%;*margin-left:105.41847889972962%} .row-fluid .offset12:first-child{margin-left:102.76243093922652%;*margin-left:102.6560479605031%} .row-fluid .offset11{margin-left:96.96132596685082%;*margin-left:96.8549429881274%} .row-fluid .offset11:first-child{margin-left:94.1988950276243%;*margin-left:94.09251204890089%} .row-fluid .offset10{margin-left:88.39779005524862%;*margin-left:88.2914070765252%} .row-fluid .offset10:first-child{margin-left:85.6353591160221%;*margin-left:85.52897613729868%} .row-fluid .offset9{margin-left:79.8342541436464%;*margin-left:79.72787116492299%} .row-fluid .offset9:first-child{margin-left:77.07182320441989%;*margin-left:76.96544022569647%} .row-fluid .offset8{margin-left:71.2707182320442%;*margin-left:71.16433525332079%} .row-fluid .offset8:first-child{margin-left:68.50828729281768%;*margin-left:68.40190431409427%} .row-fluid .offset7{margin-left:62.70718232044199%;*margin-left:62.600799341718584%} .row-fluid .offset7:first-child{margin-left:59.94475138121547%;*margin-left:59.838368402492065%} .row-fluid .offset6{margin-left:54.14364640883978%;*margin-left:54.037263430116376%} .row-fluid .offset6:first-child{margin-left:51.38121546961326%;*margin-left:51.27483249088986%} .row-fluid .offset5{margin-left:45.58011049723757%;*margin-left:45.47372751851417%} .row-fluid .offset5:first-child{margin-left:42.81767955801105%;*margin-left:42.71129657928765%} .row-fluid .offset4{margin-left:37.01657458563536%;*margin-left:36.91019160691196%} .row-fluid .offset4:first-child{margin-left:34.25414364640884%;*margin-left:34.14776066768544%} .row-fluid .offset3{margin-left:28.45303867403315%;*margin-left:28.346655695309746%} .row-fluid .offset3:first-child{margin-left:25.69060773480663%;*margin-left:25.584224756083227%} .row-fluid .offset2{margin-left:19.88950276243094%;*margin-left:19.783119783707537%} .row-fluid .offset2:first-child{margin-left:17.12707182320442%;*margin-left:17.02068884448102%} .row-fluid .offset1{margin-left:11.32596685082873%;*margin-left:11.219583872105325%} .row-fluid .offset1:first-child{margin-left:8.56353591160221%;*margin-left:8.457152932878806%} input,textarea,.uneditable-input{margin-left:0} .controls-row [class*="span"]+[class*="span"]{margin-left:20px} input.span12,textarea.span12,.uneditable-input.span12{width:710px} input.span11,textarea.span11,.uneditable-input.span11{width:648px} input.span10,textarea.span10,.uneditable-input.span10{width:586px} input.span9,textarea.span9,.uneditable-input.span9{width:524px} input.span8,textarea.span8,.uneditable-input.span8{width:462px} input.span7,textarea.span7,.uneditable-input.span7{width:400px} input.span6,textarea.span6,.uneditable-input.span6{width:338px} input.span5,textarea.span5,.uneditable-input.span5{width:276px} input.span4,textarea.span4,.uneditable-input.span4{width:214px} input.span3,textarea.span3,.uneditable-input.span3{width:152px} input.span2,textarea.span2,.uneditable-input.span2{width:90px} input.span1,textarea.span1,.uneditable-input.span1{width:28px}}@media (max-width:767px){body{padding-left:20px;padding-right:20px} .navbar-fixed-top,.navbar-fixed-bottom,.navbar-static-top{margin-left:-20px;margin-right:-20px} .container-fluid{padding:0} .dl-horizontal dt{float:none;clear:none;width:auto;text-align:left} .dl-horizontal dd{margin-left:0} .container{width:auto} .row-fluid{width:100%} .row,.thumbnails{margin-left:0} .thumbnails>li{float:none;margin-left:0} [class*="span"],.uneditable-input[class*="span"],.row-fluid [class*="span"]{float:none;display:block;width:100%;margin-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .span12,.row-fluid .span12{width:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .row-fluid [class*="offset"]:first-child{margin-left:0} .input-large,.input-xlarge,.input-xxlarge,input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .input-prepend input,.input-append input,.input-prepend input[class*="span"],.input-append input[class*="span"]{display:inline-block;width:auto} .controls-row [class*="span"]+[class*="span"]{margin-left:0} .modal{position:fixed;top:20px;left:20px;right:20px;width:auto;margin:0}.modal.fade{top:-100px} .modal.fade.in{top:20px}}@media (max-width:480px){.nav-collapse{-webkit-transform:translate3d(0, 0, 0)} .page-header h1 small{display:block;line-height:20px} input[type="checkbox"],input[type="radio"]{border:1px solid #ccc} .form-horizontal .control-label{float:none;width:auto;padding-top:0;text-align:left} .form-horizontal .controls{margin-left:0} .form-horizontal .control-list{padding-top:0} .form-horizontal .form-actions{padding-left:10px;padding-right:10px} .media .pull-left,.media .pull-right{float:none;display:block;margin-bottom:10px} .media-object{margin-right:0;margin-left:0} .modal{top:10px;left:10px;right:10px} .modal-header .close{padding:10px;margin:-10px} .carousel-caption{position:static}}@media (max-width:979px){body{padding-top:0} .navbar-fixed-top,.navbar-fixed-bottom{position:static} .navbar-fixed-top{margin-bottom:20px} .navbar-fixed-bottom{margin-top:20px} .navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding:5px} .navbar .container{width:auto;padding:0} .navbar .brand{padding-left:10px;padding-right:10px;margin:0 0 0 -5px} .nav-collapse{clear:both} .nav-collapse .nav{float:none;margin:0 0 10px} .nav-collapse .nav>li{float:none} .nav-collapse .nav>li>a{margin-bottom:2px} .nav-collapse .nav>.divider-vertical{display:none} .nav-collapse .nav .nav-header{color:#777;text-shadow:none} .nav-collapse .nav>li>a,.nav-collapse .dropdown-menu a{padding:9px 15px;font-weight:bold;color:#777;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px} .nav-collapse .btn{padding:4px 10px 4px;font-weight:normal;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px} .nav-collapse .dropdown-menu li+li a{margin-bottom:2px} .nav-collapse .nav>li>a:hover,.nav-collapse .nav>li>a:focus,.nav-collapse .dropdown-menu a:hover,.nav-collapse .dropdown-menu a:focus{background-color:#f2f2f2} .navbar-inverse .nav-collapse .nav>li>a,.navbar-inverse .nav-collapse .dropdown-menu a{color:#999} .navbar-inverse .nav-collapse .nav>li>a:hover,.navbar-inverse .nav-collapse .nav>li>a:focus,.navbar-inverse .nav-collapse .dropdown-menu a:hover,.navbar-inverse .nav-collapse .dropdown-menu a:focus{background-color:#111} .nav-collapse.in .btn-group{margin-top:5px;padding:0} .nav-collapse .dropdown-menu{position:static;top:auto;left:auto;float:none;display:none;max-width:none;margin:0 15px;padding:0;background-color:transparent;border:none;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none} .nav-collapse .open>.dropdown-menu{display:block} .nav-collapse .dropdown-menu:before,.nav-collapse .dropdown-menu:after{display:none} .nav-collapse .dropdown-menu .divider{display:none} .nav-collapse .nav>li>.dropdown-menu:before,.nav-collapse .nav>li>.dropdown-menu:after{display:none} .nav-collapse .navbar-form,.nav-collapse .navbar-search{float:none;padding:10px 15px;margin:10px 0;border-top:1px solid #f2f2f2;border-bottom:1px solid #f2f2f2;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1)} .navbar-inverse .nav-collapse .navbar-form,.navbar-inverse .nav-collapse .navbar-search{border-top-color:#111;border-bottom-color:#111} .navbar .nav-collapse .nav.pull-right{float:none;margin-left:0} .nav-collapse,.nav-collapse.collapse{overflow:hidden;height:0} .navbar .btn-navbar{display:block} .navbar-static .navbar-inner{padding-left:10px;padding-right:10px}}@media (min-width:979px + 1){.nav-collapse.collapse{height:auto !important;overflow:visible !important}}@font-face{font-family:'FontAwesome';src:url('../components/font-awesome/font/fontawesome-webfont.eot?v=3.2.1');src:url('../components/font-awesome/font/fontawesome-webfont.eot?#iefix&v=3.2.1') format('embedded-opentype'),url('../components/font-awesome/font/fontawesome-webfont.woff?v=3.2.1') format('woff'),url('../components/font-awesome/font/fontawesome-webfont.ttf?v=3.2.1') format('truetype'),url('../components/font-awesome/font/fontawesome-webfont.svg#fontawesomeregular?v=3.2.1') format('svg');font-weight:normal;font-style:normal}[class^="icon-"],[class*=" icon-"]{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em}
+[class^="icon-"]:before,[class*=" icon-"]:before{text-decoration:inherit;display:inline-block;speak:none}
+.icon-large:before{vertical-align:-10%;font-size:1.3333333333333333em}
+a [class^="icon-"],a [class*=" icon-"]{display:inline}
+[class^="icon-"].icon-fixed-width,[class*=" icon-"].icon-fixed-width{display:inline-block;width:1.1428571428571428em;text-align:right;padding-right:.2857142857142857em}[class^="icon-"].icon-fixed-width.icon-large,[class*=" icon-"].icon-fixed-width.icon-large{width:1.4285714285714286em}
+.icons-ul{margin-left:2.142857142857143em;list-style-type:none}.icons-ul>li{position:relative}
+.icons-ul .icon-li{position:absolute;left:-2.142857142857143em;width:2.142857142857143em;text-align:center;line-height:inherit}
+[class^="icon-"].hide,[class*=" icon-"].hide{display:none}
+.icon-muted{color:#eee}
+.icon-light{color:#fff}
+.icon-dark{color:#333}
+.icon-border{border:solid 1px #eee;padding:.2em .25em .15em;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
+.icon-2x{font-size:2em}.icon-2x.icon-border{border-width:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
+.icon-3x{font-size:3em}.icon-3x.icon-border{border-width:3px;border-radius:5px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}
+.icon-4x{font-size:4em}.icon-4x.icon-border{border-width:4px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
+.icon-5x{font-size:5em}.icon-5x.icon-border{border-width:5px;border-radius:7px;-webkit-border-radius:7px;-moz-border-radius:7px;border-radius:7px}
+.pull-right{float:right}
+.pull-left{float:left}
+[class^="icon-"].pull-left,[class*=" icon-"].pull-left{margin-right:.3em}
+[class^="icon-"].pull-right,[class*=" icon-"].pull-right{margin-left:.3em}
+[class^="icon-"],[class*=" icon-"]{display:inline;width:auto;height:auto;line-height:normal;vertical-align:baseline;background-image:none;background-position:0 0;background-repeat:repeat;margin-top:0}
+.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"]{background-image:none}
+.btn [class^="icon-"].icon-large,.nav [class^="icon-"].icon-large,.btn [class*=" icon-"].icon-large,.nav [class*=" icon-"].icon-large{line-height:.9em}
+.btn [class^="icon-"].icon-spin,.nav [class^="icon-"].icon-spin,.btn [class*=" icon-"].icon-spin,.nav [class*=" icon-"].icon-spin{display:inline-block}
+.nav-tabs [class^="icon-"],.nav-pills [class^="icon-"],.nav-tabs [class*=" icon-"],.nav-pills [class*=" icon-"],.nav-tabs [class^="icon-"].icon-large,.nav-pills [class^="icon-"].icon-large,.nav-tabs [class*=" icon-"].icon-large,.nav-pills [class*=" icon-"].icon-large{line-height:.9em}
+.btn [class^="icon-"].pull-left.icon-2x,.btn [class*=" icon-"].pull-left.icon-2x,.btn [class^="icon-"].pull-right.icon-2x,.btn [class*=" icon-"].pull-right.icon-2x{margin-top:.18em}
+.btn [class^="icon-"].icon-spin.icon-large,.btn [class*=" icon-"].icon-spin.icon-large{line-height:.8em}
+.btn.btn-small [class^="icon-"].pull-left.icon-2x,.btn.btn-small [class*=" icon-"].pull-left.icon-2x,.btn.btn-small [class^="icon-"].pull-right.icon-2x,.btn.btn-small [class*=" icon-"].pull-right.icon-2x{margin-top:.25em}
+.btn.btn-large [class^="icon-"],.btn.btn-large [class*=" icon-"]{margin-top:0}.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x,.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-top:.05em}
+.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x{margin-right:.2em}
+.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-left:.2em}
+.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{line-height:inherit}
+.icon-stack{position:relative;display:inline-block;width:2em;height:2em;line-height:2em;vertical-align:-35%}.icon-stack [class^="icon-"],.icon-stack [class*=" icon-"]{display:block;text-align:center;position:absolute;width:100%;height:100%;font-size:1em;line-height:inherit;*line-height:2em}
+.icon-stack .icon-stack-base{font-size:2em;*line-height:1em}
+.icon-spin{display:inline-block;-moz-animation:spin 2s infinite linear;-o-animation:spin 2s infinite linear;-webkit-animation:spin 2s infinite linear;animation:spin 2s infinite linear}
+a .icon-stack,a .icon-spin{display:inline-block;text-decoration:none}
+@-moz-keyframes spin{0%{-moz-transform:rotate(0deg)} 100%{-moz-transform:rotate(359deg)}}@-webkit-keyframes spin{0%{-webkit-transform:rotate(0deg)} 100%{-webkit-transform:rotate(359deg)}}@-o-keyframes spin{0%{-o-transform:rotate(0deg)} 100%{-o-transform:rotate(359deg)}}@-ms-keyframes spin{0%{-ms-transform:rotate(0deg)} 100%{-ms-transform:rotate(359deg)}}@keyframes spin{0%{transform:rotate(0deg)} 100%{transform:rotate(359deg)}}.icon-rotate-90:before{-webkit-transform:rotate(90deg);-moz-transform:rotate(90deg);-ms-transform:rotate(90deg);-o-transform:rotate(90deg);transform:rotate(90deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=1)}
+.icon-rotate-180:before{-webkit-transform:rotate(180deg);-moz-transform:rotate(180deg);-ms-transform:rotate(180deg);-o-transform:rotate(180deg);transform:rotate(180deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2)}
+.icon-rotate-270:before{-webkit-transform:rotate(270deg);-moz-transform:rotate(270deg);-ms-transform:rotate(270deg);-o-transform:rotate(270deg);transform:rotate(270deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=3)}
+.icon-flip-horizontal:before{-webkit-transform:scale(-1, 1);-moz-transform:scale(-1, 1);-ms-transform:scale(-1, 1);-o-transform:scale(-1, 1);transform:scale(-1, 1)}
+.icon-flip-vertical:before{-webkit-transform:scale(1, -1);-moz-transform:scale(1, -1);-ms-transform:scale(1, -1);-o-transform:scale(1, -1);transform:scale(1, -1)}
+a .icon-rotate-90:before,a .icon-rotate-180:before,a .icon-rotate-270:before,a .icon-flip-horizontal:before,a .icon-flip-vertical:before{display:inline-block}
+.icon-glass:before{content:"\f000"}
+.icon-music:before{content:"\f001"}
+.icon-search:before{content:"\f002"}
+.icon-envelope-alt:before{content:"\f003"}
+.icon-heart:before{content:"\f004"}
+.icon-star:before{content:"\f005"}
+.icon-star-empty:before{content:"\f006"}
+.icon-user:before{content:"\f007"}
+.icon-film:before{content:"\f008"}
+.icon-th-large:before{content:"\f009"}
+.icon-th:before{content:"\f00a"}
+.icon-th-list:before{content:"\f00b"}
+.icon-ok:before{content:"\f00c"}
+.icon-remove:before{content:"\f00d"}
+.icon-zoom-in:before{content:"\f00e"}
+.icon-zoom-out:before{content:"\f010"}
+.icon-power-off:before,.icon-off:before{content:"\f011"}
+.icon-signal:before{content:"\f012"}
+.icon-gear:before,.icon-cog:before{content:"\f013"}
+.icon-trash:before{content:"\f014"}
+.icon-home:before{content:"\f015"}
+.icon-file-alt:before{content:"\f016"}
+.icon-time:before{content:"\f017"}
+.icon-road:before{content:"\f018"}
+.icon-download-alt:before{content:"\f019"}
+.icon-download:before{content:"\f01a"}
+.icon-upload:before{content:"\f01b"}
+.icon-inbox:before{content:"\f01c"}
+.icon-play-circle:before{content:"\f01d"}
+.icon-rotate-right:before,.icon-repeat:before{content:"\f01e"}
+.icon-refresh:before{content:"\f021"}
+.icon-list-alt:before{content:"\f022"}
+.icon-lock:before{content:"\f023"}
+.icon-flag:before{content:"\f024"}
+.icon-headphones:before{content:"\f025"}
+.icon-volume-off:before{content:"\f026"}
+.icon-volume-down:before{content:"\f027"}
+.icon-volume-up:before{content:"\f028"}
+.icon-qrcode:before{content:"\f029"}
+.icon-barcode:before{content:"\f02a"}
+.icon-tag:before{content:"\f02b"}
+.icon-tags:before{content:"\f02c"}
+.icon-book:before{content:"\f02d"}
+.icon-bookmark:before{content:"\f02e"}
+.icon-print:before{content:"\f02f"}
+.icon-camera:before{content:"\f030"}
+.icon-font:before{content:"\f031"}
+.icon-bold:before{content:"\f032"}
+.icon-italic:before{content:"\f033"}
+.icon-text-height:before{content:"\f034"}
+.icon-text-width:before{content:"\f035"}
+.icon-align-left:before{content:"\f036"}
+.icon-align-center:before{content:"\f037"}
+.icon-align-right:before{content:"\f038"}
+.icon-align-justify:before{content:"\f039"}
+.icon-list:before{content:"\f03a"}
+.icon-indent-left:before{content:"\f03b"}
+.icon-indent-right:before{content:"\f03c"}
+.icon-facetime-video:before{content:"\f03d"}
+.icon-picture:before{content:"\f03e"}
+.icon-pencil:before{content:"\f040"}
+.icon-map-marker:before{content:"\f041"}
+.icon-adjust:before{content:"\f042"}
+.icon-tint:before{content:"\f043"}
+.icon-edit:before{content:"\f044"}
+.icon-share:before{content:"\f045"}
+.icon-check:before{content:"\f046"}
+.icon-move:before{content:"\f047"}
+.icon-step-backward:before{content:"\f048"}
+.icon-fast-backward:before{content:"\f049"}
+.icon-backward:before{content:"\f04a"}
+.icon-play:before{content:"\f04b"}
+.icon-pause:before{content:"\f04c"}
+.icon-stop:before{content:"\f04d"}
+.icon-forward:before{content:"\f04e"}
+.icon-fast-forward:before{content:"\f050"}
+.icon-step-forward:before{content:"\f051"}
+.icon-eject:before{content:"\f052"}
+.icon-chevron-left:before{content:"\f053"}
+.icon-chevron-right:before{content:"\f054"}
+.icon-plus-sign:before{content:"\f055"}
+.icon-minus-sign:before{content:"\f056"}
+.icon-remove-sign:before{content:"\f057"}
+.icon-ok-sign:before{content:"\f058"}
+.icon-question-sign:before{content:"\f059"}
+.icon-info-sign:before{content:"\f05a"}
+.icon-screenshot:before{content:"\f05b"}
+.icon-remove-circle:before{content:"\f05c"}
+.icon-ok-circle:before{content:"\f05d"}
+.icon-ban-circle:before{content:"\f05e"}
+.icon-arrow-left:before{content:"\f060"}
+.icon-arrow-right:before{content:"\f061"}
+.icon-arrow-up:before{content:"\f062"}
+.icon-arrow-down:before{content:"\f063"}
+.icon-mail-forward:before,.icon-share-alt:before{content:"\f064"}
+.icon-resize-full:before{content:"\f065"}
+.icon-resize-small:before{content:"\f066"}
+.icon-plus:before{content:"\f067"}
+.icon-minus:before{content:"\f068"}
+.icon-asterisk:before{content:"\f069"}
+.icon-exclamation-sign:before{content:"\f06a"}
+.icon-gift:before{content:"\f06b"}
+.icon-leaf:before{content:"\f06c"}
+.icon-fire:before{content:"\f06d"}
+.icon-eye-open:before{content:"\f06e"}
+.icon-eye-close:before{content:"\f070"}
+.icon-warning-sign:before{content:"\f071"}
+.icon-plane:before{content:"\f072"}
+.icon-calendar:before{content:"\f073"}
+.icon-random:before{content:"\f074"}
+.icon-comment:before{content:"\f075"}
+.icon-magnet:before{content:"\f076"}
+.icon-chevron-up:before{content:"\f077"}
+.icon-chevron-down:before{content:"\f078"}
+.icon-retweet:before{content:"\f079"}
+.icon-shopping-cart:before{content:"\f07a"}
+.icon-folder-close:before{content:"\f07b"}
+.icon-folder-open:before{content:"\f07c"}
+.icon-resize-vertical:before{content:"\f07d"}
+.icon-resize-horizontal:before{content:"\f07e"}
+.icon-bar-chart:before{content:"\f080"}
+.icon-twitter-sign:before{content:"\f081"}
+.icon-facebook-sign:before{content:"\f082"}
+.icon-camera-retro:before{content:"\f083"}
+.icon-key:before{content:"\f084"}
+.icon-gears:before,.icon-cogs:before{content:"\f085"}
+.icon-comments:before{content:"\f086"}
+.icon-thumbs-up-alt:before{content:"\f087"}
+.icon-thumbs-down-alt:before{content:"\f088"}
+.icon-star-half:before{content:"\f089"}
+.icon-heart-empty:before{content:"\f08a"}
+.icon-signout:before{content:"\f08b"}
+.icon-linkedin-sign:before{content:"\f08c"}
+.icon-pushpin:before{content:"\f08d"}
+.icon-external-link:before{content:"\f08e"}
+.icon-signin:before{content:"\f090"}
+.icon-trophy:before{content:"\f091"}
+.icon-github-sign:before{content:"\f092"}
+.icon-upload-alt:before{content:"\f093"}
+.icon-lemon:before{content:"\f094"}
+.icon-phone:before{content:"\f095"}
+.icon-unchecked:before,.icon-check-empty:before{content:"\f096"}
+.icon-bookmark-empty:before{content:"\f097"}
+.icon-phone-sign:before{content:"\f098"}
+.icon-twitter:before{content:"\f099"}
+.icon-facebook:before{content:"\f09a"}
+.icon-github:before{content:"\f09b"}
+.icon-unlock:before{content:"\f09c"}
+.icon-credit-card:before{content:"\f09d"}
+.icon-rss:before{content:"\f09e"}
+.icon-hdd:before{content:"\f0a0"}
+.icon-bullhorn:before{content:"\f0a1"}
+.icon-bell:before{content:"\f0a2"}
+.icon-certificate:before{content:"\f0a3"}
+.icon-hand-right:before{content:"\f0a4"}
+.icon-hand-left:before{content:"\f0a5"}
+.icon-hand-up:before{content:"\f0a6"}
+.icon-hand-down:before{content:"\f0a7"}
+.icon-circle-arrow-left:before{content:"\f0a8"}
+.icon-circle-arrow-right:before{content:"\f0a9"}
+.icon-circle-arrow-up:before{content:"\f0aa"}
+.icon-circle-arrow-down:before{content:"\f0ab"}
+.icon-globe:before{content:"\f0ac"}
+.icon-wrench:before{content:"\f0ad"}
+.icon-tasks:before{content:"\f0ae"}
+.icon-filter:before{content:"\f0b0"}
+.icon-briefcase:before{content:"\f0b1"}
+.icon-fullscreen:before{content:"\f0b2"}
+.icon-group:before{content:"\f0c0"}
+.icon-link:before{content:"\f0c1"}
+.icon-cloud:before{content:"\f0c2"}
+.icon-beaker:before{content:"\f0c3"}
+.icon-cut:before{content:"\f0c4"}
+.icon-copy:before{content:"\f0c5"}
+.icon-paperclip:before,.icon-paper-clip:before{content:"\f0c6"}
+.icon-save:before{content:"\f0c7"}
+.icon-sign-blank:before{content:"\f0c8"}
+.icon-reorder:before{content:"\f0c9"}
+.icon-list-ul:before{content:"\f0ca"}
+.icon-list-ol:before{content:"\f0cb"}
+.icon-strikethrough:before{content:"\f0cc"}
+.icon-underline:before{content:"\f0cd"}
+.icon-table:before{content:"\f0ce"}
+.icon-magic:before{content:"\f0d0"}
+.icon-truck:before{content:"\f0d1"}
+.icon-pinterest:before{content:"\f0d2"}
+.icon-pinterest-sign:before{content:"\f0d3"}
+.icon-google-plus-sign:before{content:"\f0d4"}
+.icon-google-plus:before{content:"\f0d5"}
+.icon-money:before{content:"\f0d6"}
+.icon-caret-down:before{content:"\f0d7"}
+.icon-caret-up:before{content:"\f0d8"}
+.icon-caret-left:before{content:"\f0d9"}
+.icon-caret-right:before{content:"\f0da"}
+.icon-columns:before{content:"\f0db"}
+.icon-sort:before{content:"\f0dc"}
+.icon-sort-down:before{content:"\f0dd"}
+.icon-sort-up:before{content:"\f0de"}
+.icon-envelope:before{content:"\f0e0"}
+.icon-linkedin:before{content:"\f0e1"}
+.icon-rotate-left:before,.icon-undo:before{content:"\f0e2"}
+.icon-legal:before{content:"\f0e3"}
+.icon-dashboard:before{content:"\f0e4"}
+.icon-comment-alt:before{content:"\f0e5"}
+.icon-comments-alt:before{content:"\f0e6"}
+.icon-bolt:before{content:"\f0e7"}
+.icon-sitemap:before{content:"\f0e8"}
+.icon-umbrella:before{content:"\f0e9"}
+.icon-paste:before{content:"\f0ea"}
+.icon-lightbulb:before{content:"\f0eb"}
+.icon-exchange:before{content:"\f0ec"}
+.icon-cloud-download:before{content:"\f0ed"}
+.icon-cloud-upload:before{content:"\f0ee"}
+.icon-user-md:before{content:"\f0f0"}
+.icon-stethoscope:before{content:"\f0f1"}
+.icon-suitcase:before{content:"\f0f2"}
+.icon-bell-alt:before{content:"\f0f3"}
+.icon-coffee:before{content:"\f0f4"}
+.icon-food:before{content:"\f0f5"}
+.icon-file-text-alt:before{content:"\f0f6"}
+.icon-building:before{content:"\f0f7"}
+.icon-hospital:before{content:"\f0f8"}
+.icon-ambulance:before{content:"\f0f9"}
+.icon-medkit:before{content:"\f0fa"}
+.icon-fighter-jet:before{content:"\f0fb"}
+.icon-beer:before{content:"\f0fc"}
+.icon-h-sign:before{content:"\f0fd"}
+.icon-plus-sign-alt:before{content:"\f0fe"}
+.icon-double-angle-left:before{content:"\f100"}
+.icon-double-angle-right:before{content:"\f101"}
+.icon-double-angle-up:before{content:"\f102"}
+.icon-double-angle-down:before{content:"\f103"}
+.icon-angle-left:before{content:"\f104"}
+.icon-angle-right:before{content:"\f105"}
+.icon-angle-up:before{content:"\f106"}
+.icon-angle-down:before{content:"\f107"}
+.icon-desktop:before{content:"\f108"}
+.icon-laptop:before{content:"\f109"}
+.icon-tablet:before{content:"\f10a"}
+.icon-mobile-phone:before{content:"\f10b"}
+.icon-circle-blank:before{content:"\f10c"}
+.icon-quote-left:before{content:"\f10d"}
+.icon-quote-right:before{content:"\f10e"}
+.icon-spinner:before{content:"\f110"}
+.icon-circle:before{content:"\f111"}
+.icon-mail-reply:before,.icon-reply:before{content:"\f112"}
+.icon-github-alt:before{content:"\f113"}
+.icon-folder-close-alt:before{content:"\f114"}
+.icon-folder-open-alt:before{content:"\f115"}
+.icon-expand-alt:before{content:"\f116"}
+.icon-collapse-alt:before{content:"\f117"}
+.icon-smile:before{content:"\f118"}
+.icon-frown:before{content:"\f119"}
+.icon-meh:before{content:"\f11a"}
+.icon-gamepad:before{content:"\f11b"}
+.icon-keyboard:before{content:"\f11c"}
+.icon-flag-alt:before{content:"\f11d"}
+.icon-flag-checkered:before{content:"\f11e"}
+.icon-terminal:before{content:"\f120"}
+.icon-code:before{content:"\f121"}
+.icon-reply-all:before{content:"\f122"}
+.icon-mail-reply-all:before{content:"\f122"}
+.icon-star-half-full:before,.icon-star-half-empty:before{content:"\f123"}
+.icon-location-arrow:before{content:"\f124"}
+.icon-crop:before{content:"\f125"}
+.icon-code-fork:before{content:"\f126"}
+.icon-unlink:before{content:"\f127"}
+.icon-question:before{content:"\f128"}
+.icon-info:before{content:"\f129"}
+.icon-exclamation:before{content:"\f12a"}
+.icon-superscript:before{content:"\f12b"}
+.icon-subscript:before{content:"\f12c"}
+.icon-eraser:before{content:"\f12d"}
+.icon-puzzle-piece:before{content:"\f12e"}
+.icon-microphone:before{content:"\f130"}
+.icon-microphone-off:before{content:"\f131"}
+.icon-shield:before{content:"\f132"}
+.icon-calendar-empty:before{content:"\f133"}
+.icon-fire-extinguisher:before{content:"\f134"}
+.icon-rocket:before{content:"\f135"}
+.icon-maxcdn:before{content:"\f136"}
+.icon-chevron-sign-left:before{content:"\f137"}
+.icon-chevron-sign-right:before{content:"\f138"}
+.icon-chevron-sign-up:before{content:"\f139"}
+.icon-chevron-sign-down:before{content:"\f13a"}
+.icon-html5:before{content:"\f13b"}
+.icon-css3:before{content:"\f13c"}
+.icon-anchor:before{content:"\f13d"}
+.icon-unlock-alt:before{content:"\f13e"}
+.icon-bullseye:before{content:"\f140"}
+.icon-ellipsis-horizontal:before{content:"\f141"}
+.icon-ellipsis-vertical:before{content:"\f142"}
+.icon-rss-sign:before{content:"\f143"}
+.icon-play-sign:before{content:"\f144"}
+.icon-ticket:before{content:"\f145"}
+.icon-minus-sign-alt:before{content:"\f146"}
+.icon-check-minus:before{content:"\f147"}
+.icon-level-up:before{content:"\f148"}
+.icon-level-down:before{content:"\f149"}
+.icon-check-sign:before{content:"\f14a"}
+.icon-edit-sign:before{content:"\f14b"}
+.icon-external-link-sign:before{content:"\f14c"}
+.icon-share-sign:before{content:"\f14d"}
+.icon-compass:before{content:"\f14e"}
+.icon-collapse:before{content:"\f150"}
+.icon-collapse-top:before{content:"\f151"}
+.icon-expand:before{content:"\f152"}
+.icon-euro:before,.icon-eur:before{content:"\f153"}
+.icon-gbp:before{content:"\f154"}
+.icon-dollar:before,.icon-usd:before{content:"\f155"}
+.icon-rupee:before,.icon-inr:before{content:"\f156"}
+.icon-yen:before,.icon-jpy:before{content:"\f157"}
+.icon-renminbi:before,.icon-cny:before{content:"\f158"}
+.icon-won:before,.icon-krw:before{content:"\f159"}
+.icon-bitcoin:before,.icon-btc:before{content:"\f15a"}
+.icon-file:before{content:"\f15b"}
+.icon-file-text:before{content:"\f15c"}
+.icon-sort-by-alphabet:before{content:"\f15d"}
+.icon-sort-by-alphabet-alt:before{content:"\f15e"}
+.icon-sort-by-attributes:before{content:"\f160"}
+.icon-sort-by-attributes-alt:before{content:"\f161"}
+.icon-sort-by-order:before{content:"\f162"}
+.icon-sort-by-order-alt:before{content:"\f163"}
+.icon-thumbs-up:before{content:"\f164"}
+.icon-thumbs-down:before{content:"\f165"}
+.icon-youtube-sign:before{content:"\f166"}
+.icon-youtube:before{content:"\f167"}
+.icon-xing:before{content:"\f168"}
+.icon-xing-sign:before{content:"\f169"}
+.icon-youtube-play:before{content:"\f16a"}
+.icon-dropbox:before{content:"\f16b"}
+.icon-stackexchange:before{content:"\f16c"}
+.icon-instagram:before{content:"\f16d"}
+.icon-flickr:before{content:"\f16e"}
+.icon-adn:before{content:"\f170"}
+.icon-bitbucket:before{content:"\f171"}
+.icon-bitbucket-sign:before{content:"\f172"}
+.icon-tumblr:before{content:"\f173"}
+.icon-tumblr-sign:before{content:"\f174"}
+.icon-long-arrow-down:before{content:"\f175"}
+.icon-long-arrow-up:before{content:"\f176"}
+.icon-long-arrow-left:before{content:"\f177"}
+.icon-long-arrow-right:before{content:"\f178"}
+.icon-apple:before{content:"\f179"}
+.icon-windows:before{content:"\f17a"}
+.icon-android:before{content:"\f17b"}
+.icon-linux:before{content:"\f17c"}
+.icon-dribbble:before{content:"\f17d"}
+.icon-skype:before{content:"\f17e"}
+.icon-foursquare:before{content:"\f180"}
+.icon-trello:before{content:"\f181"}
+.icon-female:before{content:"\f182"}
+.icon-male:before{content:"\f183"}
+.icon-gittip:before{content:"\f184"}
+.icon-sun:before{content:"\f185"}
+.icon-moon:before{content:"\f186"}
+.icon-archive:before{content:"\f187"}
+.icon-bug:before{content:"\f188"}
+.icon-vk:before{content:"\f189"}
+.icon-weibo:before{content:"\f18a"}
+.icon-renren:before{content:"\f18b"}
+code{color:#000}
+pre{font-size:inherit;line-height:inherit}
+.border-box-sizing{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
+.corner-all{border-radius:4px}
+.hbox{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+.hbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none}
+.vbox{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+.vbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none}
+.hbox.reverse,.vbox.reverse,.reverse{-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
+.hbox.box-flex0,.vbox.box-flex0,.box-flex0{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none;width:auto}
+.hbox.box-flex1,.vbox.box-flex1,.box-flex1{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+.hbox.box-flex,.vbox.box-flex,.box-flex{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+.hbox.box-flex2,.vbox.box-flex2,.box-flex2{-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2}
+.box-group1{-webkit-box-flex-group:1;-moz-box-flex-group:1;box-flex-group:1}
+.box-group2{-webkit-box-flex-group:2;-moz-box-flex-group:2;box-flex-group:2}
+.hbox.start,.vbox.start,.start{-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start}
+.hbox.end,.vbox.end,.end{-webkit-box-pack:end;-moz-box-pack:end;box-pack:end;justify-content:flex-end}
+.hbox.center,.vbox.center,.center{-webkit-box-pack:center;-moz-box-pack:center;box-pack:center;justify-content:center}
+.hbox.align-start,.vbox.align-start,.align-start{-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
+.hbox.align-end,.vbox.align-end,.align-end{-webkit-box-align:end;-moz-box-align:end;box-align:end;align-items:flex-end}
+.hbox.align-center,.vbox.align-center,.align-center{-webkit-box-align:center;-moz-box-align:center;box-align:center;align-items:center}
+div.error{margin:2em;text-align:center}
+div.error>h1{font-size:500%;line-height:normal}
+div.error>p{font-size:200%;line-height:normal}
+div.traceback-wrapper{text-align:left;max-width:800px;margin:auto}
+body{background-color:#fff;position:absolute;left:0;right:0;top:0;bottom:0;overflow:visible}
+div#header{display:none}
+#ipython_notebook{padding-left:16px}
+#noscript{width:auto;padding-top:16px;padding-bottom:16px;text-align:center;font-size:22px;color:#f00;font-weight:bold}
+#ipython_notebook img{font-family:Verdana,"Helvetica Neue",Arial,Helvetica,Geneva,sans-serif;height:24px;text-decoration:none;color:#000}
+#site{width:100%;display:none}
+.ui-button .ui-button-text{padding:.2em .8em;font-size:77%}
+input.ui-button{padding:.3em .9em}
+.navbar span{margin-top:3px}
+span#login_widget{float:right}
+.nav-header{text-transform:none}
+.navbar-nobg{background-color:transparent;background-image:none}
+#header>span{margin-top:10px}
+.modal_stretch{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;height:80%}.modal_stretch .modal-body{max-height:none;flex:1}
+@media (min-width:768px){.modal{width:700px;margin-left:-350px}}.center-nav{display:inline-block;margin-bottom:-4px}
+.alternate_upload{background-color:none;display:inline}
+.alternate_upload.form{padding:0;margin:0}
+.alternate_upload input.fileinput{background-color:#f00;position:relative;opacity:0;z-index:2;width:295px;margin-left:163px;cursor:pointer;height:26px}
+ul#tabs{margin-bottom:4px}
+ul#tabs a{padding-top:4px;padding-bottom:4px}
+ul.breadcrumb a:focus,ul.breadcrumb a:hover{text-decoration:none}
+ul.breadcrumb i.icon-home{font-size:16px;margin-right:4px}
+ul.breadcrumb span{color:#5e5e5e}
+.list_toolbar{padding:4px 0 4px 0}
+.list_toolbar [class*="span"]{min-height:26px}
+.list_header{font-weight:bold}
+.list_container{margin-top:4px;margin-bottom:20px;border:1px solid #ababab;border-radius:4px}
+.list_container>div{border-bottom:1px solid #ababab}.list_container>div:hover .list-item{background-color:#f00}
+.list_container>div:last-child{border:none}
+.list_item:hover .list_item{background-color:#ddd}
+.list_item a{text-decoration:none}
+.list_header>div,.list_item>div{padding-top:4px;padding-bottom:4px;padding-left:7px;padding-right:7px;height:22px;line-height:22px}
+.item_name{line-height:22px;height:26px}
+.item_icon{font-size:14px;color:#5e5e5e;margin-right:7px}
+.item_buttons{line-height:1em}
+.toolbar_info{height:26px;line-height:26px}
+input.nbname_input,input.engine_num_input{padding-top:3px;padding-bottom:3px;height:14px;line-height:14px;margin:0}
+input.engine_num_input{width:60px}
+.highlight_text{color:#00f}
+#project_name>.breadcrumb{padding:0;margin-bottom:0;background-color:transparent;font-weight:bold}
+.folder_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f114"}
+.notebook_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f02d"}
+.ansibold{font-weight:bold}
+.ansiblack{color:#000}
+.ansired{color:#8b0000}
+.ansigreen{color:#006400}
+.ansiyellow{color:#a52a2a}
+.ansiblue{color:#00008b}
+.ansipurple{color:#9400d3}
+.ansicyan{color:#4682b4}
+.ansigray{color:#808080}
+.ansibgblack{background-color:#000}
+.ansibgred{background-color:#f00}
+.ansibggreen{background-color:#008000}
+.ansibgyellow{background-color:#ff0}
+.ansibgblue{background-color:#00f}
+.ansibgpurple{background-color:#f0f}
+.ansibgcyan{background-color:#0ff}
+.ansibggray{background-color:#808080}
+div.cell{border:1px solid transparent;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}div.cell.selected{border-radius:4px;border:thin #ababab solid}
+div.cell.edit_mode{border-radius:4px;border:thin #008000 solid}
+div.cell{width:100%;padding:5px 5px 5px 0;margin:0;outline:none}
+div.prompt{min-width:11ex;padding:.4em;margin:0;font-family:monospace;text-align:right;line-height:1.21429em}
+@media (max-width:480px){div.prompt{text-align:left}}div.inner_cell{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+div.input_area{border:1px solid #cfcfcf;border-radius:4px;background:#f7f7f7;line-height:1.21429em}
+div.prompt:empty{padding-top:0;padding-bottom:0}
+div.input{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+@media (max-width:480px){div.input{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}}div.input_prompt{color:#000080;border-top:1px solid transparent}
+div.input_area>div.highlight{margin:.4em;border:none;padding:0;background-color:transparent}
+div.input_area>div.highlight>pre{margin:0;border:none;padding:0;background-color:transparent}
+.CodeMirror{line-height:1.21429em;height:auto;background:none;}
+.CodeMirror-scroll{overflow-y:hidden;overflow-x:auto}
+.CodeMirror-lines{padding:.4em}
+.CodeMirror-linenumber{padding:0 8px 0 4px}
+.CodeMirror-gutters{border-bottom-left-radius:4px;border-top-left-radius:4px}
+.CodeMirror pre{padding:0;border:0;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+pre code{display:block;padding:.5em}
+.highlight-base,pre code,pre .subst,pre .tag .title,pre .lisp .title,pre .clojure .built_in,pre .nginx .title{color:#000}
+.highlight-string,pre .string,pre .constant,pre .parent,pre .tag .value,pre .rules .value,pre .rules .value .number,pre .preprocessor,pre .ruby .symbol,pre .ruby .symbol .string,pre .aggregate,pre .template_tag,pre .django .variable,pre .smalltalk .class,pre .addition,pre .flow,pre .stream,pre .bash .variable,pre .apache .tag,pre .apache .cbracket,pre .tex .command,pre .tex .special,pre .erlang_repl .function_or_atom,pre .markdown .header{color:#ba2121}
+.highlight-comment,pre .comment,pre .annotation,pre .template_comment,pre .diff .header,pre .chunk,pre .markdown .blockquote{color:#408080;font-style:italic}
+.highlight-number,pre .number,pre .date,pre .regexp,pre .literal,pre .smalltalk .symbol,pre .smalltalk .char,pre .go .constant,pre .change,pre .markdown .bullet,pre .markdown .link_url{color:#080}
+pre .label,pre .javadoc,pre .ruby .string,pre .decorator,pre .filter .argument,pre .localvars,pre .array,pre .attr_selector,pre .important,pre .pseudo,pre .pi,pre .doctype,pre .deletion,pre .envvar,pre .shebang,pre .apache .sqbracket,pre .nginx .built_in,pre .tex .formula,pre .erlang_repl .reserved,pre .prompt,pre .markdown .link_label,pre .vhdl .attribute,pre .clojure .attribute,pre .coffeescript .property{color:#88f}
+.highlight-keyword,pre .keyword,pre .id,pre .phpdoc,pre .aggregate,pre .css .tag,pre .javadoctag,pre .phpdoc,pre .yardoctag,pre .smalltalk .class,pre .winutils,pre .bash .variable,pre .apache .tag,pre .go .typename,pre .tex .command,pre .markdown .strong,pre .request,pre .status{color:#008000;font-weight:bold}
+.highlight-builtin,pre .built_in{color:#008000}
+pre .markdown .emphasis{font-style:italic}
+pre .nginx .built_in{font-weight:normal}
+pre .coffeescript .javascript,pre .javascript .xml,pre .tex .formula,pre .xml .javascript,pre .xml .vbscript,pre .xml .css,pre .xml .cdata{opacity:.5}
+.cm-s-ipython span.cm-variable{color:#000}
+.cm-s-ipython span.cm-keyword{color:#008000;font-weight:bold}
+.cm-s-ipython span.cm-number{color:#080}
+.cm-s-ipython span.cm-comment{color:#408080;font-style:italic}
+.cm-s-ipython span.cm-string{color:#ba2121}
+.cm-s-ipython span.cm-builtin{color:#008000}
+.cm-s-ipython span.cm-error{color:#f00}
+.cm-s-ipython span.cm-operator{color:#a2f;font-weight:bold}
+.cm-s-ipython span.cm-meta{color:#a2f}
+.cm-s-ipython span.cm-tab{background:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);background-position:right;background-repeat:no-repeat}
+div.output_wrapper{position:relative;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+div.output_scroll{height:24em;width:100%;overflow:auto;border-radius:4px;-webkit-box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);-moz-box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);display:block}
+div.output_collapsed{margin:0;padding:0;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+div.out_prompt_overlay{height:100%;padding:0 .4em;position:absolute;border-radius:4px}
+div.out_prompt_overlay:hover{-webkit-box-shadow:inset 0 0 1px #000;-moz-box-shadow:inset 0 0 1px #000;box-shadow:inset 0 0 1px #000;background:rgba(240,240,240,0.5)}
+div.output_prompt{color:#8b0000}
+div.output_area{padding:0;page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}div.output_area .MathJax_Display{text-align:left !important}
+div.output_area .rendered_html table{margin-left:0;margin-right:0}
+div.output_area .rendered_html img{margin-left:0;margin-right:0}
+.output{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+@media (max-width:480px){div.output_area{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}}div.output_area pre{margin:0;padding:0;border:0;vertical-align:baseline;color:#000;background-color:transparent;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
+div.output_subarea{padding:.4em .4em 0 .4em;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+div.output_text{text-align:left;color:#000;line-height:1.21429em}
+div.output_stderr{background:#fdd;}
+div.output_latex{text-align:left}
+div.output_javascript:empty{padding:0}
+.js-error{color:#8b0000}
+div.raw_input_container{font-family:monospace;padding-top:5px}
+span.raw_input_prompt{}
+input.raw_input{font-family:inherit;font-size:inherit;color:inherit;width:auto;vertical-align:baseline;padding:0 .25em;margin:0 .25em}
+input.raw_input:focus{box-shadow:none}
+p.p-space{margin-bottom:10px}
+.rendered_html{color:#000;}.rendered_html em{font-style:italic}
+.rendered_html strong{font-weight:bold}
+.rendered_html u{text-decoration:underline}
+.rendered_html :link{text-decoration:underline}
+.rendered_html :visited{text-decoration:underline}
+.rendered_html h1{font-size:185.7%;margin:1.08em 0 0 0;font-weight:bold;line-height:1}
+.rendered_html h2{font-size:157.1%;margin:1.27em 0 0 0;font-weight:bold;line-height:1}
+.rendered_html h3{font-size:128.6%;margin:1.55em 0 0 0;font-weight:bold;line-height:1}
+.rendered_html h4{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1}
+.rendered_html h5{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1;font-style:italic}
+.rendered_html h6{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1;font-style:italic}
+.rendered_html h1:first-child{margin-top:.538em}
+.rendered_html h2:first-child{margin-top:.636em}
+.rendered_html h3:first-child{margin-top:.777em}
+.rendered_html h4:first-child{margin-top:1em}
+.rendered_html h5:first-child{margin-top:1em}
+.rendered_html h6:first-child{margin-top:1em}
+.rendered_html ul{list-style:disc;margin:0 2em}
+.rendered_html ul ul{list-style:square;margin:0 2em}
+.rendered_html ul ul ul{list-style:circle;margin:0 2em}
+.rendered_html ol{list-style:decimal;margin:0 2em}
+.rendered_html ol ol{list-style:upper-alpha;margin:0 2em}
+.rendered_html ol ol ol{list-style:lower-alpha;margin:0 2em}
+.rendered_html ol ol ol ol{list-style:lower-roman;margin:0 2em}
+.rendered_html ol ol ol ol ol{list-style:decimal;margin:0 2em}
+.rendered_html *+ul{margin-top:1em}
+.rendered_html *+ol{margin-top:1em}
+.rendered_html hr{color:#000;background-color:#000}
+.rendered_html pre{margin:1em 2em}
+.rendered_html pre,.rendered_html code{border:0;background-color:#fff;color:#000;font-size:100%;padding:0}
+.rendered_html blockquote{margin:1em 2em}
+.rendered_html table{margin-left:auto;margin-right:auto;border:1px solid #000;border-collapse:collapse}
+.rendered_html tr,.rendered_html th,.rendered_html td{border:1px solid #000;border-collapse:collapse;margin:1em 2em}
+.rendered_html td,.rendered_html th{text-align:left;vertical-align:middle;padding:4px}
+.rendered_html th{font-weight:bold}
+.rendered_html *+table{margin-top:1em}
+.rendered_html p{text-align:justify}
+.rendered_html *+p{margin-top:1em}
+.rendered_html img{display:block;margin-left:auto;margin-right:auto}
+.rendered_html *+img{margin-top:1em}
+div.text_cell{padding:5px 5px 5px 0;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+@media (max-width:480px){div.text_cell>div.prompt{display:none}}div.text_cell_render{outline:none;resize:none;width:inherit;border-style:none;padding:.5em .5em .5em .4em;color:#000}
+a.anchor-link:link{text-decoration:none;padding:0 20px;visibility:hidden}
+h1:hover .anchor-link,h2:hover .anchor-link,h3:hover .anchor-link,h4:hover .anchor-link,h5:hover .anchor-link,h6:hover .anchor-link{visibility:visible}
+div.cell.text_cell.rendered{padding:0}
+.widget-area{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-area .widget-subarea{padding:.44em .4em .4em 1px;margin-left:6px;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
+.widget-hlabel{min-width:10ex;padding-right:8px;padding-top:3px;text-align:right;vertical-align:text-top}
+.widget-vlabel{padding-bottom:5px;text-align:center;vertical-align:text-bottom}
+.widget-hreadout{padding-left:8px;padding-top:3px;text-align:left;vertical-align:text-top}
+.widget-vreadout{padding-top:5px;text-align:center;vertical-align:text-top}
+.slide-track{border:1px solid #ccc;background:#fff;border-radius:4px;}
+.widget-hslider{padding-left:8px;padding-right:5px;overflow:visible;width:348px;height:5px;max-height:5px;margin-top:11px;margin-bottom:10px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-hslider .ui-slider{border:0 !important;background:none !important;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-hslider .ui-slider .ui-slider-handle{width:14px !important;height:28px !important;margin-top:-8px !important}
+.widget-vslider{padding-bottom:8px;overflow:visible;width:5px;max-width:5px;height:250px;margin-left:12px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}.widget-vslider .ui-slider{border:0 !important;background:none !important;margin-left:-4px;margin-top:5px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-vslider .ui-slider .ui-slider-handle{width:28px !important;height:14px !important;margin-left:-9px}
+.widget-text{width:350px;margin:0 !important}
+.widget-listbox{width:364px;margin-bottom:0}
+.widget-numeric-text{width:150px;margin:0 !important}
+.widget-progress{width:363px}.widget-progress .bar{-webkit-transition:none;-moz-transition:none;-ms-transition:none;-o-transition:none;transition:none}
+.widget-combo-btn{min-width:138px;}
+.widget-box{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
+.widget-hbox{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+.widget-hbox-single{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;height:30px}
+.widget-vbox{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
+.widget-vbox-single{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;width:30px}
+.widget-modal{overflow:hidden;position:absolute !important;top:0;left:0;margin-left:0 !important}
+.widget-modal-body{max-height:none !important}
+.widget-container{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
+.widget-radio-box{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;padding-top:4px}
+.docked-widget-modal{overflow:hidden;position:relative !important;top:0 !important;left:0 !important;margin-left:0 !important}
+body{background-color:#fff}
+body.notebook_app{overflow:hidden}
+@media (max-width:767px){body.notebook_app{padding-left:0;padding-right:0}}span#notebook_name{height:1em;line-height:1em;padding:3px;border:none;font-size:146.5%}
+div#notebook_panel{margin:0 0 0 0;padding:0;-webkit-box-shadow:0 -1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,0.1);box-shadow:0 -1px 10px rgba(0,0,0,0.1)}
+div#notebook{font-size:14px;line-height:20px;overflow-y:scroll;overflow-x:auto;width:100%;padding:1em 0 1em 0;margin:0;border-top:1px solid #ababab;outline:none;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
+div.ui-widget-content{border:1px solid #ababab;outline:none}
+pre.dialog{background-color:#f7f7f7;border:1px solid #ddd;border-radius:4px;padding:.4em;padding-left:2em}
+p.dialog{padding:.2em}
+pre,code,kbd,samp{white-space:pre-wrap}
+#fonttest{font-family:monospace}
+p{margin-bottom:0}
+.end_space{height:200px}
+.celltoolbar{border:thin solid #cfcfcf;border-bottom:none;background:#eee;border-radius:3px 3px 0 0;width:100%;-webkit-box-pack:end;height:22px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
+.ctb_hideshow{display:none;vertical-align:bottom;padding-right:2px}
+.celltoolbar>div{padding-top:0}
+.ctb_global_show .ctb_show.ctb_hideshow{display:block}
+.ctb_global_show .ctb_show+.input_area,.ctb_global_show .ctb_show+div.text_cell_input{border-top-right-radius:0;border-top-left-radius:0}
+.celltoolbar .button_container select{margin:10px;margin-top:1px;margin-bottom:0;padding:0;font-size:87%;width:auto;display:inline-block;height:18px;line-height:18px;vertical-align:top}
+.celltoolbar label{display:inline-block;height:15px;line-height:15px;vertical-align:top}
+.celltoolbar label span{font-size:85%}
+.celltoolbar input[type=checkbox]{margin:0;margin-left:4px;margin-right:4px}
+.celltoolbar .ui-button{border:none;vertical-align:top;height:20px;min-width:30px}
+.completions{position:absolute;z-index:10;overflow:hidden;border:1px solid #ababab;border-radius:4px;-webkit-box-shadow:0 6px 10px -1px #adadad;-moz-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad}
+.completions select{background:#fff;outline:none;border:none;padding:0;margin:0;overflow:auto;font-family:monospace;font-size:110%;color:#000;width:auto}
+.completions select option.context{color:#0064cd}
+#menubar .navbar-inner{min-height:28px;border-top:1px;border-radius:0 0 4px 4px}
+#menubar .navbar{margin-bottom:8px}
+.nav-wrapper{border-bottom:1px solid #d4d4d4}
+#menubar li.dropdown{line-height:12px}
+i.menu-icon{padding-top:4px}
+ul#help_menu li a{overflow:hidden;padding-right:2.2em}ul#help_menu li a i{margin-right:-1.2em}
+#notification_area{z-index:10}
+.indicator_area{color:#777;padding:4px 3px;margin:0;width:11px;z-index:10;text-align:center}
+#kernel_indicator{margin-right:-16px}
+.edit_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f040"}
+.command_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:' '}
+.kernel_idle_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f10c"}
+.kernel_busy_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f111"}
+.notification_widget{color:#777;padding:1px 12px;margin:2px 4px;z-index:10;border:1px solid #ccc;border-radius:4px;background:rgba(240,240,240,0.5)}.notification_widget.span{padding-right:2px}
+div#pager_splitter{height:8px}
+#pager-container{position:relative;padding:15px 0}
+div#pager{font-size:14px;line-height:20px;overflow:auto;display:none}div#pager pre{line-height:1.21429em;color:#000;background-color:#f7f7f7;padding:.4em}
+.quickhelp{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
+.shortcut_key{display:inline-block;width:20ex;text-align:right;font-family:monospace}
+.shortcut_descr{display:inline-block;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
+span#save_widget{padding:0 5px;margin-top:12px}
+span#checkpoint_status,span#autosave_status{font-size:small}
+@media (max-width:767px){span#save_widget{font-size:small} span#checkpoint_status,span#autosave_status{font-size:x-small}}@media (max-width:767px){span#checkpoint_status,span#autosave_status{display:none}}@media (min-width:768px) and (max-width:979px){span#checkpoint_status{display:none} span#autosave_status{font-size:x-small}}.toolbar{padding:0 10px;margin-top:-5px}.toolbar select,.toolbar label{width:auto;height:26px;vertical-align:middle;margin-right:2px;margin-bottom:0;display:inline;font-size:92%;margin-left:.3em;margin-right:.3em;padding:0;padding-top:3px}
+.toolbar .btn{padding:2px 8px}
+.toolbar .btn-group{margin-top:0}
+.toolbar-inner{border:none !important;-webkit-box-shadow:none !important;-moz-box-shadow:none !important;box-shadow:none !important}
+#maintoolbar{margin-bottom:0}
+@-moz-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-webkit-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-moz-keyframes fadeIn{from{opacity:0} to{opacity:1}}@-webkit-keyframes fadeIn{from{opacity:0} to{opacity:1}}.bigtooltip{overflow:auto;height:200px;-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms}
+.smalltooltip{-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms;text-overflow:ellipsis;overflow:hidden;height:80px}
+.tooltipbuttons{position:absolute;padding-right:15px;top:0;right:0}
+.tooltiptext{padding-right:30px}
+.ipython_tooltip{max-width:700px;-webkit-animation:fadeOut 400ms;-moz-animation:fadeOut 400ms;animation:fadeOut 400ms;-webkit-animation:fadeIn 400ms;-moz-animation:fadeIn 400ms;animation:fadeIn 400ms;vertical-align:middle;background-color:#f7f7f7;overflow:visible;border:#ababab 1px solid;outline:none;padding:3px;margin:0;padding-left:7px;font-family:monospace;min-height:50px;-moz-box-shadow:0 6px 10px -1px #adadad;-webkit-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad;border-radius:4px;position:absolute;z-index:2}.ipython_tooltip a{float:right}
+.ipython_tooltip .tooltiptext pre{border:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;font-size:100%;background-color:#f7f7f7}
+.pretooltiparrow{left:0;margin:0;top:-16px;width:40px;height:16px;overflow:hidden;position:absolute}
+.pretooltiparrow:before{background-color:#f7f7f7;border:1px #ababab solid;z-index:11;content:"";position:absolute;left:15px;top:10px;width:25px;height:25px;-webkit-transform:rotate(45deg);-moz-transform:rotate(45deg);-ms-transform:rotate(45deg);-o-transform:rotate(45deg)}
+
+    </style>
+<style type="text/css">
+    .highlight .hll { background-color: #ffffcc }
+.highlight  { background: #f8f8f8; }
+.highlight .c { color: #408080; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
+.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #888888 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #7D9029 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #A0A000 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
+    </style>
+
+
+<style type="text/css">
+/* Overrides of notebook CSS for static HTML export */
 body {
-    font-family: "Helvetica Neue", Helvetica, "Hiragino Sans GB", Arial, sans-serif;
-    font-size: 13px;
-    line-height: 18px;
-    color: #737373;
-    background-color: white;
-    margin: 10px 13px 10px 13px;
-}
-table {
-	margin: 10px 0 15px 0;
-	border-collapse: collapse;
-}
-td,th {	
-	border: 1px solid #ddd;
-	padding: 3px 10px;
-}
-th {
-	padding: 5px 10px;	
+  overflow: visible;
+  padding: 8px;
 }
 
-a {
-    color: #0069d6;
-}
-a:hover {
-    color: #0050a3;
-    text-decoration: none;
-}
-a img {
-    border: none;
-}
-p {
-    margin-bottom: 9px;
-}
-h1,
-h2,
-h3,
-h4,
-h5,
-h6 {
-    color: #404040;
-    line-height: 36px;
-}
-h1 {
-    margin-bottom: 18px;
-    font-size: 30px;
-}
-h2 {
-    font-size: 24px;
-}
-h3 {
-    font-size: 18px;
-}
-h4 {
-    font-size: 16px;
-}
-h5 {
-    font-size: 14px;
-}
-h6 {
-    font-size: 13px;
-}
-hr {
-    margin: 0 0 19px;
-    border: 0;
-    border-bottom: 1px solid #ccc;
-}
-blockquote {
-    padding: 13px 13px 21px 15px;
-    margin-bottom: 18px;
-    font-family:georgia,serif;
-    font-style: italic;
-}
-blockquote:before {
-    content:"\201C";
-    font-size:40px;
-    margin-left:-10px;
-    font-family:georgia,serif;
-    color:#eee;
-}
-blockquote p {
-    font-size: 14px;
-    font-weight: 300;
-    line-height: 18px;
-    margin-bottom: 0;
-    font-style: italic;
-}
-code, pre {
-    font-family: Monaco, Andale Mono, Courier New, monospace;
-}
-code {
-    background-color: #fee9cc;
-    color: rgba(0, 0, 0, 0.75);
-    padding: 1px 3px;
-    font-size: 12px;
-    -webkit-border-radius: 3px;
-    -moz-border-radius: 3px;
-    border-radius: 3px;
-}
-pre {
-    display: block;
-    padding: 14px;
-    margin: 0 0 18px;
-    line-height: 16px;
-    font-size: 11px;
-    border: 1px solid #d9d9d9;
-    white-space: pre-wrap;
-    word-wrap: break-word;
-}
-pre code {
-    background-color: #fff;
-    color:#737373;
-    font-size: 11px;
-    padding: 0;
-}
-sup {
-    font-size: 0.83em;
-    vertical-align: super;
-    line-height: 0;
-}
-* {
-	-webkit-print-color-adjust: exact;
-}
-@media screen and (min-width: 914px) {
-    body {
-        width: 854px;
-        margin:10px auto;
-    }
+div#notebook {
+  overflow: visible;
+  border-top: none;
 }
+
 @media print {
-	body,code,pre code,h1,h2,h3,h4,h5,h6 {
-		color: black;
-	}
-	table, pre {
-		page-break-inside: avoid;
-	}
+  div.cell {
+    display: block;
+    page-break-inside: avoid;
+  } 
+  div.output_wrapper { 
+    display: block;
+    page-break-inside: avoid; 
+  }
+  div.output { 
+    display: block;
+    page-break-inside: avoid; 
+  }
 }
 </style>
-<title>Key differences between Python 2 and 3</title>
+
+<!-- Custom stylesheet, it must be in the same directory as the html file -->
+<link rel="stylesheet" href="custom.css">
+
+<!-- Loading mathjax macro -->
+<!-- Load mathjax -->
+    <script src="https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
+    <!-- MathJax configuration -->
+    <script type="text/x-mathjax-config">
+    MathJax.Hub.Config({
+        tex2jax: {
+            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
+            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
+            processEscapes: true,
+            processEnvironments: true
+        },
+        // Center justify equations in code and markdown cells. Elsewhere
+        // we use CSS to left justify single line equations in code cells.
+        displayAlign: 'center',
+        "HTML-CSS": {
+            styles: {'.MathJax_Display': {"margin": 0}},
+            linebreaks: { automatic: true }
+        }
+    });
+    </script>
+    <!-- End of mathjax configuration -->
 
 </head>
 <body>
-<p><a href="http://sebastianraschka.com">Sebastian Raschka</a><br/>
-last updated: 05/24/2014</p>
-
-<p><br></p>
-
-<p><strong>This is a subsection of <a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1">"A collection of not-so-obvious Python stuff you should know!"</a></strong></p>
-
-<p><a id='sections'></a></p>
-
-<p><br></p>
-
-<h2>Key differences between Python 2 and 3</h2>
-
-<p><br></p>
-
-<p>There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,</p>
-
+  <div tabindex="-1" id="notebook" class="border-box-sizing">
+    <div class="container" id="notebook-container">
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a href="http://sebastianraschka.com">Sebastian Raschka</a></p>
+<p>last updated 05/24/2014</p>
 <ul>
-<li><p><a href="https://wiki.python.org/moin/Python2orPython3">https://wiki.python.org/moin/Python2orPython3</a></p></li>
-<li><p><a href="https://docs.python.org/3.0/whatsnew/3.0.html">https://docs.python.org/3.0/whatsnew/3.0.html</a></p></li>
-<li><p><a href="http://python3porting.com/differences.html">http://python3porting.com/differences.html</a></p></li>
-<li><p><a href="https://docs.python.org/3/howto/pyporting.html">https://docs.python.org/3/howto/pyporting.html</a></p></li>
+<li><p><a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1">Open in IPython nbviewer</a></p></li>
+<li><p><a href="https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb">Link to this IPython notebook on Github</a></p></li>
+<li><p><a href="https://github.com/rasbt/python_reference">Link to the GitHub repository python_reference</a></p></li>
 </ul>
-
-
-<p>etc.</p>
-
-<p>But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!
-(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)</p>
-
-<p><a id='py23_overview'></a></p>
-
-<p><br></p>
-
-<h3>Overview - Key differences between Python 2 and 3</h3>
-
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<hr>
+I would be happy to hear your comments and suggestions.<br />Please feel free to drop me a note via <a href="https://twitter.com/rasbt">twitter</a>, <script type="text/javascript">
+<!--
+h='&#x67;&#x6d;&#x61;&#x69;&#108;&#46;&#x63;&#x6f;&#x6d;';a='&#64;';n='&#98;&#108;&#x75;&#x65;&#x77;&#x6f;&#x6f;&#100;&#116;&#114;&#x65;&#x65;';e=n+a+h;
+document.write('<a h'+'ref'+'="ma'+'ilto'+':'+e+'">'+'email'+'<\/'+'a'+'>');
+// -->
+</script><noscript>&#x65;&#x6d;&#x61;&#x69;&#108;&#32;&#40;&#98;&#108;&#x75;&#x65;&#x77;&#x6f;&#x6f;&#100;&#116;&#114;&#x65;&#x65;&#32;&#x61;&#116;&#32;&#x67;&#x6d;&#x61;&#x69;&#108;&#32;&#100;&#x6f;&#116;&#32;&#x63;&#x6f;&#x6d;&#x29;</noscript>, or <a href="https://plus.google.com/118404394130788869227">google+</a>.
+<hr>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Key-differences-between-Python-2.7.x-and-Python-3.x">Key differences between Python 2.7.x and Python 3.x<a class="anchor-link" href="#Key-differences-between-Python-2.7.x-and-Python-3.x">&#182;</a></h1>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines &quot;just go with the version your favorite tutorial was written in, and check out the differences later on.&quot;</p>
+<p>But what if you are starting a new project and have the choice to pick? I would say there is currently no &quot;right&quot; or &quot;wrong&quot; as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use. However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Sections">Sections<a class="anchor-link" href="#Sections">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
 <ul>
-<li><a href="#unicode">Unicode</a></li>
-<li><a href="#print">The print statement</a></li>
-<li><a href="#integer_div">Integer division</a></li>
-<li><a href="#xrange">xrange()</a></li>
-<li><a href="#raising_exceptions">Raising exceptions</a></li>
-<li><a href="#handling_exceptions">Handling exceptions</a></li>
-<li><a href="#next_next">next() function and .next() method</a></li>
-<li><a href="#loop_leak">Loop variables and leaking into the global scope</a></li>
-<li><a href="#compare_unorder">Comparing unorderable types</a></li>
+<li><p><a href="#future_module">Using the <code>__future__</code> module</a></p></li>
+<li><p><a href="#The-print-function">The print function</a></p></li>
+<li><p><a href="#Integer-division">Integer division</a></p></li>
+<li><p><a href="#xrange">xrange</a></p></li>
+<li><p><a href="#Raising-exceptions">Raising exceptions</a></p></li>
+<li><p><a href="#Handling-exceptions">Handling exceptions</a></p></li>
+<li><p><a href="#The-next-function-and-next-method">The next() function and .next() method</a></p></li>
+<li><p><a href="#For-loop-variables-and-the-global-namespace-leak">For-loop variables and the global namespace leak</a></p></li>
+<li><p><a href="#Comparing-unorderable-types">Comparing unorderable types</a></p></li>
+<li><p><a href="#More-articles-about-Python-2-and-Python-3">More articles about Python 2 and Python 3</a></p></li>
 </ul>
-
-
-<p><br>
-<br></p>
-
-<p><a id='unicode'></a>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a id='future_module'></a></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h3 id="the-__future__-module">The <code>__future__</code> module</h3>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Python 3.x introduced some Python 2-incompatible keywords and features that can be imported via the in-built <code>__future__</code> module in Python 2. It is recommended to use <code>__future__</code> imports it if you are planning Python 3.x support for your code. For example, if we want Python 3.x's integer division behavior in Python 2, we can import it via</p>
+<pre><code>from __future__ import division</code></pre>
+<p>More features that can be imported from the <code>__future__</code> module are listed in the table below:</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<table>
+<tr><th class="head">
+feature
+</th>
+<th class="head">
+optional in
+</th>
+<th class="head">
+mandatory in
+</th>
+<th class="head">
+effect
+</th>
+</tr>
+
+<tr><td>
+nested_scopes
+</td>
+<td>
+2.1.0b1
+</td>
+<td>
+2.2
+</td>
+<td>
+<a href="http://www.python.org/dev/peps/pep-0227"><strong>PEP 227</strong></a>: <em>Statically Nested Scopes</em>
+</td>
+</tr>
+<tr><td>
+generators
+</td>
+<td>
+2.2.0a1
+</td>
+<td>
+2.3
+</td>
+<td>
+<a class="pep reference external" href="http://www.python.org/dev/peps/pep-0255"><strong>PEP 255</strong></a>: <em>Simple Generators</em>
+</td>
+</tr>
+<tr><td>
+division
+</td>
+<td>
+2.2.0a2
+</td>
+<td>
+3.0
+</td>
+<td>
+&lt;<a class="pep reference external" href="http://www.python.org/dev/peps/pep-0238"><strong>PEP 238</strong></a>: <em>Changing the Division Operator</em>
+</td>
+</tr>
+<tr><td>
+absolute_import
+</td>
+<td>
+2.5.0a1
+</td>
+<td>
+3.0
+</td>
+<td>
+<a href="http://www.python.org/dev/peps/pep-0328"><strong>PEP 328</strong></a>: <em>Imports: Multi-Line and Absolute/Relative</em>
+</td>
+</tr>
+<tr><td>
+with_statement
+</td>
+<td>
+2.5.0a1
+</td>
+<td>
+2.6
+</td>
+<td>
+<a href="http://www.python.org/dev/peps/pep-0343"><strong>PEP 343</strong></a>: <em>The “with” Statement</em>
+</td>
+</tr>
+<tr><td>
+print_function
+</td>
+<td>
+2.6.0a2
+</td>
+<td>
+3.0
+</td>
+<td>
+<a href="http://www.python.org/dev/peps/pep-3105"><strong>PEP 3105</strong></a>: <em>Make print a function</em>
+</td>
+</tr>
+<tr><td>
+unicode_literals
+</td>
+<td>
+2.6.0a2
+</td>
+<td>
+3.0
+</td>
+<td>
+<a href="http://www.python.org/dev/peps/pep-3112"><strong>PEP 3112</strong></a>: <em>Bytes literals in Python 3000</em>
+</td>
+</tr>
+</table>
 <br>
-<br></p>
-
-<h3>Unicode...</h3>
-
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
-
-<h4>- Python 2:</h4>
-
-<p>We have ASCII <code>str()</code> types, separate <code>unicode()</code>, but no <code>byte</code> type</p>
-
-<h4>- Python 3:</h4>
-
-<p>Now, we finally have Unicode (utf-8) <code>str</code>ings, and 2 byte classes: <code>byte</code> and <code>bytearray</code>s</p>
-
-<p><br></p>
-
-<!-- HTML generated using hilite.me -->
-
-
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">#############</span>
-<span style="color: #888888"># Python 2</span>
-<span style="color: #888888">#############</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(unicode(<span style="background-color: #fff0f0">&#39;is like a python3 str()&#39;</span>))
-<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;unicode&#39;</span><span style="color: #333333">&gt;</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(b<span style="background-color: #fff0f0">&#39;byte type does not exist&#39;</span>)
-<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;str&#39;</span><span style="color: #333333">&gt;</span>
+<center>
+(Source: <a href="https://docs.python.org/2/library/__future__.html#module-__future__">https://docs.python.org/2/library/<strong>future</strong>.html</a>)
+</center>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[1]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">platform</span> <span class="kn">import</span> <span class="n">python_version</span>
+</pre></div>
 
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="background-color: #fff0f0">&#39;they are really&#39;</span> <span style="color: #333333">+</span> b<span style="background-color: #fff0f0">&#39; the same&#39;</span>
-<span style="background-color: #fff0f0">&#39;they are really the same&#39;</span>
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="The-print-function">The print function<a class="anchor-link" href="#The-print-function">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print function doesn't require the parantheses for invoking the print function (it wouldn't choke on them).<br />In contrast, Python 3 would raise a <code>SyntaxError</code> if we called the print function the Python 2-way without the parentheses.</p>
+<p>I think this change in Python 3 makes sense in terms of consistency, since it is the common way in Python to invoke function calls with its parentheses.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[3]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+<span class="k">print</span> <span class="s">&#39;Hello, World!&#39;</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;Hello, World!&#39;</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&quot;text&quot;</span><span class="p">,</span> <span class="p">;</span> <span class="k">print</span> <span class="s">&#39;print more text on the same line&#39;</span>
+</pre></div>
 
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(<span style="color: #007020">bytearray</span>(b<span style="background-color: #fff0f0">&#39;bytearray oddly does exist though&#39;</span>))
-<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;bytearray&#39;</span><span style="color: #333333">&gt;</span>
+</div>
+</div>
+</div>
 
-<span style="color: #888888">#############</span>
-<span style="color: #888888"># Python 3</span>
-<span style="color: #888888">#############</span>
+<div class="output_wrapper">
+<div class="output">
 
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;strings are now utf-8 </span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\u03BC</span><span style="background-color: #fff0f0">nico</span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\u0394</span><span style="background-color: #fff0f0">é!&#39;</span>)
-strings are now utf<span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">8</span> μnicoΔé<span style="color: #FF0000; background-color: #FFAAAA">!</span>
 
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+Hello, World!
+Hello, World!
+text print more text on the same line
 
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(b<span style="background-color: #fff0f0">&#39; and we have byte types for storing data&#39;</span>)
-<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">bytes</span><span style="background-color: #fff0f0">&#39;&gt;</span>
+</pre>
+</div>
+</div>
 
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(<span style="color: #007020">bytearray</span>(b<span style="background-color: #fff0f0">&#39;but also bytearrays for those who prefer them over strings&#39;</span>))
-<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">bytearray</span><span style="background-color: #fff0f0">&#39;&gt;</span>
+</div>
+</div>
 
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="background-color: #fff0f0">&#39;string&#39;</span> <span style="color: #333333">+</span> b<span style="background-color: #fff0f0">&#39;bytes for data&#39;</span>
-Traceback (most recent call last):s
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">TypeError</span>: Can<span style="background-color: #fff0f0">&#39;t convert &#39;</span><span style="color: #007020">bytes</span><span style="background-color: #fff0f0">&#39; object to str implicitly</span>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[2]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;Hello, World!&#39;</span><span class="p">)</span>
+
+<span class="k">print</span><span class="p">(</span><span class="s">&quot;some text,&quot;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">)</span> 
+<span class="k">print</span><span class="p">(</span><span class="s">&#39; print more text on the same line&#39;</span><span class="p">)</span>
 </pre></div>
 
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+Hello, World!
+some text, print more text on the same line
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[3]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Hello, World!&#39;</span>
+</pre></div>
 
-<p><a id='print'></a>
-<br>
-<br></p>
-
-<h3>The print statement</h3>
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansicyan">  File </span><span class="ansigreen">&quot;&lt;ipython-input-3-139a7c5835bd&gt;&quot;</span><span class="ansicyan">, line </span><span class="ansigreen">1</span>
+<span class="ansired">    print &apos;Hello, World!&apos;</span>
+                        ^
+<span class="ansired">SyntaxError</span><span class="ansired">:</span> invalid syntax
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Integer-division">Integer division<a class="anchor-link" href="#Integer-division">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>This change is particularly dangerous if you are porting code, or if you are executing Python 3 code in Python 2, since the change in integer-division behavior can often go unnoticed (it doesn't raise a <code>SyntaxError</code>).<br />So, I still tend to use a <code>float(3)/2</code> or <code>3/2.0</code> instead of a <code>3/2</code> in my Python 3 scripts to save the Python 2 guys some trouble (and vice versa, I recommend a <code>from __future__ import division</code> in your Python 2 scripts).</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[4]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+<span class="k">print</span> <span class="s">&#39;3 / 2 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">2</span>
+<span class="k">print</span> <span class="s">&#39;3 // 2 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">//</span> <span class="mi">2</span>
+<span class="k">print</span> <span class="s">&#39;3 / 2.0 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">/</span> <span class="mf">2.0</span>
+<span class="k">print</span> <span class="s">&#39;3 // 2.0 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">//</span> <span class="mf">2.0</span>
+</pre></div>
 
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+</div>
+</div>
+</div>
 
-<p>Very trivial, but this change makes sense, Python 3 now only accepts <code>print</code>s with proper parentheses - just like the other function calls ...</p>
+<div class="output_wrapper">
+<div class="output">
 
-<p><br></p>
 
-<!-- HTML generated using hilite.me -->
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+3 / 2 = 1
+3 // 2 = 1
+3 / 2.0 = 1.5
+3 // 2.0 = 1.0
 
+</pre>
+</div>
+</div>
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
-Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
-Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
+</div>
+</div>
 
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
-Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>
-    <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
-                        <span style="color: #333333">^</span>
-<span style="color: #FF0000; font-weight: bold">SyntaxError</span>: invalid syntax
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[4]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;3 / 2 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;3 // 2 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;3 / 2.0 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;3 // 2.0 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">//</span> <span class="mf">2.0</span><span class="p">)</span>
 </pre></div>
 
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+3 / 2 = 1.5
+3 // 2 = 1
+3 / 2.0 = 1.5
+3 // 2.0 = 1.0
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="xrange">xrange<a class="anchor-link" href="#xrange">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The usage of <code>xrange()</code> is very popular in Python 2.x for creating an iterable object, e.g., in a for-loop or list/set-dictionary-comprehension.<br />The behavior was quite similar to a generator (i.e., &quot;lazy evaluation&quot;), but here the xrange-iterable is not exhaustible - meaning, you could iterate over it infinitely.</p>
+<p>Thanks to its &quot;lazy-evaluation&quot;, the advantage of the regular <code>range()</code> is that <code>xrange()</code> is generally faster if you have to iterate over it only once (e.g., in a for-loop). However, in contrast to 1-time iterations, it is not recommended if you repeat the iteration multiple times, since the generation happens every time from scratch!</p>
+<p>In Python 3, the <code>range()</code> was implemented like the <code>xrange()</code> function so that a dedicated <code>xrange()</code> function does not exist anymore (<code>xrange()</code> raises a <code>NameError</code> in Python 3).</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[5]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">timeit</span>
+
+<span class="n">n</span> <span class="o">=</span> <span class="mi">10000</span>
+<span class="k">def</span> <span class="nf">test_range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+        <span class="k">pass</span>
+    
+<span class="k">def</span> <span class="nf">test_xrange</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+        <span class="k">pass</span>    
+</pre></div>
 
-<p><br></p>
-
-<p>And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a <code>end=""</code> in Python 3:</p>
+</div>
+</div>
+</div>
 
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
 <p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[6]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+
+<span class="k">print</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">timing range()&#39;</span>
+<span class="o">%</span><span class="k">timeit</span> <span class="n">test_range</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+
+<span class="k">print</span> <span class="s">&#39;</span><span class="se">\n\n</span><span class="s">timing xrange()&#39;</span>
+<span class="o">%</span><span class="k">timeit</span> <span class="n">test_xrange</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+</pre></div>
 
-<!-- HTML generated using hilite.me -->
+</div>
+</div>
+</div>
 
+<div class="output_wrapper">
+<div class="output">
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&quot;line 1&quot;</span>, ; <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;same line&#39;</span>
-line <span style="color: #0000DD; font-weight: bold">1</span> same line
 
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&quot;line 1&quot;</span>, end<span style="color: #333333">=</span><span style="background-color: #fff0f0">&quot;&quot;</span>) ; <span style="color: #007020">print</span> (<span style="background-color: #fff0f0">&quot; same line&quot;</span>)
-line <span style="color: #0000DD; font-weight: bold">1</span> same line
-</pre></div>
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
 
+timing range()
+1000 loops, best of 3: 433 µs per loop
 
-<p><a id='integer_div'></a>
-<br>
-<br></p>
 
-<h3>Integer division</h3>
+timing xrange()
+1000 loops, best of 3: 350 µs per loop
 
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+</pre>
+</div>
+</div>
 
-<p>This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed.<br/>
-So, I still tend to use a <code>float(3)/2</code> or <code>3/2.0</code> instead of a <code>3/2</code> in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can <code>from __future__ import division</code> in your Python 2 scripts).</p>
+</div>
+</div>
 
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
 <p><br></p>
-
-<!-- HTML generated using hilite.me -->
-
-
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #0000DD; font-weight: bold">2</span>
-<span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #0000DD; font-weight: bold">2</span>
-<span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
-<span style="color: #6600EE; font-weight: bold">1.5</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
-<span style="color: #6600EE; font-weight: bold">1.0</span>
-
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #0000DD; font-weight: bold">2</span>
-<span style="color: #6600EE; font-weight: bold">1.5</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #0000DD; font-weight: bold">2</span>
-<span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
-<span style="color: #6600EE; font-weight: bold">1.5</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
-<span style="color: #6600EE; font-weight: bold">1.0</span>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[7]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;</span><span class="se">\n</span><span class="s">timing range()&#39;</span><span class="p">)</span>
+<span class="o">%</span><span class="k">timeit</span> <span class="n">test_range</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
 </pre></div>
 
+</div>
+</div>
+</div>
 
-<p><a id='xrange'></a>
-<br>
-<br></p>
-
-<h3><code>xrange()</code></h3>
-
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+<div class="output_wrapper">
+<div class="output">
 
-<p><code>xrange()</code> was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than <code>range()</code> (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch!<br/>
-In Python 3, the <code>range()</code> was implemented like the <code>xrange()</code> function so that a dedicated <code>xrange()</code> function does not exist anymore.</p>
 
-<!-- HTML generated using hilite.me -->
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
 
+timing range()
+1000 loops, best of 3: 520 µs per loop
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;</span> python <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in range(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
-<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #0000DD; font-weight: bold">66</span> msec per loop
+</pre>
+</div>
+</div>
 
-    <span style="color: #333333">&gt;</span> python <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in xrange(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
-<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #6600EE; font-weight: bold">27.8</span> msec per loop
+</div>
+</div>
 
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;</span> python3 <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in range(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
-<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #6600EE; font-weight: bold">51.1</span> msec per loop
-
-<span style="color: #333333">&gt;</span> python3 <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in xrange(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py&quot;</span>, line <span style="color: #0000DD; font-weight: bold">292</span>, <span style="color: #000000; font-weight: bold">in</span> main
-    x <span style="color: #333333">=</span> t<span style="color: #333333">.</span>timeit(number)
-  File <span style="background-color: #fff0f0">&quot;/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py&quot;</span>, line <span style="color: #0000DD; font-weight: bold">178</span>, <span style="color: #000000; font-weight: bold">in</span> timeit
-    timing <span style="color: #333333">=</span> <span style="color: #007020">self</span><span style="color: #333333">.</span>inner(it, <span style="color: #007020">self</span><span style="color: #333333">.</span>timer)
-  File <span style="background-color: #fff0f0">&quot;&lt;timeit-src&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">6</span>, <span style="color: #000000; font-weight: bold">in</span> inner
-    <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> xrange(<span style="color: #0000DD; font-weight: bold">1000000</span>):
-<span style="color: #FF0000; font-weight: bold">NameError</span>: name <span style="background-color: #fff0f0">&#39;xrange&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[8]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="nb">xrange</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
 </pre></div>
 
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">NameError</span>                                 Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-8-5d8f9b79ea70&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span>print<span class="ansiblue">(</span>xrange<span class="ansiblue">(</span><span class="ansicyan">10</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+
+<span class="ansired">NameError</span>: name &apos;xrange&apos; is not defined</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Raising-exceptions">Raising exceptions<a class="anchor-link" href="#Raising-exceptions">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Where Python 2 accepts both notations, the 'old' and the 'new' syntax, Python 3 chokes (and raises a <code>SyntaxError</code> in turn) if we don't enclose the exception argument in parentheses:</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[7]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+</pre></div>
 
-<p><a id='raising_exceptions'></a>
-<br>
-<br></p>
-
-<h3>Raising exceptions</h3>
-
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+</div>
+</div>
+</div>
 
-<p>Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a <code>SyntaxError</code> in turn) if we don't enclose the exception argument in parentheses:</p>
+<div class="output_wrapper">
+<div class="output">
 
-<p><br></p>
 
-<!-- HTML generated using hilite.me -->
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
 
+</pre>
+</div>
+</div>
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">IOError</span>: file error
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>(<span style="background-color: #fff0f0">&quot;file error&quot;</span>)
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">IOError</span>: file error
+</div>
+</div>
 
-    
-<span style="color: #888888"># Python 3    </span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>
-    <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
-                 <span style="color: #333333">^</span>
-<span style="color: #FF0000; font-weight: bold">SyntaxError</span>: invalid syntax
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>(<span style="background-color: #fff0f0">&quot;file error&quot;</span>)
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">OSError</span>: file error
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[8]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">raise</span> <span class="ne">IOError</span><span class="p">,</span> <span class="s">&quot;file error&quot;</span>
 </pre></div>
 
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">IOError</span>                                   Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-8-25f049caebb0&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span><span class="ansigreen">raise</span> IOError<span class="ansiblue">,</span> <span class="ansiblue">&quot;file error&quot;</span><span class="ansiblue"></span>
+
+<span class="ansired">IOError</span>: file error</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[9]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="s">&quot;file error&quot;</span><span class="p">)</span>
+</pre></div>
 
-<p><a id='handling_exceptions'></a>
-<br>
-<br></p>
-
-<h3>Handling exceptions</h3>
+</div>
+</div>
+</div>
 
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+<div class="output_wrapper">
+<div class="output">
 
-<p>Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the <code>as</code> keyword!</p>
 
-<!-- HTML generated using hilite.me -->
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">IOError</span>                                   Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-9-6f1c43f525b2&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span><span class="ansigreen">raise</span> IOError<span class="ansiblue">(</span><span class="ansiblue">&quot;file error&quot;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
 
+<span class="ansired">IOError</span>: file error</pre>
+</div>
+</div>
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">try</span>:
-<span style="color: #333333">...</span>     blabla
-<span style="color: #333333">...</span> <span style="color: #008800; font-weight: bold">except</span> <span style="color: #FF0000; font-weight: bold">NameError</span>, err:
-<span style="color: #333333">...</span>     <span style="color: #007020">print</span> err, <span style="background-color: #fff0f0">&#39;--&gt; our error msg&#39;</span>
-<span style="color: #333333">...</span> 
-name <span style="background-color: #fff0f0">&#39;blabla&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined <span style="color: #333333">--&gt;</span> our error msg
+</div>
+</div>
 
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">try</span>:
-<span style="color: #333333">...</span>     blabla
-<span style="color: #333333">...</span> <span style="color: #008800; font-weight: bold">except</span> <span style="color: #FF0000; font-weight: bold">NameError</span> <span style="color: #008800; font-weight: bold">as</span> err:
-<span style="color: #333333">...</span>     <span style="color: #007020">print</span>(err, <span style="background-color: #fff0f0">&#39;--&gt; our error msg&#39;</span>)
-<span style="color: #333333">...</span> 
-name <span style="background-color: #fff0f0">&#39;blabla&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined <span style="color: #333333">--&gt;</span> our error msg
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[9]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
 </pre></div>
 
+</div>
+</div>
+</div>
 
-<p><a id='next_next'></a>
-<br>
-<br></p>
+<div class="output_wrapper">
+<div class="output">
 
-<h3>The <code>next()</code> function and <code>.next()</code> method</h3>
 
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
 
-<p>Where you can use both function and method in Python 2.7.5, the <code>next()</code> function is all that remain in Python 3!</p>
+</pre>
+</div>
+</div>
 
-<!-- HTML generated using hilite.me -->
+</div>
+</div>
 
-
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> my_generator <span style="color: #333333">=</span> (letter <span style="color: #008800; font-weight: bold">for</span> letter <span style="color: #000000; font-weight: bold">in</span> <span style="background-color: #fff0f0">&#39;abcdefg&#39;</span>)
-<span style="color: #333333">&gt;&gt;&gt;</span> my_generator<span style="color: #333333">.</span>next()
-<span style="background-color: #fff0f0">&#39;a&#39;</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">next</span>(my_generator)
-<span style="background-color: #fff0f0">&#39;b&#39;</span>
-
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> my_generator <span style="color: #333333">=</span> (letter <span style="color: #008800; font-weight: bold">for</span> letter <span style="color: #000000; font-weight: bold">in</span> <span style="background-color: #fff0f0">&#39;abcdefg&#39;</span>)
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">next</span>(my_generator)
-<span style="background-color: #fff0f0">&#39;a&#39;</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> my_generator<span style="color: #333333">.</span>next()
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">AttributeError</span>: <span style="background-color: #fff0f0">&#39;generator&#39;</span> <span style="color: #007020">object</span> has no attribute <span style="background-color: #fff0f0">&#39;next&#39;</span>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[10]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">raise</span> <span class="ne">IOError</span><span class="p">,</span> <span class="s">&quot;file error&quot;</span>
 </pre></div>
 
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansicyan">  File </span><span class="ansigreen">&quot;&lt;ipython-input-10-25f049caebb0&gt;&quot;</span><span class="ansicyan">, line </span><span class="ansigreen">1</span>
+<span class="ansired">    raise IOError, &quot;file error&quot;</span>
+                 ^
+<span class="ansired">SyntaxError</span><span class="ansired">:</span> invalid syntax
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The proper way to raise an exception in Python 3:</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[11]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+<span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="s">&quot;file error&quot;</span><span class="p">)</span>
+</pre></div>
 
-<p><a id='loop_leak'></a>
-<br>
-<br></p>
-
-<h3>In Python 3.x for-loop variables don't leak into the global namespace anymore</h3>
-
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">OSError</span>                                   Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-11-c350544d15da&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">      1</span> print<span class="ansiblue">(</span><span class="ansiblue">&apos;Python&apos;</span><span class="ansiblue">,</span> python_version<span class="ansiblue">(</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+<span class="ansigreen">----&gt; 2</span><span class="ansired"> </span><span class="ansigreen">raise</span> IOError<span class="ansiblue">(</span><span class="ansiblue">&quot;file error&quot;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+
+<span class="ansired">OSError</span>: file error</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Handling-exceptions">Handling exceptions<a class="anchor-link" href="#Handling-exceptions">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Also the handling of exceptions has slightly changed in Python 3. In Python 3 we have to use the &quot;<code>as</code>&quot; keyword now</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[10]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="n">let_us_cause_a_NameError</span>
+<span class="k">except</span> <span class="ne">NameError</span><span class="p">,</span> <span class="n">err</span><span class="p">:</span>
+    <span class="k">print</span> <span class="n">err</span><span class="p">,</span> <span class="s">&#39;--&gt; our error message&#39;</span>
+</pre></div>
 
-<p>This goes back to a change that was made in Python 3.x and is described in <a href="https://docs.python.org/3/whatsnew/3.0.html">What’s New In Python 3.0</a> as follows:</p>
+</div>
+</div>
+</div>
 
-<p>"List comprehensions no longer support the syntactic form <code>[... for var in item1, item2, ...]</code>. Use <code>[... for var in (item1, item2, ...)]</code> instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a <code>list()</code> constructor, and in particular the loop control variables are no longer leaked into the surrounding scope."</p>
+<div class="output_wrapper">
+<div class="output">
 
-<p><br>
-<code>[In:]</code></p>
 
-<!-- HTML generated using hilite.me -->
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+name &apos;let_us_cause_a_NameError&apos; is not defined --&gt; our error message
 
+</pre>
+</div>
+</div>
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
-<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
+</div>
+</div>
 
-i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #007020">print</span>([i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)])
-<span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span>)
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[12]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+<span class="k">try</span><span class="p">:</span>
+    <span class="n">let_us_cause_a_NameError</span>
+<span class="k">except</span> <span class="ne">NameError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
+    <span class="k">print</span><span class="p">(</span><span class="n">err</span><span class="p">,</span> <span class="s">&#39;--&gt; our error message&#39;</span><span class="p">)</span>
 </pre></div>
 
-
-<p><br>
-<code>[Out:]</code></p>
-
-<!-- HTML generated using hilite.me -->
-
-
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 3.3.5
-[0, 1, 2, 3, 4]
-1 -&gt; i in global
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+name &apos;let_us_cause_a_NameError&apos; is not defined --&gt; our error message
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a id='The-next-function-and-next-method'></a></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="The-next()-function-and-.next()-method">The next() function and .next() method<a class="anchor-link" href="#The-next()-function-and-.next()-method">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Since <code>next()</code> (<code>.next()</code>) is such a commonly used function (method), this is another syntax change (or rather change in implementation) that is worth mentioning: where you can use both the function and method syntax in Python 2.7.5, the <code>next()</code> function is all that remains in Python 3 (calling the <code>.next()</code> method raises an <code>AttributeError</code>).</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[11]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+
+<span class="n">my_generator</span> <span class="o">=</span> <span class="p">(</span><span class="n">letter</span> <span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="s">&#39;abcdefg&#39;</span><span class="p">)</span>
+
+<span class="nb">next</span><span class="p">(</span><span class="n">my_generator</span><span class="p">)</span>
+<span class="n">my_generator</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
 </pre></div>
 
+</div>
+</div>
+</div>
 
-<p><br>
-<br>
-<br>
-<code>[In:]</code></p>
+<div class="output_wrapper">
+<div class="output">
 
-<!-- HTML generated using hilite.me -->
 
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
-<span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version()
+</pre>
+</div>
+</div>
 
-i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #007020">print</span> [i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)]
-<span style="color: #007020">print</span> i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span> 
-</pre></div>
+<div class="output_area"><div class="prompt output_prompt">
+    Out[11]:</div>
 
 
-<p><br>
-<code>[Out:]</code></p>
+<div class="output_text output_subarea output_pyout">
+<pre>
+&apos;b&apos;
+</pre>
+</div>
 
-<!-- HTML generated using hilite.me -->
+</div>
 
+</div>
+</div>
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 2.7.6
-[0, 1, 2, 3, 4]
-4 -&gt; i in global
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[13]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+
+<span class="n">my_generator</span> <span class="o">=</span> <span class="p">(</span><span class="n">letter</span> <span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="s">&#39;abcdefg&#39;</span><span class="p">)</span>
+
+<span class="nb">next</span><span class="p">(</span><span class="n">my_generator</span><span class="p">)</span>
 </pre></div>
 
+</div>
+</div>
+</div>
 
-<p><a id='compare_unorder'></a>
-<br>
-<br></p>
-
-<h4>Python 3.x prevents us from comparing unorderable types</h4>
-
-<p>[<a href="#py23_overview">back to Python 2.x vs 3.x overview</a>]</p>
+<div class="output_wrapper">
+<div class="output">
 
-<p><br>
-<code>[In:]</code></p>
 
-<!-- HTML generated using hilite.me -->
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
 
+</pre>
+</div>
+</div>
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
-<span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version()
+<div class="output_area"><div class="prompt output_prompt">
+    Out[13]:</div>
 
-<span style="color: #007020">print</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
-<span style="color: #007020">print</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
-<span style="color: #007020">print</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>)
-</pre></div>
 
+<div class="output_text output_subarea output_pyout">
+<pre>
+&apos;a&apos;
+</pre>
+</div>
 
-<p><br>
-<code>[Out:]</code></p>
+</div>
 
-<!-- HTML generated using hilite.me -->
+</div>
+</div>
 
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[14]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="n">my_generator</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
+</pre></div>
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 2.7.6
-False
-True
-False
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">AttributeError</span>                            Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-14-125f388bb61b&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span>my_generator<span class="ansiblue">.</span>next<span class="ansiblue">(</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+
+<span class="ansired">AttributeError</span>: &apos;generator&apos; object has no attribute &apos;next&apos;</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="For-loop-variables-and-the-global-namespace-leak">For-loop variables and the global namespace leak<a class="anchor-link" href="#For-loop-variables-and-the-global-namespace-leak">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Good news is: In Python 3.x for-loop variables don't leak into the global namespace anymore!</p>
+<p>This goes back to a change that was made in Python 3.x and is described in <a href="https://docs.python.org/3/whatsnew/3.0.html">What’s New In Python 3.0</a> as follows:</p>
+<p>&quot;List comprehensions no longer support the syntactic form <code>[... for var in item1, item2, ...]</code>. Use <code>[... for var in (item1, item2, ...)]</code> instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a <code>list()</code> constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.&quot;</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[12]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+
+<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="k">print</span> <span class="s">&#39;before: i =&#39;</span><span class="p">,</span> <span class="n">i</span>
+
+<span class="k">print</span> <span class="s">&#39;comprehension: &#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)]</span>
+
+<span class="k">print</span> <span class="s">&#39;after: i =&#39;</span><span class="p">,</span> <span class="n">i</span>
 </pre></div>
 
+</div>
+</div>
+</div>
 
-<p><br>
-<br>
-<br></p>
+<div class="output_wrapper">
+<div class="output">
 
-<p><code>[In:]</code></p>
 
-<!-- HTML generated using hilite.me -->
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+before: i = 1
+comprehension:  [0, 1, 2, 3, 4]
+after: i = 4
 
+</pre>
+</div>
+</div>
 
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
-<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
+</div>
+</div>
 
-<span style="color: #007020">print</span>([<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>)
-<span style="color: #007020">print</span>((<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>)
-<span style="color: #007020">print</span>([<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>))
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[15]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+
+<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;before: i =&#39;</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
+
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;comprehension:&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)])</span>
+
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;after: i =&#39;</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
 </pre></div>
 
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+before: i = 1
+comprehension: [0, 1, 2, 3, 4]
+after: i = 1
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Comparing-unorderable-types">Comparing unorderable types<a class="anchor-link" href="#Comparing-unorderable-types">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Another nice change in Python 3 is that a <code>TypeError</code> is raised as warning if we try to compare unorderable types.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+<span class="k">print</span> <span class="s">&quot;[1, 2] &gt; &#39;foo&#39; = &quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="s">&#39;foo&#39;</span>
+<span class="k">print</span> <span class="s">&quot;(1, 2) &gt; &#39;foo&#39; = &quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="s">&#39;foo&#39;</span>
+<span class="k">print</span> <span class="s">&quot;[1, 2] &gt; (1, 2) = &quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+</pre></div>
 
-<p><code>[Out:]</code></p>
-
-<!-- HTML generated using hilite.me -->
-
-
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed <span style="color: #000000; font-weight: bold">in</span> Python <span style="color: #6600EE; font-weight: bold">3.3</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">5</span>
-<span style="color: #333333">---------------------------------------------------------------------------</span>
-<span style="color: #FF0000; font-weight: bold">TypeError</span>                                 Traceback (most recent call last)
-<span style="color: #333333">&lt;</span>ipython<span style="color: #333333">-</span><span style="color: #007020">input</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">3</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">1</span>d774c677f73<span style="color: #333333">&gt;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>()
-      <span style="color: #0000DD; font-weight: bold">2</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
-      <span style="color: #0000DD; font-weight: bold">3</span> 
-<span style="color: #333333">----&gt;</span> <span style="color: #0000DD; font-weight: bold">4</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
-      <span style="color: #0000DD; font-weight: bold">5</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
-      <span style="color: #0000DD; font-weight: bold">6</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>)
+</div>
+</div>
+</div>
 
-<span style="color: #FF0000; font-weight: bold">TypeError</span>: unorderable types: <span style="color: #007020">list</span>() <span style="color: #333333">&gt;</span> <span style="color: #007020">str</span>()
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[16]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&quot;[1, 2] &gt; &#39;foo&#39; = &quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="s">&#39;foo&#39;</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&quot;(1, 2) &gt; &#39;foo&#39; = &quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="s">&#39;foo&#39;</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&quot;[1, 2] &gt; (1, 2) = &quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
 </pre></div>
 
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">TypeError</span>                                 Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-16-a9031729f4a0&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">      1</span> print<span class="ansiblue">(</span><span class="ansiblue">&apos;Python&apos;</span><span class="ansiblue">,</span> python_version<span class="ansiblue">(</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+<span class="ansigreen">----&gt; 2</span><span class="ansired"> </span>print<span class="ansiblue">(</span><span class="ansiblue">&quot;[1, 2] &gt; &apos;foo&apos; = &quot;</span><span class="ansiblue">,</span> <span class="ansiblue">[</span><span class="ansicyan">1</span><span class="ansiblue">,</span> <span class="ansicyan">2</span><span class="ansiblue">]</span> <span class="ansiblue">&gt;</span> <span class="ansiblue">&apos;foo&apos;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+<span class="ansigreen">      3</span> print<span class="ansiblue">(</span><span class="ansiblue">&quot;(1, 2) &gt; &apos;foo&apos; = &quot;</span><span class="ansiblue">,</span> <span class="ansiblue">(</span><span class="ansicyan">1</span><span class="ansiblue">,</span> <span class="ansicyan">2</span><span class="ansiblue">)</span> <span class="ansiblue">&gt;</span> <span class="ansiblue">&apos;foo&apos;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+<span class="ansigreen">      4</span> print<span class="ansiblue">(</span><span class="ansiblue">&quot;[1, 2] &gt; (1, 2) = &quot;</span><span class="ansiblue">,</span> <span class="ansiblue">[</span><span class="ansicyan">1</span><span class="ansiblue">,</span> <span class="ansicyan">2</span><span class="ansiblue">]</span> <span class="ansiblue">&gt;</span> <span class="ansiblue">(</span><span class="ansicyan">1</span><span class="ansiblue">,</span> <span class="ansicyan">2</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
+
+<span class="ansired">TypeError</span>: unorderable types: list() &gt; str()</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="More-articles-about-Python-2-and-Python-3">More articles about Python 2 and Python 3<a class="anchor-link" href="#More-articles-about-Python-2-and-Python-3">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up:</p>
+<ul>
+<li><p><a href="https://wiki.python.org/moin/Python2orPython3">Should I use Python 2 or Python 3 for my development activity?</a></p></li>
+<li><p><a href="https://docs.python.org/3.0/whatsnew/3.0.html">What’s New In Python 3.0</a></p></li>
+<li><p><a href="http://python3porting.com/differences.html">Porting to Python 3</a></p></li>
+<li><p><a href="https://docs.python.org/3/howto/pyporting.html">Porting Python 2 Code to Python 3</a></p></li>
+<li><p><a href="http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3">How keep Python 3 moving forward</a></p></li>
+</ul>
+</div>
+</div>
+</div>
+    </div>
+  </div>
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
new file mode 100644
index 0000000..3486254
--- /dev/null
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -0,0 +1,1301 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:b9ba6aad52c6458ba2d9ee8691fcace515fc01f1029499253de1f48cc664fbc0"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "\n",
+      "last updated 05/24/2014\n",
+      "\n",
+      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1) \n",
+      "\n",
+      "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb)  \n",
+      "\n",
+      "- [Link to the GitHub repository python_reference](https://github.com/rasbt/python_reference)\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Key differences between Python 2.7.x and Python 3.x"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines \"just go with the version your favorite tutorial was written in, and check out the differences later on.\"\n",
+      "\n",
+      "But what if you are starting a new project and have the choice to pick? I would say there is currently no \"right\" or \"wrong\" as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use. However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Sections"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "- [Using the `__future__` module](#future_module)\n",
+      "\n",
+      "- [The print function](#The-print-function)\n",
+      "\n",
+      "- [Integer division](#Integer-division)\n",
+      "\n",
+      "- [xrange](#xrange)\n",
+      "\n",
+      "- [Raising exceptions](#Raising-exceptions)\n",
+      "\n",
+      "- [Handling exceptions](#Handling-exceptions)\n",
+      "\n",
+      "- [The next() function and .next() method](#The-next-function-and-next-method)\n",
+      "\n",
+      "- [For-loop variables and the global namespace leak](#For-loop-variables-and-the-global-namespace-leak)\n",
+      "\n",
+      "- [Comparing unorderable types](#Comparing-unorderable-types)\n",
+      "\n",
+      "- [More articles about Python 2 and Python 3](#More-articles-about-Python-2-and-Python-3)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='future_module'></a>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### The `__future__` module"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Python 3.x introduced some Python 2-incompatible keywords and features that can be imported via the in-built `__future__` module in Python 2. It is recommended to use `__future__` imports it if you are planning Python 3.x support for your code. For example, if we want Python 3.x's integer division behavior in Python 2, we can import it via\n",
+      "\n",
+      "    from __future__ import division\n",
+      "    \n",
+      "More features that can be imported from the `__future__` module are listed in the table below:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<table>\n",
+      "<tr><th class=\"head\">feature</th>\n",
+      "<th class=\"head\">optional in</th>\n",
+      "<th class=\"head\">mandatory in</th>\n",
+      "<th class=\"head\">effect</th>\n",
+      "</tr>\n",
+      "\n",
+      "<tr><td>nested_scopes</td>\n",
+      "<td>2.1.0b1</td>\n",
+      "<td>2.2</td>\n",
+      "<td><a href=\"http://www.python.org/dev/peps/pep-0227\"><strong>PEP 227</strong></a>:\n",
+      "<em>Statically Nested Scopes</em></td>\n",
+      "</tr>\n",
+      "<tr><td>generators</td>\n",
+      "<td>2.2.0a1</td>\n",
+      "<td>2.3</td>\n",
+      "<td><a class=\"pep reference external\" href=\"http://www.python.org/dev/peps/pep-0255\"><strong>PEP 255</strong></a>:\n",
+      "<em>Simple Generators</em></td>\n",
+      "</tr>\n",
+      "<tr><td>division</td>\n",
+      "<td>2.2.0a2</td>\n",
+      "<td>3.0</td>\n",
+      "<td><<a class=\"pep reference external\" href=\"http://www.python.org/dev/peps/pep-0238\"><strong>PEP 238</strong></a>:\n",
+      "<em>Changing the Division Operator</em></td>\n",
+      "</tr>\n",
+      "<tr><td>absolute_import</td>\n",
+      "<td>2.5.0a1</td>\n",
+      "<td>3.0</td>\n",
+      "<td><a href=\"http://www.python.org/dev/peps/pep-0328\"><strong>PEP 328</strong></a>:\n",
+      "<em>Imports: Multi-Line and Absolute/Relative</em></td>\n",
+      "</tr>\n",
+      "<tr><td>with_statement</td>\n",
+      "<td>2.5.0a1</td>\n",
+      "<td>2.6</td>\n",
+      "<td><a href=\"http://www.python.org/dev/peps/pep-0343\"><strong>PEP 343</strong></a>:\n",
+      "<em>The &#8220;with&#8221; Statement</em></td>\n",
+      "</tr>\n",
+      "<tr><td>print_function</td>\n",
+      "<td>2.6.0a2</td>\n",
+      "<td>3.0</td>\n",
+      "<td><a href=\"http://www.python.org/dev/peps/pep-3105\"><strong>PEP 3105</strong></a>:\n",
+      "<em>Make print a function</em></td>\n",
+      "</tr>\n",
+      "<tr><td>unicode_literals</td>\n",
+      "<td>2.6.0a2</td>\n",
+      "<td>3.0</td>\n",
+      "<td><a href=\"http://www.python.org/dev/peps/pep-3112\"><strong>PEP 3112</strong></a>:\n",
+      "<em>Bytes literals in Python 3000</em></td>\n",
+      "</tr>\n",
+      "</table>\n",
+      "<br>\n",
+      "<center>(Source: [https://docs.python.org/2/library/__future__.html](https://docs.python.org/2/library/__future__.html#module-__future__))</center>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from platform import python_version"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "The print function"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print function doesn't require the parantheses for invoking the print function (it wouldn't choke on them).  \n",
+      "In contrast, Python 3 would raise a `SyntaxError` if we called the print function the Python 2-way without the parentheses.  \n",
+      "\n",
+      "I think this change in Python 3 makes sense in terms of consistency, since it is the common way in Python to invoke function calls with its parentheses."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "print 'Hello, World!'\n",
+      "print('Hello, World!')\n",
+      "print \"text\", ; print 'print more text on the same line'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "Hello, World!\n",
+        "Hello, World!\n",
+        "text print more text on the same line\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "print('Hello, World!')\n",
+      "\n",
+      "print(\"some text,\", end=\"\") \n",
+      "print(' print more text on the same line')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n",
+        "Hello, World!\n",
+        "some text, print more text on the same line\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Hello, World!'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "SyntaxError",
+       "evalue": "invalid syntax (<ipython-input-3-139a7c5835bd>, line 1)",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-139a7c5835bd>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'Hello, World!'\u001b[0m\n\u001b[0m                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Integer division"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This change is particularly dangerous if you are porting code, or if you are executing Python 3 code in Python 2, since the change in integer-division behavior can often go unnoticed (it doesn't raise a `SyntaxError`).  \n",
+      "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble (and vice versa, I recommend a `from __future__ import division` in your Python 2 scripts)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "print '3 / 2 =', 3 / 2\n",
+      "print '3 // 2 =', 3 // 2\n",
+      "print '3 / 2.0 =', 3 / 2.0\n",
+      "print '3 // 2.0 =', 3 // 2.0"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "3 / 2 = 1\n",
+        "3 // 2 = 1\n",
+        "3 / 2.0 = 1.5\n",
+        "3 // 2.0 = 1.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "print('3 / 2 =', 3 / 2)\n",
+      "print('3 // 2 =', 3 // 2)\n",
+      "print('3 / 2.0 =', 3 / 2.0)\n",
+      "print('3 // 2.0 =', 3 // 2.0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n",
+        "3 / 2 = 1.5\n",
+        "3 // 2 = 1\n",
+        "3 / 2.0 = 1.5\n",
+        "3 // 2.0 = 1.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "xrange"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      " \n",
+      "The usage of `xrange()` is very popular in Python 2.x for creating an iterable object, e.g., in a for-loop or list/set-dictionary-comprehension.  \n",
+      "The behavior was quite similar to a generator (i.e., \"lazy evaluation\"), but here the xrange-iterable is not exhaustible - meaning, you could iterate over it infinitely.  \n",
+      "\n",
+      "\n",
+      "Thanks to its \"lazy-evaluation\", the advantage of the regular `range()` is that `xrange()` is generally faster if you have to iterate over it only once (e.g., in a for-loop). However, in contrast to 1-time iterations, it is not recommended if you repeat the iteration multiple times, since the generation happens every time from scratch!  \n",
+      "\n",
+      "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore (`xrange()` raises a `NameError` in Python 3)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "n = 10000\n",
+      "def test_range(n):\n",
+      "    for i in range(n):\n",
+      "        pass\n",
+      "    \n",
+      "def test_xrange(n):\n",
+      "    for i in xrange(n):\n",
+      "        pass    "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "\n",
+      "print '\\ntiming range()'\n",
+      "%timeit test_range(n)\n",
+      "\n",
+      "print '\\n\\ntiming xrange()'\n",
+      "%timeit test_xrange(n)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "\n",
+        "timing range()\n",
+        "1000 loops, best of 3: 433 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "\n",
+        "timing xrange()\n",
+        "1000 loops, best of 3: 350 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "\n",
+      "print('\\ntiming range()')\n",
+      "%timeit test_range(n)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n",
+        "\n",
+        "timing range()\n",
+        "1000 loops, best of 3: 520 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print(xrange(10))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "NameError",
+       "evalue": "name 'xrange' is not defined",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-8-5d8f9b79ea70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mNameError\u001b[0m: name 'xrange' is not defined"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Raising exceptions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "\n",
+      "Where Python 2 accepts both notations, the 'old' and the 'new' syntax, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "raise IOError, \"file error\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "IOError",
+       "evalue": "file error",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-8-25f049caebb0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mIOError\u001b[0m: file error"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "raise IOError(\"file error\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "IOError",
+       "evalue": "file error",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-9-6f1c43f525b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mIOError\u001b[0m: file error"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "raise IOError, \"file error\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "SyntaxError",
+       "evalue": "invalid syntax (<ipython-input-10-25f049caebb0>, line 1)",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-25f049caebb0>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    raise IOError, \"file error\"\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The proper way to raise an exception in Python 3:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "raise IOError(\"file error\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n"
+       ]
+      },
+      {
+       "ename": "OSError",
+       "evalue": "file error",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-11-c350544d15da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mOSError\u001b[0m: file error"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Handling exceptions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Also the handling of exceptions has slightly changed in Python 3. In Python 3 we have to use the \"`as`\" keyword now"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "try:\n",
+      "    let_us_cause_a_NameError\n",
+      "except NameError, err:\n",
+      "    print err, '--> our error message'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "name 'let_us_cause_a_NameError' is not defined --> our error message\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "try:\n",
+      "    let_us_cause_a_NameError\n",
+      "except NameError as err:\n",
+      "    print(err, '--> our error message')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n",
+        "name 'let_us_cause_a_NameError' is not defined --> our error message\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='The-next-function-and-next-method'></a>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "The next() function and .next() method"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Since `next()` (`.next()`) is such a commonly used function (method), this is another syntax change (or rather change in implementation) that is worth mentioning: where you can use both the function and method syntax in Python 2.7.5, the `next()` function is all that remains in Python 3 (calling the `.next()` method raises an `AttributeError`)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "\n",
+      "my_generator = (letter for letter in 'abcdefg')\n",
+      "\n",
+      "next(my_generator)\n",
+      "my_generator.next()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": [
+        "'b'"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "\n",
+      "my_generator = (letter for letter in 'abcdefg')\n",
+      "\n",
+      "next(my_generator)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": [
+        "'a'"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "my_generator.next()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "AttributeError",
+       "evalue": "'generator' object has no attribute 'next'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-14-125f388bb61b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mAttributeError\u001b[0m: 'generator' object has no attribute 'next'"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "For-loop variables and the global namespace leak"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Good news is: In Python 3.x for-loop variables don't leak into the global namespace anymore!\n",
+      "\n",
+      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
+      "\n",
+      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "\n",
+      "i = 1\n",
+      "print 'before: i =', i\n",
+      "\n",
+      "print 'comprehension: ', [i for i in range(5)]\n",
+      "\n",
+      "print 'after: i =', i"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "before: i = 1\n",
+        "comprehension:  [0, 1, 2, 3, 4]\n",
+        "after: i = 4\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "\n",
+      "i = 1\n",
+      "print('before: i =', i)\n",
+      "\n",
+      "print('comprehension:', [i for i in range(5)])\n",
+      "\n",
+      "print('after: i =', i)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n",
+        "before: i = 1\n",
+        "comprehension: [0, 1, 2, 3, 4]\n",
+        "after: i = 1\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Comparing unorderable types"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another nice change in Python 3 is that a `TypeError` is raised as warning if we try to compare unorderable types."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "print \"[1, 2] > 'foo' = \", [1, 2] > 'foo'\n",
+      "print \"(1, 2) > 'foo' = \", (1, 2) > 'foo'\n",
+      "print \"[1, 2] > (1, 2) = \", [1, 2] > (1, 2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "print(\"[1, 2] > 'foo' = \", [1, 2] > 'foo')\n",
+      "print(\"(1, 2) > 'foo' = \", (1, 2) > 'foo')\n",
+      "print(\"[1, 2] > (1, 2) = \", [1, 2] > (1, 2))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n"
+       ]
+      },
+      {
+       "ename": "TypeError",
+       "evalue": "unorderable types: list() > str()",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-16-a9031729f4a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"(1, 2) > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > (1, 2) = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "More articles about Python 2 and Python 3"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up:\n",
+      "\n",
+      "- [Should I use Python 2 or Python 3 for my development activity?](https://wiki.python.org/moin/Python2orPython3)\n",
+      "\n",
+      "- [What\u2019s New In Python 3.0](https://docs.python.org/3.0/whatsnew/3.0.html)\n",
+      "\n",
+      "- [Porting to Python 3](http://python3porting.com/differences.html)\n",
+      "\n",
+      "- [Porting Python 2 Code to Python 3](https://docs.python.org/3/howto/pyporting.html)  \n",
+      "\n",
+      "- [How keep Python 3 moving forward](http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3)\n"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 24c5642d240ce9ab3db7acd138a2e5dd2131099a Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 May 2014 20:06:31 -0400
Subject: [PATCH 074/248] added unicode section

---
 ...ey_differences_between_python_2_and_3.html | 352 ++++++++++++++++++
 ...y_differences_between_python_2_and_3.ipynb | 235 +++++++++++-
 2 files changed, 586 insertions(+), 1 deletion(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index fca0c8f..815982a 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -1754,6 +1754,7 @@ <h2 id="Sections">Sections<a class="anchor-link" href="#Sections">&#182;</a></h2
 <li><p><a href="#future_module">Using the <code>__future__</code> module</a></p></li>
 <li><p><a href="#The-print-function">The print function</a></p></li>
 <li><p><a href="#Integer-division">Integer division</a></p></li>
+<li><p><a href="#Unicode">Unicode</a></p></li>
 <li><p><a href="#xrange">xrange</a></p></li>
 <li><p><a href="#Raising-exceptions">Raising exceptions</a></p></li>
 <li><p><a href="#Handling-exceptions">Handling exceptions</a></p></li>
@@ -2250,6 +2251,357 @@ <h4 id="python-3">Python 3</h4>
 </div>
 </div>
 
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Unicode">Unicode<a class="anchor-link" href="#Unicode">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Python 2 has ASCII <code>str()</code> types, separate <code>unicode()</code>, but no <code>byte</code> type.</p>
+<p>Now, in Python 3, we finally have Unicode (utf-8) <code>str</code>ings, and 2 byte classes: <code>byte</code> and <code>bytearray</code>s.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[2]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[3]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="nb">type</span><span class="p">(</span><span class="nb">unicode</span><span class="p">(</span><span class="s">&#39;this is like a python3 str type&#39;</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+&lt;type &apos;unicode&apos;&gt;
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[4]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="nb">type</span><span class="p">(</span><span class="n">b</span><span class="s">&#39;byte type does not exist&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+&lt;type &apos;str&apos;&gt;
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[5]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;they are really&#39;</span> <span class="o">+</span> <span class="n">b</span><span class="s">&#39; the same&#39;</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+they are really the same
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[7]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="nb">type</span><span class="p">(</span><span class="nb">bytearray</span><span class="p">(</span><span class="n">b</span><span class="s">&#39;bytearray oddly does exist though&#39;</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+&lt;type &apos;bytearray&apos;&gt;
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[6]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;strings are now utf-8 </span><span class="se">\u03BC</span><span class="s">nico</span><span class="se">\u0394</span><span class="s">é!&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+strings are now utf-8 μnicoΔé!
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[8]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">(),</span> <span class="n">end</span><span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39; has&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">b</span><span class="s">&#39; bytes for storing data&#39;</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1 has &lt;class &apos;bytes&apos;&gt;
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[11]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;and Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">(),</span> <span class="n">end</span><span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">)</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39; also has&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="nb">bytearray</span><span class="p">(</span><span class="n">b</span><span class="s">&#39;bytearrays&#39;</span><span class="p">)))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+and Python 3.4.1 also has &lt;class &apos;bytearray&apos;&gt;
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[13]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="s">&#39;note that we cannot add a string&#39;</span> <span class="o">+</span> <span class="n">b</span><span class="s">&#39;bytes for data&#39;</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_text output_pyerr">
+<pre>
+<span class="ansired">---------------------------------------------------------------------------</span>
+<span class="ansired">TypeError</span>                                 Traceback (most recent call last)
+<span class="ansigreen">&lt;ipython-input-13-d3e8942ccf81&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
+<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span><span class="ansiblue">&apos;note that we cannot add a string&apos;</span> <span class="ansiblue">+</span> <span class="ansiblue">b&apos;bytes for data&apos;</span><span class="ansiblue"></span>
+
+<span class="ansired">TypeError</span>: Can&apos;t convert &apos;bytes&apos; object to str implicitly</pre>
+</div>
+</div>
+
+</div>
+</div>
+
 </div>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 3486254..92ee2ab 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b9ba6aad52c6458ba2d9ee8691fcace515fc01f1029499253de1f48cc664fbc0"
+  "signature": "sha256:bce64714d9af46abdf20f98e6b5b0b51cd1240612c1dbd99a40d812aea22dcdf"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -79,6 +79,8 @@
       "\n",
       "- [Integer division](#Integer-division)\n",
       "\n",
+      "- [Unicode](#Unicode)\n",
+      "\n",
       "- [xrange](#xrange)\n",
       "\n",
       "- [Raising exceptions](#Raising-exceptions)\n",
@@ -434,6 +436,237 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Unicode"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Python 2 has ASCII `str()` types, separate `unicode()`, but no `byte` type.  \n",
+      "\n",
+      "Now, in Python 3, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print type(unicode('this is like a python3 str type'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<type 'unicode'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print type(b'byte type does not exist')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<type 'str'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'they are really' + b' the same'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "they are really the same\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print type(bytearray(b'bytearray oddly does exist though'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<type 'bytearray'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "print('strings are now utf-8 \\u03BCnico\\u0394\u00e9!')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n",
+        "strings are now utf-8 \u03bcnico\u0394\u00e9!\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version(), end=\"\")\n",
+      "print(' has', type(b' bytes for storing data'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1 has <class 'bytes'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('and Python', python_version(), end=\"\")\n",
+      "print(' also has', type(bytearray(b'bytearrays')))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "and Python 3.4.1 also has <class 'bytearray'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "'note that we cannot add a string' + b'bytes for data'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "TypeError",
+       "evalue": "Can't convert 'bytes' object to str implicitly",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-13-d3e8942ccf81>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'note that we cannot add a string'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mb'bytes for data'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mTypeError\u001b[0m: Can't convert 'bytes' object to str implicitly"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 2,

From f9d17a4f94b9e101f3789160c875ae8ffdb4e017 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 May 2014 20:40:01 -0400
Subject: [PATCH 075/248] added input() section

---
 ...ey_differences_between_python_2_and_3.html | 121 +++++++++++++++++-
 ...y_differences_between_python_2_and_3.ipynb | 105 ++++++++++++++-
 2 files changed, 223 insertions(+), 3 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index 815982a..d4311cb 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -1761,6 +1761,7 @@ <h2 id="Sections">Sections<a class="anchor-link" href="#Sections">&#182;</a></h2
 <li><p><a href="#The-next-function-and-next-method">The next() function and .next() method</a></p></li>
 <li><p><a href="#For-loop-variables-and-the-global-namespace-leak">For-loop variables and the global namespace leak</a></p></li>
 <li><p><a href="#Comparing-unorderable-types">Comparing unorderable types</a></p></li>
+<li><p><a href="#Parsing-user-inputs-via-input">Parsing user inputs via input()</a></p></li>
 <li><p><a href="#More-articles-about-Python-2-and-Python-3">More articles about Python 2 and Python 3</a></p></li>
 </ul>
 </div>
@@ -1926,7 +1927,7 @@ <h3 id="the-__future__-module">The <code>__future__</code> module</h3>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
 <div class="prompt input_prompt">
-In&nbsp;[1]:
+In&nbsp;[3]:
 </div>
 <div class="inner_cell">
     <div class="input_area">
@@ -3714,6 +3715,124 @@ <h4 id="python-3">Python 3</h4>
 </div>
 </div>
 
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><a id='Parsing-user-inputs-via-input'></a></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Parsing-user-inputs-via-input()">Parsing user inputs via input()<a class="anchor-link" href="#Parsing-user-inputs-via-input()">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Fortunately, the <code>input()</code> function was fixed in Python 3 so that it always stores the user inputs as <code>str</code> objects. In order to avoid the dangerous behavior in Python 2 to read in other types than <code>strings</code>, we have to use <code>raw_input()</code> instead.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">Python <span style="color: #6600EE; font-weight: bold">2.7</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">6</span> 
+[GCC <span style="color: #6600EE; font-weight: bold">4.0</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">1</span> (Apple Inc<span style="color: #333333">.</span> build <span style="color: #0000DD; font-weight: bold">5493</span>)] on darwin
+Type <span style="background-color: #fff0f0">&quot;help&quot;</span>, <span style="background-color: #fff0f0">&quot;copyright&quot;</span>, <span style="background-color: #fff0f0">&quot;credits&quot;</span> <span style="color: #000000; font-weight: bold">or</span> <span style="background-color: #fff0f0">&quot;license&quot;</span> <span style="color: #008800; font-weight: bold">for</span> more information<span style="color: #333333">.</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> my_input <span style="color: #333333">=</span> <span style="color: #007020">input</span>(<span style="background-color: #fff0f0">&#39;enter a number: &#39;</span>)
+
+enter a number: <span style="color: #0000DD; font-weight: bold">123</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(my_input)
+<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;int&#39;</span><span style="color: #333333">&gt;</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> my_input <span style="color: #333333">=</span> raw_input(<span style="background-color: #fff0f0">&#39;enter a number: &#39;</span>)
+
+enter a number: <span style="color: #0000DD; font-weight: bold">123</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(my_input)
+<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;str&#39;</span><span style="color: #333333">&gt;</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">Python <span style="color: #6600EE; font-weight: bold">3.4</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">1</span> 
+[GCC <span style="color: #6600EE; font-weight: bold">4.2</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">1</span> (Apple Inc<span style="color: #333333">.</span> build <span style="color: #0000DD; font-weight: bold">5577</span>)] on darwin
+Type <span style="background-color: #fff0f0">&quot;help&quot;</span>, <span style="background-color: #fff0f0">&quot;copyright&quot;</span>, <span style="background-color: #fff0f0">&quot;credits&quot;</span> <span style="color: #000000; font-weight: bold">or</span> <span style="background-color: #fff0f0">&quot;license&quot;</span> <span style="color: #008800; font-weight: bold">for</span> more information<span style="color: #333333">.</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> my_input <span style="color: #333333">=</span> <span style="color: #007020">input</span>(<span style="background-color: #fff0f0">&#39;enter a number: &#39;</span>)
+
+enter a number: <span style="color: #0000DD; font-weight: bold">123</span>
+
+<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(my_input)
+<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">str</span><span style="background-color: #fff0f0">&#39;&gt;</span>
+</pre></div>
+</div>
+</div>
 </div>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 92ee2ab..22a8f86 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:bce64714d9af46abdf20f98e6b5b0b51cd1240612c1dbd99a40d812aea22dcdf"
+  "signature": "sha256:2d8d614150aba437b28f7fe22d2e6a05b76ddb1a447d645c7d5085ab0c286c44"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -93,6 +93,8 @@
       "\n",
       "- [Comparing unorderable types](#Comparing-unorderable-types)\n",
       "\n",
+      "- [Parsing user inputs via input()](#Parsing-user-inputs-via-input)\n",
+      "\n",
       "- [More articles about Python 2 and Python 3](#More-articles-about-Python-2-and-Python-3)"
      ]
     },
@@ -196,7 +198,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -1495,6 +1497,105 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='Parsing-user-inputs-via-input'></a>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Parsing user inputs via input()"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Fortunately, the `input()` function was fixed in Python 3 so that it always stores the user inputs as `str` objects. In order to avoid the dangerous behavior in Python 2 to read in other types than `strings`, we have to use `raw_input()` instead."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<div style=\"background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\"><pre style=\"margin: 0; line-height: 125%\">Python <span style=\"color: #6600EE; font-weight: bold\">2.7</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">6</span> \n",
+      "[GCC <span style=\"color: #6600EE; font-weight: bold\">4.0</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> (Apple Inc<span style=\"color: #333333\">.</span> build <span style=\"color: #0000DD; font-weight: bold\">5493</span>)] on darwin\n",
+      "Type <span style=\"background-color: #fff0f0\">&quot;help&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;copyright&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;credits&quot;</span> <span style=\"color: #000000; font-weight: bold\">or</span> <span style=\"background-color: #fff0f0\">&quot;license&quot;</span> <span style=\"color: #008800; font-weight: bold\">for</span> more information<span style=\"color: #333333\">.</span>\n",
+      "\n",
+      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> <span style=\"color: #007020\">input</span>(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
+      "\n",
+      "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
+      "\n",
+      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
+      "<span style=\"color: #333333\">&lt;</span><span style=\"color: #007020\">type</span> <span style=\"background-color: #fff0f0\">&#39;int&#39;</span><span style=\"color: #333333\">&gt;</span>\n",
+      "\n",
+      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> raw_input(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
+      "\n",
+      "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
+      "\n",
+      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
+      "<span style=\"color: #333333\">&lt;</span><span style=\"color: #007020\">type</span> <span style=\"background-color: #fff0f0\">&#39;str&#39;</span><span style=\"color: #333333\">&gt;</span>\n",
+      "</pre></div>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<!-- HTML generated using hilite.me --><div style=\"background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\"><pre style=\"margin: 0; line-height: 125%\">Python <span style=\"color: #6600EE; font-weight: bold\">3.4</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> \n",
+      "[GCC <span style=\"color: #6600EE; font-weight: bold\">4.2</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> (Apple Inc<span style=\"color: #333333\">.</span> build <span style=\"color: #0000DD; font-weight: bold\">5577</span>)] on darwin\n",
+      "Type <span style=\"background-color: #fff0f0\">&quot;help&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;copyright&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;credits&quot;</span> <span style=\"color: #000000; font-weight: bold\">or</span> <span style=\"background-color: #fff0f0\">&quot;license&quot;</span> <span style=\"color: #008800; font-weight: bold\">for</span> more information<span style=\"color: #333333\">.</span>\n",
+      "\n",
+      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> <span style=\"color: #007020\">input</span>(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
+      "\n",
+      "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
+      "\n",
+      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
+      "<span style=\"color: #333333\">&lt;</span><span style=\"color: #008800; font-weight: bold\">class</span> <span style=\"color: #FF0000; background-color: #FFAAAA\">&#39;</span><span style=\"color: #BB0066; font-weight: bold\">str</span><span style=\"background-color: #fff0f0\">&#39;&gt;</span>\n",
+      "</pre></div>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 2,

From 3f28b768e0faedd7ab1ef73f8172ff9412682cf1 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 25 May 2014 11:30:41 -0400
Subject: [PATCH 076/248] note about print tuple

---
 ...y_differences_between_python_2_and_3.ipynb | 45 ++++++++++++++++---
 1 file changed, 38 insertions(+), 7 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 22a8f86..ac07efa 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2d8d614150aba437b28f7fe22d2e6a05b76ddb1a447d645c7d5085ab0c286c44"
+  "signature": "sha256:3442b8345ce99d8d379e475a753f23abf3392c6d4a6fdf185cf061e73e09b9e1"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -151,13 +151,13 @@
       "<tr><td>generators</td>\n",
       "<td>2.2.0a1</td>\n",
       "<td>2.3</td>\n",
-      "<td><a class=\"pep reference external\" href=\"http://www.python.org/dev/peps/pep-0255\"><strong>PEP 255</strong></a>:\n",
+      "<td><a href=\"http://www.python.org/dev/peps/pep-0255\"><strong>PEP 255</strong></a>:\n",
       "<em>Simple Generators</em></td>\n",
       "</tr>\n",
       "<tr><td>division</td>\n",
       "<td>2.2.0a2</td>\n",
       "<td>3.0</td>\n",
-      "<td><<a class=\"pep reference external\" href=\"http://www.python.org/dev/peps/pep-0238\"><strong>PEP 238</strong></a>:\n",
+      "<td><a href=\"http://www.python.org/dev/peps/pep-0238\"><strong>PEP 238</strong></a>:\n",
       "<em>Changing the Division Operator</em></td>\n",
       "</tr>\n",
       "<tr><td>absolute_import</td>\n",
@@ -198,7 +198,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -227,10 +227,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print function doesn't require the parantheses for invoking the print function (it wouldn't choke on them).  \n",
-      "In contrast, Python 3 would raise a `SyntaxError` if we called the print function the Python 2-way without the parentheses.  \n",
+      "Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print statement has been replaced by the `print()` function, meaning that we have to wrap the object that we want to print in parantheses. \n",
       "\n",
-      "I think this change in Python 3 makes sense in terms of consistency, since it is the common way in Python to invoke function calls with its parentheses."
+      "Python 2 doesn't have a problem with additional parantheses, but in contrast, Python 3 would raise a `SyntaxError` if we called the print function the Python 2-way without the parentheses.  \n"
      ]
     },
     {
@@ -324,6 +323,38 @@
      ],
      "prompt_number": 3
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Note:**\n",
+      "\n",
+      "Printing \"Hello, World\" above  via Python 2 looked quite \"normal\". However, if we have multiple objects inside the parantheses, we will create a tuple, since `print` is a \"statement\" in Python 2, not a function call."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "print('a', 'b')\n",
+      "print 'a', 'b'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "('a', 'b')\n",
+        "a b\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
     {
      "cell_type": "markdown",
      "metadata": {},

From 667a7333cddaddc647fdee79843d3788a086105e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 25 May 2014 11:31:31 -0400
Subject: [PATCH 077/248] note about print tuple

---
 ...ey_differences_between_python_2_and_3.html | 55 +++++++++++++++++--
 1 file changed, 50 insertions(+), 5 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index d4311cb..a839b1a 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -1848,7 +1848,7 @@ <h3 id="the-__future__-module">The <code>__future__</code> module</h3>
 2.3
 </td>
 <td>
-<a class="pep reference external" href="http://www.python.org/dev/peps/pep-0255"><strong>PEP 255</strong></a>: <em>Simple Generators</em>
+<a href="http://www.python.org/dev/peps/pep-0255"><strong>PEP 255</strong></a>: <em>Simple Generators</em>
 </td>
 </tr>
 <tr><td>
@@ -1861,7 +1861,7 @@ <h3 id="the-__future__-module">The <code>__future__</code> module</h3>
 3.0
 </td>
 <td>
-&lt;<a class="pep reference external" href="http://www.python.org/dev/peps/pep-0238"><strong>PEP 238</strong></a>: <em>Changing the Division Operator</em>
+<a href="http://www.python.org/dev/peps/pep-0238"><strong>PEP 238</strong></a>: <em>Changing the Division Operator</em>
 </td>
 </tr>
 <tr><td>
@@ -1927,7 +1927,7 @@ <h3 id="the-__future__-module">The <code>__future__</code> module</h3>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
 <div class="prompt input_prompt">
-In&nbsp;[3]:
+In&nbsp;[1]:
 </div>
 <div class="inner_cell">
     <div class="input_area">
@@ -1972,8 +1972,8 @@ <h2 id="The-print-function">The print function<a class="anchor-link" href="#The-
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<p>Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print function doesn't require the parantheses for invoking the print function (it wouldn't choke on them).<br />In contrast, Python 3 would raise a <code>SyntaxError</code> if we called the print function the Python 2-way without the parentheses.</p>
-<p>I think this change in Python 3 makes sense in terms of consistency, since it is the common way in Python to invoke function calls with its parentheses.</p>
+<p>Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print statement has been replaced by the <code>print()</code> function, meaning that we have to wrap the object that we want to print in parantheses.</p>
+<p>Python 2 doesn't have a problem with additional parantheses, but in contrast, Python 3 would raise a <code>SyntaxError</code> if we called the print function the Python 2-way without the parentheses.</p>
 </div>
 </div>
 </div>
@@ -2110,6 +2110,51 @@ <h4 id="python-3">Python 3</h4>
 </div>
 </div>
 
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Note:</strong></p>
+<p>Printing &quot;Hello, World&quot; above via Python 2 looked quite &quot;normal&quot;. However, if we have multiple objects inside the parantheses, we will create a tuple, since <code>print</code> is a &quot;statement&quot; in Python 2, not a function call.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[4]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+<span class="k">print</span><span class="p">(</span><span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="s">&#39;b&#39;</span><span class="p">)</span>
+<span class="k">print</span> <span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="s">&#39;b&#39;</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+(&apos;a&apos;, &apos;b&apos;)
+a b
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
 </div>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">

From fa0b5f185c742dcce5ab048e59b75874651f3aac Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 25 May 2014 11:44:17 -0400
Subject: [PATCH 078/248] compared python 2 and 3 speeds

---
 ...ey_differences_between_python_2_and_3.html | 94 +++++++++++++++++++
 ...y_differences_between_python_2_and_3.ipynb | 81 +++++++++++++++-
 2 files changed, 174 insertions(+), 1 deletion(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index a839b1a..c8b8cbe 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -2862,6 +2862,100 @@ <h4 id="python-3">Python 3</h4>
 </div>
 </div>
 
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Note</strong><br />Some people pointed out the speed difference between Python 3's <code>range()</code> and Python2's <code>xrange()</code>. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[3]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">def</span> <span class="nf">test_while</span><span class="p">():</span>
+    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">20000</span><span class="p">:</span>
+        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+    <span class="k">return</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[4]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+<span class="o">%</span><span class="k">timeit</span> <span class="n">test_while</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+100 loops, best of 3: 2.68 ms per loop
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[6]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
+<span class="o">%</span><span class="k">timeit</span> <span class="n">test_while</span><span class="p">()</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+1000 loops, best of 3: 1.72 ms per loop
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
 </div>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index ac07efa..79065ea 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3442b8345ce99d8d379e475a753f23abf3392c6d4a6fdf185cf061e73e09b9e1"
+  "signature": "sha256:8b17f6649c9b74d878c198037ee831fbec78e7ac375adfc4a62f03266d9d66c3"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -877,6 +877,85 @@
      ],
      "prompt_number": 8
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Note**  \n",
+      "Some people pointed out the speed difference between Python 3's `range()` and Python2's `xrange()`. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2. "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def test_while():\n",
+      "    i = 0\n",
+      "    while i < 20000:\n",
+      "        i += 1\n",
+      "    return"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "%timeit test_while()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n",
+        "100 loops, best of 3: 2.68 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "%timeit test_while()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "1000 loops, best of 3: 1.72 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
     {
      "cell_type": "markdown",
      "metadata": {},

From ea23193787768ff718a7e379ddfbf3ff6168ce3f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 25 May 2014 11:46:20 -0400
Subject: [PATCH 079/248] compared python 2 and 3 speeds

---
 ...ey_differences_between_python_2_and_3.html | 21 ++++++++++++++++++-
 ...y_differences_between_python_2_and_3.ipynb | 19 +++++++++++++++--
 2 files changed, 37 insertions(+), 3 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index c8b8cbe..74d5094 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -2868,7 +2868,26 @@ <h4 id="python-3">Python 3</h4>
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<p><strong>Note</strong><br />Some people pointed out the speed difference between Python 3's <code>range()</code> and Python2's <code>xrange()</code>. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2.</p>
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="Note-about-the-speed-differences-in-Python-2-and-3">Note about the speed differences in Python 2 and 3<a class="anchor-link" href="#Note-about-the-speed-differences-in-Python-2-and-3">&#182;</a></h4>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Some people pointed out the speed difference between Python 3's <code>range()</code> and Python2's <code>xrange()</code>. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2.</p>
 </div>
 </div>
 </div>
diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 79065ea..8b2577a 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8b17f6649c9b74d878c198037ee831fbec78e7ac375adfc4a62f03266d9d66c3"
+  "signature": "sha256:eb93a4258ce700755924939b5dc873c027a656b1faafcd0228616b8575378418"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -881,7 +881,22 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "**Note**  \n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 4,
+     "metadata": {},
+     "source": [
+      "Note about the speed differences in Python 2 and 3"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
       "Some people pointed out the speed difference between Python 3's `range()` and Python2's `xrange()`. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2. "
      ]
     },

From 663a0c23982ae34834e471d9b409d9324e3d6485 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 25 May 2014 18:18:05 -0400
Subject: [PATCH 080/248] section about funcs that dont return lists anymore

---
 ...ey_differences_between_python_2_and_3.html | 158 ++++++++++++++++++
 ...y_differences_between_python_2_and_3.ipynb | 128 +++++++++++++-
 2 files changed, 285 insertions(+), 1 deletion(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index 74d5094..94ddb8e 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -1762,6 +1762,7 @@ <h2 id="Sections">Sections<a class="anchor-link" href="#Sections">&#182;</a></h2
 <li><p><a href="#For-loop-variables-and-the-global-namespace-leak">For-loop variables and the global namespace leak</a></p></li>
 <li><p><a href="#Comparing-unorderable-types">Comparing unorderable types</a></p></li>
 <li><p><a href="#Parsing-user-inputs-via-input">Parsing user inputs via input()</a></p></li>
+<li><p><a href="#Returning-iterable-objects-instead-of-lists">Returning iterable objects instead of lists</a></p></li>
 <li><p><a href="#More-articles-about-Python-2-and-Python-3">More articles about Python 2 and Python 3</a></p></li>
 </ul>
 </div>
@@ -4006,6 +4007,163 @@ <h4 id="python-3">Python 3</h4>
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
+<h2 id="Returning-iterable-objects-instead-of-lists">Returning iterable objects instead of lists<a class="anchor-link" href="#Returning-iterable-objects-instead-of-lists">&#182;</a></h2>
+</div>
+</div>
+</div>
+
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>[<a href="#Sections">back to the section-overview</a>]</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As we have already seen in the <a href="#xrange"><code>xrange</code></a> section, some functions and methods return iterable objects in Python 3 now - instead of lists in Python 2.</p>
+<p>Since we usually iterate over those only once anyway, I think this change makes a lot of sense to save memory. However, it is also possible - in contrast to generators - to iterate over those multiple times if needed, it is aonly not so efficient.</p>
+<p>And for those cases where we really need the <code>list</code>-objects, we can simply convert the iterable object into a <code>list</code> via the <code>list()</code> function.</p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-2">Python 2</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[2]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span> 
+
+<span class="k">print</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> 
+<span class="k">print</span> <span class="nb">type</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+[0, 1, 2]
+&lt;type &apos;list&apos;&gt;
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h4 id="python-3">Python 3</h4>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[7]:
+</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
+
+<span class="k">print</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
+<span class="k">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)))</span>
+<span class="k">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)))</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 3.4.1
+range(0, 3)
+&lt;class &apos;range&apos;&gt;
+[0, 1, 2]
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><strong>Some more commonly used functions and methods that don't return lists anymore in Python 3:</strong></p>
+<ul>
+<li><p><code>zip()</code></p></li>
+<li><p><code>map()</code></p></li>
+<li><p><code>filter()</code></p></li>
+<li><p>dictionary's <code>.keys()</code> method</p></li>
+<li><p>dictionary's <code>.values()</code> method</p></li>
+<li><p>dictionary's <code>.items()</code> method</p></li>
+</ul>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p><br> <br></p>
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
 <h2 id="More-articles-about-Python-2-and-Python-3">More articles about Python 2 and Python 3<a class="anchor-link" href="#More-articles-about-Python-2-and-Python-3">&#182;</a></h2>
 </div>
 </div>
diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 8b2577a..bfc2af3 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:eb93a4258ce700755924939b5dc873c027a656b1faafcd0228616b8575378418"
+  "signature": "sha256:969dffb51ccca00a6136f4187304ff4a29444b347d9c38f9fe33f77a4af5f6fe"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -95,6 +95,8 @@
       "\n",
       "- [Parsing user inputs via input()](#Parsing-user-inputs-via-input)\n",
       "\n",
+      "- [Returning iterable objects instead of lists](#Returning-iterable-objects-instead-of-lists)\n",
+      "\n",
       "- [More articles about Python 2 and Python 3](#More-articles-about-Python-2-and-Python-3)"
      ]
     },
@@ -1721,6 +1723,130 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Returning iterable objects instead of lists"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to the section-overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As we have already seen in the [`xrange`](#xrange) section, some functions and methods return iterable objects in Python 3 now  - instead of lists in Python 2.  \n",
+      "\n",
+      "Since we usually iterate over those only once anyway, I think this change makes a lot of sense to save memory. However, it is also possible - in contrast to generators - to iterate over those multiple times if needed, it is aonly not so efficient.\n",
+      "\n",
+      "And for those cases where we really need the `list`-objects, we can simply convert the iterable object into a `list` via the `list()` function."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 2"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version() \n",
+      "\n",
+      "print range(3) \n",
+      "print type(range(3))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "[0, 1, 2]\n",
+        "<type 'list'>\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### Python 3"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "\n",
+      "print(range(3))\n",
+      "print(type(range(3)))\n",
+      "print(list(range(3)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n",
+        "range(0, 3)\n",
+        "<class 'range'>\n",
+        "[0, 1, 2]\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Some more commonly used functions and methods that don't return lists anymore in Python 3:**\n",
+      "\n",
+      "- `zip()`\n",
+      "\n",
+      "- `map()`\n",
+      "\n",
+      "- `filter()`\n",
+      "\n",
+      "- dictionary's `.keys()` method\n",
+      "\n",
+      "- dictionary's `.values()` method\n",
+      "\n",
+      "- dictionary's `.items()` method\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 2,

From 689e3cf3776991fbe45ebc09fb0c194741edff61 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 27 May 2014 20:30:38 -0400
Subject: [PATCH 081/248] article links;

---
 ...y_differences_between_python_2_and_3.ipynb | 29 ++++++++++++++++---
 1 file changed, 25 insertions(+), 4 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index bfc2af3..56ff09f 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:969dffb51ccca00a6136f4187304ff4a29444b347d9c38f9fe33f77a4af5f6fe"
+  "signature": "sha256:ad53a8e56dd6c7cd1c379333f92a5232a0403e9f4efbad8a765d42d71ebecc0f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -14,7 +14,7 @@
      "source": [
       "[Sebastian Raschka](http://sebastianraschka.com)  \n",
       "\n",
-      "last updated 05/24/2014\n",
+      "last updated 05/27/2014\n",
       "\n",
       "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1) \n",
       "\n",
@@ -1866,7 +1866,10 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up:\n",
+      "Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up.\n",
+      "\n",
+      "\n",
+      "**// Porting to Python 3** \n",
       "\n",
       "- [Should I use Python 2 or Python 3 for my development activity?](https://wiki.python.org/moin/Python2orPython3)\n",
       "\n",
@@ -1876,8 +1879,26 @@
       "\n",
       "- [Porting Python 2 Code to Python 3](https://docs.python.org/3/howto/pyporting.html)  \n",
       "\n",
-      "- [How keep Python 3 moving forward](http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3)\n"
+      "- [How keep Python 3 moving forward](http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3)\n",
+      "\n",
+      "**// Pro and anti Python 3**\n",
+      "\n",
+      "- [Everything you did not want to know about Unicode in Python 3](http://lucumr.pocoo.org/2014/5/12/everything-about-unicode/)\n",
+      "\n",
+      "- [Python 3 is killing Python](https://medium.com/@deliciousrobots/5d2ad703365d/)\n",
+      "\n",
+      "- [Python 3 can revive Python](https://medium.com/p/2a7af4788b10)\n",
+      "\n",
+      "- [Python 3 is fine](http://sealedabstract.com/rants/python-3-is-fine/)\n"
      ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}

From 233a52ae99e43dd58f2fef5d50222bd20022195a Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 28 May 2014 13:32:07 -0400
Subject: [PATCH 082/248] 5 simple steps for converting Markdown documents into
 HTML and adding Python syntax highlighting

---
 README.md                                     |  16 +-
 .../markdown_syntax_highlighting/README.md    | 176 ++++++++++++++++++
 .../markdown_syntax_highlighting/body.html    |  26 +++
 .../codehilite.css                            |  61 ++++++
 .../markdown_syntax_highlighting/final.html   |  39 ++++
 .../images/mk_syntax_body_html.png            | Bin 0 -> 148855 bytes
 .../images/mk_syntax_final_html.png           | Bin 0 -> 167057 bytes
 .../some_markdown.md                          |  27 +++
 .../template.html                             |  14 ++
 9 files changed, 357 insertions(+), 2 deletions(-)
 create mode 100644 tutorials/markdown_syntax_highlighting/README.md
 create mode 100644 tutorials/markdown_syntax_highlighting/body.html
 create mode 100644 tutorials/markdown_syntax_highlighting/codehilite.css
 create mode 100644 tutorials/markdown_syntax_highlighting/final.html
 create mode 100644 tutorials/markdown_syntax_highlighting/images/mk_syntax_body_html.png
 create mode 100644 tutorials/markdown_syntax_highlighting/images/mk_syntax_final_html.png
 create mode 100644 tutorials/markdown_syntax_highlighting/some_markdown.md
 create mode 100644 tutorials/markdown_syntax_highlighting/template.html

diff --git a/README.md b/README.md
index 672788f..5982a82 100755
--- a/README.md
+++ b/README.md
@@ -35,6 +35,18 @@ A collection of useful scripts, tutorials, and other Python-related things
 - Sorting CSV files using the Python csv module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
 
+<br>
+
+
+###// Python and the web
+
+- Creating a table of contents with internal links in IPython Notebooks and Markdown documents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]  
+
+- 5 simple steps for converting Markdown documents into HTML and adding Python syntax highlighting [[Markdown](./tutorials/markdown_syntax_highlighting/README.md)] 
+
+
+
+
 <br>
 ###// benchmarks
 
@@ -51,9 +63,9 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 <br>
 
 
-###// other
 
-- Creating a table of contents with internal links in IPython Notebooks and Markdown documents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]   
+
+###// other 
 
 - Happy Mother's Day [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)]
 
diff --git a/tutorials/markdown_syntax_highlighting/README.md b/tutorials/markdown_syntax_highlighting/README.md
new file mode 100644
index 0000000..424bdd8
--- /dev/null
+++ b/tutorials/markdown_syntax_highlighting/README.md
@@ -0,0 +1,176 @@
+[Sebastian Raschka](http://sebastianraschka.com)  
+
+last updated: 05/28/2014
+
+<hr>
+I would be happy to hear your comments and suggestions.  
+Please feel free to drop me a note via
+[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).
+<hr>
+
+
+# 5 simple steps for converting Markdown documents into HTML and adding Python syntax highlighting
+
+<br>
+<br>
+
+In this little tutorial, I want to show you in 5 simple steps how easy it is to add code syntax highlighting to your blog articles. 
+
+There are more sophisticated approaches using static site generators, e.g., [nikola](https://github.com/getnikola/nikola), but the focus here is to give you the brief introduction of how it generally works.
+
+
+All the files I will be using as examples in this tutorial can be download from the GitHub repository [/rasbt/python_reference/tutorials/markdown_syntax_highlighting](https://github.com/rasbt/python_reference/tutorials/markdown_syntax_highlighting)
+
+<br>
+<br>
+
+##1 - Installing packages
+
+The two packages that we will use are
+
+- [Python-Markdown](http://pythonhosted.org/Markdown/)
+
+- [Pygments](http://pygments.org)
+
+Just as the name suggests, Python-Markdown is the Python package that we will use for the Markdown to HTML conversion. 
+The second library, Pygments, will be used to add the syntax highlighting to the code blocks.  
+Conveniently, both libraries can be installed via `pip`:
+
+
+	pip install markdown
+	
+and
+
+	pip install Pygments
+
+
+(For alternative ways to install the Python-Markdown package, please see [the
+documentation](http://pythonhosted.org/Markdown/install.html))
+	
+
+<br>
+<br>
+
+##2 - Writing a Markdown document
+
+Now, let us compose a simple Markdown document including some Python code blocks in any/our favorite Markdown editor. 
+
+
+--> [**some_markdown.md**](https://github.com/rasbt/python_reference/blob/master/tutorials/markdown_syntax_highlighting/some_markdown.md)
+
+<div style="padding:6px; color: grey; background-color: white; border: black 1px solid">
+<pre>
+
+##This is a test
+
+Code blocks must be indented by 4 whitespaces.
+Python-Markdown has a auto-guess function which works
+pretty well:
+
+    print("Hello, World")
+	# some comment
+    for letter in "this is a test":
+        print(letter)
+
+In cases where Python-Markdown has problems figuring out which
+programming language we use, we can also add the language-tag
+explicitly. One way to do this would be:
+
+    :::python
+    print("Hello, World")
+
+or we can highlight certain lines to 
+draw the reader's attention:
+
+    :::python hl_lines="1 5"
+	print("highlight me!")
+	# but not me!
+    for letter in "this is a test":
+        print(letter)   
+    # I want to be highlighted, too!
+         
+</pre>  
+</div>
+
+ 
+<br>
+<br> 
+ 
+## 3 - Converting the Markdown document to HTML 
+ 
+
+After we created our Markdown document, we are going to use Python-Markdown directly from the command line to convert it into an HTML document.
+ 
+Note that we can also import Python-Markdown as a module in our Python scripts, and it comes with a rich repertory of different functions, which are [listed in the library reference](https://pythonhosted.org/Markdown/reference.html).  
+ 
+The basic command line usage to convert a Markdown document into HTML would be:
+
+	python -m markdown input.md > output.html
+	
+However, since we want to have syntax highlighting for our Python code, we will use Python-Markdown's [CodeHilite extension](http://pythonhosted.org/Markdown/extensions/code_hilite.html) by providing an additional `-x codehilite` argument on the command line:  
+
+
+	python -m markdown -x codehilite some_markdown.md > body.html
+ 
+This will create the HTML body with our Markdown code converted to HTML with the Python code blocks annotated for the syntax highlighting.
+
+
+<br>
+<br>
+
+##4 - Generating the CSS   
+
+If we open the [**body.html**](https://github.com/rasbt/python_reference/blob/master/tutorials/markdown_syntax_highlighting/body.html) file now, which we have created in the previous section, we will notice that it doesn't have the Python code colored yet.
+
+![](./images/mk_syntax_body_html.png)
+
+What is missing is the CSS code for adding the colors to our annotated Python code block. But we can simply create such a CSS file via `Pygments` from the command line.
+ 
+	pygmentize -S default -f html > codehilite.css  
+	
+Note that we usually only need to create the [**codehilite.css**](https://github.com/rasbt/python_reference/blob/master/tutorials/markdown_syntax_highlighting/codehilite.css) file once and insert a link in all our HTML files that we created via Python-Markdown to get the syntax coloring
+
+ 
+<br>
+<br> 
+ 
+ 
+## 5 - Insert into your HTML body
+ 
+
+In order to include a link to the [codehilite.css](https://github.com/rasbt/python_reference/blob/master/tutorials/markdown_syntax_highlighting/codehilite.css) file for syntax coloring in our converted HTML file, we have to add the following line to the header section. 
+
+
+
+`<link rel="stylesheet" type="text/css" href="./codehilite.css">`
+
+
+Now, we can insert the HTML body ([body.html](https://github.com/rasbt/python_reference/blob/master/tutorials/markdown_syntax_highlighting/body.html)), which was created from our Markdown document, directly into our final HTML file (e.g., our blog article template).
+
+
+[**template.html**](https://github.com/rasbt/python_reference/blob/master/tutorials/markdown_syntax_highlighting/template.html): 
+ 
+<code>
+	
+	<!DOCTYPE html>
+	<html lang="en">
+
+	<head>
+	<meta charset="utf-8">
+	<link rel="stylesheet" type="text/css" href="./codehilite.css">
+	</head>
+
+	<body>
+
+	<-- converted HTML contents go here
+
+	</body>
+	</html>
+
+</code> 
+
+If we open our [**final.html**](https://github.com/rasbt/python_reference/blob/master/tutorials/markdown_syntax_highlighting/template.html) file in our web browser now, we can the pretty Python syntax highlighting.
+
+![](./images/mk_syntax_final_html.png)
+
+
diff --git a/tutorials/markdown_syntax_highlighting/body.html b/tutorials/markdown_syntax_highlighting/body.html
new file mode 100644
index 0000000..e768c67
--- /dev/null
+++ b/tutorials/markdown_syntax_highlighting/body.html
@@ -0,0 +1,26 @@
+<h2>This is a test</h2>
+<p>Code blocks must be indented by 4 whitespaces.
+Python-Markdown has a auto-guess function which works
+pretty well:</p>
+<div class="codehilite"><pre><span class="n">print</span><span class="p">(</span><span class="s">&quot;Hello, World&quot;</span><span class="p">)</span>
+<span class="cp"># some comment</span>
+<span class="k">for</span> <span class="n">letter</span> <span class="n">in</span> <span class="s">&quot;this is a test&quot;</span><span class="o">:</span>
+    <span class="n">print</span><span class="p">(</span><span class="n">letter</span><span class="p">)</span>
+</pre></div>
+
+
+<p>In cases where Python-Markdown has problems figuring out which
+programming language we use, we can also add the language-tag
+explicitly. One way to do this would be:</p>
+<div class="codehilite"><pre><span class="k">print</span><span class="p">(</span><span class="s">&quot;Hello, World&quot;</span><span class="p">)</span>
+</pre></div>
+
+
+<p>or we can highlight certain lines to 
+draw the reader's attention:</p>
+<div class="codehilite"><pre><span class="hll"><span class="k">print</span><span class="p">(</span><span class="s">&quot;highlight me!&quot;</span><span class="p">)</span>
+</span><span class="c"># but not me!</span>
+<span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="s">&quot;this is a test&quot;</span><span class="p">:</span>
+    <span class="k">print</span><span class="p">(</span><span class="n">letter</span><span class="p">)</span>   
+<span class="hll"><span class="c"># I want to be highlighted, too!</span>
+</span></pre></div>
\ No newline at end of file
diff --git a/tutorials/markdown_syntax_highlighting/codehilite.css b/tutorials/markdown_syntax_highlighting/codehilite.css
new file mode 100644
index 0000000..67e6ea3
--- /dev/null
+++ b/tutorials/markdown_syntax_highlighting/codehilite.css
@@ -0,0 +1,61 @@
+.hll { background-color: #ffffcc }
+.c { color: #408080; font-style: italic } /* Comment */
+.err { border: 1px solid #FF0000 } /* Error */
+.k { color: #008000; font-weight: bold } /* Keyword */
+.o { color: #666666 } /* Operator */
+.cm { color: #408080; font-style: italic } /* Comment.Multiline */
+.cp { color: #BC7A00 } /* Comment.Preproc */
+.c1 { color: #408080; font-style: italic } /* Comment.Single */
+.cs { color: #408080; font-style: italic } /* Comment.Special */
+.gd { color: #A00000 } /* Generic.Deleted */
+.ge { font-style: italic } /* Generic.Emph */
+.gr { color: #FF0000 } /* Generic.Error */
+.gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.gi { color: #00A000 } /* Generic.Inserted */
+.go { color: #888888 } /* Generic.Output */
+.gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.gs { font-weight: bold } /* Generic.Strong */
+.gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.gt { color: #0044DD } /* Generic.Traceback */
+.kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.kp { color: #008000 } /* Keyword.Pseudo */
+.kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.kt { color: #B00040 } /* Keyword.Type */
+.m { color: #666666 } /* Literal.Number */
+.s { color: #BA2121 } /* Literal.String */
+.na { color: #7D9029 } /* Name.Attribute */
+.nb { color: #008000 } /* Name.Builtin */
+.nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.no { color: #880000 } /* Name.Constant */
+.nd { color: #AA22FF } /* Name.Decorator */
+.ni { color: #999999; font-weight: bold } /* Name.Entity */
+.ne { color: #D2413A; font-weight: bold } /* Name.Exception */
+.nf { color: #0000FF } /* Name.Function */
+.nl { color: #A0A000 } /* Name.Label */
+.nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.nt { color: #008000; font-weight: bold } /* Name.Tag */
+.nv { color: #19177C } /* Name.Variable */
+.ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.w { color: #bbbbbb } /* Text.Whitespace */
+.mf { color: #666666 } /* Literal.Number.Float */
+.mh { color: #666666 } /* Literal.Number.Hex */
+.mi { color: #666666 } /* Literal.Number.Integer */
+.mo { color: #666666 } /* Literal.Number.Oct */
+.sb { color: #BA2121 } /* Literal.String.Backtick */
+.sc { color: #BA2121 } /* Literal.String.Char */
+.sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.s2 { color: #BA2121 } /* Literal.String.Double */
+.se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
+.sh { color: #BA2121 } /* Literal.String.Heredoc */
+.si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
+.sx { color: #008000 } /* Literal.String.Other */
+.sr { color: #BB6688 } /* Literal.String.Regex */
+.s1 { color: #BA2121 } /* Literal.String.Single */
+.ss { color: #19177C } /* Literal.String.Symbol */
+.bp { color: #008000 } /* Name.Builtin.Pseudo */
+.vc { color: #19177C } /* Name.Variable.Class */
+.vg { color: #19177C } /* Name.Variable.Global */
+.vi { color: #19177C } /* Name.Variable.Instance */
+.il { color: #666666 } /* Literal.Number.Integer.Long */
diff --git a/tutorials/markdown_syntax_highlighting/final.html b/tutorials/markdown_syntax_highlighting/final.html
new file mode 100644
index 0000000..cff7895
--- /dev/null
+++ b/tutorials/markdown_syntax_highlighting/final.html
@@ -0,0 +1,39 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+<meta charset="utf-8">
+<link rel="stylesheet" type="text/css" href="./codehilite.css">
+</head>
+
+<body>
+
+<h2>This is a test</h2>
+<p>Code blocks must be indented by 4 whitespaces.
+Python-Markdown has a auto-guess function which works
+pretty well:</p>
+<div class="codehilite"><pre><span class="n">print</span><span class="p">(</span><span class="s">&quot;Hello, World&quot;</span><span class="p">)</span>
+<span class="cp"># some comment</span>
+<span class="k">for</span> <span class="n">letter</span> <span class="n">in</span> <span class="s">&quot;this is a test&quot;</span><span class="o">:</span>
+    <span class="n">print</span><span class="p">(</span><span class="n">letter</span><span class="p">)</span>
+</pre></div>
+
+
+<p>In cases where Python-Markdown has problems figuring out which
+programming language we use, we can also add the language-tag
+explicitly. One way to do this would be:</p>
+<div class="codehilite"><pre><span class="k">print</span><span class="p">(</span><span class="s">&quot;Hello, World&quot;</span><span class="p">)</span>
+</pre></div>
+
+
+<p>or we can highlight certain lines to 
+draw the reader's attention:</p>
+<div class="codehilite"><pre><span class="hll"><span class="k">print</span><span class="p">(</span><span class="s">&quot;highlight me!&quot;</span><span class="p">)</span>
+</span><span class="c"># but not me!</span>
+<span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="s">&quot;this is a test&quot;</span><span class="p">:</span>
+    <span class="k">print</span><span class="p">(</span><span class="n">letter</span><span class="p">)</span>   
+<span class="hll"><span class="c"># I want to be highlighted, too!</span>
+</span></pre></div>
+
+</body>
+</html>
diff --git a/tutorials/markdown_syntax_highlighting/images/mk_syntax_body_html.png b/tutorials/markdown_syntax_highlighting/images/mk_syntax_body_html.png
new file mode 100644
index 0000000000000000000000000000000000000000..3b3aab6bf9f3837d25ba25241698aad996298a60
GIT binary patch
literal 148855
zcmZU)1yCJPlPC-XC&7XQ3GVKCaSQJ5?he5rxCVE3ce&`r-Sy(`?rtyN?*8v>)jv~H
zb*4|t>8?3FEi>T?auVMVaS<UPAihcd5><kLfC~F}g}_7ogNPHqXF)(94qJ+dC`gKk
z5Ggp>n_1eJLO}fbR+a3gqCA2<bO=h(vp|TGJNkJH9gFS>0ds6XO6DJgZcG%mlr(^;
zp{OF-9l{Vn)q*7m4$!ErrQ%SThS7wccdauJ*?)wRUc2S(dcS;hx)`V2Y;``I%uI7;
zfJj8H7LA9jfQY4@OcsZIX4(CYp7ulu0g3wu+8}p)-&Ar54b5;%zw7Z4l8ms!m^9|T
z#_rQgFo|(^2NFUow{PT)PLE=ga(^)4R|BMAP^n!bn>NXzQR1-@n^5B8D7R~bqMx9A
z_D)3t|4f{3sXQYDG-3*^EX*H3)X6x$9u01&u;8-XICi+AS>D;ecc*8n!+e{pv{sEt
zI0X**4~fiqKc<w7%CM3BVj6cV#v>UQLqEl2vcPtmo2k!+k@48PL-uE!TzWC8Z_%P&
z-|oiCXR$+RG%^8{-dfmsyy<3nbX;cX9-6*KnsO8S&hH{6n#lxx*=E%kMO{9k%b7=(
z$qdmd{n+N?ahowC_(4$70Wg}7igTV3RI#iR9=%5`mXl}$lW7N)cT{4?9>M~_3`t@%
zPI7UuG!Ln1!IN)Zx&)7}TFbphTb5ajS174w{#@d5F=VUiXcF<v9$l2T*e1}_ca@n`
z18X*xjl%M~A;Xuag<WL4!j2T(gwTJO?uMWrx-pj^DN*4`1YLkc*dRBEC;DW%gb1bJ
zWf_haC1{!GRCDVIm)Zt5D8Y}5J${G|AExJQBG~PAilQ%DKnMt~qjuml+8<*JWZc5b
zbV~$?C=!TtFLxHpNfkg;M_+!8CeE}!#q<~PZu}((xGgBaACz=QczC!OB1k%D01<SW
zAYyU=rU5+_gqQ*A5;9FUK`kLZltho?H1y;jq%Fi2NIQS8T(~=bp)07UfJX!T03v{B
zuth9IAxfH{TI`qpfR0=Us;?12=(%K6nCK!b7BGzjGyzh1h!%t@NbgWtAq;|xxeQ0d
z^C*7>?Q^G?pp*@GDv>gJSq-tqLqxVM+#xd&8U1g!yxqU+AglLDY>7WXd4K7Id+cG{
zVumI%3G)9z!~u_VEz%n&N(K{0QV}a$__a*@Bo3?aXKUacF=L$k5B!i2Ln9j|f>Z;U
zuOXqD0h+<ogDM8dhPccrOm0l?sfy#AbeI)^O}%Z#+)M5@G{32V5ZWPCJ=wi_gByLw
zMw`11T(Fa04<nEU$+u=5#vBJ)bX#C`XseMp|KRoQUZ=a$c2M%;PYsywB3<!bOI#gZ
zH(v)n#Xz$N<!lMUlaZnK2e}5Z^cwbZ_uBTN8fKOXvk|v_?+f?dVKG9uhNug6PvH86
zPgxc}{wurCLJ8NBzARHkzD?$Y97LWl5@N!6qf;N&5uzi<FWxD2LFt!Ysz^ueoA``@
zWBPrcs3_?s_9j`XC|${?G<@FXgu;scp5z|kz5rczL@m6aUtOz8rEI>`Mf*63MR8d)
zt*S$TTfSB1Ip{`qKZQmVQyEhLvwD;!Nh)cMW}U{koK4kh!C^sW!Dykcd{HHI;mWGm
z3ew8E$-C*@3VQ+80)8UwKw@_WJcm5ZFb8|g)rONLB42<#6En+uTySD_B6m`E(t46~
zV!rVD1HLb@$IygH>c<eyBTkO_(WJo?>i7~@f$!is4k243yEhv&ZZPgQ+($NkYZmq|
zY;)|*795uR;9d(&tBD1z@+R%mda$%@8!(hZzFpz9<$?64#*h-Fl6mW-Zx(VEvo7B{
zk#6{f+<Igi<NXg$eosr!)w{{#zejU-pgXab)w`)v0rPU!<?*j(+_&h?^MXS}OM+&#
zQ@L{nYt!RQgAQ4Sf!oHU@n~aI0~Q&I88fAIo!2(In1il^F*0Ekbl>wvkQ06<pcI=G
zI~L=~WXOb0u#Iz>d6>DHfeuuh?Qh8s77pC#9kA#kT_Sa{n&}Ls&8729i}qz{mqrJq
zOq#~Z$8fFB&FF#EzzbmBQmw6%&56y6Ee_BScxsbjqd&oxoyO%nHvW*dd6s$3a)!Kg
zzf`dtx>VC(U+1@LaNO-&>e6!h%k`^^&iT^W)5^%G<W}SAvHw301iXbk<T$KhsAf<~
z@k~KeH(n%Lq*=7X%EIiSG1GPJYkO|1`pvuhFtR?txBA}ls!zZ{a7+-&7h;|^fj-W|
zWZgYCop9PZ)i4!#oV3@pSTVKQopGvr{ysl69D1p|%i0e#D5;C7)qh@jmhzE*_Pug`
z)IMH3iFty1#(r^wErscaR)Gc}uzi*JR*8iF%?VkHU>-N>8}hde^tFkZ{VT4f%o)_h
z*d*c<Vd^}^Jj^`#KDxd_L;8m8W#WeB$%`yw770KJHGYtDZ*z!1G*hGo`t_;K+FDp^
zl5>vOibQpoO-#NReFP8wJ^u=48(ACWAV3^z4yyv6R{+TIAd=~)W2vM6HvW`!&57QK
zE{ZS_{?xzJpWa{R0eK6Y!ppn_bQyfS!<ZpCVe4p@HZd7*FN=*vwnWO287Az+BUAcp
zhd5WX8job|mG3EL6WYUF!fb|i{S_d&!bn66Q35DSDTgTYDK{5K!-IlNds9dR6oZdk
zwH#Km8&Qt8kKyJhS$0@(SrF1B(@F-cc3iHD20SC3vE3vq$zb9;$uFdVj5tXlQr7jk
zl`xtU%Px)G$FXT+zOna*?)xeS-a5GLccRr+)m+WSY5Vnm$3kwPZhiHlH7B(<fV~C-
zE12)J%e0SG0czwmR@%0WJbw3E>ACEo;COIx&AV;ZdfTk)C3vXHeq?b%KSAJ1WE#a4
zMha#&;Nlwr-osniv*Mlcq4>~WUQS=yJ#0P3dRiLV7o~C9&X0t5`F#Od8>!`a%^jtB
zFIKw-_uF^+4Q=G4^5lagWScBdlOvw9_BPOM72CE;?LY-+4rdu^Bcbo1;c+Xso0Z|_
zTZXE(R&iHlQ#xC=qH%AC?YDTo&fk9ecyvL%a_doJaT(|%^om;{TQT;q`*3*X_nYWJ
z@O7#($J)BlD#EIh0E6(17sKO7smj{++%T(;+|?ng(r$0~jkG!Xh0wOTxomT>+k%P*
zHhLtQ@00vE<#k9Tra8+Xt5m?!ss04xT*Z)dzNON!QF3X1F>`v&m1oaIJ5pk-OyAaz
z&wX%9W?%EZf&B2-;mwq6<`l00n8wn_gZREza5W#89eIs?sh93mcI$MsU!e|Fqhn*<
zoYQsUaX-%fXkAj%P|dmS&^oiUz2<nrdAKR(+UCNs;R0Ir9y?c)UaYKkZU))wx9s{I
zj_OXX<2@d%owgA=e+oUmU39{|!S$iA5_I||-dc2+%Q6C+uj)p3>nVBU%l(?4KzUO|
zmo{B~enWGmC465x{IUH^-mguWK5_0fmt#K^&TIRw(&D<~w5JaF2)&6tm_O>b<QD`g
z^wPR`o>Qjue->O91lAPQP;Ov!EqHrfWzDq%>%!_NFIhKZ{HWeapRcDW!6gq<jya)&
zDZV{^)Sp(@P5atM%^Tt4;Rp!oqgj<OsExtON)YbOs1R0A5RKXpv5CbeJCZ0C<;fB_
zFRObue%C7JbcBM|@CpsEe=O#EwIG+mABsz76eF*MTnQm~d1$`M_-Il`BPT$|WlYZ&
z+8FK7YoYAWLp-hUF*9$lF<&>qR&5eOPQUn;6Qp=m;6rx9jhPL#WH)(ma9|#Ly%G+*
zLdcE&jc<W~fXuQ~(Qwg_mEkeAw`DLiu{SbhaJO~%7xO|u0Ni>04Q)+b42j%rZS0(R
z-1$iU3xnt1_&;Dq5~BY?ak1tj(U4Uj60vtOC1Ph_V_+iTM<gO50yvqN@hFLk{}1`U
zJ3bN%7Z(Q}Mn*R`HwHIW274!SMrLkqZbl{+Miv(Oe;D-69(FE<?(}xfr2oz2|KlTS
z>TK*}>EL2%Z%6bWzJ^Bjt}c8eB>xfge~<rOr>VQ;|C40r{6A{_Q;_jLGmOj(OpO1R
z_dim=f1o@HmhPrD8lslArgqN%WbkvaaRL4d|Nl4h|0Mn|O3nXG$??A_|JTg_Py!hL
zqrm@C=)bM?U(kQ%;ztB9{$JDcBWlWIghN0GLP&}VsklQ1Wx@Q>5d#BDhA6X9;TeR4
z;URaRB4Ob#7=*siUHs?7O(sH$gp+n~b3>?ghY$L39hd!_^T_9F?^geMuJ7D#w@|k0
zDpl<wzugQxh=>4k@MB5^1C)ho0}MluM6Fl>S7or0L_etP;%+DqaR?)<P%;ggP(9TU
zGBZ_ixEN=D7&-q!O;X3E$<QXYjMWle{bZmFq{tjcbfo&e4h*w2l|_;qIuc`>Y+U3v
zq3`8Z&fQ}Wks?4AUV{gudt+r7WFIH9jo=h}E>km&K@=I)j%pxA_Xn%e6MyO#p=ME|
z^2<n>rAMCo#irPV7k?&Zlr_U2e?sd0piRN%N!;HGR`(C?bJnTr)?G$ZBSH=e4l;vY
zUe5ny6krs!lwqYyNy^O(2$vi^9);N_R?Hv8Ne&!M4J%ar#a)F+oU6oEwy44qc_pYu
zZ;4*SV(Sz_LK~Pd)~gvFx%JsIGW<Ex<G#O@DXT|?O*-y$W<e{W+c@LOlJ}!32(MK0
z=m36yCmsiue9~WkM=4rKJe}C&u<*B$Qpl5jWP19Cc1O|+GbQWLXAcMaJEvXv&?gZ?
zf>QMTrcV;7GSyIVGPfpGtL0~z61Uc1VgIqfH7ob$06mV!a+*={8f{TELM?eP*FJd<
z1NK)Y2gP2e>V*Va+#Y*b<(PCtOoBjD4Gk!&rS6$w5=hwEdb&aa&~6TAwrfwprnic&
z@|U&F?@$)C>~Qr8uEco=Din<qHrS4iU_G9UhVlcVgP!uBxTHTRocpGtK^Qs*{L(T}
zjFrIDN+y4;JeIR~AxX7}kE<^V343zXmuEVh%us*jx15s;RWQRVdLPmLZa@EtMM`~y
zSx8lyfCU=igAVi*^dXorgCuoSEFDTtzow^8;Cs(-neHhjSa61mCe<e<>@lGVYhy#<
z+bK>8Riqg}Qa^>umYG=gaI=NiUt=4Y!Uvh2LrOj8uN*{hXOmKpa{!DVfm&5+xe(6B
zNthAPhw!x6>UzoZsAxCXwybQ2k`tz){y)G-Uvf=@i=d{zP7iaBbvLJDoy6B$ppxE>
z-!ms{QG}ogr0R?tg$Jbb4c_8(JHa%n;m$|DP1Cnx8x-oW3Hv}y-jCEdDLXP!Zxw-A
z;w_Moi>mL2Rh6@+CzxX!@=@DjhvKrF(j$Q0JKDd1s8T#BoG_N*1*bRxlbWq-g@qyV
zqfrg;MqAKcs5js>MT}l`oWP(8Wd~&3Hi{QoLbQWq9@^XRuv{ktGHKShNQ?N2_@+as
zF?WRI;<);QcoZ~ZCt7jhI+~<$n#!=X6G<U!s%!-zyVG)+AM*$jB@QBLOgUsD1n?Al
zGe*%P>b2-hxb^t<<^$bL8@W8D=E}q?rT#V4xDMrHHdZ9maImDSVs#4ZD{2^~8O8U%
zf+N7es@O3=C`p)u5)R_f80hi#w?-cnU)Q%e+L|^iRaH!?DytyT-66Cb33R7IiysQr
znI*86#8tAV4J@%)lz+G*J&@^k8)C5ox_8iRqb&{Q6SYfSCb#qQMwHM*A|%@j{g@^W
zCjz`sk!Mlgd*P}_p}i>;iRDhTskXR~<T2!g5ml7K0WQ+avtTQ;D$FXeo&;$zW1z{O
zEj7*Q7h-p<;R337D#?YYVNe=AJd+_Aq8zy(e0bX}6~(=@dm0-N4wGi;D(<M4B^7qu
zbbvo?c&iv?CzalY$&qjncXm}_2v&cg3xdzE57kS3x7=i-5d=FI6t@O1T(@Q_m~usA
zg#<D24QE2ZWEWpBI#WC4%Rq)%c-&;~*Ah*m@`EKc*mzV424{<Jzs#H@<&O}MG(1c(
z%PAqzl+0A41B#>}5gi?ng}K_<*`{#mS?W>6MJ&+2=EckP=90_%x5G2p{}qldu+pPG
zj7QTDQXR$13nNuLOzIxP`H)CTcH(a=!5~Z1g-A$=BD6y@6VJ@cz@^E#1!t-mG{Xps
zXF-0~BnhP<ru(XX*tVFjs!fF~5jUlX^lhA`K^R2HCTcmz8oEbwNFP4XOUVpD8p|>X
zNh*K#b9_YF0G5rC&7%?lQ7jb)-6DOimVAc^(2c1gj_xEq+Q%9%mN+(oOU4I~q_t6^
zNy0cWWp{H4N#Z06!k-EWz3GJ^sK*B9s)&T<R@qSl6CEXK$koHcH5{#~s8n!9wYq;8
zOUB}}Mh!f_tLu5aADdTLRm5<g?~%}onL;)TAtLr(F`R-}=9b4a6<PYJ&Z60WxzbY-
z3+DggPU542pKytqDpEVaQsOaRz-LH@fR!&#E@XA^VntP=5{)U4uj{TBlf`!mNEtj*
zWDBQ~oKUfaRKW@NN#Pvh-Bv!{<yQ2B7`=i)anWtk#+rzilcNliT67-3h?}7$2@t{m
zWnVzssIQ>(C^oV%j7Ya`$qDP+5<gs|jbqonU`CY6qYalM7RQb&M~=Q_kWu%`okNA{
zH`iZHuYBzzGF-{w$uwL|zDe$p&uXdoVAJ;s=BaJl68A(kiyA7cZ{ULrH?*J8#@KO5
zE`M?>iR0R<zUrtk29DkqW8G3Eg!X+ytE@C&7>$jmi&zC9)7a7pdmoq^!kYI62F_$D
z_NlkLVC<;*{|Jz&4p(qL>erO}V|c}^BiQ`;I6k{gw^8U4G5R@<JveUtDUJ2UlGKlj
zt6o4@wl5<U@U0hrRydP27^Z)T2;L-RpHOLVrW#GFH%hsDPopf>VuFZ=hm@AtrQm9S
zRJmBCRw|!fIWYfgUVFV7;tm88tB`5}w2qittYLJ%Of6A-RiU$5v~VjzyhY<z_PGdW
zAS+UoLR5Y6crLYK`e^bX6In!pni)e=9aQPa?bt8N9X=PyB8QY~=cIV9KaB^%ee`M0
z@f~Ye*txMc6#kO&MkVB;h9(GX>bGfR*2C_H^*13@L18CYx^|}2%m*gNEp&|vy2|ZA
zBQ7-v@VU!^Tbq6_rjG=Cll-4=uHtqqHBB&QThi^dQUtM;SL(Am5YM<6_kWTJ4MqCd
z-K^{On{y~8(wPgSAMN;(wTl(RBoH?}aU@cudJI1@TitYjPc#$yfy=BH<Ge0c{Q6uM
zh<=kG&_jqi2~Aj8RXknobDGIzwGHNe@FnYWtjTKkUXOzZp+e+f>uH)p@B%=xTSi)|
zXx1j#@OwH^_0?79H@hFef$a8b=F*%@k%DzYo;M7hi2i);;O1ObXebmGEyy~u-NsZ8
z5%cXaGA?W0kxXsPX`}HR?nc><>%-UT7Q?OvxsyhXMf|xVFqvf1h7qw?y$aZ5fSvKS
z1nJQNF`W$2o)W4X$Fde(eVgJzNO#AuP{nmwk3}+^U&oxosM$gb8u&}qvmm}PCGwZb
z93+u-ykS7X_N|PrTD?Z0#Z5dA)tLqf4=+8bPwJ^iX@sZeNmO~5><}JHkwY2suO!xA
zDk&wqx!Df0FLT+XTWp`NyNrXIo{!Ax`U<+GgCs3OXtS8yD}jR?a$lQ#Ls2&aXXx2V
z3)M=ua~riJ@6Xsi9=x=)(wlkRRj4T5y$k7nVU6~=5QlVkFjME8aO2*0(1mg(4JwvP
z(~W@AMonFC(9IpM9@LpsnAV$fe6NpEd1F8?`J?4DJw-l7NA*JaN!Z_GplPWoNs5NY
zkP_*^_-jO&_>8mx30pH{>7wXj_brBL=n<&tsfe=){oSwkp@g3v%T1w~&Q^y5ZD}H?
z!F|dC8EBeDRE;)Yp})@+s^yG!_b{SWpt<cw36mf0w7}z7%*;?An2PfJTf268I*^br
zV#{VOqPoRwG+1QoOSG*^n6q5*-ccg{7)b()f@%Ig-}~CdawT&9RWd@eZL*xqnT#ae
zCK#86?J=~R1;0=RZaa3oFh8EwjKM)$J95nntVq^GvrE<zPZZLG5lOe_&-8Ke%Aw!6
zIE?9;({kFmd)ewYpI1QW>(twx<I{0%<pVrwJ-FsQzrWf2E;=x4Rek_A^6l>TrlZg>
zx6xIG2~sD>s9le+KJU=_{1ReQzdQPljN7S+I{gFYni?y^wb3C~Zr%2CbrRHQDx@r{
zdSB@0w>djYb88Y2FU4b`>A*yU`RO=0P;%+n9m?2ccj2D|zYpK>1$gY3{&qknen1(i
zrA@Ksv+3Cs>0GJZ{#3g%5CZ<~<=Q*P_RUtq`%U*z>K42s!nye{x44<;!X)pECC`uJ
zhfP1wYa>;Q3UrCN*mewBpw9-)`I6!#?=N-urcIorY?QB#mQe#tedj3OAC6>=|Ft5-
z<|=jv?RDk~EI**u)}<bQ3BxxDdQeBlk~7=>>cu3UTG0}+6yfoC1;yaifm(j`i28nF
za{la?3~9`19_lHvZ+5H8{~;kQCS6xm6rB3qsGkrj!tsb$`uCsBbYu>aGx8BbVWF!`
zL(RM*_}lv3#Pi<*o(Z;40c*(-J3i_<?T)ICW^v6OfQve`;ETVqx7w?{BNzS$^C+{(
zwOTf9SXO0*vQo1%h4gc*bI?Eg1u9_cF|6;=UiM5^f5<K+J>gMN9C^?F_CjWK`KfkP
zKUV(Ei0iYrnB#-UG(O@~UIh_%M4{Ti0^@<ldvv1T^=`O!Z=8W^YT{z75jtiHEuWu1
zy%VY%6iV8=p52zD{LB>16xI^X0`GF5Eidpn_+);3_#*-htIW!r^0#J7DwrmisVta=
zyt!r5w7_QDf<C+PJo^+2YgePjsqQ@G((6fl74X^3#q+hu6@FkAr)fg*7d=9eX3yqI
zrl)2^OZ)zh`_^d5P2bPy`%eoy%ZLkj!dC4WQOGoFOrLMPB*914hh@~mtSiti0>yup
zD=t0N3H{nM!#~4U&zmM#eEu@#bUZvM^WNhNs%O;=39%(bB9VV#bqa-+iBBx+djI`-
zZ)`S|j^F{^Ad*u5nvI{+ZoJ3q7T$&@-;(i_Xv~Xu0?w4{Ldc7Mn_R*wH>Y-8&u`tt
zD>3RiK+R3p@qDY?4#URRnJbSR*7$2j1lTRx_xkVk)UmLjG*LmIX)1o?&9zwUu;6gt
zY-wAj&13Gg0u+2}RzdL}=!MUu6vqZU*86yQCMGq72`y-XY$xwkV}FtA=L8P9<Y@X4
zRM~Q;g3`zVE2$NF3%sp$K`n4oto4VZlZe*h?#DTMN%s3&LCm77jlQPl^F6k{!<zRt
zc?b3YR$FmiXSv4PK_nw1&(2d{$<&&6jBW?HhKn{)_J+1+jG!X6hU2;LBy6blCK58h
z>0vx=^`gP6Yg?r2<!R2(k2{V(`e(=z%vfOoMi#g2N`^eY#CAZto#kR3X<#xtUZaVQ
z9C|ytNf<|geZ`W?knL+F2pmySMf0ecQ>klCZNq6fR@H)vsG!SqREhtq$hPHqB$W5}
z#U3v`=DNC8w5#_#vqWX%i<JxZFDacfAlA9F2LPH1TbXh%__K^sj5{shw{)P~Uze)(
z2<xZv2;s{Q>m2V1@<Cb^RYzVW^!@T!sBkv(_A+>l&O@nJd^hO%DtdYuJ~usw=Qwr!
zrD|n~F|@cUEFQJuazm5hkkBS&hQmfIj!oN=28o|Mt~ZxiUUbOf!`|=Pl;UTN^nLE{
z^qlsVc4kf(j3rSO%{%29ob0Wr?CtBR-6C;Z!JI4v+({(T)UAshE#un;Pmn0!o>;}d
z>0{OPJ9?B`aBI^f5=B|ffmvF4I}30*PtO(hk36qjx(vH20_N1CW+gJgrJ>8e(72qv
zh;S3lH)=~1W9yhyRg~<C8ODM-MPTuLKaMXuo=ya6T0U-@H`~v5Q@)#j57~Ye6X<pQ
z){}~j9ul9gO$9&r$7-LPWBpht;G%3;(19%G>wf<%Hc35<?o$EeoUSq^^kFoDIs`mT
zODSqNewI#Tz-m^KLhz})La|Xu8X$?;|Et)-ucaDhgDh6N4x@v{<1X*d-Ceg0uLC={
zkNcg5-vTeF%<)UZ<0Gop2UgwjR7_QW&oNX(NQOpqtCG?Kl%Pz4@R3>o{?3*7V(Gnr
z3fz2B>==u0*y)G8XzT_c!yM2D@^`UZ-|GjsIloWiQN}I?;5>S)@7>O+2*(SOSI##e
zj;4vmbJd?8>xLp>;&6@&XD9p(nMXb_keuslnrf=)2nR%p4BwOkN{omrNB(?nHvCAJ
z497GssZuC{Nsj$>1}}t8%iPlHo<IfmZl~}PCnyh0u_gh0RPw^KFF@jpIesuohRN+D
z_&6D0brT?%PRFZwlw+teLC~tOctU>_J61z4jE536vxF!DrzZThC9qx4+BI#fO<?Ka
zG&i3VM^o;2m^roYyjuF+bdFc<ey>%)ladvTiLSj0dq-G1z_D8U9W!kqT9~S4gy^>F
ze&#O%*bGC}4U?wx`H59+f54OOx;djdzg=AIY><a3#zUEz5I{wfX_N7G^%~Pfls{&)
zuC=-_X=fQViNy++rNX3}x#pZU=Dl&S<@00v?>MpVMRxW8Y=qq=Io|XXWEJdbe4?X3
zUcuRU-C%iEyX(SFVUPU3?>$Eia+Z56dBZ7lL6@eAsXJP;BuOTU6b$y!Kn#aug#OSk
z(70K&xIDi#oF?>^z9NP;vtU^Y3D<>Gj-HJqUpAfe9KP8SytXIrBog}hZg)C{zP+Ed
zJshVM1c!0Bo2o)i>p<}VsgR&5>z%feGv>jIpqJ@gZG!q5R2rJ%PmcWt&P~4UgR$j{
z%H~s`0e$ZGb!v)+ZyxU9j#B9`zaWWAXxNU8TBbNyA@+nIm2Q+a1QR;?C^WsG@-)3B
z*R1jkm-YA_yS5Trt0vkC3zvQ9GPe{;pWcd^ecvBmfh3V^x*{h2amVo(`PtOwn#B{E
z&;@j%^AjYpEWJw01p<rX8cI5xcG}wQaz8!oS!}9g!C5TFi`Q=Jn@cUZZN*>=rur?H
z_@6r8p=IWDHCct(-?;%1>6G`-u&b$z(5~n~o>8@#4K|7VY^WPgBnQcbR%y&3(4?Zw
zrBdz8k|Fe+lCw6Bazr@@efbFt6eFt24Q*Usy1R6Tkfk4WebR*UF2zr6n;z{b&)YWX
zOZ02EAG8@e@07>^%wrY-j-vye^AP4Hhc7=uXL{2*H12APjrhdU5+eF9G=q2?l<al|
zG3+ww7-(UC=-@8m^E<2wT%>cF%jt@|C;rO?GI_GVH5=~^M{1<8&ppyLm#$}dQ9FO#
zHnH4E)x-6aCm^j{6P$x%?{2>Q2-GMl`a2Qs!QfW1OM+#NmpoL@m2643(Yjq-_`t+!
ztwg_V8W`WrJeNvSz&)zdO+{m2=F65R)4loB!GbZ@<efe#REPs`Rt6lC^7x2K3JnB*
zXKE(57@-i7J_10p!N#I9S=5L8X%{D|5a%Wyx^i-X#@qW5Xg6Jo=A^*#2Y?*FG}khl
zKx^k8$Jl>Nt8ep~9c;HWNRKdxNi3QFeqNGOQ(aS0y<1vUUa?(TU3IUhtg5Z7sk`1O
z5_)oS@_iBjDvORZxA2xAZNKaXp$UC$#>f-R!{_pQz)w6pzJa?0an`gta)dJcVP_%e
z@ExaKy=<D3vbJw`x%}R|?lbEqkh|ZHBEavJBSM_^epne8V&i!d{rczi+S)$5c~k#U
z)BK$tOW*X<AFu0X7&_7-joW9XCr}(8U@=EX>FYHM_96g{AlLGJ#u`81HqKb@<~*ba
z=P9W)lkE}EeiOaB2{#-K%M&c}f6ypTkD%v|*mJzJsoBQsV%bh(LM0bsecicCpvbBp
z<JV@Gv;NG(E3l;y|4Xe2i6Fyt{5_PcqLi0M%kF2o3vW>AFyE+IkaXD&VFE=CaIQ2<
z?a1pc?7Z{iNEu^#m=4(a_%`;6yiw}myBnh~)3I~)-Jh`QIXd(k0rPE)K>kpXcxk{1
z|5oP36;v%$UOL;BQ9jV~y-K2<N!tUW%AWsbZ`8-rP2Z7wlrzg|a$zX5oT6GLBmIJF
z_ww})zwuG(%SG!I%`P_M+V1pFH6d-i(KX0ikH@7utVZA|2v=>xd3)he-wBk_>+3)k
zM=D>2TMng4oVZ}hOf55=fCAY4CKY+9-t~Gm26wl*)O@F1{PA|m9-0j(Kb(G7!+AMk
zZw`2Px^M1!xC*M-{M;*rB(32&g5KL%er4$Nb;<em`?zyZ67PcTIxEJ>((k<M`80;#
z=RL0Ea>F?5nseQCeyi$mDl124ao&4YyuIUde5`FCiI9buZkE21smH_4%r3`%uThl^
zE8`FY562n=)=QW`g$#)!?7Vx~?gOs0-O{{1YWl4;HLutO6D(?AUwAwbNTV!SxAv&x
zyZ2TevkK>`TggvURYe8NoZ8-l8yKCQ?;j#-&iG6cHhtgk=lM|9%z~RHS6&r*cU%4V
zyt}()k@4Af_Cs@~3rB1eZSb7In=vakLscnyyY{@7kI%f967n67Z?pxlwr{)rH8C4b
zDWn{$khzr7>x4V*d*JqH{MOqjfsfpW$y>(kmUBH)Y<=4p_7LC4<rKqqnS}{+2TXIC
z7EV)=f*hx8o!_J+sS7LV5fgvuWs$US4>7f(+m2*BfA{`;?B_de&i3&ABqjwO&798b
zqNH-W;sUZk_G^k6mt;6nE*7_nq`$pC2h<&3R^_|gw{m5hPdZ;Lc_~e5**c8~NsA%~
zL3e}Oe#@Sx1D-!?A3}DkyC!53*S$wQ773hRDt0NN2pX4>r5`{ySIX%J^tId+-y?>{
z;zJaoQDJPz<EoflAk@6CA3{Zk5AA(g4!V7%=^EzM5rN49=HpKX35+jxOc-BkxQ{cX
zV*m>T)}ZG^q-wr{@fdxN+tP2%o38skZu(BTQRrNs*JCw9_MoBR0d+q2r(wp;&tC^e
z)oCW6&l^0rs`h;fU9XE7w|BVec6J@V!R!LUj%`9uTz~m&AIteaX!<@^j(0uh*m}=X
z(lwZ-n!=E6lC9B+y(I4aNq>a1Vg)%^706D9PDp*`vnJ^O${J60xTj<X{?>Es&cfEM
z+QxE@TwEt=``B}?0i4XI5E68KcB5(2FCW(T$!pab@RKC0$$0;sY~0pSnpnaK=}4ip
zOj8wt&!pm79Bw40d5ZM_GOfW-ZV8XX<0i4ZZ6Qdx0O9F2-l)};Vi#qM=~|Pb<075J
zOp)m(8kb(~OLEi~RGd}R)URoRkP#hKR9!D-TdS*q=2O}1(r)H23I9Bi5n(XPxBO*9
zB$`LEg_o~5-A(q+%C~hH=XW(@_7*zRsTH@A2~%ztv!-{)l+~=>!>^1TFVn8gxdubg
zSzuhipN*=rDqXj^zp#~@_|@MxKiv-!1%QN2hZ_=~_a)(*dTu+K6BDvw{^yT5eglph
zdZ9Ajk9{E_u&JLPeN%$&PgZk%kT;NsMPy#R*bw_H=n$<G(t^|efL6A^&#V2#TeA-Y
z?nq%G1q&N1fZ;2M$K{OcpiBbR{wam^fyd(FY}tqc_~6c<`)7@_Xa1LXk?f8J_9{&}
zE?&t8kH<+(mt5A}X~1(GSz{*fwzQS?qhl|-E;P#nu|bsz0;Zi1Bk+1hn!fck4-V?{
zl)L1o+jFq%`O3_9mPNa1?y9>mIYBj_QKzu{FWWTJz@Euf@0S<eISSc_CmTb<4TI1s
zCakFAE}86yxf+%tjgHq2+D-4fvbiB0pSz+%e((JWd%y$6oiszk9o{ZQs%jjVu<Pli
z;836S5`1{!Y0%VZO`-mZ;SIIS{fAD|wu01$JGIS&>!$Be-|_8fKwZF>-|=OS`sPd3
z1U=G6=ItD|zH;u%)4g%Y$JH3S{JP&q*I*@|H~j44x|8UpVPLYZ$G~oNn?W-v^5DW}
z+f{mKO^e6s`(vBcn-5>I-pSryx9qp`7$a4>kEiFwf4QUOZe^=q`*D2_-ur&Le5d>6
z$f3wWjK1gXt9<7fuL)7={%AY?F0N&Lex>Tduf){!U#!J4J`dG!`W|;>?{d78M+5c(
zFK7L-Zu^=e-=+Wnr^lZ|*^bwJi%~r%2gSu*ww;H2_MM-r6DfpV10J8gpL5PY0jkvR
z%xJK4emPDja<8_H_rpDSHS&}b8SJmGjFj1}Q+d`X1nwuQetzfSC6gPEwUl<C*RX~U
z;0i9tp8FiC_?F8|HVwF_FR}S>X8hOraKmTWRVm9$E7p(sI$tFEXWI;{L}rz2YBgks
zrnl`ID`$?!cHLplOqnz`RxfP~f754e4@$@F&SrH-sdY00bIEC(;{pEpUIE=uI-h?r
zSA@t<yb-<aINi>O=HJ?u+~C@r&KInpa6(_t7dyBMr>#!T=&sKxfnr^+9ZjYqPMhfx
z`;OO<Q+v3YM?H7UrdQ;X&->7v?7CHdUc$!9Z{IJ5x=*78Xxow?|F&<15PW_--F^oy
zfy`?b>^yJs;@Z!-9x9~?Ixh%WI+vt^x?b-hQFQstrbtn6T33<fqu&>U>ON;7>hj}4
zCwtYE=#k8u4eCSUq8X&=JNKN%ygvrp4gPJb0)7#=JDhp@{O0nUj+cj<iifi$5MHCQ
zUpnA^NU}48O77L;=5?ym>0YrUX3aJ9{Bd;`%*v4d_8t>IJL|Ng<~5$he_IZB!E=+T
zObEJXxaViEv3!2nm5<r*KFqGD;W;>V)V=`j(wpmfe>OGeBL-e4h4TH-*4FpglYG0~
zxUJDT-%0ov3|H6_wtOu1$w|p_lqFJ&hvjf&t4zLr+=g!+Kb^EQcBXT9AbvD046IJJ
zzcoo@10KQFaxG^a|4O{aO+(qq7(Ct7?sZ<iugBOIT_?$)7=TISx*NX0gZ}Q&4Qz}q
z&PF4dHAy(Qp>YLw&Y`(X^Cfwh`e4Si<*MF9rzX9~mHrmmoyHUNz4KhQH9#}sD8VT*
z5h9*P&pfgWRvLSu6Lt}p;H5MAmUmh&%DcGZxatY66abMq<MFmv7_)VDt`$kAq?eZu
zdLXs^KIQ0Y)#Uv7BdG%`^V*-;Z=Qbo5l`^ub!oWCqWOBQwDVy!^pf8-XC}e#<@w>3
z!Q(!TQm*ZIbYD#uK}|inJwwmR&iZG|%u5a5^BL_f1-|#k@p_7`)ob8`&&fvUirb09
zv?e9%<(2qcwI82D4XHTi@?dke*Y)iqzX@A4w>J|oRGcm}G{;Lz%ypqMd9W5Ig_(+p
zbDjD69hP`3WNFDTfe-7Eq&}I5(!G1tzUn?ovF@6I7vFb;+o?RP)pXq1l>E0yR5&Lk
z;PrNKQ}239_h*WR_q^l~z+r5uImgQqRK>XnN_I$V-6m{BW7(1<<fgC1sGg}-UNyJx
z;w-=P#8Ps6_~&UFdfEs(pH5=*eSL0OW2$R^6aJFxIP2{bzsUVbV8`qy#eVNPR@q6T
zLVK^>Akxer8>B0ZM3L);{saGA%QK$#r--!54Byl*YdfwEk&@_|m<%STJN|}}$)~%f
z=9rcp?;Y8y+|z2ay0W_CYdkyNt?}>bxZoIVaqD;ID{B-0zr&vRsSVRxoc2YkfSaAJ
zNZ-Gwx#L1D!}noSd-bPBx4gbjU*d}Sy3k$plL;`C0us$W2}^EU(7@JKAM`jgBhoyY
z5r}CvYTKzO8FSUYQ!>?Qwb^R?&a%GB*hTg}&UIXG6I0FC<}Bg3Pi%eb((35DORB!!
zw&3e!<Gcq6DC4#q$F%2r?ro{C<374!C7o73UVYumV4P!(1A;&BzWSYiTn9F<LATbd
zy&h5U+H04VsYu`GwB-bgQD2-7{vi4dlEqXYe0c<6>#zE>LfRn9fu18iv~A{1Lf8C`
zzpW=9`R;s6nY6dvD6+4~>3Y8Cl&;=9iS^m=+-W7`&v8F*K&fVPrys}H)pkCag!6tG
z)xIeVg%HnbT@}K!13hO`;!je@fp%fM<{d*(HoT1ocguaisV`%Dc$5H*h3Zb-P~<88
zw<Y_{&+$bpg@eQj#-2B$yq0AKULB9ww3&ggGU?(!z4~kEIc>Mo__Q5|4zVwN9j*;2
zbzY{lQ}mr(jjSKW1^iw;&>z!Q85exci?Hm$<tOm|sbmxn{-B=p;hadPFKB|!6$D<?
z<Sj1>c*^Xa9d(PD*F!2M>wXjT0Dpqk&5KmvyRVm?_c-;C{^O$@U)v)d|K0!`fxfYW
zpG7vv4ZkK8qr8Hk#q8^__C*)(sD0OGHZ~!>Nj0BCi-s3J=#j<aweXZdb9$x2YjeT(
z`Jvs55#*a8^6*!)F;BOhmhjm;uhT3+w>jgkPo5C)d4lzNS)JWr#1t2QN5dC=@?4AH
zZV-%FUswBZ)$`BH1a&X_?S$t}PanG2`e)e{cD=LmdcM}(FMZ3N;VD0RXz=7-oLBC&
zv;7K(XlPlcVn9zP{{kA<z0&nfuLp89@TwlcW`n*ym0at~P9VnyMKTnJY9#&4d*Ugh
z?(|B%#HOBr&+GbnieB@?k<YW#*uUPu1>W(uOTg>Ri?;2<Oojcq%e=pU?>)WSPq3C-
ztwe0Qlp1|}Wr8iewSjmX@QoRhkjvz@gBq6fORa~{T;F00-(KZ+uPR=MJ=YKJy&^aQ
zLf}3;pIM!=P4Uytp*6v~d0zGJe!Erqmye;x952(c_ur8gzv>5cPsr)1h`D;jB1{s(
zP^04Gio#fyOic$lsK%h^(a@?AA%eTfSQOUD05y1K2X>1#c`@@eXz9{o>_{%+Rjj)-
zA<0*_K}vv-9mle88;s4Fd!eFw1#RusJdq)}pZBw<DL>&eIQ<B$V}f5=nVoDyS!mHH
z_P{r}fr<m725DPT+vIBc&p`ETeCLg#-NB9fOZhG?%T>N=pO3g|X@={$nuP4O(+_)r
zXPXy$zdv0D8-S2+w0Z@M;@iya9+2GlX6zGQ_jM&3UJv_a34X8Jr}n`Bw`{v`%VX7*
z$Hv{_W{#J|DZuZmKaZ*`^C&o1xe8Uzq9Mc6@>9DuO4HmHHi<GVH$8s8kJi$xbW4!w
z?q)Q#5E_<4A)|5E1P=!LxI+nSlQGvk|81I@aZ_|O$#Kyt=fY{Jn|+c>vL0^KvIlMZ
zrG1nQ9O#DoVoO?OXIEL*7(KpSBOf88KpYmAJVj89027B6UPeZFMtVP(!w4$CrdFez
z42G-GKf!?pN0{o@>dUI#^&qLd-S4ez`xrm7qKyhyZ9_`{a-7S(>CoP#uLhI-hw>xf
zX?9m$h<*Pxr;QOrwmQ5JVwwE**5ua4*nnZ|8rt_DVEh=^3c9`s!Va+kD%K&EIYswT
z%p-^tA$mf@Tk#wR0`}!@#$WoXz2Efze6?%;Y-8rka*V?=#!uBnF<k#08r9~|RS>=D
zP{KC_DBcXa-yWhzZrC%Uu5dTJdEET&1cI$$4Q1M0))O1R4SapS3D#>!(Wjw-wQK7c
zKX_&U+P2(+7qc85I+mS(FxCj%%!};~ihqSJ-dR)Jv+5R2DbIu|*1=>WY}sz38>!*D
z9<RdI`MAKd|LJ%x*68>p_i|duk7EKZuP(poG4?}#<Gs6av-s`qxj(&arRb%ar>h8o
zRPAgtbn@l6A)H8bSjTCrj<%|8ROm1p5c;?5C6vBE*y!)K5GWxsUf_n0ntfT-UKJ=}
z1vlMAzsC?@yAk!TKXWn^{`v6|7=wq0cg)`*N)^xmrZ|NW;p%p|k?+c^8#<b+0l6&X
zy|-*f5ufwAt~hpq7`Wb^)U!R!?v^PTJI|C(QpR<-hK+=dlwDIY_NY_Hzvw4eV~o=G
zj$oN7tEr3|T>sz+`>|+StJx5<Y=(l{BGwO@6`=S8`;?I{ldU^#6ctPfG+FjQy?N}n
zw6_qU>=iU~+|Uurzu!MzjL}I(2~N7_rL?xzgJb<0)&^Ss83XeBg@?o7rE(ix-H(W?
zs>#8BA4Mgb&7UmPT!I#(EiLVKaK&lj6Is)E)O0ZvB#CWUhoMxb`9)qgogRu>Qcgcq
z;i@N!(<mppKG}|U)i+lj`$=0cILq+=6gg>WudejLx@udd!JJ$}d6j{SDH1<E<``3<
zlTVtY3PQVfd(_7VlK;Kz)3*?uaaUKsKW6<p0uCQHMA&}_cx3gokdc~{^gVp8UcfQ`
z*i65AyOPC;a)FptY$~)hOG?Z~T8}lyZAzy-V(oQ*-~JaZ%X^NUfW4~Q@6%Zs3@l_`
zveOI+KD^`7@)Ve#HwByyzqG&ymm%S5C~xTr8@uLJi6vnAex$suRT7$s67vf4B<fHv
z^6XntdB)$&sAWqOFgv}<q&dcY?PXORi~9NtJtfFVDZDOQ5<8h&Gyz1UK~g%65><-a
z&#|QFVLcnI29TpPU>kzPvwPa-{Lbrg(Vt7cg-YQex7|`Myx(X!$M5yJz7hCRfmf+0
zyp>1|sRJV0@Xb>|YDf>ZvoRYJcHOQ;l3R6euaet%75p6l*jaH;IgeR&N}(2z$L)Nk
zcn?7~Qi4dqgcWC|E*;$`8egHQ=KF}kzWBM=Fm>|&!D2I5)5Xe<qrR3$t|>flRy=qT
zfg3JD5zqI4vE!8P>(=+ttd5UGG~Dz%io<dGmp%kdShuow3TE>0lIkY<pe-)o0kS~=
z4GS7~E6kF*zGT_)6Xf_srUDs%!8j@D2o0q1T{1qf9h$bxIT)Y0AQvm-=15p>E|n!-
z?WE?w18Jq{Qb7@?-4_%E+hm9H0*)B9nWS+|7%*KIEyyjLkbJYy7*3vk$_FlMH|bzv
z2iSqmn=@<YsHz(`Jl@jon^)@*>{sD&c7F<G=#LY$JrB}8=*a@umE8pWM_0<(L%+V=
z6>+BR{&PdyH>Y<!Vw=8KBmXkW+V`H<a(&_MUJo<tJDCec;*sm*pV2QqJ@^<90pD?d
zez{6{?BElF)Kk0w^~9d$_&tGd8QM*zNP)a3-RtsvQjCr1v=*q%<Hn;ErB<a#G^0)5
z?wvpOPR%#M)vl`bwc0FjYxf6jhq49+1LU;5%Lr&I8({U|2sY}uO)eC2Lr6(@xE7R$
z+LzPSf1h$0eXgr%_%sy&mrPG*UKtj>nhpjBSAO3EDB|jDF#*1?8RmU28wR#j)rD+X
zO--|Ds!0Pd^fpV=YC93O-vvHwW)nBtTuwv6-v)XCT}vvun!B|yy+2JsH=^Uh0Siyz
z;jd4&j~aE0G<Y`crtL2;F`Mpi_*yvOd*iPQFfLnFSdI&WB~|zL*m@_dM!G{NRG1Qa
zE@oZZf%a`zH#2xP&JS6f4mnr`2dKoN0a-88ve~rr-_Lpnz)M!D8pl@PQmFB4{@q7?
z3i>%`bC==<^|PiE@avfw#Y-O921XxZ^>Gc)b0vzNug^hZ8DFwy`5}+b5-sJPVtYy~
zuiHE<7%~?#zi4`GNoBPfk}=3=;l7E8lW_|xJTZyFebp)tH6~fKG9_n>E>E0L+pkA*
zYgLtnj%ZSya4lxq^SOtzMeU+iBL#DHL0whd=c(6P=i`9@tgmeq8H5$N6-rjf&fIB~
zoQp#qF)-MAt64}}{(gq8ky>C6cVp677roVk|E0pp6geX);zHN`>}Dp?-bFM2wRVC-
ztL!s<Y2agPf--0Gc*}=bApBsD%=)H?h9<=P0KU+cFGPE!?A9Z4SR}G%*eeB*KsFo$
z{IhjL%DNJ^ji*AHa*eQfBqmKq#-KQU)S)4#32_OUD+rrjOY>!TzjG&^0l94cpuA3(
zMJuseYAo)ASwWWmFH7PLtDv;9S6CjqVFV_t6T$_SqUe#C_evuZZ%6#kgJ0J1XVc74
zG68ol8is$Hz%K`zo8X0o#b)c$^6JSif;T@2+1VFtiuJs&X5=DWwXFvB75ny>m}0K7
z$SJsw_TOs^<KtS@BR(|gs;6tFc=|#*KJJ`Vt1vri`pW?}{o>{^pzA3C^9{GYstbB7
z)^l^QZo38Ni#Ly3J<qL~m9{o%!j1-@a_D7e>eDHT0CUMSE(nQcf5{}A4uAA!a=$s`
z8&GOH%t*{O9nm6j=ZEQLqBkOBUjRk%0mY>Z^<h{kw%Y^5014>Ligqf4wYl1Ph+hOY
zB5L|hMBeU)_1{n>Npqc&4d^iB(SpdzK=J%UB=7wZ6bziC;HHpoormRkzMsd31Y`8l
zyP0VX(2Ig{B5_d4x={<o#b$%0RB^pMi(LRJXyRa#EhSlz4flYO!dU7`mO}kSaxA}(
z$yX1A{jFR1J{ttKZze5%*O<xsi}U6(8J+LLe~F@P8Rkd=UTvA)_9X`0j#f-xXI`<8
zY&)#3{>4SNDm9lOeMStmxjNZaJDL3@R#04eBAk!eW-_iNz0ftsdkTv4O5*(G<Mz}*
z?arYx;~Jm67425rmbtnD1Y&51DhQ~6WDP@{=}zxgpJazphrvU8kbpzKexscx%F=$h
z8hEk3&)qpQJJ-JLFuJ1|CcLy#Emep>HEX>%GqF;J^5nz<v^UlK5c!R-&HrZL^pc@L
zYRXhy@AzN0{Ba=Y>-$;NBTkks)+KOWYRtKA()e=UU2X=<alIbXqdCgBBNmnU5mO<*
zdY;?xoByZ-&ffZH9`fE8wa&JJ>SO~RRr3qRP}(5l*Cv^kTfMEr&ELk_whPiI0{#>i
zpgo<qhE~_a{OR~HMz5PBY=WkDJ@#*|1#)~?T)=S#h94aR6nz}4PhsZ|nGQps3KXp&
ztiwSi<#;Ft6NU&&S+kuSi*L}bIq)i8d&Rs*>~>DGXteiQ`*UsrKWdL**Wx%RLnvV}
zD^@wbF1rV+6Zi($KVFxnzS=6(FFbwSo8YM2Ggr@S(UmXlZ%L-qqPc!BzwEeV00Um1
zwJ*2N?W7qPM5+6KddCoL<bG@=n0i%*6&y9yZ>XdROicmR!5a1n(v>|X($yWpCDpnF
zpzUaOxX0b9Xkl6Q=3D~M^<=j7UAg5{BvD(Cg3L58i#|afx~Ul=m!+qL<-TiXI|JzV
z4T``svnXB+`dXnZ*eO-y%EB=DCTAHxd%ZUBtZVxUdr0<Zsp>YmK%$O)x55)(^Kq~{
z^{<x5pqxfwb&QyF*EFigNyS(kz>5mCP9Hm1S7Cr0*9#=D^{i~PakZWJSET*D>|{LO
zEVec-ZPrWO`xU)V%6zK%$%Fe%hCCyxNK0v|GA~LKFZyXc8xrfqT+KZq3223!`}d|Y
zB@TY)AjtSD8aX!(tA8R34`$psUacK`=m0)BCG<4+qPteX(-BKJdS0+CuIC=jm=m%P
zfJca$0W>OgRaRDQS88vum>pU7&=GRp;S(C&R<K^Cd^oAw?j*4TbY?JXmq?8O2MWnx
ze#J*OueM_f*n44TO&9PupNK@}+L_51@_l*HMu}<N&BE3<tY~>?6RBw0FMT@R-2Cge
zmB%V^%x+^B;d>4C(z9hJU_x~|3!k*Jvm^MtDp1dHIViF3l%`!ve<Q##jbDi6SUF!|
zN5r+H4tA7wijx1aW1Z)KjoxxoyUE0~OvSS_nxZ6BQj4AYgiAllc1Ku8T(DCuUHx&C
z<#xcq__W=A>HBd37pLKlvB#)1D+yCZdx5^yCxrty3FzD>pn!XPHh+77tNE-1<TA$C
zT((@@{7beUnuc=logad8!&^TJtCpI8)g55mv&JWukbZ9Bxq8(+0~gNJZdHHk)HFvO
zOB;G8&exgKk2>KRRUXFXL_%bE1nglX_xAeQKH^u<&GdIh{=22jR_9{*oR9t2j;peE
zBp)!|xHfUeMB|DA1H9Ivmi@4D^LoR<w7=^4eSluF2?&${@$W~$S^*V<lYP_>_xv=d
z%^IQ+&2Eb@Q&<ch@@F9%kCpeaF27WMzsX8&=Ty~-it=TRN_PCJ&U8*|<l!WC_KfpR
z(#N7M?^_?czx+=3Rcm87+*ZxCgnpiDzesQGp2woKeV=+yb2@Jx+Ia0OGp~yI$~~tM
zwA<3hYb}V>Bh)Iloc`9p_}=zV8q0zHloc;(S^pmZJ3z$0a&LL7QG5RK?f1{zaqoip
z3qE-6)pwqHe(U}AU^?TmJx@a)x%wYdfBkLpcRxLG<DIO^Yp%~5Z8$_-zo4@1yno)r
zh4p!RAEp-%<*Wz^w30S>je&t@`6(D;&(=v{hB-_A_doym;al%axaR>vW!mpkq-P#c
zR+|i+Nj-Y6eBN!>KXUV(_g#H6Y^O|`^zvg9rp}oD$DBX8rtG=;YGGn|pF$h%NFGYA
z0Vc5K3Lj`gP{HJnUw`9Q0(lO4<0<Dqe8X*ro^^74w@xUu>){9SYWkS-FZ*@Uq&f5F
zfBN2s^XAT#d95uQ?XVTEr14tDUv}8i!P2@*AfJ2bFOz<q-Pria`yb7nH&<49;&eo{
zY_s1Uc>Ae4?&q?SN1Jax^}==s>=`2yOFw`2!w+6~4RhXp>V;3=cxUT<cMG@3YdR!Y
zJ9G+&^mixB)>XaLobUpbP1u@)`~UmXFMADGTeDG13qMrATlDL!JA|<>E;xZ+(e_EC
zC_tRUKg6lQ!+5m;Yu7h4j6LVlpC^9%$|FzS%u9R>d|?&0*?-TUzx{r~?e{h=n9ob~
zGH3ri<=SGg?_Y-Ua@N-$e%5>VK&os1W#|+CxMwW}W4n4A0JRRwj-nJIg>-WID3x_`
z(mWu&jOY$`ll3XKRuy(RbpIC~di>c39%TSdn=#|F4?fcE9fu;;VKWDX&))fvSN4Fv
z_0$WWWPX-5Aegnw;ZQ4-)B=ORiitCo!u5hX8AGayO!`Nb?69@&ZOjhKX)%gT6}l({
zF_Sovm$^x;DZ0y0gCTiEE6<qiCig9_(elo4Pn&^oAYi4~AJNib$?of~xBi#keqCEz
z)6me4??<WGfh&6Ejyj&NR4kY?ci@15Jfg=<+Es|F&ViVn1<A@$!slqyR%~p3|M^!q
zj8MU=9EYBE(lV>{Tyn+b*BsQJUG4QJoVmx5hp-cP=Y^LExbw%|iHT;xWl)jLPfbnD
zekUG1_L8e_JMDsg8?3v}@kic%$yJZte8&cR?AXx0U9b&TST8h*2$%n)K^##OW3`s{
zS_b5cITyGjuwuSr$f1Lf;hHgqE8g}t88od=jcpZY-*Mv=$DelgE(g}PZ^w>))EUR?
zc^fw!<w?n~8Wt-)-fRN4K5AD=XOI|_yk&FPkj<ij{H%L!S!2k+!_GYUx-&0$WBfBc
z`}98OZzr66(BVW4{eq?75NcfMSJu~Uf8ahh9)CuM^EzMk>UfK0h|7dbkj$4k2(n%|
zT>ZXq8n(^mkBqrvy=_J|^F?9t(tM%JhJ(AqF{61VH0JnoZaevWJU(F44G+8Ke43YB
zt!KxsT~=GWPaALC6pOuw4PbAf2h6xmB0^@FxRMf9Dq*Ax6yrIw2AFJBG|ejhb;T?Q
zBa5=8vqJ0Jk3K#26y8g3;9=oGXXwjO*u2h&^||+Rc>m3YZ9j6~3r^YR%;Ro8>D=>o
zI*2E@T$vqz%|*+u-J3p_S5N@A=8pr~cwRfCw!XH{-~sjBI@0NOU?~erl$+KO6BmWi
zB8FK~Pq4o{e9*`aT{`o~{LEXgU9Qh+%-yT6H|X#S&iKdqm*08rWxVh_VwW9uKK4+)
z*vhTIGw-<J(j!kkZr$xrX~i{qoiX;hX1HiFl{`75q2_{r-E!<UdtH3MF(;0_Duo1N
zRzH86uF*lG!dwE0r<|6JciiTc$DTa&FI#o%vG`FJp7q<0KcBSa9@oD<KD<h2>CI4g
z0*)tfJ-X`f{(rmn;tT$ILT!D0zx9XTalw^W9(@{nLEeQPx!11vSs_CDPrC&rMT2~t
zLefua5d!G*cRm<Ba8vGNEV1m;EA?LW+Q;u*cFkVAP65r*YpinaKd!y<lye@x^{$$_
zI(~%mf``V^ad2FFqxE=Yb*;ez8@hL8Fu-@|KC7O6+x1tUa@ONx?i6zOZs$LCC)aQ#
zw-hSZSa0xA7oK^``Ip^(?q$UA=DY2%`w4$l&yx7Dnw~3;yY9A2M<3rfe?H%+9<k?6
z?2v4j+MS}tXeC9+s1Vi*wy+2I?cO(@eqq&qd?i65R#~S{#T^xWh7VNG5}G^+l7qD!
z)W=EM#?%8$*QTx9Lq7bRQ?EYlTwV)UdClIV&p7dnQHQ~hMJ$<f-etF+BXc&}b%))L
zJ4}I5s91LOmAPJDZJjkMs`+)~VxM6H8H__W+lWw4I+l+!sV<wMN#-qV2rBtvHG)dl
z10ZWx6$W^t0A$zW58=Br*B^7%jVGK-I1L%O@zFQ^U94rL7J8K{+Lm2;#fPuG?UI9!
zZ=5%u=M<aly%W#Uqsdwb+bPonWwQCpa8&}u_XK@6*eFJ14C<U&AY`zkngMhE(U?^&
z7~DkcQ%)j@>62ibLJgfVyuv+SzomKxf|*qa8Hq<~7Il(UJ!9_eH{QZy`Ho#W^G*Te
zv73%Z&g@=iO`kbu#-E3rc<dBjtrD3Qn#8#*0y6=bcu8_(RkYPqR@F5XfBR(;@8~YJ
z#1e}yy(Hh4;$VX4T-R>;@4t8I*0r&+t-7je)|9CYojT5~EJD()zCA(t2aghAT~X1F
z-(Hx@GoDs%7xU&$fv0XP6dmf2(xbj32*1srg)3xrq0p_iU2Ro0Z_vyyYAvCYb%!OX
zW2H3+WQAQx0~pd=rjcx7eN|28y80>e^(<4IxxT0S+gcZI=rE&kzTU_v6neDlBq`kM
zZqt97>s;5+GLQQU^SUp&#B46-`K57g_gH^9Biv>!9T>h_BhlinBTGo?TH8=p<$aQs
zq-AEKUUuWRU~1b6+`ZuUvF9{5q2V9%=duswhfBNGwfk-UY?)N3=ulJFI){s{`NfV6
z;_5zgK^ZBeIq=+52^bxl8V%#QeO0%*cD#i*7n8$PSXx^T>Wt=jMlJA?LPJI6ACrIQ
z;VaKU|7@BEXUtlno?mTioe5fHB`^7RuWiq_*ym~+Q%Wjl<3v#ES+mZeOX%g6F11)x
znASKKU5HjxYFAa;vATZp{FwmgHME^PkNbOt?)B}fiWS`J>(YI(IW0~6dL`V@Yq9$F
z{8&n5hlZwFJt&hcO)ayg|JiNnCFU0S`m7#2lZY$cqalQ>k93(eZTgAp?|kgl7xdnA
zI90hi6m%)3=LAtpU<fHQ$UrxA;a+eh!%fc}np#_UjaE;}Wl>v0b#1%Knm>M>yx0<p
zH&^1!1>g|F{00!V&z?4;rnaWOQwIW1`!HThtg0s9f1f`a>v)p@^VycnY?=qWYfZb~
z7tFHNg~e*yvE7){#HBT?su!zkH*Eot)5gQU&Su+rv9A2UKy9&Y{`~n(?W!3;z<*yb
zJ2!w{#$SIn>TNjuOmz-d{lyjnkFO>&d+?(OvziyUU&1f{X?X5|R8WT?zz)Pqoe2BN
z>Tnm<5j^wUaYFHdQ5~x5>Ar>z9qQXR%xF}kK~C#p4ec2qGn(g{oQ^HjRabYcsplfQ
zp<{ct%QKqhvOLrksyo*(u4k)T+A7*r*R`vv<|Q(I0jZ_B!0j$EY^%88m@^pV=iGMP
zoar;!b5zvVV62o#+!3nY%KO$}8D*F^?GYb_sb4GFaH^DVn#+|UVEeFv!Q#l&yyIb2
zskgUUE6^O!tfmEu{epsdE^j&)wLyWfww0fG__I^@ZuqBTZT-~7Ic&2~i20#iRm~Iy
z&l0?1$y<%NV%_UHG`AHA8$=TT#0(bk3WE5DdvjFltjtNx84jhmAQ7NY(W$zD9G+N5
zERr`=)wZv$o6@LggkMK~BBQN(>Tka<vCNV!m4#U?ddsp?wVo*b>38mKJmPfxeD>|~
zGdFJW^K9ctkQn&GiLs$vVoO=Bu%Xlvj$=ymwNR~S6h%w2N292UiPGjOnnPH|T?|Ao
zPASWj6Cfowrm`1|02B&$fB8m$6FI!1N&K-f6H~?qW$J5crhW3wb1yv4oc$c%002M$
zNkl<ZSB-gCs0##M#^zNmzT8|@#lp<{^jmDPRqZOv_<CF;fIl-%53YirxjQq01Zp*8
z=aa^1l{Vf8R23A;*+j-`E4qip0S?#xp>N(3wn75MfK?WH?SZ6&wpDm4BTzY3)y#4&
z$xLb?HBQz{Xz^AuG|?(rk*8(ZjFqJnCWlcnK@y}CAB+@>KtxH@1fX^k34?b2Ob}u~
zrXFMolT()1jtZDpqYx4-2+sHcusBE?6(o)~_<)97O5SA$onaYAwJ4dkT(xqs!Vx#M
z3q8)t1=aZUSq)LFF7Ts1>Tp0JP%}!&t=j6SEIPM|n{YtVIdZjTQ6Zjc$i@w%RD(*n
z^iV9Aivfb@56akeY1OUZ38OCfl5lfFa~3e-8C}7!5{fQX0s|yR;xa>p;q<TbuX+2i
zXRd$i3BAZ_-BLpsnJPY64eM+H8O-O*Byf~c)iy~}8)^+beQQ+?E8VCbhQemUS{3D)
zb45<Fr=#drV+qcx3<;jiG0d)l<ypQAWp&5M*DO(mPA(~(f~DBoMe;UXto{&;r2fch
z1C-|p?w$hZs(U#UNua<|e?*sBVSs@|I5_|^d1f9GH7CGMsD9hgu_xq+OiCK9L15wF
zIvyk|)Ui=F9(NWC#?d!ijKgV&xD2iG5Ea*?LUuOeg)`wKEH#uBRaHOWy_5`5#%NB6
zQcm`S>=HE(k+?&q<Y_Q`FO9$?yyN;RBNNN=lo^uRHL(WgOjQMCCQHcDPbvV*z~QV^
z#XrC`-)4HJW|Q&b7@F8+Yp^g6`|ReU@bmnm#$3#V?3lAP!pmhDuS~2P$*fo4Wn(A-
z-Gek+o>g}-(>%apljxEg!sHA~?TLj>CUQNLkLEy78=r+K0qMR1KgsE(Qz+rW#3?b*
z@{}ig$ZE`MEUwsp&CM$-AA9t%&Rx4~@R#-1+i(N$UU*K4-RjP}?9#c{iqq!Lj-g>n
zWq!x2Bm(AHmoN)UM@=YcZC1?djb>@4oH(FHPXKpdr9l&S*g{M9My4`!M4|7=&?=}U
z3#tk{l#9RAQckkLUX>~9){qd5FFlBoxJe{x+a{<ey>XBiOCct95-C(A)g;^y1aSk|
zHF#1{nCHn<)SwZ5(v5<dNDYH?d=<>iaDy{FlJX+V66Lf$Ngb?;;1|pC6eMs|A^*sb
zFh=%g+Q|t4Epd?K;33;p0-M9EOdwqTjYLLbcc??aP<W;>sb<sU0)ns51t-f@4}B~~
zEhI3cuny_%+}$d0SYjr|RCF;Z>yDZ9NPI1ZdJDTjI3}qUqOa+RZ;}+E%EWW)Z}yaF
zyB{^WiTCULc23?1q1ARK`Xr4)QpmtE<7mkGH!w21unu(nU~<?cF1DInbFrA`lEO5Z
zn#rQQ90BS~k}y)tmnp1eRf8pAA%YYy#YWjSNWTb+Tu>C~VWv_m0FI~UaANp2_pqf1
zL+lq#nh}!zYIkp$n^{S=7kb*<lrbp_c5u*Ex#+EqV-HjXr5bQ8tAh&xAvdmau<QVc
zq(NK7%4@97OD%r-9V$mIaxr$CI0nTe^;(8vB?NbiWI9PTr9g|CLKMkLKD!wmlIy|6
zDw|VArmY%?Ne7j)xmQp={BVp2Aq^>j7EP(U%XN_%p>a4NG<7*CXeP!<R+zmqIY0Bi
zfe9h{qS%(+loSJ_yP#6fHGA<grk+EkWF=e<KA{xlsM3;DPfF&sM4vm0ogyeShXtGx
z2ieRg=NGC*wG1Wrg@eq+BpG26n}nuQ&eE;wL*(E7@k?T&?(iu}Mh)A7*f{p0H4|+`
zp-1~pD|YF@4VFo>rcas2yS{Bpw(q)J=fxoTdirlu=grPyE0~LaCdGYkrZFlFmnbB&
zNNH{MMhFNyzi34wzv^4Ulzw`TKDkb(2s$yzMV4gdl#*jfpxaU;__`WiS@3G&U<4@z
z4iqpDX0Y~{r_MGLo%oAZ3gJBGQpw~|6CNc%9u@)yt<+gWJu3*@(lQqaO>zuWBPnC8
zvW9EavxXAaQpq~lFl%5V#I+0~BQFWE9pMh=!jRSSh=^6s8Yl-Z_=o*ENBGc2l2^kL
zz~EZx#3nQ`?+n?^V&)Duwpr7j5t*T+>Qk~sT`C>4%X6P59^#}wHD@VUaTQ3Pa@}Nq
z1UCnR^<M~E^h)MYnt1~Txfm7-UFx`5+QRMb;GZ<ibqu$glZBa~SwyKMQFR5787H45
zbBr38mLg7=s2G;*xlDye2$X!wOZ-zUW9ue!5$imtOaiLTx{S`0feH3U4Ce|<S0($|
zrIckD7E)>#lfW1IKuWfjQ>UiTKC!T?m8FD8cd)7fC3hjn5?C^PtzbNboDQw`;AR~v
zKZahRl5s%@cB;8(SG2FKBXIQi#w#ZcDK^;Jc+AVJ07-x-6Vn7wzEUk~v4oHzsKm+t
z3R5$%9%>JEU<6nwe8fjowjnts=2k)V5t6hwW1lH5oUAe+7>d7fjX^C}S-esLL36e6
zoLmT5)S<|yJ2A+eU|dZM;K#O$g2+VSQ_9*`)q&;j$_GN0r?ap_FOiuaXQePSGbCVk
zlx4aTrK}4&LvJ&JkMflAVqP!@TdGo|Xe0-cfSll}9F%-yc{;Q#VHOHw-g}PbSX&Y=
zof26px{h@~Nx?P9xnsjZMU1#XqU=INa$Nh$u(G5*T{tX&;-Zo?&k7KN!u=K`g%pdX
zniOD0h{QRj9tt9g%-nK><PI|uP*OsY_(#PC2nZ9PR=~FC%1VDGkcQSI$Z?vg+{`pQ
zHRV<$Z6rY-+2{BZJQn6OFfA9!AtQ;BtSmN6Ee27LR3#A1IJg_2mgwn(E3B-jb5^zv
zvehcZ2rq91)p~L+qsge`6(<?)1Xj2%Yk^8_P~<w=K-COl)>AWtVq$K%Ma`*h)>NYd
z)M8pglm#v&i}4CUF=|2$o`Fu)+bmI;pG28Kw0mL_D-8r6TA0<ZP7$--0@7zvW8oC6
z#MtgkjUrB_nNgI>i$+*jQ_<p?av6CUg=L>n?2K>@b(xZxBX{t>SVDDr3d21{L`I5j
zrJBoi4X%MvmIsp@Ahl$L{xs@Cp9oxWLxo%m5lILi8IT*1Gs_BAU00+AB)p(17a!ge
zOQB?FHq&Uz3=PCw6ZJ~MmfoQ8Nz6<^quNF9o7iqN%V-uSAt=+?DYd7QsDra^2bYd$
zh8*+sTA`U_16^;I05FauNKPOK9FoYB##T^3w;+z-%$nqS*q9S#Fx)0P^|AWqZy6aA
zpG6ZYP)Nx{fL$dAN7SUKP)I1o5ICO@j3r!Y=v)%4Sr*hOy3H_}ym2%yt0J{kka<eN
z41YMcIfAF=I3;$1lc@O!vVe)7aE<9AB@?S6VLH_6vgrd^VEh^=#z_7UH?vC;?D$mH
z$=uB&6O}+oix6jmP%a28sIcnpn`<tTI-z(lF7cEADef#9tu*yCi+O1Z0xRP<)O+o|
z@Xa-kf@vd6?1A78Uk8`@gQ8oz?rtlUix!<{0D}`UJ%xMnT+l^iCU*o8If=|fQNcJ*
zSn+UMYKNL)T*gj=LDgU(9`XQ5NCK)R{gMJ+;ZrTSP%A_%65=dweC&=Clmhq<eo2~D
zwFn`LMhQNVtRls5j;2deV2MS{#DSXSNvEi0sH&BCrCO3EWdd7W<12{JSDv$);m+_E
zm;uwtEHxXYf~171uuOd6T|%QGY6qNU8eKgcHi9Z-s!}Nl%DcdP1UG3q1C-l|sR$Le
zjpA08BtsU-p2|voAxAx{s~#m9G^w(}#aa~dqfO0L#=u}3I)w(Iqw~-S3DF|@Xw5k-
zR0uNxxscJA!oIf!$qcIy7SYDL*p=~(T|j&@KFkhr3I}(lNa&OxQkyudmVuI<fWy;j
zGt3M$?E;S`T7p|ftlqY!%2HxxPQo%GS8bg}Z%EJbjLK(oDfgRoQBw#ZkFd`moZZvn
zR$?8<v7Zi7;W;v`N@lTLA#JT&*ktvRSgVsP42e>Jq*b{R?`v%~NOr-8c;i`uqb_-j
zR=>kwxv;X9M({F~c?wPV6KCY4i6hBUga3e=OK2Ts^`^!pxNbGS+#YcB=SCse7h$Vm
zrW4Q5P6&2}3L#XoEH4_QzmWVf5oQu;;f5qAW`Nw=CF#jnF-bU9AoyFfSevbCE|5tD
z>qC~3ZAu*)HfCf(z>P^2qo)WBkPR|8uKE;CaYSTc35cP*q`HkPik6~UCO{~Wj1!0<
zN#MMh25Dtq`jmQwXUxUuK=r*lU#MxS#R=gku=#2}wU83hc?jqGeS{)!Q0r$##M#<-
z1hz3XsE{wPs`^4W*1anIuon0*x7SV18xxYjzw1m33`?c1$)OdpqFm7%I~Y$up$spM
z7WjgScz|R-sh6`PqkM>UxI&An78##S7%%(ik+i)78_R?zOHuA|X`vB<5rS3nGutuO
ziM7p<vi`^l{R4K|3k6;#VqPzj@rz>eig0ur-1_orq+G0_EyK0e+W{ccuBA|>NFp2U
zsDP=Bs@N#0WYr4(?PqkTu7YKi(0t=cRAxp~>S4JQZnDnQW@t%Rm==DFf(82UiXAHP
zMuNT}459kKEe@No$|7Lt+bE*oyAAZHR<)9zXzC7?@5P5dg`BMP+9^z2pgYRaMOZqk
zpq{n10-9s&iUc(q(DV|3f<&DpzWh0|Whcpe)I_5`_Nl038o4#%=dm<#>J!>XuC0*W
zh6SI<t&*TpOH9OF@voD>wvkHAmI5b;Fjcgau)zSqt*u!p4#mTLV!zXh93Ba}%R`<$
zC=2NonDC#`E30_@qKbQpyr|4i*0dIguv*6!X5uB@=O%|UV^LiOBn6B9@iQ>JDFiQj
zGwhH9VXoIL+xWE~KQ+fUwm}Ri`CeC8CB=pBq;Zr=Mpv6`6ie+L*h0X`gdxR&lM$ny
zIs;pzGf=vNZqi_pGnC9#<?vR>qg}(t5rTZ!FoM8T?}AFm<r>ED5hvMW)ivdLs79v?
z)wUW*l^lS(t9ciWpohPhOG9}^7s~RTY?IRP1r6nhZg>gC-U&HLM|n&wni*s=J;RIj
z(x%YX#MeMUgMs?k>eB@h1QDa%Glc?G17Cd?g8=|ZBiRLgEy>p5!Fb!t<Y0tnB`H)^
zyh0NFWER?ZI8-z(=oK6x#_Jg3t@jQsL`tBb=RMh`d0k`6b_vn82?zAh4`lg(#2GiL
zM9c#gRBo4N#LRA#`9cuX&En5NT@Xx>TH$w4jfdBgEU5KI61thHs;~!B4WNQ64+vss
z8pGluls#oJ5>gVKRE0(mG2ut}Fh(JUy`ia~Ftm&iE{>p4@h5y=UD;4)Pivs+4|eH=
zRKAm;&KA1D0}rY!G%G9{Gy&@6F=kPICdknvsL<7=v{vxl4!*tM?vgHT%`lM4e650C
zjv?jGl3}g}_Y=P&Lac(SZ<cDbD;B+2Tcj6M>?Ha$3WUIW;pzJl^1c`=p|ZTE5-Qko
zIavi+OWcz%PZd(3(83Q_f-E9{G%CIkhkHdsTzUQGt0ThK_^z(1)3Aa)>S}Y~KGcu6
zYW3#dU-A_|wZa?5>}H5MK_h+?=#;Ec*pEq42L)n}kReJONnfRsqFTIlx8F%Jz52ut
zp1`n{fBnUuc;gy%8p>-RNf*(2_CN?a5;t~3RVD4<$aXpo+l<U0Q8Q~5-(=)>(CO&v
z)~c%Xi*FK*BXvn2ZpcABriD-pdr5SNUP+Smff9`A91rv8uaG9$;J`<1vK6X=?;=}&
zD@wC9BcpE9M1UHY1&!u-?+2x|l?M@*+B^jvUvDLvq=m#uX>@L@ku*n#{#TkEN@KKx
zP50?n;5noo+~`VIq>C-nEF-0rWVMPu7~7(9$GO>UQrvwZuNYIla>37WFht0~L4%KB
zBt)2LbWqZO42{%}`ZZ1R*x`Yz7u7hyK`(i-QD6}1Ee(4b&$vqw$tclpVikzV=GGSe
z;|okQ`6{SH4npX`7Sj{1<cbd-fWLJhmpkP9*g#k<PiSHx+cnXl2?uS;NSTJB)4g8J
zg}~w$B*1bXNrJjlXM?PSg2zA3rTIZ^M1tkXWJe^fu-=%4MX<Gd#Zq8nk)erw>47W$
z5<yB6F3t%x>2K>S)sf@gq<@GLp<^CnT15_hn@uVZ6gt5a?FB*yO=56F2fZF6H+YiL
z;E@X~S>|9A6Ry@*7G}*!L@#2vK0-fsB=}!Is$5#p9}){si6Dge;&lQs@>-Cmc-7pM
zCx|5iko;w9VCHDo3snYs>`*~R^{c~|+ewN#SrikL1_A*D8fwc|s*9w?P@FH_1YzPs
zfGTj0J_|-MTY!)!MVh$;vIaR-+Zew&4kP^m7G7$UUa9-kL4vDU;Sv*uS%{4}C4u2d
z0}EB`SWH>qwX+&L=@pP2Pv~rp7AenLf>=?MCVV1sPDY!XI*xi4@@a0AMj{FZwuO$9
z-w~=sxR$T&F%MPoopF{}X~36jJ+ty=Dh=l6>B?J22bJYxfY?YuuMplCFf=X7j*~qI
zVSRAF(NO7MXVUb~Z|Q1iNgO|Q!QS28l3dC1a_c_iKL~gR1h<AC&bGM5CfueVCUE^)
zo|Xkir-VZeGkS!<9N$KVIg6e9MoWY!eQ*T<5M;PWMbLCz%NZvJW3M&@V=c25sD5iP
zMx8tZ8x{?%{pM1ruWitn(WqhOl>S_`>O#P3ld~|&P-hTBrgcMJVw_50)Ml4y1#qjr
z#V=x%$JJ=)<rJYLtpHXYgfJ82%IexWO}}``+~KET69b;vkD+L_rG#805*5v|s=#kM
z@hclRlSWP3v<hF5LS!f~n0)vVR%yae6_l^XNlCezl}mhyB`XcJB;rn7&cDHtAsEKb
zawpI6kDDsAN2#pgn{Twz=-t1x!aml1jcR_&tcBei_TxJ=5ya@yn*-Z)^F_)?D+tk!
zuX0P26hckU*z_=OQ0rTpY+GO~_lcRz5UFzfOaUjMl=x_iB)&3Fzo_orm?8tCWLMTf
zFoK*!$2->Hr3qA^aIX1Vc_f5zwV*ew!Lu^NfJYR3Bs5q`A4n89iYSTlEN|ZYrf!m_
z|3RW#B<?f|q8o9W)Q5ztXG&_sIwFKK>mM5!i&nq0%oVB{_@}A%0e+mXuOlj8(MeLt
z=}lI6R0beE$i}O7khrmh(|73E3^S%tlHO#_P@vjl80i!cS74ujQp|CquP5l|JorWi
z<gxdpGbGm3$kzQis|2kyqoO7<Z&-v0Fe4D14428cQmA-EL}o~nd<(fpiCAj65*pB8
zLPfU+ge7k%1&MK}C^0U8n;>F_XbU}rUTx<Km5=cJJ8Jg7`xlZbt!vQ=3gd{r#d&-a
zKx0q$tlHrb4Fv2{KWc<jwxXH%adS?4WAOQAI1^*C6k8d2bclX2#{7jH3AVCPO2^sd
zFjG#kn*$XE@C({&<QPQXgrC^q1q<1pbrT@Qg51vVBq&h_G>vm1X#<Q%HU(bbGnPq|
zNJ0o|`<EU<J*%oY6k-nrl_gT{umS2;f={ONrzrP0rL5`LXy~t~R!fRKSa3T@BUfys
zkhoRx`$jeRzq-0co#b(VJ&bw!02vk{DY(#6VT>+`gbDF)X5z#G2y5T?RjWr+GDW(F
zlT0VUY^v0E_`Z%GnNUYov=ICzz~j|5+Okx7VMciDgxzQ_xJ1yPl<V55!PJ&TP}@Oi
zx2U-?BAxM7)vAN3UpIbG0ISpwct`i#G>E~4lU8WOAVq&AL4n5oU=J~Q+^Vt&x`cI#
zNiZ~@YC4tutjq3M8yy62u5y#3kFwkcHn~`94bljtJDDDo#0k!oH8>X<?yy8CY41X_
zEfyNs>=5}c)p3nR*Tj6PDJ5jFKHH5BhqQau`YrDw2Iq61o2dzjaE=9pc}7BX{h|mJ
zOLT*R0^>xvxO!z$8AlRLautr@PK74OBEJP8hl!G2iv4axrYJ~o(2xU5PUO~yu5Udh
zz(~VRBaaT^-*~chhmX2QU50D$h?p~9&|83DgJ+0`kf>fjDtk6<xs|voM>*Lcmm{=V
zGn)J-TeU5+3pD3*^8yrroHat-!U-u@CzX`Mi*A>Fl*N)FiFU=XdrxhcK^JfXk)#?R
z@k!%Q6>)<p+7bW3lXj76-)K`js(~1e@#+mVV6ZXi67!`(DL9&ga9ou^LbU>+rY#GY
znW;T&6KOaGY77?(KDy>ILO2`%M-eZE2z9jR)Ugg@oZ^{UokE$rJmOK&su2fBAMmi7
zo{M{k>LPr{%B@EicBMHggFWOpQv@Jt5|E?GG|sbim_e>JU1}o}I@T7QfOwu(F;8kx
zvwW$OXn|;NA?C8nCK(uAF4}CNQ2;B2q%a-`+$<P=E`;Xbi5<QVM7-y3!G-%|njrQN
zXp%10j8vbY1q9GWr$_)u(1->m_yf*D!@VMvIF*840BAK3G)e*^wMvwVmaj=TSfaiM
zlo?=@Xz(H4CTV1ej7iG(B<SlJ1|>TrDXfvG*%=OCssn6}&N}38vrB;o97~eLnuE<m
zNZJt4ibh(MAYyT#f8;Jr!pOHn)I{>kO6P}PyvwC;xPZ{1WHkAUaFB`unY9|J0g;;-
zVH#OVuBt#xV23EQ>Jlm{0S-D9)h5-MyS%Q`b##h>>T}{tF>CbzqIe8j?Kx0_22ID@
z?5t%#;7N5SH~-aLo{-%43T$cVV1$(dL^i-s9u#*q!&$Ic&*TD%AcRf&pRx`&GFvYK
zraKgA<6kRjW@MAa^vDFrlj9bCHAa&yC=#vbZPr#QU_U?Qk9sVU3}Nl@6g)ggm303e
z5Esax;XWW}rL77|gGMMJQTr$D_+^vSm$CeICY@q^1+dp8Ob084yd^vq8rE|5{``v+
zIZp#qFS$_W3#L+4gF~}G(5f8?PX76~<QGlW5k-nN62#gTFw+py>6cjJP8Qryn0}>?
z<=iS=d=et`B`t~-ajNCNc2rjK!#ZdhF(U_JH~x&N5g`%)0BMEBLa|m+?}^B_&Se5V
zvEne1d2Dep4Ui^3-8iO<Gb8?BM3>4Cd8nd}{;Nez#)Fnldlnx2F?TAMG(havusFg6
z-oTVZqvfA16DCqqZ<!A)G@fxQrfQ)J3o5yqq2lo;uhNqm_i~Va1`aLES8iaf)xR`w
zP@)I3hBT+0n87Sv7F1X2zQ3YX^Szv)nTSr|nnQOWYHBp?(^uR9;Xk{D-imzKV10TN
zitnLAQ_RH{sYMvF50jVREVhP10<A6Akdri6lc@vP-`3O<qlzXuS9sx81r5F8E;es^
zu-YpJy2Z2&HdaV8bCNb-hWAudL~uxsmNm6W8DO$RboL(3a+~pqJ=16iSxL|}V+v^3
zi#^PEWI|`rD{5dEbH+4vouZO{u|1mBcruNPbY&%dLSGG0ZLYO8r%}Wvli!wV<Vkb|
z|N2K0Di;wvKCvD0GQ8;=Ea9hy<yvB0K}xBTr=sj)VGXfwhtQi<iak$2aIk-+n+%bg
z2nmw9)Lv6ZF=BWtLp@MH6L|=m(42{c0!?rMrqK+JxZn|Xabn67b*}EsTo)`xAO}Z2
zlP&z2A^Nk!5;<-U>onxtY-p%RF~o4|j3B@eDVu4PCVp_jx0*jm#IFl-z4TTNj&Dt(
zrDt5PD7IRL>LOB8qJ}K7<RGcyo+<{=t6FBn00XLO_}7vm1EK+i|G0T2PZA2EH6}G=
zDpj(EGBeAU7LcrmSRE>7%RFtFhyaacf5>Rn2qKn*p@GP6NofXe0aIHCJuy=+bAv^G
zg_1g&Fy%6~>-1}5NJVM?k1|E5RC1uI16fJTSxO{bkpd?*;i4M(**bU*k{@(UB%9Tb
zaRnv6(H8`Rf#MH&%+YYt#Hfla1v3@}9IbJpaR)!CNYb6V2!;axG7d`FjW+Otqh8qn
zK_SFPj*CSS&!LK@NAXY$Xk_3AO^_ZEG~pmMyJuuQfAV!m#$Vb>F{6kvhH)R!jw}@F
z>l*m?d_2=_t8N!z$qhvAO%bKwxA9*uLv?O`D{$ejNQR44!iB<<R)K}mw8aF%46C@(
zM%bfOL4dlgtnpHge&vwYF+$g~;>ZdCjD&`MR%0FtDgLKAdqVE^(ulLU-j|h4jcg8N
zHoBsQos6aYY@x6ei!h)kxD_GRV9J3S;i!T@R$k2+>Q4RUA=>z#Wu(5=-b*a81Wpcb
zkvk^&w|WS|847A0aAZV!h)W3Ci_~hNmrT;-7(dnyl*x+yjZ|P-rA3D>^EDLs0X^<y
zD0DqzVx`XYRosY$L4|w=mN^&rQlBi*uc>J1@O`j4#<MxPm*hXVRxv(FT8#k61@NVh
zrB|pafRM3Ok)rT5ppw@D>KJ?cGXp|^f3}1p>A^y+WU!v=C#9KycoUEZnvxNnpmSAA
z8(Tl|(WsDUXs`}LM1n*0ijqha{pu-ZAYU_~659{fd#=@)3L0DJd723%dQ}!srhC;p
zaKUU14!_eN)8uY`!j&zgW=3{(zD)~>W=4sVsk)AaND(59Vas5W-!;u8CxMJSb&5ur
z<O6`Ex=eIMT!?X+uHgzGJg-QxSG7B%MH+^m;Q@hEh?7PQ*l;uyH<^u`ffB%6(%p2)
z5K{w~7P#>wHY6bbkG=N*w6mzz{@;CiPwyR)03iVqs<c3m-X&56)C>9{0$vdn6#>x;
zqEhrmR03Y)N)ZGh0fj(FAP^FS1QG&CNGHALoSa_1-!soXlbw@7{Qtf8(|h+2=B!z>
z+N@c#X5O;*CbEfTB@lK>ZKLc{G9@q7*r<%DODSFd2m7T-p&nv8J${lc7=&RSS~w6E
z;tPG-3g797yK`GOiiGZP=nJMW1FZ9qE*oW<W+My@I*wiND48(`-YnBO2H8O;`9f|`
z7@~|gS8***D@d8;V1G&hC&x3nEZ*Q#?KwOGlR9PwN{K+QWoT;%i(4`g;_yOy;6*53
zGa0mEY8aVPPBT{<q7QfR1Slh8Uzj`@Bllwn;Up?rd0KI-LoG3l5z=tpl#!Y!WQH+n
zL5e~*{CSN<S8)6oN_x}82Z)oy<@Vr$5X|{{=?lR+2L$0dASu_9YB_!g#atp+TGZlB
zX8~C#h|nHxCR~9cuD&2s7fYTtfu^;O5jITZ>SJ77rD7T*$kQV}p-5J`F%oQSZfI%N
z{ZzhHlNSkt)qFK=BN*nBFVHP05E<OzsI8w%W=BGd7A&@Nz|*otQ`qX6vEWI$M&@cD
z6kSeegkVBbTM6!zi4?6Ae5Fuo;frR$nT#$aHl{7yDh;-wiGN{aVeXhVrD_;3Nnyvd
zB2`-g|0Fhq2-%q+qyxe{vL1l4l#-FO!Kz2-3OWr3hbqfMCF{rvdcY+A>&MH%K%((u
zDWgD0C@@N6h$WGSm_ef)ol7R;awJFBn@k39PD@jPM~s1u<Uy@&%xKJItn#SN0-V0n
zHlbSBaE6f`n5I=2e8(Okh+(7FW7aS=;|xHVBR?F|!h~^(ms%ZnHCml;bc7ITP>Z>^
z2_jg90!9|=+<QN_nt6hVaP`V@8ZInhO=?zj>pb|G5DWX6CSEH74x#iSiBzLjH)&yz
zw{anE_OPXs|3!t5RpA9ZArFOt687HpusPfz;4fApK}vKe3B!SxoDoj9vX27_9qMdJ
zKfVLf{bU$JEQautZrzn+T<mZ`aBxi6KjamuK?vi3L_T5|@&T&JH(^QA;Hars2r($N
zXp+CQUPUbh5P{ZOl5fx}A?gn+Nz=oVM3$e@K$x)taxXgc9c@r+FTiX#u}IYNI#=Rw
zphel7aFk#4S_2RO((!}h1T|s|7NVcUVN%#+EYhJ1d4%z@Y!nQP7-#Al0oGuvoDsZg
zdEAnc8dFOLQAWlIKV7U<g;_zk1VD&5&O=J@Fd<2O@~%e0-b@<9HJTFy63St5u~-!_
z`d=g0`qV6FXmXzn(~!0HFj3>v$--KtHk5!-AOFJnL_8=9VJ{p6@*7S-l1>jyqZG=4
zP^OJl;z)7|;S8?UCJ_AKnFkGdpwT)+7XxP4nmehc4yjF!4_;+j^io_A*QcaA%wd<6
zT?|Rv3bk03w<JKOp`8g2=N(OwhbM26m)Ie6xRI-ZRkB`uY=yzc8e#}ehpA$Swh9a)
z#QZQGVA;vWC>&deY{K8TC**I5w4L-zm1kLF76&?l{{#gm2rDC~H7MaFHq2KVP8%Sf
zQxupAS)EDb6MPW((1R0j3o`g$=~{C{%!w-$opW%D=F48dJ}Cx;4xMrsBB~{tv@GUU
z%Sbu0Q2k;+oMuy({4y67y$XXtB9e9Cuf%ICSfa6_m^q~vSXsg|hYAN8q7}_hLxB}i
zBKNYyumm|xToy<m*Jl_?FEh$bS5OGkenD?ACs2hrRVLNww9jBLLa^DvE9r7kD3C}u
zPEbHeh!l!}5^Umc*a$+n1lxlNp*xUifgk~I%O^s?FJVCoW`og~f=II&W?JQ2^+6u|
z5@IAIIb?>(!sw9OAR(5ubbttF!kxQ*2tX-a+8(4rJiOy9LqMZAEHx@lTHb~+OC2O;
z*kl<jlAw1-c;ydo19N)jib~I~6x-~>P;r66{EC}hLuW(I4?M0?>}Utj)+qh#G(3ya
z62)#^3u<7z4u~ABh2Z7js9>KfjYd<Aa7`1Ua<%nnOeB7(#JiFcyhujSGLQjU+1h|m
z%!!%DV9;uN)DEg~DS?Pi!K33#uwM#{M1e94#>#eiA$W%qrQ9ylHGFKsE2?!^kc+92
z4V4OG&&w^y<vbwj@HAKVU4w%plmF?IN@9$Ax8VX5os0m3ic&h|i7YhnKYH|zwYCYw
zVThIi<3cS*a>Qz`rIjmrZX=snp3*YLEBY;%Fhp)4=%FvHDoE4xNT_(wh-kUNcn?cj
z#DIp-1E~h=J1^=IR%VNwNn?7H{S=~H)#~0gQ-#ab6}0NMfz$?{;Zf2`dc849vQqf?
zhc;$i=*U)tGYlCi9(=(cj-8r&4GNs%Oi7$TLqE3i#5*`U1dFy1&W=QeZcR~W$rKz*
zle7VqOu|sGA_$o->=E(|bi#7Mgr%$q7(%9}C;TuPTdn$oUIiud)soa<lMMAvAOeZf
zwK)A+oI25pSe9!p70}$U2xhBCmTJ&|;x#6i8R9{`oY2B}=+}0}_&F45$~i`dk!X#E
z`3do=WvqR}%U&VVDqd5=0E1G-D4~inY1Oh4A&w8a2o|gZ8}6XuL^-X1#l<_CEzNwf
zsHQ{os0<3jlGPZ(J9l)Ui6z1^WqKQRCmaPjifZX-L@C`06<yeQG<;2{u1~aDF<$#S
zYQu8_i4{qZ8@y<b=)CK@PO?@G535&6!wlrJF#9walC2JBR}pAxh@s$zkW7U1E()OV
z@)$L5>cX7hJQ$Ef#7m*hgyH75ZqhIf>A50UT%?4fy0oA}BMXwkjNxAd25V8kz9u3N
z`fv-=5v~)$T_W6qHo8Pc4_*?bT&F7vhO3{&3CU4cOu9=(jA=&ALR&M!K(rMEEg@{_
zR$=WaTZY?Ckb=-;Y0@dOP#8TBV_mQ^WeqEriPS#8@+U6IPYOzqTttRN9U@*HL=HnE
zGvfw*o&%$qVeUSM-Bo(|2T^+P%%c-U(5pD3ger_i<IpN(3Ya~Hh<xpOq6n8i0_awl
zDbkcRGz3MkrZYGsGyy}KnsAecB?Xul?!t*r@sCfe9Lw<xf|)KEF844lAsA#xw~_!T
z3;9opE5!pmwnShWG=uwtQCP+1CEq}=3Igt1hjA)Ym0URKFo}4LY|+4@B1Ik7>w4J_
zpXWDo53)JD8>XkaVX`nkM~^o`wCfdJj8lTM<qCXuXhua3H!5Lo(3#H$%Rs2P;0O>V
z>GmeMRM$5ex$NbVi2TJu4uLBVFevCzlujJXE^7cn#*>@WHu2*{VJT^%<WPcC%TN!b
zkwVA_-B8PK6QNDg2r}YHRx8esf*%Te7(j*cDHBfeQv9TCCt&3D=G?*p-}=|0Kp1hM
zlL1Qku}dD7>n1aEpb2Bx#05hW5v0V6ZZv4!VTV3!toso7LuWAUDU3o>h0$RikVq#;
zeRd~2w9^Krq089{D19c%s`YtR4J|2P9XOhlp}ES@Z3~{7%X;wmL<1sn<?fI=9U1V-
z$*oZbARWlWG#G+RLXjbhj$Err(1&8fy=Y~tum}<Ac?t1|hcN&@Fe6L^(iC=@V0Jz&
z2e5RipT7tIg9^?V;%RBF3l9-7Lk@yF$Y!bGPi=qU%|rqao#7n?^1Z%XS8n5DYhg){
zF!W}nyoA{jgYyI^uGr-?;IwcK$@v^oakLI%LQ_0k?O~dTFzP~X+H1V>tkN+?aW+%V
z(j;ngv6s?|8f7BkKgA~LRMrL2PzKeSPh>M>kt(VKpiV?GjgpGpI`?R@a2m845A(|;
z6T^&)^d@p}A$h7-e&Vj4z~FsMAZWuI8&W7O=`63L2{B{TJ@AnK!{#5}8^C*960wSM
zbBh$@?6l1_`bZUPR{2GiV-NnpVj@-7SL|5fW-CMUhtL%8I0+}KM|($@1&-pFE!VJb
z%Q3+@h-MSOLI~-DRKXm2sl&J!lUyD2;}XY*SU4H!_KR+3ac4*F(6A84_(&r3$V-wE
zcvzs`<3P%{>JW8^c`X`1!TggCb)r#d$X14{e5HX)mY$i37r|O!fKq`8*r_bQVh9+j
z5a=vLma4|Z)|khoe;B-G7$yx{PwsW_fj+b)J)ESZjc_Pn>9GOvTt+v4D0zi>N_nCR
z?o`ysAkDJI)oLGzAW)+9{7FX{b{yZJvMw<yxhly)#%fcfk+Y!Wc9P8F4jGZ4M63B`
zUU8OYImHBkRI1}-#4EAkH38j)#2k3|K&c#rkxYrS@y~7#zK2Qy5oR6#Lkg$Ihb<Bk
zc0wRvY(|W@75>30;s%gVBi=+StN5R}v;P(6GE_s>DD*!;!ORBPxI-*VD^cp*ft^h8
zt$7b;YVj)Ug9UKnW(`hhN#|z9q=?{>QO`GY%+fSKPVA|`kk_P7D9ZpD%J{XmcrGLH
z_>>hQr-lncjglWsm674)39TZS!ecdbkvnvj(W7A{3EmR}XyB=?HdcpWOZeF}KBdg_
zJB2<o#;$>xq&e0)1A|ogOXh(_fRs@)5D1BLxk*}yM$~XVkW=w<*eOv%QFH^4elbac
z=v92Hp?y%PCOrZ4vRm+S0*}Rzz?Rj}q&G;yB!?RW@`uj#7!p!}q5+l64c_ED$mdT&
z)W}>EL&m!f2$LZp+NBef(x52g8jdSEg%TY;UTQdDX@U%Iq4Sqe)bC;ihai@nL@-N7
z3te%FCPsxTkfTF|0xmr+f&-W&rp+cCQ!$_a_!U*f88gLZga6sJb>o!~!5z9BRRHoI
zZ3iIinqfV@LOG+B&owN$6u-9US#((CFv&$?<0gTmKpFNV82C>+uTBdRZ!J>K>4=*w
zx%@zd=8RsQHAKUdaLAK~bZsDW$c>UMFH5w>9n`V!>W&H;_+JF_C7q&TsNA;0F&dUO
z4za0dH2e#prN87ktqy4pN|7NuG@WJ$<F!m4tU$>lbbO|1({SW}{!oGu>Ij*PzJgRf
zpPk1JMVDN{RvjLBDlmhubXt+666Bf%8$uQR+HmDtjVN#+BmAfF0fQ4aljF}IaRTS8
zODOXn!4G3_ik3|P;E)-(k$kA*m_VD3H)!c<R0Nnp2x~pFyoK;C#5@qfq!uaFznpsZ
zV?`jxO<f1Ut*FxxS+~`;a^wyL1N7#$9K%6@g&ge1^O{mclMq29Hpy_shO!ifbN^KL
zyF!d%R0smPb)g|SOl%M)DavwcaUp^_?3Q3n3!#M*Xo^E)(`gvO933};n;2Q}76L?%
zo8??l1ZOCLh{1P90Ma%Gq;AOzmF5b)I!QB*pvVbGsWeQe#A^l^IHZ~qUBj@eHSkay
z9yweurD1wrxId^SGeZ}1M3}&)*@2MI1(FulucYxm6D#xB3JbVvt<#a7>hqBv@vvSH
zPtiS39KuX;GT={8heSeD>XFGG5(J*sWJrnH*e6#q!B~gJ9L%;vLIwwF`q<NR^T_8g
z=+qrVv79v{Fd|APEwt-wAfxq}H@#b+H5`;{1~knKLpex-<l6v+`2_<r9hy#N0+UQG
z0iq91Qt%@ZMNOQO8D<C$b;GJ196f4Dbf6?_${i#nl2Ir)PLOo#Z`fuCwDd6ml!7mG
z>1`KvaAd*;T#Xnftq_B1GY0upEXpmS;2(c^_O39MPei63mL7lE_e12fw(tb!ws7p>
z(mA|~!$TT2w)BeCq=%)XOd;Wz9ws9QV~X&X0zACw5ZuNANGYF!r<G?z`UxY4U53c}
znnSg0y984((?b{FIgt4}NrwTFH#Dvw+{BCzgN#)Il>q5=KB6P6l^~vJksSVKQYAt+
z(%@hP5}a70Uc{4V&@Dc^6>^BE^I2y4%Qdu25T$}g7#(Jm;20Ff5ZQ3*DulcQmBLLH
z>yN|>ROp90hp^~86;xw8y%>Va1B?cd>;J$7CsHI&9KtDqd7su5Cwl>o*RJ7p71j}C
zSSedW;XbqCz*b2rc-mMR1j$iaVKlTjJxkIkb($)<KrQ9u5NjN&6qHEe&Yfz(+Ela(
zf`4G7X?}HMVZeJ~iaMlq@gYA1kCJEz55=|6Au3>oG)NLka3~r@@FAvoSqF1N!ZU%*
z3Qo|B(M^)W3dvCPA}wf=B5aciEg5u>4uo)jSQ6q%D-5m-c4}W&Oa~TOBOU%<_j<x9
zTkU|Ca2Xu_23jR)3<~TFRbBiS?9xyu>XMlh>DOlHj1|sf3OAjU5dw{lonlaYlBHyZ
z7NndWX2e$)pcPqQ#6nh3_#&J28q^^SXG(^Q@$QRee4v|Ky6j``3d4{o0?3wnr2}os
z=iy<JxNWix(paPxHK8LU+c048juVs;fjU0wJR}Rl83hmrwI*b2^c<wQ<PK$jcAt<f
zf{)<SUr7#u5kz4JBcmupWt$`bgxyE-TZ;{3NI`~4#X?iA4USO*Uj&wb)=JZ1PqQ0b
z8D_$?I-ZDLbr6E;KrfHT&Y;Ph_%kF3IK?9$V74kROAjfC(UfWscwKLQYH?{4@F*K-
zenW%`)%rinm#&~G%)fdVlcFcA5o9O^gR271Hi#~cyfzsB!l4n%1@@4B!&GevK$xYd
z9SAf#;;^o8B9IzcD2tuSgpff{sS|9va7QJ$AM@4GiVlA<z=V=Sk>m8Ak-Ej8|0t>g
zkTzTRG@Z~$4MPu;p&1NwiOJ@{Dkxf2gA_ifHkYU*P0ke|hVUm?5h4y)B6ZU#%$ruH
z4MLC>U@I{IoT3p*YZH(w=3>54KoQ4HDqY!%m#>uK`+tC$7=97EatqhUe6=LmfW4$K
zX~-|q&u-V(H}I?o%d}0<WS)Xz-B4uGn^TRvyN6DG(VhDjni1)7EMb=>EtH@Vt+G}x
zaEeN2K7J@$;l}Xr1icu8I0cBJ(VxNK&>oI~*st{u+^8#}aD3B!dlpE3lUyf-$mO5D
zM@8p*{J`l)^Nxy^e7@;LbV@=w`9v+}aujL3%l-zg5_H7K$*q&BaB2!Rg|v~&7o_N}
zI6}j3o*)rUkB&U*y^vvsJy`bh)89HDU}$qoJx?*ip3MUq%_8>bT84pxMoQXj#-uDL
zQXHp=l{Z4N2muC}iY}Ex2Wk+eNmhc8GGIjt1sTfE>DPHgqXmsru_zQoBx3&Y1#Q^C
zB^plSbyCI%HLz0vMDR%=V}V*?T6md|%XbE@7ifjxg1(-+UhE=@6i(b4Is=la*k*N{
zQbZiatG_x?AQ9_0l#r`M#eld1&`9);|AhmY4tfGW6M?aqjEBNa=KMmAKPU+vlM2|7
z<{@K-9$|#Rw(w{<c$gUH;30mz6ndpi|FrnhGaWF<l}(xrc|cy~B3cticX}EC%s?~0
znM$bSZn-m6$I4>FIs|KsVeTL|tf>OTpE?0bCsmLxc^qo;b(BI0cRG~oc$QIsJ@OfB
zA`C3dk#uPOm}ta6p->0~JHp;-o$`c~b1TU6sAFU}@~}k08ik*a_UR2rq=z#(Vi>ko
zlCCbaKZp6`;w@~|iqM?8;1cQ7#}0Je#l5sT2&Go%HU3kKqcu1`kkiFT!u#LBex5Bb
z*;@J<gX{?>NdBlWM|y6d`G-3l6iA~BOR+&CbEJWif{=8iQ9Vo#nSswE=$?@bl^>is
z;Kv!z1$WXHM2aF1+R0+knS%WokUS#$f_f&xS3f9`el7fP8-`wFXki9s9pZvW<|*KD
zE02fMAs6ltx2j|*1miTx1S$9b^6GSWq39m7(ix<A<OBsDYlNr1ww4PoTOEQ^aRMpH
zQzoQRwOpQID^@a>8m=}%C10kSD>feaDHy}`gYTy%sXAR<s1Q+FyvWtm7^iCn>#YXE
z9Yo9uT{e)kK-_~s2B{fa#HN5$1dCcl_ZfoQ07b3q4+V+hO#LA|f%sP+YH-M+4pTv$
zHb-L#|KMZLNYM=8At0tkn}xVM9U8PulP-CJ5{$;Mbiqij!Q>E1Fh1P>_1zyN(G?Vh
zZsy2jL6pQvt<($D#uWOQ$#!LVC57~abcZzvfv`ERFgJYfQgcgTVIfcExKpI3`dT1O
z^;@cYmv^}4taFapXBg=RJHwb{uz?`^?@(Ew{1D2H!rhgrJ+kabj12eg%CgMfOn6uH
zdx%FAl-ScorkfFIGE;t{WJ}vo!cIs1iPCmO5apX-N8z^F_(*Kqb|glKX?Cy9y}PH}
zm4uz*qmgZ7x*3rsL&8oiZzI`b+)=`w@U{`Pn`V@6njMAPX5%BVZQGF;A*R`t#?0PK
zcvtj$h({EZJ6dHGQIW|^`H7M(ZAS?^9rY(l+Z92SZ-O0#+h*e<v2ELt7$K(Fy*l^q
zo^n?bc8ZTiwvp*(M4Ai<JGH!xWRG!2346lZM$~SaQNC$*6mFZ1kHofZM`DDSW>*?B
zdo$r((eEK1QBdw^l~qJVCX>um^6G+F_r3Al^39tHDoVpCEgaN2Ht7&v$a_zXjrEP(
zDbr5QTL`>El$%#y)3{g1F85t}T}eS<eI3VqofM*LGA)P(f!wCG)jP1j%PEg8N^Bai
zz@18i7(B&XJHp0LM<J2LNHZFlsjdw@O=u{_XHr^@?FY6Kj5L>7z`#x&i-piMnE`6Z
zbl?poYK*+*qNbRbdN;5om_3s$Q$F~t|1;f~^}j<UGr$NTit!GZNaLoRmW}`DB`-18
z(O2|FJV+B8X*Zg(4ZSI&<V;GWxtaC99UHwA{cIP--iWtXU7LpJrMV-$X`*DBkuoy=
zSNI>{NA}%sc+b7B{NueVw^rqr6lk(BQ164~<nD9siR>3#J9DE^_b=EsbPp@lxV82h
zC!g21LznQSBifZQ-NozEwK4S41O}iOaF>TIjln6AYe!1x={1=p(8MDVX~1390>)Zg
zg9H-|a5EAhIZ7}ROk|8?06WS7W8{u3#>X0fJH41~N}4Vf3k(2(fmoX+8gFBa1a5<O
zGZP6V%}f=R48~?qQ%vSA>Ihh!6`9PUkeT(r-C|$aQCa^xJnRiTlj#te+4#4A>=}EB
znw5p4s53E!GouSkbg5yQE}86LB5&6JZCnxkhG*PG{g38<Y@_h}tZR9P2_L+jUs@b;
zFZb^C#Ey?`@yvtIzZEt#*7MFMFZkv2K&eh2$UL;~Al6u*PDci?SDi;rGY%LElDO%O
zkD-HlFs_Ys6WEj%VwsSEcZpndQM^e2fNv#9OP~vUR74Yks4)_o%uNfVrfD;xIxxml
zb6jCidUg^Fpr)7w`Br3%xqvOlKqLl;FqE0Y1V+9w2n0Zag&2U|g=W_O(TzAN>wiK5
zhd8huInga{w#JVBn#`EUMbkp4Ba59(osEAx$L_*E0ATFH=(?=`aRpAu=6{Drj1!tM
zNFodd0%{5#JG}Q`KJup5c6iH&nl~$Wpv)s{UOwRZj`tw+7+m+UdC6bDP=X+e*Q)me
zi!*GYLAVySo0%aCFG$xG;@Xs%Il@9rj06j@E_Y>OfGsluG`XxtAjDRY86PH&Dy$bI
z>?Sj8UV4!bZG*-FZHy@`F!H(?Yx9~5q?;y=HW;ASWYH4v41`n|fk=Y{+eVjR1Jo2G
zP`A+%O`<D~B2vaNiodnM0)c5fWcOrZfGs%!G_(H4QCa^J3EB8(T{sfFK)hI5S6KRN
z1TvBthJY34H6GU{G;!rZU`h+L3Sc*5o%Mfo>_3SAH*elNcI?>7%1V9`B8EXtP0g*h
z-s;I^`{fO1eXf8Q(92wUDNP@qrma^l=>XO{ZM;aSmqD3(3v!OOlo$aUSFd`1(xmxs
z|8?cU1&HXm*WP=N7_tAz<2v=|2@M5_6ofcdB7n@z8&}Wy>%>JLPG7V5Bbr_N4;--X
zVf&6au48BMI_BV|i>7M>AlIy0`R2Q?zW>q8#mkp)2flyr0s9X;=!gT4?bb!S5Kza+
zj_k%G%40D;%8vlObj9%^CyqKwfY|b*5HJ*)#!yFM3#1poTow)n8j3hWF&Chu1PPJS
z05$Y<nZ{hM>544Twjh4z{x?mg9I0$Id-wi_EUHEYsxzh8!c2^{M9V#vD5MSGftbdU
zsm&WDATW+GT4F7hMjeSQkX}op3&32^P@)8LMLBduN(0m&ql(A~K$qD8qHU7CllX5~
z`u@zSiorwrR9Khgz-|fDyYYY1mSr>N*X(=n;L^Z`{HW2=z#t*mfTDM|lnva!Z@VZN
z8t6?K1f<tj#CRa4v1Donv1B$j53<b=$1z$+6on#-vKhb@Xuw<+4h9+ufQyX;1~42c
z4NyZ*mub>$;SXJrCE6CmXX9U<!AZC%>wo)!+CJG4a3E1fj{!UCV(ifsV+=SJWLhkB
zB!<1by!^WBuA4Y<VgbLZz*m5T>Dah&BaDzy3lsj=8{Z&k^09*cryWB}Z*K<<y7;yx
z5RKK<=MF#0Qp|4=*aWb6)_X7C|C{&5KhwUxc4&_teagzWEMGQz%9OQBmzH<yR?)f3
zHkD9^u!y!Uo;TywXMXw7q$j%MRUI_2>wvB$HLDkYIDOLUwad!dcWc+NJM<8np4v@G
zNs>A9-+kiG|MBvSKPMd;`VHyUp?B%Z&C93Hn7U%svW^v<J9q2`4K}-&7uZNQfh`#z
z7HB|vK1^mB&kAG_8DSGg38pt$L|Qe?Ou&;G1=8gs3uIOo%>XcC6id_ok17nb(QY}J
zdX2F(YKmE+oTwrS%#;~>&FHQmW}6(E<~81w(T3OrV|Eiq7Lak^ktPnr24vPIjzTQc
z1QD5OQdIQ++W)oB{mV7?6ppxfc=u?Y#~aOdGjl|7Tr~@xzUHQT4?g?iE?VF!CW8go
z5(1j4=J|iQ=HB8%E<CWSd!vxZV*1#-Yya0if5kQT6d!W&aIG)P`KR@NIzO~=LUfLO
zLX%?m#vw;Xxyybw>@M@R`d?EM@lh0*$YOfafZVnJv-wA!jE=Gc|5^Nxomk$bOP7y7
z{+Q(B{8L+7J7~}#4}|>xyBV((_!%=fKYXYwudcpUpWW4WXXp-U*arD%oIarDK%oo3
zfkh3xY0c`XzyJOE8GrrSDJS(hVN~IfpW-^Z?v1G*zx?vp7hirel`6mXx{lp@*pIN-
z6!UVe{MJjqYnVIbs;f@#@~P8{Dn_ESZu8_d)5bqG_POb)ma_I;I&>0mRBAa0S+joS
zxXF)yu=c&nzjE%U_dBI)$$@mOs($CiInO`(&~wS8mX7T^cj?$Y#(jf+B2XXuL$w7O
z28nQBYGBI;pcrGDqoagqjsO5a07*naQ~@Ri2?4|=^HXP<E*>4Qq_m#vBVb-?inn2E
zKfMD38BJ@kG^;jjJ55GS7xYo7@o|jc!MH@&y&xm64Ko=qcnhSC!fk__P(~HNCI({_
zt}Ta}Vw$Y~?cweH-(1``r*8@0ZkaB)>u>OXVf)<Nz6JWQzK-p?_kU5}oIXYQRs$tk
zf+dZ!o`3kgicz0CrXLuz$LX^ro<1mJ0Pta1MD;DsCrTkUSvLMZ(f`@_ckG3z6By&o
zG3H)}6LIB2(?ASAnowGZVH5ll`G3QP4NpDw)ZoE`vD{9e24+DqG<kzhC$ouC_^`G<
zWZsmgK7Hq?zKp<5;RD?)d$wZ8qB|l0a^9=2u77L#`NQ_@(XUUka6#?7c@mhH+p~Y)
z^Y<J4<n-xtCr>_X^k?ljGol_E7W4d>lN#ntIp?$?o%Y=?sa#RB@ppkYztcWL&N*$-
zGk=*ff94^Fop4c{6;FvwZ)th!z1QZfo_Wj}2Mz8w@Z*NX3)V|-VP3(I{`(z!#<JJO
z&xGLAk)y2(mS{6D1(_58xacwru`&3;2AiE;$S9G(WYi{bk}{2J*fSrsf;5W(bAds!
zd8vV&;K1A3NM=PwA|V2n2BR@nVJ>O|AmeZ{#i9#FdZ`VVK2sF(DfH46Z7=}d09~+n
z6aWxY2fp;Nz?2qiI8!MYb6FZSm}sy~LMBLx%>Xc&S!i0jC|V0L{~7$xK=^_UKB1_u
zD#4E&hwUg2(4*PW|IjmiMf&P*MIiC=N&c6jt-Rfd>k!A!ty63En-=`>xrwKYyf{Qc
zbnou{A8eLs|NQ>X#(y^d{CDS{KmF-Xef#$1J~peE<C=3mD~|POm^8=dn8UuyC9xK`
zeru9@*L?j8-^ipdI^yfL^n2mFm95h;oDg|I#ze4Y#@j>7io5cKPIKnuRkX_~DuSS?
zwzhH0=B_P``<E84o$*fUv*$w}bBzJaTL1AoL;IGMmlifIo|{`%kyB7C-iF$ysx2Vt
zTDt7*rl#{vPmKX^xjAq7`&|cjD6J@(yXJ$kqIL!0S$cg#U1d#WNkwtDeLBqjc=oA@
zHfBmp{jj6Lxi2K9^d$lq@L&K8!vzLi24d4RCZ?vC#$3Q2scVQ$5D2&JE8y|2eQ0)S
zOMsGInpR{vG>wU7m=_j1#ANWAzyK^!S|k8tJdD_Y7Fdu&4IaRhk=}T7K^*;W)zoxB
zYzYuhz{CPOg<kN;&-x$FWcXj-WR<F4^p{`W{m^TxlcenQ>u&wr5j`Pjs{H8jU)=l5
zyKA5?JMir9Tzlc(Wg-4I%zEJV+g@G*y8o$Xbjryo1N!K>pMK{r6+gM_iav^+=C#v*
zcg=4qZy$5@0KP;h?SE!Br=tGjSO4(ae}8f=BG3NUADn+kw~xmE^wFxE<jEgh@nq$|
zi*Nhp89gT6`Q2IjUpQjpGxt5enD;%d`pGRl=05uUv9F=!n9IL^^{GRM7p<R+mrk(O
zQw)tP4=dfZgTdpa7c#@(v+<7;?2YI>Fu0atU)i7ZW;RU&27sD^kSGB->wmk&HJ-6^
z?CpPq{8L(5$^+(leOYG1(MKQs#v5+{W3A_1X0uH61X)+j`m$Tz@#32n*`xV7z3STP
z>aAO=E4S7+)br7)Mtxyf`sPE71C|y-Y8NdUR9aM4Q&Y5YeZjibd293!plH*EGH#uh
z7S=9V<QPJNw1#3#^_C?)`*bKQ=Bi<9Zc}A$L-^OYB{!)8Nmotf(pHXiAV%xgS1s>3
zpmVA?sZZ5bH*eb7u$h0=O<U?yH918sJ^FQ7zjcMRIQnB9?YAhJ20U{#&4pf50%&eS
zahKu9XbVA_m)gY`gquE+SqL>HcCnm*sSTJH7<QzYk~&HTZ~uddra};{HDFA{+XiZ5
ztP2UKGkvD^NNm6?_LU`AwR!0Upax0w10%{T(L%U@DfA-8qG%d0FHLiq*utq@++{d2
z+B}fvrFJm};iivd7D7#lT^=~K0rLXGjx<wJN6Fy-9{z7GD9u?je(b}w$KQ6xZ5JJo
z`{!TW@aIKaTUu5;_~q}9pWXlFdmnsY%#A}9kNe)04=itBa@Riil^?yb>BJw7d2q}R
zPo4k#jPNC)Exm>g%~?Bf!rL2yg_SQq@Ivm0Bm3uP`ae}zma}TyFMs()pYPmp^Lc}7
z#@+kE`kb8p$DVswPEPw{FZk{qcU*Q%UoM54wyyi@WB>l^Wg~ug$9IlxzwX!H`sytY
zZo1))+b%mS=k<r5oL8d&W5$R%T;Yj}j<uugBI<tz|F_;e%*KB<|Fo`d7Lh~RF%0Z1
zLG3O#>HWm`ud1qo#`JNS9DexWyqk@vQKLo`6%`$S{PA2pJMAd@kT%1eY<(t*MZ~o+
z--e1VZP)q?75lY*B&WW%f$L`fm>8ynm7iX6h#)r==jWFf=N6Zh6_l1FMa4;B5#qx4
zpX%lAik76fR@b-~XLeX%^r(u8@(MZ><h3u)uc*i^C{E=S$P0YdsYqW<U(lhZXhXeu
z?Hx<7I|_^P+Le`+6_=D26&K|e7UmYBqMm~|U&@`^(yqLIOHqxvT+<69_M06WGG!v!
z!(mg2uC!|ED8~jE<6gR;_Z<m@Bg9(3Ll8%53D^)tLF~*9U}C!{sxY1!jBSoxri{9*
z5hMbk2QVCU0Z_~y{qLq@!US-c41iu>V`6O<)PbSUq&9D?F(48|WFXJ_U(08E|F<;o
zQpX85jJxhr)wK-=-txV7F8I#NZ!bHx@x#eEITze<!$H0H!Lh@>{ew#{_{Jlz%{%X`
zyy=s2axT2-$|2p7rtT;I<nHS8zwt7@EgW#nx$S=U?1Z;2I^@LMrBk1)&iUf#;oRK_
zFNtUpNGbJv0CM2P58eCO(wc_u#}0dT(fez+wsh$@_<&xyD|+lRc+g(lscLMltFOt)
zIrK}9egBlCrlI8QK~o>;e$SKN>eX0Vf8hm>&Uj5<zD|Jg8j3<g1>sD0HvVI%XXD?2
z;pFBBv2&a~wvT@vs%TF#fr*_}s6iSNYfFgf#gx(W2L7S^|IkAZU3%%I?b@|tC;sCf
z|G0krdbZ(Ded<&D@4r7A4WE+o266i7r!z(hBhsL_N6iyuHc5TI7de(M9+5M63!7h%
z;Z<z?lviWDejy_K9)L1FY>eDBw8(8YXkg{)RY^s=q(i%;eaBRJ1v@}XRb{eqQ<A%}
zvZ1-6cQ+U%I20}H&}1=JblH1z^SZvp<+&wgImP8kemP{RhRURl-Me8+QdH5UkGWiX
zN<5hEo%%OdRu%OwEH5Z7&u>?ft9yFYO;wxgH#6qus-$P90g;z30s_@8*bfF60BIH*
z2L;}}u-H`BCNM87FhWLguP+m0IGW5g0-*th%fcfbfMNm!5~fg^%q*7Q#z?c!WgA?B
z#0F|JA`=)I1IE*3UEm=`5wif2Ca{U^DD#4GfrbJNwbcPY1IA6rXi{i`1faGqDCy1m
zA3spG_dkm*r+bHzwHr3!uB5(J)4=cB^9(rEV^D|2>N-NPp)S<{G{1A!#4iZuwCi6|
zS6$02*VS&-0hhyILrvjvmmW3#*Ux^i_4u5ZA4ye=KDI}5sv*<=O{v<Pp2rPMt=m|c
zB$YMQIXR_yJeR1it>N;wg%qJvEWyGo_88o+e&ZG%de=48<n-uUwsm7wb4yLF=9FJF
zrDksVpMeCHZ2YJ5K=4d9{<HZ<D}rEPb@2A}AO7%%(@r~W{P^*`d-vXJuf5)W`|S}U
zMsV+%Jn4Y(B4RaxasJmHoZc~UCjBjWwKcUQP`*&4xTpk5E|eSjeJK8`Pu~`cOVNs{
zJ03WE)hjO#>eMB#L;KtwJ(G@|m7zDRYtAXC&C6f4v8wYy9NyELG3;^kKvBn@2d!QE
zd|h5{VR2rnu%e}~Jp|!>@S3{3oHaEK5SWF;gw&AP(uaeA-S?lf;kAP1{E}2jJ2vJ#
z@#Zz^o4#wBYHHWj4e5Ru^dMc(i$>sxL?8tWb*5zC(4#eGdN3GZM%Q#PsA!etMB!0j
z#8?ZvAS}S%6cL3G^Mc2`r~)9uHU>%<UFb4bq=DV`L=}++u?_<BX8PaknWCUEGOD8&
z_Bc|DH3k}*)ToH`cCwrFdc26u`hREsC-~TM)3MD@`OqpW;GGU)b?y9*w+<Xoi2v)V
z*4Y0^Bd>~7FW%6wPpLjSQCqozZ_VZ81x=U3PTcGM>3{nD!^yPVqyBYdZmnWB)BpMb
zr@9&=!>P#!DOBgwhE)(|7OGqx4e`}O$keH!hF?5u=4O<BYlz=(cX;SlHZz=!|7`q+
zeWUeSf|$(aAC|e+PAJ^cZQs5<yYhem1AzHh6aYP+nE&-wHk%g@nptYNx1p(_uCA`S
zx|#>mdfcqbXZ;|cepoIPB$AX7oo+Wb_dM*dhQ0QlSyNxtsat*fPEBPM{HyQKxw325
z8MXC|d+pWh@WUaF86RGo4zkz4BO3|^&04gos=kSzgXHR+fBZxS%~=c9)aMWCJ?JQJ
zBxbi>YRZtk4(XKJXW5*!Ep_~+AiuCo+Q<c<39?ge-~IR2#!P|N?B1V@2|elOpx`0!
z7J^TK?b!&^<x!i<y^w*#JiCIXG0<3G9JS?mAeO-HWp(BP39)N15sq>Itfh_rt;=1O
zY_Zf7FsQaP#L|ql@pM_8C0Jc18NDEFzvV}10N^QBL~Y?%m?^=qyDTd90(e~3YY7O1
zn3~=Q(*+F}7x&t>e|G=#jCtMD54|#bMQ!c+S0DY=vYd)@jvZ98*QbW(<UI5H=N4?N
zu3SI=>Hl~pCujJl29y`~+_yXUKa88ReEG~*9{TprCv)M;1mKp&oI&S|tXTfm8_RQs
zpE;~U*nqcAzVEyLdiR8CJVntGPdio({TyKk)KpIOE0a*vz|SaB!U?$5|C$qE`JZ34
zqY)-RZINc<AK%Csc*QQ!L}cSX<Wjo@|HO=ebBGz&#?u9c+QoSwlgu#&u^H)t(gXnD
zDgWU9=T)zvLx)ncu@W2H%EtcP=YM_vkrzr>av|rj3Fm3ptU$Kv^i5Iwaa)$e3(&+&
zijJ~QUG_fpjHQ!bdHti0dL~I{E_hSPx|OR}Ec~dcqN4w?ql(*iv_s*A(N|PpD5d3{
z`wu;L*@xq%PM_PO&&JMO^>rfa)~s5&Y)yS*zy8BcgMh(!@;m`(rwQDtU6&*FJ9)-}
zS7uKApkJ?=u3h!b39HwvUi|Trl2qq|_y08CF@iei&1fOi%%z6ex|YJ^>LxITK@Twn
zfV&DNgT=*+t|2fZU72{A=0ZMr$iP@E07%>8+89$tm@fBPEO-~10N6&a#R9v_$Y?)E
zXiN{_ky<8<nf|vu)D(o61sa;bu8{^{b^|uUTHpdnp-G8KGhx$!L5T6uDmUT!U+e$+
z+ML{qyzygh`vbm6l5=jn`{0uLs-|vVzxUgB-}IB8efK3zM9JaT-*Z*>q^7QD{~NDA
z`}(`b-FL^!Niy{FSAP282R~wZnB=6nZs@1a%6a{<E~lK_tFf+5C+ghhEt_j=_-X90
zikSwUT!p~MDJWsy^7P@Ul2Ko}<eA$a{pok6kG$r`qX+W>DDRUgZWaGYNlvMvS3#`z
z$4d|-D}9X5I5}CY=hu@<NMN~Hsiiz4kf4beQ@R(JF89I?Aa$Y8<S$$xE!GkuG8_MP
z7d39AWc^QU;0@vgZy2U_T!C@?(4_nq<sZ+8=K}c1Bag%YnzqWYdm}6so-s3rvQ^*q
zr8`#xZv4DHR+&l)3yS!$Xi~L){ko~8X7##fum2$;q7(~aNC@K9vZku4HM8GaH-CQR
zs+Dw=ckj`8(4ekE56CGk<>p2hdwQ86A$kg!7sngxE7yKJZT+fQm75ofw_X2E-4E#6
z=kUCOa!wgwphpWrtlfSjaqxAc)%BJ0m%Y1e?c8-6S25(y?R)g;y3gRg2Nma+p%-3?
zz2Mpg8)>V6E!o1U!Pr9UrAaX(i{At^J(unUNr9ebOhe|P3774MfTq<&V#{<FwG~mD
z9kI5YW>BK_2JEJZ%|$OrFaWW(UYZnOb3t$7$U?6T1AqnsYUoWs({d~s8phuGI0Ql!
zx?*pncNe_IP)9Q?(CVloI{;iz0+`D}%=kaf|EaQy@`9p*yi~*1s)nSnG{1J!<{CZk
zNaYu|t0<`5s`xK1EvnwQu|{_yS_(?rcgSztRMSvgUd}_{qT;;D_3LW+*ken}vWdU>
z*|T%5yyLzDN_7{9jk&y2r=nEd+BKV!oV@Z*9Sa+))^Dk!tFWR|MPbX9b?fU|QhCJ{
z9ZOPGC~dB-s!b|7cPgmgvc9q&fyM2+l;zc}U$=$0$t`Nvu{>`pW;CTl(@?c~Llx8E
z*^y=0_z&xjKy}<Yw((Ck{&58bKj00=3AJHr_x_K}KQF%cVg>Ithx|{@T(oG>S!bOE
zCUddunEy}w#SKN>J8}BeFFW%4%aj4%x-36WKPpwCj}((Hle+5q+`Ob__4=o-`vC+<
zq>dTW7@W%<SzOEu+WZ!k)+$C&*jBx>+72)=n_CX-APWl%OG-*2FEhlR%G)S(L1s$N
zB6>l3B=Ayz(TfDSpozRZUC%TU%8;sLKV&qiDX@d5nB7RzNO5bff!<96u{J&InI>UF
zltvdc&{K>@j++Lo+Ax$f>5XD7$Gw?#=Fq^K-i#)*u1E$Fcvt?XixTBq8Z`nz8c!1d
zvNrzr0GU=anm%$toH=|C4I!3nEm{9_ONYQs<?yzbJ_y0H9D62si8U#=hyOGDpU(#m
zm=8$P$xi;?^zILDesE)-&))N`qj(zR$=Xx@bFYjCLAvf%@f=Z8m_#sfPBW(%IFKu7
zv+<vef5!tc1CY%>UK+fK%}s0^VxMf?x^>;Ubr)ZJvG-<>teu#9VR4c?<=$@=_w2+^
zR<QiSH+?0_4*H^O(gB}|;AL%=s+MPZKNA@I?gek%o&)kVpV<O~8uS5xrx>OQqb)F(
zYn#O;YQO|gA`J|y9WqOx>0x*euoffvr4Dv+dQD(nYeW?EV4#N>Bu$VM>jH_9-~o)Z
zOfVD#2HI`u2}~y51W`D|U@X~P)CdHF7UP3pdBchrqrHTY9xnuRMJ=X}>{eld%q|P}
zSb&`<E!ku+W*T6mZKI0<hT0g*-xL2sgXNT{CE6CGX8oVuz3|3P{ZBWMmlM8L6CZBx
ze}X5yPlI<XDgN<x>%E8Q|E9)vXI}F8ZbODQ)#~%kl)uUUjV$<Jp%n!I+4%qD{1g3e
zkKw~?{AcqIVc`7#r7wM{&9Wj@ZrQShAfZ4*{Op+jVWEWY{?a#q>9RS$kOxdGY0mL{
z2$IiE=o|m^IcLXRYwkuO2t-pe4b+xJ4N^iwFJu&Vna0)_X-pu|35x=TVj2VHbw-2I
z!p&|nxS-)}H%17aVMA<6B+zBC0Oo=K0GcR@CItf1Q@fZ2c54S|vS=O{Xeb6;M^mi`
zr18`+hSqlXx*-FJWFI(?X8Vl{;#EOlUf^he1wv03cxv;S#$>kHy^+^Qa{<RD7+{7>
z4U7a>C~;86nBMa3UTXXgi`@kQy_RnRgqSPqfBaxq*gu&~Ff{QPHSpi&|MtBPJh%^c
zsdz;M_N@OMpSU7~aC(tuBLB|*&&Gc?|74^}C%QFj^v!+bPp?N{3b2zl@XqtU(xvX7
z>C=lWXWhdt&SO*NJ?y%cx}4@*dO1pKPY+j?7Uw{Bs<j$w@BGf-j(%I?ObszKnfbzS
zTsBdf0n;PL+pU{OaG^<;cQP;tp@0EzrN-Db;3?pZv;^-KV4`GWP-o$EnbKTsM&Tv|
zmJExFx#%?)1mJ-oiv%>WJd+VJOtosWxR)AY>jI{WQJ5e~qv@`!|LtlVlJ&pCFB|_X
z4KE*JgTU}g^a?Ue?WGih$AIZ!&*q<O{vk-(M3I}$z2xLJAw(}Ml*~vojq#tD{|QyS
zK}~S#ZOx|U#^&5k#~(y2>%&puN?LebVkV7M)fXHwDk^jM61ZTn!xp%9OjCQqhycA1
zn}r%Y1tcW`X0i1k88gBuA`Jq88C7f=8O0*X@=XwAYtQqpY#?@-m)h(wdfS653Is`s
zG<0DAwRs^k>^ja6ES3Nng<cB*_J|_JWHe(zzRie?bOA%@+D*e&6itd<2I$QMfUT~L
zZKk27m<0j~Dr_zCfhqI?XZ>$);G(Smi5a}%Ivf99L0&s{3z%&FaaO?<amj%3A_7T?
zzA`0b6p;UG^Uou1PAF(s%oVe4SmP>gT!){#XsmD0N2u7TwOOYc^@*w$Z5ZTbCnI|^
zx-UA^9(U6;&f{QQG!NnOni|(T>BlY$r0Ex`edI^43Fzej>j%=FOYpu@L>e$PG_K7B
z83gXa5-X)?c52fDnBD_4J+MWY*tSIp&|@TBmIh#(EijYOuxrbN1$HO_>;c#*#xrVQ
z=qUh^62Rs~n{5(!7$LSm%L&|K&-&k<ah>(Q!-E=!P{&wu6gj{gPWF{!%-)M!4%BS?
z<A-ei$>tx{1?BIU|G6G!b&}Be$M*<un>4SXTEEQ99>EJFxrx4_GbcZfZvbm-)>nvm
z4!mw5^4bPz0F0Nh_`~K$7yBr&YXT%d;M$a#O>e_sC@@+Gu*Dj1MgS8vS`ocqqEZMV
z4S1wkIIs&C6GwIcko20-WWbq{-OF%HNmt~e%T0RWvRKG~Def{&guxp_4Si&EGYXGn
zS^wi1ypi?4LxP&(NF!PtHeh1Z*<I9Nvhj~+viXNNp%61MHfTC>viZkZojAcEF2K=0
z##8?{<R8BFi%Vg?Tu;APp)d7m&I1TrO!ywK=A1^JKC_!b7v9chd1Jlj)fN-$7|JW)
zOO`BIyLK(FgSQT9+j+bV#-l}!dA(YoiC!=afqh|i_r|EQ_Dn7aA`KtD>d~V|uU@^n
zb?au9NDm8=`4AUoWAMixf6ViFn+**TAc!m$Xf4K5Qz9N1Vm`**y?b}m@g*nj!ch)%
zdV#rY1z5Fe73xsR>+3Xwr0qn3v3u#I;F;L8SZZFrXHb3m^zj#}*oP2X3j`G8FsP+V
zms0a3ZdOE17nH7%{E7bOb7<(|r$hP1R~rKtiey}{pBg;h%)V^dvK1><peyvGW5|lY
zIAEd~7D62f0OFJ}E{lZ#MqpbGjU$b5;mOwTcHVBXpfo*<u)Ap(b_$qGv6lS)`|tC?
zf7^o!k2KQ;$YKF+IaUO5yeHuqcW<OOU}Cx=yUn1sI>_8>fZ7BAnY_Tz8$*|in+y{P
zaM3gnoj|jdR(~O25}`EGia=6e$pkVPEfW?aAu}B91aBmD^r2yU#=@cZ5a^1c%uAj1
zKW?GeSN4ym3D{F+BsBkR{zv2MufINQ*f0R=wMo?2N&HjWJlhrxZbNK#*A4?ucN|Nk
z-<327(1rX=%E)fXZc@`ETY4iPgGHNB?fehMidL^)O=hN!8$tAc6uwiXMw-~W0NIiZ
z#`<#1!|u!s;IIcDhk$k$<bVXmnb5NfbGMf3Wo}SAnd)XQ)~X7>&&)*dVGez4N?gLj
zhK?v23>kUWtXW4Kam1;op32W?i1(8U5-hC^AxOG*h1_Xx8)7&4DUXE<7ry!Cn+F|q
zP^V6vu+}kc;r1Gv)_d>0huNo`atfa++flDY?M7+rLfxW8i>6MUdf<Tv5&>3c7eRv@
zG_oJho;{l%{MvIbMfsUy^w9VTPu7X|=6DOmQ^P<!G=9TmSGu6FVVRPF+i3K-Sd#}F
zZ~!|riij!)7L*>K$AueDKKbN5oc%URt(T7wc*^+tI`7Sv9|y$@vT_`0Oc}b`Si7Ux
zJ;C6LJMX;n?6c2iN$d%ImL}_g|7{*%4U*Q*KmYugGz{!2XMUo8_AvigPF&^@_(b`U
zfUL4$!2&h{G8jX!!3>8a<{^+|IOVeua5Ud(2HTC&HeJq^=pr+F7r+c_K_f63j3zLE
zPp&4w2JqgY=oO0);S<Z@rZ&kskE4k8WoX&5`3x1u6ZU3)z@|~peSLA@;W0ML<*Y$o
zCZE#uCV|x5%vtQ+{q)GR*1-U{m^Vr=fE?_lrkIP%FQ4z`&70SuLkA<F!Dc){lY!W5
zli6{W)~ZT>5opPoNW+=LsAKqGKK$^*cn&}j>L}jNP{*#E>7_O@qreEXQDl4^+Wh(R
zxl9KM8I#Houof7Dxu!%Hzbr=6Gh$J$qx|Tu%wAv<P;<qM!3ac`!<s=skQr4R%i`>(
zqpDRx6x&84O~+^?0N`wE=P+eHaCp8VF~IWW%MUy3FprBzBpz?35G%^8+svT5GFc$t
zn{Cieor%xFSr24AU=KL+v*KuGF0SaGPh4e@GD-k|3u&2MhNB9aZ4yQ7f|p)5qxLA!
zcnF-qXnyq3M`UKQam@dbovgyH?2VvJwv3V?Zd0O)h$l0Xn!vLQbndKggAVCGo&Uj;
z&?s?4!0^%pK#>zTO=VXuhIy8plhXAvuV(X%nJ*e)Pv(~(N$|Q>=HpWA;rzT5zsekT
zVu+$m7R`y{)1Usd;b<i^h7H(d)MlqP9O)x4bLLDgb0d4)Zz6%aB0+3M;T}27e*OAQ
zo;-Q@@Zs=Uj%Nh|CSc{tl_#8Vg7FA3yL}XO0mQC0fc3^v(Bvk@q)C&EhmkI%nF|1Q
zcnJf*8|gag%4`C+!ABP38QN>Fy%v4WD56H>Gb1OSc%pkPDk_C6Vxo#2foK4d85eHk
z$Y~YsimEfmW#RU};V2n;Y>3V1b}*T?af=>Z+<34S+!f`(g04}cMnxZ**D|5clw)?+
zmXJx13B$#g!m@y~8?!x`t+O6z;{h&XD6AR^U9K|$(?@(}mytWJO)%!#X&ps5QDoA%
zD-y(}1=8Fx|Bx-o%xt}x`5!VTF>eGh)wD4Tfl+cMV7-ypWYpdTFqkgn8y|tF#TRUN
zH6}(2v`P6nx%|c}z9#+g6q$Fkq09&1J2APEMmiVOJc(kP)^~x0f@p?B;Q$2!2JXxA
zgH}<u6+~BJADCDhZyMW#s7w~*Q_PYnM8>qri9+Bt-WY4~*uZcZ#8XeCfu~pzLYQoN
zF`F|EP18e=Darz=`NgisAAg)nMiMu_qxt18e|gA|ArQkzF_(#vhD?w&qf(marAZg|
zMB8BW@H2&E!dCH<pZw$--}pwR7SkY#?>qBJ*4oixfz*r|0+iBaIk7e|LI6x}w-`@t
z`I(j=%3rKzdM_0OBA?ku7XzmYUTZNNn^E!)e(;0mpMRboSGo1pTiFY(%VwLH8eR5b
zG{Z{i0$|7x7z2T6=!&HcCXO0BG?~40Sq=b&fl#8H2)s9Y!^^LJxNyaqlCmP!Qgv1B
zfL>jWKXUK^Lpw&AERglU|7;JiaJ)`gGG0V18DK^Y;IRX^i#pRAnfOR-8ZW?4%s){}
z8~HThr8jD{E*oYpcR7Pu5g6xx$jD$MA`(tHUppU$6Gg=#@IKH}Pd&xe0vFaGd68)3
z$dRK*kM`aXM`U`~(UrOBfI%;c08l{=v5HvJ1%c%N8{mpLmkro2dTD@3UVh5o%E?tT
zyBD3J<T6?hq5~WG%L79k1!d@nioryc)<JEIo6s9zB#|8g3QdrP5y%)KF-W*5w!t*e
zV~M+9q}S}W!8Bkj1Zi<c0R!G-7HG*)ivh@Py6GmK93OYwam)no3rw6ik*`?0@x~i@
z#u@b@iel~5;Ege0U%3~7aa_<~fGvTNVj2i+7)^R#dg&!zt-9rwTL>U%%mt0<IV(Uz
zVRRtbr@=^8>x|eing*yLe){RB&pr2CHb_RqMf^9v`AzT~t<VbqF+j8vGi(Wr#?!QX
zD}|B*27F|vNug<CfX5ztj0ZW~VCRVQ?QehkjyvwKsc}*)1ep*6qYI4)F@PKZY9oXA
zv_|MDRut74Ne#VwT_YJ4Mn=hSxkN4l|M`d6uS{9?xhsyp@z8B=PE21=HTJ$KhacGG
zbLZ>XO}1n`@Xzjn|NPJYJpJ_3dE~dNfv`I0a<XOBu!xMI_Ih*^m|ib@>PTZuq%nX%
zFF>zlYMLMoXr1OHRh3)wipvV~(px@^R&5sOZBkSe*-b!`F2evZ)p+@{k%1&<QiDM<
zl)w}N)YGO-Bloh`@^FpoF$A)UlRxnoc(NH7m&k=2V_*kBDRq#l&-&6NlT$;60q6w@
zB>*%YkrE@{hpi7s$?eKtY|LCg=aB*PxHBHEx9}C#vVF^GDr9p+BiF;TH_?Q27ia(i
z?Y1gnP#>{y!EQO`f(%cA2We^4X0Zg1(6DKYp{7Xzh%TlHCDK3w&_tbKYAXUy@hAW=
zW7DQhfBMs(=;bnT&YU?v`q7U#BH7&+U3Aes_uO;$-FFWjJeXS+aKVBo8;^@2qky5y
zqXV#2(9i`;0d|w^$_Fu9#Z+;}_>e;m;bk-kphOXOTE6zRud$PHMuQBoQ4u#SkPi}T
z4R{FD=rSBsZJUut9(jbzR^D~mci(-9Hl7flamE=a;w1RfpZ=6L7~!(b04@$4^wOlu
zy(o<;Os0Wt?|;)n#zU7Uo_J!fz4qdM1Hp5{4LAJohd=zzcfNxVipj7Oz@tV^L;}Yy
zkWndkECGWA6VUWr)5M3=fBoxUE#Ea5iUk57irVbz*uJnUKlAK{LoXQFb6~|gYa7>X
zs=?9gHr5oBlnfqq`0xLHGN16j>|)*C-$`j)JL%E;pLlb5uY)fB`jtl&1&YRvvmbqO
z{&AO{+sEylu>Frj)lPr<iH0LSdsL5qtYJ6SZroT&(#+#Wy2^@na<e2(#@{q?e-m+3
zvFE^lg?yGLZ%7?;%rVCvdn_qvSH^f%#{A>e%JQMm#X4TIWa0YS^lyw-bnM!#OGmEf
zTuclMag>iJCzD768O6w!rurFEr|y0Dv0X}K5_q#C!LaR-`cP<60G%QLEQ+QD8Ze%k
zjm!eUJGoh`bwTjfTW@i1kH>2Ggo|Ll^_ebSy(V))&;36x+YUYSP$<pf{6lRy9-UQN
zDT)Bd)zDK6nAk!fra%Ug!o_edNhc?tPfz7EH1pjee1?j5zHo+q43ZBx^RStJXtHRA
zgbNT2GZ+B6C?E~ErV9Y|?xNT93l}c@^{;<z_3i?u*L4J7GzJ<2zxmB?;Ee(!SEd$V
ziY=kWHpa}>0&MRX2`^od3>G#yn!ICt*=3ipb-nV+E3dx#DjOKbBJO4|F`SjSmCDw_
z6MjxiNU$YAp)90n|AH|Lk|84&z;)yTpzzWl2i5lN+p`n1O<r=zB|P^2;DZmo``zz;
z<};t+IWb-AXPA2R)mQUQvWJGL03hv8t1!l5qb^%wE|5@Oe);7bJ0Rn&W`2@uz<>cK
zopchfBeEs4Q$j<bmzp6Muw>K`G~g|bCdDp=l43k{1Z*Bnwqcycb6n<~x4Q}Vci(;Y
z-h1zj(x9>b(HO;gT+o1tlqiA*dTJ{Lb~8$#$s6eu<JBGLVToLZ!6TVbv?<)de)9K!
zt?$sgyjS_GHOYd_sYxGhUbs1_DX#eK<fXrQan9Ve$!~x024rm*-JJIySDkX@q#?&1
z(XOcB%`qnx-ToH%x+RmZyyi!%v^Mv&Y<+)B(II0tWsvWPNi{9}YSAIjN$%FizgSfC
z)u-!P>E9nay6B{_TU(odufK2He#l8<-``_tO>bRv<*Ogp?lJwY2)B+Y!jYNI*cIhY
z@Z0-s(}cUeTE_3gs`TjGzN~2U!+5Fg{jo(whrG3ezy79)`vl@`leH5g_XPC`^7lkU
zD-Ca1KKS5+x7~Id>x-o{apJ_E|NQ45Tl?Gojcc5R#`0m|P{)-8P`_!#hK9U$?Mmy`
ztodNpj5p`6ASpyH=wm|g>Z9oe7^Oi&ag!X^zOaz<vsJhYI0}qhnMhy?#MD*?>`cOV
z3@~1Jfx*yAO*3W>izPF2p~9=C+~sD!`_-?0HFoUS2OfCfzWeT@36i5f1iXnI6e=BA
z0s?_C(qgUAG7Zy36WC^uR5Q&0Pmy>1^_yWIW{1wJZ)zalW3TFP7Er}4?eG!9z$H9e
z9SUeVS$ptk0%JAc-K3^~q>F8jhwyypnX7fi6Y&t!ix4m#6#y6$P#b{d*=L{S_26TU
zIfndhMbz%4Zo>j58fMtUkl7v&!AJ;bBA-iPJkG7%zx?GdM<0E(AFoE?@E&~d!ED>u
zX(6$;E5Mr=z!(T9Ah92NfL(*(@{#(|OE1MC;MxA!lG(I)TY*;<*varS1igFr=A~|~
z-7zeV5db-;uqD!wA(<|UiIG4TF!ed-oWm^vB;!M$j=@tn7N8vum=YF{W{jgiAiXpp
zbC(B8ZQ{s6FU1BB#~d&GyPO*~Y`FR6o4E`0z3+XG;dJcS@xlu)M5gHx>+vFo;-=w9
zL(@tDY-+3#0@9*9G`S!Im67g+9;5{VL>AMy{>y|J@4VBJ->{(n%HBEor3Lw^frEN-
z=WFWRHH%hPH*9Ih9Z<UXqwX(H{@}o2M`tnyxbgir#t;43+h4s<_qwYVl)lzDNceoR
zOvk_J&F?wS{&wia_hsH;+!0f|VB*;1wD0xMRYm2NB>8xK4d;64qijhw82CHZOP{`V
z_N8+M{tXg`CWZf>-gs|Z@Vn9>&%C+$kHa;dqEla+*f^;CZ_t}Tn&G#q$FA;v`PiZN
zPn>i9;r)`@$~DX9zqh1B8hVYI_?JTl395bUymn8V^7bFE3{NXOal5>d%uoZkTjI>g
z+MaDUt^Xg!>{=?n6U=kdUH9*#hm{i7i*o`P){)mE38pD8w`<Qny*d~7-@E_fw_g2l
zb2VF#@>fRwvD~=mym*mj;b}@%B{_l36@BbaN~qg1m^D&U;z9<7#$pZAjMf?xiKE$A
zPQsz-4Cd@7Wl8ePGtUedFn~M>FTFfZjKXP>USNcP{o;ZPF0eqj02qq_$m}I<i*mmZ
zdM@+$!AF3HAAXptBYJtHNCE_aI2tJ~MvZUDU5rat&Ph&79=}<^*KdaRvX$nVdF-4D
zw<OqCFx{i&bYQL2*a4C*Z#)3lXJTWh`D87oU>`fsKKtxLrUr=+*8p%)B0U1JHHrmK
zFI?P4z$yI5C5}U;6+sCB#q>~uaj~a>qqD$JAhyR%CfyAUQ>IMeGrMfe5O8rkYSbtM
z+BV372Azz!*2K9SHaBkE7<<7Z0WFw^WZXiH9LuMs3kfDO7fpK)fXi{N%WuE^c1|sP
z`iR2^l$;4L02=6f_Uy@ZC!3tj0E2w@A{iR!X?jr9kYNus$OwZWnAn@S7U#hUqks`4
zyCyW8MIt3!7HA<hp5XwvwhAP8j4)E*f}R4e3rJ?1Cs;57;9Jg^94~zGAUl}MToBW=
zK<KSPa>L=*s-p_y+xXv<2;sIrg{CFjB${*qNB>)zb)hbn*C)*{7}hVhU-^o4>x%tr
zsx2)O-(E6t%EJ8SmXdB|x!hmt+2P$eJGo+>_wbb$J(A46{&Eg*l+S$Y&X?w_wd61+
zfpyDXzu{oKUU={q#w-k1xOEGkJE`!b$KTJqURyQp*4bB`vVXLS*X?(}q&|G^SINz%
z^bABrWG7xS&tR}d)ZH{X=qoIIIIUvc(>INl1%;!Ze&3g&TOYsr3u7isxCz38xlCX8
z^uK)}5EOpl;ceH)O$(>~BDwbXfl63=CN{-RSGQv7T|rwM@2c0wo@D8TqaS{2T`+lj
zjZO2e8*(KtUtVz(UgC<j?!70*&Ri>JHLZIq4(EoklYEC@-s3lnzUlEvkKZJz2Y=z|
zd70PN+x&)~FCUwHbNq9cA3cy4@r%nl_S^UHGfy5yl5473IQI6ZRy3vFfB5>#$0y^z
ziX*O`873r2+AV(bX}jZwhi58@*3G-=;FHqMY+ASt)((-H_i}o4H$427h+9kMWCepC
zetOK+X@fNjtHz8z_=d+@uUXf<KDzJ=cm3NQ7=!Vv-uk!n6rVI^LaT&Uy>0$-S<J3X
zrs6Rc_hY>_dEJCZW+d1)#BM^v!pY1(UOUvRsQ~l+&CLxpTxxFFxUP~}PgN_utE;~H
zgLfuRm@whR7hhe!rN+qW$_>+}ycCcxez<UXU0A5B*Og1=O?c_0mtT5m-ohp8biJ%3
zM%lV${ZyzYOn7Db?3xCNt*@_J{?Y73D^@R^KXbx_*Qx_Dh}kax1lyFEomi+T2qbqg
zH1HI2fn+SyG`VfbcH<2gO5^E58chma5b)hZ5YtNmfB@l$C9hHggXguR>#n<w8+g2A
z^XQ|Gg1PFdtG@cxuO4~ikuzq@0Ap#;!%htWtC&a3T&I!^xpd$k-z1W>G&VOiH0ot-
zZeTZa;nD=mk6l=$M}lIS-VXqfh`5;V0PGHGcCQy+c!8}HGCruvhsiuzx@-h^VA!cC
zG=Ui;uuKfQzF`l5kBIrhwY)6%=Rg11Hxr<AvG437xL^lQ4IZSOMH5l<QfxU&`Ir_D
zkCA-IC6}0;2s4YxprnB5-@iZM%N9-?GdkN39x^0H767`?=t+{YP_V~iqy;^_P*V8%
z5xzZ*EtxIzxZ{rd%2&R^vskWT*`B%4ef{;<+vl*uhy?Rm8is*Eh<jbrgi@k2@ZOKP
zQpW=>U=Z%Bb4!LD7GMCh08m?_Yr7G9&`TFJlrF$l8j(E4e(9x`tdYHW;J|@kIA(Gd
z!P_Q7ECrazF-(mO)I<yFY>Xv)IH&`#Va9XXAkpxhDrcT~CToTQ0ER-Bxhw~<umigw
zq)l1yaeYqLlG1LaE0?cbwR&CBT=mw>#k^>DQ2$PSJD2tDR9My_ug8eq4W-+@PAE#~
zb=(zKB+1ZgzxF^Vmv$TX$3@$|d`)DvbH6@t)Vr6AU$W$#OO}6qzwf<>6B}z*PEIEC
zo_&<lxOD2B$rYpaE!zzyN!C7p<LtXmJ1ElZ6qx#(QFm_n;w<#NFn-?(F8vx``ChLJ
z@7!<Nk|i(Pm0URDy9+dpN&V_azJA6T8?Jh6;sd9qX8-BJTOS!a?W2$0nR3O5Zc*%q
z6CX&fJ-u&e@1fYj0m-eOIb*>oPfZ*@w$mf&@m6oDJAXX-XN|dd+!f#b6F=l$)kb4p
zug{OY7Ds;VXHy=y?l^Y$6%!v>UM>IBu3nfN`GZ*>Etxp>il1NoZ|~_>;FE^Rb>r^5
z?BvU8$4?yp&E$~_?|mhVNnM$qtX#>vwIk0vvWGghFO6H~Ju-P(bz{>1xX)k9rSipN
zrcC=ve_cRR#$9;wg~Ohj_|)we|NM$?&O&2j^|aZONf7F+-Lef<d-7$U`NO3zOnK@q
zh>yN^%39%6wwtVmdJMke!dLcwVaf|P4|wFl5&t^7w#PvulAmApq#0)a`R;Mau+Lt)
z2gcOSy|&-cw<ceFXUURjj~(~*Glp5;u4+DMNf&RhvNy2s_uFqjQW@7FthHUSfM?0X
zg=2u)>o>h5*(}2KOLf(zj~3PB_i4|%ZD^={cjBv?I_!VOIcJah)W8|j-`*N7E?9y4
z96IWZv(7s4n1fa?n+KA`xM}s0xr<jGb;7A<oPKgeQp+CWwcl7*_4=D{?|tlvXP<rA
zz7?CN&0I*vL3`3vwRrBFmH9nJop4NwUv>!FDsr$V<{w}$a{wnU#zN|Y0DvxPL{Xf7
z5JG{N0~1}OUoO(<asf{hBwY|d38OVeEfxzUU1($<IqR&mIHU96ge1=eH1{mIdgfEX
zT%Um9!6?FQ2?Al!k%8B`;MUJnkcjz$5ey~oQ_Iw_H*+l=nsEShFfgk}0)U1RlDKOK
z3~&eF$tR!WHYs!O3=1*EIUS8=2Lk{LG>nI0JOofuY_vBLx&ZiY4Q_;TgO^dAciwqC
zsRe1h)UYsA03=hB?!iES910j<y3k005}DY^jcblJzM~5n#zcRaC2;FKscWvehAo*3
z+=m`|h_er(++~?aGdo;JU^lWc)TpBuJOyIg44%TBa~||^XM@-OumMW^z`b8y2m8Vo
zzCbiYE*K4?1!)Y;(hPus0N!Yl?Lh?qm-~2wt4W&*BPScKXt_+s7`Y?NqlF*=nfL(!
zN@{u`qc<7=2FY}JEHvqY%j&oRz*`c$!e%FU$O8rpV7}OuiB3jpN^3Mb(m<L8jK^!e
zRJ~(hW=pd+9NXqZcQmn$iJggUXJXsBW83D$n%K5&Ol;%J^PT<d_nd$0M|ZE)t8`U$
zRW<A1B0>2P1Wf*#$y#JXBv1^HO<uDakbV*{P%66>Hx4>>7-^!Mo+CT>3ci&7;H**j
zqe-2u*)FH$&2tVlmJB-hWNW(LV+R?ot0`DV9H-8_V?by3F5wuQwDC*dt1wjK$3p$#
zVZi&m8erP?{c53BlKCO%9cLS~BvQslp!&PBL#TnCrTLR023U>$>}3MY=T!Hb^^I#k
zfTm<4U7PopgnJEXQM1PP!d~9I-*c5EAruJ$>VHU_XG;@`@?8eIw$d`W4(Go;mrV}&
z+L$}^^tTG&a<Zy4L+;<bb{0;(t4%zy+z&-?IK3cncp0rc^)4jXrriXwos}(%jx@^C
zJ*}jzPF&*Nj$c*iX}|0xKxey82qKhK;W1ko-(*|K;hFdp4}v3aQA%#|Z4dYLRh`wm
zThf-@7+}AftIw)yt(`wPhElkCbe9z}Oa9JMxvlrUjrUQ@azFB>d0N#HfE>PCyGgU%
zE+D4hD;+G4N|Lk>t;<)I0;awq9=s#*<|;AYl#(GMv#4XfahkrQGaz10a~=oo4+?~3
z>7nMT#~x92_uLEXX<yG?XRe4LcAoZ#f6Ht-#vgLRymm=QDm?j~kk?%8eu9tVe#RHG
z#eESVNwn#BaBNH7tjle`)6B5$dA_F>*>+Rr`Eq~y0vxn@x9eRtK2F@yVTn{)ci-}P
z8rP~7#dB47>!l|8VEqbB=rqCivaTA_{%HwQ-@bFl1p_y)7?xG-{CvpQG`aL4izZxc
z(;pk;ewZJ9I1hEE)~)w4Q818Pnw?)vKUvQQn`{b}ynSi>1%D&8dW60Bml88Qc|vE^
z^=Y_$@FXfD{pC!+YOk=2U?D@XHDi&Uf#LG2rXU4VYt-{xr^ZE6)?3&o+)+@G={UCU
z@kCvxQN{L0BUO*-A<<fhJF&)J5Y&g|?!MlZZch=4ZE}XWJ@QT94&wT-&*%H2BXkDG
zAqo;M^>F0;^fE|LEIaB2?;wk|1Fg*t4y)^h_Gb6NXb?P<^fsoE%juNQLwyaI2sY&(
z&kgRt_U$C8o0>SS*B3bJY?_3RJ9KQW6W9#U7b>c&?H}9<h6FfpIuwk89~}>?zqZ_u
z2yJ-kaqapAIgD|v#b{YleuaRd49xyQ@Pm)M81T4wcTmgZDC&s%A&G5_vxk~bY6+T)
zT~S&)%}9%(-fagXgttb=N+AZR)7^xK2R>W^kJ%nSRD}sxHpKz$&KQueOke!vbtk{S
z>n&jG<7JEsqL-JV+p^4%#&{V23ehy@W*GyP#!g!&!W%;3Z-}+$?jD5?z6b*^4$5x2
zX}|pkw@||_dO!$dFmf35Fa3#n_u5ynvH|eJjCkGWD6?mPCSl+gXk;`x>Dp0}n(oL9
z+yYE!@TyV>YtXVj#4O}laR>CQ+i<=C%Jlw5_i(f>V<pusSbak&iC;uIX={~DAx%93
zo_Z5+SFU4Q@8r4EsPvVL<@?}aLbTOV9<A+SeBH<#dkWS*oo3DNxj(^@`PNpiXfCM$
z@AHh01WD9v7CrmrO^fw`l#8<-$AyZ=$k#;%pRAr{G71gG`;q<Kdg$WnlX#)*ZvMK&
zKHg_89Vlhux2IF0_J`I!IU*kc>>H)*&zT*<@HWbkcQqn`ixbs4eUpus{U<VLTov{=
zNBNDLQ8RkZvt&T)=>X&s!Is)nBa5xfWTsE<V;dh<27u3_MBpJy&HvDKX@TO|_Wdw|
zQim^%#;`w{H^)*u>TiMaiy-+sebld#4TqT-f$U7Xri?5Eb5UE#kc&AU?HjfmyR4(t
zztQJ!i=W4P?tbbyOf>aAJSR2PzV=3aY*h|V_@*`VfmB?#)9<V&tAvqEDf}X}RZf<A
zXJS4EC51|PlVOSM2}K+rXcI`})koVb;6EEKhqX8x_&#^I%%(o<nH~PD>lT5JT|~_U
zp37OVNpAiqQCE*`8N!5(WnraGR}*v@-7gx7H#isUUM+mE1RLpC3;9#53r)b%ptU)!
zR$?#U`M%1$F?G;BxBfPHsx+L|ea>@t*bnxe9QA7}>w$f%N^kKuEdyoFC6k<k7F>Av
zqLumrc^9GKQq|@tn#o*c#joW$cW`O1o3Yy-Yz*bZr2GByV9%EQYnK7p)1?yy7-nmg
zaRGh5zog9|Mgld~#T3|FW1zxfvi=BoSK8gv`(9Jt8nX<|3JT}Cojoa4iKjXc4h#u+
zy#1UIVopCS4B;VwGA_wq)eTYU*h4LW6P=T<HD?pr>H!83fK+HiynX{h3#)Ou^#ZwT
zCKIR;lltGOph;cnn(5D8EPLBN>{_hpRetoBq{m{l&Sax~O~CmFNbrydVd#!8XPZ66
zdovpsJccF~oFDE7ac>U}5akyuNTdvmzA@#YKUFn)pR_{>6f!QGam`v24max@I1=me
zL}5@|FD}@v{@3xI0|>9+F`}S_?l7_b2Z|i1tuy;K1WiFrymkzrO~;3D7(A<!f}Z^V
z<{h^PiV%a_Cerpd9zB!^?-B{yt=<4E^U|K5!ukk0wSwD1*jRPEHl5F=o%N10`JxDE
zI_O0a*bIlB%64(c1jXWDI>Qa6FyM@=OWArbC~}BQZjmJ;Byli@nCwjk7n+VW+p#d9
zTP+cd<9OI}RN*()t>l6I%0X*I$yO}11awaRc_DFN<LBr&{;2SvY&gepxZ)U~RMd9v
z;Tuj2_X?qRo~F8vq6!8n(U_PAMFRs|+8E9>mWPyzF~@4_)zUk23{@J;l#ko*9^E*t
zn*M%pXg$+4c1mzI`=mb0L5(cY^0szWUTMA@ov#%uWWH>fTes!|7h#g+x}OJROFp+q
z*)dT0H{i&GY2zo@T@T*3vm<@cFJ-@rd7@i>neIgqbzVi~l@ReAjt}CmO-aYo5b3#X
zxqE9B-KNlt2em~uB|OegzHH8^s4=*`HO+iF%sz^%@Ub>fX>gwd_s4I)qLM3Cp0rJ2
zOGjWz<qnufK^?bdx^A-zfkfHwH=?ifZpZ817jbGeby0&K4$7kCNtoe+YLCMyF(3Aq
z(5mdZo8-?|LG2ar;Qsxg7gw(MnO2w0;VvC53tn3-k1;LU;$TugX8eqCQpV!t=9o#1
z5i-obXZ{v7#q<8FsHmI^{UT|7zdKA75Dvx{fE<L6))nIC*_>0ETcvcmy-5Ep1yFG3
zAviRHtnD!NRY7k?w4S5=x*q0GD3X*c(GkexpgjA%!hUkx{uPoUmWS>u#;IGQ54zT;
zAWn!0YYsMNGDr3e1YtmWN;)LH*uqaEWF2w;l}zj8gTeC%9LL1|@Y6LxR)R6t4-|4g
zCKuAVK=hX=@<#Vcpe4r#=x1C*E?bx;?70fwMWOZ8zL2jPq8eg8TLZZToAz3N8^t-B
zps;lfW&>=pIl4uDp>94fULyI7;@R6YdxN8G#0JLKuyGQ_^eD^y&^`NkGUw~zD66^P
zK<5ZDWn%NbEoJkB%l77pMcp`Xdvwa%;7vlqLdi_2zEkIYck|lu^SaJ)@WF}bc`q9K
zxJJCqout%EM$s;=wJl{xGprlmby4GTe!wHSW!?Y@r#SUD$Cg*<EeE6G2XgdXq@rDd
zpKXi)cR{!U?%gsK859!&%oFo0njab%E>jDjJJm00zrXOAMe4u303a(drKgFN)YXSI
zDJoM}4V8dFP%QnL#P0S0RACp`J_u?NI1bQo!i{lx#=c6c`u0$g*UiVyp?8#~NLM>I
z2I&i(>4Y9>!r>7^b}Y^ilx9BVz?+(tQHnmzPql1`0ZQ84qN^unK_wMV*u63GUUfw?
zb5oxYGQ`5V!{|SDetv$&JBlrm?EuN|ot&h&(+VK`GB;F!%3MknFH|^Crl4TTl=9Ma
zF%gM!42bOAWu5!it<P^E0Te5++u%fZ(<p;0yHwbigOLdUQj#X{u)tOkGz#6q$6KH{
zQ8~I@Kk04+h5;nsEpH|Bx$w>Eb-Y@5;1oIBwg?~C6^SB29K=<y0IvnX`t?`G9oRdy
zt{0U`iUE;?=}3wGI8;3dSVt)lLE@t8ICwBgw7kTu&XkM{X{9}CC|1T2jr#0JrolWA
zQLJoGOktm4dUCYmY22#B@0#H-z1RZ)qLgVAG0<Tc#{_?}LHanr$_fcO5(LW9)cafj
z{M50>+N-~#vG(rqoRJ)nSrq5S>SqX9-V%3T&)zs0-2`8WYu78+W2Fyns6!enk8_!d
z<M}Gxy*2Ft`}vmM6ny;V%aN@U{gZu_Zq0%F$)&HR;{-OtMaHvOSS`1%_Zi3U4Eh{%
z_$<2*Ox1~wtydE|#EPnql+U<dI!sxunZBP4zO7xg*5fiZ39Q!fG^e1aWm;-{U0slf
zc#mD$TUa>8)~cI>*uKw(9c-p2X4QWJB)os^dGDl^Ty(NrX+BJ^M5mfo`5b#nDUU^H
zU3hQ4EUJvB-C@ja&D4Dk3G@R9O>a1V8uqqW@UFj~OGo`cCtiKlaA+1AeC5`3`7ry2
zeE4aXJE7{SpGy=!iMzz-*{tHWME-fRhrKorrywqUtM?)+Vc^iP@G`Mx+ZYUwWR%VL
zVmRip<66zi*7>wx?25>8*SUX0<ZDXlj_+#I**vJ3R*p>qrJUTm%`x?HW+Jup@V0kK
z`%8AUM8$jR<&BX3w9J;z#QSs7Z-KX%S#plPYw%@4!1po*#@eIxX{Y2x8Hkh4Vf*dc
zYTBRxAp}(1PnwjR+K~RQ;cVbpCs_yca;wLCCz;d3V|#7NWTw;cb`Fy7c4H<Ph1O#9
z?2W+ELf5qz-)FR2jG>B~WJ5*OGlMVJ)2KAVXVUda=@Ke>1~17)RLbZ4M^D@&8PQj|
zSFb06xyKuj$J)yFvS*{~nUFSBx&1+tj4b+MW1Y3M=SM@`5RoCwS{C7iy|yAfSo~-=
zxiBtMO<fjBjq=E_Fjg{(8%ww(OIt(D=vl^IyhJ>4tSd7tJbI5mDgtF3a~Mh79+`VU
z_bN$sSKR^RxWO>z$b7Ulht+p54ukygK6~A9`sp+Q^)gCICJYR)vwCj63=}Ah=Rf-U
zKLERN;w03O%6aA^C^8`8g^RN8K}m(#-ym326~9vyr?TF_AwuML;6R1#d>#Ii5EBzf
znEc4WN0bV9#(@8>0pKx4i4f_P03`X%qZkY)I;^6Kp+q#JNs4tF1`O;{y2kOs`i-hi
zse;>}6-&9jXYyo0!ee#o?Sfa{Bac(lUPI{1;pkZ*#E}xJLbR;c8bOz##mJ`u6#S%B
zrG>bZP;$`^1G?7JSK@?di$ahIrh5*daD{3kMB=4%<=zViP^KX8m<uD2aUryKgTO&s
zfC=0}xRPin#3;b4!u&KECFAl8HtS3P(1TH&)Buijw*><%6(yvK$}dL5T!bD}5q7Ok
zghoMyGj_Dz-79sQ7$t0&X8j7-41^6sM}0~u%yd6(REp5^dHtNk<Fc5w*c0P6+BG7`
zQZ<yfoiq5H^4<I8ebm9=<pO5xk{s(t_Jb~vI{EFmQEEb0A@jEo@HeIIXR!0fk-+_Y
z$M=hL%FQjNxs$ik+eO<$Pe_Q*?0i}2OP`JG^w|Zdeswxuug<~7n>nLKoPpu=hO1M7
z&j)|}-)+bEFSE{%Eb?^OfAzIgW}N>9bB}7iM120qoN{sNygg)zzQbqW(lX3(J9tks
zS|C$($CE28xNSTQm@Bj8eH^t~gllh<7qERlRZ<GPiAIW}lWzL`)hl9TMU#4}0J@8R
zD{k6;iQ{)G$sQW*aJj8--~EKGj0WS!Ic4PsTFK;^OEb-e_k|_o%xtblLFZNVr_W_0
zu2Od0Z;kuQtt<6MAJglD%S$`W5-p>^TqRJWg)c0+1a2p--Tr)jMB#g$99iK|bVaLI
znclChqW2fhA@YySJBR9)osq)@TbHNrQ1JI3yZ2;#utO8==fEPh%~PnQwyt;UC5|rK
zy0~tS_my7>&KsNW<Chl>Wfqs+qq7{8Q)XU=>j(p%=|{Rc_<}BP0+oe7p}XF>vS#W+
za>tixvb`<Vs#0`PDw%X&F1vn;##}64_dP^>bw%Q@&XSb?NIn!1qS)N={ArC8KqKpj
z#{nB<;-``x-H;n9y`G!2weWl{67YTqho5rL!))_iy3|_-hs8b8{#e1GxyZbw_}oc)
zw{>#LcAX(V$~j+eyF^aWeps<*5qK|)=3{vO5Pdl_J8WCC)D<<QC2~5hIn=pc**f>y
zaYb~t(-*$W95ZENXZr2@`sdFcI8F6Ks^8(T5L1{!=*RCyC9(`oAPH@T0*rn(F-1gT
zx_?AT?|NkoJ~qoZZ!9dDBt$Mrk_8lmI?4^2yl!0d)H2x5ygdO%7W^KNdScMwNV%^x
zDgxo%NL&^-h2RX5Vj5%PR5X5&vX<WX0?|8y6(;+8wCsEu+^6AweHJ`dt<J8_w)bDi
zYr%;DB>3d&kO%!z@j-0-apu)6<{2VrH-LDzej{y#2<2S^a!w&Jv_}fw(<GdD0Cq}H
zPZ*nrVZS7Rz{>Cra{vHj)cT_iK>>nW_Qa`p%_S-_PZ3FxE5C6i;vbs3jWj_Vtyr_z
z<0-!U?L=^=P#SLi3<8Ud3dy+X=L0<BFM#4Qn*8_Rq@t&v8pmX<VIq#4EImm}I8-g>
z@HguLc_2(E%h7JVH!g&AFzCX5+CNEAp&fS(qCZddKGR*7VWv6NEukQ^(1&B?=vV1k
zX}bi)0L~D?yP|yh`_!Dog0vb}?h5kX;~=fshe)y(9Cl&1MS`Z$DWE2wP;$TFkVY`d
zg@YGuiwV@40Gapeqyd@1Yj=O=NCZpm#KJN3ME3eJBT2J7D?Kk-p*38}A$*?JKlDES
z4!h@YJ@(>~p@|PhJF8!BUg#|n@IAd`ER7Sy)v6QDxT49q5Ry}nLlB{^wYRg~%C{Nc
zXNY$(GS$G?B1YD}P5IjQv%u@VucW+8t#(+`Rb}xPzl<2Yn66tT8TzEgCSvTD2D$Ta
zn@7KsTGK7-F6`X%po1M5HoiaXuvFt-%RMfAb%74;P5x6bL0nlihliezhN{|&gS25t
zAkO0aqS_)dQ&><nAw^)vnWCsevzgK=f8F^LVjT>ayXDoAfL)xQ^+x^&K#o|N#eY#Q
z<MT_Iv(zUzjsEwRMFnAAon>~W??}C#hwo5IWfS`mRj_ckyWYpHMT*9n?)%QUHyaOz
z3D~`bntH5WgI=;RmoRVz1_B2m`rR-VC?Ruc8f)e+TbXi4?NzQa2-@4CaFmJu_~X>`
z(iv?;Rf~V0pLuXBy?$!TdvSjR7NPfUKXtiz(*r$3(kzq7=B`v%oHoJesedTO-24gN
zs3(Z}sJ{Xkaj2>Dbux8-zu5^}j3%V`8hxti4Z%>I!B%G@Ba)y*J_~Q{WAgQq=9Fp=
z_%w|!NG=ARnD@&F#!W9!nxO1S2sSEGKek5%lkABA3#k6{VZZJ(q%1uW*?iJC1&A2R
z%=82<(RB^YEM{5>GNBt^Guo~)kk27Z8Y*Ep<L-!aRZF}MRlAppGb7b4UR$)~YuvwY
zTvfrp5mCU2TQie6*Ev6*#^{nZvAS&ejVMR}Q>+{zrKUic6K4k<=n#}=7ER+U**;GS
zNd{bS_gC~oDYW9ZBmPDKF^$Foj4?74ryo`pSVCep4`Lzx39ENSPRshVPp+7x#N&_$
zmH5OMs%RKH3=%aNA1pC!tq0bNZewh`7_EVs7v_hP%}I6LiovgFOs|l{s2CI?jpZ4P
zAmy;*2<xb#IT4F9ERj#WOpeJ66INS~LJTEEssLof6YJYb?(OLP3fxow`D-R%`GAa`
z8P!GQr@H&|TQ{UGvydbfsXZ8^u`;)7Sie!KyD~UmoVE~y#x^jdA4Q(tKqz$*1e7W+
z7=(#)I4GVG7gc0N;vm`|B(5KZV+Pb_{aTP%3XUnIe^2OI9PZ0RQwCUlN;m(Z|Lt?M
z#L(Vkl(Uo)%Sr*Pk7`#Mihdh)J`zo%S<?tb3&Uq~u@d3-9n)jbP~o&y+zL7KXR^G%
z&$Q*eYQ3FqCraqA-J`sZFx;ISW+q0<U?rvp1T=dI->`CMZBac0jZa}7{9FcWAw5Xi
z=}x9{4ki-<vo8p6=p~o3G)}Zycx8rnPbGqF1_3>m=Z>BYt!-pDBVA^Ki`TaoCM8o&
z?#eQGnsOZDrD(@J`|urur(3ec3g)AThLC}I6`Y1L(N_$@sRW}J>h+`gONawdJT`o~
z!{f&0i(o86d-f3qzrOovnJIUFnDaUQL{_k+1&utrbnJbny0f@Ad-KtD8Lz|5i7QE7
z_BSd}1T7@nEVmbhs4md=H(9pwMzu=4!1{8SIn}j&|DG@`u7MXbS|ERrC_TSH11T($
z&=k34f`X&!kX&B^NeZ^F+wu_iXHOusLP&cg*H`Z5p2l~0e~g^S{eZ7B&cqZ&bc&A%
zy*E;d%=T3GLE*&KuQO%!Y!O5^X0;F*CoGTP??&0_R5Z6^PyKzK_h^1~qr8KZqU1j+
zT>>S)x$TH}mSNK4%DAsf1A<8?qlI?E@`8-}QE}nY1pQ)!okvJb$i(Q28iG)N;7~k~
zrnmWm0pdid3kg$5h3dMYjb{+?5<!0&5(gotqmRg<Kzv>CYD&X^wu}JVjYuiljeQRg
zV@<@ND*Jv>m5WN<AK}4B31yGtkc|}x?B9#f5$D5kpnl5>tq=2qorF@QClUN|+Qc}e
z#p(p1py{dETn!A9fIU63-Tv8USExB^;dzvps=Y&<iL-G*wCZsR4n0w%u~~pH>Xi9T
zx`6IJWPwOzJ|Ayyz6n+gUwlyY6>`!@7~>w4rU1XE>DX|Q?W6#7EVf(>>iloJ0OHI8
zG(1vZUZkM0G-Wmp1{rmFA4m=yK0%cCzS~ivugBd?O`$*2nPKO1oOo|o$g|<%1hRMO
zjhv`G>5j|yr*9lFFiFlIbzdzPif6zt5Z_oX+#cjzBQr4n(A%%?hR_6VX#M?pN7j#$
zON5$RvD$~R0Vht0iKqA%h!RWau?rg}?{`+nECNH+ih+bi0q-sv<n%@o{mJan9ps+^
z9RQ&ZNrd|e8)npys1U47B1nYMZ6;xcG@{CojK2R7-`+nw%JQ}&iJA*2fG}<ds>g_O
zQ*~z=nHEGAu_NZe(!5b5jUyK&OF=6{sM$P)A@kviPq-Ak$rPN3z1Ov>q6$_xKwaDq
zY8#F<tm*f+YWjgH`8_eLACxF@T7nQIHa!qZmAyrKSArb=J2TKYHx!nMvyAarGN9}0
zeiGUs0~p8HIu$fkhX@D)bJ0lSlp8WgLoXN4AKg5>!$NRgDA1O@1Yo=N+LoeV&180c
zaZ&)u23}*-Zu{3j&bVH3gqc{spLK|EM!*GsO4g^Vp3uSM04FE1BwAz2Z{j%A+N8?y
zxp|fv-e`EckZ@GNYhN`QKSS17<72^a`OAz&h<cx9u0kWVNwJ9xe+H0%erbPWN>~&9
zFg%&YYe?a|vWf~}^e}t7!0h}V@_y3xKf^KTN8o9|YS0j16R^QDuL>~$Gr2{hB8WXi
zcwsw!KY`dkYRCoSsX*jgMbHf*kP-Q>Il%^TA@~U(#G-Pzw7ND4=;>`{$5+6yd3!DR
z&uH3yO=}$M-H~zQy(@SmYlq8!A4MI^T7E*7zv&GEK>}*$gLq@*asJ(Y3i^9f%4GLO
z`OTxQFSI>UAEAr6sZTZpPa#QpkUm5ygfk2}`ZJd4@M;_L2Z|jq5O#b7pZg<h#vf-4
zjQq|VCQ8=gC?K$=H>EZV#F+U*$r$QYy~KxFY9iJXhczn^-1x4Oq3gv@)G7v3oUaBu
zXAWeZH|Z?^2^3bzba5JJygRiKIxLtSG=m@xfOyJB2W`tNrO_Y;VYX3(Q1yCBkaCH?
zf|Ukbo694}00iT&un{$!d*BSeEqhk_A*_Nxz>*U)k2;FswJuW+MgB}Q{(ulZY}AJ!
zzrzqw9-x#d<a8odp$H%*+ij^J##EI^jEoP)`F3-Amrl#8ca+9-9b`W(%>>MIQY7^v
zkQs&n{hmjO1QssFV(3U*H!K8zU}s7(&NFW}9#BHWl4-#thWY{6fyaOtCQUJ>^h4?;
z$_4<Lhj)`Bs2Qr)<KPe_?Z}AfQX_ev%(Nh6s4!SGXM)@^sl>#zsBrE3{ko>Rqm-$>
zdNtmDb)mb0!lo-B`iR{r9Nq5b3vHn9AyU2S2Z~Vbz~b5|ppe=tGj$S+(SzmtnfH>n
z0tTO;Km!G-z-Bd$f8L>Tqe?=w^si*ec`$VX;tu);eVJGHkl#?Dg$u)u^CCZwiAVcF
z4T5Yxm&tIh8G|x#6zEZVqB^b4dQlhyvbvv;iGrAOj3F^J2w)+#+K3e)0BFQ$f0;ji
zk}|_-1AruCP<Spzr_dwXKk`5Yj(%(|DxTb=>d0oPfKle$yCHWdr9Pv4Wf*7rA(0sj
zO;7Q`XTU5E%lWlW55vgWv|=1!u;>&hW(kGJm!n~b$oA&(w*uhf7=yB<dnMv<NMZNT
z<qs5YEar;s(IKXVU~vP2kZ}|sybzFI1Jyf|J?UY#{Spl0Mdw2TOX)gc;+5-Ax?>&J
zf;qoP5E_pLOGJ%_I|D}@=6O58!6eiliwd0vAtWmCz4NL$0Y+cHg^cH`S^)MZoaK*P
zF_g+enEq)}RFR^Z2GW;hUXlWlX>{bG!f+&!RXbGSi8_K_I!ML<%(8qmlOOb6-D!+;
z@#SVJnPBl*I2iQ`XswEhBlJQe^t)HEMoXY5_AtR*nsLDZ>WJy-lh|P5@DN(kV1y>S
z*j)+1W8jeFOn>v=bGKuSzYc914bX}sRNKduC;VBF$ASR<xM>vJ)emxAK>T+&kbtk7
zcH)G{$OHVq!f#?>%@mNCQ-z6Bp7JUAuZ>Y5hfVevcCSQzcW0tjkI*Kk6Qo=6pi)HQ
z0g5QleEchAU+z+d1PQjgWKsqoK5R&wyHbK2gouPJyXIK%`&V&xp|qG+9;F~Jzk4ud
zKTLdNDM=YSpQOkxIyj;X!Uqw>N4KtmLQiA>2phR9OpbIJZ6Zme{P$??_|bTrGF6fq
zuAc)!6lFhm!-DdHgx`PuxN07W1xriDW&fBHk|53YkC=&92;K=crS*Dg^o1f2!(2SU
z$;4F|hQ!qb^2bB!?Oq4h7s90#&Qg@fW`vOVk-)+5l?z#z5gERAwO!TrI}U<}YL<&J
zKTkcsoUbw#VEKwv2!wVW9cR#KU1~-k%*Q|T^p`?L_iy|8f$imzd<&IRL;#|CP#vHN
z%)|0&z(_DjBuP`_rzA@C_I_FCK}mWrk=+7JG9-^(X@o!+g0Du2q}e?%8R7Enk<3?=
zm7FKFfi)7fNpXIJ7PtM2q;esQTGoZ&FlfODs13*`G19CUNkK`&n9L(`%bg-o(-P^Z
zt#)=;Z^8H}?ClU#c(c@mUK05tc@XK|IpS!fB5;WkiL7T#MX~I%prU4BJ2C)8Vljwl
z2iTDKugH>qfDMEQ)ADXhe@~F|@D(*Y=mp6(5Mx9d%RE-jPrH|ZIuRahCaAp^Mas<q
zDE~d>sU}zkB2L6l8kI0ypt_^>2HAf)Oncb@$S^xX&Qv2x9w#Rw@Ftn0jTR$P2e0+T
z2lUsX24I}=#NEKdr##3XC@El&{fa297!>i+=2v;xHYng=O!*=E<UkyNgQU87>CZ1b
z<4!`1pj8JC_GBFAruAilM8bP}W2J!@Z?Nbw$cTzTseoP)G(i+3JOWmj-eyOj(+(_X
zq6EhiIJ~(Z+J$hp9@1s~wfLX;FjkA}8IhAXzkUg8smAd5eD-S*v_J+Vj|CW^L0`oK
zG`&&tX70K<kTdBW+B*?66j^|-7^5Ldki0Ir7&20S82?XZW^`DsIP!HE>DQomWd*I9
z-H1aWd9>&qcyX*=lw5?U%o@6Md$dW6WIzb>jZh2{XK(C}NTjix=gBa~cu)z^n;@>m
zoqbxl`4X6unD1t@G6tLmqVg1w@SMGJK_swz!&435#S-M-awOsz)n?yOXpQs2<E6tX
z8>w(b1ro^^S^<j2u#PZxK}85(n1t!ewa6cl<CT&sKL$b1qyjQe3;N;4YWE9A#-LkY
z7qv==DUu|+mz$H`)dVQ^K5-fn#|pvzB!<#5RL2bg37JMACNo&h?;@52B*qgq2FFY9
zUPJm)z3#}DgNZ&u*9lVd%F}(A(<oleK<F8h1TQkQkZ699wI@d!u9v)@lRg;v2|WZh
zWuKIX^=TRgJs!drRU{rG2h$dY7~R8w%D{_&Nr>1h7vpUwFi>!wnE0vQKl3S}hv)|<
zYk%|oXIO*pG$;KVHDnD;Ipy4kH9<7+bEMf9v8$(RYCou{Pbom)3mg<g2@sLJ6(!8J
zo4m!jAc?G}vk=FI^N^qWZa*PQg(X~z_re$^=Dveh@_pC=`70HT_LpN2$-%A9N?WnZ
z=$1XyT((0HDu#AeLSxpQRu)hs-IWKo6`?>$sgVmZ4)a3<V8VXD!a4%c`cb-ngvg^o
za026`mDMoXT~OMW{QM$&(-^(3S^<9M;ehq9L^s1>`gLMrwEHmB$sk}5NoNhIU!Y(*
zp}|c!XV}0W7~$P>QWS9VaoFty0ILu&+!{n798pH#bcEC}8p!p`j}V2M{Ol_L6rTOG
z1U*bx&yb{x8(Lrt85#F-xIHX~miurJ3j}9xd|XY>*0%g>+=E~n^LdUO$p)@E%vdfc
zKg6+z=^N7?a+Oeswim8ZQI8*jB=hXIr6;|Yq4MP!NrX{L{lUfBrC)d2ouB2Q7T@S-
zxZ0(QXq{b$;xgzv@GkmS4k5}6(WXHt=~QT;B^z||#90U+`eRc7cgcQPfE8d08XRbu
z3xdH5$d|_U(Mku9ik2qo#Rq{)ehG4dwvg@Xmk(AVD~Ny>uW;8N3f{-cYA)Rhkn|d%
zQWSyqqAwc00??XZ{CMgQljrS6L-wVP4ojf@I=;d~+eWj8K==>@jRXtxL+}W|?Cc?8
zEsO$%G$8Ip6<)S^2B0P%nyq$$kXK3Qqk0_!*?&UV>61$c$&_zXTO-Gk5PvhfYKCTF
z`H;S>UrTC0yvkdVfR5z+_;kIGur~wL%7jx#1B)!U4CQl9<8kZpKJWT*f90=gzoJeJ
zsFmyf&d5aw=uNK?k&?z-LCDgULW@!vRC-!2g9yW*H1MJ-5(vZWPmy?nk4g-!QIwlP
z`b(D>CC$A0MQ`{NDaQ$P)h3T#IIzrpw8EflWqOlVU6?BkGryQ)c|V+%9rO^EJ3t;j
zVx0673OG@3|6Z8?w;bAjbc%vRQ86)8LMd8H3Ep*ma)%udT=(l>Oaov8yt#BDfO++8
zg!JD}pv~v%6bWBNiI@~w{jz#czH=Xck^J-@DY#*DLZo+#<Y$u3E+oH8qHF`O`Ku(3
zB@bpJ1q2^~guXrsxB0hsbBP9r^4?0LhPO+KgJx{|uWel6iGCg>X+~X0jc;}XUHV^r
z(Sjw{;zM8pv}bGfKf)+X1XjOt%yZVQgeVAxsU{LZ;-;s)NRlPY!{TTSQS(TE632NW
zSe@EJV(<1AOZm$db=$eHh!SEma3S&VNd2)v3>q+D7^sv168U|($hg5WV6z8>fjtn0
z2Ifa~Y@u{=;WB=-59q(l!zj@v8BvjX(*V1oj)7f0dYTlStc-IX=ekCNL@XcE3?E2?
zp9k{3l-HTCUMf=ftKT;DOhbaayHm?bCas1Bzm-@2n0@f9{Cv*&?2-8SXef$<%}8g+
zQfRxq2UuvzMFX#uQ_fbWxE?rJF4GR5sIN+d7&eI!VJ8Bc9=j4rjYKJk?;kEgSu@Bx
zLK8>3<J>Vyfyb*2m_~`6|7i#$!Tj-LSJT%^q<4W?s4YY$nJ9E@J=qJ9V-QS1ZY)F=
z5Cm(`+Heop+_t?c!Ii+rqLO(nfd++%?!;OhD}>%jFy_Dy1J<_TqyX*%m6_A8p$WQ&
zA_W2h8_?TGW=vrMkwM=Q2tFGJ7A~dAIMP+X6T!ul!wyIBl~0zfif383z76fg2++e0
za=_p~Ab@>gXTKl%oG4Ts_c`+-$?nDJ0?bGKj4Y7K@XO0ai^=z_DLkN!sa298!ti}_
z#QG?V|1-@W1nfk5{AEsoR-30%{lTQ(tE!Uu(<yaI>li!ZfCmeT<7?gSf5wNPgR)^B
z$}<>MU>9Wi-*&q7kN2hMHj-Crx%eWMV1+dQI}!nm7{#luva6%gbh1P4tmdUfIR>-9
zMO44eee&$MNAOfu5sK~qt~^c$wTxQPNmuk0RmZA*Hp*y%E0tuKRY~-j<jqXS+5G=^
z_&;Cb2Qs!``PsO+UX(+#_MqNZkj;o*U{&uw_P_SEBkE^1R;h9tr3ce{$Nenih+J({
zJ3Ile{8AAs*J+jaVD{J%#@o(~d-3nWPlcp@Iv0my3YBK);VM1|VrkDzrl~ZGO$$wo
zE3t&j#1E3eqls((zN9Y(WUc)Ut%?6qEX%#X#lNi4nk`|`_n27zD`BB}ry7Z@=by3s
z*Dyhuc=_kH3^`2ECi3hIEG$}wBXz3~#lV)g{sI$_f2aW(*j+=Go6T6!SL5k<$3c*J
z!i(DBHnFea+ts;L!v8Sc_e=T9E&hkx-NcOTFpSiLC>5?r_mfs~j%wA6Mx{yQs<$Rs
z4bOLpFUDwjUBFwHhK7bRTa(Nh9(N>4+EruL|1wPXHE}nY>GPat{ep~!w?Hc5iseQ+
z<)kIWL@{GFgHdgP=Hcr3=~805&*E_0KA*!r%!dRamaLNu@$T@N_^bm_)aTC0Wf#{C
zt*8y5`<muUjyrq_0LEUR%Vg^C+EJ}s!gbQWF4R=C_#}n;>}H=UsFnr$-@lEc3USw}
zo2q^Prk-;?U`DhOHF1|3I_9szN~CR^#KZn_s@UnO`+Y<8yN3s}BRy}OO<yv972gWi
zQK#nHauXi#Wt@XUr`bHt`Cc(&pJnUqkt(Eh&zERPH7&DkF{#txvyx`;?wU~Xc%KVF
zQbw87_Ixl?Z=?OfA9u)C`a16>^7}a8AJOR!4fjij7$Y;eAH<k55|5m^JUwhvY;Tas
z-+UfmX5abYY7(QW$x>#0pTzt8bFvE~2!r%}Co}XLU71Z4y%VI7*?1dSGclhV+i-Py
z=Ndi9CK3{#-3>tq{V(Do*VpouU+iy{2ZdO>ueF3o+3zB$(*N-O(;7*xi_hPLTKD`)
z{)b`Mm6Ni1kFR``QKK@gwBw!)gK>ugd7^YSF<hbQDSRPs_1}Xn7f+Y*Ry_QX{n6({
z5-l#y(}Qh}zuUImO2+;};df`em;25tR|bVKumbu0XwvZ1oo!svL?HV=0^bD+W{H@=
zpiHd6_fs`mxN|vZ<MfC}2}wNCM`si`GPoTg%g<&#ed|ovz`K>|N!!MC;25#72xY6r
zsI_IQt;fil$ZMuyd&ykAloN}h<K>p@qeeNEN7P56yNFfQ>GaGl%gcR%QDuY-Jw<{w
z6#Eamc83P}^@T^MXw{`*R+KRvF5&JevzYLinQlf}4@y*UlmT?m?{6ocL!BY9b$qPG
z((HPKpP$XVES0;?3igne68Y<|XL8=Jzgla`=DZfEAk$|E1Ogwk^XZaoHDFiE-8-&7
zTi(VT;~P<Y=N>M7j;^gS+bk`$v&R{>I3Le*-p}6Fdrj)&d%1FWLqWQdS2sRY+Rs;Z
z>Dnx5Q`Wq%pB=F`T9)hW<OJNC?!2|swEwL8gmj*J;`2N1z+5(nj`96A7TvR@_VsZ5
zs2?b5v(&!0yJMi!%jVaUDpn#(p*NXYg4(kfWB-pL{*Rh^;w1BXK0mBbJW-))yw{}D
zlb>wg%VgMMe;#Yjc3i4bx@dPkxzQ@7>pECBk*1(;7pt;I?rBsz3f13TS^GvJ@BE=Z
zmFoB%>Hgv6`z?#`lX|O4Rd`%ANel7C2m}4(m;=1*rfwD<#mPPwmAeY4iVhbfxygY0
zH!Vt6dQCBhb4YIy_QoXLpH<6jYLL<g)Rk1QA@!%+Y$c^do{lKlaZZf({|Mt%t;qHn
z>tpNVmd6En^ThBUZ;wU7xvuozE*{s1%q0=qWuGS#rhLAtM5%c6DO##38nGshdMRqF
z23$uQx5g&XYte}ldiPWBYfywqO0D<V0}(`SPG-(OrvI!I`ge~w)>KBHoF&zf@?Dg(
zPW28TV_ZzY*K9Q&zVf8Mj43Zkj!AhuK4{p*tSh{k<F;*ScDCvNmMN+q`?!sM&i*V#
zv%$9+U->b4T*O1iJtgrymGi&<IF*zbIe8)bmsQQGJ0ZH+KxxSst#w6}-pNMUa!_h2
zDpNFP9^XCW1U1jyb}!9g(bftFkl*ULA(JJZ@-e}6|7s1b{-QIJPu4Oe?bixN?ReZb
z9h%;Rqg>a_J`1%Ul5_%+N80;`;%$Vl;G+;^FFt#_fzPxw{7(oSCayi{-C9PAiIRJH
zF4?HW6)RU(RZDp7PN?n=Kvt6fe&G{YH=ax6>f1o(ay7fh{ljq@^IFZq2&on5>9zA~
z+ndil+(OMnYQoU3y?tyg0uVOyF(^X<RyVeoiLAubCKpH9x#J=4EM#q$$__K{%;-?z
z-oMyTEX5N!k~MpFYK&bb&;fv<C-2(>Y^(6!$pQ<D^B)>pTF1lX?-T205E|KTf309C
z{%h}vgdvtUOv(iAWvMX_>tn2L6^~5DplWRNV!A4%(++KDAFo<|ci*{5tWP;Iz!cGJ
z)G(XOmcC|x^buza3jc*AB7#1O6!+lK%0ONqEdBPnc3E7+DK?>v!6{utv_+yFVk4o~
zxIMc<U9W@%A^)@1l6&)!D91)P)?DHrqqS`m;zz4iQBD6y)i)m5I=AFJe|dUx|GeO0
z)rgrPl2b7BIP(-P+FNPjOwRTGbF=Q|M2iD@DeScfv4z(Li7<4O-S?~B(&=ydto_O}
zS2ieZDBksEPau2Q>h|F&!k>qShe?y)g?oz=re^<xin@ufT>br4fl=oNeG2Vpn0*{X
z3umpN64^{cXa%l*W$&vZ_KeHo-OoI$gFYX5lfs_2b-f72f9rYHLW(!9?T?q64Zk`N
zpWcPgxlZO~#jMxw59W;k1=;>8VXxI@5&Dd!p*TrON}e><?9ELan*96L#{dw|wmbjj
zlRX7qThpl&ZYvSUJ6*<mv<30X(gk^KmWM;qqkb-AUd{Y*$6}t`?cZu!aC~;Oe^!SE
z@=Vv7&mZF)Io1W@bWCTs#HD9wmOqk<Yj{EtV?KEISKHc3ON$2toHj^rB8&Zi|DZts
z7*w#lqmW~cB10p>+LHpz6D!mXXc_r(rX6AWM0qX53~eGfLp@32JI2Z<&W^b;+`6q_
zPTRQ*df1`fP?@m<QJu@*-yik9tUem!lsuGPn2WE+AO3KF#J49t3#6V6^<)**=en(v
zWfe_S()?dAL;5KnmzVhXV@pvvP7~1ZDQnOnT5YO)H5SlY^>?nUSXH`C_p?QwTldJV
zPTstaPC=zc0smDyOyap2&D9!%rX1{^zn^z1t$bsqmNpg^_Q?Gfj-&t98X7Jjc)%pD
zTw4Ds-*_KhQVs4_d^x|wSYU5-6Q)%cpX=Nu8XoHjzdFFyy|{YK&|Q5M=h6C$?DMDO
zx}%s+z~hf?!NQm)33_<CXu@o`%aZF;M^u}u<nPMY--K>RYD1D=nyHf~Q0Es`KbIJt
z=3I&U-||-bXv{_u(Ryi=bo-5fG9_$lyCZ&|frjT`61~giCd-~>PVx29GHIf<B;6WU
zjqQr$z{KD3Y5dvX5rp|cuz3H2&bo)-K^y((91YHx#f|D6_;xNb27RB4_uFhFI%(*h
z<>YT1rh*PQWYm+{O?Q%pc!i6&1<qP+sj%F=q5T?<+Ju#qIlJ<$x~WN)O!PK#5GXuv
z*Zcn@Egdo6(j078+98s`aLXZebYww0ta}I0s+mfvsMttQJC2?7218Y^R@A?k4kh!n
zE%T>U{+3C}i}||pMT8J8gh%Xe{%zvff1L*Z7#=WmTf3=t+mZ2VTCU5FfqK;~kFPRz
z$H=L6p!nE$!M$0@{4b@GmblIA-(5`&$@d*0Yi6qa>#@y6AF;Nxkw92$I@zkrPS1X`
zEdJ+qY9rDrLve+(C$6WAlhasMhjk4l$`h8VcfxyUq;Eic_r-nOkw97X4%w(sqy+oB
zeQNZ+zv^8!Zl~{Loh@;D6!KZA7~1DV0X8%4V${|Df|f6~POx#xahY^$(s^`x<lHkd
z%B+oseO#~9H5mL>QP4FnXLZkZn5}D)>U+Hwqp%WL{x8scS|(xSI!{+nqAYDr_4M3Z
zzQM#lRo1F+<Ws#AA9-M?Y^a+bmB2UquS@g=!tF?NJ0FI9el;Fw+O{n+Fhc9aD>kJT
zaC&l@i(De$%ZVb`{-fXj1Kbmtk|&mLgjm|@2Kd3FvWdqu$W9JS_x?rc`Y1Ry5O0?L
zczcmts_;vh513?1d)>WkaVOTM1Wg)KrJ-76eOv#!6*>A4%Qh1{<CcpEFjifMNz^6<
zNvKFnecwy>_<JPM^6~$R|F`=D1@R3!n&Hu_R2#Eulj}p(iU>BMw?n_>kw_AjSZDq(
zcdRc01WFxNKV+OE&B%j5&UZ?o1CpmDRTd2^4boO)_M5IXg6qFq^2$fuF2J;ERfP^?
zq#2~wVmc{}=pIwBB%L5ghiQBsTRxC?l4c72ug*b+2c-;kC|65KW2*=qpWL1^dEeJa
zJIo~|LCy+PI9&R7XHfo1K)==5qOPk_%0WJv+$>n8YhIl}DaSi(`Sj=L;A*Y&dDR>X
z_R0S>V@JeKR;M;~OFgvj@A0)!p=Z`)s_s1#^@GRr^Y38Hqna?TliSyE!w0*?pdIUf
zX^66|%y6$!rhBg--uQ?|Gp^}kh2X*+Vo|TTb_jC5nQh~XoRiJ`UvT;##Ku?&^W@pq
zcK`b~PLq#8FkSJGmyZS^%`7@L(b|L$Bhri*B$)ll(tn}He+VCf4BA!DT+uxAmBqc7
z+OZ;&gpMwz&Z~U!cfh><9zJ41nQL<9RJoe>|1cLLY4_O3czhe(*wQa9!dffP2w;@S
zWaZ?J@?vX-fNH}E=3?LBxq)(j%|DR+KZsJ0q?;9oX1qCNfxAg_^bnRsKJk4OaGPct
zjjeF0WU*$2!t!6Y?kf)9hN|z4H&==LcvGfU=#i<jiR`7*;X~Bc`<80VZdFefIiZhK
z!tL4re>Q^+s*$J4AW?>$pLt@zf}B-5!&mF^r;@gLwcT2^F0}dE|KbpR$NOZ4v2Yaz
z*|NT_%vyWPc!FPCf7;p>BF4|&N&VT*J6XCB?EV!&Mi>NmuoKw&rit2)jduD0^3#R(
zj<c|X*f`xrvW%r74^BBP!gn@WJe=bHHD?*7)HRQ>rxvNiLo1weB)R^yG-hJ?Y%#ii
zcm9YjQHki!bdE(6OZxu{P#}Pnd;>M@>9ir@`EsBQ-T#c^kI9)NEU+>0MR%$d-eJ`$
zRr~Fj|H~zTf;eDSr@L&_x`{W}AIvmsKv<xh83E05mQPMjnM0+|1Hs#&vbw6H!T+1m
zT_#Fwm#J1gb@Jf)>fKK7Jnx!Fs7JP&$@}Z#{Nno6YeQ#a<3l$GdGG%UV3gkrQoRSc
zl=pxTVSLqME$q#>Mz!8v8FpD1k&P&V@Bfp&UDWM)hG+Qp5`sQ8y5*24dni2qg)^tY
zL1xcE`d6;j+vUm2bMs#o7Qarferei<J}$dk!P!b1DTR8ox#bGagvT-p`HCx9TN?!h
zF_ueU?^{`M#YM)YA_`|Vm;LRZzipmh66^o|Cw>zdHk^v>Ta`lr4FS`^lcpXgyswgx
zQDM`vf}R#p*+%fu2TxZx)TDUTf1rmEhu5F?TA-ZdNV~NxUEJzCwZR9_aM!kCrMbGe
z$=>ZNs{$dl1kc;F_oP~_qh{P#!y6=iDb>h`lQ|=5xqbBP^yV6Yn_?2R&$R@~`6D@9
zwnAwSO+1QFz~^jP!5&F`Fg&tffh#KT5DxHi%JBs((T4Q8tUsAX-lGmIkiK$Am733O
zk;xu7Y-Ak!qtaPllXF5TgA_ztt~hKo<H{X$Klk>=aQv5QM9x?K;5kvy@sixPFEo#5
z^8JNh3t<O5xms!N2rk+QqZSPos9xCboosZH+Rx`#4LKU&#LlR9&g_I`u-?DCgA-S5
z{Bb%wON;58cUeF2Qf79OUMj8=!aA8%tz@XSz|9W`^kWf0LLF(pFG2v>T}<J)FS%Zv
zrW&Z)&|MQ?o4xPMZ={ZHxlX{kh}i^NwG5w^V7BO%Km+bb4lwl^7jmNw(bIB1Jh><a
z@S~>TFHPaL-L2_Kr|~(@H)cTJ%_M|uKS7NrU||FOH9M09?(^&FX4)4C8Wu)1D<oMM
zI4cDBZBREF&)4uc_)Qj!RChL^a!eOA)tDT{*vCAaB^>pSBB5`HZq*m>f}W0rz^Esw
z;Xmt1LnA&KCaID6wNm!#D($a_9)VIviJqTEN>~IXL}r6VCmvuB2nq-9q1iOa8@{!w
zmk>`Hut)1z2}#YSy0fv&JV#rv6TFYtMpWZu)I=IqiFh%Zt#Pd)QB_+$kMVTLY^!Uy
zNdU{<#9(9$vv_WAeXUGhTN4-4XwOlqk#$vJovfEQl5*1$Vv?TQ;mLB|Kj}$Y?xjmh
zn?H3N7L%PL6bGvri0-hs)$0TO9GO?i$FCBVn~&NsW1Nv~PF*v8e$GLc%4qAVoRUSD
z{oQ72=W?y|<W-BJ0;ido<QjAq_YtK}c^5~^*j0waF>oMqK(_YcQDKe=jq#mYtRhMY
z(Qdn^Ikmj;(W|S^qh&Z9yp^AkoFRcB*z$AdWhBC`+Dxi4n>g8*?IasuZhGmn$G~wk
z`=GuZ2hEz%9_lnnp>L*DlkEH4WHJjUW-}dpqg<oWS^QzTE^H6GM;YBz*(XfWSb04%
zf-*Qn{y-_8YBzcJVK3b3LLu9gs_V6km)|73<1WN%t!10_T)iL;wVzcZSizCo4Gu#l
z0@%8qE<mQuL?toN$B=%WtTmJSmdcozYCbAHiC^`_uQtZ)sD|%RYqr|GvUv_et`QXO
zl|k|ovTE+=U`r43vys6uywB6^!1Acq443u9_=i827*PmKkvqtlSn_K1Xma@0TnWW3
zyzUFn+P}x%MenpCr_-b0CP!$puYMmY)EDc^AE%4#J*Hg1F%tEUKVawBg?v9?T7LrW
z#r&`jkIynn;tmx6bNv8^SJwMub8$mN@a)EFe3_k-q-mqlxK76Z()|YN?WF%4O^6=d
zeT|Shbm-lSA(IH6VC*wrQk<CdaN8Bi!sSW8N4C57bpvIjBP%T18WG(9tr_3jv!B9O
z9@Yd6t-D*f*l&xi2ZZGEc}ta(mwdj58^yWosxpz1nlsjnJSV4`y@eFp_@t`&$f#E+
zUd<%R9)?e^IXKzG^jhk#*7{mengxrBbBi`r&CuD=t5H(;2h59DY_L34Y{|Bd#S0b|
zWi1OCE^c%eg{mcmg~dh1oQkST%LJ^oT~+o&4->d$G^3A+)p9E9vx_NB4GXraLe$46
z-qt=HV&cz=&5IsY#Wx5-)YSN{$19Rr6E6)Qm-CpSSM)keF79P2^`^LK%}Z;<kk<qo
zHx>E!;Vk7=n}jkpE1k)ZT!gJ<yiUC6g!qr=%kBJR<kpTE2i03Tf9#z#rB+|gwU$n3
z%S2xTxDuHww-wQW(kikLK^_?{(lZ3qxLU3SF3J?(Nipic8B1Pu2^zzLy8f)V^z^v6
zxa6jMZEF(q)pk-cHu5Sep#csb@00lIZ{}2TF-`?XRVoX>0-c3qGft;eJgd65C`&dr
zQMI6h1FUPil{tJRHW`|x3xyMRt%oG+Dmm#5nGW{kL0Qe3u{u>cSqPjH8<3yh#m72H
zIZGE4;_Ezq$%^lF=F8HOm)NYg>vEP<ZP3?$)mai&<VmT_;AWhSst_ojpG4Q&dq(-8
zgD!(wO9BN#PF=B2UY}PR=2;=HJvy{$2}z8+pC5cCm`ORMRM(;>cpXtq)KX3kuA>L}
zw5T1V1|JATaWVRL)N3u86Ag1+HvcB`YzV)lBTlXARGADNBK{sfb4u>DI;vjjMEy~A
zCnYX5!%uw2v3R!G&TcetcrzO%CAS487_iV_WK{n9s39`yPUuAH|D)<H!|H5yw&9jS
zvEuIT?k>fhq6?Se?(SBK7k4PG3wJLL3wL*2xI28@_wL@$`~AO;V=}pt$>f|fiBP3d
z&scpUb6%A$<cqc|8*)KDFMe6${9N+%lR<(A@K2#{D_E?o@r<Zkd$EmTB5(^rRIBO;
zfgV0T2!W4W(u$#>E&%eT;AoaPp&GUdS1o0(@N-@N)GXUHr^Z}`EE+*sge7{Yc~9Y}
z0P;C2XW$3MM8mo|=cn7t^%sCK4X<QyY^gr9l5tR-vE3**K}<r*YW@<WqCC~{(jy5d
zuBdM*PWos__6c&f&C^TrnnmV<i1|L0>|AEhzhsas2gT*f430tobUye9gxP5+)UcIu
zS^s4o92r5_wB7w=*SV3U%{Zu&QR65*EFfFxw~5;>M)0R>#3g<*UY~$OUXlZ_`}LQ~
zT-WScpj>xF2Aq1^@(r*>OZn(T;9L1zoPdWHELQ@`81Ka5vL>Lgr6e(rS|r1moTRdf
zn@i<x;kw&Zd_qt7vs(w8n>w9zYG3d&61o*>U+uh`zq_I@1sr<<b<SZ3bRZ7=&+<Ws
zJbf3o{P!Fcj+ok(a^cs>rK%;4&QGgi)u}os40Ux3)8<{$)~ehZ?$9c2tRgimAv-&|
zX5ORllY2|`(-oQT`vD@LL|I2KU0BZ+r{Oeh?ZkV*B2a;&DHhg##Kg!JuOoUPbXPf}
zstHP%&jT+W+*OU*A_pb(@FhE9m-07Fr47XXyGO9<UODRUKA&pNJ^T+~qAh5+GWR+3
z`m#vP{WlwKyzj7Nd9OLgS`oAO6c7UAQSCH`6{>`;Ncks{l3P}{eS#{S%|N0yRIv{O
zNdg}Q4d{oWLP+V%o=kmQs#>lVW!#mnx<WyC<s2{}VYoff^6QLaBEh8#r|O<*OY=9i
z!=v))vfn%=&|2EdUSuCbsL4$LrJn2Q`>wmH^+eRuUp8WO)zzsP<}*VZYQob)j&ErT
zlzr<hErDKY12pC8@}l!Ct)&8vXV?o#gM_1M`%dG5FbH~HnT)?rp@3>F7ErC)baZ+$
z-#gQyU=R@`x6|}ykB@FRii=2ai}%2B2957gRDH*sndA0Ty;YIRo|;-ok<wSlt`9Jd
zv`FZsW96}u%0XbU#WdN1+_BOt4lJv=-qvZ1ibE*qQVioPOZ`gcu0Yz7-L+wzDK-~3
z>!gnSCsX4W3n|hTvHFOyIQq*LmqcZ%jqQ(p<^rVU!I@qd2gYMG6{W{Vp0UG@k%m-S
zR<I~o)jWrUh91S=R`h1GT^8o`a<u$gkA<Cza3gcSq?4>rxJyM%UBl1jt+be@dYAyG
z&OXb8@hjh)5!(~iQ9SWlRLuC(6;-tB-n8DzkVnk^<|<R)A1i7U1IGRg@}q`0G5lo8
z&YpWo<UbWA9x2}nKK7a(>v)s6xl638C2Uf{*+XX;xmzv#a<v-4G}88vixE*I&M^WE
zP|zs&{9CF*^XuUlJS7NOGzzS@$p{k(?692WtupTT%wg~8D4U=XVcith6@T=>3#aw!
zHaydMj#CsNlU>t`yQ0TflhF&Nc=|{7Y3X(?gil>N@A3)FLRr6P;$&i~>;Q=VOX<>b
zg=BwuV0)D<kajYYr~IcUHiC>rrK2ngdf4gbEC|&vmSxSZ>Ul{iiUJEwvWUoMbRZ_B
zp-ebP#f}y}Shrj~gWl`e!>2E9zS_DZ8~OVf$>&V^NV-d1q5C(BDUfwjHdr}uBsTsx
z2;sPa^+#+LYTzVLrar_|9KS$?`RFIe7JeVO?t~bfFeOWVomFC;Pjq*`2y58tzbc6;
z+W!7{y=$kQjx@udU@m(goAm26BXUK?zXA~%u(`+j!`%X#weSoBR1e?~X|B9H#N&W<
z1LMJ11jh!T?j3qSZ=9Af(r-(Y-U)x?_6%kll??z7+sx&4YOg!7189xP#fzi!>{t`l
zfL`z`w*7g7?PMa_%BiG$EF&<klPrl&Ci?)G70<Q?M;CqD4QVg+knbpK^kna)cImkt
znlmElbm(?5;MJDFk4BUPG9U1NXK;={FD2^ew6q*e5t|XfeYp$JI<Tp*cj{D@EV{PH
zBkh;aCUeWGP*-!(QgrkE*b`P1w+!VFT5Mt)0nzw)D}P1&2ru7|1z=C6UagrCaHzzU
zhG+~^@)Q%!e?43&`<(KjT@i~#JIeCie#;FdSLwc(GZ=Q0qCzfZRU8Ua+=f^L4~ye*
ziZ&lH-%%6C(!7W-n%l-Wi-^&5{3KKBMa(@7>Xz(dUi2Yr3%3One@1XJSKh%5W4)JE
zfh88stAQRlFc;e+Dm;|dq3-csM=)FWn_kp!z9_F;@i&Y<yt(pk)^6wTBLXe4c#n|&
z2<n;vjmEB~fUrJBt6dEL89XGg>*#_QI+*-Z<RcAW_|9GG@lp=}$uNuqX*^5P`_ebM
z)9x|>C8_Wpx6`w;E~e7bIJX`x(?ns-#?r4VQE7%oUNBL2yFyjOd@qq3DvQ!sK0ju9
zLf1X2t<!g#-OGPbHWMyt)>tdlwBSZ5YtQkk$NRm>8<Ee}#4$>R+!%qFqMVFXGdS&{
zGm*)w6v&pF{Lza%T?evVwKjX!3`&l_boBh$XmVmSR-&}IT}Z%C%xE0R^Ps!z(fu0k
z+qoSV((KgVUFkWvq@mJI6vwuG(NfM<Nk>OV<2Axos?SK*v<KY!#0+k+Z>hM{I4d0l
zxC)wT?q_c%WPHS}Ke*~<1hKuz<+ipIG6eVx{Wuzn>4wZj-X&AtntUAOVEq2{^oh=G
zE1A&r!@}iAWm<XkdgV%@$8yFwf{jh<0Fy?twnuFztlPP~|Ju#cQIxz*&IjT?9xEO$
z{N;|PCr;2)QDz-#*>%`@gXYa!;?Y>ElT0ygpjg#r+}6#_1zh*RtU&C4djSMzDf!E!
zv<E-6*_4^aIF_&7t}#5abSeTrL6gYINE@M-(v+P^HEj>A?I+!9j1v0Y&`zl(f}`0O
zn^-JHGECmfC)s<#SNKTlDJaa4lXctbpF8nz5m^*VeRr}RAa*k%)oknIbha9wI|moz
z-1c|VM5RWT@O%DT5Gn0GUKde9PQ~jU+WG?@Bj%^X34PMKu#030Ga7xbmg=qM=^8yx
zDBHWs^J2J*R;oxdHyR3B(|BEd><Wqu`osp+zE4n$uP-k19L=6x()I;%6kPKdru}XS
zMWh`3-F;J$2;$OMeB)>0nP}o$dxaOv{A1ScP-OAQXodVTpyTkV!AJCzX(GooW+~)$
zVf$^`upWb9RsFu8S?6<^3Mc!*s!UO^*L1w%G>SrdSWYQy^uHcOrECR@hMr11aobJ3
zyH&M|3zQr-@7aqjT-E+GTkm>Pc<I?d9f<=1&L6ovDlJ&Bj({mqlw_vMVkJtY2zKtZ
zYAV!L^BxmvC-{1xTcV4Dgq9;9V7dlut&}FQJ)`batcsH@g8+?>J(rfU$M`IckfA1g
zEnd7au7^yrH&s)kw2h=cx5lt5dBqOlX9@1E@n}TZ77FUOBw|!yTNq{atv+`P7r~Nb
zd)@5w!ym?c+`ohNN0bPDQAi1yT*V|hwvL;-iGF+${^7PX$qr~SX*YR5>J+#0X~)RJ
z>(4v$*cnbx1Goqt##Ll)JSth$9|aUxHP~p!)kLY?LGRyK%*F)h+*(nP<azpC?sy>K
zm-0Ftt`xL3l&3ZC&bFT@j^V(Fy~&ZxSJcL#uI~}<&0Y5ag3%?ua;OUCT0iOQ1)Riw
z)E>)_Hk-VOtGsiE;8OfXbLyj3Qhx~85=3|cd><f^icsJ7S?m_gN#xsgqGJUP5I(&&
z_*nwo_8F1ba<OZInBrYVXW8bj9o&znN@CtGBZAK23k<U+jjLur%}Z<Pha(I{Gprpo
z27R^rnY~!6a*okR4yujZkGt3WkFDW{`r1{NAfxxm4aYGQ9g+esO);5~z0GK7&#hc*
z-ZyDe-Qe4?rjxMRWVf%$_K(d;H*k$QpLF~TUVQg^KibgbOMfx=O<L#;ehyRlj2zYM
zDc@i<|Km)sk?eIb?O0^~L*UOga|Z`YWcTJh1`vmAJULitQ>y9>VZ?Q(zO<A}BL3*y
z&?=Ufj%0(EVL#*Pl7Cq}kui-u{BUCJ&EHT`A3k_QzteSW1VJ6q$#lITauJl*(Dea?
z0I<`UdEVf6c{}0PJ@mL86!aJ|VoGD_e>f$6RBRRy^643-@@;t<G2_bL-;dI11|OO=
zbaY0~zNZG35SO{&X}59U>G<`FSZ@u!(L&ga7DVpuehQ@FOb(XP5Baj%lhL<|(liAh
z0zvoF2e-K0T)d7=tw!fR@<()BI?r#M=Ogxgk>9?FrlP9f386@o1`v>U7rU2p7F(WT
zD_Hhoxtzzu3E;lB<jR}7|1eA<ZV;Jh5gm}?mUmyIpNvXYF8HZrt>C+xYa{lFIlUP}
z$GGTH!|wR8#i681r_obZkC~2?^&!Y^v7ufz)!$-!hEx2gdYE!3&+FDavWyMUscQ4j
z*(~(oQDbG(L)a$~4DbMS<I3wg9ftEp8RJ2jaCRcI*;iiWGQDXx=j+|Qpk-A6w%e=D
zk8T8^*b_i6m#yL`HC#&Cb+={xuO^CJ+xkrC`D(MP!k1~HXC)V1sBt17$FlGII5kw-
z_iIcGB)J@~K{XZztMR?4Gd<UVv9&qW8+U6^<sGzYqAG(>Y0=|NpIMg6=Wi!V=e?)I
zx&wQ43H!K@huBhe9Gkq@Nt6P6xevDB%ExhKa~e4aq0(Ge;B#2}KF~xGoIwSlb!&$D
z3-z1ecM8D=(;ZwSV}z$Loo3it^>gn!=h56<ZS{3mO^wf|b;_sK#bsqy=H|s#+%w-Y
zk4&it7nki1US+$>S(`IdFwgBBK$qfljRlpuu4k)<a}6AHjrr^QbFx$~3y5uzF-NVd
zarVZ)+n(ksUQV8&v#tqBhb2-6I^N<wl(QpO*f3$tYaBZDHv(203uof}HV^@E`NeZv
zes2gH`m>Fq9_xD;cy}(!yPv!y>l$|MBy7KYg2#w-c-_`LO2J{&t(u{261b+k#vYw2
zvL!ABe`-&yvdld#D-6|~`Hk$0rW13zQbGi{xcVJ==y_XO)xLGBW-EU*HcIGTtKWK}
zjb~XNcNsc}k&+E{uIdqJlTM6ld}>oXtlDU$zm@|{_4Z`>g`sSvQfl@k_;S|f&+o@p
zTfNiI@ny*xQ0pyJPR#?Qn)Tkgx1w1cD3aCH;<AlXN}GB_KRjI(zyE9L77;UAB2Wl2
z#CO6DS^*m`6Xs=w;+v>03z6xY9>rQ<E3=zC5H>uJrNXZ%tNx+Ytn)zv;41nQzgby*
z6VFP!C3W+d^=|B1vWN`&`WnkHKpuxFn3*n<Rs$oLFV2(D?^T0*boDr+Kd)Wh>|cF6
z6gl(9hxY`iZ`34Ta>B7I=e=hBVGBge^7W)hJhirzXW7c*i0e#YT=H&|Hg9t!%?(Oz
z*p#hz2)gVA#@+WiohgsIHI{pPj~QE6eGIZu_q^C2Yb9qJ7$tUk2?yTT*gR!DU2uM}
zSd4Ld-W@q2u3TikTACGKZnRrXVR0H-Mgj@qXm^~7Gn|#+UNjy*G$t0Dw%LJsY>$o#
z!hbH_mmek3siZv!@O(b*vWvi>T3GgYIJpz4GW|1yf!By>qld@kGPJthLi6X6qZai|
zJ=tpQlV#(0!>+!Degz!rdOf(qj9Hv*&+kERK|X83F2h3b9ye%TdsK#63Mw52D_}2Y
z4yvv}k^d@w=~2LnsS_OvsiUg4u!OARC?slpcB)%IAMjKg7~xYcaJ<r3=^TIjOVvA3
zrDvsLer;-lGfAk$F<I>ym_F7)p7^!Md35%eXT81E+Xq~nXeQtdiuC0r^i>fu{xwV4
zM^&Y@3G@hH*r<HTHqrI%0g5pyYHI2&&nQ2Y5&hn(AhM=;nV!9KNEdld#ETeqOR!bI
z=`?uVZZz5@WmWQ4l1O;7a0`}cJ1ZRI&?^g-8;T*Scf{z+&}I{OcE8+5$S6O*9Di@0
zP$-wmYBbzSGkp`u@bFB7HgMe|T8*=<Idn;mhdLckzh9r)9DH@EZGhG>K#y2-#QZJx
zag`so)3r*iz*0be?dkHp!$-#v7wRMtb*EbW=ATQ-!dk#e&_&~zygCFnnM~PRzO5Zt
zmPl8{XjiD|m-*Ty9RuB(ZtZ@UpUP9PzSm~at{?YRw$qApMzhg0ryY|?=T!k6RgwP7
zB21TQ3uyy|-N{^2Z*L@!UmDIya8&`X0Ns!V90*MT=4sm1x@um`33IKDgi#2ji=uMk
zIKr&D%g}j!bWAHwVHAiYKrGj_m=Lj378P@bc=T#f(H^qgs&JM<TJe>RkDp)f#m2H@
zT5T*k2uNg~>*;(`^RSU^1$PHD*f{yDrn&cR&qDofHE3H3&PZ@%$OBby04i5IDuF;>
zP+DRn$5DsMjHjOJ3r6J*of?!@)^Q(Oc`)cSFeK6ADbeSSA?O+=k)X)#ql~_<d^+lz
zmD;ki@kW2HzNX$=7vFsqT26g27v-al7(|rM;kaLYrR(_IB1q}?b!zr;PJ^@eMTG$S
z&I#IkKJHT<l#?c}zhodUL`7q9ypT`k!gL0o)rhSmqthE}`Q9lO5w)5E+N5;dKz70b
zmTWwi6Cj|ar`>5ZP1<hwWY#&gE#3Q1!V^j|VS0m*G8y?3D3LoiNGMgWP}CJ4Bsvo@
zJf>04$JcK-+IH8V2y8nEOl;J>PF!YeaT^Muv+baEx`1+9Wp<eq`tr)$dLyiv#3x6k
z=(<VL?(<{dPz|b-LHjTj3=F+~-q5QMg-ZRy-y0v72x0`>GJ*&VFiZ*n?2t{@G!rdc
z%Xw@ECv=)nYL-@^OjVr;{8<^Smv!n`WnWh=jcHoPen2d4OlboO8}cmnhby;IpjbbX
zvR=`a-kj<ug2x`Vi7s7;1y)C?W7TlzW6T|kMQ+<^BJ`DXOH9Rwn>UnV%N|)6buFec
zh`blcsPyG`SZkd?rRgR}3ZAZr`Ns`O?W!0b8MDHDYiO^Aeoj-|qL*d13|DXJY^f=$
z=NxbRBHs-~AB|Vh!Bu5pt8AjzpZ|%f3!y&SH~ejJEHNule`HT(U9W^Y^OuUF{|UE=
zQtJKRJiblQsD6}2WcL>3zIevD{i%?NQAFwtGFDd?MVSVAS%TH16%Oxa`h@vOUncO|
zdAOZRs;x-Ey2|;s{OJebik0xsoLTmOd>zlPPLRK6#wb={j3W~10Cul%wrz14Nn$ln
z#l-eUhr>*<?TW{Wg{<ijwLzDwivhebkk<JP=nF0fh|97InyBT1?7vEhU=*!PqIak8
zwjeBgIG*wXE1^}9po{Vq+QmrtAg625nKeT!k;r_=C+b(4XQ2ZPTBbnVd@g39%rwOq
z%ONYNeWm(tgo6&y#+UKBI{SR+<faj|OUohJw_T(!vbQ7Nt`mU0l9%aW4@cU&(qw`<
zm3sej;r(C@il*8tIHbBqQm|dnv{vfL@SOJMfwNjO<058J9P}Z|pA~Brr05T+4<@ej
zP9_+QlGL(ur|XEE$6|I2UmcXvRA2@*Lp&9A6!c)VO?QDf{)6fS{NO4Jr)1%vHE61;
zaHg{*v3OL&{P4A}?@9gdonMn9yJPv3(%i`SO)Hv~#A=@aLqxeTN}oRajkY2MWWP=|
zKxN@`FiWtKiWXn|9;UkGn{(=0yz>osS$J;<=CEb?YU?^O*cR28>@k9WbP%Q_>8w?)
zKoyfAWNumE^~#kqi_EezH#OtAK$Lud&*%C*PT=EY{%dqY;hZaoC!9ylTu)*Cv<`KE
zoWi~RHTahc%-G7U_87~Mk#QPons|2xXm#x{0*`~T>Y3}8NT0iQS{mQW*o6w^kCN@X
zhI+U632bPj%#Oa_HIc3#Utpz!hQreMetkd!l?)H4XqoB9e!jeQb9T=gx^#7Rek^~b
zxOJ<mbK#|t6Y7mAP=0f6dSakhs#L94DQRj@@Y_n+AxY@$yhNd6(TZ9|g%tzp_}NIT
zFr)%!uex!S%;$1`yy$hZX}siY=IqUD(GqgT{}c{stivVbEU&w5c%6q*=S%r9hkedh
z5LE(Yo?Zr3Xl;cbkKSO53%d|tGbBQ`pCTpuIS8}HyMEiO1VPcWF!<X|<>IgWDg$V;
z4?vUS-W>Mis*C}*k6P++KVrVCKo2D2eOq2~k%T*o=5K%eb&WL3<)}%jY9>`^4(y|R
zAEIYtb#?TSynMr1x|6N$om|anbNX61%Ee^ecGT>0MO8;+Dp$jcCu=q)u(I^@`!qSW
zz~3`CmbNX(95x7RRh<-hVy2YkOFfq4FLMN8V%6d5377%_jLm8bCrLS(y<U0s8U}>{
zO7^SM<rW|B{c66s%Ypt&l!%o3dv`(+qZIwOhiDa&nR8~Cj1LfN0?U4v63+?uLZ%EB
zs|!yD-Dn0EN`uO+YBuVnr}VJLjL*~u=rcLM!so?yU97B|<T9+6P5(2=+DBWWAoO>7
z&2N-<ilW0zft7pDD`N5r@zwh{t&wh?4k89l;NH&ipvL&h{m{@bt=061a)Z<LA=$ls
z$oSF0=~g->rlh~i4WS8ed42TM=AE(RM+VvEH>_9Vos2q$S4{*cTT$<P3SD27z!`q^
zx@$Vx52@TNu6eOpwK74f*vV=19BurgvD;~*>)l&GCEuCmvRy!9lnf5F;;2izZu<dY
zl&C1*e)yudU?NDpJ5edhlkrZh2&$GO?{&G@t68d9?NN4RGF`;pQweI`bQFOhAMrR9
zHko}zD%9UbT*=EuPVclSe6uo7zp~fVY@3~Y+*<~&g%PoUyoWCLX-(U0<vWaDGbY53
zI~hzR9wku&SOCw(wGV%UX7H_#WN_Jh_sZc54>F^Oq;-XoWO!zhtX(u3^d=hcD=Jq4
z3{4$ckBSwu*{6D_h##<>2DIPizuhj!n4m;W{f~xnpF+$$=4A4`!&cpskpT1#?HSS}
zT^&jA*uftVTLhoX)mq-5r$LYY|H?8VVTwPAp+D)}uxnPDT+#E)trO&p4LJAq*Mt-7
z?`SC8pk56vnY?o?@;rO0JCBMhfo5#y24l-}GpIC21|ACh^0Ih3IcR3DD4;`|a-a=-
zQa!M`W3uD^CynI{%fCdQY;?#OtODlI6qEWV+0=Dg{mQrE&a!E@>+{L6+q_Hy$;F&e
z=rooUsxLA|4-$gZ+!o-QC+L*<_d#c7V_6Revf#E5n6dRH8nW3~Zdb!UuP`M=yx~m8
z6;jgD_<SV>LT5$KK7410HxJFdCAD@QqgF~^y@l+KXHtbcPR=?u{MKhJW&YvJc|W5}
zNTenwc`@98S%p{x7B(4$8Wx}aDA^56=AGyUd&M|V@3Z)CM9e)-1Rq{}f}tAMPR&f+
z&SJF`4v)51Xy&>jX*7{XBHSfPJx1^2r5bzN+qUMc6GXSjk-2)91^Vb??%|E?X25Wk
zGa9}IDIT7=l***u$LKT66LCCFzzdd}u#X`8!@BJnK>A2uEf-Qfw8VJV+Z*Fd868c;
z<0-afUc|=IAAWM<JJEM)UC(vgs~h;57LLKSf$g7<15e~}nd5<$*YUiEUlu(hi2oW8
zjt1D#;E_D~)D|P|5<Yz-J3p%X8btQ-%TmJ{@0ETTw71e0Iw)AT8wZRAG0c-WHfi>y
z%_>f8`!6@IEA;1w@ub#Nx~mInXf{(pIPB@QO`Erk18B*XcQ%a6Y0ACET0Ho<>*yr%
zJP{9A(&%hsDPvjM6RWPJQp-aYVs9xk{5T~6vRoMysUvgoY6oQ7do~>(M(J#;Td(F_
zdj9vVwvDMNVO6P>B}h%}-)C+pB0nt#j^)`YI!53iCE^DTcBt|O0(`Dxd+O7YGFhvc
zhY<q#`dx866&2P7OdA;ri#}H$Ob(iiv*jiWaV=aE=}~InKA9cI(KNM|mSs4Zi@4H#
zoVVo3=~PtNH0vA|22fFjmsgoHiFNnp|6W^_fzOYQ%od9soWec()6}j;hu~BTJnr+G
zDX{@?ZrT)v5zj@v=_qU6^IDP}DGM0`gUyZl%*}kVDYehs+~<0+$)weEWqcayPERiz
zp9fE|Am1e|wO@}XR!Sl6gKhGy0MX#Qs?7m#9B-G0EQV^?r~ND>DNdBXZpy3!W8hgm
z%6(%f`LZ<=%xBAcbc;v1wwLOiZU!dEvXB)pEAn|g#uIego6QdMJT|jR<Q)h2p$nX!
zxC#C&EJ3<}IvGJlYy&cW8`yXBx82pdiG^8{VcHg6{xQ5F$AuMuRCL6&;B`rtV>gk#
zsldeFmD+FVkm^L?7+yvTze0=d8D5F$WDVtQ0+w36rtL@`YpxHEK=qWuE~RT!?#+Cg
zw{(%Q@#TPiBix|3P}GAmJJ>4aDOu7LuDTK?A*w{eR|SSRWmt-zuN+>tZN+ut_r)MJ
z6PeSiXGbZIWmZIsC@?K(r;I^s(xMZGfQv2N*4Fg&TVEGiTaQ2J_n+8Qs_Z}h=ZTch
zYx#!k!!-)`@<Xqh`Z?a&$IW(d*<ll^iyWsu6QG%AN2~c(CV4l9y8Znv6s}-QPmnzH
z7Z%~Sfsr4+^zRXd{~pTgWz++ti@~j<<HVNrmqq$T2*S+!m2I01OF`q}0hX>Jz3LL)
zZ1t>~d1R>3qo1MqbtD}t(__1Pr!HhD%a8Z!(GUM+Q4uLuG2{_lu8UB0#{g;tV_2ci
z$=}Df7~$LAhh<=7_QHqt00=&v(Ak<ATroK>mxg7Pszyye>kmfiC3!we-%Ox814#0R
ziF5+ST14`Vm-GeAD};5M<GFu#<dZEuCg{%G(iCa<AknpwpGSSVC;B!QGj3pT;cfCj
zvH2tQTKz^dvYfR5&TIs*Lwk$8s%Nvg;xXLi;_ApKbTRCH#iXCEVzXTzFrwy9Q)3y~
zOm)BcadIV5x6l?zi>2-m`Z_9*62Tp)=25j<M_G}6Gjyrhk`JdCrJeX_|AVg+vYO&D
z^`na@bBf`fZBFy%gun6+84kn0R6gv_kjx`!z0B)gFTqcVhS5APk@@-43bOdIzmT($
z_aouG{f2SMI)pG*bszdFYp`2^kp)YSfiA5uLQkDH>aq`|X&LEeA^aLi1r{#^+=hEM
zi=Y|;rX-tMeyo_pVDfn~KG~#ESZ`uC&r1v5rIKtl0LNl#@IuXJK$qVRb8z<SOz;Pf
z8oJd{$??+Ht;F$)C_z!54$y0EK)vly+(7xU7e5=sbrQ#E20V@G(s4BnT^6@ps#zB_
z`a2L_yD~ih?{k?L!CWCjj^rr0-FNg&WV+1K?jaRi3gW$Lsi}#p8Q{-TT`~l5XqEiN
zw=31Wg`Pz-b)T8P=G;+t(U8Z`wya@;&PwFo3&+zyT=?|_|E*-PHsm-);8lpe1M5Ez
z1@sH=BM3p4BSP`P6%`h;$l5iVEmzIBy*%-8Pd--0$lv3Q|B^3@zI=3b?(4nmW)1bZ
zcrm$o7zn=SmUOG^S?>ug`0fr~neG88YpoficcPl=4kS2?)(Umb^4<@Ow5^bp&GD;U
zAHMKS!Dl0;%o&v(^F3oWoZYB*xC{9&aH>9P-k94QbpL$U9A44LHi*McvqF<sJNH%N
z48`OV8Bg-EZmm_%jaB8^-Jr-~Tq6HjfT)@oV`i)Ke3BYlV_Qq(%TU0&9nx+vKg&o?
z?2>S+oxHxcxD@QeAm}$E4Flget!}-I_Ctt?AN}Bq9QhVa<9?By#zU{7TlT~8kL%~Q
zSUP~f%gEf_eDz+Xpw_V*FQ`UOv14ufuvMHlX+}y}?!RnqZy1<sO>Nk|-oAcvZUVCJ
z1Vt466j_);qz;fwEKcNR<1Ayt3LPH0n72lMrUnRo?*n#aYk|5)>b6ztp>n*?H1gi~
z@dqsX<0d@rJ&LZ(C+hFF00E9XslER9aJ}gHnHF;M^*7u>j2mE<Yf*E?tZo$`e^#sN
z8V={p(OE?gQmZwbY?<<uUa0lhZcSdTu5GLr6X0CCJbL-FYH9cohy{dh)SRQ3p21hk
zSos*q`w70;st2t#Xp+GD(|PmfaPFm23s{aFpIxYAI?Vm1at1mqAgz2Sqw@pFV2J+w
zC}>rEbn3sTB#+av8fbpV3@m-}J>sq+QD{=X3XrPhnm^`2`u*e9Yz3FY8sZs?Elyy<
z|A^)a6cE-ATPsXAn6+>kETPLW?mn-2W^ysLq4lf1N@QqglEsRwL`PC%IPi9T#uvnQ
zPDAQ7UMz~Xx{FrF7Q0^WpG{6HrUq#_cs^((@74zapY?p7+V8C}<#ekZtS0yB9aNH6
zUV!PS8N;dx9nWbEsIT*-%pP(OiSy$Kxx#^T@(eJQ+Er?JRDwzkj>#s=z+Z_*va+>l
z>v*l_lW%*?4?pTs({HOtmcq|G4Y!@@HKfPqXSc5>ixxlA7x$T4%}+1i_#QR@t)|>J
zyx;bV&J(Bhg2IK9N(OY|(xf%#bTS)Uk4{&2k5nXq{70MGrsN09KelweZqaR)7;kH?
zLo%Ok6K!d?@)(-J^gT6fbV2V;N_2gtIxcOI8~GO_%rsz=R|`0kOxApN#e6m7^_{al
zVcPk3hoi)I`DD0?BK4WyE|xW;<{dv7z~wR!IfLon=~!WTsFkaKu&Sf}Qq2R9P(HrA
zMm`>pf#w2ws;HP+O@?d!DQGn5*KHhW*^h=-H_dqJqlm}yGh7k!zSTf#_MteeXzP02
zT&s}-tgUqwuC<_=01~ct)BwpePr!_#4XU@pIg&_s+mNMJRX`!9(m#+4Z@%ldBItKa
z1~eR)o~^6d=ZU8&6qQT+`+)bzcwNO4vww1!*7#1vOuPclYM-X$=-8SXUdCxZG2w7^
zo0dhOGM86>vHkiO-*;uP?^A<#z0tj>j_EE*gWvoj{z~eD*v1kPdTWq#MY+i0`E0qH
z9ZIIIqyMN0#-hkp2{$^u$-Z9hwJ&cUd9D?3#MJ~KrR=3vK}Ux?$I{UbHLM;1Qc|XF
zr9~Or9rLbe(ie9w^>MdUT=CB|MKMX2`Wp+l#bF{Zyj{Bm<}>?zkDvI>*0Wu2OYsPX
zaJc%<-J9pb-83BNS*~c{^%Vj8lVpBAPbZn3vt%C5<7YR^e8nO?lTKn%7hcOtd}xwU
z(m&Z5qJ@cYF=*M@@$(POEi6y2siHGeGMq(L+VwhxNN7aXqDM>evfArC_XYOr>mMYe
z!h}&&12`U|-^SPQKJ==sJZ#sVC(*--kpxhZ$MxpN7PmH-FXwYQNYBX5Fsg@5LRu<g
zpx!_%SW&N}-a-R;4qoIoDgr0zt|6_1muluQUvGt@*+V!3hx-F*uth(04&zQ#nR2z-
zxxHnlHntwuoH6`@j6aBU@UUAOaqGBB^|<n3yUIM9=eO+B5|f*`muVSgrG@K&nfzY;
zfMs+@9X1xz*wNv`-^1Npq8FAlsWc;C8m=~=+<@#NXv3`L>&_efLDHYB3k61kc&<vT
z-Cl5S)c&OywfOUU_h}59MzrN&&Z6P4%w4Bwn3m3n#N+dgXEY;kWgMjEN~%J54<_^P
z-^TzS9<acECk)KAjDHl*hVl4jI|C*164EQ+8WF#If5i#n*qZo=JMMsXXrB9A3#+6=
z|Hs_!Bqw?S5A)gY=rVj+atsR{iAx0C*wK9KIotAC5bt^Wf#YAfHeg1GA?)v@)GA3*
z44qVht^3z%v`#R^vFd786&405$#sZ-26x{!Wi5PhbQX_mxZ!O7wF)<+qi1)&)jta=
zB6$^=n|JJPvH=jv$LX;(Ea^Ljj`R~bLUKdke~j+Jh!BP>9ICKyl-VuR9&sMPQ6N-n
z8bAnU#T;3*paE!bV{8JfIm}WV9IOW&ZA6WXAgg2A?f^#&AW=qR#h_H`Rin&N>7wHc
zE3QYPs<h8gSqbmT+<>u%te}T1rl-_s>(5w3aEOGm`S_!@C&XS&tm&Yp89(>HK0^g1
z1uus*9a<AIxar2iqDGOCUHPZ9o}E<3H(Gw7znj-#feHRoE|mgzE6U8JuCyOR`-g^C
zb&Q!G1}O%n=PNCjwb7#PzSu>%bMAe337*-1SLcQR;6qd$?J6nsqnO@if54HqFJ6?M
zuUA{pK*Z8vHz192C;V3f|M~kVUm*`fIO{r9fAz;L%3C&n+LIv85HAe{BEn}=6pw|p
z9wV9ujS`b(u>Y0I{#}3EQ|J<;h6rdfi{CE0V`$~NNI4Z`Ct+D5?Uyot!lhHdI6*p$
zL1oQtx;y<x+yAV^3=6?c3srA@pc4v@l0p)p`T#GPnv)#$z%o;ld0&8LBCV<@5-GIz
zuED?ZZFD31lb~d1Qre>KId(Oo2e{GT>-_Lg&_D|iD}0l#Sv+(mBS3opng7|!yTOn{
zEO0kOSbxXg%he;AJy+iheoclsB6WVkDeNr1P%IAkbBra#$Pq99$KrpIeK-wSVGJNJ
zK%}z7eh3uC%rz?`aBb{4bq*&*lnp^kp8h)OqKrqz|39(bw-OIC0tc@m%9cL%Ll?r@
z?t6?T+DBOY3ertP?izcZ>fa}*)%o({yyN6l8Jz!*-pKjoD7r4O>K~v^tSmpsO^CsP
zHEE-$7c!%r_K)|+c)E$R#(8vI<w`vMv7^6QF!+oD6H2bW4gDSG&47_l&VGSFqtBI>
zAcf56qjo{+E(}(tYb^N3#ea<NKgKOb>hCnXiAa^nqN0z9Qp$`C;nai2re4$IkT49Z
zLMHXqN2d!B7Vw%jRJc*c^N&CLqe5YMJ5<`FLr%6a$*+_~Ch3gkqKkAZC(0-p1&+-J
z#%X-XTdN^|jhq{TE#?Cv&2|d9_Mke?W_7pt3k&=?HN|%;9H$*-wO~pS1P10idR8jO
zy@yST|7~W&Das$7vZL70S_3=ak9Fw_MFyI~54a1%F=qVi?(YrowBI2(1iO=Kgywxh
z09k(*6NG|1z~xMAa*j{6Fe`&R;^7NszhQ?G&B#4)<}RVy)3l_0pQHKzsEiWjcTG~b
zi8bjohK&wvcBIxJJq~4ON~L6mfoo`UFOTH~{@noo|IZBk$HG`>Fb!wu<O<ZwHrquo
zFu$s9euHol-3KA~VTuGJ6B06Jp`FY*Vd1e%nx@kj+SP__U_wm@@yq@2r<QTgVUtTr
z5ZeEb5CN&;A0%^rX8;4UC7X2mVNJgRf22?<EWCEFu`O$ws+h(apgrdPn2a_vnrCl_
zY46|#2Of$*phf+#Oqe!cNfbXy<Ae4)_rqNEw%Q-e+QFvr)$07jfbsO^Ma&vUzSiNi
z|0t%fSKzK6DC<jtNU-)450ExCHr_f}Uv3s$&@Qt1U10y_7r$^s_^>v%dtF`d7=55G
zFn2}fm36wG-mkaLvqqeY(6GVi5yk!K-`~G+732??YdH)skuv+aReUhm3>a+&gAWc;
ziVw>6M#8cL?!X6w2RtZg5dy-JBNWEF_?7GkP%R7DWy-U+baIs!sstWY16z5&QybCe
z!GjNt$XkX>ro+l5&b*0?WpmC=Uy^Bf)g(fOk}2**QUv@KYoO+Cea72acHUHJ)`UhM
zbe27x{$wHI!Qtp~lHRIs!s&onOZ(|K)mp&4CLmJ9H`6nxfpSMpNJH1}$BRB*NU7~t
z+iXQcpHIHmpG3M&C-)byl6>j}0Ze8TvOhK}d*J_dJvpe4F<Z%SW=SOZ)s^^^Ee5v{
zv_4@~A1Qe^%;3O=6Z5XEj5Fj#qo`F?`0;JiC%$@gRHx$UeRQn(G8sNGzmUd<0mY4v
zC7;OmPt~>|#DD6RQ&GM5O`kU(8gNaLt-U`L2^unXw*<{6t-70;yw<448(sk;256S(
zVO~Qh%N3T}z1-$71xQe~hhUV#%`6VK+JsQvBI9+eFpF05I8y5pN4+ZMkqr+Y8~W4I
zwdAchSC7jy%)x^0A;GhJZf9fTJ{c)LwJKE8HV$>#-^jdkiA!FtgB25dwO<2N?VdE}
zi~Y2GNB@mJ?~WPzK{xA%bw8q06SHiTADG$Qs6YDMr(tI_%(%<_z6wv5u*<p9LaJaC
z=Fy0&)r;fMr0f^$C8bJ~xZ_&V7*2Nt_UJOCAWBF`mZ}VoBn%3}qJo`intf|oI7{S*
zsHy3s)FdU!)JksJRS6?+2`hXqOvp$^nE*IPM^;ux@*QD>6dc$3e347tPf5@tZDy6@
z-p{yE$X<#az8>uI;@?k~+5*hEZ1g+%X(=t9$kJgJor~19wm9dp3Icf`(Y2QQu#G*=
zoz&xw*0k5xH{9c^#fp@TUa<AP!j&S@jhy;G1~TQIdLJm|`tbX--vYw}YlI{!R#Uux
zm`@{Uk=I{0?i9Kg7a1qA7>IPqhArd&DUaby%pvgimJmO&0<tHf2PcN`z}AL%a0zO;
zpUkdF-EwVO?Rv(wcX~X|!V>q`{rBh%hnYYF$vrIX=KJOodm1vN6KYHR{M|Zw{wO8J
zN3eyGC+0l6s>!o7*Ur}vwUV~HHfMjLz1XrqC5MUnHcKFI!f~Z_2W<5a-MLHWOScN|
ztdE182)@ShTI%S`secd+jVU%&Q#UcSy6M;(lvF^xO(??S+UF1^)EIq{jA;u}rCRg2
zI;p70lWZNCyO1scHo6dztgk;~5aD>{NP9j$WIBi$x$mXcI}nE1IEf*v7y+w?%-@gI
z6O`O~Hrk}6$oo;3`G!n<2Mx(3tBO@uC-0Xo?w1cW!(8q4nk_SJ*Dbf3v9VbV!|TTy
zce8Ol!$Z5-FaQRq(AB-Q?9-*>%&W^{ga!IM*_XYf%F2cFxwPS|&DhP3+Z(@eJ&g2a
zrXT)*Btc8uR+^{wqBK4}Bx~+EhV);}YXu)h`-|?1C@i<;&U|?pXqcTw2o!gIfs<dm
zDV&HIpQHah+DDKf7PJfX2r?D0t~)YF=W0UEH8&MP;f@wH+WU_+3os!A6mkgya}6-J
zDmpC)SyXS2*A+r<W;@S(6@ga#x0Oq?4$_V0)$uxu0u8#d0OR<1Sqv1^B&xnFUCgFA
zTAj{EP_Xgq)AZ+JEt_?f7FoB?+WD|njc59;kL{M*g+z&`U$pKC@xWBQzv@(Nzv_Ih
zMU&)PGUyt4Y-}8A(>`<pU5L&Gn-B4IdX>C<;mkjD^w?x-nZ6|l?WjlA)OFbZ+G#wI
z){Y`~uxjMuXltjgZ@DX}m(Hq$=*xIT3U)8=ioaWmD*BT81Fe(yx^%R7^;;fEp?*S|
z)awS@G8*;Gtm1s#ta#;<$=#a3kj(j`%8g!3$9pkbe$Cm0Yw2m?^AlmN-p$npPd(So
z{kr(PuH;~V(A#X>h?f2H$Ih+4;RZ*z3*qRU$AF7wx^NGNmYKWZ+6~V(5<W3%a>&f)
z%@ImMIU|ib>6%9WGxKEgTS|M^K-mGe=yeOz<E^w0(hg(QW9f@y+dA4fX36d`j<r8(
zYS{65bo<wM5dTG9HzO4Cgy3tunX9tqV!FG*Y?JDg-Rtmhff6QP*w3cwW)E4N_Y%#K
z8m~HcT<kOAA|IZE5MEkL)VYrpDFzLxPj8QnF|$|66cj&Zkml)^bGd94I4~QLY(1@#
zZM6x9%JU!(tZ%>$LB-t}9(Bl=37eVc(!|kG-{0K7=I_Hxq{wUc@)_MC$G?#2h<8FC
zM_&{mEE@C2{k<J9!a*1=i1nAJ;J)T2wI5h=(mTsY@Kz5aSRgEZL6P^-G#Jw6F;9vO
zDnSAea3Ub2lsXa#NY%CUnoqJ?65<QWD`exc#~>m^c_x-23mFsoy!FD02nw1Iav-G^
zQ1$G^gbncz+8%H`+<&rw;sE20)UA8m1@{FK>^}xZt|XDfQ0qNJAp~dOBv%jPvu=Iz
zalX<%_9Ma@igcd|)UMCJN$?q(KV`>0onlZUdO92KKQ;XI@yuZK`NCpal8}&M$?<|k
z-)BbnRzPW%#Q@D_Z>_M1^O%CCRox@__sMLL`oa5*!2v%)r9A~tR31MH#%n8EISrQq
zOi!1d`rp;y3Q3ruCtvXhoEga{tE<Mwxa(y=KXtcNUO5ut>nf2R$%X900$^Qjio<{t
z$r*j67Uw!tlH9vsYS+((2x;@rb6LO8m!0VgjlcKkCTtjCa#3oPY5i*?_<5n%b9qHy
zss|z>>BlkAdD~^j(WKEZoI8faq?V0a?bUF@FSH_$8PCpP!JO?CUJEA;b0(hVGJe+u
znWzE%8UF4o39iviz|7BzEiz?!%TAe^W+onwxDh@ctngN!3p5&+Cjp`NTb$@vtoJ|h
zX!+9N1$L#LBb_n91u&24S^8@N$M&rjQ4pf0NV$pI>9PY>W#g4N`C*z>w^h&&RP87v
zueYkWKRX;F3$Y=SLq>y?xBlD?JGj>Ij8ZB2iMzLg1U4S-nNIS_fmG=N2*G_`rOKa&
z^v8zd;9}oSO2i6B1od5ZRQ_-G+%O|tD-Fz79U+?ccR+DIE+${c2yj~DQ*ae?%ExpJ
z6pt~>TFvo4&oFiMCqyZV9Q6<Vk6rNEA&gV6;a+aa2G@P<Mxn**+pc2d;P;}-0|Iz3
z{&<nRMq8%(CO1RK%ke>RF+7O~b0~q6Z~_0Y*%9Q?L}97aYIH_R$BPB1s89LZcbUxM
zStTWvWh|KCaZ>D9VZ>cD&+nn(suAGu<<`4$#MDTkoxi>x5hUylwgI~zdzDss5-<jr
zL6Uq?$Ep4kDIkt-_5&bgdM2Bu;I%*VRs0SQ-Yr$?wjwfeCi3wWhfR{iJeN|&qzd(B
zqn2Z5V^3dEKXIv(Fs-;a1wLVzm`bd#xA$Jwq!%HZTS00p{!Z_=>g~5q=8|KIt-DqK
zVr0X*ZJ|3<(b;Vs+RzVSLjO68urNM?8v`FI4HO7SJfWmal$=Fp2W;I~A~NFmO+QPa
zhiA1~>&nYJiUqIQhBet%n3<`Oob<)a(XV^f2)O>F_1B$nzT0x@r>4Zs%Hh<im|)(Y
zYv@?nfltlV&%xNY{x@$6kPZG&*quV&N|Lw=DN)NJpx!6oj<q*_oi!mk(i<jZ%bil=
z=l<C9_A*Ve6^{k_do#ih7>9zmI-8ZG&yX|&rObZ)C<kK}uh8I(aAJ+i^pba4m|t?W
z4}qm8{lAn_2NklL8S95|zcr3B04jvFa#bGYJJ(2570r9gsGoNNa6`pEgLtJ3SkkG4
zexk)8`wjlzS|kw2BQu;0;jdj_O7+^3U2E_-M1YLxPcwWg*FQ#VQ*v;>fOmcO_gM7#
zZ|N5ol4f$S*2<jj5dXJ$2~6=yt*T*K6*Gdhp(v$_JhPPfNY>(}>9e7_ICG^bsaK8G
z>u$a1(v*Df^|HFMC)qqU9Bgt4y*OYFgBA9F!esM7ig8tkhA#H>h#&YHDv$KCHYC1K
z4ycMji%|!$KQ157wOi=(-#WT_uC|jzDJtV~C(%Dh1@pQH|GKf2Qkhoew&y4D`PI|^
z;X9fwwT<RL<TR~nEDlNs8OETZaXQY;m%3w~0D$j*HJvR4S*6NQ`jZWxQmnhGSl)+^
zS66Nm<kcNImYsv~ejTOTWOHsJB_pHbvbxg~;oLXbKO>tBK{CU@No+_Jcf!JF=thS2
zCI>s0?yOM8lw*Wi!XJDB^C=}Ot#d$L;cN1$$&@DWQqa*so#M>WP|V1X2VZv?zK1k!
z>k;ZBgm!nvw67Uc5;-`UG%b;cL!k2+c!VK2I~AN2!EQHl_i><sEfEI3X5d-jqt^DM
z`gEmU1y^^hiA@Ae*L*(e82|rL&nuWF0@B;qOv7Fl*^{3s6s0{?=_5&XIVa+G$vK}S
zGIh955Xsd7$u{Zk-tGsQLD7IC+4N;wfwfYmlZVl9r^dBh=klhf-4icqo-g`ZlAeV6
zqH=aGnPKOX;ZImL%Z<OQpLPZZhx#SW>^eM@`t&i4eS~Uf-E(VzwUqnUe}d@JThpht
zufUtCj&(+$IF~b^9X5V#paRd39)>PCtTm6Iq?KE*-Lj<RQj6lPi=Yf*Lt5$bT8MF$
zLXKa%L|AH0Qm$Hh4Ux?;j*|v->(Sa$y{(ESw35BCbUBf$8v#cqM7rORXY0|zWZuB%
z&HNzf|JmOJ4l9otLTy&2QT>=}GikTIPWV_NNkWNE62uE=ri=d$?e(Xqd}W7e7_p=_
ztfpVv3-@dJ;bTvBefp^y>Ii_snz&zA(Z|kUw;Kwu`fcfaXVqC~>GG6>jOL7HHAPH5
z!Fokk>!B=Y{8jPoR-Oy_1mVys{%I{$60Ua6%4MK_C$h&-aLzRNy)(zt&3NJ>yOU6E
zF0f+#(mC*Wtzowpezs~*w>A+6wD2;uv0_!~GT(<RJKzlGqF<S4vX?h6Q?2p)<^E-E
zKT9AlRdx^c`-m;xYP;54Yyl;u$2i+iJBD4m&HB}d?T|S~%6`EAu#E))^^jJnCJ;hQ
z%(v%mP1BDZsU^v#&+%i5EMHF?d2|^IaB6u;X~FF+qq9rTX-#Cyx0awI^&V>t7(ir1
zN7m%TFV;)nv#OJAx3oJ`v(D*lcfl|ol9}Rx0uRWY^!Flu>>F4f_&d@oiUL>fOBCZx
zU>Sh1ASCwZ@!s;=As8=lBwAW^AdJe|&lIU33JFf6P>*_)wC77+MJMza*@-=&w1>rO
zQ5iri2tXm36r52oyV3DJ3Ff~bl|NFzIWtK{aZl#AGslqDKKo+RY5!FA@_=R?jj`JU
z-_00h<gGcpsW{X4kvOU|4VkxNIX3Inw8AJntZKBH*8)#3upc6De5KZGeEO{ozM?QX
zyHCq?Ch#Ws*d44njhxUe(HbeVXiA=LCyj4<3Lm-%2sk8r#vqY>TR(?}ZE-=4`;>HV
zw_?(CG*%xm*p(A>Q<NY`$Ug5at61aqmT%PcB|ARU3ckutZ`m&0HV1CRyT(<P*TQEY
z%f9xHfkx+EMAG(~Q`IZOdG5hr9Laaf*DL=%)|quBC4Yj4S6J!`?Y~xf&rgaR9W>HQ
z6U;+qieiXa-;MM3o5c1?;OcCh{jI|<<qa6+a5v?5rE=B=K5CSLUt#U+i8?1<5a5J~
z>5L2gn#%60lv(i67uJb(SwI0wvL)gr3!Upa@+nZR(z+~XjTF?X$T`vZ*_{>Nn(j^;
zW)5qvF2wOW_nb<`snvHIL;3w{`1TXdF#j)XWKq5|VI)NrPWBld?U7`%1NPd5$pk=>
z&Soa`GvAF1+5gH+PgZrlww!eAZE%Q&#0XO8JoQ?aeq2Lb80JJSH#KVcQXOz&LT_#K
zXL+|FT``96p%#z;`TCyNw!Js^a<u?}v_W%~S=YbO6=W8FN(AL88mW`;ng&*q;rr#z
zY|6+u44>#fUO!_m(dVg$^75MIdJR}$g2(;WISLd=^EN+O$&}ZU8Ik@<(UO0G+e-e6
z|DEHA8(h2`k#!&|W@>anUExQ`>?-#O-OF2Y<Lz;y6pvxurW=~|+PvBC<O$hRVP;Q8
zca`=1jd#h=5D{n7<C;zWIQQMsd+!k74o@s^ayNbISc)2>xvkM<B$?WO?ekwexE5x@
z!h!qxcd??Wj*=pV>L~nVT#hxTjcK?(NnNLDlDI<V{~R)#3bI(Z@U-A}s62vEC@dp5
zC6zGQvF9_k7^k<^gulL^*(<-x7d^mV1vjC?2qUCF8Wuue)6Fwdm7Bne`A=HoN;CKy
z?nT;|%KbiEm;JMep<WnBacVImf}UD!gJOu=yc4H<weEN^9TCq#Dky-OtOAaJKHF8L
z8-ssGlFo5NKExZ-#vDGUz|iI*_C&L03K+GfW||eg(rI*Y*3PRr{RGF{5;D@!4vvm8
zl$yiZH1R^lZb-i4cFoODFc=J<{wOl#VQbG94i1{NlH;xH4<x_ia)VD+Uh=bdWoN}O
zVL2!E;wZla`H7i)B>A$UB3EWm;(F0F>QwV&_`tiM6p>dD{m{&2`v~^_@jsYqHWB1+
zrxV8(UDB6<V;>eMn?|>e$j%f7SvE^o0tg(3F6>sz)rbLUYjTh6(QPwE=WZ^&1ZJ<-
z8WZ^Plcy{%(M9ZAcSrY*;%|q;f#OpmURtV^<m7rjUj_5VGAQxtB4!)dmgMzu8yRVD
z_hm4Z`palWQvKAf0@_bR=PtP4=q$Nyb~9+QmM_P#?HR;?GnRmUJ?hjCX(V>Wmbk3#
z-J%5zNuhuMn3(^-`q_dIsxN6kRkTL5K%6yNNG^I`!{pRMze#=m&;wHvcE3QQ;9pxp
z9d%|p8SbnG9IEK(NJrh-dB~3=j@{t8)iP<j-paJ`F7SMB7yehv+-C(!c+3Bfu6GQN
ztlPr1qwb_TPCD+`R>!tgv2B}`bZpyp(y?vZRtFW^IN5u@d!O_D`08g}wdz_mXU#R{
zc*Z@S!Nn*7H5MoT*@vt2F%h*DV(%Nv>LD8X>kb79d7ZyH_S8um6EcM8S@G2p{lDZq
zW79XHkeH17$GugnmkjY^xVa$5t#>&I>1$bao$gR4Cv1FK#ci?pdoNcFFnyQ4pSq&C
z!=(!Pyu9CJ_}pRsl+q)%TdW4;K%UpK0IV$C-GCyOu>bz!*@YtOoh&9m(-`;ZHLeBT
zS-6y?@*wrreA?a9bLZD$4HYN&RTfqdoCNcc7hHk-{MIWfYRRH~CE{zKCXq_ZMH0($
z!Z?IT8a<jF0yKy6o}-GG(AHT+%jzsV6UAS4Q|-;Sa64igUnuSyG0P*xzHwZKKYKJQ
zRj}1AtbY!`#GRcW$R02CQ$gcqFxV7z@3*e13E>D=I*IrmK8PaMp{%}KvIGL00ME%2
z*LlAx8T3Y0*D{dnH!@rdiosTOXm|v{`>kT=1`S0WB$r22$dc97|I{bG84f3nW`t92
zY0%cufFz|%NK9DQ_%MbPu}$B!LVaKLuvCw9Y5}h_G^ZNL;pNIFRtS*HoI&$QU7OX6
zlWYT%$}X(G<W-jmjZl?YHjTZ*$1bZh-oBe(oz>I?C;?`i2w#v;!TvV=M~L=9>ISfE
z$OT27+C2y3st4V(Bvmq!)n%IeEY9PaxctEM>x{A6JEpJFk{;eokBhhMIBUeg(8o=@
zT{Hm!Ze~wbgT+KWR;hgI3^!Dz5c-Oif_&bUxiWN4H`-0+W`gb~2S;~Vmc|hTwBKXf
z*Tn3P9@oyq%D!>jxu{}jQQ^72@U)ivIWnICq$78=oUb;<6C<36drma0*+f<0BZXE|
zJR~-H8LYe9l=*Nply=lrwqMRX4$Cu-7rI`6KsIZ7Or5TaCuxEsXZc>h0=55-5GD~;
zoAwfo=MKH}Dea*qa(dK{StUX-`{Gm2l_s76$=58e5v^WqlEo0n3%Zu)?%&`mC-6Pg
za}$2FTT2EGzgi@1&dkldy}=h9DDEjb*L(t}RjoS(3ul<-AManctsbt4gdh|UhSoLl
zZO_kf@J=apUF<fdt|kgF6D+7Mlod-w!>l76jJFe;v3OLPXl-pb|H#n03n=}K<NHqq
zg*g-~u{na5r%Sp@Nl-0CH1NqDlQp|EFqUCFRarKff)kVoK#E($Q6XvXvb62@H#gtB
zlaxAvg_iJM%c@@(MsITQRP0*|2b6neYosSMze?c`A;--fE_eOxwQ;UR*+9CIw83Ax
z@bdWV<hlK=_98V?M-~R%WuJ#uBMRRSX(`Ut`vLHRl}D%qf%cxqWi97mFsrIENgQ;2
zAC#H!u`psO#NaQM*1RuaAO6Q*YV^v<uYGOk3QDn6Qmqa6vAS@fjKlU;;#BO?E<0?+
zClFTN+2qj@MB^^rRhjv;Ay^+%%}sfK%EaD#y>*`S(1_*tMMet%$S_RJdYlLiY=PCw
ze${<vY}-rPhyA0WqT{#~DWHk*PEu;~Bqqjs<f8ED=hDvne5*m4U-_M-fx>+up(+-j
z`BI1Ggxm3YKKmMr8mlWY7g!-DFN)Xm@9;=R2Qe$llu0D?%tT1roN;JZkeX#tWJACx
zH<)U|o$GisZ5;08a=-_4h?xWDm{+I?Xg};0mod$<+jZN>lUJPn-QIka%-9USb3YTL
zY#R^HsQ@l0y)u{Q&HR&9v`61ixIc1YLUT9nkEwAu>yiG(b781gKx;Btw08zo8xT%}
z>@jtp<}a|CTE?nnjpQQ@?rl1K@OqQAcn&I|3iC`32b;GVuEX_se2Bbc>Qi!ED^*4@
zNy5ONK6?fjPwB19y4~SF0D(ed{Vo}5s)1=J993ztweLdekwquUiXm<9o3^H26*@@_
z+WcfHzqkIAq{9m8X)U&f^ilS<{Mb+kVlP@7Rl<tUVf2M>;m%@jmL5AgdKu^2qpWi`
zGlO{+!R+mNF7Z9}{M`Tv_^%g0UYTJx+3I+QjGXndl!Hs<Y)F27zW+h=PTdvoro?@C
zeDdsT{Lko`h_u-0f9q+*Y6z_cIK5~23^=eL_lXXZaQB?TMw0TJ&87v;nK^_1KShp$
zIx<ZzRp#%7|3&^^!n){gc2#iIyf2}mVtl_JUww*Y3B%nJWSNCN2Qvrhnd~_2E};G9
z{9E`JuF1PESS_?dd1g;wFFW|~KEEp(cN8oC-irAr$Ti=A(O+b<3k(n8wJ$^aw>QZQ
zIEbvW<{$J#%jLs;eW4guYo%T)Jdc*#6a>1UzY*X%Zp*zcjgiOIWs?P+B+*AHBX-Ux
zwP2*F9w4e6rld@|(km)P#;HzBv!pFAb`t%iqtS*=u4q+nB5EvKvIX+fkw)+RTNq*v
z2OAHshDgC?C1HZv^hcyyZ+G8$d!TK;=nULy_1Wn#51(ZvU-$WFZ#YTGNP5GfUV^?%
z)tT#?jbuqSdB!`svHZGYF|%rZDN++Y!Yv|8A}el!=(tjHdT(DqCw4*GFTXlX>+Y8D
zP|yC^Vopj*+{4@bq1v#bZ9QH4Ib7LwCCrn^f3}a_ct548$iW6*C^p`6aY^mekg~4Y
zWSUb2)H5?3X(@*`%T;+UdW_CY@Tn92F34+=Y1v(0w1L^^&auUJlq)G^?WmukO5Z|V
zS&}~@&7-F=$^;({_$+RhQ3A%?7vR*1pBh!@mR42!9;I!I<^L&z(_uk`18m#QLnoHr
z_8=wNnzWi;dgx8~3N&7)Rhs6$u~?Or${bGKVxryAIj?d%{0(u9$(+<ZPMHC3Sf4h{
zD-xRFtIS&y^B4?;%AUQ({c}a`cO!~U0~C9~Z~gA(?Y=&I!)v_xJEXR00;HIGDGP%7
zaTA1fYQd}&TFU}|U&bL3X;d0OmBja-*+~BBOZosZr4PbJpQP*a`BCAC4DOdQnODeT
zT-70qvEj?RMIbytVg8aqFVW={Ht%oDG#2Ps&D0jc4$Y%4v|1H1b^yqFpsa%yHq($%
z8V{Ub5EC)i<p{~Xp^$GZ>T&XFA6^nql0@?4_WSpySxX0Idz<OKZ-ydeKOifaB&7pq
zY#OZZm=(^fZ{W)fpR@bbWXsNfB*ae|#uO3!NONv(Y+n1zLKiwg5eS7fq07$fyHDy2
za#?~*2h+{}ix<EVfh|}Df3}C{oA>ibB4|#%4HSvJv|~37T0n*|{AoAH$;QRb;vZo*
z`%h-^uZycQjc9ZYP2jGiU1LG5jpS|!ViNBuph0HMt$W@Mh<BZ4=t1h7xD1j1CP`LA
z{Ag9Bmjif?DX^R?cI^ATS{&o>t`jz{ZkXDO_u|LYP+-TtR|hhg#GY~VYFE;Lw>yi7
z9_an|54?~FlF+7$^%mZ%xIXZQ3BSRyb<)Ei=KuZ(j3BUIZ2@-RT*J5W34LEyaLB^O
z4Zj4`_kqwe8HTV9NFd-GIICzzxZx2aB<Ub18!t_AqqvuuZS;&<c_|V@s0ZZg1Zby#
z%G%Sv7$&s1IsU0MYZbxp*TE_pKDq}8y95XJ^dx|tS+Z&I8K|Zn8_XFhI#pqf^a9J*
zlMRiJN{v4t0drT~!_sEYD>Jv{y0&+U(0X&83y(87F}xJUuP9W=s$pU~gxd1G6sbNk
z`RA|R&t)=v?~Qv;B)r!5mh9dm{|+vH<e>;+R=89C7|g-4&3E?_^(rCn*f^&U1e6Dc
zzz}-4^eZdW<q_?REJ-h|3vW;s{ZL*&H!je#$WGHV$txmU(NgJQ-uuPP5afUy!W8sw
z8LXh`hJRO&jOhoIAAKZMBKq?KF_znooo(R6k12nAkDsXkakPNT#cZVK!vg2KQOuV8
ze_)kvv{10SCb;M*Do&xG272Vpg1o&0;i(1T$uJl`=pl5Zz*`FCX3n3EASu9rva9L7
zndZa!GOL57ji+W`gRSw2K{kWFx%$d~??&9Jqw)pj>HVNeTEpJGD?qY8ad-c7M_5K_
zOmkiy-ACdMvfL{_i?1C+{f98=-VXzd2;;{}r&BZZrYYIEQLtOVrP}?`yFrvxPgpU9
ztlGSdzTE!|P=d7avGIEu7|nWHi<f^t-g50KUSz?0Q9V1xB%O+wyX?lC71>^v=d;ow
zr_G#~O^Va)<+<)8L;J4mV3dkfs8B{}8B?OkhBfSHD%cX&`~!Yy?7xcq4gc?~#9lJy
zo+73S``x43u<r-e3{9MV&1d=uZAYAkKfWYN<=CW7<^K+k4Q$obLgHEz`ZT=J%P@nP
ziujDrTSGau(4wRmzCK*{Uuh-te(6wj#HlYP-$;cdNcQ{h39;8gp~gSv{OY*z6*urp
zT8C^oaQ$bc*|iXNuPgEsrppWIQZ;Fi27EiN7MBseJypNiJJ3R7_FOy-k)vs>V{P-t
zb?PKRo24O!R>c3qX@!l=puErP5o6{YIun+KH8lpAUlHD&xU~#c6?EUtIrfwj0l1c;
zFis?m5<}fZ!&R3(@SoM^6ROwM8839ule*&iPYOgzl;amlp9p5TT+O&!-*V~_OY?M)
zbb`0+GS|1ZWW;lyD2Crcxwm8dV59D`bui$;GpVE<#=pUP?0C(i1JeL<-ssaa^!pHi
z*f2jxWu)+S`1caT1v;C}@3LxSuu(kaBs@rkXod9$uX*7yHUzyM@E*e|EXEqkN0L3}
z{G?mVyN@J%19+?)`uu}7ZC{HL@H+R^4mIh#7(LIDH)`J;C0XLF<!Lw_0*}>qj$sHL
zqOhgbWs_gx>kczyXBZ9MeS$w80A9dlDyP_c)%7+I`px(kbqwGCr=VlbiG7+p-B(3p
z&mAAB>SRQz>0Z;N(_!FfJhgmZaK)@$OX*JX-v`4x3{CUHJx4eO{{!r^Zr%jKiOw__
z@qOJP@6%&^^rXDwiXE*8E>oL!o{&wI^FjHtEzP<yk&)`)Obf}~%60udGl{p6Hk2sO
zqt!$dTDs5MR8)OGWypbER{)-uQRrT(ReK(>6i4-d?uQ-oNQGyNlre{s+kXg{=3OND
zzEd)uj?*A`*<)?VLX^&GGhV08$(4a<-gl^)>Zxysib&(KR>Hbr46ZA!X=#5$VA?iN
z>^IgW&<g08!qfIO<r;?&6uwkb!W@KaJt_@{F4<UeyT1&|;Ut$<56=B}_1z->Cbd%A
zHd+)R$gA0^KoPcxS5!hqHZ3mkHxak~=@2x_+E4)eCYa%}r*fLB(;Cz4VYpjJ4Xll0
z8Np$bO$z!SKt=r(Tsm3bAN6E;dZeh4oiOF>a8lm`&Xbsd0%8ep%TR|J)T5Vn1Hesg
zHJJVz@ekDrCbkWM90SFiRIQJP<J&zs+#6V<Htdp7CnnY+!u6ve)3oM~bsy^uX&RHR
z9-6z$KcQwDMvDVRUN_1H<6v~BdKByH$O%fxz6{~`848S+#%I6q-#b+d#jBuqqs2r;
z`bo-vCiN5b6o=AN3Al#up(!EA(N-HBY-BE)yd+19!mlvt`wL^#Fu2c-YA>P^%luzB
z=cg|<G&p&}pB;s;QG-N{91~Hf8E|d-<2H!&351`zE(YhP2&SWJs<IvI#s3SQ%|ysZ
z8+7FpK~J@uf;`j^b%qo^p+x9)HNBBfwP2y+Ds43t6Avj>!{`Un|8R}nXn|mhr8ON9
zmBxo0hw8~(+*&!s&Y%P=k7czXmtck97`pKSP)jLwnMH>B{~ZQ*7=E%oFPOx>WlNWx
zA=2%vW3#=4A3Q`N59vETpR82!WZQfqH1%Q1|CG7I(BEr?2*Ie~3~b81MTe<@a+9ls
z8Kp^$YqXpjpYQ9W66T8hv^n^{;`&eDRN>#F25j|J5^C%;J_^QGFlM`c9mt%%MYY(l
ztJTSo3s$V=SrR(z*8jUPuXAKQQ4!4V40M9Fb^%zQiNwO#zFmu6J}Q--5=I2}mzVYY
z0r3AJfZcG!1m6sOVyXE*s3Rl~7<%?=xwaKYznw7*TV#}{>6@^-|G!@iL#_lP^pb}3
z>VJfm;R3zT?b2%HLf+21>%>gsw*#&_`o8Xpj_^OqlpSJT?hE{F4AUFFi%7|jB3E`&
z5!HGS%0on55A6T$;q&7}{Kj<e#Xby_MV1Q?M$G7SeWrmu6e&kPDiNe}w0HFNy(n>x
zF8{kJ`RR)#0+z?FH9I8A=wxw<-n-yu=DIB0rWt9dSjLdd{8KE=JjWsZ|CVR$piB_J
zr>xXh1qNFv*=y{&$%^~FWzzSy%j^vQfQuo})Al@g+)nr(-`R}}1`$^=z{~U!{l`2w
z^J)3{D&;Lg<n<`x>{Puh$vS^Cm9R9GxNM5uYInN(_x^R|p7CTBBKr*YFC%1N(=|p?
zuI~uyh-s!tg|*YLS4(}_0FAEY4AcA?7Xy~bTt%~>!}iS9mXAGSO(eoCLIl+hw-r(9
z4*8__&36gT%l-LM6HMKi^)1g>9pyEC+l!;(xiM4B1T0cHv`+5bRE=jr7*vyT)2Ybj
z=VSG&o4kAzF+E02r+}5Setp&2S7T=Mf>}pZfH>uG!vw_?vxrwtDHL5JI&I70azw-q
zKjlDp?4hlNibDJd_rUWwl4qm0VcBG#oWk6+u!&U>4gK7gyAIUs(GAxH^dqI?M-iN8
zN>_gcMOqK(+dR^Rp~1gqTJ7xiIW)u~XjpAE*UJOM9$5+fzGNCFk2xCQkffzl&bN8W
zh_4<&J79;6NpTUwYuE4je>KB*^PO8n_f^tVSZJYbHES(KT;@cG*eYwM=<P!=%9<?J
z!j!4+yK*lx?KzywZpvS79adWL)oLte*vjdxv<h(L3d6K&k9}RMGGv)}a_Dp{Qs8}q
zzaQxeL8hDQ3f;h(7OeM!eoW1{*KR~PTOXMSy@X?7$5Lw;x)td+S7Jur22PwWWy{VE
z7q*`6p|G9*8G}k5Ga0=2CGXMEG5^^wdhP2*R89N_JS@WbMnO2e1vX3|Jc+3yJ<8`O
zrR@g=o!2<<80%kT9e>OI_EvSyE1n%Qdmn2$8@Ieq!Z)J%@LrF(0Qbc(!o|{^`HUeg
zwZ|hvgz)NWxA<<`WKas5UJ)cIzW#VI5$MJli$Q2VmD?#uOL#*xFPTaY@bUrP2em%|
zH|WoNI@{`)OgGhEMAACUI%}HPix~`iNZlR=qRPrPL9mfX^z*w7ZP(1OccC-aCAzYi
zl39J>IOQz|HN6?L?F|YT!YXy%Q?S+p&(p+xUlK3XN2Q}h38$$fy5?EQ@8k7}UQSo!
zDp*fA#uGNCM}sWIe>$na#i~vX)V0{FQWrHt;F<l=j7MNyU}kL|56z)4KNu$!RLWbF
zU)7FU>lo-##8Vb6Z>)GsKCgpo$1;+0OiR=oEkC`?CWU)FY*e}b9b+yo4}Sui<t6E}
zi@KYLr@ih*m{jX%GAOc{&>R&VF4PL+{I`ETjb5wH%3vLCeGTE`mVj}+J|I?hv-9Kb
zaE{KZ{_cu`r1)&*!wI``vl!<&(sSwb@k#v{&9+tGDlkl=^D6Z)TPHn7{I>R3X|D4A
zc5cWT=<#tLIaZu)FNhEU=LGgw9{^R|L{n)j6`TkHu-?(3A-w2X3~?00h%mJE=?~08
zqW|h6ClxytC%VUHP5N7O3<y6i|Gvy=CTX4gVRu1l2%;UitjII@F^^Cq=PKnRWfLZw
z9OoB-WgJ|{g6OD9d#(t$;!oBzj=O$fOO!}eWRh$hr@1L9r9g9!l}(IO#-TVnl4K%I
zxA>Jx#bky|plZ}_<(vamo>`Xg?2S{C*@L<HXz-0zr*O3F`10D~&jZcUiNjc$nX%Qu
zpQPvYgoHQ`LuBpYsa~Qi>mxgx?-M1n#!9$fjSGLHn!Wd2o%qcs%tu5h@r#lV1&=u>
zex9ukOFOPJaK#Lzw0$u;jcio<3jzOKT=GvWZM5cB&azSY!-(f34H`m1qeNVlG`y80
zWMC_}$l)2+-@b`;K#g2_YIam<27O4;GfD!)sl0|Y2HEoNZ;+jcfT)T{d@P-E=^zH!
zFQbye0KY81(Zp(a1WhR{ZEP{F&8uz%d<8+HM>c%Xmt%lZ+4}S5dl8Y5U~cJ`yCamh
zyL#RIMf`LNIUxxX2D+Y@<Fm*fX8}&>E(ac<c(UcSevexNoYMjXKv_~k1Q}BZjPEyC
zTc1oriMgn<HP)`PdcsKVACJ{G_4pqPF5*1uy&n_0$;LBo%95g~r%LqA#F6*;M_NvI
zWcl-F&c?KF2Riw;5QXS061Ng1s(K|fhI3IWf)QNj;&)K(M%tU4X>G?dvCavH5piW6
zhCra0C(kB%jzZudjzu1Qq^#;J6Vm!q^y`8V6hv7B0_?YyE34nhiy4a@Ig25JX+hi-
z%pss?Y11P%bOm0f(ckxA9bbv-oxeu3Py-&2XFVJXsG1g;nHU5yE<5)YY|18NWJ)h1
zH5`c1Xhn(?YkEP^g~{;#SRxG#R=>e2NC$NSA4G~qBVypRe0Y%OSNd}`B4n+c6bsQ)
zkI?^AHub3`1ik|_shwXE)}wM~qBP9;7ZY*uAb|ExIfb1>#H8h=`4ZU8fbfWenfI`Y
zAB%!5+6f8l(+dEFgj}ggOn9C~O%G5b^4X``Y2NpGYefuN5k{ywYaEMGksV4>nPBQ=
zoIEMoo)Vfs^IJJ17_!L{C{&Qao1AExTUepmEHXH_Ur`gKKSX3qAdt`g_c`P-cP*K?
z=p%*n2gKMMgtX_*C6lu;c%c>AjEUFbb0s3V^@v)F`^oi76HN!3bts|r2#-1J()!ny
z${&Nvf2ZVEL!o#g3$tpt>uM`o0EmsZ`-Z3voE}Wy7!#T}*3aYNImF|1=Js5qEXz3h
znk|Z@GWR!z*;3@hz1Lu1oRW?k0?(S+S3+nwKDGIVs@PFf0Q-@PtD%63?l-^1EbKhm
zBCOXwaV{D2l6oSC3TE6Toj+d%XzReS+NgVe2+C@j;%G;at_}(aM=unhTI(~*$P1}2
z<fjp523F{;o0uF6G~L)*KPqk_Y(lSuv6O=Q_~y1{plfbU><&GvAy@38*jAwb8WK-W
zI$9fAHw41-19X?!xtaX9zn^kogCj}U;=45Cl1ueKsbq191-0QgEVH;-npv8xTcr17
zN@EK7CpI?CEhr%#B!mLGR~Hrg2*3q|Ib;_JKP!r}qKb#N96=}T{6}-l$cZ{DbJ_@I
za2p5@;#_7iqxbQERO<n^tPu+MIg$uD`NVzv2@13ge)mS!zXVlTcM!i|E*ZfFDDQqz
zTUvfM)b?C+vrQ<6^^hfPF3Ts)8ehU*h44I6lOwjaz`n-8u{5(bH#Ngg7_Q|?<Ja}}
zQ*N)0RcViRrYijqulk0Pv^b9@^)zRxMrfD_CVU)2Y@LI=96Pvbd97~VjRwC!U_2nB
zt?5Z>X>wl_;Qi&NAu+K2%QcSJps1+m*Aof}p<*Sm)qQaj8T?gk3+(L$-lG@|c?+N=
zzBRCA@?%1%N`=XYq5%mvy3_17vzK}{gp<f9iS=t|aagABt`M9XB}KvQ7sI8f8k1*q
z5RPY1r5oQ{-&5zsV!ZW01rwlhqn0>Pu=twVr=TSx3JnHS=nT&iIO{Ak#XtRG066*p
zllc0l3(C21QZj<dsX0b%0o^d*=bw>cH$tN%w#Ef_$)U)mn}WSNj+H_F!qR#c>ob<k
zE26F<^-;mKRgc=Dggc%^j>Ii?OL%K&&dG-b3ACjoHcuTde45!|0_PSyj>ePgy98XP
z*|dWo2hLl}(KhpY^E<g=0<7IIVWYy(Xj3h?T68L-y{vlk^f>}$>k6%K0hGPKjEGBe
z#)%V=t<}qcA$+Z;XhiMoy0f4Q@0Y;~9_h~4%yD|2*A%+9SR6c$$5z@kl>(mn+fCY~
z^+fcK24}*M7g!Ux{8@@V9S-P%G&!heirbmxpK@)H&W9)PiJ2vMxZ!t!*5a(|qC-y4
zHfn99zoh8Ta(SxSifRzAeI<#6>>=TuSTJ)xDW(x5pb%o1^RFZ}sLsp!zb30a-2R@9
zxp+%(7F0>2sYrLXI1)B4fKk2f8C8GSXO|tmMOBj03q5}~S=>RUriEiO`XZ<?oS3rQ
z1c&h;28~m<&%9Cxk`^;pAYFHJJP6yof^gt0;pFp-&7^tJO2vi~6jgCH7fkJ`I+kZ@
z1bmuVbLy<g7Czu2Az($V__HCGnV_Cui)lnsc#IAtSjDXrwW+*?WHeM^|6RcRtv-xX
zHMjyc#mZqqXM#$<WR0QM@fh`tWDu(tow$BAa{UdcMB3lBAF-z^fh;?;Y3J)`dI0Mg
zcSU?oyM$-j+UhsBhOAozm4VlIJ;^_GD3TnBnQpwZo86s+B;Gt-Z~!6l6T+-1E^9{4
zKTs?uvIymdDWiPA(kAFAMg=jx>w=|%d1AQW73hq^J-IR%2Bjl$gruAVZ)P`So22iU
zMctABqgvJ9jX!X$heF+;ZZJ>I;+&HCV>V&v`zlE8$s||EoxG=S2>Lwg1c&;T(AXyu
zHa)1EF7?T4cHiJ<uwFqg(HEy_tO9oIT@ScU1*;M<qtQx|nN+_+kyqgzunU{@i<Ekb
z=)f<+9TL%YzMop9EN_b#igU{53Cq239#w}{iK?^~3zv4?R$rKaP=hCY+P6*&&z`Ro
z!aLN1Bp0&)Tk&M7-)%GU5zj!#c@<_t?s8z5r-NNPe^>4gdj!PzN;&7wvTOVILn*}L
z!46hFW!|4wA%@q(-P1W$c!>s*OhmtjoorA5OY~a}#sjE`WX85_@1+qqAD}h;8hh2L
z@bJ(~eE-m2KY4qJ3p4{jR!veF&-Rqa7e689jF9V_u!qeldmk*H6b)JKTMY_BD9)Ix
z*sSCR-xEwuZq8L%xvJA}yXbHbi7&fM7EH#I%IZ`_${ou2Jis(@8xgkz{5O8fNVaY8
zcID<GU%RIgOtu|-rLidd6SiQ?^DuB0gsE%38D8b{LP=6$(@*5+v%jgLh5roXV`MW<
z|9<hXq!zzyx=wsD%{6q%m0fE<esf`CWo5>#7c%%g*q^Re%XI_evV3OQBf1`?@A>Ih
zA`oO!V6p7_Ai4e}__ejHXe)!8BgFRg+|=byGV4<ubM2iIX^Q~K?!|Y+B=5s=8e;8R
zULiJ^S3v^L-4$lHd*i{cGCzuCmJC~J-+(yw(}_<ljs;;Q=3mUJ?^J($zGh-H<qs5d
zn=;W*E+%@%q|2Hb5Y#n343l%2sKH0n$)@DipO-ck3tY2g6iV6tVhLywm8FIso&t!^
zruI6Wm!g0qAK8n8-vByeTU$ec3vEl~S$(r>5-{(EDqG=%rn*-b-TtNi7P+{OAX{3a
zdKXRSi9Sor6$wvlsV{-?%^atadKOS;PEVf;LSd;G%omc&Mx~r7?XW@y;aCU`K$E~|
zpwK!mZMV2=d&LiA+2BI|)!2pGQ+B-GvWfS@`GicK1s5Ol#!jf9BchqBi-izxiuOCM
zgheonxvPguZv(})Md|M&5zMXv1`zeO4CZUHORX;|@#DgH(M0K$(kx%0zY7aG1Q(yR
z0V30Bsn-y3T8f%@*&fhJ*D^{8^_xT}g<WO=v*+BGS015yM^XdunShL~7X%`}8750%
zY#46>NQox;L5#;0Z-)Nn2+$7%qNYheOn(3s{?rm_p|odBlEn!3F`w6ve*`R8-NkXE
z=0*k+%nqhecO;t0V4t`@6ikwLv=|}ajT;tBJC=TrAa9$0HnpOu`M}&ZXIQ*@9dx-m
z4aRiFj-HN0*571tn&*^Skw>_;dbuHx%#b80TH@Oet*cxN&D0V|gJPf~IqPmOe_r8>
zMp@I<sTyS7bM(O^ee0rGO!g*MP#1+lN}Y@x3*AN$d5m=kLrB?n`i7q(W}k*Ns~td5
zBvzt_Bh4a)O1W$z6I|<~v2}79?v%k1nEUbwL+IN|zPl6mvb2w7**a8l{5-78WoC)g
zQ~eP=G1l2e__FPbdxFruu}z^7vCMLE{>w`GQsIY)g7`AeGOy2Cnsp^~o$61oV#m{`
zzx>B(1#MR%sjf#$n!}+@%u?Y~CWjsJFZ+hHAQt-Fe167hx{9s1tj5*ov?E;k>Mv7F
z-xXjP4*E9?86wJ~o>0ET$+p`V3+Ca|g3QNT+r6w$I!$%WL)nc+t7g8E!jSB?q0Zkb
zd(1h7nZ>Ia8F5FlpKO%^Lste?T-VmN@-;9`Cu3wRqSyOTsgdKdDOi+DaoA{kgM)K#
zKKycw+c`h;G7OTcWwGy!rY=gR6|2bWwZ0-}nyR(tZc`dzf9%kVEq6|@t{I{`?A;^m
zIH5j55fh6q*6!BJgAegnlXY}BJyq|+`O2T&=vBs1-awDvmH(Q)?garWrpXd3!$n_a
z#hY9n8rzSLCt59zrY_XEHix_h9_b7)gk~!wremUk3VRrPeW@PjR@i4)utSPogdpli
zgs@BRifU*{T=NX<I9d&NTtZv-Z;@kT%?<JG5SuipAEKah+C|gD!p*wcr{cy@T(##G
zF7s==qqA4kQp$}jw~L1c&nD~UsuJrhnUBV3qeWv{M5{rNt4U#=*{gf!1vp8fC{>zx
zk>Z!XnQlV5Eur=#!inU8Qbeq)%4nW92tt(T*oX^7qVMIp$UOs3JPBQ&4Rl}ZDYM<$
z1+mXi_dyG7E@xD!UDozXym{8m4<3cwfY<uvESrx6QaZT+Z^>ct_;2G|*W0Khwl}#!
zMSBzU2dYVp7sG$ta}8YZMsNmU2s&0Bn24%9<#MogSu=2>{RZfh!};NYxk_cM$l-iA
zQu@STteaB>y`@uy<gt8yu;02FL*lwa3XPAlluUK%^2Y{zvCrWp&0l0)eHYF`wj|JO
zPyj@hyq8s$=WHXMxbj_KT1_n(a^7UyF3%(Rz1+Wwb*XaRFPUz9Xvv9*jDR#7JJ0dV
zbK0_?KMpZpm=G!0&)+*Z`cpde*L?Bc%pRhwp9-8W{s2jqkR*2)o-Gm<TQ6GrbR?6)
zls2fzc_57g>gj&FGw~v6y1w-m(;L}HNO;qwK+?x<clDk{pANKJtOZ#YTod}3GqCAx
zBa9y9=+d9P33Wm%-5%L$qZ23q5gE12AG+Q-*S>UT^UMwpuSRzIm*bBv*401nzJAMQ
z2|2HP-{D0yH}k-5;btc3$~v^RA%TKU%_LuAuK8MhK7-C&NZ~E5`!v{Sc>C2Mt>fyp
zj)3H2^~LqbccgWq;5wL#*%dQ{(sO9@wHO{XAgJkRJButasI<d1K;IF%e{^wRRzbnV
zY9%YV^(C#wUHH1}1Cs!er@y<yiRD=|Gqiww=*5>GUwC3@7M`)weRPda{g6QFP)usY
z3wYq)ly(K~baJ=XARtmS8(V=7F0E_4Qk<HmV)wo3cfa6tRZO!D5BAyBa%>l=(LKA)
z7U-k&xJr~aiUc>Rz2xgmd}PRs_a5FU6?fyY?ZwKCToez!7Z|;fDVPk13owB_^CH8M
zoHAZ-I}&i?z6CTQqA2)Ij3)I9A+SmD>3UQvBBO-tuawl`3!Xb?r9&claQJNP&u6-~
z7|wc6TAf-%xyNBSQ16^0xszU3#U@+0=!o_UC*f0^HzcRL1qxPZGZ4wsE>sdSMp!%4
zdNK74^YRBn$9pNiR#FH7z09jxPO{+c*Hf^c>ow-8&K^DYL-<nU3+`<QMSQuY7ZPSz
zjK1?qHl>oRez(DIfJ!U1ms*V}jmkr-!&4V>^jd9|a(J<{!v5MWs{#VkaeN86QY(Uh
zO8OOPu^hG6b7<d{&gbd9km0pzlJbJG#ytKGqZ>oMc0}4xX5zuY*1n>sLvQH@mo1)a
z3&pjqwTClv?A`#kJK1f4Kap)73mePR3}>45Vm8)5pt8hZ*o4oALJ@1PcfsF8op!09
zi<t?d3v3(yo-sBXMgpR`^GSA;VmU|-Yws*M2w(sQ3B*ZJR%cmM#1r5<R0N}~wuQjL
zPM?ej<xCs;>yht1Hf2wD^)E6rRvrO1d)K2*9l+YWLB;fdt=h+2Fd0oC=jpGS`XoWv
z?{K}>kxItCW;f?r`yc&ofA9X#8LC%GB=JPDqP0aAb>NgOv-vJ>)t4uV=Y>b6nexVN
zlHb3-L|1!@sP!z0IaLfn&}TL);?3H|qKL~-o=J)KHvidNUQlTBOh$%yoT_5EOB)JW
zu|sOkaLXX6csF+?jafsILo>^Fa?P31xLHJ-ca4m?#~)t{BwK%pUyY@wx}JJ`;<R=&
z7?#tN{nf+`fkJYVTNUloB)vLUAP2UWei4?C@o3}8@^<o8og$ySm%Ut@{fNa|oK)p=
zYu{b{2rG~e`}MowgYFTAYL`8q3c}cf)osz$L=Fmm?lV2~P1Z?{aV+89RJnx5MrO)>
zoeBeUiHPG+v@6zKA>Z`uEAbFGInX$PGj97M)b6;FI3VM3Sh6mLk$IbsQ9RxDbMubA
zwYq*LCwY9<xLL;HDRBUQlW$;{|5NA;m#i)!gz<HK%6n#QOW|@My=O0!xGFt}#y2y&
zhVboQF=0yS{*t%$G>t-Ic%_rA&5tHC@Mt^&d9O<M<oK*aqX9C~>~)1c2a=9ZV`+6p
zK~n|)71s;zg6o)Y;at11nw}3(b1}LtyAKW3mLa8~TVdKJlCSJux*Y9wO-JLU@wSzW
zq>S(_xvH*asqJo>UZYi`tTWx3s7SZN==5j^8jmyJVNRedhR-$Yw2ypKB$}ON952+$
z?YiLFNT2>Lwd<*4``R{RIlIdunD;O!B&1}SV%D=h-v=g=nXy_l;WE)w^QSk4%!z)w
zuXGy=E5_%>hA+?Ek0F*kFE*JhL6AR$@w9gko6<@;ny42yb)cCJXyAW)PCXI=1~J#t
zBiR7iD9peZBw)qv(ckMldoT9K=W#|EE1Is&N7LXKGW_CwakmZb+lQU&`jQIusgC9f
z+=6C>_XX_MoD^GXp>w3pf-unAzkqR%cy>wLnOJQ)sY!}cpf3>*sA%o(Ul1xR`O%aZ
zH7=iG&@~|cE?9jpkv2u+>2V_=sR^i*oT3-@0D5Pt-VLheIPI8;FC_ttZLzVL8+2rT
zy5cY7>VZQ?wBeM#7WGU|o8q37&eh?(nmJmOBx&gSvw>4u#mxWwS-re1vmc%?+Edj)
zxFrU8DkCf;$u_GZULO}1_+!WH{x4Iz(i0Ej6~R3PHeCMgPcOlk4D7NE=H=6c$R!Nd
z2e<Nk8CxSYiTNu_9uQDiAYV%$Rv=qlOu4awCvcA@{<W2ND2I(vDWlW|qxXIzeOcFN
ziU!|FZB4TD{TF;tZ=-CP@}wU+d~hvh`Y%B_{G%HwOApbXwXZMiKh4LM4w#kMk9Zp~
zEJ+*KpO;sU0+nit1!Oin*I|!KJ2y)vN^#;#r6zay5||HW=kDqY*S?b9s>|W$o2%({
z%&clj>zn%CqYt(~uFn1<<B3no){$M9KoaG=frmp`%=|ptVP4;tpB?=-s8_U{tC?6G
zC!A*(>$~MDb~|nPgt?jF@o_zgwf)VAeAREmYHme2RDjIjpv~4=F?|6IbEPV%^ntB7
zn2Am6{L80H#ETUPb64|D6a5z#Z%fBg3~=iMYWk1#;u(L=O^KqO!$P?a$<gB<Sh_JM
z<8DVw3XT>jI{NzNkdxAI%wODMMjzUPcm-z%>bD+7lgDloSfIXBmPr3BP2f`2bJb6~
zM<IPqU-|0twwl&KA0ZJ)HP*B6s3C89QhEcY1sY%}w0dnn^MrP|1@t^$ADo+*JLPng
z=gzUwm^r3-(`N4E&>J|qrau_ZCJ}`<brpVGn(`%i&`ju<-jaRy*emhA4v?3)a|VJ}
zg%%@P-lbyJ_^&K}T#|B9<_I8auNl}5Q3pLAIz5{jK2*cmV|D?Eh{$EUyrOmGBfF%d
zTl4bCH(VzGP-7ui5p!LVk0zsG+K9AUchj>&tn}Wu@BNvYN>cc%Uo+_wHTSs>?OL?^
z_epHXUF^<^`F@^vyFQD+=UF?AV`Jg&rFt{1vWAO<7)Y(;O?+*j0?o<L?U!I3CI_Z8
zV@@sMPDF9DD$I*R{dGsP&fBJ){?OoUU{k425vzo7i@cVNX_tHb2>Z0QD#ye^JyZ{W
z@L>%+DbiY~0?6{(cAGZPhoFbk8UeW}X%BLdf8eIuWf{MG&-e>*CX|N>)2(r7<|z|5
zbis&(P0X**jnKZmWcPP&cPko$NH-Y?I2>Rdp<UqkH+d<Z@t5OpC%^ncZY(2_FgY0%
zO=bw|=jqYhB7#R|R68IzE+y0pxX9yHiiM+dBpZBEacQM#$zp)pGao+POxN?Q#vuD9
z<x%}kX6mxZI|*3_dN9@b7W?HewdazL{%HhSpc-@p^<oB={lz!_;2DJ@xW*ZU*dFxm
zkbgOPjau(~M7`+BjaK_Wv|J5+w5&H25|KVnC!H_3de4kCYEjv)_fyG1)7?_U3fH(I
zS6<-!>L@UMud>cDpNqS0k5}FbL04Zh;Gw+uc7wVhSB0SPIKRRh{f;Hg{R^4HoGI4z
z<9yHOOoELCi!ruj{jJv%aG+I(w28~8U4%1AMU=s13hUpA5yjGnLVnGlm_di(lD!Y3
zrI(Qt8k$ORND#oS^d<&uNnjk00uaCs?@i!6>b*{qCnE3r@MH;|ONNAnr$;ayi3pI5
z86#q`Sy*t+n|JB=yrd%(JiPYnQW4{H0*Ztgsp#}2L4poUfx*{j%z+UA6atdSK5zim
z6ff=j8%JW5R0was3)@r)aV&{i+M{C8Qi+8@f;5u=D%$*s@pYD%n1PaDHDAq-sg6R!
zhepWf-2*qTsoF^stYe@2yYUSxTvYR4n^RSx)<yhstgExy9b;x?Pcs;1XOuJsJ`Z3n
zNBR2zYNxMo7=U9iQA`#Ns;v6WD!X;rH()luoXu-$49QWF^-nlBgM%|;E{agy{s1aD
z*v~<~V8heciasiKGJl!iv$Y@%q#vA{B3^A|bv#M8wUtFIY=c}Z8ohVFn+okGC;nVX
zWW9%5z}G4sP!Sn18A@VXFoWptQ+UMS8{@`;%Dglk`6g~pgpWy}L7n&YH(5|?#x#6<
ze)k?71jGgF`AP$^^G4C>>mWY4`&M;0e{y_t2~5O2{(F^ac}?5OV|t&!eHopFL^e}R
z2@d{P|NX~T78*E(Io(V8Yuo7Cc@o`uLSBm%P8wsIxd9JYg{l&DyIQ6*=G86h4jFo*
zCP9gY_D;w{P!aAQisoOs8!9k+z0^eJYr8(mMz1q#W!EeA5#~$W?0q7jY>Ys023uy-
zJ%`pw^{LFd)twb&3*9;*BBk&PIe$giT_=FHRYr2J0c+B5Cf&<0XeQq$4U3icLg~}t
zfUgmsJiM!(*S**2=_YF2SovcUeUDK}SP7~oB0{UYiymvJfR_urNO|jVjG+E^9y*ZG
ze8It(YX$>goW9~T?_@BKhlbik1bXgoo<jNU+Z=L`r2A1t`aSa#Z$Pmhg2d@Ke1eP*
z>}<>1_S0WwYugHb*Oq>r#c`H+e+kdE<Y*|yDD=_!L8Q3%&<mh#K50~kJw*1}4cPhn
z!@HL-h0XH|bNt9D1+y&Inp`#>&>!{ywlDqgf97QT;!x-~i&Glx_<mR)B^EBtk~yZu
z49Z24t0WO6)mssjCjA0#!6XC!wVV4FJ7Sj$Q?ekKKo*CM=^I6YKxKF!J-?&$5ME;E
z^u5lwc(f2@-eST^|Dc^v$BdRP__fh@Q|CC`*u>x3jNhSvB^R^M?ZfC3%;<JMb@uH)
z;{WI#Uz*5XnmTU4kIQT6a)oKFAMIh~oaXvnD6nGJ^EUC7RuAf?g!Ttwt+8%s>?gc2
zZ5CYeLqxKukj%CR=@<PV@c?g*ygE+Wi{s2P2IRVwRG&$m<{3hpkdwY_gnBJ3Q`)h9
zaIk_g!zfPT#Jo~53?tCoy+cc?sX>0oB34R;=hEi)PEhz*)$mU;QM%Sc<Ln+|mEQm_
zeh{P#DOIA!Oq^$wj3N^;u@i?ua>e67oA1Eam_xRBVg_Uh%?C(o720Q$1_ysNgv~~y
zg}5_2a%@bx=B`koJ5u$s;iFGLgN-gMI#Q|lKqWKU13;jee6~?iihfR$k3n0?%X-F3
zgV4}jLlzS^!sQM-`I96J$X{AJpIS=gFf;fqe`wr(m{OMlg5k!vDg1C!;1Q)*3hjW*
zNjsghnqa3nhjW!EB8L5FkQtE)#%%GzJvTB<M-83u=GH*u+X?;$I14nxl&K~luj0p4
z*6MSlOh>cidUJZJWh!g%w<f-Y{&g>NlUf^qjLDJfJQQ5Qz!qF0wy+CPT$&{w^*H5(
zX1Q0d%uQs+07oDeZ2Nb>W|)?mPo2|#8HV+wWmN$guyselwxAeUhf77cm=#`Yv5wOA
z&OauThDfxnA;aAzO%^{$^PK7yn@}Hk<!g{Y5bt`8o)J-Z<MU@3I)SRq<0YOgx7i#K
z>~M-`%AF8~e@E(vKGqtD{m_ny^pqia<DhnJBM)^d2R;bIY%)1tI`Ct{SQmy%)k0@t
zVV=BS`fEh0;OXmtfC$CFTtL3Erpl{S)89RjzILiukc}9OAH%~jwF=o@$*KGpuyuYf
zO3H_X9A>1sQto04Ue!EIKwNgM?gR8|#Kc)oWU{SG-J;9$23#;~Ev>2QK7}PxV)aq#
zm}9;flx3Q#;3yJAx8vV^ESDbp2o)V(OnGq1cCKBwcWA0!m5L*#Yt5nA7~i=4LyD`*
z=yMixYju~wEUisRm`4s(iKgyAguh8oLWb3k)h#%M1|u+~R;)yDbc%(47CN*MROW&O
z?OQ9eZ@4AF?((avSbVY!n&BfW@)Dwwl3{eDO<6L4%bnEnxx(!lNildb;^iQO!y^88
zIOt8LG6ck7g@j$JK?o$lS*83DrC4w#f(mLTqA@T@g;K!r3y4YAM-hvQvsiM30u;9<
z#OONHK;zO$S6HywfAA%*G>dN5j|gwxMPC@@1rbNeLD9KX<WBjEcvNtMb`O_V50<|L
zg9Vl3of0Q$xk_3Eyz_#gjP!19Nu%NJHHle>P=nq2AXSAVEz{Gz6D3Q9(4L$5U)GG=
zJ0KXvfaeOM$-w#V*z5K)yquEIHReeAq=M>JV`XPI)U*=?I{4Q0+M@06p79bspZX07
zo0Kh<HKoSQXC*wprW15DgVaG<qM(-BBitHvsW<!i#KBw$XRSwx>!3veP6+y?B8p`5
z+`N8zrVNBAX-kTJ^$u&5mQgP~+AD47Kp*2*wN@7hqbe-OgJBU9r!7H5aogorl@d`1
zqHE}dY%XjHxJSfh`fd>{e0q`|(O^~qx9pElHq37MHb~U2gH>9p7G;vTEWXv10AZ5t
z&YgN_aXW#50ANl5`9ezv?x!CaL{MHNYvTvH!TJeYQtMgRTFR4SK&N%veYKOGFi^bD
z4Xc3rCV_9?ava^Lr1#luEZZTnh~T%m3C`MEPmk3GE~0$j%O-=wCb;2w*gTM-!Rz02
z0?9u1kr!H|q>u%crh3Y>9}{F|6T1O#1h{1IddpqMn~cadb{~Srm#@C>9-Q+8S!8%D
ziN=IzXI~k{<k&C-;VXHsX9EGokns~NT`I6&k(^r4f;;kK#$g!d{U+#@NA#cbt}a2u
zV3!MJ)ghO5G67&0jyr;w65<^`J8WGI+gbC0ThiKY(L1oNSm?7pTM?R$<#{7hHLTT>
zUcZS)7=R;p`wYs~pUyL0xXuRNOuCk4{`>Q>tlTuVV)Hphr>#Zg)14JQSj1<K!^A<7
zX$JqQ@?Sm-p}_S{c$h#+KV=oXFFNw~t&*qO(<MaMybMP;B@yY$ur0^FE}ZNTuG`ll
z#6FEz0W}H7>khi3_2u%HpzKYIH605#tJv{bRwCSvFTmQ{wB&9Rw<lH=AuZb|)R+!6
zVZy6yTuqJ1V4%}sk}|RtFZRbNn;KKx*-g)fBxE5Y@vMr>#9=4~UNA+hUrI79fckrv
z!!Tf{Ci{6s)|u7=2{DbxNuC>qEFe6SR?8A=)97SpAI^<=ulm2gbQ+&Wo7FAlkqn!o
zKP^S3llmU31#M$V5q|84N*5AyQ~<5u>#A!^jf|{3Zr_mPkL!su$E#T>@v+RI=PROH
zhCHyD?<)R~bk)>|GQH6~mE+$Kn)9eDj#fZ;YScjkd*wGpUmPL7P<0H>Z>WtNVRD+U
zK`TL3Jl!=hHcAML@*HfHj_hU(exvs-W{S&qOCaeG_Ex@Ca~7qhZagUXEw^Kxc=p#n
zGO*tCN3SIzygz@pJSqc)bVX-=EOxN0Eq)LP6z^1jbuSm{ce^G^d-dsr75>z>U*9JM
zbo=g(YdCQWKf`w4ZP==5!IIWdqg~rzLvazbZ}WkkfOYh$E`VP|&tslLoSTTnw$!9B
zc=_I79-52O(BLvhlIm=Rt)~6;WVX8Kww9n=7vGLrDG6=o=?eQi2*a228)CxOzNu?s
ze#F5qtB9Vj5|<}(@V@=?YpQ3GUGJe0C)r-?ppmns^KqEIznBO`)l;V~f?H!~L6N58
znIaiRCW<nvhJJlN7jFt5#m&`KBN2HI?q7#(+l1pDJN%=^?PH=a?i*2fuxzT>ItgXo
zb==PAd;W4WGM3jddSUr^|CkFa`HLIq!4CJt{Knomf59Q2<ylOCpk&3#4+k6FW#V;~
z`4IVQZIcb6ade#CYAPsZtt>FLxrCOlWd3Anub>MFtDOArAZf@C+`1&>YdkLyiS@Ww
z*tfXY!=@_j?8yvlg|K%4)_flbt@6V^B$a$0+7~5cAm72Eco;@i6BD6bo)$)Ax%j3J
z-FEb%4vr6h^H|*!Tu=CnUPkJOx@5}{?#5D<%eD`8{@XK=`LW!X%R5_l1(mRed}uD@
zG5MMn`z!mng-<b)@zGV|p-M*Q<q?rUL3-$em+T&g5w;6d7Q3~8v&AEX(V?mPnvE21
zvT{tlONM~Qg3_0y+4Fks+4b3@NdA3v@(xU(vuPNG{JU_`d@R&NUUN&yyz*7Jp<c4a
zpP9s?7vfSS73u5AMnN9JHUq;YtCAB#V1zKK^*AtPv0?6cd@xco`-Au`#|l_Ot0!A5
zvEJGi<HCymtow8&5O;IIwPXPLW&h@SeHR2Tp*wA}K6zCA!x!z!=;k%F)f5puZ^V)Q
z!}q>;hAo@9&~$_M97s*TxG4cOqAS<!U(&C63kU#MQu#8-n#?vOBCW_~RbL@{;iTWI
zFtf0Q+kg1(;y-e*h=KbqG~IR8hCVyI?^ryOk)L}PVj0l9eEf1d^ohvNEpm1*`phm!
z_Rz@fHLpQS9K8wGz12i#Ea^6~VK-PeG&cAv9rkyFVqN~CT!yYO-RN+5M{4Gs_!C9A
z3BFOX75C<g1r-9(ZVgxnIePNXIi;7`+d37Wj&=URYp?mg+G!Ld%)9dNYoWbgVaE&h
zb<y$7J#yt&e*c<{Y_z+)Go77vd9RHgefQ^kdlUOk%s;z`@l_6?vl^`r12Gv4JVfhf
zcrUndg(9p)MtC0f3d0m7%rT6X!W(^vNr<^eqk1Y|1ck$e=lmTdW+K9AvwG#ISymk@
z$Kb4IK_cejl$b>u5Ogp&7!(9_tx_tI>U=*do(?U?naO>jCY&Qo_1Tgemsu(5eTo0o
zf4{@^Sla=PBks}$^cI3VVxd7j0^9$V)P>9F9`Mt^Ag-zF{dsPT#Z7ss>%5Vy<HjmX
zvV#y20%!v@u{|;zL@(f3?wfHRgY`k2ZU4&g=Rn&L@=;nFt$fVmJbVqFF#?*&_0KP=
z-HtYARC&gKRhp-g%-jZf?*C?2msRLMnEE*x5$tMK;S(6`FTm;GdyZbc-`c^mfqQqZ
z7X|~+Ix65g9N}he%=gcX-Z#vm2O)#ZC<{-kyzbEKP^Y}bCsgrr!=HM&y<9VL{{-2~
z_!t%V)7ZNcea9VH)K!p|mlqbY5x`1v<jIi8GAb6xb~u9nZd@~Dd81pvtmWnXc!uuD
ztt@3M1_Ch8tVRl0O!0CUJk5O?p09lxo@YMwnFew7!GWWQhfpe9ucVYPQz@jIp+e`G
zGjOWOU|Z^?zc$2}v4OYAkZcqHLlfiPRfSrq8J#g&Ay@*fVc!UBT|?WAXsytc$k50A
zkm2f?f*2X}gHpMeFD}-(wj3{@P^L=`1IBqC)*Z8MGPZagcpoxuG7ilvhz8T+2cCDG
zr#dydXfyxNc!FR+gz(6Y&)zKmhpTrCuxwejM!RgAUAAr8w$){H*|u%lc6HgdZQFdc
z&)ui*{oemMXGX4!%oStA$Oy@Z`Kw>zX61>{EI5oP2im=576iCHHG154BK5T5x7H#+
zJ*w9VvXnfWL>%lA#a?F^IWFg0pKM|k-kU2KPa#`y<<Sndcf#Z@XAes*v_;3ip^tx6
zUcDg<B)@HK4k@S0$}2Dnx^&`f<_g@`0j?nANzOyk--r^%?Vd0mK>5BB=s0vzbsEl=
zfzc1sl79e=rmWUv@WFnKT~+og)HjyBYR1exvg%g*nJ-f5Cf?b!;kb%iS5iIIJd^E_
zEiLCzbsk&p!~;cd$HCmU*R*cUlRtq@7ZiC`QLVk;JzHWYs<8(3iGrZUyGF#;PE(<y
z1KiEiGHPkc)kc0Pk%=OV3mAf^Bd%ITULNeoY*bacmzS}el}l)DYH~%#Fp@uE`FDow
zkc#k_CfW2y%x~^>tq4qZW~!=iG`U|*nOk#vG8DfOzn&uYo;IFEVgU*Qf8UbV?m0Rc
z9|-6GDMdnk-t;{AzUxbGQj@9NO4%eC0wKQQn|LgVDy3d;k>fbIxS*LHeWBxap842y
z2$cwiYp861Esd~=;?+rolQ=@zj5S7=>)b)2`DW;*fB(^Z%}FB0=siew(&I{kTFiys
zv~Q^q2<NDq^X$qC;vxRI2b}h^TRPvpj;q-GXfiYS(DF}He!%hR&yF5h@w{d+#AJL#
z+LhSnT4znot$aon+ltIWNQ(iZ<F^-J)LF++_JAwUkid>#vJO1$_$5ksX&%&|q~!;?
zzb9kuU4*kR21zw@rV9*iR?rbx&a28e><stTPzvueX)t!XJVasfm0>D>U916<PJe3k
z%@;EA%$Nu;0CV#&NqaEm`SI1y*p3t{m&MQdnVsF;4e#`QX*@LU=m?%Tanuw2WJ9^&
zv`L#p*!Yc~>_j7t4waO;E|qp)*s_hA({3h?==?cr43ZYh+p|wt71VVM`d9(c5gV{>
zwna5XzVgyrEi=Kc6Q`(y+HC(M`K98z^J9@=yD<G4)}s$`*D({82<_Cic$&7KFEt77
zcVr^%PJ3QDTJ^`;{^pO)ymPina6L|B9N0IZYCtFUoo+xK+*(n{w^Rsaq#O!;rfL0p
zDfleoY+IzCpd1iJR-A78_j%m3&W}v;zcSs+S7`)>nXWv9m4JZg??xi-(fV5zUACdz
ziNO;Kxjb@y&}KSn!tvgXS6yg^i{1HV5*JL9mDK;H=*X6Qe7CQzHnP$F?4xx6Y1yak
z2!?c4ZFo8S#Tj+@)4bk6^`oX==v4Mx7jWqzZ_g&yBDdfQA;ASZ4-NV3+l=FPtLV;C
z4Aga{pKsMqgw7l0|E&d}jLsh)#5(noLe$%Bx0A7*Tl(<q+-%7H_*}*8BEgHe8Hc{J
zm0;E#-f-uAk}^k14N#%g7DIVIbVSb0wBVdX2ldRH++jPKtQV--r~3%fiSKbDBz;WA
z>2^nG*FI1KduFmSHd6DN1)y0>FD(c$dXLJ!8z)2-5Q`(8@BX=<N^hZ{8{LPyu0>y>
zJd=`Ka79gyU2v9{{M=@9HkG*07ur%vbY_}TmecGshSf;&ESGlnON4J$c1t`isdBJS
z{&rMc3TmeNX~}qhQEVNk4C_4o48H!=D_+H_*YdhHQ~g^{%VIzx4t*_oG`7ucK6@7i
z<6!yPzPU}A)U<`|6Q?NiAn3p{g4KIj#0B07c#r;IMjV%^MOLMrrJE&)`DkLrtMWW-
z)r0K6w|S^oEM3s(JnbFI(-YK=3gSZg4)V%kZkgN14=m#o=~W_~Qz1tXn=;;smKFnl
zPbgFUg7ibFKm1ZQwpsS++6jo>O1nK(>D^Xz(`nr9eA6q(lvlgZ4^KunSy1%j%56*W
zeR_q0Bf2=wUG2S*kYH_jTMEGra&^M~aHI?1d~sMrNKPMO&i{wVSW16XXpaLkg{>la
z#%s5ruG@AG?Z#J5$@@d$+hy?KQZfryjK`4qN}&zEE`+R;p{i_rNd$kygSKG4T*qu8
zga)h(MQXTa&>x)%$HIk3=?4SMg%EV}+;ouKBjPfl03{}h5}$(w1y6V^?b<mM=2r$b
z5N!;uB#?#1MwEGk<{a^z_6S4CT3?01R}(8wR{&$b%dLI}+GXl;8=Fd&>GGgkw-PtC
zQ9A>&VibN|p#B_WR3S+DRKH25vXLNT;WR9}>e+zEQ(%5dQ6*1?B#2k7EMJBBI6@{;
zK93X?n9SOuM-q9-F*&)d)jdTnfEpk{?FS|()bg{nFNzWb2%q4BV6q3f=FNuKi;D;2
zh0I<V3))~7c_;`D(!5a!HQF*$Gczg{z6gcHwrxBU8tdF<@uC-o76?r9Aiug-k$|G2
zq{S=%U;flh0%?yLton>tp?wYox}%Ue+z=M!h6a`7&p9|&NSA=k`yOZF6R^41a*G^@
zxp-lvDf5;<0T^N-fOeT-bO8$BKlxd1`#0t$AcPRaq-73d4lG;RB(8-h3kOWzj&K*^
zfOwmlsOvGSG#MbT>BHvPa`IWanxeofauPP0wl$8SoyTK2fusSsGV%*#qAh)5n28>c
z`+VR<(t9&mDkn0Rg?I+01rqtO_zf>V^-i-0sE*s&-OUkb%^??@ks1kOb=G*e)(KBj
zF~iEhEc=paD7kuFCX||+RFW59-IN~#swlY>dAJ*MIMHwN!Y%!-5)BF<vv6+)5=9p}
z`{{&KM{@y}NS*LsA9Kyi@+eBK3xYc|<jJPNjIhoKWukknQ0|TFW=YA`7xL0aIskgx
z73qjkv%P>(y}1>?GO1s?yoFS2l)pAA+pa!v#F=>#%mef$vzlkvt~w)^j5b7#?H7US
zyMF&d_G&Xffdc7~lZ)Uf93v;IzPj(UgJyR?5L2CI@UcF&tnN;q#tJ)Y7fKV99uSC=
zRi`f?`Q;f^HD2y;YBoNXC@-w6TjQF|k?SI(nB7(U#^4fU9v*On&JHcC#EB&Z&Eysj
zJ<y0kaJ2PemdlSPa64)){2-Yt<Vhzy;wS1YrrQtBA&83(zct1#j0{%77lzUlRyjE`
z)^nGvNW<ghg^F+kl%lAiO>%sT8I~Tic>=Vk3F1>(h?)8Ogr6x>S{O7p;pQOhDs&*1
zV{7Haf|Do9%~B2kg%RJsDu?5on`fH9gKoOFZMKMK<Zx_HggKy3Kp5?h^XDMw6Ds*G
z$VK08p#n|?xx2t)Q}?W4ewl%se(7id-jp~YZO;dTXcV4}S4@~qr&$<41F&n>j)qvT
zjX=B?QL;hzTzb?w*u^<9$B@7r%HkqkOKD@B!O$ot3J-&4%-`p{$6#bI2HtlJ<`%5*
z1m}$Kh?bcGMFiE)FlG~SmC1gSc=KUXb+}iA#uoRFsl&y5fB|}Coh}W<3*K9O9IbMT
z>K6S^=F7=7(dIcXC=KDerD$o%ihT0qCDu;|P}o+Ukvb{Y=(8r6VWVa3L77P8l-But
zsaJpQ8MoT|)V5Z1M6z`-!@NKStZcBIr>VTif;)}@GoB6msD;}IUgZcQnvzZ-JM8F<
zTabR>p+g<-*_5DVA#2_Gqu$qaY$C6IyBcL2s@Dg%oG1qCVpz3+Ex`jQyfYYZ=bhhi
z%`vG@HvvE0R)>3dr)~MCyXJ%Yr%Y_xha;Jrt6mrq&rj}}g_AJ(-;mLWn`1oY?van!
zsoiTADV*WaOL~yh%b^W1lYPX0Y#5@iRl)}bE#egb#T`Ja`5V8l7);$EQcC@Ew)4nP
znVjn$#wi$SN8YHMkrf4jkY0BRq;M9m?;>N%4E}i*f)rs%YjU%7Nw9@5D^*fvVz_pa
zIvj%Xnv}=hJn{5_sDDT1+k}N#;Nx$-=Ywrn!;SFRmavUzPNQa#N$2~Mq01pv@RAVr
z=SyhO@Ai*pqOSC|sHKBRL$m%>{2?jlQ?-EB53;)E0HiKpgwiOXGg#OXh>sbl!~Nsn
zm{c)s0RW^$Ag8#;ov#+(h`eN3a3$pZizIn0Yrn51uqBy2d>Du>Q!CB{Ii{t@Q<g7k
zA#bdM{gIU!EUTP6=>TVUU0JANOfQ<3eGwx_w@<<>l@*yu=cBUDSl0Dck=LCVQywWb
zxIN~$49r_xh@Bt<zUgXqG8x;R$POd*_Jv-1q)umnLV;vh*O|a;hFqj}SKHx1dwW5Y
zY9rhvy0E%Pn8{$UA5#Q@L~P2ZFg~T06GCs+bkwNXfWW9;o*Uz5hYC;xH3Ox2CUxv(
z`H>*Tg<zE9)>78)2{F^QKTdA1XDIu}Wao7tl19mPWuMTFcJbpsehCCzdH&{iQa_6c
zP(l?0Zj(oM%ym$ZkRWTlW~AZa*>w%>zBNgYP!fp^MUJp!N(hOYC1?fTO_+PmPLhjy
zM*)>Q&=cvM1!9`9)+GcAMW8>2gXsE+;0g%gWqq6pLliu8-u33C!bt7~QSAwnd(o7h
zzUAR=AC-d*KTj!e^$Yz*5+U?lY{wFu8N_XB{}>D@{<xth*?*Tpn!uq*NuwKS=EScq
zP?SBgrJ9;+!yDGkH|SY`dV_grEzTz6>8olZoSM7m2?$!WTqiz(;?DP&klHQ-9=yky
ziH&AD{CNMdsZDiLvAA@Lri6m+{x@<IVcO5^IJeTtiy=HA3jbKiq;HLj5^)a1>1>=T
zX;O)}c%lnP-id)E;Wl&;#XkXp%MU-!(5JY0EFbX$y>*@)Hf^OKUD4C>-U~l_^1|T!
zq^_7B(pGLcI%xwlcf^f#)e#RCUUWv1AK08ST?`I9{)NoWmeeH5U`gh1dXp&WLes!f
z?4LyQhx3K2%)~-@N(>M8)PIUUKUyi2R-W4x>#uYPt0Qm%%f+?ovaFWd>}jcE6uH|d
z!qn1%CuEt*KWK&1n_1^5Y9ktVkSf!Z2jN>Z5OSiDQI_F_8|zEmSej@AUnEn8QUB0E
zb*5_5O`h1nW;b_y@~T=0qU)*>0xtGkvT|+(7n#kj2eO_dk5w@V9e7HF2$kxapEze*
zvRNq)m4`aIxYX|C-k&6mcNFFy%(D-zGh^|D8>DWc+CP;p&Y_`gSWVYn-I8r2XulHR
z*Ib`rgf4lC15{dqIMjiZ>xSdH1r&SXY6E*w1sGdH7wCVC(Ocqv+OMHOU@xi!{SfrQ
z?}pa{_m~AQ@Z=`+@`A)x>i%NL99DskDu&d><;aXgU|P&N(J^sp%8{JRW^%6J`QMj%
z;C+?@g)P;d^W0MsakHH59n4Z;6U-<+>CN6j&JZhFp#lE$@U<Bs9{N+BfEsv4I7fB&
zF?#vbu6K#=jhg?X&R0XL@dG0;I^chv-L{YT!Blf%$&Av!j3m4vVTXO#IAB*7*OvI3
zpYN_0JWc#?%1%^I+7O)XpI3a#fdP&+6=jee)YWaDhWV$U){<g>gu?hnfNyXTKR2{U
zxR_Y}=gEHz*}e<&VF+49?O|dLnsjJKb{c7S=|L*cT;?UNVBKS2>cUIottP8+qW^0R
z1wSA8c|x9{L5Lpa_Dd|8{*h#epaLa+f;MjPP|uxAa*Y4S-_QHt(`ke2HP=DiEiQLM
z|BP1R>O-pT6_obvVXs#x4Cp~1wqrNg-d`qGgGi?lLD&BKJJJDv6aN=M(*sP7(8Wr#
z#wIEI1+Pnfk7zh_f(CumL@Js3=u#=CCMACj`yR>)Tuz!+e~6)|Liodyt@gS*1w3;+
zNQA{hEyvot8cA}Ter^zyaQu61|7)Od_k`dN!4F9YV#~5XX=K|zz;BruZ8X;05)Cd(
z*D;C7d4^v+6zab#@sD4m$N(D{Aa?-i6yhbIWJrMIj+X+HUjzx$m6*+KC_R%>1WA(;
zMzH+rL;s9(eOj_VPl00TJtJ()<`SUNq=%##(~nNpC&mKISk!PL#ELXkG4@}J1ohky
zx<_k4{nShQH;qY|d&OJm+qIXu0vk;@;!uGG$1Rt+{xti^|N1)z{Jo}HwYL@q5|KiA
zvxM49bhzRW1TH=W(Fs34mc(HPvPj+_^#5beT$s-gOe4)Qe6Ckv0);tydKz}mrLllx
z4gEkQef`un8Ww}>S*2b-#{amV555mVE!Ji-#1(;zPD8Bp6MeX#q1J6BrQ!|Muqi0U
z|NU~PPq<444K8OgyV}H~i;}}0^2RC^TjF=`u$y2m>(e>E(_m?<?F+T3tSr6eJG=<U
zW8XkY{O&sfotkT>(u#AAe?KNg3MluMXTJn#nYQb?)-t!Vf9%3ulO{DZ!4~)tOAeRR
zbQGt`+7#8<M)4VDCiqXEr>IA>nzSgQ<MdT9uj{XIU02n&>#TbsdQXL}57KC!uQjjA
zq=JolLs65ou5TF2+gFUc115{}JW?H^QZC(o<j%>!i~zmzCX34Zr5We(VYszc8=%vq
zvXOp$X5-Agw_N_^x_Dg93CJ4Ij6@U12P~ZQ&mr;8vMC1qASv1LN|jOU>p_O0si0O2
zR`hE`;1`&KQqpxl6|U!qjWK5Qm}Sxkf8G=b%qvngafvLaxTvKm&-OEU6zEr8Wp75<
zO{8#A>=ejdqP%}$E>aYQpO~in9i8?rOhBl$o*^85xS}Dc<6=nnT&Cq_n>7}?)(+K^
zh1*Qf+$ZyVDbe3u$kWU)T@yO1Tu7}^!2Q(@M4Qnhj<VCz(y(6}2Q)O%d2=)DS|{UN
zIm(ka)<%q%2yyZy7)#J>atkfCFgp9Mi|YY`^Yu}x444@#*x6}Bh2M0Sfd?=U*+=-T
z9z@9Zmn51eDiqp=Yi5%2De#1EU^3KZBy{%exyq9=<L+IkWDmoSME(&S7B!qLXfMy=
zkb-2X0tZK?dC>r3Kbt4fEha=0ayL%1U`54}CfQTe?5sO3dO#5lPm>;;aMs4kF>NLQ
zR!wU&pR3Ytzimbbmj|muAV{X!&yBz95%Po5A*gC)+4IUADJVBxwf7IQg<{miEVr>{
zshrd}u~Bn4^>S&QSLXb_)Ij0kml7gTrjn-bos0jB$Ri)KV8?T=NPN*$9a~oUwWtp<
zIkx-nQB3Cv&iBl5?f{CFB+`Ic$)O@$83E4<9Zp6sg6bqL|Aj)2y)HFUMW0SVk3GSr
zrfyHi3bfa8-b+XVg(B-u3J1~G=~>JnW58A-Jzd5Sm11V`3WuMslP!<Vgoy;&h|&5M
zbv~wIhNKjB-eye!N3~IGuJbAkgfHDyVp&pNURuid0_K)5zujeHGmXOAKxDL!lbrh0
z?}$nB@=4Ga2b>m8w9)K}Gh=KuriGZ@;IU??9$-|t6sov>*yg{Y2Mdt6=l~84X?)E{
zVi?TCurY5dRxPLhiT<eZsGJptvGH~+fxWlneP@^FPC%;ov^e7_`dOe|kM9|9{5yhU
zzwt4PYW!MZYM7Vc2NH;m#KvWW(Z=sx@JpbWDQ=CsA(J#o==Hi?zj-$6N9kxZ+N=Xf
z<fu|za@LrQDOTxDc6_5{i2v?ecmNbMpz@5A7N=Hx@lpht5+8XxsV5QnLQ8Dzc|deW
zMq)|(fx&k`?K|HK&EP8vORG}d_eE>Z+jtVnRQI9Bs>Rcl31SP?syUvG`AsG5*9T+2
zAJP-9rR4`FjuX;9**}z+DQ_un)5;~%8loRy91@S1EH^dbT=f{AIzLxAhaDd?S+_2a
zT|ORQ>mwyR>6hEnCBa?Y?k+lkcS_VdzXou~g$z9&SD49lLam(3LQ89lc7z1`LesXo
zL>yp!!x~APFArA-s6G2+)@&^vb}K2579YUAgYZP&ld6%WUsn^QRydfrm*%C<8;_07
z*c`;!+OLqT_V{e)OwM0plW`j$T{X+T69gO!HaxCJvyL{ERmYf^xTj_l?`xMRBY}TB
zkX?vR@(V}Q+qc}yO8<OZx9w><BgGx3Lrz;Rf4vtmNlP47eWWJ^qsp{BjQ}b<-0G$n
z!43pw{^y_0*G%BYVXZO+F`B=l(X(+8i|}@jAEc?d4-)2TP2z;1m%G-E6DP5Vo=#Ou
z0+w}_bOeg_luds1gYwtPOP0iz%j-~^GWnRzoIY`Dp1nhQ)Z}o{uQ1`3dE&A7A=JRh
z#^0<JZHX}**_W&W3+8KCb(yaSRIx=>E06DK%2&$FM%;=`ga*6r4mHW}d$)lKNV__h
zesI5ZZ7gO4mc6f8Qa#cB=yBV3gu&yu9>LKzN9pmW$t0t7&9<7!y4luB4pdI?a*Bj$
zJbts%8RQmCnI+!tUQWtSiWW)kU63@wd@Yj`1JGQq)r~tD^iwX6OGDnqHq({mi|})l
z#v}?%*XK=25}pg6r%9^;jbAgada`ZU9SRHTo9}igrN``@k!9|MU%$}gE1zBWQ#k7O
z-_NQ^_ph_RmeOd}dxoK@0{;`*(E%611ElQFmo>uoFT)3FE7DQ-l5<N$lNF$(Oseei
z22IX$6bpC_M|g@Kjdbj@(f-m}=i-g-un$1L*L|#xf29=@#(Q&Yo`h-J)h1|+iqxCT
z6oWS6W%~e~ps^zk34=f2<9q6t&D!8MHg;a!K>;W=TK0IW3%pSQlP|Y1H(!mbdk6K_
zA7R$ef{}KPyZc0B$_tYX3UKtJ<fmJk`1AGF1G11IJT{ki8f<}TMbBh8`Q0T1XV}f+
zJH?_Crz#0CJ#$LsA?pIfYS=_8vA+ZJMzk$Tt;U;G{NUANYj+>@EHdu(8xg^p<OA21
z4M-6SF;xxmzq=Fx(9kKp*SZhagm|61EJ2184nvx>p*9P8z;YhYHG=pM-6)hKCDa6g
zJ?W!zRI#YXOVM~|Zzx12JE>cf`p`G`ZvVX%px)SadRMVZ;5BFE_H(5^K#T2HQwrF`
zZX*62hyeo}I4P41jR)Ce&Y*5^>?7w@R-;Dku2wT`^Ywm?Dl+ZcDTjavAcG1nny-sD
zoc8;n`__;^9Uqt1rZ&f{$7Jm`<yqw5+dY(WvA5>TGCMHCHcmw<Z3e67`GI@^bz+vo
z(4L6Gt&y>2S7vj1yDV4Z<qz`Z)?P~Bi!L|wQm=#2pC7JjS)4avPLm8m(B(D~+ICl{
znC*7-rWGw$^a77Op7#Y9^VENsh1b%=C%MPsjCL9o7r{V3QK4y*og@(!7o2fVk6F{-
z)*d&yz8gFR3408z7J6Txf!gU$j&}$9I`AZo{x<;d{jq^3{H2$I1dt@8Wi<0T0d=y~
z2X)Cz({l6~__W?-xhFVw>HWN_>^xWa>x2#x1z(U~K596grnt#|c&~QhF!0)x%mPi4
z6*Zt>ub{$n+0cSDY$OOWy?zdBs=N`x*bP(i!}vC=9IRg{dT2#4{C*Zy361$V6pL~-
zJ?zgz$UAg18dg8>Yr3M+Cj0)qi)pxkpblEx!Cd+%U?nrMGTijIK#d~=TG*=xgnX1X
ziikl8dLcV#x3S%vPl{_!BSCjmRZc0~MRH(s{W?@u_E(*g#O&hI{vZC7j$#VqnD+w#
zZ3E)jE^QRDgrz;3C<mJVoxFdyUu5c7lZ{53=Uko*cZ2PkpyX{6S?7DB=(<B7hBpl$
zlpb!@YfZPu>Gexj=V#hANA!E`&jkyduKRNhTvxMGg#c-#G#?y<*t$jrPW`tIjid1N
zUJ@HpWqKz`eQr*Co==Go_y~Q`OgZxCr!@`$>mrFtFL!>csk`(fsj$M3@dOU}(%n7M
zCK`Bo9WnTjfZt@9GwB>Ia`atI4UP6wWhE=i!v8{x6indn&W5?Etm$phemDsFkr-p+
zyU0!Hi+pIwrRGhN=*nXYD{FEcb3#s6;}a8;m1mhO_Q)9|JX#d~`Ol(R<s?HaUnaL@
z^F0$Y{X?YMxk{kyG(6<w<Z2XzR>$WU0+5||+b9yov=5Z=qdWxsTQFi_h*VZzZa(Og
z*?J1>kYeZmvky}Yfbo0=D&=#a5XSj+NA`9%mU)C9>PvGi)eex0I<$W?lhSBtYG+od
zaAK=GJcMp0Dz}a+W>u-*n(A8I;wk@V&)d07U?f45V&sv0%JLNHi)hzygPf~23i-sa
zVlzzt9}p`6&PUN+8cNGy6jX*r6tdg)hMV&{1=8@?EZjrB7!`Kx`a1n08!@{=<1hB|
zz=v<S^mR;gU4l0cR^<QI>mT=bH0oVLk@kvB2~I@G`DfGxb#=mveT#?w!a^iIpEmn`
zDHawA!+xrkwB3IGx*BbYpr~SylktFCQd>qflz+pmZulI=W$Usx5CCYn7`l)N{iV8+
zc<L6n0<V<X)X{uf2nu~@`oG3}AJqr|!-kfjemBz3@qWF@vp9V{45T<e=kOUxYEDKO
zKlLh*R78tn5k%7dJ=^~YWuG0D23xf0x<h%3C2+tnBA87O(A%^j<JX@He`AW;w7DdH
z{I&fXIX-~v$x*=u<_1V_i1O139zfYIIUFH-+2y3^LV_rR=jWw?{J$1~gf5uk`DxSe
zfbtX}<H{5&^?(t9&Mb=v8`Co{K=@wtG@9-E$C~^FX@Wj6cI}y1`7%^&%n##IiCz6C
z77(@`_Unkrf6wKAvBY4H+=ZV>)3&0-2)jOZI_5{O4Eg*wrxuZssAQ22lTr+BCld5`
zs=vXz*Yblghl2K%-8$Y<atk9{tx8M<$u%3mMV8W2)sp&R3jb0GdZX~RY#G>2lJXY1
zT)ZqnASN<9QMs_xav+V|0td|ZwSj*WEZgT3b|%69;%Yub0JS$ibxSdQkc19{1iH}?
zatP=My^3#~%pp@`ztRF7pJDm4o+yAS_ODTj!9OZIXFe2S#?bK;r%)tl__={N)A93i
zOQez2yqwwtu`g4?6;)~T`~Me<`v3z@4@8A4A`Sn_+jj*6oyJBhSGcYL5RrE1?LmH^
z56o1ghlI75q56xIzt=4j{F;l1cD|!Trb(huL2nCrQeP)vbqH0t_9TEMXzJd9gI&Mj
zFOcnq_sO(GLrp%BV)0Pz*ITaGrqOAbtsJLIjr>sP^{!wvgj;C&Zprm8lkyKgqNjnd
zohgGBU1&rDdbd~tF{-O(8$`m=@W)^t`{DKYH@hns%7z2Z|KB~7EiIu@MjaTiMs=rO
z6%>dq&Fupu%<KfxDU1!NK7?b&oyKxjFhiR+X16wMLZ#rOcfj7>h#`>-POdJRyDgz$
zRK+BSZXv#8B97pT{L;qL-P^$`HxGl2UK3=`{XY(6!>Cs}Z^ZTKS_d^I4$Cj1v_(kY
z3u=rXzPAfHz-CYM+K_(A+)jfajUeFM#Hd3uvQhmQ2-d2D*$Su1rxsl5%O_zg2Bg((
zP}m#-x@PpoAH4xTD-!p96kUO@P(jE*^H0Aw*<cBif-{t`eGo}qS7)VR_m=_>WzX9o
z$vf<EMVI)C%q_JxgHIsk+-~6H=l#9+5yBV9K&Q`-F8Hg_hTnKgtVNBpc)Rg76&nqX
zm+M!bMXVvi#kiSTr0?@9#?W-YRloq~=(Uk67fm;I!9Wx`=<IVBitv*X=Jvoqre#Ty
zxlBfNv!vP81)b&1RLPG*Ctg7K;Y_FCB+R<pavE|OoU%xE1IaN>`l?pAGN^$lGJlRr
zk*JMZw)5Ry3IfWWn?^@3$x-c+3tY{zJn5zlUE-OSJlk7xEU(g@x1|)3n^Q1lv|zEd
z4v~1t7~vT-E7J&Mh=!Nld$4B@UW0^U&BW6@3|m_U0;pP9Zt7MbT4{N(Y5X<dGe;bI
z$4bIc^ue{bzi%K=G^Qz*=DZe0>>MYsMA*;DGYpO_(+8edEr0Y1u{<({H`Q0;E*57X
z(YT`O2ooY+nbUaAWgR`)NQ9~s9=<NHw^Tu*Y+?@)PeP(DCd)$hyJNvtY<}L+mjFm<
zr~H@*y~frf%Nrq&zV!B53L^H!Rz%(dljSLx;@QjK!cOTrj62fe6!s8{uT`>Ee~r#G
z>=i!g#MibSRiw?YU1tt=Fv7;wqooYkOxD9D5v56(+~?TYH#ca-093^QoR0&?f$8f)
z9{18WlJew+eC(_lm7+FQ^zu|(?#wsBqADVM!GHuIu|`>Wqi{bdiMnFV<Y=R1{W=Z}
z5^NRYb;;XOo!_jX70mX1Ux6W+o6+Ll#c=!R0W~`24Inob7SX27WV__H3sC5Hz08Fz
z1>v_Bm1d!tlfno-m2!c~)ltOB<O3DfDmkGeEPPVx%k8!FeC%l(^-Rbh$S)E!nOKgP
zP0+k1Ijp@;$25mECi}hCA*k%GX`CAapmkAhW?E9tNe^yvK?r{qNsCVk^O9)QIS3l1
zDp1yyYis5Nfj>dn7}kra_}yJGzSnmdOco_<{n(yT7GUj~^A?WRgDZf9SFN5_6Mr3D
z_>n)ph6Z=MzOk$F7W`7ndSe6=A^)AQ7<jyI`6m1J1BWykcYNX3Pmp!_@boh|W|-Gr
zLehSBe-Yo02ulYTPkm#t_?J)6g?Gn^fb-2V;l1O`*kfNW77v8*6Fds}lLyF)MGArA
z6gq6Adj*TD5rKK}w_<{)Ve)m&JMw!$?n$V#Hg{QU;P_+hmA6yd(&nf~vVynMoS~ri
z{R^1>24}40Vs7;&8?gosz9H*NHg(k$->40~6_tHMdg9$I0(YZeG*dCAq#{wd5FtI&
zozsl9WFma~1Wi6OVTY8Ox8GdQ!Na7hJT7&IraY@IyYn+1ujps>Z~!%Q97ny`{0z(T
z$&%SN3Bzn^b3s_XLE>?F9;@v){ije^rYzqas46$068Id>=soY%Zl;=vj>nb1St?dF
zvypbYQ=g;7^;2U5v-)#-`5WYKNia>)%%O>LoCg<?c5}yu6%g&F+*O185FdO}xKMT!
zZQtF#%KNn1-k^e+N9=x>1F|T(X*Isv(@b31p9Bd}LyL1{@D#Z44A0&{YbR88jz@H2
z33J#%i}KO+)u^@KMb?ch$VD1KxMgHj4O6>OQYuW%%IKy$Hc#K@xmbK*w8*QhtZt6%
zoLE%$X8;GCe0fK4XPEsGeMQos<BTJ>bL#yJd`>l1H6_Ryhy6udL^#Ln6c(eXR7FPF
zSA|Y6G24SI6KwIscDFIC>ERVNA+bS6fuGCTdBD2}X&b@j&da(zbI{!~v`r(b8ki%F
zawlH_;R}1zwq}XebuxV%d@%X}rJEj<lTqaNK^hLiqg9UJwe6777Sv%}LUSu{EYWs&
zntrg#x~KB1LYw&c%`MI;g=GvxYWMZ!<DTH6C;XKE1xKtcC%eWvO>~cT?>nrss$-q=
z@?+hUvNEp&iP&<X_oA|RV^l@ghXn(8F{V^lG$OO%Cs?nkEcb-`IvW#L1SNx~VBG=$
zLDr7b7Ld@bDH>Cg_q3njF?XZ5lS}HLTY;Ofw@Y=&@u^waRo)-&Q|fCMA`YozPl!JL
z(@@+pYJqf9vNG#3+F~-SF-~rL2P*xkZ8=KM>u{{MZiDvgb%;#SAY_J>RTwh(mtip9
ziM;RB1DJ1s^6UQFBFUDw^DL0Xk>z9y$&rQ8$ZIg#*4=imdmWk)58oFw*4$<1Ra>>S
zSnCNad#C%g2C*-igDkZt0HlFSLgyZ|@jXU!U}qaw7Rl@2W9LR%m>JIw3NIvr2CUin
zd7pbKy5yNzP902cC#@O*Oya4~4*v_Lbu}G&n7iDFTs*)m=IrtwazRwA1ZH%lA7tb2
z*R&@#@~&9f(n=h4hXHD)?^f~^7-*p=khFO6bbE!+BDcqsJ$tzfn0~q;&a<*WhV9d%
zGfrJ}uyG0kO_cb=O7arIf?_d)xsAwl=%e3V+0V|#j)SAgE_$!T=q5Vd_-L4hR=_Ml
zPQ)xAcURtMP>p9xuJwsL>;veK;m4U)$O&X^{H~O7Rn}J&VIF^vn)g^#UYiek;xsub
zRN0S~1lT&Mu!hk=DFysYe+myqOuF**efPYdgviEWnppb@T-8gL%dm{+;E&JG%V)V=
zg9jZ3tWT#TdmzeBZ5*m{alz=@cAd-TF38S9YZ`NoHCu@a_3V|Yjq(|0$x?jJop^fv
zEodw9xzYf-_=z03eRQwT%vaY3{9Pmfdq3-VMkV(*043>w^tlVZg8&Eq#q}@|*Vwk&
zI~iK1zp>bi6cEtPRy|M5N-Vx@BmR#bE=r4winmJ{uWq`x9Y_17bxO-{KS=aVuz4XC
zue?b;La^Gd7mgwjw29eSq<|OJji{|ygtqP?@&0JcuTT<)hr}4puS;^NaTR8Ru*T+H
zKE~alulnQ^*>e^Y-VQCn^PhkGi?0S-1K6;m*dHDuW2VB{vf>c^AN-f3jGAQ2Dabw7
z%BwGR{Uj6^bRU+Jy6&`s1RAYvfDS1GPyc`@H&7L~kBLpXj|AYL7LLO!u<1{<Bu>gB
z?G`A;8cY)r?^W6siKbX);<K=akP{3BntKQfM5&6H2OJ}M(!C8!?CD*2Ri>gd9^obT
z)!Nz<Zc~0oxxWyciTD^No-2OcbAI`g8!!0%A<@rMEg=f|g%H+{VF08I*uW!V6oo$*
z3|M-->wlFYvcjH_;y=Cuu7d@Kg~UY3VyLIl=q~J+S97{&BUGXACg&`2<;zp`+^oYb
zHC(5jk+Mxmoe^k|=Xx(ZMa#-bpU&-kVXcLRmD+I1@6nWy@Ck51--sjly5s%wCsED3
zNk!S)qEU?L7)>h{K~xSfFV|DBZ6TNVTqbPuVM*6tH*%21Q<j*cL(7nKlM<MR#u18$
zGXhRM><2E+`_nGolaRf62hp)Rpf8#vggicffsbhp5+qI*5y>lV-E({0Z(G^P`)EWA
z1v1?1w6wgNO?O&-Cp~bjO7LeEQo&~hVcO{M?sjC$-wJ}64*AI1hJ!-^HIr1dIXFLO
z9<}ee71IJaxX6xZ7EC5?BcE?K+}~L>hgX{KMkR)vu0GT!DvM<gmAVpkDcjvH-1dT$
zwlI7_<h?UYO=w8=sXIpd0Jqv{J<TA<2UAwm6Wa~ib&Eic;-v~O0BIE|B7}2swPM!q
zx98e`G?%y?Y6|xczcl?`-rk9m-#TPr$jz`@o?V~4#9FsZp*`T`?*;me#QJI^*5p$=
zez(^`+k*~S)HU+LwsUzFE8S!Kqmo4_XoE3{ocby?n{i&c;Bj(!TkW%1;>>0p#pm|L
zWqbY9EHRc+8VXxQ{+!PZUb?g@xx{WPJ#C<DdUI1-WgWAZaO%7+SG12x-cLv#xLE&~
z?NZiZ^C?YcV(-jbB%C`HE2Ioo^k^`;EENh)wy1yxS<&ec?gB}k(I?6>-APUI#52Z+
zzBl^T_f8Od@$DcaQTNi&0rOyilj#S~*>LoO@K?x1xo4=E4;Go68wUB41CyBuYGQda
zSj`qx`{P-gda5d|;Qyl{q?q&2ZTO7dyuKxe`$>ie)4pWI{XWjv-ef~fFahS26eEzI
z(=%3zr!Mi2xKtMPY4L#fT|m_b)O&|JqV8K=>RL{WBYFqdsNPoYrDj0zK>8@C<JMK@
zuan*x4R!_Q=95N?s;7xA^75ToBHB>B8VY!7R`s~f$}h3GACMjcTG}@>N960vwzw@o
z3?0G#+>Gt%0YI4VxI>9oHH=J=H7V%Y_Lo2(c6qs;Yry@wbdvJ=xzz|tm>fO%y3#Q&
zrWWrxEGZ|M5~#VMb=yn6#e6nn`9V9?aJlxhmD;Z30Na3yLC5p4#Ofh^W1KJA2-&UE
z28I~tpjhM^Q#Sd9QX1k(U<7?|Fg^RazX@vSx|HAMLLaYjkXbOz-Lb(ZrMYBHg2KBV
z+q@wr)!zJ*Y}E$(H6w@WMKFi<CEkx<w(=5c7|UCSH!kk!ehLvsr>Qm$xxg(@uJsS$
zDRs!Ae!$zVl`k6D3aJLlBNJ#g=-f?Qc(y&E0gO%7-5(5aD-?A$Qh;^>zcGRt@6okN
z#9=q`Iv~OAQvtq#QbV>xg^^GZ;bRi=<-LMlOm9EBK_wPP3;sYB93IJ_1FU94H)oqo
z-Y2MNi6w8rk5Rb<f2OsVYcIHL&j)$+I{k2jn~v27a-xA6h+S>f23Bp4DKizz{<NG@
zqmw^@%#MzU!M~HOKi3$Qg2&OAPQK(U@%EQb_s{bkVML)GD4liE>nX&M2OAw>aIBcy
zomjSkUZ=ddW~>eeR!n`7+E18E#yj6~qvM{2<yTg&58DL@pPe;t9DU7fu7thU9riVe
zjb<EgZbn9K47qKb9Ks_>enx{MDh^*!6Lm}vw7ld|hr}eG8;P@@ELS$QR#qO0Wquh$
zmJ$W_CD&yYKr*a7IOCSF+tuxqcwM%Qd%u_{^y5I}z5Kj?SoC;LjK?9R>}p>VlIrqm
z&8=kZZkk5ic{Awe<Rn0T9HaK0oF<9(fS(5=3Mbu&iv`<%stV-!Dpe$fH6c3Bcp7J&
zHPDoN%s}7&ClA;eSyjtMO!tUZhG_0x|1r^&CRm)zO)v-aAr;GsyY3f%@tR561~#$b
z{m$MdayJ#t?4U+Srw(j_X(5}ss`EVKgzWm%sGFI?aiK=s8qXjQA)KBBKVcP}>mAo&
zvx$*1lk4gy&}UKY<iLboN-zEhbD}agkirKad;SxVcaSOWNs03Qyatx{?DD(n(9CjI
zMW$Ny_3!TZl4ic=xX^Pt{-?rA?bYHAw)auu7oj`BE0ve7y}(~`medeMds~$<2;%M$
z=C#;sZvyGvM|yoz<zg}yuS_yY2^$UC={Xaq4Nrfp48elB7s7v#RMZMW_M!=>ZO=+4
zRe6Jlt%NUPpqXrTb=&H$uEN}1wK;#Dx+_?BP>({s*#4qMsqI(eGcNW$b>MIZJ}^o%
zAErcON+5(_JF|B%Rys=z8sySWEqSHNT5}U}G?4JyXL<-MkLI5#ZxSm>T9g-Rtj{H7
z!Yi{SENp4=qliJc&CEu{3;<^;vA`NTfkNq#Q~H^6d`_9Ki^#N}$ohJB3~7!zhQ}O`
zGX@0b>9-rrDlA1~eOhF$C_tCw==6&2Gy6gth2DYYP$>N?t;$EqGg?|n1msWND-8nv
zRy8mzU*4bIH3ejH5rk4sM5+4n#kH`E*;9rcEO57h9<Hxjw6CS`*xZKtu_Y;O8N=yH
z3{!GArk7|rxu+yL)TOSa{kh0!GFW3TUGhB25>$t5kOSI0q|q}8!i27z@vA@d>?p&u
zGeDll&(WEkpn&ke+A{nvlwHrcU0|;6Cvt&LZ?dX7v?V_TO0y`M=jhfI3MkDH_oi1C
zQC#*6B&MtDLt*a_B4|}TWJB;UXIX6pi8XM9dKVS145eE<?-lyF6ECkpmVxnRmNiT_
zgymvRQFLg84rpIA@fP&c1g<211Q!9cbrHOWIz57GyzveHIOtN?VpM}9Ztwo!e*BpW
z2aCBu@pzHJr9n`fL189>Pt4M<w&OVi>^uHX+zs!m?!3ve)Z%6R_?iHACEm%)xRO$;
zR(pfMMNBsuYG%n+W7|hh6xnkA6VCfS8TN`&<W-Lq4tB-G+FHYW_*a*Mi7tiB5&Kqz
zf$h5UhQZ*cF%3DfI5zil9FXXyInIETvBz~l-iA5x%}oN-ru7lQ{Ul%@Db^lXG*3tF
zSFJAYaEFOQe-OTZWtaZRX{`PfZrho?!cLvoN+oKRB%%hYPAW2NK(huFi%xpNZe|h;
zt<aRenZaN*=?$PBA1tKk?-EIbwqB-9^QL7sD*>@Pmv-a%ASN@BZbCQN?Nwkmyf#o-
zrkfjV8`2C*USS$o*Xg{mU;x&B8Oku&aHH~k)Y!IS1{|HDS_S0&@(4Gm0#2!6Pl%zN
zI9UguM0gHgBl;i}Xjs7=N_^^vS-77E@!@dy3AP638Ths+_;hEJ4htDZWdV_aMifz@
z1~-3J=-v-9TTm?{`}Gc)Nd_NpUY@p|ugXK7Wc^VrhH?WE-D~Y%<ujjYw!aa(nnvcO
zhwB{iW*5S`41Ov2DwXfQR47k5_;`nXq1z)Bn;TKeNKx4eWZ~F}@>^b1+6|Z)%$9<m
zRn3$+WpUKiv#>?&aEo$w#E5rFv>!rg;lkZ31qpRx&kS;0TH7PN#D8^T<b-^x$)%#A
z`ZZF*^#Zl$m%%yl7mwZ0=y3IVbn!5K8T9m!N5g@>&7Vh#hfn4uo7OCUtT`;~`bj6W
z+unm3Y!eGcYS;n`NEE5Z#{Q*WRjC2(VVuk2;^@Bb>>B9{XpVTA=~Eom(%0jZE3~*I
z&z;n*3=PrfV7HPrY95qXt=!WWiO*Dy+?z9L>r*R=okFtf5By8BZlCh$iByo_(<79v
zIAt}jTHyw!`-fo`)Cu_Np8p<vWq6nF0Vq%uW{s%(uYl%zYTZl(J`aW<T#^Wgf(CR3
zkMP9KJ9`L3{qW7wK<X7?2TF`4-Hb$bI>=VaGK8!VO#h}b>3}=8!P!n^$Wq?&MztZM
z6y3PA3=9BOQgVMBIvLa>TYC5K6%VnPV70=V6YN1nVO#M0eX|I_x7M!smqUV#dO2H(
zi)#WdEK8@BOq)MOoq7+H{VKX7ls+c99?}1ykHPVLK#Xxs*g}UYzr|T?+F^V83|sV<
z`#8&EQq7Y3@Rf(hK@?@}6M%mQK?nSnTdA)1O0Y1~=i#kiU%P_X&3q^-K~aI$r8%?H
z<#JmWo7kxTuOdxQ?-hO<d}zQ{O;3fKJ{so25`+~9H;P_P1>UMc1ECM05HjDq*q@Z8
zX^_j4)ldF^Qw+j*GUZvh3jz<6Dk%OpwOE8E&+2`^teNXkJ9sHO=3#nSSn5U_JPc(2
zXF}iEu#yehLO`cKC2nZN4hV3?v;Fev8P>@Y2MR5N5d92x?Sq5bGKW(R=6t`D{>tF+
zf_;F>bZ|EhqIN>D{0%t_nmjEX`RMx4)gsa{v3zgQqyz;0<}N1xAFe(Ue0d0`%#KGy
zn8}=(sg_%Un3a)SA3JQN!y?03Ko`qB<D_Qw;t=@f;oLA3;lzJ41HQCefO3O}_|Xt{
za*P=%O-}aWBM`9Zh92wmETHLx^g@LNuPf8zCVnqaTiWP9{;TQP0PoIBfGX<3a}5PE
z5y8u4FXS)@auV?)*bKC*54W~IOz5YdjMki=n}Erm`ronG0I?UNOG7OK`+Drl(4{NV
z?AuRBOyE5D4Ir@=+2~6nvDF*~kN;n~9|-`Z*sstMIvGEF19y4Y$sWF+jt($)oZQd_
zc!+J6M#r~&ZUiF#?~K0l3e2><lsO*KL&Ni}`RPZ4=N{09jiGB%0QFF%62v=+6nV3B
z=@}J6e^VXbqQDwX?BxzA(n@-QN4}HrqZ)C@{9mlTH?*u=>MbtEL9R*^#08J{x4k8Q
zvpw5T(LQ}U(?5XQrlAQ5ua$G<dT)~@0{_TkZ;%CED9i`2_{t4(eUq*OYZr6I4gJ<f
zJ=7FA!4QFFMk3#R88tX^2eqw9ve4}M;|34n^bY<j5n}+Kj;-6S0_2^4Mr)#;%E(Mf
zVPfQ=m&Ykr9x|V=t)-CJvm25-xFCo5*6B5@e)o@2f`>`$R+&SneB6oZ`7Y(Ldc;B1
z0ND9-+}Z0dDmxlF1i@fPzkq^@ip#z7v!|hPXS%c36Fhizd`I9sEL3!!1qLGN^U_Na
z{b;ewy%BE7A-xP&Wy786StRt<4YTFnJ=%?i0}#oA3N>QQ7L9mNHJjfM8GS=T3hYz?
zs-RvTwqUw9Z_e!KL_c6!iUgs~Vo0F$@;`Dsk+R(~3HJ(0JT|6!G(Y7;r%9`TX$Syd
znph9p=WeKCV;noF2n=Pd+vS};+75a$1(;XI!6|9Yf{&a6Bbza>eEt)Gdx_9FxNuYO
zfF);)qs8Pa-P-NkWI#MfCXvZ_$2JS)M+EIf>KlNvMYKz~3r>U=N5tVCgMXdXHWl2Z
z#-rj;$-LLqB?WWGwQ#~53k$1$#S+$1CkTXYbAZ#O3{#}Ip_**D5+7+y{DhM$3(Zky
z1VbRTSyOPx+VBeDHa22y<g%<7Hr_9i641|IvU8DdlB02C>0}rrSd9JP<|-K}Z&6@S
zy)R@AT0RBhkjhKkM8DP7)C6zF9OhF_Y;?WJaOv|3>mwVGf1oOb%~V(ruY|%Ixe|G~
zKDaLQOe|Ia&kg}U6oIub01i7Pn?2f4E=2dsK}LKrx=qkVK@Jqv_<z!!zGFf^;+G^M
z;%>lXQMIHZ;uNEz*)0v7hE3YFV<uqH9@*NDA?qt{lS>(NbHB;v{|M^tc2)vKrFsjF
zw?_oo7Ji*`F4)z{RZo+cDr1O_CoFsl4wo$bLS6(8GIc^KjBCFd?zBu~;V&GnIxDLG
zuHB}{O#6}N8&jmK#4^9IFh8Fva2N*m<>~HU^_)wLPk7Zb-a%S5XFKwQhLJ=Rvb>xL
zYheB^t{+6<i)eHFxI{GoJ*ws&jcNil!R}zBTcwlB<`M^+kV!Oif95^8M2U#l5cZz`
z(KEZk!$P2f`kaU_b8f{9=Icv9_yvwxp?Z9G#wQ;4TnG=Y6jU9B^u~aUf#C+CQ93a>
zwPay8zXUFZXi&wO61nE{1K6A+Ky!!E>L-4X<7@X4jPy|w3R=OSUA>K4hszC>u85nK
z*Oe0v`{NaLEF)M?t6Ijtu>?uvk4HL6!|m<ZHphav)V&0`?!iHm<~eTb`kP6+Jckmd
zX>i3sGQ<7h>g5#O91ETr&$Daoz)I?e@U7KKS+7|mV*9J*%>@na_Ew{dQODKd^wsn-
z$*xW*_?%L9TE|ANKUJ+KgvHq<Cc<9AI`+!q@sG8gzE!swK@;w0_~n*|n@hUxV6Bs#
zcZX{@IMrR7`b{7r=8u<$hyq!g9_`uCk)}yk-gcspGw9i#PSRnz27SNP`%-C99$_4k
zFtF;p=eLnc6~<$F{2?TrI~@%TE#3^5z8sy!QOHZY&L@>@s3$6$aW7BLDp+yXUhb9z
z`!&I_?uFvm%sA_e9~M`sAE+zeR9P=*%<zPVI=d!0wIcCwaeSvSTdU8>$jz;Flopp2
zw5M&)m-nPE7F>&dvUY^SMW(H{rdyyj-@|ZP^;Ir}H&<n7F8$<VNQiPNTB-SZkv2<9
z7}tJdrG^`c`8|XrqM-07`n4oItnAeL@4v!#J?jb<5_~%m0|PAq52^^C(w%-kGWNh=
zRk!tJ(gO#M8f}SOhjN1jhaGhiTXNQQu5@(DwyZ4?-L3M^`aAB#&YSya$8v?Ft)f1O
z-@-@7EI2q3g}=gx{uSLJAZ3z0fLsPCHzUS9&3z0q2{c`hua*AniL*UTvzn?_f6!4D
zTx2xnBp~S&1#^d>r_n{H2RkFgbm%W|;y=M9EU?&w8+EFFAAYQB0i^DM-g+wr3TSx$
z7CCOaDZ7=olG_~KD%Bzmfs^wz5h77*cQQtwn_X;lkr0<8)sz6+jRU^BIt$8=T}LsC
z2UM_c@)T&7wvxlxNnuLDM@R9gd5W7g7n}@tJYX%Be2RqVdz!SJM)*dQgg%VlPzX5e
zXYzKebNB_#1^Vu)a<E>Aqmi>s#+$S|Z}LVI&6~YztBc(BhFE6DQ1ty(854lFaHBKp
zcUPq32C~b5IEl~bHw;DUf?VC|Q8Gp?bAX`GyZ-L@;7U!#KAPwrn7I3OkeNcT;LYw3
zHt)?$<+oz8QEBgw_{U&PqBsOc$iU^P=Y-YNDU_05DS!-z?W}4@6%Yn+BgY1ip7}XB
ztR(!I1|I!qeX5w{b{P$-zYXT$7R4~vO~7XvnWwT+uK)}9@Ez)Lh`c^@G(OR>zhGJm
z7huA-l&HqX`bRD!piBmcxMttsS~7J+Ge?Mqr=sFqQo;f2bJ*Z7E^Vz&m(VK*1JOCo
zpSMIKyu|di0#s=K27pq)04YxjE;ak5a)T2gbEdWO6f%X2e1w|$Gi8Yy$s2nO!dN(F
zsIi7CTV-6vnCGm@s61W~$M<Fs9Sa7y=p3?>*;sU%pkUNIg_H$wY1CKQ+>*nA_TNp|
zr>S%>7<BPS?tE+o_V#;&cOC5C9nsCY?k?5KQO+tI-+n{M!nz6H)Kymz8WD-z53@L#
z`XGME5k<8`w-7O|$5k)@jija9mDJtJb$V$-sPChkINb%_6-5UFcUAx$-W7FaA?-Rb
z_WrP%U2&e$fJdW<BZ4*S`aJ0{T!FfU!>Qy_Na=X}kxDJ$#=c92AI_)d?|K$&g2c{A
zXIk8R_CA!)C67dB8JX>z9L1DvPxU<NmVd+ZA+T_=rNZO{8#-{Y!TeeY=2Z158V~M*
zF8|HpfIhWpRvRm*Lt-Zl{%7;iU;&&P1c?ov)-s47_&*qTm|U&N*L>b=JiTXkeXSa9
zy{vw@8hkWmrfzvyUa&Tu!06r$2g#REU_plc77}q_Z_E_Ljjm*&4WJiPZx_p{#UsKY
zHNNP>)ohoSVo_So$lP?%sgx$0n^QZbjaSv;#w&8;n#|HDNN&n$`~S$g>aeJ`uWg`+
z2#SbEE8X3VNS8E2OLq<3sDQL|gLLQ6HFORj9mCKtbi>emTov#AJ>SE>%sG3neAm18
zS|_(fZbpsZt@g(-=3&VEXJBqw11AGTPEA8qonQ`47UVhW>i~-=hmaFxw?vI`ezVQI
z6A*=HduQ)UgtM!9e$av4!EMWAAMjhTg;Gk*BqJA6dqI;<Vpv{M%z&tFE{&B%Ne-{#
zSAS>_8nM$6M^0`7u$$_2g+st4yyC+97$b@YJ$j>*`R5wl?MVH*J3P&#7;1Llh1G_B
z4!HFY+`87KYdq2EI(gAN?1pcw#V^F-S=y)f(1*`o^IF)e^It*}Z;mgT-S|k21o9`i
zJ=dpveJ9pYTgj<IL@^!25E6H@0Jz0LDn=9*sj8dqwu&a_uMUI?oOg;(WasR*UPV04
zo2|RdO0>p5*?RdpK4UmW^sQO=x1dQnU!3!=r>BfHf>7DTr4EOqZwjAK+^v(%)>i|I
zhb;Rjs>Cr=Qj-5%wDNxTK*#ef3BLpVmeu>mBhjjbY++$g%~PNzel^r$X~_{@L(!Sx
ztx_dPCH(b>{najOIE)7f1OYc)b7bUWN80_R3~gTBbOXnhCs*e*-?ylRA}H6#do}B(
zq|(Hj{R)i<_ZhlGsh9(#U~H2F*<GF4S&3mQ%Ep@N)L*JtA4KN90<i7xOJFDfGWmq^
zg2}GtE^j~WTj23kVx{NPpxrOr>?1{TFY%o{J03HapP5J6J-BV_=o11;eX~SGRGwTL
zSjL{SLA4awe{bf6i&E3~cVeylq;;t%VRQLT?d^;9pyV6vhsgvAyn;DA6$<RR{pc}M
z#iFH(pL@yY)Aa<T*Qr+O$xXb0+6b&FJ?_B6plbFL80eE;#^$=N_mIIa9F?kpwDy5b
zv?s38vA%3vIvFrizb$duYR0~6^V7B>qB-kPMeD$G_dU44<ti`;$^73QpW+8dF*B(c
z0yntb`g`xQm|2^ZY;m8ZE|_mDc7GdL|4L^0GNV0}^GDNDXQO{hw0U5VH?qDS_G;cb
zk$l~Hg=4T3m*)__9!{;Z?X#VPh485W-_<hPSdwW+Dx&udQ_|Zew|9?FcI)@C48{K5
zj&onY@HT#GqFK+&_(cZ6xTGpEN_hOtK{K6Qg@=TmUAE8k+vmfNLAvGU5YIQ;Foyo*
z3l^Gn58a)a3B#!7tt*P>w^cyC>EX_Y6YT3tQRhuvrxI)UF}ymy(C`i}uRN(ivJSJa
zk$z?Mp#GLo>E{piDW9+YT(h9uiv~@yYS~wM)u*E^dwtNgR98-`ckaPLRWD&ggq9P+
z13cZECAz`!D<Q|to?1^^7x%js@A?oQ@7^@v1+CLt_v0{VQX-cNi~duR`!GCs@bPD7
zdzJsik3mR_0XwpjBGbnXKlL}69G60&A=(YEp2f`VTJbaPt&H3h4(4q~LT}o#3eP=#
z%QoPn50}0^ba1T^@7&Iy#kl3o%C@<VxMh}G9ko!%Ss6^lSiYgd<boSDM!yhammh!Q
z*2r{Act6&rJW2#ip9nU7fpZ6{@B0b9dGK>RooX~I19cV5D5#R{X}ZHk)dRvb?oZ(Y
zs<Z~5*3`tGhJ*x#&jqtOZ<ttFd49U4=k%OxaB`AI$)mnjxfTLnWS2??U~UffcX`>p
zD1KWO$pJ3bf5DZjEM5~lGvQ6O6S*fV4ig*y^qmVQ$Nz2HNcs6ztB3E?Xq5uxS1RYL
zkh7KqkE!?Rl4*ljG-M~&m=0`cB^-&KS3z};PooOCOd}AlA9Y2!iion4Mf~XR9vt*W
z7E$he*UR?~MiKgC;PnDjLEiaDsr}`}*Db%NQIpUAWJ-5ij329<3%eLzla+<bxdKLE
zK~{R?Hlz4ol#LDUaX)oVdap#EIeWbq=BZS(CLNttn72~Q((hMZJa^TwHw1m(71EN$
z^x1;nX6IZ#c^kRBlf5L#E3>~4U{?4wr**FJdAMVjzCe~XYU1NGYxdjC<$kVTDoZCG
zwmEg0j!9`cnNDwm27To+=o|8fThql)Oipp|&P<#-<5TlZe|lFH#?7P6ku2QuoO^bM
z^Xhyv2boxt`U1yazc=SF0dGL1qCyorNx;zpplGZpdpOG->9Dulf(582vfjdVn10M{
zJ~v&<zA2sv#%H6EQOW6rY+r!Np8O?>N`(RwVD0W~U&|7&Z8O@ixk41Mf)-c|DiRSd
zQk>dtNXb>+Xo~4|K2JFvBPudGaAkQHZ>&BzUs|^WzA5Z;{iw|-{4p9GT=PZNX;a(k
zyv9tcXZLWU8ulY2^ex7s;)s<Tg4<U*aN{I=6&-CquR7Dmb)|_-YO&|DHLf^Kqm>83
z`61OVHMBHI*3x=XS!I7yfADIep68@+`q07^F;qS4xaH!AT__;-d<Lx!FJPxLiEW*4
z7ed}~7SDZ!Lx5xJdD+D*{d$eQVeYaONRsg3^KPggj4np2p#AJp#L3_#GYc2DrDOD;
za^}s0$#+<oU)IeRKkth9qMG-<VQWi`3L@%yO;s?mG7m#XK9QJOh;VUNvZBmW=;{Ki
z*4Ij^DkUFGk3XzCm{x9ox1P!L#xBH5-f5hcfXf^~ZMr#{&IuQ260~P&d_=x*^n&d3
zXrs%gAZ*G5dtcStGiv9DS|`W8OdH9P-Rn{Tj;upZbH7M#Jc+=@*FZ5qL$qb3KcvSB
zaCK4~5rC*C%x6S=cXgA`Tf3NwZ4dnudzxQ-e}C^fCi-c&k%g|K5M3fJ&QRw_qk=8V
z(#uF=@_}GE>Cpknq;N*zf)F&tjDw*i1?G^%n6B^!;kjC;cYIru2JS~gGIdejuZ|3{
z!W>z!_UjQud|W2%iL&^B+6kpSh=+YTcI(jSX^wV~L+R2Paj?*Kto_9s$@mm`u>V(g
z-lKPmCuzWb8xKz1cl(EoCa~*KV75>J%xc}_v}gQr`5F7OqQ;idBe-XQLuvK)8b56)
z;yj`)O!%SK7jXAGp`*4~VumM|V00}_5Sc$Kcu8E#logjn)oaRi6&yO&xN;>p@AiBq
zRk@sFQ&8B8lgn}7FF*DXiShPsvmNttS!okQ{&3QkIt=-6QcYQ<g_=TOp!Q6SV)rPx
zFC-3yDYcv5Xzh~Xz`eDs{KV^N5%DF&EMK#_`hK2uW2x1(tZU_u^kWK9YLX@ENj0;X
zMk`b1MNyIl<Xw-;NxqUDNea@Hq0Zqfz_~svj}NqKrKeosCYC3bu-V;tTyd^Kx!Q{c
z?+g@UWlmHyCoqFkFH@2VR{bF)xmkZ=kR51y#SwJqSfce^uyzP12wlK2B#!XfeE0OC
z@Fgkzq^FkDIGushxYXR1l$MyI5nnWq`jlE7jd_QrHHN;>_H(O4kM7)tbGS4e5B|r$
z+sAm~oU3ecOQuZ~{xH)mjlR;_%yqEX(hF9{DOlATW0Ev{Rwaez#i?s{E{t^lUCXrF
zfsFBLf-eEZoO;x{mRj@xPD0?e=$_-Ns@iVqEujO6${!CDmyf<H1n0gYv9$0Z#*iiZ
zlAkI#YE?P;js1Ro*?TaabA5+w1o>8j=>xVh+CN6PclKev4*|nWbw$f8M-?^>!xJs9
z4O+rZ3n&T*@l6hAnp`B6HMDr9l2}9()~ELZObSG6j7zDNWDgsCRP#1iIy6=?bE;H`
zQ)yNtrqLkKAmn{sURnz2zK{@?T!hIQS-c_6AF^*A)&E{hbXr)*eU|KU82y$J`Al5@
zeQ07_U#BRw5FoMn9DB6tT@Bb~uk`W1#ABq0w-q2;-%y=~9JHT$;oOazK};QO=P3D}
zHfJ&FssA?de(nr*iF3_)PgTg+Z>)d&LZ3nW2Mv8o_O*FT7_oRpqVwVLi2~L;9Yaje
zI2f~qVplDzt(k)+zHQAD_}tu4yg9jk+}~)DiiV{3E`%vurr$sxS0g}6QBxWpJ7+YX
zjcKW2gl~rsIK7SU{;`&^<7&!e<aRhXfr&3*F@;os*Saf#71@EO)pz~7OiV;n(VoQc
z4@ukb&JGHh<qaxEhdpgROTDCjsMu5*Bt71bot*~kRnkKOX5S*=yk!9!Uzc&zv2tu`
zw^dG!nw%F}CqE#uY^f7*)s&Q$?#&NHDy~W8-7F8a-ZGkEa@sHrpKStw`Zloi$mOg>
z-J#@UA;C&<tS#RyOtj;t=LY4aF(L05(We-VT-fkQl1k#(>*#i*$qJP;3Fv0#W}M(z
z0c~IVLSye6<E?+zr1J&|=~Hkr#6An~tT(K`4O^5}UzR5J$MPblY|#njD5>a5fF7*|
zWiNt*v_$P<7$>5T%(}tFh_w6O1lD3tM?av#B&Dcw*_Y#kKSt~iV-5o4wXhF?nd^?b
z;@`}0-T{?WS>Xb9DTO0i1?6FD<yI)Uc2b;8%SMp+Df?5w%^xzj=w?uhsbCuAXY-4(
z>-K#H)ge-<9%Okh=^F9v>=5d>vwe9!(Vu={+L6!qc$p3zXHDUj=<vzYQ_ewPaamjt
zd#n~$Xn-Z98D=%H>?4ijq(y}TKOlOW=y9s{EanRe?Ual`KRxLI-(5p=ON)(snR=CQ
ztK*VHQPN<0T^3gMx5#cf%Sz~a2MH(7vPqvv<K*E;kmVX2Jw{?SvuEgCE7p%?bx!&o
z_0{3k-Wn%^8X96G)ZV=(%i47j1!mRtY?9%=-zb`)jkx68yPhB~Fgt$|d2^W2NKkU(
z=k-9_k7be0N?c6T_OUf_0uf5ilD7jixH22fB&)<vw58fMOFoPIa+l_u5N|Pzg4XYG
z!I&IX%=Iw(tYOSA)pX;rE;*>8711BNSEH!uA7{5L$5*EEmNVS=3s7X*-ZXjmo}G!w
zIv1%Y;G(zS5MMInzY6q-PG|YxM$kafdbrq9SA{9|>S@hKrIiW$K1_r*2CcgPaycTE
z8Y1X|6iti}=O7?Cgi+w-<7!hZSE_Do%y*R)3&r(^tj-Q;BFlwqQ%|uGe1?hzLn*^J
zx2-ti=vW2k-m;j|9*k<mKNqHR^>9pXMwa=SDDF#4;xrfT@#EfW+w}<0%>VTLk}1SE
zxtmgedZhfD=yY=_3)f#9Vh(#zekqTTSHtABY)<6)`EjFMp<s9uYu%S3UCyi-Bj+cv
z$ER*sSqf*AR352`yzl#HD2B)F=T$u@6CLlr^J<!DzsORj-d0wu6B*j6L~79fSqNkE
zM<?WVU5s$*MrP*uRXzHc3f-%ZT+o$7$<R5%uFrba8!u7D@^XpsfsA;tR1zk~A+F`3
zMSo#b4Gv4*$m96ttl5f<al?t1cH8~y>rOBagv?=H*teYocaM|Vr0Marg@xjnz&T4D
zx2`lr#`?;g3$T^%NYKMXRpUnOx#?Lo`MUEuPMqF@o|q~+)bXZRfB>Tz2+b)v5=Qe9
z9sQ-}9t2ucqKsj;vmJ_lYU{qejabX9D|8bxm)V)=uME;S_e-u?7~km7P`&LS^Pe5N
zY}Ki1_E;W|rn%nNR55pxa@k)3Rt|~Ap>pi3Z&(0~&77?v@aGS$i2q-6Gm%!{{Uo%D
z)VZi^iJqBCuMxYl{ULlE@|PpEhDCfHMPnlV)t=RDw|C2c+8bsru3EqmF@6=wtN|Sh
z9>HG!B*Nz7b~8QKP3xifA=-xOf`Y?!hwN;i{x!52!EUf!l)npdNop&SD|t8&y0!>g
zmu}b$$R0>}_qKFxayoS>QG&g@QfZ(d+djx(mu(~3TXnh9uFi3D_Mw}TQRCJ_`4}n3
z-^bmzQybzsR~M#+wiT%K7Fo+qy8vfQIc;r+gl309byePV?ECG`owV|uFJ}&<mxf0X
zPJ|x?%1tA!CC8>OLHe1whJ)G4Kfae6K?BaK;l`>C0+mKC(-9i<na$#kVTWgAFB}VW
z(cFex-0L3Q&TzEYooKXaFP6A;MtWuDR&>GQw54M%&gB>r#arLjh4M~feRzkU$T(E^
z+h#Y9mvgNH68eu^Bx=tr;7H0*#Y#0pqD=5g(J>F@O~LRZsL_FinL-S$T)Y=wgW%%>
zo7*;Mtg1$@y3~XRb5%JOIi5(Ut@n!`aAkBTtPtpM545(dAXMmvJ|f3zXn43DYHmcV
z^yq7Y<2{F2K26krf-BB#>YQY`uvpU;+zfPn6G8UUewQu*XyWat?H2ru#2L%^e3t2E
zf7huRR%I{?5m^~~4to{-G^($stE6b84ZU_=8MsW)cg>d00r}ax7B4MTWT(xRr9tXc
zLb8b}DWO(gR%miscyc0hp1X}g-J1Rj8|ZSsn1A*2g7`Z`HV5rB*C)BJz*-khBR>-H
z>b~2?FyEjqZR)VrmC_6C3+CV_w@j$NoU9!`X#q8V{C7l>v*JiN#U;76fWY(%)=XO2
z*4O^Y*aDsP*Tu?xuN8<QN5>ZVZWuSoh1RB+HZ9A=0tLAcp?>`~lW(5;hnOA9$jE5i
zvfOV+`=M$64ij_>?jLxtk;8<N6cFF5&owoEMhH!$V>|_uFP*-W+HoA}&zfs7bna<B
zyW|0GZnF5z?Gl#jIOnW4@4$BLxpXx7<8M0`$kwm3ny!U6=iT6<^hsGt->mHYWo6?4
zI_G|#a5^llhhnb>l&|uZ2OKCEe7&Wm0-{u9irj;icO348{(KW_*Y?FL)=j9A(xKe@
z>+mPzUs9TXiZQRnyKDF9A&Xy!tUu;^_4Ne|Cn~*p426Y-Xe$o5V^=c|X^S1my;Plc
z7nL4kFuwoGmoFc?T4^pryFxOTdwQ!pd8|2{Qk~6U_Z3QkzJAqev=3KN+edGjrt}C|
zFEp9@6spxB-6l;ovq$?}X-LUsGGisak=>q~GbOXe4c1o)XPGyrWp%*M2Fr_#;Z4<f
zPzyFhY9Ut?xMTo4in}w*blVBNr|0nhJE0B9g3gkrK3uc!@tfVg3Z<K|M<cvP@9ag}
z3OI$wpV+9T=&ivw_7UE<%|c7dpC`hzp5aU_pKdavxWHi`6&jVK&6AbaDfr7-_?im*
zE(MPo8oW+u^A8uvnh#=c?{?g9$HiA052jqr%>e|v<I8@e*?bt|ZRB`;*z9{~lTOua
zbsxvvxMO+?zBE$(ks8_SyKr}R$NbrW4Rh#WyYp3qd8G0~>!jQtR$vIxQ<Jao!&IpX
zB@(zYi)&EpZYB6>-nYu7$<J6c$5N3r^%*|I%28RrXB*^w&>eN%Plqw>eEqbgmIE{O
z#|W<&t3?H$ueH__0rtkMGmEY}9h@whcB=e~HR6-)u9Dwy^?e<(6Mmw>HKz4`U?{ts
z_vVqX{IU1QQduS5mBjnoZfYgJ!Z>_c{Kz(~A4~hsDxy$FGKjf^Z5@Kbe%e_2w5SG@
z6*eh$@7SVOp4R%fb!8L2yRdO96JS{F4C>|aPs&JMX<A&w0PPGp2X{XQM`LAN$KX<1
z49Hp+9GY1UQTAwWn$jMFkh)?YRt-I65YeYtoF!kbfbxcXuT4uU^mt<r?R)R-U)^tF
zS?=(D&F``BW-dixR$WqgO78Z09>WO+(<^M$y@xncD#NH4bRyF>`lc^<_VTCry+<kO
zBAO<m8thAtl~CPRD-?3oe$!H^xDNuVa|X4+^easzTGi_<n?ORqbx~1ZmJ{C&Iq#(m
z#v=lr5rTm?%$U{NOKCnMH>ta1Jn<x=HW{+HE249lY&d}}E>sRR>cOP42+kHil{iI-
zI$N>UMv~i>>kXcF&Lhe;WU3OH2?!Jx<PY<cFWm}6Ej8aOa57?&E#<sl$l}SS$3e1=
zLy^FQ(vd4fT9{_4$_OwF(?w+3NjL%?|KbDpOo_icUq~w3y}V@X>dYU*MXMZiS%S&!
z$s)7McI4t)oyLBaR&g$k;W9caEoodIF^IYS#M~Y6Hpue;g=R!w(TFBmGE|X@GY%tQ
zfwwd$gzh%Bm7YrT;>+hpr>OZVJhJ}&FNyi9C$vWE<BeTgNt8y93n%uoK!^?}@MH#O
zn)CQ(Zua6H3;;tU58k-#7O1}06(*>!vR$119x^(uox@p$>9Zt_zZ=MT9}`bLkvm8_
zfhjkc>UBIbL2g$IOy+wI9;F0wgor_%o)M}gvK)K%Gi`<x?sPzpc5W9bs%ZOLJfEYD
zMz0jFi?iziAeqlB&HdG<GYBW`^;vidJZbJMd64V&@2uo7-qMp@BSaegd6#hNE0)11
zn<?356wdn;^GIsdQARGk+1gawpQih5Bgn7V)^*?Q9+%|=K&q1DH8^O~(w{o%-Tik`
zvnI;NGxA`@0!j3Rb^vDdrnYLK#vGozfZI-dn?E`|hXm$G=Cy4>tk)6d8+hQXFV8<O
zN%F?zOlh9(7@3~^@pP$oh%{T+jd4v;RZ$Ua`Z4MHyhC`9-pgx_WyuyNT0lh~!tJxq
zM>KT>5%541T4smlFtw3OUnTGGwBSU6Z+3}@o~2h`E*cBCwP+Zfayg9Szi-{|&!Oji
z-t&7;s}l3IZG*);mSwS6IauZtTZ{@ZTa@V}Z!5>17^@zEt`lY#>snfIjVy1Rqpukj
zkU9ZkNP)aLfaNb5MErFv-BNsC>|*u+Ck#xrvDfp)hvwjeQ>mksfn;w?n+LM<Y?435
zXG$cr<nptPGHWRhg11E@0+YduUx5jSCjIM;9U}wku8yZlu4`<#D2hQwbaY)AGF`ED
z4>BwxYY(*J!)Erwto7t}fnio$F0Cw-s+_Sl|Gch!^{d#&@)sAAarD>2@am+CaE8k-
zv1PTZ=3HD_9RJ9%*^ErE!+r0kP?7#ln6_8v=ETtiX`5{&<ki;7LS{k~7LTLC6uxAj
zgjX4!AAd*T$p&Y~p>@sG03Q7kZQE(9rOs?Y(Yqo#o+X3MSepk+0ijPwgDGy758T)_
zZ_K$(m(EsCStyCldSkg@-!Un<7VMyOj?F8y3!tsM2vyGRgAYt1c|d;mGrzTQ0hjq6
zIL#XPR=NKOr7KbppD6`0v$=n|7wL7_*oC<AvNr^YOB$=~<s2(mMy)*R%jLA*P!3qE
zC4#vg`(2dOUk)43g11|ccdmQD4Q5L?Hzo{PeIPj_cN7Z7l(lcoCP&>@I>G6KI+wGN
zqV6j|6l0WBL-2u63`JnxE1pARuOPY}r^~6K*^|ipP4qttsg!(cr5G9;DAUMpWkLuv
zc*NtuHaf<Z=xHkr95nzi;fy$XI#3&hUY*tBgPoym;G1>e&%=fr;GoV5-tN`-t`+C*
z5rBfF=_}?NIp+`aX%s|;gPXts{`KNW&;Z$G4oxZ>7s*cmGN8$}On@dk#wKX@rh9em
zIeN+(Wx#~lHL&r|N**B%b4ro0UzknTSVn%U;|iLT`%1VYpPaem$*_wp()QF?X4KQ6
zu6(^`<+Ot%sqAN73SGm338}0NbB2Qxkz42d7Ummc0*uo}>nq>SPa{Gf=NDj!W~y?>
zzK8{VC9?MV{Dt=6SIlIgS_D*1d1$@Mp;kc0{AT4Jf3tZ|;_$%yV$=0%gloZek{<Ln
z@HO+*^qY}-v(C2tG%^TKnI%BGaZKT=TBKV0G!F`{LiBVYW}CJ-tV7r>-20~EZ$(gu
z`9_tOc#nYS^hFMC)BKx_=z$-aNe=X%#BZ>Cft!y`Se}>jPT@qYEWgd+zp*Td*4Ye8
z+=TyfcrG8N0^Jer9WwKYLV{O8B30z)zEUNZ%@kGFGi2a1Ahy?PLlD2mtVrs4Gg+*4
z-BF;^)$pq7`U_fui<<R~ZCIHXUe(t0A!|az{AJ6x+7ZXV1=|w}0Uz6O44)MzC!f7e
z<X1DJ5biH_x0bQbrg$o8{IS^i`-&}->usQro9kYPRh@H7XTi^zzNr4kMw?L@I#H7L
zXAfsD1uQ%_eTm#IM6khUtznJ(ssgXUH;Xq^Mx$H-fIf))YkZUNW%-0mWUsv|zMJ*g
z$cf{UhM*xjJmkom#;YX1IJmJ2FR{(~3F?IQwV&1sxM@v<O)N|xwsgcvWMM{Q$a7UF
zhDNl>^{DuA8+-0#FHJ5LXfHDnNDmwS9i+4`khHyK2eEQWauD2lP1txqBOEtFgqyi*
zqpx0RHiz04g3G2_X42*Blun7HJ7FXIoCYDPs^v0eh^_57k#U&lnuIZu(f6Z1m;SnQ
zcRSds@pO*wdU0`9hxf`J-H*TNrH(x3r$Up|M^p@<j>o&#XB|UW^`33!E7)CqT3r9c
zW$h&-v(-;5t(q(g1)mDS^J!VCZp=(+t1WSF!J7ObxM-U(NR@(qbN1wr$Em5Fk~}hN
zF?my#Tj(51rTj)Q)qSglC1n(;bg2NU%2^moR(#$gwd=8k?RBOEbOF@WQT&PhR6d?1
zv9Gx*ZK6x9Y8Qz;ce`%h^~0;I6thkBg(_hRbRGUD0>?VwYdA&p+8;l@%}+kDGV@!9
z0!c|tO{)f7flV9bAxnk1LAG4kn{~kyTYM2^AE|ruvxhxNtbTjkKfiH#N!hi;W*d<u
zk&MkZE?j9F!LWQK<i+@gbSt(WH;<I+T%=@f&=in5qjZ5FY91TDzRHN?al|fpb|1aQ
z-By2|fr7JYH6`OHHY5DP_8+6Utd{;^!Kq;OIggxMJx~-F0k1V%6k?D9aZ0G4yzT*M
zm-rt`NCXp}Wq=0G+p^Y%ZuSd7O<Z6Xb&9d2$_3q{Qx6Uc=t&k2c%NiE0r_OM(h^#w
zv6E=~H4423|8iE?m54h$kFarN+LB|&Zf>7J8sDJS5?UD-{aLN<;d<<xLqiGNmrdjJ
zFsU($D9lSl{c7z<TQZKOj)3pFJL8kn36^zz)~_HOP4=-G?P?s;K`V^HT`qrlZ+Kzo
z;J|>nH#cD<GkH`BIo4*hy<ej3+UsU(&T4W$++^3QL122H1%$o(RhWD+1aDBliv<3$
zeZQ)ag6dQb_aMk_Z&+4+xw(t8>2N5v1i-F#Iv~TLby$CMCdL~>qr<6xmYsM#&tOEL
zxW9;A0~r^Yt6#4ZI33^PRHESRX4Y`;@<ZF@Tq0$eGeV`Pi1qWx*LED=^s*Vj)?sgq
z#a?6G%b3e(tOg&?RNpM<ByBaqbDx9le#u=NDKK64w$;M*i|2q{Ue!SF!q;T3sr1Me
z!x(uo5nNmL<(lQWp3mHSL?v}Mbsk=DjT^J27_G2me(3QWw{_n@GEoYzEn;N?zrh6#
zpTM%dh6=P6E)JRG1*&b@Yr@hFF)pt3NmJ)DJEEhst_vt#p$C#q75C02LD;H}k;yKz
zyE9d1p#t=eY32l`&O#M+f&?5|suDT)I00WehKEhUA6axIOd)F$ZU(l|dmcJjH61Kr
z^x9~vRn$0toj`eBzNK?ig3vs27oxdJpu+E>X!iX4kbdrTC#b0uct`-gNEiJWs=AKk
zGay-ywN2A^d=-g!O;GX~`U{$Aqx%T8*sVNbQ;)2nvRidB6}DJ)VN-4ke=VbEzOK|x
zrN|6=?|fiht?4*5ZmA*Qr1eV6y<Zs$egm_;%Bh;Q87%BvU5C5xMW!~G^sOR9@%Rtc
z<?PN{0F}<ir>gY9rfOYy)i8a6R;g+oQDL{G&Z6oO#ipAy9){qh7C|w(6tC^%i!2?7
zIkeLn8)zkTG0Eq^8M-|l$$OF6fkomlA6^1G6Z3MFd*yK~XAOoI*Y7kO7B^nhqpxR)
z?Lr-!dIpvbt3A)cG1;>uU(DQC->jfP?FCoPG<IFr#0#8mwAKPv%_mm(eVzCX>Pm70
z0Wm{$jW=E3={hFz{QYqa!qf<@1#hWZB9Em3ya5XNq20a-?HZ=p8fA|m5=YcIw_Wgz
zBfr1m%Qe0*UhC3@(=W7QVKV{2A$S~C^@rj5x3WN_6v1iUPt4j~plP5OZwhDNv>NN{
zoOw${xgX%^L`V-H8Ws>Ydns!Uw{s5`i;a=FX||1~Ftu;cjKMV@VbQ<c>Rt1-wo87G
z4n9c-I7(=xA=ViG=S)T9!iO7C%njqj?*xuNwcpKiG$AIyd%l*|7Xt)|j_$`k2ru}x
z3=iS;bFYT&$eXKh_Nk|yN^(E@xVZLD%}-*UyBl51UhINB;g#6<&dX_HC&t)UhYXE7
z*NsHyYsl+V49~@O!Dl;sVSHDUJ6Zyli&<XhFW6*GK&ewre0A{b8%F2(dNphC`oXrB
z&v9YoYt_+b@C%}=)d_HaKlu8@>&%PLb?tV{w8_m+R+!h^VB?jr*I5&k(DOV#2YfOA
zXb!wF!&1fPn$B66Vbrt};51ujjBp?VG@bHUpC54Keu>>_*eVQix>|l1#;1s$>S6<z
z(>_lD?QpLXk|P98ixUYwEe2tU){O^Y;3?&08hjfZk-5_YvfX<72{I>vy?Ft*In|Q$
zMZTdE4KKLxcyBh$Y0$}252}B0vNh>G=&KWVw)!gfXz+Tk?fLH6rT>ZNaX`k^W2vwi
zm+?;2SOGiOmwIdUo8!6qo$-s38p};l2_1=3+AR=z4ewq^Si`!<1g+PW53F9`aDc+9
zP9Sg|q0nC_Xq*M3bwlOgS&~WA$$zjj`%li@BY6?0v?S5d%?t$RFJxx7ai5>jfv*lz
zYYra9T3lTuGI$M&RbMvhT({3%tVqos)1;pAz&%V>u0;Ag5IYT)6UES~4@23T)=ly{
zP^X=lN_c#v#jLx@U?}CZt%+fpn5F;oxlY-i7ri+p6e$`zg#3rfO@!=I7lbno$3`as
zp~M%HWfwSy><uo$_DfcnnmeBow~=!`@wy*oqw$>JV|hdr_)~>j&$952kEK9Mp-sEr
z#?p|VoOROAkW`5=oXiAY;C)e_^mM3a-ytNNE-LqI8{?R3Sn9-c+N#-qdR1qOkhkvA
z+MX(-s=IE{_1MMY+$ruSZ%2r~9rSj%cnBL@*ue`EWS>_>Uh%Jgbn}g9d26=JRD|Hk
zx!MJchy8<wqfDgZ+?e<jNfX5jPu`OpBEbTaKF39L@5E}nP-OH<hUSa;ki>K}4mm}J
z_UjH$LZUU)uhk<JiH38&_9lpc$kPt$<(*&Im?5+|9)7jO<9VeI*FM-!JzI`((!9j8
zzNic{u1M8ahp<Klkryvjtm3c6DCWeWh<>Vz=n3lR08cyY6W~?t@*Zx+YF96;YQaS!
zdjfOF2sf|Vx7SJNFW{Os4)$l@vy9Y9-!X!EtE{+&#l`BSv!0S$Yrxj=LhR%?T^~)G
z%pIH0@$&`m$33=q^DfEH{!$`H>^)#X&-6_9RU;$rhOoAmJ%m%^kho6$LTo&~NC*7x
zeA8-wC_8BGA{2xjOD->e2~h>R_Qb<4kDhYl>sO|UMJlbz>`uQT*edQP{zt4_B~${u
zHk5&^{ESJ#8ECM$3PuZOIQJp&vUwNdX5o5ka6&|m)<+0<k^e<N_f69%;}*(SqA-u~
z`>|dgFP6pji`VUwHx#zLmKS!T&+rEL;2|TV$+O%$7pH<508@@W{%%e8Hfu|;?T8#|
zHr}rqFz@Y)uBH4yC}mMzPh<?!YLsMLvE^&26;l-huEVxK?V-UDchOIA$u2FcK?8tq
z#izs8UZ)lw9w+Jj!Fbl3KtH$sB<{WC>xUO70@xyhd}Tz34J`vp=~pp*juv%0z2>vk
z<_MVTob4E!(@f2Z$2UT>)Rq;w#_fLd4C5x3LGT%Id5Y&}<|=j9K+jp@RL{WrjGWXO
zf%65>MQG?l@NGI>YUS?K+waHzFjRfBsl!0WwCFxj8EaX;+p{sFb0+;t>+not2P#1P
zGHkZ?Y6Kys;ki!$lH4Bhq)u4N_j2s>klUF9BWQFy2ie?8uCc-8HK%?E>u)<=dI#g+
z8>vK&o8C^)q4m`TbO&~L#PkU~Z&z_sOSI#0VmkJAXt?Q0VCI4&kV+o*yz8MHM)w@=
ze*1kv!e-a~SbndLBJ3uI%~-42t3K#}^PI!bW}W)wy2qf18$zH%sg`os;b=xth?xH@
z=WWXTVh=qRwHDzdc(<15{m53OiOEul71ZWt#hjtx<b((84)EL|P>?W+*|R}BnlvV`
zISV}$z}n+?nQtaR!0gte5iH?vDFc!yZY~jnu+x+f)$1uHYi*&+-c*Js_A1l;<<H*}
z)vYAZfSvnmRbeiRymjS|tv3HDpq$r{Cf9@B8H`I1WJ_rX_t+k_$X#d1+|1`cziP9D
zTc6D!)1Pg*5M4lAz>a6g0@v|iw-zzus@Bxi(L5P9^zDosN3k{GlyxPJ!Y>FlLylcf
zZU22#zUSb2%!&GwO1U|yiFJ&w!L5ery3*R}v%sG@9>=h`sai7e*#=|hwJI5y!$eu*
zutX5Uv#q$fxkgpK;ucNYs{xPf<tF4Dh1#-(jS_x##bc5#Hmihumt)K4x5s*eTTPyd
zFA4|gpw_d8M-^_TZyvR-G`THMgO(PA!(=j$;UoubS{_zSboQ5?2%ltV$)sJ!2?3Aw
z>`Tki{m*H~seSzepK8UDyW$FVvoRAC*;1{Ge~P!*$RQuBHClbtJ)Qi*Q>D{r*f&<a
z9l?$(6i21Wq_#ibhxNRseZQ@q)!>tn;(V2<*xE9~E{s038ZhYc7WjEE`eAaoBZc?~
z>|VQO8PJG1RS*UIt5Cib^XzGhjCJGQEvdB8Ay9Uf4&pyA10N6Rgbm(|3XG%c%Jakr
zJ2X7P1Z^gy2j~-lubPAPiQ>o3b$U_xolk@jS1V9@PHUA5`A0K-Y{(}>YHq9R@hay^
zUB%c>k?HI*M}l5AEJ_4{JW94^WQ`z)9UW6am8lF74nkcF>#Y&W9p+(x+9P+llI9Yz
z-Rbj_pqqN*>A`*y`Sgc6Q0LCUoADIkRG5tdI*nbDZ8KSby2uy(NSv3nG=p=VnAU_&
zUgw!UGA;e+_P!N~@!3*RQdch7LrNMqj{l(OZ_Utwo4o{%q>jZXa#=stqyln%*Q#-;
zPn<2p9|M;L^j@>7OnZJr%zCuAfRZl1o#dqAn8j>8A|pknYdYngvcGlbc#ka_%Gz>Z
zGNk77(v(WnR@Yqn0U5PdLvXYuVJ&d8HkQA}v~BhRF+2%99Jbd2EXzYT_UmV_#)C56
zdW`ggJrC4zz_+XS3ID){JAIU|etjRff88NMW0S<qwR4e$gxmkougoWUc&DaxruA2{
zzMXdZC@MzWRB@}K5EHi-AhT@&=RI+KRPUf-n7o36#X^&>9nYxMdx?_NxL&<GdyE=s
zvhQ^kk7&F|V$qqp2LgF6cs06BAY#&4nob9vCn3`FS5yUkuKjK-&(lwziUTojrNikn
zLG2?Cpc}QoZ*`nJKCc9{FK@C1nfvwm`?swOEubU@m7b{*L_;2B3_Bh!z5j93iI&Ka
z5NZKlltc_(&aVb(+vd^#{9V$&{aMuy>JuKwTN_)~{h2YyP1A>>VPhu-=bO%T=oe>k
z^O_O|DFXk|$}fPwD(2nMGukYJsTye#p>$+seEV(g!6~d6H8r&#%tjaCJ-MGUwuVf2
z1^6_}fAbf&KfC8sc{NK)mCub!Q0UjEoYa9IP|?h#n8DOIW0+!$Z)@8bZyYMtHx@Cz
zT=%Pd{qw3HdHE@Ig&FL|+>Ve{ZNN)VwD|3Ge-%ll!=Oq9hwLR9BP&K`f$hI<@=+H1
zu?@$sD5Q?Roi?IRM<%JLJSd^8qO5GEaOga<i&$ILcCg`Xz1xf|lMkmJ>S|1tT}q5)
zTdhJxbbMz2L0-)o8g69*juBxw)tj*+s`36Wcl$4$enM(N5K&qyo@i*>qT0s2s_rJt
zOjIwsJ~-ZPc`_Jkzn4)Rr>b$Xcgp>XL~bq+@;>f)#xP=$9TPeooT>ytW2v;n0@f6;
zL165<C4PZ~*?+|3GbZ$Q3-^7r`B)kC8may&w$>C~h=FJ_pda#`WixTJYAAH-Kb!&A
z%WoNemLFac)JoID<8p5@(3*&a`xSA~D0dz?fa<uy$}PrSAV8%-kvlqDO&O_EUTYMq
zivf;;nl%~0l3iO^W^i6<N<We+Q^H;Z|4P8+p>mSni%SUjO@cVHJuyj$6%Erc(^YO(
zf`pGVkGLeWO=AV`8;x_zZE)(bl&T5~KOgyR8cEI6#4pJ0_W|X>T*Z~DCk9I8m=s!5
zA_5aViYOs#w6YmW7vF3CqhIkK;=?ux8II?RAB{TWaCD9Qw-Opf@+A{}%ErVK60+p3
z6Z9o(On$G$=D`cyCTuY}^Rg%vR+fqxBlQ~T5=U5YWyZIy^TrwO3MsMGe^Ssd&I}Uk
z9YPFJll%g)AZdL}0%;xy)O$x*3ZHnwg3piG5~@O?q0nX;<e<~6yOTa~Um$Hxk8~dn
zE)x)xn1J&ssS%=(N8U5yYhhbz^id{%Kg~Ef_OD4Zsy@K%i~wMEa01_Zv3o&!jVh!1
zQI>>XOBi&?ovkYXWu9Q~{8^76k_ja!OSP4dSh{<>DG>vi5x&(mHLi?F#%3&~Y#3AN
zD_wl?AJx`k=sH>S-vr`lW@kxnLk?E_MEWiz?WzW?;*0vDwgTCdBFn;<2I&YiOYhc0
z@;wWXvRV-f$b#rmU6&7;q>m6)N(WvwRXEnt3F}X*kD?F!(ULc29Nv91J-;ZnDEu=2
zZ=zA4eP?_zmxR^Pw$Vw}98lDD1s>Y_`v2%jy4lQ7woI31fHH)uJ$-^Gtn>ENq-<R^
z+}I_>^o=^OVtvPP>0jB0H`nC58pdZj$Qz*5TQPUeY%(mn42?hJX?iclvbjf<%xeg&
zgy#ka{fhOsu3kEgVx#wJsZ08Xx`11jt2lktzDo(}Z787xGOl9pw@Oa{=vEY}^3a8W
z@3f^T;(fx%#;~nlSMIhIp`|EijM8tI+*jBS0<cCWQycQ)-&svTg13qUnQ2NEQ<p?Y
zXIu8lw^hoXC0aSP2}n|Doe>Kok-qj=Z9B>C?+xU5--C0J+sjwW156!NA=*=s5<Tiv
z$=#ae493-{Z<IM!Ur{a#{fbYw#-`SfH2FruW71wvaoaBYm78H$S7f+soZfB;Q%qr;
z`WC~HtH{t{GFSzuvS0Jt51aGeGUCU4;nrh~ml4fP?5pcJ|1xX9o7ani3}EC4CO=$Q
zX_H1NkeoZ)R3Z)VU--SOf_T9QqooMIgp5+v*8P}F(@rEt^`4n;oumEu!!s)KQa9{)
zVTDEtgid$+&hhq^{jSIE*!m8_tt$yCUx$r9)}^WsAyuc(T7kGLMcUateRc5m&SMIV
z9&F*Khm?=hq^?wi^i}n6iUxW#Emrl&>CBB{!QY39X4FZFCOuj2{MU>q?pGbRmKIIq
zCcG+C%Fat-l&kNI=m*sr)Q2No1Tw*euUyO)%YPq0{gbwGyGMwPKX4^|Ok1K}>&?DM
z5J*-}JDc8suky3B+Yd+mDiwZtu&yT(Yk%K{4sJJVM4mq=C&iVzgS4mheQF}Up%6)e
zrKlPM+y7*kw-0PJnQDX7YfTl&J#6bnO*h9u66|;$W6EDlp$TV`&v>7Uk^DD6MBm$t
za<e$-a5da!NPk>@@<1xi&k|UXm&>&kPS-+Cc42(My;_^?i|X_z^)<&N!X8hu#^hzL
zLk0?kd9t(zBGg7eQ(`>*Vi^kyZGGsFe<ee6pS!59%18wp?Y5N)hXaQuv!Y_^^H{^y
z=VTB-4lCA$NmW_1=FWL3nn&pqdZb&5a}(oH#>>^F$bHIus#d=YWf6=`g5(_sm=ymL
z9b7P<1f>@eB}#Eu6v?h~_{-ShvwVTbM;QZBE*_H{%07+#W6H9E$>Y;eEW@k}xP7C|
zc?29|%w*CaP=Qh5L%O)c?MN_h{hb}hvG}m-0hDe#zht&&Na_65M(K=MK*!iD<!s}S
zabk(yBn$OH;CM&J-(0edl<S4*TC()PUz}+`bF7LiCt=XR05)w4QDXsC7^?h_vkEqE
zXpGg<>R6>)^d~%Wt%!f#xsO$-Ra_9!$$VJOlp~1q{1<@wby`R+C>&3vUE#}>-}+jb
z4Ci65blW&<>4z>BVSgjG3*!<a>HiqHpJ1=H&bVB^3b8s-M@&+}x1w3|jU~UQ@!6md
z`jH<e#aoJzl!Sr5C4KvSYu1qXFap!qt(x&p9eI^8$eF@L9CiGSBPA)q5_fXr(F)U@
zzHO+V{CqMFp(i&0C4PI<9g`%ZJmjDSGLuZ6l29?|ac0(QK2tJT{&XiowGJdb0zeu&
z3v8?+F#cO|8B3MSh!K<_xmR=8B-;r(V4~gegLmUE^myb`Uf3s*wXkGE3mh(QQ=KKX
zohCH|Fp(wJPT3n5L{t@V%lRr0`rSpmF-h;bSWE)0eBypZji302_S#~W@9Ab}(#hqZ
zbw8;1aKd7Nb2M(1MwnrL4vvzjAK-Ts224!fPCr&WwCh|lVXiu{&0tB$OU3cdzAwWw
z{k)7*Id5^{_ZFJ-$(oxc8;QqMrc5EC#HKOjp)RejEXU>Lr8N2rr{lS%Pprp2AAGwr
z0ZuF=`*i)7G6XrO93|{#YIu0z`bR3-<I(;t{$b|*pld4}f%e7!iDCB|N6p_zWeX=_
zDX4Qq8!RoD@Zu71JKJ2HkGyfqb~U;wA*FBI+8%q>w{({~b!U-c{Fa2cJ_RVrn;c*Z
z4*;}}wvdAS*qOT+f#?P?q%QKy@wY(1$*#uzcj!~wM(QmGsN>o`1`etA<9Er7?;_S4
zip^dtC<il*)F&@XkIC9rOq;q~-bE%z{5^jL1|s^%!v0NzpJDN<(PeGe$F_vVqdoX0
z@}W}fL}_AfE-t16y*18v;k6*SSsmR)6WnB#u~3sQD%07i6Pf?)BIIYKWtZBh1w$*u
zBGgNByMp)nPUEZ6x}1b)(=j3no38`O=@qM94;>h?4`lB{yv$a2Xp)%Nj2$!^{;FH%
z$EZ5x#*9i|Ong#u`_%b}91)uT(!PyC%#rDc6+Pa`>aqc<HB-qBWU=hG&H9{SQg$6b
z_u9h;mIY~h!KQhMmT@*?;y?G3ZtXj3GIeW?(MILl2m20uw*ODM4r^X)eK5vY?^@>H
z4f0P|kw~_6^c{!~gsMB(Dpn`m>xm`OV*)GL^xSQP`vJaAI2IM2o(7imv4-&=AHt&g
zeokkcL$MM-mUR+h1Nu)OPeB@P(DIsouo(-ead)nmVO5^c8>jkit7PLSGC)s4HFntI
z$SfzLS@ky<zTiV_wQ%zZuGH836xgg7nMz~PsJ0^5UjCL}lNWy}Lc*O-!oE71*8ks_
z__A9OIu_K(xs(|K<s2*A8j15Rnwkz~2RX-sC9qZsGY^WlT*!U<?jXkt5lGYO4@1!e
z025n2xoKZZm}#dC^G)D=Hyg94T574+ADOTq0ly9$e`QD@S#zfOsy&-B3vNg+yt7QU
zP&|^Ceweq2BHBoQB&}Z;G*H^TnjiQ#UCbkyyv4@h32Jwl4WmG*w>HAn%~sUNQO&KD
zhH?d9hL+Ah5r_Xd^}ZD>B-)!=TlRY(7m#aPgzp4nsL;a5(=`4S3|eSx#KTC?TiAJ|
zscgG&r-P(sX9fCL{Q4w@R~>cQx1pf(I`Na6#kj2et#O2I#wpUW@<BuX%9K&=fec;&
zVQ7K@FMWB_Ymq6O*Q`-vYOCP)6aI2NDaPD27I%g%Dk$6yxyk2B{~W{YF~Myr2@Uuu
zYZP?5oXR@!v4SGk>Hj=d>;WB{9haHeSV!L@sjQddk`8`8hNxVjxiNVart1I2kP^Dv
z2`#O4l@);$%EvQ4#u?=4dMaQx2j<Ai%A6+-)%{2#tV|sLZjTqm#GirO!IV#_Jnq@F
zWM9@=)wmwjGi9bRR!c+CG)v-1UI*L1H-c37d?WzbhCOeYgVJ8Rd8`_k%S2USnI=d>
zefB3xr5s-_G{;I6onJ2A`;B*W2aw1uzIZrGmpDXJI1J&@J}alp!j(0l3+p<vj!o)?
z%o5`>W%*V7k0HN4M>U^FjrmC$s-Mtf#-&ID+Qn{!!e}iVEDP|UU2*XE7xd6O4DiH+
za%$B@o`YpQL0KVwtb4R~|E<<fc3s1%DmEDtBK;HEI~Tl-aDEw|-1gqWReowEF9Q@P
z0E)xLMRZ*2{j0o2W?Q*3w;y0J+jpT^KDY~5w_xpyb_@NKz2<}r4}ee+$9NQcq+YNz
zJuW8C1d3#jX01e+k+0bP&1FxYe1yIhY01`BYO_FO1;!`T6jgqXuZ!2V@=L{W$-Z9%
z<A&WOGMfj7q=H3>@JFfB5+6Lq@QI`0=gIle(uPI+mLA;@Ma<akzgYjnN~WH2l$n8J
z5B>R(x^ZEcUMn_FScOA>GstlrQtp>xp*;2<H|2V%+i%yZWKji6k|xiHh>XWHCv%c9
zuw;C{=IP#nAKCo3R^^oRzHe9=SV>~s3BWSrFn1fE&GwM)<9fz6;ys?9SI)aJSaJtl
zIeoZZP?lR<ojU*$5C>a77uuqY3>A6IDP%4C@<K!xze#c%Z!+xmrU*Z%5x+C@u9tC2
z{Vi>MtO)=%WnjXiBE!!3yx{fGD&M~R%-d4*#WYVv*dzGFokgw+d7q6NDzm8*XSEoZ
zeX1*Gq0`DH;t8Y~NXcJYT#Z95vP$uPOn+Aj-(DVzxz{*`Hd0<0#g!7pCfr|17dk9y
zH1^XXrHCXu-|und|JYVJs|Mxy*N@R-rMMIlt=h!zn$;fm!?=w$He$REN81hWlCI4I
zTn?jz+vEhE3o>`S(krppzA@ym0LrpORk~DF;n?clE%={nZ)ycKtAcsysvj&UBeLT|
z$jr;JVG~vQ?3+2m?8U5jkpFnH+=HcZNt0)Cy;(yn)4<4h9ok5}n)m=+rFIC~NIn&J
zMPgvk|DgH%>K!t;Lq7e0*A_Gp^6yyAg0w(+;qe!b-X<G<(weUL(MSUY2H&N#vzI@-
z5>TdS{T6wRWI;c02h@nOdgElRlIgMFBApbd`NI%IZ;G{Ejs2a18illv?5K6wVZkHJ
z8Mr$LWCyNK0<x<HiiRLPNhnYhlm36Jaofb;PR!6^UD)Wsf-NT6loqMk5s_5?NqH3r
zp*P5e^%rjYgnB!+7w3J^AOA4qCWByeGwidO)`zyw6q+aVW^GJScs*jhUjJj_x>iV|
zU$mI&gU+jYaOD~9-Q)Zu@j*yw0LKvh7WIU>#x;VtA^>iNEW3S1MuReF*js;n<6Cor
z@3k%rH(LW_0`QoyV7$d2^b@dw$!YHoH|mu+9x7GFxOclfB>OmSDc2X95w&AM?wX|p
zNvg*;#{d->jT7q51Q6$MoaHYL-?=}hPr21vf3~Qs+ABl1Km$O^CO#_Lcdqu&b?RLj
z6(anE&yxO!sE~*Xey(~u(Yqobl1VDVo)9#klZ~vQ45BvKY%_|&arz$*70+<C?f>kc
z3KGmW)UYt;8mTVQr>$F2$WJ-j)rSD3pJ3m)zaY{9EB$H7tXYxv_%g#-D|<>|fR57O
zrCfzoZ@y{Hf7qDt=4sDqIfnuLA{^Z#T@Z(Hj0(3W0M0PtB_kET2dz4AxP374-v>88
zSr#ex<kK*qh~!S0^tS5^B|b9Ek;rcpQR7r*s(^R{O+<Lu??fl78;UgP%R*FssG2!m
zUezJXOu*UEG?{c`5@O`p>lf1OUOS49+4?uMyzn8b>f+Z_K!dxeyy7nY+*oc&F_7IE
zc+NREVy7@1UDN^jk3d{r>SiD3lHO~|FSW0Q(j)?OEHW(hA#{C7iQH8ZpAJVm{`#ic
zF;az(cmnf&LO!4`jkEnd-?;RM(GtUE`)XwU`C;*va;g^o9YK}oz50n_3!H~0VqdN_
zVq_0E?6P4|wgZ&2y-73%g`G68*`ED%WkDsm-kybb(ZEbTuAr|yI?q@XQ8$S!*I7*i
zlLDq71B8Yn+E(!Y2;5%~9DDqj3$xf1w|Xef72pTTeueKbA&t63Bg57DxnFEG<BOET
zUs9L*0i=aK@Ppg83ML931qx#kZIeeV{mXTkX)`86QGSUY2eI&fO_f}b$gw1LBpSBa
zwItakZ5X+)R#`UIuhc!-^^8;MxlT9l?x@Y5#g}fNFjCbgnS2Q+mz-J*I+l;m*CRKn
z-a0X)VD?`vSM4j{FX6oRTa~v3#ps(V{S4ggldKhmuf4REvU*mS`S8qfN0R)yqA_=B
zr31#hpkJu|;7F_BsS31aOf*S)pi0QFGchBo2&^Q7ZN@U;5VKP7I#O)rPd0AOd-rn_
zF;S?|N4kZPqxO^5N`E`LD7w!9z+egi)Z1kPVC<2S8eePwLwbqny4XAajM<`yScX7k
zIggownI<e@QPPh`rm&>1B~6$|EK*Vl>8I`zKBo7*$dUrTL#j!yNLK)AENI{0ePp~@
zjp{P{B;+kHBe{8aH2lASQjp>J_EKzrOExx-Ar>&9NDI`h#sUpsY%PS9Z`!fq%X?ik
zY}ICe{R=7Y`LNOqIA7t#t+UZ{O_vqn=x3kJq{&+r0d!lW!S$)v|H8?JMWjj552$Go
zr2TJ{LphD9M@+-EPR2$tS3jF7V)XZ%Fs-WMH=^4Au>c!hpOu9J8{PrC_-`oM6>-Q{
zO_rC+GNSr^KPqV`uqrGeE)8DQ{|ZN2=z+xTnZr9{wRT(isi&)3^J&6n)F2c!Obd1N
z65TG0BD?<*f|1^xk;=#uhHT@`%o=utt~>u%)p_`{^}he#7JJ348G^(nX00Gb%tY)_
zds9>uU4+=J6?@c*5qs4rEsC10k!p=9ilVBjD6RJW^Z0&$pYc8az&Yo>&i%Tt*Ymp1
zbqi1Bw^>@-%>K$3hy3X9{`_A|Nr}8@ESQA3S}?;ne9gudMYznh$}o$zK2$|eb-@gY
zTec&k|5^;MoZkD*Kp20m!{QD5g)ed==?W6O&r(N%iaty!eJu)3_Csa_J$(I^dHU18
zkce9uRjMn?cUb<bmm)u2{2GA!+*Szo62~qLL(HfD>zPaFDM-VN!Yf5}Pa%QK_ow|p
zft%dcFFHFNKD}nt_jJfz%e@cH9(nmcv{^0gtyy{VzH7^?Jv6xE#TbXJp?BJsMdjP0
zza-CgNRAMdKl99`kz=C)hX2XQ<R4O05w&3@v%WL4;XzHGq&HjXPhCAHGL=>`Ml>de
zpMP%2Ncx{`=vVh}v*9RzQl!2_>SOxz?jZ96X~|E~_Gi};TA?Gh*I3PyIH9OB;$icd
zGvogo$gAg{e&O??rA83Jd8?zLW=32zrd2(VXpR=3t&dcC;h?kKF=&CnM)CZw$g~pa
zsvXvx?91O35xa><doTZ@)`w4<yh79}99)*BS*IM<N}2*Ng3<p{-5(O=vVo5z8>Wi*
z3V#%+zaC~TXm@!L;jE<eq98Z;{_>A^uk`<SaIX07B;71xMyId&lbFY+8A&Mzu=kG_
zb-BDjH9~h43tMk~KAzvOc|2M5?;x#F@fcYwC0YNzOHjV`g2(e|m0%Z>Z&8iL*S5uw
z<(j0Ofd94@Mpr2K*#of5pLeLdtFSi*J<h7ME)MfPy)ost4TMGwXugQ}k9$og`_YH_
zA$~QIX_fQRudjCN@!2D8syy;pULZDpDiW0?HU8IkFrB4awv8E)_F5D#LFJ6mEp*<U
zrku)dZ#1oWfQax>Wm*^x3VF|O6E^p6u_j~MPB#oQ^K`i8(cz=+xup2jPo~(zudo!+
zuF^zutVvpl{y#GC4R$T5HON7#1^@|xU6@McJ80X~1~2>NXT3k$J83@X75@)}6s9;B
zUP?%{*LfeU<SxkbXh)?!JC$yQ*j9LkxVz*uUFP{OeY|2CqEn8|tGk!mjpf&`PpW-I
zP33nbMOIrJidC4;7>e>G{P%0-k|~E3ElB!gWQEULT}?d74#M=oPeM^4WGP*_p?D?V
zbVeEc?-Yt$w7k%jct`nly1UM6gC_6m?Zamij~77R+`%#p;r}u=SFSckcLt!;P@UcQ
z<g^aA*YX;cH{BXum9^cPq4H6~8|mc#-*GgKP5kbK9#wdy&Ab$!BlAL0qea~*)i7eW
z&o(2H$K}jZ=yL(9?DD?^7htuNXc1`h0yP?Rkj@;qw9GbbZoQPLBs%Nz`Kjl>Dt7Bs
zR~1XGfzB!J>kq(mNycp)zCN|a3ZmCNpigPax;`eC`fW@RcJi73qvUL@g{~zo6_K~H
zxCg{0?0FG7Dqnr}J!y)22@KbKBK`gy>8lLekEq<ye_m<R_~-fittmzspJM8<X16|5
zJC!H5Y({baQmRl2Vqnw$TZ15*VN~As!6iW)=4%mFao=#ch;6s8()a&b^{|oD`l6H@
zU5SPMFkiDFAH%b(#kQTexo0yE+ztRc|D~2!1b(wqHt0*sPI|qH&u6)=^pzfSwS%mR
z&u%cOmF}$)U-W;Ry{*8C33^$oI5B*Nx%B1t0ygjE36-s&Oh=h~^pmIm$Bq=tjwZUg
zA1Yt9?YJtiAcpiHr6F^zY&>7R1H6CrU7T_K(99YBcj6NpyF3QVRTeF#Y4h<`BBhrm
z7OMZ4lkUCdZq)GKw7`yn7^keT(pRsnQ=D43AXMMT8}hnuK^omv+#y{hQ2I+n^p-ix
zzshC}1!Sj<i!$AG61J_6)lHTj=bR}%@{OE%-FdCxS8v;RJ3gRy_fPFVUvyeLoCR<H
zTbRJ1AKyLC`aGa{4R|3u6;sdn=l%2~v-LobXfWS`%8!PLG~7QIJ38t;5mC>Pz5n(b
z4cUVggqPR<9$79nx;$ABw7BpUH_dsv2!i}>VKOA8T^XI*0-pz7z4+&4n)VONzXyN+
zS@HF2nbiE0^}kRlu9vl(_V*j1^IsCpt7m_FnWbf$-@>P^W(X{GLG3SYs=14%BhR?r
zF>j7P_<pB(?>)b$Bi+`Zz~TmTqo~=O{Z*grrPwa@xjgsx6T6Ej-yo$L^|fCk8tJ!w
zu*=`}JluBtJ6+e}vXWHUm>1?>#P>wpLFpCvYxJ#!VU`sbc&FsCeD@599Y*(@6YHu7
zWDMC>q<U2bO&(nv7ExPJHZ!$bzg*ZOwQIA7h7uLX7cvJsl}%N1T{a3gKnB$vZFPz3
z5mT4!t**Ecr>q>G(l4TMCV8ZyR4tIREK_<v%{OaR4}aYZz?Y){h3hZ5t6v;CX(W-d
zSQ6DnEL|YzM9Z9a2&0W60=wmYL<;Cs|7<Y-%q1V5eg961r2m-&2rc}{n%rJqJOg7Q
zBX%f>(+iD*s;mzO%daY`W0gmT1qgE|wR@>5T*1%5B*q)5#F9IC&WX>ALMBAA^wrbC
zKHIHTCkE3BpJEloO@=ld)A-DqcYN1pD43W}CSH_pqP+DAcI11?d>E-!n6TfLj{!h1
zNm0y%^IoP~jE@Y@RA{NuJ^S#tLjx1Vld$7duMyU~NvdW%Pu5>roJ<AJk4*)-eW*vz
zw|SIfvd-&^h9D=9Sm<0QJ=<xjtLv8f9d#vI(G0RoMv&K&g}@I0+aW`ECC1qG?yqSl
z0P4AZkU1+#!2!v)U|d$H9pCQts;_avmS@UoIh&^?(;8MBi4LgJcMk8fv@nx7jC%z3
zoL=A?)(ZzTHExX_I<`(WRNptgKZ^VfJsu57<)YLC&3a90zX|6W0U~U@dYzSLqMQR9
z7z1qyl7;>54<)pc6cvi)!ymz$*#SNGL>`|u^9ULM-TGL^1YN||an&qBanY)6?@qc5
z@K`EYSzBSAfHd5J2_=}af|cSQ{cJY+fY3+20`2n3@I1|3wplqgrY{f8nCEo6N=nM_
zPWtqS(il_|8(u%FWc%Cj+=}#AJJ-d+3V^ic)&456OK8I6ModksrMlrngrh|wdIM|_
z?$Z?q7q$AOp_*@vVx~lnhwqrOk89bxSN9<tL97Y}y4fb5RTdZguZT44t<p^Wq`&i3
z4V>#(OkX>9B-{NBKQs5@*`oB0$&>47(Z>yj877AI^KsL!h(4vV)F@5DR+s^2prufw
zx;Y@upBF!%A@h9er|8{Cs564i*2P)lTG}khq8|6Sj}sL9D@D!r>TpG~_b1mc>ywkH
zLkvb{BkQq-J3JXf$y|noX5aIirZJfm>q)6)huedSf3jf0Y+FOXmk!hA&&~^_AHZ*b
zv08;_t3m<waLC&Tr=RAxw+q%sC@|@G=4{pgf$@oQJ~&9PxoLWoSHvee4tD5h!wsrO
zTA{M&*z_U2Tzq#a#3oNbwN=IK{oB$dv|RJbC01C~Of)m~i{QYU)aO>8Q>sl@82!ie
zQ{Q)u0OHbjXv4Xg@YVRy`H#o_Sy?P8BEN|SVzBB-gpKv)mY1WC!d5Su@eVy|awWHO
z)U{AUQ>16TES;*-J2b9}RY|17R2@Y_>UJ@f4wnXW;4Lm8R`|BoXeM-}v7=pP=fPd>
zS++Ozw_bXb#q+t$+AiZ*!H$OH-wBwMXYaH@ZsB@o?_BmIe2<48MmdUAC;tV4lOc7P
zEz+enC-%hD*kQWn3p}k>6CCye%PyjHOF*%BtI%qIeIgnYruK|eVpCPQhRt<bZv1uH
zCV-x<+m4FkwVX-UU~a={v2c+e`ms3{WFaX0!#dE4D&tkvcl+(fFq(eFe7*UHR3`8o
zU3m4~OFaE#T@Q02t(db!$|kOnNIxM;E<;TyReD0`XA@?)T_2q#Y|j2v*tYU0`N#<M
z=(a-_5x#|y<G+_wnHacbXJfttrsSs1O&zrLcJWA~Q-C#Q%hnj$XC%}Q%cN1vf5i$-
z7N8S<CW5~GysJsKYZCE2VLY4ipLFWsEdT7jpYjpAr@!lxC|^^HWgp?|O{L~e<afk^
zi%x%2KS{t?>~BPk1X6{ef-Q!5Eb*Mq5sHM|?e`@PdqaZOUP^-W{(#$QUQy^1fM;Xf
z%U;fEj1lq%eP}MFnr+#hWI{pO0qhV+JIS~O9v+@Ym#{obr?P#3UnR0xQNB#_uT9mh
zm}Iq<>i(KU>Ksv0NX@BGl4=OyWHb^-pSVX|s}fecCT5Da*&2iu63e`M+!5{mWAzp^
zrVeS&K^fv2DDIVD1b|6iI*48`ta6zTidt+QqWu|4ZHJJx(Esr$UDsqh9^N|@s;CZE
zui74HvQnF@U<IqVAWVspr6-m{#kTh#KrO^p@Kyv8LQ}(FkzpXYWkD{+LC-2~U?0->
zbnr>~{U!ak>eiuZZ4$BgF*(7Cw<EUvRalB_pAoC*FFaL3HSuTHw)rH^AfLJL5BBxw
zWUk!}%kfB{Qu=`EtQK)7;aRB6S&-eh{zQC1@#*R&-_LtlyD`@~`6K}>Lq*r>6B`oV
zo8=DC#9iVk4~q=jJ9{7x-jByK223nBgQxhje#MAOd$Mcfv0H$SJ(?;F>&2$(>6n2c
z)`?~f+yf)W4HI@JGO|6u$mgvf_=%d-d$$F0lwN`DJy8QxH?Zxqx$evne`OB=x<sUb
zyy@E?E)QjOlMQVqds*v|p^1PN2xkV~6}xWui#I*I{>1tskqV~bG84D$)7)6iZ~~xt
z82Q1wR#sds=P%Z04ooLj_^>o)GftFuDdHU(WIx%&W?PbBPgB5NUu*I)yYAtJ6HMbi
zzC+%Yw5f}nqUyhw^pJjK)uf=A;^7CT)P3y=cEOa&(69Bw^@4T-K1DI%ocS2*QYifd
zk_|zbCxT5a+AE*s)OLtp;q?}{BWC?{KC{0%gon31M3U6R$m%-4oM4zv3{_Wh2v0|H
zu~8wxe@}Mxhl4J_D~FJ)6CBZIh$r=;>UE8P(0X>G$6qV95UBdvR<QZd_iIckokZ4X
zDny1N%z(s9mw5;;|C((4)vT*=+E0B6i+X6{ypT<p#f)L7fY*4{%VQD!P|6UulN9^I
zNnV}aYu%*79L4SVWilcUzB@WdhE~$Fz;Q?5BfVtPms!{Kw&D8wo5|;nZ>N5@ou#K{
zv%6;_sO@3YCX6g#Ma;IN;Rrtco)rJzy5^py5#?)X8H&<vOy89(&qO5jK)hJ!c5y=B
zT9z1+jq?_-Cy30;e{CwIC1A)5=U{~($xr6xMGkSNlJmN}oTAFB^XV3|M(Z!rNLeSd
zUgUA5#(Py3VI-zAc_(}f4S#N7RAolbRVs`-j7{kIaO`%YPwI(wT<y&#y7Jq-sa$Wj
zR~ISGGSam;kQsb*<cLb_A`oLcSLt=?TNkb`C_094tnDdZ%;y*5xH;hZoyO_7S?_NH
zCNqb9w}wHlo11RR23v<0-s&W1ziJP@h0AD<VZ<AKozVnBB847Zs6@E*y1mPr?&AoV
z6m1q3#~oO&MkSv5cz$DyN#_);nvqdHw;Buog7LC4P-Nqg4x7NOeu^)nma?|F8-Xlj
zjuDwFu04MG`ozj9)4<XJ>f=cL3W|)HYlc?4vqEqB_)%2$_Ul^DSec6MvCu<a{t(@Y
z5s(F&|IKY-OE#)cC6M0L25^-PI)CV2{en^UbxwMcs(Evjw{?0PLP!W@<3FQ9jrgtj
zk6(MxlcrRu8|!Q}n_TJIX5!;=3l62ZZ}lFQLopdE>9aQcK^FAOU40Rxr_R<dETFXo
zXDQ~Br1DkRka1-=I$!ENfDKLVvn+hn8&<c;-oNQw+26lzVwuLc#EJ5eIH;fG8BMm7
zI>p(+?bSxw7jm8)&v@3pqI9UK8gEdC`=?G$PI7LZ>(k~U#YZ$9DX1q_JD!OMYYL`Z
zCjdRXtJ}2AYbZ7-z?=^Uw9LO{f9xU69El}6wGMS_qVjOptyRMt{mg!_!^x&9Tk2+2
z{qE2SsNZAvE7`JVH%#u_B3jzq{9$9ZAVtF0G*K0YrsZH>=5Th^HJcIz??Dq?_$gnt
zx4YiB$fJ%!irOT!Z}Pq@8WrSMHGOhsFR%h0GFB$&>>;p#IA<t^Q{^0gXRYh)hJP+i
z$hHP5NhBjSV5Wqh9ZOBANc!zNBDNHP5nR7fa#ZpO&GJk`q(d+`uac*Vj>UrgXAJd5
zm=zfg)KtKg>(4<_cw?pn<))zqTk2R!fSYot$YCbAH%`Wn<yvt);U3A#8aWND77;*>
z(aEE_mu)t44T_F(tj973lTn%pnjZTFPgRyf)#lBCf&0pTv~i3$UX5FKX1{^GH3T1t
zxd_zSIG86^h2>?uS0~;8#*eycVm5T2c9xG&m!c5V128OW*=_D!&3(T?oxu5QeIN(D
zdwt8aSeXda)(~Wr!B%<<)Nr09*C|W4vMpR)|70(cDL-8AXN&o$Iad>>XBeghq|+Ca
zH_%~cq1s~jTNhakF(eyS?TtSXqx$lNX%&b)^k5LQws}D(dt}F?3Xd{3LbSXS6}TlO
zBCV2I)B|X*^!vw2<HX9f-m)b{P+={NhZEKD=p>h-uIZgMN%@I~QTg#$EzG1o{FEk<
znabjZ(wQ-)GUpkeA~KJ~5qY4T##*-^JsBSSxM}zsW&S-uG<0tqa*r=ki|f*w0%Et9
zg3>V{6Psy$dSV^f-m?eh%)jIigfz@AfC3BWODdj^-uQW<CrCk_nYn8x&t-P!k#c=3
z<pZIx$|GuBAYM1m>b<1ercexh^^@N9_=n?jEw9(j3)d3W+n60UN~^8kdJly;HP^Kr
zuezedXlYaB$X7UO04r%7CkFl7YqD2Zd(xQ-2!nlPE!WWMF`^!GF_Y^fA+<h5>iGFz
z=))(T+4wo9@lF`%uD12uQmRV;SHAT0R2f+YZYK@frULpO(Elaf$uxKVTHnF1docdd
zOPgMH5(4oylin8>_5#Z418C+-c!k*JnXHA6K6lS2$?2i-2Xw7Hv}@wVPeXH3cZ5FA
zcPSaIm-nM-x_9~Ww-X|S<#q`Mh8fLhN#lIn-()#-F6tSUh8C|g`GkjJhdCwQe3sSp
z6J!9QEXG8aDIa&wes8e*2&(7jxs+Aj25$ttyv~6`)uYM3Bb7hwfdU_l3-90j2E}O3
zslNaIbh?GrSX#;u1*hoa!AU~Vw+T?B>LV9`D?O{WEqYpW=~GZ7y`w;gp=0N^5p`m3
zJR1vkn**1awKF300(;d4t<cY6gI&;Vd!Nkc5aS~(|JlZ-V!Znh2`4E>%eb^}A0Ns8
z>7KmIQvy5tL#s$uZ2^6SOhVa#RBeQvVdV_Sc`uIXMK0D5@L`;#Z>6qGp9@!Eh=RQ@
zJiUA0w1h|C$he15I4mP@()?HI9+&LFA0C18PZNchh+!$)vrDANTqZV#w~{fprkzkT
zCotSj`<f<Zm@*Jwf8#ra`>h6D6bCX2PE}+K@H{i8rl_Vg*q~1&h9-0OuT}FxzrX20
zmeaVMXS-Pu)$}lB>?|pH0jmdP;(@WB*d;tTcX?GVl)8=mFsG~>l!SbJz1Cr~N~UKB
zdu==_x1#e+2MmE5b|WfQg?BX8A2Hpo%?1&8>Dr%0<p1K?1}f-!-o<rU9faxUXm0RI
zaF6pQ_In(1LJyO3CT}nB2!v7{grjAJ1UJcQR}YwWr4|7cd`VX8v9jmly4I-5+Y1j8
z6&DUC^%TRJqG?1j=6BJ?c(s0Lw!keWD=(EGyL^sp1YOpS`h+*qRC)rSm+P^mkh%nN
z>VEvQE~qszAvY<=*&-Bh%YS4+O3ZmLzcM&ACseZDI2oHA_iQMC)b<61IIPtRG<8hW
z&(Nm7V+$`oW_rjc2vW>c^n`~$(i5K?F=6sgWR<Onn>68m9yDjyYk*709ep1<S{t2L
zcBxcQ^N^e2?o5R@$@Z9gl49Ue_>l<sa5Dc;cp2&$!K4GMrrY?5>FcEzL7E>j(BSyt
z`?%uB>{P?k<3esx5SZFQE<;?Hq;wwS_(%?bPp>xNzYx$mt=(jOv)Z--m^01p+-zbd
zY@-lQLXK*E@x>!a{eun0v&=@5H9WG$jL)k?bJR&Cm^awWL@H}H`o}l-WMK3dmU!ul
z_#Bv3E4`4fUaj1-Rz@Z_b-S{m>^6!49vm>!=x?Th!G%8fhujx8v6)q<?!_yu0LdhZ
zgo0Zl0^8RvY=;UM7HnUC_q4!%u4go*&Vf89e$W%sZ!}g8Z_V{o!vm?;ctKdE=b4g$
zXcO;D*n->%A@q~rKazLa?BYjOZ`Vtyvsd1nvJi2kH%Zto7%$p)-ZVQur@DJyKbQ`M
z&Tpay=STR<o9L1J9zcpsJPZoQW|e;EEXQAmoDO=kp*9jf#V)jGl}shaMd!R8NmzV(
z>A@KuS2Avhrps(4^rp(OBYKjH>5}h42&%!#kD7>B#-)jUbp*!y(Y@rj=Q2UUsc|D;
zoc?s%QW=YWvBLmLQFX&^qE(u_ThKB?rKlPnLZX#m=Tp(!Uq&>>@LtKS4<{?5I-_~s
zS~YQ(i0EeIjG|KI@pU^*t_4E~R?Q|DT{M_X++^?*NXSp$t}7HG09*7@$o&1(Ofo8z
zPmmV(rUipSh-GEoU|UyZk9w0Cx%>bm;eRlIUlVxyU9BntSL^iy#s=iemdY}oMEdi(
z=w}Mia4gYNBN7}PlI0E?j@FZM2gNgm77Mw_lBR2D9l`2yh@q-cj2t~uc}+CPiz2Hp
zP1nCdiqT{D+zQagO_8tE+0nmz)t7I>F#(bK-u@epqr+iqBX@%2wT&CHC*o%6FZb6U
zJXw^b`K+?iC_|46DUv*#q@)n1p`QqGGc7uP4K58TD$wH;?t4Ude%TdaP8n;(W)b#r
zv43xZg1YW8HQQA;;RrKKa`kcE8iW%S$?C32sNEy)@wgEb@!7)GLY#g=u?T4GS5u%p
z%aVHeG)NLoyosqyOKKNdGT;Yx_GkU7X;!dpY`WLYeai%;AlBX%j%qS~|3x6H!;LA4
zx)w8J@sHQh`@Nb?`gl=?_gW6Sgu)>H@EhYly_BMrjK)4{(zG}qGfL^ejV_!lvjnZ~
zvUT<(oZIi7{grT6+nLE*vA=bLqIdmH=(J_WLVoX@zKAETD|^&=KW^R{<eO~kP@eDM
zqrY@dI$<FSO|HjQ?(1@xSv_wgcYSxN(SiYWG3`;;EqtYHVvWbx<98=-jsfmp{;0yT
zlY84G6RC6)tagKSQ>Xm+AQ?H!LYg#aez2YwV?3dKqI=%9dn?@!1xQY*Bo5*;ypMw8
z>Vt=H)o_eUDTx?i3~it<%GEGLRMCjQzt4bquv|M1mtfm}AY7+G#b)7R+x_#lTs1VM
z?88k-h0`3GDFDHEhFLm|JM!&`LRo{31N45Wx{7@<E8BW~n{%7`A`SDHqqa8VLNr{i
zcgXbtNqljwd&gJ|<C(OAZP}&LQR6c7_v#Mgjc8}AVm;5{S(UQKNnah+krgWKUq#9N
z=M)OCquR9=G`GB4cfJ06EV-Yp(-p+2j%VHF2Vzt@R+8=3`)nVBC1tN~6Q0k{Zb8f#
z(u%o)M-!h*4iFt>QLJhNT703mF`@UW!uhg*LEww%X(IG9ElF+tNdnDrOe5aa5L=m3
zW=Il=;Cq!`HeM0CvXCVFmI>xvUDO9Y9ar!qLa(~doZh9>P?-_k$Y}DQ!TLlj7>j5u
z7KzlD5dH>lNnx=;ysoK`&6sw{IUWY|=o?Q{vk8?GGo;+H;908TBnI%7UtrZbZ&L?|
zbw-1Bs5KEPfKjbWE87iwE;!%;PqD{p{Y^^B<BZU$*>!^eCncZitvurN0RU&f?(ne$
z42uQQ{ymcLb!MkP1BL!{wlqt>W1!NjeQnirw|S;`+gsf#&)Uf{2Fbc-oPD%kZe)m3
zjp!pdV^!1G&D|nPNHXJlhM!9>MTMXjTc{p0tp}gX=p|>Ge#w_UVg~Cd37kKo%V0bD
zZbBgzT{zcK^HV3m0rN;vbpn#zlG$e0L?TD68CptNV)(_VIZ~o7<CewMFU;gzE`1(o
ze4yBp>=ipI%9p*%Eq0Bw;(7ka5nhVF!C;ngFJ^PzE3Om)Yf8VIu&*{;-n`>-uAX)`
z`NgJSEQCz?6k{lPtG<iyP9bR4uDQ3#*^eNcB4_$H$;)=0c(08=_RhAls0E4I0%tH@
zj|9cN%a=E8-KJN^=O)k?U1mCC=z+O^hq_@PvcJGecJoBJ9G^<I%22W8dr7O_qgqdm
zw3c!a4oKRHn&?>?PIJZwUO@Uc@;`@f<}}w4tMMB&$>$i1*@yL6DpM0)e+!hyg^_h#
z6n#3;edjrpb9AXwV#?u*Y2geoXMi5^9X;g6nIjvdMY&dgAuU@<WRKSqu}|}Ft>~X`
zq@vp&<293K{yy{R<sl4*XR5FlDzYzP_mU;xX{6}1`;#+ozeVZGT0!W*+su!E1&qhB
z{s93TR^!sP3OwYUc44Baky8<Ec>U-;?YI=J#OE5?=NXC4UzR%9%vg!ocLf>>v-jv1
zFg$F;T5?5|1Ru>B6F-qjb7LKK+aX{7+CXX6Bud5#N~#np!pBw8wK8cLgqU78p$?PJ
zv888sGr@%mAo$2I%6lknKR47V#+Yia`i<}#(kx;BX2-T(u&8(VeB3Lishf2P39=O^
zw3n7ff>pYQDIM-cI5iP!l3a3vP9HVkMLmpYh02-1L%}IXn8q#=*DPJvEtgiTSqVO*
zbFlA8<H?;c_Wfh2%+K^)PeJbo!Zmn|QnlA@BU4W`zLr3E$p{zH=mdtR>9un+M1cF?
zQlqx!p}d{B+Lu)#o(ga}y<wq$F1V8+zBw4yR<$n!!}AHsE<Ijk4&TPZs~D%rR9<cn
z`+0v>^)&ClG!;!<`(BL}s@^m&D7phOAn67?kZPzJHD(TE!u+_Gxl~^Cqp5SaDRbeO
zg=N>$QAIy-S5gGXG}NIP%zaN%$SHAAi+7a$L)cR_``vA=Mnh31hpLJ4H@AxXfkM>M
z`obkbXT+x3tV*Ge^NFnb&o{fhj#;|dWiJFUr;eY)9#d$3pyCq*2e41cZN8zbO|NWZ
z*G}m^bq&ps3Sji6I6eA8OFkzhiK(pXhuZWkc;DTWy6KaoIcjSt7nv{{x80RfAFRb!
z?NSb>(VgYHGq|NMXV!`ba8&xl({qKOyd3WU%HOVt8EJ~ZZZv5ol?}{h3PLzT#(&xd
zB7=Q`*dz!~waD|Pq9201FOonBwQPdbS$<sJ3R_(!efBa?(&k|CfFM_h2wjHZ6SDcB
z88@aL8%D>4M*v7KK7=%x-ea%m?+>pG`*vu)q`Mb9R0iNw^r-l0gK$`zI3bs?$X&HQ
zfx7j;a|(6d)?{b+P;e_@WG(YpOK)zwvl#~bTn=#;`jhE5@Q%IVb#f{JN64bfVdj$2
z&WtyJct4CxGfQQkFN!+$@GRt<sCi2I(p(Mrp8dRl)wM^NX=OfPw)R)3>fB1hfFavE
z|FK+cahH#m-yJq4_&~c&tHViq{zHYgs&{Wcd7!}XWN*gEQ^vn06(o`MtAd3<tABq>
z)iEd=CmC&E(!AA~mf<Y#ME-f~a@U1_JcHk<=ghcx{=LbmsB%tt>6%>V*u(bLHM#=|
zUn7tlPCn19Z?M4e7l8adZaH=bGjF9<ja3k%LgcLKt86nR>|-w!ZF22YWKb;)eBC+R
z-om-C5xXZo(hp)3LXfAl1;d#KWRU?!PH;)gp-J-g9GUcZmYRC@8!`$BJwZRX#E4|M
z%JH#Vuxf9+&Qws$3lj$?wykyax25o@qzh5vhunJDz|IP0!zc7wnOxZ|pRLw1;{d5o
zD}fezKZyn}4R(**zWB-T`<w4pw3R!YVZ?om{<g8v=1L#|Os93eZv?5RQ#;G!!*Ygu
z7W8EX@irrt7b1=BUDJ?Ec0{CSGok4FrLKYlkyK8qc_;@Z4(XDIDw&hsSCrPN)EH{h
zCAi8C$QLeeST8v(>03$OXcO}-7i3bkA@{*}RTP=$iQSqQ>4%l4rS?CrVf!qug{NzY
z-H-S1Q=0irtt^%>5teLC)U*C7DAhgv0-aJ8tCwR$hxzsjfYyaKvED)#Q`2jt55oNV
zxHS4PlMtH8b1r%AVgK8$o4yxrzpO(FsS};Pix#Q8(z|)zzhJ2r9s;R`r{YnjdzJZ~
zxmQ8nmYUMUex&23{WGGh<py-2%1QXi0!|hb^7tt4)^bdnoBJT<`RzLaKrFAfs>7#Y
zbh4>O6F;`=^np|uetNRS5INfFRiyA{@|`9rQA%11O?|!5UPZhmS&U3;N}aE`5x@Az
zL-$3(x{1KwKui`h%#m0ZclTgoPx%`UI*T#>F522Ua67f918<-eay)$>S6!c6EvwyA
zePIzfoL>W~_jp&#!-m-ca!+Ae#RAB{;4IzJN7tN9D*2VYAE^$!s;qYXx~o#li(e$2
zDRlgXd0M=EZoqDCii)O^IVJxHKzk)y)Kz5Zj$l&!*dKxWwDE&65ly;ynzM4pqT8rW
zGvNueWt@q%<F&Kr{^)I{_(s_r$D#?-&qmxEMxph>wc>c{D?ar*wc)RogZ4qw4F^+j
z-vmhQ@RNf^#Em@0GoE!5^{Egnn_S+w7e)4>7&4h|I4e9qm3)V+Kz6Kj|M8%kZVZh$
zS}bZoV&fP`Po-bc^vl30KWT&k<>7}^f@_S-b3Ej7oppWZQ^E3V!cy2ta?7oAm}_IG
zx6|OEM~#lwFP^r_mzG4f$IA-I@`%&96a)oEpS7Lw=e9nOO|MYnt*N^`HoU@j1Z@pX
zsns$>>tEQjJbn&%g)!htNzz}4HD$1F$%z6x{xX5cuvl$uwB7L+=lHJ1Cdr1H<<mC+
z*GXy5E8tm?7qhb)#rv$%Bv#3CV@H@-!)rYaUcnT3i49S;d~RN?B4>^=AnAp$zJsYh
z?`G0dHCRF3?-6pTiyie!5l<j66AN<oX82PW7xL4Zi(Y-UT?oh$&WI#nne1WFPLCzv
zd4q-WQvfa&^V(KLGE1r;#|`_v1Yp87l?R)ady>BW7PTqM_3kn7b*M}4R7Sb<hV{7=
zy-&&wLBNjA5A$akI=5u~`R)`)4hU1xK{>Crzu@EXLqMS#ChhmmkHozFawnrTRCDr;
z&EIC{;Vtg5QELx>N|75@%p!L2d~Ho9=YL>qNj;{+8+o*gH2>u8U{4Kf^OJ};ntf*M
z!{^mm_MKhHCEZXdyHk=2MUttD<G=qU4dUazXrHBIRX4^J%y)=OFi_I`q<=w{-t%rn
z#88#5Z@Nhv&id^ck^Qa3dd*)#Kaa702OX#Gb)RtY(hCzsH;rPOX|)YO8wQ5ntV9g<
zkaHj^n%C+?64J-`_7eah%>tdED;qqxzD3h?U0Jzc00%;^D%Lgo%WYaK=&Ai$K*2FE
zsLK6poSsMTP@l3U2(QFIhfy~Xffx~q3onre0A2Dd>r?S+4?~tVq`@e@dQlEH962{_
z{8mk)C2(;wlQLq#Ph2#cZ&&nnHN!(HJr0gX6NA`#V^ak6l!OKKB7dN>?ci<N7#C4+
zW%3m)umDv17<<Ny;qO&bXImBIJM{ZqmP#TOZyFGla-F;}3SV%u;Z>pIN7Ni@6@HT6
zqu<P0DS52@ZhKgHhvqiS@nMSiQA5V)aS0vuC@hgHA)JPJR^r5;N4J8B95N_ta?boX
zUf{2>9wfHSU{4XpCidEk$An8aJwhUn2cdtd7a`4urVm3J*>A?(70;9VGtuNh5E7rl
zWQB4xx^3Oqpxy`vU%!p;(ECV1NZjTyb>V@+yp6O(#Y#Zt$K$T^2XP|nq4}2nBHO1D
z<->J4S}r0KJEkI^QUk3M11H9+fGF1Ftmd;AgJ(m14tRUuc0`W@v@SwEo|v5k7bB&|
z_aUpBbJN9<T}2oVZY=WZ_YJG~E-tCt@T&N|mONT!QU<~`=Q?=6{MOr*`+f!N^qZWh
zdSZZ~dte99+=uzJde}RlVKMzSG8L;Pd5yJtv;J)bcC8+m{9cK-QZKYePctnTBczXB
zuddK0DOdAMy_j%QQKm774*7E=Ab<?s4_6Qa(U;mml#B3Gb#cp^Ztm_$o`NhmVV(Y$
zP{2JltovdYZ-rfFljKm=aV(NNx0;7tS5$<ZBV0Rw^+2%#TtGEWsR}d%Bz^p4Z6SXx
z1f-A3!r;>H0y`Q(+vomR2;hVR446A^FNz^44qf9#v6BYOWRm3~vOL(AQkdE~`Bf1q
z*)KJdB2=l|J(?nhpRwBWZ%sQp1waw{TzX7fnVb{uPXm+4LZ{@%YoC5-U1M&AME<g>
z0fm_W5-V8*HS4sUwzXme-^vRILL&kcO>Iq~GfQ;8>J|)J=CTv=Wl@as$ORcokvG(n
z(0BUrWysqpN!3F+Yo@=nZga=uYO%S-F+5Qq(l3&!NIm?a1mL?qS&Uc{&WPNg9}u^i
zs_hlL<7moQR2Rt=ah^#RU3Wg3R4HQ9i!M2@-yOFQ_lgT;L(+UX07f1++Gg%8$^3$K
z<9|d|yn7#i*glk&XRhKXG*yfv@-#R+J7FAVYY_>al7h1!Of*cWt>@%vylpQ14mB3)
z>Jm=m+3&a_PdYrNZ8jzeoHsnG9X3abb!Kw7hilxwqmy@fiq>7<v9^7te1YNacdNtB
ze}aG~Ui)#ZX9h}tmb|rT-x@><(|QR?W;&QP9Tnj6G1me^wxYeDf$tlXezJmcYu<=Q
z3$SUnZ1{uQB;V@|Fxun%ZhlH%tIOQEpS#%9-qlWD{3;Z|RX29J;k}lC=P-XrP?+-#
zylNYABFUQE0H#6BuCUYB<A3kn_}gggAdVWUmoUzn!(ryZ|D;rfA{#xt_o^uzAEp*p
z*21=s-}5MWpV7bbF&)zbU`HT4Wz>5fiq!9cv!aMrDzoL=Z0NgucY18THqUM?8Y6Eq
zn3ET2WCO(SM>-ZwqERAA>zr_hy78Rs`JyMQ2bIRG#}#~b(Bhm!S&M@;Ae<FK#s#;A
zC3vxa_2dhEwK$s?Na`MZ<Z9PuJ|OI1c;gCcvc=9#6_ts3%e9Bh0tL+Rpvu5X$(xF$
z#Er)|v6a^q>Q5_a#3M+Vsu+b^stIJK-jdeXTC1g$yAgCtvw)aP^;)=Na#Uw$o_Eyi
zt))cICIjIs13%4qk%uzOO{Y$CJ{>`?N(N(~^|~x`b_O&27tDKDj?v|)M1!{EPd56+
z2^kWbRqvP^*mP64SAS@>(qJAm;oK#kUGiqL{T_U^w#a$IRY>0+YBPF6TvHNmF~|=b
zeWqT7+;F0kiI+2_;CwbDe!0bWTY7TH5tQJ{@?y<p8g8VvD}L>klueY3>hgxIm5JHG
z<S{E95kHzZsdr}NS{)T0H8#w^tHneTi@6LMi*5ZPzF`b-NG(60&D*?V@c0As97ghk
zzz5q=;=`gzAiW9b?I_>1<H9n>+=6~nHcocwk*WDtsT7+vKgA4Z*^DacCu0R2k7Zua
zVb_OEpWuv~NYB6Or`N!ROZHcV*9`>7q8M+!b4C+TOS`423-o)LTz-U#ur-L^y#_6V
zg3crf()4hw+FjOJ`l)idtlf^r35~a}T5Sz$`p|#UpPuzY-9R(N61>^hHo`i$fIMMc
zI#~IsSO-+8HpO`t+EG0XZtYDD^V()&lwm$ml4RBd5NTi%49b3?`$DnR{quR;fS5t1
z)Sb*U71>91&Jn4Sg|PLw0ZraFR<_1ku+~K#2kgE&$=w)LX<}jnnzahW&#0S+NOhaY
zPqp;}q%vkPZis%6be6%5=C9ErE>@$ld(RIyx!+8F*!Qx1yk*k;RK_mvT;y}H6e96q
z1*?AMgbje6A}(3NuxFS}mKw3BI0+x0l|g7lbrq~X3QfttZ#d=eZ`jcmq+a`#{q|jo
z;t9c>b)xI}3>Z?$nxfK`d%hwp5YWo=n8J1ZNcc1tvn#xPvgERC>3rndZhl)mlOd~Z
z8?w4+E&VE+u%ehPa-DZAa#|iycnt4l;LIgVSZQ9Izg3HEH{>a_X`9SUzT|k^$JpuP
zgsaUhv9+3wSNPg;fR_N^JjtsV?DEoj80$e>HGZ{^*#|56H8(9?um7=;no=0MS7<q5
z%P7-!5b_xtYF@BP85bMR|GR1Y*pkrSX9u)fs{5SSvqxazh}z$*W2RsFOKaivM3%K9
zpHb^0TE03H@e$b;J6A1|dHeTMqt=8eh2=ysD>rn4T#O3_34x1o%bczrSz8WdBY#D%
zpOjBBRb=acbw-3#W5Q7KJk|y;LWy#`7kbY<LT(~{iMS_-fbnBHKn7FZ-h3iFxd*A0
zu312(SHTEl0$%5ou@!{8i>R1T{|5|3L3Fh@FHoWHt$cRP^`OU2bb;Q=-SUnwQEHRx
zGmpsVADemBH!>g=U@A=B^$dT$TY^vy@6cA;m`o`>JcQovcuOz84-i20F1w|hPKz(E
z$;@Wk#}jAw$K9XZ>NOVK3{tDqqu75u%rcr0O{G=j7EFbKvnAE<KmmP>&9BV&1hub^
z>eDD@?Iw&-i7`#P@EEbkvOPXD-ibdkiE_@>yc|y2_LVhPRhYwgo9k>SM&VgMSnUE?
z#SGr3V8cyibbbx*L1bW?-1m;eMY>Yjmlfa7858EM{VwAuSV<4X*$Xc__=*_L?B~Dm
z75M)RkS=75eUga*Y#<a3@^4lbAEqZra#=I7`<NC~x8`<M#PNi4L>7G(DNSzOLNauU
zc=Sn>nip*q7ELmTb9QpFz0Dm5r1(?R<O4c2SZR?=>}BI;n}SbLsag9*P*Gx9@S__)
z&Hfz4`Pqo??>=>ei@m&G;v|j{#8R>i%F&e8M|)KSiB(DNGpWrL-+7OutrpXS#3vd|
z8$fDVSx8Fa8+(Szw}+|WBDD7RlIuYPOKO5;3X0PF{$w^3-1=5j_zyNhz1`VufqjmN
z)^G+S8#l(q*;B!?>N)Wcn+?dRll$8cg~GG8Jn?H}J>?eObfO5YvSUuu&%)8B?m1R8
uShi@7e<LkiFMQB0(<mr#w{MP;{vRDx6e)3$Fv~=Cb(tV644U=au>TM9Q>p0y

literal 0
HcmV?d00001

diff --git a/tutorials/markdown_syntax_highlighting/images/mk_syntax_final_html.png b/tutorials/markdown_syntax_highlighting/images/mk_syntax_final_html.png
new file mode 100644
index 0000000000000000000000000000000000000000..eeba886e654b0c7d47ca22e5b3f36bc7f0077914
GIT binary patch
literal 167057
zcmZU(18^o$ur?gq+1R$Vu{RsrPByl!jcwbuZQHi(ywRI{d-30UtNy8)sx!^$r_OZs
z>FIC<ISE8KTsROA5Jbt}VoD$&V5$FZS{Sf@lrOCp8xRn<F$+;q1xZm+Vg*M#QwwVo
z5Rl)9)hTW&%Hvq0r`@S~=CJW{X9VXEacG_(kmm+u<N-lw#>8Q3$-@{LiYj7#A&e2!
zZJ0tUe>Ljrs5w>UAT=SDT<Q%(j{#uPTaUcGz*~Uh%@oycyVL1RR=N`-ND@+wSORDz
zNF2>f$}gxl)<bl(^j9hnP+WfqgS@F@6UjAHRKq>}UH|};oT$r~EcUt9_S;J+nQ80*
z6hu64aQu^Ak8*<QcqH+6BdAbNnQarhHtDHR(zz15a1vmG+a*HLPe?xJpfXWlKHj%X
zo(Tj3E|pFe(w{HtVhUf67B^HxXkBg!D_qeu|7sZB@s0Ylz&bm<U1J7Xfm8lVB5TQy
zIW@B?Z2Y){*4>inOvc&JPcel&u+#cs_PcR>DlY$&;|(W|L7W;fM$8NGX{urYE0k6v
zi;v1%3oD;D!!)1%r)h?Vrtg`i-1M;%P_$Gtg>W#(wB|=~uaDSz)|o{LV~olWmf2MN
zZtOUI5LnD#NKH`1Mb8N8IJRkzfwMM?8PwsK^pmP5YH=hF5y4=_WN}(Yxp*krm$dZY
znNKfWLcoXC`oP(qMK;qNa++zt&tLJe<eTcK5(z9Gy;P4_e;{a{s<Nnux2!FiMC1=c
z#%?c*ddYc394PyUApDu1Mj>AMFxEh+P+&-foLh*oy4^rt8B*vIBb0*IWjNoJAY@|F
z%&exJ>l)p_guZT$1VFlcnBQ`Uq4ql|i^2C=KtO(;b+*i*`Wu@g;TGLySipirk%DA+
zxwBf#sPIK~4HndD;>-n5&Vm2x!(Rh|-UH+FN6v77fq|YU2BnAKBZf#9f=l^}VZcBQ
zB5uI8hD6&(SVx2pCeiOO2QlLhzX#U_Y8&8{2mKTvd<Pcw7hr(@mzYm1*gTH12svFy
zEe?F>Z&w}&H9|xXS{^wy2AU|VIb;(d?O&;UICCNucpzAI2&2$y9^)Cw5^}VVUEVA+
zn6d#+6@2CZn<3^@i0Hn#J7^XhQ^4b%w>!EHlKP;;-mh0MZ}1*yKtIzS3k30>pa5)Q
zP8j%m(Sdj|a>#hn$~ciCgz{e(@t8#f?SW4uO!4yA_#q*NM%K)PX$CR~A)%UoHG^qJ
zR1A;|aamHC-I#%Cic?(l7?pv|10BZPYwp&xt~4zm+9B2bIRknlJA+6@yNC8ap=J<H
zBj86U_7?0X9fsR<+n{vlYT&v2@dgj?Gu-LAs08q5hs_S*?|APe?oRJp?*m_BAy|cT
z_k>``$<c;_T!L5!3<tOeYz9yav&ux+NjlI6!@Un!jbQIV>Vw@AfBwd&Do>dDol|74
zgloZ2o~0t+A#*{|O_4Yr@`vj|ry;B>L`P2GSC7;Um0v-bB0Y_7(%TOl6ZB)^;^c?8
zhZL#e3?-kk@FnXDN=t@k(r4J`LNwWNweZ3rb**ZZ@})9o?ek<-#dWdt>MjLt`F5GN
zpa<FGR9Z0%Weh=#nhDxuspLi4ZCc|Bc2%!s`(>GBqvgShRh7`?JIfMFP)qM-?`EJS
z)-sei%yig^#NqtPBGMe=BGmcM4xDUJ`9iGu*ahD6!VAj_xr_RX_KV~Tv*izLn8Boe
z!#~VY*rPZAoLsZB8G~7rskNVlz9ZK-MC?r*-s}*#!MKRH0CoW@Rt|9XMUGZ;P78sR
z0dq~u>1C~oX6?&{6=|D}mQYUlPKA%Q7cv5kQ6(xRv-TO^Y@}=!UH)xi-S8W^?Z^(M
zXKYUaPYch@r<wEUv&E<GC-L{qr`bzEvkJELDFjpQM>MA;p;6*BA=A3qyhVepxhdum
z`)tF&ePgl&)Jf`L^GwCe`Lg<+d+S4t5torznJ`Lv^!#z8MAt;*64Mfg5<Hnqnb2wW
zsh_4ErY@%4Cn`>Mj}#}%CvFV(nDmj(k-C_z^oG)A()lLE$Fg*56T?z}nkOqJajmXR
z8Cq&uZd&r!>TDdXFRb5fa9RvoF0C`I^{3f$(tkQlPQ9e>US(agULmbLuT`#xuGKc$
z)%zV9ocB4EIk#Q@c0q8~xn8?^-58&c+-urA5BQgXS{|WJIZtaDYZ#SMJyTKDjaSK6
zX;*DAvoZQ<O?6!cJKj2~U3m{*#<z$0H-T**`h=W>=Y*mBA!g~*Xj44QR(*?eiI?rO
zjkA&G$w$qrm9v|DnU}iPz@^c#&|Bq0wxJe-()!ps{kM%bDIfVa-#aIO_W9~X>?`ye
z*1H>28RQUz3Irc4JAw>i6+AwoBa#;35^fYC65<Zp*7W@G-OuK%d6d<-WRg@7nta84
zjC}b)`oSVYhQ|GMlE&7Vn`~oN3BFPq{2-@+))2uM=12{+`%9gzt+4iFr(E$3iJCC$
z*aC5e2p;@rfeo$>@(!vIzF(M&n3edvf-RgcqFH`A7CQQ$Q?JSQTxd;bVzATUuS07?
z8AJ6RppPxHcv-i6y#`-ENK<%6EFJB#W@h94b@7SFwn#a0!^DFGBr2c%5U0v^<MFJc
ziX+7wB0K0?$lcK1XhG7uA4zZ_N_@&v$|1`9%B>|aFx|l>1F58fios_tTJ{?`O~~i_
z=g^B(tOu;Ptgz{l>7~P#2hR7!!=8~&SZ<P4<d6wH6gSc>OgPCQQdSLlRgjv~>&{Jm
z=W*$izH!f|?#C)8-a5FQPhvHeH9uR8(~ldXCqo{<9)0y<G-tGTS_TY;H!y&7>vVwX
zziJe<mfAK=Jbur68F?I`D+wzlwLqKf?T!VP+m+F3yYba&{Y1e#(K%!nNGZsLzc+}4
zcrTw}Z;DUGr@uy{dAWS)j<EEY8t7>0-j$~4dcG2Y^2dU*)>7+Bng>b^UTn6F?vFr*
z9c`rKij<RNB<t+%W(Pbc?fn+FO)Q&UwG)*VGiZxYYYBZ14Ub2;!|Y5q-*ObSt;(lr
z>#~LN4UK0*EWg$3ZGp~9z}XGW#-m5A`E8(&@CR<CY~|$3;mhfjpDXc;(8p|5u9a1j
zWrSr9;SZuK-X9)kO4U|2*M`}H6fXAJRklZCpJc5u??g5=t>wF`edg3WP%+~%{NEJk
zsUM@Fv8~w#*=2$jjtv(-u2l@lmfETunk3hjR<q`|TzHP0wId}a%k^#i_}xeLWR5kT
z8!1kIpFYgWX3g>nuFzWec#u2~2yGU$<V4<M-Rfm{l|MS39apM@)#_NAwdVF-csx&W
z0IW)D8*8|>?c3+q_O~1^xK4NFTsoXNcbvP|y(h2Lq*tqIoLal>^xF>oPA7C{w($Tb
zTbCU~PT#_S&zm0TPv}8pHo_j?q(}2EGg+pV*1P(N!v-oI`3k?**Y5n;;#=!pKflq%
zvQmEVt^h2*Kfrqv=5L&5&Goo1h3mS(yY%?Jc<tF!ej;xY50<ZnJ^5w9O1<=6p10Il
zeS*UK!ob?%TB@BNz02NSciD@bE%jmbRJUxqv3}H_WpDR$R4b(~vktkTBdNaqel*{f
z_sz%JXRSNoQ{k|%>J!;jkSI;T%1R*aPADLjU?5G}AaO}07YCBa<`pRtIPaTB4}SM5
z*Yrd}Rxk>UQ2ypi16rVK;V&g+^NNvo!Y)K0ygalBGCrC#F-VCJ@tJcAMb<_K3|hzs
z3?Q!?{46Xx>@4@qP}RFcpmXoO6@;l?mH422(37U4Z8^;zoSYcvULQomA0ToQ(FtuJ
zAfVY6DjLojvNAlzb~cQLf9#A*7~O5`|JA%8AbjpT|CTl;&W6P9HrBRIJnsCY|0%)q
zZ~Y%+CQ{=66mhoVC)JQuAQrWAG$H0-WM^b16@ViqCgyYeW6Gl>_UpgZ|2^@Onmaq&
z^Dr^Fxw$dAu`${?nlZ6(b8|B>vof)=GW?TZaPqKqHgspObt3yOBmbWrF%u_aM+<vr
z3p-ol|FCOlWar|{PfGe9j{fKRulF=@xA<RAwod;Y*1rIm{)5BB!pO|@Keqp=^8E*u
zN5R6~#9Bkl!p6kb>7Ne)PEIzy|CIm#asJoi|IyU^Uri1U_W!H-e>nfG$;b2`0sNnU
z{tK=Dr2d0T0FIC8f1nqDQ}yH&1_2QQkrWeFaR>FzhV)lme)&F?Lq;rE3kV1+b3S8J
ztnUOze_r2ac)ybKHsa&0>tnpS5vqs$L319?`}pnMYn97}wfphS={B?Ob-iqL{nLRd
zSWpXhQb)_#djCvHQ&^S=!A*1;U#xq$Eg;tg1B~Pps;en-=dZ^rbrmtP+Q{@=<FCCV
zC8<7-<Qb)IurEKwHu;Ja;_;VZ5bh5(^MUay7-Oe-20ylIKZ|5%Q8hUz>dH%eTAYLc
zf>g0{^cy%EaPC_;cjxrPpAe}st$hp}e8LaP1}D=CQY8wmDF{_1O(IhF-)8*IGwkxP
z=uhb<Mj!f3A~YC>o-_vaJ7^EX_ELM1s!ksHqT{Gd*jRmE5(#@XSLkrWNK?Z>8JVw1
z5ue|09xM}LaWGE8+_?ru2}&JG!uj5EsL4t}OR586<kL!ArK%JVN$~vQvK-2xMw*SP
ziDk(!lQ<Do5VnkwL|W}IU;0^5pS$=`>TombN6AVo1N6f&J!nh4ZXMIY)W58T5try)
zoPv}b#t{P1PW$YRl_HcxGD!ZM7P%TJ{e2az%*Y^B#k{qRHb&wa{SLIbc-P-Ti~SC4
z6O*D@`F>H2ktzO>sdn-w|EL7mDphQblc*;59i2P=Mr1oaCOxClcu>|9LfMz{<G|S(
z<I1xi1pHf0V`AB-xJc8?wWQWjYQ2DYB#dF&A_q+>e+ry)FoYn@Uxf1Y7bYyT|8cnl
zg#6ZXZssh(^N?Gvikd2DC^O1b(H8zs4byca5%e-K^T_ot20~50%k`{fc4!u4wQ6il
zM0X2yJqfYj{k<=eMn>L-%|lZm%}sE}hN(c*h6M#!K_TmeEt=x=_)E%PdG}baSrjxJ
z;s+&n!0?$nZ=s)XX`_Elr+Q7nwOL}yxZxroQIv(#hQt$*pmojWmTXS;B1b65VpGIA
zrO7RmlCsD{15h?P(Dv*jW4zBe(CPCa1nVuTHMQ)KWD9IRhc$4(3?=>i7w&I2P9nH-
z$*3qe`HTUa+SR6cAWrAUCKC`usA{w|O^oFvq(|S~vvXNg46CS+MjA{A{wWwKa6(){
zWT*7tyOvsIRy<;%*y(09vFQ+8EK#5RM@A{qDofWaIkO-sba~ASd9Ej+w&UF(g+~Rb
z7sz`$x%hNk7yu=(oegafwC(<UjwHhNiwXD+ZfHj}6%nkM`*AsLSxsqydj?8vm};I>
zS4aywiB>j<>_*RWNlC<9XXcq*6mY7wz&OB3OaWTY1U9)&_Ftztl#DQS0n#Z3NV>=H
zusla2a%r}BzeRF&ipyEZlshzfSu%?4Y5}pJv1;t(p^9cxHA8Xy>YN`<AqTJ+M=do2
zLBiSg&Tw3RFdiint^y1OSUjg#-r&^0eo43vZEAAZS96syuCJ?vaa!(KML=cY!e*xY
zmI~+}b5$JfIQwB5l9C-AflL?4Fl^n2L0%$mC6pwh`FP+eWgs0;0GFs488?^@cJk(M
z@)g=6aMP};uHRiy0G4FxB0|bdNa(RaBB2)CTwLKn7>~(1-z0De(hv)4Fk_ShF8xY>
z(m-6-CYEW8i_Jj5pu%%{H#hgEJc4AnNOwUn?Ih8<e?KIUU99&mAd?kzS5hS`@f{QH
z=ga_laAX|VvI;5kwo;IG#&lJCRXer2W4ZZM3OI3FGFH<XOpY3`EP~upiG$3MJrNv~
zGD@t!90Nt9;M!Ov@x9tTot+qmSu<@DcfyN+1|xp%Zvb9+yEs)3wcduwnNbjTPIXZ*
z=24L|tk2K)!mCL%Uhc&R(4{??BZF%)kePudfq9~ZuM(k3!9u(U>m;zTc&AIpJoiNg
zmIckUeRgS?Y5dIzA+&>^6ZoFs3F%uER8K{aRb2@1%ZdIl<&8lS!wbbA$*=ZEB0oF1
z*k^edSdmDgV&=%PMJTd^gyr?X0Cs&{T(sOV86Jg6(!UHKm#MFVNCm=q>>%`J?s_wx
zk{<E}lZnT#z|y+@eA-7cvmGF8FDblb0Zy90I+;*dFA&nk(707#4ISG4UY3d1zG!=A
zi3>>3u`^dr!HEl+k1a%*A&slfc%elO%Nv3GA~wkaA7QC?W2IsWz?sXMJF2Io#xOMf
zoo698m@+na2a>9dBROWHN|>WaU!WqlLUO(@>1miKWhGm#x8Rt^R@FsDa^%j*mxRT5
z7uVeeRn6cGqZOz!tpPe#k-juhLdxcMkmhzGHeP!HU83fsnLnU_i3CM{8LFYFQguWm
z=YBYSs#jK4!*?^K97#`i4C#$PLv8+xdJX@~fG3o^QYr*HIMJwIN!v;uCXQN4&cqE~
zgiG<My*afdxGAU66bVZfB#=s0dI6_Rmk5T&j+B3{RKp;$kWs+#@9I2^V1}4fv28{R
zNJZ38&t&Iu_FehYJp?U3h{@|;=#Cp`4b1I?nRwzbDfNiqq#jfZ)P53y-*zNFHgw9U
zy%n9<kRVsynp^_8b!hO5wP9`hmd%LMc(kE&#p7LY<;dXn3^MC~yK|~gyZ+49^efQ5
zBFB{+n@Pvj<elLj|K3g_ho3yCg#8-}C_9+%$nttqw5#LX<r9OzAN`V5TLAr=5F>We
zgJv(;2|+uXsWnwN!-8)cW<Y`AG!IHMUeFeBRJ6cIK4j*LX=+c&|H|CKX88)I?ibt!
zOA<otv{XgI=X^j<)yMpb&6xM`4d`Fkr{76(ju`p&(7|W31L`;IrIwSg%(dY-6eY7+
zU|6G^C}19D(a;l*KnW9fGJY$t#<)h$p_yhWk&Q|vi&;UTo<&t9=ko?L5vH?NV^orF
zpmCH<pbR8GE1^RBr^8xikndIS2!(J*viAS|-4ZdQ$xmBM`MY4AbxDS9W}TCfS%l>x
zgEg=uc|oOxJ%mIvtBKXAJQ_ntHb>t{3f-<vTNTa?M^K$69Q?N(Bl}hYH`Vy0jRPbc
zjU8GVIgP89$d6xYLQZVCk_|CYZj5xdVNzjZ8V)Jlnrx#Oc?}xYXvCUH&lEaxYRNYW
znl{zo3o^bBU|?n)h!9_gr}}Q!E7x6-j9PZDrtle=eJh;(K?9a9ss8(0Xxmj~t8d;I
zp<$J8Cq|36y)rp$R%)d+x*gSZI{AOgloNIa#+}72g6OljzH{hmGM~<|2zcBGdv0%h
zxb*aqgak9uw8-fa-CZVYQW|3Qy{ulI&aDv7Hf@HF$v6*a9O}mpWANZ`)}`H@+DO(m
zS9MCPZb>~}k{Rn&XCteuEZQG}7F##G0L@TO`Rd!Qq33tXm8J9>8Rbyf$pL+2XxPLn
zc6=y{&S&`R!R^eWnlf|8kkwnCY@sRTg5U3Zs$2y2H#7t(X9&A}>>V7EO2w@jM-|$$
z=CIVm;Td1&DMAX6vS{Kz(U|&hR?(p5s9uD(CkB6)pLcYVf2!!f8la^=AD!ZJ5&Kz5
z*{LMFIHCrdzEii9ONYSDfaaQAG+Yfw{7PNz9Zp-+*jiJ_`>_(St7X+J$NW`5_s8)F
zoxZ@f1PnJ()VY9KTp}}tQW!6dlNz-2<3qj|*X^TwRF}7+K_Oh+K34v>`ic1<u9pDI
zk`a-Jx0?jlX<mX>en{eiCB<^%VjJ(Rm8Ip@{v2w4+S4Ny!QVgi#%YX0C~0n#$I84C
zh1sjeD5)zBNgHU=4(!U^L5!jY^hi5&@F8#|nNM%(@=-k>uU1SnwPh$e8&n}Rc;s3h
zEcd_b)Q-i9&_);FTp~H3-C(FjCoA^I-JQcJ<Hb}_x#*Qea73__g+23d+?9Rb-vrfl
z{MO#goOKE!xgivP^6fr?25B2nH(4V<pf48b<x2JSGoe<ZyB$Y~P!J8@+x^KC&yz))
z2y@=bMrP+=zxwG2F}_}KtnV?M1RvT5jqi1Xa!Lk`VODyifeB3mu1d+{G2ZsQ3i_$Q
zE1F=GQqzb#jvpSzsGT2!FnzC}91@sxPK+Os4q|QCC7$g9O}`2rS2vNVLHPC~^VR-*
zYfI+{vrRx;su3v*LLGI8xn?gPF315O&~_ZbA5y#Yc(e^5a@!RpLn3-xV>{gaK3J4p
zJ!@74G|O+9<gVu6GSNz+sv3_|6>0xIF#Sr2zlSy(NIZ1Y@W|F(J@cB&n|9S}o+S}&
z9s6xiPAV{LR45?pO>K7Nut$KUv(r?TUs2%r01)Kl;F#n~FDS?mXqfW%FTUuV?R~1+
zMt*;7=9=cOL(9hxaQmxx!?hS5|CTRP-)`~QjclvXO`iK)nToH}J+ybhSR3mXQ`7Gv
z9n*OVs`BfhuyyBtvURulf>@+7tYKj9uuW`PsVYGh7BfG!OvS1mN@ZsA*ni(;@^QVv
zwVlB-b-QG;qMVM;WQC3D`)i27_+&OmboJ7B)JAK$+Hjv#XI&<Uy#F_kQdN44q-anx
z@elQ&ffcoG+J)mP0sz0^TFe1yf!NoaYAb%n&d8H~+iJeqb)8*(6rS)qdol-xc1Fe+
zH_5Lr5}}u|q6Flz>GQ#wU~PW<eIfC?y_VPda+t@2Sqt7Pk1NwnNQib>lVRxE4&TLy
znrKoxPn)a6K{(w@JWS5kphSXz5pQo44Zi{B>I<d8j3z0G#1xrX860Pn?<$-zrK<JZ
zIjY*tk+XrCp<N*s;ZtSTH81o0$2gJFZM9pk@v*{Q;DM9Bp`2^cIs!`gjS^d>a(pXG
zu!4O0)8ZQ5DErdn7>qFlX6+;lVVHQaq=S&=DP@4tk#b*H-C)(88N~yo)*kA(TaMRx
zwF48%DRzW2ETJqfZ#{fTc&sX%q0%JonDN4tLuzzGu)NcK(ltP@w+<o~aUyliVR%Qv
zwqaWuIhMkJx;j>YK3j1aJ9#Nl0XO+`I+|ueX=wk2__vfy&eLHb*H>qKImtl0#oyd{
z^713kAdQ~Lywl{c=GgK{7;YOdo){X#m-FuaW~v+$BoSVZP@fj<n%3!5`5pI*b=?lY
zAc@(n3yf~@K@}lFzLZ3%7(0de`)#kF^Wj;$lc^}-_|;M|BXT5uE-&*#cA$v#zFV47
zl2R<s73H((&v=z^E&K#Bjyd@a!(2bx7BVHHuDn@(bzQ-_0(gwfU2Hk?i2k!K;YvV`
z*YQ6T-XmzCh^<;w7#zx3$<yqQ&}CN>uL*zPO5G%@4@!f=R($W)2W5^)8g+u>u?-qS
z4!bar6Dp9kaoPX0{3?n~6mM+0tiTp!FQ14mXlV^PJ)o@iZYq12>W)?UdmXF~eJ$G5
zKleA+23zB?-C3Hhmv<qC#Fk9oZN!6#|6WD*qviPcc-QwCBUhWLCkm@~ElYiQS?x#U
zuFvx`g+#Xd(bT&3O>-vp2UF@;h$vsT73A3@h!A!mro_`;h38Vg#B4Ud*9Y+Bip}|s
zE7$i6xQZ?rVz4~Hx|^MpJe|XGcx|Q^G+gFoYqi+JPnHEqs5kzTCjoWmmRPh2$47eo
zgx7A@YTHFk&)DTni}nOK5I^9W$oVMcoe#2H95%xPM$?MeD)6ZB5kufTSXy;SOIJr7
z`}3wubwwv|?QhY5iL=wZz$zvK#2Ix7;~4z+qFjVMD<m)lDx2p%O>}trA)-NKw&ya(
zbK7KuPDRCDT?Ng=?RNmVYb&;bwO;)P?MJr#kELl@*(qhW*p7!YD<<2;{O;qhF*T=`
z9+r#{2O&<rs_Ym?IVZzOephb0q5Z|uulf4T1cRU{E+Q|_HO5W0W4pHJ6xrzLsiK23
zQXHc**&bZ`hy$snhAU@iM2y@4JRIZzbYY%!C!u!#gF%hyxCPEE8W4_DeVbz+xdG(1
ze94%FvA^kw*=qS$&k{Lb&zX`RS#EzBha4)106$C`rAtJb2CcDSvfBAk5GRmVgoTUg
zUBiCyP$~xp!FEGG^F9<b?sy$Pf9tp$rr!G6kCxV!4?=!2^Yv!?+iMO$kU$_5i)oa>
zl&mMWJ~<=9TAFkrKu9!j=VfVZ5FErXeini+ZY!Zf5M`s=f}p6_Uy8L@4FvG>K}<ZU
z4E{c+c7ICuWnmA;;t?9O$SWf*0%`$Qct1`mO=r{IHe&?^T>#*xR}OcAO#E)M#>nXU
z(pDHa9F1u8(njR2H+9)hY2D06KiezYb(9ro{iEWHK~t&hW0DiEhT<aX!1+gGckJ^$
z7;_YXoY)V;nYS#8bI=#+x4!I;v4W*QPLptxlmg&5ogLD~5s`Hg=Z39&yx}nyj*)XH
zA_*?RkZNP4u_-sI4+UuJBR+j4Ef7LfGg)B+;-pwdW6Q4m_S+9l^jvQsqJB}SY|c_f
zirlq`IM@WQOUJuSW@kP+@2cIm3`ws>@(ibgN;AF>(`x4XkhoZ9i0Wf9Kocxe5w=OS
zf-e~i7oiU>If@Z5ZYf|(MUvG=iV_h9ez2347;QrcW(gV-qQxn{WCW;<r;6!(?4Mw-
z0jCuVcUjP3r)bdSh}dg;P2}43eH$;W++0z;Bkd^h$LF3DUb3=1Uv1r0+q4-aRR5Dg
zkX)z2CV>e9@i0%l%6_nhInnd+1fOz8(ZS&I&ajHIxo_fCXHIXBunYoAsFLjEnwa%6
zI@?Q9u!yFcv8gg^QxQ7_vaX@l?@Y3S)KH%8xJl4Kv?$Dvj->7V1WOW7&>1!k^(306
zBwKYDOWUnPt~PCJRlQR$ZRUvF8!&EabkttD>;$TG1n+xr$)S>ix~j&YBpb!*;NkWp
zt}_TK^clZ*Ra;f#(^<TF6ELkbz3BW~67KIsSY1$KqIUi0MQD!SJD6*X?6AeZLhvEb
z82Mw>gTrRgNog#Q)7@m+fWGxG_=m0-C?nHt@3ZN0Ge@o9`_fA2HB(SSLwc|L$H6r~
zzT;Wy<rm&n55GnvOhF*pl!<7L9tqhIm^}7=iBKYA$cHr>cpnS(UP@Pz+t|2L$hLz>
z<I7TexajBue+SXei>9~f=nf6YS(cRG{(Oa(<H}o^^e1EKcn5LwF?wbN9_Qai=H$op
zlTF-_L5YQyB2Aj<QRZVg_91PMQfuZb%>O=r-P!4os>b#AMM`-bw^`BW=SbShifG#I
zVsD=E2|_`G3_R%$R9sk@8Z3_T3t^NNmiDxObdY=kGWt_`yka<lBCejf2^2CZsiP$|
zb!&f;2;t=pmqgRLU`ENt;g*$Gd(;r|Owr$Vs<PW~4wSOC7Q43VJ64S9)K5Eq0&QNl
z50kc$+pen=@ow9$6DAoKBe1ptkBuZOl2{sd<FhY~Wq!e%#S9mOav)mZAkF?3Hv)V{
zYXMuvf8Z;Pizm5D(^nykHiy0tecXCsPGzbqtMgu`jyIOh76CE=f`nbu%xT_F7nvXU
zem@J{7x`PE^=2WxGg9_IT$r0Fc*@RCeJcLKginWiFuIjKl4kA-H3W-bN)Qx?QM8hS
zgJH@zgGMCAuIG_EOtF_p<(n*0s`F03b@;@K#SZP+dFv5IoHGU}DN$sSE2Ds9@chy4
zx9^C51g}#NAg<<w#f@4X4E#+NR!LVKT1B>J>w%)N9fUt9?STQAKC?S!89D+DJ$Oj0
zfVKG3YOL=yFY-=~?{O(t@T)_|R%r|vKIwV^0$Wx4<`U~qhrYcL{XyxVE$;N)IoqwG
zsjaLzSlUrg5+QHm(k!Tov8;NWoNTk%Tv=7ccb?UVYuQF08f#}^(%O3fCAMW;|07?l
z_(z-l4d2pn_^SkwcU@k+FC&qzmK8R?<Kfw&>y}xn>dLn7dJdIc@97bdugGLLk)P*|
zZ>T6Mpq4unVdFC@8vqVi<l5=E^Y^=H-3ifVXx|6`a%JDWx<{I4@H>s<gNG3C7(C`u
z$?=$McE@g8Q?2PauQ_}mtQ|!=%z6Y2Ds&2&C8J>+qYpaw83*914Rx$2SU;4GvDofk
zvpFxq9_Av0E21?qzr~Ix%5U2AR;OM%?Cr_UbD|sb4l5=+-P3HqlD7ZQI`@qNyQU5d
ziP)mG5o>$?a(Y-|bHk~hl<JNpXc=tH^^O6<9T`4B*7LOa4#;F@tonYd3|A-cV7)_c
z-FgcP7&QHUGYcYkD1xi_06%pu#PP>~S<d1aZn&60Y$ceX%~4_C>;=BERlZ!TyBuQ}
zI8R!f>U(YBTpVPy=I_@uemCm_KtF00ZhInYS$yvgx{>@IMp*J6p=WzN-FpppeV;Vf
ze0PUe1)SHTZzlLi`zc}^b>TuQ%F!k+{$?h0d_IZ$8h_kwp4|4{Z3z2tJ|_m3ocGGL
z-`punI1_pISqA2|-MG%>bllHy5#oJchvpJ2+P!jwW)+QbaPqsKN0WKkG-<Ck%dNYo
zhTr-mNPn2^GVFREwByAJSk2mAMt2_P7(>~1+J6lkb%cy4bFN<xp6V}l*ME{V4;Hi~
zQbuBOv3VC3f^8|O>04*1SD95kYq;6C%8_IL^8LKXlt5ZH-HfRuShE_eU)^PlQ;w}{
zjRXid)|z}=HxBewA_-W`OlgwLO?I5#pP2sYA46>MI1N^BtMdXR!+s!bTmfj5XVyOU
zuGO?ow|tPP`1QKJ>jU5IT<`o1Yx!TA%wToaS>Lz(2pWfHHW@ot&N~V3j=hMa&W-K^
zQz@gc>&|D<d-+{2w_`SpsQfd0Utcn0JwIQ(VhDRKs|3G`o_C~cv$}u+gQ+^kwarl9
zcf+d=n+o*K$oS3(%<+XZ=s3Wo#{<uxjIo2X5F=wB<-vlIcgWxpV@B@Kv6$Y=o;H`S
znMWpqpXO_7+n!nVAHx3<hW{u$aty!eq%OH=us<guoAoKOzDM@&u8%D`Xyi5T%o?kl
zb}JDm_{#B=ART(&hYM|qFXK&{)H8><4!qdzd!#{^w&y1JTEUyD)zKItZiC3|lpCMt
z_)-pfjXzF*BBfntGAs}z!=L)q;6j_B0D}>=JHRxAw}<T5)tg<j^5c}ZU*juJt5ur2
zJ};`b(NM;FE~Qz1Z}Vnr#?!4ioEPthGjGlVo1@lUhc}f-U4RBWqolsi+1-+?wrjnA
zPypxh2YbKn(9;V)91@=Qt?URIzE(0_ZhF*j5s7;CU+jVFUa{UsC4QIrFajR?%c#H*
zpU3%9)IeUQo_E1ieWr<o0t-EKId3HiY{=L)>pSOS_7#L5!|(NIl0?Cc_2oT=Pu!_D
zD?WWSbb!u}1S^iMzGViKVkMMq_X|jMBBv3n*l6Cm?>d{58<*kuQhKc>l=v1(L6d1T
zieg<o5pJ&I?yldHRq2Of!xD(<3Qws5th1OQoqOcbiNQSO$`pg8tK#9=mh&wSB$F)>
z#A<)E!NHj;Qgjaj)JKvf)T8j**T9U{M+m1uP;1DAADb~&_cv#ItI8^ll=U?GI|<(v
z!sW%qN972tEGs(QZuOe2)lX32r=NbqC70oCx5z2HvC^C0aFh|LFGnLrkJ{Bw0@Zg^
zddmbBi(hxxm|U)wERHm(YPp=}LYaEM&JbJktv_A}R&ds0Rmv)A^!y%W-))$G)SwG~
zI=?gty5ZLYdPct<Q^ON&f4*&vj6e<by^J#T?mO)Zgvxq<A9i%`q<!Bn)(W{BST7EO
zIy#)#_qn!vgDNl<V&`K(!tmJ3Fit0ZonP8vO?|<;RuKmX75C(S6%ha|R(pVFW<*47
z_kuG!r*UsiryHONd;TU1kYhgRGHe`64L{Mki>vZ`eBYDG7fWT*1w1ASg6n<Rv)eDj
z-WtC;u6kG`vYc?0wW<9RtdkWGMx+@VpKd~l5I%EHb3F!<u~wTbM9KfuZ09*`U=q`T
zu9J?DCDbhIm~musZn$*L?t0se9F?==Xby92Sl~rL8lTuHn|-p1m;8tX=(#^{%JX@x
zoLR+ofBqe<>3OcE$_@5CKTh$nx^i!%UHEuZ-}QZ1z}r8>y7h36-mwamKk{MQ*HDtu
zQro=vtuG)0!dLiqK56&*`MJy`=*R7FvSjf9-W@F)`IP~fRNqy~e_M(+F8O}ibeR=!
zo(C4L67azMgNeTi5C=M0+wlOq8ntm)CK`SP#L@?>skYks?Fp<#dDU$;OlEbQ*C<l~
z>D7i0X7zo&0Gg?*v*~{C4C)A5M?`|&FU!!gT|YK_-!C==eP3K|g@OGDzo~CMWx#$X
zVo-Ej{>uM136suY^9~Q-W8DuoL;m>)QHVw5^DsP~c{@XvnVI8f{hGrY%l)x1T7wt;
zZyzC++O-3gZhiTHD|E5F7X!C~pP!!u$gn4WWQzKy1WU5(CqXl<dA;{a@X(4Nold@9
zpNE`Yc)hm+fU|8U`gVN4D~}JN`=@IFq07y=Rn_ui3!4NBVAO1;3FqU}>4<=dPgBtE
zt6P1Gc)490>P;P;iTmocKhxTI$z!A519kNsFw%Bv$A5Zo$i&krS}IqSp-$xWYnKF^
z$oDpRv9_nkDOIi><c!7b1mEVPka0AFKfHu5A{Gp9SZ|-DFa$=yzN4xE41R}cR|d@R
zU^F+kd9=TipzC}3knsI-7H2irn-Sq10q5>b`_}K}q_h7rFjZgTdI$X7_v8N5?AGC~
zCiKd0@imnBE(Coxx{fu^0c&gS@LvOx&!UpQ6QFUJVn#^NGdg2D(|uKs_r-7+IsNDL
zW48O@tho>%@Lsp?b@eor_AwgUPLv%-sztO2>YeN#ig^?NMQA50o1LQRc#e_s@lXxO
z&wBl$>V^Vba^%g5-C7BH0k<*KZ+CjEu!7{iO)j2tsf;;vOh0*^2eBx1yrwh<-F7WT
z!CyLnSJEY4uZ=tz8FD+OwQp=@`j2<~61jdO=&iejXNOfKC3b?q&ou_O>z<Q~!d$%0
zXF}dx*O_8uT|eFmW^5|Hlm4}>&-cUGz1)8lg|6v?!b?u5=hF>_)Hh&WZsKoJl`PdU
z%-iAs_#&jBFZVsPPtV(arJbovc8H&G-;Tq6vK`;o6lANu+X4obfcr9o9p77)7z#+L
z!I0X=_NkulS;poI|9f5a%a+I1KOmy_tI66mz^VeOTql_UM>vOs{ouQ4#Yaoh4uR1!
z>hV9SSLkc%dnn_0$aT6;7`75(6S(bF6dA-Wek<;|v_z4QJcE>v9^qqR;C+=<)jp~I
z)FVmyv1>DLzGy2U;7aj+FlGa83p=#^$nEs?@IJl$baj#t|C-@mDwkbBOM8Juf3a0O
zIWY2T`@^4sNwW1J0egcPqO23OV>NH{e60D**{i0Y<^5vxhTph1UfJ*Mc?O=z3wU2D
z-+p|7p{5U6Q8~IJ{Toj^<HN3DA8)pc+3@OBl!>SE@?rAIee7#i;Ldy#=^BX}ph8Kb
zx4r?)g}!xK8iU$Kr0aS-pwsugV-a<`&IfmpGauUUID#}wQB|9=sA%2((*lM1szgY>
z%Z%E9TW>N%=|yiF{)b<fP=Mbc8!<|;<=mE+z+?T*u_CP9X}ZxKBf%f^>E#v67wEd6
z8oRaVTTUU7<+wH28tnlXR_9uCl@`;^G7loKb*5O9kb|c`Zn^ucEk2HP6U3hP0#{5<
z&UX7gd*;;oK2+EHe!H}@^Z5!-Ub%)GA6M7$9=gkJavNeek*}_3F>U8i5B-xYVAXoL
zP#%D9V>tlvgcLj-8=|)$?^s;8oJia8jB9co9olj^_cdLA7#h1}ck`r~+k1bbsIDsp
zSApfct;4{%#Kq^bNC2Jf^gt{6ZhvZ-{f*##xL^8?y!)p+Iz+&CQy2bpwzj<T<1To0
zW!LnPiL$G4a2^`?Fzq(g+r2_0<UB;x$j=TN=3d$24hs!tW$|_rC?7-6CA&jV#jL#Z
z2vi~+2u#%Pc`dOSeQBO_vFSWmSewkdX!35I+3s*Oc3Kv`H1Y)wypPtl`(%Q@Y}}j$
zx*Ir8oK^04oE+D$Zn=&npNbXKu5_N3x9a6furW;gaN)j>h#o@OcH=lw@!buXcG~Kf
z(W=Wo=yX(vh|}C&k6;tKcFSTY_yxTOs_(2h1^Zr+&$K;P^k~~G7=-dUzsc&RTzEdn
zrZQREZdJR*>i2%^_fOhxozww$Jl=<Md$Qbt@Uc~__5@S-nz}9{HPAi+`ve{fq9BDc
z>xXgCt=o>bi7|ghOV8Y0h#Usu!CSYI?%O<Tn@heqpHJ>%Rhr6e7HhWQWBK1cVtXGb
zkdnBsHzt{THK=XtH|5lG9p!@30wh<%4r6f}o=;@E%=*MmKR+*kv5fo&MU&r79}!ly
zlD5;?z}4Gcxf55nO_PqA&Z&fGt-skMD9S@a0(D#(LT{%PTS8EC+xWHBLHaUa@MivX
z{f5Rom$T<%o@X7K?v?_d{`@R9|FTifQ`@3k*S<bMhs(bs&m0}HVDm~}k3#QVpN3u}
zjfxtd_qKUltAM?H((Zj<Hxs|wm`>TAJ?#C_M<2$+3k{b}OIf?a?2gqZ=XoeJW3NN!
z;7e?P;%dqCa_o1hf_C%z4X@1Cd1|blr%xp~K;`HU1YZ@uBE>;k5^|<LbjsY5L_;;N
z^Ioc*pHPi2@QbRGz{B8_BDL){L%Z&&adqdsbzkAd!qI=dfjl(vD}&{+C6(~DWn+RK
z&X}?LWiVBbNWf?oS<gM$<TYk>#N~DoFJiH#`zcPgHPR)JU=2;$h5veF5{rZDdbx9y
zDbcqL@POBX*Dk1k=kc_{>m%5E(~YIKX`jg;-~r6mjp2ShwWD+pG|qm8_JrV8IeQ17
zl~6`D%AFutObV791VY=S_j6n-nSpRJTDo72u!LrNImSMJ1a$5AL3O|1DT}rcmXz|l
z#!M%9-95U<f0imJ^IOLfP<_8XhBC>mz5Da?Nl67ub=#NZ!Njm9kgk)XNwslR&@67*
z>p~k#`0+q#H6&UWLs3Ie#s{1;?t5%AvxVw527eW)(dD<NF+-%4E3&t|nq4(;jr0z2
zO&_%Us$_Q{v+L?o4lglm@s}{;J7|XR^1l`N5H0Ld(AC|p(fvTWepdT|#ctkWun&ew
zYjtL}&D<o%qhU;2me?5*%7!3piYN`CzQWLaQsE}!Zu%6Q8cX0b3&{0znRxNi{a!fC
zPSv|erAf@~KFycR^<jAg2=?nY8Gsr`9oO%qw%?^~wuO=5pRm)r1INt--S?Z%mHocY
zHzh@KdWGChn&c|4{7zumGA}1Dijx~4kY7zB5S{~3w-{7B&*YGrM4u2?wyb&F)~Q+A
z-{ZfVF4y8sWkX%B;HK8x<{Qf+)<HPhVy79RACUzy(c;?6S%9rHGT!XUsBDUt_!Lb>
zPnQ9I%&W~v0>V&zII$g%$=sm(b-LX)r}*?VRoAN}0BwkMWs-tDm%~YNh=iX3_4C@h
zYYOXg#AYbqa1n3*zF`W=aCh@H(gHNXFp%e)_8-+do}cLqNIM_f4zsljPSK2X43VS5
z{jWzmOGLg}O$Pgo0RD#y<yjGq>rwp<#!hyxDUJ|xDd0mipo6g$0o<#U42jQcR<zyW
z;p;UO+NNHz9<1CkW|49UR<sx{Vgfd7Rnl*4ab7%U-F|FYej9mgFD29#+h=k;)%=+9
zw2_XXYIDC16^qDzDje^&0l+=_jMGB$+2&!CvejrZ&Lji8mr$x1?R!3nH|2|^r_;7F
z$ldK(NJTnIZD{z)v}5md$ggC;yY09}q{?nJxZxX_%#0QE8ee0n*MTf~*wQ>Q?GsDQ
z$O0=V#5LdXJABsc-F1I|+mz6K`_jI}cL7p7IauWb_CWnO=ppiZs@hNUdS<si?xJ=}
zfe*(+6&V*Mp0z!7<)CD0H=B{>Uk}4#D5NHgysxjSYWml4@N(IsjB59We&tCTR)zgS
zDB~|RX0;rd6_Xwe^;I1ajNA40xU(9}m(Qg+K#v2!zxITMj}QO;pmY)G&r9nTVdfxk
znbNO^&2_J6zRUMXb)v1%j~(oV7SYgb<TbOUcq%#fm$&%S?2e;{NovOQcKf*DzbMMV
zle!N{mk9y2+?GEk7zW1G9F$X%r;Unn`269qw+%Ac0BzSV1%a9JTMa;`i(B1P%?yHN
z8!SD6hXOWSOooA6_1Nyv%5@<DS8cLc)Q2Y;KWAOr^R-?!y)f2Rn-!cTzZlUdZ7-w?
zuCE>mQy@$AqLOqTjDA>kEv=P>rC2qgkEftL@T_D6iqXA@R9u2m`Lv=Etb#h&1}ant
zqiJ=or#0J+`{p=i-~za65<0zjFX)^8d473&<@cbdbCjuq)ThK<U2L*V0{*W6-Xaz6
z>KmzS&j&5``~0M%3^4JmQoG&1_3ks)`Q!jTEw_@(rz!6)G_2(H947JkJ>ggNzpiH@
zj1=RLbmBYgoqG`u7<ij~N{e!tF{6PL6<Sudq^l}LpmdN4nlD+hl9(3Vhltt{0`i_x
zmZt&R_8%^0u}0=HU-9XNpkLm>>G=wr&mXz%rC>Ti153-uRps`n1w9S_27$-n1b3MP
zxh_gM6vtxXkg98vtaKeYlK3Ux&gl4mm9jW~$fP^OBQO{!N=3=|52wv5WY*_MVx@44
zrFIi*#FhVwuRTD>s<wDg(_9CtZ+YML+LkRP*}$`LBncV)rZK_L**m>vAUtTCAHnkj
zZV$N<@G=jK%dsaqGAbM%M~J*d;js0%1AsZ;@3f|Bf+Y46>`=BG!F!b=@*pduYb?!}
zwD1y9JWh)QE!-}<f$PQ(8n%nuu6%}<+a*9AkF%7_=YLxQ2O@=Dq6~#CEJMe9psM)l
z9evjKEuenet9t#ENYqsJEwF!z_~;i4#Z&sGg1)92g@YI*e;WL6j}dTUAO0;^lF~6=
z9xXG1<=~Tj4v>nlzDwcK<P@u2&E1g^8(fPQP-)0>Y>1QsUTSN9R{?wdHIll6J=ASA
z?O+#iJ0x+~`2(_k+6f4fQV`?P<U?$dBlf^INU%)s!V$>GjV2p)HNsW&bM3GWr*mFD
z`+=PX$L%Ty-PXslGIJ_)!4J#^3icipyO$S<ng}}K*8Ujn*j*EdRr_21=g$yc>uvwm
z;;88Rm;B+{a^xMy#bmD<lAMf{&3qvbm*I`TcfYTTO_{L3mNoa`XV3itNg|(@FE*xI
z_p>jH95@_`(J8fP6v=rKm&yD2y239F_F1V(i{}~8*F%;DFh0nhuCCKxfwtb~^_xsx
z2Myh^8~bU20KP-)?j5U|y#87X&_`oUaa~yj#oC(6Yyj5o)?-U^R4UFQkA!+}HJ1(d
zzK$yigw41~M|IPVmOFvFyR?KVfx_~#kQl_z=IBc~q=l|7=?_EboBD+eEBwY~+s&9Q
zzu+7z_LWuj*79SE=rZjI_diIqvyIQmlNXk{g%&qf7L;ii$HU^XS2uH*St9i&2c`Nl
zIWJzNjJip}IyRm^80k4xQq;L^0_=^oZ=G3KiTf=-hv@wdUc9QejGLodb~|Kku1oE@
zp^>_y#A4{tk|CY<nh8#eZ9_SG-ySNvL#m0GGy<HSC{CYnc<xdnYw_FXT~D`t&|4!x
zO_B05r09RJA5kKbi~nWlq#bb$O4dTVh8lA+n|VKM9iMdB#mIJ8pWs*9b?xP!66@6)
zuC&+a-~MYQ#}6o6-O1v&@oYU#Iq%V9;#`6C7|N+0BiQ^`yhF@6E~uDS*_GKUhhmXO
z6YZ8&pRBcoWilzLW$f8l{T1QcN2}L^RJT`A$i!r6`GRVntVuA0En3U~|A^D7<$qvT
z-@i^dYjVuoh?t0o)%OjAzswPRxBRw=4;v<ZC4sd-jH*qeIQtcDRPXyGqW07<@u0w)
zUdW{Oz$xs5I3CE|QeU8TQrAdxxe;C}nA`QBR$APFv~HBB`d$;QYfZW1ds3{6v`r)+
z$xel(nOJKz&&<arL97dI(3YIl6UUXy*MuF_rBQJ-pY+=VxSv_wu!5UMr$NB8_r)yh
zJ9T6P#+IxSk_oI*?W61?=`x?^DH3eoN;O*;4*2b3i@V8DD2`Y)#>Qowm!XAgLS1a;
zpHlLLB5F7AgU<9wt<6gewLJ44)>%<o`%zh^Ic~7h1o*s~Tlb)xEVFzuP%^dECj-X7
zFN2hPmaOKPUQ@TibLkBL7G-2?Y;d|@Bqw{cgsqILsH%<|y-twHJ8Kk7veMOt%}|C0
zcmx$PaE<rF+*TnbM$u!|(SKBG=#<OXK8GN9KDJ}gWlXx0L{fN<)ydA_IX&w8P5a)J
zY!Yax2{81L8W3H<dhY*)tQGhabCI1sj@{DT&fiAXm(6-uWRekj`bFz6#ouB_3a`8l
zZ*N%35>l<SR+DJap#OQkgj0Ccv;rdJUwdvuXm^*ReuhJOF^u?2sJ@`KZKMMJWV-fz
zt!Fjwi*)gfGCF<+KuZr+o)Vxi4i$#PGm-p*ho7`4D<dHLlNiDk<q`qMeV2m^ct2UY
zt)@Yk<k5HvQ=^zW!hmryjJ<}47>!`ANE(d1oAn5T7KD}%pq<2^PG)fk_oou^V$q|V
zLcimKWT%1qsep||8%#DZq|I+UIps9<#cVpQ{qRnfI0lj3iTdw{HTzTZ=xFohmc?lN
z$0J>T7vcTN;p{zm17Jkd=pYWa`e6yhEL+)uAIbM^F~{+Vy_+^m3LUICQCVD5n&7Rh
zp{{(*blBD`s{QN!((d%;m~k|gJQ$@<6$a)`*RuFn=;M7GS@Sl3;EQSZ?O!|B&pck*
zsC*;z%G0WHJ*JCaO&-X#>t&`V6DX#_*<VqPIsHdnC_2gBr$GHYua@ub5@xu=Ru{zY
z`Q&4=dd25WRsHQ@8@bsXCAA@iY3D6&e%)f6ak8p`v=!7{r57w#T1$(v=bPr|<g4u9
zSbXD`&vm*NzT-_zsLtycfeXP#zApmli@S;VwhL&%J;;yI^k2w#k+O%kz96sSZd+SQ
zy7w2x@`d4rsabT6t^+elKeRlDkLA1H4~$*6N-Jtihj2#0M^oB|#lc%Qtj91j37ZFZ
z4sSgTra>=ZEn5}FBY)VF-98W}rSJW+Bc}E?SWs4bd1Y`z<+y1^3sd#1dU^XGdfxOV
zvyFn*?h2Jnwz-6|>6i~1%{hsT=i51vBTTxI?g8<LjGlaBz{kqLjiY6$ISwyz>;evZ
z5%COVB=vfqf_H73oKpl-2fN3`6?{)i<TC8~nBa>qu})s8;GkCq1~OPxwT@G)7$8a4
zb|{6;WaqS<*EB=F?8_#Js8VpX=Cr-t+*m!ASx!#Ub?hj}p%A1q#m<HCut8@18<+R!
z5^<hAUX=%)V+kT!?1zVR!k9QfPLX&tMM%FfStG^9t=Yb8yPwe+kG9?}IGo0lt&QF@
zND#*P0nSr>I5l(2Wzd_QgQecJj?M9s)Rl)pgM<JiMGn@&X`)4S)e@PumGt_Wx*SgL
zQQIv$Kb~z+TVWH^?IYL&A<7rl9~pf0Km`}aD2>Zgme{PQjaNd*VE@d5zysOn_=csl
zU7+WFd8|zShH!8jU<nCJ$cjoAqQ%Ls7<;MKbqgBoDv9_x-yZ2{AF8p)1dI=Oz%Z>A
zFyAiS^=a0*#0DLTzb}Pn4#=v<E&Y>o;fPKo%$FqId}Ucl+*HxfP}5Q)tIhbh@u1WZ
z`|s!HYV<P_0gvm|RyrP;Tb3UUo}{Bz6Ct+|ZGQ^E-Wl`XqYDB=62F&`%~X8nCG^&q
zx6f%eLOh>UuUZ)c^p2bNK)U91nvdfTq7nYjGj6gcB_4ojujeT^(=;xhJBZ^|_>{S|
zH9lYseKp5%=G3lNo@opBvjE>ELdU=cg}`A@!mv~2PYm+tYZ0WHy5`@}QtprGsHtD;
z(AsZGv}ZqFz<%srJ*4sw_=MU_p>&)!SP6REcE3eO`o8an>ifM)k~{iGtc?>u2ti|@
zFHq<v5UNe+{@#ygYgKx?YcpKA^?UGLtN?mGroGif)_y*BO4{)~Pn|t7A`KP?G$Tq@
z)-+;nvi6MrKLAKTx4&XBAu4364yMuFX#zI|reZ6N<3@dRk(=sI-0<&}daTxQg=PGV
zLP)qkULdK;3p*Wlz=TJie&x|8=gprtbLPyiKjigS-M8qq!9ZSn;dQMw26L&=yv_!L
zp1tF~wFmdFXsb66vm)#5-2{*pdL>&fQH6P$k1)g&&7AxWHxUFzN32Pp5R~KxvE>Q<
zZ{PnkbK$%>^X4%HS5{QA3t49<T79!%&ODIItE#rxb7yXkfBp7{+@XE^+S|EHG<?J!
zVL!h0xRFNY-Sm&0xBmF$H$R{7?)p1#ZJh9?J*sxl30u0F%&_R-s1m(c!G$2-sQm7u
z&qkjyig~HWI%{)_dz%q^|2FBz=kI!8(ZWR^z4-cj&%U(n0edm`@u~q&a38<+-_xi3
zKJ|xRp1%2RmhiPV<dr=fqC{O(-g4o8ZsC&s!hMg>o0W2w;1XzU2p9tcpYj_Q<esgQ
z!VGiv2S5J$^T+SJH~zkdNzWO7Op~5@Mp<nRns>O*+;i0n@3`^tTkn4G+FLPl+T_Wv
zJvn~*teJn#`-`j9?rW|QCYHt&+HgnmP;w10fi+k7gEk};O#YnmJHPYDv)h|byXdi-
z?l}D1Q)|0)K%w1_JcJkZM_+Wsl*yClEnN8d2Olq(KVRlGH*K=hHoSAkt0e!}X=^7-
z^KOBB!R5bAo-((-{<9B1nZIDZtnw`DjB48MfW7ecvv)nj1uKtZ-+uPx9S+(%Mkbbi
z`ToZrz5E8|y!-6SpTG6qw)^i9ZjsmYkzj4-sUXtdBr#hz^^SMK3siP&YY*)E{pVlz
z=(nzBqoyW)5Q6U%thfFka$Q_-0#PAMCO`q^G5Ci#b<k66UZdZ-wRLr4&cFQEiIXNg
z{>-hsrAU<3Q{3*ry?>qb<M=xts9&^@uNcUj1NW9|i^bmm7|h#e-+uf>&!PQkuKSO{
zPyhSAbr_6oYHa}2`dD^Ur4S{gb0kKou9J)A0qJE#2i#rOr`TLk*zNEGUw-td7ao3s
z0XSpUtS>(LL^qD~yhe$H&3veQ@!rS0lL-8sXJ7s-^Ru)8!K_^mhgzYe78oR!=gzDa
zu2;*&7*bVa(mxK#4qNMJV|G|hk5O!@&_yALnZ$`>iJR1$vbzK|7;<C^(MLlssLaf4
z&5dVg-#5<E_r)E1IYvH>Mr<6I8`3r-yp4NRV&_FqyQRBsxWR^BPx`jHx~i_O4PPx&
zx3gRu4<?!#o(YvLnm50Hzy7>~fKRoZ5LcZ8F*^&A)uX0R0V&oue)!S^K1R^HDu<tW
z%5rOTUwY*g*B;Q9{ql_`owL``hp}yV@8#FX&Wpz0jj3kA1y+%5RaI5h0Vf|j=CW(=
zICE5=jn?1q#G~)L?3yQUy=$YrcCKsNCfJ7GtQVTt3(9&qCw-zS#_Da|#TkH?#mD#3
zu@9`692uYJAY`~^jNwWgd?f*z7PI=6vh(h``KlAoJa4yyYumQrT4BW5C+Zm?_dexG
zv9=l(D?Z-F0=7PuZSfqe8?odyq9X=vVSo<j-gnztgZdwF&Z#$?GwQ8z&v)<D^N`a|
zI`5DpNipJrrQnd4+(Rg@t=aM5{cb+->~<G+yyo?B7R`Jh6EZ<EU*?bt)+>jXZa5{i
ztZc}3TRuMet_`*u-pCiS#Y<_XgAE5^r{hNQ9BcH6=ihPaMR>g5<{KYz-9>aSy+-%;
zoja|uZm$+zDJm9w4(Z26L=VMrofwBqDUnL65>_f<q)Q*;`Ctt&*{WzxMkpKz6A*?W
zb<KT+=69cXZp>-CrC-Mb&O^@D_sXz&y<r=2SLw*UTMXH8`2M3#+wYtcZaw9Ki*`AL
zr_NmSop{|P%dgv$n9D0DfLrsY5-mJq9#mah-D_aK+Ai%0x*gbh2upM{turQWxkZl{
zW<`-;GkWBJ;q5wg<T3v_w_m?PuQizH*W6&hk)zK3_eEFUeZdvH{yl8BopwF`aK1py
z9m#X<y6N(xPd#D%9Z+fIwR)UA=7vVNXy%nXIi#*?)cv;|zui8U9(3HvW3EXl!I;&r
z9jB{*&}cC6K;rqSY15szoABf_hyP=nF5Q+q=HhdI|M}Nbw%+T;H^+rn36@@cbr5ho
zg)80FhxR@F`b$Us^Q7w9+CCc&y=&CfS08&O8%N#@AHL7-_*p4J`ER=gr9_8(oKn(H
zmk|Q!%lAGS*?)8Hku0_Rva9r5{raaKSbpsuyaEEvvTLn&!N0G+`n2<(y8WK2ni_s{
zbkw6`2pk;O-DCsa$z5k)|GKW784U1Uw%6+C-Erf!r=9!M=(~m7waZ0M-p$ovt6K`?
zYi%&_n2XQ3?V>C0yx<CQc*{L@+ViA;DzX%QL8$x6V{f?Q@{uRjFI>ns*N5%B3)?9h
zrhb=bF_tNjGOC32f-UX=eyaJc=U!gD4`0xbh}G8XRd!cduc7^ww3-<F^=sWJ@t?gd
zZDZ;IrmNm&Ze<^N{u$Svc>%97th#p3k!PQL_J|{3$cmTDx$ufR&zCt{?7q{UCmf-~
zD3mR~=BnHYSYy4l%PRRX>|(DW{TYmdx7dVKPdb*4GpR0{qD$s2ZU`#*GB}b-TWXNC
z1><8kC<J9?yPtR%-z2*6xN~nl=>pPe(C|%<z2#qGEhOy<R4;2;e$|y9yZ(;L4n47c
z!9pIFY_{(%JpYd_YY{9B2t80Ho4<fYBO>9ujlR!ps1X^1I%gJm-b=>*&8%c{u(E2Z
zf}4na%J_t8?$6lJm1sHTc+wRs^{4xom`)(ItS4lqt-=G7-c#Ft5jdkqvh|gfF2Ud3
zswTxMXU)I!=G%Dhpna!~yfguM?9<~hIs4){GiT46_19r1A3u$E$3&)8DRIv9C=rl}
z4<A^os`84OhT`wPP3Dc|C6-!h$z_-3t7d$dO*+=Jnfb>b9lCU`FK?-=sF*WtdR>S1
z^UI5nbg6Ai2LH+9Pjhd>4`R&cxl}WEt$8)6z>`8&m3B3#(5<#TD8Db9gWqIzq0pte
zO?5>j+nj|(Ej(ldGhGxpQ99~*HCEW~(o%3yn}bcPt*Gi)Q#);;p1+DScMh0xTAG)v
zYd5QYp<X#D6uPzPASv8(=NYHWscTxmor?usmtJZvHwyR_dhR+U?;yZ#vzE@HERvG!
zPhk4j5jt1b)l_&>WhLpEU9Y$K_<^44mIC)h`0ez0jSXn{=fe5yaQU6u&NXd*UpQAL
z70TLG)ilrJa&BR<eVw>EX0GT!39V#wL`rAtr;gNV81EWXbg5~>YlibNIb4OM)wLKi
zt8sx*3%poSS62S#)IWG&%(L6S8WzAA-b>Z;yL-*EK`SrkD-T_(+wyAx^R?$`EvjdO
zN>JKax6Yx9Jr|vws<Egrqkg^u>ZzkmMRohi+Nld?10ZT>J9PoK84F!&+g23IxaHWX
z>k{*t8u+nNxS`h)wQczYoAP#b4b^(MCtDht=FI%7%d$(&FY+~IJuIh?SG*`h3RxfN
zGH1rjlQ-P;_-jY?+<YiaxjGbdE#~qBQBPn9DKp4GcXZ;GbUDLKk3Slkn|P^MPxoa}
zOI>AkoARnZr%YX9sU;iBapod$$YFk%3ESt+m{nC>RokH*iKk5)?_E|@lJI{loQrk5
z{eb!GVP-ch0N%N(%^!>A*y_R()os|D%xmDH9afb~)U=tgh|Fo>fn`Ut?YKl|e#oJ^
z*s^fp!iF}Lj3D5DESj4eK$P*<U-f$34?okK$Nhw26N$&SI+;EA@rgN&iyW8m%YPf5
zdmvR55CqtPc&P(vUtSsR!a9;?fdeNLe=w?DWi8>WYuB!}ZQZPTWg6u4E>YK(0Wzy`
zp~(qsp{BC3eN`=&?{)3lvbUbqFrT%grcl|jig7(xVQDFAQ(4odqLR1d_*tr^$^tji
z#IU98rsK|Ll%Idc4fAHsW`j~zTZOSwB5_BkqLs(iU>RkYclePXhHH6S5C8x`07*na
zR4J}yEjU%mH_Yc66tI0*$6#@0VnexyRk>bIYc4}`Kyw=WA5RyQ%=3BgyQtj^gw@Ub
zoW@@rx^}@o?W=32*Uw{5hC<8_Z7Qm!DS4JER+RDDX0BM*ns$vXMbZY*Bmg;sMZ7i<
zV@vMMQLVEME_B)VvYrxGFA<Lpm336$iFM>6$GVE@wv{#0>XnV~YtK)Sv{X+2{g0)V
zTe_*dFsDhck#?xmgRQ^(QsK=<orRyzyHkGVmN0&vYa9s@1OIViZ0Hy|WE$lP8$c`J
zIHxpU3!h_dqFK0UiePA>Fg~hDSTM852&HKfVJH{;e9Rfr@`4cniO!}sXqd6Qz^1uB
zM6ilae21z=PBK$kTklXkEHk}@Wd~uQd2LnIjL*J%@#UBJN;eNUb?L#Y>AZ`^m&hwB
zSjG7&!d6>tQ@z@9zI)<zE=^zQVOub?IzC9O<Ef>SJylYyLR)$SstE%1ENOTzN4L`W
zfW!R<aq*wo*@?DE3|Nw7r(X>7@*GwKf$Fi#2p@>2<aq;>onVbqwTkQg43*GHw1NkN
z<U{xSkVYi9u^*5GyAXQqoipd7Bx(Xszlo?wWB!F!7GtrNY^X5BEfqGpBF~#q33pf!
z_^1I`g5?lZ#DrHAfr{FP*!G#+FxPy@nEg$)Qn8uFNqLD#|E!s!82n_cs+9$PmPrA(
z#bE$S$*tNKC4X4m&|KuC3ixrP>F3{qi>Er2N@_tbzQJ8qQ%|7?aI(bAVvB|^@JOXE
zC6jP-LvvO;@)=#huq-@R&az>E<ValVw0vQe-G9+_?>_nbjqg0I*Lp)I!_8b8wic@)
zCLzEb8Kcak)J{#?1U^LvY3R8gBV}g}w^adve|TOM%TFxrE>XBhG5zVp60Ue~Ob~P6
zH9BCF0P0?puZA)lX(ZO^l(Pc>io8vT6%Pv$UdAc`N=}KV;-}&!%2=;}2^3h0hY>?z
zfU?&&%u=|`BMzawF5+M(RKL#Y+!JzSCN+&#Q&@R%bUsK{$Tj@t6V7FIIrgSY8B%ma
zUWQg`Ck81n^k=ZtRdNzeNpDG2NQ#37NfbkLU^FL0DJOeEb}P01XN`EsA(NBuWha;<
zPh5;;WWp#dnIWlP6KinJRF#)ASwfb2QUO>74riq%{sFG}Hq$$Gn~WbJ(ZnuWgN1q6
zXSW`MpBEl8`cfuj=bWt(UM|acC1Tyl!FmN=Hii<=5v1Glth%e2<^dL)M3>wUCTCdc
zPb_pYk=vnsbO(a^_*<M3kYAVxtJTXteRV`f4K~mdg}!M9iv$W_@^=!Oq-ib7iy4b+
z*@F7w%6-?~vb_AsC!Xxsxzk4f*l>f5Hv;eF7gg8?@4DM=9eb=iW8qvpgDIxe!7s73
zihxxXKd+9SVi_1-$TdLpez!R2NYc<EY_?0oix29=v=v&)#>_}d_I(=*RF5lit<eHG
z)YBa(_u62Y4GI_WcA6V<uZBt7tFl8wvry2x4B@oqqg8^beQ#fjKvLAeMo`s)Ck>5>
zyqJm>N(6?E6f?IP1_v-5%`J?`QK=;5WpP%h+tZ;l>|&%O85x#@Q)D%#{7<@vF^X-b
zotzL*6Zs&?!NXn@FMrI+1VZEgBQZ7yyMt*}Rw|d%09LnYask0t=;j}8G?&oFV%#>R
z6xK(2`{=MjE2sd0%1PP946dY-9#WWMF|?c5I%ds)^9qJg#3a~+oJ0qVJCJjy&Disp
zkqx{+=+}>O-NG4p6XcMVIB{|*F%A(~d_(0pN^)p`D{-dnf>yT4Vl+qZOq`g8X);yV
zD)g5k2&Y7wl=DeLb!ZLNB9O2UL5Wv{qi(+99ce*-2#N|3X5O^|fIXLo2@%m0SMf~v
zs{XxbMo4TW-qA8QvywQOE)MQeMWrO!!2v@@;FzKhRiS%+6bDT+T4#ikY%s1Uz_J58
z^whiRT5Iwmj-S|vv?NoSNQ{^$EG{W(8A{GvjO?F;71)6mq)JgFtN09t%<%LtY#D%I
z?lpDDjk&G0p3S|W2*|Jbh!E0{V#>u2wpRRA5|)(ZqkjR{G4d@G%#ej|3=`@g=S%VQ
z5n^H?<%=!pRZzi=?uu&N*Y3gFrg|Kfij{C(-9i^v{kh2@sh*U~Td6yD7TZct=ne}w
zB@R-IznovF8qE@v;FnvW9{w`IBsK|6r$n7p#39mEA6X#zN<Sr`Ge>|>s&MCLujR3l
zM3Y!p5R1Q2OZ1avNgD|&D|BnyVdYNUxX&|r&dg~Gc=Nes>9(C$=(q&5-_HDf`hvOn
z21z1R!U(nGV4Y!;RfT|1EpJ+(y~P5;o-)Wm<rkkbR^ottaYH630Z83sqF^ey%$zdd
zpqhnIxOb~Fkd*0i9Zpk`PA)J&7ftjOPY|@(z#@Tv(JOnR+aw|mozxVb#A2OJ>IhmY
za_!<p-4wwy7YI#q40Nx!sRXsgb|On1tz27c*1?8Z0~;Z(B^Yol<?AMZaEFfq$!d8-
z#A;_9)Pt8u5+e<YRvYEjummu;7M9qACgu$z3T%BUt%+hWA~Td!OU`XkNI}$wkAFUz
zn|SJ<y0a4UxxOT(T#=baaC0(P|HZIHuM!;dX*yt#i(#SAsfK&DP26=4{+Vwwe}}5(
zWMO7#7Ex=JsJ0TwjFb79bLdKzSc*7dqH<Wa=Q5QZnQu^yj?9MC%G&NS7vbbdWfD+z
z)&+W|3`(*jM0|6FrK^hl>{>cx7#7mnFD8L6_JNdaEu~Ifp?zXuSL=`pB4xQ-0p;jQ
zkkO%*5PV@FM0(;AQ-KTu!<;-RsGb$673>TNUVz$G*N`}RisWS$924;fME{|J1O`uw
zmb)?^W$>hQhMLLaY;o2YHLa*KH0h!KU<auyMgb^}z?fD!B&WpOYJfRw4i<m&<@{eI
zGaaVlZ(JkM%2gJ>R6x*NEj$-j)SoITRRD-V?gZm%VgN1nXjDXoj5q~FS=)*lu>6|@
zfsh^3S=gbM$Z{W<+A2UzkE=qErusOa1<ey%SOE}*xTTU;^NKkL*SrEPIys7afH;M_
z)u$YD@pMQ#Y}Zje@ki)1n2p-%hqA(LAG{bC;pte25<i_JDK#yd1B_iwDhR@S$Assz
zknJ}6TM~6oPr?(MIBB;S-c4<bvasL|y6*()TdGip6pN;r6kz&@B4nu}A(oWHW&O4x
zlr|$#scBH6Q$vOr2tC}afM`{-x+akQ)+ERot!i>l)$r7nTai?fDxr=%EucOTkr>6|
zCKE!2)sTupmVnlhBSZxRGY;+|iI3YkT`LQLa<C;rZ<V|cS*(|a_2gVa6M8D0l1v%y
z0#>*#>w!vrP#9GQ8|X^3^0duVloKQ`+9H>_5dig=%n%&{mzu?R_*vIP)T(w+Mk<Dq
zlH;1F&DJ|fHHf7@TE!Jt7c_^!`^A{r1Dqd27b$9YCb)=`X=aqnY6VBlN(YGyr06C@
z)O^hAXbp*|aygPov3D{&gz=yH7%h!Kj}hTfBG1)bs%vl!jFMw8$pKPJR_LEbedrU3
zYZu9_Ikr`mAc0E3B^c48i6UVnyr3!-AKpW7DZ9uJqv>GcK+H9fhce-GfyQ5AW(qph
zFM8igcc)oKw>SyGKIZI_`qRl?p)Pfp6!K4WLymcRZ_-S%fvz`V02oISBqtCAmPAh0
z)>`M5s|Wo_&SVU&#S#Nr_sLHES$*@j4AEPX&XOJ~P)Wr^mJL7}=qRexppsAoqj;*1
zs+*aH)>9I~#+1}4yUj4VyvsDbRgv1N$UGHchCh6aXaGyyaZ2ojPod>ckOfTqglkM!
zshL;}3DZZtuA4rP1;)pLVvOVuaWlIl!H!RLoy^@lGEoVXv<Pt~2<3tx2@2j$3#3RN
z6|i%Wh1K@ll9s4sfiI?lk$ILXAQa*F5WV6kV4Cv0j)ejybkgc80luWpWk$ucQi=*J
z5_e9ajR5rcS-utvN%g^~qVjZMuy6r{arrTT?lUXZLb)JHLg^D}(jcwk@jzZ`hnnFv
zDdeJ&LnYfxP?rZN>Q$Fo(l1eXJCIt*h1zy_=izvW8-Fq@R0%-XY`Z0mztBMNWQ+Mo
zY$hwXh{m{LB-~_}Tdjv&!$4|1hMbHit>7c$Qca=JIv#^xX@L*K!0t?nLYEY<H`JL|
z5~}z~J@ch*$wpI$|1z24Y!3;`stXw?Sec+a1m+_+e=0r$l-o&6MKsjU5ChHNkV-uH
z*&;<&*HbDeuNhbic?h-q=u@|q(J3lPr_eyhNSkDiTqaDH=*?P-pKYr^u4FW(aN44B
zk|z{fW^Cl3e|$3<IKMy;X446=R&i@05`rQoF4jt|43zYQrqyV}%mCNO8PR114sjw@
zw5_QQspwigsu_`+woap0a#RvhpvKW$O8q9B0IK42$;d1gs3R?It*qUp$<7WiJqXTB
zQ)d?271Gwag+oAy7Gy!0S)ryr6s_OocwY;-L9z=z3>O}0XwYbbCUXAt3co(ml~15?
zBGp19XEfoTI14Q%jwDMR{s-J#LhHnlhQ_5t1$Up5JwWu&okFlW(Px<H#51%Lf}NpC
z2$d|$iw@~8B>$NRGs(1YLlP7-K<@2U>F`$J7_kVe6MujuY}GP02dMyAD<83-l>!<z
zS|_uY!pR}g&H`ZuHpuX}L_~ipBmokLCGZaAMc{6AxN0eCWdh7I@6D9Rp?L)wX=Ph|
zN>K@4i6dq>GCn5bGscz8`ek4ccp7Wtlqk*=@lEqG-5EFjr)sstR$h`8JUH}iNAk9X
zcNbwGH7vlxwS{>^6}_K;Zt8(vm)NmLR_LeEfYN6*P0lMJR$af@6T+JL&Xp81f2@cF
zM2evXvsEa=YqSNv+hZxV7YY!vBBT5X?Tx0uup*@Z(d|)H&<g{0rIiMg4+ex7tD%_q
zA%$S&`~-VUjACtbq^$q=Pjm~-{APq*%-d2d0!0q|CY-z?9Nmp~lz358F4jCC!?iK6
z13*@gveZQ)TX?%jgVc^qHcDqltKi=SM~BJ^SXKzl_s>LSW-<b(RJ6h{ry*)H1Wm;>
zoG}Vy^x+*sRN~DkeG?l(?N8m~v<a&$0vg|-5d~ihA);CWB~L}Mj)2Pd;=@0soUFvt
z;TT+?8{5)FSURgBVrwg)ImQ-BP_qF|FD@ua6eRKGpEFyzLopw9(WyU0*4$(o23jM2
z;7s|TKOvbYtT47E3mSc)k}$<cOx%&yQGsnEz?fRqn;^ne)l$L+0|d9WW~JO0568rQ
zmnbw<9KbLVqf>bT69*R(zOun9BNg1*<h5;nN2s|#hE+SaFcUBFb~{HnGZqyxASqe&
zADIKwt5B$BZ-yOmAR6j@(iVP#$xr(6zHJZ#YQBpWR!MQ;yJ#Gxn$gu~g@J0L!G1&W
zRbNPP;$+0Ar(j@<bOuTY36lm(azKbwUwA9z(QmhiC-Py#2ttOU3o5`|L;KP#*<;l;
z<vAmrUeG}y1y!Vk{|vz0mAvdn(!*cOrJ+2dOK=Df53=e7E%PWxcEd|BHd@FLhSU)<
zJ(?M0GLhkH2GXX`(!e)p!STFjZDCn}+Kd?OkpT$Nz*p>IFaV%vB)g)y+t%U1c%4mp
zNNjjkl0s$WD<ldCV#Pwc6sL-&1-&*T#CU~7y!B$Gg=n;(f1$v<C)+fyYi!vrA=)<K
zfFAmREC*0H<3`oUdBB3o?NU+9>_$#T5Y*k`&xf`km?E|8RiWBo+?Zqm(m#sO%~VaL
zJ(%hM6<m2h5IfTt78j{3ck5@dTrgGxO`?(u0PFovRFEE_L7>_aRb!}90S!9;*`=}*
zq1F(4J?qmzXS*D^^i`JE>DyK<O<qbB7L98C12w&#>t{I?Vh?kTXp0h>t&_oGN#I(T
za`~Yrr<$NbSNvrXUyI?3AC9ebYH5T4=Q6&L!f*dj^5@?+Rl>mLr(H;FQ1uO7c|nQm
zmE9t}`r-;wRYOJyyd|H$YNAkyu?i|f;F??}OB4}RWG!Y>0IK26CVr<BG7$k3vg}Yk
z2mv4hWk#8P;!=KL6|d14ggxqNC*gqW2Y$8G^FOrm61I8^EUzgZ{NP=saz`BCPt6*M
ztpHAY6o?H)hA3o;zJ(?$gr|oNE|Te`Dt?6qhSmIsbNmx;T%(|&yhdFTYQGU61(SKq
zZm8gYx)Y9U_p9TfAtOrNENpyZl;6lF(Aos1-|UlU9I1;9aYGL3F^7a=k^ncpYbyC}
z*+^?aaIjU(g{8SwquT=>B=yNwsQ4dPI9Y!y3NJI@K-;E?05vcR8Z8LkJW6XT4<a$O
zc}hCIMoczIpu|B-sc5N^H0eWLRkHZW&t^+M*o062LjWJh#7!!zk@ThtMC2;6T2+6T
z2!!asx!ZjraJ<ooUOMC}SNu2$LxgMyIuS?{pks?h2VEMRp^@Uq@~PD@wu9j6O*~F;
zD3tNvAF+gcXQaUnjHoK4h-8%LSIr9KWMgv^|8)>dG$kmhL=Hme!G6@Uv*hakB*z2r
zw+<W~K~27%{;ZZ~Ke3SQ0;35Z`gEXV8Y*VG<*bPlSp2L9SdNh-D4aSQWF?e5{&_Xc
z59;$Aj?OaK8Hp>bH>P0`Z0%mL6xdiC(8Rv<z!iUqprqLu=Y*QX+d4~i>~b`T4|yVV
z%wtTe%12)WlnOXkCzztWK<J=J436laS9;_IPg0(xG!n&2=3o>Pt`=ifam`6&FI`+r
zp&#29{4XHQqISF%3r~q4h53Se0x|NMkf(gr+?5xIB?6HAWouxjg!XU68tAb@A27w$
z>C25OWt}XF2}%Qj1Og3O`ATh(R2ho%rJEqE%?MBf?h&(K6te{gc~PdBOCW2I(}Yxk
z6`})fZBtX7`lRqFKtiK9XJf)JO2RZp;e;nmUZ`MuW6A>W)m7n1#)&S55GkF-QK=-J
zmJ0wvX#<GAD4dhg9sn_BlsD>DT;nV{^b!LCZx1>co<ckgCEiLWq)Ku#Uy^}B?dBGK
z<hWWwE5xNz|HWtpU*u;!28Mn_3Z}fLONU83w|P_Q473_xIM$GOInjed1dz#`?PVEH
zgT=%u(or72tVN{xon?(fiR1T2xGE5)r+>-v+U$VyUr~6v1h<MGRTt;7`UZR~X=J%y
zQq&saRF-ha!NJvx{P>9gXc!p|q}G$r6LDOd00bE>QV}#=F>@v&!PwCvbE}!+K=nJE
z8jpMxDUw<L#}`QEQmC!2(*V;DW15xzq<}6lthS=%P+LK{2{N<=4dx`jS02dNju_8B
ztsr8Q$JJ;kzesV4!ffS12s7bRURhnExf)NIJF!GJauX)XS5nAoLqoq^5)8$%qQI|n
z@zW<blTOX?kO-e#R%R$Mn5p>9U1`F&7L;!m!W>rmny%7^djPoA@+=W|;&T4;CBZO^
zpKMQ_<G%|l(}t$JiZ2b)OQ)|Z6e#Rtqo65(U!!Yc>lXvxGa{HmmkE5ZO}BNVjI@Ff
z?fA;PL`fmkR4Ab2C!`5Kph7i_<vzJyW{6Zr{7eNWp_KS|J{2c1VqWytO__mFvMUP^
zj3B2FcsYlHZv!UHR1-qT)7envs(8CsJUmm2TALSvRnUi{5(1VYWl$5R=+gJ}qRI<&
zns3xfUeZ_K@Qxgy>vC%9#5y9)nZ=Ubjudm;SrZEtb^O1+_5presBdyAVR5~rN|Tmm
zwgadPKzzchc96KS-_vguuybZiqa@K}&QPM-V|IZA8X}<B`>Z+{C#7_IP`|Ol_fsH`
zjVi&ASYdG_JjoH#$*P$N2k&l#2{a=RU5W`ZtOQ;W2Q#G0gN-FnT*Ojqo9fa=2Q_r#
zK~;G}#SP%*VI>$Bz)c{hv&gY|upUCMwDYNeWIX>8p8fCmLQ<hcFnU2@91&Yw#jDyH
zd%7Fe29IbUV4vcs5mMfaW)x-|ej8u@<fZ^akPu6ZGJugsK=d;{<}d6hunqlfXa)>3
z<rKR)fr*Ert=l=`3+HD4u#dL9wg-^p*nS(qSdiO6ASqD?G>vm1X}1hII|%OvGL~Yi
z0!au#Z6Q<adR3G&5PK-7E|GGFF%(vkPp0!vS?+O4UDL7A&|g`to|JpA;6{~3uGmN+
zajW1*r>gLOWo4Cu<Z*yKjCm|ahJ{E9F4R<rFT+}pX_3E@Cr)9FZYhhg^i*!DDjlL`
z7fL=NG=B<99OX?=wEJOhfv?gKIvC)Z%8F{P#cd9Oan#G27%8%jwXeLgN}Hi_ui~&a
z3vrfQ*tkZ9VS(G)1HxQtFp=tLZ?e{jRugLs3k<1f)`xXM_n+W|3uGo*>DHgdH@I*h
z@~}rz`ZkBMlG`&Lj|z`fWyN%jd{F{Rl4QXR+hmB-0PIj)C(=+|LQ!41QspSt2={Qs
zMUIL46p->NU3zNIi4=?_avl=47*0wx_MZH&>v8o5|CoFoJIi7*w;P>1X<w|xULHaP
zoX_oc<}r{_D^?pOCF=?orL$Pd`Dg+>%L=M~Isqn*B=zJf9K$^j&89_upu_nrXX`>u
zq>;v=phz!xf+Z(%Q$<(Ko<CruF{&KHWd5gFE(+``4NDYdO5rk&UM3f!1qe2H2L1rS
zl&g`-29F$c;d&qCWQSa?<YO19=L8zjctJ4-RSN{SML+?_S;N^qoRETbQb|R;2)m?F
z7fVswsmftTPkop{2pAX?wE&4v8nkMN8%)uT{12Y=i&UFTo8l<|h~XHoXgHnZP{Ctn
z5Dhg>Dr)KErVLWs3WS=rEMR7){;<t-b_~=Q9=6e&_DSJzIe;6hcripM(3V0FoW?lC
z)4GB}orwZ`6xWCYq(AVmTh|>9ltP5>SmyNv1(Z<RTwxD6&J+QNngrx%4jSj#qRk-J
z20&^f6G|2$oq%{!H?QoV4t2|yI*C@XNH=(7mrXJ-LN3y5RZ;@0grqPY2;3|fey)V(
z;E5f+4@6~)c7@?SnI?!m1e&}q){Io2p#=ocMyE^wNYaP~Cinx+Lc_fxl{nRcUI4|1
zfuK<m7^zpHRJD9f!od>tJ)q10qeO!b@is*xOJqz^zS}{ps~D7Qr=+k(qGo3}fT;l3
z9G&%%zs)Ws9&jv47HbYR6Cr8$Krb3;34@5mf%wQ>nuL*Whp3C<nN@oj`^8&gr4ZXf
zJ}O3&zi_)$6v(XANehVF%m~xSQgKriasoR<!Ejcps0KLbR8^l;XYTSQfY1>X0~K@f
zOF3)x0HSydTWwHKf(}i`+(xZtK;TJ*lV=dIFvHieS7J*`4Ai6`mEjd<xkx`1MK_$~
zO)6xSJZ7{A8Jx&F+aB|l`~aqY3snl@OOZmOd~<#VIv_$<Y^kiO)&Dr8|D2c(_8VLh
zu@#rL{8w*_Jnd^)Lx!Fa+BE5lv-m$DFp?FO@vhyYGL477mZx8I;?i5@_?Jd8L7O0%
zNe;0oLoFPmYY8bYPs&C7R4Ng~Axf(-#lp;!M{fdw$EpN>t}6JCd8D^SvEJgL&KG&5
zsz#Eg3=v8RCc8))e{0-B9k^!pNI9h@1!C<X@HF|Kex)~VsKE_|Ndgi=JBtck*5X3~
zuW&|{|FDrCyD8_Vk<c^}Ob*0u{2BA8buoopT3;wuYlwK(@*RAcpn)pIVIuR`JYyOl
z&6K)-O&w=Ovcg#tB!(!sWi`aF8Z{X_T4(K9c<_&jR!gBq69zbc>5kl@Ew39Lxy+*M
zuP_k~ikA7nLW3W-Vysq0L1}TFM8kts;U<!rB;+9dke@b>Dslr0Grz(uVgcCc_^O6}
zat5<>!BJVR#}7{NxM3vouvAwwy0=kPr8R<Bac_nHd@%IZ$Was}!A{5b(4iaV0<pX@
z9t-=rj>$%mHxv?RZ8^z@L#3=H3k87ut*RwQ;X|iBGDB`v(hwDgShgsFm0o7hE#`Qz
zox|b|Cusv-cu!S<_(+b{K=nx(V6sFO*?Tz4ea0vEOrs%WB|+EpDxh62_Aukg4Z$KR
z>R=di#x#XaSxH=MkLEz0rsE=Avq>Lk5{fvUm`WPScwGx}gPkAD<A2TKKb+F+%jF4A
zacqaY3>v|~5`KnSt|ix%q}0lJlItr7I*`bOK{P8g+B9Fv6ZUUZu>^?L23-os6KY}_
zLy|X16oCSo$V1qKVoW4d=z<F{jb?Df1&^@h6Mx<U;J$&pq-R4Zj8MrkdhJmTf*=J^
zs<b5QG~~jMf{L|Eh~cIjNq`|zHq$Dt5^xOPYG+F!e_fI5rMJZ~)Jbh==^59{kFAz0
zU7l)6#7r7`T>(FEYZnWNs@5a`3Ry)J|NE)PfM_bff7}+6>qrIB8j}_>Rmxd#nVIQP
zJ}B(A9^%MAUq2w8eH_+1qhgPMJdGf7Ndy{*{J@uH@Fp;|&(MQAMVVVS@~f$EsNOJQ
zY}e@b;*g3!|Ic{JP^siZRRCE@k}8~}DTSEY^Kwy@{A?XO2gwh*fRfFMV_a^@Z^S}_
zR6vSm&X}X&q=``t^u%8%9~d4d8h7xMiWDN7lA*wVR0yT)MjLp+QLmYRpcLXyj*CT-
ze(cfoC?1k02NFC@kpGXp^MJRjs`CH2uh;ZKLP-J%HK7X#(xO8VuplBR;2$UGC>s5X
zWfT|%3nQa|1yPAQBZ@FIF(3*U5)8cs2qg(gNCKp{_g-Ev|L<@8?%TP%mxM6meEuWe
zon&Y2wO89~uU*bQ=bjTof+`#&4pm0DfKVYc>)1ol%5h&vVN<QKt|Ssw1c*X9QK85A
zagWyRcsxz<q)^G_sw%7bf+Y8$d&+7e-gFC_BQio-@yAzOEvl@9ElTvW!d@Onw2Xlc
zO&(BcA$6W@q(gCrVKKCE6TW6FwCk*g3vB&W7tvx}DN7>D2{^TY^-+8wL-E%ED;K@t
z0eXgah$<l3+t^7&L-WxKj9Yk1r5vijRohX=D}@nFZS(?tf+;9unbriOzSk=*u_nH{
zBH!aq?dsGxT7sk)3u2D|=F5*L!i5?E`in${Z}gayV7*wwEcF<IbfL#v|G_e|u^EyH
z%)!*?uMQA2Zg^iJhb!tE^_n_f|5ufA9*lrYzJn?x=lJoYTB6O0)|ohtt7Py4<$Fo~
zLux65nWUvFoT`lkA*A6CrGyC~V%kcN#}Q>9N_do^l7Y%sF$e)Z@r5Jl!CZxOu=wkB
z<IGk(>c~x4>2SE{|I$vjjY1MHvN+m9>u88raHyWa5{rh4dWxWot1(PsufpblQvl|T
zwoZDUY65~@RSSUWUiA)AXtqK;p2JWC%H6!Nm;I)uQMQ0_S`3S(QK?f?b%hR-B19G=
zmXRmFYfj8N31SUn+^r!g{U9`p$h$BxpyX!F?MMY7x8qPcJ-n)|95u2~DVz_3Oz?ji
zHBcilu(&D2faHJ7Q#HgK>Pv@21cEt)BQ1#`fg!SqWz9A$t6Fne)MQFou9GKVVv|Z)
z_o(uU73vY&`Svq2gF%>L2>VNU=_T7bx|&yb;_lKO_AOBzJH#-B9$=iu+}bJA)Em(>
z=-8vdqnVFJ@aUX2JsLRD$$Wt;`4^@W=St4yX$2|09QNlFaB@7I%is;4YJuWToYc`X
zQ1VBOu?*B|t%r*;LL6Rb4_<`wR#C{q)M%NeoNjh5qL1@I0h+$CFFH@g$o+^UoJ2*b
zEG`ca8_}yO1{pq-k?JU9MjJICWs2@Q@aT=EalWdPUt#e9;^c6-JzNmMT&}0u5Ufpu
z90<gnL`=qYx)lnFzC^CHsKgm+1+!3y(1LF!T!A7k)gV(9L!L5$rm>F^HcaHIqg`D8
zVj3+d)15}4n5=Z<5;k^s^mJ>f;U%g(DH&GtCgM&o^e692uBZ?h+_5j$=fC5^lzxh%
zR$VvBi8^A97SI=JOC-!vGtsEN6E<JeQi3~WqH3j#*$<i?-YX2wWK?Nlqub&THf%!^
zf1_p5cPP_T4Fi5u0MM;S)snzpiH#6pb|wg^fRL3^!=70RYRwKIkb$R=LzL!$2mudX
zyvIJu%X`+{&OTOBq)QLm$h~qvB>Ex0VTWA4lvM}=P;+Hd^d{pE5&V(03V@g=x-bD7
z=uL4Vh~A>-xacYA>8h}MXiX=Rrltb8I$8QjqSYY-xM+a<3^sO0@-MB`kPSz(pa{Z8
zGAA5_A@T<DbQg_2mkXkIxjv9uZKNexdna#BFi&bR8=pd?hKdVNvnMlrDB)sQ=fL2i
zPmzEzm}4)%fwbrp;xQIKyF`*%d9~@)PC~wCh{%g*#5*_vr$)1-lYgSJC*hDM<e_LN
zW5i2jI9ZLrxgkMHVke@O<2ojo*f+~Qb~Dr>m~kOR02mwbqPVBOgp_U_jv|!hFa(EV
z6iAv4Lyg#@W8@>6As?Wc$tlJ!1vaq=K?S+gs#C&I&_gW+5P{afgOfk#QlilwAt=)o
zH?yjqx2MM{n5eb!;%XXdEgYB)rzU7|soP3<8jdZwdW1TKt3ukpQb4Kvpg2K|7{fyJ
zGm=aSn~X&|bTR4BUdEV$ffnOTU46hBw#pgdRm<aKOnqi*>A+^Dr}z}Nh9G8za0!4A
zacrKn8o-1k)ycbR2@5&#glp6%2qctavSjb6fYCp-oI#+DLKgprLiZS5uMSoFv<<OV
zsf`pE)$unrGq6I;Ad+ry5Xf&h0l#Q6p&O-84umpqtghU%l1ezk)rt<86K73wkjR5Z
z;}Bgmm}aZ*q?$bDK{-CWO1J2#zbdYLnHw>OC0TYM3gybh8haZA$TYOm;juYVC%Jj@
zCeO97oM<N`SA|uwUW!>a!^di31gAn#ZHqD}F^CZJ?G4+>Dk>c7jcmf-xJND~NuKN!
z$bar-wK-5RI~ocn2rDC`C?7@=8~r7+us95m&q)eQg{(F(@`)xWDMn9>M~UK}cJR>-
zhpjl7<FqOe5-^1q$fQ_vn?|{IIEq7wW?EJV_FAS3F=$mU2H7E%G5=^HEz#pU7$hQD
z7ynDV+JYr&D~jn;YJoLNC}c^*oaE}Cq8Vx^utG}YUWOQ!Ag7CK2npn34nwJ>N4YBo
z3Sn9=sFgf&s3J}^ld5%EXQE@Z!aH~+T`r0QiFD%x1(bwHp=6Q5CjO6wAi^bV4-=v~
z$TUEZz#yO}f)joT3oSAz`o$v6W|(f3Z`EGN!!NPFiAfHbvINKZ(g6}(d1~p82|6EE
z`OP<TAZWyjhtpdbA`W$pWYej%KTl)(^ImhS(G&K<rHLatR?{7f*astSkubL4G2OFn
zaIFFhtGJ$A;#w`nJ47tnSk9#n+SI!u)y%G&@7e)K9;QNuP?ja`pJ_bPN?m2j3p^Z&
z91WZ>uD3DKBDIT=uDC$B`V>*6TBp<rtP)a5S4d9u1=9zDBJ?y3XthEydvR{vL94Y?
z%PrQ>i2QP{eu=AV5lADI8mhYQt4Ub4+q)>!Gk$UkDm4#a6JAlSorU^?9NADQ(RLm!
zDCKs!sN)W_j(@{JlF2_RrILW8ALG&%o!CKuR^_yR6j>DUKYH}Uxz-fKVTi^IW}}wN
zYs8{5JkA*br`cwfr!?mAioQS;P2_MwH>zP(LYlfqLdAndM9U4vyIImA1~fzsq-rqz
zO;J_C%50Hq8-M~Dt+kr#WgVf@Rk&PLp;ZSGQX4+QqqsajIB6wQSp0HEtGF(qWGljH
zhKv*szTiJDbJh216gb71W_$t-{pwCWvK_`LShU`-(PYllrj<lVrr=mLL6s7(c7YW^
zOpCFg$TRsd!=)IOa)zhlEl5QdpW|a)Rw48%DCw`BtPPuFsFw{9NR+Pf#fp2h{S~n+
zS6?ciISL80RU=E)Xh88A6J|y{sFv+qw1;}FeYBq)l)7B|eXG<k6{+G?!&r-q#=R_n
zA}?ND6B8S?v{4+2GS!Mwgg9PkW<NOxY#ij`L>iN;U~useYEL)skyUqy9+ja`ei0+d
zhk}$CA`DZyw^Il5DA2xDLq{#j=|mP0bh?a&PaUdhN~0CywHZKd+`f?55FcJuqr*z-
zna*Q&khOAnj9yI|W?(*x-ls^DY_+!1!LF_$hQbdqnTX3vuGphWnsAWYCUq)n#-o{F
z@wEC_Cto`Lp&RmjMX)$Y3Hy3!K}Rw?dJO*}FswxZi=c===%+sTGcGFP{0X<9jVh5*
zqsbgfxlWfKG*{nOipf!zS2}+q#@r=hp)AkQ5XD4jp&3#YiLs~IG7hvLl`c7xMv-Kp
zFuE_txL{<;8b&T1sda$iPh2uTDJVhmWKaz1h<J1;cxW0kGj7nYTQHg)<`6uVV(H~C
zL}3=HdMAp|oA0VHRpvxHL_-(Qdo&UG7$1I9|3s^(R+uT$G;62{Py>eu33l6}j8MW&
z9+nhfo+ylMtl}S^glJigZUoa^GF<MVT|&^(m~JIOgPi}s%KIz^@eqL8tQ5h{;KWEB
zabtw0P-%rBV@iy6%5$1rG{hr7<V9(LT1rEd=%pMJ;sQlif_&gsD{fB*x3?6ce6m50
zKGH$u-5iB>b7W21;?N3qgqG-*ObiV@unbDGAqo(zQNnVL3#j3|6!s47CiAV64fJ|%
zbg&X)Iuoz1oWze(1`rpFj82YqL@mq1cogMl8Qj8vfe?3<X02U(G7F=E6d|}+yJNLH
zq!3@St)rDMVxdjah)9A@Rx2ox!ii&rcMjn|JlLcA9}2d-?Yg*7&y-eHaLgqa>atLN
zi){o-`5r4b_;uE;DM@{$Wso%-r=kN6jZ8%I4?FauZk=%84^~694VyiJ^n9HgKqW~1
z>?v+EYPD0-<!nWteoiJxtEoDbKNSobHItI|a~!X$Q+3lB0y%}N2E3sb=q@Whz>yMX
zC-;V^2AhylBRfMTK|_<JiHTXApdTN{k+`O|7_kU-zpw&pw6dzwy+k_h((;Etsn?94
zYdATPO4ai}0>Gex4T*Sqy4&K$CuYb&aEENhCH~Y}8IN9SKgu26a66IA{T7IENGBTu
z=y4nlmzUIjHO9LU#T83pM6rdl{VwMxJvdssHg!D7&DDaZj)+!Qa_>Oxm1i|~hZ2mH
z>u@L>_ELTZK%+>)$+TS9kc?6+kZScOvT3qN71aT#5|MPHq++)=N$M<(4C$3eI1bUo
ziWufXk%NnwR<-gIyMBS;ef&hw##1IzC@uLVLGu*Ev{6U#=!BAg%(il#0!6<>C@rm0
zkh3gzw`hve7EE?TXQ5se6RAuta6yh6X_|k(r+~*vI9WAXXlN4ANbpCf={!Y<a1Nqb
zG2n&}`H(7@qn13{MVsX6(2q;xN7^cYZ^h$8-Rt7a2xK%1akP(J1~u}Mqy!Hmzzd@I
zB}iDPBjz<|00r|`KGgO{p&?souI4K>T(WfQPP_=#00Wc?Ou$af0t|-0kVT-=c!sLl
z#X4HXp+YoXJ?tr*T|FY%mO)Qngd4o=k~YGjf}zK1#qA*-E0M}7%QWRlXt+~RBZJh-
zYFDp)AVQ!->o%D7JuFW?`m!!DYI4;i2N|PHk;a?_B?nb9hqE^#LGzUQn|{Sv>g60A
z08&#O+bC`V#iI>6-^3hv_${s+gpqWKwDH$&58p$jfXFu&|H@l#AGSz{HhF3pFg7Db
z+zS7&inswJ)QC6H$}0ZRclN*HT!yO2YK8t06!dJ!#vNjzTZvMy4y<H^UQ7wG0T-{r
zJ}f|h;weTRl5veXg}g&&%@o``6ht(JKJIGbSNok%hX(^VnB=AxK}Bb2R*XBc{1@|q
zLQG^bR%U8AXsGdhaSAkmKtq}8_yDjF@pOoW99G1wKmw78M!O{4&K4gJ!U1$}Ct9np
z>_$L*8IW7)+;dc$=w6l>nMc2>*;1j&PG*4)AHax#1Rej>VS%OrQ1TdoTB;sZfVfw+
zF34);YD|(KYBd;C(4wh%Dc@UR16v#npuODG&2@C?5t)dHIC{Wae$Y<6Kp02~D(OgO
zA$FSlbjOavTrGEW5<8~+{Vfd*=|rV8C?vT$<XVwts6yQ#Yc=8_1<9T?qLbdx*Vw`#
zP_XO>rsFg#sH@OKs}z<Fi{Ns*h*%^s<7sY<4Y1~T4df05B|XK;g#TIMb^fc)rj}-g
z1#&M2;TeUhdRVvIP)@7mbMT5g)G`Ay&&HGh6B>ez<A-P*V>$+j1kiBRY73%2%A}sH
z6vsEEeCb49pjI0b(e!YotMg*b3kp$MEjLkvjTnF<90l0VL@XmEUj*_cm7=1loF3vB
zDby+>HYK%&ziObAQ2?N=6hbL7WCtqABSmb5Oz!VM$!&jprfySnG%Wd#6h^2bh97km
zxpLl7fE|i1xrB8+Zqq6->26-3Ky1iT33AT9EkYIjTAk%vwJ11{5r1S0FgS73IsW-p
zuDg?sF`>*Kf*;0kiiS-9aL5eY$mFTLn?Re6Hz;Y569J|$Z)-d=yoK<RML!V2>};eo
z+}h6Ktq5dqsw*tG6?F`Ma6P4)*q=wj06oVp$FQr=MMfkIC%GOh<q(NYGF-92ataPF
z>rp!@sn9MD4m{2J6?*_ob|*;+9inj7H{el+-4d*B(eOeh6k(zE#nI9d?M=c>v@E=Z
z0MX-SIagi@XJ`VEjXrmgu@bHSmFaOyf#wp(WF@Bwq4h=+5KeN3O1yf2hC`|@(FG4n
zU<Wti;gQ4j)Eyei;^a|9dWI_c2+^>qcOWEmfux1;D{1_rV`TvAW(9}m+Dhq31RiP@
z590;#BxWH3S+UFP&VQi}iG--sBa{D_An>$0LrPS}KDm+(#yT{XV74U^GB{A(#|EIZ
zjQJb}ZP7y%!&yB7Bcik|L%TK-GFm?u)bkJ;!=YR~pl+ranu8=rz6~h)7Yxi)s5|Ki
z&9gx+0iq8lDfkhIqAoVoG&6!j)vz+JD9Til=%Ccv7^?kThiW2tL=cp*%n)e#Jp;@N
zzNpd@I;v0vvgr%Cn)PHJ0V_b1&?;6mw-}=!;DH~-rYF<Th-<aIl>f!24KFi*j&o$M
zmU9~%u-vQ;i+z{WS%fq*L9@5kA*W<O0sy5VB*vYl6<I<%qvw!^y8zXf3Sf)~AG5X6
zup`MVvY4xwvn5U@Q9(<vL?y}Ok720{KZR~GNg>{&NClIOWC$CL1(Z9e7FM&Z1W2zZ
zFDjzFA)Y>z9R5YSDX{Rt!Ehy#u|~BBVpgId;!^{qh>Qy|bIC=$hSLaY<nfpdX%xmV
z4B*uD4U-u1Fb0jGI>ZaqIEj9Tg+r$>Dkh=W8Q^l-Azn#}U<m<mVxr`U17)1g2t$X1
z(H#w~QYuAX$Zmy<tfcKS1Cekft0WaX<ydbu4+CpBg4IZNFG(YG6mU*(FiYu5Le%WQ
z=oFG8PAI5jY@$g1fsvy5RgHy#_b3*1tO4RfeguzZ+z1cFMi{6f%xZ-s2`xC1S`j`(
zm*;>mH)cl@h*cJfF)U(2K}E<@^ims|L<NOZ;5d>d9wA&GLqdA<3d5CQr#1m_h+okn
z&sh(|1WfGT!xO^iNlY5Fn$a;R*yU2H_!D-CKGb7Em>2rBa(a4;&6~na+iHYBqdlrj
z!ETlT>5~!)vX{))EubA;5=#hchb3K%SG88!ai(O*7%$b>g%5OWrVC0ID)}9K#4`X>
zs~$tqh|uJ!l~~*)H6aWVAhj?>Ma<06VDOF;loEm3YijcZ7K`x+AP(xCy|K}Kkmr&+
zv{bNE#cY8B^@BQ0f7NEmQR5+sq!5*Dk^m4(mE`v(8z?c7VN$VB<Ud3SM*&|1OQ2(!
zK&#vmu8f{At@bjaR~|xO4|;h-qC%57@n=jBaEix<hxHzoZc>O*mqH=0>+x0%F0}$4
z%?9f6h)|(=|1o^23QejHe%hqyi2^bdgEWxX5Z`8qD)z@V82`c%a|Wi_L;B1v5S<kQ
zEk)%ZQ16Jtx?=kv;j&N`dnyw`?tn^_Qt!lBO}HQPL0DgJ9R>ihKuy1xP?AV;oc<W8
z!wdZ(DGNYK+!a#Yiv}4@kIqmJMqgsGd9X@sd95IY57mK-wo87XOqe9oEz+%Eri0Yk
z$QQeSV6yCB2uYM>A$8EvGwu%}S>rFN=wiB&zmh0p5nfhXUQIPLU0h!(JUJ`VHFF5!
z*H-Ofrx*Dgd}9Mw-@Hkbse=`;GwUd?(l_VZ+dH_wMI*GPP^44CLY-@3M|ZB1CmFG*
zhsWEyxCFs4$s|+571g3o?T^sb<E*06Zj!IWD>ey1?4=fCAXYR<f`7ik<v?sO<zmJn
zaHp=dVq=*36uP`iK`e#H<*#0DqbmoT#=c5Hf{LDU-qjPqu4|ogLYy5xiZnUM{tmX=
z+K-f!wn>$?%eG0K&pM|^`(<`dd}#+1oFY-MYR*MJt&Ig+_Ve9)Z6+|Zn;-jWea45}
zRMH71a&?tN!{sI0EYUiCk}@s83W@MW^q?{|O-UDFD#=j@!&LRM`b}o5^GV3iWS)Os
zNi-V$NEM4hK|~_vFR$1`I~RXAjfZP71ZrT<7mBLhVXXTo64O)8K0G!zdYo7YjtY2E
zlWXsY6!!9JIt`Mk*k*NX?M1AHgfZHmAQ9`>!Qeqr(IBoZVmMKVw(y_Y8888$j=)%C
z;31t>^psa}jfIl%m{h2}GAe9k{nCRa($vH4@bEA(&c0aucqwWjh`&r31V;39V2~@D
z)Eh_)FLQaWj+1%rM!FKJ*l?>kZHz2bYsc(n(p!)lR#yRn(sogJvXCx$jM{STyEH<G
zPBHSBKlaFHu!%5O=p*S+|6v1Spin48!n#<9ty7+mawY-yqxPIwOeAS8QK+FsKM#!a
zoY=r4hGuId(GJ~K)g}*ih8_l>h?s8+mq@36grO@sPU+eplv-`>_@fwyEu0_ZRMC=n
z8b0jj76+ZJp|3W`p4e{kpAvnf+b7ZpcRnbPMipjY1OL;@q!{gp5S419&2$el1HaDD
zX_O3=9~71PpLT}>h38+06h#o)$*R%@gvA?>2|@OSdOE__RRp2*Yv9KL54Fh9zzk;X
z_(G%xeJIp^h|{#_`#7lXRmo5Y<J8FnDJP3%ZQALObkwZ|LR!Y0px|SSaHrbVa;9jj
zH3cYUM9SnT6LPs$4pXs}D*BSL=AO8er@J|0;Lf9hkpV=&#z}3KYtyBO5*@9<i(J^%
zASH~q-exeIO=4D5*+8ZR;vNJt*yv!3codL|U{NdS1S8xAC~94-C`c4%s*msl;%|H8
z;9`CSBg$T#(J4UyAC0C?J%opV=o&2+66o&GlqhydkS9oCG=}99Mrz&38%=GD4o&gm
zl2@5}f>zuifjmT6o={;xUOq)_iltVwIaWf!Y79+mfOyy|KiP23qQz8}SJc<!YIGJ(
z&y>{<r~=D;+S=Tz6|uXgva*u9i5ytzu?3B$uJ(rJo$3dF<~{E_a*s*ORoEX6Q0okY
z$$y2+0`vZ{v9FivszU9FWs8=$_VqG0mkMhO;ms8PB?wXQVNV~KE+(YOO!<a-EUmwU
zEw=g%rEQHM$u~iN;kMcML~Pso6C=bl+g9h=ZBuSd!WQw#$UZV%Oh}U<VT+dck?c11
zm#{s&eMD`enB<$Lzi``Zd?L1O{fQA`nyqOptSyAMM!%hSL_yi#Dyv9}OlHbAlx%7J
zC2X<PZzyeR1WCRL`U|(s#wTLi)}I(5rrEYS*KV6~YZA7IPe%5U>0&~f3<+DbypLqJ
zvA=}v;q4=88^t8wH2sC!X5$mFZR<~r5Yuc;V_|I}yfymm#3KsI{#IE<Qe-kyzM*7G
zGcVhvzbu~n(+8%%x^6>ReN8uatl$09OJfJcbqALZot^ESr4@RDP0KfT@4DLCTUtj|
z*Ije=XKN}db?v2nT)yLNM>01RAxI_~hK2+$`$wussc3-QEdg*XxfK~ma!h6#!zoWf
z5(`!R1!9IN`_P**Nv4=6Eywl)16V0Eq{ITUP<7%m;40`%rmb|O#GZJevFb!@31;jM
zTS6cCAzPViEwkwV<kF)5?FvT-IYmOn(c)q;{_PXIK}w$K14pH@KK?Y`5|SK%<kdd(
zrb$`~DJ>jE2Q5GnZmvF}T${KIAlU#pa54V-IL)5?-|v6y<cYMF&i0SraK(y_hFo2B
z>8>Xor4^f_Cp~b^1v<}A#4!qM3`Yfgx?@x87mokHE`x{1n_v_@wA0DK<lGo)DFOqK
z47kcZA}3LmxPtI?l+aUaGE1OH%q64&S6K@fYjNIRejx#plLXU)NsQZ;V`~f9Z2(+J
zDYYhJ(}UR-Zj6Zy!vaCVSc0o8Az6~>``}$HL_$e1i85e0HiMjGGV(1_f<%bPOhB>d
zf4jv_15U-F|Lu(wRCWl(V*KM7{6NAd_7XWGiz0a;#xRr#31CJe$x|w12bp;7dFSAZ
z{<jZt1wKiR{ag1xZJ}qmRW!1G@V(DHURqVf!}GdRMhiLV=Wb(Jd1XguyB=OIV_7fd
z_AfttecLV*VDq%@NCI|<*iDW|dj|q$eA62b#vVkyX~>OCET)th5!}?KSOhj%qVbAi
zVrrdSOBMK}h$6Kv_7duUlNkejDq2WV2LZLt+1to>-0$h7ca@2)$QaWDyUIW!21qcJ
zh0O#;zA*^2*~u6H=$(Lz{!ebiQAPg~5;(+>?8r%Oaj`dc^uNiBNn8{yggmj>$>hcO
zw{z?+`~v{SK1{AF`X5)|gyQ_~@JMk&F$GD2!9YOn1ibz8KNr=9joq6A^fKO|$*W2I
z2ompF;pqyl9C^?~_xZVh&a(%+7`#mD76~0htrrjLHjD)Tw!>lVF3g)&un>Z=5a(tr
zoKOl`5M^oB<*GsqFjj$207cq{K#*2pOH5$mq{4bZQfo59=A{-1$u?*#(8ie30u!%`
zDL1dVK)Tq+Gd6}=lO;>Q(-3+z0f`0)wv8&o2FOWBAn&6knM74;MWRe?6n}4m1p?E!
z$*w8H09$ebC>H&Xql*3~5{mK9xNszRfOxR<j<Ebj3S=TP3;`p~V?2#bXwt}qz?2qf
z6~Hd0yy*Yr*uPQ#Z`!o!=Rg1X#*G^*Dk@T^H#awb;~U@boXqcv`6aa8P(u(Xpm_8z
zRzy8M%R&ImpZN9%m+GR4WM;=x3YYaOS3Ea!=7J|4U%qG|i|XiIciZj21NS-ln4zOa
zE3$gm1B5uR5`f5!>sLPi_>9FbKDz4Vmne=HJAT}rhwORaF++xl7fP%NgEn6igNG92
zsx`|We)|6BUYhgrt4p{MFm|VL`;6cJu>Fo2Ib6IDkf&i_u7pQ|+hRP$J^-~~QhSk;
zT5ZX&kSss(8cER@@<eQb5CE9V!ofg8GB3Hg3>Z$72FRgLDiR|ARf#3p7UDNwyBGT3
zG}dJ~uv-H8w*3!5QjH2!7fQ2*g&1o|mb)!UNFTrrF^wgYn>R^7U}|Hs#9AzkJP}(U
zwU$N|fVrR{B?;zAa;Qp_2FO7s6^RjmDzgJ5+a$er`%CTi`+xO<M<04-S*~{Yen%d)
zbB(+WI|%@R2CSW8(r7XhG`{k}{D!&%CXVsTEMPIdVd--Vng;B@&o0KB#iA_M4Iu|<
z$>ipBZerTFVaePDjrEA)=bKJQun_V@Y=ID18dU(MhlZ4AF2;ZKuia%A*<<8*snDf(
zEX7#z9so2X17-oXKm+Eoa4_<I(f_@(nO$dh*+t}dj2tgf>m0w4{zv^kV8DQ>Q>V_D
zF@ukUkx+;B_3PIsOTf@?*i$<Aod@f47J80fNc|ol+QA6peOn+po15Q%z!7d+>THTM
z0EU;Jedf>C{_L4MZXeX%I&svfo$KlwUR^eK_UzS5m)4IQIbhguvzQ(FLJMA=|J41r
z|M;buHw`aq+JCWr0OJ4vKmbWZK~(&RaU*J4R=)h=qcc~oURF0~<iH_2gp~-$`_4sj
z((?<RzUi)i|MOFKWrI7$>^*YuPPNN7zWV4>552Zx*^mLlh7B1B4N9E~Z2;pU1ePpu
zy~(v03BqKivDt;l1e>^5Kwf52VLW2{1CD?~EuP<@w7O&lfF&fc6z%_{*MO}}WR^o!
zGQb!Y$w_8OauRP6SST~qn$cAuX0sfM<~81w$%a$}V|EiK7Lcjoi6%9~1{CHdPC_ix
z1PNJaQd0E4-2a>A-*)x|-+y9F^U8Z?H4e-@@T;3TCLA_)kOni!ZggXTT_kr^kcei{
z9iPAW``IaH?qAs(1EySb=LHvCmA&V~2gGM#Os?FuZ_59ukf<~F*Y!egpS}hBAFWY{
zlI#|@&Uj;71?(a@wayKwqIbdpFcK~cF}q>sCVo@?f2+oSa?#(j|CyskjvTpc*)pHY
z;)<4*mI)Ik5GS7huY2O&(rVuOLz$m(ceJ(X_4|5@2){DKgR!Rb6GZ(G(1Ah~fa8oD
zc*Cld5B=shYoB`jb0-|P<2xr;9{zT&;M*R2=#@YJdD`87emIw_pZeJ$JB&ga^dux0
zfS0TA$L{@A$Gq8}{Nzc)-+ofnfTPjbwsF>~NA9>`+MSQ)dg=xZA3RjNNvY)^WYyZ`
z(`ViI!s=%}`l<K7Yp)YV)a*yqismQpe*O=?{f|4dnLR@W4I4gWhcrk!^c73}yb|pe
zXc#2Ifysd_AAn?xZB7Q57$gJ`n+*S2EEp0nWB`&9<KM7p%u7!4noW)baxl;XSc_!!
zw%xGpG#NQ$(7TEviDGIacq7RZ>{>91*A|)**p^#n5)Kjq7XgwAU{ivz3g?zXPBKl=
z|M-FQru@I;iF-;*&$#K=pBS9wnxFmcDZhMTr#9Zqpb?6w<Zys(_`jlhU~bT$j2Ai-
z#0DK&Su?O?@E|>c@%s2*`ShLt`BcSO=N>n>_sRp|(4djtG_hgy8dFt^sKIFrPz#xB
zQvf8uyyT=}{3n;<F<|#80Ld7<^oIT~&Oh6Te;kNc|90n}HEY(~a?35d@4mYmLQY_i
z3@A?c9B5kyuME|jrul)n))BprR&R))n#S`hdz=6VFrKoY1c01>|NUzpd-U{4dyX2j
zbGB$<>-_m3dFABjvAdkU*X}nz`sln_vkp1!gZ8o+EriHhFlT1R{Mqk&_ufPI+$(E*
zt!4eMg13C=9(%v<-HUJk<Lm`<4nE|aXWC-yfe{G?9((35&##<w<jMQ*K4$zY9WO6j
zE4`Iv6?>1}>!_2L{pF515S)1QY1RcxvKg2Ze#j3Sswjelm>fBtTmZ~YEwGCwNVy51
zH$lpgS$KOGyjct+tB`D7a$t`}C~a*b0|T6zkN``A(HN^R7r6nDsX2vW(FG&5<OWP%
zD9X*Dma1fffz%MHU~ww|ASO2>RU}hdtl>hXV9aH)<Y1D)W=AGS5=9fF+yvHc8t2G-
z3;4gTW?<Qn+OG8**5l+|KQLp)nVrpzoAmDByjKB8G<IA5=cgxnhfV%|8LlI#PkDcf
zUT!V_cklw`_)Zm7O^ffIeOLAmXS{1rWuN%(YHiIbD`S5jL8Z4Zhb8)QF0)ALr;zy;
z@V{e8zADClasK)LH2?hWcfT7mW(;GEG0vD`-e!=dxlXX&NZ(!7(b29~;+9o#3z{Qz
zUf{z!we+egeU*S0ee;aERw+j_0Gw&oSoPEs6YHu+@Jg=dpD!CQu%wC?$Yc!n&W4R6
zdOG*1tzP}qlerI`4t>&X0E5=P^5n!_>gsDNJ71nxS~sAiqFTHitzAtGAgNlq?1`?f
z(@jr~0dToE|JCP4>^``5K-IieFVs~HtcZIC?Hz56EsZq;sz>fIc-|{>Pt3H6lbEOA
zg%NgCPy}fVMH2vkp^9WAIV{u`7ENPfa*}Dx1?-kOhu8!`xOou*k9X}uvy)o_l+;qR
zBFmv@jEw;{FD!P5$>24C0mzcHL;%Kk7`>3Y`Q+dcXv#!yytyzb`QNH-GsFN;LO_Bz
zC6FX)!6U!ue>}6D{(tF?%fEiZqLOUUr#}9B`LkwdO_rVX`G0xW&Q**5c-0T)?e(c6
z+rIm~8=JCh%;Zme^}J*1dwNzref#%*_}itRvTTpZpZLF1jt0qFponO_^RAEoc~w+>
z&!RcEUHPwhAz%L3k=0A?yy(VN&|h-yhwJt@<EpQ{XT@Dt{@}&EK7PceZ(el^V)s4k
z>z_Jc&1Dz-Xi1jU?R)CiE;x0xJ{N`xMH@{kj2{Ps+Y2wY7zSUAe~$>e#p4JJfMNAp
z@7!V<7fk~UKu*G?9%6=z{tvI@GdyGG*xP@L^G|hkHIKHnxAS7pt|N{(;(-SqXlrXr
z4FS-L6PaTfg6uYVA0&%Cr{}yqvAMOmdDABL{p}s?{OYVz@2ATzdnsrbhq$VB@!|=!
zRdp>bRqNMQtXWyMN`C>WHms{_ZJkhC*}7!00}ctvzs58-EE&D?;L2)Vk+`X}tFg2r
z{&qH$W=$ZeYH3{Bhs7Zda&6PAqsI-)RcGzF*5>XFn>se~x4EmKJ=aoF)iY|$@U@#>
zvldSrDNpuW6h#B>W{T!Qg((5B+{smj6QeBzX<l+CV-RloL}nr6q*O%@0h1drFEH##
zGbMSF4Bq|+lT3vmS!=+Ugtrak##k2;P-pr=?TOfcS?nuIuxj&C3qTH1ffa2rOR^9y
zU=p>+u_%fL%uCT+Cbn>LCs!Fxj5ZIXdC8rOLAdD?nT3#(Qk5G{Zos_2up`Zs<ViC4
z|7rh^o&3?Wj~rN9dgS?ECH>3!$B$UEW?5sKwxPMMmbFj(=F%(gI{BY3z2J;V%Vu6X
zYf%d}x3{z(c-F-~{>3jY{l`NV%)Iu&r7d<NPp4wJVE`zxzocYX$;unQb@^Q<^$MB2
zq`i9V;b-i~Vch6{y!g_ue(VT7T$bx>S@XoNzx{&;|M8-4f9Cy@p8M@LKJ~>PPx$1;
zmrgyYdG7D-d#NED8}Eh<N7+TzLXMi`WGI1gBLF!mRoU(GehdWg@B+gQU`lde61Cv}
zr~M!8+MJ=C0(y<IF~Ar@UT9%4{_PWTJI%R~$$J3qYj>zUS=jrgOu2=;wf(<g!-hf+
zIR4-M_P6_Sw#kzx*VNR!^PTS`@(bgi_`^Azg>j0`r-isw*Rh*k2^%lA<&d|%wSy~i
zfA0==Lv5#%qXTi(<>mF&rPXzH6}2^4RdrTb#e@;>#?_<l1A4ORR$V>Qg=U8ZMqupV
zr|K1hE6N7dmk$_FT2Y-Vi-+m?nOK#+aZoY1rD|QfdELjB;Et@UDjQf=S65wATUA|E
z$~j*pD%#oN^N!ilo`Lo44ON=L96IDA7_r~%*ia}F$!-oK5CYR%HF=U_1B`JkRnT+O
zg?1qvA=UyOg49w=Kx`5PF)S1TOiV2-Brwih3v8QHl_`@hYXoT<01PKx03@>~|GVge
z8WX@}G5~6UjY+v#PzQ!Yk=(o~$M{5$kU?Jbzj}82{a-n7mjlL(ykYK`{U%J{dA9b-
ziKREpp-;lAZ6zh6F8J{e-#(<P>%g;ayyc?y&bFSOid~L8XZKvk#tjWs`yMl}^r_|+
zkd=&PU5gWDuaEzpthKas^cSxA;h}>%yAM3;hMO-$NJr1`Lr<7^%deVu-E*%A72R#E
ze6YH$v~=Pnzq)L%^5)K=Z~Ms|FP{0WU!69-yuEAS_-pR2)wg+hbVk2SMMa89j3hjY
z@$Vr}jDN?&n~Hx}JiVm+EkFNUcina8oO4cXZEZtC!_7C}yl&mPV~#n7>+gvZCo+`y
zfteSYlTJDb=cKU~Z{X*fX7R-l?wu*-7?FGxf>Sas$@$n_yI!mwAC}O@*?x3xK?5g@
zZ(O+|8!#{%JTMzHBv(Ix;oH;Hn62NCm9B5>=pL}s$kYu0bfk+IG+_8{8@t!+Qe9tK
zQ&&=5pOx1mGuP3WF>iErG-Oo+hVKk<N?sm3HS92SY<FW*)lQZ571j0S18Yj_;BD?|
z+StC4mAJbp8$EPfVx-C|9NJ0P55|Cf0cC0wc-O)L9@u$mI~Xj15rE`cZ@^(O#wwB`
z%ZJOt6CQvB5&{JJMx)8hVrhMhGz(R>!8u55AUC612aN&asj@Ec-mlOLASt5M#C8<)
z6jOqR1P!^>0YC%BMJOqfD1rnaw=O8DE&3lnkhb6d?4X;wTkv92Yb)2p&CRX2lxV}l
zEWjl@96PD5sj-F2@|GH{(vtk@W55036}P^Y%he96ZBAFQ`qoH3yrCh{JM>6*2-&;3
zp@FYew71ksIKS3x-_(LqMqzVX9nZ?pdFVQHbobh(mR$2DY}j>=Q9X?<ZCx!*&~X7k
zAjQ{Ax{0NVV*D55zjrRphgrlMaD@Mxi2pSC0GOBf2Y3t5Kf{I%yWxf#Sfr;<pT5&h
zJMFsbu1`Ms<Ut1=#QN>F<3=y!c0%428n;;)UnJw>v6VFytt~Cgw!Gz~x~hhaHJ9L>
ze3gtp?fG}7+q1aRJw!fazXMj>_vZ;ihnMlZ>!{J$kYSq6*RAO;sc0=LU$MSv*!~AF
zalm5Uw9=qx$mso7ul_?@S!rc;S*~(GPvszJ;_3L7wz85{EgcYm@p^+)_ay)V$M3Mu
z^XvXn(Ou40T?Vp>mx;Hmvy`{Mw{*3%u4&tQheMK53qd15LL!F*hP+TRaMWl)GCdeO
zF|kvH4Is^HIjJ@YOc(%=bO0>CZV_VBlnNg6z?cOf!7!4bq{@jZFu;ihcH5IwBpSqG
zt?6MY^uO5)MJ1U@9ksBhmRhVa&`=~tMWVNpU8L6SwO&_QDHtS^41gq2w9Kd}`XArO
z@$8}H$4K&*7T{b-Yga>CR}CKP>0xQbkv*+1{`mX1H=Xk3oBr|bCEYK5;e%g>J;(_y
z?eHr5#6QAYyEk=awfZnrjtg^y;D4^ZOG?(ZZhl2XNl8n0dskM)%OkWoYw2w6?5c)R
z#-TVm4i)*afJVroKl8yHHWcGO{{|;KlN^Gh3f)Dq82|Q<ClEX5?`QtuaCz|H!FSwo
z2kSSt!x^*V#*K^d^}6{#&FA@8(l^?8evM_C#jm}kql;ggwXyQ^a~9nq*M+&hS*dRy
zMnWzkV9uAjyGI{#NXM?b&1q?G8alFl(9o{B0sL(rJgjlVh^JcHJ9pi6$3qW=nEtRz
z=Z@DM#~<ENG2z+8E1KH7_+qJ^wBzGx-Q5(QUAU^fe8Nr>j_|5ucIzc4?Y-;4LrZsF
z_WbIewp>Xk#|eCRvJ`+K$f2dX?6aFzaT2^{cjJwT8g1*ajT{1IB=W*>m0+seYIA|N
z5RjN>S4c<iogNEJt+pID#1dG~tj=5@A$ATX!ATB)we;~nEUC(pEtZ@F2Gy2^Sc)k(
zo+_)eaH}gMqZXv?xBMgx06fWx$SoWT3ndtKl|`jm02^<;mViKr$*D~+RnUNOa;<H9
zi~FCCNAorB@Cp9ls3yM3iD%06*f|yQKkrQDlSW*QS5)wl#fJ3@UwUr(H9vo~tgLxM
z10Ex?d7Cl-K3w|epH7|n?fYMC4?Lfn<l;NxKgS1${qf)2@KIyS%Gy>hTiw#qp}v5<
zEcrjj7GIb4h~+DxWo3NrP$9u*J>y*y?k0CAc=x;QFUCJ^!4+~{3`X||Ub4H2@ozsQ
z|KKH(ody7&q+kmN0x0_5TJsN0V<F#r@4dPI&Gi^-@v+Aq>j)tyZE^m`N30oqpj6l7
zEYQ&LRTO=sHh(tjsekj7N~l4T!cJO;4&Uv>lb6oA?}3+I8l7dsxRlOiYnHEkZP80z
z0|tyeYI60UA@;uK;&ed<vbKKM*op67_Tu!19-TL8=k>!z=p8R>R;^gRY*l;bn6Z=I
z4FPt8L@Df+LlJoBz~P7Ob^KEc@0;_`3uAU{88Ko6_?4?xzWmCPn%uC1_IVdCe!<Sf
zXd&cuyqej2nHZ6vD-{fb9?2vCCN-16;$%kW5SWpwLOextAs;+sU@R8E_9Nd`8Dq)>
zQ{`HV1@A-=z`Pa<>?$LZ{UDKGdH}c7GGQ$AzwIF>A;c`uPy}|4Gyt<3uo>0@7f2FC
zQc_w7n+6O*j89g%2v^bnTl9a%b#kpbf;cWM<G7lqJ`}wbl|$$)xl#Vl)q*e0x~s>Y
zdcqz*y64xIJeG|<<iyE)te*An-^yw(J*r};KLZVYStTK~s<E{-<PC}IXWXA-=+{WN
zx^l<?CmvD%$Sptm<}KMVmtOhalJYz|WY|zrQsaf6>v!~uSzl2SjUuNQ{{?aGm@mdZ
z9w7F}i}R2D;GFQ#c#Fw4!Bl~9@$kbB_pDBlQATc0;zq;dB*r5%bZ)}GOyQ&Eo$c)m
zN4C`bt*ofxOX<uM-1p*h=PfJO{QiP(hQ|CAl9)q1TC1w6x|*6+&3$Igf(4B$mQz)~
z!>C~sCXAT4Z%J(}dx{w8nsm&S!Ws!YL*LonxcZex)~<NAapTM49XNLA4*QPS`Ovb8
zdNw0qY&t@$5r9dbRhBo>HMch|SoZX?)$`V@Ux7u#294Tz#2&lvvVV1X9eUv<*=5e*
zwHf5L3Ia>EaB?uV(0V1Jcd5pwCa?`&5vc`9f*u$b8!(px*XqDiw7NuWnXV$I*2HE<
zEJYKCk}de(T+}8Ot4N{%3`1Z$O`KS$wP66zSRHyzK+z^ql%QV!yB2y9Fv+!v-c`2W
zIRpUUEs!EGd140ug9HG}u@E!<z4^a#z|a8|9qZOMa^O>1HE`&F%C?5JjqN?9wS$IK
zcduE!QLZeh8a9-NIoE7x>8Tw&q`YHOd#<*wLVL0Hj$BFirna&HgQ~h#uiAiHE9-~U
zSC+Ilu3O*SQ8jSbz^cx*s~hOHo?PY7q4gaNYd5y?GUm!5!|K`^Hsz`+S~oV9VMArd
zx;3()ta{MU0p*Qr*S7KKdUx4~5raA#*TGUzKg4!oPci<B@$V7sF-z{`f$wqcVeZ_u
z#rX#h+;h)8oT%d#e8Nx57B61>zW2S4huw%M20D0Gc}g^lSKs}EFI4O}G`GjsSjD@!
zJI)VM%FFaAvl{($o57v6HMf_RWi2b$-ul@~5pB*iy&8j17F5ph$-S_#%92=vy$r(z
z3mBGOU>oGl16f&F$pJ#*r7<kPykw3lh)GbI#X>xln;jUvNN_t7FD@uF5@IhKNJEqb
z0>BQQWOgtl(@1ge!VJBO22yT%*b7a<h9r$DXrLz<j~o{bShZm&DN>unT8?WA^TMWq
zH@z86W?hL4B=FY!PZcT2w={ADf;65Y0AvU$Y^)ndx1!PXi3{Sw=DTSKv1Ds0`X7JF
z`+q(E^O6Tnxw*FH2~~FKjBCf=oAf^?mF(cz@^4|Al{(s;h~&bMD8|3WObnJ{{M!Te
zMsfa0!#s^3{F9K3Cf?D;iFz7Vd<umZx1D+BnY>LMA0{qXQfPrc@v6^Pj2@~turf(=
z9jK|AVaxxV)#*-~@SymIXn`TiBCO1VmL6(q(#s}LfHDB^B*W&lx#n{2Hn8FvFaau%
z28O8tGE1NcK<)*@T8wOLY-Gu&)&%CYMnpjm26~7=dK&@Wx<FzicmN|U6ATG~AdPns
zOd;L`NjQ{XEZJ4$2n2%`<0UsdX+^ZrUcyMXms+aGEkV@1*EeB{RG6T!%EH|iV9%<S
zY%;fk+(qZM&0@g-7-RX{<9}$doRqXA+bp)||Kyp%0QjHtKW}i#YmA3k{;U4y5I-5N
z!9sz+BN=e#QUsvZoem7f&2$yHBdQqxc2RNuaa1{XY&e#{02pIm{T<FfAOHBr?FVwk
z4mndRA2cKPu<{VIf2bufvb=nkb>+I*d6!qPYBCP-Cb!}E^@yH?Wti%X5$5Q1v=vyE
zN4R^!-9QdGwUCiq1;(`rX^e}QLb4JwT7r4gyg(Hg%QP8WU|L&wz*e{D+*$x?E#G1j
z7X;KIFo~i_g242)!7zB(tsRWXtk-046*2-RQ>`eG0oWt}%XAfF07zzdyDdndXk>_2
zW(-K+WPk-iZ#=nqO=B|K45pCRNMnFg5ezV9kOLzDMp9~&F{ZbCh@m9+kOQy{)LOm?
z5Mr*P|M7!eVgD2s!BE6w<iJJ$NAxMPL+mjJp8Z2!jDNeuc^az4`Nt0V+nRq?u3YJO
z#&}|WJL5loWZF|!Uc*B(d{&(KSkH;`RC-%aTS<2*x63+vx^=Y}*Q{y4SbCakP{||?
zm1D@m3>Y9GZ59|i7haR~G1McL95M*JHoFMMi6T{A_P`*@(!g7(F^*#>N#KpN1a~c%
zB-t3$SvXauG*_QixCntI!{THvYRv@!cwop90Yxk?WP}V;t=cTEC5PC$fT^MtCP>mK
zx~k}ZyBdcS{qOKA#y>;D!-v=)Fgy}Hf((;;D5c;rV0ze#^G|X9AxQc}k&B*tnUnj3
z5Vf$73M0)l#{c!_f1;Z=z6?9=AXLVWP89e$isLAqyW%XDx_Hexo0>m#*yLoHW1T1m
zgV8qExd#BbSC<4(3$a<q!IMCe5?~fL6(kNqI7y@-5EvJvqLE1~qAcG8Av^!eY-pI&
zlE<yoW|$q=Ya~@kAV?C@jG+nx5-&)@&XaIhOrOlK5Q=UQ^nJvFaYDY$fEWy9<Tk*$
zi-xTznUtywBwhm`3)_VR5=c@4ObHnYq^(81NQ?gOy&|+Xa8c3!DR}TnG5+y}L&6cF
zq1F3l2%@Dp|D+*<mkM13Y+n`Upa0tYfBhqOm)2LYo6yB}2X9O7<iWDec0M-Fg4N9<
zGJNZ-6PPP>&*=_RoQ~_$T%Ww}?s3sHUUb1YxwqKiy4EX1s<J?ee!Scl6X-PowJ`c7
zx;q}cuR)OpOb(56b3q1ytFXjMDVp6)Fw*qcLoEp;utk~Jwj~MBV<c4&lap+-4Jc$Z
z?A$V8fgMT!d%zNmr_~gpCjlf%0GnqORM{l(FhXpB;9WF((f{_0^P>M99^?*>6ibdG
z2bkl>zH*G&dx^_|T8w}EP@I2?^AF>K^#5`GmwFDTJDOOEcpEEkQ{)8>!gX}>ZPISu
zF~M6W_zhYo?{13AL;{!ODZn|sXaF{k`H-o#jVcyh0^I~ifWWya3yWT}!H{6I5MYZn
zfNEDIjbKaw#;^%MnuZ*Vg#$Z*F>zuC07<PGP1Z-UYiW)tF)49T<s!9kSuAA0Bv+Xx
z!QhP{hdwd7n1m;?qW|#>-YELtAwf=Zq!BF+8!##A>@IRJ#rVfF#rcOgArUhvHYhrB
zit~?Wb>akvH~}aB7*GDcaQ?w${_=|yna+aP`@52OTD`lZlUF^m^zzG<cvhXcfTlVK
z+&l&bt>S6;B}<m9UcH(p<9j`z3D-1zFvcUtydGN66w(7jK;DNJfrZ)&xgaJ%K;n06
zqehL|amO7;jvPr57V;zt$^8BevoZLUS6*4QYL(4~1__WQ77Mf%<1HlNjpXO@JM6Fn
z>Ui^vt8kP@1)_ktAO={mVg>3@%ESB=`-=i&_fkv3GpT5?<otG!M(w=w&VI{{eF(9&
zKtMtcjas^NDLJp<vm$b;pmdJpH}pS0;X~IhyX-Q2_;4Eo7m8$D*iR0A?b@}=mMwej
zwb#%UHEJ4w6#=-*k{K34o(KR^m(ebZg&=ht1SCfq?ZT7%D!~FR-|DQF97fn(G;9fA
z!1KFA!h?44*5ZVR449sB10h2TjUu6oDytwzNJ?<QVqRDvOI2n`MF23*J@*`6D1wp%
z7r7nlS_`)j6M#=h021`%g<@SyHc&)Dl5fd<=xHMaMw${X5<&_ATVgWHOwu4Toa}_a
zNOCvIqJTXXEF5|_fm+Kq;2g@tYXB}A;JlCv3^eJq$yy3+PK<B?+d>oAY#T$KY7LN^
z0HBZ;*v5lT$pl6JzghnigbzRbFz+cV^sv23AL0MPu5{l)>3A>;#Ks#)G!!97#Xcc~
zWXFFZ07xtbU`Y}z+<0;l6C)D{lQ0tqMb8+8EMV{k(=QYLtFOMwa)SzMaa{GcKLl@K
z{`ca-JJp#OZFZpth(;8}Ps+M^KO=Jrr|85w?TMMFTV7yb@?$go;!IPV@X2Ceo5D2r
z?6c1vcGzJjo_ONGfdg&LwsSkEKaKRgeujc3ArsJ_u@8nv^A{~zM2PIa|NcXV4yD=n
z4jKzjE`H{jXE6JO6HegQc>VQS<~Ee3D%34ry!fGq9@=le{aEm<&c22QIcQ|@o;!E$
zp@$y2{a#A)3tQ)=J^0{*lO|2_LY_`?+%+_W=%Hz>7F*K=jSVZ54BSVf+x5T$5A3_|
zzAWb`B4S814N5oA?ZS=6AAkII_I@9w*2_11++}>d#|ydTr$*6(JizI8(PilBV{L!2
zTf?OApk0hZH^&izI>a(ImM>p!UB?}FT>o}KgYd1z%+~ZmfBEH?^BM;ho~_Xo@kI~(
zop^w;#z;T?^wT|>X!RCHbV4TJukN31NZw+VCq^b=CVVE4G!GbHKJjGbWpRrW$o4eB
zstYG878lQlo*S)B^M71@msOUOxASCru0us8D5$t8tRr^*@k2At)OD}g+g>v>o7?Yf
zH<-kkd?|WG*<#C+-KiEizzLrK6SxX@6w;(B+u$NF_L7rK%!M6`9Ps@4^ZhCughPYP
zOwizIh|M+`u7SYPk{JL9wB$mh;X-26(R?s3zW8F=G<cVUMlXJ-V+k+xQXiQ~U;_Fm
zGCnnJ!GZ-G0Dy!HFS&PtF_^1WbnUaxJ`~*}7Uew2PwpzL1vUXWTTTo{Ai4-3=xG!L
z<X-cjVGoyj9~;0V2}!I4QnW^jeY%w@&KT?*y3D&1_g5kYun*lX?$jVHsW4zgg*h~Z
zJ?JXy0!Ej2U69#<7)I+TvV*2w4v)g9E)<@$Z&6FCO#o!P+6e9B30VY+9{Bt80K<xT
zn4B?H=z%oEK=zp^QWZmqS{Kvk1F$ILlgv~!0mVLIO_|6NW0IUO2AG7Ig`l4|Z=Np$
z5J(ks2mlifIg_v_5Q=FM?yn+APE`p=y1cl&@WKn|O0{YIg$!TtBs!l`z+YT)m-g_>
zabCE}Av)`~Zjozt)0?39%6Kf>@FYRlN0EcacfIRfhOO8%h9L&9`^Zz3Vbc`mbLPz9
z0@_^QJ+~GTxGE8(ViN8iqBv&Em|3%C9dN(_Ne&Y<^bp`yF1GmGy5)m3JFVzrSgPs+
zP(*AZrpO8H%$YNdcNNmi1%Nud<Q2k5=c#FhMc_X8#9};6`^#Vcl6+1pl15CTNB-d-
z{=u~t1v1G`n53dVU>fj9rd_y^%T}v!RZ?BpE(oZ#{|%EnL7#jDCb=EB&^8XK(8Uhd
zT5uNxhGB7+B_EoXoCJ;Ku&em>uYb)6!ACyw5qG_76G2jkuKVx5KN)5%$!V!weHblG
zDtiqx+^1-132CKxhW8D#H!7~;$!SI4@74n>;Uq>}8Z5~b3FC<(;Q?Hg2vU(D=Gy<x
z{F7#bq!IZ^av=bo34{rs)e2(lWPJolGA{>*T!p6@47?|SK6!sdV3H8SurLWTS((_P
zfIV3W(9`Fc-Z#SH4RvW*c~=jYTwIjPnx3qiJqByKF3}@FiJM4V41u5<xT(xb<J=iG
z(aM}}(P$W@J7#u7xoCRilfb~3<QO(G5d**rO~P1LY8w|l+^Izxc#;)CjK|@^Y_?Ps
zO%Fk#sHB>Y#NBw~ja-Z}^B#EMfuH!qCwwCZMv}Qqj5K6|q?nXaL@!0EuqW9DqnlqS
zq!5OH;=lddzkT5gUntaK8btBNI&WsJ9W53}POBk6DOHw}auXv2!1Q*D@#L28+(<<E
z)p(}&xI!TE>3ww3aH=4-7Q?A%A>@}{dg&kj@CQE8bIB!_u$X~Ci_JDMIlAn_WQLVe
z1;FzJ5Eui2X{btx!~dIZx`|76?tFjmd*5R#^T7{(kQ+--LQ_bOsRS*$Xbv_|guuN9
z0EQ;hF8eUaPePz~GtCGd!1AptvGl=H)YP@56ZL22uKV--FD`m*RZU$LGeUDy>$n|<
zAA9)j`%WB^q!odp2i`IsV5E5XdnmbGX%MB61>SI~N|dkzrxE9|mZZ68;zA(_lML8m
zt;wtuH~}W6$Xt}x7m&;qo|+6(#SG&q1>!yusAZ;NVdl(_DxRr2`skxiJ@wS|`qe(h
zd$K}40%<iVt-rAQ4Xb5*kQ9g)FPG<d4vW=^>v5JkmU20eYjjT4Z4eWXS3%1{E+$e%
z4h*u7#mO28>RmYiND!MH77|5}h7rg_My<I(0+4KjX`sgvSHVcF*=>Vqz*q><(((fa
zyvZ!kl9Lt#kX?N7#oX;a=9ptnJMA={P?#}e25-pw%2&R^ec7ZJQ6y_82XBl4`^vQl
zOznaO18fPDB-21(!zfaF@4ff(?AX`8{&ki;Xv_tT>DgF7L!xycS=7NuR&S5kFp37q
zA-?Ul+ur~F_p_SPB8~^X^rbI>CrZ!@05L$a6EkcHjK)*6d@F^L1O|L!r%0k`Vt^ZN
zxPjZuob$6E`saWC=YRdzf3>NpQ!E6T5CfwNjR-M-8~}17LwsH%^rS!i=})NO5Q0tX
z?z`^}B{$k{zWHX5C!KWC2R`rt?sR}eGAa-Pi*=DB4UAP_Px5MN7X;Lj!<C!>4AS-h
zPz0vdWM(mqF_u8?L{(w>^?%O2Z}zgYrX2f~w{3p1<k5vq)2^9)=zhb`I$gJ`imB*<
zzabBBB*xI=hUV66W*|_-DDxm<>`-K|c!+tNn#Q9E7-DjYB*+-80H#Tl9)sYKW?}%#
z6irW8Q{$$x>bmL*4g5ay4+JF3OcFdf*tS#!;HkrSxC)0qND|hP0H}3N5iZsgrgSy|
z+&bi%kSZ=Gn3nO_*S_{Oo@cTa>$N>#NMQP}zaa4%LdsgYc=vLyyd<m0_!vq_u8h06
zp&rNLrBYUS$ds7+Sy8Y?%;6n!d()aI0+1J`gmHUZ1sBe>957~QDFP4DMRK!P0x-$2
zX^bJKNCLn%b5R6vKLJogo!QB)2s{bA4ItmJVZ-l!_d9C28hifv=fCx>Z?Q{qXYf|~
ztFF50$}6vAonW<q3tmK-m+pa#1coZN4!~AHLlrOy*hSVD@5O8tJ;cHF!3Q7A({>O*
zi6V}6Kli!MvFvea2pM9NBDQ152Z^-?um?tRbQzYWf*!g4`s=wMe(!tVyXT&J5<{Fn
zpM3JkC}K1C-S2*v2QT5W%>YggR`gP&%C#s>Domz^z25((hm6Ms*m&~YdrmS4o-cm!
zi{Jd_H^2JTuOftGvSg&?BqYUD1u`iGk0py1Et)=kI(r>lH#QOi)!-}}GGqud2zy>m
zy?*=K-yU?(LI3nm|1@sgI17Zpif|pUxuS9T`{GF#fUN8t+sXgn%}9R%JpTCOV2pGQ
zG9_aWk}!n;OZe@-U-z~T9X)#dfG1aX@@+CYb<O&gikh0;Cm;HoAI;*e4d;AVcQm$;
zI#<v9?X@>O{OXSTfB5s~9Z?k&o$KfR_T~l0oc;ctUETuQTN2g!=xsN39QMH@M*Z(<
zc4zDQ^^IH<l~vc**Hvxn!Nxb$ac_$FztG?RP5F%ag$oxRdE}8t9d#7Lb87}LpwbxE
z_~|?Ubgf>xcuh;E*Pj7HM(i+RXi1zC!v%2+O=TX=0G=WMEQ+G4d&h6Cr|qeS9vXkx
z(WU&7ItISUfB|e|5}PoE#v_WNZBBTKw9yaiK)_6bUJ?>W)^6fQ9(jbrNNyTJ&+Npz
z!@+Z2$b^h5zVVH3aOrr+A%|E+GCL{izy3nwX@%S3v{KFJ=;nnloU*gL(=6_pbF*BJ
zsq+^dm<0=h+y>~upnxh8NCVEP0`Ou<70L9>yg&WvPu)Uu0aNQd0WcZ^je(#2>}T*M
zfr+b73or>G&an-HS%<&?U;qF}c*zqP#B=~f?x&t}&N-}e_uY3N#~rM8Tt0G;i@Vt_
zafr=2#B&X7nvh^iq%SO_Xn#^P42?A=Qxg{eiR(pHnn8mGv23%Zax0fx^*qIJ;e{7+
zy261lRV;p(dj9$6bE5^x7>TI>Ani}9Fven`F<?u~1ro}Se)Oa4QK;p)YmS{+)wp@X
z<K?X1EUVCvs3oTf1}qtMUcW7kBFQd=l4Lx20&E^d)@+={{b2f?XWa?nr=NcM```aQ
zS`xASkH#d{?SckOqC^oiaFGL>X7uRMY-w4x85s0+N)$O`z=7<IV8L2*nH`xV8U-G}
zwFpcJWTFIc0*R@}0k9?Pok`T1zL!uI0w>Q<>!zWoFmaH6^KTw+AG}ljj`h#3$`)?S
z&3tj=qK#Qg^?+Z_TKbc_pP#on`{fTGgshM&!Snz9lP8=vbMK=L8(7ux@YTmvUG^AN
zZA)gI_nB|4$WLF1c3Z%v=dP|g`05P>B>geDu0@}&I`~e>-E`x}s;WMHTU#&vbJI?%
zI&Rvg-r`^D{|7HS__%4$ZMU?p$Id+O{#RPJn|^DAo35_Hk%i9K8s!%7ulL)Ad#?C&
z9iK~98Z~TCUDavV;ib0crd3rP{8&GKy{V3S1LDn{wFM)$2lWQ>w<3ZkMt|{(U$7bx
z?F9df88d$HgCBt03b}?P$)kxp0UqfT+c&(nzO#JLpt`nIt6q5asR!r1!d#Qc;4K_w
z9(~jTFpw=d0Th`&23N7k1C&z8oTvH6cvbf%>#}5W(1kg*Bvi00(neZMEye>UG6OI&
z;d8OXB3)mvk8WRc%{4!d^pl_bgd#{T2_S$B7G#<c`AGs|`>el^u~M_k;pHw&$y&JE
zyLfD!19e@b+a?(n3p|q}3?%Tt7z*r}-OWITYbk<vk(>kzRjiR*81dtB4lOY&;UT6L
zE-*9|$pBzXKyCn*-~ayiJXFm7j>Q0(u=E<K7W82OlMFL#V#sWdn_wga6p_zGHhyIG
z|KlJ3_>OnH!?%Q!aCo_@V~xj73rV?M0p7#_#y~&<iTyS>neE%dXP<pG4uOWf0_!&`
z91pMYbTrEzeuj}B3v#cQQvnQ1Z3I9LDr`w8+3XOiNG3)CwZP>3%!$JWB;!LCIh#rq
zdkD1S0aF@p#?%S~QcKYt1nkC=Tak0{Fp_KlF~?rSpN?I(ZXHKPoF-j#(M2?eb@z-j
z&Peo#b$gLRa?x<2p=hN5An7cg8-VN1JMZL&AMCd{vA~&Vhm3tLdoLb8;k<!>p_VRy
z%MC#SWMGmHZ8JrNG_|R}5LIXZWcG<|up5yH-WXsATqGyKP8G?;!u*f-JoV(0J>?w>
z$1dNgq`bDGJU4#AXl`pgG;h`7mCYRu9i`)HUw&zaKhJt$ze(>XWDL0Txd-o<_`N4S
zeTI(Qn-<porE`Mt<&_*pys78lpa1<YCw};v!qd<GnAU|ere*KGXq3XHu_4P|S<7$s
zdoOz%vUSq@S4~TAyJYU!&yRl-GACx0|E1^uI~V?{cJJFC-gwIaYERXPf0@xaq5e(i
zEg&uMThk5a@9@!S6R(}|{ON~|$yys%y}IC;B{kBp<K!8CJa~ej)*C)B@TL=<xaGXK
zY2uFC>V<288o+H57k1X`*|t&oZx|C&@4owPo=)9{g8o%$IC}(nq(J7u!no-yEg7-n
z&N~gS8N1uqmmj--USlg4F?#<-??8qaxPaw#^N;ZwdyG)F>`WknHM;qqacW-3Jo^EV
zL?5-}eT1ix4jENJn3v<Wm>n`FiHii~?YG}PZrnJgUl<`~DFiS~(H9{QaJv4X4}Hj7
zu4Rf#QwkW0SZfPm^h#O6Epck}0kbkbe!|;U<0*AbI^EnV&v9G4cZ-2#Ff+}0BC}|a
z2=%OI>A(pC%rBU^1mswi8Fr67_F!5Ei4f-i5Es%T5aW_qOM{DJ2b#)9SaAn3v4kop
z$w{V14j8A@PV5H;Nlt<|`3__#@0dM%HjhWKFhIa%_vFcw5op_>2@TT4RV+?qzqx+>
z`c!KnXu&)rJ2;R7-pLLGMwht|Xzu~A*s#W2cG+e0E5A=*rvfD#2n>J*dM-D)qGd(2
z8DP-jS|mdQJw-Q)9I`@x8YU-1Sjf4`=dO$+10+i<^lV%Txh&9(HlF4HIJXKUxQ(_7
z1E43t>ja)2=k^$k0Qi=lKe89`Hia>7GIK#p(E_2j3QJC^lM3T;5d!H5&M@%LyWjn8
zOaepq;ZJr#S6p$$S!bO^1EIlt$VXT5KPnIii+c^3V4wlxCWA@M0VwppEg@&$K%y36
zBeB+u#v~XTw+k&PJuq`Y#iTK%W9na9v!=Q>&^<jfo>($t_M-Cco|=($rNe8>Mh|}a
z`7K<F&%f@xGq2C)Uhq+NmZVd^eEGf4ueRjypTM?d4}9?;98h`CxmPcWYv;B_cOF-H
z+>Or_UJ0(4e#zWVp0JO3BkzyNy?EzOvag*mI;yLf5^}kU0tRbD-NmPczRJq$@+#Kc
zcJXPlpz^fap7RCnrW?;c_v(A@xfsHOH~?64+qcgRg35ER+kEBSwdkQAW>b$HuPJZ)
z#O7EQ+g^L<iqMwYyW)Xq$60#iY1cjGdGqxeyXJp(@AI<kjwwgrC9aj*p1En-oYitx
z*P6#tbG|rjrq3Sc-}uGTF1~T*jTcMmLFe8!zwoMkv)}OZN2g_9y5r7|zGFN;OQ^0N
zGG@<1PdR=P6KYq}qG^}i`dU}+x$7?Y=pES|pT-gA&xsDnvVku@e4E|z#p~v1|JXMF
z;)9OMJF{!iW>`yCZvLP1t^4A2kBPW96?Rq_eBEtVpPx5ay|Ciy(+>LLjlGx1YaTeQ
z^4u%F{RYPL%HHz-TyV35GnCjgb1dv3&yzRY5M<uoA`dhSMvq62pfrvchB2CWnP^W(
za|45V?W&C>#6olaB&@BqY5vo*@44rmd+(jSwxPvit+{dCqqFY?Nt*xiau%2n(!O%p
zOVHhO_ucarzPyS{+YW8hscPM{;h}p#-#z1zXInbFlyoee_w>ulS1nyI=TCo{)zqf7
z31g6N6%<J>f+x`m&syY#^FK5uCWnk7=d7$^&@jW0(sY3YW2k}vGODb?ys(>?94&Z^
zIUm?7h=g~sv(e?YW;Fdz!<o*Qv2~8lB~ce}DTnL5yAo8wOwo%60Q#)Pc-sUL%kVyL
zU*X+eTnzEMH~JEKHvkweiqad7a4kp@Fi7%L3m1*#H*;LW@yyq)x8BM{Dr+9gPa?Kb
zvqOvoV63u*EF`G2d59&s77SOw-0)`QW)p<t5Xh8GZc|MR8Nk@FV+l&udyd?{^PTTt
zjGF)@1n`;#jTnFgk{RJmjZJ#74Hl~S=^^jG<J^t)m7hpWm@t83cAinkW`IwA@{`yD
znVV*r_CLbWh2*3f8Yo>f3?(ekc=5JpU^M1}3obwrD<52tB`pxRXi7^UH(;+O?Itet
zKV*pFiL-m}y_a_37Sj0f;}<Smh?8j}@c=z?;3e5qOmf10sxXzJO(F+rJoMm+D@Gts
zi?Bc7R><4l_BL+9J^JXQT=cV|b1lyC&wu>Ke?0csV;nY2nKFgGLx^=@p0tMdP>pTC
z0JaAJ`Q&b_a}xk_4}uAiBE&rE2=JcwyvLI1rn~OC3u(y};2}#AATCS`UuiFyR8u>$
zcKNHTSFEfXF{tXXIjhRMbNi1S+S<|0@yLqO?wX-Hb!=Q+$YprPW2Q{Wu77yy=YIZ?
zNnoD6_Ry;qUvyz1UF*EhkDt8YvOAW1c=Yw>9Jtp-yElLLxXza4v$9#I$Ckphol76O
zJe%^uo^^?}KPJmo|KTfhuXseitJy|+=x38JZ@A*wB_HT`@!94948YF&F52;o%fIx<
zk{@lj<<rL>c;O4pKi;h@YhQW&=TE+V%GEc_IC#I@+~1vX$@SA7dFhz)6-%>`N%)I1
zex6PJ>Ml{<PO+8avP(`qdCJwd&iKT-OHQ7$*D$rWc|+UjcRcc|Ni}zTWzv)jPdN3S
z^Rhk~%c}p+w5ijk%=p~*ells$4r~Qpn{j>HN3KM3>&ivh;g>#p<w2`weq-{KZ=ZhV
zv4_-U9gS<IUw+Q?Z0a2|?o@qcKwQtxH}3B4uuv#oWN~*Z6zfu0w79#wJ4K3naV_rd
z?(XjHygbiy@Bh8`%lWc%W|K+gB)?21Y4AQ8H1T*B7!?wnogWYMG6%hxC6d1Sc1z{s
z(Pq_v-)?Kj9@R|h?%Cc=j(SXm7VeK0Gd-U*b$-VeWZJ&m3|pn!9=DhlDn3q0DYowW
zls?8b);QUC|K+j>lHfm1Q>`)e{{6bR@8xln;2g1ZJBz%=20EB`R+r*~Q%dm~GWz(n
z5We01g%I9YiR<P04c<6YXn&6-)0Ps&{=CV~&FSHn33=m7q9x(=s{I+_=J&CO(~m5o
z;BIsw61|GnJAK~uzG+LDFnF}?NH)=&16z1VgM{TqQjbahz~WZd&YZol)jpBV?s<C?
zfr++f5ddkM>Tt2dy}-*uwiL<NZ;1x+ZO@(kQ3PtsbzX-j{g1x4S--16+rN#*Sv+np
z1_3B#gLp#RCZ6)<^3<!Oqv-rEJzi3;-3y(&$eC%68`#Kb>)s#+>c8&O!`B%VL^b<-
z>yE-ENoTb{Fgiq{OBe(k1=yaQpen*M-WMhdPx4|+z}n_<^AQh9Yid;?)-_|;0U;Tf
zESdZ?5@nLcgv3h^D;9+m_8Y0$edwn7(x=v?X5l9<z;~uZ_ku_Q8<}*NwbziUnrVy|
z22Ru5RWTVcf$TPp?iUy)h|Y=C35Jt1)X<(XCf+U6r<;$ac))cNaTu^avXZlhXs8dH
zcN^F3=K(<z@4gQO2_>WFQxrE@B_T@ZWass^IPNtEKO6&&h^&q2Y1@wC^V;Lc++2#1
zGf@vw@_w4e2-p}M_Fq&9fJp=si$wt)d;y(Csh#mW9s7HnfRUB0UO4(o=n~9(hmZ3t
z&=ZgCB3al^5u(VyoX~m{KHMoVa0IvzBs3eS<)qmnun`tD4eG`m0c?8GYFY@O0S8ts
z(!Vh(a%|ujl>B8VNS9}F_9(muU&8T&MMUW!i<Ji38+<vQXwr$V#Kw`v^?!}41O{q<
zgw4cdaU=6~3Qy>|YzCg-y5QnC7l~ZipbB9c%0&UkxcFUu@_l0$XbCoh@niErVhRY-
zj)9<Ox5$Kmj#==#nBarB7x(xcVDgp?FE&CQ4}-$Tn**V|Nhur)vsM(a{Z|BJp@CLb
zk;ZLpIlizY(~P>bAis0N1ga})DofB{K`kz0KA)?JY?Xg}Kkjcit<0@~H9!Ym2&ea}
zpLja#mGn{scuj}jD-Bnhp06OAoVj;RE?Vz#??#dHA8wP9lC$9awb`?-zJ4?Hcr<=|
zWm~+GVjdKHIa#{}OO&I&L7M$`rLtBH*rhYB`Jvgw?7XtGou6}CM6Q#OZg(4+J7nJ-
zC19?9mrZ#6X8F<s`VKuHRq@!cwmWe%qq9@d(qeT`l*U(B`=+!%{`7Q`S_*dWWgaV7
zYr1eR{CU`ZbhM1l^M01U&g=EUkVzO#S*GBw1O&R%={KwH;hU|TPY5gwp1VI}+vcp%
zP|qZSUh_EGzc3tISdzM)JOhlAnmZdy*@g=Qm#<m{hO5~!`FF3(e{4q%wGpZu)NAbX
zheV4;?8E|7=eUh${f~?7cr77$2mg@Qthb5P<VN$nOdNC;Tx`!qmp?r~`tt!=Vmx<+
zddax?EtU2Jk+L_RLY>>4&Z|nh<DJ{L@aNyRVtaLY>;gDj%WJAZ-wn7ftH0HIK~hII
z9rei-c>mlB*GP9>N~)0?%cwfY9AB`!jZ`hjbUyxO&==t%W<cY?IEa39We+O#574<)
zN4WkOpU*ZB1NL#*Xb4m3##&UJx?Wo8$L~d!b6R^ReLnj-+mPK_Q)asD1Bo5{txT(}
zy13fOP)A1YyE)-K!4aJOY2P8pwA@WkO9JKmET4s$(0+sMmw+WPm(yA8eGlrrP}fgG
z53W+39~#^RP0dEVVSSK%krmoCd4F?jsyX}pS8un`ffn`IsYIy_etjS~!FcGq7@xta
z2?FAil{-~fNP7zDz0Dc=oeP}Q#tSTNJXbqH8iF*Nj}vJ-p(HJ2$#<T)>(G7jasC~}
z%8s8LS@6cNEaBV@ra@gl$wGFKO-3{bhI7W)G5?w&TI|7aaRm8ws>?Y;51_JYDWwB%
z2LYKoH>tn;Q*dsTo4~A80aQI>rYJrm2vsvg&>Jwh=BAWV1teqFD-HAf^hJIs1}oA5
zG`tWqIUyviL14;GsGw2IDb3VuHnvZHpz16q2!sW{{n;3bmViR6TP%aH#-ko&(5|=~
z$d;A`f!L>*FIKi-J2VdR@rdv=ye?0;VHOp)2eS)t2Wr8gw3}>9H#?r@{7U<_J9?3o
zmDYN=`=-d{lOn%qLjxfq2r&bhHDU{4*2pN}5#_FgP&X4&6%b9t9q@wLYNO^Ss^8L`
z*Ib!e|48yM?VPdaQTAAhQ+(@SUUH|13`SwL;;anU57kHSQQa!d5_;K0*J@eZ#B@ws
zuE{ZgP`ap!xfb?W{dRz-_R+KrN*l|E4JXz*|4en+)WBte-PMN+>U!Wu*Ts&|Cp{K5
zam-<C1t_|R3irP)`U@RHU}*Cr^Mk;ZrQ6khQb8LVoy8itBV&Q;s=6GD%dRhC^Gy2J
zF%53Mno;bQ*D{YEJ~G*TUm;2Lce4A<ntF$tmczG_0`JFQq0bw~(Svw`4)%AO_fg}z
zEXKc9zO;EU-1l<pwnr2DTo|xC^v>|1^B($`*MzE?yl;LFMk`;V94;4lUpU;q4?|~M
zo-^(z6YTuc1U=Stdwie%lr7elVWhFrdco0uM)h*Ut2?OZ>LA1twfvcDSn@RC-PLmu
zf>Wrq;0vSoi3vI1*^8D|LHgKaVf^iU@_CBa>vZE&Y(?f{)cMHJg2UKo-ymy#ki`}m
zU+=6lfc1VWa_q-`ebqS+*RYi}%fT-bZ{Ek4ac>LHev_-6Cl<HLG9P-rT}$_(*|OIN
zwj{5%YF<=QwNp{o-3g1I`9HdakEXTQc~sA1h9u9uWFfw}lf61Rxp=!=dY-30r=J{1
z?cULyTCGK}QJQ`yy#Dm;V(f)i_Ib)x+D(&9m>-&8FvQ@mrS<8G?K<^6>9@<7(qLku
zxeBdsnPSS$&PLAn@#rIW$wZtk0AQ+VV@a#)Sa)=NV&WWXDI+5TdEcb-oQ{@GtKI1p
zC2fGkT%8*>2fM0^v&>;1s(H%HyWP6npOR7%O%3iPWNPf=j-&D~CF%wcOm&f$sSw)&
zygmStJ(+HM*aV8IQO+6Qqt4jLGq1F<`ul*CAk<n7TVb|`p>MFuhTllJFdAYC4y5QW
zW84ix|5^iMtudB~$>AmTpS5=TO>RxMJ3z}2{Hp3&{TLHk7G5q=>b$~xMGhY}D=cgv
z=!B1TD-@8i;WuT@i}%n2(UJ;J4Hb*tC(Szd6M`TGqhp|YlmV#9(NYStvq4E6jN-Js
z=L8kcuaFA&jLaYsw_u}3c0$7_isq9F-3m!RicAR8uzc0|Jv|aW6fY(bjzuQNfU*{2
zC&s6UYQH5(n_m)>QVsH=+v<mL|Fa8kA(GwXay`UP-yjkcUYI`RUsy1TYG6v=v1_9i
zQk%=I_x`m;2+41&kMD}?h=@V-o7sL>^fP|INqzFK$clQ7o&7!VKY!)<%~34vR$1n6
zwoh?2RD+!v!6hOVsAU1_-E|60mOtzHiHgC?eq5b&P2#k&M|8jGWd>tXMqY&WUyQAc
z**P#^z8vueaO}U0oxgasUkg;Yj|tqbXbiAZlgR3N-hM-?Uv0S>_;)P;NUBCYOfMP?
zT0F@h%D69=`5vJXJc1(=Y#DNi)w~>Ofa!=u{@Z<CgpvbRR*gPw*+^4hn8l-hqjXS6
z5dyxn1iPSOKyDy?qhX_82d)OFGyAgiu&QZ{NRS(~1?1yY@GQL-NBUN|s3jJNgHg5M
z&sh|cqaD#Hg(--Lh!IGQA`~uM$C-mMywui%g-7O*_OnMS%1|m?C6MtfY&@dEgCQn`
z%TP_-c(0OUYC1mIDm@)f+F3IxT8Ui9Y+&4L+3kR3FiAlmDPU9lu7b`qG^PP#v#Y4f
zD-j;_zIq2z{K;91EeQjBYq{+BMJzNb^|Px-Gi>&DB=>vL+B*yX^sIb0^W(`c^;ehY
zYv^;%_c6!b_99oex!QBVa~6x2+j*9@yYOhxISc<W?Qqj`QIedsaZDwty+-(FGJOE?
zCI9<PP5JZh3~i3{@)C{lqo>VYQ}ye2HO={TJIZkp+DV_@f+`_pfSp5Ir$TRD!hXBM
zttURet6smQ>TfTX?ZP=mwaV(uK18HH@=`nuB)`uW=6ZFR1kd5UZ`av|%+gL<tV4{I
zw;XHVPLaP)HW+2_CgPq?>9*KBk{-Q5CjI7`v}426m}<l!bdsizXdnsv3>V-sQ}kV3
zyLc%;&o#Qu44@{;t~y%>Il5pqIEM(geb6<vBpJXle|N-^+*I-zRqNWTa%0(9;OQJS
z`n#MruL5cmr3^S!`H9%SKs;(cRY5`Fe=@HkC-*kvSXHQ|3$T6savwK-GH_xrn}WKa
zVS#Y4gu!lab5DC!;ezg)W9#pmG2T-T_EHn*9T$Nm732bHs7RG;6-Fx_n@Z!+8E?*+
zq})^!?l<FnnZEt=-S>Z?wYt@;BXVu0y5oFWHMLAKUyb9hpIn*8wv?gMcVCZn*9DzQ
zgMaP&+$Reu2(<PQd}V+IQh-r%KZn<p{elR>q+r>a4xJod;OAH3ce$YOr)bs`4hl5H
z(+%B))yoJJ6Q|B4wxEJ^gh^P`<`Yb$z@Sp>!IGBknlQ|lBt7dwHZ}~BKpC@EJ#>JZ
zv8D$AeUAhrV6Y@0>yQHFNE{k*zKr;0vD)tz_~!~ONTMULN(a#*hR_FUS5MGd=Aoyr
zWdh*&ox!8NBJ=99c3n9{I#ik9&>avKvP4&oD9>dqA~dA2BfpZ-5nK<en6K`YQ4+0q
zRXjb`SCkPWOLN2!)QfJ0x=xl)4LBv@>{vaWBNPC}&nP)gr3l$(>;d62AyjzgGW1=D
z2<?9`fH)+Ha*332$`4@b2>BnT8wKc8s`hI3H6sLcL^AZBr0LOskeG5<u|d%uN7z+R
z_$n7Mc>O#>cwA}&T8s!LSEJ&Dym2IoDpJ}HjVKJ$yAqo6y;2r=NX#F#O7*s#4EM*<
z<(nA<&#;nNRs2?1stdY2(Z2+2CJOvoK?EZ%uV~su&ji9S_u8RQM@TMX;Vd>GAVAM`
zQ7i?j7N^)yLBcE1_!|-$56$CQ!M}OkNwo7RAHOcUim6U<jLQ=NBbo({JBFhO@&9N@
z{pS0)nhiI0;(2U#PwN!t3(sD#$5g2f`fD~Fj_&DdA^Y5^(G1Y#A2fUFwanD^9`+qt
zzVW|E6nd$B8)DI&;x4SyXHcI<KK9;!incVcm^IP0Y5eAWe(xn<|G2JnXz_I0j{iML
zsF!Kddt;ujv?w!`J%x{cG<8$EZKmmR&S;*${jhlOunhivo_p>K!&73{@{3UW^YH+q
zkkH$;2FS{ir}&xwoI{D<>pnejGv!^l7cE6^^||BTj?2@r`gUL8{;lij?kisE?au7_
ztkXunkp4@PpJ@??q`^`MB7e-=YvymAq-f;(riF8p-E|;fc7C?5q%>d@#@piH;ifRR
ztR8dI)QvN4OlDrtvEK1mCn+Q8X=L8I#UgondG4uL-m%->`PYMH^KCQtkMoP>8EqR-
zQliYF(Dljnyivi+GGOI)<n?*M>v90QnXJiPaz7*Te(rnpp2yeT9-PFk4QHj-lSbZa
zo$g|CElVv%d!e!+_n|NK&86n;uMM~U`(oQ?p^=BGt3sRxeJGN)T!9#!BdD1{3`yd*
z`)In;TQ;?r2({_SZ^2b***VMma@!Dr!*tPQufyRcsLwZ*5jRzx8)xrNqb7nMIgBS8
zMXdD@9<_}23#{wQmKAVOEw_DOtL5pIRk0~msr2-(_a0(P68JV0L%u>UY>qnvm;wSt
zMT4d(xqh9m?q2h}moN4+Wunp6(nQT%hZxF3N7YO4iegbG5WR}>fmTY;c}iLEG3D}Z
zTvt%NW3dj%5g_>nnc*!MmS4W;Qb81vk-i|R7-qk<I9g2?X+LZoCt){A{p@fcR09O#
zQCJR~rJL55fRfr3mLrukK^es9^mGE*H5!gAa;)roJE=A5EMXFqJW<BJ$#pQJC*wA`
zsU=*Vt*P_zfE1}X0YV6jKB2V>+2BpmGX#lBoOoO}Tp&%IOb=F_92D0Gh7>_bLkR{I
z;$odb^mr3JciaknhjFS&sxK#^{}<u{P4xdck+=#ZODyxkv~oT}__z;@K9xhN0wX=l
zBhCcbNF$#>01^8cJvMF-M>Eu>Q}<K60*>+E7rPjW2RQ<+TAOtUOe9mS!A)O0hl0I4
ziROpO&JJ2d8VupzJPgzkvGkCTaFc%WNWXpKnN(Mp%C>1_N5puD5aTgb`tw;S0$BBX
zAv`#{sNE1T<TM>Nslhpd!+}LYe5?ybeb7*Am?k@W!URqfb_T;!Ks!A=K1AwFx#dCH
zpVEn!*&PwgSJDtj#kPL!$wRb>l>|OYiqX37GE`TH8#H_mNygDFx5byZ<E@w8`zWM*
zcM+Ik=`uw!QC5rs7#6%+?~msmr`BzrulwUcql#FVHhJlT-=wq1h%F9Hij|v=CzHI4
zbZkZo$=WsYo319EofPYu-k*?<ooia48obZvP?68P1~YEh9eR~Z>xABO*8c8HYk4|F
zr(Z{AdKYd6nX|kV)b%;uV@Hpto>VrWS+sIhfZ|JG+n>hAFJJM;KRQtcP@nh7jpH8W
zj<<90$_d~l0v<3(S6O}#XS8}=AMO`cBW+r~ys?yeyLotMu2(^Z8SO4O?$sn^ltf<f
zzYK93^G-FLJ?;JC-qC$(F#7yF#;vpdwAZkhyY4b`RAKuP@!7fgY9&)Mn?f6d%X;DV
z{f6cK$1kBX+v6dl_bY{H$8(c?&$IM&#bbF!Mid}yOn$+96i-9``0_*F^uFh5;tOLK
z?KM*b6Y(&UrtTq>9ZIv)D7VIn&Gw%y#!OF>!Y8j-T8F=AL`a45Z&SI*Z8jCep!2se
zXVChSde0vGaeiSrQU37V86cx|IG=8&Dlq@t&@1+RQE7)UMMLnls~Dp?x4j`izV#}-
z#8Gq@Sj8qQ4As8}a0)(I`Wx;PT+>K_Wu&_V8JKdGevzLo<DRF58MCM4gIt$TQxagD
zY<t&(yyo%z=!u&7c#=?4@#1&Q^5yTipqQPw!t=_$Dfc!r&wKQE`-QB=eI9ZR3lH~h
z<Nm#ZqYlta{|><SezDRfMES^mKN9tXzrgRsbRaahoy_}sW#TMwZDEgnntr;OnR%Ae
ztWu9ESXWS@)utIj2-71|g0CtQ=#K`#*$fs5d00S64~j-7?T->VvoI_~6Tpq<jQi*c
zoQ)1HhKByRAzpAJ#P0WD;o0*w0)lH1G+HUQ9~x$n8(|UIz4_*Hxe5$01c8mcoo_#e
z@8|35JWoa9V#%r>LL`~Pt4Ne(af*6CAqVPT9RQ4Qh?qeBh#rUI9{qTN0QrTGEmO$x
zLN>)(=}*jH>iv<btPt!RR8Srm3pP-|d}I)zBS_eTr0Rj>l*$;p56dd5VSK|D7Trn&
zJ7b&2u9M8}$D)1~lq!v!;zuIK;c-R#4<-O#<)Z9jW5*VvI3{wsCOe}N43#4|I?pd;
zSK9c9slqJAwhC{@^(h=uS+6o3hpQ<$IR_bt1C{589mx|QCdKn+?fv|{pctk>iw==u
zA+aOK>FXs9Jej1_0>xKJgaGC}NNT0Ce0>?L7eJ=RSEN`+$Bgf}=@RY-Fg7_*o5~1c
zBJ6@m$?!qd>#1^sXlZ`Lf7j<|`}ByTJ7k8_G4$B8+Fq`P`GbO`uah}VvCy{{+s4`?
zKA~4R4N|3z<b*rAREd;!;XCGp%kkJ^Op2CUnzKRBXUV7W#Im{t`k}{0V01`qMgJ|m
zw+SE-|DF!xb#vpZ`@VYs5PA*`r$dz?|DCp5`L(sRy<F&SNUlPM*H!wB-%);>foD<u
zS4Q?&bBNR0la>jnY3r}Sd^DmGi~&R9JH9U2RnG~&ms5YjK~G|RZdvPcKULSCO;3B*
zJHM)Ks?XHaOe(ZjSJi^UU6Vb6re4-b^XKx&=@xUB&+t+|cy~tEti=`G;i(ecM6<I>
zbC-$8z*sPhmOT9hk8EPK;^sJVO+aAjP4f0^!r{_ITaRbZec;vxUKq^l9QA68dE&td
zR~0Qab%DcEUJ3tW8F=#H5!V>l>{V##l-5=3Z84Xxw0b!<##b7Jw$B<UHsG51d_|a9
zRo8NnyZj{UM9~*8-T!Fh{ZVFYBqYRuGTmA7pn2=!@DbMle%K&^NP$Vs39N4Fz?ok|
zCoc`0np-SdM@5DkPiLiEo%Foe0F%AgC8Mpw&p?-M-_Gmi;t!1s5ykRG!1u07y-O0y
z%gI?=3HJQ)I19LBXoZyedbLb?Y7$BE*mqn(O^93DV?Rc0{+PnSYV5A-^~kD2O+<QL
z>*PR^&pa7m`pjy854l=^D{<fu!CynI&fJSXoR8f{<AyTT<u3q}9Dc<b>&b5+q2ENW
z18HGFp=Z}S%wks!`$>-uamr>2aagmDw(21dceY;Ou56}?It`C+p)%l2=eD@CjH9V|
zbGegZtlaCi19bWtOGa{-BmAV^HP9u)4Uc&ku-UKgku8@5Xw*N{mB&<$(KEs2mG+6k
zfJ&Ad505q&lKzzZO>7p8%SD_Wagalhe*%^rO#NA0Erc46W1~lkCrwm@T7ZcwK8yxq
zv)&UiL^v-t<`$w)PX$S6WT2-rbUc3`mdQS}D8TZdafd_@>>sc80HPu>rNTh65LfAN
zQVc|eqj$oBQr-v%kqM|`#eA#7vtl}HxmK)-G_n~HLld>(deetc52gONI&u+*p&0ka
zLTW=PyT%GVT_w$bdK2(D=|T?6P@fX#z^^@kPz&6F5{H2iDK5j5L?e!=MV<ItK|&Ot
z>jEdu6N?|;WST@4ob{|HIrb1qFFWLAWx0(WO~BGc%hV8RIiV8Frb%Cn8R&GtDl85K
zR>uqsk%5u+L+Yx!C8wO|jb)u$wJkZ0YvT5P9tr2WclR_U?B;k8uy*e^L46H27fR8?
zBTIP-*h~OxY1yW{SbDcQXgx+$c`i>IFqmOJyjU~0KE-RUDM`D{9aOzalb{H%<A@nT
zgl9`&xTFH96wFI*N0F3zMs#h_#F+*d)qjkloj!im;6cEWCd8Y=E>qFp&NWP3OVV7(
zeQ{|BBoOvKk=YTHE%gMObQaJ{cP&Oz^f54b1C*f4H-C7!IDA>SwhkZqJb%8Y7|FEY
z(jguqq!MGweI^e2^n(a$G=Pr!o7@uSXH1iOA?q(+CRc2xh&kJDutt^h>q_6<UmZhg
z#c3#FI{2(xZ|_pA<4C+NL;c=uriXK&G65|ZU<$+~-N$wRq2D3CcccFBGE@pbcEZhM
zpbBq<_z%Qkxwd=wp%y5_7o;ULlPBc};L~~GqS+5iT`1^lc$(PfkPk~W8x0FSg-QFR
zPrXYE(@S?OHmb)FW*#O^{+XM;HFo$CF#No|k@vf7>8uYurx$I?o<WBI$L)s`x(LmF
zUQad_JZ!{9j|fe09OJ=jRxFJ^P{od60z(oO9DpDR(=acEpi(DuVZ-o=2GNe-u@aRm
z0+Mc0(T*v2z!L+dj*UwFM`Dac7|DkL*$Q{X!b1)e#X|B~X5>NK7Q??*hJ<yDpvB=&
zMj+L-f?`GY1<8JTh_xsYi$Y}rlb~oSMoEVl%1C1^#B|U$8XHLl+0gUkA@W=-Yf0iX
z7i|LYRH*vf5>`H9!{V{ZYSfWT6an<fgGmW(Wquw0=z<~WLW2GzxNKP7#E{~h{$fO6
zwJc=c5L}p^U|&raVRW4x#RrL~p0Brq?5VddS3Sm#JC)Lb!Wei7l>rbKi%UgtJIFc{
zp|~m1@nA9zdgT!15L_t&c+9~8%+y$62-Q=4Q3^D!P0GX_)|fCGs{WszK)4g}t?lXb
z?YFnh;M^R2hLgj#$L!#o<hAXOdaObhZAW~hi=n~GhcWI`>{~c<u*mQCEZjzYE7A{u
z)wda4D{?kkyWZ`**>p1~BG@wjJE~IzU1n1jtRP=jvy1##$-DPjsReNa6i^(9h$tx_
zl+@*&8nG8*8UJ*k6E2p;*oM19>|5M|3Kj<C&1VO72Re*EQL_B^PO<`W3!H6a(i`Ie
z9HeP<hfe=!Y{#5fw8&0aUY<#-=yq8Kwc9ox<*9BPpH#8d%wq165?Uvc){?hlVnlJ&
zwy3|FSlQ@CnfPHUp;kLv9ojAvNTkNhzLF!=a(;3oRWy{D@=AmJqFEaQWDbyOCZE+5
zX*RKZ5#TYQ>=<%7qH0UC^gj|YqHLg~{RuN)m02r2e3@%0f@y@Q@wy;L`}3=*BS@!g
zdPFJ1tcY}AY;cM#oZ{tR^s&I3JF%a;Q4FBjt6<1^A1)TOC!#ESmX1QGBJlFE)p9n#
z3PH1<LRf{9TDlpg`l3j~*-G!Q1MwCvLTM@QF#41Nge>x~S|3>bv7it)LV5wbDg^$*
zBYIfTECfjC&#YB2#w1f40P_p%jjtqWIe~IhRACS~Fk)SKfsg?9L0A<-P?-D(#>=KJ
zAtHOC(noRy>rZODNbyj}fdn}V>InKH2z66hS-%aXPhH}j6t@5@@0&P0#0BcO-LD>l
zIAD;)Yauunnb;<0<^j(49?}<_3s9P^h?P2)CFFB8JNYWhIqvR4TjnE4^Erx+`jr^6
zRy2v8d6OngGB`dcS}ECBkt5>tJv*G9M*0f-PG~9(ES6MjJII@~6f?0PgM(&#aD(3B
z)}5~z{?#(;=-T!sC1S{d8Urj`t?9>kXn&Ap1HxjI$$Q5fR-uI35UY~NR>awq(}qpL
z+d{EZC+4tH)HdQoXD2rKai*awDubrEB7bw$S~G!?oB%}&iO3;Xb&f`b47;M0g$oR$
zWx5kd7?I9s#9?eylRThd)g#2F@Q4fl6=$ClBc>TLkc37=iA5p1BSx8Mf&$T_NyS%X
znWaLLhSKUVVUY~LjaE7Q5v#<cDhb2Bk3EsC7n&TVl)SSwLG^-A*pBD8;)fAV9}=e=
zC%k#cq_iT_l>w1qF9wM4Cs%n;9!TFoAP>eE{6Yr?puts3o`RLAo1~#)Yq>a*XeiB3
zPPa;Nxk_!<Az&rMH<58*q^Xi<06wTx<SF8zYzO)vL-x^n`H;Y1`aH~-BSp~+?rRt#
zO2e>bi7FVNA_{eOSwa90{RRuaG&P<g>yFdrZj!FCpu*^3u#g2013Qh4Kh<Bkf`<@M
zIz>)>E_N_9GdmQfiBfZ@A2v!6S=ygml&J?f3DE=!Vd@<op_~p3UhNj5Mod`rz5|C(
zEXp+VFc{1#2%%-~m7yLk2m~e=4?NVu#FWod^gk6iX-zAYL#UWg<NHK$1W)BcCi4Gw
zF+k>QV4Bv7h<qcWb4b;%U-WuK@3PG!l75f=YIS^AdO&a}ERO#vZ&;Kery;K<ryI~w
zIpCvzFSOu~H^^tYnPefGNGtWg^P$mEv6N|JVfBJp9ZbB{*M2Y(`KrM8_uUeeG>z_8
zhq+=UKo;FDkL1mo&;3Q}E-ZS8=Rs~Tl;G4hhoTL4j}x5wBVr7m5dmexK>m&b;`A!H
z6aDI@DHS-Mp1((yXA1C(#y3Rxp^+v6FI$)<0zEwruHd6;>;xu9IRd}}3MmPPQmdn|
z*<>EK<iL&_KtL&8ErZ3DN~2%*kRz=xBkCin&mIQ6i&yQykfFDuF{kPIRGSqAUyFl+
zgSDj>FPw)I+Kd0_gac^?eecYFNh{3Up__#l83#+R#(<+j&(s?tP$w^f=yhNtTe<)-
z8H9{PJ1mNPPu_u`-V-LX1KT<PY0_1iu6)BZ7B3Pf*2zjtJIToVYbPgRCkHK)l^YTw
z1eicYQX(yyup{B}$MHZE^QeamEXIUI!-s|8b-*+D`=Lw8HI`uzf0wpyQ8@-SF8&Jx
z3#&N3z622@!5{ty<p4s&oDj`4O?}k{@hp&C{O|a9>Y^-vT(DZWD~t~sqMd0t?Pmu}
zsv?UQ2~t5iOjyzcde3Fiuc4g!vHrjuapa?oegN+yt!!Q=97mC~T&aVSxJZA$ap*5U
zJg$QQ9O4k#?2B+GnGp17LnL5QfCSD6eOv$(S{2VGKvvlg3cY9GEm-p)CstE@(;!v0
z;(9p$Vxb_;{2@?*20PFnbyGS<&VwcdUB@C(4bHn$`3Rl975T+C!PAD*V+v^lh8~UB
zgO6gD-XbAtRMNE*8di!RbLZ>l-8fAK5im8=FGzCZSbrr=voDZom9YWzDv>M>9yHTZ
zY|{VpNz4$p@lkZ5Iu-&0G2GCfKAw4-6XDUEX>@4~?^5{DBZtIBXTdNyw<Yhc(fIk1
zq?<hL7sE$IJf|Qd^hx^d=%vy(lDCek`6aIPzev41zESAdRnVbRa1>LMVm+Z{;)p<T
zM2Uw}xk3L^WFZ-Z4n%tKLE5%T88Ip*<ssS;o{x*vRD$Y|%uZV$4n|x+9o4}N2nSja
zD*vR{n3fI1A`xyG0b`N#Io_Ah@$NMFrwE@9#z*8>84Cv@>FY8=2>m3ZMVxup9qpb3
z7U7_Dpjh<=%Tz<{Cav(?gyLC@7!Gfk;l$Q6e~vPx_6E*NXu(@7@)c|>3Q`0sH6fLD
zRxXrO8;qDt6g`x52N#q+fxazh3qzF2;$bHcf}3POJUovUk^d(^_!DLn-W9CBrl>Y1
z0)V}-2aqV|(bGj27DClQi!zYsYa0qAxsnJR^&buSB|oT8`_WXrr;9}kL*jwTR}+)U
zAwU>E>_Qc?Asm25h2vrSljidno^+`vgDVdGMsRS%Kt4jOS^(u%cST@eWgwNYw02Hb
zR;*mt1B8TcT%w!|GBmgci&m>UDJQ_{I}`_+G0&w01tcZXTR`@9w?7^jFA@Wh0s|f$
zjSOdVxk$WuAOKRaR_?IdUq(L}r*@#~kV@g>{SmuR7C7t<OjzhBnk{T|U8)#4zWED+
z7s$~*!kabHHzxV1qvuT)BLU$T8Ds!0)k{JwP2mYbh#{037Mo^RoTf#PD1@#VwP|C3
zNXS?8&^RLcS^3%tsaTL3Iwn0_h;W?TXTKEq{TWwG`dK(CND+B8>~Jx(q#RPXE;~dC
zNjwkwWRVUlsR7@(`vvEqH0~}!!>!ScGOQ9d&?B(pjGK8ezdZGqc0UEG00SWg>5an<
zwt~f~C^wyGf3P%;0r67^?XOB(20GCmu)vFc7)^k*4^Tw=D<UR&6N1KI7b(1jGzBlt
z$HEr}M#W+=a#6qlB=u4FS$#tC{LnZA6-#@isx_rWp;(7eG?_5H#5)<l9wayfG^$WI
zA(VJX$TO@IsANzyG!dF7_%xJV41;Xnc^U(vSRT3;(Y^I?>6=uk5!0xyZ$2UjIk4n%
z7ZUi|1ShcEYRGlTG!OPNk_`cb{rLls-dzZracVTd@Z@Beq6r%;VIL*DIgq*_QZK@V
z5x2Lq5c~&xRWGE_(BRnoA&McfOJgjy$7vB&P>Hp&20rEx!h93oti;Gvdsy)4(0tJv
zOyZX$qW&g<B?^GxDJ*$27?jzJcI}}0gY>rG4-FYZ=<*5(-4Nd3dBA3LobA)q6Tj^^
z77DY0?j%ov^JXpd$ba*$e5mn+hZbzPUFXW66KjR~O0w{kco0WWNqdvG^_JZ9_37O(
zqo*K`#^i@>C{fGepQv1FBvQ!SW}+bJv1(qsn}YF?9;QQ&UbiY-d&)TYhHUA382=qv
zH(F-eOc<NWP3(`N`e7g_->nJq>V!KubjVnF0^SD@mhczS5@~Fefb1AhUMB?UbykRB
z7Y1esOAf+@UX0*xR#OSm5q6wV;RAiFBpx12MTks#C@y~j3l3rYkIoer;W7tU5SLXV
zPoOFNjDY~CXdV|DTomOC+c=Vxk0(tJ+%hY3tT8cqs&o*flqMDy*8}lOk<eH2KN<yZ
z)AV_~(8%CJ&W>ORh4CAqg7<87>7;NL&r&sl7)n{UrTg0VcZt;+1`{-ev6mdO-fzdd
zO819m{=|}(Lk&c8?#)qa4vX^gzQ)l4$@JUsds}(h!>nRBxH=DmaA<J!d9-2jvK>)z
zI}@;IV2Zf{jDk*#63*KowBsOpSy-k}EJSfzL{dNz1}#)?Fam4}7c>qNjrvKC6qF~B
zUsJA)M%F_{nt-edGE;7logGn*OafC7=Q6vS$zg{YGVD)0O@IU)9@YkyBMi!$Fk}Q&
zh>yhuJR~tv*VlewnFFU=uG>sA>QiKT8VK2s>dRCIei^J7Myd$YaG`<NQg%5MvV312
z{6mGoPu`zGXW)wrKudq6&G0|knm(BxzL&hc$X{uh&}8y%f9<n^BV3i5QYeo7{(6ud
ztZconGES@8!Bd4!O%X;K5J*3(jgsGK!=Qpw>ChG@K8PyBORX0yLq7@)N63B|7~%nC
zBfDpb3-ePOLIf*ovP(2xCC&y>$B=4>OG&)bCteMz<N|v*zf>CmT5#BNK!m6YCW`_!
zvvNi|r!rC{QErC<Qn72oR{6t}B06-@B(w~y`uvG9#N!Bv;uH`4LDM0r?SUzw8R7(I
z8Hpt!`dv_lCJ{p17OO89TZc7d_dyh=28@?Vxg?762IpPGv7vV)WEn8$n>&b6kRqL$
z!>~9SubHFQ0Z9t>n85bZZ{&Q9z7fr$e9iDNej-0Y8SsG?Oja&J(3ln2CUFYdZu#YG
zvphI#B-6C6erch8KAVZFF?`Z#5WMVF`|!^8Lsqsk!OUcy-*8Yq_Q^yDO+3E5#7qoH
z#)isQ?Q0>Ta6oX5pH|3)a4ZeGN0%6m_`{}n63*wO%0UDZbYAv9j7HjahQe53-w?zR
zieexF={;mbg=<5AlX5s^$nMhg7FaQN3}u*<`i*~LRu&V*dJtnFWKIerE`)<a3_n6a
zR4Mca`i-6I8`ekdwR}tAJk7JoZ~ZqywNcUFckk!>#azL!=j+A@D*g!mh}RN=u{rCc
zCJtcw!fcpQ-M6cxTJld}D7+{d5n}8e(qdixGEAuQO~lz9rPRNq64Y}&lCu}8K%zTX
zWCvXZQwQML0%TZnuT=VxWSWG5S|`ni#-7-fGuW)<$2iu$ZfKQSadzZD%@1tiM8kA-
zN$3*`foZ%MOCQo@EteSZa3eA!1jZ&)p%FzG&bI_O__udvnh9HGTOr_lY*0T47?UUR
zL!kUSHG=dzA{}U}A;E2`LO1_yqTg%a$@_~H+yhL`kSc46bKf8hK9A#F_#22Jx{r7C
zBW;{IS~}no6$%d0-$$f_LJxxg1>xnPV0h8DT+-^zMY9_{-nwn19Izozi^yuKUjO9Z
z2{Q2i-;t~c1TZL2Tu>-0Q8POiFW)2QA{NaM(1#vIxkqmxD$txb#y;lzzfo|$B7qR&
za4x#?DLukv#+y&k{abF5<%+tq*0HH64_TkS7)1OtsW8Q0N~xr6%FmKA?WY@u41Yk8
zrOC+1?DIBc+m~|Z34#HX|Dcy20fuLyXSq8`=62`1w;@PrXg03tNmVh>P&E@oy#Ajg
z_(DXty0Q&**4WHV80{oFI#wwP-~uG6-~v1Q5dOvg|IiBq`FhFzaiYk;Z5;{#3^MGR
z-HmRH#@iL{%GagISo}9*oq;|;hc7Y$B=Z$1wrI%y+y-2QZ5X*!M(#~WgeL#yAJV^r
zLR9#3(zO$h$%EZMI1o1Me&mMo2xW=gDmc24P+XE&^*^{$MmPYcjXPXdYf;FMmKSHl
zUr+H|+u*9Pgm+^-F^Bn%{3lxhK12niir&#0=}BXlJ!S*y<_Gd4vr<k_BAHKS(mwNj
zksSXks{fU-o-*Zz^j>zuuHlP%>f~$>=)E{PYXBz=u)|NgU-X#8zD!{7|A`j<dHy>-
zX_%m3cK*i<4|7FYk%Z&h;rQB>Ei*?%>XH7xNBVxO!rGuQuluX-M5<wFLCLG-NYCv3
z$jrp(eamCpU)O018{D+2zJJIztG&Bcbg`~z*N9xVFjtj3Q0%ic-gUE0pLKX0`~jW#
zk20V@`J+NQ?u5tZJN!)^GdG634YU=&-Vd`)cXX;r6kHe@S@?%uUqP5Ovp;BmVnLoL
z`P_dB_FF{C*EtRLiTCS^gtq@<c?skZ{-DQK`<*GLF7WvL!q%LP0JSof2VGD=^HKxH
zduM}X|7*=yDJe>J`JdJI>xxbfjxf08UjOJE1JxrBkH&faLvP>+p^j`D>_fU2Q+pPf
zBNmQKd2;KY+IYHoDpDVBzczb$dc>qyjj;V&8Xf&akbNg>dJ`#vG=JyM&rcEm>~EoY
zqL{O0)%~rS$w=(6YoeAI9~PR^Oc^2m*lhnNK^sa8!_zcuO<tpKo*fZ`+={TXHMVq{
z5r?fJQdrQfDdFhlejIcEvpN5Fyl^Xal$<_UIhx#jQd)jF-W*|ve`}jhdtqU}QmrmW
z+k1#>^U@OVr7-7yhBhtCDSqxBDV@_oMa+-2KSm!dI#u9G&s|#;tf3s4?d%OUj3Yf@
zd86gqF8}`ZizS`Ifqq0Pnyi<+xLd!bAOJyXo#*%W7n`GK^_Pvm+57ZP#|Ol7e(&@t
z9Lx&I&ySYw+)){A;+U%vsI%zuq2seGDl@x9av@kI%>PJ<H5o%1sW|;GEw}t54dgPd
zf6HuRkHN(*27brmP#$#;$mP4*#{I7LH(!=Y09FE@RHxNRmAs^|@&KDft#th>OWwGb
zdG}xYeQgf>1*Xi`xpo?Y-sZPwOo4!SyZfW!{dVB))uyK%JPyzi5&CO<%;0iE`2ja~
z#Sh@&&biQ<MhfDulz)E}2xbue2$)B#n$4(tAH`9ETAht`GZ)_Ed%GMOS2Z3Npe5cb
zn5ZPy{3X!&L0a=aG|rGz0izAA4NNQ^j%n+E{Eer+Mt7&sAI;Fx#PMS^IWJciG0wl0
zo+f0#->BaF7&s~H0ulY6#5#iEAp5Lu3)?(%-k7B9*uBlD&=eH9E^nrq!sK_NScB$Y
zZhRTKXa5}Dx>7$x4b0lipT2+7`Z=AeX<_y*y!4H+iyP)F$DD<KF_dRZQAgA}70#>G
z`+D_y)=zA&x$#vxYp*pXMfexxzf|hOvGYx!F_HaRJpHvInN1J=^$111uC9pI=}XYH
z<)t2bVa-vI>&wf7+m9;#^)#;>5vP^e>y<feYo*ed1kE^QNrmqOD9kr*ucoFdHAmtv
zRX3_h7>xE534LXZ%-?Gz?2QZ33U+VqMfZfS^j-d~#urps87}a%1V#I+M;g(4m~b4(
zN$|UvwF4WEx*#2=%Y8-ukpi6uVbepcOd`CLL>uFw*r#H^oWM(sQ2Xs%Na*yrrqg?u
zR($f*`D#<qo+G0Iy6+F`qxH{EL$xZ}q3uS?&hNLepZT2T=bQOqbsKG;h=raPVS9Ix
z3k}8P1RW76T`eQs-)i)SRdG|D=D#!7KgS^#jJMimn2gnWJz1PSbF;Kr#J;Y058uyu
z-Q`fOA==}+f^u=9+by5(xf2Dysr+qT+Z`KjHkiz5(Td|BB^GjCbZ>X!PpPxQ{<q<a
z#)dN6CW3xwl(XV}&UiFoX>)bxRLDiPBKu}@eNeH&^E5^*$ucDHJVreJBZ1Fj3_m#y
zy~lCxmn6i&;yfWhMd$D`BOL#fhXm!xOct4HN3-|uscg)-vWw1|s<w(s^~z^{+Ghug
zgksgCtee>&c}Lt|-_$h{Tr5&odNm2h9TRWa7FX1NXr*J#YZH(CD)f*t;sLo-ewUL?
zZk~^#=Ey(|GXB?hoE#aZ&&4=G*G>$JTujCL#B95R>lPsmv9Xt1Zq}?xP(z2S?G{zG
z@QWHq=eug%3SMJVUh%NqSi^qRZ>oBQgjGugrT1%Xg-^wc9^);pa^!2@H1NofEa#Lr
zW@t6lHFP$yv<S#;CCW|4TJm?FWkt7~D_%Os65oKcj(uH28*OcyYt;4RO3a4ty35Wc
zN=Y(q;9Kt%!Qt!kzf_i|krI3|DLl?5G>L@cFk=5QYQE8^P^Cp2%Ey*gGS!P8sY0Y#
z<s{g*K2HQlX97@pf1WO0?U^%UolVwnUJ$JRFgnluxqf00aN9x6E^Vv$X&t^Kxv%~3
zIq1?QdKq`SNN(c!JhryEv^q93;+n<jI(Kypn$n;vplv;2#s)Yniz;+)y{uZte$$oy
z#p>i|L%K=e!qL3o?7`ot%;pC6XSLrk|FT^kFBlUX)^w|8g~0FAe=F9xp^;t#(%AU*
zY%8DKxx%7}%B0xaQC70C5YRoUV6n(ZkLQ)hQ!|>)3_RubsN2r+GkGWXngnG4)Rd<q
z;a!OKCJJ;5j(LEzwQ6W^BKAw$-3kYnl+v;;(J}G6#(Kz4{Zn6cE7>lpb<Z;kRJwn=
zUgXd)Gi!e-kPjo)(Y$W$nuh8!-0UGD$TrsHNZdrR&mj*V^4y(hbZ3`n7Y%|}?_Y(q
z0c#+9S!X+}w0JTkr}}y_!3E`{+voE=W4<7T3}WC0-BgrsAa(sdORDGM%t_H=O?Us2
z=B>{8gVI=?FT)qmznsYnOi7>mu34P4^EkTa&%r{RhiIcMq%Ed&$cIOZ4|>9smP1|s
zza(Cn7HbXn%80w=W?#4TnU%Cf=Qocc*HcD(n)QrkL%OD@lp+RvvDUnHaZ|sYZd^MC
z>zJ6B+>2`T$t)X(bNt(JeMqLlke88_m93f~@2@ImXcTNKLr4AMVXjSn2#x&M46H79
z&3e9Qi`!MnHJw#$2ErN749XlVwYx0rz?R%baXL?K>Y%T3y!ZJYhIW6sQ%zc!Ib&|$
zl}B!lh(NiOTpe6A9%ldN-x{d03YJD1g3)AT?dkG)(gqVb2JL_(gLfz2g=qI5xE!;d
z;Lnc8TG@~H%Es8H69qv<eOFM<19__~Eo82SAbl|<S<%<T^M!Ly{EF8jg1SlC#58(I
zmb~X>?HntPIl-6f5cBhlvhT;sFVOX$%SXiF2%7$_S!$RY(8hx8EDQ`O@zbQOoj<%c
zG$}Ta;oN8Id%qR%)+<C_GnJ#yJl<d!*!R2Zle(s9nT4$l?L{n1BoYFPN9Vg2%P9dC
z3@J&P-`=mjzXoX#89W+n;*YOpX8aisMyv_;QqmH2X-+ZvHqXCb-{hom8lpVO;X^%D
z^7f2AI=ZY8P3FG4zQ<IJ9$)Xo^1TTR$OPRwf3enKbKdiVeO|jl=W+IAzju;IHt_qm
zHn53Nq(Jg7rp!zaFP5~}6{wHTim)Eh6GVZ(5ziPskPT3r-8<ECp1B^F4^y$0K8>|o
zSz4H0OQJaIe12y1YUtf_JxkeddU;C}I(@EDD1O>hy60+Nt-c>TzdxmMw<5RbQW0{t
zIGYnt(DAaq&RC5zd-T-k&B&W`9KvEMIp@;9A6jIH0^4icY5n+X;TC3KYal*%LLyEF
z5^#TcXf$8#jRpyV)m?i`^y-*W(|>JA6LNWVKCQYMJ>vxlwz#FO+!9U$IJlNEC6lxM
zEs7pYJ*2S(0oTKH?Di&`kycleb^GCiUhe3o=O7hlS4rlph=y*Ct0-d0wN2jZ^TLv<
z*M{<P&Lb^H8XH<qsRqZyCTat<1_vp}k;%{Vcw;ByK{1}RR)4H^ZyWulh<KlvmYR6N
z6u*kHF03l=oT(Z%B7HJ952v=%0(r7(n?4lRSh=pZEZgC$FPIFImd?2z#2<YsSL<)u
zRE%lvVW(~UH@Mr_4(ez~EpO2q9F4lvZS|klad{}Ks~Tirfdo~w&z~FErss;SyBw#l
z-?W~saKp8>Q(AwY2Cpy37)$||w3pW1j{l4UuUnL&h{>$k1bo2&md^Dl2Mh%<sWc9+
z2*LA}_1T$=p~-~o{WNaVnahH%wZy${Dd}X_wFb6H52jplx7YOCwuEfzF=8P)>X!*H
zL>+Bn3PpR6#lNi&7^ZLCW|we35-oBoVtvdIP3N0QBkW77843E$6I7>xVEcVV+4;-f
zVl*oTo%z4?N)O{<f-+0$vL;<7EvcL=06smfFk{E&s@FD3$B_qWP-cum^$LO|+w<Vc
z7=`QqyWSAKSsdv~5dip?Rgzg_+LD?dPz*(bt(`P@)3a-~P}T7-1`t7mIsX6=K_-No
zqx1tsfJr4CpfI0?gS6fSgG_JOjfGrPW-GR#Th^9UirfAYT`q-)?_Yp=W82SXX>?E8
z&MpF|OS_9x@59p3F-8TfNalS&hI^2fjl-1&hT``LF)yTe{LB6G5!vvU6dtWIDj0n%
z@%Xv2#nA8plD4CSSxnkky*Ls%V!S7Uh+hBUo)tlyvV*Md14R%X7<JTn_<nfy{qxv2
zHEgR_99}iLtDOYn^yet;Xt=*tK>vV(4vLFT%B(i;tr>e;M}Fdu)@>XGd_2itI_ezh
zwvNQ;kG@^`lC0fz?sonBk2S-9lr$v^3BD`0O>Fv=Q?Ndmm|I`3kq+6t$K^;MUjanw
z`!6_z4TYBBR%4t2Gz<|e9_{b%o2AbfLEbj=23r3XN%EhCk6-M=`w#r2`N1No&E4G<
z8xE}0INM*D^!f3xr8otLM3h0c3p30-|ApcB5&k?nQFJ_7RO^jF+pZDJzsEt3Om_#d
z2c)X-qXa#E`A61zB0jeK$vroz_&u(c`~sg>59Ps>t;^@v9JPRw*fiG$!ftpuS3<sj
zum^^uaQfn&ywly&E6ThtS-aS&4KkXHtzDQ~CHKbtqnrDTUGun!Q+*&CNBcj5ARs}w
zDUR21p$2{L=HgOR)YxyfNod;L;O0csC~Qno@vuoquIl;+TY+I_%nj2<`nVkBP*+;?
z3Nqrw{6t#)_T&|fe~eL_oudNS{{P&p2q;J}FP*|l))Wp$avkGEhgWAQzoYK2LTz<f
zt}pX&9A}=gD(4Bn{|4lc{=(!?W}Be&@}Zn2x73JU`@bAItqfzh)3(em>KO?Zl9z%b
z@&o@+;}7dgk=x)^7*S>g`C7-5%VWUHcp@W1vfYPwCycdUMf6{h3^5=0&6BT6XGV(U
zYh)UQA6G(f7(>w`IFRe7RD<<wurm4nEB4bF-~-&~#oU>}C-X0<h-KeL1IFD3;_fuM
zP)v;a9+{=Ao~V$^?;2bDgN%Jj(`q~;MYP{KXlfnG$)igItWb8%JXilqy@;XYtNj&Q
z>m!}V{LtF9H8r9KXB$QzGJ4c>(vSNL|9dbtRKz4dtTWKff6(JQ|7R^r36scH?g`IQ
zsmL{72=|p4VQb@U|DuJ6&;JM%0@CplvGwoEZqcUS{_ydJJ$t9@x8*6ngNU>K(UgyX
zSt){z4KcRen=MI6<|Zh5p9H3xbLHy0mo7Sd14EAOKhu54kPe2VH2YeeN0<Nb@b-HD
z6!FFdy0st5!#M4)W6*gmrRfne19WQKbE^)ir6nn;j#s*jb<#Zz`?00D)gcjuchVLv
ztqQ}7&gaO;QCq5<hAo|#>c-Pmnh3y&K$h!w?X`5*x4MG922EP7sb@iv*sLCGTq}M5
z?Y=b7X*k}dwW(E<27dEzqSUR~=7SmX%M$SolsbzKk3$i3X?h1R27czfcB-xIEm19$
zc<7f6Y0vd~e|zfNyDy+^3rD;5m&^=aFU={bS&SpBj`uga`sk)nTEG7%S|yzj7(MKo
zzT%owv;1OtORT<=mxn$~BmiVI*9_~9ZRZj4)Ks|^W<RjjPD(wMCih%Tga#Pib6bx)
z@&rYaweJd>;MBCc>XcI(Ei4q(zwiU8!WhQ_+g-c2NPb;4k!OOsc&gX1r`ARpg-uS<
zoQ*Q-&k_e!`+u|>>AePf?+5jwIke6z)6icA1iDW(xYd7FB<FIz9$b+Ka^SSq-M7h$
zAoX7LB^V&$W=ZCEyz2Ho;xV;{lY_FJEhdqw9oA_vcK2L{K;=i{flk0k8eLYCfTyq;
zy-R%KUvvAE-|%gj=()eId9B^?TOGC0N5>p^ta+cPr?O%IA@Ov454MXer(5z1iIm%1
zS~d2)bz$CtL2QHTXl*I%`lI&U%w>1^62Y{j5}{xGF{_f+>LErJnVWqV^5*~%(CF}E
z9^pFW>ESKu^`qwzEr+0aAH?{&?Tq8edKx>>ebKGN<Ns0hmH~Av&DJnMf<tf!79hC0
zyL)hVch`-(6Wra|xcd(75M<*{a0~A6aqn}^bKmd(%+ySEO;xY%)vGp1i1D|BdhddH
z!|Yd#wHT2s-OsX{nq0ew8z@Rkh$N%MxQ8bn;|aw>p09a^*Wc%RNz=??rR2E_lBGT$
zv;p3I`<<yU6j_Xlf6if*3a)N|@;ZFBH5h01yYb1b4mS0gx=i<CVm^_(Pj%euke??F
z{|Fgo73f)kCeN*N+?AD<vRiS#f00j3IXY7qNn|o!%Pzr!8BmC+jy{;wRM!0Qhlo=S
z-D6%wy$pmeZMkd}A$85y1Z)W@HOE>cKzZUp1*_#YTHUUl&e*gN-}~|EHVl6{P_2^9
z`cqMLLgz+{2fWyvy~r&*DwCy_)0MV+7QHS#+=B12YOVDv*1V;v549{KIPd6om1ItP
z1)yi`y22b9MG-rqR{8Vl@hD2f`Csf$5~oJ3wLKE6FQ&Z|I4>3)C+>0T=cAwsA~Qe+
z{*T{-&*#30objVl-2Np=HvC3T&V4;Wf|o%p8qSlWpkU(y{o0#rGVCLem)B^3-d^*H
z$f#qgRWl~~?)%*21bOmTx{>Il%1NvC0lg%uv@Dh=$x2JIFfy`6BL1tms<n)_e^^uF
zbly@P$4?uJp#fRTE6J))+Pq&fE~3gzuB(&YE6y$DRvImAu8mm;`f<aVj_+WJ^m#0l
zv(q)v6*055$fl!XapA}lE-&XOXg&fN-$0I_66CcSd)*$adj9A9;CpP!UB!_Xjh*)4
z%G+o<+d;uFQ982(*vWr<DyzZ8;3}uoeqiBp19nQPq(aNe!c==I#)&~uT8}yn810|(
z;P<`36eGyxIIOI@#B9EnZWX_MW#%Oqrc<;po|WQWBhNi`Y!80zkhw_lms4h8j@DQ<
zS+HT|m#l))O7I-c>)*rhB2vKpTh*8|uy8NzYP+P-c*WB8O+hYFV#}0g0%P3IfD1Pp
z02T%w$2R8L?{E60E`}GEaH(Yb3{Pw~J+M%_oNNpfdA_!U19AJ)1U=;~p;1?Jn2Wlu
zdN053r@pRUX)&urn{(T=oHuK<Yv0*V1!hkkFRDJRPdL)rt@H?EFF9W=p2d3+&?(mD
zJwJvGW>Qo7i@xBG#(6iZ)-><etgM;dE9=FcB(sn0)vm0N?lgfvT1s$KjZ9AVRxj1f
z**L|wkn-|Ua#F4)bQf-<=4ZSTPV%<eO-yTb`Ci;%am#3*XI8evmBz|Y<LT9d`fJk=
zMh&L+C2GM9{CPjz?>ch4H|CT;6rA6AEiTnfHRz|bRn5S)x>7rK@*Gk|e5|W}jc&1t
zG1U0?Aw{b#)y8^1FgR`FPhSRx_Fs89YeuK1)|ZSPv+h)@brQ02I?Y%2)p(?*7GAe8
z$8fO|sUvS-D!+@!bkhUA+r*u|OahpA3w~<u?qVAkqN7?Qz9x^|((0<OT)Q}5U0=Jn
z;5cZk5@~ZVOihnYt$!`s$^tR_y$Ny<RE|V@XdK$94n`K~v)fO595RtF89!!$3+-DN
z;Ks*a|0En=)Q-<E(QkBVEdR0l5M@4zTFZ`2wsBD#TUzd<w^<Hy4JbG@4p63ExpNQ?
zs^ni@rsY`lV87Vx2*f(ecEewc__L<AYPzuuZgPRq<&Me*^)l#^3#ZiBbDgw+5xJZK
zVk=~2Ug32?I_jI#8hiVHXd)I5)oiTb9&K72PBiFugNj_;PS-|?h?(8RQ}jzNtT#F{
z=qwH#R@>S0Vz>yRIZqRG2ec1zEZeX7erweDj@et=eR$9f3v<7I#GKdvwZ)BUtU!&Y
zq4gYA#NXvLHx_t><FKaRtZ;Osqb=2_?OroJvzMI(ic^=;IvSR!Tx~zbE+uY>Ht}C2
z;wd~O2sasQ87e(%5`Z>Sv5v4>Dg8PVWd|F#SY^A6ZrSe`NW!^xV|)#Ha@j#xhyljd
z(V-Jn0e=M}?>RtJnPZi<RG?J1TMzzOYff!`Zg3rrs=k#GDYTzUB|`8#!WV`}6Xn!a
zYqndxvv_Ci!OkI7_~1SUQnl>Pn>xHkow|BU+1wXSn^_$A9Y~@ws>|C9E=cf%xVe78
zypFQdi+rXc7c9Mwm)LrYKvAh579}$xF30d;B8om-2sP;JzisAacsf3-@o#%gEb+?I
zPxYALZ`Idiy6Ee4=y=F|#PQoJb#kfsO}ppRm1)cw29P<~8L(2ev4ckv2Zy(<Xs8P<
zsWk<M-Yf9K|DXl*8j6N$&)y}UlKGTp{=Eu2H@xq)2A5Wo4x(*;CqX0M!y1I?@H$BG
zpb48|L2r{)YwJ|7qACBjw)~?YBY7gf_ESe$(`P6YHJTEyJ>=~Wvo9W)?e(=aSPO<8
zqIgJH)zGL|9enKCusAy?U@E(n_Awjl@wCpp!a5&Gd2uwO)+vR~DZkMkcV#!?s=6aL
zx^EpRL+4@6<7#rQ2fZ?<t~hF~Ev>Y-z6uAjC3IEgR9Gc2Sg_Jcxj2xGHM)D^Y<DFm
z0;>AT<ySS#R^<#cToAHalW{B_=|ne#s*!0LiGrKkgfcybOZvMlRkPYx{+hLY+Iz_B
zks&v%0`?>y30EL^G_eLN{dcV+ss^o;o;Z7-PkO_3vSd-QaXD|zj<(Pl5I$jcRN9Ti
zIm*3U#I9Niv2vVNZBopoVw|Q@>4ry7HnL(_qiDg5UgOrt;JzSp2a*$~lFD}-_SV=|
z$xRdhuwWwkQ7)ol6iajef$=qz>?}l^u>)fIj*p!y+RxFeQU?F*9iCUIQKT$XR4()=
zvS(lMsIj|NblULlw#F@Wh+T=*)Fk5*s9OHzK|Mw~R{yOJ1gf{SH#er1vfa@xw{87!
zB7$cR_*V;nt;Valx+nkQr#DqmpmjA6i4#%LY%-1%xpYWip+mviLfWp{n+jV30x2NB
zytF`<-8_~r6Qx2zwY+4W>U<B@w;D^E&DA5fkG;04KtXuF;u@ypQz}@78rCK%lJWOs
zz1m8meb(h!8ENFPj2dGHZCcVob#yYzCt_483Trea?$eDjAxbADaXWc~x#+Ys3|HPr
zYuS*FHn4On+_-4q&RxY_Re#SEVddvGAij2|nniN{YRpfD-u}w8QoVBQy#*~=stWrw
zcN`$OpCT9(`0cTceR)ydtpr+E9F~_?t@ETxxH0ByleyZ-T1iuC2swJ|b-Y+UYh}M&
zh=j-^GgY@>$o8VH-?_$zY!1lppv1ty^+=GR>!D|rvFF}>(YMv&y&rNhQHPNs-TyN2
z5eIvX&;C3H`g3!*wr%xs^-jBbXlLhl0{U09!P=~!opkeP&&R52#!J_P%H36EZOYuu
z3*kT{emm62w74GAJfzBM?*OG&y4%(BPE?j2Erw1ISNR3H<!WXR-+Vsr3Br1AgKy=v
zD!d-dVAE{SRb54|!g?$N<7)Wf-HOcmRZ&Cv^IBc%ly<ABaQz|2E_MGvU9+~e2ch|>
zCJ;d3VFu-D;AT}4b*}!+QwCfu*MxB`vJSk+@1b@|b0?sK&F)H!l6gxx_EXqI7Aw15
zP7kS?#Ud<0AmP_O>BIvY*er6sfh(U`J*>!gXk}d~i|J9B4_|KJBR|wTNhqi;EnWH@
z2eHqM)<HOjGm^I|g9Qk`(|XkG(tq5GCa@PUYKwG`&rQ<8bZ`mjhUx(EP>YR6AnFvu
z+I=h0$K=x$H^I_vI-7P<<T0qpaMQF_LLId=U74LVhN5^}-eN3Z*}!8Od$4Gz&Srko
zLV1D?@CUNwW&%~`>U~Q4x~u-Bj%0R6G5VkfOH|R4c)#kDnr4US)<RCFTGi?mi90Oa
zW`UTP#(3M7>q%w#1zq#(0-;QcWV3zF-tQ4gWEqGUW`+12<Qy4B2F3M4tDkAaD6z-|
z%5W{`jxH`PKP8Ul$bQDic^^8b9U7~Vmz}w$rOxDdTDP@+@km$RC!%3vth=a-Hv#>{
zJeHQSDQU24?+8Sx%>C}Fn2|3YqI#Tn?pqlQfBEc+ji^?O&B#c3>^>hWE@v01jGacJ
zdhaS7j!@4iN3P1C)QU%~7v5g*DH76((OT$0Uq*5Oyc4~uGHCbhxRkm>BW8Aev7gqt
zOoW{;N>XBwF#$RUq?+;tQkAB&UoJ+%=TRxOCmu)ZBXXzK7s<Uk0iB(Y8w>&giKiFx
zSB*=hEuGw28V8u_w+)`?dzg#J?`yh0o80-{W!5Z#C(Ra`Wup^Ff&~+8#tgn1ud<9i
zKlIGJ>?ap!&#Op67w@|qdW4wu2d*n~b8{0?Qou<27&&jAedMF%K!<tAEM79tuIbkV
zx9v!a(7f;Q-4BfeMBG>V@2U><d}*feHen|PdMSuyvC0}5xXo63PfxS88*IA9^t8Ku
z1Y)%Yu3AxK{=;~zrORkWpHGkyW8tivDi2~`tjdrDc90Lo6t;NLQR2AXNf*ML%goIf
z{ce&F`L$b&1!pn#5AS)6li@rRGOy!_5;+zZ2?NYbY4qdSuU3wa$Bu7vjb4?two5$3
zQG3hB`;I#LOu9=VrZ2P|v~aTbl9Ne@n(uBV?8jMczW>S1g}+8KXun%QM28Ec?t5qp
z^{k@yi;P}*^RwNKYu^?2j+R$^DIGk1>Q7vUG04R3HmNoFP+S8>+t}Z=Zs~g==X-!2
zlwD*cze89$DQFF>kEUL{74%=dnc*}KRC?ds6ROYoV|U7)6W~~deM75h8}Rh?ZRle%
zvVhE=EuA_jbsTS~=qI3U>GF-<$lOvHpD}`CZE!hiI0j~Q%-Z|YJ>2*ZM;{kiFp%kE
zRoHWp_3_q*DeAP@EJ7t!JyetF>nVH3?IO@}IK0<>^!j0i0s;<mwIKJJUd-&9^Yuo7
zG$5K4Rp*0)rjUrlW^#$(UBSw$+atvs!YJasF&8M?GoZe#1@|>lAhY<M9Jj;VkQz|E
zTFHK}64|>zCn^2|C)RPrZ>7R?>U6mKJ4BY*Ug9pPnJ&d<Hq$Qg?}c7m8IPW(s5Nn;
zWtL`!qm)XQ_1&J6M=xlH8sst4sJ3@*cw&{F|Ghp+W@Yn_cO!TXH}HevgtUqF4@JHO
zfH^vE<6j!akC!?OWgiLW+L3U|Z2Ri6QsN@-DB^fTF|xgP#qAK$clY+h))l%|&`YQU
zM9a^`oyG>y##1T)V`A(3GpYPpqJz!WgJEa#wm{t6LEjZw*jtQt*5WL%8fi^xD$dTW
z>gCswQLw3AWrz6fxLUcJgomphJg<L?Sm+m_IK4&mLgv5d7ZwWuw~Cg0-t6vSZ}{(&
z<S=dgr?=O*e82n4COj>Pq!i?5EjsGYi8gcCwVQJ*Esi_Rj~1&Xu2z3*QfG*&g)lr+
z;^_49&i9cnl}&f8u!axeJNx;UtM-MIZ#I@Oi8x3jx5`DHc&WU;$qzx&tV3($nswVh
z#&;>q`Bmhg@yJMQvd)t)kJlXfO_09|WuA=Q@hr~5(6-uU<aLJ*zCT;sXn_?2bnk%5
zXre;?cS@|OL^Xu%hEGB3S*h|HH+?-*!=;0eGlEJ3%_rG0H=J$T?d%R4duSO-Kaf@9
za91CXuOs-sHkM6&Fp$n%=iP`D4;2s@$h&K)n%Q5S*DQ?QMNXf{oFZkq7`0rf8XSl9
zy4!wfgWh0u-g>%6^}4962h8RHd)$dN+1y@cc_FuHu?7aJt8c&dl<p1k9%p?Q#vT>b
z?ES5V?-9pkta0dR{e5<Sr!0)A^OSmQqkr1F;d?oai!{jUww-$tXI*@6pS`au%e$$%
z{>|kpm+3#<k!4X+MN@lns<50Y;HHG?V`bsj*N}VgMmKZ|yKvzV7g-^Trba3eKpOKr
z4`VVk$b_8N-tfB_aORP>k@2MJYt&8~Mp8p(ts8X=oeT6|cb1=_JL=Hj`fBfAKN{Dg
zsq^eiQ4vQ_zh8O-Ua}wOKI=iBPBl|=8AdTFY`)k;TLNo9CH!&FX1BZ221YEmImo`v
zi^JO(N}a4^ob0!32#T&(@lLC{=khiw60`n^rbPDW`H1P^b@j|v_t7H3b=IqjX0H-*
z(KNc}H;k-Jtw|nrP>v9ZFZ|h2r?3X6%hNuYy}rWlvjHGI3HZuIB#i2OBpQ0X6uf@9
z;tyU>DDH~ux{5?R|2{g%=`+{t-Fup~FkjXEXLI4|(D|}h>IYdUx;m$<lXvCPXY?`+
zJ|#j`at}yK@*UZwM@p$&xLDk~5OSja$J{Smm!WABi9Q5ZeXv7GT%gI#)!lpIYrwnv
z#H!n=rUk0eZTn?Na2vbXhA)xrYQte<x819)?Ml1dMM5J(koN7`$(_(z(NHuz*?g(I
zjjw!k@yxmq1zA07XuZkV>!uCDHK(19&vg-UAJ$P>80WY5&1+uEGY#D(jTM0p41UVj
zw9r$2fo1hjU2Cg!W<CHjcf`5)CU%!5?68rV1Y&1n*h^WgxSfz+_5i-hwAFOua}uO`
zz9+fCdi<T3*KXfi_9N3Q-=L`Jac9JCFVZ)gYq+!5y<f1f-^8-5RgJ&lc)b%EQH=yY
zVR%<!w3+_lw)g}-Z;n5Qzjm{mnDl%4A&4)U&_b>o-mm-~ZooG#{|J7qHPvQG8j7bW
zUzhbWb~v_K&0sy7`I+(>8qyRJ6R#5y-;E6#5S1?S+rhD4Ngm{ztJdcBm?_0Fc#>0v
zv>$J{;B)<GEdG&a3He+fe(2R_`4w4i7W5l*vsuGYrns!x5=Gc+vw4iBcBvs=bn{NH
zG&qlzc0I3oE0yLgEsf77_W)SohhfJ${lS%+)7nqz7mju$Lsjp+dC-Ptfl_A=D&pNK
zoOZ4jXTG#Rz7pitPL}TyVC;8ZMyU6Op4XLUP6qu}P!?&t9bx2g;wC<bWx$+9&gpGk
z518S?kly`^rj!0E?iLz6TXxQ2oh4G8QCC@bPD>$y3^F2rDQzd(XqFXZ-#zfdgShF^
zGy*)@jjv@c>gnS0vRbZPoi*R8@7=*lVP!ss{#CW=bIbaMjd{wes*`_y*3+Qkq44IR
z8m^<~B2-_Qg}Mg>MC`6yba*A;%b8ELj`jE|_L9b{fox}v`(XKe`Qy+{fT`vs(lh$R
zVczQjBW+&X!mV>?wJl;aJPg5WOeL03eVmFJ>b_Zh<VLL-ETh*#ImwGP9pN~$INJSs
z23>KI&>o7uXM4GQqB-&6pifJR<8^KPU|E0(zEoi*K?bO&j)<&j?{_hJdEB`B_Sl!>
zLv)Wf4oG)p8P=I^vx_f9d-o;2AW@Xkol)s7+>PDqThI6s6t_}~Qj|%8#M~p-QcA5+
z;fqag$WQD>ik4bKdsi!a4M#<bYGB#c?K8`j77B~2)3Ny*#kG=LX$S4cLw)*!lH+il
zs;(%YpqG_!LKe|c$6!_#56_+8>n?pv2Uu|9@p#0>sMdh@u@XzC)o!(m!T;)olPEb>
zs0F5Dw7I)FYI?KfJ}*tRUACvhV)~3xjhbTrQKd$&cxXN7VpP~38C09EVpMi=VBd)o
zmeM%PsNd>!GNeYmd$9R>mNiRfK#K~kf8jK#S=qvBr{(&Zc}7E4!9RWu2y(KSt*Tv%
zZo8g3U!-pCxgzC1JE@kb*t>vVq+}3rldk5nv?2E0p^TLWX!xG9Ao=WN=-txmRq#(&
z0Xh=)w;sb*0<iU6Ow~*l-(J=5O+n>}!0cm;kLgXv=T@)hepz=Ntj(UIHCdO#f=cx%
zQ`om${?)}lNIK3_!L4$~7P|?HH;pJ-H_FZ;C*L-nESA2!@4Ra*9N4#E?G&Dpuj1>h
zeD%K;m0Nr5msj)8@FKov`wgnIKe@|M!*7(F&b=M6LyC0zP?W<Z8+vczR3O>#P<cUj
zn12Np4=ZZ6MVYKr!w~uLm9QF@33qX_Tg?_b?GDL*<uqRt<HsRz>uqE1f8H(_K)R<h
zN=YV6^TJELt_(8nBfp<)!P_+Vi<mRRrIC8oE($U5DbX9W=FMBZU)!k0_gSnC{!L5;
zQtP~XJENCNW(0-v!kl0fB}4KgS9XBP0UxWH;Tj7%CHK>T6NrgwU@XA#y};A7_2NzR
z^o#2=v7}%3h<txWZc3+<4pt|KQO8+-V`6&vQ_C-`Rt7J8&a&dN1Bd-U_2la#?-jcC
zh$tl-zFMW}bNa<GV3~x(3Ydj~%c@2<mtvtdcT&Me`7x_SMcgcV&UYi>cV+tLkN=rN
zM4U4pRE^)vE<+Dbg?}Y-Z>_xdXHpoiowh~oY_rqt2%Y96LGZ;KV4_oI-B-|IC+j=<
z)#Z`Q%`Bf6(6o}6li-WhJ$|0Ts$sXfIWyB_wTJ$c34ZTreocZu(`b<J6~@Znqt50W
zm3f}fi>O1ee-_!4pK{U;k%^nTM^u~cE*~a*k8ajsV|K-vWn^T0odmqtQ!3agUButC
zw;_FfuFQKk_ut)^RKYP40%Hg+%UW4^51x0c{;{*5Jmh#53m3h`Ki9O8=My9&qov(F
z>^-;(SDc6el9SsjvNN!^1P)Y>8s!{w>SD0b+Z}Qz?>B_Ea&nxlF=;p$ye~Vr`lGjs
zp}W2*!MSSs5}FqZ4{(s_>6m&Huau(xl9iEBzdmI)=sC-VY0U;acU|}JUA(+x4X^vn
zg><b&dbQ_iZs7s?*$<}k0C!c>#P3$V0MT1#57p0np?l$A28`_57CAK^+FGG*rbLae
z8&&RYRo}8d^DvJ`FJF%<*e|11OQ~<fiIPy0%08YwBk=PCZsav~RjHrpU52`^CJw2n
z%4gnnAb@vua`%oY0thi0f+;=gw9i`Srqr~@mvO!$p&`h)&mVRFrdiOtIS@=e7RvP=
zIndU}n2OhQ_6)Z@RcVFgJ+_|rk>$o=amNHo#E+|_Hl>DbCQ;L<sSE`5?HqTgFPkQ7
zXhPEhrp+3c;(lUI$N6<w%ScDeao?$y`;L#*zv>^-SbMeGDkV_uNt#Zos48J`)aMr$
zcBrOBDC@ziyJ1<YHs;Vecv->dIQc#oZ@G#onL$0rR$?GrisKQ3zF;JK<_*Flw8r;J
z7VICLiTgHjk#vK_TdD^m$%#*+seit5b+!Do2w&b(!3zu0s5q%~!y<jpZ6J1V42N*W
zy5Jg^`f6X0JWgv93zfW*Swb6lO5%cbvTnDXW0p9JP725#6S=RbnUz^y8??7WXlyQI
zs@l}UrMdR}H3XC`uuC~FpG;g&+1}}gq<+%$T(OCwOWU}_i8`TOrX24<W8wdrJr;bN
zg1!uy(W?8Yu}jx(wm3E<Gqtm=h6JgUww9+u$%7a{GPK%QFu5~dr1|7LjMPJlQZN(?
z);GL=!uCNuYta!gZ3+zf&PcWA8<dIOoTbaHs5B{XdmQdwiTNALI-3ay=V2u6CECeY
z^U)6Hj6JkQq<b-5LPO^?(bu2miHn{zJ5&C7n<ziOX~o{VW)CaJ>8inJ`XYYu?0XHp
zksExa3iQbM_-!euFj3|XdYo4bLaF?#XWz$aFGHZHcTwIinw~DNWGUI~TpA~+$aAdz
zOfqlQw9bIU?}a35PVH&HI8`^@6o0uj$wAjQZFgcw!PjPfY|kjwVO)nVctVlk@_zn)
zl50%YYvi0<Lu*c1-elQ=;ZN$+v?tQ3g9X4-8{Q$KW)x6uchQ-Le*|4)+jd+_>K)lz
zva5yqLOK$_x=8C)kC6T>C)EYFv1p_zyKBR)1a(^K`h%B?HcpZ~eBPSfFNvH6|HBj=
z-o+eX(aLDH^!GQ7DP4G>=c=}L=j+tW?vPl<?!CLKdl@P|Q2~>mcR8J`_v>3z%<i#t
zIuB+b(s;$yaFUsYL8`IYb7x0aq^nnFM~AQaJHd133QtFlX({mzB};`r-|TCu@|i~I
zN<F5oWUHX>L;0saAkd`~BC}HD>R0&hbh`c~eq;17XKt*8G)ktkfnl%uAXeR%gzdo6
zMRgjymY;1x;)_k*xn6Yjcg^nyq3V?B5~;X<c}wCd+#Gc(VM<R|33AbzeehA2mO4z0
z5?nmH1|xROou3hgr=3yw_aoOle}hah;13I7^IJ?~@ub|Oznen1{%&Al^D)h0!4*oB
z!lrR6vzMBgObEBUNP0~e8km;N-ieV)E^k?)gU9V6B=lPk6f?`FIP#EK82XsW)30wf
zT<&vEdhmC9FD&Os9wt?t?^lk_#W%rTES|CSU{^YSjD1I-CGzlehn*Nze!H3(t|e_;
z(M*)^H|6oj-w}Uazx9#XXLJ9_rW^K(?it{edi(=#(FDUaT+O+)WkW>(`nqMx8K-@5
zW(7FO>3$Z3LyT1jhJf?Dw|^cTuh><7@AC0`?%3*g5h%`Qvsdq|KklFvU8RIo;ov!L
z6>P3OD0I=!;l7VY?x8HZS+_x<ppB&uh6t>AJqgomNlZer_ZVGgi~pgn$dI=WaL%n@
zzwm#I=`t$4UfZ5xox&X}8{iIX4j<mlZu6cP?&?~+Kz(@WvK-<Zek2`xcF3Nw`~pX^
z^(wplBHjsxaUk;74F4Nf$}jkIj(zpx)c_!&%Sq<#Cbf4K?8d0R2?~L|J$5@5HHUjQ
z95?=HGo8+rw08ottc7^P+T(34uRmJA!W!bwv{x!%RobG@Bn6w4eSGRVK7;S-Sjhuh
z|C!!Dz^Z*Gpvrd_4_W{YdbX5*{f7!*%K@X-HT?Wd5RO(s>ENC4IQ`Dh&8-p)ep|sm
z+h7j1#Ra*}%<-0XndW#LRaqt29F?V8CA}0y{Q5hdcWU4L9)DZwdSi-2{LyP7boXXq
zGTvBS5ZY^p-s!VtxXqD<C+U<cAA4w7;jniHAW<dp0a|97*+An!Y2(|4SL18WiZ5{x
z5+25Xk4*io7dD)a)hNwo?OX2EPxyv-u<%77$QXujsyKl4m&atHii&68OL|&huGIgJ
zz9a@$=O!QfTNuS^x4U>_<DAIABYyMbn)uoo1<5o#NpxmD>X}*q=f0JV6UZt3+*wmP
zMA0HaJnhA+y|P2jub=5bT-HpuneV~q^RwiJgOkHO<a%XhZQj-ZiA6M)`yblL7veyO
zlS4kJgX)IwlHa~?Eu8S~U)<jPojciWOB4D@!beV?;^p;n1C`I=U@*+dI2;E$7n;yq
zYxC;;Q;|gcmc#>17I)3*Ff#2_Y%xjA`-Sq(HR-|lX6N+urmEfy*0cGb=&XwujDtQm
ztk}ILUMv3TGIF@;GRgmHM^a#y_{^BS{eOts0)B8zIH=Hx1a!sM+ZbWax8`_Bj>Ha9
zfKKj^6r9cF^&!{S_36RK>`sAEV`FQiuK)51=qMyGvUktv{eIbLVf1f?L`=J9mQOJ^
z_T0khFjn7->lDxUD0sSTrZqfLJi1T00u=RDs-B4#^ODs<S@tj9oI2l<7oKx3@7dxd
z+Wwc#_i#w|3wMf`Sa{|lf|8uPr1(2Ia84a8sRT(-dyZP?#UFZ6oIOT#%l3*)OiR9R
z8x|w&LgalKh-ZSXaFe5T+;&SgW5DBe5k;;-eEzmE8c*9^VSRfeF39(_FaJL~{S5na
z-<?!)(V5TaZs`&BG%ZK@Xz#FPNn;)UA!r5Te2TN)4dKX*X4NzyH(`HI_X0BO5Z)K+
zvmtV&hf@*^cB=uM8(f?T;?^pko?;)DLtGjf7ggDH)USCK`U$yLGrOK{R>{Dd2E*+3
zm;2oa=nkuIXF3{x_HEaQ^_4EHb<?JIYMdW|EiEC$%i4CgtMdp<3?9JADzUB4<^kaP
zq@r>{6motGakXxq&Q4&7)ANEG_wr*_tNjRx^(BX|0ylH^>sptQlRbmi=xD9=4sXb<
zjq|9sy+R9_#pS{wC6rz)pmL#lWYVC~?IcsztcVPthm~aY|Lnt-#2Pdb%B{3iBV7Is
z6>M17Z8mm3*G7Trp<Ayvw($k;&?!LG7_@((1+^aOSWZk&PU_X;xxC7&J&xt7#TwBv
zH`z^x*-CN@)h;b8HTfb}3JZFqj7iP4sHH>d8O=9Xz{qj2;QRhzsb`+jDaqGH<G~Q5
zSo!kr0h79K`5dj>Rm>i~Rueg`xh}(9%!mi^-6yHn=RJGbtNcX=2CT<Jz+u0m4n?|b
z&b!R)5Xrul#mdH9ezRlE)hS<V=}d<^tMh&zfu8)t{+}mf^D|=vHM)(8zIGu+kd@r0
z4?pCc0<)qMeVrpbq2zLBzdoQIIs86I;dI<+MvoXNj9STLX+;pv{aXdjUV<=a=CS9l
zZAo#!Bcf4KPM+;2{h`;MLUKL}TByK?kv-wU><Dxj;wR_jGQiUqrrtki30g<qO9gVd
zMzpI>DRuJpStK5$sH&#oP<y4SZT!|P5Ku48=2aw2%F$@4<%^$&IQtQh3^bWoCw~t5
z*i=J6^KgSQ@J4YbQne81jIPvKUF{6cd6I^{gUinG(X{ir-Tt}qThXIEtVNlyU_-A$
zA%0&MlW<XMw}H=;{9~h(LgiTZyx&>K*DII?*r9ymeP^|t!?Rlu1m}J%y1L$6@S!xD
zIT8O4MHYzzTEalkWXJcq+T|kKf2z;x!0&Ot@!Fn?^1YPVV}dwK&|$)^{a~uo{03Pg
z53rl9bbd6<>>K_Bp=9}YNonBc_C~?y6osCUA78l*%j<&!^<$G+3?IIZo&FUL<Fa`q
zQJT}zIJ`?fvjH1a8~y>#nC20I5&DT$(A4*cHv`Ut_bjRF1Pf+5pj`G^iiCYZ(p9$=
z!m6m|;#kMDwxKBn@n+vZ!pJE;ilN&x8hYAz{81R2H9fTrUD_Fa!wBw}w3jizSqM60
z=Pj4kCt->5feE?(CEllCPH1m7di4P679RGdFpwchY9|5^+%3ji<g6du1N>b@Qrq1E
zU7y`L=mvZf>vhgi2`w9{R`(4Y)YFb!k$Kb)?Er9XP)${fpA_^nx5vy|_x+Qdnc%+Q
zI-TK6fewbgr%r>3b=Kz%zKygFEzb^_T$#3Xkyl6_yeOXb{N3|6mG_ioryY_v1A1Wi
z9fPu}TF%CgqwRCiFPYYA#Say+qL^x@^$E|P@{Z|K4-)AjM;!T^fX>@ZO3v&~^LRX4
z^V5qqI;Udjc3Q)nzx;3DV)EYRmr3rFYWO%e_kg}&T7%CMe6qeqQ*Ufx&E9V>x9Z#c
zxzZ$l<6oWL#7${Dtkv&!Bn8`lV_cKRB~PHG0(LC7iEl&OV+Lf%@%LCRmEEYS_h6dm
z{E9n{>m~Gc^z^j4H(2Ec3F~z;#-R5e8UozJe5<tDo1}?udxx6`oAuPk<K%BcVd^DK
zP(UxAvA-^0zayv~-`kUdpCtW8Sp%v+G)gy_a}GYoPM@~$HA<k*%&{5?Sko@*0|FCs
zm$+FaZl5jXjrB49JpfAl9TI8Ux_n>7+y^qWK1(>eNCRBs%F%)B#1lt;edZKr${!bN
zrBY+Y(s5Ohw`tvdbr6)eQ#7tF&~rNn>%O1jFUW>MOzA{5=JChv^H|ynJ?-(XJkJ6V
z_~!a{;(dDK9zsm(&+GKLKqo_+W&Rd1CVRrmoW0CD+bnkTY{TFp8|`)?kf}lCaN}mn
z<8cGA+i9sAdjrc-&yn)SX9pZt9MvFbFT>9?HbI}MziU=CjMbbrOgDCdE+!f~Py5ax
z-=xRzoIKofGcJ4%t|wSGEzEnaU(qWLMP_)RB$!i6!oe0leJH5+MY9z>OahM>Mp9(l
z#8;yJh`ZG??7B@s;@DdQi8}3jJEGRJL_PdffU8&=d9swsKZqFzb7nn179;Sxy?a!&
z9O?hCj}#`d{J7j-Tbt!3mu#H7a82X>6FG6%^w26$^|h0eo`<!c)9;%1VqbUdvkvMK
zJSQ1`{O{3USTt%E&+|gf9l+31C1@&0%ui9j{)$2<Hlj@o*7sw48nG*o3Q&wAvxVG8
zA3xUmxj!LAJTsz?wpD``7;i5NVbo~4j$hl=K<%!FUb%#w7lxiw+ITLrJp0`^J@+}B
zK0xiL>3-ZuBpzx}8y7EWdb&fB+jV|)Ep>Y9=_uvXPJ2&B`??bJ^G^*4x$EDhIIPz)
z=zorz_Ew{^9!c4?R;M>F|M=Nhpluv)bYG<F5l#!waL;<t;1sm0T%M+(zMKSY%70U!
zo!zKxpgif7--D(9J)k62?Uee|eFmeuU#pN%w0=3xi6bd^+Er&%2Dy19X_6w<Z9nrh
z`8E7t$Jpquhud9ecAVzTt`{2N0_*Yuc-w-Wc{TF4b&W$P2>kS5IFAqO3936N-u&<l
zgm?g{<Lj4iNsbQzkMvL$V?QI%Sd7ugdWVO5_3^n4!f=-C1eEw#czl<&e>&7Xz6gCo
zP^}4DH}{%V9}c+{!9i0tH6EX~Pe`va#_*<8aO=C*Q{+R<(@B$CuH&Z^q}Adxn{4#g
zBsZ(mr55lBioiS<V;_;6uzKGL{Zzq+P%G=8lPLNf&vPcaRtSaUpeX>^-iT>C4)N2t
z{yXLAZJBuJdDW*|$M4L9OK-wt5vpKWO&G&@sqm&oRu%-cE)}V!;bv}~BEzJY&~TAo
zC-%eC*?|W1_j&kuuy)HGR5aXWm6dGlvy6T(wDIz3i5@XQA8oE*azeWt=>_UQH8q|V
z0&NIEOqUfJb@uwYxnsM15?lP!kULT-P2N0-8<%Vg+0lTV+{nK$z^if2o!D#5LH^Zi
z+41N`Okr7U&?B@h!cT8I!>b5u20SS=U%#06yy@4yt?iu8|La>JG~6LLLuzD;$x?y$
z(?1p_lmgB6eNU9Zi-O$TccaqzRXz4@e|O^*;{05%FleF3$Ud(OT0|G_;c?hm+el~g
z1uy!wXlF&$ed!ZUQ8F+H3gt(sJiULf`p*3G;mAyr8(0Bt6PfUBy2`%)>Ca3{NZUjr
zdM8aAUqQg(=<W6h85CDuL8QiMlmdiw6#A7=_xO+0xZUsNQJF|#sptT^v6w>iLfLpm
z35#T~g)s?UPBNsySaFb_w*il*tRsR`id{a^=AZohnDG#3xQ;N$68=0slK4f-lW)gX
zKeJWmAy!<>q<yf}w6eNW!nCDG5rWerR)1?{d{?B|Wjv~!+k<c6k~XqwtT#LmMW}&U
ziPM1=u8Dch&!3KEG87lBx>;0AnX+4FOCS|O%ZK{`U4sE>qCuC9d0#G+@S;-_&T1V?
zXCtJJBG+ShY0sfkh5d;P!ogywQP4e^H#O+;@PfioVyb|2pTHvt?1JuC!20m;D7!A-
z#*ag$kxlCsw;FxKewOojiqg<H|FoeTzqc?)b-%PqfOgj5V&&;|dh)k5x1c{K_bJ^k
z^+5e1cBbDVS{Fe*O0|2ql8|MK_&LCtMcaE>1eQ~SO`ZI-HA9nZePC$IGit+8UE$OS
z!vPUeDsg7~zi&?vSdY3%o5L1&nG!(8S}>q!$`YlOl)|1dNkCbrEk0sw-b;pHo7TVa
zDAY^RM^fbClkd#1h*tr6-;<#|kjdG-Lof}*$iW;xtq6WKFIHA8x!iFTx51yVeKo3B
z0YJQ7{OWNnW;rm9k&|Gt#J|2@fS$m6KPs#~0_UV5M#bVwe1=@(wV2W<QMkaJI$~4!
zU#C(*_QQSpIYo*V+?_yL0Fyisg8gMNe~ZG{2-2R}g!Y6$03Z=ph)mWKjzK##G~3Fl
z5r8{Qr#h+0;s5l-v}DozqkPv!Z$>qW9USfRMtju`9*X}H-E9e9AXe>SU|!NP=pjv0
zo|tyfAgPB9Yt8vfbBWa98oGvUnKK-|%Z1IVD=gN4+RKZ@g5+{GeQnxIIu>tlNPeVJ
z=T_5ac@1GUIc8ihH<A3v*P{NPqzxgyl@<#@fD)dx74w^>&REi%UtIz^`pBFWvC%<O
z5=Tn1j&#a)r|v}g>l*1VXa@c6m9rFDdDEAE9AIB<G6KEgKc5aVgbv9c`;1@<De=7_
z0&k}j6MJ5K>)W7QOk1Q-JMuugqyrVm(9&}5e{046<U^(jG>9x)O|f7noP?0x%+)Tj
zaFboqpBsi~pR3verE#4debPBY8zlcRfEkF7@QF5IDyH{06z%?!ZC3iWW@_-6<Cil-
zL<t(S;LI#H=&z$X4DAKK3Svt>V*H<?Bmr?Zm=MYw%)oOO1PMGOSW*dBu`vIkb6_aH
z9wf#@Tb6XW?TbV0@o$#yCp8q#Pe2hNIIY#zoPy4@ult1}IleX0mcbqj#$gjdy1vB6
zCSGy;XJqo>kW`@mMs=hdowI5FQqKAnGab?Yf{$-U)-q@0$I#?RHmHkwk!G6S64W66
zf1iT(scMPBP~!R7k#6{_d^TEDSHNLQfH_aPEz~53BL~WmmV8()J{sYQ`TyB5ffm!u
zi~Ue9L*v4gG%xr?2N~<ide|Pg%+Y|aK(R}JYe_g%onQ4&IsO>gAu$MI7L#B5h;n0+
zQew*Ws1IiKzlh5_d-dC}FxNv22PO*}0cAbRN`=*>|K~7K2t*rTO2UH_Jq1eBvK%&c
z?^mAFsQ%B~+~O!L;O#G&U=jK{6i}xmcj13-3dAS+G&RBOs7esi+Pl*M7t;C#sS=Ui
z@I%bV89O_(cZY|Iq%82CfA?cl0*yi;1cc17`bbz${V?5!g;Rs+)$4XCu!B2U(<@vk
z{&v7qXo>en*^ME6gvoyr78PWjHHvfgkvU+%Fj&nHEA~r<R$yrGtqTw?rqu|{xrB$)
zfRwV+)cVi3Lx>xCl0al0)M-wodn3W=CPo!g)V)IzO-?wjO}Ja?2GEH}ZMSU?a_xov
z*8(RWq+uyCWPb*|VcR4r#mdpQU%lUG58I5&%(n<(IL4gXw(CDyBmO6_{$l_gUyQ?q
zMU%_a7!7uKXiy|Uv#&0#qn(Ip#2QU%0us>^x6I{o+wFjg^Plh%LaTHBR-iSs2F0NP
zlMRtwa1)T4r8yDv^GUNxf~nYkn=b$GGs-Zv=6|pM2rx$IUrUHoMJ8FmI%1SJMJ^PM
zLHVt0OuybS9(~6HjrH1&Gma_CF7=jWZZduDI^0-Sd_$)0Nl14O65`?BASC2}9`@Ho
z1cI&v5heyELntN~8aB(0z`_iTCBv|c0AGtTw%*^MJJrg@9h#1+V&N>0vD3KluRuVt
z@{q)^>*?exkmkVevUBKpA#MZUc+U4eUY-x!ISW(l!I4S0*s<8B5+%UoE8%6)y^q*C
z4P04_hCrS9GZ<x&;;!obxT8&r^1g@hN6<uKr;$^dM9*IYYvXc$*u}dA+yi2}O0po2
z>&ixB=7RnAWtnWzz91Mf#IeuxV$u>Y^s6{5(Q&%a*&KhOq{0@B7v8ix?OC}tAEgTE
zleoiSd3D;!#DEEUF-JxErB*+E9;OdfKaox?B>J6j7xw9%kA1h^KRom-8gBJItNQ?@
z>(NS6%u8%3&4KvDAcai)kax&pjB9-HBCq<nHvtY!@o376fzqDO>B~|y+%wzh5pnE@
z^Ug&cWIw9-Dd6eiq3P{+(2~a(W<rZx(sk@=Me~o-lM{!-iMQ}xzl`pPmjh@-fNNn%
z>ur={h_;|K`oN!iQeHL=OZ$H_`aT@oFXN6-4C778u0s6zi1p}T8%#}87%aC3sQzO}
zM`!oYF5Fwwj8%m}!540zTiV*XTs-UX*;VND`YWNxLB|=n9thvXzz2$)j}Pb}!FYp;
zUBqgh2azW<&|3)!ZT0tI$;*7QYfJJ1mmOd=vYVti*%gTx1fa&drRXNArCAL=tx@}^
zWfm`zR3gTJuWxtB3RjMGR9kWb?OPB|R*qYVLX8wmrXD-E)aVLI8&7GfR!KwmfsWyt
zp~#SoG-25r>dlUQdM+IH+0T96#r2?pwuM;M@7-IoG{{yZN5;goog^N75zdR<I%RI;
zLjM273BnNO+lCI{6yl#NVt%3xcyGaym=7+Kv(K_Qjl$m|<R(WIV<R!)pp_zd!P<18
ztK+TxisGiuYI<IUB3ru$WN~ZoSCIRjt0YAtXc>oW)2x-uDd~T5MwN_-1?#_6)}bMy
z+L@@m@m^q&sov=;I2jrF`4wpI((SCi6Y0<?YO$}IDO>>Nf=^zHCnCt><Fh_vIK#cE
zXwZuU%34sD$-X#b+|Db9q=v1H)i&8=&Mx>>!fg^0y?HIIB!$BV7bE%95mEHL&P~6%
z4ENM6n;{Gy7#W4X`Tfce=ViXwZ1_2xD;9X-ptMox1<>)0Rs0L(GD+n)*54|^tl~1*
z8{Q|NK{aL`p7OpwsX(fBi^V(Q<Eyi}jgUT^7D_ANe<(i;L|M!eFShe?8Skm9QzXlM
zGrtgcyAAt05EG|&bkZ+%z=y}>)`XabaWSP@I9Z$KrTN)uRM!9BqIW9-;gi`LgTKEe
zri%DW7(u}QbM9naure?djT!Q<+A+z5x2I^(%bi_47JP;{*eMdwDozdAQ?YoK>84>T
zdRzPebAr8|o0_bl&ifYbI}91ol!N=K&>s}siT!%1TT7QPwepkD17N#So?MvPa--NX
zrQO&(@}p@Z-%7xB3%~qvo-;hYkU8fw+uH_~<FA~9!6nUvupb+lyn32R8|ZHbI^Xv8
zlQAWEhGl-5Z;tva1SaarA|oJCGBP|RDN1Hr>w2B^RKW`$4i`2(HKX<U4vUJ~-VhFU
zOY+>mmEP?s4!<&U(m97+J`B6n*JB+tr1ju#cdO<B`;=6En#W5tmOnP%Gs46n(srU2
zNFy;7R+Q@!QTn{xd%T^YSuM84j?RRi+qXYI+$nhstYzT)e1F<AvDrkGTIcszi?;w>
z-ak}^=9hTnRc7Ke030Bc{W0zlM6ORWN>mbCOw2_%!tS@>A2&hFpQ@K#Ue3VV=j!rL
z=4GIN=Tv2q5CjDQQ9SBW!wM6>eDRd5g2ml5C5Ls!)$1DLu-5sYDID+YOn%aIZr|Rr
zl8nDhpMWrEW#V3y1Oy%T37wZ-r)n0f({`~g{toiNoA2CYd|IFTx#HEFnZfT*B;w@n
zr-c>t`-vX!GKv981OunW-*@}v!Z|t(lz6ENy7kZlu!>Y>sK8PiO<97T(8bb9s?lO|
z<F#h|0WAmxLay6$KCvj;2v~)4p~t^*0+V`SUzb6?QAE?ZoyQDRyZD#DN8UI>&&$^-
zd9)Ya@o(_(!G>fIM<{ROY!|{u8UE}zx42L^kFqoEs4SC*mygt-KDf^tSZ|A`zT5R>
zU}={Fn9m{8pE}zx1gf&!XnVL6j@oI?W)^5Lm9JX0<~{+v0*Q;JiM{f=hCLZXYM?hE
zFGL-DZmR{d*LpuW&yD|Eu;xgGid$3iy!)UDAbo;<?eBg^up%Poq7LNR^<oqFmG&Np
zyLs7yD+gF+C{~^L#bUk%5&i4ROIYF27SQ^1&Y-C#VXs~R61-!dI^U1Nz(U7@4E%NL
zQOXxhnNLaK|0H#x-R?PfR#X}jGA5qL{nkl*+isRxR7bgbS#w0!aYz+NCW2Q5ZrxJR
zf!0u%^Vyb5bDWqD_#$;rP^>Z>N@VYM`L}J2k%CaZ+?N}X|3PxX4)p~T2@-8%+Kt9|
zgeZelvJ@)E_XJ%3%*M{azU%V!wX5Y7A=WKcB*K?pWHi4KlL|-*uNl3;`DdzwgbH1!
zjUUv`0l(zVgT19W6AS%8P~GR?=CGG_z9-KKbC={#Q7N%^P&@c8(1<^ZMdJ4Xm8?#<
z;-~Vg<}6oNo842`r5qM574vvPe-E;LtuEE;H#qvOD`qW^{}{M?NNQ=b`z@;rx4=Om
zd-lXAt&549qP}aLqP72AFX)Fxt&saLe%^v_4Wd;KD1h^{9g+SZA5*;}EtZ%tV(E?u
z#GAI!WxSkDZG1)vm@4PZkB|l)Em$Mz;Y{MJd@EdBVryP6d5-sWCi3=|?boQz-&pY7
zY3yjTZMr&w$QoetyYJj<wpk>4@0ertc)`j;n<GL0$^{VlwWIb0V><hDAMaK#Zb-xN
zM$O^pJr29;Q9H&u%k2=nH|*|~PI(hy9L8p=7>j8hHb{C2yH*LH!#mna`J!d#w6z=o
zdKeYt7i+ntOA}ye=PgiF@df1&pV!Ohab?~AD`QK4Xnm3Inn7vBKhfpriw1#aCmaKD
z5!gTD6bSd7i>@Y^Cu<N%_VnA7kqtprr<RrjhCbOu8wPeVz4CyF$)6dRTVlGb5iEN+
z%|?7q1{zGox)i+TXU^@|MPEi(I)A7#(R8dPWC2!dUd|8NK^8d|(bI@&|3T~OPL>6p
zdv$8zFG|^2(KK2(X3Vri&bb~{`a#ygsImUcM?@%QZpB{D2PP@?5EOnK9?qV#5kxNi
z`$e;8&Z(^D>29}^1){EJte(S>VS`L=RLf(K9*$FKq#~gH*f)aD^Wahtmg#(M*zV=T
zHrbWW+lrQ&#E*~NconK8qo#gO7pqvHMl)2rY4IC!n~T27b$=jB`XpoDSq3YSKnMvf
zsWUGqDh}u!n~b8%4KNtv%BVl$^x*W5Qt8nnx}=ZzIDuHe{kPF&+NHUckV0#w0B&s-
z9&mzrEwX>Ms@KSNJ~B%ug=TatA3J@PvE{>)&A9vIReA?u>$1i_cqg;to$&TATsVn^
zP?u<h3tFhC`o^>+XxV5aYOhW!NDCW@(&z*X;I9R%gFOuw(o6m_`x>sZ&Gp{`6XmAz
zqye#=f5mROmHg^=5tsCUfW_=^eZsO~S_t%v2@3CUg->Y#{rWuWhr5(cxcPxiCjN{%
zw`21DbnO5`>ho^(sbVlmJ#P9<?(~V8$Uur{?c=HnOfSq4_*Q2hFe$;P{10Nh6*fF=
z2u*|!6hVWs|FJcJr)f0k{}*`{UyNJ*<1`1?lJdQ6c4Ipixqd9i@bG}xASQ<aD?Uk#
z>VifHlxf4T$De|A8v*Ov7FDKiO)+v5-6CixZcHo{AW$P*c+>eDQ;~sJwDqsZhhKa$
z0S{P_juWI5J2#2j-RgFe2CXD&Q`f2?fZ<5}|6tffv`>83=5>1<a3Zt|QsE`8bp4Z#
zFB8~Z)~&IbEmVw*x}?k*H;FnDuk*6Cvqy?8s<2Ha$gQ*)v8y`X%^=BYDKE!CFvU~S
z3_hOHax$(TdD44cjG8vu|K^)E610Fb$Mg_H3jW(RytOo~IzqQ}?<9Gr2qO&pFNU!n
zI*j^Uj)!3(5geKS#Yz2`fz0FBap_Fz5%eFDc3E@qthRcD(0o*_fwMZbI>|;WNMqRl
zMQ-+D&?Cm&mc{*1JlG5Q32~tu5e}ri_1!q6EA`^(fl!OSpLAf^^&IOHc^tVjga2y<
zGq8XxAZ<c8WEW%m;Rn|;;V|WhkpY3MLm&)v2ONMGAKI$swiedC%S2@IeS^6IDoeQN
z{{@*g5fBj;ff0eIwt2{=+__FBy$YD~^-RM@EvN*S*gWy)SnKUbIZrsz`iS}FB3;6v
zQ@&koFS|UoTK`>j`G8(A5V=A9rCcw8Q9MgOV5>QNOxJ9~6aiKZ8}{$p+d8Z6hrf~m
z=B`AZqxXR+P?eQBa^5I0Q3>22MB&B+#fv0MmTl;%tsTK+MP_aSv(99v>waTrNdgv>
z3gyNbDAw%J{DUXY`|r9!CWJq(Y!jN1aW3-hmrA7n3;+H`L1_KpWc}7!&m_4eil~rI
zf=JhdaZmX(PBG{5V2J<AEYO3nnvX737qY10U_<fCd&JV*iOmYexu@LT-D%pM$okAF
z%s~V|9?f$ag+YBtAfhC*{dM&|SJ1ZsdXdcQbies%t}MHD*)QDbkFz>rB${7R!ud0l
z)ZJJRJ!9_?<@xXjO_IKQg!l*X{mL0=<B#2^UsaEH-4<Rug--P%XC)a~_c5@oCBe09
zM$7eQF0o|9PUCV1i@foepaI#xGl+Mgj0Pj=^X#>2Mg;e(^+oY{gR$Cv*R0EPy~!OF
z&3iyb#wH>r1MgbyzafK`i3;uhS8TN{2YlZYlm3}c1(1G&j%FO_K-8BR)}Mr~!(j;d
z-T4#6X_|MpK}UU5gYv;z@XMwa%fWt9)oJr;-XwhIJqpXZE7V1M<&uk~#en~<SdXjg
z3zz9aHaWIV)3ApEudlO@=4mUU!4t=6OTy+^hI^OU%jEq~!-`7MqN-w!yZ*iJ(!>(F
z!Rv)$-fZx7?J;NCogBU9Pgk@<=%O_1Ic0vG>>0l-_M;nVHn+(|4?zvWkOr+eSi#)D
z`-lS76^_S!K%U!p%$t<8-KDH>WzqfX;vh`D*E(1h^z!g6aiYUZiSBv#-I^TK1Gt{q
zaw6Y+H9)hsnr=rdLp@>?KK=j5dI$E*zU2KkwrzIUaXPkb+qP{x>Dcbrwr$(ClPCW7
z%zS5l*SXFc*jRh5s@k8rtIEsfZ!0RLFuEK_JNjS$qYk@FNVS2*tnXRaGovASwFt}(
z+`wUH9O*?taACVS&23#rJGl;U{Z57hEjaLLTRDE+dq2N7)I5LhqkIpEcbwhsXnBjp
zVaEB<I-d<N5Q~K-;%lOPOyVjD-Wq-+iET6dGH$8pfz4#Yl`st(8=%j^cJegdJhXtx
z2CKu{@M)iil5LkRkxTtpqagq%7(=4IWa^}G>L9dZUwS6ZKuY8aa#!ncIiMnEqu2k<
z$ApI`S-k8|Wt-k+Z~FZcYRZ^J?-!a6&AouV;f#CfJYaMS*x*5)to@1+z10lf>$Iu)
zlFmAFnEs>r^mh7pp;#-e8#jqt9JS2<5j}mRUem=oG>T`P+GNbya@nNCJ3&q)|0#!6
zqLDsOmO6Rrg_Y`yln>YPoz#-JDL7w!-hJuOt!D-~0oI@7NfDQGS*Hf{=b5@3UZ;T5
z?{OHS>fw;u0y{*Kk?K0=i-{m`b>!~R1bEZBVo1|@=1(Cz><CXH5%xCYtkXY0ADp{%
zyNS0%eE5SD)6u@rC0O?OK{aTY4g+w+KfT!X`<0`RH^XDGy2^!rF`>EFrFq30=WH4M
zq`Pu&*t@OFYJVS0oBc1-`iovMW-uJpwf#@x?YAdgm9;7Lg;x>a(u{|Wm$H9tX<wj#
z%jWcWCV{=udJ(hR|H$a{dG0F59$zxb<~HoW;Lp|@WL3${2JA|;RC#(l_n#nhLJJbU
zMiatIO?v6^xe+vOH{!F@WG|C^XHvN3egkVKxhA@SII*$sAMztTYkw%PUh&>GuGzq4
z*_T*K0{Rn5o2u&NZ_btkm^>=svT5C~!U$Q&b-cVp?oT#Ey*F&}EQ<-!+J5<N8;SdE
zq9F0wbW^*VQo6bBlm&_HZWX-mix{8Jq8@SKg@q#F^AI38E#%V$>F)U6x?ekmS!KNE
z3H$xyX#Wy4ez4jXrV4PH+Akc_SXE|qeei%O0X#xN$XA4A^(xa*W6l+iy&ab8F&)6;
z1OC~=Z%4TjnZ)6&p5#i+ju{C>E?!-aM<fLUQa&;I@=l|(E%)E$N`yWq2<rRCLbF0*
zcCa_qo+iMRS%nTA$CDgyGOt>Fbvtj$ljfXFAI(K^-kGjGDz#&Dt)#<rUpE}%AvvZ8
zqqFXhTN|9cXRHX!BXrZ<GT6L>k~heOk{Fdu9R}nZ^7Utj35f3Jqu;NYYMsA)#t~VM
zO*%;Yyu6<Lb^>sjIOy*+E`6sQ^Uo{rrCf^qN55N`m&xUF#IKS!ZVUDPG3*TDU?2qQ
zL^()AX^q!Zow%~DJ>Vac8-lV~eVpw;+8GW!8L<y~{W&ci`sQEV5!p1k>kfyfrEPAA
z?!$0|7lONL6XxfvP(Gj7zHJ+_Sku4vN`;pxwtTy0*v#h7W})~Z2J1ApDS{^Crt^;2
z>jrCTAm@oy&jlM^!Xyx3&!cOSh^4-}&IpX~AQznN8IL3VM=l>h{^zu{k)hOmXTl?l
zunljA0$WB63}dtN^mSYcS`q5;z&K8uJ!Wk|9eEchP|2$D+T1!&UT&oZJBE`aKQ_g^
zht0&56h-$>Zt`*1#-ihTv@G}rCAZ{<B~a2A4;1}3NZ<|*Qt(^SElB=%zc-TJ-gXW%
zQ^3NWel7fjb>&#-w-6q$M};lX=sQO7fB&ohMT1+bq5PLv0Y<$g*tG)QxoNbf%-f`p
zuQ4sdcOv`PL;1QsI<LjaO8=#_{v`Th{QV4*Q0jXSF0A$R2+z8eAob<4gY2gzPBh;B
z^SF#thV-KUpF3Pb^%^imq#*~=af(~cs*e0Tt96Zi=^Mk$DL2xi`UM98T!@VJQcbDO
zPRffk<6zJGH4ZX@U8neg4guSED#_6%h*52NR{?pmbQn4RpQ!imNvFxkOh~7!Ukhd*
zby!8A2+V}Cv;R#J+#_%qWz|!&u9BA3O2N@oTDx?n|7gNIf@w`VBPlH%8n3eL8B$bj
z<6yMqNSKT7@?7Ycs+9f}EUD1+GT8Rvl3TI;43l4Xb=4T8HNUR86=iv##x;RfG40tb
z!Nq?4NTEtaP-On{-(CRYtlj{rgoB_kH~ZUJ?SJpaT~AV=0^Haj*aYm9sk%dqeIEN&
z5KRAePH205JUab>%59CK>D1UU4YZy36bH_$p7RxNm%c9NJK4c2m0`EbMvDXZB?~*B
zt!q}HT;W1asl3%SJ>N1nQEFFRC9}MJhTy<KgiI$N0M4S^Gs%4}D$V6wM9TJZ#qBf;
zAsj5%+9e#-Ks0voJEhh`P#=#Dqq}E1UAH2-yC^r0JQ@x|Z<pIlpGk4M#o5s;$IG$M
z`!@GxPm!1V`$-|ye_p^W5NKH&6$U$fa>e*pohU|xaBUhL2o+r*2M0}q(S!|P5j!W>
z3x2C)@1E3c36tm45_i*jF_Rra)}W0C-DcU!x$;})8qxpmNhYhubHRI&@>NVjtw!6a
z)^PD+@3BpK`DYDQ9dy}N79qv_Qc(Dn+>in=jom;w6X{Xh2nX3ql;lwdckdwfQ?C(y
z)Mw-~xj9m^h*i6Ji|K$KqdyQ^`>2)aW;y)H7u&!kZ0JD@d7<*Yr==X{%^IZKv!cr&
zjp%IxwMh{itJa116IMd#*DJsIcLm;JIN~yKS`p$OmDrC@i6@z}9mT(+P7n@*+IDn>
z+7BZ)FcSW>b*x<PxhM;e{<9RJK+-+fKg~-Aw%Jp5V)dhbd$e7hrXOhbK&iRupES?Z
zaWq#qzcge1IcB?0G&Pbjsoz)0Rq(ri@KH$s)F{?3ThsW`OD0r@Zzk5*Qb&6(*=^>6
z_V4_}x1U;GbETT`uWO=aRNOs)6rc%TTMIYa(KhmMu)QM=M(EU2S5M2;Q`ZhkjHxOP
zc^wL)Ui@~ism1#JFtNmt^&p^>hGSBKlB_2--*zk<!k_1#8#7e)7upgM))lM{th2}!
zGn#?om8($}z(5nS&y|K2js6Sk@&l^}OM%igGZz}Ey}dEabI3u+;!i2D=CIo{y@ST@
zuOmKtzwCuky!g7y4iuUQ7b=Wg;PSyMU8)7+Z(gK%HWPSIUM_@(-_v?!uN=~547gYg
z@Tvu;QkmXFLH*ph$NoF<3$GQ`iw_=l0~6W7{sy{4`z-}u&W}^8j!zp{#hdf}j4&G$
zD(il&;0;x5S&)_Y+m5^H`yrjD1<l3x`S>pbejgZ^Fb%`^eh1p|eW7J)ZuV|%X{sLk
zs6ov5g0{c<{7O5}?Fd*IHhU?X=efCl?eSB(as0d?mdXE{Lkr~q$?JuvI1NNbftEhy
zpP90RSfa+5PJ^x@AqFuzCpxa<QJo2RzaHfBv;G+hr`K5gS#6*tG2s!u@@aL&qgRA4
zRQ|16REb+QU8==<yyeYf7zkp^YpUqy;JIaUQ*{Ot*^QoWbmDtU2fL|URi(jYGUpZ*
zweAcdWF|zu&OJfjgzs=g5vNKxDsnnG*WTW46o!EJiSUlhoK;c7Rm2Nw++D*3GZq%x
zbT`n%%SUc5(>Ik&n_ZdTz<G1*(gKOz>=+I$`;o^4jb78?vwSl{Q&U|vDeXZ%S`m^`
zhG=I|TBBV+_f@@deIU$fCFH6tVyl@xvp{PDpFTHa)r<kZD1DYxbd^u6sCP7ctI~2?
zbv(^tWmSu3BDbi=?kKF~HOLRwa8Thp>dmUGP%oG81_(+0-id40369A3`vNS>AYTSJ
zO*?8n^ugjh&i5Zsq~d+J3$im2z6Yd+c+-CJrBg-Sd;kXGp<=45HXN4bKWc_6%dy|j
z3Augc>!?8T8$iwX>M8-(PYrxD8i1jZL~<Wbnwn*Sa=$?(sV8)cFx^q8w~3L2kqc7Z
zkAvbB!=F1JFp(X0345K-;#FTX+I9=&aoUwtOU+!u4Y=+etnC@k-v<*xdcMysayXv2
z8@WOmg8xqchT?({^%&qxYqzh$59=YB9lNu*tYVoNRAjBFU_#LfHmpTqrB;8r_^g~&
zDtPVhxEDAnQ8NB9wcS>Nbku3PVH1h66CCX{y{u?pTHoe1b$9#<vbf3dK3Kt=#jRLp
zok|a%v~yy$vK0anbE-rs6~mRq>~311#CHKkTm+wbOU2}SxhFB%y;h>US~~GuN&6gV
z>BQMib2%)vn5f9i!uWWicnKU#EwtlZQ`!nCf{NRvx>>2&Mj>E&n670MPu9?R-1HXD
zBJ7^!a+>cwV+IYfOCSv!^cRU;dpIFRdpSu}A%?2j`Hft28Ypt7TivPDsFy7Aaef`%
z(F5gWd^u4w)wWdJ_zH*fk~#>zP;5SK1W)g0le!jd{YI-efR+}>S(6@@h7^F%qni5B
zbpvpioSb52rEzQEGpIrHlL7^`g>ICFwgKm}c_zYRL0}c__Btzma4LzxVlh#)T*RvR
z^IRL<Bk$zBWCaCoh|_*BTm+TMx=J<B5|(n_Gcy^wuPt?Sm(7_^=<tzoSL|@0bFFpA
zXJ<R!Io%Ssl#X(<2~`sw1Vc(e8=h5hgN5AuV{#3AD{p*W`adR?lVo;40}FLx*JyuN
zO&iG57UcG4>{N9>7(T9QSd3%11`Px43r-OUH+!5u-dC4~uOjJj%YcacHwLkNBYXhy
zJ?R@T(jF27MgP0CV|^-}KOiQJkQF^$e@nf-&HBu5dvJoCl%~7lj@*&ZLSXsu3e`lr
zj+Sy|6Ps1#4;yjB;c|U_QvkDBB5c+jw<ZeXN{$u7&d1?$gp$1%T`uOK^5R*B(z(9^
zkLx)Ey@n4HNaOAyJVpOHG+2$+Vm-Lr<Z4}M$&s;~TxvJdyS3<n%*Z$1ZWGj>H44B?
zdXrJu-n@oSCc@zPS~T3R65HvjoF8r7du1wSwx6HbdLI_bd4NkRM1mSekGfMK-0#O(
zMYML~$!IFmRtfR_9<LVr$En#6tQlI8uj8#vD>jzjKNNbWUT8b?X22lZm$q+}qyPLn
zIt%#A0qbT=(cb`XvA0jj(YMI3WP$<0#^^ySHmfv{^rXJszvt?anF#4YM%7iiKa1tQ
zAZ$E(xXE6-4h?UqsWY&7U`@NwU%I-8zx55ytI_>}CrDG}Ugpp+ZK5HNqa9nWC1ke~
zHMbdinHqE}=6ZLA9?oISh~}&NU`J5VqmUF^&V#Of#GhtCVS^Mdwzope!i9~z_jC-b
zGzvv)ENEwTx{2Fno9E%c$Ga`T_41}zG{$fhqcRh$y@T;ovKVFB-hq?PiHDT$>f#!t
z&bjz=Whyud6$%O0Wre1^W8}+5de4XxRo_Xa2WN6PKt+COZk8fUi4-k22E5@DieW;o
ziaaX^SfkLGoyz5~o;77d@^U{urrgXpVtmFiNSLy$%HXcd4HrcHVcnFE(yjPQxn*1Y
zQ$U2<|H5vtl!u$H+pC<0KDi$Dm!Tr9T-^wH<_mcD=~9l3swM67_Ha6LB9J2eYCFXn
zj^}-?{kDpeZh#@6c_#)80*?#oI*m<FcpiJf<jc@!%Zzrdmd~d;GjETMUczZX@hn&c
zR$&49A=rbhMxF8O`s}jhsN_olanju`q3!;Hu1k04V~K6USLtQOG>+vr>=yhtV1RH3
zu<(=K$9vr3sLu=AGx^^r4-OOzYjzCeXN40Rc_6T=eHcjddaG>;OD#Dl-~@#MC13_>
z4@$I@&#U<^QI3b9a%S!5<sx=FDwELM<MSG6c7&SlZEO5(>m%g-k7;+qbK6<LFnrCl
zX9pX-C!1)CZ+XcW-r<6o$lH(_J37Au{AG1IoMp|pW;3O(O-F(Bn3ROKBQbFit+aO2
zF?@G4b>V69iHc*IlVe7Cj6jsT?O}Nlo9hqy96F)(_C=KzyM~20+40TafXPO}?K`DG
zE|v7z!EWBIhenl}X~(I!cxtT{wYupOSM_JHaqGWezXtr1J$wWm%eCsdL3~#^VdOMq
z#ud+-II8CoGqYUW8}MGzuT>|w(YbN)U9N$?y$*I4;&kzCxrwn1NPx|T_ClmSe*Fd<
zuajaUz_U9q+&JBQEa?znsz;A>D_6E33X&@;yo{wS*xigw95fSYw!X%)v27o=@nC+X
zyWWbZBZeX2U~%areo|$;ypMME+}Ee=)D&BpDB4WKeg}WLYgIj{%6{t@o_0d(Y8ubs
zl}3a&arkDx0BCD$)8Ss$olal7ofm$7^HUlhSbfX&(PMc`OV|7xHwEkk^$v<*yve|!
zZN5Fix>IezbnKX}61AjF4omUu!hv5lzXJNHe$Ev)@VTe#>t=#Jj1Z_!0>(Nm!#v3L
zjz~&Y&g0{9D|a{;yt(0?_y{4)x_9+8cZ`heXW>+LKeJ{kJjxzCbpjtWizEm{YTSm|
zJA~A;1x`<PPC8Rw3S`y#e&F>+vh%E_4V*w`(|LsBJk@p)-`!lmFALfJzHBZIMmV4V
z&+Kci2W<>L80~U3eo-au68*QE_053-t5HA2<&I_4Vd}?d9RitdF%a5>$yH5^XK_d&
zU><>@Sp{vzykf19Nge&@n(XYvz#kM5J(f#4do#JB@0Gw9zN)ifx8sF!SgeW7We-Sb
z=K$w(C)kZ=pQcF$=t*c{|25*h{Xkzh((&U(LRGUiQtu1cc_MJkt)y>37GP6C|MB-!
zAiM8i&}p{Wz^cpG5u;{B?!oF{UI!qA5^ii8K4oH%3(CY;&#o*&Oww9HYyTuk{>rK0
zgZ+V~gQCEq#VLUx@gTD2`ZJ33T?WN2e}Ws(;TR-8rqR-vnQ9b}xPTH1WlH`}TV@ak
z^h{<OaAd}aHyv=~R2_B|knB>MgV1iP3j7sX<;Va!5TMBLUzyFnP!t>mn7+W4p~-N}
z12c~2qfX$pRI2nAPb!^m`z%>MtA>ca0KSy-^$9B`Bt<?!^WRBvZ%LT{)1_$xgZ#ZZ
z#s<Hd1sF}ydn}#-1HfNVD=wnL{tktmi#d~4KNr2I_4R}WeMDB!PEU8Ejcl{)dIJe*
zz_#7XWxY@4{DQFM4UiXKJj2t1CA*+=0%W<)ieGav300NzPd=a;6l6D1nOl%DxY&8x
z*ieS&7Cz$~WRP)J1+TNne}XxF2ZN!=7eB2dkUDJl$71G)qJ0#}^V-%f18z|0Ir$Q*
zgWQ>rb^TXW$KY6Tl@N_c!#{+emmUmw^#fv|0&XHhn&%GzsdpX2ov11^XtTON@Bp$S
zbL&R1k$I}u{3Qi&dCkLYK07p-=-H;$AFD~9JNTs8UllLP$!ct6S-OZ506Mc39^{uK
z1apt4-|HhX<(C0FA~Tv_d_5$l_P!`Z7G(bpApS}pqmgR;5gqIa5gfIC1wvzSdG-hU
z=hYQ{ij4(=h(Wx)M`N=H9{<FnLJQ|{pYC3kQ80?jO(^4dEn`g3H>IO?DAAN^JmTR(
z#1BlAw<D0sH@WhQqfIHPJhyAA>d9Ojp3jB}z{)Cm3kQ$TP+4s*qu9@xcZm4vR_!~^
zVq+^~zKKgN-14ij?pbCFoG<xD#%B=jKS%RRJw!0Y#6TgNCG+n@1OGT?Mo_=qGPKEx
z_n4&E`v>eZuKjj<zKcD0==(^Ux(iNHl!~8Gw!HNk$h8XaKR0u-!w~u;O2y)eTdRhL
zBYy?^wj56b<8JHba9X_{n@Qq0SLRoB?}~pE9C2B3w+lTfmwD+`Y->hh!**VHpi52w
zD&6M%_+2gjtLp|76A=xzTY(HmFRk|KW@?Ow>p$UjruD_PB4I2E6a?v~YOYpWnfp({
zrb+K!ZuAP7yAE7jm<cw&=C1frs3zgP7kYR1P@Loo*`u6~8BrOCuOQI<Qf4keFn5>!
z));2BuXVk19@64!FDk4(Xx^VnUYF6@>=1gAFQd?_Ks8a6qb0K*CrPQ8)zT5N-#r4i
z^^EvuPx`7tG3ZlmMW!L65VDi~RVNzVF**eBo1dQroN>UKjTzM8GwHBBv0f58d;4!)
zFduMrW9rdQ3whwO$aZYyWCwfRtG|8L)i|hJS8lP-H8~Y(1AocL47wa2*Vlh{QZ2@5
z-wAj6wPWg4^bP{qDlYAOj1v2G_&9LMJ!by*8TO#UgHw=68jwtP?b-1Hp_3M;veqm^
z)7eSElqz?1w82pDxQn&ve-tguUYC|QmM>maoo5Z2xstY#49*lj%uYC>tgjeRSy--i
zN%gi-xHq~YTyGYQV^qh8qVJ@UZ&xdpVbG`L^R?Nz?IDLfx=G=gKTEuvTDcIhl~tC(
zcBaV{kZiv4&awX|pU8-Wq~6PlEBIpLsr#Dgegu2>(uL%<13N>_#@k^(u<*kDaM7_+
z({rK5=r$xg)9yzFb$@~EoGw`;EgpG%6UN=1*A2QlyJXhBO=M3H*>;gCn-z>>sp4&G
zk21czvA_ExsWI%TG`e8{B(B&*hV!?zNhS;tM;D5k8Dbjt%HLJmGc=QEj}h=zNtT!2
z7{<I4-_$9#NlxGWy+zQwVo6pzUnA0-vaWG19>xSsR`m4z>rLh#)5!(_(icaQh1?^_
z;P$D60u2yAjuPG_7=o$K4skj#wSEdQ(zuN&c;6d2$n`PBWZ*k$VU*f4dh2b6wu{p1
z*j+X%<E0p<cl8GDuzJcPZx`)a2GUjR6PEs4ZyX_{wfV>EvFW_|PTMJ;-C4#p$o6w`
z`@r7TX<qmrQwlgDW{_~ny(uCxXsJ6XkX<QW(K~0wjxlBbg@CcB(SUvvV`N9E4}1Iu
z_+>>W8=+P>b;ro4z!);MB8!Ym_;7tvsQnCx<!<zv603?`a;U-QhtXBst92(tMiALV
zc}a@(scOgLcu}QznIl}zYYiMJgL(-?`LDyQgYq{~L1^3%eS7mySFt`F0tsE;h2p?c
z3*-A)+SC`zu^JF?25{-qUu+cd@Yq9%7%NKMWU}OUxvQ8gbbd>uT>`#RAE@fF?jCp1
z!Qi+)b|IscO8s1H)yAl4MEVEyYP*2Z5Y_D!rSFIn+Ixari}gW8gBP_d^=s0QFhB;y
zWfK1YYI>^BeaeISTY1HE)~Lo^%B04kH2Pt(vZL8Pf*~=GO8elRp!=^XhJR^^^*BLl
zA7bP(beM4X#kRyEb@HL=urQf0q9VZMOihuo^vJ3u{m$fPpZ`x5Z5HZ}4INF6N{Yfr
zRPKV=J_wf;0&#GLQ8)(Ys59wrrRc5v;aOGsJ-J(S`M+GnE`=~q;~XvCrKgb@1&FtN
zRu=pSp|Jt}e9eO5n_M_>RpdV>k`jbZ4lEqd%(N?ycYM&zgk4!1G^R2KMa*ktdJIRS
z{@mz)gekA_?ZF!F_}`uP_cdrhP@sdpUt_U=6K!i7$DzTqB3K;BE3l#)V1h9kM#pm*
z3l%>wH=xZ2{t?jcnneP+BVkH`MFTbW&@#L>`@7;|$w3ygg1zqnR@2chD~+<BF3bX+
z%pz$_{%bBMK~`{pS3g7fAY?-a3fk*7MxWC9R$(vWf`C#PKq5xRH8|oAh<0mOekSq$
z56)A92npsvR@OVvER+om4+zLhc@G)hO2dTB2*W|5I-y!I_02IeTeb5xZ-g#lWu*Ua
zA=uDB^f~dpBbYIk;o492Me&_C`TM4#uc|Yq^uZyxvow%htwk$)Hsxn$hyS<G`e~#<
z^BOcLMorEMNQ{QFcBp9@4EqpT2RuY022DCZWA@2?tefWleE)w{Fz93bReGNh6^z6X
z{tTY7gDqjh(YK^CtlUa%-}}MAI%|1cWSGuRgd-}gr25a|{VS0Gbdr`8Vj>gWtOWbM
zuTQ@fxLd@&Z1aFmH4t_FV|zeT##t}R_5Z>gIA*ZjaO}V_HfujnBaj53>DZ`X64)$M
zHYboMejtj`KB~X%a4#M^0Sp~3N%;R&`R*YDali*)6XQaws*@QeC?5w+)`r`jE$*&y
zgxh*w!jYfg@mRPHxM9Hjt0dyz!5A}`drx8X^ep_J1?<`{Lx%kzQ{%WSb;U3nr#|sA
zL?b-LN%oS<|4RsqiUBn)^x0v9dfMy)i%^20vogE?0xhg?kHJS<jHtsDZ<}){xcbM*
z|BWkag@MSoqoL~nlj%sZ8PFQ`Dlnm&p107B_H+n-5TLAIOf=~w(*Ji{hDCrbYYb<K
zFjhZVGvPv|Y9r~8r9|JxMWf?;H956|gAT|W#*BeaOnU&mO)bsmVRk>mIJ^*c*=4*s
za-2S#3Flf`5VFR6=7|5`G+kxvIjTM3yi^@OUd6;zff}$5hI)-6xc_2f)6<cTUSA$o
ztqOw)v#BqEl-$`|<>rSZkj*~FIhe4>z^$1tw6wOdUi|vi^??(_R5>~Tw>ct4(+E@d
zfXZ-hc**!&OP*rX=gK)bAp%F!GGNP5+2~dOW=JC&yYF&%Yi4X&=%CJGUpv{DVv7ki
z*r%k5*Fg@-n}@a*g%TlRBTlPg1u?e6VmPy^jaOWIm#n$Ior+!P8I*l?*gxE@w)I&(
zA*Tv8D9|?%>v0XC>B9Sn)w4_0HsXA3uHE&k+Qm$|sFy-t<GHRv?2(Dep{lrbN#Ij+
zs>wDECHyncjOTfdB@~pMEyXVGuFdX3ulZZdK0cSEZuLP2N0Ird<0g~IYTWZlg@moB
zou0lS6|$VsOy{(6cBSX}R*@--bLptb$GkZDDK2Ch#`nj|_^`<49T-S1RRaOn-NV@r
zA<*-;BMc~h|Da%uT_Y3wjLMP`sJ_JoP_I1@VK5GL7f{UNoM86f<+`-zrP5*KluF~3
z?|L~@UA|Rqff+(6Hq_xk_X|JN_y?U7_$~ZgLSdMS)S5LkbfkRd09zX=KLZt|w0Kej
z6<XR_8<Cl`NuP@rR79TIrO8YUHc~cuZ_uU)ZP#OJ@}keD)zoHw%mMTE=Wafma{gMt
zjsCi)V4koKOVGuw^wKaE%#ricfZo)8$nd;)L^Bdh`D~*BuYNuXfnKX`p%RIm#fn%}
z+N(&~)^7C}TdtFN^3vT=(aX$c__N`hd@M5W)ly7RG#K53FvF{sn7F|141I7EkkQD%
zOGPJwy?;XRw6|Ec>E!OBRGgSABQd(#F7GO0TpZQXsgqv)$jhXy0MBM`kQNYBJG?C>
zz=DquJAhcRHdw#KbGza+X5Q*Nrmw#@$d2Xkfah(C1CA*MzJ16saoa=EWg_s)o~qlF
zbWR*vRD#nH&Cr<GQAN~;{9`laE8>J$JhOrI@#Sd?tix(F=aIWipo^tr*yC&NZN&xs
z5QKfK&IfT{O7g8A2s?-z0zUqLTE`TGflFsan-?K+E3cP-lmhwvaf%k7%|mmi(du?X
z{pr}YsNDvJ7{B}L>9a#sZ*BH+Hvw9&>*y&}?cn&cF~7}yBXG6Hg*jEBVgyo>)n~1u
zLsyr5j&#mosf@1hNQedna5zz8r^)IQUi@YjmH>?C|Ld;{)+g*N8KDs-DiT1(UtH{q
z=d7#`YQp>=>?347g~&V(x|5DX-)%iE-@l4bh%8;~&oZvZ!%f#ap~1bBnPsz$tD`+0
z{&CWhC=t-z#?7dhA08bX$jDUzD8`i0;r)n>4up*y#ADIxZgZy4V`nn;$__<aE>|kd
zqd7zswjFgtk8u~sjE|vKt!OZ`N9U(&!aDA&+Ax??LDE>Y79OU<x|P~qi><(fVm(}3
z^;kk%cZElb;F5AMiq2@i?;ji;y$TsBbLTSvtt7+Qs6Zig5C|Y4FM*--g7NtUw;K1m
zkfhMiumFgO`>J|99Sa>DJ5f`!m6)g*tE{D(Fr3H6LzN^i060RLhP>kOZ8;+=d-gc3
z|K3}DEU?zVEV(2i)d5FQv?A}k%EAl3)ndG1{?I{UjzPj1Ojf5A-xByTula*Bt=*~W
zyu9)-4PK*o$bCq9wxf|q<mzaH@ibw!^~yMRNWB}iW_G}*^Se$RQXzQrwx%rby&@b9
z3I!H7Q%WfxNf{?u8%n334e6y~u-_=?%0fO70_(&ey#8hQ%O=2^p-!stiCCmyJpg=9
zWk)j=F&>7b#jF^_2+5z&AIP;)>LI_Fk>iz%Sf>t{c}-U>+>5P^tn8J=TkiOd?_+Kn
zo?QnCyJ6w#N4=leJgd-B#O#t()T?dHs>XJu1<f&GUg@G@4)6UlRO3yw27y+9yu;<E
zLNgGeMs`<BM!VqYD9ohO<a1X4et&<)xuAOvPz+wH$5k;PUxBhYwdMKd1F~BM6BHyc
z4TtA`{Vp1a-j>7G%h8unS#jOgD4Rc;Z__#Scwc!^Y*LoL1AQa_+1pJs3RS3Z9;v)_
zODsOk4i_6L|EK%*-W=VOTy*55W3=YAD7AJ}rmP6{2;rM36Wnmo@kt^ui2j!D=INe?
z!-BEkD7^Mg&iTq(>$5jj_UI{jxTMN~jhT^=jKev^DgEoKcT1dfrNzqf;R9&Bp_vNT
z>`7zQQHTa(0<NT$<ZHlB>L&F<o9T5ViEDX7eDa~D<z%zsVakH~#_bVTAJXlvejeRA
zViwqI8w>pEA*0io=DIF!Lh86pUCm^X93+uNSFGe$hTtz~@HbQn<u}wa($ATgUv(-`
zb%UfJU`W4WiW#xYp>Xxz6Rd_d5@0eb&nQUo(0l{KRe)?KXM-tJ<-9-Uc1M<_J)LE4
z{kU-|HL)ud!E4-&_l16v)94{T_TFdO_Q2u>+#xv|YnF|dhFrjtVK)cx(RI|*XhGY^
zn;Q0%Ob6J63;j*PjA>TUia^Jcan|%%9;LdXauTV=sA)4_<ZEl82udVp%eS)%QOT8s
zic3MGNtiRJ!^@DV1M-qsE1C$FLz36{XJ_NX4$oTsolYx<IaJml#21%Dg*K;EaOI<Z
z1_nN@^`Zp_kFQI1Qb!+>*~C{)M2X%fF=Y`WHk1dOx-S2TkK8lU&O$j|_SJ*dOJC~s
z!qs3G^XSdBDxDczGF?t|)<G=`{FQYQ(+HA!TA4&@(llLeMGf*JnTiZQJ6nDj)XN$g
zD7c`4t2aVM3W5O`cDE@T|49V;2SO*hN(!>=kx6fMW?m3(ih@oIri)FQ5jhjk@^~mW
z2sBdgkLF?^rXR@E$?R@|s2d9Ukb>CwkP>ri@K5PLG37Z;uBxE$Njh8oD;nttD+f@;
z2w5Sw(8^o)h%E^<?NATal#>4SlJl^;p+LOU{0{OwQ~cK~p>=uf^(}1eR<Ms**1P3E
zE33bMsHwC+Yw8F=cQ3L3NKQK3Ti&0R-x^LN>#>J0Ii{MwO+wkc)toN_d=2oC$1{@F
z5M|R7W{XkJnSc^`hh0{J;Z);G{2j(FYzeBpCyIV2vL_~B;=<ZCbtrN71y$QVP3{f(
zTawcyJ3HG3Jgula#g6pak&><k`kKqn3aI9sHKix$V~g$AUdK)K17H2ifOR0;pX1_#
zJxF*yrQ7f%eq7=~lcZMO5&|yJVaT%G{NDI|$ml^09c<soBr<q>2zTD>fg|3V(3ElK
zd93aw)fUp^FxewzKQj$-f}cdfW@u<f#kY5zq07EAQR_nAknx+nE}eR26;q)K;E0&q
z75C0q#9a5k8FZ2MnWU&;t(yiAy-p%RM@ou+`$syBJy2IXTpoxh>SF7G?N|v(=|Y@&
z6*+nO?!$Qicy}GT%lz$Hxf>>*ce(WU#2~sSSXzxbh$JG`0%SAPRFfjY2vkgY=y;31
zpqWPD9pZ#HB~c+~kSs{uC_|#XKDHeMk#~OS{K12ez@pzAgEB)|VdvFA=XehpQMg65
zBIoVKk-Af{3RJ7k$cxOlg%WpIDhgAD;A?dJ0R8!K)4BmmF|CL<tnDjKGD87NGD}R4
zkCtI?8F)p0ah{k|eyQ0Dg19-JBssdQ5CIP48xQxS{wQMhYu8pYS8X1wA1sPi#S<C9
zT>(c>0agkuw!Rd&+(e2gN?Qz4+X?$RhE+m7r68ESjb1&}1SP`i!niZOYA7iHxS0x(
z1fUS?S7dZo14V;<hYF*=+t8mQ<ibgwrUBL#$;2>X+&QrH451r2z)T7$Y9CKwl9J#M
zL6|1*$#UEd{(OQuZqF#hLr~3h*MSVya)}M)<q!;?RaOYMKu-VDTkV)Ew|Y62VtSDR
ziE2A<;7Kri8Wac)&_t6T)052V5EbzgdE6@S?l)N>1z-7ZH+mBY+i+kbF@UnE>41)+
zN`D79;ku;1D+)osgMBz8{l|mH?{Jz+%Kpwf^Mh$2PUuB8eYpl4OJJckT0W_av_%l<
zHY?aTML}0u?VCzE5_&xDo@P0avhtKiq>YX|ZhTHUM3ytz%7Clhe#LZyn*dG{%sX7-
z?=<7pA7+pq8x37l_wc}QJkCE-j7NLvv0Q^E05F^kwVk8br^vl-Fo;uK!q$q~VxC;z
z)V)Eye8{e$5;BJphfyzQAN(_+aA#8loCKYbz$FeJN^xXpo#6MCt9)I~5N2=F{r-b@
zw5No0Hy)6j-WTrTTXD)coXN1L*{!%gl5Q>Vg^`0NowRc>XJnqCQmxE_G?2K>!6cf&
zEgQG=Fc33uBPA?bmPTJvA?|-q^&lpU{r)`CRjazhZyHT39w&cEyz%;wo*Y0OVeR{D
zl+adeqHgE|hr1tm9d0w*NQcCT^YwNLUFX|Eag!Si`sHAd(>c}$76RuWOi%`e1DG37
zoJ@?iCw8RullDIB*1%oqzvlpDT{JMlixAPTf~POk>N|xsZ`A;cR;PI~39Z$c>;r`f
zC_QJmheGF^p<w5If|PPJ@T2pWiyzb5(re_!dtNM(yRP@G4Gj@_$`i@lD**eFGOG&k
zP`^Ib@C_W?gpKQaj=tMuvv)h`9WEa;#?ew%2I=?48Hh>ehFo4QFa@V%?PM|P8sAaK
z<Z+y-(G7-cBdcBB%Ic~D+4thJ6U~^PI!_YIR=Ib7hJKN6{LD1E<-lk#6f`2FqoShr
z5ITZ{rKQomXgf-AxTQ+Q!9`RiTH3E;PqjG58C6Mow#WK7C;ScLDEYlMKM&Cm_NJDr
zHEAauI%2K54}L|0;E-E%ISqKyDI(JKx;b}}-$~}F?ts0YB(>fp3Ai^WtISZ%WhKF|
zLyE>3V-t?X^$)7!_{qV+fgUz^m>mlWK5R>Fd<5ZsuW;QS>FP72x398>OD^cDIQop-
z>fW=&@Q{h00CM-cYKT9Jf*K!uMM8P}0<HHZ60rI!=`j};6uZ0fD{KLYarLESh*ZKC
z^}fI7`y-iwx4763guB$LHe(Cd?n^4J@%6&-AxD#c6k&BioNl%h8c)IyPQj;ixWfE;
zog@<<8mbf7t3w@0iPh1-y@ywrDCCJ#Q2I}JB<Qqc#uIC)UH|)5^djIBSbY<pWqdox
z7<nR~{LQ#xdKiAcMHz$;5XXCFuXB@6diNsGe^dG{+lfux8d3B2Y(Q}F!U3RGnV-Lh
zvn!vJDZC|p?yi#7an7<5hje1VR;J_Z>fo^*m;Tm5Dt-eYSy5z;KCg>+lX3s%R_Jx{
zpSGSkx!(#oEOa-d8-vo>en1cgEF)Xyh*Epc$&`?oToUdp7{xng$b6ID<{a5)x&vkB
z{`+uXBGyzjFj5LXm8-n;3#cn&%B_`)0*(cCZ>MmE_>%3}9LlfO4!8)v4+|3P4d95u
zc`z%kRwEzWdM3n~xZ;4DaYfs67FMTSa)T5~4uQ6F4ThzM3su=n2b}C@nwb&pWy{&%
zs{Gn15J26(Wv>M?9+%-5O-*T}=N;X=i$#VH-kpex0af%(rLW)f$=E#zcQmBh;o1{~
z`fK3Lo!G1BVVbpyoa!FPflZjVmZl&<=R~6kA*lZHV%v=yosOsO&-iVt)yxdjxtGGD
zDs7<^>tCk@RYM+6oI$O#W~wxz_>AQ%-#V;UEmEvEzND^mxMESOixTne`g5S=CaCym
z9I-~^|No9j*OEG%4+idPTkS7}O{a@<t<jIyQ(8OIxo(x*oHzLHLAO};ernY*x;#4(
zIA(-C{D|>r#>NK*{xI74icjam987J$5!%SC4Z~4szskQ0swD0s8}SK~q@K$Y?+-tj
zT2UN63<m*iT$Q{*hs+WMa!9hLuD&36=@Lcn!Y)s4>QhA(u(X&P!8F>8MC9H^q10|p
zclY}4QVL67^hk>@tdRBgQ)7Gi7L?$o2iCTem8)NF%T48tA{(i0)*9h+CZa~o<bda8
zNajPWl6U5lu3N%;VN++#4=qB~n5|5V?DRd{p4V(QCpz1pkPaj>&R9vF`-OLM;ce?%
zT9*xsGH*GvF7Ji*s<t*Ov^M(;{fYi<-s93?Wau<7LyJCPs`gXy^O(n2pM8aH_BB@f
z=VL6k2U&NE->W;;qu_G*WvMvEum~OC`J}FbUHvTfdq8piUE(`->4qlljt|SF=msWs
zI1NSy3TJ!ipfCJhCpS`43pZM7U$WgcTfbGxk}X<3so;mut<$bOal^LLc~n9c=pQ`X
zh|esF`qMX^sZ;BH{C35v?Qrhr4}tggBP=c)L2TnxOnOm{w?8Oz`8ZYPdN@m>t_yu4
zS9gTB%&u+XmAe_4IoWu5ecxAI63^%3JI#-*<xBvN#C1H2UuP+;UL=67ftX)s10VQt
zcEVaKm$cM88qczduA(lN$DJ(=-Yo@H_zWPoam`%Vd+S~Avz?KA204ggeSP+vr+-M1
zwsy180yJ?aCljDZ)Qz%@rc5y^2jH|TF4#>lf4HZ)!hadS=G|G`1pR@7*0wS*mhM*2
z>T<ARq#5F-O1+yJS??TfKRl$zv|Ar)R-vFWD0p0{i7|{%v(WmQx5g}<3c)4tsmLd{
zdVl50*|fW&>E~+dc)=&V`Jh9G<ZY(~Mz_<gzm8GUT|?`jgI2MQk`s!cwmVPtWfQPL
zx3E)LqdIFSKqG8i6;*-m*#}~_cYmr3_uI)wDyE8q^Q+*4bS3%hNiUYO3&P%LC%ce;
zMx8D&N}8bDsUeEeb1Bc<z{jl3QWTYTLhgx;VrU-I+v&-*7VTZZ@Wmo~f#<7z$X%`c
z;gNhM;j<k;)W-ONXkn?W{Kk}2Kh>4hUiRrKB)Usf-CWFxpo)pXqg368))wXNO<Ber
z>gO*{F#zftc01nlF{e(ZUvCaS{x#s_P)A*~jn_eYW}-p}ZB*0lCwE{KBm(+wFKkK{
zf8I>{mQk5K^2cUPX-63XSJ&L?x<@DYuL{h5W~5OMvsA&^JsuSb5NJ(91Czf$V0QVL
z=#22orIJu@a?Z?`TM@!e3c0~c3ltt(T8v2>hyg!_?8d=xvsZu{gd6sgH$7e?YVQvZ
z;q2?@U@L^{x6|}Z)3oGMm-v3&M64yBr!DHo(Mz@eRE(LK&v!OenMg~;_nJseHn90U
zeYT+^>J@!nVZ3J0drGD~$4p01G?>u2K@my1XMU|4wU`E5!n9Hw)KoU-vcuT21rM8t
zek%j*=l0HiOLbQ!M!%5jbyCnr9!Yb~MhPevdJ~@p`<Tw=9Q2&_5y~mQ0<%Jiu?4O*
zc&Iy$zx#R4DtY!=1h5vk&_ymmcn4X3Ybv=c%`PQsJs2yA>j+)kOpL7V_eaAVrJL2q
z^=XXceU^JFvX8h<RIYkBDkEL5sUD4K)R;MAdhc)C({k8gdqq4#w2^Iv&J0D00hsf7
z?Olz*KrrI^NfTv4AeIYMzI)8eTsNLOZs8WX^m+p?Szpz9_wfv~@4ubpx$s)3=%`8+
zS2~~g)Wco}0}!Qt`jyIVD6S(YUA@i#yPqESW}5QAWip&M&c35Qz`{B;j|5l0?j^Ig
z4La+;L&n_hmg)B6?mibAdnF%wz125ShYzB2BZY$`R|I5nTT}>X>-euxfz6lL;A>qT
z@Ufv3DcmiKD2#Eas1%na_}(4pd04k6&CIU%Arj3W;oc29Rqp3py%rx0X+-9cMeTSO
z%tX>y^{2nONNFcn>Hnc0;KUa1ehSc-R*OWutf~09onGTQk)uoi&h_WqM#^y%7HmZu
zM=!vgLo9T)TIyp!3<CCKZ?y~twAKX}7DC-@SPCR9?34=t_fd7&BIZ7(Bd@Nlme@np
zjaBrdmYd9K>5OlDX|J=y9YH?r*T9P#uZ0r46k!tizV}DPS|>?cy<~~?W*Y~w#eT!;
zHJh3HUJlR(x#LtMsmX&capR67p4UooedaTUU0ZT@13MHZXP<`WR=|nrx3MGm)_8PC
zc5oc?ciiL3<IoH@B7-!}iXCO3LUHRCyDyIwoi9EH19J;a!->P>lwi&<3~xtEw@T23
z;MI`dgvcB?V(%Mp8!Qe8OG(*lF5s^O#Ba>ubvhA<!q=GF<;U==&;DcrC{%X4ey>Qp
zEa+{n<v@#Fo*0mto`UL)bmYy|@NgFRhWut$UA;dL_J*!b;qq-d)PP;ryRVUx%)L$v
zXj}=xrCQ5{4i}$6&^Hk#w+r{vbjkc|;~0~+gxkViyKnP(r?Y9l_?=kp63g=8WMoLD
z`p5Nq9`X#Sro4huVoyWux733P_suLqw+qGSSKxa>WM}a6jj}9%{3)gKoh<QM<NUF8
zOd!wSvEqSGYC#VV`^96rdydYm_wH(!K0Te)+$SxWOX8n#JVPu~x2#b3{a-nKY@_L1
zT6eKZ6nSD<XGE#&Ox$aZYmxC`?H67pkei>VBP<kCkt*bq3F5}UOpETNf7<YEWq0^(
zN#bg}n%iPg?X7IAQR5w2LV05IpJrfNrTL^cQ4`uz$<qR*Zq<ecz6d0#B2tKtzgnHc
z&8)Ti`QCGkgJA=#UIz@D>vy3|=8fXybh}*d%UMth39?Jy;-JfuQ3*6S3_>hFgvy~2
zc|iOEU`e>GXK+V1SEdUcH#X?0UI`tr!O2`jWK0y1Yfuh}=9L4J`NZSRR39Ye@Yh(c
zv^-3aYb-;Ux=VWp*v*3zW}IhjgY?b!pL!+kPU|tGGgt7VWF-SW#*08YY!eCvdHXR$
zCYWo_xe&-496QPrnxKD1!~;RLT}ai|jtHnM8qczzQH~)3;wcUdSUUfZctIp74qiCa
z>WdEKOeBo1faZo4{D6eluc62B?w%e;`*G4F4ghPORF!O^cqhZX^Sw0q1tmHMecK&?
za$ozVsFnU%<j>UA-M5YB;SAjuS%Q!oit$bchjR?`izUecT%BTSHhzABNs`*!aN{yv
zU=R0ajevTO1;=aXzC*fAWj+(ARpLROWLf19=kt%zy<q{JV41AELChoxY!CSCO06$^
zJXclnvBdCdLi0e?k_nG4KJPVo!>~moInZLCcL@&pWtT@$55a(G87-9Z?#UmMB4R)9
zva78x7R+pX;7*7G9z}&TI773+HnpHmXij4fl1|yR{hY~{OM+%KGK)y!<FB*gbz%}V
zHY~tLt0k!z)!*HI2uZZVbErt_nk0NqkE2FXN8IyPOXTP-?_VM7Z!jGsv6HGU(UiGy
z`;4PaGR*-W*R1oQ;xIVjr@I0vF&Wq)4UrXNep<y@ov<aDtRlyc_}p&iTC;WOjaU&=
zN4mZNGS#+5M%e>9rr|KNIV}YV6tbkpC<9S{;2*LB#Y@)ngdzxf8;{O-J$rE_r?Eg`
zDdx?)72R)^X?MVKxb-Ua=Q<?!Rxfms&%heY3ngy#soZPyOfSpp<F*zoJTQ#{Ja(k)
zeo($fCa0S`nS7l3d#Nc@8L7upB>4{Jn)e|ZYWNg?8vHinNA~iI`Woo?9(YmL?#x@w
zDceB}YifEAwyuo7gt|>zeXG^Rh9q_MYfkoC4KhPwrxZwM-`c*L7wB=J{A?)d9)NeK
z6+ey$=d#$}?O+R3!8x#gOS2YgB$ai1OhxVjukoQVU+brusl@O{!|eUtHUt7WU~so-
z2`**3tBF@rIG~Nkg}vhH`tdG>*A*GSZ0Q0)$mh2)m?F=5{uCWuS?VN8dKz;_A9C$z
z$zm^?b%*$P(hxr{KRoK7nB(sy%RxG_`qMeKW5!1A=n<zX09e=Lxh(ZF@`0^n#S%U%
z&M(hKs`quBXumij&`-GeQn3%VgVf%$YtG8cn&T<sS+<l@f8S*Js?^>eYLoB>DG|_e
zv`n^*Ka*@KFfl#|8ul!XewHoXTN%J@AH2;jDwVe=bSueSdE?cuApya*VWrzYuk0$D
za}W?B?W~cs7wwrHfNq48mF^IsCm?p;-z|kc&A0d(5&3&#ok=YUUPpZ)i0gZ$r1>Y`
zW<xv!!VDVaRUh-hOAbdO7Q=Lt8BcrV(-~qjEkVfz6F*t#^8PHiPWCb_-6;|$0cVgx
z#SxS>T{PB{X6&6eeO9i+3BVtY%gmnbi(StRbLFpK_DV;q=erHtwR!8PzU0Z~UVb-_
zV2Bvo17q)+3qpqL%<9|5npTIG%*je>y2}@@t)*76$)(WL=yT$sOPSZbnZD5Pnc_P8
z@AsTfbvwB}z-u|e&5AyidbIpN&~(kc`B5Nl4Xy!?W<ZP&#^x$#iA)5G<uZV~k5REU
zsS|UqZNZ@E_uVas{3C@nu_4?(bs<;1|6EGJlK$E=|0Pu@Sm?J?CtFwt7AY(oUO3Zi
zVgQhv+NM>8s!n^$@-MTGvm-lUw$5Xk{(P*?dAP^|8vFgcdygYyB+RXyTmiJh1|Y$Y
zyepFay>+>v9_Etk4Uo@i)ZfNp%~F@38gEeqf7^W_$iOGGjSY*Ne0p|e)~C)TK%vuL
zzEll8Yw0yuY~hYS))TqEl4Z;6Y=5|jtPB`)px=vM-B|kEq(gezCO-Wgur_qwsW!Z~
zY^2_L{H8ifee0CEA!O#i7dUg(50!r$8%bO9nT+p?@a1tMG_wU*ppfdxHU2JWf<JzA
zZGJa#kUU*DB<=&}8UGSI<u>P3=xJ6s;QQXWhe^7ZMe<GUe_1XqWA}EJX=L*u-8HYZ
z)>Hkyl=3Dbxh|5>Tg>W+bJ|R?vu7%xpR2gTy(|^-=W$@@vjkRg&KC!}mcyz8<IdLy
zXNx}&OiMP}V#BFM2BDB_9vl&+)$HadJM=SMCmgX-#w7DvgEG&o`06IbbTyXx(pZ|=
zUo}y}hX6Ne;l+idhLsxBuZP980~KTO^K2)k@4SJ1Mz5pJY=%-*UZDcHOb!=%7;Z7H
z`3iY<Gcr2c$o$C<%hpf}5le!=@lm<^d%KAa+*%^+ZPyF-8=O}l?0Q0iBupuE^ZpCG
zTJpP-TKMgOm0OYD)Clyv&uZCF03P;iHJ2Sh72~2fs2!J=4MEZ_`-BT|2@WyG&q5oE
zCFZ-DH-dah2XEVq<V(uxY^I$j=4fc-5hWe$jEl=owHhW2)wCxID8;#lcN@Y|wtg3z
z*-YleTIfSE!+Nn4u}scFhUq(RHoZXz#7_dKeZSvgobOGLK4{wT{4#0tdzGnU3v9)D
z+wZe-7ldmt>_RRmFyC)>Q45_pyM?2x#nE1^=HJf^F>)i9`O|&Kkla3+2rxIIkBaxT
z+sfBm6ie0ylrp!62<uoeRK&CMik`gBcQuvY^gl^O?@y2e!p=Bo94YQ_qm`ms7TdbO
zNZiWYpnS~EXj|kDzvpcTx^lyB!rU#Mj?4R^yj@V}jF~mX`JfCo9YWi)souqTG)X(Q
z$(+G!nSt-A_Eok4FT04X{z_a`ew;=6Qrt&>994B_4D~z#7#Iw!sM^|z>$R<Kujw^0
zSIpVPSySCwv+1n#8Si5rr(-A6W9HlhV!ZFb6s?xVRiRKIA%EgtBz-6bFgi*QMQqa<
z;(X%TkYYQ_M{u}sQYW~9{0(x=KxB$_dP+M?In&Vga%xYI*;kj)P|iC*Gu;{h#w});
zZE-1k=nYTY{KE2l+HXzUqf7adPW)bmMFoDA=J^V4y}g`sQr^?0r@ae14;6l;h{bs%
zXr+^Wm|sUo=f&Jji83-NHRHSuq9fmfj^NcAlo!T|-{qT*`d!=K%V93>!1j`SV4h8M
z4<k)u2ua&n`2yZ++P&|&oac6*dNX8qxi&NBvm_G7Z^VGj3g?8?PM3W_37zpqCXCUR
z7*uc1wrvI7HbwjpS>XX8J0D9-kg_6JG-lmnl}C&1{_>9*SUZUYWslJ!AY6;tM=jj;
z{ju~;VZvd*s^fj>D#mVft5dN61|}|znSz8{nJiAL;lTQ_W^?Vl!EHU#I;#~24LRB4
zG<t(!rxw?&2ZV~=!mGOP0WxBSb+@ASu~R34+E6xi`{2W2lu}O~Y?6D6knZ~bQT2^s
zngu(z&6&1s+xGOdZQHhO+qP}nwr$(S?ZfZf|Ml)?zg5|(WUZA1Ja~bnKZfH=XGBs+
zdDxaC3@LiD=FrG6+g6KGh~55jd~gA02rO{<_?RRn-Sr!BL8u=fm|Qlts@i6^G?%2i
z2BK?(9Tt+sVJf9Rw;q=q2_{)SYq~C!m|ePi?T3g^SIR2Z;2v=jl=~UTolwx=$ojT6
z7j&uVy%Lj_5J$IC1!zP&Qf+SCR>yo7vc8;XAj2ts$o5d%S_d%P9RU$WS#ds3+tT@h
zGxCH4m#39RlR@f4Vu?09am`!48#E=c85i~=#=dO*<^D4kB>UY0xC?^Q_0?Y)zn$?4
zU@A*6>5h*8AUATiMZqXG)Eg~Q-uw&&++p693?iaN4!EMP;VsbMJa0^VH5-S<jt84V
zg~nmW5J&X|q8PrB6D9i1z15{IMviSfJfpYOflq104sGK*4|!)LZllKiufqoXymfc_
zDDY@aVf3G8I+&CD)0PxL^K5sISd;0EPCVnyEr(U_P9sSC(m}y-()Eg!posK)7VZre
z@2d`DQ2%Eh^fzeS7S!(ubZ}fVPL+yea`QV@3DsM*;2AT&{*&^{NeA0#=9Te{DVnsO
zW6ZDdH|pKeDmOuXzpyhXeWL-g`*Q&mT!AuCG~c<abb;4=4O2{cQ1cFSg@56h;9I@k
z#$YAn@Q`K!^^U<pI*~$8=r)w5q5I-5y~aE{%Xp)LAQ@@ekB;$FnfkXGNh}Z1>7xAp
zZ-UT-HY{HVMzXeSEj6G4FPob_Puv1NYN&yb6?CR$1^EVrg>1rv$co01<z{hdgj6Z`
zJDRb@R3)4cmWLosCgm?dt`Qpk=fUNR@$C*08V)P{tEk72sNhw@tn#=laBi`j%AfK>
z%7=)=)jhhJTq;y~djeR@`e)}%Vo>NVd`x3V2+*Vf%L<zcetEMLm|+&JMTQGSClBfu
zi01nEZUzTXsFiAUE8wuSKUUN(hWAjp7L?9xO_)rF1lnhL%NEoRR5h7dE{{}R%`K;8
zO>=2^#M-U4>Rw(}yUW_n^J-iaf+Gv@BC5bR4=XV*Z62-z6`cVUT19;7^YGF~?fJA^
z^`9<Bfu>O{C+OhdFRLm_vMmfSmRvOLV#Vc@<6CWBK?XxdmYA}3EiI3&ICHVpl<S?A
z`g{%>ESNiOWs?n_GRZYO?Y_Y@e1cvq6qJBXecTXx29+e<gj&@BEo>8EkdbWJUEYjK
zkWKBGvlcv;y~)k;<;AyGn69m{_NPqJgKf>@YHhBNTz_pwbDXWqg$a+(A$o0qY$DXj
zC4MnksHfr)oZ?+ozN~Go(+N^_Nt@qMasd>DLiZ6MvGfRk-$o(Bn7y=J@>ptqrODc+
z>jb@HBpOIM1*X(gJzy(PINGWQvWL?!pA_cXAFMRMVk<DoRgYGdk#B%i&0gjx<{*}g
z7HUHGnHpFQE+vKtrUOP<<d|b^I#$Bm<qa$AZsvosdL}^s+WFb-b>r*6jt2nf_S#>h
zs~yo@CKHdIAO=_y?INQsh2A*r1_!qpk^kuhFnF?ljZ>>LTJsvNBj$OMtw(sY#o;j3
zk!!F2dy|XypiR^vcca|=$NA{bm%Lh`X%%t%kzgB5JSL)pP5Z9enp@U!G@&Uurib6s
zb%OkT%)@`E5OZGd2LfN0wg>0I6}9*kIje*L`zNXZDQgjxD0YA=#kDFhn8-J%OO5F^
zlK{iOImVPmlE1COu4X<-YSdJ0t)gfR>m@Fn%y^KTZZka~pM?_g;;0mnF)W;ciK(hw
z)Lv}PoG&0*ey+9<$o}vYBX1PI`fZF#MNeobuk;v61-MB~oDt#g%DjgIF*jJ?oe{EB
zfpT#Y*sG$v{)VV4Q%jeKj<(L|0bxc_ZEKU6S-(cS+|0@b6u@|($KUdn2DU^5In<uA
z`bKKXtsx0llu0rDasf`{0kYzv!FjP#Rk!NF=g4#(NOW*CrTO{4I}4|D5|{anF2pcM
zortHB{0yh%fu#!xeq%>RKLzX8AU($UmNdu_Rq+o;M+m&s4fN*fhJW*f$S6f=$VL#N
zL1zkP*Bl5+t0%<}sf5CzB}#5r9`x3-?#X0sRgn)t(SfjYWm{1}3N+T#(`%IY<}8JQ
zW$c$kK{d62)OFVcg7n*)%xZ#w9d~5LdCcpDd)#lE+$;|sLCHtdGX4CSOUr)))v8)$
z@-6SBLzL{^yu=Bm5LXv=Ms|9JAq{Nnxry=T%~)UtqH0_8cZ!&sQRZ2P6y?;(TGuUN
zOy=bXH1y-9Yr&LXRsGXC4M8nax*mw4pIF^`Z#s)ox}*WRR3!DrWz{^{BjZy1S+P-C
zd)rnjU)@w=77C$NJdu<}`kwSz&!P}X>;$-CFeCijuK_7$X=j{NPR0CQ->HD$?~Niw
zH7G6*I3qoZM=f?Lxwz%Ay!+TR_>LFo%cDGvHcv`^gJl9(y0ImRW@rwz3VHnIjybyj
zPP`?$o4tGnNg4S{X1yTBm{W;M_vXb?>iU>{=A*FwgCDP)Up?-9X%3|Vq_0$9Vk0rB
z{7|o+-0md)mX^-eWwpLRP2@BZ8-4dPxJ-qouy)sH{Z4Csd|a8IfJU*OLTBe^gdI~_
zDqVL^DIary26`ax)&Z+u46d!;d<%PMG?-lR52naE6@eNxmxrX8xg1ahzmxns8sA;}
zJx8n@%UQVhb05rfOhg0v_GJglY?j#Qwfh!E(L&@61egZ(7?%U;7zILz^;5WP6hIfh
z>dODy!q;S51a|KrXv-!Y+faL#=0T@J9|Qsg3k8P+qm}tXpquewdK1ll|5KXxLjmix
zG_rbh_~on2YZCY12-m96{@PvpwIF@BWqI#?AhP<KBYS`*3*tz4>YLFmaOI)-DjgiI
zqZy@oAQYCM(@Z3Tfq)7v@C?;U70e>Zz1%_jK{NItQCVcdbC4W^Pbn}+Dm795$6^uK
ztftc1O;yDorKeNt9|~ztmXEiT!#0J&$3_Zai)V&NTGE=otT{#9zPd2!{W00^r$^IP
zRQG?C9#f&7fYEP2>WLe*gG{N1zuMgGrDxAR6>z`k34V;NPBCt85bS3L<kK3WqN2H0
z3PIf|rg!oFRO;MX5)YUY;&L40c!(%lj37O{IJ{0<ih2?#ofe2gJGL$5=N?tIK3Lq5
zlFqZT-|aSs;oU2J6HfEgA<3~INvSKxYTO7wY1tw)v88&A^*>TjUzp4(F+Qi+>cXs7
zmJ(0GPN#Am(|QXPHeyH)bpGPz<iKYgj<&mk80-0xn;@hpo=EXSxs`&C!uGGdy+kJW
z?$32%zP8wsS`d_`?i>HvliBZl?N0SCYto^UEp3$Frf0XZD$Q(rksO6y)AQjdo6k<n
zpE4_7eZ$l|Za%i++Evw+-mc_rv_~{RAatHlvusLTwWSfoZWexE#h8PoWT*qJ)vXS~
zr!$`}=U711DZVro<6-6paRGTgm_OH<V)iw0E#q&3rdhU+CqKWA4$0;S+nC#j4Vz^`
zliO5KlTT*!7XTl$@0K7Y<f<wWRW>P6sN$}E)_V{tH}uFMUsu<hWLCfY&eX}%C~qy@
z_z*DgP}MHu>3zak{(#91{l)0_t5tM_*9ym&uU?3>o+O!*bGF9?mAmlBi2FV6+<SHp
z;2=qWY}<YQeB~}SH&=_09$;`7ly;ep0{LU0#4tif%c@ZV{p_YD?iXT`=0jPw>o_OE
zfN!H|6uzMc)E&o~W6q64oDVi@Anqvl92EghO6YCuzJ1?@8zF`plm+45q!qkpBrE5w
z@M~b!u6?9Nr8jm>`JG=ogoTMXnb3beZwbUY5T2z5g!vg3EY!QbK84PfDz?K^4Ka=P
zMn{Z!v+YcrdF~K!wo^VJusQeD2Mq()o$lIFoOK*D2+Z1h_`x^u9UQb#4B}SG`366L
z7NxI8)p7Wh`z;*;Y?}Q-=r6m+Vrw5v)+9@bIp$;<Itkcoe-n1~S`o<M<#XEURA*)_
zuU)`^xbw~RuZLM9(MDpG25)^L$!vU`2}27!FP3$Niem|bu8X##Xd|T}4#U)OL&*=D
z&rFF$vBM1-e@PGDC|ECpB>o1wZk}bSI^n70ZAvWhzf?Lf^^qeQoOd3(%6N5kaCjnn
zG9Nj3P2pKdcmteMxEL<`wR+e0?I4ieNyT_>1?Je_Tg{m3zO<cXIFPuEFGa~y;n_*$
zG}iJ)Q6Y}h?(B2p>8EUSfb|C7+FX^lp$waYh2bi?O47h@6RM><H>kJEL7lQxdkHn1
zc-FBS^`%a%gmb>n9cK6UrbWL%p?g-N%FAW7ZDL|QpHV*CyTjwBQ(WKCV4H{ez5~Iw
z6#Es5L`3EK<CUJ>!}>tOLe%29)>M{@iL=KNWkWXNMuI8p;FK*4kBhS3m{&659khS~
z>hN+!G<subfeyiDLMokCAZqHK;*8E?$8dlyE02aYgUxD!p?h;RPUC0$Ts&pqMCNq|
z*GhH}7<RZnt>tRejJeW4k*=1lvQx$x`diNB3hT=yoIp0A9_wzEt=n-imkG)+ZCV7r
z&!+5$#WwU4-f$!8pz~gl!k-wN>{heIvPs}%O{J#LlKUg=-JFBMpBwwiUnFNaXuuhZ
zhWs^l3UoK;rS*=ckPYz-hQ~MXxnlsWPxzOC$liS5q4cz^=;1?<R*>#A(l&Qq=_AP7
zJmKU^mpuswp|%dzP}*xbBO@d!l1F;17^sgjD%;ze3m?P1coaqwWN;d%9`iim*e&-m
ztX{$RJ6RXTrk@V!{Qmk`W$gSwENi6%k$Tey>;2IYhU6K9#InOa8@navZ%Qe+M>hO4
zii}?lEf6YIxX>7T*K0rv%280eUZ?Dpyz}?Ni5<i9RoI%JYnYkbad4b>Qx7ka<ys!b
z+dZ1;d-L-j%9UP+i`d1iI-;P(Q;1t3uv&a;m#JjI<+Ac7sCoGk?@nD1=bV9`p>8%g
zXT^-?fES{xjneo&vrStjc@!2Y^vR-y84^dN`$%%FxWnWIE&X||c>V40`+eco$pL%m
z{Kt6?@6DFnOnR4?hl_#_H>-}R@9X%nPgQH=XRUxiMVpLzT1dq<wju_}U;IAg7P~=2
z4>I*hmab=|!OB$PB<`#gYXVCH5M!MgSedUyjo+oU#TBEPtv7>Wq+K7UxUsuu5uZ<{
zdv%tStk=s*hCHpMeh0`rJaP5WE&^472~WzS<L&dFeDvF&>nz`Pmvh`pM!PGIm)ThH
z`W8}muvs;Xq5=4)H@;Q=TANkfRXqrIRl(K+l%8TIa`etNhZ4|kn|HV;CQCGGfECp?
z_(|{5HJ)sHI2P@YXRsenR&KW0pQSLrt-BP@KXz(@5kYhJpRdBNL)RD6R8`KyF@@8R
z>(3t#Yh%U4W`xae1ve<+A##oR<;t?x>wn+VySlbi2p)z>;a^9&RzFyh2Zld)oDlLn
zZ=E2Tx}t}kg-O~sJI)3+99D71$os~(_hC<fiEx~xi;Sd@yRiR^$BqOiJ$q@}KnGvd
zI`c6TTQ`;+D0$kB84af+@AO>USbV*0)`=2xVB?s@>2*>ZA}TUDDS7_HSSDTjyteW|
zQTA8@;*d79*EE}F1$zQ{Si4*o>tM0NZ?)SPdvJ;Cwz>+tG*8kv7n@zQrsUzKh)7sq
zV#ARgl$>0Em)ZjNPJ0pp9dyV69|o_Pt!~pQK$ZiE4<m68CIM;r2tjm?0suIio%P&R
z^*jrtWGc{GZCDo_ihm}q>bV#_L&l85Ee<zCI~$$|E4~L0w){lec43O2f*bpBR8bqd
zx2h`3A~;?UYl^_X->(3nIHvhrWOM;NfdT7BMS`o7savS`n$`*StS~vgn?hFBY0_D}
zS31^d-sLq0GSmsJ(t@{rm!)r8n`ldpb1=zDDrgWIB}FW>4D~UY#urTeFoHwFZWH#7
z5Nv(LGSd`g5=et&6QhUp+Q<>ZEb^v;)rBnmqSW<Z6kTTL6@?t_e7XHZWq!I(XCx%X
z$N!biMO6^Ih_<Dcnw1$-iJu%5m(G)z@vKux`_*phT1c#l@um=;^;;9glEaGWl!=h6
zzz3~iOGBjhoG`aEq3leTJYgtky9hb2SWH@m6Tz`v4-}cS<hp<$*(H}vYHyOsoNN#|
z5hc{jB(0z$&P*0p44|p2mLXcG7sy@fRP^uu1f#}GRXt*%@!C9JQqobOVIm~wnkCkm
zU?4NuVh9Jb4mW##OADSLaH_cDFU?Fe_QDjU_~^a&9!Z(rd7{EY<Q`;X<Ya|qNT@dx
z>a9>#1>L&E_>k(LgE97x2GBeS^g&|P(r>Rj6hPQ#F>AJq#4bu~ZP!Iq-6W<|ia2Ie
z`I1~iavfzDUjdPz92LdPU^bRTMrEQ|s1l{ZnU#x12?Tk@x;fL23bVC15MB$#Bej?e
zu?_b1^bxbl7}-32Y*7%^c(L-y#ZyYqMvk~&%>2E~N#Sh@w|vlqC6njsJCGpCYYW?(
z>t~&@>trElLvocW(#KjK`*}24K{+$VWmAB9qk>luo_TMiefDDM>E(igYLa$Jon5WR
z4SkdmRwU&GM3mIUy+xbkOq#oyb(DLp3pv8V$EqOi$q3@iDwTS}i6W0{0?Q)G&$;``
zlq~pb?`)2mfvlUA=7I#qm^Z2_oVISrWr{K&Lz0y_vMK&XNBO@`)-p86>Ako4@%EBV
z`>(Wk^H8>MT-Pt*n`y9^XsvFjYx$3cgf<XReYew(1wU-JYS?Rc<BOd7Jq%$kJ$INy
z$xVFDUJ4#M?9`tNLZg@Q$VyvqESD70e68k><nAa0(;@}Q`C>F3=c!5MwK_N^i~P)~
zMHGrd+jjJ@Ixmrt<AzZeIkX^uhYn>J<VFiZNir-d7QbzQHsMK%Nd=9(`cGHSf(eM|
zsYR7UP!x>V7oIThHj$cELl-X*`2p4<MoSm5@!~5?)Cf>HdgyU!ExkD0YC6_{MFaP|
zEL1T4tyhhg3%If4tZAYGuz*>#enh_Fn^#DvfR`KWioGpK!NLR|8^1IhPF8BffvD9Z
z5|AM^bUb}H4-c${OjF5JQ#fyY>9Wm#o72<~BEIa$3Usuj-U=a2WO*mp>%?zG(!Oa*
zBniVBFlKToeXu%dm%EEow#-`53%u@F1B$H7aA*%$0l)&4rq0Rsvl8(NumoZ;(UUg;
zE~m-CSoD)GmUpLm%VWaa7m&n6?h?KLUHxMDR4bISux1f@EX3do&_w0)>Cy;Eu!vu@
zDm0y_m6b6M(71(GrUoNFP}ebM%(C`Hh0`cq``qlTjB>l48wqSE%24sxaC)_9l6OS;
z<If`7@yK_(7f(hZs=9ZD=5DZCR!v>g8?YRMA|IcTq_}EvCaGpC4?f5BHa7m$MaWX!
z!4%omxj@_v%<Pno%e+G5rP%K1U^NlKIypFcX7DE0-<ViX7H{E2U(kvL&@)IfyR02g
z+ClDe05ExI_}Vto3H?G<A)@x;*ucwd$i*Uj0nr=UjKjM@x%)xpklWwk3FoVOZw&K2
z&No`ZiI8*r>z&puAE#;FmI?x@xi<`Rik&gtR-iP|+VrciA%KMuH;NUFE)W&1WI!V8
z9Q=%rts2T9Ltok@Q>pw|5np!-k0UmPHhN9o+;eKYEQL}~xjPfl67eb29$%M7x_{rX
zjb%dScUu*g9z?bsB*spReA~qE>gZd`6G^Udu4yS|+Bz625<AlXrQwzjb0DuTJQ1l_
zuwO;Z3NPRR^vtqj(T~kU_}XD=#5(iXKHj#GRA$M827;Ni@Z26<OWvAt@N4dFlsq^#
za(=-LG@qS<SW14i{Z7Ln^Y&o)0OHN6ETp`}32`wOVX{^Z&X~cm)X75kWM$|a6&I25
zH4IfOXn4J(6v0$;t>zWv+sd(=&qu&oyh`JIY)39Q=imIA{5!h)#yyUG34B=@c`rHT
zvsCgMS<!~=;kiBT-uC)&@)BeD5!&$)^45dJu8ZQYm$07wYvh(T4DDdB>%EZ!VJ`pX
z8$7EO*)NdGw0;Z1W3>^Sc?<`&taHcB)v%zXqovu_+qrT~etG`<tmD2-=5db>DowIC
zstR+i4geQaa-yo-EjNIP5=!@VCrwlOZ)=|(!)7Vi77IFKCd?8b%!pF)3I-P<B`Jzw
zDZ2|gMohyLdsax}0*8J)H!)d}wlJFk=K#(*J72h+D9+ygWiB}6WTm+i!2n3249hA=
zM|?*Sl@!j_DrXIy9N}YtQ=@Pv5&|qyxA>X*nVC3<(1(M*KzXTCm&OftJA)I7K27vf
zgsXy3yLHG@sD~R}bIIkmn~;4d8ykM}CG?F&W0&vm^!DU}4j}YgqK1v2qR!vlo!nY7
zCN`$XSC6ZKF46*Am~zT20T~&U#}g;*2={X)nz-#RhhsV=%cF`u!}+tV0_SIk342LB
zRl8B{GxjJ0IAxV%TaAAl`gUN8EBmd;mfrySHw~+y(9ibG=cLxg8<p`|mP*RCbB<?6
zpx^DCze++BJkk?V8(t%=*q+F|bXI1vFq(mZS>a)X+$72`o)jN}BE{pM-Wr^J$7*u5
z+IW#PsY-umR4I6(EHNW!0W1sYxgS*i)*;wHZtl8fRd<O>6Aiuz0(X25G;8lo!h8xF
z&l!Muw`%$0Z8d`!`If7zg|YHF9w*C_4MtY2oy5n$8X;%dvq1Z_j7K}+@io6`rXmfM
zfk?1vV0U$DZvNEyWkFI$<>I~V<I4J`BzYZF&gfFWnbvKw$dQJ}Q*LR;%5Vc^qBT<z
zbRBQ4U7Ktay4b*u%4D8|&c74)7oZQlI2)>!da7wY;tGDabPr@sT@1Qi^k?L*Q{1^U
z^Vf&rEwQ|rQVgVzPgU-j&MW=NW>5fZH-lToG-$MyDm$q_+vx7bT=iO}w@5LV0-UD(
z{b3p?{jqw){aW3gCuzhk>r^Y6AzNiqQJE)k@S3p1a>=T&2kT@4w;tGw5FUe-QBRGH
z=)nhQpQ_OcAq;)gzDxy5ZxCi}PZb9c_0W;F6!yw>I*fbYKZ!jwASl3~o<JeIv!+xp
z{$F--DQbcFlzV7y;#3IriWK-Y$3edA*aeKk|F@cVo1YiR(g963QRgg!Ca_;Ay@pB3
zx*|m~7^yjveagUfyPRtX{@>K$#}&NK9>#C}1EqRU8J#<<I4oeBh;eKP%ZDuR+ig#N
zT>v_ef4&2+Hos3)kFGQFm_?rmR-8H?6+%W7!PZ<adsF7DHp$St+9ti%KUV_q?>t8V
zDD_H`ASUjuU`QD@gYes*G>zt@Ne%BQkt;g+>-*nsyUXhy8vihtKR@y12?lub>o0&L
zl`5Xc-ea+qbX*LSm!T61W-2bT_Kb2Kg8MJg>9q}}b1`YlAT?vqpMuG6&*2Fp{G1HV
zIb676{;Sck>~RR|ziZKeJi!1Zvd7ucoMT5$j2J6C!@XHxPD3<e&OJl)hamoEp%h5X
ze8m2T4GP56_F-vUTTTUxD~*T))btOs>3#I?zx5BY^+X*^gvdcNLnS9k>N!RBk!1|8
zXnr@*76GPj!>?avCBkjHjCBb5zmKkgi;9;r$Qbn7KRt!ZxT2`%RmZl&TGBcJEm|PP
zf??G^->Yc-_Z9>2=UC(WcpAb@XDsRk91Lx+JEelJS;B3Dp+?j~^xK=QMY8`7=lg~L
z<PZV9ZYk#{KS;e|B9yp#LS7JaEC+c}a*r10+}G4LYH%TL!Kn2=ub)F<88e>&I)4hc
z=RmI^_8cXDPn)8Wyti!>p{PM)976xE!~O@cJYx*!6UcG_eS8xwXrqg*q48aGhh=}h
zkbr4XyH|rmGWaf$NiX!@XY26$(D)<%b%)-=BuM|MW$+(1W7gHe)`cj<{dKey1@jC2
zzkBj?E)akmK+SMf*?fY*TPWhey#~4EdnzQutp!HqiL7Z7J1NJfwDv!i{~w0?0UN|7
zD>Z_mS@%;tjM+96X*kb^Q^e^F5?-2lX;NWY5{x}L;{F$*H_d75%pP<?-JlNJu|&Ip
z-Kh(82LRHv`jm2@28T**+`kPj5P-SXtjUHXR8>Z4xQNlD{g#a&aH>c>_f3epTwk6Y
z<hAv%eXl=I*;5eagPK-okx`dFJ?SNw%+{k!c^DH)p!C3EIwDwWr9S87Od7X5t*d3b
zinclnZI4;@*|*YNyXs@7MM3n#_{-3bWDb5+xrMOL`l>eLqvN5KYX={bh4D=*5*wLR
z+hcR<r<sUPKm<<b(bkt(z&XtD5YyJ@R=^Z=A*b>5%Z^qcA&oU2R%Gvg?D|0x<Y(%k
z&PhU(V^j80b0eCR^UY<ZQRl24lgb(=b&wS0&^X=rm^$9<bSdL)WdPRY<P-B(GT%3K
zu07;<$MN)XMQnMK@)?$NE1>Mncml9r0OOiT%kB)S{*VWQ;{++}CCB3{ByLn{oBLVH
zv=+~S$lht}8?1~VbRE`;YuyFua&cFm=6*)xw#|!p4Xfisu>RHSDlb*A_BR~Vx9J(_
zbH^5D$cBBwyRhJBgP<g5*F8r=(%9!G|HCBux3sml1&1UbiGT>_x<P%lU6>8u8IMR3
zkpX9{<8;vGzYVa*I?6|)=8bQ%I_=hYh_6Mp<_id$bs|^)neC{c^vmrUhJ=}^(FB@j
ztiaNAuGIs4;NvzE5!RD_b)9!(W~snY1Wo3gEht+@>MGGj@rY1eWR7|t7+bm!AKuQc
zuL43b5PsdPi$U)uEcB=-6dtk~x=YXLAZ}bj5;0gR)P@NESEg*SeWn*|hu7Z+VAzN*
zOC)@&*Ea}Mk=qm`8{)l!?R;r;coxcu4J4FS&q)B{SRvD@k7*%8$M|nadYIZUP@pXu
zXYSKm4)vuu{AFpE9p7?X4XLyfj?m(K_&8h#O2v2?v3=9zx7<^L>dsUH8uvztbJ2jn
z|9V?qfFi}u11yEP+IXnnF>>?eHYym&aOj)*LY6gi>sA|Iu*yQkLc^U6B>0NVC$BQ!
zk+!YFCf(JQYbc%c930G6!_7YeVj}b#50{&HO3H_T6mu3HOzh8W&~Yubq-`C<HxGSX
zJ~M}hdhqjYjni&okDl&>ba8WDfn}AXS5-hR;b`Q8<fCIREtP$W9nt=C%DY8!NTD`K
ztoP?`SJ#-h`}9VG88E1kUA`dN6rnz&=;a87fCy~wXA3?fVJ-5h23AFDwGUl~iL8{A
z=wT`zJ2;@pQ;*cPTc|&eU-j7$t$Ab|Z~OH6U0$E|+jC2z8=F7G!mu1dlSjqTSWz--
z!nLyChC^d&fZ(PbaUQz)lh<o8J+81YswTbBGe0d8q?}^&v#M@KSMJx2R>Xd|Uf!Rt
zdB~_pkPgyt0rmI3HR+*<SDQ2L?aQ|TLDksvX1%WNFnK8z43>^;Ol5x3Li3Tc<1i1;
z)K)<w)_=Pin_l=Hm5u0ldxlh`^KCm0mwHhv^2Fl@4O^(;N(6h>I4)l1otWA^8GX>x
z?1$(Pd1(dXt=pjoX&j355~!gMizNE*>DN~xysssniRZOwCdV5Ad|23aGhY6qVj(;A
z-vw<ocV#jjkOVK~OI$1zx1VR1(Vlltoy!&6wl}!mRa3tygE5vTzkT}dCvuJ&^DV?T
zgUUM7!A9FYIo^796ZG}7aKqcj<>jz+J4FAc@vz@{Aq{YAw`T_og83V5#<~~bRHk_!
zlCB2@#An~LB+_P4d>*{Z##-Tm!Vq+EO}67ucz*Q%oi>O{rC+=GbT_0_y5LTve~&Hg
z+H;3a{v{k+#F~~?wD@6f?7osiFE|h08Uaiw=^lPeB9{E|*|uz@*z_gS$n$kB+8E!p
zjpbq4=a>q=J@CiN<aTN%a=jYo;ePa*uLGtl@Iq!zRQOpkVxx;PLBS*W1qmPVGB#0m
zG~F~~<hNd%9m?JC@3@gmYme`{JhUdBEo3aG>3;?>UhW_+7i9kXr6?SY+d+Rg@?o$Y
z-NI6lvAGq-=p=#<QMD(^iY;9l|KO0|x}UTTUwiC_pQ!LrY;kVDF?g5`%?aP^ES>l*
z32%+K4=mJLO-}$~#R^tQxDbA}{ovdh->KG;h4|kgp692W+Abg<k=eLQh>w%5q$mXh
z9&Qe{!)~7E+Yd1CQ9Vt+B9`@sp|1-A*mFP6>b1MxB-E7z-@p+U+i<0>@--SfQZ&Ex
zoaDKA$VX~7ATS($Njcv&!j8s8l%r}~<02q%JXVcPPqA9BuX+US+nTbW0Ew(8e=|yT
zsEU3m$3Ms7Td0M^Wl)H)#uG0WZy;l!?ESfWOZhe$8EGG;XqfA%nMnaJ$sBPWt5UB(
zFHrS(1ZI5&;d{5-<UvrqS#y~j_?y<wbp45~`e=0bM~|v2_^$iSmpUr@ufL8g`adH_
zP=0{3W4A4VXxzdj?Ev|SfDThw+NtXKQwLqGO;%|SQqLk*T~$RP36he^Sef84HvHzn
z2^6JD_Co#0WvTJ#^ENsh9EvGlLLnO;xIb)oDO+Ukl!jt?1RS0CGKTR=!?4&n32ykb
zg##h4XXkQPg}8*l!JaPV@}?3z*J(Q?02939NQit*v<i{f(xwno$#Na;Euk4rq;OSp
zyb{O@bDHXXF<I7SrtGs6*mPV>b1Z>7DC@TQSUe^Ry=gx^^v6CP>a63%aJPl5yn^h+
zdoQmuuy#H&x8=F;aFIF2N)dPPjv@#_RM#3xu)Mu7t>lm;=ZK>SE6an=S&^FaCIOah
zj{Xy8cKHAxEeG_A2J&QDVGf62z4zCCPK>4No`V)gi|5`6A#b+nE=TjMGjLGrewN$L
zQ$Fi?d<>5Vvv@S`&%$c;`!Dls)!a@#(t4^3>%+@E9xgpvSABO^Gwyotb{;%hTWxHb
z;8`M9dlLqDbm(t4r&c%a=pG&QdBO$y`d1Ad+_`Umc<OHrB~FX^eHV^dHjG}<d#4DB
zl*{AE-)(6QmnzLUbO?qAc!*z;7o7FzS?}z4=?*N+pgU|{j2};;!x1lg-3*0>3B}Nv
zrE@e$zAeokCAqmcNH+DBG~dMdij&4=np1X{`MaqP??#UyFphk<^I0Oy-|Xw#i=)zh
zeeu>iPi5p%bjP-!BRdVo%%no@#7AQS?qa*y1L3caX>F|o{*3aQjD?0x8;Rr>3RZDg
zi+Ur0WZ%@<Hehp|%c<AZqc`D9YiJ9~mKD;BZ`wCNY(f_y%;)#@n5+M@%<Q<}`S_R_
z?c0^xdi-eBfBI_xYo+>p7zHR*|KFkhHWpx@0!jP`Td4w!Da<EJtAJV44T|270SEF1
z+-;v6`$XmQ$a$U5lJVJn^+ona-yU?lzsTUUplPIg?AiRAWVW6bx5MmMIf3o^&HlH0
zQCd(xw%Mv(NuYqFsy(px%cm25!Rbz~KFIBEozy}>JSMQBay2IIyQ$$9;^8Zp1`Ch1
zQj4WYSpnf-=iu*jl5I4Sf}2}?HW#?K#F*-9OvlHvH*1`Nue$H4)YqPY^6hQKCX3tT
z_$&nZUsSFR8v-*6pEVX|p=R_Kv7Vf}t*>p@*|3Kl!LV*S-O4cs*Zaod+vIn|n8j8t
zqbzgG3t4Ny2jY3CGVq&~@<CdxwM9osHM|7S8A9WXx^!LzvG+wrEZ)f3E!Ww6XEm?N
z9&rq0Gcunu1!=IRBe3?xUjVV)Zm41Y>qo3tl%L0c@hM*2D5y_kG!17F2}!-`Jijjn
ztc|SsLn7p=tJ<z3@vGYV2br$ANyYw<64}ZnT*eQ4;<x{I_t60BQ<X*7MHf0in+$Ei
zL0E_O9$@qBHhA6oOWI`1EqP4hf8^{-jS8JFXD`DdXRyy(n6r_VNi?HVH%Y8#P)2zQ
zplcv4)U8&+8Z%+N>_y2at-;yoj#|RPbzwa{TM`WKM3IwPmL#PVBD-~IJ-BY2CcW~X
z+2kn;^+?a6DlR>kjg|s5zvDND&!0+p2QvGu_E{|w1P%(45~?#dESa8S{;g>B!UQ3(
zo(q7&6dK;HnHoY250xsIAGOzAPDSRou^=m^Jc2vY%JlNSx!=d_r)%@vJfY70{`k8B
zddPfK4S9gq^@HBXKxy1rpsaemw>vhy9@LKqKtd^eiNQxjwDKB?gp`8`s|?deoE%3-
zUfSYq5$<OI4R|Ml6;sI((BPtP9+z!?Z$lF~4Sz}mI7cJ8kNxX%`f2)BUf6GOP|}J@
z(GmD3VvHJvJb)EDfL}I~BO9u0$jerK3S299M`u4$1ATW!@;-HMgGVMxLrooFjhA`D
zYtrOngW<3Mg^Tv^$aQKxd;*oOvBgbtq8j}}+W^$)g5jSTaX1uU>qc~%2fE#SrJocB
zcA&hiRPq3RUtp!N1D+LgQ-eu2dFKek@QUHTzN;UMfH;zOfx7<VH|)Uyf|QIvvK%!_
z+jay+IV9q#LV2W`&gEiPfj9<*65IbJRqP;CY|#nfXlT9Z9JV1XUGq9a5bP7B`iU89
zUbQME;Eth!L50@;U;4}c)4&6dJl00YzBn<3I3s}24&2(8T?x&h|Fz&HUS}Si+fQRD
zxW%aAzd5ZBKEOFiCJE>iDl0~$%J1PcwuiNW{0X{V8mZFxRL5B^%Kq?wpZPzqfUW=^
zva;5vCz!HSnibdFAmviE^cGbthRv``<4hql{fz%F1%?5l+cZjqFU`iCB2CnHMlV_}
zboix7pxo0J!bgKRm0Q&78EiZR{y(WF5@4ys7-oc=Ndq{rt*)Bp3QH6|8+W|Iirp_z
zN4F)m|1aw#2BG^nGQcuElJx5-s+ff~s_7Sl?ghgf#_uYfMg2*E<vQvAFTDl;Ap(&<
zW=mbLvQ?yOfe2*Ls>zp%7V-ULX;aS-wO#tZl{3w7JzX|b0rq|&MJ&pbN{m8VXi?&U
z=#Csv!;EGri_P#$U5W(%10#6Rg3aK!2&y~{*=nH$^ZP*ZVe#9nW3+lgS#^JD#poUy
z{KpP>wS|4kYE*3&5;fSwK=!Q(VVD_ZC0Qj3<oPSm!sb^UN)ME=*H)Kxbw2)^?C%f=
z^bDj!7fHbgso+oRIw}&x+REwW-k&}gAhlU;7(E6~mS_hoj;k2`+lz~10A`R;__iu?
zJT#f+=}V(Gj)k2%?gmR1XwN-PL;MGfLS+Q1xdsY9#j>W?{nGe>%NIZ>CyF6C2TyO(
ztK#KBSxF{okIalZ82^Pj^bm&vJj{I}t<Xko?xKXpg8iDOPnNRm2^mtYWJtw3U2S_I
zR{gG!`A@J;Psl60+9$<m=9vi7Ub*5L1ny)Tjt+FPqQOQQSQ)N8@i^h~pQ~RS9#F*;
zk!gmQ0bHU?+<<|cH1M}KmKWVB3^yy(IQ_6y7gG4O7KwjTuIMq2!1X^>@0G?a?qJ(8
z%E!d?_-k!x96va~S7O12eFv{|=l*IWdNRTNKUDG@>bIK&pF^xcL2X5lgGtj(wz+g{
z^?he)X>V$gnSz8JGAnf+Xh7ZUKnlM#%??k0*^<NjiKYBN68lpqM)T}%s3c-LQLL}y
z6)JAqWo6z66-wbCHH04xF>9HP!^~sm8rZuZ%AHeX!x$AE8j|Kbq8UNRrA7cAiP3m(
zlMeU%qXnpMyIIb1bvi6HkA*&Tj=;dRH80|%t0JfA$lmUBEELEKOL;C*p*&6m*0$X5
zFgoM=n1D1dQ9;4^T6Q+q2>CeLVl)rvW^a%92i7Rya~wKoka(w5I%{yXaBd4!+B-8_
zdc7klghdGx=m<&*dj|{l@Z23^p?T8LGV(5uy9~#`S9&v|_8JUbDoPISpNJ!U0T^qK
zGbK7O8A1~pJmpnT!v+m|p)j455QE(9JseeLPX+5<u*x{?gm%(s9-G*IB`GSV?yR3<
zUSNN)taN~y1&y^a@t!R*568fZkG0|))pE)=Bm?~8^vf+BLcO-EraNjfz7GQ;I;dVM
zRPD-9Ibn?dP6ke;GWXSwgcIw6X>@??kRe%6#gBzZ_`$cX>apzN!t`aHwS<oEI|$Hp
z4pes8<bKXtH@cm!wEjzeF0104Sb@b!j5B?B6_>T&vh!6H_jw`e>mvCTL{6!74mWY{
zFg%{hbq+eYb#pN>mp`H_j_ZB3%TAa{KNTBoz0!%taTRhpvD?GMfy!}HXFm|^%&>+X
zF8tZq!S8a?Y3U;EJjFkqSF@1=Z#E}7g{9)_`s`*qxH~a+By~K2v7350pvMS8;{~GQ
ztjK{k@P~;GPX^`0SkcC)b%Kf!eNqU~nh6~`GxIXY$-lO~J<g1>sVW0MR_99vq1j6q
z8k_dc5&6BTQd*_8lFr*6sE-w8X_Xx9Zh1N+%lF0h6&ZOQA#XPu9=nqs7ygVz-$<Q-
zusLW-dl|{95Xm;XrgdVSz(`)z2@7XlgpuV~DQBFo3%S(+2NUTQue|~spZWccwK_CB
zYCWZ0pPQI8HCFRTKeHXYl)QOIG9tgSd}Mlyi$AVrn+1&JV!Ku-1fXik`1ttv2!+K*
zm7lT;5JKy(#F^67iZyM;{hPVS;$by9a&u@*=W+>=c^zej0BANVR!FYyHb!jvNdWe{
zkL5DP>w3JmZT={$L3MOI7&2k#x(a^9Vf$T0`FgiLr5O#rvvX6(>{#VH$V7Y1e#_zc
zn3MH-3LJJbw2gCm2RW*$JZh(rd9Dg88PZBNd~gwbr%|wa?Cbw%*LO`OT}*vpST1lZ
z2Zam>qN9>Zk_-{}gwr+Y|57e4UTFDqnZ73wS!@QkS*`^;p+&wkw<<CRV)hcLDQ~=K
zaFvY~QbX)Mah))G8=i!D$kkf0uM|_S8n55TnUP}i%dcK-Ff-27+>@`zC92HdmdFb{
zTigIa(*_pq;vjLsn_%$}f3?Nphiph9%c$W%7m0UXocZ}5V_KT5WOiB-kEyg+uZz85
z2oGES($PH0IC+DK3#!`ydGjJ1jEG!)mxU)wQI<=P=11OIIbnWf!mx+(6^8-bGD)(9
zu-o5+l~Vtjsc^?h^LqB5zjoubU;bp>zVEsCX<^#+uyu~Q;dnR+NvK~m1L{yMr7cw)
zk7&PEvD7q%#XLw>Dz}{yk3iX17%DBG;~`YY=x7h_iIScxETeA9^^xX46kJX#LJ;c8
zM%sviBQ{)CO#=s3xBv-{^=Gosp^l@x1D$Bj6iQMweNiDDSldpPFb}yHZ@s`QzOfEZ
zatA`f2np5D#6&~g3C2vRv*%Y<!9fBN=U&PfBm;t}--?{?pD`R+RG-8*APlHDQ_!6p
zE`L3ri!kwUd2+@Oe?FD<v3c8gr7*JYsD#qWkhw)91(q{%-QxQ&|Hf7oei1itn-Xcn
zo$)fRtwtd&a>qi|RA09oG&RXYvIvgG0?6seLswBeF-;3C+gO=SGeA>P#vAtViXU~i
zc+m)?$-C5660-CHxgeR6E6NUWT7z6oW`rBnFRP_FrbrIZi59l_t~N4cdU*(n^z`+L
zE%h964;RVB1P3`BYHiQh8LgRLT<WH#Z9zl8HkQOp;SY8NGsfVH>B8xWJhVSW<zN-~
za<yp#N%Kw@4wj3qOhRIR_3at6UQ95x#4m~TENL3Md5L0KdRON52U}W}OSjDZpJi3b
zo0Vjj8x;){lu!>9=a#lCkyR?SFe2PL9eR7$F*j4<BN$|pGZYh&rDUTdX`9<-XPsPs
zV~U@IXUoYzqKF4ROPO%-h~UR{DfokARd+zHOpL>|xWI$8?yi*#Hph{VUv=Yu`JRsB
zrZ_DF&tJEB4{eqoZQSondI#Zkd#}ZHYaluwkBH8DAlG4d(%C*8EwVQ>BN;ehUcxb(
zHt^51Kieyr!(VP$YU;{DM3Rp9vy7}egjmT_bJ!xJrwL;t2iUkaR_&G3D^iPLj49ij
z1_{N>MM_|VGr?WLZ7Rj%<>{5G)SWSwfJhWG^qS>;xOL6F$qC37Bh8a3#n((_QW=pw
zDCHpu!@>w5n11Uv%K1iBgY;@kbrb4|2g)@iau79#iy-EGV#x8Klqy8EuwSz!6af8_
zbkow3($d2C6hjcyU&gR4R89Xq61bOQ=39v;W-3L)wa#HB>(dCXE!+1y43l4qT*_aP
z;I0AT;?pbnpy-UM1~oegSj#@Z3+r*Mg0@&=xLDR!k7AkvP8X#F?2p^}F+A}_J1EEg
z@?08Xm#j%jB{!IcV{ZV2MdS<=259E5TAxY30}&J=VSOBA7E$&D;<U!fNw)hwWl&4#
z(-)N7Df9IkEi-!G&t-+w-4T&$R@Z8IYv?Uy-A?%);hLZ(*s!2;_ceKB7fZUT$Gsr`
zYSy+&ff0?KF#%gd%q&doW+4$wE&}jt1~!!Loxo@#+yaw=x3?<XZl{>lM0}fCLO3pR
zqme%Lx%pyFwT3qBG&SkwhNi}*Q>o^rhI0-HAp&ARz%Z{~K)}C%Co&!EKblo~PWHU+
zu_wB{vGW~s%ffKKF`W;x6X3%4FnWB}jYqJfU`9x~dG+{OUD}U!o+^CFz;a4Clqh+G
z{?hB4GAS_90TU*eiE&CFI^5pdgoG1^;Xl=Qz!zrv`1Iq?7p@*e4B+Xl!)3V7oAr!D
zcdF-i^p+F`*5YKvrtJSxGD{?nhtSogWYkP)@)eJP)6b&I&XV)<%?4tXq8xbu4-mA|
z2?}(_L~@6&KQ70b5ufRBPB_(V+0H0>b|nu?VTGs&?cc0J0&Ewl5A96l3uQ17{#)>D
z)O09&P7&~GgpY`tB}nE6JODHXYH}Iz($Y4m2T{;QV*PRjHwW|6x7c8ZdZ$rG+#<&A
z;}Y%8s0s&{NHu=dE_isX4cifsM55i)*Qx5o`pE*{K*C!Js-_s_YJ*;u#&h&->5@dE
zELd7S@>ma_pJaNNb*g9qRwgU#a~;0xF!1{{gvDLeXlYYkAdjYdC#(TO6KKcL8gonh
zGOJG~?<5kN6RcaTu-84)&Ge=VV*OnS|7P}|$M0l74&>r^W8L4oj5$d@BRb;+6oo1e
zu7%HA08m{I4*{J8@_SvouBX&~UzSw(Y|W-5URh2Aht0txWlrgtrSGB-u0;W^$93<`
z2^f{frz}T(g6^Kccb4U+!<_`%8kCyV3Z>Pe^NG6jxhL|fOBj!7bGOgfu33j7smEYb
zFy-*3pY@qz`<A9elvjF6(ps(C=J+Zi>}Axn8}Y<XrFKKQ-Z50;Harf*BTVsyGduov
z&i3`*OWGrSK1(c8#E@hv_`Wh5NR2sHwJ(!j{c&NO`UjAWq)1i9cZyqoohl>?ignwW
zc6v=jjorbj6U=c?f-_c2Pw&eLSUM`^`G;HHN$aRWv!AYEtA+3hGo{`<9cC>hr_`fF
z^6>F>(wOMv-iu(oCemuJ#f7~#Qsm2d#o~H%MM~4*I@i2?ZCA{dAfyH76SUna+~Z^$
zHAJ`deV^(1UH#bl!=1+B3-S{RimS2PN0B0JSH1O4mE`TI=~uZ@_uX04JSJT!;%Rub
z)%~rknRF#XzNkl|$3kiHuj}#}9<mn;WQ}J<8x-iFaZKutc^++%Y&Z_}IxdwJ9zrk6
zUF4_K0B#*HjWjUcdg!9nf)yGolAj5ZwQ4tiC+>wVI2kcGBQxEAr_*!zfsTOz3@4~W
zE<fV>^P~`VF{6#-08PG?oIIqQC2^Z<_z_06xy@ITinH8Y2I~y!ocqfp4J0darYA=6
zgIFp*r6V<MmJg~lUi1p%e|f(NM{Fw_Uo;zvk2O_pbZDz5z8(}o#3{e91(aXDZx%*3
zdYi8Bd}c0O@Q|+e0ZigtbOe=r)4|?fJxBfd%BOE_x-1mj&zSajM;ZtXB3w$1haf?g
z)Z`?`WkbWS{*e!>>dKE2O12vhbeIH-p%JXU*vu?kzMVr%^j^EY2t|ZSVLR5iCS}nG
zHW6M2s>ygOJZ?$SUe*7Zw+s*WIu{vzd(58ncp;Ye6s`M3xc!Y&gF1*4sfBC@ux<}Y
zM07BgiowS)?hRKOV%cZuV6-oze%I6XvGOohc*aW~$7hxPB}1!`i;p~Bv?;}q)j?}3
zNL8xRsmFh}UrV!%lRzA7-$X1N6F%%-YM$9Q3cu`Iu*~r}Chq^Ns#1;A(FlQW&#&67
zIHTzge>SKDgbBm5ZQ`2TJo~tm7!|3p(*u5Uh|8sWjSfbQjLt&`<}RQyPcUOUj_IHk
z(<p0z8q_lw2~bf`uJ9#i0fYJ+EMNIBCFS4i6M7-#@{mcgMTFsUuQ+4dT+u^y-!|~Q
z0Di4_Xv~hLMm$U?u}uc%Ulrfu#7B5c6&3i0{<w$^p-45r8qo2s4XsvQA*vhnJOw#j
z=+jox$?7U<5<K7P`;@$l$k=%-rMu7sY?X(Y$x7YotBHAbxuT8A%Pxeh)!y!Y{K@!F
z2L*2Sk+!FT%HlwH{2mZ#P}p=Xug8lqC-Hh>z8}l?j&3g8$1Z|Ln9kGY)68!5irX^@
zgPw|xj){qm@MCWsMeR&Mp(0#0pNBd@?lbDA<mDA3I^RiZT;JO-e0KNe>*=ku^AlJ6
zzK&fwgDYSKU=zUy==6F>=VR+|m~7_zKu&%xvw^^)^#<CFcJt$_x=qfnwl+V7k8akx
z=j9o{aH$>D7c|qx$#$?%Y`!U+>7dlkk;-AsHJ{}JeC25okh}9DUFZHT&p=FLkV=e?
z1?*Ta;`AjHmYa>4(dW(ZB3suKd!@f4Vk4hAr@f!6>9CQid-<RoDFn;Y+7ao<v*i%w
z7-?lZZqSnY*XHZ@;|@3}5de&!?C%s$-7nshF$Xut*GO{E-c(B)yy*C~qv1jbhmFrU
zo^b=5);3efvurdp-}l^xhOg@NlpRe!{7;cNpA;!eR-4f6xa4W0lp^2w)!fdzbT_sF
z7sUH#!&V~%=5MakCWwG(oatZta<r|%rs;LNH&LtSn3JqHQw|;vkk>gXYI|bCy2w5R
z2(GY;9jjeegP%az(67~oaW5I^1nk)bP{@=Df6Lf(Y5glL{^6({;Cv<(|0MotWqInN
zv{IwDTdI>~M2*NoXLU2WI6-gz;qbhF3O|OXqOc27Ss%(oH%kDRl8<FvCG_G;au#Kv
zmE{|A$|U)jLr?v>nn(fvp<|6a1CUpaDUm`%?j3aZY%I}`6peYwTdR7roiP{}MX4)v
zC6d>4B;#8Qhqwe29UaZVunXXN`M`+W@#S%q`pa?Yxyv-_Div0=#`L!OS5wc;moSQh
zyd4jkK*;7y;6{ehgk_a)U?(Auq70R!Wu@WHjuTj>*W12)=!rK|&7t5#IcBVUDZH#T
zCpklzoWS7w(GNI=Y_cX1NmMRoKs~Z$u41A#k${JpQu0N|FR77o+Pd?V7E>r~;ZDq*
ze(@HRMp0fn<BIA|SZ2AoFhp|ny>_HazK4*#iDg9|-cc?o2b{*F8NM+_EAuROGpG`}
zDJ*oF*MpXNCMs%Q&59Nez*l~lwD8*FUQ`&eu5oS%YJE75gIjvWb^ryq>`YqI-nNdT
zRpf!+Uo+F-J{+H>yNH0p7XbBLXei(Bw^nnLFT|sq87jit85gHH-{Y(>j^kNpFR8A0
zKyu#^Z{>`?U$c_#KHq}Xy(Z`uhD5A;Cz~x<S#i5$Ti0xnnJz3a+#GK^{o)r)5nuR>
zYWMoKleewR2|}&qFzFo9?WqD}Bc`0*Up+<(>`2Iwe}v~Ca9~}#ZTQXGB&i<jz-adA
z#730cQjwh^A!E|`v9@$$W{rIjnBH&4LAX!edgsv{+E9|vk#1R#(O73JIX5_~IAfSD
zQ#`e&XU%_>S#-16P!UiFqQozHrzorE8+H4bW*oKVbLL}vc*j7k>3$|TIZh22pY3Bd
zQD?|IPpe}Z!I+sZ>eJ;_kK-~IHm<fMT~%=tIx7`GaZpbOf)SsmrLtmyRnMC}c2E#a
zuyAfL!*@CthK34ffk5~GvYRNp@@-<~YNHRGr!}}qAs;Al{X-~9IKfG{17A7(T7gOp
zC+SZ**25dc3O8lUDM$rlE0b3r42Z0kgYF0$zAWdQKA@iYW^%&=aUSV+P1RlrbPYXJ
zsnxbOvyXXcFc=xdsPd4D1U)%p0vU$2c0VrsdH|G3T}Vm)!zQ<cz=LIZMB3T39KbgJ
z8A*4o4<Pj*?UK}C)y&P2!{T?;+l|ch7?*|)7UyXTfpnu$Sx&TsX*0rhDxp;j$NsG?
z-=eFa6mc3U$nCI%)0)jMhtr_?A4$@O793a_Qa%(e-e3_vxp6E)y~Zi+y*xr-_Ljx}
z<Lay9ntY@7zZjsTfPl1!g3?GcU=}5%ASERsNRDopih#6$bSfp?jGlCNGq%yAM~zzj
z_~!3@-~ab~_T0}o_c`~uuIoN$j1>SlmVIs$T9`8=;NW}SlD(Xh{)s!^+}z#J{h>Sl
z-5-?Jzm?uol?_Q-(o!FyzI~^$HWc{>r8d5}vG~e`vaaFVr#AlrRphkG)|&`g)<Kbk
zht=<1301z8xxxSN*FbKP!X5cU5vxPii|$%8eYPo<tIyk&qnhqj6%Xsi+~1)c=6VEB
z_56n*_=IG=B3(o2*DmvU<rU6~Z+T{fZ8D6isJVfj@lNiGdkN1zs|hk{zT(8U{8-6r
z?iayCi8=BanQ~;Qj>_Ek;F+tc{}*=cyPN&$OqGG0y2Hu0%rN`j3qCOmo|cK(F|K&$
z$M=#fVi)?;qSV999(xRv|0`llBERAq{ER1bFMrA*cvsTEShx4e8<MASPtreFf9`G!
zcGkYKTc=qHmmJU2cGaW)kD@T%xnWR&?iT(SZ=u6%LJBbuFre@G%_g=>Cy;C;3K2>$
zsC?zrWw<i<AC`S7TnoMRs#hlEMZc=~SB4H3eusp#2c48RsoSWV)@H3pp(#lQm0|yz
zMxeWrtku(h*<PwPZAgzB^r=#px$<RL*T*0Ab9R+4^S&hYhZ%1UMEjcYQLP03M^-?2
zuQ{`jb3K;LiEeOL|7Cf>7LQ`Qa^%QZebXlOpgw6J&zdd1{a@YIpM*%j%}>#q&qb*5
z-(1)vzhBDZ=oVf|z@5b(3CE5XdPF2V`OIA}Tsh=z{x6z;>1?;cbcfM*3bFt80#KE1
z0FtSeeRGwLyM1NBL2?`8TlmI0aXDH0v(X?#tmZ#O-lbSk=f*GG>L)&#neoZ+LJ!U8
zmKE2O7o;mc17AMKl@?CuV_>u_xcRtM>tHDPfBa>3OZD?&5US!@^zO1Xy(lYJeV(&{
zi1OVx)1q{cX9HA;Z~vup{}kwD3v-Rm*^YbExXnI@W^lQqE5$nWO1r1tm?z#mfDP6s
zXq~B2Nfu=b!Q}Z`HvdNp{SlhE&(rdBq|b_{(D+`w>!@M@n1@Z-E_cNx<wdlITd^+b
z)zbfE)}&j%$?IS6E#2;a64?}NE@dT2qwC=yDwYzNtQ{(Ury=Wej|1TU&^Os_q@L20
zjg@Cq#l>%a8F6(U@GlMN2}G@g#}A1pp#P6L@^|HuJA#eJ>=%PaO8bZwmsFZ}{A<mU
ziWYK3DQ2f3(rVR_?*Aj#%+@ZtFHNP$aE+o^FJf$R&i1#lEVbeit^&L;Ql!0~p@eYQ
z+j(_ivGDR`fV{;Ll>XZN)l{Uas-fs#`iv)8&;9@m-mdsfu2U>BZvCl#edyNGtf6)6
z?_Ak86XsbA*dEb?;TqV*n=Vo7|1A4Ni|abM@%HA#r4W_n736oSY-B}h$*mRXtnbd!
z`;_$CuCqo!$gqP5UEXrDKifX3SiyJ0FO<GL5JMH$%;8tS^3D;*y!zK2O4qLh1)f!O
z^ixz{tb|ZC{p|z+S4h}o__(F72CJ-TYYHuu@Dvx)gDXh5*Q{P?hMG$OHP=I1tWk-`
zaGDv?+%mz5)9|7nrBkC(p6oI2|KMd``LO^W(-O~=J2X6oGmybHuC@}Is78`njJN0p
zm^l?X^qV}Dfu7nZ#tk1!>6+7l+7U41)FwumT{#O*SPR)DleEfm*y`UX=h=&e`P>kh
zoT{^Haz?n^KiM1coXYGET#lcW%TdceTXt!%H2o)7oDJQ&adJd)d>T(zeVf#DFxga}
z4<B^(3ikTInrmd|=2M#rlIO}RsANTYniNAMYk~|RDej}781aq4TE<mEC(HTdZUTFB
z!NSM3eYFJql>`0|S3)i!LI)6i5x4w;<B7_b=7OdCHw;!jrx98Gnlgt~euI}yXbDC6
z>ltW?OlMaMzYF`YiSlp4Jqtd$-MVq}ZVFN}(VTn!4#cX{CaZUU${xR{0svOk$_?r|
z?#<r#Tud-9BQ2eo;omhd+<1pzi=)2k($L9Q{d=YS&Gl>7u93R3DeL!FIz^{=IsiJH
zTWn_LCdcjeX*FO!ry3}iEC^>KD>N;#s%sDGerOa=HNDvV@Fe%${>qT<dxa#Wz2^7L
z(3KzJ_b=s#0!4R;A{JEvJYOQsn!eoP%NJ~<*5Y^X4|9=XKi__mMeZ*-A09gs8Q^~7
zAm<=bRzH?We5Xv>!&Q#MJ8&0gx7<7Qu`hNQF=TY3In{B8SvJyS>na!G);BXHm2V4r
znmu_plYji$6-xMILy(e}JWXjR*E`4YxaQh${If!7=#727M*ehGItC|+G^&f?XvuV4
zyG4!Wwm)o@gCR8&0;2tcN?9H2!a#r7gS10hd^v@kk62oG-6rUPT&1&jMctO8h9>pv
z7bPjxxgNwKi74-y#WZNCKEI}eI@U;NQ|kBa<nR0LJFkFpi>zvTR=EiS%E-qr65ise
zpJkp;G{u;@rIc0jomLAw`yJ0Ro##>U1mU)lx0Q?7Ca@ai<!5g)4i8nNXwft1b>MZr
zh7&`v&q}ko02&{}vBvqlmR5^j+_%^qq!G}f<ZJ^4B7Q$U+I)9Dsi-4G9Ftj|oB}7s
znG&6%TnVLI{W(zpnqUX8f|A(iGu#l-4{{&-g!TTykQxtBOlX!1OO>%VPHl|6s~O4b
z)nbV8{$V>=``qc~xHVd|t0bM!+|BROu>{%<hkg;bWQ3iMRe;~Q-Pfb8cCg*J%M>a$
z<K%t3PjatV;il$yDKq<v{f{ZvXuxPQ%Wl$7PcZplV)AS5P7WFW8M7XBN|$^G?n5JE
zvIXz<>*e)rxy#?_3DOnJM=Y9Bdoh)ERa=;4zpPB=b;|b&{}`)&@kR|RWuRX58`yY}
zJlwajHsN7O_#eUGiheDIMKmf#A^lw?TaiYqV8uo8XBy^Wq`kiwj5y4zJoI370)S9i
zfLrkNfBbM=#tL_wiPN>8M<K#h?X_W(B<JnHp-~*~`0$fwo0Zg*kFOUj_D4;H&QoX?
z+s{P3b$-AV6HsMDu@tbs=2rw%jC>wt1i~)H0z84ongLCMH$#5u=8fE4-_@6X_Dh+W
z;mAK7_&QQWHPZPdfRfK%mQ*+yx2f}-XBt;=?uJ!yUF=0=rR?t~<d0}IMUH7Dbm2+~
zuU2OV!IZ4{fxe8;UK}tcMQ$`v4HLUa^s_z$&^!b#7@ClCG`X0D#m5WhzL6s+o>j*b
z&P31T?}~n7)|c~*+68n5NrQ__o1oM_$<+GxP??{C@;|*YemM^qOKu3Ce$mVNUapy1
zaX{J;O4be`TAmG+_7Fo?*_HPih&`ud8fRA6ox|O&sGw-UR%qk!N^wV2>OH>mAFpG{
z!9zcjufKNEYUf$tRLfL_A>;3TGP>v4@y|666dQ?wzbLbyv3>EdU0t$wPJdB0Zz}Dh
z$tiOMcE0Udd1_Aig~3oAt)^2ba<9JN{rOlZN96t0{N`xZeqaFFd16r2?Qvpil^1Sh
zdIKf4l7B6Zmr5xmF4~i<TzytLKq_V5=Tp%A8DgV}4(i@&->eM>l+Szc2)!_IxJxUa
z;(i2;F*~`a@T${{2|N$-(SKqq`%1BA@29=J^rs)h*`?uISo#tEbvmc|i&)^hS2Bdk
zU|y!IklRex3Iv?3zH0+ln|M%G^?B>J5?gtj6YhMPor!s!DSI5?{3cs^UDER#1C7?`
zlOjb$IPMuHiOQ<T?ZtL%G1Kywnv9plH-}~w%=x-|^n-Wp=2{Fy@6rVhn)q>XNMp3(
z^EDrG9L;|0;Y@YTbF2EzEzFV~$yQ3v9azn)=nVB&j#m26tpdK|+N`1J&Ko$dta8Wp
ze7{4-@!_KkvBIHJW=!v$*yuHJQICssC)$;I+yzp^OV+2d<@BcU_+#OJ&gkiYYbDnv
zP{thx)+OI2FlZhz-UQe5Yc{D*!=DGkBjs1+kYnp?Bz{&-jHHtp$L>1Ji9a`Q2)@}&
zKg{5Clh5VKym@ayu$FU1_m9xf`SstGge~FTlEpt{!)f$LAH5HJq3C|08G08O_$w~1
znx+`8(u5pc+p17Wqr5#0mS?Ik(SCK%Y03U}F>MAy$0Yeaunx2Gaf9pHmGR9l@)%c}
zwhXNW|Hmpi@r*B>-?&Z`5=06dPBe{`QCosF>BGrR_cNrfF!JnB<tTpa{&~0DF=p+k
z`?_-B{)79vksDBx92USr<}kRo!!4IPWFF+_ReE+{lli5-^!>3AS47vpPI!al2JLej
zUKB3|^6~)z&u}aKl{&9=ab*1R2eCV!J_&5>{GP7f^R<84-6F$N`rd>XJ$a!W83SsR
zD_LqfC7jP;7mqM86EVdqXABW~rV@7`x@LR+_#T~}?Tsh6u2X^zpaQvLcvDQb$Bd|S
z<NOSNbLOEbsIBAkP%y<~r<6O=A9mcIA+S6FIp+QWLv=~8J|ljp@g2?JwPc{Na&IOg
z@&<rqUX^}xG=<^Q<E@_C)g<KSuRQIyN9k88<IgllC2Ak}WKvMPj!c)cnk)`2P#8Zv
zom<^*&H~==SB||h{0rnLwOxBFnr-s~aLQ>~^3aMh^r^cJz*hdIrfhV&I3;=Quh00a
z?-5~c1X>z1zs1<xIBvCPxUH@6*1I{`J~LD<{_0;a+%LRVb6vda_h93kW~FzIC-EI2
z-si>@8#Ngkos-Vt1#}H9)k7uPw~j{MS}yhmny;crbw&dwu%W%~p)~AxG<K+afRvi~
zi#Kj5W`INZ{_14*VWuwbVL>uNYU}Go$Uvk0ZHpHt1OHlvmIR!~S)koEU6h{cu;K`7
zH{ZK%6ZiH<tB?eJdgSK`?MC*-a7rPnk2i?N{e%HC1OLlm#@Yu*`bMl=V|@h}Kt)x6
z{f5Kqa4B3pZ|z?iOP`}x6k(}V$E2+%;Pwy1R})KBmS4)g(8mQVzmQG2^NXd$33sf;
z4-BYEClzKV?x<f*WiS8o;a*-BS#7-2j`dsCZIp7W&n@k6e~*#T6vHGKj#UX!b57Hv
zLq`LR;GJYoez`(9S8F0Ks**vpa8?TJC?Lc0AZ3g(>n`T7hP@p21m@B&4u!@i$X}pL
zfI43I4T4!r;p}K+y_IkDZUHCOPgJ}~pJ>^A)zY7q#soLBEsU-AMd{CftL)%xIh>`w
zU|)`?_r1r%`%H*9QfelDcG?m%rv6zECv(3u;zp0M5@ri(F8ERI?BxWYIw7~#4n|Vz
z%XW@R?0`5zxA5Taf_}@zs-RbN$r}b$Rtqn%FQIo{Pzt4z)ZOJ|avbB&vzm4dwaAM3
zAdvh|kvJ@T<;_iYSewoLlHHvR2hfy@D}TZFe*At<ich+h-MU7`tN>!(`*0Ky(|kBS
zedu{n9Kpd&!`a{!_yt@s@TPHQ!-lz+74XjfMd6_rC&6RqYuX8_t*|@E`cilKUI-EA
z@kX$<AaERLJ(u*YrAr1|k?2LhPg%a6h!)r~ytmxzmFfIaQCQ)Z|6#|r>_)SHHkj!i
z<JES&sLU8^&&=&dN~M0aRo>^)utT!y90cov0oz~6-+7q!YpR3^Gb2w*)#}se_@~V9
zT(tO6MzUHDA0!rXCu-Kh`Zw)LhNA{A|A}9pDb(zu{898iF9?Z0tAE$Cdwu_o9XVbZ
zFJEvn5FBe7T3|EuOnFNFbW)d!OT)b?;FxzLrlPzkOG0tx%TN;`VDW(autHCV{^tDI
z<o7OaldKa*==ts!RS34gvsrwA>p0~$8m8M_|EH2w$ne@vYfXJB%HV5BZjWz9bFW=t
zV5GP`xfaM+SUF+U`N^7xIe$J+NjYXWI@oT9Nh&XMajoK)#d6=58%1HBTp6yb>6Q=h
zn%YC<Zv0(%(sK1U4#!}sl>O~8$`gg4fRpP~nUdgRF<@#KpLhG0{YtnHS$Y5u>d^zB
z#oSSkwbsGd-C>>lNP&Nnf%nrt29V^yV5{jvXkV@YDWvx{TlBNGzK3*$>BEbjEH0zW
z)NCMI2kAHJNm2i+bdI?z;yOGS*b!p!(CM=hYe|`>=ab6DmYf`u{BIYJq=CWeMc<ZG
z#QGY56D2Wsf6xBe>h-TG@OM}EADS7HtUhH2D8`3B`Bxub>Z#N2z63%wuTJ)<vv=Od
z1fA=e&R9rQO}nXL3g)e@2Hh4Ebi`vP1jqhoRqd>B>o?*af91<4U&QDz1mfYj5v3G=
zvnN}HYc!AUSA~(wvh*P2ChFU1NDybZse!h^h!msXOa9!Z)|Li(B%}QK+mk&~yQe!|
z%Wqj?vOZwh6HdLfOG}dfLW>q5ihr~leiqZZF><iGeqttT#Nr|CS5j(HPOkTbl9G~!
zW(zG$bu*Fd`mqyl(!mB0Xm17-yHCW7lx2vuKx5in{EuvU0?)?GU%-{cXBM+wnx(ZA
zzM9qEWATs1b#I-49r%JP>yB0<m1pvyjm`T#M=vsC|AV}EuZT3gqygM)DB)!6!dn^p
zxhVrmQatX|4EY8_4L8eQonh&7Qk={3l>#g<9j&K<Y%R{(HnzrChKBg|fG$=!YyVq?
zErFfPgAgeW&<$-ZfEbl`HKBkpaQ}}}<H6*8^qcD#8t2!xC~z4e?1iE?fM?2&cIP{l
zAhf3Uf`;04^l{cjUU1Sm%^q55VE-uQ)?&2~4MsC6_%D_3pXkY=0Br?}Gs{bxf<7zp
zqjc5vk#jX&^1PCGDMML7cAlV5+|n(##dhJNQsRjU4M+BCPo^Ni4`Is#QFE6Qj92@J
ztx#EW#0^j))M%?1_I0!RbNWyolKA3E;-C>O$v%X3WsH}IzkqOoy^mTAd4b?vgjV!*
zs*C5{0oC83Z@_PLn+nEf8J!nfgjv;P11G0+ev*0T@K>C56kJ{<^AmV@_kOksmOk2)
zSstB`H#6$^dOZ<-9RxHXM=ah}Tko1sSA`LT2VyehLV^Im3*6DIOkTWjmfw^Jrx;C?
zFku^tu7PFOdmg_h3;d(@=}1|N@LNSqHa_2--H~tH#-q+d172EQ$x321IxtXt2+b{y
zD$akZk9RlHS+C`4Gl#Bd$-)30rLw!56LK~f3cYK=q`y|vI~_zfC`;#0E;S8}Nz45t
zmK?_7Fm$M;dHgz1w&>uah4w{|7cascFT(k=ZqI;yFu3(dZ8Wthv9y)X8HFux4SW+5
z-&^n^R+9bG<_XuB8)wVCY>i9#<&%NFi>jg^vdGo>Pq~d{OZ#En6JM8LRIK2&cQ<Sv
z@3;Bs=h=l2k2{o+V}*xirVHsO?vD(?Gerx^9L8V`>Bj+N6i3~4+!DIw{#db_H;H*a
zmR<$mT}-5W{g+a=McT6K!Ar{zf_gu3))yoWF{=j>)_L(`BW6Z~6Md;6q8kE9Y&|NQ
zBk4I&z}N-_^^m?rjOkN4Q_it#C5QfpKH9{5RKFMNrma~n_5@5t_Or$yErE~uB-!Pu
z_GRP266w=+bt~&)(DN;vux^7Hk?k%>;NI$6!YXkDk@j2F(AbYSwe3D~g|g`(Ir+oz
z`fQ8OEC~Wz54I#&ko9nT$@mQqEU%IeC@yBH)lS{wOWLn7<0ux5uP>;!7?>#el)^q&
z-FSExdvCk<SRD-~qS12lC1O|YA}xPeYy>uD7U`gHTW>PvxBD7oW{O=-;?^fZsXpW=
zr)2_cbl~P@zN>>=A_^to@-;C~N|RmuHgX`(1kHuDpKUI)v6*=z<^9Kq#B&8Vxan9Q
zG#y`iG|XJj8t3XZ&%#73R~rFH_PK+BQhB2{vo^Kl(~F^NjpZ(DGJC79vxptXO*0O|
zjS2xmvgm=rZIDYr#%Tr#j4B9{`X;Wa-8aL3`rD^d`Soa4kLgOhKB04pZQNsM7Mu9n
z)($i1hs@lIlVu7R=Fv1nTfT6ONOW+;D?Ep_17bcZ|Gs1`_HW?WX?v}X!k&rFm)hHu
z-5q-Gk?yI}n0(WwRyson^ym$H#D?tmJ1xtS#<}?l4JQRU>(O!ton1Ql%@gSQvFCIe
zyXm=l#>AKL*ctO6{#6!)&ul})NhxZtG)cdnUW+;s9s(Wo$O4#2IV~}cr0m*}o=^qu
zY`G2nlo1N-{9zrNG?KaLOSR4E;-+8jDxu7_@L2MjAwiOU%8cCUDZ|x+texo+?>$Tk
znwohU+3;QqwV+QnB_Ws$-yl6UJ>^ji446cntp6P25MqqtnD=r<<4=(7wi@V9NPzWd
zViuw8uVs2)le+?Dy4L>V2J1k3*b8H@Zl$wKyLtX#7*oAqvjKV2V-pWen<!WAm#jkb
zd1$UMYklGzh*Gn8#W$+y-D+QllD~@qQBro<Y3PbWrr&B9FUskIL9_FGf+eR%OEjPb
zG`=P&?C#W6B%u*y&l$MS;d|0zq?FAnB?{kT8IOu!8gAZ200PC_p0hT8IP_aje^7?d
zt67`Y40O{mkussnTn!n{pUE<==ZaS=PbT7K(3x8oQ7pDzITEF2>K`#_EUeQ)fp+6T
z`U+>-M*K@+tcc9ZD_1izrUsZ{7JXz(zo1F|=+unYb}JM8wFof@Wg6C#&8K*a!ZomO
zAfs2ro%k*k2b)*N;B_xKG5!gj2}1BcF5rDl;l>r(lP^uLYU=!WPlB0_8eZi-N{V6Z
zKPXb+qN7m!`9nn@gi4rchx$M-{zLLPIzBOya8d|v#-FSxnRq%!Y&uFfSf8&_bZfaC
zq;eE)CJ|j_&7)-<CclV<+G@S6I{g)}0Lp9uZB+{y=mo+o#Wb66Vgl+guU=87^+^h+
z%$L8ou7;X5f{l!37Z!NY*iE`;^4Nv>=L;PWqB(u>bY@nHg2QYW5%u!{QS9WCS@hb2
zfvy<rc)2q5&3R@CEHx{0)+HR~)CMhUMM5+IxtN6ghOQaHq1N#tGDS9EwZNdXZZm8K
zMKkx!EzP>s#A8z{I_(H=!<mdLd0jq-+RnoI4^*`-%wy~rF0XkN=*{||E_cVDZo}~j
z%6X-)NTp!jU}HlV%zvAY;boU`$o61=@H3gtLdUW@d<iPWY>%WsZ=3utC`$8<J{S|)
z$BW`a@0+Ex9*&xm)jC}<ibzqd{wdhdp^>k#>3%=Op=*)BO+!(iAqFzl1riKq$E(B|
ze(u!_qZ5iBR^&oB(OW6I0&v55GK)g5TXJ5MCENEl2vL%H|Btugzj9TJb<QKJB}XHw
z!THPc`SADAj{IWZhjYFA`f{>$L0lz%X@H@h9syd@k0rczDuNJ9pfD@`;y$~#N4Mux
z{9)_}H2)95PevM|-f3t1dXkwt<3)MVoe-paYcM<U&+zc~2cQXlVn{LDKW5w?d{2nY
z+&N>|OXl{YBsVU7%A|oE#-td_@P+>$QoOIp-oEm~HM;W1DPzmBYik{|v_2H;FZ)KQ
z-Z^<dbxHio^`sv*U*Z4f33H`kim?mRsROGoYa1F3>V?KP7$St;JRGC3>F`!<IliY{
z|Nm`567xj)*~e(lU$>*DJxaqLaL20rstA#{E_-8T>sxqRDCyo0I#heTN^8H-feFee
zY{=@SlP7+$>`ia|hkM2kzq5rN_&duEGmxh|6Cdk;+;*y1^S$r;&#;Zq<<)=(XJe!U
zY#Vm$aI?_wFAspf_~>#dl7nhc&;O<`+&q}F8SKxMHq@VE(96gUiR*s0G+U+*0=*?H
zm-9xpZV|>T33BF!X%S1F@s*`*iSk}Oy82Pw@Pmb_(pGl#BLGZ3&V9nh|IV;BC_~}6
z>i|>=-pAQ!LW@mqi1DKvUFIdE%8aKkgpLQ1-5MOe(_53F<{|ZkkYN_t^SDq7&Ny}D
zuvYB8=j~{DP7adGV4fXt?Wa)p>#e2yb%)khC5172@NzdurbOSCG#Mnpp;k8F$XG`<
z%?bY2;fLw0NFfZQb#~Is+9T9(pj<tCZqy;v4hpLu2>sx3O2GRSn6)0%&FJku79n|D
zjlMy}4ynec%J1d=SW);M1y|gX^_ohUuWf0{UNk1i>w|j{OAeN+)uE0i`=$osj-%NS
zCPxZe@%d7nm7LQOqls>hBGI}9&Rf`ufQ=73A30)4On`$M?LvwOp(p%tPN#uV;1c4L
z?FeBDlW`S7jUHZVj`=G#oqf6cZGb8HEWZks>B1D&_(9`-R@mBjrF!(yMD2k(Y}oxC
zC2J|nV@S;#-(_iyKc@(FVk%2kT_Yd!-lfL2^P68Xg;D1hLYUp;G=-kbfb&YHKwNBJ
z8f3m!P}NGi@Uz<Fw9|uIQELl3dUXjf+A7C%?k2GW!J8tILr<<-P8jLTuEQl`bd?)*
z^css4VBWQIjFK{Y9;#cn8cZ5IhTANiLMhB9L;4+T%5z$V8?EpoJ(rVaN|XBDOM8O=
z{bAg#2f+}?(ak_-s7Bv|JKQ!aZBDc6ogayTw!eq1$a6P(wWH77nm#L5LcSnVCZiPq
zYK;$Ga3%GBp|1r{q9J^-VbgC-FTY9ReO%B#--PQfg^B0Vu*nwP!JUA;xB;&%Mzr6P
z0CbD&jGk_S*qVh~7$I-?4QPVFx><ZLbVRNXJH!tLAAJ#}8K#jPO3^pHBYzxXRgg8M
zixfF7;=MeSC#9TW#X<;H8>**3X}_f}VotxdF-Vgb96r}1dQK8O*)t^IcTg1ntWVZh
zz<sIyd@F!y9Vm6+MQ`S@mj_f&7FlW|<5l-pM5Mfr=aG{>n0i#rd5-@Q<_$bsa40(Z
zU79Ac9?8gOBPZQ~qlhSDVIiklL*c?Khrd3E@0%SBbBr`0d!vV+#@qh#Blt^U+Q{MK
zk57+Zoo=Y^b&c(fn81nC4Yu)o<@1Da&(^cSEh}-CULW}j59-hF5x$$deuS?K$m1R?
z(&uam(gSP`H2J94?cr$;cG=Vk3L(7T$hKX9xXUNCxXfo4C`Eejr5?a6^xmFcM7oj5
ziyim&J*Nru?94`$`Mf2&sM#e4MS<-`vu%_klO+kIj?eNE5r?$})EpN{+@W!uI%MEo
zxFgw(x|{kw>MfY=hHDh<L}jBD;?u>t2Og|hgsJKvYQjLQvZjLfXibwT`6|fd=U`PH
zO^lhOrTIY^P{^OHzFRn}`a!wTCrI$fISoQ?eI0Ox0d7c%UimBP%09f3yr1tcls;fB
z?NFPq!`_qs;La<waf=tx2~UdIlFHWi(0yzi+29X2S`PL?TnuUO^WIxN;s-T*B51OV
z8$Y3#wbHShC{X%}CNyv(W~4kt-m|+9z85I6_EWxT56CNpJ%51WJi)q2o;H8HYPnuw
zHqk2h;d--1d3qD_CORF5N{HG#?)HH+@srL(8Lp8RBQnnq)u1cVCn;jqgULr*YGTMa
z^@J=_pnZ37wf+<9^T-r$sI1KcrsySY8q?XM^w1Nq`DVAj@5%tp4wz7pNuS7I?2axb
zoV9y6EuVLg5>eqgW|@TcT_C}C#LBN_Sfw;#&~x)qnNJJtI`?L(P1Evi$cfU-k*o=I
z^)kzzgFW)WNz>js36`fwtNFOMnws)6<FxSnarn*$La8AYsJYVif_2kfEv;afL7(Q%
z#cK8Q9$Z!kCl2YwUwtEh6nAR*T!=FkY>Th+^o4T@8=+dga0?J+O+SZnZWEW`R7Sqi
z>f3PiY7VELH<EUnMPCwh^Yz=~P8`6L?QEw+crZiq_>gwERH#<#K~@YE@id4t@bC=9
z3A4#6SeFwCpeW4XoR1TiSnJjoW`!Lk#bk(kO@9pq*<EaQ3FEdvFD^adI79=au2s5@
z@7JhCdV?;GHHtqscNZ{tUzL;`^4Y+liThZj0*1g0(CWJO6j$)qw%_=A!<qGEB!Bm2
zTy?l$XSlmU!<FDAJ)x)OtF`$>bvlp7*@UcMmF8j9P+9w<IDjmixN8KhIyfZ3(ACs$
z5SV8%^jY{AVAfzEs>`hrDdN4;-?W}P0yv?NLf}tI%^aN}nYD+9&oAniO@e!cLW{jR
zo#5$A*?~`e`@X=s-_vEVym<|*d58&$hhl>0HFsQ47G5h<$a&drWALhk6q^h&TFQF8
ztYPWUkrbx`is@PA+(O`xSTn@nmTO8eC((R*ZicM^{ZyG|Yh4qHUju@-4di0v56{?T
zE~f~WSHm}t7PtjZYYrYSbVSwnv^ZD7?5nWHeNO9XPD?K!3Tqd8!}S-Eo%YJc;{Kic
zD6)V`EKQ(q@3+#&w7K=|n2R-CKlX3V*Z>~{jj4U*+}Qk8YW3XH9E!%>WS=Qh?uBh&
zhN!QP2ikNjsO*k+Ut#H>mP34*+VO_(yO{C7tBH<oNZ)!L;Z$z4KiZF#8f`XZ3k~Q_
zH}@KHV0CP=nL7*7U(dwopnNwJR~806xa;E0(-s|7Qr`w*<zEO31eovDcROdyKToOD
z5liBjXncWDW+SX{OCls}l=g~pg!97cjmq{uf|>o`-Ccb-BODhB)i#h`?32Gz_+rkp
z?E5_=jz64RzoA&FKRmoGR52oO+xn;6y4_Z>9pF@5A{QCuOjPz0qJ6z)%m8=&Wv8&w
zraN7yEDqi_=H;f9z`C@dM3?H}VoLyZ*Yz|O5h!;w`{c;l9jRzD(erBLF5PTBWe~br
zBCI*6wnEg$=##a&!@2Z9dJY+;SYyptPQmJM)y<F1y`qKBO0qtdn;J>y;Kk`#6?HTI
z@z>Ul9n9Xix`fz|*OLOrp45Kb)?lAXz^xh0=K~G%S^@7D1$nKKS~G^6G+UfUwknUB
z2Jz^!T_xHfc4ItO5UAk+yhgURinhAZcgMi1H}ZK%&HHohBf@S8!*jyX1`0e~sO*5z
zf{L7W?XoZeMyE$4?|WQT@?@<yi&b|$xB#Fjy%3zqIgV=IdGAWKq3a?}q|XK;g#3l4
zp(EJqR*?9868S5a-}1NerziO`tV65q(K-eVVDa0UHWWhwDj-c1xqv@^#jZet9c7PO
z*t{dw1K=FzbI_0mJ%yewV?$Aaau=sLyh3xU_sC2{l1<UyM;1K#f+lz(&oRH!yiMG;
zNcueJiT<0{li!^-p^-q}W4vXiQB%a&@npWo_L(23V<&_`uH<cj=!n8|EoY~bsy8Dm
zngD#w2qzR`BuN;)$v^m0vDlX1AAE@ODD)V}^xI!oQ_`A!tRm}oh)m4WE@o_T?b%T4
zRySU?^NW$5)G&;NfzD7`Kzz*)yoDP_6ROY9-Y}}x79Q?V4Npx_Q;|`LeF1aL)7>l2
zfvZ7BvB%yTU!eU@+IXbQzSX~nI;&;54eYx(tvk-bPtkU#=F;4llQ?6$iS8thK#cv2
zfSJj|JVai6y4k7FD!IM>I^3@zfv)EJ{P(vI+oQO=679twKA6VzwyLGm%l^n<48pAG
zV!ybY^{`ef!{|GR=Iog@d~01}J5>^jVwpbp<B{~pP%C(N3erUl#9sbT;V^V&`u7tz
zzwWBDOf^cm>^g<S)Pv)3zvL`a<e){Tr!jvV&x!1|H)Owb*;IK4Xg8_QLDMVj?R3Ch
z{VqQsEL^y`PA1`{-~}uVh>Ky45w_#iFwL`GXh1HSKPPj?@8V9L*k*kNZ~v7!2?sZv
zn^m@2@;^M)4-1tR&B*k5I7scHsf8LI*Os{*-MxJ7A8j-|Z(UaLq3yJ%Y|3xFE6Zyy
z91r#Ig<h<T1QN!8W`64>0AcS7P1AsU<Sta^)JQ)3C@VQHtG*|3;VjuLD7BnCu<jt`
z0&XY|m4D#vqcp<mbGF`$E2r6V>+ffB0<K;5m(S#EmObd9!PP@Ct@LCW2crqRnPT+?
zEz$uySz1)GYtNyFD<D&s_COD0H*s{Lya{99vXO0iggf3b>>zb-k<NB)^MII%m1x-^
zK#kP8B3@hX9&D5uClk9!98bqzvM$(B6jZ2x(Q=99htr1EQ|YX}_Ma=2zq~Nh`}MGR
zi%W0r2z$O!Nfd@k>{eA1TubjwnaJh0DjZEiGa9L@z2`PC)dU+jq^uiEDAd1ijn`W5
z=|BPB{=oBDUJUO_1kR6gZ<$OfME-)x9T&7U2@%hmI2=8G+>A{j&VQ|byJra9=>jfF
zyxWK*?&ecDP@uprXZ|^KGK6rh=Z;q%+-+R3*=?OMX};KsApR65JFY*Y&^TQX+qL)0
z5-0r3AQh<VTreE}u-gCxd|30ke7f`V-AP&-4(DBwH6<7w=IoGI%IbG8pv<?_OG`xk
z26LCg+{T9CyO`@_81nCVEx!pFe`iPO8*g;Gq`f7M=Rn5v*0Sk<SC0)Ny`<kpD=x=Y
z2yw>Le{j<|YGhEmoY8=yX_vzHI4Cfd7CU-*%#!^mkq(~BS4t`=hTB@0z&fO_J1P^O
zDX~s?9$V)ybMJe=_VlsYlmTB3X3O8-s}<uLH{D8K>*f%v%ah3O@W23eX6%HQJ<-y1
z)CO(x9h<K64s~its-i8l<sguVjWSE=OFQ?U9&Gn##xv)GUj%<%!z3;O@|VGXmzG)z
zSFq7?qHoPOsoc_Kc_HQxq*wC{1gyiCR3u+BsCBO7Rj@JQuadKtmx7v57!q5jD8|xK
zP>XPVH3>xJ^$9B|3Vzrbcmx+w7W@G$Lx1?0V6xvdLYdadAWd6N6sc0W%m^PG0qj24
zXSe8T(O9-wy$A4=VWIosN=5_QrIF{ne}RGfead$;`rM<GbMl%6-Bxh!GW5l-`V*_p
zjfFm6M=9La)BZozclN8^>T6#$-#TA?6pvI7gFRbz{blfwQN}=C+d@6A!(5gQmdW%V
z&ggxuVz|uU;R~6=-~#7{^bw|K4gd;et7(e&@JKrTgb2zyvVm$jtG_bg-^ir<?Q0I#
z<XIdSnp1cs({zVdsK?j~ggGAFzSp2G6yqpd)AaOL&e!UMn9rIQ2p31#^bDczKddjy
z|Jpq+Cx#fMO~<6?d2DJ67O$xa#iIA_Eqv5o1vh-~8&y!P%1^}AP2ic37YW@1WIMjJ
zj7bjSjPw{s!5=o2fQWgc#7-BDEnyC$MD@l1Ahr~g(Twh)4mY`nXkX|Vj<WgtH80UK
z(rf+ZAtMqFnL{>*4FaJ>HdQI&kkmElk*EWAYNMHqM_G6EN5e`MzO}Ln6cA8J9`J5>
z-I`VM?oVgImjXVh=2BrKTB6X=By>KJ=vj!qP~NJuw8-HVgn2ySZ2xCX1ZLXBk4%H^
zHH~GGgXfz<las$SRt%L!kGS*-0Cm}wX3eko-2dRuG@SWXwmp6!S@_Hsp8w1wrm>%+
zXr2V=e+n=w+@9{|sZn2P6!+V%J890*m0hUSDaOy8Lz^!2qWzhhYo8<cBns2rlFa#G
ziO~S}-NjjU&6os^+O?5lPmn5nDRpvw#LTa4QHO|BWDA-o`r~E)zbMXGf#M%-Cd0Xg
z)7HE}A<rwf4DO{Y@@aY|ZFVL(OJ{!7e?V91&7Z%)`LUm0%cNZ*-v)P4L;;RlrJmRl
zwNYR6);D$;OEwBQ+$usj>vw^tt7l64TtKeLDQO1_50<jhFED<}rl-%G+wYKl*KFM9
zeIAN%UD`mJX$RPkH~}Nza`5QY!BStBc}~;y->0|%8m%<xgS?x<LQ=~NZF+F8gU;t*
z|LNg^x?O*bEu88~NQi}TTxw)+S>uW#`^wz3$1lfo{_bI~Qr8L%znNT)<Aw+t4===N
zdlC5nVc;JtoZP=QOT`iKqT_)zl;VL#M32Q|SRbFD5l!d-+ZkPio=dyY4!2xm9{X?m
zICy>cab+z;;Z?vI=Bkjh({d7;IOs+tP#fV<=F-f99q%1!6+g#i@;+M~qGCOZ)ZG=k
zFSS;gKZh14WvX%xQu4QUI!Vt*!fW<-?aBPzzPimQV0i~Utfx9h<N^*%N?J!;)OVO|
z6ugG>#Oe@v87aVEOWksL)Q0CQg=wj;)8L{``PoJM5zEnLLN$l)3XC^hb}vD){JG<H
z>f|)~?b^o#&vMmWmq9O0FRADN-|cLo&>V*367u{LQd-Qq{^=nqSGnjX+xKBvNzb&o
z+YRe&Il5zXNk3M}3;m1un1zECGV#lo;^&Sg8^qaE$aW&GhpOZOb|Fg&>^t4rgLvz4
zxE~-)ytrD95IH9CYBMWuCeF%w=?l>dQ~=#OX{>i(QUqB4*v9Icnt?trn@d}pFn;f1
zf_N6xu|<YI%`kKH-Yl&F)FB}+bF?_6Jkk8{S%##5YMsh&Do*TjAY(-^{y6Aj_b|75
za>5eaJGO%sOmT$?rqO4(yTCg6O=sQDF9Gz>`R>d81+SeikQ}@8@LFz7c{sez$1Cv<
zT=m1!q>g{A`}WpDnbOxWx{7z+j@Y<%2}ot{EAz^=b2%+}ce)&_NsgGQMS}Oe+R7lB
zZuWdvap%p6r5;q)nL#V7jgfNUtg`M^8=C8qjVl6AM#>o9v;n5B1yRImX55%cSM8?1
zv;Rz1S4D4l*H{51aoxc&6dvt<({uUZp48CXREoZOCJrui_4D`nMrscmqUYY*;4l;8
zcv<X_7eyIDD~i9P`RFe=*6-GpG3SQwBOgpyEm8h=Whv%f25X1e58t_@nwXPJQnB@~
zDp`L!u}hz{k9lPsr1<D;aDttP3c*yu(GA#jvc<xsL^M9|I2oul`dxaRnuv@cEQy^S
z;%3vN+%&f8ZL3XIcCd&ub_J;KIz)IF2XFBd-WrNGrKGHx=WRfnDJ-pvdS%fP4dF?d
zse;dp>pT}!giaThD$=IQR7>Rv3lt0ArQ8BSG6F9)dBJw?wsvjM>HFP`+t3w{UnCxT
zx^P13q$K(+d`hD5e8JjlgBI&ekGN7oJo$Tl)K}c=WhCVXyZ7TLx{8-8iwwls!oK%6
zxNCxshZ}ely{{D%gB^O)&8pAy6X-+T8}`oCy2S!BZH<p}5*`m4bW~D+aAPGllF2QC
zi_rb<G(F8RR*f&nvor{k4GYqD!D%kNdJi*`)QoFJG?3{DjI)UAW4nYGhSkrn9<>s4
z66k@Kb8cp+I0Znmrx{r1kqwG+vZ0{gS~IyPJM(U<G&W7FBa7zfOd=BZ8L-&wqD9D^
zU=H%TVr?=)Vk4KLu(Ff$`4Z#|l|6uAr>1u07j+;lbB>){3H*<Z4j@f>mlMPdOayML
zd9uqMA_6q!6PomNjsjt|sCOYdAUO}`natd?AVnjV8y0r(YG}%`(w^@E@YZjz@1J1w
zWqkvrzcTir`EmowZX(OxrN~Y!%L<$Dw0ze>kt>>jl=RaBeeG7}A!dOmoWedu<^)GC
zn$~H&Rhv|6rI$y*a!%v-2k|f6s2we^MjhHv`JBCF1kD;a;Ka$pU93>U)=72kYcl(1
zNFaMrcb`!M&*kMtJ)`T;iVx2me#@K=+T%`x>V`KUEaQIvy%6UW`4F(|bgliy*^#j2
z!weFPh}wi<kDkE$YUb&tgnH_(%4NZ3AN*E>PS%C;Kvv)bT(y>?>hXn}Bw;aIukIu0
z=DruWSHM?9W)T;fxITjLBJ03;X>A433aHnMcxqyvZ<=MbO#J*vqcqxc_zy3kMR)nP
zF<(7*S%%gk9Qao*0nwg%(eB}ttvr=SbbL$_I>UX!v9mqq-Em|D%n?oL!_Ef%wBnJ_
zotF2G^S~R+og^s1fQD_pQfI7mC$jc?Fn$E=x@n_zz~evj>ipI}agdgzagJ?|u2fae
z?HWI-V*RmCVo7z799>wFv_S?f=2ciQXh9T_c=leZrBt620Y2L&bU?II&SQY2b?8d@
zi$*Ep!KM>KBfNlHb8~6on0mmpsTq9FEM%cV>pn8m_OD=jn`BTNlKCQqLpYH<y4t{U
zP;Oft<1aeG=JW6~=S%|QIhI%Pcf{I%oQxLV6}EwVThQM9s28)>hvGb_3hnb)>*Q|n
zSpkg#cSEupFi~$6;iK>De|vF%ra4@q?G;(uk8OCi7w;*$t4tcqyYn~Fc>4;=L)7m&
zr<O*KUDGV}%Q+e)!zK^Ap6TkQyLg-=nJ=DGK;(b62FX$IXr1@xY6RE1jW7d@E{C-`
zKrGf5w$TA=!o>=?VS+=xf!&zX)_KN^kaMwC?-j9$iKgF;<_AC1_VC*Q6A*cRg|Bfw
zTVwiV;QfR&4u6cWy_baqWXVtJ@&Km>=$RL4s0#xz?ci}A0&%Y`H#>fw297&OILOw%
z(;cvFEhXL0{M#Uw&+f+ejgFC*8#gqYA9bt9Y3mq0FPF<2l8|&57Jol$pEzJGW7V>-
zUUPO5iJHbc*f|~dNzkMHP``AjcdGCMrO<HJGAQ9-#DsWe2=u!U@T<2wtb<iSNO1t*
z8lq+Yg4fG-x9)zU^@SPGYsKNRy>b^CFaLr<Lno=kY_d7t)*ozTtkB}K73bxq`_`ep
z+Nh<fudC^F9`pKiu1`&}wZ_TD`lV3p4())tX@x@f%l2K9AC)dGZroV3tF5_Fr_37^
zzRgtC%uV{dj{<@JxDinC0+Iv|WZ-D^>i637rN8x0v}dOxVc*miQf!F)E_(b*=7m-|
zz5bNEjd|lPr_FkE=cePlbgMmNvC9xmDThnmi4#lnl^0LH+68ltZ4}M|AOAX?HpZSE
zx+$~!sB7eIBjEEl8tu{QmTu37J<xhBIrvBG)8gNKy<m->oA&qSCCUQPt7W#NT+>3}
z^1abd5~bHs1k4-Bg_Z#!0UPvFzo`a|9%o;*Vxc6%iEk0pQ=VFNPMJpcf@xUiVHz-R
zl_ckhknh{tM%DP%^EU1nu(g%iY#p@e80nSkS1`hb5rQ|j-a?x~u2*acr@I=MpYJHI
zOH$1it}*ORqY0cC`!u^X4OpiMph8MQ5>(Y}e?40T^Gl3|1$b1;ed4sXCUkDgJ63mC
zMnxQC3E}dcI>(<ibuN(oNmX8!{vvk6UuE=~d$*3?6pE$|DBd$~T$!4hGT3{TxdMC1
z_A|3@)@9vMB-yZO^#UlM>K-#<{LS;Ai-=kDQ?E250>y-(`xS)Jw1%@qk{0sfU+nfk
zH}9-ZFj(#`U7ut9CJKyA<CmP?Ih|b#<uP75@XKOu3g0t1M9rj2|8kkboDTU4ZV1dA
zw-Adue3chIqlgz7k65?u;v_USC5AB^(yL`h((?jG{WWW9`XFZ^{IMEEBV%}(T8s|$
zc3oGrW75N!3`g(N^K;hxL2mei7%ltWyzr~koFbE-Z6r$?S3WUoUAsCc0q-$(pRAOJ
z-CJSND|2-3D%5JdnAk*@xg5ADQ(-SEvRmTL2UadmUF_MbgthNlCNj+}o`Z*7k-E)N
z>-sU!9QP&yZcB@%w)G&~w%l0GHXtJ+UBs?4p`%Vb?d4JaP^-uIp_ehvECyzOzO7~!
zfj-?>3!N5sz@@oKXvu+zO)UPv(}j3w;H%m=Dahi`lvCE#MhI@TX|!Tzys{b*2B6Mu
zBHEKV+75}AzCA$F+h_$sq*oVPucplatM215<$;8SM!hh|O}`&Ya5KcI-F2lIkHPmn
zZ1($%EK+i3vpZO(Tw{$HH?q~~dQ<GKXK``xsU*E4KYIR<sm*v|FrD>reGi%K%NmhY
z=s&REmnV*o4dD&Z0n4!}x+O)aW^cBa)FqEhEiHMnI-vG392o#F+qJS-N9mXD+X0(%
zLB)&?JEHy@USzoy%n%dL9Vs9Z3fksrrgJ-AmMF`&$?{Z}CRxb{J$)p3!t9Jvys9s3
zTzijQesh%|q|YX^HTpv}X?}LWtM}7NYrvsAipE)EfYUS^;~qeEy18qdQ`~UO%Ddoh
zF(W~--V!#{pNBN9S(g&iDQopyDGHI{XH9f*dJ8Kv$K=nT%Dn|q(OY|rLiXN%iYwNv
zk-a5v_q)u7lMw;G9g_l1ehfqtm>>i)$cUXyiARdlb76%lE|)b$WiuB8f82^N5|W%_
zA$A(&cM@`5DT=%dEJPemqsJrAVjP)Y2l<+PeDX{p^0KHnlV@U|g*m1-R#mxHU_X(?
z08XF8ZOmU@*?goCuu(X`+XAqdTmk~!vXTmf0!j<FldC0uj#gL)<ha58PMc6wS|+mt
zQD9q)<gMH5Eg7tjvj^&SV?UUT*h77f8$@Sj3nv)q5f#oSDPAoHPG}M!khuSeHPN!2
zxb}U?<~RzX*(^9KC2)Q?tB=|RoMjek`Rr`bGM&ty1p!cFclb+8+_waUky&M&Ze#mn
zWDGqc>Qn3s0k9B7m`vSJnk3y{K0x!(dK%>0)38G3iQpk2Zv36h$giAhf0{zQZbVX+
zRNs+jTlQztt4)vzuKi~7tl1#aHPOhgp!K$7$;Cu*n!QC-d<LLsACcRjIKwaIr@D7+
z-cl!V6QFARwn{|TYjja?-MrwBPs;9DrmB#~l}ScNU1aNQ5=AHEkg=(qQ_kZL5ruR7
ztTwH!gjidU%$})R>{Z`iQ{TQeNeDe&AGeIrXna;voTk0=#VeQa^C()=gjfP_Jo6&0
zfz?-7`O(O1%(vW&2R9rRquAx0`E7!2cw5&_WcD+UNK-$A4GhE4VNLo1j_z=yEQ#Tv
zwD>Z>&VJ~~g0lF9m#O5@oKJBoOVz+$PyI|2#AXnjw|F_>?N)g8v`zGORTmvdV>~m}
z%xw8m8HQ5~6)iE6_}!uld;BzWW8Ag^HoFlQiZ-DwsxUvmxJ4jab|!@lkc7};eSe$x
z<(Am5hgHGbCgoSbzUw3+U=_4VhMLn*w#7<u+5#=sNm%Lr7!))s(GVL1iLAEak*jeh
zt|(w4Vt*>s=r6?@F$OF>#ONTM61<yrXVt(yb{xB5S5553S7+SDdI!C@^!l6Hb!bN1
zu#|y^_RFJ~ax(?L@#X-?{ivC}hG}|1bvt=9d9l%H`KoPY%IjbA&tWrb@RfnkzmI)j
zuWRn9YQMg~qn$s>qDtfbkV>!^=m_@zk&W4!bC8VvwfqnxIT8KFLsgRC(BH(Z#a7|n
z-|ls>3-nbf>8oXLefChyEeyM}Z{p>0!8QwTY2Dfb+V1<T7*T+$-mT0)ebuA*HQ0e`
zA$f4n1!RP6yucSz0}XW3KKthM(tYTsu2Ad94@t@<|C5(9_DI50FR1x0w~(t&M6JC}
zdLmN<s>JG7HfTUA03|7RQih#5I)z!_tToMcmjg~<h!e-C*AD)9eHk*5M!JJxBwkCc
z5(8GXjnN7XF$0MgviahTH0-Hz7Eg?L(mw@<^wp@5&JFH_c<7iM4!~5*4^QN>^WMk7
zue~y%5vAWV-b$Hqd#<@**T;lN&H`+m{bqZgKO~U3pXW$vwc=Oau&Q6jWwa#ILI9hr
zZsFgOA|_agQYcLrG|v8rP}kYzx5w`d<3W(`1g{l`V`%{Uk*$^DEKF3KbLBQA`8r9*
zA<763l<DLZkngUT%xQUmQ5=s=611tRaMlBjz;AoznE-g_^$@y__0@Q}(jr3AiZ4WJ
zQ|D<v*yk_lObiJLwaNR}tR6q7%MW_!)B5jKUUDvL^Qoz+jaJR!O)o1q9bK4q$9KQI
zKumF-VcIIajCboPs1<Byf4}bVbrAIcC|Qlo%}=JT6`MJirKy!8N1KZS*36S&XTA9W
z-2sJY0EOPrrqr2Qz>kbH=p>blH8QBNsY}d;=uF3coNHOqI4kEitfUWO6ovR4rfoJl
zdDcFXN-v%1J-yIK38~%AOg-+`r4jl7KJekbY|&uyT(;VKsS382@pJ6V8`ZJA7psgK
zwC{aCKBU9j;=Mb#&_I02pW^``b;|HN4x2lY6c;wF-zciAHZI3&p;@(fL>p{p8n6eU
zXulW8)%h+pqIh{>`+NTHFDcf`oP}(sOT_Jm`7)_s?H5#OGvW?`R6c&)EUeo04ncvs
zyy`pM5j`!B0}lt)%CZhshS@;_1DBPxZ+;)9G!}1fP<v>o9`J0Wgvz+`hfmv2i9yr)
z?sW-!{<*QqZ*jzh1oz{d&D)04o!_2TZQDvX;Q~gGlD2!LUUGrwlamDHV=M7I!R1j9
z`$@B(jiPndQ!EGAJ1bBk?~x9ZP?X-~n3<1mE1YT9jrkdc8wk_PCHH?JKM#aFU_#)x
zYFBK^zC@Wc==~jnA@`?8Qe4*IAbC*_M2X4nGK@i)(DJuRMdj8zMPF$~iVWwa1ozH!
zrV=$&JfeP&5rwOZ+v9OGhWa8Rv}&|j(gOeO1<?PjVJUVbDnsG|#yi-A5BA8>#Epq;
zKdxh$lw*=3E_>y_(ep?l+n!W<+y~FMH$G@ELP($f)FH^rq9>b@qFZ-r;wMo;8Ue?`
zGb1(qwfRRb2a}T;U0gj>L~GyawYvD7a8T9`N1#(lh+VvtiJ0r1<JwnyYkda3sI9Fg
zQ`h+=vpW^FS^tl#ua0Z0Yqo~qu0aAtf`{Nzpg^%efIyK1cXy`+N?Y8W1lQp1PFpNE
z6faQ2TcCoqrIf<g``q_F_x`^B&pA1nnKftcHEYlG1RhaQd@MP~GsXs}6My|#EXE<(
zD*CoFeCpYIC6YH@%|DXP(?K$2!J$$!AD^E3<mBRhrtiOR`!Qk|Pxb32|If<pocGn-
zJF!$|-+<pgkAKe@lh7P*EEQ6RTPhbay;3}Xv40H;*h%>IDD-$lYn4g<sDtO}iYt!!
zo8>0`^b5})k1qz|9gJU#*<2KUG<nY4B;Rj(@WZ{&|8=q4`t!$AsVzErSARI4cTTL{
z$~2vY|B9HHQ2JA-_a;mG=DI&qou{z*cT29#o3%H6!Y}-%Yn2Ui-;3Xz_SkG}rLnx-
zuM+yk{_v~o%Z}T}-n944mid~7-?xSjKYRO!iHamJGjo*W_V?7Eu!(m``BGv}kt344
z8p$awf8y`_IW~MAl6SSR{QA}0pP76Cn>zPc>%O)Bjs*cK>id`T$Tg#jKY#Y)iM(Z=
z0gju?et!GAGK+jB_;>WSLU;9RoAO<;-hL|xIw{n2#A{}Q)u;clRotN2`$3=8!MY~P
z)9~G8G11gwY2KUZi`01P<Bo>VuQXgXb>-ZzW94@pe4Ye`6#g;1;E~FA>zt8Y9vG-B
z*(P3+6Tp3wEF%(R`V?V8_jF*Zil4eBInMLpy>|$Oc=-?87N{RG&Q`s$eapmOzARlC
z+^<%?*!$jK{^ptAo9OSHMNdaZ@;@rJdd#lY1ibq#YVFxd;7IM~xdWnKKMLrJYt&uA
z_3ZZ9Z<sr}%22bP?S=m7ewEyG`tYd-&!WkB^PFPrRDFL_=w8$1yxe2X|JuzvYLp$*
zP4M>rOp8;aYv#jsV*V1wqq;EjKz$eIAZwC#CUUd0?%tHO;Ct=~HXH-afZnDu&V~C>
zhP32XT<AS|GSTz*^Nz3Y(+_xu!IB?4ShW4zF6dwTJ?G6=x<GrCCYl-kVfGswyZQk)
z*Yo~*gu%%r4VoPOlP&tam`0P)yB&9Rp}PJMI)?BS|ItSiF$_I2>yd3TYdfdKcell!
z2Er<i!#?(X8;$8Hb`uUARy48<{abrrQu!Z|nH=3v2($WP!DdX@+oDC6nI%@3t9K#v
zZnxsB(6LX?;}*^usIoUVt#;A%ui@X-I^UtToNtruk6yMMOSInhSVKx`mCK&J8dkED
z8h!TRBE{mCuJozPn3l@x?2CWMn^Hf*)<xL7&u+usG`Dpm_K7n{e<H&3nDzZzF&iJ(
z)P7FK&u$8S>#Yy&{Kz~-esEpSwP1Sp%qcNs<R21Xh^N(()Gggynh}FfBTp%3nz_S3
z4%5v{@R4=w9?aO?@lOkUBNC>Z@lQJXZz1cgAYs<(X^0+p^X)pLy^A8_THWC_Ch1gz
zlI8$cu`O7wG}3nT{-4j*e-YqAJPVn$cETYtCU3p&Ru|Oyr+GTg<Xa`k_<p{Vt|9)V
zH*Ku@Bktdl69+!!-8)~6I?c%4IR?#=G^JbTe>IrjImdGU&_K<Ouby*={PWQNmeu~1
z$hPk^v!{+@8=WdRISw?whYTv9p}?fKN+sGgA3WFDljF|ieR21Y<DoA9VdRH+?2eb(
zb}S|i8t-$Y)GbSh<@7EzDp<@IHpW_Uzggc}{0}kqk0Ml^JK|JSJdFdZH9y77{Y7SZ
zxZ$&uqxie2dtS$zy@!qJr(g0<|C>nruj^H;{jPSV%&cfGCcogV1+*CiY=iF>0M8cP
z<VBg!?f$P-%4y4f-m#Yosjtfqv{^SEzwVvar-^q2dAUv(5go-^@t4kAg`QGl{7YE>
z{l}j^1&NC7s`*dwpQ=@xp}9XMFuxP$7#><ydX?+^n$`B_{Qnv<GA#yAcvH?5W4y%k
z)-h-1<AwxBKpUL5x;Zp2_?X+~P&)V+`-pd5N$fwNutm4NOlP+~IQH~(Q$M)q!l)Qg
zDRt<@|E5<S*XDg{Ce4%_aG(5V;iB<}<?^$$e+sYv*eIIYz^~}5QHrM7=(wSGl@SW7
zFs{?%u=G`R(V-_GFW9$gdip!{_;c5Zf8Xx0G{7S7D;A1!&1MxC@^cEykGv54i3up_
zr$&nT2MXCdwFyr*VGtYqPv8Z?30aILl4=T!XHy00>#gc$$&PRig7j3LuUUVlIL>zc
z#WC}@j9(w~|A*C7Vu_@$E-<z&U^Awyy{lERnh0U)YV%TJcVw{WWlRK022J;Yaj%lU
z4SVdse*T9FrA|~)q_?*;N6&4~K3S%D727q3xSR4ZS>ff}y#MIXt^)6EiO|2^_}oXT
zvGR=?=ILQxF>mPI=OvrFn159CJ_qNwEY_ljG)3WdQ8%Bvl}vR9_x@w#1qr&Lm=60Q
z`EN#Ypyo7ze8yMKfwAFfzsE)uET6w!c{_%d=YQcyzSZ|{6b^ADtGqC4gv3`M9(#%u
zYR=7Eyd2G}opG3)DQdIv+qV)eopbqVmNK~YuOmO@k&1tqJLy|1l@hZ#kTr4{Q%9r@
zzy%wNUDhAwG)dW(yb4C(E1Ld$!@46~Yc@)Lps6|AE2M2~R<7Z#+=7V7hbko0ugsD^
zTZn0dHMRfozq<MVR*sk4ee!L5#U;(EYxS_`=SZfveoy(<@U2_tkYcDKmdF1~=>LJn
zP?gj#BV&c>2>IVLI#qs9_Qf=*5-(p?*%Fj*(cAH-(&;(>C(GO+$JYzbomq5fM$cCV
z_CcBRV1C<v!NF?JgIG`D(j1Nr=J7jkr2bpy`46&Hg28Kb-J+XamZo}99}lx#^Ep1&
zb~{8i=t!KvOll@}PlNb(hyS(eU$wV7H!UPSk2pPGBhg!YnOoeRsVY!{+%&lZQ#4=@
z^iA%~<e#Vi9{_vRBHuiiUJR0*`BTANym?@@9!HlRr*6^6K@Q_-L-4;_S^50`<&O*<
zpp`dw!Q7{LhPXw?HbLu?t=XU^qx23RC9LtP<0&<+$94Q)U~u54NNaVvYq9=03D7Q$
zFse44kKIja902otVaIYL{|;Pv-u=&Uf`5?b$Q_ZI=HJAc20Ucna`~C(u4$$95}i!h
z(Xj|+N?p?WW|s2U=ij{7I>9qkRhbwft@EnT61QPs0%4Xou-F{`;lEbTtYiKzWke_V
zWxsd2DHF~A9Bc{_|68fG$zHGu@tf4`%@bj4eQ)M3@^Jb70nWU9zPW>Xyzl>_S^@Bx
z+jD*&tMD7wN2(OSKN&2gvnbCY{^erHyp4n9m(m@m*vo&t{5N~Ef6fxaU}LQ!`~2qh
zTI%Xtp0EkD2GEc{9+M}H^O0*<@xPPg`zqz%TND9Mt(p&anhuf4`!2_F*;~+%Y#5E>
zKo>T3G1y1&JN*A<*hN~nNgzCoclxwp4gd9$?D*QR6_$IC7lJ5-`K|7j21~Cg-Tz+%
zjM8mY1~G5TqKE95+jcHSSI@_vo^x8xKO4Mw^*`nL|M~(d&;y|GN9FWyhIiXHaB$gN
zAx0KeF-_^(=40IMVrwYV|1a5y!F!E4BtH!>Z&;R>QJU2WP75z*uz~DLd}$04KC6>@
z_aBn_pI0f{<r<<pL>>7AE>Iv~a#JEEJJ-2Ja;2~Jsv4258t>v(p98<i^&QCnFS2Ou
zg!&sp3_Ule+3_o=oC~NQq5MsgVAE9)eq7*&SI0{#obdlzP=OurR*WM_k~!^EbT0t2
zddT-qyFSp&ykuUa3Q_p`je&yId}!Q%V#Y3AOQk0l=$)#-HgiTaD;v0X%_`oAa8)cW
z=?go0tcc{m`!^c~sSr*tdIjbXdHu3#{!BilA!%OhlvH%+m*yu7)a~;U&HdR^9yC;R
z4*idhhDuUZ`WVUL`>1f3zeTQ{ukBW{#qMvfZE~-|e&qY7unF|GeMr&rQt0^fSk}TO
z?0<iWkud2Yl<iTbhik}~51lKZXl5**;njZo!?>`-U7HgGuV?k(wS|`b+w<*zD?Lga
zk*+l_VlqV;XD<4JhbVsB%Y=_L)PomU_=}zu9ct1PGs_hB-yD_Pm#r@N8M2^=_it!i
zUec`>>AVY?C>AZ)3(1AQ8rC`{?l3B!O$z>z+99sQJ`yFxQ{toV+QBMY`uz3fds~~L
zwGPWaPoMn$^n&r*zxdmtyNu@<Qc-w*(lCDg62rD8ps*@g#G&vtYVl%F>QI_F&%Ruq
zf>>%k-y7!;NpvK8|7&m9;9v2&QDM+r{txuAB1@B>7*o(A9c|ZEl9F&Rx)@kdh$O9a
zExxJF(^%@-?-na|Dk~%V6;*tqvt;ih-=qF!JJLP0__%o?X`IMw^M(npdM~;<X7M05
zYNYbC<=11~B=KRCC~xI{7&vV?{u5GAmzpAt`EQeSlWZz{HgXMmCKE&W2p+fca8ji%
z!h|xxa*YZ}y1}ocm{*Tyt1SdjNpIBA4q5L>8yEf*Ps9y%BB&YLg8<%i<aN^)EgOeA
zXOS|EF&uB&4wd5Q1;cn{l(k<a_}LF)2^z-QGcde80MRfeOnL-2g@ELkc#Y>LVTS}z
zDD!Ga8b@$V+Wt%F@<v4&nXPuqWXfzpBYnEwC~rYL<4HnakvqB%76=E0{c*-&ss-b0
z!uQPHlv5gvlX*Mm@0W#SCY>=XO_!5ya8E%4CuJn1z(@T94_@CQ#U<;wwKsuA<6_)o
zYL8z(Rh@|8qO#IO?5<@PDugk(wg1vp!H_3Ukr3LaGf5$a^`w)=KS5#ZJbZc7y<mE`
zW8(la#j>0w^93$eUOJ<Q7A)HyUTGH6j)*(9u-u(Hn)kA)GKFtFf*@Om&(wkSLs(~T
z2=WD$<*#myMuYCx<>Gh2S#mo}!r%B9{26V;K4;Uw?mqZs`u;GORs-*irPJ$*#i(~%
zPbetPUJDzp>&(`!QyfaRL;}8$`-{gBLgT$e4El!XFhR~N87lzp?)Lx?SFJQjvy-VV
zOQ>^|Vvie<Wb7!gyXy0JFO0z#{~7%%hwx{eTLR`XyNf;5BUKwB-!C0%aT(LP`_)03
z&`l|SJQSs%|Bh*Zz=_1StvgWKT-V>eY6<r%nCw!yL|S_ACE!zVOWr4W4oMWNcsoP_
z7K1x)>b2~41l|XPTU1T;InZs%+;8(9BzzfVfX{PACv!vTy0T$pfa3#Zam32=vp<j{
zQ~XPTNVCZ6)b>s@*IGpHsb!6ttCGedC&4FErE(u>^aN>1oL8%lC8f)OiJGBgs=s)I
z9t>jFiMR^7=(5wUY_~OZA|~l@a&?3<`l>n#l-{zwqtP*xv2dmx9V<+PRU?Eh9mmIq
ziO9AmGV+K66IWWzYx{LS)7qWVu&(Jh3(k_(*`WxU$x3<OYu0t4r*PS3L8++gcc-QK
z8(&DK`Wa@#+2px7aB669tc_CC(0oP{4n<lm><RCvj(c$oo|y=54=3o~pV=j2-yt;<
z)*kE{srDl4G$}h_cA~c>bx`C2PR!bqR%wdXZbl@yZ3(VS_6v5?t%^y!<vOgb>GXII
z#?Q3x1c>$mD7=q=#A=FLD9t<#!;+{bvZPPo%;XR`!X&kTt-@HMdb7SG45iR8gEnrq
z$3Y(InYx;!FX+@(%SXD98?+Bk8Pf{^q9g>k_5s5Lwtu*2ZH-*D!R7|$vBpGv*8GBd
zWG*Ax7{16u=_?_=s*$K*`85$@7|qIR+7Nzm6`4#RW@mVwC^{t|cAPxqhX7W3`W`h9
z&KpbFAyp}2XBV*?<6z-F9LQg|&w!(a8QV`qkg#hZq0Zq$bG<wfI_cVCyce~%)pP(d
z=I}_opJ&=peuOH&czZ1<2@O8^Dd)=Ql9f8lV~>R5uqVu^tUpW+^h8gsHG$k-HC*{F
zEJBj%XmbE0=G2H5BpS&ubKn%_51Af+2{58X;Nuyr2W+;jrNohyD{=Kfi_|Dqv0rJE
znK<go84}d*-OH^Z)s0^?r1p;pC1eJxOw)p7vi<RX$HY9?3CL@A7P)AAFS%_`2tMH<
zVltK6&%rj(X5y>z@)5~psT1k<Arx0*uZbXTM1-ni?RBXej8KOn6%Ckb0?#j8#F;fO
zy8Mcf0S(_^{=M)mNvgRM%QUT_Dg}=ePy{q5{@s&G+g!{s088CA@6&h0>*(o=S83#^
zqmv|BmW5{eC=0!;rHLzb^!Q9o-r_e`Syi7&PCKNvk9U|MF%?V#nCB`uV`p$k#l=b1
zeIp8Jpr(Ecf*%-ypB(QnWou}s`XFp2cju%us^JQeb8jSiI_Z0F2+i_f8zHHVtoh1T
zPbQFDgeImorc<M~oeO74T6?<YU@2l>)hOvR&Fz!I8PZbA)MZsNeczxwfbUT()EO~W
zCZutxB0pSIAjQ;iq-JHtBh3XVAz+EZs)jhKbZ3UYa_yj3TJT#Y>CAp-90DNlM`bKL
zeL`-2$uCFO!CJo-6<T{!eyN^ID{+|+_eE?{V(sBT_=_>astpA7-%1T34>7eT7h|Kg
z-hG(d5I3uD2Hj9SdleTHgBown=)P#wr8Gbo3iFKNF}x--%Je>{JP$X-$)xMlQEFx<
z*4Kj)vhU#nA{9s*^NvO1bblGO<@0^|ML%eNTz45kl!yljS~7hW!y4rfhkp^IP^j8M
zXS1QXivMV`z9a|bF+^$ySWSA_uUef-6e2M&Mv@P1t$iWF$d089Qs*ny>x`BoM3ao<
zUFUFrRN^*p7*t7Ei{qM5ZhMs)Z;+snMlWdQt`OPpm%R%t4Q<$sAdZ>jJ0Z{j8V62t
z(Stb{!Q5Wr$Ho?bXF=^-b!Nz*G0GJ+i9B1{^YAE{;qOelbjfsdqP6jDuTiK|{bGg0
zq<X^uqFJCW`RuceCjlC1V_N_c=aNM>T89H{mC1e@Xw}3BA%6EJItDLweOk(}La*jW
zG$FWVrC>zWB6n<5Mq%bbtSNX>z+{SSl0H?2b`pOlAEAjPiVi4Bi4!h~O}vI+(%#qQ
zCAb%uY3hVKj`KN{?f4rcmQ@g^k7V(dv3A$TlJPs^=!c@0>?h+V!J1HYp(3Aor}nu^
zHnp50nP=Sed-Pw_aT*U%6H%lcJoqSALHWr{sD~XngI3cd#mywc>Y|P{v6w-e#*)Pg
zaW<o`Trx(6Bzc1#YqSO#x+-1SAz9T#?u_Sxk^4rSYGP0kKE=WM9})uHbACyTbe48h
z$U6^Y0tp`QaZQLd@YrQllfB#;noc;uu2ZPn>nZbRe_}SABx|QqbSqSuGa#by(v1xr
zhN+iB(i|q2H&OKoWZrDLnEJzrOC<Ekxt0l$#F$gngGO5ZY)P&JEzvs2ge~k1p0td~
z!DRMy!G+#HqYBa1ryf3nE<8{`)QJ<woQ!RZTP%Z(UT2k_R<$UEvF41w;0#*Z61t<8
zpqcW1nh+cbljWp<<4>&74o@M>EJf%{hXsIXRr<9fCR6U!K*6<@6P4#0ruwp)2%{ly
zfAQG*5&g+>uW5lRc}9ot;IY)rg6gSXQNb3CH!}Fhl`=s?z$qL*ZrGTeo{SKqt7^BZ
z)_HT@dHq@B&axx{HmOmkXf`XvfuU68G=nS);z6$_M?6P2RbCdycRfkeN?5c&E_^8N
zBW#T32w(kN^}He^q3(<ZWrM;s7>aqgDe?`FD?O9s2w#qDN9r(IR5cY45|r;5*vJHr
z0jSsDV_XM|MW{;JOwDBf$8~!|SsjuhH%<e3E{1pUQBh8A8m4N*G0`0I-0;`5JEIGD
zPM@vii|t>@timEA+R+x0Ox(bB4j$Nc7WMM6fv~kvY}ISv8HBEyzY@sn)()UlteZ-U
z&1At#s$}`>R#^{8{IVtD<<fTEZJaVf^=vU@u$#DpzE15<#a`Pcw4V8nJhjmA6YV9^
z;@pLrS1+mY57{!L!I4iyvtg@f77@}_^IC~H)OzlaVVG4yZL=PQv_|y372R<P8!thE
z0k%AKQ~}mAUqd2W!4QuYgCJtOsXIeGGPg5VaTcJd#UK8qrGrlJ_|e{AWft;WI2xLm
zM6+k|LkO(DbQmwbM)SugKc=?Q=txzmwjC5v`?vX~tY&#6A~=3<(c0hH{};MO9+p~*
zOHw$eF|XyralWXZPK5ytLo)0SXx^XjMXEGpjQ0Z1&kX5FF;s#hRAOk~F%#lbWv+&-
zsFwPG1`TwKc9Iv9jt()M@iUOUOA(!RHdho#vXhj1QY@??OZ@;&P<}-c&nuvMM^-b>
z;vwmS?M5Xk4+_CubQ*bM$;$6|&ZO$~j)RU?R8@-BLXF`vEx6JYg{o@of{@8!@}>uk
zj2L@61@nki%VsI_I-qfQb{51Zc5N{AYg2ZEWPY+kkq7k_j1dniC_5zSNkyNQX`Q2;
z+?pjIRSK$2+S4qX8s=rv4EPc{p~i;$Xl`wT&FcE>v05`j<zy}=5fMhVd#M~;B|B>7
z;zk*0ac?2^SUm&CLs(>}^YcJ_!PR}JQxG&kzu-rU)8Dx|j?$qS*T`!664`7y^ftaq
zO45kIl6$CN$CT~EiaoR}2A!HoOKCLSST@PppaCGM8hSR#GE$$0w9n_<4W&09*Hq@^
zIl>?egWc;}pyI?(I^>8>&-h*xqSXZ5uHEFM8YLwX#bHF)tFq>{D-bYZ!->J?2r0`3
z@oM0?H&qF8%0CtIq!if&der8Gj6c|IuD@Kh&FzetfZJDJ=vyF+wZF$*3dGfBCa>97
zMXf&2HM%h56GHb&)n)3)-*Jm`V||limxM+e;A|Y~!xN1IRF;Hi1b^#@U5Y)eGP}&w
zibM=H!(l(Qhmu2=`KG9V{=BYG;V<MZF%@W`zix<8)RnLs4d=!qanQ=I;=FuQBiCb7
zUAqy>o`8s@QOon!HWt;T@XnLxBf$AF^90&!W{UIG7E$&W$S+XrwTkVge%N6SYN|J#
z8S0-DC!-H3=%Nqq1qj$AD@n|eT*H|nRF?P4IYDG2rwsaA)b?YQGG9sE1J0@lHH@pQ
zoR?a$0&#vhrXY?5%X$(#15pQ4J$LlBDuWIecSP%MLlzGqQ$rku<K+=)Z3unbjl`ev
zP5mx$Ey|;tgGS9(F6t>W_CoBuB;^cnnwJECJaiKeW(tw6>M&$0E{o(s=;`koRoMn)
z<V|B%Pyw`rc<53Hkw-#?l|d<la`Y@GXIeo&G_41mpQEk?^4e;c)%9%JG~p8JSeVG3
zEYt2+;X|OMc&p-MxphH6aa*~eyam#OyTPK;QBLU2s+P~jV@b=Nez6&o^rX~dh6Wa6
z1nL<~Z$Al_mU*tgB6}PRR<>gl+g!23F!Nhpqr^T+wWt<nt{p+fMYwLrx){EjHYtG;
zoPGK3ew0@w1W=a=Gjw+DTL354X_g6&lPY=91NE@Sy0_d(79*WTREki6F^)R5MZhoy
zLLC62VrEm-cyeaz`&~ebJrS$>trjzeMvhC{wqX3pE&Zc}F0-|WbRQ!P$5ahKuxs#w
zw#G<9uUUXm8uq)I#yvj=bur)@w*;85ZW8pX1);R^DJ6?rqEAes8~&WJHiTA_h#yY_
z)kj`fN2o?Tojh3ohLUoHadzAEiQP2d1X6OCuYwm4o9(tFqD24x$cn(KXvR{TjfT8|
zqv-`Y?oWJN(GkIm6KORO|EaG@FYOGtCAcr@EUIf=uvHGc@M$AT03E*{ejLz5NJ*s9
zPV=*NE~Y!-947a>(-lP;#v_WL<+#kIB=>T#By@%k38m0AoH1#`n8Sw*86l!}_&bc!
zSZ!UsCQ``GxHg5<IDydAhdc=ozGJ02-RSG}1ItNu#5Ot()_&6JZJ6>1G=9RD>e4KY
zXM8Or{Y<r~qVpsoW14A)%+rM#Xk~%0uumdwQV`4^%+k0{HWXht0o~+OsZh#Sm5n&s
ztnJtNX%k}WZcL*BoMiR9kNnK}7G+|kSfe@-iivKR{7!S3I=*?~K>n5zi-jSA8g<pN
zNGfCB)puM;1@ueD<k`G-N8TwymIM=)YW6@{;C)P>4O*uSz@@Fjw!F$)vo2YS6Y{0Z
zh|o{21=q=qS9AD@3rdJY@$ynp5y37=v~o&}I&ZJrJ=)LML{kAx6C%=>Uz(-jIVp#6
zi-vI{QkUBnglI)IouI@rCp2cfrrk)HskaJms~n-mx++I2#F3!qsd<02InP*6Emr+Q
z9EcOU@&~nFJ2Fj3Vb`B<q79{lsx4|tzLRDH3s*=o%~c=HOS0G8wDop6!NwGJ{31Dv
zZ`@M0i4qOBpgl!tkE6xt;|%*<Kw?I}BgjZ(7*_^ed98-$=maN4)}={TuACs?nqiIi
zZq-<#V3vqSbludvpIFF->^=*7I?`*jT|KvIcF5wf85RC60Oc)e055;4J4=pf95<lO
zChTwN&n4p?S3H*|j){eaMeJA7q%uO7@kbb0fL4=9t=e|vf=`U*>c}<cgm+99TM<i;
z*JxgOqXP#yHW}Jd(8StSjD?0s2;n}b(<z}4DX0Nt5t;aRG_2T;#}-2C<9`0JVEkE`
z2s)qEUpi3e?|LKhsiSNuNx-V0Y#I402j(%1eA!F}g}W{{c&y?(@oyM^ig$upea&|&
z&>ac|j}-=qRHd>f*YTX{@M(}7RL)usN}YHV5Z0db2y)F|B6C0+Ls;b(BxgqmLEnv8
zVI*+G%OGh`oD1NMOTfSfaea(qmrT8WIAn$h7KutqMOG)cC+Nu5b)lkKVZ^$xL*4cH
zO;VjZAR0uzZIh27!(0h0!^v3eC<R85J<0#{XylilMD>9PGPEY)s3cwnWf2#eWWWvp
z(bFXG02iUa{VIQ!?4{VeTbHY31vVpEBdE)CO7%(_8E>J3sjRun$*?Y-oe6c$mT{jj
zC2FX_3yfFHCw||5kH1R&Nlv(s<#d502`Y7=R?&Quo|%k~i*v7__>WVC-q{*C%lMs7
z$VDb`6|{&6@h<?PQwt*y+<!(;ESp1F%Jl(|5X-a?4Xa>qdH19~VD7%%8Oz>Q7lZ~C
zzp&V<TE27wTt8?9fM|>%M+`#V&N=isWNr7`xEH>Ux8F@j9-{o2MAyN0<SY2GDk)}H
zdst9aGasxk?pMY%^5P_^K&7J4O!_^r&a&+54$3dNpOyc}($d5MlgX2&V^CU~Pyy$S
zv`{HU0F`s3OQ#jKGQJx{x$(=iC`#Z}>fDR;$?qg0#f!aNW2ThwY&hfs@<TLH9c@Pi
zyvUTw@!!s=fEla=(5V`Y%v#K1FoxLIJi6)>!$?o}^-@3>C~C-4*A1aOWI~StPTOSK
zpEmPVO&OhI2|(I;HT(>t?M`6Nhaggo=b!S{_;UA<-)k2}O3JyB=sRi@1>|_~;#f7_
z6HM25nIy8$MILUZWQKC|%5$%DqF3{>C|2iczzTsSi4lr!$hRJb(`;~Ie{OCW%H{b8
zdV4fDKq_VKiXm&fS=u-xKOD*^SV_2A+o@)`*Y0>zT#wi%a;B>6@EGa$r=!x(ezasL
zLH4FC1m&GNu9^?~D|^3C&h>Sh9Ai;PhaDez)YCVbtDh&&<x88#YpZS^)&Ct#ibBEI
zmgkbN?u?(SM?3l<bP6{gp4RSvOiiWS$c&p6kQDzNnY?tLte%XLQ-<^<i?6HFz*>y>
zLL|vF7L@S1{=<TV3_oyw#aNDvrxO+n*a|719m&i%BY8;H6h<djrIJ}KYu~C0(ros$
zFN?B_#61}O>XZ-=7fv-%zv5?Cw9u(yVmf-4X~V@^=69MPyi%hHz-L%Ak_S1AuOsp$
z>U@q&98EMoq%U!}8{)n_#k*m+U#!#TL>D?@IiU~3@6M}9QICFRxyh=7Thl6NjLdUT
zK@wcYMjJ6Vf+kmMtsNaJG;?%_?D&b~jLj>04FDJzA+uGUow{!4N)g>XdR4L2UIM2o
zoNI|c+q%g^l98#7k$4H*gDmR2_$1yANlmDcY%TKLd3H~fYPShth9%OQF$reA*DTlB
zpJi$Qw2tUr;(6ixcY)I-#|9~{2XoO0g+K+PL7@nK$Q}3Yl-l(wowAQk3?H@<=^>Q`
zwGh{99{T|8`~;L*JRI}@ZSVm!4;FVC(wP&I8PbL~JWb(4DTpWF>CD_np<spG^fR+;
zBdv|Y3H6$^3!pWZag`<s`ts*+Rn(}-NA^)V-qo=KHj)mtSq~dZDMWTbbtmF^NCg@v
z`+dMZHY`3bjmr2__Uh^h1>Gv4%G@>SZEEPAj1rw@N}E)cwz#{Z^K>hd0Hg}h`H@dX
z2)}MREs9Q8kTtGVlh_E3uPyy69g;t6FuKk%WSmhrGAyu{;!fTqGA6><;ohZtspY3h
zx_Z*Hq-qzjT2{+q@;hR0tOh;aPWb{ucE(*QlrpV}uLqtsV6bJ4kxCd6vwOGHkrF3)
zBs`4BCMdD*j;NBJi+RS9X+dBrt*<arUONJNS#+Ox(%H%zpNm}AsCw9G;M7HN1g{ML
zWqa~>o;50irK{cOi>|!~LW#$A_MmMUegq}TI06tfi!$FWvD@M<)M0N>N8oKz7hI2I
zGL?c|v`KXtiO92nVjiL#(Ud(Z7#bMeEl7}FyFJZ;FLUOtV}3N}mVW!uNdH|;y=yi@
z?BvvzP!wrhqh&vFwK%eci{vpRJdDJVQ8Z?mGrP%R9cX||Yzt~?6T0tMaaQgV|6Ehm
zjhjxGHs3Ci6rxanf&927sFyWi!+@up=+Kx%0vOGU8g8wlC3o`H-G}ndf(T;kb-I=o
zef8XD1PU=qZPK4YGtAZU@1s5^=tW3VxC1frUdxOD^bGan><=kjmQ_F2uRbu2&rPUj
z?i7UxH@H4Whs4;}$B3g4VfFC76#ibKiqGLzt86zH3dj`cWVU2V`LEIeI%HUN#z*{j
z$4VeP%gAiPUxEq%{oAdGBTJPbAhbTBE@etP^?Qoqv>h7mESf;ZM%;y}KG2Q8J0o|u
z*I>cW-t3_nXc@Hww8lc&2Bn;V_bVCptTN6kxqrto#z>D`vY`x3!_+VjlzPT6xqnPq
zIB@qkuK2o#Nl1*Da4dC|WbD=BVV?R$*_{q-9brZV?Fm@8(=wF-qh2x(48yBKDXGK8
zySm9h_uLroaSxGAR}kXi$T`2A%#hwIO}!$^U>@F34I3T~-XVee_Dd|rYD~IfRLoDz
zrq9HcEtR2>Imior327(L3YSYr6r4P!2=kE4p}d!v^Mi);EP5s~t2WOZ<k6m3y=n~U
z`4LSDa?UI<%w<?`$|gM<{;Kl_VO=SB%}kR8kbvwLbv7Q*+eAibztf0v;V#8<*Tb)q
z>x(Yw*~^SV(RZ@TDr4+V8Z!lS$22j5mmn=kRc$MEtOLnT{TD<7rYvMPwlP<tX+;Lk
zHG(DfHkWD8g-Qqt>Qk&-($V?VYBk21YRB3xc?!3fIB+#|QSP7=TD+ntrU&duj3i98
zF&=p3O{oskcII|h4Q5MV^%;)O+HVO9M2@bE!8(?{7xw+-oCkH{T=my{G(vCGRJ@^c
z-WGS})+6@fX^jZM_=VGqbLM%>hQhcj>Ltpj7L^hAch}KLa3=Nqay93?g7)D&pu&C~
z<QYolfYBxr_Min$@KjhR@x}W|h9WN4i|DN|K5LWCt!G1NWsL?T&<dR$;CJ4#L|0DW
zK4t{26O}|8vQa9{Yfu&;;+|RLW(fyx!>w$qm(&cfle(&(xhGkEL1MSMo-dNDA=op^
zco**{GiNaQuSQNYI}e`PYm+nnVkHuG$Z20^5Zgia#|u<#Bmjz0HRP)GPCc4oV|^h@
zT0_)Rqcr?UVn~6I==5000}TI7XTNkU{FNDBRFazBDV|Pba`pj{2pJnqMBF6Y;L}i<
zGg)21?1?*!z=uqgo}T)K)JcP2D#|5;#iXsK+n|QtX0??otFk*}4?DE{6x_Ah*uK;a
zjXJH__5$&Ynff%$8*&!dLb|{LCgr+q&Rg^kL|z*TXo$Iswa?0wY!$6S!uIMZxO@c{
z)IPoeK|GFYseDP_oDrt>GCBOBBya(-GKK`zm<vy^a3&7R-Z(_<sf;md!?akzk(ff0
zwz{ITW!~69)v7DN(_gCa^|rY3f+XVP2(9S|@)m%aQ~r38o(LVMJ2!Zuv0*jvM6hok
zt_Ms)PhX_zXs9c6_9f8V!}q6{CXudOz*bIChLd8CG*kV+lZt{L&>2U(u{0(zSut@}
z(fofakaB<9-q#Fh+>mnWs&ve{JiO$ijY6&_+E10HB4;`U@0T0SwszR+!N%DmGVHxA
zZ0eo23^NW#Z_e#;Y^x1+qQOgo80D&hE1_#5bvuF86Vw$kqi^VN4USn2vniiZlo}^)
zvs8&zv3shyGsH~^XQv{6lNEAe(9~3Je>PJuHkq?Bh5{^EqfwtK&SE;vH6>FKMn~82
zWuO&E?ln?6fX0rnF4nMrvIUA4aHjITft=x%<vENnoW-oYc5BoQqLygyl-t^xJP)Ex
zvo`?pGQReZh|<ut+L}p7=JQr8$K$1n!(ZX0(&eqtGnpnu#~^ZY4E6|{9TK&aNfUz%
zk0@6+80AnhDs^hjsQZx$i84VFllS~t#C=KGc#Kd#zL8M|Wb*RHZ_!OUPy~_kAhq?+
z=pt-27(NV*dOXaBh2;~{5}EMgR1ll`H6<TZ0K0>6-?w5<?I%g4rk_h2E+pH=8i97!
z93aO?1Q%<QLnfF17$J&zp_WEneTBT=B@PheT}-9!IfUoTW@<c;BsdN^K9)9*=CNNL
z1Sv~Q(dDm{iMo<giJcklgY7B7vL6q-!Y_w{G8AR-*~t}zDL@)9S_gWyRKZMU`*s6+
z=_1kgoaR_C5C_zLN&-cW6p}pE=)8;cF&P)67F{naQw`YS2DQ1TWgb^n1E?M>z<zeQ
z8d$@K>g1k|rmxd_OO%=Yu)$8*f{lfxUe+kgkyl5Rsm8^w5&}jwy4KaIt*S?BdS&%S
znF9>}kV19YWw?CF+yl?CT4(K1My6A>euLEuJW*<Zyei>XElk-YB|`y!6*tKVBSQkf
zR`bE^vFa^G@MT7cxoI7tU)_$>9B=*jZ305}l$==HqCS3KSd<Go)s-dcr{Fo90P=35
zelsSu;RTGWyC2-bPgXW&i!qNYJBGqp=S{%1&ox_OUi{#xeI%NZiDfi>Hz5Zu;1v;|
zX#$vzqA$+!y{Jose)v@%J!rTAOQas=<SQYQGZO`fVV_w=417n9Ag3TTC(I)!$FMj+
zrFULM^QF8A%|Tw&YDVlT4?i|;lSPRnRN)v&HH23fbT7d-zsd-~WJu%|$98GPQ<IM@
zT!@<~iW6Mqq<}Vo#o2}@Q^;fdMoW+G0d!RM2{n?50uZ}3n?-seJiuH4oQN?AFFaFk
z`msNcNUMcv;X+2bS>;HH)X|f=G-917ce}1UM0wL&RUEs2G|6M3RTDm}<Q(T}3{VJI
zuak6AcZY24fFU0A2;e51Wa4ggsFLxi(}%0(zSQ><F_{GuUHKsiR+M2YZW^)-aBOo>
zU(KP6)G+Czjo!k=#C3U9+Nr3B&!Ii5wNmh6`Gk>h<5n!SBK^kup+3^O(BVjCRT4&p
zL1R{92P6ys3oufrs+JgrCW&4<B0a~p5^oE2ht!g~cLS3!sZJWIh;F$oL7BSGWMqq%
z9djWn>KJR>{N49D>P|JGalZubJ=-!q*R=4)V+7!x5gM3m8Nib=_p{3q1P96=xNCgR
zpDVkxC>C1Oi!4JVmYu*?38^oTf6g|m1<jkwpC3TX{AQJD@@cBn9Eh3qo{+OO!ta#x
zU+(&s#B#li&dI;7iiVE}kN3-XR7;E(5wFvlr@C2^%_S85h~%*{^fs0P*$HsRW|QBE
zadL`DWS4OM`Q1VtEjec9vnm{wb02_p<f?n$y$PN+m(*ljIm%pDj8+FvW!xDcHh9+Y
zSHbI{Q5&$tD_B}7ngXb)D#YUT7#Jv*i#bqjRH>hg_CUDt=gornJP{<|Q`#@YOeh19
z1n4sz0#iY!DBKo*?T@6L-epyyMM2#Tl%6pk@!(05n_mJ~Sv7>DN*vA0nH)+*YAIa*
zo@RZr&a=FXi_&vCG^9L2IC?Q0r+Vlf+i=P)(t$B1G<`g6mo4Tl4C1zwLyGXsV#t*!
zcA^L{Qe5iH3TNf?*=;C<c9nN0s5dU{%&d!5p=IA<XB1kcR<0IhNGAar%yKTv$*Dgf
zF+I`}GF+f-OfmID0wWU=`oYxi#~~NUVqz{Mszk*W)I$XQZ=(6LC@NiKincexR~&o9
zakdm?;dH@QJDh2KRv5CPImoE#GKv;YJT<oF%5@=&2cZm1@Qs@cMAu9@CaWDzB=~ar
zjf}=}@S`HC6^R(e4B<8&6hO_~F)AazX<h#OC~`DnLdS^0@M}uolPxb=Hd%l<yE&|k
z+`j8>riHbu8=b}vi&(X_;?#Y#dmSz1aD+k4$f6?M=$l5!MEcNdr9C66d-0`1{+wfb
zS?8*m8GqTCeK!exLAR7~kq-PKBE~mWh|%nfDbWOXr0|-rE80}%X#A&atyVb^dnf!5
zJC?shU3|I9NitQzBwn@SqZE+E1h2QH?MBZ_Cp*trxR<zhlE+w(H=?ppLv_yTf-{P&
z$n{~Z@4e9C;j~irJTN^A9NyKu!obfG8OrY-J&p)5h)xuZ`K~46(1#b%E}Fw$D@e|W
zOyiCEa@}395^$GfPK6B4sMQT-E@FW>(@`fm=n^n<CEvlgY@tyQN|Anj;U#INkPM5)
zdJoN)+oW1;(!r5RBB8Bnm9>Mw@(8GZbiW3=O&D7j?YnLB(pfpficn7t<Bd}Ab9|r@
zk>X&m9Df1OW-x%INb{zXX%%o)bHw%<4CnMXbH~8UuOQ<m+1=X{H135G4fV4&mfj-Q
z7@0F;r+T&$wQ?uDsGT|CdK2>WgcD6;(EttbPqH|DNn~XjkxW3ERZlq5nzt4Q^dlPF
z{ACMdGN4CUeJTfu;?ocwG?nf5sEn-Vxkr-7q@!~h<?Mzi>@RRNy}(_3%2|;*3)9qW
z9}ntIe9jRwLQi0_w*_}ldu%H<I$N$s+##Rt^H%N;|5L(+oJXqtv=WVzVjH@w-i`gk
z#O?sdhlPP@8_8V*D<NTFwj?}DRxk}uGtMoC0YX0AK9obE&47TJ81FL<<q)?^W=>Z7
zFL4H0ur(n5o{WOo2+^ib+WjGRjH5Z<nW@KPFfdb%OR!i*k}<2Wx3Z2!;p1>^?DPaf
zpjs@?M>6#TH|`p34f8F<<RKQkyUSsMDd5Z}vZnKraKdpfO9rN|Q+VH3><L5#>oOM2
zsteu{I@xpKSH?yL@NjJV#nm$o*V~<XwzGDINs|Fcq5T&V>~yQIBq?LMiT0N*0BBOM
zMW+shnKmtMx;Lj4&RJU^G31n_dVYI1*mocpYFY3Bmgwr<^t|CH@v7N6g6j3X%XYgV
z@wdJUcT;@`h_A3$d^3;5v@6pppWTX8GZpa)1iEOZoU=&mWMm?3x&6RWD=c-Z6WYiN
z0tZ{E^vMK#fSw5lF_Ra?z8y*RJ>@lWpTNQ(%d<dftKg3}?LFgFM^L(~WNXt<xwll$
z+J&Fng+Lx=-ju!Pawxl&omM*3@YS3=XzbiK^I@jURrW}xCXXqgjb{%xF)rOmlA6+*
zH0F8m;$6Q@y6K(4%63{3dzM#pohPzkBx0aDy1P1e%NeeL%7>fVl-{R#?c@>J#=70v
z>xHHxF-lz6uOshy({~Mm??A1+-~Ay{!Xx$Z-+V9GwVe3E#iu=~oujrwrUJy3rqr)j
zHeennH|y+ccDgK?5-axikRpXqtHrPJdGlEI%7lem$=wZ@D)L3ilVpq(MXLpK+Sd-r
zU^lByJY4(Ow-%)nWz+P9jT85AY>^=D;P~}2Lvi$XI#>Gw38&Gst1nA1@pndTAMmP~
z@87rvr+}s28@9iA@+qn2Qyqeb4DVJ!vN!GAI?|TQF?}(ksGsOP5;N>X=kw!R02kEZ
zI_S}+*@+*1ze@a8RF4O6=It-jzw^EuZzeMmisEd)kOAu{=0t-eOxUTgj3DfUj0!8_
zNlIf=rG~a0O+3a>ONAv~S>uGq$K9<{%;Ee<&NItZCOw?IR4ia|^wm1K@IhIgB9}Y<
zDJ)A1l)V7`RYY}S_Eu~4RZr!Ws3{%1X+bRRKwh2hS6g~WtQZ8XnG>2mmPnx{e!UoL
z#5;q-v`i1}-Tu<4CwV6`foBs$Cn8uIpd8BkMYrnhm9yk((Vv*dIQsVctb4QF?ZVO=
zg1JfOEaV<T&R>E;+SfnTz41%f(_oEWP`ix%NfAzM*6?<Tf#xFx0)1~&c;vC_PPl$L
zN1}L$(5<ABzY5HU{;F!Tm<nbh5n)b}yqnB%V)zmj5TCE{w>=Myw4S;vnM4pu-smqc
z^cL0Vc$;p=BlN3T*;!fwrQxx$xj$|-YihDm<<7%JqEot;&{noW{kqX7E(O`rD3bNz
zTJq`N#$NV5#fF>gBc{(CyPhdMh>i_`Wak;J3VlQRmw!A7fxZ%`DS0FCgUL{Ta&gJF
zv3|7+w<t=x0N45w@L(U}kH^vTsu`VVPMVfEc89JuT@fB#@?2VNCE~*FioK*BWhCKu
zI~bIpW76?T{uNjM$=B6!8^mX>$a@RiD_Rr}Z!$IMY?VmWmL~jg>M}tM9u73YNu}BF
zgyYyuQB79_|5Bx2ebAR{L69j`jTago>RdqU4}U?k<8~L{^+(FxPMMNemk8!z^qR?!
zzkRKDS&B!E*A=X)x74@I?IpTJA+@va&kUo)FN>u`81~|-`HR?9E*Q-xIwgmZkxf;R
zv2}9n9o*`z<T`voxZ^@qenr@`ryfE5PLo~P4_;Tl=JgYf09V(8&ckvg{4BQdeZkCZ
z_c(jDi~A_w6#Gl2s@hx28t*Zbi8M9c(bMAN)80UT*o~h!`w=jf3aO|$u3avhecm!d
z#KFNKX8!V7obOTe)2`Q+GNn(~yFXjpTpx_*apT1b6>2-1#|9RaZo3Z5<VOgoF>_xW
zzIYSm7}>d=yUGskz_-&_SVudJ-9M}#pLg?WlP0SCyUThzbKIc>yeqfSe*MGF{aR~D
z2ze;o;3hm!<<9(Sn%aBW5zm8ec}pOrjLx91dYYJi+M<E{X(+Lhg@hpRZTagGk3~Ow
z(>i|n0r%cjmidBrn#S1z*xivQx1{neKko!tdGNniRFsDFOvxXac2=4b3=rhhfmU`k
zS);z(+nni$r?+YTI@BmbB#VDH!!svx<ipV=%W&di@D9&T@z;vso=Xjj=?6L9?Xt^I
zF|JorjuQNx$Y4bep8f{B+haX$a_L{4<GY`jh3q4rU<uzuJIZfbKBi}Nq|wj94Q^DA
zqt9pqr#|_rX`A)Pt~6@o5BBo1-E*ws{d^1^TWE`%WEo#aFD-->TbnV|mul7)JUG&g
zIyS(6xEX~C3%t$a2@jQZKIOX-cO>@<X4qlPls!5*5yxA}7$wF7c4;CA=5}3<Gpr}>
z$;EyALg4&`Y-2raF?jMki|Tp!MozPKS4HKt;nvB7WXm_|G&BnYym&kL=z1yH`%=>r
z@?Ay>x;%OI?U|y*XDqb>w9K1p$mEIX7b>d=Pv)h;zl78@l<%1(nD%F&wfN{Wc}6~3
zuJ>W04czGN<95ADBOV)xTPd)^7sh*(b~bmMh263<;s=P?UjDhfKFt4Zmb+_TdHlQZ
z^|x;ZQ_(MdUK801sPj|nztymd(^xIw_%b{`)b7mx3S>8PDb+R3BG_W6Tzw2NEKzq$
zeCiiOK(}H+-8MlKTacVg@yN9JYxDc3kALxp8xe`48zNjLy{mTGLB$ik5`B{jQZC5z
z0sQcdQSI+ll<gl_`)2!I+;$FlmRF0Hny)!8O-k@TvX18SVVV}u^*0iChi{dqDtt4K
zYLZ>$#V++~UyG}7^irT@s3`8l$Oo22y<lK$$b=2#JY8VAi*p>$YF)dT<+sK?ZJMO_
z=Sb=d_dYrLlfV3U27G&Sz5mOT#;ac}9-088aIGm+wqqS!7oUm$$yp%T*VlfYO)UTz
zV~iaBTLgJKm?sb4a27V@z<TF|GIA3EH=bZ~;(C*JYL`uF4J*15fwb8TEuGE)3(n#f
z{X&n(5=1ZW_ArKphkW))Io~<M_$~YdSt|=()|@unl?|j2dw<Ls*l`zX{(KYo<LwHX
zj@n0abguZ>pR)}>zzg5m#G1j)I-Et0@BPoTx$ZtSt_L4;|F*5^vL!b6NqQK!@NB@b
z`eCUPRU4DFP(15oOrEU>iMeae?efbTX16|#A4)=0%0E2`XECCpOjkh*fttsp;SIN?
z%J<utg7VT?qYw2v)44~(Y+v#L{lI1S45Z4MKd0B;H80Uv4yE_n#(Q&75u{D<)w83*
zf={2zg!WOQ&JxWQS+sjI$s2{0Qa0YpJ}YU&_VR;mevT6xGH0dR0U1WF?6!f{4*3^E
zib)<mE^~4^k{*Mhj{~2L1~2Mm4-Mb@X|Clg)d*N*T$pe4ka$X099-Ua_9?zM=LJEB
za`MUkU5wRm$rGKo%;i-=%=#HpWDm!%)62`QJ3~5;8^`V5KKXrMu2uQd4ASQE;m5)A
zhEo@iR0=8Ky%+41&cA1jB$jEDtSyn7((jG(XYVx5*^V(<x>K@8<dGHFL5@guwT{VB
z{Cm3WwUp(ql~+jBgC|YZBD|RKzNLv4)a-0lwA{QlhZr7GKXfnNx+k?>@)<zep%EjA
z$Oac}HhiwNO`pug+H2rid4|U+_@ujN^CE)){GU^-zpsB5$H`2bn{~i--df3L&nQo&
z)FHY**N}kT`MhcA$^>)4-_6a>w2IqiAvvpJfPsb>)|%~8nPhRWEBtFMSw_I**C>sA
z9k7J?U1-|X!WC9;mWiR&=OMsIFMPYFW1~UjB|M#l?woN<L?p3(1@&9)n2Z-w*-4=-
zfeCt;knoQEk3C4Cb1dw;g5<7<vO;eC^J89H%{9NqAG4#5-#NSCJV-o&*iZ9#f4hrt
z&dC788hcEtp$~lS?Rx#;a$lRY173{Sd$!E#>;k5S9??9^jGu(IyrCBgae7=3HD1+`
zKXdY#ydIJ@R>-RgFRcigsH#ZFYHX9Q!)riDM9%S&eK2=zYtWi%ApNZnuZ3`B;*1&f
zQbYck6nr9zg*|H`t7AvaemNUhPqn8KpUls_<u-m{ZKA+v^BMW)@6bLyim*)^fFnfX
zcClC$fCDm&^ACTmoV|18tSxNin0I!sz=KK3GrpM!+Rrz7i-&jLS6f3B*)pV|;^ywg
z05<a^F24>KvUsrpRlfT0;mHE!h_dVP!^%#J1}jzACn85P=AaIW&It9|B(=8j<{TPZ
z>5`nl^@=-730GtSY#}$`tAbh4sfa)9v5Ta5gN@wLo%!(WmjNZ-&$Co4)D`+{l-?Jv
zC&tYszb1|Rwmrl{qgOEChMXvS(LDv}{-MOo{&`wOhlu;{;q@A*vjoi|X4M;Hc)d7f
zob=_zlVkiN8k(@d3#1^Lr7{|g{@%WEzeL~CpO0)l_1)68sUXkAJvAXnQacvN?KpBo
zE*AwoVR(Ee)zUKqD+W(oO_g54(3f7IH_s6-;v-{7I@Ruxv&~A^?1nZdnZX@f@c<2y
z$#ru24TposD?G!&dkru)fs2R&ZxXTor=f5axz?5ylcuu;K|BL#D#RX-WZ5Ut5sruP
zDt$@USIkH9s!d1X?XsV8X(Qg?g~+&*9TSjQ?)cI9@`w+_P^|Qq%$I$jnv4_wYW>P7
zGU2G!4@(ALkudLm#Z2RWDjku>StCdODG5Y9wL0@LPJmHrV?F6ml(##jTp3who-Meq
z@23B*d=5+y_-WR{z~J59j;|AJv0T8H{quI7^`%*xQ*?H;vpCYEJ7EpWAP0-bZBNOk
zj>NO51ahwwfP{#gK3Y_)2e*ZMI-&88pDO#wW+OnZaP$@Dw=04!>3#d%df#;5z?IGV
zjFkn_oa~px9!C%#E9wtB{+X-=3US-Go5@)m&y0Aqc%dm<Me>$~z-GZ}TW8EMF8?xG
zFe13^u~u_mgmB+yK^=Bd^oHhlp`)Y@yO?Imp9<V7Fp=1V5p>=s^+%CDbKcYyJF~d&
z_Q+O#Sou}ojX>AQ5dQW2eC(k&w)X!8^E?d01jGl!O00NSz)Wf9D9t#e_HTXy7ghMC
z%i5MX5D0ut9>2V6&8Q2o7W65Xwl-=PS#BVlv=V%Gbr1GP#mIGwJn;?yebfR(mrrNo
z?hu<$6MUow@kslyS&c67Xwh&yW~agF#(t{=wPK~)yY32DfMz3+xud6xog}Be|GE+G
zu6C<naXI^SvZ8}BXhR;!r~EHild((VVekqCLj?RRb6+k_nIyG3$#AF<Th&8GR}W3o
z>D-b89MP|)rx{h<`LIm1gtI7HhzO&WJkv_lg&$IFj61?F)L--wUN=*FOw0twKnN<6
z!p_Zr<#u3<!qna|jQ&-8WcrwW02o*4M={EoaL1dE;C9z%m}~-LJfOhlcb5ax-PB^O
zLueiB$)^IdE}dYq>HaLl_A!ynRAeUHUfGs$6wnhy14b%0ZNrX>6@q$3%S2hS4egf-
zC8SG0O3HkOAHD_opa1p0cD)|Yx3Wj#EOeCN$fsnhQQ9GjsltgF)G2bJ5&<RSAqTaU
zJBq5%0(vYN&HMgknM5T|pJqxMNiiU&sQiN^vsSw_%#n&v0H{u3DaUy1`VrKGj*O_>
zA;rru*iH_^aN+?mr!OnNN#{zahzE?gud%J+w2ZUPP_BsX)}EyChll&wc!H=EPd!HS
z+WXpX{g{N9JPbeR4yvP^!3~$Bp8yhRCa$+S+``7K-YTtZq^Zd4cCn<o%5Jnrkg_}8
zQor$d1chy*E{dhOQqIiGE+`~ZZu`{;a6N2BuaT2Qg5Aq+tlF3;6wk+7f>Ii*t`FT8
zpN$Gw42-=jEYTzd(gqAdMduu7SyG>nCyIb{yF!%$;i!o~Fy%%lxfbTOWjgvmZeYN|
zOLcmqzy?z457jFj49_DS^|Po9s!u&6;9|^?kdF?(eK~VCRs!ZV9HRja3Hn+`^Z-jC
zq(QR8%mTBNeaP<Pcm@=TUXEu!mKr}L4Ju&(Q>U~uMyRmkr|tfzn3R&ytb&B<S=K7x
zDrHV0={<jmWtiY4S7m0%vgzmoS!bI-6{ATT^&yxf6f+<Zb%1XGHwJW+87JmPoBt7%
z+)@e60m9uB!=)q!8${ZieP&&rrzolrqt{~L8-*LgIGvKl;ux(pca>2#1=i<`?Z}I_
zGJsozvF!$S$e?!r^gl+^%%}<?8gvul;%_icm~ty`n0+v<&F)O|KlP=GN$ss++$B{N
zXJAFq;$P7K)8M*@zuWukhb@MRr~D*(jBC!(@k(1^q8rjiD5W*ur{L8aJNMYyg+v6*
zTqtY_v`;>EMwKG7hNMea6Uhde6OKi2URf(EmEEz4{CO>gj4b|=hN}H14QEW?gsEvT
z0+p!1ib4^>eY&b-JUIk}EE#Fgomj!x8=OtGHX5t%7CtuCND7B)m?w&<jNO0<kbrfR
z>YN6HyoRKIrsVeeFF=UvYwPA*OL~suPhoQf7L1Na&USxmY$-!w3+{>?#nBNKCos9U
z3`QxYPC=Q?-WR2bphgj&VQ{KUsy&Np>zW0%xMt56kZn~`8UcrT4N{WfXokLG#4*Jo
zF-ME`enpI=;CIiU)^vsB=o%^BCD9UQQX;Wk4lERX?x&AMdm!gw#F)o8-$se;ONitU
zytSdGs5Lt;5cD{Pm!xr*g^LoSBS|aD-*LN}>&M6U5>$9+_B@j83eiXhPviUk!(*QK
z=FQXdms%DO#s4?&9v|+_0=#{>Hu`Ref!|*JFaO8?!x*?uQFVuaNz(MFeoPl_*=3Rm
zWZtp6la{YYpU|?ZLN`Z=GJ;tM(K&!^!|P=lk%*skAKv{y+w}>KjJ#=T8`l`#zIA6q
ztdfJGb^<QU5q>{v?TD)Eerhzklpfj2#%4h!dJ`ClpCB`}Dyb9RR9kXX=G6Kc4SAz3
z1{egjgRW*K{j9$+9fMPRDN|jZlI}@<!#e@gSUQplsOu5<mKqT*v)h*}1=9#n)u_Eg
z9r9l5P;L4sf_B;w3MKhjWos(k0#+4m_E&EM^BS9<-?u!{%j|mjU1V>%MmgmTahGNc
zK|!4avYVQ7;VR+$sh!d0DqVOCOq9sSG8oHHzeZ5}I1<`qK$4@D8+Ey>AkM9Ft<f76
z^MT7-WNUeK105U~Ax9atL&rFZN@+<THmSiV8;L5aI+iTuksd<BU*xaRl~17u<8-QQ
zs;~Xi3b#z6nycGZzB9RVC04S6!x4G@T1A|PTWUKog<@txO=I7Vs*KzzP|G5wlyU|R
zC=+OpB{z+#eR+D0K`1tg4rvKVqw8($hs_4qd%Ds%8GFe{EIElgvQiq$FRd(r2@N-Q
z@<JphW>!;daMZ^2m;KbTW+d2d#ZkfCyhGH}W%1_NOm7nMBAlxSQ6Xz7?0)?836Y$D
z+(5-$;Z=Wu;Cn-&VhzlG`r{)sZYOsqNm{3P-<)1<)sFK$->0={n6PHii+j-+@kh4x
ziO}};=@WC7)Z}$hpas$nZ|^zmh$Z2jGiiuTnAp$q{$2=8O@+*39LMBFkpMQM$OA|T
zNFI$}q#dK`ViVQtkhbCUjpe~v^BxmLeDv>cCQ=l`BZQ?Y$YZo-Nfv-HXQ(h4Onz~<
zIt+6%irKEEr4k_L$I81J63wJ$7|AHi$0-7t6GLiE-hmj<ovtMt&@DgW6@tD`Rp`9;
zGu86MLpI<a6f=I3Vm#gTQ(sGtn32otDcvl9fsbr@sYB#WcEcGu6eJaWo5put=*l$H
z84fxx)L-4T@&@F$U%Z4DhbF2wvf_#a8-3}-TVj7?<Vcejou{botybDAycqRyZCH5G
z(1Hlvx+T7!2^8WH6@kbZCf6>6+_mH1bDX4yJSEV-(A)D<a@*`+mi?!IF%aK!lr^KJ
z#}3v$P3Sy}8*qx<P2Ht@13b6S&)lZLPI*FRR2ev<C6k|p$cO;v(<BABn@TWXm*JCY
zKYQI?LxKzXDbENPy0T@zHvVo+NvqK;EswqN9c!7<k<oO9fOxNgRa3z!CL><qqckAd
zS3Z=mdHk835|i{Zg8t@Kw#HIK7y|KEi<>AiPtWk%syac3+{r?)O<qMd{cvq%IAdgc
z5rAK~#W!Wk-U*?a>|2~i{~^$St(IM8^XLVlpP!$yNKfC9Yhz0Bnr^9EeiNBdnv}N)
znRSn<0NPL6U8pj!GqN*S$@Le;&M9-Q+v1F43Q2B91~<|cKv#AIEW?;#7gB&@B^zT&
zPVY=-CibfKed6YxVZ{MW!9qL<=i0V@+B!=%*a0F1!E1}z+_R|drWQTTziVEq4C}09
z#99NA)H5N{Ry;ZyrBK23W}CizL2%Qi=8Gf|2Bsq@eG3)<t~`ly*)oA66h#y`oH9hx
z9v9U*i;<`=EbAzj*9>?4F2_tE!G$O5%a=wY=;tnQgz9>21unNEDS8rXHhg;9Q)~}1
zrajhPyU}aPN_{w2%3WzJ)J!_eAik?*RU<0`z(4&12nu<r$rZ=-!^SYyMkchH)wE>n
z%O@ixuaVZ<at{G{N_tGrMkV+}Mi@)!h83=S9niWFXVlL9$a=kRw~gUR^wWSIoB1}?
z!R>(-#%E}&QfqN0FBmAn1zjiTGJ+(>)Ucw{ju?*i#>%4P3NS7z6+^zGPdz@Cy1DK5
z)-GOOS+K)xT|#-}RZz7E@SnD3mb(~;=yyckg#gzA|EEsXQ3at^*4#hV@O*n`A8D&K
zD|b-`MoaxH&mOc^t%C9Lbk%G3B!8w7@tj-WlB-U%*}|o{nXogLS8s~qI<V`DFFa;J
zvPW(kRqik=AGL0Yu`4c3qDnZNj`hxA%IaldnX4u!4+Osot^*Y*JN7S8d*+TjTU2GR
zsfEDk^k*>1xj=u$`ny6FG?f9Rok|8ybpX=}2Mn~+S+&bTyBUX>$cl4&y5H~cM|*^}
z4yhxr8KR@clw#cF>e_e^4YTX5|5FDmVLb<ePn2T`wCb+wflIRMQ&DL`GG*Q*S0Q_1
z!@f?TE`g%YFRwam2Bxst&<_~RP8yu|H!{;mQ2K;C+8Hf5II;BLq8F}Q-?Ev{jQB!{
zb7xWIW1)HNMnvvX_safXOur@4>P9?CcXDB=Qs-73;swvmpaLmgM-R{zpo!LL0`HuA
zrzsWKW()*`GAp;=S|YirJ?FHG>HVgTVJZk9i%33PJv6%V)1boj-Gk*IEg{qwUr?-n
z2yvX)mfPTOf}cJ?Ir_j8sA!J<W<8mDVRV%$ln{~xgf3?w$ZI>U^I~@Oi+9OII3yrV
z0KuGLlM=mFD$#s$5+tJDaR9YE!3)~Hz5Dk4-Sszv9)j<j-*9CsNE&-<#9Fdb69Txq
zu!)tV24ZxjO_Hz}MjK8N7ASlL-YNmov0x0rO8TU7_>8EW*vop0QtEbareyZwZ3hgo
zl{g@)25_zw^?+6~$Epz?Uh)*;u&(eWz)e^sTynZ>8jyl&9INl6e=$N($fx#<61WT?
zhJdSapvB2&JjN}+k`W>zDBr$(`SzsemuS2$GjagGeYkG`w;=n*^M(z$y~@@z`D~2;
zEU1<rWyG0W_^K*HKb>8phk87gXBYpIu~M2MO`k3)8Co;4DFI9r_Pfb!EszWc6lldo
z?onLOj>k~F?$R$KXeCr^g$Jt{_G!XN)_>{37My`Y8JA2vLsr{|EN@x4zdvDdMKD`a
zV#zaWLk_O*U)xW_F2#M;hPRjt*$3vLWU@9c0^^TH%k&CrcqR*MW#^Eg&LvRBG4EB7
zMR%+RV)5}0>-24vw7lJZZicyAOsg(#NJewmb{w;OwiIZ~+?vffhm5q>_#FGp15x#^
z{o~YDweOkIncODg*hiAfs3ZOEIc7GwE%R&)$1(NHtm>(ktF&9gEOV%9{9=!uf<ZLc
z9|a_Cyjnm(uXf_qh?S7A=78=L$qP;#%_oL`HXI@7y`*%xil0bzJu?V~PgjcI9*0?K
zr$fl{4rI3oI4d#(bZ@0+!7ZXhgCDO?M$voqL*rTHDqacI7?6ch^{ta_1DDy}j1NO*
zG^7G49Hb-@5$$He9!fIvsmKyRBC#V{L7Gr&Le7yzM|!S4Jl#N5c8<J+7o;j?--^2J
z^JrGFM9+s5a728$Lv1LcRZu&w8~>=ohK?a<5^wM)*xsrWGUNJj9CyrZq$bfqy7a9G
zkQe4;im5KpkZ%XbB{8{S<Wp>twA0u0S}!qZBYwWwm&U^h3rQ{a_rO{qFfwR`<GX}Y
zSK-c~Cq@4jFF_;uy8Gh1ljc&zSPebJZ4hNziyEub!|b5rQBkfn$t<Xh<TqFL8OKW{
zVeNK;TDIpKg4!StY9(qme6QJFaapW4$~S;dNx3$TNF;SB(NAuR@<5YV&81S4FLAcl
zVN2y{&1g1=@HZngzj3h%fGB$X%I6#2NMKpairV8Kk|#Ilpp?r*s(jUm`u$x-DsJ3*
zW5PYN*z9-%GrX3*fo(oV`27$6+keJRJSN&Ko3<|&lZfcS`qf+UNe`~jH(`NO`ScUa
znPvhIP15YK48TTf9}ZMPkPHi{`PM`R$<d|IiBLa+EHVm?i(7=~XzU;}*MbUX!9h{d
z9u0)f!QCzIBnF4Vs|Y?*B0>$h%@Pm?QkO*GQ-+h5$pp{N8-|Tz!TW9YCTSwLe~?O;
zM=OAVF(wSJD3sF0wD1;4Pg@n2m$rS@k6#C0#SfP8kTCMqI=HaJMXRang7kFUz$xPL
z7%k}2+IVg_+VXP6boJ0iGW?A~snidBX~>miF<J$PP^C&3-*~xD4?HP@o{xY00r=+5
zGd6N(H;SdQjRPm-S8r?+x82V>@JpDANuTr1LG^G6O+7z9an!6OX=5-j_;#xA>oG;G
z2X>l%HfrVmuVPiv0nx0^DS^tyh6)N8`l=>TF8~Z_+ozCb>?`uac3>qNWFZ<*JOaLc
zX=~C&7V#tb*HW#CBCZxapdW=z$kmg3{P5xK<_^)mUR3<@^2JU^7#_=1fmH4B<KqX~
z#vgM+9ZGpZej>(LxKoSXLFw+Ex?5MDQd3bbMm*vs{Ep%&f)7ku1E{qMQ!chqqd?`d
z;l>i#Nrq9zV79vkwV2z$U3NxZV8!|5-%Kz9v9Q*+sVco`CG4U0{m0Yi&wSC{A7ATF
z`jPEMyiR)0=O9&O`$T4|q7@!P!G=qHI_3g9*;hJ5p`S>;j58G_#FNI(RvvgKNMHiv
zOv%}dk2>3)5bLXJpFe&3{A5h79uP6Pc6)d8*S~%sZIo&Km<Fvreg1|@ZP3GqyWf9*
z1P;q3Zei59CvO}fgNpzXeIeeN0tK&K;u<%U*mIk3rlx5MGj5OFgzAf`sSv!r#%`VZ
z`2)Q|Chnewa;k&M4D@BiMr2XF`YuL;WAV_9(+=UC@QM6_v>+4q;w&E2KTf>@F2!4A
z8pJk-Slq8WzJYWCtxwV@hq;&9>DikKAvaA1+I|oPl#9HBsbus~mbz&V{=!J!QHC1R
zScv=Zl4$>qdm42`0FugED&e{uBJ<s|i@TtH-VU%4sZXCj(Kn0tm<g*!2g0Pd+yc(~
zvrwiX!y{ml1~$z~SToDn=K9Dn5o4na-V-wk2%F%GFoHN@QRwF~-@4*t?huU&m4sO%
zJnbCi#)i1v$UkZ4-e&PG6QBrIR{N<Y3QG>~&4MIo%~^l{DStBZ{r<z<X#zfd{>*qu
zY(gW+qwPHoR=7J)@Z<OO;nvFw{XX!8o)guuyfb`W$5w>!Xn?GP&P)$p{l|a(Z?)(!
zM}JM3RW@rq+jzCn4gzmJ6}g#TzE+e?G<7-goYcyG1A?^rRrzf)?XI%(lNrXYD5ZeU
z?$R0i14}28bd#4{l1VvOdcG1^ZcP&^kRgJiR*ciU9i8Qs4i6xDfUoH0*RlKtMa(Gn
zkbK~&zA}p?zX)?1qGg~7Ic;LY<Y5b`YFhC2XyCf$nphivMeXI*&XZNqAqi+bKyPRi
zvP@j68N&fhB>h`)1#=HFL%&zF<!K0I&A&!o&@y3w5oAbxrN&qfZ_NOr5Jtr;Ovu!@
zdhQtzsw~N0V2qGaqSYiuB(%yYzRpLW{PsNx>DV;nSGD}A68ia{RwE}*q}{{_1r};I
z@b1bCJ)On{K?^AwSDwuDUFAcLJ@5!3(p7W>aEOoCM>dqJq6Th(D@H{Rq^i_^KtpG2
z#jnu7LdurLmcreEnI0!P8pt{`R~gtIR|&Jg=UMJ(!Ddn4Bw6o~lUW+fxU)h%u`9>+
z(sA9Ra<%Zd72!BLz{g@)g{#Tk9gI7i$96?iSzOd9$@7yeM`-`D5Y?CsxMnzMtr7s0
zirpGZXDNb6d5Qk$Li2iDe-0E~NmIxS1oBc0OcSNpRNElM1eNC`K8NPK;n|pC@3NWH
zwBS$(t;NA>KBBxI1aN%GW)L)g2=7XyDH);8F3tV)n#_-&;-^B@4-n)<mx=2HXV<;w
z>FLQ<hEmd+Rno%P{i8gjjF<d%-(V3#KR@Z}Fy^1dw$YX*>itWOjUtgeB$-NmCnr=C
zGp0W}VOZj`A#vmRs@t~~R(INdq5JXC$D)V=V-@YRxXy15yCIs`)52t_#1xOmS_@xO
zXQ(@}%TCYj>|npJ8E^_+DPhco6u6bE49jIRe?|sn7)rFcnOXId(H?YW%71P9BRDvc
z^hyGqK{gx&VhMzQp+j8wI}EB9@#}zI<{eap;1r6GLzn+!E9z>#yU#KM7p}s9OH{0>
zQ%iB!`nGDupAiL}!W7NcO{dDf$c$nhKX}J;_lGJmAYdWZeWMV1{K=ppj0!1LIE5sv
z5g5VD=kU}B1Fj23XJ|PB#jb+yss$kOg^}`XKc&C*Skf=sDRTYz`RsM1Yx|VhAV+0-
z*miuxOO2#8k1s#=uCkI!iO4idgf*|dnpqQ>n$z_BUB#fZyMXIfT4;is0W^B3oda#l
z*;+6maCti|gEk9qY%bLjOM9&2fKBR87`cy-V>}53kF(_qhox3!{2Tlvr)utl1oPhH
z3ML5IK-Y>OiKH8qG;%<?lXi|MtI&nevUtj_%PWC9EHrAj7L=G0B0A_6Ae`zta>s!=
zwE`caBnY^RzIC7`q8G@VlyEzC6L*(FE@TM}0#^l?2=8U&jAg@)?1Dj~eOg8I;ULR&
zwiuN;K*0Tvx9ES(%hrIA1(#^7N%<q#a4OJ}2WA6vfR^zUwrT>iC6^s)WSF>&M;CK?
zs@bLl1zfa+EQ_nrFaazmp9Q2*9QG<Chq2lyi(wDq2?|qoz(ymvT&1MzhJrKbZeuLj
zj`}Pl;%k~9)%JP#X8<wIEgDnSGpOyXds4jx-HIpHOFEJ$gBM@fOS(b~*W&s$b#)8^
z%wVSLo`QCS9x)C)<(AqrO^uO3M(oB#I5qxCyOBV)K8|z^e|MutlU7!f#`L_PlP|d<
zUx5*0zs6ekrM|l(9rQO*M|o0!E|zdG!rEwIOYv4CHJx(lI>ndb;U=d03kou=>yU_P
z&63y@-i?7yAOX2FjdiflDapga<dKe)1)hzvxbr1f*)jQs(kM|~-PQyd=Yc5-bR9#>
z)6z__mhy>=Dt?-`+ZcYbkF3;mh>Km{^50A}{x7!!GV{*Svj0jd^`|>VyF%&PPdERp
ziK6U-=@-#@?m*6-04I3=^>UP`R+YBFi{L4W;?*9L-_Wy$Fa(FhoTy%wMyU{fJcO-`
zgz07v0i~uN#xu9`5($)>p#GwR{~x7?o6sleWYA9<-FYd^w2GC?p>sutt#VaY0eLg`
z@|XZ5q2$3pe%*7HWgYQ!kLMP=`c3z{H8-eCi5Att#5~gcP+o~PY~#}5A)I=gI1QQw
zOAY7wA!$feIN>zVu~#e!a+CB>#O`z%JL>;pU`E>g2-;Dbl%rQ?qxXt-XnqwYDRf*E
zK{v5n!Fn|{?J!O)hoZh7IvCS%<V5V3xlf3Sv1-}d9H7c)!fx#z6EB>oFz&YWrFKIS
z!!AoPQE3>P+xY1i1NGO(->5FJpB&UEmLUFnV*)UTo9}A%HHpk;e9?u5e+!xEOs|SM
zr001GuCL^Z^-~eL^m-}?>-bDa6%&biylo9!v7%<F%MhAi<o54u-9zQtjhE5~r-_^m
zgClrdUw<ek66s8LOk!o`<ty{~wiKxLB%W**{v{jT?H2M{4P@FSYgnyrxd?W$%vQ)9
z0U48nKzZT2-!$XpMU550LJ}=sqy;g{hGq{(&|cP#O&N-A6EYfF)s98&MG;02l1WF)
z2cUqmp-vf)ZQZ0g%@IPG4jpkegG32^5C*V-=|(FhKK%Zdzm!#d|Ni{>`RVED>+`oa
zceecX4va2p<Ew)=57xm%%x6R$g0#<Dq;U-&9V^+mN}GhJR7=%k=2rhcz*SxCE-*iw
zXUYwgcHJuUuiq>^{qe)Yctqjg%3oiMB}RS27}4=+%>3~|_F4vae{~PS_4jMT%jh$2
zh)7hFi+HM#x7W+OBOke{@g)*MK$>mo4Fc2V`}ZH(p|4${S_<wix*O1;;6)4Hi$;`W
zV<U~O8Ty+C1S9qHAD>!|k?-Lp_2@4dB?FvO+ZH7Q^x8bb=_t$TwWeF6a0KzSvD}x3
z+sB9Z5`cHoa|tI?OiHI41o>Gd@$$pF-Rz8ZAMZ%Vjj`w-7W;W@4?gzu*n<yK{afqo
zo0-n4Nu%Z*-}Y=H6A*Ru@*!r4uI5ZTY{BG;0(9PYN^Og}<tX(L=Xn%o8;6>Sw+6z>
zi<GW6zqcT?WX)n-RiuO-sA|3=cc<SFBjN4)5AWG030tGiUd;gA-caJJk&|zRmV0{Q
z*Q8a7M~OY8+G&-Yg2oig2pQ~;`n3o2g@J|&Yi}~e9;usw=Y&}>MFCr8tW1_;*zxxE
z@%{T=>fqAb>(8Hh{-N2BdWx4}u;mSPFH8$9NQN+@@lRk|3#!RUeCZ#9LP;YfEP^1T
zXMcH&AQ5^+%ZwElDK*BokDe^p#?L=B-i^;CPIH4lzI}Oa%|T)7%J$D(hBmHmA8s0(
zI|t75#n-Q2YWVN&z52y;)ittd-FUM#j4InA#{vam7{OndIAO4cmzVpSM{&_^gf!zv
zON7zFa0121X@KY6$+b4c=LvUgH33trX?x5|BJ=j~m(wK+R8|_<T@bB(QKOiRNosL*
zH@cjhCSqUK=_|ArCCdCY4|Oz^>Oo7^<hc^#4^<%7=EDVZZt&@ofotaW_Tm10IshwO
zwCt$${{@+1HIt5%;@{kqMT9logafm!h^#C!_noRD?owd6t^x?F9gB8z$ECCYfC&Q5
z&K0##fJ{D;o8)?h_4hBYADio`TOgY%j%F;{0*wp#I46H=h(SznerxxAWHu$C6@PQ{
z_2miY8(xa~{$8$hqrodkG)voqBt!ts5};z#LW7of6WU&>^6kgNLyjS#j2)5T?c2xK
z2dFsY7jD_`xa=THUo!%??WHmD)=FxIlNIV#)eNODbBdxgNcXoa(@bEEEonks0n6RL
zq{fsfR%**zZf3%U-4sC{uWpQ2uv0<1s91ab_UfPi_y2q7HB&S@&4C1{=(Ywq<*gw}
z_hG9AQpJR6=7z)sc*I1<bqyj&auQ7waeQ=nWOnAOYF##Cv0ZZd-jv=HUzU&&RuZHr
zsapoQixLxDu7F!z_m%n^>Co7;1z?0a0zbJD2mwSknrg!uXJlvMI<pZfDqZgncZcMV
zgPB4&p;)eB?=~Qqv<oaV7=>D$gcY*kilu_x<xpm>A1|6#m8e4%%}jGABB-z%*4N*E
z{|!b{@NK0kV0)olt#9)PkN1z}=6Np2ZlMkI8gk|==zOoWI`grNxY44}>xYwD%2Rs_
ze+u*sx7%lQlP110N-eT7B#jSC)yQCaN3D<!4ip&ltSv`7oYs*=3)Fm|>4k)pd=%yw
zA0FRFghxINjy*k#AR5S&nRV+Z08Agomdh$RrC;+P<dm*;tcyeizN%aTU0kHPV$Y;L
zd#IQV^=M>vf<6LC#j7<H@Y`06*&mI+MC<iCX^@$v)Pw-}mNVzHS4HlcnX(47Wo`8#
z7I&=s<(s#+)^Nb)V!Or3iX>QHzGj)xrFywnM{TF*P<O6H1H&+JX{2E?8C2p6QCK4x
z(M5DsNJw257-Y6_u^`<<{B_$ejEGNOj@F$aIOzysxMD#Zfxa?^Vs~bd5UG}nJ6z7R
z(lD7EQAAAO7=Xoei*sU|)u4N7!{ux&>oB;-aC`ETh{&NC<!f89$^$pe1T<}y98In@
zd?_Euj;%0)iwR9zSeWn=YJCHO$nqEqW#m;5y2CtPB+yNhmwE68g;6gPz1BakU;{jg
zrm#R|l}+?8T{qEmf9HwQ<mcJt6DTb&p>R>uyoHhgqxjpKAMBK;DmXgB1v{Zla<I@E
z*Ex-JVn;~HN~aU=`TypvUX#nsN~A=81$%`_BMlBPwWpfsKE{*3F%Kf`isnjtLk;3&
zLn#oTMax%)oB@xflLkj$CIH~vu@ME<v1M$>oM;~bTLF`Gk1Vx-C*X4h)Hzfln6<5y
zrcO-dcUcBJC`@r66bSvGs*(9Iq6Mn<Vd_+cII@C~1x5D!G!WJHA7f7G?5D|crna$U
zVnDA3jns+>rJ?eQ>$mUk-#&f++%5x&aYDfw?l;HeS`r=PlCzBFr(D#^Ao$X9IdKX@
z7klF=^+0Y*)(fj6%3VA*y;avm1ej#O^=mf8$dKp3o-R9MvLEm$#DrdwnI?<@(Rd&m
z3aX;NykZyebyVgVx6zs-GVvpfStAY-T4}hjaQpE1+i%VK(U*6FH{G4h7cbIt*=CAJ
zoXe>fPa1oOOKd5k{Z=fG=&}oxJin;{$YnN0a74qZs)>5Ox&c765T$6UKuBUJ*of#t
zwNVNcnKfxqm|6C~RKuS2s*#>3Dhn3A(;Z+mUiPX8yeE=?m+rI9(HaQ#>n=!?uu#*~
zJ|z5hlfEte$m=2_JS5SdHddu_;B2=EPR0m$<fUnc2vGcpvhGC<WnkW2_7HQ5Vy436
z)<A{rly40^n0vc5`<T<Nm8|i|Aw3b7GhSY6?ZH8SW5@EhcecC+rp0$z7qGy!-!P$=
z{UsI@|D<O}<+j&~z+iAxw6%{tqP4ZS&+AIPFTu1R>m|?(dz?q}sFeWe2<QG~ybg(F
zwd=s{Q=~{l0%#v!%BLBY)R|S>h?uwL#h;(|;-IAA@{wE0Vq0Ffv)B?koS`(^E0ZU1
zNPTw8kr^s7&x{}{ASec$F%*2rHFY;_;ZYlcZ_K8xtKHb6swtt|zYe_vk4y+qfg>KE
zB1@@g;I<i}JsR?4lUSCr)`D=3h}w+ft)1!3)Y^wxk3Wzi$Ea3Cw$w&!i|Ke=KW>^$
zV|X!gk^E8u%sUEd$akGXt;g?N>6&52(5TELlWLJCDt)!#PZG2o<ocV@jX>32S$__e
zq&?fi7mO1oFv7+>>u`aO$#it$C6!c1Zu%V^n*%XbQUk`k1)}w}-rT-k+YP_Wp-`?V
zX|bxRJfO<Qf^vXj3}#(K0?Wra>}E+COBqtk;-yCNge#&=Ftx_?v6=ylNzL&4+lQ+b
zgql<QVsN5{_w_qq1f^mTVEBE}llE|)N95n!e0f$9k!lGFJ$dr*aNCgdhMTSVN;>_T
z^MOi_FUN^)T_=}DSdI3ibwSi!Rm>}AAcqw~BD0x?kK+~5R9f0qJaxd~AZ?26j0D1)
zA8O$nl`NU5<>=neb`j2*o93_{?RZejHFA%A%vErZc*8pxto^n~X0V6(gsc|hB5G!K
zNP%0tqQKyxGm;UVqgGkFq{iB0_goQ<V<n~{zqV}|ld?TOpu`x;q4Lp*Pr3LBN~g!`
zQgaZ(TYIr{QfM4krU*~3^ASb(ofir;HcElgF1a~U8WVi9%F!ZSQH9kLS@C&CSzF?R
zfX*A=(hVG%jAV+&%erLBBn@U1JxL*gFgH@OEcCAF#TOKbo2lmu;WI%so=+b~qoj#L
zd~w)-Zp?vZOKgK&ET-8B3v<z+c0XatCQHbS9k(9@^DEF@|GZ0<;{j5K>N-Wxc^)jH
z%y%~5B-oM-jA*T4(*&`C<}Fak+ot9WQ@!k(Li0GuLW-h4I&5xE=tV+Vw&9&LC4C|?
z)c6rPAVUIqaHq7>wi50pY0O4vCYWmQ)t97M?4=#DuS7#G`HeQq{hq3j7q%D;z*-|+
z0!$LkI8RF2Dl1`Jbk~qz@lw|x%ho;CEqt;6mtd=RdnhbDSh7j%>a%R5;q}Lj8HyU5
zMmDCB5<@l<9oag`rQM)3_EdC{FromEAw`vYeQ(a@#du!2>WM76voiC=CO@A(J>f(z
z>bLiXRq5sW_TEAdecA6l{_jPycdv$M+U+P6y;@F;Bx2Q>E<bLn1$>!Y>zo-vW3x4i
z4Hf>-y;w2(ekgqR#&BZ&IU7XiO3r%A;z(ST!Osl+qA;c?wD>$Ho>eXE5_=2!=dQuP
zpeo;PzP;F!m+b6hg4638BS@LJV9z|z7@du~pdk_ZRT$R9u2u%cSI9UAaJ$besefs=
z5g(RNBw<FtsBH%VgKv#16wAETB-*LXI;c`?W7>Q?olr=jtr#o6R5k*R5gm_8x;w6$
zaL|hP&d`PvmcJV|I8<RCdi_)jzk{j0;y>diYMU9TZDSP5J8>tCh*o2Uf4NdP(GKoF
z239}_+7_!PA8fXuz<iDE`tuCY14m^Dyz)7DxK>)W$9o`EhOV*9*4Z{_)#|s-w<aNp
z6}HM|7&xtvZE{GQ(!tEsP-IN>*TM*rs~NA~9{nkV!Pf-_#It-g`A|mi7;;^S8kM{r
zoU}%__^oNp`2+5Vb%Cf^!nOBm6u~MANDW(2Pa7j3q>sD12SMA|)79(q=kMN>lY@E!
zt=phMn<;5eJRH#qhf?1wTNxTV@JbB4B8xLpiu_g5>xoQ?Xfl0LYByBwl$F7blJN>6
zmS<2iuZqIq=pPNx#p)|ogzUO4`hjpYtGYBT81n--&2#?=Q}jIpLg5@2X~#SX)*Rf9
z&?yujm$=(VKnr*;z=t_n!q$N)zJYivq`g`u)h%ag0~pJq?ikfXl!wKcn&gixcx#<{
zj4d)tLb+p=S%#aj!m0D$fRcBt0wY&;i{^NIsa*kCy?&Bl#$Q;^g7O4E1)m+gSfQ%@
zX72s>dF_=~KLtN;2hneTr@6SF*=X$>EyEvKG1j{j3W*h7I;KpE?rNyWVhsW12m^C|
zxq-Ff@|xt#K%|m5X<(^vu=3em*$*0yJq*wx9^2%QhZac6L+F@Nt{*PS#ZdiC(l^vf
zsRmTO@+_8(v9}MmMgilVL*_0s1qwZp$OO;&UvC8S25d7c^v`XQoXj#TLZc^w^!Et#
z?JXgcx0n#S`j`Lfe<%d{I<zS$2M@-)VUng4tW9SSkV{>DBoj_1?1pOvu{7h03+NSW
zq9*4|NPbRu^d*H45z5@>Rs=&rzOFFnl+=OCzJ$OeK)x&nC4uBG0BPr*8ZzCO8Y?sI
zQ4Y=!^HCj|G$<DHeqW7KfBbCWaR6MD%OR^7hV_6s6}l+_a&4>}Ao7ZXqlLn1<zvNn
zt<&0m=J->TcUW3!H;nx4_EyYT!!UE6)iYKB6oun4-*)6yQY9YrvzY}|p1p<Yb8E|q
zu?=brqrDiHe){9%AFiP4{qMgUzpPW;4y*~rFlh2jatQYddQ<fb<@?>n(NC+xs5I|h
zp-p{S^ZSPfuN4}pcM7Wuj{c*A3Il^rcDi>A=-H#1Tysx9<fBl!ROsT;7f(=cci1hr
zS7llc_F`#1oOI<jv*KLVkxhkbt_ilg!a*xni3znP?kcX0EZ<%o{gMS45<>*B=<wUz
zCMMXSX6dRe2Q)=xA?Ro(pU@=pg^!OPKcD;^noB5s`&PD;*JQBAVv_;ya$Mhf2Uokf
z$oPb`oH!E@Sn0=e9Jbf7h+RcCSBu!e<>I5Z^$5Xb(M=s38yhp`O=E>ZO*tG^(IdQ(
z;!C4@>}0M%M5Qa!3z77IP%7AOYKKxw0FY>w{;0Uk&dj;wm#sFpSpt%CqT10?#2&YU
z)YS}iI%P>MX%;HdL2zhbOfFo|g<u_=Bcu9R#wW!FxuypqRFc%tX#o*6GQVH?@?~Ky
zn31Pp?S8YiV>65JqY{yl;@rFOFD^zvbiB~K@V4<!5Im3a*@I|bzJB`nY>13lkXlxm
zIEurlP(j!8w+{~}XM2D9f1ej9-q^zK_MT}`1*u_aW|&bG!%w3M4m&aeQVC`?y()LY
zEEK{&RA_VO5*GkE#Z^FdX+_L9#ccd)KTqp2c$fA*1b`?p96OzgiibirGtSVOXCbcS
zZ|Z`#M3yh1h!UdB?^a+cl3*Es&Q1_pNYwV5E`E6T_<oB#&?DmIu?_4cg1D2@bi{N%
zXOzl11a6YE)*QLjzw%0-3L!B42+a|>3?Q@9N)$*V42h&=kr&msZl?KxL*~4-#f3Bg
zTD+BmvZ8o1^GU_tGRS~|c)0zISbqO-Z_t{U_t+SusDAzO?OEfdHk&cOIvTN8Lk>kd
zh_%4h5)O&r-d;}6J(yPpjke8TeE<CQ8K<}=!)+=eGV`edEP(n+9LnstXo7)>Kb}zv
z`X1VCaC8^Sw6RThZKqks92oO`*%^6ox5F@r#&8m?rR$=jRwkl^owV6j9X%cDw7_KB
z81tKU===NxF8##kzx=0v%N$}B@P?kKwa7JbtEQ-DXwzJdAw%?2sK`2u`|$z3x@kOD
zn(USA)K{w0Tnv&Qpa_mqoU#d23rf)%0~yP-JawN-YBV(yPnpIPb-ub9-o}AD++XzP
zZ#6HW@%>;-C=ewFW_*_HYvb+xOXMR5%i%|DuF{j@^`Ijg)sd$v0%CpL$k?7WcU2fU
zfA1fAL=}sgjjU}mLujme!FL-vG3g$v?k;={5=bI?rnBiKC9{nl0oN4W{_*j%HFW2#
zuLb$IY&c=-AnGA$lWdIokAM9u`QSL#Gb7~XiiAl!k{g1;zLdgDunsZ%E;oybl5~RO
zOCKIS*v+nJnG3a|5Vz>bca=-lr_svJd`*?Vbnhf}LvFy?8!*|}6b*ldna@~_)@eWd
zoIHxkd?Pj03ePOlVd-}Q1^on98pR6j%wR5QDRKsw0~;}R8{j}W0Tae0-P6ln{KX`!
zaHFZ34Sqr?=C^JP`)Q~$W93gp=u6j{+F%8xx|4fL3itwbpGG&mPd@Af2O0m$O?oi&
zV4*lj=?1>YFkuxGMw=1&(#1@76eo}blicFK$4SOAKkgl8#DHZ~pU{>A3ddDQ)I}nZ
zb0V$)Xhih%vDJty=lzEV2Ja#zO>{AZ#zQ>dRdl7c)~q&QjZ1O8;~guI-t8-|BDL}Y
zSqqG8*Ifr&5!#^OVjutbB-~~)i7(M?xGD(f&T2Bu#8x=M2xiJ&+&)iQRxjR*Vl|uC
zbXzGR%~i{F8#pD;gsMI?t=O>ivUmX#@~H2-p0~9>M8!zRYiuhnM~$$IjUualC~W}Z
z#<DLik}x6a??v|Gv0Lj0<%qhl+=pP24cb9tQV2@2h=YgkR5M);vd4Z>k*|qwDt5jj
zv)#!MrCv4g>uXdwcRwp|TF9lFq&L~{pbWYqpl4x8Rn-xaQj1_qYwExe>ZDGLA?Yyp
zx6{PDpT+R*oB~<+7?pDbywH-iCSY6+ZzI+Mh>JL37%BEVJ35V$17KCDzZmTG;k64X
zK9d-8eV{$kFZipK&f~8T;Vy4&ru;JqD*xrf?{=kRI9l<!AnKLWpU&NqL*vuSAbWW(
zGRnq`awD%6+2Cc-&@Qv>jtsLS8McT2uaqZ6wD2o3rH1(+fx!Mw^RdvK7oc+FBXyv)
zsB1yxv?B)UE1MXV2V2zc0g7!R?p7mgvtRZfzuca9g+8Jz2F7`da~GQ!Yim4FU~3Ep
zm46f}H6jh=d93T}4{c0}Sj|W|M}d6uE|w(rn)TYP$SDrN8}6itfG(rP-%lg;0^;sK
zkE*enQkI5J=K)t^X?VS`5x}?>u!gz9^mi&}%?q8wNS4LoOsVTVSsU*b-b06bSqO&D
zXuBbtrVV`M)U8)HIB=s$Q=rCgs!#L1%~n;1Yk6p5YAu`V#2a=?0zlV__~&-%2<zZj
zOu#@is}e+Ok|P_lG1!OeNkfw<QEsb9O$lkZu0kIr%IPKvVYo*3uGCRFm1X7EMI24O
z6L55w)hM}>c(Spg38&ofLac&><hpn9gBok0)vxE+?z7|rxcrbr?7j5-?yKdJb|6Fv
zT{GFS>l9Wzm(3D#l%<T$(7;&1=M^}-vj0JzPBcXF;=!k_^QpqAG`@;w<52f5eHBW!
z1fkPoe>;hKqIWEuChX;NOe<xiCHS33hjKw9r4$d0MVpP<Il!5Nyt}piFPE8kxoXXv
z7?$B6<Kje7Pzc6p60bV}PVPl;ZWOAHxi)g5vl4c|id+%1qGN9a@f10~<V-!oMx?4l
zw2D@>c3E7wa+yjL@Qw|HX0(q5WXWW7O-D|cCem<p5?k(<WMr>$rn-%Rm!p}%KjUFI
zD3UQ#x@&|?(G#;65+IT0-+)e_L(N3dfy_~4Kq7gl#^_=M(qH3Y8$-e`IM2`Di&Zty
zt2t^XD-6~J>0coI&7`{EJR}8&&Qda&_~bq(6oxVSh@2p6FV#yL6p4=OtJ|wNk%1Fl
zTX!N`;XX&e3{Mb`QGYl{4DOfN7c@JX+i?2hw$Ptgq4W{|A<EP8S@8<6w1FbWJY02E
zBhb<RpmwDrm4z>$iNuFSb=sRG6pIm`p{w?O`oMcvJD4g0HDw9MZy-=GVXBr9q!_he
zj?kGy!8)flAka;GAI!N+t{TFSj{x^BIsxO5jI7S1h`0PA1zw#2Vn`CnBHH)oy!M0l
zH0SJM4MYt1Tj#P+wRWnz(LLy}h0m9{KsfsXy#Ic3Khah9#)AoO^gUJ}{!>CO)}^qH
zp<WHR2Bo^m-LI);MKc3AdNiuxLaDuO&YT!B=(O#gb*V)8KBp;cCc_!Di==zxACcit
z3*)qQnNV>K3s?)i;Wyfw%hgI5#gkqH!mP#QlBko9tF6U|1*78lXXQ#us(~Vx;_H$z
z<R>WEnI%ks_Fr@R?&^R3KmU8WA79g`N~(-R&5{Q&XSGHV*2(a+#=ZSSG*@~X2`)4c
z6`exo|Iudo;{?`X-Da$zTUM9Y5;3lJ0E<r;2W&b4MthnjE8|v*s$q`d6=a(6<4Ub)
zNA0&l+9$aWWYn4_$i>oD4+Lz)kAnXCkAMB}!C-Q^jHa?C%@iOzjc;ShGWoQTL<zKT
z&<t*>(rQ<{btMmvj~d}dF4h^vf=-XON`R3-GQhgCeoVb*r`{K98&p%90IK!#he2`k
zslWZ@H@m0F0YCon@$Y|pR96?>MRp6um*?kCA3txD`-jH;<aALWNPAt|-R(d9(?1vq
z@KBK9a$}lSOPb~eB;|RGO=9@{yFJ=^VCnAe@x$Xk{N*piM`EtD=b8nU&rd*+6&9e_
z_;FhtefUcY=Gi$Z>!6XL0q40&gE8RZsG%h(Et4ZVL+Ww^B9t67I9yUXMqTt%3M}Rt
z6<|Lt<TJ4FgQh^y1Xz~ecx*atG`N8*A}Z+RZ&c|qk5PA}WSfiOi%*TqVrzJH7fSCs
zKqP<em-FA>*QTlP{kt&?!f{>>R8Y*qFtOm@yTvlTj4oqyGX=Jll5TF@Yb=xb!^8Uu
ztk>?Ai_BX^%M0{GEpt;?IB-iD^zdM~5Ll%I*$K(lbv8*!fT!<@dL2#KX>&L^9Zk*s
zZxVQxDg`{~fTyT>14usRX8gR6W2nA&Hlv+D8XST+nTECg1d-un4V8-PkqJ&TF@@3)
zF%Bk57)Ab;0t6c#zzsRDJQYw!l1ef?*;v=`>fitU-@kp$yIoWRBZyF$Pbi!)C^W;&
z-6!HeC@|aD#^R}E(^nN^X{tuHXeC!kGf@7D8+E%`X1eTUaVerzQPqU!Bdg!!<QQ6B
z)@a$;c)0F3%vp<2AqgHC9s_xJPa!RvsToadVy@M)Ld4H67P?Cm^yD6Th*K4$h-(Fk
zB#se4Bl4TK>ChjE2=Zn>lxqkfbj70>QX|GImJH5kF-IMo^vfQiAR<^v%Gj1cEGVmN
z1Ihw2OHiWD2b(e)Ozc@+<RJx0<FcW{vGIl}oENhVRw&Xf6U|0Q5>|Q?lt$5LLU1Z@
zAzH9A)%A%SS6WpNdz98JxB`m!>0eyNS2FA((IxAEIT95j^CMcTz$Wi1*Y#=+3c>@!
zM6z4p%qIN&&eXg1#(P#gUKDyWIXn`ng^a~~VGY~J1m4n^4YajLi0Bl6@b1<EqZA;R
z#vxgF*G?N>&U+#vU~pN!%KQYY2|iJ1+t2b6lBi&eQC!Kc1JA8N+w081Y@%Gs*qkIL
znxjkcDrG9&f?oUg-U`(^L@yc_kxRJ$`^P_6Qp7pRn_EZy$up>UYo&;e{*6U{1}Com
z*Z=fyV=AA^C9;XS02EcLgB8P)y(_ax7v`lu2drWgk6tWwgeP4S(-DmViK@8Mt}Ro>
ztg1G??<qQcVJ{`7r!!9#s3~$H)>K>^1tp;i{KXIv>Pi>~+1UoJap4LL2;W>ivq-t{
zRE|&~!in8F*D5RCO21=aX)wEJ4&KiwAs7I%{3&E%WsfBgh+0DDDU6{tc*O{@J3v5H
zH3|XFz_ZdAIwG@d(P}kPMVLlFOKIS!kV@McQ%@A;CAELtb&Xf;H)w>AA{z^R_wfGV
z{crE@ds+PTAAfxO+u#1sVo^k3;k6LAHGCyI?;r0x|D7>idx)GD|M>i=sYS8beUy?z
zs2fB<Yr4C>zy1C9-&q3j0n4V~mUtX?NHVE*Bwjxqbvjw!P|mEdCdp7m&-(p`4>A}8
z$Z{ZpL@xIE?|(ys`xixI4rR5;@TUhVtkp~@n(`>wBc9Xt-f&e2gA4uCocT@J95_J>
zOtTq_iS!o}JIN6yilec0Ev#Mg)DuWYs;ojmydZCqD<L@wo4`G3ToMQy)vEO%je2-M
z0Pp(rx4+}t!2(q2kSKVgB!+F`-?J_@C|r~54pDWZ{ST6$FXWN6;-X#}IAXICq=Gi%
z$rzOU(?i%EMxiLA8*Xvd1dxz~>RDiK1uSXn3kRRBG@L{{<Jzi%hDLFpQ8{rhTz>iu
zdkSrQdp^s+5xE;wlsS!TNrPf4^c+m>de8*3@MaNJs9KzguW4fDvP?&;jet2zja^<+
zt)tcx)8{pHQ~>CiMu``HUd-8Ig%pmowK4TXn3nyTtQj`IIcD(j`O`DW7%5Mnv8O+`
zRy>3~=1Va6@qBsFzooI!S<&;NnI>x+%TlkU7`3i`@U(G)7$Tzh$gnt{^IFx@194hD
z;=9{zG?95E5%DH0_*F8hTC3H=aAl_9PHF`Eu01M9)r^}@*tSCE*iY#-15z0clhVj;
z3?}oUyIvM!lZl+2PS<tJ9t7$Uxm1IHu_K7p`u3wmwNY09O9nnC$Uw?!8AY;5wL6g8
z{m~Ho-Qy#)m6%UpIVI5(Yg{XsjJUrh)P|<XujBy|Gd&_sIyD@uLPOXMU)0K!jViMg
zj-1@oLXiCLP{)l{6yB&4O5Mg4s^&(Cit`d|^Mka~(TWQ9)d2+jk&$H8#K*lQf@SI`
zTcV?o1A_fjzm_z|5z@t3zNq)HB1tZmwgiNm6ae!SGr5yf0XTVit_C%5cj4|vFr<X+
iX+beLE#QcKivNGM^_Gx70YljU0000<MNUMnLSTZqJ@dx^

literal 0
HcmV?d00001

diff --git a/tutorials/markdown_syntax_highlighting/some_markdown.md b/tutorials/markdown_syntax_highlighting/some_markdown.md
new file mode 100644
index 0000000..44b0230
--- /dev/null
+++ b/tutorials/markdown_syntax_highlighting/some_markdown.md
@@ -0,0 +1,27 @@
+##This is a test
+
+Code blocks must be indented by 4 whitespaces.
+Python-Markdown has a auto-guess function which works
+pretty well:
+
+    print("Hello, World")
+    # some comment
+    for letter in "this is a test":
+        print(letter)
+
+In cases where Python-Markdown has problems figuring out which
+programming language we use, we can also add the language-tag
+explicitly. One way to do this would be:
+
+    :::python
+    print("Hello, World")
+
+or we can highlight certain lines to 
+draw the reader's attention:
+
+    :::python hl_lines="1 5"
+    print("highlight me!")
+    # but not me!
+    for letter in "this is a test":
+        print(letter)   
+    # I want to be highlighted, too!
\ No newline at end of file
diff --git a/tutorials/markdown_syntax_highlighting/template.html b/tutorials/markdown_syntax_highlighting/template.html
new file mode 100644
index 0000000..4e0f049
--- /dev/null
+++ b/tutorials/markdown_syntax_highlighting/template.html
@@ -0,0 +1,14 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+<meta charset="utf-8">
+<link rel="stylesheet" type="text/css" href="./codehilite.css">
+</head>
+
+<body>
+
+<-- converted HTML contents go here
+
+</body>
+</html>
\ No newline at end of file

From 6a512b84d4d16ca927dd0df4ba16764f9578033c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 28 May 2014 14:06:19 -0400
Subject: [PATCH 083/248] fixed link

---
 tutorials/markdown_syntax_highlighting/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/markdown_syntax_highlighting/README.md b/tutorials/markdown_syntax_highlighting/README.md
index 424bdd8..75d9e29 100644
--- a/tutorials/markdown_syntax_highlighting/README.md
+++ b/tutorials/markdown_syntax_highlighting/README.md
@@ -19,7 +19,7 @@ In this little tutorial, I want to show you in 5 simple steps how easy it is to
 There are more sophisticated approaches using static site generators, e.g., [nikola](https://github.com/getnikola/nikola), but the focus here is to give you the brief introduction of how it generally works.
 
 
-All the files I will be using as examples in this tutorial can be download from the GitHub repository [/rasbt/python_reference/tutorials/markdown_syntax_highlighting](https://github.com/rasbt/python_reference/tutorials/markdown_syntax_highlighting)
+All the files I will be using as examples in this tutorial can be download from the GitHub repository [/rasbt/python_reference/tutorials/markdown_syntax_highlighting](https://github.com/rasbt/python_reference/tree/master/tutorials/markdown_syntax_highlighting)
 
 <br>
 <br>

From 7e2a6f33dfafe85c27d677e5c96d5d6ca3c10ace Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 30 May 2014 01:33:49 -0400
Subject: [PATCH 084/248] fixed unorderable types section

---
 ...ey_differences_between_python_2_and_3.html | 48 +++++++++++++++++--
 ...y_differences_between_python_2_and_3.ipynb | 16 ++++++-
 2 files changed, 59 insertions(+), 5 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
index 94ddb8e..2a5a0c5 100644
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ b/tutorials/key_differences_between_python_2_and_3.html
@@ -1681,7 +1681,7 @@
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <p><a href="http://sebastianraschka.com">Sebastian Raschka</a></p>
-<p>last updated 05/24/2014</p>
+<p>last updated 05/27/2014</p>
 <ul>
 <li><p><a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1">Open in IPython nbviewer</a></p></li>
 <li><p><a href="https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb">Link to this IPython notebook on Github</a></p></li>
@@ -3793,7 +3793,7 @@ <h4 id="python-2">Python 2</h4>
 <div class="cell border-box-sizing code_cell rendered">
 <div class="input">
 <div class="prompt input_prompt">
-In&nbsp;[]:
+In&nbsp;[2]:
 </div>
 <div class="inner_cell">
     <div class="input_area">
@@ -3807,6 +3807,25 @@ <h4 id="python-2">Python 2</h4>
 </div>
 </div>
 
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stdout output_text">
+<pre>
+Python 2.7.6
+[1, 2] &gt; &apos;foo&apos; =  False
+(1, 2) &gt; &apos;foo&apos; =  True
+[1, 2] &gt; (1, 2) =  False
+
+</pre>
+</div>
+</div>
+
+</div>
+</div>
+
 </div>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
@@ -4183,7 +4202,8 @@ <h2 id="More-articles-about-Python-2-and-Python-3">More articles about Python 2
 </div>
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
-<p>Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up:</p>
+<p>Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up.</p>
+<p><strong>// Porting to Python 3</strong></p>
 <ul>
 <li><p><a href="https://wiki.python.org/moin/Python2orPython3">Should I use Python 2 or Python 3 for my development activity?</a></p></li>
 <li><p><a href="https://docs.python.org/3.0/whatsnew/3.0.html">What’s New In Python 3.0</a></p></li>
@@ -4191,8 +4211,30 @@ <h2 id="More-articles-about-Python-2-and-Python-3">More articles about Python 2
 <li><p><a href="https://docs.python.org/3/howto/pyporting.html">Porting Python 2 Code to Python 3</a></p></li>
 <li><p><a href="http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3">How keep Python 3 moving forward</a></p></li>
 </ul>
+<p><strong>// Pro and anti Python 3</strong></p>
+<ul>
+<li><p><a href="http://lucumr.pocoo.org/2014/5/12/everything-about-unicode/">Everything you did not want to know about Unicode in Python 3</a></p></li>
+<li><p><a href="https://medium.com/@deliciousrobots/5d2ad703365d/">Python 3 is killing Python</a></p></li>
+<li><p><a href="https://medium.com/p/2a7af4788b10">Python 3 can revive Python</a></p></li>
+<li><p><a href="http://sealedabstract.com/rants/python-3-is-fine/">Python 3 is fine</a></p></li>
+</ul>
+</div>
+</div>
 </div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">
+In&nbsp;[]:
 </div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class="highlight"><pre> 
+</pre></div>
+
+</div>
+</div>
+</div>
+
 </div>
     </div>
   </div>
diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 56ff09f..a3fb088 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ad53a8e56dd6c7cd1c379333f92a5232a0403e9f4efbad8a765d42d71ebecc0f"
+  "signature": "sha256:c21b89c5879797a1dd3fbd08797ca9ad15665dfb0cec3fca1ccf94bd601354b2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -1568,7 +1568,19 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.6\n",
+        "[1, 2] > 'foo' =  False\n",
+        "(1, 2) > 'foo' =  True\n",
+        "[1, 2] > (1, 2) =  False\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",

From 0ce1669ed547efdfa8ec9b33a0beb107d3b6b6a8 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 31 May 2014 17:41:22 -0400
Subject: [PATCH 085/248] new link

---
 tutorials/key_differences_between_python_2_and_3.ipynb | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index a3fb088..19d8a8c 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c21b89c5879797a1dd3fbd08797ca9ad15665dfb0cec3fca1ccf94bd601354b2"
+  "signature": "sha256:7d8ee5c733199e70a4c5c71b6dfd0953ab5e6c33c6a0c7ef50dce07b4d56ae32"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -1895,6 +1895,8 @@
       "\n",
       "**// Pro and anti Python 3**\n",
       "\n",
+      "- [10 awesome features of Python that you can't use because you refuse to upgrade to Python 3](http://asmeurer.github.io/python3-presentation/slides.html#1)\n",
+      "\n",
       "- [Everything you did not want to know about Unicode in Python 3](http://lucumr.pocoo.org/2014/5/12/everything-about-unicode/)\n",
       "\n",
       "- [Python 3 is killing Python](https://medium.com/@deliciousrobots/5d2ad703365d/)\n",

From dc5210aea9c6daaf340e5676c9460462e7b6cbc0 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 1 Jun 2014 12:59:24 -0400
Subject: [PATCH 086/248] reorganization

---
 README.md                                     |   30 +-
 .../sequential_selection_algorithms.ipynb     | 1419 +++++++++++++++++
 {funstuff => other}/happy_mothers_day.ipynb   |    0
 .../not_so_obvious_python_stuff.ipynb         |    4 +-
 .../sqlite3_howto}/LICENSE                    |    0
 .../sqlite3_howto}/README.md                  |   35 +-
 .../sqlite3_howto}/code/add_new_column.py     |    0
 .../sqlite3_howto}/code/create_new_db.py      |    0
 .../code/create_unique_index.py               |    0
 .../sqlite3_howto}/code/date_time_ops.py      |    0
 .../sqlite3_howto}/code/get_columnnames.py    |    0
 .../sqlite3_howto}/code/print_db_info.py      |    0
 .../sqlite3_howto}/code/selecting_entries.py  |    0
 .../code/update_or_insert_records.py          |    0
 .../sqlite3_howto}/code/updating_rows.py      |    0
 .../sqlite3_howto}/code/write_from_sqlite.py  |    0
 16 files changed, 1456 insertions(+), 32 deletions(-)
 create mode 100644 algorithms/sequential_selection_algorithms.ipynb
 rename {funstuff => other}/happy_mothers_day.ipynb (100%)
 rename not_so_obvious_python_stuff.ipynb => tutorials/not_so_obvious_python_stuff.ipynb (99%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/LICENSE (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/README.md (95%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/add_new_column.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/create_new_db.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/create_unique_index.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/date_time_ops.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/get_columnnames.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/print_db_info.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/selecting_entries.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/update_or_insert_records.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/updating_rows.py (100%)
 rename {sqlite3_howto => tutorials/sqlite3_howto}/code/write_from_sqlite.py (100%)

diff --git a/README.md b/README.md
index 5982a82..f4e300a 100755
--- a/README.md
+++ b/README.md
@@ -1,9 +1,12 @@
 
+<div align="center">
 
-![logo](./Images/logo.png)
+<h3>A collection of useful scripts, tutorials, and other Python-related things</h3>
+
+<br>
+<img src="./Images/logo.png" alt=""> </div>
 
 
-A collection of useful scripts, tutorials, and other Python-related things
 
 <br>
 
@@ -16,7 +19,7 @@ A collection of useful scripts, tutorials, and other Python-related things
 
 ###// Python tips and tutorials
 
-- A collection of not so obvious Python stuff you should know! [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)]
+- A collection of not so obvious Python stuff you should know! [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb?create=1)]
 
 
 - Python's scope resolution for variable names and the LEGB rule [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)]
@@ -40,22 +43,27 @@ A collection of useful scripts, tutorials, and other Python-related things
 
 ###// Python and the web
 
-- Creating a table of contents with internal links in IPython Notebooks and Markdown documents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]  
+- Creating internal links in IPython Notebooks and Markdown docs [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]  
+
+- Converting Markdown to HTML and adding Python syntax highlighting [[Markdown](./tutorials/markdown_syntax_highlighting/README.md)] 
 
-- 5 simple steps for converting Markdown documents into HTML and adding Python syntax highlighting [[Markdown](./tutorials/markdown_syntax_highlighting/README.md)] 
 
+<br>
+###// Algorithms
+
+- Sequential Selection Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
 
 
 <br>
-###// benchmarks
+###// Benchmarks
 
 *For more recent benchmarks, please also see my separate   
 GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
 
 - Python benchmarks via `timeit` [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)]  
 
-- Implementing the least squares fit method for linear regression and speeding it up via Cython [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)]   
+- Least squares fit method for linear regression sped up via Cython [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)]   
 
 - Benchmarks of different palindrome functions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)]    
 
@@ -65,14 +73,14 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 
 
-###// other 
+###// Other 
 
-- Happy Mother's Day [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/funstuff/happy_mothers_day.ipynb?create=1)]
+- Happy Mother's Day [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)]
 
 <br>
 
-###// useful scripts and snippets
+###// Useful scripts and snippets
 
-- [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to all .py files in a current directory.
+- [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to .py files.
 
 - convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)]
diff --git a/algorithms/sequential_selection_algorithms.ipynb b/algorithms/sequential_selection_algorithms.ipynb
new file mode 100644
index 0000000..9032aa2
--- /dev/null
+++ b/algorithms/sequential_selection_algorithms.ipynb
@@ -0,0 +1,1419 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:422c66e0088094cc07058647ff0a8c20c5fbb08fad34a18ee957f594d82b1e53"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "*Sebastian Raschka*  \n",
+      "last updated: **03/29/2014**  \n",
+      "[Link to this IPython Notebook on GitHub](https://github.com/rasbt/algorithms_in_ipython_notebooks)  \n",
+      "\n",
+      "<br>\n",
+      "Executed in Python 3.4.0\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sections\"></a></p>\n",
+      "<br>\n",
+      "<br>\n",
+      "#Sections\n",
+      "- <a href=\"#intro\">1. Introduction</a><br>\n",
+      "    - <a href=\"#crit_func\">Defining a criterion function for testing</a><br>\n",
+      "    - <a href=\"#sfs\">2. Sequential Forward Selection (SFS)</a><br>\n",
+      "    - <a href=\"#sfs_code\">SFS Code</a><br>\n",
+      "    - <a href=\"#example_sfs\">Example SFS</a><br>\n",
+      "- <a href=\"#sbs\">3. Sequential Backward Selection (SBS)</a><br>\n",
+      "    - <a href=\"#sbs_code\">SBS Code</a><br>\n",
+      "    - <a href=\"#example_sbs\">Example SBS</a><br>\n",
+      "- <a href=\"#pLmR\">4. \"Plus L take away R\" (+L -R)</a><br>\n",
+      "    - <a href=\"#pLmR_code\">+L -R Code</a><br>\n",
+      "    - <a href=\"#example_pLmR\">Example +L -R</a><br>\n",
+      "        - <a href=\"#example_pLmR1\">Example 1: L > R</a><br>\n",
+      "        - <a href=\"#example_pLmR2\">Example 2: R > L</a><br>\n",
+      "- <a href=\"#sffs\">5. Sequential Floating Forward Selection (SFFS)</a><br>\n",
+      "    - <a href=\"#sffs_code\">SFFS Code</a><br>\n",
+      "    - <a href=\"#example_sffs\">Example SFFS</a><br>\n",
+      "- <a href=\"#sfbs\">6. Sequential Floating Backward Selection (SFBS)</a><br>\n",
+      "    - <a href=\"#sfbs_code\">SFBS Code</a><br>\n",
+      "    - <a href=\"#example_sfbs\">Example SFBS</a><br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#Sequential Selection Algorithms in Python\n",
+      "\n",
+      "<p><a name=\"intro\"></a></p>\n",
+      "## 1. Introduction  \n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "One of the biggest challenges of designing a good classifier for solving a ***Statistical Pattern Classification*** problem is to estimate the underlying parameters to fit the model - given that the forms of the underlying probability distributions are known. The larger the number of parameters becomes, the more difficult it naturally is to estimate those parameters accurately (***Curse of Dimensionality***) from a limited number of training samples.  \n",
+      "\n",
+      "In order to avoid the ***Curse of Dimensionality***, pattern classification is often accompanied by ***Dimensionality Reduction***, which also has the nice side-effect of increasing the computational performance.\n",
+      "Common techniques are projection-based, such as ***Principal Component Analysis (PCA)***, ***Linear Dimension Analysis (LDA)***, and ***Multivariate Dimension Analysis (MDA)***.  \n",
+      "An alternative to the projection-based approach is the so-called ***Feature Selection***, and in this article, we will take a look at some of the established algorithms to tackle this combinatorial search problem. Note that those algorithms are considered as \"subpoptimal\" in contrast to an ***exhaustive search***, which is often computationally not feasible, though.\n",
+      "\n",
+      "Therefore, the goal of the presented ***sequential selection algorithms*** is to reduce the feature space *D = {x_1, x_2, x_n}* to a subset of features *D - n* in order to improve or optimize the **computational performance** of the classifier and to avoid the so-called ***Curse of Dimensionality***.  \n",
+      "The goal is to select a \"sufficiently reduced\" subset from the feature space *D* \"without significantly reducing\" the performance of the classifier. In the process of choosing an \"optimal\" feature subset of size *k*, a so-called ***Criterion Function***, which typically, simply, and intuitively assesses the ***recognition rate*** of the classifier.\n",
+      "\n",
+      "F. Ferri, P. Pudil, M. Hatef, and J. Kittler investigated the performance of different ***Sequential Selection Algorithms*** for  ***Feature Selection*** on different scales and reported their results in a nice research article: *\"[Comparative Study of Techniques for Large Scale Feature Selection](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=02CB16CB1C28EA6CB57E212861CFB180?doi=10.1.1.24.4369&rep=rep1&type=pdf),\" Pattern Recognition in Practice IV, E. Gelsema and L. Kanal, eds., pp. 403-413. Elsevier Science B.V., 1994.*  \n",
+      "Choosing an \"appropriate\" algorithm really depends on the problem - the size and desired recognition rate and computational performance. Thus, I want to encourage you to take (at least) a brief look at their paper and the results they obtained from experimenting with different problems feature space dimensions.\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"crit_func\"></a></p>\n",
+      "### Defining a criterion function (for testing)\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "In order to evaluate the performance of our selected ***feature subset*** (typically the recognition rate of the classifier), we need to define a ***criterion function***.\n",
+      "\n",
+      "For the sake of simplicity, and in order to get an intuitive idea if our algorithm returns an \"appropriate\" result, let us define a very simple criterion function here.  \n",
+      "The criterion function defined below simply returns the sum of numerical values in a list."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def simple_crit_func(feat_sub):\n",
+      "    \"\"\" Returns sum of numerical values of an input list. \"\"\" \n",
+      "    return sum(feat_sub)\n",
+      "\n",
+      "# Example:\n",
+      "simple_crit_func([1,2,4])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 1,
+       "text": [
+        "7"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sfs\"></a></p>\n",
+      "\n",
+      "## 2. Sequential Forward Selection (SFS)\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "The ***Sequential Fortward Selection (SFS)*** is one of the simplest and probably fastest *Feature Selection* algorithms.  \n",
+      "Let's summarize its mechanics in words:  \n",
+      "***SFS*** starts with an empty feature subset and sequentially adds features from the whole input feature space to this subset until the subset reaches a desired (user-specified) size. For every iteration (= inclusion of a new feature), the whole feature subset is evaluated (expect for the features that are already included in the new subset). The evaluation is done by the so-called ***criterion function*** which assesses the feature that leads to the maximum performance improvement of the feature subset if it is included.  \n",
+      "Note that included features are never removed, which is one of the biggest downsides of this algorithm.<br><br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "<hr>\n",
+      "\n",
+      "**Input:**  \n",
+      "the set of all features,  \n",
+      "- $ Y ={y_1, y_2, ..., y_d} $\n",
+      "\n",
+      "\n",
+      "The ***SFS*** algorithm takes the whole feature set as input, if our feature space consists of, e.g. 10, if our feature space consists of 10 dimensions (***d = 10***).\n",
+      "<br><br>\n",
+      "\n",
+      "**Output:**   \n",
+      "a subset of features,  \n",
+      "- $X_k = {x_j \\; | \\;  j = 1, 2, ..., k; x_j \u2208 Y}$,  \n",
+      "where $k = (0, 1, 2, ..., d $)  \n",
+      "\n",
+      "The returned output of the algorithm is a subset of the feature space of a specified size. E.g., a subset of 5 features from a 10-dimensional feature space ($k = 5, \\; d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Initialization:**  \n",
+      "- $ X_0 = \\emptyset, \\; k = 0 $\n",
+      "\n",
+      "We initialize the algorithm with an empty set (\"null set\") so that the $k = 0$ (where $k$ is the size of the subset)\n",
+      "<br><br>\n",
+      "\n",
+      "**Step 1 (Inclusion):**  \n",
+      "<br> \n",
+      "- $ x^+ \\; arg \\; max \\; J(x_k + x), \\quad where \\; x \u2208 Y - X_k $  \n",
+      "- $ X_k+1 = X_k + x^+ $  \n",
+      "- $ k = k + 1 $  \n",
+      "- $ Go \\; to \\; Step 1 $  \n",
+      "\n",
+      "We go through the ***feature space*** and look for the feature $x^+$ which maximizes our criterion if we add it to the ***feature subset*** (where $J()$ is the criterion function).  We repeat this process until we reach the ***Termination*** criterion.\n",
+      "<br><br>\n",
+      "\n",
+      "**Termination:**  \n",
+      "- stop when *k* equals the number of desired features\n",
+      "\n",
+      "We add features to the new feature subset $X_k$ until we reach the number of specified features for our final subset. E.g., if our desired number of features is 5 and we start with the \"null set\" (*Initialization*), we would add features to the subset until it contains 5 features.\n",
+      "\n",
+      "<hr>\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sfs_code\"></a></p>\n",
+      "\n",
+      "##SFS Code\n",
+      "\n",
+      "[[back to top]](#sections)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Sebastian Raschka\n",
+      "# last updated: 03/29/2014 \n",
+      "# Sequential Forward Selection (SBS)\n",
+      "\n",
+      "\n",
+      "def seq_forw_select(features, max_k, criterion_func, print_steps=False):\n",
+      "    \"\"\"\n",
+      "    Implementation of a Sequential Forward Selection algorithm.\n",
+      "    \n",
+      "    Keyword Arguments:\n",
+      "        features (list): The feature space as a list of features.\n",
+      "        max_k: Termination criterion; the size of the returned feature subset.\n",
+      "        criterion_func (function): Function that is used to evaluate the\n",
+      "            performance of the feature subset.\n",
+      "        print_steps (bool): Prints the algorithm procedure if True.\n",
+      "    \n",
+      "    Returns the selected feature subset, a list of features of length max_k.\n",
+      "\n",
+      "    \"\"\"\n",
+      "    \n",
+      "    # Initialization\n",
+      "    feat_sub = []\n",
+      "    k = 0\n",
+      "    d = len(features)\n",
+      "    if max_k > d:\n",
+      "        max_k = d\n",
+      "    \n",
+      "    while True:\n",
+      "        \n",
+      "        # Inclusion step\n",
+      "        if print_steps:\n",
+      "            print('\\nInclusion from feature space', features)\n",
+      "        crit_func_max = criterion_func(feat_sub + [features[0]])\n",
+      "        best_feat = features[0]\n",
+      "        for x in features[1:]:\n",
+      "            crit_func_eval = criterion_func(feat_sub + [x])\n",
+      "            if crit_func_eval > crit_func_max:\n",
+      "                crit_func_max = crit_func_eval\n",
+      "                best_feat = x\n",
+      "        feat_sub.append(best_feat)\n",
+      "        if print_steps:\n",
+      "            print('include: {} -> feature subset: {}'.format(best_feat, feat_sub))\n",
+      "        features.remove(best_feat)\n",
+      "        \n",
+      "        # Termination condition\n",
+      "        k = len(feat_sub)\n",
+      "        if k == max_k:\n",
+      "            break\n",
+      "                \n",
+      "    return feat_sub"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"example_sfs\"></a></p>\n",
+      "### Example SFS:\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "In this example, we take a look at the individual steps of the [***SFS***](#sfs) algorithmn to select a ***feature subset*** consisting of 3 features out of a ***feature space*** of size 10.  \n",
+      "The input feature space consists of 10 integers: 6, 3, 1, 6, 8, 2, 3, 7, 9, 1,  \n",
+      "and our criterion is to find a subset of size 3 in this ***feature space*** that maximizes the integer sum in this ***feature subset***."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def example_seq_forw_select():\n",
+      "    ex_features = [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+      "    res_forw = seq_forw_select(features=ex_features, max_k=3,\\\n",
+      "                               criterion_func=simple_crit_func, print_steps=True) \n",
+      "    return res_forw\n",
+      "    \n",
+      "# Run example\n",
+      "res_forw = example_seq_forw_select()\n",
+      "print('\\nRESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] ->', res_forw)\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Inclusion from feature space [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+        "include: 9 -> feature subset: [9]\n",
+        "\n",
+        "Inclusion from feature space [6, 3, 1, 6, 8, 2, 3, 7, 1]\n",
+        "include: 8 -> feature subset: [9, 8]\n",
+        "\n",
+        "Inclusion from feature space [6, 3, 1, 6, 2, 3, 7, 1]\n",
+        "include: 7 -> feature subset: [9, 8, 7]\n",
+        "\n",
+        "RESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] -> [9, 8, 7]\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "###Result:\n",
+      "The returned result is definitely what we would expect: the 3 highest values (note that we defined 3 as the number of desired features for our subset) in the input feature list, since our ***criterion*** is to select the numerical values (= features) that yield the maximum mathematical sum in the feature subset."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sbs\"></a></p>\n",
+      "## 3. Sequential Backward Selection (SBS)\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "The ***Sequential Backward Selection (SBS)*** algorithm is very similar to the [***SFS***](#sfs), which we have just seen in the section above. The only difference is that we start with the complete feature set instead of the \"null set\" and remove features sequentially until we reach the number of desired features *k*.  \n",
+      "Note that features are never added back once they were removed, which (similar to [***SFS***](#sfs)) is one of the biggest downsides of this algorithm.<br><hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "<hr>\n",
+      "**Input:**  \n",
+      "the set of all features,   \n",
+      "- $Y = {y_1, y_2, ..., y_d}$  \n",
+      "\n",
+      "The ***SBS*** algorithm takes the whole feature set as input, if our feature space consists of, e.g. 10, if our feature space consists of 10 dimensions ($d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Output:**  \n",
+      "a subset of features,  \n",
+      "- $ X_k = {x_j \\;| \\;j = 1, 2, ..., k; x_j \u2208 Y}, \\quad where \\; k = (0, 1, 2, ..., d)$  \n",
+      "\n",
+      "The returned output of the algorithm is a subset of the feature space of a specified size. E.g., a subset of 5 features from a 10-dimensional feature space ($k = 5, d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Initialization:**  \n",
+      "- $X_0 = Y, \\; k = d$\n",
+      "\n",
+      "We initialize the algorithm with the given feature set so that the $k = d$ (where $k$ has the size of the feature set $d$)\n",
+      "<br><br>\n",
+      "\n",
+      "**Step 1 (Exclusion):**  \n",
+      "<br> \n",
+      "- $x^- = arg \\; max \\; J(x_k - x), where x \u2208 X_k$  \n",
+      "- $X_k-1 = X_k - x^-$  \n",
+      "- $k = k - 1$    \n",
+      "- $Go \\; to \\; Step 1$ \n",
+      "\n",
+      "We go through the ***feature subset*** and look for the feature $x^-$ which minimizes our criterion if we remove it from the ***feature subset*** (where $J()$ is the criterion function).  We repeat this process until we reach the ***Termination*** criterion.\n",
+      "<br><br>\n",
+      "\n",
+      "**Termination:**  \n",
+      "- stop when $k$ equals the number of desired features\n",
+      "\n",
+      "We remove features from the feature subset $X_k$ until we reach the number of specified features for our final subset. E.g., if our desired number of features is 5 and we start with the entire feature space (*Initialization*), we would remove features from the subset until it contains 5 features.\n",
+      "<hr>\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sbs_code\"></a></p>\n",
+      "\n",
+      "##SBS Code\n",
+      "\n",
+      "[[back to top]](#sections)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Sebastian Raschka\n",
+      "# last updated: 03/29/2014 \n",
+      "# Sequential Backward Selection (SBS)\n",
+      "\n",
+      "from copy import deepcopy\n",
+      "\n",
+      "def seq_backw_select(features, max_k, criterion_func, print_steps=False):\n",
+      "    \"\"\"\n",
+      "    Implementation of a Sequential Backward Selection algorithm.\n",
+      "    \n",
+      "    Keyword Arguments:\n",
+      "        features (list): The feature space as a list of features.\n",
+      "        max_k: Termination criterion; the size of the returned feature subset.\n",
+      "        criterion_func (function): Function that is used to evaluate the\n",
+      "            performance of the feature subset.\n",
+      "        print_steps (bool): Prints the algorithm procedure if True.\n",
+      "        \n",
+      "    Returns the selected feature subset, a list of features of length max_k.\n",
+      "\n",
+      "    \"\"\"\n",
+      "    # Initialization\n",
+      "    feat_sub = deepcopy(features)\n",
+      "    k = len(feat_sub)\n",
+      "    i = 0\n",
+      "\n",
+      "    while True:\n",
+      "        \n",
+      "        # Exclusion step\n",
+      "        if print_steps:\n",
+      "            print('\\nExclusion from feature subset', feat_sub)\n",
+      "        worst_feat = len(feat_sub)-1\n",
+      "        worst_feat_val = feat_sub[worst_feat]\n",
+      "        crit_func_max = criterion_func(feat_sub[:-1]) \n",
+      "\n",
+      "        for i in reversed(range(0,len(feat_sub)-1)):\n",
+      "            crit_func_eval = criterion_func(feat_sub[:i] + feat_sub[i+1:])\n",
+      "            if crit_func_eval > crit_func_max:\n",
+      "                worst_feat, crit_func_max = i, crit_func_eval\n",
+      "                worst_feat_val = feat_sub[worst_feat]\n",
+      "        del feat_sub[worst_feat]\n",
+      "        if print_steps:\n",
+      "            print('exclude: {} -> feature subset: {}'.format(worst_feat_val, feat_sub))\n",
+      "        \n",
+      "        # Termination condition\n",
+      "        k = len(feat_sub)\n",
+      "        if k == max_k:\n",
+      "            break\n",
+      "                \n",
+      "    return feat_sub"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"example_sbs\"></a></p>\n",
+      "### Example SBS:\n",
+      "\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "\n",
+      "Like we did for the [***SFS example***](#example_sfs) above, we take a look at the individual steps of the [***SBS***](#sbs) algorithmn to select a ***feature subset*** consisting of 3 features out of a ***feature space*** of size 10.  \n",
+      "The input feature space consists of 10 integers: 6, 3, 1, 6, 8, 2, 3, 7, 9, 1,  \n",
+      "and our criterion is to find a subset of size 3 in this ***feature space*** that maximizes the integer sum in this ***feature subset***."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def example_seq_backw_select():\n",
+      "    ex_features = [6,3,1,6,8,2,3,7,9,1]\n",
+      "    res_backw = seq_backw_select(features=ex_features, max_k=3,\\\n",
+      "                                 criterion_func=simple_crit_func, print_steps=True)  \n",
+      "    return (res_backw)\n",
+      "    \n",
+      "# Run example\n",
+      "res_backw = example_seq_backw_select()\n",
+      "print('\\nRESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] ->', res_backw)\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Exclusion from feature subset [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+        "exclude: 1 -> feature subset: [6, 3, 1, 6, 8, 2, 3, 7, 9]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 1, 6, 8, 2, 3, 7, 9]\n",
+        "exclude: 1 -> feature subset: [6, 3, 6, 8, 2, 3, 7, 9]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 6, 8, 2, 3, 7, 9]\n",
+        "exclude: 2 -> feature subset: [6, 3, 6, 8, 3, 7, 9]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 6, 8, 3, 7, 9]\n",
+        "exclude: 3 -> feature subset: [6, 3, 6, 8, 7, 9]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 6, 8, 7, 9]\n",
+        "exclude: 3 -> feature subset: [6, 6, 8, 7, 9]\n",
+        "\n",
+        "Exclusion from feature subset [6, 6, 8, 7, 9]\n",
+        "exclude: 6 -> feature subset: [6, 8, 7, 9]\n",
+        "\n",
+        "Exclusion from feature subset [6, 8, 7, 9]\n",
+        "exclude: 6 -> feature subset: [8, 7, 9]\n",
+        "\n",
+        "RESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] -> [8, 7, 9]\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Result:\n",
+      "The returned ***feature subset*** is similar to the result of the [***SFS***](#example_sfs) algorithm that we have seen above, which is what we would expect: the 3 highest values (note that we defined 3 as the number of desired features for our subset) in the input feature list, since our ***criterion*** is to select the feanumerical values (= features) that yield the maximum mathematical sum in the feature subset."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"pLmR\"></a></p>\n",
+      "## 4. \"Plus L take away R\" (+L -R)\n",
+      "\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "\n",
+      "The ***\"Plus L take away R\" (+L -R)*** is basically a combination of [***SFS***](#sfs) and [***SBS***](#sbs). It append features to the ***feature subset*** *L-times*, and afterwards it removes features *R-times* until we reach our desired size for the ***feature subset***.\n",
+      "\n",
+      "**Variant 1: L > R**  \n",
+      "If *L > R*, the algorithm starts with an empty ***feature subset*** and adds *L* features to it from the ***feature space***. Then it goes over to the next step 2, where it removes *R* features from the ***feature subset***, after which it goes back to step 1 to add *L* features again.  \n",
+      "Those steps are repeated until the ***feature subset** reaches the desired size *k*.  \n",
+      "<br>\n",
+      "**Variant 2: R > L**  \n",
+      "Else, if *R > L*, the algorithms starts with the whole ***feature space*** as ***feature subset***. It remove s*R* features from it before it adds back *L* features from those features that were just removed.  \n",
+      "Those steps are repeated until the ***feature subset** reaches the desired size *k*.    \n",
+      "<br><hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "<hr>\n",
+      "**Input:**  \n",
+      "the set of all features,  \n",
+      "- $Y = {y_1, y_2, ..., y_d}$  \n",
+      "\n",
+      "The ***+L -R*** algorithm takes the whole feature set as input, if our feature space consists of, e.g. 10, if our feature space consists of 10 dimensions ($d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Output:** \n",
+      "a subset of features,  \n",
+      "- $X_k = {x_j \\; | \\; j = 1, 2, ..., k; x_j \u2208 Y}, \\quad where \\; k = (0, 1, 2, ..., d)$  \n",
+      "\n",
+      "The returned output of the algorithm is a subset of the feature space of a specified size. E.g., a subset of 5 features from a 10-dimensional feature space ($k = 5, \\; d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Initialization:**  \n",
+      "- $X_0 = Y, \\; k = d$\n",
+      "\n",
+      "We initialize the algorithm with the given feature set so that the $k = d$ (where $k$ has the size of the feature set $d$)\n",
+      "<br><br>\n",
+      "\n",
+      "**Step 1 (Inclusion):**  \n",
+      "<br>\n",
+      "- *repeat L-times:*   \n",
+      "    - $x^+ = arg \\; max \\; J(x_k + x), \\quad where \\; x \u2208 Y - X_k $    \n",
+      "    - $X_k+1 = X_k + x^+$    \n",
+      "    - $k = k + 1$  \n",
+      "- $Go \\; to\\; Step 2$  \n",
+      "<br> <br> \n",
+      "**Step 2 (Exclusion):**  \n",
+      "<br> \n",
+      "- *repeat R-times:*  \n",
+      "    - $x^- = arg max J(x_k - x), \\quad where \\; x \u2208 X_k$  \n",
+      "    - $X_k-1 = X_k - x^-$  \n",
+      "    - $k = k - 1$   \n",
+      "- $Go \\; to \\; Step 1$  \n",
+      "\n",
+      "In step 1, we go *L-times* through the ***feature space*** and look for the feature $x^+$ which maximizes our criterion if we add it to the ***feature subset*** (where $J()$ is the criterion function). Then we go over to step 2.  \n",
+      "In step 2, we go *R-times* through the ***feature subset*** and look for the feature $x^-$ which minimizes our criterion if we remove it from the ***feature subset*** (where $J()$ is the criterion function).  Then we go back to step 1.  \n",
+      "Note that this order of steps only applies if $L > R$, in the opposite case ($R > L$), we have to start with the ***Exlusion*** on the whole ***feature space***, followed by inclusion of removed features.\n",
+      "<br><br>\n",
+      "\n",
+      "**Termination:**  \n",
+      "- stop when $k$ equals the number of desired features\n",
+      "\n",
+      "We add and remove features from the feature subset $X_k$ until we reach the number of specified features for our final subset. E.g., if our desired number of features is 5 and we start with the entire feature space (*Initialization*), we would remove features from the subset until it contains 5 features.\n",
+      "<hr>\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"pLmR_code\"></a></p>\n",
+      "\n",
+      "##+L -R Code\n",
+      "\n",
+      "[[back to top]](#sections)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Sebastian Raschka\n",
+      "# last updated: 03/29/2014 \n",
+      "# \"Plus L take away R\" (+L -R)\n",
+      "\n",
+      "from copy import deepcopy\n",
+      "\n",
+      "def plus_L_minus_R(features, max_k, criterion_func, L=3, R=2, print_steps=False):\n",
+      "    \"\"\"\n",
+      "    Implementation of a \"Plus l take away r\" algorithm.\n",
+      "    \n",
+      "    Keyword Arguments:\n",
+      "        features (list): The feature space as a list of features.\n",
+      "        max_k: Termination criterion; the size of the returned feature subset.\n",
+      "        criterion_func (function): Function that is used to evaluate the\n",
+      "            performance of the feature subset.\n",
+      "        L (int): Number of features added per iteration.\n",
+      "        R (int): Number of features removed per iteration.\n",
+      "        print_steps (bool): Prints the algorithm procedure if True.\n",
+      "    \n",
+      "    Returns the selected feature subset, a list of features of length max_k.\n",
+      "\n",
+      "    \"\"\"\n",
+      "    assert(L != R), 'L must be != R to avoid an infinite loop'\n",
+      "    \n",
+      "    ############################\n",
+      "    ### +L -R for case L > R ###\n",
+      "    ############################\n",
+      "    \n",
+      "    if L > R:\n",
+      "        feat_sub = []\n",
+      "        k = 0\n",
+      "        \n",
+      "        # Initialization\n",
+      "        while True:\n",
+      "        \n",
+      "            # +L (Inclusion)\n",
+      "            if print_steps:\n",
+      "                print('\\nInclusion from features', features)\n",
+      "            for i in range(L):\n",
+      "                if len(features) > 0:\n",
+      "                    crit_func_max = criterion_func(feat_sub + [features[0]])\n",
+      "                    best_feat = features[0]\n",
+      "                    if len(features) > 1:\n",
+      "                        for x in features[1:]:\n",
+      "                            crit_func_eval = criterion_func(feat_sub + [x])\n",
+      "                            if crit_func_eval > crit_func_max:\n",
+      "                                crit_func_max = crit_func_eval\n",
+      "                                best_feat = x\n",
+      "                    features.remove(best_feat)\n",
+      "                    feat_sub.append(best_feat)\n",
+      "                    if print_steps:\n",
+      "                        print('include: {} -> feature_subset: {}'.format(best_feat, feat_sub))\n",
+      "        \n",
+      "            # -R (Exclusion)\n",
+      "            if print_steps:\n",
+      "                print('\\nExclusion from feature_subset', feat_sub)\n",
+      "            for i in range(R):\n",
+      "                if len(features) + len(feat_sub) > max_k:\n",
+      "                    worst_feat = len(feat_sub)-1\n",
+      "                    worst_feat_val = feat_sub[worst_feat]\n",
+      "                    crit_func_max = criterion_func(feat_sub[:-1]) \n",
+      "\n",
+      "                    for j in reversed(range(0,len(feat_sub)-1)):\n",
+      "                        crit_func_eval = criterion_func(feat_sub[:j] + feat_sub[j+1:])\n",
+      "                        if crit_func_eval > crit_func_max:\n",
+      "                            worst_feat, crit_func_max = j, crit_func_eval\n",
+      "                            worst_feat_val = feat_sub[worst_feat]\n",
+      "                    del feat_sub[worst_feat]\n",
+      "                    if print_steps:\n",
+      "                        print('exclude: {} -> feature subset: {}'.format(worst_feat_val, feat_sub))\n",
+      "                \n",
+      "        \n",
+      "            # Termination condition\n",
+      "            k = len(feat_sub)\n",
+      "            if k == max_k:\n",
+      "                break\n",
+      "                \n",
+      "        return feat_sub\n",
+      "    \n",
+      "    ############################\n",
+      "    ### +L -R for case L < R ###\n",
+      "    ############################\n",
+      "\n",
+      "    else:\n",
+      "        # Initialization\n",
+      "        feat_sub = deepcopy(features)\n",
+      "        k = len(feat_sub)\n",
+      "        i = 0\n",
+      "        count = 0\n",
+      "        while True:\n",
+      "            count += 1\n",
+      "            # Exclusion step\n",
+      "            removed_feats = []\n",
+      "            if print_steps:\n",
+      "                print('\\nExclusion from feature subset', feat_sub)\n",
+      "            for i in range(R):\n",
+      "                if len(feat_sub) > max_k:\n",
+      "                    worst_feat = len(feat_sub)-1\n",
+      "                    worst_feat_val = feat_sub[worst_feat]\n",
+      "                    crit_func_max = criterion_func(feat_sub[:-1]) \n",
+      "\n",
+      "                    for i in reversed(range(0,len(feat_sub)-1)):\n",
+      "                        crit_func_eval = criterion_func(feat_sub[:i] + feat_sub[i+1:])\n",
+      "                        if crit_func_eval > crit_func_max:\n",
+      "                            worst_feat, crit_func_max = i, crit_func_eval\n",
+      "                            worst_feat_val = feat_sub[worst_feat]\n",
+      "                    removed_feats.append(feat_sub.pop(worst_feat))\n",
+      "            if print_steps:\n",
+      "                print('exclude: {} -> feature subset: {}'.format(removed_feats, feat_sub))\n",
+      "            \n",
+      "            # +L (Inclusion)\n",
+      "            included_feats = []\n",
+      "            if len(feat_sub) != max_k:\n",
+      "                for i in range(L):\n",
+      "                    if len(removed_feats) > 0:\n",
+      "                        crit_func_max = criterion_func(feat_sub + [removed_feats[0]])\n",
+      "                        best_feat = removed_feats[0]\n",
+      "                        if len(removed_feats) > 1:\n",
+      "                            for x in removed_feats[1:]:\n",
+      "                                crit_func_eval = criterion_func(feat_sub + [x])\n",
+      "                                if crit_func_eval > crit_func_max:\n",
+      "                                    crit_func_max = crit_func_eval\n",
+      "                                    best_feat = x\n",
+      "                        removed_feats.remove(best_feat)\n",
+      "                        feat_sub.append(best_feat)\n",
+      "                        included_feats.append(best_feat)\n",
+      "                if print_steps:\n",
+      "                    print('\\nInclusion from removed features', removed_feats)\n",
+      "                    print('include: {} -> feature_subset: {}'.format(included_feats, feat_sub))\n",
+      "                        \n",
+      "            # Termination condition\n",
+      "            k = len(feat_sub)\n",
+      "            if k == max_k:\n",
+      "                break\n",
+      "            if count >= 30:\n",
+      "                break\n",
+      "        return feat_sub\n",
+      "        "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"example_pLmR\"></a></p>\n",
+      "### Example +L -R:\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "Like we did for the [***SFS***](#example_sfs) example above, let's have look at the individual steps of the ***+L -R*** algorithmn to select a ***feature subset*** consisting of 3 features out of a ***feature space*** of size 10.  \n",
+      "Again, the input feature space consists of the 10 integers: 6, 3, 1, 6, 8, 2, 3, 7, 9, 1,  \n",
+      "and our criterion is to find a subset of size 3 in this ***feature space*** that maximizes the integer sum in this ***feature subset***."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"example_pLmR1\"></a></p>\n",
+      "#### Example 1:  L > R\n",
+      "\n",
+      "[[back to top]](#sections)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def example_plus_L_minus_R():\n",
+      "    ex_features = [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+      "    res_plmr = plus_L_minus_R(features=ex_features, max_k=3,\\\n",
+      "                               criterion_func=simple_crit_func, L=3, R=2, print_steps=True) \n",
+      "     \n",
+      "    return (res_plmr)\n",
+      " \n",
+      "# Run example\n",
+      "res = example_plus_L_minus_R()\n",
+      "print('\\nRESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] ->', res)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Inclusion from features [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+        "include: 9 -> feature_subset: [9]\n",
+        "include: 8 -> feature_subset: [9, 8]\n",
+        "include: 7 -> feature_subset: [9, 8, 7]\n",
+        "\n",
+        "Exclusion from feature_subset [9, 8, 7]\n",
+        "exclude: 7 -> feature subset: [9, 8]\n",
+        "exclude: 8 -> feature subset: [9]\n",
+        "\n",
+        "Inclusion from features [6, 3, 1, 6, 2, 3, 1]\n",
+        "include: 6 -> feature_subset: [9, 6]\n",
+        "include: 6 -> feature_subset: [9, 6, 6]\n",
+        "include: 3 -> feature_subset: [9, 6, 6, 3]\n",
+        "\n",
+        "Exclusion from feature_subset [9, 6, 6, 3]\n",
+        "exclude: 3 -> feature subset: [9, 6, 6]\n",
+        "exclude: 6 -> feature subset: [9, 6]\n",
+        "\n",
+        "Inclusion from features [1, 2, 3, 1]\n",
+        "include: 3 -> feature_subset: [9, 6, 3]\n",
+        "include: 2 -> feature_subset: [9, 6, 3, 2]\n",
+        "include: 1 -> feature_subset: [9, 6, 3, 2, 1]\n",
+        "\n",
+        "Exclusion from feature_subset [9, 6, 3, 2, 1]\n",
+        "exclude: 1 -> feature subset: [9, 6, 3, 2]\n",
+        "exclude: 2 -> feature subset: [9, 6, 3]\n",
+        "\n",
+        "RESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] -> [9, 6, 3]\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"example_pLmR2\"></a></p>\n",
+      "#### Example 2:  R > L\n",
+      "\n",
+      "[[back to top]](#sections)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def example_plus_L_minus_R():\n",
+      "    ex_features = [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+      "    res_plmr = plus_L_minus_R(features=ex_features, max_k=3,\\\n",
+      "                               criterion_func=simple_crit_func, L=2, R=3, print_steps=True) \n",
+      "     \n",
+      "    return (res_plmr)\n",
+      " \n",
+      "# Run example\n",
+      "res = example_plus_L_minus_R()\n",
+      "print('\\nRESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] ->', res)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Exclusion from feature subset [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+        "exclude: [1, 1, 2] -> feature subset: [6, 3, 6, 8, 3, 7, 9]\n",
+        "\n",
+        "Inclusion from removed features [1]\n",
+        "include: [2, 1] -> feature_subset: [6, 3, 6, 8, 3, 7, 9, 2, 1]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 6, 8, 3, 7, 9, 2, 1]\n",
+        "exclude: [1, 2, 3] -> feature subset: [6, 3, 6, 8, 7, 9]\n",
+        "\n",
+        "Inclusion from removed features [1]\n",
+        "include: [3, 2] -> feature_subset: [6, 3, 6, 8, 7, 9, 3, 2]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 6, 8, 7, 9, 3, 2]\n",
+        "exclude: [2, 3, 3] -> feature subset: [6, 6, 8, 7, 9]\n",
+        "\n",
+        "Inclusion from removed features [2]\n",
+        "include: [3, 3] -> feature_subset: [6, 6, 8, 7, 9, 3, 3]\n",
+        "\n",
+        "Exclusion from feature subset [6, 6, 8, 7, 9, 3, 3]\n",
+        "exclude: [3, 3, 6] -> feature subset: [6, 8, 7, 9]\n",
+        "\n",
+        "Inclusion from removed features [3]\n",
+        "include: [6, 3] -> feature_subset: [6, 8, 7, 9, 6, 3]\n",
+        "\n",
+        "Exclusion from feature subset [6, 8, 7, 9, 6, 3]\n",
+        "exclude: [3, 6, 6] -> feature subset: [8, 7, 9]\n",
+        "\n",
+        "RESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] -> [8, 7, 9]\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Result:\n",
+      "The returned ***feature subset*** really is suboptimal in this particular example for L > R ([Example 1: L > R](#example_pLmR1)). This is due to the fact that we add multiple features to our ***feature subset*** and we also remove multiple features from it; we never add back any of the removed features to the investigated ***feature space***.  \n",
+      "\n",
+      "**Modifying the \"Plus L take away R\" algorithm**  \n",
+      "This algorithm can be tweaked by adding back *r - 1* features to the ***feature subset*** after each ***Exclusion*** step to be assessed by the ***criterion function*** for inclusion in the next iteration. This is a decision that has to be made by considering the particular application, since it decreases the computational performance of the algorithm, but improves the performance of the resulting ***feature subset*** as a classifier."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sffs\"></a></p>\n",
+      "## 5. Sequential Floating Forward Selection (SFFS)\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "The ***Sequential Floating Forward Selection (SFFS)*** algorithm can be considered as extension of the simpler [***SFS***](#sfs) algorithm, which we have seen in the very beginning. In constrast to ***SFS***, the ***SFFS*** algorithm **can** remove features once they were included, so that a larger number of feature subset combinations can be sampled. It is important to emphasize that the removal of included features is **conditional**, which makes it different from the [***+L -R***](#pLmR) algorithm. The ***Conditional Exclusion*** in ***SFFS*** only occurs if the resultin feature subset is assessed as \"better\" by the ***criterion function*** after removal of a particular feature."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "<hr>\n",
+      "**Input:**  \n",
+      "the set of all features,  \n",
+      "- $Y = {y_1, y_2, ..., y_d}$    \n",
+      "\n",
+      "The ***SFFS*** algorithm takes the whole feature set as input, if our feature space consists of, e.g. 10, if our feature space consists of 10 dimensions ($d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Output:**  \n",
+      "a subset of features,  \n",
+      "- $X_k = {x_j \\; | \\; j = 1, 2, ..., k; x_j \u2208 Y}, \\quad where \\; k = (0, 1, 2, ..., d)$  \n",
+      "\n",
+      "The returned output of the algorithm is a subset of the feature space of a specified size. E.g., a subset of 5 features from a 10-dimensional feature space ($k = 5, \\; d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Initialization:**  \n",
+      "- $X_0 = \\emptyset, \\quad k = 0$\n",
+      "\n",
+      "We initialize the algorithm with an empty set (\"null set\") so that the $k = 0$ (where $k$ is the size of the subset)\n",
+      "<br><br>\n",
+      "\n",
+      "**Step 1 (Inclusion):**  \n",
+      "<br> \n",
+      "- $x^+ = arg\\; max \\;J(x_k + x), \\quad  where \\; x \u2208 Y - X_k$   \n",
+      "- $X_k+1 = X_k + x^+$   \n",
+      "- $k = k + 1$   \n",
+      "- $Go \\; to \\; Step 2  $ \n",
+      "<br> <br> \n",
+      "**Step 2 (Conditional Exclusion):**  \n",
+      "<br> \n",
+      "- $x^- = arg \\; max \\; J(x_k - x),\\quad where \\; x \u2208 X_k$   \n",
+      "- $if\\; J(x_k - x) > J(x_k - x):$      \n",
+      "    - $X_k-1 = X_k - x^-$   \n",
+      "    - $k = k - 1$    \n",
+      "- $Go \\; to \\; Step 1$  \n",
+      "\n",
+      "In step 1, we include the feature from the ***feature space*** that leads to the best performance increase for our ***feature subset*** (assessed by the ***criterion function***). Then, we go over to step 2  \n",
+      "In step 2, we only remove a feature if the resulting subset would gain an increase in performance. We go back to step 1.  \n",
+      "Steps 1 and 2 are reapeated until the **Termination** criterion is reached.\n",
+      "<br><br>\n",
+      "\n",
+      "**Termination:**  \n",
+      "- stop when $k$ equals the number of desired features\n",
+      "\n",
+      "<hr>\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sffs_code\"></a></p>\n",
+      "\n",
+      "##SFFS Code\n",
+      "\n",
+      "[[back to top]](#sections)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Sebastian Raschka\n",
+      "# last updated: 03/29/2014 \n",
+      "# Sequential Floating Forward Selection (SFFS)\n",
+      "\n",
+      "def seq_float_forw_select(features, max_k, criterion_func, print_steps=False):\n",
+      "    \"\"\"\n",
+      "    Implementation of Sequential Floating Forward Selection.\n",
+      "    \n",
+      "    Keyword Arguments:\n",
+      "        features (list): The feature space as a list of features.\n",
+      "        max_k: Termination criterion; the size of the returned feature subset.\n",
+      "        criterion_func (function): Function that is used to evaluate the\n",
+      "            performance of the feature subset.\n",
+      "        print_steps (bool): Prints the algorithm procedure if True.\n",
+      "    \n",
+      "    Returns the selected feature subset, a list of features of length max_k.\n",
+      "\n",
+      "    \"\"\"\n",
+      "\n",
+      "    # Initialization\n",
+      "    feat_sub = []\n",
+      "    k = 0\n",
+      "    \n",
+      "    while True:\n",
+      "       \n",
+      "        # Step 1: Inclusion\n",
+      "        if print_steps:\n",
+      "            print('\\nInclusion from features', features)\n",
+      "        if len(features) > 0:\n",
+      "            crit_func_max = criterion_func(feat_sub + [features[0]])\n",
+      "            best_feat = features[0]\n",
+      "            if len(features) > 1:\n",
+      "                for x in features[1:]:\n",
+      "                    crit_func_eval = criterion_func(feat_sub + [x])\n",
+      "                    if crit_func_eval > crit_func_max:\n",
+      "                        crit_func_max = crit_func_eval\n",
+      "                        best_feat = x\n",
+      "            features.remove(best_feat)\n",
+      "            feat_sub.append(best_feat)\n",
+      "            if print_steps:\n",
+      "                print('include: {} -> feature_subset: {}'.format(best_feat, feat_sub))\n",
+      "        \n",
+      "        # Step 2: Conditional Exclusion\n",
+      "            worst_feat_val = None\n",
+      "            if len(features) + len(feat_sub) > max_k:\n",
+      "                crit_func_max = criterion_func(feat_sub) \n",
+      "                for i in reversed(range(0,len(feat_sub))):\n",
+      "                    crit_func_eval = criterion_func(feat_sub[:i] + feat_sub[i+1:])\n",
+      "                    if crit_func_eval > crit_func_max:\n",
+      "                        worst_feat, crit_func_max = i, crit_func_eval\n",
+      "                        worst_feat_val = feat_sub[worst_feat]\n",
+      "                if worst_feat_val:        \n",
+      "                    del feat_sub[worst_feat]\n",
+      "            if print_steps:\n",
+      "                print('exclude: {} -> feature subset: {}'.format(worst_feat_val, feat_sub))\n",
+      "            \n",
+      "        \n",
+      "        # Termination condition\n",
+      "        k = len(feat_sub)\n",
+      "        if k == max_k:\n",
+      "            break\n",
+      "                \n",
+      "    return feat_sub"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"example_sffs\"></a></p>\n",
+      "### Example SFFS:\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "Since the ***Exclusion*** step in the ***Sequential Floating Forward*** algorithm is ***conditional*** - a feature is only removed if the criterion function asses a better performance after removal - we would never exclude any feature using our [`simple_criterion_func()`](#crit_func\"), which returns the integer sum of a feature set. Thus, let us define another simple criterion function that we use for testing our ***SFFS*** algorithm.\n",
+      "\n",
+      "\n",
+      "Just as we did for the previous examples above, let's take a look at the individual steps of the ***SFFS*** algorithmn to select a ***feature subset*** consisting of 3 features out of a ***feature space*** of size 10.  \n",
+      "Also here, the input feature space consists of the 10 integers: 6, 3, 1, 6, 8, 2, 3, 7, 9, 1,  \n",
+      "and our criterion is to find a subset of size 3 in this ***feature space*** that maximizes the integer sum in this ***feature subset***."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"random_crit_func\"></a></p>\n",
+      "#### A simple criterion function with a random parameter\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "The criterion function we define below is also calculates the sum of a subset similar to the [`simple_criterion_func()`](#crit_func\") we used before. However, here we add a random integer ranging from -15 to 15 to the returned sum. Therefore, in some occasions, our criterion function can return a larger sum for a smaller subset - after we removed a feature from the subset after the ***Inclusion*** step - in order to trigger the ***Conditional Exclusion*** step."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from random import randint\n",
+      "\n",
+      "def simple_rand_crit_func(feat_sub):\n",
+      "    \"\"\" \n",
+      "    Returns sum of numerical values of an input list plus \n",
+      "    a random integer ranging from -15 to 15. \n",
+      "    \n",
+      "    \"\"\" \n",
+      "    return sum(feat_sub) + randint(-15,15)\n",
+      "\n",
+      "# Example:\n",
+      "simple_rand_crit_func([1,2,4])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "19"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def example_seq_float_forw_select():\n",
+      "    ex_features = [6,3,1,6,8,2,3,7,9,1]\n",
+      "    res_seq_flforw = seq_float_forw_select(features=ex_features, max_k=3,\\\n",
+      "                                 criterion_func=simple_rand_crit_func, print_steps=True)  \n",
+      "    return res_seq_flforw\n",
+      "    \n",
+      "# Run example\n",
+      "res_seq_flforw = example_seq_float_forw_select()\n",
+      "print('\\nRESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] ->', res_seq_flforw)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Inclusion from features [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+        "include: 7 -> feature_subset: [7]\n",
+        "exclude: None -> feature subset: [7]\n",
+        "\n",
+        "Inclusion from features [6, 3, 1, 6, 8, 2, 3, 9, 1]\n",
+        "include: 1 -> feature_subset: [7, 1]\n",
+        "exclude: 7 -> feature subset: [1]\n",
+        "\n",
+        "Inclusion from features [6, 3, 6, 8, 2, 3, 9, 1]\n",
+        "include: 3 -> feature_subset: [1, 3]\n",
+        "exclude: None -> feature subset: [1, 3]\n",
+        "\n",
+        "Inclusion from features [6, 6, 8, 2, 3, 9, 1]\n",
+        "include: 9 -> feature_subset: [1, 3, 9]\n",
+        "exclude: None -> feature subset: [1, 3, 9]\n",
+        "\n",
+        "RESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] -> [1, 3, 9]\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sfbs\"></a></p>\n",
+      "## 6. Sequential Floating Backward Selection (SFBS)\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "Just as in the [***SFFS***](#sffs) algorithm, we have a conditional step: Here, we start with the whole feature subset and exclude features sequentially. Only if adding one of the previously excluded features back to a new ***feature subset*** improves the performance (assessed by the criterion function), we add it back in the ***Conditional Inclusion*** step."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "<hr>\n",
+      "**Input:**  \n",
+      "the set of all features,   \n",
+      "- $Y = {y_1, y_2, ..., y_d}$  \n",
+      "\n",
+      "The ***SFBS*** algorithm takes the whole feature set as input, if our feature space consists of, e.g. 10, if our feature space consists of 10 dimensions ($d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Output:**  \n",
+      "a subset of features,  \n",
+      "- $X_k = {x_j \\; | \\; j = 1, 2, ..., k; x_j \u2208 Y}, \\quad where \\; k = (0, 1, 2, ..., d)$   \n",
+      "\n",
+      "The returned output of the algorithm is a subset of the feature space of a specified size. E.g., a subset of 5 features from a 10-dimensional feature space ($k = 5,\\; d = 10$).\n",
+      "<br><br>\n",
+      "\n",
+      "**Initialization:**  \n",
+      "- $X_0 = Y, \\quad k = d$\n",
+      "\n",
+      "We initialize the algorithm with the given feature set so that the $k = d$ (where $k$ has the size of the feature set $d$)\n",
+      "<br><br>\n",
+      "\n",
+      "**Step 1 (Exclusion):**  \n",
+      "<br> \n",
+      "- $x^- = arg max J(x_k - x), \\quad where \\; x \u2208 X_k$  \n",
+      "- $X_k-1 = X_k - x^-$  \n",
+      "- $k = k - 1$    \n",
+      "- $Go \\;to \\;Step 2$    \n",
+      "\n",
+      "<br> <br> \n",
+      "**Step 2 (Conditional Inclusion):**  \n",
+      "<br> \n",
+      "- $x^+ = arg max J(x_k + x), \\quad where \\; x \u2208 Y - X_k$    \n",
+      "- $if J(x_k + x) > J(x_k + x):$      \n",
+      "     - $X_k+1 = X_k + x^+$    \n",
+      "     - $k = k + 1$    \n",
+      "- $Go\\; to\\; Step 1$  \n",
+      "\n",
+      "In step 1, we exclude the feature from the ***feature space*** that yields the best performance increase of the  ***feature subset*** (assessed by the ***criterion function***). Then, we go over to step 2.  \n",
+      "In step 2, we only include one of the removed features if the resulting subset would gain an increase in performance. We go back to step 1.   \n",
+      "Steps 1 and 2 are reapeated until the **Termination** criterion is reached.\n",
+      "<br><br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"sfbs_code\"></a></p>\n",
+      "##SFBS Code\n",
+      "\n",
+      "[[back to top]](#sections)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Sebastian Raschka\n",
+      "# last updated: 03/29/2014 \n",
+      "# Sequential Floating Backward Selection (SFBS)\n",
+      "\n",
+      "from copy import deepcopy\n",
+      "\n",
+      "def seq_float_backw_select(features, max_k, criterion_func, print_steps=False):\n",
+      "    \"\"\"\n",
+      "    Implementation of Sequential Floating Backward Selection.\n",
+      "    \n",
+      "    Keyword Arguments:\n",
+      "        features (list): The feature space as a list of features.\n",
+      "        max_k: Termination criterion; the size of the returned feature subset.\n",
+      "        criterion_func (function): Function that is used to evaluate the\n",
+      "            performance of the feature subset.\n",
+      "        print_steps (bool): Prints the algorithm procedure if True.\n",
+      "    \n",
+      "    Returns the selected feature subset, a list of features of length max_k.\n",
+      "\n",
+      "    \"\"\"\n",
+      "\n",
+      "    # Initialization\n",
+      "    feat_sub = deepcopy(features)\n",
+      "    k = len(feat_sub)\n",
+      "    i = 0\n",
+      "    excluded_features = []\n",
+      "    \n",
+      "    while True:\n",
+      "        \n",
+      "        # Termination condition\n",
+      "        k = len(feat_sub)\n",
+      "        if k == max_k:\n",
+      "            break\n",
+      "        \n",
+      "        # Step 1: Exclusion\n",
+      "        if print_steps:\n",
+      "            print('\\nExclusion from feature subset', feat_sub)\n",
+      "        worst_feat = len(feat_sub)-1\n",
+      "        worst_feat_val = feat_sub[worst_feat]\n",
+      "        crit_func_max = criterion_func(feat_sub[:-1]) \n",
+      "\n",
+      "        for i in reversed(range(0,len(feat_sub)-1)):\n",
+      "            crit_func_eval = criterion_func(feat_sub[:i] + feat_sub[i+1:])\n",
+      "            if crit_func_eval > crit_func_max:\n",
+      "                worst_feat, crit_func_max = i, crit_func_eval\n",
+      "                worst_feat_val = feat_sub[worst_feat]\n",
+      "        excluded_features.append(feat_sub[worst_feat])\n",
+      "        del feat_sub[worst_feat]\n",
+      "        if print_steps:\n",
+      "            print('exclude: {} -> feature subset: {}'.format(worst_feat_val, feat_sub))\n",
+      "       \n",
+      "        # Step 2: Conditional Inclusion\n",
+      "        if len(excluded_features) > 0 and len(feat_sub) != max_k:\n",
+      "            best_feat = None\n",
+      "            best_feat_val = None\n",
+      "            crit_func_max = criterion_func(feat_sub)\n",
+      "            for i in range(len(excluded_features)):\n",
+      "                crit_func_eval = criterion_func(feat_sub + [excluded_features[i]])\n",
+      "                if crit_func_eval > crit_func_max:\n",
+      "                    best_feat, crit_func_max = i, crit_func_eval\n",
+      "                    best_feat_val = excluded_features[best_feat]\n",
+      "            if best_feat:\n",
+      "                feat_sub.append(excluded_features[best_feat])\n",
+      "                del excluded_features[best_feat]\n",
+      "            if print_steps:\n",
+      "                    print('include: {} -> feature subset: {}'.\\\n",
+      "                          format(best_feat_val, feat_sub))\n",
+      "    \n",
+      "    return feat_sub"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<p><a name=\"example_sfbs\"></a></p>\n",
+      "### Example SFBS:\n",
+      "\n",
+      "[[back to top]](#sections)\n",
+      "\n",
+      "Note that the ***Inclusion*** step in the ***Sequential Floating Backward Selection*** algorithm is ***conditional*** - a feature is only added back if the criterion function asses a better performance after its inclusion in the ***feature subset***.  \n",
+      "Therefore, we have to be a little bit careful about the ***criterion function***: If we used our [`simple_criterion_func()`](#crit_func\"), which returns the integer sum of a subset, we would trigger an infinite loop and never reach the termination criterion - assuming that our feature space consists of positive integers. The reason is that the [`simple_criterion_func()`](#crit_func\") would always return a larger sum if we include a positive integer (our feature) to the ***feature subset***, thus let us use our [second simple criterion function](#random_crit_func), which contains a (pseudo) random integer between -15 and 15 to the returned sum.  \n",
+      "In order to reduce the number of iterations, we set the number of desired features for the ***feature subset*** (via the argument `max_k`) to 7."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def example_seq_float_backw_select():\n",
+      "    ex_features = [6,3,1,6,8,2,3,7,9,1]\n",
+      "    res_seq_flbackw = seq_float_backw_select(features=ex_features, max_k=7,\\\n",
+      "                                 criterion_func=simple_rand_crit_func, print_steps=True)  \n",
+      "    return res_seq_flbackw\n",
+      "    \n",
+      "# Run example\n",
+      "res_seq_flbackw = example_seq_float_backw_select()\n",
+      "print('\\nRESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] ->', res_seq_flbackw)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Exclusion from feature subset [6, 3, 1, 6, 8, 2, 3, 7, 9, 1]\n",
+        "exclude: 2 -> feature subset: [6, 3, 1, 6, 8, 3, 7, 9, 1]\n",
+        "include: None -> feature subset: [6, 3, 1, 6, 8, 3, 7, 9, 1]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 1, 6, 8, 3, 7, 9, 1]\n",
+        "exclude: 3 -> feature subset: [6, 3, 1, 6, 8, 7, 9, 1]\n",
+        "include: 3 -> feature subset: [6, 3, 1, 6, 8, 7, 9, 1, 3]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 1, 6, 8, 7, 9, 1, 3]\n",
+        "exclude: 3 -> feature subset: [6, 3, 1, 6, 8, 7, 9, 1]\n",
+        "include: None -> feature subset: [6, 3, 1, 6, 8, 7, 9, 1]\n",
+        "\n",
+        "Exclusion from feature subset [6, 3, 1, 6, 8, 7, 9, 1]\n",
+        "exclude: 6 -> feature subset: [3, 1, 6, 8, 7, 9, 1]\n",
+        "\n",
+        "RESULT: [6, 3, 1, 6, 8, 2, 3, 7, 9, 1] -> [3, 1, 6, 8, 7, 9, 1]\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## 7. Genetic Algorithm (GA)\n",
+      "\n",
+      "*to be continued ...*"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/funstuff/happy_mothers_day.ipynb b/other/happy_mothers_day.ipynb
similarity index 100%
rename from funstuff/happy_mothers_day.ipynb
rename to other/happy_mothers_day.ipynb
diff --git a/not_so_obvious_python_stuff.ipynb b/tutorials/not_so_obvious_python_stuff.ipynb
similarity index 99%
rename from not_so_obvious_python_stuff.ipynb
rename to tutorials/not_so_obvious_python_stuff.ipynb
index c36c0dc..c2e2948 100644
--- a/not_so_obvious_python_stuff.ipynb
+++ b/tutorials/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b37510e2c48c9811f7baabeff25c48a186e0e5f154b5d341f4eda6701df705e3"
+  "signature": "sha256:d87105a74c8f25016f90bdec495a890a988277a3f51e0589febbbac87720b033"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -15,7 +15,7 @@
       "[Sebastian Raschka](http://sebastianraschka.com)  \n",
       "last updated: 05/24/2014 ([Changelog](#changelog))\n",
       "\n",
-      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)  \n",
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
       "\n"
      ]
diff --git a/sqlite3_howto/LICENSE b/tutorials/sqlite3_howto/LICENSE
similarity index 100%
rename from sqlite3_howto/LICENSE
rename to tutorials/sqlite3_howto/LICENSE
diff --git a/sqlite3_howto/README.md b/tutorials/sqlite3_howto/README.md
similarity index 95%
rename from sqlite3_howto/README.md
rename to tutorials/sqlite3_howto/README.md
index 9549f63..7ef88e9 100644
--- a/sqlite3_howto/README.md
+++ b/tutorials/sqlite3_howto/README.md
@@ -6,7 +6,7 @@ _\-- written by Sebastian Raschka_ on March 7, 2014
   
   
 
-![sqlite_python_logo.png](../Images/sqlite_python_logo.png)
+<img src="../../Images/sqlite_python_logo.png" alt="">
 
   
   
@@ -29,7 +29,7 @@ _\-- written by Sebastian Raschka_ on March 7, 2014
 • Conclusion  
 
 The complete Python code that I am using in this tutorial can be downloaded
-from my GitHub repository: <https://github.com/rasbt/python_sqlite_code>  
+from my GitHub repository: [https://github.com/rasbt/python_reference/tutorials/sqlite3_howto](https://github.com/rasbt/python_reference/tutorials/sqlite3_howto)  
   
 
 * * *
@@ -123,8 +123,7 @@ there is more information about PRIMARY KEYs further down in this section).
     conn.close()
     
 
-Download the script: [create_new_db.py](https://raw.github.com/rasbt/python_sq
-lite_code/master/code/create_new_db.py)
+Download the script: [create_new_db.py](https://raw.github.com/rasbt/python_reference/master/tutorials/code/create_new_db.py)
 
 * * *
 
@@ -135,7 +134,7 @@ lite_code/master/code/create_new_db.py)
   
   
 
-![1_sqlite3_init_db.png](../Images/1_sqlite3_init_db.png)  
+![1_sqlite3_init_db.png](../../Images/1_sqlite3_init_db.png)  
   
 Using the code above, we created a new `.sqlite` database file with 2 tables.
 Each table consists of currently one column only, which is of type INTEGER.
@@ -208,12 +207,11 @@ Let's have a look at some code:
     conn.close()
     
 
-Download the script: [add_new_column.py](https://raw.github.com/rasbt/python_s
-qlite_code/master/code/add_new_column.py)
+Download the script: [add_new_column.py](https://raw.github.com/rasbt/python_reference/master/tutorials/code/add_new_column.py)
 
   
   
-![2_sqlite3_add_col.png](../Images/2_sqlite3_add_col.png)  
+![2_sqlite3_add_col.png](../../Images/2_sqlite3_add_col.png)  
   
 
 We just added 2 more columns (`my_2nd_column` and `my_3rd_column`) to
@@ -275,7 +273,7 @@ But let us first have a look at the example code:
 Download the script: [update_or_insert_records.py](https://raw.github.com/rasb
 t/python_sqlite_code/master/code/update_or_insert_records.py)  
   
-![3_sqlite3_insert_update.png](../Images/3_sqlite3_insert_update.png)  
+![3_sqlite3_insert_update.png](../../Images/3_sqlite3_insert_update.png)  
   
 Both A) `INSERT` and B) `INSERT OR IGNORE` have in common that they append new
 rows to the database if a given PRIMARY KEY does not exist in the database
@@ -340,7 +338,7 @@ drop the index, which is also shown in the code below.
 Download the script: [create_unique_index.py](https://raw.github.com/rasbt/pyt
 hon_sqlite_code/master/code/create_unique_index.py)  
   
-![4_sqlite3_unique_index.png](../Images/4_sqlite3_unique_index.png)  
+![4_sqlite3_unique_index.png](../../Images/4_sqlite3_unique_index.png)  
   
 
   
@@ -407,15 +405,14 @@ Download the script: [selecting_entries.py](https://raw.github.com/rasbt/pytho
 n_sqlite_code/master/code/selecting_entries.py)  
   
 
-![4_sqlite3_unique_index.png](../Images/4_sqlite3_unique_index.png)  
+![4_sqlite3_unique_index.png](../../Images/4_sqlite3_unique_index.png)  
   
 if we use the `.fetchall()` method, we return a list of tuples from the
 database query, where each tuple represents one row entry. The print output
 for the 5 different cases shown in the code above would look like this (note
 that we only have a table with 1 row here):  
   
-![6_sqlite3_print_selecting_rows.png](../Images/6_sqlite3_print_selecting_rows
-.png)  
+![6_sqlite3_print_selecting_rows.png](../../Images/6_sqlite3_print_selecting_rows.png)  
   
 
   
@@ -550,7 +547,7 @@ lite_code/master/code/date_time_ops.py)
 
   
   
-![5_sqlite3_date_time.png](../Images/5_sqlite3_date_time.png)  
+![5_sqlite3_date_time.png](../../Images/5_sqlite3_date_time.png)  
   
 
 Some of the really convenient functions that return the current time and date
@@ -585,7 +582,7 @@ and entries that are older than 1 day via
 Note that we don't have to provide the complete time stamps here, the same
 syntax applies to simple dates or simple times only, too.  
   
-![5_sqlite3_date_time_2.png](../Images/5_sqlite3_date_time_2.png)  
+![5_sqlite3_date_time_2.png](../../Images/5_sqlite3_date_time_2.png)  
   
 
   
@@ -651,7 +648,7 @@ column names):
 Download the script: [get_columnnames.py](https://raw.github.com/rasbt/python_
 sqlite_code/master/code/get_columnnames.py)  
   
-![7_sqlite3_get_colnames_1.png](../Images/7_sqlite3_get_colnames_1.png)  
+![7_sqlite3_get_colnames_1.png](../../Images/7_sqlite3_get_colnames_1.png)  
   
 Since we haven't created a PRIMARY KEY column for `my_table_3`, SQLite
 automatically provides an indexed `rowid` column with unique ascending integer
@@ -669,7 +666,7 @@ grab the 2nd value in each tuple of the returned list, which can be done by
 after the `PRAGMA TABLE_INFO()` call. If we would print the contents of the
 variable `names` now, the output would look like this:  
   
-![7_sqlite3_get_colnames_2.png](../Images/7_sqlite3_get_colnames_2.png)  
+![7_sqlite3_get_colnames_2.png](../../Images/7_sqlite3_get_colnames_2.png)  
   
 
   
@@ -757,9 +754,9 @@ convenient script to print a nice overview of SQLite database tables:
 Download the script: [print_db_info.py](https://raw.github.com/rasbt/python_sq
 lite_code/master/code/print_db_info.py)  
   
-![8_sqlite3_print_db_info_1.png](../Images/8_sqlite3_print_db_info_1.png)  
+![8_sqlite3_print_db_info_1.png](../../Images/8_sqlite3_print_db_info_1.png)  
   
-![8_sqlite3_print_db_info_2.png](../Images/8_sqlite3_print_db_info_2.png)
+![8_sqlite3_print_db_info_2.png](../../Images/8_sqlite3_print_db_info_2.png)
 
   
 
diff --git a/sqlite3_howto/code/add_new_column.py b/tutorials/sqlite3_howto/code/add_new_column.py
similarity index 100%
rename from sqlite3_howto/code/add_new_column.py
rename to tutorials/sqlite3_howto/code/add_new_column.py
diff --git a/sqlite3_howto/code/create_new_db.py b/tutorials/sqlite3_howto/code/create_new_db.py
similarity index 100%
rename from sqlite3_howto/code/create_new_db.py
rename to tutorials/sqlite3_howto/code/create_new_db.py
diff --git a/sqlite3_howto/code/create_unique_index.py b/tutorials/sqlite3_howto/code/create_unique_index.py
similarity index 100%
rename from sqlite3_howto/code/create_unique_index.py
rename to tutorials/sqlite3_howto/code/create_unique_index.py
diff --git a/sqlite3_howto/code/date_time_ops.py b/tutorials/sqlite3_howto/code/date_time_ops.py
similarity index 100%
rename from sqlite3_howto/code/date_time_ops.py
rename to tutorials/sqlite3_howto/code/date_time_ops.py
diff --git a/sqlite3_howto/code/get_columnnames.py b/tutorials/sqlite3_howto/code/get_columnnames.py
similarity index 100%
rename from sqlite3_howto/code/get_columnnames.py
rename to tutorials/sqlite3_howto/code/get_columnnames.py
diff --git a/sqlite3_howto/code/print_db_info.py b/tutorials/sqlite3_howto/code/print_db_info.py
similarity index 100%
rename from sqlite3_howto/code/print_db_info.py
rename to tutorials/sqlite3_howto/code/print_db_info.py
diff --git a/sqlite3_howto/code/selecting_entries.py b/tutorials/sqlite3_howto/code/selecting_entries.py
similarity index 100%
rename from sqlite3_howto/code/selecting_entries.py
rename to tutorials/sqlite3_howto/code/selecting_entries.py
diff --git a/sqlite3_howto/code/update_or_insert_records.py b/tutorials/sqlite3_howto/code/update_or_insert_records.py
similarity index 100%
rename from sqlite3_howto/code/update_or_insert_records.py
rename to tutorials/sqlite3_howto/code/update_or_insert_records.py
diff --git a/sqlite3_howto/code/updating_rows.py b/tutorials/sqlite3_howto/code/updating_rows.py
similarity index 100%
rename from sqlite3_howto/code/updating_rows.py
rename to tutorials/sqlite3_howto/code/updating_rows.py
diff --git a/sqlite3_howto/code/write_from_sqlite.py b/tutorials/sqlite3_howto/code/write_from_sqlite.py
similarity index 100%
rename from sqlite3_howto/code/write_from_sqlite.py
rename to tutorials/sqlite3_howto/code/write_from_sqlite.py

From 0a8a955016641d49107c777082d0eb05ebc6fb63 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 3 Jun 2014 18:49:29 -0400
Subject: [PATCH 087/248] added useful links

---
 .../markdown_syntax_highlighting/README.md    | 33 +++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/tutorials/markdown_syntax_highlighting/README.md b/tutorials/markdown_syntax_highlighting/README.md
index 75d9e29..69024e9 100644
--- a/tutorials/markdown_syntax_highlighting/README.md
+++ b/tutorials/markdown_syntax_highlighting/README.md
@@ -92,7 +92,26 @@ draw the reader's attention:
 </pre>  
 </div>
 
+<br>
+<br>
+
+Note that the syntax highlighting does not only work for Python, but other programming languages.
+
+So in the case of C++, for example:
+
+	:::c++
+	#include <iostream>
  
+	int main()
+	{
+	    std::cout << "Hello, world!" << std::endl;
+ 	   return 0;
+	}
+    
+
+Since the CodeHilite extension in Python-Markdown uses Pygments, every programming language that is []listed here](http://pygments.org/languages/) currently has support for syntax highlighting.
+
+
 <br>
 <br> 
  
@@ -173,4 +192,18 @@ If we open our [**final.html**](https://github.com/rasbt/python_reference/blob/m
 
 ![](./images/mk_syntax_final_html.png)
 
+<br>
+<br>
+
+## Useful links:
+
+
+- [Python Markdown package documentation](http://pythonhosted.org//Markdown/)
+
+- [The CodeHilite documentation](https://pythonhosted.org/Markdown/extensions/code_hilite.html)
+
+- [pygments.org](http://pygments.org)
+
+- [languages supported](http://pygments.org/languages/) by Pygments
+
 

From fbe3659fbf3f0be8e6af4491541a60006c64686d Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 3 Jun 2014 18:51:27 -0400
Subject: [PATCH 088/248] added useful links

---
 tutorials/markdown_syntax_highlighting/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/markdown_syntax_highlighting/README.md b/tutorials/markdown_syntax_highlighting/README.md
index 69024e9..0148ea1 100644
--- a/tutorials/markdown_syntax_highlighting/README.md
+++ b/tutorials/markdown_syntax_highlighting/README.md
@@ -109,7 +109,7 @@ So in the case of C++, for example:
 	}
     
 
-Since the CodeHilite extension in Python-Markdown uses Pygments, every programming language that is []listed here](http://pygments.org/languages/) currently has support for syntax highlighting.
+Since the CodeHilite extension in Python-Markdown uses Pygments, every programming language that is [listed here](http://pygments.org/languages/) currently has support for syntax highlighting.
 
 
 <br>

From d2c0bbbef3434d44a6774b1219dc3090d484fe42 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 3 Jun 2014 22:33:59 -0400
Subject: [PATCH 089/248] multiple list sorting

---
 .../sorting_multiple_lists_by_col.py          | 39 +++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 howtos_as_py_files/sorting_multiple_lists_by_col.py

diff --git a/howtos_as_py_files/sorting_multiple_lists_by_col.py b/howtos_as_py_files/sorting_multiple_lists_by_col.py
new file mode 100644
index 0000000..7c534cb
--- /dev/null
+++ b/howtos_as_py_files/sorting_multiple_lists_by_col.py
@@ -0,0 +1,39 @@
+# Sebastian Raschka 2014
+
+"""
+You have 3 lists that you want to sort "relative" to each other,
+for example, picturing each list as a row in a 3x3 matrix: sort it by columns
+
+########################
+If the input lists are
+########################
+
+ list1 = ['c','b','a']
+ list2 = [6,5,4]
+ list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']
+
+########################
+the desired outcome is:
+########################
+
+ ['a', 'b', 'c'] 
+ [4, 5, 6] 
+ ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']
+
+########################
+and NOT:
+########################
+
+ ['a', 'b', 'c'] 
+ [4, 5, 6] 
+ ['another_val-b', 'some-val-associated-with-c', 'z_another_third_val-a']
+
+
+"""
+
+list1 = ['c','b','a']
+list2 = [6,5,4]
+list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']
+
+
+list1, list2, list3 = zip(*sorted(zip(list1, list2, list3)))

From 120c5e1d967f3a04b12d0853dada64c92e85af40 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 5 Jun 2014 01:27:18 -0400
Subject: [PATCH 090/248] imgs for matcheat

---
 Images/matcheat_julia_benchmark.png | Bin 0 -> 48090 bytes
 Images/mathcheat_R_logo.png         | Bin 0 -> 44284 bytes
 Images/mathcheat_julia_logo.png     | Bin 0 -> 28422 bytes
 Images/mathcheat_matlab_logo.png    | Bin 0 -> 40206 bytes
 Images/mathcheat_numpy_logo.png     | Bin 0 -> 8565 bytes
 5 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Images/matcheat_julia_benchmark.png
 create mode 100644 Images/mathcheat_R_logo.png
 create mode 100644 Images/mathcheat_julia_logo.png
 create mode 100644 Images/mathcheat_matlab_logo.png
 create mode 100644 Images/mathcheat_numpy_logo.png

diff --git a/Images/matcheat_julia_benchmark.png b/Images/matcheat_julia_benchmark.png
new file mode 100644
index 0000000000000000000000000000000000000000..2af6e460fc2f2b9efe51da017ee8e82d1af1483d
GIT binary patch
literal 48090
zcmb5WWl&ws5-yBv++Bh@1PksS+}+*XT>}KS;O@cQHE4j~4#C~s{o5q(d(Nr4w{F$P
zuf1l?^mO<1Oh4Vzi!gavF$7p#SP&2p1PO5wMGz3ME)WpVI%qIpOC=hcB=8~3LReT{
zLRgqc-qFs~!rBA`gbJe~j#mm?5c%s`#3U6AHkmnW9K>hZ)9uL5()B}7DWRW3VJ+QS
zGQwhGwj7Bc!H=!$k!{d#7(&@FlMHi%iJ-gJhyY+D8yOwKiVz>5T=y2;%=k?q?Nh@m
zlb1n$VN4Iw4=nD^v0brj1S64#8cHizdD6yf6vh(F`3811>5}`-%RV0Y9OO7SFjqUW
zp|JK7g!r1+>6VrFGm3vnt12<sM6D@4{3_usNy{poc}42bm4k%J&WVNhx}8zC*slbG
zeY6NzhedsILL9fJ=V<TJpri2!mq3XaBMkM=RsI&d6N+A^_QsJY<??HqO_wfPby1!4
ztmsoT`Zs-OVNn38;rB+d2e7cxPP=2waEb^86V{~Sq?^_f91<zD8I8E$ZO+tzi!2AE
z`dU|ppvmM))eZnjyug})+HfX_+nvC9NtsM0cyC14NnIs&xD(;1(9+9%Ya=sZ?G}U{
z-u_#4`5enSCnT3x(v6RaMy8c#w&$(<BPf}h#d@w(dpvMaGA)$Voz-Qexs2>==nahR
z3{B|WZR~-I1_Hw4&ISBwW8!Q;<Zfea>%`^GOY)}&7x4FcGXn|HpDxZ;yd>%}@<hUR
zjwVE`^eps@Bz&+$L_|D}#-?0~BBK8c2R`wVm^(Y$b1^Wuxw+B1G1J>QnlXIh<m6;v
zWMW`qq67AzbMmltHgKo2bt3&s<X<`>CQe3<7WU2-cD6+CbPWvcT%37HNZu#<&);7@
zP24U1JCm)`KVks|WO#qW@QI$0;Xky2LwVj?x#TU}O{~>LENo0{oq%)ju`{yp{OSLH
zy!r2p|1(nKzav?gS^szB|GfEUBoD*80RJP<Uv2$q1*(e=mWSa#s^^2HgJh`&0TBR^
z5D`>%2R+V&(~h5g>Kbo!V{h#C2L%^|Xy4J9Zr?7HXQq=Hg`J-93pX&e#A-NJbDorz
znx<uHm|-cQJx_9DVPz;G4=<*YHtzBV4YWC4xwU=Dw((~~Zh?388WS6M%DT(g;~Bqe
z_gXXClVnbnh6Y0h0TTd$1_31k0pJSzucq|HV*Y*d2LZk^1zQ*B@&N(-c>wlfNdw_P
zjuM5$`L_WC)ShUa00itmG-N>VLPbdf;s0&$2M(}D0s#fm|1&0v0t8VcKTZ(x-v$8?
zJm_Yq|IiB%MFWA#n;awp|1W2VfMxLi&=bgq@duwYH;x7UFP~6gr~jEiAZHTL6=2T7
zH2BYqz>)sopm*=${3SAB4wQ-o7svSD1`yx?f3JVE0OZ7+1t=A66g(3M=)YV7)xZk^
z_?JtTH2+}^WHsN=r{j68s79p!=}0HwZw{Jzi$yK<dOR9cMDks(9;HIQ*a1FDv@Xo8
zQ7JzzZ(0Ye0fzW_9Aa5AUqP;jIkO_q>d1)6AdB8U!jL>l6g+dR*yc-Je~L6D?kL1{
z-?B6XY1A(28pXW3z=mDC{Ge0ghpG4*hkz#B)YpNG!V<%vUq_`>^vU+VP(t~i^m&gI
z*IG8=AxU4uQG?)kGG{G2q=;sZF0@jA>Nj_3iR6E31t?~Y^%*l|ylL67q*1}vsMDYK
zyCp&;h~~RhQhpz>+PWTRoh{UuZldEmAMG0(T>cI6Uv=Aqfytf<40fho4^3;iA)v#$
zSoS$V=&iyWqAP=d{iEYeK*sL?@h4)o8s*gF_KoyJDK?2X>#bVWrSUkrF#znp9YFvD
z0$n9fdtf279#uXqy7@+qOPCaT;s;#K^TL9%6&K*<y(a&zf~`<jiQYh-EO<VbhG0Cz
zh2F~PtNTpvhAkMu$iD>Fe*y6J1>`M*pAS3F5c9A$zq_(BD@&kuuGq`w2@bl6SRXg*
zx^I3~VJuD%|K68O1Ooam`Dn^@MyM|dcrV-eYYeL+=vr!SIR)8KX)e|yQ$808#=qty
zYLqU2Jo%_H7?2aM&glG*qqt8jQt^{<uS}R`05_P!JkltvpC(x6%;0DaDy0vaG4DGP
zXcjnWLs!JQpW8Y^@lMaU)qo0L4EFEEgO<II|JWEQ?e2TcfXO{GkBBGyMQ$x}lc?{2
z)k1T>(Pk|(4VND=TN6A)Z%0~LG<DuW5E5Kh<>w;Sx1Mp7@;%;;%$vbYuI8U*h@{RK
z1(;{u5XG&(bSynSe#pGxc9vs!cJ9%mN^X|YDLT~2Sv^|;C9vOSgzD*0EfXGWtyKTH
z=!Xn@UP?4}#4g%?qK1=YudpL9qk*;HqR*tGPfQ)o1hgfV?h+Cmb7JaD*)vq}n(Qqa
zI0=P>N%GjuCVbtakq19Eb*)Qnu#z&K9I9Uscn(FL1x@u#V@y%Z)mlM|GvUd!sc|$`
z7Q=#k|J{GaV2G|n&cDKM$or8H1gNeSAR^WTc67KRNOaxnqh%u~KEUDx{Aa&Dq1dap
zaZyX?GMVXEQVVHejnS)D?*=-2NY;tbU<uA3&`C?MXSO{3sYa{Bd~8M|4hHsag8_5I
z_H7&y!`t;JfCgCy`EX(A3Hy*dVc1T&+yd?I0^YfydT;9?;mhvqvRI4KQo}p7XGXPX
zpW7y|p&K-Nw;VD<SA3KmN=ECQwds%eTsai#UG5{KA_KaN;IxgI>Ad)IR5KROcNRx!
zd^mkuc+im_7Psv9aX3fl*^yVJhX<fsx9Z`fo}eA<u|(-eIY?@n16GAA+L1v4e~yu8
zvh%sP9jn)eZuXS52CFA}q=t{*lOx|;#sVVaqi0}#*?s~W+ZCu!$tA-6d(vrs>-te^
zwWhs{G9gN}o^CetY2P_+kW&1(iOj=93KOmtUle$jhUA8P`J=p3k@fq`s{kQ>wsegZ
zDt^7QbVmtQ3)vfS4p$`L0oW|Y`vv3AW`#nNKbWCtJ;~v^;-YdEZ6UZ-aJ4_F7r9s$
zs0XOT!o2f)&H=_7*lNz|`Jqn55+OpChb+84EJTT&2m~?cgEC19$a^Pyo&cCkv4#z(
zq08`6<;jDR+MY`Y@YgCDNW|qZpdUC@PS=NfH-oM?fNyFun?L<GQ%uZ3Nmxhprs+a7
z?&w$h1s<_OfJqZJXb2tItRZHA{@=Z64<8`wyOC^kd)n%IF4--^K(SZj3J%$LaDuIp
zGm}IK^eMy4Us3-kHcLc*N?2~wFUz<l<TtDiHi71aKHERW7qE*A^e&v&L=690BN91K
zf)G`n6ohvPfV|~!zPk@Z-a)v38oGpm4re151IZK|^e;E&h(Q04CX93m_D{pPJW!$T
z=KX<-e@{OUK$<Yn5AIF`w*T9}lK@n}|9@8W86y8nMLrPZ<<c(_9Ljvr4JF>>cx9OJ
zR3lzonG_<U84lDuz&<$*!2L&xXo@Zl&!Brx{EBiw1B?&};o}X<?tnu*UP=DVmrJc6
zATM-ZFoOQ@&l3ewQ5;%xWj51@0DCU1jkxJSTm$W(-Z#$8gAg3D00-2MGE2%IGceZx
zYrX=5@rm_U%g2t3b=`ujC@&W!%V>rGYGgSNw;TFhL;k%m`V0Yrb+cdeNg%-kzzv4`
z@yEIY>_apGAO5h1NU0ACE#Mhgfm?uy1J4*eg&Coh5eUA4ISVZc@~%@nnXdam5M#!?
zyl=SS=*hTH2%>nhQQ{m9aPOA;ne_|GAH9a4>DLr%d=l6FatSM612z;fxuP9kMR;?`
z5>f^UTMqzLhsq-_{v!Q{DN0nv+v7sLSDcqVn8y<>3buiTvgC^kDmlOg4jXWCoEv!6
zKMK(d_8-zScDGfm$VIMDWJ8~71?0fxyLbp&{JWUTUw{g<Rfy6D{A*WpKr<3Ky8Jrz
zM?M*Hf%svIR2-JV+7d7#LgZ>nP;77mxiliU_nka}3NUv&MdtR+GchG$`(V-VGvrBO
zx`>qE2l+3sRDqlTKn_G+Kn@YkJMpeh0CNb45eB}ZL~8kD@tIuw!-XvRA^l3t18kcd
zWg>YAprJ{ko@2dJUQYmEd%2P_(8Hpp&IkPr=ojh+t*=#VzbRBYgp>4u1uha#;rDJ?
zpgyz{Kwb`2mhelf$+g)|B*P;#0hVo0@2i+k&<zXhu$&B1C|0jIZiyT9eZH+~R6i9C
zx!6Ui)qgC9<PT$k&?&#0#10*`0wpqQj3}4HIiSEQ#DUzX1|fU~eP2afxh_UiTAJAW
zTFW~2i?1q&j_TyC7qrA7yucmxA9d)vf&Dz?Ew-3eum%10Yvf6J1HY7o>oDqpLQk(P
z83HUop!Xf|*{jB){*e(I>@in(&7)G-exINAEg!$P%7?S1>LRx?S^+{50xS`rPh9dp
z3X!Imj<uPby5y-`DC3>h%Acg?zdA9|_>Hus%2C*vlTd{0j|nYTVo{9-q>e>?zA@yo
z-~|nj0ei2_mQIa+8Q9|;KNSN!cP2j5fi<iIzMo?XG5Qcd9oR~(?^$_qu8th;q5uN-
zdRJY*@j?%k*P9z)IQ+06Jr3qa`CmLVqT91lZXfH5qbp>fe}RJmjff*bK*2*IbZ|`+
zf+BuUWp_!WhZ@;j25f8<%weZ3HDW%8T#L;YWyxo0Y9YwEmi0hD;~#quy@vZZzrgqC
zT+k_|Zx(U{3G2=TO6f*a-0qBXh)*jbzI;{#9Tc9>xJDvZ$y1FFlu3^0-12Pb7fF<?
zo85{;q9o8fGJn5l6tVs&C=e-B<gD<_NzsAk@W6un<av{ef-Wd;{JRrBwfL<pZXwwp
z1OT+?b0V-$K6=u0?Keih=mV;-A|&Lwgbm^hX9vjxg`PNwgSWT0w+3R8lhc#Otf#+^
zPRsGHl%SQT90XyQKqY1@2XtW?o^Aa4GJy4cv-i`PGw544Vj?05kh1+JFe|Yi{9KOv
zXY)wLAFtX%oHL%Un_r1}GbX;Sw7KcEyHAeKSJ|j*1$O69saP*m8{8f*W^xPLMWCsw
zIjQ7r;QSm-oTPc6?oVtQpdCr8rgfO?Dh0!UWlfe^=QSxc%(wy>U)ptC94passQ`lF
zPQr7Lv%|PCk8)Kjcb-b8LsgxR9jgLG)7bEoXCGP0$|v`mwN+hqGOw_kwzn<<$KEQ~
zab?g=^lLKS#BeJ;pLlW{9UWDxbn#dDGDbV^nm^K0f1f@$Yrm4cvZIU=J=^NF>corH
zX8@CZ)mGB`{p0T22+wH%vMcV~2kQ7Lnqw2LLC|+200DhM5toz0f(@SSfTH~{Flfxg
zL)PQF<4<NryuOSGNWC~H(#srSPq8SOlG7F4ya3NZ>(w~Lp8$W~2J%t<s%3@qzg_VL
z7_s3qW!312@mgU(q1dVv{s;PMV!2Q<klVZ-$a9G<olXwb6(MdrcvkANU5qYJKPc$f
zQs_aaRIisUWc>$1H)93F#>W0=@LO6apjX>^{hpIUl<v;q0uF}gL?k{mDaZHtvN<Y!
zaUG@_ojf<DoiXw0b?|p|kn!kNUDjwM_kH)od2vfvk!h&}_#<J=fCKot{+}qbix=kl
zc=4($030G1A^<c%74F8fLns%U&%4<R_xM7U(-oOK@%(vTPqbX3?*!KP_wm4<2i@}Z
zEZj!3qVZ6+j80n1<KWM%=f~3Gf&zHrd2E1YHjb?GC>I_^?C%;$Mn$akFes7_QTHKv
zaK+(AkXA>qfj@XJmXzh?tDW``qeKJ6gw|0O3kwT7yzWvNb2rrtuYRa?cJhs4fuSCM
z?lPc5#ssf#)+%?DrVBPhKswOfXo7wI44rCbJ4a~F0y)xCOsJ4w*+UyCx?w49m=ZjR
zEEy#UUdM<VTt*yr!U_kTj6tw^+Htq~bhqYr5ebhOF`FwKGB`N+b$=q3#T`>r_?uvw
zD(O1VE2X6-Ja80=dyMA`K-(r+`mZCuFX}oj;8F97)jq5yTcCfSnZbU7xXo<V+HIJ;
z^YOaL(c*(18Zz#3<?3sMsXBtje;{&zDG(yV0sc>j%+OyY{h`VHa4J%8U)s>|4hhX{
z(-t23UpMCOkA`Z0O!SM(zj%j<d^1#}=W8=tHL3|dARPt5h$s!T>a?5@GS8NNG&J}V
z@~FR>n<{s7Kye!gcYlv$f-9H>`zHhsnWc*Auh5%eeTls^AHLyD0mT3T{Pprg>+7G8
z5v#Go0bu`5nf9I^FeL?m{@cJ413XZ()b{j0y*rh6+-~}w09b?;{7(QdP6~u*d$XMC
z@PFwF<Sf2JG&Uh*kbfGwuAn(qSY*e`z^dP|vKVT!`^9D^x7)t6r@Om*HlGh(32Xua
zKsy{b7rly34lQu5TT*#d)kceHiuN^@^nNyCv{kUf>x1b!)6q@GmfMd2GEmSaau6MC
zTdf9tY+(Nzw1pZYF%J(9b#rxneSLX-`L@QBYUUcJy|G89ZWW-=<?m?(?)NxZj?Dh<
zbY)~jI<EL<5;uw8WP{Z_EG#Tf9P|P_0QcQvi-HAi1EmGxrepH+2XDwsg%#W{ME1r8
z1_nTY4he*zvOuIW69z#7TxEMFFg(PNyyD-Yik`4~rT9pnfI}C6NVW$OIs*t<)=>al
zE{6s4M~RXqE>==fQU~y!#>U3~S3<OyN2z96L!-lV)YRQFC>-cmSbIa4Sw6QK>`izh
z;9#yKAk!Qt0MtGZAYDtqSDM7tf?a`{XIDYKxekQx6rD<6@j{l?G~yfvWP4~JUcmc;
z4+{arDU*O-!#HieCm(Q^)w-s!qgZgGg!75%16*H9Kd|9V=DN&HVgPoyc|q^Larj^a
zd;yA34+8oabu<_e0^p%dmnur{fWm8sfPO+`w`&v#AVP(1{_u`RDnz>p-ob>3{|w33
zev4{}(Hzo>ISNEspk|6?Kw23snFFwa9hS5GsaenyspABLuxAMZxp);zq-}mT|9By=
zA%2*D#DKa)(dfy%>fao@&8n9&CDf=nwF^RD4sgQcpKl?Ti1)M6_Ij@TI-&rt{d{mg
z_MAjphPXHZrT~Q=3I>$#1SaKoBkS#P7;Jdlkg`GfWau{ePf&1aP*7Vt-(g&Hp&btj
z8--!aDfoj7*%<!tZ}GBtor<ahibSKx)<eH8PF~&Jm@MwXm_5>Ve|ZY{%FY`o2y#*)
zW%0!oS%69r$a8@~Z3+gha{+D5R?<J*V-_7bi0L%1U5G4x3*5+D%W}?|owX@(<QoB-
z_H%42ft~cjx7NqP+-=Q5zd6Yr_TrRv+x%C%Tab)|#hmRZ={_U^z8#ag>m(%5%%=fb
z-@={YlC~gA8#fn9fA#l0>Yt9f?syZCG@+zuo{AGH%ItD%emu7#BV$8ecS?qnXi<-7
zv*69pIvMoA`MSw<pIg@C-nGK2S>hwLirc3IS(DBQp)&zVyEt99$=fJ$>JWrQ3u5=%
z9q_*(nC^4eLK#-i8xc#oKh2Wzc}$HKS@I}T8=NyP>~-&ta|=$Wn@%08W6r7Lykl~K
zL{H<Vq<g~3ob^cth7|r5zkYjY8U~A#--K`XVh7U~si{te*+)9}^Sh&d!$YWNd#2Zq
z+^^|dsio&Ab;)CXG&!3XrT11_YGqAi)Cx_Lb$gv$KGhR>MA<r?9(K>A_klEm_X$l{
zKPLFG;})0H-ZBDNaL-=1yb}8SUel$Cd|ywR_oMrd*7R&Kxwqdi8E^@$QPLmgZ(Y`2
zT|b9&-FkS>O)b23<1sx|optgT5wUjYVaG&UKU}b+b-Ye*sA5YaDr(;!E*bZkZXp~}
zGGrRv)!;Ye3S)`dP#}Z<J*lE7az<ESg5k^V<P$Z>$bRim*hr`ZUE!@0hiK^at@$42
zBNd5kpFA_}_V4&<Bz%nYGnHN@%-CtW>l=qzy*!O1=eBzkb(TL~^RU1BngVlHN9gx@
z0lgE3AoYVM&PJkY;dRsKN^QlHd86-pjOVHL?eDM5yQ2%$*H&e_;~YU**$-roXMLB{
z*=Cyxuy|`@8|HgS1qCirX5LxMrn}6~Z)1L+Or(E^?dFPQ`!RuKE}%Bu`fFIPPBw>q
z(lh^AwH$xmdU&3clG-NjbQ+56Okv@sxSW9VqcCHz)n{|hNCOiec}hu~D#>QC#iZB4
z;LO+Nn3t(OFCopZE1-A?eC&^B5Xg}LM0&P={c58Ip&JJ$hI=E+fePlv<h*n#wp{$$
zYgYMYA`0^BckLqsr;5Ajz}F@7RV1m_u=~t`(f$icUoM$oglr=M=8dMqs%_5kD82Lk
zLtAK_9|i;zn(n5<SZ}!(Yrda&q_MG&ql}p9!Y?S3PVY8LhmZCm{cOiB-Rv)WNRGjX
zeBXwy)=mTEL)2oPTil<WnEBlYXxUEJv*9RWvakBZOlveuYtj^*4pG3lu6jjBeH!pv
zeQ3bN&U-0jeFz3(-fp|1^=`+!9DXhreY~|nu~)cV9fbm?cylzl`?B}gH0BMz-_zv+
z`cLXKO{(i-kgEGnebUb*Y(zbM2=y+s*55df>kUtZx{7hY8_T(}DO4Vwy+A)+-F!51
zOL3v0r|-+=dR}8PCE)<6Vo_z@R-bZ<I`5x+9+c2(yoNiBD6-Z4S=4P~CB0XIS8YM}
zt-oO~cO*;H{t0;rGca^dRU5Bk`}BlVucOm<*kF02i=k<|F4`R$&4BlwnP0=-q<G{M
z_HiRn?Z^Gu@3OEx&D_17yygdB7(;_@4?H5?eXtCrznJ9i!({E1i-90*8M>m`H_U^;
zkQM<YH;+{KqY*<?=|+vayUw?rR9ES#%P|TL@8>|i0ZYXSIf|ucE4>geWczi%HG#DQ
z?#L*V`0V*)lxk5=MhQu=?Wv#3@|0Dv)^e5A+*gK-9|7a#x2<!;OYr?uJQ+^CK^Xbd
zn<S-$<q7j7emXh2T79<2uz#7lr9kEkuH3-3&5#)4@)URE;e4s>D`K}q+v#ISD|M^r
zM>^j_1*{!|77U?;Fw_HlyQ9)wWJ}hyv=NLQl5EY?PT=HL+mmd3I@HL_p|@#R?R&py
zlpiJsFL=2-4kY4ld5MKO42Xw_wJKW`bVWx;%{2KvUd6-^2vb?iiHTWf!lf77a=UDL
zHe|;U!MtNP5P)U?NTBk4)`P13ftlc(hTu<$4ZS0GizltzWeN-fg54Bnvgr&p-`z5o
z`H9$ON>Yx@0hw-aly8Qs{El_8LfZ`;XL8;KMxepJMn_>vg$fI80%!;5KkFJ$`>MEk
zxq5PEyf!r5ttk^p-pnU6bl#8Yp_dGlT%GQ!D{5WsVkD`r9ZR;L=PDM?ZcU{3>q?Ev
zuk-I`sayT}rifAUi|6v9l-Btv+iz!AT+;4TuEn#)@wmXFK1f4Rwvoy3vgqnDXH#$W
z;rcF}<2we}sB#0W*2e_XGZOSap_I+_G2O!0B?~THvXzVK3GHI%`wc@UA@mmmy&5`V
znTok{_c^}4BxF1GRL>637$T$f6O0T#a~3Z8ro5fS)Wq3?Yw7QB-bYcQXrFP^X_xfE
zWe4Y?k&4^*U5<qsIH)EpzbDP?ZeyLB!rw&UOnUdS;&#L!RXsSCqtg3597iGyB58>7
zY!*}Q;@?|-cX=(?)SOy|ahRu(^gLer(Rt4Qd>b(=t4>0&<Up}o)UAZ5DcjmnXCx-w
z)HD2J%+tW5|D(@fnU@;U({&v6Ud3Xz``MD%o6kva__o&TN-UQXG=Z}PqFj5ENxSh+
zEA9%IwbfcSm!Hp)HClY<QtlTzM~nVA%o9m;>bgy5emKle{S4&?ld(;5Z;Ti^OMJ#c
zpGVsJBAAz+nRnvmt9MoVX%rh52<?e9;cZyEU7vkb<1OEW?TL=Z(D9(#NFktlPQ_1d
z{Au`oEA~6|NO~`jF1-XRl=m<(8@ca>I*Pv&EY1hWo2)5AI{&o#NVP4=y9pUOB8*cC
zA(>0{X#mb<vy8mNK^*f}qI9&tVAb5TJgH#5991G%lg2%4@B&QXxjj5sh$x)JB)sf*
z*@)k;JwUhw0>qX5(6RGQ&AN?Ma#{Xn3A_8ua49Glqo{PMzFUB+-f}NFhaeKIOu8@7
zxf49tdEEhM@=Y0#aK(wS>ub~g>l|CdzacbLtrUX)Wcq;ElncxmQ=v@?&uu3Ko+9m|
z^9~2Te32S79&}L^iTvNF1{S~(BSI4)dE+j1ZJAqNkbpr!BJ@()-<_NO4q|0!Q2*ZE
za3p~gnQ@=Nyg0$avRFoGprO%Ft8VY@JkjHAsrTnxW0I^#uF%jK(}V&6=X<DtNlp}=
zu6*paewq}|0y$dD%orFfs3*M>bv&6D{KZ}j1xQ_CJ6HP;+WCNb?qw9YW+i8CH)L*2
zF+2n*+e&O3k_JEw621`Lcqa&YM=HR$fYji?U)M$Q0SLesKmwHB2fwTC22FlVa%h5x
z_cHnzhrXP@B)=+6g)aZvH?E^+^rPega~F}M*A(DyriN&p4DdQONw!AEFpYasg;Y4i
z_cV}R6T!C}Z{W9an<#F<gk+=p{Fukje7HQ3uMr#oK1u5xi}Uv+$%qozq*KxN-VI%I
z8aRo2sQhuW_&~yKB_5h*xR#9(?tc7>xAjB;BsGiCapIX}ilDaC7&&h;B1vpZiMxp}
z1r8WN!7}54fc_DD810@tJENJqXktKaiMqC3IH*QvUu;jZQ>x}zRmBAFbBPLUaJWc;
z5KY6eVsNYwMN1;RiuzqmsiZ(jA!=AT$y{1?!Und7>&uUQYQ+k9s%mBdgBH}({*O7v
ziebu?okhWt^b$YF<JFqsVXq*yG?@B304mfUk*E}d`_s^<j1@~pS|O6bq*#-K!0BhL
zu^K@?n5%YKA`A~W&aM*Dt;a8DeU63ttL69fXJ?DH<Q*FB3rz^cxySFEE)1-2BQKSo
z>oyE^F|s}uDzk{o(~Yl~X^dx9{p&?RW~hknKCSGXO|>KeM9Pr`OysCPlABp2rHT3r
z$&D~q@UE}LPy0ov!<(yQw})Knk|}@nb+nO6B*ELIEPCuCpH`rUNo$0g+N`3jBvSG>
zee&FRg_(vTv*qP7T$~Heed~Df1WRi2{+V!9RqlBg9PKjry>?zY&7~Zjn6L(W$hffB
zYi8o|dN@_l`qxBM(Pm1>rkQuE9OKK;8bzWHlgmNB;>DhrE6<47E<TSIkpP?soB%}J
zAA=)WCjv``Z4gbT<8)u94NhW8D`nXX;%{Ms{PB{#>gzpFUmW%V7IBLg+reRXT8=b_
zD43dDtDFcUTDjUndNJd+5_jy0XzP%3+u62nxYxq1#*oo={A5n3tV=$5>yOr8epN3|
z2IuSg)LpQ0ls`GD6qs}>&eIIF_uFlX$C2dj(szd2)%LjUC=X)bwh%S*r_CZ4LT;$b
z)3?K00(%0I=M|lg5YVHVFE6)&1FK;|C7c@{{iL{B*%5P<P&qLkXL@EQ!3qkYC%Z77
z=0a^6YOgpskX<2RH~#{?t^mMq7-(fDPMd<+Cr6ksvSpQt1V3JUo&k6l^(Ibju-v{b
z-eLpwLCrW<a-@BYETdYeL4Zvok^+WPur8>C?X%(Hrv-w!l_vFj8C^Zb^@vS@I8K=4
zV5C4gIwF-8&z}_5kvlez?ynzUBpup7Xmzbl?Wl@qxL8IW4xs_@A$>ZeBTH!^!C?^K
zPbr0$KGp2bylcxPLe(GLc$#i?XNY@j6~@j4Vw1nI(Wjy@-#k2|f1{tBW;JFfBZB_B
z8?}?VKCZOmTpB9dYRG=j62!Uxe7TTy6AdO`Nax}GYm>PgLaTE?n6%-pE^|uFuf?b+
z2Pc5*#=%+oxRS5Rr;}c^Tr}!KWJwbT)NYKVj)NBTQeG72l&7-Ig7KAdM^)n3sX&2E
zcGLY;WU3XLtvV;g#?|MtZ-sZP5cL0S;Q=)FM)0>l!B?uQ%4C+1%=qEfOireh7VqLh
z@*G?|ipx?iB{RGqp@8fk+zQ3NILv&9QuOv_(e}@)!NbEiGJnDgmGB?ewTC2lAi3jz
zqOGz4i!e;#r?tTz#ItxMmzKCClo#!5f2|H36z>NorOhPv<WUvW(Or?K*)Ds?Rnr2f
z?@%|)>7q-4j)eORB@T=*bE2~|492$3kc^i5k@_U01<(2X42_5=dkS~zTNT%Q!ZPZ~
zkU;Wy2v11&<Nd(F;i)g4Xh~LL#&p1<8TGZKv~0{tKmn3s+7pU1M$yIGu=coLs))Ny
zD@<knh#IrZfzJycvdJ5Lbr^ds`#R00h)&O1NwRvmF!KAh^$HJUU|hF-fF18E)5h<C
z(nk8M?+Lp!NL1lWjvNS&bi6zxRzgs|*iZ<!X0)DtXC0u8UIE{}1=`WAR84))7(~G3
zZV$xD&c0QeYRqoL@6<dMTb6nCpyZy~dr}Fp|01M7_>#-8sRU>H?7_D`L@5??nBDSl
z<dWWm&*!}RgDH__M?Tv8e1iV9p+)Mp>d{>9JI#*i#QN=kYI(atfh0BDVGi%D#*~ZB
zFPv8+rMAh}-*u!0OS#O_gllaBesWg?JdMfg*JI#6&X!%(QCVC?Lbz6O5{yR^JW5Pj
z^WTa+-Dq?#E(~U)j7|1lOV{S{nial1s&(f8OY53T^?)JY-e{P`XWS@d&*O1x*IR}q
z+oa`%kQ=|eKsS6vtB-44*l9PJlU*~>;-U(nU6@kw^CRl543BMVW-1>fBFyUSTY|2*
zk#VDvhAYgK9Ra;xlfno`?|q4&vD_nyXK@K)mgIZ!l4Yyd_0PJ=S4nWAqODF#TyArw
zH?k32U*<l<Nm>fA^dZ&l$)Ozcu46Grn2hhaz|BShs{mMluuaYc>c@))um&TEZX0D6
zCKf$L@4~9H(vgZs;TtKsrlP!KSV6&0$lW<S5pfZYBo)6dCnl7T0b=A55*WVM2rUY6
z*3ai0niih6CWO+gIMQq9_LG4Hn!ApQ@CH5<%JGYly-=1@@Qb%YlE~p&OMKB%w5P8J
zc3zNQ8<$s%2wenWf&Q9agy=5l(;0^?xlnVrzbJD9PCvBgOqOjBT<*EM3XE>Q<TxNk
z?=3wJ8Me$cNKp;(giAx7^DqZBPxWKk>yMQ4$9|%|4Yl@34iyHkFGjB1gU5kdZs@f(
zzOI%E>!HCl!h~cP1~@8@DwZ=OewiH*Law%3Gr=$@lW@9(v%wQqEYd%8b%e{#1>u5x
z>|o6Usu|>H5j2U8qi3S&kU7Pog3Y9Dz%^ERoXWIHreYJT5C{a32uwqKq1u`R@Hic}
zrwXMc3l+&w5pY?|*Si944dLjJZ%Ga;97|XG$1Iv3&y!))>qdgAR$1Qq%gjX1xUi?W
zIui?d?m>X*DqfiVL@LU~g%bSqGnGlRJyQMpq8jFyLolc`Y}!4`ko(&uJ2*Hv4<224
z{aDp9;8lyAtn8T6IPYGT3%F_vG$<4oa&xbQugjVn)FhmPQk$mz22OgqA6G(j%n~n`
z$L&l2Pha}&(x>)XNWdSM;R8c;meTC)nRus<aXM<os3D#h?0u*SInfErqWycLH>Bnc
z8CCm}Z^rPBq1<LqFkB`uX*roM*1IDhNu;j_=4xJIl_pfMy-x}EdiTm+WSg#b!WJTB
zWk&znZhu<ZpjV;c->LC<JWxbKug=g*`Hp70d;!vs(=Vn2DI*_2I2F1&zX-@1O_ET=
zAJCn@p8a^s5SNng2PTKhA;1{WTMdT?gZ9MU41;CA+la@y=-pe0@$n&Lw_9(&7A{S#
z;{BfX)|6x1;4|IJ4h|SFGvGb?)GI;1p{o@H6~Cf&HY;&ms)dj&5FU~UOmD(1v{u+>
zsn54aMY+3z_ta6OVW|s>>zK|!q^Xa1<*^hfXxSP<VBfezmf2~0q)=A+v@I99FN3>c
zS%;g|sFRbEyT+Z-sGKp4Q>2$Vt|^6ZB4ZY4wp)ts)WgCugnTOS%E4&pB4l`s`weou
zi*pNfQtO{q@182AK|W})!uTdMhnLdlQr;4r*W%LWmX<9SLSD&1I+^cc^?pQtxD#4*
zb9P52&%z8ZJh=XTOrp~Tf~&Qly`hflyQE>chM4#4{c_$6g;?~nPe@Vf>(}7lJ9CbS
zrFN!KORQ~=s*+zJ58~X6$L+`Q6Ze4k6`H}Ctsfo3&yx%j2_Oi%bx$5&5OoYCaLU`I
z$^rh!x==kT$}R}J4ol#^eQn|=ZIFt`0t-i4gbyUYmMz-TN-u6r*In67N`Pr&-e--c
z@<789&nH?k*89os5JddV3lEY0@UP<elk2-7uP;waM{~8w8Be^$C#Y-cRW$;xbLKOT
zP}fFGgcAuX<~LL_w~PEqcD&@Fx~Uv*8=_`z$?%9DzzraZl~gN-hU}h1sJL9uYgu2P
ztK6sh*IjQbo!v@UtKaX<o{&RRq|ewwHrVkV5ZGP3GEw-3x?}pdDsY*M#pf$^>gwut
zK3~?>)_&>vQr@Ov^fNLnGP0bRSHx7}oA1i%?)hunP)OFxgV<VGGJg{@nh-k16W=Op
z;6US^XS1<q&!FJd=&(FH2M-ZkYNP%{sEq|(LqtNaCXu+r<u}eF#S5^N{}eF&Uv6i9
zSL&?le~<kWHcbeCwaeo&pcgd=>j{HPSqs(D%kgFjpjR_rJ?v1lV6C_*--U!AS$x5h
z8&o8FV|bY8n}}tMTN7T;Ei9$o*&Luvs&<-JJ4=;Quv9D*!y^`y+)3b=X>ZRLqM)WV
zf)oi#!%8TX?D`~L2M4{z=+Mukqz*|PzfBCPxPqoOfTF9Y`1$bSOJa~vlFpYez5RG1
zLj{ZIl#I&ClM}Kf+lwva+CLa;2ABB@<u}>xX;Xz(RJ^OmI~&b|l@*K4U{itwXn$@Q
zS(<jYnpmZD8em3HsifkeQ7I{+r!z7fSnsVn!%39<y?44hEG39AATIG-XW2ldC7T79
z-<Wk9Eo&FT^*SZjR{nrZ_r!2BSg@i#l8)MpW9Fywd8jAyTOJP*rxR0M?jG#qxKI3U
zsYaTePDkdl+jpGzr6ZTvrZkdP6`lIm{&+`%0!H9nPs;NY&GTu)R|_paIGL2Y2E%iu
z9%dM*cD|JlYg!&NX#&VJqKeO||F__w&sd}Hnssg9wj5pQg(0=waraUrwb{*$Z0K7e
zjly28aC4I)nfn?oi`7-uekIRCOVlLXom>hZk;Bm>%5Wn+{EOY(b(OLD*BJf2k7cVq
zxu>ap7W^ZATgFcs;8*N69p{42U(WWFhnZCCjAw*cc+=t&?76&8Pg!~AI-bWqQ8y|Q
zo$?;WvpV%!Mdp=M+MvZ`R(3>FqDFk!<z<;P9E5z2p6sc?hHbOwJiR7`z(!ZW5%Jg-
z`R=20pVP}D#w-S+muEh+NB6IAdDPz99$Bd?UsoloMX8amNy)2He8N4efcGx~t&9v4
z*grTKaFdQmTVSX*9|v8GxY`|i)IUK^@Sv7bN!8yz%;ZLu^O?sVs@z-0gt9zqi=cPS
zYIC~jX&LT}xfPtNU_7dg`+o->$G^69S7ST}#-e=Obn4d^I+gRLo9VPJYQ<2FZpl~1
zy}F{%2iU*gdii=gABRUp6kYtDAI$6YdL=o0^|Y4O_J(h5KY^URSjil&iJMGEYaCcw
z^Xj7D$OKnVlM%rC;~AV_@M6vOeQb&xN`rKt;Ow|RBv_U9gvk2c{jAu&DP6KcpZ}@7
z%z1U#sjB*I0BQQ5eip8uS#<%ApQ}UkL+Af1NFl2ygNErF5E0iS95!A4H%3vt3sNz%
zi9UqTj2TtB>kmk(biZPA+0-9)jw;pWH9}+idCBI(^A-gtY}Nsx7qhyx|5ft%Qve$3
zr-=5m^W31|r=oVKtxWPVvkjN1#hazFd0%zrjlw8sh!ioi*xiFDalLF2?~Wl*yikh#
zz(`>D2rSXzJ;AN9Aaz=#?EC8exD(zzc_)K_bx5AWihVAt*C=6Le`(2IVpGMn{~Vmq
zC(Y2>>ZE049?M3%FhTCvBy{=X8m*#4*0|^(`19w(%ka2B$iUp{=K3q)Pd5iSl3JJd
z3H@g3aE;zEA)<5Ve5GdjmhIu`O>T59hg<t83pzf!Pu@C_>QT5H*zw`srn9%B4rqD5
zn&_fl9`Oh^>=_0bY%;E&+v{(~-<olnjMZ_)XtR(li_g9CaNPljBH)68OhD*>E9qau
z9j5+5&ild@r991Shjy{X==!=mF%nye8PLgR7<A`TiNj&CEpK2x$^n)ALf=L_ot)mL
zSqc^*5Ajqr+&YnFCC~szxK=mr)x>0HD&cgtyS>_+c*@{;y#bo7LT$pUPf}Hi?eg;?
zLj3%p4K27d(n%4|#y3+<E~j_vg_>g18J8Kl@U?n813-O6>o*eO0`;}Om+?pM$M0*J
zhz<oU^wdMI;J-ps<_R%Ta7x_ugLZtX2Bb(!hL0>5<O`qIn%%;=5i;AX2SOC~A5Tz~
z>7-5%UIxv6w-?JRezTez)XrEO?5Qp7CfLR1h7{G-&cN?)sHOTg!<@RCSfJUm{1Y=|
zK=D&*(l2Z4u{jyLmv7S-zr<t-uRX*2Epx_C#f{FzxxD>;n>1u6vGqD8|2BXDJSL(W
zKFLw>z&|_^(L-Hy#QKZLb(mh>um(5SbYi#{S9Qz<RMXT^zAQ9;l%T8~vqET%_B~0f
z%2ff=RfL%D)R|M$U%8c}Y0>{WRI<F*Qk_D~pmp@5?C%#f_WGULt_o_#&cmW8iX1XK
z?dt5}jzy18aGBFzWo_ikS94hfQN)2uMPGSKhe4y0o^0|I5<7!4+m6-4MYj?eM4r&g
zioz$A-w9|ff<_^3=bKm4zLLLhryG0KhF?P4hWO8n#U8zX93sogQyu2>Uw`(t?2h6e
zhvI$M-dA{GX7}r`y=v?Y8GBayu2_io3k8ATcmJ9wA7A3J-pypEw92p1obHpD3U!Zs
z@kB+;89}e(x`LzD1m@>~@2>eT4Q66JjlyOre4TDFZ&|y%?$!+52boa5Yb__$Wah`A
zh%W8Sk%z-te!riMHV>o*q6=O0o>!d=ynR%X%<{f`b3I$=<h-Cq(N8FN$x7g+dbD`E
z&USmfA86G1CKyj}RV8d_)Bgcw%Nbbuk;_dWmnZM^C!VTg8pJU*U^%?C5;sK32p05y
zVtqNqH$Hi+FjOvJylB5UTjU!kt7L!fieB>gI9LnQwyRAiy?3cT7<69VCTR0W#z#1A
zxX{hsutgg=Qw1OLqV$I49y`3NL<=dLzol3|PQC>h@%y*Q2CNrvj;a*!(z8vOK)2>V
z?Q<q><o@C4^+r|0&ZYxk6c#sXb)cw4ckqR^>xtWPQHUUbsv&&y;^<<8Dp)9Z&+Kmu
zCZeZBgMx|@Z8A$JzFgh6;Q%dCD*h$rZ8Hf*Q3i!4S~V@JtRyq_-~$0Y36)|l$%Ffs
zVphS=n8P?Fs!G-bgdYUIg|4R**`|#eXa&z>F0`k>LZndw9Kk_>+nNS?;_^~{^mFnk
zRx_=pU}xQ@w8p*6rP6ZOxaOgi<?yg3x|VNlTZukWjr>oAN+qQ#IoPkg@jE(i#)ViD
zDklh?<5q202PK9r9ut{YK~!(9XDia8xPE;V*3{j<FcO_F5Uo4!CN51;+r!zJ417W7
zv7mFSEgviof92{>!pXA+j&e3=PF}zD2hB4;ju&mH-3w>X_yw=0tRONyt<T{HTWgh2
z+4uD<<N_29I@A;EO=}38+%ZtYN*Ol5M{9Pif9(YxTZKyx0xxg<q0o>Y^YVnWo-X*%
zR!3hzEkD+I>?L1S5e_}KIhie&DNaI#bDsqTwCh+v@e_0s31QFiT-=U~Zl;S@eDWVn
zm%T0dCUqD|I}GwxiXB3DeTzA6_cr*GTWvV-1?6<G%ZNV$ovO-S<%fC5(VQWyJuuN5
zRomt)dtYwi;F@gd7C=9-WaE}DNCb3lbYfkM_Q<-=NDSL<RnR<m0vfj7*OOkKWbK_R
z&Xaz04VXrPMGRf<cocCiUW@<eyzXIwb*oi|IOZK3hWG8rw7s9-(-Cc7^NqFB{Uq5E
zplEkPOZn<;d`YF?p15njE(<I`?a8!6vjzo=rquU#4*kTMn3WWcH6!dkn64W0OZoc*
zWA8pCew8*W!aXem4^@?>NI$Vjj=Qw&i96KK`mnq*rYLr&K@iYx!^vURuig+lYe)}#
z+s=r9@7JdHAkPfxv|AI{&9i+0%Q^c=6d-iSbTZi6;Gi+20v)(k?vEPrabF7_C^EYl
zFJWG^&Nw~<5Bx@ykFzW+{VCos#e~5$MPS9>wOD_`6LHFpjiKl%5zv?*5zH%N$3KRs
zWU4tf!Whl&jzkwSFF_^@7Zk4yC<X;Ahx;4MP(6>Q%3N+h^F?^>b!_Qj_X*kA5u6gq
zCzKes^dAj)i<jYHY$!iMAr(O(xzR)k;K%`M%Y%y6K=t{N&(*=M9)Hm4U+}$0D4ZJD
zLd|yjG2`9@D<{-35MW<P6^V;D@=7^T1PXM)su4vx+>f0nb*IZu3dVf;ySR|=K2OiU
z5i}!;C@HYAWaG~V6w@$?rN(IC3jt=6<jY&<X27{C!6%h5aMQ+B2;`%5poHST1z)4L
zX<@IlV|*6=!nN^W#wI~|%EjGenPw_n>tT&+9&o-;3f22w3PUs@iVUD#4)dqsWaU3)
zH!ArmI)R|sVcxC&ra_9UPh{7XvERPj$(lT6Ex;!y0|pdpj%VRd*}J_M*!B(HfmM26
zw_s1Sp>?)q;n>R~a&P)aZv*?p7WYh1I^EJZQsemAV^kVO9&|aZTq-8_^I<0%wzy(R
zE3cebN!S}{(0F9v*PJtl)V#XrsBxBbCG%+B#DokVOUS9o&@a)MN?<R9`|nGI(uy{r
zLNwK?>J#^C_kiWx*0TYdf0N<z5&oNdN<x0buVx)RTUTJ<&*4A#RaXLHGsCiL(9f9Z
zL-$uI&mBuqqayh)>F=^fnUh`vyz|`fBU|*aM1Lb)t#K^+&UHP@Xfb$KyBype3^T>K
z-&~CDd|373Cssl3U~;HjOOvRJ-WYB2?lUBdPs}@25FVapFq6E9RuyK?3in#SOlW|2
zT!l{gdC=|xp7y7X*<Zj2W`D}DIRp2jhx<i){S*nBNiix0ZiD@b^QUmnt*85jup%}D
zL9xAZSxm9fsF?03%p2?JB96{a4Qp3h`9^Bi+B{KM*bQUt4r~PG-8dX?PVGn>F{2_P
z_ib&eu*hi*k54C1F&${ZicURhuSw3%c6dTXWU`Hq?ijJ(@qrfx)6IhPz%%ub)c$&Z
zvd(NgDJcmS@C6tu$KZ1}cFx!%Krh6AkPeFJrKss-$rh2ed$8pF{DtFQq8#8d%ueFH
z>3nfe0FMz~YNS?v_$Ag}Us`0ds7}-u_mj<HUUHDQ)gt>+?OhtEUB3<rGzC&vn`jEw
zBrF^nT+d_567xMD3bf9++UE@d1UHxounl@v7*`O`BI5JjT|(JxzR|1A;3lLyOm|?l
zuEWdYjch(X)#fgsosvgUTU#<IUX}Y|o5@2Xbvy$6h_TJFjwUaljs+RxVWjjVeTzaQ
z(M}8Z>4ww8C6$foI;nqol<;@B^3I^^c*WtO&IL9ev|2gq#Nsr}{teP&;Wmd*^M;_K
z7%DB|Pa7m!%!u*5t*yK~l4Xhwj=~&cBoZR*+3B-c6gQ^2g4$<o4d3r%5sPIuU0Pe#
z$m_dqCXz`zbPO*p`O-YTAhtOABn;`stg<T9Z|tX;o3g@t^h*~8=&inO0Ai=Mz=tLA
z-PKst!r8L8-=2o_pCfpo@Muq7Mm$|%ns)6A<su|13mErd0X)e9uCBKM;80W3(=rrF
z!0JF{W%N^gdp5je2EE;@dv~+p!|;u{P`|eqx9!2WS<($`=z3(($@WjrD7-U8wkgh@
z*}5kly#%HCMD3{uJ5{D54_ag`M1*zvKGt%Ij_3JNsn8~sk1qDhaQH`qLhf#&sdn)A
zgp=Gk@=VwHZd{vDKg4o^`twCYxyAI81zT>e_`foYKI)?oFt*&^*r_svYqzL;otUzN
z^m&<BWWA@_7-j8^Up?8U*9Fj$sSL;%-chM(6hUBqtM!^)av&~=jEua$ch}3UlBX(|
zG`DQf)Ft!V45Ji{G#>=qL1}#l)<`4h*cI_vl&Mx2PT({@<a*{g%6LdfNN8(o-wb}o
zZvtkOWTp#VwX0*5U%b=o8*1PrX7xmVyD25*^@r7wV2}x$w_O)#re$dvC8L+oez$XP
z4$Tn{4)0T9|LErz4;vi7u&t6cr&q3}b0ZMHQu^A3Jx*M0c4zCy?-wU-%!<>7Ld$kE
z&i{5iTc)N`z&rsB3)@3S&W0EynhygFZ4Y@P07axvA@IwtU^v0-u1H36K}ESt(4Us0
z*+q=F%`sF2f{6|muZGc;O3*%tb<U})hyWG?4;rb$opGrFdOiDWDkCEr-;$-w)1f0s
z`!wnU`&W3JO`ctNJPt;3atI`!u0xFm5{cSsbw1B)^F0MrmJ55ZsbR`1E}8UM9k=K8
z;Ls9b8(!c?en1aS5M4~X8wd_87><R6=137}5fYp*hZl|VFjuF<6*RXkBsFs+8jH7h
z^OTy<*(SZ-sOEHs#J}~O@^+LxthyHT!5cvdhXTU{%LS(#tFY39T(>|6)<)t6!=ZwM
z4j@Cb;84fIyd~_}TRy8##T6`=J4VXhQM;J^UX834>MMZ4@VsJOc8qS!NoJZR``Dhw
z@fLS*Hw2gj9=pv1KtkG8V**A<WL=jih?5pdaByFQde5OHKZ3SskFm*jkoKI>Rqp}J
z&&QIi=@oWmk{*<$`vxRyW~4PNq9m?lbEu0!0R94X!0&ATUSiiHg2w-Ng9`xtEV+|8
z0$<tnLGIg3a+wU5jkHEdjoA3yWNtvA^DPp;(sNZ)lLt;`seq*mN>6pE5(fw{sfz$T
zlTvm9dFu}&(4_+-&miMbQP1+b_$*JMK`A+EBA$>&vWG&NMP+Q*9<{gV&RbTYU?w(k
z_-v!l)!EZT;h}t$fh+854kWTU6{sSy+KQF9mp;8DF{j(1cABz*gv8L)bY+2dYXV6P
zt=di3a%D2`v$^>)=7|Mnm=qH3{k1o}v&It&ER4~?algo;&)GOe?v9allkTOxMs^4p
zE|ko2NqXh-#g}lBwP)y^%SJu*SzDPhDiZ@8FveEDrV@fhmExgbO3-wD={;vm%ec=4
zR1;V{ex}^{sn=ge?fKPJ3YR44J`lT3ZZ71PLYcab0w#ht(rzF%y~se7)U_;Ez4zE(
z@;B^waOd2fW(%~)HBO#|^FpFOta%h%l_B7^A_OGVKI5p}en;P%uwX$6Azbr04qB&=
zYKmspmvz>))q8z${AK}1kUj5qm1Uvh5Q6Vwy~uQPUDY(u7(axQgvgU;q<p>HGO%l1
zW#%>h6y_S`4zz;&ECDH^bxlB4DE61PmZY(q3NPg5GHQHhPv)Vm9QruXFLBv=*FA-6
zzFzka7qi~b;baYDVH`qi)Wh_-V7<blRo>x>@ie^A<l-e6s9_J?fqGd+t9@TcR5ZT{
z2@%T?$0x{lZshJ3=2NNQ5MY<n0+WHdj*?k)g6;GsWSe1h5gjEaQ(z*4;pHC;nxQnc
zK6V9ElY{O3b5-67P&LWO73mZ|wqp&LqnKD9_1gPcbBGvpEd3l=_;mht6{TYdLswQM
zFYZEY72zWf*G-{{`v*8H_uU4dnY1B2h@AaqzZ{ULyG<t9CdDL8El0o6YPaZ*pDE@1
zXbeqGAIZB^=a%w37o}Jrt<H7yvrlerL|l*4WH!JREzbuH&A8a9P^9Sz&WLEaQnH)q
z&%Fl}8b{e+uKktpP1lyB^5<D7;UG~uZFZf=yw!U?Uz^Pm8R3aZ&a(Khb4s<JB1G5M
zw_DubKg-~88W?y-Vvh@&qHe&sRrIB|jr_S}Z*7X<|M!0Z4De+VHM>(KZaaIs?Kx&}
zc{=snA~<BfbUyUUlSMyxdOd5O2D_9Yp7Zh7x}H8ayQqWRGA*+JKUe&Y0cZLLyYPGy
z;EqO`n$NfszzL$ICqPojgpwtOu6Jfb-axBicAx-9a+%3@+}@&G4=tigUrMxFECSw_
z>tDt;TLrb`_iCa0lQCT_!+&GCliRPDW`W<0>Tub0gsUvfo1ko6cH0NYZc>2aL9>^L
zGyeH71q8qt0I>nS&=Oo+3?LqUhEw6BR0=0O@>gon!t5^HGCV1kYyz|)Bss)k2!o_F
zDCLz#$8uG+$e{y2tWrwWhk4K5fk4CG*gg4%<WgygE;kD=cShKn@gCyW7Wd|}8J{od
zrG7K9D2aV-RiGf?2SJK7&I`w1FAporJe(cmVn-|b1a;KN!r@|&I;)|to`GB^hq6#X
zNVDQfwX)2lKrztb?CFHa4j<Xv%rVazL3SfeVVhh!$+#1=E1wWBRBo@DDc?bpD4hf|
z?Vi|Mie1DzaYUW;aU_ZfSLXh;VEV)(B8@7rL|e>g@gcf)xv(+ag>wTzpAj1e{{16w
z2DD0_@=`RWdW9h%ibpruw#S`OWgPjj^3mq$c0KH?SJOlLQE?Lq6bj2yI90pjWmmRW
zv~H`f;|u##F~rf@|Hsuk23FE7ZJ;qGnV1vXnAj6@l1yw&Y}-4wZQFJ-v2EM7bN8J0
zyyv_3{;j>cdaYX3t5-i&_0+dOQs_<fR-FQA&KMjPCl~0_8^sap$IaT$g2dyAPld7B
z_t$ZDOq*1X>FS*I)x7$Dp^3&fI32tp#e#JDFZhgRd)3)HyXHS$`L3r-OCnXGfdvA6
z)@Q*JP9Fm+bGalkrVGx2B%zH$DPICoNBh&zR&I|ipc~9WrcCNJtqk?mD#|8M0sr5i
z@F~yHMNx>hb@zr}#|ClhD%1YJ?#P~EUgXZb2~9HIK5IT)ZJcv1@NlpO#hY<yY;oGR
z>qu9_eet&G_Mb5E=!TcJ6u;Adhz1_7Ys#y25x<yjN~?upP)vj>M#B?z9b}J+4i(K&
zhmIf4IU$HA_zHUvbv-1AiaX!rUs$BY<w4*3@aMspRBeoVKI0%Hp%Nn)Fsb!Bhuv&0
z5qmi1U#81N#Da%d!W2hok}+%wIB*rox@#-4r<K{9WI~8PM>wS1oPE?wz_o>@p7H9k
zL0t0Dg9R~IL~15=BhVpL$Vpf3Y`MF@`D^MU)gJq2cui?o({eZR@BLJzqh)`2c~xGo
z%@2&nK=VIS=ZDw~D{oy{0!Op}Fvb_dX@5==QtjDtG@nb0n5k+B`E|Mm43wCwYLWol
zEZ~G~r@fCCyJ)C626f#68a)fmqk#1(uW#}V`f&btYXof!H1BGy#L7u23PG*!X`M0b
z)<SW>Bf~HGaS3PyVf&-`l*LFbEH1y}$JG;AdTQoSar(Gx56nOAo{-yo53YO5qkwFK
zg4*FzVqYN(6SUG1lQ?J>S$qrBO@9SM2SS4?D`G2j|6srJ=4!;4I2|9iAjnFu7FZC=
zT9-m_TZ-GHF4>=tk8mhg9pO-Zx#9WK0VG*mBij%wSEfBBczD6&@d#>WsMUBX^4yTX
z>X1kBD{rwq<|s77spet@e^K1}^Tfi(NL&fiao4>_QC&q-lLQU0W7s#TmU2Oojz3Gu
z>tJ}6L8u8;ZJBwT&NP(WwJ1I!3_O*@Lz}XWchm`5d%~fuk<{w7Pix02tBt0Z7&D?r
z=ck7>p(dCk*-6?QuYspnps8%WKAv7ithxN+Um(&$x;I<)ewR#4<#ae|H$Bga@gHDL
z@XFmC)}jCSnIWKKq!FVmCE+<E-+IBYwf=|t`mX09CGThk;g8lThhHFlk>5jN_%4ku
zqZvH55mSl<LFPkaI82!;%<}%3U3QQ2<=b{Y`qT3QKPKf(mo1G1ceDR^qmWn{c!giC
z`QJJvHq_v2LNIrxk@?O}^bMhebpcRgtBu@n^4G|qVw6pPa2`{8>(5W&4n$~ZDChfg
znJH|;(wWa>ceRxXC;^K>a`C$-T6jL_rkce8-F$fXw18-H3g6^bnE0|fwV$iXmI!IR
zy45WEoWNqB{;`H#3z}7`ik!JG%Pr@&wyU%0^1Q!B0$|b1BLYV%0VhUV;$M{(15QHJ
zuQ?QAt<M~pgmF>Ou*URikKhBR2CF;`xRoa3f6RJWiHNKfQBL?Eyx7SX^qpdE!WkAA
zl5<7eitlw$a+ooKR;mPdE;NZ2Eq;S{3AC~C>u)v?Y&cs;h^I-S*OHr?elF=W>~xTj
zUt~?HZ<II1wE0hIAjaF}yS2=^^1K{#LSVNJ;S?Gecpt6zNNc=j!&yFCvMh|M%-#7A
zRST{-iHH}gXs)ZR>rV#jj0;WH`R(c)h$EiH&DCAIeAiIDfN38b68KPJ5i6(6tMeip
zYOtAk(y7LGh=N+xoNjaRO}OWPo2lyW7BUfD5$;JEH{xWh)R$P&jx5Z}U5e5*XLAdP
z7+oI%q7{R%xhyF`Yz$tW(^&`WY-`jU(<C=unw;CRl}ji;XzK0ore6#ETnMLJz!)44
zP|a9d(-dk}XtnvOi~SubUh_ctA}|+xIafkmMccCtD?|jBA&@C;0IVbGNYc#0yUR47
z1iU)Jc{5d;t&Z<bu&Y@0W~Gn*ALB^J(`uY+-Jn99EhGthBl(6SOd_Vk8E3c^nd{b5
zE6NA*9_u!yDF-KR=M51n_EM~z-VIOcGr5&Qhn1T2qK*~zBEg~z>i7}J&Fj&iD&NJ!
z*tyEejMSeS{dd+Pd@$oa4xh58L!L#NNLe~DrOTL)?yT=IWiHM%HI4%y?~6zjTw7K~
z%LX3#OOz3D9?IM1C23ZAy$^oBRf*f-GjLoi;%f#S<VKxwXb}dtr!;CSy8t9y8cf+6
z+^hP37CPJSvypdkO(r+0;e6|ZKQqyu%=>8=jXTH0!E)_mcFFyplim&tCzPoZC<UQr
zpO{%oy$@-RV1L4~cnYaT@7-58^lhwA#j>jdSPvo%vi}?r1zi)4qb2Ew$sn~w(Ax}E
z>l37`6XNZAxdPNNQ*@sRE%nQ7Lh6-3GzB$&p};I<@jH7b?tKbQjt<qit}p@?^>fyA
zwIgDl9~f${QI6rAT#A;l)qeb#<4Y%`?reN=#*f3n!vE0R-4}@b0>)lh(dOowBA{a>
zNRBaDB$kE9#d}9RWr_gczeaki;zUVJVZXGN+!KR%Q7VTz7&rEZs<w+dE#pQqcmR|z
zcoy_=y9I^DC>9Fu5Xt4nOR0^>7jU>OKRkoSoQ%ZE!A;+p$jJ8`U$wOD4rb~!>ALEX
zFMKtYlQb;0AG@HNUdl+yd@&lx{ShcEON#iGBaECVj}B{}x!RY3HO?Cw&7-?}?cB>b
z)V|!b3W<5tQ_ZwUl>^s%e!*AtJ-9#nt7Wg&+~nHTmj8xd!t9pVVjxshd!iSKR4U#0
zXs6R_W30iC?>C4=IY(}x`Gw`pFPmaS0w27s4@b)lMXG!6mjNQ|m7eEri9Q4KF!55-
z9!?P|Y29IR^n+<>>r#EN1WiojU^y{m&cJ-oPI9o}A?+#({GXv&I7HkwN`UB>i|4T7
z*VCHsw%6Z}ZW?i@W#)3LiUNpP$my?}@LoH^MA;uVx`n=>B_--!ZogO9-X2zp2MZPB
zIwuarcC9$L?p(~<8`Hg7!EmM82sl;gK62RsFks|k+YKgK=j8nx>C?5Ub;qH1&FZ&X
zRXlxI`LprCgzz_0lI%GcD3<wS@bFLYmQ6)@L$>ALA;fDL_vJMWUL(OeyHn*U?+TcY
zsFFSwOGYi$_7uFGnosH>#xfwESqp!eI*Jxzex0|Wj>b<Q$%^KGLm<6IcFS*bO<9cY
zjLNni!*RN89XNZTJAXqoKd@X2qFzBC%lqCBIGC1$Ds)}tO#hj2Uj-k6`E@W8QV^G*
z<{6Q?InHmYq=b29)i!1g+Hr`v$w9TUc{K=*11bHgxC%kE1;?ct8ipl=-zJUXs*Bt(
z;3q2#+K|k3`&(%{^eL+~0MSoQHQHMpmnm1&jKdZW4D>zt4o{r`v^PxO3HvJELX8Uj
zZpcsD>E#<Z-F_0AXjkUdTy4b<tA{j}+bhqCCKnV|yPbTlv(%rforZ1=o2_PNw#}PZ
z!Kw7io<t2xkfDl0Gi)8t%LyaLbT{>WS>q{Px)X0?1lH2_ZEAIN1IQ4-kjxYZSTlN%
zOqG5$oZkc^mYHn7dAg1GIX~D3FBVa63Oi}OaN&5YX_aAOosh|S*1$O=D;TP)Qo23N
zXbc&kP_JAN7}H#WO~yo*l(ZWk9=m|<T|3j|t#u=T*b%uKTqnS3)ak*bbghIhk&;0B
z?}_{Cqd%d@$O74>)*ooDwO}i&I-9a9!lLqkhy>h~x2~$s=mgQb8Z_ZT3gO8$4$F6j
zOFmkjCZ`!3{mMZCo#|@<oPupS8+BD_?qFwhvYJf#9O_Dxq^a4^Su#aOs(h!X5x93Z
zYNY@!PbBT+vJ;%j`PltblpO1*1C^A5lW_v2o{Z;ir8^74UMKxzuXQXk_OIk$AR9>D
zT33LN&EA$6&UNt6nB(g!d~?ULNBz^QnDV3gXRGV4>7Ilb0tELjWESprzx29_H}pu|
z?ca>0^ry+GE+lm7oTu8`%u$f#y`F|QGDZhH+sWNddPGR#AoRXl+5V!>dQtP3!MT(c
z*WkNtv9@&%(%ow%(BWyY(*{e#Y0K`l)`_Z2PMqdfNasq~b>7XNY2xF2zd9k|cDotw
zu+LPd$!dIMml};anVFLdgR1!Syy)$DB~qk}cWATf^yu<U#g1wkHe$qGUjpWX(N1!q
zZpasdn&QNhZYu+~A+>x~K3YAQ^RxNhO)CTr?T0}lOg|{9xs#A^QGeT&C=<AEx{S$K
z@NG(-qf}-r-^{Cmrt5bmvb@wvI2IctmfRSQsXU!zI=!djJPFh`Spf6)+t#RFG`--Z
zTyaMDyU-<7)-)3^Wb<aB<GZLXP!u6BGW%ymT;<(mWM$F6R4Ybv^;U{34e(sGI7j}_
zAz@`aY}~{KqHqdk_)<eL$nCovnT-Ur9ep4`2s*H(Rqw)81(c##AC7OYy(Y{G7O?2h
zet7MO;R`Ieol3{?*m?_?O;jLgANrY&jLtl479gxL3bN#-c|xIXpSNp)m2@=TTB)P6
zP-AXPtdd2aIJl9|@@>B4$Z^Y1bVVn6&8@CtrkI4z2(~BgZ2jJ34_$pnX<KmGpqIOM
z;l6yGwu*5<sOljh<Ullmrf}&|@3MzQkbUy8pv!eM|KivV{@dI#5-f`4ds+W=6#vj@
zlzAG+gchKC@KAE;+587-CuuMtLR{gTNfxGC6-SMocv=mfzms{84J1l--YrV@Y{+kt
z&!I_fHPS90%vcmiczBp^d+jH_M*Q8wFdQ3z>bDhEL>>W;jj6*nYYltOu<(l!fs-^$
zo31(T-kywbtm0WhFw81TS3pgtP+O#jwn&^`IpL%9EKb)3*4;+;Hykb&aa>X`Fgj5}
zN*An;MZFyXMkE^zFocWbq%$36*8?`5tGzti-Nmyc|E{a)x9`i=;aOeBZIhW1>ko6U
zn-P`jigT7_-Cw^doP?8@;;cYHE4d;riQDQ1`<N>3za0fa`APf-gqbASdJ>3lNZBpP
zu!AH+3c09Y(dLdJ6U_mJz#opkcF4j$gbe5tHfv(4CJkuo6H=DGC0Tl{b3N)ZncpdI
zi3X>w{Hfx7w7;#{`HsYs3FlzlvVa9czX&OMvSbS{e0$mJM%oONBmt@Jz9aGRu|iQV
z^b*iFfPoG=|DI!u-Gb>~fb1~S@xxLf?AG0?5n{<hW>{111#}}w7CX}*<T^nrXOkeA
zpn>Ma^a!zkOMpZH2Vf9`u<OAA?oDbAu1$8fMaUL~`YcQNx0ann3ihKO+;V{gRlxKq
z6GBkQs!oq%KkgLn`h4F(>)D)Ji}q1N@LR!JBa~mG`{Jr?BAT8wWO?B;uObg$Fx`&Q
z=KcVA64a`q*G*yX&L8Cf?Sx&7le$(x{NHMMNVwTmd73R>LuAUBDWM?J<9z;P^R|1N
zcuiDRZ~0Vav_e1CSZ%60pFBxTlvLcBkR?c7uC@4ix3Wl)-HgoHGRUkOXiL|MX0xPF
z{BwN8PB^7pY(7J#z&~Mk6<+6>8}jJ}%^SdPb@`|$Go^y~Q%Gq!NMA>Q-9C{cDX?83
zmQCBMc$XCtPm%~s2`z+eYwku-st_xRA=7$(Bq32k+i1+?jq2BfZHHiIjuK~b3KWyE
z%4duc_c~u`(D8n6sIQ-E6<z|;xx7E^MzK%Z$qdn;islLsH7>(WA3cv+Q}F1kId(tj
zkxUHNLq<)Wwz*E$S5~sO0}Dt+6<J6v(SRqd>Y7RIUu732!zch*G4i<0;<M-_N7y}F
z<{fVVub)5IBF4TO<zs_EO|9@jo@Ypx&(IDt0K8Z?=;3{@P;Uy<LV%w>MyY9OZNGa1
zyM4MN@wj%%s1wX4GkI|ljHj~I!#twxhSyQ7#G8MS4U_k9eThjWy$@#CBWaKfQz74n
z3qTttxE~N~M=~@N$Y<fzxI{<7H`C^l-156pP79VVqgySies($PA_Fw}NFnF`irGgm
zODr@tP4?Q}K2QrlD^ZSdBhQ-rHH;2K#Hf4WD5*!|(fY|x)Xr*aU5Z|S^jHc?K{(XC
zyG?_iYg3Gj$9xcy)~R-J5gDuYE^A++1V~xd+xgufaYQ`-1T`ZzxaBz-z{brlki%x~
z<WKMY<^CbJIhmu<$zecH{^r4_V}OLmY1cOZjC!sDF|3~8<f9oKeTKwb*i-Mr%um~a
zqF0F7t`AH3WeSBPZCZ41F9ZAICT3t&6T~zP>fw)Yv?PphHATMPdg3<(RQicz6PJLd
z4L#<R(D*Ehj!d7asi)nVph4LdRwMO6oK+MC*)>NedL6Heb6crV3gO3CGSWg`yet+9
z_jTX>5SJJdsVjWezSng4oW6h>WA~^RgYmU1YOw<10EJJ$Wu1ZwA|*70W+eYMnSS(-
zx)Jy8mpZk2LY>?zg2g4}5m*462UmPjnI+n6w0e#;Lz*~8Kg7jG$LPUaGTY?s4mt+L
z0v3D@2fOj_9tCqR*T;lpbXZ%Lj8s5IK#nY(CLUe)Dh*ODfaam1W(;yqm~MrK4()o$
zMG)KZXjh~}PO3|X#d*X9z_cCf+9d6->fgyoQnYzz2t7IAm~R)E_$AZT_GRsfw~icw
zMEJthy@X6^Y;C^lC*?Z3@p?e9h>kvBi9}+lRd;AfEa^-WAMoq$!2kT3{(x6^^$(zW
z4eSaOnEjCf6a73CrvIvYtv5SN%#FU~st5d(PzSUp&zY6}WS~<R3cDfUmMIGb_)&xL
z@$tENMrbiRaN#A5R&+wAah;Z;gakPvr`X$ilrDTFTuRUqnP)RCjs7WtkS>Bs{?K3a
zd-|09gAMZWcXgl1D$eA%S{yU8Q+5k))}~Q@l-;vGGU|cH=10M_Ak+Jnbb3LNi0{!g
z)_2Ly9R35_{Us7)$8VVI-jqT4BiAh(Gmv@`hk^u+oA#LU>-tJ`WC=Q%ufJz5t7KQ-
z9~x;~RMcSjyF*~A+fBJLEcN}gt@T!oaabv8>*p0i&R9rtcgNCzbbKu{V6I+HR+W2J
zMqL6r&(L?~tazHm*<$#v!a2W8hQ4^iStF?y(&F&A*nCATcW+)i>1A*GiXMU4P?eGq
ztFZ`oO(BM5h6fIqE8cuEegAckUI0DrD)A=_wyRtlV^*#9^8M5J@+U|t#{x7T00Je}
z1{Mn)@mIS@nI&&2U2a8#vRoOR?6pgUv1i52Mwzl@g?fY8rcOV;D&2j)d;`8>n7Tlj
zayz=>)fT0l$OJ!1Z1mUX$%_MWPyj#5dt>6+#DvG~_+?tkyOWWV^Gz-b=j_)Uy_^Bg
z=#T>OZgw|z1ex67K^Cm6n$NGeFt1H9F@eLbO>S1+_M_v;W$08A{lj6Cm531$(WqbY
z#GUMaX)0m3Q+%|YzD^kE)Z!HI#~hGt%_E{KQNt!|1a)sV(8nTaGj#i5MNs_E-5QvZ
z>vDaKaMputvSQz_xL$0%PhNDfopc!SdVdKB3_RG|<Dqi%{d~{3b#8MyABZBbRyDEO
zk^+c7^#X7>qLI+Ilwew=(~|=ko^qiV{exe`QSAyhCP#$Nx{=?f*7b{S#bpKDT%Jd8
zPzpE@9bq$Sj2<20@9q~VZt@=+gQ`05aldtbJc*uh4kc@eA&O!mYUlI7Y#(#jZfStG
zZ~<0mB`ES@HN%c=(s(;ZUXF?Qf~smSzVXT*5I^3MVS>zR$aNll{ml8Uzi1Br?bEzM
zMQ!$*hr8z{h4F&B{dz_^wvwHQPtfN^_2=pKY|HfvcD}!g8cuih1pP;PQ-ii)MoX7y
zY5_)b-BoqX&m8(n#ai{e%1ezyCrE8WOFVZRaoc$3bM_;pg{QAb$H&Jm^mz(JGJpQu
zk@^axAfsq+;32m_yTU+tlrT;@&k}I^x{Alwy3e%pJ{^Z{_;DYQZlb$~Yw{shtlK~_
z#Hv2okEwoiBp`<|C=>FeF0kKxioU(CQ`>PGY^C~JukG-?JYJLwAX{r~JaTufO09FW
z7+MxuYG0G9#6HaDM^!?FpJ~{U7rgu8rIClqda$~G#TX*ET|OB4{$w6=v-!bGPK`|z
z^?O=O1&VWySki_tyi}1HPqoYwzth|8qXhi}bgn%^StUR)B_>9t`iQ|ga-(c2(_*O#
z9WPjC#>@MMmqZ(amq+G77`otZ?5;p(?~UWYV*?^K4kV0j7QUrW1PJAwgODscIB$7I
zJJn)K?yTUXUOuySqXDWv0LFvg0VL**zm$I!$RK~UBRq-E=MDMMPHOB{OE7`m_@HB~
z?^hDW{+vv<eKuU@Kt^$enmJ)X-?o4jq)bUnN(}qw;ciZ<`?5CvyDJ|QQR~ngLZU?T
z%?Xkz?z&ZExxK6|d%x=dx~t+tQ&^d4gQKINt)?dPsQr2A)hUDJEvn0LJ9K#({}vrN
z>5sMWIvQSqlmkMW{i^5XTr&J~e>`wrjRu;@Ds^X~>-B>n7y~c>IdV6F_Re>yi}gvt
zo<6Clp%U13a=>`Q8E^DqOM7cOG!vAUr+-aV_x*Lv8SfMm9x&(I%?}(v#4)xIx?94Q
ztX_COK=2-?MAOcR|DXV$BA=<9KI}ZG;4X^On_ye8)hLIA%VQ6$u?}}n_sq60itMa-
zyLw#HaD8<^IQk{;ur09SJgKRc!yhT@gG5e|B%{N)zCRJ68RUV|lSt0kk<m~<AMNZ3
zA?oaUu0x?>OJ<SjqzzI;NmT)3wO&z|bnI?)g1QiXs5W8HA-{N(G(DZN-S0pzgK+id
z$Vm7WH~545*4UZ>5u;-ygSx6N&Ys5F>mhG_-HpCl2n6%Sa4}Pn`#Y8&1ijzNQBcPD
zFQs3@FA&MTHMd3@I%l^;$ha_Kwf5Xzrx7Gcw9+ptO{|>UK7;rpB0D2?#~)O3PD=2F
zsP^JPnD`;J?{6#&etGi>$|Zr$-waereVLRg2orc79N66=5GQ)(6SU;jb?@6#DUlEl
zJM4Fc2{`RAHoyKLChk&YBMN@KP3x3KgBu2P+Y5peGUAR+5p=P5E;dJavGQSKKf7{@
zfOouTB_p%fzk|5b42=AS3kwq|C`hQ+bJ^Cg^%5b-s=U1S7~N2W^emV1IowdI^n}o%
zzz(u`&Tt5Fs%5@6xXvn&5nkj^@ouC3bYj&4m;It-k9u7Ms|yaK8}I_VLKA%tPsQ(*
z!{F4orh<n*e+>We8P`m&LfU7}({Fd&#w+%DweSsLluM{@{2EPEP7c{i3opxh921`^
zftrFMu&xf=<z&I_3kmz-lFnhFBka-GZ*%-^IqEWId;CYdE2ShW0+tR|wNt8a_k#I%
z{ad-FiC{xUq_Sess9NTa<xbvB!fH9mT~I;aUoFinN-}v&)~du}$JyndXrjFw0u~Bj
z2)|mSl>+sliLR8B7O%SEDIP^`Mk*`7R<6#R4W6bZc2#ES=jOJEEE~G>-nl=q2(adc
z`M!_B-Uonjeyk1-7R0sjHf}!P^|n5|UHx8p?5t4VmL;U#3ze4>d6kJP-{DoArljhh
z=iw96JL1(nuB6vwPnwH8Dda{{R-E4!HdH}z4lP?(4gY<<R5|hR2KL=_Wj`$e_tR^=
z{mL(ik7quweoQi@*0HJcgU8)M^FbNaC#FMfPi?ly`t-s{JD=dAsV)2M65(8HjU$0N
zK}sTp_9f_&Fa1`IUxiyeNs{^Pz-j+RYJY;3yf=g>fx7Tv>+`O4T1wyy!tpa{;q?V!
zWMYY=bj)Vg$;);s-iKb{Oqf06n*}+(cf~Z`iPM#zj71@MIMy**V^aopk<`vbSc_Gq
z+1ov};Qc&}Ho=2qQ$%c(J(57S4}TxyX&;PtI_D)W3LHUpjOA2HPe>4byYuH%ATT2Q
z!F1>gD(zVjX&Jg!+u{p8UE5B?&P`%-`_|DVCGQ5IWn;AVyXJ{niM1&r4CnPeuPZ(H
z&7)I`s_k87f2M0B;lmOsON_d(m{0vm_|5(LPt%#LNs(vK^;4D>D2p7_Cax5jh`4${
zGt?wbSOImyNdjA87{ByPPYxLvY9!{nTIjDu{t~!*+aSqn3d}DxRS#Ya<`eMhBU%KR
zslHB^!Ucvy%h<mUf8V&9%%#25&1LeOH#I}+M^a2C!bl05ifO{V+$QtsC((V<ZIh@J
z3VEgU<onRsz(hL1@dwhLCGoFR1g(R4-#TGFoGcWnG-YpPcD${+JiFjyluc~Gg=igj
z+(-u0TAD{Z`mowN#xz>G6HA3=3D(o_;)!7EMBa}4(LQ?<BuzANs6=Yp!z{->3t@1H
zl>gE;bE}#87KMvlnHc<lUu}2ckz279Hkyku&&+`MerbL~CbrI<mn~D=fcRqg<K;HJ
z2BELxRwV`+WjLk?angIJ{yEMq=5ZF{-ts4DuXWP|s$@i5cMKIM7J!iqY;Y1CV0O<*
z-^P-i=amWB^(3w&L|IxjOpV54?dPrlB#&lLNE}rf>UE)9T29&j)23OBwIEZcIbF!b
zrF|f!bF;^Yt)q=heREBLgB-PAZ&tk6uB$&3sY07(>QTafa5kZoN{jvAz%W8HhJ>ue
zqj6$)J7~JMAEIuWMKnm_^klU4WPQ|tgqJEN)}>vG<2^uZ{_)hdTLg9S&WG>nPr${A
zGpVs9@u%6fuF`4Oh!^Q0v%_}u)#))jtrCrG)08S~r9C?v1c_0sd!1x+-7giHk>m9E
z;{3!e$x?O^`AvpC@Yz+<;H8z`&$rvE0|KLC3-DQhMspRGf7LqYwkzb%1*1zbW?|W<
zv}oaimz(N9O#^2ePLXp(JKt-esNk2*;&&min<ScKIR1mt;d2=K=}$G$NZBo-NuYEo
zQtFlEnJ?&CJF3pOX5G^qPLb$yZ@^5ufbN%G9!wZd85kI@r@2mgbNcxM<)!5>QnJ<<
zS^NIf;a$%fdGaAZx?&q>7>ziXyL(5-%Nz-al0NX&>iJmed?xKwb)p9&Zs>-kEAWNB
zQ8=pOOLdb`#R_37E3Qibf)eo=HQ~$oyriY@TbPhmih<9kxit*Z2;KaI4R#+u>u<+~
zU#OYpV3u6)C1@&XgvG_Qk2fcO3{oP0CJUQy_o9RIYG&~WH2W2@lWi*#;%;o*vw8!0
zYnITWGhD$)xNQO(ZMV@dPO3IhO2U2xmsLJPjH7WmU{P1`+np!TU3L43TX1;)PPWu4
zBl}HNKtyH#y2YQI2ljjND_}xkmeb189`5Fv#MmK+hFavg=WXLXaTUi8(sTWX_psl!
zGd=sM^~NUWk-z~C-$}3j+nKcN)S)Z6&EEL}$u+SrbV_?VuGPb)45w=ulw0FnZETW`
zsCA@hN|fC1@6@z{%dW*jVl0NGAzG^9T|L7#1VPlr*KpGvaDt>RIm|qhrh+owFxI(q
zbtav>Vy@F^j+?^E9y~P$G@T*x)Z-m@hj+2Lr?~9SVAhhZpGoXa=ie8pUo_sO;|*_8
zZ(W&MB=OF6XX+AI56>#@$tBBd%7lU&HiXs0FqAp(dAdSwv`d&8Q~^4q_h#IUn5)aw
zJT=jQ9U(W<Lh-4g@se`|aBkORsCawJ6vooe>cP>vcNCk_LU6_JIWeosol4l(FM2sa
zx<}Cun5Y-Bo9&h_ggjB8X|6!(ndC#g^{K-JKru=w4SuN-)80v#$E${Y^Jpl7e}1dk
zLWrR=lh^5(eW_yUEp|oMx98`4d~5Xr1l4swb*QpY0FYrBLn;qE%NW>AW9md^i4i*2
zN-c2$MLuUpZOc!X3?;D$5mQ-K9ZVzuJd^9Xdzc()=ryl!%Oau(6IF#NrnYd41sUsN
z?9#wlb}5RNky|Kz7ac2Jwqrh-vMQY8g`kthS#hhHJRU#xF9oF!M6xC@$76gJIuT>u
zn$LU#Z6OP%&&3J`O{;|JMQWYb2anR`n9$~UBlCRsBo@1@^1D{Gi9Rv(j8~V+%}`Ya
zh19Dwgf)Y6*TO~_y+uQfQ8e%0OrY-0zg*Fq54Ni3nVO1C=KXg1r!=bO1chbH)U8Hu
z!ATWxF5e%yX2%^O7iRt3>j*0(VXa;28(ay!f!SGS$v&OlP5NX4tk5LC@`P*U8@MO$
zddBQ+D@V+y*+=H!WJ*enqAy3-SB@U}i|Z#1u`>L?|7LZ8AY}uy8GkDxqp|i0nPUHz
zwMJr^nG`@sSKV8@RD9Z98o=@T6OOuiXJ(08P!JVJ*)gB+B~FuuCVw6K!Ufx3f&&+5
z6`&%YNZ|AL4NWk>!D@wSvv1s!;bSYQ-qm#=i@4x{my}Dr`gRV2r;SlkSd_s}VZL~@
zz+WudlM{)V##5a}P4<iAur<*yje_QrkZKE&7#|N1c(#AWSIrNS*+cjzUTG9c^QC4^
zBH-;k8oH~rT<>=GbTE^r`TLWn3I)GG&cz(|npM1ldi}_-#PMmr!FTM~(o|nNvM%PE
z6H^@oh>w-akkX_gj-rjyAxC3-MSsb1Md9x{@JY3$=3Tq36U4m||Ct9F*i0<So5>*a
zgC?aWo`ikXWGm^ut@tINv-R0!zW#>xMFT4?>E<`|6IxmUhMY>XhKA@Pr;ICwqBB0#
zbwKjA{~HuDSG!>=buK+Jot?~jo^g=|JH2bCmz<VeSL+OT&4zBnb#Zjrsy)*48<hnR
zZswo{aLeLTD$X-Bp6kDY4JnWeh~{FaMUBBVb}%i>g0zaq?r-Np-^g^<KW<eP7dzZ{
z+dn0A;s<4}K?A)$A!0Fe+7n-h%2s}O&LV3?3LS@TDHYcI<z6A{Eh8p{dv4IaSj^b}
zN2`KnXrH{^vhl(glmRenZps%z#!yrB>J>AJ1}+)CurBU)Wy;%|$3_(HK!=9LY4wpJ
zT-a}Qfhz|#`bW6CyXehAog6RT$vx^8fAm9K8BC`9Thv{165OX<sT}gp#kzB`zJ@GP
zrR8M}INEtWZiGRde7);4i<KTEOuwA)H^kec-$nVPii&|Sg6oBJ*Ht1XJ|MlS)rdg%
z$wO*Zc{3TCBzS5B9w$1$7qF|$UiPByFDl<s-pNlw`G@wuyfx4@7L{oxYsfr7KAk51
z4D(;^PoUl8_r3wog&<0his!&r$W#%8dGkyb&wON5Ii5H~`bltuG%VuU0?AZ`0LsS`
zwy4mO{`fK&6Fj^CXHa%y7nZDj4msy6DWdn5vyPi1zM_z=AY4@bexXWR{a(5TpZeAI
z5!=mD%^g$e)rU}pt*x{)RLhR6_%0@2R*`uRiugPnBu(01R$!Nc$<8jiDQN|hPu9>F
z3e3@mUkJv-+fs*y0WnKCRo18Vc3lx{zBOzUhYP7kYMMETKrEPGsY?I)yiJdV=_RT_
zfX8*_1hEtOEB3A{75yK}aqH>#K4aoSkBF-CJ2uZpgzLdNf{09f2k*(4Ra8VW2Ff4T
z^JlC*SIfT5h!bO>A&8fz4>t^5_4j^`V*YPe5wC;jvtZ93VDS`aB!6m2+JY3x-yU^r
zIl>&7i?m%oPYW)gOquH+bzHvq)lab;ivScHRX!hZBhyfOLk9QnFP3WJ1fL>rB0F%9
z?&>f=*)-NGa9j`<dSLB*d@5ixhh=Sl<E-P{Hm8Tr;3lXSC=CB#u{(ta{qpYO?l2bv
z_X9!1_`q6{IC$*|V%wd7F1dl&jB=zkhU7PNdyB0Tp`i68Hx4(i!%i6fzA^RF?nYV-
zA(z*JKbv^$%X2Do7H&xD{l|>-_8UPL2kE?_ZhbF;7B2HTfvuLBI)H@zJc`%uw}v(8
z&&j0fw#fAccP>GIMihY~;Uy?0@UwGf!jMz!y+gWdd1Ydim+qY+&kO|mp9EXoO^B5n
zk22VfDxZXY9h-aI+YD^cu;y!p1CJjkZJ1*|AxkOE@l?N|EIDy-;xU@+4tm01TI6Md
zQIv=ft@$(<J<okJD^?@!kgC!c^?KgUGYZ4?3^!1S@9xFwFvoSCMAEUIo7JK67K2ch
z77*c#8_%P}B#JeQ5rb8bBOgvcB%MbCyksVdVPNAV=1Kj)m^0L3ONsqJmMj?oBSI<$
zMG8v$^2$+-Z_ZG)uTB=#FyjB&s!d=3rk)g;oT=f)^h*{u?vGFC3^R89uM9ugNQB(`
zjbo9KWAi;<Uc?rKFmYEJI$rl|3mQ;|o-g~~na&PUve+-4EbLYWj&hb@m#gguLA1|{
zCq-bw`J;_6N;&jO`uesK<V`z6tx;ftWM%m|oP&TH(`(a^RH>2n7;W-{RzJhAY!<E_
zA2@`WK{fm{He;S2`ft0*X@V{u5@zPE;$L_9)wJwo%~`UOpYHl465p@Z>o~}Q+*Y+V
z0C<9bo&e=zb&EAZ)egBuq_=kXkrTNe&a-6k8>{%8FK#v_PcOST$kfVB-c(2--b>CY
z;e_6I^J7)DRJN^8P}~#oVS*bqb<x_oj*!|#5(mEV%?09Rx)41eDZOV0E%%pKd$^Mk
zqq}B2P;w$b`s$jvP1+zTzYHpMjD&2jtNggGAMaYQh<g_Y2Z>z|!K+dkYJh6V>51}r
z_>-+KgwOZXM6q1AOYPN|9VDB2&Cwkc7Scq7ZExaA)V~{V^&U^>&^RYOR_;lDV6W2M
z84S2`%JD9J288f#Fe(^e5;(Yz0-FRq8|ctrcO0#{{?UZJ6>)GGJ|%&84}5;jBe@-T
zE`L30!c&7W9M@WZ8AP`xj%1hoa09-ifgl|q@6geD-v0jJ7&&HC?1G#Dced;Od$%Co
zBwx+*AdkOO74I6&8zY&`=`<$PL0>ct#b-nD=pSp*hEvqw9X=dxL8O=ADDzE&G`U55
zKde^vr<FXX`(siR2~J#cPhg>^mDXY0N9UX6Tc5Lwp{BESCQX@OVYHNR8})l<ic_UK
zO}lSlN!riCEt`5jLuvGd@MEPX$5}JhI8zpdrRFz$M2+Qv(YCqK<IFPQ1I5`?8`qYR
zJXO`5I~ngw$h>b9vy2If)Vp?^fkY22Rf;={woK_6c)G>1<YfmoM=@)KwH{3FL0r2b
z?3)?mP8G_ENum9$ooj4ev=*hB)V!IK1Do2`Ze>lOOpY)mC32|#BmI(AaV*E$$Em&W
zDY3^rrIoRDT+c7%o|6UUX<TS7FA9+}0>cK9h=_GjoIIXt_upq5rL`gxae=AOq|Og}
z$MnnoiupG8eb(Wm`6#|+h>H}Y|HTK%iS~#?(z(bbYwJeG+d;FI*%-4woPVL%QT^Hb
z#_&0JXqM!T@kaJKtrUy*_>P!VcO8__Av%`E-dL5*sR(z@LTSE~s3ei~l_-8gM!06=
zJoRVwcEzV!zq<)Sz`3STgrb0=;`_8;!VtslP7AZ@MDsA7O5)G*ord?ezKI0r`&994
zCi;|S9jvX*#JAjU+Qb6}t+wlSU%rgyeO{%0x}gUA$Qnj3iOQl~S#Om#X8>(k!0~P@
zc#^n|d;WgD^TT$Bt2-lvS6y1$?lb3Cx(BhPyn!r11r0y-$euid5ur0}`yycYmPOre
za*h*K_@qSWFf2Mm0_{r{cGe^?UpyzM*I@C|;7EV9Khu*NNq9r0YElxn<ITmPn8)xj
z-&G;Y;?+@Ehh$YPY^NM^!?W%5N{HQyftu!GdgX^(H%DfeSK)|y3lD{zO_3AB)N!qt
zA%75vib^T#b589|E%?cdH>|GOFtavXsm#WMjeXmMeU$k;SH|F_JXwaK`dpHRni~@&
zZt%dec0I}P>fmZwLg8s|WJ*@Iikzw{vir38MDW0{$ArTEI-kyx<4gI*ys@k{TsW4|
zkpK$-p4(w#W{N!DxjEnIoL~HsjFk8z)jLS<wDwgd^`gsovJnn>TED`;f<zw}bNhHX
zty*4QuB5MgEQ9lwh?B|WSzcdXr;J_41v1C)?eEv9mQQ4G!$FKQQP1|(Zd?r9-CWPP
zAjZ>LGg!BXdsJCQIEree6%&wJvw=~|%|B|jdTvxSg+X_v_BtsmTGKu1><nZ%-rCje
zKZnC5ZjL=%-}eW^bA1m}EeC<bj}hTfX?&7|0)x(#8+asOvHg=RTHr4X1#z|l82Z^6
z8eQdt>+`x}@+T8qu>I#eu*5^0^yTCAa=G%XQa~#sJsrlk`vU?&t=;82A4|94t5%3`
zt?^{$U=)FUi)Op__SJT8Z{Sy)A71U}tjtr{t*z?9Lmn>Y@I6CX7@N|?Or9(KqzJfN
zTmW;=Ht&s6ij%p{RIx^_n`dY1$xor;JcW7aNNqNa<4OKyi-{flcNrHKeRNfE-b0Cx
zlUkZadFUHRFc#1@C&D+kZOCx7J9oA#1*R0|fN_JyzCK~LwDTg*pzoOtmgkGD+0|QT
z%XK88ON%hu7wc`;w?v>BU?!6TtE;aE<7vPU_8cWm`OkU`ZA?te_JOb7-_njP6#tl+
znO)UkEsQ|!El~j8*WHO5*mK*VBLZ>Rh_2Rqzd}Lh+LEIpo_!dlee8QxAbDNKgoVkI
zZ}uDMIBw%w7r77H?Lr7q6HbwWJvh2s%A*NH0hR#b?V`_Lg|{ON(4H=6#%sWc>FCBI
zb`ymiG=<>RG<iMz?5@ai@q)%BWM$dqh8u&T@OgWfF_)&NBf_>|lax0qVq;^k8kQSd
zQ}#CU#G*na-@@y>6iO83L-O4pFJ1_j+%aJD=bMRTD&z#&CrDWD+h6Nc_YUZ1AB6%H
zUEGjdv`XRbli`Et&S#FbX|jYE9?XG389t<9KcDHZdU)3s_9GMygDZE-VMQax{wlZ;
z?nte=gRi`4La02(HF2zW))YS=@%?QaOJZpdFl=92?Z~OD*~Oe95<@zmD{(M4*ESWN
z_e!5>6;byTH~xIBax&gKJZ!Sw?3|9R$h@)y=t`-7!#LeP4p=l3FOZj;j??z9HU1Sn
ze+ve166sFby^Ais=ff+QNjtYtapQ_+x+Xp}<9X9<S9*TI9j!=(r1$@h!C@u=#%52r
zhhjSHr?81HnpKq{{$oia*2!*=9ai=VC5Y|4$eX8u;+|HT|Hzklm>?cTgi<dxszuDM
z0_+qp7`9B?b&^eg=Vv?=k+)}S{X-tnR!&u<v%!C>1-%B<y~$3OXs4{jN~Sc`ti*$o
zxvT4hs8d{n2D5DF`twweG|07C@};;O9?rqh&Mo4Ut5Lmx>$AwLPrS?kRPZd0-z9qW
zoQAk0iNbEidmw`XMf&_}ok~P~a=m&$x_OJvPve-L&H+3eH6HPUY6y!x{vm??@+UUV
z=gKK>t_YbAM*25DJm5>#@EBxrR4$GWKLg*>$ursrH~xHf4Ds7qIc)KOKH{AI={JSe
zY8aiY)n`$^D>mSub+<ms=*iw1OP!vG+$He>Zp;w!2Aq>UdBbvkF=oQNw?TT084Q~>
zGc~NFU3~1>i7JzWLH8W6mT;mc&knw;HlE$=4x(Ht2agXBFzqBJIPK>aFt85Z>Dwpv
zSs6UuIChX%Fr{GGRg`{1(apg0UX~URK!zsKC4u(EtJhVS!+ABD+!VWw{j70?zRCKS
z0J#ZM_TzifL~44v<CUB8adX3c!xD6KtoSn%n3laZj6SKETxjn|MVkC2AmuSMWuW{L
z>(msTlZxq)sWGcm3kL|#-Z~m8y*72lR$!<owQ-zUKb_1Dy}6cC@Buc(Oh4{sgG#YU
zrD=mgIAmmAFhv*UiqqAU1pxgTQWLY7Wys&JPho0wB$1?9rAcb7Oi;>88=~>?_d71!
z49DIn`sI7Vc6lnwR!;!<3n-CEuf_AqCih?WXQ!T;zn9Fjet}#rOl|g@8?`jjllb~D
z;2@<iN>eJ5FzXAUoMTpjP#V%-&dzzDW{n#>75jG~e~Bn6tC)tBQV(8IoUN$ef-&TN
zVMzPg@_;pe$iLO%{Z^>@rb1H`({*kTE7<}zsfksPM8h@OS6Qi?{4ILetelvlA~$sS
zkVbmsbl{J*q!+2c`+I6>DY}kf#l>>mbv_d8P|zYPj`jgn!uJ;_4M<4sMm==SdaGE}
zroI|X^b|n~{8dXuoy%uyj%#x|@VUf1fi8qQ>eJLzGa6#8dr6wNsA4(hujEYM{3qo1
z4zH9JF7I{sl}nO76GpH1hSSwF3pAAqo{puvHYy_8Xr+cu;F^@7m2~)|d82d;*A<bY
zH%cMSj;X>uVvPDh)80P()0!Mt24=TjjDLSCx`LJewze>|C(~cR-1l&g2K8|PEuVV*
z2R3141EUxjbyL8QSWz()nnfISXir>K6Lqe^s5?wnKPE9;;bZ+cHUav%AQa(z1$J$1
zp5hXt9kYPi?@>wh4~+67etCABe(2LBNYpCwdE&jnds&CsIGk0==bbmjkeN<^%}>(o
zhI7~@I|yHDPu6{TyCCU{4&&Q%Tvr!awphOA#DFP^DyVv_Oo_9FBs=^pMG{W0v2T-x
zL+BIzWCT1T_$SMa{SGdv;o9Z42@+B@Zo!sUjrVpjO)D)yDiu`8MKMbjwsW9>5!pn>
z-3R6i<DE$&U3NiM9z|Bx++`+<d|lU*-5Y`lhc9PCPI-a2OO-r)Rs2u+XWkSZiye#N
zInnSiEZ_kA4f-b?W*Dd#{_L24o_tTtmYXq49tC7}Q#*;fjRJwoN_7|SKZCoNfl-)!
z=X}fub(VP$bVV9e#@$VP?Y(MAUdR|2tu2tjG)2fVRgTzd^z_wb46@E>ZDTH1TZDdc
z^EG%oa*qW2=#-zs6eUrmU*22JQ@Ijmha?S%Qd%|G_c3X($;gUJCD&V7bAN>XCaSIQ
zEt^=kfBbv6P$z5W;la;p{fgk92>tdS{A>)#q$R9g?!X1v(BTX$k8rYvMo(;1gp7EH
zpf0iTN^Ds1Cp$*k?H`}T>$ID>FW7k`obK|<Ir1&8SjQ%(;7XDw#*0-Dc3PdKB}7+c
z_{e#9IjWD8?*(N^zvgLRmUp8ISAsOL%$w3ARkwQ>J^3g40}7N&715O@@UQlA)>9GC
zfI-aefxqTbrDXk}HDmr!ZOW%_C1zxp5_%|F3G=(zY(ZPRG=2>qiW%}CuF-^2O0CDi
zuwrO*;8T9xrIM9(eQ!@t@eKo}5Vy<f4?bQn2ISn;STbm5=%~xUm`U8|-oyp&6@GYj
zH|T%x&SW26x?Glpa<V80p$vaANjb8HwWtv+MF03;vp1*}Oad7({G0_!Y)!J9j-YQ!
z(h>5<XRwbW5hYA-P1@0k%E2_$jv?0yzGMjrZ^Kt5e0V5JpkTo7`(z?r9TFrXG>aI*
zNBZ!{-!f{0XdWSPVR3PqN_o+X4k<fh*L77&kXBU>wA%OP%2Vg%+DV~n_Q|Yc4<a3_
zTHYz^d3bdl$7xm?{^`BOJr;-OxBGi<lL#(Dd$^b??F$NuWRAy+)9%|QK@2!(f7#Rt
zr_zBfk*20Bd8dZUg5oHpwdg9h-H9*xCVn=0x+1E|8S8k>d#eriEi82UBD6QMo}?02
zL8o{v3i*1QPDtK_j~+z1PdB)?jr6L}T<mkY+VP4Z90o0~*A-4Sf6aFJ3lS0mtJm*D
z?QO-p{HkYloxS{IL;~9HDqqrMK0q@wwH*kv4z;@{q^NX0xoYz!T~E=R;_;FyEj{?Y
z+M`(t$o>b~<VC8k;`DMS)SKkm+s8k&;c?giuPDu`$`((iP2g!X9v5K$2Xl290D{8X
z;!p`Huxhjlao;mieqxA;c7EXe7n222^9Xm}nav3<3$jHJZw;IlXX~t+Mr+Dd5XLj%
zcjXCSo&AFf1Cvd)ffzpvgU+esB^F~zA(VGs-q5eJxY(Hoyun(|im%|m3I19T<ZoR)
zuud+1JaloSCnG)CFX;Wrc*h{ccgt4udeBlN!OqyIEJ^2iVuTaG@y{qhz#LJ0kb}9d
zmo|qRKdd7&l6Mv#)=N*6LsJp7+-ScsU$doIs$`v2|1ir&{-@ejH4P-3Up39-c69d|
z9J;-|=J^(Y3k1PIz<@SZB?Tct!kF%c{Z~DIHww_|v@rmi|C12^mE;0Y|3Q*~!}cKq
zPEeH?eEa_-B7Y^q;1vHV1_6}<`bV`e)PH*u{Pmfzzmi}m84%EabtnD>;zQv5!T%@0
z_!|;H{+bQ~_OEW*zfAuq*f7xlCqV&9h`(_Fv-kd;1~HJyhl~jc;r}G0K#2&84cLDz
z4T4lH45R{(G9bkLKZ!U9-=QUP1aFp2t8GS1oL}-->ILtdR@+KlX*SM32I7eW4%&vX
zj!9A~dDFr$K7qIqPM$g4JFE(uBRH9L^-n11$$z5(eyBcF@X*YJw{4eLN_um{1h8+v
zaCGJh@@6~QCZN4+tMz<Z|C!X*)?cNh;9TnxhG?)7ivmqGs>KOGw**CUjt%tiPXA7e
zg&k=5)U;~WeP%m*<RE?O=?K5%iEv+!S)0Fo#Qu2>Er7aA*y_iLmgdn*=8YgwsrPow
z$Z>uDrv&k@k|6V2@rvVV)P%5T&EKS*Kl2}~3anNaD3ShVM}z#;#X|A-e{q!!hWZQw
z_Xj45s{VcQc9gk=Pzi~<Li25x>fAK{$K+2Bc7<iE<#2YDZg$faJn8)L4&nBb-t8CM
zN-^I{bKxeOQ!#Zh#6Qb1$q3YpJK5}l1oo+qhDk&&b-usv0tpiFPuq&_Yv7u17qcn8
ze&ZQCE)D?c_^(_Jw)-25SOf0Szx{i=1OIvuk;l-t|E{4=-d|Ucql0$)*Mq8wfcLV1
zA*lV=K3m0qZLK&8*!b6>EP#znVQ_ka%Ky2|-&n-o@y*+1v;SiW5Kx@IY6Gu`%>Q-N
zNJ-!||G%N&_5cX;iMRJXX;dzx>WHZRH!2XMDP$WT6}8d2NspG3#Z$yu2xjt=yFk2u
zwebTdVzIb;3<AO-B_S-Jh^q_Xi+6SKqBULj7Bd%^PWZq3@^}0Act-MzKmNpTqKJ~8
zC3%|~Ud|Mki$GfBw_bw$uMdDgScrG?;lW&Jq<P~OlGapncxAHnfvf#%6cDfjn580c
zfX5rSNBDdNMlV|6cVrH*|6Z-j4Tew+C778}e>7fVi!R>IZ8veUsY~X6ZU(mp|IZTg
z^9yy$h!Mh{_aeZo+cC{CLOlFyJrK}U6cblyu&%kBy@_i}is?2Q5;ysPq)m=*b&qpG
z?VHyL-eVzH5MgAN8We=i|JvXl#l#dk_dyEJO4zfSl%Db>s+iE|;b6P2r&c*r^ZVKH
zynKln7QAT3e!VM1dC|CIu?isd8|wzHppqs=&=e|(2+XH___$oO9`AYA{BEIt#U+wB
zUIFXx{K)C(OdPtb4@pZr(m5$S(E(}vdpJ7T%m<<LYUjOFLVP}ir@@sNio{Ea*T-tz
zn=4bcHDSC08e4d1Fr9NVTz-XGE8W>2(&#moxF^(t)aDwjndHurO3+iNDspb_se;Rl
z2%SruU*yH6X8|6zILhsbW32^vFr^lO4331QNZ(dgqv)Bt(7hgZtb|4;b!(Czycq<M
zsLPFoMhw%MpR8b=$$l2rS=L&MSI*B%2JweNOQxq|O-vwEtEUi};et_Vi2$dh;3qpo
z6wM_GErP@dHXW^9`@Fl-szWkFe2^6SRZ6l0q=ymY|FsCO#JV`pf}irx+Cl7Zay!GT
zED1EwJ4oMVu1yaa&fv&Q!2i3FThByOGrK2G1qfLan{G{DL~rszxgu#Q;Tb3$-!0E}
zg7;JWrVB_UI=VP+0K+u-nFRX$68|nX#}`nj%wP4;S_Nc+-}PRfT-b*4kVEouBf-l7
z(ZguCk!8F#l^W2{>fl5PBh8vJ>-{xBb&>_We}66I#Km;?QEo!GyTJ@n1qUo*G&i#p
zOO>@9jXN&LaZ#rHJl+P4dS+K)HSyPUFIm$h0G$gHT|$fn0sY5ms$oG_%K_CAazy`Y
z+``|j4sm7(kvqLFM>#~aBT8YN;_s4L+^Rm$<jP8h$&(epF6Pn!ruNs$I?8}e#a#L2
ze&OHij=gebRQG1&YpJ-|^ZKjY!gP*kv#Wz1?(5@6&s^pV=7gCHC%@GHbyftKzv*>r
zKH2~4bgzK~+#xXLi2ifR_`8Dw|AGqx%}!VhjO_hqNA1S=yIDax4*hLD{8y2WAF!&t
z=g(J|lYgJf>c6eEEd=mx{%xZ9&x^qZ-1p^wo0UNKwhpPGqe)MYsR$Fc-6byWR!DSv
zzHnsM)bD#u{_H%|=)`zJSpustcEa5kQ=CwL;p9ZMe9~WsoqxW*x=9P&Cxl7DG$KZ=
zPdQ9_cv$!br5K3KU-#=~sV792lj>pL6mr)X<3piM89|sS@ur#12x1?Soqt^GrBnq;
z8Oq&HJ>LdBr#(m;Mt!U_lgoVTydLTSor&+*zTLsV=V4vwQsW=Kjb#(lv3;}`3q3JA
zs~AE~i{OfU8teo0#ps!)T^>@1x_Cc6nx|IW$H2NN?Fu`*dK3iz$*DnR4DORq$q+dC
z<*P}$s_Vx7k@V}Vz0?W<9?nr{;5dAhn2xJi-;y|s{%wrw|I^+%K35X8?S5j-#I|ia
z6LW_XYhrW9oY=PQiS3Ea9ox2@GxNUZdH#j-<@A?T)eF_twYqB6^}G7Id!okT<clhz
z=EG%LGuIj2uC(nCZ_3b;xGz@*_dNr*@uukUr;mPLOFD3UrtVyX=<r3Sb99xc$&ZxT
zbQ@EbN6@*kVNO*=d{J^S0Pe-XynXY<C-?&@Zu+QZw_V{AIsot|cI3M2xIG8i%wS<K
zUOW{QV_<iCUaD_m3s9G8rh71O133ITj^+-A(GuajEYot&W|n=cdB#h4YqfZ|SbtGV
zzdpYb$#2pK58GVrA(?jZ!FK7*NhMAxv%Z`UOwH&Q?Y(SJkU<AsaOV^?D4vDPs0`Xk
z_fRDbEOJ$@BvI5OPr>)pt)0HsvSv)0N%!yX2hp5D2SqR0clz|yc_P3BU35{E37N_c
zQEv#4IrWzU9>ZBUG~uIOT-@s|^5{F>Pu;%SXnNIB02ea2zTLXiYB5N!A#)l|C!X$j
zfY68RG_VvXS-J(@Oh(SI>)_|>X~q(a1PNKF=~<fjx!kd;;K)qRxU7DN`nA1i#$2_@
zk16{+`;XgB4?Q-_Yp3BU1ktzZnX5rHo`F?Wq(6<qXspuE+y|7)!m%Aj(z&SZJODha
zxmIdS_wZn7{$@5|a`}iFw*Kbdf1=cRE1OZ6d5H{Se<;-i5U&c+IC<Tv%>5|==_QLV
zL{WHFCsek*Z5J!$kx|-k>AyxXJUQ)<Bp0B%eJp%uZtc{&@Kf9xV}fFaYyClrDh(S5
zTP8+}8g@mvVT_9uN9@*lz~oci`4Fv0KJIhJtbitsRuU#;{i)aE2YbE#-omettuXb>
z5<z))QyeIBOIR#0iaASb0I2m)_K%GS6p71>{jq}w=1`RSC*zuiwi4eeJxkp%6BDK7
z%d)#NZ$ntPw$z9DW{M{v>_Wfz1Nmjaau14?;GLwQO|;wEy^@xeUf7(-i3B7Y)Oy>i
zA<*SQ4rvcHKK?8uCXm8W8Ph_a=2;<{gBTF`snmvj9Vhs7%7}V*au$Hh<#c_pAyqy{
zO*-Wn$`Woa9sIWMxN%A(*|jki5}R;W<j9~65Xo=2@^O$(%A8KtLDM@8IbOCi_sy)1
zDJl8P#lbU$oet=qqe`4yT5(ehpT!wtL6=%kQ1{BApBS5Dj5{((-<ntZT4^iMRueD;
zz+b84B3c)4k(P>(dgjPpyS|*!eKpY`ON9;VHw<(PB%SqwS5C)EkPf}6yA6bfrYKxS
z0@VePv(J7pt|y%e4|I2T%FUD`$v+bvV5tjI8lc>>36{$cDob&Z`fi6vOOm`i_b_~S
zhP@Tjav_*)J+;LG?0t{BIs#}p^F|!JSz(|ZVXznH^^&FQVDXn~#%D3i^({!+P3uXI
zzcG|<=5wGx46B{vpyM>h=U_c{L&TWVv}E(kc#;=-NQfdRn|(X8sujqO?~rV<vZ>4d
zMvE@i1P*4i+Tc$ef|5u2+C?jt+tGiNW)*#F<^{A@-cLapt5%2Qd_$=qil=boipfbT
zg-fKW{Mf>qiDk)7*+E@fc*7JZedsqL14CBjCK<zdY=VpU+->Ex6jl&R)cLqv+E+#E
zeTCnRF6<=mcJjGc5I>DJws5p3;+cDgzqpSPo0Pra@{$W_XSrTcA>0l@{HF8COQ?%v
zMikYKU9+Wer>)*#@z<2#kd5q|;ccz%cKcOGCLv~B`~&oQB$=6(SFs@YKsKatUHW{s
z?<=1wKxr~0X+F4edHAhPdB0#>6Y#;%+Cj#D(;tWf5pyQUW_W&~&?cD4=gt7lu~WY;
zJCu8#V47J9`$p<=5mqMq^-0djW}T2^J6<}6QRlhmG{9f*)twFIl&-DT{BEZl4sDf^
zKpy9PKr+13<AX2(GgvQ@m$YBBfAfw<2>9%91_u3bs^Rrog&h_7VMN1wdvE6Oq}A@T
zz_IzbV2iWb$hh>wMoeyvh0I+ZUd_erVZkAXie%1ar%M+|G(fL~k-}m8mMbfH56;)#
z5{6`*ouHwbNrhgpp*<yWZ@f6Tdg-<9lA`Y3fy-IBzJ6Jt;Em54AX`Y#F@Ko=WvhP)
znqx&vjr(Z+c&+C4vRxl9Q{VV}+1+sCI^Eq)fFv|DfJ(37ap@rjBk8!>4otE!3mkJD
z+7GH;Z_K?vkZyq@*&{(FCW&6)bJCdaTh(-Lq3?6i(ZC^Ac%Q4Ovc0Hx{_u$WRlZcs
z1S^_T%^PW-B*?Q08pgMZp+7ULd<bP~7!E2A%jY}1i6!pn?z*v*VqcBHko$hB*6u38
zs6)Pn!gK1CdnJr{lhb1L=`W$shuS+whGE{nhjsUDq5d<_)_kf5nC}`uE}db<_z4`3
zT$Dc(6g+(eET7(CzBx|R5N#|xigz<U>L|7g5NalLTaLzLhHYmG?ZiW0aJM&9wbq-m
zLBZORgGmzbTKI3od%nypCNdb@SqEvQv8wX9WaVe|Dn|VM{vnc(t`mDc=rgUZu>wT8
zJ8e5z+%-q7`Q!O57S5#I_}wu%DCu7@roicC3&S<u7zEq5`3bq#qK3$ub(nmRKL?U;
zS@#7pEkjK2mXCgnbe_9q@?cR-_Cib=%$f7!81&Q9i!roEO$kmQ3naOVeo_-L_5}P{
z$@46dqeK}qmZ8{)gPYnVnYK%yv2BHO^ZkT4pLQ1ovV<vsWy(Y?6pMvKrKbFv+FV8D
zBxUu(e_<@V$v^O@vU_s#O;$Dxj(OQYaePXD;reTM3@+ivDP!por3X$o?^C{&iSmLx
zXH?h+l})$XSPWH3Cm#*IW<#}`<(DmsmU9N%uC)7t_<w(2s@`jmdX82qtaR|=x2lPy
zP0#z|$)X}5w<3;6JXWPJFu&C#rfZF9m*v8wpof*9{MTuzbJB0<?7q{+6n!Z8X0i~f
z)P4jPxx!p#5uy25iUXx}p3wUA3zvbak>d1xE;U4h$-_V3x2G`z{JO%mQ0rHP=`&KR
zFGV(nc|6+Ks@AAvMG6c+jY<KThutPli;-sS1%r6me6-5N8V8=!)!#qCEip&FeuG=r
zjm1T$+RbNJlU*rt$Ot@Rm!p)kux%%2EhlS>T6M&{h$|jkQ^C`imea17CqzKI{;|^h
zBZ;nm!fgGycaR3I%vxltHN7`hMRZvr8xy4J=_<9+v#^p%KVe}yom^ilY~es>#@Hbh
zL$nyan)nMY$EWjss~Xgv2md?xZF>a9yjRS_IeY4A89dQRKXAC(52&jts5?}p!yU;`
zGRTm4mzpzErY2xU#LNA-p^!l(x9k_o6!9Y>lKASD_)&xLi3uarx3ZJPyM0S|%ZjH#
zt5??ZuuJgZ+@N3s|D)$R!?hN^&qKx4)qj$S)QPXMCTw|k+X1ZrYARxtCFXVaB#3Az
z42r;e1dmd9xYy@sh=_?ev5BklbLk6ZGbRyz$>Gy5o0;!&gNutD`5GF}Zgb=-+{hB7
zn<H2w(x=N%Pg(ZAeWB(18)9=RWX%j(t{;e;!b;(W8QD?GF%una`@qx<7|OO|+VDQ>
zoeA@<rJicWia{T;ktGWiYNqH$lV5-o^%LXRiE;BAjo+6TRgt;9%;?k3SsxfKxzu)}
zCJ_{*`S>}|uk;}$f*3NRZIe3JAdZnoWRBTDlGF1OCI&gySti^00}PeYT8G_tJLFW}
z$Pu4D)LPg-+s~NIa#QaQ(H*!Zgd3sT3gdfNVG~2F(tt#ID>>=v9R7k;%)D|x(aT^B
zBW#RU!^KMDelU^wD1ZZuVgUTxnr>`~_e-E#ZaY+qSJYZ2k5^`$*58vkagkQyy*g$#
z;>Dl@_0QcNqGq{c3v*$%hst@vh>uChBm4gGpojzCq-{U+AU7v?&JdC-d>&*AA#Zd}
zFNyNZbiu(aMia8S7}ui8BBC5NBLV4^lD-ng*1bCO#Rts~DFaH01pi?in>`-ei8{i)
zpF(K*7~zd*DXg{Va8Jc4UQu<$J=_i>Q3MH;zKIJKH<PKydvbsXTWd$otq1+qxgwB|
z(?eH%#&shFzv=5yQ{mvssbAe!*9Gu8b;~<SU+l++(_NRMVH6cJ?w#I~tL0n7&a3U<
z%6cf}sEmF>P9}Vc%k_?zkX(18E9c*wIi8yey6$<9wch@sea+;*2P_2`DND|Yim-OO
ztrb4Me7-Q!rPBtc7J3%1H~6jW^7^z2=))tzUo8rt)4Dk;+a)%7gBIbk3t&!JxzI~1
z{ez)xvGphsT6fUFw$UJ6U4O5GOy!tWtcaCW>X}ce!72AQaP#tRyVp<&8okxe()R8J
zMn>gqpZpX$;(Wi(4HlKzAQ(3pq8X1wjvLHB4DTcO57Fy#ru!N`M{d^Lf1s2()!Qzx
z_NdG-gVgy=wwL@M^u&(VT3=tD#CpzQVDFD7$rx00{B%v%e4k-r5purLn-6vkHwfgP
z(C)E#Z&p$3*gRy(={(!U3SuqQdxB5JK?uika1^rP!GUILwI^rvT>HZ;TBeW%S}4V4
z;maTYnDvXh9VD+6J_)^X-JUu3Z~n(#hG_0(MIMA}_k{a>eN~A8`EcTb*Mqsu?TWN0
z;>szL`>N7UXHQNyqd{gD%DQD+ZFZw=rp!Nr%0A$YdQLxE_xpip@wB9UG1SojcE#)*
z*~8Jm7)o1{n+j@CheW1G@t(_Bj9HKGD;p9F3PsFyd{lOp6V~A1UODNomC{c>&Up2m
zQ?KEQER;BJQek}VSA7Vt)W6n=9r%gH`TENx7AZH<p3^MhJ%Fc;AU@G%L_{~BfLB<I
zngzt^e?j)Ly&qI^I~<XHcXgA*MeY%t*!ChiuACV!s!Mu{c!mr=h*XF`*_CNf(-bRX
zd=aB8VP@?Wjl*suv9(TS_7W0uWL<<Nb)X?UuRwWhMT$8FuGbmgcrK5Ar~~MT;P7&I
z)1cq&yGpsz=Wohuoo7c-2~&`*pa$W>C(<1G!@ul$w0lu`!*$g-80g?4s?RdOj#133
zO-tmB_8TrXK}DM?);h3}jfE`vQ699oEoz;kR11jo%H3S>J1rmN!HNSktIObw2PfJ!
z#~_nEs7ThsP@r%y%#R|naP_fGr(xol;C8#@@>d{|6WYvnoyj$BubE?_y&<LEtlqvU
zw;cVg)4W?V;*7~|rJwL{w0J81qxSGedMJ8q-7CnZtdM#MKk(P>Xc8TP*nDyM2+$~#
z@7~Pdc%s@@D*NM{i;OSUJAqEtaqBQkXb4}LE@|ZE2Gy78Xe*(O@kGn{p_TsfkM?Kt
zovp=vTD=KbaTI-|K%3cRHpsQSBP}MuXJg9KSvf{6_%S0&Ng><&rW>SYOf&tyiM03n
z6=$IngJPg;jE-slv%&OKAL`Nd^U|~$O6imEX0Ly-BHBz^nK8Kuhr6W$%g`gYA?NXu
zzvEER1!kj>0q#X*Qp)Q{<E$%{EGDgsW7(pckimSa9ba6+ZR=jS1gI~$$;8l}cq-r{
z##N4G7rEkG=&%FT!IuBt*=Y5N1!U>onnlWa4}9qqa6hYQ68xy{+G;xnO@ZLy^B2$Y
z9+sQZ-n~y_RW50;n24^4!bZe{s8VdmDCs|~y|{`KLR6O1o|ak!N*HVTI|y^$IVQY^
zhxM>&rb4_~IyZm&c6>k3$WW-Z4VR^Ghub^{jCH-Qx8>-6TCct`HJ@{qx~i=$RL*!t
z_n(<Xej3~NFp7RG;{w-ia9IZV9+i79ugz==b3Py1JEp}qKMqyB@YIrA51Wlbb_}Dh
z^a!*i1x93FY)5^O$jEt|?}~kgufDi;0#;ty&z{Avp5sY0a5tR5_p5q|d^$9<9I6^7
z5b9Kvu3g72KQ#Hdch=#U(st%)Ee0=^sW`ivnVZ^JDJ~RytbkN*`!^e2&6`E@Kq0=w
z2y|DZOj<lnpC1)6Dyeq&`}%7+SEnBWC2ZI{ArHoCGRomvu1Z-i##S=->ncTl)slhT
z$<F*Q?L^EC0yyu2k@)9t6LT>aYHB&Hw8x8$*6$~v_*dy2TPA6r^-O!B3f;Sv_IK16
zy!rW_ZM$ZV(z2y@{lscr2a$ZK5umU01xz(=y3q?An1yG?2H}2*nlHL{J-&%Px9-;a
z!j)e@NXy^=k@S*w%sh1f`+ZEK+^^M}J%@Lq^jSAq$Ft$}b}mL!Gped3Eu1dLb0H_B
zdUlChwN8<j)<ds{%lQ%}J$@WK4A+V=tXIzNogp#n&y;pmyUiN+sXQ`IL&EX5gU$60
z?c=uM#SMa=mYdRjS}aj3WPii3e3z2|cs@%&^C>^NuAHjWz12C}Mi!_*_9(JHf9RNS
zQ)Wq2P?Yl&#~)GbkM+`MA)c*Kw$C&_S1(}8q_$7OLej1w{2-e@o&Xeqo6gjIQ4^r=
z>SS?+xK;d3AL#{rbSW}$>WoNM;hTT~<5QStXO?tt7@^J*<GLm5syLs(4l(j=jZ3KL
zr!+PHbBj<|CiCw<iXFo;Eie%o+cX8A;56d`O$zZSf7^4?h%h)>tQIp$%0`c$(8uG=
zlV(viD$im4QpaIl?nTqL5e2D5UoQ?+>E||HpE%C-^;ZV{#KluB$u)(^AgpzF9-1j(
zF+z~VSnbDWSSP&aZQj~Y$@mipT`xv`gZ@M<_Jhc<auQy>2Gv@pD~77IRJ?n8d_Qyu
z1n9xhn2z+mKX&porlOb3rZjXeF&NT=<2OSL>`Vu{bNm5*6W=$s@ha%Px3GDnuFu|y
zf78b{<n{S$>XOrRsA_qy5W)I?^vd>s&`mcTD$x-c+&u+}sGY%fUs?BL_B|lfAQ?IW
z_Xn=Q=*TWtBH0E3UaoH7Qtlq||5U}EX8p&oa3*6B{x5*>GYkI%yh9&kGvT8C`X4Bh
z@ej%rKal|cqsm50|CL;a8t}~j%ZG~pNv<?L!~bD?zl{FPVL6A^`mf|l^G|XOCu02%
zPTWfU$A{$F{TBW!x&C8489O24{}sI{|M4M^Z7#?Ef0Y0KR{me@m0|$UPWb?;Sw6c<
zb-{bDMVP=1fNH$oFLp4$Cz*d!$1lBg6c(^jQ%RqVDVeC>WiMNpn21r3cNuv)&7X#C
zv5|<sKalo3>XNnE^ebJ0chULRgwsb{sg^Rm)A93Q3)ad4M)LxXw7~(|TpJLtz1*#_
zfgP5*#aDCqWTCr0ML0FdCF~{x%NE0;f>h}9pmHexWVDWTPl&}@5sh9R-T0tp#rdI9
zLCpPNf-X&b9>b*6Xt*T>&s36*q=t*-hR-Z!b2!<}74L3=Zk?bd!cM8Ge^-04;_cPB
zy1BQpRLvzPZH16a^TscW?{Q$z^7A}$S^A>@$;eP8B5^cQgJDG+N2Z-NfZkyYU#t0>
z`e?c=n~D5gufq>Ul98Q*(S9k$Ne36H0>>YO8GvFd30#SA!F@k^77r9veDpfOXBv=s
z{)nyO(a^d%&dotmLJ=i_jsPDiIl1v#qIj(|SG)p&9Aj)+SjBiu@e>notnGm{C?JtX
z2^xcpipE2SDBLQiw0Q30O+{9krXDGsE<SFS=P!!-5lnJwhn-cE5s-hkmvxF2g%|Rd
z9P(0<+?yJn`R#d(&1>9`xya|)$NGxcvTJijsZQyEXn>rGl^!#Ke3jx4aEQxAT6MQ9
zvd4O%7dUEmVJkI0eY4AV6bT*ZBNt*;+R|d>?;P=S=F0P>8GvJv(`LCH)J*9Jy}@is
zNt2nVrPL+7mx#18VrQ_3E{n0#O3n1T3?=d&hI=O*aV|8M3t8&f=d!vV<)oagJ2baw
z59g5r^H=hEnO(e)bkU>>LzJ7+fY7|E#wBq!mCd6uX(>ZLdCz+CbllVqLb}3=aGXLb
zOw@IBPb-2@BZVSYP0}=PZx?oY<4Ub^&c-@}v%$;V)iBtv5JB{S7ROx)WqRjdilB}P
z-nkKl-Xy<{vf=I);PFd<xU*Q0N+YZ>K1GHyuyv)e&J!B^4CF`LldL=E6k)z5pA{0D
z(*TB-+QuA#R*)hQ84)U&WDsI16-CJxe&9k(%_Y8Q)eIS2GNL4>n-n#OXKcd5^Kxn@
zOa0>H9!kU2uz@fY#X!<B#)W^3g0&5PY`G6IQR7SI#u>ADYAlSrk)JtH;o*&YE>Tmt
zpSX+Hb&C6{?@g<ZRiEY(Nf+!!^-z<3@V#_?^er>9uEddO&eBqjx{w4>>|9H0brfKh
z<nRkOGLxW!OHPyZ*qak0T6`W=eVeZu^Bj%7d?%JFac+H*yKnF!cHf+)=-d^BVypnd
z)_hNmhU$nQa0T&`>H>u>@hKIH*KU2o$n&ssM$=RIR(AOQ8X(WHjVzml#w(C5ABzwX
zK!$oSPXZ;Fh3!q>YrR}5Ee~$@656z81YC1?xUe-|=D=I65QW*4m>P%=s#4{|mx8PI
zmdGe;)vwPV7iWhD?!J<t|KUiDJbc~($HB)F=i>R}uEoqA6S1HF#T7z1y}|hh=kt5u
z^YzmC67%IZ8;G9sohY2A;_iGI)6C|#sdc(5?txTwfEsixw!Pe~>LB-=$T`R4VOcai
zkU1xFxk(g;sX#`hWTj=awXRe|)7mcBxyXf~(#AxKiFHwHeyU@%I4{O%V?DYL(dVJc
zr#5mix9IenggPK0b_nyv6ZhB65ZA>|IQrc->0)+;0i6dY4WKH7&guBkEaY&pIB~e$
zqzQA=1}&FBp1|J-o>P11PAExRSytQ3ag_Qi0ZXs9MsORar^<+5@4Uz!l_RGpt?SF2
z{BEKbR3^`C?&_q6<54>7t}AmjTU*0?P8hL5g6J4wR?rcB1}xYHK++U%TpS(_>yI6U
zgUPT=J~OSQtF7UHG_mcGR+Yn@y}j#9UDQBU1-k(hdsxKOc824d+Nd7mbKH+x-Ew%t
zT0GJK=`wuG2$pk@jq51Ky7a-Z4XC#_*+#ZkWZc%5V1qYmE@S{U8_!o?Jui%JsA(45
zfl*swbI5`X$p;6M)5h)Us&Yu)HcMuI3U=dR(I`v9eS4p&1iHSoF37v>v{txsN*18S
znc35eM|Or>RL{>wNEbXXvF^fpTq4z0Ilk_a*YWlt5p}iQUWI8DI7al@f!oY&iI8nd
zOG5iG9n9=y`&4rM!k-q};%lz8)X+-aP$Qbw#&+{G58TwOgOmO6c|L1Z=}RV%3)2^1
zu%^{)&~>Gd^7$MqGXNDM5(%0uW>p9wmE2~wUHiEI#i6(YB{tJs7Sg7uP1Ks^az3K`
zm9>nqG7~QwEhV|q!B9w>*q%qS$_|5r^~U<SvL@6rAt@U@4#`0?S2%r#))W0M6s%m6
zX`H@ef@>vS{6@Hpj$T^us*r6v7}gnRoHmm>buJ7qI4Uhf?bdEfYpp6Fl7l-wYms@*
zJfkdswpD6%(7P=jGA%#{)&-vQI%UCZ*y*E9FN(sIvWw>`-CJV2g80W33E~${*zL%b
zE!kG+l)g^ZFwrkE>hJr3Exy(TO<`NSx03+r$~H;W!kkM?tWDm)=eS1Krxe!))wQpr
z1@seysoOtC<eXl{jf{cBtGfz&`^X8uD>(^3S9TBY_1ai*HjvHDCbcQfdzNWkY79|)
zHkKykGK(^ycMN0J?aXt~uV9Y}UA-jUYDmXF_h9aQ$iTBI*h3@kdu+(9pZ*3(BPuVL
z)%utCxYui_`<H;XFuDvi)I4$;9L85wM7V6@QZRMY&_|whFelZRU=bz+vulU5Coq<7
z&*wU1Y1j-y8{;-<`&eOBvC(&f6)u_@v_FH^2n#Ti8Q0R%6XI%2u9Bb0^z@vhwA)A6
z^@IaNkzY=hoC&-U;)zp9r<FwszD<S@>QOqM)$@wdkF~lZBfVQl&<s3>y<SN{b}ID&
zM)A+D^YKh}@MeJQRA!P{GBO&t`B7~Ys+Ekq-3oj>BHzfv5tIweGFqvQ>S>i2x`YvM
zn+28V<>%?!M1jiJp@pUt1T;LH>rU($*}dPrF)21iqy#VkvO>wC60)0;;gK@EyHh)X
zsw8_KNvtNC9_WmOjW*o&A8%!)$d3B$f(@aWJL0?fHD5eXsSNdbF^fi^g3kDpGNlv(
zL81ktb+>I8{<4M%)#UV~poG;Eh_J($+1|s#gx$V64aX5xB7Vs#dR`)7iM-z0Kx}{m
z8K!cNH|G`<0W{hCGkU)RUxM<tp|mka<rt-et_VZac&6;`I3=}YV1Qnh7~qH+Sk)h&
zwpBTmA4oS+D=Sh8fnH~9b_0ki%D;>y>l)33^rAJMXUSyJqn4D=jt`xXWY7*BpdOoz
z37Si%VkO3@OGk}fT+l==;!N+I#&$yCltbho`>x9Aa58brhqdGNB9_A&Bb!KuX{Hla
zWF178&H6dXvSM4oqzsy<d(-IHGSc}Wkv97~&!P1QKr<=i`>U!RWvGfgM?q!~$nO*%
zdUV_3B<^ZBVu^-o!XL#@e8^<UM6DD))=vrX(FpyJ!~;`1TpHmfA?jj8)WpQ2$w((t
z^es@qhTG%g&F|aFATtb|i#>*gH=vy@CX_x_c;JtugbKDPPbvo*0Qxg2X`f1YJ|<h2
zwW5iUwUw-0cwXuEXH?|ft&?X=XiwC-7el$KEYV_J-K<wu?KER#;tb)QBHD?$!MO>3
z9V^Q+#!*vU<ZXoc5lKY?8|&mH=*L%&qze$Z4)9=bAb>q&UeZAq!049wT{Z6;j$x5<
zy=^hlGC~@ixNt&+pC4dG#l`S|<g`VRPyF~WCQC+&`Q4SCXWc1CQ&Zl<(&I#a?TeK&
zLW7hhwC1Nn#%z(Ch6t)wmhtrPk&G?!kOl!aE9uW}E8;suNDJVEDYGsE*`el0RQeSf
z2x0dcrS+#sT9*otG5t);iH?zDB&p1a4KyW|oQ0H!dZ!t$m<WuN$E(|?)$P0@5h(D@
z)}fKG-OG#DqLCmVtY{R8>S~bOoN+?qkt~Z~(NnIHqgsbKFGr+EQXj^=q4}~at=U(`
zjq{G)*Mooa@_nj&mVkHO>QS0Hmw`4KvByTV-7$)m>=;4`uksJ{3u%d(vOI*qR7qiG
zs`#7;YM&g7m}A+`;g?&D?<ZwT0n*0kv_qjzMK}B|-Zi2|jsy8O`P-+X(S7|~BN){y
z7$k|=QhZ}ll@NcH$_m}K5eS6}jb^8812Y%L39*z?8jE;XM_s(tN-*Q79HVVRi#opX
z^1P=|;)qu*kMm%nWn`+zgZ-v}r!#GV7LiE*TT?A-jQiX89k!fdW{;L++l7T`IuJ2-
zu!u>WThi;h)@WVcefjLYqBf2_YPlBUJcCWH8YOAvJbO?<g}}Mdv3T053TEsgQUGN^
znGrA_ZEQH4hOdP-+{HWUG`rX--Qs6C?v{u6Lu)dMrV}weu+)Sys)$28uY#!Yw_miT
zwvBh5g?gCfnQ?A0&#r0}re$1H1oQ8rLA9pTY3ufBE5jOBBZOtqD3FDTeffheEsm!C
zQ2b(o1)1UOX{pq~m)WL*lt)WUrpvjFjo~9b2l!*J3<hyfg<maX-$%bSmCL+xANyVc
zq3CA60ilF;AXLpUi!s~m17~pjU%Hb_IN!(&K>B4)nciX|K~NU=<!=qREn{P}SN_}=
z2NN8gFR={O3nx<MuBG}-8GPQES=zyh?s=y4Ts2WJh@8>kF-Z~@*I*&(QG>TLSu4*j
z*y@RK>0T7?%}@e?$9J%_TCCG}NWtAN$2`0S26@;le7>rQ*>3D4Je}q`cm*UfSyz@t
zrt)olp^w8KTT0UIg8)Z2hL0S4NBgXv6YSuawK#;dK=BWg`eApSz|?i<xQSo9!sz@x
z?YrRb!#cbQ(W4{7+#R_>8zoI&gt|;)Duurjpvv$|;cgvD=iHvIVO`d6oT8?B2gjW#
z_NLiYansR-kMp!}(5G^^!{B7WFDg1dZtcVh+R3G{cd)r5(nUN>_?CQD=x46h+<5Qi
zX`b-&1jtUBAC1Zf&zj6;N;YJt>!dPj7=EV`dLbVZ{hOModIGGzp;7+YO;Jd5fq@KW
zfN*WS5dKq0(CBH6Jjb>8Zefba$}v}pG_Z2bVC_`}+WBWXmI=3gm`YvyXRcay=6SP~
zD0u*yoIIl<>2SAG5lEfvoltPq;Ouqj$E)BICz^Ukcasec7GD>Hie5;bbkAIISxEY_
zh5MjMrbQ0L3lu_DNc7<KM;qB(Kb3EyqqTY<Ay#6Xkh87hwkY6!USb#O+d#Q(j!<t#
zAFOu6yzfKi#obx3(mE(nUj3U?LW>G#L!?wjz_Fb!zTAYdwPb*VrIJm;8v2UM`F(Gs
z!;?-?VvS<Nm9bW(mZh~x1l;jPfm%DWQV_q>Ey`B+H5R+dZ&O2T=vHNzmGpVLuNE(f
zA--J5#93X+(jTZ4#@#3=Yhg`)4U?`^W#~WRJ5hrc8_)sO&=RO8=lvf1fWEVj52Rvs
zh7-3-e#fiahvHhJbF>yBM~YL&kjaf=DhtYV#bhN(OI(cTC|GWP2qkGf_*6Y_(wM4f
zH_dMQ@lyQp3qE+%5qM`aaX#oiCMT#9v^o#{8+8NB9Xtw7?%WsR%}H<Q&b6!|F~$`^
zhL>p5Y1uMzyEZNr!{x5^kVxzoLs$KPJ4}}}ow|b^Q`y+Xd_jbm?Tjz<;VW}Pm?s@z
zUdpR*YARUptZ^<NC&n|xEIvFYMvz45y>{6&Buc1WaV|zxjI>T@`r!2`_fDx7z)*>l
zqbOe}?Dc0QWZ(DCg+ji%!p}K`{oo#nS4LcgVe08wzh#}$;obOa$gt?dmnC9CyIIWT
zD3uxD)hhI)&FA4Xqk&QiLl_5QHN)r=f86uFy;?gXgfc%RCPpSzyH<94?V%CAzH9#l
zn)HUC?dS8|gudV_t;ad{*^xj{p7SvWpPK_tYl&+disYsIF{$T0E}=hC7q64HXXQ+G
zZ^V$T0O!~KMcq2|xmghax!z~iPGo>~a_|;{_FR0A4bQa%jiowLrTt6ub|`L754Y?|
zDPbcA%@L(3B@f{RYo&O)#{Il<1RTSnhWAGn^jAtGA9<+)yqu4(9$u%W!j<0xZeP|Z
zA%4$C9hqd<t-ml16Bp0ezR!Qxj>|4btA|aSyc5*RWJ&s4lIsbn+UNBK`(X*Mi3UZw
zK&wm@NnZ{5T=)1w)&a!eiM`2OI|cmvhdObpT-0vl+A{vA7(uU~K7WS|gH`LU)~{ws
zxbC<|Z4x}&!V)v%J#<}}CojuNtZfTE6+nffnToUn>v@t0o?oKWSkVJ2YJHi0zcDMq
z$4cXAHMm!Es}*fP-(fvA@LGnwKBLMr{?i*~Qzt*S$)@dV=pT9cxn(c<tc}p|Cs5&z
zlj;J6L7eLWE(SI7H5SMz#cCwT2zt@h250!`C5j%Ulab&u6;_gWJAUqyVI?HxE@go6
zVt35fYZss9JK3}pIF7t+s!sptxhLCj68?3y{F;Uw%;JkDT~Nn+9v5h>al1_)LSTd$
z9vqZ)x3P11IM!I0>MB1}vKDCoX?OWN*g+IsH0p?Y4aF*WP#%rIP^0E@AikbrUfnb|
zp??q;MK_sORC2>Lg?Df&8@(2Zw;*}?UD<yENU3n2Q^yRMhfx4ME;)lT7YaL7mq(*H
zN92Dp6<dsY)7B9g0i>cWlET|HAZ>-;OZxa&`EuiWH7<2yCb<+dSRr}&_H^+JM|y=c
zPU+|y+8oqI82sxY3%kX3TfLj{+Z;6hc7M><w`NQhfL20|1aWzQsCU{)MGi$HU=GA?
z$=Yy6vaGk>I_@E7ry<KR%dS>MmZ!%8KqYSl-ro>ddgpTnzL~fZiB7bizfKL3_u21U
z=S9E6tYP5=TRb*8%jw8*((@CqK>oUGjI$I@blZ_8bT|2Wlh+1XDXJ0SV6L_B&RW7S
z1KQ|pGSf7!^F6rjf3CJ?Pji<u3u4eyLw(K=o{0bUR~*##RE&-DII_4dOpT@*@sP7+
z@rP<Awon+c=nU*^Nnx~v?%5(KD^3DTN3~SYTl!m@JTNl=?P*oo{GQ(3Z%s|jI7e({
z;Km*ikI%+mH#|2lUk^IDF5fp6)u*xmCBQ>{p*|1A>@%YEINZnf452F5V-w$#RxFak
z#m%?kThG<*-bUx^H@26B$@R@`tqF-syHD`s^V6SBYOT!C)G+y2(D$MJFb!Re<9_yA
zkm7m8<CX~2%r$0d_4%r)WxZ^T&oBp^5gc1T25}G?0a(fCYuX5-SnyKZvvj86PNGxc
zw}{M6`1xkZYU{d!{R%O(X*O;L-&0jMRu-Mph>wX)awTCJzX9uJAN>E2UF;P?aBxyP
z^`D`JG+xC;n59l)Vs6>})JGyq2i^4kagKdbadZ{cW2Ux4hJYn4(@YH!g$^QsY0MP6
zC*g==F&k%WJjQ~-%I}5XO|qQ?FUkcXq`_Zt&WC29r^NmtQzENp@_Rcfe5}IQ?fHxP
zt%ZAEG8!Uq<}Z}_DoyeeAKJo+!c9Y$B3xR*x>I?;ZZS&sq4V^IM1B+Ds+yC6zV_Hk
z?TyFI%}N>8wu<)<c1tEASefEr2AazlqaP%{{{gyD3@T6l(JrZhL3(kD6^vB|qx=3S
zdWPy1<QH80M&Hk#F5(PP((#2X!FmbiSI<x3FiPw;A(m{7_4_ugK)+1~^D(d1oQPe9
zpC~M4{?cso0CxC9q$$i{XHY23dFt(|5qPyG_#wg9I7jh(Gw?MrE>5%6BOC|^DfN|V
zqa~7iD%w8`1aoH^5^>b$yse%IZlmKeIL02%Lfb{&YJ}ve-b}psXCzE7A}!+8B-|Hr
zjpxt8TU_eb3`VF<Yd*$U%l6$G#kr;7)i!8hj?`Zo%}DQrBT9cJsA>t`bBmZbBjCGn
zRY0xDX&Xaw9nl$l%AYA1O5S(whJ^M^p9XcY8KJ~m{lSMSN-o*Lx(i$ylIZ8Cm#Atz
zT|{x})7sWU1(x=R@F+wKLGk6sDIz{6eBUycCr&jBX_}`I4_C6bf1mzEk_i*=q~;_}
zUYskTH}F_|IEUWLZV=mOXM6Pr2hQAwsAsR)(uIHf7ytA+Fr`X#a5gUzve<t4Pr*hq
z1_Gw#3?=N<X$9L=Sy?<!kdaqHsF0V{O9V%{K<QVEoBIc4nt0~Ou%JrCs7<eWN2_lk
z%1N`hfcbQMjMVIv2)c3QgN~$!^OeM+z42p5^uX5@_1hE54<j^RW>vDfDbf?q@ik-Q
zUd~B1Zoq<%Olecivx1m+(2z^PYM`{AOv3b}hGcPeW6E9Fmtj>MKc0|qa2#n2nKcaa
zrvxcnGz@{(5zHjxV^W&=>eE4VQ_L#)n(Nq@0~@hh*+}I@sZwOgv%J|~dgXZFSUkb<
zg2g@a;p{ZZ-&HsBRFI%ODBTpx@}74rMi@e|38Z(rO&xedWnkcn*G^nRBn(jtcFvzM
zu;t=IRka8Z@nq6wp}~D@1;s8WI%SuS6`l_>=4fsSOmFwMVt=z+P%f}q;Qqv~w$eqL
zkl#j`+m;p{m>w)I!8%<**8~+<ugHRhG$gIYQqZMZrg|{B1h_=4VDO37*%&knW|(8k
z!&UFTwJ?cOm1A=ab3|=a&4Pgy_Ax1e1+fmQ2MzDcadug$UVV}8$N$0ed@k65x1{ew
zsQvLX3%fi83g<F9adrXhewy@~BOztEXqiBDG@jd)E#L~lnC}=Xq$}=MS~Mm6vyfcw
z@~spDu5Vrej~_O!tE+01z`y{nPkfJNySb8Z{j~R#b35bpl!dx?sth>tg_91GyCKS+
zX!R*2CjAa-fP8&a9@e&X)}af~5OXJw?}<=4=OBC^Sl#>Q)I*RiHp5^pV_NEocSrtP
z4?_L>YK?eE*Qeqr7_6u{0Xx&W|60Z|{j&bEIK^$y4-D6}pQQR^V6uj}aUW@k%dZ~#
zqDHM2=imFq^-ooJv=xvt5%{NzAZV6{oJv%B!2->wXc1_Gouf_n{gA(zf7dylM$BP!
zs5XTdXp0gh(}3H&{%!eOeEB$wPj`q=tQ{<?!#qHLhN43BHb3w|RYJE_mv2PhNj$X5
zGS(G6w@LZl9<q-9l#m^BL>1D4J@f642v62h&v(Mc#!#J{YSEa*k>e?YN-QDDdus^G
zaTLNv0wWqON;Fu(Hi)Jiy>K-z@0}mLu8cu)*s{A}%72qxU?Blo_H1K!_pQD@BP`_L
z9giV@Ytl+Nh5pE47ZejF<;4XTXdWd|5FGdhkcJW4V!?JABJR9epwJV{@O^#eiXhT|
zKI$`?()(hWFVL*hbvKGlO-&06PDX0q`}P}$y{p~pWdsEQxwaFf<#j0Q#_})$-{*0|
z@#T2Z<9?Kn=u6M_E0><<r<U{k&qKC`79D<cNSElGwweV;JDo1<+D9UpS|hz-mQ?e|
zi15euRbQLUuG`J-M?!vwow*T^nV!!vdFDVOIV>!=|DPH0pC!(_tKFVM3j98A!k5|C
ze*;bqc@9QrY-$_ikbgoBT7Z*qVn9NI34cdMhJ^H!4E&R7ZNN|3P>B1#cm8z{LmGpV
zL=*p8@fY#GMwE;gEi{h=?f=gD*TljFKK*a!|BU$>^-GR8I9Tq#D}xDd`G(h`M3NE0
ReSZ0SNsG&iRf+uc|39y^PU-*v

literal 0
HcmV?d00001

diff --git a/Images/mathcheat_R_logo.png b/Images/mathcheat_R_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..d4ae2b453accda8e26e2bccd64271d48bd9fe56c
GIT binary patch
literal 44284
zcmZ^J1yo(jvhChLaCdhnxVt+9cXu}K8erq@4#6FQySs!02=4AK!GivrbMJre{5$Sn
zV=Swx*;TVzM)z7Bsr*SA2_7FF001D#%1EgGxsUxhGhm_rTniRTK>z@}C`epfSyo(}
zRN2MR5@crq0LUQaq<N~VEn|mIb#r(BL?<Vuy2bZ_Bc0)w!GcnWlm#n8goRLhNPLjO
zM5EI*SMLK#hm)I0Vj=_wnJ&WJpem2_t4K)hugBQJT~>4c+Q@nx)!AG=Y+YQoy7XJ@
z04To%K@GTR1Hl{x+L#xo{rcz_--jL`a8ZHC+Q7bTOG_x}M+u0zmu|t#%yOBL#`_B<
zuzBa|$6jS>*M;|hL~?F%N@xJQ<UE&vKQ+!iM2cGDFdo}G)c9>G9a|VH8+HL);aBqA
zp<289vIB<BZyD*-dj<G9lXzVmxiOu}8(CD_MKMqX`WaMPRz;s-EV|WYvjF=Kw6n91
zUlaf=n3+aD4yg<5BH1J{@h&27AONr_L%dxs$kD%ft#bihrB-`UP?55xrD%&8p#7sU
z%ctu`wcn<!3@L+(+Yw$0Q@2fsaV$9CTE{d=AeD}Nrgh?ZHR1WW&ccE9@80E2Vt=()
z!7-m_jce+&sa(;RT%Aq3v#-qE*noRV-E5-9o>UD$lRdP^{`Oa;OH#ni2SC%tmtVwT
zc7{nqJBTz0(Pw9NuE0E7CWa&K=e!`t@!l}rhs^I6&zk(DIKqJxvRLdElKZ7)rScL#
zejp;JPno|ypuX?+@q#A44SxKR*N1Z*dZeLplmZtLY26tr5U8ENWDXw^Fuxsu%S8i&
z=bvMXY3*(V@C)TtZj*%>a})%KQ$SKW2>U?zo$8^o!jJWISyw04y2i8lyd6G~Am~Rq
zY%7S8s~y}U#+$oP6@J7k+6=Mlhd@t&am$D22xMY`_-Kqo2xfPJ+#m&nfMrl2(;%5-
zAV4Be4WdFzf0`ic63`>q#tASN=<^fmQxKOEv_6zpACeOSB3N1kKCEA!1-KCif(^Pt
z#VQo7Ou#Ni$rM{aCD4WS78h0K!Gs(Zk4!*K68Zg;x*V52xUxW8S@4#^4Zj1{AowXC
z<|io!^efOW1T6z%)&yw@&;nkr<0pib9{9c`;f3`D2B&XzTdoaT2)T6tXPe*(@G;bA
zAQ@I<3>I=)N*k8oT0$&An-YjdW}8S}3{fGOm4IAK&>kz3=)4CB9bRCPfr~wYw;Qe#
zvu&zrvS2F5GM{0Vp{am#z-5Wb9{4+`#B{#CRGa3jum>C$T6Qeskfw3Gv0hzzJ>-(f
z89yQNXdm7Vp_6|bu0ERsXAM*<%3@3>_;e6yoBG<Ti+%(1G6<#L^~&@!Sr2>wVF2-F
zJjqBeO#+-Qlx+|SSsE#&9i=)dKI|dvYY5r^he}|%k{fv#s`rq-DP4Wsl4zGKA@!$Z
zOxZ9>992rX%%(tfSsQW#YC-C5vgB`k=F%>>IwFQjHI#XzMpP(C!pbSM)R;<ggpy**
zpLpfkMZ874#Y<F?${pskD>FKBbd|a#{N((YrIXpyf2Hq!t4^*;v`sxt_oY%wSxom#
zH)6`8F(M0)dn<iugHY)g=~oLZs?#ix+HM*U43(A4DoizvFwHWJG!5S299E&~N(xMB
zMOUg2Ij)<bDj~xcn=SF4X=_q!;#;v;VIh*{E`FX(J|4a$IqJTJv!Jrj+o#<p+{fQ<
z99M-NU@?AVSH&xdNsj4^iT}lM&_1<0waQfsGJNl%OZ`pnK>s<FJk>H4m>OTHpy4==
zIiFK$U#T?5Vnc0XVzX@XJkL}WJ5@I2nLCwZ#PPoO5`I$G5$V(RWOFxoB6yOFpM~Fp
zkB6^;pUyGDiI?S&^(|{Z>yTr#$yyho8A^9w_psTJB{lQnhq@(IV>E9;b<KGh<Frum
zdX-?cS$U(jRne!$NeqJ|COvM^R{d5rPcbEVW^HDpT9wNJA(g!9yy9NzUcG?vzMrHk
z#?hkL{pAHw1-&M@CPmw36qSip)AU8UMf@W?W-ZQ9*Fw8`)OC?LvPrVB-xSrB<~G!g
zJEoeZ5N!{vsX3}RJ4Xxa)^v2fN-gB`4hoEYF7nR)&43^ntsR|%y^d}4@%*D2<LO5d
z9Xg#4+V)K+bx(sULs_;4W`3h#HoInxlcnni&D9l*n;yOAe*9Smb?exjpE{@Rk?+Z$
zsh?S41jD_;yOAH3+&pMIL;U8^PLH<RCa+_5VQ745>StxkHgj|)=_ZjTix3cTu5jiU
zab2dJx7!F^5}C}IVvM|;3g$FcQ+IVwpMEGV#vXbM*$=HR2`<03Ju{%lqa;d4kEWC*
zAL36T7)GSmY9(sbwkAE8UfNui3GoR<3dIUx^mz1C`7uA5yh6NsJgxs)ygGbZd{%<u
zf%*z}1YZm#1*Hiigun)e{=pq;upijp8AQT7V??PxhYcb2BJL3?0LO`Ffc4~x%k+`s
zEA^Ron}(OamnRgXsicJXP^V1%*vy!8bS$<!wx`rL>0!xvX+=q2$#hBibnTBijeAlt
zx-q1sg4iSkSiY1VE)@?m>+<2dXFKq_7fkH>X^m$qF3tND-=}I4jQVw)>4^zQh&EB*
zBQXXl_7!SwjoLl;p%#-n!=IB5M;H!cuMHk8UJ{|wLmEP+CDMlsBF$uLWU*v-A(TX$
zx7-_yZUXlzQW8>%=-4Z!D<(i8O^;2pVpJwkbqogVf;8RCgB(RH;vM6l`3nt<LXqi2
z1zJ}f%g&6rH-$Ty7|Lv_U>$@;n}!W5X^XsZl^<vaw|iN84&RBs{rdLo&uPi&>2B5I
z@fP;d&v{{sTphYlqrckz$<v^}WBab>G~S-nUS|2~yVUns3xZ9DsEs+5e3iN$W9!Dt
zgAuWIF_rCZ#z{S7yJ0&lyYqF+#%i0@baKP#JJatwQ9DXhYe}b@I(}Ne*MHoHoOhSp
zmpprJdBA$Etsi&%(#|y1F<kyxvt>}d|Kq0crnOV+tVh#C&)FCS6mK5i#8%$WEmvzJ
zJzsWGd=iKspLNiE;@f(jvogB6*t=;TFbj-@9EK4h#w6?t7<*8*troQ(Hd)*LIyiM&
zc+$~p6<`z~aV~S6I3*n_T{1cl?>^5zx5!=2ox(K`n>13t>pbfCx^*Y+i&1r~%wE*~
z2*pFjd%ZLINOGBrD6s4u<yU!3^O|?x@>4fTw_ssyA+n{I3*@WdyLk+r%^{j3VieAP
z|9l&BpPAJ-ZY4jJIEnQon>WwX|3+*i^g22cC!9~qO{k-3ZziXvu4dzVV0x|Lb-PQ&
zYWMStMtYT14X**eUyfh*dDv_00&!<<n$ej*=(zdPzWdmz@ilF?I<Dt~-{F(NGxNrM
zhu?nOdkz6)H1yL2Q;+Yv;hNyF-^S}SOa+Re@DCrJSL;`^v)w`(cqRJWO5y6)`Nu`e
zitgL9{6*ub#t5tUur_h#_tf9DN`oJ!DyA5unxsyotEH!6e#fl))ZY$YeUC|>-3{%f
zduw?}ovzCB_k8ZQGW|AmWjlSEDJ-{9-oyK}aBqKTezZ613A!vTi`uB_5xx(6EqFG&
z9ZftwQ+iZN&5ITG{0#SU<3(+6d9e8q^6Qz6PsjhyoD$ASM#mKZV50cz1bUSSdjJ4X
z)F5?jH*G})J~KyqCKGc<Qwt_9d#69v0DyoO-=9Z&3pW!|FMB%&S3WO6^1m(k{yhIB
zW+o^7+r-UQkX&0)nN-}-#e$TRiGzuSTnL_&lvKdQ+>%dKLh9e}KW~EM)^2W2e9X+A
zo}Nse>`aa>R?Mutyu8dTY|LzIjDIW`UA-OLOuQH!Tq*ul$p0-z!ot<e1?1!ga&#d5
zt6URPM|U?ta`L|%{m=2QINd;&|Le)Y_20Dq5M=&K!_3OW!u;Q07G9wL59}|^{}*O%
z_CKtg++FPc=45WhY++|%Z{gtP`Ul7QU)}x5?f(G(pCG+Voc`tP?<D?<o4{X@^Ql_6
zI@-Dal>!Y1ked*zz~6-aQ~bXf`4>vu(ca0$!qxQ;Oo;Q}kblbl(_Z_3-3YO>u>2eG
zPw9Umlw3f6sG9s0*#89ePuYLk>-<*_|CIg%@mKQsltEq=cG?o4KPmm22isq9{a4L@
zBE=o;99`6$Ow26)3hi&mKScj&{kI>T|MkPl$@Wh_|IqvcX>P{n>TYUdVdnOa8v9dk
zf46@z6D#I_hy|GctF^$N*1)IeXb!UUmN0R%5MpCt<zQstVr1b^XJg}I;o{@q7GVAl
zn}0;`mqy&h!o<zdMcvWSPUvqM_J6&SvNQc*@SpmBLj{=s>Pi3TS^w&`e@p-LTOs&A
z$p1SA2*FRR7|8+vA^=$lQFSliSq==?K(hP&FzsBhb2+Qwm`Ift-3I|8sIXAz!~U_F
zdWtZ;5D9%D>6HX=P&_#il*E=SnmAhV1Gbo$&*nPiYD4#B_rv?^%R%ls5_JNU1e^k!
z@1&r`<d@9Nhs6VKSFuQm9k;_Z&^Sh_a0=ZvdVK=I{OSH_ift-4%|mvRy~@GbQ(9Wu
ze!8I#Xmuo(Kzx6HKQizA<<Oak=2BTzFFgID4tlji&E#gCIjFp}gqO>2<~HapuI5XP
zc7VWq$&-ufm(%l8cgax6?V8-01uBFzj;Sn81~!+yvB$4zY1E!$WY@~HO+`~kL@r}w
ziIEsIx;bT^a<X_%%8PE;*x2X?>9%hoqA`yPhR65EDV8b)<>rV(SEkh7>PoSEt{KEm
zn)X+I{|wr#?#^?n5u{`(H?T<oDiqy}IPr8fjJ8vOCaXU)I163%LAJYByAO7*tjhUz
z_{cv$KgpvPczFw8ljKH>M*BWJxrl8$IS62XkETISl;KgIqX0-yOt-mc1_T7y2mrfJ
zzd_P%4;0j$UR+!tg^Ck_j*qvkX$dcnk3nN&KcXIPJr*sBt=xupB0H>kR7v7OY-eX@
zxK3BHzQnw&+}w1nc>nseRM>Hkf>}Y-tyapl`|Z!-Ms<~~n_vrfkad51oP=r<f&ma=
zy;GE87nqR+3HUm9c{iS&0{k`-7Z;cHe9_rhT~TeX@R2xgZE=>nKzM;$lN6UIdNgYH
zTHAz{GM5leJfuVNvvz=4ea7nn?sKBkP~_OTos@1zjZ;lyXY}{hPXltyi^n}NF$yr?
zr{`yV8=EW)6MnR2gk$`9cNPTWH(ovx_*RL+=`d?bRjgR4MX91hi({STsz$a(O=7nt
z(O3*=1qquNRcJyxlO#3uSXp|mudne*x?2=rFSez^3AX)`aR!Mo4Ti<9Zr?RO(p6Se
z;%zR^v+^#GWS8&mZ_ChPO%kIDNGOGYF`N@|Dp6>}3qoKgBDA20Ab9M6G9S@UoR#F{
z=1NM7+j2LAX!7kP(^BRa7p+9(B1@OJN^Qxv>q3$Q2S`h#k4;wQjbv$_lE<&DA<;zO
z*^HB@57pBrKy;u}s4v4*CHoBbUs|koWANDZEiElW9kPoNwU-X)1IlN^;Yf+di}4ff
zvdhc#x;M}~ucZ=|QhA;ZQmeGW(wY1kKa0;l$@<m52}*=g47<nj6hnBMVwu3h(o<t1
zgo}qk*W=dPC+2R8cKY;fs-$d70?;BzHKPEeJ6BBfJMqH+8HHA)4ftixhr5id)Jj``
zWWbtyzBYL&LwD2F6@b*x@|dfyQ1OaH!28EZrn)jNf{vh<&|SS;_&_WOR3SeSpHpY^
zbSuamOTsUdmYp3<#HQ<Ae8aQ<aUVrvWgXaHICR|vyIYcyl0tQ&6XV#I__W8a^l3x6
z+2NCc!MWk9pI@Un1*SY-#2`eNe+Bz%f^DyxPa+|J<phhfU?~AQa`_$k&4rMjs^AT`
zJBu>Vjt-XmlgM5{guLtr(j+k-!-O`NLaCq-DrW$F?>E#YSpo)_fkTjHzTmt#q?gm&
zC*(8N7!u0V5@EP)HteJ*+D=;ZD2YL6?Gswg26k?S!-?^6*64?fmcyvy*@q)pd3i`m
z617~6Id{*^i!=b5i2z;dk$(khfSOhBCz`kI9^n)>juzsD<z@FK#I;}v(jp4-u8dnB
z09j#lbnb2#Bp${K4kz7d0GlX}Y!tT`O5Cc)7F#@J#Ox)WB43oa%cE(xJw&~<Dmf(j
zFs*=2xg9>RY!d(>14ubqfmF~aA%3CuB}4=U$HDOl;5N(qzI6oJ@^U2zUb(>;Pn!tR
zG0E=ghukM(^ykt6I1l#rdpf4Ryr%tF_w3a+G!(K~(zhu7;gIdY0FIzctF&@daa<=e
zHFQ19@r<*vt`RJ4ZGD(s7ZiMkXKCNbWJ~fT6QYya&ZuFpT$IX}QRS6}Fs6JUYY6<c
zN*=yf{bk1xmZeQ-38h7!0_-yoE%H+MDZ*byOb)P7S0O5c3LFB2MIFquMuPW<Pz5@P
zG0)Vfd#Pw|(|^6;->cj;)iYYG@Ybjh1$9_&zAtKWscDZ6CJk6}51U24sZdBWXR`ZF
z&gl65IQ}s;MN$%eUi77?ORr=dNUArt7%iE{Ty90&aaz-v*5bxRv^l-;d->v`22yQS
zb@tgD3W3s%5Zq_lC%+3!q&x#>w8PpV0z&efYy)_HryI$VF6FK#tu<muS!;%k2OS}D
z&{NSaMyU6y{+NVo_m@SeK}bStfyLh4$aRFe5XI5A(pztscZ6LZH<1U)0<I{$t7Wkm
zP_k{VUNrlrokifS0kv!;-$vpK{H1{MHa3+~2|EtA<>MC5XU&%bI*kWSd*+A6M@q`j
z92<&dIVd(yq>y{eHn+X>%&LX~uZoLGCr38I?PZ;ZZF6%egmw`Z#F+$e%V^|I`>+8D
zG5~n@*7pKGs(Z+t`8Z+hv&X!*3@ku(LJ|)0dZYLoP&0Rv$frUOAs$mw)Y@yfjvL~q
za=Xwx$xf=I{b9*HWge{{DqP<L9|>h|)#l4$yy#Ca{?KcMPClM864V}pDt3Wrmvc|m
zO2jXV&Z~>gCqV5s!ql&|y-K-z5%LdAr(Qas<3Dwgako?eL()Uez<_{r6F8oSc=B6x
z)gKi&Qz_M>{kyc2q>EprxBh-_vSyMwdW?Fuf8Ai+%{U<G*G`yRVUXaPBT@(e^QT^2
zEkkU?vTlQ`ly5{%U@&yu-V_i_^*s>xwM5^zH|gPbgG#0h9bE-A)f445MBzajGy!Xh
z1tjcZgAvP~V5skWgslM3)r*$&v4j%7r8pMN@8)4k=)p^5X&)Nfs3n~qJdl2xm%OGW
zh$tgl2Ya;i<U@39@LEr+`r&8Q2Ga5}uO|`bWkxSy)4M49Z45JmQZt_)`0evg06BN{
z^Bs}r=C!H#`hi-fudkB2mHC~Ur@SPqFi0${wnFJP5bf$GBP!}Vz9NcUyXvCL+&eeK
zYSdQyLiu{9G^F^dFn#_irRdWRbfjRP^i7+fyoIP6<#_(tRx8mQ31oCs(_tl|-3NJ1
z;vpNHWq06oW2l#)iEY)U_>^;i^2Nev`{d$i+^NnSP1_~_Ers!$bi?7TuQbGiQ}q*I
zN?SoSR4THxRF-eNKOs5i`3mRL%fNS6%kv4^SAl)jlxtBL5?pWuajsFX$-4Z*RIg)0
zofGEJR~AG*LM(V+a*(<BkKC=;Rhoxt_N7Hx`z5@iJfM@)rxWBwk+vJZr_*yDi5Mi2
z&1jJrWdW0N^xP{q{my_qF9&eXo=$FkbyN55YKO-WYs6d<m(<=jzDy*Od?3%aF9KHi
zgf4;)+3MR!cQvBC`lCHD?hL^+DoiKXdVL51H~BNpt4c#3FlE!cII=2%DzW=}``8~}
zAG?Zjc^>-)JLY<%%QFPM4tial(qSSxKsp8n>rqyD0hXo{r6=Dk^<Z(yi~Ttpv<gUj
zDbZHbu)}GSN5ee8!j71>5>?kIkU=mKLp-@1Mo`;uv6J^ilh`%%$XS#uzCRKUkzYP7
zSRJ_{>F^oP(0B)R*fH2&KDN}X7nsdy;o$ef%m5)!B6LII_;9|UfVWrsFE~)W0A5=x
z+90W}@q1N-e9G_Y{8aW>BEcsgv?Hitt?2Ejsj_rkk3_@QU0-N~mKmmY;3`)#h<xG$
z1|;9302QkIU$O9Nsw-AdefFEv@2fY|+2QILAqAMks8IlrbS6-on5a_kxsTA16JBkw
zm?A8X*v-|k^zt?$A63ylZd7O)GRIhsknte*dkuO21X7PveGkDdZz7_%7oqVy>e{R)
z@#p3_m_js6ewnb`>LD9_L+b>h3Yhrr9QZM2yImPyT~57`x`K5`Rd}rWWFZh&;kT5f
zNL7q&M)ciOUo%jXBTVP2K5!Fzo?W95y3WzeQ6(xJ5xwt2P`zsaX2_5|(s6&7*%X%q
zJg;t!)ao~$`MzGi`QZ*FM{`kA5R!l}*-%7GB*wjRRYlbMHKlKr0wjN+GD8{yf*;H3
zTHL-${VXZHv(_(Oo#WK+ccUgkcxbYsx*UarX8DK$6qndqIl=Y(K#IGByx?eUu-VaD
zy_w6yTU5*kIlmRZ8Scc}_PSSv)|6Z`3w%!fx+&sE=E0UEa{r;m$`zYZ6k~~wTE|V%
zc!<JJAqtiS(zyOt51|<c{bEL}*62?6Yd7M!)U5g3;lbWQ&Q%4*Wg_cl!3wMD6kxAc
z;;9T$0_w`j{ipl*!~0u&Z{Ezp+6zPE;qKPK&d!fn<27=8a8Pb2>GI43WKPw=UaNZ#
zAMtabb`ghUFa5|EzFy%5IjFOvLtk3D)66!%jup18$l*{3j#j@`>IGgHZq>R02A^~{
z^NX|#R4?zruY9-b-U?MG<OcJA)1L4?GL}A;gk&fl5hvN5-7cW^0|4iqSE`}+o8R+9
z&-}b_o|*V5?vS~OXTQXclCLeCrG;i3WrFcNsJq4jaL_wcL1NBHmw`y=<vP%5l*T`}
zL<^076pd66ju(9dl$$xT(wRD_vaz%6(NB7rZKy~a`M*q$ejoRgNtseU46uAE14<xi
z6hU|DS5CUu_B`1fRv{P)Xqjwos(FdGH8$$msMC@`%m*l`yHV%J<1ftwzk!L0k|X0Z
zek6p3y5_9K<D(oqSKC+DDC(G;LZhn#DLzt=GJo*G-kN=ZM?nt++Q+%@BI5!o1Ertu
z`MsIikjZ~Z;Ld63$>0SokgyFwk>Mql`=iD=>+G(S(@F_I`g|4v_rGd7z8}z|fXX~%
zxI(36Z_HgVnsC4k9aPN3x=Qzo=r_4OR%)Wd#;PgZe3c0pPhxlB-gor)zys4=ZX5Yv
znoBMKc8pK0gLeXr2GhDNAQ6A0``dj)Rae^zS5{)?wu{TnLBLbUl%dPdwP4(w_l}#T
zy{fLu>;QO?IogLxaVD*|)!5$Ni|F%ndmOsrLWmtK-fxe{lEIVgpaRSKO-OZP7y;Tj
zkehLqn=T4sv82pQ!7L{?Hv=~w4@ad#hztTuh)5>tJ)mGv)}6VRaVE7o2Zj(8o<8gI
z3u^4|CzaKtEma-Lwl=f|yg}3+Ft`%pnr64>MUxA8O&e+#80b^@N*VDL@m!6ZAY#?y
z>*tkVvvCOg%kQ0!6f1VAUeaPlJg_1Fmggrkz0PdNH}1t{y79T18Wg}FKl@~$t^_|R
zh}54F!G<2ZZBT9cR792pOn^z6Y?UMpO+pW4C7Hs+Tv{VZ!Y7O?EvA-}mwS62_~rlF
zU~`MLA24pb`;3$zl-M6iUcqpg8{nz=dN83CNqVqdgT;N0@$donBsqUD62?S(kcI-{
z4xb1Ht*pF=qiV8*LA=RLSzf+zwV_1CJGw#z_kr?)b6z&b2Hgbw2n>t^BHMSPF;9Yq
zw%~$f<$#X@+>#SFcR^0EeUi9tF8M*m{CA**eC|iVG9mz!2n#1v90OKl06-iH3oy7=
zO-PVb-~t)ivViTwe4!PsosA29W)Ji36!!o>zP-+C`3{ot*U5fj3wtB}XfZ}?pO?Y{
zrV=C?NpVP$nr{%gGCpR715IgEL-L1m4Mc>?Y7>ykHlaYm2^cl_K5;tXPfSjFfR6FL
z$MN4M<M{6!DY2|xt6Na}bUPluLdkS)oP2a2*<alBn&9T=<7Z0cOI=u=US>9h!F*yb
zZafmDdagpYgt;4O%aF?BEL#D2d~tUuvx^K1^U>03UA70UpvS~7wF8v_5Xp#!rukBt
zx3F>9Apl&J(_4kqYB&jOF(4`sEfs8*-TEB>q`4(#h71C%6#e+@Qd{e*gbTF|+}2=d
z<W8!LfC&}(GwrMm6V)e$s2D)jTM1^g7xg2hhuxSXA!3EnDf;afr{)P+7$v%+lGV%a
zUBD%}ss{DUg4pt8h8|J<+GwU<QXKRs%MXEVDKx{(f-~uT7=9*DVFoa)wYI^jEUArO
zQH_DJ0&B4YC;J?w2s0rQk}^~G;Sr*1x*iAQ6R0<pH<W(+$zIZkO!_=-hu7+8H9tYS
zuh6WntQ?#3j*8QX9xg3(h+aAniNf^P56H&lQonLY_l>oLrPv(Ue27xa=FX)5kmw>f
zWM+d-iYi*?xb<_PONSn1>k&>Ke<&$rY)l4+wBgH6IAM<u4}}dD9GoakA^WUV3+FL7
zqz?i6Xm<l4toUlAk|v}etO^Z!1`^b-8G->G435~9iQD5WYy!{*cJ~WX>)h3Vr?SQY
zcD0W7Fs#W6IqC#XIj@=oBHbPvMr<NS9>4(gd>F{i75M{^an(|T<dj=T3phZzYsgfe
zJ{x<(*G=WBpXkcI6P9Rw?iI~dFkveUbfVTqB$m6YHNCCOrCi8xl-swrKlaN4PH#6a
z>PLTwyFXi1oCx!E8v)uM3#1Qvn>^0581BR*vy!LdS(%)E4)s8OPA}hf54~%<)Q^#n
zc&rODA>Xh=)BItFF!AC_z&+o#u%Kz{<^Y8_#DvEa#CRVBOO#;$0Uqzh>k}=;PYYXu
zavODEAs49(c|L@fF%%e9WSRm*b_ZaBrImo4o8ns+0j~f6q#kx?_T4r1&!wsvZn6;H
z?%n;+3D=59M218loUBRWviZtAc62B=GBHuvaT=zag}2A6EXpG&mG-`!R!^T_ZIR%M
zg7O0(63Ryr-DEP1jqC~L!h3c#0f2nmOYm`VRrN6>cxHLoq%wmQd1Gp2WYxNdXj!SU
zs;Oc_g<dOfT%jNjS?IA&zUOJb7A@Jc@P=bQK>JA=at~?#<z9H*^DrXpDQq^PMuP@J
z#=6@)C-Y3kcQ7AA;s^klXpmrJjP&F8K^k_e$GMG`MtA@HHebqi;CAehsM8gJ695+l
z-SV;kf;M9E&o{g60B%qsecjJY7rrbTb9z-{aZ*l7Mjr)LSk#zoN>Q^MxKa5a$RLy$
zhZaj$$|=`a_YS@_O2ortyddt&_!d-oW9AGSs!9N90#VXnOUH<QJ-PA<7!xq$e?=p^
z6*w`}NNTDsgKiJedMHHkNG9=VS?T~NGdChsQGHCYZgpP6h42NFg#<Dt4o|yHYF~SX
z|B8^(HSG*uAa+jArbQOZj2t4WS-OD{)N5>6R$;tAl0@w%n7Fnkke;V?)mJ;&`-mQR
zsVullxj+48a(TWPfGMI4waw>ULfm<Gzxi&(LFJ-tD{m_nVpp_<x~oZN?Q|-FRv&Zm
z83^eFNa=XuYjv5IE2C@BgA`vB2j~x;abJS)A>bz~*jegS@;F9U#wXMuDFoYycZT8s
znBqt_#W14)&i;hAFRk1^%ICac4Sp?lC*lF<dErTLk5bP2(aO<e&@9k8D>P+Aei8#&
zf?B2>6R~L!$@-N{Xh9iO>!ch^yRX6DgP+M<gfI1fDXI&bQQDx0l<!L2lRFi8;6_eH
zmx833?b~xVf&IQtk3H-M(Ap`-N&$=yRF@j{YXmZQHM<t&7_SB16inhaxb}S+n8rX@
zeJJH15B6{N_Vjlem-(Jb<wmBa9@A@QYbUH`)SvIq8Yd^`uGKWAreH4L4DB~v!R5(m
zm8_H9yK(&3+|679<zWP5`KYcG-zN}^ZP&n^5xmn^sC<>`{)R#brJ2alL)!H&?G`bP
zuoHX+Igu;Il^A;K^4Berx=?)yM55A|n1B|OBwSwVn{7L{Aehi%2z1fd%fNv>`r?o{
z+m{emaIkA|E?oejtZ9-Yz?3ep5BoYN8cK8+9T*L(T&tWzY6`oS6d@`yO$G@OLb8{j
z-kiV`4@N?lK=20DM9UKJG*AcQi53Kj<Qqmot!-49s>mX)5QyW69jX|Hsu`%0_kz@t
zYND%R+WO3*Jymj7bR^lysWv-Tut`<pU}CxVs)g?mTL`7IwEY0t2B-aOx5OLK^SVbQ
z#ZmNy&Xrt{az8C4`1)dkguoSj`o;=Llu;ouW#(3g9ovKQN<FuW>643>jwg7+KdZW|
zX2F}b;5yoY<JR^2=lg^EgXg9^^1=&^618I}{GaGZ70Aud^FsPU+%!{ID9l_HqD5Z~
z8&??pT@$Mkb3`E%x72eVyT{E{Az8``63M|(kgNvK+{U<EUzJ<r3;s-U8FP`xVjii8
zy6P2SpooCmzGBi4ULudk0~C<39UQqq$P=^zPaLBVU~jDcRSZLmn=LRdAk+ldVQ~>s
z`eJipr`;Ecn;4a#s^RI%9DTJ6gIehsP7c)F@3zHP5o%z_LU$k(>Y^;*WyzMj$f2UL
zn6A0VEj@{yj~$GLzlOpuj1aa4*+v~6BFlgO&P=;GvxIwcd>(~~ld9h2=}{c*gs`_J
zqcOPvuxU9-sO5uDfMh=pQM3RTo?}6lwZy9l2T~WmCIB#@Ie0{r7mt)O;_c_PhzS9U
z!8qUqBoX~GUt^?OI+6Zr`OfB+7F=Zj6lsB<2}@~9<)=Bt5&o@Xt!L?JSa^i+JlA0h
zt(>YSf8F`l`WwQ1&zucg@XKN>x6$Gp&S@t;gsS*mbUDl!5>eeququca`b$Hji^3fp
z9TR7TN@M*9<7cx#K*M-*b1Q+#^p+dc6MHNuGj2y49rqwPeI<p4(`KoTT#M2vzsg5!
z_NW#gYb%pL6Nwl+xQdg6u4@`DE04Rr!SfJogq=n%(p1G%%I@ib&(ozI7}3ROVv{U_
zYL^_wg)W+i4IyD<q@DkB?V@cSE*0hH4O2t&349n*6|vA~uV&~`E@WnWLp@E+6&@FL
zi-+<kmF4!|?H2fa1Qi=hDwg;R7fOt`M_Yv7Ozc7;)4BL8WvhwK$o-GP5iQa%sLbZM
zPjWR3atyw6&pTAE)|nXCiMzWS+WtzLIY?VTBjC2Dud1yjD7dG^&@Kdzb}87%gDD1Z
ztW|fGFcB}{XrXaM&{_Jb`^fV%FlV@wzbB~bE6sxt=8U^iZV_}LbD4AZ=b6sX!Q1CN
zeMSDUgXK^)P9`eT&Arc?w!H<XD9S=wQ$p&Ok2N#Ca0#dHZku^oS=j_r66yoabFf!f
zYG1Xhrnu8^2A%xoDz0}&<V)}`vgUB3(>}T&7zRvKsNL?Ic&@{@iYgil1;R+Fs%A9h
zo2mYwLco>Ku94v-F&=Z5ubRz=G{abEvoUri!C0lHf`H36<BO+<pk3i#(a#nz4=D#A
z#}$a-S!t{<kz)lZ!1!p$mG%sDerQXmiDYpIR!k*xS2ApPd+F-BTju4JABiLA{k#w(
z$TgYOto3;)0__8YS0?=rQe64q!nlSHsHx+Z(l$TED^uWbC19Yu7=3ybCnogpHNWa&
z>D8O+^jC6LNqseHb@;Mk6?9P6Xjj?HdRhDS=}6EN0uf!x%j7dc1uLx%#GP&5Y6M^D
zFX~Vz?4;?iGo_s)-Bn$MLMY-sLv@2>V(^eJ>hL3a5~XvjmZV%*`kL=V>LSJ<r{3?+
z``+W7Up5%P`tRv5Z);kM%Fl{{!8(4r%+e6*Up9-Oky28KBMf5L)$?wW*tB$YFHQ(f
z*36UyPB!!#wCQ6MgKqjzrA2Li8iS!&yxiqh_h@S(6lV&@QZdsr7;v>12}f5kmQXf6
zU0rAw2Orm#S^|j?h1*gC#YC(Vy&M;;KUbZRh#75t^6N%FMn5lor=|)64xPczm1!$f
zQB)>(6kR#FvW>~7rKZjnU<q$5*-m~*)opjWJIicbrKguF<OhbiSQHQN><6^7IL?Er
ziXSSq95qi#qkXj^L32!o9c_mdWyIf&n!K(#c-bwh6r&PnP+4S2kEJ`xFGH}FSs@^8
zu$lnM3B<w6RfaFr#_C#`i}K$QGQ}aGqip0!t`KXnVUi^91HqNs!9v3>C765r!rr^h
zt%No7XBD3dZkU4i41f|LZ7R&Z%l56CnrrK;*N~j}hV(pe<U~cHv4NjXH0bBZq<ZD$
z<aFQ$@j=HoR2P|txgG>uUt3ur9e$mpMB1Yu<CT_XXU{2rMD4k=i>*N>pf*m)f*KHx
zt0yT}L4D6h`&0(^(JfaNsn!%o4DQ#4-=>R&R}y{K5GSYupoqZE(Xw}ZKrIgAlYrFj
zzP{KXb`Myxqg@+u5oRJ1HW`o~C49ZUd%pBEmMQENtA&CE48mFf8aj(Peb5$&^vID7
zf34WonhU?r{MxnPpH$DDOsUKX!-pC~-+rJ#Gv|(y79(3CmBB$l#Lbt*0#ir>h^07S
zC(X=6xLLk3D#k9PqtEoj&k3{C_7xNgoy1y8X<GV>%;x>xJ46w@9>TCX{*4p)31vN1
zYSRLIRKR>xfTQpCs^R<1`Z<%ncArNv<D~i4VF9obss~HyWuzP|6^na5{+)g(vvVz)
zxpynqZyRs1Fkg&Bh>Qr!ojw864vCOW7Z{G8VXdUzn3ROBrK@lG42B`+3g(3asHI;?
zkj1(GSjBe2N5RI|d&;-9vr~3ZN7RZwP4Y8o3B@O50U$%dZqPFG?j(uS$6b6HxD<E$
zqJ=TqE#%qzYQ(p5MNv5#n+>j++JRzE9c2j6R?Rka^JA-BY68uMt-fEjG?mDw5z=1>
zYYpW>&JI9Q-L)$Dg-w`FuK;BLVXbs{OLw^}lVqttoZw+_X;06F{@_Tt0)=vK)Upmq
z#mi*Ph2gSDMf<XM{HF7&L3pGliZWU{{bkd3PZhvh9p!oX^4)1xU>NtUtGn6{l@KDt
z+A4xJm6a*Zh_<NR<rY^<Q@8XgLHFTkH@<Dr!ZCl*tgSFOD<$D!B-#SvL+V81Zt#v1
zjHy+MEF;4Lws<-=B%oYO1`!3iR`<i!RTx!^O#EpGf~+b_i)!s7W3hB=aYU~sSma!l
zVs=RewFNvdBL3t<;iVUbgWZ0;JIc&=zqD&8yz_H#B|0ifC9$-Uf~N8vFed$txA^<#
z{Z6K~Et;w<-OhMNhh-O2TfJ2>-A+~r_GAyV^zj%^#M6yp_?6)0EEzNra<JDklp^Kc
z&EC1WIWZ^hL706I^_GUQqCF~+^#R?+$VSs`(_A7}5pjy5ON>a6=c$cFPk6&1s0>&L
zBUl)f>ppAG{Uhzf56LFgP{NH&Zg@$|<w#Pd%UDyhbgh-jVQuM}3p>#Bq(GY3>3bS;
zV<d+Y3^ONGOfh|E6?QG=mT^k9sG!@lYK5f?2{bCJ{{%_gNr)|1`<;zOz8r`F1n?9M
z_<);j%iXDKA-3JwB%z!y)xQpHj~>l2pQZH5+Agu}Qb4|}fUi`+zjNgspZ7A%Y>&%s
zNys&%zt1{OQgYq|KHB%69wg#>#j)38ddx$hUU2yOJIM6<6E<4>sILqOEbUh*nu(Lk
zKnO)3p@11VQ+nb7Op=LLHn$@Oc#9a?4e?~)$DI4ERh~72<o&Tbw~^=R;_%s<n#HRP
zzsCB(cFu<}4Ep`>)^+FAtK5r8m=#V4di$hWqReG9Tw?**JGM(z$=|Tt-aF<9*V;{w
zNZjuQy=`|5+{J2{DS<zcU<2u~02H8*>^am?Xxt@&kVvpFdk<bP%bhG~fio0TBWsc&
z@^N1hat<^i1U@$dB)X%SaXyV`AbrUvwuIXU+u{~x>U4*c%!qR5-O)JT>u4i^<7Mw(
z2Y1C~rH$yL{^LmxBqc<4fuXA5Z`5^NAFFMeFDkSggr0G%NR;(DuQA(Ql#XVd@Q^XV
z<RDWIctmUv#4ZPj9DGFpvE>=4&J^QQ*ftHNP>oHLPh2F&o}-IiBFxXvplhi57F|`_
z@kTzss~Wg>%CGG>VWqLsAB}U|ak+7K*vZAcFyG76>(!AW-Yd}#%auSVgK{ea>pd5t
z;-IjvtybUk7}WV5DBStf!RH!2fOI8M@QIAk&PdO%Rcx_LXUo{ZAr9}B;=6R#Gqy&o
zikXNBq$f4!l`0a_hyKYvwRwg=qs^a;bex>nsFf+#`uHT}v#f^riG&tNxBI=~gOmH>
zJK%v0g<U(jd2f$rIQM@>Rk?0{pN|OnekmRWA0*7bMs%z0h2eaabyF=m&mNCA@K}~~
zuy?=n<Y+&~yp!gt@c<SmaRk-!Ks6$DW&Xr#3SJ2<`#uo1hb75wZEels`(Y%U@|p`|
zRM^?dpO}|TLd@=Srp5G{w<%P{r(bzU(n(QUKL|+7e9{1gwi@~l&PO+MElD`936bf{
zjw@Fsd4$u)5cL}??U&1Oo4imGIbT#*<+-5<yx=UZQJU%V72%nI`vE)sz*a6N>n<=@
ztX<k5GXm2IF}XEL#l_cojxL$d|7GAlk?^2kp!}i{D9DIayu>K^VzX5*x%Q!ZjuNU5
zpe++GGBbAc(@R<+j|D;2lrky`-Z7WWdEi~&V_Czu>d8oLBS0GeSCOqp#|KS%<?C8>
z9M4GOysn)n2D$VaLl+-C`R^dpzV;;y_%bwQ308=x<s#D_Fcd!dWK2Dc*eOu1NFcGf
zxq0O5NrJ{253GpD&Qzv=`|LiF0&1KRd>G&e+zYt-(Y;YI!f&oD_i>#p(PQ+E+sHNe
zM{J~Ej-TI5WeYH_T*KpbY}#SG7&P!{Oz`@4H;M3}ec?dUHQv;5E0)bk#x1Mbq_=yn
z&vLrFf#(sHyRb{FMLIB2B_jLs$y>y0o*ou2aLMoUr`idGikccW&KglEFPdCBStQ$!
z;9}e4SgFBbTqw-Ns85&~;V8@<eiF;&u75t$pHOqIeX|)-yF(_SR+{6V{_Zg)FXk47
z=P>s{a2&p-p+48s1*C+^gF8KOc6|I%UU9-p=KOV6zP9_eK|@~i3t8>XLy1obJZ)r3
zllt%hr5y)JzK9+aCaJ8`{O<Gs+K_W0tMWP!WfW@_EA4)o84kms#pSob=|Xk3p^nbC
z$Fb&d$13{Ea;V=AdT(ABRmDd0(;NLk_CePR(Cx9#)*nq{1T>Oq6r1z=ta5$F*WIUx
zYP{te=^4HSB50i?S91jEhR)$aAe%ZzMMbSeL?zbf=hBa0)!AOL{Klm_Llk~kmPb>%
z^2#a3$9vH3bcn60s7PB~kVk?<(;ph|%U|pf6P_z)jh&IA^0@0lQ#x<mTr>LeYt}VF
zEVo)pQ10@P_uP@)-Q%Ivp~e>eTiS6VRf)g6wcYpP@~7~)Uz=F<>w)MzH6c024?+n6
z`%dbr;HbCn?db{Y(t9GEF?+h*mm^=k{HWY9X+IxCaK&SxE-q2Wn?h3o#=z|}peTlX
zSEs90_b;kyJ>8F)iz%CrYhHYNTVajH?(vTcgX$UZBrIvZ-6f_jgQHv`F<<^@qgLC!
z1#el`DyM*63YVj;+56yjcPr~IUxBOQ{GE~eW&68nn$_t;yEwzIg;yfr*zET-M*jCm
zE8=YCJIM=rzhI>uNWq^N!6Zv&p9@{gOR`z*QB0%P5SWE8FJ5|A4GgO7kfEa}kt2mt
z@y+sO6j8wB-^fzwVQ^}Ku<G@kc1;IS^~^`O>E}_EQqj6)A!}99kX1FB`*&JV>t&GE
zM$0t<Gwz@TxTJf*)7anUe^GzR&~|^^md*`Wh@UK6MbQ*|nBiCSv86zIWz^drwlb_<
z@!Xz?wcNb=$QSAP@{_9D)ln(k*;(Bj`bnZwHMJRursL*ULs|^6#?8o3Jr={LdfL47
z=ah4q4@a1E8A*<j<`vyQL^7`S@z-(iJ`!uiB2*oBQ}01k6?2uB>$e#rxhz5%fiF#X
zV00w)ngY`P2<apM`WMlxmeaD28M+&Xe5?FsH&Rm6in%`HRJ|R0WOu7~m4hDViGQ+}
zxwz&uU#7kcDdA-4i3;CW&KWr_dK<;z?)xJH1Zh>vuylpuxwccurH;3)d<+yJ<#ZEd
zpJ3?-hEo_G1_z4{x%_e!caqw3SuTi%K$RNUhTf>N)GyLv)sJ<USmAVtEevSopl^${
z1;QXDt+^1XmWmg^X89(tiC!BeQ!8~mrmZ@38af0yo^XEB*hqBLX=-x6)%QJi|1~ui
z6XNB3eFep2R{ADJNJ5`b{_I?RUu9Gm!<i--!gevAK-r`DTR!Ay_sd7t-j7IErQrn8
z@HfN+>>X!4gD6?QlU4}kZ9;wo^TMz4X+MY4_iF4L)ta3`m17mQ9s2tYQw?tDcidXJ
zART^#n`O@CB*!w4>Vn3ef1{R_NWYaV7>Lix%3A9{I#oy#5&xNNO;h7I8p3J*(__zs
zB=05X@-s~Q9!cx99nsS>{Bl2808`%kRvx?LPnf3W7SNj)&xceRK)8sRVYyhl360_x
zJBl0zO-9d$^?=r@no6#qf}IV$P`-V8WVI?lcubXui*aj8x~4WPTu7J|94Ri#l4(FW
z`;T2pZel=rb*+09N21N^lRFo>Llvc*4b+yg;75GPtq9v&&I0S@l#1lCk2yRJXIeGw
z2U0=T{^c+8WS7sMI;%7`;Jfs*PYDKpd$&i@C6|R&Qgydjdz0~<3__uAXawUxM4|LX
zj$_C~bcL}2;*BxlZM*0U^%mu~L<^pthMF~+X_!8{TFjS6?PTBQc0OnnMY9<x7jaj7
z^poP}cxEJG(e0H+AOswR%ql&<-63Fq<ataJ-uT?pBk=j7XnjyAHuGXs_7jF_wbV3a
zaCitZ>pJid$wWbhfB+N^iV);%uC1?NJxu+;Y`DMX`6;F8NqG7OXXD21`M2f}a`!>N
zqI&i3ZnySP5J1D&cpi<ZPz+Tf?kps7mpV&#2m&5ONMMuEs9s+&n7T9z0W&0ei8SB4
zrdXGX3TBJ+NwX-&LztqNvaK5?yp`2UwvmWsWF9W0VynlX=W~VIgiUlysQ8tA0?>(6
z_NQp)pTAo!Bsp9bS=n6nC(W&!J}_i3(&gepwNI=+TiHR;A*j$@tdWXI#9piS%T_I=
z5Vh$H?cXBAQ?3hSmQrsK{7h#cVIur}35JDa8Q*AeQtXsIypHmBuI!Sp{mgRmGS^Vt
zU1rd9;PJaXnyKo&U8bm2HOhBE>lg6mj}0gKRix8s$-AHI@BB9PX?knb(dm0M>wPF2
zG|qd1j&<%=YF0*8$s)L}OfPM!Z6~raYtEdEk&(Ffjm%BK18$u6%V2unQ6TwFk48kt
z(RHt&OYT|NSxp?1-$x~^tFZEU5>}}_yZv%jY_BnyI}9bk1H;e9-md#Di9qqOJ7R{v
z7A!2m`X(2a3G&sZhOi4&lG~Muu|J#dF7#T2veKyhE32wF(0e6Dykg_q&TmxB0F)gl
zQ<`Nb>(k4*4U1>I=SEL6>HbbnAS==Z9iAQIzD|USPHyaUlZ#|a5g8wi_M01WZqHu4
z9d&pE&?%<bQfQJexD~e%U9GSh2z{;;qo<YrZue)`2Me#Wd%MS6Kbyht4by<nv!yFb
z9d~}-^UZp1jW&a&wHKXJO}~%@jVCQ^!gf>QRnxvTOx#jTTkU<x`{H|zwWxnR7lkG7
zI2-X|F4!rY!bHuaV}T#{1m<g&A0LA=%dL^}<}^8(BjC(aPDRp~$BAALs@X(229X4u
zVzg3;ZlLni%t_lwjgbnLXeCy&0D(i45Ff@ktuAz8Kz>Sk{>Kr%=XYA`Me%}#a(Bz@
zs30ym;&P27{G(#rD==iU0;DsSSRCd~$(9%gJ#(0xh0Ze}g(1RoVTVufz|N*yHkSj(
z6xKF5HE&X7>N06%u)3Jvz;X$p|J8PMZTCe7gM74nyz=m;rt$M963p;o66w%#4D+~l
z_fsx82xQk}>ePy9PB9WQ{L_m(yF~aqB)zVXHofhYa-pSw+Yhg^6yh7V0Tp8`eg1bb
zUR9b(F_c`68*9A<9i`<x6?YB$-xq~lZe&N_l{9s4LCY-f)KL^R+m}g4zZ3$CADbM%
zc+`KIeEc@`c8W4#KS5)&gOkgy)`WhNZcRCi=w`R|XHRFPeA&F>?GNLkF`k)L3;Dse
zOaIFzjN0~_4q@3U9+?;nI<AD44+(m1)4vus-0mpy4W6F}zou0wHb}m<Y`=BsD|(>w
zMEs=cIoXybX(-o@6y%Tpv!$O90zNstjz57X#<h-Ayy74Tzyj^%HL2Hc66{m12>~Bb
zcC9mE{%wZJA$hI}**hL063wpD+;t2q!`fy}eKzRji2H4qK<Vx5%++vq^-aC~^H%ld
z@Fxij^WfFxB0*(N{FBw;9MyFK1e`Vvh7I*YmUsR(mxt9TRC@si&eRH+<#AmyDRv+y
z*^dQG5k~1O7MZFK{0c}TSR^q{gHprxVp;sjN%*JH%sMUfhK&^$V_}SX2N#@HE8j?j
z?RwwQ`*REKb6TqxW`Fg5{Z-`twu3PFJb&o_6it#hPiWI2(u6ugOB&55k<ox4$i|MP
z;dQp;d@;d|lbtmzQe@3GfEnJk($SdqQRk-qn;Oj~ld`}4(JzjEn(3n8a^m9W)XSdp
zxNH=A$4db*=Az$3iGl7e?SSLN(rE`j=%olkoKv+af#WnLJ<}nv!4KCOkCk|*0NMx+
z0Zp0cZOP#%XZmJ<1hb0{J3Sr(l?VWt2gw6gegIWd)qtW|ZVEsM9^SD_Al^}xN%&+x
zJdEz1eY$hwD&6z&KKVHN&j0GWLvi6d_bezE@Mmvc;WiPtpo;&((0?1``DdcjBOU*2
zjKyxhK<2PQ?79{rJ8Li;A*UrHY^w_q?bOuLKxvl@EER`Z{Z?0q9x(5=z4qnINy|!a
zb9bsMZGL*$&r?r+REH+l2`Z*q@3+@}uRO$PKOc`M;w^V!r+mPx@cVjOf#vJGY9>b|
zYAn@2WQ2e*G`eZC(|a5~=Sp=-Y>fIb!b7N<+Dyh#y6ZPbW})Mb&Gjp-RL}gIp}oP5
z-y0(?uOcao=}TG6*Nq1q*)@sNP@afYZcQZbZ24&RRmzFgMN)BWiz|iPn{~WIPZ=#}
z@F@2!(lb2V1W?D@T1@7oWg)|OO*61I8Mzg9Wc$#tBHOWUh%UmQJRSnX00?AYc%(fL
zg8zdP>G=Mgyxm$I9jnF4-q;%CeXsIXUuzE*w@lA1lcSTt+v|isFM&^%hiT@Ipq+Y$
zkz7}SpIFr$t5nlK8%{G)-Q+>3ug|x+_SLZ$?+%@sDl64K`wH9-b@5e1_t?gkZEU=z
zQnC0JNg?%>BaA5pp?e)$CJ>QFq*R=OR9Xypf^WCqP40e$7RKiJzp?+CtVd{pM9*Q+
z;_Y2%#_)d(e(yNyWfpp$MT))iWDp}KFA<X3GRwbg=n(!Dk?}6RMXxF`Sp@4LC+_!J
zQMp*p*Q;!SF9Nj|tAJ)u(KA2NT?r%jFe?1&e0fmH_-oHl5f)WX76lq65rTp<W0Z|g
zU=JjVQk_u%aXT+{{8<L2>jN0-p+>735WZuf@PgbbiN!Y4U1`Ojg(!m}izJ|ZRKXW!
zGfyfWRT985hL?h%-yXQvmukCJzLJr_=I8PVeti&r&e}9+)wy&{yLrx~yf;uYi|+Eo
ztFfD-OivfqpBZM4f9Pg=kAM-QGlhDEkkGQ~Y?1lQE8Bth<=lmWfd<dX=bFPo`#jh8
zOej4|?*N(aiiO)Z4|2^*HXV&ZDx(rfSr;nllXngv!f2t)h@p0GB+u`-IixAdC`xox
zC}5z=buP?HrIkOQX2qgDZ04Qs7tNu8|9lCJb;1>K;Kw9t*+Tr)an_jdl5dvyYP=(B
zeUTE(3WgNFDqgclI26#2@}rrbCg@WK6_!o6O{*y*6zxo63rc*(PU7NPsxT)jaZrAR
zV(M|HdxY^9B9k#)3-s?B={;8JwH>*uG5@$Hos`?E!wZx_z5&~$n*ARDR6wi0B92fJ
zCLQTn27s-l2dWi=O)QAumw#EcdX@RH@sgQZg)yGYfkEC--w>}HGb%D^;>4c`9Dmdc
z-avi-X-VxwhYu?(btA`2HSS|=_)n|Qd;efh%q&_b!WJ{LK+<ll{?gX$+FBZwx7dF9
zs0jv`wX2u9#~yvq&6=~fJLs@u-IQsw+~~?mw|Ubh%@9;yiHRfu0_eiUg$0RcUwmD2
zjY1b@rlxE;?$|?rrb|-Qtw(O}ZPe$eV#B`kturpX?D9)KDVy~^F<MEGs9!)tH!~8i
zY0)-$Z^_a(O$(CN6T-2ua^;Gz=>A`-9L8pbD=&!Z^=i<fM6fk?KYVwdi0{)7t&*gN
z2L?Z|J<=5(QdUxK1kMQM)6YKTx;o_6)H$MU2c$<5BpBzWT1o97=#bG(g&iA+M8s+k
zKdm*eIW#*_;9#;6MtP?~1=h(!v?e*u;epYM&7mBCLvdbxGRRX7-2cGe-Ezxq=LzWN
z>0Qtx|0NQVHAwG;Z|nWWy}D@|35kfU>D42G5YnU&WUG&d4X?01?3G06Xm55cP4$wf
zn_XVPa928_(sjrdx&8N7xjkm>?G8NT7+0d8R0du*ZB7Oj!W>gI@Yo#>6&L0t9(m#g
zG1~Ceg~de&&)s_l>s}2L0ngb+rGi5TsLikVf8Y83Z?3!fns2XJv+7DY2c!$7n(PCF
zU8-AIS(&Z5CWj7;AD41m2K&^@uljB1JKs9}&tlTUHLkyqkbr6t)h1m~-&)&rf<Tb1
zkaWews4J4a0XK%@gS;}0SwO@K<knrOQFEcgj38pI2IBSs$WX&v9u8&!l8L*bJUEZp
z7$z~iYQ-Q9Mmdw0usaAJVdft$LhKEnSq@bD5|@n6sFaksAKULCf#X|hyw9qh-wj&o
zUuTOnBkx+G8_0BSuiB<ptE||T{awwJ5#()@;m0t%gg^t|y7vAT;PIJ7@vtRXtx>AB
z`ad%#-xZfty0MdHxoI;GbW``(&y5;0#g&a5=O#~^>1sDtyIXJkU-$Ye&&hF<EstiI
zA}h33m^_{OZnQYNq7p>9C51V$haZ35J@Mo-e^l!&7VTW5$9BA3ONwwSb^fyd_{ulN
zPM9$1H(H!Lq_#7zuX9F6fU<IQrL`&e5WU8k0QlrnPkchpxcTLmU%nwFl=6s_smMxw
zDho@pN;B|&qOB1yEdn72zfql-!Nuq|#LEOImcs!!_?68#2PC@A1I4RX18d@H$}9nh
zRLrD@8WS7Ct5kl39Dh2}gFv%&>(<&NrvMoE)d^e-z@e2V`?oYSr5t+1A!~o~<Fh|=
z_n+?lnCki?Wg<v-n?x5F<52hJWM?RxM<10D^La0lmSa(W;pzANOquizV_CS&^(j1!
zHNlM3Kk66g!RG$x`mF`a;QhV~{`#d&(JlZ>X0}#8EUqwVvbkoBfRO3BWy3uFx4$S%
zsL9RQ=O6<~l{}a@YP4XGi{Qm%Du|2qzWDOOvYT(c`&Xx&{FzflH~XmEAMliSNN4{0
zNnbnff{T86uXxL+L`!|5S54Ev<Jgs4N3F;LYDL2lTL-PR?dWU&aBa!lxpQlO{_~$J
zY+b0-xw^aWzB^NT<XqnnQlf_>)GG)b*DT(P6{}X*(xAS<n7k6Kc9>k$Eopl&?!lm7
zo?N?(cVLgu8GwN21oj5A`2Y)C2td?`yO1^T;|C2HI8-)CqHVuCR&|@|qkHW$$L)8(
z+|SOLGp9@i1MYX605jyZJ^;H4*zApKk%&AubrOTBnmDEzmz|w_nt=tZ;qAMYxHlyg
zTS<V>cQh%7V@LtonwguZzAz-FD7Um~%`im?Xqi)!^1&pzq2SuOCY0tA+5#z6peQLH
z<8pF`xw=hjUG1hS1IqHHZx}FV&YA0q6dFSO9o{jRu09PS8p=$4>Gd}g<0>n@@%$?b
zZxE5+A|M|t`kU;Z@V0;d<3~UE(T~sn)h}m@#zrAYBt;_3g=hV;(Q^CZY*LvaNxqms
zlE3(sh1fZ_KJmm8;pjEjToXC<)KfR#c<YT{SHx{9^F8FOGW&xT3qSyYV?(qla+SH|
za-*4~2&4xu6G#yoAe8-q=L&<I!9><ILVC~vWG_8Xt;C(oDEA;?;D#_|KgZaE!b=bB
ztK%54)uL{(5u+=jQ>IS--udU8zfh9z@{~x*C*HQ1K5!>#qmbKkYNcY2Mr+}U6xY-e
zm#!|Cq`YJdxV_tiDnwu4{0N}Nj8AMq$Q$WJXa+&11$o()HCw<n3oOko9qKzisSwm+
z5D>wU!L2`sbRuQu6u6NBOO~X{rs}l@`ZcQ=n@Dj}rq6cy8UpCm*q4I1jQMIY8COx1
z*Z=%W3sT098GX)vd+m9Q=;sfj&2tBzvE9A0NF?Qp$A9AZ^X~i0eb-8jlHW>BEun<q
zS7NmYYOJrTmztH9fbOcQs*hG_03;E8^wCER-hDmY^9&HyG&~G#jeoM%RZ-S|LINyb
zxlDtCZ<^f!cmN3gWE#={f{G;xf^iNgSkMiSFj0zR>XjMiUV;D)uUf?=&b#E!t<og?
zT9X!tdH?|@104L;q?dk;z(&W9AOFZLH{EvWjo01$b%EoXR`_o72ZJ@__MSb#&DwLM
z)E(!Rt!$G$)+uf-PA(4-i&4C*Bw8hikp2T~4Ag=M{?SKdBs=OIAi}empP%Q3X+%0N
zJ6-FPg<N~5Vx2mAq{m~l2QujGY83!Q!)v0EqS8vyMY^lmu*z(X4eM9CENL4hr6c79
zYccN^_DDF~1?{CQUcN4|WciAtpL+h~{l)R`RUJ<j4NiEwCnnDwIdhLW8{S;J_=?pV
z)|{r{0+y+YqRRPUi;)_D*0_jNrS|r2g`>0{bmyISW`FK;pKFiab=O_7l`B`K{q?bj
z_Exum3(phuF80c5G$aC2fJR)odZiICZe2aZUJS_qNB|PxVDgOrq?x#c07CrL*0u-S
zk#P<`))7b)R?S1T>X2uQ$|LFl2OwczDp{hTt}!}m&dm5B2OfIH<BvWuUS0W~4hHT(
zWB)@hfor2aO`lTfrcE8`8n*Pib?aH8NDJM`8&p%ZPConwU4s|i6Wp#=h`Z@y#xei{
z{fP~!FU;B;9~>%#lHjrGmOZjrgR-@?^|~Xj*`6@JEEma6ac!M)^GfpU7)Yos#HA;z
z{&~a7?786$YwUg2tXgLD!nA9cL8J%TlHDp*Yh+1Y|Ld<WPMJJ$+}97<Z|_q?8}r4K
z&!EH|NMHNX7r%1hMVDN-N=~5hV#@lTh(KS(8ayPDT$8C-r7oc`rV%f``r=HL`HaP4
zz52$h(^TWpc#lNL1Fg;uoj{Nwk^uE;l>kEYrh!8Ch*z<^k&XlbBmhd^ld~5^&0?^S
zasYz)&CUK0cT%SWUl&5$*$As9JyB_s9)QF41r$jdd9C^ji-)C*8#n&9KmYm9mz;Ih
zS!e56`|d#Fd>}4?-f5GfVR>%Sgwa}Nlg~g%nreK3afCPIImDpFAp<dE0Ke-bI`wXS
zNr7a4VrN=Es^7J~u!RR<OJ<9^qgK^7Y;lbZ4WbF+>T<2GyTw;6NOOlI3AKj0@OjDk
zkYDV|q}HKH#J0p-)w)%#a{N?_?Na!O%~*tfv;cVI^`&dvzH8SV*CKbXi1#_&zM}xq
zrS7!Pe(J<q?)cN~<29>PQNLj^-(CX;YpyM8S_bXLH0Bl+6T<1^0itn*S#S;!V@zU;
zmAfd+5*-1N1Cq^}?@?8?Ua#iezPQIs#Y%1g4dpQY!D{A+FGkD(P9U~%RpQ=7dSD0w
z8bmY$HOwbr`~x>K15W_f0HSTIFUI7Xlo>N-v`iQ`;R2Pnw<`FhWHon-4NMh^88f<E
zgcuSyYHXGH^=p>7m$XD{Z;zHO>as;91P~%pjlb>ky9b<*zQ92Or<T5M1c?+$=}t9O
zjyNg;5euZN6NOz~T52m(V%)OA;jn^yi;i%S)LjHg*yR+IxZ(0Fv8GkG#wofw+Kld+
zRUZo`7mcCUr$!U8s_L3yf4yh^;h#O>*ma_V27!mUB0G}yW?}sR06+jqL_t(?$BrF8
zWz?w3MQhirnjt3KFZ#1U+)}+01BWmMA~qBqGK&JOg{6Z|x1LG<G4W>zSDge9R3tz{
zEEJ(1rd|ns(%FUpB(OpFQk0^11*i;VrsGlu$x9Ewf#evJ9sq(RTbKpr{qKaE_z`tL
zLfDBsS}_?^VZF$e7hG^bt6uEiRqiA!csKb2v%r;0#B+7cW^;45w{CH7E?OX9v}t9S
zde`3CWDEoXMn8Wqk_U(;63Igbfx!3_7iIQAYFdvNUb+YuY0e6jovu`4VIxM2G`n-d
z`n6_I(}xAaigmAFUEh;Z0KvDbrI#ckeW!2xn5Mxx20SNUF@KDUbal2GLx6b{=4K{V
zYjRrkhH9KLzY;i_wcSyGP?5t9I^d8SHGyhIPj@f89@Sj{A7)yTCQ_O4t{6XIA_)+Q
z>}hazJ?!t^2;wHF5Z4>_0gJCOaf;(`v7sOdjC2nWY!Q86UJ`|3@s1!VAOZm5r3V91
z2^$$_iJtcM4wEc^!WNH4f?&kQ;)$j$%_*~H&+Of2uYIpoy}z#Ke~TB`T@o!%TV!hv
zH!~({YBt*UNjBB2*S1;huJ2?tH)w6Kdf5XV8du<pOj>0!$;jSKJ9t~LKLIGPA*E`C
zh_y1?1p6e-ub*ts5Rq<DrvFE2pba3bS+&ws6|QHj!$pX*(?vUxc=wdFEQRL`m()ap
z_>U(65L7>CO*_&^(!#Gv%{rpCmS~CJC^Lm|r_#uZky$y}xl0u#J_W5wDT0<n7}Tn!
z)gh4^p1o05U3FD)R22(f@w?xS03g~QZ13dzQ85OSKr{;!pdO)089*dKOw_Bfh|@w8
z(!kgtfC`{+j6?$jqvw7c1b44LhRc|*2?0XCs(NyaM3Mt9iuA--c5Y@=q0{%Aciwq3
zRNQy0(B0^dn#a^~yn(2jEI5Ftioq+mvn_-s*Jr!H(%splrIZMMcu&(1=9rx*C>t3o
zFBvZQ#R(FW0>RuM-``QGUaXKwUymC<(Y#=|X*E;O)V_k@BlIrxd-A2;v6><~*)Ae2
zjiyVLlc$XFFo5f6NK#ES24hwoLQY;hMc<Cuyt#ftY3cBtL!z(C9@D2yfB3~$UYOF?
z+(J;KMR>5P&eA1I^#m<7)z_O}RaG@2I)DEBJQ>QvAzD>pppk;7!u`o@?X9K;QNA5B
z2?8(zNrGYL!9>F$Imko+8#v_o01zfY^encRXh;*nkjd<$P6d&?KJ-KrZ`wOT<>lpW
z-jVYj(&JB3-mO$C+LYwEQ`{yR!I0t_EF%!m&Y{vu{bBI6y|uwjoH*Id-Ty%IIPEOA
zwgQLz0i4nVy3*1zG%nbiFPC~qV;#{ARkd!#+D%%{siaSl+4|ljlh?ee#k7OQhDHql
z=enuW_YlEuGWRoDM5+R_vh#{}Qik9xP;iNuYn}koq=g3I<S-0uBUF6aM_PzJ+?W~D
zrz;{gn>J4tKn7@b$Sb|m?!-N3%)0*h7oY1EeWvSTMXrZJOu-OM3LxRQ8Y0}-SXZir
zC_0%pk%wderGv>s1OQO6cBxh@Z=+p9GFS>w&6eyvT5=QWg#d@?v+7t<AVCZmfJ0da
zr9x@oPaKocTGTM=;kU9;OcjgA<>HO#qny>>`qpXbdVx=RLK>{;?y?8t;obYRI38+N
zSWA$J8(G_8kkg(yxw&$6yU?MB9p3)k?|k=oMNe#wh5OSY@n}3ND<el=KTB5-He5dp
z!%`b=lYo%yV)B~L5G52mJu54{TPquOZ)s`C+_-U5-?F7EM%QfIeB|cZx=$=$U7fO_
zS`*RAGeT00l~H^B$50#Ob<~)Y=6&qt{`%lOMg!QL>6y7A_6+M|Bbs;YB&rG1)SMce
zLi!LZQd6HkG>s)-%9NF#mziK4UVZFnyl(OXZ0}h9Ru_b`a<U4vO8nE(YL3+WBFTtE
zl&_WHSdx0B@4zUWJ)S6y)^2Ie5YeW10tP=+QI!QBuomrNP@@JhkjFWzEaKKgt_KGq
zc);Z=BM*>}NkQ3x1OX)2BNUH27vNcVgN_3VT)$?sXe$Fs9iJ7h98>wc#4VnM_r@}{
zn*FQ(QQJT)G*B`@K7a*1oFx0Mf7-N3uc$BDblo93gx`L&GD~TSGR*;F7t^cyZD|N|
zQBNo2(+|;}7!BOx%8Coyl-JLhIsFy={OPJyRcAi_)N|)7TDt1w1&dZm1ya|E;S!>P
zaZ6-1QxQg2j&bD`BefRVT9a}G3VY8jl)+A+JCTqYnRr3<AUv5!4wE2yZ|DwsB&wiJ
zBj0k|NSCu^PIux7{!&qGWkJ!fNBisg1rWb<DTCX%vs>Fb5@})pS{kT;)#<W2kp|lu
z<uUF>SXRh58o`1d9BNkGGq7*h0TgeCc-ODDQ>ab=kid&&Qp9W?;(mH8MjwteRJs_5
z)K~zAsgj>K_W0E*>f@C6;YiT*_Q^}kCe+Z-$>*DiKf~R9gfam6yEFt8Tb<k9ab1J~
ze3Ht(f6VyuTP95S!k6d&_1VW1V0h!=W$U%zg1$kiMC};2>FDUvw<gQnR7L47S-eo=
zCi>2!oC(=^MT&Y#+3J~Z|3lLW)#rHAGPP1COc^GO&5Qj*CTYG=Xw3!|>}xO8PgJB`
zNhRaPPFS>QBet9-jEEUyJ2NFbrpbBGV`?;-6V1%XEN<`6I=1>ss21KrG^<+(9Z&})
zIT-edH=2AzC+9F^e85^DNs_8Spl%5)yqqPqBp^Y87)Z2F(MS;n2;o8o2!ST>ba6d*
z>0>C-9DN4i-~$it(*d~TM@cO})20_?#<aVq+Uf}P$>{gGVN~XEVtLbh`g+EsMpLg-
zc>I~MMC!%Wn;PQc>YB^s7pAbq!v&O)qsJHr#_QIqNmIQ&oi05q-}>BG|2tIBNKQ0@
zx{h(qR)lDFEbM{=U9jKR&KOY#;D9c6C1p&VICa(Y&po?N_En|I<#X2IT+Mog*7+2*
zLhwt&ot<scXiS`I8B$ds;9B|tv9hX&SFZ+Re~j~};`Wd2kW8b2bPpc?T9j%3007VO
z&$Im?*2;rNR~d+SHg)1>oI%PuP17vgl~&lD?@8(DA+(>BJ>GM03~9G9TCL?5>bLKI
z>ez#B9$Q)QN_I}329qN()F0Rwsuq%-^#(=>B&`~&>6DwcTj4QK)XpSzzwYph5}IKS
zK*Vg}2%gSRxx6!}p|LSbWitVOS5m(|)6p1BjjqPysz=H?$Bmrs+9WVg@oP5K#KM`G
zImQX}1c7LfK>a=jqyOFpsG@?Fv@wwIgF|*0p!j{iwTle#R#p;lNDzKQ_(&!jHCF=%
z*RZu@4W(gyHBNa&d7X@YLfSt{Ls~%n0SCqA_=hI-WV*OH<4&I`t0=j-RPJ9$#azEo
zmNL#2COnmEaR$~XU^Ki_qv2ebB#<4bqYsqCF@(2{hz#rxBuPMfMki7)DNF&4*iQ@;
z?sQT)ptnWlz)FFH`>jx()!(g`Q@yn`RWz)HzUuj=n+}9A#?6q1i20|BI0lCR1XPk^
zTP6Lp+728X{580jJPFa0`Dy#OdA&plus2W`H|t^yeZ|Q%>Q_FueUt`UK>b0_nsGgN
z^dGhd5Rcq@kFiTL(lz+2Z&@fTC4qO%fT9j0NOhP(QEKZ^uQZoO8HTvC$wu-SU>32G
zpgCi#(mWVzfw7V_vQOc=8u#C>_7i!>*BSstczE_qT6uEkTQdz|jXAU@iC*==`0_yT
z#I3(pIq8Ku5g>U`dL*TWRX6#^uGfAN3`0*1?gJ4n;J^MDIw)%}dsWRP2$`3!x<xl6
z4VuAHKH76kncf*I_}w<y-lHl?QWXkQlaZnMJt@%y?tJO)3Qh6hMMQ5Cw1_H=b^ya2
zV0t_FbeoZy9BdAUa8-Ly01Q+gf;CB+&jL;Cl94VKg8@tPoVw9ca60iG&^U=Gkfzp_
zE#bnVLM`DHGDC-#G6}*~gbp~69(=S05W&qMVte7_hQheiNL@S05qZ0wW7|v=ojehM
zZtzI*gdr5KMvQsz{Mva|w9SX_UI(|qqW+ODI7)>V$g9*rESRK+saZ%7JeLd#qZ0LL
zo(Q2T#BRa)tt)q^Rv74T@~{ESd#*$SqCsdAS^z8>qcHN3Ax)UcE@>7NN0qdSPYpv0
zV#tUS8R&)^goQr;SJAEzvD8M<wUkAXXh7geLmBRl3WNa$D$JO%3%Qv8`low>tp}J_
zne4dE+2vd_G;_P%$7(6W*}RQ#&~+&q&a#3&`m<FFz`g$oiMb2O$0pgF`bZsWG+`%t
z8^!6FLOtZz4pfvP&{UX(4t<aS7z7-sSUg0M#7-6uFT{?>wF`Igk0J;9lFtf3zkYV5
zu&z3E_x4tWPdMR(W-S+Ak7Uq=B$FO&E$b#CY^gZ20%@8gB>Z{WfP2&!hH|}(Azf(X
zH|K)HecY{smMP0mv_h2*8a;Wn-G<akA<Q1MzYU6#_k6VZN4Ef)Mwh539s2;T3v;6n
zO@fH~`_Pznb#YSQzolE>zJl~Ok;MAGp!%PE=!Yo}7ThZ7fdugko4Bn^*!`|?300fA
zXpF6bx{LnhTm>Mq&_!!#OB!C-B)Sj9q~~ow=T~J&R;j?<3Q-3}IwOrVjHmJBb#^^K
zKt1zlw~0^^i;Rd5O+(v?T9DB<g9%kq7m{`eZek4(=^P{b8Y^L>ck9B27W^UIhj2X8
zdEXk~jkeC74zynSN_IyGU7b;HmRyzDV4?5S#skL>@ImbFl%Nd+4)fm$j%C0Q?ZSuI
zYg(^>flY(DrFr9te1+C4Bn9WqE*a_ai<#}BHL^0Su3D9xI%>|kZ8bjFHg@FL&Bm35
zmX%y21IPekF4zDl+JahM5`ZwOIRGNNX3%ay6apqf1bmpf*ByEAwv(3-#J|8TOotVk
zEredbbxdP3aRtG(Fb1-#{_br*diOXg*JQ|BtnbhJJp96I>$R3=N{U>uaWSwES_LcK
z=;NL0Kc{tX>ykC$D*%H=?>klxgovK9GSdaFh}T*qkr+tEmhs>KAevp;)27u(M(Wzk
zb-Jtl9n0S41tFpkRO4_3&&5DTi_yBi39Topx<}WaamE=QAc~k=fLf^=aB2?%Kqy9%
z5LFA|!|A>F^T#pdy`=Gy1AB!RApns9MpQGvG??@d9;8frkl+?HF#0r744~rXGgNC{
z0*0EtTB~SdTinjy^wD2y0ks+6*wvBbooYpO<Gs4!j>hKp&v*4GEF&!~PSiF7Ys}})
z&Q3Gwv1-K%E0=y_?JKrkS9tr!K_{sB`FVbh3t9-jyA<aY%u4|%Fl(hyX=w?INUYR8
zOS0^A((`h3lMMXCc%H2=lMrKl%idU-7_NCC1;YxK>-l_UM0P+*%4_I#GJg1`g07d;
zz&MhMgxX~8HEkLGX8?!9HGsnp_zh971SHBL#)=o>Jc*aJj2h(+SQ+Sl+1XhpodzU`
zK*Dilyl*O}ue`po-adV1g?;qLT0rfL_hc72=#Dp{`ueZxzDt&@tPgK#>ij%w6NnZf
zEJe$=LGnb@*;ygiq#2>lKJ&aAGhw<WKS}k<6tUBRJCL9!tvaQ$awRo{dGS@oJlZu4
z0)VhUHaQ+M4_1V4%#0jav3v)L-sUBuMMOrfhB?MT;|e$F)tn^27GjAj&9{G%8x0^I
zoAa^d5^Ebpml9!uh6rEOZ~CnPL6rw27%qe&<BAh?C&4oaB2+A-2K@#a$s7yP07#Nx
zu?mR8;5#RKseuFuBuO}=FPX+vPGs@oMOgBI{0@rzP;c8}G1SiYeCE_!8ubD7+que5
zFJ7_nx~BF%5poJ2s0m?vKup65G9^)*TeWhPt6KS_%T<tKW>&7tP!^fIV-h)tPU2b^
zJx`IknVNef=ah~SuF|1-NVtssz@wHY>hHA@%SuaYH2u0pY?{&ZT}ge5mMt132F~|I
znAa3i;39a45Na>h{2YaHAiT#Odvr_Hc@>cfZhfp2dy*nZ5>=i;AYcaW@O1;6t)vIn
znjz%@!stB9JDMS>Yu^fv0luOgY!Dinh0Kr~0FlhT>>e${(&1iu>7|jXoR{C)*?m($
zQNecmZ<^@nKs+x}U%a&at9Jd2y7U&^4LF{?=i$Y-)zo*+(fS4b3IU0*DxqSTG?!Zd
z(fT5t9ewV}#~!zbMN`tHqLi451)UhRzfCo`hQ1zIQHtQO<xbJ{o3?0radJc+fb=Oh
zHq^$WDH&QjtMoNpDpqYD|9qR$w|lCgdj0x|8oEf;2)aI}-ru=l{dyA^TE}hEkw+fA
zh&#hIjWre8j#sqi!vFvvAT0m|6%R5am?VgqFH8*_N6j*D=pGDoR4j=^B6L}ygn<|D
z^7ASd&%hJr2Z5|UX4^2ED^q1-f6!Rw{*^-j-4MIxxfh;g68%!E#BTk^vyP-9y8Sm*
z*Jutk5u}fZfNv6EJ*@rr!X9DatNGFkexPpp#mkE~MsC0Vwb$2dZ2e+mQ&TKGJvEA|
ztp%aQ@e5t?a4j#ZkijL3m%E#P_X{_6!ZeqrQTAcO%hiWyF_8TB6VgM|!d*pak;*F6
zDk%yS6g?5>*r3mR_X`y4_t=UjXq1{PcId(SfYXTkv_Dq4goC?~)@-h=J&5{=33p4q
z>U`tPh2fn197)82=T185B&2gBT98pNN;@S-9XW61AMd)OUv`C9x9kut?WcK&1_U*h
z#EGv0je}9fmz|I_gHl9>yuf8fx>PJ?#PJ<LK!W;YUMiIbK!#?x3o$V0lzCdck)<D*
zX4!`n!QH0`^&*x;b5qmwHS5;QA6rSA?7j)d$AH9EqX%#iM=>c(+mRyC^yr$Zh6(j8
zZB?Oo_@O-}RxC|VkKU$Jqm_x9!ZjWYF!^G1U`T>;$>_kiN(txLtoE+2scq_hbLHj{
zTbjCl*VNi`#FjSc|Gho^83G4hs{kA&#rmAy@Q9|z_P9H5zf-qsjVn#HEh(#X`GwH!
zJA(r|H&ZIj^r>S+O9@TuZWUc=b^nc3vOiixNBU@zmOAo*kNp%FsjM8^am+EtF`?`G
z%Da*}6~ez<>+^mQfuQk}_UfwY^t$RTp$U`6>r)K{_fzO0ha3`$N+L`UnQPYM<mGz#
z=hP1eNeaY5kyzB>Fi;cIDPmY=v_s4+A_pOZ^dR%1JPaEIwTcl*R3k#=S(Pyw#dg^m
z9N02&5U2pqv8qIUeVuepEntU=m2~|I;r;p`WXiqw{%O?r&N%aB-L*F_usbHAr;)n0
zHm$ILI*aOQYmLe1m2?eFn|-LOZwqDr@$M&|)rgjuT0HW^GYdN9R&7yJ5}&nGO*4od
zeIYgy&XvM5OlGUb1hmYL7@Su9jLBOi$&s$U%+{*tMGF5ZR1hiNM&}-y|G2y5n)BTF
z$$M(Gl>%2WdZMPxW*Kwco_b~dj)tNoR_Dx`pzj&xXi@Zf(?FV~T5VXr#um(DHJ2^{
zq(@Sut+gShrGTPi$Blg;KR@eGz2uj6>O}`H{>!s3JewvjU@7gbp|;ND%a@mPT1)IU
zA9ci0kKcOpt<yxi^>COeBHKMVH*?R~FTS?mrGrq9G*_9K>;eGlkklys!wriqfuW8%
z#PTe7vfy}(boy8sCiSy2qCU~ZODW`;1qf(d?lq9897g6T&l*q-h5f_Vs{j*IAdxtM
zEuwF=+jr#t_V{CmfA_m*+^MyAcUyok`N*WO)YN{hVNAPeN`m@<b*oa1=(4r=b85yg
zS5@1bsy0kbx<2(mue5^g5u_UW9TPyYfnfDbYBZ%^?asJ_oSdohSfzW(A#jun9Ob1j
z%8+~d=_T&8uY6O|U#n?m<Qq7OiZK_rpH#f5-Fj8G*Ua&5!njhmadW3ua%q#D(V?#d
zFOhNHqNPAvEqCa17@9dxf#D+#Kiqx(#81Dh{jVt7XgDW!BH{VEZQa_{-=<9^AX@7i
z8$z!yT#%NXo29k5^8b4B$tO<{<DaDIw`-x$iOMsTKQ?#Yxor|2UBWd}5T#}jJ*mOI
zq&<SPfIAVBh1z66WisMTis%|7NFYV<WI^b-1jzt~&I_c7*&!xTyz0Ol06r~UTwH86
z4kPTSSd5PdC}G(>{aX2?_~C~hR5`ASKf7b%Qv|djZQr4G<NforfLeC87L6__cEgIw
zUH-6AacX^bIU_xho{^f+A_)oE1{#+L$5Nu9_^xJsF)Egp8jWXXW+XB))1v}KRK|EH
zS2H>b3x{cjU5Oh}&iC!LR&!3sz4FRh_oMIqP~EWJ6<5r3!^=i%$<P`4%t5et+@Nft
zuK<R4t_>n<{c$&U-$`!v?9uwHLci8rW69Yrw_@31w^8a<x0W2`3sp!E7R&4I?m(Xj
zPo6yGr7>g1h!O1a?9$8cy6d)8iorTc3&-K!ZCtTzdG6+_di!?b7ys=`&#3G}#mo=Z
zb}uBzSY;H(5+P>&gb5QL*9z{(XchHXNMeMQH2?tIAD<Wd7}9vNI+!&kRmuQ@w@aNG
z*dcI2BoQhWAH-pWchs{$iU0`9Iu?#-A!Y%LF_sd2gQUK`UgA|N`X#|h`NCxgpj~_Y
zwMS}x`W>q8naaCWqKOF=XZcU0beq}@E>~8@ii285R&B~CGwKVIlcaX(4&9^nQv-!)
z-*=lR13MFy4tFPu7_hQG%ZnBjWy+Aof#Td#Prd1Wc=`|A(uEJZu@h&x0@)msr|;_u
zisXqBEx-(e#CwtG$6hVWJ$zWc``A9?G#Wlki*v{HxeI|q5#y^RIhHM5WUEN>ox}FF
zCM}lNBxandRX5wipZok5+^0Tu+?6Wo%U0k{|A}8ABEGh@tLth8sP&EWUah3q|LSY6
z0XV*ZyXurLpMq36Qgk{(TPe8CRVrJO5Ig$tgAc!H`HE$(Kd#S&46GPK2O!UMuW3b^
z9AhJ>+3GY&m;ou`xgXvvQ?v9{C|0~^(fX}?nF@6Qjz}ld5rnOn8l~?nYUvu2NO-f#
zwX`K*fH;s|2EbTc0xQzLrMYp^<-hywZQuFk={KoLrzr1M2}6~y1v5g8r*LESdVSBY
z#EqXYNkp<m#}uz5Mv^HB4iR&}j~9A-<=r+04idnJw-RtL2v|5QTdrK{<fI05xrZNo
z%$@#KG@M2^PM=uIFD!G@XCLhNU?&WadIzAOK1f6@%x~{BV`_z)F`cj6rn;()tkx+*
zT&uXRT)x;XT=0_iwSuG;d}z~>t8L9%Ot4#PruB696&IJL?6vpa_v&Lwtm3k-G3=dq
zS>5x7n9TKe-Fw#*(dSptXtadV#@81t$Y1dC!cgVd(eBfq{tW8L$CYO%i=GBR2Ev5}
z^o*jS;^|{Yjd^kTnic!XSQqvCkLgH0=u*Y&3nZvX5Vv`=BqbR0ND{F)APc|Az!tab
zje054DqWJ56Ens6;6{Wq2lhp@-;Z~|A>lf7x_sR7j5OJbeN-VrPSJPd;;H(6L%VzI
zvBysTw=aI_n8I8_w00+u;Qd!<j|^7}w~_7FpidpVvET*wvHcHsQZI~Yz+AipQ81@$
zKt5<n+hMo8+XMpx7_1M>I>Fi5d=f!{aIGEgxu+MqJ8rw(-TTMO1Ry7SW43*?bH<!Q
zOtr$9fb>9Mc&81LM5?pCL0CDm$W53~DhZUW?}{Yc(&c>bq(h-3En0i{HG$)0EjQYt
zkA7;sD@l*m=0@$;382|=IW1B?|AjBKfAy6A_-_^XO@Tv><(*3c$aSLI1=rkg^#ioB
z%mk!I>r+oZWd@T}`=(RBerlIWJ!(kF<F+M}4?N`H!!KX4X63#@K7J-}VPe&G^xOXF
z3=#p5U?dtqkR*Yzjug@Og>*gXksbCiPSi5gC>9p8v5w@J2NK0b&&75y-dTF3qP#+#
zqK~M^mN9XNnx)njLG(2?)))Qghd=!H>#n}xw|en$YOn)`ywnGJ7vh4%(lYYI;qtWN
zWV2hpb`=9;?tp{mxrzGDcwteoK1Gmi(iniil!$z#P2X+8YnGM@vr-hbKD)4Td9Bo*
zC0Z2jK{x+TS6T66C+O1zvWZGZOmO2S@2wAWj%URKF-pcM<hEB`w1}N6F3NMGwAMy>
zS%K=SSS=al>#Dl6GQ1zx*wV1cEn4`BM&Oqv7w2y901-I0G;G#4F>@1Aw-cZH+!x%*
zC!O?@g2L=C=mDo#C3fPEXyh(|<K|oMy!DGcU0p|aYMCMpDb&39;)@lpy|^$^KBCN>
zc;booOSPQ$)?&8>5LWKalZby@D$b!&q15`4B2u@!_(YBJ>J|+_XaM30djpVc)nSq%
zed2c5DX2`1k|F>q8ZGi1(u{PZk?&kUqTqXzPKEMll}q==14fyE)RHt19ZP5-?Qi|(
zkH3HWMHgIhmyYi?05RQNDoRw#cNUk9vax_JsUVGYHSY3DexYSPXSzv~XGrHSO17J(
z3rHP<Ss~fqbrM1ul&jrbBZGN~d+1)8moYQvD#uPx?3D&|^UB=FaWmbR@zXU9BBx54
zmPA#~$;t4aYRi}1qK}PcXKDVBs?^>Qb8FV}*|u)QS@CsZeWZ2Ga<_QV>-s2Aou1b&
zwaQBlY!8+(Ezp3ST>kwB9(ZtyqH6zd@4e?7q$+&IDzbBb){Abhd_vzF>}YGdm`HQA
zS7&o;bMK>%Jz^?IS$S#ux4(HBF>N@jwo2Osh_2Z4$it7m^8Wev9NN|2ZEIzj6~Z4e
zDdECE0ztFb7SXuqnPjhOX+M%2H0<*#6a$9M`uGd(VbmxKSrK@=p)LdpB>nbV_e#aU
zt;n~m6`!xq?^by`M}(t)A^BKh8i$FjTDAH{**YgmEqGQn+kdM!`+)xt*K_G2#*vki
zJ>yZ^(r#62SGcDi@q=>j$14_>l)2peF*Z7$l{3s0mQ8Vc&f3S#oH^Bvs3?|Qkzr#s
zsdyaqQBZ7;m>dn=J$-INjXandWYqGgvPlIDd8X<Fyp=M>tE<*&tcO+HWn*YDYHyG?
z3NW<-M>gQ-oi%Gt>iOsY^0oc;+jo@Sb)Q{%U(w@lC0U=i{-$g0(YRZgwQG7t)f10D
zK4SUeRq>IdM})uo?bH7vr^qqeDt~(b*<-KSd!;?{w}<YMXX?c6?p_IktrrIXB(kB6
zYtcyf?)fUM0Ys7{5c?L1DdN{!z;fHQ+xJD_c1R!eU9&}Gvv`|?P!)}ln6%PIRTR!J
za>OXJMHCibQiK<jEYs6nUrTfIxUYTVl%M?m#@p`J@43ooD7!)8HN42o)rV!qPf@H|
z!Ek*Uc7p{0mXwc@)b92Yown8-32T?Z+jg!Eg8PCkj~1mf?vkFN)z%A(UGA`QS3Y8*
z+kfs%H*MNzQ=u?@T3XQ#h_vOyN1D@}{PwzMmc-?CyAG*|b(=Tp<4fx#nKqf~1~@q0
zq0w^Oxqt-ek)M~1S{2)CuYFQ4`t`3~Kk&c<sLif|;~GiTD{ub8jX#uO{;AH^P8P=A
zyn5BDwEO1Yqt!s1n>Ky=#*<I}GOWK+`R~&90K$czJLSt?yHcM<_+&g0OVzX-20;d8
zxEj;!YMBORG%EJr=8Z^%(OCe+4;%*WNk-AT&E?BD3KLWc^7UC`Ne%DKB2ZCoOo0hi
zfs9U#S8J3$)r;G>v5^U6(v4wLno1(Iu3sPC`OMFL@zd4kp7YC#bnHKs?Iag`uoJIg
z5>rvJd2ZyGscv}bXd4q?5DkW;hBk08>n_kh->Y{991Kuq=^N*1=~`t_tLyLou}Q9?
zB2Ttq+^t^QDvhAS>`RuUHP^1H%=itUU>-+{yioP}YFM2zF6)@0a*51ElJE{l52AIo
zc!)-`d!?pDV+{=rkxv|VeDvGj{`Q{_J>(GRbDNy~o!(O|cduyZS9kvD4+l%Fx~RRa
zosg0~t=hijfrsW#?by;An>cxV<eZ<K^PsLfWqakl9YFRkD=BN(f4_sSf9}PnzNL@b
zz~^NoB#RQ}M8EPS!)*M=A6ZYeqeE3QTO>Jf2(haJ`v_sz2YnblZ;CXT^uQp@(Kqk_
zLg4bn&B!-G3}6xzcxDO<3(ajzCv4fGZ}eFea3y9$<RMEeT=e>X{^Go!{rfp*pL>Q*
zeNVa4F8RR!@r~_?Pv7WM_&8zYFd+Wff3*p}-&4Mz$6{~NFHC(Mrtc6Qa^O@ks|>ep
zLzmlJ+h(C3t*wn(gSSxxuaB-`Td-K1Y)w98!Gh%eb9#KjLH#dJC*dMQR|K(DNDsc8
zm6@5Q)!N&Hc$a$0DPNVl_0#7Z{qdu)4^H2eE>zn*BrW5dKmYm9DGGkPkGc`Iq>qxW
z{p-UIk9qmI*ED4}FY>t)Kll8;`v~=BIBfe#ZwC<W_~g;^j=p34`ZY_|tY1A<go`NJ
z>YlJVN1vTmgGMYWm#`B86$hn=1{TFtLXrRgNn<lMHlmfYMf}Vn^dAYu24VEP-zIc2
z{y})XEyu!Wr;V_iog?ryH=$-h5`Dr*W?T+{(95sAeB)VX|M2h&&-?XvV~N<=kx*op
z#4<1aua^R#2nhbgwSkKHzg2g<P1TcZ#c$r`I)4fwO?xvYGiT;Fao-Gm{<~T3cZ-SA
zpx&E`ms%?@FkWR}0n_!G603U>|G&NS0L-d9`~G=W60&y)Bq56cVT7qH0SAt{TCM1d
zT05N8TKm=7zF2Fut(|Y}_g4E_t=269idIFdpePVQHi$9<WRSfGWIp5j{qK{9^#Tb<
z!1m*YlV_cCpL6bW|L<#G7t_SYcVe&QrY@mhEL9r-(k88DM;jGvnLN$SyS?DcGj`mB
zkg4ZQ-SYKouDWwre!iaF?Ok5v;dl5aV>4sfvW0b3brs7{G=<7e2e(qXe*L<b=U;xY
zmy>mV(7-`$_uTW74cz99hwsMzwmYMH_4KQ+f9c^zexDDEqd9FaSNzo~=2loQXORL$
z0FVk-tG|RYt$a4C1xPe#$QK==D2{ep9&uJ1R!YuqV!PD2$D_f<PtuL@42>4%L7a<C
z#1~ff6w@Caqc(0d=L2}cs1L_T*Up{A@BQhKKb?Em9e3RWK!3+7_-_Fco1?TX(Nu%b
z?RU0)Z-QSO=9TFpAfcAmVkF979@t^_JoGD-b{?49$G8A(+XlN-Bg=NRwS4I!<caMR
z_z@tq@G}#*lwe{7BX^?N)Eww&7dW6E07YQ&d11$V9sb6KS|nJ@la}7kbHN1{1X#&`
zbJI=Vn#Xt3*=}Py=EgIBhx?nccJ11#ja!PA0**e?t^$tQit>uUt8-sT2?>OBq-Upj
z?wv8?8N55XWZ$|25Cz6uarwnxn>FjznbY9qh}jQNx{Z`9{39Pcz7E_AIFaBkvK)@k
z-pp4Wsz?O&aKeX?=@^jU?aQF*VL7OKEmACSsU^Zj*@C(RcX{)A3>ait7RVeND~*|a
zwaj^)rSP~7_ItPia*g{cDl1$oRxDfa;6wMH|Fann{DN-(%$CXaFWWSqwJIA+G^Xst
z1eMh$EoV3HNE&kE7l*1{nkAKSW-1$2*I?xcJ!#Ct38_s^CY5Maqg7J6%Lv!2mKRz(
zDbw8EiW~upBoG+T(V1i+ZIX`!5-ATPpO{Kn{*FLxO@*o`>(X8MgNHiDj2Yt_I%L?h
zlc%2j+tI^@Ny+jDwlmp|xzX=yT*v)|g@uh<wr~0bS)&guUI6FUb~uc>nX_K%U%X>i
zN3ZZu&&UxY=3RX8rMix^u1?e~fY3=Op1!e#A1`^ksD0Z-_(@Tc%g#sS4Um`tP-rcq
zRFUEJ48Mn_N)tKA7Lf$Qyw_lQ4Hc0%!)hWOyIBYWAUzI%*$#?&K!*A-Ij~nkY2;a~
zv@lrOp(HXcmeCKTq0I#FIy-=;<*Tf!bYXd&fA@?ZT=|EG9#)aZKe2UpKC$bUr^$<2
zzDsd93l8X?idr>+&{irS;Z8~c<D=e`hEz8#mRC-ra-nY~hDYJ74UKzHpA(@6Sac3E
z!4K$7z!9a2O^jsBeO7TvIfAq_QN_mfr44Y@W2@MM7D@uS7Bf9JlQ);=6~GG_fCLcK
z{SEb1!An7L8REX+{PVG>_pj)mmpg0H#0ifU6cki(nRl?s>v5k~2k*Pxm?yH5|9bA+
zm)-SEO-nIc_XZq(%ntz4U-j0TZ}wlgbd5hG%<UOAw4nTNk3aPyoePXzw(bH%9i4yE
zcW%A&z6bBk>u`4DNFE8epd=z4G!V>Rx`UdCLdes%7gbYRA+=_ri;^(%<~<Ll!_IbU
zu(3PP$g3T15+_#_Q4f#QmjVau7K84>Ay4iLs?i{JOs?^=p@^zTyJc!$AqWvG?c3e7
z+f`Ot_R7zG@zWnYc%RX3Y4G=J;B>*q(V%+<%Exi&aU3EIl?98FW{n=1g;hP2%0~h0
z14P=@;J?I#KN{-oS*fh+b_ld4y~>#p^F$RX)#8g&B_(8vJyr{pMYm@6*V!uvD2YNN
z5q?z^>{Q@VbycOUp(2DqS}55p<aO(z)LJO^t=)}H{`S^-mN7Hd8gJKtfrAAIz}>g8
zUuNdw?5wQW)23ZH7f|kI&WvO}{D{eY<8cg-4r$}~Uvd1yb6%S{y1ub-Ucl$?MGT8i
z@$y*4%U*r;mGpPsdXG9KVa`DV2U_>s{o}jgh%V#U-rYIpu>eGxrz>ul{*8N|diKc|
z;Q8Gyzsm>JKuEB`fmnt?5A`f8l~^rN5>gtQ+(x1#+z9NJEwuZ*o%}G*Lytt&TxBuW
zenEdN+r`9VzVrL-SO_l<E)eM8T8KhK1+0}zfU2&M2XL#jT`ue_J|YlZTQ+a{Js|lT
zAd%<qSJ@_YXHZV{K3df~aPAv1B-<K2I*SFuvbJp7iv|z1DpqyX-)Kd$?Xq<)NU|Qw
zr%e<?a$yuPvT=ClhLHO?F)jk%s5Wc&UOT~tfTXofiy7-2=Td>yCMbd}N7xw-rDc>l
zmvC6D;M(Ap1L+w#&iwp|P6fV^sb&wg$2X9<@RN*;v=1gvzMwEAB@$znWnF!>Vf((p
z+gq3uVy%v+?d6~!GtVBKHD~5+#2x*Gz;Z1dUMPnfjq0v>^XB#b$7^r7V&h{wl2ejA
zci!>6-(PacB~v+$E9hQ3CV<f8ltF`r)P3#pD}VXg8?WBq(AeNa=uz@A9x3Tb0fwyW
zV!2>*p)LFa6rrs0l06Ldz<plwgyKO&{z>e(FOZsH?xZl@eMubT0Ow(UL!h{TKmdva
zyJl;-oW`-v=}a}0L~inuYfvSlL3Rb8B>s&XKfCd^@7?_2kAFPlp3Jn&fn38>9?Dg8
zr3@9v-o(Q9VABLuOvr$obgN((#b}!R*7hC5MF0p1$h*plslQQe>_UwDQLjXONv;cl
z-IZ}x@8uoc(11+|bvY}NRIJ2yL|L(sL=ck3yp40~II&%ZEyI&xHx;8u;b*>+YF_o!
zmC`3MxsO%v?qwB>92>aq+N&M-Uj4)2Z+$jCKJv4I%rO<Q{bCPI)#Up52Ol_ode&Lr
zVq$C2QY_N(wuiXJN1vPd%wsKWEw^ZVFa|<7dbz?Q!mAg|U(oxd=Vv(+lH%LpW<u_}
z_r7<oyY_l1Z6+Vf36B*Z>idfEV<!%Yj*5L_&TB7UT2WDsV*yFNy(BPY@bu5{O<+VU
zksDb-R&!!YtPZ38;{nh~(6@`P4r*f6B-jg3%5)+YN^*xyXYAjkZ8iZ{0vOAj$f@EK
z6qQ(88)8;H<RpG_B9F9rtEwv|Kk(qs-p<L&x$&p}dH*-*Y%b@ynF0P^&S?i-%t`*B
zi75&iPj0Beg8&EmkZnbk#-32Ja|@1rD-Ae;i-RWd(J>+qUugzq5vMdWK~^!87>kCD
z6&FN>CMa(eK2s!FfTAb}j!fZzL>vKu>Np?DmLa2`s10SJ&8A>2jek^30<j>`R&r{l
zV`~Y%c1_lqvC)yOUU$S>Eg`PAO(*sr$E;biJfD5`nfLxFQ)G8IkL`<q<A0W~UcTh1
z=bl(cc=pgeO-*2<!x<47<-*s`|Moj?$36e|7oENP^lnFX4Y~X7y9)tF66YLcUrzV`
z9uGj&dme?@^Kgq^`@+l57Hr>9<mDmQG3@*()=Ii(u~cdj;bkE7zz%tt{7NO{fZR(h
ztS3xuM6_HyY(Sx=yj+?DVht36tJnm}bQ|hIv2KKX`iJru!Cgub{>z3EkB~1w$zzz}
zC#<?HV)fi|+plk^i`!b{f91{}e(#C%&%WR;NbPDS<6ZpzJ#*6T{gKAMdwU+|UZRwg
zIb+1!c)6AtC(n`{TdZXte`NUmHbPQ0E{0uYJdf4{AOS>0F=d=ngrYp8V_=!&WSByw
zsD^qsTB<7ZeJtQfOiHvuJ>ga>AZdqX5o;zv+)J$*CLmt9v4)a_QNzzTtK!uwYEoWV
zYn4^hrV#ftEk3qp+NRChTK@X%8}l;K`n-7I*(2ZJTH1B3GiJ<)fK@;Gg&*yvEbFfe
z7rnoC^OnsE_58_rf<JACM+rZ8me$<4uR7<<oa;=>=-XaeS{5?({PPz6;QRk)8(n_q
z=-nRv*DrFRR%XA<f^T1c<4!=*@AHitI~>wKLJ`ag$HgjSR$4AUY?lU;WL$=#2s~sO
zFo0lgn*legp{@~-NMM(yMl6_|iKB6U7nP7zU8|uejCf$NY@#^kl|#ke$-7uaM+F?g
z0>YvY?%USx=9>KE_~n6j-1PdqSI=L*Zu#vG%y{5l4wI{@Uc1x0JOnPhXPWLe5;yUv
zs0gdHv=X=QYE-H9hH}UQR=!FKB$6sst{`sr(YZw9;Q;kDEEbDKXqd^KsWE_kYD0ya
z0w9vig0gMvW~&lvp}rN56XL>48x5M+0gdNgTs7jTL70%#8|uLV#n_8u2*vAI>3u>%
z&7u~-?%~^6qDaU<ub5r+SC*94Ub%5o(biYqT>R+NNfVxfV_rk23)r&QMzD3at!r#*
zeD9z0-<gErag40v%mXji;ZYT2c?rM#@=WX9c?+z*>3!Nui_1c;{*P&&&3y6YKXQV<
zb@wEP?&tbB?a(ie<ueY2vDuk9rR9~o;!v@QVR8Tf*c%Dc>WM$*zxiOmAfRa37f>|8
z1rmO+DNgV>zLY?z6-=AnOSLPOO<+=Q=6D{I>7RbIkLi#8Wql8YSEf)H{=e!=H5y$G
zNHQ60TH{+=TBd*S;rp3xXW)_S>^y<wEL}zyZB<q6-nE}^DZRdFZ>yndtcDH+7bV2U
z8A|u*x>eT4ix*m1**R8XN}3golcDaltHv#vNf#d%Z32~KchG8IUbYj@*X;%<4Rw`P
zV;!qK@e*?1P&jnZze%qLZt{8Z`)oj%#l$=e`#QvyN=8(yj>b_XoFkzQawNolV8$E2
zC?>w*cAUn*r^SWT0Qh31vk>VwA`GD%xNjJ+isj-qc{ENZZbK>`5P<Svo2m_zSJWl$
z-raoRhK(C<-nFY%?BgSxwwTF{D}-Zs7h@wg^ydnfE^d5(;ryEcMqg}kNE>K*U``Sf
zlaQzCtS6s(!un+83S#E_wpZ0ug<O8w<(q%?Uk{x}P~ErLZwlLSwJ$_Lf@fra)23f_
zUGbC8K9R9?>sBA?HH;iyhg?hoehsE3FB4S>X;_UUxQpci5=?TDa<o!BDhZv+tc?iH
zL!x&*J+@{86lF?jw<!b<C(Ott`n}VT$U~QsK<5yPrv5`Qtmy$O##(OkibiDRgi9l5
zC)ybj-2KN(b7o)m$*R?VoN>=jAE38e*;3e!)EbeiY(DTvdmQKYX8v+)2$7Rbtkw#z
zZDmClNNj_fv`EcRkb0c_>Io>);Mj#^j(*A{e|k=u0oo}g);~EZ1<u#)gehl&cRGaf
zinb4`>@l%7g9Z<=)~{QQI#=~Lyu_oRg(M186hxnkwr)g*iL-KZ`x{RP<<Qmh^CH+w
z?X2e`!=We?6dSBKAo?m{;Pn-kmif07d1K2e>V8#PUh$m~!}7m-_E`nr=h~OCc-;a}
z3J$ps&49OxindiOD*W(b&XZ<RA_dxbdOoGAGO<M)H*U0^fAJZ7x_nkrOkzjzj*^hC
zUh&mWfBmQ5T$q=ZH;q1SI;7uY`glSBqBHiPyy^65*ROx_>Awyw+qEm;!H~fd!41To
zz%Dl=nU%+U<{^n+6eZCX1c}tVfk3EX&9ur%U=>32Pm^Bu5Cakt8c1;0CQzxV-wpwT
zeIXhaWrK{>LTrHQ>4`OyY@!(Xy?dKoCapfmg#C)xvUSsaw}1b(o!_|bhF8uQGd6`D
zFQUtE)A9a4CA_WJvlqvr9%`bDYD$J-Yh|DcOg1(B#@cLTtfodUL2Vh<S(Bzvejv<H
zp5U=&=gvP20S<)iuG;$DuFcykt%%kRU!UF{d`H_IUKSazn;gpAPHjz%q0R)#Spx@k
zSZmj;FykpXC#7!|DTbmLKl|AgE_&A*7dY1%G<bxWpiF)iA3S$iaI^qP?o$Ar$w|Pg
zw*zXAeTpI+l>Kb;mz0*lf|X@g)zrOHUQzML^s6qoi)$}}noQ$-Lpm>gV_A7w4TkB-
zd-m+UT;Rj*#^d!X+m2iEhCsM*;X><$+0R(1@%;iMmh&P*dCr}B_QKgO&wc<3_CxxT
z%gPs;<6r3D&YRiyl9Qb?^qQ-t&wKo-$G*ZUsFGGbKTC+zvSNSuYy$&u1c73NO+X@g
zBJGsFRr_J4heZ;I4wQ*8&T3^cgWn74pb0NIC)^Qfv?`d2p$dS6i6P3NbI`G&DssAu
z2*<F<YN_MoZQ9(@f(F?aAmY*2*3sVYiKqYi-8bI)$Hq%9zVz>Bju|JRGM_D(epIH5
z1mDh$#5*W|QI9lDa^0V5Ldq~FS_(4Ey@{<6$O*#@)K-@{F8a#Fd%pASZ(rBAx4D|@
z3Som*AKcnVOv40ArRr=!RdvnPTX*crTC;vDvPXzAloRR?RmML`=MW`Pv}a~kHdLy?
z+Op+yD7}(%C>EtRvI@a#xRxka)oZh!wzd|mw?>Q_Z>9IkCZPke39dpU_cNXiqJVlY
zkSn*YR(@qI!3O3ecF0M3AK!}ApE_!4>hEaYyL;S?-@NK9u7538pSy0|y3wD0zV7ST
z*}fsx5Q+~e!WiK5dBRu>luf?0w8VPrtv9VD%a&Q$8QFn~@^U}t_uO#(4GSK8@Rv*J
zM=adngGb}|J{>xy$JfgNM-3Y~Jp1ccUi0%=vtRl-7EPDG9VZ-ADVnS@5}GU}hML$i
z1p{Mb4}n1-(SF8%hl%2WH43oOiJ1T@OnUHw0nP8X`D9TOg_|nU&P2A+S93FJO897L
z&h%eKHZ1<8Us7}$ZI^^sx!0>IlGMTI``xHQ`~-Xjc9!fI__wE@82HTdPj8+$?yNc3
zd{#luLwH7#Q&#!ge0>kN-5z+WyX77wa|s|c(G^2m3>DExK;FW`y=R^|nz96r?ohOu
zjO)+lzA^v%eNxx_=9ASM$*Ivn04HfRa3EDu46V}gNKlYk{*VH!^~gJD0$t@K%RmrM
z?42%+kzx1bptx+w0&C854_ku@Cdm$B4IErxWn}h8o*^+qvMG9G`je7GRJ&y_cfj>4
z6oR?#ibfH!Zo`JQJ$vd$kv{rwG11-^Dk^J!#(X$a90Siq1Hqc=#}*whGLr0RE0Hr^
zeEC^q(Qqp(J<GRq=MGm+cD9SCz}a`)@jVSrcNH!g#xC0l2MA{zIA!9L{vL<t>X+s`
z|7Spw%tUU7<Y|?&{KzAE_yUR)Lee~mCpG}$cgZJWo0NW3ES4yVHmN<;44{}dUItAR
zNu)d^qzyU4u6`wfS=6HizoY$)vDV>mwK(S(;Z~GHS~1cQS{?ASfXXSu7=o*hYsRqS
z%=>W3!WkbgTT&SpoA69VcIMMJedDI`Eyb<jjy0S2cYVU&*=*JUusG-pnl)+=K33qG
zINr9bSh2!WR8-`@^wLWwB2V1OVskFvN3cOZwM>}YyKBF8>3KiO8;~<9GU~l|7cX6(
z>_v}@3K+J<_)Zz0E&8;wva+mU1*5G6@4kUN)M(WZ58pQ<&q^a`OGd&n79>%#-hDDm
zB;c&)|7!U=8m%Ft&auuIJJ}jEWQ3L4x1W`nnTZT#yQ(M#$gxKN;amW$qZt)`cu45l
zb)Wei{<i6pCJvsis?M;#en794!Oin0t0T$)o=+VV?!ylkTJzp}$I4F2)^qCEzID52
z<f!4+b=Q64LBJvViVVOfwDkl)bb;4Sm@vULbZGwf@BhWm?%Lbhe5SmB@u3pi<&+D%
zA8<%ri?a$7l{JG^-T(-I2sB31#iJE95uYn+Lf(7!m1~Kfyx0-R9SHPdyVyr7j4bb>
zDxydNNxRhVTD;ijz$S+}v2XH5Hrg>dRjSRf_I8Ym5;i-0xJn0F0xc=+-i~{SU-{7w
z@40>U;ze()@_Jpc)hvgnut5Z26%!@MT5rDjrtkjy@4u0^zh^s~!=J!1p6!Fdd0F2H
z`;V_pU<8*JF5eK#if-5~Em)$+O~?`TOvb**I63_XS(`}Yro|^Rs?v&2?q{WD3^1Cx
z+Ui{>3d&6u5^3uY<_uU~c;<hwbvdnb&pY25Gwy8DM@B{l6t9<6US45jDkY(i&@Z)i
zLR^G%#o8@_xVSL?;6dpcR{?{mK)N98GD`Tda^*_vgN5(o7uICuX5<FyYU@}(+dXGZ
zn$U35O}E^B{k7MR<KC`jJK?RT0HO=MCOSH9^uxdZ^GlCC{^<7gpRd10p10h%bk__5
zAP`WDh6_ppPz;;JchW|Y_0ImxOb{jU<B{qm&z7hONj42oG>G~K(yVsBv?l-w5NXvB
zb-?Aoyeb|ZjsgmgtG)igTl+b`++4JJ>Uf2@OM|9s&^Zta4UaC?3sK$=$#>Jwv{kD=
zp5~4k7LaxZvUh4h;t=0rjBi7Ena*}NaA=ocli}p?oV27{&z?N~GF)0d`ee-}S3E`x
z#=)V52&IK_P)x!;GMEf1N(Cz4uU@rS;>klMfjue~U#n<Rk*4Jsr^P1J&h@nw)bpq!
zPgW}IQM0ve%LZ%lhx4s5W6vU8(FIm!&Onk~r?9`gx3w7Zym?O2(8WZCI6m80=IYZs
zo-F?q<H7k+>{xMeiB(v**jj^TEhZ`f=gD5a9Xoe8Q&W0-@&*k5@cHLn{%cBNq7*9E
z2XF91zxE_Rbgj8;_uYE)?Xx~y^5H`-&3yh&{AMCyXS`4X;-H|e;z>m@5aeB*6va>w
zfrsC0l_V1vAOv8iB*c1I>D)|W0YM?UGC~@wI`RqboyJW?-y1gz0HfdATngS1=&-A(
z4<>^^qU7964U9KzqA1`^Dq_hf>OUmJM;J>On~&4C`k-h|R|E$Es4q)n)lK1A1d{Hw
zUa+%QPB<g~k*ccdd-k+=e^<1<)E6yQ%c#@6hVm*PEeS(n-hg~--P+~GO{JOS3L#+;
zNJcC&Okk;iLrqeLRB~kH5HDY6fFjBwH<aFe`k^7Ku>St|AFT=G^cpdGBK<_75a7AN
zrU@JZWf<pG*@KOn%dCmx2SdT?tW~R4Su0m9hb5zKA;5&PbyQSTdPWosx4!n(uRea?
zz4upgvk$Uqj`djUX@CSTeA?s*lb4PhHu9H${O><pQc_ktQ9<-*g1u7RIhm+bN3^|w
z6=ut4t<X{nn{Ztwtcfh-JLB=oWYz>%4G{wp_+hCb4FwYQ;rBMEg4ixiboC{fLm+aJ
z=G2Yk?&EuD%}~(;3Mh#*Xq^<rxVo^)X%YymZuCO}Gqqj0AQCh4eNYw%-r%WxWx{K+
ztY@3t{>6NF0015rNkl<Zo$KJf64)>8ic6>b{x46?y{x8o_e7BpR(6-%<E7+~VNjZ>
zK?vWU!)8f7Yan-31exXCBykWGklbiQ9Ozgm9t;9ZTw<z8B-dD1X;oL2LTRdTYD~k1
zLQ-{hA2?(bnz&dNE5H$A%b7ZLudp_2`_4+vvm2_l96OL=)g#2}`f-H`^a>60oIB<0
z@)<w*=^xKI=Nvg5-rSvg{i6GvEP&`b$D$z_dH=l+%zl62f~_-Wzj!^4aBc>QltLIx
zSjw?2nblrGwy1|fZE=F!C&Wu;(dsTBsQf6rk6D2<*v9KuR7G+Mzl;4ce6?Y{Y`G>#
z8RYjx9SkG?c+jW`NXQiiFrrX8HxtIZ8ij&9S;fwAPRr?0B)sOwDQ|&5gt>}`9(rg!
zGRTWukX$#qVzX-4XDJIzk#1njsQe#RRyQo$uxY0Ymo0AF?~tRY0;?pjCy`7%fADZB
zNUb)qNK8zv6^B+!W?j44uR*ZL4MiY|z(*@SsjpREQ)bl?TqS_*A&x|4)#3r3G+P3r
zRL!#WcsRN2C)>8;?&6awJ`7d--j3S(8c+XhGC1aB|9#d=bKb$%?l#UlrSs%H{-Q^I
zGMjkdYtNZ-?)iWC^~29$YgmiA(t|by-8kGnaiw}VvaFj8{W<mtG+NT5!y>^SYY<I|
zY4#(*HsXWrj-ptQW&})QWWxe2L!cf2Dl;?Dr4>`(q8e%fkeT#=DHP#cb``0LjoKK6
zfbn&L>KHZ+!r|e;8*0R>+Ns6|TnPefjalGGu;-dKZQ7GwZ|kL8+8nk<Hr=SSLE5km
z1?qTxy4}})Bh=w9)2DkD^Vu_I_<&DRQ<Gkejg8fK!AU|vFdk|gC9I2556T~AsJSfr
zq9X02Bo%c%xao5cEG#hm3&RzqcY1%yvJC+gB$<;_W7$py&yVed5NlC>SS|C)do6rY
z2!!@Z4iuLbu~<4i6UR>6HD$`A%U^rtAF~0+4>-w`gU{QupH3zPIq**U5Y_d~J8rvk
z(aO~;UwroYzh6>UUmHy_;Q#_M`g!c`n)nVo=WM`(P{)HaEDsZ#AIS!<6@9;8Z`@Z{
z)+NvjOeT;@G7A2aMotk`f$WGqG7mTSJT=qVL|x?4EmlqERzG6DjPa890wT!<m~E7B
zfG<AP4JxY|g_`u%Ew|ia%<Z#h&;E#y+Pd42x(EUvp7&8zb;A{F)@^d&7iyYZMp(g(
zEhB|I07Hh2G}+f>IE*GPB>`E)280Ii(DAT8%Q&pxl#wwJ`>H~JCCFFoDkceCwvlUO
zQxOG`EP~rL3t&sY<%x8R9DbI!cS_7-H-G25_g-|-MG-s?Wk?)rF771$IaL4&-r`xK
zMvUqhHe}eWS+BgDfHT^Kq=a#|W7(EEmsx9qdNpA+skIqbdOmAH8(|)h=vBhJRv;s=
z^I&YsB1<u>lmvExMH|#W`-ys(K9NhrF4=A_dZ0E~luh0PFeQ%|RsfmC<ad(2S|%2I
zSF``g{A05+iu7yr%$z;*@@HOns<yeMZFfz1Sz}C6GBSkipT!%j=zIKlL5IcSvP=2o
z2ukNAL#N{iZ%g97lG)M-KS*A=>Vu3u_<TumLdYr_L|l51Lz-Q(j7t`YM-~}oYImqK
za{~$Cq{#~DW#{NR^8E`aj3EjZX}#j&c0=`AOftE+xJ0N*BA_vWPAo>qB!EREzr3p6
z@%76t-1FOC&iGHB(H<=F7h?;#<jCjgxnE8-Ky+u!ThF!EUUS{%3(mXnxmj~w8nS%l
zCnMyf$LfP|11umbGn>tXmcWc&$*@S0RYXN3d>dd$t}uY<q%5K`(o%I&74%OAU<NEf
zn~K0<`jKmjz+%fD0*oH7aTzz@;uG0d+fhe1l9A<9HGt2_L#)l)Hb<mwOr9x<r<GY3
z-cnKK&~*o!7M8v3t&Tl7If_-%0wLB1PRiV;WG{Pca09`;JtACo2Uc><pan?3%UwjW
zh&+B}JY2qPk#R+l5T2NdyPDDF9gd0wuR>$RSh~Xun<aqN!2a%nisa<xo8qkkhu0fs
zs0cEkGcw%M@%g6hv48vf%a3yK4LF<j=QS+Y<H>yeG5{oazj?3>dAHsAz4e!r7SEnD
zXXc1?8`kA29-4=)I1DN%xm;%A3Nf10XuI?v_X9<MIRGq%s-So>?3lnJqoOU(wCOv3
z7p2m~F@Z(A*S@l$2q-dSX#)9C9~<gL-#Q<sr|qAJ!5cUkujbUO$yx}gLr)EPOzO}>
z?!zgi1sBd`*n8=Ej7+z?9l`_ru~3V8vQGf2%DUik$dkYo5ykP4YpCYVGDstR=>BnP
zG=*5}>Kj=7<8WjgY7B?6t~a2HjgC#SLL*|;U6;46T|!Br8jpKAK5!<sMeJ5=T(XgO
zY&J`=^Bt&lW86#Ed~RiDrhjAcvbD-sg4*oVv@a7t%n0ykhi0T@4E@1f_bmN%{n};E
zJpcEx<y93qiZyq1IJ~H9dCp92t^7=88U199cTk}!2m~XKbb><r>3jRtX2%SbktsuR
z29rtxzS&1$5oIwHL#&u=DR{90ECUui-7y<#{?PVM!ju2#U|P5*CO1i5WULGXv|y;&
z@UW?^fUJ9UIo;^t6c2+xF!!Ttw-*(Afzde1F)=I*r^O~GL1t|%0%CUvlV*P<UsKB0
zKIG>eJamM$WYGdMsa4=65m^Ly?mIJY4%0MNTp5L7qhZZraX*nFpnO*e_7bsP1U#YT
z5;(L$MI70Ele~*QUgge6@AF?1&KUN}VLCl2Uw!ES61;1MbnMWf1!Mp4+ds`*_|d}m
zUU_Zy`HhYB*(72Rix6Pd$G*s8hGd9m)<f2$5n%LcQx<_k!o4OyCzt??0gGP6j&&YK
z0c0KyLZU;HLlkO1b&=nZCRV5dh$uekPzcW{q9FG8_%UEoNRx16T?g08?XyT|9@2Eb
zqNJMO!TS?M&}ZFS5JtS=t(Y$41m>??ev%WYd)^3zLVQ<BSqaaQ6BM5kL#%v%42P;C
zsJN7Re57O$?1h!PdsKuZQ6b@`wu$0%r1Gt*C`Dk;vLc$I48`(>$aqK%k3g8aw!X@{
ze#53Qn>KCxc))<1Vy-sBT=Pl%^Q8sI0b_8@lu1(>*ygQSv-+QNUVVA&_Tud$5cb?S
z6>8$jD$jfX8fIcJIao~%U|_+_WbXuwdCzY_3S$5wR!ysHC#4BaC{Y(Q&xX3#$04-C
zdO1C+{~g8Ub<V~EF7#x)wI{fpK#v-GoYIgp0|aw^K5D|NnMtd+Tq-7{&d&ma*r@&c
znVtC~@g_0WZPk;#v9@0B>DVcV=V)t%oeMz&1*?^wo^A~pI@0=J{##aa6mcN+)u?5o
z5JjZH?>t$s!-HOJU_$GRR%^e&UJ09ZfWU6FTDEMIk`(X!@Z;51_N#ARd*6S4e=gnK
zcu<!o;lsZIKr{?W!!dpM@DTyF*T{}BkFpGdNyKmoHW9ZGQt_xlJ50c$O;z?ybXH6W
z;hI=xq6aBPCz$kI5HN;a3sM-fpMaxd*-$ep9tFJ`(;%5icWM9<oD;@?35e}UDFBE<
zsBwLor-m}vuQr%vo6tM1TmQwHB47u{g`WV9j`fkrF#tdqP!<T?yYWmwt0jP-p0~1d
z2Uwf%(Nbo2*^R^|MZ}O2Rwp=Wvq2((JuV^1#N^0tt7PX^D|f&!D-PR82cg0qk8EGc
z3h#ABM}&Bntyq`v(V|cKOgig~4Rk;FsHZr=KmMx#BzW5z;jiWA=Z}8$zaM+N@RPzn
z&z}9#Ib{F44#QSIgjS=`fr5yt7?J`d;q?GY!o(MK(F8g0``|<ou!6rC;1~)cswAH;
zSOm02vdic~PGz&z&#;#EoHG;MOn*KL1iA;^lXfuq0k~LE6n&=&FXclUlTjYLiUpCj
zN<mIa;HRRXo(R0h2}G0pCAmQRxUqhV)ymAww1#3A`RCiOnbHTMB4OdtmXnEZ16CJx
z<fiLT4Mrt_kU>(%SC;QIE-5N;EpT{3u#K=Nk+aLadc&5$+&A9&%822EpCX~fX@DGg
zX!re+&bwcXKV$rBZ13~_))!{Ikh5gTq6_QlYR|*S6(%=nu}IiWI&eK<jsOdRMU+8+
zIS?R#z)UNBCxGZ56KVfG{3>|`?U5%8;W(ztrE=ee_S73uHpxZ7sb~`oQ2?6?h@!|A
zBpE{LTG=%O26-5W;%bvkLVj2JUh<bfBk21&TCAwJa4RNZkdX%jGO?rDZ})Chr)?lO
ziaO_PRh8v{#-<`sajD&=A^`;ZXf#-HM0UzmzP7do>w1c@u&b7c4PrTzdaaD!3BHdC
zSGnJqH-GH(t1q9=n4Z^D<9cYn|7sNE(ChyyUudhkcG|Vu*@_6z`RlTk%VG<cd{nUR
z)Aa?|0z8@wngC3GQ5LaH1~3AF0SurJ|6x?eoz%u&fo&jZ5(*#|DU5um<3nIU_RY*g
zuc(JV8@U3gD*n5)w8Q|ySpFq1$nwv9?%z;p<6>iLhim|7-{2;e$Oe=QqB4Xk?w|cg
z7*bQQiB&#@tR>{}mC75cqcR4B!WtJ8xuy&mHp-g!k69+MoHDJbz^B6l6;a1s4G{Zy
zLL*5G(bp&l>L_<DxBBGXX@vGh8+E+x?JKRJ-(6i_>7D<<lKhF|Mz6}q=&P{j&>kLY
z)BV3XK!Rh0P$0p;I#WFI)U&6yv#kIu&wjkDFl2Gz;(St&4(af95YwR*)I=L9YETw?
zgA<OGUBFNcLp?gdW8U+y#g^e~0*Zq50E26H@DfhWtK<X)3o4YgxTM&g*i2xtLb4G_
zHZhYv_`dU1ESbI!g2UK%f?$#L+yKM|f=<!ax;s!^vDu%IJ(k-h2{VCbT1<j2C`!zi
zmY!kckzHlQ#x4>Sjcp_WD$?apXzaKi{)Pjpm{^2;a+p<6;8UKy`z`GI-B5ZbB{<(-
zywb{>_1eWhx%>869D041r#S&T{JQ|;(4mk@WjtURfBw`9nlG4o;WCmNKTTm3$CAQD
z+3P=DKLpRxM2u+eAbY_h!-(c4PRp=idV;(ayG7%VG+m+~mWz@NJX!E_Y8vO33R=#W
zs??;_UDsoP5d?(+!oh$L0D=G#`xOLA5HtV`9FWt9tl6+*c=UQ*O?#HPqT>27X;a9i
z6fm(n8ZRZhsHv$nYGzfuE#6sV5;`;>OGHG+q3P?@4Jx7m1hCjfA}A}T^ry=HSYEah
z(B%V~I3tUAkwruixjH9-RUL~zULCin@RRtn#-H&S9Vn?rkG6kTfb1JI)_(jUth1?w
zc=p8c69a7PSUndM7w>3Vv3h0fy7lXFp)R?AB@&Goap`ygx62iSd=V}dPLRNc72i!v
z6<MXwu;{^_DtzeN7{CoHB#ZPufCvD(=mS7v-X8!A^Sc2LZ~_gCLqIGQSx20(VOkl;
zj325>u(+K>20Sj`+WefP>efK))XJ*5Kp2aMQTw)gV72h<MHcCu+Q)>|7H!)k|1c|t
z?2j=CeY!nDT#gljfFF-mOKST@3VO;uB3~^D@bY{JTG?Zxqur}M-E6)3+B+AI7(V20
zk>TN|0kUtVpO`nA*9B;T3$n6uEVeyYT`}!5G62piDJ^MUwQ6PL`VH$zqxx9}?)EA0
z-R@S!wX<plsKV++cy)TBmG{B+c|S4Nbj-bovdN!H*CtA!W+;RJVsE^Noze?n5Ou(~
z%1Z2DBh1Fs*+wC!oMF9;KNJB;do%iF7UkyTem-Q#(Cvc<4c?iZolTUJwdubfe`P^c
zP2&I|itIuzP1vB1Rusj3h8B#nb`)(j1$F9btL&hvP}z%^{YPC&Y$_78u*cxSBCm$>
zUB#%5<BWy99Yul<<6+Rko{<`lg?+XA&9@eex%#S07O;GstNZV%_HR!?x)?b5_?!_E
z5_U$8f^xF^H(mAhYc`<$dbhH&yzbKtpSoAAU6q2}B?HQmMGmPXe6bZO+}W)rz{zc9
z$}z7bQz!!qlNJaF47~b?iS&E1agq|lzD9_G?Penv8i7F+f~|@A^QF1{`<D&O8&p0x
ze{f5`e*MC*&MQx94qGrCXa(;VPnj^{vC5j=4{zR99EhYJ$sy8!iHbDh?VCbSRc_uu
z>+=n3abb@k^cE_T2%BZV(G^I<joB7<QIWm4*w@3V1ug7GU<Z(5wWN^?4fJx?RaAN3
zU9hNN!r0Nzrl+U&sKEaJJ3x4d2Q`_<$Ku8^R+$UgN-w<NE9-&7+l}@0pZw-G_iXQ-
zG3Jtn*0|r*H&U7vpRJSLm~SB@(r-lF8WtWve{HLKrC?B|NonGgBo8m&4l=mcWu#}+
z;6qiL+B>zTPik6yW@bivLQ+yxSV)+Z8d8^^$)@{0T+3tfW)B;j{o6%L*RLoptsG50
z0G~U=A-8(tqousKQj82H)sMnzNmNI+lPV*8E5U<JMGn_>@H?A|z&P3#_IRpi0lw<$
zN(_fduwDr$RTO>L$!hVMEDL+#D(l5rb1(k!55L=^0{dxz1fSF4U(@(H4gJ-Hc(cCr
z+uuI%=(f_fxwBt?_jk3FLq<61dU-s2zsLP8VgR174OT(HcyDUogykTl78P_=MtXWv
zzs#)WoUE+2?9ALSq6~Wzt(k@R)Q^*8ut_Z{<;5{Ij@KBB88vLcuPJfx%BHQwj!68#
z<m5>La8rFsk%uW{EE!1T;hL38i5#SARb!0_%|-pk;av@r4H5^wZgKI+Mun~FRT9{<
zB(TG7iE0_+p#*j~y{K>r?5h&S7p_2HAKhaD`)Po5Z@TCSevNH?(atUN#op+PQ_+wc
zm~Cy2kt0X$`M(eU;X<w<mF*%nu|?Vhh)lK<)}R6fW5*2q-TKeBzP+om{vudSulzR^
z$1kOZlDa8}m4d3aZ2pYUXNu{NnNhLn-F1i8#Y$ueB_<`o1X8siu-BqKt~S0}64+%l
zl>8%kBqln_vvU2Wz}(m0zIbFo{*xpO>=A)o@IKwTHv(d+b5dhsNd#=KiQPNw#O|fF
zSdB?b%G9>ry!|%sf5z(#Y?IkC*-mJKiWJac{*Xbrzf4PwgXL)>U<UID6IMbs6`qU>
zN_>2xafg>CN~&PhGYMe8v|I4Xk929BOO7n;#F@wtsoEpuyGpoO{KN3mQdUQMBWXGN
zWuzwfJ}g{m{o}3ol&*Bak$V0j-=7A^v5j7gCz7lJUy_EAb0n8?1rs=~r!NV?fAaV<
z1{G$e_nt*MActyy_>I+`ux65LD8;DU;``?*N-zQTtJ$b)+nR~vIR>MVWEK~|ko=*F
zR_*Ox+*vRn5-cAPL6&)?J?%{NKuI`vL*s7m{Pzool$Nj)r1eCP!)bsV+lUgbg*5pk
zX{f8EamqazmxFuGSDCz%hYs%l>pm$lDysl{juIJdnM$-N@>9k`l_nTAd^CWex)*j4
zxuDooL=>WXRK#|Pmzp;o&0C}?Ki%5WWCV6)T}11M5(0$;R3tSy!TAAH1cCi4oZ#gX
zJr-Y@hkc@NtcUs|wH!8tZZ@r)<{w9x_qNgT2=BE|H#^?Hn>0};tMvs7H;x(Je^+)^
z-zQRgr-*Wp^Fb6u9>J871|;%(%jrK5IU>nupBey2E9K*KZ@NA6dem`X#L#^>@##{k
zplE`$<Vh`e7Uf=+!0zTZv0BQmNvO0ls+Xr@$<kGcg&(g}1epZ(6V^@x<QRsM^-po}
zT`hyAM*%{@d@!-LV#DQCd*n?CxI5De(L;Bz!s>leW3@4<T*syT;GFSe9!X7#^5N6w
zk&6nO(Q3(EzKVjN%7{38<e9b;BO12`V$b~~X6V4hMTJ54<vl|a_P^ijchyM2xilmK
zfW!laUQnprSng{O*j4H-ypz=m13Vr;6YWM|w`R|M>+<H-*2Ty0NW0wiv=*z&Av*RQ
zWT)@}j;N?eD<uivBxaUT(Hv>o3k_1?@OyGntjQuG)`bAZqc-|I70>S{X=!n-{d4>M
zxvFmOT{UD{4`r4rp&#X746CKAiCH;$)&{aMHj*MptX6uz0J(Zo2{q^r^zGZ$i1bTK
zre<S=wRGun<MTCiSUi*^(ZsMzNg%BjYF|fsO1yW;(lzdXyz%atS6_AM3A3=DiUB_A
zL;rIBcogDsz)$UacM&g<YmFN>&g$1Mvy&Vgd14rRbl!MOw1@iT(N<B>P7G#yU^k9@
z9EY4L4&a_EFPZc}(T>vQvR#$o9;hnUNo8;1*U@TKSJznmD0wh!_!#Tmw_i6lj8qEi
z3>w&<#e(~>*Mcq|wbl%h*;vDd=2`jqxz@aS|3r<v2^MYu<rt!EmG8b7_FnE?SlHit
ze+d@$F(=Hzei|TMKBjK$5PaagDy!p!sn=Sg&X~mX4Y7*L@YXQYp^JVrZ#Z5>Jd=~-
ztdXNeSnJlTA-8QM2Obq9U3A<dKZlbB;Ompd7tF4#Zn~kes>a(3w*natTjYL%Mym-m
zZzle3Y3cn?+a^%B$VJ{=#jlv$@ZG;CZautLsFl_)*Sg_beW6Ngi9W1`szjS}$tL3K
zbVEr_igSLjWQ8?w&g&Qc=m)pX<RmwA|3ru1^E5yXKQ71ct<b>~W~G;lL`+<gb-~3~
z5@-}ba<pAc2>sr@2`*wGxuvAE+{(_%um-T=7H!*T1Y2{GL;k>q8DFbIzCKwW$4oe5
zXkp>94VC4UH7T$<T)9P#<^5@bQd2{XBcm7DL%Fn6(yg0AanL;&!z>p0#KbsUS_-Ve
zgWIf4n|2sqFxv$MXGRY|@pU*OLfsvO%hn`+ylhp}xHCt7VGH|dfE@HFj{ZR!sY9F1
z29WiGm2=ycP3ECFC?;A@2&tNmupwkVcKJs@Q@H82P3vth4fVly7{a<d_7v=J#l`23
zd$Y2p@jJz(<^G5WDp{b_l08IJL^48sU9B-B?kX)l%;<IVigo`C^accXRs?_&!<bOg
zg1pOeYf%ilTv0?#;^Jc6OIK~M=FFXU#fXBzkA;PWejy9{X@GR|83lJk9y|ags3YO2
zC7`Za`3W_~S7KQ<X+SlrgNGdRs}5D7IyI>_H7S%`<k+BQ4)5td>G=cth2~}V-L|W|
zHpdSYF;f`FNm<e>%8IRu@?z{DL}Gq{@z70ZG|l1xP_}KgqZ?I^QZ^AK_!Gs}!qO^%
zy*C2;vNi74-<m)A>MJjv&!Ok3kK<{l0dfqZ#wxB@rOuhJ3?T|Z?ZC}@wY6pA8e+DI
z7cmq07&<+|u0g8N4B=3D08IkhM!BDo812{*jz5WCUw6rc<6bD*S&_4?sKg%`?V;w8
z57~(<?q%Dctos!W`>*`?ug(C4MM;#qWsg`k2Oz<PKa6sFj8jOMWE{VHdr7%(!TXCx
zpEYjulYRQ6D8~A@JRnX3<QSe6*dPHTw4H~e$*LSqB}JcG+qbMWH8V`SxSrezYU;_j
zv-9A^Vky*iA3jgeIZi}3eNqzAMi%63E-kOg1025Ajy<l*@*P%n`A#F0pNMlDz7N5L
zkwN;wKxL?iC<#f1t@OUh4q4dqX1#vV-FMym5<7nD@O>Qp+tUC!`uH6Bt5bD8mGQ(&
zy^G3{ZGlZ2mPy@6IIiNngN}KJ9(Menl}*LP0~h-sgPgLms4+E!pA&yV)B7?1nM_dC
zb89#3%=>)f)_||QF;G#m1$Gnap(~}GxMkzuYqNVdFtJ*iYDx1%bt*!5!^0zug`K2x
zeU`6S7e0FQ&`-IRAqOAb^{3MS>3WO~+@nRVCUV4Ewsf(zdf~I3b_nRYDH?L%t|#z)
zXbRzjVzww%)+<VL%|nVGy;3sv%h8e(WA|ibBradO{<AS}y))lyHEulRbITQ*>t6lw
zdMhb0cG{4^1OC#hSLks8WHUpY_vIMoFa1?)cLY3DQK22}qw-=xM&#ZK3qkH$D&I<D
zW2<ye+&$PDtpM@g%Il@Lcvvo|SJR$mdAo*lrY&qe8ghCJchfp_VE<oL>?(OJDSQga
zlFvSdLwjOZtsjgawZ3CxVq8m?uT499(iyUiTyqR(+boo;NXhMNr$KV$k(0IV9k%(|
z8S#tOZQeZSoCyU~Y9bj3>{Ua<?mkpk3;`f|Dkrr0JN%BlEzN1LSoALp0!b<`het+l
z;zTRia@oGg)>%A_cPFuNNlmaW|3ofdOi<P9TecME?%q8y){Ko1e#%=rLVTp7eQ$d?
qmok72HKTPIn=1Vtt%-0v`u_mayS|eWE?dU{0000<MNUMnLSTYB$~5c%

literal 0
HcmV?d00001

diff --git a/Images/mathcheat_julia_logo.png b/Images/mathcheat_julia_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..dbb52866576c2b77734c2d21fe081ba281c20973
GIT binary patch
literal 28422
zcmV*5Ky<%}P)<h;3K|Lk000e1NJLTq00C40008C)1^@s6isyZQ0003mX+uL$Nkc;*
zP;zf(X>4Tx04Nn>U|=$Eba8TJ5@2A+%_}Jia(7aQh>TKTzreu3z|A1cV9a2hm|R@o
z7!csY00epYd8r^86@0tKzz7n#tP3&=RgNH!E4iSk1gPo{5F4eW7AG?>Fd6{a+2th#
zKyk)7K(<IyHkiEu#4ZX6at4YY0kZkhA?zz4b`pgB1jH@^dIG5C3rKxBn9aliVkbe^
zG9Y$IY6Zw%2Iu^O%A(Blj1mPS0|NsE$CUh}RE6Nm;*!+dVg=8<<ou$7{G!B?)D)mv
zi2Fdg7z7~-K_RALXlZGo2^OUy0g4BRai0Uh;mas?1|f#9D7mz#49a6<GG<^P#7CGh
zM}mRDe<=e4%O8Z8NiPG#0v!eh{;3EtHA4ml0euFBeRBZAuTXF7C9Jdn000SaNLh0L
z04^f{04^f|c%?sf002M$Nkl<ZcmeEv1z=pqwe}}1XvIusl9?I8Ob#`imlV=AZPTP_
zoBnMBZPS$d+O&Da6v7QP%xFr?OfiEDlEt{IMg8BIyH{Szk}Oz|>=}D^uP)4-IdkT_
zXU?2C6W7cHoiqnD2TmCd9N*<bAt3H3A9xtYe>!!Z192KqJI^eAr{+Lk<$wvIadBM(
zNb@<tRSm8uZhh;CZ_#&X4rmVaRSuYd7z(7#2*BlTM1!jy4NZ0M`aEcE4j?|>ibQ)N
zQWDdV>_~y#>cBD2@gC#vu7Pr2<z08^)*5PexTc@c9Owfa2na~V#Vbmi(Twt%!`ORd
zCw7%?!TyTRP}Z;?_3p!H@|Pme+yq;E7Lsg*$Z!lmzl`A+o;MD|3&&t!&QJnuT1$-u
zeB*-oww%-liUS%@wIFB?oMapb)k;Mh-BY?9t9C5H^1W|hcg@@I2Y^Hi2%v*ujY}oy
z%0M-akQ*Sdy5S8}!tF1Ihxer?0Fw&u#N46_F>&ZDWTxaOZiRyNNyfe&RYwDAkJzUl
z)*R?v90=7OkJKE(f=zGWy{&)3;l?e<vJXR&wSd@3@Kjo+9ExRBhVTNY{KxY$MHSb~
zv(!R;0XJ$rtI$MnonH8PTsrwyOe>lPn<atwHcM9OS^3sU_-4M&G&eK{Gza=L2SUMg
z&8|=I+_HzUvHWG^IVK{}nniGIA^<ic!J5QRB5YPWEVP@pm<0%sfo4B^elI+JH=4Xo
zc>Nw&;_QUmcvQI;!$-88KlWF+V%Dw5O)FIV5+K$SR)*+kK<(3ANE5F)U~s?$Q-8A$
z@2`Cs|5|oE5U?Z5F^@p%Qb3yG$V75ND(uuQ<KyFx0YRme2AL_xlAk}|BdEGKaa9AC
zw*l7pBm{WwpmQm;)thnK+4o~u!D#iA233Q38c>5mq<=IA`YH#Ep?4es(2LhsJcCD<
ze-U{JQ#nhYLVIW<QWG+fo|MhMX%Hoh5-rv6zT^7JkYDa*zSLTs?grF2%c!w>3CJlZ
zZCrps>F43j3x1E0{U-#6|7!f-akeM<RSl>oc?0X}o!lHS!BlGb_Uh;GrzJO{AaORq
z&_dAkB0ISdDV(7fd#Grg-Af$s6J2HaU)4~C26r`01{o-KevE+`7vnpZJc7ZwBQ&Uz
zxrR?g_qeQ|(j3qnh&cxgt#Bo0@&CE(M&vrCDewe*$WI%Hw8RWjXiP$NpH#7}24J<=
zX*cahZ8r~YUk!4T&cW`gcktAr-=U_llI6y!`?_CF-+xjAYTs`|U4v7U0|Ig~EI(Ye
zAOBhQ8(5Lc;dnANQy=ou1|Z3v5(GH0iuODi<;5W@IhR14OHi#vzvQ#Ar1)=mZ{2@c
zuHpB3D$jFkK<&Al=^Qi%qRs&UEt#E{iK7qJzl`mbZzCgN94DZh$WAE?0n_l2Cc1Dy
zxk?$Jnq*H?6J7<0)9~uX?_q24`tTLjM|0AE+D98sldU-*9N2MaGd|e<2M+ZoE4Qdr
z&g7>$(jzwXHbK=AZ$VZ{9yMgTVOdhS;;J4WZF)s{1<26<RMk>7pz409Inc*AU`|Ad
zcDj7WN2qia!@)@+dO{&RF`Kn9?5dHfmI0~(wbnl)DOY(C<R;F-lD$8}uESd+RcxQg
zR|9IFXeLdh=D_hBI9yeX75iR8R>G*DcABM}^}9cte|#AopJ}FFYZ_Ng*|~%@0Sz8t
z-JVaBc6_R8sTxo_mKc3ibD&poKsnIMOcc}ZlFzWeZV6XGap=vSEKd88)=pKy!7tI2
ztrjcGO{cG_8|n5TSa;xKRMi~`z3r4b)_{6SHMg#8pXWeu!rn)p(48flTKpazTuv&Q
zV)Sj7aY(c!D_5;VOCE}AU&o=!;y%yj?p8npYIixOpVJ(OAqVPRwb)y+5y{pZ<tF8@
zB`HwjGN>q1)l4&kO@VnFqgJ_mz`@GBiUt|>N1vW3SHD$p8c<s?ME|Kd&=)vR@2o{x
z<1X0a^A))g{V5iSqkg(?SrniOcv^^|rM1PhqY_z}<oc<UG@zbJt*z^N(r`dD)CPvt
zv&1{pvQUW{6`Ld>{NiX!wW;C-P^%luxeI_dHsr*(R1K&xZU9}p=0JBiAW@(^epx$i
zRYQGQYahELYyB+>SmQ~C%hRY99K?>E*p;jSHFmt$C2J0Jn*)IWqwCAl3P=S^<rBN{
zbts$%0)DlkO2|9ak_OaMt;KbHPYMoLEEZ~otco&H3bYEChQeBOt7$;F@onMo-wawE
zT`FQ<ng-O^w}GZWbD+x{NVF%zK{us9vsY<@61XIGiD6Nwl?t{8_(^6^77P7TPsOAG
z^;B$iUFSZ}fmCkwoN6!PmLx9ad+Osd)Yv3r-M%ltWl~JCMM_pmK9{B1V?$1iOVxlH
z;|9>hYYudW1F1>rC`cW}-AO-Fk9&P?{u)E>?&R0qXT*dnF$FwaXdosB(US`^2J^^(
zmYB|!b+=MIcuoUq516MP)f|Ww2Sh_1o;v}JR7~SwW0a>x4!ap2mH(odq&xvrFE{lI
zG<%Rr@2LSfLlp_es;%hK6V1A}Rh9<Swz#6tX%6&O4g~Q@+?0w6C(sX7^wI!>J&PL^
zwbUr9gc4go+@!#5r=7li7?yQ2`ehI7t=x+^T^dj$&SG7F=0Ny3AR3{$UVm8OXpGF7
zPY3^fEWpCuNo)BVy<xvN^m~0CG`edD><Os#R$%I&vyqXU6TS-fv79uZ_OS-j<Z2F>
z9LUe;huOpC6UYv7$){Zbo0|^*;<6MiSgq$4(|(#7+`c;GCyl_AVRICD0{X^Uev{f$
z<5vUfsnO25mM0Mh1gH!*sW{9mIunD_=AyxSm^HMb+F7BteU6WyEmgo;K-=YUqSjTx
zcml~NYg&i1i@t^-`6EvvPItX38c@5=GyR0-K+HK{PCf~MT{P~isB-Th$l2iZ)S=c{
z9Wft&v6%{(di`E*DP78A2A1}gU{KmDoIQR%hyGS&k~kH$QxOUcs4?#vngGp#u5;k1
zmI|Cb@lwnla2v{9i>NK8qQ+SY37!(})k=nDYEf=d6%8eH*>V#=;!)+<hR;pC=akn@
zr4==xc3qbB6Pg3Pf&<D{N?WB&0^K<8E@V2!af0d)tQ>+@G#o;mt6JGH!=)`+sG0hx
znO4-7!0E2z=6oqAZd`)P$2^ELCtRX<BHOQotGr&p<Sr7S0kw+^)3<95^ePSn-Ka+O
zpNQMfdIXH)50|%+pqhkof@)Pm88u3OP+RSuPpAN7oY+jgP8_K_fJRR}60B)B(69if
z5BeIepZ7ILfNQbq%6+GDisNitv?c^kWj^b=YYs$=1EDkOOSZg=|66!5;+qE|%|4tv
zk~R?hl8}*<gG9ST^|z1?T0&K?Z@=p+8?EwbZB|-oJzcV70*aAOIjptfK;vS}8+1Fq
zdfxrW&lqq@+f73oRs(7%H}tXQKp*8mD5$R9xdi`Qd@l~weL`2J(^#W8&iprV@23=`
zIMNBQiE48_rFjOu0gi=MBiM<TE1;Zo+Hat1l*9_KQR{Vj%TeLpf=fp~fSXRg1KFwh
z8caiEMgwXHGxVY6KwsuSD5&l`vK=oi{}(>q`&%S(6rPcAx~iHi54FTw6sX#)2{a8H
z4YemT_+lQA4FEm<CU_VhKr~q^cLi|!8&J`-8o5bhan02GaPFilsPQIiFl{9;8c<s?
zME|Kd(3d$73aU<bJyvXAi1#-?iw#Gfg^ecEbo*r3IhiC$maHCKSta&WQ%@By)_B@P
z{oHh~1l69s$Z%M3=CB{(yopz#sNZ;bT-i*+?Ma|A?!#Yw9#BtY0aj+9KHNZ>^i!Av
zLE$!GPB_srt)vcG{%XY}v60G3C1Zl9qV5Pb?^}gcdlq0*=^J#f+NttM;ALBUE+?##
zRL;%ZSkK>l5FX;Jd=hAG9g==ArVYLj(}tanqW)tUmd~NeGG_BTn1CYD2Hm05%cDUL
z+V)6Sx%b_#J_)FyAlezg?CSk}w>dTCeVPLlR1<y-yzCft1T-bJcdyL4hiZ^ccLPeQ
z4xqSvH;OBEa(B}GsBx8X_P(9~9H3p*jud+)vQzqFK;|$E${m4$xkHhgUdVi8GK(O|
z`xGd)(eX@r<h~QRPIcv8$*-@ZJ_V=&3Xr_tTDPS{3HLFwt>145SUg{x7H+?2l`R-s
zr2ua>+(24g(0^(UoPYxY0`f8B=XlvV!Q<^;HuDw1ZJp$r&!@^2!H<{lQnZ1#P9{LL
z1~P$YxfTkn@~jN!n>;SKy-jKsU){s<?X;^p?1?R!s9BDY|52^iJTJe^(gDei)@94J
z0Q;o6&f|FQ*%H@ApKOnIdA_fuJ^`qd2bD}9Wh@3%R(o)$v<Zhxnov^aK~;?p&ZYo`
z2e8vvl$K&aey$z;3KG!2z`^4LBsz`?tN^Ptuuhr-?QuXldP`>~*hE5Fnk9y52#^Lm
zZZwvcqyF$=G?bLURdxiPS_UBTdMU4TSV~DnVoolS3ks0ZuOCwK3t&%8ZK;CFPdbX(
zS%OytSWWe}E#Pa(pw&T3{afWHx3#_|z*$%2MCFlsR2^wRZABv*Yu)h4_N?TdJ<*Dk
zj09xlCL=396<K+y{M#@gw3VNn>uagk18OMnIb8v4-`#*U8|tuflM|cwdr(>*;Bq^F
zZN=G0X|_1Jcr?@h4rpNT-4uHqit??PHpYQjlT$HaOsWD`X{gpf-PamV(;I#cgo3C5
zSZE6P{iv(0#-81~5ob@uq={px`5AK52Hu1|di=3~HP4#h<}5Em*``e>UAY|9%RYhY
zlXu~-I{q;^BhLwC38zg%+N{%&H+v2Wrc6cV(4j5bYippE=gbreuI9P6evLY{{TdPQ
zj0-9xt_BZ^ca&k*hEnX`P=Sg)jc`{}tLDErD|@66D|jv+FBKV{OU$t&uP7D6Cg)=K
z#6t8RmJOR2ZROUGyw!<5^wnztH5Bap1jh}VYw^y)D!jYA5hVoC0jbnN=?`LM8;F3l
zl|&TSHjIkXj3a!;<&VReV-j%L+37fKRu-}{7+#!7%Ib25P*3WwzRm$t%aUIKHxuB@
zdNnrGqwGj2_U+w+ty{NZ%cc$Z>p!2sL%(|rKe+n~>Lo6wsQ4}jQ0RnCQ*{-Jmn=r{
zt1qMGv8R+QTPBZ0e0mn*Y}}Vgb%daV%a4>JAA)<p*#Q5}4QM9tCZ2yf`d@WDhFowV
zGKY^)<aY$BX8pSQYw}5+^Sb@mzV0B_eYh99msY@2??XbS1-4}F$V$*`H?bam0;{LN
z4{wbh$%P3Rb!I-Mo;?i1#uc!BQf;<}9CVOPyZOiPLazi=ks(t)%PKs0^_?<2^-c}f
zCI>J$%c6$#qJ>JbvEghS;@fDOF^BN-yx3Dcw5xjAaDLt^+N-4Ux&TTWn{nB+B>c}+
zxtKaJU7hc0r%(^+uRg~C6F^PETLY-y$F=GlavwNQj2+vzVg35`Sh0L57B5|8KB9jA
z@VDo1$5*abauo_P$K2G$AE6*uvU(M^J^n9L{Q2*QADRN|uqo8$EY#vWO8(;ssPQ%%
zwLA;|W{gt`cC+j*>h&SOdqiW7vr;LbChe~*gLlnVBwuhQM%?~Q48Q1NSd)T&qC$t+
zq1vFSv!_DqS-QU(OJ3QE&F>th($b8i9Qw1eA2zFbj5t`>FD;}yKG-j%w~FQ&@C7*E
z5g_f<k_0cQ<@#u<bC!9Llxf5CtA=3qC8Lp^EuIH`Pub~pfEo&>pY3eGpPwwj+v{8y
zn{VNIVeVf;kQ4w-O|l?~8ma)QOhC1k7s*FHUf%C^2hia3D;ulW^yQ^@pejJ)dmO%V
zOD--wFHhy8K~>4{N%ds{s0jwG0aPm6P*;n?hYn)ru3gkXH)8e5m3Z;BH;>`N_=%H{
zoRoxQhYcSuT#VoU^;z8c)f>75s3w?-opk$auVVZC-+-^O3ic^ysIr>9ZdenOU`<Wq
zZz8okD<^RTOpmH*2B>nbfMDu(H^JM`0AD?4=?S=Tb~}7W4#B^E2L?R&BTV?p?MNx?
z7u5DxL`XizRIU?$nEaDlx2!0}dyj5JCCzUs{S%Zn8ld80wX=^V+hC{9s?|n%2=qd`
zS*)U+eNw>G=k>$w^1<EcrScO{0PH2OHXia|$Z0va_)C*8aAdBcEVS%SkQ&oldKI9C
zg6WzKwfNaz593g!4})_o3P?o*%}TQ%g+mG}fmHsP_8@r!woXPv%3ai0nRyc%th&yJ
zMryhVeBMQ%-BuC6eK%#}rmG9#pktl})z)0>dw&}p*N}+Tc2KE}r^$tiigN7Vw+~yl
zY{r_kYq02}_po*Mp_a-H<*+$3J)JLaD?7X&9)g#LhWWHq2Uf0H+hssC!PMt$#HPpp
zja_%#1>4-Ih)+pFv&#u<atac%vZz&Zy*)uxCCaR-g>(!v2|$G$OTK;&S2oquz(eyv
z95q_;0%-bR5wgGhc}%<aN5~jD%m7sr+05rV`5OvoE8gFMcmJ@FGru%5&}8PP!>WL1
zGAEZ3l5A9Nf<r;(ZT8L3Vp?7fQVCW|fYsH=C9a&jlKBz~m8FIfZuZBC<SXu;hLKbH
ztH(pJAvC|<cFd`q-jPIP%=TWtxeoXL@enmiKZX!YowR|bCC4M1LxKd(K$&m4pt!YL
z0YeUryQKY+snrT-)^WC~f<|-k2AI^(g8QGURBG!j*Ys1GYF}x*b}N1gUN{P(hUurJ
z<B}ILOs*!_96odiyLRrt`gLpZ>8GFI!-b!=aH{{HA(%L65&@J$Y@YSgRmtONg3HAr
zHic7ZTGY6@Vk<Q>H^J23)P#-y{wH?bc{d#Ap04B=1Zqb?Arc7Q0;H-Q1j%+2iIKjd
zk|E%*IvlX(=flR?{w8___+2g}UVJX9XzN|&c4Ov4zeM`rAu8`szL?o`^cxDMD+#7=
z{&GDWdDKAZG2^E~lEhj2lynDykU&bp<o=FQ%k|)GR+|N>nTc>D+0jttBDi{~RVTn%
z<;F|DT7_%w<)reYeg-zkGc9kEj!KE$wO#|LYzc*^d-gl=>qqG_!XbJA4cr2zIT=<?
zATi1f5m(+~-A#ta0H}Ewz+74(!;NMJ=e6KLf~u5p<wg0b@q*-Zw<!HwlsTXP&wL}Z
zY&DrgaM`y<CX=>c)v6VE;_2s)Qe{cU1P<d9>^21gqJ_$*$>~(16uHsJQ--a^V<LH7
zl|BVX;w?ad+V;|m2AH0A27OEE21Ni(95{%RIEJlLfVVrzd`|MQ3>av*TGdp;K~SxE
z>N%{-%)+c6J&45Y9KKwRb<;Xgc_skMF#qj`HyU6{r9@`y)AN#$l$xNj5c_C%k^)#h
zDl_~U`J}(P2@U)tX4&Da^x*YBuECA>C!im_0OVB?qMvuCxQIW~D*#mq9i>(@$H!kN
z#g+pej48CDv8fq(*;WFeL8eSDnZn&&&InLSc}a;D6w*XfQtnkCJt03HzkjL%gZn39
z%7nD;X0M-%5eL*{5{K~zijB#njT<&#>8DGuWcivFii$>!p)94bQ*y$RlR7?Xp)QwK
zy~|A*l$%WuX?d_m2L>7+So$fp-*qeOr_WUOPXW{90Rv!hBr1^WQ4zt1n`w)bNvtGl
zyfTR;CIH7p=b-G7zu>c?QJC_zZ*UT;^IMdd?n^QO_1>c!z>Q5|6)>ecVmiSuF~zPX
zn!-an`!-E_X<4)#^S7?TiDbG~RTn$){!<%q?cH;bOiehnox)LWxbuiPpc<N>A#@*q
zQi;bFHemK3f~mV1*%=lCObLSW?(Qj=hB8mI)kFuK|FeO^To@px1Ye^cPrh7+Ap;YU
zN&AqY(f0B&Mrfr@v18GRq;-mSeTHYAc?N4YZpMFKel3IxDVR+E%tS{5wNFlfG`Z2x
zP^SP?ZVCm?(BqH{dUR-NgAJu6*!H(Sp*bTR@tky`K^95<289(&6`^E?OjgNcP-0#l
zCx3jD=|&`+IUD<a_$}m4o{WK~ozbZ}Dc~ZoiEEV1=2z`?B5e@)$bKoBX*j{Og?0f0
zCUN!;P?K(`YC_ro2e!X=1gj_R!kJf%S8djtK{e)psyN86;)5<c^F|fMWOD;B&deus
z2H*4(RbXn~7{*@#OtGuxWLi;P<wHS6Jl<XF!kNpf)X-nvlxC+5W3f7`aB#qQ6RVsK
z@7sxcfAVv6JaXhnq^5B)i7q=nt}be#jk21PK9U49jgR^>qF)9e851=7K6oG1G&hL0
z*i66H#Qp=+<dZp64=W+8qXKejqKSF=Xe{23W($>rWMJpB&mec~I5@I6eD37XfBV{l
z*!=EcuFSG?0*WI$+Dv7b-`yd8XX~1hk*L7j)8s>Pe;by+xEn>2`=Q^^tj=Z;llzWN
z$;F`fkUoLME2^=f*o(9z4k<`WHtm_>4yEpiAZ;S}X(?9af+c0;Qh|79VGWLyyH%-?
z7MuuH?Uc#dYBTJ3Gv=PgwoHbLGwC&)^=_bcW{fg8nG|&4i8fPe_wPgTvrob{bqtyb
z?zW5!*wZ+(-%Xoir|1uYswE)-3AuSJ*9F^zGf?%HM{#J?N|l9L?kWP`Mp8q4<{>Mf
z)_%0t;32(Y#4SoDpdy{5Pm2GlOje1_)JCPJmI~K~#ru>ITV9IokREB$PMCj80oA0g
zw9JhUm)2ttHAK-)WgWhQvxw3eBAsL&K!7_Vm9`}omX#8ZW!pX2xV2stZ7vpwG#xts
zo^ZgBlez{ceU6Qw$O52kxlHw>lukd(-fo%@4imr{-uW1o98Pvp*u*u;Si2v2*%n}u
zb^aD{#UcoSTI!(>--oBJPE|#&x0!-gE?G{xZ$%|1h}btcd7G4$pl0*ieXh;Sx_44Q
zUHn;-((GKsz_Rmf*t+Nt$_~{AGi-<U?iCldr+NjTiUa>fuFFqO5SJGqRhF)a07M~4
zF#_DONI+bw3^%%Ftln4;7yVWxiEpY-y`KZ}@>9SO9noo*A*QsrYim)uXaOuks914U
z-$u>UlE8@-d5cFQ39xhWD<LBj&9vKEr_Myxo6n$n?;fM{Hu{)=rP2V|Cw6mfzPr{-
zn%OVt-Xu=@O6!OQ?NSEY(ZPu?(N@Jxt9qXc`#w9ON;h9v(ITa@<-`n71)~fw<I0Qe
zjTAEDZvi#a*R->h2*-WW8B;j45T!VeTI%}kO&s<&8Cjb>Bpe0lJWd%7)Ezv4+9mJ9
z(to<VwN<1jLpzNk3)s<Qv4C2}>PUiTTRAE}+o}jqpd=kJGzI^Zs8RbjSMnu5-&pod
zN6=|M8rrEzj+AAinL(md37Ooj1^YIa(>89rkfKS8Nz;jbV+g25%hm7=xP7l1DPjiW
z8!1s~iP5)_NaF~Yi|dAH$~#MaI8<WTQT4F@L}H|`hjPHwo@(~&hHt|jE(zuGOs>nf
z&;dW%#y^p3ag`EK<+Q(wQdP~>VLtlXN@@kCst?L}3Bgy%s8KeW-$daTP*VqTF<Obl
zNq>}~zbIc;jv`BU)iOk2qd~!O#7B#;_REPGpqeCA)p&4_fe~b#K6wYb#Nv|{j6qU?
zR>b(um4CH#4>^3q08}AG-jF(J4xEq!rl3$Gt>c80DK;`eBy01{zfYJ?7;g}Ow<L<G
zfq*)GC>nNbgF9$P4J}4UH(v(TjAz<Z&EY=#gBo&k$R88pNuaEz5@3yI?2UT%$r{F4
z?X4+hfNGj=YPq()x<R&Ikf1pPGd7?!T(kvGgh}kT0;-BO{#{WePXE2N=`>xvkprgO
z`e?IsGC*{k5#NJgU^l?Z1l%YSFB-CdWn6L^+=q(ct~Vy8jwQpSo(e@JSB%k3N`TD5
zP;C-vKV;GCv5!UaFOnqX%T5J?4<0W5sjqPvCB{hc>ZrOg15{HDZE!NuK4<aRUW#D?
zYQ_??j>wMOHRY<s?CJB5dWqK<wAVzgM4j`=$N|5{3oj?7;yAM|NmfP~Z))T3Xn#xj
z3Q!5G{_=eUriOM&5X={ssb&Ez>6ZjEg<VJSw%unVaY1RP1XHnB1n33is`sd3Ol>+6
zWp+2u7y_z6vWH;G6>~;o^OUK%cDF)3eNNg<1d6tB?zzJ-V=BI*<QgMs>S=}a^D*H-
zfQt$OjA{jYo*~mBSg}mfvE)OIrMb3JMW||z90{-Ib^8dUyn|w|fH;O7(<oo4mCnhK
zoF1-y?WHN^fGTpv{|c@YWW^Y_1WXk@{1w-&7|KyE6&X!dr#K)&B*J43@nbC4$I#4P
ze?qzD4ZVrvQ2KkLW@^$GLqIi}%fTo!5^Y~*BjqD*OR-GilPbc-sBLQbtIZ7cZwz{3
zNt!OZ4{^XEv-&c#?k-oDDzY8Rq<mk62Cnsw&+M=KVcVleVwxt<<|RQ;lv2ovgjmvV
zU;}9p`iWS!1Y^Z~ENN<wcQFH0Q@&E^mXYF!Q^S6l05SkA`fgLgAfd?|;*04tJ&o=r
zGD)VB=0F!YVBsW`T^#C-1$P7|qLV4~UCb*Sw@c6zS$!l9{+7If$Ly4PT>cdKq%=p1
zf-NB~73neR3bIN|%n(9@1fWjliml$7Vg{%tLoyhfG>=iGxJ`}%Q`rMb(g}woW(HyU
zmGUG!pe%hAV3&Bg`MC@>93vqLQ?+z9QRaXtE&`}Y{rjW2+8{u-JyBX>j07scsQ%Kk
z0u79lI&cUQxXnuIL<3jLsq~yAwL(fj*dN>jD)f5GjgdZ(en~JDMN5D+A=ReV_^V<K
zsE9NLUCKXZfNI)7vNP=%+RuhYx@gE7$jynMSg7(MEMC&!<4|Art;$c0NB;ujO(=B5
zs2_BxG4;*8fCHwW@Hc(Puoj5&GsZxGT52pnNr2qPDE_hxHSpO%q>dho1TKR;E)l%U
zzL%Assy5z}ElvUiGTEx6r837o7{L?ldlEuW0XzF=dOrs;a#A8#V9&CS8K9btBjNML
zj7UUdP&1YF^F9KaDVIH~Ugw{dmz|Skiz&Xw*^F_6?dZpSZ6ujOrvA`hngb``fGIB-
zLx;jXZ>rieD!?I_fSM`yC&(gfmql?Aj|0D~j0$$D%(3ICeHzNbQJpkcq;v$Pth`j@
z7Nv4)Xo;^%MThQ<(VnGJv{N(nG%*M`Y4SM(C>)uAREG9bDcT@ml-l8U%m7syQrTtr
zJAQNuvbkrJxKFtWtOBa02HPR^VY(>oB3s+awktwcIRSO**d*j+1%<8`He8shuCs|C
z2U0oY&phK?_&!^wmVbKbOfR-f6WAhH^wF#-yIvK>kXyX4{Dj$)kTZIWx~_n~wQL6w
zR)RbanV1V77u`s5JH1Lvjb^`;&M59%9_}qCNk|(uA)oGC#w2!Y($!5d{)!o(hSq!d
z&}7UVpNNWjZs9<#)zYC}CWoTMFcA^~Q_+l@2<U}r@t8I#Rjr&7p^FwBp*+#YeU}44
zOky0*Wb`}ZEc$)2F7*HOF}Spk!LFkPN>Yw!rylMv6=!$Aw|pIPFS!yKg9jVTH8q+L
zR?9P{hCO0pf!Z+7OF)wd`r_;#O$}Ry`L22i$H=)2VnW{NR16-K7ea0CJB%rynm{E{
zp3Xl#1GUm#ltw4Fd9LRYvq%e`$dmY*iru!l&aXoEmDDuj+-b=eJt9@L7U8t_wVI}=
zcXJ>FlnSOyL;C+*4{z}fZt<Leriw~qn-Wv_BUuxc;o)knfSVIi+`-k72@E*rJg)2K
zK31YA$g;!^QvcyOD4Lf~n%Nhr={UJUNi;?|j`Z+f%5ZbPFINLMGo+_YQ@IZl&KZb|
z+~Cr)UfM4^=4c1Z4%a2~B{_XkI<A<Ngaeff!b{hP${KE<#y6v^-!<<ESDBKJ*knYW
zB-Uva%M$HW7GNaPh3n#TvVsx(897|zX*k(_%H(B=O)|H49(vtP2y8ErNapZ2Ho(oz
z^CF4|L9q04=K?o3E{r39dKNB2{x|PM!Q?4M8+6dAOf6MH=S@9#7#!SR&BraH#dS$`
zBo)n3foddLlO0pa_j3}gzEURA2&T2%o@i1UCd@H3>)t$Sj5(mni(1+?mD`Y9H$NA4
zf~nY11@X!%Ib5MbsJRtNxZ00dt1xDgUo{&`Obi0zJInpJ`J#00dYT?A%Ip@wKlP91
zKxa8X!8QX)4?5#a<bU^WxZhuh_>@#MaeHe|UHu6)#LgCV%zXl;0^D?ya+Q{HKPev9
zRKY%NG>WdhzGchm6KSVrp2No#VEUCq&{*P8KA@s8Hq->;1e-U7>z5r<fLdIn>IkM@
zjvNIayw!fpyLKe93XD->EIcJTTg8|IYAB&o#;4)V>$9=8#IK@0HPKDt2)B8b03`yd
z;YbQhB`baeOv|f%NMu;B0~LOpGr@tYF3DFzf6-i}F?G@$=q?8Yv_zXqNKeO@FWv^n
z+{y4BIfOU{dUhQ?#2NJPgQ^Lp&clZ})OQnHt?;ebfRX?2$H<#7QB|cgVy(1P0-N-c
zIhT&ckU80CDD|jqr0cmGser27BcE_30aMX_>lo=u0-)F!rN3r(6Q*4^7!&6XRmBKD
zBdXo^urj>@P^C@GZALD?Fc06JpN>_<1UhPsE)F$hn`d+Q-_!Q9s5hX5=JTP99|6+}
z+D~P1!8bqO5Bb~+T-q#JTd?%7{q*C#oC7A@<c=GU@elqQ&Fi)!z@a!p(ZhN00NgTR
z6b@jLJP6?8n&mur5Pq5bN=!!62a7QHH^0Ql`Bx}Tb!BmZ0yuVt^z0;DblYU4<T~JD
zj8rQFvDZ~N8R(fI{s@5K82(EhvVfqbyb;b?H*AUAn6J1A!_LUT+1E|ra#PtkK$IMl
zqrJ1?dj+5hYXrcgagxZ$FWuM=UpzMzD+#K08l+|Q)S)9DYLrHNQn9Ocg|Bk2A+$2#
z<yQb*MeQ_+k@=6*_!-pNf(P#CkFg_rufuTf6dz612{}OFQ(6^o9D3db82{3X@O`=(
zfktYljzqW)9fFfXb3bR}1rWOm7)q}B96Jwfv(;+bXg~L_lDwPV`xyNn_#q~J`F3L>
ziuZJOsBhNG99|C^nTspFGfizt;;N#i%6=;LR0%{aTRj{7tBYVK{gQoIQd6S`RVDTO
zC13}^v~YYn=6`*fnut<%)8M@(ZN2fg*8r+8$C!X>Mi!0WcYbLA?zoJa>H$s|(G5)k
zsFYQ5TM`0m9X$X{w<>wYj0$A{r+}vc&<5}dP_w}}!6i0XdEP_YjMz-Km-tYaZNo3W
z-5)b18+KDQqUfSr^u~tOH1!@12-ujQI_k=+G4Zum(7b;gy!*G&rkci?_!=}ax_?t8
z!|D-OWlXEUH3+aKShf7h`QWtx{aQWrFKwh&>MA(|Mm14!1Da^B9rW{`V%pvJAR&`6
z1t{2E1yjXA_JtPOr}W1)_sv9dwgdGCnv|w0v;I|w8`LmejwLBNmC_@MPaB`$HQB35
z{98pUtuE!3-$En%dCg822G7jK=e{!w1%raLOQ-Gw$q3uhiwKnlkOmY`z2gf5Frd(m
zzr0Y1(%Jw9=Ws~FVL@p*0g*%XB-&UKIQ)<(`!bZ56&=EM0YzDtFF_r|B}x_wm^+w?
z##_@Ez^*DkZaO^~U%I{!!-p^`Q1H1v(r$`g{nd9kpyWEJA&$KKN-pbMg3bT?E7ZO6
z781^!jb^Len5?1)fHftBS{gUjV>|%aHbrR&BA6oB{K(>f0JTtGQxkn#8#uh@Y`8?^
z=MdkshP$kCA<>v89>eI*U3+vAst1B?_DT62Ik`V>yx)QMpWJ|*AC@7B%f{`=Ry5YR
zmEF^xWaS=EHl@jm1HLFVrp;54Fa45zSeBD|=oTf~tz5UTKf5@e;jQ&!`i(<y4u|>T
zBBfpgW(IwrxqBU;3eTkB%;EoyR~BN_uq3?rRs~*J?nD}QJ}k`Osw#qIEtkcCA89mB
zX13TpO+b}A<XB`$+O^ijt#JvYGltmkzt`vDyfd<qo@#~yGw$mHZKg@=qZ|+oQ2_)6
za=_fvkoxeSvE%f!aPZ;p!FPb8uzAzbObt^!06evIMx=ZKs!TK~Ar?I4zEI5^vNijC
zhHMMqa^lIma|r^)K+YYvW8^JgK>wMu)H7l;?UrWRipK`18f5ezo{ek2Jr}Dd?!=0h
z_n>B%3yB2;R{E2R#_8s;U54^%+Y*AR^gl_Wk;(!Ai49=bUU`yAkehQDZu+_wOishx
zZ;iyHc|#4GDLb!}+m}+W1XN+5fTS>8ey2`MhpeVL|MXgX_(=^Gu6LoFT4rt%w|=G_
z)NbW{J~BRz5*JJYq+%9u(o>+COG!Q4&tc{;8@_OP8cv^`i6H}x^}wcp_px>}tEqqc
z7zYes5;<k?6s}mB_RVi$!0BgV-+S+%?7vT;iT2g_e#FSav1m?8L7dpTL^uUklz3A~
z5JiEG`hn_F`nQ%U7r@M~e;I==y8;8|%!MN}OK~M!U~011>?6`=WNGSIpPPW9$^EeL
zlYQ8-;1H_!xDZc2ZX1`8TVw}e_CNC(gCMB_|FKHq*XkvZf*_keAq^A1Fc9Ns4@P!j
za6t|0XxdlJ^83PXuLV?Lo`9(Uqa@KJ&p9ny**dS=@5JV94cM~7g*}HnD50NPgE$_`
zz5GZcCU9t<!-!Gi`dKl0m;)2W$m*vQ6y_W2^CeFcu;o}M&4Flfz|^_~@ZxPY6i%N(
zm!=7*zWN#*S-%byYgeOT(+0S=E<k{qtE6BzP`Db;m_61hlaYGbg~+;V8uF%2L(Zr%
z1X3=Al6!-lCLEfn%COa!dMN|}Pqfv3L$fgToZ;B_SsC_iDl@ivE@@&60cz0%%ocjs
zF~tmEXPn`j!71oBDg(nN=0hgDGV_j32rDIreZMcJUJa<iQ1cBZ25Xsok_7;h$EB%H
zL!+OooQ&CjS*q&dl2wr<JMA$kwB<;E=q!c@RDq(|kY+L(IB4H7PwHRIfhcppGyzDl
z77q1u#*9@T@8ws(Sy6$;@^ZL1QRSt-s+zf{$-u^407L^#%E>`;ZXQ=LWr*g{LV38g
z(w2-{9f<bW0+c+?ADE#&Q_mcZ`f4X?DjQKxw<;&s|9d?~&>@?{s)9qOWhEgaJDFN;
zqT12a%vu2{Um4~L$2{McetSKjn#>e1HMLbaCqSE$%xM3~i5S#B7|zSQxt(A0`M%s>
z?Nm*l?xP$qWw8aEDA=~-6r{^%@Q}{cih?Z~tf&l;(n{MRq)poCFDfgV%vDWoJ=gkN
zXx&?1>uZ1a89)`r3W%D?1V{64`8BV%bxowrydL_DKGq!Q4ID7RNwfxYcrSFdoDU(=
z1R!<YxJiL&$SqMlY@v~aA4~o5^oP=Jklwd7($<XbQ-B&ud`IBe(Y4S#T8_;(e3u_=
z$w41z4uppTl)(_aA(*E^D&ttLcS24DxH@xRc!&<>B1%ge)Q6T4b(%s8kHT@E1=ODM
zCiG1yjeNWi_p}84yyif-IG~WK!^}eES;1N-K)1sTBYg2#dLvU<EWrDe0aR%~vk~LC
z0-!as#hA~N$DNN%Q|nwc2YSW<LzYFRjbZh1^33UVxfUNXVb!w|yZ^M=@6>Nld1*~=
z_jB*yN$K;vY?Lp3+u&6PPxScGNe!sNNV5@*<*CQ&p$%NJS6*I*9iMH%-qI?3?sL~5
zHzQR&bD{=s$-Bo*(1i>r5mJw6CvnD(b`t)ds12e`Q>@Dil{o__4UjPDaxv&i1w8}y
z?AneETXy2oE3d-vLEL$j{UF-BJf7CJ!OYJE3Qb(`<@InufV+vFI!z4ODG}z3k6f3$
z__X?1xX8iE#SAu^Z1K*;1YAv);BfGltF!2WmcYHTY_ixvlE{$yIofATx*{cCCl{cS
z@n+O2lVPEP<Y9wV(jDbMaWS@U{|swZug22lOR;LrMr56K3FcpMrD})He3|)o=H9-3
zPjAq2`Z^E0OMhzs2$g4XrK+M^)uDq2uw%z|tX;DP%a$#};-xE8ac5p|u~Do5QWRdq
zCanT~;zJ%vYcn-wCxdHOSJ$ADG?z0b?2)oEl<{-u;31T=UshLD;_%@Tl=FIJ`2jR?
zPs8?7enBBpxfOOUUB~kBa*>u!&w?!a#1<AJJ39xtc|4z+gB-e-Wzs)4EiF|6ZD_wW
z=?y*CUU{88-{%3<WQtT+I)11IC(HC|7#{5~Bl_&xwG&%5Z^H7WOYz3LAGYwPXv}!z
z^RF30`W^FZ%MI-uh;Y<5G)8SmC0e3`YqitUjygcMLt0o*2ZV?01YWKm59MuG3kk_g
z+EY4Y8e@n$66_&YPL0FX@_cM(XRJ_eX;NBp7<>2Z#+EIcv1-K%JonOTEzB7(bR?su
z4MbC75#w6~{Zo%BXf5}J^FY8-noauOQ4kecYf#EaTEz_fvU~S#g8mL{+OQ5EEcm3|
z;&b!z8F9{mf_@{Yfm*nvoXc)QlJo;VWzokix!p|-*t&T?mesW%@OsSnDVRQeDn^fH
zfb9_@Fl^XR3>eTKIoVl?KPJ7B@3FiNDYOfReGX8K4rkQRWPr&OIV(VSq_hP4_wB`&
zty{5v-CBJ3##7i=ag-|)CQd?fQX(6MVaK=*dM7V=xXrJ0<r5{Ny8~-J{TL7a;&;gH
zH-O!rtF+WItrI=njqB7bY!s^f`}gCE-}nK(aO0(F7!zrwL-;IwZEC2)-+uRVe6)5u
z`sZdUK6RrOoqEK=p_<#-0G}ll4?grDCXF6OzH&8@2@IXerElF7D$hc>Dbr>xJZ_iL
z;)?g}!#0kpHmqBZB?~^ry3a&vjWl}fc=e)H>-@P8wdP1E_V~C}iFrc=eoeX!dId<O
zkY<##Yytbtn>S<Kx^?(;$pWm~e3VwXYZP}~O-@c$!*qI=vmZ+TIr@>j&HT+@c}|iH
z_a&KqJ|`!S$5f<5Bc`0lOGK{usJ%BaXiR0rK|J;NI`xeCb=^(3V&=>lm^x(&MvW>$
zzx=!w88gc>^EaRA>~F6JRI`Fcg^nFY$T=DIRC3=O0h}G%X*=4m9?O?~iY3cdxA36f
zpdpw(BUh>XUayC5HaBYdrk9DRL~cpZo#Z6Ye6(nBcOLEbGxKk#3whRr_K;H%I1~-_
zz|JlB_`UZdkbC=C-h2Q3?O*J}SB{nEqrhNRNwmEh4t)<CIDnlysnKoRh*c|><L&ns
zn71G$rvOu@O=rY48|B}yL5k)opd_Q1fWLw2V$1DeJuvAH1)TcYYHZ&|6WXekSiE=<
z{`2CiEoG)=7hv*~sc<+Fs0c8o2DfJMQh9MVxga)Q$uBf7d9d~0Et$1(VA5k=GXIvB
zuxPjWg^XA?co?iamcCz8Re=}&^Q8L7Gnaqv2AqHXd6+kMF2)dC9d-i}&3w8-i{#fU
z0aYp^AF0go6*7hrCEVg+Cx;N5H;cfn#LKV0E%j+hV;CVlm2W|Lb9y}tEylgc8z^v+
zT81X_HfwpJYX>%lOOrHb-f4`<!adLVmW@P``X_McyNKYO<cR(dSjtb&7NbZm&N_1p
z19Tf(Uxd4^(%yD1zig;3LA|x`xS?LS%Zqk?9StdER8_`N)gIC8Hf_S1wQKO!GtZ+k
zgvcVU)la7#(8?v$Y66FVB+p3_%}m;<MXn=&re>cE9W9g`*oPG>mf^kk-o@Yk`D6>V
z!;3~S{AN1aFrKthf#Ii?I|xV*G0jFoulZ?tup0-GM`#ND+cXWhsHsYdkGCK>H3Kte
z%~IfAPm{#!FFmI|$RCa$eD#aCV*aI=GI;{yB^#!Ou8xp;C7`xubQ2q+y!;4?_w849
zqcy8n;iGq6!EQ$QHq!_;VrojVvb)GzUWBfJTBHgS7HqswVKATT?r(r)NUE)4n6&`o
zI5H|wXk&Ia-{YTS+}9*V!(j3UUkJzF6WKFfubTywlb-`nS05R>u?TJ=qbpS&+=+u+
zb}1eBWJ;m3=cA@1lcNU@6l2@Atysg!q)!(u#L5j@TdF*G=r9aOPvfSQj8{os$gH=E
z0IFm_z*9a1(r6?TR1EoUM)BTVSoqOLc=nkm@b1Fpii8m(M>Bv?3TfhSo3u8PR(Vb|
z8lhDU$D=`Pt1^zo62?d7K3n2dAZ7x4LK0@on4w0h)s<!V)kF8I&jUaIU)*r*)fihe
zRPorXf2*w9{IgdAsthx$f&wR#N$VMbXYry19LBF{sl?!6!!c#bh@emzb{J9VB{IJ`
z7#3zq14klJL{zr&){AG@Kud)BzhNJWPRD7FPe<!&45K3P;xO7u!oHCTE9)DXW^1pC
zkv&g9tgEZRVa{gn*|UpU=tg|{=_h#o?GIYY&E?Q^(xk~sA&`^O2Yh@<H&VM31wj~M
z@~GvZh#wenOHJS|J-8nqFwEeifBpkL;oexVXJaZOU9p>~`G_WJ%?d<?TGjVrE4OX2
zN9dA|Bo9Zf@{vBAnvp}ZU;(#asl!9}-GfJ-egS_Wz+QII`AAFVE(q!cFVD7>>(O()
z3Q)~(dWZM!z>n|yKK}ju8!ePt({q(A#!mk;Q<ayuwg{U!A!IV^1a)r7Gdu@GQsaL_
zFu-t44iBY0&Lu)GjiYo085!|%j+Pk7BU7_+)>q+g5B~<=fABZQ6gQ5$kS23RTkext
zYmw`wCXwl*CNFtFUaHYfCV+@S;&C-#@q!QW*FXIMFTb-0w!DFu#bL7y=RFdal@qdZ
zzuEt~LTl)A^1Ohz0&oF7W+c~zKk_mAx(SF~DW|P_&3q(Z@qzVt7^vRrz^vJ`QMzY2
zZocMn-2KCcaMw4!jG+Sxgc{Odye!(vPtNr!Kvl>@Ha7Atp};hC5YC)37)@?zI5&qq
zZe`3C!cAthK9;|A(j4eb9Pm+Yw{2XhNILuMv*2{P)GV}2&@^xh6BATIX%3a|(DTta
z7JxB9{LsE#c=F$m;@*dTtBRh(0M;_0R7X=6Z)#B@x<jwjTjnCfNnes^<uYMhS69nV
zJp;rCX!A^A2+k}d^2H-F`0|V>Qt|@oPMNBpHonZfWRknK#*OTO(@{7i4uAON1B|=8
z6Tki0Pv~_rlw}@$d9;yDZv#|=L|FlG*5o1BRB5BY)uWyszNR~bS@E_iqt9s$^hOQ{
zIA!KC3_p|12UJznsG+?n%jR!ypglC6>guHn@UtJ?i`PC{j)_yIaa(t~WO2_c1*9h$
z`*u(n0Yw>R%iMt5)rd74Hym^KIJz~84Owij!mm}UkDB`A#z~W>Ac-ChF20zk7YjLI
zO`;*ogrA!@G;iKqeDKnfxTUNfPyF?_m@;lSOEfjr<BRQO8md8{G-_kjK2$mfVQ5@R
z3z!~HzCNQl&=)vBt%@ToNn*{A+3(&2qKYtnnI;NuT7`FB{Vy)L`bKqWHiy72`UlrI
zeTsJT{_gyW_G=+fC2+EBHCHN1y5`2uW6rGU7+Ev|1@sA(RZX&lR16(nYK--C2`k}Z
z23eT1;G+-lmU1DJ38irazTj@DVMbK})d2m}D=TX-mo8OHm%M`8@3G_YKmQhEhYb{4
z1gSe7$$KTB3Y~F6S%fB(n6plr15xFG382S!!KjkaZeap+%63UGef5Q>an%i9MB&g;
z7?hW;e75C5c@gv^8DPpGK0T7wtX+%iZoM5}y7eYZojegaSs5K_nv>|udclPk<EESc
z2P>9-g1`UY@9^%DRhY(EdJnZ&F%OtL?g@>d&q{tW#ILNX#@u=Hu=L&M@snQ^;*noJ
zh=T02Hcd4><4|bn;XalZfP8e)9MBwyC<hE1t^oz_z4oF4)8V5hU|@C%!L&{RRZqc6
z4f8FcnZ{w=`i=O}FCNApfA=d~c<vd<%{IZ*uusap$A73_(K)}c|3F-M?Tz@`zn{YQ
z@A?WC6Rd(YA(8&MhJ7{sjFK(tp<)osoiiIR{_78T`i0lvBQ^<7gwD{^djU0+N`0(3
zpg9mB4hSF^dh_ZfAK;>^ZbHG(F~~}>qb{7WDriSeN=%?;x&hyQ;FtLJonJ#iPDaQK
zzCnkf6^drq`Xi%kW5!?Z96VwS?)~x4@XH^52kX|XhRu;k4Lef$Kl7yq-{5j%!ssFR
z-Z$>Ws`a0>*&Z5D+hmJAsX1_JaKKpj0vz7E9Y4GOyQ<Pd@>0VZ_(y;+F)<OVR;|X&
z3$Df2Zu<f<QXI-nOw0`O>5K*}_ZVgg!|yyTD-U;k_j|bKYqwx6?Xgluq>_~F<)Q#b
zY95O?h^L=_8TBs1(;!q!)qonQ7SQ=>4)k&kn08aY$BCDodlIiL-h|n6<|vma1zcgF
zpNn2N3DxW~Cgr}#<((r3)2EXlD>MDwNdj(S&y`6ui52jbuYVJhh;s)k8;q4@-O035
z&v2y`UEL;(8G*n2`hKijw>kLK(P6&^)J`!{-=sNkGIGEeB5z&48b7-C7Z^NxA}4q_
zYu?7}b$4su+#IJsZpVgAn0v{MIBm`hm6gOTCNB0UnOg102~%;`y+1=KwQL&$PDd(9
zd9tk5k;1LJnLb>w2u(Dmid&qirD{Np6!&$$nghL<1EQS@Km@3rF8uIa)R5NxS;-83
z!=;!#MY@oB$I-4S*){Uq1?QfH0R=hgstJZ6w|4nZ@=!oVm!NrPorgl^P*LB+sB+!Y
zd^<yc@GZcXgcujz`)DDG4;@jDX+Z4^_w+rQ11Bj5#35c%3D@Vp^~w`K))08O0*d4C
zu+~^i2hD!3%5&V9QQ+DDHQWyepoS7UaL5SU{FUpmciScvoz2|2BlMnd9m^IiMPo-`
z-GW!KbI(4NtvMeSUhaj9Vx5oXfaXBtI3V`RpccA+-)_9O`Y<L9%~MOf&GX?ZJf6d0
zZ<ECF9gO}3GND5}Ro~(LN=nbf^x1P&rg3!7lGRL+Nb(|(VSi$9*uV9&9m<7@;k1;t
ztN}F=T-SMO4)kIU9EF+y_V3-JC{46mA`>2x0OWCTx$1}koP|#cJ6A-{HtG|P!9#|t
z{GA>@1C~V&+GJ-0#2ag(wru?jwe?O_t_=GlS8CW<r!&+X&>RRS2bvkQrK+M-Wf@1?
zAn_pVNi;gIvknEB+>JCC4L_`fh^8GRJewQyjbP?gb<SX1<OozKNW4F|<<XA4dr?zo
z1p1as8c>4_(m$F5ry2(W3>WBen%b2Sb2Sn?5EE(=*PBMl@!o{Z6h`A8GrNepsMgS}
z${L|TzpRjAi~+A7MFW6kn-6d`nGxt-v}Fybk&qyrr{+Lk;een(1e0i}F1iq8IT-LH
zxNT6_iwkO~Ni-=896W>}y$-3RsHWx`_VQ?<mB1;9X*4kuFXf6d15gENHK2x{+d4<h
zfj+?j*=5#|Y;00##6LB4y)cn4HbqX>lrg))f_w(kmhBiKmuv|(Bxh%<0^($Ce}syX
zcMziuP~$dyUOhgKQ6|4Nphk%4I$O<wUe5tWr_U?kxRr^qrqob2oHPrd#Y;7PV_;-k
z7iEl&I<{?_9ox2TcWm3XZQFLmMu#1z<7Dc6GxNLZ;+*Y$_FC5zxhxPI+VRre#DUF?
z1k8Yj&d@k&N~@QMB(y>~$V&_BqE~^W6e1Z)H5v6y!(|`sARucTcGZpQ**tk=pjLy-
zSVc1#J}_7dBNl5+&%5)$5F%N$*m*tp@>R=yqy~FNvj+aI=@~oOR;L(cK0Gs6yzfoN
z1qg2BbP3SU8q~TN9FBIp{8(-bomNQ3(k08`y^Ko3y2aAripOW)2EqMB^6L?%bmC;2
zI|v9>i&J=Gm<>#h0;ec5!-O9~U)wfanN0(!wlpxRsH9tzHI(f2z2q<&6I(Mmjwqzj
znB+|=z(m~iR`x}x;6KRob8cqw0|B9T`i~1h-K8MjS;%~kTswJSOAAvsh|%}jM|{X2
zDyBx30loCd3n{tAL*dWQ{-D=wuN?rEs%6c=jZs_gmlHxoZ(k%^U>11dL4dY}LMlVF
z_C>q@Eh$=KhJxTuBwbJynbzyFTC74Ia>3l(<RE1}!)Qyi{Qx;;PHot`HMo!pyE=J}
zj{%ONLLUc!9hfMALBmhr34XHib?(!{7sz)4fqErnrPSF~si^!El29wwRY&cV$RMfS
zBj#BN`$81$zWQ&IC}P9)gf9ePPxB|t(+5w<{J9_Od>cB7YYHv}(0B=j{Q-p~8y0#&
z?dpC(myEo?Sjjk^)f>JNRh*KdT2fZF^p_eNF&x}|3@<f4z`|HLsf%I_KMsQHvbxcB
zL%Ad_E7(IoKg{J+UhTIGGG(W8)x`#{2ftd$IpZuSuu%+zy{o<Ea+weiyuO=s>_2JT
z9;3#aEZ)1(ldY>PEiiKG1Xjw@{q7}L0jw4jHFcd35Mc^>-fDE5gk3VZn>vz6Th%9K
zN1=3)S3V2hp8OP6R7C<RYwAuH(`?}GBq*w?SrSeC9+A(vjmN<@9K&cAa6CRuN5EmD
z3%%R@W^im)C%$sv7C1}GWkw+$ixUdWsSMv@&apVn**<?ESTf6kCW5I9f9#)PEUDMM
zC24{lY;~PvSi8R=Aynj=r}ini>>vLbP?A%)Ds;l)vkiIEbv^f4QPn68(~;(6;~4nM
zk*2-b!68V^t%dN)%EFS6XHI8=&VX$WVRE|U=sY{25s)JPsLM{|svV5@?{Mno6z5%&
z0->VuxQ{`{r2rE+zowq*F16sybS4?-^Y$c2+{*Hce14izcM?R2l#|q8a_?npLo@!q
z$vGtjZ|t9)PN&l@hAAezs7Z0Mi{U|WVYAy}z^0b``8$wUf*1&BD(c|HURECpC^n>E
zeJ-Ss`Axwma((oGVCJi5#||t;M_1`}8nU(|>b07o2pPXrca)hNsoqY_29EB+&{ssJ
zZ_YHqttGY>*ZyNis5p1B+@T;89&DY(?Q&ISQ&OSjU{@p!Hdob9Gu$KCbmXiVclILc
z`S)=|1uH(A>@6OL6TFf_23DD!Y}S_%P`OYDs@74E{9nM3<yrtM5?xr;b}|#={&&3*
z`;>$Aew;k&dB5@9w!mdM!6ZwNIEOEfEtw@m_s+-8&W;`6Lx<DOhZ+lxP_ek*#2^Cn
z3Hw-x8Rif!#!?JWU;?TssYdhHdYb+A_!x9ASd2X%Uehkc;_+hAO~`UI1HQQsqC3Py
z*6SKN_VJT}P%9Y?dYDz*ju%e*6&%v2!qM1;*r_LG>zyXNJI(4Qi$jnQi<~+ctVV}q
zWX^*Dd#AzagrJHWB-)u!+E%UfNTatvWIB9iee^0G*9JGyfJNB?ac^@WVz<ZNkt_gh
zqvIeL-(5cD>=u*Jl4%JU`OxLvg4lFySXgoPekSJ=l-)`R`6CdL|3COBa7Q$`48hKs
z2gWr)!V%G28V9U{&c;3eCH5aWHnb?2j1jYvB7pukP7Vin^hSW(Z}^EGD$7<N9yr+r
zGaIN93jU`-V8wSz%wRDWh)kM14E6kM)wuf_DviSbghCj6?9tUcW86HpSB{204&uXo
z^D#Kgg<1j9K4hr_J^fUmA`yG*($u{&yU6!0uftKh|3~z%PX1sSRmx%eX6o*WX}|<P
zZp=6!FTgSz#|hrE6qFrGc1;Y3VwA;804RZlQ6!|{(U><}DBRxr=MSqe_7qmph`g!J
zEkI-SXj7!)SmB~yR`D&P60yIY?O%hqk0Cko<ler1(6?l)6+cyjvTNt8p8StIB*9Le
zY^)MCf*ktU=&mRf2x2V1yU-!5+?3B20LQ5NkwDx#eNtkRf=n3@u{LKMIL9=pDMg-r
zzk#hVVBTzeQ<h3~`cEx06r7og6P2J#H4|hn6K~ip9%{wWN%^#x+cC;<|Lk@Xrd(Ia
zD<WBz@G!GHZ=m{yPEj|5+lB*QrZSR)gNBn5b$e#-x=A`nSBIWs9rY@r7<y1%e5lIA
zoOP}PX0QLy$9AjX$G6Wtw`?Yh828(AOHbWWC1`6k-tw~zVontvQNW~mB?^UtvWOnL
zfL;rT0{=fozK5x;i|t2j*3KO%)_MR95du481#Z`?Sq3_tq`W+_;yd8Yck-epnqfS4
zO)ng7P7o6^GxFuA9<d4q1r^_sTQ36ji7QG9+!`&jHX0ZQs__)HE7y4=6+rX<OWg-l
z;=ghIKemplq*cn~i~nFr;F<?#nW7NIo__Kx%+Azng~GP5%I-y_R;#9+>bsNM_r6l(
z_>N&?Eif8nB7NjRZ7TE*l8g%DewKi;zwxb+12Zsc#EfKlVOQ$ECEvc+*xRqY?`rrI
z3Yk>_D?7(w(aCZ@Bw@=AB%AoycdbRty?#s~UXA1hqi49Sj=o&j<&VFf=7a`9C`(bS
zzXR-R4?vt!?b`d;3f!aZ-iU#k8w^fwxOp`mMGkknW$=N<*xL?aGNvFbU4$T4N5KG{
zYF9qYs^j3SCd86B{QhE2n8v6B1&<Xi`1Q2baa;t)WIO@#zyVq2T)!@z+RN7FSye38
z+7ctM@L#HT5MT>!$F;%9zH*p~nKvlczEfB?pUDa>FMjP{Y03A)lvv^4vEsm@aieoY
zen>8@x{!PI+gz{U==ZiKJw+^Ttt?_vKS*yBv=EURj<v!MgrjC{5d>0JH+5iA4Gt|E
zkTh9dMGAe_{_TzR{Q2>W#q3Vvtc*X_q=<Mq%pc4^1T-A&p99iC=f}S>j~d^v2Rkih
zi#c)%G^+ztZ99!vXhXVt*$Tq4i+KcrsoM-FxNPjeBJ{KEc)h<D;n(AcOa_k$#=ecP
zn;bp6q)dj5r6&?F%oldK61t<OA9N#q;I;eTsiPsVhdmoBkOw_$6R~3#fdY<0z5sY!
z0PcZ<wsql1o%Lp4R6oyoJ<o&L&<uK!k2fA*0YJTNSOt<34Q?9T4{U=re++sazOFew
zeMDEJZmLDMtUJ96y@y!v)FG*=CRYIK%mrp>v>X)K=bP;y@9VcHu;osd(}KP>DE+#2
zq-Q3b0XBx+pq3R=$TACEXZ*ApAB1*&k4`WZP~}d_?GyKz&^$c=j`jRYc|OozQgazv
z2cm~RADlja3-;W9{g}z-#Z&{RS^uuItUZv0Qq)dJN|I#Ki_GqK=Ja#D;m6`0S=R!T
zQ0AD4lQRB)|6hRVG62EoVM70PR~~*BkGVSrq*n@NmLg@2U}sa=m@^%XQhS-wL;5Lu
zGZyecXI;{g8nyvRn^m(GfFoyr*~A9zg$*5tSebpdIz4}|YakFrofBYPQ-1=sln23N
z)n7C_>xB1*l6v;B&0#k@fBReSaaxX3x6o1np`Bw}fc&5zC2~#SrE-Pa8c4?^8Uk)j
z5XJ4^X3u@y{bVDFs|gXkHw;S^sf6=I^4StyRs~y;+VSCUKMpR~^G=mPqfv94jy;P9
z%wUqYy6AvTcm&j>V&3u<0pR&{KHeDH5{sV@3k8MF3|<hDtV_+v&5a)3UL$nT!FqQ@
z=!X>ieo|rNmt$FWKu0J3`<&Q62CF_ANU=c;y#n7|s8Flb2Z8kUr4*CThtFUifoUJ^
zTUnlgQNzJ=>W%A^;C<cul1*rGv3~Fi1FKG(`9vb)#~O}x23BP=kQYh@FQGO=jMTP*
zdYG2+OCmAvB0?g091~l}3|30_Z;Shpy3c!{!NHJ=yoOoSDh@1&M&|gw3@{vK)J3oS
z_X@%-%k-CP`yTNE@vtfuMTLE!cWJjGL6+sP+eIaHA0X8`NE&*6u*&%A?681zA6edI
z^Mg};3u(aff6P`RG2mlfR8UxV9SuH{!eSCp{PNs45@T`L(A|I_J8v1st(|tGkF&(V
zE{U?14CXXQ8=+Y8XSa<kaOPtnF)dgO{|^T0GbAL=+31;t?8V|5^=roqb@p6d8z>eP
zvW3W(B47n>^l({3eaCDhY-^l9UQ$&qSr~?gzugbb$haNRn0!y#R*5h1ZvE-6V#5F1
zW_W=(7%e{Athp~aVM|-&uSo30=|99pD9)1?J1}l<p_b_zk=bQR$<%*rS<Tqq?)DmN
zlv~(L3LMc<iYT+~m5Dm%2)OaC;(O_6ol7LDk@Du%psmR<*1p*==Bi0FW1i{VSvY5Y
zD5=U?5lRW7m`T`1thq7Tp<3-K=W91sLw&y9u{6!T@okQ#9R+W2lszOS>Jjj>JCkbQ
z)*77{DSWtpg_BmI`HQGrRM)4gs}BB6xuYg3{0XrAzQ)&V+5i1W$nk>!iBpKR6)=%p
zAQM3Z%_k?HLLg8g&9pi%(ZFPGcd$CY->2dyUdxl247I}0RtM`aSy_1@MQ9>g)ovew
zu{KJl;Mu*NO=kCL3teS%jy{Vf6)Rj@K<v1xk$Si&_(@>%Z)t;^7SbNfT67P;%KVKM
z2hR~zae8`MRjH6PK{ADYvvT$T1Tm$saj`UtOY1}u`0nIXFgfR18yN||#x2u%Ww%b+
z<RV~-ul{hgSu*W@-*~U<*jl0|;=JQdBU5AO&VLRq#~&Q{_XbohcLa@%^3S!pZ@QB{
zRW^@RYqsgwKe8p=>-Oc6K6_#8qh(4<%KlNpj!Q|UwHFgkE2+<zk~p?5nQ(XpYHD|l
z)qBvZ`Rv5R$XVvHqo;$MV{=2>&CR(MG9O|4shsq*xjJ;C?WpxcfdO7os$Nw&WC&k<
z+h2rhz{3|V<H7V^7p4h?tonPyJ1LhHvT%?<3<fmSgg9L#j0n<+1{zp(EDp_bp6{r%
z->55^{cEg6gLU>S)2LXfy<JNx)Cxz&(m`cGTADaQtK|&YuD1BbPmV0bt%K#AE-aXr
zPCKofGnAKS)_Ifnyn?^c4qyLGaBhexFHMQv9Li|ygv=0=CYm0>*KQTJE;}-JwI7S%
zoyg0VP&hl=sJCX`(<Z)>*Xs2nqXT|5t6{O30Exu*U=JZxrgtF8r1Fs$;@p9BRB~~Y
zWThsDA(3OtwjGjfxt5i3a&lNKCOB>MzPOCNZxnnf>7JQ^NZ=LH1QZ!{I{%paUPGgu
zs9FW1oK+SpNRX_U&&x6%hsNO(692Vno6b`xs0NHrcZL=H((=4Ba4>lm&<fgoC^=5^
z9TvU`?5bkndl)f!xv9P}p6R%=v~?Uay+i|&#I4tX*^eobBNmTi2W+1G8$?;T?#j?B
zcfdqgl0(bos)G@HyI#9p4UYyivpn9wR!lYj@wx!W?2iBC%#T)kzh|<M6<h&0*?ELq
z0<56UaEGS?LqdS_>&Hy<oBIobH5-J<nUC0<8pdYm@JFwIH9yXOcvh;>Z2S#Nqxn>z
zSd!h?ZnHulcp3C?+QQ!p-Ae?;)i8XuG>;GiZW4GM5{`C=4x=FI7RTR$zhfBg*@{$F
zBq;<Qs5Nn}@I~BD7Y@EiaOw4G?NozgOzxu;ZZV2bJ}<Yk>5iY+n8tuzzL^|mtX4NS
zX>i2SUxfL5cnOB&zab&3gq5_wrii(~wTxhxKg#{R;dR<>*fTc7gn)^-=RasIy4$yC
zg`=?P@}oq5zF4be3nKQ#F=rA~=4#g3deBx@UF*}lnI5nl->=c0i;&s5r%64uIQJ&I
z=N6Fi=0?y?5sL!Dcj+A82aVge_vpd>iwisY0$Q*&<OUj|6MpA<7&8$$a)R&oe8;a3
z8%5p<7}hc*VLe~|2Uc7CzbM6wk5-85C@9R114L@jCWu5$0A$qQ>5YhwgXaCsCaie=
zPd@8h0`hhiE4L!ksn*KW>&-UU{+CJLj>9e;#Vh=%Oje7bzs}CEwTwmjoRmc>;1`ix
zD`Re;q>$Q9>N5hLbk}5vDJxuwhM4rNvCu=F(hxwrBEjb?2Z3fchK8;u1;Oi9zhlFS
zqeS8*g)d1+Az`5wDD_RDKWv-5ZfyRx2U6cltb@tpi_{8%vf@0*aM?mwqHV4k<3WI?
zVrs;g4s*>!#M^Nxba;<0X|Cwi1uhP=$)+ja{R1%L$J7tZADOV`0|l6%gLVli>8F0&
zuccgeUHs^||48oe`?BSG?Dro+>v}|(ofhIx1@TwxBh`zKyI%{~ME2lus<oVf*#2Dj
z9{#e!P0Yv<&f&S+YD3*{_l$<E;tno1T6$T0xyCmbTf6>1)c5+dLnGvqeOFQhFNK)y
z{UZTNf1V6bh!=dL)b+iF|F~w1H-Xx_hP?x2>&jxwaG!Wh-Y){743t<ZARcBWN202=
zI)*^sxh2u(ry*fubej4#3%PwaihKJASML;*id+d(B`u(1nGmTTS0w89dTDPxGF2MA
z(nhVZcl-Owc4qiWt9jqfd2kd~r<3!(J_O@%lYc>{pk+EMj<I5_+)(>Us>Ab3?d2W3
ze=x5}8(z}oXND;i>c$`RU&+KN893&%S&{8u?=5dR-goF+H#yBrM%!3u7#KvP^avbd
zMXpX<s=$dE+_l<4n{Qu4LI%C)ph2+=e!ggt4<5jHQDLd;jG8Zd7T&}qCx`_Q9_;r$
zkI5WCKqjYO4pt_)MDz!e@f7STvJ#8a8mrH7IF;)6OMS<ei-zIZz>tZM(slvO)Kr7-
zySmTgf%nTfqhGmXwtx#pmM_7|$|}u3YTvpJkS>j&HKb?xh{^f`&FAzW{1)IK7(m>7
zN|a}lR(!BWS#a^-rFi;eJQkBV1^Nv<d=K_fm7X5Xxf7FgCM&2qgju!QHqJSHhW}Bz
zcZrBRl_v8B>|6>tdvW~9b&3O9;i|@Wn$_h-hjc{ou3G0sMLGB4vS;&q`CEUq<FPlY
zmaZ2v2yR+bgZ<>zO10aLWTR0(GVRb)tZn*I(fBAUc`^q)DT&SudVOpw0d0k*Cvnhe
zyc^L0vazJD9Zs$3k5t2@8?DBiSET+WD_Yq9v(=fWT{db>+Ayc+E1VX!3Rp4RSW-|!
zP+@Iiww}=}DJ4_EJ-O{Gl!tVWgo@=iZ1iIT!()=rxaW2IqnpEl_i`)*S$0BE*ce${
zO%+fzk()j2hHWI5&w(Dj>n-QIS#^EJe3X!uHM7rW&vT8dZB-%>V7IuK=>p{W8!kn>
zMm|90Z*`oY+W!2^|EX9np34g&5wrtCfdYAeb}uS4DnLH=o4bt#M_pZ>!TJ6k;le8K
z=U&g)nD`O-fH#S4D$^8TuQv-^L_B7!cFz+g-{%>09U9n(b$1;R8y^-qRu$I-?T1`0
zlT-0wp(uy{jS=p<sNS2?iP@P(Hvjl%rMvQSHiy*;b)aO7@;v5&VNmev7S5;5iT)~%
zjOQKK&}J9s)nGV+@x;>#DgmG-U%vdJX!7LQK@(O<D@;Aissg8ubXlJq1in;8Q^BSo
zlk0tFmLWm!wz11nLPlAMY2QL)WjPH>UQALCR?%d;B2$3AVP{)Hi*}-ZeF^7W0m=@a
zWP<p4R0G>_@L(|J>#-A-N}EQ?&L51S?(_{KLaaA4gf_6+pF1nAtA2$SH+khyqn_)U
zrtWL-%w>p}7q+N!5=ZzE*yrC3I@OSM7vD4!l@m1sTYytwO+{Vy;a(Ni3*riFP*;`8
z`y^ZX-j69vPK@e(-w61e{CRCLU<eg0jf!XWxscD1xw>KU0X<5{NeY@u(pzsZBGuEb
znPR~8zF>nqI1UI%odI1yMN4-?+|SPM|4}W9EGehr{?9QDeX_??o+yvyqCv(sh^ik}
zTTK?+nl|k5Sb;eOj_BlxFp5y{5!dG+>!C5FfF`S|g8_SJD;b64y~YxXMJgdJEeKGp
zPHeW0$9K1~%;oUU)OMpuZ6g$6AeIiF=lRS@h#D+q<1xpt<FfnNcn${fGyO(F+^cE{
ztyn_pS^9^fIqFEySV4fzTCIp#ZSn&&1^}r$-DExw7*tum@Sj^eN0bB>{m(2vuG;;_
zIQw1sftCGO#L1+jy!2J7Fbxs4VSZYk{NSU}>pjrvM12n~f4{KS_1?3PX~(wr*}kJ}
zJ%2l0tx^sRfG$TjTMoH?TrYBFn!u_PR*9I4YuBe}G*1<Ql$2Q%_1RK#*lq^h>^+m}
zx-Dj|cQ~Q`T17@x1O!;O$#?jT#uZ<c&7KLCz#uJQziJ3}LEEU;3W<S}UYoMa2!htM
zTY7<&sAuD6W@c)(I+L&3t;ghl+#|iL8TiFl9*I-UgCZRjN~2`W&S5gRpZgfkgD(3~
zr_6A_EBic-!QJ9-$DMS?6Y<g}vk=`88!MDc1x^$$Bxk0Eis+=7JQsR>Tng6jd+)tc
zh{GQ*F_x8(fmN|sWGIN80=t7`FEueoD93HsTCbny@=qZ8p7%;udyT2XmFPazWu%F7
z|B{ShYgmFAwXJ*xV$D{HEONc6^}U}>eDZ_1EAm5{IWCBtG~9SPxeWuVn2}xQ%+?Gi
zq3caj>ak?f=_R0k0qmo9n0QhimJDp&!&#(&Xqmn<_@1v_5})%i+QeD<s!@7H@+YW>
zuS)}PYfGEZCr#Z9_+o!yWt_U+lWEQNL)2}*X?7qD61M=a?r3DbDHlvl*<bdh{i@xo
zn&MEA=aCXJvWDau-9|864u|63hu^m|{a<m@jC|Q+zNDI=4I(_+(OxLNukbd}IXsze
z^@SrkqwApTh~>GQgp9sDc<s7w#FQH8gQyV_KPYKN1*8Hasz?l{YG2gL9bIJoRce)G
z?cb)_Z&#bY$3EcZ6y<1{HOMK1=#t&9;+dXfH~dK>9*#2~ZmYwi9p^&R_Y?QNQS$3_
zLu-b9VF}wQV!%Y)<fijROW*2(vOt>imnguJKA`f9)RDO>ZrnM~UI1uL>vw-60UKrF
z=%5q!?7Q_vTGOBAEE3*H=|8S)^kI(bxocD#%|yGI;Qk6@o9mr$qDYz9{IUCTZz4*j
z7!|qajLRcDkOvDB|JuIp&fdT+u%6iKpl-hH7W@Wp{v#0T;CCY+cI|nsoH#=y)HIo&
z5%htl9xkIU@=E-a5FZnV2a5=KOww+gmh?DLtl;?hojNgHBoVYA^gA^n`96kh6#AaD
zuG5<lGgZplKv|qOj~94GZ=#Jg566{>keiqrerf+jVm_M}{=Dmj`u%lk&|o}^qqdpR
zc$7N*1A3EYsI%rLGe5YjBfI|=slpWVbdtB7E4h@Uq@w%I#gy~acCP}z*L3>L0K%F<
z^zUX1B7SnI+~lTXY!wrZ>7Tf?3AH@Rc*A|U5l|1Ebss-w-QDrf&<5@0jIeY4H%lnx
z*r@su1QMU;1b5GQM}u-wsCxDfVOpcgo8xi*f{>9?b7vB*w2q?Z(u;Xg>EomgP`xEl
zxOeA<<d?ypU?7gH-Sj~>|H%!r$p4MgPdOls!{*9$@7%O<hz_cblnUCf8Z&8&^q`S@
zwLeWa{ek{d&XE5~*KU0#vK-jXzS{6tJh^@9_79lfb4E3n%?0^=GR+EBDDKUwiJWdA
zBP}gWaYnckE1V0zl@5bt1uwrE#sC0L9dJ;1<Rnt-f@y;_1c%3uXx#MRo*MWtr_t$$
zaPIk_GWNZx%TSO?#EW@pS2;JOZOCf9lJ(y+L(YGw{f7H_PbyH{<Q;JJMdfh))`e5E
z*-%KnSHJh9r|b0UGm|5LklDg5#qt0Pke-D;)>fPJ`_hUGjmF3>C;jMdmWhTHZBB2w
zU$$Hv|2vG^Dax7==Wh8h)7W3cokqAjogDk}auhnQAG2<-6@Py#3eol}n7ID%^A~+$
zpi0?1H@FF<>=3$!L2&z?Cw~0L!93OObSQ{yaCoecy?YtXXN8(Pp4p`FPbaG+WCX}7
ztk8`sxO-X_%B)g&fL#nF9ShxNeb0x-U*yi@lwjWe2a%cnE0mc-`C+Tc$cvhdbTWS_
zl4>`AxbA+uz?)`n^q2<fr48^Hi-NM~5x1&#>ekwF5$mb*Z0WV*XpUa9*9$$(?~PjD
zZzrO`BuPz?D}eRxeCiL)5azrEwKq*9QKaK!*O|CIT%*ex!2|OX@At)}(`u%L;oQ{*
z7N<KKcnKCxCG}ZZ0O5n;EfOv7^3_AVsP!V<Qx1q%Ns=pFemiX73z=yNjv-;q;hk8R
z?$6q9@4Y@aSnA9bRi53({g;?7XNow+4lY%-HAC0M0gSKkge4l*qbNXH!0%Io;GD$r
zJ96qQmE+&*wlXIZq#Fg}XesI|xi1Q!J;jXMB`K~sPIHhM%r=Pj9j_QQ+O5hP8id|&
zT31NOomV}=V75>wsHl^SW8CNqXgEOwH1{-&-ica^w&aQmuY>#*sYG_f=V3a1A<GKc
z<MhAFOdcx|B@bqL$$slAM}z&WG3Xf$gI!h_*4vvBQ&;CPiwx8tO<9=^82-B8p?o{F
zXiY(pIE25@mLI(y@Hx8LY{KE3&ksy}3TF0o#PHZ<1DgoTW~uZd35T*-s~Lk_^9POp
zd^LJ3CYpGS6Qa;{mE^v+LWVcLvd9@{84I9e*p1)e^+3jZ-Dt$WT&*8mwIf#E0!2R6
zbH(;B#gAVsmUv+OF&u^{_RB91CvVcEO81L~_=YI|dvCWDT0$-N&HyE8_D`!6(XrUX
zA5a#OpAqX2Ff@QFdKI@4QfprXbhjFJTYUV?!E-pC;5c_YixGLdR`oAMn{ss}Jncpd
zu$TQ(CE+0%p&!S&wn;m5kMDCk&EBxtSET8tPW`Md&%3>+d22U$pMO32T;`rDsj3KX
zN>{s@6yxLB^iAfgq8#xFy?Vi=pMApoG)0oPByBJx=iQDA&7$U^eTXPt&;{qQoWBWb
z{YL#)sY8gX9cq4TBO-orBpmN-I!C=-7_o7x2=25a#d<#*pU%xss2-;yqw}`6Audmb
zIf(t{E>l%6Dk?%rrc5V_U-{7md79!c4;#`fRqrj|a>DuY3|FdtB#4MeQuroB5bckC
zo#EpsaS&XeEJ;E~Hr(&ikk^_IFYcOTC&{6+`s>fj=6P58m*JbGxJy7zq7n(tZh69S
zR+YJsixs~W2R`@4#?(S+{qKtrK}$s0{jTIH++knEFY1!u*pmV4I5O`mcQe2Gn(@8`
z@b=)~ETIzC=i+TxRDcrusq&kOOR`!8VvCEQ9p@Z_v&fvd!gfj1QWDMls!P7HtY;8y
zbWFj+4>0|qbSY)GAC)yTzn*kxcw0c2{`s=IdlzCaDs{*o61%>znOMbk6u<FSN9qEQ
z5+hjRk?g`4hmvMZ?b1R>C&x1Q<H$xQ^37nUpWhKOGwn?#py_=EKIVod-DR0#j^kN#
z#9#7^j7_bdj;=|03Dl0`$`3g3NfB^>7Lnw4S-&hv`}3EVi$R39uo)AVX_-0E24$N>
zK<P<*mO)&n%bC~3gCZo7yvdwH1zX{@gs^?#JrCiXddr>9%5;uJjfrKQO~;z~_Uvce
zH|(nhdR0Bn3D&T=e0^}D!;60f4}Siv8_OPH?PUk0su$!B8}><7?TT<M`Gz^8YtK`2
z6UnXvw__KTY5ZSA+7Ki+)#6g(U5=BSo13xvU0jqa1INkq^eUMkxcq0uv{Dl+JlkY|
zGt!^&zNu?tV-5vMVXO1U@-u3e`-hnDR-HsdFfnL>9Tcjon+x0FKaQH0t=fESpF!gv
z&7{443H`*<fjG=k)bf|k76#x>%uNVO=M*!y=_yHLLNNFMe-ifmlbW+p-9BNnA9G#_
z_gyLt2Lbxa(DrA9gK+7SYmZ*Ch`oH;IRZ>;)<A*`@P;s`fiL8$+RtO&hwr+%5Ne=f
zr!W7Gs_<ouiYVh@!GZ#a426l@VW0TmB3p7av9+&X{t!t|2F0c+zV<-?um1^A^oz(=
z7{E&$#w(!`SYWkIRg)Ejgkv))x0^9&evRk>0!RlU(}l(o_F9aFlbp#=fmcNkLI^Wl
zapJDPK*HVhpJYCqVLsv*S8grG&#5QCwwtyZaH)1Kj<6cgIv~3CRSdyOl+Bmq%F2m>
zmzN+zK!6GxN(!4rv-D7EF#4=nTCG9b3Z)$-PsU`%ev;gM>FpKCo0Zu@gkkp_Ll<Y0
zN$9u_lm;^FRW&=O3zgk-siyE6DR6|iS%>|<6$lei6|xXplb8uIjK>j-7K((Y?hGG&
z^Tw^Nax^Jz%0wR~CrF8$e0+eVq^6o&tW>(XC&9r{P!6B{Vq2mqKOp~S--I!TQV$3O
zU>r@123238ndJulC|{-$mXp*n$OAm3e*ncm#DUBet%xv;8>V`3?s33+Pu_p<iQwt+
z*}eXDew{#Hk)a=J=CKz^&bbpoJtsvs@b=dyOxD&bK#%hFVnLLUIlG9i^VofJVr8AO
z8_{U+a7$5Jx#C_8@k~^-6~bgv^k;+w#79WgM9M_SNwZ0K_T5^#!H9{%hrE&T^sEHn
ziaTuD#B1zoErMK2V7-3+j&xS@ZbHP<<x`xm^&-8a$DAXPhP#vq49*TOE$SXN{FCgH
zptOEY)p_ZJHL8Zj7yu2Bx>T^S3d_%p#U9iEt)5Es`EOlyKbc@sVd6)D{4ZntTaEXP
z;dh(eI!u3)v?j<2xop|<eDysRZu1EdFfvphqG%?J(h<HtpLIHl+@W_jIuQ51uffF1
zWY-JBzQqwh+U{qGQ~#~z3*b8jpx10i+$iy{D<o|JN|WYtRYF~HX|XE&FuI~6I`otN
zQ$Gezh!xqGRaJuRZrpgj2Gzv#kJkqyO3q4pdit~TzfZ4GoFz*1saf+eA=Tx$<=Wax
zD!fQy=LSd68i0ddqziOO*|IQcBbXVqN?a#?fu`og1cTplB`xZZ_SLc5%hx+Q0a_Zm
z@tMozl{FjP#crjVYC4k=|2>!t0tFDmhpf)oY2f?yY;!vwk62J=54x$Xv#@ol)Ezm5
zQ>Rjm5G1E)MX9G8OoH-Jr{hW}@a7pm6as5Q1*?(FGY4PrbPTjD-ah3gmA#r*ML)X<
zR&yjXkE25yC60}S{II0FT53IKpvnh2u!4;QjyV^;ecO&?wbmD9>5jO!w4$8c9;>>t
z;5wXNSvrVJZfg(Oa}s#ElH+9ct6M`VM?sA*kGBvgGve_(%kr}_M3SkpFB9@fm3U|g
z{~7(@&*keDgpI~vP{b(HRG<yPq0YMR0lFY~aiPY_>SoCA&D*x9X7U{I{&C!0vZeiP
z*}Y8Ft*uQ(UI&7Iwr|ktf=_tzEzeZ*;dE|@h}!I1I~!qTz6j!MwGx15XhFhR4+=Sb
z&aDMcm}&nz)R`>5#KhQ8no!FNax0+P>~94IkgTAN$L$Y4UG6)|lcv}Aez8}O+8Lur
zWn9U(mHlDq>2gQ-x?SRVM8LAq%~S<9Jtw?9nmG7;*|Nb^<guf8UB7Ntb(SUb7}n=R
z`WKjOuNhOrW<r@JSx)M^oTLww5*DafMJ+vL<I_*YD@js=8+{omy1=lP9e&@6x~#xd
zB*yXSOa1V02-Q;S0%9(t;a+_Tt*jQDv*EJXo8nj+^#nW0y@%uJEt2y@SU!G!1YS#)
zi!TZP=*^4FC2-&(gnl-lfwtOCi78V<yBA;K!%W1crHF={mD1<sk@1@A`ygL;yHNAl
z1HUHH1rZmcss>RF&1L)QNJ^mHh?9yrPuA>3CT0CiM#J%jw3@hib$-Uvna`3L)NTKc
z%<`8cS3JI=1Pv&DLl~ehDJx`x)-Ni75T52{N46)i)3MIdE_>YPZ$Fx5>>r|+sS9r|
zfwAZzxLWVSuJ-%nN&wQSwTX6EhW_fPh6`e!@sHn1Nr_>*cTnE*0b}fG!f>&Wf0CFE
z1&SO7Or1DENaUK;+rw`3xym|G$ZI+WEh~kD-@^6beOM#9Rw-7dZBhRl!sl&6{^>R~
zx-P7{8$G+-KWbZ;QW~ylacj#Wk>DBrQ;IC1cG(u#%Lxs8>mRI2jVB5xGf@XULq{B5
z(>8|uZ_8kifKtIPk2|=_ryqZ~-2FA{@u#S^H+nT6XS#A|n&P8e(_%_oO*8+zELWr3
z-9Jur+*4JKniRh({ISel=CMo{kr6OY%u-9qq6dW9c;0PLDsgig*jb!BRiOM|p`d_{
z(!{}vb(r5F(Co2BQd{SDj~4s+=K{!36;$2a24$8^+}zl!Xrvc!oV6s%$b4C>Y*^?s
zed+V`pF)0>iMEhsy<L)G0^=VMjAm%SCiQ40fW8ZAI$b~9?kPJlnK$n-3%txsP8}U{
zB0Ms+R=U&jzB0Y7jU`Zrxp+QIM_Kvr#FV5)(s@j%;0Uh27ZhNbwi4pV;n1go6^uTa
z_P-#g0kM)`fG`}n-?!O`;Gw4yY}fMOG8pt~S?c>?J)V+P8D$rtNjWwAQbPyr)vev|
z9xcAzb@i*p^XfE1U{oqKepkT8wq4hn5-7dKpaHFrsnpHJ6L3DyXA)cckj;}71(cHN
zdO>qa$H)9%+S)voG$;*jTkfHMvn&h-@Y{D|KJF0Bga&9}xzzP@Rz!f-V*H<Abm`?}
zeRf`65&0gDt!qA~aj7bE!VX~(9~Wo3Edo|R_%?=7WlV@#ojdV5Z1V~gN}j5zO{ucl
zAoh>q1(lp+?7N|#_qyTT9vF(<TG2$3XzMNTgD<MyCc=SxIf4Y{CwxMY9oh8v*s15~
zdF`&+n{Ws;ik606;vfVsG^VLSp=&Cj(2D%KvLo?lEro1q;!W_wGQ<=vNQR^uwrHJR
z%T0e{q>Y4nt$hc!$H$AhZf67H>+e3VsU<4HC}$L)u`CMh(+Qt|u~%Z3oQRnWV|qG#
zZoNDZ<`2(}IjH95oS+}Ob0@>c#;6>RS!sq(LQI=Z<3haT&Dl(ZF=;86P{_I8k59Lz
zJXS^~UyA<rwv<w1tc9@LUPGH;sD=>KbBC+zyP%n=&5IrSfECAE3$LTw!1_rgSNA{F
z!@>x3fl(8*(|_^HGXFrj-V3jNdRl)Rr098FqP1K^dYgj`9i5nTr$VZb;=_B!4<_Kf
ztftUom?k;~70c4g&E@htJl2}OU`$`O4v}HZ6=BdaH(8CrrlO@46VK9<sP^%6mNvit
zOqlr3ZeviuL{Mnb>&a!wYu{*AHg=<ep^=I@tq8NSvd%4szQCau78;<(<B-ZW&a{<u
zFGR(DV@E}nP^QX?OHGoYVWJmybYMY-LS|&7L-TglafBPXu>;rsKaX&V8)yWphADui
z^;^o>%&I9l?%zN6mc-BHxHv>tH;=;R;^aAg$L^h!*hbUHQbh_2{=$^3tSHy4T!r}w
zp{yN5Z*3erL}-?CZ*g-9mKT8?8BVE!E_t8Tr$Zao^9K*u|5;ZI2bd9ZRll+}r!;2<
zS~%R;&pdchq(T|M$P}vFK_c;`6?pSUie%W+a4$$CDp0yg5=1P*_^Q2wrjsLB1l)*I
z6VGZgId8VxfB3|}5aKen(Sd)`6gCtM;ZYTt39;-W_~^;>nOzIcPrmuz6Ln!=J_rkO
Xkw@wr2y1~~X9JNElNYTMHVpbdj|64Q

literal 0
HcmV?d00001

diff --git a/Images/mathcheat_matlab_logo.png b/Images/mathcheat_matlab_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..1f87fd034325670a38ec62d897bc15c630aa7209
GIT binary patch
literal 40206
zcmZ^~1y~$Uvp0(C;<mU$fB=h2aEIXT8r&_oySux)1&834;O_43u9yFN&Uemx-}~Kt
zp4pwLu3uMGPjz+mOl_FFtT++^9s(E`7?Pxfh~nq(z~`|K2lM%D3?^s>28JMLE-WlB
zDJ)DP?_g_UZe<JxCV`Zd?4qo+gdH-`!O<~^PDVm`i{}ncGW|^g3rZnO(pMfLIFRb6
z2(%a`8jb1?<!*5C5HcfCOvC_x!v*+jRQbUk1rgD`wMa|&iz>F8_4KzPjg6&)=7lBG
z3$KMXF!@)1s9q;^a9`Fub<FdV9&L1tk^Tn=ASyVrI(YY%i3ybWqX@+8YX@&yTB*cf
z{rx$E?~nGCuU+y~j`JTrabz6A6fj^2qI2v#-c&ey5Q$3l1Gvl|P{X%LG|a(F%-DJG
z`ITfl{WVtiCHr*ke^XPacJuHw#&LhMW=FQmucuRP6+}YiX{S<dnihD%8h0p5ri1N0
ze3_Yf%#;QLVW#Pg9#G|3g)xg_;+}_HLx900_H+GqK#q9gGRp>YEjHZ^hYFK4EJj;M
zHQzh@WAc1euk>WdM3>knvlZ$pJ#pIrhz8;K*4QSCf>UZ(r<ui_RS}$>Y0U4NJ#{T@
z5P2)T@&0jtQMsZzoyZo9%+}bjI;~{vzy`ag(99sL?@UqwYp{k9*xRa<yC4C(h6Za`
z&%7ZDw$hF7-v&@4Mx35nIfCa{GSD4zyyW=X4tE7}J*17Czo>pI#^LuPm&9T*7Tqf@
zDgG`pItoBenfUScfcml1&1F9Jx9@9aPB+d~(4mULVIq8Bm|1%ekDq!h!w-Z&pSdj`
zAUicIu6LFtrkS%Im{$;&e2XOPA6s59VRA?c8-8~PuM;g)CWJqozs;)RY8+#j-QN$M
zi4nEKZMLKZ$&~i*0Wm)uDD%JK7HkAs^+2G<!aC(5u=+88AinA&5%{v$L9Ua41^P;$
zLMB5pNI;kiK>ZTrTl{oErbY0d0Ty;(vwrTAP_q8)b}-sdYTZb7hyY)40fgY5?;!AX
zKXW+$OH{0U!SYz_LX<S2c~pE&I5%NIc}`5o0pYM%)OdlXNvcvHZ9sXRusrWAxf5O+
zoKC=VF6<-;E6f|XS0Gv{#Eb#bB3P5}QtdYaIPu<*O%YeDOjw-ml`W|jY(C`XUYsrb
zOR%p&dc6s70)OBjr^M9Z@UKLKV$~_Y(MT=h$O<9KMAKuD3-Mc{B;xFMAz?!D3{rvE
zgSa~(8j)Luss{6hQlPn1qf}LCoPBl^R2IJ{|02V=x?*+eO8%el>}VNL^!=*(G5T7y
zDRq#G2B+T$kcYZ)w+ZaLTY%ckHf+^U%_s|z?Y<{{NLy4_uD@y5F)#d4dK@neJxMze
zd<cAqHe!eeyQpK~HK8p1QAm?XD6A-yQSsmo;NAk!dRZ0xO68o$f>GW2wGC<Nq89~!
zOA=7YCSXbiQ{X64(4;l^DN9<A=}_@fb&w|f<^Cb=0MroBm8+)6A<?5miRYJ3{6d8(
zCq*DCv?R+V)hgg7;3iz8h*WAbt6rYkmZd4zA>t+F#VDS@l5&%>^S3IYBF-}DB*lYL
zE^#5nB}I=Rhgy%+N9w)!wFN?<N1#W^ub@^nPi(89mp4dKG(A7bFw`*JFw8Jun{7aW
z@^`#nd^5USnZQx)G-VMfp3qE@+jL8VOau3_@iK@|oTKn%CgEt{miVya7T%cBSZnXg
z9>E^oUj48lOfN|Pkwp==C^8|kJu>Eob-#6DXJUoD#$5N~H%-zLtqrYb5?PW-5_nQf
zxwMMy9OhhBxpleREXab&!oXt5;$@DZB5I;!!X<kmOON$q_ci3Wwk^!P<=Nt{@0j;E
z0WTe|6Au?p1uunlkPSE8CjD>vUity+P=lE!Vk4C1p5{TLE+{GOd{o(lvOa<<ud4d2
zgno*zaIJ#3%BZwn-LycqejGz5o<WO4uvxoV$wf%+JEJ<IUX8*<9-l%^RZd}-c$b#X
zaQ7t1vVMeMMo(#8cwU!5wn4#`5qWuB#T0FUX2G{XPNOFK@GHI@EvnkEEXjDusJ}8w
zi?i#>`fU>p69CJ7Gb+{!w)Ua?+EopWO0oG|u0EbWo&|0hPjra95$X|H*lXB&U(dcO
z(Vu)J)}YaVR<~|Au6^!X?oYSWG4dJ;w%9SMA1_|pZ>%b#-}u>e=JhRIr*;jyUABGV
z9{HZ^h3bU~mN&#Tqyzb3(dp-x_CT*Ww3EZFmhr2|9aw4)>be=pl8r2lahh?Y@d88u
z&Lz$)J<wsweyfGRA&%h(L!_RoUEZw9O45$z$@8epLe#;}e(V0VMc$>imKQpd?<jHN
z5krY32?ux+h`OOEHEMBcHO=u4h8GqWC4Ag`VSG`17@a>mE4&yV4c;K$em<|=EL<Ku
zFTBV>aY9wXA0iY&i9xBt@*y(AqeDAG_4R=FwEGh?PU}%<&tgLey$b&f;(=!a=wLlN
z0vW!tR#Kh1wWzpyyShNp8;Xhu_qR*L{F(kE9ubB89ot3hulRuIoVbjrhiHoE_Z0Q7
z8uhzkk(!Yt#k|<W_*fnkKOM>*rq{lQ?3`{R?3^>OXeZa7E;}^tm5oeP$LjTH*wYf>
z6BBNreuQE4mhDMb-|Drx>_IIgw1>RJ9}LnRL|y4T8o$Otr3C&8oDxat*9kL{td_)*
z+<}l2Y}|DIrFZSOTb3A`SU|&4HdQue9@y~MFe5~15ME2C!@^75!Pv)I01|E+HlI6J
z!N?bwij)50sA1Bc8vQPPClN`JK^dTdSa0!b-BjE-XINnrZU1&Reb;7$@bAsv7jHHb
zHWz2p&Y$nWuRUz%mdI5>^VQlbt+FmUJ#AZeohLEYB-Rp3mm^{$QO5WiHsR~D3b_ik
zo%&|=7yE-ktwIW09rWW`$W{YZSXO6iCiPVoD=B2UQ+I|V+u_@Cl&kS48ya3}Piv$1
zfoB~>_eC!*n?K=PR@aW&Zq(BZHFTFIt2cG3_C~MsubbP|PCHc%wCwd!%wvAUG%%O`
z>X53j5T7eKE<E<bi%H+_IQD2h%UT{<S?JoZ_L%{Xf*gS5Bf=#3?ephB-m*&2dca_H
ztFmw6B>%Xr%hX5DN90W6DsDnNNW5sMH^zDH+w1~IDMuoEZ&dtX-H!c`?c3&^um?uP
zkvvO5>mw8=Dc9BZ&?E6hHh^czE!?a8i25z(zG+f3Tr+Qebv~@Akloxv+GFF$cP5K)
zoRFSB<HPed@;)uSe%SQ;MBF%5W(HS|i}$tAV9-@W7)}VcniF4J!|rreb#3+fh+j&L
z?p3Qp*-FRDt4d0RX*HM5H?J(Oj<evmsClCH>}0)DZ}X$Z3+s*}yZX1}ovP?gXs?53
zofpRS`!=t==#MOX$OxF{bB0cj58YMXBd_(hDcCX;UH(yb&Ns6+qtl&yY6Lmj>~j99
zx4Fj!ld_K6)7%C9iTY5}nBW#+#*d_@8o55`iLwbgu?DeY@hb6&$fw9P_qyAG%aO>G
znVq06n)jxMq^XJ=Z<m)2Q^UXgmzGl}Y5Y>_rJY>Q^Y_*VKMr?iT+A<uOTyPHI{EMY
z-tu0IZinKIPURlul5(Q>T|D7muU)CEP4+h)0&iZJxi!2$b4qwS2@OXuFy#J!4)7#8
zWPC6%$Wn7<btiQhX>KE1YX*ZKwuZ(GuGV&+++biluH2ue*2Yc-B(Bz0Hjdn`yk!64
z;Ql=Shs;Pu@-G%AOI|W{8F><6TL)tjHU?G(5E&l=2?+_0!w(Z~MG>+8hJRl1l9@R<
z*>N*6y12M7xUewTI+!vtadB}mf|wbZndv_{=pEf`oD5v)Z5+w}tCRoJkBG6Ok%PIN
zlew)8$v^!X7}`2J@sg4KBj|rV|E;IB-Tw%(ar|$qKJ8<4HLzo3VgNDz-#||0CjYD6
z{|og`%l|LT*wy_12lh|Pf5HAW82_as&*zA8%h>)fH*pg&a5Cm&1~IYHgV^aotjf&H
z+#q&tW)>dC|KRyw3;%@{b}%+@vUO0lwYB1tH+MC*vi|3igoT0mzmWgl{%_EKtl*Y&
zFgN}j-hT#>kBNuz|3mvveho!qM_ViBfAMp%|2N>@iT?=@wzal%Fm`nOM*!P@^Zz^T
zKl%T+e-#^Zr~lFEKgj=yQvcs}zW-6{---VQ_~$lo|7)DoMa->@ZT_ViJ39vt<Nvkg
zKas+=R<;gGb_Pbq|0wq_$iI>PllxzCH2zl(Cl}Yh%lWsK|3>mK{^QI4=G*@@asEa9
zoH=|5pZE0t%tAhdgEyKPFfaiyNfAM1SMbvgSl=vh=Lg<qD?PS~rFi=9zl@FWjUvql
zp{!;g15t(0C42*Mt~)NNC{R0ZQ(_?`J1;v)0|O}%!J<ifpsc!~t#+s$)+v{0o2-&_
zs+v;TJzOpxKC%wDCdPHwbgJ5ZY%FDcI~z@NGZ{~FEath{rDe{uIi{Dl^{a%wPOCXD
zYZmB$u7p08R-g`aM^tVU$f7C7D<rCH7O>Y#*SDRC(Pn67JS^@eqFH@WI_Q{l9u6QZ
z6maG(sSsvc6{PK?N<TUVyY!nkqFQC6!w@AhTjIjf%`p>4(tIGw#P+R3PUL(jp<?2?
zuzjje#@yN2F|e|#G~q3i;LAZlLPDBHeNn{qfwNN*bJm@X7|7qM)_D9%W?Jr{q?BM#
z%HYD?fPsNAkF0xfw25rObEf$P6F|9VXwG=7sB;0l*PqYBW;m8-NGX{mJ!gEKyQ>H)
z;T~UUnZHr#L%(Ov#jvH`erSHhO@k-|BCNGJTRv`~_?ST<K)T{30daBR>FMdW?rMCz
z)LR|)D(QBT&X11~7W90)T<@1mg*`mlnPB%6$Gf`)T+b}^@O59eaaJHbh^9@MnO40X
z)^Kdv&zdF|7gO1m4-d`EtgVr3Keumv(?#DOG);#VW@e1eCO0;0Bc)#RKn+|X3baYR
z6Yr-J!85Yo#(Eq6R_nC)Ixpzp{5)U9U0z=PN~E6pac1k|!TGeC@bJv@qGf6ul9!if
zch<NNJeI+s_09ctb5q~72T>Q5q4ROO+jr-xo@T<p!^5L5ienwvWVOT`L}an$`>qf3
z_06>xo;q-EBqhY{X5ZNB%++$Q2Dbz-gU99s9nt!*?s4>nduE)Xle~59sUd<C)3<6r
zM98xJcJc9k5gxh-a?Qp&EEWk9_*yw$@%n!8(Yd8YeWoy;q^7H3znEtCEfOYVctrP}
zxvyTo$3HyO1tqg##_7r)e;lenuy}fP)vCp|br0JEJ8F2}X0@g6x(CI3a@se--U18+
z%r-7QV9I@ay!z?79Y%fcIyhJ?Rx#qS@v=Vre0NgsKH3BvdK+b#Q09C3D~HE`^g}7z
zwz%o;{jD38PkstacS=74tAo*+si06Enz;`y$lTlpek7R@v5&luw1j{3(-0gY??;1r
zThAT*zSUO82X3L#CMrMF9t(j!$Ne-@7%_deXnU?{)4pJ&ebe1loSjRu+{<oA6!rPJ
z3bl~6c9)EP{Xm^&8=c1^IUhXleN%6$(KHs*8fO@IR~$xNt4Cnks|J1kafqoCJ~i49
z9M1REW2fwnni|H_#X55QD}rE83Eokfl1y}9R@`nW^ST_e2T11_$@}@V2bQ?ORIA=Z
zM&LM~zM-*MW3{>SuIdu8>k^S_&5ZHd&1bZjuOX_Uu_VXa{qXd3)XevKEuR(}Q8a26
z%Q9em99j4y%GQ%y|Kl;{gVXc2fSBzsQ-%Yf$-MLy3N*_kJSt;Sf#B}|BDdYR%ULD4
zp!x|ukD91WB+-%$pN}_HVmoIiCm^34WIg|&{m#1ELDm*E|C1!=Reu761>SM03ucRg
zu=m-5_t-2;%!6r_3OlC)AqaHg6dWBSVNQLyHFT*BTn5}l)rv7i+z@XL4I35Xp{Zrb
zW-i#favq@!$t~wGqY^`sj=ImOsA({PBwSpY=i*Ftnyk_WHa_RfRxctW01<~_)wDSD
zh0w%^q@W4}&~0chl$<Z_XZb@vxw*Q^V+ER=HsycOFe;AmdEF6I^qRj*WmD?MDk41u
za0eqye>rsc^p=NSkM*1ENSZ%8S>gjmJ6Dl*IUlbFeXmb9kk8!Qtb~!#f6UxSU|`yx
z_A`)zSU~;90I%1pp_NeTJNiuLc@36vwBMZ)!Thg%{D7{vl`d;%sMbaq+CL<Cp}>6c
zgO7cVE?k>L@6nqn1s;6Dx3me*L10`{zb|n<3;9s51^=cT_fa85=b){|8Opk_btR+H
z*ydmA9Dv@-@<OEq4DK_6d-t=i{frF9((ArIB1392j7fR@4E_Wo+q78#7u{`8V6pac
zoFB5aP`QWeeVS=szG#HBMQ<i(TJL>NFPNj_NM=i=#yts(14G8$%VZ2Q8$1O$;7{pS
zsP29^*?yicDnXLiv~x~aBq&ej-QvVvv)4>aLKYH?NEkbkZ5a9Bqt`?D7<S6sV?kd{
z%UBJOzIT5^6B>AZ9O6g5a2@&<Sx28g7_$IfoEv9q)MUMaZPRh5YJ?!h85(R_!lHMR
zz7VL^^*mQEn9$=&_zTW$b3pToX;kp4uk<O?9G(Xw7@jxX4}TU{>`tXZ4H^b-c0Q%+
zb#F<(p7AcyAP=b!qnyloLSz}~>~C)$o!-;%<>z!-K8B>X+2~w@!Js;c;k{Sqd{}j?
zhX$YcmX-16`bg0<G$de46%JpBBC=JDj#v2ydvJI?ZJ?iIEc%Wy{m3y&CX?R;3^8ao
zPq4@N<*XThrb@GMc$=~9G2d>S>i}rLZ9g67L{y%0u$JSn^X%rmsy;w=YHshD`CxX}
zjw#&rG49f7G*@^r^`!UEwjjyN%aq`s6ehCGM8f-aI|KVFVV}{up>t^RH=c4U>%!C2
z_0!QzjeA4@p6i2^7TjLr#mp}Qn96WAfsP7-BtpvZSahm^6*Zm9{kTC&1~tFcf@@<S
zv~t$*I^XYVR^Viej8NUy!I2N#On2Ogk`Xk*2T1(nnfR`*<N{H=4uz?_s|e<Tp8NVk
zJ%I29hqy>bJmQTq*RtrHB@=;weJF<Ulc&`znvX4s0-{^Wz=6^co+weo=hOTM1_Ym5
zaUV?Y^8@ekY@;w`{Qb=DF?x9KZyO(Ln`Pv0+Y_x_&Q$zoL03N=dqdDvtF_I|UtK@8
zOY|O!PTvd>-~hZhNs!2m!%}-Rm<dQ!VmeN|($2<kGXkKgf*8tj4EOgV&5(~Iy(cXE
zzvi#W^VqzPx}`Hxsjv2{{U9kF@25TX_`D`F+&Pi29z>Nyq<gXG)id=7d+4%r8B?+2
zEXrEUZmW@S`68QZuIm;HGcgU{bMh<<<zbl^-KQcTZeYF^mv)NGnOj=dgWi1Iqsoe*
zBUXoqZ5eIJQ|F>eR6-P*V+rcn5`d-|!gf(eQS8^uT%sI<wVJ)!V2%qqE=$GH(8W)J
z&V~>O;`Q;D+%oy7eUYocWN*48s14RH#ZkeeIv00?t_$t!#`xTHZ0WyuDH-d2O9va+
z^)r^w>$t2Nlcfyu0tq>-p=0s_TL^)yotHt3K)*VQ5l_D)vHj#ph+sENIzhfCao+jt
z;G#Ti;?OcGce~czT%LUvo8KGoIrf9Ns^W4h8SQ$~Q+y?B^N(pxznXmwSWRX{gA7X&
zu)w7&g$BsEFd8nrAKI6O5dw$PUG((2_A2JUl0jo~t8S<KAUQDDJKw3)P*Pmr*5dab
z5_(@!BFE1aeNhVU9Z0-&P@fm2PM}}gSKA(KAR4k}2}loMUmfKy@rrKTkwOxfgh+?%
z9}#&ISWfz#P3)<jf_cjVi68v@Ogqc?am|Pyl^>-}nApTouP42QPe)?ve^(ddH9G9~
zjfOy2_>rfF7%By!{`PrN7zLLAYXD>r@OQpO&y+mwpsruzmD2{8FkaxN``C+vS1=8t
zn-jE=FXH6(6A{#W)P~SmQtLA`x%Zq9UwH9rYTFK{tAzKQehydr-5VMs@r*1PgNsKr
zOaZ;+ao#%A0RH-7q}?l^cZ&nV?*;V+^7(O;K@U!$revh#`WcpGie5T4UU)Nv<6)OG
zqLvto?al*JQ#T){$qK?afQAXf^URenwNC-nA!cuD8*e%ZASW<%sH>AK1v;N9P9Od2
zm(ZR-l$&6V-v$V>Vr&b328L9Y{Zp}5f$geX6D6F_zMaKhz_02e26hI@CVYRHse_$D
z6aAvXI08t?+0G_j&vM?6a*Cx}9vYEGcK!}iEyH=pjVjo2STFy`sNo*Oct5{*A7_O~
z-dZr%H}-yRo$zAFbXj*xYuaZ0)qMTAT!n(3;_(ZZMfS)N!+(W^AVAyOdj=>hadD@7
z=gbH(FpMBV2>5+l#MtPCw7^@CZ?ac(^XjLNvG~WpIFg)*6#+d!*0C(GaK{Yx7|(dx
zeteW;)lPVFDa-D-tOU=9dtF0imkR;PzWydgkW63RWvQgw6@M!FAJ2lcmdL3uLj*|o
zmO;ai%`UWb0eS%Zn?Iu<kv1drUh}IwnU72v`{jgvQ(CuzaCCvO`UJOyK1?r)M3<k@
zjN3v?7oXH_iT6i?sVN>6%`OHOLbWj$rkJEyR&G>s^{HSg0n@eX9<IGBXafuSp%W;E
zs!RgFa0OT{Z1%ezY?(3y$^w+SDI{7&BXBglRi)J(3XL8b1$*AKK|Ia)vV@*@6?%;%
zka%>m09xr<rUtspOgoe7D1+JnKe3_swV#C?v~({bFU!RM^+eI7(_)qugFLob7a&xX
za0o@8*7|j_)i@-;IpSrFwmBu|O)-a{1P3Oi4IxM>sKw}qiIY9nuz~s4`5HBIVI=?8
zIlVWPgs+>sq?($VEG9P&CMRTRBxZmL5+GdHlIkz2Z)vXg8rrbITn2wslcGX9uLkk0
zHPBj8IpO$o0A6A%AZmT%gGiQ%+5vcMde6qc@DnTWe>WYaWdTkX=g4$ymsiVv6ARrZ
zm-W_in{^<#(Qbi|;fV4m<LVxg!R|j6RCiRr`HcxtmE^w!BRqve<s(BB^oFwE%Jp{V
z+w{PFJ$q`Ux>f9FpmGCi#ZF&CLE1w>7A9#s0#OAMzh8z_3D3f&9YgUDN_xq!=$p*)
ziIEjT!|-@JEZ9#}vriCyS%!y<V@m0MjQ=Lxli3s1haOG6iVEtN#5a_;)(GENV0U(9
zjn4Ua%4x=EDeb>ydg})ZEr($tE91mP#ONgePT2Zsyb_=)J@zRgW0{y@KvK6GU=52~
z6ZN{JX&~?U;*9QIO71ydvs#M`F(sT3`iDMH3RBzMQ)O;_fqVr8u&`hV_tOVErQ$G+
z!pq%rZ6jxuAx|{2E+?`ZUg}?Mb-kZ-RT7d4uv;!=T(kGz{VXB`lHeXLQWR})QAThB
z>R%6h-VYLcOxVxFbPk1|A^bxw`QMJ@R^Zh;OflY{az2()1EH~%O_be$+c-W~IDP*Z
zv(gQS4s0s6K%7bH(sK;g{G%RZ2-Wl0#EsNb%`6TUOHeB7bHm}2Amh(}9Ty|bwW@-6
zA#K0z)?M3QffF3b6(<4&q7)Yjb{K>{cshN#fiHw2C%a<kQHR^i@=Zd2n@|g&mHehp
zV$%QygR&on>VB6Cjxm+}wY6Tp#}Lta*pGLXb|0$q=wJv$GT(t~XQ+>i?F{r?t6heM
z{FQr6?Q>17%80E|`l8l(jg|2Hac}!!qUpynxIWB7QoQN6Rtqj>3@RlbVcot)tpp<-
z1O|C|_jp1Ez-1n>R)LRQ3P16X^vA4!FR7Z=4szWG{HESgG~#AWj4}8H!vKUb5epJp
zt-(|pZDJ~++fk1)Eqv&JpT&3v3YpG0+XnBfHE4;OhLq_L-1dXEOl}f&OfI@Qw)~es
zfTq$-pxWO&N^F_M+#mz4`3VbLI}j=@52Aq2OS8|n6Yxjh)Lx4cVjoW0`>v0Nk0zGW
zOuwrTKPD1p5vE{C{BooM_`w@3ePVAjC%_G+T9z3gH7+i64i)Dj5e@%ZkzZ!xR0lQ{
z;to0GK!D(cO+F*?;)K~yd%SM{p;Mo)TcEF7zysYRuy`%@xtdRfpZG|&Rsz;miGt}#
ze!%-E#rtn6ggRplo*V%Tasd8Im?NH%oe@_m*2H(lj#+HmSn1Nfz_{n%UC2fkRigpD
zn99-q@_FjygZ<>J{R2=D7|EvwcA2ScmOkGje!p03!v<`n0{lHh3M4~x&zFe%u9E!T
z!IcpIOk6V{LWi1MM85TSeBHbHxhJ0^SBtCNBtaR}{V<dpe}8))xPwilh-*^t5l4l3
z_uf>+62>wpCF!i}nhMtU3UlP=2cyt9N%D$IpEo!mXn;UFbm;p<I7o<<49nH*R&cya
zA6F+6%~w!nNekL?b}65ScoarSzZ*r^^RSPYdFn6Kj&u|<G9D66BN&+gZTDoPg>=&r
z$?`&mNF}p)&qODBMl%gVKu-*-!9ec)G|}l(?QtM$Hay1@sRSzoRbS*H2<B|~nud01
zW&NJLASUf{v<Y6vV&GO(qqMhZYS$TTKe==XG_XD@Xb|3l3Ix~5mPAR)2RBD0%4WrT
zT=02YKuJIDU*;I81>pmP4w(G-Zz5T$tS$~)qR*+Lg8PHgaAnLmfqmZ)2lWRA(w1(B
zsu(7=sjdp#r3c+K8&4)8Mkt)mmK*c5FidWpDR`{A=~~Pn>kSZQF;gKPlyaUE?-1Vt
zq@ekpk>E#jC3!nukKM;G?@f(23y6CJKMeGxlnqRQR4=pm_07MK@swNzS@BLjfT~|m
zJ6M^IkORV<NksOe{+RhD>X*gps8R)=T4Xovvl>a@t&TJxj9vCQY(Uqvk!U;LsVtR>
z1^5&5COwa1F8~Gzt|@8?Fh;F#AzW?M*6AN2;8PozQ&Jr&eUdN63npGFpl`9#p&T&)
z=s;%^_963E4OjLD51pPpTJ*pRO-SP&5(z4Rvq9`&zuV`w{=oO1#!y>%k?UuX)3?yb
zIg&ys0;xT1YGW0Bz2x0(2Sa|tC44^x@V;vTW5tvu^;tfEMbC-u2lvfP?pKcK*itkJ
zL?Hf7mZ4w4?hvluF-XWz|8Pn3+Q_eDOr2fS;#d5TKcqYN5DTbbp|gRmp*tU;p*t`O
zo<n+Y0JecX_?M}L+HsKIqn8_Y*Fk};`j&`_k#Xd^sjP@j2k+vfGYr!YmRAB+Nwk!n
zzIwOba8&ZI0AGfo!L^Im@r%V)vdxFVIwskNgdzN}J$B@eCxYN8&T)k&o}MipTY4N%
zb-b80>*EnI66tM#V}|Uj!+x;QUs%J)$9wZG?~5Y81cgPU1a%5l@@uPQ90*g?%Yqlw
z8MXy&qo*SdfVi>tubJY+Ru17MpqbwjBvgbSjdqQxfUxDIr5zymSzPpr2YBDQs9wa-
z$up7-XMy*<<5XOKQa02GYl;#B!^1SZW6q~5wU>C&0`*NEXrAe24{@y&*(nY_<PNz|
zPLKtK3^z^ug|>GZj(pn-m%T;-5|Vtn`^E70cnb)#gzj^L$(X?qc`VT9P()3qFiV)9
z>)m+Fr__4SV-$7_!dUGr_FD)G=I;1-yVy{}G$;~t%#I5}5wnHC1Gw{-7?6I^8dhHI
z<l*pwH>#8OGpNl2)Lg`P-xgecZueVFa=4=E)cu16Tf2=)SR!!f=Z|+nr;uT}<7mG=
zUh49dG#b1w-L6%p_2TaoVo@)NB!;&#93(*vB8rMW*Ym%yU^FTtV<BkiaQV2}3D|)!
zlS-}*?Ksy*U<Th#Wduqk!W6j`neBz0208z-XbGn|p^NnVz(?f!X@~2_<q`mOWx9&5
zdyK$jU^Qp5t`4L>x^5}ny<A?umP<j_P4Ndn1nO`rW|zpM<hW-0nHmxMClrg_h8M_?
z_TEhJZ;0uMIXq8ahyHOaqcuWfd6lQWfW7=U%6T_`tmt~Cn?a8iJLufi#-G&}0>(V|
zi2!|wUOQXAw++4AY@x$;)Fy*CS%K-_$5a{0+4ecScQ|^f`L4of+BcIyeG{KHi(A`l
zb{l#Y6YrG71M&Rl5Uv&6h=3_!u7>xRJ)t7}l4%fWJ438yKkRaa^QR~MY&bC-^HsA!
z$Oq;gj%6%&1zTLrka?}0CvG2idLMJMV9Maf0=Vy$$Vz9uv)<<;8>~*iY-b-N>u@}O
zd&%EjEq@lK>SnSDk4E<K2K8pQLt8dt8k-LqBd1U<8!!Tq5)AZv7cZL#Ad85<P)7Z9
zT)wV%u+Lt4<kAe}9(!u@TBFEjQvz9B2V(?6n&yuPKF;9k7b_VX`d<e_q5yV`1zZcK
zo(_xbLqO(`{N!NnWERK;kJ@u+7=4=kU%h!y$_WebL;yCXATf^YG~@y#6Qk4;?aGKP
z`S#f4&0c0$J0ZxJln#ze<RMxFMBE0%XBOvW;y%a8pAt;V7%(Qf1L0_qA?Fhm84f#q
z8P*!RH$AcByk^J%vxbPBfJbw+$j_y)0ry630cb)yb2G(2KvROEW*!HIg<u|fb#2nS
zWA5>07D2va!1=L_wP`?_-r`6|!|+r)7yKGsz>TDXL3PP2Swf&@4404+eH(|q@Am~5
zrR|)K%>VRY@&z9zx4!!<*ge-!9vKRAValLfXI69>fCE8+g53wUiP3fJ>48Yj)q}Xj
zz232x#*p=VnaP9WHA<Vk(;sBO<4JNRr9<jXQMgke+dvm7<Kdbyt=oN_$#m*8v3Yfi
z*pi7B>XM1fg6~ozUDp+As`S{|{n*#4MRy@>-neNwKnNgcyd<h@Ay?=kSJ?M!z`48Q
zzCa<L2&XB{#lcidhIlBM1d5;5sK6Y}?algE1czOnS9D=}521ZWiOKkDOi^i!T-eDn
zSEH&?AGXtKvBewfuA0*R`P`S#ifvqT3$p5_GA&<q>#oHekq1XhU(wu~flucx?U=rn
zm|osA{LQSvJ^A1#;%wS>>sr@c$w~}%Da(L2t0wP=V>`4-hug!dP4M#!VKjEB?i%-`
zO1`kl!SnG<p2ZUPx=|DKar}1bS({P8wgfB%MEfkk#1)}6e-OwG`~1)?n_G#1*Xt^?
z9DY<EM%<L2E4{BI<4KeLAdOSD7b!Zk%3<(|2Q(GO>RikjU-d!2fZrSZr!`Vb&Bhd#
ztW_M?XYaILG{niW$v+JcBs{K=Yv{dM6o~zmrl7RRpNoC<ipmN5%=xQ_v_plv^*H!j
zs;eI&Bv0+Q1hQj`P7R0j6phx4@Ay`=O2_ZMB?$L2dUCNTBSU3y9wo7hCDq}u@F&O%
zBfC8+M>u}QLCMu5IyJnupa^V}l0bt@Q)jC;SpG-B`!Itcwv}HfFqzr-e2dUZ1RBui
z`aCj(Lwt<_Qg+W!S<YS>3G04wQ&VeXChOYU!NKrAa<+PGeAzz)?FTm0Heg$PYCiUW
z;>(fiWCjh33(G}qVHf`^O0v%A-DU~Jrk$H`0)`YW=!GU@db<{c>J-OEEikfxUL_OY
z+WHdLW9s>qW|x7mf$Pxy>hG|y;j1Bf0$%g6^er|<H_1-t@ysWPW@8TwVvn+4Ea+-u
zc29)+&E6-?r$+RiA%SjCIcwWl7b=QHEiJxss2{{$!IsVIC_Q!qw9*3}Xr(&QGw3L9
zD4uqiG_e+yZEokyz70kFhhtrx73ZCf4<YY6@jYxg&&uXc-^U5NB&N4`-7#(po}X`s
z>9s))M<sl79cN)kS;I_13}|M_{5{Nf3kgLGD!1V@zneqmDyp%PtX2E<rd%kPfgNdP
z-W_G?Q^w}O+UQmmq#-mEl7c$N{@ar%LgEd9l?l#hA{WLe{5)6Uyu8+qVp36-PBj<s
zcttpwZQP^QUw4<UTRrU`qPLT~9wP>cdEU)Ff`~s{AC3<6Kx*Va>ByTWP>Mb9rm7^t
z*YEbH;TZ%s-AUHVMHDYJ8G_Z)S_>i;yxpL&=fc$_?Y%CxLEpn}<5(yBpQh#4+-#bB
zSwxIanylA8+#l8kCPuSXyr&*TyvLFFk%DZ!>zQjTwQM>&A2|3E=^XJ8@~VpHSy|cl
zf-kg)9S$;?T&FgC>K-B^81@Il*bhbfxzD~JaMdw|8266R6#*GUNisG@2I#s<rGAe`
zQbxJdyX~cfJ*=lr#1iG_Jjb7sS}YboS8?{jxr&>bPT<(+Xg!X`Q#!8y`XSkQHziIR
zkPdZX(V@MQPnl<KMZqc?xreY9r+v7RJ#u$mIqI{7o98yNV{OTQdvY_s;RKm*NeYA*
zOg(=hu~|<NBLgw&@0!*}BS!PTEKMwr1*~%aQHy$<rpa*Cjs-Ka6Q8xJIZUg54M5W~
z4G)+K7pFls!-)ps5uA4ZI9m>zpy%}-*HWt<JyQKOJSpcLZ#g^UtSQIC$C~s#Pa$zq
z{!fUQl~M#>#<%PdXX~<tDlT5uI~(ivGb7@xhh1ySCy*QNVC*+~qn3tTjaWEsh9M3b
zhSpv5cI~tsqDonN=Y8AvecR>6!rh+ye3=OceV*Imam<j;s2B-&GSDo+JR7V}!%yoY
zl<)yATb`ZBR}ne6qEAA#>zqoS`PS<sYu<}$pR3uQ?_N`5y!371O)`$_`&8A;7Jq`W
zON393aRPgVPnOSLGrDY6>bwcj`&53J=OAH)KJ{~SJ;}U)$bDBY`a3jNe~j-F2Di8K
zy<f$I;NOXA&Q^(7rzdZGoydDHV;RV^*&+|vxO>0yAa*yqwEcYNv_3MyRZ^!AsOkQC
zr7f%W!@*G{fiG^AlMfqjfJY`j(1fX<KxcaFPQ$utwI%~)z>{g$-9>qM;v0RUkE_bb
z+Q)OztNz0|15rd=$1*A`%dZb#)=eRvbGu$UL0o`edjFo8=ew^KVJmU+OWk(Fw8d66
z!-;&*iL`yG<~`iDsD%9Q>X*}}W_(+#bKafEWXiTXJCTI7-*BK3%gczNF*%@62yS>j
z#8{1RhD~{jMU5NSgJRI+vi4M<)`E)api_jSUeQ}5hVa6MPu|-U9m9d*YL7Cy%vPjG
zJ$k^AAv1%?{x?dib$b__3hB;Y{VueR4U8UG-J}Yy8DX<)SDXB+#oi>BcFm8Z3=`rO
zI(+kQYnC;HFXil;>!r4c$%BKmNy<{)Oi_4M_~NhwfOH_8Hl`8@X)*(v@Zqm3G_RLJ
zM27|Fp&}{aFERIf4vw;LRgS`a33|^ZZbMy%nLSvHTro9Qr%bhqu(Q<3`U+7kbusvT
zt(2&J!<ZPBh0qypt5AQULP}+IeIR*+_&=8rAtayBaOXzz(|eB#!l!KvxzTT*?}-_A
zSx-}Pytz{r+zP`fL=_gS*`uRiywI62i;hWXG$|>gwNQJnz7JrYai8R+35zz{C*NJ=
zT>YZzftKlj{TVFg_27A1{h~?qJjgMKU9Gvc(4^n<Y9VBj_`q2$yGP|@L;QmhVP~Yu
z)9irweT(>)rGjZaibLqKHNrA)i>H(Yubp(GFWl5@@fjhZ*T(Pr<@@1sl`6yrNt2N4
zTS@w?$@;lcm^JrtZHVB>*X2Tyiu>8<gke_OI%OI{-6VN9?)s7!w;}|Zs$7+_-_d>t
z-`|qvXHo*!^d5(jscnA~v6Uy5b9<C3fM5d0r@ua|bF*;H*jazU{}~vA9h}?u=`qrA
zZ?{QU;L8>Xp_Z4&8MZ_p%aigW7GA~r4ILe1Ye5qMFkWobyG|Td0esPmu?dOt7dQi$
zGS8a6d;t7?bH1rs3FA;2hmn3%#Uj=_xbzrHN-39dZ2Z1{3BBq`KE_ZvJa*lm8cGlI
zCC{ZvEI+h`L)TQufE4WGKoF$)IEZO9?s_3@TcYu`hkjwMc<P1N`?KJ(d42i*my#?E
z_HCzsGM6Kl7lwSzZ;deMGdyxiJds)uR3vE++WrI<M(&z~qH>`S^iqDRVdr+Lr5$&G
zTL>W)!8x*wX$^AU#=Gpub4B%b(-B&zwh8SX2Dz|idbUVmSAP$nak?0cX#cZP1KBZv
zczD|h!{ed3y0y(f$;fCVK8da`^k@9dGTo&3seiJY$Xz<6W&9*i5<W-nE2>gMH)gup
z?vJz6hF<z`)94-!Oho9Y!*s2{x~<p+>|iKq`<0j)o2AAp$>@xQMC^WI%g?|yq#1Lj
zp(kb<e3X~7?ryoag3mF=|FWBKV>FUn2Zh<2Ob;&%)*n9Os-Y+}`?Vs7+Im?M-OLtZ
zu8ah~AIjt^f-BTArErr4#WPV7vb``!HB71_?<iBL_s@gRV(nF%r3SlHk!Vsj1?y1y
zXIQG6H&VHC*72U%AM@I1J1V}XbUa{hG=q0?n#$LfJqULKum$4V0I-nmM2{{`b#d6*
ze-@iz=CS;>ng2~ntre#XXNX>J2m>q>Wr!pn8z~f(9ZjY~lCm%(7FL+hb~D}?zdPZC
zAX^8)40w#De`a&_I@A!e{Ld{^6_5*S&T3ojyu4#0Y;g>dQoEBbx41f9l`(yCq$OT{
zFp}^Y;)RM3bMQj24T6zqkB2d@``aO0{isO4tPNACijFFC_QCQ!&z$P>7M#w_^KJ_&
z@Yr>H=xZGAS6*}7RjUGx{(h#U>c>^(pF#bOco>E6`Eb*R?^0l8WMw6ybs&OnZzpDA
z?{W0J59Q{HJL65N3qA^~Og0`}vD|72?G9dYiVDWeAAEOTANlJjsTf>5fm_iLtDBh$
zH*KP-DJA8taWNBn<48PTx|X(=jO9?<kjj_d?Zsql=VEzAq6o2ox~kjTyN3s_hy4uv
z3m~q!2JL{JB3X)QtJ%wauCS<}UI+@8*JwxSgk@NE6zOL%NxVN+5e(9^8+sOLE<*1(
z&@fo+DUP<AS!HS|Mp^S#6>mZZL#*a^oB51fjnTq+YRW&NK8OMaQ}o4cT3^n?Z28}u
zR-UY_TkDdOrGjD{!@+yooT(^X+56!!PF)!cq)8!6WAX+2ZKMtQvzvlhU?R!F6fl+S
z^lQJkruxftX(OBW&|z~MR}@;S>8&l-d1pyRG%R8+)$M%_<>E18MU`ANhs_d?>rs`Z
z*0Po+-iqspjH3S-mlq^w9J+g*xSj(5n!XQPBUpwKDr=k;-9o+q7?4!tUm1pGi{h&f
zw>^OBrm>GHfw+&g+uSND>$fGMndt50d^31bLw^`_4o*qCQ`d?<P_Qt@Gz(EGBW?1%
z&2uw6XI=jBUZTu6=;yqcD~_Oeoi)(s`fo0WOMJ=poh*~6pqsR{;#UUp4kl=k=s>#@
zYj7Ei5~tLFRv3VRbet68Az#WvYmjQAwkA)VHx%p8v6ONa&0&YpkaW3>%R1Nfzz~t8
zTa_+-*5O%IB;&Md!#;8waWG@c&3&|F7>^^oLdk^;g@s%%a1>>6%;efC_>R1COm(*x
zC?Be?A8t4d<g58p2;G)4F2a^okW@-nW4>BmaKy|Olbm4Jkn!pFXS|qm6LZD%T1~f}
zZ_g(g={SrwhBZ~QGYKp-d5}M)D>dwy$>9yl6Wo_ns1vZs?p@=vzSh&i0(xAIjfy2G
zp{M<gweqC*v8sjO3Cx)k@Fw>|*2hp6>p4~z7u%gHS}@Gx2o(E$DpNm&k@N0ZB&>@b
zKGprKW~5KilG_>}?a)nMkabq?kQ!oi8rNdH1uB7Fzf0NuVpGjJv;#sWg*^pU2;VCP
zDFu87EC+LbrNvMbb<-!KS}E1*e6*-hA)z1F#0u`Z8Y?b2Z^gLB8zq&cS^|71*j5gT
zd(od@IW?NU_J|YVKHYy#DzAZG#z<H?*zD^|X^8F-QSgoDadio`ZS*8iOp#@>7T=Vq
zi(yM;*;6FaAUdzY>d`pRdy9g;1A6fK;MjA@H9<jy$e4Gj+*DJ&Cc2{2WE<09<;B*`
ztIMn{?IS;1gSJwcKbLEmw(sBOV@(bhm!`b_Ixn!<n%UZF<-1^tJwH3R>F8HJaEfLf
z4<q4IQ7`&4N+{L>PG~VA{fOm5aeKw0;_nlHKtB!&J1-l-zlIPwYLDK~2M}%t<CXfU
zuaJ0y!L%|Z4C8mY&yD-mD12b-4Bp$C1d1{61w-W-1+CEX8JxO0MEEDI+s`M1d|e(c
zE3Mq<y77r{9Ua`qRq*T{7^?%rlIlQVd#*CYuYC@ij7A6wRWG<_I*OPS(2<vUT7MRC
zA@{olC_%xS(o%nHj}ub=HjwWGD{BHC0(<>;dR@i!Mp&BH=jG>Valb%0jM|BQdTd?6
zLd!C%`_P(8%=?bl!<f%qIF@#v8coV=FTQl0NfaS`v!iS!r-4YTn{1yHfxw7*Aow)+
zh&C9FL4|}o*zWQC95pb2vlqCT2n%oUv`c1SrJ>4s$5^lduY<_Or)i#EqIA-{I{Vvl
z215b?L`mXHxc9j#iMzlGN?^1_G+yX6Xz_UW!qX!*^s*{499#>#2l*Lz%+b<($^XWF
ztnR$cYo-H`pM}c6qBeqPEoO(;7{vs^h*-mH549EArSvC~#$qo0hJaDvVK)MNd(PwN
z`VN`b;vn<oZTETK`eCi+t{fDP4dqC1_m5a}oIG}g#`pF0cUv7d<M-*Xuz95v1|;E9
zC5qd7iccT6Z*r7vUX!Z)nUf)`D?LrpLB)rL$O61Vfa<CcY)L~937fkKS6BQ(9Ad<E
zY%s*TyC4}u!EbDY$;8ARI|45ziL`RjQeS5K!Y<=U!7$y5_Fk~`8R^VU3DG*Ksw20^
zo7WsC9ye^4m=EqMRQJSh++0Ohmhzd{k7Ci~*}g3{OG@P)ewRAT6lHgjk3_m93Bc6{
z!{1W!8AL_p2W(0E6?V)0rsJnY&>5X$H2q@4zBNV>%vwZ%aXu4`^7e5xF_3ja1iO+-
zKS2bEan7w&+8j+qo*&YdyLIJ!GJog2H<*DZtK0f3ueGs}nVT<L`&YlVP)%|$7F|3%
z`-Sx7GEIVwOsztaRlTf4B$~eOtfnV-8%D|)r5O~kdydK!Awex1Ru%hi?h9f(`R;We
z312g6AOORrOumh?ymY1wqa)A$WzA)8xX55-lm#r{o4e1kw0+_o(nen2=`<UgUi)@<
z@JK|^eS+v|pCWH%Jtz1GU!Yv=4x!cF2ulvC+!L)5dx|`s(^)(j(H_g7o%t33?K{HW
zT`RQPnrwC|mNx>3eB!ax1lU&6M8E5@=K0-mp?FNcq!d8w$NTL4nOkDs!E+p7i8Ss_
zP2~}PR!p^at<)K<<Nvc`bjpfZ@Qmfo`=e6f#I2HGJ<fH-jF!S0X5hqQ>!4U=S`d&Y
zRQa^$$E2O~OCcM|G}PiQ3{(^VrX!7QZZPUgONrPi?ToH{Z=!3SJ6&tx3~aqV9*iC$
zhU~K=gDN;s*qt&li*DmW(DeNap(ZmNsqJ$!wl}fr<?>=F$KOH1TcntrIC-7|r<NFz
zIQeby=MkNv|4Sywou>DmO607VEDTCU>W}l>-y$f3(8bh%-{TG-U)k^<w8~#;2w;N!
zX=1S*kYMfS5-G;Z4x>vwBN)t7&zg>$HO}l-jx@q~(q--n2*#Yf#^;fOC8*~iEp0m*
z4`$S~ry5-6$_>Ab)Qpnr!+piDnk`YOD9jEuB%WtJU2bf17GAI+p@iB2_*stTss_xB
zqFfb^>N|oz$v{m0VxW=FGQ5Ky>^AfbLDPrRa9z|%{S2)=crCj9GtG?Ys-s9={H<;F
z3mD7l(ozsJ3YMSud$)ZMH~;lA0OkIDNv&(SG9@Uzg)(y_@${OB0t$OB%(CY!Q|Xxu
z?e5@^_ChIiR^Qi^NDwwdSsu@ucuD3B4P<VTNs|em$rU;TX8H$x;fjQ9ly)o?LcS-Q
z(5jbE?3r#LXei_}Yd2qgx)e4%hhHBJ_^WfTerfi+#dNX{*9hk17n}P?xYBgzx%X|u
z+{^Q5`iU-?u?9dp#tj<=CQL$_DqOqWfBD;>L%AeF##$(R7fK0LZ4pZAu^1GJR+cIX
z$8{2T5@{Ipa?=O4m`X~b)7%{=_#_jAE}Ys=nyD!;3RY4C?xCZiW<#dVec_Qi+3w}>
z%QTM91;I<1%RRA;E1tf)zrLoq`Cw(^!~6X^B;?Kbx4T&qHxSKdVwMUKyjhE$;rx7D
zn&sHLSAKbHtiC61*bNlRD9Zu$p9G$gx1;fnf?c&2hU!eOe&HX{-?TH|0f2kcO&Y*6
z(=j5crt8*z1$Z9m;r-;h=UkLJ)gY+q6^c>i2x*5VfnmT7CUtLbhwbz}D%!$$*x<i#
zF3HIzo|75WOx|NI5#eqr)UTKQwp>hGr{OXqqtWPSnE9v(LGBF{Kuyy#F!E2#&5E_o
z!ZGnxHh?M=VH1vx_B&+SL?;^Z`@Xn6?SS6B8@t*6N1z(iOgJm^?Yv9R1G`8hNNN!T
z&KvH1_r=+<MGLHWbNA%W<>jW&XPwJVm*dTt<GRB*7xWs5Jg#ZcUt9<pa3xUBVC^xK
zqaEJQ87k{}NsB^9_#FL=XNulJBLlcMQ*Zn=Nu_*AMD#UoBTh$7BUCDS6M(6&6JMNU
zHtV|lF)URBgwqBlajV%h*T9;~>ilH=9Ssp&Bn<fM)DN@vHZEy;FcwbX#33=*>fih6
zbsugc^i?>YFQT5yJ+zDyMZfeczJdpL8>$Bz`&YSqIdz`Fw{0*hc>Z<IF5)$a86JdI
zu0kE^h!PeTPd<R_CiFUvig_aKcM?fj!xFc{8tls@B1y&6ov{r=u=^8h+)mGo=Vlgk
z+cA>S#QO>dOmJzTUK-1d3C{V<p{jL#@qbKn^><UI=xV-qTzJ)-ZP!oOn9bXVqA>;1
zcW|?`Bm(CK!EfLhC^uQZy7W1eU^pW7)DG_e(Bys{sn?Uq!!3q#Qfg-){;{P+fg)fL
zq3>crMv28!OIc+r&Lz0?XJJ3pu#>1w^FR>38HXh@!#h8c9MA)}Iz5^<w{i8;lX&a5
z^eel2w0U&d|E1hR(Y;^eviz(N3pU9zn08)sPtn|kAeSIyMuf~2yF1Fn;CMF=Q`P$k
zAZ?`~$4xHMtlQo#$H+2A7i4fc8rA>aAay(C89s}eX-~(`Bs@w%(@lBQy`NR~Bd!pv
zIVGc22&q|lP%3GE??z}MDTTwudR@YN2M%3zBAQCcG+cNQ|I(jowiAV^$#Lkuy2kVE
zWQVx@sJPX(^RicH#~(TgP2rdb0B+RMzqx;zAS?9+3EqCi8a|;A!*0r`iP50H#!foY
z31naj^$K*!6OSl22@m{1jZ0a)l6bkp8y>l(oST$`nea}9VRNPt7(DP@_-H*O<0s$q
z7nK<gY#4N-ocJ3FsLyB>s<~DeU}q^L(~QFVepry@^X8I+nDhIl`pMR9fbJUajgI>U
zRL0x`Cjsk{rwa79v{ZLqZlnurKbNu?N%A(t<7QON`ur>O&M=;RG1Td22#caohMFx!
z$(2R#njp2?aeY;fwENX=$f<$~*s@UGzn+bY``W;Z*zwk%Eoy(T_JH}^WSgREW>%3~
z5GmgMePwQ}#}b@8%c&L1S+XLgs8h?f)p&E~vrMIG<LFrP_9s(+OQ&#2jEx}w-{W`|
z|8~!KOvxUoj0|q8dK+Qp9Q_)IMyV!EXNS)R&!ge1nF^UqcJ#<3bLusEsg@!a7E1Jz
zThe)+vad@b-&FE`1AwsnRL;9-lcnqRoW|ydAJhEUF1AaBut)_K2uT-z=tt1r&}Qbx
z{2Bzfbtb<XYf8xgrt|Vc^N>y!Q1l${=8_oooacTA*N<DcFO`+328+yA%cUv%KI#Z<
ziqrc`xUMDc;7ixK`S|1aH`~ZWyGlno@`_y}HwlnlrjllY)+h<d*dYdMV4E6ld!lIc
z{TJH`!C>60@|OU5!vHG!Zk;|&)wzZ09$dod?15eoUsHXn<J$A|Uq&BeNn*b#d^}YM
zm2cah`Q6I~G-{_VQ@!^r41HfUE1K7Sd3}M=Vf2a({>Lj$=cKoA>*~)dOI-}jLI|XA
z(nwn%mVs|7siz^5QY}wl;-=B5w$oq;!Np{TGjMn+JW)?)N6~kh#lN3e3z|7NmcQ(p
zB5Oqu;|qrg!mAC>Ixe{=vXMMt{btl#cgvOMtQ0KNmOigR-A?p3JP*B(=c^~`D*dwb
z;wu92*(#QSP7eN=AQ7s<ND|B)$hWt?Z!V`moh}=ly>hym(YG1;=C7^;mbx>K(JB?a
zq3h}RG{<wq?rR+T@=4k{z0OqN2cahTrIOOMTu^_$VNCF3NWia6_b^wkJ(u`HXQ}@`
z0D?e$ztL0XS6s#*iigz90+SN1qKqp{YpKN$)lA`p4I(%4Q;w@*@G^{%l^ww$2dK9J
zl!hveFIb-I`?oJQbiMaIWibWXq(ms`aRH9t@Va%Tn0<TO@e7?=#h@;gy1TkYckBGo
zvs<_3AGr74v1_invhd?OZlAm6?e8eO<rja^KB+a()KrmrkaV@wo-mfGORh3GbHEfZ
z43yQ=6T(?%C7X58cP4DOAK^5tjkKmRp*F>;lQ9N_s^`LH1gf~$50^maW?=vi;74^*
zH8K;YIkQlP3NR~JT=2W926Ipbac+zOwPaN(>08`$kEfVMtwN2gJpY0beaaZ7prj~=
z=RCY_ohkOx-t-feRV0W}{^6&edTLC^yeIC^3qJSWaYw_Ax8L40vSv;Dc`tcMc<cZB
z2gVE~F~dZIzE;iZ6MgNH0A?u3>I3f<=TZW-34@eiV!dira=e4L>R=46uF>u_bBXLU
ztjGu2Gy0Q1BYMR_SvG>pV~ispBiO1mn1SkHa}d1Sv^t8JjI%)cBYegL;KjliaUJk0
zK4Z2toAgah?DhOIddhs)z4sOR-nM;w!HSb<TLzpI^2j-LrkJ_yZPU%(X%HFj+_`h;
z?tAVjf9JBxnlJgrH`?Z(cV1!5%9Y`@ANfdV6492m%sQ$oW{ep!=^!d|X#hhQQ-~pO
zol#&2EY!tZ;_S1NV;!7t^36IVi1I)*z6=Y^IU$-~VMxks>M=%9qU-xcbEH)nCfgZP
zzz`sGI2c7tV^)sGCx_mXYMZ1`9%By5Q(m)CHnM_*iw%NQsAj2917G|1hOYO&uOz16
z<ZKfI(2-`0I#VP)wa2eICz&{Coa`GI7`XT0hsVEv-F5kYyycd;r!QRC_|{K+A{4ZT
zP?kg}H#X)5nGnmh&*_2<RR}_dA)FC7*oPk*O9+?)3gY99e5zE(#|`Gb`;rTjkrLo@
zDIxTxGARqRi>3qpVlyToEwxUZ4ugw<&dG$7FbNU6_+bu!tC>Pk7?NCVq73-fqz23p
zF@^yH?`3i90~KoC37nj5?0ER$+{#y;Un({=Hvy8O_H2jUtusZ9Cg(;C<_=04+tSfF
z_?=sBp7?{i?rJ-^p`qz#-}EMRej%_EEs21tlgmlnff>v=GeGFh05F4F;z)xjTtc{%
zV6#ptm>vGxvSm_ApiU`2Rf^{jlo&GsHKx+l)?r9@qIFnE5T<|<Gt~(uz+ym#0l<JP
zWbv8wfe8dh*zua1Rs=uBm``q^LXAm{8V^kc-S%<&ZOy$u`_}Tp)u#yN$g5Cwrm*gs
z{6T!D>eA2y+qVz><30B_f32sd{Y@fP?HX{^OtKR#>T-Iq-iEMU9fIJU31Zh7!r^eq
z2i0)s4=J&Fb;2;rCGNjJdH<RTy6R<{b*e+m7^1^W)A9P?f-;sAwj>MDk|9kuPs$KR
zur3jtDI8(U5HVvIg0w88L|SIX911&g82&78szQwnR2ok|DL3$~|0;HV;5SNQiV>+$
zY6N3HFFh^g@Hus+h}xPyV>A{`^j|NA_|qq!Z0l-iZvQ!{Go^C5GBBn~?zA^yXO-en
z-c=!&3`~|`26u@ECT0rHk7Qx^X2t1cFH4qPw{1(*ChCTi7*l@L3wq8NA|*iLs_hhp
zP@+o;bHVB4hKtpM8_|;H41pV^TYN^;0H(=;F=n6~7$ddL44?oHZnhs{aXR*)jlBH)
zd7y6W)GE~K3tlze*rvmeR0ae`$k3j3qZTlRNZh`kbaoEBy|Z&}dEUJC7aq4T3~6V%
z+}xbYGm`U_6NnfCK||~iKbFy+eFe9K<208V&OEp<2k@L{VlDweu-bHwcCg!Z?uoDt
zC&3I;CfF)I8<~~nes_(g6c1?`fl^-?3n$*>p@p6zvR!TYtubH<_s6d1O%a(?P;+EV
z0COmfCi1u}i{h<bia>>8(vnMFKFW39aa*x($4-gPge5%Bk#N)AJrYG9J!R78bdl(w
z@0#7a2S3`^H~(d=tt}_&&ZU90he@~Y^7#M+?o7Z0nK$CxLgd7e?lzVh&J^rfz!ZF2
zf*b$1_7W+<HJBs9&o_G6tP>?Mf2fuc)X6Q?vpVX>h*4o@5GPV2)Rbr~pMIR)$)8CB
zB4LWy5?5`kz_dJ=AjM5Zr~~DCU6RVh2v}v(gj?5ZHooBG(D$AHF7Dd6QH_zFg-WUx
zXE<#B=RH#}sitq$|ASZW+BNvT-Mi;6&*fU?NdgS;4G1S-SBD^S2p+;`VvMb324@I&
zogrdLfrLTQfCuRVQ!r%n%{sm_f#kqQN>D#z2)Rb!xWuI{ek^dAmB$EE2}Z1`OB_J%
zDrDqm(jxU|%mGt4*HpW}${5jmS-2|4nJ<<iR<R_@<gb{*ZYXbQqL`*#k3L!)9qdP1
zByBMXd02h4<@460t4uuq$lA`%q4)3DG5_>(xn+Tfd_=^Whp3(4odMisKLI~P4GgZ^
zm>EocV)aR?5)1}2xMc#(k?=Fm3_)KfvTxQQCBVaxaioZ-1xAP^1q_mn!jT`sqeHVa
z`4yEgj6C=;?t&XX43QCkwREV(7$Cub6AQ!1g&8u20CAe`E>UJO4<KTCsZh=BYKpZt
z=lizrPy-!V73z6A(H0$4(q-4thdMe2|7!E*w$=H3%R&+2kcgKnD2<4HbobF7wWg%`
zgU3VY?ssCx^%W-k5cjyKD_uo02?6!MRP1kXqywsY?6G7SmbnB5DWPbaOUN~XSG2p(
z7Jgj!#|)AA1w=Z=<19cUU==XNsAd4dp3#6TSMsBH>gMS%2L6~iye#4XwRbScExe%P
zmy@#-rNLZ-j>r#u?}~=5kN&T*MW?ReER@8jfsysFn&%}`KnH0*l^@x$WAIZCJ=C(J
zQfWO|y*DHx=E|5Udkio{O#CnbVYd)IerJT(g&|-Dm;%Z}Wx{1uiPuYC|N2QO!SQhL
zaLqnAR7sylxa&-S>lUiP8WE;`BSb~o$Sv22)kk&0WHP3pS~QBRt_qhYwJ-&RU_1^t
zGKL2Hit{qvjmwU*WM1;<&2+6oNh;6RGfRc)c=(Z#RocjFY-?}i6tb3RGx)>hc|J0Q
zGemg0t84K8Tf4UL!BVO9Y^^X2J7GiQPUytJ1cYN|fM^{C*BAlfS3G``4uoNfDu!UA
zk9Q=vp@ZY$P!V$pE+s&1!YB_DY)HaXCtQI+`0)q~HR?Yh$Z9uD%kq#ei?KY3H;aQQ
zrcAB@f-!KNDddffF@P!wvspQ*-x<Tx!GYhH(D4}}twUARMq2W2o|nt*y7sC{@7vxs
zy!g~LZF+tl|FE0q8&lA=bjif_uC9SEU3+cA+U?t0UnoKx5s_9z%qAtA;BjFJh<*<S
zNSVSJ0^-+!J!1-Igld(^hh?aP8^hoE&g3N`BnC%1n5Wn`>x5g?gbA``SoxulTGj}U
z9D;ERock4>`K<wnz@+eAEsKj812{M`W{B8thz(7Ke-GwRMGQ<oHX6w#3>tozQv{ub
zn$S168}weqz*W~ZcmK|&Q85Ku_^dVnN|wWBo^MQHjoa1R+x0J(Utak9h7EHs77>nW
zVmu+@&c&m<5xIynW{@cikfxE2MH(10z!>rqcI#9}7`2HdSl+06(TmjGs?y_+C!hLd
z#fh-K28`pAYery~5;370;fqvIc`P2kY$h2LaCj2%ljg>&6}t(wI)q$R*^ok-*w{q)
z!41dac$wy=VajzBz!1pL0XU{ry?OJ<-@N^vdxlrM{1v0k^XIo5iDMtn8>Zm+2I|Pj
z=xFbymtQ{d7dPB6|1J9d^!V=Gm2vG<+pT=g6pZ*b4}idBA$oUX2Jm=ehKLCWvl|Ar
zxw7t*hw8+H`okX%Vfk`>F}@>&2U`WgSpWb)07*naR3A(}&&6DVyfP5H%_DHra9-$)
z;1!0BjL=hmOn?@zbc=I00ng%$dKKq{>D9|9mjoDsiex~MUtTkgU#`s|V2o+vJd5Yc
z@TxM&D%4n|>4Z7C&da`4?Ed+8jGZulzSm$%)<Ye5UN8k038b%!4qbEIb)`>!``Zg%
zf8vP^9IY4|9ZeKhr~-m?0<Yy(r^jJqfQT`i%3_xTgq@usy!ge5rFUTvED{8261;vB
z)hTsh4&q3LM$M|aG4Xl2xhbr{E5g(8#OVSvf~Sh+#l=F3v7bzs6fOx&{#ta38>TfC
zN|kgbA#R-b)OvYV8OzKnl=du!`*Q_tVD>-oSaa9LO`|8A`9f_J9ex$+`M?wvUJz0j
zj@^CVeFJ~-xzEi#ueG(YNt<vZnm9vXX6Px19M{zzcb!2z>~0+I`EX+ka>7sq@SJ}A
z=tq-lYy6B8jAC<%Nh!fx!leT46!l3cjo@)p3yk@3-KKDHSW$^ll^}AehKZ|eD^M^7
z2&+w+&l#k;O!CC5PFdM$ok%AR<7Foy^+b6%<mN1*Pa1aIeRpovt1c+DEnIlmRjB7W
zQ_zKULFK6}Te|<@^Pg`S>+aSib@RfA^m<d7k{(HV2x6zF+;zV*frs6N$T4v`VbU{8
zSa`Xaqf@F9^N2IfNb+#TY2(J^eQYR+H|tbe7=n&5^+|phmxsVLzXBPt5l0x9G~U9g
zpM>gkfvQf4B_D?Bnu%Q)g96=n8euY~*2_ggm{ok{;!iE``geY^xezFmIMiF6JRTdZ
zG#@`F*K@^X4gJ6JD`hc7buE<R(3<BmQ}7<Ombvp<{p{`e@|V9{yzY`q+F$yHH-wTH
zLW$}Gbw@_L>Yi#p-N&3DW)g?7IQ17@2As#cYtd;u<uL!)u2rUY{=%e`Ky6|J&v&l{
zW`vJh!)C-K+)7R?9{h6gdVWh!<4EwlxP(<RTMaUoY7)kkwKwpX$Y4ltV?Z8AUB|5;
zxV_#PgTN@j$R&*xD61&Ah?na{m<m-wg<2H)A6l<3s&4E%{>-y<t7uL;oCZejLvEgn
zOrbe-^3og(A%DNVG+O@KZ-4u^i!Zt;R~{LuObqJ6Yer`46*)!?qHgPSdMWKDT!<gz
zakUu4(;dcL;(_OM?WRp3y!_?bz1F@25{4BgBn8ak2mb_)bf8m=xKunYgkxdFLls8&
zsh=<w;{=c2;FVzc9e{)x4Ar)(%gt7uVtC@KE$Lnfxh^55l%Fz&!vPK&GgATr92*L`
z_JurSgI1w(D_?cNSes5NAI^=u=Mq!U&elC90Yq*?<#P9}k32Md=^bmAyyn~&7mDNK
zl~PYn!Vv5~IPvb`(p^GFJVXt(f!)qm<ECi_MuJQnm&7D_`HU!=e%M#PTDOHS4q@}=
zWV5cjD>>%DN|y}8UOkr}&InsB4_6&d9(a=YZB(|%B+iL5i%;=bSbl#@cfBd>c^MBV
za0WwB7{}-J@$&X?4VVmgWGF+1ptYo_P^C)KqUK!B)jwz)e9wDe3dH(eYGP;(%FnUR
z6n^atjbY8Kh8^7A+da0iYgf2;*UsWCn>H?LnK!?=<+#C6>g&(tTidirCnAK{Ayi*t
zrT3r+cfnXq=uR=!uEXM5*QU69F0o-l2p3<R%q2K1$nG^4oY_aHrKhM4RNf}jav|hw
zJ|X`U!d5!4Q;02!bEYvyGQde#fu)&K*CLW1e^hsEvJI6QrKa#6j^mU>nbZg2YxTm7
zt<t={RNnl`18!5N9G@agh0>RgiYFW&2JU*Wxnsi<BTG&{gYR#v<5atJ#3TEmGRGWK
z&@O5inlWjAev|QL=$W3~Lw9f8T)1!Nw$`mfLj|>D<9uCXKeqgYFsSoO4KRW>*2*G6
z<`mgp1UO+1+*+nO-opl-<{>Vri!jI!evX6>>BKA4a=``3u@2@DylPrw#mN~V+m{d~
z<xH5s{SXy#X>sN!E{%7hGo~O905YDQ2Ca(jj~~bUs=5`-qhSGbzq}U4#gGOA$TeHb
z?{RXe52(nOF$6dii376AxGp1~a~$E~j(hLUuYUEb%dHEJGmr$v01w&EvBVVOhwP<x
zZ=2Df^4RdEuFmqhT^;!cI(M{g9_Vk@p@HHYZIUfiLyu{+9$vIi&07e=O(N3qr*-@F
zsMLCW8Ufv&i8RI-CY2!cZfu-t;yC;sk4v8FFmc@M_pWy(Z`E;(gLwq%6O(I-Q63zh
zOPI7!gpc%~a01U5L2=|E%`E;XoCKn@8t}mI<muBVRZjyrHtfhX<3I%Pd)Qs#tpZ7z
zi~*U{pzO#R+$1<T4c{axeA>(#)we99LUsS(vZlUYe$S|wf~^jp{#&*~^>b7+MJg?w
z<Rmz0`B2vNWTox>eWfS5cTGI9tD|vUUw3PFsnpo4J2e(->9Z&TFYAJV(R8O;FOr7h
zg88A)Pz*z|l?@wBGK@=?*M+bNL!j3?QJWNDxQvXa5k)=XFmdEb>X-PLT=TBP%9SCU
zcV05tX6G8!iTAPXgYYU-Ml_VDPC|=+4=Xx>G0ZAm-kQun@fn=_ggp*7$vBx;F=lv_
zYMT+iQ!Dx1kL&&fg`=KYhVpoQtS*A}I=O4j7~=+pjl7Y*Tygob(0Bj(rj93`7(D(3
zXGnZVg|eF3)1)OIeCMcS3R97+0ckC3N8D2H=*Y<Cp53K&yLUA_*4y2@WoWP{!fu$O
zyZaV5GzMKNpr!taN@;wYZ^tDQYPEW9q8u93iTSqHP&j2($QK*J@cIp*^z3$NO(`?L
z9M;+Tv7S<i5W4%5s!)$4&f}Aq<k#aECdrFr;Lso6zJ@_yh$o*&KDv_T1Ffk|a-D$o
zkQQm06B2ncE06j0Lw;bY-6{D}VkS>5!z0*ZSX&4pg*RA_tIkX~wdUx@>Y--vvQ2VC
zVS@)f8Ni#mI0P!=n(s9h5Dmp`H{YCJeg3OP+ZQix(<D;;nm`%oA^16}n8KJrtr*Rv
z#`U$(k!^zmV~=<53Xk-37M~a#oZD9(E9xq_;yjJ|jT0LAr3Q`b$hM?|#J<kpdkb5K
z(txr$o?I9k4=oM4Xz1h<Lmqu!6Wz+DExHz7r(jtY1>PCKsl#2-cmjZ%8t)sUA_eFy
zBn}&OPd`0*oCAt@Ld?K2EOVC@ExY5&C?%UzqZ?GuCrCHK!gVW;`BRr-4NmFK5aa>K
zTzMxm6PTpKB$WKxqk5vd)`l^Z{8e#jQAs^j5vy;N>vgd;Br;&AU*c1{#b}vui!@8a
zmFANd<#zv{|J&O0Yri@yrbsSgu3de{binQrXNr^&7%*Ht;-N}Cqa%Y)_H<Vs>+5cK
zyuY`3+i0mM;xD#|z?NvFM=FS6Dq|Dl5(>#iT*3@sQm<u0khc-duf`TJHi>fb%a(+q
z<`@ON`8K@a$uP!;+F_K83Dipv^At(}Zxuy=o+!{WM^WAEm^tyfg5OTVKJ!c>C7#v%
z0)~L`WXR%D!dyidfu}C%5IZ(w43%YmC6b=`3D0DKm91&w$HFfl9@FL(1jvYsvBbn{
zzob=_>(7wYM-PxOtHAM3D9$zsh0jn&Wh7xuxb;R}K@;q;r*<`UJ-M;8{Oohex^%AL
zQ14kBF{aSGz*;kv3f?5c9V0`7_jPsT@9o{)^i*lMVQ_rBK~f<<R|Kj{DK!v_8N^AF
z3S4VXyQ@z;y8>>A@}4vx{m>g?mYha&k$)&0H#Zc|IWrXX*4MxTkAzW(SJKCbmI*lD
zf97Z^!x(fNGX#F0qys`(5IYhF;=l01$t74;n^=M+ol~+fj|{|~F+&<A&aBYElNhVM
zy9#9CYCqv9E+a&bkJ98#jLNa<D}-y7IN2Cxk0u7N$~Y4Mi=X;qEk5>>ro4<>Q^qcZ
zsQ?bYrb4NKG|Uus-hO*-;5DxuowH;~iy<_aL-TW3nZg)>hgK^kglrF`*VZRSo*f(<
zdZcgn#66lcZyX+KQIiz)>Qz{xNmn8jDwR<fLDXsU1#fvD(yBYj!Za_`EE9PxAi~rI
zE1^|4NfcJE(0eY$(68IzOHXV}dQyJtAx-NiDJ1DG3sWxdP2fU(5IU1<E-`=#Uir#o
z?tsd~T%t=Gb*NRdkrGr_C@3u>Y>y+}ZB?AXrg2$5hploAxnSM!7!V|``x6dvh|~oR
zBYd?C(Oy5&@n?)d9bghTf^H!!YvmE7>?B$)DwGD0F{w}|&d=@szklD*_ks79#1v!7
zl6KB?wGfg6Fo%UHCfku#u-eiNBfCmckp_Az>j(Q99~&HK+c7pOjla?`U(2Tr8igzD
zDTs)Yxq>EGWHn^`$a-s9P(PCZR>+heVB7#lPl=Fo`o3JVMx(+>%R)m-bLf-Q7<%xL
zFu{RAj(TG#hlzOH2jmAQa|o9J{+cOEuv}S#O2&#4Cu8Ygo*-ok=z{qJk^+Bh0mH^|
z8NmxyNr?p}h4nm^&*Ol@FV_qn*{mtDge2LTGfgyyA1rWbyg_=r1=61sH?Wk6JB1n2
zcuONW4I5hgiu3MFF)C6j)TmUbMGL~%Gd<0n>o*LaaOPQKS|~ZBD%2rk3gh%dEA@8Z
zS<4#jN1ht$AA6|3r}EHXfAiL{QlsLE&AP&MzKDwDRKAgAQh~XGGGWbltHm8eg2yLN
z(XRk0J(khd`u3Bgfn9{H4{wFJi;ojyoE?gqXAIo?Kq%_~Uye&yZD7)59I0Vh(+WV5
z;D*qV61-N*30TfJ!BFgEBPrO$MoJiCB`92Na@QC@s6g+`fPY$>!diNjpG;14q-SBr
zXORk2w<EAJG^bhlr_3uXk)KHeDzp0WGPClC)58!?Q^oOMAj;yrJR+hxSx^fo74?By
zv+&H1uP&~9^#x<|PgqXQLuL*-Q`j@9MXTltBVD7V(G7!r6A$(G7T1-ATe~L8g@Tx&
zRjsD?)up;?1<5APnkS@l1$$~I_&waTO^Bz}J-udAS@WlGRV{B_qn;CCYjr%dwKj+1
znQKC!xheGCeP1X)wKeHHUIL;F)FjGtgCBxVSChhPUmL=*Wg%?eo@~@HzYs1n);NiD
zuy?PO)foVyF~3|;W`1Q<&<ydJRZJ!&Vm~knXK-;I_meK08S0hREp?J40su~lOp>Pj
zM#%7HW$Ph~DR7mL)i(`;n|MQyUmhR76tsAwBdtO;p1LU4^VP4n^!+~{DTyh{iidW+
zhEu9@pn-$J6s#brl1`q<lR_p)h2g>R2L~h-h6Wm+Esr*qBoXx1NPdA@3&P;7IBXdl
z>@D~c*+e((4KVLv;<y;jn={T3!=~|oS&yV?>M_(c-mz$w61PC}j0Wv%^gX&h3_qxe
zGRHNTgp-fG4Gi@{M_|>87ltmpF!|sLXR7>N3E+TX{()DPMt2E;3Pqd|jQizcC@LeQ
zG;GzzFOT{4lfe*I!-5}JHxA=rTw1|eLO(o~Tay%V<~S4nC=I-X9X~F0v-+tLSv+74
z=Qylz1>7YbBRZ*!>de~QB^t7JHSKu#;lUHmK6|vGxutlhR-q1vDZKtJ74)1$l}i&7
zgByl=2k+|ZX?|p6c+Rehq{4Wm(5jYb7YOVlsSu<hCC|;X<MW=d*c3Z)7@W-Saqgz+
zz=6o3`N;yLRGBD^9s;*q%PMKmD5{;WW!t9Kmax06CG_8Zk1ib89db<)<NzTtFi3&j
zx`==q{x}7zvsSj!#2F{lDVtmyR;x|S48{;Dnz)QH<k!E2@!-!aVYkHNch}?Pnl2yZ
zGoUn#KdDWUU(sg3LxQE@I#3#KK>kxJ%@7DcZdS)ME(<$j;9}Eh8epIV4ilGcX;M4I
zSQUNCBDd|DABEH2^rn&d%TLrQls{RL?_mAzGgDaQlY09zP!}D_t_<!hjf`#>?hlU)
z4HO?89Xf7cqSC1QDGD6zqT{t2spZrOJ#@kth5(6x^{%2tVheU_C1BiN<yumj9>d_@
zA4A1N^bl>^+_q47(HWtrvr2vUJs8HdI+fE-Yxdb5BmnozzWd$5zAeE`9n2%R!UmNK
z3Qd_`zC=pMb%yX!o-_!6k=aLAi&Lb<yX$a-YuF4=nnq^*Pr}N-hY>*5dh)m*m!^+9
zdXKzuL|PV6o+neER57csy9T7V6qYomTvj%bl$$upr{$N@P-tAeC=6b9c}wpn|8P)D
zp^dyeE9+zd^q`qpGX*7MX>=KT2D#<I@$$fifxe*!hWm=^OT%+^jE}3LxqORiya+(G
zDtESyRZ9i1WW+Aa&KSg5Jn0To?I$5J-g-n?-Z&zHb8nnH#jgPQi4!q%l9p?4S|kmM
zr>_o8^X7zZeNUnEz(X44*qIi~Z{8BZ`R6BJAOb%-*G#ZE-=x(nX9!Sc?@A~D;TvNp
zo~;mw$CyE3V2yy}Vl3>g0+yD#&Qw{Rs&rsM5a%H(Zow(dtuzdAdo*R7F-3smxU8tC
zEXD(`pSjKuDGYHohe*TtePZu8tum@St5Eqli*<Sbu9j^NJv6ZVMdyqbo0}Vx{!+$+
zXl7&zdtkK;>Ji1JMeB4?WoYNvNcpjWzKMGW`-&UJ#}=0KeZuDPO0iu~QZP)1w)ujP
zWO==%Ggol&(_1{5W$=?|PU_F%({zJz*aRGqeKXV!{IN;n=nSI87!B${)TzdnmxeAe
z$KX8=m^8>OJT8Qv`I+Q^-<B=OTmqNXr>NRKvCh;d6#-E&$QZ%m3f_oE@zlVq;+$bJ
zxHuiZTgT<$2u?|cP*IbvJsc6KsS&1$!EeUX-Blz>ZVg5O3+pF)7MLMrkSq>3ENPL1
zAg`gXDx;!>7!9zMCVfe*<C-g*`rrJf%7PPDC`R+ptb$g91OBPW6iKC0W=IO8ZOX&r
z;{%(9`$z5@=qqRiY0i#`a)X|y!2O1E<bi~=fFy3#f~+#|fC*VC08%2SvjC9huEFtq
z#HGg4<#*SjqjbwlB)DSN0B8R3-r#v-{MK`N3P}w{_V)R6Lh;-ehVFTDL;p={!}yEN
z(XsGT63Kwtv}Lm~kugOi9U*@5J5vxgtKy6~a1}>2tjJn^#Q~7PxIfKfa|w^bt+He%
zGv#37mdgS(U7C=kyGMC5IK!YH;!^A7Wd#7EG)u;ftttUCc$&ebWm|D1$dfu^o<bPc
zErN}w9UuDt`-<jWzxBJlVv3?(E0(ynS^;`cOvV)c++a2AJb{G2R**)wjSi1KKGZ*c
z|6qS%%lOy=Y0wRg6BRK7I<kJGSHtWRI*jKJg4$%QYb}gl*y6ZqKk5@O7|UaxNx#Rr
z>u`JIAz>d4kLqiIo|WgPEbmECE~`^KYl|k|#nmUtdqL<q<+M;l)!MWvxtYV3V5K&t
z^#nCi!X<?<g5r!3a8nB$ek%^Q+A4xHgHK(9RfIDYdGI?91+(hMaTZS+p44Q`=;0{B
z)EeSFE``~{NfBCE_`NQ%tNNOBsE&hxD1vx-;%=rKI|E@?;4CHBxc%OHDl5)AuiT`C
zjf1@k1yitBmflKFc}d;0L^}M$P=D$6zTL$~MuwWZ!h{Anz0IlmEwCyI5UIe#S((HH
zw8iAp@KkO+p74~pLke##0K8fzHWr`45bxpX{8S|CISUX`!%)*WJq_~&aT!x`+S7^Z
zbPbC(`^tkuVR-pTA-7~nsBmVAc?2h8*|~-q(r1B)X=Ee>1WPJ$SSKD_!Wd^3MI}ri
zPtvf4b!r~+rRgy)gOBooSHTPqVKYRgCh|@;0bK$fZz>1!uxE~3tB1ly_<AS{SGiN_
zOd8z|?x?KlF)ArS1y7V&y~*vq@y9Jg?|%1$n1bAPLd~m}!0!Jq{RKntn2^JGKX0-P
z4V3R2>@D0gJlL+2JjG^x%3-da4qzsZ&@~H^Rte>Dm*lKP!R7?!ehX8So5=6p7&gl<
zc=tohnLmz~pNfFTamiaF!eZ^m;U{AfA3aGKRW58_5L1+OEluZeIgFfgLTJ!gr^=I0
zCJez%;!M6%wFyFxC4{R_PWYKCII3dAlKKh97F<-v>~wxlui-i}aGJ?H3elu#w0n4b
zkHa-^j{r9Eljijz$-*9|fIX)(Mw;l)0z4)QD?RRBOVf|l0Ztp~4~?gs82Wy6U32H}
zZs=QZ(h7Z@Gtcg5R!#C8h$%8@)^h5^_K~5HM+f@~4-EG=ZW<eH(UxIB-J54Q6*gqj
z3?o>{sx{AM#-E^;8^__V6^4L34dj`hKpLjhEIx@=L=u+;<8YkU0S?TSG}Y)QHA(MY
z^mO-!-lZoRgJfzG(!!2%BxTb++5ABPBMe-{n~nUy66b_P7{4)zTql?;y*e%p$MCT~
z%P+hc#p16rH1QOZHmT4j`KBb+2&DB$9wsfyGAGZV=Cc)QuN!rtZtmhmLd4Q3t`aKL
zM0q3^+K!`$hHZD;mS6GGmzJC7EoeHJDpc{#-p<SJ>Fas>x>9N0pia5-nRMPaXj5&H
z<<yCwQ$N<0G(#dvqpEsK)^a`0RVEM+=hhg4NH?U(@0MtXjX;vPG-8i*GRve+5~%+5
zMC*p?#680*1BQTb2fBMgXUkmakWwO_*{V0`Bqi9B;L04%KgCi)@GdEgK@^4{j40%%
zbcn(k!rU}I_E*DM9z|yH;2@m3?kAjC;l(J*jJwqVYkK{do5JGI%;1{IkZk3-tA`*A
zGi3Epm;pbI!*ALq2q-5lgfr25^rJF!i(t#b#$4xre7QLAp7)l-6wT6h5X4m5v!_`0
z-N`R+Y?<GZ&sRFe%Q~AH3iDONCM_OKpf@vLfFPXkaBWI0H;$XmPkk&;1P_B?E#3g8
z;>x-sf^k^KMK&{nnT>HU$5ShhH>LNyyMje+%ImP=SpPui7%J;_kmaE;D8+K)#)Kib
znIk&zr#{Y@!Tbu-W5y7;RemEF#Zd+G%d>|Yr=|XwupAyFv%x16Q;oNg$EIPLtUAd-
z+%^5*Dxb!rbs*&Rn$}fgWNE;sdB__Z;xXF((nj7uuCV<0FtqO3mX38B#`W@<zN6p}
z3EYAF=2x`NdFuz3uWtI}(v!9?%NKgP^a7|R*<!m~C7Wqj%g{QqPSA1OOnz67s3maL
z#IeehHu)YnQh3Ybc|9(2<u&8^2^dPpn<Ur%O<`&$S`PuCx4raHky7Z|-5dHfxh|*!
zDw{V4yOo23f1G{72$-&M=>QWTA&9eywp?QjMZg$N48|lL1_pm*<B{KB!8x6EJf5E7
zkz+VBmvjOc)tl$cy2m1uc=wuXKsDkOmEppl2F%4To7Gnj;Ll@Box;bkib_<dG>xzu
zL(Ti!dc#pWLB(gT`$>MNr)Pw8_ADIkKwR3m*pSP&zhc3nMISry)UnsL&gt1*9xIRN
zq-sH}l$S+XSc~nCOYJ<LF%%3z90@X{O<|lgj(9V2@j4EV!`&l3jdSFN!hSq~TuHvf
zH{&lwqBgPG)Z5t=I_E8tD4n0YS;x*bn{*rt7t>h%gbj(1S#?p&1m;(|6E-frXjVTA
zLYbeiTVU|J>wbluz#O(Z?8N5j;J~<>geGx&m{#+a!;^J~15~*gY>L>+^XK=lxq5I^
zDEB81mWIhMn>v$qh|44Ms9ru=*%mtg{tGQVTes?XLEz;hOjea7xbMI8i)h~PG%jvx
zUijW+Cl}tcU~z|T$r$PpF<^=$J5k226LTC_%MX$)o&ZM75$7ignCzM4w>*}>PlSUs
zi?HbIC(du_p5pK|@;FGf3oiAVmgd6HuI{iir!}|}mW4cT&~YONl49r1L`v`+GBl=7
zu1#7HPUGk>(la8F-{YJBYK1LLKSn^2%P8v%;5aR;_-a@WXOyP0YZ-5DYsM77;|Nwa
zsmECzQ+Thh5w_QtI#J)K&eSP}F@F-3R8#5I<33q1wu3!3oXaoZ<ZNi!@yNqre00=Q
zC??zq)c*gfnv@}RKu<55Ys?qszGdl2^FDdvDFbIUw)F4P2l__U9CWU3$}qTuSZN|z
zYqeRpMmTA+A~B$&G$`K;cv85yr;-fzn|RamCu34yT0mUR9x&5K6eIygwfwqsXe^BC
zt4Bp0{Hq`#Hf~CeiksS$Ag3o_h$*U*;%H#AN^?K3SR-Qm3RoO&mCXo0g?9$1hRec^
zuL_UhkJ98#3N_EvuCS<DZB`xU;S@P_NFHSBbAT_apBlmaraq<h^yehM;h985b~ABt
zygx5cJNEI+_jGN4X9w#+HahJ=Drx@NI`oqn<39Od42_O$r_Gta@HbChQ~9}h3;Tx4
z<D-2O;}t0q0hP)mi!p;`m`c#jC}1@hJxVof%n_s+cK|o(wLIX+A}uia6XTqWO~D-z
z3HYZmGbH77!nHgw7&><Kh5i#)NJG*|&COy4-;>~D@tlGcgF_1p8tmg_s+0Uqz%(-P
z*73NhB_9Mp*a^kr2m_;^EWXO`Y3@#ei|~O6T<+pG>+z&bXVUh`x>0ahcU9%FGSWE5
zpA?)vgVLtdQ4c*OOnp*obxjdwxX3@HObz7PvuJq1ve5heAGUUE*dVp0QZ{v0NmD@g
zF+hK02F6I4gJ&<yEjG-3*NLkdetX%<?h^|QgPmM4psLw{KS4|~hoGxl&ck4zA|=Gk
zo;Jk=kY{m=H^y7#2i%`MhVwX22A!D{@075oO}ac26YB~S>+Kz3_u}J2k#{6`iD;9U
zg8c}Nhd06yHZU1cPmvJnJ&4t;P-leeez{Hn#t><E3UeqwL;zeCALaA33=eR!9?o3l
zF(OM{oS3@6Go49WY`Wwr@xB5@xsKnws=TXCS)CMDv#wEIkOS}CMN({xPRsiGSCcNd
z)1JlF8?Mg{_w*typv!9TIO|^hw0A9oJ_T~A1i9987cO4-krk(opWiyCzpFetJ|xz#
zkx!#!#u!vTCeGMzkEV3gCW}vt6rQPJPnV0qiSbPFN9n-E#_|!7;_<Yqphvsw48doA
zyPw?=I_By-3Hk<`%^gsio_;EM&D35^O>)tI&JaGI$e2NC)qZ*_jw4LxgU5&l*ZuNL
z@dJkyu;n2=jkCN}ev3<CL8O0!m$Qc<EjC$GWuD2@8c_-E6vWH%I^kORa^twHerX+z
zc>qn}KxcX7I+=qiGbE02=WJu>{Oiv)_wC#cQ<PJfTGh<5&g7vlYMNMT2%va;Yx|rJ
zo_t#F*A_49X|CjkyHTf9I+O5(F$5J8tuuhRq+yehRla1D1s;RTsO$MXu9~O~*nbS6
z2ddoaG=+(p$*AmPgRT<##1K2U8EUz166C!I&NU$=o|UxF?H@*!f~WDE5i(bCaXfx!
z0y>K@?zC13^tHjIeh-5O|4cR`zPJqV$y%Wv?_u0&Ox9%Hd|AXa8HB55-2`4Me#-Oc
zfr0V*PideeIxEzxP6nw^xxz`yL#Z5^x88Tp#Q3Ou#2>(fo3`JZ(cYaTg4(sESe*0b
z<ty9%*U6_1ozW=992*@^F5yu%MbtH=2J;6ec6Xg&DB0uU2G8QsNSr;aB4W7MpFNSo
zWD%(cWU*0L4at{}hIj1_J2hPxUww+M+0+sP%&>WLA|bdNR(IC&taJ>Ghm<fYzt0<B
z5Yn)jKaHc~^=|}Ve!^~vuSzq*sfq(8mKHH_#eVP-woaBym;$W4#H-)flLY-YIlYiH
zJ#p2oW^mxFnV@(ttID;yQz^w;{nGRdK1m{IR*o6M7%$G!^_~S`9e&E~xbjDZ!R~JL
zUIqgAJ{xPVz2!>h8Gs?hn%B&mKmUK8ddApW7cA-0Rrq85TDmQ&r6(uh2AWjOo<KjT
z{%w&iF`SUC0)p|dyM_h?wX9{D{>QWI0YNz_N=r+^d7W-8_4I}vy9dMY%GEj!uF(c&
zU}DX2@J?Nj%$`4WlcC{aX5c0qlNM>36QdKRb)bI8N1BD@pW+fP3(OQZjdxs@A2<PK
z{wf$$5#Y^`Ht9`$SyGlZB_U~+N$N@-I|~y`qY~s%0hH?&obJbUX3qT7Gp%b3N2LgR
ze%xtQC|*^4!NSme>35ntpLmjuJbedRNTVten60s#vjO({rHxUT*U-H1mshT7`5((p
z?w*^^4Rn;p#IqVmIdL0H4I^+RIH5~$nDH<H_q&@wRK)}5Rvw3)&YkQs$ChNydXi}H
zsG~GT7}s)ZM@M()(~fmfuaQ=^ZcQ#AfFbw*yfJms3P^+odE>bR4QW>K&JegJJv>b=
z7_imwF+rFgI03pfoK;~<6QUT#{Zrf+SL)w`6Q~qsO8d+rY?;y@>KBLQpHdGVI(mjF
zX^O9YMneX|YNTnDB?(DRA9=~Q&IhdNnOkqJj1CMSEkq*DMucW>sOCNyuO9{K<(fGc
zoUmfvZ?8Uc_?7K*`+CY_;{)U6+@#b<RVUOXOh&+|-{PWp!k8E?>m_iwHK<wqcn`}p
zoK;yAozv(KLv-%k9ePgCUWC45VYhHFv4%1DqN%=uWY^mW2Mw0ZAJiBw9Uy*>2gVu0
zI!<9D0CyFcg^2?mD-V{m*v<R`a@E7~SHXfPj*tC9pU{?I+>o9`dQ9ThYO1Ladxi|H
zH+NM%vU(|-KTB3OJyYiQe8dTMiY1L=?I)pxEJ%gR`qZ!%2|F&kv~8ek7dza^&hsQF
z;IlIH#XcLWthK07Y17KN^A~>b)E5@sx9sFzU05*KC8@!a@>X6p&!EAmJXtdJiD1e&
z)WUj$bOM;v@4ZBP>^GPiKbF5LZ#9_k<aCeI_@MS8Hg63bd`^lFyzkQCK2#=-aIj;|
zCw*-{Lf5PahtDCL0ZbAofzBq~Z0e_pEZ);RUU2g6NepS+6v81c#c#x;u%&yMLKK%{
zFfqIpl)zA^rQ-_EbY`Zs>GBW{?&QG|*TY$vlq+j(qB?S})l@+0OW17+m*E0=YA6EZ
zu64D-5)Zk;suf}I-p9DuKx&JoyA%e!?4waN4#*^D2K7v>4GryYIB9k3$4)t`|J=5@
zJzZm?6YPXbol*fT-KKgIbu_|;Xo*WJoyAcf(jrTg!KJY7Cm6%I-|}0gsXx||30^K#
z9%pmziKpyLQ-0}^kkiK_gFfbBD@`2q<46ZRD7-Y3GsKj+gpy54DBU_6H}X^D6srK~
z)r8_<PcvM=DguA3cW31gpQcd=euI_kVaJ<BDIqZyFK=}`sWdC`vGJBCj%GHX7(jk-
zdPdLh<!1#|!B|i~4wuzS&s`l?Rh$Kr{8FJfl2j-%;Vx|X{&yRF!o5E#6n$_&hDs%^
zluW`;S+H>N|9HXK;cW{~=ou`RM|-vAZ|W2`2%1!I=17P$q3_ghHSu^_J~c+{@)Kni
z7}D$qPh_k{Q8@D2s+R}{h8QV@&P`8;UB@jAg;gs<{{L_9TcG4B&NJ)Y+wbmqXdZfv
zo}d>JNW5et18W;>5D4sGoU8${HpC`2al9L{*<Fs;yE)$Y9LM$^XOm506NpcUPYA&V
zPGD`v4{&?}*cf84kwFrYMxe(?qtWzC_e@Vertf~=f9t<<yEPJ$5JI4?nd-mlug9%h
z_y6jz*R4B>x8`2n7HkFQLLJ8ue3E!F%#oTwK@oP9tiZAoqk<}a@}ibm!7mm+8ee(h
zhlhfof~{0}5(k-zAGA4~e6_qb^QIB1Ve~fy1QEz;W`iY0KmeCiz2!|u^l4~5(KwPn
zezbf-ZG>sV7#HDc>1ZxD!)QWU9`}>W+Re~6{w{rB+simg0GkBas!T5^<WFlP#k3*E
zz_hbdI(6%{#gj8WwDhdh?<`uD?}^1nFm+2fIC1Q|C4i($rTL4%+v-%e)^5~Kck^Qj
zS#U@=GgY^xHV*=l!!C{?s(6KTaOWO#pl6P8c@hUShR^ex_>MI-1Ob6#Z76lzB^-hQ
z=?Mt>Wwr@!1qdrrgw|3%D1GosMTp5)3x}iu)%X&}@|i9SXB=Ead6FmmVK~E@!<l9p
zi_0G(VC0EG{AR;E#IU}nF=05ubQ)KMm`NpE96{oSGNH^<6%xeN`_On29we1tEsnBS
zEH+(!4d27f_41h{`ycy}sT2#?V-ATp#x)BT>4X{W!%-aLuA5oche7yRYsS~L&ln!A
zR;xHTjf3;Zc63}?h1m*kP?(573xl_<yuc~`29ThtBpQD-j&&k$zMSG=Y*5LMn!TI*
z&HfzToLjiiI2cn%cZqM$5fHTGVXFsTX9+a~x3<XMCR`>uxE7&ASOZ0zs2|=?I71-@
zQy)5MlutJWBjnMo{)k)B!QY&|IbZPyRS{g%x=C2J*D_M0wptGxmt|%kf7BiI3lE};
z!l<PxmFPA;-I`t~!X+<;OGHL!G$EUD@Vp#Z;l{3<FaG!J@ZR0n*AH#M$_%8VG4Wx1
z68kV=$HkT#4sMOrn4H(vIqTMy=Q%gcTf85yxsD#df*4=YL6<7%PVp*HKt$cqF!IHa
z!deofWq>jy($o(Dfo9!>B$xILn!Wvd%y8dA<DIeCxSUnuT10<;u&0UBuUw!5cmxp0
zr9n%e;;9)}X=34TrhG``k*d}B;>QmkaF~v6%L57B0s=&ef+GY5(;!UK9RecFj~UWU
zVtH@|49kmn!?@%%7*_4k8hbsxs9ak%fZ{MSQ-MX}jeXY+UUGJ1f+$!B4S-_HiE9Pv
z0>65M#~+4UDsJHfKk_|`d5cYL)MQ?L@=2_4S9qag(iVI+Z<Mj!DW8Mz8SBqj)^Ypt
z)#EGDxxzrTTt|lr+tjh|7D5|#r=oyRcl3b>55*N8e<Fe6#gC>#7`sf|+{bGacI`F0
zUfpL#7cMrj#f$J&y>hS?!D&~5f#V1nSo2-%V5__WBGeGLqG$<JGAS!hskG|wi-3|^
zw+gNRfoWjzKV|8+d<8;7o(v-ZXf=O0M98BQVa${u0trD7y?)5I;Y=ci0Z}6nrx?w_
z42jfyLqv9;DBv<9>H`3B;#mUE0cS@h@MZJlEF4nZ<2&(ASiI{V$MG0UR4=341P%<+
z%l*MazuGAWBTX5WMdC1>oFq8uW2wpAdG9xp#i1cg9~yivmocU<HUpxD_56kzDM+w>
z!gA+U^vvqH?d<a=ubMeGUzx0x@P?o7;VHrb7mQ*}DA@FaFa9vl@<<p)K7Yg^wF5XH
z2!f4Q7Jckh%5U3gUfnlhN@tyIV)N$*-6M9Fs3iyr0)p>ZL-P1i5kbJkIa#2CTY#Vx
zHKsx<6!9o{tjLgBH`wy9gbz>XXE+ilpOnu`{h}QZ9_2}#rZf^?d89TC)2YR!0(XQ;
zLFi|oHH_b2Y;`v9j_83vN{;+CIo8gc3_vg&b`Ai6(>LlFXy&rxjrJRNEbn;e)Ob~D
z<aXnwXB#(%@m(T^t77y?Fv;`ejo9)lMMzjyTV_k9N>?tL*s}Si_}IU;?RxGPXn%>g
zLt;(EZ*uYZzoEvKg(ZTR`mvsDTgMG6&l_F7yL;r3SGLu5j1}6t;t412dVUpS4#o<L
z9|}E-i272$JTQG~5kBK^UlK0rVtS5Kt_S;HmAy><x#!}8kDbQn20sp{2?$bd1?Q3Z
z$eU+6;UmIRAiz}x5Gaszn?NCVQvhi8+c4fU{zb@1)rLaPaM%W5AY&1H^3{?Tu^ik-
z{b73WB|n?5cDbY+;DrT%lWt*IzLu3ihcm2cMnAk^DAvo^N_)@%_*$V?#zaM7hpF=u
zfEDbI=GM5_teVNZyJm_*u7B_aw?2l3eb{)DV;E+3;}*IWIBqs)JT#W|u@UsJs6B9o
zvRqw%r%J)AvTSOkjQH{K#a!dXxur9{^WkqL7hklloS88r%>lQajnX93Qg(5o`A!?@
zL@{|O2&~p|b<b$N@aWbTy`LP|o5`YC&*5p}&;*nnh1X({ie)L(t<;u`wZYjU%vBQ%
zG-bT=FxbBv-<Ie!?y{x$uzc3|d=G*^;3*uW>^5QNt20r;Ru6ny4EZxPg_1N1h-U4e
ze#qk#NZ=OXw0T6`hX_2bp+YTrAtRa50wrGopgi;f8^TfCAWnoI#!()q<fVSiH{#c8
zF|B~nUO#lBoN^zQMnM#420jb!s%YakY#j&RDzA?M4EU`1WXF{?f6l5&f7Tq=@0?wC
zI_H#3duOd)D!CPW4!T~O^r|C6>G79$_+$UJ$t^$eaICT)AHh8vM`3o&Mh0cSRvhAo
zt%xWf3(YDOi9UW(Tv?Gpw^+tYwwwCEE>j*fW0!A!sWfZZa<quPyjg9#QcO9m2_$IR
zpcBuug+cX7+2eEDJ3DV$eZlCw?OXESeRX?ouu@KULu+8Ss$ZK#H&E|hiozPOmVWzm
zfFKzk$b(BH@x_>mDem284je3)v3ZM(hZ!awPvalJ!khwvT4FEPA_k$-0SJW0khg*$
z3JA!E@I#8y9}6I&0wscm^oLLi>vVkb7zW#r27bAHgezq{#!<ggO{adLtUjs3AbzL@
zgvkIH>!y%J*%Br{<Z?F!Xl$16Oro<nf~_%iQ?4)i?W%M3#gmC8=hvOy`7x7i!)!c1
z1I5N$+8aEy@HjE2K2|IjcfT?*y7@+vf9B`0!uP%ttNwUX%B;kwvwN|rkK@^4(;xa`
za(@&9H7rHMt8C<6GR_>F_dED%X5XewrQS2noWveCw0qOWo=OMTN!1k5E31McP~aZ_
z^}an5k8XP@`F#FhYDO&XrJyy?ost`F1P60L8ibij8N%fFu?&uSh9;Q6i5vMBx0n%p
z%cHV<x$olzp-F-ZfN%(i?c0OJIooA&G4;x6S`W%A1Ojqba6rzc+^BstrVUy_1kUm(
zY>cmhNw@IGpUxtJ@ImEz^wT{JrB+~EY0YgM(IDj052_l4N7<@-I{x9VA)vB(OSxGN
zTvP91ic&v7u+Z;!ymq|1+=G)Her^1594ox=W8==+4JEU3o#%A-Wl;q<9qyP+xjLF3
z&u@LHy8p?a#twY_%c;tyJsI!fv!Hp3eywl-=Pt>ULNA$R2mqmSuHp_o0hvN6Gu>w5
zx$UOo){VpO{__9vvRz$mNXT~tj##KS4HPVu+yw5(YLNQSSh4U!RLGC^?9AdQ&vYjS
z$~eFohkf%S@&X=olkg4+X4&GN>$|y}Q|g>yh5?{GyY`rufA*}|)z)EFtXXYlcFi!k
zDpu1mqg3hd_p4jBI1UHb*w5tb8^8*70f6uVK%gKL0M0hCx)c<UuRw4uPlTAf2%l~d
z7|T<)3X!CV2$uqY;S^r>|CQG)u%U1xP8b#udgZC`iyz_B@*{Z&xOlHNFDYXroPL%|
zDC5yD7V^YtR0p%1@j1R*t2%yumv6e(mYlV>Rm_D~PMY5NID^|@O34}yCg7B=mUf*f
z#q0;@E>@KRUL-K}daYU*eD-JKJMR74RQ@mTZujtQPN!#?UoGr&e0+4%cjFKs<P-dd
z3V_!V%RDh~pUm%Z%IHPC`}yYzb62myZcla!LYcFdYb0+fD6}XnBx@a2LI8|D`^t{W
zgD-E&y)rhI#<4qWq5WE=>R<qdR~qnG+B?liSGU=Xr#hO?S7z>bwLWif$j{F0b0-%r
zsPDm3f1y}(el;-Q&F}AzFT+4OgGITC9Xot~V8Dru7m+8%Vz?l!Y6-}J_$dUqX5a>;
zpJ9hk%R_J|I2w=7SK?c${ty7*3m_<bumpfyH!9SYFJT&oRE1f2*8iIEG(Ko}&8VAY
zg<r}_KFb)EAAkTDfCg-n!5fJaFW{{=Amr@N7R{yCSDZ7}V9r&(6@jNE&y%u=)W=|u
zj(<#M8_LS~{(=1VhaU3weChYw>f6f+@8WYXfimDvP83m~;6p~d0xu~vAd-)0l~jir
zdv1rn<gfnX;DxvTcGgQIQ!-W0%0G-bB|yQlS@dc}R(1>xmcFz31^3DQdsA7*jklpy
zAMfZi2fBOBV6Ls!U8<DN-9K1gJyxon(bH?X=g*JF+j7`PV(KI1a&=&2q_(5K-}$$v
zpN>8F;Dbr8zdyAS-6?1PKHn4zP7KFfVx$oYKZTXmbT}FX1fR6})vYutND_zn!m4ml
zaCr^Y5Yk#R4h5T(;np1rIsDTJv9$P%Z*4S=q_tp)@*CU%y%|F0xSn2vd?_#Jp`f6U
zmQjXwA15F=PJS!a8qAn~+253%b-xiaxf#?BL4RGf0T{<^M8HMNd11d^t&Hz|>WPV$
zzxw6)=)>R5B`!R}1=F7_4gwfSJVo4y04lg5lLr>R$xc&!@fG7<a`D*v{_Y<t9ewlK
z5f?QB!yBE(oHC%$vf(ylixT|xLV2R_#J0`V?-ufz&D}kz%#3b-z8f#EDVIy<5e(T}
zs$=#n_FJJ#MW!_yQZ#F(ynk?T;+M}qU;Fm`_b2}O?z=nA>FM&~*{tsm4LWfGgMx^N
zO6jI>u+~IKrlBG<md|Pxp;yZMnk?go0<XfyI22k^@?cfCMcA1p!k4(#AB`JLt9+rz
zyTPw%Bl&7tp)7}%1+=02#Fgk+FC+=AbK#(I7k|9yT>i15S#ePgH<1fOkaBn!DMQ^H
zr=e`qphVcAv;F1#=5PM3H~2sQxYJvNs++Y0-J@L?5n}v~o8b3SXfzQU+B&;giHsTh
z<*T)GAAV?P)iu|4VnH`)8)1px{Bz2I!WIx^w%;<tAbeA$RDRy`(sOXS?b33k(w9g$
zP9nj99A#Iuj8+8wT!3pbrM<g$9em)M-}LUi^Uj{do$awiR~LY>&xzrk>ktqW3R)y)
z#-ve!*D&&V1p$XmL%+neeu=AYBv5~|zzZMY0tj4GL89>*1RLQh$O@pS|442D7|X-5
zsD)pFCuO(gl=~7lyR>+#Ej9;pnR^{GHc>b4_(I9OeB-3kyMT=m`Y)JB!s4r~5|B5*
zOi3^fAR8%cdH5lJ=SOeJq4;rk-6~T%hzS%x*2kIo)EG4U)d9DY?e-^rvE9kteB<zY
z{`Ais47fYEX2Az&yJ?a}e@<0Uh@i8OIta&2Vnqh$v0=_b3lBO4o(Pjp)?+|0K|(2j
zDUOZ~-FMGD^{?M?$BbpY-LYg_&i4=Q7hs5RBF-Vy^6&t{Fba+M$zzE~VYPLYJiug_
z2)T8OFv2Z7E8r@Wp^!6Ng<tbPK9UzH!x+c%LVh$(v%4w2l!I|>{#srwzk(tFM4z89
z0K{l%!d&)$N8HPAXmfI1>@EQhSSV#?C&%zLXLOfbgYHym$M?TKviX{8(|}}$_pa4u
za=2e6VSJ1;2#f$4=x$IB;u*hQ9&t)9mnxS$_2lrvi`L<7G83P|TH_6zv^#3N>}&f7
z23i&zx5eiMJ;=T1xM#U-$pcXkGL5n61rS6IqvYD#yKlJl*7zsC`qlit!6BTQJnST9
z^!RSQIyJUHyy-ND2oAzjfP|`GS>a+jvEYl4D?b7N5oY)m1W_siu7F_PHlK#Blg*Q1
z(^(N{#(||<{puEq`#?|-4mz!oDc3$fULSDkBLKvecMo{$Z*>I_tWMPFNdyFoNI*as
zutpe9q|e-NO=itgPvjGqU3{?e<S&ieK3BR)I8;`m^8RRezXl(AaoVtr1Tp%*_Z_b~
zR^;IZhc(_G!5lX96bOh4y*x;i2~_U&Xp3i&Lu@$7R4TP><;qH8)~vB#JoHb=wsg$%
zQW@W^mIXj6{36`AKnMcsW*Ib&2qhRI=usgBk8#-IOPGx#03c7T6?z4RtVh^<G_C-H
zK%tFXL-M7c*RuIU!yEWEj)X_tAyAM$bR(QQSsiql;${d_t`MLr?;40*xiRY`(!Fej
z5&6h-nn(L!jy)dMDPZs*QHE6ey!nY4@4BQ|n5@={5C3y2);9}**hh!=DsTffcia%8
zqXNBZ!HjNt)}MXd4TWrX58j-XO?Su&aU$lF2L;Q@A~zO0atBB6#Jx*zQGl^z#fphy
zDpmaTqu<MR_I5ii4|l_!8SZ}yg&r4$yo6&5;gbg|LTlaPCr|klQVPEMn?Mk=LL7o4
z5+2nGQDBHbTVON;qFLCF=tr5@O`;sI-0BYe)XrR#10eiC0HQGATy@vp#QK}FPCPYJ
zbTk2RLLSG_Ke%X26q$v`l$7joi>JH0Gu`K(Un*u&<<a{eNqKW;Vy7VvTf)caF&K)-
z4S>--%T%@u#$xBLF3w)P8lM~XxWfL{1O*Fzf{XwIc{zzxD!pjw(qjMcaB1+dA7`_D
za~-EL4ghew2kO%b4Fy}=NE8ZN#2pI03OeW^La#gsHMIf3Ak?PO_{?8{z&KIuAbcAx
z<$(LJlx1eP(uiAx-}>pse*g|~2O1(_4q(ehal+Yf_dpUF!YClH0fKc0`=%Q_Na@0%
zKyI?FJ=MK>b-9qslt=D+I2G?BFtA2f4S*qqmCcGt-`O|f7E8s+dFwZnlerux3s4)4
zzz~KcI(>flQ03wF_VsmdyyK3s+=`{e+DqF^9B<=c7$>y^nH0(j3WW<P6$Ipkf*N%r
zL8t-XrocA<fq;m{fj<O^bxV4iR^3Mg0In|rt`uoi_@jJ2V)4_V25G=WjbOWWY&K?@
z@=pCAG{n_+?M+^B(<uoEEjpWc7<P-@Om}bRdAEPspY!?uF^rie+)ElCZ|uM~tpRl0
zG-Aov!aJ+SjQ!y8?BGi;a^J4LXAx#e2deM1G}w8Gc?%4@maJMebHkmVEnwtT!>f$O
zOQbQy1z<qxSvey7kQo63s0wEY2t-oBZ5D0;1G=`Pae~2GZsZlWrA#N?CQ!|b;k*{F
zqg35m1_i6k8rgWlV>w|%K;S+?H~5Db8C1{qz4}3?K0G|(0uYJ!-;_g_38za-`yoGv
zq&qbj3JfRR-P5_|_D`D-81anbre^pUXrltSj!`o%R=82M@!dc9alBfd;Eu*a465?<
zG@Pd8azCUcd(qV!;!AJ&*hr~=z{GM`+kpfT7$W#qz!VgEXn}TlREQN24FG`96AauR
zihYM6C>TfLqxJ<H@HbP*7kq+6<43_EWkXm4Afmv4n?Mk7SU3G*l)}T;LXFpp`yIlx
z>uAZl=CAjr*Kf?a$;?{;5cY;}s~qcKB83`b&F#Nu=6?Q<s4?*IMr<w3&I~jL&#=Td
zBe~K$_}xd7V}tuS3x#=J7-|x0P+h05;p7AFzN4q7eZ%j3aso0oUf;CU#4>D6g019-
z2qJ1syFXDuj%ot(#ZOQ~!Jy!XP-+bU2r2nUtAJ48kcOHB*AF!X+@d`w{B&DNcT_VR
zqBU^O63(sPPA4?u1h&=}D$W&uDS$A^^vpm=PMtBtQAjCg3D}haFrYC$b-Od~i(eSQ
zF4?+2jyL_<W(zP(ZQOWg_nFF5&!+bL{O2VM*xA2gg@mX;^_|v6W-(9$iKXYC-*M^x
z{?Zr@3-V?6Y#bZkR9jf#)7Dvp)e5fwzzTU37z6{q>mnM$3VZ0begy&f&3Q(zC2lPP
zuM4*bzNHO*dA@<rH-G_rF-mc7IKmEV4cI0yyx;dO`;$Uu{l>OaWtPd7_E<^SmCE$=
zcC7itC%q+i-!)p>Iyzb3)9-j4b2<3NY^{7*)!q562hyeB%c=q<)fTAM)7zjHMRmq-
z!r>*q^^w@DYu1ldUhFq9ys;<3h+v@*M%4@|{G_3UU;{^DQRh=#CISF{<q<5Bp8Tk_
z`4b$WwLlOpK>#33t&~Z_Nm0(I28n`(w@70NTx=9^XoTB_Y1h5Rz2H9=ve(=acQb8o
zr7jb1X*v@EBZc=TR^510V(B;U9joobTL^o%JF(7rJ`RNPy)%<e;l6)t-}k~V(S`DZ
zBQ|YrsLs>eL{w+*y!n~SZvWj;w4oJebdO1-vwqC22Qx^hDGG4|Am}kF%oG3s4yQ>(
zK~$%O&{Hc2C_tlb3AbSi0*$9(%2&{-)(E>&6ncUMl)~N&h!6~r`rvjNH3grr0D|H2
zxY2Ej<4}RZcH_?dKq2=Vx7XaxUK~$M^Oou|1w}seSAYSwA4_L4%Rh8|@~j6Q*jL?S
z3X{)mMOO;g$3Y3G3vb&4_r=Pi1)e4tjw(>aErS|hXOtC@Ty@F2vn%iXFXNS0CX9zo
z>v#ze8UknHQm`SI3IYW}A_544fqoTw3A5n}6jA|~rBR?o!4YZ;TvNcIV2FSt1cL=j
zgP#CU%R39u00-A1=9+kEH!_YFvp0NhEH-B`cDgc#R6-b&$=<>yxDPzocsi3@aou&P
zRgXL}>{reB<j-CNFlLz8JmU=i&0Xz7+qMxDc#|#IR@4GT(^Jp1wY6Qkabq>TxP8Lx
z#@a<H7XX0*Z-qVynxwE}JygH}DgYpbO<F2p1PS~KqOco9J_SAk4sl0$3J3y0Edc<x
zbg9TY3?K*)f<la?j;uxWVY|aH-g_<<G8-QrPOi9+8Y00{P$DUUbZ`A%4!(I8n1%AX
zmS25U*LnZ)_=vlFUcU0LzcBGL-+`$Wli2b9{>huf>j-kYXct<bun%5ZiD2!*ox5aN
z`mB%tL9vb_W%<rU9B4@390&?Lg`Yw%f^XeXo&rKaqQFqUPyzyAU>L0eMY>M}34~qU
zAs8Z{(C(9HhA1%b{5iN*Ktw?yC-Px&&Z_|sg`sj{<3A3i-+3hgfn!dfC7I~$X@Vw!
zT>-rNvGCu$r~MuGKT>MD{sZ~azdUUcXZAP)_urd4uyqTM*sODtqs(EwJ?rAw?~3(C
zaKKFN-t@xIqu0N)J>iU}lHF)s>!X-5#ko)_oK_ecgqE!{EafXY@D-jFSY|)1#YL6R
z0<aX~x0H;ipW#R1aV$+)529gy9&gP#KDIsNM<!DrzNe78Vx!~45*-qW8NdAv&rZ^F
zpDOwO{^GB`aZhaT=l{5!cR&9tU);a;_x@7`udC+Xw#~!wJma8G$!>)4W}bOwcG<^2
zTXbHcR*0KqDsEB<-=vZ_q5>8QWLhiX)Fc{iwMwW#thR}23>|Oiz?(GMl?tbB6_5lS
z+`52B5JqhyV1Nt<-?MdNn0g)Y6C3`ln0@bWB~LRz@R<rAxRDSh(Ya_*=Y^m7kM7dH
z{`w%AjEP<M{e4Ga|9&}|u%#(jMG+I#76Y4pHS&|2SGVE&<?&=EKK*5&EuaFZEuww;
zP+LTVlZ~%*Wk9JZq?M<e7T2SdmYzgBWY{qH*~n|0C|})SS~6%5nejzA;77DXpek@?
z>uksD+hV*6|6r*7=KmJ=I(qn?vLtJ`c)&RgesS~Y6!6~hX}A2=AN`>8+_fL<Sn{<$
zpSa)?x3i6EnP?5@3<RjxwP;~#?lqqo8@d0_I#aU}00qvy#T2cFy&lqfv&E*hEqHTl
zO<^ZbfIy+8)RNi@Dvk0Q$`V8m0AeF+8l_=oOh`E=AS4~0J5S$;&Bs9lTa1}^?Qrfx
zzgq(!c+3k66GRiw>Gp#sArnm)f2Y%(D>hskYkTJD>fkd!^~VMWo^NT2Y2-n*1v;UX
zosa+E;LD#_-;=v=PAnd)`JOw0UC9_*pg9pRSWr;->2F>|J{%x;SrKfCCQT^ifmbBk
zI+Zj>A)h?S0zYj0r!3w;!<?g6+-B<O3x;x^yk|Ul<~g$v9UVBv5{NR3)7i*=DO(lw
ziTrTY_1rBjP)s8d0t)gSoanfJVEDy5Z;X4xkGChg7XcK*{1{TG9TXVSodPff6rhq^
zvIxYFA}GR3o`m9uLQJ;?D4#6yXhW%w1gbNVj$P#d5NHCioyYh3j8no%>Q1?sz3s;%
z$(8S%fn<1D`J}#AttCCBX;YolTivH=9#`0ff{bT+dNQ*=@X?X{*B;NMll<g1j@k6^
zPB+IERy&A>ARrVJ6l5#;;#9v1F=)gYcq~H%n`Mgl#S6axOB32SjL(x@!ggQQBIfvB
zCGSj@N5;~({ip>9*3}UVr(xL;(%{+iOpum2Yz?5IP({g}b?=W&K5<1cG5-DbcxC~P
z*Bp^C1sC5G5HgsihEOnovYM@T%0Z^J@)*?5LclGckbp*a6eRZfF@+qqF4y3XkL?>C
zK8R8;IDYX!C4JMk#?t4#rvo7wF3z3gg9cc<w~S#!XbsLcZ<%RpAgCk}KR4MuGuQRr
z8%Ju7e7`M~cJUrJw1r!R+Q4{?HK<@<z0z$>Sk1~HRS<ykBR^uIyog(_PCpH(`Xvf}
z*kz)2;)d6{W7i_^YQKMAa5A>xYsJ*M4Ot)oi*p)La%%k?xdy@`4~`t|bPbhhI|whn
zosm8FQm^*R!tr>lKWkD8AZz&e3jqOb(Eth}fmVeXPBH2ii3W$1piqC*ZFvG34F?sF
zS-*}U*uY`v1DnK=mMLd)a7W#{>`MnT@B2s++rWd>YO1l8IqDi{)z*=!YN`|OxxIav
z#5?~GOTrMsG|-R%8q$zZp07=-XKmBX*Ws0~lsqixg%)!_oCgxWy0PU$UJHv-0*`(L
zpao$71_A=e#kT~!M33Vf++o~RpBYZC{|~N{?Fe?gMqZvlE-iBuHNdKFnb)iV%-rEb
zW@pX%qS-mSU}D?b3{KuQ9=<WCnu5)XASRL-TXh=Sx;JPC^mZCF1^u)dS2f3BC_&+J
zsevE*cIRNVdb9D*yk%ed!*}>@=PVpY6Y49Ls%4I$23nfpwJJ-rg-DoZy5^+J8K2-e
z{?H6)`qPj%)fUtQYNOghd8#GoR}Dd)Y6+#P6`HAP3I!gh<KkN#SgnqaVsWnL>H~@2
zytC}iTF9waola>10+n&BrnOFSG&RN%F2TW%yY)XYY@WGvhRMvp5;rzOxj2nZ7AFuK
z5!kTBXZ~#Mr?b=wsxg|WYKSNwlS>j`C>H0O`S|Yl7E^CqKkoeduaukxt7IE^)4V$p
zwal^9K&$(7G_@sofRy4p8K!r6+RVIu!j!+AFv&$|ld%n-`gXeR6Z4)94CJY>tDAmW
zY9UQSKd((n0Pz|Oa{&l<0lq%E*_if=ithD)QZ)-!ar*|&TaYtenW|-uvj$qAII4;h
zR4}Gv(w#{&=YvJF``h?XIzFYrr5<!+L|agxn+=6HsuAc{0FXBOl}Ek;O#va`vl?Rj
zRSv+*=9;fm%;MF-X9@U$28U{^WnNDWv_NsxwP$6`#m4TLYvU%51CwGq(S^c`@Xjc9
z2qGgDV&<thih@D~{&1ks?vVn8c_=t|8jn}QwdUhH5j)_<$!*s>Heps=%vVW+H|KbQ
zTg$xe8sKAWnWL!z8E~`7hWKau;->p^V=_$|PvZOMki{gzqJ!zETL;^u%2%obY*O+_
zImlL!2?a8E5LIlRgA;-g@9Yut!Kd<O<$Hr+d$@{+bS?9`YoImIK9(vB2i&IW=YKkG
z2G-$d$&81Os$*pvZ!_|BJGRx)_L?|iYp+(dK~!7Nt$cozOf7m!riz5MI1cja#;3Km
zV!h&`QS-jP7&8mc=NKYAwX6+a%bbuJXlaULsYaQBqI8?MlUo{da<^j5VjR<=Sh?eQ
zn(U0Sm8Vq=pc+A`Y7Ny2$|u!nSkVs1&jV6onDdQwLPG!$Gd?n4uKLC(0MU)uEkK|W
z-U!nI#j)0^^q`Q)>-0?9hwwo-wDB<ruk0O1AO%{1p;Q49r3wb+5l~DUTBwOwH_q-v
z_pH3#n3<nCU@rf{q?x+{hZX^H+}0@@pWthm6IuhUf%Y+1C;LWxtIbO|nR64SbQ#WJ
ze5}Jb3&6*IC>84F=7wNmH;Lieb)uWyCU%#ofFv9dIh*Cp#$_;oFCD0vg<lvpt8aBp
z+iWnAgS_w|Y75?$Ing!H{OC?}{%^nhAd4cCoNJ2pW=#5{H4$jpbO$Mytrxg$)-0Qx
zXqT#4b7*%-v;?&bkU^lt=fYwO$>f2GS@frQbHS$)00g^C2!)1->~jXMWlnGnv^2$W
zRcN3s@I9oMNv`FAS{P;T#K58tDg>=NwF09M7^F73_*Em2M<afM0gPA<>G37pI>rxG
zQ#8vSESd#Z<#1FXr(J1~b4x=6Rq@9Cwlu|Y)NQ1pj-gfeO#Hu&;P_s&>?y}!DM19C
zz@Sl3u%>CL2}Co{PcR^24D-3M9$0+5-2f2Y`$x?3C-Y|Ubpaqa>m`7Y+~{tZH?Rg;
zpg8WzmD4aFz#U1Gz8WW4<FAe`Pc4Nn=Rk-#K2TujHQ2}tH~<DN#$nfpaj}1ogK2L&
z-dHM3n%p1e&62;Wnx1t%NCpkTOo*%&AXo_}!?e0j$63qVVia^_9Gt7U1YM{1jhpfh
z6UJMN9C4<WY6i+PyGA11L<-yvrkwCXoE!<_Y-do-#BV5?+%0uKvo?+M;@c6C-=jiY
zWW?kxbJA*{1&ZUW>gemLCe{@<u@4u_$PcmFjI9>f5+#BxTQjH;U>)|GxJZG0<rMT-
zzs1Sgag+M=h)G-tK+VJvjt+<4G_8G1K|P%G-xerdSC!i)+)X7+cAaMiG2nLg;*fw&
ztS=NCIaZvfrm>skp<5+r>8H-XfO-uMkh*!yc-P^>-HWg~?GiD_5TRmP18Q3lC!MrF
z@%pNq^Rp0TwC>r3Nt60q(G>ny#$++2TEm8<S`yQ?&<g%uTxEg?Lgck&$Dpg^T#IxI
z(T9q28IO@bI8JGS0TuD4n%3T@*I)f2&B&%~_B}Ofw!W*=bhgD!27_+Cx1GXVRC0a|
zoA@fm*?<!UuE0QH5rCjzkOCkN?Q^~U0=05@eKmm3(oc;TUtd<IZED-)!RWbTY1-s|
zzi3Kd#nCd$$|iAk!MK;XCVoDK3QO>sUMChIcv_SU!$F8A8fphBl9qXsYk+XTHa5o@
zMnU*qZ_6Bi4FuXE+2xwqzxlM;z5fL>^R|@fJm(w}Yg>y!HZ%ip5q6XGLM<R@O`IB3
c8mavM2XlB@riAj2Y5)KL07*qoM6N<$f`l3H0{{R3

literal 0
HcmV?d00001

diff --git a/Images/mathcheat_numpy_logo.png b/Images/mathcheat_numpy_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..5ed48c094b5a29db2880bdd7011ab69dc6f9cd80
GIT binary patch
literal 8565
zcmV-*A&TCKP)<h;3K|Lk000e1NJLTq006WA002D*1^@s61ad<n0009!X+uL$Nkc;*
zP;zf(X>4Tx0C)j~luKyTP!xv$j46V&f~d6?l;A>(v?V^!ics5Wwbqew82iv7$TV|j
z8cj1XnHe95NI~5BT8bMVh>I%XLb_0Jr3;Zl>uXU2ait=NLN`*z|0eBBDpoHf`Et&G
z&+CQ&RS#2+V?{Jz*v`AjuBdkC@PM{>0#&F%HMmlS=d>ph`+cTXKIgCLw@{>IvPs5@
z^&Ob_v~BhMOOImj>z?-dp85KDZ#6Cx0*XfKifq7JB@YI?SMtfclc(m0WMe3mCOhOt
zw<p;_zCm7^4e?-z)50@=sy1qJhT}3$WCgjUXk=x~QL=8QO`AL=?P<>#riT4Se#lwS
z#+8o&t!scqFDteI4;Z}*tXf~OHPits`+>1772D6GKOd!b+{<j)?1xjTqpayesq}L_
zu=pA<bG20Zb*ogGxy>A`<C$d?+@h~#Vah8Ys5jWn_{lk1h=ZPdRSR&FbL?LN#4Z5W
zKLQ&b0Lvc(i5j412O{U$ZgAzrDb5Ax$T_E7Gdq;mHtYI!t(`|Jv~JsIZq!nirTH#T
zbA>0|qDVJm*eY<&{#wR+wXk~+^2z=p=Y+R=mX(oos*|%sGu!%-7M;7vjpTb7b5A$9
zf!9>V-P0SeveC?U_XHg2v#mJKMQS2-_F((~nei3X$w%cpB#(H-gEFRXKbG2;AlK8L
zJf4e5O)dAfof+!k8A(>GGeaHmS$^#nk}~f)YVJ6e|IM?u$a5Tq1pyN_?>00f(S<0Q
z;J`%=8G1}AOj@O1&}zcN@xLl9B+9!iD!TBQdp`SnPN2ZuGJYSTaf~#UBWU_N{hR(q
zzp3BRzh0>?G*mnz?orbizx<tXWoB88j2qM_<I9?Y_y((Or`JN3)?r>jPhMHy6s|5~
zU#Ty9G?_zOj8BKU6roH_7-T%zwd}q~w)iIed~?|rnjW<;&OWKD<a(oy9ht0taW-5H
zYyY#WVA8Uy@Z^n<E<9_Yu2naw-D-=v1Dd)^ZBtv-PQL9__m_9{a#kizvTm2vr`Usy
z)AUwOx-we?{}z5R{4aG0Tezm7$$x0V&1FnW_>SQAA9qOkFZ>Mwg%u5aOFKIN000Sa
zNLh0L01FcU01FcV0GgZ_0014}Nkl<ZcmeI5+ml>Zb;ehBpT2ib&wZ@BC2V7iZ3s37
zJCJ}e0Roj91oDtdRo?T@<S|c4s8j+Jp(-JP;BbvGU>h4-7|WJy$+l*sx%N!o@7?)*
zyXQ!)(e%u;W=18?^j4iY-F@!+e0%M0eQWKlz~6uC-Hr{*um%oD4FnI!a||D2SOcyG
zhJYRd;Xu{E5YPwebq*h92<RaO4pa>c0ezre=kQ^??W(}D)^@wRXSO#j^q#rb;f>x}
z4Gb;xt<~G`s5`U<wzbf^z3*TkV3APABH@6Ay?}*+K?{Xgf_x7L&GUj*t+lLLZ(Fh4
zvP%8n^a#7HyW#PJ*TB94dTPY8wbi^e0cpG1u{t1j|L7MyKs`B@uvpl$Ql)C?sArim
z&pLsywL1Z~2>2eLuLJfr;h!q!DG2m5fMFTdz-BeDFMtkXb;pxt*_EsVZXguk+FHyy
z05gEquGE@#_WYuajl@h=yu*gxkfpQifH|3nSiV@p!h6n|r#N>i3>W5@uot#i+_Orh
zX+f?L2m}Fs$BLDf<x5R>z2Tq3RRentXb)hG#@n{Clmmq8PFN38gIMW0mUec&;A(n$
zJh5KmUBK&$=t!Z|u(^eT9hpu6+@RHH0M#0x=Uf86&Qq>7Eg276v(<KuLceLTK**xO
zu+`c_Cvd%T56x=Nox=B@OjsTY5gAQbC>eE&$J&cWEEEY@2ktPpRJ3Xn%>kgwT01BU
z^ep<G%hv!rRHfOrFaXtdjIRT0hz1koJZpuTEi9L;&H2=D^t+0_w_8nXH>zkFJuS&?
z9sW7!HL#}^`s7r^E?l^3=NC$Vw@VQm4+gklKsOo<Svg;|e4}lhNYo-R_;NgIYE{m#
zVre_ohAjc;j>qxr?`a=E8eoxS7Hk)@6<b;<b%CrO*rwtj-Nm(efsVC;Eba9l+#bU}
z2fYUN1kj=x(<4D!Tv)LhbfQ+RTf5$L0COP3>WbSgf!cbhZVP}GlWK7QD=HzH5b{Dc
zk&0MlrC>|!(+qi*hBvR`|2bs@l_bV~tzy+y%N(DCd(%*Y33>rAtLwC}=<Q~&!#K3i
z2OYp0pKsTIjzxkt5^V$Kti@0&#DBrBTkB#$WIA!&d=Zf2<V8WB0NjEOwD`RSxMf4D
zji#+F!;Kp)%i`oU*{@ivSfg5FdjRJ$WFyJ2Ro6;(6@J~u;>YO)LMsu+u|sT6M}rpU
zTtgVE<_~At6`+&xkWGz*?8?Hb#gh@|m<5p3q&5gt;1-#!ZY$7?HsFo0k2rKo6ak(Q
z1C+Av6+oTCDvOdtkm{UsN2_p_nY9{7z6@2#mo33b>sbFfmbi{}4|63oh!&?jF`c&5
zXw)?zX(qb<upF)$xMe^mV<D43F*m<z*=EP$peKg2R)#t>Vo?iYxr^D7%>(8_yGt9w
zpe_UuD}XPGMQsA?#azW!aPVYTLv7C*fUpjA0O<g}+c4w+7AkeFr$!^IkCg}I+8%*+
zs0279!u6e#sYc+==r#@i9HJVyB|s-*L0f_^Un-XDO15lez!b*P21G9aX#qgCu(ZXM
zqE*^$t3<-+#3&j7KPIghs?fsO%N8om(&pjHAwVegLYBA6S^<{|4IsZ-wiT?rwudSW
zs{{NZPGu159t?O^<8wpKBrmOkj8{Ra(`Px7>6B;Hvc3(=VX1+E0UaTHJ%4rC>Tuf_
zz?6loNVQOn0p0-4+1gs!If1bb92sy20j9Q>*|)<|>(s0XK-<uWAOJ66jnx=h^^hg`
zy~BAstk?1t3$$8R3Wptk@c^;tN(%?ki$<)#J(QpwT+(z0(WQb_)+7zKZGtFdJWWe;
zwpuqgEQg^61_t!%O3oVPO4sSbGK##adskO4WG$>!Y`IdinM}ewfEC95liL@7CZw>u
zlGq9;6$*W)0I>+Nta=CSI7kD))RWUuBPdd`a1%9yQVBJ>R+nuF8bq7p5;O!42QtyU
zxZAGts7%6z#xpS&z=6BC-*)@w-V+In(vbAXx+ILrE?lm-9F*<Oox;~ReKg+H$h)^l
z{TaPWhjWy-JN%B>e&$#lM;ybW_r-H)(QDTtF+i~ZJy(OCxKeA|LbmRrwzu;E2724!
zK+~Q${Vu?3S-DPk&Jqt+?P?JUQi<EU(1WXmswwDPz-rgAt~CJP;9;XYX`PB#gJu-5
z+8NOfYD9ckPNXU#!EX&fx(Z*8LsLXMy0Jd2Hs_Q>7@_uxIEBGl!&3Y@%09CH0`mk7
z=EO{ja5>RKDitxh%Rly@M(IQ>ll1Jt_oV<fH%mElv||79%<}eU=Qfm_H8U2qdyXeu
zF3RsNl`Na@HrVY?aA)k{QU9q#$nL%?W;5eacT<u}m0zQ0QOd#iNZ6)E!%lh94zIsc
zvLC;=ileSKxc_A!Kqn(Ld+gp>K+=7oLfJz{*6g>J$L*h<KWdABsFg`!waJ`|iQi(K
zWx)Y}2MFiUr-ics=q*waq6Qx4su2K5fS3aGA%|2a;H$dGNiuK%Jz<S?7Uc+Vj2cWR
z7Qyynp$UDn*-@M$Gigf`m2A+E_PU&VyK6~T3r&0KqvP)Mr|yc|V-Jkj^RMN%yqvNK
zUwpmbEZN_EIc{&fTee^PHn-(5cjCtL>%ED3mB0Sn5qFEPy<M^&{d)C=+m*%s%p+s=
z!0D9Te=2E{nXrBH$BW3c`w!}Y0PSI+MH$?pN8rYjaM?O9tb>jmr3Kf)x-<rXR+5CA
zIR#akr5cetxNp3hyAi}1R{^J}i1uwlL#A!bK7J&JbtgI)2AFlx5x{Q|TWn$3<ZN;s
zIiWcki+T!=^Na3`rQ<Hl-r}75SxVKG>vH9ED#zqw0D5t?Zf{?$ZhKj(!8x(&j^E3r
z_x?pMHWoeS4*_`uL#7^Sax{XZ8r@MHZ9LbY-`7p;P7mvO5daaX!RfyWZ)dQWX<o2E
zt&)M~jzSx7=d6V~5o|P&<#<wOnYD&rV{wxNV^q{2CL?uw<k*ruacbV4eBYdX{=QlJ
zi-#`Tmp?cMS8hOa2)J+yZH^V4QN~`0bIT&s$qi8sh(5dX(S4{*?MG4f`SkNy9);eA
z3x4in6Evuu*#-qNoesYrH+GxOsn_V2t)Au0*^0lL-F-wMul+9rz4CReNC+m1*RXjq
z<`!2_8Zg|_mg~ISI_r=khRAXiKvxw40eXXDT5MPA73gG@m3Smd(ju_Z0g%{bYr!w-
zH7OpF@jR3italaZ4}KP<CZp`*v5tTY@$w0rztL3G0ZRi06q$5dG`)nbwO{3}xr+Vj
zcU{^bPW`3dpL9HVyX&djy&YZecK5NH?P|z=>G|mYbZxDk%P+mh_`mJPdLTdt;kp5m
zh6=foox4!93zsT(d9H4Y%N3g?h_Zk>;alKrsR|#i15DA8wv`2JiohHM@IkD%z%Jc6
z0O0EYu`P|79hBP~A!?{2Dx{27X(j67Wz}Wny>`pe)TDAh)H@Wg(iN<?)1he`b?AuB
z8(+8n?!x=<=Y?|x=k(~*W9hJc{^Jww`L?>0lO6ReTNI7#Q9W%{<YaDD)I0T_L+6y*
zvQ<eCU)b)vI=&agTRlpKIDlW~mJ~eO|8o6`JGfT08MR;hCT#CqAt5N}Su%_U3}>x2
z(3g421hfNT+T~|RfpbsJ?;Qm`pcJa%KoXr8D<w|d26(D;g~RJ?Zvb$~V#=~>1L|72
zo8aU=^M(TCIXAeTmyBC%EQQ)pciDTYU|)x>N{8l$F)a7((4l|y>#XU;9hr#Y{m1PS
z50Bb2udMB$Hm~m@xoaHCGJ*4S3I*W|65qf5Z~;X~?r*m!ML-II_Bnkd=DP5I{O>DH
z9@8G!1Gilc*vXmhIllR$1t+0>_OWq0KGp5TH1Of#m5TlR?3%&`uKwi*JcN3qWHFWH
zwYvT6<u%fLHwU5Wdne@>*HkI+9xpFc?XHOk&f>VET)yg|=TiCJFP9mV(RJpZ#G#xS
zk2qyS*HUBr&3PG*?Iq5E0Nw1w?djJ}b9p&yK!}UYW37wOg`>zgMXciNYKcm0lJ-ej
zC_rtk12zg@Zbf0#d~d^j9k}%@&Ws@6fn<;Pbvo5?iTl+Y2}n2`qU3T88_0AuT1lGo
zD9m1>F?d-2jXJczJTVq`BVp?F3=kle{VQsK|Ml#${nb-5mQHx~d-tWuU1`{B$Z|U^
zkG?<cbZP<Cf5F{*zkY_tAI#Ws94qZ3%~z#&pVM$uU3-+ql^`uaMfdZmkBs4GG#wap
z{0P$Hhwn{0ko@$eRoANsc<;<t-LcbS5$D8-LjCL0^G=d;C+qj854v?NT}MVr^&`!F
zex+{T`PrhQXGbTyV1N4gm79S26YfhA>DQ=JJ*keL?bEn6-qt{Xjs%MKH=nv<<yssX
z5F$e8+4;+LyHfOQt_WvdDceY^ip3>pf&w9sR;5KlCE}^Liz2RON!+I)gaEfL-r?n_
z+lr%>)Iv#nYcXR@97xinEf#_{c!~sKo!NJcV<t#&DgjjK&}kgM%(@OOE-oGVn3L|-
zb?A*xyHDTc^?&#0%l4Hgr(NXq(|~BL(6B4Z*E!@?mwpb}Pm4hN(GQHcU7e2JbC7TK
zb#7TxDMxBdei5hFBJ2ETe=y~K{(}#X+V`=1OKaEl?mxm>OG^77lG^d9m`Qf~-Y-|2
zaneBo;-7Q8`~q3Z9qH^Gr(Qz?ouzE3H+cA-l&b?-`rhlSuAbqyuYP96-J{<txG(E}
z)JBxgBg_2DS93cCvko5!(D6u{8>c&Mqr0vXv@@eQdvhsm|M2VyJ04%PFP&bnd=+&g
z9<xdb{@e0wxtX*VR>GDCgo^T!d+)4FoHqhytpS~grDAre7PeR3keml|!KusyS~kiK
z%HQx}2^(F{))P#W_%D_%9a;uU+*CSr1_yCJbm;ZE?z>v1NBO~XEB2))Cf&1rk@YvA
z4GKE!w3Lwq{o+SG{qO$T->08rse#0Pd5Lbi7fkwH)=yHJdIACPg>yG*=(CG8I|s<6
zA>XwgJn_%B)mpv52hYUZc||e)_l2zWsvNz?&J#hrikKjpcHfD(qZ7T`^;sjIbbl}7
zs|`{H0<`l~Wfh!d0F{)u)QkewLft=O3CH3!q&uQ`BX#pe&}zeNn<F~-tA&&`L0+#8
zt;8j#NZ=HD=+IM^w<nHub?BxSfn!tYG>AO<M;t5Nt3$7ee?vjSPSr3QO?QWU?Y9o?
z9=gw;OIND)ECCboXjz^=`NX7si-5vrTJ2`1=#{|4Z#OGG(0ljo-LLn!MS!!h=w8-P
zs3tQJTh86QUHdO$(H~$P-p29lJ@?tSil$|!<?Ne92WUkc<%8sk*Zl>ZLk;R>8f3qQ
z+wVF9sT>M1Un??;A*sw*OL*-l96@9IJO?mIa-M~u2+bDMqzD%l0HFnP0JT6iI2;|C
zh@y&|#wj9^A33&YPn^7JPdOd>vi;@5@7UKKoFj36gAPrXRECQe$;4BsLsM>-4(%@3
z=g&)T6kIp1%@cs;3p-Fg{8PDV;fpgI{*N2hK4taS8#X&C;{T>h#LY?_KC9BNq50fg
zDZ2Yr-kc>%bA39B51jG`N_Vez1m{tT%peWKzt4dH?H|;uLodJ+D>#EyY8ZR#CJu`*
zwLqAde;sR3hBFaXae2;6w{fi~LKvspyHSV6(xXGyq(ft!Bg*I4M28MTRXn(P464#k
zht`Gqz4U80u}>d<{9+dV+*Jq^Q26ABM)&MWeiinZmcHlqm4uBuI0=*={q@G}z1)1`
zwwu1+BItDuQ5%&9PA7c^>%KpDCS|W)EMlZ~@|5n7Ykvj;bP)0@9Xg*Y+qv^aJAbii
z@60u9VX<nL8MIohR-L1wZf_M%nawu%O|o8x;EQgq`#M5c<si;n84Gfw4y`04!sR#v
zz78!7y1{;`e5IyJhYqnGWzU;*X!PhRR$B>9dYSv7L+kyibK0ut`Q@4C*5?M1w=XBj
zFa0pE&1v1?{^<68`-;N(rRSsnlUw`y`0E=sx-Zv(wAr)vo_DkDH=l3wUtYafa=lRs
z11Ob7MH!9C>U(8C?(AP-AP!w`MeOXkfW7&yC`H5)Q8^$0$Z2yO8=>dWvXW!eHg$q=
z1)gvt=tPUahqCIFm@mrFgeqLqq1ji#5J^1hBt6Ck`Z{!w6N!7*U8F!6d+=rylgMaS
zhpuRN7soptdKu20>(QZ8I@|RZ>_7Bw)_$7gcIS{oBjw|3pP6<!AAgAAplmc1)c8#g
z_a!^$SFnrfaAWcJQI_InMb+e&e*3@*UGdlc{{Fg`YUmtoj6-kf1@z{3(Yts5&*IIO
zaYCice24}weO%o6B5I9aSN)IPe9J(9uAo+Y=hb^%^1Vy_gkWR~1*~t|j*Q2R^k7?f
zB@DouHkJ&y{EKL)W^46`8y?kGJ~TCOlMW3?gHQ<R(8+{yY01;bF?|?TUAlXtPV{es
z4qc`Jgm6T;4Cm~om&#%4)uA;4eLx{+^IGcpRe;tD*Gt_Da6P-al*%9C?SJ9%N%jxe
z=gIkyGpA!WD{7d2NzkZEl6!iJj?>Fm=>=}jF0jGtC-?R0-tGGA1Ge9GzZcNHx(<x;
zhT<QuANxJa4F_!TMbtvibb>`eBLVxn^lrCW1_E@TQ?@TZzJ#1Y5T!;$uo<=svjIk7
zM=XovHoF`lA39Aw2iCg6xI%zh1E`wQE|ZSBh+z&LTGUAMcF2iVs9W6I>Cj%%E-r1N
zLnF}XHI3qAP9n>d0IGE85opIX9U3%@Z`7ex+dUw;)k^9;PTHu7);r;N7f8^VdOdHC
z-aq0bEm_!pAduYUmxu33I}O(VE^GQEQ8xJ$D*eFN3$}is_kZsNzkeOA{a!$yyZhW&
ziGCey+*jWREwaYSVAL4ZKYo0|P2sizUA@&Z5TFy0mOXT43Cge@ed9SFoXOd%b7S`P
z7mnH$ENX*%Xoa?w4;>Mg1>8zR(mWkWMxmTYR&1^5x%AKg$gX!7p+eSP6dn2^`t{4N
z(@;2X6ghAd9Xdl`MI&CqvAB)1kK(ny4(%b^CE#DG^xLIFPm;W@q7d^=11HLTRs>J_
zuS!4es_wT0EzxLt1z2RU<Xj5G_j2rSv1Ua;|JVZ=cMd558@uKaQVaYdZ8)31-bi|X
z*XQ1S{cTz+k>4+hELLha%6op4Py0*}SyTfV$dU9<&RV^aoI^Eu<-<t!+)Wt>&>a9o
z0I+)(x+>2tLSH8aFB+kVU^F_#{o^SW1=QF;Idlzl=2NM%efsWMR1s!H0`Se6=I998
zS}SGWzI4pSt$+@_W_c`s8y#91chaE?42DghLyKl;BAGG`W(`L%pnPbqBa4?ohYkby
z7KuyyQ9esjzw#{99V@t@(6B7DB6y0>ZC8Fkx~W3a0z*FwEzR}2&n`J?GRn(UPKOqO
z{26jD&NDFJ6DTH%qA8O2r=Og3A!UV+6|z=$Ef8yey_;^>yJgRj6{yDYl}}GQ>#N4E
zhA=@G{i{Sbv*e738u>-T%er|r9aCgc(Y^m8y7$QaX*)_VL>63iATTOK{kLB{>YQ-p
zV=S%?Ze10v6rj~ZU4lpNrVIq=8@g|`Vt}THk`A3=ih(pYRO5G1jtoFk@_mgRDkKp_
zp&lgb(Uj>fX&6yHjHcn)v6h7kCBRJu6O?)M_#%!USzk01O--OA`a(Nq|MKc7Qyf4O
z8@POE0h^qW2<J}|L2RHyuQ4S;a)bmaU5C=o5_Y!^_E(Xw-4ucK+do-!!;`i;j{x!Y
z|Ct9Unpj7}+}Z>{NUvRgrk~S*v}`Q-Ql)QX&fizp-$_vxp_Yy1`-I5%qbLbWsiqnc
z<3H!EUS}{s`&YrLmPidPP>Fe>PKxd_{j<x5_W5xT%AjD1rWnwa1_6dxDVtLkPMI0$
z6f^@8-3F8mB-<!dA`aq}2U2I)O9ZD#>5Qm^R>VP6KD38Qq8rvZLo^WOQ;)&TH3O;i
zY)vQD1+g@7btE*pJFILBMfbbC9KD{F!%(77sx)y6jkDZs83@pRK6IYBINo@Z5zq84
zRsJk2mz@s1fJF{5=bV&=9GY+w$IV5(0DB3GDwuW9vg3585gtH^`;zcl%wyM4KPNx}
z_%LA3DDzC*48XUc8yX2CYaQm?nt<^rNifq)RiJ4U)JwRs-n_MH!p(;hHSQK3x99i=
z&ybV`ZL856?Y0aA=o<OZ&%NT=r8xizz~VTG>Pr25=s5Y%8Y@`C0aQ*!7=UZQYa3O<
z<wL`7lX3nF3XUnzjW)5m0(lv#Qh=67W|MOfg9mE<juL?h2Y3u^6|i@(^y1?qO!qZ?
zbevHufL#aph8QGkyM9WhG_9|X^xam|@c8|z0jW0+lRfyKX!d(i1_E@0eCY3;J?*Y5
z$Aat(rtvB;Q&I~ZI%;VgwJVxr2H;NMpn2da2f9cowHSa*kRuIIfMT><&?QQ6Nc%>y
z)E@gqLrf5p!QlYdm7IoSb(eZo$^Ojoz|m98Hk#yI;^~SQYJh>fEZvWbC~{Ak@r*6y
zZuai?qQZvf*xMTT2szxE0%12&UGE(Y1Ze5dUwL8$z(vW?U@F{J*v`+IT`4fFS1n>?
zIb}F}V*u_LPMzklTdQD!#dW7gY_1Wve|{+jq+3ixLk1nA$b-zHRE9!~0MuYKZ5J8N
zrRjJ_Q}h5H0T(j&NFH?omTBnB5hjt*MCf-bi60JLW#o&(+_LVcrjl6i5$DnOKIHK8
zol^rEOrSuDqJqksmIV3lKi!$Lx|63E2+&;}dJ(JGqeD;U>}BTV_|DnmmcwZ)GGmfv
zHEL&)4$uKca!(KtNn(lT$nD4nQcM<u+~*~nbC(0WH7x9D63erdn0@0lBs;#(@PDtw
zgIM1t!aLS`ES0n)aArAu`k%8hY|rq&Yo*}Qq8u6#F*cU5w;Ad)EO%}V+)rkm1{X+Y
zmw%<9V47iRugXAxzDbAXE;0;MogiqSh+RU7NRT2;gB!rf)2Qun(n@OpvYF?<p#Y3|
ztgQ6vYMTF52&Ezb4N0?~hj^a~tlCqjR+0J$s*Iyyqkb&qImQxjYNpacwQx@6`qPUq
z%UUb%X=HQ==Dh;Nex7HUTx$*YQsUNIq>xG<-^(%}pndP_`YohJ;&x;zZpS7PwnEoW
z+zxq;2}UN9Zr%+|YOeX5nWunoIZhm4mqV$!pc4@^V~R#?2hsk<Vs-o2-B;nmo*h4y
zC9_Tv9dko7$;>x@bB1XVBrz*&)0LW?1Gvtam-B~1;&K)C9{%$RiTzsks|*C_7F0u4
zl1~15k+Q%dr;HVJfyDMSR+vbj6bfb6xk=2?i&M;dS0xw|Lz+7|nR3)Y$4sSAGti(t
zczF__*NGHPWtcaQb12hemMpq>5J``Qprk0;jRu`rfZUCdv9v9365hRjgTqgUsRjlD
zbhXW}ol4rK6SOcP5elKjSxmkvZ{q}(`Sc9@NC2lM-8Pg6DHezzL{`j>3C!yfoH(&4
zio8AyyPHQk0WY2$Bg{-=kS#tIigHW>4AY=QGqC!P+%sVdC?S3#{b71(!)NVZ0|NoN
zf)4%s+xOXt)V$54vRK|GQXFy`(<KP7$EQdz;lG8@6bIJ|h*k((`OZT3{l_yljpHaP
z;uoJ6YeY^wii3#bpd^3Lt629sZ&vf5(eT@mnNgcxYrBD#{yB#0VGXDT1_HDWxLTUB
zTyw_Ws)%}sW^{uak3LEugV~bAhik}n%1v%yv6V!lNn}#wTik~<M>Gb>Pf{GmgIUoC
z#0rL7wQL%x?n4hCPBT#>5ffd`l>pI2)IcZAoj5*0zD8;YX1{WV>l<odU_k3=jT6+Y
zU)>@`9ki?)tjhcwQB5|^e<=aKRoZ=*A`tE-)2z&dC;AV99OuSDE1Ia8kTh&+{Ny-}
zV$j|p2PhKM{Ll^NK4;uuq)Eh&lueJ>f=-X2nDAe{^~@;b-^Fk}tO3=)EdhF~s&k`F
z#2vOQm}WPlQCH6%VMvvx|EdHt_G0!V)|jDKNP0~cQ4_J1B<Yc~2TGTEY<c?O#zR{J
zdk*MZsY#_sYPqop^moMV-FREWZ+CPJh%XGwum%oH4Qy)y`7h=V%)<^J<lxu9&_W;l
vcYLUxa0uu_^|}xK!wvy`@Za&Fdcyw)S^@*l6j=D%00000NkvXXu0mjfNC1kt

literal 0
HcmV?d00001


From 78033d3f3552b308cebc714dfb32508832c97cdf Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 5 Jun 2014 19:02:21 -0400
Subject: [PATCH 091/248] some progress on matrix cheat sheet

---
 CSS/hover.css                    |  26 ++
 Images/matcheat_R_logo.png       | Bin 0 -> 32182 bytes
 Images/matcheat_julia_logo.png   | Bin 0 -> 13752 bytes
 Images/matcheat_matlab_logo.png  | Bin 0 -> 24758 bytes
 Images/matcheat_matrix.png       | Bin 0 -> 188597 bytes
 Images/matcheat_numpy_logo.png   | Bin 0 -> 8758 bytes
 Images/matcheat_octave_logo.png  | Bin 0 -> 9558 bytes
 Images/mathcheat_R_logo.png      | Bin 44284 -> 0 bytes
 Images/mathcheat_julia_logo.png  | Bin 28422 -> 0 bytes
 Images/mathcheat_matlab_logo.png | Bin 40206 -> 0 bytes
 Images/mathcheat_numpy_logo.png  | Bin 8565 -> 0 bytes
 tutorials/matrix_cheatsheet.md   | 506 +++++++++++++++++++++++++++++++
 12 files changed, 532 insertions(+)
 create mode 100644 CSS/hover.css
 create mode 100644 Images/matcheat_R_logo.png
 create mode 100644 Images/matcheat_julia_logo.png
 create mode 100644 Images/matcheat_matlab_logo.png
 create mode 100644 Images/matcheat_matrix.png
 create mode 100644 Images/matcheat_numpy_logo.png
 create mode 100644 Images/matcheat_octave_logo.png
 delete mode 100644 Images/mathcheat_R_logo.png
 delete mode 100644 Images/mathcheat_julia_logo.png
 delete mode 100644 Images/mathcheat_matlab_logo.png
 delete mode 100644 Images/mathcheat_numpy_logo.png
 create mode 100644 tutorials/matrix_cheatsheet.md

diff --git a/CSS/hover.css b/CSS/hover.css
new file mode 100644
index 0000000..e04a4aa
--- /dev/null
+++ b/CSS/hover.css
@@ -0,0 +1,26 @@
+.hover_light_dark{ 
+    opacity: 0.5; 
+    filter: alpha(opacity=50); 
+     no-repeat; 
+    } 
+
+.hover_light_dark:hover { 
+        opacity: 1; 
+        filter: alpha(opacity=100);
+    }
+
+
+
+.hover_dark_light{ 
+    opacity: 1; 
+    filter: alpha(opacity=100); 
+     no-repeat; 
+    } 
+
+
+.hover_dark_light:hover { 
+        opacity: 0.5; 
+        filter: alpha(opacity=500);
+    }
+
+
diff --git a/Images/matcheat_R_logo.png b/Images/matcheat_R_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..ce6c19201f885681c88824a61f3d8c21578b73f0
GIT binary patch
literal 32182
zcmZ^}19&D)w=Vp|wmGqF8xz~M?POxxdE!iL+fF97C$=Yca_0T^K4*Xb_n*_(Roz`{
z-FGciRbSQB9jPcUi2#cW3jhERq`r$O|GfhL78hv9zvooxgi8PbR@h2ZR8dM)lt|Ip
z!Q9H$3;_6!kdx-2s<MU|KGV(FJ&Q_0M1GI!4MQ}~`yB&ZDN-sx5i~4>!d(nP0v&}~
z-Bh&?L^7PjL>wJ1ILLSf<`!9Tq+dx){BSeI2Ijh&{cbDsV^nK<?YMPi&End3r30Y&
z9t1w%stFRnmamC^b>6RwiZ(Iy1d5Fef~X17w`*<=F8M45y7=DBpOI1aeWda6iXp(X
zbAzr|k-}y9%RiBXQ<Mx604u)4!RJSTbqJcG(m0I6@&!JApGwUV#>|454^vP@vOiR7
z`&fEJ+c}mFq&UdO)tbiXV#|%`RNTrW-z|&*&(}>S-?1qC0cF;$DwPR1e4<)dc+Qpu
zFrjA{OdeC@+eWg8qvKpf-hu+4Q--*^oDrj6xGi%5o+TCsQQ(nM#w92#=~jm)zsz56
z8dYA5nQ2o7<#r=HWoPc2;NzIE0%{%7#6ifl>@qA9FRSq{FSVABEMI!pwh8@IKKOrm
zzp33&T+HMO$K+~l+g?;Lc4Gn_$+WWw8hcXJ08Ms~LWjFm3fDw{TL?hYR`wlXn5}-&
z&>lP`T=d0-tqVw=4Fl~7=UZNo!+38P_fy8i)tfqR36@|WsT2mQnfPHzX^D*3<Rm;I
zaK`lG3HfWkkK1Z$Y>+NHuMg`c^h8bRBn2iU(y}v@FHkdq!4x*ce`(hrn}ZSx$1leQ
z-O|kf;2X-V*d_(_%Yh#tN(x41FX#>Gd#;1b4Ew96%d$GL)+L_B`}6qq8=P*G{jRJq
ziOSI<e7vbMc>x_x;dY2^KPYMfl<QAewm=3ZP&z{d`~X%*uq`4$NWgbwurx4+@1Ryf
z;0?k8tAB?e^D2mYu(cy#G0=M!Tt0}y5mFaiqYuFm4n9Cq2sW%=h6!XV&<Z-}4>Cr9
za76-U5mJW8GBTbvw3n!`A{RQ?uxMlga+1)?EJYbMO>jlNs3QM8sVi;=v|jM*PpDZU
zHpmYU-w>2^&;=ueRX|I?S{*MwwB*3Vj+iG#HWXIh#;$Z5rT}8=0M;(v9{^pb!9X&!
z&@X7PISEZ@yc;o*1Whs!6k?l1k|NM@@yrCoBE0t4?}<(aV36VYM(Nm?BRKovS~0uE
z>PE}P(o9R~Ch6+3SVtV@$gF`cLB+;P^(C5=Rf6s?94J|_^h4@~@rF8ez<RJ%qYGYq
z#L+&SJ$y&MHf&uMd-fXeR-~1f&Vchlgk6do&n}uR^y?s`ewRPSKZtu^{qg+?x8uK!
z^in3kXoK4XArYq$k=c@|BI80ILw|&z46rE$mMOTBgduwk=^9hl$E^x?N#Rq-C!<S+
zkzpy5QD-y-s!Cat=uz-fbQ33!@t8_FV`~ZNE7XwX5gCvpB?&5~P*I>ONaKr(tjTjr
zw+nd*d5IP)Bb3=MYF4Co<Y+5&i}_0XGD;@10`GwPW7Wx(i8iU{Kp%32log-{(10P2
z(ty}s`m^M{4OFRLs9z<puueT+Vz+64KU7LQvmn(t!Z_17(l~gJeOQUSD=9Fk6;+{J
z=(KL0yqFkQWTDt=zO6~FiD%txoryq_v*>Lh`E>aH+e!C5j2XF^&LPzy{vqyR<G3>9
z0F&V}t1?b;Oma+TO#B_&QTxpP%mzoTmHt;3b?OU^J<X3)l2r3lkkt4JSv7|x^rf5%
zy9$LxCTj|7BkMKmw<U(k*qPE9kKCCY1GcY&_wcj2j!5seSL=ttGybz=+)UgaTpV0A
zTp-&BJ5Hv3=2+%o<}ur7lchFXGr0Dl_Hna5Q)<T5q^dc2V>EYub<Jfd{hUD2W+i{M
zNm-+&MWKA-G@4!#gAS)~t8S}`hlqj<qb8$4t<rVAfKpy{UQw@Pua5tC-z?F(VYF~o
ze_4K1ey>rkQQ@u$X+>h?98IBiA@2y6NsCj|jljMRMO|c$RFYKen4HS$;+Cpm$4t`<
zyv>m%1zRP1=V(FQZ!N7ViRGW%gM7b!6nbU7(8BRYYewf_ZekkHUDB!0pVNKQqSk`Y
zv}-!6dmUUK%Cyll@f{7b-ZyESF4;V4t}dtFcJICP<;~Qq+r;dY@0@u=d?b0Jcw>g*
z5BCi3MtoXzb*JhK@m)eWKiO@YzKPj~qV%DxUyv%@&e58to<^81goDTWgSAMH?L6nS
z+lKF)$Y9D4W8mqSzo@p6y03lyIw`jjd+a`BH?+CRzxL7gMvEkalqeZJno^p4j5`CT
z9|5e@NYtopO?ooEw!SVE;1P%vh!sHVaqp@0WqdaJ0R3=(-Mm}*bNssUrU1?bUIlXk
zTLdlvt_~#t#{z>2;RZg~57OTm^o?=efJ}E06IA3~)IF3Bh8<oH<JAS5fsU<;;=-#<
z&C}1*1DxJiTugMR^LzZS`CpRJv6wQL9ui}c!{SSla^gPXKyewMCY@H}fkcdU3{eR`
z<~KYHA2N66@~8PtnehFKJ=py#23Fm)#*205=EL%dnVJNHek~^&LcDJT+sI#$XanVk
zvNiVx?H-5VE6JVVZ%M}^w8ybGde3I>iQvGHhLAZi;E-OViBydghSWZ&f^hSWTZ6%E
z;6ZsxLP{YuYx!LHlvPO6bJKzdxlvRdtsW~sWjEs>TOpHZ$GFwfl^R-s&|IP{m5Y{n
zXL{VH?8EmMvMlmoEx1POhAj(8v%GPoNtC1egUkc_34*b^u{S?<b9N6miyrsSu=jrU
zD;vb>(B&H4jdpntz5b5fho19zJ0iR9YkwvrCSuL-w(X;~7L|S~)%6%!HeMf%h_s6+
z?RL{o>mb?=+hW*WZkjh%TW<hK^yeOoC-$QD6v%%koo{RTYP@VtK89R&7e5xidF;4D
zd;H!!?YPs-FxJvvo2}W=t3I5(Ex2v%)VSzTH_~x3M6!xEjc;NpYv`7)wU%5eJu5m3
z#Es89>OS*nz06r3-B{_}w)0;Ai3J;m5+Fp!@ACikq-awuY&UH5d$($E=Dgsnqu0XU
zz+de0`%U7EWT<5E=s>*N67S*)XBlS-$3Se-Nd3OksKdw3gQyQ$<*6cTVf!;U7cuwE
z-stnU>s)xgHLob&ic`vuyvLSV?I`X1<=@MZEkztwKC(XBrvVE&1k(icf>~cb?qeP^
zG8@M&WM&elF|xC`^E~`+MMgqzq9d`wc{E%FI+_mVb86~pwk86BwfZ;h&gC23Z|`cr
zN{bq9Jzn1&-|ow>kJx3x&fGME3qPyV=4-p|Q^&@SwEgP19thv#SG_mJt;Y`E!?>>;
zJg{iU*DHn|pD+F2{HMNKA9GOUNcw`4-drD+A0`+31(dJ~G`SUm)gMdGE9T|h_ZL4`
z3}+f6EaJo3L>a$QUuqQwA!f>FXeF8?&LpcPXJTGrHofcbhyP5(02lT{d#OKLo>J#3
z^ZY#Cx-E>yhW^;hoo5J2Z<Y0Mzb-%89h;sUEO=O5my||rRrUxz27ctfncR;io?a+C
zE2QSd3VQs2dB63furoi}ehRsJW8u;A`@5!uas0030st_O{!>6aiv`^Q0I&)xRZUk-
zIawYP2RjBMQwL)+22VT3zi0q}&y(lxt(}>x5s{~zt-TA6CqK!*5IleH{}D5i5d90{
zYQs;WDW^yz>fmff#LmFRz(gVdOGHG(=WJ@uqbw%z-}JvVeiBPpS4SR3Mh_1U1`k#S
z2WJaLW^Qh7MkW?U78d%y2znPUdsib*dV3er|7zraw<BieV&ZJ&=xXI)PxMc_M#c_q
zuKXk<{}}o|$A9~2=lFjN*}MFARDa`R^fYp0WM*Ju{J)W0t<3+&z5fsEAI<-hX69-2
z|DgS&`7hePlJQ?Y^8L*ykDP<4mARLgk*k>i3llRNJrf5#6Pqdv3l9?q4;v>R<9}fO
zC&GWRMV-xzTpgTM9UN>06s<hXZ0-K360tI{{1@}z_5ViwCkh?~XDhS6>HQ~(0?d4j
z|3BG(;<c2`TpVoO{)J~{V)}2!zf1p<A?je~=xpZV@{a}f|Hl8j>_74UV?fQ`%Ju&^
z^&jH@WNH47Uf_TH`giHSG5+ZYo_}RZQ_RZF%>G}#vHTOw|5fv!Oi>402WJ&WBNMZK
zy!#jB-$eh3{+AuC|FOf&{<mBH$IicL{+r3i_|Itmx6%DyQ|DjOzjH?b_HVEL?|CQy
zt9w=a1po*Eq{M_(JwXn-p|bVGyT1e#*0y}6>^ZWw@W{gCse%H&V<;lmMj_OEqHlE5
z=!<vh7h?%apU8P5kTIu(ByDY&do+@7`1rK&&D{C>AMsIYiGb$g8hlAj=iX(rSRC=@
zy|lbc<129C_Y7jYpy=?8j_lt!K0722q^Lp=n%bD#{6ti<UBc(AuSEzIvGEWSug*KT
zfp@&(Kdm1nymNDN8-@a<hXDiYf4;iPPcJKTY@%GpLqh|mFE1@$MaM-)$Hm8uoqWUn
z<2m?QcLNc-Qc~7G5;k&laS>C;cyW1odU}O@pB`9tQs(DByPcJsyuj=GdY>zmOwoPa
zO(P~LDe3L%dT(lwQp+}uriRRtuUtFI8V32R^tV=%=$;Sko5P=A=g#NT;x;62uDL<s
z_g>L8>cYOFgGYeJbv8RiUq4UFlx^p#`^nS1S|u(=m2NHpHZ(fo1HyqPKLz6f0+x3S
zWitN407VK4Y^X}GsAy!rA{Q|};&6zlt!_+Y9CN?oFYsXDt+Ot9e<(=Em<X73SxYMH
z+t(jmyuT-Upc0zAy0+XO>GKWSAbu|1bcSssjNL@CXAFCFGIH$)5jWsWaAerwZ@{;3
z+rrrTpk6~u^1S!BZMw`5d(YB3wj;QIPfLVE1OrW-^9cZrvSF`F?JsV*e|iF{sj9)J
zYNV8MvMpjYac~|ZB0e0yekf@LkkO;VQ}_r0v%v8KOg)!_$k7o4b>ksapx1|nC9?Ya
z4LvQP>BMs3AVkd#9s9&2#eLc&X95Xs(WyaxGEoV6^678DiS0qBzWvZQpyh12+1kXh
z!eMXc_F_jHk+a5QHJ8;@R-U&^-}4;jh@uasAE&4EncE3IVY*lr-B;X!RZ8hz6kr=K
zR_+Q<Pf;mH*#kON2UX(<J+Jew4^Giff$>hbi)mcY%-CFCV1I)}Je*xsVl&L%K335;
zx1@rO7>X`q#Bqv}Tf%g|oSqj-cl5)Xs4v*nP58yi2Hn;siZwh885B&h{;ik=L^g{t
z<3!!>PV)Fc!C%by)3dqVt8ds=Af-NUAEa+PEiJ9ZW#C!r!9*e+Z+vDdR$59b`sw9|
z*8y)5ee{%^DS4PVZw`H{&o1(MI1=?VMt(Z3K6s;8p<SlsZ1KBa(&c7LOH2N6Ej{7(
z+!jY+XQ_|3g2VCir_dLd5HjVJ5uP@J&CNcHu>WnNZ2bEop$Ne19C!!Y;AU`o#VrmS
z8Z`3lkO^s`y{+mvxSzNs;pXk*ptxrY;<(qhcI(pC8%uZY6Ve$GT1^hhnK;CS>H^`G
zFlR3w#`&vH$dF2ap}yg#q1ekw5_9bSNbEOkL3h62D+c2eO)qO<lo%qXLccfLU%9{d
z$@<J-;H+{=wnYQ&<8lo<?>0TQH*fFn;^X6D72Z*$|IGQ-I)EZ%Uj|9(j<v*0fgKS+
zgMi}d`V)v*TKK~YIx27p6%Ie;?tCvmoz8!!13EDlm_+j8<O#wGP#K;?fgv6m`QMFx
zqWyH8EV0NoxsAi$;4fRC_x|>voV8kTrcH#r!`~B@UDZ`$m0apEoy}b7AhsLPR#sLv
z*Tt4fukGN0HghE-`80prBZ%HtTv9(2!r;qxfa%v8deyXhD9)&nmTH#=0p!{^ev%g<
z8~IKa89j?kOt`0&n-Jqf^gsv-i!MLEMH0~MGTmK}*R=u~5&Y|8y&nRCs5XAe269)u
zLqUoM7(luciP!G6QQ0B+b=mC`SiC(AnY`NpMg!%zXZzTCh0<5_btCEXsf-X$$u!GI
zN`j9^u#l6Sv*Gn@<U&g!m;O5k2*kp`L(38)h9$w{-LK&BsVGm*^LPaY%7lcOTO$Dh
zjLttcTkJN192orVlw~A0oRD9mo$8%#MA_&JRSLnYSs1Kky0^dx-%J$LADei3r}P<T
z>qWJnG<}YpjtBm9W&4mD2LpaN6coIhBcCf0DdOVeqYJ>}qZ38g@EFdw^AkDC-)?Z1
z4kUy4lYW6qGeqf}#yn!0z9N)CX#2#y3**cu`{&n%dw?|s92OxHDJE#tnZLJ1+JJ04
z!t7JT>^2|Qo1Hx*78#3MU+2F5c>IEOEN@uX{Sdke4&V0NkEPW2InR?E9v#k+XjB*|
zY9#>yh1%5wEx59<Fv<tR0^{ihdJiP}An~O0A1T>PIRVqN^?RN`;z9w_*I7g2Oknik
z4K)$Jd^NK<z(Dk#s6^p#YJx!;&?x5xKv5qtT|wbTAN_nb+rxW(0*(RJ*lo6iua^Oc
z9#WdGxk9l#LQgP_-`GU^4PPSOQD}So<~Y|W<MODm!gcXbv!q+bFSZS&d|nz~?z{rw
z*0uHeAX2OgF)L~-Yd6~dECHc#vv7}wD2L3D;NhcKBdWteQo)b{#ebsM-5d0}fn_E6
z!3D(m6?ONO#roKip6P#<2eug;KkxHhn;WyPzaerOT!vG2pNl3pXk0zA<KNWq@BD-Z
zEuF}yV)+JAlv@uO1gE$O*9J~;nPcMG1>FYV6IvJnBiu)mL<kMG+aHys;(aJ$OjXEa
zBPSp=q&b|5OnGs^0Oql`{j9|BAJj4B#IUontl~W9sJgRX?<mJM)^T79fz^H(^Hg-9
zYi$5HwZ#ty7bpHCpQd0MTM8JMzzHXnN|EXZQXxp+9JRV)wE{3BK=nf^z1*rQeH`jW
z86tlB7CB`<eS_~KdEwq9x^)GgEr{gGhQka*m^KmDvxH^}J@x1am-E>yeWPzhWpbMa
zUG$Q82ys=H1T)+U%=csEVq;U(=JS34D!fTw8Z{zK1ge8--PU<0yyR*l@qU~V_@(VA
zGuCIr8aILBwxcfFZ*{aAo-fN$E?(5&Y;3G#?TI_&gNUFowRep#J1Pu{KnC`QpI*nG
z2=S=n7PKPXBWa?nJEZD+*QPjpYV9QSV#!kwoe}U@q`LZpcct~9I9g$WO#kWjsd_>X
zTy}s1?ezF88-C1QxT!O}k@)m^92BA<itGeIwW<46%khh%RdO{*Zf&3&T>1(xiJ|1J
zIUb-AhDbCV5b8gt02Kx)>!F}wxL$;_F)eVH-BGSmBBd>BLK}k~z2fISa%cg<FsG44
z3;ONoxz=Ozq4l#yXyy>?BiJI}(!!g6c?hY$K+Hz#N6YT-5tY)VdLfi4WEol6Dzz%{
zccGu#0NI6wO*?NNpXyfK<0W<FzGBOBZV<&C42umgx_}ZF@9NFUdQx2$l8{RuFP|b@
z<?w9C=7K4Xnn47sA!rSyLdpAzU)_ARkwib+NB73<Kknr}S`O#y$MM!G!A>4Pvms)5
zkh9on0Lg<7_$V}S$3c_MOaOCk{NM0b5c`PUK4AQ3s{F;|h1>k+lP+J}HX~JR<E|h&
zHS`-6A$1CPx&=kr;vs7g#I@QjwrjMs>hxc*DQ|MBs%$(M`)z-`!o?$rojN(Pn!@*m
zOua_-_x9n<Tgdgl5uII}v0M_^h4q_H-ulV>fa`;nTyRkeut6##!Iznqw|fqgMem`8
zvPG0!xVzfWo7;CpazZ}!cLZ|+r=GHIg9a*sNr$^&cz_5?`4G?;EDg)0F(rDOt}bVB
zZakfLaX49QFjH0P=#Sf;54t(grwX&-<6pGuZh)}E(&<it${CvAHJP;@uAC2OZs_xi
zzG%C++!1!)etk69iuFe%x4SDB#rTlP9XwHP`DX4k+()OJSUrCEe%P%Xi@$XBnpm3X
z|D4I35l*plV&kzFTd7ClLf9QVh>wrvgt>w79Qj!OnBCg4&&<k_wb7|O4b~~Ysmg_z
zBPW5tohVMp6C=bYqT$H|vET^6!NRjKHa4NF8(P9IT-arspD4;hFcyQZvSy3ajK!1Y
zj?*+^t7r=z?8g6daj5)C&sKgb{TN}6s`;2*>`fTUQhtW%1qvL@!AIuVaaNjU%mUU#
z%I{gvEou<Z^H5~+NW!G4n~saKmNhKE*m92<@MH>6$sfC-(;C-<RsAwm6(XQ)JDp^m
z_P(t=)+=L{bz*j6ZN-%bv85LH%u|5V9aQq4SgzLjy4@Q%?)|%5_9gG0iw1f@lW@ZX
z30uqjm{UX`Vn|I$Fht!)qvz^C_JHTfdVSq=tZ<hh0+AL|W5oPJS64-a0_9u-BL-^j
z?A)*^IXa}NM4D8t0Vf*_knGSB%yo!d5%TW?9p42<v~YgFkxaxyrY>Icz#rB=&T*Oc
zg5xSx$9#XKXJF%lfuH{vB1pxz>r)o_zUVK5hNW23d>*9G<eVGKD+*P*KcrQ6N_d+4
z(ZZmf(JB#x0o*fAoF=JEjzg2kgX`z^EyK+3dJih~dz;v{9~&R%j(soXb*PuuWDZnF
z5kP69L*dWe?en(XEeL(G+vs_ryt%m<{rm0^Z3&vT{iqs6Bf=GoC(k8gEuASPEsy$J
zRz@bed{fWF+QtSsFy~R9c6_K9oMxgJ-*9Y`sBU;<IHPDtEIBD@Kw!;TAQq~^c8|~5
z?tb4@73uo?987T?fU$!*_Y}c1k)x|8y$meW)v@2n{6r02*VT*mIN^V<-%~1(pG|B-
z-wB1n!Uv5O9JlB%c9|V&FUi91auP!Qb+6!)>*OZ7v@$=tBzMVAXt~2$U++YL7{_Q|
zeH#JC+i~&}j0F(pKN%%GH|Wf+so`ijONl#&=KkY)W81*<y`J(Hj1Op*#M{`JZ<;_U
zl7O$RqqzP>PkD6kv4K`oozeND98htsX)AY&*qf#a1%n`n8=W*RWJEzhhO?ZyJPnPV
zlRYEn(9B#zt@u>9wL$uaD%+l%kca|mDd=~GR@885S?Lz9qT-T_p&_!iZ=$xyqI*@}
zoQVEFtM1AAcKagMaix_xisjPLj^aimZv@RX&+8J%7^`4r!L{sSW-0d5<_KCt*RBkR
z1*BXRy`JTL!pWlA&CgX~KfP306WN=Wql3)<u@KV*QTn>?;s~U>?fSS30KVCN&IQ2#
z1mV~$?D+Ku=Ke{1Tp@>>(wA;e0UoRa`Toyt4>s3p#6IEI%(qWZw47&|u2<;pJhp5v
zE(^rl>rv1Zfj(_%Mz8mb<2;_4=ITS+8n2TzQC0hyU)>le$WqyohQFrA>kSi5HadJO
z%YUocckDWYCIsElm03d9`%|~@FbWMI$kEJLn45!E!HXI24Lz-oAoXQbm4OMR$p|ar
zfb1#1UY@BU8qHOb;XcH;(w)-P)L@*h)nHICK>Q2{ap!UG3vo>zYPGAa+=I7P%$FHG
z0~93l8}%34ypsxoNMrE<6ayAqvXc+-Ta3T$?4IuTHd~h$3?AynQ6ipd0)0MyAg1T2
zW1X7Ju@{qkXHURbTiCGUO}9zVd<fu)hKx{^hw}Xh1_{9F{S$Tl(Tf+ywHGl+{gv9K
z(jTqLY)gCIwY&4_XX+Sf*rmu8VtUk%fX6|n+2y)B^Y>A%@bjm0rXnnMnm4`}im-SL
zS(}WXKFH2@cIA0a`z=cjyERK5US2>#iUai~Xwo()(!@Ln)(}u2EiuE)xxq!&+qy$-
zUJVdfFmon<B8O6UuT{Td5lLZq+7+;}vSPxm$+!a1Apj4r!6u>u2OStP(wY#V8ASL<
zxIyGddPy$?$iTJHApP8tU&vBFxtyAy8D@Y6pI2cCTKyqv)_nfzJRcp41dPUIVOq73
zh{ML(UR%2gX7Ov2q046Ps43=1L(Y2ciXow0$1fZ-u@Yt~XtvE4$3_l=5J6c8TJKOE
znmZUU+|MiolEl$tgOELyHBwNJY1c)HdQA9&aiVdifso>slg<&(4nvk}AuIUT(UH==
z>3D2=d(fHo<jR4>khCYQfBc-61l0i7bRLqTVAQMMyW|CSH(p>#Nku06_`J?m|0HW(
zIagy=(*z%%{KY0t8dV+H<u<C`=)45m*lFJik#wjUAdQ*pUBAz;lfE5!qMV_%M!`rq
z6D6c2W66oh)qSGCAlbmP2<U}ibE1YT2_UZ7banI4S_%D))!ogXXi;P{X4>kVIt&;3
zC}bMnw@OR&IKh$%`kS=k5&a;Q1O4Eb)7-+mxv12_9UUtbJ{FgqhJ%)tmYbUyCX37*
zaJ3+j1Ljxj4?qbUXm2Gp1t(fXeS>;4InDGO=l6;@9J;EhAq?v~n;3U@)mT#XTHn}E
zj#QZl6VR$ApkITY(L7B!b9Gu26kzD5xjS({8D&*$Bw0qu@eziU1UH>ud)^O-L1e(K
zh`Y?WbZsCM!gO-@ehOUry{({H<ap+=(P6X6x!m1uLfXqdV=R!BG@+So@a@l2VSo*n
zbF0Hv$ITkK3@|%dToWD&y9ieaQ)3{l4KvGovIr&_IrOxop<<xxVOqzW6<<cWezm#T
z8<4m+3n8Kv3ku{?<It@j>YX($u~^lotRemwn=8h5ui0)$$e!9Y=I7^^^|R!>w6j~D
zM=u-NghT=1!l)LRdaK6D%4tg~tu7o^y$pf#lJ)5SHQ6(tbaUw%7IkAXswS7kL&{|T
zyDjAY{+>)s4n9k)=RjK^Yvh9MizsgDa&BJ9wM)=OW+i2jTdz4kejkwo*6?hHLBDEE
z^M0s*`EmKGqC)S(%*^a&W1{6)T}=(Msfm-kKL^dYz{CWtt_A{AH5~c(c-%0kBVsX7
z3U((gQJWYmW<B(-LlDMSO}7TFP%1~kdllfFt_p53L@LA__Rd`I1XfKjwe9=RUd*V}
z!n;-!O-+E_hlr^=$g0zAHc`^5uhVjm4@e2FF|HN{kx4}@TQnJRk^siE>?xA`2?gdg
zFB2_T0}}g&AkzV*k&#Kcp)64gY^<ac=$J79I)~ah!uvs>@k2Qr8j=lt1yIr&sFJ8Z
zGwV5Yv@;RrDjeyZs7G;|ywT<nr^%MDmm29Wr%KFP*B2MFy&iF<SS=>)GdS$D7lv;3
zeWIeGy5{EUal@05zB?&{CW(az-FT5ZA=}ZIlIp-M+smlQnJwr{Y>O1H@)dNnG}KkN
zYt;Pxjw>}NH2*8am6OLVogX!s79jImmL{C1QHa7<t}n$j!4JB%>0bHR1;1&z8b!?Q
z56TKsHLhRX$qVPFFwT_`Zz=-u<L;ouH`p3q=XQsejS0?bt=2tQZwZiyZv}aZ-cB^0
zSba$>`l_0?62Q3Tj;W*-%nZ)7ZV9h!ebQ;NWUXnT7i(P$3)l{9lr(cO;lyT=u{mvS
z^-@rHOxYUexKbQ`h>!fyrk0k>uo1|2V+2f9XU;N8>0Y74=8m=@0^L3pJ$)JO7#vn7
z(@m*6g|*jEMtmhfv+)#tF7$;iV{jsbaLZ^TUc)4Bp@vs-AD{`rpU)3H>}4*JdLKC7
zJJCj)<0I#m@!Z^+M*=h6Zkv^wIPK>dAZy1u@6le;ceThOR%2+0ZbwE}mJ4t$Y=BB>
zT40gS&{9&8@{T$4@&l=MM_SWKYy><pP50R|yIbnE|I>q;g7UH?^*(dgmuU4bBru@$
z<?`c9ErXxiU|f#Qr`Rs65Elyk84cnnYMm)0YkOY`IxyY(huVz57rL<{S{hoe8SNuD
zm86$P0EWpPN?^NVdEzi>wSDp38UO79*KcYP*kYMo^MsyR1X>4s`w>cYo3nbZjg8rF
zinGJ>5Hx+ofjY7mKWnX@<QFzskTcnQMK;DLvvvK}%S$=sGTG*m6BASA<K;{J_T<-*
z6nwOOo~7dSv@cv<7rv<b<|5u`NB^*t1?sG|6f)3x?}l0c1^=9Un9Q2Y4CDD@2SmA4
zqwQco>I%5oQ>1AU$R|l$QdTNM0wpLi9pNG$ofjg~umy>E>NR?XmZ#Gk7<|3foW~zY
z%<zVN^geug!Jmm75uldy^goU(qsJ#&x8;bVL47}->-awT)nT+IBDuZxOp}3G4)pz_
zc|ZQXy;h6+OE>XLbc<r<_mQ?U9$V1N#8A<JYLZLX>bdurToOYb{waFWtYtM+87BYC
z%*>K?b89XyFAuY6cIr039|uK6!J)uUPY9^1?Qv^8wLQ7yf4El}SKz(8zh%sGmvHd&
z>;E1_Ga*SVP%~|!2Z=0$K<@3x)%C&UV@ci|4cD>jokLN;VA$y^x)gQ>b=~G6t@Sd^
zNUD1@Dj}(WmQZ93TEq17uHmVzKzTjC>Mo^7wQ`-UnJu$sTD~nE0=P~bVlWv^TlUr7
zn#*fbwl06+1<DgS_$?vW7rD)Ix+H5``(c^DMx)vrdK*FA=$c5EV|?5m7XI&#T2|&~
z2UgFu4O6$RzzoT6{;Qc?plLLQ?dX1aT=(=Z297$84r$c%)Z)D~4f<{`dES6{ba(C#
zHcaXX1zT;S?&=fePWQ_JN_C@lb%Q)sM_L9-#RnwBkR8psFDjKK#WnwB>f<h_)8cJg
zF6XVpn}Cj#%ovjm(knpX#&?l<;#i6eSg6m!t)GxBuSBWqvS#}GtyNu7cD${j4xNW-
zb5utbPTNi_F{adm!(j;&0*k0{ET=|w(#cgyi0h@r&BM|Vr<Kmd_pVRiAQkcT>qkvR
zO{`dSo;!}Wy=ih%O+UUNJi#%XOsCK(noQ!c-K<=_ylrw0jgIDyrEdcaqgwj*8qkO7
z@H1I0@F8{N*rLDMfAKvI4L(QB`t8>l7Zu6WqSld!qBnrw-QG@NyEgo2%gzqNO`7}J
z#Ffe_1~v-H2CA;%QCc1*bGcf-86`Z?0F|BYO?}5#c%zwT9geYT(9^w_*!yw0EWZ8u
zXeK$Uqp<7ByzzC7YR4s19Bw(<y0%?sJRIfm<$9TCzRUT-oHq5NT|$hDOFd#*ax!4w
zBqd!{M@qKgAfR0Ia<(jtz2fyXVpq6BLfoPE-o`-i{GySuEvAc6gA)RH4YvB(XXa4g
zNVD3``FUBpuY#&l+fRU&GNI{*=0sbsHR-}?@u-`em}{l)eJFm{H~3^&|5HV3R0Mnk
zgvN;`)^|*>5ae)RK!nwNQS;oI$bS^JDc2Q15eIRM%9+$=BL>d$7G6E6&GrE@QA`J&
zQHd?G281}NEH6VSIvVYD-Cf&$-k|5)D#$OJSerIWK5?O?XP0GA9;W2_fNvhTbhy5D
z=kuZ>x6Y4J{Nd_0;kI$Zqq=85i_Ui%yPnjZ4X#Izwcje|+Cks6Lh$Zs+dmIc9sm1&
z=RLbAJ8v#c(k2_L0^FpaNk}3_Qo1*R<jX|bIqI};OwO<rW<G!EWv&;}SKl8UL<p((
zePEAv?2fplaBYi{a2tvtHD@5w<^v7lGqT~fdIj{>k_OZGS!)(llaM#Y<++MGY?Y|d
zbFA{H#>e3pmIPTy3VxlLp-scqwhP0jyqsFS6<3dEBuJ@;VWOIZ0kYDD7`EDTD7LQB
z8^{h>r1QYSLB&8`16fvP+8eNk7o^9EfB(qn|7gHy;JF*>mh!^Xg0>d5mhv0c<Ep~$
zsb*UjL!57wC`3XJy{4>OJT)#G>SFuX%^#J)8o#S>(A0#`R!%-cymkY942j3?sYn=R
zyNvSsz^;q?mc4@a$HfCr>NV1}D60wd$4F4TQBWg@QN~7pkebDZPQlM7YW7Z>UO68*
zEMiAB@Qe=gRKL>&=PhZ*wA#9g4(VmmNg`wkv9Jiko75%ykI&^)jLNY$?%b6aM+`Tl
zwMPix1~&1#n19b#zK{Ga#KNBdq)3M|YNskOu?-{E<MgD&!pW=&1J2ehr(_s}uxhFp
zNZrYu`W@6(=TR`X>(x1vYClLb_zpZn;n5_j5S{j@P7zx&P)_@GLpNFy4f-;+XZSK`
zZ@k$M@B&(1?KqyZ)m}cwCosLp$T6T|Q--a5yc)l%u8z{y)9p5)CQo5KX*Mg@DppNp
z<&z=T+5~dgN3SF*K!kX&rEafiVxZj=EMF91kM{n-w1kqwyl7+v1Ix5Fo7;Et7Ue~Y
zeo+?x68+4JVP;~N_m>mYs13JYXHR|4{<X)a-S4<t0<40A(S_6Cn|depT}fpvsH(6F
zG7?%3H4+l`l(5B<%ZCCLk7KNubpK;J59yD#sHtJ3aO6C!_Qj<w4#>BST+iFNp_b{E
z+9xQ;@EuJ*HQBx-u#}8msy_=;ud(Noa;^Fn>)IRDNeni#pl}vt0pyf9uoO0qzfW!`
z==rC-kB1|@ak3VW<qdvf8A|mIz)Qf}5A1fhonz|YL<*m3I<p#9gM(z&mRIYioyur@
z+&-=NAM<YGh->*zm=C(GRXR)Wb=muNoL;UNxGXGSK?L<A)tg9<QSNN0>EWsGN0_vO
z*C`P0`48P3I@c%S1_!%x=rRRFm)y;t+AczXWcDu}q?f#B$7sO8xm*bxH<2EOeBf-5
zzm?}^H$DAs-l;issMUOAr5jsQ=3b18$=)$i?h{VINURz&a#vH=#uE2Xh^L@=wn{zD
z^Vuujo}G4XsNjDDOa{q3lc2RSe?FciX&6v%v|kDk${&zTfDwy%MxuLqZ5~hY5AZNJ
zbUb+X)xn@~42$jdMnS2fhTmpU!AqdP-x|aYKc)6Rlc26R6T@K(nGFhq<434%D_Z>B
z^fGA~Yz7yI6k4rq#)UHCXLoDv5t97*^rrE{zyHC%-4CJrLUr7i=P*g1T!~1iJtugO
zf5-aj9Pj<Z;L7tRYB_FeJ|^aV_V=KLCse&qr44R%gnzB<Vqv&FYF(AL;ZlgcO)Bf0
zq&?(-1vEH5u9)lNThJs)lq9`W)Dx1KSqZ=ERmNx!M+Mgxm9@J-R#;`#E6j&(=EruH
zLyy<-M?Bp#_vMG&OPiw+_*_nPJ=wJ%%b*1NGoy`l1pqtvNsk{ud0|8!{q_E4=V3#(
zZ(*!%pKFZ`G9kX<jDC9Vo?_RH8BL6W4DSUa?a_pmre$O)!2lJwduox#X0uYuECzz@
z6^P^Rg6=V?*-muuM1!joQc6ewXIKsiqUEu?w9|%TyMs&85ErqqRH?C^&frwOWgif#
zBSLh3{SE$dy?uhyX#QD`^K(C}RGw>>EcSTw7baGI^!QbjDB?D-E>iLEWN9vj5V;R~
zOK-*S2wwoDk{!>+6&Y-}`1&oE|3LnGaUHW2TMv(ciw$dR709bS(tZfxY0;4Rz#61T
zlR;Z?^UU_|Ww)Ct|F6+8$*G*j6AgLZBMnt)Ko(wp9#6@M$6=x*VaD*|hQrqmUhVM6
z&^EY->`1qX8p-LveB#~RT{WAPscMK)8nWk2^+wy34lk|8XT7WGsr)S+Ib$?spbWmu
z#M>{P_wB4Nzrmr!p+!b3TSP-E!%=tJj8Zh5khFBsIZj4;Z;-t2Q4q_7<O0SR6h;{1
z89Wt5=o;cJ7^MxL7h#hvMk5!))5G=4Ray{r?#h-VsI$XjY(bF4U162um0bqC&jr=~
zpWD4vGgDJbZOfV*<UpNB6JJAP{6HR51>sZ^lOH9G-5ah=Po&;iQ#LY~U<^m>+F&w8
zT<B;!K-a`11Sq3WFbR&Ni?q1>6ku|&c0V?;cOod`VWDxk=t&UG{#}@oa8S&*qo$@7
zgL<QdmScvu_`901F<Fd022->;-2mU<{rTSQh8;?vuKO-9303(zoP=~YE!eS+>U88b
z90LnWCvJ^<vD_lOpi4=m=5<p|<*N7iv-9~wlA~Elb0=w14nh-YuShpBCBZdM91VqP
zRTZV?aBW|zR;b+PY>hJAsw|xpK|pe&o_h39C;)f7hXf~rrcOAp%%%<j;()sc6=C~C
zbD9r<y-KTdJw@Z9314n+9zq@9TUpXD;&XOd+T*t{)yhWfJyVoDN8@U$GK|R6pH1g%
z3oMvm+8<ZdSjw#Qf0zoM-dF5orf8p?6x=msHNP-rE$~bko1|tUCnuGYw@17~I?I^u
z(NQ8&-oMej57Wk(l`P8j+njnj)_Z-ZefQS2GD32D<;b$6o{tFq22tpBy0!7`_2#m5
z+j%#v`ur=7ojVf5KKuBSsyVS}{yU(>Xdf~-|CrzcA)S6>Ij`64eAyuWo8YJ3Z9;If
z%FeowxfSS&jEsJ!c-b0EV2?m-g-&Amij+Str+F8FFmS_YblQ%Blzo9msCdW*t<4mN
zVjpjel!^%nD>TfDtn&~aV`UyWF1rv^9SS5EkE6O3Uq<NcRCp}hL@Mv(?1F-eVT9Ld
zyv^de^SH+YWVx2QG{CIB|I=iGncq@LVtana%lnheWp&rZDjh}BpsIDaH$J)j_ML0y
zW>uKNqD8@gap6pH4@`5NAOyBrulaY^a}hSAUplD*dn2OYdQ1*eY~Q5r`Dz+%X>q$&
zSPCbx0Q%L1E#CUyeX5I2Og_J<Vyryh!w_b1>tmTUx>loFGdEMCEsr%b!RK#b9}GT^
z?UWAr^rcFYQk=a-nPKM#@U>-CLbSKceCQ0me|Fy}KR!J)YWLR$+gnHCed`Gkhrt~i
zlj|nF9?&pg;nAICs;b2bERe9Y8||C5?q@V)jdhb^6I>zlhuhJIFlV5BQ0EHP!csNL
zG+DB<h1ej4Aj)>}Wlpv`?4s9N>}bV6RU<@J+nNbCczJ>&og#a}mMgHZx*J~3Vq#+O
zsH(#3beE@gOH-%}&aWuWQ0=586j+@iyoP&ap!Vm5$?cojc*?Q_2jr&TazsuvuM>-@
zXqC+1D+XECnma2uU0aA@vpljk*)fhBIQ1-U;hZPy?-ZC>5qV+d)s1=m=!y2Vkn%Y`
z<KkNK<u=AkwmrVk{GV@-7+;*+-8*#AQIn+yhYuX&`L2fEWA0<VdVKn~YvsR3<k3j7
z!4Far$STpr#>U1efed=ST^?Ehx;hi|qi^ohUZ@?p8Nzx}C-iB}Cr_Up6=Cx$L<-cM
zDO38K&DN|uOWWHM(<||LXHSPrF4Yb~kA7$Wz+<PGP`-jVRm0TG92%=nnbT9lFuzNa
zlV!&$6$!*@Q`}OLZYMvPp^`Q5I2tSc;c>BAzg>#%qK4WMPX^ik^)s1<)^{YX_B8#B
z7yNmL&NYt*c{#}L8G1?JIp0WwK0vkc30%+;T+II4=mTpP9jwJ8Yo9F|ge*xqDSs#O
z3OFi={4xY(7E9s!;*xqg=eDNpa;=_KoZu-utM>(Z<&(O4iq5HM)@!5>UP$m%`@=Mp
zpX*X>%`<@)X1#Bq+<ZSq@}Bv2Cb!*DdfWB;pMD6WtyEd?i|=$rL}3{U%cS)V<{upo
zxvM9d3-ik?yGI-Li0|*Ef?wA{OtaUNl?Y5jTNUdStbdYA_GO&;ZVJ$wfg&|Vgxu`F
z?hjmi>S0JQ8irra3}G;Prnk2wTA^A)9X4%}_E3?6ya+ktCa$Yi^0%qBTMZejmFlDC
zh*-%~Vp{O-Zmw^mu>>ngiMeh!1`h3Jh<#t>KWy?OQNRDbG~A-RMGlDOSLl+r=8HSz
z0^lfJAX0!`5yMf7E)VYukYtWkT$vf_p9P`2ZAM_%69}29c3wzjW9<}JhKHC^)DmsM
zMU7^nkd9_nMA;p+mlCqO^we!k@?BVlZAqj{-)|7}@R;;}W^Q%{kBvDUe>{wI>VJ%3
zeBHp^`Vlfw)&qI_qolgy_f05d(^K3YGmiORP6P>GD7@WiezS&T?oW{$x6#lqEiA5v
za6P;lXm$NbH_&~F-cS?xVg7jtemTmj;8^1gX{I)!>zvlFu3ARZg{l!q!gtpIS5<Qu
zM!_q}52HSoTq(i>94Ft72AEBmYqkZgn2uY+W*m`^BNz!o948&;q|?>@FlN8(tkHbA
z8poz_1M5o?*g4apxwZh7HCR_)4kn8P2bC}hPRZsnfx_`X5Mkd-L&VW=-@C*;J*~7e
z@JKQPogy#_FQoahG4;e)J-l@}b-N4E&b5??nu15%avs;RYIz#yjM%fjB@|m=Fg?VT
z`&g~HKI1ROPqiSFPGwp&MCMeQ!>{n}JQbCY?LtfX%gcN|RH)&_=ds(|f;xBWzVLQX
zN<Irp;i6`HK3}vS@{YUn^%&i~IxLLn<F^?tev8+Ye|T`qokdP|im{l!{=2GT?)_Y=
z_4swS<+9@IpCy#N%%$Z-CeNJ%HZga=gdi&r>!Hyz6>~zC(XuS!8vIrCOUBh&J?@Ca
zx=$sw`4gKfEya`akL+iaw@Qmkcr*NE5iAH?FMLm_p|x^FZ+qeH(d&503U<9eB*~FV
z+6<It#e*;<MGlLO<xz?;tH}tJ&~C7R?_xF1N9noYTzQWTOm(0!9MuWgbZMNdP^KL~
zut~j1#2%9r&oLp5W4No44)nV+-lZ>&!uq!Nq`d!NIe~>U+~`itcMhi}6R$OO`1B?7
z$!9CqFjrcM4<+W+sa->_;hF+0Oon!F$IZy5wgCC+hZnOoDvdwLW7){ZaMPV9N15jp
zY3}YG1eM<n)|UsN2(W`??|%A+GA~Nh7<5aRbn#Y+N3^<8XdNmto2Mv}9!i-NiAIXr
znb;Et1`+Z3IPh^Cn~{%`*8Yh>r)aYl34<j8mAXkurHXGrS2Ax(ht+H_SBqYnSfhkP
z&7tL56+B`5#aNck>~_SCeija}!SuQL<Zs@4Hpms*7xJH#&Cvv}8)@(p7u>f)D9J4g
ze0{a%%`(>%#aHzz&Wh5<ZFKq2biT2i)vQ3$9h5j98L>1^nPI9j>F^m|YpR&}{C<!i
zn<Nq;BQ?|f$~+0SBGWFVs52Kw^)TBDg$O+PyO)T1$afr6>`>ivy~;TGBOA-#A<tX$
zahTijxcguauJ`7|&SQ%ukPBkO8fpOk!3!1^j{iGZChz*UlW*Cggh*PBH<R!vh(8BD
z9m^`TPk5|ko3Q{KgX)L7>L7<hDu)>DRNS>z2a%eQ^X=)2eaQ{h*#T@?=zE5s8ZI))
zMrY{V=mTwtYLNLiG(IkdONUDs{<_`9auiQ~S+G=i82fihZTBrmQY0<+qZgCtVU5xe
z^yfivE1BagM+Wwq!|xOB>+>+;%Kb<@AcS+0Y$~={Cp0{;;A3O1npIcALS~do$Xhv?
zNw#d;M7VHR-|j#FBO6OU$%gHQDNA(-WR;6btg+-ZLj{Yq!2r`Ip>N<e6*iKLT1}=L
zJKB>b+a@ZIKC?0(4?0O?5E=_Gql9k1Ux#I|aw6Q#uTh>vqP*BZ7q+)~4>N6Qr0%xw
z7WvCXbAnCG)|2RlhMLh&C>1&>>aN66hS7KiqsG-i#1$G@rP$gBg5cs(e|Z*|>qaUX
zZ`?Xqsmvrx>ffQWZ^|5_cMvBKqlz+rYJNkL)%fjwS*?xvu04^2#B3PJR1yANPd7ap
z5xs9k!;Hqmo@HWNQ;f9PICQsLLVWRdSN`T<F`QD05JV>Hw~)6!tha^*J_yK^>u+>(
z<wNAJv<W`&-tvLS&BaB_)H^B)Dyu++(HO_R`%g|D8OC8svvd^kV)vSoArH{Kq~f>T
zmK+Ld;<U$dPIA&{g`JntRW=y1hYzo{M103{9QLO_q?zf9x>HZ_5=6xV)MI0WM~>d(
z<L=`q0^WmRsTrSvae?sQ$KUJCp)F;oM8@xG^i8oK120FFk;O{r02YM>9I!$CXW(r>
zSH{2%C5GYlrk)@$hN<{U6P8fGux4tZ$<H=agk9pULDdwEEor`Gpy&0lJk**(^Ji$O
zD#aWnvB1b#Hb$!2B+{x`i^Ha&k3VH1E`c3A1qDN{*4!KuoSQi_QfWVFC)lD88(KHy
zTrzZn81B#zQ&)2O0RisWQ=Mi+b45n0p|rGQmec-7Ml9ZPMvj@U=b_A>DKpLJ2SA3T
zJTI)Ow>sfH<tsRWwyoIUcLKXiCcN|VO3|j3C4%9yF%cnWBFA!hb)*raTvn_1l-sZ{
zaneO*HZvw#+KoX^7wi%xYytv}od-lnmAP1F2uMOzIS3XwI{L3)aF+*Cks{?L^C(SL
zDZjmsNCN}aB?C(KbWuY=)40+faouM<fw;ev^oi5;Mn=yeG_(G$Vu+zmYdyP%e!^Zi
zNr$k756Hu^58B*Ir+Ex8S&2@1pat>&l63&7tTC5tGk)V)zCXPk7DswP-4E(nEfY3q
z>fOdVMCiMJYV>;jX^IrR_T)H2BrVy8MR;=S=F$9;{(H~L!Hy8u@aU-z0Joh_s-}~O
z=|G{7VcxE-Mn0nCAPGK%`147DmYtSlaRQV{^Iq1y`H~En_`^p#I07a|tb;9$jk7h5
z9I1KK5ACI`Li%b*Dmg2`zWGeTkThm63)<5Gn=ue7@6=LmV;T<OenQkn51FQbBT#a$
z3Z=AM$QX`TlJvLCAlaVbnHj7F<}YwAxn^LD$MT75TUyGidO$HT*f5?J|D<!XVOveh
zw66Cl`E?8-|0}vacVJNGCx#!PNMsnwR;Xv7Ok&0!fF#9yWjC(gZj`Q29@mfE$vS2h
zJj{`6502bgHb?yfa-GY%=&?nrsfkH|j&UxpN57VnW_4>RXLflxBPyS@u)1+(dwv~r
zd=9G07>y6r;_Sj?B#w2TQkIQYqbpWB$?`T@b15uxxFwzA_~UD?V8&4_d2;pVZy{ss
z`9f;^-6^G#n)<Pg-|Ho7O6pkK1`HLjQWz0aQ_~N7N@WENAse0pA@x2<DEsai6@j08
z{zP0U<Pf+mT^1lHsmg@L)E4vY2Rd{7x4}}isIygD;(-UUsm8TF_=!~Z<lPg@r4WjT
z`EJm3Cx6U?&zRs`p<_3XbA1E|NnE<5W&3l~7vn|gw33v0+%Pfml{nW$(c&3tRGA7^
zIo*FhPx*HoJ&gu7;YSIkRwu}@wiZWF!(}1(aGti}D$|ag5IVvCTGD!(r<Doga9Hz_
zaI@Rjs65k_3j%G@<nIoqHoz6Y<F!v(YcUHh-%<;E2=`|uWdz;wH4u&?3l!lOg3K6+
z!q_D;9E(t_kFbSt*iZyPJ7L%jNuTO3x(EsyShBmU4zCZ0HhAS^{;?}D!qVyE!x|aI
z8A&%`?34e2W~C4EohOOnXOs?%ZRP{1i&k2Z2{%tvcS#H_N?nPPXJxjvbP^3|fHh?N
zK?6jNE3c`nheIo6B~hdK>*Og(&kt?sx6&AwOM@?s;zsg=_!D#~p2aTNusU>+$yPkk
zFOcq@{wYwPCML1mTRGlZgw3BAg3(x0ELk}@`?j4f*6Y0|M*H^txfQe);I^*Lw!Faj
zh$lGHu$o*t+fvuBq5T1rLRn)LW*2~D%&w5sB`IQNfI&kn;>-`%jX0Xj-}pbYW(QLQ
z#2-eek;Wk?>_Q)0Je?KHQ{(WP?LNXDx<{_1D3FP&A|i7--7_uoIG3VX*~E#EOP!)H
zUSx!Ifq`PV`XpC>Hz2#SWsT=?9#sU?=}07s3oTtrqURPKbdJf5$4c{C=CYj!%6+R0
z3!x<*wClB7X9sC-9h~Um;(lQr<3LpRNUM!FR!JDoZlVoyb60J5f<V<EL17ABw&n6{
z)aD=S2|iE1Oi;w;Ii{M+-KdQw@HB^o5|!d;v1B6hiovhgr+n&fNL&kdJ8w{)a^u*v
z$QfZ8qf1+BB$bMq)65~gIw4bYaJK#g^{rgLA$qR1ys*4T>m*g{?mr?{t=xq`cKU{(
zYSR$nN>&QEXZ`>)^=W~oM3Yo$*l7TudL((e7Ela>=<Qg|+&u=upizD&D-qBAXoX0t
zAoYbLsYK*{mylQ^t1EByJ25}4N%Oc;lSJD{pZOeV?D;V{et#b$=X3)DN+Qxfr^kx(
zG&M6Z_*&UW%!LSoBrBl<{b}hco(q67L^M$(_@3L6hM8knw%aFV7F+aFaLZt#D~d%>
z-mNI}NO&E$&$O%RStPxn4HHSTenh_`89-c`uU}F1h`Bb#rHuJR{^)gB@wEQqqJIAx
z)si&S<7wPJ;n%#tW9H{U?#gbjp0e5h3PwA*#MGJ8?Ksp@0ZxlKJy+*GbQ68t2IhXJ
z$8$hBhT)&X($f-GX{k;xt`0{D_u_}d2S%VkV}m#sq}<!+&XdFbssL(8y+iX#r6=3s
zM%r6yixT6%0=C*k_d1QOAH&kH^MP1zY#VA*jh1A>Fxex;U@Tn>J`Q$TB)V^>1XL4%
z@G?MV6?oB1|LxA+=)6jm(d+kDDSO_zY)Q*)s+hLO*&q`+w7$V`Lh|$9KJk_HunVqF
zQW+6S4XrdiKR2U8$~Yp8X<u3;h?!OdXU5~emJX(9U*7B7eZarYdaRBQQ~DY556R>h
zT8<>d1EBU${G1rZu*TkRri1u5`}yj8r&Jw@MyGuR(m<P+IGi@kU{FS4w=UOlIM)F4
z*6?phE=S6(3OHQOu?Vdwja+mv=G<uSqdd~bBJ$jpPQcZBl;n6NeHcY_&mo^wiAYPR
z3w>JUb!n5wvItCUmEP;nQ!!sMu4HI+vA&kp+1hU>kCa3iX=Ah}at4WnC|6K{2Finc
zR#~^OX02G5zTfzv$ucGI+11_Ct~yKKmK(wwrMa!~Enecjc7xo0fs2jzIeM3zs@jBd
z`8K7HD>HYKH7K1*BGt7FRebll_NuJDRv|>&-xOD%bHS#;YtpCI5+6#9&+-P<H==uf
zim!2L6M$|Q<ql_%Gg}>A7sHw)8+3ebA4*D!DA$pF7I12UtpW>}Wo+A8*(IeUk(tA}
zKC~WAFY?1dwS<7hgMvTP82e<3MJ*;ye;YlZNf+Tls16Q5T(^N{_Bh~(Sb$s?w-TrX
zJ{$P}`W^7Nv=FfDf9s4@7_{=YmL@>eY+;hOr>{rs*Jb@5T9-rl2GviTfMoxC3T0J3
zE<rc6bqz(wG2o1@6pm+AK&{=+YL!Isb~6Y~D3ttFl;MMt%BU%Tx=(j1QdsjhHN+Ia
zfFtoG+Sq3HGPFK4k&CQ+Y4FL`i4Jr+yE{Gcj58DVz6<%C*h-h!rG|$%4(GA}(w7(D
zT*!CDXbEg0p5rLBj!+7=H#S~?+mlpDTTf?<QZs_2yNQcMIGG3+-O}77x!9@aT+XKB
zf5$Wo2@)&Ai1D#&qdY{#9o>BcueEm%jn+^3(-ei0nwmuW^s)584jZ4P6|n`L17TbA
zl`kuSv0(XQ)m{qTR0z7h9>UTJo&*NB6B6Re$wae)l;43mifvtPiN&Ft4leSg&?`08
zH+oW1lADl&Ro=@kyQ~>5v|VbI!@0zjcsXDaXVMUX*3*(TqylLZ*OGrlAsq5!$seZk
zB85b#`i1Su6iv@-bsG&Yh#iZyvqxS+wVh@1Oly$zgDRrShjda4sBWSkhiVwd^g2*W
ze{gM-ho1M5oz{Bz>EdN&$;8|pITRcW!4-VztcRAI>^IH0Al`ZHDMFib88lCThU%p6
z&s=RG6()&LY)#YGcY@S@JzX7$MMbCt7;Nq{6UL8Wd@y}GIUKB?%illLpO4N~s<y|8
zJg|Fry~2Wa6y+8*S?!K@ua~Zch3K&W(lMI~Aa<u9Ks{`MB|Xc^XlD>wV^s2q35eWc
zL1h6J=|y^8frKocZ~bM88CPXXM~`H#+m`sY_BQKNp=Y_8=KHC0$enp_3yLc!YE+9?
zQPf6$N4T*qI6oa#<Ae6xsb{r`i~?3Y%KF92S5<FnZ0Y_Li#alm7-QWJdAUUVBHgyK
zj=xemLqANAx4^Pww`bq`M>=y(b_Pa(kD(vp^I5AygW-d?Fa`oW;ok0c`OKR5fJ8M<
zO57bx8fb2A9zmf?gpJmgmT(`l<q_Q*i05k3Olx0ediFN<^pY@@lHTQTFU9~Z5ERBN
zkXYqi*DP1ltR9v%0`ZD6)q)t7GSp5MjH9$W988=`Gqt6N+QCx4&yzy1+G~qm&#J7b
zkbG|y;7H2ikWRLgw*7|{lTs^~GLD5iIJ<$m+{wN{q+riahYlaK*L{(#jr~Gs__amv
zl(lXyZ+NJ+vwt)iagae_uU2F%EKDFOqs2V`)N|-saV9OZz(mt|)oxBU>>^3;RL4(5
z9Oma{TVN@kuC=uReXY|@PwVOG49j1Zo{{lxc6PSR#w_>w1MJ;)YTi{@QOa!m1Ugoy
z<}fBC#u4(Kwo1?WdGXw{zI;~St$(|57q!a}vRKp{PcTD5a44C)@~3D}Qu0|MQyfhN
zmO`j`hl<zI+74Pg*NQ%X=tNOS4m%6FQfNCdL-AYy3^y~Zk@u}!wJL7LjG3(*r?Xjn
zvOlDfOG$|wdEy-(pnFy#kp=AAyHFoelWJQ{T3buqQ=^J9n`5JVGjOwKgD?%L_@p=t
zXV1cdq$$^En-J?~c^U%)gOwY~c6V*w)^JWwPw;~7{$L79cL+Vkmzj~x5((0a46}OP
zFt0Cq#2h*QEW~1=?fi}-)PcWCK((V{{%21whP%a>)`}X_-N_2rb+r~)eE>{z9_O&Q
z49hhvr)?B+<AfCNi&Qt$z5AWjwN=x3CW@$+#_ij-6RloQ8=V-tT(>ZA?VST>3bW4A
zmYqAd&txdamX2XCYOWp`wC)I5C(X_1R`gH-O#TtYW;p}XT{!aEEqCHQ7GMs@98yZ(
z(nkw4s3~rzxO`ce6Q+zcFTC_j(FGS=P=)A4VLfyms^S3ZYyqYZ%d(Q%5RDZqT_+_i
z!<WEfwH*U-Q|^7>>6HN%xxjYpMfp|`S2QG*lj6Ot9%-NmMD@`HEgXZW@TGcuF{udJ
zMCr3cp9Wcg&8MNBQ6<?-P>y0XpzY?G-(PF8h)yjoooZv^WoQk5h03d!c6Ilg31bV)
zs8RVWsMm%l>@Zc8+ldEb)#jcKZF<m`nwDC1{`u!98sh82inhNp>wYyX5mU8Wh<Z7S
z@T?(1fxMg6Z!z<ZoVVto1rPPybNfAG1g^Q<n8UJ2E6Xdkcj1gn!oZ}mkc0w?h;rO%
z0;CoI6M+f2woW@uAJd9|`V0!Xll?$}^ec%_8=gbA6jW70eNZZw&#dS^0g%m0+<o}v
zH(nUOVdMI(UzjsbYgT9|Nc7%cr8xaBOq58IUk!B*z@?>QB@>%2oih<&#V0VOE;`lU
z*w&K+^{ije+bjLl(YFpP@n!x{VF)ElT5)tfA5p1MQ9&ezhwvB+tkO|AtRovs41A;c
zGr+1Xf5RMi%0;GdB=aP&=+!RDLrI)eBC?}r(2O6Q$BI7V2-qB8mQt(PUA@CpGQF>#
z@a9fF^Z*ybH5+A4I${3iqM}00+EF0Ef!aXlUFLi1D~n!^!J9CGchzp&zQcw^pL5PR
ztGVYZz9^Ra=3p}0gp!dZo!L40+iPm7W_gC-{XQ)V5NTtwb7SSDUun3&P8~xuYJQ#K
ziv${N@^M%%GQ`*3I{x5NQs@f`3e9eoOBxz%rq|nV1BOTCjqxwI_wEsZ>n+OhMYcmS
zi4xK^sg(tR)@#USJq*O21F%FUQ_ukkoP-6**m8zioXWU_+<(>Ja+0_ttwl<NZ)}{0
z5M-azAv0N3CWGZceT0M8nm_&iI`hJlx0_jWPcY-A%wu*>mIaa|h2A1i^)XpjtM<&B
zU23LI9cenc!mLHcCurbU>(;J-Tk+|dwno#}-5y5d`i?#R*v2!?I76WLrY*(?gG;1%
zcFpEBPvXjWK+8BZx3&x}UbHY_;^c|tmt1m*viK(PdewfaRV)y(%{%6ZW8PZuz+GD6
z*Kz;aKri7>!v=o4x|~?GzN9)_?~<ooUmt5CYE>Lg!D#k7i9DkP_4%5gzUWDeb(URs
zf=RfR2sD-_m07}k>Sfw9&pvh2m(Tc$*z(bA2kJR0%D*IuMzXjx2}6t9?Wn2ZOKO>B
zA~98+otm%6w~mOyWYP-bwOWi#U$D@^CI^*lFV;Jx-)f=T^o&#%bID**E8l2jXy(n=
z)|kIs^B42RqDRflImel4bG~SbO2+NsQZgv`?u50-iwZK$^r^)rCnudq;(nG`Z!zUN
zHkp-gzi!%Fi1$aC*VftI6dFBtg72uKkA8IY=u%mTx!^(37%q%!i#`AVFIh=MK~#4d
zUU(}Gx!l6_KfLkUYxD8M&p^$o-m+y&N>^LJeEYZGUd8;?6M1Md_hc!)$c8v`BAtcW
z^7*vNw(7d-iJ%J)zNVSUB}G^>aYRzs6MGY_@-8;4K~L#UF8698u3TZR^2lqLVE}d=
z6*T~x*RaKa!h!AD0?o}$tWa0YHNR&lCOVjrk`a6TE!Q1=#8F3Gotc&?#a`U>kOP<s
z6vShq38!*JtzNOr%$avAYcWhjpVSv`lO@hj75Gp&cZg;O11=#;bfzr;7rv-?(N8nD
zJ6Of0+$?@&k-6bdT6NNxS@TXZlV_e_MvR(lSb4z&`&hgVH6#fuZR{wm<d_YYOGC1F
zO<8#t6Jr`p+4lA3U6x{KZK@;QztJ=|)WYJ3L1sqpoH=t>{rt+GZXoMlDu0z>-vpp^
z-t7Q4-Er@|t+esaQHes$Ev;~^#R{9MJMX;n#EG<gl2VStmLd%aVRENWow2;EeES%0
zTtXCt2@|EmbPqTcEL%NME7{0Ow7S<GfZ1tc%!KJA$iLolFD+SV*Fiuu>Ny)-G3dd;
zmTF2ymY~12TjPL&`}*+0A#UcJb?zB6USG8QMhbQZn?rMFufqPzle8m&MV{<}!V<G#
z?c3%b58r8Kva0o@sk2Q6oLPWUV{3ka8;EyM1y{!CO0w?wqz~cCJ6T9%wR!U4TkYfN
zIit;lne)x`nR88%X8mCKX*N=NDzn`)Sf?YM#oz$t01F@2)&*#X4pU!SMJV)Yvw7oM
zYat3)O-$bPc?LuE&CR}xFTS+-k5~WCYPjW<yrhK9Ro3l0hy8KSzs<Q@f`i?EhI<!)
zgX;C`*JV^!)|%^YxPEhfUjBHlYvpKhKdW;|;!As1UU{=;9p1EX(Mv1wX?+0ziF-jV
zCy9e;g+VP#DJ9;~y&RXsz(ADsd1)C%`Nb3xqN(TtmR3$yxCmcV#vW&SIstAa7zdik
zt=2+>c<q$4{(&Dq_uO+oJ^l34-{6f$vB^{IzTPRszxuC4BSS4Y1*Mj2s{=j%%s<RS
zcmH{B$$W`+abdT2?7i^8M{0uB7DQ~&4qDioCrzJY64J(-qmDVo%sb+68{W)B4%>0H
z45yTLxkoe$yOvmc`J`KrAiHaJn(bTGnccf<%wV9~v?B&viCBeWh3cCcy%&A$>pj2w
z-EYbWm;N#@%8mfyed3VwcIkNb-C*SBx7~5$Nle<klsOSywY9b8q5B?589k=7=Bwv_
zRpk}9lq{HR!$p8)(TPGj{m6O8zPxbpi?a}C(R!&kwsRv$L+6mkUZd$!T&;M7gK0W?
zKRvdGHTU^Ar3Im^Lyb*3;!zVDSPud2#V0;cQb$@tpmJBGElj_6fK^gV>P@#^d-?3y
zv!Bn)%bURkh6WDUoAlhjAW8iWAsXXNQRx)x1}G|-Xj+<TH9jXZ+Xn85HnzWtBe}_v
z-2Jity||-X9I0uUtXh*{lG6&zvB%6d)2EMv`^OL+JOJ14v2Gw4RSJ2Q#j06J?X1()
z2xqFTuHY+fb*u-`jb+;ju=tiwM{~HhtA!6k^!v^}=iJucUG>{dBS(&$L@A_(SOD#(
zM0?JIkt^@K_crFhMc*t)3=9o4FIloQU(Z~9^&iWbaXpSh+n>01FSsI^x~QFd(g`P(
zm6h*!V`t@#`SeqTSmK_Kun_#B;zA1Y*W?W;uJU=Y9+>rENhD~tkG{Gf(I7FX;5O-A
z;$%bgbXKH5ye68`QuTg%91~_qS{NZX7#x~*#ZP|Fc;8(Q{)TlZuHgJgHZ{>fG!1us
zF3ACyEa426EJ|hgrDSj_w1|Xvv)^xtsl{Hbxkvj}3yMQS&A#?W#hRn$O=QvHT&tK@
zSJyM!8fDxrKc$r#HH_0`mlDwI?3NZ5Vq?6Jwa@`LD?Q^Z)duK>@Mrl(j~?TjGIjd)
zKV1EXSD^iAkjes!z1#M~sppD9o%_w}ufM*S2%Tq`E{A^Q*}Znf%GB4EydHDOH!j|K
z=9kZm=AJ?^A{SVCXkU@zHsbjgo_EnJH{Et^U2A(=9uI`zQ~F@F1S(>I)teD};#wM6
zx8l)<Hy<C6XF#E@UOQJ&;F5`~!BQ#sgT#iZN81g9L{3#IY@M=ob#*F_kGY{CV)sux
z?d+3l7QeXUyA<(voS(zymQD8^0!aW9;&D#^hA6M>q7O-QXgM}*$L3j8Xn#rlp2a-O
z)JY|Lcq@lBDcV`^q~7c*--J71hwapo9L@*0^mP%fMbGF79aW%`*PWrAFuGf)wWXe+
zolO6%na7$LGiN^ai(mcxmAvfi%XsfdHWlH+fOU0wdHKED%eUOaiaZkt&uZqk=dQc%
zk`f>I>6KSj6Z1EZV<k$yH|+~9cQunJ#sZK2${FWuxpTqo*;s`7S|6%+SpHRJ5rF~-
zD)FXh35h$a-~%MPoFdn}eO|ltk`(!Zyh7{ckB)Y%OIet@U!auYKYG+?>#Swn0TJHA
zCy_$&G4Yq4dH&h`Pe1z1PbsdHe2GcvU>Z~UmnQ*4T)QVQWQWLDuHEiG05Z0Y&MJhC
zWoU!`tOS9eR{M}n&w?j(z|>?WyRvX@MP(~nmD#f49kXWD+YB=IiNOn@HpovAq)f|3
zYOsJ_Z#SP#=?u5FHiriTodS(-{`?cs|I*i<ddim`zVy<s2RQd5wsHG%8JD1(-y;e+
z_j`bJ=j+S=`FC{Z<I#&*7NfoY`DdTc2@QnJU$6b^5<Y`i%zcV(c@Ni8e*1#UX<Q(K
z>`$6JX==)x!@lt5o6BE6#^cd<A$eZlRIg)`GAkz<kzNkCSgT5)QJk0K)a$YOm+sdB
zyf*eb8a+)O{)jmB;Lb=(OXVY=V=Q2qDo^KtzypJ!Sik?fC!c<N^wW>M@MlD%0Hy&|
z0Zct)e?5-VpM4S@H1s9)V9NMHlx*e<dc>6h&3;}_^=*<rP-C!N-CEGCn*o0~4)k(p
zWhrZGW+NC_ldHX>8+SsLsjl2=)~<fn^mMkEZkDDL=hq|)!e#i-W>*-$VYt0{H?I!b
z!QA}(Lb%kirm$$l>eQs<JAeAq|MxE3>pv*Ox7cuUe6WrMz~n){^4)jeeP+|P_4jam
zJifaCOd;^hGtXqKUA5jnb^7FG-~ZnCtH|pVw*98}flGH$O+`xxXyd+o+F8q3RWJrF
zGnY!Uo(cSmZz@=VagTbR78B77Ug=&Ulo!W>xRZLfl0IVK5G*?qc!-55@#u$>p{wZ{
zi-(!glF@v_CK@k(9Yr9Fhi@nnefiBxzI9>xZ8zU}cSc&qrDT_FGj$Ywy})@%<m7>N
zs?LF~eU$U65})-p*%>K(NkkuTNZ}K{VYoDd_bgekzn9ooQvml<DlySMyA)Fv&kAU5
zQqbve5}Dj)byKMpyLa!lGkbR96YR(0lPjUVrV@@hh;F6e3F9rsky@CaVM<5lG5nU<
ziy>5BP&nePg8aOPr%ai+oR1ca=Dus#PSgXW;kuM_#`)_?1vyE-bN{3FZ|@KEJq{<G
zg`+e;xjGlV{Bp`0OP9rPd(FM~-2Vb|ATH)uRo-u&`vsTY#S4TVLRRCC`rfy`|M0ap
z{oe?F7qB9V%}DUlxTBwmF7oZjaq4*Q<!%6A0uOqX1sKCavR=g1v}URtjapAtUVe!d
zC5cxl`6G&oEr(;6$Ri6#o<4fbnLqj2k5>HZ%Ky4;?AY<e0RJ@d&9o05pkk40&Q4_e
z6q4#<*XRNi^N*Wqt-75Rovh!`gkPx^|C9|)+s~WmD;7AGq_)oD`U+3hvPh{(F^E@|
z@zfftxE18O5fHUbe-|Skibk<fz|bfV;5Q4<oqypa;pD{F9q3y-lakUpS#N7Qi{Gv~
z`Q+o?DJdzbS0SL&H=*zU(Ba+jLY>|p$9I9%$o2DXy6rlx3-%I)DI8#a1suBn)mL6g
zdj6T0V)Bb}yYIi}fu+-@Pd|%$$AbgLCim;nf4^AN+apPc6g9?-9zFJ}KmOsBYwvmB
z?v%#HIzC#K81~`<3uzLS{&AOjS{zEg^aZhcHH4Of@DLxMjQGlx)xl&P(!;C{W*6q=
znh0uVuW&4oYO1;xH(^aLk4Ev~f+3u)hyC<dKgyXq=jdyHb>*+*S-eEV<2`bh@3-f0
z{!>e;k2(k6!!d`=Vom;Flv%rOx2c7b?A*Q%4pVPIqx<%k#L>07OoILLMIsK*MCid<
z95|B`SszO@i6ie`fCD+~VkhWMfT_oJwm9xj&0vi#Tp+2$Z%vwUtf#mzza}#?{jO6_
znEedz9UeW(Dl<c8LZ54RuQ<Kvqk`>QIJK{m#Azo$yz2J5Z<ztGu4W;Xc%BWiPMhb|
zR~E%R_V5$LQJaAUcip${_~Yjv&HY7e0@l8Ha@t7;<Xn;KdQb%zJ7&z-;vZgidHsD4
zE%0sLx-l*#IZfv=0Px@At*{PLSxs;;jXhdjLZVUsBJbA&EiH=8AF$tC)RLD1lXM7d
z2E+oL%HyoC$J*x>fQ(mqT3cE@TJ4!wilX)FS6%;=uby`6$;Y1df8YJ?cV8vTo7luv
z-QLIHdy<V&c+Z)S`9r~z<Hr;u`eXQ@R+ZURS;lzX5*B1DhqF*_88U49OB%%2MAX>W
z1l%|&c=NMu$V^6hs{PtUV`G#3FhL@b7DEg%+3zG_iT9zS36LqNEPVvM6f2*cmTh)a
zHk<OA_Tw{BlV`73v$^_~yPsK9l$ZPHX<s~iCG;MoEOLydM#>^^?c3Hm=W?;#t*xyu
zKKIhoXR{95e7TtD_$(z6_APvAq4)7ep0L?mcinX_o_NBErTh@EhCgqw@B3%p7w&sq
zzg-J5h7XVzUHbL^XuJR6d%f#7t&2%XObNr8?D9LppcRu^UI{|#SX@kfMc}dk({B*g
z-Y#x5WY=8PK&|7}a2&FFPNGr~P<CR6KOdtZf%UzbeOj0o`S0V)wcmed^|BKep8w)6
zpL+D!Z&5}8Y%H7b>n00+egx@(GU@F3;WxN99?V{4iba$&ACH0H-F(Eo(rnqd%IvC8
z_)0tCtIdi@yw_48-<Jrd@x%Zu7J&5(F%?X!|7iXFLezkgk`lsT%FT}L+s&xbQ6?!R
z8-oWhA~Ml?=!h96f5b@p{Ygcq#Kbzo!Rn-p?IpLgdM7otbWf_TY4}cgRn3bJ{$t7A
z6ks7VzkzaJPW@x~4OVQyIBjVmVD)lt_1bl-J891^Lfb<0My>b2_k#L-&p-csc;O4L
zhR2N?>n$EV^7bG7=Z~6rMjTP?=@66?2TH!HNC&#;o{wZ?8;hZxSiVo)RZ~;#-Bn%b
zXD}LKVq8WDDlw_oDxOP8R-p<*1p$m!P2xKDid&&4S`aFRNtTxYVD&IY_$4Z(hiQOb
zLrI!Ii=F~O!eLfoA=^+E<6GxF^4R^pv7;w0!b7ZHDvz-$t<9E~eOq6D@a1<_?>G(v
zG@V9fdFw=P9-l91!|C|cBM+Ie6ZwMKh|zFLTq|mTeds1SN+e2rCB#KD=)N00tA=&n
zci^CGz$;#bqAjm@J&oPWqHRr{=B9d2OLGJJ22Ydr%`G0*miJJFAocYSM92qAv?fPD
z`VHYo1t{sAHd;4^VVyXZm838%=35PohM03Op6>x<z`Xz$++K7MH-xUT7xaZPq(VnD
z#=%6EbiZiDnysu^;eB{yQSLL)Kpn3@?MUL#rVSVj8y78En2IDinn^av)G^4|v8SP-
z(fi0FkA&a&=Q5L%m*Wk^hHhTHeDza2^=meLC+b7FmfmnMoJ;p9iGxuoVkxq+)4q1W
zC3Uynep3pbcn0$0)!05iq9FuG)XV+oV*;2&odCnvYfTL8rYKZ+6xp!?PM;Rka8`EK
zkdiD#k0okn1eC;s@r(c}{Y*no^jr^Y+uzyM73Ax&UUZNPul~RPsXz6k)7GB(<*zIx
zTtyKT@;plyQ>tOsOsl|ZA?KX?$bWg->Xix?(szq&G%EE~2as<n5a=?qX3Y*xm^itY
zcj2?aVTvgYi0Co`XV}A9^0Dom0bdJx7V`{)Q7qLhA60c#rSX@HHsdEv!I$-pDc`xB
z#oYLQCA5e|6mHwR-YWUi7_^pEEnk^NL7Iu9C)CL!Zm!XSsOW((j@4jARhzfDt@8po
zqt~q6`d?Rk@7#+h{`c8tvGudcW9RC%tFkL9%I2cJq#`WB=vHA$>f5kkqj~J{M?<xh
z_2O(p=bdxT12^7yvtqp@9g5iw3Rv7{4vKT>MkNu9BszOwoe9MwidQas`%QSXCjo*E
z(GziD@NV^fZj=8@NdU9D8NHuwXjjuyOI!BBy>eSXAhbixt<!>XF_w8qZ?l|)d~BBF
zr8u$3mpf=XJ82|D<0fy~wsG{^?<|k&@9W=?mX@}Hn6kCpzKw11?uO3I8@5(`70#84
zE)vEn{b-zvO!WS^Shy=(L|?Sk!XNGJ`t)2Uj?LZ>A8Gb{@#K>?{P2fYoLN*_dhe{m
zX53#~SbSSoXYhvRrlFgLhT`vtj*EIOFF&W9NfuK&I(sqcy+c|}S$X&4H&eV@Hmf36
zB5E}|Cx!9FxY$G+F}ix?GSgUBg<gm22US8}-Oc2FJG@4R7OjM776XASKFZZKw7a1x
ziP_kvvovVe;j^dyVa=K~11nd(Q~LJGw~l1d-f?tnEr}RH8hH_io<)lmn+12?7lbqV
zJkdje1$Ql2^s}G+LPI;3vuWj?g9aA&ek9JN`;}ZB%!?J7clmdJe8*pI_|sVo^^6N@
z{itBj6ZD5zcY@W4QSL*S)$lZqB_<Wn#xMn(OoOu_xEKpp*-Wuc=#hhPr~urFFV4cL
zBuabGOH3HYG7TV+s((Jd<frQZ(ZlF~59eekF3{)c>g+6d;PLwko_OxDUDGDbTt0K!
z?8PUXaKaO%MVUcNeorVoXy5fo{|N8`gi5Xr;T={0A%TQl)2B~b<L1wO6V5&N%V+XC
zC2~#{-LgghT=v?tFTH+sRZY`)KEuq%GGqMGtrTT2Zv1#NW$FyG?2RQT{)o$>F=pJP
z*_IYtHm=|cWp|pSj8Wz|JnxgH9&SdKj4>ld6f#F4i*Hx5CO$y}l+F_uN{GUX5PW;(
zYF}r2<4^p5@0}g?^iM-K$<fde0WV>OZ7g>!uc$CDyzqiqvwmH0WJ$5FvSL@uGtWKq
z{28a6Ax<Yrp!AV|r3XHWSk(PC!NDxk;YZ1g=`-g4`44}&@4B1*dP-em-I3A$81*0|
zW6&F9ywM+2*bO=qY+F4_E(>@ZqEmAnqYT{yPNjiufC#(q15Ap`kL7zpT8ca|DM7lH
zCR`H4?ldMLOO~T)7)o3b3(6~xHJ}V9_9wC6>tONb?VF0X?AY?fN1wd!^r=(6up~H?
ztGH^wfc5-|)BCef$AD=>0jL<r(~nMhDMXMfAZOp#op9U{w~s6-{obvAf9TfAs)h-C
zKA5>saXzh9!`wd<{4pr_JFr5_Sidx#!SNz=tt69MP+}&}m}eSxSDIDtE;r9UcDorj
zd7hbj<a{%E+F|wsstR|is;o4P_{s1Q`jX?L!>_-+Atogy`lLyGN(OzErtr&u>1k_g
z=R>M%%rh@P6-tR=v0R#`s%6)fmtT4Lfs;=>`E<%Xnfh1}&#`M+?pvJ${{Bc}ai3fe
zVrnc;0pI(XbnMZ`ujy#-sM=n>y@c{G5Xt;n{%}m2A|Mfmc5l198pj!%lYdJ(nvyVU
zamzgtmD0x~Jhdjiz^7~O<20nr!#=~2xSWWFPUk^k)~X$R*3Tzj&A67f)-Mi(QW9Cs
z*rR(C@c5Awdh(|(RxE;Z<_ci#VvKaO>60w&5<#p7((j!#c|z?eC!c&fGoni2s(+zu
zqp82XU#JR-0vc9R!DkcX*O`)>bWdGF%PB4G-Eq<MKom3x<KbhRxi;!|4MRS$vC+68
zm`;|&ENi}TnZZ}e1hS%$<4n=0i8j>fO}N|&LYTE6Np^M~94`rwHmh9dt3gi;`ftUq
z#$ax4s)z4g6NcUIDJ$D)UV7;Tvvk?gq1?1WUu{*b=bW?8dG*!j7QHfk+Vn3`MjI?Z
z*`3DX#79jZi*vazQNs%|>~mqU7k&Gimn|7HdhA2@KXUKUI6X_Wc0Ifoh7=@_0xrf-
zk6kN*V>_)M{Q*~s;@9kWq(CIgP@GI+bO=2};M2W2C!d&%1it2C<H_XCus$+z4GTnv
zlL0Ib<<SIRJ~A0#nSBqz9EyAVY?J7rgacTnf0B-A8}OyhudC`D;X#hY;m)0V*yKg)
zHdXvL)x5E>xt)ovEGz<t7Dvf17-2u-+{nmAPF|7mE3_J1%9$D$pJdVq^O1#HGG;2=
z>oAroFE`8HTx!b7wwa@kJJq^*I{Em5W@xb@A!C}YzHQs8nPJ^y*6@+ACCiqCGLo`P
zW=gidwsBY24L4l(?B$pLr?~EaQfy3BnpQ~v1nCojOYenXDu64Gk3aV4<I1N_p1S(M
zhwpD$`sP1p5@+va0eprgS(}`>wmu?vq_q1Yppm!~*O9oCRcIGC;d&Gxll2Ivp+N*P
z*GY+Mg7dmx*K~dm@hYE>V)~s$Hc)PH?80qOZq)(!_M?FO2?+H;k2)=YsCTB3MLqub
z<6F-?_uL0~<|;PLQ`qx3$BvK2n9V~&f$lR;Is9%uVEWarp22z4A3yDozmYFwNMWBk
z{V?;w^H18CcYTG-k9S@!5es7S)CZ%qrDx@vyuwoZ36_SsYO{047Bg<*bd%0pN0%<i
z$?>LqS0j4W2Gh~JIb4ugjKQkVsp#d8Km3n{M;vj)(G+riq@Zql?kV@jIrzljio8^;
z-fg>4_+yGL|G|&8%$+mujRzmSzqGuftS}L?ns^}Qeln(jA4>z?(fHi8e2+w&CeAuK
znY94z7#@RKkx(7PV#LMaWk!<jSA)q~6ukAE04pwL0~ZmgI<^ydZM(whxZf?{$05`Q
zJ)?Z(b3*DVn!s8OaHc=uL?MpbcP^LZ8gXCW#_`MCw=gOyu9{=><nwR+wY9zP@S3_N
z7`8vm{80R3O(rjYgee(0ib)nV%rwd~Y1t#Fw@B?Ig)FIW>r1z3nfXi*NoBk;jVWl`
z5Q`0Pw_H0msaP+*Xkj~IW_nJzzo&)e6r#Ig<Ku67`XA4}!C?1)k>in+S#<qWfTa?C
zTHsP%ZueB-l<$<oW*^=%cFee)&p!Xm?q{BVaw1=x(6}TVCraPY*2=M>jRgnm3OIWJ
zrP)FfpRy{&$(Xim{bP>qg~L_X0T|jmKm#<qQQVJ(Uk%z8qBzEy<`!&-1(=%&5MFKd
zHI?);Nb+|L3=KvR^q94A<HmvGjytYr>C&Z-@+xg^ikfm{O5!&!x+*0tE;0MD#~zy0
z+uxTQWttC9N|3v1&>tO5z#pBZzP^EoiwwBeZ1c?1k1=*xV-nH5qGGh*h)4|KL_Cr}
zVMj**PzGE~3ykP<ILYW~8E`Ls4?7qmu09ZM@9g)?Is7Qrqm4cP=Id{J31Hnr7Dp-@
zby(6~^y=3qNS`LS++tWR7U=~UM4M)O{i2I^95L_6)z3cnbk^%{E-CFE>WbD^vrxuR
za$!IK%5TLU;t|3+o2*P710;H=T~!))g>@Ff#pH~YS6^eA`U-_ag}@^QAsX4SPOfQ@
zJTPo}Iw%%a>Ygs(miANF<8}8|RMiq0Q8#(PgLf|p1bPRKIA+ekQS;|vwMZ+K-XMb@
zE|$8*3<Uyl4ZVT5P;)~(R!?Hh?p^rIh|vNZgc)JA;eugIQBe^&x%p=N_(`U+a;M2e
zS<lFkZ}|P~ulma{ieiW<hv3Xk9JTfJH4HD6T7y8kS4@x|#td0sRD9H^(!=t@;af-W
z%yH_NBq3I!;8RPV1-Mi=I)Uv4V9YABV@8#XD!StGAMaw|bIp@aKasWkoi__n&|x-w
z6c*(u=w;e0l@wv6v^xNEz$V>Fpt9%Ta06bx!yQnlk7CW?YSPURi}pUAS7VBkh}aT_
zngSp%`e&8Mn#>u6=<kc?6MvKKAU5T4IE<)4t8tSSSqosi=(iKlM9I8bTVC;930_>F
zW_KeCwa}U559C`smU~T}GQ(`&zJ+OFwTwFwQ4*(*(?_`1K9y39lNjXA%`dd$o@wdr
zOg+nD0tcfWs37U-X`$^qt9*=f{HCU^p|3D6$F3dflyaZUKH<;L3S4fn_JE8NF*NYl
zNfW08#*Lk@{jIl_zxnV#9?IUfW79+iroAytMgSo4wo8}N$jKf>-vOEnI04N8)}TN*
z$bE1$-7j$|kO^#ZUFbQ1i;)+%JfGp7a2&n?#KO2>T0xW8{{j*JY^R~)z=b)(rwY`G
zC_BR~NOj~ow_)q>CuEx17!G4nE$rORj^gX9j63EtvwI3(N!x<h%Qcz#iYk6T_p+#^
zXKCtN23$^)Hl(kWj+uyokVU?ISh8`xn3%rM#w}$<ix<Cf;U!-?M=@L)d;HYXXALg5
zz#^a(TI`8l_D?<PsG}QZ&pxbd&6?FOJ@>-X#p^b$9UC2lkwF+_1IwWBF9I@gEVqGN
z?k16GbuTMM#nl`zdivR;^g3KkK;s(2N$$(t0)3kM2$$A;P7hQ3eNwdLkyNE^5~OH!
zE4svSBr5e!95zxi>E;RXJ~JW@tFBrOW`@Kf0fQPAnmB116I?f0-AkM)HX-%E+>2Ml
z#-}hpw2);jH=EYx2Dlf>y=DdxbDx;N;4~KL3okD@`h@vMzEO~u%koWj8lT9tr`S&6
zKmOm(3tTGU@Is5H=ipJ#nK$o<&chF%vuVYOcOSU*j+^qE+nQ%$8D<MO9{gBvDSZ8O
zaU}qxjxK-=2b|?>0#}5ixnfoKi!b;XH3)jrdDc4VKR&fS^ht{r2QG-@J%AR9NDeFG
zSlpRSNq`f;WI@Yqz(5fCBGdJ-c=cgVzJO-x_|Rr?aS@-wn_<?kTLt&ZgLq8o+~d`-
zSw;RRU%i6}S3aJeg`-#8OO913_lge-Mh$w>Qd9oF@~$<et@8{Y8w|!^u#K@HTniXX
zLIZ&Sg@m??ddb#m(k4|+SG8(2HCm-<nzU7_rd@vQSAT5Mq)pqeshgIzNxEFxE@{)2
zv}toK<N_qIA%TK{k;IfsFy`Xed!F~$)DS};A#2in$%oJJ@j2(aJm34h-+TLl$6Gs%
zyZ7wR`{~OsQU_ZdW?`Zqc1y?qPjE#Slr%lUkEMc=)z{ZI<RZV`)b!D=rb7qUpFHzL
zH7qeJ5FIBTrqvWol7Xjdcm!ZFy>MwxumO59HIixx_{h5fptyvo64R6c+{lWutI^eo
z9HVS32O$#%53g`m7Agg*MY$Y}4oR6f{n0dBoLXUj&#m@+<HL&H=u{F7tH|8T$`NKf
zqB|@tD_2~ub_8zg6KTD{h<(mu@?Korz%nH<S;??x!_Chtn~&z@m*76ckpS<f*Bi7}
zW0JCC_Xle>Zm9dLa_wp_%1qO7jsz9?Ec`8`Zwg#2GU_6AP=~d;ecZ1%ZF-~?`7A6W
z+dDhmp61gn8HW#lR@&R&lLlr+M=5<Wq|{gqa-|@14ZtMN5<UjcBz04%7BP$!6kOVj
zdJ17zq|L5tZccejj17x&iwK2^dxD4>2Cj4?mLF8m(@=1dz``*_Eh3X6r2`5fSXB}y
zI+e^E6Y2-!!oZA%;2XpSqJ@QpP-<5x$B!OD_@-45ph6!GGbDk-b*19hWbSQBenGLA
z_0W=o(Fqw|{2H7*;B^(c*fXHnzH@KUnlgtAvO>zbBK;EMiOc)yn*>*+q$<L`uGLyx
zSZlFZ0(EuuW5^TFJ@dTF<#tVde)u!nsWUB>?!GPqEFjEjMw5m>BmV`81@SDvBt!#e
z1Q@6oocqvW*gz0`&TYaLR;B1hI3g1O=g&hMfEUvHn$mP;u3Be52_g|sP<c;4g?TR=
zf~p~Fl%dfJSP^Dvt#&vJ1DF&eP$fiLGDm3{0`0azdF>I?XG2=Xg47EaFtviYhwN(@
zyV(w6wRvArDVTe!m<&N5pvlTGh2A}IID13=`u-=s_h<)@Mdp4*sMnnUE&&;b3mY0A
zY<kikmW`5a3v9=7YHMnzkS}0kgig214WqW!#I|#-N$sw7>xG_<EQ3B-iwr?3rcc;>
zLH0!54<r(SPy*aM(V>K!yz6%S;lhEtM=(wq`6nv6MI3_ZiQp3u;S+$up?WS;B)|vi
z1?uF}j3d)U&wCkDqro<wwW=r)8VwyCjVbpccpVOhvbLgHIrQnrGUe$R(Gc~GO}#|S
zCTb&wz3lApiuJA8Vi&7dGWQJ2FnZOeY}@fpY0dg|&32nLAMHc;v0Db%9Rn_QIDo~E
z2nf`<hTo_>04uyE8TRy5>#F?7eVA&dA!zLPc)sj(b-FcJd0N`fwPuZs4H>Cd)Ws#l
zF;XAa1wtj%$IJ6p_zx)r<yZiL&mRf^&=Kl{aUxSUc2b469ZkCdq)C`0BRI(A!BJ?4
zlt6+-2^D3ROjT9X^dabN52n9?%yi2>ci-d_9bK1S@J&oyLukr4Zoo+k)WYIY<@~ue
z;iI@qtPu_y1hd*q^Q(sMg}Kj!F(Lx(!rm(t)~vIVdrZ3->E__!W>?y-J^Sr1Z+-3@
zp0ExBfa87s!<Kb^M}ccz$>-DzW1TaejCxd>VrmEViEu*;AY=M$2yA)kM_0zi#=h|M
z_xju0+B7FxnvFd@7i=)Ku|qUqWrNLo$BLY?Te)KOPmpqrVM>k@D$K|~W8<UBMOQE1
z1Ex>$=ND#8=B+9SR937D6c!eblsZa=OB~J#_#A4X&49&tk_GjoabalVi0||t|F*Ng
zvwI>Ns+%C<a%gD~SXxqom1Cvy$-xgni3XJ<coT6&Op16tz*Od52j<?EnGJ9fVEsxq
zc9*3dl7d}0d}uS$l0yI9d%)S)Q1f|txpNe4EM<uCKzy~dbSHyrP7T6Snv5p35!HzR
zUqq%)t|(zaoUs~tvZ}J$^Zau!oSvHUz4qqLcm2IrbZ?LP0yZF&Vi0orE0%~@Rko!G
zLBr#}bY&1;%gShOcAg*c&Vnz$RHg?t(q-E1L9^N7gRh|vF*2ZZ&@q??c^ppvT+2q6
z>j9G~dHb4@{Qva**Si%4lyK@u5X$w{@UZYsLcRKW5N^q)7%iE&ec?pOeF~)e*x41{
zOBcb+kHTsKl6%^GVR~dpg^uq2_<wERUHp6JD=k<sZQ@`=TKJ(ixN_HkE4m)x3QMgC
zG@NtDa^$ix_&q5bJFO%!F*)g}r?>p(4}aM+3Kk3wqUvvPZN(DUs9?G!Sqq-99i#r2
zjSY<@`T6-JsissbcCV+`4Q;y^=Yn8bw9Uf<ClU;#X4At}ueQ3nAMEYF;$&=ysmVC)
zB`CGw?ggcGwQ}J7_artLsaJdgw$WKYG=U33-K@4%0O$~8XMLDn%S3W7URYr6bM5BP
zzK@RNY^*;x0OtN8%3Xx=-t=Hxw4!+M9iLPzshoQFSf&RYL*^~iV4Z*5w+5><fR6mf
z?WmF_L^)iVG0pqxE3XdW4O@_De@;Ge&Ktwv;T(_u>ZS0Yz)$fV#s<zM7A>w9W?0fv
zGp**oj{E$7x-v8ZcLbew1W~go@}P(zCnpzP-@P!o$y5+pR-O_2kILNZ5p_B<D_11<
zbgeUyxu@O{)XM<o{woAXd28o;&YGI47JEi|4xV+>%zZQ&j$Nj^@Y^uRB6W+?C&s5x
z_(hgvPEW_G+oIDz?7QK>qq#DK6<!kTVHD*j$QzK^)4YKrpIN9Y%Z4m}!`I~u$FoVU
zBH(fOi^n!^u6{4wl+p%o_IRu;v3Q(8C;*0=(qiSfUgC8zh;9I;*CKwIebW2`6ajKE
zk=$GD5`rKEc^4JiI#^^N*qp{@H3yHJa;NUy`#uG#9ee`8KmSQfIgfO}Qhs~4{)S2*
z)KmI4Mjd2YRjEiASZdb`2!xX05C0<x!MW}kl&>-LHheER9T$XuKzDc^vW=OS+d=Ys
zOu0XB<mFYE(u{vjH>YB?IfGT_OlSl#G=@OEa`IqLpI?B8I35YFq}Fqxx`HY~wiaRT
zX|m+KJb*~hDZ<>xV^KiKy(w9zytQjzS^HV{0Lo3tJw6s}KwgWP?genct|eyO)h{)S
zt94EM2I)J=u;<rjhC_!U88VF-?4iP@W`jq%5a-hRkaa=er0$!;sV&?oi@bc%OR_YE
zFqrZ7Ui;bBAND01^vCET2htU!?(q^<jub&uRMv<JiSbaU5eHTDl%g46dFN-C^Pmp;
z6f-tRuqhY2Jz(m`pk7k9)#Iu&-E0nadoIOqduvzu)XWS;6E~IadY)^kwT{AqdW#?f
zm<aV_wPjZBU+sYW2L_BZPf$nd?4yYsb{HfD(h*%<U17PIU~2aFFpuY8U9mO=IQasi
z*@4gBJ^6Uup})Uzpk{pB2eoNDOqjVTlvk`Dii#XcSJy>lb$KN$D4d|x!n=iGkGp3R
z>#S1i%JO36WOKT5{6vf5gx-?VBfXk}*8<usYg%ak$H%fCYCPP#`LTz)ar=G9oT`^h
zkXEcAX3Ksz97yA$rJ$n8bv}rV3N{I2Ogk}7Wnsw7sR@MZuoR8MlHI73V5N5E6g=GV
z+vr4{CQ_{1qkp#CV|ZlCGPt&`dbRgN%lXzL&F*#BRRe~+0WGN)RAovzbdLQ415gQB
zMCy+DFZ`a^#bG^-2#xvZ<|iT6a6o$BNfnxM)sQOcCR$O9j9*gT*uJN1eO1LNtJO@8
zYx0q-bnJ(Q-x&G0=fK5AC6A=)`a;Un&%B6LQkrt!<reA`YbuMhCUt@A5ZIl=wcZZ<
zsxu-yRok0QIu~q_M!wb0s4~?*@pwbiS+~b|)#ppn#_2-iaKrb4dRY*PskUwdDC>mM
z3J)Kabtwr#Tg3`P!iEj-*oJ8jH~M6JRm{5@@OZ(aCtappd-tVo{qeJB@w{c3<=hkC
zf+Gl5iV$$2^D)!V9J=d5CsZis#6sZA$>TAn!}*=V;mVi{+(Lzs|F{|X)nRmE7*n&v
zV*ZV{e$H3(E#|`fth|PrveWyUTI%U=fw*RfqT>~t`62HhV$k;pKe*Iaijo~Jab_$Q
zV08{q2H=pJA_{YeC$uSgpR#?|{;I|ewI5-d;$Z-tvO1RKhSckx09SNFz(Q5ihYoO^
zZH8{n10P86%S4_!rQNVkNJSWIm#Qa*8vyl?`*|acFuqPVZ2PZ&y1b*c-+iR+=;`*Z
z;ekP~IUYU;Bf~?Q!Tv9mVSp<R3iKuP`|KN8ZPvfD1A$=hnUa!>L4$}wsFw~xS$H6Y
z++BT|H+Q__`0cNL-U<tphh}fS!2wIMBoyA@Id}49$Pv(1m<nm#iluj`qx}RvJpc#x
z%9&vGmzM~{DPKtOV{u5AF52ufMXXEZWaY8NCvvV!%&?krA9=9S^Us|J%`_++L0qtb
zp7XdwMxD;T%deA|E8z*l-chh?@Qz2FxNkO`$2A4n*3bt{M++J^*7iQSsgbfTYCl+(
zIl~F((Fsm|%Yk|Ht#{8c3{bW4@MhL9ut)2$eIZY8caZVGxC6z_3@xE_=tMX>$f^uM
zkl4`iFbt$tfX5^UXxSA<7@2!cdqoe_mN$KJv~Bd*iBoBVy{E7Be%Y;|Atk-mZzc&G
z@oa*Oh=C8L)WIkg6d9qxk*T=X|FOeaaerBhEyG%XpKwI+wuLGwN?*h%QWyvE+m<2~
zpGK63k<j#L;^dH9$x7G7+tb%zYKwSOEx+2*ngEX9kNu7i`Usv!+Jd5Tn)a|?=uu4o
zMBcyr7c6o|<hd4;@sgu3bN@hZ@|J|@)VSjO2Y~0B16J(Uv6iR;2MJOh^x|;vuzlEc
zWYx1zf1k0>YLOQL7H^p#<=Te)EV3ayDscF%NF3!O=!|#a?0>L0*Zk6b_U{y6l{h(l
zEr^Zc1A5qfqU9E&?6KMbp$PB~Z4PESU<S2H#v!HghE$8?DDLnhQ?F=8z8wIgaJ3ip
z`n1$pxGzyN{qdB)Tm$DvL{Ytci4+l`Lm{n+AMj64Cm{0lFYvZ{<XM14;fDlk+y4Mz
W(snByPZ>`D0000<MNUMnLSTY1=7_@p

literal 0
HcmV?d00001

diff --git a/Images/matcheat_julia_logo.png b/Images/matcheat_julia_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..2339fea16586a7080ec6808f29870abbc360c7a2
GIT binary patch
literal 13752
zcmV;pHAl*cP)<h;3K|Lk000e1NJLTq006)M004pr1^@s65KPBl0003mX+uL$Nkc;*
zP;zf(X>4Tx04Nn>U|=$Eba8TJ5@2A+%_}Jia(7aQh>TKTzreu3z|A1cV9a2hm|R@o
z7!csY00epYd8r^86@0tKzz7n#tP3&=RgNH!E4iSk1gPo{5F4eW7AG?>Fd6{a+2th#
zKyk)7K(<IyHkiEu#4ZX6at4YY0kZkhA?zz4b`pgB1jH@^dIG5C3rKxBn9aliVkbe^
zG9Y$IY6Zw%2Iu^O%A(Blj1mPS0|NsE$CUh}RE6Nm;*!+dVg=8<<ou$7{G!B?)D)mv
zi2Fdg7z7~-K_RALXlZGo2^OUy0g4BRai0Uh;mas?1|f#9D7mz#49a6<GG<^P#7CGh
zM}mRDe<=e4%O8Z8NiPG#0v!eh{;3EtHA4ml0euFBeRBZAuTXF7C9Jdn000SaNLh0L
z04^f{04^f|c%?sf001;ENkl<ZcmeHQ2Vj&%_Wz}9wxst)fP@k{p@bF`u%Ljwo&CJO
z4H3nzc=kK>@NDND9^Q#(!?R&QR6vhz=ppo80ttciP1|fX<^SG%JDbgtO?I;z2=EPT
z_S<h}-psst@6DT;H*b6zDL@S>23*8|Bo`kapH_1;Ha1Ya586uCDv(nU>3<5!P>VQA
zFu=qw3A)-`h2ruP^eI4<xe~rUz6dddAu1#mks;CW_X`lMW=8O#xXv0>2I#Ck(~0UO
z37?6-xS{~-b}z?o`xaww$&V<nJ%M_w4iP|f9;F+O0deCoHgyt)_8bLWfB`apCu)+d
zDn3*>RRyx4Lw#_>fF#~VG-CDkU-0SLS=dv$6!H2bL>N+`*95`O*Iy8MT|+I624yIy
zUWL+HU_!V1aO>Cy&?BKQ<)+a?0B>D&axA~2f2$18(HK61;Y;FgXspNAt3Sj$Ywkiy
zP#WSwQlQrwi17WaMBUU#CiJ?xI#kq@Bd2U3K%>IUi7SwvdVZ%I2~-AX>O1wx2?oAh
z^C@0mbsN$mCn7vB0s$H=r6f{!qC6-%e<P~R)i`d<L7{mAUb%8T1}CR?IwnvVz)7c{
z>#rMV^2!t2to@mI;`<Er3cUc)G{M!-<d)-H3BJC*f(depcOXa`i<fWs4AG%+or(!M
z@ATXi46A861OqJDnfNPAMtr_zHlnpX5E&dJh`*lHAy-3vLjw#n2~G;@i-X2xSd=y2
z)vTv1oyq{G4Ao~|LE8>(LRP_Nh!5!xt-n@Cep=agxgngAnaQ9tASGlhe%bv2@=K12
zrBr9?lgq%plu%^=FCp@50>jIlJn_vsl!+i(C=b*IlE&Y@z>{JgO~MQ@$gj%6p4{#2
z1JpseR0ilE#Ga{OrDQIzD#hM{HHgrq)25cj>4evnb~5v5H9F{tnfB%HpkGZJYOdt(
z$TTVgbR>4qVA#egUeDhIgC^9v``g|zfRWHBmP`%)-EgA(2x{xhR?K(;(>usj8NfSu
zJnNt^k(%pI5aDV(ZyV7$t5q65J!&kKsH?B@9Hy5bs0`pGM4oM6cz4&|C(z|3PgmOC
z&{!vIgnVfOt+ND`0Xj=hbh3H`>4Oof9SCz>l}Gx&XckYZ8|q=I+fU{baR~5N4CUBb
z^mGT60Zw-hbh7e^Rz(O4ia>JsNEmB!1o3)e(a9id>n*6X0NrC!X;sM2bEfhV-c$zg
z5+ctYFg}7f1N?Ou(mewuwMR*BuMx7ky;D|R3av1e!9c4({gMU?RPnpc5L5=}4871v
z>oBCpD8w6vqPXf9>Kf|AdcHe^EB64*7Bh0omtaiS$I(5ur)aIO9BNK;+f@cQ%^lDg
zi{}*^-Vcb6NW!&49>rec8dOwQ&^xjZqJOT)Zcf9hj)hboK0{DLFD@H)vzP?S(_)>W
z?J5IwhF<8zb(lExN?e-!0CpBF5KPd32Kcpk!pldbvDDR~w6X+SOP68ls87+aYZ}`|
zbgC0#Dg$(4;yayHQ$~RX=mP>V^^#{XEdKY{QurN8DvD`k$Sl_Nd1B1znm#l>2<9MD
z=vot*Lgyj7coFVP|2r-laf4`%E2GD$Y`1C-eJZhi29_nwqRT3a@!86EF@M){2-g@8
zA9NmqbRp2tlBS{-HHtk0GfkMwO(i%|v0Ruw+%@JaOdNWZ?bH}EOJ@qFgaK&O6M?^*
zx1S>vgfxHOnNW0=*M&MobdT&L?g-H>O5ezlfTewXg9V%Rt;M1(Uts;Q*{GroT^;EV
z8v1Z*Gg(So=%_%9z6-|nn2Jl&u0=|GAAvxgQ?MneC`|#aAlB~rbTCJUGk|Eg3)Jmu
zy0e@d+tobAot0{ZI^rsjMzYuUp+hTe1qom71(+=+<eWH&!zcEUnsXGDHAbQOgat+;
zF}ypH<9i_?GD%E=mApy`t`PXCrh^V+0Q-j2m>ZCnZ$j>|8WfdSP+Qvot=0#T;Q>fW
z)FUxo2Q9l4pum0U0>3C2J!k)t93?z4j``G;eW<Umho4{TrUv$X(@<wY#j#^B=H{WM
zxCnJMHSp7s<z{3gLJ|`ZoR9!Njn%~oF${N(BOxoRIvmx!E^@U;rmv~4MPZ&1#V0CI
zRbGQeawnk+3_xg<0WpbT2#pLBO%e5D&EOPhOC7=h4Gr8eJ}?uZui03NpO%(l)wUXJ
zEUkx*8Wl*NO2TCoHFs2mA1+AKWAX*z7@ig;7@>hXR^<YFx)#JP30D%7m0D0%R*HNg
z;>}qb5fBuE>#m+G(l<+Ig(jtb107sHmbD3oeq4y61z({ydyk?DS<^A?1q`5*V3%Kq
z<jXEcV)_X9YRD|w$^=R>9(i~oZ2iStspwD23;N4-U}E2zc^KKh<{*Eo5#|!BF&yV3
zI`A|2B0R-_KIg|{*o8fi&^<!rm+iFy>wpKZGXUd&5t5r<gMZIEhS!!<V0eTtlA`?)
z9Ow%TDU2M?=`5B;lvmZ`NI@O;l{aA8g~7P@wgkk->S+62VWJLb^BDn>JD&r8J4$&o
zN$G-u6F9JcKeD%EW9^zutXjDO$4(UE)i>Y6QxD(MA_GX`uPP|OmVdv8+-IJHZ#dcN
zkC=dfurT;(wL&H0boFL4EJh=0wk}2eF`&y!FJSQ9_aG=PzD3y0kjhnO;-+w5TRs-e
z&Bl@6$`IH^gWzZy?F}0E`70|(4fPGEHP@lK%naj^8t5YeF#i8iF=kR<_y;H^6D={a
z0^Re!US$AD{CoCSV&>~daF_^tQ1<|Y1pCu!fgC!mlP;<|H;m6$*VZ+lyrK?S2W!zM
zQG;1ebVEuv1Fb5Ydn18D_>8Nj&SgWgvXWBd<Q&GXT|2R1!v-SwmDrTMRV0c_NJO`;
zU9oQM8vO0u5AooAzgHxT9S}+UMuL&~_@k)!>Kg=IIT_k;BK&{=(HBi6Do=tDEOju`
z2vhalPYAqX3P!&ECL(+FZi5LF4%oc%0OmhOqf(e3B9a2357Y|67tEpXw~bL6NFAy*
zTTot9jiRm9NWY^SF28#Obi^zjITCn{0c0g}a!r`_{2^4C>XF(_iy#B}jv;l3CAzb~
z>JrCSRo7$tK{FDg{PE87DTs^FQsb2o#aZ67^?g-k1@iLpNXqZS=FOW)%CEr>KP|E~
zw^wQ(golO@-bjMh)*>K4i;WvM;H}vo;Gt=MXvP4tZ&>oLy#GE_WqyN@ao3W%6l9b5
zHoy)KJV|CosI9C-#kv*H_8f%sKlq5|2VGhuOj*{B^*Q+D{!E0W>JT0m1g%avSHSTt
z1?E*2h;_7#RADqBFVl!o_ax)0dq*lv!`bYFLQ62(^N!4uJP!(ylFm<)-8rA+;b?Ik
z#`M)8D9DdGh}1mdZaZOgQi1hE{M@!+I)FML1z5ea2Je&l^u+XT<b$U9#OkEXXW5J0
z+qdF>%a<dYi2nT#KD4ziCN2@fh7A+y5KnY17P77+qLe{HQoQBBxF-QaI{%h`%|ZFs
z-yr;^>!1w|654trsUWQpmJ;tn>4V`jYCK9m`x;x`osEp=UxY80X(yp1@fYWpW5JtQ
z2u{`_B0iXi-@jD>$n|D0jYJ22)ZZcD24Hj}*8KAzy7q}g`h_&&&_7;A^>*+IbxE#X
zpH+&#EUm)O9sw`}D#XtfZjJawytP$~fIu2ChNT4H-CruOX-k=ik?^g_&fYK52bcW#
z9i~4p1B;d{#o)ndNKa3v&v2R-MZ#n<5z$wnhUNgw1g!-rXBnp)@@c}G`|R@wzG^Z_
ze(NE3LA<R&p*TTvyz115fWSZmUws*lz48|vUB6CbY^iV7EZT$e+!{nAvgB`@_-!?y
zvRGL9`uQLzOpoyXdaU?#H;l#Af{9xy%a*sD4_;vax#F{EMH$A>gqL;x_HZlr6|eq;
zQvR+%z~U99VxruUlc09utz)tg0T_aU1?+A~3DkD-wMkN*iQYoGIES``iAcoh_<@L)
zRT!53hkp7Iz6mtp4Wl!@ymZP#vpa$Li1KlMf101=(CSeGsa|p)fr*p*spyyy+ZN{{
zyoW(dcHJdfZXYwPM&pODI0K4yRblt0qwdtyt+XC908dmo<R7oWiY+E|i}9xuKcqKW
z-@8*AZal-&1o)s^v<54)O*nSkBtWzSoI6E#nzXFbbEqX1M+8Djdj){fGeCJ>E{cBq
z7P^5G#F{?q^e%IfQVYRRoX?MzLUsMepm@<j7-^2eLBO-7JG`d=#=|uT3fGbO16!3^
zBi#Jrd^`cx(dyxz4P=gOsc$zPS}WRP29Usy<eA9(S3NCt(k6@$KQhMsZ)FtlLv~?3
zZJ^ay0kjj7?iYVn<Pg}hQeSy)F3cOZLlYiuli2NUBr&9hmP{?#J89W6#|B0)DHWQ#
zw-}lrUj&fq<4KRP0QIE}Jp(O^9^YF|Y7p-l@u21jR9kgz4<0jsRCS6<YM~***YF<-
z9#xYvGn`TqRHa6%B=nT4Y)w8l9z-wjkV>JNc6d4X(Mpjw0*?Y5s%bAm1Zz2UG@&*Y
zn4lpc$ZD}?M4${(0pv`mx`<T43h7EkDbibiJ!Sw|5pyl6E@HUmH#}o+e^6%pKsW(0
z)ks~{BbI7^WzK?}bu@YAz{}Cz2vYBta3xIM)Uwc~7EfZ^tF}p4{LR9ZVbz-(QBNvR
zM+C1ifR>i8hzG1XpQngs-LX-O0D4{Pb}b!&l(P+{zg8!j<Sq3<t}OA+v3+2v&%3w0
zlH;AS2o6n%eQ8d}JI5UnyZ}{^%+XQ2W2cxma78_l*UUb(WF`?!v>y?sY*lzRrgQ}A
z&H$KJeOVtFB`V9SHr@ygv^vGX5EH9_vzFuM?}yN6J!)w~Q*3;CC<hsAEp4b8A~i4s
zD@?2u=)J!lGk_#?w(3j{^A)x|%>ACUotHz)$y#n8aX<oDigNG-p7+pohC#CG<{^m*
z2pH5GmNME`u@Y;$TSECQ<>m12*At;hU2R}QpHn9#^o&3q^__(d2YAwFtU}e3N>xpF
zEyeT<L1=_xDe4W?%2sK5@R$MQB$y4@G6w2!q_B>z$f042m%!{xa^ac#hVxm-Ht0y9
z1sQ|%NQl*ooYuZ{0g9SLVn7lgn=OQ2av5s&ZXsRTqO1h9gArKwucti))Bc?ZyXaD3
zF3n}x6J@v5Xc%GxP-U!XbN5%)^Q3R01MctI8VgJ(Ea*9uF6Pj7j4vV28GtIv+|Ty>
zlg5W5vxrs#$gqvq^JNE|)IcXMxY4{sZ7ipan{wdN@eyJ*$UFL^lS0qt*Mv~U9qa1d
zFS`PD2Z~@e5@MP7+apmZUu}5>>UQU%8^QC}>y<qKf+d+CHYo!AFHS(ofohnEfQ2Qi
zJCcyAMnbTTgrKpY8ll~E=$D?<VZG!r11L39NPXn6a6B_H2w4Yc*^11ZB~sdIzl`TT
zsibpuY!y0VN-)xggbDBxPKROPECwuvP|`^9@iX&DRDSaVYOAW;-VfkD;CvOoe2<tJ
z)6s40*pvE3szGC~=u2kJ8k8Qd5~j|)lH*da5<gFTE6E_PWQPgo-Ij{TxDdg}9YF_P
zVE`sv7Aianuswe=?cVJ@QY(m=hXLLZY%R#cFO|eJI}e*NsGA0VxG_PPP^;3vHO#pX
zN1Y>faA_1sn|42R6EaY-HA`4`a<9lGO=ple-bVbpcR|x<DAHy;+)Vn*yqu2vD>f+%
zQyxk~DH%1E6;{$nP$#5%TLkPwjKWK)mE|VnZ!SmL&0R2ZVyYE<>og?m;O@6q7=R09
zCSbLvOS~ShJ=s+pep$1t1|@U^(n2P#9MAf_@@aZzq;Pz3sRb){)F6gz$N%(5SHwlr
zl{_Q~+CrMU4L%psGZ8Zrgmg*5sM-Ib4KW?c=6wZIX_+!7VD~0W=q-HMj>uG63ggnR
z;M3@XQSZ$~cyfwZp5*k7LH5_saXoPPOM_6jsS1ThjHsrKEYY7f8X&dF?wJXMb^cOf
zn4@%fruW1cTz*eFnZ?LMF^(m9=eJkPp=DKAlCuOz?HPo3Urfe(pB%%VmsDbOq(#^W
zv6(~w?eH=)@QA@no#mDFbhx?>>x>O}ZelR*xg|lI5$1Si7BzSW1Cj}%`}V{6UoODr
zx8FqJ-)BRUngCtuAZUU^=*S}(F<P00&k9>ADp9j<2Wod7LG-k#81l$ti0IKn^l@vO
zYKki4gYz!$kB|rh7SGv=oXir0rjUtzq)wPg`0)WL`o~ORsU;>WuSMAr6B=pbf831T
zm~ibtTKeSkARQ|Ny~+UGRV)FxQ9Ken^H4G_KCcu%FE7PvIuo2ja|22N-qWBt4jl(=
zL<&j#OGX&*CQW#Ur-cgpe<prr5;b@i1A@2-x#1}(IDgh(apa0Cap?PR$;SVms4wB3
zQ9guzv4ZDEhN)pU-G%ONT!U`spAU_J%&1);NHGd95CS+jBN<(LM<Z+5er)+Q7bmtE
zY1E-rLaQKbpcO;V$PyT*!NAKBkv_2pQu@b>hPzUhNa(>IuQLGGg(Wu=G0zFk9~}vv
z9Os{?!O>$T6ct;<!5N*_7g3Sy6+$PLI&C&YE+E*0_MY=unK+rScm*frqKnZrV=T(1
zKZvr!hsn050CjX0*q;u%7-C`&*1fw}>(|gZ01j>uq*Z`%p77`(TzEqoMqHeV6Guyh
zXNbx&I@3UcQXi;6L~JnPx`rb%o<<?-sP9%;9`tI5FaXy_syW;cW`yoZ2JvxLSE=ta
z-cD8Hoz>-Rf3HYrwzz^|geW>*$scFIX71dMVOcU9!wwGDi%-u%bTGk5Q0Txq5)tj#
z2T^bP7hZVoFb3edF}@T8@Q^C^1+1b$RGw`{WIBGK{IE|UlBs{Zj{)i&foJLf7C78l
z_IT%aw)Y&zo!QPH^9p!O({`Ygau_ggo|2PAyLq2ovSuYKa2OB;YrRC;cGR&$89=F_
zmB8&<EgC?P{O#!S!$S5dmDRKX5)#@B84{MvD}T4=TXsl$@^YTeP>1%`{iJ+$#4Xz;
z`^BCBtE(!-4t*#c#BXm9!jMIwUB9=~7rfidx6I4KL7YZm4qxH)OXO*R*u_X?*B)2u
zc}f_7E5eQD4_96`n!ov7MY$2VM{{xDz<#V)zLXAJ#NvggpMsVyQ*#g)w!E%1uA|b(
z`gaukQwdl03lo@Z7k@JmmX($u=SU9r?B0zZzW)xBuDKDn-FQ8BrcLs?Qdh?^WWSrL
zt58%_L_UrRQCw1t6UUE>-(_V+GJ~(7-C&C-itDD~lW00ULPA3kMy_0<qoNTR5rL@a
zXvD-slS74I_>(c746ZUmhcN&*S|W-YEI(X9a}Bx9rp6vVd`NgdShsF1{`<w(vhJAv
z#2+cW^3z@l@tck7GO=^denN&$Fpn)$8xLH5Elu#^lag`4xUn|)ZDeZ}E&Dxt(*|U3
z-$@7f^<pCDSjT3W?0$$k)S3|y6^n~5ynt4`It=}Gr8a+TtzXy`%dum5*t=&hd70aU
z%*@~L^OFDB09<^<@5HmCP_iX(cyJ{AhWa|>=N}Uey|!%Kif!ArV%e{Yu>WwrLr(X8
zUVZHi7(8eo(uSm=fB*jEBPoeCQ5D?sDB`GP*hBA-9^i&^XG<z$ACV=+#U$mAV*B>(
z<iBPk{`c!*Y}>h4)Fmb^0cmMz2qxDXt5&TdPCofKokV)py5Df`j3?~d;MVgaGhfDd
zVt^)D;ZAm?7TY$h!yoQ_z@3cE(q4btJs3A`oL~SOu9~IjxF7cY!V<QKR(=j0I)tr6
zkQ+Cy$AWLaL_w)?fF`9!FAN<z6d@rYSWd25L|#{_M*Yv5qH<KJFqUD<)@-a;whRk@
zSb((~vlS@X5cKcW6+?%S|4};rDEE;h<M1~JE?1Uqt3Q`P#LbQavNo*2xAVUgX_HcV
z<JOyR!lX+t#jv46V9?W<1xhP=PD)jgQu)&%48WCSX_B9xk9~XhAUk_AGJng&!k-u0
zitf|5FS+{|K@Dr95y32+J5`bH&p|fame}sW5U6-f7*7Pm!=$SrfJ_0luU$#bq{zRG
ztC^gn(+85v3JMb@OhC1@&Q6jxd#$BWYx9Z~h>41{btrXjvosy~g9TAJxjSS*w0qBP
zWNq4n-&U`}iq&gGvHGAe^i1xB<lZ#0k=qqBjhH4Htyv&(P{>g{I{hOVnu)orqzJ31
zy<dLuIX<<H@O}Eoey5F4f?r4F)X<4F+2_nOd=^+T0~5J37ci1BLc_?fES*hYCStYf
z)jz)|KKD$UfxGVf1JZ{M7Fd>bv#-2Y8Gsp<nQZCeA2H`&bFpFlDjd#L&Z9=f#tUh}
z$Yic%Hy>3rv9hle@wt87T6~rhqe{wKRi!XYiwPZG>huPXX}MEmIwWuPXCt{P<WNP%
zVdZ2SFQrqfnTUM!b@iyAL>AJon}|usrq;81A+~|8EBfNIPcZNEukqv0O&;dDCHD}%
zhqwq{fZ$u)o3%+)ukC)}{8HUuRcG~zWqA9|*YWL-OW+q0CCcD<CVIzyXBuG7c3N!!
z`8+Zala`^3tWH_ykBp2EngM&iV}^9ZHRr7(eDa5%nR$ki12KO;U;OLczlqPBxgX=U
zn{R+Yck<4%R~bNIvj*(kz6D=>^DV|?j6rmKvOo-1P)HQwcKb%iXRf?!GKNvzd{|W8
zwEovN`nlGZtpl^ha^2;p%?#8$+157GdNqN<VD+T#tX=u5ApTLKMhQXCz#itPUzm{@
z|FVAa+ns7i;x8*L#@sox@z~SPBQP=!BgqQYY&MB9?BU5WSXBz7jkdgl+i<+ZjL6tT
z4D6RmtAN3juLcM9?ZpmKn}HBwqEz80i7ILj%J$4KdGgC63J1XmlyvHyx8u-}JpA$T
z8DyhICfs&y^)3T&sZ=00hyx>--S^4H@;m!y{@w0C{i<}Wix4>RX$+^ic!Bq@$Y_Cq
zTAGNPOmz3J9U<92yNf1C{DsH!@YEBJ;e$^;$EeYxVJ1Q>rxiYDu$d5fAz>5E39q{T
zX54YxE$GvmPMXnDE32$*h-akHfYs<PKKm3O&ijIhdN^uG%^{#|R?|f4sSm9rj?Kux
ztQTiOPrKhwJu)3ya=Im%!21k9)naePB2WX>(0MSxOPew{BT5<X>NgV->+NO5g?RF@
zhw&e>GR>fozoev85Wcf=IG@2_z$Ti9JpIBec=&<)5EY@E|FAEcmpOa%?1c<Ee=u@H
zI_{hC7={fW1{15lZ7_i-o8~=5B_$X;HUlp_^$-RR?2oG^Ut}xdWP4Uyl;;mB`FoDT
z4uRmUjv*puBCDr4-JEycun~W8afx`}THRT~=k4Ewt8V-Q9)9qCLHs<DFk?7=_<e9l
z81B9QLA>+YpJ~L{gh29a&FNeTJfSw0S0Oby0WZG%8uE@8iTZK=4rKsWDyOD74H#%!
zdvK{ztiN;Eux1rzzBEgyJuLZKTX&a$c@N~c5xD!VI}jC4Zr({<mdmEhRQ4Z!&rIOs
zr@>9P-+_zA3_)ITxm)`vb#!}Wcx*RhXa0g^D}S?ALuCNjAL_Te7+~p1gjru(gC7@s
zD{@<Cm4+y{O-O$J8dQ`PVDOj=F|e;<gvX}ZtpzTN`7Mej$-le#HXJ*+N30ULItuXp
zWzyg3GP+!(%0!NR>0RrkwpJrG?wrPeB)o!x<5*2Q#yxuX6U*CelRsIY`uSt`{v+ty
zt0#F9SN2p~L8iQnZ(!OGk-Uz~sfC{JN@hyKORxj__QcA}^(ZJR7P(Xga1nWGvL*~L
zkxT8nysQKV)?_1ymKWK8t<6AUT%IP=1scfXo9pW^lGvkZ(<_DwsxjA!4YM}Ov?k!i
z8GTSF_HSNIPL~zGrYZxtiaj-5ix}XeljLZ{70pNFXU36Ql3uH)-CIe5u8bW@71(4Z
zJ)=LWc`4V<d|HWh{#O~b6eE}tGQQ&=OLv}2jZrbs3I=qBV029(cj>gt?aEF*k-yk4
zWsvdlanJ?u2*9dLD}^{jX=w#1BB2XvYRSo$8yS@%l(CEVLO7@l;DB89XWJN1q_{#(
zk))0c96TC%MWxNmk)1<f6-Xhw9x2@fGgudsoM&-}VRHwq*5C%ySpnEF8G9n(pfZ3J
zH|m#r7+}fEDn&?GB(Ayc29zDkK@eGu+Kx{;0msDe+o)`m4jRzUNvc!#T4jJ!hwHPp
zq)C@wjfbY*fz_*4Ba#lP@L3JHrq7LLX5rtw`4t@%g>@S?;ZJ{k2Yt!xLB@-R)9}aj
zDfnrqoLXe(#(*3J48fsz_D{3ug1->F^7=cX(tXJPkdCZ7Wg5QLm9Ku~F#VC4n0D9i
z!5$NN6yY@P1kS^1lbhLEEJ9^~7SW=<bQJ@Vz$2n!@Z6tX#f6tl#v(GeUbbv0Hg2{a
zd?8@H`wqa=Y11)j@@2Si!Z<R5Q%>}`Lj20=O%t>5oyctNY@vMsPlL(;o?=kVZ<F#`
zodFkIbO|!f8;4>#>sv}^ds*`HdEQ`}AMiO|y<WNRp)JWTiJs9bnS{UDto}s)ag-S=
z5TxZxSv+F`l>t-&_6i2%NWj3fbR|<lVi)mo1TG0y619Ci$MJ{P_U+^Obm7sXN3ea{
zHf-Fm5t~SrI-GM9p@BMaqRbwKyU!{ExQj70y;BTGqLoD6QbL)=KF;2QgN(1BlXiy=
z9TbQ2SFTu&4?g<TmZb~bOv49eS}VtvxQz#u0op*28tpy?BoVikQ%ZZWT4dc9sH`X_
zj}iOmjKD^6kFy*f&->g4B5lYJvi{Tvt5H^~_#~S*g318iz@iGnWeiBE%}l}{KBSXF
z?s3>7#gfH~G55ny1gwbYIB|xY=MQ{V*~}kug5U~sxm+1as4{>%*izHB8w2))&p_B!
z%2slfvSiU>eEh*|96nwF-@q^oAvY}S(}&Lk*AT&*?9Px&reUV2qftPfM`(AYD5J^%
zo?uVS+a?Btsza&xR2GMV6UWI#&Psg!<(HWE#eDIu|A2uQ(Uq=OC3EFkLHKMjqy$Cc
z3*!su=?Eq1K-iSE87Vz`K~MV(lCiAG-TqP;z-^SN8C=BxpW+faK4mB=Dj;WQOYq*D
zf8u{DGU*L@c^EN*Oee_y5c!3wsHm{1H<I|d9A4!#(7gmaP1ryyKpgI!dLIt&+l9S{
zj*{am_dEm&0Cs;=2Czeh`sf`DNU6-kZ#Gq9)yid<^N)XE{=!8_CU0{iM~)OOa;k{v
zY0;AiziF*s)_`FMUqJyHtlzLfyngucCvo#lHzOq}7Jq!~0W8kmi@`yG9!-c<29Ow6
zzrBJ1CR{mD-LqpW=KOOu-lluW61pY}!Z#5^*c@;1M7V`gS>a)u|Gb>N$U9Mn2OfC>
zx7_qQqzxKCcTEJqR9PxE*rN3TE#!9ZmC69_;Y&^33I_PdBA>HuY^cM}KYWiVS6wGk
zj~GD`pFO@=z352>aRkN8<4@;>*`MdS_3Lrz71!aTM`mE`=#fPD8e4L{S%x>u_&QZj
zgUSG&Vo=R5FrY|&R(Y!M;lKZhsWTo&zd>mTq+1><XnkL<;5b7U&lh--YcW+4@o&Xz
zZ_UQ<Z@rQHs78n^!Yc%i7*0kFXK>qkuQEVe7*gZg#DG+LYN{*o_Uo@==JT%z7b(?b
z!cbXtvOiBpfK1$h<Z$>z-X7#OBw*36%W?6A<HTVVN%%aU;Bvej)bjw|#^|{Q&#Ln`
zl0)48y!Q`5{OrZ8ysV7ubXX;7Ny~3S5}QFV@gJbw--Oh$$X@zAE)m4f?#~t1D{aAO
zg695{Is&+lGc|`D23RIDiU0ER512-F{-Z}{pp@={Aj-E(<>Zl=CxARa7o9kSl(CnP
z>hvzTO6jgR!D631r$9Wb4B(`1)YoleKu&s(9XW_6AGikH$v2RxiX6bZLi_}t9oPCa
zSdf!ji?4oo-A4SZ?wqP{>Pk9QEmF%lg&1J+X~e?szd`n4AT~S*HDsdTYT&a0*;}{c
zlP^9-dfEWe@ms&n$W^%Rq&xQ*!29zkckq4Iq-EWm1%3X}!<hH+T=b$7U)AKXS(-ew
z)lgpUEXvQp#4B#V#0w`_le?fcwFTJC_;Zf|TpXTT!(#_5MzmI3r;KZ~;&JD;Em*m6
zH^PGru6c$Nt>InX036OM#ATBwAto|ZtnhT?WY-xI&p8HYAeS7T4g!95*Ks;S?AiUE
zR%e(22$$QpZxc=BYmr<HEI{Oi*oj@_t?{1rimRnJPj|*KfK7nAIa<m!WFzfH){z&p
z_607qQCKu`e(9{WeX!2_Ts3q9%R#!r2ncoaXkt_6wOi_YiC;QB1K8SW)jNX7MudYv
zoEy3b-!z!qC5Eo++%67OGYrWT{M=Tk7i-!8Uy;hoeFF!l37xmy9oSZZojKmxDQ0su
zPMkP~@Hj<<alOjQwWXZCvZB(Ot%)hxf+PE_(e*4&L3VlukZqKWj*pKQjcK6PyF+|@
zCvJ3n5|;lwA3JvM5ilf7r`WMZ@Z}Bc#PxV?4rV^}1Q3}Bf4bha-VFw9tBf--o_A?$
z>n(%}_sEF*O<sMv|KMT5BMCGY5Fq=D=MSt2uuYJs!M&P$TABy>C4#zi>y8oqlVGf@
z7QSKJ0a;t8Ang7Bd`$UG!d9I-v9-YQ?2D2wfSdW)L-_I6RY*^cqx*B|f`!&Ho&#~b
zUauiv|Mj$d>2Ba_a(i`8z%Rcp#qk1V<4h9x$*`15vBmxXogY}ad>K})&O}mln47BC
z$&k++2Cypsv9Splcflm=+qE73bTrPLz!r%k=n%$#KX?~&-v0m=QfnBgOyrDw*#Z15
z-Vtz)KXPb4X54oVKK$Z4aZy`EWtD9b=T7-&O<F7GYstMi_eTlcb>z-y%y{5yTf?Ih
zv1aK)tX{KDpr)lcl_H=N*NUZdsOIfAX5r$=SE61QB@WZbK5H*-&lm>aF^|uHYRLK0
z_z4$?_G&cl9f{*k;*&?|!-wOkho|GOZ~h&{rA9&I949-GznSpK;1+9FFUPGnUXTBN
z_Y)ES2)b;I&aP6N8UUR>5Gi!P1u(*oc6Hqi)pX{!UusW0_vgQ0&%q;vS3f&pWcA)m
zxaL!1>({Kt)VuG%!%zGX!_(8DBO8W#)}1^IXH-v0m1AVad6+aVoeol$HnS6V1s6=*
zRkV7+Oz_7i9>QHyr{RYm7vV^59$79{fo~+QqPvav?cRyEUV9az&zpd}QWJ&`8%o4q
zt`fgMvf>$<bVINYP!4`{>7Bdl%j^+C8yJF|O{?(q(@$gH!5l^K2w~()t92H-D0>gy
z{OeyZa?E*HlDSPBs;RE36#73U^EjMQwm?k0Jmf{jBw+eOk7CNzHzFlrG-^s+yP41)
zTTJZ3hzT~VSd8z!{0xJJq+|HdG(^%}wZ$~le(;}<MDig+htY-27GfG@&R`GYTzzJ`
z<I$M=qu;>6NJ}O!57lN`D4<c=jfpAe=cUwsgyV0%n1{Xl4`If%d(b5z2IWR0wr$DA
z+<(nNK1~sZ4Nn)>vC2a=-opJE#Q^q=zF^{HJbM3~cw_clp_dzJInC8=c!tBsuO=pl
zitB>JZYcucAI$x?t%yOi%o#v8ikpb=d7zcd=SP!1-o#<I9Q4wb{#<%sd`LC0YjO|V
ze)scu<mnfYo<1CvG(Tcoxf=Ms02AH(K4i#HRF>uA?mO>jmX$q5BqnQ7&9}o-a0hlX
zKu&&~aSWgkVgvYUo`)ZwiOn0=V0VEL36a49`Lbi2w71!7hQ(gSSnbgSXoVvt&d(Ds
zcF|Eq*XT%u^E<uXfI7Ms^k{w|5@KUXH+6sYqge&c@E?p5*4wYR<~qRumIfcOGUx8{
zB;&k>7AW+A!D5j@qJ`nJZ=fm?RI(q41%!iLGxZP0dm7HDIe=^|pF8EoCwA+Bzs>m&
z0f+YDz_DVhzex9y!pP-yX^Cw9mP5QOGc+_5n>T0SL{&X5I{$o_>GYGl>PMzI*S?u>
zNfSbk)B*V7<M)xhX#+yT!g+_$ZB%d{u;*u9q^P7h7=JV2b3bz&tG>J(t<wdN>%>hc
ztMJZB+!@ILj9mr!jTn%YjvuntV?ex)&XQ6Ggoe?IMEj4=HAxy}r<h%H1O^6T-MV!c
zk}(mB7kq;oue}1iffGn)SY2I)b6qKLn|4lf28GzKzTp=9<)vq^di83Ag@?Dh4<!Ei
zTRt<6b$UH~Xwxl=oMJuy;!8MxWExHsmx*#bLBJWy0NiLs5F=**O<+I!dLgDyy%THK
zt_9y$7((8IdH0s%T_sY6Dbw>M3@r7J?%PZbnKs~smtVsVUww+Sf&Ii9wk+R1kIdWR
zw-q+0cQv#K$J(Bg<6MQ>)W(xxD7dnJFQ8JH&VuX%4Y@>r=&`5p=Bz&>Gm{)&(OfBn
z%vNN)v;5B9%R2Bq!rTu9NB3ge&fS>5;AcGc@B;`73_xL}Ma;+8utKJ9{rlv>>5Q8L
zI3kP@%)?}t?maN;^|vs7`~<xE@}EdzZ$q!tK8PUXHPB8a>-jvyGJ>21-dJP5GYs?K
zp7kg)0yo|Mf4J}7dogBII!PNvj!V^ty%;b&PTyAGbXq$8r=^7cG?X{xXnJB8JhE|M
zZ)oDgl#8(WUM>zYzxd(shlweICjEQ{m-kRyt+#<)5>jvmo8j;#mX`JfW<2@?diU;)
zM`ujOwj%}L#e^W8Rye_B2e;A$!l7!mpA3r^8Uo1eA&=x`B}Le_bt|sC?k3Ei`6NaT
z9ZY_K%ES$k{gM-?Eqds9F2LQ|9F;bh@`3w6Pa_EjdB5-(&j8#Q?igkQ15Ki?{oO4X
zN#{}*{j?Bs-~Tt(u2*&$qvH~h7#|}XuW}y8z#UVNpNri50ugo14L9NT+iu6W^D_`e
zqX`E#h~rVkM7k-5<;#|dZ+HI`(wW&NMzrfWdgRL>IILK?(p|73eFf>p9PFSfzp3+!
z3-W2#HW#bu)1JVNYMlk)4~WX}_0y0)pzG0pU>fGn{~9ko`81Rk0lj*s(x?#Rl#v)V
zul*Mn7SO)TF;Ud$u@mso^Dkq{<Vz449wy$gAUl3^A9iltB7V2$kBKhX=5SUp05^o0
zKoV7#uH9*aYdWS}aW&m4vIXmDZm@CVMtrwmAt!YjCSP_1?#md9jEoEn8aM#qVId+d
zGZ!y?N(nfSRIa&m=aQ5*2*z^?&>@0H4hyYbM8w1qBPu(m&7?3%zmXZ^Xb<8;=t*jF
zxvmDTTMZFt3Z2Vs9u%?YODC|dxZ(FmpK!VGvoCSgJZ;Or7&a6CfWZSr0%w9&83~y6
zfbO(I{K#WZ;+ku(Ll#Y1GuNykRdPL6{kB%bxAuqc5xez{yF@<>8Ip$h*cg#q=?A)P
zl19=yAAAPa-)R=BPVTp*8iV?$3Yb2WS*dYrHF7adkva{S`|jJg>;6Y+*KP!P18Z`6
z+8P+H1``y|@Mp|?4zpf*4%&7eJW-m&lPIOcdOECOq>Yurf&wz%EkHRrYN{cJti000
zmWAwwFE%y~@o{korwJSrI|qrHW<;uhRR6Yt0mhv@-q_uFX-Nr=A3u(wqGGYqQ)}Hz
z@h8=cRk+}wU_?eoBPKdZ=>6;_okK_VLnj*n6v7LjVbs@66I=WKkcel*(SM;+Mnpt{
zKdF8C+9z%CnI#RwkZ>FmDRA2_H_k@bjyRII6S$&CzBeo`K0$mO(>nWYe|3^$8gKPM
zCt?61eyhX|(9lK8QhYnM+>PG~p(z<8!uTCCMKkd9n^zJfVM<lUSy3{bvv^P5pQ*5&
zwOO=>A{qYz%i<h|v(!pDi37RV&{ArdrlmM{-gO!VV1l&LKSIbJhvu4$4F^hfr)ka8
z+9RwP@pCd!wk*AQKJ}M(Fu?d{M4yCv2k=TdhnF4rvs3N^L|P}GEHl)gVxS{2(5V<e
za(`H;vic&{@;b8hD(rI}11+mSq<XX@eI<c9#TsW4nnXL*385#_k=w{frHFQdaaun$
zLdAds2AVN|oG9`nuqE14CNiv+w-if|`86>?18o?&8+aq7&O)0LL46S$qpbUKc}~l6
zw*WQ0ih&j|U}FGDY)o)$>&|QCY?a4{m$kG;%SL9wbRQE3nbr}WpnoD-GwtSyV3p{O
z>00{jM;kQ-M>b>1CAZL}2xL)6K_&i{Fr~ipCI$opNP=e1zL}Y;v2582?AWmj`w#3x
z-ti*z=#hfd)V`QBWeP5zG6^BX2y$1hrOGfoCD?SLmXg`taeBT2C$ALvKe`b-ls2X~
zs0?63k^0a<81R+EpHDt-o_^wCTtp|WUVe2J)@NmrHsgyf@v*3`EXTZ0J`{(;AARy!
zoG2_7W&zE&>J_5rcP4U@%0a|C(Uu(BBekmG!-mlIwqoGsm`44rV!+!N@MkGoURs3b
zo_rARf3^T)>2?o35W?5f*Ap=_S@Wr%G?L80K|y%`-M8onTQ&an<}5N!B1=ngi$~Mu
z68nfW8cCE>5Z=_pQ|o`&`meg86o~^zW6%J_mPM8Rt>a9M^$rHaGU%d(-`R-2sHl+6
zUsj8|S6PC~4<8XMC@91jG8CKl;d^-XjlZL&wocg1*dK)E^PM@QI>mHNY18zTo4_Lw
zn-y>0wGa2-aVxqe#)+1x4B(v(R-v?x0pIG1GA#c2CqN$~OmSJ-I}2<ITvA+uF=IyK
zjhCOLiw)+Bq-;FaK)zcz<mVm5yziDEfhMx9IHBe3?uI%u<xIroWb(>(Xiakn0aE>=
zVxYq?;L9sL`GrO3LJm%a&fZ2*OzdUGa`KZi1ozy2BR-%1J!;9@GOIBbviSSvo38}~
ze;Qlb@{qyhu_flFtZckB`$hEc(}VJ=OP_6Yfg0^a4EXcuB!80ne50SsflmLJ%oe1P
zefUk+U4^Hfc>x(Char2zT0HyGD;S(co|8$9YE6w1#2*rZgLH&(%JsM7mK(2Awh?I3
zAt%Hx*GEmLVxXNE@M);4ZG8FpnRxc)Hx&Om<>X(-Y7gH!Or}&{I=G<|HvBT}5SlFW
zB)T=?XB8(XI0S`x`{<UCAY||O6@&Zr5+hJsmYA{}^;^Y&H!vU$#O&Idg?@vFiJQIk
z8efv$rdA2qv0&aP<8}PnT8lW9<V=J!;q%>4fq@2bcpdQrux7!h7&Uyb)6F%<vee%y
z2E2&@A5ul=x+fnjTeJ`tOuC$0-*-c=q<FC7A^zA;NL!QYl+dQTIOgGrwN|So;`gIF
zWDCg!-a*`T--CGUk%y7mv%5<C-t18omRlI05rC(v%5K$W(&7I)>s9=B{sIAH*s$T^
z5I9dvc}<@Wj<fF0AN#<J!5>y%_~XZ353Gtjr)6iRJ9q6CS;k*H3Dc%d#iUCvMi8Co
z7q<;k9yO>KI2{;J&IFV6CN2;}TV7UzRV!BDn{VgigOBDltCLnAjD)yoI=`u<v}7=6
z-Q{J*;4`1>(6qQj*#xuC*k?c*E+vAWFn)rVXT-)tiD+gHRp+<Il=^(CG0^N#ygfm(
zdyaz#4v;<2e)>L$Lx&HGjj-b4A{5bH0K2c?4KSAU?6oVLT;j0<qtLKWL`6j-F|iAh
zlDZ%vAwhgc(_v@!xx+!KNOF`o)s0rmQ!yYh(2N22WsAf~BZP^Zmzv3w0y{U7a|2E*
zRUdXCs39Nm>~Kj-PL0^xf@5eAwqqjow~B#Nh=G$BK$b0uRs1F?@1jkc%x+^O+Y5S7
iCB8j2)aTBCf&T~An4QvX9Nl~X0000<MNUMnLSTZ2y((J(

literal 0
HcmV?d00001

diff --git a/Images/matcheat_matlab_logo.png b/Images/matcheat_matlab_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..e6993c7361f71245e451fcefc78b51e919479cfb
GIT binary patch
literal 24758
zcmZ^}1ym%>vNhU3<L>UzxVyvPu7kU~ySux)%K(E7GBCJ14DRl(gUg%m-22~q-g*D)
zwW_-+V(*N|$cWCYtcp@nkVJ&Tg988nh|*GGDt~{o{~jqYP=CMOuK`;C0Gx=GsHl>(
zs3?h&vxB*ntr-9yg_xJ_p{BZm6*1k()%hEZjD+$Y&l{FxmQM;3s2n97s01D!3UU{N
zl)ylxQ8!iV0h5d%GZDvt4+%D2g1tpi8tPRR6F=C9wSm2=;ket(ejnD_S~>c;v|@4P
zxAY63^cD>4bJYY3WG~XhxIF9CMMEDScm&5m0YlOR>)A0k2TDGPfzQ8n3S?z{lNxG#
zxMU18ZC|7BRsy*!eg-6wafwnu0pP?JIQjiS*azULs*QuVte?QK`!pKXa28gqBG}^Z
zWP1a3whtADbnT;=8KC_lJgq6*4)*-mcBRd1%AJx}V3BSn<+eqMFSJ>wnshec;E{T6
z?kQIez>JY)FmVJbvW;RD$H2Xex&;Toqz>?OI3vY8^H}BsJj*Tiqk&P<#^tC>nN|nK
zBjztRjjGSaEOe>;@;i~9a?|%s2=UC=fpre);$W0oc3GB57c~SI7g~#lme1WQTSWe<
z?*b#<uU~IK=hOKjvH4nCw&&lOI<Wu`6xun2ja_M90Zn#L!UsFw6|YDDw~&CQ&D=Yp
za9jQ4fn5YD_?Yu^TNkhb8%DZguGfNKhq3N(p2w{5%U5;2a%`a>a%oI9Gx3A+igH=8
zi3tRxjA_&NN0iUK9v-X7(SG{ef*$Ofu;Z`F$EmQPQI_pt{6U(DjHYm*0Sh|;IGj|_
zxc+%I7?y4Z0KYIErB-R^5eET)C^-a$y^uG!-<b{y3*1Oohh<Gtol63%_s7u-F}!ZH
z{f?Xnnd;#KLV~F?WidT&$yTUsFF0Bvv}++8dk`ZtIK3evK_HtW#3l(KG*Ai!A{~NJ
z3fxK<_(Mc+`ELrcEQ7g+SUUpdgS>wO6@ob(p>%;7J&2C*2!WEqaN)hO%wU^ARxrWW
zD44|}Rf$-o$XQ<&QSh~4yhKHmxG^9GMWYf?l7*jtgTCR=hEx@aDhb?^yW;(V(F=Jg
zg#Jy!4)qS^7mAt*K4*lu3}^{lsplhrk?b4a7W2f+g~sk#+mUI-5=8phhrNS;4WJJ*
z=u3eS9)W?Fk<f&}zY+VAs7V2aN@|ltRtjDzo}GwPir*F|mE^P!0ToeXl!=2igu54^
z6}w}sZnS7D!@Q7blBq6-eaLB!!WQ%#TxPt`P_9Y!UC14l6E!D}VL;t5!BD3@qXA;s
z=$wxLX}AY>m%!1#6-SrVo}(7{6L~4NJ@Bj_aR+qc*+ILBaTSc*>vC=EOWFk&KoCH*
zl|VeyO_d0%4YUbHCQT=yu%%E#!Gk%1c@IVHV^<FPrszr*j^Z_-YfRG+zbw)rO#o6z
z!H^E8z*eE4$!ZExleQ+)0||gSNmE97O(mUiw1o8)Ybgpy3@DM4g_KgMK^TfM1ma&-
z6nJFXguR5lM9WkVzuC`gR%QOm(^l*h^ONyol1yRCxXaiZtx2g)vPnD3@S#*pUCQvt
zFkmd8G9V3*`6z#D1y}AB?o|ycsaG$O*lFq$2$L4iE>1I!G|o1TG7j107*wY0NDfN=
ziKbX7d{RG4Sw@QYWv<L?wzWyViFeg(m6=eItMqj)<z(=l__*^P){N3j=YaZv-~jKS
zaZClOkJ<2vO$E0sHYK(_HsOx_ux)y8dX2NrO8>KiCheKlp4K;wEX_O(EG?l*?yJKB
z#zJ0|U6tZIvo*-t$a=;4b%C)uZn|RHBY!&2fc<m-E#kEPSCn__i}j!WQ-RYIyllKK
zJY2l5cp2<N9Jtx`*`wJ9*+=ZdO_tj5%|Pt~?W1OW=CrKK2{m)d#u%QWn%avBh8e-q
zjcS1!lW&ch79|ReQ|Nlhj5=H*KXreqdVEooWzu9as8hZw5>zgzDJboh?A8ew>-kNx
zY8WGu)BCL`x~SVI->78AguE)LdWN<{yM%9u+oZ)Q`bKb12UH)GC!H)EH!82XJin=C
z_-ndp8o}n!62xB3(LP*UzpkbAU1G72r=Nesx5O*wnGRkcMl&W4YXi%G{(@eW;f$VG
zi$)7l)2`{X{-u9)AlpXI#BVs<de5YBs(j<Hxu%j~%f0);k1t!Vegmsrp?&%R>4EGO
z^vVJ)5aAioiS)Sa>Q3Dr>bHP;cD&O%brZV>P31$?FehEHm8UgDGle)+0*`=wjXlqR
z<2>WE(@Nl+#AwPGYvAcvH2-xiZBP5`WkP-_?#O+>ZeU|sVCB8_l@3`JIY}~RIJF|>
z2yYr*KQg0EBT1v~XY!-*mGxDHAg^GQV4NU&mwQ*WAJdc3JNUc%%f{W(_0h}Ht0Isa
z_#O5bt`sN%REHLXXN5(BbOZMHg7vls6En>kQ0UHMfq!`ubr0i*<v`HGd~v~Hq-XyQ
zI`?Y*>gn(40c0>17ZV+5mr59!9g&QQ!;;1FkQkL56km{(7xxj*5SPu+q}OWPmx$Gl
zB`FucBF4w`p>TJue4O2ojo3Tih1<JiWYbM=JYRKgKByd@u1z%P)pDXG!Y3x&LivnB
z@2fnJtGzd9^Ed!5rL;%9CLaya9mU<~J(;~F0W(5>gwBX%4CqCfNY_eZO7DRyiZpM#
z{V=!<+OJGaOf8{dtDLEvv<hu{YMT2(X%t;gr^hBh)ydS)UcxN;Ys_ll@+*3=@Jx~%
zwTqT{duIHH+#jh}iX6%iE%-+3ADb4EW(8x)6R3yx``P>U<AkGkqp$uP<{Tbw7G3Ti
z;cvYhmo`W>VT-l8Yi$Z1dcD7P{&bxs*pb*ttz3^wjK`VbZ`nt0&MOxx*LN9OHeMYL
zeQEomywk}rrGsQUXp3okv0>g=W4)F^ra$w?czic{SCMi(`D{zePvd!G;vw{+v+SYl
z)nnTo#$$cs<ky{Mma&%p%J153y_$oG+v3}w?HcD@>P9+FhR9Y4rU^}~-+pw;)LBa|
zRGgNc2H_=SA9kMl{Jh9p9bQ}N-m(jr1B-(ggcc;iAm|7fc~r8g5wROITHpEJKYdnw
z`m5U_z#u^ELh2@IS~5(sY`8DMZGmroiR&9zDra9@@=(K`)3C$)_8(Cn^y(8Owvx6d
zAU7$`&F=6M@l`$o|B6?%U)2fKd%;7?Z|!L9qQ&*asFqSrD<3(Zt&_mHJi;kL2BDl!
z-}~5ytn9`y3)$(UDa_m)o&pd5+b=_5H!)Gz5xg3%g1?&fXY*?7Yd6P(GV1hi+MFxb
zI$z(uW>j0$^62sT<@t49gullv61C^28=U)FoitzBb)GmjzNhci#CJjZ9lhwiGHpKm
z@;iwC%)^I>fqJ=Q?DF~4Ul%y>+kBsau0+-sn(*d+w|qA_-z%nqQ>4wW5~_J$cv>>A
z?7TlOTr!+)jI>AyZxv<wOna_V?1!ALoTig#k~o#Dk(`cwj@|HXxF5V8kIk6d3+tx&
zXn9PVsV?yMc<r<>9v!&0nK{c6lG*&$#q+ZGV0UDCyg%n*byZ#wy;<EQ^bqu3^lEZH
zoOE)o_@tOt5GUl}3;TBK39>Uk+<FYXdu8R-^8dT0gmskCasdF42L3s~(&&)z0RV_^
zR%)8An(}hICJuItMy3wNW{jS8j(^br0KX^i-%~p?S0fTnJ6n4fUQYqCe<67Pp8q3e
zA|v@1#MMTCOjBNoMAX6AjD&-cospSL5RQa|gx}fJoL5Cm;=k#CuLQ^}U0oe{nV390
zJQzLL7#*A~m{@ptc$k=3nOIpF{vsG$yzE_#JQ?g=$p5R6|I?0`nTv_Dm7}YbgFVSV
z?HU<7xVZ|Dk^N)ne?I^1r=8>f7_xWy?_T}wACsq%BNGcFGt>W#<Z5O9zwZ6NSpR7L
ze`#i(R{uY=e>DF^`&Tgj%SZmd5#^P4FtsxG5;Jl&6J%v(VP|0GWMF1jV`b%K=Hz8%
z<7fI0%>VZAUu;olGb2|AXEg^0TR|l&PcvJ)e=bSb7+L>|`S1FFqyEziUPWgsv%lf}
zCy0V9{7nBJ*?;1-RLoo)Y~B8a=i&Ts!oLgulOXC~=jd$a;_{CHj{nC0yX-&l|J(jo
zdn?!f@##Or|H;z)U%lY}xb^SCe<S=e47~q}lct!Jotgc=T;t^A;%EB5YW|Zc>R{{O
ztm<fFV)l=7|Dybx=s(f_vZM9CcDQ+X{@u>MY5tqZ&-70&|66YVSK<6i`nPff;r@>4
z|EWSjxTANPIRHQyAT1`M<_UJu3Eirv;l{|jwCYz-OFnw4*FvvTt6AF`+~72N@FiG2
zFmyc3cR*Uo-ZS*^pgqC4UmhGMmg!*A@OFC;%r+a5ak=4u(e{t=*RyYrbB~)AkDW`e
zpACna+7_}cW?F;EQ>lmfb`u{3EanFB&}I_zQt$+OSdIx!FXW>MO@U3quBC(u?^u=?
z+VOh?k1`2>i&n1Zo9SyYKy^bpxv-6GlIn!Ky?UjZT&I3Uwm2Coddy(F4})Z&`+CmX
zvW1kRa(gWuiB79y(Mr8hNacd{VK6)+bB3OKRw%0PzWZ><UL4?J`SDd}ij=>c7Y&8l
z6NbXM{{8har~UGm?AXn?!H0@mIBMjuEUG2u@!qs-f(D{uvTsia!lx=tYbsmVd+4^j
z|Lt>!Qt6x(ONBxn?cu_bu3fo&4i>=H(sG2m)?}8Z;8H$WpxAH`>`x`i><YPz5yh;s
z4Ol_QGZ{i&U#}YKuO1|_<8S@CmrA&F)K-Fz>n?|UThH+;O;#z1`7D<REU@2h_J(8p
zE>@clH+`-+Vz3#o9&^f=#3Zl#e~6lm&S=Mzd`P91%!9kdm%UAWvk3nYw$yU<{bKo7
zGtngS0$)ycNs9T@gkmla8!Su4HFvo;gwwFXq<Q1vibVm3$ClquuaD=WNh+adX*QD~
zgu9I^e~kf4Ma6`tfIFQn));-SLEj4-1Gc-PX|75&nI799r&Mcfin+c4$@wT7ux*4Y
zb-KFMA70yc$U>=nkMIW6d4y5(f`Ip<B&mY{U1#Z!wu1NXb%~Rv!cj$O#NxYQ{_~&B
zOZs=09d`;$S#sVu1yTOnLC_svinbAvki3PSdXd3D8%&11i9er)I~jCZq-2`C8B%v=
znt7@DB%p=^z?gboPHc7}Co0p&%Z?Un8QvcKUXx`#&ZRc5&kVAnrWu$^D&$x$%B^=_
zTXJp-D<_|Cv7sHF?%Hz?<CQN0bLK`?0Pv^{@9*#CuMZdIhK4<+thjR<8w`yTZ7xSL
zvUS?cWAyqR%dz-eQ%y}xnep*R{VcjRHfJpRmia-)1Yj#SL%$%yRIAlx6X^AGKb6|n
zowaB&JH1BH(0z8L(F?d2DQ^8hL@ecQyQWN>9H=YLgG?U$Ig@`39z6_>9l&|<ddtZY
zdWzH`?alPPPbmANz%sbhYjv&cTX&$?5L1FB%B<L5y8V8d#BaSlTx2|?N{R3w)2jMA
z4MwjQq(#dw$rTV?XtvGb|GE-HC!RA(-ZyRj5#@uqclf|N^QD06u$V2cbE~y|@7d?l
z9WEq9bl+00T6rcx7F$hEPw%jx`(gS}@$+60(oi;ok)z;gfNDbEaowcFZVkj<Dxk)T
z=7|v?5$KMAe<z>93y6IwudO}La6gxt;67USoQo&i{QdsOr)w%jg$~l9$u^zfn4}qQ
zYAKal-zTK{Iqj;;y|(hc5MXWavOQhZER)qZ8zNk+8%1{ex1@fwT|qx)t=YFv=6Y;;
z9rnR9v1{nLrA<h6KMgWvbJ#2bWodd>$8ywnM_~TCV0z&$)?lgx<%sa`i2QzqZ4g`Y
z*Hd#wegp>1ZLwav<P=b*DGhQ`R5QbOTlXeBHfyzwrluje3A!vPr=GJ+zLk~xtoB(w
zc!S*^sq_KVk}4@=&U-^~hkp}3D)p{q%^q8s$Ltj2E(qCI`0cX$(?29C#8HU2e%Eip
z|F8XtY<JH-V2~sJG`{zJ+fnqRt1G(!uTk)*iNN^tqyx7mr*xe;+Hd8+2H5jBS$$Oy
zE?fB=+evw*6KzO5yg~*msOwbcvGmpb{941@PJL{rHN)$y=4bXWk<WzhQ$I0hhJk<9
zeqJQi#Da!_o#1U!!ie_TtAkfSy=Qf<qi1D8K78X8#M|33C10|N3A|?Ef=1Vi_10fH
z{f5&~-buMwUuxH*z}`o{Q05`*-ejiCrt7b-Y_x}&ZEUe#OZ<-P-<=;WILyzmG6is!
z)0jW3aV=QKxpHU>K3}%eJT@>BhW07}BO{s@3)Tyc8@BV`ltDxZvPrErJuB1}d1^9r
z@g`4feKYY!d)BiAjwvPxhA5vURQZv}lX*Gz{S)=EEXl9e{jB7&hMzS0b(t_Np~*c1
z$L^6+u*>+l=5j|ECPVJVZ{aLD1HIr5G)ElMmTY`oPtW_cU+LgSDzNnV4D~12@Z#VK
zF-(D&76m}u3vEV*qXzcBf8+EzONwiaIZI)X@Yq<N8k{-#AsjWvoM$}K{3a8p3m|a&
z^qysUEGKykf!SGLA($o$U{a0)1SbH6@U3Q&t-*BO^B@-8iqOp1i>cwG7n-b0t{jE}
zg8#!dWrbBSI7&M$S{*$0)YHBy0U+<QgOhb^&YI)Bl+F;c<$FFeaB!4|{bzM;XXYTY
z0$ZvUHZaJ94WV01RX4tmzBlNBdWZRM{f8Og4>?IIwj|3L^-vrsaCUBWR9Fp1V{#}h
z)JMx|QCr<c^b{k}{erER_+*}JsQ){QX*N~Kr>uu==+a|F_}{c|4eHC6Yx9~<bDB-S
z<(ZAgK8}qK5ylLzIQI_Bpev(LG1K8dHe}(&MV5RI-w3l06QY7ut{14#W@jV|;$Qt!
zZ6FaH5<+QCS0QZa6;X*=NLYfDg!I@ZZFh3IQ0CD2<~(lZa>RSU)>Hm)&SD-kJz31n
zl>SYlca#83o7PB<t#>{)*vPAYCkfh-HJC9D@{{vvco(O_2zCI26~tpveDXf!vA32%
ze;AcuO|B8L#rNg!ys7CHl9u%j0nt{_ef@>tHTq`xyOus=k8?euDUsIzcU~SynI?;y
zS#(_q-BKQ)3fyBSr)y=lf?hV>wwwg$(?(rW&sHAB<@Dj<2XhL&F}JSvpj6)}>|2uy
zn@kh<9jve@IrZ)iE?n6z|8`y6A1!A$3f_D8gPXgT#1@ZA??6#yCI|4fABqwN{}%K!
z$qpPKgSA}mIMlC;1Q=n9Ugl?>ff;ZHqKEVqO6LpAAoyOrRuYb<XNYL^j@T}1UL0#&
z)l_hUH~ri+cff-%VY_d-`PE|2@NxYJKbKKDhw3nfQMZXuY8bTm_I5PXTWsB-4aU_s
z3N=1yyexJ!E0No?n`Ke{MV#*)MW9GHq+kM%4sy@XFyh+{1M)j<I`Q%JID%T?#K;u_
z2Y6G-I<gx*OJWWt+;bnqG`%DC5qu>}qFWg*8$pERcc+(y9#@G3&KYMGoSdj*ptcGe
zdnUL>dcqo%daMLkD}^h<4!3=HNFwUqLGQv;ma%u&>w9_Lc!q`ugGuxCEK~|lk~Pp$
zvf<B417ikGec<P8@n9H}o1KC|E^uU(CHaWLaNIn^k93qJy7@cz&m3WeL=kUe!nkp4
zj2Qo-eI>4%eOn;@usINKA`~#`+>Dn#lZYZ^tV86(u03W3AB&}bit-5ih6}dJV(Cl3
zKjC}SX0dnV;Vx0Kd|5e_e^94$<109QqnK-1To&xecFqa=8>W>zkql7$ZQmJ3W7t(|
z0(`PrcrRW>ZGh@gNl%r@On!I5sr+;6Qt@2pXS{GV2T^?J7OX0{FrSP(GXDq=5P|EG
z(D79KC`F&24?xx42d9}j&+&UTMe8{y_+BD@rdcSClZDNJZNq~~@-rI6GQAphxixV*
zpKO^KHBvbwJQq0!7Jx^8kn1!@gMM-%Z>x2ks>6`+hHGgFhTWi;X?!C&zh(>8&MXV2
zq4&es?4})`vq|4)2sdeVvvWwUAYO!D;-;?4dO}svXwI3wHLJU{C<PE4mc1P%Y61!L
zft5qd){S$g1xFE%O{#V}gxywFQH>cH8X)1Mxpe(J%^1C`Wn|p8mKzc*f)giDmaK&A
z5b$|G*nHhbZ&+@1ilS&$r_5lR%Cgw~fg2y+c^zDUsD#m<425mU!%4mjbOd0FNF*J9
ziP??Qu?~)l29%BfN2DaDB$arVEx8!ec}WCIN``aBX45M3n7R>+k*Pq)f&`<b@jvmv
zR@ZEucFZx7GR*~_2GB1d;^$b)YBn$$^bd{AX(Gm2>^Y#I5uGX~#jcV5Wp3^U7i>5x
z(2?x{X4q^Vm>C?`0qcrUzz7CQSVDNiog|DJ3*i(?jyqs%I<2ZW#OCwB%!z@{*YG#z
z97^6XO*1#r3j9BwXR$f$ZQGn~O@FHO{yH%n!f|FJGn^s2=itT9IcadVT26-I3luKG
zK^Opymlmfe6~UC{4D1^Ut9a&0)CW0;geF%X$k|?srTA=P%fBu<2#;x=qJ-va*2e)5
z1)TO}c<Z{n#?#+^=KC<ZeKc7qQxQE@=~C?_5ekY2dN56-D9T_l_Fy&3;$dWfv%Xy`
zFk9pitdF<GTw4P1+VC485Zmx9atY>)Tu5*#ZoD8=55VnXO=m8q86C$(SI~N=grd<a
zHJ4D`fDGHjc&_s1j?9k^-LH8o%PU!UAwJ1v&MQvP&~aJpusOTSn8TE)jmBX&5(zB!
zH$C7CEJbG)aID~riO#_6X!&ecRn$<iB+LR}xzL>{N$>uyp=qzIoOE{AFj3r}tM$>z
zhhNhRQZB~xFu&sqH(*(LG&QT!hyqS4Pd}F%E1vmtKbd;BdT|iez=v|(($4|M6g@$@
zvBu+3ui2(D^R~X$;Nk!XMXf7JeWbyeW645P-F-`xA|L@35Lg*I*IKSl<V;pFlyiFf
z^=3mh{Z}i5w!n;T+#dpEhayPo;PcV>Q##r7)b6L(t)IGlF6J1?lR{f#xFDu$XR><x
zJQnUM`JeFDPBpQBIWxoqYhd;=E|NU`s92y08)3<e0zf-Hd?z!I4e(9u3-d1ic62t*
zdfb}2===M=HI-U}Ci&^LPT=@;DPx^hyZg3m_lH}D+fb|~nhoLsZ3}iosZo8YQMo(>
zBna3_$_UpuNezP0okgi9h2`vsRL~rW1KPH5nQ{-ZWaSP9N+-_9%MWr3qqb!M;t1l`
zm7>Fmi);8zja`Pa&Obd2I(+(hZ@+YukN}ehZax~m@@=l<`#vY7h{$SOEVnS+9lTz~
zToMTIvLLaIf&+$vx+3NBZ*~w%F4p3nIa+a%6O4kebhZSkL>E?$c?C;oiqVBp>n#&d
zbgM=eFiZ#FtO}(S<?B(u??-=}qa*2lac_8yKPznLq*LeCN0f9fQkX9d6em)Sm;vXP
zlZsCyB$#+Rn+_n-`@Om<2#?Kl*E)NcoI=C3XD0W+%LvQ?%N6L)&W5+g3L|DlrJsE%
z48$+h6-6)XVdRe>OU~X_nUVIf{L-UW!8~GVfmi%=BrFndf$8{tWaaaoE$KtAeS}ys
zbGA6I9y}KTcYK?8*OEGA^b<!>_`UA(a&f#|kxLYi5h)uSTl@n~HNkp~5|OcRsI@LC
z9qTd-u5qR^B1!=gKS@FuRdZ}c%{4qwLX1{YHBs3ZZk*ENfCOnRuK^c*;&(qX)kB@m
zw3-Z+bw%JGLS={F;Jp0<XDVR<pU=}a0iCv5<7F24C+gHPy_45?EC4X{Y1eF|+~&ht
z?sT^6s%SZw_1PfGxNC|)f;eq)Djxji=~+WGOQZUk+FT@cvafB~fJyK);X95{OU$*r
z%xq`spGRpKdACXmjP=YfH|pfVK6zM2njnByrKacc&u)eQ_mkOsAI8?6ni7lWt}xNS
z%EKaw_{nR*EPejo7>^;yjJc9ka9VH#ujnD<UhyPJRSKeU(J43TS_z?GL@T8feX&~1
zlBV(R=)GTHz!Mz2<YKuu5`^;4x0-YI*uOlq(qY(%akK;?Wwym85sx9gpC$O~J};Lf
za2U8;wxNE1%VBNJ{GMTy$RxZcmy#O`-*mS0+BnH#5=*|0AO{%;z!Gch4a?~*2Js|2
zQi6wtDJRlssHS{W!LZ^h?$Q2+5vKioLUE@VlH*($luh<gr_bPMMkf`N3_N_4zLsT_
z2%Gx$`1l8L{jr2t<8${QBgdij$#^|R))5yX#OI&!++$sJYE6p39aL11CjoPAj1GuX
z5SB0$Mh6an;p-2YEfa5csnk1}iq{yb#!D2E8R<#F_f&aB!W!Fp8A+Ur_!`S!Jd3g9
z+)zOh3=>LnfU~bH8!5vker<7e`}484;jz@+YVCIR%Gfqgvls0tE+P^5H*1$;W$|<i
z2hXApiz0ld!X4vBU3Eba0j(?B;Pje6*K5_A(c=0~NC-G~@YsiXkOYe2Km}y3?RTAa
zXLhB)&<Y0^1=|N)(Ca|JfOk7*%oG1+g@e}BoJvC-hAume0txL|>(_j}r#|?s>3~1g
zqr^x^?z(O}VG5L3cU5W8d(Qk4fkx9Ray(@$$5%GV=(}k)$|xlmHQqFuL*_6jQ&1_w
z@PU$!N;*AG2vDGX2w9%2Y#=_o0^|!<K7uA|F%mQxdF!tJ)ha>A(>_wW9GvFWUKumA
zV5eHE6D*@OwbX>S(_OiI7&NgbiSt$Cbc;gU+wF2%YmAm7sb}kH9f@kvs6z2_>G|&J
zO08b2#3KomDT+$rU=Sf#cCcJ44y&>IL=uPxmur4dF?wfD>cyZ}0-YVL63i0#En4m%
zIvM${P=fuu>y`-)D}7YVn?-$m!{4VFH|K3vv2U+X0l$!@hrWKfi7fWI%G>DZ;UaA?
z25aFe;Av-n_VDmfcJomX-O7e7Q!<%NByp7`D>&BP9Yw#3^C)*O#OB%Ak(U{cV8Ok<
zZglv5q&E_EM}N`uC+#r?1`sxdoCs)Y%1%!xA~6~I4a2u7MITMnJCX^{6{D!h#Y%C+
z^93u)$9K!*!IJGdgm|D`=;H=FFkI582R)0FO=#$}36LF#5NDOs7%k%Ow6<8&e4*1K
zmO-zzEAViyI_g#;QO4MDY<vWVa5~Pd8lHl)++O-^z%#a3W!Q|J1~j^}EVAt#?g8<!
zC-hePWw;F{!Pv3fggsc^P1BT>S41R5J1gOQiYqv7=Uz1+202)*O*=8Y99DI<IChXc
z!2S+r`zKY)=fXmm)!k7>p431<5ArCHjhhTaO;F6&8>oPtv<!pV&&wo9<7xg^TUYru
zf}B9g7+kK7Vci6ddb}ZZ&!9}<+cq2#@x+^D<coPA4rbd*hga)zOipJ9a*vH`E{Y=>
zKz%U$tAfN-nXcTm=>{;;nFB130>KeQZeMDC0?9R4?bqnZx6DLHwVugsq!5NeN*+|`
zxD@fJ4{ZHNQ-}7>SzddB+BO1Ww=r;rfx!-bLHr-*3kwGZZx5)CuS^}k+5OI!j|Xz>
zS-EbWki<+W({9hvmp;;y!DC5OYe>ksO7ct^v>M;go5m)8TdY6Ef*bV<O9a5z_NXQH
z6l4~Wb%j==${EQAhH+;pN*Pm^+klnl#_7f@3bVq}>g(T-sCKeqIZU6kw@C(IUcKK<
z?*rHg?$USmgT6c|$G=?Bz_JHV#1C4xd;8o8d0jrgXr$9yXJaT1*tfpBqeC858KM{j
zAJPgD-Tp~<O4OmL9~u1CzsI^`evDW9(S4>T$hG$iHy`F;IriQcPc5DO_g7#J015N~
zO@_vOEl$G^%a1#f5-tY7;K9R9EFq8!3f~3@G_eVS*#u!7n*R{(Vb00lhq*b~e%E1J
z#$Rjnly<!h!`3K?5>s21l^GFBh~qs791`+{|8cvzxp}^n!6?6JK-;qJIO^MmHL6;_
zEqTm5G0dG$=1~kP#L%^-9f4t10E|ppCg)7%MlHoS@rF29DS_c4^^H!t+Log!28k2E
zN;dYO1O`b72MLG6|Nc%>NH&s*f=O4Q&3_#t1+M=4SS&CQ(I$8?0VafaO!)fa?{?Cm
z;C;lw!_D5auRU`h<w1jzZHTG_?8w_H2KM74vcK!=!~T_ycD8+%;D)DX%=bXHcYRfK
zd(<5ow~yyD+vL|&e%zd(@=*p##(`M_5lIEfmrZ>W^XZ`*Vt&mb^d^2J3X?`wUVe7Y
zMgU4;<wmv<3NyhbI6`_sbPtN-?+_BJd6`HOY51sbPy0oP;0kvDX~UykhCz$_bJ+5y
z5eMVW98MV|=gP3qVWo!riqPAYZnd&j>YT~-g{7ta@;P}IM_K*|{T^L?UNxvY-Efjb
zpzgBZt{K`X{dpXTs&e!NMO42V+a~n3>GmLYNDDdFo-zq$PG1{}Oo}iX*Trw^3QbBb
zbVR(_h9gKxI7VKz&{;)rBbAkVc0@28YQJ*Hi_hPo@Kn6Iz79R9RY?Nl>f0q2ly84X
zw6cPJyW7swfHnQj{Qkq!k@>aq@_ka%TwXCyY9g4N2LQj+5*`#ItQ_c6r^2&R6exiz
zb2D1)Vo|{G$aW9~F~vE$(sQz$NDu+@`*%UAJF8&GDD#CY<lhB!aEO1E$Z5jNKyZOX
zA`O~oAUZSo--ReRf3%9Z9HBZORil5Rb)~g4Mn4Mik1PD|K8oSKsTQ8fv^UBh8NVsd
zZeov)S{xj{td&m#xSatwOkq#57$_lL%QQ$|DUx-pxGyl`c+JV7_o}EI0#uFgC5Oy)
z3n`fk0yZCy)__tOJ*4%0*<~sy-vk`{MSCFe!j<Xx&<~3V<mE}Zg+-mKjMfp%U=rps
zzkzcbYQ_fs8lc(}-iJVz_$Bmy;=C3rJ?wk)PzseDjBufKb$UVg62W1E2GOkTtzBd9
z%w4=kWF4qjk6DB(;TQ%cQK-D9otv9}uw9V7o2(;u<yR*}v^VK8#p$&M+e6{GZx51l
zW_2w)HgXSz9l?>{69G$kuhos~At8B!{EAzGYPzq>jtY`_KUNab&Prd9Q{)E8Q7Wg1
z5!E7dGdu}WeYif<XsKrVKVu%iba%1xOCLJU&r8Fz6ROsOJtEy>cg1VX&xwkjJOjcU
zS-~ZG#cIpk!DDn|E}0+E9D`z%#Z>4<mTfSqTSqD2n~dxw0Kt4?$xvgck@6Ta!oZXR
zRlqmBKyxc_URgQ;*}|DGN|os&j%>o<MqgO$z^Wj>%K>4S%b`;uk3#&{Yj4}|7@iXJ
zkUIts@A?0fj&Az<6#31X`eqy~SEgWD+M>~$cy_kZe7sZT$MXO1<J*en@B&Cp#n5=D
z2v<sf0u1+c0cnxa%e}CE3Q$g?`bEd3mIE5w6T}~+U1@VC)2v=HA?vYTMUo*02=}Rh
zFlLQ@=MZsbWyJo$uz{%sZh>VYEH+?}U}eH4!D{y<D(E+mYV0E9&vQV&GNHe*lp){K
zB1!Qnn6b(Oedb7_3lzLNm;n$gc=9qsiK~>7Pm(@!Q-$Y-MlzyTUj3iPZ#6RLPdacj
zzB~mZPv5*seg{Ll{7Sn$uNE&}Kcm1L#$kp9CK*3e=B|KqJ;UgGK3gDnESF*nv_j+N
zol{e;%)^7qdw~QMSLeBkku{W%zz3EGl5I;-i7|ieO*o;}KyQE13Q$jgQ;1PaCrl|_
zWZPcC-+G^gPpZt&Rf#Hlz8Nwpfq6=ixdGB+n}W6Ui6#i>Kfgi~+jjGyIgPb@<nVS8
zUpnT+{F=6|-9@L5V;jE?4O~~?(Z|h=E2Mx~S<|I!zmW*AmEapT&$UAPZOQXdrh@Kh
zD;LZFAUFrh>~o*}!q#QES|jR28A#rW=SKcpxEL049RZT`I<;DQm6SIKU*K5*A8sq|
zip2Dn*+b<@%FDN^l(>=`2>tWqvAzt0sesFXkLh%q3Wc~uKs_xt#$cw9s@PQ``Hf;0
zNCe$If(Pflvj2Oidwe&H%B4kqKo_!lo)s%X4+Rx`w-bmEQ3yOfd$0&giDNk}eMfDb
z7EP(w^nsN$5cf%kmlVXnk?L)uk5N!;=WoQfm#AM%7O1Ezek8{!`g!B(`}yJ)HdU$W
zbbYxTIh_J)X=v;VTD;L;Yh`&|sPm=dSaI5=arGn$L`kI43=fJ3Y&296rU8^mB7?C$
zunvN{`93NYpU=!=Zho_HxMRZ@+8d09FdXw><{4eX^!%;_=LQFN?fYX0RS2`fU@r^{
z2^|B9R6-w29SBDa4z(gd2V<K|M7j+sP=LX(ofz6~j6<s3D=JU&lg$vbFmSPheC-()
z^X=LT8oI&3M1=~2eD0m__&hKut}gHb`R%lC+1+}EE>lu}!;p$C`8A`s4kWU|ffbS=
zS7>d^7-chesppop9~#^UgU3CM2=Y0VQ|;}KH>W9eF)DV5h#IWd5k>{a-BW7d4hk2q
zQV6W1x==ekYNt>%2L-}|G4R{Z@l(LvnPf-q@5IF)TkhR6=%bCFuj$}%xZ#yAskovM
za16e5?_m@Wlk_ZO0kPTFI(Rkd-G!d|f^YY1G=AoOcHzr|*_a_g{~>U2l8qY2sd9^p
zEsyQ(lf`pK7@|ZZOh81h7>UC_yz-k4(gW1yH3#SH<bz(N#X<Oq09BHxoV=+YIyLJO
z!Z3<plvYxPX`wSyGCGspkPUS8^Mu876zpAxBC2ip7a~TM7QDp(_T1yh_yTLVZa<2}
zP9q4vHc$BMz!56HobD@}c+kS^SW)pT_tRR<qD`3|#QOoq>jN)*y9ZUq)k<16*Q;Db
ztAmiRdanURPcrm~CmQE*mXZytygZlf3-0P+353(gif4(HO9o4Z{weX?VT&p=CqxA~
z7X(eOxat5%E?BauAem`wd)Rxs+Y$^XQOad`ISN=>G`ib|5+1W#Khn!BKO+ZDo5PGa
z%9rZC=bfDj5t<zK#w-M=fw~#>d|F#+v`*vO%eIMb5c`p(m3VGhiH^i>#nx2*hAAy8
zyHFJ-Q`SMnrlkMFM(6!`V{NuG%$OfHrai?8jT63BMbrbu6w{G{pZzGIe`I5%2tz5G
zXk*sIk{3}`RVUg02f&$gwXS<p$U!1$^v>M(vS*5&ClVKmxwW6f1X>x|-%(0AkzanM
zv}j|t4<;3B(gEKyW-z-cVk0)p_3!rN6Fd%xb%)752YVEm`b~tKAwe=?$neL<{@zS4
zvVT3V_e*RE9P6P+UmzBq&oe{3ka23ox<D$O!{t>a)79o#?fIF!xonvjoy5!DGZ=-d
zD`w3)t5Zjmp%!06gO=YOh(2S-3nZW;TIiTQx(Vkk=W{5G4sCRAoPhpaH}}9rmD`U_
zGRXas|9S{Fhlt&X5k!_k2y;OO4~aDi5JP~sDzbBgXL!?;nR%Kq`SLy3ksV4YNFnuC
zjkLqeld_>z*J<4|wUU|Rm&J&|HO(XL6rF3|VjoovSYf&naHoePW<but8cRIiZ=m6~
zwi-ZAWqO_Z*c$75*`Lmhsj;rEr*dD;@EFnt&ZY^?T*AAaUXm|megwMs{T|BlrH%b!
z(?ZzDvx9Q}fACa`j`ul}MhH-jlcB)G1{W9gRRFWH7{||5gu`CBnz~+P$SV09XTdlJ
zi~x)5LbH$t2yx%pCVL}VM_H==gr?O7_7h@P{_`E?#FzV~(9~$fXLQrxiO}d8i89ay
z4Dzh0K531yHe1q~DrXl#{_dPrjU7V#ZkQC?-tD+d)FIZ}0t<xs+rxqrn0BU*R^UR_
zAMdiCnRWJf=XFdc8^l@}gmTF0v@@VR@oPxRNKh_m|Cm1}p`88TsZ2?IGgu3ZtE$Ub
z>ruX)Zfv!)tVSlBMKlmU2xAeLvyCXU>Mp0m3y1XsK`?6a$z$_m%hvmX<MW>0qEJnf
zY)67a+<ebDJXoF=PkJ;H+7*so3_TVGW=BbQSw{L+1!Gy0K3z73DQIz;sg9Uvd1w9}
zs3c`@YbMCa1gR%HEN2_uw;&veKMM+~5cMVwVCQ&hNJt-^vaSq)+L|{%bTA-PEnM*;
z$0MV*t5K`Q{PhE3A?;njT-=<bp;1k0N7f+;OU<3%;1ja-NktKN``X0G7?3l9tYfrh
zjD$y(7HK%+R37z{J*)SiTHXm!{R$T80Rs#q5bjB)ZFe?S+FLG;=YIv20?%fb9e^E}
z8)wp(PgIcOWE)OKJ>u#s5vbgld#pOsL|nQ<npf+(C@&mpg-ZWEt+JX77gDZC1QP5A
z$^m3%dlQ1`0U40^<I~I^oh%G1rWRu5gJ+(evJ9+N!8G(bs8{;kMeOd2^!`n;)9#_K
z2`uqagl>h>_=S$T!hd)Y)ylGKQe|^oa0wbN<39x15~r4`8`Qsd$M@oZA*Vy2Z|fhs
zR^n9kh2QAp)Sy-2>qHq9CqWEY69DZ}hIVG9`|eH7)1f`k2XQGfIAQ3eQIde3jxMMg
z80Zl~RH&`o^lP%ZnSO^^hEC~DrnXQr+2FJ`eThh>x7OHz8AL+Yj@25P`%+jiQfj0a
zV2wE9sp2P>l9a;jd{anAmZT<|om#=Y9GZ(_6KXh7lyZm0&ZXW{GTYfNo3W^JtnsNW
zlDU2aURq)FDN4$RqOx67-O7%3W;NXmLgu<d)|)?rUt}xw<9?dpR}z-w@p5B7GFAh?
z@zXHP5BwmJx6l<<7e0mSm!B%&<I#KZa~TM0K;9?f=7pULrdW)ue>z{UouC8bqQ^<V
zQCpaG2w|P)2t8o-_s=mka9l1sAPkC3ei76q<&c8#X}3+UNvKldLkUyCo}{X^0mI+7
zXTIS|h<_@T4JHV|v2q-&<xQxqiO9$Gx@w~Qy(3*p<dUWoYS^~W#Cs5vtZ;h4W{9?U
zxMYaWTItim8mM5P-y<yY))7u|hQk3cchlcj-D`}c)rhXFvI1<a1XE5S9%UBgfa7`P
zh<ON(UI0?ZvYucX1~~BYLU+vwI;bBUMlvC{5$=CC4mz|pQx&q8+g#)j60+|FK7Ca6
zy1}@b=KNW8KN2Z?z0rfdfv8G;UGlz(_fJ+JwmQ<F0|2Np;^lIHZF5*Wk)h<sA!K>5
ztUvHC1x77*sQYUSHLcGsFoTCE;vm~<2fqQsKjQtAAj>C@zK;1+UtH`IXDp?bQk<MH
zvzOS&&gqeT-14V$mj=D@^fXDu0xco!-Mh#@tO<g$0eTuw2&hNC@#nE$wnIAU(_&m$
z(I>wuC3Td8V4q$+n0db$9cr3Y-+)>JVIK-Hvk=l+s;+JWnmO>^DIB_!Olcv{gCQt?
z7v{}Ty%smQq|z|z$G^?=0h8&^ldDM?iKq8~+abj4=Bw=F(+W4P;kH%{xde!>?Eroe
z*>EyLQMDK7Y<tVMN4{+LGpayfk*nu&f_s^Twqtd2d5~z1RCeg(mu$zo(2Lku$4Lzl
z&@&rTHLk+sVRA#ZJcPpjuz+sIXLxtbyKIJ}ELT@us;$ki4+n{H>CYl0d`o|?C)rEN
ztl8dPKPy?%41-j{NCvw7S)Zyj>Zgdc_-yf>rc)gF=i;P*`0oV8l~}}Y-*n_(*xanE
z3gereP9lY1gPCW0gNVwchUUV{{oXWFI$l`k<`#`v6}t^{`2}r8s1Q8iWkfh#%Ddrv
z?C6Os_gAwBAbyPFN+rip;JKq_Im!;N&}#u_o=~r~RHGsI<aL^QYW*W=^QM5ul4gjN
z-JG!U;N(h8(4k#=a|Gta!eio?zPX=~#`*QkcC<*oo`g(1KHqVlP7?Aqg+8R5b~_$0
zUUC+^bx!4j%H8hHD7u%is<3^=%65moOlf@`kx<WoV`%ec*o>x?-!HJA><<8sWiySI
z#Eg?-u;fYQvqjbrwVukgyA0x!yN7abVaJ;bqn0;;-H(kq%))t~f!6mOoL{nIp3)>R
zozFU|`T1h(Jc?%G|LNm9fN8=>yDzPDl3y+do@m>HtJKtk6wnfD*<6B)Q=3=YW129N
zFu)!LrBF#GM{6`?OY8Imx`^eJgY9K5AQ6(zF$=^hoSFAU6)%JU3DLuPt2<hAZy1f$
z*(tg@tZU|aUh*?;2LFLS5fR79e?4nQf@L>XxH+CC2pKu>t<hdl$El(x9#g}{jzD`o
zOLGN|%hphDb2lM{>!ZZEA6n{M0-!~C&({mZYxLGheNgQ0%#6eu;)D8M!eWO&!pOU7
zs8DwtzF&B)ho-X3(6((m^PbWVXTO4yd$k&K9EM=yc)Fe_Mp{X?Hp7RoZb-2e)YNFH
zOgQK7Gp0)M2aSnoERUI7pOlkm{8vvnbIY&;e&LE>=Jc;xS))VdBdCJQN|NVdExl=i
zPPdEU#v$}7gg#KjeOZeq6}j=^k!B#~CP(uB?il8Kz5i);9j3Ow3#udCMXfRtzN0e(
zJ6y_b+*a{uOEeHlt*N8mPp+07oKMt9_-S;=osN!%5nWsbjspAmrJ4YMt%A@)Sy_Vg
zJ<VF@v_~F~Ru+F5|DB?6C!!OQX~tH5^&|qV*cT`>{KNwC-}qXNZ8%n^Y1cu=GPiFU
zxE}kO^+wUG_dt@Uri+wg*Y`;xOls#2=GNLcs1SfpMZjp)Q*iK=ms7OR7QIrZ4`+%y
zPG%0K47)T&E~_gw$lY?4rRrwRSg=WU^Oz6CbKCl66gB02u$#*J^B&y`zyNl`gFqK2
z?#aeQUV=pQLG5LfA7)%z&Z}0V-h%O?IW5fA?<-5e^4ID?ayO<$aJ8R-i`5GPk&?X_
zswCTQ6-jRxet|dBLoGPaP(Z@8m*?iY_xlWrAoKM^9m`VRbY9yE?2Y-z0dHnvD>HVY
z9tjjD*a3kfxki&AdeuQQbdL+|J*SOWK8UwDXlPSDAe;R12@-oU<|R~$|Af3SL<-Vs
zM3feaH1}Ire`2h=EW9X&d~@SiRd9JQ^m|0yT&k2jMWIzSjaLam@=peM5s<w*@?0p=
zZR*eYdQU#<;L*Bu)U9@7FZ<mNmuh>8Qz5AS&;(&r%gC{S;;|6V?{0faEibk$Oux$2
zq|)`MW)aXj-ol{vwl@oSiVay^Qax=E35}u&?Qg$WQHqsHQ`$dOqj6c)V0}vbM5`Wc
z`u4R5NH4Z58v>rf{#%pa!BLPQflSSOV&JQtuDMq5`-Fhc8+1IF7--N5SA!O!Vf0(o
zjv}5@#Km=~P-D_}LzP+T5%w<EdX$%>Fd}st!S|wpZN+a@Xti#(FnlPZ>Am2xw-&g^
z5(Eq}sUjIXw%TJ0Bh34O;4(p+jt@+}q*>h;E!HknsE+&5iZ#X~=a?Rt;nrujS{7cU
zXSGZj*#H50`M*!3Y71C~4N^Dhv4e`t0cgR4;jkmV*p>|2z{o<<te2=f|EAvt{JuWd
z)&4RTRw3714fSE9lDIJlbX=KiuR1f9+qy`CtEV@8uolG=_}XHi;(FDQwosp$QewH8
z`n_(;6aH0Pg#bkOtl62CMoNCMGp<s31gNjAxSIBE5yNU<ECUjA4TuhL$GAq|z37%<
zSe084d?n>)&A6;k+O$y%Ch`}|q4NECH`o5Gq6A7<{@{t163;+zXY%CXa~$-J?fgNc
z4<C|fFKI>gE${m(xfWI+E1KCYrVV3rgzAOytQ`;52CtwhTVeA}ChPmR;3w;!Mx2_K
zNYA3+>E}wJw&KL5jGU)*+{v9=^4YWsv|>wjY@0vKKB5r85p%QsgcU$0tlU6LsV}`4
zoYLdE{oBQQZ#U%b_+0f7Z(-FWxmMKNhJP>xiuyQ`&4VN67sFrGp#4Ld5*2Ywo9H~?
z&#zOQ@QT^;o!RB%ey)($pvN})s+w{Q)u%9dYYfKS-dXyird~SAd&u#xz&)Q_RH^@c
z!H&++mw<A6n`pcS3+H1wra2U3@#CZae0K@Qd}GzLv`BA{EBlLNc0HNE`e}{3Je}Ma
z7J3}<nh(XWPK`5b3zZK0N`pnFrck_<3v{53`MQ#19!?yY>X3Pp=$WRh81*_Z2>@U2
ziqqKOzpKdA^t+)^l#p@ID6=dGHBh8~`+yEZyBeDv^-B|Ms+>Gn6a@^ijdivP5d-u@
zVEe4%s8!LAa$c`T^o#Aw054_srKONYG5U;si8CFQ%X!!FvQM{J`n$)y^824{3jWCI
zi#!<^NtK0Uk}7;QAKva(g!{xuOX<8L$gXJS@>n?xG}%H;I;s%!xJ{vohmtBjAyMfE
zttRyjJ@>Ih5#pwftzYThU$Z+nCD}=ck<`;tV4;I`z=niOZbZ8Zu_nm~;MirbLmNeL
zr%#yyQYCom7Z%#c>e?RRzlq!<Dw7IJrRNT74B^MdnGr3*3Mt|pn(dqLT1t)Y%CpxM
z{qeTiy&1%*>h@4=Nyqvd$5>GKziz(OKFkmXu=snQ^obP*cr>f+Ev-Fi$p0QuwgM^C
zQyb2!1!P-0V5G^6-%Fu4r8e8>vT$S?HR;TG%<Q4~i)@3z@3NQ!h3W2*GCTQg7uJo}
z<CceYq3nlb2lrj6@q<bm<-ipH;px~)Go}?i1@_rS?e3e*Btc4Im@tD|zaoMVj8<2q
zZWCo}h%)28E&&G2v%Xo<_JkkAu|{@tNK-eJ{!CYL?xI?p_XJ?~K5@P#Tb60|q{GdN
z_>pxvJcUG=2L3({Ra0H7pqh3U;<47nT9Zf<jMZ7b7ABu<Y33(T{alxl{avZjBaH`S
z0B6ti-&I29t<ej?AzJ2`S>hrGs6bxjUG%5)nECdQ{xE?y&(dUDYxc<;p%_uKMRT1$
zr(iL%5GoAtMMrW+=!c|ESASPqw{CT9nGN5KhAGK8`Eh6TBGg2%PTxWq9u_4UV7n?O
zIyT5o>6BS-<KIghh={EnS7N1=GSlfiLqSz0_sHarb+y$HQh}g^5#GTOcCJZ6Y#W%e
zMiAG$;W@N_Z$YKNG!Z-C@>+!%i_5YgUO|k&Nf<(k3WhvZxGpC$h(HnhtvC-)h#ix|
zwHl4DzlIp`k18GqM|SpN!GS9YmW}4g78kQA>mHRcl_5$<0KM6Ed{7g*b^&^>uxarH
z(3HJKPdJ!;MG6k5xv_Gg)@ar06p0smp#QCOVdb)gT8hp#f=y{c5;KR?GCG|1)$PZL
z<*y>&ztbqR7xaNCPbOG12Tr*=S*lL~>Nx^NMxDa>Kr5Ck@sql8Pp<E7|5Ni<^TQA8
z?6mHi#x>sT591-NKWnSJE<SVz+C+^%ap<5)o{LinMi+HOC~FLRv7eIS{UhlZ%=+!L
zgQyqP5sqzF1<Bs4q+aS}OcJjRDM?DC20XI8AO2)-HlWL;#x!e=G2<_q<iO?}ktX5t
zLr1gsuTuk{-XQpXq#!kxyK63EXs=~iYcQgflV`ws{BUns9C`3i@~Qb1&b63-UnNcM
z)j(hg*9?6S81R%Je1bpTgF$F7y<?;+m$kl;z>hZh=fOGcjDb$wBa^ph1F-K|{3j&E
zeBjf$Z{8MtQ{lXqP#BjP8mAr$JpS)~>rAQfS?C2ENGK6#e3!?`@DkSH2aZ@WzKUFI
zgu;!hok+#?;&H|Hx;nhuf*}ZaIJN$a>G3QP-JAyywq*$BQ@MR%LbWuO#OYt*Hkynt
z%^{RT;m8J8Lt%fhk&MBOuCoe#N4`8)av`Xil44S7k*munh*V0?GgGrWLzAG{Qw?$m
zLW)^`Gks`k8hv}IXr*k9+D#dEnB8PYl5E&OQW5lkkoZl?vLvt1<NjsNKvAgLSNfNi
zqeh$V-pe?cautCL7z!(_FfAmGyc;y2X=k_2z1-*mFJcFrbql2;Sb^a0Etm65aNB#Q
zxkJ?k_quVPnV|!ud6uW06gRV_xakk1-*EI<K$2JhJU9M%e-GdVfKkM}qw6S(V@ZLe
zt^T$kpW?qrtWdOsRIZce-t_CerCuIfaV;8+pd|(x4K9P6I%YB_fJU2fr^+$n3}YWx
zvuEMStKh*Y5eoF<F6`*05DDoI>8ywsv=dSbNzRhSy7P}>cFwl8=07QH)11WkZ1&7C
zXHFrSHhbSwv8h5O0ALZ$9By;`spc&~-OY54?3W_!_b@b-H0_aI01}iaaa+2JS_wHf
zf=mC)RV#k|thJU6p{$@9uwc2COceQH3t$eXzaiPzhPhC4M%PBwzViPkDIV70(g)l^
z(?TT9*o1qiALGMcI+k{uE=_S9Fz6$7@+~2&ZO&oAp@hUL34#KqmP_+I6gQKwP`NZu
znwpfU6iosz=kI^&>Db_|-8km*mT)F*7*6*fyNFUhRVN*ZwcR>(cIt1Y&CX7U#)tM)
z%D@<xLF~9q$86jk2<$!x4uHzGl!8pk8=}z?1mS+>e!W1a^<y}dHC=3qm+_r|JtfZ+
zCQUL?ypJXz>*~VWXu6c)5B`;tDK{er6OH7AqroJ&s1;c178CRepdg%n>JOp-fa(WR
zJNqEh=0pDjJqx>4wx;Sl6p4f}x&{u*1LYY8aJpG8t0m0Fl`@3V5i1GGSreT#!<3(1
z5Z}875(1-OVgstNc=S6+A%!<rOpe-ex72x^<J$k~%z56eqsC<V@h}-D3UOu%a7n*Q
z*7y;yFk+*4rJp2tfQBC82meE=P`HB3){)ywB|KfVt2-kz%qXtQc`MNv8#m&jB6b0+
zA_vpKzkm!y4gdzbHvBLXaaea&kD6g>DxJ8|k}4L(q?`01Q@9J<npR-Kj|$`mE4ImD
zD8`le{9zud;TA9aNLlcg#JLdneTUJIj)YKBF5epb^37YElV@7doi8nnmNFSxQ{y?c
zfanb@wu)~+5eo9T8^DBej!C7P@0d9^`j^w@WUzHr*pG8v@?IE1{pSe?3~Ejc)J~HL
zT7LzM0#X9QwAL>4!wO>4KtXV*vE0{>Ay}UooHWHm`FhX_EXb}|i&Gi655O0L&~n`A
zh@-Xyn2g70LX(3L5W>k|vO!=RFzIKtbN~p%ZQ~sTCeSK}WSGQhNF(fzAW@`a#@4XD
zHZ0^~^NChVC`=2jX=vU8D5f1spZ4${gwP-@r3oiXJBLvQPh2JEOfcPF|7K#}rq}#Q
z0sRj{y(OSAJuZR}K;&*%b#J<4+{CuOJoCKrg-xx4yGljk9%CTTV5}w_kRVbqDh0A`
zvExU>NGBS8Y{ak%+&&F~OfQPd5?1(k_kfelI?KfPXS(K9Y%gK;fA=o`X?F~iFmDPt
z8ON16+f;B77;Fg9YPTkg1HcN?fH>e*q=lQ+mX&nt){YD~2jd_O(hbw}vsx{5zn%)3
z!xAVhoA03WYML-i&69c1uI4ZxDm{Z~rzt;NQ^S+`3E3}7?0D&=daaC0vmmmPPH126
zhxLBTN@k@8GKfuWYj6GZITx6ZPMp@4uU1Qg*e}Iw)$S-DdA0zRf{8E*+XXh-e4#LU
ziMNVgM&l_GTzrAJ%<u0pJ9qV(%=FoKIs+%3cuRdf#3d#H++uQY*Z{XjA=UwYBqf7?
z)L1!Lz<CfRU;`Z+L^uvjIMmM0P&fSo4gVmnr4g}28XG@w!%sUkNSA)l(wnry8mM6o
z+Z5VN0g<(6T$-ZMN@_<V@)nxQ<E^3*2Z1uiQW}~lZ@4T6XMsO(ynCC%<K(-3^uy%9
z?wyA{DL~Rku0)ig<572~ITmlbb>^Jrzd7r?!O6+wz#e{30;Oi>2B1=K9nw@fiNGHy
zi!|G~0y6ZPi=NHx+--LC4VmnjXPYP<f{{m!*Wx(#wypk!VA3HUg6g2bKT;0B(O_qT
zh-SmnjvvusyZV8UAQC`XJJY2fH9L)A>_DNj@w7u39%!Vm3f_{rn(FWW(FLx9Es`E%
zvBB`}8OBs(agK|RtVQuA2~7;p1Z<uk?&oho$rPCe4gQ2pI>@g1?k#p&N-Ni-w!gB7
z<D!z{fF!S{&;)N!33o#hKBqCMd1J?Q{KYvJdpC|9m)Tb-RtmT)7-P#4kOu*b5pDhw
zCJFg|7GnZZ5K&xGR61DY@87)LY|qzBVeUC523GEI(9cW1Tete_W$0ag)0~`)S~wX^
zs|5_OtLO}XgzN)gAv~~${ZSQyN5G^s>Sh@IV6w_IMZ+%ul#LU(kv6>KM@-sZM>U3P
z)A{7&^Y8(Hz{>I01-6qIk2X64_`rvs5K1!=9cA3)SmQRqKZ$#5EC@k|M6@r1vNdEV
zH0VzP4%edG?Z0_Amh0)sF@MAzUL3zr!7rAT*uzIh3D{Uls8Y3>U%Y!;{#RQzrEoQ`
zX*8aN;z3GYA{L5+K*vV1n&UQUG#Gf3gNpCe*713r-mYy}jKxU|d|@tvo9A*u_En6_
zEYSFkfhbVGqr+iNMz9eaVxj;7Hl|BEO=}mRSXv1~xR@3+VU7jd`+$Q<yJ+TtOw`R8
zp1Nu0(ZNV4d>9?WR3ja+)3kX@*;pR9VTdBU*lr%Um*=H$m=T}eDdVA>>@HKqC#$M?
zNISe+SV7ZQGWa~{03OQ3+tQUJZX=GxdSiN$Tr8^KRe?I%6W2n}PdxN0!)(7xm=n83
z4E%yKdbqutTf4jbuHQZ0Kj%H~#r;J@B!TUJMhPpn`%zQ;i=ZGSUJ}u0`l=~2qf=Tt
z^1t4=CcCh2e|l^z9*MaTuTsOYCIyw4C_><do$1hxsRcK1UHk==`v*+-&b_!UhriP4
z)3G6jB@7<J=Ite{{+k^;v4V}6FM5)@7Q(%dl$r{xFgHVg9Skz`8~QrnW^6Zhuy81b
z*^UoDKwzjJO}GP22DfrF0SVli4%1altkACL5i9-R^~Q=66pzek43L8ZoSAKWuchjZ
zzO53y;6me$9_Pe5CKOZ6t<~z_pi?atoKpW_a%kNKFZ<k+k=lZ_aWfH&Z|Y=xl^NVB
z*NZfxC=Ym`GAVS0U`PtXdn4EwXdcZ!&e+~XixRW0zNQ?F$FUX8vEZTAQ3V>a;9nRU
z&2qQ+l=e}v|8l|n?DS3RGr!r<)r^lV$J+5H&3DhZ#U%xkB@jl&%!X%&2pDj_2yT<(
zww1~5*lqUbie_Ne923C_S$ClS04Jl^#0SA#j0vM)55qu)mLO;dwg6zX!w*6JIE{Ar
z1)6U5oAqNnx+OmSX}4~r3j<C3NQZdrf58CV)=$$_GXNX%MU-KHgVR?G7^djK-p5i_
zqyv%C#?5Y_v*MlknSyii%_cs5u46jRh?ztZ)pu}@P^t_h$>A)gIyg|tedJ@M{?#js
zy-z+7$^F{{DTCD^7l&ZIY`4rX@#v%f2R|%tznpyLDwf7;-T+!=@1F9gDO1_$wgL?i
z;5@3LvoU#<%H(taRcZC!ouyxGSnF=WqkCiG2~2|g+w4+wj=}KaPZVPSCzUn@oa-OJ
z!s{Mf_8p8SOv`!anU+?FOl*voz{Tnimkw6_o$cF<o5K_41Q{A%K{bq%p<e=^p^)rk
z1)Fxbm<$6T1YDq@AD2SJ&zkZE2WgOi*sVzsC|r(i+Cd{G#iXSdkx75}1>rWH<P(a^
zJQ-igr6$V5wsi5abpa^eZFjM@WwO0>Z_*t_bM>DT&Dj@6ot9A;`O8GRu}d1=U_$j$
zzNfp?y?9Y&+qb`w%s>57bM(Rqd~CqfhI(+90PmGR5ku5sCp*Bg5GOU-46WK+z3`FW
zX3xIqW`Y@i=bYK1pbaYWC8!3!qvm9W)XW?+(CE09w&;1I&nS4<lwH-=9mhJHlOzqs
z1~Hrr@zPDE?~L)LtEr{l-`ihFZQfDq95tqvJolXHmPEY12p^b;z51%#v1yZuY}sP!
zbmK+t7<wcIHm_qcH3e*_vw#e>QUIafhL6ceLq7rncIjN$)g5pw5QTxJU?U&|RE!V1
z_|puzH4RA<VVuCxFPv9kXBli5`%E|^O=(XKCosU`Ab$v4%uFbo>%W$DZokWMW}V;U
zB=A>?qytpIL3|*(D2o-bGf5;CjWsnV+h?EUwqJR5zSz-L$v*H{%xywVar(t8VxT9n
zQJ_E}CK&=w#?<x=y0vLjE0gBW$Ky<#{vR4>$CRT5<$^`H8ygB%OcaU>x4u^S)yB2)
zfoeICZfbFQMvpa_*0%E8Vx@G+K*pOBOL$|)jf=%cb+`q*F}iPXu(D<I7Vo(yei!}G
zz4xZ)x3ok@#iCw)px+_T4&q?QQvikx(WhidQ8oYo5?e_`K~z3~U`l6NKXxLUPr#*}
z&M^90w*pH6B5n&30T}2MTw+?c053!r4m${lng&g11tIMOnGZ;CGAvoR(H75j5{}!y
z({m=@mNU2f-!*gAMMNHsjG=jz?XYr(p`>5Pg;+s|)k=P-u>Hx$%dh?SFC=SQx27U<
zr<z*M|Ck%{@cWU39PY%MOubmgldip`tA4+{FyY*J_-MVy=0-_|=YQOQCYj@h6|=0h
zyrp~Z&_e^g(N{-xrsl;`<%_Ge;+eI&JEo;2<tAkbT550Oa(q$l>FFuH^x})<|NW2u
z*j!q=v}xRQG(>MV7KpKkOAR6cm^GPz0EYmAU4W)00o9-tWReCunP6dDnsBo}1Yp*!
z{sFMyAEr@&GH#fLZjCE;rmbnx&TyJ69}3_aJiZbg<3&xsV}|mcd-*@*oNMo_n$Z(m
zP#x^B@g^6D8#)+ahpW&v@(Co;7x%4Po?H8^Z>4hg|D-8)@hqHH#<&=3Xr2pEiasP~
z6p%J`LVRxbrhog}{g?jPpQW(xoRYHeFC?UnJJ6UnCjqi|hG9S!;K2uQ+)hjmFt)^n
zQp}y9gvHwmiO8Vm0hM31Y+3G~|L*Uax}SNbY0}K;rq;7pwxF1<fCaPy5SU?bp)aK`
z0)R2xcBsUIom@*hT0sy7g#ZRL;uiu(;D(>1VZ%rt2Arl9a7)sWMgdK5*)qti+ri>@
zbexITcjM_Td=UNC`%CUsw<nxLnxl14+e(S$?fvldwB>HK%<f(JHTT|I>;A^w&9Mt-
zN9*N5Q!k*mB4D2A8$61EaayNWS-;b9uDBw9-H-ph)G>K-M<|V;Fee0P%w7H(U|Q}b
z!wCH=Var*6l8RD#ns~#Pe$}<BRt<jbbDvA)7A#1Qo<7;D_jlu}K=gn(WQTzeeJJ3f
z$*4?UK*eyYm3t_#0$?CK00sep`05VKMn6kO4yB@2(hJkkZrvIdm@E^*_#r<BAdAGt
zc>tLMkmcPEJ=Eu1anmRat<ut=<X}h6#n2LmRA5;EFxEH9nM~iS|9W3}`{zDCIyQGg
z4EGgHJ=c%I^W|Xt!=%8&LACs=yKCn?__Kj?|MQ2)VGsI70jhIilyE6D5ce<uFn`6y
za=J#4v4l!bkO6GOWOVL%=e2#|>tD}zzWd!ng)KXrWJjmx@@aT<5J4qcfn-fqSUH!1
zil%n5iZ_}B31^66hnv8nor|vY7ob^xvBQtxlC<b=(-lq?vJ)3RkbI0Vd>CHf77s38
zxMz5g+J48&;@ZZCe%<R{bqii+#cC<qKse9Q2N^Q=`@^ea&e_^J>io~#l|K7{pAOWz
za}|T<z9KE7@sLKDWDrkSQv>KAh9Wy2e=LeG4PqL6usUHJ?ck!kMIVCAM`UH$OE2}_
z^GAQw)><#7;wfy7<TC&PIU7YZ`U<er&I~#o{sG`<SFl-t1R#W4MO*+Z(uRN}v~)u=
zeS%SeJIoCEYx)W}p+j5_ZOF-FOcopK#l0SE>=p~rJ09weUG;t}nz`T@{6jl72y5&G
z6@xH~TNgZ)U-g+!w?xXjV{Yd}ubSJBf?`pLZvndrQ(U*FH2=A02d7_oRR_99hCs?R
zP_2_xp$4c7Ia}<aE3Rny=(oS!m+2m?Mq>%=4uHdBa7-YfZv*bZ&JEVtU>7(2KqQ<h
z#J$w5+$+r4l%Bv6yK*<1mVhP%sxX+uPCzwi0#szW<q}w#1}E!wOs1H3?|7&ucGWG%
z9b`m?a80x`%;fa<ytnE6fB9jy7U`_i`}g3PsWBL+fwR@{mB9qIzpx>+YvICJtz4Fk
zcE(qQra5^Pu;y_86us8;?wf9Ey6mq%o6UA@H}N)HO2OmEtQ^6n>uEY(5|J1Jn1Y31
zBCv4c$i!)H&;mC2$-*ncNDD2TPC;hf0yg*!0~yMqT$I2HgH8Tq2r>^H7a5J6k$vD~
zC4lVLJ@KpFk1w7%$CZ-}Lj~EA{82lJY1dufbl!dcmaRs{R_eXGok;6=JoAY?XIx-O
zjE|V^#~({(_Uz_}XqaMOoYcyW*-Warx%I}6|4B79VSK)}dncB7+QH4RjfENtXCn{@
zI+_4Oz@aRd1Xu(P7(N$Pxb&z%696`Vj$!l{&@mqEOqcoDJVcbjPk=}h{sbNYrT`SM
zp$FKPa*86-;YGZxsb?zr=$*grNxb`(4#=ky2Qo!R2yD}@zX6=>|IOBu6U()&+ni|o
zBu|bHbxtzH=U?P!vw0Gt97KTw)js(ZDnt?L&YCx`>Ahe6N<N>d)T7BXfDi@9uq|Z)
zASUe=Gz3$abJ0zZDZmIiel>_Dzey&VacPGcXr@UZS(A1F68$W=l#>a_fGGi(HR*7$
zIqpQ#nCjsb&HhX+_Mx8+0A#o`2$1>Q1k@*31;}v{)33X(`NI2toR6G6zf@hZ)p480
zpwc+-iYo7T>@kBo1@iik3@6z+C%EWGD|>94iU>_#e(NoXnIF9~UtG5t--p5%@USn4
zN8bWa5Ln8+#EzEX*As_vGP<D!hzz&j65zot+zcR6lYmo@k;8>Km~d2rO~Dtk3)rLu
zaB>xA$Q-xL@%r|A(c8W|l)CnIax!f029utn)mCpW9fD2D<i@AG`|9S4zw^Cv^!n?{
zwG~@TB-QH0rl(B*cfa4ff5UoCOM|Z&pDIA}Sp;g<K4xs{+E0F}h}I}a@NPsr-Hbg@
z9KKY5S(BWLItf7WI~O7@2`&W~Oyy>QA3<h;rXW*Oxut?kMYOol%F5A%(^*a?EQ)rH
zC*!!F+q1>Q=6^oZbmJYa6T|3Q*a18Cou;Hrlp@h_=U>o#$vxlBCf;{bU+I-~PIU5I
zP72~%AAQ8Fl}ZJ2YY{i^v??5Fc>=DE%AY-de){5n`f8!r)nnqRX5mZ;08GF|P(cg8
zu)wW$zQ-qk(=ZD}0U+!uW*fO%AU-W88wNN6OgWh4XyUd&<Jg~re<wcKIK3N9boy=m
zO&`3g>ZaT9HAxvyzXL%Q^pKq(;Ij>L=eEqh_uG}$4}WN&^!!pUHE)LLe(-^GPuC_a
z1aegu%S=JEQ^2BN!#BZGm)v%Hy=7ce(Jf?50?)c9Vs!ym3P4H`RL}$)0VW#DZ}bam
z`U^J$9f77Cj$Eu!2Y}<ajGDl+;8XD_AY+RM@WCc|Y=Y-zUNg?9D+kh__~$}&%)~aI
zf<yt~JF|C0j0(ewcg~zS`qFQFvpV{3{;IFCdb3yE*p=M2;AyvBsT8?HqH9s_%!(XA
zsdRx670f^Lz3-Ga{O$kM)^XKLCsNsiaT<<zqw_@r3~CY>vH**I6+5|*YWPu5P}5KB
zC>QNi@D3J(=@2^lGeF%m)eoj@Px738w9|8nyKw5~;&S>^|0kC^>jIwX;{K&g0Irim
zVY(t4^7UeWe{Sv1AN0EZ{I1r>oEfES9)7$45aNSUr<mejk6dJl#Zq(e(X8%^eprs>
z*C!IA@I6m2gP9}t?FqI9peeUeQ&#8|C}<<P1F*m%IHL!{sPPkkOWih10SBjm%n^{a
zdroc0@oHl#sXzWk5g;Qc^Usk<LZm)vl(Yoe-A#6Mw4VPbe^P9oGO6#CTmK-ndBM|>
z&KWbR_V4nfsnB5-RO;=FojEf#<&HZmk&Sra9p8B3<J)Nru>%cp8Q85qcd$fLq{2>(
z^L;XYi>oN57;VtXDP<>tAhue>C^;s9h<`j&#$^oGO*v-&KsEKjZx5y~xDqLIGec(1
zhgrvW78#|eNK&viJQbEa^QK!;SHJi|aj0v9H?V!{&Qp$~vH5kZ7Kta~Q?9rspU1t6
zwA&NoL@p93VyuPbjz%EhPfl0sRYZn4k-zKjckVET!zoor3B;}2hda5L6_?oX_nZXE
z(BXJ{x~lO%_*PHTHGja<Q2?53^Ms>^QaxF$_OFTSevXr#dfDZP&Z$$eK<I&W)AJNm
zI#w$V^bfB8htE1@<4;;slcwT+&wh-}@Js@MhCeeg2{Q6_X(!)KFtK5Ro+nt?P7SRU
zv|fWhNtKq2ip`8q$0I`=kKVtj9=rPg>1+9;zjd8d(<r&yrh`6-c=G@FSPNROdP;IM
z=_bo|lI<O-@wa|BGx+VFRGU)OXoN>vB3Qw;i@3o;D%-@i3!?^HivXi$qWlqy^`k%a
zA;OpzS15QzRk$Gt-8fdyyEl36MSnHW{K3CEWkJT~P^n_={F^>Cm0wYu7IoZs=LMI=
zoJ;N~Bzk|=5^KTRX-+RD1-Qin2E#$VjO;DQ1Z+f(x@qT<7R`_*z~~mBh5Q3QMq?T{
zfbKbv9`W&>*WcxM)9=iu|LDI(+-7|Eok%>zI2mdjC>gW)E*wEWg%t<iRMGa%RL8sj
zbI$qg&uY=;BoKybV_nT(t_K0h6j+QvMetD3B+wLW*5vmt8K$Ptg#gF*nYoPNI3=8X
z9_N|N7N^?r-l3*D|Gpd@GpXGMB9YTVy}fTRzT=xn23kae6OWy9scY8#r=lr8gz4u*
za1tCx=5Y=?HVc48un=^O;NsO7Dw@;^D$=Rjf{$@sZja-5Ki;|UoP57ib7tjJcYLW3
zojM0cevpHI5TFs>2s$;@Db3M_b=jziNpr#NS+nAyq-jn?j5`R928u?}sQ{v>U<-qb
zetZC3!)P*{u)9KD3XsJ|n@EYz5M+lUcm8r9HuqAzyduwkD4gLuJ=CcWv;$l%VWwT0
zGdnNDZRO=DLviM0fCwOse1(9cQlJrB3Ldp9;Mi(Ban%@3KYXRd;6jOs0c3dEp<c{Z
zqaS*>;9hhcIa$)qhEE$#hB}Kn^?@eU6b9mW9aEZ|N$;<if#pe)!h)%bTiE1A>~`&X
z<z$WEQjpOkpxAN*n!4!+ami!N1evKIVLT`8-uCk$Gyi5B_i^I7<O0gmPU+wADPD1+
z2n;NSE%k}ldLGV3m=x|O$`$~S)r4R^BtT8OYW3qw7T6syYSPZNd~CLh#x8-Z_PRK%
zK#+0GWN2sI+;CsPTy|RmT=TRB8CpmFE@__HDiELGU!7;iO#f{q<NY>eqLa}A_+$^R
z`UhMs%+biD!W=7X7cq%&9j_5&aa`6Yl^v7cQ#RNA>yWwp4m|UNrwXuQE0;V@?N)xn
z+MOn%Gt>sju~9ST&XVc>EuQHH0(m|=0(6CWyN<mSOouWFFfvNgM&L-Efe#$y9Ix14
zcCP)|fVt!Y*jmDsZf*;l!fR!rmVHC(=>s%!H9>_NciJv0nbJiC<E~5Nd(Pl$c$k4;
zAc-$!eNILWMomyrD@WrYWfJ1;HUViI6N~&d90Y6`a;|-3z?^$y2Uu8o1Y~R{UxojU
z)$i0bDHf>`Q(V*Z$9T}9fSW5Aka0r5aTdp1YH}Q=V=<1+bbLk{xf}{M$Bok1cntpl
zve=b*^X}i~%)Fa?ke%YI{%D1_q)s1>#%2+&#`hxPDJOk(*;FQiPxZw4Vxo!QC37C^
zSFT2SicGc;O@fh}jC)S}NJbQA6cf19Ti%381fGvN=PPA1Zk}By0})?m8BswCoL(Fa
zD3VhJ9FZkv#Z2^rMO>f+czD?eVvx`vuvF`;QaK&pcqJI)7?0s$6XR{Fna;oLGw-^m
z3XpM~3^9pBL3Gf_BbMPq;M1Z#f{k9cC2kTo;LU1`zUoChT@5s;Xyu<DH9;kyBft<C
z0hZ!p;kO-#-Ci|g?(R3|e9?GqQ+Ya;F-Aa!MmUsWGoEJ3ay17RDH9hYOy)f$Q+o<8
zlj62@qz}&!K}Pab7;-mU(-Y>z2Z`lD#Vwpq)J*&jOJ>4<shYN{(>P9=Mv6X20~DWT
zO?nvBJq>{tv<e3BWAJ(6oAW03RI{m*eE~2LfQvxm#R5Y{#qnF052X6}1=IAmITODj
z;YH7AMg%<g3|avte@4{nwZLf!G=&5v1#u^RRo?5tXJ$+eEBLK=Yzen{qL^+*IQ;~`
z05Q*;S8^hF@j7`cpfv;UVBn2s3E{ZzG#lo!-bbKLOQ4B3<iyay(Z}Z5amW2!!Q{S?
zF!8C5Q`>E4l@O8cyTIN)id*mR!5f;hQ?dXn{0m%+Y6N6{I~>L@-mGAY9|q+qM?$bj
z#Vcmlvw5@QJ<SGBs+iUbhD_^?cu;pf&MnTx-oXs~F-AG>1hHJYCO}WQ);N|Gl9@i<
z7t5jHxQ%=oDj8rorqH|3Z2IOxlNwtv6K^>iM~LPaw^^5Qfhved0p$q5-a1I+k8|k%
Y2X#vgD6}bW0ssI207*qoM6N<$g33smxBvhE

literal 0
HcmV?d00001

diff --git a/Images/matcheat_matrix.png b/Images/matcheat_matrix.png
new file mode 100644
index 0000000000000000000000000000000000000000..e9860787bbe989f947ba7a094595ed2680d9b1a7
GIT binary patch
literal 188597
zcmZ^K19T=^&|qxa$;5Utv2EM7GqG)RVjC0N&g6@&FPzxc&U-)h-#xp1PWQQ8UAR?s
zyKi;Xy-`XEl8A74a3CNch|*GGDj*=BhJV|yFi?N5Q0QV-ARutU)}o?H(xRdyN-mBT
z)^_F~AX13A=^kpTYgiG0p6@-gXrv?*_jum0B=fvdm=MZQ(t%1~;h~g2#2_UwP-!&G
z)cQdsBS=lfG2lajO;%uUQItjpl*Pmkw_<H!uWC8&wzEIRw0G8y+g8>rul!azL6qKu
zAqL&FKm*wewJ<Kv2K3O-r-q-va8N*zv_SiT78Vea&thPU?>+okSrt;FO^=t1fo5GB
z^nFT{uFGEmNu=LJ$)P~t#Fsev{3)>y!BSP5MsQibASUnAXjsEpSg{IWi)u*shwJSg
z%a7=~CNeW94+`<LXK=gO^J2S{wzDaK#jy~DdYKfvmc_o%<~?fC*&v5c)C&vGIdUM(
z7+HqX$CQP3QLN$^xR+74U?4E5!`$62NHH(mR(T+vWtIog5K+=5WvDBe)`x$_EnaV$
zR9{S3=u(H|fsvkafcs{IcxLRtddGBePzr7PEUTo8T7ruU?d2n@m%g<fB7fBn{&DX&
z^&84_K%Q`Hp7xI2c@0w!7RV#H&QHRo-ZXWPW_u{1Ltu^K6$!{KBuMji&K*&>ok8;O
z9s(77%=x*UD`>tgBi*0xZ~4KFlYQabPgzr!ZyLO1*n&Z1(wJ=K;)iABWwK(^(+Efz
z0JD!Ll&}4MZtLF@L-aZM{n$5Qf7F%#q{4<qS#^c+1!*NRn!$wzECB;>IH{m<{c~+G
ztlSMj{KB}E+NGh#9r-~-$-v1S1iiuh&U8^&;KqBqt!k6%T@zTnKaXFD;q{^&fO5j5
zsz;9q31%)7MfAAEJE3+1U}%ZZZUu1cL5$2`^hSsTfox9T+aw^Nfl?^o>EMh~VAetq
zzk~%={|-TxRnQ+HHclXmLEf_v3c;LCP<jxW{fJKR2!WD9aNz^8%%Iyr)-b`>D40dU
zRf$+7$XO!GDEK-sUZTQETo~XZqEU$`$wDu)lodF%AytK<O8oa^Zg`zA`XR3c(6c1$
zP#>Uvp{SW)3&x16AgzIG4ZH*}l7mybVxE{e(AfPOK$&(d0i?D;Y#{zM2z{90U<!=T
zI1Kolgcc0`jhINH7C9*DH`^rA60l0~>_nsz{Ej%OB<BNgsE9)2OdPCH-2DjcSfGi9
z@v?~w^HQd1riL8$5vK(TThL2zsmW4fnHE)z;15_%)Sqz-!x}~jM!F3djo_=s=ez_+
zWBs^$1Wx|#IC`uO9CZ+F$Sbj3foDUAK*}4>ZrW{(t6<~-*J~5sZ@q8<1OY@l3B;p)
zREe-U5Vpa{-_l9Q?a0+o@L-N%K0;9k*_DGT6x~R}QM`urOlTV8SB1N!2`Cj(Fr>rD
zu~o=vvYLa`q-{v`DfuaTzNJj?m`S?eXbTxA){*Cv7*Zf73o4~jQ(`E}5QvMcDR9el
z2zd#4iI%D$RyZtbRb_VO>L~Vz`N{Y(Nv5!6+-2-f)TUG?*`}Rk_)sXOu4H&*7&7Kl
z8GZ|p`7C>H2U8vp8c+=?ZqO){05%Wuhe?ZP7p0j*nq-?qnS|_dj3`rdCkG|Bp($1h
zoixl-lzzh#St#|IZ*P`w=2<siXC{>VUh=k(ax!vH{HNz0)||pz_mKLK;1KVyX;KAh
zklE;&O$E0!HYK(zHsOx_r~|MM*x;<UHu&nMNqeDnp!H27O|wV?O-rbfQ+Hg#Sjw%k
zuTorOwxP5!wpp`zTVkw^1C#?i@&LJp>|Y1(5vL8EQQqyZHV;Fm{HG~+*?7HpxOnP#
z8SJARxY-Wb6WNE^$LwRxRyy!45IToC$1MiTX<3)kY8DhtG2Df<br<Cfa{?t>)%>-l
z6-`=}#R^R`==#Zwy5EJ{^x9NCL=<J2w3rO*m9Gi~l=ExzOZp`HbOR>)XGzwLVuXJV
zR1`)R_8I3H7XwYns*<YbXp41<c}KZSTb-kC1om|)8=`WhlcnP(<W*M}x7Ca~0nGpe
z+aoJV_G*r<v7&}eZS5L~<pS;@zH#4Tub(e;@cc1aF}YY<Scdc$^r{SJ^u*dU+K^iI
z&8H2oL+iuYw)&=iW8pUYrcE<tTSqOml?*#S`Y!x<v-KOcu(}kw0FOwIq;HgOEYSQB
zo)JArPpfV}sJlY_mQc_B0NZD7V)vn`e5e{1q|0}5wP$E%5NC?v5wNeZ7a4F|=A41;
z1TIO8W{k0ho=$~}>KkeMI%lub@+)!2KZfmxw^sSrKHA^tkY$mRBxA->%Ttc=0PqHp
z8TFb;n)PkTPbOD3SLFgc0#O2S0_eRzdaM1Io{c}iK7PDz-K|_7zplI~LU2LU!2W?N
zfslaEfEIvfg++sOhZq_F9q0-sW|}u7*IUE_6L}Z?5yl70fuN81>Wafi&t5}$?$xgD
z>F?<Q!C)dTCOX_Dl`uX(E*TStC5z=DF(El3z9cCx?jxQdE}Nl6uibPY5vvnRQpS%(
zjF0I<{==p6X?{yKV*h*(ZvT>zO)tIaeBGtxuyP7emuNVk?MzFAPfWOj@)d<XSa~Q{
zcW>C?aR{-J(iQQRd^}2b9CxGtZ2q1EkrDbUbWSW|SU<{Cx=tEXdLK+txMkP<m*H*D
zL1k)UYB3F4<y_@&>(J)s<^>T7<LCxDeKvlo9;PAoVrJ3KN$aIcb@U>kxg<GiS8a=~
z%=k~a2dP-{pA;e5@J%+qwk;*i^Cy+3QIGBqvJV`l2q*3)-uyW%I6T}fdw+a}zYlO+
z+9K74E!XL7bSQY}4|D<_de0K<N$jQ8uBRlX;>_`P9HO@ul?#*`dX21_u8u}UIz*I#
zJq$CtNOmK3n06Oi7EQG_8yTbqa}Op{d(nG}6r0IsJKBDlFI&@(p%*=+kEL%OyFXw&
zHn&bX@3gW^v<=o~>vr{P52tU7Zri#v&wDkDb)AintrN@=nprD;^~luQNG_G1mYfFR
zC1fA<ocgp~<gSlxtn}^J2P}Zbfsa575MdB>2aG={+13i%j~H(PYlZ-4MW>y8mH~zV
zVi!_3NdU<($<ndG1otK0#g*?B-%~jU<B~@k_npTaKXxBPebB2<l-P<po*}rtao_BX
zJriH$A@HqvMf+8qP<`Y-w$AEA>l7|;E=RSNa9aDw`RtqoF60u<5Hbk<{PMkzeay;k
znzWP!B+X#v{N&E}@V^xq4ZDen!j9n4bQ9=oKA6v~YpC0v3d*QAxan}I+~|3GSI?-n
ztmD?_^~?3^xd{J=TPEttOE*0Cw?1jPvhO)@YWhguuZ{18^gDjle`DHy?DRW~|H{P&
zkAZr<WbF0%GT7ul@!S5GgRVq25S;er`mp*iJ>M^)f>Wf;s}ijJSbAQusO-5vFIX`G
zG(}n_gtv<_eWktBD-J;dDgks7%@U`QwUU6?m)I@u#`}@$so0E#{jffo&(^23x$1m>
zkGCF6lZoML+qttWL7DA}UhdcBNBd*5KL-mQ)>mca(c9I%f{#HTg>R<!V@W6HiqDE^
z`Ei0CzOe7No|N_$M>|iUcW<mb+WvoIN?0c;ZC4Nwc=UfxP>}2#91svNXlpesH!XQN
z9#cnqMq@Ka6LUsSd#AtHARv66Jb#<^=5EF$p7wSQt~{Rnr2k^@{B8dOW+El|7sbt%
zpHxd;iA2=V#hiqLk)4s5Q~-{IgoMw<%z{TnOyWPu|K9PFTDiG7@h~xYcz7^+urWHi
zSTeD2b8|B>vof)=GW^A0aP@L<GxlU~a3%W>CjZ4p%-q$~#oEcu+R=gJAHK#Wj_z*!
zq@@1{`tRdE`srqE@xLWGxc;YFe-&i<=Lr)FBQw)~^ZrZ9_YaguOU2yP(a!xJ{sP}w
z`Tj-yKY;&R(0>qU|Gx<SC-DC!P;{|2|Et@-5?KCI!2g8(-}p*aj&6>B?d)Q0D(&EA
z?&9R~*XI9H|5x|^{}}(ti;w9aQ~eLq{fA%vh5mJn0309Fe>+(K4j*{H1p*=jA}uDY
z<_UW42c4rY>Hf6&`MS}jU(LAo%dTS&YIKy{6k#9~hk!XZ6mJ*YWL+{4!$b${n;|t4
zEJbLj2?9>T$tWi$V<W@&j;h*{3&*Dim$RoYfKOAWx4}fRsnEMW-pR!#uj|otRu-G(
zOb(4^Op(><(}zcx@w3bj^XRUm2n=E$)#A5&ao_A#Cht)extEy0i0gvur#%4(jJH5q
z9q$jW#7WgKLYZyai^3A+7ryj*C-ge(ub@d+l5Sc;V~kIOO=y{b0<N&Gm4tF-uc`*o
z3EfiuRCe0@v0F@s!G}FTB`>5<%~G=1!JU{lp%Y|`awoa~8m+YQXx`Lr^>tC{;#)6z
zLm;IdlfOFAp&%9`CUJ!3vyU^~s^|lzKpFT9j35I{Fs!FxoBbPc^N+3%=c!-Y;Gp#H
zB$2c0Z!!|(YEp@R3hp$m)q+E$WpUEtKL<*bp92hgi<Ia5nB9h_yqK>Opvy8C)$?f`
zF%>r>MBerb52Iv?tpqT<)xGkm@sJRa-==zfeW{y?bqo~go2zV1__eYF^4)81{rTJk
z8#&sre_Chz81g8pG@7>`d*#n&|2`r@;e(3mE*XWl_f&@Yg$l9~<CO0)*Z`)+BOv-S
zy)VRCOn(J3V6w9L^aFWI^WkoT7W$^?Q_IoK*CNw6(~9@Y*?hy;Dt;<~?nNNVf8%h6
zswppniq&xstaysX<O#LoVNv;eZ$vDGA%y@gcuwM)B6+Q>g(vxSn+~{LpG@hzwK99f
zIS<T~8Eeu$*J2=*yxY8=9Aeenn)SEbk<HDVz`%5=`IF}HLl>0>TCUg0o2(P+0&E%h
zB7<sKN*QuA(tH4X#d?fL;X)xV4JKo6nhb?<tp+5A^Wf9NLzEe7N2bDIsc%}}prm2g
z1+qlg6+pAy`=-LF>@04Bm@)imzGQwrVV@6!06N$lfAh!G?_V=BJZ}VZc8kLVr!4_a
z&&tgNFv!%BZq%DUbgjY}=$pvKF@N_Qk}|5da!s4Gm%%T91Uw5z;G!1ps__iYS2_ti
zjk4GUFe;#Pr??yMN{-DZejmaCOh)DjIur0KZ)s9vQ>G?g>SBcT<HHex6P!tPFeC81
z7!#BTsWWb0%7xGh<MK9wKIhpasu`e=pMfT+z$y#ri&YuyxtHE*P3{<W?44lFl+Hhe
zUmzQH`}SCS?>t2mW7j?Dm#Q`s!)^7ntLcQv0^^4WWwO*dZ*H7#W<`;s>^SLO5`SHE
z-_0x-S%Y+1WkCr;8*JLz7*U#O!EF-q{(xC=hYVvJI(z8E2(?|VK~3C_0aHem&7baZ
zJyuYz)DYubm}u6hmd=rbp6D~PB>jwvf@R_5rBkU)#po^xda2iGk-2|*V#-p$bPEu;
znXioABF=%M@+D|}(jS=9Y**Lf%Z2kP@=eQ_EY4uWO?Yi%F73Ej)*U{mt7Sbiua3tW
zBknTbyYI(pya{Csg)J0bRe0^m&Fd%JWVwhNRB7K!H{2vYFIL1Vue(6EXF|by4*4Z^
zMB{o=l6F<&I4dlfAPa-QN-z6-DDAuw!nT*0!WorQJ1~vOxXf`EzkgP6VHM<i8Mk4`
z?>6uLsdAKVIXcqwlmJg05}AT8u1`QQuTYM(*KlK*w(@yPvK@7yJKUq&8r>=t*@CZ#
zMdgTts>*wb?Xle_snrVUzaobAV!z&OP(n{j?lU#n9r@GK!ov~MW9zQLN_Y>cc9TQ_
zs@qh|<T#yogny%48IX-*5KzNqJY-|6+H{GwE^C_e8RBK(t=?<k8KxUX*A8B)+z@g4
zw0G`DB_NJ`qD3o^cWgQ`W{l~e-n-59Km;%hBOIsd&VEsS9&DyFr#9bc93!H`)Eb`{
zA0}pq&k0r9FWoJ*5AMh4byF*jvRkRsH@R4EiFv8MzxTM>X26RJUv=B0q_Kh(^WA83
z%;+ml5b!2O)cPTZ@Ih{8VIg6++8~Sp2RVU*aey&i0$ChZA}U`_Ts@dsUlao%P};jf
zP_08}9EqB4!;beNvhj!e?zg9Ug6Z%;6mb?7H%3E1+vENB+ZpWfwhTSOeat6*^m2yp
zh#EJUqt)g>_@^MewatJXPIC%_CkxNqjMti(jixsKK~Yt_NdwJWOiNbiKDxh{Iz)(r
z!OA|@`z|Rx9ciz)jOGNvkEQpJQ+c8WtDK-q!?OZfH)J=2B${M-t*j;j2CoP@PH}-(
zhlGe+{@d+iAq6<K0p_jtQV4CpCKcBMY_i6tYhSy)k0{?mJLVpc*PpRWC);xy7e5qL
z8T=Avz*C==FPU*sQ<CYVnHY$$fR(2IbA+R>TQcYMzB~qW4h4|`jR;N=^sLAsr-Y{L
z$qu!v_0?p*@l3314Gqe|xrU^svp>PCBUzeIMn?T$bU7xjvh4Y#MG(tkXKKW{+7*Ol
z<keIWL)t^<{rPn#(#=814o`MsKv4talbR@cB`jL?Q0WRzqh}$2&zBHDXW)2q>`8R$
z6>!r7?75=l_Zyv~>0IhUi2cy6s)@Y}({$<qvZ%#a6T!D)Lj!5xV@8U`kAA68CObS7
zZ<ekZH=@4CMd{b7V{<6{82|nA`_%PPwPs9XBMXffjS)@ins63sm~QnF+M$bfjaH+`
z;K~^ymi8zjGP0~I807Qr?yf+XNiVx2N;oByO*cKljS31n6?|c{Mfezeb>5nInuQR~
zAx`}tz0kN3^!VV){VO_Zb%Vb%=w7=y>AP_^T!J85-W9DMfyBVbK1NM&%)TaoFz5D)
zl<e1*Ls+Hp$}^|)GH!WP!(=D+-b8sGy+iMZ1I$#eiQ5Pd?9K!>7^_v3B$2Puc@BNl
z`>c%fdz7SUXfD>zs~*MFkI>bI6S2l3Bauulm~BdjoOaR0xL~dmF{`?CrdG!e+4CG)
zSh6JSF#?YqoJl;B_W=fzjpx+y*g8ynjiX^dj~&_{ki216P$v7gvExW)%oVmptWC0;
zZ~YopzTr5!UBU!C-eT<0KEszLsyN2_v5NGOY51H`?o-f0mJwrGMA}RhihS4Z`QDZU
z(5P_g6VNUq<x!ZD_869e`v{Hn>e!n6N*Dr0O0V(PkR&aOrMbWQe(>-$akR9qzFxO$
zwVcrGl@x1`f}OOWtjAG7X%JB{>IFp44n?;EvmqPz4&;lNP$P96+JzU{BxA~Re;L5^
z(OmW%>L^bTGAL{`E7S<QiZw!oC`>+@ZO9i1grxbIEICsdW0^*c|Bh+#xJVMo<&#N)
zg_h-c#OKj=K%?5)UIR14baY?$-QiK~mS{KdH^tdfIpJZ+<q8Wh#N$DsMOuxrCf0K%
zIMsfs(mT!VEmE9Ak5G0F-S|nhV^TN8Mk}D;SbqArOl(~mv#<U#ve2j}6RY&8JUUBL
z>#@;lukzSouQ|y$$ugJ-^%?ureas(FYw!nMx<w^T2qU>!chh6XPlc}ZBDhr-PHElw
zMRi;XBaBiWogKBpX|LoXJ)RPx7d;``l1Bgl08F`ss@^jb!v7|AUiicpk~QHY9HOvm
z`-L+Zl@(oHPaAhltH(dKpAj81#DEE44ig=r3rK2ZL~x#~WOp`H*<wh6OBkwR9rjB@
z_FEtQzT*|K>H@HE9=KyffkMkDFALkSiIEHV>t*NS_K(($0(~fxms?DgA&QXHr%gJ4
zx3eFOC!y#gLX_W<6-XRDU+Tq*9dosksv<XtSIN>%`xyjX%G*6UDVA&S(C?6iGdr~#
zO_-qtDx*H<Sd_Q@e`caUD$iE$RaHA++A0c2$39pZXR{8UlWmA+5@Jf$8d9OvN;?ld
z=nh9Co{29~!!z(w-@XR7UzA0ROv;Z5$#2otb-Ypy0_suxyrnmL{4{E`niNXhtaepq
zLI)!(qV&0c=}&yGLBEaiO~>Qll`qe0?2=5i^oXiWaZh5=YkWwmH8K0mVsdDNbuoKL
z_`Fz`W$+QH%0JbuG=@ec){4qGLbgGKx=>n`x&9j1Zht2_ID;4|!WaZvhWMKkW#jt}
z`6Zp@@LIV82A)$Xq5fvvqxB!$If5Pf%e?!MC@KP`d5dz#*;uykpNZ{v+qHX{=!&Qx
zG^*@pcYDCMOSF~Gd!9M^8g$E?X1w4HaF40Mi;Q|qCK>}}U$o=$=cefLoj0<0Pre0)
zaCacC1hyibapwnfUhk9k9L^J_9JK9m{rO6PICmQM&f3m5FzgeRlM_0fr=WznE%=3j
zzBVHAPsTh2z4Dw>ZiAK7sPl-_{N{^fDr<q#3FDXB!`m@t+8Xw?<UXq(t1<A3Rivr+
zWeugRvL%arH1xRGR5>tvCpe0ppQ)-Rf2x~(H86+SPs`_7@)Ur{X)rAk8lK<djh)M{
z*=nA$j6*X#ljMNPTxz&`)|iO2xwlUPOCwXvhiW|H;0u+$-o`w-)lY=h`wcx_tbTlX
zO(_r-2xIx4$x>7cMRAqRUo2zN3^cH8f#CQDjm+TO6RCD?=_w*3a+t#f4~eTCwlX;&
zK<s6UVY6CNxVS1UnCc=u%%U|Hab4M%lsvaGu8U_>mQ1^-YKNa#7xK1ZhMW|X7RRC0
zRG~4IEe^;rOW@(F`~ozuuJ|KHq;wMDcOp~u7<8HlQKQeHQWVLv3S>#cAOs52fy$MI
zn&RNurg-3Ivy=NHYl;G@T(yKIdzw!%t?;hlGwDV1?If2SXZ~};sRR~kxTBqkcra>8
zD+?ppi1;IuNjVr5P+o|Xu^KJpm)$qVCIXELdd<eAv*4P~LgOQK1)R_u7vM#xnOX?v
z*JK;9Ul5_+Io9Xge-0~~`c)_{|D@A-$iDt<?EK126;tWsfYP?@o`8eHrE6AL*(!s+
zcgauzDCb8G38%`QeReIDj5!uuz}k7gJr;MqroZWoVM2lAMPXP4SQF<F?~polx;Js1
zPT!3vhe)qE7Ycr3WYBQbq8s(VDttGR;112Ago3j<t@^;sry+`ps6O6-GIV7p^?x{P
z7LAQq>+AGWos+`BEXz~RfzuQi+6Tbp^}5g!4VA$q3k<!aw7<m8atWj{=6%ms=N~cl
z$<GQJti4up*z!8g{U?%A3E%R2+ETH|^||aYS(;SemDQx>J6oyG@KS=MFi1h5z^#o*
z9n3__A<S0!Sr!-+<e$uhs#-;d^ZHH$W=&=Rni;QCMVbu37Q24d-ydkkI7+ie@l8rr
z70N@k!tFjPVZB*rH)ia{>x$R)M6Df8HEjZi=SXzS^KcJPMer8#t=E9y=Dvn(zF;0b
z{XujL)XZ>IazWTrT$Df~Oj=C#-B`C)yG!AGy)h0Al2dba+pz=baY?Gi@06{Sfg^1u
zGOc=Lat&J9NquXE5gpL8>t*sDq&u>-vQ0h1yHA}I3L^c47~N7_J)%s-O=`cOFagO4
z_Z=q18y&tfy#jEB(${`z;QXozKnba}ZiF6bPVsV{fzM;2Y}=ypcRK`(KnC2}!6+Cu
z`)MwNPhkWJY}Lp3T)l^BjptCuLk3)|t3x?D`{;%Km=Qg1FWR=6_+=yv_At7J9LcF-
z(nFx8wBQU%enx06@+vl;9-QF?R1clSidZ2c%k$rCXumn78`(mx#V&UwKt~<4R*kPo
z)pA{Iq}DITZm2s+tVRF|85_^;&4i&?Ap91|C3iJxaFj#9=-;>1fMekg>I|BgaT^hR
z+G5z8Lo~T?QfcnLYc_2w7!hy+e3)ER9!I4akY8@xGGi@kn1Zkj4-RWMFZ}k1G0lJU
zZ8T<&@SjiaB4lTyStbrhZYU1Frt1CHdYOY|N+ub^Z1a*lJR%swUOYWX^LY}d3Jg^R
zB#f*K7aE=1=|8-A>JeDxAPrJ;g+<)Z$_<Qua1jjo?CdwZ`NWq;38v_;t|O@SaWR}`
zeGW{b=zzA@iF0$XvXaWzq<ZMBC~^H^6Wl9ho6t+1L+OX98l5cZA7NZ~w5BX5{%zvo
zqSC7>gsy=qGfV(#Rh*lhogCwC69Z{KFf0y~Q`dtf-!sd@kdyn)&(8oU_#10jyl{{?
z9z#@AG)Cm-$zD8{x4t9J&WkFf=1sEy(i{;u@)@kuHL?4W{E+X{-o-(om9kf(!?Wtu
z<kjvObKzj|9~A-PtaJw!{S1YN87^eP+-!mtUF5;qp1Xj8+;7kq+=J332YWcs{YY#=
z1x72P)~~2H;|T0*zXzhb>2i9N8;xHoJV6#t1zd4Mvl!Fsb-p#f=CmKD>xQWlWAYMt
ziV^sXzK|N5;D3|24-12w%hN@{gbA(PXl!eY1ZA&{^5vny{&Oozwij@@N=kG}jA|;K
zoyWH;?k99+yAoaWr)@;USy=8e3l$}*2(pk)9)X(d1aa5nG~w{OI93RceDfBq(Hu6R
zS2ldWfh^;CQX4qTC+&}IoD<U#Ys(zkUf17D;yic0&YN0DzLDM|Se<%?VC}pQ)u(=_
zG(_zrQUf!>{fPFBpA}1cqq+fP4$V_Vq58<x8vN2`Dd82r0nxQjS17jmew63EIBtR4
zoa=i*8;)pQ*<LrzmVDwR>uk&v?>cszgw99KqI*eij_6PFXC_w1SdBiisx_;g$y$gP
z9==ojnSMsRx?SW%HI^f)>Bz8g;81Wxjvn(<`EVP@S$wn?+c)!WdHaWIfcuwvHiU~q
zTikfA6D>vk92pS0^B8sz=r5*T4{P$A=@_o-tH$T-hT%dCv3aa9x*PYz+j_T9%@Z{o
zzbwb^LO`{f+n$XvHJlGmyoX-3j|L)YHku9Ued>Bn`@v2a3~357OoK4hd6+^N-(On4
z3TTtT8r2vXE|<(r(4<4(X+S5LdD7xVQgo-NE7WoJrEyw}c`#?1t9GXoa^qSJm#9Nq
z=<(s*z*TU@hW@5^*npTbo;X7%I1(;E>2fPeSe%l1W<7onk|$QQweVb}QgdHuN5|qO
zyWH+SDUVzR57W)Lki;s&Jddne1mVuKisC<`Gfo1_o0*x?>-A__76=8~^-5Sfar{T~
zW5`ea0hjtk^|O5yL9XQ?J9W_~S)}Z$`Upq~k-d6wU3hvdF`%t#w(wL75yJI`&9dUk
z5wmutC@sG}Ox8$#6o=+jOamfKmasec<X{-5p<(4>=*;H=u~xsc)4#s4!Z=1}MbQ9p
zxvd<mm8+Y@$^w_(H88<n5Uw9!@q;I^)2Z(%Sl&Q;!JvHl!g4|ElfdfNSkOhgiPZh5
z``QS#ifg^K*+t>kz45XsAM*iBFp9F#g(BX>0;=w~^}6cyu^afvdCl_5xj)g+Rqsf(
zaj>Q&pc+vkwn(XMy$XZGM}ixSv0)qi5L)Ob(<obWAEF>`kqcoZFS0AxMQZX(#ENyW
zmUy6WZGeF@1#!1Dx@wJgmSv%kcnDnJIQ>w@qR2k#252zlR+rruhv*}Lv<7Ly19O&{
zE>)l2JNrO51E8Aoss7=B$D{S3_Y2Q<hL1BXfgFB<DsWQs#y4SK6?pHnwcV_8KJll=
zn9!>C3Q*}%<vI8($3_(_4Szkr`k0WAP~GTnGA->P`~pQ^xfweib0jG$@{bm?dX|__
zT&e<TKkv|<nDT42=WFb70vPYMmzp=^)H_W1?LHfP**t1|JWy_mcwc=RK~o`Y1gO6G
z>6=CwZ}~UND!88+;5hVDru>ZYx$W6Yha<oQImSFVe5cVsd!etjSUF(vbLOA~5sNnu
zLe1Fi^VfL)xzjvNIpe%+Cx@I*CFla=Y!x59h4pM6ceOBLNaBo1dSL%<I;*xu^S6!s
zGMJc+Nud;a!EIPTtqpZx&b;fl7$?s2?94AwvN0&O1IdfjBh)fU6D%U>_VJOAq}4BY
zo<Nue45Lg7(X$dCKKI{iAe@>o*oVbVO%tZ`p}#zqDU-%N&-?0N7p<YuWk9Fm!Ny-n
zqIf3F0xj}?*mSdHr#1<QPNP{_T5Qc?7!R@!`^}&st9x->np}5tp3-1&mesp_=WrIf
zv)FJujgop^m2SeQmkL}Zh6Q(eJOS_Ea=fUoQ{O@X49pf7!SrF&!Wr^iaoO?l5A9#c
zq%-SvfCHn(F0QgX)rg|Ew>{FZ{aT^gG#s`%)hB5tnQ4>Q(_A*_Ex%+Zp!XJ?NP@8i
zq<m@FUgkeU<<k*`CBKd5%SEzR?R6H!Tp?KEm6h)EE{p*80XO%wgm)x!E^up*N}iZ}
z9~3tYGCX{=!clZc*a7kTD$nD^W@?b-zJJSh%0OCuzC33)+StR>unZ6uoQFDJ*o1i8
za`XIlhYp-k-#=QG81al{+w2LeC{X9`cJSgU5W~d1W7aCb;Q`RqKxXCpzfroTm$K21
zn9AhK2T-af<gr_lFcoU|3n;j9nUY(Tcgp=NVvs$zD$6v3mz#UIaEN)g!d6RnBW<WO
z+c8T|K0|ehQChv+<{H~&S4kKT6*^z`B&Bwl#<i%^GAhX=8q7E}s#9TyI8i)(B3Ckp
zlC<ZiTzwXl*|){E!BQ)AH%1ErpUAOF%fqq$15-Xd{)KYY-bq&)JaUrH3a-Bj?QuPK
zZf_u~dmqxRFZyh1I!7CfR$8XD1#32(>c7C{T~*OnZQii^maPdtut3VX(!C{nYg%La
z%Ukz4o6)MgL`RB~n~{G)i;lZ_7V3w`Lz-p|Vk=f(6OJbg(ec4i-R&!4O7Wu;=b!AI
zEcx&$@XPKp`Z{Xy5#Cfjm*(vo-?+&&VK6x32rMYiHrhV$q^q(-|IKu#=hLiu2^0Ud
zKfqb?S(2MP<z>`}_bBy}pl_pBQB2=sWRz6#e!l2b=_S_il$!`ELfPChla|+bpZWF>
zBmhZkrJ{J7591TOV3W!wpqY+j@5^tcg*Jt@x=MDw;*X{?g6a)zZm=<d{*@0^b1_L`
zJp~{HfT7p@4p%;{7{5yUU|?9ffgfB>)VmF>*v7<WXf1i!N_SU!#-RA_S9#23$ktON
zIWT|BG^1M;8~WYalp;{39l0?#8N~sWlBj<E0ZWVW&!=)0YP!OasPKZ<g-~PuFkT^g
z`@Ny~u}Wh?bPSN9yU4h*nKqU=`p8pT9XDg}uEMv=tMZ-|5w0RMH(2u~=Ak0;&+E-I
zSFFqqc&}6&ttDSiu)1|x{b0r|GQ<LY`S!^I4PN?S7tSc>l}+MXi7KJg&4g*QX}5#g
zwzK!%&CRhm&ov4_5UKcj*mwf|yxYdeY*WRFR_^6(_m3AhikxGPcSdhjF_Euq&m~hX
zoF(3aTz<Ses)x*O1+%(lkHEfqX0W<H!FS`mkGLymNv+Sk$WD!BbVEl0TEO3wZk_Mo
zMZI3^zFe;0%>4;W^(`sVC)SYRO|<6p9R(3iKzibqAT1x!sAYJy97ZfDa~_NF{?%y~
zaFRyPneT?{p<7J{v)rjdf*-}s5wG`($#L&wgvNM+sX{dkpmL%v(DVERT|KyeqNZ~+
zlpm}NU=5fR{-#K^14LKgpNhOB#v~dm-c!ff$&kU1F!gopsd)ZdC}G-H6S(11^JP+i
zoN|LR2&?oM9nt3#`0U1z<BJ7^QW<>iS*y1P7uMe<jIngDFkt>Iq-g+8<i|YH(jU;U
zC1eMxnA0Rm%ZUM^v=At}Z`HPN^q=LbZlenxCOiTAe~nsdv}f!azfifCnE<Exj8lHA
z=?vGqVo2*x!`E~2TsKc-r{Bcyq^J-Y<H|~v@B8aFL(Ap^N*{Ub#_aQcHcne%qmBnt
z+DlRc8HZB|RQzIp5+2tdyaIZ}-sce}B!OdYfHb3StDJ*Y!S&G^OkLM?Vzvw4f|1FW
zov&ad3b)t-gvwAo?_MQ@>}%)=wN9>8nni%I9zuTdK0}?2Fp{ZM_{Tk%$CFrBClDOw
zScmXrRd#FJchBoO%V}{3{+T6rk{2n$2si##k{2eB#gC(v{xa3HFbBb@QdBpHdcJsm
z?}mlYSUnFy!`f$P?+VVEuhv^b0H{r5hZT8HmZ|^|eA8E)Vl`#n9nLy;8qXt+yG-ih
zQ?9q}Qh}^Ro#EMDjgqhqw`+_OAm%kpSQ$jdaotx13ZsD-L4UA9o4N*!=e<m{Bx-*P
zigA9w0U6D*%%k~@M#)r@6yJlkK{sUI>|k0B28n(J#t2;55QhCJbPeKrjyDLY%$1R_
zLo(QIFy#4pbgclC0gij@MwNw1^>UkMipuxAG>tz6gy0Yq#QXp~q)YDX8C|K)clJ|;
zymrFQN5d!y3jt@098@4*g}>p8F@y@Xf{b_VD8ype&E*A3!SiKN?cf^p-`hZQihWtj
zEG#h8zEChYeXg>gDZ<GYLMrd4_d9-}bQOC>=^n$G@|};dIL_9V?sgS~m758dLuHe}
zI7$<TaY3=*LgAU=0JZ8%06~D)>LJAf1R6E8n|YQTr+(=!t5bRDt)1=IcmDM8K1O3)
zSjFnkhLaA>vkE8%H-nt-zmB^!?p`}Jzp`P@Elb^PMXlHcc3=)3nN|y?EiKqXPvV$J
zV&`!pn6d6svWQ+>{~ba5m7tk~)IhA?m>A8PhJG#W@3&JUH03#@@)S=y0e$Vydod#^
z`R`^~;+CR}C8<!x(NmdXJ3IGT19BWy{+wdaOB`F_tztk)Noy!zL8(zprRm0+0}S8E
zY+A>azk-e77D~xzVUCdqgE|_<6F-U9(RpxE7xR{;zMyX2Yuqw>2K-PyvdHmsY#wjh
z#bV#`;}Go+`Q3X=h^cM9*Q7DNHsQvC$fQ%f0&Z*sD{#k#+`S$7R^1i+q(;*D!L3|Z
z7IKMnQ*M=A1iQUGVf*tGh2GZ?{<zVM+t1xDDD;`mmr%iKuW)6XR{shwZ>%iNuC>Ye
z`*+xH)Nc*7RzzOYvZw&QYxowU3<3)?uq;QcH%%%o0)|2BSLXQ{>es-Sq`1KLOlxSA
zyhgAc-*rN<Fh%;NS0>nBDUOb*wU*{Bn=ckB0LtIzkkm&namhc<8+zaKV+9aRr9QjW
zxCm#|vY*NZF%4Uq;@I$hSFS{3o#_t=ha@3qcDz=`jozP$PvML#vCW1jwEz1~=Vjt4
zJz8bsds59v3f!X7c+$jnz+mtAFQfLJU**J$g5XTCuJ_|#_&%<UjBf5kr3Xk#lEti(
zAiYVn_`}2(Mdrwc#}0fCjpsI674E>f6yT+&+Tk%n>gJI-lTc*}=rUmNW5<mBAkS?b
z<&zz%rTA^AdDc|#=(E67&-wf8wl%LX4*}?wfQsa6Gd*drI@5gR@V!VH#v`<Ro2v%7
zTY3s-Eq$)DdO5p$yaM^tr!U3Jg_qDvN9|3z(!jU=<e`t{=d?giTASuk_MzjF%^mUl
z$Gf?x#?IZl+p1OgUB3Mip5gJpv@e&ZHRZ8=u9M{#7#K~Q{cSsPgTG&;fP2)o5W^oR
zC<L2hJjS=t<R6k<Wn5~k(eTV<IG$w-$~%H(=bPHAGQ58_TJq@^r!*gZhKM=lby)ke
zLZyCw-HNo_R=P3YZuzqMsjy+nbzPpDKj`d3uT^}(!&15avd9Wy=(@j<U|1Y08jLwx
zGg=-4rI&QvQMXm>t6G|0NwD(6&N^97pgfDCbKl`>skwTNHDpyFoLl)cDG+=qN;l3=
zY$#e+Yp^%{)qYy{E(C0`D0)24I(=WsSb=85oh<n>Yu)OeN4cmj8+s{cs7HK1G3tDM
z@Az>ixjGal8lf`eZ{m3clm4%>mB29uF`9K-?X<fSW1*Rr>-6dNtuR8bLxx1zBjg{3
z!Zmz{92}3ujaeR=8}F-#=Ndqm|Ma^=%W35R%8i<rOCJ`kjmz8>Yt=uvskR{P4|*iF
zX&2pq7)6C`mhgYpNngm>aMP;%KP1YN{0taVd{L7~0tin{kCp$Ng=&1ZWnW#$bVTi^
zw3EVDPglQ=jK9_THY?r6<R{QR{B*~*Jh)%(={0=QRztVonf^$E1P>zSyt?tNuV^`4
z@%p{)A#TLqw}ay^yI2l+(XYd874OTO6>*M$&h-0F6i1$Z57bm538<w-nBbLUR{4|V
zGT-0j+1t_h;Krk~MrZ}rA>i{W3fXv}n57ExypN}ysvTwewI4Ef=c@8#UNV_BpH*8h
zcpc17a=b}zeIFW+H5OIR^%pLwj8>voL+a}7a*Tf7ti}x0>uJ@SHh+H>Wb=VqnHDgv
ztH0dP+w*hwD|Hy4(c_Y8evW+KyJfC%mYjV*dEGW!S8gKW2*|##Rd%SmOjzbKdc^;M
zNb8=RR-Lh}>+&Hn574jqyFUQ8p)`Yup~ILC^T43WSs~=6Vgo@I^ImH8@0_aw9-#rQ
zPZ2km)`b^8#J}=)(Lj<&265T$ApSmePFFUQw-+(xZ#LyrueU&DPxaJCN?V6JEQ0&;
z;z*4$=qLj8Q#<l(U7L$aSw0;W_m5Z%fjdmRi(280pSBR~(twt{n2ond1LiM)f9<2a
zuZEQ(seN72>H%eY17mG#5>!f9fDeV8<3#5p^VLD{dB|@SHr@4mwhfMq`OI6M!n3R<
zj<L{M%qKXgc0{P~sn+YKc4M{1_jy51{ikoJW;3$ix*0e1gRn0go{iV~F<F>9kfdbc
zE-{6?GwGYvRDX6m5iD=n`5hh=iU5~d<Q@!*&_e5p_yY03D!pmvt%mg764eRW5Qszs
zX}=2QGJU$@y@JXPnc(R?%6Ibox_0nBHA3-085q2>%{})I3*H+Wj2+~(JDQw!$c%<s
ziPL)5YOr@gz#>!2deE_d+g1CZDwKQi=KMI?oN=;zfaJl885Tk_<#zYMDc$BWIRL;X
z-{t4mcX(Jhjgd^V#A&F#hFcvN=1HAR&|Y9``QEwxUKyG}0H>zs?5Gzf>Xl6@gv8sY
zuBDLN{MT2Ca)@p|=stMZ>#g=1lvme>8tj)&1vJ6VZu&o&3|6~tsJ!^<h_h-u*#dT~
z+8jB-dXUlYbUa?=QNlJu`ME}1i@4@`L0e~Z@sl(aym$n(mbxJ|+cs(|9`-SKdd(61
zK`dfrZ}YI4Ni&Gmg&w;TIlQv>OBh`?Y7Kw5L@cSS5Q>TH+XZysg%c};O*tqYRrfJ(
zD0ks%^kzP=0h$*sd*ZO5;6=aluF!?a^UwbZ=YFOkQ~yfacK9qqg>t7`yr!vrjbmV{
zX)6VG+^FkeUOVu+HDpTt40iSwVbZhXm8#EzT(NR2BMHSl2ohj<--+K(0ECS^?I~n}
z*%f0(vuS|j<S>DRh4B0#V_deVm{7bn6IF|@!`0Ty2&qS33|N0`3Q%Vb7g(7r0}Q(#
zcFAhV$V%V|d=us6o_Em;%mSwxu2n+L*0I9K<eZO157p>WzH`OK8GIot<nYG{tz1Do
zof6DCeQH(o88xE)rD&fW$E)YKi>W+{Gh7SmSEk&D_`M}@nvGQi&}RQ6tTzLnT|{$^
z%ug5BkbWqDVmu34iIzP3U+z*voIQFw)$H@WNoKJb;0vy)n@)R+;LQ?lPcML=aL*(x
z`-sAB#=HEj)vkkl2n$pZ49iSJ(oavgT=t%A=eL{M#p*lgBq<YEtqYZqxe5r$mCnaB
z`5>~s0W$Juu$^<rSNo2L)IPwQSlh<wZd_0|Jlu0<_A$BjrY?2&VJ@OF#t{|~$baZ}
zp$TtI_07sq#K+Ybb>pS?p=)q#?qH`MiC=tY#7@2+wZs?xcx_1IqC6&7fVF=LH(`Eb
z@*mMlYkV^_wU{r5n&m7M+S@ySwfE+I+xt}8%Rw>pnG@S1-<aqVl5HW*ytXi?=0Mog
zigXkWJ!#67dv$ZT75UasER1OgXLR~XEo$q!V826k43XH|j$y7#XSKOkqySdy?y+Di
zi1FM4!00&zoO>UwO}KHXK3WO@$y#iiFReOj-@M@2Rrh$Uf1WX(HMwi<a$MQJ#OX5I
znS2+!n;L20Pr45m4Az|&j@c$Q{-OLB0(F_juPIBFhm~0>OIN!tUum%;Yecxix05qu
zg7<i7T}%upC)MNCc%K?)gA&>7N{g2e%vOXfD1QDNUvVABJ1Qf)ltLt^4EoYXbUP|b
zg!|K~r&a89SjuyXCAIx$yxOH>C4>20cxjQ!tTmPKJYrFH>^fLBpKMj|FdPhwIi9)s
z3(M$1|IaxOGgJX4mhsS^h&2<UjAJ7x1Yt=b<JiMk_3vCSFMyehsr*4->|fvu`tn@u
z=Oxr>%0^*>Qs4UPVd$^nFfQ@?IjBWR%+sF{c8fzOVcH8we}hVm8I_2C7DYlbcYupr
z2G7Q(WiDSOuT-K0P+h?6mceGkNc3gMzv@zbaKlgxgNNUX&>h&rJ|oEC92Nxu@PFK8
zQ1so{%gr4YVf3f&J46UujjwJE5MsZA@GZ(}?G+JK(L>W?rX%~jrJHIrNFD`w=*bK>
zNf%4;md=yHXrzG<Ctc18M{Bov%HlA48KK^vf3rWRj}tqLrfGYHBuT0ra6!dde}cKq
zv>=*a4-5{^_M2<K@&1`k?`Phr{uT|}6jQ&(L^ubvLyiV-nB%b2=MXwdB1gMPEI)?8
zOMQ4I5MYM+Lv*p}R-NaeS8;IYT71rn*{zt;8Y|;cX`WDFL_n((Q;+EaT2-;f8fnZ!
z9ZCGB_usX2n47|{d~@LmAQAa*o5xU2gNQN?BH6g@#~D8idhf>29X4M`6x$i#b{r_P
zA#I#mc8Xdz4tvD$r05?wRwEAwBKbl7R&<TQVfK4hum(v0MUc8?JC<h`8@5S4h$Y9Y
zkvr1E%ARpxL~qfB57Ci6p{U)Nc@dzIj)~7G!awOQA!8{~TM+;_I0dWHFZgl$xV-4>
zc00+e7*j4TZG$iiBnW$HO}0emvjs7C;kGygq<;Jm0M1?Apl8lq9B76_*$6zRAtL#o
z;iE1t8YELN31Dzv!o;_FVU~7t`#%`yK~0oIa2`1%GY?#^nBXzxQITM+*(@#VND=Vb
z_z33u*`A2W^vHU0SqeniPLn*%37sE<V1s+H9@qI%+H6tE?XF93eTrJreC{G=Dc5;s
zd*X_~GkA`!5t^b}i9VMdw6FloR1>$?Gvc!hPjtn3B`IOU=zgt`P%9&lQg%c8{WYM_
z0y{Boo{}3<uRv*Gg)b-9U-T7p2xYfI3b-Y~Uyp@9nQ>Mqu8f_vJH1j9E_fGfLs+-D
zt=wyl^b3ZjO}YP!m)q&i04;>RzoEU<Y)pHK3|yPyhXk181HfiPc^*0ND7~nok1Ds_
zU|xH;=4D^k99IK`y8`u+_76<-u%Z0&e@Ue0`&r<@PFxLnjuPh25k1pm>vt>ge1rbT
zczW8az67?c81Op1o|IEntj#Y92~QDUbiXT$mT64qqWEy^gv>w%Rt3rx=XD7<uA77?
zm#1zKOp>&)5$Q#e*pYY34Bfl=md9Wl3^d&KA21ub8EL`n9UoR8s5t#{f=PyRht3$4
z84buvcRE*-=5ulaWpkF6WyQsDMHQ@AnxB=kwUbd&SWgMml#`_$ufQ-+hjAfKTc>K-
zN88DYf8%pn4SS{u?P6!@agT!mYNZ=cSwry?lvue!@#9$_66Pd8g~#bG$AoK(4Rgqp
z?e2ma57fG%LgQjCw-|d2nsaOM?m{=fP@=?y)MIp4bb4_&f`QEnwGJ4enotITl(4ar
zg$%?-h)dl8Pr3e>7Pc=q;O(%_3>S6MYed)xm|P>;7$witNZ}oJjIj3iI^3(dM9x53
za*tQV`*5XaQJjNsWhOP!yI70~6P68$3s=2DP7*Bfyq!+mnMqaA&@)9>$WrV(q4lT2
zqnPA6mi_v>$FU<ol2(_Rm^x#me``EffW|W1O4d!n&4}4P!qEqNk=T;=Hb(Gy)yebY
zVFh^?MLc+=v+8Z4V^v~6wvF2N=J(J{@J6A<crZ<QURN)Mzsean2<u|Rbp-s6=9H==
z)zXuZ!tE0}oaR0Z2wJ3H@*3BBK2JeF+PEU|ZG+l9AlVm;PRCIJkXmU%&MHORmqcAQ
z@Wkdvs#mqO&J=jhr~b;FW846^hvYRF01juCsZs7J&wI6(GWI?${*Mp01JaYN&c#$V
zc073cU@|2^X=O5jxaAewjt}8U^|t^5yQC0_oQ}t!i}a8!sKfNPRO6HQ0&7*0<Z!>i
zL=9Lod-kYWTV^PZo{Gnl{sd;9KeLF$xf#&XXxO|Gk6>eH&zru7`1WlduBbfN7Wa$X
z%7Yt7Ki`hsh~44P;clZd9EtG)5}52Z*iT`aAkNmT8T|Y+t#&S6e%FV6gFYA;h=z{r
zuRr;I5I4QxKJ~LgtW;L?d(KMnwBZ4wkNuF{JMHx*u?gAt8)qef*eU$<8tM;;h2|9~
ziG9iE$OS|z4+reD?w!KikDy>pL4JmLMa17n0Hd?)R`DHObgf@?HZAq=Oui@&5xsL;
zU-v$CQnLVUW=mZh)Z2hk(c0_cQ*0;oubjP&9_IBxGMD<%w1+b=GV1;F<qSSW!#u1k
zY%ACRMgE!(bOBbFLV42@0hc4^bR}3#o3`c;mFs!s=i4@%glQg@1h(QaIf)<`AE)j@
zVz%Fo=mi_n6`+jL=;cXVN#Vci=VrTJUBj@AAC%<z3rFUqe`gV^hVq-;(-lt~5-TDc
zeoC<gvdq6CCJA)y5P56XCGsJ|d!MI&{q$>&$;De`v7$#pivbd5KfY8?j?-XT;<0GK
z9r_L4#v+m3X@~Gw&+w_U&2{k5aM9nvO134@$_Q}8@lVI6_&FF_+_ND)_-95XlO~Kz
z70Tuk+=vm7aP{!(5z47PONeFqY$dWimx&298dSBqT=7sxkbO`y$mDr>kGgSY{`jhX
z`&GR2o#aRG_*&)?w4q=kv1d>E-%Vs-7BKhaLkme6@CS!_?9Venc3&gA<}(Fa5}A`&
zgf07ri%2QJKjjhgrWxk1DdmNEM{Y?x)c`zy%ictPgW<3Yso6zOE-ps?5R;1>VV3ud
zj^1;&20G$=jMNgE&tho==A*R`lik{nlj$b3iD_=>4ZRS`DN$>0;Q*88d2(;6X5LJ#
zgI@or-GDDi>yEdflD&YgL63kAVn$%@(Yh?+MhqS(0d}X1VfBj6itvzj)Dv1}YWX&f
z<{0Y+tzq?h$x*9^@ZVVI<XL4{-o5b{_jk5nnp+%SbH5<S7%g)Lz<b`koYC!D-2fgg
zOabMjY!mF?wAB#Zm+HiZ*X{k_fn+SCZOzVb5JuC>p?1I}ybgyAm6DcG(HaoB|AeH?
zUAIU1{j7g!r6|iN9T;5CZkYxg68rwNvT*rqeduDVTV!7^B?GQ;V}EzCUZ2~U9_jwm
zfQTn&5}=Kii!b&!7i0Uox<$z_j#FO<mx5`?5PacynR@cWzAu-nHLYKyvA$G$vn<n7
zq3QBcrq$Sl``i<Ei71O~cY_=7(@V6Ah;yKA6$S;EQ$>%T9VuUYYz$HpV+5>*Z_!t*
zR^K$lQGSCSu{tGF=CInMJ|$rRy?)v?(YyB<|6m57QLXXFNrnix>t?g;+CH-*(;pSR
zPGlczHiI0G@i?s(29=Ek^nd2z-8-w(SVJyB38>M-gK<c@kWUKV#p;FHH=wyO1SI9%
zTiW{XpXrH&hT$BG!Z2)YQ(?MzzS1)x<rK_rY7II!Ews*hb4){7ZKF1oY58wi@%ZVm
zEwp#c5hgdjL1;367;`w3!#1pLo}5KzScil4N$@t%os{_3UXvzO`M0UKS~?G5oR<(j
zihs-wZ+V@N@Z@|3GjbO7h7;wmUwli<P3Vp(Fe1pmwKh(^ef)f{(naUKtIQ38@Ok={
zc0=!_U-p^%#ZBt#Q{BA~yQQ&(PlNq&t<8SUn*Qd0nD#SPLR3_Dw}yVNS8v-5s=Q%5
zW9S3<EuB1*SN#ulhEh>O)UC)A_LTDI2{pm<bVaV#4l|X3dgUwp;^V;XL4jb!{I5W#
zWQ)|C+yrwYFIy|Ia{(Z!dBbe{V-Bxo^(U*QLVo_iHeOb6Oj3XoB;^eX2Z<h!&9jzk
zw8**1?udHWcT9Rqd)_221|a$J$;3N+3T(V21SK88ay&#;8XaG;M|4Bnuit@>$~iah
z>dLYCy<PtcSU{)0{K!~x##%R+$Xy&_B?ODD=&Q15i?#B*FXa*8+gs<VF<=Vv=ei7-
z#QtD6AB}!@C^7IUVQ~+ywlN|4KGttLD>OTEZZeg5qh&_B!Lita6k$hLSI_9heptR^
zS2SqG|A$asFlAWB-5nPUX=6Xfg+Q;y9%3A6?)$`;J@6)@Kb&X;x!0u4<O}8UW#msI
z4FwpTs=LnH;rkao6t-_ri*_nRICyDAxxXJVm;E4-(9*EymmD$t<-9ZaIWuJ~_pPwP
z?<&*b+Kd`isy~#1cAEga9N-`m-!z;?58AnC_}jyY!GEC`#(MbA=Kbl9HH(d#ofcNH
zLT?Xl73?I9u=pC`JF17LV>@CyfAEtI=S3Clqd#R(jQzBb%)S+;MX{GY{TEq%<jeWg
z$dyqB1rNO))Q=<0--&@18+4??^m{7Z5#bxFcsUZv8o>yfFtyBinzZId(*&}j<mq-g
zc^oF5Xg3|-Wl!SNUF*JPJ7^fXb4-9;#b#9aCwvfnATV+E?+>PjMm|n64jN|RGEBxX
zEv3=KI3}T|&#*G~a!%8*94v7E@g3C`F8<UKCkBbg3vjq2r}h1@a5QChflhIFyH1m!
zbi%@7TJrfYA{8l=^vF3mVW&>fQyT9*!GGMPXJpQC62Z)LIQ`xhc5Z7T`Eo2eo&e2>
zH-#uCnMS=FX1=7Y!^^32b;p3oMDD5Y@Nz1-NiH)*?v)Es`6G32?oAc9^u2ba{B_){
z2P1gjN5RX(ffcmK%dDouq<oz^*CJdsW%L<l{$B(yp<+Wu#?g+JI@8KHtK|+kp{FC=
zInlw4t!9`1Yr0ncwC0;nO6!GJXttgV^bj`~{=S@2*bkgP3yYG;AD`9xfm7{;W7<PW
zN4zQzIqzY$#=;n{+){9hafs{;_$mZ=mk1+nIC-#x_=Kyq9yFTvXj-?=Z)mNbfbW*K
za80ILY5au8D-7Mgu`VWGSmTilm0B}T&1$LRBg3DlH{H8<Z5sp^wrw4hBUl%cgztUd
z2MHo&d{2USSa15pcRVOKYzqU^YVN(Q(3{j+&L-R7*Z<I6aVoDC7Qfgml`qo*W5m}>
z@0oW?i_4L+7wV}uC3JHbM<wrX;n)RE+Hs|sTM8e`?L!O75HH5Trd^6J9xDush?sBV
z=y%&qpO7$9XS;gqM*-R?)EYNbaTj!}9W0<jFL$-+m$Xn4151d!dx-mmU~PJeW#9r*
zzjbFQcW=vsGzUYwD(_PC#mjpjxTVteh7p&dhpH>>yX58j%~oPf%1B(;&>B~w!JU>~
zy3(t*Z;*F(>)H?w0wht)N<+-N0;4QI^rI6Zbqem;p02mtySu#1ZFL9ZR5=uE@B9A3
z<8Uko-xnQTWIQ5J`+W2_<F1%pTGQE(z}f=>8@z+15iNYVT`GM5S4!bTZ1odmuks1K
zo5H|^G|<cqNnx+_bob$AQ*QX;?S*X7-?4k_Ll!J0Ps#8k6Hf-gCKlnuk6cmYsVwec
zrDjMtSvg%F9;c()Fw*j_V4h?<*i%6&Pv(bxraTcRc*=;Y>FoE4!4w=nAH+)|L8su>
zHn7j)=^Vo1q!`Wb@)Q||8!{%s<Vlw?JQELp+>bonp%-r*13nKcaAkNIl41IdHC2sQ
zO3K{;YXO^sfMwUj9mo7Ukv#RqEna{p8}q8p$2<weyjjPz7h^k+EW=V|MU;f&i5+Zy
zl7by4c0g3;6c11IF;9e1?sGv!o;c)*C2p)|9k9F<lITeC)E`d~V!x{3sXNpo>E&(?
zXn?T!LIcE=$6SEYR!V%Nlp;8a9!46Th9u*1QqIFGq-DSRkY&nFJp){Bp<o?xN=p&P
zdXh37?~G?o^hq<}_zoK-Ogf#i(!Nf|6NBI@S?}Znj)gqAhLJ+U$v(-mOv_Vg4C5&-
z+HI-O!%1FVgE~Xo(eY$kGzWhbere>&c@AyC+ffIsD?0KJ+nhY4rsSREGJ2msOv1L!
z!N)-j$;<2uI5D>*FZc31_jt1%*+%4Vwk7$S*PgQO$;%2K9yM-l<f5T!M{KW?Cq#LI
z69;Q#{jCM<4?`<SbB6$)cO%^}(qIdh<n5h+<E{bbi~cQ7ld^B1?;kWTvn^O=(#8{?
z@qUX}*`9Hp@17!D<O{Yh2M@`+poKhu1<^(RCeO1@xJ`_-^Asp)M!mq69qIgh7Gwb~
z3}jmT!86Q(;R9$#Y!Q@w0BKl<@*0IW?*ZMRI(5k6y6di!hb;K<OrCJ#K3{dn0<{fB
zf|U+SL`xnoepIFqd~&0`yte>0G7O5i=?tR`HW9m2E1@YQFygS0@LyIceU=wTg9BVI
z3z$wi>;{z=cdLRvDzk2SHVU_m;Qk1VOk^+Qg0piFIC6ikN-rA^VYuUVA2@WbAnfD|
zI^>-YJRt@sNsA1_-L_GY>G+=xWx^i=heD!RhRd>PSQYKk!7bR#7^k9em_Tk!@0@7s
zhI6+7tf#wq>Wf>pQ3k@|-Um6=V<(mM!faxW*@DR^6rzQ_Cye9<erl1-lIbKIFydYa
zOqD>K)VMX=1jjw*>O>wOQQjd`cN+z(aMhY8o3L37`D;UHGo;0n#K_$Z;cl{nqKq_y
zS0LO&+<*q1kEfzE4ho%nJ6Kl;VYzteJnr5=Ko=jv2pexB=CYI{Hw=IlX;UXpAx{bc
zc5c<SOh-5jzQKZQH|DHq*cuef@ttKLEl3OcIy}&!4=8kY!e~ylFKXaXVJs_nc@+4j
z7V*(kukl17(Rc3W=&Z2e#yEVbMV;tlW00@FE8RmZB==E{VB}E<A=)GN;ZgLB#gk@C
zxIdmB0N;X7(H7(>eEtf5U<G<q-#{1#Lw;d~ZGv7Y>ncPYbkfFon;>)~5aHOeh0mXW
z4_H?64<aB8gFw{_At6H0797C#D}ZHr0SoyMl{&nPbf6jO$jdUjuD>MDBM$2gsO4_^
zvW2mu1_MZd>u!OdOCJ~m_0_|ZxHkrAJ10{KNAA$I5rlDFLZ)R|(T>yd4jBo9*3ocT
zRp*YA{q<3h^;qul2w#rj`*$2&aEvl6!6x?Nw2^sMV7G2{-TIfgx{pPQ_oU1SpFpnv
zKM(s=$jYc>vItKna%&bw?c96%ak{2z)vpQK?W@qyG6<&@hN3U&*x{t$MP;wW1?`vN
zM%`*TD!w7a9hKeA%_#!mBx8cnP6ping}5qKg1I+`?FhFz_FdL*rB3jy=H`lD`XDYM
z&l+0c1N~NVlV>&VK-h;h*F4?P3$HVhI&EQ|8@29={QBjguOE}t`d(pK?!C33`OPRa
zPO|m>w<MF+2)=)IOU0{x)bOGvu8>^gh@lUq6V^b>sQjqu*8Vm0&6AQ^|2tC&v#)8C
zzt?gbU%~W;0+@oO*Rc**Utrb{8iI9hMx@Tut=6@*!2JbE$dgw*5-gb-<sAbfP2j}R
zn{#IVxt`OwsoE}oJj4QmC35f2S(zuhR`rJ!xAsLtE-E-L$ZCl*gxR>N?g#f$$k<W#
z-8n0JcH6G~vf8eEM)y*$Pgv=*a4^}0psM5c%^ycz>!(qed}&U1jtkAkw;R595AJz;
zen#uNB!Q3Y)%At1G+XT->7j8#TFbxGu&ouo=}%+V-4_Edd>C|6uq-u2WjSQKp&WBb
zd>$!nCys8I<^OE7n%}{_!|Vj?+;rqU;0YJBDD$SgVf1@??O&TdHvdQ2OEXsHEXxc_
zwP@k<e%rr|b(o3<Kb}mQxr#RbA+O~<489mkYlDB2NSY~xy$@md#FM@ztqp$=<!}&o
zsn>F+j41!qah-Yq06+jqL_t)cH0-^m=}lL{(#OMgxE=KlO7kD?H`Aws=O3yC3-5~B
z&V@NW^PDhgWJ0ZZU)!sF5+*1f%f$hhCHa|62Thj8&PA?IjC{^W3AYjz-ygKXY4Gx5
zJ)x|M=huOk|Gvxf@5Cl4Xm9p&3wqz5;n4d;DJcFQ+)jd-f^l|UA9@?aTB#NkKM=HW
zx(g;QoTxe@EwblnR_izQ!2Kn@?RQZ>?3*I(JyP=L-W|E-nc(@?#Cbjh&qsdZl76G_
z;>c;=Q}P$kU$lg!W!{)Hlc)KX<_A&rXRg=!Huecqfuh!~oNQCPpsb6$42{w{Gc8gt
zFs#;%;AP(H!jprnC&KTS=h5d=IFa@?+e&Q2LT5b;tKZ{kN+Zm@x6jHv)wOEZRNU(4
z$=`uyKL<SdS|_DjHErSF+pYFB$uR#R%XZfKW>E2i+P6Kg{c9Ymc^~-YDV|;bb*)|g
zWaKAbmNN1$bZnvFwB~o3Zu48XIycUHs%*by|G>f*nNwSes>vw-%7H}w+)`?0dmCdS
zR=+PBGe_U&54LV^*K7YzUKOLulQSIQ=rC=KaEXnzBBk}e+|JqqGup&eQJr_rYP-;Y
z9jHhB<r#8LC2@jGth^$fwFhff6pe=VkEe{?&kgI;OvN7PbG$2p(+bQRmj6dOXJ%`r
zW{if`clQ{3USu{>r#L1K514~*=}#HSVx@Sg?Iur_Vb;)F3;2TPe0kwtO+R&7!qzar
zCywh+w9nkru3es(ANkB^Hh+G-Z{a^0bB%9$Z41YSlPB7yp4QFu(~OiBE|f|iPkPJ0
zJd|-(Hf+CW2gwr}K}{sQ(a#R0?Gc2{J3;cKmM;(9@Z=YSt7JS`a0*hq(tH6n65Bby
zDnz57Dr8fqRMWFTZ5CH=hDIJKuw_OHENY|p7F_DJea7gA^?Wd|g{jkqov=Jr36lje
z{0@-|Do&I<t&j|>3#IxuhWurp&J^qw6=!m~K3lmh3bXGnm?Q6r^8QRR7&<j!2fJn}
z#t0TDPJ@xbx1+=fT1xjP7g|3ZX^s6uvd_$y&D{@(S@)hON}Qh3?UkBoj%Dm+Cz;mx
zkE#{>DMMP}m44cvt%m`oa<##R7f|?e;cY45%URS4bQ=i4fy)a{;ZIulY*OA&e$2M_
z>{zI{x8uSiOwBvy0hAY7TjDh<^s!H3mqmHrZ+;JR0~{euot84J<v2Qj&VcP4TQtX?
zqE*MfmCvL$wVfy`X!bWs*7&JGdGu@fbn=*%gDnl3eFehKjLOTtn#(xr+vs=&-M+G9
zj-TaMB)(-4+IGN6tew|)pYPZEK91cTFOM?b?skxJOnqLst=vn7&FnL9Y4vyj<BE$*
zk}-(~CJnO;>Seu7?vDm1<Mddx;wPD`v!doi-bi5o_l&vgnO+U|HftI$nu_!Mw_`ki
z#e}i@f=qqcXOdanY8(2~2X&)=R-bxhy1D#|nXIlgaPRY|uvbr-yPpeIgDq<GvY|}w
z+(vftJ+&EcbDy^ClTJ<xU0mHdh?(O|wRoxNF7o^)@ceyy+V#ux<z*jpa-!{O>9Yp_
zH&-pbJJafaZ6R&+6)k_#_T24lze(*e0iLArr4XfKn<`!=8{<2PJXJUH>A_6qDb?)k
zKh$UEuECNnZ!RQHup4mW9yH@co<F&onGNeRtzU$pc}kxccx^Hn`3rhbgfjHwmYaEJ
zzd8I)Ef-X6EP8{#YYU~?*ZP9df5_t2>;~?@PKNfzw(gA&g#E8Il3}H+g+<e={g>-z
z-(N6?-Vx@!U8X<yj+7J5PL;LgSXVttw~PVDOr5SdyB}<py}O`ecX<zl{Z3qblDBk*
zU8d4tZ)x0AYm`0_dd4w^*0{PH)NXdM(1KgPDIHjstXSGdVSA+g1@{4q;@eB@+AVfN
za~?Y@NdS)X;sX&;;*vhYUe>mz-cg#L|8K4C9G`QXlflTlkVp51Ees1$@-2DO8f=*p
zFDn-pzOOYBPs%0ip_Z7uV7fW={#Mm5CwzM~zmCxo(=VB7;?Ci!UrqY<I>QKeHlkgp
z7t7;kin2H7d(B(;jf=o2Ur}ziwjqJZd5EUDH`k+GPnoIiJh3$&)F3QQMaO>uwp$(V
zIn^H)>w7M&RA%26H8bzc*v`?eQ~my2efOE=O7Wf4xI2S|EF0^R8l45r4<E+#9}2@@
z8>aoJK7DOVD}ATZa(8RcIyiNJQ}7VdT8h)hM3lWS;e>Mw!H(nFbMBNGWS^h4QaCml
zR%V0kNBdJ=u`lSm1Xt3-kXpK`*7Rl#PnTa6N{89=?SwZz@9x;tp7Tp~RQPk#seGj5
z?mVHquyB>u^d{VP?U%Ho)wR+OYYlI~gmA$?hiOgiYsGNa(~8x~b>Q|?QnSuPNjS}|
zhJNWk7wUVS*R1=$0RzJ<AhlQ3Ib}h*e^{N8Q3_G!EG^l*so2<loWCHPq>+Cejy6+-
zLED!{xG{+1Nw~LG!pbiiO}~N5@sV0bZ=ZYDd~5v7nT3Tbor=9K15pnRf1{D8{is;m
zy26>Y#`^5c%L=-4l$NZ1uUOr>JTYgl>a)`??$hn{TB7=mVg+Hd)|!4N`^vzWys+3D
zKYezgc0;;mAD0F`TjVd)Q{K*bcj_-n)rs?5?skyn^>??tiC50ore71bbeP+u%^8~9
zl8(F#EzrK8Pj^;%_Uzx57UsSvs`fEiCwU(BbnQVZ-UJA{qvZLQgXc%V^X~-De^aa5
z8#7Mkt$l_y=sLUC73XHZlBuN5$|szYX@2mH+Rq#H*28++7>02XC~_a{x09P%34Dwe
zGc!F(yfOy`q-AY8syMT7Ri>7CPBs;_Yhj${>%R&c>G$L<BU3lG9bKBPUYo0fPe3`?
z4Icw<-BD`odRiIZW7K?OIixR@0X77A6MU;Bz{`8U%MW*X8Mj#*YhA5$MWxjm*V>Dx
z0SV6*<=OzA$yNHAF3<0gaKtL~91gO$n1o$hYVJC>Ty6d^>8GESN`$q!ko@h=^!d39
zbvysjgl!JB%!U6b)^?v>tyDfOeCtG*f$(*l%GIUDu4mS2?OX8SgyRG>=vJ`s6&OHW
zBTO7G%*x-Fyz$d!N_*a=m5ly^o_tOO-owBmk1dmm-;Fy+iAbhL)!AS+G9iu|y1y=k
z?QvY#*y$6U1_6+qT5wvyMI8!c+Mb{tEU6kWy4RtDvYnh0EG=vr!SKh&a)lSqCBw4m
znHy!ZL#qM=HAXmlg9$h@SoXDLefjf?iJ<DZ`f;%BJUy$8{d<4T8m>6_#vBY4_^`!!
zIc@png`71|#&_JpHf@N=O&j?ZqzQJ|&%Q;pjn%GJ$M@v?D&{qE3l$kmW`(UcNeACk
zGb&ezmUC=UH~N}R_QJl@>RbE!`akH{nwb#hK+DPgNg=sr`#|5oM-3~iLZCn^lr;xo
z#haL-b}B0TW4}H613NSH1=BH)!4CNCO;NcFU){iB7B97t4xYR<0-A=r))M9gGY7$J
z^+)TrJA+0B5ddi4+4QvRpt0&VdcQs9n?{bS6%A4TwCiC9M+CD9oUP&0p7<=%ptf$i
zF{oP&82Kobxm=ep3NJBJ?!85~`C|jERuh%)`nU!k3m4RM;KHqe82tQ*J@85`?@ufQ
zty^FcoN4l&9c}2{gZWjzNo4i)C>t*~;i6Hu2u!P-)bufU>T9`DCnD?UF>BrSW`ERl
zJuSf%MEr+N$y>%~w$RpNZF);3YHY@CQv*F6gOKFJ39Pz_l#x9*NVOlrSEjH4(Rw&&
zr!Od^SKaF6F}c_5H8rjIK-kK>u#j1Go14QO9Wt!-VART9+?QSTKW-kjdtt5TK<DEV
zAX(g9;U(Lf%g|%{BeT!9osC9v#T7%j<v&l$8!p;Ao1s3CGFM+eP#F0yY*cEYZDFft
z+b-$Y2T&a6RPwSO4qQ5x8Thke2A{QZ%~jo0G%JCZSzXTYfNPn-@W;UOe^N|_b=!?9
zmB^j8-LWnCLg58&O2^kzVEzr;J5z4qwV6WZ%vRccumze6df{bys&;j`?oR8_G()X*
zWi9Z_Nq_ZS`TpFw9!y4NWS`A^CC_69R}*vZ6|VO%W-hg~zxvMXK>BpRC7j4ewlsIQ
z8>XHzlv=wzSIAu&nZX_=WQo|nQEr%_qh>PVes}Wn@V}V}^Ca>zPJez1pYOs~S-{Jk
z+F=?FuWVcVv<%>O#CV=IYq(%{GxF#BDEFcvd{;5o=bY6t8aK9Z#CcFBe>*FDOUH%e
z)+j!8IMi|qe?E|2{ZJvFd$TazYw>L{?7{DU<zRO8J?XrCT4+?ihrub6Gsga2!>4|m
zdh>277u+kT>w|AGlm1*atbY>U+d|O<7Y2Wa?x2OQ<c@urjJ5&XF2uDvJP}h@0|>D(
zTiW~-yz&pGU~DA7d6U>#V1~y0<)vEvSNXP{!`@k}VzcGt>N8fP>4I74jaHx+KV7ai
zZplH@^eyBa`M)ZKJJ(efTH6Pkh1bwXfzPI4KjV(I)rI!1fmZ+PrFkC0XF#OS2$PNL
zYS^QIL;X_l(Xfx=;7dmEqVPd{a{eMrv0oz6rLWCa>)(PFkn!=|G|iv*<AsIVZ3#E`
zrw-PRJ#50qC*_`6TBzTd^s+BXrlF}ta>0$dgscb9k9;gl_Y*6jL5^XnU_#0<Lpb;m
zE}HMuB!mrz4gBp!bAvppy@73RZ}3WfEg2d8EwlW0v+b!5w@UT9G%!5sq|9^BKt(|Y
z=S{O^{0P=zEhyE($v5HJch}MNgv%9{F#Dl`NgMV2XGMct$;2AI4o%X%hvhwWARQmQ
zc=JSi>Tj(%YwduYdxx-F-<}J`Pn}rU_N>&Ly|IwYTogg4M>;N8E#*)axf;D4+aoaJ
zb{{Hx(;u|Unhnhj8M1<+8Cg`xTRc@L9r^{1c1Pa-fFg|O0WQZ`_Cwt2)0_`?o#Wx2
zvX(LVtWvf3C9l@r4atOksX@6ZCeJTdi(f_91f><~b-p1co`tag@ayiR5z?OSZRMg;
zDjfS|J{|0yb@#l0o8?e10XCIg*Qo70Pb)horBnHLL`Lbo6W;ctXJ!|!DbzC0NI@V&
z_;3ooKjWQLKETVn!OM22mH)F;uUwOB>e<i=<slF70zM8PcWVHRPo4)a&tG1y*RRX9
zaX*L^Y%imX)l%ihQOhr5wZ7(t=864AYJD{^{`BJ1!Z+<o^2zDcz$f|)XSwSn&%`OU
z^;$Cff^<skDh9hQsg<hNind<?A7f*c_iOT!<oQJ7S9O$C*WB+GgWa#Im#WuCfJ5KK
zE|&mnIa3#nH@2?wrv2JLn14~kp)ch61etU%FY6G5j!3Lbk(V*T$@U`v3WsJ~==33Z
z_qXI_rUnk0ruI7UJT8ZX5Q*+7YrCJhtGacCKBJ}kjl%zlQq_;nw<bT_DAhN2_&Z#{
zs0aIYFdw+vH`I$5U_{|ni9|S6jCP&1yRvnKJ!6hyrt?Cu^-G<EKRM|?n0F`qS_-!g
z8W?iJp#Ptj+0MF#HSzLF*=N6B3v#*MAp7WE!hV}i2QPeCmkn9%5jseee%pwdIo)!c
zqs`>-<v1C{sdU|m`ad^nE&HJbT@g$rv7-mqk@#Q>MU4mloBxb6yO6ip7y2pZ1l#s!
z>Ol*Jd)_#<>}O-C;jc>FS~R|kPe4RiaJtA(I>+0VzpLVVw`9b!@8?s)|7oPH!M5q`
zz-1x0o@?1v`Q-4|jTFAuW_o+LP-R5J-$~-jpG`6ML0+VYUY>MB;ghiatrkxD+*CUE
z4vg^|3u49P8GISIVOT4&dj7p4QTb5abtkg!@aOZn<WbQ7i>;vcktS~W$hjlm%H<PB
zK~tFWJP4!C7BUJ#Zd=1~A<e95v#*$L?>xCyoxHHw^6v{Z<E)Gr`bwYUj0q?A;&eLm
zXE2NG2qN=^!`jMQVLy8nhqtC^-rn}i<(8TMGc1V=8D{h5mS<m(Gty7dO?w$WjHflj
z@)e$|ADz>OKbLc05_J0iJe{_WZ{XW*o@=c$?EGu=go(55&6}I3gRB^Mm6moNsI(hc
zM5F!4zM!BD{$&PVSJs@tze*vkA##6LFqVBMlg^#zCnL8Vwr>_e_R_qy47S#>&-*z{
z?fmwGT!Tg#p@%Ky<W7|=nW7|G+aoV=l1SwK*iL9>AnL#LA*%E!|EV!^#ji7IeMJ}r
z4+SFm${{0vft_$x>dxquNeBQf8vXugDtCU6#}U$C_$w)FqS8eEV@cZ@wsd<fG@w0Y
zxBNLREPOIyTLYHvtP!R+UTIgpq=$u1IgZtDK?{xyZ)dq({hA((T|bQP>v@@A-VBHT
z4PVa|^P+L9)kvJ&pB(*XBONsYzkNG2*Ut|mM!%lVV!<-y?{*=&ku#^NT_O9ZGD}|8
zwANS3QP48O^aXw@`6S22AP_YFo4lNfMz3C;9QkiuiY&?Vz8Pg-=p`l3PgR5V4UQQ2
zxaF7`TeC(3y>Uma)w~vy5_GtM4?CH}sgV;^Ouzco*<kX+^<w$^b^{-X3fvt{a9dJ`
zX~{OTj%E$`Tr9>d2SzkPp4Y+iABLKQ3bme@)blR`T+Y$V0avU2vewd_v_AT+fz-%1
z&72Lhp8KHO39BomO7e0DUWP@n_?l^V=gHOT#Pgah?>3C9r>DdqZ2I;J;bi|PmCn8e
zi=!BD^=A!<6}P}9{a&0t!`;Z{IUSz2$@4Esr&E8b8P3INZP~XA>Cx{AtoXJ?YqN0e
z=O(T6lW{HPD9%!7t+4VF*Q<WV0~7X#%YU5AoBa{??0SCU#W`cy6@|>$HDOM}f>Zmh
zFu=lFXbxqJk+0|(14@B=f9PfYa+x*sW~?JDG{e5PXPo3qaBJIkR0PeLVBg=BJ80pH
zuRjR;c+*L}FfxL1Eppa@$;az{^G=*I8PF|m38FBwH`z|&7<u(FzM7zk^k1aS#PNX*
zhB&A)aWAhnMA%9+{Wo4kOX6Ch)<QV_KHA+l!}Ekc<6~+<NeSI!OF5jr%q#1CS)5jy
z52xQ6RIw`nrVdlbci-&#wUf=Mmx+dPDz-q_mY&!c+V!7LHK$(+Nrta@x;tEd;Y;Ou
zum|&6?$&6coGU}4aeJc)`)g<|*9{)Ytlz%SZf+5-35LQ}JB%jPXy#*;8V`x%dvZbX
za~?L|RkWFp)$mz+gw5eQa-V8pTR%4XLAg@bL1IXP4i3s`^T0=yvBgRbOZXTmZWr^6
z`b~|7U&aSLmvuEz%t^f|j6)@E854T>gJrjLg;{rw)11b&bB)<|LG2sHDW3I?WiPK-
z3;$ZI*1jeJ=L~$1Gtt)Tziu`Htkoo*5gMg;m0Fb_JB`Gq$f^Edu3r2ARJ^0j|4-a|
zfLU4<`TuXc?|XCZFnP#9%wixX2`U&?5hE(FD4+-^A|Nb5SXL2TR8RqRMNkw)MM3>3
zh=KuKU6srXlX|A}?VI2H|J1qN!^}8?;d%7`LQnTS_q-?8sZ*y;eXBUuwY}+21med`
z+4Wf?9Bs|M(Yep-V(4pHI}!h_73uaeuE4E1WAbCOEfrif7aG<whe2)XQ`bzl`gd?^
zJcPx<{Hf$GCY({z#wgkdjfqz_JKg`wdZ}G|_UspCo1L#1!npOgL4WZh0qj$9YxZ*5
z)b1ti`@=qJ2iq)Tn6a*vw13v_B&n2X4FzKT2`@7B^uCspG$!MTGkeIR3~*Y~OedM1
zh&G+x6Ux#I$I{a;#zo}#M6~ggKKWc7CLFcz>vnZiq((Wwr56#WUOU<E{=x2BBOP<@
z)|qziJ}a>Zj+)xT=Av?Axan*UTOP(q+;k4kDSy#jVN_xpEQ;Yx7kEuvY@soj)g~_L
zWB4ev9BJVs`qBxZWu!i#`AeXA7n<)u^B)Y9uGuUC#@aV>`oG%bjlW6nCu=5>=`Tbr
zb2x8l-9|j|NgU$*xudtP=|r>V&jmBL_L_~GB}@Q9dDS-4FYul2J>$LccSwuoY5pc?
zex2xYkOSusUxvx|T;J*3HQt?kzwX%w#{`EpOlQ}~ZvS|!H~CJZPkcjl*}c$FdX8H$
zYFVIV6-*Z93s%Rj2Nn*Ay1_JbJrZO4L^d<j!%Yz?4rv+hZbS1nrkZj|Z;sejYDwQ}
zd~IU~U9)f2;PER{c4}!GTA6IMzhp-K52ur<>-sITW|_ewlitMJ;=TzJHW%5ZRqvWJ
zH%_#>-(cBXJ=1Uh!tJ@wVZHpEzMX0KL`65+_sw;}Sv$c}hoh&o4sugxzJ=cX^M5}x
z$O7_EvC76%N%7K(U4CuPZQj!A&R!-*Q8})uR`Uz1!a_noN+-#EC~Ijmjimj#0bZ>7
z*<mw(VAq}fpH{zdCAXf~*(rm1d3$@0%nQp<>^TQEilT#rnZg*9eKBS`13N%aIpTFp
z%#pDB9K?EqV1(Im9ArJHV~ngzWsix?>mW@kpfCsBHD+&vSaP%<TgPOQ?>HrKn51>G
z_T{&jNW&C1FEM$a=)^n0Ls!Yo?q-`_!&t`aYqv>LCPcg<N9fIcrZ;lzk~Gl}$i-D#
z)k_mq`}hLhJi=IQQudOtYuX`F7M^4W_OZezW5XOW^msz*T<9<Ft)THwDSOY&<q4xe
zn$)B9(l2Q<@0MU5+&ZfPh9ydL2B{xLJ73<QEi(3l`Cgz-sRN$~PYDf3n#!lEm~g^)
zW$)bHBiW=1VQ+G59N2J6iXjoAWh_wjFzEBq6VR1MFsH#=gSMcpFz~P-b@HQLB?`1|
zM8y9aq*1oe%2MWb-9kQLZCKeXCT+vEX>wtt3?%0p2Q5_Tud)G`bk!N=0+o%j2Npg;
z3#B~9O92SBnp=ulc%;cf%bRU=#VxKn3$f|)B{YfG2+D(&MYj$;Zb|dX#uZgKhH26c
z>8#kD(;(86?XxP6frB=N_*R-q_o4|L`*YVKbq(ltz7Io#F`kdep=e%fKzkBeOVPKQ
zOT%2w;I5H0`G3A%f?lJJpn`25`nM%5Gq(x0x`Xm$0OTFki>!MhpV0dOE!X*GU{6mS
zl20-+Z>;r!J+w+wp7sW|^QtXmgZ7BNE-JMFZ76zH^FhxQ?aY^_?3a^qX^UE;ln-;p
zHXC0ZGU(DUDUbgczwNHmL5{!W-+#o;m)_0?(A;4SLdp=Cy5Qn(2u;|ku?K@g2o9Dn
z#{A6O<8%WXz*q&iq&pIx1ud!!;euo!mJrG<;|}YZ|3Y38q8*`3m?CxuiNpv~p>)Ka
zSd{)EaZE~nKJUW65=DV&C4O0w%u)aDd}Yt4JxLmJfUz5CLgX^?>qz5r$k<1<P)E~;
zdW143C~t}Mg+7v3oO5WTY0F82$W+=!-rYD~o~^nlvFXt5^6pOL%aI02fiz1t2O*uG
zYH9ISk5n(@Cl?qyQDzoKk2f*7RhCUkndJyt2w6!jbdizv<g*x=;6Fkk&A3Pdk%dS}
zI%5?-%6n)e^_^)i+Eh`n5bG`xM4Y~oe6pU<i^{tR<7w={dlDG{iA15^Fpbk!&@ick
zU&(4lOxnr((jJLck;ISjx1bl1N;vQc$}Cawu%r$0{I&i{9Xm<+)H#nJV-yA|8nkj6
zb1P~JB{>RONq8Q%T|#H7y@EcIhk1)1rY*~1hYM&!?g7LPv0T_Gx)7RW9u<0V5$|vx
z$^42CK%1#^1)nRF2VEy~^y4YY6GWKMU!HXA1>;ro^;)!<F;nx+9H}wiQD6C5wNhvq
z=BSWXf)3n7Jy=0VISNI_)F>+gidJ*Wx>B^<AnzLL-Ak3D`kM$X%X#WqqzQdX1++PS
zlD}YRVmTr7r}~R9p~gxsy=X(Sdj4HmyC^PX+$HvzY6o<y%8Q_jKa(a_Cz2+Y<wDD{
zUS{|#^Sh(|GEJ0AgQ<5ne&`2r2PD29eP&FVbLI7~O@Gm^s?RbmGRCZ}WDPH4EupXA
zv$EgdJ!n~k6MZ>pQa9tLP-^I&#m}~IU4B66@h)qeH2&co?w!;+khQULK8<?}RUYaE
zh1eBq72$25{gm$hnfgXm&-DCrn6Dg>*%eTn%do1EHHTnI?5^~jAJun5A+qFPCklBP
z3?@n>gZZeu%szZx478hcf$w+t@Va6~0x;kp&kjl*NCpxkjOzjQ%un({I1zF!v_-Z~
zJk>9>cfNfE$N^>@aluck_))E<6xn4Lq#l*3qZgtDe50hl0Xj@w!VzkP9g_i=M~u<3
zd1*mP(+#9>@qG&#i3Ur(beJS{!wd0BsoN!5NteYr7-LmFBf1qTNOm~LV}3(ITG|q&
zCZ=qnZ-@C&$)H{gO!VT`X=a7Zkm%Ahh5v=h*hhuopp}kbYMbA+cvlc@6CxcvEmbIZ
ztTYv=K_-ykErXN8Ag&oHeCD!JT@cFrN=tV12sM=D!bE8~)PN%i0z~1ZLDP6&3Lqae
zGl)S(ol-}g&yx%DND7THYGem}wjeR7G?;8Owjd45y9}yGssce}$^mbwekh5XSbPLe
z8hoZ-A|S|SI4=>>T4M5n8XVNS4b2Np?x?>qw~V$hazHu!DRsz+4O&J4pq{O0S@OvL
z3lDS!^CO56X;KNFrOWDcwsk&{%;!;w>V2}|vjKW!tQOAGk|xD(MYoi%kf<uoo9Y`8
z!KnjV`^aAyXPJq=K{{NZ<zedGjF^MYWW&ZnNqVnnSrtonV9fJT&-qID2<IAWVJ<eW
zQ^Qc;qNiz85O}d{u!9n1T;xBUe0ntYbUt;ug~TF+8ODzv+9GN(geiel4^B(eDl|_P
zB_v88B<J7x{3FbFNB6XYs%esf7SD>!VBXBXJal@PeYCFAqso6_tN3iw3diQ+=}SQ3
ztY&HjMyRw@^ip7^+(Q>XQnj7kT6X)pq1XSK9*K0Kd^Bv?!G#V>8b1mRgA}TlT&;Y*
zn+l(5$DL0{k+Km4#w@4K!Qzk?46XdJYAXBOFxC5WBba@k?x9xG+&5Q^%3HN`uo}Dk
zcZ&fWh@I0iR(Ai;kpkL3>U;fr+_?T9gjeC-0E8RL{m5@$BZfbqXO1c8nFC`RbXXAF
z56ya!=Ddou{n3Gaxf{fequ0;QWUQK8WVC|0kLZ4ktw8$ZvQ_%G*bN?U2h*oxn*cpM
zeR9~&jBeiC5Xhwb!*siQcPrcy6||Z067D71FhHplUygq6ftJ_!NX&?&w8Hs$yKr#e
z^nTs(X3vj;XvWcs7v)m<{XzEpvg0)_jDplrIiviJbjB=naT3(&cW%USHez7uvRo?n
zVn5Zsr_*m<u7xmOJ^jw2S$Z8hR8;LV*M$Ax-T_JG&#YKRhPYlIjY49(CAs%to4PVI
zo1X_B_ZT!F^FFxM{>#qFJj*g6yiGKnODfl9Tz8kQ-MppK?R*LJ6q|v%aaKe?KFW$4
z7D&pRWfuE-^OGRlCb*Ql05sozRw`_^lE!sm-~TC?#$EIJ@I@|t1G#pY-|PQcOY#`!
z7mhH<*U)A!k9y%BX`h6N@={6Zg0x)D#+}d0l0&U_amLmIOrI|ILbUp7zV#tDDZSUV
z!zY_@`z9?aUQo4i2Wn0@)`+Lh1aaAzr+ID<w)+SzD<MMY)J@7)x^^_)R&nxxrz4Hd
z)3Tfhp-_o8R{N+0{EBh#0?m6Y3|g0)LG8LsnmZQLf3z7+pOJVvp1W%2l20UD$@fSY
z%w3TL&Z{a$^;9hvPqm||%Y%N%d^oQq?IbN7O^|kC(ARc%jG+&K)?6H$pg)7g2ccW$
zD|>RyEdE=Z4jyU*Q}3q^cGvvbfCFE|dI><d-Q{_`o0%gyo-3=PZF;=Gco)I6$BZO%
za@8!oK1}<6YWmY>ahZ*fprf`JXpDGQ#s)!61cLNivB}<}Z?!(x3*t4z6Z{XnknM>s
zAB5Z7H&~~4rlP`m`BdQ$P@VtV_Gix3yv&KNoj-=HiEGce?<bufMSl0|sjzra-cdCF
zK*yW=2#8D8M)|5D{Gp#}-rnptuQNk)89a!SVD9t^uKZd?>r%E}p-T}SP-pzB9@$Qx
z@zE;~E3p*|$5-6aD=gf6$*?i0yQlCZWEuHmYq{ZTL6yiZq~azCq46jiN0jFU2>6xN
zT<P_iqw-)>uGXOBkv|qDO^yP)@WattoarFNB$bbk*dynpYo+6`mzDBG=cyzkeyeSR
zAzNW-&d!9EFb%!(mTJEC*_fN-qCR?gs+d}yqzbRC=Z5|#p?&GNbV4nad4a{PIJdvz
zDv^r;XD7#UmZe}cBiZuXfFrOXV(541vTAPV{cbtMkq>gBJ^6U(@=AW_%yh}#9jAn&
zDNVo|W)1%P-f|)T-q^H{EE*%1q`>|n<iC12GxWvSg|HKCG40*Kqi)ObVIiF!zO<Gd
zdaqs0zLcF^szvpo9%0awfo?L%!t935F^Xn2WmZob&KJ*0tlWD^JJTo_8AmU_b108Z
zrky*hWYteM@<!Uxt0$3mR$?146AK>&)}o9(d}*;@Vy|7gqHNbr(aD$AYp2x<xzi#`
z->sGmpI^w^wb(K%u4(To?Lm)tU|k#eXs%j)tIK%Cv3c0AK5}uXR6oTk+B+*TL>+tQ
zTm6^6R6iKaX+5cZvFKKg5AybK!5q4%Tyze=y&r)(7i@$@G7-PR>30qrBOl3Cs&B^-
zw<g<=t~q;P?3y)=+<~>s$W<0wzLN}JpU>tFjMDD2O6lRxrx`g?_k_c7G|%l{O^;lk
zA|DnMIK$6hT(?Ir%+`y?m|WJyasJ{FYvjUgwQ_<b27$3Yf|LvW70$1xM$gaI^M@--
z>BQL+nx~2RqpO+GTdmDBFY@GeA5JVvj|-11e;QgoH&ZXYnhi~cN=&f_cEC^5G-?QE
zXV-9+*k<9F;Y{sa7P30f{#{aZ)-g+Otrlw6Yfkhy!qBd+Dfx=W4reNF6W3MQWb{+%
zg1scR(gzH?BcGJjlGPS{g0#^X^xklK<hv*eAGPAtPPS<jU8{OZrC9!t*fL&FHbyS8
zi&!UE#kbV+wQCb6j&pJOgqn+hW!u9TGqGM+*~l>4?WugD)PeC>oVdr8jFF2i0>Bwo
z@eL!n`hUlczyUQ;`N$)A*>E^Nv#u)f$VZE%`g_s^F7y&@?{ZRpW7)-u%EEMw3k_^S
z-dD8i??+pk0p|a<;avHGD8<oB#>H}W&Y6{W){D6VVmo^}Xtp!vY5tUYKKGu)DqK=d
zRZmRv)?&x5Usx_?-xy;sK%u+Vwb75}tCbTS#z{^VJTm+L!e>Wh^K=vDd8yokcOU3<
z=ROsO=5C=ee(X$d{Q;AW&TldxaWZIZXus&_1i=FP6VF)2&WSd2PQ!0wwxdGtNU8JX
zsS4z8IdY4xcq&RFLCHLs#De^h-HBW9p#8sLcYqQgvkZ|OSw(jw(l0zWS?_BTubJ#m
zUhQ|nri1-0r{5FuhE?+HHG9rX&3rT0%Dgnk!H*YWBMDZu{ISCuoyKK)piOtR_4`jZ
z*B)RrgDF4kJ%H##4dqXkoYdlu9Zs-g6DEh6;>u@=jy>G9{P83(D%eKe3F>YC$@bb8
z^k<`XAxysn(OOghC|El`@j;g1im@nes99(*3|hSXNA$6LYb?WL=cc)yx7P4L0}isw
z_^8#+FSahIBgq}%y3yoZu;%&wS#N_G<zAjjS;LshAb2+Kih5Dkh%IiNtx+yOJAOPT
zX&pUIEz^wNmuKR!!y4Vz13LY5Gz+~rPI%0y<wtXOVFikLE7fV03sY&s2Icm(Mt9>O
z?N;{-qPfdzLzh9QAf&=3Rt+>MexRAmezV>2LBfgJMRicxyrV}JuA=jDl6`^h_I}am
zu6bV2(D2Muc#|<exQQ+peYHSXIW}tE_ZxBJPSA5*E0VU!<>&ONGv4|GCmXYOq?-29
z9Hr(ocNf#?+&$A?wNq-+s+a82`!sXvl!;*Mh)$=!#`Fy=(x6JsI<TsjUYlgXMh}Tk
zS$OH4+h(KYPy9}VnHN>)(V94Mj;k5PW8zFWi^&$yYZ|UkpE===y|vr+Hrj!a7w*BF
zFfu!QWGto`W9}{HZkP>QzX>`RuagQRTcBlGFG9;A{Z4q=hQp`YYxf6nIT?oC-$45)
zFUJm_>a2U&+^n}c-%r25X07#-^#@J0*6y!^%nZWr&%>@!w@e*tg5CrDez4X6)o5|^
zkN%0E(%t(#EO!jwEZN-9b$NHPv+l*6*<iZhyU){7&P(&Ak?ojI?L9X+_mf;FdyoSg
zf^N~WA#M8`Uf$@;U4?Bsj+izh-2S4|^aHh<__WR*3qR<$;x*6h&h#EK{Pg}v1$0uy
zK@?DmkoPEJ&a4^NZ_N^<wrvDc7<$77l_e1fx88$u-i9Na?cNWJ&_?P{y1=>bj=Evf
zh@fS&dt)<P`~2C)^w;%nK>WeE&uCt1HxzK~0WC*e)Pp%!OTP#h{lsMW)ZX!U*epi)
zuVDPr(a7~~P77N})BwdPj)dfqIr$epf1UZxH%2n5qY~Tz#goj8p4zW$rJwDkdk?hH
zV38xkl!-DOJs1{P^Mr{)1+yVxF@Nz0V-d#NG?MJub!+slOn&qZC!2XQ43FF9@PSqo
zHc(Yypsv`L6d{aV>JT<57g6}!w_cvnM?O~0*~2Z4;z<yE9VFp|>o0$}R4pFuWp&%q
z>^*$ld!iSocC9!o)>X=ttB}N}lwL_6`BnzBPdAx3jR|~3-72x@THkDShyWjR^hvCP
zur)n0&dlA`>R=|r6v-M@k6!t-U~StVX*A#eh91S;bX+|xRd$zoDenn6(9v|c9Grh)
z+h1ZYsRPAGh&9#zq=yY1g(DlOhhM__MNh6QL=g8a+$8FDS=(uiTeXV}1jpI#y%26H
zYYd&Om#u8i^q#`G)Bj^7rgO&dIeOX5_6+ZFQKw)d|M99-d#hE98;!&Rw9x&P8JLBf
zHGB>bsT8QNdo!U`u}o{U?^I5yWtKluF1knJIP9}!$0@dq#&?P3Fn`e#ciUR`mjd0G
zpKodOjylR$$^daal(Gv~;^ee<2jRB1UrG8tFlOmA|I5)7eWOxcEC%7WiTIq4a$PD`
zQA#p_NwtL!=gh+4QMUh3i!HyC^dE}**`q7DWq$}tW+oO>J?b#Ek-LW6+)IL-_h^d)
zB@ueBNN1zfjj;1OqfZQCX}K2txfKWN8F_dwrPH)*Qa{hl1`p5r-9NJ#QXYw56H@x3
zZwzHJ&+29Szwc2VG!N^S?RC2kUx@c-(dUQ1gO;ac(G2QT`H{QTB`>s0L<UV?p{miV
zpUvVRsIO0dAOeMHKmnY^&M2hp#obi<hXD>H)#j`ZeJPVMQ+<8vWSs3xCULMS9hTpm
zDQ2GQrGpJYfc`TMCf%s=wsa}8Pe1Li4rnu7q>rV`^Nv;MSk3R@(c#y!&a&Z(Kjmxr
z{d|ycsl?3mb#Jv7xzDM&E7z3Er3(OjtcNzagxua7tI$Nuog7JR+(;yGF4^g+Le*J5
z2jg@xG-dvTj;F~}g*B|^*#G_^JBJxu<JNv#ws(1=`+G^SF^F?V)H5rdD3&vCi0#&G
zmQTDGeduhXY?u00{}G{OnP11KzB82#pP2Q%hcOEIoe}5`HoefYmF?@n6aBdRK;JWK
zL)Nkf^;&9C#BAAu$a7Oe9kX#e?pU}mA6(p1;*RD~8um|k{$}Q@8a5z7c4%2|`DEmp
zY}Wt7M1Rv;lCHYA*a!g$R-OKA<t}gjz3sMtUX7q<%)~A*>3Oc$MMC28PK_`084D7Y
zUjyc@N@18-CYUve&j|mA&3Mi3vyJ|v)oz)iS`74M-Rql;nXe%aP8u-8I0+x|w8?j_
z>#y3mJMOP52Zf`!i$%p?EU)1-I!bqb(qakVh3(jaW!L>w<{<QuD~(LyxQtsmA!pfD
zFO@qa<2n-K>fh36BcnKb>abBfI!@1AJJsxb-3}_(WnD8hV?I`Fj<p|j`}t!e_7`oJ
z<Mj59G%_wS;nH9AkRa=IfN)c>;>5~CmW$kC%Wejw6|tbLt>4$11>wdwx>h+U?M5@>
z(PJfVj7=vlygKhxPfEGb_{Q+Dl0O#AWRl|R1o*<8+u0j~b???E%~8<Fr#W`{HTZj2
zGv+^53dW)yfQ$1&Hzr;MpZcr!=*>hC(Bsp~sqC{;Hcr)3`2(Gdvx}7}eN_1q5GxWI
z#IN5eZJt<@3?v9?^R|y{Mci)3Przw7!26HP1Y_@V8oI;oD5e<nMNIkCMypOXg95|{
ztsva|d4wD8cH+?=l{3lobh!S|sBM4>A3JKMJ@Ij`*?+_g(Z(jh8j)i{ZS1HG-Pv2S
zGnxI;8RxLF=`7cr)NTekKr@qmPAZc<K{zP<Qx?Ah0cvsTxQgzsh@I4)b}IcW@;%?p
zq>nW8=#hT1e*aCa<`0XF+%XxMTP2|@#<Oi-7e1?Tm|xY*ZPFjmve1LFUQzkzvFt~f
z42t?GcaWa$|8}nLuN6}=_JsYadhOkIuKm67&h&>3&sYIR#}QL@Kh5p_YOcpyaqjG*
z<t}YHPvmDd&3xT%r}xce3g_l6XSkJmoV3QbQtj*^c`R$A#QnFrQGTPAX?$^_)%u(f
z#=o9P*1Z~UOt&Y!a2kQ5$0@Yi)5hPpuD5EZU_5A)qx@0ZAlw%EH~(9Wp9oxxNhns#
zzzjDWjdZ;s7wI{lYcwV3h_wtB4RlNVlpLI~0YTZh!_%(2!bq3i%?^yxf5Xeyw^ywI
z;g&8Lxx);*bfSaWVpH;1sW;}WPe-NKr=T(vBWG7F03i14mm<WytX4Wcm0qBEe=3ty
z&M?!>OQyWBbGwb!&kel6p>~?h#@T(Hls)X*xkDXv>1L*Ul`Y#Ku2YzYznvq__xvCK
z^*2{V<HXv4QHAZyILVUV*7E6nysVbE$<R%1Mtspmv71z`97!#@TO4z!Ya*G9gd1kK
z@^%_2EEPLL0|}&a>3F>MWzEj|L)*RX7qNpW8CvnO;Z$~S)6G2J$<(if;o&hU2I02o
zr;D@0UqOaoy_+vUul4<MX5@A!Z;$kCAB;aDxKTUTanpM^R%)?tcJ6JmCxe{1qn258
zhg(Xm=%u`eC7+elKjm`J?Iu$v3&PDXnzxPz>)+RzYJA)75DF;tpX`UTmrn#6-rt>?
z`yS{aUyphZc71c7s<G&*Ow}~IW_n*PyUvWA{-2QG+>EyP4!7v+65GK#9k~|XDR5ia
ztG9jROZ4R8`_usKGkj~wO)b`(;t9E2{xsW)o{AFtb;J77-+^#bOrnRR>G{?o$Ex3G
zW$n5FC}S@4)`pQ)FBpq%w~7`w)&3Jf7;GSIsA!JfZWgUlqI+Q4^Y1TPi|;Dtswc%c
zjo=gBufxcn2qPTPSdtI_2U@t!kD9jvXfUhBk{`Pzi!CZx(~LKsGtt>_W-{G>(()nV
zpnY=?%F(>`?o3(`N<-IWbM8^d>0|UsY*^{rwvR%%vHS($)_W}R-4~i}^=u2pPN=o-
zj^H8aSua~`FZoq2V=RXsf^f^6G-Q@u?W9x740q9;8MYuJ8U5aHws0t@y0)2&d@W6@
zb*+4L)`3oN8JeVmwGD6XvraU8bIDost$Z%?BA-PW1;PKDPBvaJJ-hKdYc6cqVKf=|
zom-I6&Kj``2ZEy8#Y~TU)y>2YPitf6&CZN}#KfzQ6^7$ozjwD2kKSH(M{m#M(|dVt
zupYk|Le#R^2+!QkBMem>QpJz3sb4&7XP;xa?mnr^&~>y~`9jVmqd#1n9=Xj(;RqFm
zXCz3y;-b`;`Haw~_#nt7OK&ceQlmX4J!jNEjZcZEjCknFnY5Yi88e>%I1S4P*RtcF
zT3z(xOd<QS#EB*}-}}9XqMo!LW((;TCr-F7Vr#>{$!BwAE7P-a@MD!PL*cPB!=}r6
z*~>xj@Qk^;x_xs=&RF!FT6Xlib~!cNv%EiJ#3+L=)*ahj%cP4kicPe0FTO^KNTr`J
zbE%d1WBYwuuE{lnxPVLUtLO6X5xh{K&wPHuTmK$^s(ZiP2kISle(DGA8}Y;Ny-IG;
zkF=6u3!*a)2oE2dC82M-Xb<C8W>v&$%4*ej8o6L~(`(%%yI^1hV_|3wFVMWE)8-)k
z0D3@$zm~jJI3^{Vt)2MYXzq??IC;fncl{ahwD(BL<G>qq{$Dr{aVSNpgXqP-;(2Ck
zhb>yb6;t9o$9A%B2)o^{bCUSq*)UiibwFmJYeF1TkAbrkI|Zbb!Nnwd74q>pIV-(S
z<n$f@&$vA*#R7!8Ri*PX#jaH552;s}vRw|1&TqPXiKf8Kw|qkeU-MV_5e|}N`@~Ul
zhzVIrPc4eg_7667C*BF-YZ*s7C_lBrvpe6P>Q0=hdlm>cHhtY3P0Y>@LF}BUVJ&GH
z-XDR*-rwX_6vKYZb;6%^lo_j-+8Wu^T<04E(q7lmIyV807m7(+&E^--f9>KM%^y#9
z#q`H0VlA7EjLzi~-ptOGR`$iV>;2!PH+7{i7MUn6=_3ce?LN{g9?V@RsIRwv)a{5@
zGb5)cInw%AVLX<E;U)ndBu4Yn25N#rJAZKOw!SynX<Q<0nqsVqU1u*JMBik0<~$ff
zZ#uDx1>M;LD~2CWfb5C<%&DQJyFfI#{g}9_-SmHhPwp$G`=*)KQ@i2|`1|9Xnal9r
z+L}zJ_pY>xM<#CP-pTIt=^^72q6T8j9lh7vGZMnqvM)uiJkw2@cX=I#dte?%U$(b4
z2c2Xb#xUGUgK&GzTvxj_t+^|D*3`A_X7}p@gj=-HkG;n^gfblRjzC8V%M`ZOEAO0!
zS+(^3BXejYQ2?Rl)Nr*(wgkow>Jp&xy2)VTLjf!zrH9zkIx9gyJvtdoT_O5YES|oI
zyJrnIxqHR!q=6r5siPVj{l~gN|FJlrA4c*-FJW_lU3iQG#*!P=p;(W&<#%>Btp0=J
z-o#mA+rr$c8+<8c2aoCn5$8)0)&+#1r(Z(LEEs4x)cn<cgjz|KJztq@bY493`MGxg
zZu}c#qpfrI(fP%UXJ%vc%$gZvWFki;Z<QBKKRMU-A21RV+g9(k$!Kb~YBPUS8tC(E
zICDd%=iQA^aecK}INZ&IYiGjgYe4bsoj2O|&h*i+E4oF148?|*%V)zL?ORRMZ8IOz
zJ?HQU8z5E@u;jUKLj$Mcphis4Hf(2Cm98USiS4@pgL+@UckoE5SvuCtbnlz$O}yRc
zLv(n5^b_xKJqpKJG;}}sUR(C##9yWRVN0Lu+^EIQ@gcY1xY*A0^ywR$?bhe?$S6!j
zcD2}cpOrYBpH1|jWjKambnXRe{bT%KGH!<B>oaFe_qpokqE%=fG~4Ns@4huQts0#(
z8~Wsxy;ko#jH}kteXcAJ@%p_##lCm9%mqynE#ANIL4ErSAPdgBD&oLIDk<Q6rTpr?
zi{7(0cZGzBV8YZ<LK=+i;!teF6HbzSziTE_c%u3rk$pH4Ups8(_Uk&0n{nR#F+j$k
zY?R(-p`SvWl2g=Ja{QKbkrKI=0NEqo@FR&BG*mbg5VeApM*?+<X_AU!A;deN@~6>k
zvWF?VMMH=)Sts_5^8bL_;dD{iCT~XjC^Q)hKu+?aOUC9j>J5-Wa`UeAjrf<9=pD3#
zOd^HL|ME!Ppm?|u9w4aXxHv%DJtGQAUSs*o1=DsBZrh@Q(5Y);k1?r6j5-9dgMMv*
z-jlIXY06%bKgE)1kOqQ`%Rp?G88~I{C~e*X0VMs9afYHv-v$ZBpp%B}uUKD*zX`=A
zfFQ@9?|4bx<xBY&azZCP0K<?VC2sjhHdTw{<fP;)J71Ne)K=)m=Miqq5_Mv~8*vdk
zcxzAx>AbWJU2KDJlRWxT=1(Eq2K`lOGLOP;L7YiMW}yX%J|gZtx2l6hq2^o=kZW*u
z!`vE+cy(;S-d5hE4l%71Nl=Le{NC<EUq!*CBD$#gAQ$YSz_4P+KCmx_mhrkKXwA)t
zOQFXJvMQTiq2)pUsZ2LlY(^JynMb~Hz7>0DJbJM%_~DQ*cDW;@4N!T!FZU+&i|?=;
z;<G$bCgqvf$BL1U>c|2@a;x=1#1sf*j-nTuq2Ax-ANfvn+{4rhz=-msUc<l=N$L9s
zQ8ti_KvU9Q0YzwaNMfz3D-l|rx22zNi`eB-@5JDHDknXGMky}~{Q!QUEPY`9Mp<gC
z)EJWwn#aC4rcN~=3w^^arT}=v0x5w8B%_&|-$co%`X|5t&gUPo^L=LKs~W&yibh1F
zHx@2kB!Qmo$v-Z;Cxj>1@c40()#aHCI#Q6Bogr(8E{Fv6o(jFg0pzbfys9Q~IYWza
zyIfll2_j*KW~8V`l>Ty%5#mH*G<M|$K?O-gf^ZurP1+}EGF{m8C9|aML4LWop#z(d
zLc7aH9G8mopK<BueEBdsZeMj(UUNQ;bAqT4k|(H06tPU8^vPfxA`mvoCjQ=wI^;qM
zB2@Kq$-q9rOZ;r_k#blwa@(!%%D8elDN7u*m^d&J9oYaC1mSN+xa~$k)T!#g!Md_6
z!E%AtB21xgf0DfDNfn}RaDlH7N^0yDkZ>Z}3wlWE-9=`UzvbQ4gE3f`U(vyW_(}%)
zNogZLa+H3Ew$)%k%i%(7lrfS$<B}*4%`^6wcG5qwSkZT;z4V{5lVsIkOcj&g65+N8
z1}Sh-c#3#ij{m^w+k@tU1XROGKU7<m(3Xmvcqk9QAA+{icj6-|7okxBp-3Idc?wLE
z_-+c}1*(d6@Gkb}Vw)l+Q3@F)PF7&nf`r5J0{Uid_Tg{t%GCq<EM}b5+)4s}{>3wB
zA(d0kQu={`zZxe&t_dx(<`r5K-mflwr1{JE!5t(h%|ykWgZ7NQ`I+x85l_@wGoD4^
zLd$~86zQ82g+j+I(6=D^CP)*y5L%&Zh02q4uopjq2<4IRJjqkkDf0@D3F%MuRn5;z
zGRTsb{~sft2u1SN_WDY)e5byK_Eo*I{#Z|fTvI+<M6h8)RX&i#gP5H5hWU-^MwoN_
zXXavrG^{FwMx-90J?K)?SCA(1fgtdNzC}qWYYoMogmO>_Iw_y>WZm*#LYq0N@aIzU
ziLVgpD|9j7A=HIs6g*k21Jxzfc)tG!o_`MW6@uWlc7Sk`-m^0zy+$CQWM=p*dy1jL
z0|8G}cA6w?jc^mLfb_sZP)788zr#lcV198f#G?`lR|q$`gp==uVF{0?R3%lQNGDab
zMVexw7m7gGY?b!3R4VBrH5(E<kv)~qsvinXB$DdWC^fbl5e6lfs&J4`P;hc1FPDq)
zwCt?&NVpn1K9JQxdF<LM&Y66a8h7wWe?(d!HroKXCUu}5*#Z@$`YYX&MGT~O;LAaz
z0N&$EV;IRr#i^2askg?!@Xa9R*cSQFtw*>E<U7fy`ljN#4J7HoyVN^B?y7t#M@e~X
zMq+8QfHo78KCBL8s<zH2=0-|8NPbm|)U@qGt8rEoHI?KmUxK!yJmzD-%xFH-G5{!S
zRR?{;6imf%Q*%M<m>_P?HRfU7SHvKVaZ0$r+M<2)bx4}jD`UsIY~<0VZD<*3EJi0w
zYZm&ZP6^P|{Abl<Q5A9QpuaonCl#m?U<zBIHDO9yA>7p5(jFx8flmoJISK7izDVhy
zCMk&@DG>U`&ouBI^2j4;&;T?ukZ6V2=GP>V83rdik%oCQ$IBEoKzeRb9_qHOe2B>9
zF2frsR{`@hg~~zAkE)LEx7TO+PD#Gd@@9HuQv=3}x7EQoZMk+O<{F!ws*Fa9L?1r|
zgH$=CFx3;@SU{r6u1-Etf8YxRt@zXLvgQ?hbCTsS002M$Nkl<ZTr4T3)qKkMZ4vX2
zl`NlS^DOZ$&WtEZdLjNY;7jsRKJg=|f9La$FyCj^`O<Bj35;i~Bz<zmDjXf+9jV`M
zekzJem)7n4K`8OZXXD9pu@PNG*Ept$ghUqy=p+o<OCPP<nLV4Sxo-#k?w8Qtlo=$E
z2Dj|sLWd<y#sFzg%*phfvs(Qe$MK(RMXirTq1fE2olX`eQp3QWn3!H?maW{uzOv<Q
z-`De+AGgB%Ie9C00BKyxweKMf<XZ0Z8mOBj6Rc_k6Yn4r!4g}qUxW$Oo|uLGe$Jmh
zoqO16Q%j$OAJMnOnP@{doIJ_vX|<F-bSbvKyLhSIZ|3|Nygo^E)R+rE_swNJ^O7Ld
zzNPJZ_gkQ^%69&!(Di=X@TWlA85|2$1lEN<ZaJxvCY##=a_zjLU3^90dcSM>Q}0Cv
zhr!8}o$`kv%YGO#B>10uLeZ$4i0uuwxU)BOd!3t|uyRe&&ArgWN&H;D0V+yXLHZp<
zqx>eDQEThd*Y<n;Z|9=wwYKZ52<&J!3>!D}J@1}ORJkVWW-$5b-Ph^Od^ik@z4LnQ
zbc;(a@=b=p?6tlZK1f@UvWgefY$H9V&7B_v#`6k#<vk`BZULcY{9xwl(2F0UKSBp`
z9N+el{zBaZgu%u$pVV&3q}`pn_S~(VZu>J_*Hq^h0n1M$FLBUf9Z}ZGZ%s00Ei{AG
zUNCi9?3FGprg8^ZskqfjX0Ho+{X2!lm9(L+*;%LFHD+$?bvs{)WB0XXqxia*P&kp{
zKk9+B!lmZRN_yp7J4Xx;ZR$V#ZvO$DSZy_{aHvKEh(<hpY19jTrxQt~Vit~#K#>t@
z{!cxB=0lcW`&=qxk0Nh+QGzK~*mxgGD3M;pLRq9jc^2k*ftFQ2=+7{hS{J%bxV{;+
zu7sAwuYi~!D!xh?Y}_#$w|Pj_82K>gt9CP<x*+HUe>Ric8D%SfD71`Aq4vE!uYI)}
z4c%&GO&1m!hDraAxLEuTI~cw>o3WQBmj7@gnErQsF9Pw;od(UnVL<bf&^*dry>M>M
z%I-&bVVtyn-0Qa(dxt;+#p6x4`#>}7-XLm4iIrEiipNEu)EfTeyEV^{5MG#yZMk#@
zou2Qv(DHno!QA^xR_S#C@3#D@Q@Kt|L(A|)1>KglOJtU~iP_iXL>)QEcly6*cvI(^
zAlK4%;WeE9WNgAfYk$}8dk<ye;S2K_v(m9zKWO!vS7M{j5v1}7=-@@3)4FS}KYJM_
zlPfqiJ{2eVv#ORo+A-#?@`7m6P)N4)KB3k8a@!9cV4Nq?xE9U3`4{(7t-EpfbqTin
z;@Am+YGF&;+^>J)AI~I{scHoZ3alk5yt?e<4@5J?t*lU(z0}n4tQ2SVucb%7Fpu^T
zXD$nkl_Le5C5Z_5cyKjSJWhf4FfiOAk7Sk~*)7HRdMAEXl7L?VmR(Z0aM-S$>Q)O!
zDTEsnM&5<(Lg|MkpNn}|*>-m~iPMa!S_ujO@l<?OnUFqQS#XPoHHBtAyKD@dYZvkG
zZ{&`y=Z3$Wq@(q?36!X`+?to(T+dhjE3x&R3pyyZqSTmq*^oPOOOlD#SvW0ca=K<x
zd3!Zqxk9rgbPAn`hc7A?>L)v5DUhU3tSE#Hm;s~srlEZGV@XO$08421yqv;cPd_5L
zi!KpWN#^7tC^Z6qbAMR?ZCAU7t7~z5A}SUcae-pu70P%{i2#ro=9U684lz<>%PC%5
zb}Da5KpNOa^}V3fP64G>uOvg~kuMt>305c;x-;{TvQ~IS-XT<rX69|8L;xtcy69Ah
zRi^daSfS(2z-{A-N>VsF=NefqGO~zw==&Jy#5!t7AHFbCEx*<k#X#chSJ4YE&$@UA
zMqg*k(Rk3e9Z{7m&Lv1xv-p(#YB60p+RNcgLmxV~TyT#B;RYaK0pX@X>mUJT4y~Bk
zgIw4QQ&}X$j8NlZbS0R$xzDS)Lzkss^;kgMmCxd5H?8N(#_+`jd@^F+a%k1a?hg*!
zu$ezLeNX^H*JN^;X9X#Hm$EbbSwX@{oIwjG^U(5(YUz>xwiOZ%6q}iSiH)ZRo69oh
zp7N|}N;9#^CSR)ZW>?-7@pN8V=eMHe!CY;oWf5MGu*w$=Swp8e)#5Sp2se?ygob&P
z+1Zqg9q|Z1<K|OKJlolGBsFrKLEmgl2dlQb_dI>eJqHz86(W=5EG>WNP`dm^L1q%r
zw2n?LCIr&)(DJo$N*^W|=Xvuqe-ud1n+55a!7(}l$4Um&V!ae)y`+=s2Wsi+yHeHM
z!3JVC6mw$596H-9;5pC$C7Q2)1`7%~AuT2Qj%b-9zr-0#zp-czpKAayLi_m2k!<~Q
zAU(yu!YEyoL)oZE)moDKFzCvYw7lV%1PHAaif2WH{4rURmSLAOdg*oe{yhp%gv(8Q
ziesPU0_lfV4oh@Fu0gkXMe}FW3Wd{RWgVm~0)=@|zFaxM&Fc7nFjiExp>v9b%86FV
zc{X!F=Su%sa;k3+^IGg`)l)%#p2@qYHAQ9duY7j2OrCDT=7Em1-Y+}7CeeM(T{L6*
z#7TeRzr0>Bg)u)ka5W>c`-|R1@cl?W#;&NX@ITxBwbfISs+2mvK2-(sw=iDO6?0UG
zo|HIHM8?>Fif?5^6xykv^<XHdpuvmM7Foc8fI>$k9(nnTGBISB>_$`n+33wm<W)&#
zki&^VTH^}M&%82cnz_EY_Ib0@v)|2vdMmozcqE%%GlP%Ys2{bgNSv4$qL3$AYyVGy
zkDpZ}6pM3LHv!StpZf{<I;qIa3&9$3?WTO%%y&#sZ-jdR^7;G>TTaVZ^TO_A`@w84
zdk9QJ7O3cvl?|jcc8f<E50jV8B7Zn>*k)Sc`j_?Q`cLXX=A~(tC+~_XM{K7Di#RTx
z>f#K%XJJ0qpZ?ffr+tI#r(fcx;>L8e_Jz@;Kb{NohnaTiLdP-lU9){pvp;!ptK0e<
zF|L-vM0%)BADRm`zPjBFCs?o*5N?ey>i!=0Equq(RJ9df199&UbN<HHce~z?<+z>G
zhCX6v<7O)in|MDbn?hxE<GA}^BiQ(cZZG%^3c?}gUQRaBw{0I`HgZ{yEvx%%&FTHJ
z+1;=o;d~QEFT8dh;RgM%1#PAi!-fM(l$)P4yiITMdUAA}ekTaGBc_^-@7XPTGy}&e
z>8YLZ)BM9$dlNoz2}xuW&P75MeRVsE=Ds`Yjh~459MZ9~On2JfZnReK;!I<fWED=*
zjH#2ckhrYh^CvOVhX~X|6Nm^B&E7iOpE#9M5&*kRubb|zKQxhef0!Hgxyq5bAvg7?
zN6|8~hQET%w7d@9yh77wFPsXR_lKP4wn4awPG9a3=%)Lwl4)TRJAGwmuJOI7YcDU@
zsh!|f0aquJy=dycW_z<2;Dfo^*T!B>?6Si_X3lsD;U+}3@#Rz9^#`{a{#ed)_Rwwn
zu$-|#^Nl;9`4?r?>5vQUB;3>vCqC5ZHLfrMYwy6Ax_r9deH7{t3lf^+JR9EuM5<o%
zg4R^?=h<%N0C8H6fdHyM(a&!80VksnZbzo=q}`6!zqH@zJ*4{tUxh*Nu6X@J=R1gY
zLBQ7Mkk4-<YhUC~_8uvCMQqOdx3LEHYs^jGV)TNIeZTbsqHx?lr*C>iv)BDSqF*3h
zq~OQTk+_8en)f#5!@{d%g}H<ibF&HVlEWFIhVMp4o@!}RpC`1^CV(1B&z+02FSVWE
z(WbZd0KXZ4vdbNALz$xOpnZRxBW}0r|48EBT$KW~%TWp9*Jd!%;K)Aw(P}!qhnMa@
zGLQBVrVDwAdp0#e91cXQK8ORhCD)yADn%*BJut4^dBfJ?d$Wbbcc*jNw{!gD1~gjB
z$_7z}(|*Y*6e@g5-KMKh>_~&$yx~}+!;!UUF8z9~JoI_-EfysG$9vehmdvGJtCg$&
zt)<%^ZHE4M+F$WRxmG#~iOU|CmZgr(>Gd-US*z3{q`Ve}Kf?=N%Z*FNI|X+a&%yaK
zy-0NT9eJaET0UpxnpP7un|s0#j%>no^BbME;E=49vC+P(9fZANwBn)kQ11D}!m^PF
zc~@?uH(%7ucU^v|Jp~6sB03pGx`;=Wm20z=)Nsp)y#Xpnbmt1)CTc8h4GiemtPv;f
z6=iqH7lw+Z4-&BRyO`tUhK%KZH0sVWpK!#v1d}X@ag{0@TS+guxmqlpt6AP{orGX2
zapB0J^zz4uk9AONc7A61W;tV3-=!CfRNn~J1CV~Gs{yizi^mRSS3Hu#;u_W2O=Z{4
zwT!tRaJ}c?n4V_&k9Lx9GG?rXvdjONE86>t#S1efmJ!>VZWgUvc;LVtF<whjyN}u{
z*9GOo^Fb0YvGP)rFGT+)`fdL2KinZCke<}f7|IhTPHZXoFJg`4&Eio}w)fzyr!7^h
zK1siHk-OxJ?GiWKfmt&3-X8%@^(<}p{NciqdsDEu8Ljtwt(SjerLf{5zihf#bD{5)
z<J8cVH8-=5p8*BQd}EUKs95Bu^wBGaii_{Ue}wXS#-Z$ztm_abZ}$2CD=Aq%v`6|X
z6e@QkgyG4aJ!~%iX|Ay7hv|Io6inBif|jSd#1}(!*b?C;XiYilP7H=CwX4bHvK7@n
zGFm9VW+oH2tiXgZ_8x-1O9gZ3E%oxyje1&qV@Qp|w`4H%?CX;!C!RqhWEq*_Wkn*K
zv{TJHl74V&GxjzPXuf!)&^+|GQ=c*IMfTE)wd_aLQsrtK1^qcl{CmBKi*0EiMH3I@
zr|lqU<inMZ<m&mC1!*0@TTTz!6yI(~kWjR&EKU)qf!`-dK3TCQUvZW<o<_LA7m`U@
zIR`}{BXxh$L8(?rR{Y7Xr}qqSxWeg4iN=mmJI&69PvKtRW&@i`5Plbw&B~jcyyiD`
z@17v;+}8nl2+jYWQFm7cDHw8Gx}xeji@SE?+iY@}>4{*Mu%C4A5xZc{3yO)@g1FU;
za^2W@onA3*Oqyaj$RSWI+*Qa3{0pAHnfW4v2~7?jmSeo$OEw(1vDLh*Fqb<@jNcJ8
z*Q4m)Ns46b7ff*GU->J)q-3=1Hn$-bM9(LsTZgXY48-`z=qQ9+ixFJL&k!$Q3oPf)
zsna%iPwm~B>^@xVXAiJYPI%hXDWvVyne07Y^zw%ZRA4q*e{^GR>e{I7Zv@rT4wGmT
zPNBXtldN9R#7{!iE4^9EWKPXncBNrIR+`y3cem5Y9F)z7)3feP)6uG>of&_o7F6G-
zXVcFmj(pj(vWKI$Tn;+tZRvFI{poP+F>|xiU(!0k3{c~yPgl5XTugshs@pzFPl!%X
zI87vrFz9GcE$hw%lQ{xc5_d*Um^^a3kF7I*;iU&1AB)dhy?1k_zuJjQZ?MyyPfz*l
zPi)RMzG?Kpdtn1j2-hwV5+*SteO7BGn8GpAu?|PSnPkJ^W6f17?J1MkqUi%2tN1z)
zZmY-q$E%xTTshGr)5-cnH_okEnVPbO0FYcvG?JZd%dAIs{$LPpJ6W0XWtQ#VIhCwC
zaMRqX74DS1xZq?C{VP#m83VCYi62Ly$8PRDJQ=Jx*q$XIlBKS%g}`91#?CAnbc+Vo
z6x|yp<FzjxpIv`mcGlS=?>figpaXlmMfa4l(T1sD!z;lSuVa+izq&u4=#QPAoinmT
zRzXqHyJ>>c!1&CjYqN9iv-0WGZUZFTrjPXJI)4N#!sVm?7^{*EeC)6d?Z)l-M)r`b
zfW5J&9>;gU#MJcH3$66?U~;lJF|jBdZKwT5XZ_B%2vRAhBs}PC^Qb#GEPJsXfu7~w
zZlEfIGxOD!2sg2!SweqT43?Hps2z^i=H$hbNcH8OQH*V^6D6}}jS+FWIobZh0)3MY
z$GDk&lXUmKIh<9pSY|^l{l>acIbI>xn$7<<LtMgX>-KESO)k*9>?f;yZSv#|pcng-
z{`yjoKUl3Q#(?g@cVnV=$=|_uXPMhZW9kbDC{eVv_z6Lp9Kef(Np16}HH&O4cB0r<
zL(E-$&GPn4us**P!cAGKFlJ(M+PQuvT>CnZsJG}{BDLVcj2MV4>+JjBi9?<}dlMcS
z)TN`TH80x~tUd&QcDhc8q9on^7^k0;q50dPc{a6ZHWjD$aV)|hS)3v>&I&VAIy)uD
zm_$6`N#9rxa27%^!HOfI-mG4Vp=#tB1_yKcEY1Pd7;MwEf7-_d4eDR5faRbA8D^5W
zja-%1M!(~xQ3ads-u;ySxUX0X7Whf+!iwvxoHjNbObD96jEeJTt~|F>h0KvSK1cc9
zxnd%2U7^FW`7FC%&eY0Rj5yh6$7#Y&BuKRJ)X6=7i4|?>S995L-B`HsgxIs5o7P7z
z&1TuLX-n7c-V>sXNb8HfpCzIn?c2R;<8F>}vqt^u47hblTjE)>Uum_Bol3^&4Y-5}
zO=4X2%+B|<Ubu!ZSAhAK+!L3@LzMp*vdGKw#wf1-k_c%s*EQOYXhGxLO+oMeXv%#N
zo|I0Ht@bZ^o^?RMte=x|2w!7b#jZYeg9n)k*A&LOz5bJ?!Yg@t`=>oWcR)J5=w82&
zTB=#?J0$E4T8TFf>!at#*~S;gyN!=y@pX&m<qs;k%YM^E!JL@gpZHz(^@Oo{tLYjQ
z(-c==bGL;7*G5U<h;n+_FZ&gHm?*B_33}O6u+=^u8v+4vc>S1IV7S){jNA(<&Z4`K
z0t&I#`c)!GIJ@{tz?Q|xj9Xsh{a**CCgUB`*4mX1+quj>Ufj4K42zf5?EFjOoKX$T
zXs#dN#u1|+=8hTI-Xm%NfrEo^)4P9my~2KOX5>bjHU~-TJ0hKMVR_!7RN=*Qsj)W#
zAb7&mh@0l5cMfyrQeyFK?D^gAXyNGHwJevZh3*4(YteU%?AWo>-QE);UiJtl>+EF*
zagT61A4<adjdfR|-C8R>+h5c3{KxdTdVL;Te`LDP2_5gT*?#-$996#stq_aU-Lr28
zPxXDdZy3I%;@XukXYby#K{?`3l+f%K7g8?3olPIY8PJyVDqRv<Mi>y#dhhCqxO25Q
zFPf)iD7pmlGyD7mRGOm=f04Vj6NQ#b$>RShWVG3h@rKt%{r>fhIDB6rs-A-f57Wd6
zRMYSM#MKskH?Qd1*{5swe=X`0GpT(QzYb1UoB2;Mj^t#xQ$bsFO|ER(4a0qIA*_9}
zqs_c89Sq%`P2-@*a29*k+*ezOE+E(LA{L$n_HB8`ZhbdqZKU=37i*5YlkcW#8PmU{
z+iKn6hV=_^b?4}Wt_Ml?IiW{r8N&&MR1`kD<-=kZyIrw$B&2;%I3k@Ly{}&YnP_!>
zNE|4>wqG%%FS<g@Hovi{J9CK^W!}N*<<)k|AbgYVbhO4defpM5s_!B^Ehxp-jlwcn
zxhEB^;?YT#P>on?bd%0KAlKenu*<I}mh%G5f3p?%YocVPob-z)Tj|1Kq29X93sOf^
zjQV-GOlncrsh*uLgzLKP_B}bTc5*g9`V($Ha<MV{WAS-0-#1mG)cVhV{$}Qz5QfQl
ze#*(cJ?yr>6eb>q>6tx{Oo$CN`F5>0Fag4&)^f|XYJx^%vsneK@`AjTc`jBGKQowQ
z5ox!1*pw1ng@{=ZaYgxqX`170QC;W~%&*&(alzk!nr_YpdiM`z$VHY)5Lk0S2pMNN
zjOn`@yOXDYaN8AAoEf0GO9^Um$41aOr0q;NTC|JSa?fadwbh>apy69D_KaX6ouq1k
z-uQB(J$()pY7^STHI-)m2s_>X->LreNBjLa%IMKF7cWIGnfbz8d**z@H_WcyzXN&g
zK12@7Y7#ds)<zI^e7uF_A^gv@TXP>nI#K`+`Ye*XP~esieN~7r4C=d|0Tom%HVa2<
zZtFV}ovHVN;YS6VObNtN3;eaB**10m$1}<JWl^j0X4CZ_ANMEU$p&;R99)c4d(C9>
z(r&AFH&*kU4=1iHHp~BJq&q(y?@pZ#ocHa%Mc7Id@$GoxD<Ir{PRO*?(|tYW-ePy(
zZhmvTJ$;cD<larb&`ILyW;A<!r`@~VWUL9*OxtId=)LYP5zL4=na0*9c9LkK5l(-+
z*Y|!xP`YP|;U;q}`uswqPrhNUmwds+8(GJi`dFta$RilH5j@$8yD9vzh8z+Zv1k$l
z^IdH%cw#D;`K%8^bM@ZuW}|pEnv;tfymx43Ce+7|Y<B8jb(7R^)0nxmgWG#c3#Wa}
zS#D|GpT@nJkISZPa@iLq=B}F2|II{y;&=kz3ze;lVthCd%Wk4Sb-q{=+gdo~6W7cn
zkme?D`XT{0nmR!<<EiW0t<DWbq|6A%XrW@f^OLG*nG(585QpiCmMINADIR+Qz4J31
zCXiqJb3h7Y@U-6l^+R;z%q=1ZLj0yP?Pl#Pm}7T3p862p%HTHvwCt>4Nqm1}cj_b-
z0CCuG>=XL`JJ$~WJg~jj^N}_OniC5Mov6nKfD2iHL_3D&E71Hct@iW>gyusd<Z_W<
zoO<K7sm{z9x@YdrWO|;~{C>NoGp10oo;=JUl(wfzJ#F@eR(tkhBT!z=*3;J=(J}}(
z16uC?&ulbNFU^&X!(Z7CCc0Cg<Aj#uMnCEPR)<N-B1d^7CVt!t8gVO`zPi&?Xg2O3
zT6eX2-UB^0a;XTOzDBUezydAk_NsQbbBhtCj}K5Rpg$0rzpUHpe#?yUQ>_yzFPX#3
zum6M|ct1t8bzEfNLpg4Iqfb0YVk52~iuCyn&mj18_Ju)e=7!l;^J><q1QrnW;J@&B
zW{`y_N$}?zP2#}&LnW*H8qWcK*q^&<0pSL<@u0ymFbwC&lF~BoagAiWmGr(T^abYr
z6GLY1rCn$4mUh4S$s`hU6@_qH=%iX=QV4aaNrf<3hGM?+G{Q|XV_z&52l&3l&VaJF
zM7YUmMOJ|<Y_7f);%kevGHJXg8jSgTy15(zTO(=mzcfP1=MUC;r0j;p7E}cG1v}p@
z@`;@x8-y5)C!16mvUn(Dn>rNZRZSII+x{a}!`k#=`lW0mIWN;lW#`K~M9!tu`M6i`
zO<Wa=xf#VtK9Q{COd)yXe>Dcg8)IEVy5gpje7r<HEHIM()1Os;C2rmLp#6Lv)GO8>
zTfC?80d&zgp@`*0j4xE_in$m^Ggy33r+lOaY)!E*R>3p3w>Tx!!XtFWz5BpJoJv%-
zmI9oKL4t`1f#{hBNGVZxNgceyA6v&}ld8!p_O=8+-pbB*38O;D_O0Gs#N6U<ma<fN
zif%Wfz_y@eE)D-8QVXc9`REJ)f1qJ>mw&;8WG+eGOw00-9ZQ_T4`@p2l}G55dD{}<
zCgH7+ng@tYMSIdSjN=(61;iTAH`ECFj`Ec4FHBxc8L<)p1P=iSeM=+c!5%n^5P}9u
z#)$Oo)4bU44&v3xnD8^`fSf+JS}%VWEz>@Qa2wE+vh$@016mgS4G6bkJ}cVWqJ7k%
ztsks!@+jn*Y*8`3PZsE1^<x>ts$wN>LG$A7lFd+5eDi2lEL_k%Z;swF7#CK8Zs2|(
zLYN`<fad>yvh#gr<|}hZ=Y@C_iiZBlB2$LS44&vnR4gosRR+RF>^o(rl>s7#+W>un
zN?kTm#-1=c$Tp&sU!V^uf7@r_J7GvW^CPBX)QT^bBwoe4NWohn+~o5LiWGmcR1JtD
zQM%{7C|HnY^17hj@GPbfK;l1wvOznkhUh!-o>~*=;~pkSOyqXq-9c9e2ph?QLvd0W
z5B?YQkVri|3Xua-+5%0Z+CjaEAa>?AZ}r~bjcxuFMns|FdT?{RE#gl}95L|>C-$!5
zf={8Hu%RXe^bl!+c!G2URi=?g#k!Nm5!X#2v_Ke&85?sX`I6)jzJqZC9hM5W&bN{@
zc`)Bn@z4M$sCZ*CM8tZFpWwH=#+-{On6yhF!<LgK2r$M^5Q2g{`s@9r9<nK9;%@vF
zg*su81kERX8z9{HQ!ZviN4%6YY{TU{kr0!o_zT9WoX`tGN&LwOG75_j@q?h|RuFTm
zVd1+$b#lQ4jj-WE7reIumMi8}WKE{YCl`Q1D}oGT|5S1JHu2})qzMhkyF%Za_()W3
z9IY#Tw4fPPN9KR=;V++s@1*EG`5^QtXU)y%INB`hh&@c9HTfEPlyyXA>J`+asO9#O
z4~&nr?<vv*^(N&ZrHD#TW9$^-Yfs7&`j+g1=8`)Y>6@S&h2|fo5UeZom1I!+5^0Z+
zCN#f0YZl}p-vFqh<j1H^#z5Yc*k_FAUj@&onrW8Mvdpc}==Kn9aw(7A7nC7$Mvb!X
zNz65&QTZtSkTJn0IH){mj(mc86T+F#CpPcg`|uBG^E5x;4dj<KgZ+X+J}x4Q#DSDY
z*30Vo@!OlW$y_jg3NbgRT2-O){+<8)BOr^v&U~TM0nj35mZYcbpejHl12c%RMwjrd
zBj5?3iwYA@!`0w|78}HH6Ds~Y5N=G~cF{gC_sxOsWN^@8DEn6;8q)~=S7WSp_!vw+
z5shgttRD%&*etmW(nP9|?}RVO%9G;2uN%qcviMrkwvw79Me1M-Buq9$yMQ8lx;$xz
zdY5rxOhj`#n0k5nFCY0%`ny^36-q?n7Nc}Q1&O9c3Dwd}G7lsiKB!$=N#=|)WGSEL
z>jjP*k4c)eMIGIk9~lOu3UyO1dGQOO;-<+MsCiZO3VKmP62ixr)WHv;wZyo>9B|$b
zhHRw72cz!_1-3=KRr-q*HfRRFclgj>@lnx|v1jBM&#mIW$$-HeBvce;e@K1<#23Y>
zehBl?NfJLB^1g|*{&WmCRVj!xZ2AGD@I61|=MRbSJ3zgOWT_s5*k>Gn6(@Q+AG9(+
zNN({i790bqRMCqjDl+ABn75wk8JJj7UMLq?GCxcMe+V*jv)fgvd47%~IaJQKS@O-(
z=)ydzcej`JG!kw=%M>?|kg-e}l*hl&vYZ?#Y`!g|<w+xk-9XBfo-k5E-!Qwu$>Hhs
zqJ%KH-H~o=uix_O_Gnq?hoMyW9@0-~lFW&meo)B_+6Sr=kPWVb=Fw;K>j1>1;O(D|
z!$v+#CSxvHd2wM}2J|iQ-xOL?og55v0ED78p<`+EfPVgB9bm_;{E?^}{3Ff#JD-1q
z`KlJ}upW`hfHw-lEqg}QT69+qLukXo{7W4njJ=3%a6=`XKb=Sl?3NN_QFQ_dO$Cz#
z;a2(hP-^jAZXx&Yi4`|s!eWLbleD7;Hmw5xA(hz2;)3qymEw}S?3}%?NY#9Y!H^mp
zhFx&_!YuWjxd6OlD6{Ore5v|L!mO;(<Mb(YbLsu1;^Ob6v#A4^lyTxpol4pRq<try
zwVnf!J1K4WD<kf*-xi9)H^rHlAcEFQU47Aas@bK#EEI=sjMHH0WBcfgKKlJae$fvK
zg++I!^Q8-s5$KCBUSd@l!#Cy9)?R2C4@v8b@2cjO{xV;ze>!s5C=#(*6~({~A0bvj
zxQ!D>?9!psvfma<!(YHSkDK^JBoa;+LaZv9uGq-dvPtc;Rd?}E@}))JPUmd;=AMu<
zmfTa#Fa1TPSiUq$$<f+*O<rGo_bC0y6iXLiyiNerXgZx#J~QMj`)$5l`#5e4Rk-zZ
zQvXstz3jnUseFE7#{@==BI@`;-Cg!zp+r6upqzVWEMjZb?BOrv()PYkJ&ZT=u9C6j
zr$f1=KTj3&r$$x`V^<M$ci36JVDSxT!%50l)$O6rXIx_`OJ8iE#eP%>U-~Pi<2W6Q
zV!|ukUd=B1Wwuy2gQYo39bC|&TTiN=t~;d*F+>#SJ3E(?kvkas`*X#@nYt;Aw|H^g
zT6TY}u;fQZ-Z=`0Dt*&mP|-&3WUPLkE#}`1gTY%|?YgqN^npro(f7%BWCYW;lG^7;
z`(>rL_)b0N90GHip-qc2(0kpf{HLAbBuD~nM#9?Bqv8xgW(i=J;V)M*OMg`^j^3_i
ztvzYK+~Z@HBmGsIjO0VmseN`hwd_IieO+W!61kuo`C2uz>{rF&@U4kT(}%gVyNxj+
zZRD#-#>g{2te5;*b!+J_%f&^v@h;Fg5N=7~12s$4`!(cHXux=(i)+<NcImI6`7g#9
zOx+UeFzEZvdHVi-I$yp7n~U{Eod3WO@0Lo7zUgF$N`s|_%-x{P+wJpaS_YJL!H~V|
z!D4CT%V?o7(^F36#Y9bpP3T$>ZXG+RTmwBqXZ24Jx(HQDgmbRam9h>BrJ?IVx?$}R
z_fkn!=$7?e6W|g$`qZ&`F4adF1L*tWdHSA&zCSgr=)3+&`miC1Gw;q@OYW`am;EAB
zD4h@DORa-UQoVkNb<aBZPv{5h3?+q&B>WFB-tCUX-;nrBY(c%wv7h$XN$#kEn|&@F
znu0{wvE)X$^x>*gd#6*$zQ`DCw-}tsquMX(0M6dwj1{F{RB;MNiD@+>FU)9=ZM**p
zK_-(h8I1ZzKb|U>*{)$N9=3)*De<fzMmefbfH<_lGa&7-li};?X?M5K30CQWxMsu#
zlo(QW0eE6!?_4KdnZPpemrI-~%@1OGchasVP@BgNnZn%YDLpD2H<Yb?DoKS;(iMzz
zqh=~Dg6yeY6{iA_VAwgL1hb;tLA7+@NI?pT{<KWQgM6WU0&&}RA`gf#u?LNN#I1XC
zEnoTwFL1=%jGWorarz(*L{`vNj#z?l%biBdwRa~4m`jv9Y9v$p3<$SA?;@G00Yzma
ze^}2c>iI(pDT0Z_aUaWnj>|^Hf{@XjSve3p<$UH<fL@8N6Ez7x>$^$q%t}7>V)lVc
zO18vJi>Fdi=}q-~{@*pL^y!jQJTlBA9?a~$)q;CaVpKkpcS^?v*>HV2D7~d#$iI(!
z=4vgj{(G)adaYToR>(vmQ;%|~H4%VgF6cuaF6Qm$aj7{;zr=gk4j#P-;|4bDyrrhs
z{ykGFzS_(ZW=EB$5RCF38?eUlK1x#Y=$*MtZr@&-JM6gr0nkk^=K=+_LhjO<J@g)@
znm^d$ux!SQzMRDhgH_rsMfJ0bMdW=ho^Z7Plpor=RPB)~C5D@sj9!<{xx00_P%Ovw
za|H>9rZ{b}=(RBYyt*@VQ3?vgPc(Ch4nA~WFyszhB5~q~^>$XBcwwnZ;pOJQC|L&K
z8JMpkbRlIPE_<lMh_VL`W$IT6=8`TeJ9YVzE-R83L?Nf>C*v@Ca6LWzO;(y%3PJ1P
zlojQV<lUP=5{#q9O9398h8~X@%2q#_z}Ct{dO(wmsaN`CL+PR0P)4OIdi^ZYFeJ$w
zFq9s-mH0Uia<K_eFlwaY!fT-UPsFM4zIpnNDDQxJI)A8m&@bvkALQz8E{dGz4>`lv
zB%D=PZqTiI<d^!B`4%;m(DEK+5VYpoOJ@DO+yY?&mOW%7O)SGSPB~+>4rFfSWYH#;
z-bIyE<pg|7_l#H&p~Vj-cKXDUSv!SwFk?oAV~4Y)OSM$~{EA(9eUyuPR$O{}y-<7~
z$}})J32|(ruA9_PSEx6m@Zq9edCP#li^mV=O6LOiOKgB4>_l$PsJ*Y8w^rgR{K{gg
z{BM3Po^+zp+oWD>TxFfs2VMJD{rgv$FGDu}L<vsB`|U5oUV5k*Pa+b+OMTbd==TLe
zEQ~>^0Yv{O3>4c2LAW(9p9?y_G5dpwq9`HQXL`gCpZ{Ipwx;8ov)6mg=8wa^y*O{d
z7?@mMM&E<Q88L8@#6CK2ruXsF{*!ZF>z<Z3bz0On4untg9ln0Us6l|1U~nUhy>JX@
zB4VfG%p$9%r%fI|>veyh_+lTc%SG=*HyL}yRCny<UC=`r-+2~0<C<PPcH~TZ<2hD0
zobE?B{-$?kGITQ#Tte@78$cal<nDiucV9W(*>H&0j5@h6wWnH|NZ7Pj`Yk_7T7q>G
z);wC3#NoqoG<K8BLByIG?}Qr<_gei`NsxX)7MT~-i5#&x;&0ZalFB6xHoUEP@?EWd
z{~j~$f2S3Xy<)n(;dm?_Q5d=})2!bAG~&q*bo;a|#Pcix0@6w5#kS)=(e}rV^jpNt
zin526OlP;09ZgIJ>-I~g!ns^rJQ1lLJw3i_qkysESQv?u^<;WRoBCW6puQDg!bdMm
zt$o>aJo({%-=Cxo7RED4f%JUva4Ise6ZgI{l}vxB-4e8%9KqECtqG%1s4*O;prqEE
zT{Nfr^KN(iGOv>)DLr?DC|{~tnK*3zrj8f)6+}>vLoQLK*={G9-yRm@`+_Qa|5P}0
zV~96=>{N+ZEOjKPatHw*h2#?_|JKq(pM3kYH+I}qtNqJVm*Apo-=RM7=9#{vbsluO
z)?!H`4%<1CVdJi#6{}cp6elX7`KC+R+deXmO44@%ZT$7q{jpcgH2aUbz0}GSGS*;W
ziNuBcFc2o%fj0HFX}|r`*dv4$>`G*szP9Pr)4j1*%r*TrY0ueJ&{+Ls({WS14aYQ_
z!TOAs+R3o2<FZ`R`qqY*G-hYNox%5QA(j32jA6K*x&CFO-I-~pUs%Kvw*!q%^~R3u
zG=uSU;OuSKu@8L{31sZ`(Dw{xvv+76<7Mn<L=O%(sWpU@qtpO{HBOTU^vqllTTrYP
z1eqE>BC5U=knl9pWGCMAwtlPkFj1AClS6^Tb|V`zsvl8bNT;1W!KM3i2}rZ)-eXxW
zcUaEOADXhofAhv8`mO#`NtoHUV7bqBh|SiB*B=m0duwuj;aEH6eS0>Zcz37ouOHA}
zJjWW^Ka~nrcc5>NK#h8oKf+0MuA2=v95dbC_(4_@`>K7QYowoVr-H{i-q=wd>%fS!
z2dVjhFlgUj=ZM=*^FRFgZ>~xq+%VOEXi%V(lKT7Yod5e7-&<pZMghA~c_51f{gJ31
z)bT{wwsI9MzDobrhmvG!QadZOPh9=LaG`YUY&ve?IAc^~UgYO7`56>wWHa0x`(WW+
zXXpb%xy3(7=fa->G-##b++j}E-PL!aRu~EPJ#ITm?(kIB*_kw;qm-bhUZ2$~Z_Q<_
zV#n_NNo)k94*1;-g|zwq@%A17mK9b0|IIJQe*HR3hv^}z>nbWLih^qd7vqZSn)7d0
zS+f{1peyQ{STmp)P!v=UB#5A7kUV4tW|%zF)7`IMzWM$?r(So@00Xk_U++w(dUY$F
zsycPf`JR()T8&c@96`&JnELp4(gLrI7LM2a+%a{Wy9Al`Z+e`oociL%Zm<&dkzZmr
znl#MCZ;DwEj5t?T`Bb@$gat^vd{X<-NP6<Rbb0JIIKCkZUcwl^qBgxTTXjY|M(jEa
zDr^FNg+&>qwCgl1x{G*ace`QgAL`EZ9hFMq-$A7)zqO_@{^L|WT$qoWAH`QR^OS!Z
z;opytpqGnUaCa(3Ru717NT1n^9yr|xwH<4n3%af`G-9p3BdI%MflUl#zz=q4meV*@
z!P*+sTR1N}g{%4Z@{={lCQD=0Olai~l!*d?Z-G`T4*skTIaoLObnlOBescRsC@<KC
zyqNuExjZ>wlwubYmB^0GL)d3o_UR{#I+H(e%bFL2aZrrME&;+fw+JK}6Q+EugxdZ_
zCvf)7q>KL)<+Pxmg!jp0n0oa+6({=-Q8w5H8klhow>n|^G1+wC*-@5!akN?D5ypDz
z#X~mCM7DcL)0L}_j9>bdH9L2(o9$dAkx-ZoWI_1bP>4%1$h19*hi0G*WCn@CTXW#L
zGp*nDQCSLaPAX_V<*pNvos6jQjI=)Vj&jBsbyMEmIN)LTmUl~^tq|p;n_B#(a?me3
zA-2Py4o6z4=9#e@p1l~m8`JLehDxpSGB1y9uTBiQ$YsM`Eom?I#HU$e6B%1-8u~8u
zU20p6AI1XN9?<tWKyy3YXz|NzXxs4O(+Bo`gp*HA`c616kw#b>q6p)CMa%ePj{CV}
z=;V>q<aL?y_&GLU6jULC)`SU|y0TD7t!{yEa|SWou>41P2`x)5%t$ZjcyTn8%-mzw
zvybuc5n^i=mL$5vI@px0+BGz85M1ZNwwGYrH*=?1PwxwYQ_dfr+`CF<>BXsBg4wly
zeJZk0z#91~^lewM$A`1Yu$>C4?HHr~W#xk{vr+d)8G;ee;mVL9>%dHb;ELolARnvd
z(aou<{{Q^#QIU_TMMiw-$-J>hOz7B5Jw8i}D}s(6l*y(KN6E8=wm*@T38$tMO^?3x
z<)RfsAk;xPAW&YBG5g^it;Mq{J)<1r%!YNs=vW8iL+9~>SCSqJqGt#5U+c7Hzh!n)
zk58vcA18LzD6y-mjh*v9vq2~oGNq5@EoZo8uN!LYSUAH`F|IHaOx#~K;`{e+FL+yP
zZuxZ*=sSvvA05L>T9}zTp|RNe6`UC?j`M;cJ+)t$YX7z`T(6!zC~KM--^@Kfmq~$e
zD;||%2IIK&xp6D|WG}VzBh7|;C3dg-Ky2c>bdN{K7g<|`K_VepZQx#@f&}3fwzc)s
z-T7b^gj=SM>Vb6!D8YIL#EQa81=`%u)_1&Od-wjw7;|E;lznCbODADv#!_~@V{Un3
z55pW&v-UT&<|lRp>t7VlvAG(hk91Q3a`~veklZ(kp^}%?jbhJie`Uen{Ay5nr&$CC
zO=AC^Y*;?p%m#OF_wF6@w?}SHD-rIT=Ckq0K9D5AI@-?P#-ypu{|8~U-e=7@`_@wV
zV`A7)@?a6M*GDE+=tyL1{DusX;U+%s#qym;tQ+v(YduZYE(guk3=KE&{*61ljnB$7
zu$HlnGT52AW?EC0oqwvGNgrrtGDp?)!ZX8c^R&6<x)FWW94<QL4<e9Z57}|lmfrkN
z@(bBRI1st#=bjp5=f5@IST|zKS*uE^!m%+sd^s8Yd?wewW@mrrTMTS#xdc7Al8hXe
z&h@X@*_(Z*-nafyElaLbG(5onh)}99uhOSSc*J3pC_ht2B4+u2R=$1qY<vD=rf1GD
zP+Qnqp8zdCmi2;l2cb?0AlXSk)-`)KS2~4P*g}tS_DywSt{K_IAJ4X$|7~K6j=Wvc
zUJ07*<hf?+n`WSA+uG(QG@9E#5w_fo20#<Mon6?Dw>+`Y+VSbA<!uJl37Xd<8VO;O
z%;NLo%=}m8o86xe=-WDUNKYRKeV^BrJ8M#Web%vx4RhUaV|(k8R4;cJp`GL=Mw^#q
z)q~D63~jFUOSCKzPl5%+gL+TjFx{T_HWwrOODv%^2ed5bj(D(hPi;h-4sA4MPc%AV
z!-&Ei9)WL#z*^e+sos1z4@|z?CsJ`E*mUIL;;xe<AMpP<a4<|q>(e{J4bO~s8O0H!
z{ONAWpGOqeZ9w0_eAI{QxAD@$+88-~ByHt8#^!$rFc6h%BeXR?AFFi`Wv~_ygd69X
z=p1BD|4z@N-X78IV%EZ-LNmcFJCiMMoNH`8N?#0jS%H6FpX)D!2EwiSp^?<8vqf&k
z-)coMVXAMBvPbX)Ot0>q(un<r9o9deDV1JqWc|B3zI(Z>-C{Cw;%KgZoXC)|f;q1h
z_ijk_hu&W+*WRq<F@*QKzs4s-kdCHrC{!i<w|{@+b<Y=>LnfNOzBp8T0dDf{Mm+J@
zf-!mJP+{V72lq;Ct9LnEK)z~@d@N0jE6*yvvXmM6pE%9ZiE1B7<>Dq>@5c!i#Y{C$
z-0E+5es^x+N2WwH4%!#?UFU_vL{7D<riHtNcb0(Q@**6VzG|>S<r<2O0nXRD`og<+
z1zVoi>dn5v<Gh6n_<hea$~klD&#6iZw}9PCUA7{m?f+8OSDjodz~LI$Y$n<Fl}zGb
zm8Nb;hIFH&b<Wbf{0Hk!<#k3CC5th3VIiOUH-gW;qHIoy!st73Dd~2j{>4Gqy~6YD
z>4H6Zja|05tu%ig!ogSU;ZGH^m7`ICbXwv3N4ueae;C;N4A~QxTSG=R(t1~gzH{WT
zS^sD@=Zx#=k#FTosY5Ki^udZfbyc-ge@{@vQA^-nuEVLpL9l%H`Uv&F1;+P9TJGH=
z=FnSHS!cpXuevH<%)AKY7Yf?aCs*04zL#epWSeg2y<0UecX&QG{wt3AZWy&r*%@tp
z$FA1K8EwJcX!%6C_4?-^3D}|gg%_7{Q-4mXaW4$oKXszfGx2M2d9_@8UOz7=HF1y>
z?$rI#YYGL%ugvWyXn)a)R$W&vWFF_|tXke2KgYt$Eb-cYP%fq)Ls~s=jh~guM{Dt6
zal)8WJk-u)9^>T3&yoO|khoX_4H&V9bXh3?b3ly0Ns(`3-<6DglQEm}(y0TA_UP$2
zC<00xOAy)<$Fd_Q5NM5!O&=rv-yer^6PM-6={4Pyd!rz}3fkxwa^+OrO=X{2a_Xmu
zGsa>vc0#T~P}fxE&{C@YeJq4~nPe0s+|XMyne=McZl2pgrI$0u&Sh@jl+LCm`&RQe
zUDB$?=%+IHV0Ep+QKdBA$`pOqk4t3}+>|VQ2&^?!>fH|b172k6n!-@-sZq+mGnTNI
zx-#<=$`nf+0`eFsS6_VJtfJ)||K`fUpm#6m5!x!FPhL+H@BI?Hdqt12=c0Agu)gY~
z;X?VC$V5bxWZqgZM^7%~s;?!EpWOw_(epcha07EFZ%$q7RGd|b)jG%X(yuC8BVWsB
zE3Yx(7!bF8Dsqd*LVMR#i>ppUBTMk({-46Adzlwl6ZrvsS7W33v%oDLH|kVgjX*q(
z_3_IK`OJ&kR_g-awfBXgXC0WiP-~yNJF2siy+33o|27E{9qj`TTeRH3OAOnhljL4q
z;0i|IK;kue=RqnP&_shEitH%tT}qhh+l0vQA@HtT#wmBq=66F6ivW~7HLHA7oD0_V
zg7%q6U&TFN>tSo;V1k%Y*d)MXyEmGFbAWAx8=K+myNSz%L{qjg6B|eG9yboI;tI%Q
zcYf-I){_yebM>A7PQUM8-cS6l9oVC$L4dtr{;U0daE+S;ebUAZP*nKKJ-+Wob6|xI
zvcLj|TI-yJz984^r(@QRXBH2+D{6n;yCWdR8_KqEqR9WQ;{|JR$b&=gWIfP%!svwS
z0^BWxLHA@bV|N$@z0lu;Ts#PM_f|f*4uC|84T(cVaY=F@3DI&jBixjD218*6%IdNp
z4tyuDCnK|Waic%~VdThc^2U%TVM5Kl-j9MiQM6!2X3toL1i)<kc(K>{p@o;Z*bL{o
z0UJ;l5z?xEbreLH{W<&Mkqz0~{QtCj?gd8dU*Y37(ZS(RV6=X{*lnGth1R~Nk=h4^
z>0&dU`!BZ(SDSd(_+e5>$5tuOJLfn03!hFx=OEiKN|b%0>xD#9@NN#!I8w(d%M7=-
zqFrC;_53T%!~l>X&VU~7`X<*^vGuke4@)lr5^wUsDs#iVE{vj$T4Jt7+|=>o-UU&t
z_Y%GHOJbMpmr6_)=lnN1UH2lm<;@}T3TTh+_OFYB;09L{*`hg&QFGEu8b59JdgtOL
z<R4*vs)pg+w&2gc8#nSz;^Ez^seGE>ix*C9_PiT#Il9`_ymdao<KUIIdvX6fxWOsJ
zERx327)aXrdYC%I0j1TAd$%*bXy)DX&}0$CUdajf!ZAU~16yToO4=kbBG(0e9K?FC
z(U07V2H_^L*Cik%5bn!a_RzQe6vB-Ws=j;4g1hVg5texdMMR-TT)PNDi*@h5AoBl&
zqIin5doi|CX@2BgYDCUqwqX@KqkZloewlUO#CA9JI2+Gqp58hKkAI&s0_R!KceDq6
z-wAzRfz}p3yqyc$o_{kj;h?eLvL2vs=y6PEBgUWa@j&Hg5kAW0N5o395ao+|p;5q$
zw8T{&TE_HK53coMuWbj;YR&9j)^O+kQ_-?qJTTqF=f(|w6yC;eTs8I7V@xKTjraSl
zUiS>R-?e@cMASPGnVsL`#qBuebq)FUC!g05<Y>45dv27|NIoW@?{+Vq|2Fh}7W93M
zA4iT8+M}V_KC97dejQZaez6(M_5u#wIPSyw-+>zABsB3Q#1yr_X#Qfc*Zu}E83n*4
zW|Dv9Q!Ey>X0w^R`OR<6Vs>VsEfE7(7PzpIyT7D(;Y!&v7_h$-!%fgMa~5VSWwZ2B
z92fr<q#8a3aEu`a*a|Tuaw$U;U<<aEc}of#Qv%~%Ak~0y`|_}vdnyjIf6?{Z-v;3(
zHkl#=eYj_7LdCXtH`+(7WnyOWA#OxK{7?W0H-T_M*CJ<T>sMD1y17T}DkU1D6{I}?
zwIt=ql9Bi1zf_8CBSe>MTTWYUAV{GJ2@@n-KXuCHLJTNnZGq-q<gwJf2f|ItTYh+V
zDZgB`gqK@>lwGIXJ1AB{{_Ku>1$9%r#9ZTs#33YVVy+!B#u(&-vXWwUw|N@nqOxgL
z@5!sGzpM)Ki4Cs$TPNBBzc6-$*dc9D$BG@Kq=_7kaT0{kRy78@^Ff&6CXs}tdI#f2
z&zT4*Yfz_wS)#J@eNbPQ`%E@z7Kba`FR<_jmIs<kITV*Nmu!FK8YpeX)#acL=o#}u
z39zc6xDi>m+<pd#Gc``T$7LmQ$$$oAzQvv4z#dq}MIPp7oVi^=y-Avsrx_^amis?Q
zs}prjMQ_<X&f+ITIS41`!#-kKNAro*wla3qfoD4ASJ0YbG$VAyLHr<O<{)mGvOC}N
zUD}*Sxrm5J+B$ehBMj0YhMV-4@)aUYtqcBMN@K&?jp|%(G4G;u9K^e$9SZeG2`e`G
zdyL<ncJ_Ppd2h5F3ovDw&Lk=etsT&^T#Z1lF@J;DY=d>H+RXZsU<re0FiSM5Vk^qr
z%70lf^bI$g@p^T!mc9<sguXeGmf{;q>y)i>%(`7_ha|~A1B4qM6ZcpL(6`!?r1-z`
z`D^TaX~`o5(3FNSRAQG2LqYGCkZK|UWFu5sX^>@u7+iEs404o<M8;tW;TFqPd-*-2
zZlX|8<MnWzWGrPkm0jQ@zl$3?5t|eCLpD0uY!xa<u01Gb<WbkV3DO1BpyD*~`?R!=
zcZt3r@tu^@lLN>s2q5{toymhcH1;J;lx54YtI!8aJS&OICS|J_R~tzalP|Gxlt+{X
zTnL36bV1NyqN31UvWe~wBP51MJdxCa99-P(35o6Xky>O!5=5H7E0I#sUlo5!<cop|
zTtT>TV^~4^2yF`T=x%<>yHk`S`zITpibgAoULkw<To6F_@RKy-Kz@q5y=5d^jeKML
z%`7RD+InV7`Xt0G^ohHQ_)gNQ6uuhD<-m>QP!MF4pvvHQWNjyi43Z7xmyK5*e%<TC
zIur@7*l6R?OZ8WfYnmug5l<<al5rL#BNt%dl(lhw3X)FZt4aHWwqOc4j0Nbh5}OS~
z9}`Q(j9+{g-NlSg&8@UW;;=DR`YQS%@N0Qg8u=c}?=mJLixxV#hc5jC>4KW%5g!Nf
zTJGWp@~6;`<de3n`wRV*x79dM-s1*kDWx>B_rX}vUomfz19+A`VpE~-&)<WyR#1ck
zYnS~<T55~ff2;OAiMf!mrVjC0BH`&*5R%ZL&_IG+yr3Z^{+o&&Cu!pRNBS#hJ6=}E
zzfIJ;nlA)-m}Nm%d`>?7F*4tj&!4z8O_GiLB7J`ZT2@^aZdm#uYe;Bxlaxzs%d{*A
zH}oJfkNRrziJlgElra&co**;91E`|n+nBQv(ge*Wb4z*4G4>SdP0|Dry7cZKb{r!p
z=tb&PW^m$tj<qC@#G+Gi3k5L;Eh_}4tc|}!w}aOo_WR#v^A(wvIPyk_PFH9pCl4qn
z;)qGN`7aNrr$S5dsZzGc;PT$e>AA$YDHNP!S1;}Tn+!RVx5Nce0kV48_T|Jv>B=RF
zXaE2}07*naRK!9Z43yNY^74ZXWPmgiF$B1A*$e>z@D}e1r$A0Nh(Sy$+1*guQ{Ga)
zsADOgEO0q_Rn%l5F}X0nal%!|ge}*BZGxd3LUWB;9tV{sF{u>1dgWb1MOFn$sU~-6
zwD@cAEVp0epo3U&(q8#YaWYB^E_r0blF^pA7?#sr%>#Lf<SK$A`ilsJ9CT8$t9D~^
zJLnfFm?=w`6q<!Nf;`e+q|buvQJj_hEftY(24ooWNZKL!RB?k?bj#d_%(>jOu{2)c
zu9w>Uzd^XAX$$6nGB9G*AwE(RVr)<pfAEpiBxZV2Q%^WiMp;4)F;<vKNFN666J!)3
zgF%E~nP2pD#ONC!@&=jYT`tX-HY{`8@+!0;q9VW-v|~^hiN-i(sYu0=M;_kARzUnt
zJRskL>2Z+x0pAr@NMA63qrHO_P8S~%`B?Ia8WU*5QXRtold8D&<=dDH4s*n2I`G*b
zRmmK&CfQ5|T;71bp<8YV)GNxb!ASCh{_eiTk-oI9u-#pb;U;~RI%G};w@Ln7u0xGI
zuc<kb^anlC6j{4<=2pc>6B=6ThqzoughLZZlB&sv2tq}WP_(9^ebW&>V?8Y0GKjc>
zBeRvL+fo5*L(rN?pfUKA#;C&7>~iy3@_E5cl(7SlHMkY;LElsCUCJ+nq^XX8tb=J2
zk;B9FOKSZK&tJ3oK5})?v|8*7fGj>#vnMVql*%8$r$5A+dw1PjbxEZ-d1bLQe4?PG
zkN~prigRcceIW}W!41_;9Caox%akzGw-h=FVj8UJhv^OboJ2#2xgMupTQ{a|sFWu!
zO&3znBTZ~xWpk$rRfcRCOB-+c)m44$mz8YxwOq*vAL6{EqK}_Flq-K6gDPxB#mO~@
zCwuyqa%I)o!j}sINY~OwR~4eI^g;3wdOLBQr1IY*&g2!P$^;Iv3-6`f)GH{e+`DVW
z=r2lH=edw2C$wz5x%Uj4qrWI-?Ps$gbO^`tP0Vht;@v;w3c0r>#!8>1%@2O0uL9FW
z`Jhm0m(_~XHx|mn--%MvUXgt(d7=<29aizNc4cBgsja@PR2}^x=ppu8fyd^Fw>5cf
ztu%Fgx}14UWLnQHY7>7N%TM2&Emyt(T0xLKjcigobtJv|wqmvZ9h5watakJ)BWDcf
zR^OVh)J}+<XalYji6u9A%}{CTI^GrZ&@L=b4y|ZoXV!9)SC=Z|ze(qe@kq<OwQ5XV
zIZ~XuAyqDZU=M_wS|bnc?BH+NWW+W!#yDP3OQWaZ=5rdZN)T?s@k&=!f9Wxt68>+w
zb>UL<hxPQ(3D~3(@g&Js6WmG`rf<%dOCRKPX8hugl??x2BsF~-h^+I$e1XcWom$FF
zURf$lTv9BJo@rx#1sd=TD19!3Zm$ACb&`<+_>k0pP)$$YR4q^Z(JW?O0m5yYlhl7v
z%1vA`cz5J<Gp9-UKN-qQ-&`qA`~f7~3lvT5rpLW>S0UUOV(j3xkzWqw<lXV}^@4pc
zeZq=L`KO@2%SbrRL#_UU(bV)>@|~mSjWx0kU=$vo)y6NtvHh(evc9XO<8FqqN#(-C
zrL^yl`BLpW*adCT<J^1e7VD)laZy&aFJj%6zc6A?US29s{0VgGM*;l^a_u8^Yx*Yg
zU6{=~hp}Jc9Lhejs89T<UYK5+t5iOlIL701`l>&bixYp8G02swUto<h&dP_z!_BX3
zsDjp<b);U0xu}etfijH=-95k3Ve-{v<4q^kZ`A2;sWSG9R0ezRL~e0{L@a%}ZVPR&
zc@mzhU`|{)RGhjo4}ulssL*l`l-f!4^y*tnmC+yB+*e7P7Zm2OHU67oE_DQB*A{jc
zMda!AtQxy7gO1yT2fH_xjmgVK3ez`b%jM&sZ$V}v26@DNX)p8pe@%Mi%~#sP=`O<E
z+;a-4^gf9lY{XENR^|>ZoAx-u_`3`VKO>oxj8Ymj*ee)djzJgnSbG18Q+Uo2!cDHo
zl4<#oKV%aU<l7`))BHKht_qZG?05`c{cd6uYR1SHZ9%xPddMTKmN}FwX-YVmd_45Q
zLcaVeyPQHEB(C<$Z!9~dmssW8!4{Vb93Q@_YS#WAi*h~)LF}YQ&O!yv%au2kQl+EJ
za`wrB(vxmvwfwURHWI$rUvG$Se-fpRN>Y121pG3qXd&X@jODAE48K2DD!<ez5F?Ez
zujQW&v@8fCe<MLG71Kd?r8|3lC|asy0K^SpI2#4QLOLq{YpqaxKX4<QS1VMI5H-$Z
zqHVxMKUs3BM+XIeo#E$RIFcRuYMe5rs#@(ouzhXxBkyk82b`=K#PbR^x*>-2QSF`8
zLh4zZjbA7^<)hrZPae=sdEmNM<-~HjaHyXScQ|4BEyIQUzp{w}QV6%A;Nj|0<z45=
znqlu>($Y^#+u_zY2<I^Hgo{CWs=4>z|JExo4&{RL)CVElmL^&@7bVJIeno_U6LT&7
z`XQY-TgAfjjV#>fU>KIgI%=%Q%R$Z1DKy*Ao1ALy5ED28sJC--`ON+uC){2NK`jwI
zF4oImBeKYIwXAnT;71D%{1<H@AV@e%*sEGeE50zB6DK^mXO>N8jcNLK>z*>5qh>4?
z67R0qclH~$hmUo@m;qDlnh{J!V67_a;+s|xH#%1D&ct^fTTj(LIf(V9LX$tzDpK=H
zC(sXicWZv;$@SFm=gfy7+!T!fK1on$^#2@snWsXdCrUsrGamUyEt8rI?B1VIRVu&`
zn|Di`=4(OvaaFtUY*`fr;<e$CdBMXTzHZb%Ey0P>ap5p%*#YRd-t^-pOv$s0W*yX9
zxXll&>6$hCDG+7=-$stF5G>R++#7XQE;aaVNpJ@cMGIz-dCE|#@GN|X$~38)EfXYj
ztLB%v710%QE24%$JVH=vZ%c~dhB(MPbtGLs4TPIarHEK$t)o{Exi0Qz?dluiymyE0
zWe*+3@*>Veizr%G4{O8k$&_(ipGEw~<rm|>9TBE3*|_?Kp+fqf6QlIG0w}dG=dLxq
zJjlQ5R|Vx)jw`Rr6$&pl3(kRXvz*J-Q_C3wbY{Zsc36G`XwA1EZ~-vl6O1(kK2Upi
zwU9oF_<^(14@G!?w{MR`HiJeiZyel-+?)Dt_va#sWY{^;2}4atg~e$8*hnNb$YV(3
zoKx(6kHv|ss`J%$(7hRi+w!wSjLUzj>j#2-QNTnSiQ%>ZE`}+}{6`TF^S<6ZBj|Rn
z3OsWdB-?Z0BzN$p$bdxoCJ@xET3q>hIb{#`Om7pa86~{)^_{Prbvrj8!Ln5s6Kfyr
z{!&CT_@`F0d0_?wP{}cIX|(;wU2b<Rl3{#$t4E-*?5lEAw=Zf1trKH<YQ~`1GKSIC
z{jEre(y}A7D1MpCh``(1ivCw*VX}o_MwrB|-a4xpbbbH;Ws=1$+J8l;l4p;HG`a5J
zL`QRf3?k@X7W&(s7j|Ml8#((d-BAVcB}8EkPVwy1Q0H!OgSjtyJ%4K)rXHI$%g5R&
zGv71)E&Z_b`<B=Jp&9w7FD7%J_dIm`F}SxVyf2$%o&vJxUe}xZvfm44lPLQvHli8Z
zisp#&@qBF|T*$?xqfMvt^;Wq3l?&ZnU*`^!!e{>Dx!cJM(uBmt_v$;lUNnd8{8tv@
z#qSfbm?J7?Gf>w`>E1HU@Kaeg;Qcqk|FfFkO(-WvG1mCWu4LCqpsZRp@Z=?;ko<DE
zqL339a>Dh{I%G&MzbwjnTYD~X4aiZ_oXObkU*q>0KgZ+)q+<SKStEZKzB#(>$lFFx
z1gu-6#KtiVZ~M!WcGv-(d0nR~qUwR$QpAMYn(_SmLj25X`uy7#ydAHZ@AR&>`(|BY
z!iD-S;>2x#h0%$4t9NCuBS-E0n<d{u$Gy$zn<HrmhFU~E`Y@|V%(3K}t%Gpe^*Y{t
zb))0mn{v%DB6p}`g~~a=kRuQD7K?#4|Mo?{f3Kjnh|VxCu0)GQ`f+|H*wzlbdogo8
zhJfh^K6f0w(A$2r-HSSI+&(u%qN?d(gX12t8L(TC_o`V?s}%$4?&ZypD8(TzVhI*X
zKJnMl$J`D3+SE&Pwlm|VgH7F_zqTDVKY^y^K3CiRvPNh2OST&|7}GPw={hW{*rUtf
zsxRrAaJqxJ4eu6i1BBbNKx;Po;r!SAu6s`eimre(n-SbWew4<`kLZJlV8I8_M;nhq
zDinM=9zAIQW(u|wU+&3-G&;ExLyx^}hvSMV&c&%Gup02kKzJ!Aa_L>-+=;umou6B5
z`&+SvR`J?WiL=@vI41P{f|!Ud*|7L>%kKYVA)5b|kBJCl+=h`5>kWC-hz)-IdwhQw
zvUzCL3gNaC!%bf5-PnqPSu-4Z=U9H?QoFz<OetU#!VT1pGQ<|b6wT(tA>4#8CGJ(5
zFx8(OGpD$6Ph6cX<X(mDVFX!EKm_A|2YY=4*w{838CP_fb<xI)JQOXz=De_O0yWL|
zE^7CqE;2!6eKLv|bFhIzF5;t`CD8xPLg?L>ai{OE43}T*;>!tW=^=7D!fME(1YInx
z)f#=hdwnO=t+F=$wR(BzYe_17bloaEC&;$X?PF=n#hEG3{`-iXf5z@KYgh#0J;tRA
z;%`V_^}Twz`o$;}ZRScdNx4+=kVn#ZOK}7ELzY#%>f%f;?#yco?;`9xpNevikm8!Q
zRo+G$Er%PS%qC99R<p_q^Ku0A#GI(=+L<Sm?9t;#O0}<YMP!NOUtcf_hq8NyEp?SP
zvA#A+6=UKTR>jP^u^`-9XLtH~Y1Euq2f}TYZ)*<BrJRUb#;AY9D0uhm3OlE2LA<@K
zxpxG9`bndyse8-i^h=3yLCXuTD_XfH!O8VHT8NpF8bmekG#=p&K7=*vh`Kg@$xwdk
zmTY<W+oq$53rQ$miB`n?ehD!r2bl7dH%}ArBoOm$^lQ1iw%CmNx8lGadwpXcS;u;k
zdLkl=&$=btW`{|tfMGMhi_yp_9Z^e-|Fl}p93^v$F7nuXDzhfC7qR)2ov?Q^NW9H@
zP<ibbPKQ$B3QDL&&%6dS+aX@IbHO4T_uwEdpODR@#sVPjAwm%XZjdkcj$tQrP(Rx~
zr^P`}K5iLewEu_?GEAseD}H=9mENzLX`Z2gI%@94qDSd5;8Ue7#vu+-dy6m=o)c`w
zeDNT{2j>IZUhUY`H;?2dF3l^Ene=7sY&deF!OtJdZj8)2aqT4YWILUj3LVVmm>y5>
z-vDMgDFGrG!BLc!@y@b{FJ%*f+0LX^g+vREW22fuwhBsge@vZ^8O@KKm4W-0)#DiB
zUjaczVAtl?iT5H(s%0fNw@%g!w;0fxr;etl*JjJBE_4XNgdR&E)5uiDPX^&O-7x_{
z2?SQj<zrEPi?tB{!ws19Sq>4Vt&C0hp8zO)>G)bYb3o5&ofq^Hd{B!S=3rMewN+Pw
z;M_GAFMeM0<CYa0DaXt`%Or@cZw|bp`FgmHev24bUc+fb`<!lH&yN^0_u!6bGH`TD
zaY<VKl|{4gbUTxH9o_%49CAEMbSuC9Js#OdAKfFAy!7b%pzQt1u4v2QbB%?wbDi}5
zY4{ZebsGc!G<;qh$X{|64`_J#wTJ$cG6x;jqd81tHyU2J+2Gm;+1wR?@`{X|@7dmL
z{Hi)YxS{=(4a$D51L{ji6%P5399jfyx}TX(r`H&n+%q$2b6Rb?YE>n~1!N}T<nFGv
z`6=_Qt^Y4v^zP>h*7g(sYAJ#*a$rKP^68Vlw(y@jyiHH@0Q?t%?7@1<*`L$H^o`u}
zK(kF5>B390sq8;;60slTUXUN8y(q&N;49*7JNlcR9L@)e`6&Ao6>5+b%hXCudwE0#
zs&I8^z=uy7PdO7^Yun4a+yiiQJH?oGEiLsTRPp-@c?9gEG}mux>sR;Yqb8o?Q%#9J
z9EZD1f8)WeMR$`OWuL(Y2L(1ixg%VE*fPSc;{?djqt1M?ej2c-$4wyLHM`$x1RI~z
zSeX5`)rsI<!>t(M0x}psZ#b(5i^=Av`*@T!jqs0)+V+?3?A-rYd%-N{t@IORcFkn|
zUEBOE?=|O~HABt{!VRuf^_N3Ro~Q$YZ%g8b8!ZoR2F!}AJ#PE>R**A0xLEE{JYvmp
zfTQ}0FI&w#kjV0Y{j6~d8*?j@J^&=;L80vMdjHyO-o`cA1`dSmqy-Dq(<67S*T4UX
z;i6GaS-HancAQE|FHL2F4UM3EGSLe5(Twgl=b|mo-PN4^LAK@Wt0MA3v(=>jv2?z7
zDQLETH{ojKAkHGHu+!ch`2P5vMDhsr)Dc5k?Y&N}d*05@+y_io#c-2@@ev-8+Y7=i
z^BUr-y}`(}elgoxJf5JXh{psiW<3j9ej4jV5N<U#_^Ug~=7V>&dbgB2g_i>OxC!(+
z2+rmy-Oi3L=^f|5RN6j>yx;~hPfSwXKMM322R6s%ZD+J?-#glvofA~&lS&z<>gx0F
z-QjO~dT*h>A@8UEfqTrG@}@o1wC){m>};ItbhFQt8=VCAbb=l4Y_=L_oBi~YQgWma
z1)^d&YXYbFYZtDU3ju3&rMK!AnMSOwcSd2r+=g4tKq1`bm{g;qub*jxa4X@v1IHi2
zWnlJ`I)VV32XNp(W5s_Vy0<$EBCWlne`h)<{Bse2h)uYO&?vlsgKrY#=&OitPygya
z9Ve_)$K3Y39;{t1DZRqUM6)}>`=)x^{cV}3_@b29V8!9x1L+~o+*!}uyuY##ZhV}e
z=uG^G;9_W-pyNz!@!0MDhF58G_SleBd>=XyQ9>`%t-Sbmcm~J7qo_y-K0%`_!=K#e
z5W}sUB@~Hmuj*R;TV!(RIRjdG|4=r)X4c$%u;1Y}!R&|<OHRqXRKgyq%wgbidMECR
zyC)h-Ji+P7Of5#<hswd|H**ywle@L;xmQaWAl&||kPkO+4Yyro;OJ*-)cKh<Z%vo<
ziBk#g^XIl3ZcJ(Q)9M+)izoZ#occ*UZT<tcH+p71Z^9%w(>;6sTV0lV20sk-OnP58
zody{bortmb57NQJ>DeO68D!cmv+)zH-TK_tsC#NMmp-aujec4)yI1%8#V>4)8XrsM
zGH<L}qaRDm*13JxKB5GAC{@4>q-j;!#{AEeMJkdB#!cw-ArFaTkT3SCx%Z4%)i)W{
za8u-uoKz~b&h5C1$Ai;*UELV}WRzbxb$fU5Ha7uD=jA?GNKfAqR;@~;&!6rG%`bUD
z@r9-I%$=#4QHYGzX<@tg;fh^(4W6xV5fLwTqTV?GK7239A6qI--<*Jb5B2#UMqd7X
z!%pRudfFV)?5NcUXWyxL)o<6*#UtX9cbnM<;Z&UcM5miRtY#A8s9+R{*EZh^TAwk3
z+P5pI?9=14Nm{t2<@YWiT(_|C>Jkj4u#Xd#MoTVlXtS4#lo&ctED6zQ-#2R2-e%{V
zRlW48>k6gWPj|ch-|1291EZ<JA<fk0+x&KX*Sx;<O-3ucJ{3<~WtOaL6!*@Yi+V?|
za_Mg_v(`-0maz2|H$rmR9e&4XarLbj820_JeHvcM-d-i}r|azcg1;!7Vtk(wkDf7<
zai$~38tYl!9UZKwop|zlg;M6gJ}9w5Z`Fl<Ve1R~-Qsa&+Zl819siAs8-$=Sikp%D
zE|fr?WwdrN89&#}Z~x~2rT}XbF`ZIN{ecs#Od!SA38(AGf91+fz2hVYm*cTt^{pMR
zlo*E<ZRCVvE?L|bZFzCvTEj(s{46JrsK#_A+gAI6s3YN=dsh{Dm3Ix7N{AweG3dEB
z0h#?J2)nqg&HuY{Id6E^cO#Tg;p*GsA^cqc4)Wb-A*W4Vo-cr1H0@PwVz?!3|7KWS
zZcD3g(P~CH()~LFuX{=mxo;TlkNvbZL`NhlHMRt)xc0sjI+BI3alDE&C_Z^sI;cb{
zFhU^QwC1tBa8WZ9|Br%0@BFsw7hjOiuD;E!nnI7i>N7%Ia9&c=$G)iLTW4(R&EFiv
z?k)YGco@pg8@yp|EoSGues|~Fw|ct>T3z~bCOz~<LLGfA0KqwCS6-`Cf_r0c>^H^2
z{C5Z?bfxFzj;R%<u8+{nf!6%7=ULAfGRMA>&pBh=)bK}3rNnEuwQ5<b{0rQ1RTS@y
zXy;osukuB7P|r^?pf}BMS2yVWff!Gs?PRp~zD`%V{r~GPe;MH><}`wEdsW`fJq^W7
zKlI$|w8VO>*t25q{^xmb*K1=}#qR1lN%0-Iw3Y4Z$O8Rj(ZGFM%_{wKl=BHM-}%*E
z5N@(5BQZAeOeE$|hB!|a#Y->LI)Bo9dyTF6_cVgp*GGM>`&=ei+|!_o?f~)iXM_MP
z<garmSJr!2iA8?zLd@1Tb9g=}(cL?Myxx@%GYa6>AClO5vW5K(#Bye{-#kGN%zel=
z9UJcLK$q_!--a;uFNy^jrR!lBH_uw=H;)4)Q=yJowBQ@ymOG)}I}dJp7dI8+%7vRr
zYm#<4Y1n|8lW+*0A1(g8LAWI|G$p3lPWrRA!$-Cv4j=>!#Aat9?Ckp9{h;I80ZNxB
z6cmtA#^gsw1b8(L1@6TX?s`C1a6(oURNh&Qe)Ee$^gX@5G4ZVt+|^vW;LUw72{1p?
zEtGE2j&3yf^={9<3?;+W9?rjTnp5<(#nYSp_Q`r=U~6lSVwSbN70!Lu?ZKTU`bdZ@
zzvl(pSnTsS4!amN5LY?7MjW%P?QrhP-CnQ`R1`o4t7vM$`ev}}GrW5X2sbg?5z{*9
zuVRd3)iJkR9~Gicbl+^wAnU-X_mkeWo*V3BKe!HKtj**dLGd_8otNUgn2UX~U3RsA
zAa-Hc|1+A*n}M&M5e51L%!&_skG6#CjKsUN9Y+~EFj4^7=v((JJr+ky-d)|G^R*;2
z2oW!i_$6MMnKhC=PN8<amAG!MttCVKq<<UoaU_Z;VnCniktmVewQaw95=b=0Qttu8
z8Uj^zdw|C=+>qXfp7COK8m3#oFnfFCs1Z3hdGxs4i+C6Bd59R-fhzlxAl#s#DMX!j
zF{f9veK(+=$B02(l(a7Z<@jD*p&UD>_d<PNJB}Ax(ft4P+;AOu<_d_*OFEu+qYfgC
z^4InfcPAV*<LuuBBJFdon{=V?$FrVLTeN;R-*0@>2(%qN&0S!eSZn^;hQI5Jfrpuy
z-n|%T?rK8(-A^pRFS2oOvoH@L$oE-YKfDW6C-(L$rjOEDnOil#tlcu^m;@IPXFe#-
z_233C@pf3gjXibu68dp0u3jZK!VCh{L)$?_)CwIWDiA#<Fbizhmo2yp$B6ETdf=vm
zMaFk?=)1q+Mp_3IxV`K9tc|cq{7BH1-S73oDCLA^26*rvK*D`22?VVP3RCM{)Axd{
zN#ubr4Y`dws}16~H6sw5y>LB!p<Pzl8qo5WKxSUDgv|Uun4A4=5N>5|hVxiEyc-Wf
zFY%rBxgD>4qKH#PRH}q4vd*wu<b>~XWqgf;m-%iITrrrT;(yi5+>?5C>kM$)r=VvP
z`4^tL9`Z1eOdZK=_5xNw7K}*=+%!xFkE%nAw8g$pOpduas7MiFn2#(|ZZ%@ZtL*(S
za~s%&lBgcc2f6A--b$NwbBZ@M(F@g&-T8LgH>%wOBog@$gD5k)1<Aiuhax0eyGNSv
z{xp{V(hsqRhLA9xmkT=GdgvqBxX|j1v3R!{ygSGSNec={Hht2U?4;#lMc$?OSW+U_
zat4k5WA;WBqGaJc0luKECIkOv3lP~ExslR700AV6mMbxt<h?<@)wJV&B`%lpd1wjm
zlls8&rf3JsGcxQ^2mKJ`;)DAu{p6AP<!Dh#ra@e;<*}05LAYV3DGs&8GE{CqV*9He
z6pry$nLFB2q!7+IWPECzDcTUL3o+AOw(*pj#C~^q>`XQXWz-K%MVKk^%AhQM5y$;v
znn*qJ#e<M#7)^nS(gD;~*|w4jeGlY{EBQd6aYJjWq6V+-?YrnT*jGWD?penmY6CXb
zD+sp&*%VS!ji(ekuurFJgLG-5lDH@YXN-BFjFYJlVB|)69TPT`G{%LT>aHpDC_MQR
zEemh~CD&4%JZX>0CuUZAqRr&p!MLDNXEGIJ969Yn=2Z%ihc-T7?TV?WfSu@)d}`7+
zVXzPZ(w-$+#>PT~O_A~!Bl95*rW9FM(6jjTrs2uJ&Ucyi_>4!b4dMkxBHmDQDF-TZ
zq2e75=wc}!qc3&Je@dqcV>NACq3@-&QG;$pmi=J5l|mln^Vit<(whGb!cEvt4rYYy
z;^bSQj#x|*Qa;(pCupq|f=#+9a!x)IcN`D|n<6g?*CP@tK6<2w<%MvPGEh{pF^Pgj
z250XGH)#=VQ5s|Fl#N|!RcA<-SXeTx5@dHBWvwBtM4BLi?&ELi$1p!d)`M7A#p>El
zo>ipDh7N;4tTzso2i38iLJ_5Cl20ZKgq$GeU_do}UwS8X^5FV1QC#mMO{$Cqve6~9
z4+Y4}6d~w12tvi9l8uu^u0kj=I10rhF~>lkDfAHasx(1qkxk;iDHA9T7-fYaC_m|k
z+%N>;hB%qUkP!b(Ec(QNU@(_5PWP~6P;4sH8zjp!$j97iEO0DY;Ov+3iHVVDuUVsT
zc!?+l74Bcwx^!c2k4!F$ZYds`^j`WuXrG9uq_1-GK?bV0M$}7k;2<GV<0SnM`(HKA
zWJ|X5u8fn=iWuLEAdR*tWYlW%2?9_0&lo4`$urHDfM#GilP2j~_<KLTL_5y^LErd$
zAHD>fBk#i5CL5OW38Y=rGfQKj7A#fnwZCLjkI?cA?@Rjxk;lz|xPt5lq_gqK`7cT+
zawrQSxj`wKmkkVVOo=aq)@YAHnlaA$B%6t>Hx6ytTNTYqKLk}KeZwX<nWg+?T88;Z
zHjvh)4$&VJ$S3_07_j79&ASrs&n8XicAhjTkB$lbvu4zdMeXBMxz}fp{)(j&BKHA}
zBEJ{fRsG-~Rb`<tXGd5;n95uzl%vpqtVv|$GXFx$3jH}n;W92VAKNHT`f(s-$ay6H
z=?BJ3N;iT>YM6cA6&(?(QZZRnd(|@CiZ#tR@3LN0|HXnz)~GxW*SEo^5BvRZv-vWS
z4?(!8m|4rG>R@Mq1H<vA>LeW)3=W&$66$E_wEN#9+$c%p?SYUb91cYbu`O?A4`>_a
zysTnc3unM&;Pi%5R-JgfwTu8_YzN$-VBtl}EYL-w0aK4mA=ehMX2j<B0mwC+j);vd
z6{|XgOOwNNz*We62-R^YQN)AO2JyvsO*W*5d!!#?bUr6LuW$hoWpVvnK?U(5sZtMS
zZgBOIPrjB=K?+&-z%&^PIzQMxW%Puz9L6kXm-InO!)!z1#6jR19OqKBUOcXmW|OZg
z#tiZ>c7n|pX}oZb18!rGPq^?EbeQmAG@1?i0VqUO#?m(#CQ4m4KO^+UauWv#NtxTh
zP?C?C5;d;!ZLvqbm3VDxKBUe)q8txKNd_BRXM7@4i0Z4<uSQf1X=%b9<qiB>@S>zq
zCmD?i`n5$Au*(k|W*Kf+MbVXftZb?sAOxx65$BeiV)cQzVbEDT7=ln!@!O<$>94d>
zXkdl&U#4Yfo;4%~=>P#Kxu8*)jfc=T2W>=TX?7I8mezqBVqzPixcH@aAB1{?GMHRK
zEUB51j>tU1`L3YUcGJAL50D@Us=w0ay*<*86%?kTWf^%x9TY-CgDM97-Qy<0TLw2a
z#Y9CoS|TIsaVd>1E&HudX-jK#fP$0u$-J@ta)<`ijYt}{MDxN))9w}ern?epT};(v
zJ=2_r#M6_v|F_Ryv-v&}*$90Npn@P}3KEm)x?-95W!Qx>IM|Pkoimo3xu;Ye2Qem5
zixt;`8bO^vbXbhoYu_DnKuT4LA0j@O#NXmnQv~;Ly^|qFb}0zA%sWO*LCH*AohxNt
zN*ZbtRR>k5wsBnmrIzGAG-6HPTB%N5iEF}th4i?R*)J^Wlb4SaX6`Ojak(c5AdoUO
zed6L;p15D2*4S01VKCTubH@yu<L6fLnU`Y2#7QX|Z~lFC(#rYt%Lj<3(&tB=nS09B
zi9e*3sgM0K;-+0yDG?{GJauipTs>ZQ>?3me#5ExDNSnNdv=cD-!kAoflCsG5_Q)8^
z=02B>hrTzGo>^BK8vnI8Scm%%)Rz#X^jC2IB{oHnJ@p^e(=&IMhsJ-E&KM)uSax#B
z@K5U*NgMlR8on?}^fe&TeqGN^-w4`H5Kk|mAG->1{fv56>JW4sw-E7*4`CX~@OMX3
zqd!fhF{@9aE=tX_>baS9#p(!gwD8v<o;;ZoE2*C{njZO~mDY(+6wNZf-yCsP-#%2C
zy3Q#%FXZqMWZuwHn;+a?`k~;$Tt5Y%GJa8w`6*UwClD<}5R$@?sKv=*q8GC9YW1OH
z)t_p`nL7*Bq0j0A+Dqk$Yc`r5J3W&DX-C8oI~hK$oSC|=T#>OKKi@9um|c-ZTt7)G
zmB-I>3I>Od{<L9j0`#6B`br-n*4x%7Au5Y8b=^>T`evt``!~WAZE=#3-<0!|$6WI6
z+4$9ELvUU_tWRE7El=N6EEnHL2v3RqC60a`{791eqCbd464XuBb1K@Z3+nmduUigI
zyb?KB#OH^aTiH5<=J7|6d8g2PwK9EmuBd1pb)Rg!>DLYE<G&lq=iiQNQRv%xPC=Uh
zrKRZm2hjH>BQAadr}(#*t5cWfi|Ln9G@KwCuf#4i6n!5J4J_dH^&6waiDO)@E|iKN
zh%J4eEM?UStM4dR#sMF(nweWWS)uuh(s^qiuKscZ6mfyj<6a*=7h2v$e7<jtq}UsV
zR-Nl`ALZR;@!;~>l>@(FB&Fl(*2G2SLhg;w4tqzuXeFi3koJdiKKoh@DM@P)vg~tp
zd*Y%}KJzL;#6aPKf;(>5n!cr0o?e@&<lY82s9Q<KezQ#TnS%3T4i%Jy04Rv}2YsIg
ziY^1aET%x-jyS8%FXR<kRc@@0*sr~I_Wzvt$eS<4i+=_=O|#F;;jB9{{k3v|<F<ZA
zEtNSia9Y0$dcigeDJ`rRoUc4`rU>=P-H8xAp*>zP^M?|AMm}Nd+6%eL3+fS>PlD$z
z9rknN<7P41g&+$@5B1LwlT8qAf|jDyA}SE*rTLV+G4cVU7;fr$db*|!e+ab>8tnRK
zDp@1jH(KW>y|5`v2g<;g6`aC98Kvw$4r0#HKI#Oy_Mh28@dz_-O@Ztojg3JYIwn&r
zzQ_WSO2DBbOXe_frMz2x&z`E=W53gK!C6rhY(`19-tf|otvb1<M~;6!&hs}VzQ4ir
zGXGF<atDR5?Qo=U?0Y9z`oi@`xaz`3h%5C<Ki|JL>8GDEk{bTe5-LcpX>uS*J`S(S
zf9IXzOM+bYPewQMj9RAj0WDSfblxuivzP7u(d=fQUCUIyD#)mOoPK=T^48%36l9hE
zTRpCRqnIup-p}@~wEOuN)w2?x4moL@em#!4-)2{G&$QE6md3SH3hBbry5vjs3ojYY
zS3gaRt417W{$tpzyoG#EPY>*fKRj#|UxLN(bzQf2Gl&=r+;|4Vs(yXYH~OKC$q0i|
zJ3pIC@85R%H{{&%8-|Lx*Tp6#hGGDq`iqi<NlV3}rvrhVXxi?r1-J6nYAJIVKK9>O
z)eA>jB~0qlqV#2C^l&=}vU@^Tw~KN9&|Jov@bqA-9(s3^mM$jwL(|!q;EZG6Ocktr
z)9^Qz{o1>8#Bj@NwfC_x>m7VR6O->atOJk@5M?9q?gWHUE`jEBxZpd<*yo%gF1$g_
zRRU;6?neWckzCc)BZLj@E3pVSDkYL(CZOh|_^Ogsc%fO!JjIlYJ-ab^m#(@Gpm|Vg
zLh}j8n)+u5>A*n><~A96Pd;Bd(kwcYQj??AKUdA#1y|8`uv3rB{Wx3RV-)eN?P>X%
zKKyaiSjvXItSS0F(kj@9AjB{^_q4nP5Ca9K?h-Bv=p*KS8^gf8#lm;;NVf9%#4!&p
z8!I$FS-0!o63c7k$KnuauM2oLEfZnF8hUM1=v^PVsmG3_M$Sm^LCi3Sn2Ht%Ly-_6
z+ejC+;!AVc{Bwb8?=KNMNSi6(yq~ngjbds)bP|$wK#{a;Hvb%zkCZ4$|7+E#zA2p#
z*LQ;6wN@V|Oo{zZc`g080nNL&D|9N;0VDQ`x-s;2==M;|(NOl9PXY1xl60<cxRJ9a
z)RZZEZ5f@v=kpggUwS%t(3pC+x?b;WmM0ry_Q_#3Zguz_?ecd#jL;ZZSqSiK>gvh$
zMC3lb?{>o8tp?JTrE->kF8%qCACy6sBz+9)0ikB^60P4`>*5EvU}Rnh!c8m(5G#mm
zN~Thb@`GJy?Q@7duow}@rS$|NS?uSf1!aTrf86{~;+qq`vEyYs-S&+Pza=VaED|?@
zT>Ei8gHv+dyB~u%WJL<ic6u%?CyBQ)@#3zL<es0g;<gvH&Wqgs-9cc_l;A^q+Rj%m
z_7;ER_+g`ibpxKdQP1x<da>I)kF<qu)cCPTfw^dj9Qq+0B|%Z53~(yR9GtSlok751
z=K33>F!h)`jpQJ}{{-?^X)X`Y5je!CVMf*%AcroRCxR4)z(xsF8e8L`8F%pD0)k1r
z!6{)+sc*K=?-B>N7-tT$oZ!B|YyHl1i5L*2pC}gRg`{?jkqb6;Ji-Mb1<Y%y15L-j
zFLYby_x)fVc=(gVw^cbAdYhi}H*{RFmt>rh!V3^w7+tW)zSh62>$~UZC{$@B;+(xb
ziat;(iSXE-)NF5U?6xm<{fM9)xx+2CB1TC#LN+c<+0TMz8&7QSmZaalw2w)E85fQ=
z&EEI7Me{#$n^A8d8bOwzEq-v(-}zc%-3B1iu5EX{E!a7KxS`VUp^HQKMLc{|6T22$
z{p+-T=V}+#Q%Wm*nAm4U-RfV{b+^6KZkc6(DLAfQco*-!!Rn%4*SeQ>;h!*b-6hg$
z8*7}<h}2lol|3g`uG}yW%z-%ON#siqnbrY`KKCCx{N_cdgq7$DI+J0L{Gu#`kr0~C
z2+iY<zW2ug&70M{I4(;nUoC;K=$Y>QsHc$Cn@=ei=>xn>xX_DYx0^IS8u?n5189~_
zI42@8cA|!G7TiouEezreZooI2(3{l6Cq_Orz5AL`=VU!hw!7NS*Eif9ujzHeW-72o
zxWT_9YeMs}qWN_}U`>{oAh^zr6d|{<2R~A_()HO%_K>t4&0!RBUg)~(gTUFZh*M%{
zc`JFw$S6Dy%R<jJ^=)sM@3{AxF0{c1h$(_aecRhe+n~}UACueAx4&zl?cQ&BhREC*
z!~Bt{lv(JQQ4gz#+dE<RN1VXo#gP9hLd$vGz&l?=M+L3^Au|_jhQ8+*To|GF`gv{p
zr<!eVyA>$qv@~bWd6d8Z4$oiSe3u#_k|83n$`G?br9l)$CcbJ3w^L7zpH?o}`*SWL
zL>Hf=qV!T)%RpX!1ccjC*$?<Zi)0+^aZbX(OiHgC$`zi|N)hZZ#5{&i*vPg}hit;~
z`yDW0kwwfm+i^zlU?Tg+NCr20fn|?6X8DyP*;Sx|65y>2yF`AZa5d6g$42bJ^TJ&B
zB4QDVTnYt2<}D*u@h~{c-xFs`#Gf<bkeip16p@OH#mxR~EACmL^%NnNv_A3?Cl_t+
z1nnQ_k@a|$W{kYw&P7|=e&;j}h1I+&CM;6#gC2G56a^%Nh+Gwutej3bo047c8gbud
zY4TQ^vs)Al3xguK`h)DT%=BaVUI@&XZ84s+V<PYxk_MTXAjXpD`VMA*ec!6pjaA>T
zRI+a*C$})8Tr|eMXqC-uA5J<Sj{K~gvvGdky%N;~Hqh2En+sM(#!%TDJAwGWDK`lh
zi_yq$ig_#5*1OkX7*~{9L0+Xy-2z#cdQ8O}{d~PtIu@~jIA1|wx7P>GSd?Q1@%i{X
zvDZ}Xkxzs~*X3-Gi;>LdW_TSzL2-*RpANmzedoX-r~bj9Kqz_Oa!fIEtWagL_tFy1
z3$n4yK0H9PHGi(qY$~nzalkjLL#uHzj>(^+&a*i4w4rqEt=&9IvryL;_0~mkC;ti@
z_59keB4ojAN~nZe#@DhLXE-n#--`jDQZK<eok`P9O+#m<2+PR^xv9C8^?^K9L~Vsn
zjhj=q6p1#HD<YmS#0BCYBU#PCw35IOl3x&RTL$g7gyuOvORt6I|EZM*79M2Y1A_Bk
zl6?P)cAse5IzB{9y?{VF8(5am_si?)iOX|^<Q9vao^`OlrPe|0;tKtUQH#jbx!xrb
zYs<i-P&Q^)>s;9m;&w5re`K^Y@?*AbrO!ZbeMX6sXu+EOSIte$u<H@ceVOKyK4y~#
zs8%yEOF#Gcp%l*wrVA}|chJm?HuBkAF*S+upeLb<7$s4_3py3XOag~0?{v4r*o)X!
z#KSU>Tab-kCpQD`3nKO<E!oLob{D^ixLqMu0dl1*WbU5rtS*|vAE+1WIQR0-z$W@S
zXnuIam^igsNxxh-YCo!`o#BqvxdP^@pjl#i2MeQ=txC+lvIA1p-}zao`Y+ZiV<TVc
zA+&1O3VT<2z55SJS`dK)SIQLuW$AO%n)Ot85fMQwPtwYJORqoD4>_;-+`CmF+{_)v
zY;A#X>to#S03B5KHRmNM-FcqUoF$oevnQ<T*!N8~cFq0RZm0hdWKRivN7D)BzuD>T
zI>qQY`#UN7iBhSWVYK105wo4{k0t~EG1<fNKr;1o;h(ap%;QpQ`h?m(JfC(zv*n*}
zr-E(RoZ7CrVc%T~{X5E`k#}wHdM(bp4d+6ud}s0J9^`~g?X3V}-&HZKG2dzY)W=Gd
z%|pgPI<%KD5P!8IhLMFCit9fDtgc_vnGfa)+^vPJgHGx=s6X1)tph|Hl1U2xoJz-y
zozePzdpo@?=`eq2(JsEsN=I`$!}a_2clz5iQU19O_lH)p^L4YmO;0fznqAP-`*C6j
z*W3f)wkDh-KC&n{OoI^V;PZ|Av(v<)vorZOha4`lDeQ{Z&j^?_MIMw(890?JmOsx-
zWt`P^y7Vs3ikflcZ_&NY&+IQoeOu2Rq5|ruE44c0=-Hl+T6B{f7Wd(MfRoL?XIrp-
z#+lc%pwxWS+zgcUjFgssC5EwxAEIl>hqlJOWvjP-CJmYm$2#1q$%mmWb4e-TI!;t%
zs<cxpzbutjXtq;8v#k+CYntn2D($ap1l=>WF#01KCh=T5=Wp$Ac|)mb2pooeb?z<m
z{reAWFSIVTfv9uCyG;1ftX_Dom2t6!?O%vZ?m;CZ^B6C)^COFm`;Rmm&SWW-e<wFF
z*@Ktmh4MR%3pV5K;&!d?-LAXA7I-m{fy-XT79@^yXf4q>A&G{eIY*{|$jj%@{B6*D
zI<kc37k>?_Tksrf2(1|bGtvihh|C!2+|yHO)9Dh6&j}V!>2zklYxIB~JB3dY>$IlU
z!R-7m>`wZLrF7wNXc_c%=2_79w2{s}1N0{%mBxp6`kN1P8{TG6Z;xZ(5a$hjeU+Vk
zvY(p$WD9hqI3$wua1WZ#xpsIr^eCbwIdC5IsKZb!Qur`H9;=&OWBnQsZd;16o@MtH
zTF%&b5yS)iV}#TP1;|ZPe&Yr^_{r}QRaI_n$ZJ)ad>-v2p9~xqEk-IKq*NTvIo_sc
zwwiFnQ3jtU;*5EP<{{erb8OoZx^-f`a5(fmWu`&Q$>|M4Av-?hE~q*Ndi|B!<deVs
z^Qbpp#)65I*3lVul;&AF^2O2o%sRaq?eM(**)lXB+}<^u9{;{Aya<8tR0p^dt92za
z^OqmlvMge_{fx+~TWoLq<WjZzuSw3mq1*Rvkk|7`{gZg}dY6(g67r@U1q+#A&AM`}
z`kF8gEZOayL0q?M?cVUm>y`SyCwNWjx&F;5ZS+^e+4R0zI(K-kF!IB6cJck2f(?_4
z^E*DQw}M&2>t5OO7Cx{!+%UB`xARk4D<rLVS>IbEt{HILXw7|vTJA|v3gdQKnN6y1
z26MHo={LX5b#De`9b&P{>$nqaYWl5jlZN5>o~WRQcAg<4T%*{1wa)DMTBfMa+>@!L
z2qi@9Ds;n%?!0JNANgFhkbe;a4|6IfyE#-dR^OUI_#B$u%e#K>8qc@t!{+K+)1ZBP
zqy4+k&A+FD`^<V}=ALLc?h?uVtSD&x-u3OViam3;T{F^vG=9D4Z-42w{`zTuu62pt
z3$`b2>r1V$^?PDm?Nf1P?y_n+aTi;sH^S{N+SXe?6U?=*H2T42#0B5-qsDiFzDAh0
znR~KzGeg*?OS%4vM3BWPJIq5Kxo#$MA~*E*rBOd~NF_USw>cCw-LQGGEMI&8dq9N0
ze7t4U8U1WFM^q6?Xy~1*l78+vmHf;-W;LF3gN64dVez<eyZS1kP*0he$v+oLW(v21
zZzT}zjGJ0K-50*fh}YwS<(P_<o^qYVpGq1xU*g1cZvs8_nsRZ~saiQePq6r9JsJV^
zw(8<ivGf8LId`Mizfy}!$CqqpxNkPTABw8Th;Ld<{7sfy|4gYob#_z<w)k%Md)y>s
z1tCYIV=x?Cc~LZbKbTK;{96mL1)`WU#B9sZYA21R3kMVI^B>dsRp&_b<v|R$`X@$n
zwf_`9bj<E)jd+>nE3bm)Z|=K|<F>>b51wDx{9>)??|>d}a9xkUnB6PwUiJO;%E*5v
z1#y<tKE4COO|66B$}7U0cW2<vAG;~Kf4Z@-{XH7=O%R@oyZvM`r%zowQe3?@T~AFn
zQ>}C3u6e+aS$!M+J~F;leNVYC{H-|Cct3JB96YViJntfXC59XQRQ;6}@A)uYDCB_1
z80|BN1BI#6%)0DwX1~a4|ISskY`thmA3c7kP$I^l$ZhSTDjIGv^N5yG<u{|u|2S^2
zo>ob4=bCrMl2qmGq#c`B#-UYh^6H{WtG=UHE`2z0=HCL?WWEruxvw&uo(Zk)MV^-@
z9^sxe-?_*Sb{@Uiy>H5&YhGyd0Q0yD|A~K+*P>v@XNWVRrRv8Q%Y}DxAA$Q9$LoL3
z=g}kFUJ1hO5Ta80f#+R|<q-rB=D2&cJMqHzyM6x}AwMU{za^V8&5qtY1@KA}f8}2t
zwn|4r47d0F&ad}^aC>lX1z;gpa4*z5XTsl1o3VFW!=HOo1Z#vG0MSl7gsAHl;y~R<
zo!k0a?_Od=fm(8JTlD6>1cr8prgwf0L~hay!;OpK{Fgj8*@pOIzew-j)b{#oW8Ys#
z6!L52)I)o`+0(p65V(JcW9<Hz0q*D9Jk8xj+Lc7A-R>p5wV;iv9K4q{+y!LFVm=cY
zVbneYS>%R+v7zoFu``0G{Y%^|-cK5Ki7O^W5ALV@EpsjQiX)(cu6J2@hEIr?r1e{n
zYbW4Hh#Z1CCrVk=zaos|4Mh6A6t|I{9q1-NIGn&VkB=h%D&pCNq#?d*6LSs6BcVPN
z>v)7s!mZt4{wtlHf2olKf{trjfo=mn{7JLd{ecmgpocVk0fj9;>fHhYYh$c=7l9u1
z$Y*#-<K$+)cRF2N&eta2O*H5(#B+Cc6K|HWNQX)5+y>#L*o=gt1ctshE<!dcd?O1A
zu6HJAwUiYQi!NIDTBq+_W)RsfVfT%bU<+{9-{V6V2B=HExIl#GHb}iMb^E~vw(l9R
zth3#ydxh_MpcnmHnIo!nZ}0luZxO0WoU8ZwDqwhX3u)gQr0IQ-RKExVZNv_vo$YAu
zGj2cL&f$k5%AktwAEBTB&~RwPg%{tl{cAgZ|7UQ$IC;{$p5D2Eco11T4mY>M`4hWt
zcpp@NLV9J6rGIk#;9f@BfY)dmA!+n^Ul5mfqa3=PD0>@%Sh#C=JJj&d@~uG>UN7VT
zs`J;3=hQOI;~r0viDjoP5@)U(1pPmveAy)ZAUyH78Ah9-?-N0!Z6c1{29zFY*1=l(
z@i`xsUaE(d?}NU1w|4>DC1NgpjGIF*jGAXaXI~JX7qKA-vi|L{AMRr8Z{bFB1D;}U
zB@SE#Ns7=sF$Kl6P7`}%)hX%N9*?AP_{atpt@V=reIP?ivCyNt2ozmtStGVJ?$o0G
zH9;J$BR}(_Wql2%r|+$cL+`dI3hyOj)z>;#kajO=w-OC)BO4>gwa(>(H2;ny3U2Eq
z{kx*TC^1i$gO2+IeczMjzt-t_=Mw8}gs~I)ZqlFILqE7N)Qq|)_uRPiC*nPBh$HVN
z(3MiJ4A+2R+@oWU?$^H!!mT7%P)p0c9a#TSf!)18#bN`Z2g{Dhi*PFp`BKCNWKJ*V
zCbzz;g}2CTK>weq8o4L*ZSdWG_b1#Uk)JAC-G}RwyvFrRA<9&g1zE-rFBK^xZImY~
zs2~Zg!Uv+@fTWi4VWWtJK!PO%_Y~DWSxjQ17<C8jm19DNS_RFMt0J!dB2V3L0fO+i
z$PrPoNIfhgWk))Qou=v-pvov(%ERuG_-HIZF-lh`C}f^1X_O<3o>cd6&vJjo)M(GR
zSHd;Jb3FhRBsPn5;Q^>1C`R>7MOlERq+dWsah@*KsxhHu5@V0C;$2MN$RjOM{a45@
zQIahoionO|%Eoi2ye^kN22WyK0SplBeS+g{#+qv-^)k)_8)+#I|3+!*K(41Qa?&rU
zWp9sA%n<X4IpI<rL@D6~kqIhf9-}TxL9V#y$#H?WJ0Hrp-E|HMCpT0ecC^cgG+Kx_
zXJA`P8snk#5J;J1NW_&O^$utX)5qQM+UPyeOhgtxh*`(G!@Rp|xjdn%!MpSTyL0-w
zNRbboW2r_S)qiqg@$eADr63czNy4I#3puHH3>29-I+LY3FzR6pgeRtVGG!9ITBV5&
z0%RpUmUm?|iBU+yR#1*&bI!Z6xl!1FW(Kq@jT)?rC0f}Xw@_NETCC=B3Nhj)HP<p$
z1DZ!1r7}qWmU8mj?nlyP?bfLS(Sy*5dRHREaEv6r%#l(9tC)|27>%m`<RiDNH90Jn
z*6qWm(Z5ONWwR=e_Cb39P$Xyq1xb75pFTk5@f&lzOy6dajTZwdGD+L`Qn?lVC8$Hy
zSnux9qyFu$vGZlt{tAQ}8fk?LlMO;PDiCfGGfRO2gzKRPvLT386GL};`M*H8$pp$H
zlQ=<{%V;Etd$p4?(U~Y)VOiuzBo#_+nxC@giBZ0wf@b+!+K7A+KM^`H+!Tsu1sTQ^
zBa;S|$LFgklhaOYOeIE~#C4Oal}v(Kz_>S58|#$hp)6u|X&Xs{ZxL$_aUKJo$qkt_
zHcOR;9$g`8kVUF1nk*iY_WCUMSIXO$9Jp4X?};Fw<Xtga!t!ke;il|E*YLOO`_gNb
zk2Yfw!GT35=oct8g$P4#sumy@dKLFdHaG}TtU840QfM|QzeawNlnIdZ4LeGO9um$S
z;D;cJloCzWiCD9+A*gr7bchWZ8lXIi*vL4GEbqaa5lJf#)v2+-UPSd*#CtoaLs>Ua
zo|xqcIDo$u!fjmIMdLstA~RO#J!M;qg}@4G2`*eY^rj!muY?Q(w4&nOjZ+<qDgn7D
zwhDR;+QcV?v_~P_?k8=AIs`E&bx5qTCB&C%sLZWgnrV-+Okge;Q-Pc(#7X<heU*xY
zmRIv0;0hV%#O2-fyo*Xe(K36eQp!RDDxd(+StYs=l-f4ZWQ-ByDLFAflw>PuaO)^Y
z`GxX8Y^pTqVg=!b4f2ZrT=p2H;9|p8^ewa^2RYJDX@^2S?$6(f2q+Kh8S%KLFVnol
z*rTt1@laKxN-@j*1zo7dSjI8yqzV84KmbWZK~#zVYORX=8zr*PiK6A1rFvP{3MI-d
zL7~s2Ee>?CMe@qJlEP(x$R~RNN?8>LkiMuA?xg}TLuJiNd^+}q!PrTC80;x+1k!D2
zbWjX)uGSiBgE|!I@Db@&)$;J){x+Mh^cT)<fVr1bi~V}QEwJ2VK<ErRl|*tCf>D#o
zA{F`9Zf<kww0krNH|gI>oeYXHO%Q||^~wgxCZ|p(ekenHOwDKr|7FF)`;bo;kPqC!
zfD@3?Km~CcOE66G@rC#wZpmP=4!AFQf$NC8I|4Vd6KYjRp+I^D+GqJBU*a;DOpSUY
z;Jt%<OYg3r)TF$ncR7UA2K-1Hl8kDJ@)kuQC}l1o+^{ZXp$>+bA3}Jt%VF$C9?83u
zPdG$D3+)t6h@Q!+mcv8*7A&QSe}e%+OYDKvybU;Cq7sXOgA({!Ld=PUXnQbL6l2I?
zG`Quce5xfToYBCygXB|5gE4(LZg7xk@Le_?beP;%c=tgu+@xvp2o0$Dkv_?Is%qfs
z87vMt#WkYem*z=K!GRwKWHdsAbZ~b>V?`SfCx$MVbv@9h1RNcRxd{2|pbk+PLucGV
z2ID66E~SaRyV~d!*Ch0&+M+gl2I*m+hofb=0kRp&JgG-$AWyx7&EapUj34w3CoqGt
z-y==MVUw{F3y~%CCrpGyk@$;gSXih~J$s@$6(UW_-(zh&nC3;ivaCEFw0Q5|rN7X?
z15j#vdv|X%&r-v^xs14sFXcmzDjYrKtsp67WI$(fn-TEqGJOlp>t$I(ilzq0HRu-0
z84@H-9@<7e1co|Bn$$5MHnqkd%{m<vve)l_&E~s2^?NN)E%s45G!H=Gv&3Y&rBtna
z8dT6O49?H56eJ$nG%=$<6cu!JeWDoQ`b0WQXV6S0!>5d=R<F%hOP@$E)Mpb$Cd9Bk
z!o%l6{Gfeu?;o*d?kNvV6T2z<8fHYUzBuMlWGL6l9;e@a8ExW{;lj*1&}Ww#Mf)H)
zZMZ5yspXET8xxld6>@K4uxCNZoG@a~tg8&+Cb5(`ntsg*va6n2v%WMmb*WjztsRp|
z{3&9%kye|!B3pD0B_9Z|{D<o1)YU`9(nnCwia8zli8}Gn)@&#aO<aiH9JUISS~3Ju
zY|Z_}+T_KV!U`%#HtVJ7e@Mq=BdSyHVkRE`@knOP{iWK(`RU!LAmLDWR4AS-`J!Ah
z@=Nm3zE$UDvc_t;-U$&h*54aTt@>>?YmBgf**8?$DPyVev$NTRcx|}9G^Vc~EzYdV
z4Gn)AJE8?K;4demzZ=f2SzoS=oq=Cwd~HYV3^CKjQj?by3eF4ZKb&d#^A%_HZMDkG
zUD@jJHzHzT5u)XPN-5%vRj012fbz?hbFWAHkBDG-js`84#`7OaQ(thGI-*DHLXy!l
z#xkn_TObb|p!t^iDlh2cw4eE@oivnQeP^|@`nFW1{1H%0prXV<dy>v2<L8a$hreYw
z0y8xZ8`cDUHgj*WI`laJ(jdF^$KnwGg0UR^g1^U~2V$(;F{8%RpKImSYl(sPb6irg
z@z%aG26}O*GJO+<_-_K?24ZgH*R|}-J=N;ejdnTrMy|qbwr{Shf>fLML%mS_l*A;Y
zuOb3?SbCPZ6~^WNW9~h`Y^kdB@5*&@>~wPi1Qa7GAgJT02xdiu855{u959Wdm;>fG
zjEXV>3MPUIh=`JN&OzY9&7tGTm8-tr+Nb+UxEJT|GtToBm+sSbD(tZK-fOS5-t}fM
zvy7W7@2fRd-d?Vi-cA$DvQ=Z}AO}l6i!ax8t*rjl#rBHZ8jTgVR_i0*hI4Ei=f1g^
zvB$YvD)o_Xat^+>$|oo6RS(vh%Wo`I^M}DsvDwHfpX1zGWcfNlgd!`P1EprKLcRqt
z=e`8FCGuQQou!YCTT3r)lnZZUY%red-v+_Cmgj*I&AoajGS7}rc85EytCU~$FTYXL
z5<#hbcHCNiO`}qNKQhtfHo14<*qO{<m@6{zX7(#Ure3q8yn1c1IdOWHms}V^###Hq
zn7#6Dk-uDH$OlRA$RlNb`8;y%2NTY+D=Xz3NVsGhZSwhwgN%Z}L`H8e*30ittjuj@
zi!T~4!)4f%acciF{VV7_*<tQ#oGz68fARMdPrkfJnGaYI^Up3gW;xOZmrCqHtov|T
z4Xz@bDp$@5Pj?iKM}k(QbJRK`D;t_w_fm}FwW?lxwkZC5vtxkmycPoU6NH<g)%39s
z7?t1wa?ACb$>=8p?Zc>*PL^O1@tn(n5#PT*3Og6+19z`+r}<eUm-=c_(VFkcmqEpl
z!xz0~|B^BCF0JC-Fc|2$Ccpx{Z0*}Ls)waz?|R=emyO#aKZH+gWmO+}TUr@h?G5zm
zh%xrXj1s|x_0l4_Yl*PjQ&@ZPq<_3&S6&_!2UkV|2NcifsaoEBMa8He9+vxm3j5Zw
zQET+Of(l}uLGX6I;Ba0h8H7l<*{X;7#qRIZp0n4eGxqar$a^J=jWjCTs{}&Q#&?Qt
z`6Yg#bB^B4J!2%-{3O?fY3jay%&5K9szDXxianllntM3NwKrMy!ZU5LcGCDs*1Mz!
z8ZGD7{-s%hn@w+?lFyZ%GjQ8y+nwSIn#Jm;W%FIlN-wgDh36QBAvYa&cG9GOxjzWD
zRio+=jgq@xXa;wsv3GMch_~gV+|x=qYg`gf@+LHPbx6_WkjhI)otQl^7l?N(_vR|0
zKvuEvOha-B4`p8DXW>BO7cE~Bb@%=l4$`=h;pr}!%^0=F%xd3kxYa|9YHm*fXXMgF
z-*DlXn$-u_mA(2=^$N+}_58~!dg&meoPVb2D7m5u@=piIfHSRq9t1r1iopnJo(t2t
zpZ5D&uBnZFNLU<5TE8)gy1$S5Ms3s>IR-Wd4AKw?X3dXO@|Ax!D$XiXwjL7Aw#!3O
zqYn89YuY%-%V<+8j;9*&$baO9=s4LaiY$wghbks9dikf9Ovy*wJ~!)!8zlS+)`_Qa
zt`|m~^R+&XDV(2mWAr^5h|4b4>t;53oJm5~sy2#z2e<ZoB2cuEkC6`yXr*we2?CJ`
zM<4sVrBH8T$7;SiS1!GPM6M1Ym#?8&k50>jEBwA*10DBak@<XDId~}ZL-ZMCd}Li0
z`j+-nc4_HbNrnS*P15XDUR=mAUP|t2ac)tyg?nXQc%;m~{I4=U{xdBf6Qx2<?x1(M
zJt)6wtWZ8SvCQW;^!l6KVn}`4=NWwx*dohhaDCN+%sVc4Hq2OMt@KZBq3~>@XbYG_
z<Y!}=csIvh_>dbw9p*Yq*vjq)kZ6@{46cQu>^kDsFq^9Ci2vvR@c5gPFXbN|tV-cM
zLFiwurRH9lHuoWNhQ7}Mh!E+$KlL$nvuS5wDTETg#RDw%Pyh6O?A=S#EUYLCzjswh
zvCHr;|A7*KnwWTBI|$Z#q_!_<`9oMmr??TPi`t1IwAuZkHvR6oaOQnIFPd`5b1R~(
zgIp^^LekUx2Vuj)7L;5YXK*{k3>q76zY%THL+6<;CW=U(1=-WP!SS5k%2wqV&}=Y6
z`1fdGu+ERoakBZZ>=|2LGC3IBsrLoo#Pc(2++qI`>XUB?`h&Y%KlkK{ReqIA?tvun
z?oFd$eGuEr;h~ew^?J%IcFe~fs`aHoK)5}_vEyw?<o_j(!%b1_tSKu*fU;|gT}v%C
zL_2JTlWopPa?ePe)Wd7MUA}?9^25L@9_|K&#{tQbXI9hE51GaAp{@@CFfy0J{%~45
z>s@Zd)?$q$(rKLBjJZ#q*K<L?#ct98#UT7%+N8Gi)16KLJBFar7%uwY#BMtEzP8`~
z4ed#OPJ1s+@1N93CXWgSEU#K{FFBIGLEctDDzQUR2gLxefh7P<irff7V~$f%Gswv0
z{cKw{`CT~Nx^TF$bgM%)f?`t^hv_WxgA=9mN%F9Mpyl<hH-HIa1e~F@UQN#U=Z87(
zL4PoGWC2mDXJ&A(<@;+veT7z9ezRfqzO^}-IX&nkKHKT}cL+T@C@|Am+OxX<8O!9W
zw?9FL1EL|u)(D{(5(%6;mb|tfoWtX81$xDmo??M)Sng`i@zz3dlgJi*zTES_hLw24
z&v{wS9SvHM6v5$LmQSAKSVzf|ot$#kNqCbu#;}vB=i$pBjim+Wh2H(PviwRK&WzNi
zww~wQ54EWHuA(LJyFSQKkx`>q(QDu2TtCVLR;%O|Sakh6c{}ToKlg4u3?4?7M@#6O
zS~m9cVonRYng1}OlrlnenMGU7*xgMw`JpbK@i-fsU%Y+LN9J`zU6pNNE|4m!I@5Ys
zJ`|#lz8$9K#Ei70qi;HJs_)%rcudSvv7!7jbW|2MwQPyZgX-KmFY_Cb`6pG(<eqNU
z{xzhh%h-d>5ef1KsD=K;ajj|(8Vq(!{Xsf739t=~Hh!E{7|J~D#FCjkIIC}Yf2$L~
z;WiB3({=C1-E{IO@=v3UM-R7y*^g_XrELCyPFDMlf72iiufd$ig77~wtEkHS@BH}N
zlkfa{#1Dy$RT>goi!`XNHGfdZTa{3s`&NpJhVPI^+1jzC8rptU*ap!63mx!3Df6(K
zHEWD*9s*u|Rkcvuzbgp0L>P{x*VB!>Lk#vZP{p9mF?w1fZ#P=@<Z($q_r^tb<=~{~
zT{D1*)ij*tG9fd#OhE`4CLFJ(SxbSb&cxnbd2h-3>PYpU1GEP{vn3B~YG?Jgl}mYm
z38@E#3^r~hZQ=}5QkNOs(`Q3(Z82E6t}t4DZa|C#o^j!u!t2<4cd6_y10(@#){qp8
za_pp~6)K3(ANkUdx>$LA<o#@eMm!@8EUahT%S5VBK}<$mfmz4S4976xxHC;4qQFhl
z=8qc%cS+y&ZopMm14a;A+UPH81$P3Lk()B|6Tn>cbQwUj!ZRELFa=P9V5p_Wb_Rfg
z=jkSisZDJ5T*oLAzq&Y~lq$=wKFh1*ra8u__&0A4d#9Kx6wZBl#V+liy8S<qyjvDv
z5n`fh<xmbwJlxhR$F!x_G|MaRFV;p+Hf?RAG@{J3Tra4lg~?02{Ve0;f@N`~7-&=I
zgPHrvrd@w!T<TpkO{$$*GVvw1lFhXeCW!<k&$12xlE;Pao^`kJFF{cu;Y^tvc{c5C
zNE|;=31aW-U)4$mHzt1mxr?2pmltHS&v_F{o}6hnSkggFNIT1W%l|R%7U64a{k%<$
zM$sbS?0BFZ#vMbmCe*Sb&SvT`tncG~ibl&}Ekc%IKPtYYUMM}kmji1RBfC_Pwh~zu
zXQT8#8h7`yRvg^t1hqFTDlYkxg$5dCy(>9)zZ=w!SX5m6TUu3=$;`|uhgFM(XLOx(
zU?l8NjEo(1<&B(sYR{H)%I=+7Y3*Ms#lk)VJ8c<J<&%t!7sW;IYTwsQ7NM0$VStAU
zo&j^*G%exDQ{<DwPEj%-26#&M$U_n&F+GA&A*s`^7Q2|T>Ps)n7o7trnmB>wVsY$>
z-YbVZLCj4ozS%lCAu=y*_ehx^!vEZ-=k(la*D@^MNFZp?9mKU1SY&0<n{OX4H@}l}
znm?-Npxknl%vW{Gews82JKmUy2$LHwH&tNyVJxm%)kCy`cWoc{KV9)aOY2?U_w_v7
zf+vpDOC;q<wr~T^*&VVdMf?XJJDC!8@kVJwN-jsX6?+=)*YZD^Isc})Ah?Yf&E2KZ
zLF^oy*PIt4Y+{_B7ry_e3bCe1gZa0G!r^9aKXyxJ?l&OZsvz8eBC4&~zvgxKP@F7y
zO$h<ZY9}nV%FmAr(_fow`B&?SyN{zMy$Bd-$#p@l<)4;wO2-u}tI@R{+O0J`cdp&f
z?NbHq)6X^?INe?M^mG=K3^^J+z!{J>6a#bJlP2c|chw`K>{$`a9pa3T;X(h(h|bb`
z2HK|QOm)^Dtjz^m%qZSIKwC~z-5r<@?=d?QY^Wum)GRR-sKh$T4<xPk*W-~0w`dB5
z)9mUG?*YPX7KEE4IUTuonX47vp407p#LJe;N-uHTv^|-ud&*!c*a*VyrInlskO_@0
zD;@+Kb}?yWK2o$z<|7zkND7nBJ4-<7z0`zH!pb^doQ&7MN}DrBYG(0RxXuo++-Uvw
zaNRy3Oqs>B{Cr!PhZwvHicJPCo<e^wEoMtDZIrU@)A6Q*wGJx1c<!?Ly9$)2lg)e-
z_$~_<6RV7?;h`2^b{*RwjJPrOy=#ntf2SU!v`7~N!jX5-hZ}|xB9L+Gz`Ry?D<~V}
zAS2Pc^TIli>q5!DWzyUJHf>-%*M*WpNFO0g*~iL*Qq0*fb8r~m4V(S-yO&x<nQU?$
zc{%G#$|TG?0Evk!NDF@2N@4!(&YYR++lyS<iL3S?y!#EC2d(o9?ZUH*ObMx0c-t6e
zD!bJE?PPcEGg^>;vrFC#ctBPc?fjEW*WJs>xd%~t(ef9z)KZE^9u$F!>M$)M%b7X-
z)y>_RQ$enYEUz2Nviruoo_iI~nWh{#6MJ8uiMPC}+aBCm@SRl_%Tc0rzr?v$_S)XP
zg}~X}a0h>i;6;ngEr)JtcP?%OX3e*~n_}{tMCPVFx3;@iHUgvS+1|}bA~58Qdv0s>
zuB(Mc)wj*OL)aT*V}FI<bo0+-Y`lrFQR*PKb5re0Tu)3$kUtG&Ue*ZZ*rbnSk(r-M
zReCL@tw*|ldk_t-Hu~O;8Tk{W0puc?NLjSe+Gzr0oB08D_rlSxZXYDg0uoNLBU+D;
zd8^Nifdc>ispR1`(VXTqv-(FvSe1zhpUZ`tUexUpKO7fG>|_zoEDX%WgOq%`rI(4^
zVh;p<uZj%ilDt>MsChz(_}9KRbyVgXEHdd`1GpAvyiEs~ZOtth?jCBlEU7+r)Q?a9
z@MBkN_TyfxW*&vgMI1=cR{Kf?>d?Nq{W~NPB$@y>9Ac7BYwsB;7IvRB*FQPvl2}^?
z8J#C0IRE)^Pkzh^bPUNe8~l1M@s4Twi%+XH;m%FHs|lLEQ?4Zu%m>S*WYeZ-^97`2
z*`sbW-)`4TR>}I?>!rf}-EiyS8@$dZ^;u(i-B^Al#)m6BzxS1H+B~`#t$qlXpM&lC
zx&b<6Nndt#xde8~Hp?A-`=^3l@03o`__uPjdLyb1lG8b>-;?VW|EXDUmk(TLyksT+
z+3U7{Q3%JrRjk-`Qu5_m`qu9g@PB77s~u5F)~px(IZ9in`rM1=FW%lw6n)qlkEk$m
z*<%(1BhvXrKL9F>125v<Fr7Uy#7#kT&Wo_seIhBhf8zCm>jE^lK~Q|gsJrSf(FClv
zS?ko^VCML4G^tr$?W_4*{ctt~KWaze9X+C#%|ZR!`F!m)X*&J>D5!pQ)UCZ59m^%?
zd}Al<eK&w!#}A4}NovA$!YBmU?AcL}J7UZl|75OYkM!N~6DswstzNruSj{dT5S4@b
zV1T~eu1|fj+Z&uYniQTM=X<}AHbS$OX?uQwmP!iepU#dwyRoZH--uc#6_V>17v)bZ
zvPO<JOJvG(7oS(D!4uaV{7z5GM~&sG`?cLox6O6J+nsFmGweY24vGNty=B)2we3eu
zb~irLAb2dFF1cK<8YX6*3#ZfWwMz!Imp97Ab$T&c-w$R#t;Mx(EOKgxW<_(vvPk=x
zZG5iZ9seEuxO?iF^{(mN&>6&WK3)2Qa>-hhIaa-A^snq97x`@DKS)Ukzg)IoEn9lo
zz~21A&AkmjGuYRa)8%&<jp%`a=U<6AfPBCtbl>?QT5*FQpo?cy@4iuQ{HwKwEF9hq
zeb2jCW~d^BoYi7Bw>jB#ZUmgTU@Se492z5uZPeOup9RQ3L0@uBxokI5OP%|5+KsQ7
z%lr-XVA)l*5u8+M|I+@T``s+AzNHqf*i=>LFa-{}-_n!XTdLtoIY-cX@1j9}_V|s-
z-0}LX{rrZx^mIe-UhMhZ3%Xh3$a1`TtvRmepy9b}KrByDUwqNL%#W3<^oM?T^4(z|
z%o!@ty@Yy-_UeMOI>9Fmg)Emc$Wz_^=ZZG=sXEBDzH1#`VX^eP(;xOj=d}%E?BiM)
z4!3@AB?QF3Lgt@0lz9kh)5*_;9eCNSk@t~4-GbVxHJfz*phdxj{j~PVTEw^-6{@(-
zuRFb|4|f74>Y>cPF)PfxI|$vwM$GX~6iW8!z#aW?rLH@jPWu~;sB}<T@UHH#YeFj^
ztwZe#b-VT|qiPgmGuhsWdS_y5k*!`^3FyxI@*i;ae~fS|ycUGp0U+EkXNK3HRguS|
zcWFNeE&w7Xh$0ratm9<mx8z&{-oN%KgpYSXxP1=}x0iu%yUPne@5BZiPr_2W%R{;_
z$96OX;r5dx(iR!9cgtKb^BznaQd<0di5n$@o3T$a8SA(Zf~~!<e`6ekn_{+<ah%*j
zOG?)NSzq(FLK}G%2&irFh2075AH?s2>s!I>2T8@nYqW6`#2GK1J*nsQ&j87Vd$oHB
z5vCRLz+W=w&wWaZj8$Ccu>HHD=X<xNp&)UhKN4uW6w%+-k7vI-82G<O5kww(HwfA;
zO*-ed{PvgiSkcg+(Mi8Q?vTk?Lh?vNliZHo|6YW^PLj^qEwB4M%EDKY1SD>e{ttv3
ztQC56m6!T6APZ3jI=|x`CqU^fymwJzL~}gj)+mgwVba4iG=DCYbID!QQ`B~J%J80E
zIQ`kcOF#mK7lWAVnUKbXaqCyuR!<@s5S(QRvfBNV@A>z^yLmf^q78mFxXr@`3i$2*
zAoecfQP}%}^@Fhcl{BFQLn!ql7Z~|0{V5!7g7_+Pk=VXjV1*_MR8`#lbKnK{Cvk8i
zvk2u$oj4613gh5%v~Aziqqt9h`ChmoN&LGxe<`CI%K!;DjDXBpgDW80djQ1TZ^JmT
ztcWB>@$_dsj~uSh-#{d}wiop-j{M-kD2cAbzFkFw2-p~1G3U>`QxfI?CID4P<V@1N
zsO8TbE9gb6`C&gETpxNN^zHubQ5;>vg#y3`0>hsUL7Fj}t)m_o+!nM!YWC<ubT68P
zO6c96l0;<81hLf$XWsEwv}Tfvq<@m+c5am@no-nDvR1a?=W7XnAgK{xeoxbO61UIh
zoI;Dq*uXxr)Br6u7tDS@aCNcKJBxFpMl!f+E|~kUoWmb;9<tmZ70VUK{C|K_lMpSR
zO?ekY+`1_Aui<_7Fg8d#ih>1dkn4_#Bcp-zRpw>>mNfM5ri{0#-8@5(vP$2o{-vFT
z!%aB)#FRp8dM1mo#l4sIJnwey6$BrAgrG9DxC<BGmB{isUUvUqWxjn5GXEoCwkCcP
z8I9HsLiqXo@Xw+_`+E19Fa*Su^e+W5_bD8uY>uF-7|Q%@F#M5MMU))ipK13`ea{!a
z%gxBtTDammUfB5#>FGc{(i_N_$}N(F_xHo;&kg+OrpIs(KDrnGM}Pfe5N;KAZX^K7
zzqO)Q4h7-%r$N*|S+)`g3fnqE#aPl0?C>P3l6|nHCHJE9Zy5GY=@WIM@D$JL{??BM
zzXoL^vAS(;-_@Zyl;;t=HP30KL|7&w6{IcB8@AYVx#IJhM+7ZAkl!fbO~e8rkOd(H
zxhC3{gpmwUOA<qfpzyzp8mv%q5C0SM3CW@)hLoUuE>hPmuocW-H+-nNpNV4rUMZRo
zG~zc^ambIlSL!Nt+SQ@z%M(;wrEEoIC<{tW>OdP1wZQXOmZ^hGT(X58$Yt}_A%x1j
zBMt{_?fi%Aa;=zr#QQuY=eQqesMzEAEcKHA6tA1~1-{h3K2OkWD)v_{l@&vws-$<-
zMf2~Kb&D_O%O>w*BHl45<!QS*<n8or+Qktmm6<<O{g{x4$UFh%Fi;x&O-R=KMf@7}
zn`#H@OFrk}IRf(}>SX~5N5Nq2;2ngU@s9a7AO-jyQh%YYOc?S&35_BYjEnf6bFygR
zoD)94`5V<c6jz>DH*hAWVj_~V@Lmab-0_O}8+QCseaqJhZKv8wYCn9hfPi>RLQ;Aq
z%OKt|vF{J7%R`Z4;Sa<fEcR#aQ}0yyLb&gdGD>(XF|M4W|9Qdi9ipgU(SGDP;W?8j
zS(!=DT%|44*x1qTI~^O`px*ztnLDI}*n%@GtHUx6<wuPV7GH^kpm6G)v@>l<8Q{<3
zwqe~AAKi|2nwL@emS-wHN4b5z-^A3zcu=%qBBKRtzAENl9=GsbV)=#n1yGBUFJe|?
ztfT_E8Mf2#9ms$JxyYE|i@)did*XZ<fqw_WjbJ1}O5wu-=E!Gpln{lW24E<qXXr>U
zw@AXH5N=$@R&1BTx=`x{p=AKU&o<<3+RVM`*_^{0juNLDOfe%Y<!?6GYQkPX0F7}@
z4Eo}6mPu)f`_}NcOe0bN>45E=TgCryy(yH?dhS6!^_Bb*qrI3*<aq)ep1%&XQ>OT~
z5Mq4=*Q<W!9pWS+PlGlt=Xisl?KW_))OY?GK0W^67IiG3f<P^0Ep`?-E#y!Oj-bA<
zZ!5$b&()Xn8SR6efS`gDLP;iXL1K|=S<~cR=_AUatXag9DukP~C%}baA8_x(lvCm_
zSzx(NrIJL893sPrun@jIRgaB)j{{6lbc%zI_iEcXCr%gYyQ5EO1LbV{TlE!f7{J3k
zk_rC+&l5)sdZ<E?$-rXLQADcJ*SMV&QjhP``}r(=OTP{mBi>J$mBVVueEDcgX+M1_
z&lQuep!j&Fs@oFYOLT!ubYcimG9a?dWG(<9wOEN<AS(iQ7N@S1D|?9VrEf(BC9I0)
z3HnOfcb5Z}%hVx#OW7oBQ!<+=?NHiJG>QdT#-Yj;N~RQ2RFnt$Qz6n;^I1$dQWj|^
zL19uB+JOn1w^LWeda{!1RG;$>k$I8Z6<jBXFaGV2<t4mZcApYyF~@a`VSRUbJD+82
zh|Duyj6JwUOpQbfsdTX-SAVO%jCqk+eI;d=G0A5&L9+W3SyKKFj8qDEBx6m+q&Vcs
z_^r@qvXF|rv%pAfd-@>1rT+*tm53E3kL$S~ppEJe`UnfjF#og2<6^Fp19{$oY^81r
zK}nw}<Ck1Ve@Jr+YEqMNO1Y)#{PlMo|Cq^F%;ri1kzjstAu?Yh{6p+E5EE4!6fIa~
z6=aZsA`K_O+%f?VvHEf!E7t!D!i@nk)Q%J)Yo7P&5v{|URmUQfvdT)Rg?ti?Ov)=^
zO<bXxQoWl58$;G#Ta}q}3dtl6JJH}NEgn^xACK&iBaPXYx~c1!*ycH)hV>t%erS}u
zS43^T$FS|PBgRQai(%*Fv(nyZBBc>NQJ2b78JLO6&mfLH3MT`V?YVlq+$%L<(i69S
zqQabHl#%;}c4Y&{f6OwfzC+X-PorLPP7Zc_N~0C60u@mmu|k0j`MuP2T+4MT4}zeK
zr2afY)q{61X%G3^q|e2AMiLypmy6Ut3aZ*lG%z9yo@kItoWKz0FZW{H;~i>|6s=5}
zQx5qple7#h7I1bulpAf3Tm%UOk9@o1y|e+-7}uzC(!a4T34Ste5(f??lFR2&Z$s@%
zT=D8UULin<d2OF}fLt3zR?x^6WQv*@1{2m0K_>ObrB3}Zr7*$q=MzmxJI0T+mE5bM
zDgJt|EL3ucB+0IjgpNqD)KyOMV$324+M~)sUkHl}aw~TYS&k9+L?)6Qs5fp^NWFz~
z@?K<KY5EK21ck|XkvtnSGFBcT^E;59(oXaDvPka|J}&*mm6V6clIKA}K*R9=yo`e0
zQ%?AWg;`{I2g*^6QzgsN>C~V5<o|SD+kW&prRhsO)DA?+sp=0YfZQ}Jh>}NgK+u;P
zWiD8_PL@1*o?I&Lo!?C{SN%;zNvgMt<L{Y#7rK7ukCc^ZF%3EEWYw>M0=lo#Y<vM1
z2Qb~ju@efhv+Dl3Ac`#6rm6T$*@j8<B#oUy^f-Fz61beo&D!Td<_N-Vg6|dvn7noV
zGa|rb%AbPIreRYwz5H87_2oB@RmXmS_gZ+bL<5?45e_vHnh<yOqVtyISFNu%mtAXC
z?fsdEC~xy5-g{rY36iN^|0;Aj+wHXRy$MGUWT1zt#iKyLYzMjaQ8=@19jP_G1dmvc
z5k4SP><6IK2tQkKQ>9uslKO*itA2XIT5)T$R{x9!!jG_~e=Qr!Z-;B^wt9Wlz4hkk
ziJEJ@prkFoZM3%P;c9c~MFJ~ez)4%k7He0B)VskudNu%xMb3&_>ebS_8E|Mmk3t1W
zy9kF{Qpy&cJ6>3`sXV&$k9nphkUglb6BmpZ*KDYc!q<lW27>87YWB)|M(V2{sx&8l
z0T2T;o3U?6TXsdWyk=u@bkQ$ivzkiM!uy)WirXhDs~;|ojGdg3E0_suw|chtvXRo7
z^|jH(zs=|M31T>fzwKv>Km*dI_c(R?S?qMbU2|9drQQUQMLQH5#iIq`MjxYL4+r9W
zrA3aU{L;U&Bfyj*>h|K_FD@**q)@W<pq(UGQ5NonLxu&~an{E?k(IKg7cU|WsO~0P
ztaR+GdO@MZZfiD{-;l342PtQ7w)l^W3Tx=MW!G92nRx3bE^<~rFw$6cpVO#(D6?d+
z>Xm}F<j;#}tIEjID+GSSXvNH89Rzais)@?Tw?JY}S?Qv4LAb4LG*{l`)XVQ9^BvIL
z>SsqSK~k>zi`yvwTPAE4Lry}<y7PlNF#)FJfhc}t!dmrEt-0!+O1*px&Ll}XFPi84
zA)hh1Kv`ASm)|sAS+k)$vgj<*3Z=Ph$rT_t9|oCuqgl-f;*fkBg?En`%Woa6j(&%+
z!J;kEDWg9gbEW<(ZnA6cfwakX&~e{F<~LSGR@?%rQ}_sH=*QE>b5j51*I8A2FRq(G
z<^`F#L*}_|yGsb;gu7;Ad343~pyOV^qJ?W+@+?29Zx<>cZ6Ld&5uVr2!znnC7a3iC
zCH=cM3x)VLM5E=9u}gMkLS?EMW0$m|ph<5kSMo<DhV|l_w)}=g<u&U|Ba6-<(L2aY
z_XyhL#zp1Ta_(0c8_<`MDFGafJF6ZXX{>%Q*DSn=u<-w=TF4{lKoF64SB9wCje0tI
z(uBKeZL_iJ{#>K@*2L5*BiWM6C(5fp&n~|*UvgfG(j>|vrpcYwO_ln8|Hl(gzS2;6
zX;m|W56r<}p7wr2&<^|gu@4%RWE+X5Z>xB<w+a^)4UBJA;n+mI7yXaW-1a$9s6oG0
zc-lO|P3)GBJmm2*R>h0}ELnwLiNeB4RXC_%6`rmY-Q8`LP|jh<R|<jLFYl05b_G!X
zyL!>|cm0mDa?BbzUMxX&mfyQ*L>s+e_O!I;-;~Af%Zg^>EooT*AsJ{X@&PmOoV4`3
zypw;LR&;i=WU0;a?<iU11ul2b^SYS>66n1d*X7dU^Kx8A+V7RjmXsyg8fCr9;z9S)
zY+x;_819<Hj&Krz67t#?TK(K}CfvsN078g@BHPtn978@UbeJ&$@Aq@v+&)^tk&I39
zl7&edy+@&fW()>X*(pA^SD5{Y)^_)9y3PO8-1^rGPHF#sZsx~E%YAaw9r;f!7hIpj
z-9LK${>^#6d`PpHdl^Z%K2Xg$tGjmRcXqe%Ph-W}$2BK@I7xAK4EopR2i3!xMSBnY
ze_zb!^LzH3nUn2S;lM_r@|Mgl9@8|-FVqU%v)jGaIc8t7jRkkcaqq&Q7d(^;a!)GS
zRu$)v)Kkn+3x~`vqI=5Lo&FVjqx7Zw<}y@NW<l6DWI(Gs7V4|+<ytf?(SmB<ue;?J
z>EJ|NbWE~P-W-RW^8+Yv3Sn-~ypvujao$Zgw_E7UoY-&oE;BvZb;MhPWcquZPCTe5
z&G(eVsF02Pr0F`#18e#_gLeNaLS)uR2SNBK$hGHbL^wE%2|;j1(d_9&oVcS}{hc=V
z)+K*)C4Q(C1X~($^TQ?h+?YUQ`zvy}lS9lRg4UFM^yvHbN_fvq5Ij^5N8krk<3$!&
zS&-NUYh;xZ0U!NTC2z3uPJTUVd$&s}#zgmT4*a=aW<6`^xYhiSNQ@;RR#xGuTFKd6
z<dz_=e;qNauTD$-OF-)7NA=OqTO_uu>diN&mHtIN=#W5le#9)fFK-wiGfTZcdOf2#
zW{!N_C^-8UjmEzWWnOa|)K@E7Pp=yF*T?0~Z@ezZKV$53!lTUY1A9Fx>P`;MBj*wc
zRsLwzt{&nS+h;@_b9}@a`<h4#U5QCdDPCd{<1qFrfHZSJnhpBxGt)sVIdxs487}O2
zt#j>O>BVFD>PIvy+8Ad&&b2SF`lUlg3#Ip_rbJHEUS}5um$tl_pBf-WSkwO!CFJn!
zd$-x1yQXAYB_cl$A2G{^nuY!!+Jm`Y8ohWDW|TvVx-(&E-QRn?_V2M!i&7{B^5n<=
z-LcbVeAMejCgc!=+x<Zl+#=IztOpO8p*3Ex-917#-h^9At`Q+b-;)(iRCs+iOjYL&
z690Y!QmNtin*S+R&mUYb+N|0fO84@Aixhi%#`^R-w+8J?;VF<^rQ9el5lvDcw_^W6
z5VD5?t@DW>&h4AGn7Q@fUa^C4AzK<}rRmx$d!~0LVV;w*7OuCV{Il#FOcZAHumBQ0
zW9zH84SKgI<eDmvOh&;bJ1XpF=FMDSMC*kwrf+OHSfPCec<xm{C3g{)b!;c_@5>XO
z&`oE5AeOv9pFXBdWJVM~odP|?yk7qVtt_@j9fYjjZ|R%%o9_8*;jR)Jm>kmfq8TsZ
zx9qwJfpYhDY@!sxERC{odlb8S7fhlBl4LHQmJdw}L@lJzHvBkO46>OIcEZ*#ajUQf
z5c{f;y~4~$Z9Mh<WRT3cap@r24lZb=!NYFq?3FpumLyK@r|TYIDf9|i`OpNOzJOf9
zR$M+9%bU|T>?hG%!5|B-#CP?RZaRG|$;N3D?=HO1-v;3(nbnm2lH$rCqm81=G%6w>
z_IL8tf^gfKZT}Vsw;mKT^rbrH%?v6-nK($HLeCvJYLuTB<_7n81X9a3JJY*g?j*Ax
zF@UxMl(}}6EcE51uwO!gQBTY7m)Y@JAFwVC{V%o2?OV_Z-Lj&!A#zdHK=Ltpd0Jur
z)Q;{Cl6b9_R*r94x!pW_aG$`KhwEPZW9_*0nKpcSCa68JE7$@SGQtOgsC6a*sW9#o
z-^#vU;@Kamu@7r9{HYci(op;x2sid8(dK*<&w?Vc3t8b`DWKSOMf;VyqWqf&R*l?l
z0T2f$*`6C(-k%dJnu#|1!FDwFaq<Vxn6aLhvVg4o*KL01YFG`dp*$LOz-h2a^)B%I
z{!O-TFD+TMujk1_5~s<mh5Yvuvr^G=`*H)8vF;hZ@7?D3?j9vu3&=0L0h!+)xQ)pC
zU(B%drW}mYaf10EiMI(lu7VGj#dQO(rv)E(D2Wvrb+X(uKzdH5gsqT@daECca<T*I
zDX`!9-7DKTuE)RmRF-6hV0gKiM!)ZAgDXrBoVt@tM``>J6oFTFv;Ga7dzxV<Gf_JD
zzL8X3<+x_0XIV>h!zv9l|192h?jW0ff8S4HTMIYNhSSHHe)VuGZx(xo4Vc8Vi8ucP
zl@QAt@xJ-z#JPBCkcKzPjvU#P$ye3makcx0oOpt1;nD9C%OW9Q6ipTh<Riz72$WQu
zY<d~|wX9}1Ba}xIJDAFDhPb0O>#)r4yh<{$h^I9Gne(;9)`|z<y}F}VF1;Va>3zci
zNkVcaAk|63h9R5;Z!<_b9zCx`8_yPgDn~q<;Pz#-hkE<MRy4TP^{TH~R9JMLR!VN+
z%RDYB76>CJw#`i3MjUvBaY!&Y)So&*=;T&409pccVA@x3Wp$l6;V7^O7{4%$h!l>z
zhiJidy<l)IK$8{novFd8RZ81a+SJkTok`ud$^E-H<S}SJ3PC2%73#KO)rUeVb}?+6
z3Zl&S;KR5+uh-t-7Oirir;(kSWt(_QfLts;v;jP9(gu>b4NkZ(T4n#9$*6xOp=rM)
ze#{@qR;|@XoW&ui{-L63FSB&3W>aUPtk_x@IU8+^y36xGy;)CkamHD|a(%F)<+S;(
z@k;Y+hKO*Qdqc%3?3?ELe;m>#DX;sy1|eR4*<M<&9@CawKT=w`7KGa=rh_Ar8X_Y?
z@gnFmwTq!%;vDT#UvXX%H!ewk#*c^Mw%<<UDd_6Pb(_H^cV>#4ZAEOge<gX}m=$RP
zVonXSS|i4Ev|4=gFwcauoDCbKlDZpFm|Ui=%SDFSZ0KX3sa5kYZ4xJLRnjeOvV5DF
zy~rRiPAtfn89p=af(*;g{R}=oZ6@nnkadf%7%5MDEU1IblU%#Qp4!zYnm<qGG<J4I
zmfydeE9~pJ>;Rw<l0HI~Hz3QKm1q;rDD4i)5c6d2Nu&0nA66@-vfVRBpALev*0!fl
zh?G_@(u92SQ)Eul{S&8>-nCAD?CZ5g_2951x{L~QOs%Z+vQoiW<JrUnr2fTIasTGr
zVB*wDv;0EfX}AWACA6Q5%-hI3lv03Y42!*NmN{DGmBpgH%(G<SGk2F)aqOhNQXA#|
z5gC<n2?V|Jx_r?d=SHXo`C8RC*~Bt~zeJp9>GfT`eSX`Iw!$R+!SPDt8wmhnEw%Tc
z37RY>gq<xcY}N`(w;SM`9r|%4gTQ27DFk3a@@e&m@zU5S7I6*KWkubrzEmst*Lors
zj0cSWc_tW?@7kNj%47d!xZclN5E1meEEr2BzBpPdzXyaH$)o=Njc}VEB>K}!dVaUi
zo&HL@z3C{kV^5Ur!cp`rW7mCMQO~`GjuVYQCJH$g2G{Vfzy1{25e-?kR<{q5{)K73
ze^E9F?_*jRm&<TIv1UTz!e?eZj1TD?iALU0)boe2eltk*m5}L$gKYDQX1l?|6~FjA
zlXV$Zg0IfT+Ya}-{)T*HEz+&r5qaRFeQVu5?V0w4_Q2f}kO+=BW;pc@e({H%=AWOl
z%pz!?r?zH0S2<p8@3J#Q`!GYxbgkwqrs{hk^!^xBGh>lGcWNkc270>Q$R^HOlDC2N
zZhezSa10u)g!@PeME>paP{J$&p0W(zA!R|4rrwZ&mX8Jc`T`N}NQ<xwW_nv+_hfH2
z^)dMdfSsNP>TPx^S@&#jCY;PAg?$AXl}ndgHCocV*=+NHDS%kh2!7s4)<1cwwdFVq
zt{~!l*MgKHWTEq=$#~;C^*M8_YUcmVwF>*1Zv606xc=GEOyZR@68SjThhuVQgj-(G
z@5N-_$~}3wUunDpgxg#vo%tj&q&UMKg>VxGn)L3*zBafd^m>2LdV}jp2BHvdG$iw*
zz*jLzNJps11M8(lz5MUCA!s-AX^t*2uNa<;!aj~GJDL0;GW5wj_gc8w9-I^6Qp{j&
zBL+Gfu1kVVFOJE-VVU`Ta4s>KPaVA_X#FPNai3CQN|=Gsz=Xvl3O2n6s!O`Mbf}FD
zpSafXqLtg-%CU`i?PmiqAPZUZD6`Z#YqHZi!3oS2sWE-jOz_Z(-c0veXOO@_>7T^n
zBn1}@c4r65hxELERiF@Vv)|d$n?1cQ2sgBD7E}gE;q66TX~9%K+YI+$*A4rZ1pegr
zK=Vylw)JF;7GGInm!F>$roYi@c^5i~@ib9@7z{8YDJUQcxIPkS+YX)VKeRHQ^&hgx
z6y&AEHzx)ps~h*(+Unm_4~2U$x+=`JymYev@Jdjf>;w}`Gxn1}xK)NS-@D57+$Wb@
zXp&G?d}G~Hrsn*$@D}C=R`dXUCRTE#R6V-FYGWqR415`GAx7Knb8l-l+*So1+-E7}
z-iaKt%s4z?I41O^@W~xF6K;7qORk^K?B${pyPEg?nRNYLVOw*mTIq$r;xG$&Cr+p9
z_JrCvU)QVe4cur$93{|)ZGJgS)f>#j-CG#8wRGf5C9Vg}_92NC8JO`jasSgfk=uSa
ziT1p=Fm`*{^!sN0O)oOKIvZhUrMOHfpP0RrBtE)s|ERwfHUNL~`J(eNOr&+S?6z6W
zzo+4ieYRPLT00Bw_r3l}vIOU{>e0<oVK)$NzYU-gRkA0yaJPJZ%puFO*v#-WhG)!W
zQ)ft06Nwd&Yi(`(*JJs@-aho?I7!YA^{q4@XfrZ04H0gGv(QzR<jslyg73S;Mj|((
zP5&pz`(nv}vAAs4K%eDj^qG^gcJNC^@uv&Hn)O&<E%?T+><#EVQVtGLJnoW`J$_10
zpZ<8quf4h)tyyP|Lv9W#s7qFZqQ2ykdO;97&S=@n{=3`T{`SJ4@(QyQuAL2pxmfhE
z{M)fF9g<ej{^P~JsMR_bbiA1l0J~jI+iiMG-RJ*Q9`2b$6F#f4<EtgR*>l0-*V4%A
z&K?(pInqFm9FvsVKN<9b%LBcA#=tLpvxXxi8qcC2p8D@z0Q6Tc?LX?SUK=mcbACGY
z+1RUmVZ<%&7nUQkIjy+Qs!xBl)75IDVB@W#Rb?B#Z8n-bMh_a_skqfwKzxJN8{Xdz
z`acR|Yo+gxfO=c~P&xr<H|_m7iTXeBll(orcSAf*;AA@YTWtB-n33Bf%yrM|W9?@f
zg9OMDws1&jAW2!Y?3SK3dkY}~9O4N8RzRu05N)mUsl`_FZAQsz#_pn1D)q@v_d0`f
zK)Ahe1cY154K4^e(e2avrUQf?GoLK_W1(v0A>BPWOY-k*SoWA_Pko62D!H2R#?SsB
z?vEWmfujnT!rUO5{iq#JTv#o*YhVB^b&URHJ=B3Tn>!`$kA7frmFsfpWEjqUvXxBr
z49Z_jmtLQ%rQ7>qaAQ6lKh>>TV*|JN!iKi=ik`da`9^oq`6F-uCZOYbM(Y=?uAp>S
zaA5aXsPSWd6Rn~*LAYHpoAz!W^Txki2jP~a17y#?Qigd!YrJEKaO<CuddXjUP)Jq!
z)gvb=mBZ64xD&9z-4$)|Tg{5;cxK~Wq=0-uJKXwOr$2G-XrZvXhwfd>EjqKWZGVI9
zExEc{1~KRu`Ia{MxuETx(MkPds==zQ_0cSfQbB8CdNGz>S1viA=rm_e+jc_O4c2zD
z_?>^1`Tn2!{r2b5xbnJ6yxV3nVJBhI`l&}hplymWfu{cW(M)v+wZyz&v`_8@r5BcS
zYc~1gSsWy@-xs7F8^;4`<Sx-lT@Y^5*XVKS^%ZO6-<&F-Br{89lDV(zgYrj6ZSt;a
ziKX34XQtxmqjj(HiL%o?x>{sMU?y9qqwd*S7+v4Xss~r1H5);blH9p<ijNxC&<oE=
za^964FP=6vb3D;!-^O^kwdAbX=yF|<PW>qKE1w#5nr|kfTb=}4n|opRr%;5+Ix4&A
z$Inua>D2$dKP43V!@GQ(C|hjvv$m5JUtO|F2S#kzqrhkN?4Jwj2Y^X%cRQT<vX23j
z2`00%;*mL9r=Qw_=;;#0^8Hb(a&T1eZw2A@+npfXM0JoUm$WvLAX)KV$uPy~mKJ%>
zl|8?ADR}|zh~oHGv3ZbnDGB4jb#ap3$2+HEjmWDc+8jl#pLD$*C>f)Hw-zu{v^9=e
zr?tJ_iDXX}l*|<*T1@n~54vgggGq>%sV8|q>s<`G?W!d31-W)DXrCKXUZKUki(7u{
zxD>RHU?;imBCflJ>uyVv<Zilr8sf)OL(-}#qYQu>pqaOH{lU$kdhP=Sb`|LJ2U%Ri
zfw&WGLE2pc)5MM~ihbPk((VQQ0X$!!e+%_{K(?y^ngrZkmqU9yNqO%9X?82p7E#~R
zI-d6%Y+^SKvcW?%OE-$zziRoy%_jHtuL5Co7l^#O1MVkB+&Z42gV=({Zu(zhGy{f8
z5M>FhPyS8aVCons*FgaV7Y`C|yAj5-LEJhO4!1KTWT>zAZjA#C0A1_MHt*0=b4{WT
zZh;=^KWS9~d$2S<N_a((nUEF2O%PCoBT7h_qDO+jGgu44?R<aW-4#c{4Osi9XsS3Q
zM@AHdS8yY}W&kwM2eA=s^5el}tn}+d&e+*pF$le1FwzAz2O=`OwwrpJncC*Oc<#qN
zKYBoXxC!>oEbjik?azJ$<Qm&3uFL%G<OG`@#B=}E_pxtk0<O_}k>PIu2y`*djZLZU
zPlJrREb@Z2pbM{$qnHeEiOus`q<j1o4k1A=iiwMO@m*?5Qg`_;O7;F3VVJl^<lWQ?
zW{x2pl0s-jBFiB7K<VAXwOl$lzl(HhVR(Nhnh_qhxlH%AcOZ9-1*HdLcoKo^%+dk~
zvdb77S0uiF2i#}3Q`=&a##{Vo?)%8lFG%Vn2)C1YL0yab7q^1e=dj67z=4PZC)&gU
zCCJS4*;a1%^zQX(07IRU`MHm=+dyD@zh%7SX+QY++FzSTYfjRCO5Nx8BM<58J{7<p
zlRgw={mZf-g#@{Kb_=pqlKjwCq{U>+fDXJ8IoyaW`ytK=Ee;?6o&84753VtBWkiyO
zA@=VX1T$ZR;BPDYPR#X6M(-K~Q=j(y_%;d0WISIKhN(^J&p(iD?`y~c9B$tIUex`H
z&>RfqBJ(`GcU1(TXB>6UZTY>E7#rw#iN`zc=m*mu@%{J#5N-`rOPPFkR<}pia~Gfg
z_YrO)I1)0(rZx9Qwu1+!*5L9%JUC6njV@GTC#xM5mAKTtMaT|Ts%wGH#G>J#Pkgur
z<+f+`&huj*hGRu%!W#O7he#n-nk10;JZTNN9$S*|UJ>{YXi(0}M2&fazm>klJ^V)u
zGOPxVIVXrW*2N(LMjkd_W_brl$49*bd#TuzIl1FH`ABVdT&Lc{gsc)`Qx2$(n2EWH
zyk069j@+tB!+UD;ohp73IsM3EVRFLT{TNh`+%4JqiTfP3vx?`HeTvw~6)H$=Lxvgs
z<-MM4W91FR!ipf_*m`7Hq>qMuCYSLv=`&(i)6Mjox^KQsaFz<nZ^$h+-=^XKWV^Y*
zgC+M9q=iRI+KF<|FC^0+#xqNuc;GG%zLVJ9dEPzwOI@nULyYhcg*T6IqkkmYRz*Wh
zt8xmekyz8=dkwK|3rK)TTEZNMh&G-iZ8yrZhUh7tFLrEBO4V`FrX*0u?KixR;C%&N
zk%$XoXH~y(4ipsEEK~;B192#!V8e6NA30M%hT$zcue01P6R>~;Bvx68740mQB`iyB
zVtS)c!}5u0A(0~FP9C0<K9axZ%`$Sm^peQbJX3^vhLl<O^VpE6`=r;5aelBE4#$V|
z2-kpe6#m2oSytBxh>1yO2!<M7r}~2S8^*Ff;@-A;4DZ~<Ayra+!M*g?!u+7nnuMvb
z7OHd9Kv#WA-_a!Mp{maobKe$evPYZ~M;L&Vg=eX<AtyA=j<KQSQBs)-8kEba{rr5y
zWnynve2!8N`c0_7=Esz@iFzL9M@eD&gz?(DsPe+;zbDQYUEuFPxQT5CVW%3Z5u{q#
zMn1}55yXj9NkY{4_y3G=lMa*vjZhc<E+#e!QxZgu3TUU%RT@d&BN?Ssn2y+oMe7oD
zmkd&33XL^fPpq(@uoOB>P&~`I9*eLFXJc{#Nv7$mxL0B|7956~Ik%c~OeO+qP;g`J
zH8`hW(ee(o9fg=1<vN@P8nMeV5LFsPiGyT;P@T?qe-pOIts+@6KxHzNa6N_CQSacq
zv^9`cRR<Oj&JbS5FVI6OG)B(JI>+Kiv(HC>@R><UXoTR<QlAwfjrt7JV^SC1sgQ6?
zgyK9@*GU^}8Qv?Cys~BTy|#((CCY^MllGtpm|6K>u9th)aY|x)8JxnqCgqU6-{~Ru
z;sa2OCM@g%BvDZ-@($`Ei?f2iO8>|^rHz@m#J(xWr)(W<OZ=~bPCv|Ni5ZptlYSOC
z5KWYrVzC)3RN1^|UWK}C<a%)e${Z!-r5xhD-|-HKG86d_SzgBEXL@b`06+jqL_t*l
z1emE&&mKY0ib@{o3&q>Fk$1>*c+3;lm9N!=w1nTXflxagZec=^ep9ifGGidiGHocQ
zkBlpkJqf=Qc@#&N)KS(?<|VYoAwrA3kam{|R=D1T6Hg)cWMUWjk+@tLr#xSow?N0C
zFk&s@91FHW>QN2}0aP+2GEdv7NqcJkoQ!9A9%JcmPEcH?+Cu8Jl>212C-;h6Fu|*_
zDzYq#0(NpG^Gmr!WQxC4*|56~{XP86AqY+tKRh;khT<j^l%JFtc@&dnhPgu_Bp1ep
z%op^TAobMynZVUp5m^@b78#ImNdAf3C}bTUpRh0G`zJX5F_W)Mf(*prs?VfK5r-UZ
zQgpT4@BwR(!fx}Wgx!eHP<d%=SS9#YHrHsX3tFHI&L=uV2pO2E(yYYW28xFO3kk#@
zw%G8O3fT8#HN{Jtvv`*vkI;Hp850Ti$8L-@d3bJwbHiK!(&UIS<rzU6fvQHO;{x9(
zqhWY%c&}1t<y?{L`m)u*{m=7cwUkjwz0^4j29G!=&m$;j{+xtSsl`L;EN_17!8rjS
z40AZhJJ@Eacd&Yk$wwyRh4zxZXiyK(dy-UHW)JC)VOiu}`9x$aj$%zQ#gYq^bEL50
z5i&y$H4je7!FRlanE}oVK1)A=Tq8l9sAvT0VFVM6gSz3Fm%O`4PxMf+8%R6LKS2r+
z7NLF%>W$|i)VrL3r7+T)vST5)gD^u#4qJJPK_m^#_rpa&N+;KWJWHvo<oXkWKq=Lb
zn{8MR5}2yGNuMv&ZO1u$o%1;>c$n&P$|)=)>^iwm>LrIb2~&wCQEd>)w1=&b-Ou)+
z-14WY>QKI=%rqA>qv;TKg_ft3G<ndj4&J6X&7d3-M<E_k-a#*`@xrNvzLo2+rw;Q|
zNWyF-kD8%4+@#&5zh!0PI=W8Dyo<~SyOw!ckN?<35XzX6SLRW2i%}ICcXdv$)3?ww
z4abY5B3u{;k2%LYpcaW;?c${IWa1T+C}!LRSys;zB?<*<M}0+>S-6v#p)3#O4_kqf
z2GwC)EcM1V$cjaL9{ndZQm8yw#(9Uzabk&_8j7g|7IQj>QVg}iVSVMCTq6=Plu?m4
zRvE67K4$I`^AU?1LhyGVf6wH*(DgfiDP_`^NXOt^{`O+fO(5J(0O1BwsQ%Sa8~(D!
zs)uro>IXm%3EGDhRjp#O^+0Qo#2uqQUFvY%Nc}4y++;!&ZB_>1!ZH6@wqa@|MAI80
z+)zT^HLkC`d#pBoG666`^}%*f`PjKvX-iDJ`d)CjT?+!}FL1rxYE^SD17QOZ<@o7i
zxz(F0BP;K4YPlC<1BPF1?5E3etJl^>R^DB$HNHZ4$+VL;{~Kh_*3!s|+d;DlpB2}Q
zoHoI|WzN;AAYw=f<z|b1JC<Lwh3jsz30kBRpIg+HUcIEaYE!wn^ok*eoAtsHNy8VH
z<lM5$)D#2X81*6Eb3vSj)#e;pqXgkA_-si81WvQ_L8)ykjV`~sI79`pqANs^+=Lj4
zHj&Kg*~s_d+j_WBU$w5#DE)h4f{N0XgHl_ysnlG24!V6P+v-}j_;OHc+sY%${+ws|
z$kOtcYQkaHSa}EcK2T{^Kb{aSR!^5+-K?zH26x;A`GVkht(Sr_x~WlFy|oCUh&>G3
z*`()W6Te=ZTfG4uvisb6?l5AfP<xl%)TplBS{Ye<wo7f8D208NPmiweu-0;JVd6Co
z8U+nludUf!9hvx<P52e+ne@HPF&wgU5ae0$eNA)aJ+<2EO@)!sZ)-$dh`L$Gmi&1!
zZ4U?6O?DNy505TpOD|nGN9b5$XA|c&ic;TI_m0$8+>)=eNMy|)Ep}E)+dYWf9*5jA
z@e041@S&|<Uu%qfle}_3&MTi=Vy?Omblm;5#-d-EdHg|{^I8y>w=JqJKHJXYOq4kk
z1!Gv=o&J>ZgT|IH@m4-PZm-@@9RVR%uYF8oyfCqh%j7MUoxhIpQT_6Sy#_?u%KNId
z$_G*t9<sC!@^RJrYIEhi^%_V$raol;I%Hn>4MpY;dA!WKAU*Y`7WHK}j#Lz{+fAT9
zpT)VAIc@PJpvpGVcGsE}dtdIIa{-@B<koC0jjp=AQpvwwje~UTbaf6yXt|O*l!;t&
zN;E0&&L%18Ta*Ze!X(^${A^H|+XNj~Dm%~RUeUsdXI968EHh`+zXh-2L-fb`LbLMW
z%*?%`Vy=9kp=5ORgSC<7R}+W6O_zeqT)nM00$(CC3-;|nJ{vz{Ne%>St?(8WBPL#B
z-w~Xni{XC*F?SuPyjP22QCA4T)yUbhi;L_J6Z*Dp^A6rw9-==5mMwG3LUh)AG5^uO
zo_O-*?V`!B(!&zq*aYw=?QgM!Y9o66NV5{$G3yQf(g+&wuMon|0AOWPI5u%GF##L-
zRG<A3Io_g5n%_%(BTCGp4|zPL5YM=5nN&Ul*@m!MK{H~xyKA7z2tAS>mlb!R#G(X<
z^b@GvyVUchf0(q*iE*>>UM+7vbHu0|l9amV^g2m8nvHypSO{_&IBRl-e|O)T`exki
zU!o=MYim~h)ls>9dasjujkI|T2%wi1jQZ>1QtNb&%~CCGyiG6W-q<ip&-aV1(}K2M
z88gSvGYSbw*E<)rg30eBo!mZSPV@Vk8(o%Uoj=TZQzxdK+|$P0=82#iL{w#)x8slz
zOKsTpWjH99N0?ASaecYsl%CtqPklX^Gsm0O=n3;2Zh|P1Ti8;k#7Gw2SvM;$w+g)r
zW(Jer%G%Lp=52!V=YBfdpF7*>m0mKE&%XiR<%uP?ut&$8{G#4+p44>5PfGLsU*aq~
zE9myGwFl190$>ReP5-!<cUQmw@<p?idqzE9|CD9d-&4w4jg~QUs?#aFXslRyZ)TN`
zYgmQ-v)t^-t?tZeTGv0zNyg73j<YdqOn%L66`t29)IXouvV#`Rx}6>jIt`}Wlw7>y
zUvf)^J*`chm~WL{(kNGsWg_fxuWU=DUm*+SkF@bFx2$z%;?^I_y~+`dGHii*^QR5h
zT^ZPupYOKXzcc$JlZ8#8p0k(u*5qe7_giyd;%w?)8i8>1I?;3?0v#t7b3ORWAldeb
zRy%Ij<Hk|&`7mjZ9RoYz#>uGj`*Oc_WThhe8+UaP07V#0o)ChV6I~vi|5;!VcFzjV
zN(onD@)ZS2WO5gWEV_bl(`k^Acao6!x@ph5qv|*Qtw8)P%c0te^JKB7bDg=0>)w)8
z`j^b~gSGXLx^o@u4Inf7m$rK0mTK5|hf_&0ZuTzod}O|(Wd1$K{JxKt`LS;}rQ{9}
zUl%hrPR=^^GD7E$*K^6b3@3Iw*iNK~yZe|`KSpybz#X;MM5WnN`|TuZXd}lOMfZ@3
zSvfQ)&Hi|R^&4)xPs}6SF!L-ZK##6AeFl_Tazs=<QL)P}@Cwu430r!pVUB)I8O2fs
zz~VAd(vW2>cVt~qYQ@1NGv4;EWvvJ{iTK_yZ6l+dKO22(F|<cah_&z5a=ATv_RJ6U
zwzJQ84g@E&NFj5dY`V_wTFxGqJsLB{?gh;nv9#9N{qEe)lfM5upv7n8Kx=mG?O!(M
z+$Yy_BVW^8@3JVGJEPa_UsoKIUp87O9+jAq`aqL~<^S+ln96qjN~x6`upUP1qlirP
zsWEQJB#V|9kzdpF&2ru-9!0RB*jcbaDNMeY9kA!b<ev@K;a*g;C*LnTPo60UouTZz
zGBiYEl5IL+<O<>T?kz#*GB)0_^AKT|d&LfjR-{N8rS$7#t@Vjc-2O5vLkE=<ERo*e
zVZ?RAITr=@Sb?*&WSVR<CAoL*DNeq4;K24T(&NHE<;=8=r=HNjq|-~y8p~f4X2C;F
zSbn8lG|Rr_->nDcVhN}uXY6HR6h35!@T>6xPwW3dPpYqS-K-yE{v8HZ*n#F<Wu)bo
zJGrzM5H%q&sMK)4&mG6mSfO^2Vy7iETntbk*Q|KE9@)z>BbxFPYoa3if1HPW4Jt^P
zAXtzJ2e^(^>)CQWVR%+}4opIzf}$<CwDdwf7j5?v@62N2?rGW4Rx5IL$D=+I#?Edf
zBfhhr&VAeuw2Ey6Yp252x2i$z$)+7|c0y-2!fs}x*xsuQD2Ci`xZK+xXYbN?(maz+
zVSjL5`Mz0vzU?@T0UAX)wU_8tyg3&+s|H3qm&VRsgdNC?BsN{?*T)=6BNn?O;ex`o
zmuxLiUm#Q+EzUg+Y=hj3sY+!nQ*uJm9n2BwAj&$Y=sq?Lz4U>Sk=rMM%EZ$vuLTYR
zotjM2`KNQv@r}ysajnrGvMuj@tCO_92=by9n|S;CY_ik-H+zZu93r<C$4+op80bp~
z!C*(5&X%F}>FR0dBt^-+0EN3X;kebq{9Je!o9Mcsw?DZln*B|TH5FYz4(Yn#C-vv!
ze#qp@zx;ba`bI1!ehHNgI}({^@^7YqVp$M`8)C<!viuv0AQ0KLNP=O>rEixq=jD;^
z-Rp#f{R;(P(N6fF+?DF><2q5BD5G7;ydd11@j#!G>%I_#n9ULi`x9em@4Pgvye97w
zXTSnwM5Wi~U}7d+Q`I4NbPpz%8WcssM+lMEh^+}K&#c0e$9u&du59v;t*rDnJt3wQ
z_o>`FrGacCqJiiWYqW~age-61>jgg!15{+0*n8XF&9REl3ao(WmH6r)oBKF}2ara*
zeKwx@kmXww3}3PubUvF9Tj?MxvXD+|n+|Ms!YyXN-fZ~x0L`9gxbjQ&0!$vF;$+@k
zS)BAsS>qjPIqe5YutAH%O9xr&vqZC81xz%?JhHHW?nGuETh0H?zoj?+M~7Gk1<@u(
zooxG*Hg`_i$^X+xarC`G6_k=Phl;%>+r$?7F$lLEaM{PcN$f*7s8Z}h<*zQXR@_ys
zF1x8zF8w=Zqx+;Ine)UfsH9v9z$xsYP=&JvLWafpB@-CkK5<T?Xy)e3sgFS{{%4pC
zF0*@$k5=pD7ltL#!u2J--a30W=w0E08DE@VbhcK)B*08)0p-l+oF$=2y7tssmIuiN
zK^F1u@?d9=%<g}CChXsoA546|*eE<RED$=TdYce5ZU=n99D!|4wIxZYL0bV+!)#|R
zQhK_?(yK#k)RL7fP0QKX*%P^y_mpdkPlx`UZzV)rhYzUu0w-tJusuT0Ce>A5azO>*
z8D?$gACkg+63;ks>EQ9g_^+^r(?AlY2hdWX^6Y=cW`{DMyE&d`qpn5rJb8d_KC@<4
z4oY(*A;-PNs>b8=e9kCB2?cnQ-AZW8e6`b0qh_|^0j+5^A&pD1|H3t@yKrjF7UJrW
z@MwJ<d=>n0Mx|gDUx-y$I2UC{EYpolN;^B`YjL!U(eYde^w%sNC_`DC1k_DVZotg7
zjZqw8v?X8Z%qc<FSUPGiIW=lPiGvXm8#91AIOv$XDoOMGBh|vIY+0VE3#xovO-4RA
zQZ2s=p+b!6-zY%%6&Z7<sHslgtD6O$Z4h2mh0E_Lw7$H3WzjA^D{^pTraEZg_L(zD
z=e($EEnj3#{4igYY-8MteUKjn9#Ts&X+H5WKe`|gU>Y#c)Cx|1FWQr6Dz@m91?71I
zvb<sV7%^U{>z-P0bGwHYWg&d3NMuO{A0S&imUp7hEz$F&GbesiS290+95R2=V`ZM&
zFOugP+2RX{kp*C~{b)(zPSw%(bsL)}IutSJfKjBNjD!6c2a5tQzSxN+vIx6qVgFpt
zT@->+1HrkQJdb0(Uc*1;e@e@$a7vm*%xpk0i_dp+RzuyZ#*5TfGK?u_qcomPKe(X@
z{5dMTU@SlJTNg_%R9srkti2MH+D!p68ta|^p7X=a)oA&HX472}qA3j*KVxs?fVndj
zry$)Q-`vvX&T0?B_03@9-^Z%u_iDCb0eu9QlC~j5$YrLpN+cc~ZO@7^_93I>-LNg_
zo@_waNq=siugm|@ubnmH&R;F7n&@#H#u`$CzHc^scy({KdxHaqNR)U#7J0C=+z}*9
zc@qR;gmW)UI=f7%C~d}IlvUBcwi|cOjC<X4l71lZyo=>cs3btqRpMo)E%_%1xAF%n
zM&Yf5%}A&U$+ky-yv!{=JIqagxzh^oB9Fmxu<x&#@*Y~9%qVWQS>WD;RQbPYCzEdr
z2I00`Y%kWV!IdF#qM^CzncLfg`)aXnd#1Ir05N6HT>sqmT>m<-@4Hvf7d&n2OF#v!
z(PqP~Mig!Ja4BG%>~yq^2Mh?zEXSqiWQbfY%eJ(%jr)1sG%S;}N6<OcMZBxjRked!
zLx?^6V?SvBD(!auAVFrtKW@hnl<OMnpV6C(d+bB?E}llz?Y)Xx^^*k%0%b7xdNLW3
z$Dt5yKdkHd8_d{VVKN;%3Kg{RaP*)`RWBUSv%{?v(U{fOKOHWt49yENIm(ibe!2*O
zV_%zjuXuELo`h5-)7tu{v78ws4j{c#cxf-)u+LO$>j`!T99SIO)y|^rxNK%}(bmHU
zKoM=t9%GOKKS_Jr)Adg^TYAp5(w?aJGN@E_`TXcZnUq%X-6YNcl$3b;)@1!tofc3F
zJM9o6f+RSvFYDFg7?o(qD7t$8o2_`=?sIdq=QzEjpC#T|v#=nW5nI$~%WI+mBg@D?
zb<T(<%`o1~2H=(P07^~6SuNu|uT>7U^8TG9fcgWmrYk_FzTD8J-?cU9{Mqg0o>I!i
zf27RB+<I4S@wXgb?HLs^;K5OtUOGsp-b0M|^*P^aFuAL$;4qU3LlUlw_AB%86OABR
znEQ0^;vhpiGv|K1bufEo&BMrHM|VrCgsqj1sTjp~3{%zkHxe3K$jq59Z|$`%ssT;+
zfxw~APk8p`r+3?P=U4-0McKwIl8v0S%qi|Wl=<M!oyj~5CAY!iea4b}ey_lt`gE@y
zPl8f-CK?nVz|Ak}_Tt%cRDM1S@l_$aztG(H%*nR@U_CZ+J!>GGaFAARc*b-)SO*`W
z+c%>J8GsVaqfCB}?pw9sqFl%f9Bhn%PY=xX&uF*8X%KE!4}=>dV0m7v9$&QV14Wj@
zp)9wG;pFSOzJI?JJF5Z6b6w-)vSZkuF@0PTh?6wFWsq%zFLC2LtXACC<8YloKj{I|
zQpgURy^hEZJ|T9&$G5_*uhxNgktU#N>xJi9PC7LmZG1u4io5JM_5omy-nHbH^>Vsx
zCfV|GqlfBXC~Bmky7QQd{p0=|svG}<=i^U=FI{Ab32&{fP5geekb6=LdZ=y8p4!HD
zE#Ek4<t-rGcHeHUKRxPT0~Xm6t&UklK0oGQLRStcbhq%Vt|Z4`dgpraE+Lo`XN?r{
zdw6J7Ic>=w41Mb%f^aKp^*2=t$wnA8P6Ofgl)6!V1+X3RpEN(*s22aZ6>NP&ez5e~
zYT20p#beHD+dkyC%)QITlAo8#pu23%n#pFqlXb%L+Nt+IEm*Z(8_U?iwNCT8ozJ&p
zc-X=<8?AB8@U!mkdOdwlP(f#uL7znqNSt)jVNpMSWWyT&j8z80YG&bNGW8X`UwCW5
z8b7{LmVVC$lkx12bQ0Ae6@<U3+KV@zeul1I>z@dEj$t4Q8yT*Q)(?Ar@wruZx2?f=
z+V|qAuP{Qm%YIHxZ@e`r&L#bxf3c>wKHLk7&#8y2wpgQtiDxt44%)?!H9!TSH<4H+
z0v6o)LKc~?93;j4o4MV#MB_=zi)Q~zj6zL4w`c73|J)g{_S4P-W9~H1TlB?|>TX-o
z@pyI+O&^*0mCrSu#^DA?xX^+U-fw*us-hKMQaHEbtl1ook(?*#o!^P#N<Lov+0oi=
zo3*icTR)ilvbfu*so3oK_qanAaICSq*4F3F%J)a#JyzLmvz~`@&7b?&u<wh9Hf-T=
z^L`J9+nt6sc3Lx6+%L<UwSfufrSCtGWi$Wf4<^1aR$a3>8HEBW?EVlQvj@Y$q7yi`
zDIHCx2BAE!cx(;A$bm8aBa;6xh?Dg=;r_c^bib#8S8nOu3kC6otKyRUpngQNQaLQi
z0GkZDr&!67Ki5m{s?^mBvj*TA<Xh6Cj^;|wj<CTv#?mW^)H`U<rN%I<(62k2ki_&h
z!n$_@2)FCU24kOZfN;YOOXe~EdTE_PRy(qq&*r?Oce3t<=gy{scaIJheZSGnuoUzz
z9Q6A?#1iuMnzwROgDk@&mAR_hkF=UzeKj&q(z7R&`Mt<Qcd%A6pfMW#Y`t21N!y?P
zRLNg-VYNce5yt*hI(<6!`=7T{|JZ7<dOPkxA7ITXey{uWPF#IsC0w&j&iV1&fBOWR
zv8QLJslW)28_o1>TgEO3w-b9|=>_F@w{5{#I`ER2Zx8th?R~4rvR3TmsQ({m_fL1D
z(gDq2#g<Vx=KQSliypR?npS*vn)9x0d*ND%wn$Ot8fNqDPQ|LnR?I}Y{ixK>9Wi2#
z9Y?y#a^G&ey<Cr{dhIM%(nfw$uNmaWh_|-F?iZpc`N$wEesG)=l?ef7gJkl1saFTp
zncq7sMC%H{%D;q-sc-bVy&t3f5?P|me2){{f6R#|j`I~QZ7JT+^@Vi&mt%#>!Lx;#
zZ}#S9zo@6V!yGfW7hG5Oc*)>wl5VWPL%P10j<XfA-AQfpp=c#cEX|Xan)M^XYWM7!
z{<fo;ELSksNKEI#;FM0}vkF9)Ff}<Zae;)DxvwLUhRa??CVps_f?uP}(Y@~BVE$Xs
zm|aAe$^@Le&m;*9i9nzcHzH76;rW#BC@|Yg6WzNsNCWJ!<`OIz2fL=d9K6M~{WSa|
zcz9lm6<8~O)PzW{p8+1hKtZ_6vVvCpR|v<a`x%r;iBU22+!3x}kH*3kmPB{6En7vA
zUzRtmTA~du^pfaSwy~>+=S)`M{`r2A+%=D!nWauBgz8ZBrd`Fcv=BXn5|!flHI^6j
z(wj9ayrZA^=dsNzAbS(G<{kht>y{u*?~%rmJv|!J$80m*KFIo~>WTSe!zdh_XNwSO
zy>CjWgW8y8=CjDWbx3OZR}GTzGE4-^E#0Kt@#V5lqYT6Wr{qPo4=fmke@aa6VlNJ_
zF*5VnmQi>iyIWFL^uC+2u8Mlc^s`4!D;;i_;hkO@{7wug{~vMh0bp5C<^R8%Ue51z
z?w;u(h=?d62(B1GQE)N93M;6HyXvaz8rHC`tO-<t1Qmo86f;VcpeTrvQON=Vk|s~*
z?w8ZO_xJhM>mCLibbtPT%fA@9U)QT!x5BBaQ|FxTIaU}uFl8t9?mCT2T0O=^0D!*D
zupTje<@ZW5&F8%`F~Y<z{stn)?%uKM=e7M{L;nHy6*0?YnY1s(oPf>C+dpc>_jPO{
zAcysHS=n&I0kS>sl}Ri4(!g$B+xELRs=wqNK;DbV`&HW~&Y*ll;4r3V^?nDPAR<!4
zhUg(4GGh;Kb@ia}edZf3Hb4@PIwt10C6Hbx$z>`Mmcu+dx|?V9*4Dztd7=xr=GR9t
zW0VMoJ|NU+{{TgZL}yUZR(f9P>+)~o@E_%>O!w07R}6G$>TB@*a=65i5Ic;}(?Wh-
zB`XdM8Rb3{bt9d6SUXe3Y+^*>fR$jm$unref(KOBw{09#{zaPK84JOkLmPRuzy)|@
z1HpNf6(-(|uMn&-nsE1>n<U<1)6qKk1nXcU>p<fo0-=%VPh#D)j=Ucd+YhkP(>jmW
z-11aE@B$U46ISMfuo);gn-@WQL}-cR-AL@^uHC!074+`X12WfiBAJh&dzpup&qd^u
zf;ZY5S@Ca7oHa8d*3O7T4)z?|BSzv$zC6}NoZUf3?}=`riX<QOWcP03G!iL1@%n^g
zyR3t+BRnTKtt#+d1C8zzIPE)GS64WJvkw9~Hd<$ZD-Ytb)^)%?9-!^gH*N0_RV{xd
z{Vk?uPu_er9wGs4x)7J9-bACQvf;gfjp2a6Y2JiF=X}{k>c2iiYtS^H%0dN7d@f@7
zJ7f`V<a&|h2QuLQ+jjlu2)jA$jENZh>t9UE?+;jiyu%dLPZOYzs{8hvaEwT@jD-ZP
zUa655M>w5vM&5sxzb$*50&Mt9HN{kdGD5v^Ee!GS$94u~kY|Dm@`te<)<`hP*v!fj
zW93=+R!bt05M%8p<w49kHJ_BnCi6c6S!kS1S=DLU0&<NGFQbBJ2<_tpa`_}1S&YZ~
z?_p69)UhAWtKT=%J~=-TH-_xph%V>|1j^<^9$y)=K79{g-rs<gZ|2h2a6f@$<E=<j
zMFTQcUqJ?`EnM|b6j48p^)*N4y(_kyI%xEw?Ilg5`pbAd=~?W$B{P&#bfqoE)>{Aq
zQ(GUMWVmX882vUbw^?$3h1ij|yOn-0erm62oBA#t0sZLHg=Q97Rh&FYN@Q`eu+8|X
zUk2f(4)n)ZVriZ=ZBwq2El9N<<KK3Z`pSeFpbRR?exwQBEW2qV!lTZ9Xmvzu6xKad
zUhF!m5!xd#sy;mxnW-19yj8C@wEI+=AEVS(^kTn>rW}%!ZP1I*1r^BB$RI_R*8z}i
zh7s13_VK8B&O}1tZU2+G#TLDfuv~eT+5j{m&Nb~8{TOeB5>voW!!1xT<1C$$#u?r~
zN(-5m3M|n_j&3tQSpLX5WTkBy1GZU>HhJ5W(;CvjXV9PO1$)8rn&cT3G|A8|H1JGE
zviS4OR|`X(7FUK$)oj3^WWrh5qAnA=;F3blNc2*t+X(PY3^zD9qozVaCRdXhj)09!
z+F=cjL6;#y?++8dx0f`8W>lzSg3n95118KMkJj;pI1P|aimxRAg7E?-E)uR8E~dZ1
z%u!}Fd0NO@_^oHuA6=Q|C^x}Nm~?}1gGi;w(8g&HIC?j2Hwc_b%CXTIFZ`>>m!@6>
zBXpSnA7lL9L_UfVgFw=ydvu#T)r&dNUnN+&Xx<Gc8kwOyn?*#Mj!gH2+|p*Mh0oX-
zL>I>HMmc~Lgyu;7ivS{ttfibRLFk({I1@ulQfJwd>b4>BlM;q;6XM695w*@31A|Uv
zfoQ`v?|=r32^3|eATd!jG%j%QCN>)NX~R<xMAH@_#(4HG0!nzTnS1qD<2bAgo@c%c
z8c|;hg`_rX+~K+9D?kg`*gNx#4n5tHOnf<63UCM*^qIb6p&8p*o;P`twS!nQGD_u1
zjy$YqsQYofjJ}Izm&e7>r}`lJ(R_=(#XT|p=2^kL)fU`ZYEJbIGq)7&)2u!`B~ete
zVfDjs-?T9@Lp^MG<o0Qq*Jz_tlC7-9#n3Y2F5QV46V{tC)}#;S(9{iMmb^*IQ)jq^
zx?nO)dvu`CPJ>nxicsa&P);-i_ix;ODo^V`<E%qibi0}7L@TOKV*rz3+y<a75Y*W8
zk+1(^Unyq#z}6-tskNF1c-E{Z9WdHk)DAY)l`vqU3DdrnzA^SIs57%}wFb0R@{Ft{
z!u8C7l<gdn2BEkEpC#HeGFyK0UegZ_A0#C_L;afP<$f4i);uuok#W9<rqmwo;7{8(
zzWPh)|2yYP<F`e#nGF*tXRs*kuC6DX92tH=ctYY>P`=HGKwDgE;1I=Kun`;OwUiBX
z9M>{iPbgdd03ap}_FqoCiH#B+5u3JlP2AM;kiw6fYmWv=N%iQk80K`_A|#aIXDq=*
z6`M@#pR)(o%DW7kp$9_yg>IyGyJEA^7wi%h!%ZiRvpePfuuZu=DR;**ng%bUi2g%o
z5X9WHfc9{HY4QmD<6vVLOljsM4<oNm9-@$7%W~ddLj^$q+F+%2)72EMYb03D;Hf?>
zFm2w;L}4}^6QPxIt0}iuXe3dWp7la^Cx+u;^-a4mM`fPfQs||}(7nkXz#!K|#hmpT
z4aT7Vtn#>`qgghKUSpc$Ih2_yD>PO><XNt!p}V6W={?fL&jwa622m6X-JQw%Qy)EK
z;GQ~Qw@1i&srsvpve`e(=_f_;jmyI9L(?g<oK%h&fDvc6Xc*ilgd0@IjRk9((Agc7
z8uQi9>$lH&9_8*?&ch*3FuQVd(kf$A40=!RbwU?92yT~;U;0{N&EZ*&5ren^+{2ao
z)64YjLf?>uq6W){w6mud`p|`vB0?L;x%%~VNqi$~qVLOMc?}62hv?!-D_wtT+?Q!`
zD=mu~hLh~aOzhLQp5yz_*$W!IZJCx;#-IWFje)P{L!a?+mT6ubo|yppZ|})$-?5Bv
zL$xbJnzOSP3I1euA&{PofkA}Ii8W%qgf=K1ha=u@(rY!hx{Y$1WTZm-dAx)9Fr1}%
zo@|w)3LWtBVa=q3_O6s;eoZ+zQ=#7MGHh^$edjTzL=Rw9WbXhj@gZp0AUH+y(B3X#
zr1zjur^_^7hUWA1=V8iikG^4S7$=Z}T>Vf3`WyKyCr27nHT!Q&f6L~(vW+nsO#C>F
z7Mks}F<chr%Ec2L*ThgdY|xs#e<(kCB`)}@D5@93F!Sjlcg^}tX>whmj|$@2CLn-`
zZmVrt1jm)C{Qa@$jvI32(pNwyv6((C!i~izT)+8W#9t+3&S@aIK;29}QYhs93>CyE
ziC9<ji;Xw?*%5E@J{%)|$hU=pO8wh_J^4^MKl!s%aqvgD4`iZ7e>0j~eOcC^f?@~q
zj;TnP`NW_*`A9jx`l@7c;9CKZ5@KfEG@6>cJWX6FVqERb1QA?d=%V4on!7-s-AjzM
zcO&1f0Tj4oFuCU5^nj3Xi8l)gmkdTOC+`om`L0|x{yGRms36@EwnwufLN*xiP7o5Y
z*{&W)uepP`SHH_<qAwy3PILtoWMtx48)4H*{3*~rw^8nvOj?^^2rDyoLn$+PLv~;l
z?gDjpOaDjS1*KM)ygpkV`x$o=kZbmS1CfbaN3%OV2HHn)xeNj*jVaQ2a`L`(KK@1)
zFx<N(;+;=~<JV54C$5dB0?=Ktw-l_^f5_+7+>#m?z1T_m#Jh7}2TJYELT>W9JjkPD
z(#moGii_eF^0fUmTq_6Ta50>?c`Uu^_sOigE1M*;wryj$$!7uaHp1fR!|udSONBM-
zlcj;vK}pWp!O#We#H!0duB}U!%BLyM4@zf`c&jcIYAI1FDrO((#o?b9(;yl_sSOk-
z|Bx)WJ0Z88%tluIb|^D>S*AR8i_4vbKFg)&!R5&0y(5KzZxF+68e_muLf`A4Z(`OB
zoQ|>5Vi*kmWF)%k65jhr+{A=qGd6Jx>FxHD))bpbz%3^E?qO@qpN0y<=i}}kahkYn
zak3_9LlP~o;up+7`s2gy<o$#B>?h<GjJ)Z9^`<=N``>)lY$7u8yK-jrC7|B!_40=H
z=40W&4_3u@e7sOz^FY2(_;TPzGO5VK9Ru0bm*z^V@5WvJJ{*wyrLFNh%h}aG1s!*n
zn~UxTok8}aS3tKnrUq8u6U)b54$aScVd>N{P^_h5@r!IzLYRt<)z<AL9WvZR6i+UG
zbk<&VA8YE$Y-!caRt6>lgIm7WErevyi^S%O9-0M#H=LRLWvV=JEyisa3kjq#aP~-S
z@^+Az*Ck5n4+l>CT?KpfABhuqEwK@=fl5$o*!yK76TdBH*W8dAn7EdJLC}2s-9>x+
zH-p(V*Qd*)m+NiBw&S`xdc}BR&Hd?o;;pQC7>eAN3f`)l3WdpAGv(1s!lWPo{OsS3
z`%QdCem=4JYEU=~IEup9PM#Yq5iaGEKCA&u$#Y_sHx|aC;|To?|6hod-(_W6w@o!G
zSEOtC!!y~~j-2M$$(gI0^f{dDc0RCXKhwkzJkyKqWG+ol#3ftlo;Dy(cO%zfQx~TP
z7aMVQEm+0F{Y>?exk~eHP(lB!xL15HHJ+}eoKof*w^kf3ai`wqrQ^E>aVMdpIT0zo
zJCW)>Jl9$O_Rd1>TD#-mZmx3WLVME(TMO>Upi_Kzz=aims>j4roj=UBH@>U8P`eTJ
z@uZJE@Iq_TC%OyK$pI(--bgg|rlgZSu$7wqDh|?n`7rmENGg6v&dI*Kk(&NoV<89%
zVd;H#GI>naO}(U%oIbV%Ft%U~d_q!7=3h>8#KHVof|B8M{2B+%N!e)XdDZxq4|nIS
zY{4o2myxvgQ9+`3mBYHLcxEb=cvdyK=>x%>Gg9!1{}G85PL0J9yVatbKN*>G$Mezh
z*^xy3f^K*2zpItSE8^AUONQd<_aVGVCPFvshI8MlF3q0i*Shy&-}>T21hcznbONhO
zi91MwzGjardFfaB8E@1<`XBd;|B_7E?tEnG<mf{3rNq%X0_SNT@RA_mtj))I^YItv
zlGzV{aMQLcG0@W`$%;wQ{o~6t<+#S-yfRldBYl=VFtpctsP1q6*V)D9AG59OVL5D~
zdsglGc4ztv)urazLOc6r;`u<d&3o#-P4AssY_H3=^M_~Qt6Em&r=8}^DUGGhrbOEt
z_Uzt+AmPr)$K7<(+W1ek`Ni|?x-}{8FcFqMlu9Oc^OH_dH;H_t{BAqbzGtenbaA?x
zJ1mol?H1bgo2tEy?^syqHHv=m=(r@sG?G?6X%(sQ{lr%cras$YbQjO@MgvfI0+aAc
z{akrYYaq|x*!;?4Q1R#(wCpF`g0Ob-MFD-kBWmc|Ez0*tH?nkjqcL?nF68q=RuP1o
zZh6sFajSWEt-krgoyEotZ0CcNyQp5DIuZ2e;E-MVl$Ge+2!iaA8tBMi$sHYX^6!hp
zVn-I8)Js~)*;Ao!5H97<5CF>{LLJu|3t~8RrzbRW+xelTPfmPq<_v}IhE6#D-CA|_
zTTUgq>u|JiT%ZUQVT2p0s*%WxQG@gChI8l6)u+F1FU9v7jAcI>c+o?!UOL!G)GnT_
zZTdv8fO%WsZ^WwStZHTc!e}+|qQQ9TpDic%u3S7ew&ZU4ygl#jG87v)JrZl**!JhX
zHd~wfVYHHZ`9Oj|v@{(0US&nS2`lPIRKcq|xO<&Fy;@niCRt0paxjtlXP<CO>>J#G
zp2@T_{rz`pV3ZIQ^fq-BKq_!vWLuTbZ|Kcm)Wgk9zj*8|Dej!Q<r9l%xZ?Qjs2@Ls
ztDl}lE@nb;OYA^rf=4<*XCvwb-ISD8-l5ML3r(xItyNra_vQw$ugMJ)ha?@Y;(=2F
zffQTfbx9x24DBY*AeYiRgbu@LMlgJFis-x*zxl@q#*NsH@1JpE<IT7;=D6jL!1Zpz
zx$j&%(zrU}7v7)3jYh++{UXwizYP@7Xd~vW_T1w8gLtqOUEbsENc&bhV6#bJ%4Icg
z2J?0^aorwEbc>j~c*Ta(y4`IhUrQ{tIjfi0JL-h>Du(%mFulL$`cve+*mc8N9V>|(
zu!Ch(kPeKeCJm?L$8hB=PRwYWZgPKI*eo?c7NJRF{pdi8jPpNpA^~S?N8T~xbyKem
zJYp93-kOx%xEUX>i<w~XQ?SL|R?vD7Jk`U^Nae(OuX#834=@|p7~`LT#Z9=F{1I+C
zzI)U5AHfmwEz^+=$5bnwS+~n=1qvkW&wgbq|N35{PGG}c6-0riL~-<(;r2}kl3G#X
z#f5J5H>fNzGa3B2pYvxy6W%NSjU=hb(<IquTdSQ&>j#^Hsgqj-x5TQ0t-~a}iEFEg
z>dWnyYkj0~MlDFZvfw73*G;tV>mVs5?`zedc5b~3iVDP-@rWF$Q!e%Tf*XH!C*HoN
zOA9chJF6O0&mfY?V9%>vvxoqr2+X~gv{IbKA9IsVvTOAoj|BM>i=H=7ck1`_YIp<=
zt<@4dGjBm5aL4-eoj5pR*G_Nwxqm7|qa&?o^L89PYFzjy;mr1uiATuI$JsTo7CySM
zy>vA(xh4A0V$gKzhA3GuYexDA_|jnThv|6O0PTJsl2Ow&I~=?a+w3Lf;W@=T-3u#U
zsQR_DI=!H2;dny~2NR4lrNo}`Wd0aXd3TckC@96_DfhJ=VK<mOx+H<<(&q_Aaw<NA
z0TprMAku`|B(X{ldw8cC9nWwg*6huPOg9?$+08z<O6}cVFPHPwE@Zh9q4~e<gq0I`
z$M@Mnz*z9g{s@UXmP{F?nl*qi%~g#^Z#sdwokx`7Mo>B4?xbJWM{vgXiN|t>bfewN
ziIjC!JFI*KTD}EUdzUr?j<Ph4bf<eKaR--NKOt1z{qp>jj^qxpQoW7MUh4+AYMO~`
zIBcofnQ=OOBpiD7tD9l<tMy*{0R&yDuP;T?v^=Bf$(!%iSJN-aRYY25b6{gM!63ES
zktuC64Rwd&Zfvzr<R`MlFo>wubB#gxZLSzP$dSctojm=yZ4%djMp81k%!jyM{y3K#
z{T2wfqggrP6p{FPtKyzF+H;{yh@}6HU@b=QD9+@KD)qh*H}k4)y0^a5bF#LZ*xmJ<
z0;sbnVt^^4M-__p;FmIR%`}-g3+sYnVWzSxFYO{9^6&`1kw|DGC1>!&G}ll;Z0Khd
zfspM)yq&=6JU4E+S>NfdWozf4h~cLKJQzcO>C7%7787LFAg{#Y#&?NE?Hn*65xbhK
zwDP<ES3gUmuUi6Nnjk0y(TeZtMV&O)1{Rcg1f`EpAV~x3R3*B5IG&Z*)dgM9gEHL!
zs4?-kx>o05mJ<%Q2=ODSJaXZzHTW?n)BHUsp8w_moLR8u&RM88|4?Wa-ZhlV{imFk
zU~0CLg%e<Q0<*zmF8F0^nsZf$m>_{W=th$VH{H3DYBj%E3`f2d7TkP?m|lr6zLVT#
zVe<D#*v8kg8{301RnH79=)=<_QwS>`gUZubFPG!$CvRs$xJg61yx1`X3xlA^M3v#*
z<yV)f%zSjnOaEOvx%eH7WS14Xx*7*f*T>f`Dq5LO;gsgoL>zl%Yw;W%2?+Nf4P4#h
zWIfv}{YyTZI&#1+;-@4e2r7Y0o#<*O8ha^($=rE^aW}dHcHyYwjYyFOfk7d}gvX|A
zDAcsktdytes`!;Y$Ot`w&~fa#ru*t2_aeeG^=Ww{*Y%@Do!sk!44ht%I165MwdE#`
z9Cfk>_?g~@HhGR0d$tGbtU9c4<cfUKa^~%+x9L(3itmTBorMeCYW#(xvElDq8Fpos
zs~s7;CY!Q4bJo<G0)YD>D<>TLbtV<oXRVo|Bkk6==ltq*v0CY)seEFO4(`*bUb-8t
zT{&WtwV@JJ)o;A{Yx_SWRx(!q@yPg3;_2?XEy2u(1<|MPYoTRCgd7<Cx>SS4qvrM?
zeLMCK(5)uk>(~V7ZHvntx=g4yAL2|~!=Llg&AX?%weJN!sJCW&UA8m)ExQ;U#r|Gx
zR&~&DP<4Td+m43`s@9hKEcyX`8)YzMf=%o=&(GWCf3Y(4>$kLP=V9Z!nu20<p1~v%
z*iK>-UG7Z(OW8|mRRdN*^V>S73Ca_T#p^e>=DwAy+v#4k`;Zh@IKz2o&Pu%(gd10o
z{xyZcT{&5v`X&t*3OFI$n!jG^Eqtq6S@<sT9DajCv|HL(SBK90SzGGW>p}aZ+Ftu&
zvD*aZ`*hAtzn2xq<1%?_KiF%z_;0|$Ml`y!<AskA7G*x!Y2DuHF8#{taFukTDA2r7
zkZONlX?8)?B=?HMJLlJYpYY@L8}WXBi`8+<OxVr@lgu_En_gQ7$pyFlJg(%=k2y}f
zZm&O}T4~&eAA}teUi(qdNU657Vc)q*dm3MvcExGj2`VVpa@Os;Sn17S24Jy+!>k)$
z1;{j?n#`g!;9~4NH}<HnZm-)HBvKQxZBXkY+@7C~6u*>qV+U)Av(XtOT*Y3yZ)=Hg
z1~@eDTJhbyXi!@SHXht2<Zj%L?Fgn4^N+DBO4-Q#yualjyXNF!T~T1vzEtT=f7EU%
z;-r*UT8K^__%J4{ju96)mcOaj?any8#4hnD!8oJwy&b1>MJ?C>+G+Z$(W+k${QC7?
zFY!D=9IjpHY&Zz_-$5dh*bNN6j%6)UYTj-VlVwa=QJ|#3WkW~q8dM_`4mM$jdpP2z
z&tngZW9(2){ITg?`t)>f@p7*gn@mKVLByr+8L|gH6wNlSovAN=miTIU%)t+Lt;J7F
zb*tCJ>Nrq}TC)DY93yuy2)8>yxcv&hACX0?H|@1iruDWQ=C_1|OJFP7egK1JFKtC9
z+^DxY>h*q)Dx9V@e!ICndq%GAWO|;^UGX>A*6h2d+l_nT!~l%SexlFitH0YzD`Q~#
z3oIHx6>^P=a)_-{vStEQemw7D4{K;y<FB?j!)Yf<AkWwi4va)U&mGO+kAl2O=$oep
ze=w0qvQf^RSX=UEm_I3_?raA0A8xng|G+J17KkZ$>3AaktY&QH_y*SV5@Xom$WO<U
z@f{k`na?&6^3l=Noyg45GtI|$jx2PhoNjMR!|&BNyI5;#b02EVR?+0ml0a-PQQ9cm
z7R8>$!}06rM55|U{foauuk8pMUO1eJ6u+2u;|Fm7Zb4A;$~em9#mI)2b%?u{2@!MQ
zZ=>4_mePvW+)->cZ#bYI$hdDG$XKn#aOzc&ic`Sudwnai@x?Q>O&{@=aF+oW+wmO~
zBfVSZ!!4f*Yebh;RAklx3cUeAjG;5QZ4saMvTS7hhq-L9ake*gD3Peg8UMAqnm<k7
zw%6bP2YK6-L>kA{a-m9de4xGq=M2PBdvyuH?AfEgOh?wgcy4L&7bErJ(WQKZ8;#;)
zH7^q-M#J0@g+zR(j=KP6K$*YxtnL!>TYAIf;$<ImEk09Z+XMj`kpreb-OFo{_T}Pr
z`WoHf@%f;7%RpoJ<WfoKv+8Ays{E-j5n=9-Orp2G-mRWx`}I@S2h~$7jIwZpcy|;u
zQoGr?y%$t|nyKgBK7y2ocx}I_Ho9w**7)}lS@+D)4SQ4m^ta`1x*Dlmk*t?KIGmR;
zZ}sQ3dXP<9qo>4j&RO8;+tdF1S&>HP(ZxvT=uBs)4ek({17cy-YL(-HUgqyX1x-7H
zL9N-FJEc>te7)MszA4?^aoQaWc;)O@y6|+cfiC^jRs7H9mhnKTh8)vgJgweNz9<{p
zX{s~i6N7B#RQX;&C$mS+${p&bf@-zays~30oLz4w_Q*$fngY2t-|P?yrfsdMg9;jm
z?zpZq5(f3&)aQGR+;Qb-e5V%Ul<2N{)Gbbbu3B+M3wG(keA;He>OKl0?IV$P^4&#u
z;DdM=i`U)advkfeRjX`yONDnw>u`<9%u9RT{Ac}6{)BS8@FpAKcgt<wvDm16Kk(gM
zlg-lGK@Y9-hl5(Jvv8i&br@($Vg8h<CdVWbaROfzxzClo!ePXYOM+DXd^$fB62t96
z%TK*J7tih!iPbJ_RXX>$k^EaTiIEd>aXVHeY|KKf^I)u3{%_Du`BpT(Cv$#RJ+^KS
zXQ}Y^9OVco6a~3<<x))!pwj=I#c-=`FMPvC4JdEyopAQ6t@_Y^4dr&+6vVsh8?A+p
zR#0PFumH(m^g6Wtn>wA|gN<<Mn$cSE@NzbHST}6kP_5T5jQJBcWisArupQofxcI|{
z=2ns*V@9>ozioe;jf!nG0I4<fW4z7n(!_s8%o_WZ71_9d9W@+q+c)Oo;R24?zG^p{
zXKV=9ogrGzp(wNM#;>}Bz}gVhzP?P~cR}Bm*Bf#hmwrW%vQSZ!|Gkh0)!Ewe56Q;Z
zU74&`z?UYrT{9;j=Y4j`N$pEHB1jK@rjV~6fXBUKiml{J{FH?W=+N=`LjAzS#>_i2
zt+CrP+2{bFu-kL~#-qD+Awoya%q6`-&GTN8&JKO6S)DqDg8>fKnx@a%W1o~DZsgVh
zp6OEW%Q}e{bW*{r?T`I9Q(XFKtv>gWupQekYv&GCsDNs{`7^s4+dJiz-kk#_3Nmy3
zR9Cl?&K-n)d}Fb_>b7zz>@|YflRK64$A-MvuC1iMF$yi)rP<Rfm0rCN4qh3}xY>@|
zSvS|7{#UD&|57SeJOVD?>)P%6m)i3mgT9Z!tJ+8U^j&yYy3l#7zUVza<-~SqCwl8r
zoz;H=;dbIO!VL`J|33&f_D18ocU?RvpEr~!ym28l|F!Dk{IL-~`YI3Bg0M*VGC`bR
zb$=BEeiJVDA7ZwXZCdqn1K%FQczYuj02dUJZgeKH=?z_p)veOGAlwe^<SG}=)u#Rl
zmC-6*w2{qdWpQdl;<M4_>#CzkW%2fnzH#jFj;I3`J#72{3@+wAvd#5gi=<<r7xsRO
zaS$R98OiIY0C3s2ah>MSz|tadM8bAbZL2~1C;)0HYP%yG;J<7J{)3!|pl*DuH<Cvs
z2$SBj>gORnQ<QP~<>LCVd09K~*NH1rqY{zi`+%r!sfVqLjW7<@AV!p1$L>8^58GD>
zF{i68Xr%XBcJEJ(u>DKqnPu{@sod_BO?{V^8*?-~D{#NV5zYs|soe$g=tgX#g$hDo
z;_c~Ly+45Xp<e;MM~Q_^xzKLk+6=nC=Xplcel`)EUlZ7!yBlHWI#4{jG9npxoiS|8
zqIi$EmF2?F$6q5P9P;n8y6|I&lb%E9r=8^0OXfY;{y&7I@-f;a`(3V(##R*b9_!hN
zQ$Q!-!7bAGzO28Q3#Q52FJ;H}#Pim*T;=bDr(<auix;ZcUebmn8&`WzkW6F7;R*4x
zps71Ri>+gp_9h=QvF<PEFC*&i<5;o1qn5v=5jHPW4kman2n@i+8AtU1P`YzjByeBi
zTCqK&R&QM+BwQ8c=<Sa17NFq-urZbt^grNA?ED>E`d&S|c}Xt_)-%77snaG<yM0a;
zxwKy)Li3xUgJRQaoFn={VsU_r^4Ybz_wkNj5yLLg2jD#2L9_L+Wwg9k(Qyy%0joOM
zRCj)^?Tf6}K+E`B>)iwjPeLzbza~jJByo)X-<k(~(>76xwJNY2pv&A|?B0eT=v{Ax
zQ4pctY8$zHJ?#8Ws8g=n1C%SldveR^J;u7dlbhJ?dX}3kbk}<nm+H5IRD26-W3U%#
ze1|cSiRUQZ9QVLQU;}6EIN8CZF6`cb<W9Eql0qyu)3kN@hZK(n{WnR^*vRlkW=t|W
z4=|O+UkkMSLTLFRyca#loM3YuKQM0F#Ph44t$0fPwI=R7^`01h!<%NU+0SCkxLmrU
z&@y_S?(NX%Jh!4ZBCN)pYU7-~c*S}aXW>hsZzN2Oa}^&9YJqOuXKh2@5AZBEac2Na
zSo(;{DAG~<z{eR=gmC6Ix^1WahoAozQ?qSA7Q9@xc=(w0UY&@f_771kp}e_3+a&pB
zt@T^N0<xr`mc6CvppZCRNkTjdTH{U_KBPOg?w73Je@a`i9ou%}mmR<JYbJ#a)*wmx
zRc-$#Z<OF3M%o~aM5MB*R45?{8w>)5JYz2^6QyM>tk6+P#(awnSx$F&Kqe$6N&?H<
zw3i%+lJ?5rT>aRloI(4rnW<l<oXl$wp{$_Z$i?L%dAf9R#bO1q6v&gd8lqHOv#oj9
zUjC0luPkrC#!(RCY~CyTV6gJKqU%4kM*oa~JO=b^hy8MTFT^C9N#)Ta&~ZGmK%V)>
z_ax=(PcDBN-G1MS<WSd>%TZ9^3HoSks`acf4}0QStRje%!537ug8DLTMn`VK*zovY
zN}5}Z=aXnkbHXOmM=a7>{b&BxdCa1x#X5WqxCTCT+cuk<6$kd#auTOt6{B~Uo=eHI
zMBP%6q2TJr^jaQ29r)Tz#K8hMP%^ZKJXZ9r=W($~WB8kcPoqT+NU7!WdPM#0KdQ%{
zl6aKO7P&R~I<NYPcYvWKADO@DtHy-A*;vI;pR9kB4EHRthBiHcXO<J6p7=v^&h1gb
z3;L8o-Gdb1u->*D?@`TvmM5RxL3^8LhbT3ra{c}<zf!J7`|VPHpPH7(z--Q9p3J}g
zkFB%;udg~aUMjos002M$Nkl<Zek*gUzqhs-(1fg{q>xj4w1;S2X<k<9(`%pT$MUm6
zVT$Hqa-LKlA{(u#CqHXu>nV9WFW`-Cr>rr3`G1@Kmd$q?u>&ucjV7B`_v(6N{%Q>a
zlWG>C$;Wz!CXs816yCy~pd)H9o1!5hfCAw|9PP$MoyHD7D(5UA{<V8%0;t<~w%Xq0
zt@Kvk@xNIpa8c$Rdhe3nL7jT9I1~ugM9nZ|X&(cvg=K;oQeGUfap)t37MEceo!H|L
zpHM?A1QUae1t+PH`e~v~2r<Kz3P)LF@trIbbf`!GKxY)BP)It1oPz_m09YV_>mNL9
zc2WrHG`F`<;fe-dkN&l!H7Sb^h~A#!S#79fY2!6@3aKSjkjDC{<)jc*b3wM*vphRW
znMK-yH3)VST%-6s(H|Eq9U}ORGFK}TCyT{kdaudR0jBqAf$**sAH)XDHPbIAs5Md~
z?Q+~sD5qs4M4gne+7Q{u&A4go^vL!S-x(xYo|gi;F~~si(b^nkMu-gDm=Yos5+fk=
zorB)r&>Z8d1IZxrHq$p~K?o{?c*0@;JtVgbo<kX{q_FL3NWey-CYjrZHaO_Sp6h2x
zp=|K{qA|^%4iKUEpfNZ(SY@6>&mj~Z8=PevL5VI@wKi>SjDMl8+P}mNE<@a@j7rCO
zM7KYNQvpFWo?B^I^NZ})So3J((EU*x5dG>WzUTdbG4PK|o?y7clmp>ruV!x7s+{WN
zx9T6~4>*5qWy%&~f;=3*yo?DxA276m=E*aq`5|Tl!G>r`peFNKXinzcFgN`n?KfrH
zpUd@|b*p#4S!xr-r~NWbQ8#Olb!qA}b1fV9mRiYo(7uTas6T|RoMBDE9Bf?!f`UWW
zCaGQ_0h`Ous?Az|+txQs9%ClPfj+@3@Qmn82dwgRi)IXzG@gbA^zMF^$^YN}p`m<o
zQUgdd4>TKp@J~kQ{(5MA1`R(1As2}_U+(8k!-|UIq#WO{c=QYt3TGteIP!1H@-W}3
z%wJ9FJjHbg$hhBMkI}#k&D4OK3aDEPQv{;=#5F>EiZQ2<FlKScuzea8b=nKP*J)Rw
zWvXu-c_^2>*J=l$4h{={3Lm)pi0wfOI}PV3Zcv*ueF`dQqU+>o@tN}E5&2fvGM%(O
z$}fL|i<_-}zfFBx+|Yd~Y)X^*2>D}Rv09|t>$~z;cq?che<~WfFC#A5BJa@Vu9I7B
z(aA3kpK%v@$e*HLuLNzk2rJJ9C2rfFjfLI;puRTsfes-k9)CI>;+O`d#&S9Gf@#X_
zk8BfTeiKPU8^5?~Syim$(Jc2R(TMwERG}LgQ*k5ezuJPqmLlhx>!qNg`ulPE3YyHo
zO8Z6mVmoQ2zv7@kP5SFGs>jWtIBe05{%K<tYD|I+BaY$y!D2FW_hT&sHqe-uHkFB5
zus7{n%X_(^%ipeiMVNzuR~sK9&}}e>jqO1wjMtD!oqDhGyo0$8t>!)A;8zl+S~9|y
zb+$jn?_wOW2cPA?LYpWEFYlmOpEih$Y(!o<h@LJPk`--G#|CI&mt@3#HsiH{XJuZd
zI(67>OUr|Ff$=r8j8%X!*96+nZWDS3;Xe_363qkRu+V-%0;CpmN(2ZKhixr+FT`UH
zBC}wYyiw{}56utuX<kY`-3I*i(C8}?j2ZK`1p?3^KaN70`U6mH(6_mD5XBq%r<pJ?
z1`hmm8}Ca0H5RgT5iRjve-m>%L*6p|v&5dDWj0E!7yJ3RCp`dTCdOOiYuYzMUthsG
znD4Ixz_opPWld^bidIz4-iv2nsb`@_F<8{+z6i0^%b*Kc%!pAm{bf8^=jH&D*h}k$
zHQ~NI#{BX(b@nZ(Ryy*`{C;Bd<@_-SHzF70zYZ#GbFN%C)^`0m$dMz5tThi07sh@W
zl`#;LLkN&}3|l)qO5V>vy@@gzbHlo`c*HFj4uUZ$eT}-Nh{g0(5N_JYz{Q)RZl!-b
zr=+W%2-0vIW$2+E0qHa9?6`igIQ#=d@-j6ON3g=H>p3mZNq#ZVxi5`)J3LY@WIhVQ
zjq9S2YpZWB6(-I}mWD3068NnRh@<6B-dZe7oSP~QoUbrFaewII!Ni&?ljYU-qPTd6
zZ#%oEt%(PU`PB~*Uu;vkjMMd`^PF^K)kEYxxSV%!nDQ4qf8d+rxLC{=3qk;a_>%3=
z=r0CRt1d{FC+@?A-@8C4VdwcoWCq&2ZQ=<d>y}&+0qq!Zs#af|9+&_f7ked}gr3!@
zu5xDMm1nVhIDT^}Gw~y0v)z%)Mqh!U)jm0tjfL#0^ONO?8$FH+q0h3`>bncMRX-;0
zdO{+Q=N*!_R)JDm^<!dT{YI#uFvy*fi%vdXD6W2>oZo3vzMT2jz{z~R=#GCsUzoTk
zTN=AEOk#WI7tYGZOt}Zkd7&RO#M|<<cpK?(;`ig(@oQo!HgG16HtR;Qy`+9{I7?1z
z?ToYb+eb5N?v7{O=RiN=NVS<SC$s6`7$^SDL3{GiOmWpk1>)1i5<Uwu_xZ9n`P)ok
z^_A)J(7A-hS@46@rzY&lhe5JERw$1A%u68K4ejSE7Tr*0;v8bzU1-JynQ{ST)=+lh
zo9WWng>FWW?%`h*Q<IP73zLry7T4ULC`3SM<&GcrR^Og3PFzqb4u8jqA+ibvelijp
zKPy+5d^}Oi{U>3M2w!C8XQk0k{jD_xE&Esw6ix@Nc|COYi(;XC0&(KB$x9r<m}u=7
zT3*dBHj>P-!|o1`3>5Om2GDXJt-1Q)Qhwr_(DJ2d217R}e-~%`Aj4MOSj-cPkC<>#
zY=p`CMXJ2|mO?)FvB32=5<Bi#X#SREn*X6edIn>^D5qBcgtk1CD8vu+ZEH;;GWz48
z<is~KrB(Mta|Yp71#x-hs^kvqLC4*n&1L_Kcyg$H3@xjl%j;GjjBU1VT3WvZIbz2M
z2fs5O+i^p_nE!NW%TbWImFhW_=XU7BoFqSxbH*<y<i@`P67E)8p~%9>FbK{Yq3`oq
zLw6Oj@q;ns#<Bmztz(%r>vE-uyVBYCAvmKuqG*ksH;|e5VX8FoD+gKzmH64BJNBbO
ze&T$PaKBQk*u1r_t{zYB@JPClI0Euye-het<g(Gk#1FE?@gF4$$$y5H@nB$yTYd)9
z6PqtDHO_Fj7AZzmZ>rT<__(i2kmaq${3`Ez?vTPL^r?kB+v(>~2a89i-g0dQqtA7N
zNWj+2k@Z%0I^Bt4vuMgK%V)RtQCu#0I-w+Ell)b(D&2FUgzvG7zUK_dl$bo#zcT6K
z7}=P@$63hHqE&dSn~qKfXdJ}KC9T3c<H^pwv+c(ZXwNPEBxu<aDLa2;EY-eiruFzi
z<b5a7N*t9V)>JDs^_ivl&bnN${NZS}dtJkyJEK{dKC!oib}_(*gWdUUD^T9dDQw{m
zc?kU_gmVWi$0>$UY+==^F!PysH1*;}V(QavdXfwCZ#AltKq2i{noYTF61hVxPNG)i
zPtC*<yHum=59`cW9yW-d5WmEzvRPq;OL!&?u^r?;7A2llHM;SA;jA-2oUKFcSni**
zF|V}XY<!P3=dKxy7LWI$xubG1H$QK0e6Ky{?lcg~e>Udj-j)H~HE(TvpEKt@YcMu&
zQV?GvtkCqA>WfQ1cj|T)t_f49`Y)Tksn0DfE?$`c9XFUrzpdvjokO|f>I>ChW15qf
z`HIGXP30Q}EMa<Sr>M{jw3vV9D!xrGpY%~zc9Qo(3_J6o=L~FvaMKmuB>Ib490yV`
z@Ig1@Z<*?D{&-?O^|EX#^`@+yKP;N=J~Gvs{_pf+`k+!ehH?>`Tq{`Wb~YYUo1Z@i
z=djc>a6&d=C+gPvLn?D~XGY{nARPQ+20x(n$ohknXVvi2Xc4ckvH4>Syl}_cxM>7c
z5)X?<g_-t!GtI?I(v|e9QyCec)_%RzS-bE2Jn`SUg(KoHBCvGYV4t*#mb<_&iJs(r
z%SK3EJS@mmE}E%SufYoEh!k<+IQq8q%V_PapfwGe4YVfq#W8F*s3jHw-I?k=I@fvp
zm94pj?}W|hs|rs3b=~ybSLYU+f5^5AZ;Pd)`w&O;HJ$YA$@2@XhjN|5(b&#m8I(V2
zndT2}&n*&)DfPipG`V*xG5y*4T(?s8%f}My?XhmK@WZ+0`geEc-QfYJ@E!@=xP=XR
zR_&Ul>c+Qr7Ms7pr6kSN_1+{6ur^XIzDCc%%_o>=BS7>d(=NBn+9)x<DEmzLt#iwR
zh!D9CTiu7+!R%Kmm6<c$rPy<ZqUldzaQ1JpX!5x=AvkC4T+U$)#R)PM+8{IW8@{yV
z-@26!%DWJQoaxU^SEs+>F2(mQ#|Yx<)vmzG?n_Hc^FNAJ5_^N{{HTN_v5+<DVKV8W
zpfqfL?u5LTc!3|AJ!xiX(}#maq3BHCR>T^n!T<X|o=i4oKzS*K%Kl6b6CBpji!7`1
z#f`oBOR)=B-a_nM$ou@p&cYR_0QzjKS$F32maI%l4JkL^RFNPP1p#_CyC)T{yhD9s
zTFm50Jn>xo*1n#I+^@qZa1vSSE<J1RLmS)G8&G!{C+^ENh}%WZXiicrVc}eY;fs=S
ze*@;RAl#(b0O4jwN1JhPN6#Di1`a0K(4#NKq6IWt^jR@)<TN(6&5<C!SImLC^i%t1
zq7iQit~wJ$_W-{7HPC4TX*<MHZ1xMfocnh5^m@-8P9yVMip=~6UdS<>-Ta1`M)RSq
z2q3F|ebDQyPj?581sIxZI;}g5l-$V5x9zsNu4K4)+N+H`x})dxX1!kF18`Xci?DN9
z2o$6R3shd2<V6i(k6r(D*!7pP!N^yT3L+4!UvGD)2N@Acj06jy;RLbWW9${w>qtAy
zVb*km<nBacV7-%v8k;Lg%cGn}+`VR4J3HvbG2U-KNL;rQ?M}F!de3Y6JuRpfECpOa
z@3ux*!?$ttS#Goq9DDJUNH>1ufRp*VPONrAvx>V!Ia}_-$F(nSFEQU*oYo`=xAV6I
zQ>V15y(ShRE1a_SGPVpOXMUW|@uXZ=Vg*<TvTv%!dJADMj^zWQg3wOcSZE;iCCo|s
z@QBlcBld=4v7wVAS$CD>B*iecb1x3q#U1);Je}Bypa{y_v6rxmU1#XbcsjnOXKbmP
z->zdxk;105>s7C)a?wWXf2W5@Uonw?Q!CSXv<qthfAEb?P&&O7kF9FP8@Kk55>qAr
zG}PNo^oy^;#0L^KSKGi_RkXeFMK|cgd+~RO@<{{Bv@GQ^HmQRbq!Qs=#c$k;w3T&e
zPv-2{SS#*MxZcP)IGMyfV*F(Z2dtc*cwydkGD~*or~T|P3706zesbTOXQx1fg4IZW
zL~-1jvFI+){HfR}5tj~4KP%C;T<oO*sQcb%3Mf%0(z+`e4t_3`#uqE*no_4id&tAn
z`=>IcV>^l7J?aXnzm@V^i<hd<=D08Rtk@x}-B%$g2L5byE7qoiXX~v;i1j33iISd{
zZUU~?3>f@M@3s!K*YMj91VBhB0e#jk4T+eN2}Vw`inh~?H1D7-SsXsGQxuMjXR=4N
zqrqduFZfvl``w&XIvOkNP52DH95mZ)_@6!p-`X1i<)D02#B;fj+i+;5+L<NVLW<3U
z11Yha8^f00%O9Ce<loYZ_I|D7@0m|e-h8*dpRSr1Q)C!`1e#_kN8ono4OhheNaDo{
zz|3-<p2f6D;l6zoG)Cd1-J21YwShkUS<(ceOc2qf-!<rtd@Yw9`ARySdIKn?2lWno
zPs^dWEJ>7fdU8=Dh8r?Qo<kOw`nO>>^YU)SpYJKiVKngq4<7~4wd-5mM}e7Qr9O0W
zHY<*p+nUvWsMCqWiq`Nspx%_n>Tyf1BMw*8gt&x8Wy0b=TB+`z>g^ULU(T-TNG4Li
zr$hIlMyq`{<~nQCAhfY4+}IBFM;L!W+;#{{1oX5a6i@tl3V_5bB(Hrc$FC=p%%dDW
zhBI(1w+ttkkZ@8QJPELYiF>t4r<W5FJkYVcw$=Qdk6^~JGH>WbXOCWJv>r-#2L8j!
zdF38nz}wdBQ45Xsqsoiq++vT_(XyrwCvRP*Gw>;kypAX3ol^ARypwo-FIKxs=79vP
z1c?+quxOW#ilk~+O|=>q5PXqx@gwpC?d(OXKa&k|E*$*#M8-*1oiLzD<4!DjC@!jW
zn4q}Y?u@HPs>t*Vi>o3>Hz$J7iY8%sfSjHFN{&U*)&2HaF%L)f%sz~Zom#Y~ztpNk
zk_D&qo?h0DamB(Ss`~lqVEXhbNS&eXz(><il@286sNVEj52f1qw-02KZ<cln8m-_A
z{$cWtDdjTn_K~7GLHS$pgzdJ&#gjB!IV-j*^tu}xo%VxP+a0xIvHgUsBaGUHpyyX(
z-Duxe$qMt^Z~xQ$G}axEv7Pa7zAyLdh#bs((-xsMMav++67K}f_8LDOEOY`lZ+r3Q
zx>0v0ng`u>*8#2W=EK1gaySrSd>~hloDOm5Sc7yHN`q$lc^UFpGI%rKisAQZp4;K@
zH?z6sH@0+_e(qK?hbOa%=XMfC5v4~Lx{>ZW3zt99F#aNOjD(sxXSy~2{TQ6(NPOr!
z0Y3Yv6|v#U`#_EHF&P#=F&G^^Gm{<qVj}512RcPx(O>rvG4fQCME_#yHql=x_H}Dz
zuRv614Syk;Y2UlKyZB|hlX+vzwY`qj_&s(sKK#Us5O!A<!<jQ@>Xn~oYuQ6a(uLD}
z)Pz>RO(*PbaNC3vvT>9}Ye04Gk+%o_mDp@IZf;f2bT}FiljBt0!V(l-g~qUU%|*2p
zso(51<)>ueJcqk1BqzqPPjdY>|1bZ1n$4F1T)|_TeGlgp8Lo*b3<0paM9WAg2sD`?
zG6KPFWe><Lh0V~7q~4RavmbG6qeL(pj)dV)(+kZDc8uci8dom#=1yxb&3(O7ZC(c%
zq2)I3NI4*{Kwq));A@eC#>4z^MK}9l8J#h**yl9w>^7`I%J#~ANT&Y>*n+uar}HQw
zwtpAtIC;oqBYCsrJxn0iJ0if2v39+`g@^r~GxN!rdS`tOrv`PRK1BT7@#&a1*mdX6
z>7W7t0`bR4m_I4Q)xP7*eWOdBW{7WG6dp-Tga^d`xM6f>AX84=+Vtjnt-UVR^~RD+
zGt(-x--@i7k1NU86ry~RajD{7+1ZA@{uP9#>B3=m6vK^i*ee$)pPupJZ<N}d$gsN*
znXpp{HXq)ucNb!QKO{QHy|>NPHh<Qu5PrSadZ5ek*tNF2eXh3Ucp=h4zx~G^0=15{
z`Ec@1_5l17xaE&%m>9-^e~%}E#hO3&pH@eXYoc!`M#jF8$@Lzc?asa@+Hm*KSUZvO
zzs3{2sfs^;9OJh$;VK{Kw!0g=Zfp_=cq!t!s}1S0S*-MMJHL%WMl;8?whZhD=GNPb
z1Dc#*BL~*eMXUUks3+@y;K3$NIoRKprh<(}Cu<H<>~F?hz$LX{{qrk}m1|>-UZv+Z
zehF9k(jZbjvEFUm4^%ZOvy6EHx_NEf#{_f#h18KV%^RI3@Edu!YnVGR=yDN7sjN0;
z?Ckz_qH*(LyYmQA>j`ia!+x}W4Pn>6X%llTMN|v$Q%5-V{D<edtp{UmZ_P5oP0Wee
zNL9~Ml5T(eYyPwuz>L_{Yo=P2>vBZf?RcFVq<)G4Hi4Eu(dUBkofbCl>@=-R$^wn$
zaiF>WZ$WF$B^c*!P`wda&JsyPbW4!eg>OzZnt#l7tytZyUf$-mMEtkYrW)-(k*7Fp
z7lPnifad=M&EL+PWgJDYu;x#iZ?D}IG#lJtcSD^mF&>HU)3mm{xruFQ!jC^ohHbD_
zQ$!N}l-~&&F5)-QIBB`B2DOSHLJo|Uez4dEP_#6EcCXR>BV`mh<GBgYe4xq(+4%G*
zq2eQx8E84@CEhHA8-|gSsc`jOAlLjQZkXB$Vpen~2M|;x-29eSy*m$mkH=X<Q0^_D
z_fF=h03#)<BU>g5i;Iz&Q}7N#)QspFQC&Wq_(?t+PS5$%ABxu9-9)!;=<PtPFVWe!
zh_7yS*5V7Ln&~ZmsKqT(5PCJxocCtJ)XiriZGV{n2Era!Cv?QW8-&}r#TbrF;&$A1
zhE7XlH@vuB891*PPd=xkJbU<zM7ZhowQ8>&4Ks)3Q(W<!`YFgvjX95J;TOW@v||}*
z(QT9HFODsYqONyMBT~6WXSe=SIbyh7oKM8Y8d0M9Mn<l(LLk2wK@k-3)k3%t68lu!
zZ+vxAQ2VMTE)N7TN?fjPtM@=JShyflFaE;_{CX#BT-Ru{uJMD#OEUGsTSrjqw8Q$<
zwR-K0PA~n2VsremVi`mlG1r>ZLkOWg9j|@&T&w;gyXWlSJN~07Klwb{Yu&!sYFx}U
znZv@N>cMZPQ*N^6I(ui*gJ)I4xsNWjGB3$SciI>m3c59a@jNzChS8SzW%D-u-ujV#
z%7h8A#^Uj{c5*MCo$d_wI18q~#zh`d@ODdE=>z>_Z%c*nlz8g8sM<{IT5@;X&>0Hb
zwcgYT?MCXoWf!<DXdl9F|7or@^PxyP^U17NLhfhsh{HAm+9!K#Hd=g}mvS?0w{_1_
zbM_PL#`&V{mO5_rGDW`>A8Lo`gX3|x+>Avy1XkVKDNcX3R_Wa};Jdk&Tm4C+#tI}D
z?owpx9~ZfLNB!d0;;H2GdUoX$R5CctHRefAnd+4;8$}viUhZ=xFZX&3%McF^d@Plp
zuGE+6Al#BK$;31J1knmH!w?Qy^WRI<2H!uFTXVgiXx&w7%zru39sEHtk={2T_EE)N
zx@f7|yul4dews-ozL$wx(FJIrYAj-BztU(7A79R{`K@2-K2~d0za9kbsU~>?*~wq}
z#qJ}u*5X$pz09YR(V$Wb=TAg8fbCYWembn(FjRwE&gI_L;p$s$RL+k26E|iu(Gz5t
zG#f5_r;dT8I7{Z1mTot3Kac|%|H1ii>6b(G@(0R=u+svqS+BPLq$gsL%>J2puu%1v
zP5|L{_NK6YwrDvQ8UJnEZeG)A_*FK*A41D-8_8tsZlrN@tx@|n<Mam5pQnQUgtK4#
zL95!jc+s!Dv(y;<?@~#K%cbviYQ2Yh;o`;Vtu$Y|s#5Pwry}D&%cc>rM1$&#zy7V=
zdi3D5Gw`uocKCBBo@!fq)5k{Yv4i5y&<UxGdsff&Yal((un@mNsxo;qC#7y;tW}pV
zJQsA0Z+@*@F~{Dk^O2#?r--avby6=&XUku2RcAiXYsdCX+v(T9Dp!~4jf<^r^6<15
zon*J4@p_}@W{UIQTB^;TTx}-5Tk>|=KrF*<tvCIdc0Kj}qBrp2D00KD)7`MpnR&0>
z%6>NKmEM8-RIg{Z(e2EA%x>jRjK{p;X1o_9yA!|emS#_=R=Up~^o8KW8G@60OFG}3
ztuD>Ir_xH?nD-_(d*y&3nfq~%W0LJoJCtEIw>z8v&oiF@S#UmWMYx>{!tKzd<op>3
zum3aR!<i5|8V(Mvt&kzTTR~JkUwbooP4X~m_aDQwWGWd9{3s8*Hxt=%gs!HRRsQz4
zlYdh?TRDHeKK0(ef^b8HVfI~?j?C2Dz1R%UJTorhx+A^c_fIF8ljwRHJizHGJ4wVH
zn)h%6eUQ;SH#NKsgt6Cf^rqWZ>teVWd^5{%JN8;amBz?hMnTC5dEzkFRy?%M4P0x7
z7zoIW_lmS2F6!1bn*Byr9?Dbwx=K3F!%luP=1&OnC5j1%BL%|EO8g^Ao3+$;GxZ4_
zBvYd8C8{zy8~PAd7)umAi6cR{{k|Fc4`DV0sz>)Aq0syXFb9y$aYJK}u;OnEt==8Y
z(8QC&9@Bdnaw4l6X<e+)+YHzM`@&1f^U3?Ev4}N>_fhg9T^7q_y14QP8uw*`dboJT
zP*+0CofJjG-A5cLQl>xfPA+i9#E-u<7sfwpiS`4`k^N;@;kkVOx$SjzB~QvjDP-+u
zvCR?K*58EiWD@%MlN7BifoGEss;~DukX6+06Wh*xcG8NzpkuXu1QF^mdo~v>kW&7m
zov?k2T(LkOyCL0rAyLIQH6!h75QLyO6OGtou}FOX&}w~GF~M|0CvTFxmxfmJd&WrG
z$RR^GF%gkL!XfGjI!+jU6=I|ka>olaro02BVQe=dx-;&b`=I;3G`GtgHp)`?FTaDU
zR-w5(j?gQF)`TuFA$0df^(KJK{m_<7JFztvi5Xg!tvJU2vMPzbB7sP%V>K^?-Y9@k
zRTw{nz}gLvlww-YmEu$=jK9gV!$oNC*U%3rGem%WnomIUmxvA!`GdA|F+*#9-;4>O
z9B$(K_G9NYe;{-N&t{be-!lqtdme&jh#v5c37D|L{W6$OjF}Xv455jH0zEr~b;nUT
zUJqHr2*`r_Su<Sg9mdap1jjK_Hj2*J%-t8En~*skW`q7Ch-5_HN3qZRp%Hcw@!9M-
z-hQmxq#sfHs0G?Uz3?*PF%h@0d669NK&xZaw+Xs^37-L3_wJ?H@W{@NjKgC%;Gd)|
zGqmLqsi2%NaTIPw?`?<OyLejGL{GO))y=<>{uYqM6PvFHPbd!-pZl_SB=Pdl>D*25
z$;;@#Hc5XNn>eE=((bX%*BFby%Wz#;iKYPU9F?@Z)hwLfbc60qJgWr`zw{TA4gw?0
zSE(o7VcyH-*}xn1jg0PPqNIrzp~@(o<`|idY-EY)r3Gipgh>DeaiG)7c>+tQ=}c09
zA7L|jEOrAFP#xOT*<>TwJnDid!ZJ!eKCd9pc$&0L`?QJb;2@d$)CO!;pHi&<_sUN_
zE+wLABW*$PpxztAj$~)s4nG^0K?rgPKvn&6NH>ig@N#{`q2m*6Kr7uJ4K^!nk{UN{
zlBPoPWWvUnte~ch*%S8D=JYpWqUCxhqW0)XC1d;#ks7;W8M<p6u`Mv772WE#ZLfLa
z&wt1sc&jXsuc?`L@Dkboa=@6Ry5L0m00Qc#zW@>_z~^fJ89idlJV<uI9QFL9p8mmO
zHUY+>Z$rF1raCBO>SpYUm-8^fGtpHvwj5aE?06UB%v@+Z_^gF#Y@xXu^xMk(P*(Mr
z#Mk=b&(d<f*?>8i<n7PYr#TW`=&}p_Ds81@ji)pPrYxTs2OUDoXf-*AG2FH`WvqkL
zI`h3iE=K3Vb0)7yKbCk_vQ;cDwyp#0ilrGP_2~yI8rkA5H@}~Ud0ZbYwoSR^JhSm`
zudk*k{a8L+`+qXlW=@_mw|psc#BKKhs_eg8cJ|n#Yrbhc>wv%eC^b_h@$bxmzf9k%
zsXsq3NhazGfAfU;W1e6Byqv2%D%JQyXHq1acOZH)z#{X8zwO80viWW!cA&v1iF5+>
zu5Uz^u3tvDVJ#=a6SJt0U5eu~2(RTX@&pTnui;uOxJArPIH8~_DdniW`TZ(j7G2dD
zNb1%iF(dJGeWZF3x)3Zf5)bi)Bq}xU0G-C#>T@SLWf?$Y*w3Ou^G&ss6S1MdP&NIZ
z)FH0SAbI+Eh9n?XvzarQMw!Uw)JNFxB%7|dyndoyEk3=2&D^|$Q#X_iuKF!+P2}po
zKG=j$$`Apt0U854bz!Bt<yxQoOJDMc##xbpRuUVHIY>CL%myb$O=23ZvKFW>Q*!yV
z)<fnMY*<nmY0(-;wuo|yzbT;p!d-E?F6WVH2$48>0cBL7=E#f-zY&}AH|4e2iucn2
zK^u(0y(DomcsB|a-i6j)u8NF&pXq<v{!+|X!WfRbKXyFZe+NP(WA_Z*XfYV%5ggKT
z%Lo*z&-}%l(H5C;_Bkj?C{3asB@KlBXB>nM1OBhBQ76B2OX5pfY;FRPS@H&{heJ7-
zGGk`?q1z7XxxNVp8<s&&^*hyn$I6`c|Dw*PNqR43Ee=={6jh7|>qxCwv4=MtBL|pF
z*3?IRrv{s#svMZ6pZzu~Zh)CjF)>?Ttp_ykrc!<@F+b)x)@8po`bgbczXl2~&%l~L
ztrZSE3yvEa=vOI*Mq`}YCQtSMwWPi>CuWa;(a`~86!$!9;L@~7?Kba~JX{Bf{zhMf
zYoq?PpW6W6@IM;{ebpvuZ2!6R0C>_hN9)TJ(Qngb?P83p0eESCQQ*?2e*ZM?qFZR*
zl5;PuA?-oDS6hh=ldT-DUTZ4k_cM@$ivH-<_s}*&zH0Cqh!ewUT`3iRDrh<I?i82!
zfx)zQ#IXgTv;$m?1&=W%x`N3nSO_;Q3Z3Zu?d--CfoqNGF&u!1_OB$v8Bs@obCW2J
zV4P80Dc;XJ<WoSLk?IzKFnOV_h)pMq9Ynd|PNeq$<%YO|frfJSj9c*oxPIbHMMS0~
z4Z2{Md@>s>0X|U(Vvf;WHkwl*`e&`7oZ!>iWQboDG-6!oVnF=bpoX{#p}3H5QtuT>
zEB0d6mbeRuTa#z+%s9w@DQz?yp*ExKB~7*#Cf7}4E7?ZuAQ8yp9Yh?w1BWx_c`0?&
zCiNHeP$1DmS8c`|NL}Hb(SuPEJ)h~hlY#3ocW@R$2KgYJ_=G=Geo_`QGp_ReJZ%Pn
zqV|T2)8U9p?vtJll0*+DWM3ozQPtKvcv4(0yNChGs6G)qbP{_rzK(Z78-FL)VeIwI
zdugkhwcVsm9IgxvKOh6#{=F8fl-oFMdGFR+k@~AK(?MhA0V*xxWqF0GV}Xh5#G(Yk
z;!PSGHuKmaI7~oq+ItYO7_<^BEz@n6lL)#s=s5cB?8$BHg@ly96HXbJsm8<ED`Ca<
zqAhpxEVQh+g%LFWiFe>WH%dM0c*iPeo_FAiCK5ajo#`M`TmNFB4?4IJg9R<d6KRTk
zI%wsaiPQ6<WziX*?dRjb=s*v#74P7Hw}Arr6)X_mB!86Mi2oQj0IvUpQv$iSO!GwO
zKs-ihE6N>;qv<yE1EP-G4TqZAy$#K?AtL7J(>#9T`y8n9JX~git54}2$VqyPcU|u#
zFHYX$qDT5GO~m%-dAU1g?P^UDJsg)@verRHLVtPGFmPfFv<IjTECTU53{!*Ek>*+^
zrHqAl0JKaHhwks8M`$?$axLs)B8I}VcPG1N%M2b4wupwZCp3S1pXN2k5^+QV)<y@q
z?NdI7!7|Nj>^W2nrU2TB^q3FIZMQ!(@Bb!cv)Rbf(o*=&cfK<T7wq~N>Jkl*7t*h7
z{#Tt`j3Yhv%_oLY1;%M)I~L^68jJ1lyKK4eFSg_3**|g2h_&PT;o{h}E@(E0h@)!o
zo2Asqak;|kt5c=oam2l<LIAqL$h@5;VXnu#rueCm=nl8#%jGj6VfL**L%2PqhlbPB
z70q|U@_B>ti616P<3A{Y3c_ecsGt!|B6F!zlZ`|g<Dm<N;}hp5N@L$G=ZO{P0aXmL
zpB-|Ex0N6La<VXRve0aJT)S*le9cY7FS{V0D||fQX^=u^j7N8#DU{1+fI5=rogfts
zU%4u==0?yo7v!?p_xboe!rkE2<<!JEiSnu|F;99wO1LR2D4#hJo%mj^FmiITQ2M+=
zrQ~o+IFX$EO|~@t<7_tlPcnaDgV1$$drAG!&B8dJ&V}Q*6tm-J5zp#(2HArhE91EQ
ztl=g+HUXjNu9`^hxG_^oeGsiN=+VrvId|mrTyE@Z+2X`a1{987lDS++1mic2XI9;r
z%HSbQ3kqm(G8n&gJhS??R9f&-@3m!X;@*6A?6hoY;`h0Xr<h)mLb&SI@$?RlB@3}b
zm{ZnI1b4W8BNMK=b1XN0Q#^$Z97Ivs8ToP{J1zv-r5FWjb&0z%HzhjEV@jG+9psA3
zCf^eZU>HAoz+Qc8rZE1ELTT(53HT7kUh-y%k9bf~mL=CLpPDCLTfVUB%E9~&Ycr+9
zp}_5Ex4mB}vgXmz!th0)AGui#{&y)c^5tB9)kW#j;MrkJNVwRcATvQ>PTUg3)jRS;
z4w~%ehTPScr%L1BEEI>&cR|MmLH;X4-l|Jd#j!KXh2aaMaUXPC?9h_6>i%+W^oz;j
z&{u_o3&Vl0497-}%jQ@AJXy?tVVUOT+hVz&YWb6r))coSgVy}$sI$}NfzrsOt}$ye
zoa-{jZcu-0SXMb689lq09yvZ+SbbNbnE6-GnhlQ_WToW9*@@D`&r`+hhe5L~L<7YX
z+)DE&(w0s%9Q$P{HE~v=wEEg~Ax&r;f0o#TKZWLR{tGnU($1I+N3I%7j-Q<@jsK*a
z&wWxfPwca+2U6qTAn$@wF8eVMZh$C^4+ft2vm{#90gXW#Yb_i)e<Hr)mRvdiUw9N_
z@N^^HnlxVQd4hN2i#Qm$YCJXh$86rp`eF3&yft!4DK&O#s<`U69&8C{&DUfNGIQ+f
zpf&G^!N-E3dso52-E4a7tDq2Xbh(@M(QL#WOzp5fT}-^APq)^v*1;1rA0aMaAghR*
zpzp_1#n_>gQ(L$pJ=5ul&6hVD<r0(>9mje0x+WS##c<=m#nm8MMbKP|;m(N<OS1IC
zT4U|YD|7A5rFQl(Egm=paW%R!O5_07YYzq1^pUkz8^4*}nEA$tBK~40QRcw4qU<Ty
z`PT}?GqYIzRl1QoA_>ZfS=9Ad3l!NHfeFLtfk``aKqtNM?Zt)G1DRIlwb)i504@m4
zcH4C8(XrOt;&*jX#_EEYShX9Ls%v*?E>zCfS&Mr%%vRW_=$j~qx=TbdF1eBFFBhxp
zp4(bn`ZPNKf6qmeFQ_Lr{9|>dvnlHr-fpMd=VYwR>#=pXVygbwPMx{RC6SH|5>F1o
z7cW|@tlzD@*!;yZDv0a*_7kt<z{1I4kpEgb7N1=59vkjVb97kce>0mIyA)%pQI}R7
zQHk>$K4AHSsKjwI;s9U-?riy6{(WI_`V4O=_T1rU_6xoasv!5}Vl=gHH|6ATAGjmq
z#Rif;iM)NfDZ79Yl?y`QJx<asEZXajv}W9ijF)|*B#zwCJRciU057_-k$gCXA`s)`
z9^E8<oz*0({fAnx@r_#+7cNayQu`I-@z(=~#h<HjM5BkaAjQ#%69TZIfTiIkDLc|Z
z`2$|s-?F8*_I1(O_#U}r;lGySze(1v2WP~PKxf}>wl=<_Hs83%Yg!2+2T$=G0?&a@
zrxNis9T04igY~SXOY5z52Q5-A-^?7GH!kY!GPf=QTi6BHmN-T6(Mm^IndWb&8e5J_
zEdZ`b$M=ZY`8S1Wq1k4?msm>emq=^4OCL!kBK1n}*jROT_9O+62Uzs8h0dd^XXiW9
z`ELG=38+xqF?}?*3Lbh&V(*n0iG<z(1siTzL47eGI;52Jy^=%``UB<B+&3%rwSV87
z54z=E{s=o^uPIxpmvmFJ$Is8z?<=(mM<>!Q(6`L~oz%j4(ENj$cKSd!6@5j?PVU`K
zPJMi7uJLH8Q+%JtI&cZYML?hxn*Y&LXnsI#i2(CWxaq%ZCZ~@F^){F3W)8BF@q^=b
z>c!2()MpxVy@jlwd!u2e!U*%T(rmT*Nl7${eAXV8ll;KOM+WOk!h4bibtUPhGQsGr
z6?D4nrd|$064OujS<K}6X1L`)r)!&!vlpYgmZGWmVmklUT+GYQJ8NGYPTPe+cksMO
zqIGT~nELpZ>eOk@Vr=(vtdC|xffL0=Ko%%b&WyhC1Cl!tntxF@ZD#=(!59$rnJc+(
zqeE~oL)8?Lxzf02?)SDsVhG8+8V(7^IXV(SxDk)x#7*6W%l+E&#oS!Qo9EQS-s9X`
zFkU8#E5o^*II9f8jVqL~Jm4PDo5zBQ1ICo-|6D2k=f5?9{7bwhZo@J7sr|}cbcaPZ
z#4mXKP}oSlN%BPoOADg&i+0euFVQN#E0a$?znN^_)@!)?Wu53S$e!mWqT{z<Zl&uN
z?T~yj{qjtD^ctK=<9W><Zdx<%nr=4lvxrlr8!QQDEOOA1IWUtM{#g&T3nGo(qPD-_
z_KF`$C+ye~h4Oyt@C1S1>apa@(y_6tgtwy6Q#7u}P4>+goL|;W`pcq@r<+)FbvH88
zXe2i2g3^lj1}}4xVYg09xGaYphT)reow$7tG)o}Y`{%7q2Q1dwQy|;8&T7&2?(2lj
zOL44#6=cA#-eWw-iJ5zypQv6_ulY;ZJlu|j<Vy$a?5lf;>eUVG;B3zO(5n2x0((`k
zCcYb#!v@%pyL9oxAM=N5)$S5T>JlfxRV0oYwA1@`VwGPBmBv0!cUu=V@V7kR77p{X
z?T3gwa0M_;T^*5WDjDSqidkzSN<<3BNgi#v-YYd1IGE9)7byh;U#um13x3y~<djjh
zZ2gMirnwTg-1#Zm{OjUDY?m<FxM8k=9d<AOrBalbYi{)>;tCqN!j}IE0AcsB%X4GK
z%7MN`tj5`!{P`cN3|&AZ<m``dlLwq4WAjErepH-p{M9|XdL730FDxd@?`Y?`57(Lf
zQW#rB@PLuP9shkIliE8FoFHs{#}9`t8%iX0ZpQ03cG)tSbm(wJ0o?EP6F-e38T1X#
za{_DW*iD_K>kuOK(VG1$v6vOmpj!iuB3o~qOF!;1U_pxm)Hh1w9yIs@0-S^FbBp+%
z-n|_*?t$iyf##pr#00O01v~>0VD0cC0!f*2#5={@R}4z}70WaaT=_{fzucC_&wGCB
z;Z$evQ}JSKq7iN16Si7c0y=v%)fxJ1tQZ|{c+K04r2lErn(DP=rvi3Zt%|kzO>_13
z;~?CCtD6xKslqF0Zgm4vxG~EdhLL3bx@x^UgESV(>)qUpIO93OUwK)7t`ZP1q1)F1
zMXiKlICkwIx>Ewip-I^IqCN0_C)eAADf{_0PtVyK_p8)8b52)aDba28)dN=UHHPM^
zy$Ts9wEf#=!cFg})u8Y0liFt*`po@!^5(nsy$ldcq50Fuw9t+V;ihXjV~qSw%On{1
zPC4$Cs_vGLHn=cCg_3QtdC9!RTs^f!wM8ypueMhIKlz}RsR;mr%!1b6M%2TQ3Kawg
ztaaxdn6bUlu2;XgRqw2|I<ei|xI2tV5qnGP?q<uc<-+nO@@!j;$jn!!+EXXk3(l$$
zci?z}>TFRr<UO6$*_9xU`HD0w%C)9Wb{C>Mk3>f<4bylSLyJgbw8P4UB+Y`v`QHcL
z+l3<oRdZZA9pbNU`lPAG^qH}R_?{!NfztxwPw|d%+AiYTUNU1ug=QugE8rx?$3}nj
zp+>Gvc!)A_uO!DM*(KU65j@0BOCrHz@Xosxtn$A&=?0;ls^3a>My}4LW5adNas@7k
z#FKAL*@K_UB%{MM@<61Gk#`u}&5Jy2ry619%1FiXTqpH%b0kEF_)3ovYsl0OG+X&2
zskB$9x`dOmxf#V$#|V>$=<ttizx`0O=?=%d=<8S<oN-2pqK3kIsr$}eg~;Hiipk+G
zWHN<gma%@zbE~(Q9MgYlB!aITC8=LByApS<2nAlDV=tIEYy(#DNSA1RHE7atqift)
ze1FS^=7RhQL$SoJjrhz-6@0D9R0XQcaE5WwWSH4?HF0VZG==&CX$7>I50krw(axW0
z?Z)q{hCAWKqL?aaK5*dPZQh+~=iXk-rVrQkxfizX>2|u)@lJeqgI;8QsB&el)ZM2h
zzB7OXUhV+31u|^bkO87VF^b_PM^J*ytDW$T!RXKODG8#t{5?Xv+Dth2y>KbEM<y93
zh^U@-pPjX0#4W4e0?j{eb>c6GCj8%Ei+g^glK8u1D!ywMe+V>WsTG+$b%o}K-GMJ!
z+%_~6%QWwc=8^axfVnlc-nDBxweE~f=&b+4+j{^?QWg9EoqKY)bK_=~ET9q;5Cl{V
zAfQAg34#Jo1VIo*L=p6veow{lR8&L-6)<7|R7|J{0*Xpd!osGT@8p^8>Gc29nYqgX
z3;KBf=h<-Q_H>^<p-!DTRrRehT}eb>6@9yPOVA9+NRi$hXM2&@MTh)M>lvyL`ws7r
z{BN>}$~)Ais*zDc>?E?GNLYZ;>q=z3>7;Nr)a(hv%^hlqB_RnO^b-GX0^85(g?EO|
z{3#3F&fTeQ>3AYMR6<)As8ks|1SaG@vuhMmM&{_4XUV&_AMXXkhF+I@vdPHB`J6Kb
z&4aGDmzr+wZ3I=NSUW&>ENeVk`t1|i=zo&0|1D)e@=tyjgU0Tut}2-}t68WV5aq4Z
zYsjAjtas$fWvSA^NxpGiy_xjLPeo+?c%7x?&aN1xi%B;whB2gAC|zt+|9ZDXV&<vt
z&lM`jZ2gj)Sr4#<+g(cny9>yo@`+))c#)WskOa0O0)L@r*1pvBIuBTZHIy@xHT5{0
z&4O;iFYp0W_T&jOw`L(0V3wDMkun}~LXkI_JJ7ehVOnDb3CLKKJ8~_{eIEyJ-ZNvv
ziS^F!$k$d3&G`>3bfX3);G|}De&U5uEf<87e&Vk;A_=%{vcSY*(8ez`Mp`j95vla(
zsYMwj5ys?f>J!|=D0Z!>SK+vsWJ$Z!;JCmZvUf=z{bJEc9me<q+a_{><{pZXD+}~w
zTaMF^v3&s1yKX3!;`xQR`f0Oc?V<ys@OqtlbdMybas)Ho=YUAN$Md^?F+6)bZGyTs
z4OA`8a>I&S&3NYY(2JQzYYdPCI(=umlz?O8Ikp`9WdG#(-UAjX*i)8OCOR?+tkcJ2
zjCiW1H*V2<v+S7mGLbtegMKR)F1T2DcU7DHNVA2*e698^<T6BKwQ<BV+SEC2$IO^^
zxK@k>Mv{KZurc!A<Y1G9`zVqWz5rZ6qrYLLBgn({ms+oTBPN{vW25s2zY|QR183Z!
zE$B$_kuH8<i9LLZS!n-kq1F3KDn#0(AI$BTLry-xR>z~Y{;61jh^s#bqq;8NHPfN(
z{nbqDiP-k;@%`#|HQ&hOb!W-E-dpS7XNmghmjou1^;`nId75oB-&2eDF0htLo;;c?
z>*Hxwk#J?jn3Vv-V3tfWV`!GUH*_MQLFe71{E=aM=)KGV#zev@B;!9=p2_UiOINSz
zHq1Rxf#E0Hxuq7adwy68W<aEEXayM7bc@ZX{^dT+n<b!s+a!AH2en|`o?0!KH$$M&
zftYlxG4l3(QfMBF9Dfzf@5s31`ZO<%x0jMRl$b(u$9KB^dQc0JX=+D;fZz{5cS6Sl
z6&RWopr+88np4HTWTKx~l!{5#{Ss4i4$LSTwU-O(1`0yb)wU~XqgNE2^y^eRbP%Xv
z`;Xw~M#**wZ$SlMI4{N+y0oP)d^PlS$JO1HvQ0(W{6`nM(;u-L7@H&S&t7Ql0t&Fy
z)T`f$JR^hQZK+6q(Cu#cX6J}Au?-sxo%R0;&Hq(sJ_WR!tbG`<g+Sfs^y!<8M$BRe
z%_6n)^WRvuJd6w%3TXBMc4h)<j9iJXZ~GommP}*hT~2=84~%;1dBlw)wR--wrCe&y
zR=9CL@fdZI++l@mW}A-HyezDdBoEqyIZ_u{w3P7iPy5D^)c~{*{%#?z-8|YFJ{MFF
zfo5vg5WGb_>?WDLvTn4l7P7&H-sfkd&XGe3*^_YkYTn#z`FF*jg2MLD3FFwK3AXWL
zU@Q-(_3`hJgX?1$EIQ<H`$XI@(ph8tGr6>xZkguxnas%7+R5C99dG2KT;BQ^$ezY@
zI8P2Y|At!JdQ-W(?0d#=G~Wv5J{~uFzi-5iAEnym(?*MMlDPJjR?~WA!5F(NQy}{=
zL2{<TxsMqg)}^6|o^p%x%5_|wOb~9>bDCayx4ga8Om8HbYWY(iCvO5zS=*An=tXh5
z_i%$ed2yV2b=d&1lVug?xdXEW@BCIhCNG#(?mA(R4ko^A4J~}W)m%7-d_S7$<=*bN
z<)h-T_MR4U2TEhE<u7zor9+dTbwv}|C)FLhU^KtY`d~Dw5`W~wp_e&r3{`@aB`u*|
zd|ja!Y-l!SPi=_a9O*KZ$?X}MjZdSMydsk+ydqBq-L~Gkx!!7h(F{$=HnAA1L4*%c
zD?SFO<1sOF%P`1NY#Vc*&$h-d94oH6C8>1pYILga?aPp<=Vq<!o}p9Us@o&|jGjI@
zZzNt@t6sqggZ(1>^@29_YjId~qtWYNP6j;kk4{winB)fQ8tJ`Dnepd#QtNh0>XlEI
zob0Y4D-$KmdHI5OO|?G%wN!KD&7+0vzJ8{4ONTM7$E_nKTKPjp^SMJoklopCx2`At
ztJP_by>Fzj^rKPP|7!z;n;k9tO+N2l#5oC{!-XpbIo!l;Po&H(6m^CWHOQknIot|q
zcZuid%+=@z^qk~y6W_w@-dQ)P)x!mHS{TD6-9>6mdVxOlrcyDpPtD(WXsSJN;ZV-~
ze9w)$bJ5gUVavaHF7Dht)S37^2sdcH@xyv6cqpwc{dy_qE)SfjJ{xU3zSp+)$eZfK
z@`2cn+5?);LGz!6=0R(l+h#K3*R+!9<L5ht1B>3O4aP_uw4?gfjaK7*QBZtkfotYS
z+-pa*ud_oVoB*g&?tNS6u7p2<iE-xG3547GGvt_S8tE4hnCCN`I^U{!?#`rv+(%D^
z8*805t_ihwZ3wHH;irzV?a_C{$=q85Kl}EAJ^WU{d?c&(@0;{z-=cN1Z_U{wZ*g+u
z?X!GAYo4e3#Y<C8<-{C@RMJ7+HRnmbw)DvbvvLYyZ^`~ycy+!QtZy~u&g`!PXx=Ry
z8U+jVD|6bgRX)B?--RP{+}Cc@KdG_7K%V$K|GUcLAPd>0m&g$}l~les>XwgaWUF6j
z)EC~JM8?(}Kp$2|A{CLp?|ud^rLE?Ue1{cO93rFHbTa(aQp%zS(<e#PCqcL^wMvKi
z`8o)<nKNVGT+WL&La?mMrz=Pruq+rqs32>1#8%c?a5qvQNG@?P(pSOKP`yeTAE`)=
zn9jZ!UOFA_!^K7rcf>)pp6BXjG0=LyL3E=rhU6>TmK;|<5Jf9&fYjxZzI!n(yTp@_
z3<DD2*;<90pt=vShc^fk&OHEB)x%xJNKiK%`%$kr`><W&+7sN_cY88+=UFWJ{tpF(
zf+Wq+S-GIeaqP)`7KTh0RMuA{A&E2Uz8uNyXM_@C0K}z8rA}JO9{GqEs_478@|EfH
z25J{i2~3>{5;1=gV<`iNKeIN;bumhckiM3GXh2o=4>^fQf7kF&nBX||s3U$#cMm3)
z)lU?<NjK2Tsd0r6Ri+AIF0xHaUmh7eD~pi1uwO=S@haJu@8<o?xALbJNslU2kv`Kb
z<s&Q-qHHMw5D?2b)<ox(q2`mXjTUlr;AU}eOrzUgi(imPB0M7zszioVsb?gzhIoL8
zZv}0U8^i+N2NK{iwgcqCQBsgj5<x4$H(cGdl)4bE1u<t<K#T3-X}w?3W-=rY4J<LY
z=GrQ*-GYXgG++q{=_O!^a}dFpZp5}B?;k}<+{4y`pOCMPZ;5MyE1=Z=G-YuN#G1a0
zHcEU0X|H@1T|TFNeI*@)4U0RY_>+rUr)<Z@jvSpqV<|se(17;V(Cv-lqzX;pQ4qDC
zNb|gkQyY@*Drk@Ps{0sceJjaf_6#(?oOiDq(DzHAZxDjf--VV@InjUjK(bjs#<d6g
zAQuJ7S?t%7yDis+o@L(fosQS>S^n@@Wy@fr(pEr7k3!3A%*vG&nv(1@F}iV~QHA>3
z6IZb`bnz=<G7IrAgXHL=gOTQwM^5Ohe+}3w?sxug`u8cnU`|Yu8ClW0(3=!l2a4u-
zhZuC_y4*LzN71=6TneDEr8TN5_~-vU8<550%vT0UfC$JJ#tR)Sy^m%D_w|zA&5LZ3
zMk+GqhkT^}$}bZKbaerBsSd&olLBUKAs`tv`a(WlM*sjo07*naRCrXHBZ05`zm4MX
zE}lUzj2hzU%iuMN0SlNxq(&jwVcyPSm&1k9XGslw7Tv32C`_Qb52m1WPB6fJnh@S8
zC>cQ%3GGu9Xad6A<ogTJOGW`zq&}qz<35N%9i&_Q6vm?rXw)a|+61zY=N8|=N0IjA
zilAs1F9BMpHV>+o@19WPHbi>WCOK*=&j{+n-A*Ct7KfkPgs4^2FnJ%YF+!+|<?-+2
z3g64oU-~P0Vv)|8{FLj$l!Xbyh!m)Xu~AO%!W8MdvS}-T$rIn978HzLJu7<`_dhQ2
zaql?aVd?%?Ko-i)i1}OuWhkQ)OiXO?i(@Zsk?~VwgArFY6WL_w0)5~=X^V6py|+S2
ziSbI1N}Rt`5s6#8ZlupL*N6h@^MFyS&tkZh@fO;`Pg`A+IhQ)*J7`8^Y&AE`tE9de
z)YxbEGyJx=z~zfA6{=B1j%_l&tP?eEn}6o3#rab%h0t~1@t^yJCi;hlK26D7&U0O8
zP{u>nhN5|X?;j17QA_Ang(Kw-)n<90tOL{=Qq+b&*f`XNE}PjReT&<bj8~Rx>J-DX
z03O7X;h*=H4lk}B=52947W*gfmLeV^aE!iDUg%L;$*H$LPx3qWETb;gWrCokI<fh+
znhflqFCr`m{U}2(b>gA32$E9cDKszZK~y5}H|32b@7`<;$teYu`?D_3hWUy-Gbl7(
zqzVQ+zQfa+cc>+zvcyWm)xdLSA4I|Lk}3qZB4M%uRh49v0u+!DHN;s*RCqk+eY*vU
zPmY=c$wd~qjN3mc!gF-uJM;^`ursRXWRc0jlUE^J%0Pl#=@SPGSO`-g)YggYrf7wR
zagXfANPXh+Bx&r?15+RO^PQx-R_zlamI<VO;-w~p#zWV~<6^M~W~?B;!eE5>WuYr{
zn~VWFk?4?B_XiYF<>J6iiyEJ~(3DLZ%Ho|ZK8XEK77eBjg)CzcDo+xmB#eN4X-pOh
zrJu|b=|sj^7<Y|(>7$a&WFVxwGHwvyBAix@r;LFxFkXh?s}ER^`J7{Zd55Y`3}^ID
zXaM=Fe~mT?SYlI>#wp$__dV0b%GaBUFbA1G>12~e3nj8>)nbu_AoRm{$_A)58QDBU
zf@YygpTvnD+UvV;ajV!EWX|L!1*e9AO1JrD03)SuvT<`u|2p$V{xCrm_G4SfW%?k@
zRNjHa%vn#-w`^o;JjLygJ_-X%ruqEO7Oc#x^g+BBVU_~?;l!a6t3c@(l{~*0ElYjG
zACZPUPLBc+7Ep>L8K}}c1O;WFggMH6)TBTgs;<Gt63q;rm-!Id5urt-JJzW3D|etD
z0oM%HuF^)akxPBt(WhmYl#<97-yyg2SCz$|gI_oOkp7AwAsd)PAY?v-KS(wY#q`+_
z6)j62WgQC*;DxeSGf(L%zJ*Aov^iei?}yy6c#pPTQ7tc$Ytks5n&G*@TH`4Kty4FF
z?G#PP=0)EW-J)t2;X?sI$daPy8(WPMI$1BzC?sv_<o|ykq&eqps!j+`P-+}!?R`*K
z%T6iBR-A7{PB&IV{rKR@4r}b{=vh*%Yo&m|D-4prPzA9f9z!zGvI|p}y{~Opt8tl{
zrY@P#MM+`POSkYrq-u6}LE^~jz!@9W2IDNkv@j$&221_?s(g#_zJMXB8-!3rDkE=~
zkvYgRq6TTI*Yi?wp97_qc_Rj>F_3V7h1qbrabA|vGq2{pIqus)h1@3w#LTIf_2s_5
z(Km@HfZsyAfEg89Qp6-o8266sTU;|~_LAr^)CBlLy9c|5`x11M_mI0Djjzi8CJbpy
z{Z{R#8^R<R<G2T7TLRUvIMgmK&(Iy!Um06bHz29$7?QH5n`Tt&CGjLT$^1Z7*)zn@
z8K}H>N~I#~1J#WnljJ0!G92TOV65UD9;Xu)r=+Zx)Jd5mIKcgZ&Iib5Wa%|l=Jf3A
zbu+w+elU+nB#E(oM$f*^FvI(}CK3dv9OF6iEWyn0!VKSIa76F-bp7QkHD53yLEf1!
z%<7qgV>9?80h<NkhL5*870LY4Z=pj8jw77k*cws=btY1yq=e7Dj(OZFB;`B{5DkT~
zy`^R3+KNz8i_6nA5U<GH2_&-{l!H1$Xb2a(#62iyxcdZ#qznh4rW?EP>@i}{fR-g_
zII+8}y$P!fs>~4j78;N!m7=myk|1<|+Nr;ZNvAd|GN+40S^?t~0U>S?!0DK@VLzt_
za*LJ;I!-iG(pQ;2nIAqo2ikh(5QhNi%;Uq{$7ws_+05}6jJ6}@E>Q-Blf*vPPUPCw
zT>CTaL)9iZ*PK_$m=aeR#Yp_9tT`AU1SUbVblGHKe6g2GGN-us91DH3Za}joaVp84
z0WC++^22aNV$~#s&CR^Y!7$5x_d%PYrsE{=jI_+LJ^`*ysP`mu8h&SnpF|HqbD~cb
z!A7Q2#%0i7k#6lhF?O$JWkci2wGN;1)QMRmwO4F}_yP-h(cYVTw&u;KT#{+l72YAB
z`Am0D-3abxfQgkaY9_hnydW{cKf;7ie<`}%F{@{fG0fl&AaFhC8#R^z&1V;B9>knt
z-p@q0H2ay#vq88$u6AHt6~gVYR9yMeaC+$QR-yXk*23HcL6|*bi7|1BQSSX?!CSvy
zml#aCKK|_`Zf1`#V>oT2c6GZxcYz+5qYNNYfml67_ZWkE8@YO^TYPn=*tovhm_AMG
zqVqMLQRmB~2$P9`92PH~utv{{%k5j{nj4P_Jc6V%fe_H5PEmAeTXQc&I`r8k&d`xT
ze)dDt)#lf8$+BPM@-}Hq5Ob5+OWU>Pmq4lAIF!x4(96axJ*-|a+o*mp6E6E*CF`u{
zrW2y~O~1QSZ(o~>m;RElm^*rzxMPI1ug^BC?-M|%5szQLBqM2prrtJJ?S3z(ExQAE
zu2nq{QC^7OV4eI^`uO_^FrJU`AgG6vZw~7It?d0Gi3;?13q?ufAlD*tW-a+~C6nE&
zotgdULbdi0Ei#FDpj0h9CxLz>`J=uav39?cF1Sm4C!C6UlV?_&L2Ec!c9m1GhXRL;
z4!xQ8FEqQ?iz{h1UVi6L(R6G2`n?<VuvOMp{GpI@R<zuy3l|nzU&XKD^olupfm5(b
z#F%}k)m^uz-?R=I)t7$8D&Z$;1&{W;^?TG?)(K<!_$5Zs%EV^4(f2nVTWjgNjOj~0
zjvqiaG<y$u-3>2kw(%rE@)`737?CVAp$AcKh;+-xczvX0-ZE~Cy-O>ZsmSo|?Q}N0
zq|MTV!OBrrj{1ZCS_h74ORsQBTBjawII>yq{XNl!uUO*bUz%jie8+12tks-4A?l7?
zxzx??A7_nR$86r%YEGXVdBqQ`vWMT$EhZ8A!~2`v4F~r+r4KJ9)MiLxC%mR^tF<>C
z-s_BAyDXJ`L6{|?uTlRl#>F!of9Wk4ueQM(Jk>So-)J=E&LzVKoFn_zadYB5X1Vdb
z$;Qlo5MP%lcuK0HpM7C~AEJw2JoUiaq74SNknvRM5I@hcH*WrDzBPSD;+so(b|dSB
z`z0}|K8n%ohlG2aNXYov*5)p6)#@KhMdLphPUrUYay$_=e=^gW`488h_|b4Cy9d`i
zGiv;Jwl#aE=9fM^>Qs)yDHJcQ&K>pc<PlbT_;X|K(6K?W{+;>y^r->cXjYr}X*rwS
zU9E#){=a9NwU1=O6}J`h?zYf!%a3L*YSvrV<S{T7bE)mTG*I(!;i|c2?J`2FE@9I_
zr@E!0`k~C?g=ER?`Fwh7KMks|H}iq{X6vhFY>qLvGmMX@BV<!u3Ef_w&D%>FF82lV
zU#PX3pK-&9?~i1Jw;(NPbDttWzE47g^NE4y>9IYg+0`%2*1Kye;mCP*0gn~4x3Sh+
zw>$Y=-aq6FonYt8Y-k0K)x33khE4aRiaCBU^j(I&*R;KLdw7l1sTFhV-2<9`u+?3^
zSC4a&FkspzC(dV4o{ie!3G$dSctAn+I0(0gI^DSo!ax+>h~;|R!{eqP2sfqh)|kxR
zBdg}-dmD>>{-6}XJcFrrG=7M#jcGQPf^a*b>3I)jaCsWwfaK`+3`HILScU|GV|SDt
zl=qhU=rcv!i_eO*Ajd2Wf!2#Edg--#&MJc-0{@dNc(^vzn`^e0Txuh6@n}75?o~9h
zdv?<sPFk4j+@14^C#E0)Kbifnna-p8c=LK@NGlwsIW}=jGrNQAxui88Oy)rQn4*Y@
z(x(htxdY>zS-{~Q=_!9%*>SgOxsMHZW_t6LX!ue)>s{9kr`}zyuRjeLxiV&yPLIX>
z6-zJ_8JjH@Il6MBHj(C%9-<Gjf>|6)%cj|3G0V{t-^ihhvMIZ`U_Mj{XA(1S44t97
z#gi<@9&MQGjv~)<K4%mU6_ZvHl`k)YNb6;c6qV8jb7$f=bP;U<IG7Ty66RbyHkZ-7
znfRfKF>Nj%af%n=QhS=4*8G{|!C`I29M3!DkLq^uC<7m!g=F39^*L*0)=9mT^jxRA
z1Yw_!9vU%btd#|K=>O3`>8UUuDMAl0UO~7?Vn|8on80`?H`~yuRxW&Os`tpKJ?rjR
zOqVYcu(*6Rcy{nZj_8SxXq!X-f<aD>v29$h9^Gbop?zn*o!uWNPspYD<7%*G>*<Bg
zpYz?^OY)-S)9n$AW0O7q(XHz9v*-6<=|fuiXf5CQ)pUE~J5mdo7ZtL(*K20&>V@7T
zt7qrEHAOG?vK-I)`ob5R-8Cy~3*JLzFLziTji}f$O5{^>veuYSW)RM^(!tG(9Hn_$
zWtiTG>4R{Y@diw%+YX7Nf42JB+1kRl(#`xKxtuc!ZEv8FBJB_K(IWQGe!SjZvjS9E
z3xwMd7D|bfp4%_TR=--UcK)2{X7_ipM3^wL`}VR6SJbMVd$Zl#ekQe~wbC(mwtL%j
z=iz0YxrNV)c9YCe2zyYDpgUzmlgwG{pSyL^>yN3<cJIygO0Rdbxwn-qcY8cU_HNGj
z8-|0S)9g(8DA!CszmuA}P*88VpztacaFoLyecZgyn?7?@R4WK6g^w28)XJvw$Z%&m
zSSZAm)1@DrGH8NQnW&~PX>tN{LD64Xt*_n1tF=C(MY(qt?DQUSs`{C!`XeiP^WER*
zk#}<|T7O(~e)e-Z5i*E4kLzmtvz=(;sSCBKkJvSLn=!j|N#Zo$*9kVB!nOahtIm$2
zZstVGEF3|kb(G_ZzQ>2{@}+6Ja5!_^8_@hRHmpx2K-6ivs7wBhWz+Ij?_8uMIif55
z8Ke%vt#$ciP`{ac2qGQJagKKS*C+ABjpG@2#|{XU9^@lSfLf4P<VYaWsnYK-^-+WE
zU;g|HMHshW{EZz0z4o4|Uh7W1qjZt-tkeL{kouKVy6BbQGh^?UWSO;I=K);~@A9g|
z`zGl&I&$3RXu38DvWK{2+cl%g`9o=YqKbD{F0{6b@cGPW%Wf<d45w)|f1>%pPx@$|
z@egDw?$#Z*d%MIX&g;Xc+3D`@YaM@`PK1hB3+F(bpd2iIMh>LJUKt^BKWBFDd&C=+
zBW29-cda^PalEA6PcyFy6_mlc|8NadP_5IOGJD2ms32ip66ZkDadRluY?6vb^<1>{
zmqt#cbz>x}HGUA2Z2({2?_(x1x>`&8Zg(Cr3kQU$=1rX@aW|r9E!_BT^V;O0AbV<N
z2$PU`lsF0a7pDy}FcZxMr1kDO9PZin{BpePS3xdn^g?SZSFitoNbaeMx#BzVa9nHR
zYe@=_Ay#)(iI@J`%SW{!G)D&tOi>L`d!O;bFjuFv?3*1ce~xZDhYFRD)&V`L?u=+l
ze%;Q6wJ@-jY=&?X1Qco=eQR>PeRDdV|7y@8??fU&U}PA*YyEiSYZIy50bag)XPc2B
zrqg$N+Q>I1QrQ=Kx$Yl3!p@So5hnIhQm#GVmNQ2IIo%u1h)U32K4Ml*t7Re(PR@>o
z;r}p9#%~x;!)&sh-*uU9L+jlj(Tg~;XCfn>BMJ)jjeLV(?Ttp#x&!%0v|LieSR_*;
zYeV#l(5|(ATF;$j*txgjyNdoieF$hxa@=M1A91oTA}~XnG(@i!jVA?byW~1lg=4RG
zE!jKxMR$ZYAtEBvC^XxfiskHH-HiV?$tM?p>dbhRGYjSH3p#yN=Y9p#9_hNN-P5U+
zccu!doy5=8(-+Pr4b*+QCju1JChpEQ>MAR&TwKmrBUP3E&2ZTeBPS`o!@@m?1Il>H
z==~-LJ<?E3d@NmYS9F~29hh#UlhE=O7qYB<QKK**?$)UB$g(o=OQ6~oR!fe8ga*)M
zv(Zo?$R;W!S-^V(XXMH$h2n%llUj+p+O@4p+bNt-%93H5(+Kzd1COQO1xfxTDK}g{
z8_iv0_{MfU!~bQExX7qmyf9&{u^xN6|La~X{=$hD#=&36<<@|{v(Pv3XPDQ-Wxk_&
z92xnHxp>W4P4Fb?mGn?fXaA-mL$-wiLG+=AeVr821_87&#vr{wkTenLHeiu^ey}#<
zrCwl(*+Nuv=p;Ep1H#xul+><vyDhxsX)no82)WC9Qyg;waUq~i85qKns2py%L6J*1
z$zL!IE<M2Gnpcj4iP<|vglUnk+<4jVbJ=iaHlBX7<}o2^x!_g=e}?W6tYtArzv(9J
zUq)o$%aTY_bKOI6Fdy;l`t@GDx4{Tf&$r$3$A+EsF5Rqdqdy!cTksofxBZFfUiI5n
zGrgCcamE5ik`2Q4X=h#=d!Iv=<f-2LyN#Z?70Yxz?H1C1x-ysrMjdx6z!ZW7kV5+s
zB8*8;YDKt<&+~00kZsglT4-s7LtUcFH?`_lU|B?g${dif#y$#~ZE4$)(+ZC6sp5qK
z3P8X6LeW~obmztIx@*k6quTELIqm0Ok#J-WOcgPWH74U9SHX^pb)D?7#h2S|T(3vG
zqo${9D}M|)JP91dCX1ZUebnA(CyTA@dn7O_=JQB}^p;Y3<h*>Ybo79{X@vlJ(80>H
zN~)Cy6~b*=`p<y%M*;UB>`$<y2zSH&rC4kIy4&;bPj#FfQjS#-x2QO2%e4nmU1tZE
z45FG*`_JiU!)et8@BR_5a$2?s$Oal<UzFh9EgB12QJu8l0Igqoz23uV&)wBQlIC6b
zrH_6c1ZSB<sYiF(=JS{7#e>;}ZC`W7O(*pnCuOamiOj7sWJ=>vH87}2`XFGh=N7ZW
zXXUcl<6sW3b9ql(r~O0+L>b0ZOYaa&Kx=M7xOIeXt$69rOBt;*uWdLq?m90knyKYI
zr+HJW>HW>}ot>;y>Q#~;)+d)<r{Ujk`KjIQlqDN9y<_v}1XG2mauzuX^D<oIL6noL
zkLFAjMTk1K1JSeNu9z5|PfZ2$*XRv*Hz(~Z4P4YdMAyrj_K;`Suk~36W|ZFBVt1rI
zPNjXJs$bg^{(lmU3ysQg9%eiEhH(|NCTo`j1X}Xfx;}ZqWNYdp=>-s>>5<DLRCo|4
zi>ZEf*fc6-d*qT7))s=}?@a89-Gfy7mOxsd$NwPU&c5UsJXz1SZk+D6t~8<8NON9Z
zQMm^jH_UYWd!l&Z(rUMTXUfaHO3Rv=h$!-&F?;+%r+d%hwZQ5p+GIwAhGpmbH<hPh
zzAU&t!mXeD5pmX@00lD)XypVD7)W4+3th{<wUvYmR%lfOvIilpx0C!khRvZ50a-<{
zuNbh18vhf78*`<Wn5Zo(ythK$sd2+fy&RPX#=fYLNrpc+;*>9eeNv(YFK+zQZ^k{6
zS`|U%WF#Z4lPtS+BJZy3xQ%PU1MO;>-QOW45^$q+%X}}a7ce3<jqoq&q;OGUHNOXC
z66B0rY^E_zAh>i-Z(i#KWD}0ep^Onc+zFdM3!57L+nn@G78{T`)9C!79!$U2LJTAy
z+5=74Z&q7LBrMZz8ntV!j(Z@6k2NfJIff5N|H<>J3^KUW=u2_$JB2-j99`~?re&_c
z*A%F&zUPoOc6G@Gp+zNRX?T=eZ{@Bze`(J%+_Vu+cJNyT>Wd|1;hg~@ksAY}G~2)H
zc<tZm9g|Zz`L|4+O-oGQnSBK|s+s8*m<*_?S=#{cxV+J=e#hzHPaCyvsE562q=i!5
zn7hRAjI3=14+V)i$~#E$wQvCnRHD?FR#y+^FqGx0#@t7(E}@33@G<e_fjop7_|(^*
zfpEKhL7Tp)(VBXjSF3(S5N;%pYr;*PIga<PM{YJNcvwVxBgvdIYK&dkN4ROYe_`d)
zQ>$G8>R8$H0U<(E`SUVnys@tPYs}Cpa}7_F{5csjdbFYU)|mme0Yw|BLs3<DY-SiT
zQMBMTNuUE%195jYY5pSTn{H@zZ`b4exoI=FyQzgURsg*b(Tf!GUt5+c9+Ko+H#FM%
zj*QbX-)R1{+w4tcBedcDgsVuon~HQ8mnc|@-o9ow0&-uUey7)HeT$j70<<%+h<a;g
zL+`;nkzah%yHfmYQpxbANA1dg3c`);BeaqwH!sa*cJb4V>w7JSOd`-DFlvAVzwZSI
z10ld5-C8AyWh0~WYfr94l4CCZOFa&zvpyS{(IYdrY;h8|SB$ALM$R&@<G_l$w|nGz
zi_wcO^um^`gBIs{Tl4-{52wyI8U#=Zz56?XM$%F<9q5g(dA+d8I>?KEA8e3~M5g#D
zMGR?5oj~Rkasx_+=<4TM$dVa+p*eanruy#aZogi3vnR6o6H?TAiKRQMG$WjqGX_RX
zn6!6CF9;jOP}H2lhzZccdjY9@Je^6pop|mXaY&4G=V0s?-z=2k*@d`yQ_8b;0gU?U
zA#K@L^5uAPA#B~0@|`_crOP6{`rq~L!nIC2@}jVPdlVR}Vq@X6eVUKDan!j3FrX|i
z`lXH?PwCwM%?080Pb~rntPs%(BWi@DclgS@lUmuc4UmFEZ*lV*u1*^HSLRZ~=o@2y
zCZ79@*Pz`<A9kEOD4$Mm-8O3%;{1eLh>-^2Rw0*gM;6`FaG=dvAX2hYJv?S7qu<S?
zNbwA67vbhS4EsL=7z5)^?~-=H5;=32H}6YRN#{%A-%!++{>CvH*O7pb41U%GDW*A~
z4V{)RrgyJ-ldr3_8?ULfM=!1nMRnlnAMzXeu0^wSY$|J=7F*%$WH5V))(P*L*Sg1+
zx=XJ%Dls|IK=!m+pRXsa&r4*hvbpp&Q>%R;@Qi(mp#5?=d(;OSJKdXnz1B^innuY)
zE|(T5z~7TD)o*CF=YOvICXyL0GFvX<x&+^`_sD6(r@Pt2Z))j1vbplv-R9i+p`Y45
zWu~4RyWTxSf)ym(MU8IiIeBZvPlMs$(WW<dLC-gLZ)%z6mhG+9^+w`aqqp$I78!?|
z2wFxfdo~ESBcpKPeQ}UJh5jCi$qdj>?&aBh_w;5x0VFhXx}7((9i#oLMyGWhS+~i{
zn7X`T%RBsgo1N;#?I3+dCK@?sq_EXG!fj2~z1o*LK~%~F%RV<;T=BiI;{CbNnLm%n
zZbHSJG*yuUi!8{E7`!%$l~lfjwzQ5h`^ju;;*zoAvM+{1oj-Kiwab*<Uw7zldY7JS
zZUe&YzC;`PsOTu;lvt_DZ%7x~pA2f57iFB`v(ek(s!_Y9(F}X3IRCPAI(=?7l@Rx^
z_O&KvA15vzna-x4mqEiluhqZR!juwg^H=4Xqo5MgyZh<pjm>7~M|QOI=h<B9ZFmDm
zbMgEYEli_X&Dl0|V|YjQmBp;HTPxhK?`(7GM{+RD#L0~yFdy*!5aY6>*-?NAx~V)d
zP^ZLyB^CQ$2fal@8{^ki{q|^c=;+Y`s-n1db*IT>QV2M-bIQSOQ2c{JV~mt!%X&@>
z?<<~AD5Ulfgxg%J{rXCK_+219IiNK`Yqmb$i8_Z4w@2PLJd}i;sPV&QyLW#?UOB%#
zbn++%>aN!OQN7h$m)4eiIRkdLWf&7~di1)G#FW)`>!9KG*awD&Kr{xkAM7=Z9rH-)
zg{(d9JHh01FnuiI#B$cbYm41w-vQN0ET!sIEkp!10`Y*_lEF>Rm9q%BR%A5Z)97Vi
zSoD^C!y1kzoBq^C6k5};mI0D|p5_J*D_$Hs1gsi<Yf21crvK1nZ{ZWVA3j`5ny;;N
zhTmQ)#VwM$ej0eAM#!^l&uHGz#9u>k{v@@xL%fErX=F3n_y>FCnaljTu?!aFfIdM*
zKlb5XdG=DTkrZ>u$k}$m%5=^4ZS_u#9Ds#Oa?a3k<TA{5%+{|Po!ZC5kO*nwUij}U
zPxN+B2sbww`tMP9=$K}v`o%_L;X)8@V8YQpu~W;^;XepXbg$N~wif(JL|hgW#<gfe
zI;niQ=vu*?HbcHPZq<jcT4EIs@p6r8s_mJ#5iQ|gK)Chm=N&9wkwW@QmdeB>OX)u4
zd@EW-BqK4<h_5TGU-X)^L|H8*hns4bJS<HH+#z8R%`sNTv_g<^Rd!VnY0pDlbx)w4
zl{y76XKu?b@SxmBpTXBFe@O%w@xtH@>I5bdt-b=y%>9d~8+lgwHuhgC%~GlZK0H$q
zX<v+#bXEE*11ybxyuYF(<A}_$R^k|lWN$qOR(zkNXpn~`1}||Lo{wA7pJhJC%DaT>
z#O(Hn)JeZ_6B0dY{}1|8sbAj06$8{?f@(ujb}ID@$U#-#F3jm)`{*;JRN)=Yt`a!C
z$MZ;)+@<b&F5~i-e(I=A*-F$JQPndIyh&WpL>^@+$ymlUPDJQcteptdeXkg^7*zrJ
zKs7u-AL2p(Rf`qvsWyvL4d*g<afHb!LB%BK6IvI|=32PfH8Erw#7Btl9NdRD4yY#T
zCY$ZHxKBMu-$k9)?$dGzEz5jMp27&Xt;gwy!NR{*c`!gnF?q(MQ{qp&3HZa6!Eh?q
z!A&3#=`XrqiFa-AUVRjk!3<9;v?jByn2F2-Z$`g~B=*GjGKWMjAa^2kOCU9TQ;9tS
zx{^2$<`S-v!xX_rGf5yIMop{_nz0p;1lKS|S>{~w!^tGe2s7nkRF5K_T?&0~7_0+?
zPN6zuC&=(`Qisr#JZBO)%+}+-<D12^iwHE;WLo@$((i(DHC4gN(dh`XF8+rcdl@SX
zzLFk@R?!w{S%ePd;eo&BGE~2J_t&2USC_G}cC@wRegc}wd&d|TR8c~&D!`Yxq$zuk
zS}XEqzUen$uzohXM$1=2jKqLO<;anbG2wuwXnrHtm>*Nz2x+BKb;^^p@PAsKjkm+&
z%vYK(1_OW=0<TT&Nuq?m2TAa2nK!<J&}7iXsa{Dlidpa##xHfuWY_3B#EXj)i%InT
z(2?_Mg20T2TJQES!Kbi~HbMq|#<Dm7GTg#MWV$x;pDbvhi^03q`j_Tm>qS`Ut2uDa
zRzbNjbSQ?29HW2+!T`p(S$1u)?FeI<AG}EFSk7nmRXM_HOZe_lt_hS^6#n8{D@GA<
zSeJhS=0Ls{e`dZ>u}KvgN`M%H_Nsf3lN7^|+n%XVm4dXusI^i4395&8_MhcTS&zcF
zpe&`rkhah9Sx{JV(r26$gmNR-WIh<5enLy0q5VqE;Akg5ReK?3m2S4doGqf}_^H}1
z<1FKor;ZKWjqfDE+Ng)0!W<bt^$u~#QFY6^2KOQHD$Bjh6OU}MP>t%V&=b;<>IX(A
z)n`GtNnfRi#4J2c+6<o;&zAm}@>y*{v>o4Fr56|4V}9ktq}s+DDHkrDQ`ay8%LbjS
z<&`qNg6@;KTFz(ssG!O)YBioh<De=P>P~2iF;WOCnP(V?qCuG_XjrWU>8q5d>#zEb
zXGJF~b)imE{_}#|ls;}w%QC;x&&5rOO-8xhjqse%t+YYL4fTeG+m<%X_cAsD_`&~J
zp+IGx<-QTFL8ppdgm$IRvwS09BY75D*)*pMT$8b;sC6LoO*`d2HD;nVqtAU`dvzPX
z41R9@gWD913V;dk9D#TXEh&Vd0x@u1*34kcM7R(monQhKnoWcQfK>`j2cJ=Gm${UF
z2#v^m$eLOlM;TM$C0+T)v(oSJ&8|tGrJX`ULf^~y39SeoK_Nw@ZH$w|UisfJUTV<(
zm&}(M#8pNd$yJ;Ik&f7PM6!e!R2&8A9E(uJS{1?(NTN&>qp2i3Ix9sbpTr+mCM*Id
zp}v>8q;8P}c=TzDd?!RG3_#L<)YP6Vu2Q5T;(5MS1SDG=6DJHZK_Zo3Wp9^Er;>ad
zBQf?twKGZ#g#H_GyNS?ctNXz8t4U;Hs8h|6NCt`_ppKJ?RFZ-$S|+<Mh0EDW^#)@d
zA4T{>&=i|A5Auz)Z?k8G05>sFd2$Oy9ut3M+;l{W8uSCHN`B`%SzHJ>ib&<{{8Muv
z8-*%-uihii4(3d4L_E)U$u-pvbr1K-LKP&Uw3j&)lZc8Q%VlW_S2iJ%Ha#m$mi}^j
z=JkX6#3xdaL{F+ujpSm}RQsOvpS*<E_kBGFH_A9l$M`EA60$he;*dvWO2koV&}Mm`
zNV&Y7yP0n>oC>powiF7C9;p7xGmMec%0KBX_feP3ryvCR!EZ{E5`T2nGpSGF49ojB
zrzy!7Ck9mvQG>erEfXd(Xu4c@N|EoRufp&ovk0^-Z5Byg!J_$X6D<paU@oK~i|<fv
z76v6k5uT{>GIhVK$4!zSZ5IFfflwsjNR?hz8YY|DfR=d@$(1TNS#0)fA8p4*`h;t2
z0E3Lyv}G`!_}>3ZP<iREtX;-SF*McY!DpFEXj!dW1aH}><)kS6Qgfzgir<9Cz)Tm5
zI^8mtM1gwzUU-ivGq~_L-3pCT=i}Gt^Z;D5`FrG!&42InJl-aB^!RHRU;i)td^XJY
z2?#f(Z$+ZhS0VYn0>cveo%kUJ4yI}>b5#t?_^|=m7W<Q7a6H#X=nA{AViYp)B6)Ee
z?vk2wkZU-*;jSYDj!tM`>wd-#ZOXF*5+`?N+H!X8>Al<78WbvsN2P9=WSJxui6G7L
z=c8{Whg)zLzan@iNeUwj;SaYD^xhx2n^Uo%Zt~}*EOV7l-mrds#%e+|9>~e#`S=-#
zWq%TPtCR8dJ;X9&pCj?Z92X@+^$z3@c$sF9C(Vd4SgX|D1>TK{$=nsHg4o2fGJRsk
z%p3*^tjdu|P;V0DOLptQZvT{y%Z3MIRDotnIX(Y20`NLMh@#C<LDJvFA`-lK#0H?g
zcB5{_I+kEDJ7tahsUX}8Vp2cESo2PM-@K7Kp0@mrV>ez!DyJ74tK?=gc7tY-0>l7i
z??+1X{ZQ9!125);8E+&m>b@Bxcbs9y*b&j)%ZZ2}GdUx7q-F-c1K}o!B7u#<;Pnjk
z?Zju1nwfs}Z?U~k{wcRe02#Eh61c|N*D{g~a30YB$rwprg%L?T!XC5K3**tpdB@g_
z_gdP!KM^gDO`F#CxDnn(4>-9-<h)BCgF*Ws5OcrBEeCgIeT+l+0T?h}g#7|3N%FAC
zd(FKwTK*KrGM1Cajetnbt860&I?h-gpi=46inYJ1=T1m-4MZBWBDuue*AS}kg@j_<
zfnzg*g(TcmW$>f_%M<@!R2^Xm1<;Z)-Q9YI&@y%?V8L{efAo?*)5kQ5iNkrv?!03=
zjQ%Dm0~u486(~fAaAGjVvc5K_`8%Oe)EEjGDY^f4VFwc$VRKBM;6jf!XMK=*f2VCc
zi`1m$FK{iR;DeZhLxXTr*WT$eu+SbTKU565dwAN&9?m-G$~vIle+A*j`WHr?;8A4m
z4YH2>cj|N_8?5k7>8~(ek#Grilsz$Rrd|q-u7{>(peg0&y%~KAGZn;KVkWt>vu0{P
zKt6M*tfuJVKG5y{(CrS;EgnM29M|?8T-%;&s2P(<At+PJy*+EXdqrmWDC!G5%W|ht
z*NZi?_j?8eOND~QixmBI>y)pasXQBm+v5f|_=kqhH-6NOhc6k)4869QU-$wDx8DX~
z?(C(eLb%;O@2%S>Xp)4|kQ5-fb0^G+i;bbi^^^7K(?R=?F+w5SM8c4r318vjaU)LY
zux_#b?S;nlsYw?Dg`sZ{;@NUhICz2bb{%4yR?_)>y?o-0gpECC+cSa+T87Ss=as~Z
zK1=k5OK&aZ(mV7BG@Vc8cdXYzt_4efF$7Ajn@M`vsJZ7<YvwH(f5|T@`7GHk;!ZlO
zKX0ZvcVWJ_^t;2^^iFkrKDDrR-9o!*6rz#yGdZi6SjlOiqAzII+TSY0%RW=iq_(S(
zbwLXo*VkLsKPOQ<?-2NUJS!=NHoS3eA?Oyg;j@crd%TkxI*GW^XL~j8MkEuFI_22K
z5cfOI_{i~5Bub%4HZ)`PkiPT=kZTw{qFO4Lxdc?u)n;Ul<7$VFP(90kDks&349K-g
z(JnWPwcFHc_CdqO#3w-P;JO^2k_o2YG1rV!6>Z6fouZlV*}<`izwt1y5k4xstq`yL
zTd8ai1?RDy8+GfTrTUV~NwDI!t#Bsrr{3Od>-&uA6PE$?umO~ukm_wXy4rMJHDZmO
zi!TP=52W1z6%;gy|G>%}N><*RE639_!MeS$7LwseNfJmUi~Ut|D*Y8|lcO}Lgv@<M
zwH04amBs&R{SkgOya(nX>I6AbOJU8(5j>tk^Z4QsZRK_8ihs{!XZ@?<R{5M#F8e${
z9|gy3zpB=nJUZ!=KZ%{<s1Uh0s}&DzdF%FXwTtI$WhsQ4A0+<$EpNk3ekXOzh%xa&
zr(mRfGnfv8wO2=t%G*ns)V8?$=Pk4O(t2b1O`11!)^IAbD+o^F-Zu7YG-l4Q{mKQo
zoI4TOdMX7(wXUsQ-I-TW_K~>yy6S$@7ylPkhzbbr*@ToFP8VJVE&C%;YoEE+)X9B>
zo1i@v9f)LGakb$KGdUX|k5O>VaB4n$^ts`D`4das(Km<X=1udBjYox^fxci9%?CpB
zje6~a!d8uV_$y1&<->Zp>GuLcxh0<rzayKq%lNpSL1cjUwyUixv&r&1iaB>Htdu<v
z$(J>1&Clne6?YZ#&I+!9BAdRrQS07Ds;6_xX=e!%&>NYCcXjLC>tMj5yA@h~a-sQ3
zjUagjyV(Q;Ub?VfCqH-j{KEXjn}=&;Oi&2|K1p&%7p>v9h`}wkqLV>{9vjpH^zd<8
za;^;*oYA_q;h0+0zZ1jZ&6P}Mryxz>&1Au;H|rl9>rGtYmaKf!4(H?E?1$<tFEt)7
z`68&dY|9R3qaH!^+nqJpaM^DN$+@~ms7{tr4M*)%C9a%7h{~a^Q#vB?HoPcoO1u}z
zY~2se^W@I`-~9drKHqoy#`r*{1pi(TZtv_P+$75bk{k}_awdeqK1OCv@S;Pf0Oc$B
zgrOH87k}kT{)wJVq+Vk=2)7d(-OhcPt~K0u|M{mo3rA*xY9!8qWg|zfvFQuv<`=$`
zZs!lqksS?Yixjq~<CXm|^`?rM-q}xYIDCG#@%tgk^_F#ZboKPEz4Y9t>kHin(q4Km
zEA1RmG&9cw#dGk&Z2J${ZsAA=l|O<a;R~BLS6gd#XjdEWMQm(!lF2g{8jmdR&7;Fe
zO2>=x#3G}unKXV-Z9ckFr`otmtkW@Qp^BB=HzXwyR&S0s@qL~)b@pUy-AUnsS*+;!
z*O2~J1_q|0iVTq)y>MoVC4<3Qq)aw3pg(cxU1V!0*31Woz3H%?ONQQT!kQ5kWFtdT
zP^yXL{MbMqw*Oal@dzMXoKmaDg<~w&DmILDhZ8X&g|zZw%Pt(1OJN-pl-i`815|LC
zct~pz2GLY%2M<Mj-}%xUr;=V0PF=E4UHgJ=)4$b>O2-iz9_GC6C~eLh&e-W!rp>}@
zGU<4JGI+3JPYDuk=;JYHHY*zaPA=`N@=_%2V(!%muulZiVfw3F=`fN;V%(7aiXM<+
za&ijhNNp?df01tGq7aW1ekz77aW;_fMlZ=^?C~z6AV`78sC}u~ePs3AeDjz2W_G_4
z<7DcEmnWIt!*kyBN1dv>LniHxGt5PjbgcLNM_1PtrVb0)4oq$MTwIoF_2`l13-cRK
zB9Rb&)$^A(I*)9%FyFbS)XBcQAm=H)_H_`PThA_dkL3OAi?h%GDW~4mY(28NJMTYc
z`EfTPJ~Bc8r$Z&FtA;=6KT&<)9%=4e2Bj8eX8+&Z!rWEqM(*W#pv&}a1N{`klc<nH
zXN>`D`piaS%{KmgFl+Wu|A|72Y$74*JHjUB6w)(sfrKlXKcY|bCF0sbkCA)iuvt6^
zf9+I2AbemA4|KKZcUGGZukh#NXe25esioXER7`ifZtAg-+H~i>ieEk{mC2l0vYgeO
z)S8jTbmzf}KXfJuQvcqKXD*uVJbJLdpwOR(i1LFDi~juSil{@-|C`J4w3k>Z3=r8y
z`gbor#&)e?uMjnZ_uEdi{;;{)`qQ-qYi!IYo&im@lc@BuvXgnfPgHg4L8{MR4C-y?
z$esP@bZyNlf1!D~7CNsbt1|ZB*-ut$kL~K!z3a#|y&L)+Jk(}BTCJ|#-K%%LrGfIy
zyUtcUd*;IF>YCO5g7*s}3hrwrYo9Y&oBbl_xb4e!`V>hMMc>%E|DELt%$L{qi?9|k
zaIy-F7ifC>b5mghAcA59+$2`ek?V8mWWE-)ZpVI&r-3TMBseyR(L)`l<aa7jIw(#B
z>bHOPAJUQBnA#WIAufoeQ%2MIy%!WJ$UTa><arn!MqRa5qsai@C_65{Bb^4aX_zIH
zWT78-Kmd)tvrx(IPF}Y^MBVf&U0}g5i%(Xv?&<|A_H&`LOHUWCh{8do{EFN4f^!6B
z_6InH{JO^xn`C>Wc<MbYvKnc;iL+WbyqsTg8=2Hrqol=xGyDcU)BbtA?QcNoz+Lfd
z57kV`Sb9~ih#ajqu2yl`aJ}kxT1i3-gQTiYpGscK_Zd#^ZK8;AGza8b+{#8P{@@mL
z3o!^(5Vqnzjvm9B7!u+~aiDtHuu(WLN;QAfZt@Cgh+g|Pn&+UTt-M(uF^rDhx(R3o
zIrxlR)*SgPCJ)vuKH8e|k`Xia0`jo^$ZIg0qKwDEAH_KtZpJSyl*hj(ikwKV-(Y}d
z8!}h^I36L8dDQ-76e=X#aNZjEY@9Oi5Vp`WrcW8MQd@g=^T)jg8FpmK;g$4tKn{zH
zSKlG6<<Dhrd9UH5Uk{rh(}Dlv*?9gdK}R9i<QXYECn^bLT)_|$5rMA!@gc{~HI2s4
z@eHPV*j*C-1*<Th?D52kX2nM{Od7woJE>hNnThxLm2hpx_owJvJpd7ya#uc_E|)$Q
zL5+xj6QOIbOq`YXmWspY13v(F5&ldpHjGh)W9ej|4(7d|C)$#mCNh~_JK6ScJPyC6
z7T)S<4r#006+Nf^4Kj|1k`V8QzHU?X;EDf<ev`jxf-M2;hzqYBO%+}-50JnK>=zFZ
zZqTwa=1Sj3*#~d)Nh(>etP;N5mk0P^#76z%8NdBEy$k!J-we}aC~4^9^7+vG7)On)
zMN3@0a+Z_v?yPy;2Z=B*bjF|FO+LYe&@S2p5?VtdADChSCa7|)o3TnfTXJzlVDbni
zHS$d-Z52twS0c-U>?rxXzWfJy@}Shs#<c@lZm1Rb#D2<eG9&0|b%D23oBGVj)vmwM
z2si<&Hp@ULw5Aw@QSXTml1xQf_&}&RJK+x>_O$MwV4XQOkXOX%=8vK8x<ut5Zt+$Q
zNWqR%{*Q8Q`R|>ay{o2&YthBP!$;36mX`dO)GsnFQ(O}j_`Ap`{^JNSE*(3|Z4h%E
zw{l@QzwB4Imtk1%{n|?kA0D$u&(=%5we_%br5=jyK*i2}YR~@9t~`<XZuS~jLVwVh
z1KE~=`KkO<{2E)YSfUqRp`ic_R6eiNb3w*z$6&69F%$enA>7nx3@Vi4=Krv$;5oR!
z1dm2xSVdGsuo7nzA?Ii<Qlo^MTVm(|i+JW!q=+1HEo&T%BmiT3uac2k?zz>g+STr#
zEib)~lQ!{O;Q+7o9*)Dfg(QnQN~D>oBR6=D9f^lxe%u&7kF>fH&WWSz*hf-1|8G;h
z*>iMK%e0KiQ#N$h9%sxMW8+rkTrFMt(2$+l*~=J1rs*y-Qu$ZAVpd7KuQcPv_eeGx
z7m~t>N&iS7)@Gqu*)WiENphAH4=*{DH{~;>qf_h_NWv^W6dAgIKj(%BN)Qy36s(5P
zdeLzyDKH+spa_1n<C<f(<t*3J`FEtO;%gjyteVNp2c!AObG7nYqxAe~3!UaK?T-6=
z;##CZ@ig-B$a``bXGz<!#x0&T(xr<`yLZD}vi|j`jXs{QW)B$2=Fc+i+>32|PixW4
ze_}2qFhu3bo;!Vbwbi-H_1#?)BA}CBsaYliaXM!Yw{2^QWxK1)RN-{fQic-I`%6(w
z6&AHaK`Wn7Oi6zW2P1M5_keG8v%rin9?|dA=wLL1xCpqS=x0(0Z!{?%ZRZ43;w-lu
zcRM?kJ(|pU>yl{bvtzEaqU}t*vxy0}qUB$dkVvxHUAWSySxX(aa5ga;E+jkh%jf6Y
zw-0y9#}$|bv20?yT+{ZeSG%p;K|^^`v6~Uc@3C(Vr>uO#+;~_AhKK82yO-3y)$zOs
zvYqtaf_jS(G2D3QEH{(X+abq%@L~Vjf)$kn2sdEvQ54PDF()NnAbp0G1>q)v<wc^*
zu#Z3+rDK5#t!wpK*XohEBbVS*cp`xa6e^+pH_XJdi!?t0&37YWjZ9c<tRy`n70)s~
zmD?{RZDybeD0M|T*L!FtAe)3A{$wVa{X$&FO%3OHR4BZ<{psmo?#iTYlD|%_Nh}VT
zpx*adapOA?F<}c);aC-?<e4R3X`D#E;t2!K5Vy8iQk+WR?4Zz^)8LsHVevd5z*1s9
zudEH9W@KAGoAR1pNcASJFJu5=K%T$cRnYgS4KTz=l`g=aS41wq7tjA!(9rWmYvfWl
z9o;>jY&dbUx$(W}h4c%?(&f)+ZtoY>WWyUbHm5$2S;*`?o+-V{C;JEM-uI)~4<>bM
z8EfvHa-yV(KWv1}pIS|8G;d|kAak}HV`QEF8_Ux$UnqkRg0zFY<Sd?Ti@LkgHmy;^
z&Yz|O-0o_m>TdWCW_uiOaBN1;qmts;N#WuVbNG|CtrYu8@`Z6=tQoNKf8I5e-a^!!
z@1xL7;mkhGiK(6krqnUK?}+%ENk*?6b1NU>%uC=BLJ8_DhGh1GIuW(>sP|B_N5VfH
zJ(AvA(*wR@0DVd8-CNsWymixipc+NJJeJa?9{iQGM`zQoRg%Gv&bEcPGs%j3#|n0l
zSPwt9y5y`gbuvk0k9JJpq@0l%7WhK<_wBH81AB8X8xu|wjC@FFysTcjRI~gCI64TP
zD5sPxsN(wFR(e5Mg?dAueczn7@uY5T=AsY@s;Bw?=tX8FFq=2~JwdKTa}5y$6XT^7
zed62YRQmP8jAOn1`?g=d5l1P^^5UZ!``Beza5|V1?=~bD0;(An>2+2nM(d`Q-?+)?
zMt&4_{)&VC&pKZ7=R`ktQVK3k9yP)%5_<C`mPHB`wN}Z}99v}spEyXoKdSf@i9y0m
zYd0jYYg=CX4~`eL!?5!wFK+&%-D%xwwIzK!iS7jz+Fxh`-rEo04#Mr$Ic@svT2uNv
z_mK#+1(7*N^8iCqKv+qk_g>pJSd7tnJ;|LvW=?!F<4Whv9dWGxai`U}BSoGrAYv(e
zjBk(Q^5sk2;S<Ps_xnc2oal>D_`VuRCyf1daC0-2U@q1&Cp!{y5HuRI267saO!FVK
zsVm2PF`kflM7D^Lt3b0IoD@1gYj@mN%dTwe#rLNztqbwAv9IPO#FG}MkI%#4Vz=>=
zrmPB0---%^(G2?g6yio+<}1B(IW9jy)1$_G63pbG9?$Tu5?W3tBUg+$!~cbL9f>kp
zqa}rR187<uIn`@K+C~=v?O6MibZ3lYq06D<*II@#h61hu&HpDf--YHM>ja&j3fRI2
z{|)qwAVdUOWC&Bqio3=N&RE-N-K=%eM}f>+MYf9d?Rf4JMl0^&Li~4j8B%?P8=CRl
z<z@?^hoqD(Rw1-Zybu2YEXia!gVuzK`?TDj<tKKRYvPHaVwG&|Xqh+XOk#vssa+f!
zBc)PR>d&ue6W0%=a&N*<8JMyCvXZ{?hov$R>yplGuIIi|vs=Gv`;DJqo)`Mgh|t&7
zyMOHUqE;!gm!cjJv?A&#b8A=6yv#JsQSo(w?6EeOtD)unK;l-I96^#p3f;THUhI`)
zYa+7LvjGW1Uz0Pk`}mgs7)Z_xKJhBf3Edg2(<gT9|L(Uf$fS@9*fv7t!9GB^-3P+$
z%UPmk^=#s8>ZKF2xs5NLYs?V5Ma25#`^0?xN8n?KqS#6HrMYx^b<12h2j{ehk#2T!
z4LKf$YyLHan-DXHXB_XV@xryE&B{@ug}4jS<qID00y(VMiL=_VBat&TI9_6DrPr6z
z?yA5IXJ>+`Q)9v%%_Xhh4z-8hK3s}>t*CK*quu?171dum+^W1~s2Eop;ml<qdv-!)
zeM~N64MiyJCxe-@wYGCm3U7n9#iApERjSy$iTq;srTy~p*{oRzt#E3xH+QMg3GSZO
zrhW)iYuqrGTx;mn{|bF#+nVTFb708L-hCUr+2f6_T0WCBRhr9k!%j{okO=M~{Yn2f
zvQ)-K?fizH*`;7D`DAY>SX1|B-=`ApfYh@!dv)x9-G^xb_k8~pwn4p@KEV!#-WWx#
zPjv~h(&(nQDVoc^)vHAFJp2A$H+AT+IdZ0xC!ev|78K71BwFf}-q4Ir?rly~JR}I}
zpNPZaWey<+bHuG^8>H~=w6BiPS2R1L=U4K}t_g>N4K;t}4Uw07f6g8{*322H7D%H;
zt9gZpkzKu}nSSwuPMp~(G+S53;65Q9aWj-6yjXPBxON4<lhwd52yT)u3TNi5^z#xo
zsu7>#kaVtlSF6!kPsl(LqB>g<HEbK*kM6JQvu8*i2XeH12{25W%qnY%?6wW_RqZA2
zbMt9?+qT>J(p;nZ&9P?rxUu}$E@8TJN2^o&gb|K?H<iuqnocG0oVM^a$+?En>EcFb
z;!}nE$XP+Ldsn^FygD7_-xOrS1)sn-g^ayZBWi!eYGhxOb%#zuM(xbPl)5~N=fn<<
z;oaW|s^{x$kW?o6>pn}8-ae%$A6-%p#WU4p;hM2#<#=`+zZKW6>@<*ZWubATc5R|%
zz>wvvN8qGors3UH^_rj717b56@x2)9zMmmqJOr6_jq=G+X6pM!ThaVVq50`x>dipk
z9-YyrZWWJLT!L<Kjm9^EuCZ0l(6e1jKnvxQbH&bYYu%Y|lLh#V#f%C`x_+iN_1dUo
z?au-3MEXv=h(OSqN5|dN5r8!hWo_gmD|lqmpFU%dj}Q+TMaxg=Ef>&nOKfR#tBh7T
ziHLXDww!HJnc@kZc=is>a}eO{t#!v<BD!o+z(3!H#efzeg68nsj8gTQR;zZ&wBLZf
zQ%Bo&@*l*-`HB{XYwKEb&!J9<06a;v5l(+1=%x?OTb0wDyza7*-P!OKE;RhYMV3=J
zDVvjPo!>XQjgRO!msYjh-b3BtvooywdNB9i&`ZCrU=1D5eT3d@{t)ErCj^qtW<0&6
zPW|6qdmLmTOkqNfxFFm<GwPI&Zf0v&_7QFzi$$wD7<F`G5;z@0gLpK65~9`YP%C(L
zZ7Lc1T+uQ8Ic@G7_Ep%7Lbx5=&DOtO>&%`9!VPZ)ZBmxsGj)o#U_yu*a3=_o(2u;F
zw-BscGAxMRPbff1TLWJ|<Vgfl*_b(^iSnQ&KChhf2ha9VOa1!f8k0{$q%L$DWdz|T
zb_D<cKmbWZK~z0l!=xWd1)y5jUsa3OkaUPv#zJZayo3A1Ih$iL$Qk9oi5?VPv($$R
z81)L~Otodv627>rs(#Zy@x_(qF-rZQ&!`@Fhs0YDNm#v?F;1$|D`gy0?oC4A;u9Q;
zRhX|$l8q*F@-~9qQx(yoc`qDKzExtnx=*2<L=B(<va4s2m_hD|HvtV{tk7%AoA8N_
z+w#u-*;st^q`!;xF?0NZZHU_uTeph6{13TCeKJ2d#H%8kjTHHn$CWFOG*J0(A|WY1
zRiOekm+CyA&~eIjP}1yza6<zv#!86~9Q|FinDlG~9$KiK`p+>;{FS7g!o&puW6)k1
zNy#RS3WQ+f4mw%H9bT>CBhRXt;ix0@!hP_n{?WL<H>mnJQGk3>$DwF27lN{qR70D5
zpD{o6Rbzub@?(pIp6D1C`#!#)&lYKUfN-N-{1r-JTV}rFg+V{@EK_trw}{Q9##id6
zHvXtVW4<?|d7(!+P;;+R$q9{$pS()lzKNE3LX>Z^4hGcEn`Lfg{+@bG=ur-OVqm8}
z(QhM<^GknT`LU@`i}a+g=NFn2bwht{rM;3nO=9?NqGj=#0P)6~AwZ$8{wOGriQoYq
z?$ftHK0WbW7TXM#2A&sn6xURnMK>pP&^`s~l74J<O|_YCp@YYtmHrAh5&L1E<_!y;
z!8K{gvn|iY&-ZcWOLLTiws<L!6~Gat_G6&ZZlj^1&X&oQGEdVb5R6<C1|kfI{Z=80
z$XkXZwvdY`*ICaD#Y>4&M}~mjpRnrxgLj|>RYWUwZK<ef;0saDaTHa2kk3@99KRJX
zKoE)$Kwp=}cY-{U^(cBXLAZ%dX%*K*0%LlZoj$r}1=l!ask!DfC4Paju%&L`;|j^8
zkWX7dE%FTevkKn6Ft|@9M~r<{o(1M9w51c4TLlVdLy<NMGE5Pt_$z`oQb%tNM(Q2>
zcuL_mX`gbQ$B$FVyCT(d3{}W6`7Ejq`HO5MZC3^w8GBLdpl4US7N1)-d30;sFV=J6
zzv$UiTjb|Du1Q_uc_F{^t7IurC^0#?^H<{Vs%sd%1Tm-bo5?(jkwwscGVW4`9Q5Q_
zrKcR!$5@FApn4ydp0U5&roa>v{1javu1o!Z<n&F>V)7o)ZjxkCc!SC^AeLyMGsaYq
zXbLPL<H6?DPnOANSx@5PCcp%d(4>80K9e}DLNAZ;tRSVNE{Q)O{oTMTmJiw?d$RNc
ziB_T6<oz<gB9}6L(qCZ?lFLoefG{Gye#XA?wW8&vaxe7>ijSgl6AH*h`pTun{B>fi
z7ACA*p2Wl`CqbEmQ63U{6q*v0ob*ASmG<Fltx%XU_vkH!KE(f8C9RbA3Mx}*S0N{P
zmibn7tmfJrb+G19n=6?_q03Z#+=p-_b1g=6g)WqSZ;A18Sru;Uj|I;T(+=r}jFBM7
zWNhUWB(z3RYge2%q<vDSd{5uh+T=O8reY&Ak7_-y;F_#wc}6yH*0|)D+tfaxJ(+ie
z9n}V*;f*|t1xN7_#!`JRv@AS9?vwimYe=^4XPisEex~xjWWH1_4HaKE&_v7?%|Ul6
z0}s)NPD)y*a-Z=@Vl4FU;Te9HG(vrs8SWCIm(H@Vo`P@_9hnUF(-o-<Vpjw=K%~ig
zMIuuWNvc(DNHANGxmi9q*@(0tpV^;5!in_79w>MAujcq6TLP1}*k*KseZM{kZO~Ud
z0(cf;TzpoVD7{y{jjB!=lfLBNx9n0a$1L70Edfo#J2t;B$CwDgNS~grsQ&V_02b7{
z`w|Gg!SsWNAbw#~%6*TDuuvh~h`z92sgkY-QpBcqs5zAN)*pk-@9AG-oTR-YymR(R
z_X$D6;DrV@)mPv?=6}=J$+NVVe0VY-s)MS&9KXx_{<F<SHr6qo2d^*vP|pt9teCPe
zcQ!{QpOvUnHai((DV!FbLVL2&<M@ULN53s#_0@d}soB3Kqa|P&PP>oaC;g!Q804Ai
ziHtAz^#uo^Z{e2x=_7X<W^$fY(f$T)7C}NzE>Fjhq%sCgA->qnH?@y;=xhqgBS<zg
zVeZg#zi*F^Gv6qQQ2;!+wyD<jeQBwelLL8bRew#PZ(-_eKKO*FYw~JIA0-G(B5@C{
z(e6B?DjS=kTNz=IIv3x`LlpI1S@&$>F*Z9!MV-1-U;DRg`G0w-%q=Ig&Cr^}v4EBl
z+7@Y9Hblv&z4>~Pkrxn<05TToR$d}JSWcO-Y&t>%GA42&5?bS$%#pND<&jkDr{7ma
zQzD=U-eA+S+$X{Z!c5FbNk&xNr`9dkpNVey`kBhJVZNIo+~@+6rvjN1pd@p+Wf?56
z=svD7f#!?SnoXMc=pRrDe~le+mpat6Qv0LD!>2O*8<WINq85)xm-3e#HFY!ymKR5$
z#pvvMzL#B?@19U((iV%5iAW?85ClY8;oTYA-JQV6w=&K!V2SOeh!(nnwW-hP6Hv#V
z=vdlNAB`mFob1VID|<9Lr2^OP;aSlta4suelD6%gxOO|&NT4WTAWQEjK<&1^-uo?n
zJ<kE;bChAZ<czX+;<O^pR}Vs@qZz&UE&@Gojr$qT$~$NvZFND2VUggt7f+%j-#V)o
z&*Q!o+;_*|zQv>3VjmZOO235&6(i%>o%4F>91Cj?NWHspXqUWKB5kmM=`4+<zc`?|
z2Rml^AkA_>+Y!D<e1*A@)b_6-_m@OKnuMr!we<X(GFJLP9Ofp&Skic#+u2&~%~>n`
zN>FNqri@8%WA9}dxl_3BC0u)$JZy82J4{B)JDPitjlqffX3;Q!9Lk@Xu~PfSCK0sa
zc#dZeu#7y<x-SA%b_Y9P7g8327m4)gifr&gSF=`<K8}n_uZsy;$$Mvc7HLi`aOtnm
z0n$s1glaw~r8#@ziuVZIhPw3?HUY{lEA=wnG*_V_KqV1-7*Eg2S(%p;A?sJLC8D57
zt~YhxOQF#f(C9tfhnrRIEm<q`YV`7drXMvE6qsSA4!{Fs*Tev-i;4sK-hM#e2k`6!
z&;ErSkDO-tb9we<q^`LYb{X*wp=Z{^)0ItV%>Y_{Cu6l2E?>8Dz{Q3y(=W6vMI?Ta
zMvdLb;dUq<H2VOCF7x%h%sErwydq;5OHltk!oY~MpWrcbaoWaR((r$W$14U;L6K%p
zN6r5_gNY`%&Y!pqjZ5}St{u&}mUQfq<Sr~Rz?g8Hmjj`LiR%>%%E#%n4VvE>nr9uy
zj|dAi66X-q&DaQ48hDX9Wp1`q7HJvAD~L;DRbDT?!?BGK(4>FF=)IW3O`a7R0Isa^
z2NJx$_*UE2M|(+d2Q;;hWnfga+!yK~7}0s7Rf`|dKwRdG!haBxx`0>bUul{I6WS+l
z6#oMc8yiP;LFH{HG|&7T1T8-XO`*f(J`8(#qi`BwJ42rR2hZZE=pL0f@+TNp^dLt0
zsli%n(>B>ipGEm!BHVZ*$6i6W4P7vr89J<yU-(>eVeaNA%)NcNx#a!Ykbm!dcim5W
z5{pP1I(Mm4I<!@Ynh{wI$QShk!bHmsj3lX!yzDr@LX-TV6|;1>S89BBzA<$Y2)A)8
z^Bk4A3yK@`QYJ%i=dvqFVOJ*Fe=>1XC6n8uL++~4F!TJ`wn7Do&RC8Za$Ie(ca7s(
z^~SR6Zsn)SCm8zd@<zS+)m$`mI>v$$Hs+JKwxU_Be=-*>yN$eDE4qLW#^UBKlOX1D
z-m*WG3h8aSsl*$Nsw<|O_3x#k;kUW$`>0Ho*tU1~ba(9m`Np!Fa|LJELxGr$>f2AZ
z=HDT*St?oj!?9fExvlhuQ|ITspsX+bekO01d_rkuqJ`Ddt?GMG<}ZV9a+G~ap&RO$
zhaTH}J_xsOr9rLvc6?kqn61n<n_mRsCONY(*ok^W^uPKZBgxpu2!okxID+h%@73F_
zzm>z~ca{pNZ5yfSi@Wpn2k|LAGH;bhD`SvPu5?5_Si7@dPaRw|%f~qxtKvDu!?0aF
zuh+;NQMSsYt1)vayKq?D->@4#yM<wG<?Y#`J>0N2p5@KAiAkG3w&)aIVP?!Cu5E`_
zy~p;%dF}N@yKvy(+QHTC`hDUS#I7sdEh+*vztTrpw4$~Wm4d9_HTYJ)r(%t~H7=1V
zQ7gWp?XP=bvz0W_#EJyWB2Yn^=&$qICEBvjSwl&?7Oj7^Un7T`UVPJ1WB3%0oL(5<
z*0j9!-|BTnzcz#qZH(a%=Z1Z;-5nIPN@uLFE60<=tqsEMA5Cw=b#Z6ps$sYA3UaB1
z!%5+#b#Lu{VG9FPHnj>9u8T<e|MB)7fR+_S{{PA0=6he9JVO)^BnyHlAQ=GxQ4mEG
zSp%qm89`l4tPw?m30yHQikQ~0Cd^?G5k<n}_|nbs#Q&%IzBe<#;O=jC{~qS??mM?n
zpAOa4)m8PaGF$rEMXlN5YnRyNv)e_X@4*9&{+7Fp?!=wsQhP=(r$u8)?ilhJo)i!-
z8U3S3=DT1@N)SOpOM3PYt$9>WA>6Wu&33lFkZgD4a1;B7Do0|-r0S&OS1)#R&mdJv
zU5mQEto5e8XY@yIT;hyg5>{H@oo#ISc?9doXp2E`W)A2Qf|Rwf|5Rt{m3n{l=1G@%
zCBw-;>+Lb<Z@o+FjD2d-Eu5OLh6b9qBJ6GbCay}e81e3!%od;B%Wiq?%xtjP(??$G
zl5-s7?IPRm-8Th7(5p}0HIXfz(ks+H-LFpnr-o6A+2f@21mTHWYO@qI1IATjDeK_d
z84zj%+~E9ZcF}aJ@li7}mq5$&Djg|jpNJ>V$K1S?VJBd{%;ct<iBr_at|WFDk=*PR
zhSU7=w&u*cMth4t=M+prho>*dMl<E<R`<7K$+A1_5^j`ca-JV<y>zyz?}kg`i|}8D
zz6)n+!KVHDP5X>-W66zX$*^Oibha06Ij7pRo;_wxz6G?V8<@rCropD;Fy_nV&xWua
zVJUt3|M_|hpBG6|RtJHrjsBlH-PyOq0q{zq-Vf4Xa}et!Oc16dWlYdRgNM7lwR_d8
zQ|CcQc<3lS6W_`aN}_%lD?qrtq}lKO$?ws95$e_AQ@`bZQk5`Xj=Bj8EnW{OW<3H1
z$5H3BvXKSlvi0?IvvXf|+u7qmxN&q-2shC)i!@0)M9iAGb_&E(s~Yu+asC9$NoIQK
zw#%!nHLJR_QD-D9oS}Qh3day+*X)Oywf-Ys-#gH9y>kktw@1faJ6fA*{o3yrUXaNp
z_qO5<PifTFA0F2t(#!;FlF)mBZMiFZ&eS#CYOvW4GtVH=56dlob;Zsc7K)8GWzy2@
zi!9q&(zUl<(VGjanJ9ZC{xW<elElJ3Bdy+XaTndHLbzR?buGVUJUY>z3THA&`6Vjv
zm9VbiX@QxN;llfY<A<Ki-VCzm;9jG9J1#Z{5QKVlKNeRS3=~qm-u-SXTDRA9ZS$MW
zs=ai~DqgGEosTx-jR$OLOy6QQ>?LI@cWG)i-qZ@#?J-^3`X0OL?mlMcuD}Hz<Hlee
z^+g!^IOa=zt*<nLwR=uir*3d+&Qr#m+$D)!du3~|c8}@m^i6Kf-EY**T!bqS1GC*<
z5{5K1z%aM?D#z<E9j%qKaw#o6-^>R0PxT*Nq)(em^KRt>!2G0(O3lR^7o<r%(}InI
z2!A(pkg6>y!NK*2+-&RjtW~$1FyL%3n&yr*vz_lxwbz`So%IeH@iWJhHVALAXyah8
zmU3H93n@p2=cAKM|Gv5I%qN|?vq#2rac7%)L9@MP_wH=}QL`V{laN=ZX35eAt9y#R
zx19~c;s}&EJj!%`UTs(JbX)E|KG;Y)E6hmrhP%3C_Hn^953T4~d~m1VJv%pZtJ};T
zlgqk<;Y>u|EVL}dA$oemE&)t*|FhXyyHBGwa|sbN9K#4U4AN+pK#;iGQ7H9Z3eVZE
z=Wlx9+)Vwu9;iG}od;y}{HaE!`=cq)dhNNXv*WIFV$RH+1Q6)^+d6A@YtGGFCmL9s
zH7^~p^2fxa1OPQxGWGDsopjTI(9?l&ErjNyEs2~j^Z**b(1lbj$X03J-@6uQ*-VOW
z$~ktaVLdwDnd)!IM5Xg>W{!1$zl~CxpvFRgOR}e$j$LV+n_oundVW$kMe{m0HN*9*
z>T}!Pt2HEXXYXF(?_OcL7C@U#7Z5=iBY}UQX_v3WzY!G9qm$vbcz}1s8*obdw9xm~
z=G4c`y0f%oJExjP=_1ch+cU|di=rt!1<cYFS*LJ%#!KtlqDRKet$G3U=X*tI#r7$*
z_Yb@t!+f{DSEx^v`=U$PhlPCmbkzD4?#e?%D)$*jkTUKbS#Q;&nM(0}gz-SFAkvDc
z43rT^*v7DS{6FOp2LJJw9HmiwgR54@fb#n-uk>yrr*lMf4|NP;mc}DjOk@kkSG}a|
z1ltjAVn9K%RuPLp$!M0RE&t0%!5M2f<TFDu!0YuFpv{iL?}?pp@F<Ch)`xNb;oM;I
zUHK7EOA7VoJ;Sz=h7&LO<7nR7t7@g)OlTj)QHOI~`2#r@6RSS+GOa)ON|FQ{^1<Sd
z`J>*RUAOy7S&U_)e6F5peYepbOz9y;4!w1K5{BFI(bCU&V=gItdcRr7dnFv{iI*}i
zVckbr2U?3`bvGZc_@h@eJls4qG9IuqsZfGq<YQwL(ulV)&>+3XURNqD{Rv4!55iO9
z{<=2(f_5)z8Zj1WDU3drvnnK>Ig&6uWS|)QSxk}xt?>Ryd-6?2DcVwtJD(&o!m>~_
zU<7d2ZL%<{a#&aNnfDR(8=SxRKr)^%3Q=xS-+`2?_hVEq47|i0<k<r>C#ntN;2zmZ
zDXca|KA_JO{VZv^HIB$m`2&`hd8sHF@nC^AjPVI$`F)*I(hLKmB8oi5f-PFjD>`P$
z;WqfzOj7@b*sBrCU}ju;I8A1aG1pnKK35t2j2!$?(@c}>=<uvJ_NEvR2q`9y!e41~
zn35aLS+y=(8M#@q#`V+I&z)}esL|Zy8~T-SU3U;Xh^A(CpgYgWvk^yb^Xna$G_H<S
zP>w61@70+~`9lKm?x)?~c)k27BZaYxd!^u!USC23DeL@`saa?l^Pc+0gK+DD*1QnM
z|Fh@(xB<fLkonaiv@EI`(fu-{^4`oL#q7$zx~1Gz_^<|{*7}#3p!+bBH_XGPxd*T|
zT4p?c{X{N%IOxxo7r3<FD)-&Yv$Fom2Xm#uoAJLXll5Y>=Vp#8_^bX}C>GAbfdgyf
z=p|;R^W%EAzri4=F>FWP+>f;KM{};8H1z57$ud6CGpbkANK<VDx<8y&Rgki~ykxD5
zx{;m8SvEv#uJ~iNXcg<|4f4Q-pk>qm%CLvZ04bZq2f!|x#dP`CfK-%miTt&g-Ndsh
z(aSILyl7iBYTT%WogYckx>CC0JI1)_wvEm&$WpNsR9sL_mjBTpP6>x`H_yHwX!(Dh
zuok_IRJvQ>=ikjoB<^JyjZhA=c+`u4Xlvn?C^AM$*2KR8#l~fjEIqWIwnMjSP42RT
z|MxeC>~R<MGVMYQ^AZS5nDrvL)L3z79XV;eW^?%HlKO1#fzf{D!Ym2{77n#3gwhO4
zMR>}>HI#W5{@U?d-oT#7E|_{p6!g+LIj?k9)<yqFJuEhuegD2N2sb(rOd1HHJc_;$
z2W@dfllL)Pzy&+$+>QNqu(cQz&cOi<ib9v0!Ys4FLsQ}OrCLAx>Z0u~?mAQNYt40j
zZ}+pux<Dh8`AX~kItt=h2S$Uw3%!}ZPz%Q=e*fMMS+%qz|Msk9S335#tGlzkhpZrb
zjO&fwRCc_*`@XXrsdza^v(tdjzAIy~Lt9(l*qiOIwZrU*4$eP}g?L|xZt!uJI;swZ
znR$d;q;>DXIcPuxFBfI7T;e)JTm)QIhyKsMujv~O+1T3f5^vVsXTmFA9*f;JF?WYY
z(sX14Spn#_lGK0cxLG<i$<#hQ+v(nA!pt?j`OaEc{|b1~v67j8OX9>ElQ{SK39E2y
z<X3N;Yxn;{+`Qm{pwAFa(rB~tInW{ki09urX=RTHyqW83?cmQQ%}0H3?WCPObl}ch
z$Gx?<Car=?ilI<0)n}$@H<<4kIj4MX*3TU)_I;YjX&q>A-2bgfQZ#htf`Q;lH^P8~
zeuVtysZ%8sg-Haz7UK^~?YgbW+LcptwYw{=!daEPdvL?te0I0df7l7Fr3tDb#*Ss&
zdf!%JILyp7e>~PMo}I-ET+#ta_?@54wPtTLYW7Oc%U?!}I{b*sukn1zoKU-3kZZb8
zy?jf!dMTN%|2Wz!y}*aQ%X;SE$ZLLUu37!O*>Lyv{mk>wvkF5I&x2iE^J{>31$Z@R
z`rv^e4A;6OFNMw&&tsVgT9a5NqN>A9PM^MD%iz&Pz3HGj5m#P8h#jTSz%(Ns3Im8i
z2#US|>(P6E2!m*w6Dzt^#FVDZueDpf`?5jh6@|Q6Oh7Ym?@P^A@4;+XyfB{|`Ov84
zJvGSKODq$*_3|hC0D{?<h320F&HrZ?nr8$=BA4I1hxYSc+9;dfe4pgoCtPa2Xr~gk
zpyi)K%UxVAy`hh{Jgo8&;{CvBkDyz=qnk~A%WHy}aN0@or`x<CA<y8b?(a{&wjao;
z#?eaWcpy0I+U8dgE~n+D#TSy<5nZ-K9qj!a(_w_g)gG>w)(2`)?aO+@s8r0{KN0#5
z0V3`X%cZ1nGb;mg_GNS3=J$=RwKR2zvw~e?p5EjH^Y{Aqgbx4jZp@SS<11kRf#q=*
z#WrBAqbs_%hi%!545#>J&}%G={5AMrUvAmfcxnPbgh#=rXY922&*RqkEj9~)$3@4-
zV4{K;if!`e&X<(I%}6A?)<;Nocy_0;<$_+T^Er&McVlrA-&v%vG|gd*=#m*&YR#q-
zUz~JDkw(b+k{X9(w9$`xB%BJ#ZVA6oix^4x{_%plxaahK3%vMTl=4_uMRFoYB03Xc
zLyp$_aR)RI;q01Nb=(|b37h)hek~dOTn0$)z^Hx{+qMX5Xj{ZZ4qz)Upi_>kckjX3
z{)=Hfs%FSDg|8?OPoY&|vP>xN0O6*5pXj4-vt3;>YI35(*Qfs%5N@NfHCPi8lfg5h
zEt2F6@!+IUeet&|e)b}XA_5}q98U**WoMo(;v>v)R>&oz7haXK>`~3io#;B&M56mI
z88w!CI8%wY)#JG9$IcPD>AwO4#Wkrz+zyf|(q>d8-iyY~Mep*<@rF9^xJ=?67aRI$
zR?9s%!-k{Vh4WqHNKRyLny@C{;#Q(Z>Y-5}hudVV7p|VPCg0$e!-pDyG2*Ar(O78K
z(p^qJaw**{f=BeD8Ewl;>g`Qubn9~;l=M$6Evy2fo@iSAHEs;x(;WO65t*Y^?(LK2
z;;&~sqXgYN!jb);5<!wY%O2P5@&_=?FqqjZ3H0*@kW(vwg?3R@t&yaavDy4u&<Bbh
zMKk@dalIBxn9=-|VEdPlppRT#LWIJ|g`uRRK_i87iWNS!PApi!lfCx$5&aRp#n6|(
zI%5@HXW8aNY606(^bHLt$SluN5y{2=tA#2|l7GD$+i(<U&LyK{h!U8TD!tFiQ81H^
zeQwen`#2iO8XL<}vSBl~K)fBI>KO@Hc6nU)&Mg`4V$*h3nQrDh!|L3PBR2l8X^3wj
z7S5#KGTI*@o6?r-03Zm*C{%#B#0<XNOa@zW19v|N7XB@i())cYu6<!p54UDeBT@%O
zC?xoF^!6dm&;3g*j%V0ZY}6B^BOEs@e}BjIPA6RHdPY!OxQ5d!rFL90Lb6;r4R!Ay
zM0o31s7n%^%)!z0ke2nMh_v^u5kGes@|*bA6FZqHKv-7mg^g9DG(*`ml2$jKy-G~C
z$jG>%iyJJMR>mFngWq=VZ)DY;2(A7@No*1UE_k?|c+V{u<*T6y^7bk9+n}TMS4Xim
zIk5VF)yY_Fo54f9*m}BU_U`S4-TNKTZ-D21D%q(;c!6gBt2vcI|3{Z%kB_a3Ho!Q{
zrGQj-CpzrkU`C(v9kVci$(maJG$+%#K5S)=WI~?exJj4P-Zu=$3m^w$3zSgS^H;V^
z^5fN$M__Tsc!skqvBQT+xvu#6PH$s8`KS09X>7D;vm9^uyTY+=O)Z`K*Rkfv1?0Wz
zwBoss4LFF(2$9-k+_pIAD>)cBT+bZgdxbMoJKDN6-1_pQZ9WZk91yi|CP@m<^Kv9V
z>`z@d+ijmT(kZ<Rgd5q^>tAfO``;SG^>0>M<rkF;(QG}KeNPCIDWjV#viA2mmfb^g
z!V)mu>t=)8!R7Ag>x-qh)riRR)$V>dNr{@&Ctk0ieZ|sR{}x4qUsluH;l<wAo6J(w
z08MjK2bmYCPDw30^?TtXBL?V<7&-*Ac}rv9?Nc<z-ZUtMtLy!lH%oM2%|O4Y*<%2F
zE8+)WX!Z)rD4pw(QadnuznSS*-=q!v7x`xXnYxP}G<NrPGMVScQHpC+T*D&iWQ_7f
zgF?7AX_^&BFP)d+o1WhMR;}H-*$T@a)VzoYlo|j&DL1`3Z5pR#jqG75)`>9o4tBEs
zIpE?aQ{t$$IS=Nn@`b&8|A%IiaF=E{(@u=hoXtIZ`k_=HC~$t(kr>=yFXUX(befSo
z7|ru4MucITzP8_9^uf`>*kw_%{rz^k@ge2MqMM_ZVU3gAa}jF{cc9V>@DNxY5h74a
za>seuM!DDU4kXsjQJyDe_1fnoz>t|#j?B8~bA2tE)8=kqg4y^oN7_Wka$SvwtDhEx
zTSu#bnk=6)mM`x+@Y+9Wby{D7Nh44U{@e^|uOp6!WI~>^((DnA<L~PdVsSc|`vjpf
zInp-1Hr^_nHcl$Zp4R+MtKGSqn6(nfT->O3aqU33)zjHejkm@wmK<)-@-02!2ExLO
z)S4jHu1aGAqX~#ROL}zH3u>G##3LYlE60b@1=0tskfvjnE{OasXQ6lgGCASS8O<k+
zW;Az$1RaX>*&BQ9vDa`CFxz_b8=ZE9?w)sbM*pb-nyaNXzuN9}zZ@&NMJUqs?>Tzo
zD<SKW#IR%u7n&dZ7Rx{1X$YId)$o%#^ILJ9;+au2^=)iB08r?@lC2)oWufIMXt|6a
z_3lRKKdr=i(MzDy1~XTSZvt{pg+!#|geNEn+BMJ{w^RrI?gew=9YGmnW-xPOA4wNI
zOmCopN=57w0j6&>Zmjpc<#}`Ry09Eg)q~pY{Xy@$t;9bdHyD3gP!1lf^`<Y;d-<zv
zr*O7UU;BFN?s}(lJ6?AG+K#;+<?Zon!t&rx_3kwBE%I;nt&xi?kf@+F?{0OPH<QC{
z8S^2A{D0uJ17rbP8^(%ti6ljK;1{+tjn6e(lEY0Vie|AV2ybNbM-M1kIB{=c(z#1D
z!~%}Dcxm~gdB^Bg)9PD?N_*mqT7?rqxZP3j%)U7Zm4$a3i*li#QYD`i$bdt-;>M7=
z#6cBl05OvvL45HJagfJKN`ut#6Q4bOnA411l#eWN#2InnkutJ}N#%0kuRMz6I4j+d
zEmZWE^aB^UnCMpv_k>XzOL;HGAz^eTC?ttUmU843)yRA~87Xq4QMVYQ@KlJ~@EAEq
ztM{TpRo?GJ^OkZ7cp|qb%df{=(mdH|#ra$U=pZ;QJge#x>oJB%w5HNO;vrC+5QPw>
z)Wi?{m4%CI7<c(w+9<td803zso$tA?4v;b`Vp~w&`Uvt9NWTO+p`1Lsy`1QpC?xmz
zk&!@FloQP3$}v(q-;;9q_%XLEA^sR%QiCvHF_y^~h=aWNEfXaUR35Jpk%@i~$3u?R
z2}0s<(IM?Hc4CO-3ChV}F$Ppc32w<e4h<6v`O9Qu=Izpd-N3Rg;Wr;sjwFeq!a)RO
zJO%ZJW@|e_P{v5wz-y>_XZq%j0j;2Km-++^D9<u}1td)IXP2fu*##dP2FsU5Z>ME4
zl`!7WvW&Ar?va;m=!Y(Ts}}d>*OjaTm?$sdM=t1A<!c+ZWw<tkZW*LaGFEJUlv3@(
zGic$SdQ{rHkdF`<Kc1SW+r_*?jSDo7Z?LlXb50Po(qrC>d!-oFx6kc-rRvxGOVwO+
z`iuvUqh;lbz7s7g+Lrz=l*2cWg<wobyQFUjfCBl2ZHrGbClYxvKeDbs)MB2EF$fR4
z8k0>NDPB1~5AC*$fM67upM_^-NGK=L{y>e>{IiUSpbe}1sYq;C<IY|C?|lBB`1$T&
zzPv#QPa$7cAZ<MrBHv6L-}@kD7J<rrCGFy*qjEukV3Dy<E>Q5YbltGrc7&Tav=e^B
zIuiY5Db_<0^Q9Z9M|_EgGV7D~Nd`eq_z;vr#ECjbc3X5s3gIUDDCk1aW0D$1V`0eL
zh$S@@#@^)F-S|$JIq#BzRcJPdOz~YgI<c>&?9}=y$_QOE&k6yAPpjlI6MxkdHy{NX
zfLZ7(hVSH?9pxY(1%E}IAqXhtr%8PZARy(0apQ)xOUBEi>Y|vCg(BL8cSu|2s89Mw
zpTxK*I!s||D$>8SS3D?$fY;1_rp*XRB5~qR%{|d2%N)V*gcS=SO(A*!6%17!!)Mjp
zB4w&J$gak-iUyYPoM^0NBV-)N;6BVlCPgt!sQDJ8nYfV2b0X;?IjCdXLZ7(xgiF4o
zyaM9j?<@ThE4b>f%(*aW;Zd^a@GqAfa?(#(@X{8AtQ)7il##I!+GFe$a*bzIn-LNf
za&3g?1mv=aa?-{Pe7}kxn5+1DN}tuU(l^F0Mf;=z*E5W&U%X?OErstk^PJF`@CA9V
zeB`~`84(|!a1DzMX?Y3Hi~FU3A!PiZQ?==ev5kgor{(4Ri4&vpb!Wa+e`TE2dgJdV
zKO1@NUi=nSt<aeo13vAps^SZN3s+yyy_I|xo+V=}?*(umm#kZb))N{>zSj5Po{TYL
zr`ATDd$Rr{g_7*-*c?=<BH2KPRY*0Dy?BNCj-{_qW%7sgmouwE>MhW+w3qj&9EkuN
z6b*={fw=6|cy>3w<2@>9yD&vDMo#jZwXBjT0HzT7(3ewQ=tsstXkNfwY*y?~>Q=gz
zaRiyEFQFXoRBK%3Ou|~qxJxm#7;5bb{nGy@?HgbHz3YF?d=WqxY#ORE<jVAsEM7VU
zqr-z!dWD*dT|yR*xJW4+x^Mz^W3|iBS&;_O`3WJ*LP8(R_eyfZS)00LF-s?(?BaEb
z@rn_HT}#%?!g~>UAUOUK#v-4^bp=K|%sD6JL|UbhEL!marUU#FX-O`==D7uQl#+6G
zAaUl)Q5d89j{IFbD+D8sW$gId>yrw_Cs0W)a+;?9NI}w6K?2fq8A&8nq?u{@@m~?F
zFr9Ipo0fOXKf5!+jSWUEG`gThL%fjH93ibeM)F1Wl=ni=3T_HhkvPQj<FDFG9yTUY
zp@IzZ-6>ZnI<Qdl@Z<nfTtM52G^>b|_RS;G*c#Zgw?8X%!Dv!$NIU<H{>tdE2|Okz
z98#}3tBBrEm;j4qh(1$dF7KdU@(!7Ai3_0wVo_lX?}-pCp&2PRe~<da&?#<=iUyIY
zm~Um=QvF4Gl1gQR=G~HYMV=LOn=E{lKTwRi3-t*Npbb@L0JP&YQX;txHyxV1%S-MH
zy^0Q85NYZ#GT%OI2&#`llTwBvVwV`6V5BzQ#s3wuj%Mt%j?iZ!)r<d!x`yQ_3n4!C
zo>=l@BKuN{^h1{z7(&O2#$^n7rK|%Pjwd{;GD54dpKlA}kGfhh=O<%Q(qQQebhof3
z)jGo@teBrP^6|7RIT!^{Mbp$=LVF-5r6a>OOT;nOpyZGo(&_LNCrf+SW}c#NkEcg<
zPjCa&By+o+<~i9Wn|9PMLpY=Z-hr^F23>CcKVJV6=DP#oCTmOv34*nb^mOrAHAXS$
z+^+^Mb)TCx>;pjh{2BE;3<@Tb`WF|>(kmUu+ygY%J?txj{}-tZmlK%>kuLm~W=;R3
zSPWhuRpcFf!6^Q{OHH1Fw`SAw`!kMr1Sp=LK%y`jRj=?9VFB}uS6`LW%J1+Ud!K<m
z_zAU(mZFl?%GddhduRwcj%NkZN$@Cr+|@jZDQgo~bS=cUTUIY$gP!ea^x>C0tB`PJ
z>R*__Nf1QP8v0LoP2;IKz4A8KwfCjmukZlqu@3jiYvpTP*V>Kuf{v3N+&->gl&-?S
zQ5tB$Zvg0UL{teqg<yAe2_pjLn~;*;epcQny}@=3(u=ES7Z7gH!~#($=^lHFafq+E
z$KWGyKd})B;a9vOZ|Ba!B+&-#^ALKwF}52B)_!_Mb3mY>u1J$A{d#G^EWF$xUhg1@
z9)ggUfT%j#v9f0wHt{?Rf@CHMQZhNiH18DKOsa4M+8)n>NPBV7EWQFZ7lcXlfS`_C
zqi`9hvXc@k_!}N3l7mYKUZjy-^;h~K^v&iW$Tj<D>4Ry;YakA2MM*nC8!{%MzTgDo
zy}&g~mxAPaMq~uP!it320<&XUcx&Fyo|#yKAM#!pb<zWU_69O3ofBKZgWLnDry$!%
zaPD(`!&*hYwm$<;xY#p`mw<9Sa2{#K2F~;Nl|$bL!oVJYtILsl;HSm6=j`mMp*8pq
z?hw&WFSFb`rskdPviXoQ;Zgb`9v#xJC%KfQ26GWaTIt<c$A1>I{8Q)=t%{PmM4A&d
zj7YnAOUu2+vi%c4cs-p!=i>l6?}t&)zx)*ivv`r|Xv9eg|6rxLH&X8SVL5T}Od6J!
ze~oQ>ga>sG#}LB3_-_~|FE3cdi)}|z)(5}ALLvc?ICkrc@t0*CusNsNIM2R<XP<3a
z?x7gNn4kDT88^?&KHsoC3H)y3>GTluA}15t{Df;^Zbk5-zlf`vf01Y6gqTY1Jf&8Y
zbt_-VB9>p`ir9mep9UQ~8(JpzZ?aW#>g9~m>ulFtgc|(!>?s(~^l`i%u4O(>C!$>y
zG$jcMiqL?fc|bJLgZO6e@92fsa_>ydlFU2F8m0!{cmE=2{t{@uIY`347^!!bZD!B2
zY}^q|dw<!;P+4wa{ADhsegA{&e}{0B7f!O9tj#3F%f~Y1)0_FZk44qq-EowE%W`w^
zwaLiffw|s>JJVK-Zz^s_=J>14LPFS4ci$jte-z_@1Rz7=RGX<t3hX%9W6Eaf`GazM
z|Jml&-=<ykj5`o+yXd5vBy>g+i*(74%UOT_Zbl2nV}HNt?$+}`(Jg~`IASQq3xgJ8
z!O}bPIl|$Q;j^SUORCM;H|Bz+-z(+aMLoPOCQ@(TsqVH5#pl*Z7kzm$TR5(l+xnL2
z+2(!ubjjBX#7YFYMxJbM?=79Fm-~H?J$~uAy%LK3>eM}PU>#c07vGl6TX{kqlJmx0
zzO_B``ckm;YoNf&JR48MR%KhO`PFi?<WqjZ%nYpb>_R%7Yt&nxoX^8HMN@?#sf7v=
z%Mn&KA_y^EjYjLJ5q<Gz-J<0MW_pGn&lILx&5zHcg0@mlii&4M0litj2)d_KwsUo3
z?N=Ie?(uo6c$(u`W0sRY3AN@+`i<~+GKS@3$rp<i!>FcfS2b(dH<ay(E0EDm!UdkJ
zhntUUH?*gYYvb==yCbw;G#v%OL(MpT5cJSp*^)g%4y{-9K)B&=az(|Qcq6`&2{D^b
z@S@F!Rh!;(ihAK>!?#9FC;!a$VB^!t>(+#+%%jxQBBQ@*PSIB@?-%UX;!b9tJgF_a
z&8_GW>5Bf+uLghQIbqy*zo@IkIzX3RIH%xb4$xe43?ti-weF@9()Prc%6|UX7$?@e
zkv+WG+xV=YHFC?SS3aj-&<2$xe`Is8=^34l==d|2Oqfe=Fh@H-+}7CqeGRv<yzj2k
zTrt{a52?4eoD+9*S1q#^ze^hlAF1{>{96E3ncBoxD_;J%9!RUPBzp*GwiAL5NlvxG
zJH+cF9cg@VTW#tdOk~2$=8y98E#Ll2%@^w+=|Feit7Ni=cC*?5T0UT=x8-?Bk65&X
zrDTBQv?xBZ1zRh<DC1kX*tEtnRy;QwulYuQwsO-FuY6{bOS%Ixar6h>+wE@oMH#Yj
z61sVjZ-wio!VL%7_0omh!_&rI;@g8qw}tD!Yu8HW7JQpLY{jz-eRjOnXk0H|A8xYf
zR{ZCW?&h{!HZ#-P;OXTTyTUd#XSwV2?%Uko_=9qN>}}<&v$W=6!wg$?y;J?M9+{$s
zU?r)!m7a<aBy%fFS(s~8^t!C8ACG2oM|bjbAMVY~{+=9ek}X_Lf!i-=d4jSVa>>$v
z9Vt2EO>4uEwK?mIQDgB9PC*CZqo0vY=0>Ml^?%8R%kCP<IZOL)0-QeDTyGCn=Az>1
zu4j!v^QRCVc&~25eJ0*KvJT|lNm{hwDZNJKDsm5AIZyMaXyJxEx^?dv7?;n5g8(MA
zS0-Nd<I~mIH<<%;>4s8YY;WWLzaJhWpUiJalG>UWnVs(3+vC8TAfWl$UfO#k#A96)
zZTy34lwV}!!qu~&o@%u%$3z78W^ge+D4ADO0@&)!RS6&k(wf}sb#Un#zH8wnPu4GV
zF%tB`J+o~1`waZ8ubP{iyTfj0o|(&%GDYdTR3tGbTjUAJXXWfZ@HQ-(o34Gk*v_9$
zwuS@AV7e-BYM*R2dJnjLZ+}o29BQ+dkJy=KgqbA9f(Ew0JJ+$CrGYd1k#@bm+8uaL
zHKknId!QGGQ^-0Q<qVQ~&jj~B*|%rj+o=UxacF;v>Ey1&wX58+9$nO)>i)$I3a9&C
z{uTJL<UsZ;>Tc^jIucYaa;23}Gz5xnNevzAO>(%!Tk!8(gPYW9c6)@$(kpPUGHUvx
zOL_!U_2TjiZPB=L-lV)RY0-P5yNajvWaQHYPY`!WVBj``|LA`-lMRP#1KFcD%#otu
zA1gVj%w+tF#JgS6b=duJ5G8Z-DVa=Cn~EQuWXT&NM&+Goi9?K3-87!QzS`JuWVado
zI}*g9q2B#dH;j8GZf|mw$)%-p>}<4QYOs1KE$}jC<rBJ#&v3l{fNhQ1H|&<ZykNPf
z;WH_{l%u>{{Gp^9;0W;sBq!<bMl}lP>~I(QYad`eApk|Ey>O}Vfqu5i@jTV%uCMji
zK4rRE`+BzN9auy*DdF8>WxGF`Za*^WPm_bo%bZa*^3P5)ogd7$)+{yV?A`KS{*AKL
z6}6GqSXtdc5l&nNQG8BcTkoyiduFb6PrmIxy&!SG^k`i_=|36-skkwz=2f)p5iucE
z=Ng-?uxb)FICD0lYN4W+&nGmyIPPGyQ)PE*`a&1B{}yV(AZr~}HZo5O{ON1ws?(ov
zTA8QkGu~lrj+=P7L?v4=LT>J9n-8ru*FGhjBMnjztQAiLVjrAJRNh}sch~OOsBSw;
zJO_IErUR>uwfjWXV4D_(n-WZ}y>!z7wZ__g<61D2hS3HI=^E(UUca@wZqHto1OjpP
z8A{uo<gTjN*{8D(BH}NDaEpK1PB-jVt*zgWz^Ya1TP<2-@hv7?#P@5h^#`_UwRgdI
zCGgEQdPkGhFbU%ob<ehWT3(Noj{ewOndrUyx)Dq})-yY^7@>Wh6`EcFEpO10^2bW9
zw+E@6LuhoJmS&DK9m8)MTP_P}vzK~%YIg1oIh!@^%zj|Hv39R^we<lbj2<R|=|Rx^
z4bXhKXk@R{%<fIiSnf^VY&5K~g6W(<2I>>Ccx=qmyf$i#-=TT+H#G)pcWcbfeNk%@
z96RU(KvKPu4e%d$?L0C&UQ1Jk%|IH#1*7+snYeQ=cqj39Ri%oWJsLUgzLvM@kxFsm
ziw*&+c?A8i(xD->?6|NlDvr$m|2vl`+9~HAYAb}>=(Ur%!f7Dfnr>hp!{kdH3(9gn
zN}@k<V#Sj`&lR#yiyf6@u!H;F-?G8zTM8q&BYT<ty#v(gMSbL@rr-Txy)yvWBdDGJ
zopBVb%LWtI<wmnl>-xQW#cYIY%Qf{N5Y!s3oV-Hs-j?7)nF|+vC^P2o+3~u+*81*K
zIkMqDyW)Xj$z4`A(#VgKX;HA{(k0(<@G*{vI)e{6y%9r?01iYF!U2<`5NYk-RIo>G
zvHa3Mi<aENoy^1vTZmWu#V?c22AyVZh;S2WVcsAlF%i5A#w-$xx{W(~oIv308ySN&
zfmXo3deOCZIj+?bd|boKrDOPK4xGlF<ZC0_h3Eo;HBo4emaIizPdT3x>K4m)yj60=
z>&E1}%H^d$GV{h$@XP%}J)Qe_*b}(2S}bD8L)B#DCiInmi7IKo9~oKn0*Gs)pIq#W
zzC0}jtLtI^JGkbFgd>s7xRi<nOSN$@>Y3{^&dANCpL;dtzEOZ%_sk`YZ$y$QFokA@
zWJQf?qY)h`<ED=6tKkDE>-_^_C)xtX)&@xc{1s4+zxdlozwpcKX`YGGed6rD$XWhC
zvzRntJdUl6-k$fgW;1EsgO3^85l4?ST_<j=uHozvIl-DPl_SV~Z6@jb4vilOW}1+C
z7r6%d-6VU`B5(2?)qGqx0;9~y{qq?=USAJ8|EYI5*bdt)Qma(GtBW~T^&JCnjEorN
ze_E6+UmyrKJFxc`l{1&nGF!AVCM^+<>PRcTv*2=~GH2fvcg>><hE*9j{xdTEs)tLZ
z(Hm?r142bHD9pTL<+{e~H8CNL3^u+YO3ie*_f`<8a5-~XUSF}g%n7aG)V~xXvzGLc
z3rxTLJ=Vb{e6Ep0WO>RiKYCl1w9HLy=A{}+Ao3d`#THJh<X8OJEBO~Pf*9RY)@_+5
zp=HrBleQ{<Rlyp$#q>+p!8rG3%Usvds#mrK<QNoi)Nq_+BNoQX+^T$pYXPO5K4un<
zjlIU5xC^%M!^Qik@p&eAP)t{R#T>H>ZL{_7X5ZPvv8<7vTX|h2xAOj6A#))K9PW!!
zMf2}~=I5$O`@<x^rOn0na58zDS&61=N$-bo9Bv9D6Eqt%&&gH1Tf|@_l8jBP2geWo
zIuo=%kE_}441%n+f8e!i=1T+M0C7N$zZ%0qW1#s<7NO*j659a7C>sTKso!=wGas>A
zWPHu#E=KARYo!>;aPobOUg$~RBGo+ZQt#s>Saoqd(z`$D_k+KAk)8#K#L^RT7B7qh
z7csI(=;2RildV@1aAgKKoC^vLu!DVc#q?HnoT)e0XIekCy8aQKUwJ<{*Y*L~v!_kI
zF~i4a)Xlv%Z`zX`XX|B+>CSKMUiMf-I<;Qtx+%v+cqvWEUzrDR(6%>U+@9*(Zx3>(
zI~kpnImmH(`|h9@&bWzLj;+=Qro-tEMGaCxh}V;WiwuhdV#CcwjfWU=gxerBWsfB5
z#R*wA|4cGxz(d6`PlA3o?*s$cCMdWUi<N}8GoI*cCEdC><&RvP_XrN;;yCZPD@?!m
z76HAW9=*02&D^Bbj6%sQywS5u=Vm>3S<m&J>bUL-%OAa7bLU<$+pT^D4YceDAcs-n
z5H)L7TYtc&`i6@$v)+MYe&J#eV!RdqW>#dHMw?{5huddV?ZYtjE^!ucI36;e>ZNl*
z?F)9z-o4~xUrGva;yG^*kR&znr?E7n<({1-2v5$<9?Y{t6rN(V0tN{GLyiYra!x>>
zty~OceTadqt|w!k9rNtbwz=tqK5V^XY`d`8Z2ig@5OANpE!Lw=3=;GiuI`1MpEzW>
zOiXKHOe;QzNC|9$-d>jH?eAtXXC_8GZN`(|7$E`%DeI3JbnrUmMf?+BAGuhiv(YsD
zrM|&^y`cMBf1rZ{arT+e^G9h}02`LSr{Vbr`@VZ9Yl6*BRA9Tjs7x+d7edRuU-kxr
zKa*NGlVSs5VE89_%p}u<@?tSWnVL%rX8?+SxH;(mihPM92mnx8^UhYQ@jbs+I5(en
z50mv*B=M`38T_TyAN&#9!k+SM36+qR>HoRa8~ly*M{L@IZ#L4Ycl4UUbSWsFula>*
zDwezNAmi-isCAG(HG^VS+$Jh%?wBOg`*l~eu&n=%zWJmL-A5N;GPcI_$}1AtZ4-$%
zEG<_?WY&lTFV7s2hn910{uqyRRB}%)k<;j3U51no%&p{xBN6L-K0*R`U0U$Gl|9eh
z&vl#?x?gy`Z<jCi98^*Ix?=~#IPv1r#W=QNVj;R-wB@W`J&E#0<yCSj+==E(6{Gl`
zgsl!-%GGnx+%0<5D32KBcSPRYCDl&#PDS&~C^WCoY+91NtSqNlr+$a>Ap+UD&~9n_
zKjzvsTV($SIV599z!+Esk;yXq+O|y&smi-SuMuUt|5vwXugtst!3GdMmJpU<@nbB$
zf5Mu$9h8ksfjYG_*o1YPXXXEOuQ+JYD82gyECGN$tjVTn<g<F(2Cec9c}FgtxO0&=
z`f<$`ze{6ZS9kY_%+4<?V5-<{9%u!FHLyI6`p%xDLMo!}NWHZ8i$*kiYri&l&<|K}
z_)dliv4&MScVGM`@{|iKn1HG0USGD{NyE+@<htJRBH?17z~bs1$aX3Du?VEp`B{%J
zNkj$q23V0jGuqT-mH2o)1bY9EPN11Mv;VHkL59UGp%Up=D#sep&ltpvc*%5s!-3t#
z)=L7M-Udeh&x6PuL-sOa%_=))xIsC0rq-!r`qCecWU?=l5sdW4w;TQ0&m@hg3jP=?
zJ%N9c%tZxb^b-Uh0aYa6T@qaC^)EJi)!WlX+;n3$f;3pi!TW3n=_O2p%?|$qR{!lN
z^v=ndqaV(?g%>bLsAHnd!fer<otw2m%sHi(IUJrl#$YWV&gDd(3T=t4Z1#TBk8_t7
zt;&bVS6fPPC6LW(etL(i55o&>VOXQ@&1zdtsCPCT(rL_I5hI}_x*WxG&+;5+Tz4v;
z#Ax|cw)D+<nt$hFbIEsrMXg|=ZcNj$+m?8h7iz`e&)pz*S%zRahVDO4w+8pd^Z_1L
z?T5AN8st*~w6?5et6&r$sr$ceq;t2S<NTJxF`ewGNtpER)|1J*mSl=2#`(dY27}@|
zd`tJR<DN2NWcIfSilmbp&~l=GZvtJVz7-AhSBT}Di8(PPbDwS1+duYu3T1`SF(j?I
zoM;{+XzWbk$eB!<-n*s676aC1NyF5$&@u<H-oLW5fz}-V%3^ooR*XT|N8*ioT6$03
zb(e;A?Oy|)M!2}Uo&;;Neg6<26%q=oL7WVJ&vE^uS&!6SX7!Vym@19v5k0Nkl=1A5
zz^L9FL2dB+!?OC=tr^GNGqC~Wim0k}z7FYpigoaiAByyvbOtz%=;_!O{EXoZw7KgE
zQp#mL)72{<$vfrunV!KRES?_rawq`J({d!V+|g%WHZ|CA5a`r1d)@F?Q2c&Gs(VKP
z=BC-1XL_FhJPh3Hc*~Ka+L9j-8SXL$1L?E%0{|GGitEt~!WFzC+!`ZQfxZMI<sHwY
zNhV}k*Zp1)B%N|ny2A41^bN8#@z2g1qc@tK(7eIMCIX*-e%_RIAm}<lnGzW;aZa?X
zWCl{%G^F{~k0sl6Mjw5pnGM%9gVtwBQo5Y}%*cpI&;N0kVvkRF9RbMfRi^D8hJ_hx
zWB!d~<~0W-v&4Y$499P1oz}M3*1C&s&E@jX?qz^wcDi4aTCj*8>4R`<nB*k{;Rf3F
z4BvEjOYGpUyFj=-;l&Js`2UgjD&A5{XKxv6jb1#OPdklx=7S3eH!S%i?M!_+A#(Le
zZFOYMad#iM;f8I&wzI_^KAkqdJJ!yfTF#|_iK@3Z+r4k275lc>;o*@52V_ee^zNKV
zTgQ)d3TGE{ak~-E-qgfgnAHfK2wDsuEfTyI&nI*5B{RM3A(d|N{CqxcfN;C5-RpfZ
zit5Le+r@LT`Djx;n7TM^Th9S-viTJN06+jqL_t)%^PC)pub$oieWO2pnLaRa>{nSN
zq^-0V%^^3~Wm}@s;MgWQGNgsZHI2aAtJtl)BrFUbuJxy1rNcCcFDohsLb-AT=gCO#
z+%StBN)O$$;qDzmT#(j1cctlMpY3OKi=0&7thej$A!RoSnY)_a_b)P&Xf}zOH>bpK
zApXSj?W~z;8Lh8_?z+v0ivLXd`V+m34v!)?Tc`OkEy%vcv)nymCs-YZ_HO91|EUp9
zpVRIx{%OG+xi~BUt?ty`YX+5%m|p&rLPmUfYM*Jgy8ni85%)Tc<Egq9`pc%1217|%
zBs{sDInK45MUlnk5qSrZ{d&(ZiXI>p5y>inhSG1@{NelVz#OcOLTh=>$xpPc=Cx_l
z-6LZbj>ml<oK0u0?~C`ZKK<T)Yx0`WeDUONw(+HQv;BpT0hXhS1H_f_>jYwG;<QQ5
zuSbH=**EK%i`#MiU0SPrIuL4mV1(QB#6Qwy={H+b@AsM$ZyYPwi`w?wr@PJG_oKA;
zeSBdsR^eu7hS)&lRAWlkh=u;ncYEQN_(MxR!gQvQ&fe%W$1WWM;noBZ+MCZu$OQWY
zh#9CHZafBOL8j~csNQXVmZR`)B<@@@NT=S@>Wsgwn9m^nH~*~xjgqhwl{2b?UiWLL
zdR8T7ycX~W8({n2Yu*00k%W1*_-lLnGk?bugZZypgQ*wk-J~_A&%RdH0rT_?hiG-e
zs1Zx?gKK@G)(!3xgd1!cU2XOm9>y!;BmZ@CIQ{(j9ByJD6ecB;$Om!ZT%hHJFHAfW
z^2eBNxV{@1MaRiJGm3h5X#;13xdlKfDlDP>?v09;Bj<Q>*JGFH+`!R#dA0BFo-;@O
zDJl?HCz$&rCjIZ!(%Nw&?c%AKY+9>Dv)6?^`^b!$JvQSD%{QU>#z)McaJ9iYD5!PN
zysO@B-k^v1s~o#{jx(hBJKLS+C$z}e56JVTUex+GNm7dMo2bd=`^2Yz*u`iVTh!Zx
zDPYiwx43EPUq<c93p<&{=bP>7)tsC;6{@jTEu^^x!;pMT5Fg)3t2w1iu=`ATJe#xi
zR#mHCCwn9b4?eTV%Ae4qu10t6>Rlk*cGXpujmlF-z!5ptuqVni?}TuZ=74}vev4w&
zm%oRnblHo=-!#S*4q{f%N;?#?M-J$c9YZ8$F(_2{tb9Wk-oa>K{K1=p=jNHaJX@x3
zHTfG+Q?-S13emUYp6J|!plAzY!bcesv4nGS;358!|8dR#p>*S_J=~H->d}^t)F=G_
ziG$RmQsvZToCUQ+|M9a_AQOCS6XG85qErl`pwy^Vl$yiJ#pha19V!C=!S9(TVlhyT
z2yiTz`8lIsEBRxcpwy&a@+>+raZyuw&ZPQbqtW)#In)93Q6G6b^G!Bh&~d{tp(uq#
z-zy-TVfW<@KT<8izv{23UBtM=DMA7;@oD=zWc-FmFrhVpl``-+*2mi(GZ!LtV+)Y?
z4(D5q%MxC*S^BvXdXIc^suFyjqrXWV%=k*Arge|24~7uWNqy2BX)p}61k+>Og>lb|
zwYu_5;A`=3m!L&dj&CU&iFi6FbrO&6>iT=MOb#<fUsYfVa1L(|iC3c9s`3>wTST-=
z(7o=MH^vz|3;~gb<<xx9F8pJ6epVRD5E-^G-|%LJQ~_4Rs|1fFi4~&Off#Z(?|82S
z@E`iR%d<kuP@t@<dEeyuVNxR|<E8ZUqGDpj%P0<?m45BChPKm{__0GzLd&A@5r1*K
zeRz><MA#DLv&^kLi?l3dcc4zWC!UY+AHI7G%`Zj+jS+iD%hW~%!yJW=8#_Vxu?|Fi
z%1j%E`0ESJ{=GSr+Ww2{f8yu6gZWZ9vxCBm!9=fPVt7ep5kADU3&KX+Nw@@hsAB4_
zpgcHl=>?-eem2U9zoANG$aj)tlLNK!G)~f4I?MNhGE?hZjg6}AiT@M&mrE?7%cw`v
zo<!K}a6wWC8&UL&K@VCPE_D88@hPJX8bF0s!B^SXn=*oc1{pnkmNN1jlFD}UnxJV`
z^0zo~z>5K9C={Nt7mbi(ns9YN%;76)?7_2AAGirgk&@2MKMPV-1r8F_jR<!W^KIdz
zs}ORyC;j&(mil(*Syb1kF;kU)N=^YHB?#bzu~ZK3LhORvl77IvQ^r+{7!s;aOn1V_
zWegPZPs*W>6#rQ@KY~;o<sJf-&~EYw<;2=9dyg1Gs85v};qxTr(30U+gprExW!zVA
zPsBYLmo?mzXJKv}Uv=J_rfaz;L@XB{cX{!M(2NQ{%Dky}2r6$q{Sy#_Fb4e16*^G<
zrX8xyvO0yKY~X#%xG!iv>2HHM+sxm~DYuX!N9vO?ka1zorB2c8rR(`D<=C|8gN&zY
zv%VPCGRoXa+qtL40Qfi(F5|?M&yRt$S&gTtkyx-plp4?cy}t6bqGh4cBK1Kd*t8T~
ziO{8(AuL1DvM}nE{0Ml1O<PV7YD|{$EIt<E9V|ZM;&Gs81wmew<H#(?Ib%=W#ipz1
z1bWfh)U|?pvR7cwm9bOx$=u5BhOtlb?kPm*D#|MNP`(3*A>K-A{Ye;En`aqMRj=^k
zzt>;+j-{_lKg3f8-L|w>){IK0B=al2+Vn#O>5(-F-HDHF3Oy=1TgAPFwKh#z)}OwT
zGR%(xYoJ4ycWC3(Nf05SM>S`{r=UmKW46=x1oz|}{1Sg|MYl5Ui`Bg$>W{XmK2hb9
z_KmOp-u1s`zOomH)QGiM+5mYH6G77Dpru>L#U_X<VOFt1yh$->F??GhJ>ie9Bpe)g
z6q1I?-3ggv7z#Z{X_>$$yQDf{ys}_+NY}$%oA)Ytn!%G%QX2(GsUiAB?eLP{iQSVv
zFwQhfepEP2$%Z`4mO5XK#Vbg;AremQ`cR5O@#qU}mS=b5bsLsZ_rw^*k!}YP4k<`H
zkI*v@vE=gZ`FIRFA=P#{u?zj3pv_Yv^~tUeo$Vw=if&FJ++<uHUrsR+zE_AhqzC3q
zp$uVQ!<Ycu<3fP8Q<AxiiTn)5R2UuY9!jDFirJ2b-`)pK9E`R?^X)XIJg+Z7dfKdL
zK*nx|6!G7g-*KLu-sxF2kMx`cCTj=XA1|d=?k8fT5qU?xfi8w^9ZGacB4ZK8Vk8ry
z5Q$W(kVVdzz~{Lqh(5$6h1TRA{=;%lJu47x6+c79MBP^>mM7ekm<KA`?r>d5Y<JPQ
zD|qXZF(fs&yb0COJmQNrwPV~>Xh>*T3BSS}(AUSDyrpfbA3~$Tl(w$}iBZCrZ1g+R
zxA@);bL=gQ0d>iX_<2k@MN^M^79^V>LUC5(VKoNaK-xqamS9(M>SA0Jf>kAx<qxS`
zM(PQd>M!qRK87(!=AV_A5VDy_gM^k*Ln-wW#(652F`+8fn)nbi1{f$8+Cq>kx*<-N
z^9VPgZ?$m<Flm?rP+Fo+COhbRp61B{lWb9=t6oma=4q5qY@i9@mt?)E?8UrMjouSF
z_20YkKViPed-_$Xditn#Ty{(Qc%OV-7Dy|cSN=fbLv+0_&Y21UbuSz?u(kYK91upJ
zM4pve)*kpu{+@owjv`Yo4m`3;v3q7;oj0>*Mh;V(Y)8n5w7l~rV=tEsqbxYZcx3~~
zYa^e?xS)c}J}4u;8=`{bdD$rB$WZ5VKCe}7$hh8-fjRgAa^t8)zO{^9ya@E&N?fc!
zt_dPdUxnZ2zj&T|D2TNGK+hIX39G&M-XdiQZdW8N&F_fBe@)RYU1B-<B+uRhDrhm9
zjMsDT<=h+N-mf4k0-Ea?SIfQ<qfIW<;zy<5&=G-;OCKyah4VSHno%0w%OaPZSD7*R
zz4Imgm43ibWAy3nbIZoaTP@FAI?x99ptlo*8xBM2nrFQ66#mKQmCVW;Ff5SwY9MVP
za-VT9SsKoBt#kmg2SpM&5YpVMND*`yu@AwRhJU0T%N#xXV$UMSm+Ky(8Q~mupe?kU
zXpYWlIi37$M)=z}wT{B+{>=qDa~AP_s~F)Pf~l{<XYwM~&YfpC4px3a8O5u~hxWRn
zReB|gQ0!mf{SdOGnnC*B<t6hh{T+n?&VWdJ6Jz?a)QKBmtWbHfk;pj7xbRUnObL7G
zo}D#3^5jL2fP`xjR`%SSUcOS|K7&5{1L;%ACzpGrXO&)!ap~yL4E{vFs?Y|$&86#d
zPT_?_yqk;Tcs2C}f;2;a{QSrsJi@b^5xkCu-d}3l<W94#JrbQ9YpL~2GDBQ!se9&b
zi5~tO@i7l-k|6up=LBZ&dsu=ZHagVO!r`}H(nq-zEz^s6#w&(mXw=J|5So2xR3YYs
zvGObjdc}A}N1J<H!79H71LWS&=+CHmfV$wIf;Mtp#&r+s8@+$mVq?tEi*L%>#h1E{
zxjVG-Q)qNAQvbb<C~hZPwtEalD30?nNXO#4a!&Cg+cB0=?q`%+#oBm7)+xS(drP<{
z$V{S$dgo=#{EJP;KOWOpj^qKXXY=rqKKd@llbnbBA0ZOKM3sy4LLa4{3!fKgSutMF
zn$Ia2BX4&+)xMwLW+1dIbRn!(ByLVI*5M9$-thQ27o)uoGV@*n2w=l;&nrORmTRsI
zHOWsW2sdMY==*%=8@Pd-5^(>+z&2b5`UvZQewc^3AVdpx{`t^U7(nwzD)|meug^NU
zGj%J*ZWn`aLmh{DY=P!6Cf*B=6TTxSA4Pl8vH!R0e~)m3*(!wFrQ_N1DUICRN5X0s
ztyJOd%gsgCB%^}|YTXT=8z4`RC+ozl>rBR$SsKC8-O$&5vW2}7?kj?u1k;e+iro~)
zc(Z)IUuy3@)7<(K5N_j;njB%IZwnJAj0NdRrJs>|4cQU>bm=ckIdAulpN12$x93!E
z3&u2w#HA2!*a%c04&1Mn{HT!kmUXe#PsE_lx>GN)`-PKnp(%oaI>R!pQmtNnXFgi?
zja<PV?SgunOx$H#y4zl6^(OCJ<Y%8AWJp>85q7oi?QWcZPL?>ueK&uSX-qrydglk3
zB!2?;#<+K)Va#M3b#lT*OYR=a=8x+Zt2gy#r+*yLYfoGB@8cPtbVaxiC24z*_P!1~
zk+=^q4b|WIj_ZqPKMQxL5`Yj+1ljX(r-Xf5&!6BYMh0Zh2SB(jMGXOi5Va4IvF4pK
zVl4i!Rn!R}oj*PUoN21nylEs{_W5Gb+pXbmds}y=abKDg-d489E(0zD-DaO{##?5v
z6#8Sy*j0c8!nQ#m^I-5mC+a^Sda{vp>8JCRluS%(Z)?`wXXOc+Zh|TUOmdXrZrrci
zu#Xzo#;znkSg)f+Q$g6dJp}n!u_mrcN=c`XWRLRPt<7p{a1TCq=pVNSRHvKUFClP_
zdlGG|{K1Mda#2WrwNjjUde7Z*TD=*q#{*6zU~w}<B2AFC-3ylJ%WkqNNuwHXyr*9a
z7{}~M1<OA;cAdpVLPmmcJ2q+OPR`rj9w65iJ9g&4Y6m1-XX0y>jQDvc-I2t9dea3g
z&=~)6*)JSVF16lhoH?-JtUt2TEWCIT`3PHiLUqT%qs>9<rZ6mAH0hR3Yvsdf?k#Wh
z+qXF3(r-g&dj@1oCBE)n_0HxqlCDGnlho*npoMmYxuD(=b%(knB3Ii5@m0$D`*kuY
zXie`aGyP4cMO}%grh*oU`lKs_0nGQLJiwoUmp`?LL9*VQd0jSGdb{KgMB;JFx;;79
znt8Q9Q1o5Wtil<lWfyC;+I#Fce>$m+u<|=AJbaj^f(>8w>V;DXmR?{Tkd)K(>$TPo
z-J~D^)a4#Add*C|UjINb9DRKy<1C%?q8W^}{c7C1yOb>XT&8H{dYow|5_=p}=M`q4
zLN9KjsSBqY*-V9&S?>$9Y#v_G7vJg=40vGVPn<~2!nRiJo#ZlH1}$%5Y~=Jpybb3_
zLZKe#x@L|T#-|wR=1uLIwiu-6hb+R#Mmp;-HFMkAwf9tlrFWEy{@zW1D&3jdy?8sk
zc+^<*eylzLV|JXMXl?SpWpZi$nZT04UFD87y!E?x>fR9maGqtt3w%3wEa14af=2jP
z5$vG(&p`8yxoG`uZA3T@Ou|#x6#h}y!Vs$W3GK)wu|0dgYj@|~0m5y$L4fg2(*1M5
z9xR3#8TWy<?YLTd-M)jl{`xcsLE?yNT=}!Iz=Qz{4#(Ur)n_lQ^*g@@;g*5W9)C$~
zGI_vJ6O0})Zc|7n38|S<_xUBfBnIBrS5{}|?y%ba;rWcSJA<~7VVc*yAquD7B_-3t
zpLNq|)tUM?tWNe>d3<#{+Q!3YTWj}j&4n{%GEeBP`#caY6J2NhlKM>Z$9B&@D&u>n
z<c!RbNv8eX>CU?S$eDGq-Zz%^^)1iZ+THZB0X{ytBy%u9+aK(uThHd+s{_0i^NF{=
zXx3t_{mV`mY(-wl%9cqEH;_2~0ioCU;&f}>Q=7H9%kd!-0Kq0TZ1W40{tChkUuEg5
z7;nT-RJ<@lPO4hEdPR47u-=V}&zsMJ0ZUi9%bXC5mlV$Ty|i6TR<G<&hqG={JV)}x
z4Yc+z+CfysLY`$nH9yH7pY;UUvwCT=H6E0;@@p~mBUPaLjORX8ZEiTa+Z=pdj$}?U
zad(#FR&8AB@C_vvMfWS!Wc{I2&Dz(EmNk+$oRc~0xh8O@IGXuDwYl+G?N+p<NE=Cg
z_~3MZ&B}O2cYL$-@*#psMg4l>Y!4T#C1PSZWfRNvemB!uy)vCOOXNDel5|HhLK1}v
z9iT|Sd;)&j(>j0X5r~3_+nkC;d0D?@I@)mXY@>6p*R_}CEc{fBnagXvbx)m|YkUVJ
z-2O$`)^vBT*cm)n?^W+G=?7A+KLQ*%$uz$@*I0ipnJiZ2+#KKmS$841-Zng~S{Jyn
zJWGg6Em+&<Uah|Myr`#-j2Pa2iC_KTY;EJ~b93IIW#4}~@zgnGsCWlL0Yb~@*u_Am
zXn75^%(2%xu1x;Gz?*)1wL0?&tLYt7@V%os63eD4w9H|WPppGjn|kfG{<;I&^>8j5
zy5cH%OxbW(1m4CavomvFD711X7qbTE|4m0g-%n}H4YrmCxf6-IJLn{v4z4!V9RR{@
z8g$$v3Fn!fw&_sH?bVqJ=gMLJRMU_DNV!92)V-)#4s$1p3kU;?PPlVVy|ez1Mx**J
zJt(~&00$t4HOrdQoj*AJ+=&@xhV!fFUxi6YlVvj%L$RPWx6`tf6knEgO{<}=Uf!AR
zJq-Hux%lhxtVF1m@sVItqC)E3-3B2!)mXb*Jg0jjTJ8+ZcJ@UHV0HDWYa{YpIf=KA
zXoLf;^UEQ96Z3-s^OEAZ<YueS#cNg#ro$>>a4)gE?rpVr!(mg++1s_Ik<aT{06mRs
zn;~>j-}G9&YK@PY*>e+^m>W-g3z{!N^Po6&)D!e(NT2G%KjwdrVZPg6$-rW;p+Ouo
z2o^o~=3LTy098FmP<0bmFb>Lk=F)=Wt%~qT>~lD#D$&WZf)r|Jy9iH3qJr6#LWSB?
z0F@RQ+FxEV$8K{oqn~tq_Ym~&Pjv(p6py`UQK59!tWU1Bz~;VUV@L|Z{^WA^0}0o)
zqscoK74yf<dHA)4&Y7}INoRIp#<og5yYq{5;2$k%(4BbcgX1N##}Z`251gj~g(o&c
z?&Lyd)#`F-<b$>o<Mi&nsAMerYN=%9YF6_ba?px(=d_Zs{Ei}=o_k+028Px%8t-fc
z_=A%XKr-}THy*+BWwhrMUQo^o8maVFJP;-N6b4MzmeO|(5pI&gzGRPm-pW)yD7nQf
z%^Cp-h#<J~p-jcDfN%@5I}mPiw8aTr${}=7Q#oDruv0O8oI~{j+^nrv|EWI!$(G=c
z%=dvlcrXaekr8Xj_jS(pfDVwdlFc||qiPwys#0418&{M&qy`$by;VJ{aVK#Z;P0fx
zVIf7@$c>Zs*sJt%xTzNRzQ^&WNk;?;_wGu0=}*J~&Njq1(-@gFS3MGrr2RppJF+U_
zKv=zL#1k&HXxH=IQWYU7byh60Rz2LUB<(QNbE13}v606ZP30rJI}Gp_pqG9IqF?TF
zT!oOd<UiAN@~*{x;iRD0|5KBVA5V`#z*D~A9~61P+761ZIBoyN>1Pff$xU84s6?Av
zfp)Yg!a=S*Yq7WN^R+_UNCRt`p_8DKO~-duZ7NkJJ|~+Q{BT>4Sc@~xZX1i0@y~$M
zn(L;)Rx>DGQpk_JCd`u9QnwGAr$_a~0xgpg>%|pg?6Y2G^wW;-9SeUt6k29u8@Xap
zwsO%d>95Ve-b?0*OK6!-5)zZBDH`q2S3EGDcbB*P)(^?aa8ECd>P|TMhkP;r?1;<+
z7Ahjamc_dFe91P_x4VB3cQNP(fnfCJoGW?Grio>O7rK<wGB5XStI)T4zhxXLC+EuS
zt9_vG-206v3CT<_6Q;T2Mslkj&X)b>6TE){Zp;C0#otEC-d;5;>G`3(C+iqPnKF=x
z0Tzu{O5*Rr)$_Fc5j3>yWj4<ygIu!mp?t+%gnnR<jj)BItDb*WPRG*yAy3fDAGh5!
z;?(&oEoy%ig<eq5mVFk4Td8Yx|5LW%wm$dj-hj-Bi1orK39(XS9pKQr>S4B37cp8#
zI$=zT@0%b;p;eA&s%iTs18Ey)WUkT$rW?09N%NnzF#qNe2XMy@n%|<f5|O9=&oA*4
zvMh`I6nOB6L)1>!WB=Oe3t0f_QrwPtWve0_qo}A5)WjU`C8ZZSe!RI6Hg5)YAj)!N
z2K1h(LOC&|+pmSXw%?flAmk@{FaFtk5J~OujFml@lreil{4<Ov%)WJZr`x+X8!)z(
zSn{Q`1OOMQL9Q6@3Q`9djjwjPgTH1&Ql*>m2AS1TI{sEC+xyj2uX-&LC^i@JOV;n`
zc7oMe66wVx;N!?m;If(P8}-gFILe)y$+2goy)V>~t(W(}xsQb9SEO(hJ?Ve0mTY-#
zzaBNY_Zsr;C{7_Qpgs8H^Bd(HvUo3zw_eh$4Ia%##g}AY7f1_ojFtteHY7GETt2-0
zGe+*PEJtKSN*IM8@l&Au)lUTNsNrcwCW1s6W~!ExfHpULSB#4|2{E;~k4NpeCrCI&
zTxyntn8PhXLa|=EY;G`fbJUE83?{}aHZr11&1oAB-qcvVzdz&bS@DWQeJh^idju?T
z{6ihj-QCQT-)o87na*8?VEu7x&R$xvi&tt+|KDbl^#^ROt-Fxyzz2?Ja<3WS@NbZ5
zx)WD#G1?lzdURX#P5gVZ`GzUWku#NG^{7jOb)Bg8IlGNBJ${4YDneUv8Do*rZ~jyV
zis87CKTrz+7l`ajZ?tXCo9Q*~%GCU)7c<Vmu^CTW@#I~CTx)6@j~sA#9M_7^jx()q
zPB%Bc(wwnZ7QEaM%sCbG?rjH+Z~8qvayW`(m%DCrv}Wb>Ozk_BcK+mCUKmC74a0kl
zA6D9h(+YSelSAzJorWZNgl2~M4A-kxLaelr=FZcyX73Iy9|kQS;#tmKIAs&4T+#A(
zJN@o&asvboODyk<gogVcfDS&uVr1ZgU$)at=QNswhsvGeg$`Qp*<{P>d-d*L@r9N5
z60j}&j?i}@9lyrS^?tXlH-kU9wU-D0C9*h!aJ!dessZE!u^6S2Hg=Vr?fz_AclOOX
zJ5;2bE6Uo~<#w*~gKgcpYmC60>>6872hFw^=fPk!8F^Kb4L35#dSs9`9vCueuq<Qz
zh_sOlY9G$AqGiq7Z%E7813jT-nLo+|TJ`2ZJDl}W1Kz7T%Y(6mp+&bX@qXj=F%iec
zjpFHvU;Fe-yL-1Aj=bIXv~VtcB-h_I*vbj;oFSTRIKV7dl{O195JuGPle;l)#eMvq
z9a8Txp!K&-huf~#=Zuk2vjpl5m%OO>&P6uaqrI7T)Y#riN$EU4<1E^V=C8Bx_hC%g
zI>cK0e|3rdtKDvE>({T>Hf-1c(j7*y)3xhYvF5iU+^QVyFBmhkPqQ6orRk1-#B<|K
z1gUvl(a!E4x%ID!qcSULDFa(~($a?}oyjjbLzb$fQwICV5N?d23Z9Ifs`b`w@rM1H
ztxbn^+ubkX0dl(pVKp^(|BhoX$(Tu_lk~nRBUDHyzq!~Szf~Phwcd9rXM>2@ziXR|
zeKVeErh`9b(v^>vOAh+o7-3jaPq)9+i8N@#+plMvlOCZGn`!IA?I_rk4T|Swv(8GC
z0ktSH56bEjpC={F@npC{_{0}7F$+0k;tLo{jwRKj9~p5~n60Om-dDEWrMjIx%J(wo
zYj*pqy)c|Dgq5rO9B4I@`fwm+LOE&)u}E-mQH<%Nd6_w9YOww(o#xgv1AH?`33OjS
z!gtnm_r!oXMy{zxTRaP!K4V;8_A?M}uM%?aX@Z2a7U`C~2ODbHvIc*VfW0|w^s{-#
znb7Rw3xK_1obt|h42vxyA+Amu3oJMayz_GA_@@f~_>BM*BCxcCm7M!@y*u*>tsc!f
z!q$`CZ_>E%#*#Ju8P}sN#Hqt*xK>LtmlUk=Tl4<NWeIvq^q{|Kk<mGh?cF2CT49?;
zMl-s92xtq<6txpwef*_1&ru<{rWR~TBYW?GV=pDcG`UNAzm|;cMp}695^L#yfN-0D
zZXQPG``i(ua4bl;#kw<gyNe%<jd8VTk9^GXtR+O{38N&qS4$@DT<jN5Ns8g>FsOW3
zAlIZVl2DPWY%6yHY!MsG;GPC(6FqVE>pIR-pNKMn-uh&cl-^%*MsCPvvWJ93@Cs>&
zo|ZqvJ3gAt_y=)s1}hK!%3q|}!FnUcYeEm!wt~*LFk+6<9gzm<@Q{{C$Ma5TdH+Uh
z<3Zha`&LXJcW|fxE$$uAvF#<qp=fv0?j54?As^w_mUt7NH5~D<G4}DbiQDroVLQoT
z2g9m^e1Iclk4f!tokc`3;*s3n7(^&1S3ERQlpKYzKya@$o#<gb9{qIIw~FK*yh%`M
zP~PI4w)|e!L5X$H%*2JOL@ks{m)}z=TZIm}>HIi<1?wp5YGa=&c_SY;vsO7YBj%>_
zoo-BsO9M#0Wly>a!3pd*6<0mw2aLx8CN5|hgxdu}THbO}zY7;2u_v)VDyHe~9Yf2m
z8LnwbM>ONuF>TpT$1?dVfq9bz*L&fJvGj&)MeDJ5k7P9WI0C)a0Ox0k$a=l*nR%vY
zPL5KfhQC$Ow^Mu`QQsC1%<jEGXf4rfdpDi950aCu6J;(en-jNrzQ3O&#ss&q8oGT2
zlJJ|IN<7ty-KS*?|D2vxy|vZ>imzy%<6JU$SeiFHiR_AQY5M=uB{M5ht}xq&9(qVy
zyLPR%X3ZMyZ-4unhK61A397zbGGAeO&YpNO9G@ETT0PAj#m+G3r-OBR?CmSz#StQ5
zuk9b#d(HRi;n0#Ps||_nFwe~%Ar5DB>k+&}UPR=(J&6|}s>oel5Xtt1D(Q*{oGPfG
zc0HZ`Xw-CeuMjM!Zp^$}@@UBjF;4P`3>W{!zpp2=HzqB2zcN8qYx?wC!lt>(GQus*
zq`x{1y+dr@-7m4D-`C^0Pp2*akdm+W>*>@x#opT0Xa1$r){A7(EY^*=TiYGdpBU~Q
zzC-rnOz&6!A8+pgXjxI@|DSx$&FSSCCXxh^1<CM3Q4qzP7L0%bDj3ie3<MQWR+Jzp
zh#8R;!<umyGln%{77&CPCcbpvyE&Zr|5V@mX5Neg?%&<DXWq=aef#w3P+eVJUEiwh
zz3J2Sj`jagL#|Cb`6orG&X1<MvuA2Og7E3-$LUW0{w~gKEi-ehWrcUw;<*oXTh?9~
z%fQyIdQ%t8H?ttjXm}Ff@I|X7iD-ubb6bE7L=}El-LKzaw{xdB8UNme{_KkkG-T{*
zvL_2S;IKj8joJl`e&c&?r|=9j)B7E<tIpPXcxOXD9@)J=a`w*Kj{7*Qs1RT?C#F-`
z!<$~?_PJK;79(`_)=ls5l$$!J?KZwJ({5a?g}u*IliDS%VBywwxAwK5+4))=b3<>W
z2*d8KZ90n|pKlWRICRz|X6op)m)eKCTjpE--T1tIX{*z{FXtCd2{O&EFSP3a$_WU2
z)Pa<)I=SmVGSL#V4psQf+3j|)r5I&T?57)_TWmIORwyrqi!u?@j2*MmavyG+jIz<Y
zHx8WjwrAw!glU>N`exWwefG)*bdf?N>KNVMclzd}=Xn35qp@AksyFqUiBmGU(DNE!
zt+u*%b(7h5_27hMJ#}!)t$wu8@V8j8x0cJNlTu(NXSBKZwxJ70b5`qi?kTC1=9Bjx
ziT`G4?i!EFV>_$2XX5E|`q%|=^c4X@IEzZV(z492t~Q%`GJ93nFsCZEHBz_cuIj1l
zSCQdK7|{+FKBpUV3KZhgt#okz_Hg^@QA<p#+<jA4=~OKnJ~R_-lS@9FI)&Sz?+2m*
zeXk1<b68@1v_oHBZ1=u3K)9LfO+5hi-rJZ2sl%<bvnICmGT{%m;A?4v7N(A{)6Qx*
zD0b5BV{9jPqUQB}v(=wFUGGLeuf+?W)*I>LGwFDKF`79)p)JUTwVnOtw_Rx&m6pV`
zYh6<dYF{$j*^|6X`0zq7bGD#0SqK8sP?tl}ZPaw-1hip22#J}OKFUaYkN3^R*Dmxr
zzw&y8=Qx>gbJd@_DCjy*^~@9yX`ycoSIqd4v1%98d(9uY-NG}COz&6K?%er$&zww5
z?@+8@4(zz~+h$v>PgsGyA~L&wYGC@{>FMLpOJ#k%{gH*JcIklT>tBWDZ$+UdO_Hc5
zYD(n8|2)mk&ML_l#|-Qew$|x%w3(Ti9To2cSup<tgc}xD?-{Wxr*=~H&o<f%7pp_7
zG>IS8M0P1WC~-k1LvNPRHOXCJhCQ$8C50<8mR4WX8t>zf1h1@obi%}cZo2-NMz{J7
z5N;q*<aT(+4kJ}c&EjkbhAzLWkRxTJ?<&%dLS?8KGYrr^nxKNz)0|Z0G7E$BSRvu$
zWFzHd&rxL*dmi)@`^Yt*)HcdnmzBft9Vb1!5;Yi4@<&z^e+HjviL!G>0wGUq7FA<)
z9F_pckaAMr($;~U;%ank9Hj<JQ0k*GIMq=%|4T<`o>1=+HH4fDM81VI^&)LnC^~sh
zBph4WsxH}hs(y(C&0EEf0d-Ighud<IiSG&dFle9r%i}UiJNiM}*74yXX`A$45`7Z{
zp45aTMRfmgKGA`SR5BPRDax~}KvWUpe2_PMRuE+K95Dy(!uT#W@QJ2?H8LuLp^~!8
zk_?};QtZHS-y&`=X@?XTL;~SKai}euKie;ii`kf$cBxjXii80$@?;H8vNC7JDo&xe
ztfZPjQCZj}%BlEo+-}NC<sDecB%lHD!m~4P1$|TKF1bu*=A}Mm`X}`*%fqfTLKVi8
z@*}JRT3KeBth%AdgBJ-ci!PTWzyvXg+#@YxyNX+I>VuY*t*+3N{1ur<Y5EjB4(NUO
z-ZTsl`BK#L)U4h?`5}F)_Gt=@CtxV9+;%UA#ut08U7uyWC}NU!$m><#RDV_5c#lF{
z8p>9ltw|M=H>;6g#AMxyvPw>Oa$Q-bUqpqb;Lc~U{ur#IA;N8nF+m+eA^r~fA)A@O
zh4C_^Cv6$fG8OIG-wDc1?_39BIWnM?0bNihbjzhCbg|nzuwkaHC=-(hQ2Fp~v&1m7
z9ZEsf6#4)EXWHG)HzXkPJ@?$Bq+dr|-H<SL)Uw&E_PEDAPV1pC*vWk5TNwieP@K}}
z>scV6$Q4p(oaq2QEW@PGIS;d}C&(K`j>8U9p>D);6F0X@2sa}Gp|g|d>nJDE+eUuG
zo8Qvd$on2CsdmtYDcT|+1qnTmR3N|&WlJnaJz-i4{Lh}H5N-l|paa-}D#Vj<kc=wd
zOI_rN6t1u1quiDX$_QwspwuK57KFo>gT9GdIUT!Y>8m^sN-a_GqSlZnv@L?l{rt}Y
z)7SEl#9|bp!^@f=@B|Ggh&0jh%4c=SAnlMxA1O&2#oTVaJV$?pxlYS?@mZpSh}n{O
zT|)j6%Lj#85<wXI>m7)lHIz%s81O2>q)3}_eNK7FVVDG|D9APOy&!8Fp?fJ$^sow;
z0gWVop`l`NAmz~DNldu-L7rVrUY<Pu(w9V)leWlBF^`C9Ng?5+1@iMq{gv-z8L56q
zWjx-YzK!w#^+{g^up$UD>HlizN9I<>P@&Jro8*TvR`UbAH<{x(bcmQM%HD}jS)Qf7
z9c^Z8m);=^9oRDE$ithMvYuJPvl4Nc{$d}D2RX{DB~MU}l84ScL1I_vIFalbKjH}{
z${EXlcWznmLd%Nw@cgzThFgVxFkOmH-~p;1B2>W4i2p}CK3=z<>MLJTasy=r`L~Mt
zgy{jqP~|p}2R$lVeT1QbfWx!<@Ozpk5rtGgwv%@Nc@p?Q(Je2p^Bik~5`4)#GT$N#
zDpaS?8K*9VgcHUqwDU-FD^<w+3N7<q;K&0S-Aa8ttI(Rz<RC^IpSiZxSMdOMJJ<lL
zXcB5YGxn@CnWF(+2tSf?a@j`gQiZm|>cV&o<phC;>r-j7MBos(GKv&_q1;2{fe>Y^
zC`^t#%lJ`3ttquO_*K?{`d&T7*MCQPH0CQ(uc#Uxo%u503Q@$4OmkQw?5H9gp_N2!
zs9LDdAWV9K>pyl4wHwhdLl%=_wj!M|SYik*J7t6<!`F-ueEz?SaFbe<bfhE!o@M`&
z=&nP+fyk$jjAFn`@1>-A4^MKoM#|-4e=vqJq9XM`q}a(WA$C~OGVIdHUCN2XwLFhP
z6kN)ax~Lx@ff(`c^3EZWO#(v>rPTr2Y&jl&(4bxal`3V!W8)f127~92=v9BE0n%SV
z52`ViHcRi-nCgZwCix!orlt8<8f%{1fn1aR>XjvlL?i~r1plcqelGYRpCwEF7{*?d
z+bns*aw{pfBkt9p9Q85n@=p27CwWdlFU-4Ql*1aO^j{^Rvr*oG_a%7;T{HxUq5v~6
zGa*!UZo=VE#OY_yj$zw`>GIf+uH?;wh{KH65YactqdxpI&^PG=^us@jvEw@cjVNta
z>(NkF27@TVsvoMHI;||x#_(BT8c5Qdt%lDsD>zvcNlz+Y8bN;BnU>}jrXZFKL)w$}
z;hK$Sxh+vK&19?)CKPj`ySv0)Bb5DM3`Fu36F3@<zE$~3R(=prTChWiZFP*}cI0Jg
zpNiKeLizxeCwcNx64E+|!JXyQy4a1DQ5_7>VuNu~^|A5FrpDVJIjR0KPn;ZfL2E9H
z@~miCC~i+Q&ln5%VTr!gd!f-}dbd0Q<2dAjkQ~e}!zf5S;X6C#a!4=BP?*ws6{&cD
z)>QLNDT#T=dgj}|wUIt@&$UNozWA1w*6hxF)s>j46M|RAikhK0&q(6|K2G(1;|JYO
zffiyGv#&$@9rpD0O#$c}p_Xhiaudd#UPoK}klzn~MbBUk3dMqcQ%Spc?8?2VXgRCf
zR{KuyCEsMB!H^YGkt+6-gv1o%l>$zJZ(1vw?j+q?qgY|Xx%v=Ah@>TEB%@Y(U&;2?
z_wDwNI)3X~I}(pu5<oENfs(m54%;71Li6~7G5RV!9nH0(h4YC6aWIIOGjZp^C?vkG
zb$^uv{%?sY@hb5(HO%`ZHV8kl<I05|F8pGn^Yd2Fyqas^HI`mF7gM|SeY5q$X3+Y8
z5gNl|t^MSz<{lDRop0d(k2qS&AxWT`yz4(gtg=)FCkD+tBBSL_GVI=u+fi^A9G_iV
zF6pGdOMNvCakbJ}8P^`^8I7;C{qAihat7WdkI!huSGc&`>*)1c{J#G!VLF7adjZz;
zE4ZMzom;XQ5NU7BSh-^Yr~Tu0AFoeQ`gGSyAB_Pw=)AZYCY|raLI1l>l)E73WRK|C
z&2P5*txuX!<{Zpqv3VBYOxy^Q_7`xqKAO=BFY&OiYwNX-_4@u@n4JhAi{!PZB>ja|
z3=nRps4gR>*?t|ferva{JaOSmLHQ058W1K?<-Xy(jFmmkv11(0)o_&<{+8{LY0bPM
zZ<@tGZ(oO^Q>tx{cXl515AGjtWZXF=rC(V<H_T;Jt&uEV8udj|#mEYznd=dSM6X>R
z_WfUR(X}F@k6h|9=M8P)tgw%>CCylq(MB#Jj|NrGVJlzcrG&?++(eifr=eHh4U2q;
zz6b+eLLo|@RUf56gP*}>EX*#ijeOin89^hdo*#q0fe{J=mP=TYoNxqaLEp6El@&X+
zpKo`5(GD8u(F+%s?DV0D)%`;~s=ot^1z~1Dj*WxxlYBDnHSg}k{`Ya8_|%e7I$cZq
z57wi_i^YVDemuUUkH1MvN89o4_hx2~%~7Jn6km)l;QMM}?H%0NplT|;hV`>wV7GtP
z3>xp#1M6VND4hk(k3;k1HLrob5yTWsSSnsh%lj*vs#4j2)_h*YD4rI2{ogl(`W3{D
z5ikmKqSmgOUjzd_+t7UKDFd4Ke~={R!LC+5C+pxD(1iB<_NR!}a=5FP&KT1CSKEH?
z-wbZZYz<RaboIW|{bAE@-XPA9Bi(*dMlYVhIzY`_zqQ}*eG6JXKMPpM$K<r#Z+?K-
zDJP_i;!9GVk?t6^+wie`2XxCOrx@@fZKO;6{a^flC-bFJ9fVud!hrdVkxcQVdUk%^
zueOmW^Y7ftoV+L+_5T>Q+cycq&DB@@pqO#jw!EZ25_tz!?M+8?o7_(-oVXkvh6p#!
zS)bJ_FZc59#B6ihH$k|Ku=i~iBHMEk>^FQJb1FkcNf<r5UuoXiueEOtyP|7VH_I?G
zRSs!KOsK|go5U%(p9$xIZT7ot&{mi^F2w~LX0Q8H9Dn2Xp!L!8;);)E(Moo~QsQWI
za;rIWs@2QjQ<nG!n!9h=>m9qjzvW1)o;}`kImjgLe&ijot+(~7P9t+9<%k-VeX>a$
zuG(VzzuY);xN9K=JLAWhIDKr?8rP!1({l0nHI-tD%c{)}bmzMFVQeYVn(VB*Cm}-F
zu<D2b*zQh!sFX_|+RV&e(Vknli@Rho--BVOg!({E7S7>mJ$*3m9l?FxF_yQjR<C#O
z$j6h{7juT!v7)vcyNCK=?^_Y0RMDnx$d$EVG2ZZj{zC3;C41}*Nj{vh<Mc6UH=dhm
z7^~Q5kH?p9;3VFGTB;gNFPQ7g`uJPoLbQ;L(nol1c+Y&@-aBt(kLQp}I39QZI5pdt
zsRl&b@b=eiYa-{Q57F&SM{$|8fLsUT6!W^jO2T+iB}H4br2L_>Q~u{(AzCbise@f-
z+s4I)|3`GaB0bl6Qgm`K@yxLq%jF&RijwOcRI@f6<G0dh?PaXER<A_!i$Uk#d&DwI
z^vrpC87r^SD*<i@H{FVY3gg1`u^H1@8(7&jMK`ry-Q4`7xHa*W5~#OUHg1iE-hqqG
z!;kmtleguw-rf=eu%x?pEn1tOU^FK`1Db7(<dt=I9eF3}&8cq}v+09cskl29x%*eG
zO~>|I%sh}=VJfs)7?DVHayKE8vMglrU(oQ9l|L?IWxZ%2D9rV<*5;GKw%nKK+aUr@
zF7S0MQ?z9C<C9MQDPb0e?g-i9^4$zF$6${>)^~`9Xgi~|#_YLbnCK?sSB_@#C-?HR
zm$Ya7@8|W&>xm!8<z)7xGSM&Qo3rN@dMj_u6s$}eL(_@G8rj;JIiuL0yw=NO-E3r^
zTrtele4~0{%1<BZ*>sh6a3P&6*4w{?zJ=zq1DeM@-(vk5eBX+sEHo*7l_U&T)IiXu
z^f&%9mZ&Fy!o1ck0@l=W$Bct#-`<*k>rQmsa+;qQ()`@JJ9E_^#IbW276}JAHs1A|
z%&}%_>!wCcpGs?(Wa6kDnm^X^wrr@^^fe`8@-n>;*AWblPPvHM&Blj{!Q^$hf?;<J
z%+C^QRnO190n44YYPhGgqV&;eH>u4u<8&tW4m5d3)=3?1xLdb&>H(WNg2UkSr8@S1
z|BsNz&NSqRAgu-A)?T<g3e0te-n*+Aw}0t(W6TpN%&^<Ky)v|WzixJ#-#0pD#l<~2
zi>1mxNUfsRRS38BsA|uv^;<v3S*#}%_TNZC#4sv3+PCg(gq?c{eS(p)5+&q2UN1Un
zbsNqFi1malPPyZB=O=TW&4<?NGcOX45?y`s;h>lvpAgR~3VL_LxF2VmRw2M2YP;2c
z!1C?YHi)Wry!jBy9h%^<7K!f<7`0<Zv+0m(?cqbUdLYmKo;zf2DK{TJ+t_$OuNt+A
zA;J0F=+~Wi`w7#XnJa^49A;v7KS9puQTam!&so*A!y4#)agq185{p$1j>g))UcaN+
z_qR#-qGiv?iA2T}HHRoZ`~O)_wj4Lps@{rbn1CAMR<roNv}=uv3C(<S^W$5!=D&*j
zAuE~qN+xAyTSg3;n2AeV&_ck2<M|J8K=0dH>IlthTwe1x9#CIc{CB+ru)truH02jx
zWT%6_%=b6!>(3KMODkV!xU~yweacm9|EYI_Z9%{LbHLbFSCdUg&o}DdH`@jlBhIl%
z4>D15`Ld@ZM1n1}Y&5y!b)qIz+Z*<cs)krdy;_}>1_(D%)u5X+agsfM!9wrhLuMDN
zpZDs{-Z+OnSR;~kD{lU{*NuF9r*oem3WVUdt)%&1y>3FD97aW1u=rjAOCGh@==|C1
z5ipapOi4@qQ<TEGqQ2?nTFstJIl0$>McqgD>Yu9C9y%#K@9b0XQUGe)y|vWhjn&$Q
z7p1Gt0R=bxtfJxV8+wa3fq1)AYdGt2ZuTXK5ep;A4?8QmPJRdB22cgG<^x!mq1F+J
zl$e4pcBTt+H|b6HfV`JF0aeyko)bpRGw6`<B$(B+Cnnt9&viFHzE+=pE}{jJ{LE?7
zy)CD7@q(TXoOPz0(~|6)%SgjM;3I4Xf&6&q6is`qXKz?jom>1$zLh>co65c_YgvVk
zy?J$gzVThNn>r%v<u1yz4!ZV(YZqth-_EylPtUT=b&~B*p6+bF7-z$vu~P>Urspo!
z#t}Pd9%!y;n6Xr&y}||5kI*Ip8wj_MzJ`Amw5Ap3UX%e<($F5-yFK5z&-OD<@wfp&
zF2UQN@}kE&-ks)A8Bj6U*&jpS$IrCpKZy)wW)km6&FFrn9&Z4MKzF}6c1ZI!2d$Ns
zo`0_A#`6o&hW+|;!Mp=v(sYVfd#*LoGpjewH@6(pthYX9VC4ZUqf0E+>!Eo90eA;%
zX8R_f>POEtYv0gYMkcE}2NHEgdNjNw9nR+e`G31I-=){l5+qB$<7fmE^uNDIfI&>7
zVakjUDM-4%@qjg!^5qxx3*qJt2A&M;I6Xi*6WxiRpX5l*2hquKrjptE+ewZkm{lQ#
zN5s~uh5mM~DDa0(&S1iYvG;b?WmdB2rTaK+&ZO3C7%7f_!eQwu9-xnYJex8(hR>f#
zu<YMs&$lTZRL;+qGmr14{Cg!hp)k2bAOCEI1AbGRJ1gm%YleBB&!qK`ymPg_xt8M=
z<qGfeTqD&nTi+j`-rRFaNMQMb>DH|J^*D<@dBnmT&1-zF)gmSpPD0cGJZh)q*om`m
zE!oh~>hJ6ld=9e#0eeZ}Kk;;>j-&EB;~Q^^wA`hm*2G&pd@9Uqt=l}`o^*(I+jGn3
zRx)e;Tr8&l8GrL{8nwNBy3XR~{1z@?VDFmVyo1m?>7u#vI-;ntA9n8+FB}r{$Tdo*
z+Nu5@7eI@pBMTj+o*y&TZAdD5EKy}JWkJNjw8Sbt%S`ouz0mL7g)?_<aYjBm;Z$C#
z6@yKSVed|CU_~Ft4ga2!Al#BojQSce2LM>Je#&0=P_G<!K)8XLRvnhjNRIKc97OH1
zRlpbc0C+;2@*isY!S8e+=!;3`HyH29cE^G-aR2E4WHIji7Jpe>z!)NRGI;TyO>Oc&
zLBbv1%XNQ+8QRh8Vj;GlC$32CbxW-fHHpoZ?P~oENk8|JLT=^9{hUNmuvfSB{zH1d
za8@BV^}Zm7yK_DLG@^iX^eA`QRA%B*;=0v!-<%W`2<?=MNuhtFS5beY&Z?MWx2H1W
zx4G#Q&}`>mn_)EK@|&k}l~+_1!p(xFq?Kx2P=&NtpFqqk`cCS|w4&vS&$=n`fnXk)
z5nJ!Pvsc4wd^12(T-3`iH`DEJ*E+qe_+r4tNAF{uD6DzmUcV`na!&|Qxv{w(V;R_a
zhbtZ!FJ+IaI&s$xqV4TCY^I_$cjrpkBe1{nh)e=`I4>v~D{n3otX$0o)hTenU1|Qi
z3I@V5p|P}FPM%_SA_%v_6-34UqMaT2u*m>3eq-F)h1q!RgCN{if^h4l2M9NWD)FEu
z>vzvI4>kT7`hNR}HF=R&)Orhg>t+K%0+Zu+?x1;8Ua8Q+M+~sgWbH#XVRk?{h2_Ye
zz>f<)St_ruWLMu?Dr8TK?Yf{*muMbd(EY98FC)Et{kT2yDzg;MEky0xP^pM6T`m;=
zfoadoR~oSNjUo)8Fvz2iy}qo%TtydWG}UY8tJ^L#7n}pK>Fmpp0x)3{wg-_Wa#Ot{
zE!DLn<8S;T<rUu&`jq3Cn2WHf%HrjMgWm@?%~FjBc|<;4{7|#LaJ$pVJ}sMd4j4#R
z9H;Rg(CnfNinA9Gm3>9iefZ#o>H0UFcIJdE9vMU+tZ3<@V(fR@81S%bG%2l(Xqh7;
zuYFesEgYn|A=KPcM)dMa^i1Qkv#mP%FJaQ1k5r@0r}h@YRwXQ5tf#WiDp>BSzMDG6
zbKSK@3cV6G(X?H5e32V=PlN7pkl6W!^o<La0oRuPa=AhyxrL!HU8N?iaoJ3`^^M@+
zGh=$`5-^}GJd7<y!G?W0v?ZmLUS0AEPfY<#;HY{WV2O2lI{)^ZReC9i#Odkq&-q|m
z)J_qm+=Al~;o{;Fk(j&I;wQw18nO67E_vo#(agnSCjyRkfF6nqR~9TY+cB%x2})mf
zE)b(<Pxoi9v5*)mZuXqmCUi@(!>P>9ZVi5u_H&sf4%~T~m}`w-?kc;9-w3Sr*$3#G
zT(0csuYg35q$d8og57Ri+k8}SU|TD-QAiKs#J`6DUbbksk4XH#kSBE^By4}`OsDqc
z>|*+aOh%iR2Iv4fYQ4ozr^VlhWu_YX*5ewD+E?Oku!sT*<`B<<GCQVGuYE1<5(JV7
ziIe`H2-xQ)M*GfsxBit}2lqs_3ir-?RFeP1T7>`12}euq3;)~4H2?x=!NXkLb5q~^
zLZj3AUcQH|GH6YaMi_lDlN8Gisf0j&3HXEBt<A>V&321O4cXN3fJVRFOg25HUhm&u
z=@d^R5Yf3r_Ff-&?jf$@F}H=MXH(c&#amBr)mp%XC7e^z1!S-h`O=Dv>IYiQ-k-98
zS&HrcC#uobv%8C}Uui)!EGPS2?~4oZre|~({rQo8`P@VdM`Q1K%V_>OBeeEo!e^zS
zB(<i6Ho*j}_Vm<#L?K$|v7_MZ4J#?R)ttMu*YIc3%Mfmit-$R?Eh(m!JJJ%(Ll#-U
z3GbN=x4e$n$L^?Bes>IJz=#{~gy!EA&+D0SqxjCA3yL<5IgyH8KhS58p6Dsj1V`17
zi0VXZ4pMsIr3I&ShV6w*G~a>dPm>7cdQyG;bTD(7F>g<f*oC*p4nj|&L7V@`w2RJn
z$BW^h@gUF~OfE^%XN_6e!z|aC46M-`Er0G@!fXfVgpLQ2+JrbeW*C8wMs80^A1pb}
ziUni(xx}E7OYi~nSrVCdyyB65ksVbroGOT-3r6kqekd6X*DAeR3+os4h_zZwrv5Vn
zta&Dyc_j`+zKp^6+a`Ls7xuH_nGE-YVV?y#^2u}>?7F^ieI(a^h!4vZ002M$Nkl<Z
zpwi%CrX82Ba0#NR*)fq-k=B#(&l9`K?rIC~<N^*6CaRk8_?J8k<vLpR649-3s6Do*
zt^8!JtoIjChh(+f`CZ%pV>TYUTEk-&i!B|9Pn@`ZBWlpi{(`$7On6=B`VaMKFS5X+
z>3qfIAl4-Y7Z0UZ7V#A4yZL9QGyP3|r*lgX+0UFXCf**WYq!nz8Xq*dJSdo%qa#nu
zX><a%3bX6|GVqJfD5a*p)*(VG$MOx`xO#KaZypl&GbgwyXR2-Xe$<cb10n)4W{i;!
z>v$AG2;7cLjKhS=>6o4qu(ADlxyf(n-TTr(;UqnsJ<$$nyHWpHj~%I?mrm8Q{XaH(
zy}QI#(=-ZK=4^LWXj+pn6WsMluua8^;Bk9O>UJL!MQx#>^*4Dz>amfVZorkSNbmfJ
z=|GgtUo~Opj%?W5&uVtU2k=e*g~?RmS#hELy{I*EbH-hKPuq8n7&Y=wc3fv==#Af&
zO3lBn=Q}SRHF75qEOb@qj(^%o&Ar^{S3XKGM(_s2_;U${8buG(lVEWq^njY0qvaq8
zn@QtxEiS&7FhX1x*cj>i8yo%B7flduwrj9&+7pQ(z9O0*^yAX2i*f2$--{M1QQTfk
zTGzuWg+PSa$X{{Jk9k%5ikY}|g~3K4Nls05ExJ6Hq@K|C&~^jApN~OtvR<;<#MkpU
zhyk?mGSpi?tS7yDrh3_@<ulHV5k)nE>dmILiO<<y+{6~R>{~<}?ssmi#?z&Ef!Kz|
z$ZLq$x3$YPG;T3ZGaW6o5`wuJo$oh0jdwUv<!pz`bKkR8m8@{nJeH;@z4QXk3z3sp
zrBH8uqt!FU^2XSQHBX@ERz5Jhn?-ag>MK4B&G#12!)LX^TY7fmy>UqN4mcM~S1c}I
zyy66>`m&BcuMrWPL4f_~-Jkb@!c**Y_LLCMbGFt0O%sb_X7{8~ZR%ZGrt#(3UhREa
zWF4E)%cmC|TWJ0{=}h-`LE9Kj>G_jmk5HGmkM*_Q16r7VS-~tkD|E%I424J%ZX*6-
z?1y;VOJ$#?Iab*+J9nar`hMEaKdGFW`U>j+lfeE~E}5;^uqtU&(LDDn{sx2CgM57~
zk<JSk7R9hK6}ONEvfBTVX^+g8J4ej{@sfI0Xm-DbqmaiMb~xXOqblJO_i`M<&-(Kp
z8f_F$EoAL<OIy5!o%OrYY%_5VBIx8waW2Q9`#}z>=VUDJfY|BXZE##-ef_PZc2u6O
znn~&aZgL-L2OKbT;J+dtA|7F~!<AnwHm_>??d#KTU2KoOE0wV_bz|XU4fb<!5EjPu
z+iL9|esN7qrFtKqjTRm|(#oBXO@p2bXFt}2ZF)v9<Hz+|Yi%YaF;~Mo9`^Kbn;$LS
zQg27w2Jx&Uj8OMaH9^bk&i;hV+z@xW(=qYNQkIhojpjGzI`ijoq}LYp`k%PjT}dN#
ztI@bk+`S_pVyd@f`1V)bS2MuAPy|Z7GPJsPce_Y}hO=5T=m^)hm^aBSF*d#OEQa&C
zz1p)4)CRR%>aFGtP$MA^svobkiq8fawV~=)uj=>vzvyZGZ|kAI(ddR-vEuj<So^uf
zu1zq~?6HB@{pEaj?)7XqWykE^-S%5w@Cn3-rgS3Gt7p`^))z8*>Zl;q{A#UTyIc#*
z!%U;|i&nq+sSw|2a^QqB(MxB4E<3_TGIw>W+5M)8gBtX{q6ghmk}ilY7dZ9I(^~C`
z|H$d7f9kn4=%@8b929Z<O;CIw)gbJMro>Ca#kSV|Dc%Coj_It&JI%wI@1AZF_pFz+
ze;xI30OP>3eRn_d!1{N79rZ2ppc5jS5)SV1Hd4}TT)i$}I5mCxf^KW#U8StOs%clR
zZ(_G$CiaMBqz@yM=E7_|_r?G@n)|7eZDbCM+<102nmI4zS#+<Lq#9#yDG^|-Zq8rT
z!SN~MHNu?8aatw%CkH1ICmzLn#jZG+ZzeOBxs9>&i&-vflex=5-U(Oc<fT9*ICqy5
zjl5&}O$+Ub_ogy-rC}@*d(gj+psWSk^!88e-mkXzXPzq-+zq{sFe2;89Ce$WJ48re
zE>?P`yQb&#eo^hso}qKNuWNH3C^yP4&1K@*g>dGzAm-M?(^e5bZ)M-^{&>DS`x>q1
zo*0{EImP%!X6rlKyEA9$UH4g7SNw56^DZ>+Z$oC4Hlkx+N+S2lln(kL?3KA4N9%M$
zEnHYzY!#mBr2TuU{h5Cr#v`P?M59h(C=H<up#(m&BD59Fd(ixTeKx-&wpOQX>>u>{
zZS_{;8Z*eA8`;)g<O$7xs@`f7^fJ+Ek-IM6$wH&;@6UIt7wRzhs<v=TxmkDy-u51-
z_7|_~2GQbT;*4etMf1Oy?arMS^>Y{EJ#C*9x}c^$e`B-JzJ=I>Lbpp&{Za@2kN?Lw
zb6E^GvAvuI;r6bIRX(lD#YCgCaH(iv`8yb6)f{eVjX_)~L9nPn$7YnH>a?5WF9G4U
zSWB886b%KYr8iHYuLCXjnPzw4ia1oU)Wk4;=N$fZ7a>czei_&n%iK!B=2*;CmLuE*
zRDhOgVB;zc5zfu#2(o2>ctVy{=3nHZj~m#A3c5+~tMq&&dE(@S=cHXKJj)K-Qu4+q
z#!^gnLy$>Zh-rqHsO%@DaRX2PG8Chf-zdlBVSKD5dtGVT?tK;-lu;7tcYv6q2Fa8}
zKXxFphNvK-3&fzYIGhb@8rpM8WD&y4^Njq^8!Sr3r~;eogh<X*nwWS)TNd$ya-ien
z?9T-lr!_bQoJy<|FkV+^#kNSn(&EGr1kfO|!>}Cc5G=Nah%c1Vs$;Z;P&@;$0N?K^
z4b~i7f?tHm4iIiUuNf+03QKB$aFb1sK1}frP*8j?KP2X4M+`T~8;lkGU?N0?v@8!_
zD>zjoZ_qw^1#*o73gI?D*73ALI}H$S%f@bk_wI-tHyqCio+b9z0F@`_8S3M*Ozgec
zO$IHL=18l=Vn;4-_+8XwDmERqrJyw#Q*}FnO*Ca--#ZX)0_YGOw~DGEQ#Pb!^tm{>
zl+RL|$RUQZo{`fOO_sJZcVx;4$QG|?Ly(z<Fv^%qX|{IqaY;vaWxdjprE-S37j(Z_
z(TL<pqnStYKrhNeOJmG;&?ECJ=ve8mBw8fjD}au=<AHtzt+~U7Th_<mz0&4^t@DtU
zWu43|wM7O_5_e3LhrW4k@Y!62VKwN=oixu{6Fz`uP8d1Q$U6+fgqFq3P0`EFxg7LY
z%19n4%q$~CVDz2!{Ws`#(3nU4_wTXu-N}3hedS-;C=3B@<sf_qyPKfdCYfWpAc(eP
zBj2y)M-2Q$UXi@blv9T7^h+3kKyU@&CPW7MP2~z&NPc;=v@{RYE2tI~TZ>~XW=HBB
za=9UaA!V(ph0Z+aYk8xff=EkfB;!CnB0Wi*E)5wVnW0VlQ$Qpt`AgWDWR_=F^CJvW
zE((#&1udnX<uP^fBQgpHNPQn(G~_c1LB<gcB%CZV8J9tPEID>^bB)Rv3Zbq|)FDT3
zY1t!nn`)JYQV?@{^SdCekZBT8<FH97vmCiV;%F<TS2~wV#59wE6hSG8?~{tjgRwCF
z5;${!3X+%doO0txTjG1EOAtkCDJ}yb=(ziN78_$wRU(}y2}mb}(t`;ol%FgpJZ=e6
zNKjSA{?sWKCR_Ls&s4lq;;m6H=Q&pN1Jt+6!jyOImZ(B4X+hDgqaC~&o$Zb`tC(ik
zzwU@%wt^BE{VUoNL>g^TG1n&HLd-`j2)sWI^On$if`q$|=l0=8P*xm16kTlNoomP&
zBK6R<f*|Ab6m5c8GIuZ?#({FiJ~T}|D|12KygbVf&TV81g7$_eHJ+6zc%&r#6<U^l
zNSru9R|37=fp8OAW{*&`EGz=aaYuh8KAp7X0QxU4VVFVg4ek(D4qm7i_(tY~dj*hi
z{6juJioUl}*Iqm(vFJdFN;y7h`dX?}Ckxtf4|!~c`Z~VCE?$lE!w{Fu?{b=#F<4@}
zkJ4WuXSpy}$Whr)gwA9gw0SdhC9wu~(6Z3kdddpTQ)eXJ2pJ#7YS7;yeTyBuqBYjq
z62eyT0XAnvd%SXqMpeu|SqEIz2+C2$UYn$xsH3?bO=P{WX~L)nV@1$fL2&*}x~18V
zmj36=7ot+e?Ykq~+UzpEtakQJiCx8@?L_RTKJ37Ki%~fYKp~y}cMxv!Ui4kd5N@)V
z6vKjO<mQz96S5VmLy!|DtdKuKMoWo&7|w9VACwdQ5jvnj>@(Fj-XrCfaPIOf?@@X3
ztVmHJRScy~m?0WD`OJ@$M3UYiSqW+j=~w_I5S^+<3hgP$D`as_&<~`o;f}0)9VAOP
z%fdVa;l@TbKqpDx25rMRi82l!e5Vjy=%A5Qci{gA<6;W*7U`OMkYPXAA6C;Z78)7S
z4;ms;fG{8>kw~8WFeXSONEJiW5+luLF$WWvD;qAlNy;IWV%jdcK&kCf6Qi>e|1V?3
z;L$f=vT70VEvrN-;Wto`QB94#%<y2&6uNTR7$_*av|0W_qx>U_Fv4Bqpbx`5k&K9w
z$JdfSC?_BZIR-;Qj6Q9_))+@e(cEs&?l=+2m<;QCv{Q`?fDEb3En~FPKZrbypgMe(
z-xw~clyV9oDE$~neySX=Lph=PCEb_xC(hR?wKMM#XnqihT8+zq9#uQkvrA(zK!!1A
z1618IT884Grk&*ww3Pj|^y!h(Qh#^S3eQ7lvN<aN2_K}tYT3|V1n`06KX@;r%G61F
zm1HiDBIVL_gpom9Nr}6MgezkwCknM5loTstN?~4yrOp7^s7lI9prK`XJgyV?g3Cq<
z3+*wcY}!iYAcBLm`0q%65Azk-W{?;k6+9D!oBP77kvlo^y1(fAZCviLHR3QiWoiAM
z)_OlqaDhr1hvv1>3otYd&?~(>?Ber?g~6c64#y-&kj(^yTmGW5<*skpt#5>W|4SS>
z#2kl#+oh8-Y0~N8k7DjjmTbkv%RM{XOeBqufN+zY6GD;68^A01zierh%hGt@O03>J
zji7dc?&}A*dgTpf%Ea9*aW<0P@1vmeVLKZ6U@GOTOpNaD8eHBPK8#n_vyL|Ufh-pT
zeX9YQtMer<8hMw2RXY>z2U_=+Jo`~A9J#*WxO;=3`)Sj!U(VIMY3sT3Jy2hvBdD+T
zxB7nP^IT&bm(?@RG@ag0T45g-m<qYX2C>JqM6B%KqF+v*Q?`oF4&CnE4Zn6Nx?_QM
zi^C6QK!w$TIl0fk+n}IxQhmL7C%W33%+OE}Os-O1UND?heY0^zFCYe)Q93(KcnO@o
ze~Es5O1y><U+(g}lR1>jlP@)U&5xU0W_X06k&CIG-uQB--}wyYV*8~*6q$~*Dzd`)
zAZp$c`aZGf(q~{*w@+j=Z*7P1eUQ6SQ)o%{wMEl#TYBs27zeTtHzr^L5B{s9<{3Iu
zj#tu2=^Enm?bEU9pXqcvpCitfz_yX@d0G*XAi!cQm9nqLHnr#*&Fg!i`1i1m&uO{m
zYbk3a;xa4<>lb6HR?H@2*Lc{ZHI0SK{BHjbwCi<6Gk+pZwCnYxdS%oL?=_RcJF-sZ
zFw+X>nsM#ws2~1`euy9L7<Jnlp|vf|xN}Pq<^H8?<({ZH;Y>ZQz7++=yq@GQEL#9g
zoNya?=YUe{@T@QbPE0$}?zF5J9`#R^wDD`W=JXrd{KZi>-o!YmXN5LoOz10qC5*>_
zNW09)Xst#(e<sMf#L_D_K;P>$vv+Sjs=Zn7>m#;ad1vZTX}*6?95k=vf^0n(Y-c&P
ze@`p!fD$z-xa53$!ATteg6@~BmrF&)we{?IDJy+Mh<$FHG{4pJd!M!A-D%#au(`?x
zxjZfJuLy4l5z#d9)m~OMOCa2Oe`o}YZ%uqd;=dtGsI@C<84#6FvzC9GYkLQTw$_W{
z`oH!4aI1$O?yLvG&1!zL)o<Ns$~jPXpTYgWao{)q1JC<+F=FC;zB-_Jd#|3^x*-VF
zCW_#gf==zJp3%C7JlHGhwj}+EqKPfM(Yk?4aKbtjuVfuOu4mV8Z}!@^ni0;F1`Jqa
zmEF3yyu1IKzssw3B!PWOSeg8bDsJh8cGlw93P~jS%O<R;%d#Vtmx=4?7;c<Nrf$YR
z&(U!z^W<`7>U%ci3r8fBxa{L9iO!2X%p74Mzo48ey@()-!bZfo%b<kN+~7xceK`tn
z?AF(jDTD~$tbA<Jp1e3cT713<lAB#vP&7;1g)+~A`L4pA@j%D2#yqQXcBN3c4zrPa
zOfB`;l<n^4TE$Z*vXygKULmdnA4|DO&mK9qTqs`^VU14+oiC1ixf3ut%G+k@KtRO=
zrAZwm&w5trCF8mBnfR||^KlQbO|#-yBX20@3m3;$9OdHD%SXJ-5w?qojcKof@_;Fg
zy>~1<aiLquo~Xo^M>e~XF_3*12~E5^rA`B}^G>_qtg@WSD<^WLD<VfZrI#bI45^$p
z7z2BMm&-@Xa`w-;W0&Ofspn$ir*x4c$>e)W<&kq;aW9hC2Z1zRX@c-dyA$uu<;^hw
zu2+_vk#qE%v$yAtT~;c%hef8fHl_1Ui&%T!_~pd7!&{VnXhkc%xR7@DbAX)U>>a(M
zdC;gfd1a|E_IkVM>@Sm{h*~p_8`UST%8yiDE@3RlhR?93_MBvKqPNCKE*bq;(XG5J
z$~kKa_V{~BdH2YOdj!6RSxJA@W`m^eojGAly*pnSIh*U5DN$--arn$Jv-rFWE)v=N
z9)l}=na3{_GP$QD>C}<M)WoMf(J*3A4`R5$0p!GTdg>!K)RQEj=}dEkzz)x<<i@VI
zm|L9x<H#s`$*4VfMRug{45E)vFm<SBn0efhJfo7Gyv-6tNRq-?xPjGg{@FywxWfcq
zK!1^{MA%SC{uh;<9Fw7AL)1u6y~HD&`f?_lJtj=2j~(}>ZnXyMMaoe`IofAa5zCXQ
z&t`I&!@^YNiQ}osJ4O4>#k0`um}^(gs^mv*gudskK_1|-N2K|)1vBTwqgRi+6L0lK
zalI&8VqzWu&|rW3FBffd<h*jed}YEq$Uw`ZZsus)b=E@5#9Gt$b2N89$_;70bV*`~
zr!t{em87roBXc@A=&PvpIK5$(Su`fEbPD+Yc1kas$c|i#Q*$vvMD-$btI3IzKl;Ii
zr1Y{%D*aEmL0#{`9Pv5-I8S$0Tc*7tWshH4%BN4m5wcc_$KN}W8-KG?P9G^sUS7<V
zQml={lUJ3>qtHD5v#~Lhyh<|l{!(f5HC|RPkjLEup=7kl_ZQ0}uXeITu8xY=mfVq7
zunzX|-0}Ai`|xSd9sz8XI#b&B4@$c*Uo|ki*dVM?_h;=+?Y$t}_A~VMSL;#pM?U5b
zgR4$GThyJkvA1x|Lha#KrmD{VMK}Fqj<DN!7=jW-zbM{~^@+ClMi6d4b2~aV5`UGX
zqr!MOT8cZk=z&2Dw|EPuPxO%W+iOAl_rw<wF;p5S3tJf0Py!apO&;iKTThs8Z#ulO
z;BU_N(nk@J1fP3bj;%H}AFb5_9AWqFj=B;+YU^=xtxb<_fqE<Sy+d$}zCL574h>SB
zAIx{Qy{z7xdo@;`4})eqa=y0dXdNTeAn5)=G{zl$`-wB%xeo`exa-BvglLH9C#$df
z+gfk)lj_a-`}NQ~ux~8hTJQHZYJ^<kd?iWR{Ah_I5E4L=eOAhioAr3(LEUQiAuGt8
z=)uC+(6Fdg$5!-hCZP;Fw=|<oN6*$DJ|JGyouZa`V$5huOsX`7mQAcYu*ZBn(Hd^7
z#ao|LZPmZ6w^6ET<|_P|9+q;W+1YTze(`kJEF=|>YmLil!KNc;>s#Nf)y#5H_g=ti
z$LJ2VMb!A(VrT19+im~H&^y5)YF}w}f(0aP@sh<^P&VO-E8Pr+opIE*nMo!`(jyVD
z(g(|c6qEFkdaC<h3!RMzg^L;ioAaj+n1gIG%9ER%pcq2`M~%4tt!_K)Qvbj<G?8F6
zShEDx8y4Ezp6_#xDr(+i^i=zfYU`m@?wmDVatg20%=xosgDp>Nw)ziQeKU_v1Y(<g
z-ePz2(X)$<@1>gVemO)0B<Uwq^x|_9zz<zKvqL`gqx#mFV9OI)t=<;9Zx&sMH`eFh
zG~3@w{JQ=&+qX(Cy`e8KTg8N=--?#Wl^AXy+ywO%;r+~c28g*8firhSy*B@Gt!3|>
zb5kb|*9$_2c)>&9Ox%|iOlP!j&s<PnSooaQc0ty8hY^QO(RY2JzcJrS9U&m1PO|NZ
z(EMSKO!M7eNGv)cynMFqckd^@3Z~T3&}c(fn|?a9d<_v(+Foc+i1vjxuw?sxtoJuR
zvDU0#g`b38N85JtOm}`r^VXD<BZ<^1N#<DAq9ELeN2m~P_lerfinGs4I~tKrHy+rj
zcJHzL^b@g&ftCTFC{@9*CeUtN;P^@0!R~tVlNzng?Pe7Gx}I!((p-D-Yuv(sdbjtI
z(p|mwg=W{^hB+HyvP3Z*EwJH!C7m&_b&YCPXrR|+9mJD&5NpnoJZ$k-NuC!^+?m1=
zClPHM;{PF?qz`l5XxnVCVP9=J>Xnk>%ZOYq8!2=po8dny4F+`gw=uAEQYM9CIk61x
zu7eK4Y)WDnl9{v`N$(+}S9)Wyu;MB|k9&39dW>jSNLU;ySAZg!3SpudSC=02?XMEG
zDaP9{lFa!aSiX_Uf|SXmPUQ<6hxp%VL_rl3q~!=VVeDLvsiU5>9%);|t;~5jAhsQ+
z^<6;#3FAxju}>2u4?D!UGYO<FG14fRe_PJ6%WbFeHLd3ygR_Pr=(s24Q>(u>QW*QF
zrB)ub8WUe-5(#TIf4*ET;UnVZ?B-YG^Jb>TaY!rzdpM=`<IK^cskIvlC87ja@t-h%
z`fxXlx~Ny=vPP10TYejSlAIk$8PAE0D7@SP=MOa|>o#O6?pU1|SSjMda1@%S3OVYD
z3Q{VO7V^vZ7o1ewYibK`(E7QzRowL6U3>BKzVaa_&p@79;a^AX(Q~X~+{D$vuY!nZ
z8NN9>X07^}UU9}E%gD>|1df5OZR9*F9TEVx{XsN}b)sz6iSK&OXnyr?QhDciY$EP&
zY3=XF%q}m7CPX(q^6`>qW|~Ih>v4+>7Y<EtMe0>e<p150Lbz?_ZR>{!w|zjkZS0nl
zo)4sYIP{`clKI8bR9}Nl$zK`?@dHuwAdL+qYS3pLS>(p#HKWCq-?6djXllKClWyje
za(30-LD57~x7DQ$2)8@Oz05IwuKTAJZ5IX<LfA~>kwL2WyE@m0EY5eQGSP!<^uI;-
zl9KTt;RbN(uCa9HkZ!j7+b;7>cG1>X{A4_xKB$}N+}#y~B<1L?8k8kkMm=-hh|o`V
z^2=@}{cI3!2V&LKH=@d$S7Zg@78AqGS~*<iqF$pWse*Wf#H+HRu`8zvr59F#4qJh_
zzm$W%UzK<4$&S<hp3pZj(xA!gYnNy~+=b>1tE*_<Tp8%y&$S|engS4^9@feh&IvCl
z=tzQjyHIzU-xgZ-bpnXS-k~Gjx{bwh@e<9B0EVQ_Tt@TX;Oa~&Q1r?#azTu0I_aj8
z;`>IN$!|Kjv0JPG!VTZLKzE?!e8t_Xh6!LwA>4?>G_Vd)Dn)wr5b^0|!#MZU(af6r
zbH((TiAmtmBzJMe8b9AFXnhcFpE9_D>*$Sl_6b_Rf{?o(Mm&G%2u_)V=7IN<<NsJo
z^6#oR?%H7<8X^CeT2gp-#dTM9?D`i&yllu=cJ<EPK2G;X?NvWZD)tBw*z>ZZFg}c;
zA`zMYe?ZzZ^Zn}=LmPtr8N_fiw5GoGh<an;(_zcs2Fw)4QsKSQdXiW)h#yYr$Wra<
zi@&Z^=y|ELM$t-~F;8<wiC-oMMCF)8#)Rs+$OWRZkb|vUU`Z|yD>sbWBF#FytdG9d
z&b02BX;&{Y`_@{CXH`xz$0VuFe>K6!vgoRz=9~m#=S(Bh_##d?ZqR&;)(LE=N3$2y
zYqdMvZuY5ydgE0bQ_}bVXnkjZa5D%P+WmAj+PI>#5G;%YmDh;^1U&q6Hg_Ie9nMCb
zQj&j`s3+7K654=yiLRW_C~DLu#Fx7#r56<`URb3t9E7-SFX`3%d1CSqj*f%bu*yMy
zHMuatG_G*jq@6mX@6Ntu5tlsKm^f^XI9|z};HBKPX1e%R7p{u>^sU>2&FATJW?|H<
zTot8i7c6wAFYDIYzcvCv8pSomOXHE7CLMQ8+uHWr2KAX*_#I-s9ks3Y;7WJSUR`!G
zr^K?A3UFWkO@iOcZ>2<3F7{+V7$rCD?PH}&mq^4DI*%oTn%mtwEG%r4DjsAfK{xE*
zUkj%H#i{GAIPVxD+}JosN`0Xq>hg)BtsvL>KNT;t#HPM(hN!pG)$Q-iE~XAGroAV1
zt!*dOTJtyi%^pVdXk!y%kTG@v(%ZI9?F-q3%oFlyi+vm-il=^1$V6MJ;nt@UANQnD
z)7v}o?0s?C@;EbvY9XC|Iu7L@>L**DFw<)MXQ7roIjs)L@qPV7d<-<dTd3!ro`&f&
z445K{Ws6Efm6)C<$mIk+c}Bqnpt=hl#2n0y&BaeHv>V?jbV0b;0}vB)&c%<iVxa;R
z>&=SHg%8%6&F>Hos2Eu|4N8oOvNrMtJJbI5OnV-CR*Ay~&1)m)T0@#QmS|oRn(zKs
zLuj7Zsxk}0t7s`GNQO1h_cAm=vu#}6TI_Ed>6gv{s|PJlzi@N+p*8-Dzfg|y&y=`@
zAd^2?jTOx=js)e`5qe5GjM`A@8_cg%jYV78#=BHXvq#ltu7O>lBvHW6=+yk}nb^n$
zT!rnVWw9dC%Pi3?pbMX}x%J>GeMWc7M&X$;XB#taTrm@FdNI-3+%c{Eo>*|_EJ}GV
z0ZV3NU1j6^2`R^3qWLv?y8OO^ojZ=a@!>j<ynii{=Lv|z&OFg&ZCI(%>kN1C{JGxr
zWxZPaH>?A+>QPn3hW5$kt^`IkoB!wk49R7;_t1_VG2Cz_o_)i(l|9PzoN>z{Mp7_$
zX4tRXh}SUunyBzK_ceN*@5+>Lq4S=y>#eF9+n-KsGWes6TZ}o!@DRT1i-e_O-eh>O
z#J!4}?-X2t%qvn3laqh#n4NwMmb?V{8o7qk+?#rYQ7k1ZzMDx$Tc)Gw(?bX`tMB#2
zLeb9FEx(ZQN3QLA^KVT0qn`ocMvTOTt0iJ94cRBJt-K{yGR;N98q4{mcelO8i<*Id
z=4h|-wlFX3B%Jl}KW`+HUrS{W@003l#83>)*%NYl30Hl{&$^ZG8}0i69qg4h#;$if
zmWE+hdS>$n;?W?jkAAG|xT^x!d|D>)AMUlA*F~WrQ6G?Am4vi>1cZpWzeV)XJ3s9O
zx#OHv`GO$jtT4^?cUs7RjOYs{^oh4dnc8Ov7<nD?#2d!V@e7?w@Rw#k`|K>Sf_mEA
zyXQLA>ZqGN#qsjvU9<Hcevtc@5*J3Hqj@4ga~JU&!p5AQvI+5I15B_u>_ye9BR_xX
zq*FQDEcbrrcS@IL-P%XmzV+CWkvl$486^TvauV;~4Q}e#iaGM?v}aFt-O=}?GjrFp
zyR%pLO@qy&^hyiwB>lMkZ3*wmOpBB*jo&RPX%>G7+FVWR{lyD1hv*&(EF`E<Ykzlu
za4TFh3Bs-JY&*5tj_${+<{4vV;h7*QS4HmRS5ldo=lH$CwIv5YO4{B#u*12&?|%<H
z-c`V<%aQ5rt6BaQLNa~GjX3$ZM3y#F2L(=C?S&!g@fAN9%Vv)0<=Wpf8)KjFyoG<A
z_qV^pT-3NX9lyjbMb%o+xx<MkZcm}-O(f*I6HWKAFGy(<pZ7q0B*2D!2PZ_(2IMUZ
zR$U-dx+b)o=$$KP;`WtGv@8*XYg%}DKFU8YPRo0UGZ=Mm7jnqrd!mp|>eJEoQ=@+G
zhDIE}Arlu)jnbOq=)u-%)ca97S+lW#9;I#vnM_c=y5}wYOWI%k#X{*3Y5sk&7zQ%X
z#8kWr32{AOk#Wx~Ufhd2-}R#Lo6K~Cb3eP>H+w%3$V1kc_>$|fwHfYY&uZNPW_90!
zIdN^m=?WJLIcWYtU9M2^TB!b22se?Obbt@@uk1jpvMC4$1FiUzE(kX#U4B!KFh!Qt
z`)N~oIk8S2*H>JTWa?j<?bfc;aA%q`3(v+z2B$g(Yqj%B%}>1~Z<SvhF^cd2vyVGi
z4Of<Vee5FzF2#Fp>7|)WxDa&a-Z$F;S=r4#-S+Zhq1pQr2)7?3U27uh=ALDztO|L*
zZ$_=3#2sra>J*;mkjI7F*P}3fZqdpd$vT)~9rW8#?eh^kzfytx^%~uchW|G|?veST
zmQUP+Y&UaSXtr<H6MJQ1g&W%e81+!ki9CXQgHf}0Na6&4p7p1n8Fj?{Vbm~VE~XrJ
zQdAb`nI2?XU!S$oz(~8lFxc<*u&pp%65mXmN`q8Vj<IEDOt^e;r(&x|TrMuzFgX$N
zRc>YjE!-NSOB1mJ5__DccYd|#o2fW3Mhsiq#yVjW;20Gxe4<XQu>nd=q6l<;vDk-?
z<9M35=V^Jqn@nE>I_?55t?LWg!pGXYIc4<z(hIvkYryCS&w>}{c!`gm(a>moX|6r@
z2BYUbE#e}|Fzqr3w>#!LbLVM&>lh5t^NiV#+a2*thItp`K%#4VOwxC3F~zBQ>;1LB
z9xJr+Pr)(d4?&-uWkB|zSG$4cZ%(2}B8D&o2|%>g54w2zL{%~FBWgrp|Jwu&h3>;?
zy&vI-h(M!p`Zb|xpqJEj0vvt_6whakF!L%RyRES;!>$|EYdg*UFRjQu3BRe|X(NAO
z&NtS#xySG|wjh(BiFi@b=ReqLw!fg^&zG0`UE=ojI_R@8R>!yV?L8)x6OpHP+;xHO
z|BSAR@PjnC$3%ZM>{juxYs@~o(H{E}UaQu0?dnHbjW#}7Wz&tSffj6HD+E3*{M(+y
z4;tvo=3PM-Exk#!3Wx*$#e3OST%U{Quj(tG9MF&M9%MQRh_u;@1K1&6274Hr>}Lb3
zoek%&7KB?$@BgJ9v!S$g&oYgCUytvP{LcMgiy^*+eSqQ2g1}`10UxKQ4lphM-kD(T
z9o#$Mi~!R*-QAzOKj0VOtptOYjRwVqIuS^RccW#MB~FUwTglAZ-Nxv@<g<FXn9N)*
zG2BFV$%%0rflQfOD47XvB-58#jnUWTGFojRp1m||6H?Xa{%SGs(^xK44BJ4z3OR9f
zhUUE?&2Qa}=6?g-W?^4d{ELrAR{Iz2o-jCK1)7A5LMV0(cTHqAzftXgX6re}$3&LZ
z4SNiP+qbuMXU{Ua&WT8?xF5!Qq0#tvXdVQoGDF-dv$B-v1xf+6ZV|Ml7XTo%&WDLj
zxuV!CKEqD+elg#hd5O^#S~jpe^0(-bLNKx@<bupBItEeLjN0w>cI`^jPoEKDdf}O-
z-PIRA)@atR(L(RpX#a7Q+4?>)5pi2<R-nCG`a5ZU8#Mo^2$8rc9<SoCYV`bvlc4)=
zVusd~{UXMRQm^-59Du}4(tD#dS!;u$tLgJsb(>xM`I=lZN>mfYWtMkwsjL47|L+7@
zKnlvqH4eA)mz1sYsXedp`9^o~vOOT&^5_5?ay*yyA!NhgU_KNtaB*~D+DztaTKf|N
z=2*UQ+|2#Ij#v9kvsZs_q8yHE^APM(2Zb?7Euwajtw8dTDVWe9!cE>Gnb?t*Vd~2f
zZYrXL+$Ib8OGT1kdTUt38>M|b14>Nkns&sqk}^`3a%<_ph9x8qy`Uevpx(r|AArTc
zW>)n_HDrK>llRJBu^Hu#a;JUlvbI3*3N56XA}`)^k{jbOR1bLOrz)w|Fz{z1j4UBI
zF}f;BU`LEe+RnIFQWek94`Yh|r0ho3ri3WSdOV}-AEj+lPQ_spk7#5_n+JLOpr?I6
zE=kk?lLtFt3>bOxq+e=Gb-DbEw=6w77-!X<aUS7-8gG#DEIf&Yg|1k=REnuz_<t!a
z<uTVfF@hr`QNEH=py-$<v0qg&+@u{envhHbds`_pc(1gNUh;X+K601M*)n?Ei(YJA
zDko1fw<@Z^60NA$0&#_=WWx}9;tfNZ*%8-G&8->}wKnGE*};CQXkP5NWup<XK&l(|
zZwD<)Hru(f--T{>LC49(G+M|=hK>qw_o54GkXUb2r4VVn$>69(nQ*DiR48-{&?CZ;
z*V34%yxnMC*{xFbV61kmAv&vc^GnadkTD-qNHawjsuwV6V`rW`JD4AJ_b}+bG-Q|<
zSy4*K+GY5}q3dq640E6#qDtI_mMM<P5V~0!Q{CK)55x}~(4JHe-7*&maTY1G)aF4x
zh*WXTvqbX-!j8HVU~^734Az0fH&m*tL7og4LIrCB`)@kM-{Cq~0pr~&U-Bcp{X5d%
zW9Pe*`KlK(aAJ^xB)W5AY2tnm3W{!J0^NYq-OsmcC@W|mXh!0ksW@6<wSXTi^04nA
z9~*<^!mhwd1uXD4lMpd-kiJe9v?3M(Z739r+_oy{EcFP1iM_ABFB8E<oDjc?&9;fW
zz4$3mW*a|X@0Cy7DgH+XD?p4$5N=XV`XPqgJg(49n_(-Dqdw_|#FY~Lm6$;7Lz|Gy
zY1bfrnV^DJOXv9>`bh5OJ$zK=MKV^jMND%9WuwjtGV^;%yh>iKP(k}sKrTikCKb=A
zg^RXPd0P@2NiHnJuv}jwFQP1UQVs+kWgsx=n_K|J_wdds$_WyVy3uMR@t?_)XXPSF
zhz!Twf=E+w-Bt|8PxO!EX)A}%O4ktjLBb9E?5-ZZ7y2M(7N$Lvo8-M2c@OpR4)vS*
zM9!X)d?(9D5N_+~D-S4y+e7j!dE!}047=v25Rpa2aASTXvWPevSAAO#>r09HmuDr;
z*`}T4WL%i<B-ub7^aBm3u~Ow8;@8JA|I%JwAquJZcj!SG7lj6tP&8^>C__1fXKAfO
zx@WAYZxz48WPr-Tuz2q_KL3pcOPAE#DukOL@Q@VoeX$eaCL0)XxAKQUmlQ$^^q$1d
zQ*qO<l{XHgJuqk48q|9^Eg1(>Mp`HJOIw7Nhnu1FO$j&x^<6;`P?tMsehbe&M%qnV
zc%PbEscH>*vH<|X*+DC7)f@PNcL>65nqQ>9lH?tGNK5@y2shRNw5KKLt(E=1+;7Oq
zMeaB7-@z$bwU5mYEg$`b1_TfxZB}^-O-r6wz^tba%!g=vr9MUT(8?|}FGiLi{uHW{
zc~Z23Zw)oKvN_N{Vh_@C>RZWYiS8qD-8Rymr8}xe=oh6QHT}<-uS`BD8&FizLjJPL
z;<^5>P(i?F2iR{VxyTG*5J?`FWO4~MTmZ}bDF~Jk-aRnNSKmKkPk3B0UTl4r#h;=|
z_!%mHVMsKKmoX_YVnI|blZ4pWS;9MT<mw=#898>g=Adl@q>*S^Ij7Rsh`#cT8gj^z
znq}8qE>#S6-{n3Pq%BOXl#?-%=Rxt%McG`I#ZsfCDlEKOSdX+wm-;(_gflH;uG%v2
z6(K|_l|a#uFRx@2caOt13@HOm@|R{wTXvBa)M~-!G1DYc)G%(EY8%h82!~^!q+Wes
zBrh*hua;u+$1t!=#YU2Rg-g{11*Uux04v_nW<i?ChWj@YH5QP37kAJPakO}LfoBKJ
zRSZd(A`ysXrsci7kO^V~73bZ9cMkJLY3$r`>4-7FsDC)ujEf91c{^xXwOR5oMPM$K
zeW;AzF6D-wr85W<@(&xv5IM+X<4*x$<5A<9EzK=-Hi%6&=&z#X9a6ItBGIMg^~*r0
z=-e2A#0qgL&(huu0|DZ6K%;~CmbM8QDjy+O2~lv+KH0>uN*Kfx9OjKO-zo~Tz`>YU
zptu6R8Twt|IpowaKF=<_W7s|!KM5lwmN)X+J*B1oaxolBfYBi>11%Xu`%rI`Li{4a
z)qvOVNswl|SK5mXebC=wo}5Zb(B8~4I>YBmGP^Y2gE82Z=7)K!5!{6Ft92^x6<UUd
z5X=S=w$PR2E#pm(v`+Wb-(`7!5A&7%V31gcgTXKo@1;32|4j6>Kk4-w*K1Mv{W&-N
zPo^Et5C`ggQ7`x{6FCYJ=fh^k#HOzL{-ERkh~XO&qwIb}eiejUQoL*gz(Uh*eKqKJ
z{!Pc(_J9$}Q0_4?4SI#i!_TRCbVZ-I1dwvG5zk#1cH@VcL}9>K-ioOUnrEsowDPqT
z!q|jP?~iy>dM)0ZnvCXw8Lj*($LaqGrTfje{!`wiBF#QIuVtQ+IGz7!hW+n2aqeHT
zR^}+5ut9OsxTDwa{St)D#a2cfXeR+q<9{6njcYVN{mK!X4MnM7Q#+i0CFVZs9DVE}
zE&(KN+)wI$?IKf|q*=%C-Z!VxL<=zM|K91fZp2ZuM0u|6p|?U0LWJr#Y$sXb-4&kc
zr}}p{`&Hs?Nvtj%vm8<UC?Zh~OHd14oVRmN3cUWWTa*iZ;#L{uS7*~!sckmyX!RP`
z>7o0yj8S?8dBv7l|8kpl=$sW?t#C!2cvgM4{aw^RSIC7Tz(k@Tc#n;(*6mHd|6gX3
zJ>Rvn$7A~_@!C2;(){;;I9Fa=xe5E!eKFg)wbgBbn3GGW@&__*>Y%>U`j2+E@jlc_
z!q8Ql_t0O}4f<Pz@h)VO%FXFiYJJD5eYVqWe_V?c!mZA8vTPOO#l-5|A{TBp;i~G|
z!VN(eon4%Lec3FW!qrxn_-faNUH^xA?3_|C@-NWRb~!S75A=e?3nJfny5uEkyBMpy
zg$oV8d>yVR_u_mfp$MmgsCItTx1U=w^3RDf7ALvxJ-GE{F3UGKxZ=dXjk~sBYY^4n
z#Wv5eSzm*{<a1poxVI6tKZ$;5>4djL&oZa-yZVte#ji-``eV!b#FdoB{<Zqfup9n?
z?}Py}R+1Fs#EeUyVoWdaoN!|^ZhZiy1Xd~DX=S5yhMtS3Ytif(aaW&!_THUNIcpND
ze_uVQo(Jkpe5t^A?GvHzr$OJ}YK8utaAfQrcF;U|y*u?d^QsZE^g^7|V{IbA0_649
zF}O;7vF-MM)d<_4N+SEoASusg9l!%+9dv#f`RxyI=OgngLcyN;E5gAPjB=ZqWL{IU
zN-rda+aDXj{OdWx&_40Arf6B#xMIBU+UFPa%n6#T$Dn@))goMZ|6I`1APL)F=R*8W
zta3yZ`ZlZ?p5lAGpEY~cx5a_;WN7|XyV3kpq4`%1Y5uG2Uh{)ikU7s~tr%{C?`N&+
zC(S$Je&>r(oH?st7GD5eVTZSHebDU_^KeH9D>Wnk_y6p&hw1Dvg67IUzw8!HvP6zy
zh*62HLjei$EBn-u%-H)4*q@tBeJzvC9OkE-y~nJH>jhE7TI3k0@YT|8q*5iSVZWI_
zxsoY7Uo2FqPmaEi(l0qih`&&A$u;%SRNmd&cfCW$-KkHT15}V8;pBO>Z9uGRFzZ>(
z%K}3yzGyT%`iU4!5!Mlxjk%+*^G0$6=c6`^e#Oiv6OX>DoE<wqRdzYb31qJL0<1HL
z=VatvT@W@Jw)4&^$FL#r!gI#5#piSDk1_GY<xauCw{7Oc@zmJu+yXy@Kh#MRA;!k1
z>PW71DV{?yV;aA9B$L`VX8*P|d#wTW^LA7tdrU%vNOJL3d?ue!G{@g!5b@D6iZ22c
z^#0h1IPPkEFEWdYea7(;BD6+d&k`_goS~0rOYcJ7&UuN+@!BY*%(1tY@~IOs!I<<k
z69+N|zD&n2Dde4l6Fv8qk~R98IB%vs+B}xazd=GDjl|=ZlnSF~TLqA7@-odDb2NL(
zGTpU#vvNkckU9_-u^%kDrRT<3Yb<S#T~R1-^@qp0qFX*KCX@)lCeN?rGpEO<YBMJz
zm4@9`s-ac%VQ)6*eUZEth`aacqE~uOkTr^VWBlS$-aBDegqzH-(2KJVgU5<>4=%YA
z*LtEk)ZD#6>_AH1A!T>+1D<F~*--K4dy?lFSzC9eEH&=gr;VDqCzF>WZ&E}8?(rsE
z!<jIQ^x+k!@`?<H)L6@&GGb;=a9xAwH-mDVdRKxZ9Oo|Rm{E7+l^G&)ilk-5#fwIr
zi3`#r*(Zuy9~vW$l$Mh87b$Wl!i~>Rh1f_=-I~s&4}g{r9Cs#f9wI`A=s2Y#M(U&f
zq<qD=jg2cvJ4@p8#>Qic#>Bhbyt^v4GAEB_Cca1j*lFBgt|oGqLjc$pkK{*g24&O0
z0|fLp@($?xjj3|#pBOe8vz+FgwFopvlnkso^41uxd!Ij=8~ZrEbG1}F^3Ji;*m+JR
z`y}ZXHavL~7nQOOT)ptzv25v|V_e%)C8rFPq_6TL40nQgmAOTo5u={)o?kL1F0~3E
z1g-q@CNiTpvL@x!B<chP4Lfn1ZBZLwo3V>4*|GENa{4H-3gLDmQ%c4zsboiAL!Lsj
ziM17R*xm#rBV!XFbUasjTVfl<-Dv*F(EQXgn!l)!cOM@z(X7Lj(DJIBS$z3uA$wee
z*j7o#-k#5zg~&A0ta~J6HJ75h{s*RAn6C_sB(5|t`)GIS>~!Y7+)O{O+THxbxq9oT
zskXC@_#`kRXGP@HKiR0yT_3fqv8<gvlUr%@IKo^No8^Ak*axqhZ>jn1pJVk6pHgY~
zj=_gL{430t$@gGJcCl~Gy`xpDeL3#f>vE2F6a>13XSj%w#iv7m$tGW$ecoJu{v&=T
zs^k1SD_lR&7jInf+keF8)es#Q;$Xe_-XdmTT_>2vz08u#kIp~X)C#trQft?*(gSUC
zhZt#~<DOu)f@%=7zm;_C=U~Z6c!$nU{a!c&lC2<%yOm6zGS{tr9h<GLm{4%{^y-Z5
zuIst&yB0gMmo(ao7Yw9V4ZZUolMt@}rx~yektgECMzra$?qc^LJxCunM7ZItO6fsi
zlS1uSP++tk#_b=5J)B=h_BhVdzq%N2Ke^Uv{S-7FrcCyF5N@9s=KUOb1HT2D<7t^E
zI$p4$;cq&!zZg`BwRN(^p$>`iGmUnC)(k`k#*)<=Z?A=$k7_J7ex&#Phui(dtFry{
zk&YK`obPWuNS}$rqE^1zusMYo;esEwe~euLX3180cpw$EkR&-M3-3hwiZ&8$L*jhh
zCyTC>qz=)%?oaET&4&ebjJNdMsml;<=<Z|#Hi>~(d*fWR{gieGP>+uxl$g}&i?5#x
zwm+@i?mbMNHQ3pd7r7c2>9;TTreA_t21%U(3>)+0jwJ6hi{0s$MPfao&z@H8ZaOxo
z^|wZ0?+;y^h0<XtI$8*$>c<wkGq1q73cLQ9=Th#tsOC?HVfS~v4lWsD@1+$pcS4vB
zs{KB)Arr!1D%g?a6azC}is7aZZfuzD^Gb#@64>+CH0#yd;<mLqXQxl$NGv*I(Md83
zqM9;SM*89ni$Uw|umd7Z5sh<fj$oL9v-N<*h51jW>#4(vp2g8(>xuK7E&s3a|Fw7J
zQIb{V{q40cUDegq(>)Bs5S--5B1UmZTo7}j=D33!aXlvoT!KV685Cm>kf0-ophh5x
zazsUi;DR6`o1VBt32p%$6{dTd?&+oWs#kBT_wxJQSFgIdsUFZo^T*UZ-SytP@2=nd
z?)UEf?zhyOg^ZPW5^Nfy+We&u!8^LEyGuO>8l@yCe*QxPoOc31AaQb9Ra2(6|M=Qk
z{q|Hl`OGxDgjiDLs+!gP6*$d86AtI0cSB1Q^I+i=rR}acE);A4Q=kXfN?ei&TERsD
zHQXW}ZV!@OsRil3N*F<><<CFGtacwzoCF+KVxtd_9Y{|(VFDevtQ-MD?AO2*(tg;&
zhN2eA59HYn;u@xXfG)340hC};&w~@!gAI57B&&vaFY}^U%(|)KFB0d!AL6_Q35Io?
zXIwm>zrWVrrgz|JjavPivgJNv*_E>;dlK%Q6r%(0-*t28jjeX)S~vy;TTZYC2ZQ<h
zzu8E9qxt|`;5ps_AXvPC<jLAI3K|^tCCzV}R_rtbTM-#HAZAnL^J*CwHg}X_pIHJO
zb-`Sx{3ABC`1H$A3k47bE=4tLeMPNIY3zm;kFE7e7jMhvUs+8GZJ(XuTPvIm3gbN3
z(qVA`3;9X8d=U&}ms_#U_u<kU?X~ah_HaN0l~fKO%#)lvsZ6{=O0~Y)=(u&dHD+0m
zBYeo}8Alh4o%4l>!Uv(B2m>nUx3cZ)iKGOZ*Om86uKQzp$Vm8wx5V=Fa_h`do;8r+
zv{0FPS1O|>;KS`2#nraOaB6QEspsbt+dp5*m9B(M3LZe>V?k(c4&o#hTR8IfH1H@T
zB`58ggN9of4&a=G$CGKYE5uF!s+oK?ze6GkdE$4dc|C8z*u1d$BxgQQP>XMjWdo}&
znP0*(4KT?5Q&BCx-pKl$YGD5`-qU#SlAe@z&8tN<-oZW$rUNkXI(LfxfKal9Xe;(M
z3jDQAyj)GfNh-Fbmel=`W2rk!#*X{_Nr=~J6pu5%2S=pklu>$X08T%Aq3t6R1qBG*
z4cw4N3c{<MT$IW;<x{hFC$hy$fDo|Jgj9jCdz-#%-lB#Zv?u!-Ze&?d5Q{Wb;DRib
zZY;&J&+Vn1{Vm~rgpHi~aw(SibHqK|CYA;EZ#n^#;ic&dr?T7crq&x;{mTyY)irtg
zlBwM6x8U2LAE*tP-0d5U8(=94l`t=4<m8zvrgAg4YAGxs1%1ivNyp+ldwTuTOqY>L
z?=sTvPiuDfmoVN$1w@93M~LAQ76IwP{_H7r=9{U^%s1lc)M=8U9|K%=py77*OlI=U
zRcN>=t}0gIX!tVvOLha+47B9n-QVu@pdAY-2LJ$CTDQYf%YtFg{yJYMyvBi(H=HfM
znAk*Z&1_7c?!+25yO1#eE8&$=s&h*ND&SBw!H~+65zaS>^F3`6i>e!1&>Bs-M?ILE
z%%1EdWnGi>Kkdo(*SbBps>Qruy=V&#Y;je#-HLetLRx_<dXBlY%ueGC){nFW4TqaS
z;mVRRdv`25{RPOT5tpH@iS=r5)EqS2j%;AD5Er<NgG(N23swt!sYt;A;NCww9>g2z
zW(lRDTl-+V>na%U!Wi)IpY%iH4#Aj^cFo6&`a~PvmXiGB$=M5m-){(UzWarBoSzgp
z4?lnZ29_U+fuUrz@ESeo{C5@H{d7+&hQ8iln<aVV-Nk(7?XV9*)({tUf4~qT(4i_^
z#UR2)$IM4lDd(pv*1<PPrUzy`jGZgZFRYnsSLfO|&@yOnTnySQiv;Y&!C1fG-=<Go
zEg_pb2YE)Lr><SH)@<Ge(_A?i!hsD{mMCnVaz%yJ0z3r6$5qpx`{P>O`UQMkovzYu
z8cPwTrQwB(OD#Y1Qdq_+IXWC|#^XoJiR=sD*%t(3<qZqg!^c=q+Mn<;aGis#ny)@(
zQcfJ_#XEO&xPA|E??~x-uBPqF)fXH0#LV>bVMhVwo7b%M9-1*LUbEzuKI*5uM<BCl
zD(->ZwOacJF$b$zq{A0~4K09J%;q38+^7wPr9cl8FmO83{E=PQo=8Eu-vR3JLx7%4
z<C@{7EaB(h1Y!8bvcGSlyJ$a>@NzFRuvdV!zw-}zbFcDO<!niwxPWXA)vv#M$(#S{
zU`;KQ)!d$L%){w%uU-%4cEIDCoq#wSmO_{hK?e(YAiK9e<jBD)K#=`#4xR%#O63v>
z@~l0z^45yAbgo%%eOtAu73BOX)0=oLi1UvRcJ~!}ivea+{tZG$9$X*viQVEqf)9eB
z#x-A!VTXWq79-Qv^8f%7gGod|RQ-$LyDr-guDJ`Zj@9AB5mt!t&143wnuYfV9^7$)
zqj}4xE_ByEk*p+6nn=W-NBJ}yyuWOrTe&t-O`HS+Ku?p@_7y9=`6n+|mo7`!6DMSn
z@n=Y|YPIamow!_C{#d$^I5CsdkE0?g96p{FkGluzUgIj+Rd+~A^CH0RgyqWG^_gb!
zsVQnXq9Ms3_tIFxeW>oXu7iTg>nGIMOkfzt8b<P1H3>Tz$@p^sc&uKz+7+SSU$z@6
zNzg!oK0e0X*+*RV)#}IBTD2SVrjmn(8*TL>lfY%1<_}weBjJHoCtgFq;Xp%LIIhvC
zT-)owwgI&FpiufX>}6gEZOu2Sspd^9t@@>so!^_)G_0-@f0m5LkC79Z=O^M5=K<%7
z!1-q*ZWrQSl1c`1@Z5V!qh9@N&~aB3;A<~%?&G{d<1thG7PY+q;0GXsdqN4a&R)EA
z8=R$)rzl?V7a{5^Zk3oCsBk8%7zX+TA8zOy4rb(qV>>nXk#tW^J7B&Ha9Ly@>^xZz
zQb&FZ2@?e3BMxN6ZvfkFAo6W#ZU=U34VY<&b-4iw8t+>YIIm<%%EbHEaenS};CvCr
zmp)9EO5kVV;8V?*YV~TW!#IjB`2D{9@Sk)lo(W%XFIPjBDAxp!O%cNu{vZ5Co9~T9
z0ZyTY8$el!1>1jE%p{ICQ=NO`t@7vO<nrsPma|%PGp8vqMgjw0m5$f>1OXM3CeELT
z#dcPe#ZzofAC4$|ykR>rG*}xe+w_Dr2Bw_c{ba-My$cikZ3@&nG|4+qb-UkU+HA1x
zi)pBbE_q9*_iW?Dv<&`ID)3b3^LO>mT@;8n<RZSt$=%p{>`Z8dy-Lvd@Umn7$ggO#
z;4z*j>(<XqJAi}YnH%tBhY8pC4pWDQ7V}Ewi{j8k!q@)o)O&{yul=xp2Lo8!Zx8bN
zANiW|>%i8Zn^8*dg%ttR3lgN*zmYui8q;&$nRjRJ1M>y;1-$*xM@MfUHwhlsrhf8~
zm?jobw%obbax%|~C#JvYq<J9JzNI6MVvJW$$=lxJr|Z|l5#(OicMrHd^_e+k_G`Vo
zJ{73#TO=!e=7g3%6{?w-?=qGzoqHrNb535*y|5Rfh8vs+`SuSnYg3M!Ix`V3-zLLi
zU7%F1w_WwbqEfyXhd0~0@zOt})2j)Zcg%Yer%EaNCoRjqj}iq~Wu1?$@(BUlxhQ(C
z*>tNHINsC=*<Sjo`Pj@YFc_$VAU`NJ;Sk3Q`^Lj0*#IfKqarQdA#-G@&xgOd()~rk
zNrRP9x=;hTb%XY;RBq6M-1}yX%+njj((9lg^Xs@Y_330xFZr5&l%w}nJFaz?;ZI$c
ziBTtCKibvZ71OnUAp6Bn7Gv=v+?2M<)}2KhoPJOCOV<|T#*B+}w(c&#wbIX^d~znV
zo1W;zDpy!I+JnsJyn;FkD9=slyDh_=v*@tS&+N(Si6_{`%7r!r{1n;!`D(Cuu3QbQ
zgtYB)ZGu}^>t3j)2&oVD7Zjw9-3bhXn|oLM_Fe_J#Lfy0x4^qN=jGpmBR2S0^OowK
zc_T+ZBTaucn+)np{^F^gCD&Cse`(oB9UCM-Vf@m^L6hDtJEhOTgs6^~osLG2#Xj<F
z|Ch{!mT0K%j<j36(n{9OI*9(k`4;@KmEWUi@E_>b&RzDbpY(D5??arIafVjf3&*a|
zZ7{akFuc>kXFo1aUOQ>Tk8<KhA+0zcFx$2BHG5`nEE9B_e)Sxlr~nNFMzUAvuF$`v
zhFe#zoMF154>wZSp4_>|^s;{$Q>JbX(#9@DH}7tf7D(!gO7iwg;KS|4WfNX)dd4Xk
zxo~DaZp_;8-0L!dyV7o(-_7{b7fwLSvm48up2;}-%vSpj%TDcyCCcCM)5f%{*8bJ%
zrq3v9`B(Mg@WzPs4Q#4xz<GFpb1mQgId_FQsqk7k<viH5%`a1F3Z_YKbi1i16^-rp
z+66u5%dPKl=f|y!7)H^*Kg7=l;!6NA1U@v}o@B(*Z}gP*bxP2^ujYF5&=J96Ny6d|
zKhny%W_$9ybW+t}&-e1Ki8BQZj^gV_>(H;}1j9M)Ls&EK$!Lk=DLJu)hFe(W;8z$)
z7Lp0zsh*=ZC;tv!XU&Sg_Kz;Drvf}qVDGa%@1R(oNWCQwUs_#?&A`s=S1RV}8Inae
zBMLn3cJAqP0%-QobF9D6k-FDYpc}5Zr(zBBCP-YCvEl?0>17PSR~OB-Gh|52DvJ4|
zuG_i23AGG}`^;c6eGD2><w7g{93x@>wBoG2zuWa=DHuV~A;xCQRrr;vokwYhmVEF%
zQfu;Wu^L@m4Jwy8*bV|_sDp9^j|xE5P{R#+Xz+WrR>-!pFHqv`+g8ohx54E#4%36W
zEm`mj=PpphtEbe@TC-&>CnW*X&Rxsq%9);>cs=&p0hnfR8@qaWyJ`Nr3T-w=7jZV?
zE`=qoyU^Ub>W-Gnv{NU!F<855*FPw^iPyqgaor3Ww^%J&`{z~<K$e@gw7Q+oQ+=Df
zL+HQ5V7gY&Rk>5~S3ctKyj!YVRP9RVBm?6}S8sf=+OA%}Ns2KN<`L0bA~P4c;P<Qa
zRzNvvDsp0iuDr5g2G_@f#ICNkb_LrUYPh)zmTx@(GxfrT6C@2_{M<N7Q{0EvV8x6U
z%-|yuVr4L^skpyfb=R(>HX9bsrUTJNP4OOHK{{x|Ar5Or5$F77#jRe(IIOxmE_mE#
zyZ!<2?xCClzAH>hx{`6UsyGiUJC#fM0me71;s(`Eb=s{f!4HIJlYc*0FZYwgIzi=l
zx77pdNz7J^>tNN&V?N3z!w4GIQv7CNJpzcVb)<u587<Bi*{cSXOKsEvSfea!A8a0i
zpA1le1p!swq^FqQ#fKN%Iw+cA9w8o9=JcJ8+PMuHX@4VI+Hrx}y0c=b;E7__2`h9x
zELT0)A7T5zXeq~<Sg4?7aFzZUv7QaoN#OjKk%))tfUQxo)aI93owai$M>`JlMFCN|
zv)vZT3WDXK%jSP|-1b*nkSPKdOragz!HI@OS9B0i5DpC3Uz+EDp{wqv8s2I-*UY~}
zO?2*p-?X>%wKY)`c)+f+*e?XLF~B#cd8;XC3t5C><q4L3^Xno`)^-F+<FdM|p}fqq
zd-2v+Ryx%S6g&B5;I}`7^EYq8`47S`AS80sW3WTNwb?bl0-MhmSNTX)GOt_pS23S+
zFAfr&dzQ`0JE19vcT^A(V@9C=0Rn(N<im|Jq$_%m*;CLaUS-AFH@B_Y#jrKcF%{be
zViTEMWbJWK2TkQOs0_>&o)w37OfxV)&v&v^xN=HOKhuoWKL-)0%Y&Xm89nfQwhSzZ
z2Nw`G3!v>2TJ`7fL(Z2%)Ufdi$xjpj!Z^@yV_TRJ^E?Bb#<v_yBi4_9)&+TG`eS3j
z7KYk+l=Ed5NR7k1kYj|kE-Gt;vcE_Hxlp<v5*|6)ERV95KzMd_b6&b?L6I<x$r_Ok
zEk>IMvds~1P&8ubhpl3N%jj1q%ZR+F02UAhXHR%-=xexPdmDP*5U>^LR1i$xiezC~
z82YgjADjI={9yn4ar9(?W>N72abaF)3DFjC*j}b%YG8zhj}k0M5k_Rh&`%x@k#L8<
ze!sTkVPOD2#6t^;hbBlRJ~V=CAeL_sEwrFm1^B_>uHhMVSVHbK%u5Y7yz>EPG1*C+
z${34*=LxnA@H_%>n>E+ABQEsChq920@Ea934G4(-g07J-Sw(7yJoU$0<VCZE7mQE~
zin|u1TjdHKrIKq5@i6)a<V4=mhZ_$Z`^y>%@AJSkl>Zj^6XG)Xn3UHYXt)vg@SzI*
z77pPp;(G%37zaGY2P0S{+y!e54PwBU&j5bdr(tb@!^6H`H`FWI@hF^UUJelQ?Lc|+
zqCEB=|M+7)&}s}}ig_mH0php7VIi%-ccc{bZX(8w=t4Y$6auw1ai|&eJ&*vZFr$a(
zwc&oSUdT>kG9VNF03Q+1mYvY(+s}se@fLlBb{r}g!tYTyFWOCs4@_E?jj<Moevlwq
zmlfwU1Fiwj2dq*y<+lvKi=6K|@nvP46l{bL;tuT^2&4njl6S=~ob!{OKfAXO%ReVY
zBO{`T1b*$#wzc?f*iOK}F_!X=Vlv^oNg&}#R%jcFTbBr~BKL?Nz9&JV$P#3SdCzS-
z4`N8*gh6LcVrsZ?QUI7@9Jqd1LOj<bAP&O*E24M+DEwCu-^f#(#g*c0WHa)fbd`-_
zUc`y_*aU}uw(yE@VLBcFuqVumI;3rU*T08E0}c+t(_BtcycVjqWH)Sqw0IMy<CXnI
zn<42Jwrd6DVeKMp7gH+;UuB`h!h<N*ODOmIOXJiKAPC!rl^3=8#Hlhg{6u^EaiZ_6
zgZ-f95z}#;u&RkHbFt4s8*MoP>8iZP70JnmrQKQ4FN_0MLE>XL$vP0vG2-Eo*H6Q^
zQ31(BdEkrEw-)eT7^WeC6L2QMps+po`C=jy?t}>|X1$d@oG5I-IKrjck7K`B-f+4o
zPNc(Y91T(nN?JfZTm;<NR|*#}FAt0{W;E_Ei0uJ9<5u*0I6a7}X!D`Qmg$+7W6OR)
z$w<Hn3=ewb5aWTlMJY|84vhLKQYbXEkdMTG_6EdQ2;zpcY!7jG2jWTKnU3cLkKjI}
zry(BEkP{S(ME#p_p7_JG#PNCH0Ol{}fjDqwdB`Vt*@QjDlZS!=hw&H!jW=5a<QM&A
zUY?<HHB4M~P(D85^l!+;k$^FH-4baF2(#!1=M@f?CE)0wZ-^8A^EgHvbIea_{t2x_
z!jpq@D4Zv~AkK6A!9WuI;CK-3Jcj{gmVkTzzZ!1ngZSPMIXTJ5)4>GeH&O&#wI^T)
zz0dQX_@77QIS7sKb}U}=&q^gAi4{EXjD@=KL`}i*ONd-3il?FiiHWQvyc~2~9y72#
zOh<&o4QZS}q<)-uIVcBdP_9_)vfwCh^q$XA9N%vUTjXV$D37@I)3G5@xkxaoUZGoz
zbQ{{dq0SKxqv{ycj~f;iJxBEpw?)8-$%fL6iW`-8(4T&vhnvSb8JK3&SVZNC4;a(*
z)8eva7^K+*KV}g1;b*8Hgs142Xamx)i~ozbQSBO4-l%jN`Zwsu$dv1mQ98m8A5Af*
z260TbvA+ZSVw!#s?TcW{xXoi0l^12;byQsc{iDibd{id$4d)%c@8>_{MWo*}U!pPq
zgW<6i<qX%$i0GO3%r~k$@eXz1`q((n#0VmBcq<-9;L!Tp-|p^rS^2K>d_i2flEFDR
z+)rRrh0n5lB%m;lM)U#T;}rv#?Lp%|I`$)bYT#WVd<0paoS>{B{GP27UOtP%7uqGj
zS5}lGA|J<3Gys@{(e2=d6LUl8BXkrOy=J?k5XD8$gX<P~`98`cuA9<{{F~zX=?5Qy
z;W9+}L;Q@YV^DuoaFl;jy`mn(t-lWMM!^oL1|QTY{V<%Oxac}46cLZ7yy5gw8Jn+z
zG$Yy~-t`kimk~r~6pSBl+~(%+c~ss}>DGbFOe0e~Ix<Q}_zmv+$;5L%GI}>+JT|nC
zSvRMN@($NNDlV#a7^YF4;WCHw4&O&<4|NskH;rvnMg)N9ndJ=EJL-8<c}zD7A4kP8
zEyHk~d>n?srgtL!@A-%8P;?$&5HQN&Av7pmQ#Q)i#j#<4Tc0_6rm;2P$4Z1SHE(6D
zAhag98B%dSNJNg{a9{_;Ry68dbRAAKz8^zi41qBO4l@X>lhghh*j|+V<g>EL9Y08A
za&RXi#7p`D@58X^ubxOsaY{1$W_&+}z!(B!2#g`{SP%$_FPG|IY%4Pgd?g+9_V4nl
z`V-(v?Cj8&tT;##OohiPZv1`>fiVQe5Ew(?@jxJ4?~?6+6LlKuE_`Sj?$+I`b>DKg
zeuFpz3`L%BqPUTv@OX+Jrx`<F41qBO#t;|<fj+YV+yfY17R+DBH`PDWY*ydvLdXQF
zU$Dg~OgC=g)$){|8N}~8^Fln1f5s3PLtqSnF$4|`fspuuuu=>Rio8;7PX#g!N}wii
zh(<*R$`3|M-gVw?;0lkQLY6`DYEZD*8B%8me;>NW@!K&3#t;}o;4p#!_zEL?OX5pv
zN!c<XtSzjQL1kMAs>5iAkjrIOq*5r1jR3_4x$qa6gq8<^M#w!q8Sj~)+(zgNVoZPu
zmvI<FU<`r74+28&7~R%&YL9?k4#dU6xc;-y9S?PMLJ~}?`_L%CKQvNAV0`~w4_YD!
za_x>*7sS?2?vCmFhYcFPc1N3b%w&;fG@)nSaTr5j41qBO4hskri$zI;Q83qq$8~rF
zhvL38IY|{i=;a}B<-3II45&4Dyj!e~nH&U_s;BCdDleX2cNQgBeAyUaKS7n(RdP4J
z96yaAFowVw0*3<xxU%Kym&BL%)hdjLAdv7P4#qB^S|>@1B)&J{|LlH-tqCv|y~0}n
z^t81Z7^$EEn}`PtmIdsS!e|L}uCb0rz2h*3z!(B!2pqN$Ao1l&w_Gku)6=j*4|0j;
zJ`Vx3RF*%Z`M!U3pAx?UkfO<rEfs+UQ3Y`&k@aDOW<ww?9~0gUAQ?Z5Auxu(;R^v0
rV9-}Vgh6Zt;l=YJ*btw^yUYIv2pxEjQPaX;00000NkvXXu0mjfJnZfa

literal 0
HcmV?d00001

diff --git a/Images/matcheat_numpy_logo.png b/Images/matcheat_numpy_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..c56d4c022a7130beed2ee67d7fff7274aff5de1e
GIT binary patch
literal 8758
zcmZ9SWmpv7+sB8J4hab<>5^Kyy9DX(hNU|smhPn_7D;L8ZV-`@?vn115Po$0*Xw!r
z%ypeJ*Swf>?!DuCe|BbK)IQ2%V~}A00D!HiAfo|455b2T+AH{M<RnxJKOuWaDQcm?
zj{r2QX!td$r>vf*ri+cIkGZ=w;N#=NZtv{iVQKDa&F<oE`{h)G3;?JBMHxvg-|Rm*
ze!i*N%jai|s@GkLStTkfo-X#x5%q+~Rz3w~@mqY|<559Ev9u;zw{2qq<DKjoqZOfr
zpqkq)zKyc{Jf?akHG_E#ChE_pDhq5*%{HcaA`>_A3R%teStY`IKE~Q9w!(_zf#ZRv
zf2ffqwWQ-oxTK`APG0?t=1Pk}SBNJukN$tN`Gv2Df|nO>O=YFBz>*WpZYXsXLZbO)
zB!k1l`}8+#_rh1f&(Ci_yF%xXu|tJm`19)|=fVW{ZuPPGtP{epen~C^W2?e3MZTqM
z!iF(fzNH+i(YVw_PnH35H7@qJ*oy<L_`wo$BL?j4hC@A(#6x|fB=Bojl0~cF+_xwd
zb4qXndJI%7HBfdOyJ7?Db$UA%r~8g)Nk{=S01rU^{@)9huL@-9sEDKr2g42lf+mC-
zP@{+@MK~mrP9%HSg<HuqTgVgxA>sOJrGfm@%q_)qMH6PAUjscRK&BfdO1^Q+rcTQ8
z#%?K5=@a$sR=gS*NOK}plv*ew3kVC_7D!U)SyrTBBY3Ww^&%y~?e<{msh6bSUz<p&
zz^Y7mY2tWM@B+DX=JBq6=fn~Weik`<x>;9UZ}TX1tX~0X!(0*WHU>vD3ddwgZ^iJP
z5lK<lM?Wmz|2NE&HZF1i>#<>BW!?VgOKnX}TGuwC1FII>Z`=il?)uf{Cg8cNw`2R(
zjNdyF*~2Vd)=+DH>mnYaTZ%8PkW>Dx$jd0+yv<w}8o(Y8$57Drz-7GU&5(@ndAV`d
z(@4T{6e2n`$i%LB8ccEX^Hwp$Z7I;~BgyzO*lG?_L*UXQf11snMj@3by=o!YQ9x14
zd?U_2QDc8*106}Q{9UVDMpUtIls)Su5g3(@0;C=txC`abRq1?~JM<(hor$rrv03i7
zs1u$(-gOt0W=Itrw)*>-%XC`+2U(JuDHhtQVp2nVcydxyF$eayu=on%<((rP3j_ip
zQJV}@G%MzK5(lNXE_`!GN0o)cdZP{6a@7#(I%<PJ^NHH-zJHtbB}%V(3)RgbS>AtI
zMNA#)RbwFrY;Uf1hEsq1`lY0<syac<n>e!D?&0n}`j+4M<m;5(yWr8TPd5xE?VgJH
z3&)2m%}x&yeVe|0qL)=IYrk|@(%1lq$``&SLPA141OSIhj6C>!L$Z9~7&}uI?BH;x
zD*klk_;Orj*fL+cbn@;j(VRp>M~A`D(QyaNL)LCmQ~DYi0YBy^SMYN{-=CKCYSM8>
zBTdb|=jVsBVwSYn81ulVP-TW~MMcHy|Be?}F)=Z#%v})05QKvsTWdQzMI=jSrqDtU
zY){R(VT)P34zn$5{uSv`1@U_440GysUj7@f7eSmGj<K*dn1z6Q8`5>|x4cn?1i^>v
zKdFz8mlH82HT;w4i;m6mY;S}qa8SSLRKRQ+U<Ss<@29~{SGIY9&GcIPB0-P77Otg~
zI2j`m_f?v`3!4H^GZHzP=<e>VV2;ogn!!XWVoI?9fhjwl=~t9maK{vD41cSt+I3p0
zFSahjX$yXN4o5=8QFzLMeJr(v|KxHl78qq>)a=sI@bg9(uKRqoafMM&P@rnrrwsv|
z(vLa#<U*cI!)Bb5YJ~K3_%YIO`~G+f>sRT3Ds@=<om*0aZ#Sbp$HvBn@8<<~tJ5UQ
zh!`R%hp45pIvKQ1mr$G#<?X>&681h(=m@y0#{(qbE6i6Jma?$1H6mj|;Ld-1It!lA
zsF)jtLUFEFTzYaYk1A^TWGZa1<n8UtOBx%sJdS3Wd#jB*rV#)POw3PtetWR_&1xq*
zyA56*o^w3pcYEVG6%!K^{JXomODEpK{?{k^l74<|$UuHu8|<MES6qS5aWS9LH!)HC
zS)l3LHx^1jf*P-t(r+iZIXj#DZHx?J;$Yb9?5wNz*;<ZGlL0d-E~WD6>8Xv#-A?MC
z-Wxk-XPcnsyM24Igh)w-)QjQ|8P~tP7K~R#C^_&f!p%wCH@+iW6i7!E!Rv$}HKyHR
zp?a3s#J9kjZhfH$b0{HpaQod|1QM|E--2lkX0bO~gw%w`gy_QKvO~?TA={f+rDBU*
zu|Of`rF!c~bSKyFqS8`B%7F9U>Z>bH`SS;RdwV5h$>{CDL`Fp5b6T3>zevH;Eu=TU
zyE}K67<zl_I~d2&y-4`GoS6_q-N?vs@A7VJrNw2!ODN}{KaMzpmzVec#}BEvm(Iw@
zSIu(U4GM3FiI0PCI@XzAHK!^w5F9VmoVf{*m-VQ;fB)VnV?>rrDFIuSt=lXVtu*!W
zF2HfE(+^?$pJRQc$atl=L7=gk&c8}C5}W59Gb;Qj;beWB8uId}t1o1OZi@}Js<AKs
z!OLmQ<tHu!Ep4B=fx)+{p0y4i7jDX8f@7%gDO#Bw++8eb;3O~!G!vry`VG4g$$^nl
zamw7{qQI{5G$97PBS+e>)kmDD?Ta3raxDae^78V{+k{t+d@BuhQ(xCyZw%_KVnX26
z2>`3BtD|wm9CEvoHVsQAd|xQ+2%y`vu}hh1M8`F_>?gGywbhgIQMzqDX~rr7uGQ8`
z(CR{ZxMd+<nNCedeZ0K5+&w)0q^?#c)1(epE>s)GA)S}Qo^OPOg^}hvii+qSTy4O7
zU;e0k_|VYY*yxH7us-;Bcd0Vf7$aBcSEb{i^=1^ED9M-7L(7lM@fB~Sy+?bW6*pI0
zzZ43<NrE*y?5xV8Rz9255N}Y0gNBwkco{BaOKrr??`q=X!!P=~ZM#1cE!xQGYYte=
zL%8XUf9bf?!WW8J2x&4&<^n=sP1uY0L1%ty4OPQpjwS8n-lgNgBE#{EBO<(Uf<rMg
zm%Po!YD3J5Ns}(>7$QRZEl`ku+VOn+fIx~-rStY+BN;=Xw!LJ^t|SlQY4?5>H*uCh
zdzJ|$5GM<PB47wgHSG5ent);=jQ)G|suvLvU?-wr%Lur4zR}eg@^2uri+TN&Ur4{`
zVpgZ;%!ElBA%#BrOqs~|)k~r_mTz{HXHBL{VckQHJ;3#$4z|my^iDE-%sZ=lk!;b`
z$x=+OMnxg1a1M-(CR^+8MoWz!gQ)0W<6%{f+8Y+GCLpNjqK-b4%!;x03AOrHr+}L!
zh8(8}CaJ-?0uqfohj$|~2c0a<RJ2I9W=?o`xCCmVRv^~17(PCJ_V!fuCP^JcYow`i
zp{Fe~WWB6Gjwzc$SuH@MdLEP@CylMcytTa@Mb2f+zR(DoC@m>@6~Jh0Y|Iwb_xc*-
z+hs!MyPA@cf<Jx3y%v8P_k6xDRK60Y+CgPE>v^V#{9DvH?8k5_Ba(>^Mnotv5fLmb
zapv#}g2K6IMfP6Cm2)0hO;67*S%J<Q$Rnt0Z_lDSef&pMPoQWS8n;mxXpsE$@xnre
zqD#^GtLgF4E>`fK9W3-huazQVjo^0~2ktg{vq9BCo5#M)^8!6R{c^}uJ2NvgLf%je
z0b`^}CMn6B5MGD`p58V|!T@@VyGe()6E)xQWPx;yPKDQy);e5+zu(k3uL{js5v7q#
zY2IWfC#o{cAoms}2j+}BB(kF#2J;+A5&SFJe>H4oCR4rYEZ^d^l5WinD+9Wh3L6_e
z)sT-*mKu<9_uJcrp5c<4B3;f57mgGD^#G7=u|~vyKS!+UA-}%D8x*|Uww-m@nVGpt
zN=gh608b%`C%DuqQwg}j6+bWN-d@Sll9mdfhF6$|hK2?CSu?im&k`AORHhnD^nnJh
zLYh!TMnjgrUKpP~o0FG-#qeabh#0Q?MW04EdX(3Df`jX|Segb02Zfsq-W#)Lcsv&D
z!DVWt(Oxr7fsV|o*PscUX2PD)z+u!%jR**M9clf53p4WJ;h|25#0DQ(0%3$yFE<HR
zm_-5}GU?SHR{`>~SR&T;k&zL4lE~y{m-Sqb?Kru#q-022e*SkMN?N`cnb3c-?KcRe
zu>U6j4q)(B*W=$2P(wq*7<^&AG^KucGuSJe12aH4@Gj~GDUV4mBjX{_C`HB&B}>_h
zjm=}kMKOWk*sd6&Ic!&945$tnCrIAW{dc|fhwaV2C;~DvvfG4&gwBD1fuK5Bse;Ki
zzstQh<yt~JT~vWrQy<~Bv$L|M!drX&k|SZTUKCZi7MPWt{nQCgEIhot7XT3XtE}VY
z`9T{_V}c%g%ALJt`Dtls^y%;+my$&Eo`(#3z0=R*7#_A%Ep_!|GfomaZEfv~XC_ks
zU`abNZm>!4FEdJtq1(a<ZZf7aF3qB;C9$ex(zHMd1zelusnG1Y2IA!;{$5^I-QC^k
zL`X@x*x8xc!+B{AOh019Z3uT7ub`lx9*6;^%<!+hn$%fDR5v%L|H19qxV+14Z52pV
zX7IS3@mcrV&-re^C9oqxLMR&(MHQr1=ly3nx1@wYM^zPfY}dWOQ%ICrEg~XfZ|n;{
zKLX$?kZmp<0jGq8F5e3aQ9uHoK0^~SG|3xqBGYwRtd(4ypC9<wlf8U=W>Qm9$OQ!j
z`C!U>X#8RCjanxUbYF)pXh_$&Z4Y?2tf*itloUkkNgRD1U?w&e!hd#M_{z=~78zC$
zkJ?ImFQnG&|K_`~IjwqMa(4DfybQ^YcZ3+CaL;)x@*Vtl5BUZM;veI$;BorbagzMS
z@+Jmv1G?#F>(q*hiUc$@G-&Pvm}2qu_4ThSD=S~4SPu*ic5H8N(_Rz&0}?s+7c&|i
z9z))AdqZwp=jvJCiD^V6-*_e_F%n}U$xoM`shC{Jqt@2eAdv78j>Olw(Y8q@#~a{i
zf*(xpyIE~Yk6*4<th@0Z##=NIKnbTFt-P9I(Q2hL>QD3BF)|iZHCL;qNUt%Y=MG^y
zn4WIrBTa|UyVxd7_keF<_onD(-^ja&a6qbeX9X1%tQ#TY6BCInQ8bZ>>S#lYSW0LB
z9s1(<30?Pl=^q~XOj1{?{u>}PNYXWlDKaN1{cBQ1w{d-Y3*o%J^Kv3~UKF9`l8`C}
zFr%rsD<g&pBf8rCXErN1NDrF^KY6i0COSLJdPB<=K5)?x;?)q#bDk}}V!#l@x&ak8
zbi5&^(-**6UOXOxdXr_cwoN{{9G1P=EjT|If-tC-n@Pbj3{E#OP)bo2t}kO+0Hour
zyMq-3^3brzbS9+1$0MYF?TK;Z<=<&JJ*7ijd}OlBsrniZD<kyPwLe4aGHG|&(#Fm=
zga!rI(}Db-EF6f{IIk8BVUU&K?uXx*M=c|}G&yk2sxB-H`v<?^rmRC;@1mJQJveI~
zHLp0o&BF-jp`p>KTlD-@hJuU$5nnf@G36KUYhmZ4j6)hbB|JA^dJ=m6UQ{j_qb>ck
z68uL16GfJ%QXG&~mtrCm`p^HV)#^v`u$9GncOdigmMdB)o<-eaaz=)&Nl#Fhe<yih
z51$+jb>{dMc^b<8cuIV(*Ci+(0#iSDwq?=#hN8*M$wf{_Mmc;R23I@77UwD_dwX*o
z9cYM~8350-mf!AiWU}fsqN1zmFxVl9Vpl0i^6NSXnjnu)$2@)$t^|vfV4k6AB_aUq
zt{iTZ<>hvW70`}c#SOfE!|b!>w_g-x@%upLglSZ1Cjne{^Q#MU`H+wsClcwaJu5i?
z#81l3SSTBh@}b`Um&~%+gOmFjJJ>szCRg%r(c^L9#*j7*d+HG^17?O)gM#vBVVJhN
zS1VMQajQ0tL^CYo<*w@Fiet>Jg*~er30PIAPVjvNgZ)S}P#O9dd)fbE@olDr^5xTc
z>!l?k_t^K&Zg8yC^iT_OqMn3?EIEJFN7C+d!)S(41?VMuMuWA1JTlU+0f;E5#6ksB
zdu-0nBm{+%Nv#0KJC+*9kJJAx4f5U_9VyDz{@`?8xfmef?|deui_L6&jXu=gR<7=3
zDkoQC+xU8>hD=@@|1?H$t>j%05hjHy5hh?3l)lKY31TP~n=0xXtwUrzTew>)6?`T|
zmX#M1OT3L8YndQ#)E}-1-C3_-=s@TC+86hrU70*VO^dT?BFeg?<)%_B?61vb@#Sx0
zL~sM;R(kv||Kda5)CnW|&9cU>S?8`8me+rOd)OA~@9+FN;ZeG+0(iP?{`TK{>!`$~
z?DldNak4J)@pFtZ5Dj9x8!VHvPj5Ep-Z7&~=?;&^C&4_yImz`kTPYhYE$p)wf?+dm
z4u)y*tSZK%9qD6-)?D|W_EmIH4ZKvwt6elf8Uzv5OH0hxo`;jNr;lbP<mG9sB)VB0
znN3wJ>BC^g_2#n4+91D+d@m>CFR>=@yDrR3im0W*c{!<Iszqo>W+l#5fD>y)dw0(E
zwOO<l`M!Z#l5<X4+Sd1bo1~bfpO?iQ`<~2KI{sbLL(si@dlcSY6#L+K|M||@$==7?
zsMHjXg`I#GEh@8hbcGLuOFqwjmJI^Xx6@Ls#~Y+m51*jLzO~&d5;v3`02*Sg+baGp
z2)FW(0fiKY@_A(3s7<QRh&MP=>@(!EaoP6sMORG&IwE!M#KrFp|L6Rwwd=e`*>iYW
z4%crPdAJS++{ESK`%qOY)1e{0OU6U&G4a2K7B}$Lh<TTWl1WX9W)k0Ro$F6!i06Em
zyfQ%4hr-LzFz8bS_w8@K;`HW2myY^bA%fLl97(+Bq(Sc&r^~LY%gY&QjK_#}zWIig
z?$P>XV=Dl%(L9VFfzvgt3t=Y%xo9E&DA7DueXi}i*shNZHzrC@B=HW@G1z&35;5B2
z4Zz-NZ>*n6+sdx=2&G^Et}8!a?O)<NRQ&2?qV&G^e|(w#>34h?R`1;ey&DP+O6hoL
zTIWY=eexJIecX7vq?*P{^D1HMsjrJwSa&qX{70wB&cU6~bLoRBec<)By*)g5P20OK
z^f#Epouu|QaHmy%bJ{gGAtx=<`J7X;{a`7WsJn0}jjpge_r@fWXN^)s`f0cDOtV|u
zO))!-t-0&|)|di?RKvE)sP}dzYQ6Z$_lUS|X^*<+<x$$H_lC>h<Y2F*XQ*@e>&7IL
zRY2c2KhX%-FkejczIVGwkr9lpwxq&Ab1HASopsR4BPs88%)K9}CH+7RAN~1Bv-qI!
zj3p({_t><p<N4nAX;j%>9P|+LAtC$a@<vpj9ak<Z^$R#C;EDUc&`>Nx+m>AaM=8;>
zk-Q1T$54<t2n_&~XnF}8{(hP#8u72#CDTb=9&$Y91Cg@_2QGCp8B)fH@g*1U2=FIG
zR3~koO3PhF#^FWgJ3Kvv{p<)ywnp{trtqcjJDs6`a^UDHb()a-ZJqAMmhx4e5c7MD
z{D3jUL@zvc^qh?9+W8-vK6`$=x(cj7+$R_ENW0OrPKcJ0yT}RlFRJ5Y6`bU%*TDCm
zPL&zYc)oD5nQPK{Zm}jtzNs=X0|31)>h;$B1HYKLQyN_pCv%RR)EQM?5uYkvygiS)
zf}DNVHbpFh^?AgcRe!L2OYX(_@)tyhYLIT$(t2Ji-78@6`FB`VIex)gAfJhG?s<av
zxo~$}yxY|Gue+wv7mW%GotCwSBZaNGHz@Mot=oKF_O}y;(L>*P*sa~4NPuhlKk9c;
zo4(9;1+LF&zSX%NA|e~8yY%`o5hRj!e%gTB_>kzw$?<ofRQ#6_W&Gz{y84&xh^XC@
zV~~kW{bCx7ZC=F4KehR4=?}<Dj+K`TbXGPaUo4ew$fE3n^!?Vh`oei$dTV>ertZW?
zZHj3p>He~^l5jA-k<J00g<UT@ySJsM#!J#tc&>h&J6$!<A^@n&=I_H}BM)*m)UsRF
zbsP4qmK%RL=)gEM9*O;APsq*1A-+0b3lfp52-EN`fD*G&>t+eT{8Q7~ElhH|JKH4>
z+%1b@<FK<=T(XE1Mnny?N00zL-e9?nE{<R^##g|Q((9GSADi;&Ozw<mt<{uaux5A+
zHb1=mfYg(?vdtPzJZNpc8D3beBHIJcV7NslN#bQx@0o@;sf0}#D_a+-YZr5J$V)h~
z?pNx;Ap>S52!Kf7I+$Mgr%=SlV!Wc`h|7Uhb%8@OIqt<HSYWW1^yA$1lYZE9b`I_G
zre`BExz<jBeDxj5p$!0m??1m-U4jIEV{mG5ZS=Caz@E#y<2~8(o`nYh4&rsd(qCc`
z00}8PCgxEd1R%R@dAdfzQ@@Er;p(PGe=Ub;OArBonwFjUj32U&i6CU`!ctiqkbqWE
z5)D7?aa2H0&_!-Yl2e>^7z3Cr=DHdQL=X4Hn&HCD?WO^MeD&emSU=D!qVnq+QBDr1
zXW7FqQVJmtS+osSPr=#yB2(e7Wp4y`G4ZbYJZUK7i7t{Em9cO6I~LKF&W0GjwIN(u
zWcC*%jbI$yfxBXaXhnzhgS+7SW0BW|DJn)|k~?T0+Q|x*djpWPOB<5l>Q07flF>#7
zcly4Xmb|v<(GPg8#&?H`<MgtE11`VVZ{tw~)<RMg%)D9^c;91;AEDeKM7eet$X^CS
zrERNYKN^AKk$})}YKW2Pja@>w)ArTC^IPltnz!SU3rnlI?Eh`*zAzF?;vq`nNdSlw
zXaVv<wEZuBgk+}ng<3I_y_TRzt!<<p>C6x?Q%MFy8B~i#uH|U6eqU*L#;Q15GL0g)
zbt^EQbjK*7vtClBq~byibJBOKj*xq|4Obr_ct+*Hn7urYRX(S)V;`Rqd3*u@o%cih
zftucu7R`dg`{13Ee%YmYcorJr?$N)``$DW%v;4VTVqYZ<jRI?Vqbi?3R)iRUBrWt*
z1rd3)lFAhS=jF*B7g9`zCM9I~G;wY?=ZHf*Y}Txf0NB_tL>aT*Cn!w$PQ$4OdaKQ*
zl$kv8=Mc1x-+I<)R$#9UwwGNh;7>ekT14#0)0<onB;{c-$z}xU&JW#{2HS5r-Tirg
zWVwI*=zvKJd=CxvWKA49hqvorQ{N1px4t?a4}VAyVTp+6U>(P<G!&7XJIL#HNo%K;
zCUdK0cw>(BIXFCn9)n_~S!9uNxGQXdpwBA+D^XcZnyYJ`IvJvz)9$Q?%o5q$;?g75
zgTkc#6SM4N0>#lgB#}>7feSXJSL<I-_O^^@0f{)NJ`MX%%%ws0TXirVM|1P^KmK3L
zHb;*g(pbj=vZ?dGHK*}2GR~1Z&izt)gL>-Fl*pDo{<a{DU7N-e{y#lCH~LJqR09oX
zrlnj>)7EEEc|l?2b?jSti33ugoC9I>H)_&<I6#Y4y&)l?tk4xMLFGs5(t9C4S6i3U
z6H*O{ETV2p9t_e6#k4DeBts>etj86~6Y;wbzjq^XPD3BDG+hmKO!8ECy521J;OfT4
zzY(@R%+3yq*yAp`V8<W!$y?Uhjn=59H7+V;>=#A=+WoJvGEn0LjFxM>o0yxF9rFqJ
z&|a`)Nzd%ora;hwt$JdFEgc|<2{@sFH0mNu)|C?xV^dbD@{)qx?3x89LAqby&mZ^p
zieX!K-A9LTesEf1M&|<{CF8kCIrxld0s+l(Xn^a)FF#QvX)c)nK0ey_Q?G#9hw~+w
z4AkS6X5hfE0su~KtBo*#T7OB&9*;6Y?&Nm~El5CsZjhVITP9f{cjEowN&v9=lKL3f
zUmVTPY|!DGP6>S9?Nw;1`js?i*(pF-JEQzW8qOUs`cbRa24QmMzDNEmBHF@ra3Tw=
zwkkXfWoKuXA$MvzSeKOp+Gi?uFV??%eTR|TY1<}rdt2zku8jmdPR|ZM?JY(~YO8w(
z80O}4W!gJ3V1N3XOY|;|pDkybwFBJLF=X{wrV{I~6L3|f{kU&c9PJcCvqoL`-9h!S
zBn;j>t0|EzkM~hir9_XxABeY=Jal`O*r#Rh;n+5km4ZkCe22d1vR651VjrC_R3M4^
z?qN{jx+AW4_=n^wmii+AYqwTCY_lhtCaZWP^iR*J$((g@o^sXL@z$2XrUB888zE;6
z7+=}L#5F=ub~X-KF9tbgF`e1R566_=2xmLTd;Zm+?P;rsLIKWyu8t64V%!g^R2hqm
z%uZG523#5Y{ayE_Na6Lm=1d<Q=QBL{*tlhZbY`brrSQ|Xqeb)q62L^T|DGOYAhEkb
z;T6F2>mzT}5%tLa9x1CsKoArz4eyR`-&iS7sIGS?)jd_vmYY(PD>)vWn6GJlwvOHR
zsP*7=|ETT6s_Ig&u|K8&3j7;P<#b`X-{tHX{WI)oVZG+8ok6#DPKe;CZb5v6)mWn_
z2gck~;VE~t#5&fwtV6UCw;e42UD>$tt0m=mT(zaLTdMz4QCTuhId54LoE5k~=HWSv
zj_4!Z5$m>NRFz+?(x020ZWnl3V2Bzxn9SLWsKNPdGU1(DBd~acpa;5NyIe-Vl7Gk>
zgn`5+a3yOYj@`jtw$dG0ewE)o-g@jdeSe@4TX~v|OpE=bGmd!(S@E2RrG&{GJf68E
ze&}Hq=2-f4(uoW}Bn$mN*w~Pm2LC;UB1;}n%L9<g&&ay<0^{~R?NitldlAal2AUdX
z_bb)XGWB{JMlD5lLw3;#K5SF<Bs^!(vtPx;Z<qbx1L+;BB2D*YTa@!3-;MN?AapNU
zMK5cR-7RBqP~+hr4xA4o6>q~oCFW$Di0%o?y(_#O>lch<3~K27B<KHv?dqJBW*zu)
z7nSwma8vM^tS#lw%@AfQn<^Z6pZ7{Sc8}A?q5g@|;ndMbb{x>y)5K!4M@2+HT30c?
zq@G3}jk*(w+L;LQA2;M$IB(;&bf;C@K;cudhmp7;9ylk)eFbzjjtKbv+aaB3-vBNp
zEV_1fStg)jYUv}iePr_Jr9FunJ|C{F?N&W+aA+!_`H>48XIY2_+om`-c17d>0HO2W
zw*V$P-f7URbh_BG_a@}1vWX$f-)5(M9ZUFGWly&1r6$bSaU(Ybph@bV(QJ<>|1G<V
zfnBv=!Dvg@FRV!yTKnh5vT<?h-kX;ee#ceKzTC)9Oteke{B6~x4ad#+a!B}P5s2F<
z=`vA{TAi^n-cmO)q}_SsQv?9KJZ<$Q#KqM!I$p<ec`J9qh$Yb#g7k4J+oT6b5|}Gt
zHbcJBV#z-cCQbXvh7U+B!CZp5p4=0d%p5HthcX;j0qGbfmy2g65%pXO0gae4KFaab
zINvF()ML<xuB_dqReVR{zjqBIm&}N8QWAu8LHXM)=FtGqI+k0-6}6rd^7M|AJAp`;
zjAd{l7jNdH9$UB+5FGLPmNkBZE2-Bz4f|S%PasQw<H#cNJyE!tTYl*h1ZIh3(@nG}
z%a@NfVCBYVP@7LuVkgqdRq`s8gSuA@ndCg<ZAee&1lxX7^7q10z=j&>lmX$_U&LP*
zh_GXn&v6LcyoJ^R%I&)FlK#iG@FxhS3~RCfu_D41`BO2>I5Q2O&-C5EEIk7?7yK<A
q_=u6qLix1?B0jAg$^XsI;wf*CZ-#_+E#M!10E)67WvZpjLjMQrn#`*J

literal 0
HcmV?d00001

diff --git a/Images/matcheat_octave_logo.png b/Images/matcheat_octave_logo.png
new file mode 100644
index 0000000000000000000000000000000000000000..f046a29a8a298f31a590c537bb1a382302f90121
GIT binary patch
literal 9558
zcmV-cC8^qpP)<h;3K|Lk000e1NJLTq002t>002t}1^@s6I8J)%000U>X+uL$Nkc;*
zP;zf(X>4Tx07wm;mUmQB*%pV-y*Itk5+Wca^cs2zAksTX6$DX<Nq|rShJ+?|L<L3^
z5h+$=RKNj8hazJ|6bplbV%G`s5KzX!QA9=M-HdAq@2xfS-kSZ#S>M^`x7XQc?|s+0
z08spb1j2M!0f022SQPH-!CVp(%f$Br7!UytSOLJ{W@ZFO_(THK{JlMynW#v{v-a*T
zfMmPdEWc1DbJqWVks>!kBnAKqMb$PuekK>?0+ds;#ThdH1j_W4DKdsJG8Ul;qO2n0
z#IJ1jr{*iW$(WZW<e?f_&KbNko{YOt-kK%hql^ThT$m-`XQO-vWxZ5MngHeZDAUvU
zoJ;^P6q#Sl=O&?Si84hL8SaVl0ssh<#5ufj4vYCYXr2Igrf1}e1c^yvrV-beY31n1
zX8Q57Q~6>sE0n`c;fQ!l&-AnmjxZO1uWyz`0VP>&nP`#itsL#`S=Q!g`M=rU9)45(
zJ;-|dRq-b5&z?byo>|{)?5r=n76A4nTALlSzLiw~v~31J<>9PP?;rs31pu_(obw)r
zY+jPY;tVGXi|p)da{-@gE-UCa`=5eu%D;v=_nFJ?`&K)q7e9d`Nfk3?MdhZarb|T3
z%nS~f&t(1g5dY)AIcd$w!z`Siz!&j_=v7hZlnI21XuE|xfmo0(WD10T)!}~_HYW!e
zew}L+XmwuzeT6wtxJd`dZ#@7*BLgIEKY9Xv>st^p3dp{^Xswa2bB{85{^$B13tWnB
z;Y>jyQ|9&zk7RNsqAVGs--K+z0uqo1bf5|}fi5rtEMN^BfHQCd-XH*kfJhJnmIE$G
z0%<@5vOzxB0181d*a3EfYH$G5fqKvcPJ%XY23!PJzzuK<41h;K3WmW;Fah3yX$XSw
z5EY_9s*o0>51B&N5F1(uc|$=^I1~fLLy3?Ol0f;;Ca4%HgQ}rJP(Ab`bQ-z{U4#0d
z2hboi2K@njgb|nm(_szR0JebHusa+GN5aeCM0gdP2N%HG;Yzp`J`T6S7vUT504#-H
z!jlL<$Or?`Mpy_N@kBz9SR?@vA#0H$qyni$nvf2p8@Y{0k#Xb$28W?xm>3qu8RLgp
zjNxKdVb)?wFx8l2m{v>|<~C*!GlBVnrDD~wrdTJeKXwT=5u1%I#8zOBU|X=4u>;s)
z>^mF|$G{ol9B_WP7+f-LHLe7=57&&lfa}8z;U@8Tyei%l?}87(bMRt(A-)QK9Dg3)
zj~~XrCy)tR1Z#p1A(kK{Y$Q|=8VKhI{e%(1G*N-5Pjn)N5P8I0VkxnX*g?EW941ba
z6iJ387g8iCnY4jaNopcpCOsy-A(P2EWJhusSwLP-t|XrzUnLKcKTwn?CKOLf97RIe
zPB}`sKzTrUL#0v;sBY9)s+hW+T2H-1eM)^VN0T#`^Oxhvt&^*fYnAJldnHel*Ozyf
zUoM{~Um<@={-*r60#U(0!Bc^wuvVc);k3d%g-J!4qLpHZVwz%!VuRu}#Ze`^l7W)9
z5>Kf>>9Eozr6C$Z)1`URxU@~QI@)F0FdauXr2Es8>BaOP=)Lp_WhG@><tXJG<r?L)
z%2EcxFktvIQW>R;lZ?BJkMlI<xzFRz+cvLhUjMu)mH8@eDtwh9m1dOzm5-`SRd3Z4
z)t#zss!!A~Y9?x7YT0W0)h?@z&!^9Kp3j|MH2>uMhw8ApiF&yDYW2hFJ?fJhni{?u
z85&g@mo&yT8JcdI$(rSw=QPK(Xj%)k1X|@<=e1rim6`6$RAwc!i#egKuI;BS(LSWz
zt39n_sIypSqfWEV6J3%nTQ@<sT(?tqLQhLCSTA3%QSYHXQJ<}!q`ybMTYt*H&>-4i
zi$R;gsG*9XzhRzXqv2yCs*$VFDx+GXJH|L;wsDH_KI2;^u!)^Xl1YupO;gy^-c(?^
z&$Q1BYvyPsG^;hc$D**@Sy`+`)}T4VJji^bd7Jqw3q6Zii=7tT7GEswEK@D(EFW1Z
zSp`^awCb?>!`j4}Yh7b~$A)U-W3$et-R8BesV(1jzwLcHnq9En7Q0Tn&-M=XBKs!$
zF$X<|c!#|X_t<oHD7%Dx)e-CH;keH6jN=C<dnd8eNvGePS<WfW4bGzr3>WYh)GZit
z(Q)Cp9CDE^WG;+fcyOWARoj*0TI>4EP1lX*cEoMO-Pk?Z{kZ!p4@(b`M~lalr<3Oz
z&kJ6Nm#<fmSFg8{_hRpA@25UGK8Ze!J`=unzN>vN_+kA5{dW4@^Vjg_`q%qU1ULk&
z3Fr!>1V#i_2R;ij2@(Z$1jE4r!MlPVFVbHmT+|i<Li|H^g**v03|$raa~LixG^{4<
zdAL=0et35TEn-DPL&UpCkI2%<M~jUXOBQ!V$w$RS)kjT5dqtN;OP5$IS+nFuj9QE!
zracxP8x?ybc5<or(%nmk<Lu%J<L)jqT$Z!!+H$q!smsr<kYB-BaVj1gA06Ki|A`aA
zspU+r^k2Dm<pkH0yNCOd=f*4NjqzRhW&Du@mxQu}(L|TTU5R5!u1OV1;{s1XwcvHK
zU-E(Esg#hEqbW0~(W%X8gtYjy(?TU-im)qPGd(B0FT*sWFhjb^Y1Qsk6QV%TkxVFa
zS!TPKj{Z#bNQ@+#C4*TDvud*5XGdk9%2CV_=Je#6<ZjCy$@9tkel=z_cXemJcK(L^
z!8Pt{4y}dOu3X!>PIq0wy5aS{>yK?9ZAjVh%SOwMWgFjair&;wpi!{CU}&@N=Eg#~
zLQ&zpEzVmGY{hI9Z0+4<v#n~|mm*%#^<vB7isDZt+>-0xS$$Xe-OToc?Y*V;rTcf_
zb_jRe-RZjXSeas3UfIyD;9afd%<`i0x4T#DzE)vdabOQ=k7SRuGN`h>O0Q~1)u-yD
z>VX=Mn&!Rgd$;YK+Q-}1zu#?t(*cbG#Ronf6db&N$oEidtwC+YVcg-Y!_VuY>bk#Y
ze_ww@?MU&F&qswvrN_dLb=5o6*Egs)ls3YRlE$&)amR1{;Ppd$6RYV^Go!iq1UMl%
z@#4q$AMc(FJlT1QeX8jv{h#)>&{~RGq1N2iiMFIRX?sk2-|2wUogK~{EkB$8eDsX=
znVPf8XG_nK&J~=SIiGia@<PUi@r#KUhdNhuKDxBz(w(lbuHMUmm#<#&xpJx7z5D!C
zm#b&4IbAz_oqfIShW(A!9=o2FU+jKq>9y}|z3FhX{g&gcj=lwb=lWgyFW&aLedUh-
zof`v-2Kw$UzI*>(+&$@i-u=-BsSjR1%z8NeX#HdC<Dw@DPb!|OKdt@M_}6Bsz4Yv$
z*I>`Hh-Z(6xI-`hmHDqv!v)W&&nrf>M(RhcN6(D;jNN*%^u_SYjF;2ng}*8Ow)d6M
ztDk;%`@Lsk$;9w$(d(H%O5UixIr`T2ZRcd@<kNR)@201U-mAVp_JRGO`(yOSk?HJD
z_)nFejX!sM3H<VSCT(Ws-}i*``!YINegFUf32;bRa{vGf6951U69E94oEQKA8oNnE
zK~#7F?OY3ZRmHWQ+0XL|Nq_*sf)5mtP*J26X(1<C%V(|9+9nmfQg8cJT5GMh+J3j*
ze&z7B*4taPy;iS<+tya=<K|qe#cHX536$%jrTBm#m0|@EF$p2h^VpA>`>)yOoE#Do
zf+Rq{GRfI{_TIB+*8H<(&6+i9HfO^aYU}HD#%fuzj{B5t{QicJQ8D&Z!_;RQ;qbW{
z)22z`oyMh{z?qCOCJl{qiC>E8+I~$pc5_YJ!F_)lbKF-%CcB+=cD$Xeo10c-O37;(
z)2i@d0vRyI@jH5f4@Xf1QgZ87$r1@c`PKYx+nE~kFEzuFIDnYNq&P{pELAn>dyM%$
z;z)|pIewa^F<m#9W|}HaL<u)5@y<@9+v2BE&jZBu@3^~OXsoG01_(Lel00Y_b#H_Z
zM+{cHp~1*k;G*AcK3j70i@IrDs$1q{&NU{T3=_U5HG$d@GXOoVai%GpxB`oaz&#0$
z!!<Y!;Ay(Xb<^a!ZBtq%Ja@lv-IokQd&24Je)7?pv)&|Z5>nD*mf&196vam>ymzpu
zWs^*!E;p}!Q-u}_-vrM1f?<a$q?2XRbA2wsK?DZ?B8-TO6flScXF@C=SIH=~3OJ#A
z61q4O=b46Xh9bBU;5ug~-l})gUB7x{{<K%=1!}bTy0}OrMh>(bX^Os~9};NI98niM
z`N~C>75%;*3Y{l1DJEUl2Qa9#)D;k_ga8l?wWmu0B>zmY1rif3fK=cafdo*HLPs2D
zB1}C8qap|0ibS~)3Nb&OY7xHsh%dBz9-cF4JEbe;bh;kSpa?mf43&JUt5X8}Lo2uZ
z3pe!JbvtDHscsMT1rsPHQh;a#4gOW-5uw7+Gl|hCPTfvI776^grXv`TTO_-5JP*=9
zwt=}wzCq4O=?j47bL!Ga0xmYqNQ8OW%x>;D_xSDY_dK{@X3t1<O`?Cufrv=tWW(I2
zx8DZ2a;x+l>T)hBXBH}mTvZ%r0*rWreuNxO#6<$25O-)K`0+la-_Ja+o%wDz5_SV}
z2e{fe$I!hahGC7uYh!fF1|Om;W+4k*$HQ~q425jNvT-lF!FQc+KQQN%1}bg1xlC1o
zGCpJ=`3y2C-JIY4<u-2Fx5{)EwY-Ol8UYwNkdP(-s23=L35EG!J>87P6hMeKG0%Mt
zf_o#n+jjKA-L8=PEH|C;^itm#7uAh+5jONpx#WH&(w~fB^BmI-&%&rN%Ls)_iR=7y
zHwkyoij`Q*byHHv@3rpP^TQ`Eo1Mx5Mm<5%j`MpCLk26)RUdew=@!kle;_hFs`8LC
z=rkm6ih)wV6z(CR)Fpn=7&HpcM@83g;r&8pGV50_oVDk0ssF^|4P~LS%F~TV$wgB5
zpMofuZH8>_c6a-lp>rGMxT&7i!e=);aQ^#VPeAx5;&F^NN2Zqz39LLj&rZIykeS9~
zfJLWDL)HX95!WcNiUAAaOi-88H8WPi0L~^U-0$w%zUJw_-gsk{K%z!yY-CzRoJpl@
zE?dUf(xnA1NGwC<OBriQCV5pwEpI})V%@5?p(&<Eq8IB@`+{Lv9|2gVpGi4pBx>nG
zykYmWFZuBWQ`g8uLQ76>;<a^2=z~Sg(}et!46^XhoOejD@&f(5`i+x?9bFAEJQZ98
zS)m#9vijjbyi@=zAE$0-S~|djY<7Q?PIun+$1Bd?N2F2%#N!z1--~`kY72?OWD>K5
zV6dsJm%3iL<8mt;`if@SHQa^zVux9$VRXI0<Sj3sRkorCqJB}d4v}AIq<#r-gX%hP
zAUQ<=>^t+<n}4Q<qn{Jq?T}G9pa>)sL5UPld64KH;6Zl<P+B(zu&=IPFzx4*RlGqO
zjd6CwU4rtWK%Cix;*-!@iOvh^p9@brdD7=Iw)u@nIyG_5?$`7&ZEw31UtRs^=v~P@
zPI$D%Svy7uyhP)!#<!N1xerZT!BU3;Y*4VMwMgll@$1bOL#KWU!;i^?APoo_K)^|a
z6v3sy%EzS?(7Yj!wJwZqpWpEDs-&tEx>BAKf~pSV5e*w}WO`!_SJR=-ZP+$@@gM%@
zvG+Z*bh0#=438TJWv&AS_EuR40rnKE+mkCc3;+7MB=ZLYtoO~nbmvCDszklTXPmiG
z4@DM;o(^yo#(YYPLor~aJE_}XDe9q`WxL#UKmXk2(;w_RcT@>S=&*75a&z{QCGJ}m
zJKL#dYa>%m8CyC!=4&ENdTvpv>ca2X*KW4%Yt34=F=y(-?#uPHOefHa5*DIHA=W3N
zrZSK@ylU!4A<t{P9;gXmTSiJE%As*Z;D8}twG{@tTn?Dqg(6&J)3?8PdDT$`iU>Gw
z%@R^0Sld-+Pt;O-$BwbVRS~qS7YYcahZKlhU1RAUBtV@op(<z0h0SE(pk^BfdCZd#
zo)m;9uVJQT3E}&sa0>znnG2LgxY3!eDGvR{*TPZF%k-@C-bsJIkZ;Z!QMf(|5hL@e
zt9i{HBm$Zo^nvigFLw=*0-^)8$E7MAA{?Bv>gzRzQRd8tUpQNu<~-7tC1!0xO;u1R
zWlFN2*ef>%U@{^wUD16P8o2L&>(5I9g)o^nsT_ik;eO0T2r?>@6H&xeit3=4qTWbP
z=<?t|97xqc)5Nz{@J6Kbg?=Gagi1e~@iB)*7bH$XXH*zb;ZzLHs#}8XLQ&{asfRaR
zan1&`7;oeU@B4V^_z4Bz0yEiQg!KoDDhZrUAAPsY4Qn1|x~ktPkdPBiY?YuSta3mR
ze{<8+0cyMFJO52j0-1O840BXwae!S!s;0`(nQz*Bn*24%D+n*<o3K|hC?SJ{{xU0>
z#2^RzN_gf($n&!?S;g!mFt90Eg}jxF0xe2T5qLBZ*b!hRR&Uv|@?|P7S$k+1FbG{o
z{Kx^#L?7vaWcE;FA8}ERlhlbG!B&PKV}ETzp9S4?7$EcmD_&8d@^K9NbO3c4Cb`Od
z3H7QM91u|eO5N5p-*I3n$vzF!vParE0X{&PBYL~BC4d7{L>Ggd=J~Eq0}mRQYCxjh
zhi!-kEBrK>O?UX*8gISy>F-P<!YZl~IXL^1*{>VM*D=<JbePsuansghrpKpwFf|F~
zLM@bsLA4rCgifVtQtXUL`2rQu2#Y11k41>b!O@M3b!LsNmA3IG-m~xgcQa`QOJpB%
z0Gl?7`FlHA=Lf#Xp1R;O-<Y!b<~23%kGz#v#{0a`I|z_6Mk_OQiC{q>YuZx`s8!VE
zFegy#tH4sDU;r4&F#skY+cYPYdhL~~W^W_)qH)<WZ5>KV(0Fi^15beD1Ug=#yLr=R
z%q4!>`zG_-CxZ}03w0$@cbFFAy4xZgt76Y@=G1;TGx4-(7q;%bc?V-RRn=m$UKs9s
zE1)<l$ehF>k^qNg*IsM?iusc~+yb{kAoadLG2*e_hT<Ahu#hHSqGxrGc}a9diVvm@
z)ANxEy@0u|fQGBj48_Ly(0&dmFEWAYSe2Xk;4{saeDmZRPU>v`F$B)_i=W^5+XUxp
zY9*$6eMWQYQB-gFmqpezCagM5c2D(WN#t)67R}3=v9YjXl6s3IeG#K;Dj&eX6g;lu
z%}wK5AA4Px(Ui-b4b7q~S(9w(&NakQ=P&|>8mqP*6O99uEP8TaSdJ;uhI21@GB#o2
zf^_qq-}gAXKbf36H%+|+p#ZW%U`w5~$dKNTA|@MWT2Y0blj?%@j!gStbU-orY;ZQh
z_}S%{H>a2bVefK%b=o(U%uHtfb}Rt{X?$7Y;I&wqU~*Z5)DxI|U-gF_A2GxB(=Y&-
zeyZ!UzdZl62l95je4TmImBu{H6RSqW!jagRac($l<ph+8X<x9^kWW1D8QdjZhyoAV
zgTx|okB}XE&32dn&J*4zs*=n}K#t&dOgL;{y72_uqn?9<u6c3iFQelt7iU_VUvRzj
zf`{gu(TUL=D_My^-kE~k61DY6>9|UFR9VzCF=fFLoemX~Tbj#;_H&r2V?i13qG9xk
zab>zF8Aqe^C5CQ&l89MK7Rlo=0Hk<hBL+82qaEk|)V|hkEfk)^eeF|pFHyae#sn3#
ztmoD6P^pYWER%U)4sb@GMBzDgvmyV2_n^_>C9ai~Mup1K7Z*@TgDHFT{8)y@nz#=u
zqW0*#GaCJ@v&xK)Vpv65eD%}YM^P6|5O*P9Bx5BUOXbw;giG%zM~@Fb&`3#OQxAr&
zMp`|Vk&pMlW$n^{^MS=f>9!Uqk37!H^*B-9SX~`}Rp6Xqst*AU$dN69rY?e`iXOj*
zs$~JS*-p2?cicbQ<rPdbtWQ&A8p(13*;eR}!(9#HRKiQ0hdc8T$d7y^sJkUEp3;Gt
z@KZE>g9&fJHGkftra@#F7%{L$pgtp>Wxl(4S$9o(|Gumdjg6C8=i~W;Pk@z20%!8V
zp-T+ouXDrLgmnj+?p=J{#*HRfQBHtW1QvBgHO-lSMhh$`n|#k>S|~K5(=VTikuR`Q
z6{Y`&kG3>6DlzN({wAjzi!`tlGe?|Hz?|T$KI~D^(xOIhm_N5eq&I7Jh;h^U;0dt$
z07aKowY3T?IF;;z3eiS7I_!04U9oCo<q6K}16U%b9qM7T*>ke{1e|`LMx1Whv6D`K
z)dwuPOu*ipoI5Fv*+?^VE%I0>mxeW68UGHF{vY=iM!lx`VBo`)rxlCK(Busm#kA=s
zIP0Kn5ls8BumM{Wj0+^iIGZxEV4)BtEe}f2@q05b<gp4*PQ_Sk#zK%UP6>QLhj9WI
zWJM6+7hDWu?nf73TK}O1W$X$;lusni%92j6tOQiWBV+~Sid;|tP9z)=b*PH9RewKB
zMlP8|GyJ#0go_WaU@Se3)ofkg=x4gQ<9Z%!IRQ6N(tbZOy9C{8u(Ps6!;ICP61w9@
zVbd}<Q^CpO)ly2t6$hAN3f)lh(X=84H$j-?U^Irq>JJ_4uW5Ow*~p|F@@K~iZvP)x
z#giPqp&Wke1N<27`_g7@?Vil;-NC%n{ue#%j3<bezbDR0fG@7wC{1uyC5?vEO~`f0
znEHtG*!D%nt=XRTWo^6Drh%EZvUV1u4W6RCA)ecUH3_`eKS7Wh+z9`F=C(80e)84q
z#mWyq@#SxS>0Mc0tMj@fN<IDqc}FK07jHFhAFoG@@jiC3p~9+LDn09rN4(p&U1>M%
zPrILWyW38OMYzu>3Aab#d7ULSlTt?cXsERGe$E0-`H-Xnf@lFh5@mZ&I+OqA)cNf3
zQ2F8u?^*i@ue)d`qrHeZ&*I}5vZm>Qyn@@_DViA`gROkTS*RRm2IP-IjX#=QiAt>8
zc~xn-WBW!lgT=*?pj!LH2lW|0UE6uh559WkYj(-cX$uW{GKnsgiAsfB2;p~ilkGCX
zaQKn_B;7dcWh;6+ZePV{KVncQy?5M(7Wmo|aguv8Fz$^N&>L`reA9e#rlo-k7JJ;l
zh{Y2f`}@~qZE)-4g%9bif2uXZ)+Jboq^%|3%v{~62tWwOBX$z@^T3Lxb@X(?X(Y60
z%zgj&hkalA(0z<m6DJ?qYpg#3N9hhcp4TzaNbPRfa~W3JX|E*qJpv@zrclgNLZBUV
z^)|s+<?`kF5_kcha@RA}omTW|Yy*KA5#Brb-iS=7UsCtRC;_mrx`R=dW6v+w^)@Z*
zUe@;Ixz929!LywEb-vtVai+TVEjNj2P90p?FL`|96j?TAHN?@WekSc>=b6rhc<F$-
zA=Q;7@~qM)t0nLP#@gDOU*qeHd!N4#3z0X=o=##>D7HblbR^FJ>W-LVQLodboAq_0
zWVEp4iqgB+F6^k`J6Hn#TRCr?KBbKKCAkui8iQR10}eQDth5>)M5lTH%AiWn;Kt?O
z4gl+rl;(t=)!uQZKPvh$9txkuvMIRQ5ED^T1)!0*R}BZi$Q>NJwmdyj0)Ma2{Uvua
zT+ww)O)JBO1eV))e#ml%DFJa4dDu{PwaUe+H{&Weu_fxb7r(b5)@GRhY#DI30uywa
zHXCo4tSsZa9@MV)okPcRTw(p^`h~C{)=Ss1$dQidpqB|S6~h%R-O|n66Hkwp7;rrQ
zt>)=hx7|1gegsF4!*T@5^;fXUv4(LCq)-+4;lTwBPTkmsr<G}Drrqfpd+E4cljmPT
zyGF4oOSYGdVH>8*;y*2|`1Ij}MR=$oflC1P0~U_m!cv_sip1v30Bh=&DvCn^Foh&w
zBET|TD<Lqi?YMs4enu{pBkca2_u_eDQFMIP&(@ckV!qUE2A2+yt&|=T;Am8Elb>dp
zb|{NL7q_IyGTt`j6VA>FGZq|PHw>_lY?%^E(n;_AuE{5S0509t4BLKDoSK+Z!Ng7^
zzsSpu+`Gs^grhEcx;!l!yPRcu{&)1f>#l99seVfVHVpd#1M>PG^HZ&ipS`Zsl=IDU
zIKh-+?_)0HIR@l{Mhs3T+Yl@G!+%#-R#<O6VU;&OvAFO;{>34kX)`$~ng3eX!L#Wb
zu=nsyW`zt)&tc0@EuvbA;;Mykpp6ay5qhgId;rEnkqd=sKVG)tkLM`PA}?w65se{H
za5#y0eyZjBVg}PgtgO5Q8*xp1O#wbWw5vb&3*x$N>mIXo@(glWX$deZtz=m}GUvU4
zMWm884w>V8eZdy&!1xky>6V5KzNX1k4ln|Vc$q*`aU4}MN&top=%#%hM8i|%|Fz*N
zm0vDTypV5fdhv*YD!Pz^z;+$dmyv&dOG<C+=7`1{L^BNoYA67=NoetYN_D3~R)-y-
z;U9o`U#nRKcfXrRX^@%$!AP58a%HP?02q!6xLgGkhk?^3GQ)hReED-fiQT=vLa7v_
zcaZSUL3`MA4fsO98<43asOW#dpyaPgkWbZb4mjYu13yT>w+Y?H{aW?UFf!iC4D5Y`
zVvp7<;xGb^S`3Xvt)`*`88^U{KE4LRAorG7k?@-G`!-yyfTD(=B&1?MzZ;l<p+6xJ
zKJ?UmQ%e3`1bm-d&`Pz+P|dL!@pkLZhNuih{*5^q;6w8&D5mrVkqMMYaOQ=+8Ubj(
zH4yZBI)xsJo{cTLzZyHquH!2<{G^5RRd_2<9u;7X04)yXm|&#g<IrIl*5s>MHQuYC
zZ$jXmA-#6cjE4a%<g2<(f?8iXH`jd2xZ_!l?J2in%LTTC_yA0UA&k;diu@^eL)BSz
z3{qZw`rd;ciCqq<a&g5^UtEJ1A8^ub&$QjhJMnfPMU*FAgU+gPhgOht3!}J#m65Cl
z*fm(u$ZK2T5{f2)9lR$nFdc!H)e*3zBJIFKk+Z6CN5i+UPw_kIYev3HAFDvasNopc
z5`PiK^aGTN3Odt$>MY!YQPR+`tCG78_1l+W#eIrryPx$^nQg5dFTBf!xQFq)<xfTo
zv*MH&$CO_9`-Z!}KK-5hCXb&wLzvjEuM8)HApm-4AIsj|dwF|%+qS`y9tgxxuLv;u
z)Bw0mzvJ2KxD~n+BVG*CA_7b&B&bk2QiS69Z`9Bgd5jTc0Fe4DzH5UK0|rT2yQ4g?
zNqsXubg`9t?lxFGcEH5nB81*)az86IY-BS<2=kOuyf+?MPsi6pW}p#fZMN;o$DjWN
zJNLEQWaazMg>x~M2FS`Z7y<`^_1f@s!(t$*#g{JSB$$0j9^=kucfj1Sf`v;?lbLRs
zcoRxAeiLMByi>!p0*RQ2QsAuMBzqeqU#1(?!fWt`##Y*MH9LGRhr<SXIKC|r>NYej
zjpUyCsuVu+f~>-Ltpw9C<c}P%vyJt134ET4{DuP2g%WatpqTnXoX8{TqJnDPp>5zS
zWH96im4Z4dwc_TQwf2vm{g`mQdjZ%*;0{v7;P`}YNdzHNs*plWT?D1%$W%|kh066B
z->Si?lt<37YU&1#(S@d!N?jvPLgf_v$P3V-eC}qj)_|Sdup7WOf&_pOLDgL)&Jm!f
zP>+fZL;)HIJ5kk04kCiLi;@gmR0;hWrMPBqy6`XVo}CLZKT6<J6&V(el7$v(Aq-+D
zQ2kIr#cb5#xIqL4=UhBU7jUJrTnboEFs{NlCmmM^CySpWu;mYH4A{H?tZ*=}><-&4
z_zCJvXS9O(J&bjwNaAB7IY*C0HYkX8J7A%v_lJ8lF!(*GtgE>_(CDelAB|9CT`=xi
z`%!$}XeqkLT<&AS>fuX8Xg@R?L{~r#34A5fK${Z!>bmzHJu9T4XY_}2-?WMo@Bn?1
zZ9$j3vZsaJxc*Kyp?MWEMgwse8(<&SVQNQ7Id#1V4`n!a(=6pU&1tE<C#yqIc83uS
z1V7bSu<!q-#Ino30t@O5EEKN9{E~6>ub{pPxB>tL5Kxo?kP_i3{TG8n_b6>XP2cxm
zM`BwIi!JVcmtDSnttj39y3k^k2G6v)W8Pi$Di<A4D|Dm^)H+o&^k(?-zm2_$cb%2=
z)*<_&9Ew(Xth?7t=ghty(qIuL)}xu5Vcc`bGA#fEm<LrS3USC;7XyIQtv=jD_xb>q
z2b0beEU?d36B}@JZp_AayTX~&jtQx~-~P_ymp*AuI1h}7<%j)6OAz?d!;}^K=`$nv
ze5GP$iXP>lluBsTIn%QFAJ3d)grnDjq&kivUU3J$8wjBTZc}D{f{p~NLXYYmVA!`i
z2HZyAs&0yEAsT=WTOPLh8BvU)SPM+GYQ}@_Sf$@%mrdL?SnVM<IZRT@!Wuw6Sn82{
zMqIWPZi!^_L9p*?I9)UKD*((w%?QnasxV4{;$ha0H7VL5j=Dp_sgX}%O5Bu}UvvgM
zP(I*|29zirlv|DbLdhfwQ&Ow+orbVr_iVm;)_O{vNF)p#ax(+EL3eW@fel&-HBQqQ
zq#7vq<d~gEC(|%Pfy^Ks0^zRb!cpCu3$1-Vw9F3!yea@N9-o&|N$}BEtQliP4aSW^
zSHo+n<I*<+A)5daaVDkf#%yb|=BC#Iww15{(+B^g${>Y~Y-oqE@Ox}IOHo~Ps0F9d
z$6$&oQ2y`rrEX;ONxETIVfr)^6`qEEbOwN!3;@d%=jFQ{{50^bu4W+h2KH{f27M08
zSI$PKE4Ab88$P1G+f^_y*42WWa*b0&!eG(=2g92r)rzNqd;kCd07*qoM6N<$f~&q&
AM*si-

literal 0
HcmV?d00001

diff --git a/Images/mathcheat_R_logo.png b/Images/mathcheat_R_logo.png
deleted file mode 100644
index d4ae2b453accda8e26e2bccd64271d48bd9fe56c..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 44284
zcmZ^J1yo(jvhChLaCdhnxVt+9cXu}K8erq@4#6FQySs!02=4AK!GivrbMJre{5$Sn
zV=Swx*;TVzM)z7Bsr*SA2_7FF001D#%1EgGxsUxhGhm_rTniRTK>z@}C`epfSyo(}
zRN2MR5@crq0LUQaq<N~VEn|mIb#r(BL?<Vuy2bZ_Bc0)w!GcnWlm#n8goRLhNPLjO
zM5EI*SMLK#hm)I0Vj=_wnJ&WJpem2_t4K)hugBQJT~>4c+Q@nx)!AG=Y+YQoy7XJ@
z04To%K@GTR1Hl{x+L#xo{rcz_--jL`a8ZHC+Q7bTOG_x}M+u0zmu|t#%yOBL#`_B<
zuzBa|$6jS>*M;|hL~?F%N@xJQ<UE&vKQ+!iM2cGDFdo}G)c9>G9a|VH8+HL);aBqA
zp<289vIB<BZyD*-dj<G9lXzVmxiOu}8(CD_MKMqX`WaMPRz;s-EV|WYvjF=Kw6n91
zUlaf=n3+aD4yg<5BH1J{@h&27AONr_L%dxs$kD%ft#bihrB-`UP?55xrD%&8p#7sU
z%ctu`wcn<!3@L+(+Yw$0Q@2fsaV$9CTE{d=AeD}Nrgh?ZHR1WW&ccE9@80E2Vt=()
z!7-m_jce+&sa(;RT%Aq3v#-qE*noRV-E5-9o>UD$lRdP^{`Oa;OH#ni2SC%tmtVwT
zc7{nqJBTz0(Pw9NuE0E7CWa&K=e!`t@!l}rhs^I6&zk(DIKqJxvRLdElKZ7)rScL#
zejp;JPno|ypuX?+@q#A44SxKR*N1Z*dZeLplmZtLY26tr5U8ENWDXw^Fuxsu%S8i&
z=bvMXY3*(V@C)TtZj*%>a})%KQ$SKW2>U?zo$8^o!jJWISyw04y2i8lyd6G~Am~Rq
zY%7S8s~y}U#+$oP6@J7k+6=Mlhd@t&am$D22xMY`_-Kqo2xfPJ+#m&nfMrl2(;%5-
zAV4Be4WdFzf0`ic63`>q#tASN=<^fmQxKOEv_6zpACeOSB3N1kKCEA!1-KCif(^Pt
z#VQo7Ou#Ni$rM{aCD4WS78h0K!Gs(Zk4!*K68Zg;x*V52xUxW8S@4#^4Zj1{AowXC
z<|io!^efOW1T6z%)&yw@&;nkr<0pib9{9c`;f3`D2B&XzTdoaT2)T6tXPe*(@G;bA
zAQ@I<3>I=)N*k8oT0$&An-YjdW}8S}3{fGOm4IAK&>kz3=)4CB9bRCPfr~wYw;Qe#
zvu&zrvS2F5GM{0Vp{am#z-5Wb9{4+`#B{#CRGa3jum>C$T6Qeskfw3Gv0hzzJ>-(f
z89yQNXdm7Vp_6|bu0ERsXAM*<%3@3>_;e6yoBG<Ti+%(1G6<#L^~&@!Sr2>wVF2-F
zJjqBeO#+-Qlx+|SSsE#&9i=)dKI|dvYY5r^he}|%k{fv#s`rq-DP4Wsl4zGKA@!$Z
zOxZ9>992rX%%(tfSsQW#YC-C5vgB`k=F%>>IwFQjHI#XzMpP(C!pbSM)R;<ggpy**
zpLpfkMZ874#Y<F?${pskD>FKBbd|a#{N((YrIXpyf2Hq!t4^*;v`sxt_oY%wSxom#
zH)6`8F(M0)dn<iugHY)g=~oLZs?#ix+HM*U43(A4DoizvFwHWJG!5S299E&~N(xMB
zMOUg2Ij)<bDj~xcn=SF4X=_q!;#;v;VIh*{E`FX(J|4a$IqJTJv!Jrj+o#<p+{fQ<
z99M-NU@?AVSH&xdNsj4^iT}lM&_1<0waQfsGJNl%OZ`pnK>s<FJk>H4m>OTHpy4==
zIiFK$U#T?5Vnc0XVzX@XJkL}WJ5@I2nLCwZ#PPoO5`I$G5$V(RWOFxoB6yOFpM~Fp
zkB6^;pUyGDiI?S&^(|{Z>yTr#$yyho8A^9w_psTJB{lQnhq@(IV>E9;b<KGh<Frum
zdX-?cS$U(jRne!$NeqJ|COvM^R{d5rPcbEVW^HDpT9wNJA(g!9yy9NzUcG?vzMrHk
z#?hkL{pAHw1-&M@CPmw36qSip)AU8UMf@W?W-ZQ9*Fw8`)OC?LvPrVB-xSrB<~G!g
zJEoeZ5N!{vsX3}RJ4Xxa)^v2fN-gB`4hoEYF7nR)&43^ntsR|%y^d}4@%*D2<LO5d
z9Xg#4+V)K+bx(sULs_;4W`3h#HoInxlcnni&D9l*n;yOAe*9Smb?exjpE{@Rk?+Z$
zsh?S41jD_;yOAH3+&pMIL;U8^PLH<RCa+_5VQ745>StxkHgj|)=_ZjTix3cTu5jiU
zab2dJx7!F^5}C}IVvM|;3g$FcQ+IVwpMEGV#vXbM*$=HR2`<03Ju{%lqa;d4kEWC*
zAL36T7)GSmY9(sbwkAE8UfNui3GoR<3dIUx^mz1C`7uA5yh6NsJgxs)ygGbZd{%<u
zf%*z}1YZm#1*Hiigun)e{=pq;upijp8AQT7V??PxhYcb2BJL3?0LO`Ffc4~x%k+`s
zEA^Ron}(OamnRgXsicJXP^V1%*vy!8bS$<!wx`rL>0!xvX+=q2$#hBibnTBijeAlt
zx-q1sg4iSkSiY1VE)@?m>+<2dXFKq_7fkH>X^m$qF3tND-=}I4jQVw)>4^zQh&EB*
zBQXXl_7!SwjoLl;p%#-n!=IB5M;H!cuMHk8UJ{|wLmEP+CDMlsBF$uLWU*v-A(TX$
zx7-_yZUXlzQW8>%=-4Z!D<(i8O^;2pVpJwkbqogVf;8RCgB(RH;vM6l`3nt<LXqi2
z1zJ}f%g&6rH-$Ty7|Lv_U>$@;n}!W5X^XsZl^<vaw|iN84&RBs{rdLo&uPi&>2B5I
z@fP;d&v{{sTphYlqrckz$<v^}WBab>G~S-nUS|2~yVUns3xZ9DsEs+5e3iN$W9!Dt
zgAuWIF_rCZ#z{S7yJ0&lyYqF+#%i0@baKP#JJatwQ9DXhYe}b@I(}Ne*MHoHoOhSp
zmpprJdBA$Etsi&%(#|y1F<kyxvt>}d|Kq0crnOV+tVh#C&)FCS6mK5i#8%$WEmvzJ
zJzsWGd=iKspLNiE;@f(jvogB6*t=;TFbj-@9EK4h#w6?t7<*8*troQ(Hd)*LIyiM&
zc+$~p6<`z~aV~S6I3*n_T{1cl?>^5zx5!=2ox(K`n>13t>pbfCx^*Y+i&1r~%wE*~
z2*pFjd%ZLINOGBrD6s4u<yU!3^O|?x@>4fTw_ssyA+n{I3*@WdyLk+r%^{j3VieAP
z|9l&BpPAJ-ZY4jJIEnQon>WwX|3+*i^g22cC!9~qO{k-3ZziXvu4dzVV0x|Lb-PQ&
zYWMStMtYT14X**eUyfh*dDv_00&!<<n$ej*=(zdPzWdmz@ilF?I<Dt~-{F(NGxNrM
zhu?nOdkz6)H1yL2Q;+Yv;hNyF-^S}SOa+Re@DCrJSL;`^v)w`(cqRJWO5y6)`Nu`e
zitgL9{6*ub#t5tUur_h#_tf9DN`oJ!DyA5unxsyotEH!6e#fl))ZY$YeUC|>-3{%f
zduw?}ovzCB_k8ZQGW|AmWjlSEDJ-{9-oyK}aBqKTezZ613A!vTi`uB_5xx(6EqFG&
z9ZftwQ+iZN&5ITG{0#SU<3(+6d9e8q^6Qz6PsjhyoD$ASM#mKZV50cz1bUSSdjJ4X
z)F5?jH*G})J~KyqCKGc<Qwt_9d#69v0DyoO-=9Z&3pW!|FMB%&S3WO6^1m(k{yhIB
zW+o^7+r-UQkX&0)nN-}-#e$TRiGzuSTnL_&lvKdQ+>%dKLh9e}KW~EM)^2W2e9X+A
zo}Nse>`aa>R?Mutyu8dTY|LzIjDIW`UA-OLOuQH!Tq*ul$p0-z!ot<e1?1!ga&#d5
zt6URPM|U?ta`L|%{m=2QINd;&|Le)Y_20Dq5M=&K!_3OW!u;Q07G9wL59}|^{}*O%
z_CKtg++FPc=45WhY++|%Z{gtP`Ul7QU)}x5?f(G(pCG+Voc`tP?<D?<o4{X@^Ql_6
zI@-Dal>!Y1ked*zz~6-aQ~bXf`4>vu(ca0$!qxQ;Oo;Q}kblbl(_Z_3-3YO>u>2eG
zPw9Umlw3f6sG9s0*#89ePuYLk>-<*_|CIg%@mKQsltEq=cG?o4KPmm22isq9{a4L@
zBE=o;99`6$Ow26)3hi&mKScj&{kI>T|MkPl$@Wh_|IqvcX>P{n>TYUdVdnOa8v9dk
zf46@z6D#I_hy|GctF^$N*1)IeXb!UUmN0R%5MpCt<zQstVr1b^XJg}I;o{@q7GVAl
zn}0;`mqy&h!o<zdMcvWSPUvqM_J6&SvNQc*@SpmBLj{=s>Pi3TS^w&`e@p-LTOs&A
z$p1SA2*FRR7|8+vA^=$lQFSliSq==?K(hP&FzsBhb2+Qwm`Ift-3I|8sIXAz!~U_F
zdWtZ;5D9%D>6HX=P&_#il*E=SnmAhV1Gbo$&*nPiYD4#B_rv?^%R%ls5_JNU1e^k!
z@1&r`<d@9Nhs6VKSFuQm9k;_Z&^Sh_a0=ZvdVK=I{OSH_ift-4%|mvRy~@GbQ(9Wu
ze!8I#Xmuo(Kzx6HKQizA<<Oak=2BTzFFgID4tlji&E#gCIjFp}gqO>2<~HapuI5XP
zc7VWq$&-ufm(%l8cgax6?V8-01uBFzj;Sn81~!+yvB$4zY1E!$WY@~HO+`~kL@r}w
ziIEsIx;bT^a<X_%%8PE;*x2X?>9%hoqA`yPhR65EDV8b)<>rV(SEkh7>PoSEt{KEm
zn)X+I{|wr#?#^?n5u{`(H?T<oDiqy}IPr8fjJ8vOCaXU)I163%LAJYByAO7*tjhUz
z_{cv$KgpvPczFw8ljKH>M*BWJxrl8$IS62XkETISl;KgIqX0-yOt-mc1_T7y2mrfJ
zzd_P%4;0j$UR+!tg^Ck_j*qvkX$dcnk3nN&KcXIPJr*sBt=xupB0H>kR7v7OY-eX@
zxK3BHzQnw&+}w1nc>nseRM>Hkf>}Y-tyapl`|Z!-Ms<~~n_vrfkad51oP=r<f&ma=
zy;GE87nqR+3HUm9c{iS&0{k`-7Z;cHe9_rhT~TeX@R2xgZE=>nKzM;$lN6UIdNgYH
zTHAz{GM5leJfuVNvvz=4ea7nn?sKBkP~_OTos@1zjZ;lyXY}{hPXltyi^n}NF$yr?
zr{`yV8=EW)6MnR2gk$`9cNPTWH(ovx_*RL+=`d?bRjgR4MX91hi({STsz$a(O=7nt
z(O3*=1qquNRcJyxlO#3uSXp|mudne*x?2=rFSez^3AX)`aR!Mo4Ti<9Zr?RO(p6Se
z;%zR^v+^#GWS8&mZ_ChPO%kIDNGOGYF`N@|Dp6>}3qoKgBDA20Ab9M6G9S@UoR#F{
z=1NM7+j2LAX!7kP(^BRa7p+9(B1@OJN^Qxv>q3$Q2S`h#k4;wQjbv$_lE<&DA<;zO
z*^HB@57pBrKy;u}s4v4*CHoBbUs|koWANDZEiElW9kPoNwU-X)1IlN^;Yf+di}4ff
zvdhc#x;M}~ucZ=|QhA;ZQmeGW(wY1kKa0;l$@<m52}*=g47<nj6hnBMVwu3h(o<t1
zgo}qk*W=dPC+2R8cKY;fs-$d70?;BzHKPEeJ6BBfJMqH+8HHA)4ftixhr5id)Jj``
zWWbtyzBYL&LwD2F6@b*x@|dfyQ1OaH!28EZrn)jNf{vh<&|SS;_&_WOR3SeSpHpY^
zbSuamOTsUdmYp3<#HQ<Ae8aQ<aUVrvWgXaHICR|vyIYcyl0tQ&6XV#I__W8a^l3x6
z+2NCc!MWk9pI@Un1*SY-#2`eNe+Bz%f^DyxPa+|J<phhfU?~AQa`_$k&4rMjs^AT`
zJBu>Vjt-XmlgM5{guLtr(j+k-!-O`NLaCq-DrW$F?>E#YSpo)_fkTjHzTmt#q?gm&
zC*(8N7!u0V5@EP)HteJ*+D=;ZD2YL6?Gswg26k?S!-?^6*64?fmcyvy*@q)pd3i`m
z617~6Id{*^i!=b5i2z;dk$(khfSOhBCz`kI9^n)>juzsD<z@FK#I;}v(jp4-u8dnB
z09j#lbnb2#Bp${K4kz7d0GlX}Y!tT`O5Cc)7F#@J#Ox)WB43oa%cE(xJw&~<Dmf(j
zFs*=2xg9>RY!d(>14ubqfmF~aA%3CuB}4=U$HDOl;5N(qzI6oJ@^U2zUb(>;Pn!tR
zG0E=ghukM(^ykt6I1l#rdpf4Ryr%tF_w3a+G!(K~(zhu7;gIdY0FIzctF&@daa<=e
zHFQ19@r<*vt`RJ4ZGD(s7ZiMkXKCNbWJ~fT6QYya&ZuFpT$IX}QRS6}Fs6JUYY6<c
zN*=yf{bk1xmZeQ-38h7!0_-yoE%H+MDZ*byOb)P7S0O5c3LFB2MIFquMuPW<Pz5@P
zG0)Vfd#Pw|(|^6;->cj;)iYYG@Ybjh1$9_&zAtKWscDZ6CJk6}51U24sZdBWXR`ZF
z&gl65IQ}s;MN$%eUi77?ORr=dNUArt7%iE{Ty90&aaz-v*5bxRv^l-;d->v`22yQS
zb@tgD3W3s%5Zq_lC%+3!q&x#>w8PpV0z&efYy)_HryI$VF6FK#tu<muS!;%k2OS}D
z&{NSaMyU6y{+NVo_m@SeK}bStfyLh4$aRFe5XI5A(pztscZ6LZH<1U)0<I{$t7Wkm
zP_k{VUNrlrokifS0kv!;-$vpK{H1{MHa3+~2|EtA<>MC5XU&%bI*kWSd*+A6M@q`j
z92<&dIVd(yq>y{eHn+X>%&LX~uZoLGCr38I?PZ;ZZF6%egmw`Z#F+$e%V^|I`>+8D
zG5~n@*7pKGs(Z+t`8Z+hv&X!*3@ku(LJ|)0dZYLoP&0Rv$frUOAs$mw)Y@yfjvL~q
za=Xwx$xf=I{b9*HWge{{DqP<L9|>h|)#l4$yy#Ca{?KcMPClM864V}pDt3Wrmvc|m
zO2jXV&Z~>gCqV5s!ql&|y-K-z5%LdAr(Qas<3Dwgako?eL()Uez<_{r6F8oSc=B6x
z)gKi&Qz_M>{kyc2q>EprxBh-_vSyMwdW?Fuf8Ai+%{U<G*G`yRVUXaPBT@(e^QT^2
zEkkU?vTlQ`ly5{%U@&yu-V_i_^*s>xwM5^zH|gPbgG#0h9bE-A)f445MBzajGy!Xh
z1tjcZgAvP~V5skWgslM3)r*$&v4j%7r8pMN@8)4k=)p^5X&)Nfs3n~qJdl2xm%OGW
zh$tgl2Ya;i<U@39@LEr+`r&8Q2Ga5}uO|`bWkxSy)4M49Z45JmQZt_)`0evg06BN{
z^Bs}r=C!H#`hi-fudkB2mHC~Ur@SPqFi0${wnFJP5bf$GBP!}Vz9NcUyXvCL+&eeK
zYSdQyLiu{9G^F^dFn#_irRdWRbfjRP^i7+fyoIP6<#_(tRx8mQ31oCs(_tl|-3NJ1
z;vpNHWq06oW2l#)iEY)U_>^;i^2Nev`{d$i+^NnSP1_~_Ers!$bi?7TuQbGiQ}q*I
zN?SoSR4THxRF-eNKOs5i`3mRL%fNS6%kv4^SAl)jlxtBL5?pWuajsFX$-4Z*RIg)0
zofGEJR~AG*LM(V+a*(<BkKC=;Rhoxt_N7Hx`z5@iJfM@)rxWBwk+vJZr_*yDi5Mi2
z&1jJrWdW0N^xP{q{my_qF9&eXo=$FkbyN55YKO-WYs6d<m(<=jzDy*Od?3%aF9KHi
zgf4;)+3MR!cQvBC`lCHD?hL^+DoiKXdVL51H~BNpt4c#3FlE!cII=2%DzW=}``8~}
zAG?Zjc^>-)JLY<%%QFPM4tial(qSSxKsp8n>rqyD0hXo{r6=Dk^<Z(yi~Ttpv<gUj
zDbZHbu)}GSN5ee8!j71>5>?kIkU=mKLp-@1Mo`;uv6J^ilh`%%$XS#uzCRKUkzYP7
zSRJ_{>F^oP(0B)R*fH2&KDN}X7nsdy;o$ef%m5)!B6LII_;9|UfVWrsFE~)W0A5=x
z+90W}@q1N-e9G_Y{8aW>BEcsgv?Hitt?2Ejsj_rkk3_@QU0-N~mKmmY;3`)#h<xG$
z1|;9302QkIU$O9Nsw-AdefFEv@2fY|+2QILAqAMks8IlrbS6-on5a_kxsTA16JBkw
zm?A8X*v-|k^zt?$A63ylZd7O)GRIhsknte*dkuO21X7PveGkDdZz7_%7oqVy>e{R)
z@#p3_m_js6ewnb`>LD9_L+b>h3Yhrr9QZM2yImPyT~57`x`K5`Rd}rWWFZh&;kT5f
zNL7q&M)ciOUo%jXBTVP2K5!Fzo?W95y3WzeQ6(xJ5xwt2P`zsaX2_5|(s6&7*%X%q
zJg;t!)ao~$`MzGi`QZ*FM{`kA5R!l}*-%7GB*wjRRYlbMHKlKr0wjN+GD8{yf*;H3
zTHL-${VXZHv(_(Oo#WK+ccUgkcxbYsx*UarX8DK$6qndqIl=Y(K#IGByx?eUu-VaD
zy_w6yTU5*kIlmRZ8Scc}_PSSv)|6Z`3w%!fx+&sE=E0UEa{r;m$`zYZ6k~~wTE|V%
zc!<JJAqtiS(zyOt51|<c{bEL}*62?6Yd7M!)U5g3;lbWQ&Q%4*Wg_cl!3wMD6kxAc
z;;9T$0_w`j{ipl*!~0u&Z{Ezp+6zPE;qKPK&d!fn<27=8a8Pb2>GI43WKPw=UaNZ#
zAMtabb`ghUFa5|EzFy%5IjFOvLtk3D)66!%jup18$l*{3j#j@`>IGgHZq>R02A^~{
z^NX|#R4?zruY9-b-U?MG<OcJA)1L4?GL}A;gk&fl5hvN5-7cW^0|4iqSE`}+o8R+9
z&-}b_o|*V5?vS~OXTQXclCLeCrG;i3WrFcNsJq4jaL_wcL1NBHmw`y=<vP%5l*T`}
zL<^076pd66ju(9dl$$xT(wRD_vaz%6(NB7rZKy~a`M*q$ejoRgNtseU46uAE14<xi
z6hU|DS5CUu_B`1fRv{P)Xqjwos(FdGH8$$msMC@`%m*l`yHV%J<1ftwzk!L0k|X0Z
zek6p3y5_9K<D(oqSKC+DDC(G;LZhn#DLzt=GJo*G-kN=ZM?nt++Q+%@BI5!o1Ertu
z`MsIikjZ~Z;Ld63$>0SokgyFwk>Mql`=iD=>+G(S(@F_I`g|4v_rGd7z8}z|fXX~%
zxI(36Z_HgVnsC4k9aPN3x=Qzo=r_4OR%)Wd#;PgZe3c0pPhxlB-gor)zys4=ZX5Yv
znoBMKc8pK0gLeXr2GhDNAQ6A0``dj)Rae^zS5{)?wu{TnLBLbUl%dPdwP4(w_l}#T
zy{fLu>;QO?IogLxaVD*|)!5$Ni|F%ndmOsrLWmtK-fxe{lEIVgpaRSKO-OZP7y;Tj
zkehLqn=T4sv82pQ!7L{?Hv=~w4@ad#hztTuh)5>tJ)mGv)}6VRaVE7o2Zj(8o<8gI
z3u^4|CzaKtEma-Lwl=f|yg}3+Ft`%pnr64>MUxA8O&e+#80b^@N*VDL@m!6ZAY#?y
z>*tkVvvCOg%kQ0!6f1VAUeaPlJg_1Fmggrkz0PdNH}1t{y79T18Wg}FKl@~$t^_|R
zh}54F!G<2ZZBT9cR792pOn^z6Y?UMpO+pW4C7Hs+Tv{VZ!Y7O?EvA-}mwS62_~rlF
zU~`MLA24pb`;3$zl-M6iUcqpg8{nz=dN83CNqVqdgT;N0@$donBsqUD62?S(kcI-{
z4xb1Ht*pF=qiV8*LA=RLSzf+zwV_1CJGw#z_kr?)b6z&b2Hgbw2n>t^BHMSPF;9Yq
zw%~$f<$#X@+>#SFcR^0EeUi9tF8M*m{CA**eC|iVG9mz!2n#1v90OKl06-iH3oy7=
zO-PVb-~t)ivViTwe4!PsosA29W)Ji36!!o>zP-+C`3{ot*U5fj3wtB}XfZ}?pO?Y{
zrV=C?NpVP$nr{%gGCpR715IgEL-L1m4Mc>?Y7>ykHlaYm2^cl_K5;tXPfSjFfR6FL
z$MN4M<M{6!DY2|xt6Na}bUPluLdkS)oP2a2*<alBn&9T=<7Z0cOI=u=US>9h!F*yb
zZafmDdagpYgt;4O%aF?BEL#D2d~tUuvx^K1^U>03UA70UpvS~7wF8v_5Xp#!rukBt
zx3F>9Apl&J(_4kqYB&jOF(4`sEfs8*-TEB>q`4(#h71C%6#e+@Qd{e*gbTF|+}2=d
z<W8!LfC&}(GwrMm6V)e$s2D)jTM1^g7xg2hhuxSXA!3EnDf;afr{)P+7$v%+lGV%a
zUBD%}ss{DUg4pt8h8|J<+GwU<QXKRs%MXEVDKx{(f-~uT7=9*DVFoa)wYI^jEUArO
zQH_DJ0&B4YC;J?w2s0rQk}^~G;Sr*1x*iAQ6R0<pH<W(+$zIZkO!_=-hu7+8H9tYS
zuh6WntQ?#3j*8QX9xg3(h+aAniNf^P56H&lQonLY_l>oLrPv(Ue27xa=FX)5kmw>f
zWM+d-iYi*?xb<_PONSn1>k&>Ke<&$rY)l4+wBgH6IAM<u4}}dD9GoakA^WUV3+FL7
zqz?i6Xm<l4toUlAk|v}etO^Z!1`^b-8G->G435~9iQD5WYy!{*cJ~WX>)h3Vr?SQY
zcD0W7Fs#W6IqC#XIj@=oBHbPvMr<NS9>4(gd>F{i75M{^an(|T<dj=T3phZzYsgfe
zJ{x<(*G=WBpXkcI6P9Rw?iI~dFkveUbfVTqB$m6YHNCCOrCi8xl-swrKlaN4PH#6a
z>PLTwyFXi1oCx!E8v)uM3#1Qvn>^0581BR*vy!LdS(%)E4)s8OPA}hf54~%<)Q^#n
zc&rODA>Xh=)BItFF!AC_z&+o#u%Kz{<^Y8_#DvEa#CRVBOO#;$0Uqzh>k}=;PYYXu
zavODEAs49(c|L@fF%%e9WSRm*b_ZaBrImo4o8ns+0j~f6q#kx?_T4r1&!wsvZn6;H
z?%n;+3D=59M218loUBRWviZtAc62B=GBHuvaT=zag}2A6EXpG&mG-`!R!^T_ZIR%M
zg7O0(63Ryr-DEP1jqC~L!h3c#0f2nmOYm`VRrN6>cxHLoq%wmQd1Gp2WYxNdXj!SU
zs;Oc_g<dOfT%jNjS?IA&zUOJb7A@Jc@P=bQK>JA=at~?#<z9H*^DrXpDQq^PMuP@J
z#=6@)C-Y3kcQ7AA;s^klXpmrJjP&F8K^k_e$GMG`MtA@HHebqi;CAehsM8gJ695+l
z-SV;kf;M9E&o{g60B%qsecjJY7rrbTb9z-{aZ*l7Mjr)LSk#zoN>Q^MxKa5a$RLy$
zhZaj$$|=`a_YS@_O2ortyddt&_!d-oW9AGSs!9N90#VXnOUH<QJ-PA<7!xq$e?=p^
z6*w`}NNTDsgKiJedMHHkNG9=VS?T~NGdChsQGHCYZgpP6h42NFg#<Dt4o|yHYF~SX
z|B8^(HSG*uAa+jArbQOZj2t4WS-OD{)N5>6R$;tAl0@w%n7Fnkke;V?)mJ;&`-mQR
zsVullxj+48a(TWPfGMI4waw>ULfm<Gzxi&(LFJ-tD{m_nVpp_<x~oZN?Q|-FRv&Zm
z83^eFNa=XuYjv5IE2C@BgA`vB2j~x;abJS)A>bz~*jegS@;F9U#wXMuDFoYycZT8s
znBqt_#W14)&i;hAFRk1^%ICac4Sp?lC*lF<dErTLk5bP2(aO<e&@9k8D>P+Aei8#&
zf?B2>6R~L!$@-N{Xh9iO>!ch^yRX6DgP+M<gfI1fDXI&bQQDx0l<!L2lRFi8;6_eH
zmx833?b~xVf&IQtk3H-M(Ap`-N&$=yRF@j{YXmZQHM<t&7_SB16inhaxb}S+n8rX@
zeJJH15B6{N_Vjlem-(Jb<wmBa9@A@QYbUH`)SvIq8Yd^`uGKWAreH4L4DB~v!R5(m
zm8_H9yK(&3+|679<zWP5`KYcG-zN}^ZP&n^5xmn^sC<>`{)R#brJ2alL)!H&?G`bP
zuoHX+Igu;Il^A;K^4Berx=?)yM55A|n1B|OBwSwVn{7L{Aehi%2z1fd%fNv>`r?o{
z+m{emaIkA|E?oejtZ9-Yz?3ep5BoYN8cK8+9T*L(T&tWzY6`oS6d@`yO$G@OLb8{j
z-kiV`4@N?lK=20DM9UKJG*AcQi53Kj<Qqmot!-49s>mX)5QyW69jX|Hsu`%0_kz@t
zYND%R+WO3*Jymj7bR^lysWv-Tut`<pU}CxVs)g?mTL`7IwEY0t2B-aOx5OLK^SVbQ
z#ZmNy&Xrt{az8C4`1)dkguoSj`o;=Llu;ouW#(3g9ovKQN<FuW>643>jwg7+KdZW|
zX2F}b;5yoY<JR^2=lg^EgXg9^^1=&^618I}{GaGZ70Aud^FsPU+%!{ID9l_HqD5Z~
z8&??pT@$Mkb3`E%x72eVyT{E{Az8``63M|(kgNvK+{U<EUzJ<r3;s-U8FP`xVjii8
zy6P2SpooCmzGBi4ULudk0~C<39UQqq$P=^zPaLBVU~jDcRSZLmn=LRdAk+ldVQ~>s
z`eJipr`;Ecn;4a#s^RI%9DTJ6gIehsP7c)F@3zHP5o%z_LU$k(>Y^;*WyzMj$f2UL
zn6A0VEj@{yj~$GLzlOpuj1aa4*+v~6BFlgO&P=;GvxIwcd>(~~ld9h2=}{c*gs`_J
zqcOPvuxU9-sO5uDfMh=pQM3RTo?}6lwZy9l2T~WmCIB#@Ie0{r7mt)O;_c_PhzS9U
z!8qUqBoX~GUt^?OI+6Zr`OfB+7F=Zj6lsB<2}@~9<)=Bt5&o@Xt!L?JSa^i+JlA0h
zt(>YSf8F`l`WwQ1&zucg@XKN>x6$Gp&S@t;gsS*mbUDl!5>eeququca`b$Hji^3fp
z9TR7TN@M*9<7cx#K*M-*b1Q+#^p+dc6MHNuGj2y49rqwPeI<p4(`KoTT#M2vzsg5!
z_NW#gYb%pL6Nwl+xQdg6u4@`DE04Rr!SfJogq=n%(p1G%%I@ib&(ozI7}3ROVv{U_
zYL^_wg)W+i4IyD<q@DkB?V@cSE*0hH4O2t&349n*6|vA~uV&~`E@WnWLp@E+6&@FL
zi-+<kmF4!|?H2fa1Qi=hDwg;R7fOt`M_Yv7Ozc7;)4BL8WvhwK$o-GP5iQa%sLbZM
zPjWR3atyw6&pTAE)|nXCiMzWS+WtzLIY?VTBjC2Dud1yjD7dG^&@Kdzb}87%gDD1Z
ztW|fGFcB}{XrXaM&{_Jb`^fV%FlV@wzbB~bE6sxt=8U^iZV_}LbD4AZ=b6sX!Q1CN
zeMSDUgXK^)P9`eT&Arc?w!H<XD9S=wQ$p&Ok2N#Ca0#dHZku^oS=j_r66yoabFf!f
zYG1Xhrnu8^2A%xoDz0}&<V)}`vgUB3(>}T&7zRvKsNL?Ic&@{@iYgil1;R+Fs%A9h
zo2mYwLco>Ku94v-F&=Z5ubRz=G{abEvoUri!C0lHf`H36<BO+<pk3i#(a#nz4=D#A
z#}$a-S!t{<kz)lZ!1!p$mG%sDerQXmiDYpIR!k*xS2ApPd+F-BTju4JABiLA{k#w(
z$TgYOto3;)0__8YS0?=rQe64q!nlSHsHx+Z(l$TED^uWbC19Yu7=3ybCnogpHNWa&
z>D8O+^jC6LNqseHb@;Mk6?9P6Xjj?HdRhDS=}6EN0uf!x%j7dc1uLx%#GP&5Y6M^D
zFX~Vz?4;?iGo_s)-Bn$MLMY-sLv@2>V(^eJ>hL3a5~XvjmZV%*`kL=V>LSJ<r{3?+
z``+W7Up5%P`tRv5Z);kM%Fl{{!8(4r%+e6*Up9-Oky28KBMf5L)$?wW*tB$YFHQ(f
z*36UyPB!!#wCQ6MgKqjzrA2Li8iS!&yxiqh_h@S(6lV&@QZdsr7;v>12}f5kmQXf6
zU0rAw2Orm#S^|j?h1*gC#YC(Vy&M;;KUbZRh#75t^6N%FMn5lor=|)64xPczm1!$f
zQB)>(6kR#FvW>~7rKZjnU<q$5*-m~*)opjWJIicbrKguF<OhbiSQHQN><6^7IL?Er
ziXSSq95qi#qkXj^L32!o9c_mdWyIf&n!K(#c-bwh6r&PnP+4S2kEJ`xFGH}FSs@^8
zu$lnM3B<w6RfaFr#_C#`i}K$QGQ}aGqip0!t`KXnVUi^91HqNs!9v3>C765r!rr^h
zt%No7XBD3dZkU4i41f|LZ7R&Z%l56CnrrK;*N~j}hV(pe<U~cHv4NjXH0bBZq<ZD$
z<aFQ$@j=HoR2P|txgG>uUt3ur9e$mpMB1Yu<CT_XXU{2rMD4k=i>*N>pf*m)f*KHx
zt0yT}L4D6h`&0(^(JfaNsn!%o4DQ#4-=>R&R}y{K5GSYupoqZE(Xw}ZKrIgAlYrFj
zzP{KXb`Myxqg@+u5oRJ1HW`o~C49ZUd%pBEmMQENtA&CE48mFf8aj(Peb5$&^vID7
zf34WonhU?r{MxnPpH$DDOsUKX!-pC~-+rJ#Gv|(y79(3CmBB$l#Lbt*0#ir>h^07S
zC(X=6xLLk3D#k9PqtEoj&k3{C_7xNgoy1y8X<GV>%;x>xJ46w@9>TCX{*4p)31vN1
zYSRLIRKR>xfTQpCs^R<1`Z<%ncArNv<D~i4VF9obss~HyWuzP|6^na5{+)g(vvVz)
zxpynqZyRs1Fkg&Bh>Qr!ojw864vCOW7Z{G8VXdUzn3ROBrK@lG42B`+3g(3asHI;?
zkj1(GSjBe2N5RI|d&;-9vr~3ZN7RZwP4Y8o3B@O50U$%dZqPFG?j(uS$6b6HxD<E$
zqJ=TqE#%qzYQ(p5MNv5#n+>j++JRzE9c2j6R?Rka^JA-BY68uMt-fEjG?mDw5z=1>
zYYpW>&JI9Q-L)$Dg-w`FuK;BLVXbs{OLw^}lVqttoZw+_X;06F{@_Tt0)=vK)Upmq
z#mi*Ph2gSDMf<XM{HF7&L3pGliZWU{{bkd3PZhvh9p!oX^4)1xU>NtUtGn6{l@KDt
z+A4xJm6a*Zh_<NR<rY^<Q@8XgLHFTkH@<Dr!ZCl*tgSFOD<$D!B-#SvL+V81Zt#v1
zjHy+MEF;4Lws<-=B%oYO1`!3iR`<i!RTx!^O#EpGf~+b_i)!s7W3hB=aYU~sSma!l
zVs=RewFNvdBL3t<;iVUbgWZ0;JIc&=zqD&8yz_H#B|0ifC9$-Uf~N8vFed$txA^<#
z{Z6K~Et;w<-OhMNhh-O2TfJ2>-A+~r_GAyV^zj%^#M6yp_?6)0EEzNra<JDklp^Kc
z&EC1WIWZ^hL706I^_GUQqCF~+^#R?+$VSs`(_A7}5pjy5ON>a6=c$cFPk6&1s0>&L
zBUl)f>ppAG{Uhzf56LFgP{NH&Zg@$|<w#Pd%UDyhbgh-jVQuM}3p>#Bq(GY3>3bS;
zV<d+Y3^ONGOfh|E6?QG=mT^k9sG!@lYK5f?2{bCJ{{%_gNr)|1`<;zOz8r`F1n?9M
z_<);j%iXDKA-3JwB%z!y)xQpHj~>l2pQZH5+Agu}Qb4|}fUi`+zjNgspZ7A%Y>&%s
zNys&%zt1{OQgYq|KHB%69wg#>#j)38ddx$hUU2yOJIM6<6E<4>sILqOEbUh*nu(Lk
zKnO)3p@11VQ+nb7Op=LLHn$@Oc#9a?4e?~)$DI4ERh~72<o&Tbw~^=R;_%s<n#HRP
zzsCB(cFu<}4Ep`>)^+FAtK5r8m=#V4di$hWqReG9Tw?**JGM(z$=|Tt-aF<9*V;{w
zNZjuQy=`|5+{J2{DS<zcU<2u~02H8*>^am?Xxt@&kVvpFdk<bP%bhG~fio0TBWsc&
z@^N1hat<^i1U@$dB)X%SaXyV`AbrUvwuIXU+u{~x>U4*c%!qR5-O)JT>u4i^<7Mw(
z2Y1C~rH$yL{^LmxBqc<4fuXA5Z`5^NAFFMeFDkSggr0G%NR;(DuQA(Ql#XVd@Q^XV
z<RDWIctmUv#4ZPj9DGFpvE>=4&J^QQ*ftHNP>oHLPh2F&o}-IiBFxXvplhi57F|`_
z@kTzss~Wg>%CGG>VWqLsAB}U|ak+7K*vZAcFyG76>(!AW-Yd}#%auSVgK{ea>pd5t
z;-IjvtybUk7}WV5DBStf!RH!2fOI8M@QIAk&PdO%Rcx_LXUo{ZAr9}B;=6R#Gqy&o
zikXNBq$f4!l`0a_hyKYvwRwg=qs^a;bex>nsFf+#`uHT}v#f^riG&tNxBI=~gOmH>
zJK%v0g<U(jd2f$rIQM@>Rk?0{pN|OnekmRWA0*7bMs%z0h2eaabyF=m&mNCA@K}~~
zuy?=n<Y+&~yp!gt@c<SmaRk-!Ks6$DW&Xr#3SJ2<`#uo1hb75wZEels`(Y%U@|p`|
zRM^?dpO}|TLd@=Srp5G{w<%P{r(bzU(n(QUKL|+7e9{1gwi@~l&PO+MElD`936bf{
zjw@Fsd4$u)5cL}??U&1Oo4imGIbT#*<+-5<yx=UZQJU%V72%nI`vE)sz*a6N>n<=@
ztX<k5GXm2IF}XEL#l_cojxL$d|7GAlk?^2kp!}i{D9DIayu>K^VzX5*x%Q!ZjuNU5
zpe++GGBbAc(@R<+j|D;2lrky`-Z7WWdEi~&V_Czu>d8oLBS0GeSCOqp#|KS%<?C8>
z9M4GOysn)n2D$VaLl+-C`R^dpzV;;y_%bwQ308=x<s#D_Fcd!dWK2Dc*eOu1NFcGf
zxq0O5NrJ{253GpD&Qzv=`|LiF0&1KRd>G&e+zYt-(Y;YI!f&oD_i>#p(PQ+E+sHNe
zM{J~Ej-TI5WeYH_T*KpbY}#SG7&P!{Oz`@4H;M3}ec?dUHQv;5E0)bk#x1Mbq_=yn
z&vLrFf#(sHyRb{FMLIB2B_jLs$y>y0o*ou2aLMoUr`idGikccW&KglEFPdCBStQ$!
z;9}e4SgFBbTqw-Ns85&~;V8@<eiF;&u75t$pHOqIeX|)-yF(_SR+{6V{_Zg)FXk47
z=P>s{a2&p-p+48s1*C+^gF8KOc6|I%UU9-p=KOV6zP9_eK|@~i3t8>XLy1obJZ)r3
zllt%hr5y)JzK9+aCaJ8`{O<Gs+K_W0tMWP!WfW@_EA4)o84kms#pSob=|Xk3p^nbC
z$Fb&d$13{Ea;V=AdT(ABRmDd0(;NLk_CePR(Cx9#)*nq{1T>Oq6r1z=ta5$F*WIUx
zYP{te=^4HSB50i?S91jEhR)$aAe%ZzMMbSeL?zbf=hBa0)!AOL{Klm_Llk~kmPb>%
z^2#a3$9vH3bcn60s7PB~kVk?<(;ph|%U|pf6P_z)jh&IA^0@0lQ#x<mTr>LeYt}VF
zEVo)pQ10@P_uP@)-Q%Ivp~e>eTiS6VRf)g6wcYpP@~7~)Uz=F<>w)MzH6c024?+n6
z`%dbr;HbCn?db{Y(t9GEF?+h*mm^=k{HWY9X+IxCaK&SxE-q2Wn?h3o#=z|}peTlX
zSEs90_b;kyJ>8F)iz%CrYhHYNTVajH?(vTcgX$UZBrIvZ-6f_jgQHv`F<<^@qgLC!
z1#el`DyM*63YVj;+56yjcPr~IUxBOQ{GE~eW&68nn$_t;yEwzIg;yfr*zET-M*jCm
zE8=YCJIM=rzhI>uNWq^N!6Zv&p9@{gOR`z*QB0%P5SWE8FJ5|A4GgO7kfEa}kt2mt
z@y+sO6j8wB-^fzwVQ^}Ku<G@kc1;IS^~^`O>E}_EQqj6)A!}99kX1FB`*&JV>t&GE
zM$0t<Gwz@TxTJf*)7anUe^GzR&~|^^md*`Wh@UK6MbQ*|nBiCSv86zIWz^drwlb_<
z@!Xz?wcNb=$QSAP@{_9D)ln(k*;(Bj`bnZwHMJRursL*ULs|^6#?8o3Jr={LdfL47
z=ah4q4@a1E8A*<j<`vyQL^7`S@z-(iJ`!uiB2*oBQ}01k6?2uB>$e#rxhz5%fiF#X
zV00w)ngY`P2<apM`WMlxmeaD28M+&Xe5?FsH&Rm6in%`HRJ|R0WOu7~m4hDViGQ+}
zxwz&uU#7kcDdA-4i3;CW&KWr_dK<;z?)xJH1Zh>vuylpuxwccurH;3)d<+yJ<#ZEd
zpJ3?-hEo_G1_z4{x%_e!caqw3SuTi%K$RNUhTf>N)GyLv)sJ<USmAVtEevSopl^${
z1;QXDt+^1XmWmg^X89(tiC!BeQ!8~mrmZ@38af0yo^XEB*hqBLX=-x6)%QJi|1~ui
z6XNB3eFep2R{ADJNJ5`b{_I?RUu9Gm!<i--!gevAK-r`DTR!Ay_sd7t-j7IErQrn8
z@HfN+>>X!4gD6?QlU4}kZ9;wo^TMz4X+MY4_iF4L)ta3`m17mQ9s2tYQw?tDcidXJ
zART^#n`O@CB*!w4>Vn3ef1{R_NWYaV7>Lix%3A9{I#oy#5&xNNO;h7I8p3J*(__zs
zB=05X@-s~Q9!cx99nsS>{Bl2808`%kRvx?LPnf3W7SNj)&xceRK)8sRVYyhl360_x
zJBl0zO-9d$^?=r@no6#qf}IV$P`-V8WVI?lcubXui*aj8x~4WPTu7J|94Ri#l4(FW
z`;T2pZel=rb*+09N21N^lRFo>Llvc*4b+yg;75GPtq9v&&I0S@l#1lCk2yRJXIeGw
z2U0=T{^c+8WS7sMI;%7`;Jfs*PYDKpd$&i@C6|R&Qgydjdz0~<3__uAXawUxM4|LX
zj$_C~bcL}2;*BxlZM*0U^%mu~L<^pthMF~+X_!8{TFjS6?PTBQc0OnnMY9<x7jaj7
z^poP}cxEJG(e0H+AOswR%ql&<-63Fq<ataJ-uT?pBk=j7XnjyAHuGXs_7jF_wbV3a
zaCitZ>pJid$wWbhfB+N^iV);%uC1?NJxu+;Y`DMX`6;F8NqG7OXXD21`M2f}a`!>N
zqI&i3ZnySP5J1D&cpi<ZPz+Tf?kps7mpV&#2m&5ONMMuEs9s+&n7T9z0W&0ei8SB4
zrdXGX3TBJ+NwX-&LztqNvaK5?yp`2UwvmWsWF9W0VynlX=W~VIgiUlysQ8tA0?>(6
z_NQp)pTAo!Bsp9bS=n6nC(W&!J}_i3(&gepwNI=+TiHR;A*j$@tdWXI#9piS%T_I=
z5Vh$H?cXBAQ?3hSmQrsK{7h#cVIur}35JDa8Q*AeQtXsIypHmBuI!Sp{mgRmGS^Vt
zU1rd9;PJaXnyKo&U8bm2HOhBE>lg6mj}0gKRix8s$-AHI@BB9PX?knb(dm0M>wPF2
zG|qd1j&<%=YF0*8$s)L}OfPM!Z6~raYtEdEk&(Ffjm%BK18$u6%V2unQ6TwFk48kt
z(RHt&OYT|NSxp?1-$x~^tFZEU5>}}_yZv%jY_BnyI}9bk1H;e9-md#Di9qqOJ7R{v
z7A!2m`X(2a3G&sZhOi4&lG~Muu|J#dF7#T2veKyhE32wF(0e6Dykg_q&TmxB0F)gl
zQ<`Nb>(k4*4U1>I=SEL6>HbbnAS==Z9iAQIzD|USPHyaUlZ#|a5g8wi_M01WZqHu4
z9d&pE&?%<bQfQJexD~e%U9GSh2z{;;qo<YrZue)`2Me#Wd%MS6Kbyht4by<nv!yFb
z9d~}-^UZp1jW&a&wHKXJO}~%@jVCQ^!gf>QRnxvTOx#jTTkU<x`{H|zwWxnR7lkG7
zI2-X|F4!rY!bHuaV}T#{1m<g&A0LA=%dL^}<}^8(BjC(aPDRp~$BAALs@X(229X4u
zVzg3;ZlLni%t_lwjgbnLXeCy&0D(i45Ff@ktuAz8Kz>Sk{>Kr%=XYA`Me%}#a(Bz@
zs30ym;&P27{G(#rD==iU0;DsSSRCd~$(9%gJ#(0xh0Ze}g(1RoVTVufz|N*yHkSj(
z6xKF5HE&X7>N06%u)3Jvz;X$p|J8PMZTCe7gM74nyz=m;rt$M963p;o66w%#4D+~l
z_fsx82xQk}>ePy9PB9WQ{L_m(yF~aqB)zVXHofhYa-pSw+Yhg^6yh7V0Tp8`eg1bb
zUR9b(F_c`68*9A<9i`<x6?YB$-xq~lZe&N_l{9s4LCY-f)KL^R+m}g4zZ3$CADbM%
zc+`KIeEc@`c8W4#KS5)&gOkgy)`WhNZcRCi=w`R|XHRFPeA&F>?GNLkF`k)L3;Dse
zOaIFzjN0~_4q@3U9+?;nI<AD44+(m1)4vus-0mpy4W6F}zou0wHb}m<Y`=BsD|(>w
zMEs=cIoXybX(-o@6y%Tpv!$O90zNstjz57X#<h-Ayy74Tzyj^%HL2Hc66{m12>~Bb
zcC9mE{%wZJA$hI}**hL063wpD+;t2q!`fy}eKzRji2H4qK<Vx5%++vq^-aC~^H%ld
z@Fxij^WfFxB0*(N{FBw;9MyFK1e`Vvh7I*YmUsR(mxt9TRC@si&eRH+<#AmyDRv+y
z*^dQG5k~1O7MZFK{0c}TSR^q{gHprxVp;sjN%*JH%sMUfhK&^$V_}SX2N#@HE8j?j
z?RwwQ`*REKb6TqxW`Fg5{Z-`twu3PFJb&o_6it#hPiWI2(u6ugOB&55k<ox4$i|MP
z;dQp;d@;d|lbtmzQe@3GfEnJk($SdqQRk-qn;Oj~ld`}4(JzjEn(3n8a^m9W)XSdp
zxNH=A$4db*=Az$3iGl7e?SSLN(rE`j=%olkoKv+af#WnLJ<}nv!4KCOkCk|*0NMx+
z0Zp0cZOP#%XZmJ<1hb0{J3Sr(l?VWt2gw6gegIWd)qtW|ZVEsM9^SD_Al^}xN%&+x
zJdEz1eY$hwD&6z&KKVHN&j0GWLvi6d_bezE@Mmvc;WiPtpo;&((0?1``DdcjBOU*2
zjKyxhK<2PQ?79{rJ8Li;A*UrHY^w_q?bOuLKxvl@EER`Z{Z?0q9x(5=z4qnINy|!a
zb9bsMZGL*$&r?r+REH+l2`Z*q@3+@}uRO$PKOc`M;w^V!r+mPx@cVjOf#vJGY9>b|
zYAn@2WQ2e*G`eZC(|a5~=Sp=-Y>fIb!b7N<+Dyh#y6ZPbW})Mb&Gjp-RL}gIp}oP5
z-y0(?uOcao=}TG6*Nq1q*)@sNP@afYZcQZbZ24&RRmzFgMN)BWiz|iPn{~WIPZ=#}
z@F@2!(lb2V1W?D@T1@7oWg)|OO*61I8Mzg9Wc$#tBHOWUh%UmQJRSnX00?AYc%(fL
zg8zdP>G=Mgyxm$I9jnF4-q;%CeXsIXUuzE*w@lA1lcSTt+v|isFM&^%hiT@Ipq+Y$
zkz7}SpIFr$t5nlK8%{G)-Q+>3ug|x+_SLZ$?+%@sDl64K`wH9-b@5e1_t?gkZEU=z
zQnC0JNg?%>BaA5pp?e)$CJ>QFq*R=OR9Xypf^WCqP40e$7RKiJzp?+CtVd{pM9*Q+
z;_Y2%#_)d(e(yNyWfpp$MT))iWDp}KFA<X3GRwbg=n(!Dk?}6RMXxF`Sp@4LC+_!J
zQMp*p*Q;!SF9Nj|tAJ)u(KA2NT?r%jFe?1&e0fmH_-oHl5f)WX76lq65rTp<W0Z|g
zU=JjVQk_u%aXT+{{8<L2>jN0-p+>735WZuf@PgbbiN!Y4U1`Ojg(!m}izJ|ZRKXW!
zGfyfWRT985hL?h%-yXQvmukCJzLJr_=I8PVeti&r&e}9+)wy&{yLrx~yf;uYi|+Eo
ztFfD-OivfqpBZM4f9Pg=kAM-QGlhDEkkGQ~Y?1lQE8Bth<=lmWfd<dX=bFPo`#jh8
zOej4|?*N(aiiO)Z4|2^*HXV&ZDx(rfSr;nllXngv!f2t)h@p0GB+u`-IixAdC`xox
zC}5z=buP?HrIkOQX2qgDZ04Qs7tNu8|9lCJb;1>K;Kw9t*+Tr)an_jdl5dvyYP=(B
zeUTE(3WgNFDqgclI26#2@}rrbCg@WK6_!o6O{*y*6zxo63rc*(PU7NPsxT)jaZrAR
zV(M|HdxY^9B9k#)3-s?B={;8JwH>*uG5@$Hos`?E!wZx_z5&~$n*ARDR6wi0B92fJ
zCLQTn27s-l2dWi=O)QAumw#EcdX@RH@sgQZg)yGYfkEC--w>}HGb%D^;>4c`9Dmdc
z-avi-X-VxwhYu?(btA`2HSS|=_)n|Qd;efh%q&_b!WJ{LK+<ll{?gX$+FBZwx7dF9
zs0jv`wX2u9#~yvq&6=~fJLs@u-IQsw+~~?mw|Ubh%@9;yiHRfu0_eiUg$0RcUwmD2
zjY1b@rlxE;?$|?rrb|-Qtw(O}ZPe$eV#B`kturpX?D9)KDVy~^F<MEGs9!)tH!~8i
zY0)-$Z^_a(O$(CN6T-2ua^;Gz=>A`-9L8pbD=&!Z^=i<fM6fk?KYVwdi0{)7t&*gN
z2L?Z|J<=5(QdUxK1kMQM)6YKTx;o_6)H$MU2c$<5BpBzWT1o97=#bG(g&iA+M8s+k
zKdm*eIW#*_;9#;6MtP?~1=h(!v?e*u;epYM&7mBCLvdbxGRRX7-2cGe-Ezxq=LzWN
z>0Qtx|0NQVHAwG;Z|nWWy}D@|35kfU>D42G5YnU&WUG&d4X?01?3G06Xm55cP4$wf
zn_XVPa928_(sjrdx&8N7xjkm>?G8NT7+0d8R0du*ZB7Oj!W>gI@Yo#>6&L0t9(m#g
zG1~Ceg~de&&)s_l>s}2L0ngb+rGi5TsLikVf8Y83Z?3!fns2XJv+7DY2c!$7n(PCF
zU8-AIS(&Z5CWj7;AD41m2K&^@uljB1JKs9}&tlTUHLkyqkbr6t)h1m~-&)&rf<Tb1
zkaWews4J4a0XK%@gS;}0SwO@K<knrOQFEcgj38pI2IBSs$WX&v9u8&!l8L*bJUEZp
z7$z~iYQ-Q9Mmdw0usaAJVdft$LhKEnSq@bD5|@n6sFaksAKULCf#X|hyw9qh-wj&o
zUuTOnBkx+G8_0BSuiB<ptE||T{awwJ5#()@;m0t%gg^t|y7vAT;PIJ7@vtRXtx>AB
z`ad%#-xZfty0MdHxoI;GbW``(&y5;0#g&a5=O#~^>1sDtyIXJkU-$Ye&&hF<EstiI
zA}h33m^_{OZnQYNq7p>9C51V$haZ35J@Mo-e^l!&7VTW5$9BA3ONwwSb^fyd_{ulN
zPM9$1H(H!Lq_#7zuX9F6fU<IQrL`&e5WU8k0QlrnPkchpxcTLmU%nwFl=6s_smMxw
zDho@pN;B|&qOB1yEdn72zfql-!Nuq|#LEOImcs!!_?68#2PC@A1I4RX18d@H$}9nh
zRLrD@8WS7Ct5kl39Dh2}gFv%&>(<&NrvMoE)d^e-z@e2V`?oYSr5t+1A!~o~<Fh|=
z_n+?lnCki?Wg<v-n?x5F<52hJWM?RxM<10D^La0lmSa(W;pzANOquizV_CS&^(j1!
zHNlM3Kk66g!RG$x`mF`a;QhV~{`#d&(JlZ>X0}#8EUqwVvbkoBfRO3BWy3uFx4$S%
zsL9RQ=O6<~l{}a@YP4XGi{Qm%Du|2qzWDOOvYT(c`&Xx&{FzflH~XmEAMliSNN4{0
zNnbnff{T86uXxL+L`!|5S54Ev<Jgs4N3F;LYDL2lTL-PR?dWU&aBa!lxpQlO{_~$J
zY+b0-xw^aWzB^NT<XqnnQlf_>)GG)b*DT(P6{}X*(xAS<n7k6Kc9>k$Eopl&?!lm7
zo?N?(cVLgu8GwN21oj5A`2Y)C2td?`yO1^T;|C2HI8-)CqHVuCR&|@|qkHW$$L)8(
z+|SOLGp9@i1MYX605jyZJ^;H4*zApKk%&AubrOTBnmDEzmz|w_nt=tZ;qAMYxHlyg
zTS<V>cQh%7V@LtonwguZzAz-FD7Um~%`im?Xqi)!^1&pzq2SuOCY0tA+5#z6peQLH
z<8pF`xw=hjUG1hS1IqHHZx}FV&YA0q6dFSO9o{jRu09PS8p=$4>Gd}g<0>n@@%$?b
zZxE5+A|M|t`kU;Z@V0;d<3~UE(T~sn)h}m@#zrAYBt;_3g=hV;(Q^CZY*LvaNxqms
zlE3(sh1fZ_KJmm8;pjEjToXC<)KfR#c<YT{SHx{9^F8FOGW&xT3qSyYV?(qla+SH|
za-*4~2&4xu6G#yoAe8-q=L&<I!9><ILVC~vWG_8Xt;C(oDEA;?;D#_|KgZaE!b=bB
ztK%54)uL{(5u+=jQ>IS--udU8zfh9z@{~x*C*HQ1K5!>#qmbKkYNcY2Mr+}U6xY-e
zm#!|Cq`YJdxV_tiDnwu4{0N}Nj8AMq$Q$WJXa+&11$o()HCw<n3oOko9qKzisSwm+
z5D>wU!L2`sbRuQu6u6NBOO~X{rs}l@`ZcQ=n@Dj}rq6cy8UpCm*q4I1jQMIY8COx1
z*Z=%W3sT098GX)vd+m9Q=;sfj&2tBzvE9A0NF?Qp$A9AZ^X~i0eb-8jlHW>BEun<q
zS7NmYYOJrTmztH9fbOcQs*hG_03;E8^wCER-hDmY^9&HyG&~G#jeoM%RZ-S|LINyb
zxlDtCZ<^f!cmN3gWE#={f{G;xf^iNgSkMiSFj0zR>XjMiUV;D)uUf?=&b#E!t<og?
zT9X!tdH?|@104L;q?dk;z(&W9AOFZLH{EvWjo01$b%EoXR`_o72ZJ@__MSb#&DwLM
z)E(!Rt!$G$)+uf-PA(4-i&4C*Bw8hikp2T~4Ag=M{?SKdBs=OIAi}empP%Q3X+%0N
zJ6-FPg<N~5Vx2mAq{m~l2QujGY83!Q!)v0EqS8vyMY^lmu*z(X4eM9CENL4hr6c79
zYccN^_DDF~1?{CQUcN4|WciAtpL+h~{l)R`RUJ<j4NiEwCnnDwIdhLW8{S;J_=?pV
z)|{r{0+y+YqRRPUi;)_D*0_jNrS|r2g`>0{bmyISW`FK;pKFiab=O_7l`B`K{q?bj
z_Exum3(phuF80c5G$aC2fJR)odZiICZe2aZUJS_qNB|PxVDgOrq?x#c07CrL*0u-S
zk#P<`))7b)R?S1T>X2uQ$|LFl2OwczDp{hTt}!}m&dm5B2OfIH<BvWuUS0W~4hHT(
zWB)@hfor2aO`lTfrcE8`8n*Pib?aH8NDJM`8&p%ZPConwU4s|i6Wp#=h`Z@y#xei{
z{fP~!FU;B;9~>%#lHjrGmOZjrgR-@?^|~Xj*`6@JEEma6ac!M)^GfpU7)Yos#HA;z
z{&~a7?786$YwUg2tXgLD!nA9cL8J%TlHDp*Yh+1Y|Ld<WPMJJ$+}97<Z|_q?8}r4K
z&!EH|NMHNX7r%1hMVDN-N=~5hV#@lTh(KS(8ayPDT$8C-r7oc`rV%f``r=HL`HaP4
zz52$h(^TWpc#lNL1Fg;uoj{Nwk^uE;l>kEYrh!8Ch*z<^k&XlbBmhd^ld~5^&0?^S
zasYz)&CUK0cT%SWUl&5$*$As9JyB_s9)QF41r$jdd9C^ji-)C*8#n&9KmYm9mz;Ih
zS!e56`|d#Fd>}4?-f5GfVR>%Sgwa}Nlg~g%nreK3afCPIImDpFAp<dE0Ke-bI`wXS
zNr7a4VrN=Es^7J~u!RR<OJ<9^qgK^7Y;lbZ4WbF+>T<2GyTw;6NOOlI3AKj0@OjDk
zkYDV|q}HKH#J0p-)w)%#a{N?_?Na!O%~*tfv;cVI^`&dvzH8SV*CKbXi1#_&zM}xq
zrS7!Pe(J<q?)cN~<29>PQNLj^-(CX;YpyM8S_bXLH0Bl+6T<1^0itn*S#S;!V@zU;
zmAfd+5*-1N1Cq^}?@?8?Ua#iezPQIs#Y%1g4dpQY!D{A+FGkD(P9U~%RpQ=7dSD0w
z8bmY$HOwbr`~x>K15W_f0HSTIFUI7Xlo>N-v`iQ`;R2Pnw<`FhWHon-4NMh^88f<E
zgcuSyYHXGH^=p>7m$XD{Z;zHO>as;91P~%pjlb>ky9b<*zQ92Or<T5M1c?+$=}t9O
zjyNg;5euZN6NOz~T52m(V%)OA;jn^yi;i%S)LjHg*yR+IxZ(0Fv8GkG#wofw+Kld+
zRUZo`7mcCUr$!U8s_L3yf4yh^;h#O>*ma_V27!mUB0G}yW?}sR06+jqL_t(?$BrF8
zWz?w3MQhirnjt3KFZ#1U+)}+01BWmMA~qBqGK&JOg{6Z|x1LG<G4W>zSDge9R3tz{
zEEJ(1rd|ns(%FUpB(OpFQk0^11*i;VrsGlu$x9Ewf#evJ9sq(RTbKpr{qKaE_z`tL
zLfDBsS}_?^VZF$e7hG^bt6uEiRqiA!csKb2v%r;0#B+7cW^;45w{CH7E?OX9v}t9S
zde`3CWDEoXMn8Wqk_U(;63Igbfx!3_7iIQAYFdvNUb+YuY0e6jovu`4VIxM2G`n-d
z`n6_I(}xAaigmAFUEh;Z0KvDbrI#ckeW!2xn5Mxx20SNUF@KDUbal2GLx6b{=4K{V
zYjRrkhH9KLzY;i_wcSyGP?5t9I^d8SHGyhIPj@f89@Sj{A7)yTCQ_O4t{6XIA_)+Q
z>}hazJ?!t^2;wHF5Z4>_0gJCOaf;(`v7sOdjC2nWY!Q86UJ`|3@s1!VAOZm5r3V91
z2^$$_iJtcM4wEc^!WNH4f?&kQ;)$j$%_*~H&+Of2uYIpoy}z#Ke~TB`T@o!%TV!hv
zH!~({YBt*UNjBB2*S1;huJ2?tH)w6Kdf5XV8du<pOj>0!$;jSKJ9t~LKLIGPA*E`C
zh_y1?1p6e-ub*ts5Rq<DrvFE2pba3bS+&ws6|QHj!$pX*(?vUxc=wdFEQRL`m()ap
z_>U(65L7>CO*_&^(!#Gv%{rpCmS~CJC^Lm|r_#uZky$y}xl0u#J_W5wDT0<n7}Tn!
z)gh4^p1o05U3FD)R22(f@w?xS03g~QZ13dzQ85OSKr{;!pdO)089*dKOw_Bfh|@w8
z(!kgtfC`{+j6?$jqvw7c1b44LhRc|*2?0XCs(NyaM3Mt9iuA--c5Y@=q0{%Aciwq3
zRNQy0(B0^dn#a^~yn(2jEI5Ftioq+mvn_-s*Jr!H(%splrIZMMcu&(1=9rx*C>t3o
zFBvZQ#R(FW0>RuM-``QGUaXKwUymC<(Y#=|X*E;O)V_k@BlIrxd-A2;v6><~*)Ae2
zjiyVLlc$XFFo5f6NK#ES24hwoLQY;hMc<Cuyt#ftY3cBtL!z(C9@D2yfB3~$UYOF?
z+(J;KMR>5P&eA1I^#m<7)z_O}RaG@2I)DEBJQ>QvAzD>pppk;7!u`o@?X9K;QNA5B
z2?8(zNrGYL!9>F$Imko+8#v_o01zfY^encRXh;*nkjd<$P6d&?KJ-KrZ`wOT<>lpW
z-jVYj(&JB3-mO$C+LYwEQ`{yR!I0t_EF%!m&Y{vu{bBI6y|uwjoH*Id-Ty%IIPEOA
zwgQLz0i4nVy3*1zG%nbiFPC~qV;#{ARkd!#+D%%{siaSl+4|ljlh?ee#k7OQhDHql
z=enuW_YlEuGWRoDM5+R_vh#{}Qik9xP;iNuYn}koq=g3I<S-0uBUF6aM_PzJ+?W~D
zrz;{gn>J4tKn7@b$Sb|m?!-N3%)0*h7oY1EeWvSTMXrZJOu-OM3LxRQ8Y0}-SXZir
zC_0%pk%wderGv>s1OQO6cBxh@Z=+p9GFS>w&6eyvT5=QWg#d@?v+7t<AVCZmfJ0da
zr9x@oPaKocTGTM=;kU9;OcjgA<>HO#qny>>`qpXbdVx=RLK>{;?y?8t;obYRI38+N
zSWA$J8(G_8kkg(yxw&$6yU?MB9p3)k?|k=oMNe#wh5OSY@n}3ND<el=KTB5-He5dp
z!%`b=lYo%yV)B~L5G52mJu54{TPquOZ)s`C+_-U5-?F7EM%QfIeB|cZx=$=$U7fO_
zS`*RAGeT00l~H^B$50#Ob<~)Y=6&qt{`%lOMg!QL>6y7A_6+M|Bbs;YB&rG1)SMce
zLi!LZQd6HkG>s)-%9NF#mziK4UVZFnyl(OXZ0}h9Ru_b`a<U4vO8nE(YL3+WBFTtE
zl&_WHSdx0B@4zUWJ)S6y)^2Ie5YeW10tP=+QI!QBuomrNP@@JhkjFWzEaKKgt_KGq
zc);Z=BM*>}NkQ3x1OX)2BNUH27vNcVgN_3VT)$?sXe$Fs9iJ7h98>wc#4VnM_r@}{
zn*FQ(QQJT)G*B`@K7a*1oFx0Mf7-N3uc$BDblo93gx`L&GD~TSGR*;F7t^cyZD|N|
zQBNo2(+|;}7!BOx%8Coyl-JLhIsFy={OPJyRcAi_)N|)7TDt1w1&dZm1ya|E;S!>P
zaZ6-1QxQg2j&bD`BefRVT9a}G3VY8jl)+A+JCTqYnRr3<AUv5!4wE2yZ|DwsB&wiJ
zBj0k|NSCu^PIux7{!&qGWkJ!fNBisg1rWb<DTCX%vs>Fb5@})pS{kT;)#<W2kp|lu
z<uUF>SXRh58o`1d9BNkGGq7*h0TgeCc-ODDQ>ab=kid&&Qp9W?;(mH8MjwteRJs_5
z)K~zAsgj>K_W0E*>f@C6;YiT*_Q^}kCe+Z-$>*DiKf~R9gfam6yEFt8Tb<k9ab1J~
ze3Ht(f6VyuTP95S!k6d&_1VW1V0h!=W$U%zg1$kiMC};2>FDUvw<gQnR7L47S-eo=
zCi>2!oC(=^MT&Y#+3J~Z|3lLW)#rHAGPP1COc^GO&5Qj*CTYG=Xw3!|>}xO8PgJB`
zNhRaPPFS>QBet9-jEEUyJ2NFbrpbBGV`?;-6V1%XEN<`6I=1>ss21KrG^<+(9Z&})
zIT-edH=2AzC+9F^e85^DNs_8Spl%5)yqqPqBp^Y87)Z2F(MS;n2;o8o2!ST>ba6d*
z>0>C-9DN4i-~$it(*d~TM@cO})20_?#<aVq+Uf}P$>{gGVN~XEVtLbh`g+EsMpLg-
zc>I~MMC!%Wn;PQc>YB^s7pAbq!v&O)qsJHr#_QIqNmIQ&oi05q-}>BG|2tIBNKQ0@
zx{h(qR)lDFEbM{=U9jKR&KOY#;D9c6C1p&VICa(Y&po?N_En|I<#X2IT+Mog*7+2*
zLhwt&ot<scXiS`I8B$ds;9B|tv9hX&SFZ+Re~j~};`Wd2kW8b2bPpc?T9j%3007VO
z&$Im?*2;rNR~d+SHg)1>oI%PuP17vgl~&lD?@8(DA+(>BJ>GM03~9G9TCL?5>bLKI
z>ez#B9$Q)QN_I}329qN()F0Rwsuq%-^#(=>B&`~&>6DwcTj4QK)XpSzzwYph5}IKS
zK*Vg}2%gSRxx6!}p|LSbWitVOS5m(|)6p1BjjqPysz=H?$Bmrs+9WVg@oP5K#KM`G
zImQX}1c7LfK>a=jqyOFpsG@?Fv@wwIgF|*0p!j{iwTle#R#p;lNDzKQ_(&!jHCF=%
z*RZu@4W(gyHBNa&d7X@YLfSt{Ls~%n0SCqA_=hI-WV*OH<4&I`t0=j-RPJ9$#azEo
zmNL#2COnmEaR$~XU^Ki_qv2ebB#<4bqYsqCF@(2{hz#rxBuPMfMki7)DNF&4*iQ@;
z?sQT)ptnWlz)FFH`>jx()!(g`Q@yn`RWz)HzUuj=n+}9A#?6q1i20|BI0lCR1XPk^
zTP6Lp+728X{580jJPFa0`Dy#OdA&plus2W`H|t^yeZ|Q%>Q_FueUt`UK>b0_nsGgN
z^dGhd5Rcq@kFiTL(lz+2Z&@fTC4qO%fT9j0NOhP(QEKZ^uQZoO8HTvC$wu-SU>32G
zpgCi#(mWVzfw7V_vQOc=8u#C>_7i!>*BSstczE_qT6uEkTQdz|jXAU@iC*==`0_yT
z#I3(pIq8Ku5g>U`dL*TWRX6#^uGfAN3`0*1?gJ4n;J^MDIw)%}dsWRP2$`3!x<xl6
z4VuAHKH76kncf*I_}w<y-lHl?QWXkQlaZnMJt@%y?tJO)3Qh6hMMQ5Cw1_H=b^ya2
zV0t_FbeoZy9BdAUa8-Ly01Q+gf;CB+&jL;Cl94VKg8@tPoVw9ca60iG&^U=Gkfzp_
zE#bnVLM`DHGDC-#G6}*~gbp~69(=S05W&qMVte7_hQheiNL@S05qZ0wW7|v=ojehM
zZtzI*gdr5KMvQsz{Mva|w9SX_UI(|qqW+ODI7)>V$g9*rESRK+saZ%7JeLd#qZ0LL
zo(Q2T#BRa)tt)q^Rv74T@~{ESd#*$SqCsdAS^z8>qcHN3Ax)UcE@>7NN0qdSPYpv0
zV#tUS8R&)^goQr;SJAEzvD8M<wUkAXXh7geLmBRl3WNa$D$JO%3%Qv8`low>tp}J_
zne4dE+2vd_G;_P%$7(6W*}RQ#&~+&q&a#3&`m<FFz`g$oiMb2O$0pgF`bZsWG+`%t
z8^!6FLOtZz4pfvP&{UX(4t<aS7z7-sSUg0M#7-6uFT{?>wF`Igk0J;9lFtf3zkYV5
zu&z3E_x4tWPdMR(W-S+Ak7Uq=B$FO&E$b#CY^gZ20%@8gB>Z{WfP2&!hH|}(Azf(X
zH|K)HecY{smMP0mv_h2*8a;Wn-G<akA<Q1MzYU6#_k6VZN4Ef)Mwh539s2;T3v;6n
zO@fH~`_Pznb#YSQzolE>zJl~Ok;MAGp!%PE=!Yo}7ThZ7fdugko4Bn^*!`|?300fA
zXpF6bx{LnhTm>Mq&_!!#OB!C-B)Sj9q~~ow=T~J&R;j?<3Q-3}IwOrVjHmJBb#^^K
zKt1zlw~0^^i;Rd5O+(v?T9DB<g9%kq7m{`eZek4(=^P{b8Y^L>ck9B27W^UIhj2X8
zdEXk~jkeC74zynSN_IyGU7b;HmRyzDV4?5S#skL>@ImbFl%Nd+4)fm$j%C0Q?ZSuI
zYg(^>flY(DrFr9te1+C4Bn9WqE*a_ai<#}BHL^0Su3D9xI%>|kZ8bjFHg@FL&Bm35
zmX%y21IPekF4zDl+JahM5`ZwOIRGNNX3%ay6apqf1bmpf*ByEAwv(3-#J|8TOotVk
zEredbbxdP3aRtG(Fb1-#{_br*diOXg*JQ|BtnbhJJp96I>$R3=N{U>uaWSwES_LcK
z=;NL0Kc{tX>ykC$D*%H=?>klxgovK9GSdaFh}T*qkr+tEmhs>KAevp;)27u(M(Wzk
zb-Jtl9n0S41tFpkRO4_3&&5DTi_yBi39Topx<}WaamE=QAc~k=fLf^=aB2?%Kqy9%
z5LFA|!|A>F^T#pdy`=Gy1AB!RApns9MpQGvG??@d9;8frkl+?HF#0r744~rXGgNC{
z0*0EtTB~SdTinjy^wD2y0ks+6*wvBbooYpO<Gs4!j>hKp&v*4GEF&!~PSiF7Ys}})
z&Q3Gwv1-K%E0=y_?JKrkS9tr!K_{sB`FVbh3t9-jyA<aY%u4|%Fl(hyX=w?INUYR8
zOS0^A((`h3lMMXCc%H2=lMrKl%idU-7_NCC1;YxK>-l_UM0P+*%4_I#GJg1`g07d;
zz&MhMgxX~8HEkLGX8?!9HGsnp_zh971SHBL#)=o>Jc*aJj2h(+SQ+Sl+1XhpodzU`
zK*Dilyl*O}ue`po-adV1g?;qLT0rfL_hc72=#Dp{`ueZxzDt&@tPgK#>ij%w6NnZf
zEJe$=LGnb@*;ygiq#2>lKJ&aAGhw<WKS}k<6tUBRJCL9!tvaQ$awRo{dGS@oJlZu4
z0)VhUHaQ+M4_1V4%#0jav3v)L-sUBuMMOrfhB?MT;|e$F)tn^27GjAj&9{G%8x0^I
zoAa^d5^Ebpml9!uh6rEOZ~CnPL6rw27%qe&<BAh?C&4oaB2+A-2K@#a$s7yP07#Nx
zu?mR8;5#RKseuFuBuO}=FPX+vPGs@oMOgBI{0@rzP;c8}G1SiYeCE_!8ubD7+que5
zFJ7_nx~BF%5poJ2s0m?vKup65G9^)*TeWhPt6KS_%T<tKW>&7tP!^fIV-h)tPU2b^
zJx`IknVNef=ah~SuF|1-NVtssz@wHY>hHA@%SuaYH2u0pY?{&ZT}ge5mMt132F~|I
znAa3i;39a45Na>h{2YaHAiT#Odvr_Hc@>cfZhfp2dy*nZ5>=i;AYcaW@O1;6t)vIn
znjz%@!stB9JDMS>Yu^fv0luOgY!Dinh0Kr~0FlhT>>e${(&1iu>7|jXoR{C)*?m($
zQNecmZ<^@nKs+x}U%a&at9Jd2y7U&^4LF{?=i$Y-)zo*+(fS4b3IU0*DxqSTG?!Zd
z(fT5t9ewV}#~!zbMN`tHqLi451)UhRzfCo`hQ1zIQHtQO<xbJ{o3?0radJc+fb=Oh
zHq^$WDH&QjtMoNpDpqYD|9qR$w|lCgdj0x|8oEf;2)aI}-ru=l{dyA^TE}hEkw+fA
zh&#hIjWre8j#sqi!vFvvAT0m|6%R5am?VgqFH8*_N6j*D=pGDoR4j=^B6L}ygn<|D
z^7ASd&%hJr2Z5|UX4^2ED^q1-f6!Rw{*^-j-4MIxxfh;g68%!E#BTk^vyP-9y8Sm*
z*Jutk5u}fZfNv6EJ*@rr!X9DatNGFkexPpp#mkE~MsC0Vwb$2dZ2e+mQ&TKGJvEA|
ztp%aQ@e5t?a4j#ZkijL3m%E#P_X{_6!ZeqrQTAcO%hiWyF_8TB6VgM|!d*pak;*F6
zDk%yS6g?5>*r3mR_X`y4_t=UjXq1{PcId(SfYXTkv_Dq4goC?~)@-h=J&5{=33p4q
z>U`tPh2fn197)82=T185B&2gBT98pNN;@S-9XW61AMd)OUv`C9x9kut?WcK&1_U*h
z#EGv0je}9fmz|I_gHl9>yuf8fx>PJ?#PJ<LK!W;YUMiIbK!#?x3o$V0lzCdck)<D*
zX4!`n!QH0`^&*x;b5qmwHS5;QA6rSA?7j)d$AH9EqX%#iM=>c(+mRyC^yr$Zh6(j8
zZB?Oo_@O-}RxC|VkKU$Jqm_x9!ZjWYF!^G1U`T>;$>_kiN(txLtoE+2scq_hbLHj{
zTbjCl*VNi`#FjSc|Gho^83G4hs{kA&#rmAy@Q9|z_P9H5zf-qsjVn#HEh(#X`GwH!
zJA(r|H&ZIj^r>S+O9@TuZWUc=b^nc3vOiixNBU@zmOAo*kNp%FsjM8^am+EtF`?`G
z%Da*}6~ez<>+^mQfuQk}_UfwY^t$RTp$U`6>r)K{_fzO0ha3`$N+L`UnQPYM<mGz#
z=hP1eNeaY5kyzB>Fi;cIDPmY=v_s4+A_pOZ^dR%1JPaEIwTcl*R3k#=S(Pyw#dg^m
z9N02&5U2pqv8qIUeVuepEntU=m2~|I;r;p`WXiqw{%O?r&N%aB-L*F_usbHAr;)n0
zHm$ILI*aOQYmLe1m2?eFn|-LOZwqDr@$M&|)rgjuT0HW^GYdN9R&7yJ5}&nGO*4od
zeIYgy&XvM5OlGUb1hmYL7@Su9jLBOi$&s$U%+{*tMGF5ZR1hiNM&}-y|G2y5n)BTF
z$$M(Gl>%2WdZMPxW*Kwco_b~dj)tNoR_Dx`pzj&xXi@Zf(?FV~T5VXr#um(DHJ2^{
zq(@Sut+gShrGTPi$Blg;KR@eGz2uj6>O}`H{>!s3JewvjU@7gbp|;ND%a@mPT1)IU
zA9ci0kKcOpt<yxi^>COeBHKMVH*?R~FTS?mrGrq9G*_9K>;eGlkklys!wriqfuW8%
z#PTe7vfy}(boy8sCiSy2qCU~ZODW`;1qf(d?lq9897g6T&l*q-h5f_Vs{j*IAdxtM
zEuwF=+jr#t_V{CmfA_m*+^MyAcUyok`N*WO)YN{hVNAPeN`m@<b*oa1=(4r=b85yg
zS5@1bsy0kbx<2(mue5^g5u_UW9TPyYfnfDbYBZ%^?asJ_oSdohSfzW(A#jun9Ob1j
z%8+~d=_T&8uY6O|U#n?m<Qq7OiZK_rpH#f5-Fj8G*Ua&5!njhmadW3ua%q#D(V?#d
zFOhNHqNPAvEqCa17@9dxf#D+#Kiqx(#81Dh{jVt7XgDW!BH{VEZQa_{-=<9^AX@7i
z8$z!yT#%NXo29k5^8b4B$tO<{<DaDIw`-x$iOMsTKQ?#Yxor|2UBWd}5T#}jJ*mOI
zq&<SPfIAVBh1z66WisMTis%|7NFYV<WI^b-1jzt~&I_c7*&!xTyz0Ol06r~UTwH86
z4kPTSSd5PdC}G(>{aX2?_~C~hR5`ASKf7b%Qv|djZQr4G<NforfLeC87L6__cEgIw
zUH-6AacX^bIU_xho{^f+A_)oE1{#+L$5Nu9_^xJsF)Egp8jWXXW+XB))1v}KRK|EH
zS2H>b3x{cjU5Oh}&iC!LR&!3sz4FRh_oMIqP~EWJ6<5r3!^=i%$<P`4%t5et+@Nft
zuK<R4t_>n<{c$&U-$`!v?9uwHLci8rW69Yrw_@31w^8a<x0W2`3sp!E7R&4I?m(Xj
zPo6yGr7>g1h!O1a?9$8cy6d)8iorTc3&-K!ZCtTzdG6+_di!?b7ys=`&#3G}#mo=Z
zb}uBzSY;H(5+P>&gb5QL*9z{(XchHXNMeMQH2?tIAD<Wd7}9vNI+!&kRmuQ@w@aNG
z*dcI2BoQhWAH-pWchs{$iU0`9Iu?#-A!Y%LF_sd2gQUK`UgA|N`X#|h`NCxgpj~_Y
zwMS}x`W>q8naaCWqKOF=XZcU0beq}@E>~8@ii285R&B~CGwKVIlcaX(4&9^nQv-!)
z-*=lR13MFy4tFPu7_hQG%ZnBjWy+Aof#Td#Prd1Wc=`|A(uEJZu@h&x0@)msr|;_u
zisXqBEx-(e#CwtG$6hVWJ$zWc``A9?G#Wlki*v{HxeI|q5#y^RIhHM5WUEN>ox}FF
zCM}lNBxandRX5wipZok5+^0Tu+?6Wo%U0k{|A}8ABEGh@tLth8sP&EWUah3q|LSY6
z0XV*ZyXurLpMq36Qgk{(TPe8CRVrJO5Ig$tgAc!H`HE$(Kd#S&46GPK2O!UMuW3b^
z9AhJ>+3GY&m;ou`xgXvvQ?v9{C|0~^(fX}?nF@6Qjz}ld5rnOn8l~?nYUvu2NO-f#
zwX`K*fH;s|2EbTc0xQzLrMYp^<-hywZQuFk={KoLrzr1M2}6~y1v5g8r*LESdVSBY
z#EqXYNkp<m#}uz5Mv^HB4iR&}j~9A-<=r+04idnJw-RtL2v|5QTdrK{<fI05xrZNo
z%$@#KG@M2^PM=uIFD!G@XCLhNU?&WadIzAOK1f6@%x~{BV`_z)F`cj6rn;()tkx+*
zT&uXRT)x;XT=0_iwSuG;d}z~>t8L9%Ot4#PruB696&IJL?6vpa_v&Lwtm3k-G3=dq
zS>5x7n9TKe-Fw#*(dSptXtadV#@81t$Y1dC!cgVd(eBfq{tW8L$CYO%i=GBR2Ev5}
z^o*jS;^|{Yjd^kTnic!XSQqvCkLgH0=u*Y&3nZvX5Vv`=BqbR0ND{F)APc|Az!tab
zje054DqWJ56Ens6;6{Wq2lhp@-;Z~|A>lf7x_sR7j5OJbeN-VrPSJPd;;H(6L%VzI
zvBysTw=aI_n8I8_w00+u;Qd!<j|^7}w~_7FpidpVvET*wvHcHsQZI~Yz+AipQ81@$
zKt5<n+hMo8+XMpx7_1M>I>Fi5d=f!{aIGEgxu+MqJ8rw(-TTMO1Ry7SW43*?bH<!Q
zOtr$9fb>9Mc&81LM5?pCL0CDm$W53~DhZUW?}{Yc(&c>bq(h-3En0i{HG$)0EjQYt
zkA7;sD@l*m=0@$;382|=IW1B?|AjBKfAy6A_-_^XO@Tv><(*3c$aSLI1=rkg^#ioB
z%mk!I>r+oZWd@T}`=(RBerlIWJ!(kF<F+M}4?N`H!!KX4X63#@K7J-}VPe&G^xOXF
z3=#p5U?dtqkR*Yzjug@Og>*gXksbCiPSi5gC>9p8v5w@J2NK0b&&75y-dTF3qP#+#
zqK~M^mN9XNnx)njLG(2?)))Qghd=!H>#n}xw|en$YOn)`ywnGJ7vh4%(lYYI;qtWN
zWV2hpb`=9;?tp{mxrzGDcwteoK1Gmi(iniil!$z#P2X+8YnGM@vr-hbKD)4Td9Bo*
zC0Z2jK{x+TS6T66C+O1zvWZGZOmO2S@2wAWj%URKF-pcM<hEB`w1}N6F3NMGwAMy>
zS%K=SSS=al>#Dl6GQ1zx*wV1cEn4`BM&Oqv7w2y901-I0G;G#4F>@1Aw-cZH+!x%*
zC!O?@g2L=C=mDo#C3fPEXyh(|<K|oMy!DGcU0p|aYMCMpDb&39;)@lpy|^$^KBCN>
zc;booOSPQ$)?&8>5LWKalZby@D$b!&q15`4B2u@!_(YBJ>J|+_XaM30djpVc)nSq%
zed2c5DX2`1k|F>q8ZGi1(u{PZk?&kUqTqXzPKEMll}q==14fyE)RHt19ZP5-?Qi|(
zkH3HWMHgIhmyYi?05RQNDoRw#cNUk9vax_JsUVGYHSY3DexYSPXSzv~XGrHSO17J(
z3rHP<Ss~fqbrM1ul&jrbBZGN~d+1)8moYQvD#uPx?3D&|^UB=FaWmbR@zXU9BBx54
zmPA#~$;t4aYRi}1qK}PcXKDVBs?^>Qb8FV}*|u)QS@CsZeWZ2Ga<_QV>-s2Aou1b&
zwaQBlY!8+(Ezp3ST>kwB9(ZtyqH6zd@4e?7q$+&IDzbBb){Abhd_vzF>}YGdm`HQA
zS7&o;bMK>%Jz^?IS$S#ux4(HBF>N@jwo2Osh_2Z4$it7m^8Wev9NN|2ZEIzj6~Z4e
zDdECE0ztFb7SXuqnPjhOX+M%2H0<*#6a$9M`uGd(VbmxKSrK@=p)LdpB>nbV_e#aU
zt;n~m6`!xq?^by`M}(t)A^BKh8i$FjTDAH{**YgmEqGQn+kdM!`+)xt*K_G2#*vki
zJ>yZ^(r#62SGcDi@q=>j$14_>l)2peF*Z7$l{3s0mQ8Vc&f3S#oH^Bvs3?|Qkzr#s
zsdyaqQBZ7;m>dn=J$-INjXandWYqGgvPlIDd8X<Fyp=M>tE<*&tcO+HWn*YDYHyG?
z3NW<-M>gQ-oi%Gt>iOsY^0oc;+jo@Sb)Q{%U(w@lC0U=i{-$g0(YRZgwQG7t)f10D
zK4SUeRq>IdM})uo?bH7vr^qqeDt~(b*<-KSd!;?{w}<YMXX?c6?p_IktrrIXB(kB6
zYtcyf?)fUM0Ys7{5c?L1DdN{!z;fHQ+xJD_c1R!eU9&}Gvv`|?P!)}ln6%PIRTR!J
za>OXJMHCibQiK<jEYs6nUrTfIxUYTVl%M?m#@p`J@43ooD7!)8HN42o)rV!qPf@H|
z!Ek*Uc7p{0mXwc@)b92Yown8-32T?Z+jg!Eg8PCkj~1mf?vkFN)z%A(UGA`QS3Y8*
z+kfs%H*MNzQ=u?@T3XQ#h_vOyN1D@}{PwzMmc-?CyAG*|b(=Tp<4fx#nKqf~1~@q0
zq0w^Oxqt-ek)M~1S{2)CuYFQ4`t`3~Kk&c<sLif|;~GiTD{ub8jX#uO{;AH^P8P=A
zyn5BDwEO1Yqt!s1n>Ky=#*<I}GOWK+`R~&90K$czJLSt?yHcM<_+&g0OVzX-20;d8
zxEj;!YMBORG%EJr=8Z^%(OCe+4;%*WNk-AT&E?BD3KLWc^7UC`Ne%DKB2ZCoOo0hi
zfs9U#S8J3$)r;G>v5^U6(v4wLno1(Iu3sPC`OMFL@zd4kp7YC#bnHKs?Iag`uoJIg
z5>rvJd2ZyGscv}bXd4q?5DkW;hBk08>n_kh->Y{991Kuq=^N*1=~`t_tLyLou}Q9?
zB2Ttq+^t^QDvhAS>`RuUHP^1H%=itUU>-+{yioP}YFM2zF6)@0a*51ElJE{l52AIo
zc!)-`d!?pDV+{=rkxv|VeDvGj{`Q{_J>(GRbDNy~o!(O|cduyZS9kvD4+l%Fx~RRa
zosg0~t=hijfrsW#?by;An>cxV<eZ<K^PsLfWqakl9YFRkD=BN(f4_sSf9}PnzNL@b
zz~^NoB#RQ}M8EPS!)*M=A6ZYeqeE3QTO>Jf2(haJ`v_sz2YnblZ;CXT^uQp@(Kqk_
zLg4bn&B!-G3}6xzcxDO<3(ajzCv4fGZ}eFea3y9$<RMEeT=e>X{^Go!{rfp*pL>Q*
zeNVa4F8RR!@r~_?Pv7WM_&8zYFd+Wff3*p}-&4Mz$6{~NFHC(Mrtc6Qa^O@ks|>ep
zLzmlJ+h(C3t*wn(gSSxxuaB-`Td-K1Y)w98!Gh%eb9#KjLH#dJC*dMQR|K(DNDsc8
zm6@5Q)!N&Hc$a$0DPNVl_0#7Z{qdu)4^H2eE>zn*BrW5dKmYm9DGGkPkGc`Iq>qxW
z{p-UIk9qmI*ED4}FY>t)Kll8;`v~=BIBfe#ZwC<W_~g;^j=p34`ZY_|tY1A<go`NJ
z>YlJVN1vTmgGMYWm#`B86$hn=1{TFtLXrRgNn<lMHlmfYMf}Vn^dAYu24VEP-zIc2
z{y})XEyu!Wr;V_iog?ryH=$-h5`Dr*W?T+{(95sAeB)VX|M2h&&-?XvV~N<=kx*op
z#4<1aua^R#2nhbgwSkKHzg2g<P1TcZ#c$r`I)4fwO?xvYGiT;Fao-Gm{<~T3cZ-SA
zpx&E`ms%?@FkWR}0n_!G603U>|G&NS0L-d9`~G=W60&y)Bq56cVT7qH0SAt{TCM1d
zT05N8TKm=7zF2Fut(|Y}_g4E_t=269idIFdpePVQHi$9<WRSfGWIp5j{qK{9^#Tb<
z!1m*YlV_cCpL6bW|L<#G7t_SYcVe&QrY@mhEL9r-(k88DM;jGvnLN$SyS?DcGj`mB
zkg4ZQ-SYKouDWwre!iaF?Ok5v;dl5aV>4sfvW0b3brs7{G=<7e2e(qXe*L<b=U;xY
zmy>mV(7-`$_uTW74cz99hwsMzwmYMH_4KQ+f9c^zexDDEqd9FaSNzo~=2loQXORL$
z0FVk-tG|RYt$a4C1xPe#$QK==D2{ep9&uJ1R!YuqV!PD2$D_f<PtuL@42>4%L7a<C
z#1~ff6w@Caqc(0d=L2}cs1L_T*Up{A@BQhKKb?Em9e3RWK!3+7_-_Fco1?TX(Nu%b
z?RU0)Z-QSO=9TFpAfcAmVkF979@t^_JoGD-b{?49$G8A(+XlN-Bg=NRwS4I!<caMR
z_z@tq@G}#*lwe{7BX^?N)Eww&7dW6E07YQ&d11$V9sb6KS|nJ@la}7kbHN1{1X#&`
zbJI=Vn#Xt3*=}Py=EgIBhx?nccJ11#ja!PA0**e?t^$tQit>uUt8-sT2?>OBq-Upj
z?wv8?8N55XWZ$|25Cz6uarwnxn>FjznbY9qh}jQNx{Z`9{39Pcz7E_AIFaBkvK)@k
z-pp4Wsz?O&aKeX?=@^jU?aQF*VL7OKEmACSsU^Zj*@C(RcX{)A3>ait7RVeND~*|a
zwaj^)rSP~7_ItPia*g{cDl1$oRxDfa;6wMH|Fann{DN-(%$CXaFWWSqwJIA+G^Xst
z1eMh$EoV3HNE&kE7l*1{nkAKSW-1$2*I?xcJ!#Ct38_s^CY5Maqg7J6%Lv!2mKRz(
zDbw8EiW~upBoG+T(V1i+ZIX`!5-ATPpO{Kn{*FLxO@*o`>(X8MgNHiDj2Yt_I%L?h
zlc%2j+tI^@Ny+jDwlmp|xzX=yT*v)|g@uh<wr~0bS)&guUI6FUb~uc>nX_K%U%X>i
zN3ZZu&&UxY=3RX8rMix^u1?e~fY3=Op1!e#A1`^ksD0Z-_(@Tc%g#sS4Um`tP-rcq
zRFUEJ48Mn_N)tKA7Lf$Qyw_lQ4Hc0%!)hWOyIBYWAUzI%*$#?&K!*A-Ij~nkY2;a~
zv@lrOp(HXcmeCKTq0I#FIy-=;<*Tf!bYXd&fA@?ZT=|EG9#)aZKe2UpKC$bUr^$<2
zzDsd93l8X?idr>+&{irS;Z8~c<D=e`hEz8#mRC-ra-nY~hDYJ74UKzHpA(@6Sac3E
z!4K$7z!9a2O^jsBeO7TvIfAq_QN_mfr44Y@W2@MM7D@uS7Bf9JlQ);=6~GG_fCLcK
z{SEb1!An7L8REX+{PVG>_pj)mmpg0H#0ifU6cki(nRl?s>v5k~2k*Pxm?yH5|9bA+
zm)-SEO-nIc_XZq(%ntz4U-j0TZ}wlgbd5hG%<UOAw4nTNk3aPyoePXzw(bH%9i4yE
zcW%A&z6bBk>u`4DNFE8epd=z4G!V>Rx`UdCLdes%7gbYRA+=_ri;^(%<~<Ll!_IbU
zu(3PP$g3T15+_#_Q4f#QmjVau7K84>Ay4iLs?i{JOs?^=p@^zTyJc!$AqWvG?c3e7
z+f`Ot_R7zG@zWnYc%RX3Y4G=J;B>*q(V%+<%Exi&aU3EIl?98FW{n=1g;hP2%0~h0
z14P=@;J?I#KN{-oS*fh+b_ld4y~>#p^F$RX)#8g&B_(8vJyr{pMYm@6*V!uvD2YNN
z5q?z^>{Q@VbycOUp(2DqS}55p<aO(z)LJO^t=)}H{`S^-mN7Hd8gJKtfrAAIz}>g8
zUuNdw?5wQW)23ZH7f|kI&WvO}{D{eY<8cg-4r$}~Uvd1yb6%S{y1ub-Ucl$?MGT8i
z@$y*4%U*r;mGpPsdXG9KVa`DV2U_>s{o}jgh%V#U-rYIpu>eGxrz>ul{*8N|diKc|
z;Q8Gyzsm>JKuEB`fmnt?5A`f8l~^rN5>gtQ+(x1#+z9NJEwuZ*o%}G*Lytt&TxBuW
zenEdN+r`9VzVrL-SO_l<E)eM8T8KhK1+0}zfU2&M2XL#jT`ue_J|YlZTQ+a{Js|lT
zAd%<qSJ@_YXHZV{K3df~aPAv1B-<K2I*SFuvbJp7iv|z1DpqyX-)Kd$?Xq<)NU|Qw
zr%e<?a$yuPvT=ClhLHO?F)jk%s5Wc&UOT~tfTXofiy7-2=Td>yCMbd}N7xw-rDc>l
zmvC6D;M(Ap1L+w#&iwp|P6fV^sb&wg$2X9<@RN*;v=1gvzMwEAB@$znWnF!>Vf((p
z+gq3uVy%v+?d6~!GtVBKHD~5+#2x*Gz;Z1dUMPnfjq0v>^XB#b$7^r7V&h{wl2ejA
zci!>6-(PacB~v+$E9hQ3CV<f8ltF`r)P3#pD}VXg8?WBq(AeNa=uz@A9x3Tb0fwyW
zV!2>*p)LFa6rrs0l06Ldz<plwgyKO&{z>e(FOZsH?xZl@eMubT0Ow(UL!h{TKmdva
zyJl;-oW`-v=}a}0L~inuYfvSlL3Rb8B>s&XKfCd^@7?_2kAFPlp3Jn&fn38>9?Dg8
zr3@9v-o(Q9VABLuOvr$obgN((#b}!R*7hC5MF0p1$h*plslQQe>_UwDQLjXONv;cl
z-IZ}x@8uoc(11+|bvY}NRIJ2yL|L(sL=ck3yp40~II&%ZEyI&xHx;8u;b*>+YF_o!
zmC`3MxsO%v?qwB>92>aq+N&M-Uj4)2Z+$jCKJv4I%rO<Q{bCPI)#Up52Ol_ode&Lr
zVq$C2QY_N(wuiXJN1vPd%wsKWEw^ZVFa|<7dbz?Q!mAg|U(oxd=Vv(+lH%LpW<u_}
z_r7<oyY_l1Z6+Vf36B*Z>idfEV<!%Yj*5L_&TB7UT2WDsV*yFNy(BPY@bu5{O<+VU
zksDb-R&!!YtPZ38;{nh~(6@`P4r*f6B-jg3%5)+YN^*xyXYAjkZ8iZ{0vOAj$f@EK
z6qQ(88)8;H<RpG_B9F9rtEwv|Kk(qs-p<L&x$&p}dH*-*Y%b@ynF0P^&S?i-%t`*B
zi75&iPj0Beg8&EmkZnbk#-32Ja|@1rD-Ae;i-RWd(J>+qUugzq5vMdWK~^!87>kCD
z6&FN>CMa(eK2s!FfTAb}j!fZzL>vKu>Np?DmLa2`s10SJ&8A>2jek^30<j>`R&r{l
zV`~Y%c1_lqvC)yOUU$S>Eg`PAO(*sr$E;biJfD5`nfLxFQ)G8IkL`<q<A0W~UcTh1
z=bl(cc=pgeO-*2<!x<47<-*s`|Moj?$36e|7oENP^lnFX4Y~X7y9)tF66YLcUrzV`
z9uGj&dme?@^Kgq^`@+l57Hr>9<mDmQG3@*()=Ii(u~cdj;bkE7zz%tt{7NO{fZR(h
ztS3xuM6_HyY(Sx=yj+?DVht36tJnm}bQ|hIv2KKX`iJru!Cgub{>z3EkB~1w$zzz}
zC#<?HV)fi|+plk^i`!b{f91{}e(#C%&%WR;NbPDS<6ZpzJ#*6T{gKAMdwU+|UZRwg
zIb+1!c)6AtC(n`{TdZXte`NUmHbPQ0E{0uYJdf4{AOS>0F=d=ngrYp8V_=!&WSByw
zsD^qsTB<7ZeJtQfOiHvuJ>ga>AZdqX5o;zv+)J$*CLmt9v4)a_QNzzTtK!uwYEoWV
zYn4^hrV#ftEk3qp+NRChTK@X%8}l;K`n-7I*(2ZJTH1B3GiJ<)fK@;Gg&*yvEbFfe
z7rnoC^OnsE_58_rf<JACM+rZ8me$<4uR7<<oa;=>=-XaeS{5?({PPz6;QRk)8(n_q
z=-nRv*DrFRR%XA<f^T1c<4!=*@AHitI~>wKLJ`ag$HgjSR$4AUY?lU;WL$=#2s~sO
zFo0lgn*legp{@~-NMM(yMl6_|iKB6U7nP7zU8|uejCf$NY@#^kl|#ke$-7uaM+F?g
z0>YvY?%USx=9>KE_~n6j-1PdqSI=L*Zu#vG%y{5l4wI{@Uc1x0JOnPhXPWLe5;yUv
zs0gdHv=X=QYE-H9hH}UQR=!FKB$6sst{`sr(YZw9;Q;kDEEbDKXqd^KsWE_kYD0ya
z0w9vig0gMvW~&lvp}rN56XL>48x5M+0gdNgTs7jTL70%#8|uLV#n_8u2*vAI>3u>%
z&7u~-?%~^6qDaU<ub5r+SC*94Ub%5o(biYqT>R+NNfVxfV_rk23)r&QMzD3at!r#*
zeD9z0-<gErag40v%mXji;ZYT2c?rM#@=WX9c?+z*>3!Nui_1c;{*P&&&3y6YKXQV<
zb@wEP?&tbB?a(ie<ueY2vDuk9rR9~o;!v@QVR8Tf*c%Dc>WM$*zxiOmAfRa37f>|8
z1rmO+DNgV>zLY?z6-=AnOSLPOO<+=Q=6D{I>7RbIkLi#8Wql8YSEf)H{=e!=H5y$G
zNHQ60TH{+=TBd*S;rp3xXW)_S>^y<wEL}zyZB<q6-nE}^DZRdFZ>yndtcDH+7bV2U
z8A|u*x>eT4ix*m1**R8XN}3golcDaltHv#vNf#d%Z32~KchG8IUbYj@*X;%<4Rw`P
zV;!qK@e*?1P&jnZze%qLZt{8Z`)oj%#l$=e`#QvyN=8(yj>b_XoFkzQawNolV8$E2
zC?>w*cAUn*r^SWT0Qh31vk>VwA`GD%xNjJ+isj-qc{ENZZbK>`5P<Svo2m_zSJWl$
z-raoRhK(C<-nFY%?BgSxwwTF{D}-Zs7h@wg^ydnfE^d5(;ryEcMqg}kNE>K*U``Sf
zlaQzCtS6s(!un+83S#E_wpZ0ug<O8w<(q%?Uk{x}P~ErLZwlLSwJ$_Lf@fra)23f_
zUGbC8K9R9?>sBA?HH;iyhg?hoehsE3FB4S>X;_UUxQpci5=?TDa<o!BDhZv+tc?iH
zL!x&*J+@{86lF?jw<!b<C(Ott`n}VT$U~QsK<5yPrv5`Qtmy$O##(OkibiDRgi9l5
zC)ybj-2KN(b7o)m$*R?VoN>=jAE38e*;3e!)EbeiY(DTvdmQKYX8v+)2$7Rbtkw#z
zZDmClNNj_fv`EcRkb0c_>Io>);Mj#^j(*A{e|k=u0oo}g);~EZ1<u#)gehl&cRGaf
zinb4`>@l%7g9Z<=)~{QQI#=~Lyu_oRg(M186hxnkwr)g*iL-KZ`x{RP<<Qmh^CH+w
z?X2e`!=We?6dSBKAo?m{;Pn-kmif07d1K2e>V8#PUh$m~!}7m-_E`nr=h~OCc-;a}
z3J$ps&49OxindiOD*W(b&XZ<RA_dxbdOoGAGO<M)H*U0^fAJZ7x_nkrOkzjzj*^hC
zUh&mWfBmQ5T$q=ZH;q1SI;7uY`glSBqBHiPyy^65*ROx_>Awyw+qEm;!H~fd!41To
zz%Dl=nU%+U<{^n+6eZCX1c}tVfk3EX&9ur%U=>32Pm^Bu5Cakt8c1;0CQzxV-wpwT
zeIXhaWrK{>LTrHQ>4`OyY@!(Xy?dKoCapfmg#C)xvUSsaw}1b(o!_|bhF8uQGd6`D
zFQUtE)A9a4CA_WJvlqvr9%`bDYD$J-Yh|DcOg1(B#@cLTtfodUL2Vh<S(Bzvejv<H
zp5U=&=gvP20S<)iuG;$DuFcykt%%kRU!UF{d`H_IUKSazn;gpAPHjz%q0R)#Spx@k
zSZmj;FykpXC#7!|DTbmLKl|AgE_&A*7dY1%G<bxWpiF)iA3S$iaI^qP?o$Ar$w|Pg
zw*zXAeTpI+l>Kb;mz0*lf|X@g)zrOHUQzML^s6qoi)$}}noQ$-Lpm>gV_A7w4TkB-
zd-m+UT;Rj*#^d!X+m2iEhCsM*;X><$+0R(1@%;iMmh&P*dCr}B_QKgO&wc<3_CxxT
z%gPs;<6r3D&YRiyl9Qb?^qQ-t&wKo-$G*ZUsFGGbKTC+zvSNSuYy$&u1c73NO+X@g
zBJGsFRr_J4heZ;I4wQ*8&T3^cgWn74pb0NIC)^Qfv?`d2p$dS6i6P3NbI`G&DssAu
z2*<F<YN_MoZQ9(@f(F?aAmY*2*3sVYiKqYi-8bI)$Hq%9zVz>Bju|JRGM_D(epIH5
z1mDh$#5*W|QI9lDa^0V5Ldq~FS_(4Ey@{<6$O*#@)K-@{F8a#Fd%pASZ(rBAx4D|@
z3Som*AKcnVOv40ArRr=!RdvnPTX*crTC;vDvPXzAloRR?RmML`=MW`Pv}a~kHdLy?
z+Op+yD7}(%C>EtRvI@a#xRxka)oZh!wzd|mw?>Q_Z>9IkCZPke39dpU_cNXiqJVlY
zkSn*YR(@qI!3O3ecF0M3AK!}ApE_!4>hEaYyL;S?-@NK9u7538pSy0|y3wD0zV7ST
z*}fsx5Q+~e!WiK5dBRu>luf?0w8VPrtv9VD%a&Q$8QFn~@^U}t_uO#(4GSK8@Rv*J
zM=adngGb}|J{>xy$JfgNM-3Y~Jp1ccUi0%=vtRl-7EPDG9VZ-ADVnS@5}GU}hML$i
z1p{Mb4}n1-(SF8%hl%2WH43oOiJ1T@OnUHw0nP8X`D9TOg_|nU&P2A+S93FJO897L
z&h%eKHZ1<8Us7}$ZI^^sx!0>IlGMTI``xHQ`~-Xjc9!fI__wE@82HTdPj8+$?yNc3
zd{#luLwH7#Q&#!ge0>kN-5z+WyX77wa|s|c(G^2m3>DExK;FW`y=R^|nz96r?ohOu
zjO)+lzA^v%eNxx_=9ASM$*Ivn04HfRa3EDu46V}gNKlYk{*VH!^~gJD0$t@K%RmrM
z?42%+kzx1bptx+w0&C854_ku@Cdm$B4IErxWn}h8o*^+qvMG9G`je7GRJ&y_cfj>4
z6oR?#ibfH!Zo`JQJ$vd$kv{rwG11-^Dk^J!#(X$a90Siq1Hqc=#}*whGLr0RE0Hr^
zeEC^q(Qqp(J<GRq=MGm+cD9SCz}a`)@jVSrcNH!g#xC0l2MA{zIA!9L{vL<t>X+s`
z|7Spw%tUU7<Y|?&{KzAE_yUR)Lee~mCpG}$cgZJWo0NW3ES4yVHmN<;44{}dUItAR
zNu)d^qzyU4u6`wfS=6HizoY$)vDV>mwK(S(;Z~GHS~1cQS{?ASfXXSu7=o*hYsRqS
z%=>W3!WkbgTT&SpoA69VcIMMJedDI`Eyb<jjy0S2cYVU&*=*JUusG-pnl)+=K33qG
zINr9bSh2!WR8-`@^wLWwB2V1OVskFvN3cOZwM>}YyKBF8>3KiO8;~<9GU~l|7cX6(
z>_v}@3K+J<_)Zz0E&8;wva+mU1*5G6@4kUN)M(WZ58pQ<&q^a`OGd&n79>%#-hDDm
zB;c&)|7!U=8m%Ft&auuIJJ}jEWQ3L4x1W`nnTZT#yQ(M#$gxKN;amW$qZt)`cu45l
zb)Wei{<i6pCJvsis?M;#en794!Oin0t0T$)o=+VV?!ylkTJzp}$I4F2)^qCEzID52
z<f!4+b=Q64LBJvViVVOfwDkl)bb;4Sm@vULbZGwf@BhWm?%Lbhe5SmB@u3pi<&+D%
zA8<%ri?a$7l{JG^-T(-I2sB31#iJE95uYn+Lf(7!m1~Kfyx0-R9SHPdyVyr7j4bb>
zDxydNNxRhVTD;ijz$S+}v2XH5Hrg>dRjSRf_I8Ym5;i-0xJn0F0xc=+-i~{SU-{7w
z@40>U;ze()@_Jpc)hvgnut5Z26%!@MT5rDjrtkjy@4u0^zh^s~!=J!1p6!Fdd0F2H
z`;V_pU<8*JF5eK#if-5~Em)$+O~?`TOvb**I63_XS(`}Yro|^Rs?v&2?q{WD3^1Cx
z+Ui{>3d&6u5^3uY<_uU~c;<hwbvdnb&pY25Gwy8DM@B{l6t9<6US45jDkY(i&@Z)i
zLR^G%#o8@_xVSL?;6dpcR{?{mK)N98GD`Tda^*_vgN5(o7uICuX5<FyYU@}(+dXGZ
zn$U35O}E^B{k7MR<KC`jJK?RT0HO=MCOSH9^uxdZ^GlCC{^<7gpRd10p10h%bk__5
zAP`WDh6_ppPz;;JchW|Y_0ImxOb{jU<B{qm&z7hONj42oG>G~K(yVsBv?l-w5NXvB
zb-?Aoyeb|ZjsgmgtG)igTl+b`++4JJ>Uf2@OM|9s&^Zta4UaC?3sK$=$#>Jwv{kD=
zp5~4k7LaxZvUh4h;t=0rjBi7Ena*}NaA=ocli}p?oV27{&z?N~GF)0d`ee-}S3E`x
z#=)V52&IK_P)x!;GMEf1N(Cz4uU@rS;>klMfjue~U#n<Rk*4Jsr^P1J&h@nw)bpq!
zPgW}IQM0ve%LZ%lhx4s5W6vU8(FIm!&Onk~r?9`gx3w7Zym?O2(8WZCI6m80=IYZs
zo-F?q<H7k+>{xMeiB(v**jj^TEhZ`f=gD5a9Xoe8Q&W0-@&*k5@cHLn{%cBNq7*9E
z2XF91zxE_Rbgj8;_uYE)?Xx~y^5H`-&3yh&{AMCyXS`4X;-H|e;z>m@5aeB*6va>w
zfrsC0l_V1vAOv8iB*c1I>D)|W0YM?UGC~@wI`RqboyJW?-y1gz0HfdATngS1=&-A(
z4<>^^qU7964U9KzqA1`^Dq_hf>OUmJM;J>On~&4C`k-h|R|E$Es4q)n)lK1A1d{Hw
zUa+%QPB<g~k*ccdd-k+=e^<1<)E6yQ%c#@6hVm*PEeS(n-hg~--P+~GO{JOS3L#+;
zNJcC&Okk;iLrqeLRB~kH5HDY6fFjBwH<aFe`k^7Ku>St|AFT=G^cpdGBK<_75a7AN
zrU@JZWf<pG*@KOn%dCmx2SdT?tW~R4Su0m9hb5zKA;5&PbyQSTdPWosx4!n(uRea?
zz4upgvk$Uqj`djUX@CSTeA?s*lb4PhHu9H${O><pQc_ktQ9<-*g1u7RIhm+bN3^|w
z6=ut4t<X{nn{Ztwtcfh-JLB=oWYz>%4G{wp_+hCb4FwYQ;rBMEg4ixiboC{fLm+aJ
z=G2Yk?&EuD%}~(;3Mh#*Xq^<rxVo^)X%YymZuCO}Gqqj0AQCh4eNYw%-r%WxWx{K+
ztY@3t{>6NF0015rNkl<Zo$KJf64)>8ic6>b{x46?y{x8o_e7BpR(6-%<E7+~VNjZ>
zK?vWU!)8f7Yan-31exXCBykWGklbiQ9Ozgm9t;9ZTw<z8B-dD1X;oL2LTRdTYD~k1
zLQ-{hA2?(bnz&dNE5H$A%b7ZLudp_2`_4+vvm2_l96OL=)g#2}`f-H`^a>60oIB<0
z@)<w*=^xKI=Nvg5-rSvg{i6GvEP&`b$D$z_dH=l+%zl62f~_-Wzj!^4aBc>QltLIx
zSjw?2nblrGwy1|fZE=F!C&Wu;(dsTBsQf6rk6D2<*v9KuR7G+Mzl;4ce6?Y{Y`G>#
z8RYjx9SkG?c+jW`NXQiiFrrX8HxtIZ8ij&9S;fwAPRr?0B)sOwDQ|&5gt>}`9(rg!
zGRTWukX$#qVzX-4XDJIzk#1njsQe#RRyQo$uxY0Ymo0AF?~tRY0;?pjCy`7%fADZB
zNUb)qNK8zv6^B+!W?j44uR*ZL4MiY|z(*@SsjpREQ)bl?TqS_*A&x|4)#3r3G+P3r
zRL!#WcsRN2C)>8;?&6awJ`7d--j3S(8c+XhGC1aB|9#d=bKb$%?l#UlrSs%H{-Q^I
zGMjkdYtNZ-?)iWC^~29$YgmiA(t|by-8kGnaiw}VvaFj8{W<mtG+NT5!y>^SYY<I|
zY4#(*HsXWrj-ptQW&})QWWxe2L!cf2Dl;?Dr4>`(q8e%fkeT#=DHP#cb``0LjoKK6
zfbn&L>KHZ+!r|e;8*0R>+Ns6|TnPefjalGGu;-dKZQ7GwZ|kL8+8nk<Hr=SSLE5km
z1?qTxy4}})Bh=w9)2DkD^Vu_I_<&DRQ<Gkejg8fK!AU|vFdk|gC9I2556T~AsJSfr
zq9X02Bo%c%xao5cEG#hm3&RzqcY1%yvJC+gB$<;_W7$py&yVed5NlC>SS|C)do6rY
z2!!@Z4iuLbu~<4i6UR>6HD$`A%U^rtAF~0+4>-w`gU{QupH3zPIq**U5Y_d~J8rvk
z(aO~;UwroYzh6>UUmHy_;Q#_M`g!c`n)nVo=WM`(P{)HaEDsZ#AIS!<6@9;8Z`@Z{
z)+NvjOeT;@G7A2aMotk`f$WGqG7mTSJT=qVL|x?4EmlqERzG6DjPa890wT!<m~E7B
zfG<AP4JxY|g_`u%Ew|ia%<Z#h&;E#y+Pd42x(EUvp7&8zb;A{F)@^d&7iyYZMp(g(
zEhB|I07Hh2G}+f>IE*GPB>`E)280Ii(DAT8%Q&pxl#wwJ`>H~JCCFFoDkceCwvlUO
zQxOG`EP~rL3t&sY<%x8R9DbI!cS_7-H-G25_g-|-MG-s?Wk?)rF771$IaL4&-r`xK
zMvUqhHe}eWS+BgDfHT^Kq=a#|W7(EEmsx9qdNpA+skIqbdOmAH8(|)h=vBhJRv;s=
z^I&YsB1<u>lmvExMH|#W`-ys(K9NhrF4=A_dZ0E~luh0PFeQ%|RsfmC<ad(2S|%2I
zSF``g{A05+iu7yr%$z;*@@HOns<yeMZFfz1Sz}C6GBSkipT!%j=zIKlL5IcSvP=2o
z2ukNAL#N{iZ%g97lG)M-KS*A=>Vu3u_<TumLdYr_L|l51Lz-Q(j7t`YM-~}oYImqK
za{~$Cq{#~DW#{NR^8E`aj3EjZX}#j&c0=`AOftE+xJ0N*BA_vWPAo>qB!EREzr3p6
z@%76t-1FOC&iGHB(H<=F7h?;#<jCjgxnE8-Ky+u!ThF!EUUS{%3(mXnxmj~w8nS%l
zCnMyf$LfP|11umbGn>tXmcWc&$*@S0RYXN3d>dd$t}uY<q%5K`(o%I&74%OAU<NEf
zn~K0<`jKmjz+%fD0*oH7aTzz@;uG0d+fhe1l9A<9HGt2_L#)l)Hb<mwOr9x<r<GY3
z-cnKK&~*o!7M8v3t&Tl7If_-%0wLB1PRiV;WG{Pca09`;JtACo2Uc><pan?3%UwjW
zh&+B}JY2qPk#R+l5T2NdyPDDF9gd0wuR>$RSh~Xun<aqN!2a%nisa<xo8qkkhu0fs
zs0cEkGcw%M@%g6hv48vf%a3yK4LF<j=QS+Y<H>yeG5{oazj?3>dAHsAz4e!r7SEnD
zXXc1?8`kA29-4=)I1DN%xm;%A3Nf10XuI?v_X9<MIRGq%s-So>?3lnJqoOU(wCOv3
z7p2m~F@Z(A*S@l$2q-dSX#)9C9~<gL-#Q<sr|qAJ!5cUkujbUO$yx}gLr)EPOzO}>
z?!zgi1sBd`*n8=Ej7+z?9l`_ru~3V8vQGf2%DUik$dkYo5ykP4YpCYVGDstR=>BnP
zG=*5}>Kj=7<8WjgY7B?6t~a2HjgC#SLL*|;U6;46T|!Br8jpKAK5!<sMeJ5=T(XgO
zY&J`=^Bt&lW86#Ed~RiDrhjAcvbD-sg4*oVv@a7t%n0ykhi0T@4E@1f_bmN%{n};E
zJpcEx<y93qiZyq1IJ~H9dCp92t^7=88U199cTk}!2m~XKbb><r>3jRtX2%SbktsuR
z29rtxzS&1$5oIwHL#&u=DR{90ECUui-7y<#{?PVM!ju2#U|P5*CO1i5WULGXv|y;&
z@UW?^fUJ9UIo;^t6c2+xF!!Ttw-*(Afzde1F)=I*r^O~GL1t|%0%CUvlV*P<UsKB0
zKIG>eJamM$WYGdMsa4=65m^Ly?mIJY4%0MNTp5L7qhZZraX*nFpnO*e_7bsP1U#YT
z5;(L$MI70Ele~*QUgge6@AF?1&KUN}VLCl2Uw!ES61;1MbnMWf1!Mp4+ds`*_|d}m
zUU_Zy`HhYB*(72Rix6Pd$G*s8hGd9m)<f2$5n%LcQx<_k!o4OyCzt??0gGP6j&&YK
z0c0KyLZU;HLlkO1b&=nZCRV5dh$uekPzcW{q9FG8_%UEoNRx16T?g08?XyT|9@2Eb
zqNJMO!TS?M&}ZFS5JtS=t(Y$41m>??ev%WYd)^3zLVQ<BSqaaQ6BM5kL#%v%42P;C
zsJN7Re57O$?1h!PdsKuZQ6b@`wu$0%r1Gt*C`Dk;vLc$I48`(>$aqK%k3g8aw!X@{
ze#53Qn>KCxc))<1Vy-sBT=Pl%^Q8sI0b_8@lu1(>*ygQSv-+QNUVVA&_Tud$5cb?S
z6>8$jD$jfX8fIcJIao~%U|_+_WbXuwdCzY_3S$5wR!ysHC#4BaC{Y(Q&xX3#$04-C
zdO1C+{~g8Ub<V~EF7#x)wI{fpK#v-GoYIgp0|aw^K5D|NnMtd+Tq-7{&d&ma*r@&c
znVtC~@g_0WZPk;#v9@0B>DVcV=V)t%oeMz&1*?^wo^A~pI@0=J{##aa6mcN+)u?5o
z5JjZH?>t$s!-HOJU_$GRR%^e&UJ09ZfWU6FTDEMIk`(X!@Z;51_N#ARd*6S4e=gnK
zcu<!o;lsZIKr{?W!!dpM@DTyF*T{}BkFpGdNyKmoHW9ZGQt_xlJ50c$O;z?ybXH6W
z;hI=xq6aBPCz$kI5HN;a3sM-fpMaxd*-$ep9tFJ`(;%5icWM9<oD;@?35e}UDFBE<
zsBwLor-m}vuQr%vo6tM1TmQwHB47u{g`WV9j`fkrF#tdqP!<T?yYWmwt0jP-p0~1d
z2Uwf%(Nbo2*^R^|MZ}O2Rwp=Wvq2((JuV^1#N^0tt7PX^D|f&!D-PR82cg0qk8EGc
z3h#ABM}&Bntyq`v(V|cKOgig~4Rk;FsHZr=KmMx#BzW5z;jiWA=Z}8$zaM+N@RPzn
z&z}9#Ib{F44#QSIgjS=`fr5yt7?J`d;q?GY!o(MK(F8g0``|<ou!6rC;1~)cswAH;
zSOm02vdic~PGz&z&#;#EoHG;MOn*KL1iA;^lXfuq0k~LE6n&=&FXclUlTjYLiUpCj
zN<mIa;HRRXo(R0h2}G0pCAmQRxUqhV)ymAww1#3A`RCiOnbHTMB4OdtmXnEZ16CJx
z<fiLT4Mrt_kU>(%SC;QIE-5N;EpT{3u#K=Nk+aLadc&5$+&A9&%822EpCX~fX@DGg
zX!re+&bwcXKV$rBZ13~_))!{Ikh5gTq6_QlYR|*S6(%=nu}IiWI&eK<jsOdRMU+8+
zIS?R#z)UNBCxGZ56KVfG{3>|`?U5%8;W(ztrE=ee_S73uHpxZ7sb~`oQ2?6?h@!|A
zBpE{LTG=%O26-5W;%bvkLVj2JUh<bfBk21&TCAwJa4RNZkdX%jGO?rDZ})Chr)?lO
ziaO_PRh8v{#-<`sajD&=A^`;ZXf#-HM0UzmzP7do>w1c@u&b7c4PrTzdaaD!3BHdC
zSGnJqH-GH(t1q9=n4Z^D<9cYn|7sNE(ChyyUudhkcG|Vu*@_6z`RlTk%VG<cd{nUR
z)Aa?|0z8@wngC3GQ5LaH1~3AF0SurJ|6x?eoz%u&fo&jZ5(*#|DU5um<3nIU_RY*g
zuc(JV8@U3gD*n5)w8Q|ySpFq1$nwv9?%z;p<6>iLhim|7-{2;e$Oe=QqB4Xk?w|cg
z7*bQQiB&#@tR>{}mC75cqcR4B!WtJ8xuy&mHp-g!k69+MoHDJbz^B6l6;a1s4G{Zy
zLL*5G(bp&l>L_<DxBBGXX@vGh8+E+x?JKRJ-(6i_>7D<<lKhF|Mz6}q=&P{j&>kLY
z)BV3XK!Rh0P$0p;I#WFI)U&6yv#kIu&wjkDFl2Gz;(St&4(af95YwR*)I=L9YETw?
zgA<OGUBFNcLp?gdW8U+y#g^e~0*Zq50E26H@DfhWtK<X)3o4YgxTM&g*i2xtLb4G_
zHZhYv_`dU1ESbI!g2UK%f?$#L+yKM|f=<!ax;s!^vDu%IJ(k-h2{VCbT1<j2C`!zi
zmY!kckzHlQ#x4>Sjcp_WD$?apXzaKi{)Pjpm{^2;a+p<6;8UKy`z`GI-B5ZbB{<(-
zywb{>_1eWhx%>869D041r#S&T{JQ|;(4mk@WjtURfBw`9nlG4o;WCmNKTTm3$CAQD
z+3P=DKLpRxM2u+eAbY_h!-(c4PRp=idV;(ayG7%VG+m+~mWz@NJX!E_Y8vO33R=#W
zs??;_UDsoP5d?(+!oh$L0D=G#`xOLA5HtV`9FWt9tl6+*c=UQ*O?#HPqT>27X;a9i
z6fm(n8ZRZhsHv$nYGzfuE#6sV5;`;>OGHG+q3P?@4Jx7m1hCjfA}A}T^ry=HSYEah
z(B%V~I3tUAkwruixjH9-RUL~zULCin@RRtn#-H&S9Vn?rkG6kTfb1JI)_(jUth1?w
zc=p8c69a7PSUndM7w>3Vv3h0fy7lXFp)R?AB@&Goap`ygx62iSd=V}dPLRNc72i!v
z6<MXwu;{^_DtzeN7{CoHB#ZPufCvD(=mS7v-X8!A^Sc2LZ~_gCLqIGQSx20(VOkl;
zj325>u(+K>20Sj`+WefP>efK))XJ*5Kp2aMQTw)gV72h<MHcCu+Q)>|7H!)k|1c|t
z?2j=CeY!nDT#gljfFF-mOKST@3VO;uB3~^D@bY{JTG?Zxqur}M-E6)3+B+AI7(V20
zk>TN|0kUtVpO`nA*9B;T3$n6uEVeyYT`}!5G62piDJ^MUwQ6PL`VH$zqxx9}?)EA0
z-R@S!wX<plsKV++cy)TBmG{B+c|S4Nbj-bovdN!H*CtA!W+;RJVsE^Noze?n5Ou(~
z%1Z2DBh1Fs*+wC!oMF9;KNJB;do%iF7UkyTem-Q#(Cvc<4c?iZolTUJwdubfe`P^c
zP2&I|itIuzP1vB1Rusj3h8B#nb`)(j1$F9btL&hvP}z%^{YPC&Y$_78u*cxSBCm$>
zUB#%5<BWy99Yul<<6+Rko{<`lg?+XA&9@eex%#S07O;GstNZV%_HR!?x)?b5_?!_E
z5_U$8f^xF^H(mAhYc`<$dbhH&yzbKtpSoAAU6q2}B?HQmMGmPXe6bZO+}W)rz{zc9
z$}z7bQz!!qlNJaF47~b?iS&E1agq|lzD9_G?Penv8i7F+f~|@A^QF1{`<D&O8&p0x
ze{f5`e*MC*&MQx94qGrCXa(;VPnj^{vC5j=4{zR99EhYJ$sy8!iHbDh?VCbSRc_uu
z>+=n3abb@k^cE_T2%BZV(G^I<joB7<QIWm4*w@3V1ug7GU<Z(5wWN^?4fJx?RaAN3
zU9hNN!r0Nzrl+U&sKEaJJ3x4d2Q`_<$Ku8^R+$UgN-w<NE9-&7+l}@0pZw-G_iXQ-
zG3Jtn*0|r*H&U7vpRJSLm~SB@(r-lF8WtWve{HLKrC?B|NonGgBo8m&4l=mcWu#}+
z;6qiL+B>zTPik6yW@bivLQ+yxSV)+Z8d8^^$)@{0T+3tfW)B;j{o6%L*RLoptsG50
z0G~U=A-8(tqousKQj82H)sMnzNmNI+lPV*8E5U<JMGn_>@H?A|z&P3#_IRpi0lw<$
zN(_fduwDr$RTO>L$!hVMEDL+#D(l5rb1(k!55L=^0{dxz1fSF4U(@(H4gJ-Hc(cCr
z+uuI%=(f_fxwBt?_jk3FLq<61dU-s2zsLP8VgR174OT(HcyDUogykTl78P_=MtXWv
zzs#)WoUE+2?9ALSq6~Wzt(k@R)Q^*8ut_Z{<;5{Ij@KBB88vLcuPJfx%BHQwj!68#
z<m5>La8rFsk%uW{EE!1T;hL38i5#SARb!0_%|-pk;av@r4H5^wZgKI+Mun~FRT9{<
zB(TG7iE0_+p#*j~y{K>r?5h&S7p_2HAKhaD`)Po5Z@TCSevNH?(atUN#op+PQ_+wc
zm~Cy2kt0X$`M(eU;X<w<mF*%nu|?Vhh)lK<)}R6fW5*2q-TKeBzP+om{vudSulzR^
z$1kOZlDa8}m4d3aZ2pYUXNu{NnNhLn-F1i8#Y$ueB_<`o1X8siu-BqKt~S0}64+%l
zl>8%kBqln_vvU2Wz}(m0zIbFo{*xpO>=A)o@IKwTHv(d+b5dhsNd#=KiQPNw#O|fF
zSdB?b%G9>ry!|%sf5z(#Y?IkC*-mJKiWJac{*Xbrzf4PwgXL)>U<UID6IMbs6`qU>
zN_>2xafg>CN~&PhGYMe8v|I4Xk929BOO7n;#F@wtsoEpuyGpoO{KN3mQdUQMBWXGN
zWuzwfJ}g{m{o}3ol&*Bak$V0j-=7A^v5j7gCz7lJUy_EAb0n8?1rs=~r!NV?fAaV<
z1{G$e_nt*MActyy_>I+`ux65LD8;DU;``?*N-zQTtJ$b)+nR~vIR>MVWEK~|ko=*F
zR_*Ox+*vRn5-cAPL6&)?J?%{NKuI`vL*s7m{Pzool$Nj)r1eCP!)bsV+lUgbg*5pk
zX{f8EamqazmxFuGSDCz%hYs%l>pm$lDysl{juIJdnM$-N@>9k`l_nTAd^CWex)*j4
zxuDooL=>WXRK#|Pmzp;o&0C}?Ki%5WWCV6)T}11M5(0$;R3tSy!TAAH1cCi4oZ#gX
zJr-Y@hkc@NtcUs|wH!8tZZ@r)<{w9x_qNgT2=BE|H#^?Hn>0};tMvs7H;x(Je^+)^
z-zQRgr-*Wp^Fb6u9>J871|;%(%jrK5IU>nupBey2E9K*KZ@NA6dem`X#L#^>@##{k
zplE`$<Vh`e7Uf=+!0zTZv0BQmNvO0ls+Xr@$<kGcg&(g}1epZ(6V^@x<QRsM^-po}
zT`hyAM*%{@d@!-LV#DQCd*n?CxI5De(L;Bz!s>leW3@4<T*syT;GFSe9!X7#^5N6w
zk&6nO(Q3(EzKVjN%7{38<e9b;BO12`V$b~~X6V4hMTJ54<vl|a_P^ijchyM2xilmK
zfW!laUQnprSng{O*j4H-ypz=m13Vr;6YWM|w`R|M>+<H-*2Ty0NW0wiv=*z&Av*RQ
zWT)@}j;N?eD<uivBxaUT(Hv>o3k_1?@OyGntjQuG)`bAZqc-|I70>S{X=!n-{d4>M
zxvFmOT{UD{4`r4rp&#X746CKAiCH;$)&{aMHj*MptX6uz0J(Zo2{q^r^zGZ$i1bTK
zre<S=wRGun<MTCiSUi*^(ZsMzNg%BjYF|fsO1yW;(lzdXyz%atS6_AM3A3=DiUB_A
zL;rIBcogDsz)$UacM&g<YmFN>&g$1Mvy&Vgd14rRbl!MOw1@iT(N<B>P7G#yU^k9@
z9EY4L4&a_EFPZc}(T>vQvR#$o9;hnUNo8;1*U@TKSJznmD0wh!_!#Tmw_i6lj8qEi
z3>w&<#e(~>*Mcq|wbl%h*;vDd=2`jqxz@aS|3r<v2^MYu<rt!EmG8b7_FnE?SlHit
ze+d@$F(=Hzei|TMKBjK$5PaagDy!p!sn=Sg&X~mX4Y7*L@YXQYp^JVrZ#Z5>Jd=~-
ztdXNeSnJlTA-8QM2Obq9U3A<dKZlbB;Ompd7tF4#Zn~kes>a(3w*natTjYL%Mym-m
zZzle3Y3cn?+a^%B$VJ{=#jlv$@ZG;CZautLsFl_)*Sg_beW6Ngi9W1`szjS}$tL3K
zbVEr_igSLjWQ8?w&g&Qc=m)pX<RmwA|3ru1^E5yXKQ71ct<b>~W~G;lL`+<gb-~3~
z5@-}ba<pAc2>sr@2`*wGxuvAE+{(_%um-T=7H!*T1Y2{GL;k>q8DFbIzCKwW$4oe5
zXkp>94VC4UH7T$<T)9P#<^5@bQd2{XBcm7DL%Fn6(yg0AanL;&!z>p0#KbsUS_-Ve
zgWIf4n|2sqFxv$MXGRY|@pU*OLfsvO%hn`+ylhp}xHCt7VGH|dfE@HFj{ZR!sY9F1
z29WiGm2=ycP3ECFC?;A@2&tNmupwkVcKJs@Q@H82P3vth4fVly7{a<d_7v=J#l`23
zd$Y2p@jJz(<^G5WDp{b_l08IJL^48sU9B-B?kX)l%;<IVigo`C^accXRs?_&!<bOg
zg1pOeYf%ilTv0?#;^Jc6OIK~M=FFXU#fXBzkA;PWejy9{X@GR|83lJk9y|ags3YO2
zC7`Za`3W_~S7KQ<X+SlrgNGdRs}5D7IyI>_H7S%`<k+BQ4)5td>G=cth2~}V-L|W|
zHpdSYF;f`FNm<e>%8IRu@?z{DL}Gq{@z70ZG|l1xP_}KgqZ?I^QZ^AK_!Gs}!qO^%
zy*C2;vNi74-<m)A>MJjv&!Ok3kK<{l0dfqZ#wxB@rOuhJ3?T|Z?ZC}@wY6pA8e+DI
z7cmq07&<+|u0g8N4B=3D08IkhM!BDo812{*jz5WCUw6rc<6bD*S&_4?sKg%`?V;w8
z57~(<?q%Dctos!W`>*`?ug(C4MM;#qWsg`k2Oz<PKa6sFj8jOMWE{VHdr7%(!TXCx
zpEYjulYRQ6D8~A@JRnX3<QSe6*dPHTw4H~e$*LSqB}JcG+qbMWH8V`SxSrezYU;_j
zv-9A^Vky*iA3jgeIZi}3eNqzAMi%63E-kOg1025Ajy<l*@*P%n`A#F0pNMlDz7N5L
zkwN;wKxL?iC<#f1t@OUh4q4dqX1#vV-FMym5<7nD@O>Qp+tUC!`uH6Bt5bD8mGQ(&
zy^G3{ZGlZ2mPy@6IIiNngN}KJ9(Menl}*LP0~h-sgPgLms4+E!pA&yV)B7?1nM_dC
zb89#3%=>)f)_||QF;G#m1$Gnap(~}GxMkzuYqNVdFtJ*iYDx1%bt*!5!^0zug`K2x
zeU`6S7e0FQ&`-IRAqOAb^{3MS>3WO~+@nRVCUV4Ewsf(zdf~I3b_nRYDH?L%t|#z)
zXbRzjVzww%)+<VL%|nVGy;3sv%h8e(WA|ibBradO{<AS}y))lyHEulRbITQ*>t6lw
zdMhb0cG{4^1OC#hSLks8WHUpY_vIMoFa1?)cLY3DQK22}qw-=xM&#ZK3qkH$D&I<D
zW2<ye+&$PDtpM@g%Il@Lcvvo|SJR$mdAo*lrY&qe8ghCJchfp_VE<oL>?(OJDSQga
zlFvSdLwjOZtsjgawZ3CxVq8m?uT499(iyUiTyqR(+boo;NXhMNr$KV$k(0IV9k%(|
z8S#tOZQeZSoCyU~Y9bj3>{Ua<?mkpk3;`f|Dkrr0JN%BlEzN1LSoALp0!b<`het+l
z;zTRia@oGg)>%A_cPFuNNlmaW|3ofdOi<P9TecME?%q8y){Ko1e#%=rLVTp7eQ$d?
qmok72HKTPIn=1Vtt%-0v`u_mayS|eWE?dU{0000<MNUMnLSTYB$~5c%

diff --git a/Images/mathcheat_julia_logo.png b/Images/mathcheat_julia_logo.png
deleted file mode 100644
index dbb52866576c2b77734c2d21fe081ba281c20973..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 28422
zcmV*5Ky<%}P)<h;3K|Lk000e1NJLTq00C40008C)1^@s6isyZQ0003mX+uL$Nkc;*
zP;zf(X>4Tx04Nn>U|=$Eba8TJ5@2A+%_}Jia(7aQh>TKTzreu3z|A1cV9a2hm|R@o
z7!csY00epYd8r^86@0tKzz7n#tP3&=RgNH!E4iSk1gPo{5F4eW7AG?>Fd6{a+2th#
zKyk)7K(<IyHkiEu#4ZX6at4YY0kZkhA?zz4b`pgB1jH@^dIG5C3rKxBn9aliVkbe^
zG9Y$IY6Zw%2Iu^O%A(Blj1mPS0|NsE$CUh}RE6Nm;*!+dVg=8<<ou$7{G!B?)D)mv
zi2Fdg7z7~-K_RALXlZGo2^OUy0g4BRai0Uh;mas?1|f#9D7mz#49a6<GG<^P#7CGh
zM}mRDe<=e4%O8Z8NiPG#0v!eh{;3EtHA4ml0euFBeRBZAuTXF7C9Jdn000SaNLh0L
z04^f{04^f|c%?sf002M$Nkl<ZcmeEv1z=pqwe}}1XvIusl9?I8Ob#`imlV=AZPTP_
zoBnMBZPS$d+O&Da6v7QP%xFr?OfiEDlEt{IMg8BIyH{Szk}Oz|>=}D^uP)4-IdkT_
zXU?2C6W7cHoiqnD2TmCd9N*<bAt3H3A9xtYe>!!Z192KqJI^eAr{+Lk<$wvIadBM(
zNb@<tRSm8uZhh;CZ_#&X4rmVaRSuYd7z(7#2*BlTM1!jy4NZ0M`aEcE4j?|>ibQ)N
zQWDdV>_~y#>cBD2@gC#vu7Pr2<z08^)*5PexTc@c9Owfa2na~V#Vbmi(Twt%!`ORd
zCw7%?!TyTRP}Z;?_3p!H@|Pme+yq;E7Lsg*$Z!lmzl`A+o;MD|3&&t!&QJnuT1$-u
zeB*-oww%-liUS%@wIFB?oMapb)k;Mh-BY?9t9C5H^1W|hcg@@I2Y^Hi2%v*ujY}oy
z%0M-akQ*Sdy5S8}!tF1Ihxer?0Fw&u#N46_F>&ZDWTxaOZiRyNNyfe&RYwDAkJzUl
z)*R?v90=7OkJKE(f=zGWy{&)3;l?e<vJXR&wSd@3@Kjo+9ExRBhVTNY{KxY$MHSb~
zv(!R;0XJ$rtI$MnonH8PTsrwyOe>lPn<atwHcM9OS^3sU_-4M&G&eK{Gza=L2SUMg
z&8|=I+_HzUvHWG^IVK{}nniGIA^<ic!J5QRB5YPWEVP@pm<0%sfo4B^elI+JH=4Xo
zc>Nw&;_QUmcvQI;!$-88KlWF+V%Dw5O)FIV5+K$SR)*+kK<(3ANE5F)U~s?$Q-8A$
z@2`Cs|5|oE5U?Z5F^@p%Qb3yG$V75ND(uuQ<KyFx0YRme2AL_xlAk}|BdEGKaa9AC
zw*l7pBm{WwpmQm;)thnK+4o~u!D#iA233Q38c>5mq<=IA`YH#Ep?4es(2LhsJcCD<
ze-U{JQ#nhYLVIW<QWG+fo|MhMX%Hoh5-rv6zT^7JkYDa*zSLTs?grF2%c!w>3CJlZ
zZCrps>F43j3x1E0{U-#6|7!f-akeM<RSl>oc?0X}o!lHS!BlGb_Uh;GrzJO{AaORq
z&_dAkB0ISdDV(7fd#Grg-Af$s6J2HaU)4~C26r`01{o-KevE+`7vnpZJc7ZwBQ&Uz
zxrR?g_qeQ|(j3qnh&cxgt#Bo0@&CE(M&vrCDewe*$WI%Hw8RWjXiP$NpH#7}24J<=
zX*cahZ8r~YUk!4T&cW`gcktAr-=U_llI6y!`?_CF-+xjAYTs`|U4v7U0|Ig~EI(Ye
zAOBhQ8(5Lc;dnANQy=ou1|Z3v5(GH0iuODi<;5W@IhR14OHi#vzvQ#Ar1)=mZ{2@c
zuHpB3D$jFkK<&Al=^Qi%qRs&UEt#E{iK7qJzl`mbZzCgN94DZh$WAE?0n_l2Cc1Dy
zxk?$Jnq*H?6J7<0)9~uX?_q24`tTLjM|0AE+D98sldU-*9N2MaGd|e<2M+ZoE4Qdr
z&g7>$(jzwXHbK=AZ$VZ{9yMgTVOdhS;;J4WZF)s{1<26<RMk>7pz409Inc*AU`|Ad
zcDj7WN2qia!@)@+dO{&RF`Kn9?5dHfmI0~(wbnl)DOY(C<R;F-lD$8}uESd+RcxQg
zR|9IFXeLdh=D_hBI9yeX75iR8R>G*DcABM}^}9cte|#AopJ}FFYZ_Ng*|~%@0Sz8t
z-JVaBc6_R8sTxo_mKc3ibD&poKsnIMOcc}ZlFzWeZV6XGap=vSEKd88)=pKy!7tI2
ztrjcGO{cG_8|n5TSa;xKRMi~`z3r4b)_{6SHMg#8pXWeu!rn)p(48flTKpazTuv&Q
zV)Sj7aY(c!D_5;VOCE}AU&o=!;y%yj?p8npYIixOpVJ(OAqVPRwb)y+5y{pZ<tF8@
zB`HwjGN>q1)l4&kO@VnFqgJ_mz`@GBiUt|>N1vW3SHD$p8c<s?ME|Kd&=)vR@2o{x
z<1X0a^A))g{V5iSqkg(?SrniOcv^^|rM1PhqY_z}<oc<UG@zbJt*z^N(r`dD)CPvt
zv&1{pvQUW{6`Ld>{NiX!wW;C-P^%luxeI_dHsr*(R1K&xZU9}p=0JBiAW@(^epx$i
zRYQGQYahELYyB+>SmQ~C%hRY99K?>E*p;jSHFmt$C2J0Jn*)IWqwCAl3P=S^<rBN{
zbts$%0)DlkO2|9ak_OaMt;KbHPYMoLEEZ~otco&H3bYEChQeBOt7$;F@onMo-wawE
zT`FQ<ng-O^w}GZWbD+x{NVF%zK{us9vsY<@61XIGiD6Nwl?t{8_(^6^77P7TPsOAG
z^;B$iUFSZ}fmCkwoN6!PmLx9ad+Osd)Yv3r-M%ltWl~JCMM_pmK9{B1V?$1iOVxlH
z;|9>hYYudW1F1>rC`cW}-AO-Fk9&P?{u)E>?&R0qXT*dnF$FwaXdosB(US`^2J^^(
zmYB|!b+=MIcuoUq516MP)f|Ww2Sh_1o;v}JR7~SwW0a>x4!ap2mH(odq&xvrFE{lI
zG<%Rr@2LSfLlp_es;%hK6V1A}Rh9<Swz#6tX%6&O4g~Q@+?0w6C(sX7^wI!>J&PL^
zwbUr9gc4go+@!#5r=7li7?yQ2`ehI7t=x+^T^dj$&SG7F=0Ny3AR3{$UVm8OXpGF7
zPY3^fEWpCuNo)BVy<xvN^m~0CG`edD><Os#R$%I&vyqXU6TS-fv79uZ_OS-j<Z2F>
z9LUe;huOpC6UYv7$){Zbo0|^*;<6MiSgq$4(|(#7+`c;GCyl_AVRICD0{X^Uev{f$
z<5vUfsnO25mM0Mh1gH!*sW{9mIunD_=AyxSm^HMb+F7BteU6WyEmgo;K-=YUqSjTx
zcml~NYg&i1i@t^-`6EvvPItX38c@5=GyR0-K+HK{PCf~MT{P~isB-Th$l2iZ)S=c{
z9Wft&v6%{(di`E*DP78A2A1}gU{KmDoIQR%hyGS&k~kH$QxOUcs4?#vngGp#u5;k1
zmI|Cb@lwnla2v{9i>NK8qQ+SY37!(})k=nDYEf=d6%8eH*>V#=;!)+<hR;pC=akn@
zr4==xc3qbB6Pg3Pf&<D{N?WB&0^K<8E@V2!af0d)tQ>+@G#o;mt6JGH!=)`+sG0hx
znO4-7!0E2z=6oqAZd`)P$2^ELCtRX<BHOQotGr&p<Sr7S0kw+^)3<95^ePSn-Ka+O
zpNQMfdIXH)50|%+pqhkof@)Pm88u3OP+RSuPpAN7oY+jgP8_K_fJRR}60B)B(69if
z5BeIepZ7ILfNQbq%6+GDisNitv?c^kWj^b=YYs$=1EDkOOSZg=|66!5;+qE|%|4tv
zk~R?hl8}*<gG9ST^|z1?T0&K?Z@=p+8?EwbZB|-oJzcV70*aAOIjptfK;vS}8+1Fq
zdfxrW&lqq@+f73oRs(7%H}tXQKp*8mD5$R9xdi`Qd@l~weL`2J(^#W8&iprV@23=`
zIMNBQiE48_rFjOu0gi=MBiM<TE1;Zo+Hat1l*9_KQR{Vj%TeLpf=fp~fSXRg1KFwh
z8caiEMgwXHGxVY6KwsuSD5&l`vK=oi{}(>q`&%S(6rPcAx~iHi54FTw6sX#)2{a8H
z4YemT_+lQA4FEm<CU_VhKr~q^cLi|!8&J`-8o5bhan02GaPFilsPQIiFl{9;8c<s?
zME|Kd(3d$73aU<bJyvXAi1#-?iw#Gfg^ecEbo*r3IhiC$maHCKSta&WQ%@By)_B@P
z{oHh~1l69s$Z%M3=CB{(yopz#sNZ;bT-i*+?Ma|A?!#Yw9#BtY0aj+9KHNZ>^i!Av
zLE$!GPB_srt)vcG{%XY}v60G3C1Zl9qV5Pb?^}gcdlq0*=^J#f+NttM;ALBUE+?##
zRL;%ZSkK>l5FX;Jd=hAG9g==ArVYLj(}tanqW)tUmd~NeGG_BTn1CYD2Hm05%cDUL
z+V)6Sx%b_#J_)FyAlezg?CSk}w>dTCeVPLlR1<y-yzCft1T-bJcdyL4hiZ^ccLPeQ
z4xqSvH;OBEa(B}GsBx8X_P(9~9H3p*jud+)vQzqFK;|$E${m4$xkHhgUdVi8GK(O|
z`xGd)(eX@r<h~QRPIcv8$*-@ZJ_V=&3Xr_tTDPS{3HLFwt>145SUg{x7H+?2l`R-s
zr2ua>+(24g(0^(UoPYxY0`f8B=XlvV!Q<^;HuDw1ZJp$r&!@^2!H<{lQnZ1#P9{LL
z1~P$YxfTkn@~jN!n>;SKy-jKsU){s<?X;^p?1?R!s9BDY|52^iJTJe^(gDei)@94J
z0Q;o6&f|FQ*%H@ApKOnIdA_fuJ^`qd2bD}9Wh@3%R(o)$v<Zhxnov^aK~;?p&ZYo`
z2e8vvl$K&aey$z;3KG!2z`^4LBsz`?tN^Ptuuhr-?QuXldP`>~*hE5Fnk9y52#^Lm
zZZwvcqyF$=G?bLURdxiPS_UBTdMU4TSV~DnVoolS3ks0ZuOCwK3t&%8ZK;CFPdbX(
zS%OytSWWe}E#Pa(pw&T3{afWHx3#_|z*$%2MCFlsR2^wRZABv*Yu)h4_N?TdJ<*Dk
zj09xlCL=396<K+y{M#@gw3VNn>uagk18OMnIb8v4-`#*U8|tuflM|cwdr(>*;Bq^F
zZN=G0X|_1Jcr?@h4rpNT-4uHqit??PHpYQjlT$HaOsWD`X{gpf-PamV(;I#cgo3C5
zSZE6P{iv(0#-81~5ob@uq={px`5AK52Hu1|di=3~HP4#h<}5Em*``e>UAY|9%RYhY
zlXu~-I{q;^BhLwC38zg%+N{%&H+v2Wrc6cV(4j5bYippE=gbreuI9P6evLY{{TdPQ
zj0-9xt_BZ^ca&k*hEnX`P=Sg)jc`{}tLDErD|@66D|jv+FBKV{OU$t&uP7D6Cg)=K
z#6t8RmJOR2ZROUGyw!<5^wnztH5Bap1jh}VYw^y)D!jYA5hVoC0jbnN=?`LM8;F3l
zl|&TSHjIkXj3a!;<&VReV-j%L+37fKRu-}{7+#!7%Ib25P*3WwzRm$t%aUIKHxuB@
zdNnrGqwGj2_U+w+ty{NZ%cc$Z>p!2sL%(|rKe+n~>Lo6wsQ4}jQ0RnCQ*{-Jmn=r{
zt1qMGv8R+QTPBZ0e0mn*Y}}Vgb%daV%a4>JAA)<p*#Q5}4QM9tCZ2yf`d@WDhFowV
zGKY^)<aY$BX8pSQYw}5+^Sb@mzV0B_eYh99msY@2??XbS1-4}F$V$*`H?bam0;{LN
z4{wbh$%P3Rb!I-Mo;?i1#uc!BQf;<}9CVOPyZOiPLazi=ks(t)%PKs0^_?<2^-c}f
zCI>J$%c6$#qJ>JbvEghS;@fDOF^BN-yx3Dcw5xjAaDLt^+N-4Ux&TTWn{nB+B>c}+
zxtKaJU7hc0r%(^+uRg~C6F^PETLY-y$F=GlavwNQj2+vzVg35`Sh0L57B5|8KB9jA
z@VDo1$5*abauo_P$K2G$AE6*uvU(M^J^n9L{Q2*QADRN|uqo8$EY#vWO8(;ssPQ%%
zwLA;|W{gt`cC+j*>h&SOdqiW7vr;LbChe~*gLlnVBwuhQM%?~Q48Q1NSd)T&qC$t+
zq1vFSv!_DqS-QU(OJ3QE&F>th($b8i9Qw1eA2zFbj5t`>FD;}yKG-j%w~FQ&@C7*E
z5g_f<k_0cQ<@#u<bC!9Llxf5CtA=3qC8Lp^EuIH`Pub~pfEo&>pY3eGpPwwj+v{8y
zn{VNIVeVf;kQ4w-O|l?~8ma)QOhC1k7s*FHUf%C^2hia3D;ulW^yQ^@pejJ)dmO%V
zOD--wFHhy8K~>4{N%ds{s0jwG0aPm6P*;n?hYn)ru3gkXH)8e5m3Z;BH;>`N_=%H{
zoRoxQhYcSuT#VoU^;z8c)f>75s3w?-opk$auVVZC-+-^O3ic^ysIr>9ZdenOU`<Wq
zZz8okD<^RTOpmH*2B>nbfMDu(H^JM`0AD?4=?S=Tb~}7W4#B^E2L?R&BTV?p?MNx?
z7u5DxL`XizRIU?$nEaDlx2!0}dyj5JCCzUs{S%Zn8ld80wX=^V+hC{9s?|n%2=qd`
zS*)U+eNw>G=k>$w^1<EcrScO{0PH2OHXia|$Z0va_)C*8aAdBcEVS%SkQ&oldKI9C
zg6WzKwfNaz593g!4})_o3P?o*%}TQ%g+mG}fmHsP_8@r!woXPv%3ai0nRyc%th&yJ
zMryhVeBMQ%-BuC6eK%#}rmG9#pktl})z)0>dw&}p*N}+Tc2KE}r^$tiigN7Vw+~yl
zY{r_kYq02}_po*Mp_a-H<*+$3J)JLaD?7X&9)g#LhWWHq2Uf0H+hssC!PMt$#HPpp
zja_%#1>4-Ih)+pFv&#u<atac%vZz&Zy*)uxCCaR-g>(!v2|$G$OTK;&S2oquz(eyv
z95q_;0%-bR5wgGhc}%<aN5~jD%m7sr+05rV`5OvoE8gFMcmJ@FGru%5&}8PP!>WL1
zGAEZ3l5A9Nf<r;(ZT8L3Vp?7fQVCW|fYsH=C9a&jlKBz~m8FIfZuZBC<SXu;hLKbH
ztH(pJAvC|<cFd`q-jPIP%=TWtxeoXL@enmiKZX!YowR|bCC4M1LxKd(K$&m4pt!YL
z0YeUryQKY+snrT-)^WC~f<|-k2AI^(g8QGURBG!j*Ys1GYF}x*b}N1gUN{P(hUurJ
z<B}ILOs*!_96odiyLRrt`gLpZ>8GFI!-b!=aH{{HA(%L65&@J$Y@YSgRmtONg3HAr
zHic7ZTGY6@Vk<Q>H^J23)P#-y{wH?bc{d#Ap04B=1Zqb?Arc7Q0;H-Q1j%+2iIKjd
zk|E%*IvlX(=flR?{w8___+2g}UVJX9XzN|&c4Ov4zeM`rAu8`szL?o`^cxDMD+#7=
z{&GDWdDKAZG2^E~lEhj2lynDykU&bp<o=FQ%k|)GR+|N>nTc>D+0jttBDi{~RVTn%
z<;F|DT7_%w<)reYeg-zkGc9kEj!KE$wO#|LYzc*^d-gl=>qqG_!XbJA4cr2zIT=<?
zATi1f5m(+~-A#ta0H}Ewz+74(!;NMJ=e6KLf~u5p<wg0b@q*-Zw<!HwlsTXP&wL}Z
zY&DrgaM`y<CX=>c)v6VE;_2s)Qe{cU1P<d9>^21gqJ_$*$>~(16uHsJQ--a^V<LH7
zl|BVX;w?ad+V;|m2AH0A27OEE21Ni(95{%RIEJlLfVVrzd`|MQ3>av*TGdp;K~SxE
z>N%{-%)+c6J&45Y9KKwRb<;Xgc_skMF#qj`HyU6{r9@`y)AN#$l$xNj5c_C%k^)#h
zDl_~U`J}(P2@U)tX4&Da^x*YBuECA>C!im_0OVB?qMvuCxQIW~D*#mq9i>(@$H!kN
z#g+pej48CDv8fq(*;WFeL8eSDnZn&&&InLSc}a;D6w*XfQtnkCJt03HzkjL%gZn39
z%7nD;X0M-%5eL*{5{K~zijB#njT<&#>8DGuWcivFii$>!p)94bQ*y$RlR7?Xp)QwK
zy~|A*l$%WuX?d_m2L>7+So$fp-*qeOr_WUOPXW{90Rv!hBr1^WQ4zt1n`w)bNvtGl
zyfTR;CIH7p=b-G7zu>c?QJC_zZ*UT;^IMdd?n^QO_1>c!z>Q5|6)>ecVmiSuF~zPX
zn!-an`!-E_X<4)#^S7?TiDbG~RTn$){!<%q?cH;bOiehnox)LWxbuiPpc<N>A#@*q
zQi;bFHemK3f~mV1*%=lCObLSW?(Qj=hB8mI)kFuK|FeO^To@px1Ye^cPrh7+Ap;YU
zN&AqY(f0B&Mrfr@v18GRq;-mSeTHYAc?N4YZpMFKel3IxDVR+E%tS{5wNFlfG`Z2x
zP^SP?ZVCm?(BqH{dUR-NgAJu6*!H(Sp*bTR@tky`K^95<289(&6`^E?OjgNcP-0#l
zCx3jD=|&`+IUD<a_$}m4o{WK~ozbZ}Dc~ZoiEEV1=2z`?B5e@)$bKoBX*j{Og?0f0
zCUN!;P?K(`YC_ro2e!X=1gj_R!kJf%S8djtK{e)psyN86;)5<c^F|fMWOD;B&deus
z2H*4(RbXn~7{*@#OtGuxWLi;P<wHS6Jl<XF!kNpf)X-nvlxC+5W3f7`aB#qQ6RVsK
z@7sxcfAVv6JaXhnq^5B)i7q=nt}be#jk21PK9U49jgR^>qF)9e851=7K6oG1G&hL0
z*i66H#Qp=+<dZp64=W+8qXKejqKSF=Xe{23W($>rWMJpB&mec~I5@I6eD37XfBV{l
z*!=EcuFSG?0*WI$+Dv7b-`yd8XX~1hk*L7j)8s>Pe;by+xEn>2`=Q^^tj=Z;llzWN
z$;F`fkUoLME2^=f*o(9z4k<`WHtm_>4yEpiAZ;S}X(?9af+c0;Qh|79VGWLyyH%-?
z7MuuH?Uc#dYBTJ3Gv=PgwoHbLGwC&)^=_bcW{fg8nG|&4i8fPe_wPgTvrob{bqtyb
z?zW5!*wZ+(-%Xoir|1uYswE)-3AuSJ*9F^zGf?%HM{#J?N|l9L?kWP`Mp8q4<{>Mf
z)_%0t;32(Y#4SoDpdy{5Pm2GlOje1_)JCPJmI~K~#ru>ITV9IokREB$PMCj80oA0g
zw9JhUm)2ttHAK-)WgWhQvxw3eBAsL&K!7_Vm9`}omX#8ZW!pX2xV2stZ7vpwG#xts
zo^ZgBlez{ceU6Qw$O52kxlHw>lukd(-fo%@4imr{-uW1o98Pvp*u*u;Si2v2*%n}u
zb^aD{#UcoSTI!(>--oBJPE|#&x0!-gE?G{xZ$%|1h}btcd7G4$pl0*ieXh;Sx_44Q
zUHn;-((GKsz_Rmf*t+Nt$_~{AGi-<U?iCldr+NjTiUa>fuFFqO5SJGqRhF)a07M~4
zF#_DONI+bw3^%%Ftln4;7yVWxiEpY-y`KZ}@>9SO9noo*A*QsrYim)uXaOuks914U
z-$u>UlE8@-d5cFQ39xhWD<LBj&9vKEr_Myxo6n$n?;fM{Hu{)=rP2V|Cw6mfzPr{-
zn%OVt-Xu=@O6!OQ?NSEY(ZPu?(N@Jxt9qXc`#w9ON;h9v(ITa@<-`n71)~fw<I0Qe
zjTAEDZvi#a*R->h2*-WW8B;j45T!VeTI%}kO&s<&8Cjb>Bpe0lJWd%7)Ezv4+9mJ9
z(to<VwN<1jLpzNk3)s<Qv4C2}>PUiTTRAE}+o}jqpd=kJGzI^Zs8RbjSMnu5-&pod
zN6=|M8rrEzj+AAinL(md37Ooj1^YIa(>89rkfKS8Nz;jbV+g25%hm7=xP7l1DPjiW
z8!1s~iP5)_NaF~Yi|dAH$~#MaI8<WTQT4F@L}H|`hjPHwo@(~&hHt|jE(zuGOs>nf
z&;dW%#y^p3ag`EK<+Q(wQdP~>VLtlXN@@kCst?L}3Bgy%s8KeW-$daTP*VqTF<Obl
zNq>}~zbIc;jv`BU)iOk2qd~!O#7B#;_REPGpqeCA)p&4_fe~b#K6wYb#Nv|{j6qU?
zR>b(um4CH#4>^3q08}AG-jF(J4xEq!rl3$Gt>c80DK;`eBy01{zfYJ?7;g}Ow<L<G
zfq*)GC>nNbgF9$P4J}4UH(v(TjAz<Z&EY=#gBo&k$R88pNuaEz5@3yI?2UT%$r{F4
z?X4+hfNGj=YPq()x<R&Ikf1pPGd7?!T(kvGgh}kT0;-BO{#{WePXE2N=`>xvkprgO
z`e?IsGC*{k5#NJgU^l?Z1l%YSFB-CdWn6L^+=q(ct~Vy8jwQpSo(e@JSB%k3N`TD5
zP;C-vKV;GCv5!UaFOnqX%T5J?4<0W5sjqPvCB{hc>ZrOg15{HDZE!NuK4<aRUW#D?
zYQ_??j>wMOHRY<s?CJB5dWqK<wAVzgM4j`=$N|5{3oj?7;yAM|NmfP~Z))T3Xn#xj
z3Q!5G{_=eUriOM&5X={ssb&Ez>6ZjEg<VJSw%unVaY1RP1XHnB1n33is`sd3Ol>+6
zWp+2u7y_z6vWH;G6>~;o^OUK%cDF)3eNNg<1d6tB?zzJ-V=BI*<QgMs>S=}a^D*H-
zfQt$OjA{jYo*~mBSg}mfvE)OIrMb3JMW||z90{-Ib^8dUyn|w|fH;O7(<oo4mCnhK
zoF1-y?WHN^fGTpv{|c@YWW^Y_1WXk@{1w-&7|KyE6&X!dr#K)&B*J43@nbC4$I#4P
ze?qzD4ZVrvQ2KkLW@^$GLqIi}%fTo!5^Y~*BjqD*OR-GilPbc-sBLQbtIZ7cZwz{3
zNt!OZ4{^XEv-&c#?k-oDDzY8Rq<mk62Cnsw&+M=KVcVleVwxt<<|RQ;lv2ovgjmvV
zU;}9p`iWS!1Y^Z~ENN<wcQFH0Q@&E^mXYF!Q^S6l05SkA`fgLgAfd?|;*04tJ&o=r
zGD)VB=0F!YVBsW`T^#C-1$P7|qLV4~UCb*Sw@c6zS$!l9{+7If$Ly4PT>cdKq%=p1
zf-NB~73neR3bIN|%n(9@1fWjliml$7Vg{%tLoyhfG>=iGxJ`}%Q`rMb(g}woW(HyU
zmGUG!pe%hAV3&Bg`MC@>93vqLQ?+z9QRaXtE&`}Y{rjW2+8{u-JyBX>j07scsQ%Kk
z0u79lI&cUQxXnuIL<3jLsq~yAwL(fj*dN>jD)f5GjgdZ(en~JDMN5D+A=ReV_^V<K
zsE9NLUCKXZfNI)7vNP=%+RuhYx@gE7$jynMSg7(MEMC&!<4|Art;$c0NB;ujO(=B5
zs2_BxG4;*8fCHwW@Hc(Puoj5&GsZxGT52pnNr2qPDE_hxHSpO%q>dho1TKR;E)l%U
zzL%Assy5z}ElvUiGTEx6r837o7{L?ldlEuW0XzF=dOrs;a#A8#V9&CS8K9btBjNML
zj7UUdP&1YF^F9KaDVIH~Ugw{dmz|Skiz&Xw*^F_6?dZpSZ6ujOrvA`hngb``fGIB-
zLx;jXZ>rieD!?I_fSM`yC&(gfmql?Aj|0D~j0$$D%(3ICeHzNbQJpkcq;v$Pth`j@
z7Nv4)Xo;^%MThQ<(VnGJv{N(nG%*M`Y4SM(C>)uAREG9bDcT@ml-l8U%m7syQrTtr
zJAQNuvbkrJxKFtWtOBa02HPR^VY(>oB3s+awktwcIRSO**d*j+1%<8`He8shuCs|C
z2U0oY&phK?_&!^wmVbKbOfR-f6WAhH^wF#-yIvK>kXyX4{Dj$)kTZIWx~_n~wQL6w
zR)RbanV1V77u`s5JH1Lvjb^`;&M59%9_}qCNk|(uA)oGC#w2!Y($!5d{)!o(hSq!d
z&}7UVpNNWjZs9<#)zYC}CWoTMFcA^~Q_+l@2<U}r@t8I#Rjr&7p^FwBp*+#YeU}44
zOky0*Wb`}ZEc$)2F7*HOF}Spk!LFkPN>Yw!rylMv6=!$Aw|pIPFS!yKg9jVTH8q+L
zR?9P{hCO0pf!Z+7OF)wd`r_;#O$}Ry`L22i$H=)2VnW{NR16-K7ea0CJB%rynm{E{
zp3Xl#1GUm#ltw4Fd9LRYvq%e`$dmY*iru!l&aXoEmDDuj+-b=eJt9@L7U8t_wVI}=
zcXJ>FlnSOyL;C+*4{z}fZt<Leriw~qn-Wv_BUuxc;o)knfSVIi+`-k72@E*rJg)2K
zK31YA$g;!^QvcyOD4Lf~n%Nhr={UJUNi;?|j`Z+f%5ZbPFINLMGo+_YQ@IZl&KZb|
z+~Cr)UfM4^=4c1Z4%a2~B{_XkI<A<Ngaeff!b{hP${KE<#y6v^-!<<ESDBKJ*knYW
zB-Uva%M$HW7GNaPh3n#TvVsx(897|zX*k(_%H(B=O)|H49(vtP2y8ErNapZ2Ho(oz
z^CF4|L9q04=K?o3E{r39dKNB2{x|PM!Q?4M8+6dAOf6MH=S@9#7#!SR&BraH#dS$`
zBo)n3foddLlO0pa_j3}gzEURA2&T2%o@i1UCd@H3>)t$Sj5(mni(1+?mD`Y9H$NA4
zf~nY11@X!%Ib5MbsJRtNxZ00dt1xDgUo{&`Obi0zJInpJ`J#00dYT?A%Ip@wKlP91
zKxa8X!8QX)4?5#a<bU^WxZhuh_>@#MaeHe|UHu6)#LgCV%zXl;0^D?ya+Q{HKPev9
zRKY%NG>WdhzGchm6KSVrp2No#VEUCq&{*P8KA@s8Hq->;1e-U7>z5r<fLdIn>IkM@
zjvNIayw!fpyLKe93XD->EIcJTTg8|IYAB&o#;4)V>$9=8#IK@0HPKDt2)B8b03`yd
z;YbQhB`baeOv|f%NMu;B0~LOpGr@tYF3DFzf6-i}F?G@$=q?8Yv_zXqNKeO@FWv^n
z+{y4BIfOU{dUhQ?#2NJPgQ^Lp&clZ})OQnHt?;ebfRX?2$H<#7QB|cgVy(1P0-N-c
zIhT&ckU80CDD|jqr0cmGser27BcE_30aMX_>lo=u0-)F!rN3r(6Q*4^7!&6XRmBKD
zBdXo^urj>@P^C@GZALD?Fc06JpN>_<1UhPsE)F$hn`d+Q-_!Q9s5hX5=JTP99|6+}
z+D~P1!8bqO5Bb~+T-q#JTd?%7{q*C#oC7A@<c=GU@elqQ&Fi)!z@a!p(ZhN00NgTR
z6b@jLJP6?8n&mur5Pq5bN=!!62a7QHH^0Ql`Bx}Tb!BmZ0yuVt^z0;DblYU4<T~JD
zj8rQFvDZ~N8R(fI{s@5K82(EhvVfqbyb;b?H*AUAn6J1A!_LUT+1E|ra#PtkK$IMl
zqrJ1?dj+5hYXrcgagxZ$FWuM=UpzMzD+#K08l+|Q)S)9DYLrHNQn9Ocg|Bk2A+$2#
z<yQb*MeQ_+k@=6*_!-pNf(P#CkFg_rufuTf6dz612{}OFQ(6^o9D3db82{3X@O`=(
zfktYljzqW)9fFfXb3bR}1rWOm7)q}B96Jwfv(;+bXg~L_lDwPV`xyNn_#q~J`F3L>
ziuZJOsBhNG99|C^nTspFGfizt;;N#i%6=;LR0%{aTRj{7tBYVK{gQoIQd6S`RVDTO
zC13}^v~YYn=6`*fnut<%)8M@(ZN2fg*8r+8$C!X>Mi!0WcYbLA?zoJa>H$s|(G5)k
zsFYQ5TM`0m9X$X{w<>wYj0$A{r+}vc&<5}dP_w}}!6i0XdEP_YjMz-Km-tYaZNo3W
z-5)b18+KDQqUfSr^u~tOH1!@12-ujQI_k=+G4Zum(7b;gy!*G&rkci?_!=}ax_?t8
z!|D-OWlXEUH3+aKShf7h`QWtx{aQWrFKwh&>MA(|Mm14!1Da^B9rW{`V%pvJAR&`6
z1t{2E1yjXA_JtPOr}W1)_sv9dwgdGCnv|w0v;I|w8`LmejwLBNmC_@MPaB`$HQB35
z{98pUtuE!3-$En%dCg822G7jK=e{!w1%raLOQ-Gw$q3uhiwKnlkOmY`z2gf5Frd(m
zzr0Y1(%Jw9=Ws~FVL@p*0g*%XB-&UKIQ)<(`!bZ56&=EM0YzDtFF_r|B}x_wm^+w?
z##_@Ez^*DkZaO^~U%I{!!-p^`Q1H1v(r$`g{nd9kpyWEJA&$KKN-pbMg3bT?E7ZO6
z781^!jb^Len5?1)fHftBS{gUjV>|%aHbrR&BA6oB{K(>f0JTtGQxkn#8#uh@Y`8?^
z=MdkshP$kCA<>v89>eI*U3+vAst1B?_DT62Ik`V>yx)QMpWJ|*AC@7B%f{`=Ry5YR
zmEF^xWaS=EHl@jm1HLFVrp;54Fa45zSeBD|=oTf~tz5UTKf5@e;jQ&!`i(<y4u|>T
zBBfpgW(IwrxqBU;3eTkB%;EoyR~BN_uq3?rRs~*J?nD}QJ}k`Osw#qIEtkcCA89mB
zX13TpO+b}A<XB`$+O^ijt#JvYGltmkzt`vDyfd<qo@#~yGw$mHZKg@=qZ|+oQ2_)6
za=_fvkoxeSvE%f!aPZ;p!FPb8uzAzbObt^!06evIMx=ZKs!TK~Ar?I4zEI5^vNijC
zhHMMqa^lIma|r^)K+YYvW8^JgK>wMu)H7l;?UrWRipK`18f5ezo{ek2Jr}Dd?!=0h
z_n>B%3yB2;R{E2R#_8s;U54^%+Y*AR^gl_Wk;(!Ai49=bUU`yAkehQDZu+_wOishx
zZ;iyHc|#4GDLb!}+m}+W1XN+5fTS>8ey2`MhpeVL|MXgX_(=^Gu6LoFT4rt%w|=G_
z)NbW{J~BRz5*JJYq+%9u(o>+COG!Q4&tc{;8@_OP8cv^`i6H}x^}wcp_px>}tEqqc
z7zYes5;<k?6s}mB_RVi$!0BgV-+S+%?7vT;iT2g_e#FSav1m?8L7dpTL^uUklz3A~
z5JiEG`hn_F`nQ%U7r@M~e;I==y8;8|%!MN}OK~M!U~011>?6`=WNGSIpPPW9$^EeL
zlYQ8-;1H_!xDZc2ZX1`8TVw}e_CNC(gCMB_|FKHq*XkvZf*_keAq^A1Fc9Ns4@P!j
za6t|0XxdlJ^83PXuLV?Lo`9(Uqa@KJ&p9ny**dS=@5JV94cM~7g*}HnD50NPgE$_`
zz5GZcCU9t<!-!Gi`dKl0m;)2W$m*vQ6y_W2^CeFcu;o}M&4Flfz|^_~@ZxPY6i%N(
zm!=7*zWN#*S-%byYgeOT(+0S=E<k{qtE6BzP`Db;m_61hlaYGbg~+;V8uF%2L(Zr%
z1X3=Al6!-lCLEfn%COa!dMN|}Pqfv3L$fgToZ;B_SsC_iDl@ivE@@&60cz0%%ocjs
zF~tmEXPn`j!71oBDg(nN=0hgDGV_j32rDIreZMcJUJa<iQ1cBZ25Xsok_7;h$EB%H
zL!+OooQ&CjS*q&dl2wr<JMA$kwB<;E=q!c@RDq(|kY+L(IB4H7PwHRIfhcppGyzDl
z77q1u#*9@T@8ws(Sy6$;@^ZL1QRSt-s+zf{$-u^407L^#%E>`;ZXQ=LWr*g{LV38g
z(w2-{9f<bW0+c+?ADE#&Q_mcZ`f4X?DjQKxw<;&s|9d?~&>@?{s)9qOWhEgaJDFN;
zqT12a%vu2{Um4~L$2{McetSKjn#>e1HMLbaCqSE$%xM3~i5S#B7|zSQxt(A0`M%s>
z?Nm*l?xP$qWw8aEDA=~-6r{^%@Q}{cih?Z~tf&l;(n{MRq)poCFDfgV%vDWoJ=gkN
zXx&?1>uZ1a89)`r3W%D?1V{64`8BV%bxowrydL_DKGq!Q4ID7RNwfxYcrSFdoDU(=
z1R!<YxJiL&$SqMlY@v~aA4~o5^oP=Jklwd7($<XbQ-B&ud`IBe(Y4S#T8_;(e3u_=
z$w41z4uppTl)(_aA(*E^D&ttLcS24DxH@xRc!&<>B1%ge)Q6T4b(%s8kHT@E1=ODM
zCiG1yjeNWi_p}84yyif-IG~WK!^}eES;1N-K)1sTBYg2#dLvU<EWrDe0aR%~vk~LC
z0-!as#hA~N$DNN%Q|nwc2YSW<LzYFRjbZh1^33UVxfUNXVb!w|yZ^M=@6>Nld1*~=
z_jB*yN$K;vY?Lp3+u&6PPxScGNe!sNNV5@*<*CQ&p$%NJS6*I*9iMH%-qI?3?sL~5
zHzQR&bD{=s$-Bo*(1i>r5mJw6CvnD(b`t)ds12e`Q>@Dil{o__4UjPDaxv&i1w8}y
z?AneETXy2oE3d-vLEL$j{UF-BJf7CJ!OYJE3Qb(`<@InufV+vFI!z4ODG}z3k6f3$
z__X?1xX8iE#SAu^Z1K*;1YAv);BfGltF!2WmcYHTY_ixvlE{$yIofATx*{cCCl{cS
z@n+O2lVPEP<Y9wV(jDbMaWS@U{|swZug22lOR;LrMr56K3FcpMrD})He3|)o=H9-3
zPjAq2`Z^E0OMhzs2$g4XrK+M^)uDq2uw%z|tX;DP%a$#};-xE8ac5p|u~Do5QWRdq
zCanT~;zJ%vYcn-wCxdHOSJ$ADG?z0b?2)oEl<{-u;31T=UshLD;_%@Tl=FIJ`2jR?
zPs8?7enBBpxfOOUUB~kBa*>u!&w?!a#1<AJJ39xtc|4z+gB-e-Wzs)4EiF|6ZD_wW
z=?y*CUU{88-{%3<WQtT+I)11IC(HC|7#{5~Bl_&xwG&%5Z^H7WOYz3LAGYwPXv}!z
z^RF30`W^FZ%MI-uh;Y<5G)8SmC0e3`YqitUjygcMLt0o*2ZV?01YWKm59MuG3kk_g
z+EY4Y8e@n$66_&YPL0FX@_cM(XRJ_eX;NBp7<>2Z#+EIcv1-K%JonOTEzB7(bR?su
z4MbC75#w6~{Zo%BXf5}J^FY8-noauOQ4kecYf#EaTEz_fvU~S#g8mL{+OQ5EEcm3|
z;&b!z8F9{mf_@{Yfm*nvoXc)QlJo;VWzokix!p|-*t&T?mesW%@OsSnDVRQeDn^fH
zfb9_@Fl^XR3>eTKIoVl?KPJ7B@3FiNDYOfReGX8K4rkQRWPr&OIV(VSq_hP4_wB`&
zty{5v-CBJ3##7i=ag-|)CQd?fQX(6MVaK=*dM7V=xXrJ0<r5{Ny8~-J{TL7a;&;gH
zH-O!rtF+WItrI=njqB7bY!s^f`}gCE-}nK(aO0(F7!zrwL-;IwZEC2)-+uRVe6)5u
z`sZdUK6RrOoqEK=p_<#-0G}ll4?grDCXF6OzH&8@2@IXerElF7D$hc>Dbr>xJZ_iL
z;)?g}!#0kpHmqBZB?~^ry3a&vjWl}fc=e)H>-@P8wdP1E_V~C}iFrc=eoeX!dId<O
zkY<##Yytbtn>S<Kx^?(;$pWm~e3VwXYZP}~O-@c$!*qI=vmZ+TIr@>j&HT+@c}|iH
z_a&KqJ|`!S$5f<5Bc`0lOGK{usJ%BaXiR0rK|J;NI`xeCb=^(3V&=>lm^x(&MvW>$
zzx=!w88gc>^EaRA>~F6JRI`Fcg^nFY$T=DIRC3=O0h}G%X*=4m9?O?~iY3cdxA36f
zpdpw(BUh>XUayC5HaBYdrk9DRL~cpZo#Z6Ye6(nBcOLEbGxKk#3whRr_K;H%I1~-_
zz|JlB_`UZdkbC=C-h2Q3?O*J}SB{nEqrhNRNwmEh4t)<CIDnlysnKoRh*c|><L&ns
zn71G$rvOu@O=rY48|B}yL5k)opd_Q1fWLw2V$1DeJuvAH1)TcYYHZ&|6WXekSiE=<
z{`2CiEoG)=7hv*~sc<+Fs0c8o2DfJMQh9MVxga)Q$uBf7d9d~0Et$1(VA5k=GXIvB
zuxPjWg^XA?co?iamcCz8Re=}&^Q8L7Gnaqv2AqHXd6+kMF2)dC9d-i}&3w8-i{#fU
z0aYp^AF0go6*7hrCEVg+Cx;N5H;cfn#LKV0E%j+hV;CVlm2W|Lb9y}tEylgc8z^v+
zT81X_HfwpJYX>%lOOrHb-f4`<!adLVmW@P``X_McyNKYO<cR(dSjtb&7NbZm&N_1p
z19Tf(Uxd4^(%yD1zig;3LA|x`xS?LS%Zqk?9StdER8_`N)gIC8Hf_S1wQKO!GtZ+k
zgvcVU)la7#(8?v$Y66FVB+p3_%}m;<MXn=&re>cE9W9g`*oPG>mf^kk-o@Yk`D6>V
z!;3~S{AN1aFrKthf#Ii?I|xV*G0jFoulZ?tup0-GM`#ND+cXWhsHsYdkGCK>H3Kte
z%~IfAPm{#!FFmI|$RCa$eD#aCV*aI=GI;{yB^#!Ou8xp;C7`xubQ2q+y!;4?_w849
zqcy8n;iGq6!EQ$QHq!_;VrojVvb)GzUWBfJTBHgS7HqswVKATT?r(r)NUE)4n6&`o
zI5H|wXk&Ia-{YTS+}9*V!(j3UUkJzF6WKFfubTywlb-`nS05R>u?TJ=qbpS&+=+u+
zb}1eBWJ;m3=cA@1lcNU@6l2@Atysg!q)!(u#L5j@TdF*G=r9aOPvfSQj8{os$gH=E
z0IFm_z*9a1(r6?TR1EoUM)BTVSoqOLc=nkm@b1Fpii8m(M>Bv?3TfhSo3u8PR(Vb|
z8lhDU$D=`Pt1^zo62?d7K3n2dAZ7x4LK0@on4w0h)s<!V)kF8I&jUaIU)*r*)fihe
zRPorXf2*w9{IgdAsthx$f&wR#N$VMbXYry19LBF{sl?!6!!c#bh@emzb{J9VB{IJ`
z7#3zq14klJL{zr&){AG@Kud)BzhNJWPRD7FPe<!&45K3P;xO7u!oHCTE9)DXW^1pC
zkv&g9tgEZRVa{gn*|UpU=tg|{=_h#o?GIYY&E?Q^(xk~sA&`^O2Yh@<H&VM31wj~M
z@~GvZh#wenOHJS|J-8nqFwEeifBpkL;oexVXJaZOU9p>~`G_WJ%?d<?TGjVrE4OX2
zN9dA|Bo9Zf@{vBAnvp}ZU;(#asl!9}-GfJ-egS_Wz+QII`AAFVE(q!cFVD7>>(O()
z3Q)~(dWZM!z>n|yKK}ju8!ePt({q(A#!mk;Q<ayuwg{U!A!IV^1a)r7Gdu@GQsaL_
zFu-t44iBY0&Lu)GjiYo085!|%j+Pk7BU7_+)>q+g5B~<=fABZQ6gQ5$kS23RTkext
zYmw`wCXwl*CNFtFUaHYfCV+@S;&C-#@q!QW*FXIMFTb-0w!DFu#bL7y=RFdal@qdZ
zzuEt~LTl)A^1Ohz0&oF7W+c~zKk_mAx(SF~DW|P_&3q(Z@qzVt7^vRrz^vJ`QMzY2
zZocMn-2KCcaMw4!jG+Sxgc{Odye!(vPtNr!Kvl>@Ha7Atp};hC5YC)37)@?zI5&qq
zZe`3C!cAthK9;|A(j4eb9Pm+Yw{2XhNILuMv*2{P)GV}2&@^xh6BATIX%3a|(DTta
z7JxB9{LsE#c=F$m;@*dTtBRh(0M;_0R7X=6Z)#B@x<jwjTjnCfNnes^<uYMhS69nV
zJp;rCX!A^A2+k}d^2H-F`0|V>Qt|@oPMNBpHonZfWRknK#*OTO(@{7i4uAON1B|=8
z6Tki0Pv~_rlw}@$d9;yDZv#|=L|FlG*5o1BRB5BY)uWyszNR~bS@E_iqt9s$^hOQ{
zIA!KC3_p|12UJznsG+?n%jR!ypglC6>guHn@UtJ?i`PC{j)_yIaa(t~WO2_c1*9h$
z`*u(n0Yw>R%iMt5)rd74Hym^KIJz~84Owij!mm}UkDB`A#z~W>Ac-ChF20zk7YjLI
zO`;*ogrA!@G;iKqeDKnfxTUNfPyF?_m@;lSOEfjr<BRQO8md8{G-_kjK2$mfVQ5@R
z3z!~HzCNQl&=)vBt%@ToNn*{A+3(&2qKYtnnI;NuT7`FB{Vy)L`bKqWHiy72`UlrI
zeTsJT{_gyW_G=+fC2+EBHCHN1y5`2uW6rGU7+Ev|1@sA(RZX&lR16(nYK--C2`k}Z
z23eT1;G+-lmU1DJ38irazTj@DVMbK})d2m}D=TX-mo8OHm%M`8@3G_YKmQhEhYb{4
z1gSe7$$KTB3Y~F6S%fB(n6plr15xFG382S!!KjkaZeap+%63UGef5Q>an%i9MB&g;
z7?hW;e75C5c@gv^8DPpGK0T7wtX+%iZoM5}y7eYZojegaSs5K_nv>|udclPk<EESc
z2P>9-g1`UY@9^%DRhY(EdJnZ&F%OtL?g@>d&q{tW#ILNX#@u=Hu=L&M@snQ^;*noJ
zh=T02Hcd4><4|bn;XalZfP8e)9MBwyC<hE1t^oz_z4oF4)8V5hU|@C%!L&{RRZqc6
z4f8FcnZ{w=`i=O}FCNApfA=d~c<vd<%{IZ*uusap$A73_(K)}c|3F-M?Tz@`zn{YQ
z@A?WC6Rd(YA(8&MhJ7{sjFK(tp<)osoiiIR{_78T`i0lvBQ^<7gwD{^djU0+N`0(3
zpg9mB4hSF^dh_ZfAK;>^ZbHG(F~~}>qb{7WDriSeN=%?;x&hyQ;FtLJonJ#iPDaQK
zzCnkf6^drq`Xi%kW5!?Z96VwS?)~x4@XH^52kX|XhRu;k4Lef$Kl7yq-{5j%!ssFR
z-Z$>Ws`a0>*&Z5D+hmJAsX1_JaKKpj0vz7E9Y4GOyQ<Pd@>0VZ_(y;+F)<OVR;|X&
z3$Df2Zu<f<QXI-nOw0`O>5K*}_ZVgg!|yyTD-U;k_j|bKYqwx6?Xgluq>_~F<)Q#b
zY95O?h^L=_8TBs1(;!q!)qonQ7SQ=>4)k&kn08aY$BCDodlIiL-h|n6<|vma1zcgF
zpNn2N3DxW~Cgr}#<((r3)2EXlD>MDwNdj(S&y`6ui52jbuYVJhh;s)k8;q4@-O035
z&v2y`UEL;(8G*n2`hKijw>kLK(P6&^)J`!{-=sNkGIGEeB5z&48b7-C7Z^NxA}4q_
zYu?7}b$4su+#IJsZpVgAn0v{MIBm`hm6gOTCNB0UnOg102~%;`y+1=KwQL&$PDd(9
zd9tk5k;1LJnLb>w2u(Dmid&qirD{Np6!&$$nghL<1EQS@Km@3rF8uIa)R5NxS;-83
z!=;!#MY@oB$I-4S*){Uq1?QfH0R=hgstJZ6w|4nZ@=!oVm!NrPorgl^P*LB+sB+!Y
zd^<yc@GZcXgcujz`)DDG4;@jDX+Z4^_w+rQ11Bj5#35c%3D@Vp^~w`K))08O0*d4C
zu+~^i2hD!3%5&V9QQ+DDHQWyepoS7UaL5SU{FUpmciScvoz2|2BlMnd9m^IiMPo-`
z-GW!KbI(4NtvMeSUhaj9Vx5oXfaXBtI3V`RpccA+-)_9O`Y<L9%~MOf&GX?ZJf6d0
zZ<ECF9gO}3GND5}Ro~(LN=nbf^x1P&rg3!7lGRL+Nb(|(VSi$9*uV9&9m<7@;k1;t
ztN}F=T-SMO4)kIU9EF+y_V3-JC{46mA`>2x0OWCTx$1}koP|#cJ6A-{HtG|P!9#|t
z{GA>@1C~V&+GJ-0#2ag(wru?jwe?O_t_=GlS8CW<r!&+X&>RRS2bvkQrK+M-Wf@1?
zAn_pVNi;gIvknEB+>JCC4L_`fh^8GRJewQyjbP?gb<SX1<OozKNW4F|<<XA4dr?zo
z1p1as8c>4_(m$F5ry2(W3>WBen%b2Sb2Sn?5EE(=*PBMl@!o{Z6h`A8GrNepsMgS}
z${L|TzpRjAi~+A7MFW6kn-6d`nGxt-v}Fybk&qyrr{+Lk;een(1e0i}F1iq8IT-LH
zxNT6_iwkO~Ni-=896W>}y$-3RsHWx`_VQ?<mB1;9X*4kuFXf6d15gENHK2x{+d4<h
zfj+?j*=5#|Y;00##6LB4y)cn4HbqX>lrg))f_w(kmhBiKmuv|(Bxh%<0^($Ce}syX
zcMziuP~$dyUOhgKQ6|4Nphk%4I$O<wUe5tWr_U?kxRr^qrqob2oHPrd#Y;7PV_;-k
z7iEl&I<{?_9ox2TcWm3XZQFLmMu#1z<7Dc6GxNLZ;+*Y$_FC5zxhxPI+VRre#DUF?
z1k8Yj&d@k&N~@QMB(y>~$V&_BqE~^W6e1Z)H5v6y!(|`sARucTcGZpQ**tk=pjLy-
zSVc1#J}_7dBNl5+&%5)$5F%N$*m*tp@>R=yqy~FNvj+aI=@~oOR;L(cK0Gs6yzfoN
z1qg2BbP3SU8q~TN9FBIp{8(-bomNQ3(k08`y^Ko3y2aAripOW)2EqMB^6L?%bmC;2
zI|v9>i&J=Gm<>#h0;ec5!-O9~U)wfanN0(!wlpxRsH9tzHI(f2z2q<&6I(Mmjwqzj
znB+|=z(m~iR`x}x;6KRob8cqw0|B9T`i~1h-K8MjS;%~kTswJSOAAvsh|%}jM|{X2
zDyBx30loCd3n{tAL*dWQ{-D=wuN?rEs%6c=jZs_gmlHxoZ(k%^U>11dL4dY}LMlVF
z_C>q@Eh$=KhJxTuBwbJynbzyFTC74Ia>3l(<RE1}!)Qyi{Qx;;PHot`HMo!pyE=J}
zj{%ONLLUc!9hfMALBmhr34XHib?(!{7sz)4fqErnrPSF~si^!El29wwRY&cV$RMfS
zBj#BN`$81$zWQ&IC}P9)gf9ePPxB|t(+5w<{J9_Od>cB7YYHv}(0B=j{Q-p~8y0#&
z?dpC(myEo?Sjjk^)f>JNRh*KdT2fZF^p_eNF&x}|3@<f4z`|HLsf%I_KMsQHvbxcB
zL%Ad_E7(IoKg{J+UhTIGGG(W8)x`#{2ftd$IpZuSuu%+zy{o<Ea+weiyuO=s>_2JT
z9;3#aEZ)1(ldY>PEiiKG1Xjw@{q7}L0jw4jHFcd35Mc^>-fDE5gk3VZn>vz6Th%9K
zN1=3)S3V2hp8OP6R7C<RYwAuH(`?}GBq*w?SrSeC9+A(vjmN<@9K&cAa6CRuN5EmD
z3%%R@W^im)C%$sv7C1}GWkw+$ixUdWsSMv@&apVn**<?ESTf6kCW5I9f9#)PEUDMM
zC24{lY;~PvSi8R=Aynj=r}ini>>vLbP?A%)Ds;l)vkiIEbv^f4QPn68(~;(6;~4nM
zk*2-b!68V^t%dN)%EFS6XHI8=&VX$WVRE|U=sY{25s)JPsLM{|svV5@?{Mno6z5%&
z0->VuxQ{`{r2rE+zowq*F16sybS4?-^Y$c2+{*Hce14izcM?R2l#|q8a_?npLo@!q
z$vGtjZ|t9)PN&l@hAAezs7Z0Mi{U|WVYAy}z^0b``8$wUf*1&BD(c|HURECpC^n>E
zeJ-Ss`Axwma((oGVCJi5#||t;M_1`}8nU(|>b07o2pPXrca)hNsoqY_29EB+&{ssJ
zZ_YHqttGY>*ZyNis5p1B+@T;89&DY(?Q&ISQ&OSjU{@p!Hdob9Gu$KCbmXiVclILc
z`S)=|1uH(A>@6OL6TFf_23DD!Y}S_%P`OYDs@74E{9nM3<yrtM5?xr;b}|#={&&3*
z`;>$Aew;k&dB5@9w!mdM!6ZwNIEOEfEtw@m_s+-8&W;`6Lx<DOhZ+lxP_ek*#2^Cn
z3Hw-x8Rif!#!?JWU;?TssYdhHdYb+A_!x9ASd2X%Uehkc;_+hAO~`UI1HQQsqC3Py
z*6SKN_VJT}P%9Y?dYDz*ju%e*6&%v2!qM1;*r_LG>zyXNJI(4Qi$jnQi<~+ctVV}q
zWX^*Dd#AzagrJHWB-)u!+E%UfNTatvWIB9iee^0G*9JGyfJNB?ac^@WVz<ZNkt_gh
zqvIeL-(5cD>=u*Jl4%JU`OxLvg4lFySXgoPekSJ=l-)`R`6CdL|3COBa7Q$`48hKs
z2gWr)!V%G28V9U{&c;3eCH5aWHnb?2j1jYvB7pukP7Vin^hSW(Z}^EGD$7<N9yr+r
zGaIN93jU`-V8wSz%wRDWh)kM14E6kM)wuf_DviSbghCj6?9tUcW86HpSB{204&uXo
z^D#Kgg<1j9K4hr_J^fUmA`yG*($u{&yU6!0uftKh|3~z%PX1sSRmx%eX6o*WX}|<P
zZp=6!FTgSz#|hrE6qFrGc1;Y3VwA;804RZlQ6!|{(U><}DBRxr=MSqe_7qmph`g!J
zEkI-SXj7!)SmB~yR`D&P60yIY?O%hqk0Cko<ler1(6?l)6+cyjvTNt8p8StIB*9Le
zY^)MCf*ktU=&mRf2x2V1yU-!5+?3B20LQ5NkwDx#eNtkRf=n3@u{LKMIL9=pDMg-r
zzk#hVVBTzeQ<h3~`cEx06r7og6P2J#H4|hn6K~ip9%{wWN%^#x+cC;<|Lk@Xrd(Ia
zD<WBz@G!GHZ=m{yPEj|5+lB*QrZSR)gNBn5b$e#-x=A`nSBIWs9rY@r7<y1%e5lIA
zoOP}PX0QLy$9AjX$G6Wtw`?Yh828(AOHbWWC1`6k-tw~zVontvQNW~mB?^UtvWOnL
zfL;rT0{=fozK5x;i|t2j*3KO%)_MR95du481#Z`?Sq3_tq`W+_;yd8Yck-epnqfS4
zO)ng7P7o6^GxFuA9<d4q1r^_sTQ36ji7QG9+!`&jHX0ZQs__)HE7y4=6+rX<OWg-l
z;=ghIKemplq*cn~i~nFr;F<?#nW7NIo__Kx%+Azng~GP5%I-y_R;#9+>bsNM_r6l(
z_>N&?Eif8nB7NjRZ7TE*l8g%DewKi;zwxb+12Zsc#EfKlVOQ$ECEvc+*xRqY?`rrI
z3Yk>_D?7(w(aCZ@Bw@=AB%AoycdbRty?#s~UXA1hqi49Sj=o&j<&VFf=7a`9C`(bS
zzXR-R4?vt!?b`d;3f!aZ-iU#k8w^fwxOp`mMGkknW$=N<*xL?aGNvFbU4$T4N5KG{
zYF9qYs^j3SCd86B{QhE2n8v6B1&<Xi`1Q2baa;t)WIO@#zyVq2T)!@z+RN7FSye38
z+7ctM@L#HT5MT>!$F;%9zH*p~nKvlczEfB?pUDa>FMjP{Y03A)lvv^4vEsm@aieoY
zen>8@x{!PI+gz{U==ZiKJw+^Ttt?_vKS*yBv=EURj<v!MgrjC{5d>0JH+5iA4Gt|E
zkTh9dMGAe_{_TzR{Q2>W#q3Vvtc*X_q=<Mq%pc4^1T-A&p99iC=f}S>j~d^v2Rkih
zi#c)%G^+ztZ99!vXhXVt*$Tq4i+KcrsoM-FxNPjeBJ{KEc)h<D;n(AcOa_k$#=ecP
zn;bp6q)dj5r6&?F%oldK61t<OA9N#q;I;eTsiPsVhdmoBkOw_$6R~3#fdY<0z5sY!
z0PcZ<wsql1o%Lp4R6oyoJ<o&L&<uK!k2fA*0YJTNSOt<34Q?9T4{U=re++sazOFew
zeMDEJZmLDMtUJ96y@y!v)FG*=CRYIK%mrp>v>X)K=bP;y@9VcHu;osd(}KP>DE+#2
zq-Q3b0XBx+pq3R=$TACEXZ*ApAB1*&k4`WZP~}d_?GyKz&^$c=j`jRYc|OozQgazv
z2cm~RADlja3-;W9{g}z-#Z&{RS^uuItUZv0Qq)dJN|I#Ki_GqK=Ja#D;m6`0S=R!T
zQ0AD4lQRB)|6hRVG62EoVM70PR~~*BkGVSrq*n@NmLg@2U}sa=m@^%XQhS-wL;5Lu
zGZyecXI;{g8nyvRn^m(GfFoyr*~A9zg$*5tSebpdIz4}|YakFrofBYPQ-1=sln23N
z)n7C_>xB1*l6v;B&0#k@fBReSaaxX3x6o1np`Bw}fc&5zC2~#SrE-Pa8c4?^8Uk)j
z5XJ4^X3u@y{bVDFs|gXkHw;S^sf6=I^4StyRs~y;+VSCUKMpR~^G=mPqfv94jy;P9
z%wUqYy6AvTcm&j>V&3u<0pR&{KHeDH5{sV@3k8MF3|<hDtV_+v&5a)3UL$nT!FqQ@
z=!X>ieo|rNmt$FWKu0J3`<&Q62CF_ANU=c;y#n7|s8Flb2Z8kUr4*CThtFUifoUJ^
zTUnlgQNzJ=>W%A^;C<cul1*rGv3~Fi1FKG(`9vb)#~O}x23BP=kQYh@FQGO=jMTP*
zdYG2+OCmAvB0?g091~l}3|30_Z;Shpy3c!{!NHJ=yoOoSDh@1&M&|gw3@{vK)J3oS
z_X@%-%k-CP`yTNE@vtfuMTLE!cWJjGL6+sP+eIaHA0X8`NE&*6u*&%A?681zA6edI
z^Mg};3u(aff6P`RG2mlfR8UxV9SuH{!eSCp{PNs45@T`L(A|I_J8v1st(|tGkF&(V
zE{U?14CXXQ8=+Y8XSa<kaOPtnF)dgO{|^T0GbAL=+31;t?8V|5^=roqb@p6d8z>eP
zvW3W(B47n>^l({3eaCDhY-^l9UQ$&qSr~?gzugbb$haNRn0!y#R*5h1ZvE-6V#5F1
zW_W=(7%e{Athp~aVM|-&uSo30=|99pD9)1?J1}l<p_b_zk=bQR$<%*rS<Tqq?)DmN
zlv~(L3LMc<iYT+~m5Dm%2)OaC;(O_6ol7LDk@Du%psmR<*1p*==Bi0FW1i{VSvY5Y
zD5=U?5lRW7m`T`1thq7Tp<3-K=W91sLw&y9u{6!T@okQ#9R+W2lszOS>Jjj>JCkbQ
z)*77{DSWtpg_BmI`HQGrRM)4gs}BB6xuYg3{0XrAzQ)&V+5i1W$nk>!iBpKR6)=%p
zAQM3Z%_k?HLLg8g&9pi%(ZFPGcd$CY->2dyUdxl247I}0RtM`aSy_1@MQ9>g)ovew
zu{KJl;Mu*NO=kCL3teS%jy{Vf6)Rj@K<v1xk$Si&_(@>%Z)t;^7SbNfT67P;%KVKM
z2hR~zae8`MRjH6PK{ADYvvT$T1Tm$saj`UtOY1}u`0nIXFgfR18yN||#x2u%Ww%b+
z<RV~-ul{hgSu*W@-*~U<*jl0|;=JQdBU5AO&VLRq#~&Q{_XbohcLa@%^3S!pZ@QB{
zRW^@RYqsgwKe8p=>-Oc6K6_#8qh(4<%KlNpj!Q|UwHFgkE2+<zk~p?5nQ(XpYHD|l
z)qBvZ`Rv5R$XVvHqo;$MV{=2>&CR(MG9O|4shsq*xjJ;C?WpxcfdO7os$Nw&WC&k<
z+h2rhz{3|V<H7V^7p4h?tonPyJ1LhHvT%?<3<fmSgg9L#j0n<+1{zp(EDp_bp6{r%
z->55^{cEg6gLU>S)2LXfy<JNx)Cxz&(m`cGTADaQtK|&YuD1BbPmV0bt%K#AE-aXr
zPCKofGnAKS)_Ifnyn?^c4qyLGaBhexFHMQv9Li|ygv=0=CYm0>*KQTJE;}-JwI7S%
zoyg0VP&hl=sJCX`(<Z)>*Xs2nqXT|5t6{O30Exu*U=JZxrgtF8r1Fs$;@p9BRB~~Y
zWThsDA(3OtwjGjfxt5i3a&lNKCOB>MzPOCNZxnnf>7JQ^NZ=LH1QZ!{I{%paUPGgu
zs9FW1oK+SpNRX_U&&x6%hsNO(692Vno6b`xs0NHrcZL=H((=4Ba4>lm&<fgoC^=5^
z9TvU`?5bkndl)f!xv9P}p6R%=v~?Uay+i|&#I4tX*^eobBNmTi2W+1G8$?;T?#j?B
zcfdqgl0(bos)G@HyI#9p4UYyivpn9wR!lYj@wx!W?2iBC%#T)kzh|<M6<h&0*?ELq
z0<56UaEGS?LqdS_>&Hy<oBIobH5-J<nUC0<8pdYm@JFwIH9yXOcvh;>Z2S#Nqxn>z
zSd!h?ZnHulcp3C?+QQ!p-Ae?;)i8XuG>;GiZW4GM5{`C=4x=FI7RTR$zhfBg*@{$F
zBq;<Qs5Nn}@I~BD7Y@EiaOw4G?NozgOzxu;ZZV2bJ}<Yk>5iY+n8tuzzL^|mtX4NS
zX>i2SUxfL5cnOB&zab&3gq5_wrii(~wTxhxKg#{R;dR<>*fTc7gn)^-=RasIy4$yC
zg`=?P@}oq5zF4be3nKQ#F=rA~=4#g3deBx@UF*}lnI5nl->=c0i;&s5r%64uIQJ&I
z=N6Fi=0?y?5sL!Dcj+A82aVge_vpd>iwisY0$Q*&<OUj|6MpA<7&8$$a)R&oe8;a3
z8%5p<7}hc*VLe~|2Uc7CzbM6wk5-85C@9R114L@jCWu5$0A$qQ>5YhwgXaCsCaie=
zPd@8h0`hhiE4L!ksn*KW>&-UU{+CJLj>9e;#Vh=%Oje7bzs}CEwTwmjoRmc>;1`ix
zD`Re;q>$Q9>N5hLbk}5vDJxuwhM4rNvCu=F(hxwrBEjb?2Z3fchK8;u1;Oi9zhlFS
zqeS8*g)d1+Az`5wDD_RDKWv-5ZfyRx2U6cltb@tpi_{8%vf@0*aM?mwqHV4k<3WI?
zVrs;g4s*>!#M^Nxba;<0X|Cwi1uhP=$)+ja{R1%L$J7tZADOV`0|l6%gLVli>8F0&
zuccgeUHs^||48oe`?BSG?Dro+>v}|(ofhIx1@TwxBh`zKyI%{~ME2lus<oVf*#2Dj
z9{#e!P0Yv<&f&S+YD3*{_l$<E;tno1T6$T0xyCmbTf6>1)c5+dLnGvqeOFQhFNK)y
z{UZTNf1V6bh!=dL)b+iF|F~w1H-Xx_hP?x2>&jxwaG!Wh-Y){743t<ZARcBWN202=
zI)*^sxh2u(ry*fubej4#3%PwaihKJASML;*id+d(B`u(1nGmTTS0w89dTDPxGF2MA
z(nhVZcl-Owc4qiWt9jqfd2kd~r<3!(J_O@%lYc>{pk+EMj<I5_+)(>Us>Ab3?d2W3
ze=x5}8(z}oXND;i>c$`RU&+KN893&%S&{8u?=5dR-goF+H#yBrM%!3u7#KvP^avbd
zMXpX<s=$dE+_l<4n{Qu4LI%C)ph2+=e!ggt4<5jHQDLd;jG8Zd7T&}qCx`_Q9_;r$
zkI5WCKqjYO4pt_)MDz!e@f7STvJ#8a8mrH7IF;)6OMS<ei-zIZz>tZM(slvO)Kr7-
zySmTgf%nTfqhGmXwtx#pmM_7|$|}u3YTvpJkS>j&HKb?xh{^f`&FAzW{1)IK7(m>7
zN|a}lR(!BWS#a^-rFi;eJQkBV1^Nv<d=K_fm7X5Xxf7FgCM&2qgju!QHqJSHhW}Bz
zcZrBRl_v8B>|6>tdvW~9b&3O9;i|@Wn$_h-hjc{ou3G0sMLGB4vS;&q`CEUq<FPlY
zmaZ2v2yR+bgZ<>zO10aLWTR0(GVRb)tZn*I(fBAUc`^q)DT&SudVOpw0d0k*Cvnhe
zyc^L0vazJD9Zs$3k5t2@8?DBiSET+WD_Yq9v(=fWT{db>+Ayc+E1VX!3Rp4RSW-|!
zP+@Iiww}=}DJ4_EJ-O{Gl!tVWgo@=iZ1iIT!()=rxaW2IqnpEl_i`)*S$0BE*ce${
zO%+fzk()j2hHWI5&w(Dj>n-QIS#^EJe3X!uHM7rW&vT8dZB-%>V7IuK=>p{W8!kn>
zMm|90Z*`oY+W!2^|EX9np34g&5wrtCfdYAeb}uS4DnLH=o4bt#M_pZ>!TJ6k;le8K
z=U&g)nD`O-fH#S4D$^8TuQv-^L_B7!cFz+g-{%>09U9n(b$1;R8y^-qRu$I-?T1`0
zlT-0wp(uy{jS=p<sNS2?iP@P(Hvjl%rMvQSHiy*;b)aO7@;v5&VNmev7S5;5iT)~%
zjOQKK&}J9s)nGV+@x;>#DgmG-U%vdJX!7LQK@(O<D@;Aissg8ubXlJq1in;8Q^BSo
zlk0tFmLWm!wz11nLPlAMY2QL)WjPH>UQALCR?%d;B2$3AVP{)Hi*}-ZeF^7W0m=@a
zWP<p4R0G>_@L(|J>#-A-N}EQ?&L51S?(_{KLaaA4gf_6+pF1nAtA2$SH+khyqn_)U
zrtWL-%w>p}7q+N!5=ZzE*yrC3I@OSM7vD4!l@m1sTYytwO+{Vy;a(Ni3*riFP*;`8
z`y^ZX-j69vPK@e(-w61e{CRCLU<eg0jf!XWxscD1xw>KU0X<5{NeY@u(pzsZBGuEb
znPR~8zF>nqI1UI%odI1yMN4-?+|SPM|4}W9EGehr{?9QDeX_??o+yvyqCv(sh^ik}
zTTK?+nl|k5Sb;eOj_BlxFp5y{5!dG+>!C5FfF`S|g8_SJD;b64y~YxXMJgdJEeKGp
zPHeW0$9K1~%;oUU)OMpuZ6g$6AeIiF=lRS@h#D+q<1xpt<FfnNcn${fGyO(F+^cE{
ztyn_pS^9^fIqFEySV4fzTCIp#ZSn&&1^}r$-DExw7*tum@Sj^eN0bB>{m(2vuG;;_
zIQw1sftCGO#L1+jy!2J7Fbxs4VSZYk{NSU}>pjrvM12n~f4{KS_1?3PX~(wr*}kJ}
zJ%2l0tx^sRfG$TjTMoH?TrYBFn!u_PR*9I4YuBe}G*1<Ql$2Q%_1RK#*lq^h>^+m}
zx-Dj|cQ~Q`T17@x1O!;O$#?jT#uZ<c&7KLCz#uJQziJ3}LEEU;3W<S}UYoMa2!htM
zTY7<&sAuD6W@c)(I+L&3t;ghl+#|iL8TiFl9*I-UgCZRjN~2`W&S5gRpZgfkgD(3~
zr_6A_EBic-!QJ9-$DMS?6Y<g}vk=`88!MDc1x^$$Bxk0Eis+=7JQsR>Tng6jd+)tc
zh{GQ*F_x8(fmN|sWGIN80=t7`FEueoD93HsTCbny@=qZ8p7%;udyT2XmFPazWu%F7
z|B{ShYgmFAwXJ*xV$D{HEONc6^}U}>eDZ_1EAm5{IWCBtG~9SPxeWuVn2}xQ%+?Gi
zq3caj>ak?f=_R0k0qmo9n0QhimJDp&!&#(&Xqmn<_@1v_5})%i+QeD<s!@7H@+YW>
zuS)}PYfGEZCr#Z9_+o!yWt_U+lWEQNL)2}*X?7qD61M=a?r3DbDHlvl*<bdh{i@xo
zn&MEA=aCXJvWDau-9|864u|63hu^m|{a<m@jC|Q+zNDI=4I(_+(OxLNukbd}IXsze
z^@SrkqwApTh~>GQgp9sDc<s7w#FQH8gQyV_KPYKN1*8Hasz?l{YG2gL9bIJoRce)G
z?cb)_Z&#bY$3EcZ6y<1{HOMK1=#t&9;+dXfH~dK>9*#2~ZmYwi9p^&R_Y?QNQS$3_
zLu-b9VF}wQV!%Y)<fijROW*2(vOt>imnguJKA`f9)RDO>ZrnM~UI1uL>vw-60UKrF
z=%5q!?7Q_vTGOBAEE3*H=|8S)^kI(bxocD#%|yGI;Qk6@o9mr$qDYz9{IUCTZz4*j
z7!|qajLRcDkOvDB|JuIp&fdT+u%6iKpl-hH7W@Wp{v#0T;CCY+cI|nsoH#=y)HIo&
z5%htl9xkIU@=E-a5FZnV2a5=KOww+gmh?DLtl;?hojNgHBoVYA^gA^n`96kh6#AaD
zuG5<lGgZplKv|qOj~94GZ=#Jg566{>keiqrerf+jVm_M}{=Dmj`u%lk&|o}^qqdpR
zc$7N*1A3EYsI%rLGe5YjBfI|=slpWVbdtB7E4h@Uq@w%I#gy~acCP}z*L3>L0K%F<
z^zUX1B7SnI+~lTXY!wrZ>7Tf?3AH@Rc*A|U5l|1Ebss-w-QDrf&<5@0jIeY4H%lnx
z*r@su1QMU;1b5GQM}u-wsCxDfVOpcgo8xi*f{>9?b7vB*w2q?Z(u;Xg>EomgP`xEl
zxOeA<<d?ypU?7gH-Sj~>|H%!r$p4MgPdOls!{*9$@7%O<hz_cblnUCf8Z&8&^q`S@
zwLeWa{ek{d&XE5~*KU0#vK-jXzS{6tJh^@9_79lfb4E3n%?0^=GR+EBDDKUwiJWdA
zBP}gWaYnckE1V0zl@5bt1uwrE#sC0L9dJ;1<Rnt-f@y;_1c%3uXx#MRo*MWtr_t$$
zaPIk_GWNZx%TSO?#EW@pS2;JOZOCf9lJ(y+L(YGw{f7H_PbyH{<Q;JJMdfh))`e5E
z*-%KnSHJh9r|b0UGm|5LklDg5#qt0Pke-D;)>fPJ`_hUGjmF3>C;jMdmWhTHZBB2w
zU$$Hv|2vG^Dax7==Wh8h)7W3cokqAjogDk}auhnQAG2<-6@Py#3eol}n7ID%^A~+$
zpi0?1H@FF<>=3$!L2&z?Cw~0L!93OObSQ{yaCoecy?YtXXN8(Pp4p`FPbaG+WCX}7
ztk8`sxO-X_%B)g&fL#nF9ShxNeb0x-U*yi@lwjWe2a%cnE0mc-`C+Tc$cvhdbTWS_
zl4>`AxbA+uz?)`n^q2<fr48^Hi-NM~5x1&#>ekwF5$mb*Z0WV*XpUa9*9$$(?~PjD
zZzrO`BuPz?D}eRxeCiL)5azrEwKq*9QKaK!*O|CIT%*ex!2|OX@At)}(`u%L;oQ{*
z7N<KKcnKCxCG}ZZ0O5n;EfOv7^3_AVsP!V<Qx1q%Ns=pFemiX73z=yNjv-;q;hk8R
z?$6q9@4Y@aSnA9bRi53({g;?7XNow+4lY%-HAC0M0gSKkge4l*qbNXH!0%Io;GD$r
zJ96qQmE+&*wlXIZq#Fg}XesI|xi1Q!J;jXMB`K~sPIHhM%r=Pj9j_QQ+O5hP8id|&
zT31NOomV}=V75>wsHl^SW8CNqXgEOwH1{-&-ica^w&aQmuY>#*sYG_f=V3a1A<GKc
z<MhAFOdcx|B@bqL$$slAM}z&WG3Xf$gI!h_*4vvBQ&;CPiwx8tO<9=^82-B8p?o{F
zXiY(pIE25@mLI(y@Hx8LY{KE3&ksy}3TF0o#PHZ<1DgoTW~uZd35T*-s~Lk_^9POp
zd^LJ3CYpGS6Qa;{mE^v+LWVcLvd9@{84I9e*p1)e^+3jZ-Dt$WT&*8mwIf#E0!2R6
zbH(;B#gAVsmUv+OF&u^{_RB91CvVcEO81L~_=YI|dvCWDT0$-N&HyE8_D`!6(XrUX
zA5a#OpAqX2Ff@QFdKI@4QfprXbhjFJTYUV?!E-pC;5c_YixGLdR`oAMn{ss}Jncpd
zu$TQ(CE+0%p&!S&wn;m5kMDCk&EBxtSET8tPW`Md&%3>+d22U$pMO32T;`rDsj3KX
zN>{s@6yxLB^iAfgq8#xFy?Vi=pMApoG)0oPByBJx=iQDA&7$U^eTXPt&;{qQoWBWb
z{YL#)sY8gX9cq4TBO-orBpmN-I!C=-7_o7x2=25a#d<#*pU%xss2-;yqw}`6Audmb
zIf(t{E>l%6Dk?%rrc5V_U-{7md79!c4;#`fRqrj|a>DuY3|FdtB#4MeQuroB5bckC
zo#EpsaS&XeEJ;E~Hr(&ikk^_IFYcOTC&{6+`s>fj=6P58m*JbGxJy7zq7n(tZh69S
zR+YJsixs~W2R`@4#?(S+{qKtrK}$s0{jTIH++knEFY1!u*pmV4I5O`mcQe2Gn(@8`
z@b=)~ETIzC=i+TxRDcrusq&kOOR`!8VvCEQ9p@Z_v&fvd!gfj1QWDMls!P7HtY;8y
zbWFj+4>0|qbSY)GAC)yTzn*kxcw0c2{`s=IdlzCaDs{*o61%>znOMbk6u<FSN9qEQ
z5+hjRk?g`4hmvMZ?b1R>C&x1Q<H$xQ^37nUpWhKOGwn?#py_=EKIVod-DR0#j^kN#
z#9#7^j7_bdj;=|03Dl0`$`3g3NfB^>7Lnw4S-&hv`}3EVi$R39uo)AVX_-0E24$N>
zK<P<*mO)&n%bC~3gCZo7yvdwH1zX{@gs^?#JrCiXddr>9%5;uJjfrKQO~;z~_Uvce
zH|(nhdR0Bn3D&T=e0^}D!;60f4}Siv8_OPH?PUk0su$!B8}><7?TT<M`Gz^8YtK`2
z6UnXvw__KTY5ZSA+7Ki+)#6g(U5=BSo13xvU0jqa1INkq^eUMkxcq0uv{Dl+JlkY|
zGt!^&zNu?tV-5vMVXO1U@-u3e`-hnDR-HsdFfnL>9Tcjon+x0FKaQH0t=fESpF!gv
z&7{443H`*<fjG=k)bf|k76#x>%uNVO=M*!y=_yHLLNNFMe-ifmlbW+p-9BNnA9G#_
z_gyLt2Lbxa(DrA9gK+7SYmZ*Ch`oH;IRZ>;)<A*`@P;s`fiL8$+RtO&hwr+%5Ne=f
zr!W7Gs_<ouiYVh@!GZ#a426l@VW0TmB3p7av9+&X{t!t|2F0c+zV<-?um1^A^oz(=
z7{E&$#w(!`SYWkIRg)Ejgkv))x0^9&evRk>0!RlU(}l(o_F9aFlbp#=fmcNkLI^Wl
zapJDPK*HVhpJYCqVLsv*S8grG&#5QCwwtyZaH)1Kj<6cgIv~3CRSdyOl+Bmq%F2m>
zmzN+zK!6GxN(!4rv-D7EF#4=nTCG9b3Z)$-PsU`%ev;gM>FpKCo0Zu@gkkp_Ll<Y0
zN$9u_lm;^FRW&=O3zgk-siyE6DR6|iS%>|<6$lei6|xXplb8uIjK>j-7K((Y?hGG&
z^Tw^Nax^Jz%0wR~CrF8$e0+eVq^6o&tW>(XC&9r{P!6B{Vq2mqKOp~S--I!TQV$3O
zU>r@123238ndJulC|{-$mXp*n$OAm3e*ncm#DUBet%xv;8>V`3?s33+Pu_p<iQwt+
z*}eXDew{#Hk)a=J=CKz^&bbpoJtsvs@b=dyOxD&bK#%hFVnLLUIlG9i^VofJVr8AO
z8_{U+a7$5Jx#C_8@k~^-6~bgv^k;+w#79WgM9M_SNwZ0K_T5^#!H9{%hrE&T^sEHn
ziaTuD#B1zoErMK2V7-3+j&xS@ZbHP<<x`xm^&-8a$DAXPhP#vq49*TOE$SXN{FCgH
zptOEY)p_ZJHL8Zj7yu2Bx>T^S3d_%p#U9iEt)5Es`EOlyKbc@sVd6)D{4ZntTaEXP
z;dh(eI!u3)v?j<2xop|<eDysRZu1EdFfvphqG%?J(h<HtpLIHl+@W_jIuQ51uffF1
zWY-JBzQqwh+U{qGQ~#~z3*b8jpx10i+$iy{D<o|JN|WYtRYF~HX|XE&FuI~6I`otN
zQ$Gezh!xqGRaJuRZrpgj2Gzv#kJkqyO3q4pdit~TzfZ4GoFz*1saf+eA=Tx$<=Wax
zD!fQy=LSd68i0ddqziOO*|IQcBbXVqN?a#?fu`og1cTplB`xZZ_SLc5%hx+Q0a_Zm
z@tMozl{FjP#crjVYC4k=|2>!t0tFDmhpf)oY2f?yY;!vwk62J=54x$Xv#@ol)Ezm5
zQ>Rjm5G1E)MX9G8OoH-Jr{hW}@a7pm6as5Q1*?(FGY4PrbPTjD-ah3gmA#r*ML)X<
zR&yjXkE25yC60}S{II0FT53IKpvnh2u!4;QjyV^;ecO&?wbmD9>5jO!w4$8c9;>>t
z;5wXNSvrVJZfg(Oa}s#ElH+9ct6M`VM?sA*kGBvgGve_(%kr}_M3SkpFB9@fm3U|g
z{~7(@&*keDgpI~vP{b(HRG<yPq0YMR0lFY~aiPY_>SoCA&D*x9X7U{I{&C!0vZeiP
z*}Y8Ft*uQ(UI&7Iwr|ktf=_tzEzeZ*;dE|@h}!I1I~!qTz6j!MwGx15XhFhR4+=Sb
z&aDMcm}&nz)R`>5#KhQ8no!FNax0+P>~94IkgTAN$L$Y4UG6)|lcv}Aez8}O+8Lur
zWn9U(mHlDq>2gQ-x?SRVM8LAq%~S<9Jtw?9nmG7;*|Nb^<guf8UB7Ntb(SUb7}n=R
z`WKjOuNhOrW<r@JSx)M^oTLww5*DafMJ+vL<I_*YD@js=8+{omy1=lP9e&@6x~#xd
zB*yXSOa1V02-Q;S0%9(t;a+_Tt*jQDv*EJXo8nj+^#nW0y@%uJEt2y@SU!G!1YS#)
zi!TZP=*^4FC2-&(gnl-lfwtOCi78V<yBA;K!%W1crHF={mD1<sk@1@A`ygL;yHNAl
z1HUHH1rZmcss>RF&1L)QNJ^mHh?9yrPuA>3CT0CiM#J%jw3@hib$-Uvna`3L)NTKc
z%<`8cS3JI=1Pv&DLl~ehDJx`x)-Ni75T52{N46)i)3MIdE_>YPZ$Fx5>>r|+sS9r|
zfwAZzxLWVSuJ-%nN&wQSwTX6EhW_fPh6`e!@sHn1Nr_>*cTnE*0b}fG!f>&Wf0CFE
z1&SO7Or1DENaUK;+rw`3xym|G$ZI+WEh~kD-@^6beOM#9Rw-7dZBhRl!sl&6{^>R~
zx-P7{8$G+-KWbZ;QW~ylacj#Wk>DBrQ;IC1cG(u#%Lxs8>mRI2jVB5xGf@XULq{B5
z(>8|uZ_8kifKtIPk2|=_ryqZ~-2FA{@u#S^H+nT6XS#A|n&P8e(_%_oO*8+zELWr3
z-9Jur+*4JKniRh({ISel=CMo{kr6OY%u-9qq6dW9c;0PLDsgig*jb!BRiOM|p`d_{
z(!{}vb(r5F(Co2BQd{SDj~4s+=K{!36;$2a24$8^+}zl!Xrvc!oV6s%$b4C>Y*^?s
zed+V`pF)0>iMEhsy<L)G0^=VMjAm%SCiQ40fW8ZAI$b~9?kPJlnK$n-3%txsP8}U{
zB0Ms+R=U&jzB0Y7jU`Zrxp+QIM_Kvr#FV5)(s@j%;0Uh27ZhNbwi4pV;n1go6^uTa
z_P-#g0kM)`fG`}n-?!O`;Gw4yY}fMOG8pt~S?c>?J)V+P8D$rtNjWwAQbPyr)vev|
z9xcAzb@i*p^XfE1U{oqKepkT8wq4hn5-7dKpaHFrsnpHJ6L3DyXA)cckj;}71(cHN
zdO>qa$H)9%+S)voG$;*jTkfHMvn&h-@Y{D|KJF0Bga&9}xzzP@Rz!f-V*H<Abm`?}
zeRf`65&0gDt!qA~aj7bE!VX~(9~Wo3Edo|R_%?=7WlV@#ojdV5Z1V~gN}j5zO{ucl
zAoh>q1(lp+?7N|#_qyTT9vF(<TG2$3XzMNTgD<MyCc=SxIf4Y{CwxMY9oh8v*s15~
zdF`&+n{Ws;ik606;vfVsG^VLSp=&Cj(2D%KvLo?lEro1q;!W_wGQ<=vNQR^uwrHJR
z%T0e{q>Y4nt$hc!$H$AhZf67H>+e3VsU<4HC}$L)u`CMh(+Qt|u~%Z3oQRnWV|qG#
zZoNDZ<`2(}IjH95oS+}Ob0@>c#;6>RS!sq(LQI=Z<3haT&Dl(ZF=;86P{_I8k59Lz
zJXS^~UyA<rwv<w1tc9@LUPGH;sD=>KbBC+zyP%n=&5IrSfECAE3$LTw!1_rgSNA{F
z!@>x3fl(8*(|_^HGXFrj-V3jNdRl)Rr098FqP1K^dYgj`9i5nTr$VZb;=_B!4<_Kf
ztftUom?k;~70c4g&E@htJl2}OU`$`O4v}HZ6=BdaH(8CrrlO@46VK9<sP^%6mNvit
zOqlr3ZeviuL{Mnb>&a!wYu{*AHg=<ep^=I@tq8NSvd%4szQCau78;<(<B-ZW&a{<u
zFGR(DV@E}nP^QX?OHGoYVWJmybYMY-LS|&7L-TglafBPXu>;rsKaX&V8)yWphADui
z^;^o>%&I9l?%zN6mc-BHxHv>tH;=;R;^aAg$L^h!*hbUHQbh_2{=$^3tSHy4T!r}w
zp{yN5Z*3erL}-?CZ*g-9mKT8?8BVE!E_t8Tr$Zao^9K*u|5;ZI2bd9ZRll+}r!;2<
zS~%R;&pdchq(T|M$P}vFK_c;`6?pSUie%W+a4$$CDp0yg5=1P*_^Q2wrjsLB1l)*I
z6VGZgId8VxfB3|}5aKen(Sd)`6gCtM;ZYTt39;-W_~^;>nOzIcPrmuz6Ln!=J_rkO
Xkw@wr2y1~~X9JNElNYTMHVpbdj|64Q

diff --git a/Images/mathcheat_matlab_logo.png b/Images/mathcheat_matlab_logo.png
deleted file mode 100644
index 1f87fd034325670a38ec62d897bc15c630aa7209..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 40206
zcmZ^~1y~$Uvp0(C;<mU$fB=h2aEIXT8r&_oySux)1&834;O_43u9yFN&Uemx-}~Kt
zp4pwLu3uMGPjz+mOl_FFtT++^9s(E`7?Pxfh~nq(z~`|K2lM%D3?^s>28JMLE-WlB
zDJ)DP?_g_UZe<JxCV`Zd?4qo+gdH-`!O<~^PDVm`i{}ncGW|^g3rZnO(pMfLIFRb6
z2(%a`8jb1?<!*5C5HcfCOvC_x!v*+jRQbUk1rgD`wMa|&iz>F8_4KzPjg6&)=7lBG
z3$KMXF!@)1s9q;^a9`Fub<FdV9&L1tk^Tn=ASyVrI(YY%i3ybWqX@+8YX@&yTB*cf
z{rx$E?~nGCuU+y~j`JTrabz6A6fj^2qI2v#-c&ey5Q$3l1Gvl|P{X%LG|a(F%-DJG
z`ITfl{WVtiCHr*ke^XPacJuHw#&LhMW=FQmucuRP6+}YiX{S<dnihD%8h0p5ri1N0
ze3_Yf%#;QLVW#Pg9#G|3g)xg_;+}_HLx900_H+GqK#q9gGRp>YEjHZ^hYFK4EJj;M
zHQzh@WAc1euk>WdM3>knvlZ$pJ#pIrhz8;K*4QSCf>UZ(r<ui_RS}$>Y0U4NJ#{T@
z5P2)T@&0jtQMsZzoyZo9%+}bjI;~{vzy`ag(99sL?@UqwYp{k9*xRa<yC4C(h6Za`
z&%7ZDw$hF7-v&@4Mx35nIfCa{GSD4zyyW=X4tE7}J*17Czo>pI#^LuPm&9T*7Tqf@
zDgG`pItoBenfUScfcml1&1F9Jx9@9aPB+d~(4mULVIq8Bm|1%ekDq!h!w-Z&pSdj`
zAUicIu6LFtrkS%Im{$;&e2XOPA6s59VRA?c8-8~PuM;g)CWJqozs;)RY8+#j-QN$M
zi4nEKZMLKZ$&~i*0Wm)uDD%JK7HkAs^+2G<!aC(5u=+88AinA&5%{v$L9Ua41^P;$
zLMB5pNI;kiK>ZTrTl{oErbY0d0Ty;(vwrTAP_q8)b}-sdYTZb7hyY)40fgY5?;!AX
zKXW+$OH{0U!SYz_LX<S2c~pE&I5%NIc}`5o0pYM%)OdlXNvcvHZ9sXRusrWAxf5O+
zoKC=VF6<-;E6f|XS0Gv{#Eb#bB3P5}QtdYaIPu<*O%YeDOjw-ml`W|jY(C`XUYsrb
zOR%p&dc6s70)OBjr^M9Z@UKLKV$~_Y(MT=h$O<9KMAKuD3-Mc{B;xFMAz?!D3{rvE
zgSa~(8j)Luss{6hQlPn1qf}LCoPBl^R2IJ{|02V=x?*+eO8%el>}VNL^!=*(G5T7y
zDRq#G2B+T$kcYZ)w+ZaLTY%ckHf+^U%_s|z?Y<{{NLy4_uD@y5F)#d4dK@neJxMze
zd<cAqHe!eeyQpK~HK8p1QAm?XD6A-yQSsmo;NAk!dRZ0xO68o$f>GW2wGC<Nq89~!
zOA=7YCSXbiQ{X64(4;l^DN9<A=}_@fb&w|f<^Cb=0MroBm8+)6A<?5miRYJ3{6d8(
zCq*DCv?R+V)hgg7;3iz8h*WAbt6rYkmZd4zA>t+F#VDS@l5&%>^S3IYBF-}DB*lYL
zE^#5nB}I=Rhgy%+N9w)!wFN?<N1#W^ub@^nPi(89mp4dKG(A7bFw`*JFw8Jun{7aW
z@^`#nd^5USnZQx)G-VMfp3qE@+jL8VOau3_@iK@|oTKn%CgEt{miVya7T%cBSZnXg
z9>E^oUj48lOfN|Pkwp==C^8|kJu>Eob-#6DXJUoD#$5N~H%-zLtqrYb5?PW-5_nQf
zxwMMy9OhhBxpleREXab&!oXt5;$@DZB5I;!!X<kmOON$q_ci3Wwk^!P<=Nt{@0j;E
z0WTe|6Au?p1uunlkPSE8CjD>vUity+P=lE!Vk4C1p5{TLE+{GOd{o(lvOa<<ud4d2
zgno*zaIJ#3%BZwn-LycqejGz5o<WO4uvxoV$wf%+JEJ<IUX8*<9-l%^RZd}-c$b#X
zaQ7t1vVMeMMo(#8cwU!5wn4#`5qWuB#T0FUX2G{XPNOFK@GHI@EvnkEEXjDusJ}8w
zi?i#>`fU>p69CJ7Gb+{!w)Ua?+EopWO0oG|u0EbWo&|0hPjra95$X|H*lXB&U(dcO
z(Vu)J)}YaVR<~|Au6^!X?oYSWG4dJ;w%9SMA1_|pZ>%b#-}u>e=JhRIr*;jyUABGV
z9{HZ^h3bU~mN&#Tqyzb3(dp-x_CT*Ww3EZFmhr2|9aw4)>be=pl8r2lahh?Y@d88u
z&Lz$)J<wsweyfGRA&%h(L!_RoUEZw9O45$z$@8epLe#;}e(V0VMc$>imKQpd?<jHN
z5krY32?ux+h`OOEHEMBcHO=u4h8GqWC4Ag`VSG`17@a>mE4&yV4c;K$em<|=EL<Ku
zFTBV>aY9wXA0iY&i9xBt@*y(AqeDAG_4R=FwEGh?PU}%<&tgLey$b&f;(=!a=wLlN
z0vW!tR#Kh1wWzpyyShNp8;Xhu_qR*L{F(kE9ubB89ot3hulRuIoVbjrhiHoE_Z0Q7
z8uhzkk(!Yt#k|<W_*fnkKOM>*rq{lQ?3`{R?3^>OXeZa7E;}^tm5oeP$LjTH*wYf>
z6BBNreuQE4mhDMb-|Drx>_IIgw1>RJ9}LnRL|y4T8o$Otr3C&8oDxat*9kL{td_)*
z+<}l2Y}|DIrFZSOTb3A`SU|&4HdQue9@y~MFe5~15ME2C!@^75!Pv)I01|E+HlI6J
z!N?bwij)50sA1Bc8vQPPClN`JK^dTdSa0!b-BjE-XINnrZU1&Reb;7$@bAsv7jHHb
zHWz2p&Y$nWuRUz%mdI5>^VQlbt+FmUJ#AZeohLEYB-Rp3mm^{$QO5WiHsR~D3b_ik
zo%&|=7yE-ktwIW09rWW`$W{YZSXO6iCiPVoD=B2UQ+I|V+u_@Cl&kS48ya3}Piv$1
zfoB~>_eC!*n?K=PR@aW&Zq(BZHFTFIt2cG3_C~MsubbP|PCHc%wCwd!%wvAUG%%O`
z>X53j5T7eKE<E<bi%H+_IQD2h%UT{<S?JoZ_L%{Xf*gS5Bf=#3?ephB-m*&2dca_H
ztFmw6B>%Xr%hX5DN90W6DsDnNNW5sMH^zDH+w1~IDMuoEZ&dtX-H!c`?c3&^um?uP
zkvvO5>mw8=Dc9BZ&?E6hHh^czE!?a8i25z(zG+f3Tr+Qebv~@Akloxv+GFF$cP5K)
zoRFSB<HPed@;)uSe%SQ;MBF%5W(HS|i}$tAV9-@W7)}VcniF4J!|rreb#3+fh+j&L
z?p3Qp*-FRDt4d0RX*HM5H?J(Oj<evmsClCH>}0)DZ}X$Z3+s*}yZX1}ovP?gXs?53
zofpRS`!=t==#MOX$OxF{bB0cj58YMXBd_(hDcCX;UH(yb&Ns6+qtl&yY6Lmj>~j99
zx4Fj!ld_K6)7%C9iTY5}nBW#+#*d_@8o55`iLwbgu?DeY@hb6&$fw9P_qyAG%aO>G
znVq06n)jxMq^XJ=Z<m)2Q^UXgmzGl}Y5Y>_rJY>Q^Y_*VKMr?iT+A<uOTyPHI{EMY
z-tu0IZinKIPURlul5(Q>T|D7muU)CEP4+h)0&iZJxi!2$b4qwS2@OXuFy#J!4)7#8
zWPC6%$Wn7<btiQhX>KE1YX*ZKwuZ(GuGV&+++biluH2ue*2Yc-B(Bz0Hjdn`yk!64
z;Ql=Shs;Pu@-G%AOI|W{8F><6TL)tjHU?G(5E&l=2?+_0!w(Z~MG>+8hJRl1l9@R<
z*>N*6y12M7xUewTI+!vtadB}mf|wbZndv_{=pEf`oD5v)Z5+w}tCRoJkBG6Ok%PIN
zlew)8$v^!X7}`2J@sg4KBj|rV|E;IB-Tw%(ar|$qKJ8<4HLzo3VgNDz-#||0CjYD6
z{|og`%l|LT*wy_12lh|Pf5HAW82_as&*zA8%h>)fH*pg&a5Cm&1~IYHgV^aotjf&H
z+#q&tW)>dC|KRyw3;%@{b}%+@vUO0lwYB1tH+MC*vi|3igoT0mzmWgl{%_EKtl*Y&
zFgN}j-hT#>kBNuz|3mvveho!qM_ViBfAMp%|2N>@iT?=@wzal%Fm`nOM*!P@^Zz^T
zKl%T+e-#^Zr~lFEKgj=yQvcs}zW-6{---VQ_~$lo|7)DoMa->@ZT_ViJ39vt<Nvkg
zKas+=R<;gGb_Pbq|0wq_$iI>PllxzCH2zl(Cl}Yh%lWsK|3>mK{^QI4=G*@@asEa9
zoH=|5pZE0t%tAhdgEyKPFfaiyNfAM1SMbvgSl=vh=Lg<qD?PS~rFi=9zl@FWjUvql
zp{!;g15t(0C42*Mt~)NNC{R0ZQ(_?`J1;v)0|O}%!J<ifpsc!~t#+s$)+v{0o2-&_
zs+v;TJzOpxKC%wDCdPHwbgJ5ZY%FDcI~z@NGZ{~FEath{rDe{uIi{Dl^{a%wPOCXD
zYZmB$u7p08R-g`aM^tVU$f7C7D<rCH7O>Y#*SDRC(Pn67JS^@eqFH@WI_Q{l9u6QZ
z6maG(sSsvc6{PK?N<TUVyY!nkqFQC6!w@AhTjIjf%`p>4(tIGw#P+R3PUL(jp<?2?
zuzjje#@yN2F|e|#G~q3i;LAZlLPDBHeNn{qfwNN*bJm@X7|7qM)_D9%W?Jr{q?BM#
z%HYD?fPsNAkF0xfw25rObEf$P6F|9VXwG=7sB;0l*PqYBW;m8-NGX{mJ!gEKyQ>H)
z;T~UUnZHr#L%(Ov#jvH`erSHhO@k-|BCNGJTRv`~_?ST<K)T{30daBR>FMdW?rMCz
z)LR|)D(QBT&X11~7W90)T<@1mg*`mlnPB%6$Gf`)T+b}^@O59eaaJHbh^9@MnO40X
z)^Kdv&zdF|7gO1m4-d`EtgVr3Keumv(?#DOG);#VW@e1eCO0;0Bc)#RKn+|X3baYR
z6Yr-J!85Yo#(Eq6R_nC)Ixpzp{5)U9U0z=PN~E6pac1k|!TGeC@bJv@qGf6ul9!if
zch<NNJeI+s_09ctb5q~72T>Q5q4ROO+jr-xo@T<p!^5L5ienwvWVOT`L}an$`>qf3
z_06>xo;q-EBqhY{X5ZNB%++$Q2Dbz-gU99s9nt!*?s4>nduE)Xle~59sUd<C)3<6r
zM98xJcJc9k5gxh-a?Qp&EEWk9_*yw$@%n!8(Yd8YeWoy;q^7H3znEtCEfOYVctrP}
zxvyTo$3HyO1tqg##_7r)e;lenuy}fP)vCp|br0JEJ8F2}X0@g6x(CI3a@se--U18+
z%r-7QV9I@ay!z?79Y%fcIyhJ?Rx#qS@v=Vre0NgsKH3BvdK+b#Q09C3D~HE`^g}7z
zwz%o;{jD38PkstacS=74tAo*+si06Enz;`y$lTlpek7R@v5&luw1j{3(-0gY??;1r
zThAT*zSUO82X3L#CMrMF9t(j!$Ne-@7%_deXnU?{)4pJ&ebe1loSjRu+{<oA6!rPJ
z3bl~6c9)EP{Xm^&8=c1^IUhXleN%6$(KHs*8fO@IR~$xNt4Cnks|J1kafqoCJ~i49
z9M1REW2fwnni|H_#X55QD}rE83Eokfl1y}9R@`nW^ST_e2T11_$@}@V2bQ?ORIA=Z
zM&LM~zM-*MW3{>SuIdu8>k^S_&5ZHd&1bZjuOX_Uu_VXa{qXd3)XevKEuR(}Q8a26
z%Q9em99j4y%GQ%y|Kl;{gVXc2fSBzsQ-%Yf$-MLy3N*_kJSt;Sf#B}|BDdYR%ULD4
zp!x|ukD91WB+-%$pN}_HVmoIiCm^34WIg|&{m#1ELDm*E|C1!=Reu761>SM03ucRg
zu=m-5_t-2;%!6r_3OlC)AqaHg6dWBSVNQLyHFT*BTn5}l)rv7i+z@XL4I35Xp{Zrb
zW-i#favq@!$t~wGqY^`sj=ImOsA({PBwSpY=i*Ftnyk_WHa_RfRxctW01<~_)wDSD
zh0w%^q@W4}&~0chl$<Z_XZb@vxw*Q^V+ER=HsycOFe;AmdEF6I^qRj*WmD?MDk41u
za0eqye>rsc^p=NSkM*1ENSZ%8S>gjmJ6Dl*IUlbFeXmb9kk8!Qtb~!#f6UxSU|`yx
z_A`)zSU~;90I%1pp_NeTJNiuLc@36vwBMZ)!Thg%{D7{vl`d;%sMbaq+CL<Cp}>6c
zgO7cVE?k>L@6nqn1s;6Dx3me*L10`{zb|n<3;9s51^=cT_fa85=b){|8Opk_btR+H
z*ydmA9Dv@-@<OEq4DK_6d-t=i{frF9((ArIB1392j7fR@4E_Wo+q78#7u{`8V6pac
zoFB5aP`QWeeVS=szG#HBMQ<i(TJL>NFPNj_NM=i=#yts(14G8$%VZ2Q8$1O$;7{pS
zsP29^*?yicDnXLiv~x~aBq&ej-QvVvv)4>aLKYH?NEkbkZ5a9Bqt`?D7<S6sV?kd{
z%UBJOzIT5^6B>AZ9O6g5a2@&<Sx28g7_$IfoEv9q)MUMaZPRh5YJ?!h85(R_!lHMR
zz7VL^^*mQEn9$=&_zTW$b3pToX;kp4uk<O?9G(Xw7@jxX4}TU{>`tXZ4H^b-c0Q%+
zb#F<(p7AcyAP=b!qnyloLSz}~>~C)$o!-;%<>z!-K8B>X+2~w@!Js;c;k{Sqd{}j?
zhX$YcmX-16`bg0<G$de46%JpBBC=JDj#v2ydvJI?ZJ?iIEc%Wy{m3y&CX?R;3^8ao
zPq4@N<*XThrb@GMc$=~9G2d>S>i}rLZ9g67L{y%0u$JSn^X%rmsy;w=YHshD`CxX}
zjw#&rG49f7G*@^r^`!UEwjjyN%aq`s6ehCGM8f-aI|KVFVV}{up>t^RH=c4U>%!C2
z_0!QzjeA4@p6i2^7TjLr#mp}Qn96WAfsP7-BtpvZSahm^6*Zm9{kTC&1~tFcf@@<S
zv~t$*I^XYVR^Viej8NUy!I2N#On2Ogk`Xk*2T1(nnfR`*<N{H=4uz?_s|e<Tp8NVk
zJ%I29hqy>bJmQTq*RtrHB@=;weJF<Ulc&`znvX4s0-{^Wz=6^co+weo=hOTM1_Ym5
zaUV?Y^8@ekY@;w`{Qb=DF?x9KZyO(Ln`Pv0+Y_x_&Q$zoL03N=dqdDvtF_I|UtK@8
zOY|O!PTvd>-~hZhNs!2m!%}-Rm<dQ!VmeN|($2<kGXkKgf*8tj4EOgV&5(~Iy(cXE
zzvi#W^VqzPx}`Hxsjv2{{U9kF@25TX_`D`F+&Pi29z>Nyq<gXG)id=7d+4%r8B?+2
zEXrEUZmW@S`68QZuIm;HGcgU{bMh<<<zbl^-KQcTZeYF^mv)NGnOj=dgWi1Iqsoe*
zBUXoqZ5eIJQ|F>eR6-P*V+rcn5`d-|!gf(eQS8^uT%sI<wVJ)!V2%qqE=$GH(8W)J
z&V~>O;`Q;D+%oy7eUYocWN*48s14RH#ZkeeIv00?t_$t!#`xTHZ0WyuDH-d2O9va+
z^)r^w>$t2Nlcfyu0tq>-p=0s_TL^)yotHt3K)*VQ5l_D)vHj#ph+sENIzhfCao+jt
z;G#Ti;?OcGce~czT%LUvo8KGoIrf9Ns^W4h8SQ$~Q+y?B^N(pxznXmwSWRX{gA7X&
zu)w7&g$BsEFd8nrAKI6O5dw$PUG((2_A2JUl0jo~t8S<KAUQDDJKw3)P*Pmr*5dab
z5_(@!BFE1aeNhVU9Z0-&P@fm2PM}}gSKA(KAR4k}2}loMUmfKy@rrKTkwOxfgh+?%
z9}#&ISWfz#P3)<jf_cjVi68v@Ogqc?am|Pyl^>-}nApTouP42QPe)?ve^(ddH9G9~
zjfOy2_>rfF7%By!{`PrN7zLLAYXD>r@OQpO&y+mwpsruzmD2{8FkaxN``C+vS1=8t
zn-jE=FXH6(6A{#W)P~SmQtLA`x%Zq9UwH9rYTFK{tAzKQehydr-5VMs@r*1PgNsKr
zOaZ;+ao#%A0RH-7q}?l^cZ&nV?*;V+^7(O;K@U!$revh#`WcpGie5T4UU)Nv<6)OG
zqLvto?al*JQ#T){$qK?afQAXf^URenwNC-nA!cuD8*e%ZASW<%sH>AK1v;N9P9Od2
zm(ZR-l$&6V-v$V>Vr&b328L9Y{Zp}5f$geX6D6F_zMaKhz_02e26hI@CVYRHse_$D
z6aAvXI08t?+0G_j&vM?6a*Cx}9vYEGcK!}iEyH=pjVjo2STFy`sNo*Oct5{*A7_O~
z-dZr%H}-yRo$zAFbXj*xYuaZ0)qMTAT!n(3;_(ZZMfS)N!+(W^AVAyOdj=>hadD@7
z=gbH(FpMBV2>5+l#MtPCw7^@CZ?ac(^XjLNvG~WpIFg)*6#+d!*0C(GaK{Yx7|(dx
zeteW;)lPVFDa-D-tOU=9dtF0imkR;PzWydgkW63RWvQgw6@M!FAJ2lcmdL3uLj*|o
zmO;ai%`UWb0eS%Zn?Iu<kv1drUh}IwnU72v`{jgvQ(CuzaCCvO`UJOyK1?r)M3<k@
zjN3v?7oXH_iT6i?sVN>6%`OHOLbWj$rkJEyR&G>s^{HSg0n@eX9<IGBXafuSp%W;E
zs!RgFa0OT{Z1%ezY?(3y$^w+SDI{7&BXBglRi)J(3XL8b1$*AKK|Ia)vV@*@6?%;%
zka%>m09xr<rUtspOgoe7D1+JnKe3_swV#C?v~({bFU!RM^+eI7(_)qugFLob7a&xX
za0o@8*7|j_)i@-;IpSrFwmBu|O)-a{1P3Oi4IxM>sKw}qiIY9nuz~s4`5HBIVI=?8
zIlVWPgs+>sq?($VEG9P&CMRTRBxZmL5+GdHlIkz2Z)vXg8rrbITn2wslcGX9uLkk0
zHPBj8IpO$o0A6A%AZmT%gGiQ%+5vcMde6qc@DnTWe>WYaWdTkX=g4$ymsiVv6ARrZ
zm-W_in{^<#(Qbi|;fV4m<LVxg!R|j6RCiRr`HcxtmE^w!BRqve<s(BB^oFwE%Jp{V
z+w{PFJ$q`Ux>f9FpmGCi#ZF&CLE1w>7A9#s0#OAMzh8z_3D3f&9YgUDN_xq!=$p*)
ziIEjT!|-@JEZ9#}vriCyS%!y<V@m0MjQ=Lxli3s1haOG6iVEtN#5a_;)(GENV0U(9
zjn4Ua%4x=EDeb>ydg})ZEr($tE91mP#ONgePT2Zsyb_=)J@zRgW0{y@KvK6GU=52~
z6ZN{JX&~?U;*9QIO71ydvs#M`F(sT3`iDMH3RBzMQ)O;_fqVr8u&`hV_tOVErQ$G+
z!pq%rZ6jxuAx|{2E+?`ZUg}?Mb-kZ-RT7d4uv;!=T(kGz{VXB`lHeXLQWR})QAThB
z>R%6h-VYLcOxVxFbPk1|A^bxw`QMJ@R^Zh;OflY{az2()1EH~%O_be$+c-W~IDP*Z
zv(gQS4s0s6K%7bH(sK;g{G%RZ2-Wl0#EsNb%`6TUOHeB7bHm}2Amh(}9Ty|bwW@-6
zA#K0z)?M3QffF3b6(<4&q7)Yjb{K>{cshN#fiHw2C%a<kQHR^i@=Zd2n@|g&mHehp
zV$%QygR&on>VB6Cjxm+}wY6Tp#}Lta*pGLXb|0$q=wJv$GT(t~XQ+>i?F{r?t6heM
z{FQr6?Q>17%80E|`l8l(jg|2Hac}!!qUpynxIWB7QoQN6Rtqj>3@RlbVcot)tpp<-
z1O|C|_jp1Ez-1n>R)LRQ3P16X^vA4!FR7Z=4szWG{HESgG~#AWj4}8H!vKUb5epJp
zt-(|pZDJ~++fk1)Eqv&JpT&3v3YpG0+XnBfHE4;OhLq_L-1dXEOl}f&OfI@Qw)~es
zfTq$-pxWO&N^F_M+#mz4`3VbLI}j=@52Aq2OS8|n6Yxjh)Lx4cVjoW0`>v0Nk0zGW
zOuwrTKPD1p5vE{C{BooM_`w@3ePVAjC%_G+T9z3gH7+i64i)Dj5e@%ZkzZ!xR0lQ{
z;to0GK!D(cO+F*?;)K~yd%SM{p;Mo)TcEF7zysYRuy`%@xtdRfpZG|&Rsz;miGt}#
ze!%-E#rtn6ggRplo*V%Tasd8Im?NH%oe@_m*2H(lj#+HmSn1Nfz_{n%UC2fkRigpD
zn99-q@_FjygZ<>J{R2=D7|EvwcA2ScmOkGje!p03!v<`n0{lHh3M4~x&zFe%u9E!T
z!IcpIOk6V{LWi1MM85TSeBHbHxhJ0^SBtCNBtaR}{V<dpe}8))xPwilh-*^t5l4l3
z_uf>+62>wpCF!i}nhMtU3UlP=2cyt9N%D$IpEo!mXn;UFbm;p<I7o<<49nH*R&cya
zA6F+6%~w!nNekL?b}65ScoarSzZ*r^^RSPYdFn6Kj&u|<G9D66BN&+gZTDoPg>=&r
z$?`&mNF}p)&qODBMl%gVKu-*-!9ec)G|}l(?QtM$Hay1@sRSzoRbS*H2<B|~nud01
zW&NJLASUf{v<Y6vV&GO(qqMhZYS$TTKe==XG_XD@Xb|3l3Ix~5mPAR)2RBD0%4WrT
zT=02YKuJIDU*;I81>pmP4w(G-Zz5T$tS$~)qR*+Lg8PHgaAnLmfqmZ)2lWRA(w1(B
zsu(7=sjdp#r3c+K8&4)8Mkt)mmK*c5FidWpDR`{A=~~Pn>kSZQF;gKPlyaUE?-1Vt
zq@ekpk>E#jC3!nukKM;G?@f(23y6CJKMeGxlnqRQR4=pm_07MK@swNzS@BLjfT~|m
zJ6M^IkORV<NksOe{+RhD>X*gps8R)=T4Xovvl>a@t&TJxj9vCQY(Uqvk!U;LsVtR>
z1^5&5COwa1F8~Gzt|@8?Fh;F#AzW?M*6AN2;8PozQ&Jr&eUdN63npGFpl`9#p&T&)
z=s;%^_963E4OjLD51pPpTJ*pRO-SP&5(z4Rvq9`&zuV`w{=oO1#!y>%k?UuX)3?yb
zIg&ys0;xT1YGW0Bz2x0(2Sa|tC44^x@V;vTW5tvu^;tfEMbC-u2lvfP?pKcK*itkJ
zL?Hf7mZ4w4?hvluF-XWz|8Pn3+Q_eDOr2fS;#d5TKcqYN5DTbbp|gRmp*tU;p*t`O
zo<n+Y0JecX_?M}L+HsKIqn8_Y*Fk};`j&`_k#Xd^sjP@j2k+vfGYr!YmRAB+Nwk!n
zzIwOba8&ZI0AGfo!L^Im@r%V)vdxFVIwskNgdzN}J$B@eCxYN8&T)k&o}MipTY4N%
zb-b80>*EnI66tM#V}|Uj!+x;QUs%J)$9wZG?~5Y81cgPU1a%5l@@uPQ90*g?%Yqlw
z8MXy&qo*SdfVi>tubJY+Ru17MpqbwjBvgbSjdqQxfUxDIr5zymSzPpr2YBDQs9wa-
z$up7-XMy*<<5XOKQa02GYl;#B!^1SZW6q~5wU>C&0`*NEXrAe24{@y&*(nY_<PNz|
zPLKtK3^z^ug|>GZj(pn-m%T;-5|Vtn`^E70cnb)#gzj^L$(X?qc`VT9P()3qFiV)9
z>)m+Fr__4SV-$7_!dUGr_FD)G=I;1-yVy{}G$;~t%#I5}5wnHC1Gw{-7?6I^8dhHI
z<l*pwH>#8OGpNl2)Lg`P-xgecZueVFa=4=E)cu16Tf2=)SR!!f=Z|+nr;uT}<7mG=
zUh49dG#b1w-L6%p_2TaoVo@)NB!;&#93(*vB8rMW*Ym%yU^FTtV<BkiaQV2}3D|)!
zlS-}*?Ksy*U<Th#Wduqk!W6j`neBz0208z-XbGn|p^NnVz(?f!X@~2_<q`mOWx9&5
zdyK$jU^Qp5t`4L>x^5}ny<A?umP<j_P4Ndn1nO`rW|zpM<hW-0nHmxMClrg_h8M_?
z_TEhJZ;0uMIXq8ahyHOaqcuWfd6lQWfW7=U%6T_`tmt~Cn?a8iJLufi#-G&}0>(V|
zi2!|wUOQXAw++4AY@x$;)Fy*CS%K-_$5a{0+4ecScQ|^f`L4of+BcIyeG{KHi(A`l
zb{l#Y6YrG71M&Rl5Uv&6h=3_!u7>xRJ)t7}l4%fWJ438yKkRaa^QR~MY&bC-^HsA!
z$Oq;gj%6%&1zTLrka?}0CvG2idLMJMV9Maf0=Vy$$Vz9uv)<<;8>~*iY-b-N>u@}O
zd&%EjEq@lK>SnSDk4E<K2K8pQLt8dt8k-LqBd1U<8!!Tq5)AZv7cZL#Ad85<P)7Z9
zT)wV%u+Lt4<kAe}9(!u@TBFEjQvz9B2V(?6n&yuPKF;9k7b_VX`d<e_q5yV`1zZcK
zo(_xbLqO(`{N!NnWERK;kJ@u+7=4=kU%h!y$_WebL;yCXATf^YG~@y#6Qk4;?aGKP
z`S#f4&0c0$J0ZxJln#ze<RMxFMBE0%XBOvW;y%a8pAt;V7%(Qf1L0_qA?Fhm84f#q
z8P*!RH$AcByk^J%vxbPBfJbw+$j_y)0ry630cb)yb2G(2KvROEW*!HIg<u|fb#2nS
zWA5>07D2va!1=L_wP`?_-r`6|!|+r)7yKGsz>TDXL3PP2Swf&@4404+eH(|q@Am~5
zrR|)K%>VRY@&z9zx4!!<*ge-!9vKRAValLfXI69>fCE8+g53wUiP3fJ>48Yj)q}Xj
zz232x#*p=VnaP9WHA<Vk(;sBO<4JNRr9<jXQMgke+dvm7<Kdbyt=oN_$#m*8v3Yfi
z*pi7B>XM1fg6~ozUDp+As`S{|{n*#4MRy@>-neNwKnNgcyd<h@Ay?=kSJ?M!z`48Q
zzCa<L2&XB{#lcidhIlBM1d5;5sK6Y}?algE1czOnS9D=}521ZWiOKkDOi^i!T-eDn
zSEH&?AGXtKvBewfuA0*R`P`S#ifvqT3$p5_GA&<q>#oHekq1XhU(wu~flucx?U=rn
zm|osA{LQSvJ^A1#;%wS>>sr@c$w~}%Da(L2t0wP=V>`4-hug!dP4M#!VKjEB?i%-`
zO1`kl!SnG<p2ZUPx=|DKar}1bS({P8wgfB%MEfkk#1)}6e-OwG`~1)?n_G#1*Xt^?
z9DY<EM%<L2E4{BI<4KeLAdOSD7b!Zk%3<(|2Q(GO>RikjU-d!2fZrSZr!`Vb&Bhd#
ztW_M?XYaILG{niW$v+JcBs{K=Yv{dM6o~zmrl7RRpNoC<ipmN5%=xQ_v_plv^*H!j
zs;eI&Bv0+Q1hQj`P7R0j6phx4@Ay`=O2_ZMB?$L2dUCNTBSU3y9wo7hCDq}u@F&O%
zBfC8+M>u}QLCMu5IyJnupa^V}l0bt@Q)jC;SpG-B`!Itcwv}HfFqzr-e2dUZ1RBui
z`aCj(Lwt<_Qg+W!S<YS>3G04wQ&VeXChOYU!NKrAa<+PGeAzz)?FTm0Heg$PYCiUW
z;>(fiWCjh33(G}qVHf`^O0v%A-DU~Jrk$H`0)`YW=!GU@db<{c>J-OEEikfxUL_OY
z+WHdLW9s>qW|x7mf$Pxy>hG|y;j1Bf0$%g6^er|<H_1-t@ysWPW@8TwVvn+4Ea+-u
zc29)+&E6-?r$+RiA%SjCIcwWl7b=QHEiJxss2{{$!IsVIC_Q!qw9*3}Xr(&QGw3L9
zD4uqiG_e+yZEokyz70kFhhtrx73ZCf4<YY6@jYxg&&uXc-^U5NB&N4`-7#(po}X`s
z>9s))M<sl79cN)kS;I_13}|M_{5{Nf3kgLGD!1V@zneqmDyp%PtX2E<rd%kPfgNdP
z-W_G?Q^w}O+UQmmq#-mEl7c$N{@ar%LgEd9l?l#hA{WLe{5)6Uyu8+qVp36-PBj<s
zcttpwZQP^QUw4<UTRrU`qPLT~9wP>cdEU)Ff`~s{AC3<6Kx*Va>ByTWP>Mb9rm7^t
z*YEbH;TZ%s-AUHVMHDYJ8G_Z)S_>i;yxpL&=fc$_?Y%CxLEpn}<5(yBpQh#4+-#bB
zSwxIanylA8+#l8kCPuSXyr&*TyvLFFk%DZ!>zQjTwQM>&A2|3E=^XJ8@~VpHSy|cl
zf-kg)9S$;?T&FgC>K-B^81@Il*bhbfxzD~JaMdw|8266R6#*GUNisG@2I#s<rGAe`
zQbxJdyX~cfJ*=lr#1iG_Jjb7sS}YboS8?{jxr&>bPT<(+Xg!X`Q#!8y`XSkQHziIR
zkPdZX(V@MQPnl<KMZqc?xreY9r+v7RJ#u$mIqI{7o98yNV{OTQdvY_s;RKm*NeYA*
zOg(=hu~|<NBLgw&@0!*}BS!PTEKMwr1*~%aQHy$<rpa*Cjs-Ka6Q8xJIZUg54M5W~
z4G)+K7pFls!-)ps5uA4ZI9m>zpy%}-*HWt<JyQKOJSpcLZ#g^UtSQIC$C~s#Pa$zq
z{!fUQl~M#>#<%PdXX~<tDlT5uI~(ivGb7@xhh1ySCy*QNVC*+~qn3tTjaWEsh9M3b
zhSpv5cI~tsqDonN=Y8AvecR>6!rh+ye3=OceV*Imam<j;s2B-&GSDo+JR7V}!%yoY
zl<)yATb`ZBR}ne6qEAA#>zqoS`PS<sYu<}$pR3uQ?_N`5y!371O)`$_`&8A;7Jq`W
zON393aRPgVPnOSLGrDY6>bwcj`&53J=OAH)KJ{~SJ;}U)$bDBY`a3jNe~j-F2Di8K
zy<f$I;NOXA&Q^(7rzdZGoydDHV;RV^*&+|vxO>0yAa*yqwEcYNv_3MyRZ^!AsOkQC
zr7f%W!@*G{fiG^AlMfqjfJY`j(1fX<KxcaFPQ$utwI%~)z>{g$-9>qM;v0RUkE_bb
z+Q)OztNz0|15rd=$1*A`%dZb#)=eRvbGu$UL0o`edjFo8=ew^KVJmU+OWk(Fw8d66
z!-;&*iL`yG<~`iDsD%9Q>X*}}W_(+#bKafEWXiTXJCTI7-*BK3%gczNF*%@62yS>j
z#8{1RhD~{jMU5NSgJRI+vi4M<)`E)api_jSUeQ}5hVa6MPu|-U9m9d*YL7Cy%vPjG
zJ$k^AAv1%?{x?dib$b__3hB;Y{VueR4U8UG-J}Yy8DX<)SDXB+#oi>BcFm8Z3=`rO
zI(+kQYnC;HFXil;>!r4c$%BKmNy<{)Oi_4M_~NhwfOH_8Hl`8@X)*(v@Zqm3G_RLJ
zM27|Fp&}{aFERIf4vw;LRgS`a33|^ZZbMy%nLSvHTro9Qr%bhqu(Q<3`U+7kbusvT
zt(2&J!<ZPBh0qypt5AQULP}+IeIR*+_&=8rAtayBaOXzz(|eB#!l!KvxzTT*?}-_A
zSx-}Pytz{r+zP`fL=_gS*`uRiywI62i;hWXG$|>gwNQJnz7JrYai8R+35zz{C*NJ=
zT>YZzftKlj{TVFg_27A1{h~?qJjgMKU9Gvc(4^n<Y9VBj_`q2$yGP|@L;QmhVP~Yu
z)9irweT(>)rGjZaibLqKHNrA)i>H(Yubp(GFWl5@@fjhZ*T(Pr<@@1sl`6yrNt2N4
zTS@w?$@;lcm^JrtZHVB>*X2Tyiu>8<gke_OI%OI{-6VN9?)s7!w;}|Zs$7+_-_d>t
z-`|qvXHo*!^d5(jscnA~v6Uy5b9<C3fM5d0r@ua|bF*;H*jazU{}~vA9h}?u=`qrA
zZ?{QU;L8>Xp_Z4&8MZ_p%aigW7GA~r4ILe1Ye5qMFkWobyG|Td0esPmu?dOt7dQi$
zGS8a6d;t7?bH1rs3FA;2hmn3%#Uj=_xbzrHN-39dZ2Z1{3BBq`KE_ZvJa*lm8cGlI
zCC{ZvEI+h`L)TQufE4WGKoF$)IEZO9?s_3@TcYu`hkjwMc<P1N`?KJ(d42i*my#?E
z_HCzsGM6Kl7lwSzZ;deMGdyxiJds)uR3vE++WrI<M(&z~qH>`S^iqDRVdr+Lr5$&G
zTL>W)!8x*wX$^AU#=Gpub4B%b(-B&zwh8SX2Dz|idbUVmSAP$nak?0cX#cZP1KBZv
zczD|h!{ed3y0y(f$;fCVK8da`^k@9dGTo&3seiJY$Xz<6W&9*i5<W-nE2>gMH)gup
z?vJz6hF<z`)94-!Oho9Y!*s2{x~<p+>|iKq`<0j)o2AAp$>@xQMC^WI%g?|yq#1Lj
zp(kb<e3X~7?ryoag3mF=|FWBKV>FUn2Zh<2Ob;&%)*n9Os-Y+}`?Vs7+Im?M-OLtZ
zu8ah~AIjt^f-BTArErr4#WPV7vb``!HB71_?<iBL_s@gRV(nF%r3SlHk!Vsj1?y1y
zXIQG6H&VHC*72U%AM@I1J1V}XbUa{hG=q0?n#$LfJqULKum$4V0I-nmM2{{`b#d6*
ze-@iz=CS;>ng2~ntre#XXNX>J2m>q>Wr!pn8z~f(9ZjY~lCm%(7FL+hb~D}?zdPZC
zAX^8)40w#De`a&_I@A!e{Ld{^6_5*S&T3ojyu4#0Y;g>dQoEBbx41f9l`(yCq$OT{
zFp}^Y;)RM3bMQj24T6zqkB2d@``aO0{isO4tPNACijFFC_QCQ!&z$P>7M#w_^KJ_&
z@Yr>H=xZGAS6*}7RjUGx{(h#U>c>^(pF#bOco>E6`Eb*R?^0l8WMw6ybs&OnZzpDA
z?{W0J59Q{HJL65N3qA^~Og0`}vD|72?G9dYiVDWeAAEOTANlJjsTf>5fm_iLtDBh$
zH*KP-DJA8taWNBn<48PTx|X(=jO9?<kjj_d?Zsql=VEzAq6o2ox~kjTyN3s_hy4uv
z3m~q!2JL{JB3X)QtJ%wauCS<}UI+@8*JwxSgk@NE6zOL%NxVN+5e(9^8+sOLE<*1(
z&@fo+DUP<AS!HS|Mp^S#6>mZZL#*a^oB51fjnTq+YRW&NK8OMaQ}o4cT3^n?Z28}u
zR-UY_TkDdOrGjD{!@+yooT(^X+56!!PF)!cq)8!6WAX+2ZKMtQvzvlhU?R!F6fl+S
z^lQJkruxftX(OBW&|z~MR}@;S>8&l-d1pyRG%R8+)$M%_<>E18MU`ANhs_d?>rs`Z
z*0Po+-iqspjH3S-mlq^w9J+g*xSj(5n!XQPBUpwKDr=k;-9o+q7?4!tUm1pGi{h&f
zw>^OBrm>GHfw+&g+uSND>$fGMndt50d^31bLw^`_4o*qCQ`d?<P_Qt@Gz(EGBW?1%
z&2uw6XI=jBUZTu6=;yqcD~_Oeoi)(s`fo0WOMJ=poh*~6pqsR{;#UUp4kl=k=s>#@
zYj7Ei5~tLFRv3VRbet68Az#WvYmjQAwkA)VHx%p8v6ONa&0&YpkaW3>%R1Nfzz~t8
zTa_+-*5O%IB;&Md!#;8waWG@c&3&|F7>^^oLdk^;g@s%%a1>>6%;efC_>R1COm(*x
zC?Be?A8t4d<g58p2;G)4F2a^okW@-nW4>BmaKy|Olbm4Jkn!pFXS|qm6LZD%T1~f}
zZ_g(g={SrwhBZ~QGYKp-d5}M)D>dwy$>9yl6Wo_ns1vZs?p@=vzSh&i0(xAIjfy2G
zp{M<gweqC*v8sjO3Cx)k@Fw>|*2hp6>p4~z7u%gHS}@Gx2o(E$DpNm&k@N0ZB&>@b
zKGprKW~5KilG_>}?a)nMkabq?kQ!oi8rNdH1uB7Fzf0NuVpGjJv;#sWg*^pU2;VCP
zDFu87EC+LbrNvMbb<-!KS}E1*e6*-hA)z1F#0u`Z8Y?b2Z^gLB8zq&cS^|71*j5gT
zd(od@IW?NU_J|YVKHYy#DzAZG#z<H?*zD^|X^8F-QSgoDadio`ZS*8iOp#@>7T=Vq
zi(yM;*;6FaAUdzY>d`pRdy9g;1A6fK;MjA@H9<jy$e4Gj+*DJ&Cc2{2WE<09<;B*`
ztIMn{?IS;1gSJwcKbLEmw(sBOV@(bhm!`b_Ixn!<n%UZF<-1^tJwH3R>F8HJaEfLf
z4<q4IQ7`&4N+{L>PG~VA{fOm5aeKw0;_nlHKtB!&J1-l-zlIPwYLDK~2M}%t<CXfU
zuaJ0y!L%|Z4C8mY&yD-mD12b-4Bp$C1d1{61w-W-1+CEX8JxO0MEEDI+s`M1d|e(c
zE3Mq<y77r{9Ua`qRq*T{7^?%rlIlQVd#*CYuYC@ij7A6wRWG<_I*OPS(2<vUT7MRC
zA@{olC_%xS(o%nHj}ub=HjwWGD{BHC0(<>;dR@i!Mp&BH=jG>Valb%0jM|BQdTd?6
zLd!C%`_P(8%=?bl!<f%qIF@#v8coV=FTQl0NfaS`v!iS!r-4YTn{1yHfxw7*Aow)+
zh&C9FL4|}o*zWQC95pb2vlqCT2n%oUv`c1SrJ>4s$5^lduY<_Or)i#EqIA-{I{Vvl
z215b?L`mXHxc9j#iMzlGN?^1_G+yX6Xz_UW!qX!*^s*{499#>#2l*Lz%+b<($^XWF
ztnR$cYo-H`pM}c6qBeqPEoO(;7{vs^h*-mH549EArSvC~#$qo0hJaDvVK)MNd(PwN
z`VN`b;vn<oZTETK`eCi+t{fDP4dqC1_m5a}oIG}g#`pF0cUv7d<M-*Xuz95v1|;E9
zC5qd7iccT6Z*r7vUX!Z)nUf)`D?LrpLB)rL$O61Vfa<CcY)L~937fkKS6BQ(9Ad<E
zY%s*TyC4}u!EbDY$;8ARI|45ziL`RjQeS5K!Y<=U!7$y5_Fk~`8R^VU3DG*Ksw20^
zo7WsC9ye^4m=EqMRQJSh++0Ohmhzd{k7Ci~*}g3{OG@P)ewRAT6lHgjk3_m93Bc6{
z!{1W!8AL_p2W(0E6?V)0rsJnY&>5X$H2q@4zBNV>%vwZ%aXu4`^7e5xF_3ja1iO+-
zKS2bEan7w&+8j+qo*&YdyLIJ!GJog2H<*DZtK0f3ueGs}nVT<L`&YlVP)%|$7F|3%
z`-Sx7GEIVwOsztaRlTf4B$~eOtfnV-8%D|)r5O~kdydK!Awex1Ru%hi?h9f(`R;We
z312g6AOORrOumh?ymY1wqa)A$WzA)8xX55-lm#r{o4e1kw0+_o(nen2=`<UgUi)@<
z@JK|^eS+v|pCWH%Jtz1GU!Yv=4x!cF2ulvC+!L)5dx|`s(^)(j(H_g7o%t33?K{HW
zT`RQPnrwC|mNx>3eB!ax1lU&6M8E5@=K0-mp?FNcq!d8w$NTL4nOkDs!E+p7i8Ss_
zP2~}PR!p^at<)K<<Nvc`bjpfZ@Qmfo`=e6f#I2HGJ<fH-jF!S0X5hqQ>!4U=S`d&Y
zRQa^$$E2O~OCcM|G}PiQ3{(^VrX!7QZZPUgONrPi?ToH{Z=!3SJ6&tx3~aqV9*iC$
zhU~K=gDN;s*qt&li*DmW(DeNap(ZmNsqJ$!wl}fr<?>=F$KOH1TcntrIC-7|r<NFz
zIQeby=MkNv|4Sywou>DmO607VEDTCU>W}l>-y$f3(8bh%-{TG-U)k^<w8~#;2w;N!
zX=1S*kYMfS5-G;Z4x>vwBN)t7&zg>$HO}l-jx@q~(q--n2*#Yf#^;fOC8*~iEp0m*
z4`$S~ry5-6$_>Ab)Qpnr!+piDnk`YOD9jEuB%WtJU2bf17GAI+p@iB2_*stTss_xB
zqFfb^>N|oz$v{m0VxW=FGQ5Ky>^AfbLDPrRa9z|%{S2)=crCj9GtG?Ys-s9={H<;F
z3mD7l(ozsJ3YMSud$)ZMH~;lA0OkIDNv&(SG9@Uzg)(y_@${OB0t$OB%(CY!Q|Xxu
z?e5@^_ChIiR^Qi^NDwwdSsu@ucuD3B4P<VTNs|em$rU;TX8H$x;fjQ9ly)o?LcS-Q
z(5jbE?3r#LXei_}Yd2qgx)e4%hhHBJ_^WfTerfi+#dNX{*9hk17n}P?xYBgzx%X|u
z+{^Q5`iU-?u?9dp#tj<=CQL$_DqOqWfBD;>L%AeF##$(R7fK0LZ4pZAu^1GJR+cIX
z$8{2T5@{Ipa?=O4m`X~b)7%{=_#_jAE}Ys=nyD!;3RY4C?xCZiW<#dVec_Qi+3w}>
z%QTM91;I<1%RRA;E1tf)zrLoq`Cw(^!~6X^B;?Kbx4T&qHxSKdVwMUKyjhE$;rx7D
zn&sHLSAKbHtiC61*bNlRD9Zu$p9G$gx1;fnf?c&2hU!eOe&HX{-?TH|0f2kcO&Y*6
z(=j5crt8*z1$Z9m;r-;h=UkLJ)gY+q6^c>i2x*5VfnmT7CUtLbhwbz}D%!$$*x<i#
zF3HIzo|75WOx|NI5#eqr)UTKQwp>hGr{OXqqtWPSnE9v(LGBF{Kuyy#F!E2#&5E_o
z!ZGnxHh?M=VH1vx_B&+SL?;^Z`@Xn6?SS6B8@t*6N1z(iOgJm^?Yv9R1G`8hNNN!T
z&KvH1_r=+<MGLHWbNA%W<>jW&XPwJVm*dTt<GRB*7xWs5Jg#ZcUt9<pa3xUBVC^xK
zqaEJQ87k{}NsB^9_#FL=XNulJBLlcMQ*Zn=Nu_*AMD#UoBTh$7BUCDS6M(6&6JMNU
zHtV|lF)URBgwqBlajV%h*T9;~>ilH=9Ssp&Bn<fM)DN@vHZEy;FcwbX#33=*>fih6
zbsugc^i?>YFQT5yJ+zDyMZfeczJdpL8>$Bz`&YSqIdz`Fw{0*hc>Z<IF5)$a86JdI
zu0kE^h!PeTPd<R_CiFUvig_aKcM?fj!xFc{8tls@B1y&6ov{r=u=^8h+)mGo=Vlgk
z+cA>S#QO>dOmJzTUK-1d3C{V<p{jL#@qbKn^><UI=xV-qTzJ)-ZP!oOn9bXVqA>;1
zcW|?`Bm(CK!EfLhC^uQZy7W1eU^pW7)DG_e(Bys{sn?Uq!!3q#Qfg-){;{P+fg)fL
zq3>crMv28!OIc+r&Lz0?XJJ3pu#>1w^FR>38HXh@!#h8c9MA)}Iz5^<w{i8;lX&a5
z^eel2w0U&d|E1hR(Y;^eviz(N3pU9zn08)sPtn|kAeSIyMuf~2yF1Fn;CMF=Q`P$k
zAZ?`~$4xHMtlQo#$H+2A7i4fc8rA>aAay(C89s}eX-~(`Bs@w%(@lBQy`NR~Bd!pv
zIVGc22&q|lP%3GE??z}MDTTwudR@YN2M%3zBAQCcG+cNQ|I(jowiAV^$#Lkuy2kVE
zWQVx@sJPX(^RicH#~(TgP2rdb0B+RMzqx;zAS?9+3EqCi8a|;A!*0r`iP50H#!foY
z31naj^$K*!6OSl22@m{1jZ0a)l6bkp8y>l(oST$`nea}9VRNPt7(DP@_-H*O<0s$q
z7nK<gY#4N-ocJ3FsLyB>s<~DeU}q^L(~QFVepry@^X8I+nDhIl`pMR9fbJUajgI>U
zRL0x`Cjsk{rwa79v{ZLqZlnurKbNu?N%A(t<7QON`ur>O&M=;RG1Td22#caohMFx!
z$(2R#njp2?aeY;fwENX=$f<$~*s@UGzn+bY``W;Z*zwk%Eoy(T_JH}^WSgREW>%3~
z5GmgMePwQ}#}b@8%c&L1S+XLgs8h?f)p&E~vrMIG<LFrP_9s(+OQ&#2jEx}w-{W`|
z|8~!KOvxUoj0|q8dK+Qp9Q_)IMyV!EXNS)R&!ge1nF^UqcJ#<3bLusEsg@!a7E1Jz
zThe)+vad@b-&FE`1AwsnRL;9-lcnqRoW|ydAJhEUF1AaBut)_K2uT-z=tt1r&}Qbx
z{2Bzfbtb<XYf8xgrt|Vc^N>y!Q1l${=8_oooacTA*N<DcFO`+328+yA%cUv%KI#Z<
ziqrc`xUMDc;7ixK`S|1aH`~ZWyGlno@`_y}HwlnlrjllY)+h<d*dYdMV4E6ld!lIc
z{TJH`!C>60@|OU5!vHG!Zk;|&)wzZ09$dod?15eoUsHXn<J$A|Uq&BeNn*b#d^}YM
zm2cah`Q6I~G-{_VQ@!^r41HfUE1K7Sd3}M=Vf2a({>Lj$=cKoA>*~)dOI-}jLI|XA
z(nwn%mVs|7siz^5QY}wl;-=B5w$oq;!Np{TGjMn+JW)?)N6~kh#lN3e3z|7NmcQ(p
zB5Oqu;|qrg!mAC>Ixe{=vXMMt{btl#cgvOMtQ0KNmOigR-A?p3JP*B(=c^~`D*dwb
z;wu92*(#QSP7eN=AQ7s<ND|B)$hWt?Z!V`moh}=ly>hym(YG1;=C7^;mbx>K(JB?a
zq3h}RG{<wq?rR+T@=4k{z0OqN2cahTrIOOMTu^_$VNCF3NWia6_b^wkJ(u`HXQ}@`
z0D?e$ztL0XS6s#*iigz90+SN1qKqp{YpKN$)lA`p4I(%4Q;w@*@G^{%l^ww$2dK9J
zl!hveFIb-I`?oJQbiMaIWibWXq(ms`aRH9t@Va%Tn0<TO@e7?=#h@;gy1TkYckBGo
zvs<_3AGr74v1_invhd?OZlAm6?e8eO<rja^KB+a()KrmrkaV@wo-mfGORh3GbHEfZ
z43yQ=6T(?%C7X58cP4DOAK^5tjkKmRp*F>;lQ9N_s^`LH1gf~$50^maW?=vi;74^*
zH8K;YIkQlP3NR~JT=2W926Ipbac+zOwPaN(>08`$kEfVMtwN2gJpY0beaaZ7prj~=
z=RCY_ohkOx-t-feRV0W}{^6&edTLC^yeIC^3qJSWaYw_Ax8L40vSv;Dc`tcMc<cZB
z2gVE~F~dZIzE;iZ6MgNH0A?u3>I3f<=TZW-34@eiV!dira=e4L>R=46uF>u_bBXLU
ztjGu2Gy0Q1BYMR_SvG>pV~ispBiO1mn1SkHa}d1Sv^t8JjI%)cBYegL;KjliaUJk0
zK4Z2toAgah?DhOIddhs)z4sOR-nM;w!HSb<TLzpI^2j-LrkJ_yZPU%(X%HFj+_`h;
z?tAVjf9JBxnlJgrH`?Z(cV1!5%9Y`@ANfdV6492m%sQ$oW{ep!=^!d|X#hhQQ-~pO
zol#&2EY!tZ;_S1NV;!7t^36IVi1I)*z6=Y^IU$-~VMxks>M=%9qU-xcbEH)nCfgZP
zzz`sGI2c7tV^)sGCx_mXYMZ1`9%By5Q(m)CHnM_*iw%NQsAj2917G|1hOYO&uOz16
z<ZKfI(2-`0I#VP)wa2eICz&{Coa`GI7`XT0hsVEv-F5kYyycd;r!QRC_|{K+A{4ZT
zP?kg}H#X)5nGnmh&*_2<RR}_dA)FC7*oPk*O9+?)3gY99e5zE(#|`Gb`;rTjkrLo@
zDIxTxGARqRi>3qpVlyToEwxUZ4ugw<&dG$7FbNU6_+bu!tC>Pk7?NCVq73-fqz23p
zF@^yH?`3i90~KoC37nj5?0ER$+{#y;Un({=Hvy8O_H2jUtusZ9Cg(;C<_=04+tSfF
z_?=sBp7?{i?rJ-^p`qz#-}EMRej%_EEs21tlgmlnff>v=GeGFh05F4F;z)xjTtc{%
zV6#ptm>vGxvSm_ApiU`2Rf^{jlo&GsHKx+l)?r9@qIFnE5T<|<Gt~(uz+ym#0l<JP
zWbv8wfe8dh*zua1Rs=uBm``q^LXAm{8V^kc-S%<&ZOy$u`_}Tp)u#yN$g5Cwrm*gs
z{6T!D>eA2y+qVz><30B_f32sd{Y@fP?HX{^OtKR#>T-Iq-iEMU9fIJU31Zh7!r^eq
z2i0)s4=J&Fb;2;rCGNjJdH<RTy6R<{b*e+m7^1^W)A9P?f-;sAwj>MDk|9kuPs$KR
zur3jtDI8(U5HVvIg0w88L|SIX911&g82&78szQwnR2ok|DL3$~|0;HV;5SNQiV>+$
zY6N3HFFh^g@Hus+h}xPyV>A{`^j|NA_|qq!Z0l-iZvQ!{Go^C5GBBn~?zA^yXO-en
z-c=!&3`~|`26u@ECT0rHk7Qx^X2t1cFH4qPw{1(*ChCTi7*l@L3wq8NA|*iLs_hhp
zP@+o;bHVB4hKtpM8_|;H41pV^TYN^;0H(=;F=n6~7$ddL44?oHZnhs{aXR*)jlBH)
zd7y6W)GE~K3tlze*rvmeR0ae`$k3j3qZTlRNZh`kbaoEBy|Z&}dEUJC7aq4T3~6V%
z+}xbYGm`U_6NnfCK||~iKbFy+eFe9K<208V&OEp<2k@L{VlDweu-bHwcCg!Z?uoDt
zC&3I;CfF)I8<~~nes_(g6c1?`fl^-?3n$*>p@p6zvR!TYtubH<_s6d1O%a(?P;+EV
z0COmfCi1u}i{h<bia>>8(vnMFKFW39aa*x($4-gPge5%Bk#N)AJrYG9J!R78bdl(w
z@0#7a2S3`^H~(d=tt}_&&ZU90he@~Y^7#M+?o7Z0nK$CxLgd7e?lzVh&J^rfz!ZF2
zf*b$1_7W+<HJBs9&o_G6tP>?Mf2fuc)X6Q?vpVX>h*4o@5GPV2)Rbr~pMIR)$)8CB
zB4LWy5?5`kz_dJ=AjM5Zr~~DCU6RVh2v}v(gj?5ZHooBG(D$AHF7Dd6QH_zFg-WUx
zXE<#B=RH#}sitq$|ASZW+BNvT-Mi;6&*fU?NdgS;4G1S-SBD^S2p+;`VvMb324@I&
zogrdLfrLTQfCuRVQ!r%n%{sm_f#kqQN>D#z2)Rb!xWuI{ek^dAmB$EE2}Z1`OB_J%
zDrDqm(jxU|%mGt4*HpW}${5jmS-2|4nJ<<iR<R_@<gb{*ZYXbQqL`*#k3L!)9qdP1
zByBMXd02h4<@460t4uuq$lA`%q4)3DG5_>(xn+Tfd_=^Whp3(4odMisKLI~P4GgZ^
zm>EocV)aR?5)1}2xMc#(k?=Fm3_)KfvTxQQCBVaxaioZ-1xAP^1q_mn!jT`sqeHVa
z`4yEgj6C=;?t&XX43QCkwREV(7$Cub6AQ!1g&8u20CAe`E>UJO4<KTCsZh=BYKpZt
z=lizrPy-!V73z6A(H0$4(q-4thdMe2|7!E*w$=H3%R&+2kcgKnD2<4HbobF7wWg%`
zgU3VY?ssCx^%W-k5cjyKD_uo02?6!MRP1kXqywsY?6G7SmbnB5DWPbaOUN~XSG2p(
z7Jgj!#|)AA1w=Z=<19cUU==XNsAd4dp3#6TSMsBH>gMS%2L6~iye#4XwRbScExe%P
zmy@#-rNLZ-j>r#u?}~=5kN&T*MW?ReER@8jfsysFn&%}`KnH0*l^@x$WAIZCJ=C(J
zQfWO|y*DHx=E|5Udkio{O#CnbVYd)IerJT(g&|-Dm;%Z}Wx{1uiPuYC|N2QO!SQhL
zaLqnAR7sylxa&-S>lUiP8WE;`BSb~o$Sv22)kk&0WHP3pS~QBRt_qhYwJ-&RU_1^t
zGKL2Hit{qvjmwU*WM1;<&2+6oNh;6RGfRc)c=(Z#RocjFY-?}i6tb3RGx)>hc|J0Q
zGemg0t84K8Tf4UL!BVO9Y^^X2J7GiQPUytJ1cYN|fM^{C*BAlfS3G``4uoNfDu!UA
zk9Q=vp@ZY$P!V$pE+s&1!YB_DY)HaXCtQI+`0)q~HR?Yh$Z9uD%kq#ei?KY3H;aQQ
zrcAB@f-!KNDddffF@P!wvspQ*-x<Tx!GYhH(D4}}twUARMq2W2o|nt*y7sC{@7vxs
zy!g~LZF+tl|FE0q8&lA=bjif_uC9SEU3+cA+U?t0UnoKx5s_9z%qAtA;BjFJh<*<S
zNSVSJ0^-+!J!1-Igld(^hh?aP8^hoE&g3N`BnC%1n5Wn`>x5g?gbA``SoxulTGj}U
z9D;ERock4>`K<wnz@+eAEsKj812{M`W{B8thz(7Ke-GwRMGQ<oHX6w#3>tozQv{ub
zn$S168}weqz*W~ZcmK|&Q85Ku_^dVnN|wWBo^MQHjoa1R+x0J(Utak9h7EHs77>nW
zVmu+@&c&m<5xIynW{@cikfxE2MH(10z!>rqcI#9}7`2HdSl+06(TmjGs?y_+C!hLd
z#fh-K28`pAYery~5;370;fqvIc`P2kY$h2LaCj2%ljg>&6}t(wI)q$R*^ok-*w{q)
z!41dac$wy=VajzBz!1pL0XU{ry?OJ<-@N^vdxlrM{1v0k^XIo5iDMtn8>Zm+2I|Pj
z=xFbymtQ{d7dPB6|1J9d^!V=Gm2vG<+pT=g6pZ*b4}idBA$oUX2Jm=ehKLCWvl|Ar
zxw7t*hw8+H`okX%Vfk`>F}@>&2U`WgSpWb)07*naR3A(}&&6DVyfP5H%_DHra9-$)
z;1!0BjL=hmOn?@zbc=I00ng%$dKKq{>D9|9mjoDsiex~MUtTkgU#`s|V2o+vJd5Yc
z@TxM&D%4n|>4Z7C&da`4?Ed+8jGZulzSm$%)<Ye5UN8k038b%!4qbEIb)`>!``Zg%
zf8vP^9IY4|9ZeKhr~-m?0<Yy(r^jJqfQT`i%3_xTgq@usy!ge5rFUTvED{8261;vB
z)hTsh4&q3LM$M|aG4Xl2xhbr{E5g(8#OVSvf~Sh+#l=F3v7bzs6fOx&{#ta38>TfC
zN|kgbA#R-b)OvYV8OzKnl=du!`*Q_tVD>-oSaa9LO`|8A`9f_J9ex$+`M?wvUJz0j
zj@^CVeFJ~-xzEi#ueG(YNt<vZnm9vXX6Px19M{zzcb!2z>~0+I`EX+ka>7sq@SJ}A
z=tq-lYy6B8jAC<%Nh!fx!leT46!l3cjo@)p3yk@3-KKDHSW$^ll^}AehKZ|eD^M^7
z2&+w+&l#k;O!CC5PFdM$ok%AR<7Foy^+b6%<mN1*Pa1aIeRpovt1c+DEnIlmRjB7W
zQ_zKULFK6}Te|<@^Pg`S>+aSib@RfA^m<d7k{(HV2x6zF+;zV*frs6N$T4v`VbU{8
zSa`Xaqf@F9^N2IfNb+#TY2(J^eQYR+H|tbe7=n&5^+|phmxsVLzXBPt5l0x9G~U9g
zpM>gkfvQf4B_D?Bnu%Q)g96=n8euY~*2_ggm{ok{;!iE``geY^xezFmIMiF6JRTdZ
zG#@`F*K@^X4gJ6JD`hc7buE<R(3<BmQ}7<Ombvp<{p{`e@|V9{yzY`q+F$yHH-wTH
zLW$}Gbw@_L>Yi#p-N&3DW)g?7IQ17@2As#cYtd;u<uL!)u2rUY{=%e`Ky6|J&v&l{
zW`vJh!)C-K+)7R?9{h6gdVWh!<4EwlxP(<RTMaUoY7)kkwKwpX$Y4ltV?Z8AUB|5;
zxV_#PgTN@j$R&*xD61&Ah?na{m<m-wg<2H)A6l<3s&4E%{>-y<t7uL;oCZejLvEgn
zOrbe-^3og(A%DNVG+O@KZ-4u^i!Zt;R~{LuObqJ6Yer`46*)!?qHgPSdMWKDT!<gz
zakUu4(;dcL;(_OM?WRp3y!_?bz1F@25{4BgBn8ak2mb_)bf8m=xKunYgkxdFLls8&
zsh=<w;{=c2;FVzc9e{)x4Ar)(%gt7uVtC@KE$Lnfxh^55l%Fz&!vPK&GgATr92*L`
z_JurSgI1w(D_?cNSes5NAI^=u=Mq!U&elC90Yq*?<#P9}k32Md=^bmAyyn~&7mDNK
zl~PYn!Vv5~IPvb`(p^GFJVXt(f!)qm<ECi_MuJQnm&7D_`HU!=e%M#PTDOHS4q@}=
zWV5cjD>>%DN|y}8UOkr}&InsB4_6&d9(a=YZB(|%B+iL5i%;=bSbl#@cfBd>c^MBV
za0WwB7{}-J@$&X?4VVmgWGF+1ptYo_P^C)KqUK!B)jwz)e9wDe3dH(eYGP;(%FnUR
z6n^atjbY8Kh8^7A+da0iYgf2;*UsWCn>H?LnK!?=<+#C6>g&(tTidirCnAK{Ayi*t
zrT3r+cfnXq=uR=!uEXM5*QU69F0o-l2p3<R%q2K1$nG^4oY_aHrKhM4RNf}jav|hw
zJ|X`U!d5!4Q;02!bEYvyGQde#fu)&K*CLW1e^hsEvJI6QrKa#6j^mU>nbZg2YxTm7
zt<t={RNnl`18!5N9G@agh0>RgiYFW&2JU*Wxnsi<BTG&{gYR#v<5atJ#3TEmGRGWK
z&@O5inlWjAev|QL=$W3~Lw9f8T)1!Nw$`mfLj|>D<9uCXKeqgYFsSoO4KRW>*2*G6
z<`mgp1UO+1+*+nO-opl-<{>Vri!jI!evX6>>BKA4a=``3u@2@DylPrw#mN~V+m{d~
z<xH5s{SXy#X>sN!E{%7hGo~O905YDQ2Ca(jj~~bUs=5`-qhSGbzq}U4#gGOA$TeHb
z?{RXe52(nOF$6dii376AxGp1~a~$E~j(hLUuYUEb%dHEJGmr$v01w&EvBVVOhwP<x
zZ=2Df^4RdEuFmqhT^;!cI(M{g9_Vk@p@HHYZIUfiLyu{+9$vIi&07e=O(N3qr*-@F
zsMLCW8Ufv&i8RI-CY2!cZfu-t;yC;sk4v8FFmc@M_pWy(Z`E;(gLwq%6O(I-Q63zh
zOPI7!gpc%~a01U5L2=|E%`E;XoCKn@8t}mI<muBVRZjyrHtfhX<3I%Pd)Qs#tpZ7z
zi~*U{pzO#R+$1<T4c{axeA>(#)we99LUsS(vZlUYe$S|wf~^jp{#&*~^>b7+MJg?w
z<Rmz0`B2vNWTox>eWfS5cTGI9tD|vUUw3PFsnpo4J2e(->9Z&TFYAJV(R8O;FOr7h
zg88A)Pz*z|l?@wBGK@=?*M+bNL!j3?QJWNDxQvXa5k)=XFmdEb>X-PLT=TBP%9SCU
zcV05tX6G8!iTAPXgYYU-Ml_VDPC|=+4=Xx>G0ZAm-kQun@fn=_ggp*7$vBx;F=lv_
zYMT+iQ!Dx1kL&&fg`=KYhVpoQtS*A}I=O4j7~=+pjl7Y*Tygob(0Bj(rj93`7(D(3
zXGnZVg|eF3)1)OIeCMcS3R97+0ckC3N8D2H=*Y<Cp53K&yLUA_*4y2@WoWP{!fu$O
zyZaV5GzMKNpr!taN@;wYZ^tDQYPEW9q8u93iTSqHP&j2($QK*J@cIp*^z3$NO(`?L
z9M;+Tv7S<i5W4%5s!)$4&f}Aq<k#aECdrFr;Lso6zJ@_yh$o*&KDv_T1Ffk|a-D$o
zkQQm06B2ncE06j0Lw;bY-6{D}VkS>5!z0*ZSX&4pg*RA_tIkX~wdUx@>Y--vvQ2VC
zVS@)f8Ni#mI0P!=n(s9h5Dmp`H{YCJeg3OP+ZQix(<D;;nm`%oA^16}n8KJrtr*Rv
z#`U$(k!^zmV~=<53Xk-37M~a#oZD9(E9xq_;yjJ|jT0LAr3Q`b$hM?|#J<kpdkb5K
z(txr$o?I9k4=oM4Xz1h<Lmqu!6Wz+DExHz7r(jtY1>PCKsl#2-cmjZ%8t)sUA_eFy
zBn}&OPd`0*oCAt@Ld?K2EOVC@ExY5&C?%UzqZ?GuCrCHK!gVW;`BRr-4NmFK5aa>K
zTzMxm6PTpKB$WKxqk5vd)`l^Z{8e#jQAs^j5vy;N>vgd;Br;&AU*c1{#b}vui!@8a
zmFANd<#zv{|J&O0Yri@yrbsSgu3de{binQrXNr^&7%*Ht;-N}Cqa%Y)_H<Vs>+5cK
zyuY`3+i0mM;xD#|z?NvFM=FS6Dq|Dl5(>#iT*3@sQm<u0khc-duf`TJHi>fb%a(+q
z<`@ON`8K@a$uP!;+F_K83Dipv^At(}Zxuy=o+!{WM^WAEm^tyfg5OTVKJ!c>C7#v%
z0)~L`WXR%D!dyidfu}C%5IZ(w43%YmC6b=`3D0DKm91&w$HFfl9@FL(1jvYsvBbn{
zzob=_>(7wYM-PxOtHAM3D9$zsh0jn&Wh7xuxb;R}K@;q;r*<`UJ-M;8{Oohex^%AL
zQ14kBF{aSGz*;kv3f?5c9V0`7_jPsT@9o{)^i*lMVQ_rBK~f<<R|Kj{DK!v_8N^AF
z3S4VXyQ@z;y8>>A@}4vx{m>g?mYha&k$)&0H#Zc|IWrXX*4MxTkAzW(SJKCbmI*lD
zf97Z^!x(fNGX#F0qys`(5IYhF;=l01$t74;n^=M+ol~+fj|{|~F+&<A&aBYElNhVM
zy9#9CYCqv9E+a&bkJ98#jLNa<D}-y7IN2Cxk0u7N$~Y4Mi=X;qEk5>>ro4<>Q^qcZ
zsQ?bYrb4NKG|Uus-hO*-;5DxuowH;~iy<_aL-TW3nZg)>hgK^kglrF`*VZRSo*f(<
zdZcgn#66lcZyX+KQIiz)>Qz{xNmn8jDwR<fLDXsU1#fvD(yBYj!Za_`EE9PxAi~rI
zE1^|4NfcJE(0eY$(68IzOHXV}dQyJtAx-NiDJ1DG3sWxdP2fU(5IU1<E-`=#Uir#o
z?tsd~T%t=Gb*NRdkrGr_C@3u>Y>y+}ZB?AXrg2$5hploAxnSM!7!V|``x6dvh|~oR
zBYd?C(Oy5&@n?)d9bghTf^H!!YvmE7>?B$)DwGD0F{w}|&d=@szklD*_ks79#1v!7
zl6KB?wGfg6Fo%UHCfku#u-eiNBfCmckp_Az>j(Q99~&HK+c7pOjla?`U(2Tr8igzD
zDTs)Yxq>EGWHn^`$a-s9P(PCZR>+heVB7#lPl=Fo`o3JVMx(+>%R)m-bLf-Q7<%xL
zFu{RAj(TG#hlzOH2jmAQa|o9J{+cOEuv}S#O2&#4Cu8Ygo*-ok=z{qJk^+Bh0mH^|
z8NmxyNr?p}h4nm^&*Ol@FV_qn*{mtDge2LTGfgyyA1rWbyg_=r1=61sH?Wk6JB1n2
zcuONW4I5hgiu3MFF)C6j)TmUbMGL~%Gd<0n>o*LaaOPQKS|~ZBD%2rk3gh%dEA@8Z
zS<4#jN1ht$AA6|3r}EHXfAiL{QlsLE&AP&MzKDwDRKAgAQh~XGGGWbltHm8eg2yLN
z(XRk0J(khd`u3Bgfn9{H4{wFJi;ojyoE?gqXAIo?Kq%_~Uye&yZD7)59I0Vh(+WV5
z;D*qV61-N*30TfJ!BFgEBPrO$MoJiCB`92Na@QC@s6g+`fPY$>!diNjpG;14q-SBr
zXORk2w<EAJG^bhlr_3uXk)KHeDzp0WGPClC)58!?Q^oOMAj;yrJR+hxSx^fo74?By
zv+&H1uP&~9^#x<|PgqXQLuL*-Q`j@9MXTltBVD7V(G7!r6A$(G7T1-ATe~L8g@Tx&
zRjsD?)up;?1<5APnkS@l1$$~I_&waTO^Bz}J-udAS@WlGRV{B_qn;CCYjr%dwKj+1
znQKC!xheGCeP1X)wKeHHUIL;F)FjGtgCBxVSChhPUmL=*Wg%?eo@~@HzYs1n);NiD
zuy?PO)foVyF~3|;W`1Q<&<ydJRZJ!&Vm~knXK-;I_meK08S0hREp?J40su~lOp>Pj
zM#%7HW$Ph~DR7mL)i(`;n|MQyUmhR76tsAwBdtO;p1LU4^VP4n^!+~{DTyh{iidW+
zhEu9@pn-$J6s#brl1`q<lR_p)h2g>R2L~h-h6Wm+Esr*qBoXx1NPdA@3&P;7IBXdl
z>@D~c*+e((4KVLv;<y;jn={T3!=~|oS&yV?>M_(c-mz$w61PC}j0Wv%^gX&h3_qxe
zGRHNTgp-fG4Gi@{M_|>87ltmpF!|sLXR7>N3E+TX{()DPMt2E;3Pqd|jQizcC@LeQ
zG;GzzFOT{4lfe*I!-5}JHxA=rTw1|eLO(o~Tay%V<~S4nC=I-X9X~F0v-+tLSv+74
z=Qylz1>7YbBRZ*!>de~QB^t7JHSKu#;lUHmK6|vGxutlhR-q1vDZKtJ74)1$l}i&7
zgByl=2k+|ZX?|p6c+Rehq{4Wm(5jYb7YOVlsSu<hCC|;X<MW=d*c3Z)7@W-Saqgz+
zz=6o3`N;yLRGBD^9s;*q%PMKmD5{;WW!t9Kmax06CG_8Zk1ib89db<)<NzTtFi3&j
zx`==q{x}7zvsSj!#2F{lDVtmyR;x|S48{;Dnz)QH<k!E2@!-!aVYkHNch}?Pnl2yZ
zGoUn#KdDWUU(sg3LxQE@I#3#KK>kxJ%@7DcZdS)ME(<$j;9}Eh8epIV4ilGcX;M4I
zSQUNCBDd|DABEH2^rn&d%TLrQls{RL?_mAzGgDaQlY09zP!}D_t_<!hjf`#>?hlU)
z4HO?89Xf7cqSC1QDGD6zqT{t2spZrOJ#@kth5(6x^{%2tVheU_C1BiN<yumj9>d_@
zA4A1N^bl>^+_q47(HWtrvr2vUJs8HdI+fE-Yxdb5BmnozzWd$5zAeE`9n2%R!UmNK
z3Qd_`zC=pMb%yX!o-_!6k=aLAi&Lb<yX$a-YuF4=nnq^*Pr}N-hY>*5dh)m*m!^+9
zdXKzuL|PV6o+neER57csy9T7V6qYomTvj%bl$$upr{$N@P-tAeC=6b9c}wpn|8P)D
zp^dyeE9+zd^q`qpGX*7MX>=KT2D#<I@$$fifxe*!hWm=^OT%+^jE}3LxqORiya+(G
zDtESyRZ9i1WW+Aa&KSg5Jn0To?I$5J-g-n?-Z&zHb8nnH#jgPQi4!q%l9p?4S|kmM
zr>_o8^X7zZeNUnEz(X44*qIi~Z{8BZ`R6BJAOb%-*G#ZE-=x(nX9!Sc?@A~D;TvNp
zo~;mw$CyE3V2yy}Vl3>g0+yD#&Qw{Rs&rsM5a%H(Zow(dtuzdAdo*R7F-3smxU8tC
zEXD(`pSjKuDGYHohe*TtePZu8tum@St5Eqli*<Sbu9j^NJv6ZVMdyqbo0}Vx{!+$+
zXl7&zdtkK;>Ji1JMeB4?WoYNvNcpjWzKMGW`-&UJ#}=0KeZuDPO0iu~QZP)1w)ujP
zWO==%Ggol&(_1{5W$=?|PU_F%({zJz*aRGqeKXV!{IN;n=nSI87!B${)TzdnmxeAe
z$KX8=m^8>OJT8Qv`I+Q^-<B=OTmqNXr>NRKvCh;d6#-E&$QZ%m3f_oE@zlVq;+$bJ
zxHuiZTgT<$2u?|cP*IbvJsc6KsS&1$!EeUX-Blz>ZVg5O3+pF)7MLMrkSq>3ENPL1
zAg`gXDx;!>7!9zMCVfe*<C-g*`rrJf%7PPDC`R+ptb$g91OBPW6iKC0W=IO8ZOX&r
z;{%(9`$z5@=qqRiY0i#`a)X|y!2O1E<bi~=fFy3#f~+#|fC*VC08%2SvjC9huEFtq
z#HGg4<#*SjqjbwlB)DSN0B8R3-r#v-{MK`N3P}w{_V)R6Lh;-ehVFTDL;p={!}yEN
z(XsGT63Kwtv}Lm~kugOi9U*@5J5vxgtKy6~a1}>2tjJn^#Q~7PxIfKfa|w^bt+He%
zGv#37mdgS(U7C=kyGMC5IK!YH;!^A7Wd#7EG)u;ftttUCc$&ebWm|D1$dfu^o<bPc
zErN}w9UuDt`-<jWzxBJlVv3?(E0(ynS^;`cOvV)c++a2AJb{G2R**)wjSi1KKGZ*c
z|6qS%%lOy=Y0wRg6BRK7I<kJGSHtWRI*jKJg4$%QYb}gl*y6ZqKk5@O7|UaxNx#Rr
z>u`JIAz>d4kLqiIo|WgPEbmECE~`^KYl|k|#nmUtdqL<q<+M;l)!MWvxtYV3V5K&t
z^#nCi!X<?<g5r!3a8nB$ek%^Q+A4xHgHK(9RfIDYdGI?91+(hMaTZS+p44Q`=;0{B
z)EeSFE``~{NfBCE_`NQ%tNNOBsE&hxD1vx-;%=rKI|E@?;4CHBxc%OHDl5)AuiT`C
zjf1@k1yitBmflKFc}d;0L^}M$P=D$6zTL$~MuwWZ!h{Anz0IlmEwCyI5UIe#S((HH
zw8iAp@KkO+p74~pLke##0K8fzHWr`45bxpX{8S|CISUX`!%)*WJq_~&aT!x`+S7^Z
zbPbC(`^tkuVR-pTA-7~nsBmVAc?2h8*|~-q(r1B)X=Ee>1WPJ$SSKD_!Wd^3MI}ri
zPtvf4b!r~+rRgy)gOBooSHTPqVKYRgCh|@;0bK$fZz>1!uxE~3tB1ly_<AS{SGiN_
zOd8z|?x?KlF)ArS1y7V&y~*vq@y9Jg?|%1$n1bAPLd~m}!0!Jq{RKntn2^JGKX0-P
z4V3R2>@D0gJlL+2JjG^x%3-da4qzsZ&@~H^Rte>Dm*lKP!R7?!ehX8So5=6p7&gl<
zc=tohnLmz~pNfFTamiaF!eZ^m;U{AfA3aGKRW58_5L1+OEluZeIgFfgLTJ!gr^=I0
zCJez%;!M6%wFyFxC4{R_PWYKCII3dAlKKh97F<-v>~wxlui-i}aGJ?H3elu#w0n4b
zkHa-^j{r9Eljijz$-*9|fIX)(Mw;l)0z4)QD?RRBOVf|l0Ztp~4~?gs82Wy6U32H}
zZs=QZ(h7Z@Gtcg5R!#C8h$%8@)^h5^_K~5HM+f@~4-EG=ZW<eH(UxIB-J54Q6*gqj
z3?o>{sx{AM#-E^;8^__V6^4L34dj`hKpLjhEIx@=L=u+;<8YkU0S?TSG}Y)QHA(MY
z^mO-!-lZoRgJfzG(!!2%BxTb++5ABPBMe-{n~nUy66b_P7{4)zTql?;y*e%p$MCT~
z%P+hc#p16rH1QOZHmT4j`KBb+2&DB$9wsfyGAGZV=Cc)QuN!rtZtmhmLd4Q3t`aKL
zM0q3^+K!`$hHZD;mS6GGmzJC7EoeHJDpc{#-p<SJ>Fas>x>9N0pia5-nRMPaXj5&H
z<<yCwQ$N<0G(#dvqpEsK)^a`0RVEM+=hhg4NH?U(@0MtXjX;vPG-8i*GRve+5~%+5
zMC*p?#680*1BQTb2fBMgXUkmakWwO_*{V0`Bqi9B;L04%KgCi)@GdEgK@^4{j40%%
zbcn(k!rU}I_E*DM9z|yH;2@m3?kAjC;l(J*jJwqVYkK{do5JGI%;1{IkZk3-tA`*A
zGi3Epm;pbI!*ALq2q-5lgfr25^rJF!i(t#b#$4xre7QLAp7)l-6wT6h5X4m5v!_`0
z-N`R+Y?<GZ&sRFe%Q~AH3iDONCM_OKpf@vLfFPXkaBWI0H;$XmPkk&;1P_B?E#3g8
z;>x-sf^k^KMK&{nnT>HU$5ShhH>LNyyMje+%ImP=SpPui7%J;_kmaE;D8+K)#)Kib
znIk&zr#{Y@!Tbu-W5y7;RemEF#Zd+G%d>|Yr=|XwupAyFv%x16Q;oNg$EIPLtUAd-
z+%^5*Dxb!rbs*&Rn$}fgWNE;sdB__Z;xXF((nj7uuCV<0FtqO3mX38B#`W@<zN6p}
z3EYAF=2x`NdFuz3uWtI}(v!9?%NKgP^a7|R*<!m~C7Wqj%g{QqPSA1OOnz67s3maL
z#IeehHu)YnQh3Ybc|9(2<u&8^2^dPpn<Ur%O<`&$S`PuCx4raHky7Z|-5dHfxh|*!
zDw{V4yOo23f1G{72$-&M=>QWTA&9eywp?QjMZg$N48|lL1_pm*<B{KB!8x6EJf5E7
zkz+VBmvjOc)tl$cy2m1uc=wuXKsDkOmEppl2F%4To7Gnj;Ll@Box;bkib_<dG>xzu
zL(Ti!dc#pWLB(gT`$>MNr)Pw8_ADIkKwR3m*pSP&zhc3nMISry)UnsL&gt1*9xIRN
zq-sH}l$S+XSc~nCOYJ<LF%%3z90@X{O<|lgj(9V2@j4EV!`&l3jdSFN!hSq~TuHvf
zH{&lwqBgPG)Z5t=I_E8tD4n0YS;x*bn{*rt7t>h%gbj(1S#?p&1m;(|6E-frXjVTA
zLYbeiTVU|J>wbluz#O(Z?8N5j;J~<>geGx&m{#+a!;^J~15~*gY>L>+^XK=lxq5I^
zDEB81mWIhMn>v$qh|44Ms9ru=*%mtg{tGQVTes?XLEz;hOjea7xbMI8i)h~PG%jvx
zUijW+Cl}tcU~z|T$r$PpF<^=$J5k226LTC_%MX$)o&ZM75$7ignCzM4w>*}>PlSUs
zi?HbIC(du_p5pK|@;FGf3oiAVmgd6HuI{iir!}|}mW4cT&~YONl49r1L`v`+GBl=7
zu1#7HPUGk>(la8F-{YJBYK1LLKSn^2%P8v%;5aR;_-a@WXOyP0YZ-5DYsM77;|Nwa
zsmECzQ+Thh5w_QtI#J)K&eSP}F@F-3R8#5I<33q1wu3!3oXaoZ<ZNi!@yNqre00=Q
zC??zq)c*gfnv@}RKu<55Ys?qszGdl2^FDdvDFbIUw)F4P2l__U9CWU3$}qTuSZN|z
zYqeRpMmTA+A~B$&G$`K;cv85yr;-fzn|RamCu34yT0mUR9x&5K6eIygwfwqsXe^BC
zt4Bp0{Hq`#Hf~CeiksS$Ag3o_h$*U*;%H#AN^?K3SR-Qm3RoO&mCXo0g?9$1hRec^
zuL_UhkJ98#3N_EvuCS<DZB`xU;S@P_NFHSBbAT_apBlmaraq<h^yehM;h985b~ABt
zygx5cJNEI+_jGN4X9w#+HahJ=Drx@NI`oqn<39Od42_O$r_Gta@HbChQ~9}h3;Tx4
z<D-2O;}t0q0hP)mi!p;`m`c#jC}1@hJxVof%n_s+cK|o(wLIX+A}uia6XTqWO~D-z
z3HYZmGbH77!nHgw7&><Kh5i#)NJG*|&COy4-;>~D@tlGcgF_1p8tmg_s+0Uqz%(-P
z*73NhB_9Mp*a^kr2m_;^EWXO`Y3@#ei|~O6T<+pG>+z&bXVUh`x>0ahcU9%FGSWE5
zpA?)vgVLtdQ4c*OOnp*obxjdwxX3@HObz7PvuJq1ve5heAGUUE*dVp0QZ{v0NmD@g
zF+hK02F6I4gJ&<yEjG-3*NLkdetX%<?h^|QgPmM4psLw{KS4|~hoGxl&ck4zA|=Gk
zo;Jk=kY{m=H^y7#2i%`MhVwX22A!D{@075oO}ac26YB~S>+Kz3_u}J2k#{6`iD;9U
zg8c}Nhd06yHZU1cPmvJnJ&4t;P-leeez{Hn#t><E3UeqwL;zeCALaA33=eR!9?o3l
zF(OM{oS3@6Go49WY`Wwr@xB5@xsKnws=TXCS)CMDv#wEIkOS}CMN({xPRsiGSCcNd
z)1JlF8?Mg{_w*typv!9TIO|^hw0A9oJ_T~A1i9987cO4-krk(opWiyCzpFetJ|xz#
zkx!#!#u!vTCeGMzkEV3gCW}vt6rQPJPnV0qiSbPFN9n-E#_|!7;_<Yqphvsw48doA
zyPw?=I_By-3Hk<`%^gsio_;EM&D35^O>)tI&JaGI$e2NC)qZ*_jw4LxgU5&l*ZuNL
z@dJkyu;n2=jkCN}ev3<CL8O0!m$Qc<EjC$GWuD2@8c_-E6vWH%I^kORa^twHerX+z
zc>qn}KxcX7I+=qiGbE02=WJu>{Oiv)_wC#cQ<PJfTGh<5&g7vlYMNMT2%va;Yx|rJ
zo_t#F*A_49X|CjkyHTf9I+O5(F$5J8tuuhRq+yehRla1D1s;RTsO$MXu9~O~*nbS6
z2ddoaG=+(p$*AmPgRT<##1K2U8EUz166C!I&NU$=o|UxF?H@*!f~WDE5i(bCaXfx!
z0y>K@?zC13^tHjIeh-5O|4cR`zPJqV$y%Wv?_u0&Ox9%Hd|AXa8HB55-2`4Me#-Oc
zfr0V*PideeIxEzxP6nw^xxz`yL#Z5^x88Tp#Q3Ou#2>(fo3`JZ(cYaTg4(sESe*0b
z<ty9%*U6_1ozW=992*@^F5yu%MbtH=2J;6ec6Xg&DB0uU2G8QsNSr;aB4W7MpFNSo
zWD%(cWU*0L4at{}hIj1_J2hPxUww+M+0+sP%&>WLA|bdNR(IC&taJ>Ghm<fYzt0<B
z5Yn)jKaHc~^=|}Ve!^~vuSzq*sfq(8mKHH_#eVP-woaBym;$W4#H-)flLY-YIlYiH
zJ#p2oW^mxFnV@(ttID;yQz^w;{nGRdK1m{IR*o6M7%$G!^_~S`9e&E~xbjDZ!R~JL
zUIqgAJ{xPVz2!>h8Gs?hn%B&mKmUK8ddApW7cA-0Rrq85TDmQ&r6(uh2AWjOo<KjT
z{%w&iF`SUC0)p|dyM_h?wX9{D{>QWI0YNz_N=r+^d7W-8_4I}vy9dMY%GEj!uF(c&
zU}DX2@J?Nj%$`4WlcC{aX5c0qlNM>36QdKRb)bI8N1BD@pW+fP3(OQZjdxs@A2<PK
z{wf$$5#Y^`Ht9`$SyGlZB_U~+N$N@-I|~y`qY~s%0hH?&obJbUX3qT7Gp%b3N2LgR
ze%xtQC|*^4!NSme>35ntpLmjuJbedRNTVten60s#vjO({rHxUT*U-H1mshT7`5((p
z?w*^^4Rn;p#IqVmIdL0H4I^+RIH5~$nDH<H_q&@wRK)}5Rvw3)&YkQs$ChNydXi}H
zsG~GT7}s)ZM@M()(~fmfuaQ=^ZcQ#AfFbw*yfJms3P^+odE>bR4QW>K&JegJJv>b=
z7_imwF+rFgI03pfoK;~<6QUT#{Zrf+SL)w`6Q~qsO8d+rY?;y@>KBLQpHdGVI(mjF
zX^O9YMneX|YNTnDB?(DRA9=~Q&IhdNnOkqJj1CMSEkq*DMucW>sOCNyuO9{K<(fGc
zoUmfvZ?8Uc_?7K*`+CY_;{)U6+@#b<RVUOXOh&+|-{PWp!k8E?>m_iwHK<wqcn`}p
zoK;yAozv(KLv-%k9ePgCUWC45VYhHFv4%1DqN%=uWY^mW2Mw0ZAJiBw9Uy*>2gVu0
zI!<9D0CyFcg^2?mD-V{m*v<R`a@E7~SHXfPj*tC9pU{?I+>o9`dQ9ThYO1Ladxi|H
zH+NM%vU(|-KTB3OJyYiQe8dTMiY1L=?I)pxEJ%gR`qZ!%2|F&kv~8ek7dza^&hsQF
z;IlIH#XcLWthK07Y17KN^A~>b)E5@sx9sFzU05*KC8@!a@>X6p&!EAmJXtdJiD1e&
z)WUj$bOM;v@4ZBP>^GPiKbF5LZ#9_k<aCeI_@MS8Hg63bd`^lFyzkQCK2#=-aIj;|
zCw*-{Lf5PahtDCL0ZbAofzBq~Z0e_pEZ);RUU2g6NepS+6v81c#c#x;u%&yMLKK%{
zFfqIpl)zA^rQ-_EbY`Zs>GBW{?&QG|*TY$vlq+j(qB?S})l@+0OW17+m*E0=YA6EZ
zu64D-5)Zk;suf}I-p9DuKx&JoyA%e!?4waN4#*^D2K7v>4GryYIB9k3$4)t`|J=5@
zJzZm?6YPXbol*fT-KKgIbu_|;Xo*WJoyAcf(jrTg!KJY7Cm6%I-|}0gsXx||30^K#
z9%pmziKpyLQ-0}^kkiK_gFfbBD@`2q<46ZRD7-Y3GsKj+gpy54DBU_6H}X^D6srK~
z)r8_<PcvM=DguA3cW31gpQcd=euI_kVaJ<BDIqZyFK=}`sWdC`vGJBCj%GHX7(jk-
zdPdLh<!1#|!B|i~4wuzS&s`l?Rh$Kr{8FJfl2j-%;Vx|X{&yRF!o5E#6n$_&hDs%^
zluW`;S+H>N|9HXK;cW{~=ou`RM|-vAZ|W2`2%1!I=17P$q3_ghHSu^_J~c+{@)Kni
z7}D$qPh_k{Q8@D2s+R}{h8QV@&P`8;UB@jAg;gs<{{L_9TcG4B&NJ)Y+wbmqXdZfv
zo}d>JNW5et18W;>5D4sGoU8${HpC`2al9L{*<Fs;yE)$Y9LM$^XOm506NpcUPYA&V
zPGD`v4{&?}*cf84kwFrYMxe(?qtWzC_e@Vertf~=f9t<<yEPJ$5JI4?nd-mlug9%h
z_y6jz*R4B>x8`2n7HkFQLLJ8ue3E!F%#oTwK@oP9tiZAoqk<}a@}ibm!7mm+8ee(h
zhlhfof~{0}5(k-zAGA4~e6_qb^QIB1Ve~fy1QEz;W`iY0KmeCiz2!|u^l4~5(KwPn
zezbf-ZG>sV7#HDc>1ZxD!)QWU9`}>W+Re~6{w{rB+simg0GkBas!T5^<WFlP#k3*E
zz_hbdI(6%{#gj8WwDhdh?<`uD?}^1nFm+2fIC1Q|C4i($rTL4%+v-%e)^5~Kck^Qj
zS#U@=GgY^xHV*=l!!C{?s(6KTaOWO#pl6P8c@hUShR^ex_>MI-1Ob6#Z76lzB^-hQ
z=?Mt>Wwr@!1qdrrgw|3%D1GosMTp5)3x}iu)%X&}@|i9SXB=Ead6FmmVK~E@!<l9p
zi_0G(VC0EG{AR;E#IU}nF=05ubQ)KMm`NpE96{oSGNH^<6%xeN`_On29we1tEsnBS
zEH+(!4d27f_41h{`ycy}sT2#?V-ATp#x)BT>4X{W!%-aLuA5oche7yRYsS~L&ln!A
zR;xHTjf3;Zc63}?h1m*kP?(573xl_<yuc~`29ThtBpQD-j&&k$zMSG=Y*5LMn!TI*
z&HfzToLjiiI2cn%cZqM$5fHTGVXFsTX9+a~x3<XMCR`>uxE7&ASOZ0zs2|=?I71-@
zQy)5MlutJWBjnMo{)k)B!QY&|IbZPyRS{g%x=C2J*D_M0wptGxmt|%kf7BiI3lE};
z!l<PxmFPA;-I`t~!X+<;OGHL!G$EUD@Vp#Z;l{3<FaG!J@ZR0n*AH#M$_%8VG4Wx1
z68kV=$HkT#4sMOrn4H(vIqTMy=Q%gcTf85yxsD#df*4=YL6<7%PVp*HKt$cqF!IHa
z!deofWq>jy($o(Dfo9!>B$xILn!Wvd%y8dA<DIeCxSUnuT10<;u&0UBuUw!5cmxp0
zr9n%e;;9)}X=34TrhG``k*d}B;>QmkaF~v6%L57B0s=&ef+GY5(;!UK9RecFj~UWU
zVtH@|49kmn!?@%%7*_4k8hbsxs9ak%fZ{MSQ-MX}jeXY+UUGJ1f+$!B4S-_HiE9Pv
z0>65M#~+4UDsJHfKk_|`d5cYL)MQ?L@=2_4S9qag(iVI+Z<Mj!DW8Mz8SBqj)^Ypt
z)#EGDxxzrTTt|lr+tjh|7D5|#r=oyRcl3b>55*N8e<Fe6#gC>#7`sf|+{bGacI`F0
zUfpL#7cMrj#f$J&y>hS?!D&~5f#V1nSo2-%V5__WBGeGLqG$<JGAS!hskG|wi-3|^
zw+gNRfoWjzKV|8+d<8;7o(v-ZXf=O0M98BQVa${u0trD7y?)5I;Y=ci0Z}6nrx?w_
z42jfyLqv9;DBv<9>H`3B;#mUE0cS@h@MZJlEF4nZ<2&(ASiI{V$MG0UR4=341P%<+
z%l*MazuGAWBTX5WMdC1>oFq8uW2wpAdG9xp#i1cg9~yivmocU<HUpxD_56kzDM+w>
z!gA+U^vvqH?d<a=ubMeGUzx0x@P?o7;VHrb7mQ*}DA@FaFa9vl@<<p)K7Yg^wF5XH
z2!f4Q7Jckh%5U3gUfnlhN@tyIV)N$*-6M9Fs3iyr0)p>ZL-P1i5kbJkIa#2CTY#Vx
zHKsx<6!9o{tjLgBH`wy9gbz>XXE+ilpOnu`{h}QZ9_2}#rZf^?d89TC)2YR!0(XQ;
zLFi|oHH_b2Y;`v9j_83vN{;+CIo8gc3_vg&b`Ai6(>LlFXy&rxjrJRNEbn;e)Ob~D
z<aXnwXB#(%@m(T^t77y?Fv;`ejo9)lMMzjyTV_k9N>?tL*s}Si_}IU;?RxGPXn%>g
zLt;(EZ*uYZzoEvKg(ZTR`mvsDTgMG6&l_F7yL;r3SGLu5j1}6t;t412dVUpS4#o<L
z9|}E-i272$JTQG~5kBK^UlK0rVtS5Kt_S;HmAy><x#!}8kDbQn20sp{2?$bd1?Q3Z
z$eU+6;UmIRAiz}x5Gaszn?NCVQvhi8+c4fU{zb@1)rLaPaM%W5AY&1H^3{?Tu^ik-
z{b73WB|n?5cDbY+;DrT%lWt*IzLu3ihcm2cMnAk^DAvo^N_)@%_*$V?#zaM7hpF=u
zfEDbI=GM5_teVNZyJm_*u7B_aw?2l3eb{)DV;E+3;}*IWIBqs)JT#W|u@UsJs6B9o
zvRqw%r%J)AvTSOkjQH{K#a!dXxur9{^WkqL7hklloS88r%>lQajnX93Qg(5o`A!?@
zL@{|O2&~p|b<b$N@aWbTy`LP|o5`YC&*5p}&;*nnh1X({ie)L(t<;u`wZYjU%vBQ%
zG-bT=FxbBv-<Ie!?y{x$uzc3|d=G*^;3*uW>^5QNt20r;Ru6ny4EZxPg_1N1h-U4e
ze#qk#NZ=OXw0T6`hX_2bp+YTrAtRa50wrGopgi;f8^TfCAWnoI#!()q<fVSiH{#c8
zF|B~nUO#lBoN^zQMnM#420jb!s%YakY#j&RDzA?M4EU`1WXF{?f6l5&f7Tq=@0?wC
zI_H#3duOd)D!CPW4!T~O^r|C6>G79$_+$UJ$t^$eaICT)AHh8vM`3o&Mh0cSRvhAo
zt%xWf3(YDOi9UW(Tv?Gpw^+tYwwwCEE>j*fW0!A!sWfZZa<quPyjg9#QcO9m2_$IR
zpcBuug+cX7+2eEDJ3DV$eZlCw?OXESeRX?ouu@KULu+8Ss$ZK#H&E|hiozPOmVWzm
zfFKzk$b(BH@x_>mDem284je3)v3ZM(hZ!awPvalJ!khwvT4FEPA_k$-0SJW0khg*$
z3JA!E@I#8y9}6I&0wscm^oLLi>vVkb7zW#r27bAHgezq{#!<ggO{adLtUjs3AbzL@
zgvkIH>!y%J*%Br{<Z?F!Xl$16Oro<nf~_%iQ?4)i?W%M3#gmC8=hvOy`7x7i!)!c1
z1I5N$+8aEy@HjE2K2|IjcfT?*y7@+vf9B`0!uP%ttNwUX%B;kwvwN|rkK@^4(;xa`
za(@&9H7rHMt8C<6GR_>F_dED%X5XewrQS2noWveCw0qOWo=OMTN!1k5E31McP~aZ_
z^}an5k8XP@`F#FhYDO&XrJyy?ost`F1P60L8ibij8N%fFu?&uSh9;Q6i5vMBx0n%p
z%cHV<x$olzp-F-ZfN%(i?c0OJIooA&G4;x6S`W%A1Ojqba6rzc+^BstrVUy_1kUm(
zY>cmhNw@IGpUxtJ@ImEz^wT{JrB+~EY0YgM(IDj052_l4N7<@-I{x9VA)vB(OSxGN
zTvP91ic&v7u+Z;!ymq|1+=G)Her^1594ox=W8==+4JEU3o#%A-Wl;q<9qyP+xjLF3
z&u@LHy8p?a#twY_%c;tyJsI!fv!Hp3eywl-=Pt>ULNA$R2mqmSuHp_o0hvN6Gu>w5
zx$UOo){VpO{__9vvRz$mNXT~tj##KS4HPVu+yw5(YLNQSSh4U!RLGC^?9AdQ&vYjS
z$~eFohkf%S@&X=olkg4+X4&GN>$|y}Q|g>yh5?{GyY`rufA*}|)z)EFtXXYlcFi!k
zDpu1mqg3hd_p4jBI1UHb*w5tb8^8*70f6uVK%gKL0M0hCx)c<UuRw4uPlTAf2%l~d
z7|T<)3X!CV2$uqY;S^r>|CQG)u%U1xP8b#udgZC`iyz_B@*{Z&xOlHNFDYXroPL%|
zDC5yD7V^YtR0p%1@j1R*t2%yumv6e(mYlV>Rm_D~PMY5NID^|@O34}yCg7B=mUf*f
z#q0;@E>@KRUL-K}daYU*eD-JKJMR74RQ@mTZujtQPN!#?UoGr&e0+4%cjFKs<P-dd
z3V_!V%RDh~pUm%Z%IHPC`}yYzb62myZcla!LYcFdYb0+fD6}XnBx@a2LI8|D`^t{W
zgD-E&y)rhI#<4qWq5WE=>R<qdR~qnG+B?liSGU=Xr#hO?S7z>bwLWif$j{F0b0-%r
zsPDm3f1y}(el;-Q&F}AzFT+4OgGITC9Xot~V8Dru7m+8%Vz?l!Y6-}J_$dUqX5a>;
zpJ9hk%R_J|I2w=7SK?c${ty7*3m_<bumpfyH!9SYFJT&oRE1f2*8iIEG(Ko}&8VAY
zg<r}_KFb)EAAkTDfCg-n!5fJaFW{{=Amr@N7R{yCSDZ7}V9r&(6@jNE&y%u=)W=|u
zj(<#M8_LS~{(=1VhaU3weChYw>f6f+@8WYXfimDvP83m~;6p~d0xu~vAd-)0l~jir
zdv1rn<gfnX;DxvTcGgQIQ!-W0%0G-bB|yQlS@dc}R(1>xmcFz31^3DQdsA7*jklpy
zAMfZi2fBOBV6Ls!U8<DN-9K1gJyxon(bH?X=g*JF+j7`PV(KI1a&=&2q_(5K-}$$v
zpN>8F;Dbr8zdyAS-6?1PKHn4zP7KFfVx$oYKZTXmbT}FX1fR6})vYutND_zn!m4ml
zaCr^Y5Yk#R4h5T(;np1rIsDTJv9$P%Z*4S=q_tp)@*CU%y%|F0xSn2vd?_#Jp`f6U
zmQjXwA15F=PJS!a8qAn~+253%b-xiaxf#?BL4RGf0T{<^M8HMNd11d^t&Hz|>WPV$
zzxw6)=)>R5B`!R}1=F7_4gwfSJVo4y04lg5lLr>R$xc&!@fG7<a`D*v{_Y<t9ewlK
z5f?QB!yBE(oHC%$vf(ylixT|xLV2R_#J0`V?-ufz&D}kz%#3b-z8f#EDVIy<5e(T}
zs$=#n_FJJ#MW!_yQZ#F(ynk?T;+M}qU;Fm`_b2}O?z=nA>FM&~*{tsm4LWfGgMx^N
zO6jI>u+~IKrlBG<md|Pxp;yZMnk?go0<XfyI22k^@?cfCMcA1p!k4(#AB`JLt9+rz
zyTPw%Bl&7tp)7}%1+=02#Fgk+FC+=AbK#(I7k|9yT>i15S#ePgH<1fOkaBn!DMQ^H
zr=e`qphVcAv;F1#=5PM3H~2sQxYJvNs++Y0-J@L?5n}v~o8b3SXfzQU+B&;giHsTh
z<*T)GAAV?P)iu|4VnH`)8)1px{Bz2I!WIx^w%;<tAbeA$RDRy`(sOXS?b33k(w9g$
zP9nj99A#Iuj8+8wT!3pbrM<g$9em)M-}LUi^Uj{do$awiR~LY>&xzrk>ktqW3R)y)
z#-ve!*D&&V1p$XmL%+neeu=AYBv5~|zzZMY0tj4GL89>*1RLQh$O@pS|442D7|X-5
zsD)pFCuO(gl=~7lyR>+#Ej9;pnR^{GHc>b4_(I9OeB-3kyMT=m`Y)JB!s4r~5|B5*
zOi3^fAR8%cdH5lJ=SOeJq4;rk-6~T%hzS%x*2kIo)EG4U)d9DY?e-^rvE9kteB<zY
z{`Ais47fYEX2Az&yJ?a}e@<0Uh@i8OIta&2Vnqh$v0=_b3lBO4o(Pjp)?+|0K|(2j
zDUOZ~-FMGD^{?M?$BbpY-LYg_&i4=Q7hs5RBF-Vy^6&t{Fba+M$zzE~VYPLYJiug_
z2)T8OFv2Z7E8r@Wp^!6Ng<tbPK9UzH!x+c%LVh$(v%4w2l!I|>{#srwzk(tFM4z89
z0K{l%!d&)$N8HPAXmfI1>@EQhSSV#?C&%zLXLOfbgYHym$M?TKviX{8(|}}$_pa4u
za=2e6VSJ1;2#f$4=x$IB;u*hQ9&t)9mnxS$_2lrvi`L<7G83P|TH_6zv^#3N>}&f7
z23i&zx5eiMJ;=T1xM#U-$pcXkGL5n61rS6IqvYD#yKlJl*7zsC`qlit!6BTQJnST9
z^!RSQIyJUHyy-ND2oAzjfP|`GS>a+jvEYl4D?b7N5oY)m1W_siu7F_PHlK#Blg*Q1
z(^(N{#(||<{puEq`#?|-4mz!oDc3$fULSDkBLKvecMo{$Z*>I_tWMPFNdyFoNI*as
zutpe9q|e-NO=itgPvjGqU3{?e<S&ieK3BR)I8;`m^8RRezXl(AaoVtr1Tp%*_Z_b~
zR^;IZhc(_G!5lX96bOh4y*x;i2~_U&Xp3i&Lu@$7R4TP><;qH8)~vB#JoHb=wsg$%
zQW@W^mIXj6{36`AKnMcsW*Ib&2qhRI=usgBk8#-IOPGx#03c7T6?z4RtVh^<G_C-H
zK%tFXL-M7c*RuIU!yEWEj)X_tAyAM$bR(QQSsiql;${d_t`MLr?;40*xiRY`(!Fej
z5&6h-nn(L!jy)dMDPZs*QHE6ey!nY4@4BQ|n5@={5C3y2);9}**hh!=DsTffcia%8
zqXNBZ!HjNt)}MXd4TWrX58j-XO?Su&aU$lF2L;Q@A~zO0atBB6#Jx*zQGl^z#fphy
zDpmaTqu<MR_I5ii4|l_!8SZ}yg&r4$yo6&5;gbg|LTlaPCr|klQVPEMn?Mk=LL7o4
z5+2nGQDBHbTVON;qFLCF=tr5@O`;sI-0BYe)XrR#10eiC0HQGATy@vp#QK}FPCPYJ
zbTk2RLLSG_Ke%X26q$v`l$7joi>JH0Gu`K(Un*u&<<a{eNqKW;Vy7VvTf)caF&K)-
z4S>--%T%@u#$xBLF3w)P8lM~XxWfL{1O*Fzf{XwIc{zzxD!pjw(qjMcaB1+dA7`_D
za~-EL4ghew2kO%b4Fy}=NE8ZN#2pI03OeW^La#gsHMIf3Ak?PO_{?8{z&KIuAbcAx
z<$(LJlx1eP(uiAx-}>pse*g|~2O1(_4q(ehal+Yf_dpUF!YClH0fKc0`=%Q_Na@0%
zKyI?FJ=MK>b-9qslt=D+I2G?BFtA2f4S*qqmCcGt-`O|f7E8s+dFwZnlerux3s4)4
zzz~KcI(>flQ03wF_VsmdyyK3s+=`{e+DqF^9B<=c7$>y^nH0(j3WW<P6$Ipkf*N%r
zL8t-XrocA<fq;m{fj<O^bxV4iR^3Mg0In|rt`uoi_@jJ2V)4_V25G=WjbOWWY&K?@
z@=pCAG{n_+?M+^B(<uoEEjpWc7<P-@Om}bRdAEPspY!?uF^rie+)ElCZ|uM~tpRl0
zG-Aov!aJ+SjQ!y8?BGi;a^J4LXAx#e2deM1G}w8Gc?%4@maJMebHkmVEnwtT!>f$O
zOQbQy1z<qxSvey7kQo63s0wEY2t-oBZ5D0;1G=`Pae~2GZsZlWrA#N?CQ!|b;k*{F
zqg35m1_i6k8rgWlV>w|%K;S+?H~5Db8C1{qz4}3?K0G|(0uYJ!-;_g_38za-`yoGv
zq&qbj3JfRR-P5_|_D`D-81anbre^pUXrltSj!`o%R=82M@!dc9alBfd;Eu*a465?<
zG@Pd8azCUcd(qV!;!AJ&*hr~=z{GM`+kpfT7$W#qz!VgEXn}TlREQN24FG`96AauR
zihYM6C>TfLqxJ<H@HbP*7kq+6<43_EWkXm4Afmv4n?Mk7SU3G*l)}T;LXFpp`yIlx
z>uAZl=CAjr*Kf?a$;?{;5cY;}s~qcKB83`b&F#Nu=6?Q<s4?*IMr<w3&I~jL&#=Td
zBe~K$_}xd7V}tuS3x#=J7-|x0P+h05;p7AFzN4q7eZ%j3aso0oUf;CU#4>D6g019-
z2qJ1syFXDuj%ot(#ZOQ~!Jy!XP-+bU2r2nUtAJ48kcOHB*AF!X+@d`w{B&DNcT_VR
zqBU^O63(sPPA4?u1h&=}D$W&uDS$A^^vpm=PMtBtQAjCg3D}haFrYC$b-Od~i(eSQ
zF4?+2jyL_<W(zP(ZQOWg_nFF5&!+bL{O2VM*xA2gg@mX;^_|v6W-(9$iKXYC-*M^x
z{?Zr@3-V?6Y#bZkR9jf#)7Dvp)e5fwzzTU37z6{q>mnM$3VZ0begy&f&3Q(zC2lPP
zuM4*bzNHO*dA@<rH-G_rF-mc7IKmEV4cI0yyx;dO`;$Uu{l>OaWtPd7_E<^SmCE$=
zcC7itC%q+i-!)p>Iyzb3)9-j4b2<3NY^{7*)!q562hyeB%c=q<)fTAM)7zjHMRmq-
z!r>*q^^w@DYu1ldUhFq9ys;<3h+v@*M%4@|{G_3UU;{^DQRh=#CISF{<q<5Bp8Tk_
z`4b$WwLlOpK>#33t&~Z_Nm0(I28n`(w@70NTx=9^XoTB_Y1h5Rz2H9=ve(=acQb8o
zr7jb1X*v@EBZc=TR^510V(B;U9joobTL^o%JF(7rJ`RNPy)%<e;l6)t-}k~V(S`DZ
zBQ|YrsLs>eL{w+*y!n~SZvWj;w4oJebdO1-vwqC22Qx^hDGG4|Am}kF%oG3s4yQ>(
zK~$%O&{Hc2C_tlb3AbSi0*$9(%2&{-)(E>&6ncUMl)~N&h!6~r`rvjNH3grr0D|H2
zxY2Ej<4}RZcH_?dKq2=Vx7XaxUK~$M^Oou|1w}seSAYSwA4_L4%Rh8|@~j6Q*jL?S
z3X{)mMOO;g$3Y3G3vb&4_r=Pi1)e4tjw(>aErS|hXOtC@Ty@F2vn%iXFXNS0CX9zo
z>v#ze8UknHQm`SI3IYW}A_544fqoTw3A5n}6jA|~rBR?o!4YZ;TvNcIV2FSt1cL=j
zgP#CU%R39u00-A1=9+kEH!_YFvp0NhEH-B`cDgc#R6-b&$=<>yxDPzocsi3@aou&P
zRgXL}>{reB<j-CNFlLz8JmU=i&0Xz7+qMxDc#|#IR@4GT(^Jp1wY6Qkabq>TxP8Lx
z#@a<H7XX0*Z-qVynxwE}JygH}DgYpbO<F2p1PS~KqOco9J_SAk4sl0$3J3y0Edc<x
zbg9TY3?K*)f<la?j;uxWVY|aH-g_<<G8-QrPOi9+8Y00{P$DUUbZ`A%4!(I8n1%AX
zmS25U*LnZ)_=vlFUcU0LzcBGL-+`$Wli2b9{>huf>j-kYXct<bun%5ZiD2!*ox5aN
z`mB%tL9vb_W%<rU9B4@390&?Lg`Yw%f^XeXo&rKaqQFqUPyzyAU>L0eMY>M}34~qU
zAs8Z{(C(9HhA1%b{5iN*Ktw?yC-Px&&Z_|sg`sj{<3A3i-+3hgfn!dfC7I~$X@Vw!
zT>-rNvGCu$r~MuGKT>MD{sZ~azdUUcXZAP)_urd4uyqTM*sODtqs(EwJ?rAw?~3(C
zaKKFN-t@xIqu0N)J>iU}lHF)s>!X-5#ko)_oK_ecgqE!{EafXY@D-jFSY|)1#YL6R
z0<aX~x0H;ipW#R1aV$+)529gy9&gP#KDIsNM<!DrzNe78Vx!~45*-qW8NdAv&rZ^F
zpDOwO{^GB`aZhaT=l{5!cR&9tU);a;_x@7`udC+Xw#~!wJma8G$!>)4W}bOwcG<^2
zTXbHcR*0KqDsEB<-=vZ_q5>8QWLhiX)Fc{iwMwW#thR}23>|Oiz?(GMl?tbB6_5lS
z+`52B5JqhyV1Nt<-?MdNn0g)Y6C3`ln0@bWB~LRz@R<rAxRDSh(Ya_*=Y^m7kM7dH
z{`w%AjEP<M{e4Ga|9&}|u%#(jMG+I#76Y4pHS&|2SGVE&<?&=EKK*5&EuaFZEuww;
zP+LTVlZ~%*Wk9JZq?M<e7T2SdmYzgBWY{qH*~n|0C|})SS~6%5nejzA;77DXpek@?
z>uksD+hV*6|6r*7=KmJ=I(qn?vLtJ`c)&RgesS~Y6!6~hX}A2=AN`>8+_fL<Sn{<$
zpSa)?x3i6EnP?5@3<RjxwP;~#?lqqo8@d0_I#aU}00qvy#T2cFy&lqfv&E*hEqHTl
zO<^ZbfIy+8)RNi@Dvk0Q$`V8m0AeF+8l_=oOh`E=AS4~0J5S$;&Bs9lTa1}^?Qrfx
zzgq(!c+3k66GRiw>Gp#sArnm)f2Y%(D>hskYkTJD>fkd!^~VMWo^NT2Y2-n*1v;UX
zosa+E;LD#_-;=v=PAnd)`JOw0UC9_*pg9pRSWr;->2F>|J{%x;SrKfCCQT^ifmbBk
zI+Zj>A)h?S0zYj0r!3w;!<?g6+-B<O3x;x^yk|Ul<~g$v9UVBv5{NR3)7i*=DO(lw
ziTrTY_1rBjP)s8d0t)gSoanfJVEDy5Z;X4xkGChg7XcK*{1{TG9TXVSodPff6rhq^
zvIxYFA}GR3o`m9uLQJ;?D4#6yXhW%w1gbNVj$P#d5NHCioyYh3j8no%>Q1?sz3s;%
z$(8S%fn<1D`J}#AttCCBX;YolTivH=9#`0ff{bT+dNQ*=@X?X{*B;NMll<g1j@k6^
zPB+IERy&A>ARrVJ6l5#;;#9v1F=)gYcq~H%n`Mgl#S6axOB32SjL(x@!ggQQBIfvB
zCGSj@N5;~({ip>9*3}UVr(xL;(%{+iOpum2Yz?5IP({g}b?=W&K5<1cG5-DbcxC~P
z*Bp^C1sC5G5HgsihEOnovYM@T%0Z^J@)*?5LclGckbp*a6eRZfF@+qqF4y3XkL?>C
zK8R8;IDYX!C4JMk#?t4#rvo7wF3z3gg9cc<w~S#!XbsLcZ<%RpAgCk}KR4MuGuQRr
z8%Ju7e7`M~cJUrJw1r!R+Q4{?HK<@<z0z$>Sk1~HRS<ykBR^uIyog(_PCpH(`Xvf}
z*kz)2;)d6{W7i_^YQKMAa5A>xYsJ*M4Ot)oi*p)La%%k?xdy@`4~`t|bPbhhI|whn
zosm8FQm^*R!tr>lKWkD8AZz&e3jqOb(Eth}fmVeXPBH2ii3W$1piqC*ZFvG34F?sF
zS-*}U*uY`v1DnK=mMLd)a7W#{>`MnT@B2s++rWd>YO1l8IqDi{)z*=!YN`|OxxIav
z#5?~GOTrMsG|-R%8q$zZp07=-XKmBX*Ws0~lsqixg%)!_oCgxWy0PU$UJHv-0*`(L
zpao$71_A=e#kT~!M33Vf++o~RpBYZC{|~N{?Fe?gMqZvlE-iBuHNdKFnb)iV%-rEb
zW@pX%qS-mSU}D?b3{KuQ9=<WCnu5)XASRL-TXh=Sx;JPC^mZCF1^u)dS2f3BC_&+J
zsevE*cIRNVdb9D*yk%ed!*}>@=PVpY6Y49Ls%4I$23nfpwJJ-rg-DoZy5^+J8K2-e
z{?H6)`qPj%)fUtQYNOghd8#GoR}Dd)Y6+#P6`HAP3I!gh<KkN#SgnqaVsWnL>H~@2
zytC}iTF9waola>10+n&BrnOFSG&RN%F2TW%yY)XYY@WGvhRMvp5;rzOxj2nZ7AFuK
z5!kTBXZ~#Mr?b=wsxg|WYKSNwlS>j`C>H0O`S|Yl7E^CqKkoeduaukxt7IE^)4V$p
zwal^9K&$(7G_@sofRy4p8K!r6+RVIu!j!+AFv&$|ld%n-`gXeR6Z4)94CJY>tDAmW
zY9UQSKd((n0Pz|Oa{&l<0lq%E*_if=ithD)QZ)-!ar*|&TaYtenW|-uvj$qAII4;h
zR4}Gv(w#{&=YvJF``h?XIzFYrr5<!+L|agxn+=6HsuAc{0FXBOl}Ek;O#va`vl?Rj
zRSv+*=9;fm%;MF-X9@U$28U{^WnNDWv_NsxwP$6`#m4TLYvU%51CwGq(S^c`@Xjc9
z2qGgDV&<thih@D~{&1ks?vVn8c_=t|8jn}QwdUhH5j)_<$!*s>Heps=%vVW+H|KbQ
zTg$xe8sKAWnWL!z8E~`7hWKau;->p^V=_$|PvZOMki{gzqJ!zETL;^u%2%obY*O+_
zImlL!2?a8E5LIlRgA;-g@9Yut!Kd<O<$Hr+d$@{+bS?9`YoImIK9(vB2i&IW=YKkG
z2G-$d$&81Os$*pvZ!_|BJGRx)_L?|iYp+(dK~!7Nt$cozOf7m!riz5MI1cja#;3Km
zV!h&`QS-jP7&8mc=NKYAwX6+a%bbuJXlaULsYaQBqI8?MlUo{da<^j5VjR<=Sh?eQ
zn(U0Sm8Vq=pc+A`Y7Ny2$|u!nSkVs1&jV6onDdQwLPG!$Gd?n4uKLC(0MU)uEkK|W
z-U!nI#j)0^^q`Q)>-0?9hwwo-wDB<ruk0O1AO%{1p;Q49r3wb+5l~DUTBwOwH_q-v
z_pH3#n3<nCU@rf{q?x+{hZX^H+}0@@pWthm6IuhUf%Y+1C;LWxtIbO|nR64SbQ#WJ
ze5}Jb3&6*IC>84F=7wNmH;Lieb)uWyCU%#ofFv9dIh*Cp#$_;oFCD0vg<lvpt8aBp
z+iWnAgS_w|Y75?$Ing!H{OC?}{%^nhAd4cCoNJ2pW=#5{H4$jpbO$Mytrxg$)-0Qx
zXqT#4b7*%-v;?&bkU^lt=fYwO$>f2GS@frQbHS$)00g^C2!)1->~jXMWlnGnv^2$W
zRcN3s@I9oMNv`FAS{P;T#K58tDg>=NwF09M7^F73_*Em2M<afM0gPA<>G37pI>rxG
zQ#8vSESd#Z<#1FXr(J1~b4x=6Rq@9Cwlu|Y)NQ1pj-gfeO#Hu&;P_s&>?y}!DM19C
zz@Sl3u%>CL2}Co{PcR^24D-3M9$0+5-2f2Y`$x?3C-Y|Ubpaqa>m`7Y+~{tZH?Rg;
zpg8WzmD4aFz#U1Gz8WW4<FAe`Pc4Nn=Rk-#K2TujHQ2}tH~<DN#$nfpaj}1ogK2L&
z-dHM3n%p1e&62;Wnx1t%NCpkTOo*%&AXo_}!?e0j$63qVVia^_9Gt7U1YM{1jhpfh
z6UJMN9C4<WY6i+PyGA11L<-yvrkwCXoE!<_Y-do-#BV5?+%0uKvo?+M;@c6C-=jiY
zWW?kxbJA*{1&ZUW>gemLCe{@<u@4u_$PcmFjI9>f5+#BxTQjH;U>)|GxJZG0<rMT-
zzs1Sgag+M=h)G-tK+VJvjt+<4G_8G1K|P%G-xerdSC!i)+)X7+cAaMiG2nLg;*fw&
ztS=NCIaZvfrm>skp<5+r>8H-XfO-uMkh*!yc-P^>-HWg~?GiD_5TRmP18Q3lC!MrF
z@%pNq^Rp0TwC>r3Nt60q(G>ny#$++2TEm8<S`yQ?&<g%uTxEg?Lgck&$Dpg^T#IxI
z(T9q28IO@bI8JGS0TuD4n%3T@*I)f2&B&%~_B}Ofw!W*=bhgD!27_+Cx1GXVRC0a|
zoA@fm*?<!UuE0QH5rCjzkOCkN?Q^~U0=05@eKmm3(oc;TUtd<IZED-)!RWbTY1-s|
zzi3Kd#nCd$$|iAk!MK;XCVoDK3QO>sUMChIcv_SU!$F8A8fphBl9qXsYk+XTHa5o@
zMnU*qZ_6Bi4FuXE+2xwqzxlM;z5fL>^R|@fJm(w}Yg>y!HZ%ip5q6XGLM<R@O`IB3
c8mavM2XlB@riAj2Y5)KL07*qoM6N<$f`l3H0{{R3

diff --git a/Images/mathcheat_numpy_logo.png b/Images/mathcheat_numpy_logo.png
deleted file mode 100644
index 5ed48c094b5a29db2880bdd7011ab69dc6f9cd80..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 8565
zcmV-*A&TCKP)<h;3K|Lk000e1NJLTq006WA002D*1^@s61ad<n0009!X+uL$Nkc;*
zP;zf(X>4Tx0C)j~luKyTP!xv$j46V&f~d6?l;A>(v?V^!ics5Wwbqew82iv7$TV|j
z8cj1XnHe95NI~5BT8bMVh>I%XLb_0Jr3;Zl>uXU2ait=NLN`*z|0eBBDpoHf`Et&G
z&+CQ&RS#2+V?{Jz*v`AjuBdkC@PM{>0#&F%HMmlS=d>ph`+cTXKIgCLw@{>IvPs5@
z^&Ob_v~BhMOOImj>z?-dp85KDZ#6Cx0*XfKifq7JB@YI?SMtfclc(m0WMe3mCOhOt
zw<p;_zCm7^4e?-z)50@=sy1qJhT}3$WCgjUXk=x~QL=8QO`AL=?P<>#riT4Se#lwS
z#+8o&t!scqFDteI4;Z}*tXf~OHPits`+>1772D6GKOd!b+{<j)?1xjTqpayesq}L_
zu=pA<bG20Zb*ogGxy>A`<C$d?+@h~#Vah8Ys5jWn_{lk1h=ZPdRSR&FbL?LN#4Z5W
zKLQ&b0Lvc(i5j412O{U$ZgAzrDb5Ax$T_E7Gdq;mHtYI!t(`|Jv~JsIZq!nirTH#T
zbA>0|qDVJm*eY<&{#wR+wXk~+^2z=p=Y+R=mX(oos*|%sGu!%-7M;7vjpTb7b5A$9
zf!9>V-P0SeveC?U_XHg2v#mJKMQS2-_F((~nei3X$w%cpB#(H-gEFRXKbG2;AlK8L
zJf4e5O)dAfof+!k8A(>GGeaHmS$^#nk}~f)YVJ6e|IM?u$a5Tq1pyN_?>00f(S<0Q
z;J`%=8G1}AOj@O1&}zcN@xLl9B+9!iD!TBQdp`SnPN2ZuGJYSTaf~#UBWU_N{hR(q
zzp3BRzh0>?G*mnz?orbizx<tXWoB88j2qM_<I9?Y_y((Or`JN3)?r>jPhMHy6s|5~
zU#Ty9G?_zOj8BKU6roH_7-T%zwd}q~w)iIed~?|rnjW<;&OWKD<a(oy9ht0taW-5H
zYyY#WVA8Uy@Z^n<E<9_Yu2naw-D-=v1Dd)^ZBtv-PQL9__m_9{a#kizvTm2vr`Usy
z)AUwOx-we?{}z5R{4aG0Tezm7$$x0V&1FnW_>SQAA9qOkFZ>Mwg%u5aOFKIN000Sa
zNLh0L01FcU01FcV0GgZ_0014}Nkl<ZcmeI5+ml>Zb;ehBpT2ib&wZ@BC2V7iZ3s37
zJCJ}e0Roj91oDtdRo?T@<S|c4s8j+Jp(-JP;BbvGU>h4-7|WJy$+l*sx%N!o@7?)*
zyXQ!)(e%u;W=18?^j4iY-F@!+e0%M0eQWKlz~6uC-Hr{*um%oD4FnI!a||D2SOcyG
zhJYRd;Xu{E5YPwebq*h92<RaO4pa>c0ezre=kQ^??W(}D)^@wRXSO#j^q#rb;f>x}
z4Gb;xt<~G`s5`U<wzbf^z3*TkV3APABH@6Ay?}*+K?{Xgf_x7L&GUj*t+lLLZ(Fh4
zvP%8n^a#7HyW#PJ*TB94dTPY8wbi^e0cpG1u{t1j|L7MyKs`B@uvpl$Ql)C?sArim
z&pLsywL1Z~2>2eLuLJfr;h!q!DG2m5fMFTdz-BeDFMtkXb;pxt*_EsVZXguk+FHyy
z05gEquGE@#_WYuajl@h=yu*gxkfpQifH|3nSiV@p!h6n|r#N>i3>W5@uot#i+_Orh
zX+f?L2m}Fs$BLDf<x5R>z2Tq3RRentXb)hG#@n{Clmmq8PFN38gIMW0mUec&;A(n$
zJh5KmUBK&$=t!Z|u(^eT9hpu6+@RHH0M#0x=Uf86&Qq>7Eg276v(<KuLceLTK**xO
zu+`c_Cvd%T56x=Nox=B@OjsTY5gAQbC>eE&$J&cWEEEY@2ktPpRJ3Xn%>kgwT01BU
z^ep<G%hv!rRHfOrFaXtdjIRT0hz1koJZpuTEi9L;&H2=D^t+0_w_8nXH>zkFJuS&?
z9sW7!HL#}^`s7r^E?l^3=NC$Vw@VQm4+gklKsOo<Svg;|e4}lhNYo-R_;NgIYE{m#
zVre_ohAjc;j>qxr?`a=E8eoxS7Hk)@6<b;<b%CrO*rwtj-Nm(efsVC;Eba9l+#bU}
z2fYUN1kj=x(<4D!Tv)LhbfQ+RTf5$L0COP3>WbSgf!cbhZVP}GlWK7QD=HzH5b{Dc
zk&0MlrC>|!(+qi*hBvR`|2bs@l_bV~tzy+y%N(DCd(%*Y33>rAtLwC}=<Q~&!#K3i
z2OYp0pKsTIjzxkt5^V$Kti@0&#DBrBTkB#$WIA!&d=Zf2<V8WB0NjEOwD`RSxMf4D
zji#+F!;Kp)%i`oU*{@ivSfg5FdjRJ$WFyJ2Ro6;(6@J~u;>YO)LMsu+u|sT6M}rpU
zTtgVE<_~At6`+&xkWGz*?8?Hb#gh@|m<5p3q&5gt;1-#!ZY$7?HsFo0k2rKo6ak(Q
z1C+Av6+oTCDvOdtkm{UsN2_p_nY9{7z6@2#mo33b>sbFfmbi{}4|63oh!&?jF`c&5
zXw)?zX(qb<upF)$xMe^mV<D43F*m<z*=EP$peKg2R)#t>Vo?iYxr^D7%>(8_yGt9w
zpe_UuD}XPGMQsA?#azW!aPVYTLv7C*fUpjA0O<g}+c4w+7AkeFr$!^IkCg}I+8%*+
zs0279!u6e#sYc+==r#@i9HJVyB|s-*L0f_^Un-XDO15lez!b*P21G9aX#qgCu(ZXM
zqE*^$t3<-+#3&j7KPIghs?fsO%N8om(&pjHAwVegLYBA6S^<{|4IsZ-wiT?rwudSW
zs{{NZPGu159t?O^<8wpKBrmOkj8{Ra(`Px7>6B;Hvc3(=VX1+E0UaTHJ%4rC>Tuf_
zz?6loNVQOn0p0-4+1gs!If1bb92sy20j9Q>*|)<|>(s0XK-<uWAOJ66jnx=h^^hg`
zy~BAstk?1t3$$8R3Wptk@c^;tN(%?ki$<)#J(QpwT+(z0(WQb_)+7zKZGtFdJWWe;
zwpuqgEQg^61_t!%O3oVPO4sSbGK##adskO4WG$>!Y`IdinM}ewfEC95liL@7CZw>u
zlGq9;6$*W)0I>+Nta=CSI7kD))RWUuBPdd`a1%9yQVBJ>R+nuF8bq7p5;O!42QtyU
zxZAGts7%6z#xpS&z=6BC-*)@w-V+In(vbAXx+ILrE?lm-9F*<Oox;~ReKg+H$h)^l
z{TaPWhjWy-JN%B>e&$#lM;ybW_r-H)(QDTtF+i~ZJy(OCxKeA|LbmRrwzu;E2724!
zK+~Q${Vu?3S-DPk&Jqt+?P?JUQi<EU(1WXmswwDPz-rgAt~CJP;9;XYX`PB#gJu-5
z+8NOfYD9ckPNXU#!EX&fx(Z*8LsLXMy0Jd2Hs_Q>7@_uxIEBGl!&3Y@%09CH0`mk7
z=EO{ja5>RKDitxh%Rly@M(IQ>ll1Jt_oV<fH%mElv||79%<}eU=Qfm_H8U2qdyXeu
zF3RsNl`Na@HrVY?aA)k{QU9q#$nL%?W;5eacT<u}m0zQ0QOd#iNZ6)E!%lh94zIsc
zvLC;=ileSKxc_A!Kqn(Ld+gp>K+=7oLfJz{*6g>J$L*h<KWdABsFg`!waJ`|iQi(K
zWx)Y}2MFiUr-ics=q*waq6Qx4su2K5fS3aGA%|2a;H$dGNiuK%Jz<S?7Uc+Vj2cWR
z7Qyynp$UDn*-@M$Gigf`m2A+E_PU&VyK6~T3r&0KqvP)Mr|yc|V-Jkj^RMN%yqvNK
zUwpmbEZN_EIc{&fTee^PHn-(5cjCtL>%ED3mB0Sn5qFEPy<M^&{d)C=+m*%s%p+s=
z!0D9Te=2E{nXrBH$BW3c`w!}Y0PSI+MH$?pN8rYjaM?O9tb>jmr3Kf)x-<rXR+5CA
zIR#akr5cetxNp3hyAi}1R{^J}i1uwlL#A!bK7J&JbtgI)2AFlx5x{Q|TWn$3<ZN;s
zIiWcki+T!=^Na3`rQ<Hl-r}75SxVKG>vH9ED#zqw0D5t?Zf{?$ZhKj(!8x(&j^E3r
z_x?pMHWoeS4*_`uL#7^Sax{XZ8r@MHZ9LbY-`7p;P7mvO5daaX!RfyWZ)dQWX<o2E
zt&)M~jzSx7=d6V~5o|P&<#<wOnYD&rV{wxNV^q{2CL?uw<k*ruacbV4eBYdX{=QlJ
zi-#`Tmp?cMS8hOa2)J+yZH^V4QN~`0bIT&s$qi8sh(5dX(S4{*?MG4f`SkNy9);eA
z3x4in6Evuu*#-qNoesYrH+GxOsn_V2t)Au0*^0lL-F-wMul+9rz4CReNC+m1*RXjq
z<`!2_8Zg|_mg~ISI_r=khRAXiKvxw40eXXDT5MPA73gG@m3Smd(ju_Z0g%{bYr!w-
zH7OpF@jR3italaZ4}KP<CZp`*v5tTY@$w0rztL3G0ZRi06q$5dG`)nbwO{3}xr+Vj
zcU{^bPW`3dpL9HVyX&djy&YZecK5NH?P|z=>G|mYbZxDk%P+mh_`mJPdLTdt;kp5m
zh6=foox4!93zsT(d9H4Y%N3g?h_Zk>;alKrsR|#i15DA8wv`2JiohHM@IkD%z%Jc6
z0O0EYu`P|79hBP~A!?{2Dx{27X(j67Wz}Wny>`pe)TDAh)H@Wg(iN<?)1he`b?AuB
z8(+8n?!x=<=Y?|x=k(~*W9hJc{^Jww`L?>0lO6ReTNI7#Q9W%{<YaDD)I0T_L+6y*
zvQ<eCU)b)vI=&agTRlpKIDlW~mJ~eO|8o6`JGfT08MR;hCT#CqAt5N}Su%_U3}>x2
z(3g421hfNT+T~|RfpbsJ?;Qm`pcJa%KoXr8D<w|d26(D;g~RJ?Zvb$~V#=~>1L|72
zo8aU=^M(TCIXAeTmyBC%EQQ)pciDTYU|)x>N{8l$F)a7((4l|y>#XU;9hr#Y{m1PS
z50Bb2udMB$Hm~m@xoaHCGJ*4S3I*W|65qf5Z~;X~?r*m!ML-II_Bnkd=DP5I{O>DH
z9@8G!1Gilc*vXmhIllR$1t+0>_OWq0KGp5TH1Of#m5TlR?3%&`uKwi*JcN3qWHFWH
zwYvT6<u%fLHwU5Wdne@>*HkI+9xpFc?XHOk&f>VET)yg|=TiCJFP9mV(RJpZ#G#xS
zk2qyS*HUBr&3PG*?Iq5E0Nw1w?djJ}b9p&yK!}UYW37wOg`>zgMXciNYKcm0lJ-ej
zC_rtk12zg@Zbf0#d~d^j9k}%@&Ws@6fn<;Pbvo5?iTl+Y2}n2`qU3T88_0AuT1lGo
zD9m1>F?d-2jXJczJTVq`BVp?F3=kle{VQsK|Ml#${nb-5mQHx~d-tWuU1`{B$Z|U^
zkG?<cbZP<Cf5F{*zkY_tAI#Ws94qZ3%~z#&pVM$uU3-+ql^`uaMfdZmkBs4GG#wap
z{0P$Hhwn{0ko@$eRoANsc<;<t-LcbS5$D8-LjCL0^G=d;C+qj854v?NT}MVr^&`!F
zex+{T`PrhQXGbTyV1N4gm79S26YfhA>DQ=JJ*keL?bEn6-qt{Xjs%MKH=nv<<yssX
z5F$e8+4;+LyHfOQt_WvdDceY^ip3>pf&w9sR;5KlCE}^Liz2RON!+I)gaEfL-r?n_
z+lr%>)Iv#nYcXR@97xinEf#_{c!~sKo!NJcV<t#&DgjjK&}kgM%(@OOE-oGVn3L|-
zb?A*xyHDTc^?&#0%l4Hgr(NXq(|~BL(6B4Z*E!@?mwpb}Pm4hN(GQHcU7e2JbC7TK
zb#7TxDMxBdei5hFBJ2ETe=y~K{(}#X+V`=1OKaEl?mxm>OG^77lG^d9m`Qf~-Y-|2
zaneBo;-7Q8`~q3Z9qH^Gr(Qz?ouzE3H+cA-l&b?-`rhlSuAbqyuYP96-J{<txG(E}
z)JBxgBg_2DS93cCvko5!(D6u{8>c&Mqr0vXv@@eQdvhsm|M2VyJ04%PFP&bnd=+&g
z9<xdb{@e0wxtX*VR>GDCgo^T!d+)4FoHqhytpS~grDAre7PeR3keml|!KusyS~kiK
z%HQx}2^(F{))P#W_%D_%9a;uU+*CSr1_yCJbm;ZE?z>v1NBO~XEB2))Cf&1rk@YvA
z4GKE!w3Lwq{o+SG{qO$T->08rse#0Pd5Lbi7fkwH)=yHJdIACPg>yG*=(CG8I|s<6
zA>XwgJn_%B)mpv52hYUZc||e)_l2zWsvNz?&J#hrikKjpcHfD(qZ7T`^;sjIbbl}7
zs|`{H0<`l~Wfh!d0F{)u)QkewLft=O3CH3!q&uQ`BX#pe&}zeNn<F~-tA&&`L0+#8
zt;8j#NZ=HD=+IM^w<nHub?BxSfn!tYG>AO<M;t5Nt3$7ee?vjSPSr3QO?QWU?Y9o?
z9=gw;OIND)ECCboXjz^=`NX7si-5vrTJ2`1=#{|4Z#OGG(0ljo-LLn!MS!!h=w8-P
zs3tQJTh86QUHdO$(H~$P-p29lJ@?tSil$|!<?Ne92WUkc<%8sk*Zl>ZLk;R>8f3qQ
z+wVF9sT>M1Un??;A*sw*OL*-l96@9IJO?mIa-M~u2+bDMqzD%l0HFnP0JT6iI2;|C
zh@y&|#wj9^A33&YPn^7JPdOd>vi;@5@7UKKoFj36gAPrXRECQe$;4BsLsM>-4(%@3
z=g&)T6kIp1%@cs;3p-Fg{8PDV;fpgI{*N2hK4taS8#X&C;{T>h#LY?_KC9BNq50fg
zDZ2Yr-kc>%bA39B51jG`N_Vez1m{tT%peWKzt4dH?H|;uLodJ+D>#EyY8ZR#CJu`*
zwLqAde;sR3hBFaXae2;6w{fi~LKvspyHSV6(xXGyq(ft!Bg*I4M28MTRXn(P464#k
zht`Gqz4U80u}>d<{9+dV+*Jq^Q26ABM)&MWeiinZmcHlqm4uBuI0=*={q@G}z1)1`
zwwu1+BItDuQ5%&9PA7c^>%KpDCS|W)EMlZ~@|5n7Ykvj;bP)0@9Xg*Y+qv^aJAbii
z@60u9VX<nL8MIohR-L1wZf_M%nawu%O|o8x;EQgq`#M5c<si;n84Gfw4y`04!sR#v
zz78!7y1{;`e5IyJhYqnGWzU;*X!PhRR$B>9dYSv7L+kyibK0ut`Q@4C*5?M1w=XBj
zFa0pE&1v1?{^<68`-;N(rRSsnlUw`y`0E=sx-Zv(wAr)vo_DkDH=l3wUtYafa=lRs
z11Ob7MH!9C>U(8C?(AP-AP!w`MeOXkfW7&yC`H5)Q8^$0$Z2yO8=>dWvXW!eHg$q=
z1)gvt=tPUahqCIFm@mrFgeqLqq1ji#5J^1hBt6Ck`Z{!w6N!7*U8F!6d+=rylgMaS
zhpuRN7soptdKu20>(QZ8I@|RZ>_7Bw)_$7gcIS{oBjw|3pP6<!AAgAAplmc1)c8#g
z_a!^$SFnrfaAWcJQI_InMb+e&e*3@*UGdlc{{Fg`YUmtoj6-kf1@z{3(Yts5&*IIO
zaYCice24}weO%o6B5I9aSN)IPe9J(9uAo+Y=hb^%^1Vy_gkWR~1*~t|j*Q2R^k7?f
zB@DouHkJ&y{EKL)W^46`8y?kGJ~TCOlMW3?gHQ<R(8+{yY01;bF?|?TUAlXtPV{es
z4qc`Jgm6T;4Cm~om&#%4)uA;4eLx{+^IGcpRe;tD*Gt_Da6P-al*%9C?SJ9%N%jxe
z=gIkyGpA!WD{7d2NzkZEl6!iJj?>Fm=>=}jF0jGtC-?R0-tGGA1Ge9GzZcNHx(<x;
zhT<QuANxJa4F_!TMbtvibb>`eBLVxn^lrCW1_E@TQ?@TZzJ#1Y5T!;$uo<=svjIk7
zM=XovHoF`lA39Aw2iCg6xI%zh1E`wQE|ZSBh+z&LTGUAMcF2iVs9W6I>Cj%%E-r1N
zLnF}XHI3qAP9n>d0IGE85opIX9U3%@Z`7ex+dUw;)k^9;PTHu7);r;N7f8^VdOdHC
z-aq0bEm_!pAduYUmxu33I}O(VE^GQEQ8xJ$D*eFN3$}is_kZsNzkeOA{a!$yyZhW&
ziGCey+*jWREwaYSVAL4ZKYo0|P2sizUA@&Z5TFy0mOXT43Cge@ed9SFoXOd%b7S`P
z7mnH$ENX*%Xoa?w4;>Mg1>8zR(mWkWMxmTYR&1^5x%AKg$gX!7p+eSP6dn2^`t{4N
z(@;2X6ghAd9Xdl`MI&CqvAB)1kK(ny4(%b^CE#DG^xLIFPm;W@q7d^=11HLTRs>J_
zuS!4es_wT0EzxLt1z2RU<Xj5G_j2rSv1Ua;|JVZ=cMd558@uKaQVaYdZ8)31-bi|X
z*XQ1S{cTz+k>4+hELLha%6op4Py0*}SyTfV$dU9<&RV^aoI^Eu<-<t!+)Wt>&>a9o
z0I+)(x+>2tLSH8aFB+kVU^F_#{o^SW1=QF;Idlzl=2NM%efsWMR1s!H0`Se6=I998
zS}SGWzI4pSt$+@_W_c`s8y#91chaE?42DghLyKl;BAGG`W(`L%pnPbqBa4?ohYkby
z7KuyyQ9esjzw#{99V@t@(6B7DB6y0>ZC8Fkx~W3a0z*FwEzR}2&n`J?GRn(UPKOqO
z{26jD&NDFJ6DTH%qA8O2r=Og3A!UV+6|z=$Ef8yey_;^>yJgRj6{yDYl}}GQ>#N4E
zhA=@G{i{Sbv*e738u>-T%er|r9aCgc(Y^m8y7$QaX*)_VL>63iATTOK{kLB{>YQ-p
zV=S%?Ze10v6rj~ZU4lpNrVIq=8@g|`Vt}THk`A3=ih(pYRO5G1jtoFk@_mgRDkKp_
zp&lgb(Uj>fX&6yHjHcn)v6h7kCBRJu6O?)M_#%!USzk01O--OA`a(Nq|MKc7Qyf4O
z8@POE0h^qW2<J}|L2RHyuQ4S;a)bmaU5C=o5_Y!^_E(Xw-4ucK+do-!!;`i;j{x!Y
z|Ct9Unpj7}+}Z>{NUvRgrk~S*v}`Q-Ql)QX&fizp-$_vxp_Yy1`-I5%qbLbWsiqnc
z<3H!EUS}{s`&YrLmPidPP>Fe>PKxd_{j<x5_W5xT%AjD1rWnwa1_6dxDVtLkPMI0$
z6f^@8-3F8mB-<!dA`aq}2U2I)O9ZD#>5Qm^R>VP6KD38Qq8rvZLo^WOQ;)&TH3O;i
zY)vQD1+g@7btE*pJFILBMfbbC9KD{F!%(77sx)y6jkDZs83@pRK6IYBINo@Z5zq84
zRsJk2mz@s1fJF{5=bV&=9GY+w$IV5(0DB3GDwuW9vg3585gtH^`;zcl%wyM4KPNx}
z_%LA3DDzC*48XUc8yX2CYaQm?nt<^rNifq)RiJ4U)JwRs-n_MH!p(;hHSQK3x99i=
z&ybV`ZL856?Y0aA=o<OZ&%NT=r8xizz~VTG>Pr25=s5Y%8Y@`C0aQ*!7=UZQYa3O<
z<wL`7lX3nF3XUnzjW)5m0(lv#Qh=67W|MOfg9mE<juL?h2Y3u^6|i@(^y1?qO!qZ?
zbevHufL#aph8QGkyM9WhG_9|X^xam|@c8|z0jW0+lRfyKX!d(i1_E@0eCY3;J?*Y5
z$Aat(rtvB;Q&I~ZI%;VgwJVxr2H;NMpn2da2f9cowHSa*kRuIIfMT><&?QQ6Nc%>y
z)E@gqLrf5p!QlYdm7IoSb(eZo$^Ojoz|m98Hk#yI;^~SQYJh>fEZvWbC~{Ak@r*6y
zZuai?qQZvf*xMTT2szxE0%12&UGE(Y1Ze5dUwL8$z(vW?U@F{J*v`+IT`4fFS1n>?
zIb}F}V*u_LPMzklTdQD!#dW7gY_1Wve|{+jq+3ixLk1nA$b-zHRE9!~0MuYKZ5J8N
zrRjJ_Q}h5H0T(j&NFH?omTBnB5hjt*MCf-bi60JLW#o&(+_LVcrjl6i5$DnOKIHK8
zol^rEOrSuDqJqksmIV3lKi!$Lx|63E2+&;}dJ(JGqeD;U>}BTV_|DnmmcwZ)GGmfv
zHEL&)4$uKca!(KtNn(lT$nD4nQcM<u+~*~nbC(0WH7x9D63erdn0@0lBs;#(@PDtw
zgIM1t!aLS`ES0n)aArAu`k%8hY|rq&Yo*}Qq8u6#F*cU5w;Ad)EO%}V+)rkm1{X+Y
zmw%<9V47iRugXAxzDbAXE;0;MogiqSh+RU7NRT2;gB!rf)2Qun(n@OpvYF?<p#Y3|
ztgQ6vYMTF52&Ezb4N0?~hj^a~tlCqjR+0J$s*Iyyqkb&qImQxjYNpacwQx@6`qPUq
z%UUb%X=HQ==Dh;Nex7HUTx$*YQsUNIq>xG<-^(%}pndP_`YohJ;&x;zZpS7PwnEoW
z+zxq;2}UN9Zr%+|YOeX5nWunoIZhm4mqV$!pc4@^V~R#?2hsk<Vs-o2-B;nmo*h4y
zC9_Tv9dko7$;>x@bB1XVBrz*&)0LW?1Gvtam-B~1;&K)C9{%$RiTzsks|*C_7F0u4
zl1~15k+Q%dr;HVJfyDMSR+vbj6bfb6xk=2?i&M;dS0xw|Lz+7|nR3)Y$4sSAGti(t
zczF__*NGHPWtcaQb12hemMpq>5J``Qprk0;jRu`rfZUCdv9v9365hRjgTqgUsRjlD
zbhXW}ol4rK6SOcP5elKjSxmkvZ{q}(`Sc9@NC2lM-8Pg6DHezzL{`j>3C!yfoH(&4
zio8AyyPHQk0WY2$Bg{-=kS#tIigHW>4AY=QGqC!P+%sVdC?S3#{b71(!)NVZ0|NoN
zf)4%s+xOXt)V$54vRK|GQXFy`(<KP7$EQdz;lG8@6bIJ|h*k((`OZT3{l_yljpHaP
z;uoJ6YeY^wii3#bpd^3Lt629sZ&vf5(eT@mnNgcxYrBD#{yB#0VGXDT1_HDWxLTUB
zTyw_Ws)%}sW^{uak3LEugV~bAhik}n%1v%yv6V!lNn}#wTik~<M>Gb>Pf{GmgIUoC
z#0rL7wQL%x?n4hCPBT#>5ffd`l>pI2)IcZAoj5*0zD8;YX1{WV>l<odU_k3=jT6+Y
zU)>@`9ki?)tjhcwQB5|^e<=aKRoZ=*A`tE-)2z&dC;AV99OuSDE1Ia8kTh&+{Ny-}
zV$j|p2PhKM{Ll^NK4;uuq)Eh&lueJ>f=-X2nDAe{^~@;b-^Fk}tO3=)EdhF~s&k`F
z#2vOQm}WPlQCH6%VMvvx|EdHt_G0!V)|jDKNP0~cQ4_J1B<Yc~2TGTEY<c?O#zR{J
zdk*MZsY#_sYPqop^moMV-FREWZ+CPJh%XGwum%oH4Qy)y`7h=V%)<^J<lxu9&_W;l
vcYLUxa0uu_^|}xK!wvy`@Za&Fdcyw)S^@*l6j=D%00000NkvXXu0mjfNC1kt

diff --git a/tutorials/matrix_cheatsheet.md b/tutorials/matrix_cheatsheet.md
new file mode 100644
index 0000000..cc12686
--- /dev/null
+++ b/tutorials/matrix_cheatsheet.md
@@ -0,0 +1,506 @@
+<link rel="stylesheet" type="text/css" href="../CSS/hover.css" media="screen" />
+
+
+
+
+# Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia
+
+At its core, this article is about a simple cheat sheet for basic operations on numeric matrices, which can be very useful if you working and experimenting with some of the most popular languages that are used for scientific computing, statistics, and data analysis.
+
+<a id='sections'></a>
+
+## Sections
+
+- [Introduction](#introduction)
+
+- [Language overview](#overview)
+
+	- [MATLAB/Octave](#matlab)
+	
+	- [Python NumPy](#numpy)
+	
+	- [R](#r)
+	
+	- [Julia](#julia)
+
+- [Matrix cheat sheet](#cheatsheet)
+
+- [Alternative data structures: NumPy matrices vs. NumPy arrays](#numpy_arrays)
+
+
+
+<br>
+
+
+<a id='introduction'></a>
+
+## Introduction
+
+[[back to section overview](#sections)]
+<br>
+
+Matrices (or multidimensional arrays) are not only presenting the fundamental elements of many algebraic equations that are used in many popular fields, such as pattern classification, machine learning, data mining, and math and engineering in general. But in context of scientific computing, they also come in very handy for managing and storing data in an more organized tabular form.  
+Such multidimensional data structures are also very powerful performance-wise thanks to the concept of automatic vectorization: instead of the individual and sequential processing of operations on scalars in loop-structures, the whole computation can be parallelized in order to make optimal use of modern computer architectures.  
+
+
+<br>
+
+![R matrix](../Images/matcheat_matrix.png)
+
+<br>
+<br>
+
+<font size=1em>
+<hr>
+**Note:**  
+This article originated from an older article with containing a cheat sheet that was just about MATLAB matrices and NumPy arrays. Since then, I added a couple of more rows and doubled the width of the cheat sheet by adding those two other languages R and Julia. Instead of making further modifications, I wanted to keep this old article as is - for future reference and for people who may only be interested in this slimmer version:  
+[Moving from MATLAB matrices to NumPy arrays - A Matrix Cheatsheet](http://sebastianraschka.com/Articles/2014_matlab_vs_numpy.html).
+<hr>
+</font>
+
+<br>
+<br>
+
+<a id='overview'></a>
+
+### Language overview
+
+[[back to section overview](#sections)]
+<br>
+
+Before we **[jump to the actual cheat sheet](#cheatsheet)**, I wanted to give you at least a brief overview of the different languages that we are dealing with.
+
+
+All four languages, MATLAB/Octave, Python, R, and Julia are dynamically typed, have a command line interface for the interpreter, and come with great number of additional and useful libraries to support scientific and technical computing. Conveniently, these languages also offer great solutions for easy plotting and visualizations.  
+
+Combined with interactive notebook interfaces or dynamic report generation engines ([MuPAD](http://www.mathworks.com/discovery/mupad.html) for MATLAB, [IPython Notebook](http://ipython.org/notebook.html) for Python, [knitr](http://yihui.name/knitr/) for R, and [IJulia](https://github.com/JuliaLang/IJulia.jl) for Julia based on IPython Notebook) data analysis and documentation has never been easier.
+
+<br>
+<br>
+
+<a id="matlab"></a>
+
+# MATLAB/Octave
+
+[[back to section overview](#sections)]
+<br>
+
+<br>
+<a href="http://www.mathworks.com/products/matlab/" class="hover_dark_light">
+![matlab logo](../Images/matcheat_matlab_logo.png)
+</a>
+
+<br>
+
+[MATLAB](http://www.mathworks.com/products/matlab/) (stands for MATrix LABoratory) is the name of an application and language that was developed by [MathWorks](http://www.mathworks.com/index.html?s_tid=gn_logo) back in 1984. One of its strengths is the variety of different and highly optimized "toolboxes" (including very powerful functions for image and other signal processing task), which makes suitable for tackling basically every possible science and engineering task.  
+Like the other languages, which will be covered in this article, it has cross-platform support and is using dynamic types, which allows for a convenient interface, but can also be quite "memory hungry" for computations on large data sets.
+
+Even today, MATLAB is probably (still) the most popular language for numeric computation used for engineering tasks in academia as well as in industry.
+
+#### GNU Octave
+
+<br>
+<a href="http://www.gnu.org/software/octave/" class="hover_dark_light">
+![matlab logo](../Images/matcheat_octave_logo.png)
+</a>
+
+<br>
+
+It is also worth mentioning that MATLAB is the only language in this cheat sheet which is not free and open-sourced. But since it is so immensely popular, I want to mention it nonetheless. And as an alternative there is also the free [GNU Octave re-implementation](http://www.gnu.org/software/octave/) that follows the same syntactic rules so that the code is compatible to MATLAB (except for very specialized libraries).
+
+<br>
+
+*<font size=1em> This [image](http://commons.wikimedia.org/wiki/File:Matlab_Logo.png) is a freely usable media under public domain and represents the first eigenfunction of the L-shaped membrane, resembling (but not identical to) MATLAB's logo trademarked by MathWorks Inc.</font>
+
+<br>
+<br>
+
+<a id="numpy"></a>
+
+
+
+
+# Python NumPy
+
+[[back to section overview](#sections)]
+<br>
+
+<br>
+<a href="http://www.numpy.org" class="hover_dark_light"/>
+![python logo](../Images/matcheat_numpy_logo.png)
+</a>
+
+<br>
+
+Initially, the [NumPy](http://www.numpy.org) project started out under the name "Numeric" in 1995 (renamed to NumPy in 2006) as a Python library for numeric computations based on multi-dimensional data structures, such as arrays and matrices. Since it makes use of pre-compiled C code for operations on its "`ndarray`" objects, it is considerably faster than using equivalent approaches in (C)Python.
+
+
+Python NumPy is my personal favorite since I am a big fan of the Python programming language. Although similar tools exist for other languages, I found myself to be most productive doing my research and data analyses in [IPython notebooks](http://ipython.org/notebook.html).  
+It allows me to easily combine Python code (sometimes optimized by compiling it via the [Cython](http://cython.org) C-Extension or the just-in-time (JIT) [Numba](http://numba.pydata.org) compiler if speed is a concern) with different libraries from the [Scipy stack](http://www.scipy.org/) including [matplotlib](http://matplotlib.org) for inline data visualization (you can find some of my example benchmarks in this [GitHub repository](http://github.com/rasbt/One-Python-benchmark-per-day)).
+
+<br>
+<br>
+
+<a id="r"></a>
+
+# R
+
+[[back to section overview](#sections)]
+<br>
+
+<br>
+<a href="http://www.r-project.org" class="hover_dark_light"/>
+![R logo](../Images/matcheat_R_logo.png)
+</a>
+<br>
+
+The [R](http://www.r-project.org) programming language was developed in 1993 and is a modern GNU implementation of an older statistical programming language called [S](http://stat.bell-labs.com/S/), which was developed in the [Bell Laboratories](http://stat.bell-labs.com) in 1976. Since its release, it has a fast-growing user base and is particularly popular among statisticians.  
+
+R was also the first language which kindled my fascination for statistics and computing. I have used it quite extensively a couple of years ago before I discovered Python as my new favorite language for data analysis.  
+Although R has great in-built functions for performing all sorts statistics, as well as a plethora of freely available libraries developed by the large R community, I often hear people complaining about its rather unintuitive syntax.
+
+<br>
+<br>
+
+<a id="julia"></a>
+
+# Julia
+
+[[back to section overview](#sections)]
+<br>
+
+<br>
+<a href="http://julialang.org" class="hover_dark_light"/>
+![python logo](../Images/matcheat_julia_logo.png)
+</a>
+
+<br>
+
+
+With its first release in 2012, [Julia](http://julialang.org) is by far the youngest of the programming languages mentioned in this article.  a
+While Julia can also be used as an interpreted language with dynamic types from the command line, it aims for high-performance in scientific computing that is superior to the other dynamic programming languages for technical computing thanks to its LLVM-based just-in-time (JIT) compiler.
+
+Personally, I haven't used Julia that extensively, yet, but there are some exciting benchmarks that look very promising:
+
+<a href="http://julialang.org/benchmarks/">
+![Julia benchmark](../Images/matcheat_julia_benchmark.png)
+</a>
+
+<font size=1em>
+C compiled by gcc 4.8.1, taking best timing from all optimization levels (-O0 through -O3). C, Fortran and Julia use OpenBLAS v0.2.8. The Python implementations of rand_mat_stat and rand_mat_mul use NumPy (v1.6.1) functions; the rest are pure Python implementations.<br>
+
+Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast dynamic language for technical computing”.  
+(Source: [http://julialang.org/benchmarks/](http://julialang.org/benchmarks/), with permission from the copyright holder)
+</font>
+
+<br>
+<br>
+<br>
+<a id="cheatsheet"></a>
+<br>
+
+
+
+# Cheat sheet
+
+<a id='cheat_overview'></a>
+<br>
+
+<br><a href='#cheat_overview'>[back to cheat sheet overview]
+
+
+
+### Cheat sheet overview
+
+
+- [Creating matrices](#creating)
+
+- Accessing matrix elements
+
+- Manipulating shape
+
+- Basic matrix operations
+
+- Advanced matrix operations
+
+<br>
+<br>
+<br>
+<br>
+
+<table cellspacing="0" border="0">
+	<colgroup width="383"></colgroup>
+	<colgroup width="387"></colgroup>
+	<colgroup width="447"></colgroup>
+	<colgroup width="469"></colgroup>
+	<colgroup width="511"></colgroup>
+	<colgroup width="690"></colgroup>
+	<tr>
+		<td height="57" align="center" valign=middle bgcolor="#0066CC"><b><font face="Arial" size=5 color="#FFFFFF">Task</font></b></td>
+		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Andale Mono" size=5 color="#FFFFFF">MATLAB/Octave</font></b></td>
+		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Andale Mono" size=5 color="#FFFFFF">Python NumPy</font></b></td>
+		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Andale Mono" size=5 color="#FFFFFF">R</font></b></td>
+		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Andale Mono" size=5 color="#FFFFFF">Julia</font></b></td>
+		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Arial" size=5 color="#FFFFFF">Task</font></b></td>
+	</tr>
+	<tr> <a id='creating'></a>
+		<td colspan=6 height="70" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">CREATING MATRICES</font><br><a href='#cheat_overview'>[back to cheat sheet overview]</a></b></td>
+		</tr>		
+	<tr>
+		<td style="border-right: 1px solid #000000" height="158" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating Matrices&nbsp;<br>(here: 3x3 matrix)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp; 9</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 6],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7, 8, 9]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(c(1,2,3,4,5,6,7,8,9),nrow=3,byrow=T)<br>
# equivalent to
<br># A = matrix(1:9,nrow=3,byrow=T)

<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6<br>[3,]    7    8    9</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6<br> 7  8  9</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating Matrices&nbsp;<br>(here: 3x3 matrix)</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating an 1D column vector</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; a = [1; 2; 3]<br>a =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp; 3</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; a = np.array([[1],[2],[3]])<br>P&gt; a<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; a = matrix(c(1,2,3), nrow=3, byrow=T)<br>R&gt; a<br>     [,1]<br>[1,]    1<br>[2,]    2<br>[3,]    3<br>R&gt;</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; a=[1; 2; 3]<br>3-element Array{Int64,1}:  <br> 1<br> 2<br> 3</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating an 1D column vector</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating an<br>1D row vector</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; b = [1 2 3]<br>b =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; b = np.array([1,2,3])<br><br>P&gt; b<br>array([1, 2, 3])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; b = matrix(c(1,2,3), ncol=3)<br><br>R&gt; b<br>     [,1] [,2] [,3]<br>[1,]    1    2    3</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; b=[1 2 3]  <br>1x3 Array{Int64,2}:<br> 1  2  3<br><br># note that this is a 2D array. # vectors in Julia are columns</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating an<br>1D row vector</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating a <br>random m x n matrix</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; rand(3,2)<br>ans =<br>&nbsp;&nbsp; 0.21977 &nbsp; 0.10220<br>&nbsp;&nbsp; 0.38959 &nbsp; 0.69911<br>&nbsp;&nbsp; 0.15624 &nbsp; 0.65637</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; np.random.rand(3,2)<br>array([[ 0.29347865,&nbsp; 0.17920462],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.51615758,&nbsp; 0.64593471],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.01067605,&nbsp; 0.09692771]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; matrix(runif(3*2), ncol=2)<br>          [,1]      [,2]<br>[1,] 0.5675127 0.7751204<br>[2,] 0.3439412 0.5261893<br>[3,] 0.2273177 0.223438</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; rand(3,2)<br>3x2 Array{Float64,2}:<br> 0.36882   0.267725<br> 0.571856  0.601524<br> 0.848084  0.858935</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating a <br>random m x n matrix</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating a<br>zero&nbsp;m x n matrix&nbsp;</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; zeros(3,2)<br>ans =<br>&nbsp;&nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; np.zeros((3,2))<br>array([[ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp; 0.]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; mat.or.vec(3, 2)<br>     [,1] [,2]<br>[1,]    0    0<br>[2,]    0    0<br>[3,]    0    0</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; zeros(3,2)<br>3x2 Array{Float64,2}:<br> 0.0  0.0<br> 0.0  0.0<br> 0.0  0.0</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating a<br>zero&nbsp;m x n matrix&nbsp;</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating an<br>m x n matrix of ones</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; ones(3,2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp; 1</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; np.ones([3,2])<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; mat.or.vec(3, 2) + 1<br>     [,1] [,2]<br>[1,]    1    1<br>[2,]    1    1<br>[3,]    1    1</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; ones(3,2)<br>3x2 Array{Float64,2}:<br> 1.0  1.0<br> 1.0  1.0<br> 1.0  1.0</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating an<br>m x n matrix of ones</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating an<br>identity matrix</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; eye(3)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 1 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp; 1</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; np.eye(3)<br>array([[ 1.,&nbsp; 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp; 1.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp; 0.,&nbsp; 1.]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; diag(3)<br>     [,1] [,2] [,3]<br>[1,]    1    0    0<br>[2,]    0    1    0<br>[3,]    0    0    1</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; eye(3)<br>3x3 Array{Float64,2}:<br> 1.0  0.0  0.0<br> 0.0  1.0  0.0<br> 0.0  0.0  1.0</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating an<br>identity matrix</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="217" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating a<br>diagonal matrix</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; a = [1 2 3]<br><br>M&gt; diag(a)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp; 3</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; a = np.array([1,2,3])<br><br>P&gt; np.diag(a)<br>array([[1, 0, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0, 2, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0, 0, 3]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; diag(1:3)<br>     [,1] [,2] [,3]<br>[1,]    1    0    0<br>[2,]    0    2    0<br>[3,]    0    0    3</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; a=[1, 2, 3] <br><br># added commas because julia<br># vectors are columnar<br><br>J&gt; diagm(a)<br>3x3 Array{Int64,2}:<br> 1  0  0<br> 0  2  0<br> 0  0  3</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating a<br>diagonal matrix</font></b></td>
+	</tr>
+	<tr>
+		<td colspan=6 height="128" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">ACESSING MATRIX ELEMENTS</font></b></td>
+		</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="159" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Getting the dimension<br>of a matrix<br>(here: 2D, rows x cols)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6]<br>A =<br>&nbsp; &nbsp;1 &nbsp; 2 &nbsp; 3<br>&nbsp; &nbsp;4 &nbsp; 5 &nbsp; 6<br><br>M&gt; size(A)<br>ans =<br>&nbsp; &nbsp;2 &nbsp; 3</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6] ])<br><br>P&gt; A<br>array([[1, 2, 3],<br>&nbsp; &nbsp; &nbsp; &nbsp;[4, 5, 6]])<br><br>P&gt; A.shape<br>(2, 3)</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:6,nrow=2,byrow=T)<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6
R&gt; dim(A)<br>[1] 2 3</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6]<br>2x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6<br><br>J&gt; size(A)<br>(2,3)</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Getting the dimension<br>of a matrix<br>(here: 2D, rows x cols)</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="240" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Selecting rows&nbsp;</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st row<br>M&gt; A(1,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br><br>% 1st 2 rows<br>M&gt; A(1:2,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st row<br>P&gt; A[0,:]<br>array([1, 2, 3])<br><br># 1st 2 rows<br>P&gt; A[0:2,:]<br>array([[1, 2, 3], [4, 5, 6]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9,nrow=3,byrow=T)

<br><br># 1st row<br>

R&gt; A[1,]<br>[1] 1 2 3<br><br>

# 1st 2 rows

<br>R&gt; A[1:2,]<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9]; <br>#semicolon suppresses output<br><br>#1st row<br>J&gt; A[1,:]  <br>1x3 Array{Int64,2}:<br> 1  2  3<br><br>#1st 2 rows<br>J&gt; A[1:2,:]  <br>2x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Selecting rows&nbsp;</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="304" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Selecting columns</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st column<br>M&gt; A(:,1)<br>ans =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 4<br>&nbsp;&nbsp; 7<br><br>% 1st 2 columns<br>M&gt; A(:,1:2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2<br>&nbsp;&nbsp; 4 &nbsp; 5<br>&nbsp;&nbsp; 7 &nbsp; 8</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st column (as row vector)<br>P&gt; A[:,0]<br>array([1, 4, 7])<br><br># 1st column (as column vector)<br>P&gt; A[:,[0]]<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7]])<br><br># 1st 2 columns<br>P&gt; A[:,0:2]<br>array([[1, 2],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[4, 5],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[7, 8]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9,nrow=3,byrow=T)


<br><br># 1st column as row vector
<br>R&gt; t(A[,1])<br>     [,1] [,2] [,3]<br>[1,]    1    4    7

<br><br># 1st column as column vector
<br>R&gt; A[,1]<br>[1] 1 4 7

<br><br># 1st 2 columns
<br>R&gt; A[,1:2]<br>     [,1] [,2]<br>[1,]    1    2<br>[2,]    4    5<br>[3,]    7    8</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>#1st column<br>J&gt; A[:,1] <br>3-element Array{Int64,1}:<br> 1<br> 4<br> 7<br> <br>#1st 2 columns<br>J&gt; A[:,1:2] <br>3x2 Array{Int64,2}:<br> 1  2<br> 4  5<br> 7  8</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Selecting columns</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="260" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Extracting rows and columns by criteria<br><br>(here: get rows&nbsp;that have value 9 in column 3)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 9; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp; 9<br><br>M&gt; A(A(:,3) == 9,:)<br>ans =<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp; 9</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,9], [7,8,9]])<br><br>P&gt; A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7, 8, 9]])<br><br>P&gt; A[A[:,2] == 9]<br>array([[4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7, 8, 9]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9,nrow=3,byrow=T)

<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    9<br>[3,]    7    8    9

<br><br>R&gt; matrix(A[A[,3]==9], ncol=3)<br>     [,1] [,2] [,3]<br>[1,]    4    5    9<br>[2,]    7    8    9</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 9; 7 8 9]<br>3x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  9<br> 7  8  9<br><br># use '.==' for<br># element-wise check<br>J&gt; A[ A[:,3] .==9, :]  <br>2x3 Array{Int64,2}:<br> 4  5  9<br> 7  8  9</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Extracting rows and columns by criteria<br><br>(here: get rows&nbsp;that have value 9 in column 3)</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="118" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Accessing elements<br>(here: 1st element)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A(1,1)<br>ans =&nbsp; 1</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A[0,0]<br>1</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(c(1,2,3,4,5,9,7,8,9),nrow=3,byrow=T)
<br><br>
R&gt; A[1,1]<br>[1] 1</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A[1,1]<br>1</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Accessing elements<br>(here: 1st element)</font></b></td>
+	</tr>
+	<tr>
+		<td colspan=6 height="128" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">MANIPULATING SHAPE AND DIMENSIONS</font></b></td>
+		</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="173" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Converting&nbsp;<br>row to column vectors</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; b = [1 2 3]
<br><br>M&gt; b = b'<br>b =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp; 3</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; b = np.array([1, 2, 3])<br><br>P&gt; b = b[np.newaxis].T<br># alternatively <br>#&nbsp;b = b[:,np.newaxis]<br><br>P&gt; b<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; b = matrix(c(1,2,3), ncol=3)<br><br>R&gt; t(b)<br>     [,1]<br>[1,]    1<br>[2,]    2<br>[3,]    3</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; b=vec([1 2 3])<br>3-element Array{Int64,1}:<br> 1<br> 2<br> 3</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Converting&nbsp;<br>row to column vectors</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="275" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Reshaping Matrices<br><br>(here: 3x3 matrix to row vector)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp; 9<br><br>M&gt; total_elements = numel(A)<br><br>M&gt; B = reshape(A,1,total_elements)&nbsp;<br>% or reshape(A,1,9)<br>B =<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7 &nbsp; 2 &nbsp; 5 &nbsp; 8 &nbsp; 3 &nbsp; 6 &nbsp; 9</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([[1,2,3],[4,5,6],[7,8,9]])<br><br>P&gt; A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7, 8, 9]])<br><br>P&gt; total_elements = A.shape[0] * A.shape[1]<br><br>P&gt; B = A.reshape(1, total_elements)&nbsp;<br><br># or A.reshape(1,9)<br># Alternative: A.shape = (1,9)&nbsp;<br># to&nbsp;change the array in place<br><br>P&gt; B<br>array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9,nrow=3,byrow=T)

<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6<br>[3,]    7    8    9
R&gt; total_elements = dim(A)[1] * dim(A)[2]<br><br>R&gt; B = matrix(A, ncol=total_elements)<br><br>R&gt; B<br>     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]<br>[1,]    1    4    7    2    5    8    3    6    9</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6<br> 7  8  9<br><br>J&gt; total_elements=length(A)<br>9<br><br>J&gt;B=reshape(A,1,total_elements)<br>1x9 Array{Int64,2}:<br> 1  4  7  2  5  8  3  6  9</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Reshaping Matrices<br><br>(here: 3x3 matrix to row vector)</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="240" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Concatenating matrices</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6]<br><br>M&gt; B = [7 8 9; 10 11 12]<br><br>M&gt; C = [A; B]<br>&nbsp; &nbsp;&nbsp;1&nbsp; &nbsp; 2&nbsp; &nbsp; 3<br>&nbsp; &nbsp; 4&nbsp; &nbsp; 5&nbsp; &nbsp; 6<br>&nbsp; &nbsp; 7&nbsp; &nbsp; 8&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 10 &nbsp; 11 &nbsp; 12</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([[1, 2, 3], [4, 5, 6]])<br><br>P&gt; B = np.array([[7, 8, 9],[10,11,12]])<br><br>P&gt; C = np.concatenate((A, B), axis=0)<br><br>P&gt; C<br>array([[ 1, 2, 3],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 4, 5, 6],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 7, 8, 9],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[10, 11, 12]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:6,nrow=2,byrow=T)<br><br>R&gt; B = matrix(7:12,nrow=2,byrow=T)<br><br>R&gt; C = rbind(A,B)<br><br>R&gt; C<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6<br>[3,]    7    8    9<br>[4,]   10   11   12</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6];<br><br>J&gt; B=[7 8 9; 10 11 12];<br><br>J&gt; C=[A; B]<br>4x3 Array{Int64,2}:<br>  1   2   3<br>  4   5   6<br>  7   8   9<br> 10  11  12</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Concatenating matrices</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="256" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Stacking&nbsp;<br>vectors and matrices</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; a = [1 2 3]<br><br>M&gt; b = [4 5 6]<br><br>M&gt; c = [a' b']<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 4<br>&nbsp;&nbsp; 2 &nbsp; 5<br>&nbsp;&nbsp; 3 &nbsp; 6<br><br>M&gt; c = [a; b]<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; a = np.array([1,2,3])<br>P&gt; b = np.array([4,5,6])<br><br>P&gt; np.c_[a,b]<br>array([[1, 4],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2, 5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6]])<br><br>P&gt; np.r_[a,b]<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 6]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; a = matrix(1:3, ncol=3)<br><br>R&gt; b = matrix(4:6, ncol=3)<br><br>R&gt; matrix(rbind(A, B), ncol=2)<br>     [,1] [,2]<br>[1,]    1    5<br>[2,]    4    3
<br><br>
R&gt; rbind(A,B)<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; a=[1 2 3];<br><br>J&gt; b=[4 5 6];<br><br>J&gt; c=[a' b']<br>3x2 Array{Int64,2}:<br> 1  4<br> 2  5<br> 3  6<br><br>J&gt; c=[a; b]<br>2x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Stacking&nbsp;<br>vectors and matrices</font></b></td>
+	</tr>
+	<tr>
+		<td colspan=6 height="114" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">BASIC MATRIX OPERATIONS</font></b></td>
+		</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="253" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix-scalar<br>operations</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A * 2<br>ans =<br>&nbsp; &nbsp; 2&nbsp; &nbsp; 4&nbsp; &nbsp; 6<br>&nbsp; &nbsp; 8 &nbsp; 10 &nbsp; 12<br>&nbsp;&nbsp; 14 &nbsp; 16 &nbsp; 18<br><br>M&gt; A + 2<br><br>M&gt; A - 2<br><br>M&gt; A / 2</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A * 2<br>array([[ 2,&nbsp; 4,&nbsp; 6],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 8, 10, 12],<br>&nbsp;&nbsp; &nbsp; &nbsp; [14, 16, 18]])<br><br>P&gt; A + 2<br><br>P&gt; A - 2<br><br>P&gt; A / 2</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)<br><br>R&gt; A * 2<br>     [,1] [,2] [,3]<br>[1,]    2    4    6<br>[2,]    8   10   12<br>[3,]   14   16   18
R&gt; A + 2<br><br>R&gt; A - 2<br><br>R&gt; A / 2</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br> # elementwise operator<br><br>J&gt; A .* 2    <br>3x3 Array{Int64,2}:<br>  2   4   6<br>  8  10  12<br> 14  16  18 <br> <br>J&gt; A .+ 2;<br><br>J&gt; A .- 2;<br><br>J&gt; A ./ 2;</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix-scalar<br>operations</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="165" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Matrix-matrix<br>multiplication</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A * A<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp; 42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp; 102 &nbsp; 126 &nbsp; 150</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; np.dot(A,A) # or A.dot(A)<br>array([[ 30,&nbsp; 36,&nbsp; 42],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp; &nbsp; &nbsp; [102, 126, 150]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)<br><br>R&gt; A %*% A<br>     [,1] [,2] [,3]<br>[1,]   30   36   42<br>[2,]   66   81   96<br>[3,]  102  126  150</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A * A<br>3x3 Array{Int64,2}:<br>  30   36   42<br>  66   81   96<br> 102  126  150</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Matrix-matrix<br>multiplication</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="190" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix-vector<br>multiplication</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; b = [ 1; 2; 3 ]<br><br>M&gt; A * b<br>ans =<br>&nbsp;&nbsp; 14<br>&nbsp;&nbsp; 32<br>&nbsp;&nbsp; 50</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; b = np.array([ [1], [2], [3] ])<br><br>P&gt; np.dot(A,b) # or A.dot(b)<br><br>array([[14], [32], [50]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, ncol=3)<br><br>R&gt; b = matrix(1:3, nrow=3)

R&gt; t(b %*% A)<br>     [,1]<br>[1,]   14<br>[2,]   32<br>[3,]   50</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; b=[1; 2; 3];<br><br>J&gt; A*b<br>3-element Array{Int64,1}:<br> 14<br> 32<br> 50</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix-vector<br>multiplication</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="240" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A .* A<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp; 64 &nbsp; 81<br><br>M&gt; A .+ A<br><br>M&gt; A .- A<br><br>M&gt; A ./ A</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A * A<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])<br><br>P&gt; A + A<br><br>P&gt; A - A<br><br>P&gt; A / A</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)
R&gt; A * A<br>     [,1] [,2] [,3]<br>[1,]    1    4    9<br>[2,]   16   25   36<br>[3,]   49   64   81

<br><br>R&gt; A + A<br><br>R&gt; A - A<br><br>R&gt; A / A</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A .* A<br>3x3 Array{Int64,2}:<br>  1   4   9<br> 16  25  36<br> 49  64  81 <br> <br>J&gt; A .+ A; <br><br>J&gt; A .- A; <br><br>J&gt; A ./ A;</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="164" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix elements&nbsp;to power n <br><br>(here: individual elements squared)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A.^2<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp; 64 &nbsp; 81</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; np.power(A,2)<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)<br><br>R&gt; A ^ 2<br>     [,1] [,2] [,3]<br>[1,]    1    4    9<br>[2,]   16   25   36<br>[3,]   49   64   81</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A .^ 2<br>3x3 Array{Int64,2}:<br>  1   4   9<br> 16  25  36<br> 49  64  81</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix elements&nbsp;to power n <br><br>(here: individual elements squared)</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="225" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Matrix to power n<br><br>(here: matrix-matrix&nbsp;multiplication with itself)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A ^ 2<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp; 42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp; 102 &nbsp; 126 &nbsp; 150</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; np.linalg.matrix_power(A,2)<br>array([[ 30,&nbsp; 36,&nbsp; 42],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp; &nbsp; &nbsp; [102, 126, 150]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, ncol=3)<br><br>
# requires the ‘expm’ package
<br><br>R&gt; install.packages('expm')
<br><br>R&gt; library(expm)
<br><br>
R&gt; A %^% 2<br>     [,1] [,2] [,3]<br>[1,]   30   66  102<br>[2,]   36   81  126<br>[3,]   42   96  150</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A ^ 2<br>3x3 Array{Int64,2}:<br>  30   36   42<br>  66   81   96<br> 102  126  150</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Matrix to power n<br><br>(here: matrix-matrix&nbsp;multiplication with itself)</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="172" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix transpose</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A'<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2 &nbsp; 5 &nbsp; 8<br>&nbsp;&nbsp; 3 &nbsp; 6 &nbsp; 9</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A.T<br>array([[1, 4, 7],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2, 5, 8],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6, 9]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)
<br><br>R&gt; t(A)<br>     [,1] [,2] [,3]<br>[1,]    1    4    7<br>[2,]    2    5    8<br>[3,]    3    6    9</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6<br> 7  8  9<br><br>J&gt; A'<br>3x3 Array{Int64,2}:<br> 1  4  7<br> 2  5  8<br> 3  6  9</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix transpose</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="176" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Determinant of a matrix:<br>&nbsp;A -&gt; |A|</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [6 1 1; 4 -2 5; 2 8 7]<br>A =<br>&nbsp;&nbsp; 6 &nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 4&nbsp; -2 &nbsp; 5<br>&nbsp;&nbsp; 2 &nbsp; 8 &nbsp; 7<br><br>M&gt; det(A)<br>ans = -306</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt;&nbsp;A = np.array([[6,1,1],[4,-2,5],[2,8,7]])<br><br>P&gt; A<br>array([[ 6,&nbsp; 1,&nbsp; 1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 4, -2,&nbsp; 5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 2,&nbsp; 8,&nbsp; 7]])<br><br>P&gt; np.linalg.det(A)<br>-306.0</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(c(6,1,1,4,-2,5,2,8,7), nrow=3, byrow=T)<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    6    1    1<br>[2,]    4   -2    5<br>[3,]    2    8    7<br><br>R&gt; det(A)<br>[1] -306</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[6 1 1; 4 -2 5; 2 8 7]<br>3x3 Array{Int64,2}:<br> 6   1  1<br> 4  -2  5<br> 2   8  7<br><br>J&gt; det(A)<br>-306.0</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Determinant of a matrix:<br>&nbsp;A -&gt; |A|</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="202" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Inverse of a matrix</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [4 7; 2 6]<br>A =<br>&nbsp;&nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2 &nbsp; 6<br><br>M&gt; A_inv = inv(A)<br>A_inv =<br>&nbsp; &nbsp;0.60000&nbsp; -0.70000<br>&nbsp; -0.20000 &nbsp; 0.40000</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([[4, 7], [2, 6]])<br><br>P&gt; A<br>array([[4, 7],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[2, 6]])<br><br>P&gt; A_inverse = np.linalg.inv(A)<br><br>P&gt; A_inverse<br>array([[ 0.6, -0.7],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-0.2, 0.4]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(c(4,7,2,6), nrow=2, byrow=T)<br><br>R&gt; A<br>     [,1] [,2]<br>[1,]    4    7<br>[2,]    2    6<br><br>R&gt; solve(A)<br>     [,1] [,2]<br>[1,]  0.6 -0.7<br>[2,] -0.2  0.4</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[4 7; 2 6]<br>2x2 Array{Int64,2}:<br> 4  7<br> 2  6<br><br>J&gt; A_inv=inv(A)<br>2x2 Array{Float64,2}:<br>  0.6  -0.7<br> -0.2   0.4</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Inverse of a matrix</font></b></td>
+	</tr>
+	<tr>
+		<td colspan=6 height="202" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">ADVANCED MATRIX OPERATIONS</font></b></td>
+		</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="311" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Calculating the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here: covariances of the means&nbsp;<br>of x1, x2, and x3)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; x1 = [4.0000 4.2000 3.9000 4.3000 4.1000]’<br><br>M&gt; x2 = [2.0000 2.1000 2.0000 2.1000 2.2000]'<br><br>M&gt; x3 = [0.60000 0.59000 0.58000 0.62000 0.63000]’<br><br>M&gt; cov( [x1,x2,x3] )<br>ans =<br>&nbsp;&nbsp; 2.5000e-02 &nbsp; 7.5000e-03 &nbsp; 1.7500e-03<br>&nbsp;&nbsp; 7.5000e-03 &nbsp; 7.0000e-03 &nbsp; 1.3500e-03<br>&nbsp;&nbsp; 1.7500e-03 &nbsp; 1.3500e-03 &nbsp; 4.3000e-04</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; x1 = np.array([ 4, 4.2, 3.9, 4.3, 4.1])<br><br>P&gt; x2 = np.array([ 2, 2.1, 2, 2.1, 2.2])<br><br>P&gt; x3 = np.array([ 0.6, 0.59, 0.58, 0.62, 0.63])<br><br>P&gt; np.cov([x1, x2, x3])<br>Array([[ 0.025 &nbsp;, &nbsp;0.0075 , &nbsp;0.00175],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.0075 , &nbsp;0.007 &nbsp;, &nbsp;0.00135],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.00175, &nbsp;0.00135, &nbsp;0.00043]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; x1 = matrix(c(4, 4.2, 3.9, 4.3, 4.1), ncol=5)<br><br>R&gt; x2 = matrix(c(2, 2.1, 2, 2.1, 2.2), ncol=5)<br><br>R&gt; x3 = matrix(c(0.6, 0.59, 0.58, 0.62, 0.63), ncol=5)

<br><br>R&gt; cov(matrix(c(x1, x2, x3), ncol=3))<br>        [,1]    [,2]    [,3]<br>[1,] 0.02500 0.00750 0.00175<br>[2,] 0.00750 0.00700 0.00135<br>[3,] 0.00175 0.00135 0.00043</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; x1=[4.0 4.2 3.9 4.3 4.1]';<br><br>J&gt; x2=[2. 2.1 2. 2.1 2.2]';<br><br>J&gt; x3=[0.6 .59 .58 .62 .63]';<br><br>J&gt; cov([x1 x2 x3])<br>3x3 Array{Float64,2}:<br> 0.025    0.0075   0.00175<br> 0.0075   0.007    0.00135<br> 0.00175  0.00135  0.00043</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Calculating the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here: covariances of the means&nbsp;<br>of x1, x2, and x3)</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="309" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Calculating&nbsp;<br>eigenvectors and&nbsp;eigenvalues</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [3 1; 1 3]<br>A =<br>&nbsp;&nbsp; 3 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp; 3<br><br>M&gt; [eig_vec,eig_val] = eig(A)<br>eig_vec =<br>&nbsp; -0.70711 &nbsp; 0.70711<br>&nbsp;&nbsp; 0.70711 &nbsp; 0.70711<br>eig_val =<br>Diagonal Matrix<br>&nbsp;&nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 4</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([[3, 1], [1, 3]])<br><br>P&gt; A<br>array([[3, 1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [1, 3]])<br><br>P&gt; eig_val, eig_vec = np.linalg.eig(A)<br>P&gt; eig_val<br>array([ 4.,&nbsp; 2.])<br><br>P&gt; eig_vec<br>Array([[ 0.70710678, -0.70710678],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.70710678,&nbsp; 0.70710678]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(c(3,1,1,3), ncol=2)<br><br>R&gt; A<br>     [,1] [,2]<br>[1,]    3    1<br>[2,]    1    3<br>R&gt; eigen(A)<br>$values<br>[1] 4 2<br><br>$vectors<br>          [,1]       [,2]<br>[1,] 0.7071068 -0.7071068<br>[2,] 0.7071068  0.7071068</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[3 1; 1 3]<br>2x2 Array{Int64,2}:<br> 3  1<br> 1  3<br><br>J&gt; (eig_vec,eig_val)=eig(a)<br>([2.0,4.0],<br>2x2 Array{Float64,2}:<br> -0.707107  0.707107<br>  0.707107  0.707107)</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Calculating&nbsp;<br>eigenvectors and&nbsp;eigenvalues</font></b></td>
+	</tr>
+	<tr>
+		<td style="border-right: 1px solid #000000" height="539" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Generating a Gaussian dataset:<br><br>creating random vectors from the multivariate normal<br>distribution given mean and covariance matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance = 0, variance = 2)</font></b></td>
+		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>% requires statistics toolbox package<br>% how to install and load it in Octave:<br><br>% download the package from:&nbsp;<br>% http://octave.sourceforge.net/packages.php<br>% pkg install&nbsp;<br>% &nbsp; &nbsp; ~/Desktop/io-2.0.2.tar.gz &nbsp;<br>% pkg install&nbsp;<br>% &nbsp; &nbsp; ~/Desktop/statistics-1.2.3.tar.gz<br><br>M&gt; pkg load statistics<br><br>M&gt; mean = [0 0]<br><br>M&gt; cov = [2 0; 0 2]<br>cov =<br>&nbsp; &nbsp;2 &nbsp; 0<br>&nbsp; &nbsp;0 &nbsp; 2<br><br>M&gt; mvnrnd(mean,cov,5)<br>&nbsp; &nbsp;2.480150 &nbsp;-0.559906<br>&nbsp; -2.933047 &nbsp; 0.560212<br>&nbsp; &nbsp;0.098206 &nbsp; 3.055316<br>&nbsp; -0.985215 &nbsp;-0.990936<br>&nbsp; &nbsp;1.122528 &nbsp; 0.686977<br>&nbsp; &nbsp;&nbsp;</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; mean = np.array([0,0])<br><br>P&gt; cov = np.array([[2,0],[0,2]])<br><br>P&gt; np.random.multivariate_normal(mean, cov, 5)<br><br>Array([[ 1.55432624, -1.17972629],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-2.01185294, 1.96081908],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-2.11810813, 1.45784216],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-2.93207591, -0.07369322],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-1.37031244, -1.18408792]])</font></td>
+		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2># requires the ‘mass’ package<br><br>
R&gt; install.packages('MASS')<br><br>
R&gt; library(MASS)
<br><br>R&gt; mvrnorm(n=10, mean, cov)<br>            [,1]       [,2]<br> [1,] -0.8407830 -0.1882706<br> [2,]  0.8496822 -0.7889329<br> [3,] -0.1564171  0.8422177<br> [4,] -0.6288779  1.0618688<br> [5,] -0.5103879  0.1303697<br> [6,]  0.8413189 -0.1623758<br> [7,] -1.0495466 -0.4161082<br> [8,] -1.3236339  0.7755572<br> [9,]  0.2771013  1.4900494<br>[10,] -1.3536268  0.2338913</font></td>
+		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2># requires the Distributions package from https://github.com/JuliaStats/Distributions.jl<br><br>J&gt; using Distributions<br><br>J&gt; mean=[0., 0.]<br>2-element Array{Float64,1}:<br> 0.0<br> 0.0<br> <br>J&gt; cov=[2. 0.; 0. 2.]<br>2x2 Array{Float64,2}:<br> 2.0  0.0<br> 0.0  2.0<br><br>J&gt; rand( MvNormal(mean, cov), 5)<br>2x5 Array{Float64,2}:<br> -0.527634  0.370725  -0.761928  -3.91747   1.47516<br> -0.448821  2.21904    2.24561    0.692063  0.390495</font></td>
+		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Generating a Gaussian dataset:<br><br>creating random vectors from the multivariate normal<br>distribution given mean and covariance matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance = 0, variance = 2)</font></b></td>
+	</tr>
+</table>
+
+
+
+
+
+<a id='numpy_arrays'></a>
+
+
+### Alternative data structures: NumPy matrices vs. NumPy arrays
+
+Python's NumPy library also has a dedicated "matrix" type with a syntax that is a little bit closer to the MATLAB matrix: For example, the "<code>  *  </code>" operator would perform a matrix-matrix multiplication of NumPy matrices - same operator performs element-wise multiplication on NumPy arrays.    
+Vice versa, the "`.dot()`" method is used for element-wise multiplication of NumPy matrices, wheras the equivalent operation would for NumPy arrays would be achieved via the "<code>  *  </code>"-operator.  
+**Most people recommend the usage of the NumPy array type over NumPy matrices, since arrays are what most of the NumPy functions return.**
\ No newline at end of file

From 4ebe90dceb51dcb8d82b0e25c4df237e08dcb328 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 5 Jun 2014 23:40:21 -0400
Subject: [PATCH 092/248] matrix cheatsheet finished

---
 README.md                             |    3 +
 tutorials/matrix_cheatsheet.md        | 1382 ++++++++++++++++++++-----
 tutorials/matrix_cheatsheet_only.html | 1180 +++++++++++++++++++++
 3 files changed, 2331 insertions(+), 234 deletions(-)
 create mode 100644 tutorials/matrix_cheatsheet_only.html

diff --git a/README.md b/README.md
index f4e300a..d894b75 100755
--- a/README.md
+++ b/README.md
@@ -77,6 +77,9 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 - Happy Mother's Day [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)]
 
+- Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia [[Markdown](./tutorials/matrix_cheatsheet.md)]
+
+
 <br>
 
 ###// Useful scripts and snippets
diff --git a/tutorials/matrix_cheatsheet.md b/tutorials/matrix_cheatsheet.md
index cc12686..24bfd84 100644
--- a/tutorials/matrix_cheatsheet.md
+++ b/tutorials/matrix_cheatsheet.md
@@ -203,10 +203,11 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 
 # Cheat sheet
 
+[[back to section overview](#sections)]
+
 <a id='cheat_overview'></a>
 <br>
 
-<br><a href='#cheat_overview'>[back to cheat sheet overview]
 
 
 
@@ -215,292 +216,1205 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 
 - [Creating matrices](#creating)
 
-- Accessing matrix elements
+- [Accessing matrix elements](#accessing)
 
-- Manipulating shape
+- [Manipulating shape](#manipulating)
 
-- Basic matrix operations
+- [Basic matrix operations](#basic_ops)
 
-- Advanced matrix operations
+- [Advanced matrix operations](#advanced_ops)
 
 <br>
 <br>
 <br>
 <br>
 
-<table cellspacing="0" border="0">
-	<colgroup width="383"></colgroup>
-	<colgroup width="387"></colgroup>
-	<colgroup width="447"></colgroup>
-	<colgroup width="469"></colgroup>
-	<colgroup width="511"></colgroup>
-	<colgroup width="690"></colgroup>
-	<tr>
-		<td height="57" align="center" valign=middle bgcolor="#0066CC"><b><font face="Arial" size=5 color="#FFFFFF">Task</font></b></td>
-		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Andale Mono" size=5 color="#FFFFFF">MATLAB/Octave</font></b></td>
-		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Andale Mono" size=5 color="#FFFFFF">Python NumPy</font></b></td>
-		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Andale Mono" size=5 color="#FFFFFF">R</font></b></td>
-		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Andale Mono" size=5 color="#FFFFFF">Julia</font></b></td>
-		<td align="center" valign=middle bgcolor="#0066CC"><b><font face="Arial" size=5 color="#FFFFFF">Task</font></b></td>
-	</tr>
-	<tr> <a id='creating'></a>
-		<td colspan=6 height="70" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">CREATING MATRICES</font><br><a href='#cheat_overview'>[back to cheat sheet overview]</a></b></td>
-		</tr>		
+
+**If you are interested in downloading this cheat sheet table for your references, you can find it [here on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/matrix_cheatsheet_only.html)**
+
+<br>
+<br>
+
+<table cellpadding="2" cellspacing="0" style="page-break-before: always">
+	<col width="383">
+	<col width="387">
+	<col width="447">
+	<col width="469">
+	<col width="511">
+	<col width="690">
 	<tr>
-		<td style="border-right: 1px solid #000000" height="158" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating Matrices&nbsp;<br>(here: 3x3 matrix)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp; 9</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 6],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7, 8, 9]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(c(1,2,3,4,5,6,7,8,9),nrow=3,byrow=T)<br>
# equivalent to
<br># A = matrix(1:9,nrow=3,byrow=T)

<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6<br>[3,]    7    8    9</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6<br> 7  8  9</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating Matrices&nbsp;<br>(here: 3x3 matrix)</font></b></td>
+		<td height="57" bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>MATLAB/Octave</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>Python
+			NumPy</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>R</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>Julia</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
+		</td>
 	</tr>
 	<tr>
-		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating an 1D column vector</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; a = [1; 2; 3]<br>a =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp; 3</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; a = np.array([[1],[2],[3]])<br>P&gt; a<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; a = matrix(c(1,2,3), nrow=3, byrow=T)<br>R&gt; a<br>     [,1]<br>[1,]    1<br>[2,]    2<br>[3,]    3<br>R&gt;</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; a=[1; 2; 3]<br>3-element Array{Int64,1}:  <br> 1<br> 2<br> 3</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating an 1D column vector</font></b></td>
+		<td colspan="6" height="70" bgcolor="#ffffff" style="border: none; padding: 0in" id="creating">
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>CREATING
+			MATRICES </b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating an<br>1D row vector</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; b = [1 2 3]<br>b =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; b = np.array([1,2,3])<br><br>P&gt; b<br>array([1, 2, 3])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; b = matrix(c(1,2,3), ncol=3)<br><br>R&gt; b<br>     [,1] [,2] [,3]<br>[1,]    1    2    3</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; b=[1 2 3]  <br>1x3 Array{Int64,2}:<br> 1  2  3<br><br># note that this is a 2D array. # vectors in Julia are columns</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating an<br>1D row vector</font></b></td>
+	<tr valign="top">
+		<td height="158" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			Matrices&nbsp;<br>(here: 3x3 matrix)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
+			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
+			9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 6],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [7, 8, 9]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(1,2,3,4,5,6,7,8,9),nrow=3,byrow=T)<br><br>
#
+			equivalent to
<br># A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
+			8 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			Matrices&nbsp;<br>(here: 3x3 matrix)</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating a <br>random m x n matrix</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; rand(3,2)<br>ans =<br>&nbsp;&nbsp; 0.21977 &nbsp; 0.10220<br>&nbsp;&nbsp; 0.38959 &nbsp; 0.69911<br>&nbsp;&nbsp; 0.15624 &nbsp; 0.65637</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; np.random.rand(3,2)<br>array([[ 0.29347865,&nbsp; 0.17920462],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.51615758,&nbsp; 0.64593471],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.01067605,&nbsp; 0.09692771]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; matrix(runif(3*2), ncol=2)<br>          [,1]      [,2]<br>[1,] 0.5675127 0.7751204<br>[2,] 0.3439412 0.5261893<br>[3,] 0.2273177 0.223438</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; rand(3,2)<br>3x2 Array{Float64,2}:<br> 0.36882   0.267725<br> 0.571856  0.601524<br> 0.848084  0.858935</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating a <br>random m x n matrix</font></b></td>
+	<tr valign="top">
+		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an 1D column vector</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			a = [1; 2; 3]<br>a =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp;
+			3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			a = np.array([[1],[2],[3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			a<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [3]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			a = matrix(c(1,2,3), nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			a<br>[,1]<br>[1,] 1<br>[2,] 2<br>[3,] 3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			a=[1; 2; 3]<br>3-element Array{Int64,1}: <br>1<br>2<br>3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an 1D column vector</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating a<br>zero&nbsp;m x n matrix&nbsp;</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; zeros(3,2)<br>ans =<br>&nbsp;&nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; np.zeros((3,2))<br>array([[ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp; 0.]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; mat.or.vec(3, 2)<br>     [,1] [,2]<br>[1,]    0    0<br>[2,]    0    0<br>[3,]    0    0</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; zeros(3,2)<br>3x2 Array{Float64,2}:<br> 0.0  0.0<br> 0.0  0.0<br> 0.0  0.0</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating a<br>zero&nbsp;m x n matrix&nbsp;</font></b></td>
+	<tr valign="top">
+		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>1D row vector</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = [1 2 3]<br>b =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b<br>array([1, 2, 3])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b = matrix(c(1,2,3), ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b<br>[,1] [,2] [,3]<br>[1,] 1 2 3</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			b=[1 2 3] <br>1x3 Array{Int64,2}:<br>1 2 3<br><br># note that this
+			is a 2D array. <br># vectors in Julia are columns</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>1D row vector</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating an<br>m x n matrix of ones</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; ones(3,2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp; 1</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; np.ones([3,2])<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; mat.or.vec(3, 2) + 1<br>     [,1] [,2]<br>[1,]    1    1<br>[2,]    1    1<br>[3,]    1    1</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; ones(3,2)<br>3x2 Array{Float64,2}:<br> 1.0  1.0<br> 1.0  1.0<br> 1.0  1.0</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating an<br>m x n matrix of ones</font></b></td>
+	<tr valign="top">
+		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a <br>random m x n matrix</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			rand(3,2)<br>ans =<br>&nbsp;&nbsp; 0.21977 &nbsp; 0.10220<br>&nbsp;&nbsp;
+			0.38959 &nbsp; 0.69911<br>&nbsp;&nbsp; 0.15624 &nbsp; 0.65637</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.random.rand(3,2)<br>array([[ 0.29347865,&nbsp; 0.17920462],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [ 0.51615758,&nbsp; 0.64593471],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [ 0.01067605,&nbsp; 0.09692771]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			matrix(runif(3*2), ncol=2)<br>[,1] [,2]<br>[1,] 0.5675127
+			0.7751204<br>[2,] 0.3439412 0.5261893<br>[3,] 0.2273177 0.223438</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			rand(3,2)<br>3x2 Array{Float64,2}:<br>0.36882 0.267725<br>0.571856
+			0.601524<br>0.848084 0.858935</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a <br>random m x n matrix</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="128" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating an<br>identity matrix</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; eye(3)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 1 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp; 1</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; np.eye(3)<br>array([[ 1.,&nbsp; 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp; 1.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp; 0.,&nbsp; 1.]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; diag(3)<br>     [,1] [,2] [,3]<br>[1,]    1    0    0<br>[2,]    0    1    0<br>[3,]    0    0    1</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; eye(3)<br>3x3 Array{Float64,2}:<br> 1.0  0.0  0.0<br> 0.0  1.0  0.0<br> 0.0  0.0  1.0</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Creating an<br>identity matrix</font></b></td>
+	<tr valign="top">
+		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a<br>zero&nbsp;m x n matrix&nbsp;</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			zeros(3,2)<br>ans =<br>&nbsp;&nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp;
+			0<br>&nbsp;&nbsp; 0 &nbsp; 0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.zeros((3,2))<br>array([[ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp;
+			0.]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			mat.or.vec(3, 2)<br>[,1] [,2]<br>[1,] 0 0<br>[2,] 0 0<br>[3,] 0 0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			zeros(3,2)<br>3x2 Array{Float64,2}:<br>0.0 0.0<br>0.0 0.0<br>0.0
+			0.0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a<br>zero&nbsp;m x n matrix&nbsp;</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="217" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating a<br>diagonal matrix</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; a = [1 2 3]<br><br>M&gt; diag(a)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp; 3</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; a = np.array([1,2,3])<br><br>P&gt; np.diag(a)<br>array([[1, 0, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0, 2, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0, 0, 3]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; diag(1:3)<br>     [,1] [,2] [,3]<br>[1,]    1    0    0<br>[2,]    0    2    0<br>[3,]    0    0    3</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; a=[1, 2, 3] <br><br># added commas because julia<br># vectors are columnar<br><br>J&gt; diagm(a)<br>3x3 Array{Int64,2}:<br> 1  0  0<br> 0  2  0<br> 0  0  3</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Creating a<br>diagonal matrix</font></b></td>
+	<tr valign="top">
+		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>m x n matrix of ones</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			ones(3,2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp;
+			1<br>&nbsp;&nbsp; 1 &nbsp; 1</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.ones([3,2])<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			mat.or.vec(3, 2) + 1<br>[,1] [,2]<br>[1,] 1 1<br>[2,] 1 1<br>[3,]
+			1 1</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			ones(3,2)<br>3x2 Array{Float64,2}:<br>1.0 1.0<br>1.0 1.0<br>1.0
+			1.0</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>m x n matrix of ones</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td colspan=6 height="128" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">ACESSING MATRIX ELEMENTS</font></b></td>
-		</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="159" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Getting the dimension<br>of a matrix<br>(here: 2D, rows x cols)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6]<br>A =<br>&nbsp; &nbsp;1 &nbsp; 2 &nbsp; 3<br>&nbsp; &nbsp;4 &nbsp; 5 &nbsp; 6<br><br>M&gt; size(A)<br>ans =<br>&nbsp; &nbsp;2 &nbsp; 3</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6] ])<br><br>P&gt; A<br>array([[1, 2, 3],<br>&nbsp; &nbsp; &nbsp; &nbsp;[4, 5, 6]])<br><br>P&gt; A.shape<br>(2, 3)</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:6,nrow=2,byrow=T)<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6
R&gt; dim(A)<br>[1] 2 3</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6]<br>2x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6<br><br>J&gt; size(A)<br>(2,3)</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Getting the dimension<br>of a matrix<br>(here: 2D, rows x cols)</font></b></td>
+	<tr valign="top">
+		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>identity matrix</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			eye(3)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp;
+			0<br>&nbsp;&nbsp; 0 &nbsp; 1 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp;
+			1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.eye(3)<br>array([[ 1.,&nbsp; 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [ 0.,&nbsp; 1.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [
+			0.,&nbsp; 0.,&nbsp; 1.]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			diag(3)<br>[,1] [,2] [,3]<br>[1,] 1 0 0<br>[2,] 0 1 0<br>[3,] 0 0
+			1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			eye(3)<br>3x3 Array{Float64,2}:<br>1.0 0.0 0.0<br>0.0 1.0 0.0<br>0.0
+			0.0 1.0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>identity matrix</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="240" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Selecting rows&nbsp;</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st row<br>M&gt; A(1,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br><br>% 1st 2 rows<br>M&gt; A(1:2,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st row<br>P&gt; A[0,:]<br>array([1, 2, 3])<br><br># 1st 2 rows<br>P&gt; A[0:2,:]<br>array([[1, 2, 3], [4, 5, 6]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9,nrow=3,byrow=T)

<br><br># 1st row<br>

R&gt; A[1,]<br>[1] 1 2 3<br><br>

# 1st 2 rows

<br>R&gt; A[1:2,]<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9]; <br>#semicolon suppresses output<br><br>#1st row<br>J&gt; A[1,:]  <br>1x3 Array{Int64,2}:<br> 1  2  3<br><br>#1st 2 rows<br>J&gt; A[1:2,:]  <br>2x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Selecting rows&nbsp;</font></b></td>
+	<tr valign="top">
+		<td height="217" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a<br>diagonal matrix</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			a = [1 2 3]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			diag(a)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp;
+			0<br>&nbsp;&nbsp; 0 &nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp;
+			3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			a = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.diag(a)<br>array([[1, 0, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0,
+			2, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0, 0, 3]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			diag(1:3)<br>[,1] [,2] [,3]<br>[1,] 1 0 0<br>[2,] 0 2 0<br>[3,] 0
+			0 3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			a=[1, 2, 3] <br><br># added commas because julia<br># vectors are
+			columnar<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			diagm(a)<br>3x3 Array{Int64,2}:<br>1 0 0<br>0 2 0<br>0 0 3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a<br>diagonal matrix</b></font></font></p>
+		</td>
 	</tr>
 	<tr>
-		<td style="border-right: 1px solid #000000" height="304" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Selecting columns</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st column<br>M&gt; A(:,1)<br>ans =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 4<br>&nbsp;&nbsp; 7<br><br>% 1st 2 columns<br>M&gt; A(:,1:2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2<br>&nbsp;&nbsp; 4 &nbsp; 5<br>&nbsp;&nbsp; 7 &nbsp; 8</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st column (as row vector)<br>P&gt; A[:,0]<br>array([1, 4, 7])<br><br># 1st column (as column vector)<br>P&gt; A[:,[0]]<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7]])<br><br># 1st 2 columns<br>P&gt; A[:,0:2]<br>array([[1, 2],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[4, 5],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[7, 8]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9,nrow=3,byrow=T)


<br><br># 1st column as row vector
<br>R&gt; t(A[,1])<br>     [,1] [,2] [,3]<br>[1,]    1    4    7

<br><br># 1st column as column vector
<br>R&gt; A[,1]<br>[1] 1 4 7

<br><br># 1st 2 columns
<br>R&gt; A[,1:2]<br>     [,1] [,2]<br>[1,]    1    2<br>[2,]    4    5<br>[3,]    7    8</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>#1st column<br>J&gt; A[:,1] <br>3-element Array{Int64,1}:<br> 1<br> 4<br> 7<br> <br>#1st 2 columns<br>J&gt; A[:,1:2] <br>3x2 Array{Int64,2}:<br> 1  2<br> 4  5<br> 7  8</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Selecting columns</font></b></td>
+		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in" id='accessing'>
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ACCESSING
+			MATRIX ELEMENTS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="260" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Extracting rows and columns by criteria<br><br>(here: get rows&nbsp;that have value 9 in column 3)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 9; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp; 9<br><br>M&gt; A(A(:,3) == 9,:)<br>ans =<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp; 9</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,9], [7,8,9]])<br><br>P&gt; A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7, 8, 9]])<br><br>P&gt; A[A[:,2] == 9]<br>array([[4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7, 8, 9]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9,nrow=3,byrow=T)

<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    9<br>[3,]    7    8    9

<br><br>R&gt; matrix(A[A[,3]==9], ncol=3)<br>     [,1] [,2] [,3]<br>[1,]    4    5    9<br>[2,]    7    8    9</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 9; 7 8 9]<br>3x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  9<br> 7  8  9<br><br># use '.==' for<br># element-wise check<br>J&gt; A[ A[:,3] .==9, :]  <br>2x3 Array{Int64,2}:<br> 4  5  9<br> 7  8  9</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Extracting rows and columns by criteria<br><br>(here: get rows&nbsp;that have value 9 in column 3)</font></b></td>
+	<tr valign="top">
+		<td height="159" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Getting
+			the dimension<br>of a matrix<br>(here: 2D, rows x cols)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6]<br>A =<br>&nbsp; &nbsp;1 &nbsp; 2 &nbsp; 3<br>&nbsp;
+			&nbsp;4 &nbsp; 5 &nbsp; 6<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			size(A)<br>ans =<br>&nbsp; &nbsp;2 &nbsp; 3</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[1, 2, 3],<br>&nbsp; &nbsp; &nbsp; &nbsp;[4, 5,
+			6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A.shape<br>(2, 3)</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:6,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6
</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			dim(A)<br>[1] 2 3</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6]<br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			size(A)<br>(2,3)</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Getting
+			the dimension<br>of a matrix<br>(here: 2D, rows x cols)</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="118" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Accessing elements<br>(here: 1st element)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A(1,1)<br>ans =&nbsp; 1</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A[0,0]<br>1</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(c(1,2,3,4,5,9,7,8,9),nrow=3,byrow=T)
<br><br>
R&gt; A[1,1]<br>[1] 1</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A[1,1]<br>1</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Accessing elements<br>(here: 1st element)</font></b></td>
+	<tr valign="top">
+		<td height="240" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
+			rows&nbsp;</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st row<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(1,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br><br>% 1st 2
+			rows<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(1:2,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp;
+			4 &nbsp; 5 &nbsp; 6</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st row<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[0,:]<br>array([1, 2, 3])<br><br># 1st 2 rows<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[0:2,:]<br>array([[1, 2, 3], [4, 5, 6]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)

<br><br># 1st row<br>

<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[1,]<br>[1] 1 2 3<br><br>

# 1st 2 rows

<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[1:2,]<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]; <br>#semicolon suppresses output<br><br>#1st
+			row<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[1,:] <br>1x3 Array{Int64,2}:<br>1 2 3<br><br>#1st 2 rows<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[1:2,:] <br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
+			rows&nbsp;</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td colspan=6 height="128" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">MANIPULATING SHAPE AND DIMENSIONS</font></b></td>
-		</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="173" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Converting&nbsp;<br>row to column vectors</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; b = [1 2 3]
<br><br>M&gt; b = b'<br>b =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp; 3</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; b = np.array([1, 2, 3])<br><br>P&gt; b = b[np.newaxis].T<br># alternatively <br>#&nbsp;b = b[:,np.newaxis]<br><br>P&gt; b<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; b = matrix(c(1,2,3), ncol=3)<br><br>R&gt; t(b)<br>     [,1]<br>[1,]    1<br>[2,]    2<br>[3,]    3</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; b=vec([1 2 3])<br>3-element Array{Int64,1}:<br> 1<br> 2<br> 3</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Converting&nbsp;<br>row to column vectors</font></b></td>
+	<tr valign="top">
+		<td height="304" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
+			columns</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st column<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(:,1)<br>ans =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 4<br>&nbsp;&nbsp;
+			7<br><br>% 1st 2 columns<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(:,1:2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2<br>&nbsp;&nbsp; 4 &nbsp;
+			5<br>&nbsp;&nbsp; 7 &nbsp; 8</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st column
+			(as row vector)<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[:,0]<br>array([1, 4, 7])<br><br># 1st column (as column
+			vector)<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[:,[0]]<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [7]])<br><br># 1st 2 columns<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[:,0:2]<br>array([[1, 2],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[4,
+			5],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[7, 8]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)


<br><br># 1st column as row
+			vector
<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			t(A[,1])<br>[,1] [,2] [,3]<br>[1,] 1 4 7

<br><br># 1st column
+			as column vector
<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[,1]<br>[1] 1 4 7

<br><br># 1st 2 columns
<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[,1:2]<br>[,1] [,2]<br>[1,] 1 2<br>[2,] 4 5<br>[3,] 7 8</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br>#1st column<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[:,1] <br>3-element Array{Int64,1}:<br>1<br>4<br>7<br><br>#1st 2
+			columns<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[:,1:2] <br>3x2 Array{Int64,2}:<br>1 2<br>4 5<br>7 8</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
+			columns</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="275" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Reshaping Matrices<br><br>(here: 3x3 matrix to row vector)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp; 9<br><br>M&gt; total_elements = numel(A)<br><br>M&gt; B = reshape(A,1,total_elements)&nbsp;<br>% or reshape(A,1,9)<br>B =<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7 &nbsp; 2 &nbsp; 5 &nbsp; 8 &nbsp; 3 &nbsp; 6 &nbsp; 9</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([[1,2,3],[4,5,6],[7,8,9]])<br><br>P&gt; A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [7, 8, 9]])<br><br>P&gt; total_elements = A.shape[0] * A.shape[1]<br><br>P&gt; B = A.reshape(1, total_elements)&nbsp;<br><br># or A.reshape(1,9)<br># Alternative: A.shape = (1,9)&nbsp;<br># to&nbsp;change the array in place<br><br>P&gt; B<br>array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9,nrow=3,byrow=T)

<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6<br>[3,]    7    8    9
R&gt; total_elements = dim(A)[1] * dim(A)[2]<br><br>R&gt; B = matrix(A, ncol=total_elements)<br><br>R&gt; B<br>     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]<br>[1,]    1    4    7    2    5    8    3    6    9</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6<br> 7  8  9<br><br>J&gt; total_elements=length(A)<br>9<br><br>J&gt;B=reshape(A,1,total_elements)<br>1x9 Array{Int64,2}:<br> 1  4  7  2  5  8  3  6  9</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Reshaping Matrices<br><br>(here: 3x3 matrix to row vector)</font></b></td>
+	<tr valign="top">
+		<td height="260" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Extracting
+			rows and columns by criteria<br><br>(here: get rows&nbsp;that have
+			value 9 in column 3)</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 9; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
+			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
+			9<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(A(:,3) == 9,:)<br>ans =<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp;
+			7 &nbsp; 8 &nbsp; 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,9], [7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [7, 8, 9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[A[:,2] == 9]<br>array([[4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[7, 8, 9]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 9<br>[3,] 7 8
+			9

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			matrix(A[A[,3]==9], ncol=3)<br>[,1] [,2] [,3]<br>[1,] 4 5 9<br>[2,]
+			7 8 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 9; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 9<br>7
+			8 9<br><br># use '.==' for<br># element-wise check<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[ A[:,3] .==9, :] <br>2x3 Array{Int64,2}:<br>4 5 9<br>7 8 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Extracting
+			rows and columns by criteria<br><br>(here: get rows&nbsp;that have
+			value 9 in column 3)</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="240" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Concatenating matrices</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6]<br><br>M&gt; B = [7 8 9; 10 11 12]<br><br>M&gt; C = [A; B]<br>&nbsp; &nbsp;&nbsp;1&nbsp; &nbsp; 2&nbsp; &nbsp; 3<br>&nbsp; &nbsp; 4&nbsp; &nbsp; 5&nbsp; &nbsp; 6<br>&nbsp; &nbsp; 7&nbsp; &nbsp; 8&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 10 &nbsp; 11 &nbsp; 12</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([[1, 2, 3], [4, 5, 6]])<br><br>P&gt; B = np.array([[7, 8, 9],[10,11,12]])<br><br>P&gt; C = np.concatenate((A, B), axis=0)<br><br>P&gt; C<br>array([[ 1, 2, 3],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 4, 5, 6],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 7, 8, 9],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[10, 11, 12]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:6,nrow=2,byrow=T)<br><br>R&gt; B = matrix(7:12,nrow=2,byrow=T)<br><br>R&gt; C = rbind(A,B)<br><br>R&gt; C<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6<br>[3,]    7    8    9<br>[4,]   10   11   12</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6];<br><br>J&gt; B=[7 8 9; 10 11 12];<br><br>J&gt; C=[A; B]<br>4x3 Array{Int64,2}:<br>  1   2   3<br>  4   5   6<br>  7   8   9<br> 10  11  12</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Concatenating matrices</font></b></td>
+	<tr valign="top">
+		<td height="118" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Accessing
+			elements<br>(here: 1st element)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(1,1)<br>ans =&nbsp; 1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[0,0]<br>1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(1,2,3,4,5,9,7,8,9),nrow=3,byrow=T)
<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[1,1]<br>[1] 1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[1,1]<br>1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Accessing
+			elements<br>(here: 1st element)</b></font></font></p>
+		</td>
 	</tr>
 	<tr>
-		<td style="border-right: 1px solid #000000" height="256" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Stacking&nbsp;<br>vectors and matrices</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; a = [1 2 3]<br><br>M&gt; b = [4 5 6]<br><br>M&gt; c = [a' b']<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 4<br>&nbsp;&nbsp; 2 &nbsp; 5<br>&nbsp;&nbsp; 3 &nbsp; 6<br><br>M&gt; c = [a; b]<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; a = np.array([1,2,3])<br>P&gt; b = np.array([4,5,6])<br><br>P&gt; np.c_[a,b]<br>array([[1, 4],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2, 5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6]])<br><br>P&gt; np.r_[a,b]<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 6]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; a = matrix(1:3, ncol=3)<br><br>R&gt; b = matrix(4:6, ncol=3)<br><br>R&gt; matrix(rbind(A, B), ncol=2)<br>     [,1] [,2]<br>[1,]    1    5<br>[2,]    4    3
<br><br>
R&gt; rbind(A,B)<br>     [,1] [,2] [,3]<br>[1,]    1    2    3<br>[2,]    4    5    6</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; a=[1 2 3];<br><br>J&gt; b=[4 5 6];<br><br>J&gt; c=[a' b']<br>3x2 Array{Int64,2}:<br> 1  4<br> 2  5<br> 3  6<br><br>J&gt; c=[a; b]<br>2x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Stacking&nbsp;<br>vectors and matrices</font></b></td>
+		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in" id="manipulating">
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>MANIPULATING
+			SHAPE AND DIMENSIONS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+		</td>
 	</tr>
-	<tr>
-		<td colspan=6 height="114" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">BASIC MATRIX OPERATIONS</font></b></td>
-		</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="253" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix-scalar<br>operations</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A * 2<br>ans =<br>&nbsp; &nbsp; 2&nbsp; &nbsp; 4&nbsp; &nbsp; 6<br>&nbsp; &nbsp; 8 &nbsp; 10 &nbsp; 12<br>&nbsp;&nbsp; 14 &nbsp; 16 &nbsp; 18<br><br>M&gt; A + 2<br><br>M&gt; A - 2<br><br>M&gt; A / 2</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A * 2<br>array([[ 2,&nbsp; 4,&nbsp; 6],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 8, 10, 12],<br>&nbsp;&nbsp; &nbsp; &nbsp; [14, 16, 18]])<br><br>P&gt; A + 2<br><br>P&gt; A - 2<br><br>P&gt; A / 2</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)<br><br>R&gt; A * 2<br>     [,1] [,2] [,3]<br>[1,]    2    4    6<br>[2,]    8   10   12<br>[3,]   14   16   18
R&gt; A + 2<br><br>R&gt; A - 2<br><br>R&gt; A / 2</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br> # elementwise operator<br><br>J&gt; A .* 2    <br>3x3 Array{Int64,2}:<br>  2   4   6<br>  8  10  12<br> 14  16  18 <br> <br>J&gt; A .+ 2;<br><br>J&gt; A .- 2;<br><br>J&gt; A ./ 2;</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix-scalar<br>operations</font></b></td>
+	<tr valign="top">
+		<td height="173" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Converting&nbsp;<br>row
+			to column vectors</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = [1 2 3]
<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = b'<br>b =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp;
+			3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = np.array([1, 2, 3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = b[np.newaxis].T<br># alternatively <br>#&nbsp;b =
+			b[:,np.newaxis]<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [3]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b = matrix(c(1,2,3), ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			t(b)<br>[,1]<br>[1,] 1<br>[2,] 2<br>[3,] 3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			b=vec([1 2 3])<br>3-element Array{Int64,1}:<br>1<br>2<br>3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Converting&nbsp;<br>row
+			to column vectors</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="165" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Matrix-matrix<br>multiplication</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A * A<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp; 42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp; 102 &nbsp; 126 &nbsp; 150</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; np.dot(A,A) # or A.dot(A)<br>array([[ 30,&nbsp; 36,&nbsp; 42],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp; &nbsp; &nbsp; [102, 126, 150]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)<br><br>R&gt; A %*% A<br>     [,1] [,2] [,3]<br>[1,]   30   36   42<br>[2,]   66   81   96<br>[3,]  102  126  150</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A * A<br>3x3 Array{Int64,2}:<br>  30   36   42<br>  66   81   96<br> 102  126  150</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Matrix-matrix<br>multiplication</font></b></td>
+	<tr valign="top">
+		<td height="275" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Reshaping
+			Matrices<br><br>(here: 3x3 matrix to row vector)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
+			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
+			9<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			total_elements = numel(A)<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			B = reshape(A,1,total_elements)&nbsp;<br>% or reshape(A,1,9)<br>B
+			=<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7 &nbsp; 2 &nbsp; 5 &nbsp; 8 &nbsp;
+			3 &nbsp; 6 &nbsp; 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[1,2,3],[4,5,6],[7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [7, 8, 9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			total_elements = A.shape[0] * A.shape[1]<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			B = A.reshape(1, total_elements)&nbsp;<br><br># or
+			A.reshape(1,9)<br># Alternative: A.shape = (1,9)&nbsp;<br>#
+			to&nbsp;change the array in place<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			B<br>array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			total_elements = dim(A)[1] * dim(A)[2]<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			B = matrix(A, ncol=total_elements)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			B<br>[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]<br>[1,] 1 4 7 2
+			5 8 3 6 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
+			8 9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			total_elements=length(A)<br>9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>B=reshape(A,1,total_elements)<br>1x9
+			Array{Int64,2}:<br>1 4 7 2 5 8 3 6 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Reshaping
+			Matrices<br><br>(here: 3x3 matrix to row vector)</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="190" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix-vector<br>multiplication</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; b = [ 1; 2; 3 ]<br><br>M&gt; A * b<br>ans =<br>&nbsp;&nbsp; 14<br>&nbsp;&nbsp; 32<br>&nbsp;&nbsp; 50</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; b = np.array([ [1], [2], [3] ])<br><br>P&gt; np.dot(A,b) # or A.dot(b)<br><br>array([[14], [32], [50]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, ncol=3)<br><br>R&gt; b = matrix(1:3, nrow=3)

R&gt; t(b %*% A)<br>     [,1]<br>[1,]   14<br>[2,]   32<br>[3,]   50</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; b=[1; 2; 3];<br><br>J&gt; A*b<br>3-element Array{Int64,1}:<br> 14<br> 32<br> 50</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix-vector<br>multiplication</font></b></td>
+	<tr valign="top">
+		<td height="240" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Concatenating
+			matrices</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			B = [7 8 9; 10 11 12]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			C = [A; B]<br>&nbsp; &nbsp;&nbsp;1&nbsp; &nbsp; 2&nbsp; &nbsp; 3<br>&nbsp;
+			&nbsp; 4&nbsp; &nbsp; 5&nbsp; &nbsp; 6<br>&nbsp; &nbsp; 7&nbsp; &nbsp;
+			8&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 10 &nbsp; 11 &nbsp; 12</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[1, 2, 3], [4, 5, 6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			B = np.array([[7, 8, 9],[10,11,12]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			C = np.concatenate((A, B), axis=0)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			C<br>array([[ 1, 2, 3],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 4,
+			5, 6],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 7, 8, 9],&nbsp;<br>&nbsp;
+			&nbsp; &nbsp; &nbsp;[10, 11, 12]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:6,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			B = matrix(7:12,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			C = rbind(A,B)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			C<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9<br>[4,]
+			10 11 12</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			B=[7 8 9; 10 11 12];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			C=[A; B]<br>4x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7 8 9<br>10
+			11 12</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Concatenating
+			matrices</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="240" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A .* A<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp; 64 &nbsp; 81<br><br>M&gt; A .+ A<br><br>M&gt; A .- A<br><br>M&gt; A ./ A</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A * A<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])<br><br>P&gt; A + A<br><br>P&gt; A - A<br><br>P&gt; A / A</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)
R&gt; A * A<br>     [,1] [,2] [,3]<br>[1,]    1    4    9<br>[2,]   16   25   36<br>[3,]   49   64   81

<br><br>R&gt; A + A<br><br>R&gt; A - A<br><br>R&gt; A / A</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A .* A<br>3x3 Array{Int64,2}:<br>  1   4   9<br> 16  25  36<br> 49  64  81 <br> <br>J&gt; A .+ A; <br><br>J&gt; A .- A; <br><br>J&gt; A ./ A;</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</font></b></td>
+	<tr valign="top">
+		<td height="256" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Stacking&nbsp;<br>vectors
+			and matrices</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			a = [1 2 3]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = [4 5 6]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			c = [a' b']<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 4<br>&nbsp;&nbsp; 2 &nbsp;
+			5<br>&nbsp;&nbsp; 3 &nbsp; 6<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			c = [a; b]<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp;
+			4 &nbsp; 5 &nbsp; 6</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			a = np.array([1,2,3])<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = np.array([4,5,6])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.c_[a,b]<br>array([[1, 4],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2,
+			5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.r_[a,b]<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4,
+			5, 6]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			a = matrix(1:3, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b = matrix(4:6, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			matrix(rbind(A, B), ncol=2)<br>[,1] [,2]<br>[1,] 1 5<br>[2,] 4
+			3
<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			rbind(A,B)<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			a=[1 2 3];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			b=[4 5 6];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			c=[a' b']<br>3x2 Array{Int64,2}:<br>1 4<br>2 5<br>3 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			c=[a; b]<br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Stacking&nbsp;<br>vectors
+			and matrices</b></font></font></p>
+		</td>
 	</tr>
 	<tr>
-		<td style="border-right: 1px solid #000000" height="164" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix elements&nbsp;to power n <br><br>(here: individual elements squared)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A.^2<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp; 64 &nbsp; 81</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; np.power(A,2)<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp; &nbsp; [16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)<br><br>R&gt; A ^ 2<br>     [,1] [,2] [,3]<br>[1,]    1    4    9<br>[2,]   16   25   36<br>[3,]   49   64   81</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A .^ 2<br>3x3 Array{Int64,2}:<br>  1   4   9<br> 16  25  36<br> 49  64  81</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix elements&nbsp;to power n <br><br>(here: individual elements squared)</font></b></td>
+		<td colspan="6" height="114" bgcolor="#ffffff" style="border: none; padding: 0in" id='basic_ops'>
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>BASIC
+			MATRIX OPERATIONS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="225" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Matrix to power n<br><br>(here: matrix-matrix&nbsp;multiplication with itself)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A ^ 2<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp; 42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp; 102 &nbsp; 126 &nbsp; 150</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; np.linalg.matrix_power(A,2)<br>array([[ 30,&nbsp; 36,&nbsp; 42],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp; &nbsp; &nbsp; [102, 126, 150]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, ncol=3)<br><br>
# requires the ‘expm’ package
<br><br>R&gt; install.packages('expm')
<br><br>R&gt; library(expm)
<br><br>
R&gt; A %^% 2<br>     [,1] [,2] [,3]<br>[1,]   30   66  102<br>[2,]   36   81  126<br>[3,]   42   96  150</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9];<br><br>J&gt; A ^ 2<br>3x3 Array{Int64,2}:<br>  30   36   42<br>  66   81   96<br> 102  126  150</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Matrix to power n<br><br>(here: matrix-matrix&nbsp;multiplication with itself)</font></b></td>
+	<tr valign="top">
+		<td height="253" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-scalar<br>operations</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A * 2<br>ans =<br>&nbsp; &nbsp; 2&nbsp; &nbsp; 4&nbsp; &nbsp; 6<br>&nbsp;
+			&nbsp; 8 &nbsp; 10 &nbsp; 12<br>&nbsp;&nbsp; 14 &nbsp; 16 &nbsp;
+			18<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A + 2<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A - 2<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A / 2</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A * 2<br>array([[ 2,&nbsp; 4,&nbsp; 6],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[ 8, 10, 12],<br>&nbsp;&nbsp; &nbsp; &nbsp; [14, 16, 18]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A + 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A - 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A / 2</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A * 2<br>[,1] [,2] [,3]<br>[1,] 2 4 6<br>[2,] 8 10 12<br>[3,] 14
+			16 18
</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A + 2<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A - 2<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A / 2</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br># elementwise operator<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .* 2 <br>3x3 Array{Int64,2}:<br>2 4 6<br>8 10 12<br>14 16 18
+			<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .+ 2;<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .- 2;<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A ./ 2;</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-scalar<br>operations</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="172" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix transpose</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt;&nbsp;A = [1 2 3; 4 5 6; 7 8 9]<br><br>M&gt; A'<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2 &nbsp; 5 &nbsp; 8<br>&nbsp;&nbsp; 3 &nbsp; 6 &nbsp; 9</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br>P&gt; A.T<br>array([[1, 4, 7],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2, 5, 8],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6, 9]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(1:9, nrow=3, byrow=T)
<br><br>R&gt; t(A)<br>     [,1] [,2] [,3]<br>[1,]    1    4    7<br>[2,]    2    5    8<br>[3,]    3    6    9</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br> 1  2  3<br> 4  5  6<br> 7  8  9<br><br>J&gt; A'<br>3x3 Array{Int64,2}:<br> 1  4  7<br> 2  5  8<br> 3  6  9</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Matrix transpose</font></b></td>
+	<tr valign="top">
+		<td height="165" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-matrix<br>multiplication</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A * A<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp;
+			42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp;
+			102 &nbsp; 126 &nbsp; 150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.dot(A,A) # or A.dot(A)<br>array([[ 30,&nbsp; 36,&nbsp; 42],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[102, 126, 150]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A %*% A<br>[,1] [,2] [,3]<br>[1,] 30 36 42<br>[2,] 66 81 96<br>[3,]
+			102 126 150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A * A<br>3x3 Array{Int64,2}:<br>30 36 42<br>66 81 96<br>102 126
+			150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-matrix<br>multiplication</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="176" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Determinant of a matrix:<br>&nbsp;A -&gt; |A|</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; A = [6 1 1; 4 -2 5; 2 8 7]<br>A =<br>&nbsp;&nbsp; 6 &nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 4&nbsp; -2 &nbsp; 5<br>&nbsp;&nbsp; 2 &nbsp; 8 &nbsp; 7<br><br>M&gt; det(A)<br>ans = -306</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt;&nbsp;A = np.array([[6,1,1],[4,-2,5],[2,8,7]])<br><br>P&gt; A<br>array([[ 6,&nbsp; 1,&nbsp; 1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 4, -2,&nbsp; 5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 2,&nbsp; 8,&nbsp; 7]])<br><br>P&gt; np.linalg.det(A)<br>-306.0</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; A = matrix(c(6,1,1,4,-2,5,2,8,7), nrow=3, byrow=T)<br><br>R&gt; A<br>     [,1] [,2] [,3]<br>[1,]    6    1    1<br>[2,]    4   -2    5<br>[3,]    2    8    7<br><br>R&gt; det(A)<br>[1] -306</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; A=[6 1 1; 4 -2 5; 2 8 7]<br>3x3 Array{Int64,2}:<br> 6   1  1<br> 4  -2  5<br> 2   8  7<br><br>J&gt; det(A)<br>-306.0</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Determinant of a matrix:<br>&nbsp;A -&gt; |A|</font></b></td>
+	<tr valign="top">
+		<td height="190" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-vector<br>multiplication</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = [ 1; 2; 3 ]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A * b<br>ans =<br>&nbsp;&nbsp; 14<br>&nbsp;&nbsp; 32<br>&nbsp;&nbsp;
+			50</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = np.array([ [1], [2], [3] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.dot(A,b) # or A.dot(b)<br><br>array([[14], [32], [50]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b = matrix(1:3, nrow=3)<br><br>

<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			t(b %*% A)<br>[,1]<br>[1,] 14<br>[2,] 32<br>[3,] 50</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			b=[1; 2; 3];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A*b<br>3-element Array{Int64,1}:<br>14<br>32<br>50</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-vector<br>multiplication</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="202" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Inverse of a matrix</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [4 7; 2 6]<br>A =<br>&nbsp;&nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2 &nbsp; 6<br><br>M&gt; A_inv = inv(A)<br>A_inv =<br>&nbsp; &nbsp;0.60000&nbsp; -0.70000<br>&nbsp; -0.20000 &nbsp; 0.40000</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([[4, 7], [2, 6]])<br><br>P&gt; A<br>array([[4, 7],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[2, 6]])<br><br>P&gt; A_inverse = np.linalg.inv(A)<br><br>P&gt; A_inverse<br>array([[ 0.6, -0.7],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-0.2, 0.4]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(c(4,7,2,6), nrow=2, byrow=T)<br><br>R&gt; A<br>     [,1] [,2]<br>[1,]    4    7<br>[2,]    2    6<br><br>R&gt; solve(A)<br>     [,1] [,2]<br>[1,]  0.6 -0.7<br>[2,] -0.2  0.4</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[4 7; 2 6]<br>2x2 Array{Int64,2}:<br> 4  7<br> 2  6<br><br>J&gt; A_inv=inv(A)<br>2x2 Array{Float64,2}:<br>  0.6  -0.7<br> -0.2   0.4</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Inverse of a matrix</font></b></td>
+	<tr valign="top">
+		<td height="240" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A .* A<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp;
+			9<br>&nbsp;&nbsp; 16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp;
+			64 &nbsp; 81<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A .+ A<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A .- A<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A ./ A</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A * A<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A + A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A - A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A / A</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A * A<br>[,1] [,2] [,3]<br>[1,] 1 4 9<br>[2,] 16 25 36<br>[3,] 49
+			64 81

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A + A<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A - A<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A / A</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .* A<br>3x3 Array{Int64,2}:<br>1 4 9<br>16 25 36<br>49 64 81
+			<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .+ A; <br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .- A; <br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A ./ A;</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td colspan=6 height="202" align="center" valign=middle bgcolor="#FFFFFF"><b><font face="Arial" size=4 color="#000000">ADVANCED MATRIX OPERATIONS</font></b></td>
-		</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="311" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Calculating the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here: covariances of the means&nbsp;<br>of x1, x2, and x3)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>M&gt; x1 = [4.0000 4.2000 3.9000 4.3000 4.1000]’<br><br>M&gt; x2 = [2.0000 2.1000 2.0000 2.1000 2.2000]'<br><br>M&gt; x3 = [0.60000 0.59000 0.58000 0.62000 0.63000]’<br><br>M&gt; cov( [x1,x2,x3] )<br>ans =<br>&nbsp;&nbsp; 2.5000e-02 &nbsp; 7.5000e-03 &nbsp; 1.7500e-03<br>&nbsp;&nbsp; 7.5000e-03 &nbsp; 7.0000e-03 &nbsp; 1.3500e-03<br>&nbsp;&nbsp; 1.7500e-03 &nbsp; 1.3500e-03 &nbsp; 4.3000e-04</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; x1 = np.array([ 4, 4.2, 3.9, 4.3, 4.1])<br><br>P&gt; x2 = np.array([ 2, 2.1, 2, 2.1, 2.2])<br><br>P&gt; x3 = np.array([ 0.6, 0.59, 0.58, 0.62, 0.63])<br><br>P&gt; np.cov([x1, x2, x3])<br>Array([[ 0.025 &nbsp;, &nbsp;0.0075 , &nbsp;0.00175],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.0075 , &nbsp;0.007 &nbsp;, &nbsp;0.00135],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.00175, &nbsp;0.00135, &nbsp;0.00043]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>R&gt; x1 = matrix(c(4, 4.2, 3.9, 4.3, 4.1), ncol=5)<br><br>R&gt; x2 = matrix(c(2, 2.1, 2, 2.1, 2.2), ncol=5)<br><br>R&gt; x3 = matrix(c(0.6, 0.59, 0.58, 0.62, 0.63), ncol=5)

<br><br>R&gt; cov(matrix(c(x1, x2, x3), ncol=3))<br>        [,1]    [,2]    [,3]<br>[1,] 0.02500 0.00750 0.00175<br>[2,] 0.00750 0.00700 0.00135<br>[3,] 0.00175 0.00135 0.00043</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>J&gt; x1=[4.0 4.2 3.9 4.3 4.1]';<br><br>J&gt; x2=[2. 2.1 2. 2.1 2.2]';<br><br>J&gt; x3=[0.6 .59 .58 .62 .63]';<br><br>J&gt; cov([x1 x2 x3])<br>3x3 Array{Float64,2}:<br> 0.025    0.0075   0.00175<br> 0.0075   0.007    0.00135<br> 0.00175  0.00135  0.00043</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Calculating the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here: covariances of the means&nbsp;<br>of x1, x2, and x3)</font></b></td>
+	<tr valign="top">
+		<td height="164" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			elements&nbsp;to power n <br><br>(here: individual elements
+			squared)</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A.^2<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp; 9<br>&nbsp;&nbsp;
+			16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp; 64 &nbsp; 81</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.power(A,2)<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A ^ 2<br>[,1] [,2] [,3]<br>[1,] 1 4 9<br>[2,] 16 25 36<br>[3,] 49
+			64 81</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .^ 2<br>3x3 Array{Int64,2}:<br>1 4 9<br>16 25 36<br>49 64 81</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			elements&nbsp;to power n <br><br>(here: individual elements
+			squared)</b></font></font></p>
+		</td>
 	</tr>
-	<tr>
-		<td style="border-right: 1px solid #000000" height="309" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Calculating&nbsp;<br>eigenvectors and&nbsp;eigenvalues</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>M&gt; A = [3 1; 1 3]<br>A =<br>&nbsp;&nbsp; 3 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp; 3<br><br>M&gt; [eig_vec,eig_val] = eig(A)<br>eig_vec =<br>&nbsp; -0.70711 &nbsp; 0.70711<br>&nbsp;&nbsp; 0.70711 &nbsp; 0.70711<br>eig_val =<br>Diagonal Matrix<br>&nbsp;&nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 4</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>P&gt; A = np.array([[3, 1], [1, 3]])<br><br>P&gt; A<br>array([[3, 1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [1, 3]])<br><br>P&gt; eig_val, eig_vec = np.linalg.eig(A)<br>P&gt; eig_val<br>array([ 4.,&nbsp; 2.])<br><br>P&gt; eig_vec<br>Array([[ 0.70710678, -0.70710678],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.70710678,&nbsp; 0.70710678]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>R&gt; A = matrix(c(3,1,1,3), ncol=2)<br><br>R&gt; A<br>     [,1] [,2]<br>[1,]    3    1<br>[2,]    1    3<br>R&gt; eigen(A)<br>$values<br>[1] 4 2<br><br>$vectors<br>          [,1]       [,2]<br>[1,] 0.7071068 -0.7071068<br>[2,] 0.7071068  0.7071068</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#CFE7F5"><font face="Andale Mono" size=2>J&gt; A=[3 1; 1 3]<br>2x2 Array{Int64,2}:<br> 3  1<br> 1  3<br><br>J&gt; (eig_vec,eig_val)=eig(a)<br>([2.0,4.0],<br>2x2 Array{Float64,2}:<br> -0.707107  0.707107<br>  0.707107  0.707107)</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#CFE7F5"><b><font face="Arial" size=2 color="#000000">Calculating&nbsp;<br>eigenvectors and&nbsp;eigenvalues</font></b></td>
+	<tr valign="top">
+		<td height="225" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			to power n<br><br>(here: matrix-matrix&nbsp;multiplication with
+			itself)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A ^ 2<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp;
+			42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp;
+			102 &nbsp; 126 &nbsp; 150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.linalg.matrix_power(A,2)<br>array([[ 30,&nbsp; 36,&nbsp;
+			42],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [102, 126, 150]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, ncol=3)<br><br>
# requires the ‘expm’
+			package
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			install.packages('expm')
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			library(expm)
<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A %^% 2<br>[,1] [,2] [,3]<br>[1,] 30 66 102<br>[2,] 36 81 126<br>[3,]
+			42 96 150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A ^ 2<br>3x3 Array{Int64,2}:<br>30 36 42<br>66 81 96<br>102 126
+			150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			to power n<br><br>(here: matrix-matrix&nbsp;multiplication with
+			itself)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="172" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			transpose</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A'<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2
+			&nbsp; 5 &nbsp; 8<br>&nbsp;&nbsp; 3 &nbsp; 6 &nbsp; 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A.T<br>array([[1, 4, 7],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2, 5,
+			8],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6, 9]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			t(A)<br>[,1] [,2] [,3]<br>[1,] 1 4 7<br>[2,] 2 5 8<br>[3,] 3 6 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
+			8 9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A'<br>3x3 Array{Int64,2}:<br>1 4 7<br>2 5 8<br>3 6 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			transpose</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="176" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Determinant
+			of a matrix:<br>&nbsp;A -&gt; |A|</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [6 1 1; 4 -2 5; 2 8 7]<br>A =<br>&nbsp;&nbsp; 6 &nbsp; 1 &nbsp;
+			1<br>&nbsp;&nbsp; 4&nbsp; -2 &nbsp; 5<br>&nbsp;&nbsp; 2 &nbsp; 8 &nbsp;
+			7<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			det(A)<br>ans = -306</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>&nbsp;A
+			= np.array([[6,1,1],[4,-2,5],[2,8,7]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[ 6,&nbsp; 1,&nbsp; 1],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[ 4, -2,&nbsp; 5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 2,&nbsp; 8,&nbsp;
+			7]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.linalg.det(A)<br>-306.0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(6,1,1,4,-2,5,2,8,7), nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 6 1 1<br>[2,] 4 -2 5<br>[3,] 2 8 7<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			det(A)<br>[1] -306</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[6 1 1; 4 -2 5; 2 8 7]<br>3x3 Array{Int64,2}:<br>6 1 1<br>4 -2
+			5<br>2 8 7<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			det(A)<br>-306.0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Determinant
+			of a matrix:<br>&nbsp;A -&gt; |A|</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="202" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Inverse
+			of a matrix</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [4 7; 2 6]<br>A =<br>&nbsp;&nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2
+			&nbsp; 6<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A_inv = inv(A)<br>A_inv =<br>&nbsp; &nbsp;0.60000&nbsp; -0.70000<br>&nbsp;
+			-0.20000 &nbsp; 0.40000</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[4, 7], [2, 6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[4, 7],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[2,
+			6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A_inverse = np.linalg.inv(A)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A_inverse<br>array([[ 0.6, -0.7],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-0.2, 0.4]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(4,7,2,6), nrow=2, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2]<br>[1,] 4 7<br>[2,] 2 6<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			solve(A)<br>[,1] [,2]<br>[1,] 0.6 -0.7<br>[2,] -0.2 0.4</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[4 7; 2 6]<br>2x2 Array{Int64,2}:<br>4 7<br>2 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A_inv=inv(A)<br>2x2 Array{Float64,2}:<br>0.6 -0.7<br>-0.2 0.4</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Inverse
+			of a matrix</b></font></font></p>
+		</td>
 	</tr>
 	<tr>
-		<td style="border-right: 1px solid #000000" height="539" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Generating a Gaussian dataset:<br><br>creating random vectors from the multivariate normal<br>distribution given mean and covariance matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance = 0, variance = 2)</font></b></td>
-		<td style="border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>% requires statistics toolbox package<br>% how to install and load it in Octave:<br><br>% download the package from:&nbsp;<br>% http://octave.sourceforge.net/packages.php<br>% pkg install&nbsp;<br>% &nbsp; &nbsp; ~/Desktop/io-2.0.2.tar.gz &nbsp;<br>% pkg install&nbsp;<br>% &nbsp; &nbsp; ~/Desktop/statistics-1.2.3.tar.gz<br><br>M&gt; pkg load statistics<br><br>M&gt; mean = [0 0]<br><br>M&gt; cov = [2 0; 0 2]<br>cov =<br>&nbsp; &nbsp;2 &nbsp; 0<br>&nbsp; &nbsp;0 &nbsp; 2<br><br>M&gt; mvnrnd(mean,cov,5)<br>&nbsp; &nbsp;2.480150 &nbsp;-0.559906<br>&nbsp; -2.933047 &nbsp; 0.560212<br>&nbsp; &nbsp;0.098206 &nbsp; 3.055316<br>&nbsp; -0.985215 &nbsp;-0.990936<br>&nbsp; &nbsp;1.122528 &nbsp; 0.686977<br>&nbsp; &nbsp;&nbsp;</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2>P&gt; mean = np.array([0,0])<br><br>P&gt; cov = np.array([[2,0],[0,2]])<br><br>P&gt; np.random.multivariate_normal(mean, cov, 5)<br><br>Array([[ 1.55432624, -1.17972629],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-2.01185294, 1.96081908],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-2.11810813, 1.45784216],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-2.93207591, -0.07369322],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[-1.37031244, -1.18408792]])</font></td>
-		<td style="border-left: 1px dotted #000000; border-right: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2># requires the ‘mass’ package<br><br>
R&gt; install.packages('MASS')<br><br>
R&gt; library(MASS)
<br><br>R&gt; mvrnorm(n=10, mean, cov)<br>            [,1]       [,2]<br> [1,] -0.8407830 -0.1882706<br> [2,]  0.8496822 -0.7889329<br> [3,] -0.1564171  0.8422177<br> [4,] -0.6288779  1.0618688<br> [5,] -0.5103879  0.1303697<br> [6,]  0.8413189 -0.1623758<br> [7,] -1.0495466 -0.4161082<br> [8,] -1.3236339  0.7755572<br> [9,]  0.2771013  1.4900494<br>[10,] -1.3536268  0.2338913</font></td>
-		<td style="border-left: 1px dotted #000000" align="left" valign=top bgcolor="#FFFFFF"><font face="Andale Mono" size=2># requires the Distributions package from https://github.com/JuliaStats/Distributions.jl<br><br>J&gt; using Distributions<br><br>J&gt; mean=[0., 0.]<br>2-element Array{Float64,1}:<br> 0.0<br> 0.0<br> <br>J&gt; cov=[2. 0.; 0. 2.]<br>2x2 Array{Float64,2}:<br> 2.0  0.0<br> 0.0  2.0<br><br>J&gt; rand( MvNormal(mean, cov), 5)<br>2x5 Array{Float64,2}:<br> -0.527634  0.370725  -0.761928  -3.91747   1.47516<br> -0.448821  2.21904    2.24561    0.692063  0.390495</font></td>
-		<td style="border-left: 1px solid #000000; border-right: 1px solid #000000" align="left" valign=top bgcolor="#FFFFFF"><b><font face="Arial" size=2 color="#000000">Generating a Gaussian dataset:<br><br>creating random vectors from the multivariate normal<br>distribution given mean and covariance matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance = 0, variance = 2)</font></b></td>
+		<td colspan="6" height="202" bgcolor="#ffffff" style="border: none; padding: 0in" id='advanced_ops'>
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ADVANCED
+			MATRIX OPERATIONS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="311" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating
+			the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here:
+			covariances of the means&nbsp;<br>of x1, x2, and x3)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			x1 = [4.0000 4.2000 3.9000 4.3000 4.1000]’<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			x2 = [2.0000 2.1000 2.0000 2.1000 2.2000]'<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			x3 = [0.60000 0.59000 0.58000 0.62000 0.63000]’<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			cov( [x1,x2,x3] )<br>ans =<br>&nbsp;&nbsp; 2.5000e-02 &nbsp;
+			7.5000e-03 &nbsp; 1.7500e-03<br>&nbsp;&nbsp; 7.5000e-03 &nbsp;
+			7.0000e-03 &nbsp; 1.3500e-03<br>&nbsp;&nbsp; 1.7500e-03 &nbsp;
+			1.3500e-03 &nbsp; 4.3000e-04</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			x1 = np.array([ 4, 4.2, 3.9, 4.3, 4.1])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			x2 = np.array([ 2, 2.1, 2, 2.1, 2.2])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			x3 = np.array([ 0.6, 0.59, 0.58, 0.62, 0.63])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.cov([x1, x2, x3])<br>Array([[ 0.025 &nbsp;, &nbsp;0.0075 ,
+			&nbsp;0.00175],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.0075 , &nbsp;0.007
+			&nbsp;, &nbsp;0.00135],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.00175,
+			&nbsp;0.00135, &nbsp;0.00043]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			x1 = matrix(c(4, 4.2, 3.9, 4.3, 4.1), ncol=5)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			x2 = matrix(c(2, 2.1, 2, 2.1, 2.2), ncol=5)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			x3 = matrix(c(0.6, 0.59, 0.58, 0.62, 0.63), ncol=5)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			cov(matrix(c(x1, x2, x3), ncol=3))<br>[,1] [,2] [,3]<br>[1,]
+			0.02500 0.00750 0.00175<br>[2,] 0.00750 0.00700 0.00135<br>[3,]
+			0.00175 0.00135 0.00043</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			x1=[4.0 4.2 3.9 4.3 4.1]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			x2=[2. 2.1 2. 2.1 2.2]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			x3=[0.6 .59 .58 .62 .63]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			cov([x1 x2 x3])<br>3x3 Array{Float64,2}:<br>0.025 0.0075
+			0.00175<br>0.0075 0.007 0.00135<br>0.00175 0.00135 0.00043</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating
+			the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here:
+			covariances of the means&nbsp;<br>of x1, x2, and x3)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="309" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating&nbsp;<br>eigenvectors
+			and&nbsp;eigenvalues</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [3 1; 1 3]<br>A =<br>&nbsp;&nbsp; 3 &nbsp; 1<br>&nbsp;&nbsp; 1
+			&nbsp; 3<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			[eig_vec,eig_val] = eig(A)<br>eig_vec =<br>&nbsp; -0.70711 &nbsp;
+			0.70711<br>&nbsp;&nbsp; 0.70711 &nbsp; 0.70711<br>eig_val
+			=<br>Diagonal Matrix<br>&nbsp;&nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0
+			&nbsp; 4</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[3, 1], [1, 3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[3, 1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [1, 3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			eig_val, eig_vec = np.linalg.eig(A)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			eig_val<br>array([ 4.,&nbsp; 2.])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			eig_vec<br>Array([[ 0.70710678, -0.70710678],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [ 0.70710678,&nbsp; 0.70710678]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(3,1,1,3), ncol=2)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2]<br>[1,] 3 1<br>[2,] 1 3<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			eigen(A)<br>$values<br>[1] 4 2<br><br>$vectors<br>[,1] [,2]<br>[1,]
+			0.7071068 -0.7071068<br>[2,] 0.7071068 0.7071068</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[3 1; 1 3]<br>2x2 Array{Int64,2}:<br>3 1<br>1 3<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			(eig_vec,eig_val)=eig(a)<br>([2.0,4.0],<br>2x2
+			Array{Float64,2}:<br>-0.707107 0.707107<br>0.707107 0.707107)</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating&nbsp;<br>eigenvectors
+			and&nbsp;eigenvalues</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="539" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
+			a Gaussian dataset:<br><br>creating random vectors from the
+			multivariate normal<br>distribution given mean and covariance
+			matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance
+			= 0, variance = 2)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">%
+			requires statistics toolbox package<br>% how to install and load
+			it in Octave:<br><br>% download the package from:&nbsp;<br>%
+			http://octave.sourceforge.net/packages.php<br>% pkg install&nbsp;<br>%
+			&nbsp; &nbsp; ~/Desktop/io-2.0.2.tar.gz &nbsp;<br>% pkg install&nbsp;<br>%
+			&nbsp; &nbsp; ~/Desktop/statistics-1.2.3.tar.gz<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			pkg load statistics<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			mean = [0 0]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			cov = [2 0; 0 2]<br>cov =<br>&nbsp; &nbsp;2 &nbsp; 0<br>&nbsp; &nbsp;0
+			&nbsp; 2<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			mvnrnd(mean,cov,5)<br>&nbsp; &nbsp;2.480150 &nbsp;-0.559906<br>&nbsp;
+			-2.933047 &nbsp; 0.560212<br>&nbsp; &nbsp;0.098206 &nbsp;
+			3.055316<br>&nbsp; -0.985215 &nbsp;-0.990936<br>&nbsp; &nbsp;1.122528
+			&nbsp; 0.686977<br>&nbsp; &nbsp;&nbsp;</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			mean = np.array([0,0])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			cov = np.array([[2,0],[0,2]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.random.multivariate_normal(mean, cov, 5)<br><br>Array([[
+			1.55432624, -1.17972629],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-2.01185294, 1.96081908],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-2.11810813, 1.45784216],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-2.93207591, -0.07369322],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-1.37031244, -1.18408792]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			requires the ‘mass’ package<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			install.packages('MASS')<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			library(MASS)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			mvrnorm(n=10, mean, cov)<br>[,1] [,2]<br>[1,] -0.8407830
+			-0.1882706<br>[2,] 0.8496822 -0.7889329<br>[3,] -0.1564171
+			0.8422177<br>[4,] -0.6288779 1.0618688<br>[5,] -0.5103879
+			0.1303697<br>[6,] 0.8413189 -0.1623758<br>[7,] -1.0495466
+			-0.4161082<br>[8,] -1.3236339 0.7755572<br>[9,] 0.2771013
+			1.4900494<br>[10,] -1.3536268 0.2338913</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			requires the Distributions package from
+			https://github.com/JuliaStats/Distributions.jl<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			using Distributions<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			mean=[0., 0.]<br>2-element Array{Float64,1}:<br>0.0<br>0.0<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			cov=[2. 0.; 0. 2.]<br>2x2 Array{Float64,2}:<br>2.0 0.0<br>0.0
+			2.0<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			rand( MvNormal(mean, cov), 5)<br>2x5 Array{Float64,2}:<br>-0.527634
+			0.370725 -0.761928 -3.91747 1.47516<br>-0.448821 2.21904 2.24561
+			0.692063 0.390495</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
+			a Gaussian dataset:<br><br>creating random vectors from the
+			multivariate normal<br>distribution given mean and covariance
+			matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance
+			= 0, variance = 2)</b></font></font></p>
+		</td>
 	</tr>
 </table>
+<br>
 
+<br><br><font size=0.6>
+(Thanks to Keith C. Campbell for providing me with the syntax for the Julia language.)
+</font>
 
-
-
+<br>
 
 <a id='numpy_arrays'></a>
 
 
+<br>
+<br>
+<br>
+<br>
+
 ### Alternative data structures: NumPy matrices vs. NumPy arrays
 
+[[back to section overview](#sections)]
+
 Python's NumPy library also has a dedicated "matrix" type with a syntax that is a little bit closer to the MATLAB matrix: For example, the "<code>  *  </code>" operator would perform a matrix-matrix multiplication of NumPy matrices - same operator performs element-wise multiplication on NumPy arrays.    
 Vice versa, the "`.dot()`" method is used for element-wise multiplication of NumPy matrices, wheras the equivalent operation would for NumPy arrays would be achieved via the "<code>  *  </code>"-operator.  
 **Most people recommend the usage of the NumPy array type over NumPy matrices, since arrays are what most of the NumPy functions return.**
\ No newline at end of file
diff --git a/tutorials/matrix_cheatsheet_only.html b/tutorials/matrix_cheatsheet_only.html
new file mode 100644
index 0000000..a03d73f
--- /dev/null
+++ b/tutorials/matrix_cheatsheet_only.html
@@ -0,0 +1,1180 @@
+<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">
+<html>
+<head>
+	<meta http-equiv="content-type" content="text/html; charset=utf-8">
+	<title></title>
+	<meta name="generator" content="LibreOffice 4.2.4.2 (MacOSX)">
+	<meta name="created" content="0;0">
+	<meta name="changed" content="20140605;223752655486000">
+	<style type="text/css">
+	<!--
+		td p { color: #000000; font-family: "Verdana"; font-size: 12pt }
+		p { color: #000000; font-family: "Verdana"; font-size: 12pt }
+	-->
+	</style>
+</head>
+<body lang="en-US" text="#000000" dir="ltr" style="background: transparent">
+<table cellpadding="2" cellspacing="0" style="page-break-before: always">
+	<col width="383">
+	<col width="387">
+	<col width="447">
+	<col width="469">
+	<col width="511">
+	<col width="690">
+	<tr>
+		<td height="57" bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>MATLAB/Octave</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>Python
+			NumPy</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>R</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>Julia</b></font></font></font></p>
+		</td>
+		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
+		</td>
+	</tr>
+	<tr>
+		<td colspan="6" height="70" bgcolor="#ffffff" style="border: none; padding: 0in">
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>CREATING
+			MATRICES</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="158" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			Matrices&nbsp;<br>(here: 3x3 matrix)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
+			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
+			9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 6],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [7, 8, 9]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(1,2,3,4,5,6,7,8,9),nrow=3,byrow=T)<br><br>
#
+			equivalent to
<br># A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
+			8 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			Matrices&nbsp;<br>(here: 3x3 matrix)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an 1D column vector</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			a = [1; 2; 3]<br>a =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp;
+			3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			a = np.array([[1],[2],[3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			a<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [3]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			a = matrix(c(1,2,3), nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			a<br>[,1]<br>[1,] 1<br>[2,] 2<br>[3,] 3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			a=[1; 2; 3]<br>3-element Array{Int64,1}: <br>1<br>2<br>3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an 1D column vector</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>1D row vector</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = [1 2 3]<br>b =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b<br>array([1, 2, 3])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b = matrix(c(1,2,3), ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b<br>[,1] [,2] [,3]<br>[1,] 1 2 3</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			b=[1 2 3] <br>1x3 Array{Int64,2}:<br>1 2 3<br><br># note that this
+			is a 2D array. <br># vectors in Julia are columns</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>1D row vector</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a <br>random m x n matrix</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			rand(3,2)<br>ans =<br>&nbsp;&nbsp; 0.21977 &nbsp; 0.10220<br>&nbsp;&nbsp;
+			0.38959 &nbsp; 0.69911<br>&nbsp;&nbsp; 0.15624 &nbsp; 0.65637</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.random.rand(3,2)<br>array([[ 0.29347865,&nbsp; 0.17920462],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [ 0.51615758,&nbsp; 0.64593471],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [ 0.01067605,&nbsp; 0.09692771]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			matrix(runif(3*2), ncol=2)<br>[,1] [,2]<br>[1,] 0.5675127
+			0.7751204<br>[2,] 0.3439412 0.5261893<br>[3,] 0.2273177 0.223438</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			rand(3,2)<br>3x2 Array{Float64,2}:<br>0.36882 0.267725<br>0.571856
+			0.601524<br>0.848084 0.858935</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a <br>random m x n matrix</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a<br>zero&nbsp;m x n matrix&nbsp;</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			zeros(3,2)<br>ans =<br>&nbsp;&nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp;
+			0<br>&nbsp;&nbsp; 0 &nbsp; 0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.zeros((3,2))<br>array([[ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp;
+			0.]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			mat.or.vec(3, 2)<br>[,1] [,2]<br>[1,] 0 0<br>[2,] 0 0<br>[3,] 0 0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			zeros(3,2)<br>3x2 Array{Float64,2}:<br>0.0 0.0<br>0.0 0.0<br>0.0
+			0.0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a<br>zero&nbsp;m x n matrix&nbsp;</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>m x n matrix of ones</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			ones(3,2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp;
+			1<br>&nbsp;&nbsp; 1 &nbsp; 1</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.ones([3,2])<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			mat.or.vec(3, 2) + 1<br>[,1] [,2]<br>[1,] 1 1<br>[2,] 1 1<br>[3,]
+			1 1</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			ones(3,2)<br>3x2 Array{Float64,2}:<br>1.0 1.0<br>1.0 1.0<br>1.0
+			1.0</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>m x n matrix of ones</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>identity matrix</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			eye(3)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp;
+			0<br>&nbsp;&nbsp; 0 &nbsp; 1 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp;
+			1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.eye(3)<br>array([[ 1.,&nbsp; 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [ 0.,&nbsp; 1.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [
+			0.,&nbsp; 0.,&nbsp; 1.]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			diag(3)<br>[,1] [,2] [,3]<br>[1,] 1 0 0<br>[2,] 0 1 0<br>[3,] 0 0
+			1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			eye(3)<br>3x3 Array{Float64,2}:<br>1.0 0.0 0.0<br>0.0 1.0 0.0<br>0.0
+			0.0 1.0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			an<br>identity matrix</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="217" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a<br>diagonal matrix</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			a = [1 2 3]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			diag(a)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp;
+			0<br>&nbsp;&nbsp; 0 &nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp;
+			3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			a = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.diag(a)<br>array([[1, 0, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0,
+			2, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0, 0, 3]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			diag(1:3)<br>[,1] [,2] [,3]<br>[1,] 1 0 0<br>[2,] 0 2 0<br>[3,] 0
+			0 3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			a=[1, 2, 3] <br><br># added commas because julia<br># vectors are
+			columnar<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			diagm(a)<br>3x3 Array{Int64,2}:<br>1 0 0<br>0 2 0<br>0 0 3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
+			a<br>diagonal matrix</b></font></font></p>
+		</td>
+	</tr>
+	<tr>
+		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in">
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ACCESSING
+			MATRIX ELEMENTS</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="159" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Getting
+			the dimension<br>of a matrix<br>(here: 2D, rows x cols)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6]<br>A =<br>&nbsp; &nbsp;1 &nbsp; 2 &nbsp; 3<br>&nbsp;
+			&nbsp;4 &nbsp; 5 &nbsp; 6<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			size(A)<br>ans =<br>&nbsp; &nbsp;2 &nbsp; 3</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[1, 2, 3],<br>&nbsp; &nbsp; &nbsp; &nbsp;[4, 5,
+			6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A.shape<br>(2, 3)</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:6,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6
</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			dim(A)<br>[1] 2 3</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6]<br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			size(A)<br>(2,3)</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Getting
+			the dimension<br>of a matrix<br>(here: 2D, rows x cols)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="240" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
+			rows&nbsp;</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st row<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(1,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br><br>% 1st 2
+			rows<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(1:2,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp;
+			4 &nbsp; 5 &nbsp; 6</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st row<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[0,:]<br>array([1, 2, 3])<br><br># 1st 2 rows<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[0:2,:]<br>array([[1, 2, 3], [4, 5, 6]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)

<br><br># 1st row<br>

<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[1,]<br>[1] 1 2 3<br><br>

# 1st 2 rows

<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[1:2,]<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]; <br>#semicolon suppresses output<br><br>#1st
+			row<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[1,:] <br>1x3 Array{Int64,2}:<br>1 2 3<br><br>#1st 2 rows<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[1:2,:] <br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
+			rows&nbsp;</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="304" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
+			columns</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st column<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(:,1)<br>ans =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 4<br>&nbsp;&nbsp;
+			7<br><br>% 1st 2 columns<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(:,1:2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2<br>&nbsp;&nbsp; 4 &nbsp;
+			5<br>&nbsp;&nbsp; 7 &nbsp; 8</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st column
+			(as row vector)<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[:,0]<br>array([1, 4, 7])<br><br># 1st column (as column
+			vector)<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[:,[0]]<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [7]])<br><br># 1st 2 columns<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[:,0:2]<br>array([[1, 2],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[4,
+			5],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[7, 8]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)


<br><br># 1st column as row
+			vector
<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			t(A[,1])<br>[,1] [,2] [,3]<br>[1,] 1 4 7

<br><br># 1st column
+			as column vector
<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[,1]<br>[1] 1 4 7

<br><br># 1st 2 columns
<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[,1:2]<br>[,1] [,2]<br>[1,] 1 2<br>[2,] 4 5<br>[3,] 7 8</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br>#1st column<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[:,1] <br>3-element Array{Int64,1}:<br>1<br>4<br>7<br><br>#1st 2
+			columns<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[:,1:2] <br>3x2 Array{Int64,2}:<br>1 2<br>4 5<br>7 8</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
+			columns</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="260" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Extracting
+			rows and columns by criteria<br><br>(here: get rows&nbsp;that have
+			value 9 in column 3)</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 9; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
+			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
+			9<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(A(:,3) == 9,:)<br>ans =<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp;
+			7 &nbsp; 8 &nbsp; 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,9], [7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [7, 8, 9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[A[:,2] == 9]<br>array([[4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[7, 8, 9]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 9<br>[3,] 7 8
+			9

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			matrix(A[A[,3]==9], ncol=3)<br>[,1] [,2] [,3]<br>[1,] 4 5 9<br>[2,]
+			7 8 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 9; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 9<br>7
+			8 9<br><br># use '.==' for<br># element-wise check<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[ A[:,3] .==9, :] <br>2x3 Array{Int64,2}:<br>4 5 9<br>7 8 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Extracting
+			rows and columns by criteria<br><br>(here: get rows&nbsp;that have
+			value 9 in column 3)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="118" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Accessing
+			elements<br>(here: 1st element)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(1,1)<br>ans =&nbsp; 1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A[0,0]<br>1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(1,2,3,4,5,9,7,8,9),nrow=3,byrow=T)
<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A[1,1]<br>[1] 1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A[1,1]<br>1</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Accessing
+			elements<br>(here: 1st element)</b></font></font></p>
+		</td>
+	</tr>
+	<tr>
+		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in">
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>MANIPULATING
+			SHAPE AND DIMENSIONS</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="173" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Converting&nbsp;<br>row
+			to column vectors</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = [1 2 3]
<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = b'<br>b =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp;
+			3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = np.array([1, 2, 3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = b[np.newaxis].T<br># alternatively <br>#&nbsp;b =
+			b[:,np.newaxis]<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [3]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b = matrix(c(1,2,3), ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			t(b)<br>[,1]<br>[1,] 1<br>[2,] 2<br>[3,] 3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			b=vec([1 2 3])<br>3-element Array{Int64,1}:<br>1<br>2<br>3</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Converting&nbsp;<br>row
+			to column vectors</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="275" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Reshaping
+			Matrices<br><br>(here: 3x3 matrix to row vector)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
+			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
+			9<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			total_elements = numel(A)<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			B = reshape(A,1,total_elements)&nbsp;<br>% or reshape(A,1,9)<br>B
+			=<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7 &nbsp; 2 &nbsp; 5 &nbsp; 8 &nbsp;
+			3 &nbsp; 6 &nbsp; 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[1,2,3],[4,5,6],[7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [7, 8, 9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			total_elements = A.shape[0] * A.shape[1]<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			B = A.reshape(1, total_elements)&nbsp;<br><br># or
+			A.reshape(1,9)<br># Alternative: A.shape = (1,9)&nbsp;<br>#
+			to&nbsp;change the array in place<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			B<br>array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			total_elements = dim(A)[1] * dim(A)[2]<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			B = matrix(A, ncol=total_elements)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			B<br>[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]<br>[1,] 1 4 7 2
+			5 8 3 6 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
+			8 9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			total_elements=length(A)<br>9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>B=reshape(A,1,total_elements)<br>1x9
+			Array{Int64,2}:<br>1 4 7 2 5 8 3 6 9</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Reshaping
+			Matrices<br><br>(here: 3x3 matrix to row vector)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="240" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Concatenating
+			matrices</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			B = [7 8 9; 10 11 12]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			C = [A; B]<br>&nbsp; &nbsp;&nbsp;1&nbsp; &nbsp; 2&nbsp; &nbsp; 3<br>&nbsp;
+			&nbsp; 4&nbsp; &nbsp; 5&nbsp; &nbsp; 6<br>&nbsp; &nbsp; 7&nbsp; &nbsp;
+			8&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 10 &nbsp; 11 &nbsp; 12</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[1, 2, 3], [4, 5, 6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			B = np.array([[7, 8, 9],[10,11,12]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			C = np.concatenate((A, B), axis=0)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			C<br>array([[ 1, 2, 3],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 4,
+			5, 6],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 7, 8, 9],&nbsp;<br>&nbsp;
+			&nbsp; &nbsp; &nbsp;[10, 11, 12]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:6,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			B = matrix(7:12,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			C = rbind(A,B)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			C<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9<br>[4,]
+			10 11 12</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			B=[7 8 9; 10 11 12];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			C=[A; B]<br>4x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7 8 9<br>10
+			11 12</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Concatenating
+			matrices</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="256" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Stacking&nbsp;<br>vectors
+			and matrices</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			a = [1 2 3]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = [4 5 6]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			c = [a' b']<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 4<br>&nbsp;&nbsp; 2 &nbsp;
+			5<br>&nbsp;&nbsp; 3 &nbsp; 6<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			c = [a; b]<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp;
+			4 &nbsp; 5 &nbsp; 6</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			a = np.array([1,2,3])<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = np.array([4,5,6])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.c_[a,b]<br>array([[1, 4],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2,
+			5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.r_[a,b]<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4,
+			5, 6]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			a = matrix(1:3, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b = matrix(4:6, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			matrix(rbind(A, B), ncol=2)<br>[,1] [,2]<br>[1,] 1 5<br>[2,] 4
+			3
<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			rbind(A,B)<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			a=[1 2 3];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			b=[4 5 6];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			c=[a' b']<br>3x2 Array{Int64,2}:<br>1 4<br>2 5<br>3 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			c=[a; b]<br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Stacking&nbsp;<br>vectors
+			and matrices</b></font></font></p>
+		</td>
+	</tr>
+	<tr>
+		<td colspan="6" height="114" bgcolor="#ffffff" style="border: none; padding: 0in">
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>BASIC
+			MATRIX OPERATIONS</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="253" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-scalar<br>operations</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A * 2<br>ans =<br>&nbsp; &nbsp; 2&nbsp; &nbsp; 4&nbsp; &nbsp; 6<br>&nbsp;
+			&nbsp; 8 &nbsp; 10 &nbsp; 12<br>&nbsp;&nbsp; 14 &nbsp; 16 &nbsp;
+			18<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A + 2<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A - 2<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A / 2</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A * 2<br>array([[ 2,&nbsp; 4,&nbsp; 6],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[ 8, 10, 12],<br>&nbsp;&nbsp; &nbsp; &nbsp; [14, 16, 18]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A + 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A - 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A / 2</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A * 2<br>[,1] [,2] [,3]<br>[1,] 2 4 6<br>[2,] 8 10 12<br>[3,] 14
+			16 18
</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A + 2<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A - 2<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A / 2</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br># elementwise operator<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .* 2 <br>3x3 Array{Int64,2}:<br>2 4 6<br>8 10 12<br>14 16 18
+			<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .+ 2;<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .- 2;<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A ./ 2;</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-scalar<br>operations</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="165" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-matrix<br>multiplication</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A * A<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp;
+			42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp;
+			102 &nbsp; 126 &nbsp; 150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.dot(A,A) # or A.dot(A)<br>array([[ 30,&nbsp; 36,&nbsp; 42],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[102, 126, 150]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A %*% A<br>[,1] [,2] [,3]<br>[1,] 30 36 42<br>[2,] 66 81 96<br>[3,]
+			102 126 150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A * A<br>3x3 Array{Int64,2}:<br>30 36 42<br>66 81 96<br>102 126
+			150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-matrix<br>multiplication</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="190" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-vector<br>multiplication</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			b = [ 1; 2; 3 ]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A * b<br>ans =<br>&nbsp;&nbsp; 14<br>&nbsp;&nbsp; 32<br>&nbsp;&nbsp;
+			50</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			b = np.array([ [1], [2], [3] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.dot(A,b) # or A.dot(b)<br><br>array([[14], [32], [50]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			b = matrix(1:3, nrow=3)<br><br>

<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			t(b %*% A)<br>[,1]<br>[1,] 14<br>[2,] 32<br>[3,] 50</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			b=[1; 2; 3];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A*b<br>3-element Array{Int64,1}:<br>14<br>32<br>50</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-vector<br>multiplication</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="240" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A .* A<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp;
+			9<br>&nbsp;&nbsp; 16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp;
+			64 &nbsp; 81<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A .+ A<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A .- A<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A ./ A</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A * A<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A + A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A - A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A / A</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A * A<br>[,1] [,2] [,3]<br>[1,] 1 4 9<br>[2,] 16 25 36<br>[3,] 49
+			64 81

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A + A<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A - A<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A / A</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .* A<br>3x3 Array{Int64,2}:<br>1 4 9<br>16 25 36<br>49 64 81
+			<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .+ A; <br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .- A; <br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A ./ A;</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="164" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			elements&nbsp;to power n <br><br>(here: individual elements
+			squared)</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A.^2<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp; 9<br>&nbsp;&nbsp;
+			16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp; 64 &nbsp; 81</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.power(A,2)<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A ^ 2<br>[,1] [,2] [,3]<br>[1,] 1 4 9<br>[2,] 16 25 36<br>[3,] 49
+			64 81</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A .^ 2<br>3x3 Array{Int64,2}:<br>1 4 9<br>16 25 36<br>49 64 81</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			elements&nbsp;to power n <br><br>(here: individual elements
+			squared)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="225" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			to power n<br><br>(here: matrix-matrix&nbsp;multiplication with
+			itself)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A ^ 2<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp;
+			42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp;
+			102 &nbsp; 126 &nbsp; 150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.linalg.matrix_power(A,2)<br>array([[ 30,&nbsp; 36,&nbsp;
+			42],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp;
+			&nbsp; &nbsp; [102, 126, 150]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, ncol=3)<br><br>
# requires the ‘expm’
+			package
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			install.packages('expm')
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			library(expm)
<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A %^% 2<br>[,1] [,2] [,3]<br>[1,] 30 66 102<br>[2,] 36 81 126<br>[3,]
+			42 96 150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A ^ 2<br>3x3 Array{Int64,2}:<br>30 36 42<br>66 81 96<br>102 126
+			150</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			to power n<br><br>(here: matrix-matrix&nbsp;multiplication with
+			itself)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="172" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			transpose</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A'<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2
+			&nbsp; 5 &nbsp; 8<br>&nbsp;&nbsp; 3 &nbsp; 6 &nbsp; 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A.T<br>array([[1, 4, 7],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2, 5,
+			8],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6, 9]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9, nrow=3, byrow=T)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			t(A)<br>[,1] [,2] [,3]<br>[1,] 1 4 7<br>[2,] 2 5 8<br>[3,] 3 6 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
+			8 9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A'<br>3x3 Array{Int64,2}:<br>1 4 7<br>2 5 8<br>3 6 9</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
+			transpose</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="176" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Determinant
+			of a matrix:<br>&nbsp;A -&gt; |A|</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [6 1 1; 4 -2 5; 2 8 7]<br>A =<br>&nbsp;&nbsp; 6 &nbsp; 1 &nbsp;
+			1<br>&nbsp;&nbsp; 4&nbsp; -2 &nbsp; 5<br>&nbsp;&nbsp; 2 &nbsp; 8 &nbsp;
+			7<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			det(A)<br>ans = -306</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>&nbsp;A
+			= np.array([[6,1,1],[4,-2,5],[2,8,7]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[ 6,&nbsp; 1,&nbsp; 1],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			[ 4, -2,&nbsp; 5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 2,&nbsp; 8,&nbsp;
+			7]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.linalg.det(A)<br>-306.0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(6,1,1,4,-2,5,2,8,7), nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2] [,3]<br>[1,] 6 1 1<br>[2,] 4 -2 5<br>[3,] 2 8 7<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			det(A)<br>[1] -306</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[6 1 1; 4 -2 5; 2 8 7]<br>3x3 Array{Int64,2}:<br>6 1 1<br>4 -2
+			5<br>2 8 7<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			det(A)<br>-306.0</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Determinant
+			of a matrix:<br>&nbsp;A -&gt; |A|</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="202" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Inverse
+			of a matrix</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [4 7; 2 6]<br>A =<br>&nbsp;&nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2
+			&nbsp; 6<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A_inv = inv(A)<br>A_inv =<br>&nbsp; &nbsp;0.60000&nbsp; -0.70000<br>&nbsp;
+			-0.20000 &nbsp; 0.40000</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[4, 7], [2, 6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[4, 7],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[2,
+			6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A_inverse = np.linalg.inv(A)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A_inverse<br>array([[ 0.6, -0.7],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-0.2, 0.4]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(4,7,2,6), nrow=2, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2]<br>[1,] 4 7<br>[2,] 2 6<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			solve(A)<br>[,1] [,2]<br>[1,] 0.6 -0.7<br>[2,] -0.2 0.4</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[4 7; 2 6]<br>2x2 Array{Int64,2}:<br>4 7<br>2 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A_inv=inv(A)<br>2x2 Array{Float64,2}:<br>0.6 -0.7<br>-0.2 0.4</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Inverse
+			of a matrix</b></font></font></p>
+		</td>
+	</tr>
+	<tr>
+		<td colspan="6" height="202" bgcolor="#ffffff" style="border: none; padding: 0in">
+			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ADVANCED
+			MATRIX OPERATIONS</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="311" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating
+			the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here:
+			covariances of the means&nbsp;<br>of x1, x2, and x3)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			x1 = [4.0000 4.2000 3.9000 4.3000 4.1000]’<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			x2 = [2.0000 2.1000 2.0000 2.1000 2.2000]'<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			x3 = [0.60000 0.59000 0.58000 0.62000 0.63000]’<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			cov( [x1,x2,x3] )<br>ans =<br>&nbsp;&nbsp; 2.5000e-02 &nbsp;
+			7.5000e-03 &nbsp; 1.7500e-03<br>&nbsp;&nbsp; 7.5000e-03 &nbsp;
+			7.0000e-03 &nbsp; 1.3500e-03<br>&nbsp;&nbsp; 1.7500e-03 &nbsp;
+			1.3500e-03 &nbsp; 4.3000e-04</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			x1 = np.array([ 4, 4.2, 3.9, 4.3, 4.1])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			x2 = np.array([ 2, 2.1, 2, 2.1, 2.2])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			x3 = np.array([ 0.6, 0.59, 0.58, 0.62, 0.63])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.cov([x1, x2, x3])<br>Array([[ 0.025 &nbsp;, &nbsp;0.0075 ,
+			&nbsp;0.00175],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.0075 , &nbsp;0.007
+			&nbsp;, &nbsp;0.00135],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.00175,
+			&nbsp;0.00135, &nbsp;0.00043]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			x1 = matrix(c(4, 4.2, 3.9, 4.3, 4.1), ncol=5)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			x2 = matrix(c(2, 2.1, 2, 2.1, 2.2), ncol=5)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			x3 = matrix(c(0.6, 0.59, 0.58, 0.62, 0.63), ncol=5)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			cov(matrix(c(x1, x2, x3), ncol=3))<br>[,1] [,2] [,3]<br>[1,]
+			0.02500 0.00750 0.00175<br>[2,] 0.00750 0.00700 0.00135<br>[3,]
+			0.00175 0.00135 0.00043</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			x1=[4.0 4.2 3.9 4.3 4.1]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			x2=[2. 2.1 2. 2.1 2.2]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			x3=[0.6 .59 .58 .62 .63]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			cov([x1 x2 x3])<br>3x3 Array{Float64,2}:<br>0.025 0.0075
+			0.00175<br>0.0075 0.007 0.00135<br>0.00175 0.00135 0.00043</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating
+			the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here:
+			covariances of the means&nbsp;<br>of x1, x2, and x3)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="309" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating&nbsp;<br>eigenvectors
+			and&nbsp;eigenvalues</b></font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [3 1; 1 3]<br>A =<br>&nbsp;&nbsp; 3 &nbsp; 1<br>&nbsp;&nbsp; 1
+			&nbsp; 3<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			[eig_vec,eig_val] = eig(A)<br>eig_vec =<br>&nbsp; -0.70711 &nbsp;
+			0.70711<br>&nbsp;&nbsp; 0.70711 &nbsp; 0.70711<br>eig_val
+			=<br>Diagonal Matrix<br>&nbsp;&nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0
+			&nbsp; 4</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[3, 1], [1, 3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A<br>array([[3, 1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [1, 3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			eig_val, eig_vec = np.linalg.eig(A)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			eig_val<br>array([ 4.,&nbsp; 2.])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			eig_vec<br>Array([[ 0.70710678, -0.70710678],<br>&nbsp;&nbsp; &nbsp;
+			&nbsp; [ 0.70710678,&nbsp; 0.70710678]])</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(c(3,1,1,3), ncol=2)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A<br>[,1] [,2]<br>[1,] 3 1<br>[2,] 1 3<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			eigen(A)<br>$values<br>[1] 4 2<br><br>$vectors<br>[,1] [,2]<br>[1,]
+			0.7071068 -0.7071068<br>[2,] 0.7071068 0.7071068</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[3 1; 1 3]<br>2x2 Array{Int64,2}:<br>3 1<br>1 3<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			(eig_vec,eig_val)=eig(a)<br>([2.0,4.0],<br>2x2
+			Array{Float64,2}:<br>-0.707107 0.707107<br>0.707107 0.707107)</font></font></p>
+		</td>
+		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating&nbsp;<br>eigenvectors
+			and&nbsp;eigenvalues</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td height="539" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
+			a Gaussian dataset:<br><br>creating random vectors from the
+			multivariate normal<br>distribution given mean and covariance
+			matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance
+			= 0, variance = 2)</b></font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">%
+			requires statistics toolbox package<br>% how to install and load
+			it in Octave:<br><br>% download the package from:&nbsp;<br>%
+			http://octave.sourceforge.net/packages.php<br>% pkg install&nbsp;<br>%
+			&nbsp; &nbsp; ~/Desktop/io-2.0.2.tar.gz &nbsp;<br>% pkg install&nbsp;<br>%
+			&nbsp; &nbsp; ~/Desktop/statistics-1.2.3.tar.gz<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			pkg load statistics<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			mean = [0 0]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			cov = [2 0; 0 2]<br>cov =<br>&nbsp; &nbsp;2 &nbsp; 0<br>&nbsp; &nbsp;0
+			&nbsp; 2<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			mvnrnd(mean,cov,5)<br>&nbsp; &nbsp;2.480150 &nbsp;-0.559906<br>&nbsp;
+			-2.933047 &nbsp; 0.560212<br>&nbsp; &nbsp;0.098206 &nbsp;
+			3.055316<br>&nbsp; -0.985215 &nbsp;-0.990936<br>&nbsp; &nbsp;1.122528
+			&nbsp; 0.686977<br>&nbsp; &nbsp;&nbsp;</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			mean = np.array([0,0])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			cov = np.array([[2,0],[0,2]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			np.random.multivariate_normal(mean, cov, 5)<br><br>Array([[
+			1.55432624, -1.17972629],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-2.01185294, 1.96081908],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-2.11810813, 1.45784216],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-2.93207591, -0.07369322],&nbsp;<br>&nbsp; &nbsp; &nbsp;
+			&nbsp;[-1.37031244, -1.18408792]])</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			requires the ‘mass’ package<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			install.packages('MASS')<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			library(MASS)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			mvrnorm(n=10, mean, cov)<br>[,1] [,2]<br>[1,] -0.8407830
+			-0.1882706<br>[2,] 0.8496822 -0.7889329<br>[3,] -0.1564171
+			0.8422177<br>[4,] -0.6288779 1.0618688<br>[5,] -0.5103879
+			0.1303697<br>[6,] 0.8413189 -0.1623758<br>[7,] -1.0495466
+			-0.4161082<br>[8,] -1.3236339 0.7755572<br>[9,] 0.2771013
+			1.4900494<br>[10,] -1.3536268 0.2338913</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			requires the Distributions package from
+			https://github.com/JuliaStats/Distributions.jl<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			using Distributions<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			mean=[0., 0.]<br>2-element Array{Float64,1}:<br>0.0<br>0.0<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			cov=[2. 0.; 0. 2.]<br>2x2 Array{Float64,2}:<br>2.0 0.0<br>0.0
+			2.0<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			rand( MvNormal(mean, cov), 5)<br>2x5 Array{Float64,2}:<br>-0.527634
+			0.370725 -0.761928 -3.91747 1.47516<br>-0.448821 2.21904 2.24561
+			0.692063 0.390495</font></font></p>
+		</td>
+		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
+			a Gaussian dataset:<br><br>creating random vectors from the
+			multivariate normal<br>distribution given mean and covariance
+			matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance
+			= 0, variance = 2)</b></font></font></p>
+		</td>
+	</tr>
+</table>
+<p><!-- ************************************************************************** --><br><br>
+</p>
+</body>
+</html>
\ No newline at end of file

From 844a1560a3e8922a23db4ead10135a88db14b5ee Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 12 Jun 2014 09:52:01 -0400
Subject: [PATCH 093/248] running cython

---
 README.md                      |   6 +-
 tutorials/running_cython.ipynb | 623 +++++++++++++++++++++++++++++++++
 2 files changed, 626 insertions(+), 3 deletions(-)
 create mode 100644 tutorials/running_cython.ipynb

diff --git a/README.md b/README.md
index d894b75..cc44f9b 100755
--- a/README.md
+++ b/README.md
@@ -21,12 +21,10 @@
 
 - A collection of not so obvious Python stuff you should know! [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb?create=1)]
 
-
 - Python's scope resolution for variable names and the LEGB rule [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb?create=1)]
 
 - Key differences between Python 2.x and Python 3.x [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1)]
 
-
 - A thorough guide to SQLite database operations in Python [[Markdown](./sqlite3_howto/README.md)]  
 
 - Unit testing in Python - Why we want to make it a habit [[Markdown](./tutorials/unit_testing.md)]  
@@ -34,9 +32,11 @@
 - Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown](./tutorials/installing_scientific_packages.md)]
 
 
-
 - Sorting CSV files using the Python csv module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
+- Using Cython with and without IPython magic [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/running_cython.ipynb)]
+
+
 
 <br>
 
diff --git a/tutorials/running_cython.ipynb b/tutorials/running_cython.ipynb
new file mode 100644
index 0000000..16285bf
--- /dev/null
+++ b/tutorials/running_cython.ipynb
@@ -0,0 +1,623 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:0e619f8592165b10fa0b3558de3c622629bfb7aab1e9544b5bad64478cf35848"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "last updated: 06/12/2014"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"sections\"></a>\n",
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Using Cython with and without IPython magic"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"introduction\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Bubblesort in regular (C)Python"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First, we will write a simple implementation of the bubble sort algorithm in regular (C)Python"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def python_bubblesort(a_list):\n",
+      "    \"\"\" Bubblesort in Python for list objects. \"\"\"\n",
+      "    length = len(a_list)\n",
+      "    swapped = 1\n",
+      "    for i in range(0, length):\n",
+      "        if swapped: \n",
+      "            swapped = 0\n",
+      "            for ele in range(0, length-i-1):\n",
+      "                if a_list[ele] > a_list[ele + 1]:\n",
+      "                    temp = a_list[ele + 1]\n",
+      "                    a_list[ele + 1] = a_list[ele]\n",
+      "                    a_list[ele] = temp\n",
+      "                    swapped = 1\n",
+      "    return a_list"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "python_bubblesort([6,3,1,5,6])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "[1, 3, 5, 6, 6]"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Implemented in Cython"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
+      "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "First, we will take the initial Python code as is and use Cython for the compilation. Cython is capable of autoguessing types, however, we can make our code way more efficient by adding static types."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "def cython_bubblesort_untyped(a_list):\n",
+      "    \"\"\" Bubblesort in Python for list objects. \"\"\"\n",
+      "    length = len(a_list)\n",
+      "    swapped = 1\n",
+      "    for i in range(0, length):\n",
+      "        if swapped: \n",
+      "            swapped = 0\n",
+      "            for ele in range(0, length-i-1):\n",
+      "                if a_list[ele] > a_list[ele + 1]:\n",
+      "                    temp = a_list[ele + 1]\n",
+      "                    a_list[ele + 1] = a_list[ele]\n",
+      "                    a_list[ele] = temp\n",
+      "                    swapped = 1\n",
+      "    return a_list"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "import numpy as np\n",
+      "cimport numpy as np\n",
+      "cimport cython\n",
+      "@cython.boundscheck(False) \n",
+      "@cython.wraparound(False)\n",
+      "cpdef cython_bubblesort_typed(inp_ary):\n",
+      "    \"\"\" The Cython implementation of Bubblesort with NumPy memoryview.\"\"\"\n",
+      "    cdef unsigned long length, i, swapped, ele, temp\n",
+      "    cdef long[:] np_ary = inp_ary\n",
+      "    length = np_ary.shape[0]\n",
+      "    swapped = 1\n",
+      "    for i in xrange(0, length):\n",
+      "        if swapped: \n",
+      "            swapped = 0\n",
+      "            for ele in xrange(0, length-i-1):\n",
+      "                if np_ary[ele] > np_ary[ele + 1]:\n",
+      "                    temp = np_ary[ele + 1]\n",
+      "                    np_ary[ele + 1] = np_ary[ele]\n",
+      "                    np_ary[ele] = temp\n",
+      "                    swapped = 1\n",
+      "    return inp_ary"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Speed comparison"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Below, we will do a quick speed comparison of our 3 implementations of the bubble sort algorithm by sorting a list (or numpy array) of 1000 random digits. Here, we have to make copies of the lists/numpy arrays, since our bubble sort implementation is sorting in place."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import random\n",
+      "import numpy as np\n",
+      "import copy\n",
+      "\n",
+      "list_a = [random.randint(0,1000) for num in range(1000)]\n",
+      "list_b = copy.deepcopy(a_list)\n",
+      "\n",
+      "ary_a = np.asarray(list_a)\n",
+      "ary_b = copy.deepcopy(ary_a)\n",
+      "ary_c = copy.deepcopy(ary_a)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('\\n(C)Python on list:')\n",
+      "%timeit python_bubblesort(list_a)\n",
+      "\n",
+      "print('\\n(C)Python on numpy array:')\n",
+      "%timeit python_bubblesort(ary_a)\n",
+      "\n",
+      "print('\\nuntyped Cython on list:')\n",
+      "%timeit cython_bubblesort_untyped(list_b)\n",
+      "\n",
+      "print('\\nuntyped Cython on numpy array:')\n",
+      "%timeit cython_bubblesort_untyped(ary_b)\n",
+      "\n",
+      "print('\\ntyped Cython with memoryview on numpy array:')\n",
+      "%timeit cython_bubblesort_typed(ary_c)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "(C)Python on list:\n",
+        "1 loops, best of 3: 332 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "(C)Python on numpy array:\n",
+        "1 loops, best of 3: 839 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "untyped Cython on list:\n",
+        "10000 loops, best of 3: 183 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "untyped Cython on numpy array:\n",
+        "1 loops, best of 3: 666 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "\n",
+        "typed Cython with memoryview on numpy array:\n",
+        "100000 loops, best of 3: 4.05 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n",
+      "As we can see from the results above, we are already able to make our Python code run almost as twice as fast if we compile it via Cython (Python on list: 332 \u00b5s, untyped Cython on list: 183 \u00b5s).  \n",
+      "However, although it is more \"work\" to adjust the Python code, the \"typed Cython with memoryview on numpy array\" is significantly as expected."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"cython_bonus\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "How to use Cython without the IPython magic"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "IPython's notebook is really great for explanatory analysis and documentation, but what if we want to compile our Python code via Cython without letting IPython's magic doing all the work?  \n",
+      "These are the steps you would need."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 1. Creating a .pyx file containing the the desired code or function."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file cython_bubblesort_nomagic.pyx\n",
+      "\n",
+      "cpdef cython_bubblesort_nomagic(inp_ary):\n",
+      "    \"\"\" The Cython implementation of Bubblesort with NumPy memoryview.\"\"\"\n",
+      "    cdef unsigned long length, i, swapped, ele, temp\n",
+      "    cdef long[:] np_ary = inp_ary\n",
+      "    length = np_ary.shape[0]\n",
+      "    swapped = 1\n",
+      "    for i in xrange(0, length):\n",
+      "        if swapped: \n",
+      "            swapped = 0\n",
+      "            for ele in xrange(0, length-i-1):\n",
+      "                if np_ary[ele] > np_ary[ele + 1]:\n",
+      "                    temp = np_ary[ele + 1]\n",
+      "                    np_ary[ele + 1] = np_ary[ele]\n",
+      "                    np_ary[ele] = temp\n",
+      "                    swapped = 1\n",
+      "    return inp_ary"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Overwriting cython_bubblesort_nomagic.pyx\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 2. Creating a simple setup file"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file setup.py\n",
+      "\n",
+      "from distutils.core import setup\n",
+      "from distutils.extension import Extension\n",
+      "from Cython.Distutils import build_ext\n",
+      "\n",
+      "setup(\n",
+      "    cmdclass = {'build_ext': build_ext},\n",
+      "    ext_modules = [Extension(\"cython_bubblesort_nomagic\", [\"cython_bubblesort_nomagic.pyx\"])]\n",
+      ")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Overwriting setup.py\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "####3. Building and Compiling"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "!python3 setup.py build_ext --inplace"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "running build_ext\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "cythoning cython_bubblesort_nomagic.pyx to cython_bubblesort_nomagic.c\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "building 'cython_bubblesort_nomagic' extension\r\n",
+        "/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/sebastian/miniconda3/envs/py34/include -arch x86_64 -I/Users/sebastian/miniconda3/envs/py34/include/python3.4m -c cython_bubblesort_nomagic.c -o build/temp.macosx-10.5-x86_64-3.4/cython_bubblesort_nomagic.o\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\u001b[1mcython_bubblesort_nomagic.c:16276:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:16427:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_PyInt_FromSize_t' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
+        "\u001b[0;1;32m                                ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14058:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_GetBufferAndValidate' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_GetBufferAndValidate(\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14092:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_SafeReleaseBuffer' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {\r\n",
+        "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14165:1: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_add_acquisition_count_locked' [-Wunused-function]\u001b[0m\r\n",
+        "__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,\r\n",
+        "\u001b[0;1;32m^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14175:1: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_sub_acquisition_count_locked' [-Wunused-function]\u001b[0m\r\n",
+        "__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,\r\n",
+        "\u001b[0;1;32m^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14643:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_PyBytes_Equals' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2...\r\n",
+        "\u001b[0;1;32m                         ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14920:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_GetItemInt_List_Fast' [-Wunused-function]\u001b[0m\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, P...\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14934:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_GetItemInt_Tuple_Fast' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, ...\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:15111:38: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_PyInt_From_unsigned_long' [-Wunused-function]\u001b[0m\r\n",
+        "      static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_long(unsi...\r\n",
+        "\u001b[0;1;32m                                     ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:15158:36: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction\r\n",
+        "      '__Pyx_PyInt_As_unsigned_long' is not needed and will not be emitted\r\n",
+        "      [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                                   ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:15627:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_char' is\r\n",
+        "      not needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:15727:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is\r\n",
+        "      not needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
+        "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
+        "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "13 warnings generated.\r\n",
+        "/usr/bin/clang -bundle -undefined dynamic_lookup -L/Users/sebastian/miniconda3/envs/py34/lib -arch x86_64 build/temp.macosx-10.5-x86_64-3.4/cython_bubblesort_nomagic.o -L/Users/sebastian/miniconda3/envs/py34/lib -o /Users/sebastian/Desktop/cython_bubblesort_nomagic.so\r\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "#### 4. Importing and running the code"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import cython_bubblesort_nomagic\n",
+      "\n",
+      "cython_bubblesort_nomagic.cython_bubblesort_nomagic(np.array([4,6,2,1,6]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": [
+        "array([1, 2, 4, 6, 6])"
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"numba\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 163058f1f17a89a43bf832db7329dbfe69b02362 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 15 Jun 2014 23:23:19 -0400
Subject: [PATCH 094/248] contain method

---
 ...y_differences_between_python_2_and_3.ipynb | 263 +++++++++++++++++-
 1 file changed, 261 insertions(+), 2 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 19d8a8c..a1afba9 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7d8ee5c733199e70a4c5c71b6dfd0953ab5e6c33c6a0c7ef50dce07b4d56ae32"
+  "signature": "sha256:a91d66ae5e233f325b262961102505a8bdaee3ad1a251f061dee9cd68c0aaeac"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -872,11 +872,270 @@
        "output_type": "pyerr",
        "traceback": [
         "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-8-5d8f9b79ea70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m<ipython-input-5-5d8f9b79ea70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
         "\u001b[0;31mNameError\u001b[0m: name 'xrange' is not defined"
        ]
       }
      ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='contains'></a>\n",
+      "<br>\n",
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "The `__contain__` method for `range` objects in Python 3"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [yegle](), who pointed this out).  The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and bolean types.\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x = 10000000"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def val_in_range(x, val):\n",
+      "    if val in range(x):\n",
+      "        return True\n",
+      "    else:\n",
+      "        return False"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def val_in_xrange(x, val):\n",
+      "    if val in range(x):\n",
+      "        return True\n",
+      "    else:\n",
+      "        return False"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "assert(val_in_range(x, x/2) == True)\n",
+      "assert(val_in_range(x, x//2) == True)\n",
+      "%timeit val_in_range(x, x/2)\n",
+      "%timeit val_in_range(x, x//2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.3.5\n",
+        "1 loops, best of 3: 751 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1000000 loops, best of 3: 1.21 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Based on the `timeit` results above, you see that the execution for the \"look up\" was about 60,000 faster when it was of an integer type rather than a float. However, since Python 2.x's `range` or `xrange` doesn't have a `__contains__` method, the \"look-up speed\" wouldn't be that much different for integers or floats:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "assert(val_in_xrange(x, x/2.0) == True)\n",
+      "assert(val_in_xrange(x, x/2) == True)\n",
+      "%timeit val_in_xrange(x, x/2.0)\n",
+      "%timeit val_in_xrange(x, x/2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.7\n",
+        "1 loops, best of 3: 661 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1 loops, best of 3: 564 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Below the \"proofs\" that the `__contain__` method wasn't added to Python 2.x yet:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Python', python_version())\n",
+      "range.__contains__"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.4.1\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "<slot wrapper '__contains__' of 'range' objects>"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "range.__contains__"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.7\n"
+       ]
+      },
+      {
+       "ename": "AttributeError",
+       "evalue": "'builtin_function_or_method' object has no attribute '__contains__'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-7-05327350dafb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mAttributeError\u001b[0m: 'builtin_function_or_method' object has no attribute '__contains__'"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print 'Python', python_version()\n",
+      "xrange.__contains__"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 2.7.7\n"
+       ]
+      },
+      {
+       "ename": "AttributeError",
+       "evalue": "type object 'xrange' has no attribute '__contains__'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-8-7d1a71bfee8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mxrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mAttributeError\u001b[0m: type object 'xrange' has no attribute '__contains__'"
+       ]
+      }
+     ],
      "prompt_number": 8
     },
     {

From 8b6a137ed9dec5c494ee53676c02d8c6a2a5146d Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 15 Jun 2014 23:26:19 -0400
Subject: [PATCH 095/248] contain method

---
 tutorials/key_differences_between_python_2_and_3.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index a1afba9..c830a92 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a91d66ae5e233f325b262961102505a8bdaee3ad1a251f061dee9cd68c0aaeac"
+  "signature": "sha256:6381ed57a43484d7eb056ed84635101d586f95e11bbc8904b58f8d2c3c5693c3"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -900,7 +900,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [yegle](), who pointed this out).  The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and bolean types.\n"
+      "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [Yuchen Ying](https://github.com/yegle), who pointed this out).  The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and bolean types.\n"
      ]
     },
     {

From b262a4e06e8d5dc0d568d7508a53241b64aa7df5 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 15 Jun 2014 23:33:33 -0400
Subject: [PATCH 096/248] contain method

---
 tutorials/key_differences_between_python_2_and_3.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index c830a92..23a0fa4 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6381ed57a43484d7eb056ed84635101d586f95e11bbc8904b58f8d2c3c5693c3"
+  "signature": "sha256:2d1167e2155bc6be8905a13729dbbbcc240cb7281e18fac55b1c407b2d6271db"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -893,7 +893,7 @@
      "level": 3,
      "metadata": {},
      "source": [
-      "The `__contain__` method for `range` objects in Python 3"
+      "The `__contains__` method for `range` objects in Python 3"
      ]
     },
     {

From 3fdc0fc62c1efdcb3bfc202362fb261098f65f7f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 15 Jun 2014 23:40:29 -0400
Subject: [PATCH 097/248] fixed typo

---
 tutorials/key_differences_between_python_2_and_3.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 23a0fa4..c940318 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2d1167e2155bc6be8905a13729dbbbcc240cb7281e18fac55b1c407b2d6271db"
+  "signature": "sha256:53ee36d6fcce5f6dbce838e41d83e62fbb143538fe3ca1b0f3cd9b5505e5d786"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -900,7 +900,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [Yuchen Ying](https://github.com/yegle), who pointed this out).  The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and bolean types.\n"
+      "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [Yuchen Ying](https://github.com/yegle), who pointed this out).  The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and Boolean types.\n"
      ]
     },
     {

From fc1ccf3cde1ec58956091e6a8d1b553923d93cf2 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 16 Jun 2014 07:47:24 -0400
Subject: [PATCH 098/248] fix xrange py2

---
 ...y_differences_between_python_2_and_3.ipynb | 60 ++++++++++++-------
 1 file changed, 37 insertions(+), 23 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index c940318..f3e2067 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:53ee36d6fcce5f6dbce838e41d83e62fbb143538fe3ca1b0f3cd9b5505e5d786"
+  "signature": "sha256:1a71ccc70829239143d02cebcb97bec031b45e676ebad340fc04c9bd4a5760bf"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -349,13 +349,13 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Python 2.7.6\n",
+        "Python 2.7.7\n",
         "('a', 'b')\n",
         "a b\n"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -739,7 +739,7 @@
       "\n",
       "n = 10000\n",
       "def test_range(n):\n",
-      "    for i in range(n):\n",
+      "    return for i in range(n):\n",
       "        pass\n",
       "    \n",
       "def test_xrange(n):\n",
@@ -912,32 +912,26 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "def val_in_range(x, val):\n",
-      "    if val in range(x):\n",
-      "        return True\n",
-      "    else:\n",
-      "        return False"
+      "    return val in range(x)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "def val_in_xrange(x, val):\n",
-      "    if val in range(x):\n",
-      "        return True\n",
-      "    else:\n",
-      "        return False"
+      "    return val in xrange(x)"
      ],
      "language": "python",
      "metadata": {},
@@ -961,8 +955,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Python 3.3.5\n",
-        "1 loops, best of 3: 751 ms per loop"
+        "Python 3.4.1\n",
+        "1 loops, best of 3: 742 ms per loop"
        ]
       },
       {
@@ -970,7 +964,7 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1000000 loops, best of 3: 1.21 \u00b5s per loop"
+        "1000000 loops, best of 3: 1.19 \u00b5s per loop"
        ]
       },
       {
@@ -981,7 +975,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -1004,8 +998,12 @@
       "print 'Python', python_version()\n",
       "assert(val_in_xrange(x, x/2.0) == True)\n",
       "assert(val_in_xrange(x, x/2) == True)\n",
+      "assert(val_in_range(x, x/2) == True)\n",
+      "assert(val_in_range(x, x//2) == True)\n",
       "%timeit val_in_xrange(x, x/2.0)\n",
-      "%timeit val_in_xrange(x, x/2)"
+      "%timeit val_in_xrange(x, x/2)\n",
+      "%timeit val_in_range(x, x/2.0)\n",
+      "%timeit val_in_range(x, x/2)"
      ],
      "language": "python",
      "metadata": {},
@@ -1015,7 +1013,7 @@
        "stream": "stdout",
        "text": [
         "Python 2.7.7\n",
-        "1 loops, best of 3: 661 ms per loop"
+        "1 loops, best of 3: 285 ms per loop"
        ]
       },
       {
@@ -1023,7 +1021,23 @@
        "stream": "stdout",
        "text": [
         "\n",
-        "1 loops, best of 3: 564 ms per loop"
+        "1 loops, best of 3: 179 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1 loops, best of 3: 658 ms per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "1 loops, best of 3: 556 ms per loop"
        ]
       },
       {
@@ -1070,13 +1084,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 8,
        "text": [
         "<slot wrapper '__contains__' of 'range' objects>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 8
     },
     {
      "cell_type": "code",

From 96ebee32dc536d489089db9f1a3f21a56f3c4da5 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 16 Jun 2014 11:31:43 -0400
Subject: [PATCH 099/248] links to python resources

---
 README.md | 45 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)

diff --git a/README.md b/README.md
index cc44f9b..86bb21a 100755
--- a/README.md
+++ b/README.md
@@ -87,3 +87,48 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 - [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to .py files.
 
 - convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)]
+
+
+<br>
+
+<a id='links'></a>
+
+###// Links
+
+
+
+- [PyPI - the Python Package Index](https://pypi.python.org/pypi) - the official repository for all open source Python modules and packages  
+
+- [PEP 8](http://legacy.python.org/dev/peps/pep-0008/) - The official style guide for Python code
+
+
+
+**// News**
+
+- [Python subreddit](http://www.reddit.com/r/Python/) - my favorite resource to catch up with Python news and great Python-related articles
+
+- [Python community on Google+](https://plus.google.com/communities/103393744324769547228) - a nice and friendly community to share and discuss everything about Python
+
+- [Python Weekly](http://www.pythonweekly.com) - A free weekly newsletter featuring curated news, articles, new releases, jobs etc. related to Python
+
+
+
+**// Resources for learning Python**
+
+- [Learn Python The Hard Way](http://learnpythonthehardway.org/book/) - one of the most popular and recommended resources for learning Python  
+
+- [Dive Into Python](http://www.diveintopython.net) / [Dive Into Python 3](http://getpython3.com/diveintopython3/) - a free Python book for experienced programmers
+
+- [The Hitchhiker’s Guide to Python](http://docs.python-guide.org/en/latest/) - a free best-practice handbook for both novices and experts
+
+- [Think Python - How to Think Like a Computer Scientist](http://www.greenteapress.com/thinkpython/) - an introduction for beginners starting with basic concepts of programming
+
+**// My favorite Python projects and packages**
+
+- [The IPython Notebook](http://ipython.org/notebook.html) - an interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document.
+
+- [SciPy Stack](http://www.scipy.org/index.html) - Python packages (NumPy, pandas, SciPy, IPython, Matplotlib) for scientific computing
+
+- [Cython](http://cython.org) - C-extensions for Python, an optimizing static compiler to combine Python and C code
+
+- [Numba](http://numba.pydata.org) - an just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
\ No newline at end of file

From 777fd12862b2c93ab3a345d09098f550995119a5 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 16 Jun 2014 13:07:51 -0400
Subject: [PATCH 100/248] upd. algorithm cat.

---
 README.md | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 86bb21a..45f7886 100755
--- a/README.md
+++ b/README.md
@@ -51,6 +51,16 @@
 <br>
 ###// Algorithms
 
+*The algorithms category was moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*
+
+
+
+- Sorting Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/sorting/sorting_algorithms.ipynb?create=1)]
+
+- Linear regression via the least squares fit method [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/linregr_least_squares_fit.ipynb?create=1)]
+
+- Dixon's Q test to identify outliers for small sample sizes [[IPython nb](hhttp://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/dixon_q_test.ipynb?create=1)]
+
 - Sequential Selection Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
 
@@ -72,7 +82,6 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 
 
-
 ###// Other 
 
 - Happy Mother's Day [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)]

From a76fa9a01bd7974dcb5d6758de3591d8d334e7c1 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 16 Jun 2014 16:53:05 -0400
Subject: [PATCH 101/248] fixed link

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 45f7886..4fe23d0 100755
--- a/README.md
+++ b/README.md
@@ -59,7 +59,7 @@
 
 - Linear regression via the least squares fit method [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/linregr_least_squares_fit.ipynb?create=1)]
 
-- Dixon's Q test to identify outliers for small sample sizes [[IPython nb](hhttp://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/dixon_q_test.ipynb?create=1)]
+- Dixon's Q test to identify outliers for small sample sizes [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/dixon_q_test.ipynb?create=1)]
 
 - Sequential Selection Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 

From 6cb30cfb38aba7b550f12ebab25ae3c17b346b85 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 16 Jun 2014 23:00:23 -0400
Subject: [PATCH 102/248] refined numpy syntax

---
 tutorials/.~lock.matrix_cheatsheet_only.html# |  1 +
 tutorials/matrix_cheatsheet_only.html         | 37 +++++++++++++------
 2 files changed, 26 insertions(+), 12 deletions(-)
 create mode 100644 tutorials/.~lock.matrix_cheatsheet_only.html#

diff --git a/tutorials/.~lock.matrix_cheatsheet_only.html# b/tutorials/.~lock.matrix_cheatsheet_only.html#
new file mode 100644
index 0000000..fda9341
--- /dev/null
+++ b/tutorials/.~lock.matrix_cheatsheet_only.html#
@@ -0,0 +1 @@
+,sebastian,Macintosh.local,16.06.2014 22:50,file:///Users/sebastian/Library/Application%20Support/LibreOffice/4;
\ No newline at end of file
diff --git a/tutorials/matrix_cheatsheet_only.html b/tutorials/matrix_cheatsheet_only.html
index a03d73f..3f81405 100644
--- a/tutorials/matrix_cheatsheet_only.html
+++ b/tutorials/matrix_cheatsheet_only.html
@@ -5,7 +5,7 @@
 	<title></title>
 	<meta name="generator" content="LibreOffice 4.2.4.2 (MacOSX)">
 	<meta name="created" content="0;0">
-	<meta name="changed" content="20140605;223752655486000">
+	<meta name="changed" content="20140616;225009144635000">
 	<style type="text/css">
 	<!--
 		td p { color: #000000; font-family: "Verdana"; font-size: 12pt }
@@ -22,7 +22,7 @@
 	<col width="511">
 	<col width="690">
 	<tr>
-		<td height="57" bgcolor="#0066cc" style="border: none; padding: 0in">
+		<td height="58" bgcolor="#0066cc" style="border: none; padding: 0in">
 			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
 		</td>
 		<td bgcolor="#0066cc" style="border: none; padding: 0in">
@@ -92,10 +92,15 @@
 			3</font></font></p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			a = np.array([[1],[2],[3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			a<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp;
-			&nbsp; &nbsp; [3]])</font></font></p>
+			<p align="left"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font face="Andale Mono"><font size="1" style="font-size: 8pt">
+			</font></font><font face="Andale Mono"><font size="1" style="font-size: 8pt">a</font></font><font color="#2fff12"><font face="AndaleMono, serif"><font size="4" style="font-size: 14pt">
+			</font></font></font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">=
+			np.array([1,2,3]).reshape(1,3)</font></font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font face="Andale Mono"><font size="1" style="font-size: 8pt">
+			</font></font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape<br>(1,
+			3)</font></font></font></p>
+			<p align="left"><br>
+			</p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -124,6 +129,14 @@
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b<br>array([1, 2, 3])</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			note that numpy doesn't have <br># explicit “row-vectors”, but
+			1-D <br># arrays</font></font></p>
+			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12"><font face="AndaleMono, serif"><font size="1" style="font-size: 8pt"><b>
+			</b></font></font></font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape</font></font></font></font></font></p>
+			<p style="margin-bottom: 0in"><font face="Andale Mono"><font size="1" style="font-size: 8pt">(3,)</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><br>
+			</p>
 		</td>
 		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -213,7 +226,7 @@
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			np.ones([3,2])<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			np.ones((3,2))<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
 			[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.]])</font></font></p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
@@ -551,10 +564,10 @@
 			A = np.array([[1,2,3],[4,5,6],[7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [7, 8, 9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			total_elements = A.shape[0] * A.shape[1]<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			B = A.reshape(1, total_elements)&nbsp;<br><br># or
-			A.reshape(1,9)<br># Alternative: A.shape = (1,9)&nbsp;<br>#
-			to&nbsp;change the array in place<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			total_elements = np.prod(A.shape)</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			B = A.reshape(1, total_elements)&nbsp;<br><br># alternative
+			shortcut:<br># A.reshape(1,-1)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			B<br>array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])</font></font></p>
 		</td>
 		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
@@ -1107,7 +1120,7 @@
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="539" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td height="540" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
 			a Gaussian dataset:<br><br>creating random vectors from the
 			multivariate normal<br>distribution given mean and covariance

From f04bbaa36e8ee696a2405f1c49807756fae5b86f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 17 Jun 2014 16:28:34 -0400
Subject: [PATCH 103/248] python logo

---
 Images/python-logo-master-v3-TM-flattened.png | Bin 0 -> 11155 bytes
 README.md                                     |   2 +-
 body.html                                     |   0
 3 files changed, 1 insertion(+), 1 deletion(-)
 create mode 100644 Images/python-logo-master-v3-TM-flattened.png
 create mode 100644 body.html

diff --git a/Images/python-logo-master-v3-TM-flattened.png b/Images/python-logo-master-v3-TM-flattened.png
new file mode 100644
index 0000000000000000000000000000000000000000..738f6ed41f499d1f57fd4db356dc1d64769bf725
GIT binary patch
literal 11155
zcmdUVWn5IzyDmd_NQaal2t$J+ol*jVbf+L4(%q$k_$O4P6i{-=p&38~1f+zaLz<ys
z7@D&@=iU$Z!~K5m{PqkpdoR{r>)mfY@00XQM~#e_i5LqDi%dgZMIQ?b+Z22sAjAis
zmi_Vf!5>^-B@F{Y@Cqe#NCdx$ywpv7v9O2_{=Fee<>J}kB;6}jlUK?<j<&B}dU~)M
zymWWO5`HKoEFmN;uHC-w4MuSP8}Z!p#j5~YA4e>0TYDc*cSnbpw(L({`Zxx7`ndYC
zYn!l(3JZ%T_?Hi3VX<Rrs3;l)=IqW#yk<s5u^m*=OpB_?Lq9Ojy1M?Uz3Y9)Vl2%k
zJ@>(%->L@DUDIuIY)C`>CCbyk_-T<}6^)I*C_H@QqdIY1*i>j@X(n9|*<id+>#px%
zxcYZq+^^6(T%6O_J8ke+2j`4c&-7?jhPr8u#C44H+_G1AD~D4&%N_=2YL#@cHE7OK
zpyP7k(<5fmZeax#@G6*@+Q+A)qM{<Z7Z2s+<SZl<b^yPqR3kZ35)%`7u}Mw;4WqK@
z0~a}yv6Sh+MLk@G8gMa@6OpD0F492$U;9_(Z&p>xhK7TKgZV~f=$6MSsIlVkN0Et%
zT?Hs9z6@e<ZRL71_g#6fB?UHf)w*yIuxIXU#&QEg$5h(UNL#R3O_=_7T<!6Tc~VXv
zL|VO0&f@?^EqW#s88|iq@u7&eT)Ywo>$he33Oa=hAq-Vxh^)RKw0WgQ9I&5eHcPSa
z2n^f&Ex<7Up(}Uv?o?(o_s;+zEAc6Yn)s0qzWkEcM?pE^Ycjuc%sa<=g3WEPM~DsL
zn}#<-$ug0df`6)CExWk39F2J*f0a+R3HXsLNCZo>w(frpJPX)YT+hAxu2mj9E-srs
zraMl*E0(s{ZkRHR1#PyJa5f2b1tA06p-rs&qqFs6jc<RYbf~WNEdKp|Zq4pHO#XCd
zHbzbUK)S6%5zG*4+o#z$US)wmc3?PlI(!z=Gn$D^;%^UGAp=`yJxIYN{)lUP8T8=s
z*<bl9%2PQnC*%EhKsAyYb&R5~d*Bi*a)-A+urqCcP{K%iSF!{xdJ3w-=yk(3&i590
zfwo|aIB@^T3q#RoUoV@U;pLp}TH$0u_9NqdAN`y;eLgIcS~YmXyJ^l1q|QZ7e}4q5
z<c^M!b%H12<?Jc3i6V9u@^F*Xq@ji<;^+<*j&;tN>?mAIMdd&0JN=s3MJZ%5=r(#P
zs14&N0d&CYtM&Z9aW6V<`Ij+I!6=n~qpBjRrm0uy?4AV7#U~|%6MPumooWuE&J=uj
zaN;Ypa-!9&9F!I57(sTB`<~WINrn{1`g&6L@L+$hYxAx-PB<RW_w*<Ze%s0WO#$)Q
zvH_mmhY?pCZf)-FdLy!5W)BZS_PYAVB|_ICu1NPd`ikJh&J=o<x{3QTH{bWKSgd8v
z(w3xoG~0vQ$TiY|S{3&FXTWh$bY*3QH*0`_kx@}DFF>H=C6l;zcYD}nnodc#+Y2{I
zi)?l+dgfhM8&mAb$=Qc~xX>~+%&)USwNpvbLsPiGFb*CRM-hh(E9dHv#6&8h=Te)R
zJEsx_<_x?#?5&2uM)ub!Rb9H{$=q*;hKBrha3Y(U9{)#^d$W1Z3^_N)vcA49Ge9bJ
z>cHLCl!9D(NQ!aZvqDBXe(YOJEuk~o_oj1J3Q0Y#XBl|JmKW9zgtxdd?NRvkI}-KG
zFyoPXv7eKhTe*9Sg<!yr*qhg{^UW&kZf~ydR&6!Q(cJno)ZB4cn|U+g^qFIQIp9+0
zq-nRoJdLsKZc3H4MH#`Si;m%C!}FkOR@k^uu6<u&!Lur)UWJj~cY|jYMwjy}g;B=5
zGzWuX!;<mk=jTDI$3cU`!<Yic<z+A1=$yy!vBy6Mrj*j*0-uH~?y4a~TN4<_Y>L7y
z8+TP=+-z+jMfypQ6!kRdKpGd($6~+M;!-~1I3+eSTEX~ouER#{-+#v(3;6higzmLQ
zI!gYBUM9SLCj%e2xzS)lUYh(;m)UlgdScT|YU%0uWDb7s^7oT(CS>t-a>FMueeX+#
zIZY6vEWcWwCSr(9SY4e3+B4^f-l(Yj`D7tTU}&gUg*3l3FhsX*3u`dcr1#m<V#GEC
zm&<q}mV^8Lj&X;gBd+KCuUry|MN57uI3c3zTLtooFqu@~_U|=|(uOw<=X{4D&N_ie
z@i!bP7e7`WKCT^Zi<Q3~mhT*ktM4E*s+>pkk2`s9_OmVhs%LJ~HlzRG={VIOP~j&<
zF9x=8(crl#Cv!X~h815%PqH)-N3_BU8~ky!Bb0Rj-Y|57Hp4u}^57Wvt9aI^NZMba
z0{bguPoSKIenRj2v780vg17%XV37-L92Ov^P&WF@klFfoVc=DfH;xli7K^p3TwrYe
z2zF*jwmkxbi18>K0<XV6h}6?@O*1d&b4I)xg>6-^jV^O|XI&LGa!L`90WaP~M+7nK
z3KxW(l2EzSwY9YZlZMV@SyQ6F%&;qL9dpK#BS<`Ti_C(T3jr8Z#FH1Q?;OFG;R8ka
zhw2MEa3>*~4RCxuu~e~&Y()0JKp5&y;^0qeCX@^#G0CiNotu3EoNXwOd2tm)YxV3j
z(lIczeo`Bpv;Zo(D#q5o1l~V<DwVCx5nJ>NkzNR&oQO631~ek_-)t!yDLFxC8zLc5
zzA5u31+Ya2zcit^Nxv}OJ*_AGdSw$c9`klgjEsz>d8(Gk>ekk|>WH)mHd3*8=+W}S
z&V}WA4{SOyBldbTGH^Hz=c-m1#Z;n3{BAGhi^pH~)sU*{r46iI92MyQx55{c@`-C}
zT^TB)l4KD$G1mp|3m;X->n1xG<v%<vMtiF0(Ws}>C~LJiwfC;AIXZvL{I-aP>c{V1
zsz32=_SwbP=?G;_*$}kfQu?w>C;6mTtb{}AvcWhtZTPESx6$O$RUA^tuIQ$CpebjY
zj>zP9@9pN|?fd%qEFCPQ^~<Wo@1I{-ep)1%^WkB?mbF=-BfTf~AA>T(-i{wF1-!(k
z`-`a-g7lhU9)mW9|81MIRw-f*z@rpc2!>=bva$6}O=Wvpj*pMOEx7kIxzNVPNB>*7
zddORr$75!Ovs!Ido+LaeDJku}eKoJ2-j4~pJVndC`d+$yRy+9y=s>6(g{q#Cv+4fe
zzBY)>aizk%$z#D)G;*g7J*y6D5;B(qi%Zkirt6P`<`RTYG*I4)_jyJR>5vg*t>+}>
zh?)u8FgKZwhBh=NN?H!jt+}>#A$C-pth6+qseA|R`Vn7MPgq2R>LS5Do6Vm_po$&Z
zwe!Ht#3Ztimw}l%MK=3eX|hifi`juN%D}MRbHASkxgYJ*^xCdkQn<j!MA$)f=Y)Ub
z&}lJrVa$Gl(QeWw)&}A8aa>#--cQ6dGBXtd!;Q{7Z#=SG+NOv)u}7R<t8;3P3qN{9
zAEcXv4pmEKRjoE5MGH;qf2T3Cq#?6J>Ir5kUmrY_hhczpV+bhX^-HpX+upA9rm9`+
z_gv)u?CrH@e)9S<AgU6Totyi1Hw114*0K7l9)3W-ctfjWWQ8Ub`3qwGQr!g7DxlXj
zjLd|*SNe@X05fbbX0e^P_0VwXr)T6zi4=<M8PLPp+S=-CrF#Ah%@J|39=mFxjgqrJ
zD8Iqqzm*Ip`hJ6O5!utD`cy~f37sYgM29QA_qOJ}Yrtuj`sD5e3N<MxQ$S-)%1Q`4
z7pw$$9JtrF6MK*xHNhikXWV^Qw*8rENOm7SR>U?+lr^1P8NSV0x2V{SiC`-S!fa}4
zs!R>|7`fxmok4%BoTca*I5a$L@8KbsX`^2@AuKC<w?Co2v@~o>Kq4fB{H_$u1?(f(
z{O8ZAMfyac<>uziWG;H)`;*M^<!N{%CafI1@t1`7((SbsU66q)c)4;aWFS-q;~6!C
zd34LH&|GQ%;>DBAUp!^N9joe@QNNv^s#>Q8#`fgNli2%_RmL7|3<CoL?Y_}tTjJnc
zvz!_{Gb;-`nU;%@46g5}XGUccURP({%Nkdl(fCl)O!M0p{<||1bWG*O9LeOLCQ*J*
zb#)V@ez1yPt@}K7s5RwH{u|=A^!G0(NHh%$$xgz=Ly+fc#-0o{N~goOu03~PQp&IG
zg|{BC6UAl=<76(n`|R{yHV72vLXQvM++y|J;>Y~F($YK0$_>JM_V5$!&47OT*GWU=
zkHrNBd_>x$f>{H74x%|&C_6j5F7J*$+Siw-yC|(lS~BjfSIvGeJD>x1*_P_fDkL~8
zPMnO##i3BB&F#&?h6X;7Ns|TnC1xLU^;oB5ydvTrI`}RL2}yQd9(}MUP>wXmrV@p6
zRIux}g;XvLUK(O>s2AyXZGansg8`WN)m7{M@VAW8(!Lk9@2ulw2Eo3>FT1T*P6pN!
zOiUmHU*KgMk+j0NMDq!-Pr^tmZD$Axz8BgvN^2Mk&bBFIoQF$favsVGG9TG0$1l9Z
z#l^+o!3+HHHR8(<mXPQvi1y>m8mL!lr8mtK5fO<xJoE<n%5p(PNnSY9v|B?--ilep
zeHs{-#<q*3ZaUZNH*YxdhDq?)YSmm_TuRF@MWuW%oSecUC|)sF?&yCvK=Ji|_`tZF
zCL=99kTwzX=Gp@RGijbeJYNj6^&8;MW`N;Ef5XXKer9zLn9#tqez_@WtPUxwc-(+k
zrH7Ha2AO~Q9Wiii5ymbG1&Nu6ghZTOHN%WuR63U}aC4hNo|edvk33KMLA>_fnG#`<
zde!&H#<WQP0r<u{;$@n~O49-i8!Tecrv_>d7Fl97^-AhZf4@cn=0!u<r)7h83kt`n
zU#hFCbFniG@4qJ!NB&~bFWM7!h+9S7Zs1uo`Qx~Abg{ykcNLFFMWSV&S#7}UCUz~>
zrM=~{%x9PM7wAUHcOVbEbTcco@grS@u#i?i@+r^bETDAvnxZw*xj+av*`(jOx(fY<
zA|G9PMuVsJmSyYl5Be0<WvjROzR~46Ig-nlwazzGRFiDng}#-85?zIqPlOt%O*3(Z
z>i-4@%-i=7gK7mIIG&j2h%Et|xDl;CBK>o8Be-1_7|6hZ^QY0%k>W2r15CN)sSO|r
zG<_?7tDQhyp}Y0UEYRwiP#cAKAeV+xwaJgw)vqJO2b{@0g<e&CzQeNgjp5qkvGLp#
z|BU{iVN<DX&FAp}9`u^dq0(?{VV2z%+`G1ev^lw@Nmo3U5FgszzWWURd^pX1o)a>#
z$t%Qi-#lSPKl8alO$V|;w88(?i9>~lwH%(=b6%R*8zm#dj5bqcpjpdP)%FF8a@-l3
zO;)5f$e|-#&STis{l);TbgpI0LuqN|i}Nq{FBTYTntRh|qu*y3f6|S`&a6JoGbVf~
z+Zf9b#UZ!ftcsnf97yC9I_ulpz|^<n)0oqC&*uC@e|mWN&j9)~BZ^6s1vf5wiplQy
z_?QYFTj+nVr1(!C>Vjnb&qJ-g(LdNss8{dH;vxc;5VcL#)mnp0ZK6?0&wiA0N{x)J
z>Q3Y+Q!}odH?gd-6`;gzrznt(#X8vGCU#V6ZQ7-UE%~-0&*xDM;*{n^L(a#iw6E}?
zuF~a@fdZYXAoHCemwPi`sMdZh(WFXx%t58}WYmYh^?9L{DP(ER4Pk{Gf=To#2fsR<
zMI6wZAhfwdNTNX~iN)IGC{E#!+i0F^kWQuV(0;2tJJZnCPOgoatFCZoMq2w5nl!MM
zf%FE;VtG}YUn4!Xl2?pgg8PV5%7TZfMqHbkFJwvKWT5uz_{)L_{z7+FE(;1FGg4Do
zsoGaT=nqToKCMcLrax(>X%ny^19B`|0ih#J4rQk=_AF<;$V}u?Cyzs9t*%Xb&ymuj
z*0A}(q5?E`-yKR$mEdQe>7Kqxb{_T|i${K8QYyY5U(WhD{9I-dJCl2<F%GMWl9L9;
z5}cN!^x9Kl+s!rKa88-f1nVGQ!a*W;U*>*!q~qkQFjDlMf{X*VdnFNCEjvo>D@MGW
zpG$Ss=W&5;|JQ5tBzxW<S^K{2QC(}R2HbMPO65>a4lWxCeI%r;tzvoMz8cg<_UPu)
z8}8TW<ydg-O7D*lMJzy>!D5>$c{~_m|MK`)KcNEYj?(*zm0Vq}JKHYxOt%%!NR*8z
zK*{-0H#Q`HiCRfT>Gkt-c1T5SMJ~MGyNPcE?vWRXheaTNbW{HZz5uhhrq@H*>1N&=
zUBI&XsqIa*OP1V1A1~WMLjxxI4Yho75E)k=7KLL(0jUrWmdJbL>MNCHKF_&7QPeur
zncB)0f|;vrSh=#Un7B9genJ}|ie+K#<31kDJ#z`b8*i#MTc0`1Bn_G06<q0`ETMvy
zW?^(Nf_||dgr^F4WF`#@g54EvsDe+CYcwNLj{a|!s@3G{8onI*E*(XO2yZv=3~V*h
zT5&t^NTtR{e12}BqV%iMvc=9>LwYn98V?l(+Liy<f_DbT(E~H3<q$Cx9)4*UMZU2u
zXOk4GF&k3dzr)mN9llIaE#c?t+dEW-IC{Pd`A+-V%~}X`<AI%des)TWJgrbGY=c;&
zrTyhRs-+7MGNL3kb+GH(21QU=LnN(u>>2KV`-1>fAPS}9mjZr$ahJ2L@%t8nnEBKM
zm$iW)j^4ZaR_82~Jx91mbWeH|<iGH;$|Orra866S$w8w^CEq=MeH9b?^wY(BBv;E=
zBV?I!EZB=kq+k$vB-f5HoU{DBPhHti^l+s*HG-15<M!s1e&LK{X)HA4dNLnLRmU<j
zLsz)7>+?7_5#Q*`=w<-55-1cJWdHzzE)3J5o6N=ORC?+R8IT=}^ZmrK(L9fB^a4pJ
zcQev*xVhg}(uN^UWVbn=un)5Mj%D6$De2v|L_qK^+m<Nh4@h`&&toYO91M-Eboz*y
z%gUCtCJeco_Wa+p2sde#kbu_>1RIo&l6w!0y@q#~wn&W>e90%rI<U7RNM7M`WgR?_
z+5Z))y(GC!EH9au_yeq|tBT-z1<*h&S@qmr<|#1c;U)na;@<xIYV^YdFEK*Fo8fhR
zGEZyOGYG$p>>trS>cphP^w%6^PSM@YwmOM$xBW1rW#EqezS>??OK=FYUQ-aQ$p-?#
z%qZrLEdd5RBD37ygtMD?tc2Ud#KO+B3F>!|T%xNReyz`gaAx)4f5q4?Te1VsWcP+b
zP*58SKO)-rjp7x>_+ixg9y}apE)eJ2En(oX>a7i}Kd=NN_Ewj)L|!IR)3)s(os8!}
z`z!DwkDkMBww~M&|Ct$mg32(qz~f8Ao7zpO$XGfD+_(E$2)mJ*ziaC%%k~r^5a5OS
z=i26%iM!b${DBI-q$W@9ev6=PJdIgD$Z2c;hMkT)v_(?;d)+IH;tD2+R=izd&(K7-
z5Q$?k)C9vM{06qg;E@jJ3ZUM<D+`5SPntVLWke9Pw#fuSUOvO2^EE=M$YUinb6<!}
zR@r*IJ3g|zBkJ9QVST;#AIczv=xd`7LgAu|tgy;Fe8Fo}C#gxi4STYz4I|>TEnh-0
z7+|Zs<ec(O*Je5@tF0`2T-3nSp9E-jVrNPiOj)BO7o7Po`!4;Lmoa8H%k6%2CP8<E
zw?|J{;;7Q>h(hDF!s6B1*hzFkOBBUKMgN)?v&qAoD@ii!`#As2B>F{~lGmm_qaF|1
z{eZWQuZv#pkYnIQ+jI=AA%eI}4$kf{zaQAjF-7j`T*F3z35IVdxT=KI(5NOga;s<;
z%DZ|KRDc4sz~_GqP|>%M(u<Te7eeAXJakk!i_dz!gwq(Zky%e|eO*R~ooNmuVoGz{
zg%fauDEPP~ICi*7yY;?e*?;yE3l>E4N9QCaSvig-OR?=`Q}IiswKD>u5B~)1baHyi
zj#ZbCkc(q<yu5TTvmUl!<smpeLgg?Q92|<BAv+a_rXj-PO32@$4Zr#&+0ec0x_c(!
zH8p&0a#uzEoB#g8U2m<#k2R*%At}l6xv)f1dQf=ZC@Kj69heEZl?I66V_de>NQvNH
zx}BztxAiVn*Kf4ct3NB(mGmtLR#~wl4^|9*vhL-)^?sR9AC|j3V)pZ%gINcKK7L)X
zQ5lgg`)`M?>ltrBhM@kns|554xhdy{y%3!#uq|HS;oTvg1`at)BwoUi2aropUIixq
z@VWsxn@h0wpr(F6qHk&&P3$t+3&YmWKJN9GBQ?8s!NIMezj^#Lu=eE<=h&wrRIr<^
zCDN}Tw_K@!QrVUF0gz1{sBrIMr>_NpaP;zz6n&ZBCzcW{yId`>><2bYvQl`^sz@%g
zssV3B)S>jkm6SW{S`G+KY1~qw)Ulgfc4m1h!8KS*f4)Dbf`9$4|NSgeqhvhiM8_RW
z{-?Ezl9I%v0frrj4`sj?#hAqtFIt@vvMYupLsP>QdDYp8@4%vQzgo@WVM((=Dn|Y8
zPPiC;+X0{@_JqKZA~oh21-BMohLT(!MxdnBg|vc7dKiF0cFsiHM9M~~dkYVy<4N1F
zfj5NzGPkP5gSxt7MZ&}1^kJ*ZV;h<5Qj@vtv*1)v?LyQ`#-ldulga}Hm=2t;nb_Dc
z0^`o&oD^msi_gimG}F0gh+l?*v{3OfjbldstomY;9_H3&8D9?_F@3m$_192W9tTV~
zD?{uyjpI^^ME$FC_-2Q!H#m)rCs63AU=Dn|1oarOd!_DpkZz8JFlPP(W#g2hF#KQ1
zNliHdPdYCrAMSKV008Op>%FnrH5S-N&{Gr04^-f2KHWc)3Hy>)7r}w7*Z<BXe%LzO
z@msYDmVD7IqQkjw`l`pvOiV;%+58F8Z&{uxdQ6-QBn7#SpxFHRbLzRyXHY!BHgfL{
z5p7pO1{C;h>Iren8;&MRmC4z?$sRwM5WBBG{JH-E?^~&h!!l@TEEhB<B_&nfNYQ_;
zD@RFzG6x6A77GmLeP#&vk>B4M+Rh@7b=h`<N_(WE9h7%sVq)!U<g79b+d(e_|CJMB
zY+?J`VhNG<9`Hus1d3Ygw+XQmtz~U->%(<&BQ|=Y0^C&70UFZF79n%8TxV7h54L7t
zl(|;9k)5bxhW~NjuufZXv-z$5_t>qiEt9S{P*6aRZlk3(3jJ)|+@5aQCG+G4a@NV~
zfOEwqC1D|<Bymby#*1TKHhskXU)UNcz66xtM59}yNBC_z&(G@VyC~QTArD?FtWJ|B
zRB5~BJr9cyY-^EE;c^jY;?2-3h!Ze0^gh|tTRsA)Magl}iCngUVb96QE8gyxV3TV;
zi~wJG6ZSi+FTvfQ+pN3N1bPua_2diHuVKA5E4A+iM`Rl{P=brcCFleWewF9c6z90m
zx&*?4$TiBk^0i=M_CoZBHL|Q=+~_o0{zWR-(g-V#t2KVswUy$K@W<|0B5US>op<MR
z8*&K%B|5`VLHfxY{-BNuPD$>u8%A9xk{@RS#ANh~T1#;1`XN9o@@}s-L>@gtW;I!H
zYFRpej0cZ|!A#7;!IX~p24FGPH%D15R-7v1;s8LnJeAZ;kF~4r>)%^_`MiT!_^A)Y
zT#}l6puEn_YR~#x&@mZLEAZ=Zg}tPGMIm~6ZH3OBmA$(^NuGe&W>{xAm7cVpUZ^8$
z^t{tuIO=TU;X&@}>zn4t_~9u&0OK%w^X|sS6DYqwE)wAQR>~Ck4VpKc;Q!!aYKj45
z>^F!qB&q_FJ@f*AeGaDnEXm{IWX+Yz<KhNo6H(a;dHoImtqk3W%ouP46NFxj=h2z%
zMSrZBhJp%ipN;fIs?q)Too8o_BdSChnyP{Ag<KlhIXSCcUsh8%_#Ldzeh_%AqmNBO
z(O265W_HZ0ny4PWsL`doL(JZA&53Tvs75lgv-{=nFCFIY51*b@Mi9AaPFg_qUL*pP
zj<9|qRBrKDxL|&{I|0aD7neL_!bRfUaaM8<r?~bpSy+Z99ykE501$$E(D!1;6j<+a
zr~jc@y1H;9y8vR<btaQSc#s{UkR4cE1G>T7>uN;J+j(8uB-GunubX2q5f5rUvJG75
z4yLsCW@;3_1TzVM>X1O+llKZ2-RgX*dUn=SvdNKn;yj&fk9}}~ZK3QkFa~^apppmo
zAwgGuFP(W90R60U@6*sxvIGTKPde8>*e2}F&?WgP)65Wu7C!V&&tp@nso+@w$?!8Q
za=EKq8^}PU@)XQFXLyxH!49MZ%F;PA^u~zh{X7VUCnVb|G?eA}^XFYCZS?k1XUscX
z=zB()zwJSlO}~-V_PutpRnP$-?VVC5O{8Dz#daB0ol7IYqMmAN_s|7`hDZ6vmt{n5
zd7@j}uF=)FZYX7HtLLNKD}UOOVQA-TlOY0g&kPBbDP`49tT^gv3e;a06u)hp5<t#t
zhk7||!k-BV3Km)js*RC+<t4s<|GwSjQg(K>f?!tnqxQo$>h$Y_Yd!!E>Ra;(`;-kG
z5N$sx5?bCW$+HUJpY7%TtOFnq;?hz(fB%Q+K|$WP%mvF`X|&aII04IN%c;wA=lo|K
z^71Kp?XcL43%{Ho?Ox3lpozlRMpKISOPA=@4`(rza&m-ohqg9auuBxTD$upnRW49e
zt#527e^{;H)%-?17Xu(FN&g)N1H;`5O{JDsQe>)n`MjFQPQK*xXWtEod+ezGr`?7l
zrWi4`dVD^KM*xcRg7TEi;f-p~#dZW{m#b#RhQULu{N<9xjVEHQ<1LTP?v1ZPuyx(O
z)8~)5xu?4sQa-DR8$uYD`k7`I-4h(u%rzs5VfJf7k2YfcuW+4zK>eSOeb9lLX9~eP
z)x9#-n9Gj|1y-o`48Sv({Ko|tb*^i7`dDlm6f`a3z?U4|uTjn6`A%cD2HHFJ<q@yD
z$cFXHkMXrm!^C(&0JX$u8qjbbJtY5yB#zv7kHHdl^G}jB)u7%;V<9VddpO&0P;{<U
zV^mfjtoc7EW}0RZKA*vKf6bhCgw&<bwN*rCN@(H_jttHE_5j;?lt)@SJZ3BtAW+wr
z7<<sIs|fRu#{uA>kg?ytyVyh~-vOPTAG9!ry7V7c+>fCh(1f+Hk)@?!8Z?fwQQ;3^
zo&hN4Qx$~yYyFWsTC%Rho%uJq%fHohvE|wmp-CfAIj5~+Yu~4tE}1~gEOcU}o9|DB
zMzWJFQjUTkp!snmI<wyR!C?N2GPqw(ebzujUSgs%A?OtAFhKg(0u$n%TjHeK%Ua?>
zUqF*PkB(|KP&+-Gg}k8mqX|Z^wnwx6bAHS6y@pG7PpMT0_wR%+eEpDEh)B!ey4$8$
zc1yx<gGZ!(cf%*p0Z|C)%!P7p6U3K)^(Hf6RlMH^D?}Y;UR(7Bv;<4*#a;#10Pxq&
zg@~$xhtxFbFTL~9oitn=BO!S2bSBQixf<ea$Ko7j{Z9U^=Td#uC7UjdNJqx%^m|nm
zy??t=Y@{e?3%y{&irBEOgZm!ydJX4^cO!);E|JPK?_BD)&ErW7@95DyJZ*u{D42Ur
zY`<;9$+YE<K+Pj6hnwI*vsOzB^0tV=_U{IKxmEnhcb;n{I=6w={Ks$tXh>MV4b{Qh
ziUs%fkiuHl<1=%$o`pwx^xNoWPhBIytd#E(Ds>A$GW|aVhSa_Q2z=pv#>~p%UVxkH
zsq^D{)Wdja^6w;S9<xV-{`PI752^IBC3*NYJld)*SxaEpMo8r_LG&3qn#xbZ&~UM!
z3-n`NN!@*(@^1;cM(JM{W<zOq`;(<?81bK3pLk--`4L6qztL{DkxQR=?v#}BX%?k>
zHXR-wGMFi=plFqi#@uE*F1WttLWhG%Z!dliUBH93wtfc>t&FqL1&*uy{k2x(g|HK%
zqf$-kj?<`Jp@`HPF846$O14)JP4Q!fEm6W=Pu?fRX$&V1+~P-ETfg$ypHN+zxZRwq
zl<xhgB}*BnFfI<-7KKJ-=b45yc=q=8B6Z1$lVacrPlvL>jwvEZ7PmQVrm^q1tx02N
zwy`Fxkl~of5w)I6d0`Z7Htr2wdkR842O(8*TNeAp?ufnN(~}~0?2pSX9v;5~I`!T|
z2P8w!yna-0a@zH27N5vtSKg2Y9v3ZO>l;G`tcJHw*T#o#=eDcm7v%e`pxYmdUw$*-
z`({+u4j-&ecVq<Msv=)TGR#j!MVr)Aqt2TIb~zkX8$oOlQVj}7fyLt)Q^8%B{EPNE
zP~_dof64U9vUj@H2_OcrMqaP#xESyhKvPK6hDy&3^f7;tnpNrI@Vi7t#wKECB36ps
zhghNbP#@dVXCM%6HnV=q`t=52_H-#06e)>zy=fT^%y6uhcHErX`v<ePe=PVQg!1fh
zR4|WQ1u+}<W!b-dmS08@U0q7fXj)>t*Qu$snB>HZXbx9~xx)m+E*swOy99z)+P>B%
zknhO+hM>B~ouAu`x!7M{5`t6(EKaEa!OfKTtqL$&dZZh$FszWhtCbfcW;gqYS^iOs
zL(_!sWej4oq>(%`MyUWxBR$mko^>kdet`_gEWT4G(~*W44}fl`7F44OfXDI>J~u&X
z7H}itbxz~8MKE-mgH2EAfoQ%4k1OF*XR<MQ^Dhu%Uer2^Gd(k;Ez$YB&1XQ$uc{Z@
z4uY(r9u04DwA;f0D}GKUNoLY}2ilvwNlZf6#(W4(T4yM>XRflYBI@oe)=LW5b?1x5
znf6DO0iyqTW$B40Xo>V*?W1xKMfu*C)tmJU4nCc22j21BJ1Wqv1fLF~jD0n8C3JVF
zl5G3TsutRb#ea*_leM+~A=TwCF>f01dBB<W3+q!uC0^Okm!0p)fV>dHrGaX2DjszP
z9HjbqasZqp|1VBetD^Y-!J;Z7LB>o_EjRYYJ;Fl7lZoVI0ADB!?D?~8Vq8gf%p`uN
zuSg=`1i*kM5?2XxQ`Y@$Z|_97L>|E)k=3>_j^7DqQKZ&@>taJ{it|f?rU{o1eq^=G
zH@LJ$llW1~g|V`#WpY9$|4Y~o`X6g(`3`wZ*8>?Me7+LTf=#)qYtGiL_v5aoVRvVT
zKDS?9{RWNlKc1wf*IW$ctx6fzOD?mgj6S$OHr&q9d_i#!7V{kiP+qLPVWbwIqi(}c
zbCJKIwMSj9qHKg}YLlC7FrJ^^lv2P8TbNsJ-fc)tO#H^iP-C~sT#4J2=J~BgCP3<E
zS-VG4Y&m-rpYxf|oW~wb7zyA<71;MBD^n;(o(iwh*N}F5mCx>m+Qyf&L&|26GI@^z
zBpadt^~i(NRPT7J!e=|EMvk7y!o%^pVYgS(y@hHws%GDJwgLYuyW-%Dg~KN>7F2Om
z)SnYsxt>mAlH$wQ3kGkRXEUNbAnM{4)9`E7>r=?iD3D1f!mp-qRhXd6ef*FTgRb6j
zyu!{<pE{b^_(`f7Z(G+j@be9`^lv8tCsrrfEQzzaZPC}0Os32%th?4_lkF2LkrQ#x
z1ZKe=t`TXp1#g2)Jl1gU7n}1mv`j45lV_?UnuCsuT^YrMr<LC`zVcXTO?tyTzyD~D
zro6n$9fr?^G@CnT?HvK+0}&vEwCkHN56p2bKK-TK<vj}zW&3?@pOB#ieusId{WnL<
zoOSIU6ec>yg>&&cQt=nfBi=dJ7NjPCjR#yH?=;Rk_t^)p9^(my1eN4Fu)2DfhkVY7
z-z(n`n{N$z9=Ml28w!OZV>c{_2K#-acx0lPR%ofH&^SEpYD3`?9MRh0aJl_#m`9_~
zJiCvCE!>zPS+Ig~)kXPT5liE196*-?wfxho=-18&pScanKhd$@t-^7yGW8E;dG1tN
z3`jhhJiT<IrlRTrv<#h7D}g+#N{{F)b<+;}=mTQY$*{^!b+07+D&K10bQ1BYl#S%i
z$T%+C2*MVI8hD3u%hz>!-QN{-<fElCI5;`C>Ep}gP-jQdxWbq19ylM6a+l{a=eUej
z`3{TZ`gC4IM%eEE_=(KQlUUwYMuf3~aQhhia&~#*aM>sA#yHJ8zR>Z|&Hy%GM4*wx
zWT3PB%T<0(Ieif>S)R17_^UCFtLorKfdZhQ-T6lhA~eaZ!h1k{rz5v`lHVih*G3>v
z5yjp#2~w5r_d!LrvD$|ux_8$YzXD5D2k4$H%r@qrbbWq-SvWCY5l|<c8AKc^cpzO2
yz|zq#zC=*{<4+R*Zx8VQC9CrPN&`K(h4jP)6lgG(_XBz?mWHa1N~Mxb^nU@y+ffq$

literal 0
HcmV?d00001

diff --git a/README.md b/README.md
index 4fe23d0..5f657eb 100755
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 <h3>A collection of useful scripts, tutorials, and other Python-related things</h3>
 
 <br>
-<img src="./Images/logo.png" alt=""> </div>
+<img src="./Images/python-logo-master-v3-TM-flattened.png" alt=""> </div>
 
 
 
diff --git a/body.html b/body.html
new file mode 100644
index 0000000..e69de29

From 2340747bee24cc95647026d40d049f9f51737199 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Jun 2014 00:28:29 -0400
Subject: [PATCH 104/248] random string generator

---
 tutorials/.~lock.matrix_cheatsheet_only.html# | 1 -
 tutorials/matrix_cheatsheet_only.html         | 4 ++--
 2 files changed, 2 insertions(+), 3 deletions(-)
 delete mode 100644 tutorials/.~lock.matrix_cheatsheet_only.html#

diff --git a/tutorials/.~lock.matrix_cheatsheet_only.html# b/tutorials/.~lock.matrix_cheatsheet_only.html#
deleted file mode 100644
index fda9341..0000000
--- a/tutorials/.~lock.matrix_cheatsheet_only.html#
+++ /dev/null
@@ -1 +0,0 @@
-,sebastian,Macintosh.local,16.06.2014 22:50,file:///Users/sebastian/Library/Application%20Support/LibreOffice/4;
\ No newline at end of file
diff --git a/tutorials/matrix_cheatsheet_only.html b/tutorials/matrix_cheatsheet_only.html
index 3f81405..659d121 100644
--- a/tutorials/matrix_cheatsheet_only.html
+++ b/tutorials/matrix_cheatsheet_only.html
@@ -5,7 +5,7 @@
 	<title></title>
 	<meta name="generator" content="LibreOffice 4.2.4.2 (MacOSX)">
 	<meta name="created" content="0;0">
-	<meta name="changed" content="20140616;225009144635000">
+	<meta name="changed" content="20140616;230046743144000">
 	<style type="text/css">
 	<!--
 		td p { color: #000000; font-family: "Verdana"; font-size: 12pt }
@@ -132,7 +132,7 @@
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
 			note that numpy doesn't have <br># explicit “row-vectors”, but
 			1-D <br># arrays</font></font></p>
-			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12"><font face="AndaleMono, serif"><font size="1" style="font-size: 8pt"><b>
+			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12"><font face="AndaleMono, serif"><font size="4" style="font-size: 14pt"><b>
 			</b></font></font></font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape</font></font></font></font></font></p>
 			<p style="margin-bottom: 0in"><font face="Andale Mono"><font size="1" style="font-size: 8pt">(3,)</font></font></p>
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><br>

From 2f3ae7cad7f29961fc277c3e0556ca740d6ffa14 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Jun 2014 00:29:33 -0400
Subject: [PATCH 105/248] random string generator

---
 body.html | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 body.html

diff --git a/body.html b/body.html
deleted file mode 100644
index e69de29..0000000

From c76d80a54015c8de729d1ecc18d9ef9a2a61b879 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Jun 2014 00:32:43 -0400
Subject: [PATCH 106/248] random string generator

---
 README.md                                 |  3 +++
 useful_scripts/random_string_generator.py | 19 +++++++++++++++++++
 2 files changed, 22 insertions(+)
 create mode 100644 useful_scripts/random_string_generator.py

diff --git a/README.md b/README.md
index 5f657eb..462a41e 100755
--- a/README.md
+++ b/README.md
@@ -97,6 +97,9 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 - convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)]
 
+- A random string generator [function](./useful_scripts/random_string_generator.py)
+
+
 
 <br>
 
diff --git a/useful_scripts/random_string_generator.py b/useful_scripts/random_string_generator.py
new file mode 100644
index 0000000..15cfe51
--- /dev/null
+++ b/useful_scripts/random_string_generator.py
@@ -0,0 +1,19 @@
+import string
+import random
+
+def rand_string(length):
+    """ Generates a random string of numbers, lower- and uppercase chars. """
+    return ''.join(random.choice(
+            string.ascii_lowercase + string.ascii_uppercase + string.digits)
+                   for i in range(length)
+            )
+
+if __name__ == '__main__':
+    print("Example1:", rand_string(length=4))
+    print("Example2:", rand_string(length=8))
+    print("Example2:", rand_string(length=16))
+
+
+    # Example1: 5bVL
+    # Example2: oIIg37xl
+    # Example2: 7IqDbrf506TatFO9

From 26812a1dfc119c9d4622f79adf4c3a8d6b75e870 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Jun 2014 16:04:42 -0400
Subject: [PATCH 107/248] mutliprocessing scheme

---
 Images/multiprocessing_scheme.png     | Bin 0 -> 106996 bytes
 tutorials/multiprocessing_intro.ipynb | 550 ++++++++++++++++++++++++++
 2 files changed, 550 insertions(+)
 create mode 100644 Images/multiprocessing_scheme.png
 create mode 100644 tutorials/multiprocessing_intro.ipynb

diff --git a/Images/multiprocessing_scheme.png b/Images/multiprocessing_scheme.png
new file mode 100644
index 0000000000000000000000000000000000000000..224e15960060e7de588be246746704056855b9d5
GIT binary patch
literal 106996
zcmeFYRX|<K5;lkhC%9{HcV`13xO+&@1cJM}ySr}OCAbsZo!}DO-8QbXb572^=bxGP
znTN~MrdF@&uIjSx^;M{nf+Pwe0U`th1d6njm<j|0^cDmJR2@7Nn6gLU?g0UTIAkg+
zsw6EcN~UCIZDeX;2mv95Ql8|jraFW>u-A44>l;8%bw%Cw1`R$#38us^G6n`i2HuxU
zUlK>-i#R$ur1+$Gn4dZ1H>MC;9Gt$OH##xmB09K8+-hE^pEfhs?oYfsT#b!{&IFuK
zms2_H$KUuY_n?QXexZjb$(55%$)Yi^m1Bgy#rpz@eQyC7)iZ|X9uNfU>&<j_24&)Q
zo^)SV>CN@H28{^tg2jcP#lnx;Gqh1f7h=<C)|aM+poPpmDj&sCv`6oDK)@jDek|m(
zO+t5nADy)`!oiLHRX1b|0>Xtom_i!Hhcj$Hg3*e6GdMzWL8cco49$rAxF6fb1>Tqf
zAk4*OnTASkLG+3$9Qyj@EsgGOe2;X(#j$Qo>R|V3H?l3F@$BftYpt|=EcqUnszXc?
zW6WLfyEb||BlRIuUv`{67Ub0Fz`>~9Ph{88=>}FoxOPdk_O3){oqS2ul5V4Hl72te
zxVz<?dWmGGy1%`M(;*d|4agxw32F5|A!nWQ35x8GWNs0Oh+PpGBjn+X+e+9qF$NL&
zCd(yD^?YNak@7Hfmm7O;^wP$h4Du|O6A5@tAK!9+9A(qPq6?QyKcE9qD5N&8aa@2@
zpT4<Cd~C9?Dx~~SNMj3Ccy<)g#>=7RMBPbv=R>w_;3M_Lzm>#1{~E3<^~qv;6>>e1
zT9*o)8j&5JWKed4V}ZP)&m$-@v7Q)W`pjwx)~E5-XcaR*;<cin1fuf^vw1qY-Q1Q5
zf`}B(N9MT#9~~n50MaI^FHxq(=sBlI0GSNR^*x-~d!&6xgD+8HyAawCNTIwC0Y30(
z&`HqDXg+FWkPKY}Rp{IPAF5D}VX(6ah#(x-;dx;-yG(6Jw4vrZQEcD<F#ca)S|PU9
z1ycgZ;Ng_X2=ZY~MPXy`zd^IT;~9iQ^y|*XqkStAAQpqA32o_zn1fLawS+n=+VQ?9
z8}t*q7_|izMa1AI;XX%%-w-SDTmZ^f*%So#PG1|CR%C^M=?x-Y)K$df9<KARO>cxy
z_59B|{m+qHpx!7^Ca}xE8Aj>nwko|V{-g#a<EI%+|2{FtOi8}jNsZ?a#|-Tn;~F7S
zeEKJH9pBQMljtZh`^kfS$9-~#H!W{h{7yfteL&3_m|SDUih%7$-ir8YfMbBc#=v%;
z!Y;?OOSwzF`_>2sL=xUdZxC4(^~u`;nH%8;fmXnZzh%GEm&dA}1q7nEML`@r&6^)=
zh_tDhNgD~B@tof}WAgYthR|%(UvRtVc`)h*O!Osh5S|-f@LWus$HUNf`K-&qzdMI7
zgYOIQ4{+*s?1ujuSNxunyaj_IM7lS!*G?bvg51{UE=i31ATC`hT<XIVtP0-&?E$-K
z$W`cfDQ+tF*yOl^?ecBW?YEbnms*H{`chw&_32U*$`jm%-wYcJOY)_q2NG#T(KPB-
zs8oob7A`Zm`nCGD20z)|6Wz1lZ(Yvs=&2y6iI(Cfit0#~=V#{LsM27WAkHDaLo7iQ
zM?^u?z>>hW4GRhr3Ip^3`k=#bv4Tx$(@4g``wJ{{iSu>xBY##qQ1AmY#aQx5b8e>w
z60s7G6LJ%366q7iX;J9&5>OHshv|m<h6fVP=)FHqs$D2;lvya7sx+5eia2J{%O+N8
zW-onrlf14a5FL|b`fT|_vuugNv96`$f&4o0(0!Jrv?E<IYmYrzpz_OVwJR#;7ZlY<
z*2q}`I8HZCYR-DjXU@K~A6zUZ?K3KK`leK-iDntI1k;74KTMJ8W$PzQg^g+10C;o}
zb`rP-+LEFJdXlBJdPN$>D)Ul2Dm)sEpKoJHl%q?Mvo%WROL!_O%Spd?NOUN_`a*X3
zn3AzB82+?8@A^5&x@1%PwQQp}wiNvr?LhIQNoR`wu~oX2wt>el81rsUY3@Y>yB;j=
zdLHbQk7>F*{$oU=0Y>hl54#4t0%N#im8omi3f#A>6YSQUq%}$n@HK<=CiO0cCD!=n
z%_}v(E`f<{>S41YWx;b{bC7e*b0>4w7IKzN+=<+cmUIp64M&#0EcAY*E?%76EUnZQ
zEyDe>ZaAxZJzU=>vo*29v!8K}u>Wp%Y&?~kGfKW|ND~oXH0@S2t&o*YoX(LE&G)*a
ze;Ri@wa(tsX#(lp)%B*QqNRdp{<*^63b~QD(bLNRl>e0Imf)6#Q4l9ueZ+0|>t^h>
z?ZQ@KQDRXqD1Q%*#kjIwmw1)dTKEj=OqWzVODYR7YoNWQJ+wWteaq{{tL&NoiS5Pr
z<=6A$Vwwf>k4!<5b-XI>Y=#&jJd#i7ca(#iQW!HN$Z&8lXYeGSOsbUXs<>bRszjXv
z0Ek?O@~F>_=uXP}xx?a$J>Prg0vv;<yEOuM0y?74BWR;s@Jxy5B99~DqBRNBNw426
z3A9kU*;Mar)`wU26%W80Ea}@;D_7T7W6F9Y9c#1K%y^l;O#K*%NHS#kQKRkYI(*5&
zu&rV9ktn*SSA2l0*K;Gl#*7#9;`pxg&b9*A8Ta;0bRWY`<b}>X)srzo1p*a9UT8(A
z0LB2!sULfnY)*33hdcfu;S&A>Q9G&2ob8f?{e)dL4>RpUy4YOW&ohaIiAfoAG~Aeb
z;c(L#)6Rv_vQSj@_)7gX5e;(uaqCx7kp;q1!8OY@^tD125t^<5t#?`pE49N8vq|a`
zb{_U#x{<nLIxEc*#$)|lqUM5gr+RSZ;d=AUy4Ien53wj-gooT1Jk-`tO!M@Ol(#E|
z`|>BQX|9BqLEh(&Ic$2&dU0zVi-z{s(fg!<!Z%jcD>d#TjZnH>vmeU3Yr0v)mvhU6
zKfkKIOwY^(l(+T0?Q?JZ+8ElfvU1wqRw0%{S@g|C@;oX@MZ#3%;7107lP+B?L)ohM
zGSv##Cla;Q1}*Q~h7~<}7;I;HK?YzMXK90)=R(gfdwUjhYt?!q$FJ9Wp530eH;*UQ
zC2Zg61bhTz9M=!Jiv3rFIlL<>)hn64Kf6gem{c);uP9rsZf_UK%V|QlO5lzn)^)q<
zM?a&ml;4$q1SSHJqW$uw@+Q1PA9qfxI}I`hQU`zvc<IIb(gL4<Le+ma!?(t+d)v@g
zcb$RAJG*!~<#nkMu7{d2$EV{eaUgf`d2_L~R&~#AT;3r>KsQqnAiUh13)%?m!?ki|
zIy`K{bGf*rT9d_X5p0>zacc8;2m>XmYNTptRq|<@A5@?5-2<PkY21n3v?>I2t$%Dd
z<h5CgtStghc_!8_+Z_+MA8+afkj}1FB3s=ny-i=&1y|c9AL(xOga_SN?`?V@jI!6!
zuay@KA_urSfKRJ{eO4EvLHFW4>AU#gp5+Z(?{7~RhLmS?JEn^qC&t%#+<8R<9s^bY
zy-Sbnz`MTfktbp7m+x+dPlvsF-#gs;aigPKlUg&~RIfgsoa8t=o$=Frpdsl{^c23&
zS<hP<Xi*$hT*=B476xkHzqu`VDjS-=vG!eqEmSEg>?J}lrpA5$jww9`q7x_zp`;(9
zy#Wg$*1Xv2(hrq3wu2t@QZaq$ePMA55cJtbTZcgM2|<AfaDY&Xyb6+1e{V*MH2E2#
zp*~TaRf`?N!qNzyyq>&7dU0k(i;}cA72>?!+sv%S(d^KiDeedWk-hb7AtYoeFNh2w
ztA93#+%k7#q(3)(dn3HR0L8#V7EcHP0hwW{_Q~Osyd1xQwI%b{Z`S&T%r2HT;G!1-
z0^q_A{%L9G@RiKP(!$D~-$ju8uMzy<-@nN$<Ya#haWEGo|0J(OCTeYGNXEs?$;?VF
zgh)n42C(~P#IGVI@#l5$e}d#D4h}Z_EG*8>&dkmn%+_|sENpyyd@QW&EbQz|;1Nvr
zu2v3TU6`!wDgI{i4<9i@djmUD8wXQsE3)5wzv^2%ItY@J|DNbSe}Avj(8ct>Gg;aH
z5eqCJ%kL*FY|N}I|KSaO74VzNuVm_CXz@wR)Y8z(9z2H-A0HRsukrtnC;y%CUvGZ?
z@0;9w?EilAUr+wL31Imxz`q3gTU&oo!Riu11hD)^^+JfUtuQkX5bq(R#YEIxAdfTP
zmZ{WTUmIC|eM(yW?1q_-K#vD40YgO==BGR<#hBeMjE2z%Q}9Ve4Xdi3mW>)qoND1c
z^d}Pnw|zgxwqxe1z>~+<)yLMe#oE2q#^Y1>wKn@R=NnM7`|haylJjoH{TRnYI)1aq
z{UtgxMvM;(B^e}w)O#q5nD;<*yji2?YJvZg8tEgmvi1A<e}<k&y)Rgea_<w8{x`Yc
zl<gl*zeoByQD&=H547oL{bvF&Ia}ox@4t+|a|&Q6l{9YYRxAFCKbYi>{MVBHOqorL
zP(1U?wsL{y@0I>dE}jvD{WtHQQ7~$UCruj1@&A>{2TUS?{x@$ENhtbjr)7p4>3@qv
z?->0@3xD&jqkv3w6F9|wr2IEIv1Q=TivAXSDb`1(jrcaqGw$DHnH7V7>jmn6>iV;n
z|5Mi=)Bpdkb>U4%Wte@=s%X1evY!xatbXWuD{%aL=Iw1D#D^;UbpHD2{aR@|4GBkd
zJ{j#*$L3vc-~KRL`SP?Utk`#e+9p%xFa5N#_Pnx_te44nu=->5x}wf3Hv_@rrZ%hX
zyc<asRruT&)nl82RJx4%xA!7L`*Mu)ZcWLu$fIP~4bz=@p1DsSrct&(pIE={OA5<y
z*;u-55w4|tlvhjcbluCi-C^<OyM84;a~T*Aez~YK)0cvGNLKOQj+OMf>mlX#bAP%R
zcvLx@*S43RCXY<~{&l>(aW##{VLvAr2V3Z(7jq<;C3;jjPr|b0pfC_JuW2vSU1j%p
zu~v0S42bD+SW<N9c{|E6QRf<bk_kik(-o#^c$+3SmBaY$EZ$iwX#5H4c37j`uGp+0
zvtD+6(Ku@?8o$EOyiu5V(ao9ey4`7msd4#oGn_>sg*9h#z2&IFd{mo(4`|))YUaGz
z8w%PTll@!xKaRm4?PGgb@7dyVu?e(lDLGgL%kKS)b4~8w;jS*>y~g9rKs0HG%|`e7
zBXI3$cec{YvFo9EFZ0Jy#Sd~09_G=dZ;rvlPA>N&dYSywcqH-MsU|#7zbtCy+s_)7
z%*vdOa-L&-VE8jToqky+XN6qCH4_Uv!8N(TG%VYVJf)-xc|IJ@8kBg!JJhSJxu3OJ
zH*ExEPtQ0j?8_petQlu{R&=~Pcqvs}%V!m{n70u+ec^&XpR=3BP1eYx5C1LBCqXFE
zGL|xpIh7S@ALppx!}_gL7~T|R=X#X^`=J@;R#9j=HQrp0?ZH^SdVo~MknWOp$(|so
zM;gTQ<LQsHXzwSA#_k_y?T;yJWgb$asT{#HsNUd>)x8MQu=sJ(AP)=zoiwhE%ZeX;
zuN^13HjJ0&HZ@z+pg!YP?ul0v8q&=x<8^l_Z(!L1WDQU|MAwWYF@aSucC&0hA;KSq
z!}zH)o5W?^H<-lDWH^D&<*kWZvW^qJ+>Zp$^Delvj@K8pq|F3kK{tohMlQL5pC%#9
zCrw*XzpOhvr=6Jyv~t!h8&@ROoK{`;bevZb_rxQ)cfpdEQ?Po!Jn^>Koa$J&U2k4>
zuNl~)9F{?+f8DI>Hw?!lDB@DE^2EPQ6b9e_y*_rl<`HVI@VZ|a&S?nEzg4i+7UF&V
z2^Z8km2As!$rnAzPxF3!R9n42iugxqc@-{Mr?l6a=k1DVQAQe$F?pE4A(F8*@AS{o
z+}#ZTZnh+;Czg<QYr}$mxQH%14wI(C?TWK%5`+$c=a8&!w-nH+W-tKx6CQ3yJhgo3
zSt=9XENY<+K>?O~lHO{Ftf7V|Ay(V4hJguZmt$%;E;?jgBH>jc?3?#^1O;GQ;lUEu
zOCnC3SaZE)T)Q84zRbAYeTcKWGhkGDI>>r4wb(KF5?As~+7G|b&LH!}ra^vW!t18i
z!#rR3DhHJc8bj}C6W1Q7e~Dix7*;L^ye@r+YaCByW54&@2*mA<Be;A}MX7e|c)pR<
zx;LkZ_j^4@ePwmM+*af00i1V2Ten{H)2ckNn|~|y?VT!+O=kSuedVWGq!3B)vL*bI
zN83$AJwRkX3idT%np9{c_fkkGeDf-nc$oTWUX;KouYzUC7fO?a&vdD$g;f(8-cMVk
z_WDfYEZXq+Tet1EZOb)M!mm#t@5e^(Uuqq8nTCTAWwdc0o2~htucm>SLJg~KM~a?@
zs;vZB5vqRVe7|ZZgaj<fMT~jnivo#Z?kBuoK;k~rO}1M$qqoyMr7d@x;d~fXa)9Pm
zk+^W?CBd_viviNC+panoN>)l3qp`H>dGF`>K3BRI5_Hlh9S%zkVU*`v?^m9%@)+&(
zxwriL*`!bFaPmkm_gSy5FGXXd0>)?FFMHmZ+?JYkkJhg@*2*zaJ4t$X-M9n{-`Jr?
zc`>;k7;Pld?Z^3~e)X#qWxAR1@5S5|c|R0M0Nd@}@@>boYNSlUjTd|?<mW3o0UNqp
z?{ZySW9tr+D~gmc47n<|byXLvvZ!ryK8D5W%T42+e0~FM-)xc)rzL|SI~`)YvfYy6
z8;UNjWX$Ube2zFngU#Pda*0@TIqSkBtgLmlbqh2yIjj+}&vKmm%oeJxOIZNcA!w9C
z`9?hW^NzxCeAd&qX{1wQAy8^MlSY}^JA?|ugJJPa+>X09+bm7%Q2g<Y-C5A~n7_5C
zk6~s-2RdT+enS+tY?FHj5+d@vnsg=QClkzt?q|Jwhb5D-a?kR5$#P)xRRYvXZ?!Td
zPogU0dHZ8cK8X^sV7yJIJu%)?S3Q3XlzF?&{dyPun)2(aU`DycwWObO@3}bGRSs0%
zC(Z4QxBhPTEVbi)S*YM1cz;zSJXQiYBQb@ss(I)8$pg$VV-L$XM(_DhBN9=dk!EZt
z1h|!(Q|H!)twFXQPJU1CU3WbFEem6C=eF(4v)Rxf&7Oy*P>qs`cmB8aM&7u--P)uR
zp^w7=1klac>(Wo**=S0OlPd?bvyxg`DG?D~otImCnj4J8_RQiMyXpc8_~dVQs>229
zS{1AeR7Ef`Vmf2cIzww;9**|BO(ISG`dnQW7KqvtF%@`oCWR*WZWavCH^mqIQH6LL
zH(E8)N#a#r%Q_y)mNC#js$D;>y^;)gTTQ2|+Vr5LIyBKXQOgkTeON_&$=DVf0ZH3~
z&cXI~tNkN<?suwRj&py9rJJcr>u8j+e{^p{-$!BcbRjNkdY#<yWcZ3|t-PUkKdcew
zDO2Tq{`J~1H)?L}JcwsqK}K9<)4Ahu#b;idx#V`$-5M<Ns1HwePo`)x4PS{AWfPl}
zoRkMRq>rO02%OY;Fw9?lfZx$-4mSNRY_|2b;bNS1__Tgrx9Ky;P4{tWe%Eu~=PG*>
z&JKn+iTVpgWJt1%Y8nh2vo}YqzlyQ(o)$yKZsCokwY!$y^a@#*?Zw+W9U2mLtO<NO
z>hy<Z0068PS0jUmU7)qZciiosV_Ch2DRfDq$=A_Mz}D833Ua{9mhQvDe8=j7c4iNp
zP`72eu@KnrwGZ#cI0&aBjvOo9b=VeJNw8ozlNTf&0dUaNBWK>)&7KAX#tT%*+EXc{
zeA)p`{`$DMurwJC7)3MfZw*$b$v)K6ZkB=_J^;oh@1;-g=}mj$%h9$%CR|Drp&iEz
zO>&O47YGPmTUxM@_kFVW;(V6t!s{N`r+kVA`qPHqVfZsp^H(0sB@CsavGCf`(HWay
zN(r{xPt_@myYpVVdaO8@4?n%!c1&DTmE$0cS7?izJ7eAV(X&4rvbZlMYUqC6u6+QT
z)zQSu@x=Li9xEBXV<oYZSq;4mJE^KITD*y0peORNE&U2rm+4Cn5~y`Ag7dVIg5Yr6
z)uUEM9^ZtWuTq(dDZ4)l7{3A{fv~Cm!dR9+li5};oZCSe+FeM{Gw`GvX0646_>F<Y
zuQ{Do8i!&<1yVkn1ycYiPbRNIUjcV3{LhyL$-t4p?M4^mG~jh&e@>?NSX~J|4N~ih
zJdZfFH=n1acy~=bZ}hnw$^a8FbLPHn)OxSx6S^ID7uVD$@4Nwm>hW6iakrrq8gm%C
ze07Yb)YomrmjKTUVP1+XycBRS^&VqV1YGoLn}Jx9gPpaBLtQo+-ZgG(p{7THh!lR@
z(?ta1-Br<z^u$v{1K1m3j(OTIA_2yUQ%zLenRtXGyI6q5fNA1+9bFn`IE7eO1%tG`
za#y2Q+{fjq-(edM%B9ynRiT$dkp-RkY4gpf`FoEia1__b^sm_#%mTo{(SQ;+4rmq3
zadjM&g@v%p_V;Eo=GRi*Iy+{*P9?Wr$y>W&MjMCQAO)p*@6oc<?3$i(K2!$OGUSXt
zFI<U2REPY?pgnBm&D+AE(m<-l)?0-WzUz8A6cy5_3xOCiCmZW4J5QjHo*bx4VO+Fr
zJ}1lKRkR``K(G&D5o+?f9HIe@i<XArqK0T}K#66eKho~RcOpA`W%}<4bS&rbr)2rn
zxCDk3Jrv)ACWM7qe#jg+Tzqi0y^uL%P`~T|S0&oX)AR^d+QPTZ6FYp72jd+swt}ab
z1#Az2)q9x8J6}INArwIS5h{HUn4%B;8tx_SPGg->m7b7=;UDc4S_<E=#gaPz4WGEX
z%{Fd0FN&XDT_HEbOZr*F&z`1dOS~27t6r}innDjBRp7KumQui&(rh5EZc3v%uXq#3
zyA+yk(hDN2NiwY0>q>9pKHFg2*ueA-B~Udsm5#?#VpHP`v*2jnO(?pb+>(sn;2@_N
zhFW5ST~Vx`&>{@xl8lzL&G5*`<rVjuCfbqkL!cYp*6++5a=G`F$9LCtCqJC^XTJ$P
ziMJ}WmjGwi+SAw?Rx+B|`gZ2+Pw*C3QN!E`j2TQLpwn$mhIi;=shGXJ6}f0Y^{y=*
zVqNbIq2LyVwy97sl*XE58HziChe}Z85n8G`qj?AfIiY*Sbe#C_Gj0-voj1RW@XC4p
zx!&L#Tpg1G;&q$OCihhprcn~>pfsT5g<1#8=ZSd1qR)rNUgRl(TW>qeR3WeC2repj
zEHA|PXGszKLeK^l$TkVBF)%!!G{Zu1Qd9$hzQyUd=}qA8uAm^3RV!T_8U=J{X&O*X
z1V0OKpe;Mol_bvkd1<~qDS2m?^&cfP@#gaAYip-n1nwlp8%^w9;|@^5-mUn;$^n#G
z%{7RU@Xq&Q=6^ySSVgNE<K}Kp`F;=+TPmR&jChn~7mzE-8}+)3&&uzLTW1Nb)%t)Q
z`c$4QQSg?$uvrcRV73LLyiAMB0uk`pR<A3S{J``>Cz3E5YR6?o95V^~dfQv*bo_g?
zkUKHaJ;Qqtv+hNg010g1NSI6erpj<F)T)0M7Y0&dQMloS7@jk3v=7LM4{6%UdQv|1
zy&x)$4pqUpYC>5>3w>oeXCu*l=fx@&^ux#W5tj)%%SbU>=0f^JH{U^ZgzpHx-5SIA
zs3$;;!8XCl<pNb0k^zTP`fXyC%tCo(=$G(KY{A`AsYD)nM>XP!m~d5|n?U;Et(obF
ze%nmCd=kS@)^SCVI|o%bDdch$nn9Y%bP%ic*lv0Ju1BT?K_b7iOu$b{1d^nEFNbfs
ziW`^k9OMZ0gcVp#0Da7g+|3K9{u092bx3R7bYR6GjTOzro|M#lGV6Mrax3NgZDWf~
znT+Heg`n!*!n>U>qC@$4;dxZo_`zzrI8XsUw+)+xzgi`!B^`Nr8ER7%M4e0Vh?j(J
z`eG<d)rJ1}`2m@;ok<hmsn3{EH1k97AuuB?xU>iY@6<;%W>FK_bLiD8h+95I()M!m
zN#)MdG<Mok?_@J>FGCqdKL^S+J_saB--n-bKuFmXX)ny|9V1N(no@ZI#Dbtc0Mzji
zDrf;Xa!BIjq^P!5p|^t8f$}cHNYO99V}fLCtFm24=SKc}F$#??*7x4iisR<htUo^U
zhuZX`0`GP<SPYvlwjyx&()kT!e$tLW&N+Ccw8phhdBA1c<a`~9UtAUvw%u~wY{_h}
z)7iK#sUtve!Lb&{g{_cz4`u!#8+>Y_?mBwPBYmdr%L6f!;byGXj$j6e6xnOoY8oU(
z)X}x{rp7c{U7lj2#(?Aj&s#HNj^(_Ld)$$x)b1N4rkVst17oSlA3pAuiKg_Ar~)Xr
z0i)Y!Z{od}b+zBb8~`Mk^(DLfa^-b)2qbM}HRTZik_V%T)EZ$9kmTp=q`auYpwA4s
zu*<aITGY=adXVLY-N=<;!Dfioi23YjGf*@P3GJ4HZYgY!CLk8Pb3H7A@0po((XtsY
zdD=q6U9eeIKRb9_tn)tzr|4O+hf2~iJ~q49dZ)Z@RBeT2tCs3JHMJKG&=QoM9;O^R
zc^126B3)dW8JxZj7Cb3&wagv4j+yIDFTiD*p*yF*xl%|F5}zo?1lP(Jt@nv%%#W^l
z*tUe02l#LV>aXIwIe^eI<*bB66*E9de-F$Q4X@?6MQF+bEfGuuv_h(IT6zl@aO=~@
zJ`eT=PEfqreB(M@=7LO&Z3mSbf6o`_LxeNTE{CX+u0=6E5=s1NmT1?+oUuFpCxC<W
z4b8p>rh#-kL4n7^P{KEGIZX7s@<hY<QW}%X{UP!*T6p^H)Iq`dfkf<PT~MJlpd6b&
zahibR6q~7^UWG0T@q3B90kzd#_pLMlwh@z;I3`7#Eq>F2QJ{`bo<%nzWSX&PdFG=n
zvGhHq%R#ZNbBvyLE2}v`UB4fMMnbo3smxlKM@Jy8gT?F=X0NEe{4sV&<=D_FlHk@Z
zr*v@yea^2Vz2fo<pma02=_5`QqHeGhZZ<%S<Fj4jc6YDpT7rLBrbZUsNPSREgdfB^
zBK4s!^_b=^7u^{%R|GNuIkxIvv)d{`q|zBlo4GC2Xf3%LG-IMD$Edq)Z5oc`%adkY
zjXaG$(K<Egy-IDtJ?m6Tpw#)2dn{&I-%tfS*HCyBoizhCqk@zC8Qi6t3W1*2sVp_=
zb!x*+;<_6q@ch&Ice5*=Jh#-mpg?=_)i=HTJP3W_NT~`&7h$NgDoOrffsHosh~$}Z
z!8tQOHH=~7*Jkc@TGf;0RnILniQJ*=E>#Mt$b8>X`uO7*+n|d}KL=#9Hj^M3qpA^O
zo}(WC;iT5{#`M&S;1zly1(FoZBa=KcS_qj4?Rj%9ypXKB>_U5omP}ZY`JS~~?XH&(
z!!?AaxH?>vwI(;ZJu`Wdr%YM$N?p;DQc<T0rO+h0N9q~1=bNN1tgz>s^qpWBQw0hx
zy0{DxV2n4eRfjLhl8tA?zk5tB`;^I73hB1OZW~?6q9|Xc+4l?^8xBg#WYCD+iNS4u
zVuB_r4#?m-v1cPR=SSxxu-`Bpr7YrI!NT4Z50fwXlEVmG72Uhe=UgRbb~km530+1N
zc|z3~_Cmyvn}9YScLt=5t}Pb9vt>MI^)<zQ$@K&r1%wt!u0Gez&KIhCUP{f#>tdh2
zdT<ql#>$)ibtJ-E|JYF?%*z!(yT>KpTHD$nWn;5FQ06;i6chSX_Yv#hXhtW6tHKVJ
zf{5srKEop6K>H&>cpZ6BB^*@Wn*vQ-mhNFtCs4$0<d!{J>C%*yz<fQH1zqM?<Y|X4
zpJUX8B|a2L7@kiw`~p4|?wBImO48+0TM~P8htIEn-bw1YDoW&83_4ggkCKoSw<X1Y
zrhF-ZH`F?Lu}{-FsS(wRLBHlI-b78=)A!_BxvD1X4-nZC4?}!hOhDX?C0sz&^(gB6
z(V7T3?P`x4W}5G^6NuLT!UAwf46`;~k2BV1Up3o%E^j}VFotK|7W9P_;8ug}QyuA-
zwcr%1+fk}F5d?oop@95`xMo5LmBOHa{ymOxYvfz$;+1$fmvA{a=b!#o0d|=Q7dZ(+
zBkkoyzKwZTz84H44p6gBbM#Y;CS3*BY>Nw5q??TB5kpr_9Jg;^>iXm~ZwKgeP@IU2
z4Gd1@TZ4(^<kkQm%+lAIRuA>o{lrpm$sVquq+T-5OPR%Qn!wM(MtPlNjrdL8JAB&e
z<u?8}oVG4O_z7XWS^U`UP*e=EK>fpxidzgqZkzoGIZ;6t*dowR<8+5dN4Vudr*zio
z`SRKyZ1|9yIKB4ZCm=P5S5Nil2wJi3k2Ol{Fj|-5IwsJ-?#0^3V3uMXzV5Z|dEY7}
z0~-4E^+y#p0HIHMWe_v)NO0|&CKLu9JydUnP2#)kzz|-ePx=^fg<KrIZoCoj<D&){
z7a^!F3O!_FAwTObX9s&bIw`u;(2QZ$Wb%U3v+g}mF<;(l-D$H;emqnuVA0iXZt^Oq
zCi{UvL^>L!AWkk~LN$5fvVX4zFR7_(Y&9f@&`J@z7ek#N|C2gXd_B1POc#(R)lUQq
z?G;UhU-=cRB?vXJmRM6I(n9=`SuZ~|F0ZUkuxW^yobNj3*|LSI=XKfap4T_`@A9&U
zohvT&y3^AM4vL|ReHuMUq<`lS&=?D@x7BP|z0+MP5h$c(-LTocS%*`j8fTgZuRHQ}
zvYdlBp)#rftyt;TA)qa2iN2Ybg?x>e@EQOq*>H@;S?uwssi=~CD<cqit<K$uALoF6
z&>OC|T*@_<t`|hum_`A<i+=}6iDlhDmU51D{uY^~9`}rQ^E8x9Zu$MaX(T@1PnGAa
z>4l|vQ_K2bB^RocsQ^^&3%@UyD(D_fONo^#U4^(T<EDN1^^UkKB+(!QQ0qjQ!$r4a
zMuj%z6kxIh!>kaYTLf--1aEsq%G7EvWTMx392;$;E7_kwPl48^pr)z%ew3-5(u!JS
zELqOQlN(&%=@}x`a_KQK>o%pTsD09P9nfo4JFH(B`-Sm%(x`XrvaOQ_8)C1t#g;}&
zN~Z(JGsW7WUFSlOS3p5g_cPTe%#%4!!+Lu>%e#nehcm&(BFp6joEY3;0fKs&+OTzL
z9j(#{Dj{1v)w6hW_Gluhgcm1iqoD|ayStFV&YDxV=@-@)ot#Yj$7)IQd#NQEB~4*v
zrkA21%RRLiF`+<w@f5b?nm4Ix-1SqrVr)#vJS0~|CgJOkUj^=Q<bTlnT9NtmunUXl
z^Pw~kuhVbZ^X*@6dH@yB{rSo_3}p5=F+^VmqaHvjGraR8rxDB9j;0PYXGqxBn2E{&
z3ED3>H}V?cs+{ft(!qpGd~JIo=f@(ua!9qCn|-Do7wvw=boYG>k%p}9smwd>__!{0
zBj4EXClkHhfgoYf1qYYR#aN_+13E%{@|dvRu)kINbytP=Y?*GNy_6wn^kaT_I4*f9
zn@s|Pi-FQd2L4){Bejy-&WSwjsqmYp9W0F2N*#58!KO7`8?pj==5P+wBmED8`{j?+
zmMsK*bbkH;#>uYx0LX+sWB(LypXkT$X3wqc>dUJ({<<)~Y#ta^IrqL*U-T17Y6UFr
zoMoIRVXHQ6Z|xk=V6N>F%Ywpj^)wB|`aGc?{djV1l8UIBZ(J9G;mDahWcjT9I8i;K
z0hfaS4g}|MhZ3^T{+jf$@LPG+#n$eR`HK`%zO6<i-$BH@72oepY$i9j=yqwwBjshm
zC8dIoxtLd&)JwJ+2PA5G8rXLn!ocTp4{`XuB(2=^^YKD^g3u?Z1g#3E7MJKM-by<H
zI-!9{P8f%JtqCM{13a(&<;LNi2n2gO!f_XA!M*$EB3l4%yc?UUj0ih(hO1y}c<L!)
z$~0?q(zhtY25Ke+I_CG{3(|VHEURctR*f!+Aqc3KZRtk+lw$P4Ep`Q&Et<>P1$kw(
z;h0R=(xL6c)c!bBF3MF+(swtF9mE5|aCp|#HhPn<lxR$c3#=MPjFEz!_63<YU1Xwq
zReKYJj+uy;o-!}`L))k+?w`|}$}TjWOPMeRd-Fdc<n1x1p4!^&vF<dol8n;Rd~BDK
zwxx#NsMGSO4k2)9;!ANOuykP^K35e|N#P)4vlsK}^le~n+0XUR{iK^pKFYR8F@3MP
zZF!Z@GZc&OlA;x+C*&}Sg?Ml1)3NPPBYMGssu`xYX)*3`-b&R&7310E8X1gmZx6kj
zEKgp4=LR+Db%h@d^(Iv`L;3tmeo7CUpbF!#uKP(nOp`s|#>e16+RSPjyzJ=d5UKnc
zS0je0WlFRb=zvzc%!XPtqQ#HVZkHr!N$+XU*Rf*TRePG4e;l)~j#5AbIs3W*WAOO&
zb(G%S$4t{fwMlV86?H9yfuK&t6Drf^Lf%GCYrcfUt;4OpjFxGFI41!1lI)6H$~`bN
zHuTi#{<nJJ8gZ8#Pic+1Gd@Dl)I2nU@<Gu+I^0t{d%LYxjRb;GGco!?+*x>$F6_vq
z-6H^R!+9)d*YI>hKd9^J!_co50Lj;>o*A55Y}<>?%YizhR)%yq((V|=r~tNPLfFJi
zXoLP2aq)B6JIrgFi_K7z($piPw4kOY2-}hmTA*VrQsH^trw45(SDe`4I4MaD<)luM
z)rRh&zVQM^9{Pwh2B~SVPQs@mT4Ic|Dp^(>FE%H>yC(stee=RPVmNOKG4*W>(vZVk
zZSFawfKoO|L^g+I-9DnimA-^KPwyqsceSK%uiM{_M^t^QS#5<SDsV+{&8IMedD`xe
zG#!8IYCGitq~O_V*7_P#d3oQq-1N$+r#wCqCm~c=^5ubL<ULxKtL=D@zjt(|abXFg
zasQ_2`&W7Ug6&nhF#FH+R?m+tBhS#{D$~oT+cyC#Me>wk2LA5d;LkfCLh;h>Xx#<Q
z@f={IREx{yh-!8&tOmGvou&3^)h$5q<_p}l+{nRuN@rSz-8M{(q<=M_Ddt&AD+;F*
zNpa=2WD+7E#kK=ZY7}#RpiiXo1yP+rUoxyMdGq%bN}xdNw(?@*V&@)!=0MY4Ux(;X
zeeJhirfHXQVNdbTRc`g@8#X;D>?l~G^jZ%FLeu8@<A13oF(}(<aB(rS+ZNcig>Nn=
zVR;~K=_hL8W@6+QhC}-Y6PjX%PAdp7u|(mrtPz+3oSRV^(E?E_8+TzN4>mX7gyja6
zRuVi<)lR48Q3!U*42am$BwrF@-f1gW$am=C_Y--D0XcDqJl;;Zp7%p*ZpQB$vs1TT
z%ai(|e6nO;FEn8#waP|au3it2dPCvc=o5e7!{NaZOO8Ud_CBF6a*XbKmM6NAwiI*g
zdG|6%%s51zbUQR|f_q~}hJ(*;eCIl&z0j6cV!+-ef=*?-(IGM1*;Y7)XFx|1J-W_~
zq@-108eH>{cQRzQW(p0`;o5#@eU+e>^`ps3kLNSrNd@n^J8xnfuwn8`z5RI_u1Jqs
zAEDT?Z_N7k5J!(k1#2tQ0VaOgipjKJuj?(SoJf2AzBas&>ndv~L9bc0mBm2eC1hqf
zha~AL@_zU6^7~Hxq>{%Xr=gwcdMW2RSBN`z#rm1+gu99ee~;Ihd1>`G90Vx&<|!)3
z&Jq%R&#^0&gdKu`0@rJjEnIaJuf)@hw!@>iULDDcrfimxuEqtcB>HP?_xztbPxYLc
zDIY)a<h$_qt1Y|wIHuUham+JT9-&+ktti<l!WzTWSw9>dj#$LpkM8s)W*@j_rJ!_g
zg$+K6d<2f#)UEIA=r)t~jhbe#VtZi7zgD=kj)2;|g!YE)1v6VN>5a`esRn!B^!4l%
zavAiu?=oHXEAr=cq&g#$Itg15I?=A-??^jb!~}2<$F5l%7BK>g(@l!860#na4`Jj%
zDS;)M%_t*Td}!%C*FmyK9IxA+`+J%xz|<rk7+7f^+$`|zh3?@Va|Q>E@t4&eQ-J;E
z=WT3aq4d>x)5w9$bPiRqUNUzsDGvVr6)$u;bi>e5>K7PeGjxJ>8O4uqizuTusasU0
z^u}x850SP;<JNevdX_cY$ch%InCLjhci-Oo9Z_852E_5KBr0t_OihfK@~$W&^<hR7
zXANRfJ5aCEHTzyVNn^5#cA~<H3%UDB+z(3%7g=3F+jpZ8s%o!IuEDg~tY32&q8aiz
z&<PCEz>43_eZw9ExX>_%=wyZCaL$4u8*3L4wBDFLK@ab$s8>FP8%ka(ePQAj9+k%w
z%2YHP|CM|HYp(UME3TziBu4oAafbzcpFgF3H*Xrk$)=j@b89mGnLp-sa>u73LFbqG
z?m_?}+JMcAzD#M>+3Y(w*V=$=+?O~$ck;EZrf((=?4Ru#hCQdQU@p=cF6S7B<B|hH
z&fh@+*GX3|ci&N<HrP=QWx~WJxXN*4y<y%h9F0^>;~UHt`OK{RWJgZ68e5`vC(exO
za@&0|1nQ!dwS|SpS(X(eeYXks<iFk0HwN+M^8!~LA(Hck{6tmBuYjLqX0D11ILwhD
zE;u&?Y~iiKX^dcxPEGNM^rO7%BTL-D$t|gTGXi)C(-DnTv$wUd;pIZ~<lS3yGMwjp
zwz8i=KxDorg0Abf#lUkleJA{AKSxc#dfgQO{xqNl6Y?w$f0M3F;+68lWCqnc0x&Ng
zg}RR~9@A$Usp_0Q*Q91^BwE5FSpGFhk!ZA}y=2tTAzk$HO!j9%_v|nNzB3~a2D0xt
z0<sIyK$%d^d6+q^MlqJ+;1^Ky=dcmI*DtPS!`GaP=S!#xoD#jYsue+E5jNuMn}I?K
z*3#5lQZ~xt9pSwc;a+Mk^dsUpl``5wO|b9On=Pt+(ri(7FX3n_L}ogf$$Ha4+{V_0
zIP2G}6)Pn<g$O~RBN2XBlA%*pOC!s&oJ<(0t*kzKR60Jq9~R^pS&bFB`rmG=L&nnx
z+wSDT`k8GG<}*%Yh4W$NkqHROcZ-0_Le`Y$w|m;@*@$H6xAcnkFyxN=?}!Z;ZPza%
z_@iSstnv>8>RknAWt@{$D(BkqmsYve4@G|k)T^gKoE8JNL*Gx4_f}2i)q=n=O&oz!
z{Sccte8uVw<Z8wk^p}*y<Kx;1D{)ctGKD>_&sy7-8c;oA5Xo{EQ>YuC`s~e!O#6)Y
zHbs82I!+q>!2>d2uD?^<VAZdOS2JSgA<^I4;Xm6fP?@-`G(~VAgH+HOT_<Gm>N^j~
z+(b7<j3UPfiWvsng>_ctc=|U(SMyW;1-vn#g7o7^z2war$+97UQbpJeH9|n4$`AgM
zGJ~Ysc(RA$`|V?SfJ~d_<H)MamqY|KG-<|z-&hs3xLY6}biYHhn9c59SJlQ?j+ZWz
z%yr5?@UIYvGac3r8=+LxwWsde-o9`ilykP7vt4mg_)pdzgg?Yr2pi1Q4|{@^f+(xz
z;LjUqE052^R1P6Y&jvMAg%H4#SCMsiIDqen!0^OudYF{p;CZvuQ)<T)#aJxTD0yH?
z9tY|t#d~!{URi_L+wAA$&Q6#VdmlOn*e6cdc*1m-%}_Z~?mO%6$jc2lf5RhvXURJO
z;-ipZ#%+~D(s>`L2{z-^<(8LS-#Wq;D|*&PWgz0{x7<FJWj!l`)VeMfcOc=(<|<nb
z=}B<eC!Vk8^(HK|<<f%e{pbinWsIFT8~~(pbnS)$?+dKJk&^PKK2+;Vrs7~3X-;43
zuL?II)t>x=fPDK4a))^7cg-$r(zNVB;kc?!m-1H3y3cZQ%TA)2eQ)_>x9Fj!wV^UT
z__#XVpVue1DJ*71iT=i`YM_1j=(sDjpBbJk)5h@D9FFe4U`-`H@NK+?Rr2E3|6-_o
zE6B1{&carX$^O#Y-yltT$@c}i%{oTG|Dv_{fpMJ-PPP6C|HX9{6M+$-J5H&@zh(IY
zblpb;V@WF(#@?#@7fYIL2Zo#`{xWp>^e-9%eDTZ=eI5a+e=TMhUFm-d{GSGYJKz6o
z@OK#d|IkScTJ#hC<HWwD=%3m>TxVOBFJGdF_-b}67v2kiowad#wTk6WtgN7^`rhs%
zQn2d67@)G}JX5S(dLto}k({S;*6Mi}@+Uy3Q~Ai~)tR6Zc#fv9J&{Y;w~>_Hvus&l
z0>xUhUcNWW&s2RI!qCL$u_EdyQNSdY?&Q6WS}uPp^LOY_l7`y9bOr6co6Ea}zxBY&
zSpw-bG=z?YtSV(Z&H0^{&-{8<^SRaJ!tV`ISdg7kp+Jz2&~?;e;#;vl3|yg#6*>3s
zF1GN85F>vO;cJ5`Z;?^kegPxW(+mX`t`ILYD{B;{+G4MGA6j?0^CTEGTTKq83gT%L
zPa!wGGi1R)|K`WJ*gru3QV9s<a9G24zO?{yX)^-P`w+ts^D<;w2fom{dg{Pm{e@nH
z%F(|*e^lL>9BPV}Dl^yTFuSK6teW9%a^Q!|)8qF<6%hJ62EKoT5P&QmJ$2i{SWYz<
zK?N(u-_YPMe~8zJSE51b=B9mi2)@9SDPAtvLfBV5yVE&__!FVADU{-OuLHd(<W$do
z0Ww-}5xndxy(0|AELBht!b9IugM<8f#?jCJZUTv6Nnagh^(tW~32>@&sW*uCN)zr#
z7XC`JX4RNqTXrsQHn=k=I}MwM<@)C7KpPy@$5$Go<o;+;9xA6Vo*WgO=z&ws`n2qH
zs03-J-URdO)g-<bNT_J_ZRnUCb%k1~2B;I<T2?lr0i7oPPl_;Mg6y@DKbzZARqS>M
z+%E61knuszFyR5Nf5=j`#)nV6N=n}cr7ehAD*Ja3D}wcX@nZoJ{8jX?xFC^DlBWy&
zm9JD#o%YIJ<y0&EJ=}}iiEC$DbZ}Z(U0HF^(@Ry9nm+}cul$`WMC1R?O=^h{mz?Q8
zpHU%dA-T+pmaY{0%QHm4yzw6=mH)vU2SY5SQT4}T&|W4m2c=#9>Y{cGQn;DT;;PHJ
z^SPyYL$1Lf15S;T#Kw((6rUkY#!0KsnZzmeQI%hD``*>fa`9@`Dl@C-@z7qF8%w!l
z&x4=0{QuB?BBc*TWP$0~qn+Ab|B6MtJ*$o*SiPlZB9AP8XY2j<P|viE+$g;3H#H^l
zFdRx*aF!e?z!=DT6D925Lyzem|0MQ(9H>^=u$d?x8ELV6$=HzDCtPr&lBn^%N_3U(
zYCQOVXLU=7NCIpw=aI>uD_d$M&bKelSATrAff7yMfp2Y#==1a%qhMU)s@BK*jy-2I
zZVx(mUs)9C=YedE`b@6}Om$_;Y3kfYQ7g>6^G1Jg>xqEN9zW+FvsFhRGjn&av|f;!
zF!OxhY*uM_WyxVQnsTu<z@pt`^U``Ro5cLtIKz=zAw9&5#u)5PZ!%&>B%}0<0pej#
zpTzxCzb#G~h9@&t)Th4LPzp$kU-ZMpJ?ERsjkCR`VlROMUWpfd8)}j&N>e4rkAVjQ
z1{~lpO}rSL&V7i}{;54I6(HmzZqvM*?NA&^IwoIZDMDU!(FsM3tV9VcJqC!KpTLm(
z4$!!Oo(s4gFC14fjnnjSro{cUdGBj5al=W&uek)CthXoSaCEP2XI^KS?x*-*#4|;%
z<GhYD(6S+CMppMSP80PAsXY6U6pWTW^VkzG7>Fw9M&VvqZ)zmwv1~J4P(t{)P^Int
zd@cOAI8p366h}2(q$s?bVt{s=)3?=VtQ2yc7NCdDC%RZngKW1oc=7+HPIZH?5bxAQ
z?}Eapil{%iLmLoSN<`xef>F}mPXpeqS5va2?#I=B-G<3JH)CMDTn2b4dM`VAxhtYh
z2L1`J;6?<8okR_sn`)UnaNx>hHyrMQBlQ4-p{(m?KknO|(nv(&gHQeuNX$)SqrVZ-
zJ*dJS&!$JW4o!^}U{wA+7}FnK55`vChEPPkFQEbAp?7RwMO0|);BwEz*ts3uyhc&6
zmKsRzcK+km#f*@>CcM$^Fz(O75}3+V)g>&1SWn;{0!1*O@ptEha^XDv=Z52rU{ayI
zbbF!8*yvFp56kLg1i$ocAF@B6{Vw3;ZfouN)LHr7dCimO?Pc1LFp_@`W9qh;(M$M?
z0@q&oHg{~4ZERGa3T4=uT4Lxp_0PSHg~c8xhrbOP5}}UCmr|Xgiv_7|2X{<Uuk1M>
zW{jiyoqd<z8{>3a*eDR^`-BVdP3q-uKu{<42Yr<nYz^&;<_XWYkF<Dxyp~i7MxZgC
zweRiU!LWY1EEx7@aNh%EZlP({jC4cc>TX~7(>KgeJdGy_1uN2famxxP`rlF9ae|-u
zk2};OkV)GMbEa{9H2=zS-IN@HL1*Fe^(r@<`C;|8JsGwG+(42Jh601tf;mae(tfuo
ziYj=OI^nUKq-BNR4+bV`fpPfzFOO%6w6gRVq45g*p(b|er{AaWpRT|#&E$&q2eYZ|
zr$dbn@}~?xh95H;rFo4L%K8QJrc@S(I1E7;Z>HR=;t|vKVZp(|YGEWw`A^uUg4$QB
zGcBIIE4Ur}$bQsQ3|Dg79}p975dui%v!`Zno44z|I+*UbOsqVv9w4btx!f5kk_R?i
z&uL-mE8HPg`l{fZMAiImgUEYP!v=D%x@-|UFUN-9PFeJ^DK2*0g?FqcPMch{Kb{J#
z`#^V(C@N^02qw+!JrM1;am>t|m=ssN6R;N4lfS;g(J3i)D-_O<?%YovU#^V$C&lYP
z!OwmAS<^N|a-yrJ6FX#P)ngIryyB>FnJl{Ai<;i_%jyUETKBJ9xt4Ag)7E2264!5=
z_G4W0tfmHLv^xeJCX?i0RWaG?V0AWwyFh{<PjasbuLZSK)jzMfSpigbOjTuA6V$4t
zz}BR2ow9EM^)Msg{>sn)X9KAa0@N&*9DCWcj({$b_JPi+9z=V;`z>Ntlx@K676SX~
zm5A1ErC~Loy@`RM7aH$Nnqu8c$;fWpj!mwg-|$0QnU0lDhI|P9SIg-tY0&R4WCsQ<
z>yCdF5^20%Cs_Vi6#<`!i0e3r!~PCl*%a_(at5aS1l-rVMPiDuR%h_?p^i^$sznvQ
z_5&n-V8YgJ1QFiMsRFaAtHrx@l+G{SQO<Br7hrBhO$f<UCmzg}(EqS6wmdj+TUjJc
zn%wyQiwn4~zDzGx!dmfK!C9eJ#TJXrwwMZ@H=|jPIPuTCEif_p)!(PkX#LhRxy(P(
z*AGzx&wZyP3ZftpfmEL*{0J_IjO<zu3W4z7B*QVEF9#{2&t3Dtv4quPkTNVorkgYi
z_xRwsW+~#V8jGt)4oWvpUbjcalz&!Bk-jO;m*M|jFwsjvypgLEJuW<@$T?c1%QMZz
zdB)(N={FL>9Zru30Mg_vdkYRRhW!rG7k2bIxx0H=-a>t|U(Uhy?zWfVyjxqyI}h^1
zliYLf;M6Q*<Ovxjv?TAq8LzEju1r2#T*w+5rmk>=1)oigk0mAF{Yj}rP{j*aIB^p7
zH5~$ml~Mr-6GgHE#3by3xWmsgn1_6`A)Ln2Wv+hkcdCkliHb=Q3v%rxl792AviGiV
zgBE7-5w#1C*siW&?mn^d+a6Z>Eu!EPtiU%(e&j!9Gk^yF4j6>V&f{_Nt%ktsC?TGG
z=rh=ERl8_1U7)HJAmnCZ=KT#Bl?j>cF&IlMreSgpN6%gEHiA3u$E=O|B|hl0nmegZ
zVg_Z>cTw}?nJ$sj)Z-yss-oL<Ga6C@)#&ko`)Xj1R#>TxlKtb+>QJ<kpY*}W!rUHj
zQ}#6D?hSN9KTGJ~-iac$id>I^jG&rbsquDktlW=rqHuVJh{?6lA_X*A=E5QG5v^6b
zbziuCm?#UXq#%~WP01`FPkt08%_iDw8s}|_HQQ~~^rdqT8p2g4dEy?S++Rq9$au;T
zfon^1cVmYLz`r6exU@WeKHC421`;V_D0$lya{;0A3Gw|ne6;I2PK#6t%s{W#$F<*a
zgT?!Pq=zvv4e<4{4_D9Q<$f=i(|d<O?;hN_m8eouUbO~}{n3Iq3(fX>{q*{R{66id
z_atR%vOhcs6NgBvw}FKgkxNNTmC`3B$?pEe!E4g-)E8YdCW4L9g1yw=+_pKF@=N6$
zxFI_{W)>gkC;utbB_UkH;D}2!A3JW7zs_7vclW~*rZ0NTn*6eCjAYwdZD038idlM>
zoJULIU}SrL5$!D(!j}fz`6-GTnL3$Q-W#rNQC!==sO$G)l%sI@S;bpQF@^POUL<#z
zALoEv)VC?^h4XOH=ZB+8RA{dvg^V;snfU&h_~K}!+G5JtcwoU_s{&i)nVuxg-&R>n
z;+sZ!lDKo#E?GL4ZykL@<(p<%C-MnXC@LBddEZZH4cLm{$aH;3)!f(_ncRsMde~1j
z&UAf!z70mHEj<Hw+|)aevK=Or((SfCie?ak+bY0iODaL1miJM4qe|&VbI;Sk-zqoF
zbtXCo*9?;7Jk>J;vp-Z`g_m;29PDdq<jn@f1~6G43bvf9q5s_Os6|7t^YC@&-PVI4
zFp(Z!z@>GN7r5Ue@;xe3)EKyt(F=?;zzkJ@e+0L@z=yhmlSQrql%@jzK|Z(-jiiW=
zFQ*)n@uR^(S@}Ndrd!+foWOpz{|mUoOJI0vIFu%{>GkCibU<Ykopb#=;j3Y59l&Nu
z_KlG6u`zos%BX);HMhO5aRB{aY`tY%Rco|842TE{C?L|^jY@Z?fYKo)N+Td3-Q6G!
z(p@5rNJ>gcY(TnOy8C_BIp96_{@*X>cRt|WYp?aJImaAx%rRg7_OVt!n;3(_R=qn>
z@=e%sLKOK*Jr<-_e7T{$R~*VwGaUIN_w$D?8o|?_V91C5{$**yf$G=!Yf&)blb0DL
z@>EGdaF5fnRJA5uu=9O1&){bkD)G%<eEX>I9E=S3g}Dxq2PX^RGhile)yN{JEs}rT
zrA=tvkU<sCo`gfZ_sd60D9eAS=ApfL+kH{elr(;%YRk@Qf8niDk~g<76{UV}M|lFK
zcEr72>?CsDefq8|`nd7>(x-XHkY<h{-*E7!z`IYz<EqcnU==1q0+(wFBp-k4yH-k7
z_7JyR-cjo6Zy683)!=wBSc>1sAIkAtEHgifnkPx<*Li0$ztruii4dYfo;7L5=wqWx
zw;w2CSF@k;um1ri@1Ez*u~p+eMc6+4%x&X#Hk5h=W{7676{%Gv6%-P=O!lIGww9$@
z{XhY;gwDqE%GgM8uRr`uwx5>XIPeM7)p2X;#$ncsM@_88zSIzSa}YmJ@u3Bc^O5EL
ztoOrD5|K*So@+iGtas6PQXFtK(^E8)$AnDTN45Xt7pI72qRAh<q6iEz>XOp&VcOK*
zW(1+CZ#*?j;wDtRLr90Ex*loRa?6H4GZahED=vN)6?SZi6;=C)OP=fg^J??jpE+9Q
z-|jxA$BZYI-+;-$yf0PCP~YBq0j$2tr1*_Qpv>Nj&B5LjdbyUo_n8+lHaFGHX{1Qw
zm*DY5&F;bp?hu%2(Xp9UnkmbMsTQV>DQ<{b^7mGa?tM-7C$P*=+zx&zxl{<_HMQr#
zy?L71C_ajkDhomAYdw~_6kT_1bvYId{A|%_z&Y@h{a)Rpw~kjQ2a~ZW?RSrb8N-Gq
zKI~uLtI+34GA|1hI9xvAVHowNPOcOr3jLpqo{x7ApY!KPG^yaAIZ;0~Y914|LZC{4
z5vzr-oN_RmQDD{H%p~>T>4+4#2^90A*#r<Ls?WkJadTBM$dvgqxk@}#dp)=+<I$Kc
zmRRJx+Yh;|8zZ-{{936KcdBKffAUt6_hv3u=pFK+SCYKs>c{Exh`Mh(+8Z3wVUmJt
z3NJL2Hry?nE+c-w)NiJtF{o{UvX{9(58=&sRidPs;R!KI?Ei!rn3q=4b2wU_ukX{3
z=@CX3)aPzql7hd#2QQ8F@1J`h`bEOYM8Cr&sGvispaZ!!X$$u|)lII|MCd#S*dgw>
z33*pN27CXeE_5xi5iQ1P^pEL1`kTmy@A)#@*cx-U8+p()BuCCX!{8#vIRv&+6Q^e<
zWu7Nk5HxrG|Gf8~5I;P{UyB}!*YmM3j(X3e=&vMb5Es!n&Ji=2|Iuj}6sBs)2~@Ww
z3FuZ#wlmHJ<s@eri;haTz3)fo82BkhwQUorQN+RYI~?sOgdYzAfZ(J~H-^dIoK!$E
zy*f))HrEX8!By-+-Aqv({UUe3beKNE;HQ}ME2m0~h9X&2?PpFNnaaBi3aA|ZUy*@S
zLNvVHazUz7;p*^Xxyc_3rhDH~4jtvpZSy#DN7KL2AEHl{qTi-)4TOMk^@s(!E?+{V
zXhf&Ql}2-mdr4`;mp#Wq__CkI3ynmR^Zn}FhKQsAUlI9zSokKT(Z7Ie4#g#6|Df!$
zPwQi{I^I7Y5{f8$#<r$eoi2Mob`~Y(c<@|pQS_bCT)o+-ny-h9tBSD+!@o};qR9#)
z@8>}xL489}h2nAYsCT1UmyXH1-xi9T&f4dbQerb^>vaMHi$UqAzH+c-OCzk8`SwRw
zb~m+_Cq0JHKo~yz{ZImdy;YtB%2<{MbF#J45l#f-l-cq#pVVoRS60@9$6+sFqSFSQ
zD&yae?Ear8ga!{=`CV!NX1K3GORF!L*WOpBWNqIw(VR1B`f7Q-j<e*#J%Xbtfx}`v
zOFF7O<kgQDRNfC$Q<ML_5z=tYJo-O;z>F(0RjppL*W!mSi+Y;<YXg~*6hjyrlRZrZ
zcbU}RIb*2rBInefy|cVMA*(V7vayUB%DPs0)!vE<Uh%E>Nv<`=*?o}6Ms|zB2{^-A
zHP?N6(MjNA&UQ+xsBTX`k|-&nHE-v^s)g}9C|qBB=CWqJw9J>A<R%@{8V`!8MuA?5
zKgi#2;3wqjL5jRm1xgn%T92G8cnDLPC=EW!w}B3i{S42u+fN>eZ^BaGv;KiX^BUAN
z_Xw?;z+hT^`=N#>qN=AyxV$QSY|d@5YfIoslj^cJqGjp2+lG@0n2bu|+-Zv)qLTk*
zxN#uup<%rIEA`hhC~QAV#S3gsl6pRQ1n6710+kzHjk2E(F&@*pyq9AEt?&6Sz9>9O
z-goL@nfM^{4*ym-x^3rpR*L9>3Go0sz74=@C_Lx*5|i@V7zg;1*w4w^civtZYAhVa
z`U4#1*0a-<R1$N%O-fcV@JXVGgQkCPy%@qnz8$!^HE0kW&;-2JE@An_;C4(n9slP3
zdpnrn$#v9;-A?98TsO_ks%fgGeZdE5*9;Mr{Tpe85WK0Zw(F1Q<se?!hFt=;M^Dz2
z#*-b8p1KA;s<#d-LMxc0^2sGp&yKqvu+*{IEcf&g<x!YPA{dO9D*ea$q~f0E<<W#C
z@C;}wb^`_p8RHX~N=F%5$bfQ<<=^DGZHlR*A#9X1UJ(OO<*@rELl@9wYr}cX<e2If
zAe;<K7zyA02A<R=aKuTRR?{`g4fz4bvCtm6aRJZ!<0CongUPMe*S)<b#?KLG55ak2
z)T#<;1-17?k^RTq7U+n$+~eiD{k##Mrq)0#&fj`>OlLzua@UP2U}W%Ap#r~G<;^(I
z`u+11`o$4nt`FtN8OYd)=;p{LkBd{-=UE7W5Q4<p%zQ(6QucpAe6)H3qmfG#gG04p
zuQKD@R5QZd@!06OT+h3`EL=<khavk(>kjZfVdV;d8FaGzAsI?mDeyyuy#{YcVNy?K
zDyyES9js~;GOo#XBWsF?=*~<SJKd6ID0RKJ=j<g00;tKiz?ltI?<m)aWS;0nCreYP
z7yX#ka%+R5CxyI2rYJJFUtrkxIZ!lNO?Lmq5GF1X0!1>OEiUZ@kQe;>R5xG1nYayz
z<7fj_9RPDZhDjDjL27kxK#~4^uZv+|6*v^|p91=Bip-zzL%;AnjDo(`p})Xbd!6I{
zqgR$L3k1i&tl}eaqH7@=0C@EE=YQ&MKU`GEV^o6W`=7FH_v>>QyW7QyEc3Uw0LwZ9
zPlQ)H5K9J*t#yU2bsM%z&Os#LA%IavJenb}P8hQtlB))^8kzAlJ!Str5DZB4dT!gK
zX=cc2u$jHs>Wf7hLA>XNsL$*9z-VpfE<j><sXV7Xx!1Gz1cl-#`UiHoVA6kpn}wD|
z<uG-U`5=1);i@Jn>lqjsU(_@np}swgAiiKY09S)OP_0Y>a{_R>h4U%KA{TxMod1Ca
zYC$!{tWk!@a&8dLFVXryhRFVW3KUug1Ck3G9c9Vw`no(pz;T19l9wpt_68Kn%??PC
z@XrTOJld$^3t|KB*DIZ2z^eR=3kURW^9Zm++4xD5da(z_p8=mX@{4RK`o%*g_!T!i
z&6E-9n>-_=f|AyzAb5Fzf<^u4Smf=47E*dc5JM=DJ%l?lr$k$?!LaTOxM+ymq8}=(
zEoT_COdVBzD>){2d*n40X2GE7P$=iL1E6c$#X@NhPk2m<`V!e6=<?18&fgungU;1N
zdN{Hj@!nE6)v1~UK1}!D_kh$>%p+|dkZ5aYmJW9;!{%Sp&|gBgwAPjmg_2jmj*_>W
z4fApjlAIEy-cO7Wg1T;6!oxhbH#d(Sp8BOAD8!Dy<hFoZkdP5>p3UI*ty(jei-7zQ
z1^z?jX{zFN<V|}!@GlF?OaA2F-oh3UJz(I#3x@qU$Bi==+1-J)Lx2*^f$ve|9@kXp
zOOjaafz40=;VzM_E^dP-8z$F5zi5IqKb_s=x=-_?%V+N=Yru1h#UmgHj;HV>qe)XF
z`H-bbdwYXv9{qOOC?*hn9mc&JZ(qNmoy^MD@$xQ0U#^aC{acdT-=R)`xK)2V4MtFd
zW{e3p-NxR<ZGuX`ER&^bEEMq|-+n>IzUTHLQ2pSr3f(~sVffyT+Kr4gnNtpWRhl2e
zBoIVJgi_BU^eQlK9}iC~+zdJYX2lRXn34x?Nh^*t??=w+rKW@Y>ZSLkO1B?cfd>C(
z$U58oAd^HTr@B{QEjH}`Mpb@<U>uePz|tR2+9<ryQ!PP~mDW21_f;)CDf>1csV5=E
zb42<Qw{`?-j$<#IwkFBjFj+7V9u26<`Wq}^gScAK-&a<6`@Jw^(nMXC-L|I&?OCUN
zyxtG&5l}5IPImpqY}?Vb_g{r*?9&$s-<%g9`ls;(ei{%M*<1V>aJXYb^Rv%1B}c6Y
z-5U)pJ4cE97pef@5EnUI?!@p$0Q6D-yA#*=U&AbV3AhAa;C%v)yx1&PA!B;yX&X~=
z;eG`VABX3sD)NB}LDw-xjk>phm(DkDI|`bzfCoY1L7Hav?a?Ww^f*}t)7u-Aio9f2
zY8T`&l1E~tG@IYwm?73ww<Fn2XQ?q%4;za{zUz*gQ&p&!i-}*=1QGem)FJxo8zgIi
zKs&CE+unI5sA?d^FL1cTAfoHno6`{}@MdujgMmkMqq5=fC^ugYUlz4l7X@U8E&aa<
zlL}?_<*#9jPiv^u*-GRR@g+I7GN^%a1x$2@(NR3HA3h|dd=FmrX~fI}$k3VeHl;v4
z+Nnny1J0A{K*-IWqF+jOklm3qt;T8k3l87>$H25bMzt;ECdRpC*TMMjAzp<8nNR%<
zW9YNw;?5bDtm5*Yns_gB3??T1(2Wk`qdf*#{>QXTISQ`!Bj^|fYzGiX4^WuI=u^?9
zJ`+XD9ZoMxA1}L8FK`7L@!%M!Hr+yVp27t5u9nxZ%<XGn%nmQ28_(#gDl$u*pj9Nn
z8PxLzlpI~?2RwcIRY(Ryrte2|$-s@gzsdOhrd)|=x{r`ItxkSMILbcGHLvx_nP7uB
z?&DO;6fmJxUDws1KT|TRaN~WNhE`3?;AbR*(04ii*Zd7AxJZYbvn*VvZB>Var2HAF
zb7>meI0m9}EX1iV@%pI`XM7O7JOu2*5pUlpdTF@3pLx@x*nZtpdi$hMUjzK7&?3s7
zB0EVnrE0lFI)S=20kk-ijvEZ({<b(j;b;?VnYtx0%#Kgw@P{46PEfA6TBgY=l?~`~
zh`ZlUs;KO=uQUUI;`_%@UaNn<Ob9CdM914%yvZ;=CC^MBb33>Hh8gy(g1041f9Qwr
zgG>kY5>x)ls<JWCOs*Ifjatge31cO*LSl8@4GY(NJflOgoW`ja09y1q&y?N(5Kx_@
z^2nl+qRdNW4ZkkQ?TM~AZg95zwiHXJlvB6NUV_JN`dB4fE>f!S5~jq!^#L-m!RoSJ
zXs-uGG1XJ96Hk&XORtbNR$>6jL~X~rl#7`3$yA{-Vbu^kocGh`VcLIBx$$JgH!>^f
zTt~Gam-d8-v|`|fvj=%R0Lv*I;5~#69CN_C2n)(})O1=2${!ZZzStPv{b>m69B<UU
z+KU4QROkDG*EX$Zs{@%vz={UYk%#MrB`N&QM|8O<dsPNAPxw*#fx{H%@r?U5$eqa}
z`MJ4K{KK3N1M`oF%Z|+?(mtNAlHbH+NWnt-c_=_TR%)Rqh0BEeL7B6Qt3KAc<6xFF
zS?jL*D2GB~fDH9D=&!}|*sTL5ubX}h*f*gkD65o|9eF>fd#}SFKNd~Gk=YS)1u~>N
z=-w#>$-Pzl1st6mL63?>vtkucY4QAK9M2na-~IVr3ekh-pvMHNj8I!;#X=jg@K;Q0
z<eniw1A*!%eS5YFMALGtHy&t9NMwF;2PR~fAoV3bH|hhOAJFXan>mX&2>S*iaLKpj
zM9}+$yjuXio<84Cz5Xl$T$K($uC87P<9%N>W(fXS9q0~4jv}9_HSERkZr45M;mx`L
zRwd^fy4U!e5QljIY($84Pv+Z#$HamIPC)ZuM=AB}aRSsuI8~Od<Fx~ZIae!aoRd3M
z)%9rIlpS<sdd&!DR;NpGsld92YxLVasi)7?b4rzs9{wJR1<|IOuoUe!-t^x`kkYbc
zW%uv7oLmt&-oC!t1_+Ojo9o-@6yPZz0+SzxlDa)`IXum)kU+bCH}(2rg$^<~+o~9n
zM}9XL@aLiHs;I*VomcDiA0xqF9mys4HDHMXZVbQsmCO4(C*u0oLxPuBN&UO8)`2mb
z>)Bc^fL7*&i@%jVySVKx8;E-OeqMc>1)=iyn8@LP(RbUg9j#~nW1w_KHAn7As&^19
z>2(|2v8g?Ni_^{kl&A~bTpYsp%TrJPppDL0HQ&V{?Tzd-cLHo{J!D~)97rt)%vvBH
znMG^Hkk9y!RzVYlSj##p^W@bg0Oj{$%qs>Y1TW?uS{J&{J<yE@uPrFL6s%+?ac4<s
z4(>kW5pXco3oJN66LexNUfM6QPiI7T*m~r)8?4(uft~GSkrCa9i43A7Kjv+M&7<^u
z%;R-Gyx;1Lc9`>obxpOk8rFzcOHvvRxgxbf3ZY`c$&<SA&UZ+!hlUOVY<+m$tYH_0
zzzS#vpt%6IgER1kgnlyQ2B=9yK`BTGU*uh$=e#l~e!?lfgN}5Ctx55-0@N^!D)~fW
zKhs>lv%)Wx5`iHzz=$V5C=BCDUS96ZKfXj|p4<}~6nqTo@wbkWX(EUsE<^g5c@~(+
zW^bKpdX9e^gtH3W_eax;>;zT7i!!6-*q5975sa^XV+fWHt7=`2rC#D4#qXZ20SsLR
znpqNgXP_hP&wmW};%9?7-YYi#XUQWd`r-2z^RN!$?nJYCH)jucPSn<hq2X1sM)Nl)
zNmAzw@BoGIqyFa>MSQa*5`HnsofseS380?EY!+9#6w4o7aZK7r$JaZ@0Vc0UJC{<y
zj4bUoc$Q)0K4p)b<Y~Jnzu80%i1j_9v;i7X5KS({evozqvKvw3sVZ>I`9M{AwX);)
z1J8TJX?s>a3*{Xi`M4<F!jAsq624L1jTfN3ni&J?jYVK8L=-DFkTn7KdTo*kFen1H
zyl9?cz2(ykV+`K}|JXi&uq@m#{cZBYeS*-bi^x2Vlil~nA4kVwZY?)8j7`#=qVi{R
zeQ1gRXxch>?*kV(5$Oe-4k9;zwzsNh+<<!vhUVvhBZe<EUJkaV6ulGKzh_;cGUcS?
z`mBW3!Ion@5xyRn^hxR%_+=T&NRq?o<SklTd^&TAfm6l$0?D;QNDAj`jcIT`h8Qvy
zMT4I^Q*fdnPV`sj(eLGx(c?Y&z@dnSFkP==AUGy_8>=wkCx6!zo}yK=osy*`_(XCF
z@0vc}=;oL(_qHdL<lV!e3vavt0^>X5PQgsoH5=Dknm~JTJZ{D!w$Fb&>HUkK_=OM8
z5cO91vB38~wX$qt!aiKs<U`fdPG+VCZ#t~<3_MmPC*A)TkU%IsocGHU!f^g6QU{0w
z2uH^I5V61}<F2TDNtgTCOE=XSj_2#!UCah2AB_OJf+Y@$y2F8c?%zLyOx*nt&i_Wg
z1KK(-*IScmvioo&`WW+c2!~e<KFs^}RRj)O8BhdksD~mVQIa?D@8wfIi<;o2@9^s~
zz>2knYve77edfv2cS0*3mq8F8DVj*mm^IcK3SUy+ut2okv>leHfEJl1;+8L&jm{HC
zI?EVJjB<-{l|RANay({5c@oVz&e_UV-uyKp%JN723slzN;>a+Ss&><Y=VOw+T+2$n
z=H#K*=XXd>;LgrL$3!~MCNUF}K0HM%aV8IosSk1U9`FJL)@&pRR=HB<p))dDD7;h8
zAQa^{5_EfzjL|%I@+BATho1ZpoU&EwBE%%fFau=B)A>G>5Mk5Yu3)i>HWmT9X@$`3
zT#iZ&kc1ENao*NC4s*(UciiUvcmqCsQD#GPjxRDig7qJne_E;RLpnaqNqR5AVpWRc
z+NqW)XlqbC^-;8c!TMLVMD40grYE;HWVY+eJ4JSbG<ZCP6xUIyD;%!0a2hIqdoJ=S
ztS!zFV<s(io6Rlf_eG<((g}`>jO7wC+9WyE)-565asM6*9+{ba{heFzlmypjqRe6t
zfJj0Z>Zsf)jJU$%fNTEgO5P$;FEAymxM<n+eMKOlJJll)jsAPrg#z?y!;V66cOxlO
z>pXD0;*0r{G|jQ>AM7S^;t%r8jEAF@WUGEq_`MbjDJD^ivHvnOG6(^)c@0m6-MR=l
zl8m2Pa{e-Tu|m{cEz!`3=>a^3TW+WvQcoF7slK&{Z|weO0WtG!2Fi1<Ei2KA`^7Gn
z+4e8^ri`Oj;$wvXqvuR{n-u{Hr!|O$*}dgi>(rogS2xF>2n}00b>y+*J><sBJ9UX#
zV+M8P_THVQxxa{w^xLojs?^-e+VsGrf@#$Ww&KQB1zip~-q&+e2BV`uQ|B6rq3L;>
z#mu~$%CdWo6wjNCn5~>utxLj-&PScG%-&#mXChI{k$fc%5sj0~&$(RdHayM44oA+Y
zY1Ebn4{zz7N6YPvkAd^z60h@#<8c!ud~RYOr*bSalW1*8$bDM)w*2kSPi?_ZSaizb
z9|oZ35PQ6NqJC~#IN<j=vK!SE#r*0B`pvX|fAdoi+^E@J`{8b**#iYtm^0-EZJL!e
z+_{4B5PWXa<v$6ab$Lp1wd4OYiY65hcDs%P%`j1#rw_Vo^gD+JQYUjb5?^&E=as(c
zcoY1+P|@<KUg6D!O6ldHkQ$8w9UCb4`T#DR`{AohLeAyGQW(15RP<XJr{B8*usJ<I
z&0`Db*k7Q01bu7=K(UxSc6TX05jxcSnqh#pKPa~Qwk3dCA+c@?W(SDx5_SJ1+5i3Q
zo+7Yo^E&Q<_aI2;VfJ(ed<ZeW(`1b`n~x5}LqN`?s^I^P#IUGe#B*5SH<Hb04Mf<f
zhJi!<;d>AI%>%7X26Yl}SM6NQSIYJC|1K_O>_uwVdKOKm1SxltKaS^%_onwPe1S`?
z7dU*f*-Nils8}+uQ~vAUpyK(3+T)A(0J+7kQ1xTPj8ll>0t5!2lHEby{<_2c;@&PR
z|J<_ZlvT}O%$jI#<@jMftg!z2PyN3~B+U#3H9RjHJB$0-8PF3sFDDp5VzV!Ix0?J@
zoATx?2cO)=lL#Rd<Q?)gAkFgHcLNw%LE!YQO09(;uQ&Kj&yO-QZ^^+lD#UnE*X5ps
zUm(5%X0OtCAS1RyKO1uu`pbu`hJRnLcZiSXU&QtGgadHDAU=qD39|n)*E8EUddR`L
zAWM8l4Q$cwMhsEX?dEM-zP%9j4aE3?r7i#&mlMsv*LagdCa=g&-vDlKh_nT5bvN+i
z9U~uOxOX#E@1Rg(GWMVcrU{<RdP5Xx<+M9*e?FqA?yq%NDff*g3>0X<Dy=g;6CiX+
z!845Zo{P@9WwGoTfI?*Bn8j<#V|>5iCuHyDgYGqemI=Lp^91zVF$(}`Oo?w@S*J#F
z{;%j46W0Sy>3bbW3^nTog%JxUoe9gn@4V4CNRgJkfl+3WMmZQ~CU0=B09YOi)n>|W
zG`$ZR9=E6(lSmMN)8H(@gI|D+hNNcS{`OU5gbo<a4{c(Xo#qY__s<GM5J7}k@3Ia6
zJ1`lRM&`Y;TUM1Eq8PERcE8-v)dl*NpML@dcmp`70EsJSA2M|d)AZs|Am2^kxjF-o
zy$5HqRUCwb2H<VEQKs3J>IVUVnh?%R1ca2pq7G5`+3vRt{jJ<yyNB?u>r>?=M1M)N
zON`&9g~gCy5bCplhO}6iLC_--K{q~xWM5odm-Ra5AQC8F&j1~#&QV!h<cx0U`>3Ww
zjGZ0L=Weloc~@`bM+i2+3Rw`?r`B<IeSNupGU}Mmw(Lgc&qkg?_a>gu5Xd>!)6#jP
zKT^(4vivXSbhF?lP=^Wt@7AXV6d9i%60?82lidJx4DP^+`8TMOAj1gYPm`wLimb1B
zBd`)eh@ojhTnF0MC(Je?!$W!j{$t>oqlA$8bIQzs;YPY4EJCbe3eX22(lYydp`Q?s
z#?4AVotI#Or-XY39Dft4pCYw<W-W1^+MwrV<<({fC2+y!QvgbD^5_Q2qpw#Z)eY?5
zhNVeI5?UfrMrRIkIOkL?U2)8X5hy-q4|xAgC+iqOJkQZ&6$eEH;B?5Ag0ny`#~t)K
z_PQYD4JhrxSjd+WL_aNW4p*Q``>GDYBf4Z(-1(Rxdh-_A;=m)G0cK#Jl8%R}9bD&S
zIc^==n`CJ{hyi@G0ZI><#slX#V%u3^+C$F`4FNCrI|oMZ2-e>=TL=o}UrDZxpaYg}
zJ1u$b<qe{q5{66fu@7))#Ckeai_wwX1k|k`$4zqi&sH;w+<-*L@fXUrM(tZVr&{dI
zXG-Tl;t$#yT@}xZ^JaJ$B*CCS*XA?cTR!O{LQVBbZ<7C)yJAylQ&=ciyR4^{=Gy!O
z>qd@1`1E5UTWSSjLi0x=*BF)Kt1;Iyn4VDxUT@AA?#{OXRVMk-RD(B>JB#O&>A)k7
zx$9e;C8bA;l4g&?r0MhKOW?Q@97<(Wf*S<}ca~%ru12}09l^S8o$=E*mw<V=0{A#U
zi=P~JZW65erWv>VD<zG>aU!gtD0+oOw~BAx^pj`c|INY$lBA0)jp?vg^Xx=A4_}7M
zrF)!)mJUBt8}*~6mP<VFWpU95h-l#`P1UH;;;_HSH}80ofRa1qY$&exS(cO8ylFvo
z_@;F-01$U>IZXf{ghVsV%m$wjh1oc1$l>{)*Ts%)2yf#};QxMv)(C1@(xHvwVI52h
z^Fm|hYGL=|1N|jtY>rBmcTVK*GC<Pa^861m=<}Xg%u(zc8<E0bbD^oIu+G5?JC0(S
zcYYe0y%RDCu49jp^2M>Vp<#*2@6(<)A-O;fPMhHUq3OfS>f;lbSu-CqW+$jmp5y-Q
zW8|TGX2B@l`{<s@8#dr`Uo0muRcZ``YEsJYUmd&??I`e`NkIqglryWBRcN<0g5PJj
z+A07N)LN8vv^G_0>0X&VKAdnO&@5WpUZMz)*~XYZ$Rp+<aSfhvGdI<?i)`wGD$0E;
zx!Vpw&?8dStfR8!uJ^_lzju+0=tjzgD?6zx<1s5VDq=XY#oikn$X3O@7=bE4^uHA#
zJ^TUlgRu4ZjqF(DrRfdHZEMTw=^Roi_cuXOXG$CTa-}%b=IUeZ-*ja}z7+mvH$xt^
zXCflHuZ{v~R|`!j?U%Jf9bI064aEjSm|~Lg-Prox;-<v2p>Vn4?!&nvTEl`3XyD^a
zR0!@SF`HuMi8sED%J%6&<4(DLR+HtSW%L}e7*@YMJc)f&s{(#sF}Yd&?U}6z6LRQy
zHBHuFLi>6x@)v%IjYMrno6fe3zPBF<yQy;V{f2bdh7XH5lF~ztr;1&BEOvon)2Z+l
zdj#REN7daBVR!*&b#n9V_pv&&Eu=PeVqOQk2HJj_t^8a|816FPDH)0n(Io=HK{^VL
zKO|`e3d4pNIyMejNS?Y~IIL4f5qRL>)Tvv3p?Tu(XX?5Y+7l?TFQ7z!*y;m;>c2}V
zKq>PHC$w8}sPzze+sYruJFwUntq?ToPun>gtbwro3n<?J3o;Gbst=NyVcvT>HCA(=
z`2BvmoHRtD3SQ2G7J%|b8?O#QtqrB+>c-1WpyAh;eD(4ggz>MG!b_p9?Tj4Qa^J@*
z!Ix`)-yo{F0WIT*{gsUAFat^o$xy=QwaYy^pjE_82~HSaZNt|fHX0+?YT`K`>Z2jh
zStOc>9EE5*l~-h1Q3S;2J7G}@7;ZnCNzH#B0A3;)fJ~^I0#3tjsK)a^=h(F(c<sm$
zc?AT9E`Y^@pcLrRW-=xK%jRn!)Q<*s?+YhWqWQU1>hGivB^j<ha(iO`a;hctxy8&<
zV4_lFYL9>ix7ig)0ioU*<njvI+B>V6;m<OFu5uU1?A<`BD;2JZIt8NLS!BsH68DQ;
zpuprrNIU=<*F)g_9+@K*@ZnkhX}UjgEr|L%zU4t7IharOo@RbhaGA$4-UU+ikeo4m
z3kU`nAAsurO)yi{39#Zn7rfpV<oZJ=UMWdv-3n+3Z5UtWu-STk=ye`z!9Rpon!*&t
zf8q~vw(KZI%|d(Fci?^VstKSQ&Xhelj<L4@mrMcH2kKNpn-1c1oYsJb(tTGP2P<w5
zc!DP5#sC~~Pm*g8&oR{Hp9KiQlk1Q&0<fP_e5f*Kpe1<<`j37ua(@7zdj;Ir*Zc)9
z^w-lLGi9um0JXfAk-Wg^Vt^%CDn69J<x=<@L88yVuH9XXQlNGXkGoVUal+mNebN~h
zfNC0n5ypV+LF)aMkdF-Cp8v5mgIJW7Va;2dWXunb$UMCX3V~Vbj{4AoX)q}PF?5q)
z83R{&u0m>9p>ttz)sHq}w*#<g0blZj<ocuo2$_lBy|_mgKHqU&4>;O60m)>FW(Dr0
zXrN%@p%<%x$nnODB^Ho`t}ONz>41SRiG-KZ{bNUdqdhRd@dEu>@_3#pILBn3v(#Uf
z0e_ipH+f`BQKfhamLC{W3dkP=BmSz9b0-p!oT!hHLh?Xc|Jl#vHd4}y!fCu#t7R_*
zyArOD5*h#lIxpNI+*g<Z&9Z1Db6L*=4$oAXOVMFqUc9D9LtMgtJO`<(Yz=BSL%L4@
z|G^4}Io_F5bVcHy^6C$+wt<OnHdiO`$F?Hx-tEJZ64j9DP30uHb20z3oA|O>PTtr2
zM{>A}<bjQ|2Pc2k;2aSUJ%VBe)(A*m)dUj1@%20TD6WIdm{On#N%Q!h9T-qamxl>&
zYayk@H{z2%bO?IQ4=_ShB~<oDs1@T!IZ@P&uoSs6RLN&4yGDFI@Q2+!l^4K=))okh
zF1%b(c_owH?MU+2p!FDZ#WCw<TdT$RpA#o|YPk*pcjd__Z!{Hs2`Qefi^j`|#$=$m
zFaiTRko&PM%UK|N>_LPgK&~XGxKRnz7Dd_Glo6nKH)4J9SH?hu0Q6S<tJ(<4t0xe?
z?W7hVLc46b^ROp{{~Wu-;VwK4qeCU2x`i<Tf5w|Fq7|hloO!)v-?&ixlKd^L@24EU
zyhqZmBN|3?@wr?WD^Sb+%wI8;%><JvqiXCizkbXa##t^X(g2XJQQ=yQ5JgVzV7M-e
zt7q)l>4-{mZtXWv*oYRN@s|kK-P5N0Kg2`~++gh8O<DB^bF+DipnH6j=8Z051yRl=
zz*n<#QqLQ0K2$;E^~*tk;c>*{J?@g6s;Fs;({g(o<%k;^r*mmufDQ`7v*ZAr^*Ug%
z{)=k?l@Ue%8gxd)Ef5E1RVDQ)DhGCJrXiSBmMc$h6cius`p4h}i>Or3L$@98VCLF@
zctUC`${-{;l4wW4Lm*vFkNU0jFd;frjt-;nN=A=OviR|BI*kzWMJDYHG@0W(3z=>k
zuD|X+>NpKWmT-Z*;vus0`^@EgL{2To%E#9{2#F`L5PTz~=IZzr`c)#4Vig(@5#x^8
zsa=t#NnvyBdOL5#O6qSy0+=UN*r}QBxh6g3>gV~^!dapf@nN%)`X{};T3NP8qg?er
zQt}OI_>ni|n@~$WzXu(A+=v!0ua0APAj2X(YvEU*z7wd+cgD7k%8Jc{uQfiLlFP(o
z(b9Dr4<Ij4Qn+L(Y`2A_zPyA$d`F|`uUKqP)9FsWjmf-ya6f(rm(BJ?RlsBF&`03`
zHqTJlB=N9R`&b&RLKcWgF_i;&2?^*aX?{$aV1#p}I2*n-ns0-4E11|G1l=7b=BBK1
zPt!xTp4X}IZNh_|K+OGD>tKCD{9GgroNXCs>hYrBUJfRoPrbS&a}?k<gPnQrI<ly%
zV{LJjOOk$y8*Pxnr)WiW41F!p;QzXPMb7K)d9(z7l&J6OUjJC6(056!Qk0~;*fS_&
zLOKGR*H+C*(+4@AxO#Y}>cgETOm-itQ^qWe<JJ~dxkcxha~<F&pO&kKFBQF~hEQoB
zasL)_NTmfKim=hF^*qAYhCTf<nS*O$Z2U~g%U19xpUdLaB3M>)?-<EthHbcCjf7PX
zi+#bP11;R9-q@HmG-pK476r(0YX2i_U3t_*Ab0S<lVPq_9^Jh4eU|9qvd;RGvDuhh
z!?&Ef^!1A0bMtdtn-oZ%|7`6uu4o>8e;~U;m~Y01>AG$f(trQ<m;n`pQ_QXm^G-A#
zzf2*Wuv<!>8pNT6%Q?)Wi2i!L$AI0dCMB8WoRXlH@}8#CHWCg3CrGzHO!<>gx_$Rk
zm;h}>-IMa^U(duiDz`(9W*)hcHn_fZjC%R8m<Fk#=VSf<Pn#7cTC@;Eje))fTB3=X
zC5?B+KP1(5%5OdiJz9MDR+Le$(mt@B#1zza>C~pKx8I2R!1HCxz~i+y>~>^JqjIVa
z#YzIPboJvLk3*6?qos!Z76eq!tEH!;k~s&KAS~ks$K`TU7OJ2;!Ustx07F4%zf-|;
zp~XOyVc@;u1chXXSmvmvk)MRydi`cc4)v-w<=1BbSa<Qgz8RU7N+U<?ZzT&B&6MQ1
zl%N*HBTlM$s-9n5yB9|IiEOF+YLNrG`8}t96kr9pOeSJ)SwT^WG)cr`Q|ooA+~t-p
zEGA~9JT4rEd2#S(<z;dXlbJ17GH?j=b`S6T%}UawlfT7BA9(7RhYk;yOUs`rudEM8
z1?7qr(EL2B_$d|#DNj$~XLoMiH*D$%Hs<~BvoDO;4{XnRJF1*GV609D#-_^;GSjo}
zX*^_h>&i(Hn+XD!sQ;ND9&o+?vw#dLh0d2*j7Mk;iqL#6PSC7ByKN+}--#6-Pb)vY
zS_Q1qK0r}}BS8C95BNu?T_>jA^|i77!hxi!;W)W2PBKqmnsF@kmymjc7;pbM+%=<$
z;LdkZujeUQZAv_7A9grs4*`dA0h%&y5bp&NK7>cX(53V%PPq*_O}sj^SXjul0N-&w
zG1mXnJ(q^d766h~Fx=7$xW<7DaiH&Mt?wsXKLv|-k_6q|f(7IsQzQaeEYC*KPzJT8
z<j+JcVB9gyQ#Eo^14jtSBBv!qu7L$gYl}ala9DIEKXI(b%nKWh8E1wr_a@o@*7vsn
z>x_P;3si(=^Z_nX@LkvAEun+_hfEVPqdAHt2V0N>4Wuvdd{607NFW~Um%?LbdY{GQ
zE;9ZZXzBpmEdtwzAS(nIX%C-N2B_z9BV{YpV}CEpuq%Ku)Gqv~x&hXOU{Kg!c!0eB
z)jDE6;74(?uQ=WJIctWsvS0@G>o2$0TWa-p=ns)74U6~@cBM+QjhZSsYgCLAhHM!N
zUrH!JCuU00LurW>)d>Q=0B(WhL~hfxuAn*oYQ1G>ixst(dOQ^nftSax)D9lm_3>5G
z+9|l3xO*dnM+#v+!h(Ly$bI-MZZ%pR?RpT{ph2_B!FvRoH`*Bs&K-yJlw*@sjV)El
zf-?L@?`>P9^SXi`Y1TuDU?UI{J!92!q53mjmIszHOQ-)D&gh+>?C{VH><loMl%l)e
zGcQeoN+!qc>Ve<8Ir}CkI+?=xlBh3i-=797QN)cBzh~BMA6-ebtugF37actNCajE^
zbl*Fr$BDsLSV3W)7vB~4@#Tw{Tw)sUXeG)$w>=f!oM-A^^_qe3)4C8R7BUHkcAsF)
z)?My^*<GL)s|n@0vkgueROQsk)`En+plWpXk#FM!W(34~c0u`Mj=hiLx&T-Xj3(K;
zQcnkx6PYQBbZulSL(~!*GMmV+{Mvsm8&`Mld%qFgUY9}YrN`?}tIj#Q?lskG36eo#
zQ_ru(W?(SQbAOa1`y<FA=bxCG)`keSm4yVeG~l9yvCRM_8UVm<3!$8SU<w7$k?~+l
z*ePPuXCO~rgmjLt%dILbOWU5Lp7t0iuQN?Q*Ve9=sV}s5*Fq!qB<_|OSkIfTHP0?g
zUpUpypQuNQR6ujgsFFE#LVLxxKF7%zB@Fv%isfC@MjI27*{2y?KPA=HV%TO`B0OD>
zttI^qsR9AfO+&TJTKu2#UQ^T~>iIe$x4`>JuIMvJTMb6Rce3J^fbybg^a|kJAUWfM
zjw?ZDdjzmee)HkQF~he2=Aa<9G~vX+JX^UoIT4fSXh6NNJrc1|%#iY?maEZ53+Rve
zMk|*X{XXjxs@*l_`t_B-#%I@t#udjtoA#B@E8Iq|?j@tg)s5%hkH(!#wrxhP{y?e-
z`$hvZFps6nJX1!q;cTxN{aOhj260&sz&k}uI!}{0IH|5T!msw+OyFSm(>Xj$?V`2?
zB9z`KtC0n?KU_j>u#kNjAFZhW^ZjFdk2Wf8hblVu-yVt&=KD4YBh%QVGv;!x6wo7;
z9(gma_d3gvQbl0KFJY>i6<31YB-=Zd82LnmMl$Z3X1d*8Zh%5(te*S;)<LAUxj8mJ
z(AfbiIh4<UW&?3%=4_<;OWj^qexA`OOK#zmLXwkOTT)L4qo>eMc*i@m1{i_&*ZL9&
znw*He=nF-D#yoY4Af+dsWtDol!iw+ZRY&lJ>5h<#hf(ImBjJUoI+d8wgn>C9k^JGk
zG<g}YoOS3tqlLirvHD_uWnTqDTH~M`hB)OyRn$XB?EUH*RQPnwejfnk{$q<)4QOD2
zVx&JZ!!Kp>C7Z9V$%qR3Owoq*#~ObsxUcQvyWfyfk$P6}uTHSSzjv2liRFCe5$;z;
zCa}&_W{qYxFC_Oy6eej+M#+HB#%m@Eg6U#=<yAjos~pN?m>!wX@I0zhMEK{T^h~L~
zg5_4wiZgRsv!c@WvTkOA82_KIsRa*xpJ)cO5xoVQqRt(=zm`mx@Kjp#j2p%wV&?z)
zvORTP&lI34Vzyx)pejE*epX03uR{~i!^8YgmCQ4p+`FlswFpb<o>N$vb;;;6d@rZ5
zyy`$vvViI(Vy2(}J}oJf7ukr=Vm2@$z5+Tx_j!5ZnUO<y#h4kP*3#SWg8p`rq)e4C
zMHZ)l;G8lGjZwyEA@W0wlL6<7?ztzJLX-8iS1Nc(6l=t5aAkKA(7bXkNb`(hZKE^Z
zdQ&@$3|XMaJinhxjqXbtL{W&PoKP7CD}3PRo3`^Xk@!-6tmxmzl1W7x(Jg$Bh%)cN
zQc>m>*jmJL{Uk|_{|fX0BH_l8?ZGRol_l&das9+8!{%FN1&x3x2LgvHSUTA0nJ!XW
zF688yX7gZSQ*7MCWOyfnS4NUzB}*D53sDgviox?;tOk+}&bhCk6QPU@nT@ord1;%T
ztx1v`=-roqB3AWn{Lf;ZZ`p{6lo5?zsXZ4L*ks{qEf8vm6DBr#P08~bgATZm6HQH)
z`o;RI2KzS6m-;njO|Gp<G&g!b|ML^T$$yQ~Gy|2kK&!nBB*6JrhC&$0f9Mn(gQV73
z9DmB1^TjY*K99s){REz$z4X_gANerh6c!ZK5q6l4gS08wg03kVuS`8Yu<po`<;k#r
z;bAaw%+3&LdZF0qMY=`x9J!!4#T$M+33;ceW*9jm_J{s#X?C)3nla^a67LLLVUYIU
zb3Tj-*9FOR_U%B$uOwtM%qw*ci3~m#RSe276n*`nTurx;6oV<1CBPu)T=Eg0&)4sP
zRH)eGgV^CCYOha9^VyHR1_wS-yc%4xn0pjR?d8Cjph6CJnieOYte2MDf~Bk?b~?wq
zR})&~-LnzeWBbqB56)8pVylDW^nQwDA?NT3(8tdkkesV484Bbe)>3+kt!rbNml1^{
zHVp~oSwDQ@>AJxU&Qv;|mqU?}OcGLbnH(Z<eIlvw@Yx*3N2SngD!D*oqk95!2dD9v
zQs8kK<|5+b+s}@mq4c){QFLK(Hjv*F93SNU7*Pzb`_0|*!YSL7aWw0>f}blr_&V3K
zIYYzpJ1MI2)ZSNQXBw;wmsI>#B^W-8emd-(T?UR?C()m!DqN#A5;5+|TBG684_bBN
zg{6_3JwhGetbKkKCe~b+3r-RAu0|o!>O57?Q&PK(xybo^HX#+B(#|~C%Q5i>i1(oJ
z$2=bE`c`KM@W^<SV62;>!e{0Ve62xL!%bH<D|u$*Cqo)}hI;wcb%N=nW>Be*(4u+%
z>@j_j464E`Q_?;5v(*`5Y_zF%QhJsT^zD_h*fuMfs4D||ORkyNDtw1IiW%#%<Ud$y
zWg?{>BfL#~nrmIBh}9cFWRmf#<K`6R$$EUmjuAJ1A|p_75MlV<oag@hUI8U}V_6$2
z{WbW%bvB6ziZHHupj^A|1FEvEHK^Q$_Kf(i@Rae8gmWQIe8OdrV|U;`%ag~e%D{6>
zU4qU@w>w<@uxkMmb8i77#=efu@bhuZmyuwsrU`h@S<d`&ZEV~wb>}<kF%qx=V-l5z
z9jdM)kv#VZL?LY-;PeQpzN{1m#AXGz&1pKVWq+fOEm&G`*{mE5N@u{*QX?ud^C-`0
zJIZ*w#M{tEXP@l=8@%}aCD*u%R?$)`N9o^i>48Wqy*k#QV9Je%nZ_M$#81?^kkh=g
z_CNsQL%@l5uL03x|FzNUa7~FK2KwwC-iq8nKc&#(LWx4Lb+Uvy8pU5TLJy@qMr?c~
z7<xa6_O|gq+gKB67rOf8ez_0GranORA=8#iN2_779)l;_02sPraM*&rza8`j-L(;b
z1l4jw*#5}B5;>LG{Mv%P<(Y)12IVh^i4d*=%QJt>$IQ9(N0Xa(S2r9MLy%S2qm0xp
zhQo7v<TEYyqzn(%O-kyggr~UM!g0tjz`Cid$#3gCxlo$b&9lj6EVK{ezcGMVjzgd>
z2E)Deed#-Kv}=Pf+&URI@3+KJSY6ckQ}83Qkw56M%2g2pVMd#|+XG=ris|W-556#Q
z<7*$Nkww52H6MT}tD)MrpVNFkrHi{6*Mr5a;cBm~+n|;&U^dp7EFERD#|R2zeUl_x
z)1PKOY5w+_aSQsZaNeyNsFbM4^?LP0E@JLwJe#6u_$CU{xe5z5e+(8HZLhKNr|O&2
zXB;b(ZAyTX{O4LG_(v0(kwkc2Fi+^G*LXA{`$b0lW&lU4#eHiIa6uYBLtN=dpKTl%
zFEGC}O%L3HNk8N7rizeqjlG6*Dm+h57fCkJFrKKA3sJMqJ$Vg@VOwGz@f&albD34Y
zlgSmj1|AJw-||O+e~FX}B*y$!;<ChmXOMWp0`irN$sKg}_phy=OabT~{``F~W0{l^
zM)Yc?RE3f(`KzrU7|P;kf*8+M7gBadC*6x@k^jUOMTBC^wCAGwwMyw6PtsBL9gPQS
znUj&xWaZi#pWed_3oqChzUwN^XY|EU#Kh%ZqM|ripBQ+novx6aWD%6(gR2TSW#wB0
zo<)qxz`bw>t?c>o0521lVHLHdj|f`()YYbrfD;UK-S{#KcwBoh#Ph^5)`=+ysNi@?
z%TPup{<s4X#|&uk8rKYc?SK|SqA_W+uvOVAbte(u$>BV7HOHp3`xk4;VWx>M44MS4
zI&CoLkbes7k1lH&R-wV=l*5V|+<VcZiPBRZ`F=!(jCYy>GcCRAZdGhyq(%x7Bkd9%
z+e%8G6>+^Dz7c{hm}r99<u(jP)b3yn&VHbRi`Njh%%}p`I|b=`Em6_A;u<LQ*jEO&
z8i1w(+T6PhX7sx!xDvx$>vUQt21BI8&SmMs?Yu@hoWc}$agTpc{ypKU2zJ<-YE@K3
zVSkoNE#8zFk3S=kG@M8=TrP?c8vp0ewuKFS!^ZrwW7<OfWAkfU193`#uMlSzG!eD>
z(EA`3Olo&^6|#oL@k><fB$|Es26+9J)#e@0d5Fy$k@ojEBMeSrZf(Bbr3*p}nX{8>
zAGknXdy^%vCL;e%lqw5ke5v{Zv#%6X1F*<XBq+y<veY*SEUg$Zuj}QHIZjNoPvXI`
z9sPMqLYa}T&V@4eTJbvRPLoYC@-29i63UgJH*CdzyG`d|9!w?|%?s9kEgGIZ?7i)&
zEp-Mtk3vDUUH0Q{rB3At+z3fl2R!0Rx{eix<j0pfV~%M~-VMt4${w8LNqyBfV@Y#~
zur^@s@9+MQp+~1<tf`AO0OL{YB!4QVWE8!28_%UZ3X05`Z5W;h{mI~_cty-m$LGt|
z#!!#{#`KOxJ->y?8|GIVy3|StHkkP<;c5PqLc!P;TJXnypBMsj{3Nbi6bd40_gUsG
z5QR&&pR|<^x`wMAW2UI)6sGOaGJK`ba()+L`MVEl2SDBpyE0=CTH>E7(0kin-z+ua
zVd*NaNCB!O{0DajX1|uexW%sE?$r?>;rgz#CCL8+vFJQiQBd>V;Wt>T>txFl;9%2h
zKQ@Wk5|D#&_4_?@8^+hxWG6&J;4g^je0%e<NF$`Hbk|4bSuyPJSh0JUXVqIg>M4zc
zRSk67>Llk?+to}8)uF)%`t2jU%kI>Eh&Tp1<7==AJC$yVAzjxER1v`hSS-H58oN{s
zPL)kq4^Tg;U8^zWS#h=TSn`^Q^&;+bWU}USq5jsv_w_Dnr-}L-a6M(3kt&MExDcK?
zW21;?6?$ps1jdSKXQQc9dfOlcVwe<sT#ngzbvAqJ1%R0+O=_{uuK)H!E!88Q>MzUm
zcMDb04MfM#M1E)7y9cw)+$Gd}{o%DZ_h8f=Qyw0!&h$5g>)#{YfvCLW{EUdxD0&O5
z#$|EZ4X)|e({k4`+Y#b5i&6C{MtpY97%Azgdh5Or?^3kyfRo+dTUv{_svh^bMy;ga
zZWsI^PxKh=^Ml`lY@vQD7OJuUM-*LM)Q>otvN5AYAtUo5za`-I-bfNwB^w7sP*)?r
zI9vN_F!HKMZp(<T!q*RyglyKJObKJkvU1GA`0mincMX>2PE1T6h1u6p=s79n@6mb+
z5Kkr1@2Mjr4Kfi0mc9Bq&-yT&r}4!t{-J``tS;*UYZvo%LL}Ue=;e~Ol(oCejv!R?
z=kln2Y~QeHys2gYCPT-Gp1=@<`V_90lZfphe%NvtrzRMQV))AI;Cnp}W*nUbm^^cJ
zcf4={=G1eA8RBbaLom*lX0A48zG8pb1r4&{V4Z`Ej%yX7Z9=Ywp8JpI?7XEbB+30k
zcWlGEcJr}1#TKT=BiDyXUE<{P<dGe-|G{W|m}z?2cKG4xD-7>8dikgm;eYV-A}DC;
zdD5_uu7FPBgxCq&@VmOazMm;t$*G(8MK#*a^me+H+{Oi46=pW|76~aT5ZEacurINX
z_49HP^{=X`9Mol>B}QZ;rTGxMjeNZa_MU@B8^rJhw^djarIyfO@qnD0yoi^2jf%xl
zx+d#h=s7gC03JC~pP`)58@2mS!Y}h-v`)QRKyxpPF)STdOKEJR<SViR1^~XxlZJ{K
zFDe#1!Z`_Rn#ibmApAEnF~b)OQqgQ-z94RMez`o^g`Go=T#x_m0h-su`^L>GElcNd
zF9n0R^wW+G8b+>n9gjnp#hSs-)LKp#PyN37NtVfk5YM;?%O+(nI#+qt^-+Ub!Bt?F
zixPgm<3Jw(wo*{)LItTSohtYXq1?i9A7>o*MXul_-J?IXU|&4lYgL<fqR&NsL0A9`
zHgs{dM$fqbcxU0Y7NCM3$5y;vFuD~Znf-g5ZpmQ-k*emiqfUKHRq}KH?Ou??aPAIw
z)3Xr0qbNTtU5r!9#yeE|d%&qt`eR|1{Lif%7a#ZjDgENlfRFF`-Y%paG_6lT7uH{;
zjrCQm5yM%td-}wK37*uB6+Uzor9%W?&owRc(z|}KiqLQ$5twxjYjp#qpxDV2WUe>$
zQLE)%RgEjyndtIvA?P!_U+9sTJ!{C(3O|()!l_wjsxW|ms@b^`b%~wE?a(L)7}U@?
zSOf*|2l6$$QJrIO1Edcmj-d_EU^ZL5$(Ga^%;IK7#{>n*T=UQW$>5)w;P7U410xWf
zJ|x+MAx`ahMw38X|M2>7uJLNAWL`YQX+0lIkQ&snQ}xqNj+X_k`#DoJvN8TAE21Uy
zTRo^W;bsd<%h}Y{FNwS()FDIH@{O&fN^P%t+?0Vv%Nw>Q{xFK9t?*Tv*S<~kk9+$+
zxRC<Z0qvlZ?6?d~we0-(Ma*U6-N%VqI$j4@mq{>&vJFN~0r|8G2A_Q!v70|9^#ZYU
zb1h|P@r$@xB+UGa3AZ(k&j}od8lXvEwgHP}ur)JDlQcPv#Io$pM0`;P5bv&Ky2Ze2
zyJ3~;i}hF)HLaM<$+HEJA!KDE_UPs-M>Nf#`Tcnjnh{4C|5)4bb#jc#=p%<k#9xNt
zOhv!Su7J&5*y&p^_nGax_QuXP!RNl=nk1nT!iBc4!eyx~<vLTiaMbZln!}Yxcx<R`
zq}T`*tzgpKi6b#PPA9QEqng0{t0!R>-u;=kQF2AT?T*7GfVYWLe}fW>V`%x*k;(sR
znX?go9-8N62J}*?RRNb@mjDn*B1Nk;o(*@iG`jrOWeZG!%osxvpy3>zjW1Z@xV9WK
zF8iQguDR)#1$|`5hCcm|-qn@{H?RZL`Uf8vFlD0~C$Z;~eN)41bY&g|Xpga@Hs{(#
zHf08nk$LLg;UjCPv2O~9t=IP>@h@Av-D~E97-TFx_0Hnvr-A4o^{~iD_*bzx1$b<Y
z76|*tbu0X~{1e=vA=tkh)asdYa2obHn~w7srl0s_Ix?aHasVoF6%j0`g!0?-FuINa
zRRt*b@-%&!csJEW2(JV1;|LireCVS4GXp%BN^PDCoDGCK*#Rjh%MjNfI#y6(xXf?c
zCj0KMR($9?Y*E|@td{`7vlsS1?I7GIx!xra`t|~`z@X;zq(czB632q%YAe<T+WqaE
z2WQWl`G_Gy2g@63$w8nHXjv=?zGYly+Y2iZLl?LmcY$^gfhZ#SO;V(Lea*qV3l(8C
zK?mHm<l{*Lg=Gt8iEjj5)}`vK<~ukmWHzlBl8oOc%hL5T)2c7}7Vb~spB_5rzsjko
zjRl54z%<E+xH<vb=Eb_*PcR}s6VTL3`hw9{lyd!emYdM?;+7{%D@UYN6iUeEn>_(z
z;EbEByzxn)e((K%sA;^zW+Ok{qc+lr`{{Lu${+Kiu0be!O;RIJczD;r9{@>=2h~+p
zl)Tq63(%+r5NeI={&G7iCl72x$hXJ@wnQil-%sJtnKwfaVSOL6(qu-Rd!4+R#I&aq
zl>>BUccC5YR<zml9VhL>-^x!vRzYZ>d^7mi$(^pG-(%pVCPQ?-HgB}L`EN@qAta%p
zIzIGKm$t;oS0Dv27p8jbm8Ty5@x{@I_f2x<;kztxzbO1upf-xMVfppqL-#~fPz&ZS
z-Hfv4{tEAjt-^7F!zcN>GB8y0wfCwCp#J1h6*qVn6~17Tvj15{Rc9#%h^@SG^R11y
zqzue)zfcTZ*2KyD=j^<!psMWQCF}*tmS3tO$Kw@_K%C<}W&TwEog`zbg*dLq?DA9Q
z)v=%r8HQrSkGyvuN;IBteFqTmiFn>|xXU7iZ^*mkhdjZk=&#?TeLVWmrOP^#7Rll{
z2V^tlKrKq*xEOhl(7}W0g`c1MMfrVYy^1&TZv@MRq+1Hg=0eA;g4H<2W5K)qPkO<0
zl0smE!7*DlX)rwyV}K{=a8>v6`{?{<x5Gy;*A$Qb&v^P(%2{;o{YKyCj|IN6X()NK
z=<YA^je2t<+^@jwAQe`L>(uZLA1-Y7CFp})5=`&-jn;43(Vuk~TsO78@L<xVEBcX0
zT&!Z8M-lpwd}}h&GjXr+Hniv<C_mL+=YRX>6I{`qlfe~mN6&!9*UuvsJ!Yc^t&e8b
z4wZ8&2=ShY8f!H2MtJp663;cWaZSA7e!{Os6w#mEb4=c!wEgBTbkk~G(<?hykLWBw
z@Vj7z$`eqFNElpQ{hk@lxi5FOKOqV>kAXEgse)r42#zip;Gj-rSIJQNU#9B^7u-kH
zwtN%CY(@nasa-+LkK1Z%n=HyNHzB~0yZ6rAImQt6n1ic)>DM*Plnk5yj&yR3m9d3U
z-MY}rm8jc_z&HjD8(%XT38^bVmdkzLb57@t^C}eTXjpa@ThIl~(|w;jBge((D%@eO
z-ZCvJJ-9zIG21G!6$$SK6e}3N;)XxtWOr&avdEN{K(HU81EFdTa;V@441!yZA^%$A
zqh8;R;4p=Z<Q2?K{U=kuS%f($a{gk$^SoI_bu;;w6q)L82A$peZ$pVuC-3W6ZkjBE
za+6XqwKBC~g@+hFs5Z#wJ2eM?)$(J6lEffH>qQDP@|{)FBoe-)>2`zs9|jfWl=G!A
zX^XJ62h~p6zD*OX=+cXTk^CnB5>diE{r{*s%eXAo@9WbIlF}gEjWkGiqlC1ybax8U
z9n#Vv-AE(dDbgw3DIm!Ix{v4lp3n2b*Uml6T(kGuYkjvyb~!6dHDr*%6R@atpbkgh
zRJV3dSB}OTnaV0jaNIJ=IRXV7N#BNfd?BZg_%D<U0!O5zMc_NTYjRNC=%y4Oi4i;@
zKwp>b8Ou5q)a5&7>Av0uIw;~C7MQAwZnRN$g2X8)z^cfXDmP2#c73A}z7qOOvThJZ
zh(6=jN7>n(OH^?okBp0qR^FJjt5^V6Bn4BSUGh_tI^Ruz2|Ia_xa}ZKX<ya(L$JiH
zU_>ZF`Pv6gr-H*Od2?YyfiHUw&$6%8uQjC<Hf?ilC`))hIPtzc^J*%2yZ65rmIAs<
ztHi0A?}#W@nL)960X~e&WJzt<J>>nHV6jJ_P@9RlvyvTqfl-pf^QMq+Tiv%9L@`_C
z7n}>TJ-XNlH=5OKDNfp%SXm#;x~#95H_MNV9z%im3^?Uh(96D#-kr4pucBuQ@uG*x
zV%ht~7o;pxv%o6<_>mZ_iN){e;_~ZashcVzU}&BylT{${^vpPh6v0dW(8AMCL9-Gn
zFB`sK7*h$$2i20-+cGGkh~e~H%*(6VE8>LSmo>hONr2Kdd8U*l+wKzvZV{xfZh?a-
zty2tzm7A(G;(i=bWg$o6X%3#!*P(T=vfoBrL|7C4*6S4XK}Jdl;mZ9N4?&B!X<Mu#
zN;@jz0D;)$W<L@%iEAm5f0b%FSS^b=kdOR<KKk3#5%4#;+N~l)Ge}Zi-#2A%?{ohc
zyC4^xaP740vecQ<Ba?t_QP8jm$)OJ7Lkjxn?!kUVV2|pEBepj{+xW)($dvSTj#GO2
zlw^P{j#w$jmlC4c^|@RsHR{v<+Cv?fS*tyD<fId&jF3E!w&*OWcvxu}7*@9Z$?5o0
zvcg49X8YVH06vMXiw@&hUchs-Qa|g(RDAaFf(m*DRn?@9A)SDYC<I}VoZC|cJUP_r
zY<YvdR|)~+dDSrJpXP9npWo%mDbUsiJ`9V91SDjn;s!CsjjZFPopaL$L2vfIrjQOZ
zQcr)4^fO73JHGO*j<rQr4ma$5l{1-*)1S}(z9N^hIqJ31k$<i_-7CTq7Vf|Ngj4Ds
zE0h&hn=VX(Bbj)Wt<X}!DeAd_$$cW9{826+UnvzahO`8J?1xp5wlWwMfDKmH4>VUP
zaP+0LO2xjpM_+uV#?rnL;}5qmqTw1KVajyBNR(DRS8Fkjv8P1Zo|nYDW<0c|pdU-r
zpbI<r60sN(OwPEP%97-U;Ov9a6c~fXO7voo35)?DZ}Fmhz)pcv<R3|#VNG$jseDd-
zB_i)yOOI;iS|r21-QYVtsY1f|n?1(k8swKGHk6$#N^Qg5@&RBvvOAH}M6LS6vCvE^
z{!Jw<tY~{1$@EBVI#PObpGimpq80@RnM^)Qf6w^m7^U(3lfbsB#9^Pzfo%Kh|Ab<o
z>Kq8@wP*`du0RdIy2Xhvpr_Mim*`Dr@X17{>tEyxj{?(3c+u1V^xAmi<JhPUJvgiD
z&hmfReM*zod#%k~Q&K&>sTy)Evp-nAb_nMhqCJg65k{23moy;D-O%=jPfzfJtrZ17
z_xb35>bqrvC?Ti#7|%+^mF<jf`OoHv&FIXz-_2He4bk!+-y_=<rf8-QLWXbDak9ex
z$n7-HjEWy3D2Xlgddz4zCD?eegf>i@#dc5gn)8pKdJwury(U>jo^>0wt9g6k5w)3a
zt~0)GFZj?=`|GmGih{sH*Rd2SFC<Hla<EIa6b$qJYFdKGuZh0qwntX7BLAdOTAL$R
z@Dq04<aS+&ac(z$U)~w*qMQEuPj7;jK9IByrw6aN6>@T&%5>>pRuE1X=XgXG9g1QK
zt4UJMnuR3jR(c{Wwnx)`U*mpBg=CuY7W^6NJI$kHb6TM7t|Pdz{(B(hb4rHi;Jzgg
zh76m?DGDe#*lin5R23hhTaU#y{eM#gFGgnc+-mMhYWWtqPk2EAogvHunyX$UFf-?s
zj;D&M{<?gU=8q?DvTFZ)`T0pwLr}p_4o=Lo{3XwSDh_)rTzd3OBgI&n#R6B!{+sa0
zvhSLOCzHMOIKRA@4)3mAn3RO+X8!k($HA3MO%`lf&V<1i2N-dWktuOaM#=J|w<OU1
z^SF-MAah%dgz;nE*HudG%JDweZWWsu<_iAj2YjZz*p?N&1ifqaf1?oL*++R^VUG14
z;o0)^-CwHa!Y?s`q~j^blwu9)chJ;$)hpnlGbzvoDZyFUEsq+SU`aFj->8rto8J6-
zkYQLuTv>N^$u_<5A!Ct?L}>}()42J&=MXuC`C(6Z(M*PQ;;G>7>Cf3(U(xXtt+HHQ
ziB;&T@UfMOGud_0>(u1GXRTi{R4T?r%K<5Y2MKr1tYPw1s^!kOmf*#^)h@lB^HN?b
zMQ)W*UKK<64_qA95RHA#?|${5dF4dKb66w$@yJ53ZY*%ZmGp-$`mvvN*yg^9|NS>q
zHpLf&a5=v(#)%*gl}JiJm*K{F-ZKP@lo-#G?;A1_h^x^VJD;C!cb^{Do`fHMgg)Ku
ztwBK8sbo<_m2pRyX011~Rt38Pn`~7{qDtUVv;H>tvtAyGUEc*r2yZPZLYtJVC5k$o
zMo<ejzVxU5uhy4>nWfT|epKD%`(F9RR`a*amd$&<X2*+_R7+<2RnTXHeBTkEzjn;3
zD`Wx2Xg5m{oYHYb%jO#?o|12eFWqt&oC9bZ@Axtxzm~`Uk?cT_1tGhZ=JGygtEk58
zEoVFWq`NIU%-7Z;HC&y)a{8VQvT6cE@?LoYi>CRnH3TJGH8NnsQwZ23s`|xEZ0^^<
z4}}<<fa+`jOb#Hgo(=z88>&Ea3$w@g0EKk*t8hqvl2Lat%k5Ff=jcwq>++v<#v07v
zV9{VcE4R}|p46<g(o%u2=#HNtgR=RLJCXm^si}6z1Z>ZN2~z60PH)LMu!Xr*@pCY;
z0A~9RERd5(*~VCPoEZxs>|nN9_Q>Q_*D9Hzubto|Y47aNYHVb}&T;#?-0U$#?mWsd
z0elv`D=ssTxyTD|tpkOkf4^_+9LP|U4#&V--(M~6trDeEJZ*CTs>iBjXv?J%O)wOK
zI5e_QqJELOjHk2aLH!u8;(Z(4grj430lAqMX6Yoq6=0roNM+Rc-2)icHsoKi$Th>;
z3otBO1@Z%=Bil)g>Yg?v=hn>tr^6@$a#yVb3lw^ldqXGG6_^+LgS_D*@Y_E^)+9_x
z6FbhLoqn>LEi<gFFLjq_^d07k_xkMDY;*HdHlQ)uk_*y>dQt}MJN;RE`t1*aX+U7N
zpnXqwe1vqxkojftY9>iI-KT$UC%YVWfTd$Pj0uXMm-flZ(^=>%0<5B(X*q25+I?^2
zQU5`T&?{fMT3Cj_8ijxX3Q4<A%!2Wv@v!mJZQw{@9!VdigGdoza7W&A!sgM9TC!84
zl|b{~$l#QwOZ^fOd>j`%<tBmP`xTgLB-h*-1Sel%%Vu~z`IOo0|CECg?F8-8Dd+;<
z|482fjtaP>Dlq7o@Ls(v*#aupL?Pq+V*o6Mk;nolS{2Oy6aI{DWJPa{wqSl^)G5E6
zv6yf_jnAf4KaGDDKbA^~jD&Rt7`cAGm)8CO+R3<R-W+)6`o^cgupJNL{nuHD6*wpX
zdWQ;J>hueHLr~+T4E$S{C3Qt51<+{b2MGT9Wg)ahDEbvPHkc$}7IX$Ws|U~Zk8e#k
z861%czK&Sj1`^(R@5L+A{QZyjTHuZr5N9BmE`V|f0see`cL`(1?~ImE-<joEpPv7~
zk|Ciu7=fhxy%pQH5x)Xki|5kyjT!uZkDf0!RL3hoB8E7EOiH20p0-~7F#!=9^BH3$
z|EWj=NZ^%-d487AE9wR0&VxP^i+{513>M;xxq2yNoxK66OM6~9xW&Q|$d<f4VdVuX
z4upb?!J_zrnFi{gW9z%fb*aj>7eQ^ps(t4p!6HQ3kWYJ=Rk5;BT9IHXFZM!J@vv<4
zuRVkzup_YxJVB@!ap_|mk3e3*N{ROk{nC^_>p@{GcLUx-gc)>_AoMn{r~sGT21xp+
zt=Z-ue#?UM?Dw9DnZ^JW^zW;XZw(wtcyQ{){_yiKRV?qsH%Mmgy4XKgXW^dAaquqL
zaLxS!qcmG!RWksr&5<%M@n(X(HC-9|pVJolod1fVzI4#}J-`R!0_KHcyE%8@h#P-i
z2OJy7sEzjrWyH(?dsD?{yVX$T%aZ!X@afu6@$`ldGMrw)z*%rl7)4O2gyXy!`8mw>
z^*REs+~jRKNH_Cu<JRCq8l6Y*xTGNGa3vyTDL-}WE7{+#ocay){z++SgXgZ|&6<LA
z1W@OA3qWflSUj{Zv}F+hJeXHNUjjqt>Dd!pUmP9a<oNp*bjo&JFf83c_JV-K(JH&x
z1Tm`iB%xWVcND|u0f^R=BEbcH;5a7`TfJ#)VgcgZ*Rgs-5yw}JbrDkOTJlC8<)BDH
zM<5{-$if2=!2370dz)->UzdH&tjt#3Kot!Fo(^6&;E6)^cm#2*8abFChni-Mj_0DC
z=h61u?bou75NHqgtsGreJqQ(DST`+%ncEls?@POfj|>5ofcP)JbxX(`&=lW>v3qBx
z+8DXSBz4^b?2ft{;ff=K*9O5;>uDT+id`x`RxDU8CTGo9F@U++2lL}6z%3f3EZU0z
zIP@al`w`|kv{&N8vy`-lw_sYD4z_7<v3W0y%*&QL!EF1WXUx~+2b>-;y}kqie)zo~
z$S*FywXyOP3_k%<4{Yfv00DuRcO$%pd8&Zp{|Z(^YAApIby)KC!*2#;VqLv>9^=m0
zJw+y%HQPg&umi-_^B;b~o;*%B*jRjqOwl0cqZ=`D*9<u46UTl7hsAIIr(gcw!BbzG
z&!|jgLW`wVoOU4CCVE;bd8X9nwheT9BYZ2fUthG^<p=Qv77?hjBSWAtJNNP;`Iu8O
zW%&fdIn2SyyiK@v#Yq0I<08k9x+{jN5{hp;sB)})UmC}ecv~|4v%oq=c_+w&V{()m
z-X|X)X(0cMnHG6;SUB>eh7xvWdKh=_a0aS4WD!o2_Rwq%$c>9{0B=Us?cO98EPus)
ze5{Wt@O9-IFRbXuK%GM*4BM@yK_76gvA)13WqB8+FIlrWlw^1M%2!wgmFOAT86_;7
z&f(JkdvoKuAh7@*S6GyQ@h~!`LKneLvWUC;FahHyJ#y`AEs4=b*2o1)?NzS~c{&oH
z>JaOGfoM}3@YBfzRN!f98-q}t+3ItfbLY>$NmA_?KwSEl&{1_ymy1-%3dCe;u=@pL
zvMHGg^V#0Oa8X=~(Bg+E93kR1D9(z)`6JI2q{Z*|jL!dk6%473EZ1R8gn1l1xtDTY
zaOSh9kfr%S+}MqWHILH+HWr-_MXb}O)W(7sxjGBn>J_&bkk|`^$k)xWu$vHkX6&m!
z9yNNmpIV^~@!QtnMTOSwWUL&F@$q@J7CoOUXIuBQ#7p6Gm0h}MVYw3x8Z=r#@w)GL
zb`xfxRDzESM9j|MyyW<|3%gf_x$5kK?P{@AnDWrfh49BTb%as2n!$(Zmws_=_alAa
zmEZnARf=skEcYeXhRBJ~SHL4HbNm>usxwCkUfU5M4o;}$>kXxe9?_U7Sv=4$rrdV9
zRIln5Pu66)VA4yTC~9CCc}$%t)niAb#x#b^!mR^S;mbEjDg=TW5a;)Xp5c-3G*pcT
zSE?4G)o$58AL&U!YzRm6odyp1;B>*{&3Mhu;Od-ht*SXC4Z9BHq}NGM6w#oAer{)h
z&F`^^YIqCNj?A&Nd<nAW*ZQYk_^@}7G+=SWBKyIEnxH&?(bDU;_9RTLX9rFmjgV<b
zdFOj2t`?w!oOS>$_H}QPx{&T|H!8GTETl;Cv6)|iFr=RC*3qX@RS{^wl&AiriW0Tf
zPfPr_smcEYm8c}Tt9?e|+pVU~S#ARi85nbxC=@R^t2|&oCa7Hy3a8bS>*cd0CvoY*
zeBaqlTj@DkQA~g;JTg_y5CcK4i565(jQ4^)vPXTO^7(9*^;tlLz|Ag{&{4F>fgxBS
z5e{y3Gax2bB0hDrw2wBZXh7G#9S^RlGz>NhDOSlo(2tJkJb1%th6f4C)=uI8U@P*@
z%l|+|V0J~A8%OiKEdRZ$M%>r|21k6gU@b+@Ht%-56R|M*pXWx=xG4@b4Ye(c@5>y&
zn)c={$?RPAk~~RUPPB1*ysv&ak0sx5!{}^Y0x_0OJ`==gHM5uiyLOC2OCGb@R7(^n
zM#XmLmxtEJhV3Rw!dM|=2cv@?{+uOZlsUogn*d0~z;l5Clh0ct47|mo&?NG2f8~X%
zf2&vEd&*<MvEw4PYo;jkX)E+$Ddv3Q`tFcwCHz>=k~pgPuYaHg4$5pX8wI_%_r7dM
zY0Ex-4x}`v&FTK6^k*TJihRX`=S_s>!nCg5XIwx(zoDA==aJ<(TpZONhx-Gu1vsX+
zXHE=h>e@<a)xHnD6LY~wuvccQzX1hW8|W6!sBD;MuQF!?{Sb+h(Ti*u6liV6Fi&1n
zZM*{m!=*XoE(ZcqM3hevyW@j<<242`2oInMaJ6AHX=^?WdX*CKKZ8(yQotK|3uVD*
zq=cBjuB;Zj_8)|e9nqN>+}}<G=I(*rk>FP!1bgulDnkD_zm#d7M~&zEcgf2M|A(2l
z0hnajcGi-GOyvTRj7~rDt1Jw72QZY9&($Y+4d;)#@r{O5I(BX*AZiW-HCtd~Q%mk*
zbc>EAIX+^NaV`&se0$RfYxbBtsOJ`0TgtUmH}}KPRo1%AMAcUIrDwno7Wm%o*$CC%
zn{O9Oas{hm0e6i590X`|&?qS)7J&%qQy$E}65<H&oM2c#Y0+>Hr4Ri1lgc7JDiMSO
zb;8Kfr;s-;W3H!^9P?4y{aclJoXnqBOEht-Y;}+LlOvB1cT$LAO|ne2)Ntg?Akb1L
z`r7QQ);*m-d@U1f{i^;A$Sn)?p7jmxiWl`P#rPuZXI@O*4*WOOD7aL1pR6Qw{jZ9G
zxNBXZ&^y4mtUltiz&1uh*^k+v%H29U(yn_^jc%X+o(n7*qkx<z7A?(m5_t=c@ZT@q
zKT8rajZcG$?q8?k2}qv*`ktt!($cp=cnWRno2vK6T;uB9(S0<`5qK&T3S{#SaMML1
z#2rpg*YSnDcMd-cyjk4w-5uOOp=mB()tcOM;tr%^2rdS|@aFl4*U^Jw=igMa1(kBe
z#lAsaY#&Cmm+#^pCoIxgYJ6>@GuNm!P~>$90q*g3-YGSIt=n6QhWo`;Pp4>)rEC7T
zALo$m5@k0k@yj!Q=}#h1nNzQy&br_H%pnsB9m5<xxuJEDYO$lT=l~5z1+8)SoQ|=_
zU+<ds<<#p~!d(+p)jR)x$0iCeS2@u)XcMCo<v-8}zRlSI_Fmr#P5MasCLGF!rl|1=
zE9p(_T<gG>F+m@2{q^}Sk|p|V^kaAlfqGD`xfCEa{p2iJCy*a@EE6T1{-s$q23Wm5
z5yH^!;GGH3D!u8=*NL!I-=SVvrTyM`^Y7c}OAX!3!)lunmZGKVm1<4NJtvO(qtQox
zN0e)^!9p^fHb~us*1Q7F>4wMpmG6WrNz`-tN3aItIp!lMY)lw*!1V3ts~(6#rZ1ul
zinn~e?WJq}1V*2yjysq1RAdmq<Zkyx6lgy#M%eIe+ry8;V$*xm|3usS^sM?@#Ls0p
zyd9zBS>ppRj+fultuRx`!eC8y4<YU4TWTlm$FJ4a#e3X}d_D1Iet(^CW7tTi_`{#Y
zO#5?mP2f)PiPNTkoAEp~X55Q(&O0mV0Q+$2J7m?`pc*7y)|SZV+#tHe?jabK;K-iv
zgJ@{~5YMk*(VcCNMZI)2s8QTSOzRCuEJgMU61DfzHl&6sI*F9hG4OPykA-xDWA=SS
zN#DtEhVP7{^{h!F=A+oLgPL#@f+TEwsXj?5jxy<Y{auU664PmKKlNdMsdjAiZ(G_!
zjfq-4OJznT+rEBXBt!aXCTZb{?<+S2P*XoTcG~LqOtHV+CyG?JFj2*w_-zv>>eV^=
zv1)n7_W^8J>7{uDM4xz^j1UDxmAg<R-j0Wh5#EN3zAO`goKcQ`rqEbQ;&xV(5=`Nb
z-d;1=vqf(OUM+qfCeB7312}8?6(ZgC(pz-}78*FgK|kO<6xbVA>=|++Ims9t-g<hN
zc*+EX0I~{}RH@5j<324akt{ld5Q)ECY1dIa6sF!?&;+T2Zz);i#E?;50z&VyFVUO2
zsgg)7ujG<1w%k?c!g<D7Rz$&`3do~WK=-%|qarU-hZ#j!7Vi~qJE|SwiwLV3+=gZJ
z1^M?;mz7!UfNUu}%v6B4Hz@D_@Js{}`1i(9+Lf8dKf`kj(NMA&#;_;+ns#@LDSY0G
zbBtJV>OfkVRNZXzMCn5cSp8ncFGYzLv1itcn6{JUP-R|p|M2{tk(g=}<#(OKJ_OvE
z-~deR#y0v>!ec#t_z4tKN9$+~LX(dSxePB;f)_IuH4Ks^v!p}K&wdS8MoWOcZh)em
znNtn9Ymc1?wZ&m8ue7@KLuVI#@g<)_eJM-~D`lk*aF@Ie{%{MKvw$RXLwM08RmdR0
z1iT&|s2e>jHZknee)3ncd-pu13lkN@5O1N>k8x$2_?9jCmSQXbczpjIw!y$MQIM99
zEd-r(<Du=}ho{BdoA!4C#sH0-v#Nh((erUt7S4&hKL1&2Q>)6%6@NV)#&HSr;rl8~
zZ=7)a%W2c2Pv*$mmziMDNR_G%6eeX(Cn!uA+$DiPHP-!QSvX^#g%N{(uBy_P<*1X#
z;9|GKCP78_OnxV|17N3k;-hZ~;<aym>MTO%$Z7^1;2=KQg+GB-lZKK+LZSIK!#~8d
zG|WAHfW-!`aD&){s+6=)yrQnC=g{Z*%T%^Wpe1RB{coH_ttz%Ix)=S#w3gU!f)r|^
z2;2QEb%rScf%AlHOtf91E-0{Sw!}lZz8FU-Uk26;GN^^z^UF|nS8OAB_XI3={0`Rg
ztw!ht2L^{+(v$~*x4)Dkv<6*{e^PB|mhdOtI0jvBS=OMZ2XsWtHx3ZVwU-LGxdddC
z31~D?6>*SE>9&VU9B#or6Ed1w2hAma4}V(+Ky&i9CgohMY!rQa*#Vq{<b{tN$(}oZ
zkL)zYz~SG8JFM9BD;`)-D=WIc02Eb*YXUAu4gE)EQHo2^d84wby<3q*1*Aq=RC)EC
z)ItOwdc{~205EEdes)zxCkNqS;8(@5+<|mIVJ!YSSs28#0&#+2<)=0s<Mlqf7VjC|
z=<-UOv(qhb-T}srOJI^Y0d}Xa8ftgP%<WgxS^I*Wc&B~PDx3W8h?x+&1n@~8w;6k8
z5Y)-XE&6$ME`uF=Yxb85d?2z~0i2g1OcMVK1DU1=h30fgkpYP%ek#(tseD=@O`CXR
zMiD-Afi~W$sE^OmzjYoNyzzc<+V7J0UWF-|gu8GIeK-e<TXGUarL9KS#D35~g<lwL
zQ2Bwa3e^%4DVUc4Yq7!GzOMw45_QXpi?!DXSElA|P_iJaz(z;6V&1v}KQ}qZZ;xl0
zCy<}C<<6PA4AxX7{cdir@VHpzSjaIIoOJh)5?lIw-~G%lP9u&l*+bw6SrKtAxz_!;
zPgB$+&l##1Kgq$W_bE-<oNzNK<CxYzVo}Z-jZy0Q9Fpme@Ajg{Py-vtIQuZ&*Dc{6
z-wbvzEFYBRD{-f~>~K9jpwV8!4{Gv+y;QuczTSINF9ZA21C1zl@Cyuj^$AC6ly_SX
zMHL}!V14OC^|M9Ym0ZVW!^o~%KpTjJC(_2Z#Z-hqQ3c_Qa(#eKR|(VvnV&esirv;5
z69X1X&m5t;$A!NqOa|b5!g@*rv{aI6H`7u#6iq6Ot=oAb>gEDsy1ZL3qdshVgA?Em
za&MXIrGBno7dd6jIZY@{GnnlOuLwzsDsnivAm>miHqsz5`wxBI!=20NL(v{bHf}O#
zmjPwx>YoPmSi^4RK3$H8zRfoD)7d3bMUK0GgWQJm%Wc)uB0IWfAIVuFmsDdeqENq3
zcxrK7^=)=IB#!lf?ji*PlPY<IE&Z;NG_!&**Y;EJSAMsiBwOpkx;-UjHW03d{ytO^
zpdHo|-sOVFZzJ0)!RP_ZS+=^Te4NLpaFZdgnoc#WJu4<hw$>$z?SaD^7?_$2G8ERk
zq#>(GP;P30z;N+_e7r7XZ3MCy4s5}k{1dcscFXJOv2tiIfLClsNF<Y3EBnuh3zV0x
z238Y9eInaz!L?<&u_x+SmwXcBo(S)fJ=qCff7c6rOx08f)kbMVW%|G@Bx{Q?o85WW
zNvu~)Dxz6IMt^kIz;OzfZ{Cu|#r`bTi!ei(Zv)OsA>Tks?0c4DKOrAq`QqD>FPOo|
zo$}n3-wYu$%`3m5wJvIffb5{DEFLdF^a4npKw3}7Ggo7loeWlO<0VF`A<&Q?gUa<4
zh(^J^%KZY+(itBx1j4-Kbnn&7-^Y%nJ9P4_-(>CzStP*=CQglCbu_?yv}J-){q690
zFgkE%^rvfKMLtNlYKLpD;SOkKm~TE7>Pgx1LtVA>+==XsNe9nt?B?T+2<)cKUfXV`
zq=ARJYcvi@Sk6}6rG5`;)pry5<NFjFfwU1C=Z_<$q;uw_n*4i((J!YpIk7(MXnMi(
zdw`n`kYsd3-%}Xh;;K9#qeQzZplD+C<1Qxeg#O5vZYT?S6Y_Fc$2Cl%@#4FJ6c-mo
z<CJWazU+!F{QfS;_yj%5`i(vh(oK>oy1DX0w)&X`xA-9dS4rN81Utw2HjP~w$<Skn
z=2JBe7s2EQw058()6k`WI?c1ErcjiBsG3_fDNdLMzGz9E->xb_9V=f1={HCK*9puF
z()e#8FA9lg6XU_fyO<v|M=#`cs4hdQAmGFp5c^C$#uRLQWK>){25N5GBv54+2g<mk
z2`PW%{OQ2D2>t+QcsQpfPJcgk?aa{WlZ7Ekxgj%v`}57szj-0`9eI+llnp&((gUb3
zG~*G|AL(wWitHBObkyA>g+y(b6X)T=<--fj1H8r&exB4Z{3c!x8&(gPpGbxoG%qvp
z8APWd@iOD4hA7!LNEWI0!(`;6`Nu4~DK<Q?_j@yq!OwQTNUsOmPAh9K7-Jvv)z=Na
zCP__-UH-ag8f<ix17D?9>Uo`klvY4rT>jQS`a??mNe90<tnJv=ehHlHf#Vq1#-o@S
z+W<Aadl4ArwF2SD0a+*9TfZ(J$bz49Tdlglrw6_9WHPFzV_F%JJ)A0>-_Ti%nvN5v
zD;oNvVH`r;nu9QJJK=avWvK#Nd%Xh(94nt<Zw}g06f*W;HoI@RA=f@cj1x%~4RQEf
z(rCr0%WiWiW!wi<nikzl?E!J2rqlqHxO>xp@C#oayQ{{^hQmC)0kmZRv4h2O;Qoed
zWS5PKa&o_B`T`PtCouNr`Wvm3i()N%P^3eZ#LrbMPR+A>0dX1`ughmE*^9z{SdY?D
zh={P_DsQ8ctZ*5&=-0m1pt-vW<etziEqqIB+;r*I|2?arQb_o<V~;<vI*eX<`DgG7
zp@Xiv=?34!S<jsxJyHO~_<jmhx<xWg0nN0F!dzX#St~i7>ze1MiYiMHvL-}EHE*Qq
z+e|bCjSk0`+spzy0gl{O$e|qj^r1FRKu=8V-EEN|`tW*p&n_6v)KeOlojljIiI?@|
z%l)VPXHTTY<ZGSErz7z~kJQckDK3|Fp`hb5L=(*{jni5n>ShqKURyJC?kNrfXNQ3?
zotJ0}8Y#Yc6*JG7Ti1bOcXpw3aXm1ROcM{@fT`lQP;aYQir^S538k>Lp+~D9)7?=p
z6c{iSWtod;7^}97GKyhHu$ZgaDQ7NM`HqUI10(oOeGupaVEfC#9am}`+&rU=iA7UR
zn@5_4up-tKR`noFdry4k`@+~R7u_vF_U5ahjT-b^PC6A7#R1i>v~`=B1SK<ZeOs+m
z(vW<L6<f|O=>DLc-WAs<l&)V6>?@X2_3U4yBO7_Re6Pz4zhft6(xhnT>6>t|{nOl!
zawB&QY!utEqc}f{b+}J-M`3Dh|Kn05)4}9rHyK}r)J7KTBqVS0t}B5@yX#8%shZDo
z?J@Zgspu8xa9~6uPhcLoN=%pr{3xy${}iB@ZLdR=Q1<1Lpzr(O^Iz8sMC3vLkGjgQ
zBd`%yIfdx(o;3|Q`B>E>n*Gm55@iT8OL^R5z5S)E5{7(%x`b>U&&W(ZbWyObsDLE6
z*Ykr;P<kEZ(95`rceOsR_XZ&DYCesSw^C1Tb`yN7Z&{6G@Eb#JcG0T@SedrgXI@KY
zWH@cAvCOeD7Zb4@XgXW{k>a#Pz+^45)e+y(>%}uTZ-h%r`=<~BDyIX>Wck9Z`7L>b
zsgcdQpD7cTwuO!W2@vp69|69SS+&*&-s&|sHfAFn>3I5hVBF)<ty$}9c2n=oshC&t
zJ<r{hZk)^*4Dyp(DQIt-O$y(0!Ci|VqiIJt^o8eAAmOSl^UxJ}vBV+_CY^;ztoer6
zIB0=^XnWaaR?TR|$&}d!HH+Am!%CgA3wV$GAl%1mv)=4Ffok>U61syZJVHf8?)^!u
zk(h)4jt>%H4N^i@2Se#l+}|P6<Xh48EVfiu6=J;bktInko!C(!;9Gr>_|-UI$v3fF
zC_N5M>yu8fQW+0B@;8%}MulmD%9~M@f8?{3xBx6T-M!~G+neHuFw4r#Tb?jUtpzNv
zYgM)Vev(OeUZ`<HF*pHig)`2sfNaCNO%D{o7;SDe2cGS^EQ#r1&N#?)$PB$3y|*c5
zcqq;t-AHLEGATeeIHX&nD8x)*SQ?H48%`s1e2k?0glM0eGq|gN0+}r62SK6U6zWR2
zOWZcZTTfWX;g4XF2=%h}b(ba95@8>`sWPT4mUNT%$5rD{f_N%lci>WX#?QrMpoMyb
zK*67)X**-E?p$b%h-w5AS<(vq0T8LKy@BiTc6Itk{T(Q%NHN&ItXrH$bes5~g);SN
z46f3Ch}eTOmpM?cS`5vv+U0^l{$*`$mh*1?JVGffGCn?EIpwcea*_=@z-wbA$QPEf
zs%?50Tbib|*b<BlukaSJ^=^<zh_RSif=;10wwfLlq&cXtG9qWff>&WW(4{GP-g27B
z;ER6}39Yle;%#w!gHILct*AXvs@g|do=Z6>!U_A{8Y;F|=IO?=nd%+tn)mj!IE7_%
z^-^kZrwr#Oa(85Nd{?EDfO)phFP9b=12fwjhN+!DCPOt?b_!MWa%2%Bu=FP>|K?M@
zgzSnE=Cc{<#&?N(6j0m;+&--?8Ik~RHF&8CBI>mIC#xX`98=2;>8jGielG**6x?R0
zg&xT%U7-~~z*u;_Lz5OvN9^W0aA143={7-=wQVk0<-$p4%R}KDPJgIiRs8)KaV~Sf
zPY-tF&vZJFmPdmL`miu+;lwqv2jpYA<`lb#v=JLheZxgB+YGy0^5-FzW%%qiDBr<p
zELz*Uc=OPNo@NQ#pcR3iSpH~l;DqZOJwhz5k}rbS3D@`4=gL~5r5SYJN|=o7DsNSm
z3&OT-E8!q4)ic{mOSx`v<U9PZdHb>g$J9&nANTZub8KhbYh+#Kd}fJe+sG?ieFSC{
zzY8D@edagj_7*Kw&^<l=5}48La)Xv}xZoA)TtVjVOq}f>wElLc;7HVhCn4P7P;U-O
zbmUUAcw0xtQoBEST&n*(rDou5Ff`R{OAEoEFOo<S^#Zi5b;1yy%!?k0q?gRdZ%-3U
zyhV4{g|;l;KmR)dUP%$1pN%|+LDJmnB&&KDr)q?*kd^RCI<0`4*GGTjTqYv{JISn(
zK1M%X&1a^pBFz9#BQ*S4)$zM$RLdrd2_g~Q8|T>EyY`4C&Z)9Kvncwzw`Mlj>e4@D
zMyBAewlrCf<ubhDrD=h&&ECiqIZT{BETW`_buj`Pw;-go?oA&+6ZYn<4l?hI=B}r1
z*ivlM2O&Gd{IcuVUx8{^G@)IOdp&12k_582TfGvsE+<rGw!3kW_IdJm^3&s4kx|zb
z0SLVqr9FG)f)TA5WTH!RG(Jfi1f0hXim^R9Sok9Xnv+k!E^xzo2ao<E``>c|=aZ<|
zfpJ+s`=rA%#mxnQEBa5~oVzZj8Z1OHpGi3B&&5Vk>Km?~9&%#6;~2iJLTeLmQOCWU
zlI*4@uG+KxlP9!}s%XDy^Da&{tNa25G5~y_aY=wW6&`nsX*Y$(-wELepoJma5T}!L
z;d8DJXYl>$k_}uCueEhjJctiM+RRi>1K2Y7qOPYZUu1ZUQUX9-7S^Lg|8QG!_K+kd
zfkI6|ujcXiVNJ3PCBHOPdKUajP$|+?irxoeSWm<cUX$}mW^dsqnFw@?x?Iqd>U}1|
z>lsJ>4ydh?M4=~f-*UqqM7vlo_t;IA#31anjk~~<Bkr)`!8^nbB<wU>4&MG=Bafv0
z<a7Lq)vQ?Ui;k-b<oOi7=JzV&U~ehyoRP@NN4Vx3yP&`-=tEEy@yvJsZQg5^(1dDm
zi$yja^oD;C=RJvVsN>)^V8*c;lFF{AISB1nh{t>wN{uK%^>T~H^BtoKV<p20ox<!{
z_PJ+~M`D1ojkJG}7b#qx&oCk<t)o!Cytm=~M*$;~=+VWR`wg|yU)72TIan!+%|lcd
zlV8<|nv#?y{)8vGzl4&p;s;(@Jo#7{w?bQ^Tx+@|uo)F9`M&p)@>Ns#v7ulnOF_Iq
z4c!PWDAR3?=pgU#6Dbwr8fL)+<!r$DV`9Aox6X{G&b;LW6KRLd7e^=ZeY@{dF0{xz
zQ0I3UdVg%cc5lNBah0HMnIZ0i^%paRcIg{HyJKzh>YZ?uPtws?x~U&C{f*VyMOQs5
z%9kRun6Tht_9F0TspDUzbCL=>zy&N3B;s)&U#8o6jl^d#B&NB|8#NDmrJ-<ni<4X<
z^d+Xhz7a8c6xhC$z_f3bI&R?)(&un~csoA;t$7}s`Te<~MJ*ofGRH`cn`Mhygq$q%
zIf-op)+zkG#TcG_aENzO-+)rT&hj5E5B(4NTRU8HK_uR<Ei`-9Axlc#kxQ+UX<*d)
z2c5Zd4|Tn}k<}g7S}K;Je3Us!9;q(w3j~*&8tMLyZ@X@(p{^0mUh>GH8J&x1>migg
z6ovP@*s18IpCeN!jM+fU?F(z}XzlqL&9>D!kzJ71iM@H6Dl_#?`jWwdg^cMP&XaCt
zFC=D2nW8=7tUdwSoc<z_k37+za21>CR>zfz<%*|UKb(`n%vt|=T(*a_3R9AmB=&5(
zF7vd+1GX-1llG`HFZ=BSw9wY*bWx9ZUB5N@!uMdsww~*-|5NNK>cK#Fqmswc6jQg^
z%6}S2NW~>u2HTlQw7A)@PqM7$z2x2hSB#cWLw$na=as*2oNT_%f|zeIQ@_eNY+%ys
zdnvxF?HbV6%^^4;T<0u)Ut<D)mQ9peS^8jGj^@w?&B5;$TY8Q|-5BozOfQH@a_az8
zk89<3@VznywZl%XmsLHQFfmr-$#v=kzJ?+c7>*{a{Fx6s^d${G=Qz}Q{C$S?kY@w!
zvz(J(S=gP^F|0Q)apzm9AQFvTHm(kWXem(BZq}f*5GxsOycW+HBJwKYmRr-*HtMR2
zW8R4JIb`#L{8Mlwv^G5A&-2YeGGoGl;rBjQ%RF#Frc+J!>h-<n9E9!+$pUWU%6f_9
z3OGFDZr{sS7nL=rrt~3e&wCEvP4S%#{xrVQMOB=J%jIoF@|VXU%k_u1!>8$YK{Zg>
z@+uqUqJyY2tjNJd>fT1@&Cj?IUhhU3z;?`t&YXR$O1D^CH(a`zwfxrZ*Akp|eNzOk
z``3<pTAt=PRg-NJIsYLWF}u*f3Vh3U0xQzi-iE#vtA&f^JXNd^wggI&oHwBF;&TJF
zFr;WH9PFrxI7B_RX*K1I5h1O4^tpdy+w$&PS75KCSubUxtOJ-%C3K=Acr4l&5lR0^
z=a!FkD}Hg@b~%2N@XwZYD4Y+OmtV^3Q(+cVGG%?SUuo9LgG!K=J`koC5)p4Ss`78J
zCbC2z#<aCs%N9^Vcwg>YrT7tYv!@MVMwn(83=b|RUXdf=ya>tYeQRB@&4;i7KY)KF
z(S<qiNg!yJbsFk1(?XJve0xeZ@q&gK%LnvuTMiRunY%-`0-W6`^RIzIl!49mgk$c?
z^|=(loLlsvg$yLoXRrd67^)$}T`LIgNl`w_8?jUu=S*SUmTi>gGrgvplw_kPSa>Bs
zE0aEsx4ratFye@eCVGj0iSbBVY^+GWP73$E>|~~-_Dg%NIU=^2m6<yEfwe`t<r$`H
zk$F2DXZC&hZ{R7^BN9C@iQ)f9bpdvUS({L{vQh6ct90yd2}Xx`k~RLG9}q$Kb?JfS
z27!Z^s_vq5CQ~BBKyp1$Maou?h+eUipw&L@OIs$oVt*WjPi81uo=eBUo<Z8e;~d*w
zy*P5N)q?gUnr?z0l!l~j0&bSlj5GpOd7xTWYJ@!Drb2JwqX}y~u>Y#a3~{2p8chud
z_2{~;$(FY*9?V+x1?PFSRWg3b1XN1hYorI?e6krO<1%OXYU?$BgmE*z<EZIm!e`%F
z+7<?xvO`{|dPjHY(mqUFM?F)!RQ05*<Hn`&z)O?p6LnKWd?!W3_%UdYw)ucCx~DX8
zb~9v0^VGmMbfzAmlk_Wg&nVc-$X#dEXvN$5Jv~&E6x|<Tirx(HLpEVq;Nn-S7j=({
zhhFWOee<zfZ2X48bQ`0I09lgJNWWHMa`LJ1#_lOE!5_T0lbSHEF;x`yH1h)XW#N2(
z{y4orJ-4)U$QQ3rAp!~Nid3uuXZ>y-OUeYVpGjX{8u6uHe8C;+W&Q+-NnXH06?@pD
z9)DZehIqGx5Yr>u>34n|8vJSrxA|o%X_4^?jB=D<GhHWy*nT28AJxmw6>0aIRL^&d
zYV12tFD?LQ=RH_`20*;lU%OMgm_g90f_r7eSfr^#49VOK;#Up(cK3Z*tN$(43`QtD
z6E&KU?TC{>6J4Moy5d?im(G%AIz)97m50JGcl~IMT5l`=A}n)Ga9?W2(sK42Q6H{Z
zZbGF=Bfa-Y=oXG)v9tH$G<}|&1~~^aQR1&vh^)H$z?!(s*#A1Cr}$U<e=GnP2tyfS
z(>ddwrD@@(M=n*t6kF`{II0-{uGFt2YtUjF?>ySj+%LANe|-rd6%54~ZuC~aP2Y!)
zfg6$eJm2lYvk$nB&JpMn=I;Pm8fqW|l13C6Qx-htwrdq}FM4jQMTmfB?rp$PaNJtg
zo$+OxU`A8(pH5e?60U@ZmQJBNlPt>_pFj%_o*W<70DDzFHu>X0|L2$b(aVx)bP*}U
zm6auq&0dH0hulSQjUIhp7YieX@ZLHd%7D+<=|C0FM;_FE7{yChXydeLhL<sf7+mk}
z+@hCOq)<y(WPZXa`h(xIE)EUd-B2ooQ!j^^`K|A5?oqEQzDo7}G48ABI`O?QC5%#3
z7M0G})l1Xb^!JHf!1}!(8P?u2V^ur<T%^SumLrzx(|1?{-XL%B;~t9N>g<2R&qhJm
zIVPoi*?u>gV$(Q3=W?4l`gC)#Zo81Ytchbcs$d_R4fo?aCcmFxJ~ZE%b_4ZZ@__5;
z1BUB>9WgNzygdF7F!yBJeT1p(t^$KY5&w@gz`Ymda>U>z&n-<K?Hn@Q#+>=;KKoZ&
zaRt-m{6cJ9a97yogsR&jS2spX9sK2UE^kLuhEzY0sp2b3)|hNNE#4dIwP{W-7Y9pA
zsJ7g2us^DoTpLx!(q%dXbqTsLwB3!_@BHD8q5s^|z;*f88<z-I;w6s|#dAWip1!KQ
zna{Y#`$avdZVgf747%v*ozmyth^xWApq+$N$?>5tRS>nH>eX6xG=6vpJgES#T84EN
zCOxIGU`VKfK<6jq7GorYi+u9OJ%AM2!W}ON<oZyf#1o@P)F`HL@olQ$>g9^<tHZ07
z4oUaYw`9>pa__!pOvg>vN0#ghh6&q;p6jmNYjOx=7)0-2^;$?aO%3<!TzzC+@~qeh
zIQ@J-TU<1P{GOXdB^|sKV8GIKV8e|1!JXH^IX*Tb%8NB6XN0xY25et!w3(-=^Gcn@
zDZ<~d2!ebx>OVuvBmvitTFw<@IM^5$Q8i%f1*qdeCCykW52v3q1mE;|4Nn9&2L0)a
zHf}J3O*K$+dNWJCvg_OZ<mW}K@%~3DFYq72Y(e#6R%l;uf3K(04p(eqmYjDwsHajQ
zJ;WW4Lh9Ys?Zf&k(?Oo0tn=}v=kQCo^`g`#^nWH@VlWQ2qDywXA45j$$T2=^%74{a
zM8V?<w)>IspaBC+@V?TIXb?}d0bnzO%{Gjq!xAgwY6z!*@0QYv`r|m<J-L*Oxeney
z2uDpUll@r{Di^2YIL@w--#3@&SZ6s;R^`l%0*5fz)Zpc>V1r({y=4ZAYT`G~AVo!+
zZ#p3?2f#^sGXnM!`;ts^)0TB|By8xdzj&M$Jp2|TY4+a9O`T|#?l$nBx(8U>*aK=M
zdrMcN4%=s{ZHB-kpYodUBKg=QaO!I-yNALV6qf38jC80H#IuLPTZn`6ER@Z6U%?{N
z5y71(12-4K99{b}YY_Ls#nMSU@qo|pA-mSC`i-t<&ZaE^yEXRnI(%w`QfGq*8WUZj
z2d7M__r-y0QCU?coEi%crfMPG41RujrU&LKzCi|<9*q>Bz}YiIo|ht;V{NICDQ9_P
z8nwC8hIsnRu@9xZ;(1fKzP5&SLfbEl?gq#q*waoAO*4GKjF@_^Zfr*S#PBDlOvb21
z<sEc4hE_af!`Ns+u?RNP%b#C7!D;r4xwW6j`U)mu`)fUr?2(juSVBiI!yP|hjYzpX
zKvtL%3@}QJbZ`3I53PWK+w-uc=S|g^#zFlD<})d}=6`!Oxg@9&so)yUocUM74HFz#
z^^^*jTS;-unL>r0Wh0U{G98i1I9bn=KKG_j0}tWf^wM4@x2+R^Dn0%H4Eqp#gTW7(
zj2p=9V5q~d<%ErgXo%0AC6+an-byyYRE$4&xEhb|-I)w`Y+O^s?Okj<g)ImN<w8D^
zt!0fhIx({<t{Vt#-7|sTaRz^hNb9D=i7p6O01v_afwTEC3cQt#?RBQ*tf3F_0TZ*R
z-JXk$TwIXTV1WUe{sCamv8U!`=>iB+m6ddg%uTTjq-kNgXJ4I%tMKIokHhVy>1SS!
zQMq6vE-x@V9JwMwj^h`Z*?wkZ_U@4c%sx3^Ip+I!qlo7fF#8>Q${R^LKmpKuj_Diw
z?p>vMSc9Q+t>U$<HHl17AKd$-c!{`M$Iqu+F<vq{!N@v#&g7A1S+CsLUbwO}MOinM
z8TT*m<VCV#&uGxbUY_>_5~|@YC&HEg6xiNnjLK&-yP=dk4}(vHI3xhNFA%QFu85v7
zoiySubNC*3#VZYBl~HS+p@dmr<nx2?gd)+XvH$YZ{JWwYPn|vhZ=$^Jc(}^U*0HcX
z34SQ=T@YokYeI^ul#5c9B}CTxmOfqr1qX3z<I2o+UkKB4vXc0md%8IM-tXSAO1R{-
zsjW9ZNKAVjWwm9l<=ty{1(^~vJVY8k<Y9%$l@e!&x!!@L2?#USeajQrT>K7`?>d|I
zzZTC~v@jl<W`k?Ks)f5*aW`~yP4k=oMo`Rmt+pag=FD11zyhS=fF1Uh^C@vwcMV#%
z#v8c>w7HRlp+=>AaH6T?pABfdd=~K;grCN_T7dUw!xf|Zs9{-+fC1iw@Y#Uzu&Qt-
zWe(Ge)WX1lZ@_XXE{rZAlC^ME%D$R8J4WM0cc73c*}Gw^89KZ1y5CiS+{h7U%tbhT
zqYYpOOmukXQ;%Hp2~)zOeV&IsTAnwBChH2R@d?W15U5Fdce{5Ild5Dx>JxIgu7o3C
zO18m&ZViKH>kvNk=n{D2hBzrTy!p6UQLQzbu83m0xP|*GKCZ6RB)RJ$l*DOJdxZM2
zUKYxw@xEUnkZl;^`@Xzy@P0OoRc->o;Oe>KUP6cdAOZdu<r?GD`Y!kNKt#?CWrYEm
za$H5;e0DYHTR!_J*;o2-dSnbPhs8!^XE(xy3(#h3KVD{<$@*Mv7(`2F_rFqpS;XYm
zgzzdqOI@!KEtHeAJxSJ=`<>!H-;gD2qo=%_^0yE$%vHi0>z5ym`&PcZ{65D16K|`f
z8bwHaMy`E{+$`l10A2kbp!hy(#{xO^;c^0Bx5rp|bO~3I_gxySI^*UQ479Uc)6IZF
zE@G2a|0k|M<Le*hqSZh0I=CMjkK`!jLg6^5mkO4w{Uk{P_+Y0&`p5TGqod64nhPqg
zqP2=rTnk2vd;!v-wmzgL(3qIhrxtyd|0k+2Ll2EQ*l4ci<&tDR89jc-b0L0ELm^LF
z(f39lZq?Dc&+5oXbHxyn+9CI06VtyP3Xi)5R<sY$2@JkqB|@#xkLc*|wv%2Du<+i5
zgxd77bqMM+dT9Tz@>IA4_n>F~-P<X;F<!?E60T*F0j_0*DH?aBmt6a(5}b$eSvdf&
zSRlIZ!)<)X&em{a*GpnJU=h*9ma5%SLqbh&8tdPH^*b33G9Xq=fHL{I5i8amFc^kW
zRaaj3sc&k;r8!v9;0Urtknn}M`dS^EFg@@x{92bGe2@JL3ff@w$XF1^Ysa=*AeBeZ
zg8>bX%savYW(^IxcoaR;3^u>45>cXb)*IpYoCxl^7kmFPI^2e<x^!JAGm=E^I4al6
z(2Lzxl=Ib9A|TKT1~b;tyhelT><RbKC0qrF6z5kauXsvUXe;Joh;c)rxS94MhZL=U
z|I;q-StIjt$34-#U}dJ#uV70`=wkc!hR`v<^-l`08ESpvxw&fCt36L-1sAN+B1;!a
zQH(K3k}U`#CGgR;lL+Au8_sUbyQj70=yXz|0hfD-D+5R9=r>?enE+=1WK~Pra{=7_
z5UsOi{@qO2V+ic0-R3u2?K>rl%*HfqgWZ~$Xce7<bdPTd@ZAwJL3qRop85IlF5Pd>
z;C7`Dgp5c(H)3PHSi9;{nVKwV33sRF|J$ogDFnRfgL#q4>Ia*sgUijTAwx#t>R9&T
zIy>5!{XU!4>=Zz@yx9Dx8f`bk04Vba$Q!W~Ge5IiQSmJf9rfR{itgMNfLk`+&8@f(
zxEQe?lm+=%-Af6#jF^H4!E!xDzWlKU-;EQ+FG;Pbo0P~7<)2@yl7{y7zwQk%eHKs(
zZ!M6RP<tfDyX^3xCZapT)kkqTJ<Ry3k$oj;{c1*qS7Lq4zTiq{tef^-pZyo!zb5al
zFzlSNV_NLWVS3p7{HA_c^67}_cl>8!r15f~4W(rT7KpTFG`d7sDWk*)(oh~3P6L$n
z_jBX$ec8z;U!g^JVK~V^kBA$`5O^u$?e=7uSZ<}l!E|@Pm)PKaVfDpjGgk3sS$VB|
za1Z_;r%klhfk0?0P$%P}b(=#OXg@cY-Tq{C+{eE2YvK8Om1<$041uv()2_SLHaqPr
z7W#1O(w5#soFv=lkl~ax8?^Gl++);;l2jBQ`Q4yQIwYAeNdx)pB^V9#%$e#3e1gCy
zgg$iRA`N*;t7h4TZ5i{naJI=PgWOWTcic#~Gz{m>lvB38$UCc=qeU~pa2b(kWv8v;
zchrsO0a?<xxVf+`_r3#b!`%s8HER^8Ey80kKdP&|sS@W~p=US{Cm)&f{rr*QS{}!Q
zy7|ny4}nAP)@bJ93Dm(xnhIUwhI%WB-+)ovW}-ww0c}z(vZi0e1J$<z$!~KEsqla-
zJe*4rgG&@<|8W|JJpL8y5_<+s)c9D}R4<@&61rm`GP!&Yqu0BK$JaXpRJB#C<nazQ
zli2+JqAHr7Bai^jL4|FM@QL*F>;))L<~N6uwG4)uPndYykGo|?nA6|yR<6;V;?Bsz
zYJc*}?0-f4to$q$uOyOt-L><L0WVLTxne!pgQsaC7~HTy6X>sBo+%JY9({kR;J<E)
zClf~3v-2^<!){C!P|rNz(&KD4;^h21@cAs(;q4N85*HhKwt-H)VztQit1qkN9P#(u
z{`mfJfa+Og>~aDJS;?Z}Qry*iTjc(7FT%?ojF^!J=`ryNJi4F}t25jh`t6VwkyRgV
zJrR*hrqTbbY{|`TUN{NVT4;rxcW7f8Nz@|F>S1WEf}t_p9x$CZg2hd#5zWh~1gX6!
zNjtj{1|=kwI_1Afhj$>!52F-WsJudQm??Fv?dit^GFA>+qWiQ~RH~4W$xrP^W<HlC
z-J-`-muVXNl^f|Caj)2-{rE{$3R{Y7{&D!EjjsiCsu$ll$Q8qAW3GX4nHpkVf&1^%
zMAC15ZtOo!M4_@^`vKyGCo70RYsIZQf$ZvLhqu#H53T4cCN)YzEsk@ad1dy_TvVl(
z-64+}A!#f6Cn-_qs50BJ%<hhxhAMuQu3qydu=x&+rzM5<4NyebduX7h)KT5&ld)#v
zhA~#Ws!;Q;ZGXDth;=pAuKE4u$FKselX*e)Ft5BwcVp=rCD)fz1UXX4)$+?S8C3_u
z!&e0mY+pg7%WE_#Bi#F*A}5N%yCPVcCHqd{CB^{GFBdp3^58qbUnTNkFl9m<xm0l6
z9Oj{@qU5HP8EM{<&o(+$v`=&wvSY|_ty~WOp3))u<idk+d_Uv|N~qIsl2K9G7pb{<
zerZcKb?CnZLj5A?0JHnk2lV-ir4FLa<Wk7ZH<#-o7eU2ufaR`}*LotFJ9LVbyl!+H
z%i?lh=6>=xB0t)rWNiaQo9P)w_O-W$zFNLudoVM-j2zA*qZCRR1S<-p@B@_zgs$V>
z8$OXap=2$6*EhOvZtbhlRiI~ce$3~p04>8PlBw-6Kd(-u0g1L$1Qp8%ku-~o#38f5
ztEI1hleX{))OkVOslPCAyqn&qGj{mBl_B@7H~BihvO-t*)*rWsW5>kD=;LM6dGQt4
zI3-;$zHB4Fz^OtDBAang%&4>d?c)2+8(00lm{o}KtgzN0zI37{mr+zoQMs{Chv!&#
zXh@TzT}L@n*_Kf}NjGRRI9UggDsj=7M35gq#6Mv?)F?g>V>dz1#RyB`^A$8O01W;-
z<+d2gno|g=viLoMYsXYxoiow?8kZTL5oF_55^d-ohWlXsl8+7ZKYr;4YwbR)u$f;s
zCam{d4ypJ}8@Dj4`0Xl>Pqz*o#2c2E*|ryT03c(VT+nw43bs2(VW3FcE2RB3ge8iu
zFQSC>Y@p4@Y7O#@?!MW1@t%C|7S1v%7pti8-AlC3MF$7R;l71aQI^^T8J1rLx}^eM
z3<7+Fdy;aWeHS^IV9Ors^K-V&2Pgltv$Qzp^2Kkjl%A*WPvX2Z@V)*Hwt#hPMVBu_
zAjL*rOB&09?E6_-KZkIA!Ilu15J({P$*os$s7P-2HvyEUq3`vdEWSHRN+g}Knvn>^
zV)n?cmMy8TReMHS{`?@($so}|if^9VmV4`dFU$wk(ENWyopW$xU9`nxJClh$vCWAw
zu|2UTb|$uMb7D_yCllMYZNHnZ-mCXls=88L-M8;O`<%Vk`mIXm`uqPj;?l_wX1KxT
z)ehLg;A;cg?mZ&~R=q8fN-5%Vdo}A>aa?$jUQ9yRNYirdXP6m*spde$pm^g|Y_kHR
z2z}eMWtSfJ$i}Rz+KYd1hBVr#Ry!azY*J4luI7{NzRf9%YX__%uAP=Z(|J=*jpmr$
zp%XfugIxd0`xE14t+w#}eIOBN0x8mps-$2aCt!tNa`QC8L~HkqC8_o?Y(o?%*5Zo?
zrJ@Z=NQ`^eOHUQJwNec1W&lTs2njHs3u${yeWis=CJCZY(>A~}vc4vF`Y6}h-wF6_
zeGH<wccRbhM3L&1oM9+Gw9o0wk5)ZokVT|1j|w(SkN>y<(h8R1n_BOyaNX54xkcl1
zAvm8D_OI!#DS~&}Iio1iQ~k#=?SCelqxyb00Lz3Xz0a~$;(46$8d&UN;ps2r@3%2w
ztU)XY;e0PGu%-NU^m8kFgra}{E1{p$=_6_MYer>E2d5cQ9s4xqbOMTGa!p+o1+Goy
z`sA0%GO^OcAnKX=x{~0WYgsVh;?7TiyxLQ?nC)2>(0bE)d}g^?dT00@(icd5nr#CN
zcI|g;ya0Y3SW1(6LgQO{6MzWn7VP;l_1D{7LGzrI@FmnKOlktq7pPso&~$GZ0o|VM
zbCSjCQ0Uw8H@^5pfXgZ6dDXiP#C=H%K6O0`?_3d*=Cv<tS2RkkNi=+JXw}I!<vOlq
zvUw;FAaX$&Lq&e?HuEKZ(rf*W&$?RX>k1_J?`qybOu#)!?kYWui$|y)fJGM<KKsT%
zBzb37kIsu4xd}AR@}kyPMP0SQR9~!cG-d-M6^Xgv_iywonJ;kF-rx%e_l3_J41Kgw
z(e)t$QFDhIh_OFQ9s69Et~Sq&S8f4<(%ItKxz#tu(o|yG>Kd+<?L@-1_GY1+>t0m3
z3irEte%=2}xaJ8UK01OrW=)`B;o4n46vW$AydKQ+27K8^SF%tP%;#)bVGM?uB1y(d
z-DQBy0P~P;-!Xq5ktUZEJD&O5+ovl#fwpT^`8lJSN(T_2ea2ceh1g1CLf8&KRHKUq
zyFRh2lc{?>?NL0v$UyT@r*FUzTBO5?s3&JdsY+{RdfgjD4@_4m3E}OtnU@Ef?z(hb
z57THF!X9;>1L{m;?h<5%YL9rdLwDGK2dRub*iGit*1-asSeV0~1TTJ2iU%O^>kOVB
z;F)bme`GwTjj(;EKh0%G6m{UqK>+D;@`!zPhN2>BifOyZeVpq+{q2*NoLTaR?9HOk
z>g?)MGoMW&MV%cy-{_fk1Hhr=SqXyexFKOzGlnI!Ile*BUjXL>-j19D>r0yvbb1cq
zOUxo2v9|_hRtOzn(D?o>7=CO$$>G0-&UYGLZdO9GZ-&mKVa?(;Cv=2vr-W&4FD&1A
zKHE<A9Xtzxz+P+-mEF(7VRZBS8t`?_+0iNC83f{>y$H4O(&_($o&j7nYI1hC<l;HK
z4qF;V)IDTwKr$dfR_Vw3<Jh9}X>gxaiPx|4IdC2`nU?1fq0t)=-n%V6f5e;^R$*5f
zU&iqF5D_sPUGG5c#+bQbcKeWrNnzyrFNV1i7`^>wgq5!QpM=}*WT0Y9Gd5}tW&7O+
zV+_z)?umsmE<s0vCKG6%_2NL-Ut|(39WVZjmm$d8r_2)K6$sd)fyw8uwoTnj<a0yn
zy&+h^MLGBk-JU>k_Jq<LC%*qV&X{6NZLmXxNm@9=hZxViDvlw5u72P*afth44f-yh
znGQA3_VcDEw8yV(F&|yP&vhrd1Lhws<`)4MG|H$h4#AXc?P;$5W54@08+VPr3jPCQ
zx{~%^FkRgE*9V!1rw9f(wj_I1Y^n!s-R!D7W>&1ss%B^}{~TxI)%+pvp@`CgL%PL{
z#78WCojVxaDpRVj1xn(&ytc~y<Wd2KMk_n=l<BV|31D|J34MSKh|LdRC)z%n!NBJt
z9dPj<!Kb(dY&k1p0^UhbUcS?3Fd0dKPLzEFd|O0Z?IFFzZ@i#E&+JNyKXom^QBqO6
zN)t8_T3!QaeHH^Ha!(4Iz6-^zB$uauH8qhIw_<4m^rxSbXxzwG)s361hw{I})CDeJ
zAD_7NYg1!UvzN0*hNt*Al$NW{maD0CDUoV~=IC?4F0xIkL<vdie*z{zGTc`%-6S=N
zt_u09>Zd-nA2%O8tK^#Fjf~5xjwqGum|InD@L)HzX%Z=JB*DkR&h&9!7XVD{200O$
z;eFc3vMl?a{2ca#?f|BpBYspG#>+$@)Gb2F#-Q_`nk{rT*b6EmS=#y>!K=!(pN1Qn
z6@pnrZ}*A>XAPK5(d|MSjnd+H7YIvJ#26^{d$95dLbg`>CXy+hu}_bsR3ugO+&KLc
zk#@_o_FTE(x;L0!6N0kq>jd=19y6ctkA1H;5u(Rb--Rb*pf6g(m*g5X{&jDPp1mMf
z>KPz^g3bGydxyRbe$_CFm3S?5j?*(+L-4k&?~ZM6>mRk089zY}k|<}@_@q8*CxV==
z?9wYP6&)btIM~2TSH+7K22DYIK_$h<Fe(}I(^AQ^Q(=wtW-3Ppd(Wi;rM>{aHN4)1
z%(Be9)Q$cK-$Dq>(V4~irv=dfECQ3IRJGq9y#RLSqAA%l_=sr#w_lbiD+P&ZOf<#B
zLv3>MfDRZM;t+X&UfT7aH46a@`@P`0)rV%pgI-HD^=alf-d*G1LVr}31TyR(qR&#a
zu(Rn&(*S3G=3C-}6<!%Do!A@fJBqkchUDIQzKi23!VTm8#7+eDR+r3&fJ78w0vjQg
z!qXehmeZxD36awX<h$Rt!^;g*3g{sTwY|s{%Y8aB7GjkYPq76ne}uwx$nN7*wAXAv
z5>;kh{+B=JRqMOX2|w19hsDiL^!bNPzG-}J;zQALsI|LMXq~T2Z(M#!o04;cNHw*_
z{Dy~P$q<kI@hOAcyQ9`i^DsgraIm{%pYERgMUmeG-|M<pse|$XKt#ELVd@zQ_>7&H
zKn2%xWV3HYrYtzJr9<)soi!YR<D?+krvX2o#G|wU7D3m?-H{3)%hi=6Z-5w{iDGJh
z@eX^>;N^a)9<n{L8Fdy5_Qct@lBw0V4#ZHk&5-BlIc6%(2D}ZnxcracNSu02q+XPm
z@Xuhcc5O_iC>?Io6bPasGg-Divuq1LZvcc)eCqFITox)sJzd|C5-<G=jY+zcu<72v
zfEw%^gq!S~?xbo^qQq`F_40BFprJgbqz3+qV2Zd&G2Z@cDt+tvib3}+6_`Ot-TskS
z<%qx#IhPv}Lt>KFc<8OO0j;<(YND_E6y+0UI)UU{e&+|6S_xf1@A244bk4RRH=a3H
zr&wv8W+xETGhT+h=3p?JcZGdHJ{WCTvoOTdIH^y=n^I?o2OQJ^7DuoQq2v<eRvL3_
z;78vuh*d3BUpc{dq+1Y{VW43mS-Uh+NpCetkh8IZ?0G0m(67bXShRZRkb2H@TJ4W-
z+|^z)8iQXh$sh@4rvn(|{&g2VV?XwS%wQMEM2-2sP5mut{d~aR$9IBzP+&TQG2V|~
zLA4tHmP~C}B|}*zDc5><pRsp^*2tvS3E2`6G);XQ5Rzp`km|JgdrOQ(tFNRM((YA6
z3CbgTJYHXN@L;$s7s9qM5s{Rf#Q2aucH~AHNtnAt<S5(xA`>T}H|%w4u9W`U0lQ$-
zw*qNp2X%7wkaAs0eFnZ=A^vJ5SDVaq8+NC%G*i$dt-^WzxbNJuP$5W>E&mbt<<M&#
zSL!L&vv&V$zRQD&MGCYqGO7Fqa0wa&$>x*0S#aE+gwaj5+T=5)*&{%R<pAUt;{)IV
zN#ARzFKN)<C5iaDSUI?msQrq3kATw?t0}MHcX0>s^P{5d%5t^C#Jujs*~5cRmTwSU
zYKwoZ8X91SXYr(O3#hldzcVX(HRXN7{))i$QZtnzn(&Gunh@!@{@*yQ{TbU9-ub;5
zf)%fFYt-bO)5-!~ju|-7Gs?bzo20T-;$8^em)g3}4;XeOR5E!_vV$NsF#Zl%Hv9S&
ztm3b&&69MWqs|>u=!L99VMy7g=PTqG2d9^ORf}#m45$M_-tOerK{*9<iV_^LOdwS*
zBU22z#J-bT+eD;MH|aQer^rnHYW`jv*32vJSC$uh%YF98Q#Mg+ceK?Ca}7INw<Fj7
zfbQ!y<{3z;qZ+!Ru?UCI3!Mg8EoNJVJm&#cQMiIGeEodJ@G_F&A`rvIp|m!-EJ+H?
zY5=pz;rs?0r*->y$%M(Q7)pyVFl#d<<uJM=G8H!k^WU%FXMw2p1ATy0Imt{hZzxxH
z@Xxg>Vk+%{%HbM$py0cL4GY)rcUuPFT3pUdU74~uBso?g*)9gv(`Yy=k~}_0-)v`%
zWNJlhYG(6XMj`goz4>HoDcMq(XRn9v0&8V@+ja7iMKi6^-GH4%6~eJz4Zj{tsA;&k
zPw0zpr@oXX3fCe;{)<xb{Q<Q2bNr3VTzl2t1tF178?oA4&0aQuJTqChJI!m<<1P?J
zWo!)pDJbq(|9$Pm9NpAD$k#C@V@$v?6)qks)NzvI8kW}hkyQrr<s9|>YgiwnUNHDu
z<;W$WrR2rSxjk>oNk=QOFUoUeG(OHLJkIMYK3(0U5RpxUPQ~p$-RSX8RZU$P-oYjd
zh7zu7{+fnWtTl6}`V~yy0qS7XWf#FwRnweIiCmTyNutk-%K(bQ9K?TbNt!S2VADYv
zWDg#k8{fTRnf+_8;k*Lg4M`X`&0awgKo-$Ns{;NEjq=~q6X%DB(w}w!e>C*yTOaWr
zefi#O{)i_KLZ{ROE>%?|jl;+(M^4dhtO<$4o=_DL-la8GWbb8$TVCtA_qki8i?@x{
zghs|#H;lbmyyfg9en#(6w4cFG#Z3DebK61V24`}~gOLBuC*0u>TIxUsIX5H3K#T?8
zOq?|RMX_0fvDA%H5qs*8k7b@F3bin%E%<^{<3?!vhl9kAE#K}IilCQ-Jd!1lYu75q
z9d|8wQQ&-N*tOtiWi4hF5}LBJvz`m49?@J6>j^=s*JE{cJ8t2D$83=h3^lbjkg{cJ
zXiKkU3X}c=Kwk~RMzYXY>D3Po!A?OT?zG>eEwq{3BAu_9d0c-|4|3K8juR-bWQYc2
zQhonKMkiggU5C&PU|Q@9%>0A>3uIEb20-+LLYC>7-5zwH-7-J*ZHJ0c(bxzUh7C9?
z%HwXKo3+g$Q1Z_2F+Lprby3EURNzx?uN&1nr{_OM1A-ESz-sYJUP!ludtgC-Tpxf2
zwvOzoY*)d1fo)^Y(I#NhF+%d=4;UhX7}O1BHy#NdLS{L9_Bk>1wB=TOoK%OX%q9|4
za(Qx)T}1}%UTT=l68%-l5W6zn?V7+*;V+jL*7^3ot&&BO{46+&)sx`}csx6DrL<-?
zg^nv6zVj3#aq?_b{+=QD-OFk8__8K2pXvp*^j{-N!axUWZ?yz-YjQRT-Ihaw$-mwE
z;{fvPzeWHM22ke#2L5#VM8&M6PK4yI8PUAgKRS*Y5^U|5%JG1)z-);^Z+10RBVMc2
zmf^{q>RgJHCI4M<0w?4A6;|kC|6<P~%&V`GpmU>PR9qE2x%4TDA4!l2>pqk;HIy;m
zO`4!NDT{M$Ia7<2Mb_M={59gt)9X4j^rU5uha>TMn7B)!dW42HgOpwasS{R6csM`5
z{E*8vCQR&$(v?~t>6k9+30pac!ccY^@=;a7^CX)0z<ZF)svx!!BvaASV|`@QB?kjj
z2AA!x<)s46(^*u|_;XTF&xDW>j+VQ5tqJ}Qj~jGVXKTg;qI_E211j~sLow4aRxD#K
zPE9V1*qF|E-OR9_AEc=JUt#mG_L2sz>uRNVKl2^!q3YlAS-ib-?}L|E&P*p$?a?;U
z8hiJyxT@JKgCQ$*0tJ`9jC3dxlfSr4t6zLFQr{>+y(6ID!3#teJnxqV-;<PZFfEe1
zg)X-Nl%F}2k`B}BQh88d{Rja@5|~fckWcrxyg|ZNy%1Qr5MUl>5I~MHg!y*qk~{&L
zxfjxoaHv+&t%(FF8$G3bxaR|hARR<aO&<W;{TYTQ?KSDaI!7$790qmcvQVv803S9u
z7p+UYvico%)cFs6Tacc_du7PthQw?4k94ux;-zEanNX<r_oC6#BC#KtVZMv6Q+_)r
z{+-7l&L&6^zfF+dA6p-4Q^dw;@xJ2SEjQ{F`}CZ_<>57l$N0b|TD{DYFz90~)B~gQ
zdn!d_;&e-OwSto+-w2Y71*;6i2(xta$wxNX=gML8*S=DAfGhaSW5DtXyEsTy*^}@F
z`YTRX`82lr&%ly&lt~?XV)Q)F!#BeSQL_AB_*9_)m>Aq9=erO#n~)k~-Bg|}`>2&5
zTCvIF=75P@@;}Ws4I<Yan1ACiBnaVl!A`3vhOUquslH$OKv(D*7JIZ1p4w{A?E(Xj
z5ND33d4v;j9u^IelU$+LBJK3)^?V!>MxU|)B0Bw*ADJ0!q>+*uirmxdGo^Q>XKb_z
za5pLuHJ0Z^e`EY04#t}nAIrQgd_Lg@WEkLBPcN(#L6C2Bpu@Ik&<5V3wk(sD3-_}=
zhTfy(&&~snfX_};fb>Uey0-;}2WdPt6K1xfuQZRaHhA=?^lkDY9n~W@f#(;T7(5T6
zD5eg`w~;*9@`qMYrG;Y4@Cslfn8zf*fk^D+NhcE81?hz_^X^1m28!z1O>uu#8f$gA
z8Q1XYX=l{KEWH%08k?HS&q2zql$x!(5;Vk`%IdX3$(~65v4s==MCGeN25MNq#QoY2
zKU$!&X};q}9LJ3qjVVh-jO{(eO6Pfk{IwdYWN!swAU8n};_t(JT>TZK7B7`4tdCBi
z+NL`vncJS?e5PFCosP9VuTeFk6_k{~fixq7cFA>~Ic4+kWptKX4RR#pB!XaUaj`8t
z&JGbr7S)Vv?Xl@hbY|w<kG1T&$gQ@#9k~(Rz)stp%1&(Ru6zy7gsvR;rrwa%*+LM!
z7hLBQ)O<rT-|I3(HbamqWC-#TO|rFx{oUz$H@{*~QoYRgYi&wem^n<)6b~FMZ9g2y
zjU&!(+tI4y{uhplqJ09Uu*0e=A|%mkn!PFO&<5|;`DJ^2)&xx~-=N6kAh}Lcw9BcL
zmI|$OlSZ-m;Zk=lF>4139!YKdUp1jUT{i5-A!h<fRiBWtp+vy3(Ve6kYg@!39N#3B
z>;WrkhUMZP-iXFb8p*s)Qk>jVo2Z{zl$(lHyBZlM)H|jndZ{4#Gmrq$Ouw;(L}=_s
zKC+`uk;ETa`QI3@qXPMsM_LB~{LumdR|md}x?dJ-qjPfW_*rHQxC-}4hJD8wx4$&O
zJ7l#2r`EhHWdT<5_bj@u{*6v52-b@UcR+TN={F>73gl>OF`eE;S%CT%ya#;^XUN-m
zhR;-TsGf@OJrWJ#^)9bto__VdKZh9a_*07fNQ(fD<DBBFzab*hg@~%K^fRACWU|RC
z#<Gb*EE0!}zIzQTQi|0tY@k@;C!<(C8iaqIg1mev90CG(EeLZjdl&78$HiN_OUrY|
z!dlhK-MY%#lXAA!!)%FzSL5+)v(w3Xk%L>w)D7Ot0^R7B-Xo%NJqgRJ2(!n@38#gn
z-~l5o8Hl|&Iyw7?q13b0+`X~^>s=&tP=zLri3Xdg(yZ@{d}wpt@aFD0&r4BVf<$`5
zvcuB3p`v+km~tvZS-;D-HP~*zC4LH?gOb$R$C+b2*}t+|J%^+|jX)fH8Bd}Zm2^9X
zf$*)xEeU_}V3~_y)N4D?_tD7yisYta^19e;ed)Tn*NFVO*wn}+dLN8JHYx5Hx5inC
zB13zlr6mflRnmvKh;G0%ZOs*a;*~xG-l{a8-i`rc+Zw;L7goKDF0HDi<dSNCVqnwQ
zh#;4`#WaWxu28OpipwZ+%*SYB_n@pfOc{;zZ6aZ+r9JjgsP+5T!tn9_lO7ia$7g16
z%1PC2WJI{kG5maI7t5(yo6fpzB}TE6sL{+^(n-`saR})&jRDy<DSOct(La-0pjRnH
z)-J6p4#pW`R59}u+<msO?O58wTZ&m3$G^ovz)U$5IoSISJMs;t#(T0>e)=}43vOD>
zsp2u%VhXrYGB`g}4sAJN?yLqElvdDw<6-^7l0sLhB!+OHHlLW$A!<CZe0)Im=CMRA
zzbHfL=-^~Y`Q@M6-Mag22-xv0s>zqP85TS<yz9#7e6X1t2#M+wdRA_AiwWd#bvw+a
zlh@#q^f|{^x3R(%R;zC<HMeBhrhGT4=ieH)PK&s~@O|GziC7Hxcqv-08!Eu}h&>2g
zk*$)D6mrf{>?w~I!zeI%=Wn6}04H&5i?L>RWzMQJt+b00J(7T_NUF8y@iZ|s^p|Sv
z?>Yk*%vt@Ejwr2IUEB6@i)T^Qqc;4j#N}6nL!9~@L>Q0enOj*bIjE{>{f7kjS;H5=
z!+;2v>|XO4k>DCjb75c8NWh98b#^}InbEyNTj}GlSfjv!q2qRyA-){j%;E^a4fYb~
zvz4l`0wK8n%_ZgOw%FBxoJ~_kD7hh=m+I)ZI1x<7+K)8|OHgDuKPMX-ym+K7Qcw1L
zExNA>@O^X#T04!^zX+r1RqBAP@oVTn3tUIR9-`0+fug|;IKPGYQfndrc%DJtvhtnY
z{TC|Sp~h?P-l#~F?^IU%qH|8ZWVQ8);B<j8lWY9ecq;*?MZ(*0L9YIpZudUZNNlyS
zbq2-*e8?^=@Cj$%{7@M)f+x`fuU4dRv!{iFA*<fqePXMjmD-id+XMTTIS$##3>DHl
z90ZC5`<-uw_W0;XU!ZZ8Q%-0I`OLTX1<lUKglao>VSR+DhH-b@8@3;Jx>zSrEbV>+
zB;xGsF!}9#${uo?Nw+Z<{2Q&+dk|3*zuk&rbdihhW0i8A$=sSSu#=m*e^?eBxh5QG
zyAsKa_{2t-IV0#LBX@FSLv+2ZnO#f2?1%NtWlkK$H&ck@k$w}6^Ixpe3Agb{1oy_P
z?xJ~<6p3Avl-kl{ZD*%UB2UnQ(TE;{kvhy#b^ZHSBb55&ioD;|ZP|Ws%+v7WulRv;
zgR4JECO+rc*`1d*WKT2;lFxSO>5Xqtdym=4iSBDT!}~3Tw;NwnD`{LAi;v5l=m`SE
zVWy*8iwSf}7d+xWz*iCP1cOd~h|mJDEL>sbtj@%xDrc|nwkCTpD-ql;8W7}xA8q&7
zO=#iXh*(ck8gopY=cDarBD1k(rvPem>(Wd#+`hLnux8l788@pzwTK5yP_N*Jw(GC8
zLy^ulvy2oGMC6;p<JhG?$z9!lmweBWCR+X3{!v-}_Sr}^`+FeFSuNxg?tR!k#07yz
zwAruGxpDtDxqb8mLTC17M9DGg+Fi+AG5qPP_*syg5j$7w?PS8{$T#R^(4>ae&OmMq
zVT6G1E<U23L=v=&WheC4csFQf>ZFaB)`_{k8ow^`Fe|_V>zyqraFP}adR>PbO(&XR
zWEqZNPBFot(v+u}wF`}uo^mf~8*uxVuaoHOQ48q&Hq7Llo+C)BfBng_$+ZNZkMb-t
zHajX{ei)U<2&a7dZEuCMe&Zo5MB>yVbsC^<>4F<1YQkbL()n*POVp-ceos_cw7U`_
zn0krM4u(@+>sqIT@y(+1-ok3;pt-m`u{0&QrGc6uFOi7Zq3M~964Hap-u-YnNVCPN
z@$fr~azBZrNl3+Trg0$dg=gEw<X2yN{mP%qz4bYZsu&}6+DxZmTryZ|+`W7q^=OUF
zy49A(Q>uGXb}JYT---?Xl=DfKuE{z=AVGtUKvYbW$nuGtHT2SkyCJ8U3cq8~i3Er8
zX<cP_R|X#i&)CUI)9{ENbH`sQ*apR1o0ZhdVJc_ECFKr>cPSx&GgWaCPm7Gmk6|20
z&;Io5<t{LI4F9(TL20GGs5+|htvezR>$T@Gq@qF#j<Qb)K}APogwb}qA_WCH*nbcn
zm@w*Qsw#MMLg3|`iR-_$O$qaD5_4=Yzx)mfT@GACnC6N@I#E`kA{o#`X}|L1n8!3x
z70hWtQE>7sGpH~Ag9T+!ITx^a^=#c6Lq6@e5)k)G^vSlVIiI`i!GyEuxnp-Jqe8R?
zc2%c<vF^;eie=m&1vVR3WlyvY0iA8yfD^N3j&Rk66Sv{cp`tf612o<)Os5nXYR}xd
zM_#NRp~9dag*e`#!j?*SMUp8%J%B83;R>swC-WTUlm~1enVzmb&NO(~QftGbbkVur
z;T^KpicU<jZ8ge%*}3tT1=R%CiwjU?Sz%7`N`krk1;-PS$qWnQ$MD`fcoE1Ppl)qb
z7>#=vt2+J(H1xz}&j)+Z6C?B?#p}-G25a7YsVPD8nz@|5;;pKg_BMc(pk^WUZcZJK
zv?h5*z!|T=lFZ=Y##3c>g)i*w-YRt9c$$oaCKCR_3H?hrpDRuh?4t77|Ag)5puWYF
zSmP2(2c$|6GM!jav%v^Y*Q+Hf-tuy}^Abi-?viNG*c&f`f~~5>c=goq&yQRq!3jsb
zqWzyQm)Wf5?5xgQ?52}r^+cu$rh$RdBR&L396Z=@UjWR7%Bmj{#sd|qiac^v(-FcA
z4V#62+k7rltwGNuwGx7{T6lUbcyCX=DrdjdgD11<DLS>bw76b2su@QJQoi0T6z&sU
zZERw9?pgcQs>jS&ui_eFZ#IWIjD~`2`I=6460~OrXIW8{fPYtMY>VuWF0!YRtEHd?
zUp5BQD72wwU~bBsdIDoesuZs1cx|D4g=!?%;J^p0aKZ*>I|a3W#SRGXB>@sr#*S@8
znpCIVQCiQ?-uh;$5_-^<ZO80))1@bGCy?@&^@$SJ8NGLJPCT%-$gPHd)n*G3rtdZi
z6Olptyf4?;iyFy`dy7A3O%)DOgB*;Ujj$dMM!}nL8gMOZ@8{pt@J(XcMx>e-*@>-8
z)n}c>)uG5#eG1ZK(djJ`9rH+SJx~}sC=I(st(jz9+BaX}gA^Fa1NE`K5D4oAyYJNK
z-MV-`xbR=0^Kan}13HGI2hWWM4gTehid+TFOmE*pHcje+HfvmRUZlaj{Ht?=f#nY;
z2<IEzf*n>~$3KoPvy4`CQQloMJ}FF`JhlvmSXhI{*9Md|J44vQnfQ+!LS-5>hI^5z
zrm@5<V>t(mLL8iBq2E#PgMFO;+F`jL5ma_i8*cucnn<){Lm}~IMoH7*w(xd`dgZyl
zVNAf0L6^G4yUS*4*ZrvbaeAu5w}>R{V%TR@g~sM=WTw2v_zO(CmxYu9L52q0zZbS4
z9BrO<%UMi4NY9|ASuA_)r4Dsn6c$&us@uEHz>H+y5_u%&*G~Jxh(qB~mJ7r^JZL6=
zmE0$_rq;=-vyXllwL}+~LVr^8FE}1OjzKb$<-T$)(AsrwX?t%NlY84@es_WpgHY90
z8XVOfG^QYw-1q7Pbx46Vv6LE*O59(KKPGeb@}ln4Uo<O7fu6#xc_fEIOm$7GwJOk_
z*%eW~&Rru*vZky)JykbfzYfUl8=sKd<3PZz_;N$!MuB(V4{1h*NJhB)or@Eqa=J~_
zM%=Bu9`>mMA-n!Bc2d6>#T*4fyfAZm+}<zCiOGz$sd;Ti6dKCN%U!FzLL&z5#=M{?
zc4&RDHIs%dv!2OfrmQtAD)7?y+VJUnd1lEqHrhG){OO>%@g!{?=7i$Sq~20K9!pzw
zBeOKK0H?pCwzDuP|N82^1{UFo{f_)kiowi4p8)~MHojeF%vljE@;^t_)pO^@)|t%n
z-Bw7P?>VY1i|E0?Q^Gxwg;9%cs{e)k&rxT`wMREqm%b;a?X=w_`n^Z%Uj}^hx5_3{
zSm>gUPy(Dw-jLMyFA)j;KE@O6jcTVbU%<9CV_UR$T$+0E?;ptGe~|=WoOiyhblyFI
zGCc!B))#T!ZPZ$;RP>H|<T1%CG{)h^=6hb@iV9Io+fx*EHbugD+n|SwG#g^X5$2gS
zWe>NNcJkoz>;!3~<Ss*E^DWfLLQNXRNr$(^I>!@Eh%od>f1OfH2`3G;Z;DbKQxgxY
zDCA>SXq;Thy>rh`L8^uoG6u;<&2=U17*N>V&p}Ag`8VYGUw;ngU8BE?LPbTyFa3E!
zEByWy^6Qh>8;0J})X~|dd$RTkEK+<g;@!S76W(gYT3g9`Cuub%g!IXL7UX2!fsC*v
zLr+6lZ_(Sn&3j?RamVxv0Q=v`zJ$#tisQ?%>n@gDIhviwB!f_moJI8apfqGAAP>$-
z2fl`ye#-X7sGZZV_(go|f%g9N(u5psw15mVynBnStv@>gACW%x$(}?=Zqu3M!6LGf
z<iX)q=l>WX65!1hE$l-)6}K;a$gvE_t_u*^aR0(_FwvSdDIb*L8i>QL+{le11?xPl
zx+0nkJ$=W-piTdF3NsRg5FD}saV8UV>mSt>OQNTt{nH(W`ls`f@iJP@h*fyw-u~u5
zf7U^va91ve-8YzWoRSmn)S!H0llrukFiPBnFBQUaSj)AG4f@4O;b;xq^~L(f)1Ati
z!JLqsmux3QF)bAM!4lD$_%RKnq9H%;j$_EE_newIf=X4asHjq{^bfh`(E1Cj=Q{T^
zvSpKt8zgT+ck!`%HvUo#>C;F`;L|tEW%V~C<ie4z>cg&z8_=kef8DDdqu{P9B6rdn
zH&9ZC`s!aRTJ>-8+NFimb`>SX@d#Y;!k_=q-^%Uj^Ur7O9Uz5i{|r&)JR^wV(mov$
z(wqB{IcP)is4t0|)j3Yy(2s4c`1g(YUxQIf<KF~*$_y51TV!u0EM;_9^!GXBAu0v%
zDN6Z0GArP}ay7M>N3}GD8NZBUmmZ;P*<h2Sk-wG^#wq1N<tgM0OD+E~dJ(Sw|F=?}
zA@1SVw{3#9!BxODNdLN`6s}5}B>mWEfVH?^Aziv#Z5(0{-)5ZhBgOXoDP~bJAV=yT
zbaZRB0bwiq$BzotS>O$OHNs`yE|H%?tbX<pJPVWB!%BD+q$0VuC+tdpqcyhyU7V#J
zAKH@E%hbVg3r9e-q2K{Cg#_i9g*@zdyS{Ji^xSCg(*UhmX;y={$10gf)A|j0oTsJj
z*d2)J3fp@gu3gq5O7k@cF=eYiiVlk%Cyjqu$J;0M;IB{vF<yN>n8Uf2Hs@<>%!XqI
zX4Sp71`WZhu`gcrS;ee<XnsI{i5u5VLxIJ%z=6jeHUD;#l@dHf^48{XA%A&P?>e_I
zLiTZ;bOF_bYuiOoQKC=t&1Ra5Py15GY##NhVKSD=bYyT<T&Bx=B*z-(*#M29R$8_7
zwSdP^3f(zMet1g4*+_0-a$QHhf&*0bAxq`yEn)*#Z<3_-7>Mb*y8Te0L2_s>d$$9&
z8p8=p1{%bu<UqMyvTk*hE-(YUcm^fB1pU-9jIOZ{CYNvuKQJk#I2vrA0_(~Gqj&q1
z$9(QU;IaPvMCyG_Y1~t8nD@P@_x*gQ`-$f##0!XvNNo(dxEsfYszLN#hVyUPMV4WO
z<NUS@paI%|77>B%2Xu;4PY?w^E2D*hvZZ=gS|=YC*W!H!t1_tr%3oZ(>dt4js*deD
zwbi{!mn%vu8&02ov8TfcwnfH=LXP&(;*oQ^rOXR^Q$8|wgOMu2iI~(AuHsdlL6~LF
z4kRR2$Fnxc3kT)Tlu8FD`(}<WOC|i$wwG75$Yky34o74_b$^g@=_9T_MT9f6ar^_d
zDEKr6u5VUwbnvTvD~zLUzrL!5_lB78ni%}WBxq<WLVx{9g;0@Oz#rLD=Vf`J1-s)k
zD|t*ksGH*i#9vi0|6p>MlEw6%h9ZevSjpju__%uDvD~N<&G=`N8j#1~1$n7Y_{xYY
zKw9KT|1LO3ml0IMIE$mtl-O}sVT1qN1=FF_A;-J=Zwwh$8V<=!MDiX!GEVq3sXF3W
zT0a%Mm}Kl@o8et(uw&!xeDL?U(YRgsvatRPwtWa$VJzcfqV5m;6Pn<OIW-+b65Ua(
zzNooGDP{aVv6|S8Er43i<2!ioHjm254RH%39AEtYxbc3M8#!24d~1q+Y5L^TewLTF
zN?-^K*sH2b=6&+kMt~TA&gZgDw@*ry9qh@R8z8;;WivWOwXVgt2XjiIm@SiyNaQbU
z)7Wl*Xf<qeo#`^(cd5kxrd0J@ruKcWMDEG-<1~A0pYGh7rIV=D%7F7ba|%DuAb9J9
z>J|5R6sNRLe*s2Y`)Xc$YC0d!3+WM^FRh&`QUv4V?YjjGOkD(k`rv@^I0sf@ow^B{
z+dd?1cI+DmO-4q>PEOLpAmHWme)H!$^!M6?_980NFgAh3^LnzBrlNw%4LY2+p|>Rz
znB4xH34cs8yrDgSZb9>If_B_c2gAyux1~NB^6eYKP+1Aj>en|x8*m}fw&#SpHgx&c
zzV!0H_8z?MI-EOQxOeM2KkX;CCM>~0&!v`2xq@yi2Z@T?9Z$TiPFz(0IYn=44DBy0
z9f#PfG<Z3;$7-E8X3;=2&D31$e4pUm4@#Azj>N5I4>33(To#CTe+Mr9gxB8q#9D!y
z_6Bzb4&Q}kX(y1o@d)IcNF~piwgp|DwA~oIKX^<$gtT;A({xT-$c?sL`oXWEcbq#_
z&8RRYG!QeQ)aSnPDS%TNl1zO3n3F>h4i{_W8U3A13Ds9Y-;xOb3?y#7S+{lGX?5O_
z@?Vp#1g_f-lTxeN6nj`dS_#GP0a*z>!MwY~B$}Do-j74suM!+K`u;w-vA=IKFI*48
zy+8U1pK}S1PyQK6ZV8IK2km<14TzZlv?C)6BwFFYoL**i#;$G<4t2h_8UDBI_Zjcu
z={OXjC>fGpc~we}bg^xFV>Z912l<C=yFl>lJ{Uf(8F0T`07=?>r@efqsS_UL_<ug{
z#fg4Q-PdCA?_tH}*}N|2JvaATV@aVwZ`Gewit(4%*@BmhBWl0b4u{tc&|plL>$qh6
z3`#~s7oLWBR{J>Cg`J6{LJBs#%De-Xp!LDAq{<0%pmM=#A<N>tK619(J-0Bp*zn0y
zc0*QhK<DJy9q(1WhO31YkES7IriJ6a(<r=s=Rn9)CJaPORg&bj0lxTiF2HXeD+vUx
zxRm?2mGi!M@RExOqZ{4*RKCAHe7tX@2Uiz(aPHYu-lt(!Er$sN<FL5}LKe_Mc$oYj
z3!u3NE3aa*7i}&DLUEg7B)D5}?Q+s~;P?7(TbPQ_Z^4TqEI{GqKU|3L_s=$B*?Awo
zc#XOfqTj*vzQX)C@qVj<W3m`-fA@Zk{+O~U4g5`RYDqSIj|qLv24P}>yelQS+wIGj
z>kqD(;dqu_<y7R02oug(oM$oC_sl_#LALc6{k9n0{OtP0Lg@8)T%_3TfM??=*?TV;
zlA{~6mi|{{Kren7lBUEQ;g@sJV6;FYE5_F(Ek~b()~_MVya>ikNVb5YM1i0;Z;Lk0
z(PuX7HKd>jc1PT6SKO!20B#TA)#l@@v;DE3;r%mrE1g$aEd(13U=};9Vdo{HP!^;I
z>LUl)=#o_^%c8TxbwtVsWPthqHtb=im<UdBa%@yoE%n)P=s{36R{lMy(JHA<O^k=s
zF#3YkbLW&eq)@r7qNaUU1}sI^=8nYp)AQ3mznDhys5i?Dn<zOIs4m~LpKn>R#j5^*
z!3ihmrAi3^%_VvEMfM=lbpe<(W&O_Pg_o;_DDXzFg0!X>+q=Iwdui5f1C?S29NsUH
zKNxK{FKY;2YWxr~h~{H(-OmFpsJV{ldY^#1i`o4cCW98j@xAIXtg3oSlv#kb#P-X4
z=i`~&+pOGM#>7(wAez=8yh0_s64kvG)x9Ruz0QID9+F-^y&JV)gON!q)!IM-@7rEp
zax%uQ9TKMtR1Y}CHJLQjiW`klcH=abTXV`L4z?Gs;UbT)@wb#gUa=Vctm=DnNn46i
zqsQE;n+X;}Sjzr+?)ID+>KtEvHzHrk+VY8HO?tMHRgV!y^z;gq&*ESZTkJ$c7S}A&
zWAkOp8;AwE8QXXrOA7fC7f(iF`C@M+8MTjfZfvWcH#A+${xLNcg%mvzaS13@LWQOQ
zzVwsXBW2@(Z)A^|%h|rd!#3gD6k!F)%xL)6iE>}izA!3}l=9H2zuj<?Z&yG-IHpH8
zW{N-hZ;<2c*Ld&i&zA@oJhm{sav$Zii^Iao8Cs-?gv`je3Gt9xx<B7cq3dpY=TOfy
zob6)cxnkol*wrB*g9Ycm1Yc8bS^@#1<W<>xsRZKDKkho;lf!BLnN&r6GqY6^Mv}_?
zX7&R7RJIGOG}}3PJiOf&S95oBmDb=lv=s3SmSn0FHLLWUz&!`9qupcpRS@}I!y9X7
z!7*C<4o;;My!glFry)iyvH{`K1?%ks|FaFc(5>~mtfk9A&O~jGA6f4i2TGhl^=<f5
zKHqUZRwejh;r2IAkf4OY3FrGlt^iz?dfIHB6Eq8a+JPtnvr?24Me^e{*K@HH7@&FK
zPIKpr*z4qH_vdu@7(aDJ0UkTJ935R3Nb#c2v!FBZTAs-=N%;Vl*ihqAxtBUQ_qj5i
zes#5jc>bsOJ?@|_T51f_lL78dG|$C#_T<D=`?T5<SUFdrvlzRq(a(zqnY`x{0l%=;
z6);5MIZdI@xIH^uqNVF$v38N(ag_e=BVyt)LXk7&pG%!%BZAQ{vbgT;?Z10fAJ0{H
zS__#Qu8X|{g$^w<g~XIUf9lNML86A=l969Fp7-uaI;I3bervleV2He7@^h8N<o;?7
z6T}N;+i`x<LEk8W&C}y(7Zg-i*$VYl%=g69Yv0t>9AEm9N!X-she&UUE+Vs&)Rq&*
z732jdv`^M{zs+^lep==+`2&mQN8Qt!YpfPGt1~CI;V~qGt8WEE5SEPxC(ga<I1yNx
z*m0^2h|x0R5=@||6nB}=sVIY6tU#Z#eg`T=ZhwBEq7uApcbJ(3#eAoe=T<mX0N&Iq
z|NG!aO5JB!Z@e~TXZx;>`K7>fPR&!rM73VTQmlvnS^VRlk*;q@XqZPR9!pq4vq<GO
zkh!96cd3+h{Y6O=EaB&ZXQ~ZcFe{zw-_-8If=>0myuv0fg_1*=6nY5Mh_Eg_V|oa`
zE>X&-&#J~Ok!}G=pC?@dkK%P)<TcDow~5{tZof&?K>8CysnrL$N70rMJKZ|dZ(uK3
z7YSla5I9X*poOITIfHvFpLnJX1QqPK;EXxy2@RqPnfCp#PYmoQp%&b|ct5`|sD35N
z!zfqUw~KbigBEuv+gT+97o?DU!Ti|PZF)etT0!gO?&NiuORu!QtI**+l8`e1yA810
zMlKS$r>0KuDc9BqOrtVjCOwI@3GsVkYplzTB1knS>2mg6v+mkIljGj4gV%4?AKIRl
zIaV~bN}D~VKYzsO88Q?FSVGY=rd5<n^{zQ_pTMmi7Z}QK8FX1_KC{PBm*G9XmX>V(
zgjB*TfL2pM<Qz&)n%{=nIoRcAP5l99eCqrFieSC&OMSo(h1T1NEw8;@@xLRx1nbJC
zFinc`%=7|AddG|0Tr{iwHB$r_`2LYDZ8P`|LqDms7uw7?foQ>r#~1I9i}1U#@Q=@J
zVPE|-_!j#VlYDr<M-RiRABFkH{>Cfr0?xlF!41Yk={l@iVRz|7XwY9FE>64wHA-fO
zxG#{RLlWLnGpu&~^<^}9h`-fVt&aI0p+5wwD^LD{;kbdJ`0R)UW<@X;6T?E!X}djq
z(3g3QFWEavZw7=;e|+w7mn(<}k_9kK1785s_?u136*-65ED<(L5Ct3SEw<cyMC-=R
zd9`9O9S8e8(ba+8M1>wU+Tn3EI=-(`1W2$sYiei&>P+fj3R9B1V3R((YlQ%I3Kjy#
z6z}sF@M#$ydkEF#w>Ykd+N4Q8b3y-g^OHY%Jw`+bZ0PDdjXtKTU;U4}g}#pHnPApS
zqs37Po*LXGqsWz2fGvxiVz+l#JCuGaZ2U#pASDbHgd)yrh)s(Ig*cu`I!ML!s2In~
ztC|}@_L356HIerKdH&DHZ&M<{WVsgpo~Cn~hX28VU#*Sa3+)$7Q9`(ELXJL(nS4z-
z3K{Q-#sr#2icEjixEqPwX0Z2Sb-?STIB&*Q(oz#FclrVsykKed1v}<O(AI7YIUkDz
z{FKO$5J|em*a=s^vmNn+{`y#j!mMmZG$#s~&L1DLY41nmkKA{?gR(3l)yWnMP-x#|
z0x;+W$XaTJIAZJL0DNnH5ff|ogjU!(Y0Br(yuT<iywDQ${~QNNnh#KOX75y_ysJaX
zOt-P!c3|p%b`d+H28LX&a-jW%4gP)?@?#QoHKFBK-o(WF>4bAD*<Z(r9B?cg?%MEi
z^*SE0An@EYlcr5+=$v#&WrZ|i(f|zYF@%q5WEYJ!2q=1dpE-nYVNS+P-P@INCp|xV
z)~FyUS$pY*v}!SX%`+S3xWSNKvLyNLRtH*xQ7w*<b>H`ND>sl>mYVgq<_~=k8hwH=
z+!#$)an88GP$JHm94V&01yl(14n|Jq^KC1%uu{H{ct0`xgbx44<F^Xa968|t&K)AT
zcH*W?cI?Db#Q=Nz8+>$JNGGC9%Wpx9nJ8hI7zyd?_jm>Vms)-c`Bos&$^FMLh;MRE
zb@AFBOAI1hejzVKqN7dYo>&s-`FOy!P1|)OZ_(u%<J1z?331Wl_vc!&M$mIVB6i+V
zoK_lS;c~uwhLZQTY)^urQURSxZLWv1VZmOs@9yiUoIx<^G*3|%k4Q5#jX#yDOOi@?
zD^QIj*#si+FA~F+pW+S8MAeR1iK~y(xB<cPIRWu6)=s#QQ%k0?6w)#DI1TaQ@6rg3
zt0Xah`EdT=9naf`k7fCw8<bvlE$3mf(?~qK=^#4||FHl`X&Yj)`R!(5|5vdx0P74r
zP=;VDMDl(_uQupUWmr(^5|gF9rD&CKLdFQ-O9VVo6~V@zsk;4g2DHQAg7OBUTy&aG
z!b<8mwwEp=bmS)bo=MrFG3RT6L<4P%06n^@Wq;YCd>n~JpJVOYx*wxJUBTKHsNfX6
zae{_C9lOBl=onP8czO@(eKVLZTfVW91<t4^J}4)<r)aWAXqB-m)mtwt9BZ`8o0}Q-
z2VZ63^lY{Lz)`*_oB|O#j#odk9^Ji23Z?7L*f?|P99FMmL7;hIa-ndL%3+XT)UYK-
z52QOUUOFbI|4yQOXGRz~PIEpWRa8^oC#4kLw?-dRQdq!XZaF8|{Au4Pwf@ra@O+J6
z-u9cmg&fP;Y%^IbKRAMppc+j40cBQG6*C<N34!Ua0cU0?GtHrb?Ih@KUzAXguU<oS
zJm}-OEpNs2O1};f254*I45+f>$gT-islojG<yu-B9RHKK-#C3963ADF6d~&^rs$_b
zDf;#RH}1|oO4TOMyj*%FWnA7qTdwl@GBQ~waB!4lEo2wM5$Eqqhp30OtSkjBA`&ez
zgO(iYhx2c%FV-G}W#0=owf-$&SRMFOr)O+=>3J9iSWJgE!7$Ow)Qt$hl^g6^|AyLe
z?7QlQaS_yVnYC2zAzXi1e`tPLV=(?91}6)(KW#yYv8H`l!&`(}G4j^x{cy4B<tm)a
zn%{fe3gN&i?#o5DJ=NMYUt?!{aH;15yMUNc^^{fx$}HYzam0w?Y+gqn0&P<%6`|tN
zsOs__)fFn%<=@2cHqF46j>XK>_oX-WfYB}xt>a&OG4DG6)V=dwG#41I@9BNmfX0Xn
z!BKLaaJazNGl;MIAXlk>Xl@?KHQ~Fy#z^}jNvKNoP-#w&1Y*K5rDI9MlHhLj<6C<N
zH)=ny&xi@VD#z~h`A@nPB7F3Lexngdc|RDQvAItmnj569H8)ZHJ>Jsz5g<ZVn0k`}
z0x?r$e*N62iwrHOZcC)gB~<}emWa)&btV%qER*EZBL;)6+-DMdr}NOZz_W2T?ylo3
zxA;rcVaTpWe{qVhj}4qIq(+Bw6XpJCIaEdZ-8dm$&y$fCE_s=X7^Oz`7v!`q#OZL$
zn%hU`!~2EXLF*&K{q$=6e3Cc~eftlZWZGmgdQG$xa+e>nA{w|}oBjl5gJ8Vho#n&j
z$~9*RY_Uk<K%s|98-z22u<61siB69L1p7f2CDmt~_v4RBVS{K-;1X|*U|g|hlciZj
z^d(z{(R%%ZG|@Ew;v9PkLwA*b#FHM4mM0xD#5hH7Dc?r-bo*-eRz+p<9FTV#I6xTm
z%E6EX9Z(zMN}0SR;gOxBbgHbe{!yT!?~xHw?bd$7wZXeDkWUmvxhV(vX-zB=JRG=b
zILKGT4<gunxLROjBQiwz!W4Z+CfWE`NCK2n0Vm8nrw}8}&hVvoo{HwA6n#74$w}U(
zi_X#?wWRDPP82c}c8_>}kQ$C7)^ndBBPh&8ZSLMcceFw}7;n&c6?pzt8I3qi0P*4G
z4vJm+3eI7p!brZ<CP@5&qxc^<qss*8fImMm_Z%Xqc+i}1>k*{Qc-W3PFaMBui6Ef@
z6RmL^FtMb528Twqicv|iY$g~8dQt19Aw<*Y9$su7i%my&WI|sRQm3?j&vY(J!^@0W
z<~bLy0~e%`F2LdroH&85lxO>)WQ^5(9ml}Eg#?`z&#Beq8l=%;$4P64R4yI%d<+be
zgV94NnOxqezN;kyqwFY057LOCi3YsF4%!i8b}Bz_cE3#-Na{Unyp6>4>0ZJH2J@S|
z%4%UiOqxxYI{MzGK**1gB(sJ`#XtSBSA!P1FPde>s*ezG<U%6_<U5~;gtP{W)Hnh3
zj8uHO2-tq!B@X>h#8iNP<jdpt!p4QCmXj)U&ldNouzf58Py)nXFqBu=d{YsOnGjng
zcdjqTIS11)3ygZ`^fWcUid)dkWqK**NMp>;DQVG{#Xj0MUfTUYn3B|og`a5c!wC-W
zPfht(Ou$vJLQrN}C;gyW#ff%)waj<zzHoMYCX9bI9?}QH^C`h_(w4D3(t8%Rt^R9<
zI5(inLHA6cmue}PiT8@ep4m4|%O=83MJv4Rd}511lC~)#KdMq&ZRJwMLw26N<-Ap^
z{cShy_w44F+R6Dcq+hLdnX9+|zcP2{JeF1behRN`ik1+@x}()%{ibMGo%fcJTC6Ji
z@AU-=u^ArKCv98FSsvj6eKtEW|8$5G(MpgJ4ziJT1O!JHK{I<)5LQ{_hz$)KsFncB
zZQ#+7eJd+>k+|HsFamZqc5d#}*0F1<pxRVC+n7SimpKDW;-Jpv$@5>0Zv!De-elC;
zLlvHV_PZ$YI41u!zdtt~Jg+~`V}7O=*CPjPxkhkufTx*wp@2YQ=F+(=s5F_Dxs<C{
zH{qT4-XZ4SZM?5|mkBfPvQ>GF9e8&i@IU7hWRCPHHVir(&z<)r$fNl@r^st%=Qh}%
zoV%qB%G>TsxMF}yGWa2QEnfd1f;7pqB?Gkb1NXS@a}gSdq1V3P1Ino3hi<EXX%1T^
zJ;YDAg~d1PNQeR$^Rd6|m*c>%*8k3?I5>MUvhmw9Uo?lzU2&2|X}Z+v*wb=W4qTXL
z`0enf!hEH5<+;$hK?cGsY<psYM5EI~`lb#+rOB)hvn&!>2yx@v44p2qaGY;DLwgsf
zdl@(k`IVetCe(aC(#9=6XO*#<Mw<@36lI1JH5pzbpi^=bpPWEHqdULS_?uEVYy>4J
z=$gp!5`tc1S@ZBW<@u>>KGsoYt;8zX6pJmraa?_@1{Fq#F-l*^r#zGyianVyOH`EA
z%X3_UXF=owVq1u@ZQ1a0f<E|)$(Y-qf@FDL!{dSqA(rAd>T|8lE{zyuX&Ei%qBa!=
zPS+5*_<*B*O;gx_pd3Ooa3o9}O1Li~Zn`q9JI6NzZBp2wi*r^sTv~Vu7Vt>&1<jlO
zTeDt0=s_3joCO%1O;K5!N{Mbg%r0z|O<JhIerir@TQDkHg_YD<ivlXIWJnGS(2I%!
z-XIB9XuTK<WD`6^+EbUd7?mCS#DtG13__EGd-8#l*MaW>P*EV5-Xf4>HTfrpX-ThE
zFoj2DShI;rTEZ|;#~?wW>t*0g&PLk`A^br!cEkvX3dy#B%IAFVTsq(dfdUR$9f79)
zmIU8N;JRcm1M6Jhf-2|3Oq2s8M1F#84x*1yOUA38=6&@$%k8ORFL_DIAL1ZzTb=58
z@3=LkBi}!NFhE(~=s;Ro>GR%ga3gqrg_x^R`1+6Q{S+3E{Y{!i!)T(-uz&JR_wq^C
zcEWv4>GsId)}JD?8O(&)H`UgGXZIKv^LJa$;1Ms0W^)j$=|+fDRqmk)a>$Et=`%!F
zEgdZcwT7u#9cSDCPqV1Rh5e7e?#OQ<y2hwl<&wPTcF~=0XEuW<q?%ii7p+sCkU0p>
zNJ_NUZ6~m=K~ehBOI1A{c@bY0v>9+-M-e$<EY(^L1%)+Pf9PKNVfrZVRs^KN_21q;
zrFXvLMw54)3oBw}6g0%<k=ik5Km5)fbV8Obl$iA&^>RuPvjqlxgP5RY;DrQ+lU)({
z|Ne)mua2tfi?&uorMpA8q;yGlH%K=~NJ@A2rMu<QNP~2DcXxMpm-rq0z4ylV595x(
zWpMUBJJy<O&bcs%+0DbHnvM}7wk!nZj;o0oq0^R@`~=rcds%4$;`;Xb61Eje3a3j-
z<V~0v(~`f+vPdx+*>%bUKWIg<7p}99BwMJ&be|5=MXtU$)j7kZnh7_A_(DHX)jScs
zba@lATzxclhMA5=z=2ElRqn9nyQ)p(<hBl|zCx5k^-qy$T1sucruFP5D48<d$c$!&
zm4`K9-VtvBuPk+*hFN=FtkC0rvM)AJ6e*XW{SJrS99rFNa-5y2S!6WHO0}sk+6kZc
zcEm~R$iy!kl-KrP>^|S)q5Xj{Tb{z&o=1T`ammx-Ih}Le+SVp9b~CqSr0FNR1S~-8
z%gk;*l91mGA6)V*zV{gRgN67Zc=~kOpoo1NeeXz><0>T6D<l^5HYSw`TWx&^->;tv
z?9wq^ocQQ3pf{d_QgpvU0@wXVfJ(wwK)$pJ)k@WT<Gf#-=Q1yq{dBniTDMb&1M+={
zEkHeV<2{F5*drgp?{_dh$t<_J#V=ibr5d1P3Jr9GeCn<Ze~mCCLk>$Ilfe<<GX@_?
za|vuJOrAe&XVcx=`C{PI$Z?Q4vLta+)X#?SdFnlJw{9%}5F<Qq58A`bdW7RlQ!SIz
zjZ(bx(RX4D=J>ZTT0ZN>GWHF@^wIiASS>=&Cx?u<Z&7?%S#F?L{ROtB(<lisfRY>z
zp(*4WlTCIy{0VE6^*z`cCDL89G&@g+0^j<JARi7(ced8Or^>)miY|11RGR~4!(jkE
zp*ubvdiS?e&7;IzOlFsB4>m4oc0w^b|1ARp<2<!q%l(ge(r4xdU{G+seo|Q{1P*D_
zWBpyUwi3$wFS}R1JU&P{A;eqp)X!jBXni@3#=>87zJaRg@ld{<XpB4N3JFNP@QwMA
z?I}X-2$vCS_Ypic7Oe8$>7BHeu$+K$y})b94Vxf-OU#IQtIyTd%lrH&$7D<yRjyjd
zgo$S0M^<i8cabW_r^1q+gNK?VinimS*5S&O386@#LYK(^;i>b#_e6iY)V_Os9|&To
zE@spmAir9EjB#5eM@0QTIRE)fd5|f1;W#p)_F9s&Rx)89_9KL|nCy|1Y{w57dBpeI
z2nWyc$inubEGBwUso>hn$=Ya?%Mxpm7!9Wa#mWuojX4HtQ?`d4UY=-v?Bti-&jae@
zNJ^1<gB!OT!FB<}rz6l<i&95LA|+@dKfzI-Z}t8H1&;eZ{#6#N{P?|iZBfD8r;iFq
zh=ixfXoYc6{AeiMQt|SU66l<wdBXW9-#OAH3aEQ}qak!J5?xfLPqo%hwH{w6pe~|w
z;7F)yX;2Zv^yu2%gWj@$V&Zt}-acWvD5&Ssk3%e&SQ=``%L4>mr)?zuLP9YIyZj|M
zKakV^>Kccka@;ZH=30(}VS0auv_P}Nj2Z^hCKOYkh3!1VS7?Rd(v%l5%{s_`>}o1<
zHHco=)bZKfkd+n3PwXpgW8uzM-F(JzZ5?tK#ZO#8g=iphYFUo=O!D~Q7<Y`HmJ{xf
zM$Ljf0;rQ;&#mAC&zRn+sCe##D!b%s3D52vlRo&jEp?NErpBf))ZwH<Mx`ESsgI~k
zhRt-#*p1IV9jy#dP0>m|?Rj6V@!hdLc5P#^I97b$8~{2ZSg;iVM9`w@KYR$#p$&9C
z8v1r@=Co3zSZxdajd?`xD)(`A(fkBg%ss6|XiCYdxzsjPIWUA#V^X?;&86eja<Jxk
z=*!!Chll?J$4;S9Xx98Nz?v10L-VurBp@*&$+t1RVMwoKEUKOGiA~gZC!lq0>~?Zz
z(fcidPF@PL^9T!ct#0>yZqL@%G#A2H;*-CTfZ1aM=jYT87sZ1MSMg5crxOE$ptEVE
zkKW9tmu6`?FjN!;lwRUk=#8LT-fJ*6&)+6x$`^`Unhq*gRO6^q7}M8e8(GcFFeT4x
z8~B_{kkLp^#jJ%o!a1ydKA*rG8m5QD{s7|z`%9A5tz;r$Yr!w&Hq(WR4&>Ol*Ou<n
zemTx$RU}4{R(j7Xs4Hr>T(gMOy^zNr)R3@n5ZP`kEWogXdu>uUBm0n>%Zu+O`f00w
ziLiF=5wE5$YnoMkxQ9<cR%jB*5|PiC(i1P;PQwb#DZ1HIg@N-IOi5#&)m)HdnSjK{
zN!dO$e<Q9j{p5@u=I`^`p1sl32{hy&2nF)HNh6-}$k@0!5J~Ag?a_qIY($ZpnE{n;
z8ml96e2*k+^kkz4y|=kB&aR0G-lH1TtSpY^yRa;n51pX86>4xwJBipO4>r6!)I#}&
zq;Nax)rspybTnz7FWmzEl<vMddq>AEO5{x3VE32zhV;H=2AEE27KdB{?oo{z>(%dm
z@NsLCV}tNib?3;b5oKhNk<&UEORP8q91=vkct6ww91nw_u9vbxt;?}-Q^!$J$qW&B
z8N&%huzG02_x9Aw!D5ikpOv%L4n;>UkD5tkB5JDqtSqKA;xAVU<R3S1hth+@sIdIz
zE+HMCC=U}(6b!9y66f4T1#(jGX<>yYpsXyXdVJ~ui%p;u+p$`tXtJkb>^XUg-O)&|
zfH;Etft;@3j`apA7rhVqZP&srr<L~CRM3>u*|>74l4x~c#V<L`iOXbDkFyOu08u*t
zmDWB-hR%sHqgFqY7fs^?Gr>6*U59L2y3CWWR~#q9(i^yYC8Eyv{A{GsTbpEv3rfCg
zKtu@33hj;M`YFqU-?t?&7?6u{)z3dHrieGA*F!LvQ0>Z;3qfJR1&dHp(VB^VUb80=
zQPlCe=P;ei9Rkvp`RrWWhdo%0cuKp_+Fv&FiWXxJKAka}?O_p&!)#KbxVthoD6Zws
zs6m}Z$S}u)=c=utS~pj)T7_!Z#mAZxHVW7v1R{Jwuw}5qW$$Ta?TVwRTPzVdDyj-s
zlq~XhPL&6BzA$dk6tl=WNmu)Hcq;+()Q?j2FitJZu4tY>TtPbFg9YX@>{8HWow-~C
zcyU?(fMpgB!wh#^k*ik%0YQ?ANN%hpE;V3&iFtS-=r8GB^c4gNgu6}2bE2OQ1U%4h
zS7stbvZ`PNiSkr-@K@Arx3q41g-bp<FIFD(h~rhJ(QZu_o-9daw$gmMdvgjw9Qag7
z6qoL_Z4Qvvl&aJCvz^7>yYaD(4HsT6T(}{GO-lU;Hb^c|_|fx~{0PNH-P9TDrwj%_
zuqOmd6whMI7VJ`#Vo-h<*wz7l{R=iFW(p-0TFo_K)M6OUsRw1(yu}DhVUnU$1}C(1
zP>rnUAa)!j)kxVjK~7oI=<_HFo$$W7XXinAa-BIj!qd!K3b?N_%%GYbtwLoUEv7F!
zGfhR40vKJ*4sK5s?Tm)GMg@tWYr)~LzySk$`;nw)hTVZ*w6mo+KQD2u?Cnid!m?0R
zwgqUJe{iiJb3yfec<VW{c(v}lTep9~pZZuX;rdC2QhW6sGA!TV_EQ_*($0uukfVH)
ziq>1Rr`p8Jg3UQ{=%Z9JBK+Yd5^=wFdXgKRJA9U&JPr4bcHwWyb&?O^DXtx>f9>I#
z&>dlnPv^UrwK*iNa%x^OObOImO$g+wH%eA@&Q@fJ7N8FXJ?+@(XwOgQ2d!|}4%)`2
z)8W}dRwDS^t!(!~`$QtrFi!{wed$mcE6I>Jq4`AWh0h&9SXTzRF@562Ki*s|HqP;j
zoEW5@ESb;D7nRbvepp};?wq<}i3BL8kK?wz)!fZa$3KGF%ef<**VDVob5>J#j{Ch}
z5OT!a8-i112PP!@d@<eqH!*Om478ji=ji;0uRj_0>9$y<8vY^F(n!%?%U~me@foHN
zW+$@bO7JJdO`KlvoQNzP#zi`vwYrnK!z1*ig1X?r{eDnvnxRKnn>MTzxCJ-6G0;bS
zY0)RW^8<rTD=<i`ukC*I#MNL_`vTy6G7<w)jmR1<4R8P&VcEXmd~#8Fs|pW))jMHo
zRm$ELhw_AH6`V<<qT{(Q9@(~;&zt0_I>|aO+!GvcKxRo|$j26Vt<@QIG+(4~u}so>
ze7};D`$nipTv4z2&v2`zkaDFGIukUbsvjm;Njs)jw<=gb{wkZ{LL8GjL$HffHNjX*
zs-vyr)8qP@flIqm8O-|D^`bN8J^Io~AeDMJ+8N3jYS^@W7y@=^=ECpL11x@v3St|0
zdRceFhE=jDZPlgbrq_~@#${&XCa1sH;DTbQ<Pj&$3xN91is)e+pE6-tPX*jkSpys}
zAV(Fzh(g^P8NtlC!k~Dp%rtw^wJw1_MhQ$nLtv^BDvPlS`211gee8{t81~$=3fG%D
zUtiM=ltk7T9XGAV6dw42Tf|7Aw%Wf$q$n&%vFSw6%u@SvV3#HJQZ$W{8mkMsF=SY9
z+L$3WRK>HGQB`R^QGEK*wL5W}P=6r?L~RKJc~{U3N9mxUfI!nRi4a+&p3OCZ%E1S-
zpD`m!Ng5n0Cwt0<9GTydi_0&2U~I=JzfhUdK$NEQO0;!MbHd|m@?o`}O&3AAz2Hi}
zeknJxc9}%XkkNdaiMXq<I7yOfimlpvDfgst3#;ZGW8!xgAlyyXTAAUdg2B;SJ%gB9
zaG2;bwK(IfWN(fj2O$NUj2q?G)^6Ay=DLS>G^XET0tniT{&#czi@e_U;RN;;7DM79
ztMMk2`2y~^8jo^L&K-gW%*VCJ`QQb2uS;7P3U_g0R(TGj@O8wyH-XfarQ=VmC~Wjn
zEQ-c147Bi;j8Pb}dFEdTewkFTQr-oTX_$ZK)D|UI3AI;T*-#epm(jBDU-i=$w(E-S
zoKBF&|M4UeQ<&cqTH9dRUCdn5xDgqv1nw<R!I8=^FOHyNgR86m62X;r3`4*K)p*pP
zHCm%k@3*hqZBy@RGgnA*8yHlrZxeB4$UUg(S-S^oGR6%>_gc;7(%r>xYFSE8<Ev9J
zXdY(&Jy%GLi?>SN_0^E?(AdrTlXXvdVFK<^b1#R+Btca5!!~`?C=p3>usJ4;;hyX#
zJp#=dXETp3uTI-HP<!+zwfyV+^}gflO=h{dklIw7M8fXu#JEn!V2LvwRmYXeU!7A9
z#a#w0E))oHT~uB4p8X<=nF6GsM-kUAyYa_rp9bQRj4-Si_z|SPSQl+9be9hol;RYf
zMj~)@g;$w1#}WE&`L&M=2EzK?xc;;hCi(~ym9&{vOvDqV$}EVWITG|BlA+?Vn|J+}
z(D$ux8mVK;;Fqnw_dMS&i`{1)<gmP2<2{bTtXg*OVmAb!0AmdcUR#c7dB~~8m6DIY
z?umHJwR!g;N4HJn{kGS!6Anz>rw^TF2y3X>#P1S&`o_6ud65vteL}jU|4dRc!wyqs
z+({pUhM2^x+*6l?cB7Y0MjWA4MSO%4MJU9ycU+0Mbm;}9-Px|+Uc@6=hG|?zQP>so
zYCSS?ny6K`RHdOpJ4H^v?T~%S;vN|5&*)B>XGBR{IlZmB8cN%5(b2?|)${_P!H}Sp
z>08@HmM3O*$aAi4AUUHHr+p`|qq;Fsn_|`Z6Z%bcsC0DT<PH5F!ZVjX#}Az(+UEql
zb0=2LU5M=WW-W0XU+OuM5@3%IB4C-n`Ld)~9RtD6-A^A%gucHhv~L=w($1>e2zT%C
zS6O*9fUXtODEPs))hZz!F3LPCglthSA$;iRicdy5fn_HhpmN1z5&_gzg-!x*AyUVy
z2&$>EtFkI%3IbmrCgVfikPQhEM~)KCvvdc7Ya<;^lX?I;iLwz?BlrfDga-pS_Zbn_
z>uZ@1({_p;>zui%5eiGV3B~w^ytGHpJav&ES;BG}J=S2jKH1hK?j=ugNm{YpwsaBL
zcQv_D!n{SI%qm^Z#5dKqJ(|Y6Z+uKfg5)p!POSwSQ$8Mdvm}oziDwFaGeovf;vm1l
zf7@8fFnPTU%gN@`V-(w9GRDFb^(F|tG5sfej|XvZ!7Tz5Xa1XyyYNkdAU(oRob(DC
zA+8r?*<PfsZ(lsztRf8QJL}hp1ggQwIVI+%IM|Qx4BD<g*SXr|ueu-*8WZU+Ft{{9
zSR#GMt6uwooR&o_J7%t9GI0Ebx_B0>XGon6YP-NP5anm&n6$6cc8VHGu8q`bIV}XF
zjjWDo!_X9I`(9meW@$7gWh5QBHJdJ4`K9lQ@S(Yo)>qzP%)A}F7f?Tjw13f%GVw5#
ziQZtj!7Y~=lpo*t1Rj8`EGu?{Ls_@qblR>ew5$a4e%d`&{3H*x=JjXE+Hs=X*(cx^
z&%3W$Lkc~NYI9Rnv6<tfRCjNzz5A9{-c9Em$D8OFnrR7d<OM<HuTwjs&kDT?F5z+C
z49uCI2dJHLY(u4M5R8^=a3d{84l4$?qZuWpp9B*6Mz;Ti3ATvq7aY!w_>0)0F>wDq
zmo`P-1><FS?0NY2pT*w`ZFjE%2~~ZmMPhSI&N>L?VA~<kO1J5y@gx6!2edhR*28Pj
z`sG!GO9QkBeeE^>3WnXm0-}mjI0;X{2*m5})7l!+aOvWqoS_rpQyZ$Ur~ByA#WBpQ
zs2F$(0r5;yn`9Umr@$Ub%zIszj&ly_=G6Gh_{_q50%Sap!Aok}Ln?OLeUApJnqLPM
zGmRQM6*(uSX7Wp@TJxsHnA<GTO9xRAH%;?QDbQVK&+~N8>y+L5irszY1+ZVc2HIa6
zhh<i&u4Oze?|F^Pd5kQr-qvnfzt2WSZ(p6g5dSvAdb4FMIady7%=iE2u~-5Hdg@EC
zJ}wzuCe}6Y03wANH%s=g^7$hY=eDO6&%{H(7ruC;_>~oEY18Mw2EWF)cO#i`Ey@4)
zivMqsT8q@f+MkuRg=%9@UIxFg+|o)tz|<O=izctFkM}fY%$t)OcFhy%6re#boh1K_
zmw$-w-Yj~a(fc!V=XG}HCi&-89!B&y9<-U8Uxk<OkM#apEH3p}s`JFZDInab$g(fU
z1$1&K`!Q~3u>UiauS0++Lz|;&YQKE+KK}?1arrL+&&+71))`~+rM=Pk=VgFNEdSgs
zuMOC2GPrcWA3Ht(Bv83wfP>8c96-tQHs439`?(Y#&8g1z+{wF5#<gxsw~COROlLd;
zl(Kn%;Cc2udiD*)&wF&wV^qzfM-Aq}@(Z?NSZU2oY*O>~QQOPWnL|FeC_1F=0>2GZ
z5AbmR+QIKByLFR(+Q916c&7I15Aq03W1PN!U28%7&l@4@H8X||#Mjlq0#Vc0Ob6uu
z|9IX&gUR=qU~?=D9x550Dqm$Ih4QY4eO}9bF8~Lufp;I->)b!EljtFa=s~*eT3W6J
z&;`7VBmWuy$h$Ad;Jz?4UZm>s$Gz>&J>#)3gZj%WH%`*x9O~|sa;x|FOV16kRPvs4
z_Dl+NMHq7(wTIqZJ4ammHlZVmZ*2GO8~}~@;P(=@=~lIrf>vJnAg|2ms%Yzl7kprh
zH1TQ%vVZElj_SN#IpXj5*5BC}v#x~Ye!PbLiEaaG3e_SVzbmwkkmaBHzcm)w&Lk#K
zPfNb;1+<={#;T)wURb;MkyY;USloJ8O#AIAhP&a$5t3L<bXy*zF;M2Yr;D$mQ}X;;
z_;QMWW$D%6{Cy>=Ch2)Kn7#mVhiVdf67MbiwbwR~>Ue)&37PCI2eL)NJ;m9&_@3+U
zh+O)(S2q$<P4t}9BoIvkcY)=mXZiN!l@afCf$goZQoS?Xa_Y$7Zni8vue`-b5gyu)
zaXpPXFOd1W(E8g%;lKU*-#(E;f!cC=L|M}0s7sD;=@<TF(6QtW@jLK-8T;F?oPOin
zyhSLdKe~sS=EwnmF@|VuV}L$FdMt9MkCkCkVM`9xvja5pzCs_-N_{msv@GW0Ob$Hr
zyKh?Fq0c@~wZ?UV#F=TNL5+G%v3thdxGz~BM^p^T30LsYozJ@)I*%Ob{dEgGEDGjt
zh$_qhsIwSQyowB;fzb1RcQIh~C?qg#!3pWz)UjBCaUb+VyLgN2;xj^b>-VJkIF<hL
z#odrh|Dss$k2w$4kcs}qGo{>#Sq0$^?wAW0xj~%*TUQH}68b9b%wvz}xr6B1tziE-
z>hFD&CAdN&O0VgrYRBt=PM^yw&}r^@g6L0-nu5xJ&fA+_I{K;|?~9gS&NN4G_Fb6<
ziWsRxH$X^pbqi8FYbm<R`S09&L;)J6JRTonr7=UJ1q64VxL<$^)OcPSV=ex*+zKst
zd}VBATu$e^$SV_^?Z{;)Rg+j%XnQ>Ku3hmaj#)Z9RPjHtBzxQerv<}vCxcPSGeC3C
ztFd>VvSj*u0$`><x4QvoUE3baIc@t9fR#Ak$Fs|{kS|Bew$o|#MONfo&U2H}E1Qr(
z^}0}mDC|@U63blT!PM()jImfH7pILngI=Fe2|qoef$rNPe%PoS8yT1>e#we?IK%zl
z`Mutcx<0?PR7HgCxlQf(o(n1rMmagGeENiS9QnB&`5EBqE72^l<va;xBOdYfm~z(#
zI)yD~9|G}9E3$WY7tmS<dk>K61JaPFtIfh2>r`DpW5L&tmBjFv%`kaI7w|;fh_j;D
z*a$Rbi4cupH}>zONPHy{^S|l<07Xad9T7T)nY549QP{UX)_-rUU)wr(&6;@5ZhgAr
zp#Cn?Q0OudrYrK2^XaAlq*&MCxePh|ky2%9#=uzxGwj`Ak@RLi(hBiL88)l-ld*MT
z!0g{#w3Jq@ARtNm@17zR_nAtRLIeqUu#!|OX*a5itTtX3O@>6aqmmF4-(uQt{C+7s
zIvlpE)}dFePgttSzB3q-a+;?#UP{a6U-I^wqN<jlNL%Krqh@R5u)qix-uAd~gQmX2
zGllN8sVV8u@~M;G4f5I#3J#?h6+?X5V$E0eN>k=caccKo%=i3>51SD(4UzcM9O27B
z>$d1_*u(=X4r6Wocu!nD`?Hb0>`KxKCGPqMugcg|*S4fv!zx6f`|{e$^Wr{pob}+}
zFihV1ihQCKwdrZD3l5-6>I=$gusHH)W@jIDw?9zqAu@UU%W4Z&NMKCnD=7cA-peHp
ziMD9=*UkC7pR5qPUqa5!Aw<-KgQGZ6Six&Xh$CD;Hn^E4*;Z-*px+ZUEqMdFnD)B)
zlNN<oF!Z%p^jHs$5?Mju8WrhK9Vy-4r65BOTsMD#;=`>1gs5iV>H?MbN$B*&ayWR>
zrzxIIKizO{<?^(~phPh^%Tpbs)!GyuO@B5tRN%LhssbDg{sj!}Trk~zvC;d!Q9F=Q
zB)2KKeH3T9aGONo(aunwm_}mZ&Bm%xp=qw4*Nud}{=x!%iJA{?j_x>IJZ|ntTIof)
zCQFBowFycH;K`}<+b>LYCrof!#<I#ZD)eG#?>&d=Ji_Ehrb{~&2WCVlM>znN&!v^G
zM9?fe>g963-JjUer1*IqSifiJ{XFP1`+0T>b8#cS=v3s%gdHOTvFa(|(LA0>O)%Gy
zvUHM;rM_0hcL{qslU$VtMFW!3(sU&)CJd9IuYufnlXi)i=z)YEDh4SLN`bEG`qDQ=
zmHl65x)zwL4{LTf;zF~6A5sv(zb>UUgKOOqayL~G69Np>7r&;7VhGbT;2i$2B`HcM
z4VG-^DoR)C`HC2r#i0eJmYE`_c3kRAtQrOD%L<c}CJ-UUscAW?LsZaMx20KA`hOpi
zH{ejrkhW<ScGZGZLbJAy`^&4dN{jNFFU^q@5N9;HNra^qFALHR6~IwDaa9H+dl7%a
z!^m=j`{eAi&+GLj8aPN{b%toR$x!hJO058Q1Ohs!g+H64_D7=!w{}X<Z895wHX7~?
zGQRVel+amZ4;owUkVb`s9WFy~Ts)le0MXvN$-loZPOv`_c(DrAdr1b+$xo*j8X&Bg
zY`FWmG++|u908$b=MPmIZ?n$&dw>>t%T`x&N*}Bi8|I%?rns*nwMhL%yQC6RcnI+4
z*`Y%npenrl6`OQft%8NSSY^be-G?&stKaOzoLnBKbCx-vj0d3*vr(=lkd6@C#grSL
zvY*%`YKVOUkpLvvh$)cLMwL}GY4=f81aj)1@<>X>hjOF4<umr;U{#FW&EJ=a>gE#W
zzYJK@G*#-lYlq`H6J1H@YHCf?c~WDktwTt^<W8GreO{(W-Yzelw6iPiv|Uw!j;lc<
zZ)Wm3B1X%n6Oj<nWpWGL9R&vs=N1Ieue_opRxs6FZ%dmfh;EjHiKI^6Z31#7V|?Qx
zP`9cjZf;L)|CG44?^FnJdEW}ddp*ZO6oM8d?Y7Y%iFJA-`6AZzfMeFA&+HxRAfsgY
zUY*#@n?*V$3m!R9<A9+l6cVbmR?}|PR~wF*P`bEFNJRSyuIA{unr^-(38L&z`()Lk
z(|^AXND7d2Wo;o99%{(J1LWy=q9QNZuOAH1vrFeEr57j=kfQL{1II-Ea)rb8twpom
z7@ggs!TFNFHR>SFKsm*jV$K&Ocuw0_gp`DvLT%7y;N}m8?dq`Gd&6ykDlWP@ZKLRS
zX8EA^rD9!_TQQ=bv$rc6#X3k}T5?Hk5Kv9~@>VUnoLh6UJvbl?x0s%pJZ8Tvhg=X$
ze~sPpBr_2RLz<+-gWDTp9u;S9svKC+-+ZX@(`HY&UL>0eckOYDYKC$a+26Ky9nxYf
zwjeZ}-+u8rOIl&hBS4R$qAW$nCn?3?CAUfAM_gvVC>bl4T-HUNRWtO2r8!#l<1ejl
z#VeLfu5HTSn)=pnZbMk|i^ku(+`QyN&)}d{pGzjVJ7$nmhGxe$Jlo#3p_De57I^5p
z_t?SxoCCCpLl2Ui`~&Z#h3xG*aVtuw4Y=W-8vX@%(O*|waKcCCt^158J`TpIvemE(
z%_Y`V$At~|;T)GOP7vDwCg<Jp;n~~9VYR8*G&bVFi*aBd&*ii9zIrIPBQf2%cEdbS
zcemjOyqvoQts-n)#urwi6p;308ANI?gPqhaS<)O6%xt3Ir=V(*gxUqJ`ux?Lwo(=c
zOB2_4O69tQ0*eg6y!zy{f1Ek$+6g>-vHX#jj{Jx9fhu?ybG5vBJUL_Wp1-3??zjm=
z?ke6Mrmy>*0URGq`o*Zs`_EcZdKI#c#Z9_Wku}9_4~mYpBr*{_;3_=HvW4dPca}?R
zc8J$*I8i7+VAo?E$9-*3jkdny_>+u}uC42HE@U`2*?;wi8b6?Ia)AzqXWL(@Ixu`8
zOd#lOcIoloO`(d8&bij$2**oxuyR3ILid(zeESsIPV2IwmePck2+GA1V#XvN+a`0e
zes{+u5`HY4XOdcmR&TPxD=lU4Xc`yOY%$^%_+LcD2$Nh&Udj90`|Jt)oBYzOM%QX!
zS&LuJcu|g+9y@1F%-+^j$Uai;(DtE2yMX>CnIxKB%JZ!!=AUrZtj=3VUqGM0t!3Yf
z*ugFKkxUwIIO`4J+YyIAIuT=vAk5$~>=dw`MiC;5h68)WPFA@R){14a!ccAkr%d|!
zN}2Q2()+zA8X0Vas+`)zb6!K2ukGsTzuiUc^Rwr}ac~!v+>Hywv|#<MUV6QIqIb8e
zV!@zBOko!au!t96IFq}%iaSlTf%h_&gp%FmV%!xSk}hu0UotV`HkzqE6&<%`4+jy(
zgQ9uI$H+$0AwFrppSdE5tXH##GnVsW#}`BpLR4JGUxn7;FNqiWv{_&Oo9#1po|^Wk
zq$>5s;q5dkhXo9d64-|cv+F9rT&!+~j+d{8iy-~h?#E=+ME(v{DHgo@K#J(Py2v5=
zHOs|kS`z>9Tvi#|D@2yw{17_dCLUjR%N>W{+v9mecFvR;+B#|73KHt9=7BLPW|jcs
zil^sZEmwzBG(+2>ZRH_Gm(X2FRrk;@K|ST#_v&27wM!*pKcy1{M{5zP#PDIMQa;B~
z%rYAZMhMn%T5%sXkakgnG15yxzdpeBWR?$xB{P+n^k=6VD&c>vMkr0Mx&+MOz9%0p
z7;xuH_qR8HlgJg#AjH+=Ej}RceiZ&DI98iLc(WX%KM@^|p&ERML$?l`Vd*TeLkJy!
zen^ulX){M;4{NA_A}GdR+;kKbc4S9UbZ=p6uLJ`b%o)2w7t;l}lZ;Y8k@zAY?pn)0
za+C-^9B`PMYZD>K*DUFx2CA%1(v4-VE?3A8K5YOq#UXYWhMn_4ALdH6(e9E~X+YL%
zfQ6O9N#5#!Hz4Kmr$yO>h;heoSf}$;?y|n-+}972*~}`HGd9%n6U;xdIpYGWO~6I!
z-6FTDJ3Y0ST`zcwr5J*~<_4L2r-i|jjNOSl_B>i(*=tA^X8r3oMQE^ER9q`8d%l`h
zBc$$I^%Hb=7QJuoF(2i4UaRzo+$+P;+$~T*qR!{YZoWtfe0Lgrg(|}_<orF1Gl-Og
z2<poA7mtWsjV<Q@YI-wk(G-wszk#l7P2}k!>q3c$1|Vd$;{886AUdcVb%*}%)Sjvv
zk_o`9__YrNPh<a1l>5!tFhfzjYF!(b6ERFym7@%=2?J_kr9(G~)%#Fu0*26Pq)?$(
zn$!)K(i6Ne+vbJzC)MSQQc(@c$RP%H8Syoh_RTVf-(b+`C#MazadzdN_HH~!R8uSb
zo?&zdz-&Q$H;Sh6!+qA?SR|2=z0E-SKCkw6ELvif%;Sf}SL4V%fiXHn-l07NI+z|t
zjMIX>{%GpAC^`~}^C~z<ozaTl))4h4z~>J#4P5JQ$<TzxF6qkp#k9C}V|F#JsMU<{
zh3`YPF-rTox(9+s*Z!0_ubrpkiyTTp5?qkIsE&3PMq;rAsuYQ%++*TWFybasQT9Iq
zv((NEeOMJPhiBV^=BC=|>tg9H)0Y-hg#ANZVb@`kmefX(^rpumF@&z6uv&~%XgX3a
z-E4-3nq2XB%`J-11JD<G6`Z16H)ypYt*fVIV`?`eg@bkVIgckF2uS*IUaXx+kWAt(
zn8D`^%rXJ-^Mbwe-H8ZSvy=#q^%64g%d+-982sBKcwpMifnF&Tb)(Pf<$)k_?st56
zv6`0X<Tf!dR|c*L7+u-vGa@cnxD-O2Y}?#Wt+gOS{Hm9RoOEvH<CSRM8weD&x|xo^
zsQY&9S`p_Q=z?xJ(nkyJ=bW4C{yk#83o=zjD`Z%^aN)*0;>-bn=*sB;GkAxghTSfP
zD{{0Qu=!<5xcya4tnh|pPNLA|Cu~{8%8l0DiTi*c&OK!Ov!{s1+PRnTG>9m~q~kC=
zdJw&yQhuGD{pqJa%MFQQ+4%avQUO3fHiUAf;-@lblpP!=;g^NHmuQLrL{4A<G22Uy
zk6kH51uL}v>^Nr+xRG?fU3PzA^MQ(YRr+;;M;|`A-fKPNBQ#pciLXbaGKvG8qGn$E
z9Pm;)7nj_~w2Cc+tGi6s+8&m>FmKn*<-6R6hFLjIJpFCCYXfWzoRgZC11UL;B;4)$
z(0!+>@>)q-aS;ZXOg!OZKt{KsI43aZ_KJxk(5E~?Xp7W8&G1u2f@hc|r_&d>q)jzr
zVB!<0^LP+ONfIyPt+bd#?p1-j!py27fttafQ)x$32FGrn*1%o1mcPmHhVjQQu>@??
z9%}gpj~;N*X286Z*Su;B5kYM%@KsusZwX-KWw<NW;yCR?%nIePMElO@E>>2JmPf!x
zL3DtT{W-#ul$U#lVAQIw4>@H|A@#zlTutihC`ck%gOcGnOdU>$;T2N7_3;52XA}kB
zzQU6ejUQqAO5x5xGHpjXr-K9C5d`drO?*IQ8CO$D@BNz0^JQ+p)&h&+E}Q-s>Fq*e
zlMp%V5qf<T%gy=y5>#qOZ&Y&U$dn=?PbXEJ2WBak{OdyCgX$#)4MfZ$2;^vS(C<iU
zerT$iul!>5ylb_bDd&*xZb!O8<*!*ZvBFl-+NmS-`ZtJBy@d~RohghhV%KcXeSa}b
zI*X0ye7ZvTIbY135s~qHKNVUB*}R{EiF{M6&74NbC7+wlsaqC3Byn68nj2cdew={o
zojfqlg2b|xzfnFAF|e)jNl3TLv6HdT4_)|PD|=qCo=bR9HfL!YnxY2rSJ`Fl1m{+7
z=np`<GUOxYirO<f7;b>h8MX)(q_45l<TAm=Ra|A$gjgO~_ViE}uCifsK~zp{6dr2H
z5aKj{iMeCR{@|cQu(U`204d}sM5~=_APb<Yp};(vCb1td>%r23lQ!;F1=S|X(NM+Y
zs}8U~V&q9#)gmg21nwd+ku73*zz-rS4iK{%3uhu~nN*NRwNK^-E9L-2+{kti@wevG
zNZG;CCXN6n<g$=U(UoEXf__RlyQpbr&w)+UZAr{}N{`>7&H?khL9j?@=tflZ9gXGf
zD^+Aj!Jj@*R{T<uR@0|^&4^*4Sc|_y<8r_lPT|O`!%SiSuNS~qdak5!!7XJSQu;!%
zO`MD|1Bd)@rGb)^T&bT0U<$bFuKz>x2%w=-H#_}J*J=A@`bQ_17lz201Y_?ru~ulD
z#v0w*QQ?9SL{83}mo^v_ggS%axQ#(gI9{#!p0p`}h#}G+%(NJDvsRc9F2Q#@CN=Du
z3n+C)jvf^Wa8_(&KeF~B5%B5Q(J*S=tj@nE4*`aA6kt%TbjJtbsX?)gdK>N_FaF=b
zvs%3PCv5$RSt^Abi=BvWRiLZ#ss<1D?*&dvt@$=lZdSn-4ogrr&0Fb}8%4MlLgyf$
zvyS^JceA8HFs@q;MOOmrV5Kho7jX<aw%_&5LRSix#I=pk0L$^U$nXkU67T^K;hOhw
zpm@MwoXE*$Q1@w9<!XIRi|R6s)9<gsHpFh{+q@kjbT_c9*)$bTza5h1Q8{{Hwwulr
z7$>R}qO-`@ohpwIEK4GaH@YY;p?Ac-B9fCU2JY7ArgzM2T^cUGZ#4f0U9X3ejFaAU
z)t`ry(%8r!^OgLKXd5JJjz+6gYmdPcgk*n|Bm*_$I5kBv6os;37{j8V4$w*pqsJ|N
zHnF1)I%1*4<fhVYOSmDIQC?iycr-6z(6|06Z(jPl_YsYPdS+w%pii8@uB%&Mybkq)
zgi}@UXu@Z;9swNEb5LscPRJa36D7U1|K%5j2&e4-t8DZk0y~YU1GCVDD@!aJrNKpq
zmIN9#PujrQR`UG?XKI0HH4{%;+r5Er)3^u+iTwR1_r(lLZYX$npmM82^3~S$@Zco2
zf_Ue4CYVvIe5Z_6&~?+Ux*{6%jaH%|q$h+wM7^6n;qdr%k=sEtht=3G*tP1qO`-&6
z{J4rU?x32k6F=<R%&ygy=ZGcyoJ4Jen{#(ZZjRa=_AQ>HxJ)o%T%??2hfalju+%Lb
zprX&m#)Afr32Fm{e?5$TgA_9wMO~^IeDu9qV_zv+>6uwjC9mlm$HF4a9+?V_4FRF^
zIve3Erg_Ive*N)XLX+TrkKd<Gp%QA{*J@IA<KGP^gP<Wf#)~Ut=ks%0kkm~qC4T*r
z>QG;<we(}HZW<_49n-gdU8_%Qd)DE2w`&6@5jjIjWW8kELkYbq@v#k9P2bi~7=>iz
z^FU}3+_3uXs;jj|KX#SjD1)Tox#i)B-JtrnZ9EZR<NFIOeu~FXK|W6*BjftYZtX=c
zosL-g3pDTqC|5Kobn{b~kAZQ&keI=oShsRMgBR+L`8;&R2v`DGZ%=i<1hD2tDCR5N
zli`h`^n^rDImLi>zW0;Km4*thG(;N!tlyuE)j{ZBQP$u^-7!YxDZqs93t4R49QxQT
z!l>9-IpXt(_3<w^oNdW41a#ruaQl}FYMJTea}EfjjOId&zCo}s8o2F2CKErOvQ5p1
z?^f^6?1F7CQRiv*rUsRK8JTSZ!P2pq0}8KCqQ^wE?j*2>03S?9_*;v^3QRg{YkVKC
zmovbUF0BF=dzy>H1RFUCE3t^v{ilm2Lt}zY&~O(eWrKOy?#-OunO}Cp@*RF$d56AE
zyc_{0RBG>-0@cKl&U;@w?_lWD%K&Z-=Onf1$Y{mb`4t!RWo3l;Kc~-uc}b7mAyRmR
z$$>{#6)Vglbq2{y>;!SK1E5~Xf$ee}<DD%y&v<J*+<pUf(J{#t<)pJz5Y2wb&NoEd
zh-J7jp>H3-D>$7HRXoi`z)^G+i1a2Y9VH~sW*U*qm?esWn{}Aw^U0MyCQ^=+DVVK=
z0PlC*29+@Y(L}%TT<+Ds<X~K)Z3`&OkGZcZB|8K3gA?0Rjz@O7YWz+(lLrrSl13V2
zTRT_dN8};2s-p?KU#45<Fs^``ub-`o$n$zp3*d6t^mtgTBRD$Sn7&lH6g42ao#LL$
z*Ay-O_*?u?9PTe(gh@N)7+wS9u4JAtCeTS(;U_~=8+MdtVRHM*I!1jC;xtIdM&EV5
zTz2NirXxc0VSE;6I-?8{lbjSA(q8G0_;3Suq&);#i+4Z{^}(A|KWDc0Kz2Gn->$xJ
zrDF9%S93x{EMOj1Qzs9W!CR3VbG?eA#t+iM$xi2sw)&cek~OE1W!qFhBL<VtAgelP
zZz@&rDOj96d^lK^Pp$m#84z7W;=FywWB9CNh_)9pxAt;dM;h)nu?iGeoeq8_!96^G
zb><OpMe9~woRi?s;<=96MJEZeMDp$0n;@AhI(V%IL0uP@HA;okv)Q!ppxIWO+5~ZD
z_Uvav(NJhtP;2Rw=z5vLTub~J&*VD?ZxU}yUgT<%k%S!?cDGWQtI=<uw&KSLk?T+O
z)V0o2;8IDamadZ31>r{;YYX6D>GoAC=vrB{9&qLZV3oz+yMQccu)ca>JGWl!0tdHV
z@n<lwUyQPHu4&T|J#WqX#e}ymtGY@NV8OpkSktI4VTH*k`v|}@h$#gMK{QZh4Mmf>
ze@xLzuZdcyokJSY+Zg(ONJ*UQWVycIsZzNPqwgd=a7Qy6m@*BxYX**3j&vBDtyo{{
z=BqcebL61R=s%4|WpSx)-QKT-@iX^~CtbS(atJt9RLiAGG=oR~hzDJOB4}xgWf*&Q
z!qV3D>}l0z2Y=ON2mg1|3m3aGL8S?Cw*5eYoqWO@Uauy{%edXg4G<}CuLys5KL5ET
z%&N-imn5@Jzo$hmQd&b9>0A(f6_MRrBxu2-<&cDbXsM0)U}i8;e&YG@MfjWgv^g?)
zQ088wY)STQq|j||Cb9$uPGhtz&DJVS3pua2wdBY49Cw(GmGuGicuMOnJD!C8@a^s5
z`FYpZKw}FmK#>%1yzJ+)!sV`N8F*|82mkgY6t!}1?hk@%P3%3nx1^L^@K?04uYr9T
zCUG=|f>mYJ`qE8Op3yv-+du4ew1ek#UK%aRTEKm(rW?zI@oxx00z^(UBi`>u7Z!T<
z|3)GL7c@Ux(&kr;kSFv?QnCPjTHF@J_eXyB-TG7m8pFLd@AXMss04AM1n}U+3a|b!
z<{(p7L8xo*OY~vA9L61szT?2{ee&pxK3rnqC_c~-&M^=b{{<_2pdo@sh_vUnt31fA
zlEE@vh*KGw?tgDdw=z9`3m%>cPX9$`VEhEp&)x(0v1gMIZuzxw+(}`SL?f$avwOy{
z^!0Q)LZ2|~7|PFM_Bbt_g^cU*F!AEQQkXgSU4i(*h8iU}A1^O%!t6dWPDM+<vBz<s
z7YCJxh(w^8X&3N1D`t$zX@`X7L-`*VRV)`Y5b1ic{W`h`m%D@VY5Vh77dHAE=!@AD
z8?fbf30Nb;jz65}lMv}mt;uD0ol2nY$2`V#dQV66iBe#DsYscQ7Lx=V*ZBNw*Ex;X
zROIoqwQ8ShZ2C$&kxU?8f}lVyy8VL?t3u@gNFc=v=-&N=XiOO{>pjLl6XF&P8dc=i
z6yWKQ6=f-sM!ALAV}ex>G(^X$$>onOQKXck0veagTG3iMHLw>|<cQP9oA<kSE^co6
z$z!LYUkf+l>&L|HW;TSd3ri|apr#(3p_6Axw%%o+rdRvc(1!OaW%D?*Iw~;{AKCVV
zbx!x8mqw6IeJ=$)Z3&!CN3S8cSJ!Z4H~mx~gI#k5^8;G(iM|n@Cj_QjhvIj8T36}3
zPI&sxEt}uif{TfN20TupIzuZC-312P7tIYyRLE48eS>iltJ-Yf(DxC??yqViqLA(#
zBzwU=3+^QYU1q@mK0*OUn%70^h*IDhU>FjF%AvVj8o@NXGEg_}{@QyM=#<HA9hc%K
zu2{xpLgKThAe8@=FLCVC<R^wtYnPSA)B6M{AJY12as`(>cR=$j(xq>3o7J>!dw?!?
zTO0XfLT_eu#?ww6@|(%11&})h^1vd+>2lDo>lZcYpg*3L0bS^u+6MR75UWvwCt54F
zjA9tKghU^A@OsrL-vug?t<-lDDJ<mT=HT;beN?RkfT}m2h&FtwT44asq@N5E6;SKO
zM2kkw_}o*j@}P>)LzF!t>7-*fpxx9MufgE#D6-+0gqe|!Hb;-GP^Kf9V2NjrfAX5F
z+w`Ia%MUfa!H!pPx@a_7nd?nH{@%8<a!R3#t*!aEqkoQVMP|c|L~8EA$Hgt<Do4mE
zPt{x?|7oBW!Q!$TxeGnq+Ve<j?K+R$)u_WzSl$$=*TaSKEmz;Dd!IG70s8mHjSA6B
zAdtJABv9%xj=XHXsyqe((ISI(h5PJ^1=0I#p{{#uBvIPWJ)UGJ;Wn-?5RjiJ($;rP
zuz>}xa$#f7*W_9u46Cv7_y#(N^4P{F94R>(eMjNxScAwRPy?5Mgekq+Cano!wGI@!
zoE_DiAA3^w4(y06T5>a4+$%#2IB@6jp)`os!vd0Lr|I79-6&l&3ZRcXs3VLzIh|#h
zi(<ayKR*~WEoC77Jy<vU1Go!_><K8*)5{kniMZa(#xZQpO3T4PE#17t83@l^nW6^2
zOQ0loBHHY>9g|W0x=kYLrIBvQ0#nfx->%^ESr<d_k`@+gD>hl|nEJ!%elW7{hQu{(
z<{KoF=noI5EBZN{%0X{DM3T?swCUwTcvcBgEkG3{oY?`t4K(-utH2f$qx{yj7isY_
zcb#``?a9=}<rEroU*7tZ?ZhXgIP{G6urn6+^^>=Xl2{t`=DDj>%9pgR5WeE`?k~Y9
z0w>lNgbobHcGFhI$JPV>FwPuE=$ZD%MY*adM-Kz9FNE@Lkx*%5=zutuBPrl4gb+Q)
zWP(@0-L8xif}j+mS5~8YY77JFBiD6+u%-))bCCujVPDzg4Jv|*{Hkz@R1*T|b`r#o
z*)YT6AmcOIWclTzToA@>3}}c!Yt?`~JcdMCCk6SJI^9tXlL~@Z$t@4|Cu;an)M-JA
zMmrJt|1uvGl=Z>Qkzu6fl!kUV+uO>pIaI6_&M)|er*=tA^K<$PA4|>Q*6Wqkm_c^)
zyOc>m5ACQ)?z=W8HFJ$|8+BDZ0A0n(m|9?OyJ{WHaECVy3EC~!i>qv_qLw5*KGgR2
zWiIq-bJcWNjh5cVZzX!Qv@T2s3R}g_k?}pNi`~)tAJw|EDM4pHEmj;>cT?J@nPucG
z?UOap!6^kZ)5#L1GxIJ-rhI=)VcvB!$6;=ii+-TMjK@aJytfK~K%`K)E3%24{uP^Z
zgov;FZhp#qT)>GdSkY_}wp#mHabws9xTjtG?OIB?`u3+_nlIQ1SF{8&qdIPh$V}Nu
z`PMDl{;k^+CN_(qIbi^w&hEr(CYaOnH@r4|o|p5?e?2V@Es8>BFSSyiB62CKYGW^!
zgJNROqvC#6ip+;XT;wIhr`{ot?j4~bA3GzEWHJTJ<P}$PS?_ZVmxl2Oa@LY%lR3s^
zz<g<pea*8{M179p>IJ=`_V(V;QNpVq-6#b@Qj>3DrRif#Fga`Bz69-e96a#fw~-%2
zyBdFTq4Dgs<Cz>`NA#W@U3=bTHwlC1KYI}wymVdWQ)BQIN+938$yN#gm{{@?Jv;+>
z-DbT@6j)LDSgn*!dueftBd4(Ywo(LGbqUD{dw6N{<<ZMhs;7*u<3HnRKVcQbC?NpG
zj|h&nbiZqhdPvF@)XR*#;+UVIBq%6dX|wr-(E_z(4AO2cL#Juz$FxP2k7@|T>HvWG
z^}I$8dgJ~1A|T~`$+S^|PQMkAgz7?rc+-4f`9mk{?(`-s>UU<Y);c@X<};jAZh7d$
zGufs%Ks;f@73{lEC@-Npt`Q^=ZUy(vj)3o|D66s>Js-_imiI<kb3TGA1SbBSsKm3i
zqW10NJm6V_%urO+X|5aoB;C&^a$eW?IgAGV3~Se4xX`j}(EusXqD9-aTGli@So>>a
z0U5os>S#f(a>%YF%|{dIq~(7+BP61?NO5u7e;;JhSx|us=Al1FC~`s*Yzf-bJY`CE
zIxpV(NVxS)tESk<+x1ZGq=@cv;`u5Z$5|qejHmj0%ouIsoeg`Zm@^?l#Yf(^jhGHq
z0?+)Mim+$hxPrirHrW=+aADcnP44(kDQ;sPJFXr2aqj@Fl2mAq*0^x<+XnaF<OEn~
z-)%MYVxK@){HPQqyB`X%!=I`0LzEPJ{_X@?n7HC6QGU+P#qestxCvDkT^jw5geJ!Y
zWjpa~!s+olW08oYZjj!F9e({HMJPiUb^Q_|qe{}Fj}>G0@RjeBr%*}s7+yZtvY%k?
zl5}C3@yCJdk|TSw34p0;2mtWf+r}m&NlDe8pE>szrZiqpB9oiJ43DM<KC%9|dh19A
zPX2Tk!kB1YS2$Pu9Rec$^ysvtaN+nkv63o~YM+^|XpcTgj6{C~bZP&D_U0wrNR@ed
z=Vu@B>?n~;jQ64HIvJx2|K?kc9IA9|&y3TliKHrt7vC`8jK|!mXBi*|w9JV58z#Xa
ze#r-nYACy3E>ZY6uCK<}aVOZ^T?iIu6>0Xf?b&gRp$AP!BuYaJ{+yS9%8zWeOWsbO
z@KV9VV~df6M3H;(Y*fC{*(Be&$)@hqcVUc*bhsZ4|Mv5@L%)uYz_JX5P~!SRq51o)
zQ{q3p_^2C1B&QGXzeD=t;$&KtTXD3ahUBH#kKA6*k3Klx`ehDR04dXvA~=qtlHyQr
zWpDe~ZLKA!_iQlCM)Z-!P;G;1mjsN}Co-maFTS)HYfY;uC~guaJ@VdMxd&|~ge45v
zg=5FR`2T<@YmH6t72yY6B^$`fcl&xy2OgZsVRz8&uM)KL$%?RKqPhdIPDkKEMqvdl
zIyE|+Pjo_-N`+_QB!VHz1FZX>I61Cg(-a_uNAz`Z=L{m~;}bg;?zduSI$?tF3}BV_
z^kJ_acEUHybM=nrz3WD4V(=gZSDy!{2j<8B4&By5wi^>5dRfaIFU!;t87qo_6<V35
z1_2x?XaH5ZOSb`|1hbnk`n^j)(Fn&CzKfXGX{J{p$Y|FQjbU@<LhDjZbgJN);0zkV
z9^%{~8?9qV$;gtMl|6Y)Es=aVTwNO+d@Ch%N@!u1MTfPk$!}e(b@8gcD5R{ZCVSWw
zAz{15bI{B+I@Nr?YYyq{Y`|I}>%U6D<C=z!p(d{dfK^x^`pS4Si9ALCbukgL7_{#B
zl3lb9slzN$Q-mVaXQ!m;nSdW$Dy1Ej;;9MXd`Ri4#{X{{M`HMLIJwRmxLDg(wH%*d
z8{V6z^H|$lJ}*0(pQv73UKKUbM1KjYHL`loo1rm`g&ik~pLtfBHR{cVI&av<llslE
zcnk{aV)~Ly*DHKA2$2OQ5aO;~3h|fIO>YdY@=>X)_&4_i-q3?7CKu$=D>Y1F$|J>L
zxhD~V60}kYCx05M=C>PgiO0Db4?|ww?{{7=`lo*^U4rL%k#ItcdYxk;*yEN7bpJQv
z1Ug&w!hH!jO-sB?*p)Pkz)OW#LT?W5Y~H{vlzNAiS26WQiVHg3Mv|fhTL-3S5DW1*
z)F@jMihs3oZEf|JLjon*b?*mU4;avpxj#e#Nw=qXFksj5A5Qv8waSO~g<Gh?qMshs
zy)CI6sv5M)4^?rpWqT1h38^IvJrLa^aSWdSaBy|PmMNWWUwA8gyZ;=2ot(p#10Win
z@7<pMMFal>yuOF(w36(n=fbWuF$^bn(hEloqrtEYn=6fc(hfiK`d;N;Tfa?159J_z
za3lIQKR1W*SDmZ*YtmELQoT|RWE}tdWKJRi<&oliKf-Xm85qvejSa(-zG5!+k<|Io
z3lL$CoA6Um2amaDlU<8m6K0(vxC;P>k^7rcwf_RJ|00xX&^Y)QYw^TxfTXc~MgO*I
zT21>$c#NBa*CJQPMO8i!*`ED$h<Lq^{^vgfUe;)pdtG|B?dr1PTB>88u3LL{_REGf
zel!4Q-9?de>i@7I|HY-Kz5&N{T6!YZ6Z_X7;@5orO#}P?$4mWhH1n)be-L+cglB2~
zKQ8=#+*+i!uWuWwzvUH;`rncNdz|(%R2(m^;(SXK_2ryeM0@_3*LUv*{YXEf<Q-DS
zG#)-q8EAQa@UOubFKP6^ciYf|n8(CuyFS-5cj-v4S1_2v9cH@<Ic)U$7-U;ZTZXF-
zGt);@s$JbiXMK=Z1^gIAht`zQe9YadN_eq!mA3xwz6T3su~}t&(+KxO-#1*z(Q_@)
z@+Jd~-bWATgGNSd{`mD)WV?o_kJfn$8<FyDOKnwX$3o4Lwfe4-r>}s?XIdF_&L8k;
zmdM;=(9r`MW}W%0rN@dKk~6$qW6&uCr`&v&G^I8>jxN<ad`rJs?gzA7%_d22{0yYd
z`@N3akz*gs{JET8T=z1#t-H9@Tv3l$;tL#uZ)Vi>I=LRzR$e~{QB@&Uc+vd7^4|Hq
zlAv1~4t8wYwr$(ColNXxCN?Lw?TKyMwr$QN^X@t4eAoM)=U@2tFMVBIy{oIMt5&aC
zweI`Alv|VRURL_ANV(>!2i^>@j5bgYm?6F;+8e$L`A5xrLj1<l>8KSLm^+qfSjw(6
zo3dOn3FF54*z;=QV>e;UoIOFOwm{~&;t!9Br$c;n3!8h0y<|14n{f?!Yvb(~!m>DT
z<`yN~UUmsE-bP15NOBy?cd#i74%p>GF3tK$(4y6PCu5SeT7i6B;Z80;^v$(Rk*=mC
ziE=}YkCUaMvvrU_j|ULeZDZK1J6Fjp?t3AOkkJG@t+^Q)^0LIjawNPwHjAmL&Ms0!
zlxqrjqD!I%|McPak(0|hYk#B{$v<oaB$t*SE(SD&Y%%$zMhJe8a=}C{DX?6dZ31`G
zJRPPcP`g?lCkG2h-dnS|^k%hbzxjuin0o59_22@#sAfj)ef3}dlN?4CdhPDSzg>mv
zYbSq>m9%=UX{zQGtzhtDG)mLo`|BdYP~2Q!4ptBCCaP;+=T*mdGp--ZTQh~(Xx2CB
zyt6OJ*I0sPbbLsIEF+sH+_z})?k4jm2eQiXORcvkgQ_3qrZY*2>UK61NrS^bmvN#R
zBV?k1*=P=rsRbp`WeoN<Ks4F#Pg8Aj(jQe1!z<B^iV6|HA}V_xV4xcDUS9UXDKQ<I
zK1`-l!*D3108uY2l`$+e9a!e9_^&Ktt!AQXo&X1vr&Yy_#&xn+!!`O?12w~4TfCWr
zL^muow_ah0Q9vCV2F<ZvU7d-47#f~!fPu^E+R~;hQ~pnm)dJ28v>;lQD|bWpqdYP<
zw_hsapv~o@mICGR^}fiYOnPlfi;7$P5><dbZ%A5J*6{Bv1^ICIglJFsWFGn=Up`=X
zEL2#u>99m?^#mCOn<_*v>>KkB*!US)ZJPpf&Q(HwC5{bL-Q9*Fk6g?K2_MHd`tGO3
z8D__#M|f#=u|wv2o`Hgnrw*kjP<SLZP;ry^rEtMUg^jz(M~P-;E9_Qpm~np-U)rZA
z_;h#E9~2@IZl+He<%z%;rwHLK|Dg>~qC+C=WvTe!3wNQMjqG_|&%mn+j<Ktcw?qpJ
zm<jM<Y~8%gjZXyBub1vQD~u*h7!R>Zkbkgs_LY;Vr7MgwsHEWetyy)>ivf9z{q@85
z71U8n<qs%74B!q46TJZ>lUiF?0{IG}!IWu@uYAoHYuHd(r8+;z*m2Ewa0l5w07AYm
z3PVqv`1tQO+w0Y)=gst_90~t8{#!OsX6@?g%Fzn=d)*r9gF8CbI-s3WKhYKo;%)<a
z_vT@`A1FUlg?aCikrrMwoD$FwD&V0a?@3iE>NvUv7CEe;voG)K%e4~wTpJ;>9D3@}
zs~MJ2$T)HvN;GC#?CC1~rnol!o-XN%Ro&%4sI{Ou&x#U=pa5`OMr%C!kD_9g*(u9y
zrXhb)h|#i$!Q715VLuGKaJl<T#ED<Q3eO1$IOZN%&3S(dDf|qxB;HvvTaY(VQ8jTb
zeOMvPyRT*95y&$Q7FfQ;Kl0R)V@qL^9nPy&pSAM0DQ$fkNVD?kZ2CsN5H?Z<pK$IT
z+e)CI{e^O)kxsfoeG7Jgc7Zk6zQUN8>k~Z^!d}Eb{h~nJZru2UgVS>t$^_IH7cx%#
zhfxtc6G3_~xJ&kN7mH((m}x(eM9wW=HnH8VuwGp-Vlwa<S&-8|KwSj_G&Wl=gK1{+
z-+=1fA3)^<)#cTVYu!$LhqOq#z~-7zN2nJxCH`|QpmzNS?`r$Z!Em&gJ-F}t#4%N#
z-viHSwphS~H!y5J-+@pl>Zj&zvkFxC0YZ_k9sl7)>fB1`nzP%_%f7Cbj6N^4xH{%z
z8NJ8lTIwqK?XvQ<^7T60&i|!a<alh@%oj@3#OY-GxsU%rzL59zR7T*rr9$<g6GI#T
zl#hO&HOhUFGgRQ>sX16Qdal%Z{vB3xQCq_=qC`8TBzS!EFB4<azHyq+_sY9{(!r>c
z`|j@`(U;Rmh84G}-{<A6gRcGXNC>ml-|MSIMWO2$wwbvoy`6{DH+5WDJxr>{lf2$P
zenPMFusSvyQ}T|y(8?fMXZ60+dgK>o(UQ|!_3R~4J2u7$fAZ_$d#^0_&?+=_ZA8i6
zQ6)|oSI`FjD$UhX#k8wbz*tDXIP3;o59njq(+BU(<W?gIs#*MAH}3xf0T0)b<)C?j
zf)YZ5i)j;$`f!$6)K~63UqhiLIUBBkhjCS2_WYb_ja(J_JO~=I(43*>2i^ECiqeE?
zsmsF9!za;L`*0@3-C2b-W^cdAnA0_k3Abz-K|&Vbw)w(<`7N;Yz$|Xiit`P9ytAVa
z28{(i^9<U4nFIA#)|?ascpoAW2&!=xlX7OY4a&T8#E9mv39CZ>u_lAWTF0_)=M_pO
zEG!Vsu=nCYkr`2E@sV}1Cri@$3^M+K7!G@X+uN*x_!Yj}@2{PBHVi)i|DvBS+n&1u
zKy~IlZFRwtjf@go87^g9A1OlbBggNLcxq$YHy&KI4l#5;-yo*`vHJ<}pNGq(GPZAD
ze+#EK&AT*WV;H?&GmAeS1B;b=T`CjQxV{`G+U)&$t_vsn{+8DBR6^jf78pK}%>T+W
z+r3=lIDFtWY3Fw)MpS8Z!K~L}k0bNVqcN-BL|}8c`0M$qm+-4&u=l08uHy3{?Y~G`
z9qj3>QW$^F{t`pv8H!RY`S-PM_7|XA?pst6`g{fsK$^1b0X3lX1<~JPDuD_`!kzCx
zz+tJ^Um#kmW%FBS-shE7YsYd8be|S+N-Nc$Js`MkA4UVR_AXHhzx}tnr_pSjF`!0L
z{pj1hv4G{L(-}~M4}G4oa`+dK$D7H&q2JxHv0VECgb^n5Uwg6mDRS?Jh#7gG8GU`E
zRp}qo!tT6{aP9b9)_}_^|9EN46Zm>NZ0G=LYj^>(i%KxszzjE2A{AgkqlR``F5E~s
z^C<Ds{}IpD$VEB>L0=!;xBj<lrfcxOvJv$pj3NqW^f~H=tvUiYdeT`}%$W5)kC}eF
z?|AmUF4y@l5A~C#nDp$|mR~02{ULwCzLqn%P(|`x8?4@e@-WMx8!{?V<i8&(ivK>E
zVU^oI@GltO12h&CNu)NN-J}1YivMPtT_^byS(0zYq`svX4PGw#36PAx-SX=ItM|^X
zG2Uy4>;^big1b3urpP8!yZ;%e-@AXmCbW87HXT|Wb~S&w%c2XY-~Q8?A^JR%vNX??
ze*^DVShmV}h9aB+5?cE=#=rpS)_k`x>bxZKLWzmHYJV(d?L3tUee&?T*XskjRcJ2&
z99^HvJ1kV4g#9rP>3hIund7Eo?`I$sq4fu6uM`$BbDW~U@lxH7!^;rA1GGG9>J?6s
zqvCGM!NWMdneC&tzhA$F-ujWgnIwDYw69~1G^Pw54&5j!bxYWYZfw|LoXLMxoj5cc
zV}5|3a0Hb;vtY2K&+@(UYf1_xTR^TD4*#rT#dsJhcv60?quI}oHy~tcSsre?UGuS{
z*NHVjjaSKL^8LNJoBL|aTx7!#ao`f;Dl(0?wFk)#V+Ck71oSJToiI_`21<5Umb0Uw
zK_@#HHP8fD+d1wcF3g8$^?ocDAHax&jcE02S8aQ2Uaz$FaJcrBB^wzbyj3q3A(}z}
ztypV4*hssj%lSW*pPS}Y!!A9rYK+#@^uDbXN54Zn$y<UOC#aLFaU~zt{ns_u_=rp~
z4LPERgz$8fW`p{dcOZannCZK-N#wJAreQujl+dg3ydCEg5EE63c&uY<kfJ;TL|2-a
zJ9#i^aPlgTuh4=rI;{KNb~}1*ZgxLeFfAXY)^L75Varp=iCom21&#&p>uvATob|dh
z<v<VD+-Gbe0k>OOt)8DEEc2+-hRoJ~?!twdZ2f`5cLJpqk$txdC@JKBA8mAn0ke(8
zWH=S^<I0R^5pMom#YR2~zMKN^%=hLErDvusnTf8)ERb0^g(H_r&r;MR-EzJ@nVpP<
z11Jp>=Ay%fa9AwQ$Zkvf0~bU2l(FzIgT#AAY`EGA{462)pNZAPBH&K^8CUxLZ+e0M
zwa=<)WzlFxg;Z!Fo$^ie@uAbr+*m`9YdqG}BlK7^Avbr@o<HS3<+;*Zs*;494&XS-
zK#!+qaMhxS9QqhDTiFBaxzMQV02-_xNm*NfpYjt~yMHWQzlrx)-W+4*As0m~N#q==
z-2bC!pZ)&Q=(+uz>3i$%|04Cl)%!KAmiMxx>hYb&|FxyHTj4UDjE8>hpY;RIl~;cL
zPr6YI)+O<Zf?4~^%v7Nq%l;F%^dYS7Gl?LY*0x~=@n5!WWvoyTLMePEJ&SPgZMvoR
z_8S}wKYR>uHHM=Bb*HX*zq8MUF)SDC^bc`qIWh}vfs@+%1gMnuBeD7OWiwvz(QVxu
zg)0dT2?|H}pB>5^t#Dg2QCkor{FCqwS0<@n<CbZ^&77>3MT3&e(5)+P7;=;Y=fgW@
zjN>%LGP-pZ#`Vjj_AOP<{vG)7)>oHyb}sz46~X^(EqYvZ3#iY1X)4S3cwM(5N+h09
z$_YkyJGZ+p`kVcjcX{nC&{)>WTE&lgfvbOBfdqmhnEI?=<|;2%4dK1u6A}CX9G3S>
zYD(QeFz0Ik6n}t!!8&|YVqXqx7lfe1q1Ws_sIqv!S#InY9pXPz!hp=<$GdQGjh6Qo
za?}1V*&>uqzaIuRdwx+1QR<21<PS_5VTlrBkmPFR6L8eC9a0=jrkYgI3z2=~?r7uV
z`BsCckf!`nKlNGdB<!cW5NEK)PM9`>iz2m`;wTc?LS1Uo3bAqqP=!z%FRYwh|D!D^
z&lJ5Q$GF$^rajFp&n9DGG1}y@(%uF}#JL7ol97cW;&NfPX!BCVvT6arAu~SHc7}s|
z>e0EV3P5jZw^j^CP$8IMm!%XL5VY&_1Lz4EE|&{$_D%SmRCX`_hU|6UK#{^PxJ6$Q
z)^b1$T2$nV^=^(bjyTlt*Q1LXnp!TX0C3kYt?;|W44@gt(LuYF2WwxU7$4tR2a3JB
z`#|r_6yE?-2q-3cOeyTnVg0|^*$h`{)Wr!4Qmz=tx7-LTi9)JisjWstS82H!EgJzf
zt<o3`WcR2s__eY-2dd?{Kr9TOIE$Y-8F}LFwezrVy?orBnZnDgZNF1;LI6aCXyhHL
z?->`1SO(Tc$S^DtwvRg-A?<rqMb0&>(P!6wlq?Ml00IMJb}XERi?pl{P)bZ97KjJP
zQKPn8^7OuSPp@3C{@%ksFd>DCjYh$gd9L)L&ygmPFCi(IBz8X`Vdan#U6)0(7dsgn
znvkfh{b{op;>WHc?i1_B9C_SY4%;Y`4u)dVIgaI{<UOHw<mxO4H0q|8xG*awYZHwl
z9gq+Ctz;&NW=rAWwul%uE%wG%;wFV1s<xQ!8YdF)oOO15h@~(yEO6rwXM!xxp1r5r
zp52=Up`4;PQ(;$YH=Va}>3sLKo;{Zijz6*je9oKCYxNxVybN3paLWSsWvFt7g4N-S
zlGcYZ$V;gtf^r$1Np_xVJ-2}z!uw^CgJNB>YCy`t3Z~5!zC=ha$>-<r-tV20-$u!h
zo)d5&G^W@0bi06ryIdB&O)V&PW+6mxeNbVid*5hH-W}W*lpw^EfCO~#4nFt@JkW(6
z+qU}UD!qDCb4@@knt8}fpMUC94@;iCGdG+9(0(`Gr@E6Ff?lSE5@-i;c!H)!D3AyJ
zy$2X|bNIg?V#b0Xb08l;AXuiA?^`Aoqi_`>e0?q$aM2cudamG8b@GrL<UUccdP8zP
ziGWVfoE5;kJFICiXeHWo2QrJeu%_vs$a)v(A(0XIGmtp}^SQ9vD$l<qzm9KrW~OuM
zpO?7%vC=9I`n;=Sl_SAd1KNf%^Be}kEuJQ~6t`M2f$I*06Duu!dmbl~vTVsem|<aR
zO=~>xYmhXtECB0xON{pur(<p~7suhfXfX6z!DaZJKCDdenm=zIcqb)rp<-`GZN#95
z)(ow=aKTX+Q8|*Ic#eUXQa1XbX^AY)ay6prpB>m|d?M2*8?@Vi>kJ!2=lwouuv4|s
zl)4OmZY_;WJ&A%oyK<4R($k1fS?9u8BZ=WcKRq~y_qRDJ15}DLQ8;2l)i4d`J@5C`
zsmGdgxHg0O5b=AQXV;)42_I<^ytBgc=4@LcTSICKmmb*mX-d`Uh654LVE2RH2aQoI
zh}D6614In%5qA-(Ng(;gb#a0pozljRGcBjxHlu*42g@c7!BIotSY*LMG!_%#^v!4M
zWAkmHq$da1M72Gt5Ab7ZBmHnIv4SA>sc{?`q)Sc$B+V)OXi-?5lDFrD4jHgxdS-6o
z!33iqC5y{v-Zo`K<Z-Ic1+JYQ>BTPBUXXx9o=?-trfA|GLl9<n69wJ>gbwMQX$t&m
z3j*!!6oVcsiOc$7{eTvylrYfn!%-3Cce$+Z&cq{TZmY6jg=s?AOcHWzg-E@oJ7>fM
z=I%twA9%jsm8qPh@Om>D!U4jivXKtod~RIx@WkM&u%W9iO~Lbiu;r`B8c1p~aIocJ
zYfjm43%j$ZMc+1z60Yp%q9DLOU2KY&o^%AYEeHn$E_<+*Y8i*XHySr0vX(wP*TLz{
zJKLL>-p`_Fr4g<^%bVWC6BwH&gs=18kOBQs(1jI43&N&W|7_`CDn6UlprCc$IcjLA
zpUu^T;*>*GtH4AGzIk(iZG3Hoxr3>x0YjjhoY@yE7F?`UQ6-3VgcT>x$63+)K}Rq%
z*yc@yn0j?3xe9VFZ<QtD1#U#ch!iU}7Uwtug1wBix*?j<RqyH7t0~;XILl9L^g9$R
zIGlub+DDAj8jxRxC$P5F^H}C7#HcT5^qet1W@`5>FqvC|D?#TKTGW(OPeveCbF8G<
zy;6b?iFG_z9C1ZXBGz60__(EZ?Xrj<&@g8PPHek~88E+K!JX){pDg+r(5|`8@mll*
z8>K}u-1U6pu&M?yqB+4g^J5K%VzD)TsticEJj%jL8h`1oc3>{6Fddg3^Sjfz>lWrS
zaDE>LE_v^`llm%Mw$2kIF|f>^{~9OhN)K@3b~CU)u%wA`zOIzO@oa-PWGWXsNKb2Z
zv1$!*>>)o_nnLmfm+r(;C}!E9KAiA)=~gr<m@2xPRy8A6V`dkS)L^TuY|)s1Gcbo-
z_V33DKYpArA0Iyib5Vj&sZ2@i)kLTW3Y5-MWt*TRCC#g&Nq3mky9JMBUI}NPApP!e
zU#+~SW@mpaj_iNR&?ZZDUfLs7e2fM@!=BfBAfMahrve)m56ttD+pSIm{F&1p)oddj
z2p7XJOBFzUrnL}C>qZ;N-*M_|cOmvDj%MC2A$?mjb8k)H#Gd#44P21S4&kU4MA9wk
zZG4wjr!qLkM>)<@Kmwf*Mi9lJhAGp`A2J3y(zV~G8b+19c2d@JXj-15VjQmztaWYR
zF4q=M!6V^VH<}jM`-e|6)|EBxfIeL5TLg<K+2oZlRYlc&_ifMb4UqwT34VUihkpyA
z5d-TsUD0te6q{gm;<@T0))ed%%fNc1Y?3(cOZA{LILvyyky7uF*MLSbH^kMCqc97)
z*lWdv!!1J=Waz$Sr-Z@%!@lAL<e0vZPKu6kl`nX|E)A9B<q^=T@=0+U8bbC6I{zbp
zLh-g$t(|(YBUXaKTu%Xz(Lr$7PX?|(ISC1hu>QKFg~AWQ+yBC}8-t4gL|=~^+$S*0
zkSgLY{coi;F@T~bbC#dy`3hr=MyKdTTh`AfdWq#4p}!LEO1Ijv%=6204SCtfaGbd?
zq3XzE8#|)ig4|GITZJQg_*2btl)a(+8<#0VuRX5<%M6BHYukFtV^C?`h^Px_cxA~e
z#cEm1cz(Ltwz?Q3FjD+JTU^Z}xL84Y5CV?#>g*#~0T^I%rffL`Fs|&c!h7!Vmp3NZ
zkdNhv8JY<{hRl7)p<{LD)YnN9b3Z^5_7A0#IusX7__ffVg1u8-Qg0c<nT7$EmVs!_
zcG_f^4#z?`F6tBY#eO|f=5-A1;tJx=F_^)cbxQ(^bJBL-G+`dwL@Dd|Wu;1KN%M5D
z&W>Yr!I@uUT-?|~lZ<b0^uI@f5>La!gzM?frEvd5kr(CJ===rmyq1<<k({Z?!Ey0W
z$$*z^7}{spj9}Bge^^u1QrnZFlYzu~s6kO4Njw-;LRQ>d2NKrbXg-drhogaQZ?(tl
zg~7i`>ttzlosfQEpAehn!a3{_wX;h0q1Amkmp~f^pd4^N(fw_adI;1dO;ZR|Oj#Qx
z2co&CibG7i6|&DF(VxTauDczivxLNSU2VByU(Kc{JlqLx(2dNsD^hj0ys7-qdGe9-
zS&Pv06wf?{$;EANLu`)i7c~@z-2r8zR5h3kA6l^vSXvnr$x()VQRFkhD4UT#z^7}|
zB(_zg2q=P34R9_32^*?7bvEeYm(9ut%uN?Xh}B8i1HY7odYUPo3hH>5K6FAtGu(p`
z?+d7U1W%LU;yN~+Tw6lU_gFTeG0s?morY>IVvW8DJoB9;LcxGL@1}6<49~PLDay{7
zTsTlocRcL{Beg(9bE7wFtj@X8UTj62jZC;|ef-MivrgKk>;c7gP32oWdZf)`z=2DJ
z;=IfeRVPC{PzYn2Gz#l`vR15VP@hLoHCOWbgXWjvAl%Fw(D%Xrf;ZWBzWO4<wnx^u
zv^;H%x-*fHK?%Svqk&jeUk!kyOBHt-%|^84+guSXR7djJ3%=8B@Utaq(*?ml$W$;O
zTlhvHYTQ^!0s3R+7Jsd0u1277-blbMrw@N!qJq;w_|VyO6Usa#?1)qoGs@37Q`j*>
zBsTz=1S-elT+p>-Fgva?Q|IkcTZFQ9V)QUS>VsP@)3_Wx(YAw?1V*r95Pqhl0aSvG
zM@xl?D-y5BlUvcGuisj+X$axC!jD{&DxMKf9Oy`>B>p=6^NL@lUp+{$M55GbD2lec
zOet<KV*lqScG@g4DanuRlk~U|5r&!sl|=c-OhiEjofHOvXV~E*@c?XSRWdBz9Ue2R
zoYNG>eUjmx6y++v4Q0spC=60MKetP&RF$(C6K;bDDpRr{EW9A^u|mvORS_aMc!9fe
z$>ya$#L+j&w+`rOXj(@hQ<@NesN$e=IIna-OV?%S%cAI<m0MoRRZu38&R<j~hRn=J
zD&w?NT1qJr#_cyL2r@)gP^G1cx+?80z%XT9qzSpjEBXbnC3VCGE}&<+xGb^sHoY?)
z`+f&VKBgeZX5X*s)YRx3h}qJN1eM&OQFCHqd)g|OO(73DaR`R?`^BnDc};3|TeF9=
z35bbZR2fh9xJp(e;S!KF=+$JT{?>+R&5A_n+Jj(XUfqbajIL3#|7EAU7l}7M4vy^W
zO}Upz1k(&}TMf9Uy&3WdmA;{c$bY8lt7u&glG5-hv1N?5C~hLf4EIpKK21lOy19U*
z<ks$o6d)&4TTaO@g>s>_mYEvI)Hg@az<oYIM!3h8C*380`WZ4GLc%px^cKF~H?V;f
zN2+3$Jb(aH$=b)?sx&6C-QnT@P=lnDZ&(khxNn@<R|S-45!Pf3(nBYP+^6MB=mQi{
z#g4d1jfRSi>F>th0E#UUh#TIXqPqMNf{5_w-;gzFLX!vWB25ZOQ^GXEr3)I!?tMH%
zG#_ty@Zlk-rV`-4jhsJVOrF+dJ3gTVbffwp3$g0`GrTUmn6))}!LIPJ8!u-c4ouxO
z7}C@5xOMFPIqD_GOiD|FGg7Vz5dQvn91}5Bb8b|<_xKIhW{d(d@0}wZBR0hCK$}H^
z^ad`%ylgt&4o8Q8QD9`SIeJNpD&K2M*CZGIFi_KzEo;?mY7^2`VnR~$DgFxha`Uv%
zw9)<e;6!DcgHAfMY#klf!y_=2i7C^Wx>0CSf!Chf-ZCjIuL<)mhQdHFx4v|s+596d
zM*M-hX)Ktp(h0LvIo>hAnm)3i!G0DCXY`IIc%&8GWF6f(4K--q2enZ{o+x?PXa2lo
zXDNq&Va4E}%6F6?7IVz22IW6YUR?R+)r4nO4YNx7ZQM#mQuLAqMW6(|PL!5{L23Xa
z`%us;X6k84FM_gIZ^^W{9dAv#NXk7RT1<ZMD*v41leW0uOJdz=65EtBQR0LeFYo3L
z|8CtKabkhVoDOWHsaP5lSM8Zwc~F!-VU^$ggp1MM5FNg9%LFb&qJ+54Uc!%-tKCWU
z>nd?ME5#HR<PO#K78v`SI~2?m>qiU|k)x)uCcv^+sIdqRHG~eb*JV`LSiBU!fyiD8
zbKkra5rUG(baj}F-uzILVf}PC%s6OVjeGm2zU8W>`tcs1cO7~x+kkJ9qxV<`PnGRC
z4kUek6117(*QZ76Yov_79-6*43LL0ob3*Y6+DH-FX}0|)F(xX@s%B6mOaK{~pIhpC
zZe4{<2Wso%ES(<3_dB`UPIf@XaKL#B?=-F{_)N#;+hKfJxnf(Ucp1#fR1rAN7=78(
zOnI~{>z?Vu(wKtPlgYsA@FE?6gtjf2Akrc9Zuc)LUThUAvx8!RR|!m~q3i1743T8I
zu6Lo-9aO|#AvXn=fw^><oqb}XBlNMe#Cn2MtcoKs%_0R9v3aT4O|5QD6RAoi`jz1D
zqo!iX_z-1;FtxA-c*p+14?bQ|7_rcDv}@Ejl>UOMnbZ(p1)<OI9r|zRWM~BJ9z%`i
zjlT?W;OK*slDvGXd1(l=j0rQ(Z+;%$)QB@jX3sgn$Exw5O3IeJYHkoy1b{QIplW!{
zbO*Qa$4p3(E<7_9*vOZI1U9C%VMKimWWcywtg3=;mRBvP<M=#Otc%kH*^?^34@M0J
zj3m@0=78Y;1ywj|daw?S3{$f%F?RuUuCEtPmX1anz7<A)?w%1v^o<S&iO5Gk8AQ;!
z&ed*|>tf0Nsu;tv9$5RnHPSI=211A*gjZ9~Gme8iun5TLuUR0Ti)4Ma^ykaRNrNi6
zNZ>&vo;g$58fu{4q-kxRc3@5(JeDx_`K74c;YO2#a>OdMaSihssY^RH1Se3}ELFHu
zH3jZ{1QjbQ#zh_!M!w~?$6aa%z5@A+mX=5Z&9EDjZQ=K>-H+Xqz)549cVi5-A8w1n
zxfoTYF@C>k7uHZm2(j3`tWVOA`!;DgzpRhiMlmc(&LLTQKl*wf&SHYoGbxajw#^bC
zh~g8im9$y_iM6j!OLXW-XdnoWm8%&BgxEF5U%3rW@W`$|r=F0xzT-Pb8#XwXqMJh1
z3@H)<slRknd2n8Sru7V&Za|~AD&q+5Pt5WAou!&g1<BpfYXk<rQR4#SBNHi;u<s$_
zrABrCJQNWn7W0yV{t?np<JgI3GUCY(qMoVPBs7&dj|u?A7!7PHNZg9Q2ttO*o6D89
z+n``_9+%y7FfMjamnXa7FDC9IiSiP!kRCrrG?Pf|Q!8Re28|Y@Ed?#XhQtMMC(KbY
zNoULe>H5Cg!ha*tt0?ZIoh?VNq!db3JOiVYmDw|P-^XA{2%Hh}He)w+dCc1Fl3tB3
zEboXwHhuldkRl*C>WfH+A0h!5(C1w9fM}YFx{8uFzl9RnwUl>X5x%;ZR*hd3xh}Va
z-P4fPl#(=~XQ)Iq!2~K(=_P$1*ASk$jWDrhAzmN`3q;B?Wi#;0$+&Ch?KJ@?uGR=D
z99C~4&Hsi7S9K;Lza-F4iHmZgo)DW?MWNhBn38}S%H7Y;>MH&#h3_4L$a~iZ4vc_T
zt9X1Ot9f<RPy;TgTlVnZVPX~x`Jcl)D!Q`HKk-4cq?6>9L^68FWUse1e8m%`F9q?`
zvO%Vo{KZ*mE273GR{plS1Cz>whwn&Q3NBVv5O{x@{j#1XWuv&XNwg0R!jcdb$<$sG
ze_Y1@nH)Cvas%aWvC>MSx5Fg@5cRbYZORFSv9JU!Hh9KpG&9PLyMG_(R2|aup++ML
zmUA0fW8plnzo)o3@0$X;nZshNYWX7ZF(PVkCt*LE{L)4fNDM=<?<<ee_8IDuAH=%~
zNBvPGg*Wt0(%=Py*@m&ZE*@l3IGEXngS9FjB0XqGQ9Ru<$l7vIv|vpRZZJkyG32bD
z*zWEzyM4(E3m1Y)YrQn6c}<^G5=Uv{OHlaoB_Y=_>PFrH{wd)C&MgTz4RolN9wR!^
zn@V8TtB6>1y{VX_89%4arvw`*QHx!pu?1!{u0&$30UV~huE+yXGj;19Dp3hS=|wan
z(_LJSxqVQ+XUX<&2hHyNqetIx7uW^qCYknaZD{lMh&<C1cuxAA1e!PCOD^46LWlah
zDkPJzRB-&>!S3{-QBy5FuvjOtsoJOu)>>?4Hd{!~IVRAywhSV+$LUY3W3VCm3IZ_|
zeQ%pR1JE?YJ1;1MRkMkD^@v55CaiXXyv<#kM9`{HUpsaTEpE$+YD#Ja30%VZZW7~#
zp=Wu#8q*b!;sb&R4`hQ?zoREZlL(M*I)@V!VCjmhKPGxZjal0!?xDrPHe6QGf@k)p
z{MVAo>>RRJv}Ya6DO#Q{V63qFJ=Rh1CEaXYK<{)5{i7xcpHrB=^jo%=K5`nZYfKx*
z@XmCe)Nsz<!@4tm!j!dKu~^eW*@mo@P((}TZ|J9Wd<@3jV}Zzumck6T+(fhoOSso>
zt^zu$nug9FAL&2|@MTiaC4vkS`=+Wi*2VNgLAUH(Q566hc#4_T9PQWyGQE0BHxw+z
z=w+@kw1N<2_`69`=e=^}*;ofpVSF_<I^0R{e4rehwdPM3z5|*mdkWVI2PTU;5F`W>
zk7@jXZJV+jQ+8V~x>NOzw4?>QPDPZWUmJ1l2}oAZGU4U$XjWK=m~8O1J!-U&<4rA3
z2rAKjjFeQ`qc|OuR6MfdO`=H=slUeX#a(M|nDr0CU#g~}=3q(wUNbgsf4y$yfxs$q
zkYuiKnO7#0l~rk~l6yP?2^=9yT(*W)_bs`)GcYmMHB%h5-OC>@6?xAOisA(`U)yIH
zUQCLSz3kkrnjx(&#4UrCzcGM4!POv_2F%ff1!X%nAP9!!y5MreHC<4WTmAjC=TPOO
z1@55s>gh;l?z30wS83`p>Qc^|e1}omJ_#d}F%Q5n)y~lIzR)EhtTO<i(eQ*l6g*3$
zrP^7;g`Q&ZR$f2=7x~_{gpCV#(&*%$z4Wjsl&Vb{O8wlUHq1$(FeSW57M3eBDNXJg
zDiAWHU)WTdg;-^e>g^B&tsJk3LoCd4B;B?s6z^P6%+ux2LHi*3NVbd3v;Uha+vS4h
zdtTq%9Yg6h57lT0CkD%bBYsmF2nhPKjJSyU8P%x3mh;6#j2vDVW{KhZQ0z=2LrLuo
zS4JV0Xr*TE&l|*cl;GT|&nKgtnXLkI(inogrkjq<T9fec@luvX#6>3ErZFjBOF9W;
zEkVMfTInSzKX}`Ciz{t{diw1W`uE_$a$_nbhP4L~%=itAzh|Rf1}T!!*!#pz@j<p$
zc{%1uX16|RIi4)pR4W`Q>xYBnK}$f{1cVt&c@`A3h99-624@Bv*2lBopk*TD`S_^V
zbg{U7;Z3AfOK2%b8rS_)P<LV0tlf_^4E`FvIO>v^Blmu)TGQ`y()H5JkgGyYf>g5Z
z4I*HyItfESS+UU)M?qQK(LA_4N~eU|SbTD0;5!>aahT20U^F+qEJ`NRlPi!xvY%n|
zTvw$h>v(1KuhZdLdpwSP-p#JPHz$c0fM>|98xo#to=A62o(^U|hd0VQrOKPW^V|Pk
z#s~QoIi^g`{`cdw*YC_GO>9FO*_`Kb)rzobSERs_WPbnxZraVhGYGLF)9t8J8Y772
zlEwUH2mR<UMM~qb6P+Kp1wKcw(-9(fa@xYdAE8o6)fnQ-cO-D#H97MFT5S$^I712n
zZs$~T!5lhNwARrP21mBm@UxEAJuQv0WTb-YgYW$9m`D>==dl+DnI>X|1gU}d6u<pd
zwzB010csz%Rv8ETK$|YjXSdSK&!gDe>IVl{QlZ*EDUXz3D8pCCrl*JdAn@qh)q&`7
zr?y*0&76q9o01s77#6_bJ53lT!I%XnvbZ*W(5=SQHk8DndscMy>rDD_RF_S07lWUw
z^&1?;1ygLRN>|TZm|3H%l^FL7w}OnzX_!BC>{m(?c84)^UrozaW!rg<s92<9B}Re?
zg$QqD2@#d7g2vKVq;|jxG6&oRJs$1>-S;}y2$n=+ghXe(NKlIE2&_Nbc5-$o0N(jH
zl9o_-@@=pY7qE10z;PvnMWBlOn)^iW(6|cYmJd%!&C}8DIiqLCAB+j1LG%(@5gB%b
zdu?Ixw<wphB(d?i=!$g3DKvOZdK=jO$_Ir;h}=l=&Uy+B9+`#lNMV`|5twrsX}oP+
z-{O|0CPs5hZOvL;w_^#-FdrzrcT^v7v^#dvYJ*tl_hym?!Ka9T=&O1`Ep07cm&VQ7
zPMt6bAs@O3(yRV*6YG5uv_d%ADv@~fJ~;siylX0M2DCkKM9PHt#EACLJPVKv1J-Hz
z10|JEsm7w~@8QYN!UFJIb%o0+G>H8x28#6zfNkUmEyBkQ019+UJ`BFB<q+%g9_lyd
zCwSEWZFRlwMe5!Y6ByeT%)&!g?p`Z;Wv@D~RyRUANnxGbz;aM9&O9r}Y~0DFxEmH_
zd<d)PP+ij$MVoe=Ai;R=D>HzR7pmro`lDy>XHMXVhQ(8xkt9(WXp-w+-`bFFZbZ!0
zgshpA{Z2i{Np`~H1m&i+Ba33EgII!;5igpe+O<T}QOWJ?;~{KkCNdud_L99Ac3s@T
z%IZ`F^yiDOX2EM#Us0u|lqO6J%IM$Quit&I4gS>cRWA!`&UgSo)(!c!s9a5UCxfT>
ztGW?GS?THdr8SP!k-P%)Vg2r`SjPH_xtEEzMnWCl#SKE+@~Df5ab2P6R{y}68&O}N
zf7YTwXu3U>!-DoR$WG-Jw7hEf-3r?GalR#Z4!_eC##S3$txM9CT4!!_)7Ah{a$};u
zqHNvtmHT!~sAgacG*<Lj8}sBlq(m$(VZ*jt*i40N$-#o+*P?VFp%>)Kk0;tsv-Liz
zvI1-kj<Tua4-^sJ5I|t*J!WPxn-21nzt6zi2;Gk>Sqo0H2M$1qU1DCs5>!gEE9yoe
zWDLlkb<rmhi}*_oPgxgjbmmbA93QDQNniTKXa!q9(<<}>DnC0|ikCf_iQJb|p8Dg}
z^rQ+-ImWr7L7}=MYUcM-D7_?11b=7D>Q3=LnIKWL1dYiruJBVT$WmKMIZUmxtj1ql
z+e%%jV4&6p@=L6oYx!I(-edcMnS#JNZ=4Ir2h%<bJ|+Y&U~^uok+F+}mA2W@o#3qX
zK#PZgP<&gEG^=+xoog%`aX>eJ;?avLDk%CB$$IAlhp9m)K}H8<VundY$O2<f#(PmS
z;3X$Lycv`-7^YCJ&M+ThZ4<&@gsB$fJ+P_CSy#DMSU=Kej3i%4$BzdCZWw_hdIohJ
z%8xc4`I@RSg7ay=R%p`3<EJ*%T~lXlYGIspH>W<FUYN(FR4qD-`yREV%FutY5U~zs
zAY;h@rd!1?OJW?YQyXP<JX>IeHs5$_OoXm)5c|NhxT)`lXDCquGYmJRZtifdv4qVI
zT?i$x;QfHUeC^Y3KBrkjChzOBz9iwesnu^A#^<A@$8^v_BoTkq1lZB+FJ5<s(~ZQA
zbkKnyk!Jy+-WT-Z3pRegAdLq>C__@W0~!PYUhv7iEZn0`b|G|1H^>a;0+POGl$ax7
zSHA2Y#w%}``T~@p9Q8dI{UU=)WP3StN?H8yf(Y!dWR~#ra)+~0_s1~)aUE;;&tcdq
zOf@f2{De59n$f%CfA@BwM}L8@{2Ae(cx`ihEkMj0k>Zw(B#o3oVGT2fC#{azU1;BC
z(A6dEwEMJK!mE~S<8XK0;t&xm_qJgnM~N06tNsZx%9+`Ez@)f6l*}TRF>B=Bb8UTJ
zeb^bjkNWpyxDx9JmSC1ca|U-bM{s;Hwu#`S^wJCwbml^cU_J<121QXPKr^FLwK#9|
zlRtom`bwT8S4)w!-=uf*cC<FEf!5wvFmogl%Y`#I!cZk8-BdSouSgYRx#jR`Alnf^
zjPaLdWe`;RqlRZo0^WF6*V6I8N~RNV-?5U|FjCzkZz&7W@51spviGt4q$quE^m0Hv
zcK(G`=4AxOivKyTMA4jdQB|Q)P_N6YL0dbqnJ&7o&-nz8tDbt2M#_dbVK;(Uh=txl
z%)3xk*7rb$iZ)tXqG81Pm}cQ6upLYV%UP``2)z$?6BR;XEI^eggS{w9gKlCqAS|oJ
z4wzo)TEZ?ON|Sa^2X8L7TR5QXf-Bl7-!ES2Zh{AYEF->t<BwzD3@uyOEg8_FlYt1x
zXWG~gGK-eEe&%!yE?d=}lq619D6&Kzq+98KrG|{r2u}in_tc(1HzV<5tP*MMG!Lg^
zP%Z70owf-Po+IY!TdoXJtO~ImFfNtyZB<~C$_IJCOKj<MXP?@#ZI1wNF(OYvDL7Qv
zOKo2O@Gvx@0+SUKL^u3|BDS63OQsQA9(i<leK4T`p#$3&Mu5xI1OtUXChURx{_cs@
zM`3IL&$2h|SE`IbKmwd8Y#Ag=Y89*3WG^TL6G-fze3bGNDvZCSu6&Xu9d+G<7{bD@
zUW75KQ@bP3tRnbCy2=B(Sb}~*PM9UfT7Q{3glSlZ(tlEdM_=rUzIK9nRmLSS4iPqx
zV&H<^jsIChiwZGGhQ?7&bz8l#)>3DmxINM*!maU;oL3DjA&oqN`dc0iPh!$UVQ6E_
z4jStYhqS;M#8zU7&|~kEUNU9twmxt0l4qez0ncai5EZr6O%bf-T3FnJg&JT)An0RY
zB;H%)zZCWUI8G#Os=>;a=`<ZYvQ6i|e?!Y~{Y>?C#$i_j6bqp66KBn@_6rhxTXJji
zCq~jJzz`kKv`z-{9<!2iu(}430TH1(y;u%6Gfx|EzbKz5z}X`DN(^7~ZQf%h!A+o|
z=FlrT<V5fg6HCW8v_qVq*_+djF$i{C<Oq(ir&9GCD$<?@pu^)re>D&s9Ywm*cg10Y
zJpkFF+wc$xyls;HUeE=cm3rYv&U-I(R4gDbEH5AjNT+~U0hVTUXo!<|)JtmQBtlwe
z>4PkhYB&q#+D>L0m0QEYCCAQlS^mZOLl8lHu3LzI@{Hl26H;Lx!4qN-&9*Ch)jE`b
ze~5^cXR`6MxSiAC*)G|fc*#NW5S}wySm4CUM+p+Aw=a0{QL7R;TSkVIMl5X{tl#0!
zspoS1;4ka>d8gSV9|XT}U6o0A$~=a{54oAqtl~~;esE78@#K~0zQ<!&%t?ZJ2nfdi
zxz^_{ou=--k=0HD6d6_)TN)5)71<|e(Ifhr%Xfco8z&WZG(dSZozu_HZ(Xu<Pmfq2
ziI^Wv!cM5+cch?%HZ`q{gZt&grEz*kD8$4MnOw7fA&%|8r^17DM3O~v@-x0P1;;f*
zrGnO$p?{((frOCi>)Qxq0SQSNEVm%z0a(=A$3SJ9-L{1EOi6JE<w4hb*MnIzKC#gb
zUi*9>H6LC^Fcp`<W4kh8(WI>bH=q0AT?2#jXer%Sqm6F_%g$}0P@5EZvuDEXJe>pj
zYqJe=xo0-F%gE+TCXF2p*tPcQ5~n8RVId^Z9&{cFnpZ8Aqg};)$3<1<9og|$U3@S=
zovuDu>t5S^P0{gbJ|HHfg`f#VJctIDdxv*A51L8yLCUUoxf~~xmR#^~N{o<wN0l6&
zss%a*aaOr3!HGCt6Mo~^xj7{^>6<N&x{mO+$NrcaQWeT&_l(-A(uWx=Cg5{3$bL03
zB_G*WAji1U-s?hSuI}4r>w?<yGaymzQ7UqMOy`L13JPB~)y@G|6%SiN?i^N*$*QaJ
zouh#CuSUM@7d6XeSiY9TF`LFVJ*cBa+`xACgxYcT!Pc{{Ts<UzN;b}2oUuRN!Xb{!
ziZ9)%d;r~?+b78EfCHz%0vFnQfEY^AIf^6P>`5l6_H5Xcq~i0-d~#rRtT+YEnSu$o
zV{Rh=M~x`IiH1KDdT7a^Cwvmm77d~9JQMrKx4O>ObZ`OC3OXxd;F>B4uSdUC0jb!c
zpiVVEIBx@yT0314NxZl9HY6v#GcK0v(<TU>nX2Khfp)zqy{d)ka@WKV*}lkB-OsHs
zjo%eD8u_X_Jk$y<s0oMYz^L<UNV8tc!QF1yiql=8SOxC$)eOF+DCaW5>G^_M@17GE
z0j7qBfCa-A1+o&g1vuLkLcFfc6jp~Ii~AR)su1hXmEnYpl15FrFM_hLN%x^xo}ZSe
zY*ohLm9{U+4DOe5lc>eJ<y4+o9|UH?@)Hzx_i6ckebL<^M2=Q{fz9FU<_(H;37cw6
zY^={|C~34EE9S7K^UE@<3PV3%0lyO_=C)r8YhtK8FaKuA&jr7iW2-?<rZRlDR!XOn
zrL)K}pZZfrcDfwfjyxS>53i8qlE!Ki8ER`$jVZw1RLkqa60k#l?8xBCC#<W^Njcr*
zLxr=MD(vIV+G>uMG{QeQZHjzV)S=5_>jUY{ec8abI<wlmN6Z4AC(EOV)=9ejSByuI
zO~dc!a-}2LeC6n#hQGbdo5xn0$Efl4oBBzxxZdhsij;vENG~F~5p)dNQg+5WU>!n4
zY(N>@SNYi9|3|ZXP3<HnWvtmHr<1gm@2(<lgRczSSjXBQ*1M(0C%I5-(I7*xxWJW8
zG(#&*(gbLGAL#mGc+rgzg}PI@Xr7?jik(}nO16RqXB*Ss-z>dPh!LbEsv{j{Lhpe7
z?Nm%sJ?WQoWQGmwEXRFDpyh^4n9k#THjM*Li!J#qy-gEKoK;L?q{sBnS>8#r6je-G
zgH5Kh4oL_7$qp|&#a{WovPs?GDZJ<r$FVnC9S2@^unP@i)Q$@?h2k>90&Gg_#*}UW
zZlPN92*rAWLDctbXmT(t9l!wbn<kAQV&_;38y+Va*olg3f8S~bcRS*mJ%1()+NW^p
zj0pZDo~6B;Ab8!eXqpvu-=6o%TXn%DpP&qzu;+!d*#4rkKKKk?w55BcBL8Y%7c#BD
zGmSrXae#&FivgLHzhAG7aCUs8FsW0bRNqT$e1pEnCgVfwG-MnZ@+V_8wvqoj%c~Jz
zqb!oFfiOE7?XUZ)2g%%Go8N_ALU<^7b5Unt8m;jp%fzmC>FnUbknwi0N<Xk|C<R>u
zDAB{bNvx{6&nsH3<>kQT<hMP~UT9eJ1raj+qyd})fOJKy#=R#wZrdzE$Sk+$=N_?=
zQgFTM?@{LNDh&#_MfQHcS5-|1W{K%+V$~NCqMenh@Gq}#u<1SBhESN3eBxA`7)2U@
z8W_zBwqNmkK@Kp=XeZvwco94ELID=pCn-u?IQU|pbAsKL^S;T1SAp~^^MC&F*9Kr;
z+abQV{kIJ7=KvM(Vtugy&l{JaxXByaVL4#FJlP_!R}z3PBC<~&+ML~=((S?EzuUwA
z94WD=mIp0oNeTA<UGv|UVzGiPf?Z+#qSx{N@4x;}4gU^L(8XmtWQjk}0{^E8|8r;$
z5%#%ot>98k{om)s^QDLdo(UuWWc_Qy|5MQv1z<rfPp&)h*Z)3G4kiG0Uhyh@wEG_w
z88eb3<bLR1#3obxS8x5_;xiO!rEV$GzyJ9ht}ua5OQ))i#q|I0jsM*)9@tm`)OW7`
zs3=_y_;iA4K4ca$z}NAAZy*iu0XFtFajf@0D(cf01wLK-5+04*fArKGSwgN)y!e;G
ze^hjW3VgcRk{xNC|9u10Ktf<+eaRmj|Dz)2G-PQdf5vCYiJ$+`ga5yM6<<W5jE-Ba
TK}@h9z@LnSqIj*SanSz(S8fza

literal 0
HcmV?d00001

diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
new file mode 100644
index 0000000..c131162
--- /dev/null
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -0,0 +1,550 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:f70ae9a75bf1eda857e582939e9a34df3db49efdb72d1585834c31f3c5f6de10"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Parallel processing via the `multiprocessing` module"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "CPUs with multiple cores have become the standard in the recent development of modern computer architectures, whether it is our desktop machine at home, our laptop, and especially supercomputer facilities.  \n",
+      "\n",
+      "However, the default Python interpreter's GIL (Global Interpreter Lock) is designed for simplicity, and in order to prevent conflicts between threads, it executes only one statement at a time (so-called serial processing).\n"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "\n",
+      "Multi-Threading vs. Multi-Processesing\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Depending on the application, two different approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-task\" of an process and those threads will have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
+      "\n",
+      "A safer approach (although less efficient due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n",
+      "\n",
+      "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Sections"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Introduction to the `multiprocessing` module"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "The `Process` class"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The most basic approach is to use the `Process` class from the multiprocessing module.  \n",
+      "Here, we will use a simple queue function to compute the cubes for the 6 numbers 1, 2, 3, 4, 5, and 6 in 6 parallel processes."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import multiprocessing as mp\n",
+      "\n",
+      "# Define an output queue\n",
+      "output = mp.Queue()\n",
+      "\n",
+      "# define a example function\n",
+      "def cube(x, output):\n",
+      "    output.put(x**3)\n",
+      "\n",
+      "# Setup a list of processes that we want to run\n",
+      "processes = [mp.Process(target=cube, args=(x, output)) for x in range(1,7)]\n",
+      "\n",
+      "# Run processes\n",
+      "for p in processes:\n",
+      "    p.start()\n",
+      "\n",
+      "# Exit the completed processes\n",
+      "for p in processes:\n",
+      "    p.join()\n",
+      "\n",
+      "# Get process results from the output queue\n",
+      "results = [output.get() for p in processes]\n",
+      "\n",
+      "print(results)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[1, 8, 27, 64, 125, 216]\n"
+       ]
+      }
+     ],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Defining an own `Process` class"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "class CubeProcess(mp.Process):\n",
+      "    def __init__(self, x, output):\n",
+      "        super(mp.Process, self).__init__()\n",
+      "        self.x = x\n",
+      "        self.queue = output\n",
+      "    def run(self):\n",
+      "        self.queue.put(self.x**3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Define an output queue\n",
+      "output = mp.Queue()\n",
+      "\n",
+      "# Setup a list of processes that we want to run\n",
+      "processes = [CubeProcess(x, output) for x in range(1,7)]\n",
+      "\n",
+      "# Run processes\n",
+      "for p in processes:\n",
+      "    p.start()\n",
+      "\n",
+      "# Exit the completed processes\n",
+      "for p in processes:\n",
+      "    p.join()\n",
+      "\n",
+      "# Get process results from the output queue\n",
+      "results = [output.get() for p in processes]\n",
+      "\n",
+      "print(results)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[1, 8, 27, 64, 125, 216]\n"
+       ]
+      }
+     ],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "The `Pool` class"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another more convenient approach for simple parallel processing tasks is the `Pool` class.  \n",
+      "\n",
+      "There are four methods that are particularly interesing:\n",
+      "\n",
+      "    - Pool.apply\n",
+      "    \n",
+      "    - Pool.map\n",
+      "    \n",
+      "    - Pool.apply_async\n",
+      "    \n",
+      "    - Pool.map_async\n",
+      "    \n",
+      "The `Pool.apply` and `Pool.map` methods are basically equivalents to Python's in-built [`apply`](https://docs.python.org/2/library/functions.html#apply) and [`map`](https://docs.python.org/2/library/functions.html#map) functions."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Before we come to the `async` variants of the `Pool` methods, let us take a look at a simple example using `Pool.apply` and `Pool.map`. Here, we will set the number of processes to 4, which means that the `Pool` class will only allow 4 processes running at the same time."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def cube(x):\n",
+      "    return x**3"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pool = mp.Pool(processes=4)\n",
+      "results = [pool.apply(cube, args=(x,)) for x in range(1,7)]\n",
+      "print(results)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[1, 8, 27, 64, 125, 216]\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pool = mp.Pool(processes=4)\n",
+      "results = pool.map(cube, range(1,7))\n",
+      "print(results)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[1, 8, 27, 64, 125, 216]\n"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished and return the results. The `async` variants, in contrast, will submit the processes to run in the background without locking the main program, which can be quite useful for certain application.  \n",
+      "However, if we use this asynchronous approach, we need to use the `get` method in order to obtain the `return` values."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pool = mp.Pool(processes=4)\n",
+      "results = [pool.apply_async(cube, args=(x,)) for x in range(1,7)]\n",
+      "output = [p.get() for p in results]\n",
+      "print(output)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[1, 8, 27, 64, 125, 216]\n"
+       ]
+      }
+     ],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Parzen kernel density estimation as benchmarking function"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "def parzen_estimation(x_samples, point_x, h):\n",
+      "    k_n = 0\n",
+      "    for row in x_samples:\n",
+      "        x_i = (point_x - row[:,np.newaxis]) / (h)\n",
+      "        for row in x_i:\n",
+      "            if np.abs(row) > (1/2):\n",
+      "                break\n",
+      "        else: # \"completion-else\"*\n",
+      "            k_n += 1\n",
+      "    return (k_n / len(x_samples)) / (h**point_x.shape[1])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "**A quick note about the \"completion else**\n",
+      "\n",
+      "Sometimes I receive comments about whether I used this for-else combination intentionally or if it happened by mistake. That is a legitimate question, since this \"completion-else\" is rarely used (that's what I call it, I am not aware if there is an \"official\" name for this, if so, please let me know).  \n",
+      "I have a more detailed explanation [here](http://sebastianraschka.com/Articles/2014_deep_python.html#else_clauses) in one of my blog-posts, but in a nutshell: In contrast to a conditional else (in combination with if-statements), the \"completion else\" is only executed if the preceding code block (here the `for`-loop) has finished.\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from mpl_toolkits.mplot3d import Axes3D\n",
+      "import matplotlib.pyplot as plt\n",
+      "import numpy as np\n",
+      "from itertools import product, combinations\n",
+      "fig = plt.figure(figsize=(7,7))\n",
+      "ax = fig.gca(projection='3d')\n",
+      "ax.set_aspect(\"equal\")\n",
+      "\n",
+      "# Plot Points\n",
+      "\n",
+      "# samples within the cube\n",
+      "X_inside = np.array([[0,0,0],[0.2,0.2,0.2],[0.1, -0.1, -0.3]])\n",
+      "\n",
+      "X_outside = np.array([[-1.2,0.3,-0.3],[0.8,-0.82,-0.9],[1, 0.6, -0.7],\n",
+      "                      [0.8,0.7,0.2],[0.7,-0.8,-0.45],[-0.3, 0.6, 0.9],\n",
+      "                      [0.7,-0.6,-0.8]])\n",
+      "\n",
+      "for row in X_inside:\n",
+      "    ax.scatter(row[0], row[1], row[2], color=\"r\", s=50, marker='^')\n",
+      "\n",
+      "for row in X_outside:    \n",
+      "    ax.scatter(row[0], row[1], row[2], color=\"k\", s=50)\n",
+      "\n",
+      "# Plot Cube\n",
+      "h = [-0.5, 0.5]\n",
+      "for s, e in combinations(np.array(list(product(h,h,h))), 2):\n",
+      "    if np.sum(np.abs(s-e)) == h[1]-h[0]:\n",
+      "        ax.plot3D(*zip(s,e), color=\"g\")\n",
+      "        \n",
+      "ax.set_xlim(-1.5, 1.5)\n",
+      "ax.set_ylim(-1.5, 1.5)\n",
+      "ax.set_zlim(-1.5, 1.5)\n",
+      "\n",
+      "plt.show()\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAGUCAYAAAASxdSgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCoXSgfn/AYLQokbxoJlvAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105285828>"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "point_x = np.array([[0],[0],[0]])\n",
+      "X_all = np.vstack((X_inside,X_outside))\n",
+      "\n",
+      "print('p(x) =', parzen_estimation(X_all, point_x, h=1))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "p(x) = 0.3\n"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Sample data and `timeit` benchmarks"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "np.random.seed(123)\n",
+      "\n",
+      "# Generate random 2D-patterns\n",
+      "mu_vec = np.array([0,0])\n",
+      "cov_mat = np.array([[1,0],[0,1]])\n",
+      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 1000)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import multiprocessing\n",
+      "\n",
+      "def serial(samples, x, widths):\n",
+      "    return [parzen_estimation(samples, x, w) for w in widths]\n",
+      "\n",
+      "def multiprocess(processes, samples, x, widths):\n",
+      "    pool = multiprocessing.Pool(processes=processes)\n",
+      "    return [pool.apply(parzen_estimation, args=(samples, x, w)) for w in widths]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "widths = np.arange(0.1, 1.3, 0.1)\n",
+      "point_x = np.array([[0],[0]])\n",
+      "results = []\n",
+      "\n",
+      "results.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import serial, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+      "\n",
+      "results.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+      "\n",
+      "results.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+      "\n",
+      "results.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+      "\n",
+      "results.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n",
+      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "OSError",
+       "evalue": "[Errno 24] Too many open files",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-28-4be468cf2eb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m results.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n\u001b[0;32m---> 11\u001b[0;31m             'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m results.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/timeit.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/timeit.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
+        "\u001b[0;32m<ipython-input-17-1745161bc4ac>\u001b[0m in \u001b[0;36mmultiprocess\u001b[0;34m(processes, samples, x, widths)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmultiprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mpool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparzen_estimation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwidths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/context.py\u001b[0m in \u001b[0;36mPool\u001b[0;34m(self, processes, initializer, initargs, maxtasksperchild)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         return Pool(processes, initializer, initargs, maxtasksperchild,\n\u001b[0;32m--> 118\u001b[0;31m                     context=self.get_context())\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mRawValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypecode_or_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/pool.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, processes, initializer, initargs, maxtasksperchild, context)\u001b[0m\n\u001b[1;32m    148\u001b[0m                  maxtasksperchild=None, context=None):\n\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mget_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_queues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_taskqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/pool.py\u001b[0m in \u001b[0;36m_setup_queues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setup_queues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_outqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_quick_put\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/context.py\u001b[0m in \u001b[0;36mSimpleQueue\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;34m'''Returns a queue object'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mqueues\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSimpleQueue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     def Pool(self, processes=None, initializer=None, initargs=(),\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/queues.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ctx)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduplex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    336\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rlock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/connection.py\u001b[0m in \u001b[0;36mPipe\u001b[0;34m(duplex)\u001b[0m\n\u001b[1;32m    518\u001b[0m             \u001b[0mc2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 520\u001b[0;31m             \u001b[0mfd1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfd2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    521\u001b[0m             \u001b[0mc1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwritable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m             \u001b[0mc2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mOSError\u001b[0m: [Errno 24] Too many open files"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      " If you create 100 processes on a machine with four cores, the process scheduler may put 25 processes on each core, but since each core can still only work on one process at a time, they won't get work done any faster than if you only had one on each. (In fact, it will be much slower, because there is a lot of overhead associated with switching between processes.)"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Plotting the results"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Conclusion"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 9176aefae0842be1e279e5a704d6145d0d8700e3 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Jun 2014 17:57:31 -0400
Subject: [PATCH 108/248] multiprocessing tutorial

---
 README.md                             |   1 +
 tutorials/multiprocessing_intro.ipynb | 595 +++++++++++++++++++++++---
 2 files changed, 541 insertions(+), 55 deletions(-)

diff --git a/README.md b/README.md
index 462a41e..db04777 100755
--- a/README.md
+++ b/README.md
@@ -36,6 +36,7 @@
 
 - Using Cython with and without IPython magic [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/running_cython.ipynb)]
 
+- Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1)]
 
 
 <br>
diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index c131162..380d846 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -1,13 +1,57 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f70ae9a75bf1eda857e582939e9a34df3db49efdb72d1585834c31f3c5f6de10"
+  "signature": "sha256:82b19ba527d6db0f573ef2e3b9d7924274ed0bf6d50c7e4f3325a8572b1c54f8"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "\n",
+      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1)  \n",
+      "\n",
+      "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)  \n",
+      "\n",
+      "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import time\n",
+      "print('Last updated: %s' %time.strftime('%d/%m/%Y'))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Last updated: 19/06/2014\n"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+      "<hr>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 1,
@@ -38,13 +82,28 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Depending on the application, two different approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-task\" of an process and those threads will have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
+      "Depending on the application, two different approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-task\" of a single process and those threads will usually have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
       "\n",
       "A safer approach (although less efficient due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n",
       "\n",
       "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores."
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](https://github.com/rasbt/python_reference/blob/master/Images/multiprocessing_scheme.png)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 2,
@@ -53,14 +112,66 @@
       "Sections"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Introduction to the multiprocessing module](#Introduction-to-the-multiprocessing-module)\n",
+      "\n",
+      "    - [The Process class](#The-Process-class)\n",
+      "    \n",
+      "    - [Defining an own Process class](#Defining-an-own-Process-class)\n",
+      "    \n",
+      "    - [The Pool class](#The-Pool-class)\n",
+      "    \n",
+      "- [Parzen kernel density estimation as benchmarking function](#Parzen-kernel-density-estimation-as-benchmarking-function)\n",
+      "    \n",
+      "    - [The Parzen-window method in a nutshell](#The-Parzen-window-method-in-a-nutshell)\n",
+      "    \n",
+      "    - [Sample data and timeit benchmarks](#Sample-data-and-timeit-benchmarks)\n",
+      "    \n",
+      "    - [Benchmarking functions](#Benchmarking-functions)\n",
+      "    \n",
+      "    - [Preparing the plotting of the results](#Preparing-the-plotting-of-the-results)\n",
+      "\n",
+      "- [Results](#Results)\n",
+      "\n",
+      "- [Conclusion](#Conclusion)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 1,
      "metadata": {},
      "source": [
       "Introduction to the `multiprocessing` module"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The [multiprocessing](https://docs.python.org/dev/library/multiprocessing.html) module in Python's Standard Libarary has a lot of powerful features. If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the [official documentation](https://docs.python.org/dev/library/multiprocessing.html) as an entry point.  \n",
+      "\n",
+      "In the following sections, I want to provide a brief overview of different approaches to show how the `multiprocessing` module can be used for prallel programming."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 3,
@@ -73,7 +184,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The most basic approach is to use the `Process` class from the multiprocessing module.  \n",
+      "The most basic approach is probably to use the `Process` class from the `multiprocessing` module.  \n",
       "Here, we will use a simple queue function to compute the cubes for the 6 numbers 1, 2, 3, 4, 5, and 6 in 6 parallel processes."
      ]
     },
@@ -117,7 +228,15 @@
        ]
       }
      ],
-     "prompt_number": 33
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
@@ -127,6 +246,20 @@
       "Defining an own `Process` class"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can also define our own `Process` classes based on the `multiprocessing.Process` parent-class. Note that we will define a `run` method, which will be automatically executed when we start a process-instance via `.start()`."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -142,7 +275,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 24
+     "prompt_number": 2
     },
     {
      "cell_type": "code",
@@ -178,7 +311,15 @@
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
@@ -192,7 +333,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Another more convenient approach for simple parallel processing tasks is the `Pool` class.  \n",
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another and more convenient approach for simple parallel processing tasks is provided by the `Pool` class.  \n",
       "\n",
       "There are four methods that are particularly interesing:\n",
       "\n",
@@ -224,7 +372,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -245,7 +393,7 @@
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -266,13 +414,13 @@
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished and return the results. The `async` variants, in contrast, will submit the processes to run in the background without locking the main program, which can be quite useful for certain application.  \n",
+      "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished and return the results. The `async` variants, in contrast, will submit the processes to be run in the background without locking the main program, which can be quite useful for certain applications.  \n",
       "However, if we use this asynchronous approach, we need to use the `get` method in order to obtain the `return` values."
      ]
     },
@@ -296,16 +444,41 @@
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 1,
      "metadata": {},
      "source": [
       "Parzen kernel density estimation as benchmarking function"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the following approach, I want to do a simple comparison of a serial vs. multiprocessing approach where I will use a slightly more complex function than the `cube` example, which he have been using above.  \n",
+      "\n",
+      "Here, I define a function for performing a Kernel density estimation for probability density functions using the Parzen-window technique.  \n",
+      "I don't want to go into much detail about the theory of this technique, since we are mostly interested to see how `multiprocessing` can be used for performance improvements, but you are welcome to read my more detailed article about the [Parzen-window method here](http://sebastianraschka.com/Articles/2014_parzen_density_est.html). "
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -313,6 +486,17 @@
       "import numpy as np\n",
       "\n",
       "def parzen_estimation(x_samples, point_x, h):\n",
+      "    \"\"\"\n",
+      "    Implementation of a hypercube kernel for Parzen-window estimation.\n",
+      "\n",
+      "    Keyword arguments:\n",
+      "        x_sample:training sample, 'd x 1'-dimensional numpy array\n",
+      "        x: point x for density estimation, 'd x 1'-dimensional numpy array\n",
+      "        h: window width\n",
+      "        \n",
+      "    Returns the predicted pdf as float.\n",
+      "\n",
+      "    \"\"\"\n",
       "    k_n = 0\n",
       "    for row in x_samples:\n",
       "        x_i = (point_x - row[:,np.newaxis]) / (h)\n",
@@ -326,7 +510,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -340,6 +524,30 @@
       "<hr>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "The Parzen-window method in a nutshell"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "So what this function does in a nutshell: It counts points in a defined region (the so-called window), and divides the number of those points inside by the number of total points to estimate the probability of a single point being in a certain region.\n",
+      "\n",
+      "Below is a simple example where our window is represented by a hypercube centered at the origin, and we want to get an estimate of the probability for a point being in the center of the plot based on the hypercube."
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -349,7 +557,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -396,13 +604,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAGUCAYAAAASxdSgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCoXSgfn/AYLQokbxoJlvAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAGUCAYAAAASxdSgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x105285828>"
+        "<matplotlib.figure.Figure at 0x109e81f60>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -424,7 +632,15 @@
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
@@ -434,6 +650,20 @@
       "Sample data and `timeit` benchmarks"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In the section below, we will create a random dataset from a bivariate Gaussian distribution with a mean vector centered at the origin and a identity matrix as covariance matrix. "
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -445,86 +675,332 @@
       "# Generate random 2D-patterns\n",
       "mu_vec = np.array([0,0])\n",
       "cov_mat = np.array([[1,0],[0,1]])\n",
-      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 1000)"
+      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 16
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The expected probability of a point at the center of the distribution is ~ 0.15915 as we can see below.  \n",
+      "And our goal is here to use the Parzen-window approach to predict this density based on the sample data set that we have created above.  \n",
+      "\n",
+      "\n",
+      "In order to make a \"good\" prediction via the Parzen-window technique, it is - among other things - crucial to select an appropriate window with. Here, we will use multiple processes to predict the density at the center of the bivariate Gaussian distribution using different window widths."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from scipy.stats import multivariate_normal\n",
+      "var = multivariate_normal(mean=[0,0], cov=[[1,0],[0,1]])\n",
+      "print('actual probability density:', var.pdf([0,0]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "actual probability density: 0.159154943092\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Benchmarking functions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Below, we will set up a benchmarking functions for our serial and multiprocessig approach that we can pass to our `timeit` benchmark function."
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import multiprocessing\n",
-      "\n",
       "def serial(samples, x, widths):\n",
       "    return [parzen_estimation(samples, x, w) for w in widths]\n",
       "\n",
       "def multiprocess(processes, samples, x, widths):\n",
-      "    pool = multiprocessing.Pool(processes=processes)\n",
+      "    pool = mp.Pool(processes=processes)\n",
       "    return [pool.apply(parzen_estimation, args=(samples, x, w)) for w in widths]"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Just to get an idea what the results would look like (i.e., the predicted densities for different window widths):"
+     ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import timeit\n",
-      "\n",
       "widths = np.arange(0.1, 1.3, 0.1)\n",
       "point_x = np.array([[0],[0]])\n",
       "results = []\n",
       "\n",
-      "results.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n",
+      "results = multiprocess(4, x_2Dgauss, point_x, widths)\n",
+      "for w,r in zip(widths, results):\n",
+      "    print('h = %s, p(x) = %s' %(w, r))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "h = 0.1, p(x) = 0.016\n",
+        "h = 0.2, p(x) = 0.0305\n",
+        "h = 0.3, p(x) = 0.045\n",
+        "h = 0.4, p(x) = 0.06175\n",
+        "h = 0.5, p(x) = 0.078\n",
+        "h = 0.6, p(x) = 0.0911666666667\n",
+        "h = 0.7, p(x) = 0.106\n",
+        "h = 0.8, p(x) = 0.117375\n",
+        "h = 0.9, p(x) = 0.132666666667\n",
+        "h = 1.0, p(x) = 0.1445\n",
+        "h = 1.1, p(x) = 0.157090909091\n",
+        "h = 1.2, p(x) = 0.1685\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Based on the results, we can say that the best window-width would be h=1.1, since the estimated result is close to the actual result ~0.15915."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "\n",
+      "mu_vec = np.array([0,0])\n",
+      "cov_mat = np.array([[1,0],[0,1]])\n",
+      "n = 100000\n",
+      "\n",
+      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, n)\n",
+      "\n",
+      "benchmarks = []\n",
+      "\n",
+      "benchmarks.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n",
       "            'from __main__ import serial, x_2Dgauss, point_x, widths').timeit(number=1))\n",
       "\n",
-      "results.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n",
+      "benchmarks.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n",
       "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
       "\n",
-      "results.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
+      "benchmarks.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
       "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
       "\n",
-      "results.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n",
+      "benchmarks.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n",
       "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
       "\n",
-      "results.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n",
+      "benchmarks.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n",
       "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))"
      ],
      "language": "python",
      "metadata": {},
+     "outputs": [],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Preparing the plotting of the results"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import platform\n",
+      "\n",
+      "def print_sysinfo():\n",
+      "    \n",
+      "    print('\\nPython version  :', platform.python_version())\n",
+      "    print('compiler        :', platform.python_compiler())\n",
+      "    \n",
+      "    print('\\nsystem     :', platform.system())\n",
+      "    print('release    :', platform.release())\n",
+      "    print('machine    :', platform.machine())\n",
+      "    print('processor  :', platform.processor())\n",
+      "    print('CPU count  :', mp.cpu_count())\n",
+      "    print('interpreter:', platform.architecture()[0])\n",
+      "    print('\\n\\n')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "import numpy as np\n",
+      "\n",
+      "def plot_results():\n",
+      "    bar_labels = ['serial', '2', '3', '4', '6']\n",
+      "\n",
+      "    fig = plt.figure(figsize=(10,8))\n",
+      "\n",
+      "    # plot bars\n",
+      "    y_pos = np.arange(len(benchmarks))\n",
+      "    y_pos = [x for x in y_pos]\n",
+      "    plt.yticks(y_pos, bar_labels, fontsize=16)\n",
+      "    plt.barh(y_pos, benchmarks,\n",
+      "             align='center', alpha=0.4, color='g')\n",
+      "\n",
+      "    # annotation and labels\n",
+      "    plt.xlabel('time in seconds for n=%s' %n, fontsize=14)\n",
+      "    plt.ylabel('number of processes', fontsize=14)\n",
+      "    t = plt.title('Serial vs. Multiprocessing via Parzen-window estimation', fontsize=18)\n",
+      "    plt.ylim([-1,len(benchmarks)+0.5])\n",
+      "    plt.grid()\n",
+      "\n",
+      "    plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Results"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "plot_results()\n",
+      "print_sysinfo()"
+     ],
+     "language": "python",
+     "metadata": {},
      "outputs": [
       {
-       "ename": "OSError",
-       "evalue": "[Errno 24] Too many open files",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-28-4be468cf2eb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m results.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n\u001b[0;32m---> 11\u001b[0;31m             'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m results.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/timeit.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    176\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/timeit.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
-        "\u001b[0;32m<ipython-input-17-1745161bc4ac>\u001b[0m in \u001b[0;36mmultiprocess\u001b[0;34m(processes, samples, x, widths)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmultiprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mpool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmultiprocessing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprocesses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparzen_estimation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwidths\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/context.py\u001b[0m in \u001b[0;36mPool\u001b[0;34m(self, processes, initializer, initargs, maxtasksperchild)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m         return Pool(processes, initializer, initargs, maxtasksperchild,\n\u001b[0;32m--> 118\u001b[0;31m                     context=self.get_context())\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mRawValue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtypecode_or_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/pool.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, processes, initializer, initargs, maxtasksperchild, context)\u001b[0m\n\u001b[1;32m    148\u001b[0m                  maxtasksperchild=None, context=None):\n\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcontext\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mget_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_queues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_taskqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cache\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/pool.py\u001b[0m in \u001b[0;36m_setup_queues\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setup_queues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_outqueue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_quick_put\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/context.py\u001b[0m in \u001b[0;36mSimpleQueue\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    109\u001b[0m         \u001b[0;34m'''Returns a queue object'''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mqueues\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSimpleQueue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSimpleQueue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mctx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     def Pool(self, processes=None, initializer=None, initargs=(),\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/queues.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ctx)\u001b[0m\n\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_writer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mduplex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    336\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rlock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_poll\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/multiprocessing/connection.py\u001b[0m in \u001b[0;36mPipe\u001b[0;34m(duplex)\u001b[0m\n\u001b[1;32m    518\u001b[0m             \u001b[0mc2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    519\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 520\u001b[0;31m             \u001b[0mfd1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfd2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    521\u001b[0m             \u001b[0mc1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwritable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    522\u001b[0m             \u001b[0mc2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mConnection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mOSError\u001b[0m: [Errno 24] Too many open files"
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAoIAAAIACAYAAAAWiA3GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nznewV8UAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x10b49e438>"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Python version  : 3.4.1\n",
+        "compiler        : GCC 4.2.1 (Apple Inc. build 5577)\n",
+        "\n",
+        "system     : Darwin\n",
+        "release    : 13.2.0\n",
+        "machine    : x86_64\n",
+        "processor  : i386\n",
+        "CPU count  : 4\n",
+        "interpreter: 64bit\n",
+        "\n",
+        "\n",
+        "\n"
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      " If you create 100 processes on a machine with four cores, the process scheduler may put 25 processes on each core, but since each core can still only work on one process at a time, they won't get work done any faster than if you only had one on each. (In fact, it will be much slower, because there is a lot of overhead associated with switching between processes.)"
+      "<br>\n",
+      "<br>"
      ]
     },
     {
@@ -532,15 +1008,24 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Plotting the results"
+      "Conclusion"
      ]
     },
     {
-     "cell_type": "heading",
-     "level": 2,
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Conclusion"
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel.\n",
+      "However, on my particular machine it didn't make any differences if we submitted 2, 3, 4, or 6 processes in parallel.  \n",
+      "\n",
+      "In practice,  it doesn't make much sense to submit more processes than the CPU supports (e.g., here 6 processes for a 4-core CPU). For larger benchmarks, this would to a slower performace, because of an additional overhead for communicating between the additional processes."
      ]
     }
    ],

From b8d1062a2ded704338e40022c921d1eb28793efe Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Jun 2014 17:58:47 -0400
Subject: [PATCH 109/248] multiprocessing tutorial

---
 tutorials/multiprocessing_intro.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index 380d846..49f82c6 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:82b19ba527d6db0f573ef2e3b9d7924274ed0bf6d50c7e4f3325a8572b1c54f8"
+  "signature": "sha256:4324cc9b055392dcbb5813ce3a18ba58a848b9da045d6c60188030f257582522"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -93,7 +93,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "![](https://github.com/rasbt/python_reference/blob/master/Images/multiprocessing_scheme.png)"
+      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/multiprocessing_scheme.png)"
      ]
     },
     {

From a595035aa46df5b3a24d96828c4dbdc5bf318223 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Jun 2014 18:26:19 -0400
Subject: [PATCH 110/248] fixed spelling

---
 tutorials/multiprocessing_intro.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index 49f82c6..7251546 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4324cc9b055392dcbb5813ce3a18ba58a848b9da045d6c60188030f257582522"
+  "signature": "sha256:71b41bd32e9a3483fb6b2a31c1a9a11b0ed8e32dace59748d17e5e2292c778b0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -75,7 +75,7 @@
      "metadata": {},
      "source": [
       "\n",
-      "Multi-Threading vs. Multi-Processesing\n"
+      "Multi-Threading vs. Multi-Processing\n"
      ]
     },
     {
@@ -159,9 +159,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The [multiprocessing](https://docs.python.org/dev/library/multiprocessing.html) module in Python's Standard Libarary has a lot of powerful features. If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the [official documentation](https://docs.python.org/dev/library/multiprocessing.html) as an entry point.  \n",
+      "The [multiprocessing](https://docs.python.org/dev/library/multiprocessing.html) module in Python's Standard Library has a lot of powerful features. If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the [official documentation](https://docs.python.org/dev/library/multiprocessing.html) as an entry point.  \n",
       "\n",
-      "In the following sections, I want to provide a brief overview of different approaches to show how the `multiprocessing` module can be used for prallel programming."
+      "In the following sections, I want to provide a brief overview of different approaches to show how the `multiprocessing` module can be used for parallel programming."
      ]
     },
     {
@@ -741,7 +741,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Below, we will set up a benchmarking functions for our serial and multiprocessig approach that we can pass to our `timeit` benchmark function."
+      "Below, we will set up a benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function."
      ]
     },
     {

From 22e323899a2875102c6ac09e5540fb6939198562 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 20 Jun 2014 11:09:14 -0400
Subject: [PATCH 111/248] new benchmark

---
 tutorials/multiprocessing_intro.ipynb | 281 +++++++++++++++++---------
 1 file changed, 186 insertions(+), 95 deletions(-)

diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index 7251546..e7478e6 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:71b41bd32e9a3483fb6b2a31c1a9a11b0ed8e32dace59748d17e5e2292c778b0"
+  "signature": "sha256:d007cd318f83f1063ca5b837d93bfb94ad30a9ac9350652246e0099b6aef6373"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -35,11 +35,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: 19/06/2014\n"
+        "Last updated: 20/06/2014\n"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -64,36 +64,46 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "CPUs with multiple cores have become the standard in the recent development of modern computer architectures, whether it is our desktop machine at home, our laptop, and especially supercomputer facilities.  \n",
+      "CPUs with multiple cores have become the standard in the recent development of modern computer architectures and we can not only find them in supercomputer facilities but also in our desktop machines at home, and our laptops; even Apple's iPhone 5S got a 1.3 Ghz Dual-core processor in 2013.\n",
+      "\n",
+      "However, the default Python interpreter was designed with simplicity in mind and has a thread-safe mechanism, the so-called \"GIL\" (Global Interpreter Lock). In order to prevent conflicts between threads, it executes only one statement at a time (so-called serial processing, or single-threading).\n",
       "\n",
-      "However, the default Python interpreter's GIL (Global Interpreter Lock) is designed for simplicity, and in order to prevent conflicts between threads, it executes only one statement at a time (so-called serial processing).\n"
+      "In this introduction to Python's `multiprocessing` module, we will see how we can spawn multiple subprocesses to avoid some of the GIL's disadvantages."
      ]
     },
     {
-     "cell_type": "heading",
-     "level": 3,
+     "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "\n",
-      "Multi-Threading vs. Multi-Processing\n"
+      "<br>\n",
+      "<br>"
      ]
     },
     {
-     "cell_type": "markdown",
+     "cell_type": "heading",
+     "level": 2,
      "metadata": {},
      "source": [
-      "Depending on the application, two different approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-task\" of a single process and those threads will usually have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
-      "\n",
-      "A safer approach (although less efficient due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n",
-      "\n",
-      "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores."
+      "Sections"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/multiprocessing_scheme.png)"
+      "- [An introduction to parallel programming using Python's `multiprocessing` module](#An-introduction-to-parallel-programming-using-Python's-`multiprocessing`-module)\n",
+      "    - [Multi-Threading vs. Multi-Processing](#Multi-Threading-vs.-Multi-Processing)\n",
+      "- [Introduction to the `multiprocessing` module](#Introduction-to-the-multiprocessing-module)\n",
+      "    - [The `Process` class](#The-Process-class)\n",
+      "        - [How to retrieve results in a particular order](#How-to-retrieve-results-in-a-particular-order)\n",
+      "    - [The `Pool` class](#The-Pool-class)\n",
+      "- [Kernel density estimation as benchmarking function](#Kernel-density-estimation-as-benchmarking-function)\n",
+      "    - [The Parzen-window method in a nutshell](#The-Parzen-window-method-in-a-nutshell)\n",
+      "    - [Sample data and `timeit` benchmarks](#Sample-data-and-timeit-benchmarks)\n",
+      "    - [Benchmarking functions](#Benchmarking-functions)\n",
+      "    - [Preparing the plotting of the results](#Preparing-the-plotting-of-the-results)\n",
+      "- [Results](#Results)\n",
+      "- [Conclusion](#Conclusion)"
      ]
     },
     {
@@ -106,37 +116,29 @@
     },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 3,
      "metadata": {},
      "source": [
-      "Sections"
+      "\n",
+      "Multi-Threading vs. Multi-Processing\n"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "- [Introduction to the multiprocessing module](#Introduction-to-the-multiprocessing-module)\n",
+      "Depending on the application, two common approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-tasks\" of a single process and those threads will usually have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
       "\n",
-      "    - [The Process class](#The-Process-class)\n",
-      "    \n",
-      "    - [Defining an own Process class](#Defining-an-own-Process-class)\n",
-      "    \n",
-      "    - [The Pool class](#The-Pool-class)\n",
-      "    \n",
-      "- [Parzen kernel density estimation as benchmarking function](#Parzen-kernel-density-estimation-as-benchmarking-function)\n",
-      "    \n",
-      "    - [The Parzen-window method in a nutshell](#The-Parzen-window-method-in-a-nutshell)\n",
-      "    \n",
-      "    - [Sample data and timeit benchmarks](#Sample-data-and-timeit-benchmarks)\n",
-      "    \n",
-      "    - [Benchmarking functions](#Benchmarking-functions)\n",
-      "    \n",
-      "    - [Preparing the plotting of the results](#Preparing-the-plotting-of-the-results)\n",
+      "A safer approach (although it comes with an additional overhead due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n",
       "\n",
-      "- [Results](#Results)\n",
-      "\n",
-      "- [Conclusion](#Conclusion)"
+      "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/multiprocessing_scheme.png)"
      ]
     },
     {
@@ -155,6 +157,13 @@
       "Introduction to the `multiprocessing` module"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -180,6 +189,13 @@
       "The `Process` class"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -193,16 +209,26 @@
      "collapsed": false,
      "input": [
       "import multiprocessing as mp\n",
+      "import random\n",
+      "import string\n",
+      "\n",
+      "random.seed(123)\n",
       "\n",
       "# Define an output queue\n",
       "output = mp.Queue()\n",
       "\n",
       "# define a example function\n",
-      "def cube(x, output):\n",
-      "    output.put(x**3)\n",
+      "def rand_string(length, output):\n",
+      "    \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n",
+      "    rand_str = ''.join(random.choice(\n",
+      "                        string.ascii_lowercase \n",
+      "                        + string.ascii_uppercase \n",
+      "                        + string.digits)\n",
+      "                   for i in range(length))\n",
+      "    output.put(rand_str)\n",
       "\n",
       "# Setup a list of processes that we want to run\n",
-      "processes = [mp.Process(target=cube, args=(x, output)) for x in range(1,7)]\n",
+      "processes = [mp.Process(target=rand_string, args=(5, output)) for x in range(4)]\n",
       "\n",
       "# Run processes\n",
       "for p in processes:\n",
@@ -224,11 +250,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[1, 8, 27, 64, 125, 216]\n"
+        "['41CkH', '5gOMX', 'qIJT3', '9CzRU']\n"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -243,7 +269,7 @@
      "level": 3,
      "metadata": {},
      "source": [
-      "Defining an own `Process` class"
+      "How to retrieve results in a particular order "
      ]
     },
     {
@@ -257,26 +283,13 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We can also define our own `Process` classes based on the `multiprocessing.Process` parent-class. Note that we will define a `run` method, which will be automatically executed when we start a process-instance via `.start()`."
+      "The order of the obtained results does not necessarily have to match the order of the processes (in the `processes` list). Since we eventually use the `.get()` method to retrieve the results from the `Queue` sequentially, the order in which the processes finished determines the order of our results.  \n",
+      "E.g., if the second process has finished just before the first process, the order of the strings in the `results` list could have also been\n",
+      "`['PQpqM', 'yzQfA', 'SHZYV', 'PSNkD']` instead of `['yzQfA', 'PQpqM', 'SHZYV', 'PSNkD']`\n",
+      "\n",
+      "If our application required us to retrieve results in a particular order, one possibility would be to refer to the processes' `._identity` attribute. In this case, we could also simply use the values from our `range` object as position argument. The modified code would be:"
      ]
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class CubeProcess(mp.Process):\n",
-      "    def __init__(self, x, output):\n",
-      "        super(mp.Process, self).__init__()\n",
-      "        self.x = x\n",
-      "        self.queue = output\n",
-      "    def run(self):\n",
-      "        self.queue.put(self.x**3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -284,8 +297,18 @@
       "# Define an output queue\n",
       "output = mp.Queue()\n",
       "\n",
+      "# define a example function\n",
+      "def rand_string(length, pos, output):\n",
+      "    \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n",
+      "    rand_str = ''.join(random.choice(\n",
+      "                        string.ascii_lowercase \n",
+      "                        + string.ascii_uppercase \n",
+      "                        + string.digits)\n",
+      "                   for i in range(length))\n",
+      "    output.put((pos, rand_str))\n",
+      "\n",
       "# Setup a list of processes that we want to run\n",
-      "processes = [CubeProcess(x, output) for x in range(1,7)]\n",
+      "processes = [mp.Process(target=rand_string, args=(5, x, output)) for x in range(4)]\n",
       "\n",
       "# Run processes\n",
       "for p in processes:\n",
@@ -307,11 +330,49 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[1, 8, 27, 64, 125, 216]\n"
+        "[(0, 'pJpCf'), (1, '20o0V'), (2, 'DlB7w'), (3, 'PxH3O')]\n"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And the retrieved results would be tuples, for example, `[(0, 'KAQo6'), (1, '5lUya'), (2, 'nj6Q0'), (3, 'QQvLr')]`   \n",
+      "or `[(1, '5lUya'), (3, 'QQvLr'), (0, 'KAQo6'), (2, 'nj6Q0')]`\n",
+      "\n",
+      "To make sure that we retrieved the results in order, we could simply sort the results and optionally get rid of the position argument:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "results.sort()\n",
+      "results = [r[1] for r in results]\n",
+      "print(results)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['pJpCf', '20o0V', 'DlB7w', 'PxH3O']\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**A simpler way to to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**"
+     ]
     },
     {
      "cell_type": "markdown",
@@ -372,7 +433,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 5
     },
     {
      "cell_type": "code",
@@ -459,7 +520,7 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "Parzen kernel density estimation as benchmarking function"
+      "Kernel density estimation as benchmarking function"
      ]
     },
     {
@@ -596,7 +657,7 @@
       "ax.set_ylim(-1.5, 1.5)\n",
       "ax.set_zlim(-1.5, 1.5)\n",
       "\n",
-      "plt.show()\n"
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
@@ -606,7 +667,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAGUCAYAAAASxdSgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXeUVOX9/9/3Tp/ZQpG6LCxNQUXEgCJIEVgWUIkRCwqK\nDdFEjTExNiwYG/zUX+QY2/fYYn5BiFEhAZYOFiKgIUG/iqgUkaZI2d2pt/3+WJ/rnbv3ztyZuW1m\nn9c5niO7s3eeO+V5P5/OSJIkgUKhUCiUAmGdXgCFQqFQSgMqKBQKhUIxBSooFAqFQjEFKigUCoVC\nMQUqKBQKhUIxBSooFAqFQjEFKigUCoVCMQUqKBQKhUIxBSooFAqFQjEFKigUCoVCMQUqKBQKhUIx\nBSooFAqFQjEFKigUCoVCMQUqKBQKhUIxBSooFAqFQjEFKigUCoVCMQUqKBQKhUIxBSooFAqFQjEF\nKigUCoVCMQUqKBQKhUIxBSooFAqFQjEFKigUCoVCMQUqKBQKhUIxBSooFAqFQjEFKigUCoVCMQUq\nKBQKhUIxBSooFAqFQjEFKigUCoVCMQUqKBQKhUIxBSooFAqFQjEFr9MLoFD0kCQJqVQKPM/D7/fD\n4/GAYRgwDOP00igUigaMJEmS04ugUNSIogiO4+T/iIiIoohAICALDMtSI5tCcQvUQqG4CkmSIAgC\nGhoa4Pf7wbKs/J8kSYjH42AYBhzHAQBYloXX64XP56MCQ6E4DBUUimuQJAkcx0EQBNnNpYRYKcT1\nRYzrVCqFVCoFgAoMheIkVFAorkAURaRSKUiSJMdJRFFEPB6HIAjwer3weDxpf6MUGKBZkEjchQoM\nhWI/NIZCcRTi4iJxEiISx44dgyRJspVCHicIAliWhcfjkf/TC9ITgVF+xFmWhc/nkwWKCgyFYh5U\nUCiOQawJURRlMSFxkkQigVAoBL/fD47j5I2/qakJgUCACgyF4kKoy4viCFouLkEQ0NTUlCYQahiG\nSdv4lcJCxElPYNQpx0RckskkkskkACowFEohUEGh2IokSeB5HjzPg2EYecNOpVKIRqMIBoMIBoNo\namoydD2GYeD1euH1euXrmykwHo9Hjr94vV5aA0OhZIAKCsU2SG2J2sUVi8XAcRzKy8tlYSDkuoEb\nFRilBZJJYERRRGNjIwDIwqK0YKjAUCg/QQWFYjnKwDvw08bN8zyi0Sg8Hg8qKyst2ZwzCUwymYQo\nimnWi5bAkP9YloUoikgkEvL1qcBQKD9BBYViKWoXF7FKEokE4vE4wuEw/H6/bRtxPgKj/FstC4YK\nDIXSDBUUimXo1ZZEo1GIooiKigrNwDvBjgREIwJDrBOSUZbNRUYFhtJaoYJCMR11bQk55XMch2g0\nCp/Ph7Kysoyba6bfWSk0WgKTSCTkYL0RF5lynVRgKK0JKigUU9GrLUkkEkgkEohEIi1aquSC3Rsw\nEUSGYeD3+yGKIkRRBM/z4DgOkiTlLDCkHxlABYZSWlBBoZgGyYiSJAnBYFB2cZEU4MrKyqKv6yCN\nKokFI4qi7CIzKjDKGhoqMJRSggoKpWCUgXdRFAE0b57q2pJS3ByJwPh8PgAtBYY8RikQRgVGEAT4\nfD74/X4qMJSigAoKpSC0XFyiKCIWiyGVSqGsrEzebPO5drGRSWBIw0p1kaWewKRSKVlYyGNIfIcK\nDMWNUEGh5A2JIwBIE5NUKgWv14uKioq8XVylslEWIjDAT0kCQMtkBwBpnZSpwFCchgoKJWe0aksA\nyC1LvF5v1iyu1opSYEirFz2BUVtoWi4yMjuGQATG6/WmxW8oFDuggkLJCb32KdFoFDzPIxgMynUn\nZlGqm6JSILQERhRFJJNJ8DxvuNCSCIwkSXIPMyowFLuggkIxhLp9CtnYeJ5HU1MTvF4vKisrkUwm\nIQiCk0stWtQCE4vF5IaUPM8jmUzK3ZZzFRjye2UMhgoMxWyooFCyohzNq7RKksmk3D4lEAjYup7W\nABEX4sYiWWBEJPIRGHXciwoMxUyooFAykmv7FCI2haJ3nda84SnFA0BGgdHKAqMCQ7EaKigUTfTm\nlpD2KX6/nwbeLSabMGcSGI7jkEgkMk6z1BOYeDwOAHLshQoMxShUUCgtyDSaN5lMFtw+JZ/1tNaN\nLJf7NktgyPvNMAw4jmthwZA0ZSowFDVUUChpaNWWCIKAaDQKIHv7FLNcXsrr0U0rP8wQGKU7k8TS\nqMBQ9KCCQgGgX1vSGtqnGKEUEgGMCgx5rNoyNCIwyj5kVGBaH1RQKHIDR5JRRDaTTKN5rcZsS4fS\nEj2BIW36o9FozhZMKpWSkwOUAkM+V1RgShsqKK0YZW1JKpWSJycKgoCmpiZ4PJ6C2qdQigulwBAx\nUFbxi6KYt8AAkOtrqMCULlRQWimZaktisRhCoRACgUDOX/hStCxK8Z6MoEwjBtKnWRYqMEoLhsRg\nqMAUP1RQWiFatSUA5JOkEy6uTLTmLC83YURg9GbBkL9XuteA5hgd6WGmbhVDBab4cM+uQbEcvdoS\nnuflDsHl5eWu+xKrN6XWaC24kUwCY2RcMoAWAkPa+JDHq7PIKO6GCkorIdtoXq/XK8dQCsHMSnky\nrItSHJghMADSxENtwVCBcTdUUFoByvG06vYpkiShoqICyWTSlSd/0t2YbiD2QToVF0o+AqP8W6Cl\nBUMFxt1QQSlhMrVPaWpqQiAQQCgUcp2LiyAIAhoaGsCyrFzrQKBB3OIjk8AkEglZNEh35GwuMmJ1\nKwVGXQdDsRcqKCWK3twS0j6lkNG8VkM2Cp7nUV5eLt+DKIpIJBLy3HUAuidcivtRCkwgEJBHRxNX\nrCRJGV1k6j5kJEtRK02Zfj7sgQpKiaGeW6Jsn0KKF7Xap1jdJdgopM2LKIrw+/3wer1pJ1CSCeT1\netMsMJKKSjYPOg63+CCC4ff7wbJs2rhk4rKlAuNuqKCUEFq1JUDxtE9RrpNhmLTRtmrI/ZEmlbn2\nqSoGWnu6tHJcMoAWAgOgxfubi8CQFGVq4ZoHFZQSQau2JNf2KU4F5bVcceRLbxS9NiIkJZoU4dE+\nU9lxMjkjk4hmEhhixeYiMMSFSlAWWpI6GEpuUEEpcjLVlkSjUXg8HlRWVmb9cjj15SF9xIDsnYxz\nQUtgjKawUopjkJlSYIgFUojAJBIJcBwnH7yURZbFaOE6ARWUIkavtkQ5mteM2pJcyCWGko8rLt8Y\njZEMI7Lx0Crt4oO8X7kIjF6assfj0bRgqMBkhwpKkSIIAmKxmOwmUtaWaI3mzYadFehuyDbTyjAi\nm486g8yN9TmliBVzdHIRGK2/V66NCkx2qKAUGUoXF/k3wzDyaF6fz+fq0by5uLjs3MgzuU+Ur7XS\ngqFYgxWvrZbAkEMEyRIkkPdZbcFQgckOFZQiIlNtSSKRsH00rxaZLB1SUGnExeXkl1G9+ZAuAizL\nyptPsWeQtXa0YmyxWCztPVY+hgqMMaigFAF6tSXKnxca0LbS5eUGF1chkI2glFOUlbRGFx/5TpHN\nX8uCydaqX0tg4vF4Wnym1AWGCorLyTaal2EYV3YIJliVxeUkmTLIjLRxLwacWK+b6m700tCNHiKU\nVq7y70tdYKiguBi92pJ4PI5UKoVwOJz2AXUTRAiLoWdYoWTKIKMpysVDJkGzWmDIcweDwaIWGCoo\nLkTpylJ+CJXtUyoqKkzPijGr9QoAU1xcxep6UQtMtgrvUrDaWhtmC4yyjxl53Pz583H//ffbf3MF\nQD/JLoPUlhAxIR+uZDKJhoYGBAIBlJWVyadct226ZIYJz/OorKzMW0wync7cds/ZIMH9YDCISCSC\nUCgEj8cjpyhHo1G5qI7OgHGOQlxuRGD8fj9CoRAikYg8QptkYJI0f57nNT/DyhgOy7JYuXJlobdk\nO9RCcRF6Lq5oNCp33rVyNG+hGzXJ4gIgi54ZqE92xSYoatQpynrBX5K+3FpwUwylUIzE2ZQWjPq+\nBUEoSsuVCooLyNQ+hYxE1WqfYubmWsgXWTn5MRKJIBqNmrKm1kAm1wnQ7Dq0O4OslDZ2o1gt3Jni\nbERggGZPxM6dO9G+fXuEQiFL12QFxSeBJYZy9ofSKkkkEmhsbEQoFCqKQkWSuux0HUyxo3SdAJDb\n5wA/ZfaRpAxBEFqVBWMHdn3PiMAEAgGEw+G0uqyHHnoIp512GrZv346HHnoI7777bsZmqddeey06\ndeqEAQMGaP5+/fr1qKysxKBBgzBo0CA8/PDDltwTQAXFMYhVQgZGETEhG3QymURFRQUCgYChaxVK\nPtYOx3FoaGiAx+NBeXm56SY63SxbbjyRSER2lSWTSSowJQTLsggEAliwYAFWrVqFvn37oqmpCb/9\n7W/RvXt32WpVc80116C+vj7jtUeNGoWtW7di69atmD17thXLB0BdXo6gV1tCgnd+v9+QVeKU1aJ2\ncVlhlbjVInMaPdcJz/PyECr1kLFieS1bo6tND5Zl0bNnT8ybNw9As+tTrzffiBEjsHv37ozXs+ug\nQS0UmyG1CWoXVywWQ1NTE8LhMMLhsGu/WEoXV0VFhaaYmB04p6dufYjAkAyycDiclkEWi8VoBpkB\nnBYz9fPHYjGEw2H534XEUxiGwcaNGzFw4EBMmjQJn332WUFrzQS1UGyCnCRJxlZZWRmAn0beAvlV\nkiuLogrBiAgoLahSLlQsZvSaXKr7UxVz8VxrIB6PIxKJmHKtM844A3v37kU4HMby5ctx4YUXYseO\nHaZcWw21UGxAXVtCSKVSaGhogM/nsyQGYRakOr8YLCjKTygbXJLaiGAwCJZlM9ZGOH1adwKn71n9\n/NFoNM1CKYTy8nL5WhMnTgTHcThy5Igp11ZDLRSL0aotIXNLOI4ruFmi1XUZhcxYobiLTCnKytoI\noNlyttuCcXpTdxNkQJ4ZHDp0CB07dgTDMNi8eTMkSUK7du1MubYaKigWoVdbQuack/YpbrFKtISJ\n1MEYTRLIdK1816OMMykFmVI4SoHx+/2yeyyRSJRMk8tiQf19ycXldfnll2PDhg04fPgwqqurMWfO\nHLnFz6xZs/Dmm2/iueeeg9frRTgcxhtvvGH6+glUUCxAbzRvKpWSC9UikYgpX04rLBQ7srgo7oME\n+IGfgsCtZUyyG6wjtcurU6dOhv5uwYIFGX//q1/9Cr/61a8KWptRqKCYDEnfBJB2oo7FYhAEAeFw\nGIlEwvEPrx5ktDB1cVGUKcrZxiSb0eTSDZu6W4jH40VZKU8FxST0akuI28jn86GiokK3OKnQ5y4U\nst7GxsaCxwiXQr+t1kqm9y3TmGT1jHY6Jjk3yERQQiwWkzNBiwkqKCagN5pXy21k9mZrxpeWVF0D\nza4OI9X5lNLGSFGtMoss1wmHbsNt1pGZQXk7oYJSAOrRvMrAezQahSRJtriNChEoZRYXANeM5yXC\nS2JPSv9+a4C8p27a5DJR6HyQ1k62wsZiofV8Q01GkiRwHAdBEFq0T8k0pdBNForSHVdWVoZjx46Z\nti4zkCQJDQ0N8v+TwjyGYeT23nRTcidG2rer4y9OWgluc9FSC6UVkWk0b6FTCu2AbM4kNdHsLC4z\nRJNYfWRIEYF0ZlaP1i21rKNSI1P7dvJeEheaU4cFNxU20hhKK0CvtkQ5mjdb+xQrLJRcrqfMOFO7\n49wQTFcKMwAEg8E08SZuk2AwaHnWEcU61AIjiiKSyaQcewTQwj3Wmg4L1OVV4mjVlgDNA3FisRiC\nwWDaTAMj17P7C6LOOHPbF5Q0ngSa20U0Nja2eIxS9LSyjojgF1tQuLVD3kuGYeD3+9MOC+oMMisO\nC04H5dXPb2YvLzuhgmIAskmpXVyxWAwcx+U0mtfsD60Rq0Lp4gqHw5ZnceVj6Sir8kn+fS7XIO8L\ncd9ptRWhVd/ZcTqOQYSitR8WOI4ryoJiKigZIB/iY8eOpc1I53ke0WgUHo9HczSv0Wvb8SUgM+m1\nXFxqnHB56cVzzGjdotVWROmzV3fdLbVNqVTIdFgolQwyrf2g2O4BoIKii7K2hBQjqk/6fr8/rzfd\nzA9KJhFwu4srF7ErFC2fPS3KK070UpR5nk9rckkOC0asUXVhodM4HcvMFyooKjLVlsTjcVNaklht\nCRTi4rLrg0wSGTweT1axs+L1ysWlUqxf7mIjX6s9U4qyOhvQje5O9eermD9vVFAU6NWWAM0tSXLt\numsH6s22kFO/WfeVTQBSqRSi0ahcle/066nnUlGeeMlj3LghUdLRS1FWjkl245Ax5TqK1QVLBeVH\n9GpLSJfVUChkWrM2qyyUYnBxxeNxpFIpw4kMTpzW1CdecspVfh6Uc9vd5CoplGI+HethxN1JHqec\nCWMXWpZZsb4PrV5QMs0tISmsxDXiVkgOP8ldzzeLy0pXHHk9GYYxNAfGTWJIPheBQKBF111l/MVt\nJ958Kfb1Z0PL3RmPx2VXNzlQOPV+8jxftF2+W7Wg6NWWEJcMqS1pbGx0tBgxGzzP2xLYzhfSjibX\nWh23ot6QSi3jyCmcSFkm33uGYRAIBMCyrO3vp3oviMViRVmDArRiQSEfGL3aEmX7FDdUkGvB87w8\nW8VNLi7yelkxqMtt74WRjKN8AsJOF9q1VvTeT/WYZGLBmBVPU7ddKcYqeaAVCoqR9ilWj+Y1Y1Mk\nLi6l6LlhXQRJktDU1NTqBnVlyjjSailSSvGXUsRoPZOZGWTFOlwLaGWCojW3BPhpc9bLOnLTqZhk\ncfE8j/Ly8rQUZ7dAXmc3ZsXZjTIg3BriL8WKUYtQL4NMS2CMNizVagxJXV4uRl1bonRxKTdnvawj\nswUl3+spazdIhT7JPnILyWQSqVQKPp+v4C+Fm4TcLIzGX0g7d7tx8vUuRjdfpgyyfBuWUpeXi9Gr\nLSEptl6vN+/2KfmSz0apzOLKt0LfinURlPEnN9SWFAOZ3GPk8BOPx20vyKPvXf4oDwxA5o4MenNg\nqKC4FL3aklyryJ08KWdrQumGU7w6/kTakFNyQ3naJWMGfD6fXJAH0PiLVVhlHelZpMqODMokFoZh\nilpQSvITSQLvZKYGedNILUQymURFRYVjLUmMioAgCGhoaIAkSaisrLRlBG6u98lxHBoaGuR4SWvb\n5FauXIna2lrU1NTgzDPPxOuvvy6PUy4UIjDBYBCRSAShUAgejweCICAWiyEajSKZTMqdsCnuhlik\npKN2JBKR9yBJkvD9999j3LhxWLp0KQ4cOCC7zPS49tpr0alTJwwYMED3Mbfeeiv69u2LgQMHYuvW\nrabejxYl9+0XRRGJREIuUCKnDrLxkd5RTrQkyYVkMomGhgYEAgFEIhHdNZhpoeRyn6QYrKmpCWVl\nZWnjjq2IOTmN1v3Mnz8fV111FTZt2oQjR45g+/btuOOOO3DttddassGTky4RGFLTw3EcotEoYrEY\nkskkBEEoSoFxKobi1GuldHl6vV60adMG9957LwRBwLJly9ChQweMHTsWr7zyiubfX3PNNaivr9e9\n/rJly/DVV1/hyy+/xIsvvoibbrrJqluRKSmXF6kYP3bsGNq2bStvbGQCYL61EMS6MYtMG26+c1bs\nRBRFRKNR2XKyyipxgzuPrEPNDz/8gIcfflhOBSbEYjHU19fjww8/xNlnn23pmoq5IaIbcer1IULq\n9/tx7rnnYseOHZg0aRIuuOACvPvuu3K3czUjRozA7t27da+7ZMkSzJgxAwBw1lln4dixYzh06BA6\ndepkxW0AKCFBUfflIT+LRqMAUPDGZ8fGppXFZSdGNnD1ICw3rtEOli1bpvt5isfjWLBggaWCoiZb\nQ0TAWPylGDOtSg0S362oqMD555+f93X27duH6upq+d/dunXDt99+SwXFCOQEpmyfks9oXi3M/oJp\nWTzZamEyXcuODVZvEJaTa3ISMspAC5KO7iR66axunXjYmtOV1bNYzKxDUb+uVt9nyQgKkL6RxePx\ntPYpZl3XbIrBxWXnIKxiYcSIEbpfzrKyMowfP97mFWXGSP2L1+uVs42cwmlhcwNmZXlVVVVh7969\n8r+//fZbVFVVFXzdTJRUUJ5kRQEwTUysgAgUWS9pT5KPmJgdlFdfS/maOikmbrN4+vfvj5EjRyIY\nDKb93OfzoX379rjwwgsdWll2tLKNSFsR0miUjBlwW+FsKaK2kIjLq1AmT56MP//5zwCADz/8EG3a\ntLHU3QWUkIUiSRIaGhoQCoUQj8dNPelYYaGQjdotQ6a0cHoQlttdZ6+//jp+//vf44033oDX6wXH\ncRg5ciSee+65vEcIEOx0wyjdYyzLgud5eL3egqq9iwmnXV5qiFs5G5dffjk2bNiAw4cPo7q6GnPm\nzJHjZbNmzcKkSZOwbNky9OnTB5FIRDdbzExKRlBYlkWbNm3AMIzphXVmN01MpVKy+6hQF5cVm65y\nEFY+lp7bhcAsgsEg5s+fj0cffRT79u3DCSecgPbt2zu9rIJhGAY+ny9tXojV8Re3bep2ov6uRKNR\nQ4KyYMGCrI955pln8l5XPpSMoABIa2Pgxg2NZHERl4Pb4iUkWaCxsREALO+6XCqUlZXhpJNOcnoZ\npqDe2Emii1b8RdnOnTS3pOnJ+WGFy8sJ3LWjmYRbmjkqUbqPWJZtUb9QKGac8EiQNhgMOpISrIfT\ngWLKTxht567snuyWz5EebrOOjLq83EhJCorZmNU0kWRxmdkqwyx3QzKZRDKZhMfjMeV0ZNb9ET++\nJEmOduGlaJOp265eM0RKOmpB4zjOtQlF2SgpQSEbv1tcXnYN7VLed64oU4JDoZAps1XMOu0RMSYB\nbrJZAc2plWZPzaMUjta8djLQLlP8xUkrwW0WClC86dMlJSgEN7i81HPp1X5ptwheY2MjvF4vKioq\nXDOoS5KaRweLoohwOAyfzweO4+Q6iWg0Cr/fr+tmoadgd0DcXaQIViv+ohQWN3wnnMCNgpYvVFBy\nwMgbr8yQsrNQsZD5KsQCcMOXWtknTNmrSk02NwsRFzdUgVOayRR/4ThO/u60poOB+vumHLVRjJSk\noJiN0TfXqIvLyW68dlTm53t/xGLy+XwIh8Nytlk2jFaB0yyk7DhV/+LxeJBMJuX6F3X8hYzTtQI3\nWAjK53f6UFcIJSUoVrVPV15T74OXycXlFsg8GIZhXJcSTF4/o0PP9DDShVdpvbjpNWjtkENBpmFU\nbuo/RmlJSQkKwex28wQtkcqnCNCJGA/HcWhqasooeE64vLK5CAtdT6YspGQyKf+eblLuQutgIIoi\neJ5vEX8pZsuzlDK8gBIWFCssFDWkPX6+J347TG0S4E4kEq7rb6aMl2i9fla8NnrusVLapEqRTJZn\nIpGAJEl5W55ucHkRiJVerJSUoFj5oVCLVCEuLiva4WsJKNmwSfNJuxo7FsNcFSBzkJgUnio3MIp1\n5LqpKy3PQCCgW/+iLLB0I1qNIUOhkIMrKoySEhSC1e3mC+lzRSikdsQIZMP2+XwoKysz9Dx2uby0\nMszcgN4mxXEcRFGUa1/scI9RAcsNI4kZxRB/KeYqeYAKSk7XFAQBsVjMdUFt9f26dcM2I6XazkI4\nskkBzQJNal/sco85semphz0VI7nGX0iihhOoP8dmzUJxCiooBiHptmZlcVm5xnxTgs1ak9Z18s0w\nc8tJXc89pjVi18oUV0ruGIm/kM+Z05l/NIbiIqz4EpNTNWlN4kb/JrGe4vE4WJZ1ZB59JvKNl7jp\nHtQo3WN2tXgvZZyqfwkEAojFYmBZ1pHCWKuGazlFSQkKwayTNjlVA82T+Mw8uZhpoYiiKHcJdlsN\nDHG/ZZtDX8yQyma1D1/LxUKsFze9R60d9eFAL/5iR984M+fJOwEVFB3UdRuxWMw17hcCSQnmeR6B\nQKBg68lMkSM9tziOKzjDrNg2Xy0Xi3K0LlAcGUitESOFsWbGzmgMpQgoZGMkLq5kMmlp3Uahm7ey\nhsPv9zs2610Lcl8kXbnYg7yFwjAtJyCS2IvdJ+BsuO3QZAeZ3G3qwthMsTMz4i9UUFyE+kORq19W\n6eKqrKxM+3BYFUTPB3VMIhaLmb6ufDc1YtkBMJyunG0tdvDRgY8Q42IY2X2kpc+j1YFX6wTsZKNO\np7LLisFSs7rzQjweL+ox0iUlKIR8PphkIySuI/U1nGzoSCCDsEiuOtmUzMzOyhfl2sLhsNxBwKn1\n5MLOYzvx87//HDEuhtM6noaxNWMxrsc4DOkyBD6PtZ0F9DYoUvsSjUape8zFGK1/0bM+aWFjkWC0\ncNCp1iS5ioByEJadVe9GIOnKPM/LLi7ijnP7Brjhmw24btl1GNV9FKrLq3F+n/OxZvca3LX+Luw6\nvgvndDsHo6tHY1TVKPQL9rN8PWSDYhgGqVQKgUCAdk62GLM+p7nEX/RSy2lQ3kVoqX8mMrm4tK5t\nRcNJI5C2+B6PBxUVFZZbT/msjbTst2ODM6PLgCRJ+J///g/mfTgPL096Gf/57j84GD2IEdUjMKJ6\nBB4c8SC+j32PtXvWYtXOVZi3aR7KA+UY22MsxtWMw4jqESjzl5l4Vy0xskGVSufkUiioVKMVf1En\nZwCQiytZli36GEppvYMKsm02HMfh+PHj8Hq9KC8vN/RhtrtDMNDcM6yhoQGBQACRSMTyDTsXceI4\nDg0NDfD7/abES+wiJaRw2+rb8PJ/X8aqqaswsvtIMGh53x3CHXBZ/8vw3Pjn8MnVn+DP5/8Z1RXV\nePbfz+LEF07EpEWT8OTmJ/GfQ/+BKFl/2CAbVCAQQDgcRjgchsfjkTs4RKNRJJNJ8DzfKoPr+WKX\nJU2SM4LBIMLhsOxaF0URy5cvx+DBg7Fjxw58/PHHhmYB1dfXo1+/fujbty/mzp3b4vfr169HZWUl\nBg0ahEGDBuHhhx+24rbSKCkLBcg+V17p4sqlNsLuzTKXNiV2W0/KeInbOhhn43DsMKb/YzraBNtg\n1eWrUO4vB/Dj5wb6mzDLsDit42k4reNpuG3IbYhyUby39z2s2b0G1y67FseTx3Fu93MxtmYsxvQY\ng06RTpbfSzb/fT7prVSI7IEkZxCRqaurQ8eOHTF37lz89a9/xT333INBgwZhzpw5GDNmTIu/FwQB\nN998M1YioTvAAAAgAElEQVSvXo2qqioMGTIEkydPRv/+/dMeN2rUKCxZssSu2yo9QSHotf8gvv1s\nLi4j1zN7fQSlK87utNts92k0lmOGW8rs1/yT7z/BFYuvwCX9L8HsYbPBMumvay7PFfFFMKHXBEzo\nNQEAsOf4HqzdsxZLv1qKO9fdieqKaoztMRZja8ZiaNehCHit7amWrb0IYDy9tVgszVLC6/ViyJAh\niEQiePnll9G2bVu899576Natm+bjN2/ejD59+qCmpgYAMHXqVCxevLiFoNh9QChZQQHSX0yO4xCN\nRgtql27Hm5Mt20wLu2Io2WI5bmbxjsW4bc1teGLME5hy0pQWv89moWSjR2UPXHPaNbjmtGvAizw+\nOvARVu9ejQfffxA7juzA2VVny/GXPm37GEoWKVSM9dq7u7E1jFMJHE5bZFqFjZFIBOFwGHV1dbp/\nt2/fPlRXV8v/7tatGzZt2pT2GIZhsHHjRgwcOBBVVVV44okncPLJJ5t/EwpKVlDIm5Svi0vvemah\nFgG9lGC3QIQuFAohEAjY/uXP9/lEScTcD+fi9U9fx1sXvYVBnQZpX18jhpIvXtaLoVVDMbRqKGYP\nn40j8SNY/816rNm9Bk9/9DS8rFe2XkZ1H4XKQKUpz5sJo4PFlI0SWxNOCyrBaFDeyHrPOOMM7N27\nF+FwGMuXL8eFF16IHTt2mLFMXUpOUJSuFuI6kiTJlPYfVs5YKTQl2Ky1aQmdWyc+ZqMp1YSbVtyE\nA00HsO6KdRnjGgys21DahdrhopMuwkUnXQRJkrD9h+1Ys2cNXt72Mm6svxGndDhFFpgzOp0BD2tt\nSrjSPabunEze71LJHnM7agtFEARDXcKrqqqwd+9e+d979+5t4R4rLy+X/3/ixIn45S9/iSNHjqBd\nu3YmrFybkhMUAglq5+I6shMieIIgoLGxET6fL283klX3VojQOZnKDDTHNC5ffDkGdhqIpZcsNRTD\nKMTlZRSGYdD/hP7of0J/3PyzmxHn4ti4byPW7FmDm1fejIPRgzi3+7kYXT0aI6tGolewly1rIu6x\nWCwmHxpaQ+dkt9VKGW0cOnjwYHz55ZfYvXs3unbtioULF2LBggVpjzl06BA6duwIhmGwefNmSJJk\nqZgAJSgoREg4joPf7zctp9uKDVIQBDQ0NLhuEBbgjnhJvq/5xm83YsbSGbhtyG345aBfuioOpSbk\nC2FsTbN1glHA/sb9zbUvu1bhwQ8eRMdIR9l6GV41HCGftVXUkiTJVomdnZPdtrHbhfK+c/n8eb1e\nPPPMM6irq4MgCLjuuuvQv39/vPDCCwCAWbNm4c0338Rzzz0Hr9eLcDiMN954w5J7UMJIJeYwPXr0\nKDiOk1sdmNXGQBRFHD9+HG3bti34WpIkobGxUa4sL3RaHMdxiMfjqKioKHhtDQ0N8Pl8SCQSBcVL\njh07hvLy8oLcjPF4HJIkIRgMguM4eR2xWAyBQEDz2q9uexV/2PgHvDjhxeZN2iAvbH0BXxz5Ak+N\nfarF7ziOgyAICAaDed9LrnAchxSXwufHP8eaPWuwZvcafPr9pziz65kYVzMOY3qMQf/2/U3fhKPR\nKEKhkK6bS+keEwQBgDmDxTK9p1ZCsuCcqk5vamqS68skScLEiRPxwQcfOLIWMyg5C4UMwSKT2NyG\nMq6jrKJ1A+Q0aka8xKwTP2nrkkqlZL++1nU5gcPdG+7Guj3rUH9ZPfq27Zv7em1weeWCh/VgcJfB\nGNxlMO4ceieOJ4/j3W/exZo9a/DC1hfAiRzG9BiDsTVjMbr7aLQPWd9UUOkeU3ZOVrvH3NA52ShO\nrdGN+1OhuGc3Mwmv1wtBEEwv9jNjg1TOWGFZVp4O54a1kRodURQRDoddEXyXJEkWklAoJLtelIFj\nr9eLY8ljuHrp1fB7/Fh7xdq8sqYYMHCZnrSgMlCJC/pegAv6XgBJkvDVsa+wZvcavPHZG7h11a3o\n27Zvc2PLmubGll7W2q+30c7JRtxjrdXlBaQLWrG/BiUnKFaTzwdfK1OK4zjXnFCUiQHkZOk0yhNv\nJBIBx3HweDzw+XyIRqOyf/+/B/6LGctn4Pze5+OB4Q/A78s/LdxtFkomGIZB37Z90bdtX9w46EYk\n+SQ27d+ENXvW4I61d+Cbhm8wonpEc3ymx1j0qOxhy5qy9a5yW+dkNwlZKpVyxUGuEEpOUMiHww3t\n5oHCqvONUsi9plIpRKNROTHASA8hqyFjg30+n+aplqS9rty9Er9c+Us8MvIRXHLiJXJPq3yyksys\nQ3GCgDeAkd1HYmT3kZgzYg4ORQ9h7Z61WLtnLR7Z+Agq/BVy8H9EN+sbWwItB4tptXbP5MYsddRi\nRr6HxUzJCQrBiqydXNuJkEFYPp8P4XC4hWnr5JdIr1eYWevK5zrqNSkDv+rHPbXlKby07SUsunAR\nhnQZAgBZi/YyWV9uOaWaRadIJ1x+8uW4/OTLIUoiPvn+E6zZvQbPfPQMrlt6Hc7ofAZ6VPTA0Kqh\nuPLUK+W/s+rEnq1zMtB8kFC25m9tkFlCxQwVlBwxek1yyrYjJTjXe1VaTW4Z0au1Jp7nWzwuxsVw\n05qbsKdhD9ZevhZdy7um/V6raE/tdtGbqFdMLq9cYBkWAzsOxMCOA3H7mbejKdWEl7e9jAffexD/\ne/h/0wTFLtTuMZKiTg4CgP77ZCZOurzUz00FpZVhtFtrLBYDx3GGmifaTSarySmUMZxMa9rXuA9X\nLLkCvSp6YeklS1EWyO62yeZ2kVuOiJItLejdwLo96/B/t/xfnNfnPAzsONDp5cgQF2c291ixZI/l\nCnV5uRCrYihGrqksBqysrLT1Q2/kXo1YTWa+bkbnvZDah0x1Hpv3b8ZV/7wKN51xE2aePBNBb+41\nIXpuF5L2yvN8WtsRJzctq07Of/r3nzB/y3y8ddFbeOuLtyzPBMuHbJ2TScp9sbeG0WoMSQXFpdjt\n8lJujEaKAc1cn5HOtUZnq5iFkTVl6xFGXqPXP3kd96y7B8/WPYsJvSbIBY9mrFHuyOsPgPU0N1BU\nn4pLoWGiIAq4e8PdWP/Neqy6fBW6V3THos8XwcO4Z5S0HlZ2TnZTlhdpDFvMUEHJ8ZpqlJt1LsWA\nVqxP68vh5GwVPYz2CONFHrPfm41V36zCskuWod8J1s10J7NRtGoqlLUvhVaEO0GMi+G6ZdehMdWI\nlZetRJtgGwCAIAlpnwenRJM8r9HX1GjnZLe7x2iWVytHLQJu2az1vjAkXpLLDBirYzuiKKKxsTFr\nj7CjiaOY9s40CKKAd698F2Ue6+e3K4PyylMxSRDweDxFVxH+XfQ7XPrOpTip/Ul47fzX4Pf8VKcj\niAK8TMstwK33ooVe52Sjg8XcZHmWQlDe+eOqyVgZQ1HC8zwaGhpymkmvhVVrTCaTaGxslGeP271J\naL3+HMfh+PHj8Pv9cv8iLXb8sAMj/zwSJ7U7CQsvWIh2IWs7pALZ29eT4H4oFEIkEpFjUMlkEtFo\nVG5Iauco5mx88cMXGPfGOIzvOR7P1z2fJiZAs4Vidat8uyEHgUAggEgkglAoJB8EYrEYYrEYksmk\nbHWSv3ECrRgKdXm5FGUHT7M+MKSdSyKRKHgQltkfYuUGTrLM8o2XWCFyRl+zFV+vwPVLr8dDox7C\nFf2uAMdxpq9FD6P3rQ4au3Ea4vt738eMpTPw0IiHMO2UaZqPEUShKGIohaDlHlN2TmYYBizLQhAE\nxy3NeDyODh06OPb8ZlCyggKYM9dcDSnCKnRgF2D++kRRRCwWA8MwebvgzLbsjKZRS5KEpzc/jae3\nPI2FFy3EsG7DkEwmbXNJFNJ6JdumZaSw0kwWfb4Id62/Cy+f9zJGdx+t+zhBElyR5WVXYFwre4wk\neKjdY3bEybQsFLO6ozuF858mk7HqQ0By4lmWde089cbGRlcNFCNt+rMJXIJP4Ff1v8Kn332KDVdu\nQPfK7gDsd0WYlTmm3rSMFlYWiiRJeGLzE3h126v45yX/xMknZJ4fzou8nIzQGiHWCREQpzsnx+Nx\nlJVZ3xLHSkpOUJSYddomKcFmn1zMWh/JzQ+HwwXP7CBuvUIhp79sAneg6QAue+syVFdUY+30tYj4\nnfEhZ7NQ8n2fjBZWFpqRxAkcfrPmN9j23Tasvnw1upR1yfo3glT6Li+jkJ5xyiw/Ymnm2jnZKJIk\npR2yaB2KS1G6kgrZsNX1G27qEAyku5NIzYQbSKVShiZmfnTgI1z21mW4/vTrcdewuxy1qjI1hzTz\nAKFXWEliRcrTslEakg246p9Xwct6sezSZYYbP6pdXk7VZLip/QnBiKVpdudkKigupxBBISnBSneN\n2QHiQtfX2NgIlmVRWVmJhoYGU9eWD0oBJq3w9Zj+znQs3rEYdw+7G78b+rucvpCW1BjB/vb1ytRk\nUjxJxIXM9CEbm571sq9xHy5++2IM7ToU/2fM/8kpJiKIpZflZRVqS1P5XpEi2FzdY7SXVytBOQgr\nGAympSK7IS1Ub31OdQkmz00mUVZUVGStZi/3l+Ok9idh6VdL8fSWpzG6x2jU9a5DXa86VJVXpV3X\nDpzqraZ8fqXLhbhZMgWMt323DZe9cxluHHQjbh18a86nZKuzvARBwBdffAGGYXDSSSe5oqhWTT7v\nuZZ7TGuwWK6dk2kMxeXkukko24Fopbeavenks75kMqmZfuuku4j0MPN6vXLNS7b1nNzhZIR9YTxZ\n+yS+i36HVbtWof7resxeNxtdy7tiQu8JGNt9LE5re5ot95CtDsVuSMA4EAiktRshPv0N+zbglrW3\nYN7oeZjSb0pe77+VWV6LFi3CnXfeKYthJBLBU089hcmTJ1vyfIVQ6HdHaWkC6WnkmTonq7/7tFLe\npeQTQyEnbFEUTUkJNkKu6zPSrsRuiLVktIcZwct6wUvN7ek7Rjpi2qnTMO3U5sr4LQe2oP7retyz\n4R58ffRrnFtzLsb1GIfamtoW7erNxE3t69XuEGVq8ivbXsEjGx/BqxNfxeCOgxGNRvNqlmhVltey\nZctwyy23yLEGoHmznDlzJsrKyjBmzJi0x7upn5YZ6LWG0eoRp6QUXF7us0FNxOiGzfM8jh8/LqcE\nu63lvCAIcoxEb31mrc3odYg119TUhLKysjTXmxE8jAeC2HJ4lof1YGjVUDw48kG8O/1dbLxiIy7o\newHW7VmHs18/G8P+PAx/+NcfsHHfRnCCeTGtQupQ7EKURMx5fw6e/uhp1F9Wj5E1I+VOCGSWiF41\nuBbqLC+zPtv33XdfmpgQ4vE4HnjgAVOewyysFjMSAyPtj4hngcRhEokE1qxZg1deeQXBYNDQQbG+\nvh79+vVD3759MXfuXM3H3Hrrrejbty8GDhyIrVu3mn1bupSkhZILuQ7CstvlpRcvcRKSXcbzfN4C\n52E9EKSWgqKmY7jZernkxEsgSAI+Pvgxln+5HLPfm409DXswqvsojO85HuNqxhlKldWDAQM360mS\nT+KmFTfhm4ZvsHrqapwQPkH+nZHCSi1/viiJLVxehX6+4vE4du7cqfv7bdu2lZxFkgtK9xjP8wgE\nAvB4PFi/fj02btyIU045BXV1dRg/fjzGjBnTogxAEATcfPPNWL16NaqqqjBkyBBMnjwZ/fv3lx+z\nbNkyfPXVV/jyyy+xadMm3HTTTfjwww9tub+StFCM9PMiLqR4PI7y8nJDYmLnl4BkTBELIFuxopnW\nU6brkOyyQl2DHsYDXmw5kTHTWrysF2d1PQt3nXUX1k5diy1Xb8GEXhOwZvcanPXaWRj++nA8+N6D\n2PjtRkPXVuK0hcJu2QLPu+9q/u5I/Ah+/vefgxd5/OPif6SJiRpyIg4EAgiHw4hEIvB6vXK6azQa\nla0XXuRND8qTIVl6+P3+VismWrAsi9GjR+PVV1/FwIED8frrr6NTp06YN28evvvuuxaP37x5M/r0\n6YOamhr4fD5MnToVixcvTnvMkiVLMGPGDADAWWedhWPHjuHQoUO23E9JWyh6mywJIhMXl1Gfs11B\neSfjJZm+7KR7cSAQKNha8rDaLi+jawGa56ZPO2Uapp0yDbzI46MDH2Hl7pW4c/2d2HN8D0Z3H43a\nnrWGrJdMdSiWI4oIXXMNEIsh+sUXgGIEwq5ju3Dx2xdjYq+JeGjkQznHPPQKK1OpFFJ8CjzXbMnk\nWvuih9frRW1tLVasWNEiI9Lj8eDnP/95i79xYx2KE8/NMAwGDx6MwYMH4+6779b8m3379qG6ulr+\nd7du3bBp06asj/n222/RqVMnk++gJSVpoSjR6njb0NAAv9+PsrIy16UyGomXaGF1fEfZvdhoa5dM\n6/GyXkMuL0K25/OyXgytGor7h9+P96a/hy1Xb0FdrzrZejnn9XMw5/05+Ne+f2laL05aKN4lS8D8\n8AOYeBy+v/5V/vnHBz9G3cI63DjoRjw86uGCA+hKf344HAYYIOBvziCLx+Nyymu22Es25s6dizZt\n2qRlIfr9frRr1w4PPfRQQfdQKmi9vka+U0bFT319u0Sz5C0UgjIlOJdBWOrrWWmh5Dr10QrUa1J3\nC9AqVvzmm2+wcuVKSJKE8ePHo0ePHlnXrheUz2eNWqitly0HtmDVrlX4/brfY8/xPTi3x7morWm2\nXjqXdXYubVgUEZg9G0w0CgDwz5kD7oorsHTnUvx23W/x7IRnMbHXREuemhd5+L1+BINBuVCPdDko\nZI57TU0N/vWvf2H+/PlYvHgxGIbBlClTcMstt7iqm64bul7kWkNWVVWFvXv3yv/eu3cvunXrlvEx\n3377LaqqqmAHJSko6hiKKIqIRqOQJAmVlZV5WyVWWQFuFDsAaa+blmtQkiTcfvvteOWVV+Takzvu\nuANXXXUVHn/88YzXztVCKQQv68XZVWfj7Kqzcf859+Ng00Gs3r0aq3avwj0b7kGPyh7oXtEdRxJH\nwIu8rR14iXVCYOJxvPfYTNzVaSMWTl6IM7udadlzi5Iox1DI+8cwDEKhUFqxXj6deLt06YLHHnsM\njz32WNZ1OB2kd0NMJ5VKGRqFMXjwYHz55ZfYvXs3unbtioULF2LBggVpj5k8eTKeeeYZTJ06FR9+\n+CHatGlji7sLKFFBIZDKduLicksXXgJZH6l/KUTszEYQBDQ2NsLn8+kO6Hr++efx2muvyRsO4S9/\n+Qtqamowa9Ys3esbDcoDP/VRItlMhdK5rDOmnzod00+dDl7ksXn/Zvzp33/CJ999gl7P9cKYHmPk\n2EuniIVfRJV1AgBMNIqznl+MZR9tRPcTelv33NAubFQexrTmuBfbxEq3otW63shwLa/Xi2eeeQZ1\ndXUQBAHXXXcd+vfvjxdeeAEAMGvWLEyaNAnLli1Dnz59EIlE8Morr1h2Hy3WZ9sz2YwkSeA4DjzP\no6ysLO9BWErMtgJEUZRPJmVlZaZ0Ly0UpQhnS6WeN28eYrFYi5/HYjH88Y9/xMyZM3X/1mhQXim4\n5P5IFbkZJ1sv68WwbsNwOH4YkiThybFPYvXu1VixcwXuXn83aiprMLbHWIyuGo3hNcNNtV7U1gmh\nrRSAf/lGpK60VlByyfJSpyarW43kU1jpNE5bRkpymYUyceJETJyY7gZVH96eeeYZ09aWCyUpKCRL\niswBN0NM1Ncv9IOYSqWQTCbh9XpNGftphtiR1i6SJKG8vDyj643neRw8eFD394cPH5bbTmhhxOUl\nCAIkSZLfQyIwyWQSgiCkjRQodCMjzSG7lHXBladeiStPvRKcwGHzgc1Y8fUK3PXeXdhfv1+OvdTW\n1KJjpGPezwcAgQceAGIxSCwLUWrOiGIZFkwsjsgjjyA1fXpB189GviOAM7UaccvESrejZaEUe5U8\nUKKCQjaySCSieYLOFzO+GMogdzAYNLXZZKGt+kmqMkk1zYTH40FZWRkaGxs1fx8KhTIKeTaXF0lQ\nYBgG4XAYqVRKzlIim5TP55Nbihe6kWllefk8PgzvNhxndT4Ld595N47yR7F692rU76zH3evvRs82\nPTGupnlm++DOg3PenFO3347D33yOv372V5zWYSBGdx8lr5kLBgGLN2Kzug3nW1hJcJOl4BRUUFxM\nMBgEy7JpbhKzUPYJyxV1kJvjONMEpZAvJKnLISJhpBU+wzCYOXMmnn322RYxlGAwiKuvvjrjmvQq\n5ZUNMMPhsGYLD+Ua1DUWuWxkadcyUIfStbwrrhpwFa4acBU4gcOm/Zuwavcq/Gb1b7Cvad9PsZce\n4wxZL+vG9MaMpQ/hodmPYfgp06BsJJNMJi3PO7NiwJZS9IH0OSKZGiU6gZtqUIzGUNxOSQoKkHs6\nXi7XzeeapChQGeR2qjeYEnVrl1zWc99992Hjxo349NNP0dTUBAAoKytD//79cd9992VuvaJhoahb\nuuSCeiPTc8PoBZFzrUPxeXw4p/ocnFN9DuaMmIP9jfuxevdqLPtqGe5cdyd6tenV7BrrWatpvSz8\nfCHuXn+37tx3SZIsj0Wo29dbscFmKqwkok/cmq3ZUqEWiotRZqq4gVz7heVDPuKUSCQ0W+EbvU4o\nFMKaNWuwatUqvPnmmxBFERdffLGcgZJMJnX/Vm2hqAeakXiJcpPJZcPRc8PoBZELrUPRsl5W7lqJ\n21bfhgNNB2TrZWyPsXjt09fw2ievGZr7biVWtq/XQin6pEEiEReSQGP2FES3ov4sl0KnYaBEBYVg\nhQWQyzWzFQU6ZaEQS4DjuIJbu3g8HkyYMAETJkxI+7kgZA64e1mv7O4jKcqZUruVr1Wur5ue9aJM\ngSXuRzNOyUrr5aGRD2Ff4z6s3r0a//zqn/jVil8h5Avh46s/RueyzgU9T6FY0csrF0hwn+f5tIaJ\nhRZWGsVNFhF1eRURZn9wjGxm5MQNIKd+YflidJPVGm3sxHqIy4sE36203tRopcAyDANBFNJmixgp\n4DNCVXkVZgyYgRkDZuAP7/8BxxLHHBcTIP8sLysgqeBaUxDzKawsNqLRKMrLy51eRsGUtKBY8aEz\nck0SL8lWTGm3hWJ0XYD1pzcP4wEncIhGo3l3BzADckr2+XxgWRbhcLiF9UJqXsx4TYK+ICqQW3zI\nKqweAVwIpV5Yqf4sJRIJdO7s/CGjUEpSUNQdPM3cHLOJgB3xknzWZdQSsOOLKUkSuCQHTijc5WYW\npA5Fy3oh7rBYLJZ2Sg7eeSe4Sy+FOHiw4eeRJMk144bdZKFkw4rCSre5vGgMpQiwywpQxiX0mig6\nsTYjzR3tRHa5gQEYZBQTOy04recip2RCIBBoniPC8xA++gjlL7wAZtMmRNeuNXxKliDBJXpiS5aX\nEXJ93lIorNQKytMYiotRWiZWzzApJC5h5trU15IkCU1NTbrNHfUww6rTep2UwfeKsgrbmkMagVgo\nmVD6+IM/Nj30fvEFhLVrkRg2zFCGktssFDuzvKwi38JKaqGYT/F/mgxgpaDkEpfQupaZ61JCihW9\nXq9uc0c7IfUuxOXmTXhznqpoJbm8Pux//wvvpk1gJAlSLIbKRx5BdO3atAwldQBZTn2GewTF6Swv\nK8ilsNLJGjB1nREVlCLByo2UxEvUdRxGscqlQzZvJ+eqKCH1Lsrgu9GZ8nZitLAxcN99wI+ZRwwA\nz/bt8G3cCPacc5qv86OPn+d5udKfiIubTsVuiaFY+ZpkK6wkYq8WfruhLi+XY6XLi7RQMaOOw+x1\naW3e+VzLjNdM2aRT/Trl0r7eDhgwMKIn7H//C8+P1olMLIbA7NmIrV/ffC2Fj1+5iXEch2QqCQ/r\nkcfuOrWJiZIIBkzBUyCLCXVhJUlHJhMrARhyW5oBbb1SpFhhBZAuwZWVlaY1jCz0OiStNZlMukbk\nRFGEKIpy5bsSD+uRO+xmQytYbmZTTXJNIxaK0jqR/xYAu307PO+/D+FHK0V5XaULxufzgQGTtok5\n0duKF3lXWCdOQsScVO0T95hdhZVKqMurFUI+aF6v15T5JWZ9QInFBEBz886VQkWYxG8A6L5OXtZr\naB6KXRhpDsns2gXv+vWQysshqnuBJRLwP/kk4ipBUSNBgpf1yn3TtHpb2eHf16pBcZM7zm6U4gJY\nX1ipVYdCBaUIMMNCUXbAJTUcZte15Hs9ZVIAqfh2EmWzyWQyqbse17m8DFgoUnU1YsuXA7z2ukXV\nbG/Na+Cn91qrtxUJIAuCIFt4VlgvbsrwcjJdWS/z0e7CSjuagdqBOz5RFqD80po1J6SiokI2id2A\nMinA5/O1aCNvN8r4jcfjybieXIPyVtekiKKIpKDfzBIA4PVCGD7csjUoA8ixWEyus8inHX82SjHD\ny0rMLqxUi2ipWIclKyiEQjYi5ZwQ4kridU6ndq5Pq1jR6lqbbOtRN5vMFuPwMu5JGxYlEX/c8kds\n3r8ZV/7jSozvOR61NbXW9NuSYChtmPS28nq9LawXM4r3BEloVQF5LfLdxM0urHR6hIWZlLyg5It6\nTohZFk+hqId0qU9Ddp908i3qdEvacJyL48YVNyIhJLBpxiZ8fPBjrNy1EvduuBc1lTUY33M8xnYf\niwHtBpjyfEqXVy5opb/mO0wMaBZRt7i8ip18CivV31MnU5bNpOQ/UblmBEmShEQigUQioZt665Q1\nQCrNlUO6lNexm0zryYaRoDxgrYAfjh3G1MVT0b2iO5ZcvARBbxD92vfDtFOmpc00+fWaX+P72PcY\n13Mc6nrWYUyPMWgXapfXc5pRKa/OHMt1mBjgriyvUnH3AMYLK8nv5ILXErFSSlZQ8rEo1PESrdRb\nKz74RtZntLmjGV9OI6+ZsngyGAzmfA2ng/JfHvkSU96egov7XYzZw2a3cP8oZ5rcP+x+7DyyE+8e\neBeLti/Cr1f/Gqd2OBXje45HXc86nHLCKYZf83wtlEzkOkwM0B7/WyqBYaPYIWR6liXQnCr8yiuv\nQBRF2a1pdD1HjhzBZZddhj179qCmpgaLFi1CmzZtWjyupqZG3st8Ph82b95s6v2pKflPj1FBEQRB\nnjZC2BQAACAASURBVKWeqY7DikLJTJB4CWnz7nSnYKA5+N7U1ISysjJNMTECy7CQIBmuRTGT9/e+\njwmLJuCOs+7A/cPvNxRLqC6vxvUDr8eiCxfhq1lf4Xdn/g4Hmg7giiVX4OT/ORm/XvVrLP1qKZpS\nTRmvY3UvL3JCDgQCCIfDCIfD8Hg84HkesVgMsVgMyWQSHM9Rl5fNkPeGpCZHIhH069cPX3zxBT76\n6CPU1NRg1qxZePvtt7N6VR5//HHU1tZix44dGDt2LB5//HHd51y/fj22bt1quZgAJWyh5AI5/TvR\nqiSTQCktpsrKSsdPj1rB93xhGAYexgNBFMB6cruvQkT9jc/ewD0b7sFLk17CuT3OzesaIV8ItT2b\n58VLkoQvj36JlbtW4vmtz2Pm8pkY0nUI6nrWYXzP8ejTtk/a39rdy0svOymWiIGRGLnvmFNdqEvF\n1ZMPDMOgtrYWgwYNwrFjxzBv3jzU19fj7bffxoUXXpjxb5csWYINGzYAAGbMmIHRo0frioqdr3HJ\nCooRl5eReInWde14g7QyzOxam9Z18p1AmcmMJ4F5H6wfriVJEuZ+OBd/+d+/YOklS9H/hP6mXJdh\nGJzY7kSc2O5E3Pyzm9GQbMD6b9Zj5a6V+OOWPyLiizS7xnrVYXjVcEfjBcrsJH/AD6/HK1svyWRz\nyjRxe9k9tMqpOhQ3PC+pku/fvz/69zf2uTx06BA6deoEAOjUqRMOHTqk+TiGYTBu3Dh4PB7MmjUL\nM2fOLPwGMlCygkLQ22RJa3dRFHM6/Vvh8lJfTy/DzCnyCb4beYzRavlCX++UkMKtq27F5z98jjWX\nr0GnSKeCrpeJikAFJvedjMl9J0OSJGz7fhtW7FyBRzc+iu0/bEf7UHsM6DAA+xr3oaq8yrJ1ZIMU\nNir9+6QVTGsYuesm9BpD1tbW4uDBgy1+/sgjj6T9O1OG2AcffIAuXbrg+++/R21tLfr164cRI0aY\ns3ANWqWgKFu7m9FCxUxIcWA+HYytsFCyBd8LIZfAvCiKeZ0ojyaOYvqS6agIVGDZpcsQ8dnXgI9h\nGAzsOBADOw7E74f+Hj/Ef8ANy2/AruO7MOz1YehW3k0O7A/uMtjWmIa6sJFsSmQcMgkeKyvD7epr\nZRdOudv0LBQ1q1at0r1Gp06dcPDgQXTu3BkHDhxAx44dNR/XpUsXAECHDh3wi1/8Aps3b7ZUUEo+\nKK8mlUqhoaEBgUAAkUgk5y+GVRYKiZckEglUVFTk1Q7fbMwIvmfCwxirRSExLhJU5nlefs0ysevY\nLtS+UYsBHQfgLxf8xVYx0aJ9qD1ObHcipvafiq9v/BpPjnkSDBj8du1v0fv53rhm6TV447M38EP8\nB8vXote6nnwfyDCxUCgkH26IizgWiyGRSMjvQyE4nTLsBmEk2Zu5MHnyZLz22msAgNdee00z5hKL\nxdDY2Cg/x8qVKzFggDn1VHqUrIWijqEQkz6ZTBbU2p1g5hdBkiQ0NjbmXByoxiyxkyRJLsoqJPie\nrU+Zl/VmFBSSZqlsnCeKYosZ71rtxrcc2IIrllyB3535O8waNCuv9VsBSRv2sl4MrRqKoVVDcf85\n92N/436s3LUSS75cgt+t/R36tu2L8T3HY2LviRjYcaDpG58gGu/llRZ7UVTtZxsmRtFG/Z2Ix+M5\nC8pdd92FSy+9FC+99JKcNgwA+/fvx8yZM7F06VIcPHgQF110EYDmnn/Tpk3D+PHjzbsRDUpWUAhk\nUyOjcAvNljL7y0JOfYFAIOeJj1agzJMvRNyM4GH1XV7EYgN+6lgsCEJaUVgwGNSsSP7nzn/i9rW3\n49m6ZzGx10TL1p8PeoLftbwrrj7talx92tVI8kms27kOa79di2uXXYumVBNqa2pR16sOo7uPRkWg\nouB18CKfV+sVIhjqrrxaw8TcOs8dcFd2WT7Dtdq1a4fVq1e3+HnXrl2xdOlSAECvXr3wn//8x5Q1\nGqXkBUUQmk/ALMuaNgo328nbKKlUCqlUSg52m7WufCHBd1KMZXWasl5QXhRFNDY2pvnriT+ftHYn\nva78fr98auY4DvM/mo8Xt72IBZMWYFDnQRAEwXU+/2xrCXgDGNltJGp712KeZx6+Pvo1Vu5aiZe3\nvYwb62/EGZ3PkNOST2x3Yl73JkqiKc0hldaL1kwRt1svbsjyysfl5VZKWlCI7x2AqXPVC924lc0d\n3TCiF0gPvpPRqFbDMmwLC4WIGvHfNzQ0yKJAxIS0dud5Xk5xFSQBd757Jz7c/yFWX74aXSNdNavF\nrchYYvbvh//pp5F8/HEgW6GqwTHDSnq37Y2b2t6Em864CVEuig3fbMDKXStx4d8vhM/jk62XEd1G\nIOQLGbqmFe3rjVgv6mFiTsdQ3EAsFkNlZaXTyzCFkhUUsmmXl5ejsbHRNR9cpfutoqICqVRKtqLM\nun6uqMcGx+Nxy+pZlKiD8kTUSHsZ8rfxeFxOb+U4DoIgwO/3y26wo7GjmLliJiRIWH7JcrQJNbeg\nUPe6UmcsmXVq9j/8MHx/+Qv4sWMhZPFRF1opH/FFMKn3JEzqPQmSJOGzw59hxa4VeGrzU7hm6TUY\nVjUMdb2arZfuFd11r6PVvt5sN5DaetEaJsayrCPuJyf3A/VzJxIJVFU5l0JuJiUrKCzLygWBdtSO\nGEGZrmymxaRcVy6YWfmeD17WK7deUYsacZ2EQiHZnUUKK5XWxrcN3+Lity7Gzzr/DHNHzoXP40Mq\nlZItF+V/ymrxTKfmXGC+/Ra+N98EAzTPla+tzWilmFkpzzAMTulwCk7pcApuP/N2HE0cxdo9a7Fi\n5wo8svERdAh3kF1jQ7sOhc/zUyJKtiwvs1E2TSQuSkEQwHGcHC9zYhSyG4jFYgiFjFmWbqdkBQWA\nfPqxoro91+vp1XPYVXmvRm0pabXBtxoP6wEncIjFYkilUrKoEZcWiZNIkiRbFqFQSO6qu+mbTbhm\nxTW44fQbcPtZt8tzWMjfkzYjANLiMdlOzbm0gfc/+ijwY98ldu9eeFatymilWHkybhtsiyknTcGU\nk6ZAEAX8+9C/sXLXSsx+dzZ2HduF0d1Ho65XHWprajVHANsJeR8Yprn9SzAYLOh9KCa06lByDcq7\nlZIWFKvIZUMgm6Hy9G3luow2wsxkKdkVa/IyXjRGG8EHeNmaVIoJwzByejDLsnK2l9frxbp963DD\n8hvwxLlP4Pye5yMajcrJBMr0VjJGVykuRFiUg5AyDbHSuw/ZOvmxLTkTjWa1Uuzq5eVhPRjSZQiG\ndBmCe4fdi0PRQ1i1axVW7FyBu9ffjTJfGTjJ+cmjpNWLkffBTOvFLS5wIL+0YbfSKgTFKZeXsrlj\npnb4dlooVla+54IoioDU7HopLy+Xf6bMyhIEAdFoFH6/Py154cWtL+Lxfz2Ov/3ibzir6iwALQPA\nkiTJlghxdSktEqBZWImwkOfUajVO3DKJRCItHVZpnRCyWSlWtK83QqdIJ0w/dTqmnzod7+99H5ct\nvgwjq0favg4j6L0PpWK9EBElUAulSFBmkdjtViKpr8pYjtVku08ygz6bpWT168XzfLOF5GluUgg0\nb+7ki8YwDDiOQzweRzAYlLOGBFHAvRvuxYqdK7DmijXo2aZn2pqJgASDQbkAklyHzINQurvIc2az\nXjwejxyXIemwvoMHUaawTuR1ZLNSDI4AtorlO5fjlyt+iT+f/2eMrRnr2DqMooy9APkNE9PCTXUo\neq1XipGSFhQldlooPM+jsbHRUHNHO8ROmabsRPBdiXJQmN/jhyAJ8sZO3FypVEqujieFjDEuhuuW\nXoejiaNYc8WarBMTWZZFIBCQs8VIHQtxjRHLRVk/QdxtAFrUr6jTYf0vvgjwPMSyMvz4AFkmPNu3\nw7NxI4Thw1usyykLBQD+3//+Pzzw3gNYdOEiDOkypMXvnXAD5fqc+QwT08NNLi9qoRQRVmRT6YkA\nsQLyae5oxbqyBd/tXI86k4tlWCS5pGyZEOHjeR6RSEQWvkPRQ7j07UvRt21fvHr+qwh49YeM6a2D\nuFCU1ksikZCr74m4sCybFtgn/082L7nQcuZMSMOHt3ClkXsR+vWD58fHKyk0bThf5n80Hy9sfQFL\nL1mKk9qfZPvzW4Ge9aJMD8/HerEaGpQvcuy2AsrLyw0PLLJybfmmKZu9Jq30ZEFozjISJVEWk1gs\nBkmSEIlE5I3488OfY8pbUzDtlGm4Z9g9BW8KWpsQiZMQ15hSYIjvnqyZiIanVy8IvXunCYYH6TPE\nkz8mEyg3NbstFEmScP9796N+Zz1WTF2BbuXdbHtuu9EbJqZV3OqmoHwikaBpw8WA0l1hpcsr3+FT\nZkOyooD04LuT1fhqC0mZyUUGbCkzuZTCt27POlz9z6vx6KhHMe3UaZasT92+hdRGEHGTJAl+vx/B\nYFDXNZYpsE82NTJjhOf5vFvx5wov8rhl1S3YcWQH6i+rR/tQe0ufz00oY2pAS+uFuFedaM2jfu/d\nJG6FUtKCosQqQcln+JTetczCSbebmlgsJs+dAdIzuTxMcx1KU1NTi0yu1z99HfdtuA+vX/A6Rna3\nJxtJuQklk0kkEgn4/X4IgoCGhoa0rDG1a0xd8wIgrSI/EAg0CwkjQRRERKPRtD5XZh9C4lwcVy+9\nGpzIYcnFSxxv3a+HXZup2npJpVLged41w8SooBQRVrmVlAHmQCA3vz7B7LWRzCYz2s4XAgmC+/1+\n2T+szuTyMB5EY9G0TC5JkvCHD/6AhZ8tRP3UevRr36+gdeQK2WzImAPyGpJqfeUJN1PNC9kolVlj\nLMvCw3oQ8DfP4slUsV/IRns0cRRTF09FdXk1nqt7Lq1CnpI+TIwIvZ3DxJTvbSlZJ0ArEhTiojAL\njuOQTCZzipdYCSmglKTCW/QXChFaUqgGQDOTCxLg9XvlxyT5JG6svxG7ju3Cumnr0DGiPYXOKkit\nCc/zKCsrS3sNSZaX0jWWT80LaTWTrWIfaBblXK2XA00HcNFbF2Fk9Ug8NvqxnFrUl9rmZhTi9gSg\n6aa0uh1/Kb3uzu+EFmJFDIWcYHOdRa+HGWsjwXeySZkhJvmsSdkVoLy8XN5seZ5Pa6OSSCTAcRwC\nvgBIwtMP8R9w+TuXo0O4A5Zfttxw11yzUCYFZBsLXUjNi1I4yAalrhQnMSXyOKMn5q+OfoWL3roI\nMwbMwO1Dbi+ZTcoK9D7fyvdWab2o2/EXYr2UkoCoKWlBUWKGoFixcReKMvgun/wLJN8vCRnPq3S3\nkRb9yrRgURQRiUTgZb3gRR5fH/0aU/4+Bef1OQ9/GPWHvAY/FYJeUoBRcql5YVgGPq9P7jsGQJ7z\nog7uk5iSkRPzfw79B5e+cynuHXYvZgyYYe4LZCFObq5GnlfLeiECAxRuvair5oudViEoZnxgycYd\nDAbBsqwpGzfw09ry+WKpg+9mrSlXlJlc5eXl8ibo8/lk9xexxDweD8LhsByU//yHz/Gb1b/BvcPv\nxfWnX2/72kWxOUDu8/lMyYbLVvPC8zwkUcpY86K+XrYT8wcHPsANK27A/Nr5OL/P+QWtn6KP2k1J\nDg+5DBNTH2xJN4hSoaQFxSyXFynIU27cTrZu0Kt8N8u1l8t11LUuwE+ZXORkzvO8bAGQFGufz4dP\nv/8Uy3cux6vnv4oL+l5Q8LpzhayLWBdmo1XzwrBM2kRKdcW+KIry50vpKlRaL8oT89tfvI3frfsd\nXhj3AoZ1GYZkMum6Qj43UqhlQAQjl2Fi6r8HSqvtClDigkLId6PVKsgj17NifUau62Tluxp1ixmg\nZSYX2bRJJpeyXUbXSFcwEoPrl16PYVXDcF6f8zCpzyR0Le9q+dpJvCMUClnaAVqJ0pVVUVHRouaF\nrIPjOIRCobR2/EDLmpdXPnkFc/81F+9MeQcDOgzQbUNiJBXWiQPSwYMH8f777yMcDmPMmDFFfVLP\nlmShF3ehgtJKICdphmFQWVmZ9iGwo/JeCyNt5+1aF3FlEatNGXhWZnKpe3IpT+3LLl8GURTxQ/QH\nrNy5Est3LseD7z2I7hXdMbH3RJzX5zwM6jzIdAHXWpddkPb16sI7EiPh+eaRyKTDMXGfKdOReZ7H\nk1uexILPF2DpxUvRq20vQ21IjAT27bBqeJ7HbbfdhoULF8Ln88mf26effhqXXHKJ5c9vNcr3Qp0V\nSNzSyWQSX3zxBQDkLCh/+9vf8OCDD2L79u3YsmULzjjjDM3H1dfX47bbboMgCLj++utx5513FnZj\nBmgVgpLrRku64ZK55lZ/yYysz87K90zrIVlaiURCTpnWy+RKpVJpPbm0YFkWHco7YNrAabjitCuQ\n5JJ4/5v3sXznclz9j6sR5aOY0HMCzut7Hs7tcW5B2V8kC43juKzrsgotS5TUuIiiKMegiG9eXfPC\neljcteEubNy3EfWX1qNDqIOua0yrDYldqbCZeOCBB/C3v/0NyWQSyWRS/vnNN9+M6upqDB061PI1\n2JkMoDw8KOuY5syZgw8//BBdu3bF888/j0mTJqF7d/2xzYQBAwbg7bffxqxZs3QfIwgCbr75Zqxe\nvRpVVVUYMmQIJk+ejP79+5t5ay0onfQCDfKJoaRSKTQ2NiIUCulm/JhtCWS7XjKZRFNTEyKRSMbu\nxVZbKMQFSGI3Xq9XPnkRq4TEd0gtRy6bNsMwCPqDGNdnHJ6ofQJbr9uKxb9YjJryGjz14VOo+VMN\nprw5BS/95yUcaDqQ89q1Gk/ajXrAlnpdyvYt4XAY5eXlcp+n403HcdU7V2Hbd9vwjyn/QLc23eQO\nA6QYkrjRSIsX4KcNLRAIIBwOIxQKya34o9Eo4vG4nLVkNbFYDC+99JIcZ1ASj8fx+OOP27IOp1DG\nXv7+97/jpZdewoknnogPPvgAP/vZzzB9+vSs1+jXrx9OPPHEjI/ZvHkz+vTpg5qaGvh8PkydOhWL\nFy826zZ0oRbKjygD3dmKFe1yLeWyJqtRugArKioAQHe6IsMwiEQiBZ0Aidvg1C6n4tQup+K3w36L\n75u+x4qvV6B+Zz3uf/d+1FTWYFLvSTivz3kY2Gmg7vMRIQRQ8LoKRdkc0si6iBgkxASuWXUNQt4Q\n/n7h3+GFFw0NDYbmvJDsOqUVowwmk2aWQPOGb2WH3j179mSM+23bts3U53Mb6n2DZVkMHjwY999/\nPwRBwHfffWfK8+zbtw/V1dXyv7t164ZNmzaZcu1MtApBIeiZuU4HurUEKp81WZXllSmTSzldkfTt\nyjYDJh9YlkWnik64atBVuPL0KxFPxvH+3vex/OvlmL54OpJiUnaNje4xGkFvUF5nNBq1bF25IknN\nFookNU/zZFk2q1v1cOwwprw1BaeccArmj58PL+uVr5XrnBee59NcYkp3Gs/zCAQCeQf2jdC2bduM\n1lDbtm0Lfg4juKX+RRmU93g86NKlCwCgtrYWBw8ebPG3jz76KC64IHtGpFP31ioEJdOLm0+Ld6st\nFLImj8eTtWrbakgmFxkZrDwB62VyWQ3DMAgHwxjfdzxq+9RCEAR89t1nWP71cszbOA9X//NqjOg2\nAhN7T8TIziPRrU03Rzsut0CC/JnLJnJ7G/bi53/7OS7oewEeHPFgi+SQfOe8qJtZks+zGYH9THTu\n3Bmnn346tmzZ0qIdUigUwg033JDzNYsZvSyvVatWFXTdqqoq7N27V/733r170a2b9aMLSlpQtDKz\nlD8rdL66WaccpUApCyjzOVGbKXTqwslMmVx2pt8qIafy07qehtO6noY7ht+B7xq/Q/3X9aj/uh6z\nN8xG77a9ZdfYgI4DHBUWURKRTCYNFVJ+fvhzXPjmhbhl8C24efDNGa+rleVFrBetOS/qZpZ67fgz\nBfYz1Vlk4oUXXsDYsWMRi8XkWEokEsHPfvYzXHvttYavUwhOWSjq543FYmjXLvP00WzX02Lw4MH4\n8ssvsXv3bnTt2hULFy7EggUL8n4eo5S0oChRbtrKnlPZ5qvrXcsKCm07b/a6YrGYKZlcdsKyLNqF\n2uGiPhdh2oBpSAkpvP/N+1j29TJMfWcqeJGXU5JHdh8pu8bsgOd58BwvWxSZ2LRvE6a+MxWPnfsY\npp48NefnyjbnhYiLz+eT3ZUkPRnQnvNC4jSk35i6zkJpvWSiV69e+Pe//43XXnsNy5YtQ3l5Oa66\n6iqcd955rmi0aickfT0X3n77bdx66604fPgwzjvvPAwaNAjLly/H/v37MXPmTCxduhRerxfPPPMM\n6urqIAgCrrvuOsszvACAkZws+bYBUnV87NgxlJeXg2VZRKNRCIKQcxaSkqNHj5rW1bexsREA5DXl\n+6WSJAlHjx5F27Zt8xYX4ttPpVLy/ZGTrDqTSxRFuY2KGyCNO5PJZAuRI5vqp999iuVfL8eKXSuw\n/eh2jKoehUl9JmFi74mWdjcmhZQ3rb0Jv+j3C1zc72Ldx67YuQI3LL8BL058EXW96kxfi7IfFXF7\neb1eOftLPfYYQIvAvhJlYJ/EaIwG9kmnArsPJNFoVL5fOyH7EenMMHfuXAwbNgyTJk2ydR1W0WqO\nAyRwTGITZHpgIdczQ4uVJ/9CEwIKtVCUmVzKn6nFhASAnc6YUkIsJq3W88BPrrHTu56O07uejjuH\n34lDjYew/KvlWLZjGe5adxdObHciJvWehEl9JuHUDqeadm/KQkqGYTLOlH/jszdw97q7sejCRTir\n6ixTnl+NsucUqbciSSDKIL1eYF+ZjPH/2zvz6KiqdO0/VZlIJZUAgmkI+RjES6AbIQFJd2OYp5AR\nQQkgImCMtBr0gihLWxBtnJDlvYJcR4aFBklCBiEJU0tkSoKCCkhE8KabQSIIZK6qVKW+P3L38eTk\nnKpTVWeq1P6t1auXUNTZOTlnP3vv932fl69LJbtinwiRms2r+FAzKM+mubmZVsp7K42NjW7HJuSA\n7V5MHHnVHAu78+Tt27fb1TGQtGAtZUwRSPqt3e7cep6g1+vRK7wXFo1YhIWxC9FkbsJX//oKJT+X\n4IFdbdXa5GgsPioeQf7ueX2R4j2yY3LUU37D1xvw7tfvYs/sPRjSY4hb1xMLSaQwGAzMkS/Xj4oc\njZHjMfIMsM0syWKIHah3FNjXorgoCVfIqKB4GTqdjsl66dKlC1MkJsX3erJDYftgkfNqKcflyvdx\nkxPIz2UymdqllMpppOgunlrPA233LKRLCBIGJWDaf0yDzWbD6ZrTKL5YjFcOv4Lzt85j3P8bh8SB\niZh611T0NPR0+p1COyaSNsz97OrDq1H0UxEOzD2AqLAovq+UDCEfM3acRGyfF3bGGDk+c1axzzZQ\nJN+hpJmllk75Scy0s9DpBaWxsZFxYJU64Ofug8kNvpPVtRoIZXIZDAam5zbZqZAgr1aQ2noe+H1S\njYmMQUxkDFbetxK/1P2C0oulKKwqxPJ/LsfgOwa3ZY3dnYjBdwzucF12jIlUvzN/x6mUt7ZakbUv\nC2eun8H+OfvRw9DD45/BEa74mInp88Jugcx3NCYU2CdCQqr0ye5FbGBfCtTK8mL/bNQc0ssglhNS\nT9ruPIxCle9S1rWI/S6ygma3MWbHc8iLDbSJTlBQEGw2G+rq6todg6h1TKfUjkmv1yOyayQWj1iM\nRbGL0GhqRNm/ylDycwnSctLgr/dHwoAEJN6diPui7kOAPsBh9Tt792iymtr8yloaUTy7GKGBobL9\nHEDH4zdXEKp5IQsSsTUv7N0LCfKT41MS2Cc1L3JW7GsFkmnaWej0gkKaEkldjOjq96ldjc8dC8l0\nI8kJXBsVIn42m43JjiP/lm1cSIrdiLgo8eKrYT0PtP3OQ4NDkRidiOmDpsNms+G7a9+h+EIxVpWt\nwsXaixgTOQZT+09FcnQyQnQdjzLIDqXWXIvZ+bMRERKBbSnbEOgn785PyhRvd2pe2LsXIqok/Zw8\nc3yBfS2YWUoJXx0KPfLyIsgvT47qdrHfx26oxBc0JsFOKXD2c/J5cjnK5OKOl/vi8/X0IDsYOV58\nNa3n2ZBd3Ig+IzCizwistK7Ev278CwcuHcDui7ux8vBK/LHHH5mjsUHdB7XdW9hRa67F1OypGN1n\nNN6a+JasLY+dZb9JgSs1L0RciGAAbQsErh0MESxHfd3dbcOtlQwvoE3otRST9JROX4dCttHEtFCq\noDzpOujsYeA2oeJ7kEnSgBQrldra2rZ+7TyTLcnkIrb8QEcxcTeTi0wSxOmWtABmZwh5AilGbWlp\ngcFg0EwhJdB2XxsbG9vFGxpMDfiy+kuU/lyK/f/ajyD/ICQMSMCRK0dQ01CDx2Iew3N/eU7WiU0L\n9ULcmhf2YoNMpmSnQnYvABzWvJDvZNfQuFKxT2I3auwMuHU3CQkJOHz4sGYEzlM6/Q6FoMaRl9jK\nd6nHxvddfJlcXBsVT+ISQscg3Awhd+IujoLcasPnY6bT6WAMNiJlcAqSo5NhtVrx7bVvUXyxGD/e\n+BET+k7AsnuXyTou7j1Ta8Ji7zTIcanFYmFideT5c8XM0llnRGeBfS3tUDobPiUoUh0rOUMo+K4E\nfC8KETZiM8MnJlLHJbjHIEINo5ytKkmNCaC+9TwXMfeM/Kz3Rt2Le6PuxQvxL8BmbZsASTBb6gQH\nrd4zMg6SRUgWMWJqXrhmltyjMbYdjJRmllLDFjMioJ2JTi8ocsVQhL6PxB9aW1sVt53nGwsRtrCw\nMMZVlpxdk6MGT7J/xMAXd+FOJERguFX6WiykBNyP5fj7+cPfz7/dit0doRWCPH9ibPGVhggw+565\nWvPiKGtMbJdKraGl35GnaO/uegl8IsAOvpNWrmqNiytsQplccgds+cbHLp5jryjZ6ad6vR5NTU1M\nR0KtvHREgKXImHJXaIXwBgEWumdia164gX0iLI5qXriBffJvLBaL24F9d+Eet2npdyQFPiMocu0C\nCGKC70qNjQibXq+H0Whk/owrJuRYRM2eK3zZPBaLpZ1fFDkbVxs5BVioSl3s0ZgcRZ5S4UxMV40U\noQAAIABJREFUuLhS80IckrldKoGOuxcS4yI7IRKcB9y34vcE4hDQmej0giLnkReJyXhqOw9IawfR\n3NyMoKAgyTO55Ia8XCQrCQBvN0I1ahHYQW4lBJhvxS50NEZ+n1qzxQE8K6YEXKt5IUe4YswsSUEl\nO4WZBPal7lJJ4L7jzc3NbvVh0jKdXlDYyCEozc3N7arN3f0uKSArr8DAQBgMBt7gOzvFNTAwUDNi\nYrfzW89zV6nuHgd5OjYpg9y1tbX4+eefERERgd69ezv9vKOjMa3a4gBgjgal3M25UvPCDewT4WDP\nA3yCJXdgn3xHZytqBHxIUKSedNirICls5z0VO5PJhObm5nbVyXJnckmFs6Mk9kvvznGQJxDzSXJt\nT54jk8mEZ555Bjt37kRgYCDMZjNGjBiBTz75BFFR4gwh2Udj/v7+zDFXa2urZmxx2DVDcqZ5s+8F\nAN54HFtgiFg0NzdDr9ejpaUFdnv7Pi9iA/tS7JKbmpokq4vTCj4lKFLtUFpbW2EymWC32xEeHq7q\nKp+byUXGJZTJpXaFORey+rfbXbOeZx8HkWJKqa1gpI5LPPzwwzh48CBMJhMzQVVUVGDcuHH4/vvv\nXVqtksUBu4hVjqwxV2EvDpSuGeKreWHfC+KaTQL1AESbWXLT392p2OezXelMxpCADwiK1DEUq9WK\nhoYG+Pv7S2Y77+7YuP5g5HssFku7YjGlM7nEIpX1PN8RSGNjIwAwq1NXJ1Ru9bunnD9/nhET7nXq\n6+uxY8cOLF68WNR3CaUs8x2NKXlMSJ414vqg5rPGvRdsy3yy2BJT88KNvZBjRfKdxIXDHTPLztYL\nBfABQSFIISgWiwWNjY3MOTp5QNWA6w9G/owcZZGdCvB//T40VmEup/U8N1OKTHJirWBI9buUR4PH\njx8XvP+NjY3Yu3evKEERG+R25ThIiudCK5X5QpjNZiYex95pOKt54ZpZsv9fqEulUGCfu0Mhc0ln\nwmcEheCO7QJZebGD79zgnie4KnZklxQUFMRkiZCHnzzEbBNInU6H+vr6DgFLtVDCel4oO8iZFQxf\n8Z0UOPLS0ul+N+oUgh2XcGenyT0OYh8Teno0pmUxIfVY7BgYd1fLrnkB0G7hIbbmRUxgn/ue0yMv\nL4R95OUO5IEkVu/tmiVJXNciRuzILslgMDCTg5hMLim9tTxBrcQAMVYw5LgwNDRU8rqXqVOnMkct\nXIKDg/HQQw8J/lup4xJCx4TuHI1p1eYFEFfo6WnNC/mdignskyQAk8mEiooK1NfXuywoOTk5WL16\nNaqqqnDixAnExsbyfq5fv36MO0ZAQAAqKytdv4Fu0OkFhQ1ZIYh96NnHSiRGwf4uKcclBpLJ5Y4n\nl5gJVe6eJlqynueer5vNZmblSSqopYw1hIWFYf369Vi+fDkzAQNtO5dp06Zh7NixvP9O7voXMUdj\n7MZZ3LGRn8XdGJhcuFNrJUXNi9VqbScu7PtLMsssFgvWrl2LM2fOoH///ggNDUViYiL69OnjdIxD\nhw5Ffn4+MjMznf4shw4dQvfu3UXcLenwCUFhbzXF7iq4x0rcB1LqQklHkBe3paVF0JMLgOhMLqHg\nLbuniZQFhNw0Ui1UvbMhx5dGo7GdwDibUF1lwYIFGDhwIN58802cOXMGEREReOKJJzBnzhze+6zG\n6l/oaMxkMrXLoNPpdEwwWmueYVLF5zyteeGaWZLvDAsLw969e/Hee+/h3//+N44cOYIXXngBzzzz\nDF544QWHY4qOjhY9fqXmJzY+ISgEsQ8WO/iuVLGY0O6JL5NLSk8u9gqKnBWTQDYJLnqSGcQ9X9dS\nYoBQyjJfYydu8NZdURw9ejQKCwtFjY04BKi1+hc6GmtqamJsQ7SUgg7IZ0EjtJPjq4UiR2PsjDHy\nP/KO6fV6WK1WjB8/HrNmzYLNZkNDQ4MkYyXjnTRpEvz8/JCZmYmMjAzJvtsR2noaZMbZroIv+O7u\nd0kxNqFMLj5PLlfqOByNgbvl5xYQurJa1/L5OpmwHaUs8x0TKmEFw06n1srqn/y85NiGHPtYLBY0\nNzdLupNzFyX9zMTUvAQEBLQTY4vFAj8/P0ZcfvvtNyapxs/PD+Hh4QCAyZMn49q1ax2uuXbtWiQn\nJ4sa39GjR9GrVy9cv34dkydPRnR0NOLj46W7AQJQQfk/HAXfHeFO1pgYHGVyETEhL5Cfn58sq1ih\nAkIxq3Wt+oUB7p+v8wVvpa7x8Ib7xnWAdhZrUOJnYIuJ0v5YfEfIbNdoIiKBgYHMs/Prr79i9+7d\nmDBhQofv279/v8dj6tWrFwCgZ8+emDFjBiorK6mgSIWzGIqj4Luj75RrjNwjN0eZXErZuztKteSm\nnQpNPFpAivvG3skJWcG4s1rXsmOwownbWayBXeMhx8+kpphw4dZCkRR5nU6H27dv45FHHkF8fDz2\n7t2LDz74AOPGjXP7WkKL46amJthsNhiNRjQ2NmLfvn1YtWqV29dxBe0caCsA38NstVpRV1eHwMBA\nl49l5AjMm0wmNDY2wmg0CooJqQR3xypfCoiABAcHw2g0Mn5Ezc3NqK+vR0NDgyYnRSKAUt83spML\nCQlBWFgYAgICmB1mQ0MDU1jp6FkhZ+hkFaul+8YdmyPIhBocHIzQ0FAmbmY2m1FXV4fGxkbG1VcK\ntCQmXIh5bGBgIIxGI7p3746MjAwcOXIEly5dwqJFi5CVlYWTJ0+K/s78/HxERUWhvLwciYmJSEhI\nAABcvXoViYmJAIBr164hPj4ew4cPR1xcHJKSkjBlyhRZfkYuOrsaqQAKQ3ofsCc6wPPg++3bt2E0\nGiXJWqqtrYVer2fSQz3N5FIDcp7OztcXW50uN2rUv7BX6y0tLQD4rWD4+tJrBbKjk2Js7CQHkl7r\nydEYdyesJfh2m7W1tUhPT8cLL7yAyZMn48yZM9i9ezf+9Kc/iY6NaB2fEhQSbwgKCmKC76GhoW5P\nzFIJit1ux+3bt5mGWCQ+IpTJZTAYNJV6SwoCuZYg3AlELSdcLdS/sDPorFYrk0FHVu+k3cAXX3yB\nH3/8EZGRkZgxY4bTCno5kVJMuLCPTdliK/ZojCwQtdgDhmRmssWkvr4ec+bMwfLlyzF9+nS1hygb\nPiEoxMSNZPWQLAuj0ejRxFZbW9vO7dUdyHGC3W5HcHAwAgMDHWZyObLwUAOxVdzsTBir1SqpK7Aj\nPG3wJBetra1MvxAAuHDhAu6//36YTCY0NDQwHk+ffvopJk+erPj45PAzE4IrtjabzWEcSutiwrV6\naWxsRHp6OrKyspCamqr2EGXF5wTFYrEgICBAkjTWuro6j144dtvglpYWBAUFMT5c7EwuraWQEthC\n58r95B4FyZGCyy2m1JIIA+3b4ra2tmLQoEGoqanp8DmDwcAUQSqFkmLCh6OjMQCa7U7JJyZNTU2Y\nO3cuMjMzMXPmTLWHKDvaestkhBQh6fV6TdREWCwW1NfXIyQkBMHBwczOiS0mZPdCgpxqj5kNOULU\n6XRuJTOQn8loNDIpzySoT1wB3F3rkGJKNXpyiMFsNrfrsX7w4MF2dixsbDYbPv74Y4/uhyuQ7CyD\nwaBaEzaSNUaSHEjaemNjIxoaGhireC2thfnEpLm5GfPnz8fixYt9QkwAH0kbJpM3CQxLWT3r6kNN\njohMJhNTPGm326HX65mjI7YFvRYDtVKmtwql4LprfaL1Yko+x+ALFy4wR19czGYzqqqqZLGC4SKX\n07InkIxCUkRJdiXclgRqFlTyiYnZbMaCBQvw0EMPYfbs2aqMSw208dTIjE6ng9FoZI6+pMQVQSGT\nndVq7eDJRfL4ST2DzWZjDOiIVYMWkNt6nl1M6ar1idaPB4ViTX379mVaAnMJCgpCdHQ0QkNDZbGC\nIWghcUEIclxNYowER/dDqYJKtuMCEROLxYJHHnkEs2bNwty5c2Ufg5bwiRgK20GVrA6lgJuG7Ah2\nfxJHNipkjMHBwe2q09nVuEq9LFzU7EnPDepziynJi63FCnOunxl3bC0tLbjrrrvw22+/dfi3wcHB\n+P7779G7d+8O38nOkvIkDsWO52gpcQEQn2km5f0QC1tMyAKmpaUFixYtwrRp0/Doo49q6jlUAm0t\nRWRGCf8tPtixENL/wFEmF3sFy3UElttHSgi1V7DO7C1IRbYWxcTZEVxAQAAKCwuRmJjYrvhSp9Ph\n448/7iAmQEcrGO79EGsFo9UsOMC1tGUhaxy2yamU9VDk98oWE6vVioyMDEyYMMEnxQTwkR0KebjI\nCluq3H72uakQJJMrODiYaT/K58kl9qiGnWJJArVy9gpn75q0Vv8C/F79TmJRWiqm5FvBOqKxsRG5\nubk4ffo0+vbti/T0dPTs2dPl67KfD0cpuJ1FTJzBlzXmyVEhEROdTtdOTB5//HHExcUhKyvLJ8UE\noILiEeyHig8lPLnIypRMHlJOpuyjGq3VvwD8R3BaKaYkiwR2oFYN2MemLS0t7VpEu9PuQAmULKh0\ndbfP3nGS7ESbzYYnn3wSQ4cOxbJly3xWTAAfExSymiU20Z5Cjhe4bTzZNvikEp8tJuQFltpyg28y\ndTcjiO/F0RJijuDUKqbUqskjuR/s7pRSN1PzFHYAXsmCSvZuX2hBxvdOtLa2YunSpRg4cCCef/55\nTdxDNaExFInhs8EnL7JOp2MmdjliElzXVz67eTErdS1bqJMjOIvF4vSohi/uInccikyIWiy8A37v\nTkkSQ+SMM7iKkmICCKesC2WNNTc3A2gvJsuWLUPfvn2pmPwfPrFDIV5Tra2tqK2tRbdu3ST5XpIL\nT2wyXM3kUiomIZQhxbdSV9oW3xXE2ryI+R454lByHtV4irNMM/YuXo2jQrWr87lw3xlyTM1Omnn+\n+efRtWtXvPLKKx69J4sWLcKePXtw55134vTp0x3+/tChQ0hNTcWAAQMAADNnzsSLL77o9vXkxKd2\nKFLD3vHYbDameNJRJpca7XDFrtTJ+LQ+IUrZmVKKYkpAexMiG2diAnRspsbtQChnyroW7x07LZ1k\nX/r5+WHz5s147bXXMGTIEHTv3h2vvvqqx/dj4cKFeOqpp/Dwww8Lfmbs2LEoKiry6DpK4FOCIteR\nV0tLCxoaGkRncqlZwU0EhBxnkcmUvDRkhU5WZFpA7up3McWUjlbqatbnOMOdeye0AJGjYZYWxYRA\nhJjtVbdkyRL88ssv+Omnn2A2mxEVFYW4uDhs2bIFffr0ces68fHxqK6udjoWb8AnBIU89OT/pZos\n2X5bzjK5mpqaNBekJTEdMh6DwcC44JLxqp1+q3T1O18feUcrdaliYS0tLfjmm29gt9sRGxsrSfxF\nisQK9gIEaF8k7KkVjDeICXtXZ7fb8dprr8FsNqOgoAB6vR4NDQ3Yv38/7rzzTtnGotPpcOzYMQwb\nNgyRkZFYt24dhgwZItv1PMEnBIWNVKtvEpchwXelMrmkhB2TYKeP8q3U5fSQEkLtbClnK3Vi6Olp\nC4NPP/0Uzz77LLOjtdvtWLNmDR577DG3v9PVGhixkKNCd6xx2GhdTEiiAltM1q1bh+vXr2PTpk3M\nOxAaGooZM2bIOp7Y2FhcunQJBoMBJSUlSEtLw/nz52W9prv4RFAeaMuqstvtuHXrFsLDwz2aFMnL\nSgLc4eHhTIdCsish19SqPxK3Mt9ZMSW3lkFsxpi7aDlbikw4xL3ak6B+aWkpHnroISaDiBAcHIz3\n3nsPDz74oFvjk0NMnF2TW9/BFhf2GIiYaPW9YCfbEDH57//+b1y4cAEffPCBLIk01dXVSE5O5g3K\nc+nfvz+++eYbdO/eXfJxeIq2KpoUwNM4SmtrK+rr65mJGADTsIsdfCeOwp6uXuXAVet5nU6HwMBA\nGAwGxk6cHPXV19eL6pnuCqReKDg4WJNiYjabYbVaYTQaYTQamZbNFovF5b7pq1at6iAmQFuN06pV\nq1y+p+zOpEoaZBIBIS0JSLEvtyUB2x5fa++FkJi89957OHfunGxi4oyamhrmOaisrITdbtekmAA+\nfOTlDiSTKzAwEMHBwcwkSpyBuZlcWqxC9vQYSe7aDi1aqBO4Ew753XIzpLj1P+x7wv2+s2fPCl7v\nypUraGpqYhYuzlD7iJAglPhBjpH8/PyYpBWtvB/c41/yLn/00Uf49ttvsXXrVtnEZM6cOSgrK8ON\nGzcQFRWFl19+mWmLnJmZidzcXGzatInxAtyxY4cs45ACnzvyqq2tdat5EF8mFzmWIX5J/v7+zDGI\nFqvL5bSel6K2Q8veUmJSb/n+jTObj549ewo21woICMD169dFPataERMh2Jlw7GQHtuCq5aLNrg0j\nCwW73Y7Nmzfj8OHD2L59u+biPFpFW0tAGfEkGE8yWkJDQ9utzMmxFwnQm0wmAG0r1paWFlWb/nCR\nO7WVW9vBzQZylDHGfaG1KCbupC2zd3NCjsCzZs1CdnY2syIl+Pn5ISkpSbSYaLXHOsC/62Rn0ZEj\nTrncC5zBJybbt2/Hl19+iezsbComLuAzO5SWlhYm/hEUFCQq44qsSi0WC4xGI9N3QyiTi3wvWaWz\nnU2VNidko3ZygCOPMZ1OJ0n1u1zIFeAm4vLLL79gypQp+O2335gFSVBQEMLDw3H06FFe63ru92g1\neQEQf4TJ3uFarVbGCkYuF22CyWTqICY7duxAUVERdu7cqcl7qmV8TlDENsVie3IZjUbo9XqHmVx8\nK38+c0KpOuyJQQ2bFzFjYmeMAWCSA7QwPjZKeZrdunULH374IXbu3AmbzYbU1FRkZmYiIiLCocBq\n2eoF8CwepoQVDFdMACA3Nxeff/458vLyHLaloPDjM4JC2v+K6WFChIdUtZM/4/PkEmNSCAinVcp1\ndsw+89ei9bzdbmd8z/R6vWa6UhLUikmI9V3Tcn0TIG1yhStedGJhv7vk3SgoKMC2bduQn58v2JKC\n4hgqKBy4mVyAY08udyZrdnaUs5x9d5CiQlpO+Fb+fPdEjfN0QDvHSOx7QgwKySRKjjC1eL5PxESO\nXafQPXHFCoZPTHbv3o0PP/wQBQUForPqKB3xOUFx1BSLZHIZDAYmBZRPTEgAUYrJmrwgZAVGXhB3\nJ1ItW88D4tyMpcgY83R8Wlv5k3tCjjABMM+J3PfEFeQUEy7knvA1mBNKiCGZhOyU/r1792LDhg0o\nKCiA0WiUdcydHZ8TFKGmWI4yudgGj3JO1p5OpFq2ngfcP6aRsysl3/i0aAcCtD9GIpmE7BiD0tY4\nXEg8Ua14mLNWv3xicvDgQbz99tsoLCyUrPGeL+MzgsJO2SS1BIB7mVxKHYNwe4M7WpF6y5m6p5O1\nlF0p+canxYJKwPH4hKxxlEr+ANQXEy7cuAv5sy5dujD3paysDK+99hoKCws97pHkrKcJAGRlZaGk\npAQGgwFbtmxBTEyMR9fUIj4nKOxKZxIYttvtzKrF1UwupeCKC5lIAwICmCpkrU6GcqUtS+UxpnZa\ntTNcmaxd8dRSY3xqYDabYTKZEBAQgFOnTmHevHm47777cO7cOezfv99t23k2hw8fRmhoKB5++GFe\nQSkuLsaGDRtQXFyMiooKLF26FOXl5R5fV2toK/VHAUgcpLW1FXV1ddDpdDAajYzNvCNPLjWPQYi9\nR2hoKIxGIyMkdXV1aG5uRmBgoOYyuYDfX2Y5PM2k8BiTc3xS4Opk7cxTq7m5mTlOVWN8SmOxWJhj\nLoPBgNGjR+O//uu/cOPGDfTo0QNDhgxBamoqjh8/7tF14uPjHe5yioqKsGDBAgBAXFwcbt++jZqa\nGo+uqUW09wbJDImF1NXVISgoiMn2EsrkstlsmvPkIvUsRPzIRFpfX6+JQkqgfQ2MEvfPVY8xpcfn\nDp5a0XA9tVxxLxCDN4gJd3wnTpzAu+++i/z8fERERODWrVsoLi7uEFOVmitXriAqKor57z59+uDy\n5cuIiIiQ9bpK4zOCws7fJ1XZYjK5PG03Kwds63myuyJ/zm4IpXQhJXt8arQ6JgiZE7ItT4joaFVM\nSNGdlONz1MvE1cJBrYsJOQZmj+/kyZN4/vnnsWvXLmYi79atG+bNm6fImLi7Qq3NK1LgM4ICtL2k\nZrOZeanUyOTyFHYHQ27asrNVuhJFg+76XskFn8dYU1MTYy1vMplU70rJhs+oUA4cdabU6/UODRu1\nbOIJ8Kcuf/fdd1i2bBl27dqFXr16KT6myMhIXLp0ifnvy5cvIzIyUvFxyI32lmYywV5RAeiQyaXT\n6WC1WtHQ0MAUNWphgmHDFjtn4yOrdPZZOtl5NTQ0oLm5mSkMk3p8UtXoSA2Jien1eoSFhcFoNMLf\n35+JRbnSx0TO8cktJlzIYsNgMMBoNDJu2o2NjUzchTwr7NRbbxGTs2fPYunSpdi5c6dqk3hKSgq2\nbdsGACgvL0fXrl073XEX4ENZXq2trcxkUV9fz6yeyaQntxuvp0iVtsxX6+JJISVB6zs7Z+4BUmWM\neTI+bnMnteE+K0RoSWq6FsbIhi+1+ty5c1iyZAk+//xz9O/fX7Zrs3uaREREdOhpAgBPPvkkSktL\nERISgs2bNyM2Nla28aiFzwgKWV2R4DU7vkAClVrfwsshdmy7E3cr0rViVSKEq47BcnhHObueq71W\nlIZkwwUFBTFFwloopiTwicn58+eRkZGB7OxsDBw4UNXx+Qo+Iyitra1MxzigbRK0WCxM0ROxnlf7\nxeCiZI2EK4WUBK0XVHq6c5LbY4yICXFv0KqYcL2vyI5OqCpdSfh61F+8eBGLFi3C9u3bMWjQIEXH\n48toa/aUkS+++AIpKSn46KOP8Ouvv6KxsRHLli1DfX09goODGYfhhoYGmM1m1c7RCeQIhOyclKiR\n4Na68PVJZ68/SH/w4OBgTYsJaXDlzmTNjUWRSZ/bK92ddRk7W0+rYmIymTqICfB7DVBISAhTA0Tu\nN6kBkjpGxwefmFRXV2PRokXYunUrFROF8Zkdit1ux40bN5Cfn4/s7GxUVVVh1KhReO2119C3b18m\nXZjbv0SoH7jcY9WS9Tyf3YlOp2OCx1osCFTiGM4TjzEiJsSoVKti4mqCgJDZqStuwGLhE5NLly5h\n/vz5+PjjjzF06FDJrkURh88ICuGbb75BSkoKHn/8cURGRiI/Px/19fWYOnUqUlNT24kLn8W83OLi\nDdbzZKIBoJlCSjZqHMO54jEmVxdIKZEq20wuY08+I8+rV69i7ty5+OCDDzB8+HC3v5viPj4lKHa7\nHcnJyVi8eDFmzJjB/HltbS2++OIL5OXl4caNG5gyZQpSU1Nx1113ORUXKYO03pApxW7XS1Kt1d7R\nsdGCY7CjjDEAaGpqYupitPg7lqsOhk903enCyPc7vnbtGubMmYP33nsPI0aMkGzMFNfwKUEBwBQx\nClFfX489e/YgLy8PV69excSJE5GWloZBgwY5FBdPzfe0bj3vLBNJKdF1hBYdg7nHqHa7nRETpRuH\nOUOpokpyLaHFiKPnhbwnbDH59ddfkZ6ejnfeeQd//vOfZRszxTk+Jyiu0NjYiJKSEuTl5aG6uhrj\nxo3DjBkzMGTIEOj1eslqOrSeKeXqMZxQVz05uy9q3QqEJH2w409qdqXkoqSY8F1bTCYdn5jcuHED\n6enpeOuttzB69GjFxkzhhwqKSJqbm7Fv3z7k5eXhxx9/xJgxYzBjxgzcc889HomL1q3T2VYv7pz3\ny1VIyUbrViDkKJPsPgF1u1Jy4R5lqm0qyndf/Pz8OrQ9vnnzJmbPno21a9di7Nixqo2Z8jtUUNzA\nYrHg4MGDyM3NxenTpzF69GikpaVhxIgRzMvILRjkTqJ2u52x1tb6RBgQECDZMRx759La2urRJKrm\nqlosYrPNlOpKyUVLYsIHaXtssVgAABcuXMDp06cxZswYLFmyBKtXr8bEiRMluVZpaSmefvpp2Gw2\nPProo3juuefa/f2hQ4eQmpqKAQMGAABmzpyJF198UZJrdxaooHhIS0sLysrKkJOTg1OnTmHUqFFI\nS0tDXFwcIxLsyYJMolp3u1Ui7ZavkNKVtFstT4SA+/3p5epKyUWLdi9c2PfQ398fX3/9Nd566y18\n+eWXGDhwIBYuXNhukvfkOoMGDcKBAwcQGRmJe++9F9nZ2Rg8eDDzmUOHDmH9+vUoKiry9MfqtGjv\nLfQyAgICMGnSJLz//vs4duwYZs2ahYKCAkyYMAHLli3DV199BbvdzhQMkt7WJLZA0jO1pOtWq5V5\nieW0UuEWUoo1atRyrxqCu2IC/O4ETIoGAwICYLVaUV9fL1nhrbeJCXGxGDx4MJqamrB9+3asXbsW\nZ8+exV/+8hccOHDAo2tVVlZi4MCB6NevHwICApCeno7CwsIOn9PSe6pFtHdo78X4+/tj3LhxGDdu\nHGw2G44fP47c3Fy89NJLuOeeezB58mS8/fbbmDVrFp544gkmvZTd8EiNM3Q2amVKce3Uyc6lubm5\nXdqtTqfTlD0+H1KmLpOKdD6beXcz6bxRTIC2JJm5c+di6dKlSEtLAwAkJSU57copBr4GWBUVFe0+\no9PpcOzYMQwbNgyRkZFYt24dhgwZ4tF1OxtUUGTCz88P9913H+677z60traisLAQjz76KAYPHowf\nfvgB+/btw7hx45gjJXL8Y7FY0NTU1K5nvFIvvFYypYQmUZPJBAC8vWC0gpx1MK52peTDG4woSeyO\nLSZNTU2YN28elixZwogJQYpnVcx9iI2NxaVLl2AwGFBSUoK0tDScP3/e42t3JrR3VtAJOXXqFJ54\n4gmsWbMGX331FZYuXYoTJ04gISEBGRkZ2L17N8xmM4KCghASEtKhZzyfj5aUcH3DtJQgQCbRLl26\nMLUKfn5+7TyjpFihSgHxNmNnIsmFOx5j3iImDQ0NjFkr0JZhOX/+fCxevBizZs2S5brcBliXLl1C\nnz592n2G3GcASEhIQEtLC27evCnLeLwVGpRXgLNnz+LChQtITU1t9+d2ux1nzpxBTk7Dwr5tAAAY\nl0lEQVQODhw4gD59+iAtLQ1TpkxhHlyuq6vUAVpvCG7zZZtpoZCSjZaKKoUyxojAaFlMuOnVZrMZ\n8+fPR3p6Oh566CHZrm21WjFo0CAcPHgQvXv3xqhRozoE5WtqanDnnXdCp9OhsrISDz74IKqrq2Ub\nkzdCBUUj2O12VFVVITc3F3v37sWdd96J1NRUTJs2DUajkfkMV1zcsa5gX5O43Wp1khGTbSZkSKhU\nwaBWjgr5IEepZrO5Q62LlhYPfGJisVjwyCOPIDU1FY888ojsv8eSkhImbXjx4sVYuXIl3n//fQBt\nTbI2btyITZs2wd/fHwaDAevXr6eV+RyooGgQu92OCxcuIC8vD8XFxejWrRuSk5Mxffp0dO3alfkM\nmUDd6TBIDAq12q4XcC9TSqiQUg63W0DbYgJ0dK62Wq2qdaUUgk9MWlpasGjRIkydOhUZGRmafD4p\nHaGConHsdjuqq6uRl5eHPXv2wGAwIDk5GUlJSejWrZug7b6jiULrJpSAdHY0fDVAUmXSab1C31Hz\nLqW7UgrB7VkDtP3uMzIyMGbMGPztb3/T5PNJ4YcKihdht9tx+fJl7Nq1C0VFRfD390dycjKSk5PR\no0cPUT1dbDYbk6KsRRNKQL6Wx9xCSk8y6YiYaLUOxpVOkHJ3pRSCT0xsNhsef/xxjBo1CllZWZp8\nPinCUEHxUux2O2pqarBr1y4UFhbCZrMhKSkJKSkpiIiIEAxct7a2IigoiHmBtYZSwW13q9G9we7F\nk546SnmM8SVa2Gw2PPnkk/jTn/6E5cuXUzHxQjqdoOTk5GD16tWoqqrCiRMnEBsby/u5fv36ISws\njDlHrqysVHik0sHuRllQUACTyYTp06cjJSUFkZGR0Ol0OH78OO666y6EhITAZrOpnhXFh1rxCG6y\ng1BswRsy4qRu0CaHx5jdbmecl8mRa2trK55++mkMGDAAK1eu1MTzSHGdTicoVVVV0Ov1yMzMxNtv\nvy0oKP3798c333yD7t27KzxC+bl58yYKCwuxa9cu1NXVITo6Gnl5eSgoKEBsbKzmUm4B7cQjhOJR\n/v7+sFgssNlsmmjLzIfc3T6l8BgjySDsBmOtra1Yvnw5evXqhZdeeomKiRfT6QSFMH78eKeC8vXX\nX+OOO+5QeGTK8uabb+L111/HxIkTce3aNcFulGql3Gr5CImbSQcAQUFBmtrVEeQWE77rCXWlFPod\nConJypUrERYWhldffVVT95TiOj5rvaLT6TBp0iT4+fkhMzMTGRkZag9JctavX4+PP/4YJ0+eRL9+\n/ZhulK+88gquXLmCSZMmMd0o/f3921nAkICunOLC9ZTSkpgAv1ejt7S0QK/Xo0uXLoxxplSdOqWA\niIlOp1OsR72rHmNCYvLSSy8hODgYr7zyChWTToBX7lAmT56Ma9eudfjztWvXIjk5GYDzHcovv/yC\nXr164fr165g8eTLeffddxMfHyzpupblw4QLCw8PRs2fPDn/X2NiI0tJS5Obm4n//938xfvz4dt0o\nAec9XTzBG2xAhFb9SjQNc3WMSoqJs/HwZYyR40NyH+12O9asWQOz2Yz169dLsphw1s8EALKyslBS\nUgKDwYAtW7YgJibG4+tSfscrBUUMzgSFzcsvv4zQ0FAsW7ZMgZFpD75ulGlpaRg2bFgHcZGqMZaS\nxzPuIHaManZeJKt+d7tpyg0RF+LGoNPpsG7dOsTFxeHUqVOora3Fu+++K4mYiOlnUlxcjA0bNqC4\nuBgVFRVYunQpysvLPb425Xe0dcYgMUJa2dTUhPr6egBtK/V9+/Zh6NChSg5NUwQHByM1NRXbtm3D\n4cOHMWHCBHzyySeYMGECXnjhBZw4cQI6nQ5dunRBaGhou74uQkaEQnhDhb4rY9TpdMwxjtFobHdv\n6urqXLo37oxRq2JCMJlM8Pf3R1hYGLp06YLw8HCsXbsW69evx40bN5CTk8O8i54gpp9JUVERFixY\nAACIi4vD7du3UVNT4/G1Kb/T6QQlPz8fUVFRKC8vR2JiIhISEgAAV69eRWJiIgDg2rVriI+Px/Dh\nwxEXF4ekpCRMmTJFzWFrhsDAQCQkJODjjz/G0aNHkZiYiM8++wzjx4/HihUrcOzYsXYNw1yZQImT\nrJ+fn2YnQU8nam7TMPa9kco12hvEhOzw2GMku7bhw4fj4sWLmDhxIrZu3YoHHnjA4+vx9TO5cuWK\n089cvnzZ42tTfqfTBeVnzJiBGTNmdPjz3r17Y8+ePQCAAQMG4Ntvv1V6aF4H6UY5adIkWK1WHDly\nBLm5uVi5ciVGjhyJ1NRU/PWvf+3Q04U0DGNXopNJUMsV+lJb0hBxIfeGBK6bm5vdNmn0JjFhx3Xs\ndjs2bdqEH374AVu2bIGfnx8ee+wxPPbYYx53nwTE9TMhY3Pn31HE0el2KGqQk5ODP/7xj/Dz88PJ\nkycFP1daWoro6GjcfffdeOONNxQcoeeQbpQbNmxAeXk55s2bh9LSUkycOBFZWVn45z//CZvN1m51\nzu7pUl9fDz8/P82LCbEBkXqMUrT19SYxAdBOTD766COcPHkSmzdv7lBnJEUMRUw/E+5nLl++jMjI\nSI+vTfkdKigSMHToUOTn52PMmDGCnyG2EqWlpfjhhx+QnZ2Nc+fOKThK6SDdKN955x1UVFQgIyMD\nZWVlmDx5MpYsWYK9e/eipaUFgYGBqKqqwrlz55hdirN+8Wogt5hwISm3BoMBYWFhCAoKgs1mQ0ND\nAxoaGphUajbstFtvEBN2NteWLVtw9OhRbN26VTY7nZEjR+Knn35CdXU1LBYLPv/8c6SkpLT7TEpK\nCrZt2wYAKC8vR9euXRERESHLeHyVTnfkpQbR0dFOP8MOGgJggobsLBRvRK/XIy4uDnFxcWhtbcV3\n332HnJwcvPHGG7jjjjtQUVHRrm8Et1+8pz1dPEVMvxU5cdbWlxyLkXulVXdokgYOtBeTTz/9FAcP\nHsSOHTtk7WLp7++PDRs2YOrUqUw/k8GDB7frZzJ9+nQUFxdj4MCBCAkJwebNm2Ubj69CBUUh+AKC\nFRUVKo5IevR6PWJiYhATE4M9e/Zg/vz5uP/++/E///M/2L17N9LS0jB58mSEhITw9otXuj+H2mLC\nhdRsEOFgiwvBZrOpXkjJRcjZeMeOHdi9ezdycnI8akEgloSEBCYJh5CZmdnuvzds2CD7OHwZKigi\nEVNM6QgtTQByc/ToUSxevBglJSWIi4tr141y48aNiIiIaNeNkqzO2dXWznq6eIo7zbuURKfTQa/X\nw2q1IiAgAIGBgbBarYo4GLiCkJjk5uZi165dyMvL04RYU5SBCopI9u/f79G/FxM07CyMGjUKR48e\nxV133QWgbXIcPHgw/v73v+PFF19kulE+8MADHbpROhIXYnPiKaR5l9T9VqSEL+OMu3MxmUySNw1z\nBSG3g4KCAnz22WfIz8/XbJsEijx02kp5NRg/fjzWrVuHESNGdPg7q9WKQYMG4eDBg+jduzdGjRrV\noZLX1xDbjVLIGdkdcfEmMRGTYs1tGkbujRJV+nxisnv3bnz44YcoKChASEiIbNenaBMqKBKQn5+P\nrKws3LhxA+Hh4YiJiUFJSQmuXr2KjIwMpv6lpKSE8RpavHgxVq5cqfLItYPYbpSe2O4r1bzLE1wR\nE75/S+xx2PbynvQu4YNr6km+e+/evXj33XdRWFgIo9Eo2fUo3gMVFIrm4OtGmZiYiNTUVIfdKB2J\nS2cXEy7cpmFSZdMJicnBgwfx9ttvo7CwEOHh4W5/P8W7oYJC0TRiulE66+lC4g3eIiZSxx24fV3c\nzaYTEpOysjKsXbsWRUVF6Natm6Rjp3gXVFC8mJs3b2L27Nn417/+hX79+mHnzp3o2rVrh891pnbH\n3G6U06ZNQ2pqKvr27cuICzuuQIonScxEi9l2cooJF76OlGTn4igmxW5/HBoaytzHI0eOYM2aNSgs\nLOz0zeoozqGC4sWsWLECPXr0wIoVK/DGG2/g1q1beP311zt8rrO2O66trcUXX3yBvLw8XL9+nelG\nOXDgQOh0OuTn52P06NEwGo2wWq2aSrclKCkmXMQeGwp11Tx+/Dj+/ve/o7CwkLfnDsX3oILixURH\nR6OsrAwRERG4du0axo0bh6qqqg6f84V2x6QbZV5eHlNEevz4cZSWljLuBFL2dJECIiaBgYGq12qw\nxYUrvuTP2GJy4sQJPP/88ygoKJDcvsQXd96dBSooXky3bt1w69YtAG0TQvfu3Zn/ZjNgwACEh4d3\n6nbHbF566SV89NFHGD9+PC5evMjbjZJ7LKa0uGhJTLiwjw2J3X5AQADOnTuHoUOH4uzZs1i2bBny\n8/PRq1cvya/v6ztvb0abEUoKg1CF/j/+8Y92/63T6QQnwqNHj7ZrdxwdHd3p2h0T3nrrLezatQsn\nT57EH/7wB6Yb5YYNGzp0oxSy3Ze7loP0hdGK5QsX0jSMHIMZDAa0tLTg2WefxY8//gij0YjVq1fL\nFoAvKipCWVkZAGDBggUYN24cr6AAwk30KOpAdyheTHR0NA4dOoQ//OEP+OWXXzB+/HjeIy82nb3d\n8YULF9C1a1f06NGjw99ZLBYcPHgQubm5OH36NEaPHo3U1FSMHDmSd+dis9kkr+XQupgQzGYzLBZL\nu2Ous2fPYsWKFRg7diwOHTqEb7/9FvPmzcPGjRslvTbdeXsvdIfixaSkpGDr1q147rnnsHXrVqSl\npXX4TFNTE2w2G4xGI9PueNWqVSqMVhkGDhwo+HekG2VCQgJaWlpQVlaG7OxsPPvss4iLi0NaWhri\n4uKcNsVyV1y0ZkYpBJ+YnDt3Dn/729/w+eefY8CAAQCAX3/91ekCRgi68+6c0B2KF3Pz5k08+OCD\n+Pe//90ueMmu0P/5559x//33A2izHZk3bx6t0OfA7kZZUVGBESNGIC0tDX/961+ZuhW+QkFXOi5q\n3YySYDabYTabERoayvxc58+fR0ZGBj777DPcfffdso+B7ry9FyooFAoLm82G48ePIzc3F0ePHsU9\n99yDtLQ0jBkzhvH+crUK3ZvF5OLFi1i0aBG2b9+OQYMGKTKOFStW4I477sBzzz2H119/Hbdv3+4Q\nQ+HuvKdMmYJVq1ZhypQpioyRwg8VFApFgNbWVpw4cQK5ubkoKyvD4MGDkZaWhnHjxjFHVs6q0L1F\nTCwWC0wmE0JCQpgCx+rqaixYsABbt27FkCFDFBsL3Xl7L1RQKKIpLS1lzC0fffRRPPfccx0+k5WV\nhZKSEhgMBmzZsgUxMTEqjFR62N0o//nPf2LAgAFIS0vDxIkTERwcDKCjuOj1erS2tiIoKEjTNu58\nYnL58mXMmzcPn3zyCYYOHaryCCneAhUUiihsNhsGDRqEAwcOIDIyEvfee28H+/3i4mJs2LABxcXF\nqKiowNKlS1FeXq7iqOXBbrfjzJkzyMnJwYEDB9CnTx+kpqZiypQpjGX7+fPncccddzDBfal7ukgF\nn5hcvXoVc+fOxfvvv99pFgQUZVC+iTfFK6msrMTAgQPRr18/BAQEID09HYWFhe0+U1RUhAULFgAA\n4uLicPv2bdTU1KgxXFnR6XQYOnQo1qxZg6NHj+Lll1/Gzz//jLS0NDz00EPYuHEjpk6diu+//x6h\noaEwGo3o0qULU8xYX1/PmCyqCZ+YXLt2DfPmzcN7771HxYTiMlRQKKIgdiaEPn364MqVK04/c/ny\nZcXGqAbsbpRHjhzBggULsGbNGgwfPhzvv/8+PvvsM9TW1sLf3x/BwcEwGo0IDg6G3W7vIC5KHhYQ\nB2a2mPz666+YO3cu3nnnHYwcOVKxsVA6D7QOhSIKsXUX3ElRCwaMSnHq1ClkZmZiy5YtuP/++5lu\nlPPmzWO6USYmJqJ79+4d2vk2Nja63DDMXUhdDVtMbty4gblz52LdunX4y1/+Ist1KZ0fKigUUURG\nRuLSpUvMf1+6dAl9+vRx+JnLly8jMjJSsTGqTVBQED744AOkpKQAaPOaWr58OZYtW8Z0o3zkkUeY\nbpRJSUno2bNnB3FpamqSzRmZT0xu3ryJuXPnYu3atbjvvvskuQ7FN6FBeYoorFYrBg0ahIMHD6J3\n794YNWqUw6B8eXk5nn766U4ZlPcEdjfKgoIC2Gw2JCUldehGybaAkUpc+LpW3r59G7Nnz8aqVasw\nadIkKX9Uig9CBYUimpKSEiZtePHixVi5ciXef/99AEBmZiYA4Mknn0RpaSlCQkKwefNmxMbGqjlk\nTcPXjTIhIQEpKSno06cPIxzsniXuigufmNTV1SE9PR3PP/88pk2bJtvPSfEdqKBQKBrBWTdKwL2e\nLlarFU1NTe3EpKGhAenp6fjP//xPJCUlKfYzUjo3VFAoFA3irBslIK6nC5+YNDY2Ys6cOXjiiScw\nY8YM1X5GSueDCgqFonG43SgnTpyItLQ0REdH84qLzWZjMsXMZjNCQkIYMWlqasLcuXORkZGBBx54\nQPKx5uTkYPXq1aiqqsKJEycEjzzFuC5QvA9ah0LxCkpLSxEdHY27774bb7zxRoe/P3ToEMLDwxET\nE4OYmBi8+uqrKoxSHoxGI9LT05GTk4P9+/dj+PDhWLduHSZOnIhXXnkFZ86cAdCWZUYKKYE2s0eg\nrY/Jjh07cP36dcyfPx8LFy6URUwAYOjQocjPz8eYMWMEP2Oz2ZhY2w8//IDs7GycO3dOlvFQlIWm\nDVM0D5mA2LYvKSkp7TLMAGDs2LEoKipSaZTKEBISgpkzZ2LmzJmC3SjNZjNWrFiBvXv3IigoCE1N\nTdi+fTueeuopDB48GDabDbW1tQgPD5d8fNHR0U4/w3ZdAMC4LnB/nxTvg+5QKJpHjO0L4HvtYIOD\ng5Gamopt27bh8OHDmDBhAt58802mC+Xp06cBACNGjEBYWBhef/11PPXUU9i5cyeioqKQnZ2tyrjF\nuC5QvBO6Q6FoHr4JqKKiot1ndDodjh07hmHDhiEyMhLr1q1T1HJdbQIDA9GrVy9UVFRgy5YtCA8P\nR3Z2NpYvX46GhgY888wzeOyxx6DT6bBgwQLU1dW57SUm1G1x7dq1SE5Odvrvfck9wdeggkLRPGIm\noNjYWFy6dAkGgwElJSVIS0vD+fPnFRiddvjHP/6BjRs3YubMmQCASZMmwWq1orS0FImJie3uY1hY\nmNvX2b9/v0fjFOO6QPFO6JEXRfOImYCMRiMMBgMAMD3jb968qeg41Wbnzp2MmBD8/f2RlJSkyq5A\n6Ahy5MiR+Omnn1BdXQ2LxYLPP/+csauheDdUUCiaR8wEVFNTw0xglZWVsNvt6N69uxrDVQ0tHCXl\n5+cjKioK5eXlSExMREJCAoC2HiuJiYkA2kRuw4YNmDp1KoYMGYLZs2fTgHwngdahULwCZ7YvGzdu\nxKZNm+Dv7w+DwYD169fjz3/+s8qjplB8CyooFAqFQpEEeuRFoVAoFEmggkKhOGHRokWIiIjA0KFD\nBT+TlZWFu+++G8OGDcOpU6cUHB2Foh2ooFAoTli4cCFKS0sF/764uBgXLlzATz/9hA8++ABLlixR\ncHQUinaggkKhOCE+Ph7dunUT/PuioiIsWLAAABAXF4fbt2+jpqZGqeFRKJqBCgqF4iF8lfyXL19W\ncUQUijpQQaFQJICbLKmFmhAKRWmooFAoHsKt5L98+TIiIyNVHBGFog5UUCgUD0lJScG2bdsAAOXl\n5ejatSsiIiJUHhWFojzUHJJCccKcOXNQVlaGGzduICoqCi+//DJaWloAtFXpT58+HcXFxRg4cCBC\nQkKwefNmlUfsHmK7Lfbr1w9hYWHw8/NDQEAAKisrFR4pRavQSnkKhQIAqKqqgl6vR2ZmJt5++21B\nQenfvz+++eYbn/NKoziH7lAoFAoAcd0WCXQdSuGDxlAoFA3jrEr/0KFDCA8PR0xMDGJiYvDqq6/K\nPiadTodJkyZh5MiR+PDDD2W/HsV7oDsUCkXDLFy4EE899RQefvhhwc+MHTsWRUVFor7P026LAHD0\n6FH06tUL169fx+TJkxEdHY34+HhR/5bSuaGCQqFomPj4eFRXVzv8jCvHT552WwSAXr16AQB69uyJ\nGTNmoLKykgoKBQA98qJQvBqdTodjx45h2LBhmD59On744QdJvldIpJqamlBfXw8AaGxsxL59+xya\nZlJ8CyooFIoXExsbi0uXLuG7777DU089hbS0NLe/S0y3xWvXriE+Ph7Dhw9HXFwckpKSMGXKFEl+\nFor3Q9OGKRSNU11djeTkZJw+fdrpZ2lKL0VN6A6FQvFiampqmOOpyspK2O12KiYU1aBBeQpFwzir\n0s/NzcWmTZvg7+8Pg8GAHTt2qDxiii9Dj7woFAqFIgn0yItCoVAokkAFhUKhUCiSQAWFQqFQKJJA\nBYVCoVAokkAFhUKhUCiSQAWFQqFQKJLw/wG01aU/LOMn1wAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x109e81f60>"
+        "<matplotlib.figure.Figure at 0x1090d4e10>"
        ]
       }
      ],
@@ -675,12 +736,12 @@
       "# Generate random 2D-patterns\n",
       "mu_vec = np.array([0,0])\n",
       "cov_mat = np.array([[1,0],[0,1]])\n",
-      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)"
+      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 100000)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -712,7 +773,7 @@
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -758,7 +819,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 15
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -786,28 +847,29 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "h = 0.1, p(x) = 0.016\n",
-        "h = 0.2, p(x) = 0.0305\n",
-        "h = 0.3, p(x) = 0.045\n",
-        "h = 0.4, p(x) = 0.06175\n",
-        "h = 0.5, p(x) = 0.078\n",
-        "h = 0.6, p(x) = 0.0911666666667\n",
-        "h = 0.7, p(x) = 0.106\n",
-        "h = 0.8, p(x) = 0.117375\n",
-        "h = 0.9, p(x) = 0.132666666667\n",
-        "h = 1.0, p(x) = 0.1445\n",
-        "h = 1.1, p(x) = 0.157090909091\n",
-        "h = 1.2, p(x) = 0.1685\n"
+        "h = 0.1, p(x) = 0.0167\n",
+        "h = 0.2, p(x) = 0.03035\n",
+        "h = 0.3, p(x) = 0.0453333333333\n",
+        "h = 0.4, p(x) = 0.061475\n",
+        "h = 0.5, p(x) = 0.07758\n",
+        "h = 0.6, p(x) = 0.093\n",
+        "h = 0.7, p(x) = 0.107142857143\n",
+        "h = 0.8, p(x) = 0.1210875\n",
+        "h = 0.9, p(x) = 0.134522222222\n",
+        "h = 1.0, p(x) = 0.14695\n",
+        "h = 1.1, p(x) = 0.159381818182\n",
+        "h = 1.2, p(x) = 0.17025\n"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Based on the results, we can say that the best window-width would be h=1.1, since the estimated result is close to the actual result ~0.15915."
+      "Based on the results, we can say that the best window-width would be h=1.1, since the estimated result is close to the actual result ~0.15915.  \n",
+      "Thus, for the benchmark, let us create 100 evenly spaced window width in the range of 1.0 to 1.2."
      ]
     },
     {
@@ -818,6 +880,17 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "widths = np.linspace(1.0, 1.2 , 100)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 25
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -850,7 +923,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 17
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -897,7 +970,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 18
+     "prompt_number": 27
     },
     {
      "cell_type": "code",
@@ -913,16 +986,23 @@
       "\n",
       "    # plot bars\n",
       "    y_pos = np.arange(len(benchmarks))\n",
-      "    y_pos = [x for x in y_pos]\n",
       "    plt.yticks(y_pos, bar_labels, fontsize=16)\n",
-      "    plt.barh(y_pos, benchmarks,\n",
+      "    bars = plt.barh(y_pos, benchmarks,\n",
       "             align='center', alpha=0.4, color='g')\n",
       "\n",
       "    # annotation and labels\n",
+      "    \n",
+      "    for ba,be in zip(bars, benchmarks):\n",
+      "        print(ba.get_width())\n",
+      "        plt.text(ba.get_width() + ba.get_width()*0.09, ba.get_y() + ba.get_height()/2,\n",
+      "                '{0:.2%}'.format(benchmarks[0]/be), \n",
+      "                ha='center', va='bottom', fontsize=12)\n",
+      "    \n",
       "    plt.xlabel('time in seconds for n=%s' %n, fontsize=14)\n",
       "    plt.ylabel('number of processes', fontsize=14)\n",
       "    t = plt.title('Serial vs. Multiprocessing via Parzen-window estimation', fontsize=18)\n",
       "    plt.ylim([-1,len(benchmarks)+0.5])\n",
+      "    plt.vlines(benchmarks[0], -1, len(benchmarks)+0.5, linestyles='dashed')\n",
       "    plt.grid()\n",
       "\n",
       "    plt.show()"
@@ -930,7 +1010,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 100
     },
     {
      "cell_type": "markdown",
@@ -965,12 +1045,23 @@
      "language": "python",
      "metadata": {},
      "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "254.60583067900006\n",
+        "207.03454153399798\n",
+        "206.59588822500154\n",
+        "206.7515160139992\n",
+        "210.72415902600187\n"
+       ]
+      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAoIAAAIACAYAAAAWiA3GAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nznewV8UAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAoUAAAIACAYAAAD0VBa/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYFFIRERERAD+D0Z2U+HPVJKMAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10b49e438>"
+        "<matplotlib.figure.Figure at 0x10d966e48>"
        ]
       },
       {
@@ -993,7 +1084,7 @@
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 102
     },
     {
      "cell_type": "markdown",
@@ -1025,7 +1116,7 @@
       "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel.\n",
       "However, on my particular machine it didn't make any differences if we submitted 2, 3, 4, or 6 processes in parallel.  \n",
       "\n",
-      "In practice,  it doesn't make much sense to submit more processes than the CPU supports (e.g., here 6 processes for a 4-core CPU). For larger benchmarks, this would to a slower performace, because of an additional overhead for communicating between the additional processes."
+      "We notice a slightly slower performance when we were running 6 parellel processes: In practice, it doesn't make much sense to submit more processes than the CPU supports (e.g., here 6 processes for a 4-core CPU). For larger benchmarks, this would lead to a slower performace, because of an additional overhead for communicating between the additional processes."
      ]
     }
    ],

From d2cd95a0ec3133be2b5981eb584e7acc8f6b5390 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 20 Jun 2014 15:53:01 -0400
Subject: [PATCH 112/248] upd. benchmark

---
 tutorials/multiprocessing_intro.ipynb | 132 ++++++++++++++------------
 1 file changed, 73 insertions(+), 59 deletions(-)

diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index e7478e6..5f536e7 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:d007cd318f83f1063ca5b837d93bfb94ad30a9ac9350652246e0099b6aef6373"
+  "signature": "sha256:4b992169e84879b172c1ddfadf3f353a3812b16f9d4e448fc4aa443cbd3db9da"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -39,7 +39,7 @@
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -250,11 +250,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "['41CkH', '5gOMX', 'qIJT3', '9CzRU']\n"
+        "['BJWNs', 'GOK0H', '7CTRJ', 'THDF3']\n"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -330,11 +330,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "[(0, 'pJpCf'), (1, '20o0V'), (2, 'DlB7w'), (3, 'PxH3O')]\n"
+        "[(0, 'h5hoV'), (1, 'fvdmN'), (2, 'rxGX4'), (3, '8hDJj')]\n"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -361,11 +361,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "['pJpCf', '20o0V', 'DlB7w', 'PxH3O']\n"
+        "['h5hoV', 'fvdmN', 'rxGX4', '8hDJj']\n"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -481,8 +481,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished and return the results. The `async` variants, in contrast, will submit the processes to be run in the background without locking the main program, which can be quite useful for certain applications.  \n",
-      "However, if we use this asynchronous approach, we need to use the `get` method in order to obtain the `return` values."
+      "The `Pool.map` and `Pool.apply` will lock the main program until all a process is finished, which is quite useful if we want to obtain resuls in a particular order for certain applications.   \n",
+      "In contrast, the `async` variants will submit all processes at once and retrieve the results as soon as they are finished. \n",
+      "One more difference is that we need to use the `get` method after the `apply_async()` call in order to obtain the `return` values of the finished processes."
      ]
     },
     {
@@ -667,7 +668,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAGUCAYAAAASxdSgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1090d4e10>"
+        "<matplotlib.figure.Figure at 0x10905d0f0>"
        ]
       }
      ],
@@ -736,12 +737,12 @@
       "# Generate random 2D-patterns\n",
       "mu_vec = np.array([0,0])\n",
       "cov_mat = np.array([[1,0],[0,1]])\n",
-      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 100000)"
+      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -773,7 +774,7 @@
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -802,9 +803,31 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Below, we will set up a benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function."
+      "Below, we will set up benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function.  \n",
+      "We will be using the `Pool.apply_async` function to take advantage of firing up processes simultaneously: Here, we don't care about the order in which the results for the different window widths are computed, we just need to associate each result with the input window width.  \n",
+      "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to to sort our list of results later."
      ]
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def parzen_estimation(x_samples, point_x, h):\n",
+      "    k_n = 0\n",
+      "    for row in x_samples:\n",
+      "        x_i = (point_x - row[:,np.newaxis]) / (h)\n",
+      "        for row in x_i:\n",
+      "            if np.abs(row) > (1/2):\n",
+      "                break\n",
+      "        else: # \"completion-else\"*\n",
+      "            k_n += 1\n",
+      "    return (h, (k_n / len(x_samples)) / (h**point_x.shape[1]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -814,12 +837,15 @@
       "\n",
       "def multiprocess(processes, samples, x, widths):\n",
       "    pool = mp.Pool(processes=processes)\n",
-      "    return [pool.apply(parzen_estimation, args=(samples, x, w)) for w in widths]"
+      "    results = [pool.apply_async(parzen_estimation, args=(samples, x, w)) for w in widths]\n",
+      "    results = [p.get() for p in results]\n",
+      "    results.sort() # to sort the results by input window width\n",
+      "    return results"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -837,8 +863,9 @@
       "results = []\n",
       "\n",
       "results = multiprocess(4, x_2Dgauss, point_x, widths)\n",
-      "for w,r in zip(widths, results):\n",
-      "    print('h = %s, p(x) = %s' %(w, r))"
+      "\n",
+      "for r in results:\n",
+      "    print('h = %s, p(x) = %s' %(r[0], r[1]))"
      ],
      "language": "python",
      "metadata": {},
@@ -847,22 +874,22 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "h = 0.1, p(x) = 0.0167\n",
-        "h = 0.2, p(x) = 0.03035\n",
-        "h = 0.3, p(x) = 0.0453333333333\n",
-        "h = 0.4, p(x) = 0.061475\n",
-        "h = 0.5, p(x) = 0.07758\n",
-        "h = 0.6, p(x) = 0.093\n",
-        "h = 0.7, p(x) = 0.107142857143\n",
-        "h = 0.8, p(x) = 0.1210875\n",
-        "h = 0.9, p(x) = 0.134522222222\n",
-        "h = 1.0, p(x) = 0.14695\n",
-        "h = 1.1, p(x) = 0.159381818182\n",
-        "h = 1.2, p(x) = 0.17025\n"
+        "h = 0.1, p(x) = 0.016\n",
+        "h = 0.2, p(x) = 0.0305\n",
+        "h = 0.3, p(x) = 0.045\n",
+        "h = 0.4, p(x) = 0.06175\n",
+        "h = 0.5, p(x) = 0.078\n",
+        "h = 0.6, p(x) = 0.0911666666667\n",
+        "h = 0.7, p(x) = 0.106\n",
+        "h = 0.8, p(x) = 0.117375\n",
+        "h = 0.9, p(x) = 0.132666666667\n",
+        "h = 1.0, p(x) = 0.1445\n",
+        "h = 1.1, p(x) = 0.157090909091\n",
+        "h = 1.2, p(x) = 0.1685\n"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -884,12 +911,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "widths = np.linspace(1.0, 1.2 , 100)"
+      "widths = np.linspace(1.0, 1.2, 100)"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 25
+     "prompt_number": 18
     },
     {
      "cell_type": "code",
@@ -899,7 +926,7 @@
       "\n",
       "mu_vec = np.array([0,0])\n",
       "cov_mat = np.array([[1,0],[0,1]])\n",
-      "n = 100000\n",
+      "n = 10000\n",
       "\n",
       "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, n)\n",
       "\n",
@@ -923,7 +950,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -970,7 +997,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 27
+     "prompt_number": 20
     },
     {
      "cell_type": "code",
@@ -993,8 +1020,7 @@
       "    # annotation and labels\n",
       "    \n",
       "    for ba,be in zip(bars, benchmarks):\n",
-      "        print(ba.get_width())\n",
-      "        plt.text(ba.get_width() + ba.get_width()*0.09, ba.get_y() + ba.get_height()/2,\n",
+      "        plt.text(ba.get_width() + 2, ba.get_y() + ba.get_height()/2,\n",
       "                '{0:.2%}'.format(benchmarks[0]/be), \n",
       "                ha='center', va='bottom', fontsize=12)\n",
       "    \n",
@@ -1002,6 +1028,7 @@
       "    plt.ylabel('number of processes', fontsize=14)\n",
       "    t = plt.title('Serial vs. Multiprocessing via Parzen-window estimation', fontsize=18)\n",
       "    plt.ylim([-1,len(benchmarks)+0.5])\n",
+      "    plt.xlim([0,max(benchmarks)*1.1])\n",
       "    plt.vlines(benchmarks[0], -1, len(benchmarks)+0.5, linestyles='dashed')\n",
       "    plt.grid()\n",
       "\n",
@@ -1010,7 +1037,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 100
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1045,23 +1072,12 @@
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "254.60583067900006\n",
-        "207.03454153399798\n",
-        "206.59588822500154\n",
-        "206.7515160139992\n",
-        "210.72415902600187\n"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAoUAAAIACAYAAAD0VBa/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYFFIRERERAD+D0Z2U+HPVJKMAAAAAElFTkSuQmCC\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAowAAAIACAYAAAAIQT11AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10d966e48>"
+        "<matplotlib.figure.Figure at 0x10f1dd160>"
        ]
       },
       {
@@ -1084,7 +1100,7 @@
        ]
       }
      ],
-     "prompt_number": 102
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1096,7 +1112,7 @@
     },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 1,
      "metadata": {},
      "source": [
       "Conclusion"
@@ -1113,10 +1129,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel.\n",
-      "However, on my particular machine it didn't make any differences if we submitted 2, 3, 4, or 6 processes in parallel.  \n",
-      "\n",
-      "We notice a slightly slower performance when we were running 6 parellel processes: In practice, it doesn't make much sense to submit more processes than the CPU supports (e.g., here 6 processes for a 4-core CPU). For larger benchmarks, this would lead to a slower performace, because of an additional overhead for communicating between the additional processes."
+      "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel. However, on my particular machine it 6 processes didn't lead to a further performance improvement, which makes sense for a 4-core CPU. \n",
+      "This even lead to a slightly worse performance, because of an additional overhead for communicating between the additional processes."
      ]
     }
    ],

From 1f2b02b7235aab9d92f5f6d86bf5e6f3f6e5965b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 20 Jun 2014 19:38:59 -0400
Subject: [PATCH 113/248] upd. parallel fig.

---
 Images/multiprocessing_scheme.png | Bin 106996 -> 100484 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/Images/multiprocessing_scheme.png b/Images/multiprocessing_scheme.png
index 224e15960060e7de588be246746704056855b9d5..9cb130d2b2e6732d8fb198a3e0a263c68c97bf62 100644
GIT binary patch
literal 100484
zcmeFYbz59dw=D{U1PvN2XhLug?he6&ySuvuXhN{y!5xCT1$SsHXyfkg(r7~ir}>?|
z@4NRt@4a8(o<Deay4UJjwPsDJnq!QxuZq%dQQxD&!NI+i`68hL2lr|Y4h|sz`3>wF
z{W&d9I5<>MYjN?fGUDPCUtJt6tnJL<;J&=gO!8DyUBC|owR5+R<9wu`x_bW&g<_Kb
z3m&3!n9TRD2q8h#9ulvm-eJ>en5lKaONV|mm3)U5_`_rl1&sBzzgt;Ca%Uyd7Uish
z^Kv!qaX@=*VXtv+!Sc*+t_AKZ^amo)O$+`zM~>FJ)1z)Z9NgjFTLeNZcnmH0t_=$d
zMCm&TglTBIU}|ddm;TzDQ|9kxtxJrZU#VSZpZ#M$a*Ka@1BWU(!zBQqCfGqpP_6AF
z`uvPIbd^Z=IfU&qehx}*`Nyr^D!ZGaU53`dlw|7docG#eL~R^dk*!}>)2KG`A`x@+
zQmED~^L&xa+tp;!;C60lr>5>Q<l$J~r5cXxQRmo&eU^MjbQ%UmfJ08`<!y7ph=A}~
zWx;tBT5g9UhRK)|V$Y>m@9h7zxWA}Xg_y80B=jh3{PdCsUDcsSvl4u-a!ithr_#1h
zwTeBdAUQeFp53*AbS|us0aPCa|9*Q=zo0$_Wr;>+X|LHGm$S6v!`*z+NhhuCNK}Wb
zvwtJ9vr(>eMga$Y4Oh3CaY+_pXAsxBiB5wSaeQp&3ZHGu%&^b>kp07Ps56B3Hg)*)
zL4&`LKsbO>29Moba;LDUP)=fG1RWz8WcGNA^}N-^Ydt#H!<doXMQ{<kudckGfD#mD
z)fy}ip!JK{3^mAqX2YM5iw21ZkZJqQ%H0soFPQghlMK>dM?pAo%2%Hpgufy99qD4R
zq5kb?v#N-#a*g@??P>3x98E9WVMAW@qw4MrdW@M1RW2h@-dd1dHv-NtB)8wF90AO%
z2#iK=Nxri?y;`M!3;O;A>s8V#<}V1=B8Y!Ph2~){$Tkn}5oqHCHy!Y898vKHm(v?P
zM9r?ZPH5=grA1Ify5(5mR|Bk(f1G3C<%*X6!q3M{6`RE()<O0b7yZif?p2?7*e|R&
z5y&`oF(G|mX^!|;!7EC)_btf!f%m_W#wj@7Ji_}0VW%KW8NZ!}YxurU%};_X4IEyV
z@WRVLBIsJ$kZr;j!e|5%Y!IKrF$Nm~<B>)FBEOoD(n2P_kP!Q&^$8yPgKg}`e1sCo
zv|kwc#LZD(Vx6~Ny$Q`RP9enaC)x_tj@&TOFrGD$Wt~YeP0^4i*yXaoVh@1)C@`6+
zDb%7V7xqBm!cLE3>eVocG19G0u6Z?Ye9TXRG0;V{N#X=(BGmirz*&jdh&dP8`u(Wq
z?FRLQR~!B6yR#pd-LB^*z8^YJ{Ym`E)?&!}J86EQ=pfqu!2FOz@yYI!8rFN{J><tA
zY#@hnK(UhB#}F*<UOf}Kn&^4aHW?CX#rStJA)g3TKGCJt1*pl`eAK5Fq;CHZKgee$
z?Lw$6VxUy{DVxHO3NudlYXU9xJ0)2XNwEb*UfE_5ZxL_t0+qML4%1qtDJ_{gO6?MU
zvVJVm@$AW$$y<XJ@nx~LiATvkR7weR$)3rE%-J-CAN*yX3ZYF1%H1N}ssVY`8aYxM
zbwI&j8OgNVM3bK;X(nMNft#Ft%2aJ}0db8uN+lu()ss{OAKr^i6?jiJ)hX2REt)T~
zl1g*uKTO3R^j(qfw_l-{Q<>}T(C(1zyx*xEQh5VpHM(P0Au5QBk8F*Mx#ZYw25o_s
zxT>rTp4;dWA@mOPzKI_bEfV1qV@l=K9cSLnWR}{ODowN6P}>;WEZ98EFqcJvia?%O
zpiD!K=WS@{VRcK`x2AiW>z+fw!}#}U?>pWTy;pyq%+b$Dl;)5&n6{I)$1zZ6rGr+F
zsI#N9S8u?Yn0h**W<gaO!JAW2c~ZnQA(X#TCRkxwT&rc7r&v3Ns~^X#%Prcd*Qn|#
zrX<Ir#bQ{ce3m1moL!Nf-znXx>p#>rPO)edA)4M@oD-hYX`E%8w_!?I8e29&pQn?@
z-_K*(;2eG-w53a39hNB*ClfWOpgKRjs%F#zsso|h?pjfElySBW<W?_hYnMyS{^sow
z`0JbJoep6@6O7P`$i!d4H)K3vRAoA1B-f_XeywF+cUXPjv)G$vt8eNz5Mr}sT02&_
zvRhwK!nEemdE&>PreD2+->TROy1}^l_(1)@h9ns3721w*JMZQ}+ZyCIgMGBW(KL1u
zxrIdILsK&)Q?!<;Jw`Y7b}SDKo#32cnu*Y5!g-^K#3h#5j5*TK%PD7CeJOEE=jeV!
zVJ>RVqu0K7WnOUMvFU*UQw}p$I$|K9D1Prf2+iPUa+PMRW>sU{t;w0qS&<N*P?%7Z
z5N?M@N0}eXo$({WqsRTq<=pw+{oI2RA`fCY%06m7q7<SAk`UTw6r9)Yh&|o#-K{^!
zStboX=}qG!h(X0Yf(1}G(e?4}T?v^PIm)Szy_?j%0A8MmOeT^N;=Qe3V*XD4m5zwQ
zm&5mz8kFvnoRL<L^pQ-KluOoP)UMr@iqwguC=|peC&u&n<l$0sJGmkkx^=vXx^>FT
zu9sAMyy#NDQ!)&y{AJj!?MzQbOisFn^&Ex^EZLE-yfSR|+(Dd+Zw-Bj+v{i8i@MOi
zGl#|^CI|fqnvh8D)eke3sg%K!*+NhftzUQlV+an|E=l;6kVnT}GEp*W9aMK$Hzh`8
z9A3?!&n`&Q&eFq?$12`3WIc1Lj+-ko5i3vYs%_Dl68$89{U!2KI#r-HTCL5WRZD5}
z>>=e5?A@#Fv~7oB(!tBY2LPu9r>DDRhsRS0w43wP7Na6~wo-4YS<zF!yJh3L<0!_S
z!v4#``LNV*lsWO5L-^{n@^9tp4kN4Dv)z8NW-;ZBcBV0147)x%JiC(>i`ojCrR0wW
z6W1ogo8g;ERLgNkYubLAkd={}pp*83n}P?=bq{3E<&}e$ORZEBZG(mJ%60vUoe^*@
zxUp69xI@EO*VzcuI>s!f?sM^<cG)T$>6xO#{KJ6vF=@N)hdzxbnTrEUbDeAU{!{Q#
zulkUL$lj5(`TxEBYFi;{-)FqMQQiYO$~|o9wDdRhmpJ)y5et$ImM$0o#<<V$PtS1|
zb0=^CqvHB&wwwnXAJ?zNeQ?VTzOv^v-y!mR;Jw%!xFbKyLKj%@4)-fPpn1%`X&BcD
z*U6b(o(*fr=d$*Z_gOpmK9xy2M#?0d{_J}dd6Sw}J7g&biXFqtNaxM=1c1f*gD)b&
z2txTZ-Go}|wkI<yt1DNB1Cpx@E}C6Rmf9bn>d9r6mAv}=ewlvlCn1kfvt+GVNruM&
z>x23;`}PB;+Q+1=is+8letY-&4=k%UEq*)E&zZ!pBHrAeGI#hq8!QVR_^m!pAeCSm
z2#<W@d9->oJ>JTtK~<v9DiyAHoVlB`C~3bs{yk>|s{Lsh6VfEk@|*~%QtEjPDgiM_
z)kz&nS4e{*A(1QJYOeauha;1xwt_q9o*HfwC(5z`o)7JoCWF1_wi8FG!m_Ky9lZCm
zH}-pG``c5V)@Ox9;j3jG!Z!hrIS-~+1F;9kN_R?$*-^rtz9>+z7qz{`?%Hk8<-=z_
zZ2)XfiQ@D{+Z7HD4fo{_9xg3|5DrcRPDVmh%?tiG6QwP9rs-KTP%17i=nWCWhj;JI
z=z*i>+{?d&6LVT{Zdt@$5j1Y5xetF$bsv_e*PK%SC@F(OCmByYq38KL=a~t35ZDtu
zYrSm+E$n5qgCG?k%d`5~+APk;*>7_?p!dlKW{KsI-?3Q{q`Hs^;VF#}|DO*5M3mLX
zk$#JL|L!6@<yWj~r<3sia{s@6Y?Oqo)s#i`{l?*6qy6h@7qI^5-#<|MqtV8<B)G-a
zfBQtVxyo1n$0T2dwFvtje58$Fb@p$sGU~Nr{P!38kEx_kAq;~ipWA#oA5L4R{LXyV
z7QM*t$3qJZpYP}D9jI@l)`u9X<oB}u0o@)EObsdhzI}L_`D@T!?x@o#fswdz7QtHd
z)zxGy1kYt>X};@eRk`Tw^B|gh&N)3-M%T_oo3euE$F=9oq?-Yuo0@}2_OTQx?rvnl
zfWA<XqA3~Z;q;&3Z|(UQ1XD1hr;s8zc)sazWYpbUa%g3`XkB|eO(p~X3U%{8Dz6<g
zYUcJN2*6bmU^}Vq!8jOo1h^*Szop4=5_9$+)?LLJbhS85&f<-jhiBnKbZFWLQSmHU
z6Mlw(2DtodDtg+$Lk!6t=SjK_L`wp^8!$_uhjgJCod2i;qB2ZBF*mLM=tdyMJ?(rv
zaXcViyOV;fcx^?wW*zdMwbc@@oiwfq?OpKx=!@zaq{-GhoMQ2Z4A+eA%Nc;<7P&$j
z_Wuks6EFFWssjfccK8sS?>0h<V1DNvYgYTRmPX1SzmoDiFLgfZeuLS$mG<&bo(P_D
zoH&4UR*TZUy8Df4N{W+i<l&?oi$axGAA{`d7~fHO-GW8>s5RH~MU21O*2A#jQ{1e9
z&k1i7D1gNQaTPM=Sy&4a;%|Stu2Oo2WIyvJrTX<!lEuzLX!`P>f(Z<0%dSxoOqujF
z!6s$@OyfO*U80#B=b^!E%N&O%?mw=+6#~Z~6H4ka-$b=^zzLfxIyTStFY&_s`(;;<
zf|BnhZqc}1szev?`V&4EY|_=p*jly~^^@0ghMmZ+yNs}}j1cjK5$ptaXwgw?6R`<t
z5PAN{K9bHo81$nYDa(`vA1TwJ6!rYq@G?S0AhxelHnDAj-7k&U0ke9H>7UG9Z3@;T
zg0#_(oi{fBE_96ckr@{fZL{ilAFrmS9Q_}b+%8ACiv|Qwop-9A4$G>9D1UjMhNi<k
zUlj=_reDst)Sb2*6-o;pDqC+wac3!~cx`%^GJn=FPgI|T*%RFFv`hcgkF3k%v_-x5
z8E{jZotPfg9Ap-+z(o{hub&_cB2a$ve|}hN-2uJ5s#UfhKB{azrB_OS+$8AylCTn=
zb(P;6FUwHbv>BPZ=MomL$X7Bg%xtRd*nT}3LhPV*-f`Q0CYZ-sGMqq4&|=qeP`HDW
zD%!}Yj5G%GEVqk3g5<=|hvhw?9UQWbuos)Uw}jv+9wD=Dy@cTpI&R~*vjO3!i`Ssk
zBy7;Ia|w$3DLkf^2uh5{z|em)sWV0Oe(({d-@dr@n%^x`W4Qlq>l)LUH~<`<E&qH$
z{;V7nV#D@%eXqHs{bsT3P|bEP4B6rNp_fJ|wz7FQO(%<~artxmk^Y%1$nVtEcICrq
z%>dZ}dp1OV0tKx*CjIrxfOx_n-(7aw;3MO{wUlZi9twfuj)zl8R&m;Jo93M)IRPqg
zGsv$}pa6em7YLn*0dTa#7FTJ>vD1(5v}sEg;(tGt&Aj6e*-2K4z{yGYv*vTwUeqiJ
zlpl_01YQhB6(P?k2fP(ybiW}2c}cn1a8TyE;!+VToeSuze;Dbwu5e_6OcWTXXIarG
zoOuoKPtJAR)VrP3&q)=T>@s;DTBl!;x{k0FtQxuuQcVE^&Y^q4!tJnNlu38&@UCe6
z*@GN9$V{!40{3dGc>d4zWO4_GAanzhrP1(5_w$4QbJaGkZ{DU$U2^-edBKywc>rdl
zV!Z`$(K6d_azdJ1Kj@ks*P8dd>(vyR;T>@EY8^?KN8j&yCah@xZnxr}nCcN(FHCo!
zkH408`)vGI;&WpZd-s4sS0Mdj|I4ZtWLtiWb4n4hRL;q6fhAvDT;MQct$cWVC*!B;
z{qCUCm)v*yI-#v*IG+s_0KHZ@XJ;#K`|}-`s@d<U1he3<nLo>Ok4wZsAxm_4n0r6`
zc49|pdP=IB0RZyU1Ez)&8nSwPlE0rVtUkw_%@2@qyZ=Cahm?9A?QNWKyG8!oBu~PQ
zVF6AV^aHcUw@A+WKV4?a5q;)W62ub<G>{z%x{)N%S35*-PZEf0sAyO$#m-LQPPN1x
zt7u$J`<WgFY2kmRwJuuGa#$+kICJUe3yTpY|GOS?oSnW+v^7Gl5ea?w8TEq<*D*Tb
z!`Ep3An!5PAz&uZ6kSF;u0NIeBW*cV)n1H$xmxA#Ova#uHB7*HV23q`dl_%l|Gvoo
zaC@vEo<X%{Z)nPUNHuHHdueHPb}(>l%wx%ZZZK1U`&hH{ekN0|*(XHFtND-T<91Aa
z{94$l?|C5kv6*qpWvOO+sVi(&olSc~%Y#6DQ!i~oCf_?ylqrqB*^1MkBOt^iZOypf
ze_B~O@vdRsB*OYaglF?7WjKGzwF-o3Nbytx_3WiAj@4#emn&v1?{A|-C}zR7IYnaO
zx(@4n-ieKD#ef6rD=p`{ztTb~o5PMIo-TXKqDV?Z&x1+;7aFvzOh)-6Fi0u=9rv>5
zqIHqt=`V&H1szKj18t*|B->L8K1gAoBL21<R9oxBY@@MPNgs0S|9q>6|AvOLoNWhQ
zfd=zki}e`pE7C)IKyFFxFmtP$%hrPXaHb3m+AwF1e4I<@viG5C_M(Eg!wA3kk0RGD
zcqF$!=X2iUfn?L}J}?WcvHLY0@%wF4k}trsbf8s_H7<L&^yn{7_`hTZ>Rgle8a(R?
z-&~~i!*Zq|vyC0<%Ux*ljj-ZV>DRd8apb%HiX8Utu^8t00s)q%<%Pj`D4KfCk_1Xn
z^X}%_t!RFan>GLEG2+$pkTFxT%M=XrDB?B2olF)Xx*LJ7VWKl>YquS@cm|6U46s^}
z^acR}nE<bJUPL+CC2l|!U1^j)dxB5XH<j$QrZ0m$K~FqV32UjR+xl)R9ReYNyl8Wo
z&`QUv&!fIT6q<y(u83Tws1t^b-lyaaxeK!mdmXQjtU^#&oUaXv=_eaL<?o>}%Vj6M
zM)6KB+(Tn+eR`<BoDx71!;fs)Fv}@uf}V#8ACEWE3o0907bMCWE>%@gqeI!E#dh?S
zRr?W|%mXT$`Z{5mzA~OBj^CTc%pMx4NmLbJs(ZZv0yJ2zm>g%mo@(fJ#}K?4Kh>f%
z2~2g?UAkh6wvab?D}KY6?*SImr9rC{oZ^afBD}<6*-9<_2q>UqDa5m6{o88Q>eigM
zHyDnwrlgxlV9>G^PKaUApYeQO^}JZYP)9dTRCTKgbMC5^gJE2b$NA(8eg<#T0t<S*
zDQ^l^WoNAFeTq_g5SjZ+8FyzGnaaAUqnDc-LyFZDM{~@=KH0{rsnq4b0tr7~3KIwk
zCAHDxWwu(N*!r9{ThcfRGePr%MY-OZsK4cZ0s!rZH_~FX$j@vIF4O2T^Cn}uYEBxK
z@P!b|U{xo^B`x<>4)ku9EQwa{tnG4)%3$aOR@YK?(Kd*UWxb6)|CV{HV?h2eKhUz#
zu`kI&k1IG+O*i#;vs5*FTgcsBGBjB~wJ}u_H{14OagcpopPT4T15EF#`OEw~7sEhc
z61yBF^c;)i61^y+g%3zB)`<v&_Pfk6z2A@Q6}MEGQ@$kK&Lp7{dc%*7QEH>Lf#m?3
zZ-AshDodc$8><9q^__~_mFUA~X!f(J`{^R&l!}&}LPVqrEv8$uqr7PSd;`<J>1Y7x
zP9RGR%gxBh==6F6Y@@Tfj{4M3m!}mN@vf0!So>DD*iH8+qA#0WqUDI}L49iZONTNX
zd_N?Iy5~A?1>9ccra~4A1bqMd4{p*M)fG;p4~~e(ZLdn=J?T7zV3tY_eut0rZot;?
zVe$K++Lj*5oHu7jZ`GhXwK`EAD-jui4Hoq9-l*U+>Rdi&m9OWe<WpCSOky#uF0d~X
z{6UC1DvX@5OYV`&G#C(%8c9W1g{nlorj7Wqt<7dtv{=Jy-y4sM+A=K_9POSsXz65Z
zvTyjbV>o1za_@Yozmg&v@C15>Dm8*YW&3@?*Wc?t@CY3nV;&WzzfPj&EO%N&o%7xk
zrNd=4hjwkgv9{>metuee<~Of7O<#jrsQy9A9L&@AT&Yi2>gP@vg!irqa>Pyjg6rVD
zCq{E-aiG<7igF+lEhp79rW+<pW!hKlP))9E$|d)<|6+igJhR0@Lw{q#6r`Kia>e&b
zj2M9j>WyYYDyO|Lg%k`J7e$%@hw&WWJ)qP+2HEm`sen#mU8$_c7>Nm43mmC+UQI(v
z@+MfZV}`Q(3uU?zp4&z{@45|DLn$_N5Y(!`xtFGR+2@PXos=;Gf6gaAOw7;HX|$ol
zjDc@EpZ!SXl5b|J0QKqFZGz2mF>l^G1M_++(!GARtv#&XqOy2@bX4#A9#T#C_gcl5
zU<3#pmbX`b%RmBDFIu+UNpt(|y&9*lF!b($pyNC^&LU{H5FR-*8JHJ%T7cD?>hw{d
zVqdrEG&p4?vTIV(2y5fM^XaHlmR~}*cdUdD&r_tPm71Kh%#@)OP?yI{zH_j>)LL}$
zLB3X8Mhp)@Au}Av3FJL6otuVbcK^B4Y8y&D_6s?ly1W50A3OcJVEl)km-9ruuj>cB
zy*wu>KY5XDriJ>8Nnu+gs#Yj-+c{9DgH~_P)R9V0!#e%k6s#Xl8w0q?<4@Lek+E-*
zw_;y-8y%^=K)R5>$t=QJp<3?nu?d^3EMEaqxH^sm92grYbe3H`T+>%Gy6$uUhd95!
zPN|-!3u!M+*^l0Wwp?K{kZd|I=&|RBw9rkpxLsC!eAm;H(x3^cUcwRr99xw?R1!t7
zcOzipD&h3EyXaT21ui8=Iw|ZJzbRYOaHQA{oI$soI;?*K8u?wXtix$OTdXLfMVV<w
zb5;~W>Mv~EknNNSdQ8GhcvH2u{n|Uvxd>Nc-}t(+^uC~IXw59y>22l6@(ibzJD@(Z
z*!ue0FE9=l#~lRSLEml43eslZUF+bUz{G<C#By7r<@{UayTedaGZkRVngD85aIK=W
z`aXc}u1<Gi$9-6IuC&0Kr=#A38tSx!I;RjX$Y$?0viRq^fW?oF508C`h*KnI%O(#j
z_<H3t946YF{a5xFY^`s|#V7W)OnR-?<31%#Q*Q#=6zL42$JI3@%b<{~$jM6f-O?jh
zDm_XHimMO4y@`i5UNBegx3h7@PP?a5pysYH00lcK2wgUBxLg%$-lx=keV$AZLYYI(
z4I0)sGq@9cSO78abk}qObjL1vum6A*gnLgz_!7@p=r^!EJGW9H$8MVQ<^-0Rlb^6!
zY0~iDZO3G0l#j#<9crs*X2g72?@uXQ6BF&wEjs_9^pF4AGw8)TcyM2-kVK4|VDsZ1
zsR_v$f+fA&btlRI^WF$7J7EqRE#c$qH&oZ}2nhIh7P>n2j++@N(aeKmhB=$Q=9K<1
z(qiTsS~4kBtoLB{5^?4g6;3360x<3X;h8nkBJLif42=Jhx(+Q>qH<<CD?zr(o#Y#L
z@#Yd<x4R6g8PsQtu_7fb8zrDoE+URfTG28~Zs7bhDAn|KjzuMy&Y>-1+`dLFJxV%5
zptWFL+Ehs@#t^z&cj1-Ff@ewoA=yaH?!EVii#dJxRV<yIcW#JG!xijo#j`UQMCYJ@
z&%7_b6Ci&<3`rPh+Yyel9rga60+#g~(#<Mt#$?E_hQFoQ&U`JXN0ZUygm|pne3?ZN
z;?K-799TO!yRA>sU9ga%iK`;SJU(JX@Y3Ar`i|@rKuN!LHZf(n8a&;u#)jrF<)tKe
zJ)Q7+(*^z=LDT1_gMucCxQ<U~Vqs_!>1z1MxCHiq$0I`}Z=<!|+2k?8D6ZrhX`#5;
zWW(fP)a(wjkgi{zBUJr(-f^9lR*)vmA?Zp~B?Dz9f@n_f^+|@IXtXcs=lnYfsw(*J
z<ggO-I|+pMmzod>`}#U(graN2ggP{qi5|9Pl1&`$ha*sB9x2?>NOFfF*ml5tR=vYi
zR_$u5AGDd_gZH|8iLBfO@;+0Md&lK!eqCva^Gr_E4E%Um4Gc5BVP}^bMspHe0Dn>$
zieL^7b^N2avqBl15u=-crher0F$zuZvJ_JHCrp3~Go&jHr}EM}dUk(%xUz*CWjC?i
z`l&dQ!^-XB<GU!NOx)5%EVaj7R|Y!An3A}ng+2NwYwUW8ZC9<GU0c3oUyY*R7!>?0
zE-~l$E#G3%q3m?_%R?s_4Sfhh`{NLU<AS1hQfKuZ6E}EPv#4jNHa-~BH*ZL{F`P4-
z<;cgj6uOhT5L7)Z5#W%QKnx1axp$A-lm;ygL~vHg%C}M(zT$P*vow-?bW~YhPxEwl
z<B#~uUG8L((msNGx%_2OD%Rm0q>6rqfREt~g8dA|b*+8ME<3Dwaq#hXA7Z^s`>*wx
zFy9Z>ClZzhkMstq=%ah;KCh?ThnLzTnqj%-6>eGDmy>T~<G&gQsP8oqaf7Y_vIrLL
zn=RR$3r&0WXLM&lc6bu27itdmW3Bl^!}RR}J2NMzTJ_N}uQAnx3TImU^W?D>57xks
zl&U{QTX%3waqx2%J4)j{6%#Z?wbV93AjK(>7ItE(=YQmgCGS)6e;e4^G5MEXw!P`u
z7H8=@xq%$Jc#r1M!*Xwi!G-1u_~t}o?MH_wenTlexw_mMtiOZGORi~$M>e9vS`uRU
zFoLD^8OV7iQ3X4`S160Y8P6j1slE=R)nKp|0W<ZQzrhjJuQhrEPK0X-cNm=3hIMK-
zDL5Q94O(^*oe+j(S*>gTYSus=8u{Iz?nWY@{{2w>>2XMoAJ5A~w3T2nKj1F4PofVV
zuC^cfxER3R7F+@%Ck@v~S@M7)hOJjY)rMn8KYl9=$8&Wu>u)<fs5KZQ*5GO1{ORB}
zYmemZJ*<79!>wI-545?WkX`Gc0F2naG;yrNC>&ogD?(D#y(??xBxo241yHoOn?dmk
zv{|RtWnk<Y;85*X0h#%HFk2IK(d-ozD9#5x@6km=_GWNJNq=(dco+ZI9`6xti%zYx
zFBEWRy=T61T3vX>YA_}}7|sszk?eC2dI#iF`dP1BUi#AeQGlS|aKIb7M|>-K9R)D{
zBW?`q=+N0Y`yz_EXYnPoXfY^#yGCWn9%jDoXc-bLcly?+?{3@b`qgxyWza8*7lohP
zv0Q4x$}-34*M*O%hzBh)Bhg0Mtv8aoJ#70@<{KulkQJAmNsl1F4jB1aW1oJ)x|ikS
za3stB7ZDCZ<N*ydLF`=J98;oCtdQZzw+GmXOQ{FfijjcH*$V?pxpvm7+NQ@xKRxkn
z@U1nh`hmVt8*8wMR40A^VmVW|+?(gQQnnNNOExqMM3lIX#dTRx9On3IjA!jf7FaSs
z5J}IAM1yBWfO~;cb!B@siFrfkx~M|<O{09N);|rokN`MGNp_TQtW-xmW=9NHc^TX~
z`_12(d!z=7g}$-c){WSD_mA@(rGv7oj1DS2`vYmfkg-xOG5wBh#u^0BDIe*>(T?N1
zgZ*pezOq5>YnoimYlFL*ldMGf>E9Pq<T<6lvLL^LD%R0Wr7da1VSqb*i>+}U-;>d8
zYV@1xx0&h&9(vWl3jHAaIDXjfvzIChuqAVGuy4My{Z**eaNcXcfS+T#DtYXONma}A
zaMem7C2I}zb7sdrEyq<Ly*+M@bET?pQ$|!2DIU9Uxd>Zr@2v9sNa&U75Wx?s%)nwa
zIS$0|1{>y0M`F)y@pbW<v=XBF{ndVX8dk=QLgtQUTK~-hO@oYA;_8-?Xf>d0C@uQ@
zjIs$?{zg!zV#%>n9&TEgQ*yR%3O>hD&LtHUov?l)IO<3;rBJnS1JqcUM1CDyF%C(Z
z>%TPVLABIDutlCZ0dq1|e6eAf2GoHQQmy#Af&$7D|LLtZAa6%x4>9wd#u=5-wuTbc
zo)5F=e~Lz98yqDJmYL!!$h1b^zF@}<{!VL5doO)w!Df;Y?&HE^I$|bVmg}x-8OFYr
zB`{+35W=~S5aaRr8mIDr9b8}$<t*K;1$3cw?aOg+A;(9V3*0Tj@trHZ?^Bir1eWso
z5<2e5g~<t8`T}P?0PB4z0*1eJ4%@-=@Aj&J)~KDbmIGaOiRWJ*i9QK*C?}p#JdN;^
z22fGzl>rq(f2YQ+Z8Al>N8ylXG7FnL;4T?1r1g$~?A{!t#o8#&1P$x;t+A}~d7?~b
z(7p8U4z=(zxQ@Y^A@_;N@0$*fOQE-u^-(%|w(I8ApMs49rn;knUl)v@VU5289&sW0
zL`SG1REzHKnF{q-#N}27E@VrL!vo-y6V>27(<fZ+bO<Elin{r+;8jm<-y==iXtaXQ
zbotU7e*;f#%qurqpl}5`e!n^Y{=^w@@*9d(P6T3|&|XO^^N?qlv)?QSb0~0NnEudm
zjxF+ixJ+Pg+71Q;1EM>kTE$Yn{LYlTOH<;sGN>QYX}s1vIOT38r@>mxkkoA2$5}Kb
z9KUq^Y893^VN5&2qA`oqoG0OIi!J7HFuk`2gAUA9!uUWE!ar{4iZWCH5YG5N*FtMe
zE3U_E<xUBe2KVy#+@%}NjPKMN_my`l7A=4HbD}TNU}>oUIdzNOHU22P9&ebVU0#o=
zsIzmGbWTNNaDG%<xrqV*(v}JMo@`n;C`m14$^_8tEp^{#s=Kd`Gw3VqtPLy8*ylxY
z(SO<`uY0l<6S3?>`N;%853=IHo_9ac<Xmm=pboSeFUd|=s}4R&!JtNH+KD6Q&Z7KU
zYadHI!h0JuHsT@-JMrE3v8#I46O;<u_k{5qE#9+bzc}jew+Ql2m*U?oxTjMsv$U!C
zno|-xLQXazC|!THXm5Da#tUM){8IQwENZc^FXh()s^!T+(W3bLUVfsFy4!u_t{v+4
zdW)Brc`FejOTCmi@d6wrfV;jtnMi^(#EF)3&St|E#W5QeKi#T|C-)7`iDE;NY*UjU
zO4A-S`BEod65)PB63q2u?nXOGCs#m59>((K*zCp288r<^POX1!K7N5@ZDg4<;{+>s
zFeFrS@kWL1cR^f<bL=_oU+s-NY)gwX7zl*hTx8JxE|<oYd>}ZwGfiOQl|`C;1$cU4
z0d-83)OpF3bdA*t9sORg1aB+nco0(-!jJ_2?8Y2+SN~X|fTf4fgNcX4H(yUG*XS#L
zRFvYk`vZdyN8jlOd60Gm05eVqEWU8DMUwEY(bZ)@BvRZ#*)lF7ovm4!q;LH+K_HJ2
zHsg1zzK5kO<>OO1jp~+}?i#upFU||Y<Ral1)Y>q*iOd=T{`?rFN{Cc#LX^FA!U|Yq
zA8CQ&T+)_60WL&7=VGt?Fq4fZmAEPp`=O(nLT@q!5}on~7gO`&x14Z_k(l_S{L}i$
z*%zWTlaXesnhi3HN4gnU86%z5qP_tO>|tyPYP_7*C|D2Aelrav<}kv85FMxgx4YY~
z>g4xlaoCk~S{8Oo^s5F^-;I{uBFL!WklcM;stj^{vedH4*{a$NoKe*XS`r|}estL&
zCg9lzhwXC_?83HmOBDy-iGoHf9tf|c%er5BJUW&UdveA8b~~jG$+H!!$0<fXa-(wD
zja>p3E^WNZEq?!HcWzM5Sj#)c8sS9lBG;WS#?*TQWONqp;}W!&RIwBqfqA7c!6~{9
z(R2;C%VUHK^#inr{K<(}<MJPn^8RC2KBsQ?g96Z{F03f71G>YK%<63+V~UQv$P^6u
zfyOLiTQHBR=-=b`OXgN10EwD9r{3$LM9HsoMpbWS@3)wftA$xn7e%-}0xe2*;9XSP
zq9DOAM+<i<Z)<cZ>YOYl#G2oLA|tsIx<gfd$nz!Qb>K7;a7&BC{b<p)s&7%fTuSxq
zfobu45jA<4&R?EY61L1HDVN3?5LPmz(1!4fIeA2I!v+gy2NjiGt?aBtmw(S&Znws7
zuFQEYZ)KIRt{=SAQ#<bZSZ*H2`<A2(_wUNAw;8dQ9KIq~+w-Yd{GCq=oWy@HN$n9H
zOV9MXS@1J8N=$a;p|$9;^cf6d{*!kx>+sVci~^8_CwkKP{1j%{mb>}*^^L@|YL?bP
zk(C*ChO%8^Sr)FPEf<g?vq~fGA$Sm9?445M{;1{gv;ya;9rtmOR?ciNJ74yHn%r&H
z&QH2#1s(c8xA>E%c|2zA^aU>tFhF*KP;r~O2B@a1OJ!+H$~q=yJAGY_ozU=u!C_3v
ztuM_jB?ZgMrI48j7Stj6!v^I*WKw&eZI5{Nm|_*zVcnD>gQHAOnI`w*UD=i~Hgew&
z<t5jq<#HxH^&8Z5P3NzX8;SxK60tsq@?Do4Ol4EcqqumtCBKo&PKM}A9W|_o*wxM;
z#v_Vp2R;vU_yc!BUuXDWwc)<Nc>Yvg;{`RN(u$f|?9FkuAvU<Yk~SJdp2PXoy#9Oc
z#%L_I`t#o%F}tD#zuT3foZ?#~Yu{-&1=E($uBg>2|Hq@&v7UW1e7+|Kiys>&j{ZK{
z?S<mf)x`!su(gJd2^%>-9}QN*F%zcR7JzA14fZNh-WEWy@3c5ATF341ma>VOD9o@s
z$5iHmhizy`U)_DkpX~#s(l{(gIV*<gHv5#k^g1)DOyl6X+e%ZqccR4KC3}w7+^Ph@
zV~XAL!?y9(<>fzqTg^XIq*dz-W55GTPq7F!c&2c8FcGO4zfN<$k#6@OtZo@)=gr72
zQ}i7XS0N3CW$FklcnwB(T+j3{MQT7jPRWd7H}DfncpkGJ`iv6WnDH&%VLim)y@6;7
zLqVo-kfDBGc|<(k<O?e5uIMV8hxr2I-uH_V{fu#b)wxg%GfyLlF=Bed?JH**)bJW@
z^(HAo4a3JAOuu4X%Wnl2Fj&ZI!?Y-UH3nb!k@7mq_tMRtxX)Sskg#=?ryncvobP@=
zuZYC)r@%zH%!y4VxdsngCFFA_3|pg9Rt8;9YgaY#em)Oo@y#KKSa~B!p}Ak9;{9Mo
zgDEn6h9rC^x>mO*ReHW#i<hbIu~3k5DQiRn>hf?YIJyV1+rXe4K8Q;&FrGh9aaDH7
zJtm}WadvP?8_m$5{%AWrhF@D`|IhY9x8mt{zFrxfTDXP5vf93k#%=u7ww9%xfnULQ
zn;3?|oCFtpc;h(!4tF+rpvHKD+vVcf7V(j{o;2ATOumqhQHoh5Fnp+WIGaCN(E$6@
zY@_;oTd^iSc%jmV<4R6x`^0>#Q7yU2W8OGa5uYT27cDh&d0vU`dAZ3Ad}z#VKONh4
z-ORQxIus!SZm3u6(68;y28p_Z={;!B7FNz(U#&rgG&?2-YiojOFvIyEys?!&GjI-&
zq<dhxr6GSa+sU@Qe5Nm;xLSq$Qr0ch_sC~(>8Z5a?Zn$hJ@j*6@@HrllF(P^9!h|N
zm-g4w?ZFZUFNk$S<+ByVTS(?DFuAeMlQvL58&8|ho57F<ErE1WF~fV-Eu25eI9OZ@
zGMJ2mnWBbwoxrh;w^yo~Cy0q0p6zQ<6H9|B&;l8*-73f&zZKz6@>F&MZ%d$r-d!5i
z#nZTj3p%3Mp=)Bm0IvsB7W>$jU9H@!0N>+$-&+VmVF%HwZB;ue49aL}V0rT^#u9Ip
z5l+%&uDn5*9O-a7agDj%<~Qgsf)WY?%U8GkrBeDc9fs4$#>HYrLVUpDU>ho5ADTVg
zBc}rriBs3R#?oLw78`LJbpPm3&4B0xTs&I2btFAZ;O^c7kG7fbXs;59cA4)or_^Cs
zl*Gxh_$~|jS+c?>@ynXnvUhAwJdIB2n7$9b7C#I|cikuejo@PIH(LEI8(HD#04G)*
zNArJ-8Oc|Fgu-A#_1L2Q0TNev?lu9om5C-()G0yye)B`nCH|!~BgCPglK;_`CGpJr
zppfZoF&)TP?u1-7=M~UaDkPO^CTl{U1lTg|_xGO24@y5%+5eSHok)PpKTSE=)r2c(
zh&<oBTCk_zwc<8VW0!0kMx4U*o`F+!gRhFv!>S3)wRSonL6w{L8(iX&r#b;HkQa2$
z=@H>c==5#YM(XCBbQ;!`lkE|={ABR{*7spO;T#EX7S=KCjH!=uQV0R_W?B<|rxE6?
zUjKgHH?WWoYnKihCXxl)2Gv8OHxt~p?MJ4DvN1X}O(jSIlL!D<w45T!`oEu)3Cv*q
zltMn_)<-pfOU@GZkZ`7DRsd6Gv6b7Qx2{(w48N>zTSEEEa-DXb^6O_^iz1U$*YA&K
z)%*9|EZ(C_iG1D-&2mMG+o{+4?&02A8^!FE#71L@P6|?l$gI_P9{b))8c2GQrbd6)
zkqR6?omJA^82IB?`sA2$Q&#g}6G}7{#u!chY6$6wM(Ome{DX$RMR(ugt5<wJ{<N)0
z(sDZSi$&;Y+rB9nGv})P%+E1(d`1jD{^_M<&*x3-!p#`8&&Mghk#>$<I+OMB>kg^W
zidFabu#L8*GUTbq2^fpozr(sM@@*<KISp-t&(ZL-;S7S%px9B-!haCy^kY2TbJTq*
z(KCaNcj`(s*cP#=Ewh!F3y5o~QZ2eenx5G<xwMl*wykeIwHbv7#0v4|Y`vo)d3UkH
zw3_Mr;K>bYJ#DeQ66%X&E$(9h=112y6|~5DB}_TAp3LH9q#|0W91^1(saJhd4}D5#
zVeUg56-6SQ&K#i@Vv_%f<fpYU(&(hHI9S`CB)@ohVhb-7!4kQ}k(2Gof?wV>0^~#<
zQ)Hss(D^zszFV<KsxdRo^Sd^;1tjz!Z=JGJqzFppb_d_2DgsJ>FMVhIwF*PNBd`X_
zwbafhCqC%0^Aq6#8Q8y<XEmz#L<&1zGF@<U?r#TQ?VK3<#Y+z)HQpv*=$1{dM>t9D
zb)#}enX}BW2)09|8fo<+vKkMQ)a^8WnL;SlD194KOR{9V=>QtVmpf!D0jSo0b-VaE
z9K0FaLl?G0)mHIWjqw6WaUdqmaWp|2zE9=xjfstwpzwHgcRn_#Ee*m(jhL*L{rLyR
zA;KIX$EviCU)kZiD5An>G;r=qjDzAEca!haLFx(-%xjbS%U@uyC&(2~*n3IckmXnh
z4#ZO;lDbF3&gUKBM-T|Tv1t_Q?>1INJiKdnSqb`eAZ2v%f%T`8!YQ_FDQ>3x{secK
zUy)#Ll%=IMOCREKgdud(fj?OIKI1SK!9#;GeYzSJ9l=eTaxE$H>!I4h9xflWCW>fB
z^RtEM9KFw|J(+`S@7;q;Pz3T(J~X8DL#Hi@tb;XCNYtA8$-SRqp6_DlaL;-{P4aiY
z*@Z?eEAiBoGHW6|-qNCb2w9OJMDg`E(JLBoyq0~809+{XO1`%q^MqcZV~VEnaF!_Y
z+)+B}WU7BF!Iej!DKQSN#R76dj6*56#CGs9TPpS6<<^?wqk~ELf|F|`g?AE<R*{)x
zHu%JV9_kta=|Yu%mxkqgQbCN@c<+6J*@zoqXB*AMg~>z^>#a#Ddn2r2B*j$@jxokC
zp$71Qjp+{IYX{J|LUObcjr#luH$EiTQh(#JhIQs@WqRZvKl2o{mk?W7bqyNjzg@+z
zwA4Ypu|(`A2!I8IOSn-YsKf(Iw0PQJ74#PI1EQlw{hNbOtsS#rUfiO^RB^3;$R^=j
zxX<nHaHGp@ZWim`J>rr<p(#OjDkK9wW3E|Kc!b!{FO>ZFmn-<Hi|RqvUc4CBFhY~(
z(2cu>8=m3y7gg0|_p2tRj1HnimX9aZCdjAAd^k>6A9-759p^q7ZxgS`-LGC(dK!~{
z*`eVc?&T^Q<ty!%aO_|xA^{C0SH68m7SBx*V=@^`8onLM*NqY4yV^k#M*K;{Be{kF
zK@`(C<(yX9gWtt1psi8eXfPLxf0rY+x00<vzBWD}PuqUlu4gcLArWkHE2cg%S5{e{
zsy5&c;3~f}zHZMpDevY<0UK?;5rEui%bOrkN1&<rb~bU7LU74jf{%XoyIp?%^+vF$
z{TH-3o|eEe_KgGA7^wnids~sxIp>wHy`$JE(QncLRCtHT?LVB%yTz;Yyc$H#CIfE*
z1U@J;OKk7zXPMZRxkx8B87CJ_x>&Bx5hr(Ui+<U(pIe@|-r){}Robl>xa2>(VND61
zJ)f98(!z#-Hbl}piS4Og*_$+INISrOMmU8WmO3i5zjSE0Xy0T$HJV<2Lkv%Esr}0S
z7N1$^b*phgUtO0jR+n{(x%A3Mffa$Zk0^rLU^Cg~UllZ}Xwqg#;33KST4h)(i_1DC
zYOU~I>d~z2N7h-btoLS#B}b&Pmj_;7u$4EBywX#_Zj+e<nxW0>Bj|e==Z_WnT+z;;
zPX@vqn#DaeFzw7Qpp^Lgs$Ac#ap8r8#>$8Y9@-oa;x*tUEOg>37l<otL**`C-(}2b
zlk~JQXN#A>Xgx00?ZGJ@R5Gy6l4&S}<S>tDR8x-|2eUH;h<~g$7|O@!Y_kz$nl8NW
zSG7W(P0+vIy|NBS87F{8oAdkd`Nd39zXcK6>eCu)@i39dyo}a7E4{0HTTdDoK!3OG
zztK7R*^;O7M*WLj`wLryXP3#vL%zDUB1((`bc>9qkri=G??ycR?J~07Y1C(Cq+vhV
z5qOlT+X{P8Bg9=ocs}mcMDq~-HRN<~FdbbsTY1#=2s=m~1fvJy+7mr_8FFOzr1z(@
zXdE=S+MI#CJFI=F|HZJCBM9&XbeLtCQbMAWvqUJg_RySFJ2KnPcOvwCz)Llx2veBf
z>n^!*^vY2s`@)qwPz2)axDa4xu@Qa*2ycBtb{(B=+a(o0-P*psMc^g7p1G|~hXAcU
zjLt^4wyQnau$yLE=qvMeh#-8rFtOGIMw2bRw#tQBCjtk4jF-&kxJDIx^Mj!6cLsZw
zSY03Nj7Jp=cUzO;3m&91#7G9)ZydyDL(LYQ2S`-`*hZ)r5YkIT$Bu>0@ykmK>#~Z5
zG$5rMfUWqrGQK@~oVlZ*G$zw&6p{ti)3EopX0UKG<NVV3rP2F<VjXPAXD%qqJK9*3
z6mbQ<q>*&mMOOo$+R5THiv+>5Ej5C1nN*i^se~mb=TDtGmSBv<fP!GScn@gq5Q}6&
zE7`|Uix)}|J_7QD-g?Fw28+D}Ae>`oX;Kw{z{wVyZ75BT`;fM5nXKQTEU;6gK^(xh
z{{7YDK~eVVzC&mUa^#BrhHAWH`ipNE31F>jtO!3IDD4bTF+nn~6S8|~<Ndc{UlmQl
zo*OLnU7ecx@K6m<<?)GypiJ&a<#!E#7-?cL6y5y9enSo(Y0r5B>kihsR714XD7maH
zRmB1Fd=eH<zFGA-OUMpM-_)qz<{niAI5f#JdoUZ5_M1D`2gJ}#0IyT<Gve+456bF(
zC|qMGJKKPRqQ~fk>CB?d9gG-GCRB(q@n4yn|3N%YB1ACt)4juCV6dF~H<zW}!NP!2
zhgC7@<z4>=Sv?XH0l#vfs60dE1?2pf|NrxWP#KX?FUG2yNBe*N>?LxAUZ#}&$3^zP
zgw21BVI+ZF?If7{dduverq+KSxa*DrMpyS88lr=-hX2QW2m`PsVT|FQ_>uAE|Hph_
z*)9;qSihiSD4PDiFZzF1^#4DuI~=2=vPV;psYr7Gqb)sjUgDORvcVG9#E0sU&%^nL
zjuYmSM4w?_Y-5cRzFFByiDe+8`HLL+7GZ`B-J|RY*=y&XEOMG?0(-@KFH^Fs5?CZl
zCD<bUe%Fd1+iFr$I)-Cpf>~%HKf>X;pJf(hWTNuh$27MIegECUCo3bL+Z9hwn&XeV
zT*NP>E+p*x09*Vk|Jy(^q^tKwS1w!WZjY(O!ej#@<-rEjujW!y=lXW!@b+PpciG0(
zZ?-xxM(^-<9>Dh>s5%^hh#nV1jQU?Bg(vJ@9)_1%(E21#fz_F6MRt7_I@p4zq4fdA
zD0f-m5;M11`Hx+K6@fOzIgf>l{06|y#ZFc*!q1Xpcywz%4CVa{@xi?mrNu<s{KX}Y
z6zA_uhmCu(jJq#d#w{>H;^f-XO=I1xj;(@d^?iVXvz>9xbIDs@w7-Sbgu+`zivF^F
zR@T~o?_FO60qPpsR4YdYx3a^`N26j)5hHf61cm++?j!?7{rk;iCM;4)(mB9JHo)PZ
z9WmHvS$RgM@Lmuwa<}R-%v4|N(<I#|tx~W-b}*s06Xl?=Aw~AU{iJZ6A9i}r4vAu(
zzIA;#X)H?AG~mUL9ev4t0XdW+!~>u$!k8YDHS$rk8BBVEgHkA)eQu8QH;$!eU}T_R
zbXa|D=K|2Z>t{^Ex83(T(CR$i7WD&EF0e=$c#xTt4EklE^CHHbGR<_Qcv9b=8tW&a
zXvyZMRyVJZ$H$sDiB_B*DzX;_ZDdz9_V3IK9*esZuVczw-WxXcmZAR~CTreszH=a;
z#zDfhoLHUj8X3>KS`%WH%q+Z53-uvk50G5)N1MaTFP3<ZJP`hd*kRIHyqlo_HB#RK
zg00Z<NGyE#;|R3*3@{-UN!9%)@9*NkFOtNV3SeCFxRrh#<1TvAnisj`DSzgXZwg3O
z_lK32+paH(GJNHq<&r%Y3b-$Z^jjX4C@2K-i97FGsD1NWpCSQ|%0$}S|Lk+P5e^yh
zaXUvQ6RH2dZgvcXRf#tlrR0KkGl_N!1#=g@>y(DuQCC*c0Lsq&%|wF%%2hsYNW8&-
z>2o}?M8-#rShCT0AsTsg?uCK@)-u4+^0v}9QYCa6u;l8OnB9GDa0Ii#fxEzFg=@Sp
z?0>|d|479DUchFluC0^<<bkI*nhOQei*9T2P#4%eH^QWp#_FL_PXF%@v3HF5UR7xy
zGgDVVdDxVXtF~;Q@|Le<L;5Lysq-Sjz4zLZ9sxVu844)e?dSSu`Jj}nrzgcz!tQ8Q
z{V|gz%Gh}}ZzCMG=41FC(R80??#+Az5e%9u2MRb8N49@keK*_fiDnm=(D${e-j;Ur
z8kl%~3zNVxx9sO$J*+(oRn+vqf6=!n9#u3N+-iT=VLALP?JFht+_oi6M%O4Uu!(IA
z*`X1NNlA956K2l7gLQhfR_*+!^T-l4U_|b9^5J{#=(_<9g7rtsmvWtAi8yVDZEqJZ
z5lT&UTRhxG_~JQ$Ev*<+>S0De1lq-8X=e2*27Hy?ZFd-IlvvE}monUjh$0^-jG0?-
z6sSC(o0lgc9b?ffnqGGILRh)l<?iHtQ94<}LPzO^i43E|XHIUB-8NkX(FlCVbQz+f
zQgFrV1k<)_K2^c=T6M5}F8;=|Ig1aGL3lVImq_{;3V<n43Se5L4;8Q>4`8T|%{Q1h
zAdc*=3#rh)afQoohr#e5WNO?09k{CQafiiU<{1il)|-6JrxB#HuN~#;lkb2@(PG>$
zbD`PZyQ$XKGnz^;g^piq%ZU7Mfh{7?7nl@Es|ChsFPs)W4~SWVY1yXSRhzP(u8LYu
zr#~1oc^-^QqX-wEky7bdray&^LmmIIm_K63O!R02sUGq(*?<?xmh*_yb_&F?xu07K
zZBB3~df|gwy=l0|@&>K~Ktej1xCIa^6#m50el_``@+x2fTts|wPixtZlbd{zm0UgD
zcKVxvL5wuQh@uHYxdczhAYF%MbH1%e_HaY<1odOX$3so)7{Qw_XW!;p!_$YDLyEj4
zR5Nn(hV3gKVLa1k&qd$M(J4%rD1-Tp?7(XRy<ZqS>))?h=GrUUwkqA|?B~l4VPJtS
z-(H66<O@#m!n<qI9lI;pRo(c>%x|nhO>1>O7#&T1G2FQys|4s{@SA>&NBZ9xNQ~k)
zm-@+0Q$rrR%s;8ri-eto*W$xsrE~Pu5|CJyg=@R|FEgyqFP#Ob6ZOJNx`O;9H{O5i
z=p_!rbJAtqj;cBV|A=Z#VbG!*Y$1wZR%a%4?#1pqd4UAAS0qH#-xqN2)P^zpPp02}
zn{XDVi!2q|;M)Me)I&v$?I-s78)q<aobR_mIRh1qP7q8p_qpc#vpA+8M+MA-4qytr
zm^LM}PUbOc9o%;23imp`=NC2C)M@KkmO}@`B4etD?56t9@On2Uz=`h}dO2k&beY;4
z)ZGXZ8vR)z<(J(sHGtu|lMIFs*~o*NB}b*AY{0jBm}hAp3quB#CSP#Gy2~+s0-u#^
zpjfiDufoljWNkixd6L0fmPzv@A=#%yhogUL1Vl{4-dJ4|>VDP00kj&{9pIjO5{%ZS
z^oDth{FLXcMAd_aWKWDbKP;beulL%qEUD+^{;)=~b;E6sZj9F0sam|4iqLQ`TeLiS
zKJUR)dXYEEP4ZUxz+@5mGa9-T`PMHqC*T##eCL01(WcT@uL^;nW`hRgXBZ8hw#|-4
zx+%WpZZjgnN%0%@^qFh+!#kQ|SdEs$-sZWCG<ls=yE4qo-`p$)k$AqY^4Hf;$Nsi(
zPXak#D}<)7iu{y-1)0ZzoZ-ESj&U$vc;?BOT>-UYv6A1R>H(9Zw_FP>mR6Y3i5=T&
zwja1VAqv<w+>2Iuy&87ZlnEM81Lr=xFd{cAyAnf~0#9}k`&1hnpGCLj0G*eieItZ8
zsrp6mi6x=D8Bca_OmTN)_^k7#HvntY!*QCy;3E9$Mau<SR1m|BX*6$Pxs>o#4knYw
zV+E{Hd$4i_@T{SaZoge^ZS~nG5`MJ)c3ozuKgl4x@4Tbs29*xhH<nHInZal$sQ6!;
zy>(QTThu*la1}(PMM)I}0RfSeI!GN-L_BmODP1BUV1eXO>F)0CkW{)mghO}diElr6
z@9%xzf4?ypf(%YQ&)#dVHRqaht*VwHvpg3@s?O_1Q4+HOVpcj-e-LYIe{}zrodPTQ
zI(^%tVBw%XwI@%@R*x`$lH0j&`zd08=y&SsT~@VMS(XSvVJkD#pZHL7mNH33JT@CS
z8~}#H1l0V+zJB{vW~Ayt+Dt00oU;#U)(|hwYi_j{j6uv`-L!0&!p<}_Sa5ssxXFbU
zuRk>-x^AHeho^TwlVkboc8I?BgG?m+#?$$4c{=7_NV<x+xf&Ok+88|DM|!S`Zhvsh
zcbj^14Q5)76g^#jVsL>`YnEXzqAi5rrmFo<uiu|DWI6zkGhO6&J@%N?Br#EjUtENo
zNXcz{px<A2KdJF@7{v4RgNA>^@je_o*3JF^oqf_jW_t`tdF-ce7auo76^|;&B}7~>
z*p&di@3<^yJbOB#Uc6^9>OWxnSu=wCE<DdDr=rnnO|)-uL&6j1YUhYIcsI3##?u%;
zmY1aZjyAZx@gvAGa}}buOn+1#jdJXc2}62i*+*%SJZoCHUsp5k-%o(+Udrlk_MgZ&
ztKV)Ulr>R?<YIEJ*(;{05%S$Z!n?m3+g8~donE!4Z)ZDPE(iSK14v)i5;^f}>uvp+
zlT8(9D*n{L%18B*q<aG7iYy;_R}9Zapho@HKmJd{WO*@~cYW*z6#3cNS|UPCb{Nia
z5~R>X&QlwXX>g2UjiqR2gG(M~deKaj5HPcrCE`|;JXe4G5n9bi_7i(F_lbT#p=dl1
zLmq!DsX=MFE4Cx6My*je>}<<%3w4k06)!HQPA#;NOvkyg6-E^&Yd1@?IY&w-(n`4!
z(V{mL>e2E*$Rn#{In7owdp9V`v)|~vHde>SGn~lb3w8|95|VSh3Psc4Ek!rz2uYYB
zwvwdPGAOq|Hq6IO9D0P|r3=Po(R1rLC6fndRsH68>)4lq{OHNy`Nvst@G||j7fX*a
zrQxx^R^7@k!_$_u8d)kjI_m6UvYfGx7;s(6dPR?a5<6>*CEeg;YlFmygt3SXNP5I7
z{*bL{l7c>TE_-#(U_n*x<m*cQX)qlu=YB`GvEDp}s{UB&^su~siZYGV`{zq{Kf<4<
z8a=M6hW%cM_uX>EhJl(uO+^;|Blhj|JLBr7{PxKa4HZCt>-QAPvM!*UQ1dG{YvhC~
zaKGqY57KeHe8yu11fZB0ayZ^<$lTXs#7Q&xN|G!RE0w*9T7|`wS{MH31&}rY!9)QK
z?sr~T!Hki1{!H(=N}A}~6haExOR`X_b_(cgOl_z_tkOcnyzf41S)b>S;-TFE!+@28
z4PK4?l$R13Kce=TR8&n%<e;u_?WAEvA1x|^6Xz)RGA5BpMDYaP5c;wz6<&q*_rAdZ
z>%LhWO4~Bg-|Ir{Z(5f!+88Lfk`nAEWYSGi9Hlr4!Q%d2?J5Q@Kkp?oU@|6;*`pj)
zE1g7dQ=10Q!eu$b+^``YlO*1ud}Ut6g6uFYt5SsP9C^Gtp4HAq1uBRu!(J2(v_hIx
zD%LC`Eq0bOb2U7(B0TXvi|*W1t$pea-cgj;tb109??`PF>dl-4D~c6spL6x?o{rSl
zMUVWFaQ*TOk-sY?WYW(B8%@3~l%;ms_7f+`@ozq&hiB*}xVf=>XXQx1#88<0jaIV~
zihNQeC{oPVMruYLffQ(G?{77rCF_8t(K$r#t6@f`X#m7Tbi92rvvI}fhlpzbFRB$h
zHmu!l&p(>lt1S+yF(0It9@%#iH_{W?3o{0O(@y_Yy{qTs(;c>|Wm8+4BjZpPJ&}p(
zj7N+4Ph^t5rsNZ_DQvlExY18KUJn`r99Nzc7<SThzs!jtSB>8F&u-S%O(VE+Vk*y<
zJ^IikRJx^Q;T~69#m8=|N%Sg3_Tj@8M()F^nm^NJ*q;;fB1e@y>{PLZ3&O1o7)e1B
z4`pQ!Ii=2ev}_v^?N@KXz>g!n8hwsq?S1hYN9^0#z|Z$n?U^#jT-)4sv<8nZv3qdO
z?FdNAsBb~_1wDQy7nMB4=|1V|<<|}Q#7bz?YCPrZ?iaxx)CSfY(%)fakG87T@{Fq=
zm&4_E_Q^N7dit)%Psg`D;%_u{Qk#f-YSXo#MPpg;OmMxRNq-{?<TZ6~h80IYHz|={
z(DAl>LHphN_NhdZ;#kfoD=mTboP@wg*}+K7BJWDsg!S5$bpPq??A;|kyUbr~QPyPP
z46&iDv3rZzc~x2J>C3&*t9qS;;T@z*=w?#N@XSmV<1bNG)8Cf>OzEh>63$RI&Mjkb
z^)hR#(lo*ZciaYRC%rWJMXxj5`U?^p>ggDn>^bLep54j`L(Rtv|LqQ5B>&Jd98|Ue
z7Y>TdFb+A--!i()&oE!>$34DU*YX2MhGyG5>jgfseeiRVC-OG<W!0`!7|Fed$4ilq
zKEI(T*ItX^HaDW#HxAcP#WN&83J-i?ctv49wn@8}JT%f6fwMhZH-$}5_E+Qt5Y|^b
z3d|iL%bP{{=(B#&&reTHh;<$}w`UbZPqyc#m(0tY6s~s530-<9{s&amk#&2qTMCsa
z*RlQ_RT<g|-nVsCYmBfiSED9s!}UB)tVMxf<JG9YVdFD%s=$iG#NOHOr{SI_;Uz#&
z^-D8*WC-?Ijft`1MPPXjlq02sOr%Q%U0fQcHGHr?3Q6>=1)F6Agg>Yv<TxE$dV($!
zjO5hGH&EGVfA>*yJYjy+m-MthL2anwEPt`h`8@P!-|ZcY3vv@0XTpHEGsp@Sl<n7<
zkE_TwE^Zp1E`(cYM5B%~m$6C2yFQ<<y1Yk4Y(xn9<!jliH#d_1;OMqo;BWsqm=9KZ
zjytr$4eJydUkMp9CKyhkN+kao;NI-t;!+%`@nji)&GIBj&auyV`rXo|+mNzMXf>Y8
zI&++h;ZvN#E&Py4@7t97rNf%p_<i{O_=U@8TO%R<k2Mq~jUk2|Zo*An8-MWo=8?DU
zC?>|nT%Wr<oGu==5GBV`0y63<>o0e4KiNF?U1V=j6%(jUR!l`ZjG{e2p5%m)#zma0
zytkNpx}5N-9}-t{9OfKzv)udX3d%|jwyxdm5~*71tJZVy8IYS)uPZdz62zg=V8uNL
za`m^>o0ebTQaHuKMi*k6d0&$j6{_inr)p6-=U5Y1aTZ##lTI6HX7*~-ZDU}`WztUV
z378ahXM1GVf$Gv%5k0(C=h>H2F44d?B4WFW7>g<^SWO=4y!DeU#*E$=CtTTVHRYO5
zeRu0CV~`9~*bD^nPpeOV4Jxq9F5RsFb(E7De<eYTd|L#aWN*+!V=3<!)ASUV#?{Ck
zcN1}eGf-4<+P|Cj+paW@UaBME(MX*#iMAqJiX)wbc8>Fj@r$SwiSWqd%7is2$tCd5
z?ZQ9GY25Q)`aJ@2CC5J>u7>IF^a%BDC%LSh0x_`jJ3oKIk^2orPXb^ea{(ryu!+3c
zg{K_T*Wla#a8D->zs9TB(^ikgiFbP9_LXq2$_73?&0bM(vGtBEn$M}~Z@B*p4m<6;
z=b{47z9#B{S>@E4ugr}N^(>3p!}60;?S!c|n)t(vQ;t*FaDIA@jvkmf24-A)veVxK
zk&$FVwS|3Lpfp*I``x!nwNsIx9f7nNfksrs?_~F#>bbKysvVUHgIYR(C|Ec6N^4Q(
zJa40M+>!P1_B*uO@3CO%k-8g@XbsF8?5CsHXJ1xz`sa2EOGt@U?qBnKDXh{m@I2=j
zYQ&_fT;Ar+ZdWy@$c_c34R1+vP?l_s+*|Obp6VVl&9QC_D5Uf8hw&GA@LpPNoBAI?
zn*Nfe8$!RdDl3Jq4dVym|Iv(OCc+;OHu|~Bvr{mK0X{Cp3c@5LroR9-Ib)4kx#EHR
z@c6~@@zQTLCdwBvkyucaj#B1S=B)Qh5O46=A7`Gb2~3J}B*qu0YnYOWqd&KNF~{G}
zxTQKYO$_TcRnb+5`L)$Tr8oJ$m55rvE=6odQpbY=>6pqpo(zj@`wcTM1qdr(+xRc}
zXVM`i7@W47^W5cpymtlC8eTBfgA?+WP|GPO$+ouAPel|5nQ-OL^dq(Vm^2glCjBFP
z<eL2$lql|$?Cg_dSuZm6Csp2v6FidfUsT@j2{TlESh-I)>+9bvFyk-Qez>ewGe;?v
zFxUCSk!3n<F_NWBuIUJ}COC$Hvin-vq!>}_eEeQz3^^9~k-Usc8uvKckT><xZ<TwD
z*l*$Vup^x$h%cUWr1;#D^_1Tcbv2PfciL6DKw@Z?#{5aKrpMu!Y50(ls-7(O5>oH9
zKRoQQoc<B|H?cSl=yLU@7fp6Quuta<{@`bE0fKP!Ced6<=-%(sb&8wl7;a{|KMHkx
zW3Pij52sKnfK8=-Qi`1Vns61mcD=KmzFvR2n%xoR5_f#>5eKushfC`k$X(cUBUg~-
zKDXXcdZZN6#r!7U{naj%jZKM`^-UMqFjwhL9yvi8{1#bt+WdaAt1rIu{QEBX!TD8M
zRx+8)Y|>V}dKe}1qx$bEE+E+!NTZhp5MxkZggo#4!zmDAryo3UPujFokW7vpYdjDU
zKnOkPUTZ}XbNdm$a$M<M>qLwj?j!HCx2Ots&=#s+J5l?j)Ac5Yx$!kuSp6Q1i+?N|
zT}j^1$XhxC(T<gp0N|6o`^>7y(Y7tX55vuaG&(&p`ZN4FzU`tv0}N?&d}*AGaO6lI
zF~U;nW5=3Ky_q?9$afYW(O`xgef1n=isr$O(BvK;^Vad4WGQW$(uS7Njf>zU*r9P2
z61{CN6pT6&cpcJucf(@izFH~P6$)!K!JIsA#C-j}%%Q$LE#m8IVT38VD>|OVi!~fq
zVwK6G>Vlb0A9}##g8$vp_x^YcVjdpkX4YFKWd~JZE3g^ZjOXhA!l8p1GHQ(XbsTH_
z(-M+&+>>Ofrm%tWtDo0;@Gg-Ozh?%&c&}Z@5Ssh%v)xT_epxlHw2~A|-xpH$mnSIq
zM5PPPo^pQ>YnGVt1;hbE_!ivwO)czF9ed5iWc3vTgKA2il|CAt5H!!uX#LKcESfx+
z|JfM}(P!K_3%K{ytM$I$OAOMnuP-riKHawsVBbbjtKI-Q#(KzIo~>5}WJT3gh1J)q
z>RHxB@@+~yYE2oSVgc_~FNZJ>rb3!w$XLCvjQby0bn*_#M6<<p2)%7$=sJW^eqiM*
zlIQoxgB5Zp<cbUwlIHI#=fj53qoV(okP(Ipt<NR|?x}>TmLARJ5)NS3ddf!(5O8jf
z*8J0{Rhpb^8ieK$N4=9%ZqRUt=k<>NsUwKj#xq!REs!fY4%z)#&S&wG2{)p_FXHz%
z?9Ax+z{9N3Or#l2X>!@)%<m_7@Ig{^mXO*_pcDGMY7+laR2jk`j0_0?bHH4jUmx6=
zvYY;2reg*_BY#Jr8ATEZOUbr=!JPPxR>)5H(xa5meJ^nypM4In_{W`2m!;h$EnYV#
zIWSeM82MX6L`u(rX;N^O2bKK68R?DTzq#xOueQ7NJ}$I#b~2W4OxW6*3oNar!68XB
zD&rDHKPDN>QEnElQRUJr_1{nHGsl_FWz?p=I!C7Zw;h;b*OX!|A{X@kx>0eEzBuC)
zX?+moSSeNA+28Vs<G-JbaIH#C;#I5CB^{4+g!5o~dY;07x5%#_1Fv?g1$o;6jeuG1
z_%T;?JSzW1Gqu{;`=T!&qf{ew%g*eHQi3U3+w<n(8A7DwFBqVNH^@(xe)i$Z#?=6b
z+Yp;caeSW*iekD4RiJY1DfOHyv_L{>xVrfn-lMFFzDY^)DJ*jwr+<W0v29krB)<oE
zN6%bK^Yr1r>2wyAvtZ<z`}OCrjR)ziROc5~-1xs9)yk1ifkH>@fBw>BBz|@%o*)D>
zbLFB9p^X@eN5uGzpK!n1{C&+-D@mj!Xvt!5>qg>zJH+>MA1!W~jv0&ijP-U#S%Nx~
z_Flr_5bB$6A<YXyLSz%feB@vRI#CfnsVzW><dUh<SlLZ$&h=H$HRbmsk<;AP2j#!m
ziXC@k@{{3Fo&5`db`a@*9R`5QwdL<j#g2fa0OVX`YDfJ3`9pNDDI<N1iY6<&%(X%E
z=72W=e8j#?utpVH_W#FI_;S4c4`aMj26tMN@i)&pe<R6yoqwMeqa>AZBOJWI>8qL6
z`tzTvcB1{n$T?IhE!Vz4OpD_xYq;vj{DbjGA}Oh;;}o^2*Kh_ZTsvV^Kk}V+KSKv%
zg#piVm-PW|G!~btg_{^=7^3<2WyS6szV+ig)ap%|!i@HBv}F)-fIq>~w{8D(T}h*Y
z{(?L;J?kA-XUzyy{;IC<B#QYoTw@dCIQK2yY;#FzvrmbqqxLzoC}ul3_ijY$<2Hu<
zW%`pvA&j4vIW>>R0$r@|QyjTgjL}JJ5tYuV<&K^i&Z<LiC8wi&@^6NDlNto#NhE}u
zT;F&>o6i$TRaHwpa?GyHAEKROX87IffsinIIcF2tRh0m&@g}p`Hecp4eb-!m@^2zQ
zq87WXn%PGO;(*0Y&OA3?XY&HO8XazdLC%E!rHGWpy|9&8!XUr$#*U*ZWG}j9B8{@G
zK5rDm8ycN1dy~B|TB7LMW%{`b)pgdH-?ivGW3|Zj$YUQTY4Nb3S(U&g9Y3uHFn9lM
z2Pe#UuHht}Bu57;UyR*jZ~18vTzQHa`wvB1o-m4*ba%qkp4lCt@FyWsL4t;KBEYP*
zD~k?<BDX)1KFNL$bk_SkmSW$gf%@Ai%9edv*Y$cjm0s|K`zgG_q%2sVZ%9@M2kwO?
z?&PK!^NT0R$%7sg{(plwp_kWqqTsXWvUbdI^J*PLh@LYS1yN>Tk;`N?Wv&`h#RmD`
zu0K|8y&;dr>@PD%ot*@`?UQ%eZ>7UweYBV<rx6_~eRYsNR?!vhZSCr{ALwmdnDOoG
zXJq$)|0S9iFJCN`Lg?7}Z+c8(j$dxJg}Y+Zgv6@v6z|KZR2zU}2b7zS{V20#M?V8a
zFY!-u*e)|{`DKNakmHU~SiD&fns6h&HJk_ZaCV)%J>gt9&A0hrbCa@umcS2p9wP}q
ziCM-+jWZpgpwCcV(q>l}n~-W@!FDr^lBP!;S_Cwqij)eAtZYPNazx~f>F9(G_Wt>+
z<uduG++_9?-0Ibg5&;}JQE`Xo$`EdqKgUMW%6r|tL-j9qFL|d-;)xpSEo{?{)~OLy
zkt!6lCy+MI+C2>n7Or~p@MSp`Ag;DoWEwRp*0biLWz|B8<dN9ZzALi`^?p<8dTPDd
zIk2E~aLnWQ@5xVF_pK8Cva2_*eXy8F;gefY*7`!nk9kcMkCxq%qC)Q9Qcd9n*+Q*v
zK16^pGPo?mr(->lMj6CejDxqqp=0mwy7LX!%qX(EpG?%4U69hl<1zGpUmbTqm%tVz
zqvAe1aaVt~>LN2DFN=OG+xk?DQHjPOr_<^fhz=KKPU0)@r35O_%z_yfm~~UVjMHq#
zE*_82YDXv4&umeSEyjP(GClkZOy(*VHY@s!>&U3kqlCAogaL63p5egrR3NNK3{j}8
zp|J&p@hbtZGHvrzOR}h3PBK-p-rQd`8?t>3U18t)tdX7JzAP;GAEn|uO3o^?Zk%NL
zqnvxvP9<O$I@N%0%u#DDYi1x8jO_TO6$PP_*jsj~IR21!kuv;+bqGIPuqvk5AZP8L
zXA-oQFQj`57!*)NCgpAmFqQdn^J`UDHv|zY#wxE{-m4asHd_tZR#{L}S8h|vX?}t%
z^6aM#-Ge!ukt~R|FYr&CQgzUS`)&W{+Rc|wRH*(Mff54f$bBw9NIrzA9JSdtWSZzk
zTfE<DSIC|bB@#q>@6!sj`BJaIA0dPs&62W}$vnYn>A`Brg9`Ga)bL2?panpQ`>~q)
z^2?+ao_0m^AnUoeSRHI3M<*PUJ6!n;MFp#R6@2?>naShd@F|%vegm<Q@=w%q;Y=(0
zxbaQTQboEY=gE`!<&jsdAtJ2f#?B{7?wtOaSf!A|eLMd;!b+`w6T&Fg5Ye<{MWR5*
z$;WE%7C2nVdmf&tjrXR8aIW-5@7`SLqiv!##>qwZvsLc~aYVIi)Xb4io#f-BrEkMh
z6^*lsLZ<%mAg@SXc)n6p(y30x$_jc!oo2`&O<FEng<#mM<nq^3v|xQ%>c4dy*PpNn
zn<cO7nX9-Ot4&LbU`Y4OR6R~p^-d`3*N+F3N~($i?WBtvKk>C=L-|t6qu_nHJ+u(C
zIEsKpsGlU!aKAhK_89)aHDwC)Ag(~Bm&7g&m-_*59JvY5{M3c;d`@pc+-FMg-cgym
zQv4^NI@Rh2I@}-K0;qrzU=W&0#5a1UNYNKQI~yLqeQOB~+5N4I8X*<W2|mEo#g|Rm
zca*FbbvSd8JiqsF%l`om@*eC5FBzT(*unh(Q(;CC&@C8W<X~91q~~-BDon)=_Hu1k
z6wORV5{GLv+8;i1xvG)UN-m-7yu)*m;KQgIsj+2n(uBa@Py^~j=6AH*N`V#~W~-@H
zVR2MK(O0_rf9H2PN>j`D>r&=rAJYO(b0y)^4Y{{dPHH=DfVyGBEO}y0NiDN#6c0zA
z^6l&=!Ervc_A-LApUM4fOF_R&+QG0^e)z~-nfk6Lp!U<iuQ+n+v8U#xL#pzi<DF^%
zF1q`DKZ&X~gZT>hXs_+Vb(~p(h30Wi`EEK%9{2Q*Ui!1(=J%l1Rmj%W&lUqg$7;j=
zTKVWEFQNM5*aM=$>*vw!DPShU9Fx9}p(1wZ+aNyj`b=>fSHfe>_C_a(cNu%%9foeu
zX`(Ze$?~s^E;GkEzH|jNsvcgI$l)j>22lI5fRuCV2khW55ej(z$uh^g+mBloclGm4
zSO%-A8+@5_M(?zx`*YR_KsGqfJNvP~giQhIKF>JNp?g>~hb^zi<mQMaK`^lbFnoP3
z>l~nW^3{COrXJG|A$ECUlv985Pm%9JwNegtVHPKPfJ~I1o;e*ZXX=D(N(X?)T<u3a
zos$pn&b5lA_?mN1LWS)~AbeYa(9%;t=R`%k)?htJuiFH&O3;7cEC*+J?|5W6cIPBr
zS5m50j3r>%<ZqHJN}5q7fe<qPaMKethzzzy8tFzuXMZay;>1$ptj!n%Rz@drvZQ-j
zp-j4h4?-qWCC<i2>b%2l2yZt)ol2>Sd}Ap&w;1nppM6c}bl&OWpAn{W8dNsUJ_fwh
z!Twjca1#C`<fq$l)7pjP%2;<NE1URFh~B&BQhW~!-ZKy2xNYQf8e5L#=TC^E`dO$2
zdHNQVs*VRQ8mYx4SBB%1J7D4qqG#(W+ZP6+t*cnhtt>yL?Xth;T8tMiq&x-tNhdQH
zLSp0rD5hGM$~lZK)|&l5?5-dA>xZqPZm7*l?XF%MqsJG@jepN;!ixt3L(YE3**rTz
zDlpxaKB&06B@a-#llLRQc6u1<#jzoLoBb2wL%pa%gRW5uQL6)^Gq;qVpW-cyYv=sf
z+HX!mtlHG$L!hfQ6y5#BIKTi<wte7?SQLZ70Om6W_$r#FLR^ZJIu_VyMtGo64B-fC
z)j~DB_Xm!kQG^^J2xz?ls7vIYwoyIk2=S{2I2^Bz?azN!03r3yd|nkWD-8xVyy1=;
zJmN3g;X0>`$m7q*_5L+9(gK@bce=Xy&Y^qqhfmc$_~bLTO+>~%%^+z}bA}fvda%sL
zyYJOPZ6mT^|7fu2I{R1x^We)%3_Uw;;TkhOt3m%&<zk*UC+>DWJQ(T|>Hm|tc2&BR
z;RvjTTh6YofaMhEM%~gZ_=14WhWzd;VS)~!(x&$*rxFUExXgZ`O6%aPuZaNWCBUfk
z<{H#=(4SaXRJ)p$9p-({`y?b?I5mdYyS1H7t9^{AJ8MnDG#1e~YL*<>4>{HK`|H04
zmcmq3*wH8SJ(O&*pKqhcbg;cr65$u_zBQ{wBMq_nz={)UL^yALd%wsX&l15#+@8H#
zerr!DZuABYa%Hvj!R?0EPm0_t7~Sh3y&j}o^XDJV<(oU;UZD5EKQ)7&BIorkD*I&3
z`23qz1K@TvY$!6D=ioyMSCs)ni-&MpaLJLJ=llbkA!lk$PF!*X$Vtw#VotY`kS}ZA
zyD!pTTDiAy2)dv4J@nUIKndez_iVW@36W>FPxhi?x7^e5dC#e6$Yr6ZHd;On$b*|9
zD_`d2&+k_=)5Ah7REGtq(4%dlW!j?NUbQVZwNk)ZIy!Ip(RAqI^|x(M)$88{NE&M`
za7yGb_{Kpo%MuGRVO`SWMCemKVw=nP*u<EMmc&)~2Wh~*Xf1gT?)H4XVlLiriLor#
zfd$W1=afndtMNg<4Ua#?q0=k6sq&Aaaph=7yp8{sU!-+i2f!|KLPW^f2aQ;U_R@B#
z^bl%vKkBopH_pRmr8JZjwh0W?Wlq5v(mi7Kq3R<5%A=c|ttdJ>7PuR(28oN`6eL=G
z<-)oKmOotWmzOTTn0Ju)k}SLGH&W}=gti$#FKKnZg?50<%yBD!e?9=@?OASrq%(d0
z)UAup+L{=C_cbGJFnzaDF%j(t{15sIp=um#LEC*9f4mc#+fl$bI6m&EW+{{~%MiUS
zXGZdVtM-VaQBYivj^ffG02b}l(^Kd?C@KmLyo;-n5|Kt&B^YMkUPW*P2SDtwZjqzO
z{e-I`4<TyuL4GgFrf?y?hAYwGmFP^5@+aW`%DPdx6qjoKeI(*-r$0Mx{^~PV$Yhub
z?`67@&_>BVkh~T-&t<USENB{JE)U6kdM@l<EL=$y@5-p~Z_6S{LTzIF`HoqoIJk>V
zU{AB7eYXegpw)wnOk?|n80|FL(!nkx*X3^&Bmw+L4TQA*;GM|VuMB?Bc#!01T$B(C
z>ldQ|k_~`aGe}jTNjV#>9kZimABcRvq3FQ{FVDJtB#7%x&GPtTde#TP=&c{31O(kU
zmvETS{f8Z)s+zJ=dXC|^CsQ`M>Vrd`4Sq8}ToR3>?Rpxi-{dGe<KJoix8d;;s3p^q
zG_8=JKPLtyfHYcdsVF;$&j-Oj)2hwV3P|EEKV#Jj{-HIPta15`S=OI^(ie$q%(`=i
zPX0Guf;J;i^rKaqLfi(|I=SNQeVDjiUlqYcqTEtvP8ec$;Tfv6uTEYDIV~{1-B%wj
zf@{WlwiHLS>qRuzsF@dO{nsaH5xqJW3SQ$~W(f-Lj_YV8dgfO+_V*#Db`YUydD@-X
z=5(O8xtz6@^qZqL2wpSn#t5bS@#>ZO%Ad%_q&|f+h!?)v=PY(~iRw(=jCW+)=*Bg#
zraiVh;p_rsl<Lf<^}jF^p|97eMQUPRAXyhCqRnm)Tms>s%rY7aSWY=q;4<W7yk2$B
z%2^GNL<asaL)0UL+?`4ykCzBI%Mx1I>xK%l51*mbs(Rt<I4-LvYI|CxO=|EryPSem
zd$6Z{m*@#~aIz8zL`Rv0Pkm6Dt!rf_diL3O&&UOaf4f@6?}-Tw4u)mYshoyc2yLR3
zfWRc4A`iiwHS>Jvh8~^OD-4^?@+npp){cs})Rcn*eNtk#uD0}Pw{g7XmCH&=LNR-|
zAc|Rwk4@*|X`05;n!XragP%S|F?ZhLFzBfJc9L&MJ+Oc#&c_BCo~RsEQoJ~@Wtvf%
zI2~Za4GH>8ojYn5yui;;ypJ`@lSfWYA(ime9LA7do~1pvTLlWJnRYU?jbeK5iR@OQ
z!<|pss=UT5On`*Kx*>Obo2MZ3iX2fW^@ANg-tM_3k*I9D8*}mU{rk@kwOjJtTOFmN
zcAu#2kXuu7w-AV=$=57@7p1{*<$w0~<4HAcG`^QR=gYH$7$6%UfNf^T<e&N)NQt6}
zCOkXYW|QwJnn&8+_CSBu3dfa>=nBO;_RE@2Sd}9NrQ(t3HhkZF%`1IV|KgkXddozp
zzc|xsua5Vgn6<^?2n=XwZ)P!o;#nJmw0b$ke{0Ih`fr1+*=xgOdbXI({#hwmwQmTw
zQ0A(E1eUsIXmW3B2IMRPl|{(CTkgx?aq^7e3z|*Os$Z`~^4*8&R)DfcE%b1!L2aj1
z`~C7AfjEUHh>j|fUA;$pW^sxq<a{%i41Ot$4sBHmoc5fzfGgoQJIk1qx~<ly`g7>W
z=8Px8HF?U-;Z)<W6+~2DEbe!;QI@faX@39b2Vkpqw(jWrL=mH-R$)7P>@KvI$%@s$
z5vzZT&6Ovc>-%$(h}maqMs!Y6jBi2Sca6VN?cf(9Y<ruJZ|ml6(4l)X!+;|CWyBJr
z$1K?Z>A;<%Ec$o%Jf5)CWWmN3^xTh|6t5NU!Egt93qH18+^#hYxLzMo$MLrm9kL!-
z#{N1bSsb|KK1D|}d-A~3gbK;x|8Px1A&M?1tD8JWMua0sWzL6}J*bT&bq=IA=a3i$
z8eAU(cvXPxRHG66ub2&^??wT{7g{AUf#KBjytWAE$%ACFq<QA3Y@RR7RqLSU^;hCK
z<^)!s_tb6bP8L;6VzO#>KLs`F?D(oVS5mdQxk!Cdt6%0AcVE)m`4PqkD43ftgOY>%
z<N2$e-xtw6HW;cyR@k%;loxlntwR;nowxNZ7Li2RX!-l0u$EK-*6y;uBDa4ojwg-Y
zkaAG)8h_~sKEebt6Zh@;)8CJDctX>`Fav<kzTH2LwmlGFGAyE4t6zOKh%hyb_1ok-
zk_MrG*!@-I{%qzCDY~t>0XP9soSBdJj~RlAW=}dBI`-3g(BIjA+cf6;u%0J|%#%0I
z0}B9<UBBnF5Bm(MO~`OyoPzFW#>N<5Pqb`TsON$c2WZz#?vXm(0_TtYtWH2WOJ3SB
z{3))66j#wE!a)amwFsV_J>YyN%bQ({#YXKC>t>NF$A?|`JHue_tC(f6;RMCRqaTcQ
zV}~fNH;S0AbDM2KYS>V<*;xR)Aeh7KF(Yp^hEQ)U9R4;~Qoss+HD1j?l2je{o@Bn1
zhV{PKWu{+2=<fLH9w5)aoQD{`X&2Oack&ddAq->pj(xRG?)Muk#&Z{+S1w?JK=^*H
z&3iWJbbO#UgeK%)nd&r^ASa@qWu;&FbR6G-4h~A=ndO_<|HQfU9i*6Jj@v)4rUR~1
zWNJzD=<agfDitmR6f0mv0a6ak%}}h`X{8yk$j()L{NPh@3o&j8ZFH6U{Z&7>ozsU>
zFgycDwtB!GVv)`*Q{dT2+*=zV{-Y$^PpRsd-CvMx6#}*q_yji?<N)T(A-c~*ZQWkI
zOVD)^ubvAwf7fXb|4;tptvepf5xG}DSU<QB(bB3;Bs`7a8ZuKeFHBVXC#b5?&z(!5
z526e2(b9RMcZ~nB2ffp5nIl*4)>ZoHt-LyZu#2V|V2zXUUuk?qo7&Nnm7S+Kd0x9i
z@aY6W*#X0e37IDhC%2@%P_Kt>CPdl>=Q$qi)wi=|Qz%3RXaL82^2S5RE?it?^jh`}
zSY@CqmVe4|peP#!*hqP(W9oNyzbFlwLRtr#n0T)TuLXCWcJ)0I!(3tU+n_W#xSIrz
zn%w4(c6Lm}u-T*}Td2}Yy~Q%GeJ;>g-i@|Gvw~Q;e@e`h@wE>;`V*M1QgyMgs-2RC
zs^(`Cu^^iXem&szb)H^*555uAe%y`Y{K#yY%Rgz`v=8N}cMqEG7MI@q5Fjw3`6jod
zmHJJHSLl+#67F;@pVMv<DDus_ct;c<ZHiCu_uJl20H|j0M9Zxj0L*e4bF*M1#q+^*
z7#J@Qh;4g{8|>ol5$29{UClHH165=!Z!^3q{f$d7h?+USuX^~sH&lnjkw#f7k#F3h
zowbdkPHwezIeBEy3gPyIf^z5Tdk^^*>U+(#19pf-=d=SJ4$jL4S`D@^MXf`?#Z$JN
z&=HX4WC{~}u69X<YX;xe4H0~{4;3Gi_}yK9kS@)P?S7~`*sFo@0;Dn(cl>fom*=_D
zGVedZ5wYQbC<!Bfj6XJs4iL+Cl(%VjNEw;s&%k~8Ro^cVu>T4Eh^-M%X|{RwpUVVH
zTz|+#P9wK=8BDMmu`{5Lq90TdOOD`ug#6C15H*TR)>ttM_S{j$7*T~q{2h$27w9#f
zp20?x^jkE?LwhzUdGLlbUDa_^XVbP)jMcbZ{~vS@MxabDvaJdn{g|VHGVDmGJf&xQ
zYnBLvdF!Wcs3;@Xv%bC3|EyFto>$O#ZQ(l4C$~zt4%P^+lG|L7IFH=O{rtX9iYl}?
z#m03Y+E4%fkq0*0{qq!)hfBo4J)0bT#sC%$$1j94kN1>Je@#q5XoHpDM4H};zo3V5
za*Sucx8I7YtG@&`SVzz?vQN1dT`c6aHw@L#)yA3!$oF^K>lIIpG=DR*_Jc0+#eI-r
zKM`5e=#hidzk6-ixF)OQ`f@gVcld-#DH=T>`c=>BpF=Z_>sa7W3K(PVOBOy!^uY-p
z9}gWOFvpL<$IjfB(pf=TVJ#=@mSz^}L)Ks$m;JWHpog_u58Z&UGl9&+JOIbj35hd{
zmzs#$sYp@c6b_&+G0Ukb(2&!iIt?0!17&-zcky)cVF$T>@b9(|C#vmNcEYHl?5tJU
za;^G>Ea?{<?(V|YYX-mgZ2vZP33(GseS|J1Gz9CPU8@PGyw;K&f{&qA*Q)jB$)#PM
z=o_!Yo+tDPvy=UewGLms#^bZ{9!j-|hNEt^@XxqT0G#q1lxLW@7@4|f1;9_wCKPo%
zC<T8cKk0i6uWirrrlGt01sNR5foSH#wqzG9TouapkID8po0y4v4q7Ys=b2%Z+GeV4
zT5uKhegJwyq*yTL>>X+Y{V>r0w8L|{i*Z&p3apdB9b~grt3`H4b!BLuWeNfElpZx~
zw)sol6cjz;oOQcV7eao>mS0men7=9+=;8}swCjeQBtN&an}7_y_d%Qzg7g9ZNH63Q
zs8k-cdzR|{R%+(-w)qSF)RBC}=vhV^qI;E>fios$*tm2HTdm|x>?*|N?f)sg1(A9V
z`OYz3e;lwrupmlozp182@xj)v4Sn017Gg{?Cs|{2`0P9tXGHKhdb}#g@|$>YY@7Up
z;fu-=Z1>fa9cu20>K~W#@X#Ho*-FYwO&Ei2Lncv+8eE^NkAk6zg>cZ6g({vI{Vumo
z?FuP4oLae95od|%fn(*Rw@&<xxpUBaJVT(_B?ju7r??nQGp6wyp<O%2FoI@$<q0M}
z$O>&%7@Z42Aef~DcvXexu+H?GM&XtjxU;g_3atK0&#zu8r1RT&72r0Cak;K#$4Xbc
z;bXeyseM*xFuR0SqDxm%1I$JI^uST-sp=4_9BQkDR9|pP!Z5QSUCD6WC14T(P_6ud
zG1GXRGHFJN4G_hp8gg0z$#2l2r|6(_{5`!}1hmU=1Xehl3~m7}kA39rcR?OWmA^ex
z+DZ^z-<4+N@*T7uVJmCa!OR=G6G-K_V0~dh>(sCVmOWgvgaM+2+Ozvq<oue2YIUhS
z2&_@Xf0)&W(K0EA06oVQC+ptTNygg0uN4F}otOY}U#vnV-Z4_s#A$Qe4>Tb#lYWhm
z+`He_|C4iZY;pQLH%nh(w9sc@Jptgc666=;y6F%f9VED#vA>1GZmFo+`B0DH{{uDV
zr8;y!O~-$xZPt2?Z(=>y`_l!X%g;eY?Fdp~rf%&$o)Pikz1jIJ&xf>}`a`Fn<zM#Q
z>IP)6<=R9u{S9C`wb-~W14ub^O9d}X{7f`W3iTRv$`yPCitDKt6@@BU)}dc@ZOLtT
z)ZP{N=dUtVp&5WD)w#^ehu+m}9B2*FYB=dIhGFqpPj`QQdMS+H?=w<I#GPy-?{r0v
z$5bvdz4^SBCO;Tb7qMs-CUjac@0mA195;Cb%zDAqSQ3tmp_Ey0AUuRe+Rb|z-GmHm
z1+m8{$lpdaNTbsUYot^1*WLk8Kvs;RN1sq!H=;T`zPXaD;W@tw;ErO}X2$AxhB;$=
zoqjH2@fBcRy)!*YiwPHJwdE|m1cHC&XZ`VYf1(CbAsA3D^@F-4ItUC|xu?Gg4U^bB
z@rxJkX2KNj^l1En7SF<lv8vir(;#ZR`mttMz%I?gF{D!nBk%`^UAcn3?a#>F97lQN
ze#=K3G`-8Qoh3bFsWJ$NARKppGxx}2e-574aF6xyAMFcxyK@W*h`$d<#S4n+Ji@pf
z&}4-HzGruu%HX0&F*}ZRvkc_3Y$F#LoMdvsk_=mNiuwBzlCK;<Ct<Cnj?}UN<=cA9
zVKkXEZ%vheT=Nkcagu$gyw_vFt2Ngnyw{o3M%WeAuNls4@B-*^q8qhyu%vmBC19Kw
zq2D*f_t{Oa3m4<JqWR~JfUIJF84eSC54yl*Xsjy?G(9l~p$jaol+jsqi-J*?zDGOj
zk<|Y+ljR^)U$#@=wXwJ=l|P<1(ie3M5o3q24`R??=LtsbaC;P5yU-?cCjFJkE`K||
z+nZa`FK0Y-4B#X-Q<ZEs_l*R)82`N?Vz<#IFy&}JwFHm|Vf)$uKKpc)Cr$JhTe#u}
z{-xL9FQ=E1f03k(-EKIvrJ!4V*?bJZeBD#X2*Fu;9SCYxzDeAWRS>=|-{+lEG3U3i
zd12bZG^z9BK|!yR065Zk?moVTibyMo1l19+1^8pvx!w+#g9c=f?}u$DH7tAaSj^->
z=mtft$)Pw2H=q4?s@2cr&mVrmLP9I4h8R09qvaj+UJs{f4r$)cIMbvOUia(8-1f3*
z^bUm86iZ0{QT@vr;Q`O%DkD9BL{A24IqguKV?h78>ce!wA?LHkG^F|kP?be0$AGd%
zgYFYV=WX6ggx(F}K}I(p4FrusGTnR7d;3#jOSkPG>id&R{1D2-mp=0H8|~fXwAtM~
z+*SkjVI_UiG+#p4i5SUm2~)LLuvTAz6oz3>)Tt}u&$OQ?H|Jen<s-iZ^^B4&<%cJ^
z0;x?nn4dC|0?Ee~<{CgZU2Y8{?E0e;*Jeq&V9DhY_P;!GrL=xxE0;qorw@<~HlXi9
zZ>K<)iI9gpea4ikJ86fA7JpDPw^ek6q!vZD^)Op=lB^DBgC1PL$}7h@4Qs5Z#Gk%0
z{O1f@RXJ!_T}S)O%Vxv8T?M-2I=gNcAP9VD`fZ6dPnkIUv~;WPEKK$a6EzmFP3icJ
z#fs&s8Mo(mn>gN=8BClv(IhZjBIF8M31_MX!3TIj02N<#(QdobKQLAbHn+XIugG~P
z8Y*A;c7&GAc$`7Ci`vT)#~l5ik@~8;HbBUCFQj1Cl$REdqaMBMIOoe8=F1o-gi<|C
z4cG$V?zkBlOEyX0opN(2-zSqK>)$NUx4|80w8(7})B*0vi4<te`?*tQJ9U}6YjR&d
zGL(Et4+4AB75S%1EU&_V?QqzBrGluwb<uF8z8)TL_9|}|cg5-@ZoD_$#XU}R@nK&{
z8MiUDbCmfHXJ#QXWIEgv=)d_5Gac>E+ts)w^fSwMxFx#k{{Hv8VZ(W^?{4h^yA2DQ
z50tl!gBq`6PmJid2H8A_Q`M6No$uaco;<wTQab8XCJNi}RZ@UL<Ppb*)F4iB0sK)&
z6gmK6dF(NhMwdq;d0fG0_NP6dpl!GZ2d}r~U*i%h0@`$eb;p*5cEU#kw@n+5z3AP2
z3aYn-7G;B!rn*1H{n+PJn<Tt)4`YnO`&rA@<q<Pr^56iFG6qA(#O{q_P$yQVUi-55
zfBZ!r6N2-%=Tc3iYc0_84RBPizl>Ww!EAZPL-XDqjcG-Hxx19342G7K$<(pb_&0A~
zhiE(g$J1oxQDR>5)9QVmK*$#bMPtWKuC`m$P(Lx`90FWq6iXc5al#<n)*FI|W(~#!
zQvgI<tR3MD4!E$1sPI(>8PWovbPyGpze_9&M1flOCxj*ORCV7iupD<Eupdh(oW@*j
zIo$36lf@QVZ8yaPfTeo}e|yKp^Nj%c58NcbwEkM6(CAybKC~`w(Sr=%$HzGPTir>Q
zO&TF*&0{;QJy1dES`*kT*nQ`QWX>ytDoU|G<e3<9ynQYx#Yh>uGg7mNtjH?Kak`Cl
zv|An21K-ttV8a#ysiQL$%KkGhx#fTdq)2%`E_3`40fUTMh54V?k=|m5E~3%9NzinQ
zi?7}D3Qm$avo&D4g-?ce|Lez(cOKqhq9~4d(Gx?SCe-sQ)-`Typoe=YhFkhE$@PeD
zIk&#uXT6hg2mkBWSyX22+YBp{u&KznF#_Bv#yPvX>LpBPO2uYK%cfGEqp}&ATX`c?
zyKE*(#LfA}n{V|}kZdteDPDAeRlR*eGW0U(z;7^dPm6<jhux<s9u02hkyc3ImFTgx
z-YX4$g_QR$-Ma&QeStTW6!i`o3aLLOCHZiSFH;eOT#$1xWXe&D=Gz=n({p-%cId>e
zSz@dHE6ibYM9<SH!`tV1qn-@oo{^T3@zY$OA8>w%^Tg9Evs?l04uUqs7rTA{RAw4;
zj6%*a{`)#o5(%ognpoYNkJSH^O*u~_RdP;!#h<@?(W)ghIei=CofQU6-jxV7ogu;>
za9X?1*PW$CjYioWEq@Oc7&JYWX}e9LZ-rQHWbbL*ehF(Gz2iNjp4`p%j(^%Exf%e5
z@^t>2dU%pzc#?PS*becw6sx|QkJ$McOW8X652l+Vz%MK80bGoG<f+SiXM3D&Bf;i!
zGSV5qV`*DC<rYJ%S07M4C(Xg>R5_YgUVEcjPa71VG`b|{I7id}&@<pe_+1I_3NSLX
zFTXZU^u6l|z99KUQj-7o^#T84mp0t%HEJKEtWIDKUp|e1ZGsRW9HF*^@Ib?Bf=a{5
z@LaXzApb<}N*DYDXC%Usrul2uY@3Rl-TcSaW-ugg-lRKbMbLSD8I0)>ua}~VVz&Q#
zm+tA~@qDtlZ;zr1ZWXisni--^-rMOE=Gzm^-vbqFn_wp5fpjM{=<`vydc;_9UFqAc
z=>=9!Bbx==*Y1t<-Lrykc5-jAtj=D(a@`h?)lm$`zM94P0;PF=Uie@C`wF4HNHC(|
zF{h^cE|uSq&#d<j=V-wca?%fPSlx(--+Hpbo~qyu2NlBy^MSMz_CJr{JG^ASG1R;k
z#}8;RXGce*&qYsOAAxJ|0GadRe_x*;%y(u5{T20>dlNUa^Xu$`nH23e#!DS6_ZY*Y
zE}VJ#bPt9%J*G=n@^ZeFHSNFEu5q~B*W|R^`;&&>$Su!@!DAEy8r|5GGH(5QNzRuq
zxTTFwVe}^qp6FSkyF<BJBL>$q)xb6f;^p5j&0D@(ISPHvxR^Ir(Xm?XUuHECrqxtu
z)|a%Jt#C24bNo#SD@hT<`+ep5L(Q;Z!sUxuS^x9w-xx2E<rU+d{=CYt@11;wn0~!%
z%!H;KwA`6wrp)~gi^TStoA9Q5KjFG$Xx4n4?a-c$`9=D3I-3<77!SE~){ms-&6*rP
z2wh6}KkqqJ47}%?al2dfxJ{BQfDcKiWvjBk1zcs-aIThVV#-{*{U;*6hi@fpL;R6q
z{x{iasVNtmM`Us)m*`9$U)l(sZSrx;4f)Op&l65G{C_tO{19>-x_>T%KsA{jKh{|F
z*o}bujZU#i*Q1bE+8R$J9)^otDt$B3t-B}{x5t{DGY6*IUO8F_*K<81;Gc0XmZ+fT
zGL-y2Q)#VqzLmgk0$&M5c=xr{!d^W<88ocNxoz&S&RRA({krP|ZCS(<Cv*52Fwtti
zsBXn;)uekv#+#Y&=A(z<m)&$b5OEjY4Bvj(^y{{C*NfsU@c9<FUnu<Vg^|Pr7RF+m
z8vqTS4%#p)#jRsOZ%Gx0PE`d6BBF~g{kW?$3fc~}?RQAO-cbE&Gc?+W%sl|sWL$4g
zSJJBi`8b&URgImh<>vp}jTcEDs)BoG<X!|g`>du$-Zjla5)bUjlF(tPbm}YX1w7D7
zQf&V{rB(X|yp_2tn*WIJ!XrkomP3!!bes?)<u-cxWI<u=0C=7qF;o-1+CuSvpZv$E
zp=%NTAeF95q87-#r@-;G*)6=MP!6_WbBLSyYw}o)A&<r#(Id$8;fr)<Grve3Xk;l`
z8_Wy_HnV$^pjl+TSM~e<i6Y?Uag~0k-m06}`XuDKZ#I(f>@qa!u_eS|<#iLalS3d4
zzq2y0T{RO-<P!cs8Ai3%^&rM1Zvgdc-Ki%|WJ*OzWC5h1dT+teT2-)-7M}gm_rJXj
zmIs9(^a{eyOmZV@4Humb*T*(NeH#Af#}&2VtY{%HhjeN&+QvG4*XT}f7J}^vjIcY6
z0plzQ%W>GVwr_9DO`e@e{+b5$6Gnd=z>laIOn*vZ_5Z%`US6NqZjJv0f3j3Q3F1P?
z+Gt^NITNi!*@N(w;~f<Fk^8WQsrjPdGsd`2N}?zCzmu*_xE*cA3XD7(!q<n-f@IVl
zU=?2^2))MEG>T0S?BxI5*$;9g>Lq61QJmqMpNK_m`*gJJHR{^QK8Qa6OQ?zY2!({X
z5;i(4bvr3W@z}jT-M7oJ3e6V0sFfUa<wDkh*~MQi@H><67>)JY+$PQ8?+6x~IWB+#
zIKw8p;63OVkpK44TI25#GJzM-Jm%0NGQg4M2~5+C4MMx-zr*S6Zf$lB7Scixz1H1m
zAf#)APU3~e^yjgwB&!RO3Ev20D!R8<pqQ*HvVXQ!{}wE?zzLvioCMOhaD%+mNZJq7
zM<86kd(&AaTES!9>^X94f}7udUM5h6Cg8ez*@PQd))=vt(L$r_$#=gK%pOpluf1Oq
zmmC&5?NF_O4)S4-cIt@$FRz;vra=9e<P20E4;t&Rr*HTj78RiI`}A{1#u<p0Q^2d^
z4{Pyzrb8IuABcRI?o<y)NK{~EeHhjU&nSl+=9+zPpkhI{kNG{KgV!&xdp)qXDrpL`
zpC^8nm=B<B?s$uOVoyv7ok1H->5LRJX~DWztP_Cta_xm8Y~KU7`u$e%a1I@6>5)Qw
z3JS2kZVPjWek2y&_Ifi>mTUBi6tS4mvUa_Qvod8i0Jf$vCMd@tdg-p7$8jS=*RdQ+
zZcG8WElBpOKy%ix){St|x_(z|l@=5tzLJSO#(=(`O#p{D9)av{03;4px(|&Ty>ysb
z18$>A<GhI(HiL@--+*^8N%wYgw%pYn%t3yC44gGq4@ZrN-vW1!GvDY?MS&Na;Qp<Y
zVowj&mi8Evfg1D|5e;7_L`h5!w|~1U;f~E*{oMlUqb|Y`a60YUowPj{7$mP2PmgoW
zas$Bw!~5CUWQenJo~#_yWG8OL#xDm?5`F0HApzSxhI1&yMS9^<cx<oF=5%efk*d~n
zylbHi7GE@X+%up+fFhz!qz?MPSlNmHNWI|B_lt}v_B&+wCr?fT?uu^wjC+cb=d2y<
zleHKx$pkc2oBI;eo_6S`^YdBkF4=Nlvd@h2C96X2ZUII+!p_!vkGO+@dCi;Z50|f2
zfoZzqpd(0k6NGbTGw~;E?%?KB0F<uh3_8vyz>=i<$alGt^68bX6NzB5*{B6Q0n1O1
z&oIy^L2Gd0$h`iZ6(@NNr2UH|brNt9(u~X{Dn0kvJtLO;jod=>>tIB99rpUe66>jP
z=2+o(MK~*MFut2igS7S3j^+<siz_BVyaH+=p9I5=V`Y*rlRmtEDXQ}Mo;kF=Zcs)L
z!_d9p{Nd=*4taDR{G{fonJi5-Qe@n*{UgZeP6M$<R}}C2=i8zBbe9K>H43idp==#V
ztgBX-W(@q8O&9)VtuHe0Kxv+BkMIOFWbHTqD4yb98G+Ho##*Cs1``Ujm`-q$&x{&Z
zXb|(GjTAq_c>ovsQLU})7(LCYOVaTL_MaC(&FJ%vlRnq5>RV4`Rh96JR4%7qp9uY&
zGRmk4`YSsYKH4F!UpQJF>fpTi+63l!UHG%b-YhSjzt~-pZ?>_EDpla((=j}ezRk_`
z<h#j^RAj(+ghPOKdyBkRpXHgIafvoq>yHQw-qXZhH=$BjVq!i|hhMnjO?#YK9RXEc
zHMln|1{9qotuMZ(#qR+tmNN~#VfAFuz=YpVB!<O|h`$(l<ewdGtnnsV*X&4p1~lA0
z3{HAIMz#wej-%?4$8YGmZ*o_32MA2G8h#}Y?09wX+9Xo&iBO=QAhpJIdia{GPR4*T
zoNj?0<T>RaHLZpKN9|I+aWlqzGKMQfL=U+Kau`jSV%gax+H5kJ)Wz=!a`0SwNvMSK
zEv);2>^JbCTdZ<gTO^81{T}#35`Q$~QF2_)o<a4)%47V`+(YC{3ST*m7UmJ?<v}p$
zT|{>%u$u(<WkI=v7@nNJSgvMiO~Y@Uz3MmhwdpbTel&uP8B~^}HC)2p?{DKNTrt}?
z29G<qTnhZ$y(E&6F6gp5E<URR%2^8gcRG_0V|DM{;O`b+45cZQ4U7NE(<;y7XY$q$
zAYAjE<#BG%*WnFv?SmP?X+N8GKTV4py?vK+aQwEUei^2ygJW;y7%u$T{dy&@#o$BI
z?u#+%s+6UOMpv7`aS*Uy5Dcm_{X%PGgy!eD?)^0Ey3WA($~H-h;FVhKwc1IL$c;f`
zHL!e(J@H!>BY8T@<soVZ@PCLXO@`m^d~yc;c#8<kwb~~+23x&}pCq!s+`f9{@e7;a
zzX$t6(MxJN4z=}}gr}hgT7FwM*gWC4gqlwI!+N>KZ;s!k7IrC!Kag(Fi<H1OjTJ23
z&Wx!@zn8r2em5a<ja8Ij(D_!Pgp~L9X9U9tJoRV$whSi2w6Ya>7gVXk3vPDfb2XOc
zuE;UcJdl0n^|K5xIPLkV671+Xf6ha0JeDLj_4DIQ3@=Oa0xB}Y98M>s_BdEuC&+1H
ze>UQS&1iu^rA5M_j)tP-Da$RHVDb;*ARVjnQ$lL6h~5im2BQjbS%wx;`Cr8{BtNYF
zxV@Y4Ffb1h=*8uouZ_klNRwl}DxZ;2rUP;yHePXZPxg@HztM_|tU*^!bX*~^z6|O>
zmh85s4z)8I`Ryx~NdE0vF{|*i>-+(?xEgKPyoTV1d%^xlF9MJGho4^T*p<eJ*Wr8G
z?~v=~QWuY?zxu+pw)XE@`gcWr0!ap|uxS8=0TC&RjEI^iLtIWO_xi<&yo*M+>v32&
z;~x*7Qxc43JPueWlDI%AFsxaehS?ubIy&<;O~1du`fpJR<@(>0pyYPfyx>bCIIg$K
ziO-{KeEttv%G@7#qlXuL^aCw&wQKM|-P|v7V=cvgjM3{EcNt4zZGN%Z5BvX}{QRN<
zStr}c{ZZ9)khyr^eu6Zm;zuS$E-T(B|76TC9*yrYI*Rv=>nFzfue{Ec>!hOqRG9=P
z{S;RlEh;eT3+u@k8{(<||H|23F@rNTGy!JZPVpAx$dx;&wrWtv2p*D!_{WP?SSdTs
zG#$&c)zT{eW2z?`b8Lb;Dc<-6QcP6&dNrt3iu4lX{-;j8NSXm<#%v#Oz=rmq)fWI<
z+;5i?eN^v#NlKAu7}glcP;M%u`vgAkA3`nK;!*pt2VPPmzk`{D!b0@(KfG^U_<Ilj
z{_7VG?nJ1t)?o0}8??(t2Ov_)K5&1~@T4Lj)tJ`pL0)mU;O+U8l;8M|bZbUw#^<Qz
zY3GaN^#N$GYcae2<gwH0tD|<d{MY4T3mpIJro707Zi-j@?Q|^Wtoo_L;;}k74}H{j
za_;zY7>jvr_}gZ1_QX8p{uC3!bNk}prd6ve$Yy^hJ$z(x37kK1Z^nvJ0qZ|hv#?D-
z&eW{^vFLv%+JEnp3mHf`%GO5m<D-S%=B-Uu76UE72J6>c`AD+Ii0yb!>=@S?GZpOO
zzzu+8z~*|ipXP}rIB$$^0=U`M@<Zz#x|_}a_i_Y-%W*A^je7)tSU#NNK-?Jw47T$p
z6nlU3>Jg~;L<iQv9M_AeQvj_zsLV>RBoR0mRH?oDbhw6?PLPL2>Y>vAu8xH7fYi1{
z;uxG1Q=lCB|494GsH)oT4-^JO1VJPu1p$?imJ(@61tpY}2I=lD0g*<LZWTqkQ$j#G
zl<w{Z>GI5__j&I7|9&`M-Z2;sG1z;py{>u9`KxOquheRCVsE*ZUgGh^W(ky8xdl}x
z77{ieNV53e8GE<Eh_Pn7Fo&Tmul9VztOjop`aDMjQIq)T@Z_ITfP8YPB3RkVse4_n
zCGeI!@4=j}-9@Z!KAq#=-lFVSzy{5Uf_KgpS@b1peKZ(AZ9m~!s@obQe&JBP;}b^7
z?`k$pDk)C5&~R1QzM0ZVLr=Xp$}ko4@9gX%GnHwU+dW$OMGyX<f*vZ_?GA+tzo&e!
zj=@b`W>HCj_~=5dB)?~44`4sCx+#O@g>|iflAX7&$Ve_F1%pkm;rW-4_<L3xl}?)*
zvo!$kSoVB;6ea!n;lJZKU{_**=R8X8hr>h`v^<XtybRz{?F3zS9}>tsupuPnG<<xs
z-R0K;ZE2`9`U>?eG}X)|RSMi1+X`CiJ_V@?;87-7G|GP<5Y0xkx}^;O6Rws_X+3`*
zZao_72en1|t@ML&IvSBV2EfVnivK)iZH+KT3&0hJpBG;dHZ}X<SJQPX-jJ)hYH=Pp
zDryq3H)9KJ+k`q6hAJPjYN73|4m$OImOF?GvifSy6nVYg0fq(lJ{aMvoF+ZWI9jhs
z{qw=--4dDP$rSW|LX1Fh&X^BPL#)M7(t6dQMapY3Nt%Ahm1O86();O&CX9UoCn=uC
zcM61fu_dBjMB`Ab0||CMLB`&*=3tqCs@@4ti)YObn6Mf{ZwT_05y_kUy>+y5-jna4
zRW6{ogf%#Cy(Es#aTA`gmz&_2vC(f1b3g*uMxiWyr3!rC%Z#tHKwfftpwbJn0W4eJ
z(yB3j;VLpnD!KdpWr;<*Qao4YVr~Ib9S3yG@#WuqFI9mpquiD8{Zom*oBvys(WIVB
zn3TE*lC=$86Xmos$Xv>sLa+0q<D&~yjNv;&Z%D{^2wilU7e)nB^`Xo$>|du>8K0A>
z))}EZVl6}e;kP_mmPa)d%WW3jP_9-^akH#M;hzgimqh90HqZC#awNcNuxEyfegi5d
zYMD{8OOtL#wnQ-T#)ohrTUD%>c1qN~w>;za&xGA_95%M?n#1bo63l0~FBIo!oVqh5
z%=%oqu{3%Jv(2LXRiw9MN>JSXr`*A(y?b#0`~tQv56f8t#OQBE;KR%OZfGXgHq5cP
zB^74RqF!nbxr+0@bKBZ4qLxk~Stnm<Dt-hRu(q-KbFB1znGM~u$>TKa*m5h2srOhM
z6?{)r1R5vaka%5|Q^*nAzi{J7S+4)@1Ce@&nGMZ-*gB0E{WRalG=2P*5GXIdxAnEn
z`V6mg8Bbj?L3Epz{YomGA-PXg;_INEEy!a_8N`(c8{@V>6ROq&v`v>$o9pUQe3rA!
zGfN`;x>%f|OZLW1;Z&Od<x~W{Uyc6<|7(<_Hno!&d0CLABzbe{wg#ISy1g~<GIX{3
zZ&)r{8}<*TENKs5<#Q{3kPIEexNbgWwCzk+s71!+m|f67((E}%{v|BNO38K=8Gejj
zhVqQG&{u&3E$c045B@w3;?x#s#%8Z%BOlRRfiBt*syy55J?P7j5)|N#Os|6l`chCe
zt2$a39ilLG#sWP^AUT4m%zpW))nnZDX@G%9dCX%MWx@#QWp_D0Z_no@W{T>7rpyi|
z{zq%vj4UrRe>~IpQ$>C3vDov&rYC+pD#tZhQbp&uMLq3GCan0)MzhM%ft1V@h~Be`
z^Z76os$<wx>2TtcY?{a_n7)cjkAMm(aecG~Vt@`Gd?cvY`&!jGQex&-g=@|)Rb?c<
z2~&1wm20Nx-(~e(0%iIbGWD2YdM;68m8XY4Ho;eA$x)PuUIKJ`cQwnXJ9%9`iCd}j
zW>x3c+N86C$;oO+sIzQ)cSlP?V3SEhhe!kZD9z1@>hW@$S%M4F7ag%Bl*S2VdB1J1
z$8j2kS#6RkuJvcdWC6mdF#g$668&-#im%SJcG_h4iuMS)ar+tthhF1rbWE;$1s=EP
z(b6BIRLL#ymI`BMWoz%$@6EMFjsu824e6aI_w1MC%T{x%+9K2p)a639Rkg4DUhe%|
zg-glj>@d8)KH9AMvhd}4ozDe9Os<Oyoe7KF+~S{HG1-T<!5ViYi)%UqyH5J#<t-Rm
z@{%uqQG}{Rgh~Bc$2~%r^H@CgKA(1Cg4Oh)E@|_^JFU}f7S-!_C+oKx^2<lB@)!9T
zM0oY#%J8%w__LiD8N^ym>yEDt<u0H7JkhuKHWGS7Y-!Ona(S>*O_h{h$+hO_)PI>J
zCFu063U)29RA*X6urKXMCdUp(X5B#R7`p2kgyFM)9{QUsKQBE;e{-_dORw7Ml=e<@
z8-ek7WyvZG&!bRd#$R5<J{8e60L3BZYmt4dANXSVT3?+DNQ8LlI?j2e7z%{3%j_L3
zw~@E}MD=<>sV`Nv;i9Rq!Die9RkiTG?2D5#-Nl<sS(&w5w1Re__-Q{sNS&OWBlybT
zrzh8&D-|oo2*F}QHCFGBUF~n3{ff$c>sv*h_?wuPj-|J>GN+pFUs8=P7PeRkvss1b
z{LG)~vb=~?53yG`Hoq3TopAhG0Jc*UMn%8TXK>_!m{iViFmxB*bKY=dA-7E8s%OD<
z5uY6{F)r_kxnle}WhA`%maEt9*=tYw91HP=-XqWV3c+c$_G|d>ime45*Hmk3oFEnT
zV5I=TAp2hAG)L&CNQSiSfPsDWk^q#Z$XW7hUN%TWL^5T3fH^{5mvL@1k*_QE2kY?2
z#m^+0I)M(FyG7+JT#_5l2OZThU*|-$IBV&ne^*yS*Y&~Wxj5?AR$XxRc9dtu7S8qU
zXD2^Tp7BJy-;~;O7mf?bR4ZDzJn4Sos4^E*Y~8u9&dcmqgs}(Go~+kVxdv}U0H@Z-
zQqG~^bm`EOI?(CR`$-%52i62oYIBn$=L%m44s)v#n-54|zjteOL%&midjg21BjQ-s
z^Dx>QkWCL3F(DLLlK3&}W`f2pzO-d)NvRzsX0+Ja!%%(r<!tHf=fSPo+7x5vVJCsp
zQWZFx#NSLZ4Lo8>-5H)|$AK(Cj2E8R%+RwtZD`zs_LOa{;0eAmbCuKP4X+2ZiL)(l
zZV`@J>Aaz|fb!ozNgbOr0c7tutmaLY0R0#E>n|awyzrRmGdTdRhq%!>E*GT=WefbL
z>G@0T7o_}mUYOK>WllPh*5*mL_#xs^3$s8e??Q0Op_)4TB!+-72ycS*YewX@%cwAv
z)=%o~@`9Mud<_vuRDT~g;Bp~)MJ89m;jAa%TobXwu*<L3z{JzctYuHtbST4;LxG<$
z9n@gU+cCf6TL+@2MX!#ZW2t9F7=T8JQ`cg~=1b-*Ijb#VPu6F18V)i)akjK!dA$6+
zd&F-<ZdfO<`ns!E8^)^WjR9rm$CKe}?!r@vxlF@xt3dg<7<Q0{l5@9>!$Ml-P^=`y
zwBHyTE}+DBW1j`OmdL4<Hs(<OEI0r~T>u%Ig*Y+p(mjryLf{%_4i#21{2?Hu&qi)=
znW_c|>eQiFL`CB(&=UI`VyV(cCob=fu0<yW2sJm#5jW4!RvFeu^#sGiJ(2Qa64OK2
z_Sc!eU1jcVf3w}CC$wKJMoTBiDcqRam8eh^-*t*|tWFMx1rIA>2+z&X=!{ywxh_f0
zZb}y;jDEbkJbO@Ws4g<$4?WkQ)z2tlZpA0!sl`}+FWGLJD~ICdCyf?deMrz;&r*Z6
z?3lZ2z#RJQU4j2~wr!?bOQm(G_0R8}KE5}bu6nU8eRjr)tF$NcwA@hnouGjYmFHm^
z3=!H&6?Z&|G1-6kq`xrSw1(xpTXX(hEEP7I+y}lG6FL+j2|O0$a!qZt#AwV)E?{9D
zZww@)bi8C)y{C*dSP-E^cN6cbkM+Wct#~X}!*U3n-Iw|(t<yfwXe*1D6~RYDxd&34
za;(VB*bS<eep+4w=#yZi<#^@&5CIsyvTjONMqfZf4qZ_Rkf7ZMwC<NWK0X}@6{?|F
zL5t<UP}z`P@F9naEE%EB<TgZKe=9H`AE=*n+Z4?E_N`_UY0>)Syk#kpT=<R`o-y(B
zOxABPUB9O$kWNzjV*P!guBou8aFhj8>&%xk5o$pd+aBzdW*##^lb+kcZi8Q*ecoNV
zqhyXAt6(6*mHlc~m9V4evQnHrQ?@wg%SC7yBFf#?aP60SXl-(RSLnTUCHz*?!~%}z
z8O@U%L4Bp@^<D7-Y$AT-ybMbr@iLBy%*>BJug-kle<n#iTou9mc(Z$FGwg6~<3xAU
zMCtS`VJ?-+9KJATC5WlZfm`C=Kaa}tL^Bs|oEYfH?TY7aFIdNV>BxYi?8Smzh0*Zp
z3e)GyVM?YIr#G#4e@a!saLLbH1x0Uv1GeaQ)=Apo4@>UoqS+fa$g*(ur*ZCyW7Fq$
z8V2Y_H`T9cEVuXhh6evBB5JvJ8k|HYQ|3iWJm?Ge&Whc8w`HWuo8*50f;)Gep0W9Z
zRJbGJ(dHX_M`wseIwU$ImLbWVXwfrt^9g{0h}d>gDvm;d+i;W~-cMbn^QhY?^x)f3
z4dp3{4Zl|Jf8j}gROhF?ksPM!Y$^VJnvPm>R&w@^2bm88$u<*Ve!pjQUi+tX@zFqJ
zUuqqZ_%wOdOa-1y>2gEuNqFX5BuVW}dp-vCR`zD{#H}?hs--^h_O5o`)(iN)1^OR)
ze*Y-rTw{|ls1oNJ!(#48qOAhKk;f>4!&QNy^i-r5VF_=zF3~sv0>0>!uEdYefQp96
zYrkT#|9Un9dv?en*osn+zid_ahIDOW!S-FPnKE~}1Ri~NcyI}SoP0P4IH<27Dh{!)
zrD|7K>JOKOx5x92`)h|zQC^{6aOS;O^K_BdOCu#&*8vPu88uRlQZqMHpP!+r%Qgn!
zbG+}%vvHVbSAZNRLza&+ntMVA5+3b2DuOjADPDuANQKI0GN4fOT)caE=;ya)n@&F0
zFEdPMw%l^0f!Ztkzm67D+<ZSWlX0kotQ$?$`PAeUf~tPy3=89z^TN+XO2NUC$m6pw
zjKXfsJ=-#p<qOSWB_UkmemEg<ny$;~%k0nMnejyuP-F%cc(vWuOV$-VU!e~T@=;`a
z=V_edr_TCGc0$WGc6#({9Gbwj19z)|Y_)SGWo`l=f}1*&YNr`L%t^})2eFFcxT?ae
z2Dd-RA{F<N#VvE;AsoFERXQ5m{_~~CG$qpg@7bOMo_7h{LH%BijHtg2OH6v3#z9vS
zHt(r>mXjmZtIjJl2<Zhle@Kxe5Hpyhmz}n<CGAAjtewz#;yvt+3bF1yZv}F+IOU4O
zdTcFBe0K#+rHS}a`@Su9Cd3TQ#$TNYA>%w1dAJ5o!K)6%fYodyqK$y|&16~VynE@T
z#*E?J<=*PjE3Y~cJ?d$0xhXxGgsk^udd7!aHyozd6r3qSn=5)^c!V9dhu{M_W#uU`
z?YFi1y8TKWT9#`0hbNe$axklEoE`7Am&BQuQ28Cg9_w;)ukV9c04^Bk&+<mWn9Tcx
zw+)d=u9uZakbVl=ozI>yrbfbb0+VzyenTBwbqb(~e6g1Y!0&7~`s|Wh0PzY4bLQZl
z+$)0#qY5}0Mg=O|?6oDEiif}XA1T-Lu%(<`QG0i@_QiolTt1v#?GoqGYNPsb!GBJS
zE`|ynWS+MIxXnBjiCHz4*m+69-YrN$ksv-r(S0*cA#7`V^vE0c@+PYAX@W&*oZH#n
zAg#vY;)ka>(yzT8_A3llw|b9oC^yYX4U?RHt;hGol;K$7$b?eWbNgm=?CO#D*Myf`
ziB+xocGcf3SbzPFtS3xOwjd7sNRoS1a917~XoNYdK?!$cAZ@JRKaUKY8;yi=_EMA%
z@AC5c1Nn;PHeo1qYzVmO1iG7QlGn@OR8C_yHEqQAvV}beuZv@DrtmgeO?<6zgHaO<
z^`x|=yc&zV9lg16R)h7&kzI+feU{*x5iTFn-qJuu_~ffAEE#GONL0qwQ+TFz(070`
z`bDJ`H08cFR4`u}*7g*-E|!69;iI|8#<LxL`j`}S*Mp69&_LbPz8{^x)y(Hoqy1;A
z5Ub-`dS{uml^?045Sv~QuX634TC#Ml#t$xH`<Iag(!_=)JE65Dcj$=CKfhMoGit@-
zZ2pKf+Ku!|SF~-dhLj%28<7n?qEDK;s9&50<LLdAg-;2Vwm{d2>J<iZz4KUzGvf1f
z(s6~pq8v6l7t~l=`OO-bnmnl1!SEeUQjAp#BkpM~zj0&r=}x<rH{BU7|JoF!o#UmD
zY$KYa8}3^V%HSYOXR-Ql;q!8SpaswNUL3druYPnw&EiG3W^EqE_sh-L0%dZBu07IT
z_c&;V@odM3ZB{Ypl$2C@Fv`z5H1(B^&j;T&nQ|Zq)7u{}7mA>)_M6}HV=S+LXog1I
z`DPJn#@G^zQJz;CQ4&0u?CRrRp3w(LUMj-i?b{U<;~8d9aRI5#M>-Ly$gnbp{RaKj
z@&q|nM~WsH2XmWFmE)c%qwWO9Zv{#8Zs(+jdIQnryk;^m(r!meX1hp6B?!C@j&S{U
zk>rp}U#5YXQ<STgwsH2}0tnG$-U*rwFgJJck^U^^QpPnI>LO;5pFrgjS=J5*i2Y!a
zXVTqHI}h|%v^%%Fhh2IKetk#A_x4+z%IX$T&M|8>+Xq0FrpOh$qvfzVK6XF3)v<#W
z{=sDTJlbT`Pa`DZXy%g7uI90x%dx?N0PU)an3ya=Y#WN(onK>QjR?15P$fJ8Ry&9H
zO|Ei03ROoSA$@dk_jxO_j5di@oHJ14^@sfuVdUDw$K*d5z@iPA{jta^@VSXNK);Ik
zhI(xJmF~dLy9Oa*hSN4J?WC_d>h*&Y*p|Os+AHEyE;(m*5P+1+(@Kjq>^C<K1~~dn
z(5)O(^S>f{Y}vtto7=bOxE%m+J3o&D1?55ENchcRb3*XVE4v=6>b(I3y9%qhu()RE
z!^b<%sowec{vvV+#{1A`Bm-|?dpY(DNQRu0ZiRtJIiW|T^D}CcS+>9DbeB8wq+Ms8
z^Zkm_nq(Uhq07B(n5zIj#rp-U;;glpLl5;-nIsj}d~e6xM-{p@jY4?;{mriIj4$sE
zw1RA3N1~YZsLJucERn*JX^$Y&iVd@V*<Zy|FQYHH{|ev~7Qk(I>aR}m>)Cf1BFp6t
zIU+Wt@^nsNrLT(9(OsvC+whls|6hMu`NFeUj4Je*LNbURwk2!uqVGn$dhj?qsA!C}
z;{Y^nbk}RwHUPkOg7eF@tAlt9wpBo+<VETGxl}W!zab=5?<##qBzof`;wEk&9=TmX
zpJB5?|5P=zHJh)#bl$2aym&1oDM7BBa(1*J%4{*}iMGO5V>P1hCAOV;rW!IvH9ax-
z>^Bw&O`8`!U;gJR!nYo4Ji;6dnQ&O{`CbiK63J10p`0ch!;gZuKV|68DYD)`!yHJv
z%V}%CrT)?Z9R6>xSLb5!b*@kr;b*K&2b|~_ebAV{18`oVx5Eq>&L=xV-+ITy4*JjR
z$w}%OUF*DYR@*GdZ?Uhgz8KtMsrg=H7py-mU3(fwo}jT+#Ia?f{c};LyFEEFNu81<
z(H;>pc09gH#?e@y!c{bYIj&3AXf*SZ%+PQARv2$RXdGL*AKqFj)hHE;-|N_o!SAt|
z3l(#_tZ%4MR(Xuwex9Bw9RDHXeNjpDm90ps>FhJ1g;@O_%ioUL$|U*($9>%^R@?W>
zcs0sqU%)M46;Af3%w6&~D@6w+Iz;~o8{4&dKfhA@1eyJwyP0GTq+Gp4K~EoTjt#88
z`ZCQFGl}jvY!KsjOhHMW{Ys|1Yt!h{2oW3VYNC|K1~0kc;xzXy{lSi_v#PO2)7<H7
zw{*-qu9B(2Hzn?KMi+Sc2G8F+;BL}>C4OZWvcmnUk)7{V=>kC7&spVq^nQ@+t;4z)
zqy#8J>!D%7^S*C)Uo>rYAl&ZI`I~eXkQVK`xI>et#Q^f_c0$fRWg*W)SR3xD>QAag
z?|GfO*ZN1BynW^3zx<(j+9j4w0C!eAyunT;l9_p!duDnq{;0M7t>&-5sG6z6VY{p#
z?cMr42vv^F7wHu8aA!DlZkZ=R9ax=$I2#tL)0g@uKc`h$Inj7BxGcwbw>xnRWS<r7
zVNJ4YWEn{60X~`1yQ36~`MS;)Z5oFxZ8}7i_qrkFDtpMGpL%Rhu=WtK@%7e`n3A}*
zQYyob!;Zh|E?nJ}Y+{y^>1Mq)cOQn!-%`AyT&1s>Su<zWXrJ8J?Nl}D%;qlYP(7u)
zu>r8b{)u{I@_Om<!RF@Zy?s%3@{Y<C7;Fdob2Ue;Xj_#vNR>SxrFjhOUo-NYi0Q;#
zsHxt4r%c~Hv7oNZ&OOU%_qeTxzJ0JKB9D?j*l2LO!ImXdv&`(Lv!ft6Op(toHhtzh
zQV6iu{E0oNdVB7>R=$qUX6!l8s?sr0JqB;;fM8y>GxGZd3ZA{@jGgxHVkn(g3^S`7
z*MZ5Q?tH|W?Zrv-6*#QJt`<;FOv#hQ0F%|ig39Mwo{_>FB3p<>GrhMpwDXAg({B2=
z?h#CjZ|BBWmxa8tJtCurP1^a*wL47Zk_F;Y{lqj!Ly{FPby<PvFR!iB3QxIC?*9C>
z+AE}i+2e?bCf8wgAkdvRbqy$<WA@TtdO)nZ4>cqpAz3*o28+lRJijrzliPL{+0+xK
z9ktun-4mC$o4$R%y=JpN*133OG!yi^T#}Z;7Z=vkLw3EhK<JNkP|%x4sO&<b+rEo=
zQ?ZwJU>?0K+LsLruqw5EF!~8pgy$8y3SQeY-#s0@OB|5qqJmbWnQ53=Z3zDs?&9He
zLBHa%iiV0t+v#JJ(tU!`xUKTS^-I2nmniKcsJyR?#-|&dFt6eIyIl9my0F+`uAhfl
zMFpnguiaNm>`HqociNadTyc^?d(u_r)IPbBZhkz$5|cB{)688zoOL}-z+JAMSGSyU
z6gmU(nuTol)2*T0062%n-A*?ie#_*s{Izz!qjx*_d3n=oG`35OOuRhk&AbtCbyF8Y
z8|u^V<7k1r%-bVJtR^F<=u9okV5JE*zq%T=j_0E%?0|7wmD35EWj_xT*(ONU5B;F6
z+ZEPXwmM)_W)0Jmw6<j3VXT#VNR0nzg(e%Zr~9l8I4HB+paU91q(c@LhR>82&I`Vt
zwLmD*xG?ayf_XDfNY66cK7Mu&HfqM~f?=U{vOV4-2NtkQ?ZrW0^xoJ@3dGki?v-(D
z+sr8`xE}9f9`0(#8qj;PwueU>IP6E|iJwK)u-ByQ3;ek_!-04*enmYSRRs;?tfJ|U
zTxxXQQPQ3-oP`Yg$(3${;)8t!M}4;awS7iEzV@TGJF;liZU_6(+AMO}bvv_<7sU{m
z$qC(jpiO#Cu-NJ6=~g`DY=8}m0M4`Hok=cdKh_rp)4Hl@I}KepFYi7#$d=;iU(Os8
zR@X<y9Q_9j3uQj}?BNkg28U{s-uAnl?~6L;@E2Nb4ig$(mg>uiZ@f~X^?-?D90cyG
z{->^cD=xK+%bYCQ%k7vSPfjntN$IaB^i;qeM9@tA#?Hi?3k7yZM`GTx*lecdv?N6i
z9i?psvM4^}{U;G@W==<9>#D3?SouPsMu-qs*e#T32%+b02)fQ$r7LmbGl-&{N=|o#
zPrUCmAI(lU`spmKt+mA=l-}0Obzn@w<K19mFnTp}1T$eEmzi>r*f3-}(jQ-o;S_&A
z?!i5ygJ%qTWyjCYI#tfvxqP$F;;q~}x`~9D*eGj_{msblycA=2tivqBDvb3wo~zYs
zOO4*Dt50WL!}A#3bRc`V>?8J3A(3Pu%a0y5i91CPywjOGMEy1|$Y|ZxF!C+kx#rV{
zzSO7Ij62EawGjDM|ClPx%B0>|n$2_IMu!SOu!NrX*7yO09oEq}OrD!Li93$bnOA8e
zcFPvf`u1I?og9a5=po)MO~~GP_HA71i(;(8!vU0A=<&hilZDntO7tk|7%`$X9frTO
zTIPjOOS!QIn~KuokzURc>qpvbxTpV7U)?d8qo8Z3U81In>vnMIn|u%PJOpjU8V#tS
zsyOki6Qoor<{VVLpU7*Y_p%l2DC?>TG%#C`VoqN|-(x5Zsh22oZt0FEk<G#D60OPJ
z6HR)|`Ws8<jC*W0UQi0**==R&{Fd?+J<AS@)QH^e8q4emBVgBoD>W5H&~JuO-ETE9
z?o?SPCnl3i_NVWOauu+w`ZC^>Sz?lYk^j^yU>ozQ+C5IwK=8|b#qaYox9Wr<X|#nj
zo_&_>r<QboT@+2IZhKeN7=!$?T*^lN<#tzdgo+)*%kR=+U{L=&WU6Dsdv$i$hRbtT
zV>$Bb!`G8RnFYmnOHw$}aEN)U;axb>Tkm&bhJV<U*%dGs7K-HhO_XkZMZS;Ds53sQ
zPA~c@Au3-BKN_jIo@3NHk-WB{wK$+b3N=HwA{<5B-HYM~$uVGJwgr|~X3cX8k|RJ+
zN@qXMf`*T_%TkYfJ!X9@SIsEYjvu$E;E>pGQCiy*LmfcE-Sc-;yqLB3jy>Puw=5j=
zl75~gNw*F?o<ll-ux6N)RW>x}kEFa=7}yK_#qi!0<T$6`yLYCNxg+vTE7UfYEcARC
zcAYwJaWYcw)zLDm8I4O&iHB)*(Ad*eo2e!f9d_R@5?h*-TQ<Wz|G7^fsB)raGoZEE
z9c0=!Alv5&cFC*1ayqU1!<dfG$(!L5xR$FBvo=;?r?+ki!c|SUs@~}jT=#F+-3Twu
zeYaN!J!`A(@^|fPrUxC4GmT|i$@ebYoE~^-&dmT)xrpm%_Vw~)Tt<|oKgw_uz6Tx?
z$5`{@dZcUHd}4iJoZTq5-aI0@w2E*>>olj9LpIKxVPe7~p1g8{aN9dW=!@(ZAi7b8
z|6NKm`r{{Y4s|%MJCgYCg`256D>WSRnNBr1btT^ScOs<K(BF3#MikIMc#Xr(u?A-R
z>&+vphVZ$u6OC75zDPl-V%##KyenNin}0$1r>o#hfK8O=k@-i#2ixj3S_84MdHWv@
zq$b1X>n&MpQnqm8MypiGyeiP2#|>R1ScKpN=1b!B4!Q!o*%d{a*XpU_FLm^_!k*!<
z#=n;DyBHjFH!%(iIf`nQN&LP+XdMj1%djoW)-H0_bI0ERsYVUrC@E6@!~v=!hG#U5
zONP9578==+F)NAIe8!EzP4myWm|b&s0W$sbPR6{1<32`Nce4H9kh&mltP0(SaGrK;
z4av}H;Q2ha&&GHaf2n+fZe8H|D>$sn%PZF_j&KOjfpKWumN@HIxHj8B52aaVInDv{
zmlsJWR7agWqaArsRDji)^?nLH0rC${FHHJ+nZW?9L$&DRt8cBy7&V%s3Zxpu?oCRQ
z1#eW>VF|$8y%A@WyzRRRMiI8HDuq1>YCE*YTz5?h0LMPbNg$J-KS>Gm*0kpwBt44b
zFbLAWPVY^v;j})&ZMV`!vx<%|3}x{0<-mm=?+qBbGp$~Xd5D@psL!3^L(dlk+g`9r
zE!&$>wxCh*S(F3<Ugm&sGR{v78#4E3(2k_NJ<&Nyp#|7i68~`d8{aUqW6gx`3N4xf
z8V+fkS&4ZJ2rn2M(A-z?^9vp|I^7V~(9<9ilAzasLs{evh224RZ39AoPo-D}OKyB)
zz+Ln|zFzzuK=nW!*0Aw~{6X$b%#gSvNc^}m%OrB&API$dg3P@>jr<(+ur8zM%wHm)
zd3&AWjWvk#Fg~L5Uf(h4c1#9^1oa7e{bUG0vQq6^h2a{sbN5ke_6)TzkTv$zW94Ns
z@lBJL%nTP9(eoNpO`->V6;8eT>l>)|(p0dG`X0iIZ#524z2si`PWSP<%1hfe9-oP7
zw<=^BhG-O(nhcPNw3@B>QKp*@J~#>%xr3jNOHwP>VM?t!+rdUlTsb&H<89LcAX=N5
z^ZWHLtsyfmrInGpSX<R{`zrku!LsFHmvNqA<(Woi)Esu8hfv(_d6FzJ{3Mb0XVwcH
zZy6ZoGB`YcpB-qii<LO{WhiXF$kwoxM4^d))BnQ*sE+5y1ZY1CFdHA&@u~lGVq4IA
zmxMVNQkVL`_v;xhMhGIV9E@McwkEm_2xF={TTY!7F*g)H_rftvAPmySDr9$JP&fxv
zT^IF#L$7?_aDEQTcg-{iiYlmz>v$djy2f=vKsAv#-v^w6xLP*0D7OZ*B7bxTj51Tb
za>9H2Dyl&UR-nQYH-iwYFGUov1_6P~q4Fs@nE)X2<BA^p<vOWOOL%Y(R&$4a$xxAx
zcFUlXTe5)N@KyY+liTe?#6F*%B+xN-cy3K|#5P`}{KtVKav2S@9oC>fvL2$<6aCW%
z386_qWw|m;@sgWSgVg3N6AoI3hCUjVuf6->@IF%dw?I?ka5gb;ni@tWtkrZr(~wZ~
zCoD)@jdV<hTKa#NAcoQq3sT1|Q3v2j86(AQ;L<WhQZxL_{4UyMvyDRp>6)S^D&Ews
zS~$P)=&I04dq;K~lov8Nzg5{JzWc=jvD@!{mLDE2Fw*l$EDYJ=5qWiLUB^FyA`f3g
z_^DRk-@Zo(GP8otB>uAZOO<qJH{~UeOGvn=NovhJogI{u7XP8hF!=%g`Oq^(v)ow^
z>aFkWSDR#}+K-?<-LRr*V_8Kn1%H5_0;l5HPtn)pwjHjtihl)-A`b{eMosGdu+9%s
zgua0pTs`6-4fMe^1PtWU<TAa0bRN`S$X72mp56yRu={+jD=kf(4(33%jX*81?DT=@
zsrFc@mtTztnhm>X4~^Q66q_s?ezxxy_g*B<+_8X~?Hbm<%dD7<Xj-n3qk8^U^9)!Z
z-&cX^6E0eHv0nv!Ctl<y<{&0cM`IdC;0hB}I4Jt&x!sj=1$@9o_S33Cf<aX0*FtBz
z)DJaDbOaqPxu4J4DKtpRiS1t;!l5#gNH8_7Q)^3cK>_SVrn2oSx_|GW|D!rj{(Q5B
zRQ}PFHh8`orBaRk%+xtw#;=~AY;Jm<?Wuh6ZyuDjeM@NO-Jwd@t+4uV_InMGSI%ki
zKJ<No+uwP$nkw3IKq-R7LYZA##P$`stUvcII|QZngqeP36P$2A2);(4_B!Ba-1K>m
zpL_R5BMvUwb7&e)Z`4djb1TigU+Ndp0r9`Rs&JF;ay1w^`{jdYSl;>s-_>s_?@Ho7
zp;F=!&|y_^MjsNrSRz#8f+NnNnxDF5Da!p<&f0Px)sz3Qvo80F2WvuNn~Q{ED(1C2
z$g621H5fEYhb)PzRlz^0D(Y{t1NbQXK{_V@hwQ0k_fCCfBxpTWhmioP0M|pXQ&X58
z29vOLb+=_I6A>(dABak1%)a-@I7r2oa@IidGD!64r^G+9N1b8HTFUj&vICEo&t&gQ
z$iq{vcUzvTp8JD!NK@KI&>Ok|ZCHY+$;0m@g5A^OLOEv*{T)b-)YT95fS{S)`kqEW
z2UjM)Eu|iyc4^<Qd1n=<nai7!iu!*wL5Tz+<%XlZpu?>M(VBB;ccQY0f1t=4LU;&|
z!9^jRT_BlGid|{zFOjO0rMv-K;Z{^r%PXvre_m(Dax1rvq?x%?V;aAX`U>N;!%E-w
zKCS$AnbC7+rw`<R<-2o1D5Bi8l&6O5L#)ut;ISdMFD_7j0fa@R+oRaAy4lV)cgoBO
z>dKowK8ke5H@=xaAEDun{@%TnEbVbFI~L#~Lmoi$*X)?5zmHq3cMr=fK^}*>w<yo$
zKvUu~_`(eKfQ0*YhOEIVYB%L~(c7}6h`w3e<fRTto7sV2sVd~uGt7<5VoM+HI^a3g
zXFiqO;Ptr~?aQFe(Z3U@`a@ZU4$`7uyMbWktkNZ!#S59f;j4lMT(pCWpHi9M6?UI{
zJ(@e7x+O;!hP^!X($k3KDVXF4ZpvTd97Hx<ZO(c}F3dsTG)?uc4iUIUU-Zfnr~(}X
zhYeL)Y7%DOgAsJ_g3VPru0Ln3CXH!Okf&Gez2_4j_Zn(7vErGM+P|V#1|BqtyLM+?
z{F~)KhYlnD7SAhR$SfF31C-Vd8pv0zWiTz$i8l$2qWMhg9*4{tB({&O4CZZutysM_
zX9S@NKRu3{3FXU$0lFglRT}~Lr+nVhrIPiH37v}$khZYgiBi@?57JwR-y6Wh&F-bV
zZ~O6HpS-4slI1bFA;`22G>a+NYiNNv$4GLP>PVZWxuq%zs{DNhDYHxW09rZ#me09;
z+TV#jv*4c<iWHnnKJO#6kj8N^NBv&ye#9(vk3`MAcM7URcw;$@WM^_mOUz<;2Ocb-
zllomY%8e_D{#o8pp{#<diX%1p0sPw&>`$QfAo>b$LMv4K{Vb4Q<#*77M9U4;Q3t4U
zA&6M($x}2j-O|-1X48zDTCq;=v`fNr^CW*nJBoM;e#jj71W=779F}bht_N#Me#c!=
zN2Oo#?*HCqL}7Z(-&#<=2`3=vHm4mWpS|^SDRQ~l76;a})aFVB!v)x3@pcSEG66WX
zJU>0!CQ41Tl}!a<^SN6Y2s&K!9l-g~;z9|<;ct;beib3}d3n$H)$`k{*+7+8<Pi&j
zv=7MI(d8pzYE&*<JQRhiQrOb~vfrtzYMEpVa5wAEoX^kgK51C{tf31V#0B55LukO=
z?<ZXJcai%0$p8KM*I0dCrc~SjS78F5L#7uR<_lPPKlyslEW$szi0rACn9hS+F&M2H
zDiDw9bdGwNqgi&rt4TQu?2|&~q?GQ=!V+>p*WyW6ypk<indowmC}tEsBKoh{mD&*P
zBAbR2$Xd8z`O&=KbZg)nM^1q@eB8!Lf{uqbB#YV1BbH{cs_{;H%^;PC-Q(E)cQSj#
zw>5_hMy<62?T`Z}Kn*Lwsdr@5n%deC$5|br8FG;6hCGLVzkJ;3<+tkh;>~iG;R%_4
zb>;fPMGiyD$0F@chnd<gXy#Y^<2g2<*W6#3p4VSuCWIDb^%G?1^_n>YY1@=FY4rAU
zk1`u-F~75lvsG+`p$LQ<)MQRbf3g36Ujrrt1>{nSgks2eOQ+uJA3=8ZQQR7VNAg9F
z(Tu$WF>F*Z2ar=@9<hQ5`g;8_kOec)w$3M_Jm>FF{$;KvW;OXOQAPaXe=XwW<n4w9
z=N*0xN`o3Hk8IQbniCmB&`5Uqt5D2x7?J8HB7mO#JIhK}K!7PBxLhUr-HZ|m^3mn8
zS~IHrM~D7b6e@xqB4Vy?V=~tjvCuWRq`9FgB|~t+@MbLOx|?*L6TQ<|&c}#a|5lR!
z-9J=(Ygx`g(~JKuk3TgF`8I+EbXjC*injmWhyU3hBCK<G{`U|6{KiGvJIVSL4%yq@
z5yZ1;3H^i`lN|t&mZyi?YVF!&!6FX$5!RZTb4t1YdC0vnv;FFjPcIiDY@!;aJ&xe`
z+Ze<J{Z#6qZ8~%mo(avjV=}Kb62E~}uzuMDlON*v+x)$toFmXPE6FE9d<HJG0x~Y9
z;_E6N|Hxzi5y{G;A=TXtbkTn>u2`F7n1-q#2Z*A61+n-_hLX=CF#)*1BeQ)K0$sXg
zDEGkDHiE1JLEK#Nyu|c~ktX(!aK382)DAR3JFF*&V^`>OORVhtc(n>7wv}4du1D@t
zVHC?CVbi3V0P&(qnWd(Rf*>Ggj`yr41hkFx#U}cuVRYTyOHUpnv|Q+j+wdpRvL3H=
zu=Bli)e+e`%s^dP+f<zb%GvXwb7U?6)1g%^{BYvAGf>r5)_9y$Li%;bV9A(7(rXbP
zJjgEa5nxby_Maz#x)zI?`lj~{INl(PY!T90`p#hcI=7mx_y0p)={JRJi*h>kyqCac
zlPBB+s?h;~J8K{Kt$-P`6RKPbr~(aexmSWH(9X7|8z_tVxXlK3MFqA4V0HcSfR26~
z#trvzOgSaFTqK;@5(sZ0%Ej_%7WHG-x0Z+-8YBn5roo*M$PW?on!XrU#=xeruH|*+
zFj#J*?_~j1a0A*_yYVWggT?)DwL^qb0Sh5wPH_VpO`HF+IQr$ly0(CCmzOOSRsPR2
zm_diu(PWwb4Pyh&Z?FZIx7&MSNOG!9QSt=(ehfkPjl94(D?(KU;SGO^H}pYH;J_@9
zvjgg|66D&=+!iB!duzkJh`c}W>sv~DNJ#i>Fi;Lv*>fS$xr4n(>jaKz*fmfaQQVt5
zhF)XA-!(Y6TTnr2rLBXXv!@&c%^jL>>b)i8{qBh#!H|_0>ypA{@>CCFR7z_<`~eCE
zrO@wFJ7NBFaGji-uJIXgVnYYxFAZ)B|KmADtN&b;&)3`ekcASRMUpuHdsdknxTH@X
zZ^Bd-R}Q;W)`@;!E=wYrnPHtxh5~Ym;5l!9EeOSqze2BVQw9%8l@|o{2WHF#i-fB~
z_QuUbUt_^`&5*>~S@bK5Y&D1PPr%PQ1{#orjM_ivbg%z7dDqN*0MFzaskIKVzzRZ%
zfCn6GCZZm;nPvZ3rXpJS5Ws(3<M;X5IAZSdf7qoEX=q3iwGL@1?fqY#HLJG?!HL{o
zb%IuS%!=THe{_KK9Y9`Jf^(@o$+qo|5UVH)8qXkJVq^tOAXR3$6-{@qhq=1&_IlKz
zUON1$8hi%wRQ>@MVSr{Ht+sY>INTOScQh<lK$TKT*!gm4HBYS}6txgUqJtjL>Ut_C
za1X-c`cTmdGAgzYqA*n*>$3U05RYyiwy4We3n7sU;1G&v+|i*CEL40lv#hz53$7_E
z(06@)^ZXG94fTH9k9OvqA)_X?$iAjzf!*;|zZb41p|wMwo8`Vh!yM!*l4&&!^H_2$
zrQ{Fsiz5`PpW^Oo0ib_MZjKX&pCTL<m$cv-`)XLzPjA5D){B7ptoh*Zh&JZC+i_%c
zwLe(Y3LEoAwSYKxx6X9ENsKo*hUmb4Jy#P94A%+63!AbGD_MkifEpyv;G)Z9+oD#3
zn_l(3qRosndb=;>^ubx|_!+q1kjceeVf^nxPhG;Ko|05bTc^9r)kM|hH_0ya(j3$S
zSHF=<omSW_@-e@bq8#`Ru`c>liP8+TlkA)MW9-7{n%U7(nxw+d<@|I9UPlrO+h;c~
zxGI+8{wg}`cwQtc*e|FmaC9@G8!QEjzj_tM{|^5D{HjBSI`E_@N~Mp+JHeW=Iy(3l
zIG4t@V>$|G2O)Rm-CECOUDcbv;S7Na(Q-7f@k0&{>&MUOmH*xO-rYtWVE;vr;F7iI
zFIc`3bT5FhBvYJOq7?(Rpc|vVZT`-F)a<())tMBQ!nXhHbA@85seY?qV=ITFid%JE
z?rMVHA(fH#`xKfRM$<Hs`hpa3qQd$g4WOe^W~FF<Lt4{9LciaCa65mkmHZ=e5RHk-
z`^@#^m-)Qp0b-#RP`sF4ID98kDTj@}QseLvmHU(@BnQ~)_Itc9)gG4<ng7>DD58tk
zl4)ISJuO=MK!71%>opCAoqg)CBelKo`QeAMSYC?}uFZf!Uc*(i(z~zY*Tb7;DSZ5L
zaF&K{Q6~gDE?`|<eRP_i+-K7G8b5mMM7bNu)zFwA4m>W9n*8eWi@D_l8se{IQ=}L-
z?K?nsX{C?%D)RiT3@eszrai?(<Pd+1%syfyE5@g=z&c-WW;NHxCtv=Yq(k{Ghxz@9
z+*{lRn&Q#6Vz(I`JaixS4kooKcukK6*SA8Ixn@vzi3TDIT?Xq90*;HKFryX3?t$pE
z_WPwx*CqB+TZmDFBg}=ANAqenWuvH~3I5ZM;^Vih#IM4JF+T3~`^+;>5dAXqg~GBM
z^(&Cduz<Lr51<B?L%xr*ZZUM=S`1C8-{8^SIOuK2obg7zFb0FnOc{*`gD!F2%>!WH
zcnv)hxXl)NuTb#*c(VxqdF_B`41V@FL@h7C9%5PRHHm}~z*eCpb!2~xZ#dLo1$hNp
zb(A0knA{BKz;_*Hi@<dUmUj%CRtu|v=}o{mogA9$dQ(5G_4GJ^z3=2y5_aazT7?gi
zfthw}zaheD9J&mZ13*S->+dgt4+I#S-ZA0x?W?eDkJ}D1+TGmmZ$1YXlQlR+lOV2h
zmLjvd_|gcq#XKCOvu%O@EsBL{NZHqdaVQc=Vg6;~Of{rp3z<AZRHc)tIrfmqQ!n)!
z+*ikfOeU+wqVnOCF%~8^km+@ML~wHoPMukc=doBRYb%GTY&?)YdC4dmKMcVH*eLYn
z{mz(Ou69zo%)L2J%6G-RAuv>Cv+?F|jNaD}vt{hUfF#+J-qvd;L3_wpiQ+$5D;kGs
zn;he$n$1PU`5;4|Pu8OLc*K*hTgK$y)tjmcUH$^O!y-hA3}WXXAww^@r-mq!<HY?W
z4P%!RZ@qx?guQI+r|`q@y#dU#U{g#LI)>~EH{BPo#j)I5jahq;>N*ac!{7(0s{trK
zz)jv<<B7RX$!GSXI`sd|0<gD6QUcsSBv*|h=4#lM(T<oS{0Y&`D3VT)t@raDYWq3O
zJH*VPMg2ZGqpJph?sJZpmq)KqbFD*LumOiY?Y-nC2`qwr?ZkP169`H>xJ<bomaDIO
zE9{IQK#4b4i&lFrEBp+JmVq{H9?tjU7dC#W6N*??so2wUeoi2QY^pH-CnG0NUsfiu
zpH0`ih+9CjAf|Ea7Cg`}sglC`)ai)mYT#4QB9;2#0X8PW_2h#eeggn=jw3v3Ej`W#
zpu2c$WX?gB{tpCQq%!Ey?q87N#UOzFnpL|SX~=3fOG7VhvQvmQ>kK0SarNe<-<Kz<
zPcDz#bj;zX8S&gJIcpQ3`i}p<#@k1aHbQBd+!%Av{-c=G=j6Rc$gwHj9OJ|`&jqLD
zd4!twv&|QD!}}LPy1dmYfBZ(mB#cnwTo69#J}S3j)Ro@U(n<jF{T>h@*#s|E%-k?@
z;X>wnyx`boEZqrK>cd;_62aJp```qsbg|5Y6sCTXW2Nj`dyCQ~H$UivCMw20=;H;b
zX<$Uep5<hWb7Fth`G#h+p24x69A`Vh&BgG;Rp+d*LJ_`QL(C%YbNb{4HokmbA2r!4
z33*!}#bAVY`N3&=jMh%k7%`P_ucO=z(-V1XR<hsbUQTV&17833I1GYlYD(Br+zERz
zh_W>$9Y)_A0bNBMv%%bT{{D<`j_aLk10Yf+l>EvoS#pdtcaSWy>ox~y)m=_ModL(2
zq_^!lGSdK(U^aWyz@3V@*Rj%>EI9f^y>x=`mICCC;P7s*-N?AaIoWr`2d%l%hHr-#
zJsvQ8essKnrzt=T2qk)TJn#8=1sO%>AXxQkUQPSj3$T*bw`<{vl?YPCtRy$qgc$M+
zga7>`kf*^G^}2>=KiT@l<SRvu#wu$d^Q;l1c|2<@$MdiV&cJc*H|hJPCRDe7J>&JL
z$g|-j=HFhRH8?JWkXi<+aEIOn)4q)9i)te*GIv#dlQNUDhd<>GY{a#GVl8$XFqzGu
zH~X%jV)W_86<otrlLMUixDVq}p>YL`7Xn^kj!-o<_pqbDNL-1m=^FlJA<p!F9oRm4
z)Db0dW`5dy+xErV%lQe=44<4ne{5J4E0N~g_1w^KF}CC;OF|cHlXdhD{E<oMps95!
z#4aS_O<S6+Y;vQ-ki^~y#Y*06bNIok*xh13>tH;Ky+n51mlAvR=nh7bcvRCbD`~{3
zts|oK{VbslVs4OV+P^`@FoL2X4Sh#EqTvjE$u}jr8TkoQq7=^gl4a^;mOpPZ36h+w
z=yx|@jS6=EpfEeZe{PacOo$Qd+?Cy6gy(NPLV$LsJL2F4B!_O9<ZFst|I+eOz^v@<
zF}MOzg6!-vV)W&_QJJ`;taV`vYfXJNwDBlF9Z=5kSJy3<Mg^OTJtj~`cs6ZnJ<ll6
zYD}ymUU5%;I`68uzd<zX1C6PfUTRxz+P9zJh33zBn^&q`ZBZ+IRp!mVGh%8BW}oUn
z>ltUHTP5sm9y{2_NlA}*BqE6&RL~g)DulKh2|Y`HT5bU7?SRY=RJ{55ib%H4ra#R<
z_$OOE?c|W5n8i2koCCJ{f+;-Rt`=(-WA)=OE@*Xl=5^@ip**1UHTsfR;+DHw^-S`S
zg|L#DLOIe|zX<R?BVKl5;CFvE&$#iWg+V|Y^Mw+NK*GJiv;xrAI{V*bzg@~<Hp6r~
z=q`$qw&9$@$rTn~2qu0Lmy;DDcF_}1$gIbGiOu*YiMQmgy}nzpG~&*xr<t6N*FSI0
z!qbG-sT1FSQOpZW(`CJvJwh@N1wK*Jm#$#blm18A2LI`5JP8?8ry*?eXh~H#xh<`*
zUE6>HBX6fa&|^=KEF^6^#wu(c`%5&t&V7okxhNYP#`&UWquq<2KU5;ASRxx(Lyt{2
zWRwE|H#mAoZDyAHYuiwX{@<nlKj5GR!{=pEMHdDRB1rrXg1~f3KEo{M5)SFql`S5V
zp2h-S9Q6P_ds5$Y1VxQQLi!eP{t57bSN!ueCx|*~aVd?wIhR{O{f>(lVP-1k++6@I
zTu!YOx&P0LzYa!GZ=D&c?4dv_MdTI&(A+jRC{+C0-5{5tMM%o;n4LEXKojBKixZW9
z+~eh80UvDtt=v$;eZJD^m`)>cPVbYSTP2C4UBGXN1xs-VJ|oy}{#WBcUgm|C#OLrl
z?JWdOsnI+}g=<*`6eQ`!z4U)EEaV5iyqS++34raELtxwOm1-b<Xu}*37={e4C~o_i
z>Hqn`447z0gjt4Pcfvh3g-O7@=>EU|H3Hw|EzW!oCfq~f{|KA;;C=q_L3bS9`2&Xk
z?_OL(_d&AOvit)wr-T9Jc+jUAb<IbVT3$cSw8hrW$E5ENVhQAKMZNv|U#@ZZz_QV7
z=~o#?R#xh5EM}N`a$xSWgnUF>#EXf<h#@t96ijO}Zv!l!qtDR6m{}gkDExme3!=HQ
zvI~PwK-fMGYhcN&c|c=2dC*1GK<N?s<sgwJ&cS|<vd3RjTo&Sx#5Jeu=J`IW(7?HY
z=0B_IIiv6uG*}i1W|KA533v5hAtSE_Vgm+-Bvw*Z`fX4h(t33XotT#a#^X|w4<u%{
z!`jeYC3}Ao6d!2f8<0TX4QM9pkida86+YX!7PO3tOol(tYlImZHVLbJn)D&WtF0i}
zLuJsrELJiy%Ap6n75Ub!(G5*|XNvHoa3Yd>L6h1_M28w;_Bf8?dw-@<`(z!wYwg&3
zZ<|Zb0VQ-_*BL7`kYRg0Z{wg;B8{iRewka?4#a~cV*ikdH|4|X0Fi|L&;}5GTm%f7
zpMk6aacU0)lVsDZ>|2M8DjB87CZnu{h|UMB)hEM>Ar@1gWRsr%c_G6jqA;h6KIUNF
zbJ0*&%HcrA`}A|C?(T(xVZ#I^y11FYALBb}RL_&R4jpgGfP6utE()(~uRlwV!Fjq8
zLRh*=@eaX6L16ZfL+S&C?GtYYk&F@$!<5w+hnRy!J{8()A+Z4!B<xw%O}(kTjL%lg
z%?N^VYK-|F0)mtsW}5Lgu+RsMy3R@*BrJp&9dH_rhta4Vk;tBs1b&Mfa!t1)D6Q*I
zJ?SjR9R{DcGw&pSWsH4?VtQs+PzX-NN3#s<9%!ZJdLKf0cFb~bYkcnJ(0*D9otg!l
zM_R+DZeHLI7G<d##V31zmtqSinlbNB?Q@_Gt0yy+vTS97cd{!p!!WN=`1D`SZF<BQ
zVv23{unhXgdoDY3x9?t4WI%sjX1T7^@P&=ay}}ub-A)sf-T{|u4x8Fb`Ro$7Ox~ur
z=!gV`MIyxnh$jAM$?jRENB09oEHU$&j$warDmEw{LeM}muJ$XgQEwN~p$kyRef9H~
zEy=S);0rpdX_#>+`TANhm4oOtslRp9hl0gmFxuf8B$*-UeA<!xZu{nW>-bh1Z8?0H
zIxX8UpYEt<{ZKO<E4Oh_6f+1C`AHyxQXriDOQxK4L`sw3UXxW50?(8P;b#_5bUnRm
zP6|0ecL+ryaDV>@lWisfngTc%VY|*`4<s*ykK!Gj943Wb$6d><sc$tr_Ih(OwAMgi
zRY6Eye<+pkM7Mq6d>qEz8H2>tF(e``XYE%F1<WP5QC@$mW2}7Wl#lX5dtzU^nOLf5
z@>l-LqSIqDv3a}gSbmQit&Yr}e_9V{&A5n7?EIr?=yZNpvx0}^e0#fl_vp@9N}!?X
z`oL+UzriJ4RizO&cd;hSA>HHr5XEK*8VvyN#>2lN#N9-GH$YyS{Tffli|c=Zejy9z
z?Ua`5GA-hjJk2pj9WdV*t2qDQlP-^=TR>G<EO>eA0fy2BGTK2Z%$Z#hyRK-(nT)S7
zALLIy^hNPi#vH&A*TW2wRT=tO_&{g1MIvwb{`S-FY0tmp-tmZ&zXejbFBACH!pEK;
zO|Qqf3m<J1x#7F03Pqh^-MI`Ld%)@RPz~VJo;}GPQ5L8!#B7S+e$)hS3saO@6nae4
z%O+-UD{tS*jivyX*kU`kuS5S+EFwlU32E;GQvhQF!=kbJ#&R82)&|y}bB!5_VV3bC
zVlYQNi(BNm`_Fh1GC%@#)lI7K?CgaIksErNhTh~+)DhelyU5b62nCGN4{vlz3#C$0
z2Jz23bU(^MMhYocQS}sA#QO$Pj_@+B`Dko3oamt&=})n`FWN*YNTV;c&}e*E*GH3e
z{_QutMxNwFvHTb>pS_+%sF{~v<#@iHPy?i3V2+KW&e@c*jXE{=5?*EscquTiG(Z$;
z9U5gqVVdWtp@ta*W?)g7W@s(CQ-X*dJ&~_6!p5aA%f;)stwFNiaAh(Mh^m}?E6EZP
zAA#E<mxAGH>lEMMvMmm5bJGlGTK)gp$Z;}9oW^QaqxOO&%Yi)jQoD%ck?Kwv!Q`34
zeBpaLgwF8ud@iYzyeULg!$UCm)x{2pyF*;K{i0Ja-$}n~w*dhj!Q!8tEfIy1xh7$M
z{qew^Qufmq&3!Mee|->jDdf`CitchxXG`b|;<ys$-`v#czD+1>hP^QAeQBM})!YV@
zo_CV=8#wbaWxAW3m1J5UxF>{uU{KVYzZ{7&Ffj=6z1l7V{7A)vfJ^}Y9ff*<0c%XC
zu+1VyTeGpqjoN>8@7j(8wK5uA#`Nstp@lI&%hN5_*rr_nLGXvSJ7V0u(WG+PhG9CT
zV~*$zhfj1ZM8P2LA*_=W#uOj7%IC0hdt=hCNF0oFuj&x^QI+r%G6SmMWZw@uD_WRD
z-`Rc1xWEP-kI2R-2lnSL$J9Y4g~z<Y)k#pDVIN2;+Tpj@p$!D4$79~-sWMnh>%O}k
z`MtooH8*bS<6m7H{qQ|_*)eyo>BL+n^65&TCq464k!{N=?hPwmdX^U2r<oBXI(0}l
zK*`D=DBm9a?to?k$tXHOcJLyN*=XgAOEjnyZsT39>5H%<EL>0QvR-;`!&phYV6gqJ
zMnHe|C6&P~&-TH5^3M#nut|hVQjAA`oD|KR5Z16M-Dd@{N_(?vjAS|RGSW(<43;<W
zb*^($prvx*&3LiHfedLUbCGfa+RUr%L_$7@S}g%Ghb5*bLpDUvcVb0AwAU{li`%fA
z`ah3%Tc4|o)zfPFFZFxpOJ}X$Pa@Y6v=mf9sWLVPsoE>a&NEnS0*dy%mC?26=5$dO
za)3@$o#q((4B+_0lXXD?WRR7QP=j#^nS3KRU}LV27GWQ}OOQ$^Wt1FNbM(Thc|o1j
z(_oe^`19^eOtz*X&tt^bUT8t9CQw3!`EfA_jJegUxMtI~{*R^*?vav+$1>(Y(V9R5
z#AG)C73xi+a)=Hs9*Y+t@C_`d2|p=ci`*bgz0gJlR;A|38g~p~vfvlGG%}hb!Xp=0
z)zlvn6F2oclu%7N>lq72%PF0{C&*QB`JVQ)1Cz5UVeWD;%F0=W?}wS^0EN|Oy8HA+
z?+uXFLQ;gGPoHZ4V+kElXKnzrxEtfPzZS<BUk|OC9<@k-;94=~GfKW^a>dp&Bj%2z
zh9=A%T~RfU1&7L!G>nOq`FIX@`rR`%A!D7%KywdamPt^f668K%fiAMv9w>G@aLmgD
z1okN2LRHjIP`3mvV{fVGunQi$5RBL9({Cuh-tPo@`AZj<BbVyiXS<Bq=vc8^?K!;#
z*xgs8xW>>MjmL}yz=wl3f78<dt#qV&$6>hmoiYDU@I2G2RkiG%=&omGdV<6ZLM>&+
zenZCS7-UH&Lei}+o`@!yuR6lZU%2hBuv(uv4`+hS)5#_-h5jZ|?o=9O@PRBA`j@KG
zGzi`dX}|w}IIe>-@0CKkNAG!9e4f7&N<;NjXS*8G96YN_I$P$&s)N4ZvTK1*u-f5F
zuQKoHSCY}nFsexCh%&F?FC|M*8=)_KRFmqw9Gd)#wdeo3tLNh<;ov$F2<+i^0`t<5
z)VtLkCf9Ss{0^GKFJ<BN2i5SXn3U84l~cbCbN&WwKKq0u1H{`*jKY!oC-vBD<&e++
zBNS-uQRf1V5Azb;zXH7sHh#ap6=wZ-?F>Nq|A(@#jH;?@+m(=(5Tr{{1nF)N5s(HE
z*rap_(k&?<(jwg@pma)iY#IRpK^itK-LW}y`Ml5fz2lr;=a&v;u-9I5tvT=es(Zf%
zW_irvjGQWJsh;|~j#B=@sUvcj;iInz{K2+!CYCs<YbREmG(5^+31Vk0MBh$VvBqWE
zI7u6V`lb0RBwB^?AGNv)sEL^5P$!}lCN}u#^(walrxcU*;Wb-lYWYV=haLD`AhU1%
zudlcbgGvxR^GDEChsly@%niiP(Yi07l%_#kU=0}GP!E}^i*&NDAZOhUIO*M>DsC4S
zK!NX&=^w+3@3?kz<$UqF0r|#%4&^j`F~+zE!^BedgyCvr25UO?f5;lxe@0bwIAS#b
zhun2#c?Bey2p;?b%4H$mt&dTkLQ?>uK}TZf=ZAO?d0jw<aE%k78L!27+ydTzn%`|u
z%^xC(wbSBzgN#JvZJSpDWbfgog_+TO@6Jej5mNCkly7FB{wY@%O0xA$*Q?>xbi1;=
za6Z?CQb=Ox%=EO8M)d{MWO4Shpsa^0^!4w9bR&(RfTdMz6pR<-#faccfQ);*EL<w+
z-kz$YA<gSBBk6Jp&UF%i;;aO80hTdZXEpl`>lM(q7pNr^1t`J?mp;Hps#005Ls1Pt
z%1H@p3zS_53{O+n0Lyj*(L=UN+7A_mYbqgI(Ndt<2YBzO*2%dyFrocLE`TIu?{_y;
ztnYx3n8Onl&;V^aYIA@#4V)+@J0N%Mh-XOiL-jW>8*&`HWc|45D+<0LNpJDmm)eD$
zY)*m`tI9-+p7#G9iph=2!(PslS4pz(3fkCMhI*Z*^VO8*^q-d?+|LzE3yhh}dj~?;
zXJn|}u8iRU^g0c+Yw2x(zDHQ>6M)1dHxRE|4+ZM3r4@Lw@R%+VKSu^_l#d6}Ux7=h
zMM1+jOH_Q83c}aZjldhxKZ{GWEnY0z-p(zQcjutd7Le+%?W%YNgAlqfUhl-3F0R)b
z%QrrOR}oGc*I;w1I|tgl20-loj-V0wUcgEsL4dWF(?fvyTRJEi%R{v~`&&DZ(_MMG
z4my(}8PbHD@>)VbFcTSaa2@2)4P*c4&0P+v#4k<L9pBjgU^H>gHxH^pkNyRR1D@}Y
zt(?rpFe6kVS#RLuw2N2z2;jqKVwu&wsv(Vhuwf929J=_DP2+yIEdW_h9EiFTwY#)E
z89gCscrZV$5is2xUaJQRg#A@vGen7w4u5!wfiFp%!X#)eMuC+t4pz6Wg)EYa-)5a1
z1SJQnD$@cHQZnRXx+?k_x61=NiE?r^Fmk0@Om<4;syJCkOSo2uPq&UAS}J7~Lw(q7
z#g?9%&pyxxG6-9T7m|hfuA3p!3{uHLkNRPZ>0jCr#KD1iZ^(G27DR<y6)t;i>+l07
zi74@luNk)`fGv~yS{Awf+Xe`aY(Zx%_a(=R7gPLf0d4+9QggxW5Rf)UF^-l3dyV8j
zpPpraEMu~R*Pt+$P*=Y08QbaD2|MX%(h+Rt;<g1iUt$Jtb_uEZMn|nH8y@Z<qF!J9
z0+c=BT#aKcKpuXG^1Rku=J9mTSkM>eI)+vzZVA+{P9Ry3)9rSGs$6(g_M7e(1$v8j
zGne^*6WABc>vRPW92E+!!u7Gn*I{Kbx{!aKQu9Cl`Bt94{`rcvz+smyR$cc*Botj5
z6=;2>oqlwm=z!4l?cQB~d(fjsA_O5&Q$-eOk?QaPint>CA{XWQ3xpKWi2S1GbR3wr
za@{BsUU1u*7{qM^`;wip*zr9ry~V`wZher^U{IVanj|J2F+=U*0m$iD(5r1)sPry_
z^5q28nwt?K;5kHC=Xc^nh46PJD7))&tCvGZb0U2K!=%xZ;Z;=fH7dt3VuINSSnP|)
z%IYjw$TBFlp(I+a4sVd_eKB<jw#p%i1c>00BtSR0_>5;tzo4#y!TiJ2FunySEPW!x
zT$5io0pZFyw_*e|Z`>7Yyps+Zpt15vY6d)KpTEkw-*DC(#_i)SYmiJP1`c<QUbM$w
zt6v~?pC~@lblC4V6<NzBE}@w1;5s-04SzilNX%tyy*45=bS5l>lkXUMe*%fMKd=m|
ze*a8g;*b<teSa%W)W^_a;4>7qWtwnnmx@L}*s*r+*PQfWoh{s`@fDnO(kmAPQgQ|I
z!M`O2FUEIYfr@>az~I43IcZ|;5l0f67_f|73)^A{THzMuHUXPqlMerTuF6bu!mX*w
ze2xs4j#|LzP7KTFUS_eHOC`O1<V$V+jVM(up&4M_Gf@8EA?IS-X7~^IbM*cOrZ5V_
z2{ZK%LJedJQtkyNVb<afe*`s>IcJuVUr!y8(!G_&z#+83hI)CrUm(}aMaUe8!tDdI
znEjq2qkOMA?(1;?Bl8{5q8}PA-BLn%)t5AGk^Mp2XT=|&NxyLCuok`}lSFID(CscV
z067(<H8pP%C3baap6O@(lcjNO@7GSp)%rCtSuh=K@Z0-}#I(VPFT&p)K}5m8Zw|aM
zm%=Y8oUg~S9F2#>EnV3qZm>B`2ETG(h@35c4$HU|4v(N(;}m?LRg11@7A;bi6xZHF
zHLiz#kn!{v^LJ%dBWjTEv?%0e?jZQxda*o6dwMSw`02U@O-X5k8@Hzd|N8!)G>M^^
zClgH*mdUAP+(p698t9{EM@t)stx#M8t;6a2eIU$HIzejlF!b%R=|<=z=E}aMZX4V*
zHM{(HqcH4Hg(RP9?-P16^xd2JeO)fWp+fvzAHlLVMSO6Y1%sGJw(@X%9p1A{<ES@R
zLTjnZyC^$&l<EnV=Ybt~7L(e;k)Hf(mQ<O_Lx)dI{BrH_V41motF#WV$-&C@DesC2
z8visiRK*=m!!|`BxL#$&USZ!b*-?w{#+Mr5&q1SFX}-c&?G|9XVRlNL`Iis^at>66
z*FaJ1NAxVv+Pv)T^U|^%`WtIp6Pem`$ru(r)V#YxG-XH?$UNDAzMiR_KkX%7WpY?d
z>T2s#Q0qDinm-0Z(ScG{i7>loAJeq>oAxa#Fi@zD&CLsePD2wup%1%Yv~$8T1uh&P
zN{wYu=gyqIX2lQ4P$bQKZ`0DCnGa4Tc(va5$V4YxkT7t~Rbh5w1<Q#r`9W<`1}Tx|
z?*wx8!><YgH6E29?8L1cz-RN~70!P(<!SXZ9Jm$KWi^r=^bQfH<>gCgG)0XOR%bB*
zqoOMrDdn1T1%rd!eOM@F=;^|w(o}!LwaEkM2lSlow56D#fQ0#*HB609%vd$=xH67h
zZN!ZPMc_5udqR*|T72;asSQOSRBx^7(z@CL_q;FTJ%<YGjSFc3r$NjQ321Q47bRs_
z^be^$*Ry>5IwxrEP0+0PzSWipT*ODWscV1ehcnx9b8QEj+*qsCdd`fTH~KL2b#mRc
zUtYo`AUB|fGt6RbRoDHA1m*D1pi10Od(Qs$j8|AfUsQLc)1i89<({Reh`{_(?g8{4
zSFbiKwL3%p`L7!L@a$_4w>}5-@g*w48xJ|igEq7?A`Bx!1uo5E;`x3IJRv4<F%7pi
zBIp@itZX8-N}{Pg7%2UU{3^*a7DmK_{MB*+LWoYE3G+-FQMlTiPuHE3Mn-M&62G#E
zGg}0HaogrD1%&K!&>B`lkp}#I{UDRB_*e5~Lz%=N9`ZJ>W6<bi=^J`1cLZTm{@hf{
zu&@tsSu>&@U0@yf*)Eh%7b=4~9MMJ`KSpHhFj(W6d(h!-qzxR+j}O@f4r-hZ*+k>$
zBETKUm$&$@kDIDH|2C@a&YPZ=Vz>ViI9s8o%{dUbxNTRH)kKI(&VTJyl-Z^0EfN|w
ziPA~`*37nlV?fSlHh6dQZ8%9kK#9}uqlF0mT>E>Xsk92~sbzf5F><9fsHB|cJM0i7
zHTQ8>0?u4=szWK65ze8Gvd|^Q2F<@l+d)k=n0MrmPqrC+o4=p~mI-8O3Eq#ii3fFT
z!sG9!Ld?$&kUJdzRxhvNi@}6-uL=juI-fY$vGFvT5#xSU%9#i^dSu>6bOeoqFmPYI
zZO0U$??A5Cm30xG#g6BFzl@v3riA;{4rBCV*n;ix{iwb#HG*mq$?5ZKueLzyhxPn$
zhdsHs*vTjSWp2X(O*3*1hC5rcQ?B7}Ysq^SkY97gi8YU`IqBh@C^qC+ZH|JzMGCSS
zbj{?>X5Iq2cG^itB3!TF7d8h=6e9#?ArCM%2#1KarNS0nOXFp^=i6D?o5!cxS(UjZ
z@RrFfK)?rS{C2WR(UHcg#Z18xS(^PIJSZXdEs3Sx*TBS0S&ddE+hf9ofPQ!c0RuxY
zNI`G?1c#<m$kHQc;!M~9PCMiy%H;*ZJs*7XMs*I|u>aiv=ZbW~709p`Q6?QOS!VHW
zyn43Sm98D@q;bX&&SmA)j{W$|;-0u~<WwZ~{cs9Xr|tdlr<MqYGVYW?{#Q0P&tD${
z*7{&`P*dRGnLQ&1x=Z-MlB$(?1gCWo5e|`Pd0m?WJd(ycjqs(KZ(d8-9vIe_qWSIm
zKI|nDOp9C6VOLkL@gyd-xRfsbEf_5T$2;QbF*I9TRtE6`ugR)V$XEQ@fNXRf1PSSv
z_;fY_in?i^d%Krj5sG~}z6|;8omnuPY>RG+!AkhuXn^MtzPC!C+u6R?FaH2D6s&E?
z`xw=q>>A#dtkB&x=dtvw{5;UXE;hIjgtN$=GkLqVv~U8`XPUiI4&wH++V1<$Gbb3d
z$kA<71EVmk^#DNJy3Qie4ih!|qEoVm#aUp>u=6t=|2>3+pJ2oCB{pv)-dUh9YCBXf
z_EprfN(ON3l+|Loxt#_Y`UgJ6l0-#$=C?rj9zEgH0{VNAHojW6ot=xd`5OM$yBqy~
z=91FWv))}4u5Adp6b^kWxSXwb+x#h{AwAf|iww3Rq&YOv<2JL3x1*L=#6gCJ#T)6z
zlkQ6(V$~12b=~pQBKzKFJDd2kW2}TV7HycR(6jc#Ov-JRmm;1$&Q&s#OLx4N-w0F^
zg)b#MHcbFnE|H)i>G7~&kc)N<QmwM~?`wX6Uq*3r(IW1V6+asO1(k3^E?p9DtksY}
zX4CgBv<!o4+a6>B4)E|8*oZO;#oF;Ft1}h)f;i>@1JnYvM=635j10fh^d%39verE0
zK6rIW_m4g-J@~0?`tb4ey>rjT_?sOb=B0ToA5YhaA3im!$$VTz%o;xiGD?(m$^60b
z@_1kBn|Zvz&edFfv2AHQR1j%%EsM(W6FFE)pbNfvq%6v%unVRGKHX(#y%m8i&lG@o
z5dyb9=yzwGzdZZ)xXge|Auk<mIKS{L=Vz8ZH$?>EZvT@C{Wy+y$k^Zpu97ZT!~Rp_
z7@i3#-}&ocrFPXlmm@9h%Z7m0l`B?c))PYb5la2!V<y#fyq2on;S6<Hq856~<riCG
zKb^=lE~j`CEbb$c2czB*`(6;F3=;i}fADAULL*^^u7Fo<@@96|&ByQ#a>Fg&T3pXz
zAw&U_R5%0ew;{oX6c^H}T=yZJ_~6SEqhlJidpl+Wi9Ch*CX&KS-IpUu6?OO}0XV7W
z8~^!sPh(r3zLIoMLmWno`^E9<v(U+ZO8q+8S?Ub^Rkiv5=dVmw0l{^Am2%{v<m^9d
zE!<?&D^v@nNWxlo7GSYc_Hph-3Jz*#A%)_(PEEj^^+_L_TI!L(F&lRrM#MpASg63u
zuKvx0)UWPYpbLB@z4)M|;h>8<<xwfaAQMOWqEZUQZ>MI#;lLB(@06|&$wcZW3o{}H
zs`%4;H$aN#<1`?D(*D4m>~SNiH8kCAjiTiok4utKy%~5nS-T}YY)|#PF8gL&Bc2hd
z98V?_@q-Qz&9>7wL`L3HG70Dpxa<yEr}ZcL%KZI{o!QPZag>&p0~!cLgb#*4(L9{`
z)Sl&mRW=u*&!3dl-7i)7ZLqNI(J?<N-T|y8_#HKxtoe3Qmm4RlOy)V(Ui$v5sg?z!
zABA&wE1^8vh_S&61N&TJ3GYavR(QfblTsBEacRGMEF<N4*giLHZV0K_k_$!D=yL{N
zE`#nI-jQs@M)qa3WXlzfZs^B!VnZg)MCFMiuDaZz9}DB&uMd~C%8qU!=YW1pjnM%5
zt-J?h%g~Rp^>uDRzf}fl+z9=cbdtM1^jmvg|Nnis1vZ+3B&^xrs6ZrZUc02uBl5v5
zl342@)Xt#iB2Qc~sSi)sP@N53dpk8kUD%yngI6E44vpH7)_`mtt-*Bu8q$ObU97$B
z#9sKNA1ByHRwE2N3&j(x!NgE3^dkv5=jTIviL5Wxrr4L+6mrn}qkgSphX<MvSuAUb
z^E=)++$`#kykpor&8PG)@sTLVt#=#eh_fzo5bpC!G9POA?l{{Nx$o_eW9U?MJ4M~u
zEpPqujg!bYZZpi-T|)43B&&(g9pE8%Wj`=2;KR8%@WJffBXLx`K3>j%H+xUyaX;p)
zJb#kZW8tx!kEGv3E}uU35jdjRKQ%csed(ogv2-~VbHj9PE5*|a^FYK^UdH>4e88;d
zf2)rUql$Vbi1S@vKjkJBM%#vo^C)6al6o4~A568VENG)vSb|=$dHPnrNV)(6wKwry
zs^?vzCD?s6jr%Mo(Z3`@J$58~e2s7RPIGpf=@#Z*tS7`<PiVvKN97*p0)~f;E{SlA
z#!d_!1CRWZd4~7N5x{R&_$G@d+fQtX=57^PtgT07XHU$zO;8lK0O+*PHkPM9Y`)<M
z#%ppEh9A7>U!KEJb2<A9P&NEsPrm#V`}ocIA~9*V-5G}aCdU!MDP4=iCIQkXmLq6=
za5uks1nidI^qgXE$tNR;Dkyq^O7E?}FB#7lbbV!Qq}Fo5_%z7^Z;Z91gIDkG0jm8m
z|K;*8?4e-O*QmiKlDFKDkhXr*a4t&QJSo@l&qpv?wJFRmHcjwH+{>@YT^%kf>Ww|G
z8^3$iJu|y%-l9~XbGo4rXGS4KZvi*L{V~>sd8hlnH?sj88G~!Jr_%DOM3(ApmFT(`
z@MiO{g45MU^GRcORhz-6tnq8Fed)P@aI*I~Bvn@GbQ<%!g>8uRw8OTzv|o_O-N$$M
zdIN*beZjxcz~HnqIzaKEFmzW0u=ZM*@qc~QnCu$^a>g!`t<F+jeck5i_iwLee6NtN
zRH!?5BU<P>h@gAIX&ZQ=fS{xY$WZ!2M6$JOra9YpsjUaChaLbmI2d^bU>=`o;K|mh
zDS!K`f~%XH;Sb2V!?v*lhL%oKj7n#B55X#NRNz;3(_qj7!#+BnX9s{A;~n5fV)$mP
zm98Mu-(X*7<MaL34*gUu#=gu34L4qsZ+1`D8v3z*s?7L)V4xR$K5o&Y(KvUiCCLBm
zve<D@O_>^}w6%T<0E|)zd2AnVQ6(t}Z+tmd904JzlMVEz?L13NB|z9IV$@}xV8t@g
zbjy+B9)*7UxA!Ks)L``NIu)??aZ!BBMA7|1Sr(H2qD!HEDOv~F5MTc>tBl)cM`9B<
z`r>;3G6zZ6LKO-Z?P8$OzEKm>&$`yEGjJY}F5IDev;-i@A$e(y4;(reC^M&1nIVV0
zYFD+cmh7mI<#^855D?!RprNp{U1TrTB3k$;dApI&{lmX6VSOkX$DO_xT3>4n2#`J2
z=R^}PmIbI$PBVzMg2o>|zeb*2j!e$09CgF;FO0P;TQo|hh>}c#RexgHyy=eC13~~_
zEqkuvQ@>lXQc8T|ucjUd*e^V#>l()$_VdO(;~FtV6p`K9$$hZDX~-aao1eRbZKe&(
zU!IfpYr7Y~%IKAY(5aS}D{)a0Hfz;dc~qLH+4#M2-|1%ma(P#F-dAEosz~pUjtLz}
z!QON<x)I7!((?^WMN~E(%#Pc-^%7SGAiBphNotJXI}J)N0Q-jm$XPa`1ZtcE?WIAs
z+1z8mT2c^U&ZeplJ&%Rfy(#LJEO{h|u=8j~0t8;cOBP6Ke$SQu;v@Grc((90C$fMV
zE(B=xH3k|15MF3dFxW-uKOuW7%>-lt4=*ocKTa*HaIPS_-7v3H;t>w|XD{hGU=hv3
zFOIksLkIbp4af$j4WX@AaR!V&kK6YKt#Gn#t-=-`I;evy3MvGqOk+M+yeh;w2m+S`
ztYhQJH?nxO-9W6+seEmwxm|x-Xbh{05LK~^r4fuZncCwW<Q-$`{fXsF4T2{q7!D)W
z-VGm!Fltg^U+dJZ)@G4|@ZGE<Amqyhkh~b9$lNn5`*@C&w}W6}{07ho*=EHbN9A$S
zub{O{j6i`2zB8{dXSx7r_Oeca)e>YtyhThCff31`WGEPVjc#5d+O{9Yg*`tX+5Tal
z#p0zy>DJ~_<mw^`$p|1?e}!JAs?cUmL6~1||F+*HOO_RkpZCB(B{OM<6zxhCIONsS
z2#_Y2!8Qr<Y4wsVvqN->J>JgKny=9MoGtPK8^dq5#)$M)05-v_*hDtxfkny+L<$yv
z5i*9#K6*k-Js3}7W|i@*2&~~}vlRUkyl_m&f=BKvMDDLF<9GUO-m7c5VN}EBq8dRN
zg9LAV$`HV4!ZAf2K|?jHfU;uuiTLW42{<WK{!R~}N)_Uz^9RVeNE2X1204B`I>A8&
zx}!l_fhVa+KksS6Bop$W1L^^)gJ00g*{>;v9Q-5FRQ|RMt#c?i)LT6-F<D)pw+C<j
zIT1CbT2b`YWFMbE52XR~q~7mwauOYZvO<wX!0`<8d@KGvdh(V+B&hO`m;DUCII0!c
z)n1MFfB*)5CDHv(4Xb9TR&gWx^m(?Sy4D%m89R^1A?dc+VP5mM*=llsWW>{!`A837
z6)yCAUzBpdo}uxn>gR3vb0VL!4my5Olb<gIe!h<|8b;Z3u_QoGv<x)@8rb0!+fH71
z1T<M2T&nXul=zR#oQ$jJIGA94lLMZ%XqAv@n?`%tRxB(Dvg>XK%2=y4;t1Y-|1{O4
zjygH)R;^<o@e%N|TmIcP2*KLoK0#EBi74npTi4&;%8Q2L+IH%>Wlq1r9o}k>e$nJO
z__kZWV;3xgb0?AP*?A2;s@H<h-S_$zP&kds`=fTAWW)E^ceke7P9gj_h*Hm#hsYgF
zMk`=Uvd6UGz=kgG@9&Rz&`N22`{eEq9|3tDK5&y9(1oAX8JH)T6%ZQ3Rai*DUFf{c
zehD=8lHv-^VAU046uIRTstP6e-Z!fxf;yDAL0P1VA2rpU5N!+50vMdcmD+!<oZR2`
ze&+RCLSr=TcNH$U;yhIT^;ph<j!k_}3Sar#gE1+OXofZ77Q=RFP|Q`&dvYwJfXit9
z-Mb4Dv|oI5TlxEMuk)dxUoAdIKw2DlZasPKW%SR5&q~wpybm9rj`=<-{o_g|lOL5P
zOK+IFpY<LK4V92XEQYl*jSc~d2T57m2Z8jhv^BJD!!6&urU1K2c?5;NL)X1J!I?HN
zl4dnfHW=Ei!1|H<r_&QAka5YxYw4==RTY7M1xF(mB3V}-+`L1Yzd~-8e6a5=+M{39
z<Py3AV$A8Cd9oFw?z-n<Zzik-v{zQ$6cz*4zu#rykWD>A-&-(DLiZmu2k+BT<$OwD
zaTwR%M}-?TJmB81-vN(gp@*Co(?3$J1TdyeIL1YMKo=$ZWHPb}AC*{%SIu?+kPUW-
zdNSI6aQMd0wk1_ZuIZgT(_pU%#sZrjU)){6Y+EqEOI}psf7W&I*#daqUjT62voh34
zNG5ZcXq_D42~DOUNNL*u2zY!+<3V4f?^!WcD~NtbNa>;oN5gPdJl!6XMd>c8G1hMY
zXTLoNtI>+D6g~PO1r9u#e;>&s5)=<<HT?AVI-e)U9NFx^HX=`x(^Td`T(St0(eAGW
ziu`lvu>=W?%C`6Vo{K)ozMV21uVma&3#d;45dW%c1xzk)JqXf9*D-0`>Fq?}p0yzI
zUDT+_^`Er>^@}?C5~Vd=$GAE#_=p1N3nc%R$&#sSy+(lZUST_V`-cve=+$0FourL0
zd^oEP%M19eHo&&5RO|0iv$tyedtek~S|dA!Ye5KsFdKNiz@Fgx#(bY0;9t;~dSP<^
zvl}`Ne!9V_wzRgIIcIcX`MPettK-Sn!=YVZR_iC|7|BE1M1A+cPcLq2eAe2}^MhFu
zuXV8lHq_9zAJ1j+5!C`l#j)ep&uvgD4qLR;m@CXa`R5h06hN=)<@kdHqdG^ySgZ-#
znuQZe|9I)loY*a))kFrnrND8>!3t99k6!kRCn~=s7tvG(yT294-#^-kYx;qWflr<2
zmI+lN#F{q26VIPgu_Nbiu3yjH_X3g0ZwEirYd{Y#xM8kX3p9N5UWW)*UtmB9Q8I;A
z%n~1!CRfj-{5QDsEIqTJYSQF<Vp%1FKKXFwIO%LpXtL>0vGojC!+PN&$I5rus~=-Q
zv7;LR=PM>D2QTzHrxqN_$iMe1{HY3&VYa?(MnzJRz`!<hcDw*81K~OoSebO7V?La%
zOi#E|X?mX$CB$U@F)GmRT9>3B=YeY)Je`X5(G?(HZdw$>)Hx)Y2mZ?`B|A`B4pAiy
z0w;9wlxhvgD}z>Ln$#b7i6v^%OoaA7JA&sSQwR&sMw#o6I!+2&dAeJ97pVe)_?e;S
zPTkt*gD}=nae*Jhx}l@8Kce-nuUuOaexsS}K?dWNgbRHuZ*<5M&I<~GTnh8V`@15B
zh<b5@A+X!EVtLVFT`m4#Xo-50qQ2%gGPX+~Ui5N8eYiPs`j3xK!<uc}iHI?}4z_<}
zrCw#om^@2yY&w{g*64HwPTy!RJjpF*Iespa!DBGja?6SA-9|a!sRdbVfhpXDV|>Aw
zO8S;v1Q_PaygSpk0;jYtiWbPUt`hC1fWGGcyiPQzG!}Ij(`?^=J%DIctIu+bz1GTL
z48=qh7+-xvvsv<u9TfyLc(`KMq`cHm^o$h(u8GyO!*{Pf2Bk!{0?SWqLIEpC{x}ys
zNO<j2x8YLz^gquZE$a77euDmz*q^qfCnYm69v5WLr>*XTGDmTWlGn87Q)j(Q>7Tc0
z%3>qWiE|P0y6lT;t%nYl;;1%y?Y7(En?>ipPO6^sCwJudF>L($VF!$ATUOCv<^Or?
z{(g~l26W+3$HCP*EG4zZ;+r9rrFG`P1{s&XWt&Qr`E0mP!T23>mG>awIoI*ka~dKG
z7h)9GL5taAP)Mbf#YTdSf0ngvTVHws$eBwN=iY**!lNbFvtk>q{O3!BT(fv>MxYt7
znQN=2g$?oi??*okyZ<9P-a5Sv6e0-%yt-f*z#--Q4f3>wkM0hfBo#cBR#9RnUwfCs
zSLydap=9B9Jt9LjDF51t{#uzC{1+@4;ACTfD7Ol42~c(-1{i5<6#zu@(eMT7eJudT
z-QWzU(D9-b5E`Zgl;V=&PR`+6-Cujx@cwHR$^9p!G8xz=ML$6MSb6yvFb2iO199o-
z<HMioD!C?+LW~*M;VyOw`mW3fC*wmxLz=4=<Z)?ZfXEy={-EqFH<L!xM(XVIU)^`7
zcp$;okO8WE3&>Wl{Ok>OZZnpXaGs$=7>=ah4ba5&q?|_xZ+;bD)6Yzz{NKkZQ;BdL
z)_)9Dgs$MKok5lJO6<vu*)ZtUtH8dT3ko+L$eC}I>d-9*y0o~-ubTJA-;sf?Tf5t5
zM4IeRle}>43ec~u^32uDIQHYqWS!c1xB8(>Ny+dzpwru)fgRfrd7=l%0FTKNP;2p)
ze_OgAO#QjcCI?0=Lt#b|lKfv+)QVMwf=Xjuw>qT=rWR~u++10-E1iI_cok1@yb#+a
z4TCxD^`J~74lpUo2Y=d83OifQnPQzfOcV(p;iJYV3tQ;`In)@ari~L2bN+%{R&9~n
zM|Qz;(hIT@7iuMjptc#BC%8C<Z0R7CE)FV;RvLi{fgVb3IoIuAU;G<B>#;ivUmE?l
zV1xEd7A)r?$3Y^2A|WWQ{?Kz1XiOeAfe{8%*{4A1{dhto`-Lw!R;+*mV`#xdyB?V6
z$H0Wl85V+$puEPUDRg4RPx_{6g7zscpc`~I{sC9pi-#G9tdj1G77)TLvw{wefe#@=
zzq79O!60KOw9zBG7o-E8DG6>BRaRsV)|kg@oA<{5NnMoquQ!Sll`ZP2Ec0ZMMW=ix
zqZAauPrI^|7xL%=z{~wOe41mxfGNN-1&$tDXzVcy0ny|jIJa6H|GYRPdOA`I#=IWb
zE~eO2$Q`oShirfm>HQ+h)}uY2j%ZQAoiUsf-2u+judmvZ-G(SE)Vsi*#dqowDboNP
zB$aTyi9Z?F+ZG#e38aJgh+ode*$&i{#Bo#qEnxmR4Z!IlhJFEJ$3G}RvG3oNO(fgz
zM)V7$BodV=^L&Kt^<ytPB3q2~k3prL!8Q)HltIC5Y;Y;4FSxg*K6I3k$I!a38pc}j
z!Yn2~Piq17kV~bOQ3OC&iZkWiX@30z6L0wCpHIcNdxK?IGZ_JMHo~vimP}DuXH$<O
z9}In&1G$Vzt04Vjn-4O2KAsu;GckWTJk0m+MM5Wt2-XomH?(%Z!^8}2vK}4W7`edf
z>*@*Q4F#?XU5`jvMu4d!6|>QX^GkIUUZ@a$=$)ZoP05n%TZ|5mY6>0Q>S3quQ~F7F
z(%dyoBKR~<0|#dxq1$7-t&5;1R+%h6Z|2!Pe0r0Y!NR!#cf(RR0mVkV03Sny+k#um
zrT$5_e}ddoQ7h!dWD8@w@(l2>Sb^hDlM8Bu{`XSnc~Gxk*3E$&@B<7}STV(yC3D3E
z!D3)z5gY`8GajG9!UWIJXFAH6fX;x4WdzR$+|;2|_$|_0%C3~^Dg373TeZvI2g;x0
zHwE09P+$Hn@+&^B9vmcJz^k`r1vUjGFon{TLgCR{5Pa3^@P~IOX%fh-)IGt8S(zOn
zduN`HU#ikr-+vHlQ~y1ss?ee9>aK=`axrm$qG+l(#h=Xs76Kivp~f^B=sWXx=Ga9J
zrkD;zlT3@S3OMModv?xV5W0hJIeJH6=2H}p32`03OP+xH>;?6pgSIE4xUN_hOAt9e
zFd?jWR<7gpP;10<a0M$JTU3@&)I8DXLv&T9uSLKJFcZZXfNApE`jW46GfK+Zai*Sy
zzLZ&DHS?JcN$!1kAsi#ewdeIK<(4#|SMM4Yb8t6E&>K@pY79f!It0f`I@_pxFoA$$
zvj*hek4xPs^*JuA0!@EDbY9Q^Sj}%ALJ9s|4jpuV020zU|B%#nXaFPbcn0t^r^*yj
zf=V?%y@W?LGfkk!-kA{O-a8hC>-2aF&<rF1IIxk2J23}L{2HEiiR71(TOD|<x0BTK
zl=&e|7rr$e4!yg{x8)bSfa?*Bc~$|!kr1=LTXdb)zGOZRxC8IyLg58`kVUvQ2Fgil
z$^iSBp$NE-UqHy5U_i&q5U8cKE0U({6S_`K>r}&-D!p~;;0m`83Y1#L@)Ns0FYCbX
z_fEhj6vec^Wc-o8@mo}OqP?<?f>@Cnv!|hTg?NKiBP&%oL-oy-9BlNz9=oQnrvQYU
z3Lz^T2FF~QE2z(GrijCt2-KDMG`>IR<#YW$W!^stlTus9V)Eh|5Idc*s_115lZ%W(
z_Xq4FI}2X&K<mrCx+OFJ5Ijan)$Ou$;|S?GsgWP6j!a(e_?b4Df?)JVo0+uBE6UIb
za15!}tpO#m=sv@bn|rNCK#4n$ev8QwmOC{=V9hU2ON-8A$F#1<`nMD??g*_0-sxFT
zNRH9noWVO+KT_Y2p~X<*h@v&#5oC;LMJI`oQ~Xe-c?rn10-H~a6zc!=@PfnnsbTDO
zaL|OM{GqPDu^%qCJbDU>(fuX&6lerYg&biuccS#SryHt<pQzB{x8gK)m`f)kHzFrx
zZb_3XP8}z<a#_HZR5R78+EV?vp->+LO|)81v<cjvQB3v8LPxG%O5a<MZZg$0CF_!F
z^GzJQmfS<IY@v$~A9jV7i+$kqN{;f*zTq+Q55`!lUQTXY&koZR6=_Wu!?iauf9!og
z;~9Bt7bPXSTu=JCS@WCX0T)HP6-H5EcU*>GO3L+7zC^X7KCWOs6?EKR5c>b#A#dgw
zz8l6dx>b!_rn~8(RjeG~Rw$j!#YrUW{`ymQ`4;1`5CvL*mfN$}OLJ+iNt0Mu{wSeG
zTA?o;08+-N_pzLCarH@1&V(4)5>~XMWw^8q@2r5zFh^mG{Xy%6>f(p-2m<C%D$UyJ
z2_2U5NRpsPo&+`OU%a-UNR<N}jjOAL`C%~_G`qG2cY;P#jYf*>Bu<W0BJ0P{tS5oO
zZ5I!12M!fvJl#hwdYwSH>E|`Xz1s?0+wrQMQ=Iq;PtvK}F%9bs%;GdE?olONLll>a
z`TgWftbelrII)+0P@|vamFWdF>*F15C7CCG`y76Xha-r?zl&!C!j^jAw>1<0b2j-$
zO8Pmd9?5L&L@alOsO5<G3ny*=u`(QJyJ<0g>Tv_&3AEz|&h|b@;eV4q3?c$e8pe0s
z04ghvl2QQz5scv3Hno#<5Z<x<rgtHZuSuG059ol|8iIKW^ZZcnVz$Z)6i40CwBnox
z5mkqjFC9#@n^rz6I43a^lV4%^p7+1zX1nabq?@$hcWb{px3TzG;#^J`l<Wszq&rbr
zPeJyD{9Spf7-Dq(qe8M)a_KZ|!r)c<*3Tp-q^)6$7$jD#KeuCt)6sAgpBn9(zgz)1
z27_CxaTag>*^P@sZY?X}34veZz4TElRw2He%g7t_i>4}EjR!EFAgxglW)Ka}-g9UI
zSxjaIJCSBCvHp`3%ip4JN4_y_p(+|Ak4qGU>&BQFvDTPOJ-jR}Yb-GknPLcZ;JB=3
zN(D90lwgp+y_j=yX<qE8;B_dI<rKF~!5Rc3m4qjqg5<KJl=&-}F4I2pNbbF=FqE8D
zP<v#+!xiO4$o*ZPXU^-E9IKY$zpWaP35n`}lJ5m<mvY*I^H`sEC40@hb<lQQ7e@uH
zk<~O{3@Yhws8>NGMz~qi;Nsa=_UfN6&*^Zi%NG$I#lV3b(o29eTA17By!t3KQL$yo
zI8(b`dbM0|55#3iXXFys#t@(7e<&0?P!u#`bFrjFRkZAkxM$L9GYvP*KozN4{vK7m
z2!!oLm#mLW{&N%OB~YnP`^fL+v;%x4BOZ)SEs8yekWDfjO>w}J=kaGSa08y90XQEH
zw|`T>LRZ+qurJa{T=J2{&$ED+q3?&d8^B%W;s^<h3vn%)Adgpl;}ZI!*!4k&dlJHZ
zdpMgDYyf$$ua?L>wp(G5A*iUKB5);q3hswMK5I3F7LDzRI_Kcd`hW^;!fL0EwG2H*
zNt-78X~TmbMDbf^_M|XF|HeasE!QNPpBU!R`K%=b(%R``GGH|B8}UwIdEBs6{#cfn
zRXTzeaxmmL-lkWo?d0%d&lj&M3eq7!Xo<ZuE0d(=Sx_?yy9-kgdccNZp5xHle#Zv%
zZ~erST=V<s_x&2gE(th)FkvwAI18hAi1tIhi!Og?V>AEXW6*y>`WD9{Jb~?)w$(zM
zaH~Wt3w4Ef!^PsxUsx<WG2grnwPo^i4S<gQjW8%?4-)T5C*I&5qsj}GO#BQnOZ8UA
zf*Pj!?TY?U(7nZE^VMpKL??M}ers{U%b)K3#zCF}l4Vt3m0C=veFj2R{*Qc;XW-d`
z=u&vF9{`Yuykor!DpwmqLV~Tn^7qiQC7o%)I=PQh?$HidibOCW{<||kJJyy%jeWFd
zM1m5sMvr-mvZ1nAoF_Gk87|f3mvT47X1{HmlFnSlA?ML2#?$*VN%ajVGDU?g-6{Yf
z-u~QW!xKa9u=FD3^WJbY-wv$gJj>5y-(2=hF=qGpoC1CFyh1Cmp+pVWPO_xA87@Z%
zd^gr7c)0erk=LO{>E`7so4F%RR=5q4V(2QLP-)0SiHBnW{j=rja?gM#26p0tS;#L?
zvL|rT_nLeXji>0v89bx6Ez-7md{vE_9vJvbd1hFsIuIL;@yujuDGI?=ViNA(mEwTY
z9Zw#rz-N-aER#t?xGvc5;>E;7UEB6XU<)wpzoox_d~sTupy$}zh}5yrh2K3Q2;|k)
z&EeYx%}_u5ZkD~1Yh{NU(Lx3}&(e0qkVGAM=$2O;JgNUmwaQE=xAfh?Gc3M@rJXwy
zc#3@(gtZ-9f}@OngT=GC|DY64?HC|0=NdxP{WsUQPz8a<LkqOf#1vsDS!RkmyeocC
zddhbd!oJvNZG`(0Nyuzx^7hL*`KZGVftBPFgk&`8m2A_}f?T#L5Qc2l&nIs!suqN;
z%OB9C+V(}b8$VouRpiNcXmq<G2t>1Sy${b#eP{UWR#(z@F`$Z_(uqJ&*a^gMEWS~m
z#bv8f9Y^Pu=e^=GR9u9AD5MtXo!%k;>IAZ4qU0i}{J%e2!0a$j{E!j+Q*+SP)<iq3
zeVWYt@NB(YijR)oz!SC7vxrKv+-aQxHFZ9$pBOH`w{Myg!Tl%)hTDfl_jUs7FseU`
zAN5MzR7>rG#nwWYr|x^_9`(%78D?+fpDP}hbsF@<M#HD=)3xM#c}DuKRal)~{~-c0
zT?o%QvSe4guUv-@syocs%o+o3Qz|%Wp%E`Ibu!?_4&D%SmxLQ7^+fnDB0(iQR$QOs
zrL|;}9I8+=5y223A{;7a5?+EnGsG-4=PaEhW6u%4XwC2=1<Sb#q0@>F%6>ah|1J`1
zers8KU30SM<~J~6lP(?0<M0Y>iatJ`GDF?f)xh5ZTS2N%6W8h2>AC=lIa-O~?8m#6
z>u_iutXwxrX7>)PN`H#AQLzll8lAvR>y*b#s0&f?s)5rLHR&4+aXi=7^g;bHRam(#
z`vm*oUxZN}i!*+E$DRAhM6IjII6FqXQ+XD-fAvo@{~MuMwaxy%uW=u`XlmRx&Ck{G
zU|)N8LmdZe2`0~VLS0xV9T<KTU^%ZOZ|M*OR7cvx2l3q_BX`w(2-T9+&r|1aq1Iym
z0m$w--aI!P8|ksYevfS$R^*UT(Qwd%A}iFb6gH>^n>Qf+_3C!6P7IZ*sp=jzV9_qP
z4XBR1`M-`R7it#0BXdizg^o!}H*~>nl4X^BEj^rx$<q*AVfHOpN!a5pPTR3n3kGYl
z%fqt6+5jR;3v^GLI&(jA%=JHbGB%gsNKc3(o_OJ?%pdwu>X5MY_6f*eQU+!pRnWL0
zkJH{ejIZUl24Ze~zM>9%;oWz^KA~PNWtr3-9;ahn#Ee~fheVuTgs1zs{Erp;Rxd;s
zHoCQP!Kq`ytT6}5JxP4Uy4nx^X)=DepkomHUI_<OA17*Y*wc<aKKW`N7&(PA(F=9@
z2y3-mDU)PVIlD(5dJd|pf;@uX3-zZt!lkWho+(~7@4^2xyU)Z=lQIA4$i?bUPnTk;
zlK3wDM}u<mP0m+$<MgFI>pW3ef8Y=2ZOD}7xHW!{StM-f$=tw&%Y1e@h%(U2BHpx^
zLX+pS$aHM^<9OrAUqx2a{mWKN;=ikBXKvIxER%lShGBz5Ra4I#GMU2o1Ij8y?+h&z
zc1ej<A6z`6)gs$eqp~8nZ@^;U8nx$>@y8Bv;Xu<a9nZkH83ab#-nTQyXvS$zweE`W
zt<J_^cQ>?uF0;SRvxsFDK|+-&?IkChzJUBavr9I|32~&iCtbH%ybd>ruYO86cKZI?
zg+h?cA{KAsYO=qe#0&*A6tVAVs(-eRA&dBuyq3<F6=_=iu*a*+Y%li<@%6*t2gM;b
zM6%S;g5uc|BU02HL5_dcCVw3+U-bg=hjIKjmMv7$*!V4-!sj`TQReC@b{|`OZkI~`
zmlXO7tpN#7vT>bxK#(Ao`aY!>vr0TZhIyxS*j1NxKS4&%dg)ntfFPXMp_!v(fEqJ*
zFJI!CnPtvl!~Y`UtNZwT=w#ZQZ)?D;fKbleNzYh@MkOq#At4KKDRpv0=gl=ydf!&*
zwaRJHC7Zz|vam&>XxO3`kN7o24gD`L0C4t^^^cN|aquUs?>uErmK2<%3@46~oHXN(
z-&MTA7u{?1;%Bg+D!3^EwK{g;6K__G(~QMkb+@?;h-%27>MeI<l3|*pw(Hi8r5;q%
zjGv$1-C>ZF*girA>(+99+|6D7uTNZeM)&-phejK~S`_-yua+IZFAjq(tdLr*UyER~
zpEC@GBQWCH8TB3F!?fF)0d;wxhW|L--FQbU&HiS_#7?5+HYtoA%51a(d8d6Y4vdfB
zgWougU9Ss*krc}XT2(ZccCSTSK5B>K3XIHktDMSv|36>3+_5-&$xk8lJI09WDDS$u
z9=9q&EgjL)5FN`&EZ!c!g?vwUS_QmojImo_4uMz%m?IkJGlqRST>0?G{04Z4a>syN
z_khbH)mmwOgX7vl(6cElBVEPU>!v<5<4p0tz(7O5GFc`&vIY4q@xg*Og<mecj1LuE
zV!=0v!+!+x>BQe!xKDl0PU}iA-8#uEUJlCFmhS!86yg}=gN%^yb+vl^wtD<YX!QkN
z?5&|c)4nsvW)GjboU^F^LKVWF-a}PVFUeVwx}#_Psl*y%n~TI_6VX-CX>tB0JP5by
zqf)~HoT3$0g8ecxq(<S=iX|&<-y){Af`1h}D^`e;!n7{_Cp0<(%hU52t3JLAbCG`=
z!hc_pFF$z^l+s_x$p^ZfF>1}SH0u!&8JEK!PeCH+OP1N)sQHX$ci+C|4dwra(H}qf
z@d}aLCW_{*c1uI=mDP`ugZO>zxijFTFUhOBsl9QoE{)xHalBca`wtIjD2VDSc=ST*
zFC+Vv{s%828HGvWy=8Ijp)72#zl=#C3?@8(HbRGG=AFgLb|5@|{n#xX<1)%r>HoUt
zXKiKvCm*BDT?}SfX6^{>9s#wDGGOdHlL2&8h;accFj3``U(=b`PGqFW-IzY;po_8R
z0?Hj9Um5qly|f<!?`!9Xm(UT+v%4zGyTf2OYZ_ys;d?IwVRxLd#TS>Q|J?s7VN|Ff
z6BjZ~w#t(ORx0CqfUb`Lr|<aYpzCG$Q(f>a`d##X!wp#sa6^piF>0mf9%LYb8B{yF
z&hBUt&iKj{_2=c2T!zd8L7CZgcK$rUG0T6pN=puOFgc*800Y{TG$#LQ4jj;v3-oxt
zTC~d08{jcXE~z!2@%<R9J}{w(7Xa6fn9ymFU|=ox;<>tBZ`!rHVifs%c5$Nq32r6u
zJ?ogUESd<EV@!Ieegk0F%rz`tr8;wajxN~X69b-wxVZAKc=YNk90m!!uK^Qm1)41d
z1-9x}QU~Rc<Q&#2+Sh7~-6_mBK$w&d46R@S?^O=n^~dbPrqOhmMR?d_o||12x9J;@
z43h_WX01SLpa7A^>{%iQV#O_`E6^a$qK^*WU3vBkCw(F*QveZDp`cmB)4kQpQYw(q
zvg!B4dF{Lf0MHu%;E;#lW=aqQ;zTqXEt~lf-m?NGf=34y{sx6z>eg0?S9FWJSj7d{
zIJeAumzxEEJS87ck~KIv#@^tc#xvTE>HfiOEOpT_?ohrOKUs3o$m0MH_c|#mT78#(
zaFRc@MC{s&xDsB3(=~9Fna@3lItV@QAw>WpVZa$sms)*l&6efci3Y5628b|HL<<1#
zI_YT<S?O1RXatf0*WbK=lFZe%NaKS>RMc(5*saOJ%$Q!sUVyB>CT9>A&N$<0?5Xyh
z<<D+`=;@2JqHkmvJ)5$#`fqPQR!1^)sC&`r7fT%6+nRKlx@IY6QmLP&MF7v;Hl&hx
zHOp`=?UCE%Jf3^dNo>VMa1N65L}$Sqnt6{CwH>e?Du8sdm>XUIUqG48`$$dW@>_}+
zf$@1g9EN`!&F=Hnlq7hw`FVdIqtbb7n@eoOF%%+JcLNYK%(#nB5eEgY|AZVwtDG~$
zv;ekZEGw8cd5fjbN$K6P*v~?Vr5li&`vq8}%W!=A!Vc;+DTi$#VS+^S?fRNu?EBA-
z)tkTWi1pEdeCG|&%@-HE_WG>uAs=-huYO%ms`8~vv=37Ff&<9^M8@xC5FM#Pl$Y+g
zsdx>Cgc3k=iAa`Az;LDf1diI|qAhYQ$_hwPlWg>XqQb=vXxNMPI*83aE>E|w3ad5e
zA5VS;?9bR30EMIPAs)rU9u!yfJqeMl-TWZ$3a4>Xgu;G>x63+4p#Wsyf9)TTO>yW*
zvdT+35Ht2~-$Vj!)v8yolm-+(9R+LF`Z{W&XJvNK7a)^FP8PM&7Tao*caRCuSV;0Q
z+jJslpk!2r`2fsYLW|jJW(@e26S#hZ0OpF^2<%{pU{263MY`XVBbkVo_aOGPP7L%X
zveHSCNjgSB_nvI1eGojAqkBnHe<QP*A70W^IP{BjJPO_lTHTm<z@b$ER45nfn6J;^
z63PI}^txRM(MR(HXRiTWzX8>aV1&EAatvRLYi27;+S-?S0IupYTW1B4_HFE0ea5k_
zbl(Nh*Nwd)CB7G98Rput7D(op$nAOLNlj|WrPfW>?&e(Pe!7XC=;z=kY@^Vnsy>#8
zyD!8T8i<xi1itRn7!>Rz;6OwdJ4gXsptCa>K@z(cP{H{dZSKhy2v``lg6Nss9DDBD
z<?vBpoXXe>&Aizk`WpGme5sMFom3c%^34MfJts7)_Z~&>GwkCFg43bQ5b<^a^OCyN
z0!6<U1_-tIrR??+T4ayVKUK-o%Lklu?#pJSVjfb;K$ZCC9gl4tktG+j=~)|Un)8JO
z@a=PDvM5;Czo4C<APMf_py*W+TnzLmqZoUq<C}B^F(i?q2R+W<+57^zD0EA~)RNEl
z?lXtIc`SbnP>eJm<Y7ksqXa!}wu+ty+WvVFS9z(9eSxC~g|Dxl41(5S104HGwKWRO
zFbUf!lW;B>X>5{=9c{#uCZ^J(2d~9ENR&ye_2hCy|I`9Batz{$RfQqMWV@mnP+b;Y
zsxz-7`g#FuoD70J>Wf%sfHVP{%&q8b?`L6#*EoxC#fgbO;}1J`#vZjbFnjd#8RkK-
z#8uYI#~!h=H;eh-Kb4#m8jM7bb2LZyl5K?}kOv7Z1GYa6!yR40@;@SZRg}N9oJJ3F
zwQpW1if27U0=s?bmv25K(u@J_GFORL^qdsWbeVU-Iu6MnKsd4chc9%FJ@-An-Hwq1
z9khMBNFe_hf2^Xr>dbn_(I0TF!Y!ElNGl$yyl_AQC%rya%ReKT^k&xoGg)mJUCP9v
z7k0oj%2cQ*Y6H{~6u69ywE~RB5LeT8xRz#`)C<02!agL|Iy?<>i@o@z1caC>gMvta
z(+(3`GL0H$9R%)@s4GUFpLK}ER%gh?fU)4b*E?=43c$mydjb0Y<P1z=>irZ#giA)(
zC7JSHv5&-jA@?xx{`hB(C9?BqhyUF{LS7@a7K7vrZPoy#^wfzE)LF$SiA;lG0djZx
zLap|QALd(+hBlg6!92+KWIUFjIQH*emEVsATF#Z&aixR&`K|COnCa#+u2vpf0WP9&
zZ@Igv6%}_yQc$!ukk;j=n)W%&zU_7;HQF8!)QBt|cma@JV{;yeoeqT6r#*j2Vwyc7
z$-T{@Wh2qY@@ERDsVZ_6HKk@{R~|)Ns1b%Gev<u^YN<wBS5c*NFko!#LMEe@-X<a;
z-VJFl(&=wWk%%#)7kNWi@2IlKpIE_76px+rnwIOg-?yWK&8Sk<ZaHkkQHjNy$3{Yd
z%r7MzVx<mLKdY{*l6$0!5+69I)0xGp9Ry-*b$ui5!tRO|Ef$J6?R<z5FTcpB^Fc#f
zhwZA*J+Au;ZfN4Pq%Z;`CB>a^77p+YQ?n-ad}^HF40!B+328hXy}+A1Wac0_|GXDL
zVf5!}ECU$}hKE;Ift^!hlL`<NmB#Q3%SKL1I9Rw?O&zP;BEd0?zAs9!_KnjwryxhU
zV)U<V3|MwBT$Y+y4VX|U$0*xM<3+C*$Xd>%5l9pLh1Q4nPDOxp<`L@&(E3^IyxXj-
zLI5qt5b^DHx-Nkw0)iOjg~3j85A+fbx<u6<Vrmlp)+CyZB4;P`r$F>%PTd+KuF&8<
z!O9>xG6%rAQ4~z-PxC}88l)tpi6Hj0=zY$npNa%9aCj^|A$~&J`{Yh@4{k<Z?=z&y
zKWO*ckXtt`eEiE8iCBL<Cqh`f8g=HkzO|7%+?j}Fl&@csrpR@ArymWMj;VgI*IhxL
z$?eze;sUnD-g71<CVnPL80=BNgO5x50Kw(a2l4=HGnB5tS>i>b)(Gpz?DgeJc;OU3
zKveOcxC;9X>2$wwN<;>ortK`18Pq!4H3zcr;P98^ehJC{n8802dQ2R3suxb?p~1fq
zN@f<Te3?MGT0-j)m+!b3a^Oy#Y4|DXQPeH-&jH-(I7mZ&`-Rq`t8CL8&P-n8JJ0t!
zD|)_s+_ZSG+)1o5&?ji!rY7TaQkh|fK-1FiTcX$}KviImp<wz68)~avcP-`LA2tKx
z^0>8p=N*O9Z@g;N*p$01<fV_IP=nrU#^zsv^Z+iX$(nl8Cv5GW=w*ogtccELgQKX!
z7o=x6rt*%<>Du2hH%1X6(JxADB-{JSdX~6gIEvaYj(~(99***6ZT%0?LnUY_J_kqn
z3_i!R+DAsKWoYPUf9B|W?y<BYI~U!{vlwts8$pU_3(n+bCI}z5_(ptHuDE<nf0g8(
zqtbQ3931kX@sT@zfx@dl!307x>q)nO+@$!x%^axIimQlepEde2uFL@fufXp8=2{(h
z`7?dBJo3jG^g?Q*_c;1GooB;FpF3s!&$5TzTGm3A?)yfzJ}iZr@j=N<tA-$+z0YQZ
z@~V^pyraCo7*r;+mqO44yW%D_Jo-A{2U4-fqoC<xe2IlCH$e1A7dI9Msrrm=xn?<w
zz?Q6<JuE{m4M-_al}chI_5R3kwPhE)lg@ffkYE|>apy!XKo(`vU}C%v2am3#sW!J=
zK{Y92#@DWYj8V^QsunxJY{s@+JSQ{ybX0)@AxA*6;Z9eDi}>ua_GNiV9tN~WmAQ1<
zB-E^4Oz(^KZxgW2GV#>L5-&&+t$Acws*>XH%AaJ1u_yb0THAu6XvUBgA&g6KDV0Q|
z)ithpMxsUgDywEC>8i~*q$}gXfz90U12#D$3Dz4-gS!{OA3r~TDsx^`(<R^6d8cId
z$f}H~fupF_5Srz&avBhl+XF`@^-dF)pI=3KB&d>pi=wmotfm_(h>*d*R9D<(IS1M>
zA(gALSGR^SCu%FPlc%9U@cBTUBB9bAje&%q`tFR~fF0bv1ANBO^+Uxg!ob@OE7(k%
z?s*HgEr2Rt<K6d1lWcs0scD1y$Bga!USCKDa$TniPhu1+HYD!Z<QF;RDn*00Y6h|S
zc>j^3TH2wL!-+T_aQV1^^WZ;C%tCHk0@I+n-YcbvYRi@Jr!kG>2{tQiF<O+Qk1;B1
zZL?v9apr>@69^bP4(cM%A`XfOb+F_nR@gH?D%;|q!Y6u#885E)tWHzsOL;o0;5LC3
z(`4;8$DjB7CB;j!nY2d49>>Q~+??jKHWgZtjA+bpi&vwG+G|-nKoeL1!bFzk?rWnY
zL^{luR^KeT>msE6>C_WCH^j$rpzcYHqvj9(j2d!J^9rDhCDF^t7GaJN5BwjUECdh?
zk?M}ZUO&{H&aAa`8?EmsJdlolw662Yipl0k!ZjE3v8(o4GEIltn=VYV!D{~dDAue;
zv5%=2+jJt9!ru>lL32MCer3B_KxY=|(%i_P!|mUAkOfoM-t>DpoEM;&nfa-(xhIS@
zi?vwOlfq70M`a}m&$t3l+5>C@9;h-gmOrH>x~);KHs%+i9$f<U_zR$=;m}xn_dbDc
zj@Xc1<<4lJtU2vTupAbn+n1gKJT+6S&&$cgs93eRPHgF^g4<Cg(sEjj;RV|}JqdBl
z9{)D5cIbO~FI%z95IUBLl>X`WL5;5yAaI913mwCgi_IX0?%cV!?fzu!?~jdr(%RBV
zKA;z={5)t#j^O*A;917HC>-`6AdbrHwtyLlHLG82a2S`h*Ent88SoUR>35iTFjz~(
zKWB}AX^ktJs=oi#8QSKl2xjfbH!#qJ634R_MZ$uyiFTuVJLo)Q7^4)f02qv`JKmZu
z-1h0+^Q7o^oMt9moo{I3tX9;F6Mv{)-<%KoIwmbdIV!e#XH@4cNq%_hy%6<(*n7*U
zs@AamS5iO_k#3bOA>AOc5GACMZln>UV}YcINQ*Smt#k@Vx3si$r_`cj!Fi@Teb4*l
zf5tfH(;0)Ymu?qp&h^YE@B6xbw|J&`uq&!8rk$+$ASzA4w;18lR_rChDAJ+$JB^`9
zJi5qr(g@mWxV&&z2Uj%d>HYo$bH2Wod;P692nDO&5Sr6*R)3Qv`CN5IJJzZOBg8d)
zErsru|ER;0h6;OuF8t}eDC=GIYNZO=<)q4zqXfK$xN8xz6(?<!-JzBx!W-r-f_y29
zS|v}lm+r$59krS%C#exq>oSW86^kB(>B9D2jMKGOEJD{Di&s5Y-5fm<Vq%qUq9n!(
zpz7F}v3?o+q~eRsNYAsIndY&zrci&vDBB-eSzJv%ev^g|CG}`s^ht)Wa1DK-!_&>c
z+O8lTjitYYV*{gD9A?i3XxM7_{uJ{k)T|_+kfA;KP6k4wLBWb+O0Ng4l=qko#?mv(
zRw%N3P<YFnti9Bi8bJ826ocdjv6Xa$xeCK5;&zAo8?H`@A*km0y>JPfw&26&-B0=!
zbw?^}%O%<5rlGX<6OuZrVG~-xB&TFcWf7Ew*qy^4RNR&I!K9og;T+x-OGT7LVv22h
zElO}usqzONdEVXa@`W5KmI3SCx2Pe5Aza`7K<W4Uh8HDDbPCD9_osxdNzGZ7U!a-p
zg^LHxgB3DyQ|#OOWy<c#jW-YUN4*B4`u0&ZqW<X8aMxIFGsu*1%O8r>cekvRZejK!
z(%P09QxaDeHA2CMmmX2Mnh^007MpZOrZV<dc<mwx@uOauhm!OMA+eWG?$vkd(6fsw
zAMrMB(OQ?_x@zcOJOJy74C#FlBh@8iH?*CqFyct66YjlKE5=6%L)2_Jq9-(aC6%G~
z@5qJCx%5-GncPpgabx-W7kf2xM?QIyKw#@t%PEvlYd{3^APAtHxWU+qqECNCpV(1D
zn2C1jouEk&Y+^<n^t#j2os<&_1)>^#6fAKUq9k?K)0dcT5*H0d6*=XP<k!u3Um?rV
ziMyZ7w`{yYG|g$CNL=^Vt*v^Kj?VGE9r5Nq&J7mIw6w<H@EU1`IniK@5SQnN&DzqG
z<4I(}EFPs7MipjuVM2`C4f%_IJkbq0&JjKtzrf1luzI0ICqwf{IO0QLY3dvBzK6Ap
zPx4=_#4+d={ICdi9?D-=i|-_WL866;=kVfdM)4AFc{jArZ?K~<GoHMr!{OWRGxQsI
zU#e7Da<Et$()@@5=V6D$cbBRCCK4*8Gw2Lt;!h-3QhEEm#1E+-$~tLp+>aH>(MMNW
zip*Kb5kvvEySmhpx?*w1TASYZEJukpKQyN&94|=r!o-J-Vi1m}C1qsB2^*TbHiL6n
z0>m2Ag>7coN5l#Vo*r2BsjF+12PTbZam&pvo-TNWucB=EC~m!7ptxDDnGk<)C*{^@
z)!W!xLuxnITLlMiviw*Ib4AmS9wT;(*2|Y#W~L@}$#16;L_nJ0G1qB>d?~95w_^d(
zu-@&s+~(4-<%F|1IW)x?Z@cE{<YQl+7`PqmLMinJ-8FKiP7j8-Yt6`H5e&F_Zm?+Z
z7%riEIf2{gR9Rngl^x*-(s}vP-xr=X9TDzOa;f|7>aPRy<h{ld4c{v*xD|AS9&kkW
z;;`Q3@hBhZEhRfhV|<SN`1?I4=Uw8{u7#>AE~U1I?G(dCjFF!-V4ELHWQ^Nd&eWsK
zcS8pWhRAP~nNeoxpk(1&-Il?y;w}*=t0bdRvU_iyM31&ZrevqYR$?<>iKpM#m!CFD
zlVQQ#NlzWTRo>!4SV(LnM0}l@2p;t+-}vyg$=oO6B8L8UQU?Zc8d;t$aTZwwqcx+?
zM0JvLgPed}$}VBC!XkzbSIa|Vq8AIL0(mT#1U`h$ew6y?axQab`0YQ<@bH)SL!?_?
zgo#T$?EipQZzN>ZrpADzVixYdd0#cCPXA-Xx4cAYt8W$yzP;0$HjQ7~BwrmoHX*8_
zMKlH&FJj5D*@YiSMX902Xr8&?M)`#hJuOt8$>1gS(oIy}?ncFdQI6oz2v^a^m^#P|
zA<K-f$XohC%@=$Yo1)n2wRsRDK~h?7k9S$HJLj;QkYKGe7RSQB9mDOzDKuuq^scM5
z>^1N30sGlq&0CtTy}YI>3_IAujELdK1}5v6o>KjRJ1966XN3A(9y)6+72?*81p=jV
z100qe=qMgI6(&YC@NPyzzbWn=LavgZTwXbU<;FMfHeMOfuVc$?8;RNdySuULR75M6
z9+6viq+V?E7@agSeL_C^R$e0B9mK2Bi&K5(Yw$+Il!QME#S{rE@1>&8@dYjTa`hQf
zOT2d|;R$SzzF%~p8H+GVIC;`WSY({&;WBE*(c9O`s36h=kozuKUhr00rq#prInL)k
z_nP9rRjqOpr0&P;p&L4~5k-#MM62X^u^a+iyQKOXvbEp_(Dr%kMEe=SA3Y53cDx9?
zugtX&!J=v@@qi{vN`JYHXD={_LB9jeY2cRLcJb@dOTR8%8&@R!-Kc)U2o+COP^c+Z
z<T&EnrWR3mY@!9H!Qrv1Tm5zTA_w{Hy{&a*8DD=_g?sd`2`^QIz%ZT$lN>d&y;Aqf
zs-sh$4!*B%?~4!l;<y&XM5Ooi(DZW$d3RMrM;|qV{PYLbIH1Hcsb5jlSepL|RMus@
zMEnXY%|Bm<_!G%iZRDxJ5a`Rn3)*3&?+`K+%SO{iU*^qbu68FHbQ&g9N4-Y*7hQs^
zN+xtN50|X=WaukD<zH$eGz&_^_vPlF2Uyl#7g)gBOwrwg^jE^lzIm)MEt*AL>J%Rx
zIQQ;t3I?&;eEm#Usm8@wAhgx-y7R3ys4Zq@1ADn1=ApQ`(~p1?*wvEGpL=-^cRZon
zZYx*!zVwKUlWX<E>=qm!G@DjYoJ_fa_&a=Ug$|t8X~|!r;82QbjRIA^Om`9U6H4Oe
zupntpMd9>?5#F!P#`CR%f#*)H%gPJoA9+mE2kco$V^&I)@@WCnAm$rW0mL*|V9of2
zI*3%!hKLAlI+DLY3G_MT5cvSWBwVCA^wd#Eby>#IzXb-F18Wub4kkwOr>UT5%;-dm
z9_IG{cgPCC!f@wZd9%{c-tn4qWTtgA)8{kB{uryY=f6Z&7K-Q`x1O&V-i|Sq(>5DT
zc3o7b^c@AqxEL!;L-g+ptX*7fcNG09xb(BuH7W6MzA}gVYpZXJvvuvrAVpp_EawsA
z9)buqbFS?>jdi80Ccpb0yTym~8_Y}0Y%<!o$}|1mZw^I_o=lyD?$!p5m+M?TpXN*Z
zzGGhNy0CW{?Dn^c>FBYKrxljRtf0|Qm-D&E<1ba9T>NK;0$(>({Y(z~^H#S&x@ftj
zvJ0$bhK1YqgkkNrRa`{&5S2mM`_|!cj@#_)=!M3L`d_2o^_HQdDyr;8c0pO7jcXSV
zlwWG8&H9M(I;vJWgNtk^>*9%(bWe_a1jEhuPs81IN%mQ7FN|oJj>6+s0eK;39Cm@s
zy+U52{r89jV2FMmXx7iZ?IrwW3Cw01dlqF}mVS@LU-GoH)<IrhtN7wwAo?Qz6h)rb
z!lg>x(`dWEVUW<H_2hpt0&_pR%%xJH8^Fd|)9HEz*tS>)*U<%<BOB}atvR|ysc%ZA
zMiMlRi~F=2xN=%fyfypTMa?SqLYHcIBv{I8`c0SLZ5Eh#6N?dwXmhZ@qp|jWWWc4;
zPwo<f)=Tlkxl3*%Z>(Joua*Xwwa+46?;63)B%f<LNZ|7Q=xu%R<c|DZF9zd>r(GaN
z8v*t2h!FTU4JGZRl8fD)3XY(0#zNu6)yus4R6vzp-{9V0_PTnwf8N5MV&HLE5<VQn
zFJHZK66@}J^K*L}TqHh@)YM2AF&r!vPgcZ0xSuW;B9-jE2Lj-SCPw;EfqR`Q>@=oE
zwaIcl!GN;kw$YX$7cv$Z=%!5HPN!H02k&s_;skL8_|hj*z~RwXq+gMUCkq!oTmbLb
z{MK3vJ}M$dUl_dO|LkHw5gi@9i50epq(I3Z(L4af8TUOVq&-Nh><h6e60H&+YcHwk
zMtQtm4y8plB6Hlibavj(Fx2s<FA-i`o*>g`58BUxdwBMbu-wSk@*;Y8@{Tuh{KE4i
zJ0t@&R2v&Js`MWWwn`<YS}A{fpHsEmZJrR4avnZIsa(hH<$ZxvGRQqfW~K-LcedCy
zkcJ_dal<W#vrDTCoTpnY6Azt^BFbv_RA|%sL8cNBv<@XoL4t8|XBDttB7i|BiU)uk
z*7s*jCwRIm_#_$gw5ZcrikK~9XrCYM;OI-xESg`ONKISVfdLSRGzaI-N?ralF0I0?
zTknl}OC+dvwAfp1vlh}1m!e4Bh)IbRMJT{a0Ygr0XI@n<eB(QKu@Dhlme6mLXj2K7
z1TUN4nAi~y*d8Ri^MDs^MFqDA4SVIi;SBKd-QA%pxE646a%e)q%V?yEK4?Kz;X@1X
zQl6ZMxL350zVJzk4!r!IUh?)8;1do(ekcYt<<hT&kl1HHB8%tAqdkE;#(Zr0!K4(n
z(qjXiwr-20=V-{sO~@SW)BSV)W_cOU*4gkCPgwc3>UaW$?`6ofS(SR)s<(vUvl<I$
z4Vg~Q)2wnAc1advXhrv!bwg(D(BE8FwTECMZS`|IQahfoI8EZ2GdAbwBkAguG_HY#
z5aG*HqQ@o5ZB{3TC5+w7*~CR)W&r4mt1_ln{6Us&RuCYB!p%EQuHtp?<l+Dm*6x;x
z3U{bu)ynkUwXdIfEVODvzOeKn!00qTIVYa=FLljg0TdiK6e<9no@=HQ5O)?$K`0&c
z^KN+eAQ(BZdofv{^Rol=3>Yl>?K&|eVCaC)`R5nsLH!cCwCBqS-Ygf8<COn&F0mF$
zl0mK;H_vAocrTXdS^f&i_DcEK3I`pK8HYrG9BtHela{y1a?Ppc*Va*~J7HFt7W-lE
zZ{Ej~QPSmF_^zkzRrRR-r21-YdO&Cq$ZYTr{de}yKiN9dm4oYboiG|zK*B)BY1~G;
zBvc}lLFtUYFI3>`Q@`&q+X0leUD*oH?rCR4+xQc#d7@|DQi&u&4RsD8N-jmYw+?VR
z#R@p#(t(r>^O3x!p54d}q!gjbUY)YR-uck}&UG?A3io{7ZU;PxZ|%+xL;+LSOz_V<
z^T{Q_9IZgii>an~xn&*2sjkE0E9<G(^%AK9mGcQHGyY^6-9f@K0VMk@FjU!j)mG_s
z(DXUu{LoPT{zY>((1=#qU88|EqCsfyZM2n*4=vu9p)CLRB+R(6Kjk~xlqkp+@oy_R
zo1-=rI_BsJ!D8jQc_uUdaeV^0EW<JYlLPQDCFUvx$C>IikhH*a?=s4BXfum9UpG(b
zA)k7PQSRYAqEnBTe+j^Yug$`trQ`E7t{Xc~pRz7d{=rNmi1EFkSayp364H-oFUmJ?
z;_U_Pkdx<`6;cE{pxsW37%*P0QO`Q-c*Z=t{vWB21t~h27=rHIXpoTi8;l0r;L`GU
zS^Hr2@>Loo?HasSv_I|buxcp|uqe}Mb=kVEIRq=e{Ih=@YnbU8eMD|f1WTe<Gd!Ty
z*D5eh*jBczsl0ec-P+U&9{mZDyyRo;47MjbSoe-TTOA#%2=I#+=<>f#F#TJ%LgpJL
zF6PH?li>TZmgH8O6Vtoj#23ZW|C{(CZ~GbZ*tA5?#XsLhtmCfF>z34H6iQz>=M$Rc
zU;{J}J!V+U^HTz{8?^JL7m}K#>)g#9NON>lFfCs0>aXcM>bE#J=S<gdhM6tboX2T3
z|0BCv<3K0Nv`o#`Vb|v_Huh1xqli|geSuWI1ORN=VAe{_1W;x9d<zMjS^MjtWS=}B
zj!TF>der}gKjjJ}CX+yZG_)L;(>~Yi9CXa&r^o~&E8x>1XZ^s%Rt|zL*$_cU@vI;6
z@IuMA9LQ$@Q7e!~$T~oz1%c+eC}5RJ@|yNeI7Als42zkFp>dJ?$Au>*br&1~!kct5
zWi%2H)t5ks%WWGWRm-VoS@1ydYZYyUBF_xk>2)eQF~E%Yits#cLuOEkfc?fgP$l%d
z4BiSlG=O}^z(yerv>Q?hAk{Mj64l5gb7+}T6)9sZI?ucS$_Rd_NRV1>9r6N7B{~6E
zQS5@^M++;B$t&>XO?#lF@BuDk4vE*^rE?KP=Nd}Z)Q`&Z-`WA5s{`<#ul;BEgd~uf
z(x^Fe+^b>_IKSi|7j6wuY~Mz1fpoeYV9hvV2rbQpB3kLfIR?3JtJM^zQH?OmBrkl<
zz}0~p)tJ0*fUG7UKB(hH6GgbGj;vT}V-{4-k`>MFIBU!CP|o_2;GuX~tyR5^?H==Q
zUww#*g6Y?Wk~Vk`16Qx3YOo74@=pOl|M6XKU?<OptW3Qi!M_gJy~d_Wg?xd7mB70Q
zL+%E^ddLpU&XsW?gNK=f8*m5o9sn4=+Zo7n)%#rm)J8%jNFwn7Fin<<g5Z5>psld{
zloj)N7YUhjjv=uNgBN7^-K<PGrv!xP(h==rHi{j=_UPHQ{f`i{qapp`7rew}0tQm@
zW;gB=;(R6zx>pUVCTIJTXfwV93LQ%zb5?}Z0uOM=g-d!l3mt+Ene|k;48x95x2l1u
z=)sm)U>5E76o_3QQ?pdvQNIS8y6*gpG%a6Y5W+Q;x89b`1bz+J@fRSz;b#S|Ave@(
zr;t>F_@a6iGP@kpZri>kH<JdoVaH_}PzAd&G207MVg<N+I)xQQ4pr~d{vg$d--VV3
zOQ5vq6F}60s;@~ng~PsCDK27a49i?7Vz&yX3;;|?)CpWv7TmfH+bZatMIT5FG*Vy}
zC6_Ld{h_wI;nACzzGZQLhRL9~h#(e+z;NU3U^&)2IY819D7NN51uhYOd0<jnhnO!x
zkL!*Ufsi0DC5lw}d6ToqX=e_c+zV7#H5Mmt5}^#fhlA7A*$?t+U5+1waRxxPa}aRj
zqajm8IY{4{-52k71hg~-Mnc%-UU=CcR2?V>;?*$In{KhIsCJ#GdIN~zq_<MY8>`lE
zzC|VuV3;>q-igt%`9{(O{6|d2_P`Rna&E|liV;z1yx!4!0miDl91E4p`19!1EWS;z
zC&upiDIUlL&&xF_m<;gn!69kX5CBx@HQkn=Jl=yGyilPlVy%%O4W!fwK9F{Q32-+)
zx9kUEh!oB#LqELRHY0}TKzPi%{~b2>h`en=bUib2Wwp_Bv91Kf{c}F$BtJCHb5O4R
z0@7_xV7#u*DtaLzc|aNMY{)Jc>;LQm%6o=#bum`UEp>oJ+X+-gH)}fq<qta#Y`xEQ
zE_aiG8p#r96?#jR5KU066a~<MO)(rdiblK-ex`Z@4aU9+ioZ7q_^TnJ?#>hnd&BQ%
zoZB*-CSRDBIZP8CumI5A-y9rtR0^^nhavj`=~@;%uh&aYpk0WzL_Hke^yU4Rz;9c~
zSV*Dkhz}4YaDAy@uGi0`1OQmPl$cPK9_<xsjNo4Bb`0$)nE9O4T^MgjkYSht%|W*e
zo@IXQG+<+wehB^Wh+?}R5}OqURHpjmgDABn@MIJ^pk?bvkwvLa)hWBM)$TS2If#ia
zuS<QL+w5L#045ZAmV*~)FP|Y9-!fXl@#Oe^n2^JWgcqO=pT7>uw96IMB4o~{cf4XD
zDPLl{r!^4C2RbA2J0L`A8Sg6~8<7R_LQnkwVThct)B;?oDxW^`rNUqHZ*Bti`*&e`
zdoW!uulmPc6E^TXRBIj#es&5pIIBEUks6xM{eN;59JR<M7K7=|to!R=D!Ro*ggU)}
z9A&)l^1$)#lRHmNfx_$|ClgdwEeo>YpH+{+F6Qx1Mcafxl?8Ku;1}b8yO9V-0*(P|
ztgKM6c*YxUWr=F=Ywi=a4amViBITY{d$at|e18R^uzyT_w`YZc+QVFIzj!ie-4vQL
zK59v+uK>w{VSfpmkS+5&$@2WCN7cH_@>`_rrV)}#3GlSN>x!&yBg~%>6Hk-*_$l&r
z`GXRd|9-|`v3}&^dzXX~kIeM+o}OhoX8|K!vdf26FRZp#cSUBHkKZDE)3s|ASuz=&
z0ohJj+j&IIQ46Z3xgupR`Ry?#vtZC<w1$4XH{$~1?PscUkE+{iI@&c#yody&$g6)q
zM2Kq9D2Wrhz!fP<rFP@<60-F2)c5Hg#?+aG3nGz{)KlhB;al(axZDt5&o7i4?QUdW
za=9hrub-ujFWiHjt!0>#b2N=;;v|4%yPrUeHId~g?IMcLf-qR57<A)5QO3TicJ&4W
z-|Yixo&bCyIs7Yst)@ZAn<0gh#uw3Q>QD2@y@B&(2$I1N%OQr<rwN@RBuKs?pIG)b
zE5@}ssKIPzTBCCWkiK5cPuc+RK~>A4G23t|XDN-b3H~hg{r)R3ARWD|VUaj+g~smI
zTQo#bS?&^h^<pJvg6+Tq8%gZVR~l?O@87up&a&26j)p)r2I02NLi%p~MZ@5e2#w^W
zYPR+^y1#Pd2kJ4`{Ulo&B{5jd{~2%K82HHNQJTx+x3{C(??mwAzAlt#ql<U>FecK*
z*HN`yFoVp<ya4V|dB~@w-fm|A8S~51JpkO<e3{%@$;K6==zp&CtjJW2)dP(bc3v)#
zM1DTw6r;`Xr?V2W(n0`T%S`jh7=JF&>JW^fUfXtL{7F(d6VKY?Ei)SqQ_K%dsXS5G
z_9~d#dSB82e1i*t<RfEJ>j`}Nd}_8zT3MzT4Gw?&FZQT~ear>r&$h>aj>=&I6#V9*
zXLS2nhzBFQ&G@s^m-RxJFqzeoaX^@Gs=<5z<LuH?!sP4yNtK^icQ-`7MdP1mfb^9#
zrf6F<fR_V)>K{YIZYFz$a{*QY&jHNoTO|7w;pqh<VR$guxCrm>2E58)C(Qjwi^EAI
z%QNaRgR%T6X?Li<WNru6BlOzG9L3}FjV|;Ns0>i7gyAQ%XE?HaF?Q1#CPvg<8M<wB
zw3g_DM)mM0%<zRSzXxkxPe8+}$F5F(w<va)W^{=8S3>;|69qGXLO94_KPVXImR|7a
zf?B-=bJq}`7jDR+XDeJ|B0QMSO1-4BByAKF>W)*IvTITQp;@OD9OR7OMqzcw%0;=a
z&F`F5T_wsE@AXJK<WG41iNx00RW4Y-*x6K%uhv0Jim4XL)ZOPrw7&}5E1YV8Kl#X{
zs8!VhLv+$<Ju)L?(QV90c=0&|gAi9!^^?$OKfQSsvy}+H8ytI1{+1gk$anbDs88E?
zbfzr(BVPHvY{tZV*_gyAE0%4>A>bl=%H~(mtQ*X}AeJbmpUGKRt8G~^c%MN#m_2(j
z&1WPDI~hz|Y$d)_R>wfKIc4ppu9aVb>5Y-!DYu428~trRZI%`+a;JjId?XH|(jASj
ze60i`N#~?QKHmr;#Udm=P^On(`=Z%qDHWlyEP1MJ8Nm>P=G~Kb-+;X?X~~&3REc>k
zG}Vm!&(j$cQqQ<Ga7QbKjL{dxST1;MNlH{NIKZl{JNKk5$7=6E-IvWAQB<<lni?G#
ziQuSbsWJa{CnL#EGWpXQ2FAm=n2`G<Z3(?`K!nAkbYW-EJ{Xez+9;jRUiY??Ly4r2
zXwlTk!!2_(ZkFLHzdY)nE}@4M!I?ygUCMe&uTQ!eKNid<^geXt%em(yrGKH+HCD0l
ztftH48S3WO!f5=qg`j8WU`JvuTQL#NC1;4_U4H!$q%1aC19W)oqhyGjbvul;Iv0-Q
z@ATngB&<qMZL*K5jq-8Z`%c!J4A0!lVj00NDlF+KtB)&PladhB3#O%54(xCMxy=Ll
zQOP0ps#PG;WB=MU#J~1cdosT??SWACCog)+@xbJ#Sfc|iEnk7>UuemByzR_>+05a|
z@`jh{b+)<03I*CWiA4F!1)&FCBSlu<m_<hOjxlMX@7Dt%t99<lq_QY~eG^RIar%xO
z$$z@>brj5C3cDb#6V=*~2l5^@Bx>%g+Dk(f7Oa9<q^C}-59+m5J(R?2C9`7^2is_i
z?2Ak}!j~z!DZlD6({_QLjIj~&yyHgN@cZ^NYnGzcl8t!>S<#?7mP&l@njIL9cQm~V
zj*OA*8v(NHR)#-Lu!EwLDlL;ZO+N`MZ>ZHu+n-DPXga$DDqxfPdx|1%@HTevz#mY5
zK3AbpwMYDjx*<Vo^?5M<U1QtFKn!{&)tjdFy5y0>jGfkI?IxHsD2(k8gQ%U-KR&ce
zsZ=YYPa7VMClTLT=Hn25>rvVt3POyraG;rIR$Q-H5fHE&p||Q{f40Z02Xenf66=2X
zwB7(%;UE;Tm>f6!XfK*-9bGS&WK$3fuzk;~qc07;wY$DaQ)~AbWa1y7Q7rE6_QaJp
z`O}8K*0$<U(^uACm-y`7R^$P0x{ibuv94VX8Y&nxB(cHY4LSJgw)OvVFwrgDwQFg`
z#vP7g1FAMtkFJ19KjD|EI>hW>*{8W{gGf)6^2cj&CH(ZV=n8io@=0U}yKdkVqINw9
zqP|cHqcnQShx3qafN@5%{l{s%$3$Q^cMJkJ9vE%g-#nA{!N0FUnLm%{mei-h_aDTE
zsT=ZTHS?0*_(YFU<9=m*OR}+mEYGf1aWJ?_sgOgZey~ctR`UvIDL+OtyP$%awu}3o
zX(q@GjfT&iclu3Q!RhPc7^2iqfHL^GW%kRduM^joz3flhar*X+7Em!f079bIqqBI1
zRFIw?<V_E7$Lw`~GNF;$ZB_MQI)ekRQ#~vO!$(AB`Lbn{s7zAxm{sN#almb-E4jCJ
zEF|l~bQL<M1hiQar*Q-I)nwPMi4;72@<`P{P9rIk@cMO@pK@}$RMlpvzSIq$K9=ql
z3V<zdsYCaQ2}7ctxadsCD|5u_r#O7<8A+MGIit2J_?}S?i=L?iWnKxwG0b-Qf{`%o
zvVx;WZjx}#-9jb4WyXex^H|~17lg~7SaQ4Avq|?N<syoxmn@Aln!21bI<@-#jSG;W
z^W?HTyT+K!**ScNccr7sq-#)HEtxNGIz6<UwwpGH3`f62&gG(=n9B9VYs{aFcP7%}
zou*8+z6QO1JINb#{cC^~9^XkuYGinHRBEYGp}p-&g2yP8<ISV;D<RuBz4l|Um`-8B
z%PmPw*XjZI{e6T(sDr^MsU*Dty^1>Nv4T#U;<P5WGqouRD75fq<QV9>`zf0_BN{R7
zb~gmY;u8(gxL9^HZI$Wwuu`onK0d{Zf=9{nJ!$C*N4#^8H>Bpk5#jn?Koe_Ga5r0b
zgmN-aPV0RBx!bi-V?IUZP1x>mq{g|#yoRnS0?uiuhG`teWDL7T|3*M2Sd4N#K#r%F
zneTLp*)!s>|IG<Y@wV~vFqC|)cCqQtDRhf=f(!#BUe>~-ZmqI<bJCv8a$6cA3(MN}
z-;ELm(>V26WZfj(3ST|*&g&cslk|(;l#D1#{PzUNiNz6TvfI@d0nbFeo83``nAD@e
zxHt1<TlqN0$|8h0<=k8)m(sSDv#5fE251O`e3=V{ElRv}+aMycwPpLgE`_lwL*u6=
zo-8lhoYfzhienFlr{{>(c-hx+o@VCNwbI=LLsfGwo-x(y1REskopUd^+=w2A_1u$w
z3tw7JXiP8%<d)n<!fTr_8E!HoOS6|>Q4w-tsUC!kLNVVwOS$0$F!=nU7RQgYJ8tyC
zN@D(m>o_l9Jnhkn)d}VA)UB35>O#Y-1PN?Eg=rUY9CK3dJh@{09OMk>q?3D(s}#4D
z*9QPPLxbMV=_*1BAtz(OMMt^|qQ{r`u*j#@qEhd+o@puh#~FZhAiNFVwwAtu8S#Kr
zt1aSwdE~_|3IEYpm$Pw|)?eH_$Ru+Ios8@@&q>IhQ37VP%$Rdjp#2_`{DsU|1tJZk
zPapjL$Bt<tQp+hV`-1ibrWt9V!gLI!6i}Cndf3(CPad3N;$SjPyTKBvq4Q*ZRo67X
zOEbw$R8vctN?C4>u&C>?q$Q`m9AU#C=z~VO9-Mfx_pBN+#0mC|YZ;5qq6#-b9C|?|
z;Uv1yiUq-t<n+uXg8?troTYcJ?dH#)<$avW4#5Ed-1#L_pIMYgFxn`>Fd%wM7mjd0
zN!}N@n)g39&wKjwb7(M+&1M(rR**-82A2}*>1V~-8g(zT3tC&Pgp*16<YR)O+o*&L
zXDjPoUE38b@)uh!S2d&)vyr=g&-q1-WHK1;YmNRhfH_c+SSX_j=@!L8#rcwZM`Yrb
zcLYmO;^C9ZG*p^H-6BTLt?5q(L;~AiF}G_m1RE9ja0P5yG0b&{#6HjDQGTxSTN1YP
zX2Nt!mMQ>~*-TkKV4wlpQ;Ja&dIKW6Xfsy?S*uQrdJO65yZ-|oKI1F=-MYo3)+7<H
zp;i@jGcCZ?|KE7|6sBwYaN1%PF<E&duez$}P4_i{{(rvankIU>L$9RFGjzH;6h2&H
zbnm3fJDEkZ00rDxy($og!J>c%@g@)c<eN%vv}FDAwD<b;4Z8G9rePVnS!9m)A#IE_
z&u869R$beWzjRE$V!ki#G|p+ES>$3)q`xDGr7X@h0Y16Vlrz8FbC8bohH0;!Yc0--
z#eOKzb;YLV{xfe1#Rrl?e4>`57YzlnW!++4y;MyFzbA#8O~(dU?Mr^=iiai7JiLuy
zfJ=RxBM|uX%A)-^gW+m3U-uYYr2Aj09JQ|xQ4+k&{!R``5`uRPz;sFtPYKYTFY@cA
zsl)8A$m_Xb@Dt?T@#7td%(_!#$e|Oz&2r7xck#3Twv7OK=Fe7NuX1U$ckp*oqHVwI
zys^xx?M3b)%*bGn@m!dCxw{K)CBXG{s;y)Q>1^zR^G{gyd(12N<FemlRt*I2C=;M)
zn6qt6lP=XblfDL{iT2FNr~cOZTQ$<t79clMBYu|~WNHjQQHFvn4ET3oUBYK^&VUk<
zIQ~6gxcFw-@PK#drM)Ky+0WwPQ1Ff-W+E<-+q@oA^Bugy*<aiYd{pBzehQGnDOBnV
z;c5k7JO^6~l3n}Zf4&?Cs=R~`3MuS03(d2)(ke9e;81w~aq>b8t$lL79V#vOt3SD0
z5G8C;%KJ`k3T^|1eSy;<IfB>~<ouY9NcE22Ot986to>)mtQ})<V0D-(JwiK@<~1Z$
zj0lQ#YNM<0s0c3F%aauIY(sYJgrA9LUV0sDA7`As%=b10q)pL3Nt?fV4ZzZ47NB9`
z<}$X5r9EPG!#Nz?Y@&5zg*Br!p5RF}BF$UfpC4i?H^9JW93Ds7dgZHlmM{-@Icq3!
z{cEBfh`{3LqyUbOJeDz3aT$jKt3mSArvD9Ve*Gqu1xRU${nu7(L4wLSidzZ=cz8f`
zJ_{Up*`e^?I)s2!CuZxwIuJw!)r;MrVEG_KzQM$&NgM^JT^?X@IdT)yO(U=%M*lmF
zngtPUAybp!Ozz=C%%-~AXPbb37qVwqhP<sWL;$qTgqd50Pv~98;TY?PA%8oT6N)bs
zcLKO|S#19W2-ZO;1#mqtN&o0ILO){w^znFUYT#!=m^_HuloU697CLP5@2Ln-6$wyr
ztCYmZEFLMBLS%Km8Z`b9EDus&Z2Gu6PzC|J284M{mJ>1ps6xaT=<X=qk9t9`c6zzL
zX#)<gdaXSG-1~3H?pa|PZq<td&!1Qv9+5~)lo$ay?p~f>G+l_bIsx(+Wv{1gZ3g&_
z&?%mX;Z*20gZvFvX4~JN0iLkZTOXpIaT=9-nmw*lSio)zd2IC!x@b_*A)0rp3W3Fw
zz(W6M*0GBB_BH=Bk|DaxEsGVg{7g6lxv##I&oUmUZ)`^4eX+N%?6^Htce9##?G}2z
zu4|{?(aV45fL|$}TbiKQ+F+b<SUglFm<I&ND2c$)G2u<44}J8a(SL647s}284gD9t
z9YmpX085@(1jK?~4jA!&$11;$s$Mv`C|Di+Pb%qG<D@cS$pIqhI-oAf0-}#Xr6GTo
z5-EgEFLl^b_?M6OX9vgfWpB9PO0n-{JE7wI%jyIgn~F*==s#Z<+?B-*bV%`W0M0po
zFm~MhB?qJea=@PgfmeJvjIrPXe?6ODAAAOb%+{Tk-jS>x0S(iRUr20{K8}9Ff`9%T
znNq(q{hx9F_w6UV_6c8jffK^k`rm&{9lBMFQDK`>C4zZIVmmhK35vCCi<-rNgLdP8
z-}mb;O8!34n-2v3hHn4<j?{B3&Hw*bp*H*fx|uv*Hv&9lOc<a#yxlwlF4G!Ts9tdn
zI8v2QI+&t@O3&-A9}IvU#R;T}G_r>JKvcN$K`*If>hvwp8*LPOw*F0tfqu3|h7M(}
zoIpU3-=hH}w)7wt<k$NRfCiim9Jtv!T&6wzQ+AKnTN@$Js{+WwJcxiYGT+s&(+%{2
zj$pHzvs(EZhoBr66~x1pe<c%vfAa(6nB@W1_#DWE0&uPxmH<Jg0ObIHH!XS)@NSbQ
zp^B(Eppv8rYt34^C2+~gK&VQEj)`E@8Yo%nck>}561WKpUWG$O-wBgQ`9YAzh})Ek
zu>;80Ob}mY-U1^PBtBeZAfKW_(TD=q7l2tT2lU`fkmX?5M*!;Q(NjRA(9TK%@TVtz
zErW8@<%CwpkTn@16h5eiN^LvHFRgsERe%uI6hNJjH<%M=6x8Yo$}%F=)`7Wy>kSwi
zF7DbkdH)^QV%_(Ow`(@Dp)76^pci0cklc3yXg0ZwP(E$QWjhE`xzXxcrqPKV@`lk~
zz@aq=5`Iw-y`$&xLv=l(7g6(&;Xh4y>;?#d8z%rm@et$&zIg0*-@3R76Fd$x89<76
zRxexyrCJj8^O8&k+aZIjgew99tuS8>JAyoohHedj=1&{|H%lgXJ8v&YI9r14iQ?x)
zs8lCpw@x<fjJ|merFb(56$H|<{oNs|1(4H!8kY|ho4gA^*;_R6l-B>9iUzu{sO%b1
zEJ5m5(GL?(26obfUy51>iqL0)%nToM55%|4I)zf>6rKm)hd^#LIm!zxuM9-mgK21(
z!?dMfhD21~B(gk#0*Jbosw(o)lRgVVpae)3NT%mJV~;Qr%Hz60TJ8u@mR~T~%z}fS
zy#fRg->;MT3F_8$65gnJ>tOH(g1OFGeV<MWd<R7(_W@|bLJEDO;nNme%BJkU&&7w}
zgoZ#YEo>f2@_>P%@et$(hCsrOx=d#A@mt(aRd$P#NbjrTtF5FP7c(l<9sS#U?YNxA
zxZPRz%A_@4U>7uhc!Hz6{yNt=a@1d(@lZRGz9`6Ho&Je2i~l`!rWLPmSw=rbwhz5i
z4o`Q^ueOebuU6fTQjjSkDf{jsLaVNQ$FJCSRxiATW@nERXUp<c72=1o<z*K;!WE$f
z7IgUFK>!9QO8?0_#^=<>+ffsQwAT2{SpqUo=88Nzvp;BPz7&;hz1mx7qkb^~*;<cI
zE|KS}*D+h(;%zF0eW5~|X+lqk7TYn~sycs(N^kh@&valT=q&M2aTli+JpN2@>dg5T
z$&hpR@!KWGQE2}-bJiQ&EO6Zu6d%Y2V3P;)$Tr56tsmB3SEhJZGu*04b=m~KARE!}
zmkJi6aUE;x5+hfH`7wF)P5dFY!I(e)S5T6?x;K!JA?C=jD~jodw3*QP!TQiX8Kf1c
zl~1bw9u8`uu{__UYpHgq^rr<LRZB1w*XODsW2ibpyzFHnzIiYBUq5vF{LnErX2C4y
zv_6iW5GEF0$sji}`+mUbu|t4-Bf8u_KZb_{0{i*<4$CRXVJ2Sw*QBxB9q^Y{c#bPQ
zma_|b@&PqJ9*SCx=GWrYY82^Hx4P|-v8(0fY~3Utwre`I>hl2AmD;ujYn?ALBNb%m
zq{6KG^J2(E+?|@^W&tTrrj24=Ups<Z-jJ7|c-5WP*SX@q%}Rd<6&`vzOz7l7LG7im
z86icZ^)zW~3`jwJzSOc9S{JdkP$(W{K8-}2+6gakdaTPDbR}<Vhgj+VZ10|XKt5Wc
zpCh08^>(rSv|a=Garb||SR)`V7#{unu@1-%+JPOQ2>P&%ZjtKAZIfL>Lys<0ojE}(
zec8E}s~%Kxsqxt3RW>WO{hs_NgdBhWSYV?*ESkFQcX&rW^(pqr%X#^;w(7~>ZvR&g
znGWy?-Q3>St77=S|C_u_W4m*tm;H7ncAU@eKVrvs4Hs+9JxI3$fBy&YKCwvXD`gra
ze*L{0LKhhD#2T;K7M=BfW%O_V{OigCndsKK{OhEvHXr}T)qJp>Xi@nJzxKP%C;!ho
z@xkU{LZKY{fAsZIT_cYsr}_SFGpO-=d1dB$%KCINw!Yefzimr2CU0&ZdIAIee+JZ_
zf0ma{?eLxyCp?<e`|Tr82rYaXFHZM|zSJKUIDtt%d#0`~q5|cOI;Zb5)Fa(kBm3;%
z8ZAEwJsB()<<Np_+8tAngm#cCXNX5oY+v`b@vGLq4;9>?CLuZ~G9POL+}k{^+f%&X
zYbSYpD`LPytT@U$Z2Wg@fI(ttirNx@uV~yJ^3?A-zJB$9Ah>ZnLhx`n9x^BVw~fX2
z2^w?FbgKNo(;I`HUWKsU$KUSJ$qE%Bd@Pi~_ZDoX6<zz#6GFj7&Cc7W41T*oEcBDT
z6p3JxtSs5smDnh#@Oo5Xq6d^p1^e!lvxD_AhcriUpA__St~>l8eL=sqY_S=3PaG$m
ze4+s7G*C!bf<a)}^cuqwTwKn$BTQcCXYu0%E>jtZdOkFe_SjL>7N-Hs|Gzx>VSxYS
zbcH--2(b@w6#fCGfu|5Uy?*<9hLkp_VKua#lTvSXeSH*+39J*>!jDVA4*b8(D5=tO
z0K}lM*d8iN)(kJk7I}5lL-P|XRh%7Ub8Q^J%?%+8vmW_jW<l)xX>c&wO5Ty@`|(F8
z8RtC_Ao5uUsL>utH|5ze@V%HpW7lyJuKw0(#Z+#=!Pcydl%k0DImF?U<Z3?xTcNmp
zj+*PB2Hs7PgMnJ&3s8GN_ITRWfNgWE$Y8;*gQD7bREvQc<kzYJ-ldjX56oR!eOKV4
zofN=0T@7J*{@ak6j=45jkN<Ej^=F+ZaB>X}e^MC&wZu|lShPOW^;r`)EQSgF(#6~a
zXAx_G<Lzl7TgnEYRvQFrt|S|9s{FNBSPo_17aoJCY`*|4AbUCfvSTYOUd)va%m80K
z>9)gv>nOud4Tp1;Ex~iP^uyzmE;H+WF=jT&p8~*+`6A)1#m`$1vb_S4dL&SlDG2B8
z)Ok1HkIMH#e|`~mp3TIF`@ePLR2@RY^CRE@8)_=fZmCTSQRLh;;_fpm3-#EZs;;iG
zovpWo(hZZ}088h4EAR46(DD@oyNdQB?cbjL*1HB?V*dodbf~;ZFLKf-nDrNt;L96h
zF%2AOvpI8H)cs(MrJ1tDt>g!Unl1;6RwgJvM`z1hl)2*pW`r>GpVix)EgHXnZWMZ$
zz`{Fd51j)d34*VR-JZsyvI{KcRKp|4G5Og!7yZq__SSX+P=LzDp(0uaFZjGKF95WO
za=Bn}(A65uje;mC)!YBKZx2Mk`&!T#1{8R>SK5zK)9#0WzgsZ+v5jeMBu|5{@-)<{
z&&pUcvK~wzgMhg_{b-C7+};Sb>I}m(z5cA<Zq*QVI;0V7Zj+%>7Z<n7L_N=zW}b^N
zKWRy*1w+=%IDv;<d@;dsj4%K0f+6Z5n1%Fc*=2v5lEuEz+d*4}y~PgmeqPvj|0Z2r
zfRoo+uy(<<#$WwX+z-?Zaa-kJHVFlD`WRN1(Qo%KfcvZ1V;E>ysMy9TX;k!~+f1$7
zsqP_g`%UQAg8ll^{upOb-2gUdas}*@9_O5QEWgjdy;5^vSMwCwzWkaDUSr&NNPW!~
zs{ty~0A>cE=n62!HFkmLr5_&%%{c%5q^@J*LNH#i+pzpOUcwu?4E8qV&-DnIZp4cT
zqP!~Xt`->k&-)wE6OtQ4$hmJf`0BZAKfDQz4q1TPN%9_hC~iN2zVU})zjIO}i_LE%
zuvgp=oTL<0aunpR(|XvAuqnoIG`)3J6KCV`?ZX~-oED^${U0*}VVt0&*#zLT?C%&r
z)tI+9VjkZV`~%`vP$N9R09S&6yW2E3!~1R0wRnIEW_X22dU+Y-*`X}FjQA}+Z8E)L
za=vT_X!$%bu!^bqn5+H%OZ2Fan(30&YKA5(%@d#yvm@xF_=avxX%MbeLfyR5AXLcg
zx38opJLiLTQ<_xQ?or_ZkinKcpRR!G=>$WI$^v0y%Wqo{R#bTY4|*?fTKF>BD56cM
z*^BO8v~s3b_uGUKC&qt|!)B_g)z%Oc6l|Z))z}Wg=7E8+U~{=MK=;p%=AS=kwxHLt
zdf<7Z*v$O)h@i)tdWsF#f$w_<(D?p+k;WJIZh7MiWNWB67LNY<1+cWPffXSjCU}e*
zhgo;Ls_<I-kAFMpHI$GIunaG2j?o+c`trptx84)TQ^7(*`X-wx)6A&<ZbsjVWS@|Y
zwqVj&ic$MV>TlAx5X`Kje`E-Z671(U7&Ym%GnPJc>*0p&4^a(V+!>`()>Wq$++<IA
zY#K6(={2?K++a}SWVw)0E3l$H=#{3$8pZPGC84d71NyGIu_nYX`{aFf=~hBa`hle$
zYbk;)W?kDf2xIVUW%YEifp!*FGmCWm_TheDO5Wy=GV{x|WIfrRFXt<iN<;o`1F>#`
z#uL*BUfY@4+5sbikfwrEiIPG!^`0CW^3e~Eau{<;jp`43`}fUHTbPldCp#U-;R~M6
z95do(JCTlH{rv>?&!50VTT?eiHR-6w{ouww`-k$|D7B+xqtNbP0-VwSc(WZ}T!y*u
z^T|~+V_vz@rmMo8(r4QCs}s!i+$LE$<7Y{P@A9M6r>c)|Ryt)AOAdt?ZfP+UR5@-D
z_e4Y|FQ^Zv@LH);@K`8$<pxFQDHgBR&}hf-{=G}HU_xc#AZ62yBTdZ2MuglVj7ni|
z<~@B7d6P><XZ9ivKY=b2yXNR(57;+eZ<F`nXL(*}dLHAuump<qY+~~jZl;<3v#0Wn
zx!0)v*pp1)M&Uxbx?8KM<vLTPSBHc>d0FSPDWZ=lr<QDtW(uu>ghL;cf1WO$W>lwE
z4PK<x&Va>F-zesv{toU+JJ)KUt<009r*kM>&+-7P^}*uAS$BVd)gvpfWZ2oq@3R3b
zm}8gcd&tGCdhd;y-ithbFtOFOve^DT#s+Sq3pj#3$Ik-*WXgKp^0p$px@z2(9V_q!
z*GQMD1aJHc(@_J3REmrjrYq)Nw{rQUK1aJ<=2P#lf|Ic{bX2~K#!9Ik&36F{%HQ*)
z-V+}TXI$M52^TrpLdFb_1*7;wA@|iTviMmX)MVJ@dGK*a+$alEAHgyqX?K1D0?B~*
z%&_%x{&1i)|DP@-dwW2ATLAvjT%G2|aH$g0>>0K9ME>m@nNx5ldpo6r!k?mcKntmK
zUod^m(daI-*9#5n{?D8n>eKh8RLNYq5aa7$kgr67Ql52u;$d%*wf=Ad=*g!lo4QPq
z`xm}q#!c<c##8yGe}97kadb@|#9hY1D3?<ld@u6g(%R0!<&_SPJ5jy<rL~Nk4*e;@
zZZ(FNxykNElw<$nt__=LNVgCQH{Mo~KA_cdD>F`Biq5gxEQ0$9$o2H^cX-3PT|3wG
z=g<3t0x*qAYqnE9e~B1xccvT8|931tLpQnLvLJEU)4mgQ%5^KaR00fR2Bjq<uWmf+
z{*+~W{RhY-)vn`<y$hxpl{jWow-E$y!;8?Dx??h34*xs}R@rN40TS`GQklVbyK;3A
z+H0HI%Oy8Aiws#r7fijFg@5#r@9kY^J!<p@k7a&G{20efGUGU$xa{+#YX6y9{(rW3
z1rk0I6v3`Wo>gJA@1*J5Ia&2a#^RxBs~-VlKIZ_e9(8p#BLvP*i^7gZ-k#-E2CjmW
zLm?Cw0wPy_<Prny=>;DPLb)=bzUy7XNgI1M5EJD;_uR;Jz1VRd`#J~&?x#~`bY~@3
z(`w%BA>?kC7odECrFq_to%7*UY{P}A`!cgZJXDy)rP5v@fBVQ-hr+mL^YhW{e)4f<
zzsCyqmT|u8<tN)9tM9bDFI3OIhkFfQ^3&@rXdm#p>}e7sDQ&9mdY(_tDyU@3eZ#H!
zj0AZXwz>u4B5B9fzL(~CmoBDPi{rr;Ts@dqC95@$?9{pcnFy#+SvIg*5n)79MgA$Z
zK`i4@G5H7H$6sf&Ajxd?1*@%_>;wQiO@_Lxr_O$L-Kp*TIh3Oq;BY8)czbNf>%QmN
zA%rp)0aaIih`H~)n*GQJl0rvj!&gv|m_RRMvAPYlA{pJ|jS*&Q2+1Y~RN}8_Qre;Q
zsvH2URF|acf=Su?g4YGSvPMuU3_e{wkgBKfUILr!!9H9!#0l7GimU_O_+94f?G@kX
zKW1t)oQ+t5;Fs?D`BfXcSA-Y%^4X{Rt2toprH?BBA;+Z3qBG(=&(P}eE3j#10fm@g
zvS4x6hoV+0v-O^38{jizX>IN(PJ$p@*Qrr>M6+<vVI<pIO$DtIPsIB)@1*si>=hPw
zoHb<^NZSs0YC?xUe{gRBn*Og`hQZa@*#03JzVLgF;`g3Ey7gl?jEZVDJ|6(}_ew3u
zC$aYnZUD}*)olbPRMgHfH3i@#?aoGuyrK{JfOxSdwAvkPc2{@2LBLoZ;<X9E&Uzk`
zP9!~aevy1*7;gv)2##g!vFp|FaNoXRBpO{0+fc4{>Qu><@Sbn>*Gk->e>;1z!XJ3u
z9w)>Y$j{}AJh!Zd!1*95X>v6!?nLF*DfbwVWOx0V3qfaFhPG;2v9C~P34C7>pF%WO
zqwW1Kd+q%7oMQ!hccp0E=8jruFFDHS!_U3|0d-H}W!tR|n0a4f(Ejy$C?dQ-^Y{$x
zUpl>Gl~dd`OfTELMm|>B>#=io|Fgd>r}AAeBDu!066D*KY+_u)cnpAjFv=Uy4vvqp
z%W0rP>CP=#N#_EY^I_%TjvKu83$501Av7WnDdV8pXf?oLfLKu5x?Ps5Uv8lcoEn!=
zcwaOnVVs80As^J+<TtGbAI9Mc6s{pg)Dz|&kVKkoOdxOXaq+k}B3(gFMIE<XV=1UF
zU#n~ovM3zvr(6kdfmXJhYxKpoWobvp$L|C5LtWzb5r~*cd(ZINO)`!<j*oV(!%iM~
zF{YIeO|I0;T(+EpoqnI>Feat-`<c@|*OeEieQ}CK0QWis!c{*+70H5E@ZXx2upd?$
zP(8lyIgu$MULqetzaq>9xpZP6LkZUo*yiVe9XU&HvWU>mySe8WwV(WU`~;;wLd49l
zrzrquZQ1$$?&)JwZ_t?sq1tz$FdCfiGv$^;gHYWmm&Lg2;=)gUaRWt<O2U_^$;bx7
zg%Q%{Xmk@xX&7!l`@rdmPMzY0@f=|X*{ZJGsxQgqr&h(o=yh)M(^SgCE6@T<d%H?&
zTejY>yp)`On8s#7#)Ngkl#?fC0|BB@I;%~Xa`CX;)%o`0`s2EbFQCpu&dPN|MHte_
z>%@oct)TuAgt?7pJ@lTxVbJ-q;R?CaFUkGZMk{3<mJfkAFYfs7c&_xspYCh``IS8I
z=tyqMw!Xe82e!#b>vwrGjE@rnt_wFN2)j9G7aKm}JNXvgW0X*8^zK4+G(K$Ua~oe_
z-go=>zn4rO>~aFbh>qsqRE(5DN3%&7%cxN@iEWVDTTwT7ujsBXzKL_;sq|a9LM`-g
z5I%UtTuq<ae~%oWa7Ke9VF%tJR7%jPE+3*z(rf!_a<cRo$CzM;x?Kj>dfIb(Q$Mzh
zL$nWg&whSyE~cjf8S!<{`R$t5nu^mDHpGs=NlFGV0oC|eBA}XQLm+4{HRCM|6-m@M
zVllc_VrQ-l9Jd1MwOa7u)|iL9`fVR#Lkc7A-yz-4%+WL*3}D{+ta}zhi*}Rl6nL26
zGwSk4_>KZdF83rLfXwpV>U>2z;}hCNl5K<YiLpV@W=Z2Qq4oh^Y{6KQh1$0)4fPyc
zJo?2X+L`rLim0diQ5011bRAUJ>#yAtYbK>W>p!*!mXsB~A2Hp!&9B}C@P-BKVBnkd
zjGeSSrXDz1<h2}OIPE(KxJjMhDk9Ccv1qx{bi>-s;?Z2%wS5daZ0g#p9_l6NG@Ez~
zmKUC#Y{UAN<m;3N0fH@55`YMt(yuRv`;@$n<0}9@b5jZoke+q0Vb^y9(6s3Be7C`+
z1K&Ysi>6fAGzmC7bxSgLknr3L4L*IW*Tq0W_aO9CH&5=YCObqaSXAnXh03BqVLU?D
z(1WbOJjq$To<Zp-)YgLz<}-KM*n+vj@s{=Y<~Hja{@QuGzyOPdezfZ}!W=H`y%G2;
zyfj|8rZhaco@e>^_`L9w7ElV;HfaV-f~)8F9U^cJe@=PB3uwUad-^x{ff{~nt5z2v
zp5QhUe@9`7ur=7jC1UqW<74&;b|lNxQTPFo6jIY5DmDZ`5%^M<U%dC>zdH>Hu^T*?
z1rmuQ-YlJ;m!C>C8tDyxkFT%nlnEybza>dcQ^jAmE^i5<W&Q0q$$XU_rSIG43|}$N
z7~`5(dOw%~cCH`XZ$!5i>zc=##=|_D*93w02$4?VDLj&LKDCN)QLM}bUZa#AwXUzh
zX^N=|B=tpZy;i03PE8o3O9ge%8pp)k-sm#Rl|7tO!Yq$8Ej4u;yi2b{dPYW2qn>;)
zq94L34sUF1xq&O(mtc%lT1PChVR8}TU#jT2q4MK>`>nUc?KhHDHRp*<gf_tq#X1iJ
zFi)oJ$t`4=G4t!#d>?syZ6lQyAycYG)k1B{?yv##{2sSk`ytGD9mmV`Wki5|tH(Wg
zAtSkvM^Z4xNYVCtj^wskh|*`1u#$dG>T|`UHK-uZ>DR_vQZe`ryCyfwwh}XNq1+o^
z>ksj88ZNQ|vK(QK=lbshL?v@+j9NvN*=+5sWs$L>oG9cNhk0_9EbJS`EO~|L`5O!d
zp3RM>QaH)p0#SRbO}FrUf-X4IK~8M&XLZ2GiBO~l&C5~|+7_A^VV*w}7sHM3$WKp=
z?Fy%WG+(ui$@lqW=cki4@wr7Vs^KQp^wKeLV%2BgoOzLq%$}Ehlz5?5d=u%1|JhN0
z!zvbN(P>$bwKNj#QlZ)pdZ=uscCgn|LLG;(e^cn^fX!UV;q4+meU%W5i4N%&tFGq6
zf*)`*&h~n+lvf_7_;a>bktA#|(5N7G8=sN3^UpAm^aJCdEME{u2*=z)wf+Go(K<e4
z6%~G!r^@bJ&~;*f<C)F4HDE5BO-7N)NdvF)ji>|Y>-+;`q*P;M2O4(B8dTYhdoGdj
zEp9viSKRU2@=0Xfy9+`!pug|O4~Baz(fu$?6UBXT_S(MJMU&=9Bb6ctz8J>_6Em0E
z)i?I+;L<ajPK}ye!hOo`wA^NLGj%>1$*hNDUOlXR5qUNv-F{TKrXhO5UDhyla^$_<
z6T$2KT27TNWwNi`a%+aYE4TKQ{7(A|QE}~iHNGu8Z;M+=z^Bo#XJ6rX`H3xSgmj&m
z-e5glp=alX=LTJ_=L#A-Y|&a2-+ZmR{$&Rhe79Z1=lacEbJr&ku*1}j8<QMf@jC}e
zV#wjFqt3;z&FA4cAN8=|&G#AV?%i-9w$ptgN0fJ^+`uAF;~S?)cc#p~u^L2q(b4Yg
zH<$lU0A=7Ix+a{U{93T(GQnns1f>Hfs}=(Lfr@UpDCP(@L*o1X$pr!l*TKxEZu221
zoxHyOY&6eM+gsjX6Arr8?<ZL^=IK?wGWH;v?0CO;{|QSWc2q{j9nZGG4nw2weSCJc
zOSL_?#!)Y`1~v3JW9m0DlHy`mPGvf(m=7({Y`NI0njeQABg;~d)?RVGv2kACCUZmw
zMc?AwwP`We;nROr_Xc<I?aL|n&cNMlJeNDW9DX}71y!JGuW8{8a#T!0$$6dGvr+3-
z!(3yIZ&lk}dwKGqp-0~qA7)4AR4(#5D5sR@fz<vw*zq{gT-P^gM}T#h9oDEsy|#BP
zu%Pp&*PNfSv|fql3Mu<+bFT3Xm`=#yv=S*OJrz+)PNT?#pD{;!9*8@6DhP^0-Yn!C
zcZ++&pyvEEz&$#Qa~)oA^ey-yC-t(1T8jH#XBl4N%OPQ`MPK==OC(d5FZF%0-W~~;
z3Jx0@(cNoImH22Yj8ZqpwTr|=A0by5(asVN+3NQU9tLH#@J{wsOul|NQfcxI`vF}6
zS5_cXY{<qyMVFd}vVlWTR=9pf6A1L&Hy8OQ&=YgFaUmH0k-c0;t<33%z!DPAuE(*d
zR&CLU@zNmDC{l~P^m~;R2&U0)af+4%$8fSac;+XaL*v$Ox$j;+Co5Z9@o>&pso}&I
z!Kf`JlY-wSu3E{z|MtV{2@PFV1FoP5HEny1O~-AQlihi@fd8wqbB|}T|Npp{PGq?;
zQ@7!koaQj)JlyKWH!39OOokA;BZo~w+?m5ta*7zGIiC+pZn4tHCgijnQVh#VZPqY3
z{4RYT`F_8T@9&S_Z~wd>kL~(AuJ^U~=XzY%`}KT(K5z19F!LWg$kjd_Zx@Dd*J|2g
zXS)2Q%z9onPV%u5O=wRaDfG2Sxj}4&4#jAQrYnaRXL%_Lx_5If4=LjVWwM$yZcuo$
zBZ`%4QpGj{&FDGK@)guQ3hnK;6M9Q*F-GpCmA`zZSAY$jjXk+w%lx)Ns?VlZ8RMC0
z>*4{{g-%edDOZEkpU=GB$%gm2&tg<wXbNqf)vnlHc}yzgmzazYTh5_~(F>|IN1RnX
zqf$~ssZ0y+-hHtBWII?C)$8?pqm=`uptO7J&|?urS2sO&VGTIH?<>yr6qZkt4e-HS
ze32j$0jhHlieRO(lA_d#Jz$AftC6#NTb(9dQJ)?B8VW{|H!DEbYn*Qp!gnBzTf9K3
zzA3y{vb$*sUDY%jRt>#sokO8kKDIR#@x#_~?CK5XMKxRP<-Dak4}m48eiuDPnzY_s
zD`D0_nK33s+DwSt)Bf#Edew~e2GiFbql~B;`;EFsOWqHK<wLXxj}A0Ds5%*|`907@
z=^+d96&z!TEoHSpsJ~_Qud+A2;B^$j9!WLDM45@i)G7V8p3lDyq}Jc1r1o2_v2o&-
z3fPz1vZ`r@hD=vdsb~IzelVv%VxYUsoY00gOJNK*qnl6mj$uhT+#*Xpu;@oU`vS2=
zrCZk>hj=lN4g+hjt*;$dU}<*&-ziYpcFFJA^GQOMFst7%Hw*FXGT^C%srPPQGsjQg
z)-M2?DN<?#vMN0&)LKbJ9P>UuMWGTW$NT8;gq1@;&viphkwqU01dx)0Lb742brg5M
zd?IdFngVLp)=-X=$-R5(&z?Jr4t*PmoRHXQ!V3}b2y707UJ$=lIPC(8JxS5=uxZE0
za%n70s&s8}`yj<2vh{iF=ciW==8x<=jNEVnBF9f4G-(;9;fiNevO}ud&=)fA(C;aM
zYb7j?O_(6it}1HsI4c&=aj#!Xe8$2p$b7z6*YCV4Gr`NEeM&6ND=9sxl&|i|w_au`
z;BZo4#SUz0dawJdeega6d2Ih{L))ntJX|N}-Q1yKTy;@CcUDxx()eZ+ZRWn!p_7Fc
z{4)FEhUtos(F(aw8E0K#6LhS%{w}TKS_iyodfu5Z4v-u@?@i(|+a^;AXI@P7d_BND
zXBZDz#8`|O(L_qLk6?Rbs@_uZqL%v-p#%p}G3u)6#XsDmwPOtSh+a3ih!khXaG#+?
z(RUe(PIj@Wbc9?&Ch1k@eM#rAM;F@C_}0=4727<r(EfQzt(%M%!R`ZWNv_11ppg=>
znC0YStX}CD5ki3&G$>jtvs2&(>FTw~T_#k%HbiiuDBZE}?aiA=aEyUm)swVqk|%Ut
z=DA}6`7n&Xeb-5&CSn+(1i$Af#-Bs(Zap%Q8v`@|bcCU9O#NY7bjE;TF)DuR!oV^(
zHuiInm$I;yYUc;PzNJeMu--SY4&}b+8Nb&pR^%M+*4neHKC9YthMVRe@}3@WpCb2N
z5scW+7S!E@trE&pze>uk#hIt!SI^;C)Z{ICWf(l8YN^GEfX*6uB<EWkGhD43%xHBw
zm;Emx##In5z=;Z&w#XwlFG*$8hUMmeOY(j;sNlI^y@n{XFxHZ02%G2MfsZDaiZSk5
zXvzt<k?&dT?q5*~{&Qi@!l8|r*H{isUgIl|zr5Z{dji#GCLuESLc^fu>z0~b`UDqz
z5M%|u#8-u(_IBynLFgBT-bbx^`HO2`;+YH=Yn2R(^Hn_&X2%js3pc&k*4f;F%Q;BV
zJ;>6wWLZYu6Cdsa^2Y|AYfu0<al6HwUIjuBTTci2Bn*9d*Y$RKdaqwRBD1AysNYsu
zyi7#szTPzM;fWjHaC_7+ml3ZJb=q#nAv}}NNA36fF{;eyMYdjo$40i;lC^}yqnIG|
zY@0n;BG$iik{sA069IE!0}Xo$NN>1xaglrNw5}aUSlRBxkSNdn2K1}$@f#}7S>!+y
zdv%3_v*-q|{JtpQa(G|<{@OQnMKr9v7QKaiBdsMe>y^}1y!okhlyl6F18ek^eHX=}
zje8*}(a6OnO8zaG6;M}c^(4z|-75|We!6Nb9g7^+_I?8D7R9KL6GZ1V!3vn~&rmF@
z??KO)USezL*T}~(*!?=;!j8xYNYFcy@lA%xB)G--<HMwH!yT`zuz2SMmzwAq7cl*s
zt<eQ_j}kdw$F#G>53SslXj|5Ys-H*gWEe;5Ait1lw{SVqN9)Vzzg0Cpw0vqE0AXWt
zW^#Q-+)pXb+hjv+CH$OR2O8>bhh`1ho0V`3>g_0894_mYfrx*fRHJr{@0d&Z>-%yO
z@RH*ELHPLC7iy;vWOXDy>~4AH7quJ6Pz>v>UYdR;w+i|T(&vMcN7MFeoh3duieJwV
zP!#SwnntW#vo(QoprugXAiq0{=dJa}O=BIj$rx_}C(=<gYaGwFD8R_hsS%5#5gveF
z(&01Bv#Wo5*%%nxV5akyii2E@sE*b-3#zsE#Q0GGGV6TUEA_<7YrMYq*5OhUczRYC
zx_uUV_pyGzx)yMx4GC_E>oEKhJGlpIaPGeV#VJ9!T5;qbsd{K(4X0#pw>kU5P!eit
zkTfFm^&YDIag_0pl^FQwL^UFXy}LofHJJWLONIy5lA5w5U7oS``Hq-64=kSVLR=l`
zfZBNS%E9;SvA=dulcXvZ7zpN>F=zuN>hc?_lCsieCyT=BMobl7Y%G+fOYnh#?95;;
zeGQGi52}0G{M8QCWp&+I$>TI+<$nEZu?YiKd{Xn#j5qD-6S>P+s?0&IS;^GnxvgJZ
zKthpAK=Y~gLD#y)!Ww_i@Gci#w;td-kJK+FJU%V#J?sp$HF)d)-FJ<npl~BaA<eKv
zX-Iah7QrEBTo>#X_*iMG<~gz%*n9is@_*){fhIRUKD|^ANq~f;(5RWtM?3izQ^(Jc
z)q9tnZas?T95P%>pzmh5?JCB+_-miKjFRsf)uGBLDHTikq~cJ0I@~Fv+{tN}sUKp6
zho(0YOI;dXQrc$1Gs~Syj4FH1tDh{)c}ginpq~i3_ZZ?(mjsGB+ZboYq4fRhaJs#f
zERnX1h!{29KjNdpxPVNQAhhZLih}w^@GSMRe{Apw(@U~j?tWudm&@(oN^YfIOkdWu
z<MCwLXv>PKWG<vO-GP8!sK7_c9%nrzg~$dN{aazvmh!>df!JWheE(nT|4|7!a@aSs
z)F4}G$X5s03vW0XXwU{^W^<_vIx=&rnyi<fwr$!HB`g=eBneE!@NH|wN4K7~#HOSg
zaemnCd3v-MNdczq4{P6kUo*p=F!Sha+YhVac{-;@#WuuV8EF0l*i5YwnonkFOPNIN
zwl$zi|Nl;pol<^N76JgD^v6U0y1(sC#0f*qL+nBQa^{4C6o7a{PVSO7{Yf3e-k-1f
zo-mWc+ALrGU*v~4ivmc*OSg5^-Ma${Y|g_ARQzsmhobEOi1ve7_HSHs+`QHp@=Wbp
zRp1Wk`32;2q}xC<(Yqbbe54Ptv+r9|hYY2s@-<>tKk$d%Jovgjc;*H=e9Fvu&P?|(
zz4AnV>yFPF=D#PN{S_`^A`_qDsM)VecRN^dx}NvimTOr*pUCWAdY^^EZN*UUoLk+j
zU;84rW*l^c6!LDb?x!6N-z2$9#yI5R9p({~&2DW3kS0cR#@FU{XYn=kYf`vG%p!oQ
zI`i2Zej{HyWY7s)KyBTD_{O}Sq91wujC{KWa&y*ktPCN*c;U35GuD9o^q_G5biRo1
zH6kDvybQ|?azza%Z~xL(CNZ?Fi`XjzA*5}Kzd1eGa~1_az%^(<y5n)wJzF{)m>%0Y
z!~bER?BIIDv-c2`B^A5#uN)l^2Dx12C|m?lC{V1L;u#<#Sg<Q;!j}UtEAl3k!H1^<
zKGm-BkA=j8&FLu=ybkeoKLAoeuQTSH)oZe94NpEDB$x$$U4Mi0Ke9|>Tb8+Y|3vRa
z#JzQMPUa$Cpn=ada?x<trK~gn>0YVV???k3cSgppuM|H$=>6Wz>FY)@!_%9mewaSO
zZyDYhe`{Zz|02Mx|27xhuW?z`A&KdcRSM;+1a3$f3%q=_?vyVLk)Uj2O3sEi<=HZ^
zB{sDGPA~6?i%k%t6=I!@*a1R@g76v_AKt={@>Wc^cpL;v)k0<1%FgCwNpVx?qxxq3
za$6g#o1DS-X4iQv;zZQD`rDsc(_NqMIODjt?v)#$>zxCV%AwxeV?*0@eD{=W+<aGI
zrQ0E{;(;(p)EhCtCK|TeThPQ5jhEdFW<*sNYQl<s&Vk<*LF_{_%_v}XN(1wd&~<=!
z5K*5kSx?e_96`=gjthAQ@Tm2J;een&Hqg~FzZDmi5Svo@A4qwhV7}0Nr){X$c`dgl
z70(RjKY=b#jzCTt6_<yVEiX>!AD);U3A{Mbf-;`}%W4M?4p2a0qxO~&$17-kR*@mx
zn!X>G_(4ftUMSB67AP~x)BE$k2)0gx`{e=H59He<f79PzUnlx7{kC1>Ki&TKg2A|b
rAVHIp<EMTm$Uj5yql9wz-gR)ZE*M9cc$*{ud`?-M{iEi@#rXdL*ycEz

literal 106996
zcmeFYRX|<K5;lkhC%9{HcV`13xO+&@1cJM}ySr}OCAbsZo!}DO-8QbXb572^=bxGP
znTN~MrdF@&uIjSx^;M{nf+Pwe0U`th1d6njm<j|0^cDmJR2@7Nn6gLU?g0UTIAkg+
zsw6EcN~UCIZDeX;2mv95Ql8|jraFW>u-A44>l;8%bw%Cw1`R$#38us^G6n`i2HuxU
zUlK>-i#R$ur1+$Gn4dZ1H>MC;9Gt$OH##xmB09K8+-hE^pEfhs?oYfsT#b!{&IFuK
zms2_H$KUuY_n?QXexZjb$(55%$)Yi^m1Bgy#rpz@eQyC7)iZ|X9uNfU>&<j_24&)Q
zo^)SV>CN@H28{^tg2jcP#lnx;Gqh1f7h=<C)|aM+poPpmDj&sCv`6oDK)@jDek|m(
zO+t5nADy)`!oiLHRX1b|0>Xtom_i!Hhcj$Hg3*e6GdMzWL8cco49$rAxF6fb1>Tqf
zAk4*OnTASkLG+3$9Qyj@EsgGOe2;X(#j$Qo>R|V3H?l3F@$BftYpt|=EcqUnszXc?
zW6WLfyEb||BlRIuUv`{67Ub0Fz`>~9Ph{88=>}FoxOPdk_O3){oqS2ul5V4Hl72te
zxVz<?dWmGGy1%`M(;*d|4agxw32F5|A!nWQ35x8GWNs0Oh+PpGBjn+X+e+9qF$NL&
zCd(yD^?YNak@7Hfmm7O;^wP$h4Du|O6A5@tAK!9+9A(qPq6?QyKcE9qD5N&8aa@2@
zpT4<Cd~C9?Dx~~SNMj3Ccy<)g#>=7RMBPbv=R>w_;3M_Lzm>#1{~E3<^~qv;6>>e1
zT9*o)8j&5JWKed4V}ZP)&m$-@v7Q)W`pjwx)~E5-XcaR*;<cin1fuf^vw1qY-Q1Q5
zf`}B(N9MT#9~~n50MaI^FHxq(=sBlI0GSNR^*x-~d!&6xgD+8HyAawCNTIwC0Y30(
z&`HqDXg+FWkPKY}Rp{IPAF5D}VX(6ah#(x-;dx;-yG(6Jw4vrZQEcD<F#ca)S|PU9
z1ycgZ;Ng_X2=ZY~MPXy`zd^IT;~9iQ^y|*XqkStAAQpqA32o_zn1fLawS+n=+VQ?9
z8}t*q7_|izMa1AI;XX%%-w-SDTmZ^f*%So#PG1|CR%C^M=?x-Y)K$df9<KARO>cxy
z_59B|{m+qHpx!7^Ca}xE8Aj>nwko|V{-g#a<EI%+|2{FtOi8}jNsZ?a#|-Tn;~F7S
zeEKJH9pBQMljtZh`^kfS$9-~#H!W{h{7yfteL&3_m|SDUih%7$-ir8YfMbBc#=v%;
z!Y;?OOSwzF`_>2sL=xUdZxC4(^~u`;nH%8;fmXnZzh%GEm&dA}1q7nEML`@r&6^)=
zh_tDhNgD~B@tof}WAgYthR|%(UvRtVc`)h*O!Osh5S|-f@LWus$HUNf`K-&qzdMI7
zgYOIQ4{+*s?1ujuSNxunyaj_IM7lS!*G?bvg51{UE=i31ATC`hT<XIVtP0-&?E$-K
z$W`cfDQ+tF*yOl^?ecBW?YEbnms*H{`chw&_32U*$`jm%-wYcJOY)_q2NG#T(KPB-
zs8oob7A`Zm`nCGD20z)|6Wz1lZ(Yvs=&2y6iI(Cfit0#~=V#{LsM27WAkHDaLo7iQ
zM?^u?z>>hW4GRhr3Ip^3`k=#bv4Tx$(@4g``wJ{{iSu>xBY##qQ1AmY#aQx5b8e>w
z60s7G6LJ%366q7iX;J9&5>OHshv|m<h6fVP=)FHqs$D2;lvya7sx+5eia2J{%O+N8
zW-onrlf14a5FL|b`fT|_vuugNv96`$f&4o0(0!Jrv?E<IYmYrzpz_OVwJR#;7ZlY<
z*2q}`I8HZCYR-DjXU@K~A6zUZ?K3KK`leK-iDntI1k;74KTMJ8W$PzQg^g+10C;o}
zb`rP-+LEFJdXlBJdPN$>D)Ul2Dm)sEpKoJHl%q?Mvo%WROL!_O%Spd?NOUN_`a*X3
zn3AzB82+?8@A^5&x@1%PwQQp}wiNvr?LhIQNoR`wu~oX2wt>el81rsUY3@Y>yB;j=
zdLHbQk7>F*{$oU=0Y>hl54#4t0%N#im8omi3f#A>6YSQUq%}$n@HK<=CiO0cCD!=n
z%_}v(E`f<{>S41YWx;b{bC7e*b0>4w7IKzN+=<+cmUIp64M&#0EcAY*E?%76EUnZQ
zEyDe>ZaAxZJzU=>vo*29v!8K}u>Wp%Y&?~kGfKW|ND~oXH0@S2t&o*YoX(LE&G)*a
ze;Ri@wa(tsX#(lp)%B*QqNRdp{<*^63b~QD(bLNRl>e0Imf)6#Q4l9ueZ+0|>t^h>
z?ZQ@KQDRXqD1Q%*#kjIwmw1)dTKEj=OqWzVODYR7YoNWQJ+wWteaq{{tL&NoiS5Pr
z<=6A$Vwwf>k4!<5b-XI>Y=#&jJd#i7ca(#iQW!HN$Z&8lXYeGSOsbUXs<>bRszjXv
z0Ek?O@~F>_=uXP}xx?a$J>Prg0vv;<yEOuM0y?74BWR;s@Jxy5B99~DqBRNBNw426
z3A9kU*;Mar)`wU26%W80Ea}@;D_7T7W6F9Y9c#1K%y^l;O#K*%NHS#kQKRkYI(*5&
zu&rV9ktn*SSA2l0*K;Gl#*7#9;`pxg&b9*A8Ta;0bRWY`<b}>X)srzo1p*a9UT8(A
z0LB2!sULfnY)*33hdcfu;S&A>Q9G&2ob8f?{e)dL4>RpUy4YOW&ohaIiAfoAG~Aeb
z;c(L#)6Rv_vQSj@_)7gX5e;(uaqCx7kp;q1!8OY@^tD125t^<5t#?`pE49N8vq|a`
zb{_U#x{<nLIxEc*#$)|lqUM5gr+RSZ;d=AUy4Ien53wj-gooT1Jk-`tO!M@Ol(#E|
z`|>BQX|9BqLEh(&Ic$2&dU0zVi-z{s(fg!<!Z%jcD>d#TjZnH>vmeU3Yr0v)mvhU6
zKfkKIOwY^(l(+T0?Q?JZ+8ElfvU1wqRw0%{S@g|C@;oX@MZ#3%;7107lP+B?L)ohM
zGSv##Cla;Q1}*Q~h7~<}7;I;HK?YzMXK90)=R(gfdwUjhYt?!q$FJ9Wp530eH;*UQ
zC2Zg61bhTz9M=!Jiv3rFIlL<>)hn64Kf6gem{c);uP9rsZf_UK%V|QlO5lzn)^)q<
zM?a&ml;4$q1SSHJqW$uw@+Q1PA9qfxI}I`hQU`zvc<IIb(gL4<Le+ma!?(t+d)v@g
zcb$RAJG*!~<#nkMu7{d2$EV{eaUgf`d2_L~R&~#AT;3r>KsQqnAiUh13)%?m!?ki|
zIy`K{bGf*rT9d_X5p0>zacc8;2m>XmYNTptRq|<@A5@?5-2<PkY21n3v?>I2t$%Dd
z<h5CgtStghc_!8_+Z_+MA8+afkj}1FB3s=ny-i=&1y|c9AL(xOga_SN?`?V@jI!6!
zuay@KA_urSfKRJ{eO4EvLHFW4>AU#gp5+Z(?{7~RhLmS?JEn^qC&t%#+<8R<9s^bY
zy-Sbnz`MTfktbp7m+x+dPlvsF-#gs;aigPKlUg&~RIfgsoa8t=o$=Frpdsl{^c23&
zS<hP<Xi*$hT*=B476xkHzqu`VDjS-=vG!eqEmSEg>?J}lrpA5$jww9`q7x_zp`;(9
zy#Wg$*1Xv2(hrq3wu2t@QZaq$ePMA55cJtbTZcgM2|<AfaDY&Xyb6+1e{V*MH2E2#
zp*~TaRf`?N!qNzyyq>&7dU0k(i;}cA72>?!+sv%S(d^KiDeedWk-hb7AtYoeFNh2w
ztA93#+%k7#q(3)(dn3HR0L8#V7EcHP0hwW{_Q~Osyd1xQwI%b{Z`S&T%r2HT;G!1-
z0^q_A{%L9G@RiKP(!$D~-$ju8uMzy<-@nN$<Ya#haWEGo|0J(OCTeYGNXEs?$;?VF
zgh)n42C(~P#IGVI@#l5$e}d#D4h}Z_EG*8>&dkmn%+_|sENpyyd@QW&EbQz|;1Nvr
zu2v3TU6`!wDgI{i4<9i@djmUD8wXQsE3)5wzv^2%ItY@J|DNbSe}Avj(8ct>Gg;aH
z5eqCJ%kL*FY|N}I|KSaO74VzNuVm_CXz@wR)Y8z(9z2H-A0HRsukrtnC;y%CUvGZ?
z@0;9w?EilAUr+wL31Imxz`q3gTU&oo!Riu11hD)^^+JfUtuQkX5bq(R#YEIxAdfTP
zmZ{WTUmIC|eM(yW?1q_-K#vD40YgO==BGR<#hBeMjE2z%Q}9Ve4Xdi3mW>)qoND1c
z^d}Pnw|zgxwqxe1z>~+<)yLMe#oE2q#^Y1>wKn@R=NnM7`|haylJjoH{TRnYI)1aq
z{UtgxMvM;(B^e}w)O#q5nD;<*yji2?YJvZg8tEgmvi1A<e}<k&y)Rgea_<w8{x`Yc
zl<gl*zeoByQD&=H547oL{bvF&Ia}ox@4t+|a|&Q6l{9YYRxAFCKbYi>{MVBHOqorL
zP(1U?wsL{y@0I>dE}jvD{WtHQQ7~$UCruj1@&A>{2TUS?{x@$ENhtbjr)7p4>3@qv
z?->0@3xD&jqkv3w6F9|wr2IEIv1Q=TivAXSDb`1(jrcaqGw$DHnH7V7>jmn6>iV;n
z|5Mi=)Bpdkb>U4%Wte@=s%X1evY!xatbXWuD{%aL=Iw1D#D^;UbpHD2{aR@|4GBkd
zJ{j#*$L3vc-~KRL`SP?Utk`#e+9p%xFa5N#_Pnx_te44nu=->5x}wf3Hv_@rrZ%hX
zyc<asRruT&)nl82RJx4%xA!7L`*Mu)ZcWLu$fIP~4bz=@p1DsSrct&(pIE={OA5<y
z*;u-55w4|tlvhjcbluCi-C^<OyM84;a~T*Aez~YK)0cvGNLKOQj+OMf>mlX#bAP%R
zcvLx@*S43RCXY<~{&l>(aW##{VLvAr2V3Z(7jq<;C3;jjPr|b0pfC_JuW2vSU1j%p
zu~v0S42bD+SW<N9c{|E6QRf<bk_kik(-o#^c$+3SmBaY$EZ$iwX#5H4c37j`uGp+0
zvtD+6(Ku@?8o$EOyiu5V(ao9ey4`7msd4#oGn_>sg*9h#z2&IFd{mo(4`|))YUaGz
z8w%PTll@!xKaRm4?PGgb@7dyVu?e(lDLGgL%kKS)b4~8w;jS*>y~g9rKs0HG%|`e7
zBXI3$cec{YvFo9EFZ0Jy#Sd~09_G=dZ;rvlPA>N&dYSywcqH-MsU|#7zbtCy+s_)7
z%*vdOa-L&-VE8jToqky+XN6qCH4_Uv!8N(TG%VYVJf)-xc|IJ@8kBg!JJhSJxu3OJ
zH*ExEPtQ0j?8_petQlu{R&=~Pcqvs}%V!m{n70u+ec^&XpR=3BP1eYx5C1LBCqXFE
zGL|xpIh7S@ALppx!}_gL7~T|R=X#X^`=J@;R#9j=HQrp0?ZH^SdVo~MknWOp$(|so
zM;gTQ<LQsHXzwSA#_k_y?T;yJWgb$asT{#HsNUd>)x8MQu=sJ(AP)=zoiwhE%ZeX;
zuN^13HjJ0&HZ@z+pg!YP?ul0v8q&=x<8^l_Z(!L1WDQU|MAwWYF@aSucC&0hA;KSq
z!}zH)o5W?^H<-lDWH^D&<*kWZvW^qJ+>Zp$^Delvj@K8pq|F3kK{tohMlQL5pC%#9
zCrw*XzpOhvr=6Jyv~t!h8&@ROoK{`;bevZb_rxQ)cfpdEQ?Po!Jn^>Koa$J&U2k4>
zuNl~)9F{?+f8DI>Hw?!lDB@DE^2EPQ6b9e_y*_rl<`HVI@VZ|a&S?nEzg4i+7UF&V
z2^Z8km2As!$rnAzPxF3!R9n42iugxqc@-{Mr?l6a=k1DVQAQe$F?pE4A(F8*@AS{o
z+}#ZTZnh+;Czg<QYr}$mxQH%14wI(C?TWK%5`+$c=a8&!w-nH+W-tKx6CQ3yJhgo3
zSt=9XENY<+K>?O~lHO{Ftf7V|Ay(V4hJguZmt$%;E;?jgBH>jc?3?#^1O;GQ;lUEu
zOCnC3SaZE)T)Q84zRbAYeTcKWGhkGDI>>r4wb(KF5?As~+7G|b&LH!}ra^vW!t18i
z!#rR3DhHJc8bj}C6W1Q7e~Dix7*;L^ye@r+YaCByW54&@2*mA<Be;A}MX7e|c)pR<
zx;LkZ_j^4@ePwmM+*af00i1V2Ten{H)2ckNn|~|y?VT!+O=kSuedVWGq!3B)vL*bI
zN83$AJwRkX3idT%np9{c_fkkGeDf-nc$oTWUX;KouYzUC7fO?a&vdD$g;f(8-cMVk
z_WDfYEZXq+Tet1EZOb)M!mm#t@5e^(Uuqq8nTCTAWwdc0o2~htucm>SLJg~KM~a?@
zs;vZB5vqRVe7|ZZgaj<fMT~jnivo#Z?kBuoK;k~rO}1M$qqoyMr7d@x;d~fXa)9Pm
zk+^W?CBd_viviNC+panoN>)l3qp`H>dGF`>K3BRI5_Hlh9S%zkVU*`v?^m9%@)+&(
zxwriL*`!bFaPmkm_gSy5FGXXd0>)?FFMHmZ+?JYkkJhg@*2*zaJ4t$X-M9n{-`Jr?
zc`>;k7;Pld?Z^3~e)X#qWxAR1@5S5|c|R0M0Nd@}@@>boYNSlUjTd|?<mW3o0UNqp
z?{ZySW9tr+D~gmc47n<|byXLvvZ!ryK8D5W%T42+e0~FM-)xc)rzL|SI~`)YvfYy6
z8;UNjWX$Ube2zFngU#Pda*0@TIqSkBtgLmlbqh2yIjj+}&vKmm%oeJxOIZNcA!w9C
z`9?hW^NzxCeAd&qX{1wQAy8^MlSY}^JA?|ugJJPa+>X09+bm7%Q2g<Y-C5A~n7_5C
zk6~s-2RdT+enS+tY?FHj5+d@vnsg=QClkzt?q|Jwhb5D-a?kR5$#P)xRRYvXZ?!Td
zPogU0dHZ8cK8X^sV7yJIJu%)?S3Q3XlzF?&{dyPun)2(aU`DycwWObO@3}bGRSs0%
zC(Z4QxBhPTEVbi)S*YM1cz;zSJXQiYBQb@ss(I)8$pg$VV-L$XM(_DhBN9=dk!EZt
z1h|!(Q|H!)twFXQPJU1CU3WbFEem6C=eF(4v)Rxf&7Oy*P>qs`cmB8aM&7u--P)uR
zp^w7=1klac>(Wo**=S0OlPd?bvyxg`DG?D~otImCnj4J8_RQiMyXpc8_~dVQs>229
zS{1AeR7Ef`Vmf2cIzww;9**|BO(ISG`dnQW7KqvtF%@`oCWR*WZWavCH^mqIQH6LL
zH(E8)N#a#r%Q_y)mNC#js$D;>y^;)gTTQ2|+Vr5LIyBKXQOgkTeON_&$=DVf0ZH3~
z&cXI~tNkN<?suwRj&py9rJJcr>u8j+e{^p{-$!BcbRjNkdY#<yWcZ3|t-PUkKdcew
zDO2Tq{`J~1H)?L}JcwsqK}K9<)4Ahu#b;idx#V`$-5M<Ns1HwePo`)x4PS{AWfPl}
zoRkMRq>rO02%OY;Fw9?lfZx$-4mSNRY_|2b;bNS1__Tgrx9Ky;P4{tWe%Eu~=PG*>
z&JKn+iTVpgWJt1%Y8nh2vo}YqzlyQ(o)$yKZsCokwY!$y^a@#*?Zw+W9U2mLtO<NO
z>hy<Z0068PS0jUmU7)qZciiosV_Ch2DRfDq$=A_Mz}D833Ua{9mhQvDe8=j7c4iNp
zP`72eu@KnrwGZ#cI0&aBjvOo9b=VeJNw8ozlNTf&0dUaNBWK>)&7KAX#tT%*+EXc{
zeA)p`{`$DMurwJC7)3MfZw*$b$v)K6ZkB=_J^;oh@1;-g=}mj$%h9$%CR|Drp&iEz
zO>&O47YGPmTUxM@_kFVW;(V6t!s{N`r+kVA`qPHqVfZsp^H(0sB@CsavGCf`(HWay
zN(r{xPt_@myYpVVdaO8@4?n%!c1&DTmE$0cS7?izJ7eAV(X&4rvbZlMYUqC6u6+QT
z)zQSu@x=Li9xEBXV<oYZSq;4mJE^KITD*y0peORNE&U2rm+4Cn5~y`Ag7dVIg5Yr6
z)uUEM9^ZtWuTq(dDZ4)l7{3A{fv~Cm!dR9+li5};oZCSe+FeM{Gw`GvX0646_>F<Y
zuQ{Do8i!&<1yVkn1ycYiPbRNIUjcV3{LhyL$-t4p?M4^mG~jh&e@>?NSX~J|4N~ih
zJdZfFH=n1acy~=bZ}hnw$^a8FbLPHn)OxSx6S^ID7uVD$@4Nwm>hW6iakrrq8gm%C
ze07Yb)YomrmjKTUVP1+XycBRS^&VqV1YGoLn}Jx9gPpaBLtQo+-ZgG(p{7THh!lR@
z(?ta1-Br<z^u$v{1K1m3j(OTIA_2yUQ%zLenRtXGyI6q5fNA1+9bFn`IE7eO1%tG`
za#y2Q+{fjq-(edM%B9ynRiT$dkp-RkY4gpf`FoEia1__b^sm_#%mTo{(SQ;+4rmq3
zadjM&g@v%p_V;Eo=GRi*Iy+{*P9?Wr$y>W&MjMCQAO)p*@6oc<?3$i(K2!$OGUSXt
zFI<U2REPY?pgnBm&D+AE(m<-l)?0-WzUz8A6cy5_3xOCiCmZW4J5QjHo*bx4VO+Fr
zJ}1lKRkR``K(G&D5o+?f9HIe@i<XArqK0T}K#66eKho~RcOpA`W%}<4bS&rbr)2rn
zxCDk3Jrv)ACWM7qe#jg+Tzqi0y^uL%P`~T|S0&oX)AR^d+QPTZ6FYp72jd+swt}ab
z1#Az2)q9x8J6}INArwIS5h{HUn4%B;8tx_SPGg->m7b7=;UDc4S_<E=#gaPz4WGEX
z%{Fd0FN&XDT_HEbOZr*F&z`1dOS~27t6r}innDjBRp7KumQui&(rh5EZc3v%uXq#3
zyA+yk(hDN2NiwY0>q>9pKHFg2*ueA-B~Udsm5#?#VpHP`v*2jnO(?pb+>(sn;2@_N
zhFW5ST~Vx`&>{@xl8lzL&G5*`<rVjuCfbqkL!cYp*6++5a=G`F$9LCtCqJC^XTJ$P
ziMJ}WmjGwi+SAw?Rx+B|`gZ2+Pw*C3QN!E`j2TQLpwn$mhIi;=shGXJ6}f0Y^{y=*
zVqNbIq2LyVwy97sl*XE58HziChe}Z85n8G`qj?AfIiY*Sbe#C_Gj0-voj1RW@XC4p
zx!&L#Tpg1G;&q$OCihhprcn~>pfsT5g<1#8=ZSd1qR)rNUgRl(TW>qeR3WeC2repj
zEHA|PXGszKLeK^l$TkVBF)%!!G{Zu1Qd9$hzQyUd=}qA8uAm^3RV!T_8U=J{X&O*X
z1V0OKpe;Mol_bvkd1<~qDS2m?^&cfP@#gaAYip-n1nwlp8%^w9;|@^5-mUn;$^n#G
z%{7RU@Xq&Q=6^ySSVgNE<K}Kp`F;=+TPmR&jChn~7mzE-8}+)3&&uzLTW1Nb)%t)Q
z`c$4QQSg?$uvrcRV73LLyiAMB0uk`pR<A3S{J``>Cz3E5YR6?o95V^~dfQv*bo_g?
zkUKHaJ;Qqtv+hNg010g1NSI6erpj<F)T)0M7Y0&dQMloS7@jk3v=7LM4{6%UdQv|1
zy&x)$4pqUpYC>5>3w>oeXCu*l=fx@&^ux#W5tj)%%SbU>=0f^JH{U^ZgzpHx-5SIA
zs3$;;!8XCl<pNb0k^zTP`fXyC%tCo(=$G(KY{A`AsYD)nM>XP!m~d5|n?U;Et(obF
ze%nmCd=kS@)^SCVI|o%bDdch$nn9Y%bP%ic*lv0Ju1BT?K_b7iOu$b{1d^nEFNbfs
ziW`^k9OMZ0gcVp#0Da7g+|3K9{u092bx3R7bYR6GjTOzro|M#lGV6Mrax3NgZDWf~
znT+Heg`n!*!n>U>qC@$4;dxZo_`zzrI8XsUw+)+xzgi`!B^`Nr8ER7%M4e0Vh?j(J
z`eG<d)rJ1}`2m@;ok<hmsn3{EH1k97AuuB?xU>iY@6<;%W>FK_bLiD8h+95I()M!m
zN#)MdG<Mok?_@J>FGCqdKL^S+J_saB--n-bKuFmXX)ny|9V1N(no@ZI#Dbtc0Mzji
zDrf;Xa!BIjq^P!5p|^t8f$}cHNYO99V}fLCtFm24=SKc}F$#??*7x4iisR<htUo^U
zhuZX`0`GP<SPYvlwjyx&()kT!e$tLW&N+Ccw8phhdBA1c<a`~9UtAUvw%u~wY{_h}
z)7iK#sUtve!Lb&{g{_cz4`u!#8+>Y_?mBwPBYmdr%L6f!;byGXj$j6e6xnOoY8oU(
z)X}x{rp7c{U7lj2#(?Aj&s#HNj^(_Ld)$$x)b1N4rkVst17oSlA3pAuiKg_Ar~)Xr
z0i)Y!Z{od}b+zBb8~`Mk^(DLfa^-b)2qbM}HRTZik_V%T)EZ$9kmTp=q`auYpwA4s
zu*<aITGY=adXVLY-N=<;!Dfioi23YjGf*@P3GJ4HZYgY!CLk8Pb3H7A@0po((XtsY
zdD=q6U9eeIKRb9_tn)tzr|4O+hf2~iJ~q49dZ)Z@RBeT2tCs3JHMJKG&=QoM9;O^R
zc^126B3)dW8JxZj7Cb3&wagv4j+yIDFTiD*p*yF*xl%|F5}zo?1lP(Jt@nv%%#W^l
z*tUe02l#LV>aXIwIe^eI<*bB66*E9de-F$Q4X@?6MQF+bEfGuuv_h(IT6zl@aO=~@
zJ`eT=PEfqreB(M@=7LO&Z3mSbf6o`_LxeNTE{CX+u0=6E5=s1NmT1?+oUuFpCxC<W
z4b8p>rh#-kL4n7^P{KEGIZX7s@<hY<QW}%X{UP!*T6p^H)Iq`dfkf<PT~MJlpd6b&
zahibR6q~7^UWG0T@q3B90kzd#_pLMlwh@z;I3`7#Eq>F2QJ{`bo<%nzWSX&PdFG=n
zvGhHq%R#ZNbBvyLE2}v`UB4fMMnbo3smxlKM@Jy8gT?F=X0NEe{4sV&<=D_FlHk@Z
zr*v@yea^2Vz2fo<pma02=_5`QqHeGhZZ<%S<Fj4jc6YDpT7rLBrbZUsNPSREgdfB^
zBK4s!^_b=^7u^{%R|GNuIkxIvv)d{`q|zBlo4GC2Xf3%LG-IMD$Edq)Z5oc`%adkY
zjXaG$(K<Egy-IDtJ?m6Tpw#)2dn{&I-%tfS*HCyBoizhCqk@zC8Qi6t3W1*2sVp_=
zb!x*+;<_6q@ch&Ice5*=Jh#-mpg?=_)i=HTJP3W_NT~`&7h$NgDoOrffsHosh~$}Z
z!8tQOHH=~7*Jkc@TGf;0RnILniQJ*=E>#Mt$b8>X`uO7*+n|d}KL=#9Hj^M3qpA^O
zo}(WC;iT5{#`M&S;1zly1(FoZBa=KcS_qj4?Rj%9ypXKB>_U5omP}ZY`JS~~?XH&(
z!!?AaxH?>vwI(;ZJu`Wdr%YM$N?p;DQc<T0rO+h0N9q~1=bNN1tgz>s^qpWBQw0hx
zy0{DxV2n4eRfjLhl8tA?zk5tB`;^I73hB1OZW~?6q9|Xc+4l?^8xBg#WYCD+iNS4u
zVuB_r4#?m-v1cPR=SSxxu-`Bpr7YrI!NT4Z50fwXlEVmG72Uhe=UgRbb~km530+1N
zc|z3~_Cmyvn}9YScLt=5t}Pb9vt>MI^)<zQ$@K&r1%wt!u0Gez&KIhCUP{f#>tdh2
zdT<ql#>$)ibtJ-E|JYF?%*z!(yT>KpTHD$nWn;5FQ06;i6chSX_Yv#hXhtW6tHKVJ
zf{5srKEop6K>H&>cpZ6BB^*@Wn*vQ-mhNFtCs4$0<d!{J>C%*yz<fQH1zqM?<Y|X4
zpJUX8B|a2L7@kiw`~p4|?wBImO48+0TM~P8htIEn-bw1YDoW&83_4ggkCKoSw<X1Y
zrhF-ZH`F?Lu}{-FsS(wRLBHlI-b78=)A!_BxvD1X4-nZC4?}!hOhDX?C0sz&^(gB6
z(V7T3?P`x4W}5G^6NuLT!UAwf46`;~k2BV1Up3o%E^j}VFotK|7W9P_;8ug}QyuA-
zwcr%1+fk}F5d?oop@95`xMo5LmBOHa{ymOxYvfz$;+1$fmvA{a=b!#o0d|=Q7dZ(+
zBkkoyzKwZTz84H44p6gBbM#Y;CS3*BY>Nw5q??TB5kpr_9Jg;^>iXm~ZwKgeP@IU2
z4Gd1@TZ4(^<kkQm%+lAIRuA>o{lrpm$sVquq+T-5OPR%Qn!wM(MtPlNjrdL8JAB&e
z<u?8}oVG4O_z7XWS^U`UP*e=EK>fpxidzgqZkzoGIZ;6t*dowR<8+5dN4Vudr*zio
z`SRKyZ1|9yIKB4ZCm=P5S5Nil2wJi3k2Ol{Fj|-5IwsJ-?#0^3V3uMXzV5Z|dEY7}
z0~-4E^+y#p0HIHMWe_v)NO0|&CKLu9JydUnP2#)kzz|-ePx=^fg<KrIZoCoj<D&){
z7a^!F3O!_FAwTObX9s&bIw`u;(2QZ$Wb%U3v+g}mF<;(l-D$H;emqnuVA0iXZt^Oq
zCi{UvL^>L!AWkk~LN$5fvVX4zFR7_(Y&9f@&`J@z7ek#N|C2gXd_B1POc#(R)lUQq
z?G;UhU-=cRB?vXJmRM6I(n9=`SuZ~|F0ZUkuxW^yobNj3*|LSI=XKfap4T_`@A9&U
zohvT&y3^AM4vL|ReHuMUq<`lS&=?D@x7BP|z0+MP5h$c(-LTocS%*`j8fTgZuRHQ}
zvYdlBp)#rftyt;TA)qa2iN2Ybg?x>e@EQOq*>H@;S?uwssi=~CD<cqit<K$uALoF6
z&>OC|T*@_<t`|hum_`A<i+=}6iDlhDmU51D{uY^~9`}rQ^E8x9Zu$MaX(T@1PnGAa
z>4l|vQ_K2bB^RocsQ^^&3%@UyD(D_fONo^#U4^(T<EDN1^^UkKB+(!QQ0qjQ!$r4a
zMuj%z6kxIh!>kaYTLf--1aEsq%G7EvWTMx392;$;E7_kwPl48^pr)z%ew3-5(u!JS
zELqOQlN(&%=@}x`a_KQK>o%pTsD09P9nfo4JFH(B`-Sm%(x`XrvaOQ_8)C1t#g;}&
zN~Z(JGsW7WUFSlOS3p5g_cPTe%#%4!!+Lu>%e#nehcm&(BFp6joEY3;0fKs&+OTzL
z9j(#{Dj{1v)w6hW_Gluhgcm1iqoD|ayStFV&YDxV=@-@)ot#Yj$7)IQd#NQEB~4*v
zrkA21%RRLiF`+<w@f5b?nm4Ix-1SqrVr)#vJS0~|CgJOkUj^=Q<bTlnT9NtmunUXl
z^Pw~kuhVbZ^X*@6dH@yB{rSo_3}p5=F+^VmqaHvjGraR8rxDB9j;0PYXGqxBn2E{&
z3ED3>H}V?cs+{ft(!qpGd~JIo=f@(ua!9qCn|-Do7wvw=boYG>k%p}9smwd>__!{0
zBj4EXClkHhfgoYf1qYYR#aN_+13E%{@|dvRu)kINbytP=Y?*GNy_6wn^kaT_I4*f9
zn@s|Pi-FQd2L4){Bejy-&WSwjsqmYp9W0F2N*#58!KO7`8?pj==5P+wBmED8`{j?+
zmMsK*bbkH;#>uYx0LX+sWB(LypXkT$X3wqc>dUJ({<<)~Y#ta^IrqL*U-T17Y6UFr
zoMoIRVXHQ6Z|xk=V6N>F%Ywpj^)wB|`aGc?{djV1l8UIBZ(J9G;mDahWcjT9I8i;K
z0hfaS4g}|MhZ3^T{+jf$@LPG+#n$eR`HK`%zO6<i-$BH@72oepY$i9j=yqwwBjshm
zC8dIoxtLd&)JwJ+2PA5G8rXLn!ocTp4{`XuB(2=^^YKD^g3u?Z1g#3E7MJKM-by<H
zI-!9{P8f%JtqCM{13a(&<;LNi2n2gO!f_XA!M*$EB3l4%yc?UUj0ih(hO1y}c<L!)
z$~0?q(zhtY25Ke+I_CG{3(|VHEURctR*f!+Aqc3KZRtk+lw$P4Ep`Q&Et<>P1$kw(
z;h0R=(xL6c)c!bBF3MF+(swtF9mE5|aCp|#HhPn<lxR$c3#=MPjFEz!_63<YU1Xwq
zReKYJj+uy;o-!}`L))k+?w`|}$}TjWOPMeRd-Fdc<n1x1p4!^&vF<dol8n;Rd~BDK
zwxx#NsMGSO4k2)9;!ANOuykP^K35e|N#P)4vlsK}^le~n+0XUR{iK^pKFYR8F@3MP
zZF!Z@GZc&OlA;x+C*&}Sg?Ml1)3NPPBYMGssu`xYX)*3`-b&R&7310E8X1gmZx6kj
zEKgp4=LR+Db%h@d^(Iv`L;3tmeo7CUpbF!#uKP(nOp`s|#>e16+RSPjyzJ=d5UKnc
zS0je0WlFRb=zvzc%!XPtqQ#HVZkHr!N$+XU*Rf*TRePG4e;l)~j#5AbIs3W*WAOO&
zb(G%S$4t{fwMlV86?H9yfuK&t6Drf^Lf%GCYrcfUt;4OpjFxGFI41!1lI)6H$~`bN
zHuTi#{<nJJ8gZ8#Pic+1Gd@Dl)I2nU@<Gu+I^0t{d%LYxjRb;GGco!?+*x>$F6_vq
z-6H^R!+9)d*YI>hKd9^J!_co50Lj;>o*A55Y}<>?%YizhR)%yq((V|=r~tNPLfFJi
zXoLP2aq)B6JIrgFi_K7z($piPw4kOY2-}hmTA*VrQsH^trw45(SDe`4I4MaD<)luM
z)rRh&zVQM^9{Pwh2B~SVPQs@mT4Ic|Dp^(>FE%H>yC(stee=RPVmNOKG4*W>(vZVk
zZSFawfKoO|L^g+I-9DnimA-^KPwyqsceSK%uiM{_M^t^QS#5<SDsV+{&8IMedD`xe
zG#!8IYCGitq~O_V*7_P#d3oQq-1N$+r#wCqCm~c=^5ubL<ULxKtL=D@zjt(|abXFg
zasQ_2`&W7Ug6&nhF#FH+R?m+tBhS#{D$~oT+cyC#Me>wk2LA5d;LkfCLh;h>Xx#<Q
z@f={IREx{yh-!8&tOmGvou&3^)h$5q<_p}l+{nRuN@rSz-8M{(q<=M_Ddt&AD+;F*
zNpa=2WD+7E#kK=ZY7}#RpiiXo1yP+rUoxyMdGq%bN}xdNw(?@*V&@)!=0MY4Ux(;X
zeeJhirfHXQVNdbTRc`g@8#X;D>?l~G^jZ%FLeu8@<A13oF(}(<aB(rS+ZNcig>Nn=
zVR;~K=_hL8W@6+QhC}-Y6PjX%PAdp7u|(mrtPz+3oSRV^(E?E_8+TzN4>mX7gyja6
zRuVi<)lR48Q3!U*42am$BwrF@-f1gW$am=C_Y--D0XcDqJl;;Zp7%p*ZpQB$vs1TT
z%ai(|e6nO;FEn8#waP|au3it2dPCvc=o5e7!{NaZOO8Ud_CBF6a*XbKmM6NAwiI*g
zdG|6%%s51zbUQR|f_q~}hJ(*;eCIl&z0j6cV!+-ef=*?-(IGM1*;Y7)XFx|1J-W_~
zq@-108eH>{cQRzQW(p0`;o5#@eU+e>^`ps3kLNSrNd@n^J8xnfuwn8`z5RI_u1Jqs
zAEDT?Z_N7k5J!(k1#2tQ0VaOgipjKJuj?(SoJf2AzBas&>ndv~L9bc0mBm2eC1hqf
zha~AL@_zU6^7~Hxq>{%Xr=gwcdMW2RSBN`z#rm1+gu99ee~;Ihd1>`G90Vx&<|!)3
z&Jq%R&#^0&gdKu`0@rJjEnIaJuf)@hw!@>iULDDcrfimxuEqtcB>HP?_xztbPxYLc
zDIY)a<h$_qt1Y|wIHuUham+JT9-&+ktti<l!WzTWSw9>dj#$LpkM8s)W*@j_rJ!_g
zg$+K6d<2f#)UEIA=r)t~jhbe#VtZi7zgD=kj)2;|g!YE)1v6VN>5a`esRn!B^!4l%
zavAiu?=oHXEAr=cq&g#$Itg15I?=A-??^jb!~}2<$F5l%7BK>g(@l!860#na4`Jj%
zDS;)M%_t*Td}!%C*FmyK9IxA+`+J%xz|<rk7+7f^+$`|zh3?@Va|Q>E@t4&eQ-J;E
z=WT3aq4d>x)5w9$bPiRqUNUzsDGvVr6)$u;bi>e5>K7PeGjxJ>8O4uqizuTusasU0
z^u}x850SP;<JNevdX_cY$ch%InCLjhci-Oo9Z_852E_5KBr0t_OihfK@~$W&^<hR7
zXANRfJ5aCEHTzyVNn^5#cA~<H3%UDB+z(3%7g=3F+jpZ8s%o!IuEDg~tY32&q8aiz
z&<PCEz>43_eZw9ExX>_%=wyZCaL$4u8*3L4wBDFLK@ab$s8>FP8%ka(ePQAj9+k%w
z%2YHP|CM|HYp(UME3TziBu4oAafbzcpFgF3H*Xrk$)=j@b89mGnLp-sa>u73LFbqG
z?m_?}+JMcAzD#M>+3Y(w*V=$=+?O~$ck;EZrf((=?4Ru#hCQdQU@p=cF6S7B<B|hH
z&fh@+*GX3|ci&N<HrP=QWx~WJxXN*4y<y%h9F0^>;~UHt`OK{RWJgZ68e5`vC(exO
za@&0|1nQ!dwS|SpS(X(eeYXks<iFk0HwN+M^8!~LA(Hck{6tmBuYjLqX0D11ILwhD
zE;u&?Y~iiKX^dcxPEGNM^rO7%BTL-D$t|gTGXi)C(-DnTv$wUd;pIZ~<lS3yGMwjp
zwz8i=KxDorg0Abf#lUkleJA{AKSxc#dfgQO{xqNl6Y?w$f0M3F;+68lWCqnc0x&Ng
zg}RR~9@A$Usp_0Q*Q91^BwE5FSpGFhk!ZA}y=2tTAzk$HO!j9%_v|nNzB3~a2D0xt
z0<sIyK$%d^d6+q^MlqJ+;1^Ky=dcmI*DtPS!`GaP=S!#xoD#jYsue+E5jNuMn}I?K
z*3#5lQZ~xt9pSwc;a+Mk^dsUpl``5wO|b9On=Pt+(ri(7FX3n_L}ogf$$Ha4+{V_0
zIP2G}6)Pn<g$O~RBN2XBlA%*pOC!s&oJ<(0t*kzKR60Jq9~R^pS&bFB`rmG=L&nnx
z+wSDT`k8GG<}*%Yh4W$NkqHROcZ-0_Le`Y$w|m;@*@$H6xAcnkFyxN=?}!Z;ZPza%
z_@iSstnv>8>RknAWt@{$D(BkqmsYve4@G|k)T^gKoE8JNL*Gx4_f}2i)q=n=O&oz!
z{Sccte8uVw<Z8wk^p}*y<Kx;1D{)ctGKD>_&sy7-8c;oA5Xo{EQ>YuC`s~e!O#6)Y
zHbs82I!+q>!2>d2uD?^<VAZdOS2JSgA<^I4;Xm6fP?@-`G(~VAgH+HOT_<Gm>N^j~
z+(b7<j3UPfiWvsng>_ctc=|U(SMyW;1-vn#g7o7^z2war$+97UQbpJeH9|n4$`AgM
zGJ~Ysc(RA$`|V?SfJ~d_<H)MamqY|KG-<|z-&hs3xLY6}biYHhn9c59SJlQ?j+ZWz
z%yr5?@UIYvGac3r8=+LxwWsde-o9`ilykP7vt4mg_)pdzgg?Yr2pi1Q4|{@^f+(xz
z;LjUqE052^R1P6Y&jvMAg%H4#SCMsiIDqen!0^OudYF{p;CZvuQ)<T)#aJxTD0yH?
z9tY|t#d~!{URi_L+wAA$&Q6#VdmlOn*e6cdc*1m-%}_Z~?mO%6$jc2lf5RhvXURJO
z;-ipZ#%+~D(s>`L2{z-^<(8LS-#Wq;D|*&PWgz0{x7<FJWj!l`)VeMfcOc=(<|<nb
z=}B<eC!Vk8^(HK|<<f%e{pbinWsIFT8~~(pbnS)$?+dKJk&^PKK2+;Vrs7~3X-;43
zuL?II)t>x=fPDK4a))^7cg-$r(zNVB;kc?!m-1H3y3cZQ%TA)2eQ)_>x9Fj!wV^UT
z__#XVpVue1DJ*71iT=i`YM_1j=(sDjpBbJk)5h@D9FFe4U`-`H@NK+?Rr2E3|6-_o
zE6B1{&carX$^O#Y-yltT$@c}i%{oTG|Dv_{fpMJ-PPP6C|HX9{6M+$-J5H&@zh(IY
zblpb;V@WF(#@?#@7fYIL2Zo#`{xWp>^e-9%eDTZ=eI5a+e=TMhUFm-d{GSGYJKz6o
z@OK#d|IkScTJ#hC<HWwD=%3m>TxVOBFJGdF_-b}67v2kiowad#wTk6WtgN7^`rhs%
zQn2d67@)G}JX5S(dLto}k({S;*6Mi}@+Uy3Q~Ai~)tR6Zc#fv9J&{Y;w~>_Hvus&l
z0>xUhUcNWW&s2RI!qCL$u_EdyQNSdY?&Q6WS}uPp^LOY_l7`y9bOr6co6Ea}zxBY&
zSpw-bG=z?YtSV(Z&H0^{&-{8<^SRaJ!tV`ISdg7kp+Jz2&~?;e;#;vl3|yg#6*>3s
zF1GN85F>vO;cJ5`Z;?^kegPxW(+mX`t`ILYD{B;{+G4MGA6j?0^CTEGTTKq83gT%L
zPa!wGGi1R)|K`WJ*gru3QV9s<a9G24zO?{yX)^-P`w+ts^D<;w2fom{dg{Pm{e@nH
z%F(|*e^lL>9BPV}Dl^yTFuSK6teW9%a^Q!|)8qF<6%hJ62EKoT5P&QmJ$2i{SWYz<
zK?N(u-_YPMe~8zJSE51b=B9mi2)@9SDPAtvLfBV5yVE&__!FVADU{-OuLHd(<W$do
z0Ww-}5xndxy(0|AELBht!b9IugM<8f#?jCJZUTv6Nnagh^(tW~32>@&sW*uCN)zr#
z7XC`JX4RNqTXrsQHn=k=I}MwM<@)C7KpPy@$5$Go<o;+;9xA6Vo*WgO=z&ws`n2qH
zs03-J-URdO)g-<bNT_J_ZRnUCb%k1~2B;I<T2?lr0i7oPPl_;Mg6y@DKbzZARqS>M
z+%E61knuszFyR5Nf5=j`#)nV6N=n}cr7ehAD*Ja3D}wcX@nZoJ{8jX?xFC^DlBWy&
zm9JD#o%YIJ<y0&EJ=}}iiEC$DbZ}Z(U0HF^(@Ry9nm+}cul$`WMC1R?O=^h{mz?Q8
zpHU%dA-T+pmaY{0%QHm4yzw6=mH)vU2SY5SQT4}T&|W4m2c=#9>Y{cGQn;DT;;PHJ
z^SPyYL$1Lf15S;T#Kw((6rUkY#!0KsnZzmeQI%hD``*>fa`9@`Dl@C-@z7qF8%w!l
z&x4=0{QuB?BBc*TWP$0~qn+Ab|B6MtJ*$o*SiPlZB9AP8XY2j<P|viE+$g;3H#H^l
zFdRx*aF!e?z!=DT6D925Lyzem|0MQ(9H>^=u$d?x8ELV6$=HzDCtPr&lBn^%N_3U(
zYCQOVXLU=7NCIpw=aI>uD_d$M&bKelSATrAff7yMfp2Y#==1a%qhMU)s@BK*jy-2I
zZVx(mUs)9C=YedE`b@6}Om$_;Y3kfYQ7g>6^G1Jg>xqEN9zW+FvsFhRGjn&av|f;!
zF!OxhY*uM_WyxVQnsTu<z@pt`^U``Ro5cLtIKz=zAw9&5#u)5PZ!%&>B%}0<0pej#
zpTzxCzb#G~h9@&t)Th4LPzp$kU-ZMpJ?ERsjkCR`VlROMUWpfd8)}j&N>e4rkAVjQ
z1{~lpO}rSL&V7i}{;54I6(HmzZqvM*?NA&^IwoIZDMDU!(FsM3tV9VcJqC!KpTLm(
z4$!!Oo(s4gFC14fjnnjSro{cUdGBj5al=W&uek)CthXoSaCEP2XI^KS?x*-*#4|;%
z<GhYD(6S+CMppMSP80PAsXY6U6pWTW^VkzG7>Fw9M&VvqZ)zmwv1~J4P(t{)P^Int
zd@cOAI8p366h}2(q$s?bVt{s=)3?=VtQ2yc7NCdDC%RZngKW1oc=7+HPIZH?5bxAQ
z?}Eapil{%iLmLoSN<`xef>F}mPXpeqS5va2?#I=B-G<3JH)CMDTn2b4dM`VAxhtYh
z2L1`J;6?<8okR_sn`)UnaNx>hHyrMQBlQ4-p{(m?KknO|(nv(&gHQeuNX$)SqrVZ-
zJ*dJS&!$JW4o!^}U{wA+7}FnK55`vChEPPkFQEbAp?7RwMO0|);BwEz*ts3uyhc&6
zmKsRzcK+km#f*@>CcM$^Fz(O75}3+V)g>&1SWn;{0!1*O@ptEha^XDv=Z52rU{ayI
zbbF!8*yvFp56kLg1i$ocAF@B6{Vw3;ZfouN)LHr7dCimO?Pc1LFp_@`W9qh;(M$M?
z0@q&oHg{~4ZERGa3T4=uT4Lxp_0PSHg~c8xhrbOP5}}UCmr|Xgiv_7|2X{<Uuk1M>
zW{jiyoqd<z8{>3a*eDR^`-BVdP3q-uKu{<42Yr<nYz^&;<_XWYkF<Dxyp~i7MxZgC
zweRiU!LWY1EEx7@aNh%EZlP({jC4cc>TX~7(>KgeJdGy_1uN2famxxP`rlF9ae|-u
zk2};OkV)GMbEa{9H2=zS-IN@HL1*Fe^(r@<`C;|8JsGwG+(42Jh601tf;mae(tfuo
ziYj=OI^nUKq-BNR4+bV`fpPfzFOO%6w6gRVq45g*p(b|er{AaWpRT|#&E$&q2eYZ|
zr$dbn@}~?xh95H;rFo4L%K8QJrc@S(I1E7;Z>HR=;t|vKVZp(|YGEWw`A^uUg4$QB
zGcBIIE4Ur}$bQsQ3|Dg79}p975dui%v!`Zno44z|I+*UbOsqVv9w4btx!f5kk_R?i
z&uL-mE8HPg`l{fZMAiImgUEYP!v=D%x@-|UFUN-9PFeJ^DK2*0g?FqcPMch{Kb{J#
z`#^V(C@N^02qw+!JrM1;am>t|m=ssN6R;N4lfS;g(J3i)D-_O<?%YovU#^V$C&lYP
z!OwmAS<^N|a-yrJ6FX#P)ngIryyB>FnJl{Ai<;i_%jyUETKBJ9xt4Ag)7E2264!5=
z_G4W0tfmHLv^xeJCX?i0RWaG?V0AWwyFh{<PjasbuLZSK)jzMfSpigbOjTuA6V$4t
zz}BR2ow9EM^)Msg{>sn)X9KAa0@N&*9DCWcj({$b_JPi+9z=V;`z>Ntlx@K676SX~
zm5A1ErC~Loy@`RM7aH$Nnqu8c$;fWpj!mwg-|$0QnU0lDhI|P9SIg-tY0&R4WCsQ<
z>yCdF5^20%Cs_Vi6#<`!i0e3r!~PCl*%a_(at5aS1l-rVMPiDuR%h_?p^i^$sznvQ
z_5&n-V8YgJ1QFiMsRFaAtHrx@l+G{SQO<Br7hrBhO$f<UCmzg}(EqS6wmdj+TUjJc
zn%wyQiwn4~zDzGx!dmfK!C9eJ#TJXrwwMZ@H=|jPIPuTCEif_p)!(PkX#LhRxy(P(
z*AGzx&wZyP3ZftpfmEL*{0J_IjO<zu3W4z7B*QVEF9#{2&t3Dtv4quPkTNVorkgYi
z_xRwsW+~#V8jGt)4oWvpUbjcalz&!Bk-jO;m*M|jFwsjvypgLEJuW<@$T?c1%QMZz
zdB)(N={FL>9Zru30Mg_vdkYRRhW!rG7k2bIxx0H=-a>t|U(Uhy?zWfVyjxqyI}h^1
zliYLf;M6Q*<Ovxjv?TAq8LzEju1r2#T*w+5rmk>=1)oigk0mAF{Yj}rP{j*aIB^p7
zH5~$ml~Mr-6GgHE#3by3xWmsgn1_6`A)Ln2Wv+hkcdCkliHb=Q3v%rxl792AviGiV
zgBE7-5w#1C*siW&?mn^d+a6Z>Eu!EPtiU%(e&j!9Gk^yF4j6>V&f{_Nt%ktsC?TGG
z=rh=ERl8_1U7)HJAmnCZ=KT#Bl?j>cF&IlMreSgpN6%gEHiA3u$E=O|B|hl0nmegZ
zVg_Z>cTw}?nJ$sj)Z-yss-oL<Ga6C@)#&ko`)Xj1R#>TxlKtb+>QJ<kpY*}W!rUHj
zQ}#6D?hSN9KTGJ~-iac$id>I^jG&rbsquDktlW=rqHuVJh{?6lA_X*A=E5QG5v^6b
zbziuCm?#UXq#%~WP01`FPkt08%_iDw8s}|_HQQ~~^rdqT8p2g4dEy?S++Rq9$au;T
zfon^1cVmYLz`r6exU@WeKHC421`;V_D0$lya{;0A3Gw|ne6;I2PK#6t%s{W#$F<*a
zgT?!Pq=zvv4e<4{4_D9Q<$f=i(|d<O?;hN_m8eouUbO~}{n3Iq3(fX>{q*{R{66id
z_atR%vOhcs6NgBvw}FKgkxNNTmC`3B$?pEe!E4g-)E8YdCW4L9g1yw=+_pKF@=N6$
zxFI_{W)>gkC;utbB_UkH;D}2!A3JW7zs_7vclW~*rZ0NTn*6eCjAYwdZD038idlM>
zoJULIU}SrL5$!D(!j}fz`6-GTnL3$Q-W#rNQC!==sO$G)l%sI@S;bpQF@^POUL<#z
zALoEv)VC?^h4XOH=ZB+8RA{dvg^V;snfU&h_~K}!+G5JtcwoU_s{&i)nVuxg-&R>n
z;+sZ!lDKo#E?GL4ZykL@<(p<%C-MnXC@LBddEZZH4cLm{$aH;3)!f(_ncRsMde~1j
z&UAf!z70mHEj<Hw+|)aevK=Or((SfCie?ak+bY0iODaL1miJM4qe|&VbI;Sk-zqoF
zbtXCo*9?;7Jk>J;vp-Z`g_m;29PDdq<jn@f1~6G43bvf9q5s_Os6|7t^YC@&-PVI4
zFp(Z!z@>GN7r5Ue@;xe3)EKyt(F=?;zzkJ@e+0L@z=yhmlSQrql%@jzK|Z(-jiiW=
zFQ*)n@uR^(S@}Ndrd!+foWOpz{|mUoOJI0vIFu%{>GkCibU<Ykopb#=;j3Y59l&Nu
z_KlG6u`zos%BX);HMhO5aRB{aY`tY%Rco|842TE{C?L|^jY@Z?fYKo)N+Td3-Q6G!
z(p@5rNJ>gcY(TnOy8C_BIp96_{@*X>cRt|WYp?aJImaAx%rRg7_OVt!n;3(_R=qn>
z@=e%sLKOK*Jr<-_e7T{$R~*VwGaUIN_w$D?8o|?_V91C5{$**yf$G=!Yf&)blb0DL
z@>EGdaF5fnRJA5uu=9O1&){bkD)G%<eEX>I9E=S3g}Dxq2PX^RGhile)yN{JEs}rT
zrA=tvkU<sCo`gfZ_sd60D9eAS=ApfL+kH{elr(;%YRk@Qf8niDk~g<76{UV}M|lFK
zcEr72>?CsDefq8|`nd7>(x-XHkY<h{-*E7!z`IYz<EqcnU==1q0+(wFBp-k4yH-k7
z_7JyR-cjo6Zy683)!=wBSc>1sAIkAtEHgifnkPx<*Li0$ztruii4dYfo;7L5=wqWx
zw;w2CSF@k;um1ri@1Ez*u~p+eMc6+4%x&X#Hk5h=W{7676{%Gv6%-P=O!lIGww9$@
z{XhY;gwDqE%GgM8uRr`uwx5>XIPeM7)p2X;#$ncsM@_88zSIzSa}YmJ@u3Bc^O5EL
ztoOrD5|K*So@+iGtas6PQXFtK(^E8)$AnDTN45Xt7pI72qRAh<q6iEz>XOp&VcOK*
zW(1+CZ#*?j;wDtRLr90Ex*loRa?6H4GZahED=vN)6?SZi6;=C)OP=fg^J??jpE+9Q
z-|jxA$BZYI-+;-$yf0PCP~YBq0j$2tr1*_Qpv>Nj&B5LjdbyUo_n8+lHaFGHX{1Qw
zm*DY5&F;bp?hu%2(Xp9UnkmbMsTQV>DQ<{b^7mGa?tM-7C$P*=+zx&zxl{<_HMQr#
zy?L71C_ajkDhomAYdw~_6kT_1bvYId{A|%_z&Y@h{a)Rpw~kjQ2a~ZW?RSrb8N-Gq
zKI~uLtI+34GA|1hI9xvAVHowNPOcOr3jLpqo{x7ApY!KPG^yaAIZ;0~Y914|LZC{4
z5vzr-oN_RmQDD{H%p~>T>4+4#2^90A*#r<Ls?WkJadTBM$dvgqxk@}#dp)=+<I$Kc
zmRRJx+Yh;|8zZ-{{936KcdBKffAUt6_hv3u=pFK+SCYKs>c{Exh`Mh(+8Z3wVUmJt
z3NJL2Hry?nE+c-w)NiJtF{o{UvX{9(58=&sRidPs;R!KI?Ei!rn3q=4b2wU_ukX{3
z=@CX3)aPzql7hd#2QQ8F@1J`h`bEOYM8Cr&sGvispaZ!!X$$u|)lII|MCd#S*dgw>
z33*pN27CXeE_5xi5iQ1P^pEL1`kTmy@A)#@*cx-U8+p()BuCCX!{8#vIRv&+6Q^e<
zWu7Nk5HxrG|Gf8~5I;P{UyB}!*YmM3j(X3e=&vMb5Es!n&Ji=2|Iuj}6sBs)2~@Ww
z3FuZ#wlmHJ<s@eri;haTz3)fo82BkhwQUorQN+RYI~?sOgdYzAfZ(J~H-^dIoK!$E
zy*f))HrEX8!By-+-Aqv({UUe3beKNE;HQ}ME2m0~h9X&2?PpFNnaaBi3aA|ZUy*@S
zLNvVHazUz7;p*^Xxyc_3rhDH~4jtvpZSy#DN7KL2AEHl{qTi-)4TOMk^@s(!E?+{V
zXhf&Ql}2-mdr4`;mp#Wq__CkI3ynmR^Zn}FhKQsAUlI9zSokKT(Z7Ie4#g#6|Df!$
zPwQi{I^I7Y5{f8$#<r$eoi2Mob`~Y(c<@|pQS_bCT)o+-ny-h9tBSD+!@o};qR9#)
z@8>}xL489}h2nAYsCT1UmyXH1-xi9T&f4dbQerb^>vaMHi$UqAzH+c-OCzk8`SwRw
zb~m+_Cq0JHKo~yz{ZImdy;YtB%2<{MbF#J45l#f-l-cq#pVVoRS60@9$6+sFqSFSQ
zD&yae?Ear8ga!{=`CV!NX1K3GORF!L*WOpBWNqIw(VR1B`f7Q-j<e*#J%Xbtfx}`v
zOFF7O<kgQDRNfC$Q<ML_5z=tYJo-O;z>F(0RjppL*W!mSi+Y;<YXg~*6hjyrlRZrZ
zcbU}RIb*2rBInefy|cVMA*(V7vayUB%DPs0)!vE<Uh%E>Nv<`=*?o}6Ms|zB2{^-A
zHP?N6(MjNA&UQ+xsBTX`k|-&nHE-v^s)g}9C|qBB=CWqJw9J>A<R%@{8V`!8MuA?5
zKgi#2;3wqjL5jRm1xgn%T92G8cnDLPC=EW!w}B3i{S42u+fN>eZ^BaGv;KiX^BUAN
z_Xw?;z+hT^`=N#>qN=AyxV$QSY|d@5YfIoslj^cJqGjp2+lG@0n2bu|+-Zv)qLTk*
zxN#uup<%rIEA`hhC~QAV#S3gsl6pRQ1n6710+kzHjk2E(F&@*pyq9AEt?&6Sz9>9O
z-goL@nfM^{4*ym-x^3rpR*L9>3Go0sz74=@C_Lx*5|i@V7zg;1*w4w^civtZYAhVa
z`U4#1*0a-<R1$N%O-fcV@JXVGgQkCPy%@qnz8$!^HE0kW&;-2JE@An_;C4(n9slP3
zdpnrn$#v9;-A?98TsO_ks%fgGeZdE5*9;Mr{Tpe85WK0Zw(F1Q<se?!hFt=;M^Dz2
z#*-b8p1KA;s<#d-LMxc0^2sGp&yKqvu+*{IEcf&g<x!YPA{dO9D*ea$q~f0E<<W#C
z@C;}wb^`_p8RHX~N=F%5$bfQ<<=^DGZHlR*A#9X1UJ(OO<*@rELl@9wYr}cX<e2If
zAe;<K7zyA02A<R=aKuTRR?{`g4fz4bvCtm6aRJZ!<0CongUPMe*S)<b#?KLG55ak2
z)T#<;1-17?k^RTq7U+n$+~eiD{k##Mrq)0#&fj`>OlLzua@UP2U}W%Ap#r~G<;^(I
z`u+11`o$4nt`FtN8OYd)=;p{LkBd{-=UE7W5Q4<p%zQ(6QucpAe6)H3qmfG#gG04p
zuQKD@R5QZd@!06OT+h3`EL=<khavk(>kjZfVdV;d8FaGzAsI?mDeyyuy#{YcVNy?K
zDyyES9js~;GOo#XBWsF?=*~<SJKd6ID0RKJ=j<g00;tKiz?ltI?<m)aWS;0nCreYP
z7yX#ka%+R5CxyI2rYJJFUtrkxIZ!lNO?Lmq5GF1X0!1>OEiUZ@kQe;>R5xG1nYayz
z<7fj_9RPDZhDjDjL27kxK#~4^uZv+|6*v^|p91=Bip-zzL%;AnjDo(`p})Xbd!6I{
zqgR$L3k1i&tl}eaqH7@=0C@EE=YQ&MKU`GEV^o6W`=7FH_v>>QyW7QyEc3Uw0LwZ9
zPlQ)H5K9J*t#yU2bsM%z&Os#LA%IavJenb}P8hQtlB))^8kzAlJ!Str5DZB4dT!gK
zX=cc2u$jHs>Wf7hLA>XNsL$*9z-VpfE<j><sXV7Xx!1Gz1cl-#`UiHoVA6kpn}wD|
z<uG-U`5=1);i@Jn>lqjsU(_@np}swgAiiKY09S)OP_0Y>a{_R>h4U%KA{TxMod1Ca
zYC$!{tWk!@a&8dLFVXryhRFVW3KUug1Ck3G9c9Vw`no(pz;T19l9wpt_68Kn%??PC
z@XrTOJld$^3t|KB*DIZ2z^eR=3kURW^9Zm++4xD5da(z_p8=mX@{4RK`o%*g_!T!i
z&6E-9n>-_=f|AyzAb5Fzf<^u4Smf=47E*dc5JM=DJ%l?lr$k$?!LaTOxM+ymq8}=(
zEoT_COdVBzD>){2d*n40X2GE7P$=iL1E6c$#X@NhPk2m<`V!e6=<?18&fgungU;1N
zdN{Hj@!nE6)v1~UK1}!D_kh$>%p+|dkZ5aYmJW9;!{%Sp&|gBgwAPjmg_2jmj*_>W
z4fApjlAIEy-cO7Wg1T;6!oxhbH#d(Sp8BOAD8!Dy<hFoZkdP5>p3UI*ty(jei-7zQ
z1^z?jX{zFN<V|}!@GlF?OaA2F-oh3UJz(I#3x@qU$Bi==+1-J)Lx2*^f$ve|9@kXp
zOOjaafz40=;VzM_E^dP-8z$F5zi5IqKb_s=x=-_?%V+N=Yru1h#UmgHj;HV>qe)XF
z`H-bbdwYXv9{qOOC?*hn9mc&JZ(qNmoy^MD@$xQ0U#^aC{acdT-=R)`xK)2V4MtFd
zW{e3p-NxR<ZGuX`ER&^bEEMq|-+n>IzUTHLQ2pSr3f(~sVffyT+Kr4gnNtpWRhl2e
zBoIVJgi_BU^eQlK9}iC~+zdJYX2lRXn34x?Nh^*t??=w+rKW@Y>ZSLkO1B?cfd>C(
z$U58oAd^HTr@B{QEjH}`Mpb@<U>uePz|tR2+9<ryQ!PP~mDW21_f;)CDf>1csV5=E
zb42<Qw{`?-j$<#IwkFBjFj+7V9u26<`Wq}^gScAK-&a<6`@Jw^(nMXC-L|I&?OCUN
zyxtG&5l}5IPImpqY}?Vb_g{r*?9&$s-<%g9`ls;(ei{%M*<1V>aJXYb^Rv%1B}c6Y
z-5U)pJ4cE97pef@5EnUI?!@p$0Q6D-yA#*=U&AbV3AhAa;C%v)yx1&PA!B;yX&X~=
z;eG`VABX3sD)NB}LDw-xjk>phm(DkDI|`bzfCoY1L7Hav?a?Ww^f*}t)7u-Aio9f2
zY8T`&l1E~tG@IYwm?73ww<Fn2XQ?q%4;za{zUz*gQ&p&!i-}*=1QGem)FJxo8zgIi
zKs&CE+unI5sA?d^FL1cTAfoHno6`{}@MdujgMmkMqq5=fC^ugYUlz4l7X@U8E&aa<
zlL}?_<*#9jPiv^u*-GRR@g+I7GN^%a1x$2@(NR3HA3h|dd=FmrX~fI}$k3VeHl;v4
z+Nnny1J0A{K*-IWqF+jOklm3qt;T8k3l87>$H25bMzt;ECdRpC*TMMjAzp<8nNR%<
zW9YNw;?5bDtm5*Yns_gB3??T1(2Wk`qdf*#{>QXTISQ`!Bj^|fYzGiX4^WuI=u^?9
zJ`+XD9ZoMxA1}L8FK`7L@!%M!Hr+yVp27t5u9nxZ%<XGn%nmQ28_(#gDl$u*pj9Nn
z8PxLzlpI~?2RwcIRY(Ryrte2|$-s@gzsdOhrd)|=x{r`ItxkSMILbcGHLvx_nP7uB
z?&DO;6fmJxUDws1KT|TRaN~WNhE`3?;AbR*(04ii*Zd7AxJZYbvn*VvZB>Var2HAF
zb7>meI0m9}EX1iV@%pI`XM7O7JOu2*5pUlpdTF@3pLx@x*nZtpdi$hMUjzK7&?3s7
zB0EVnrE0lFI)S=20kk-ijvEZ({<b(j;b;?VnYtx0%#Kgw@P{46PEfA6TBgY=l?~`~
zh`ZlUs;KO=uQUUI;`_%@UaNn<Ob9CdM914%yvZ;=CC^MBb33>Hh8gy(g1041f9Qwr
zgG>kY5>x)ls<JWCOs*Ifjatge31cO*LSl8@4GY(NJflOgoW`ja09y1q&y?N(5Kx_@
z^2nl+qRdNW4ZkkQ?TM~AZg95zwiHXJlvB6NUV_JN`dB4fE>f!S5~jq!^#L-m!RoSJ
zXs-uGG1XJ96Hk&XORtbNR$>6jL~X~rl#7`3$yA{-Vbu^kocGh`VcLIBx$$JgH!>^f
zTt~Gam-d8-v|`|fvj=%R0Lv*I;5~#69CN_C2n)(})O1=2${!ZZzStPv{b>m69B<UU
z+KU4QROkDG*EX$Zs{@%vz={UYk%#MrB`N&QM|8O<dsPNAPxw*#fx{H%@r?U5$eqa}
z`MJ4K{KK3N1M`oF%Z|+?(mtNAlHbH+NWnt-c_=_TR%)Rqh0BEeL7B6Qt3KAc<6xFF
zS?jL*D2GB~fDH9D=&!}|*sTL5ubX}h*f*gkD65o|9eF>fd#}SFKNd~Gk=YS)1u~>N
z=-w#>$-Pzl1st6mL63?>vtkucY4QAK9M2na-~IVr3ekh-pvMHNj8I!;#X=jg@K;Q0
z<eniw1A*!%eS5YFMALGtHy&t9NMwF;2PR~fAoV3bH|hhOAJFXan>mX&2>S*iaLKpj
zM9}+$yjuXio<84Cz5Xl$T$K($uC87P<9%N>W(fXS9q0~4jv}9_HSERkZr45M;mx`L
zRwd^fy4U!e5QljIY($84Pv+Z#$HamIPC)ZuM=AB}aRSsuI8~Od<Fx~ZIae!aoRd3M
z)%9rIlpS<sdd&!DR;NpGsld92YxLVasi)7?b4rzs9{wJR1<|IOuoUe!-t^x`kkYbc
zW%uv7oLmt&-oC!t1_+Ojo9o-@6yPZz0+SzxlDa)`IXum)kU+bCH}(2rg$^<~+o~9n
zM}9XL@aLiHs;I*VomcDiA0xqF9mys4HDHMXZVbQsmCO4(C*u0oLxPuBN&UO8)`2mb
z>)Bc^fL7*&i@%jVySVKx8;E-OeqMc>1)=iyn8@LP(RbUg9j#~nW1w_KHAn7As&^19
z>2(|2v8g?Ni_^{kl&A~bTpYsp%TrJPppDL0HQ&V{?Tzd-cLHo{J!D~)97rt)%vvBH
znMG^Hkk9y!RzVYlSj##p^W@bg0Oj{$%qs>Y1TW?uS{J&{J<yE@uPrFL6s%+?ac4<s
z4(>kW5pXco3oJN66LexNUfM6QPiI7T*m~r)8?4(uft~GSkrCa9i43A7Kjv+M&7<^u
z%;R-Gyx;1Lc9`>obxpOk8rFzcOHvvRxgxbf3ZY`c$&<SA&UZ+!hlUOVY<+m$tYH_0
zzzS#vpt%6IgER1kgnlyQ2B=9yK`BTGU*uh$=e#l~e!?lfgN}5Ctx55-0@N^!D)~fW
zKhs>lv%)Wx5`iHzz=$V5C=BCDUS96ZKfXj|p4<}~6nqTo@wbkWX(EUsE<^g5c@~(+
zW^bKpdX9e^gtH3W_eax;>;zT7i!!6-*q5975sa^XV+fWHt7=`2rC#D4#qXZ20SsLR
znpqNgXP_hP&wmW};%9?7-YYi#XUQWd`r-2z^RN!$?nJYCH)jucPSn<hq2X1sM)Nl)
zNmAzw@BoGIqyFa>MSQa*5`HnsofseS380?EY!+9#6w4o7aZK7r$JaZ@0Vc0UJC{<y
zj4bUoc$Q)0K4p)b<Y~Jnzu80%i1j_9v;i7X5KS({evozqvKvw3sVZ>I`9M{AwX);)
z1J8TJX?s>a3*{Xi`M4<F!jAsq624L1jTfN3ni&J?jYVK8L=-DFkTn7KdTo*kFen1H
zyl9?cz2(ykV+`K}|JXi&uq@m#{cZBYeS*-bi^x2Vlil~nA4kVwZY?)8j7`#=qVi{R
zeQ1gRXxch>?*kV(5$Oe-4k9;zwzsNh+<<!vhUVvhBZe<EUJkaV6ulGKzh_;cGUcS?
z`mBW3!Ion@5xyRn^hxR%_+=T&NRq?o<SklTd^&TAfm6l$0?D;QNDAj`jcIT`h8Qvy
zMT4I^Q*fdnPV`sj(eLGx(c?Y&z@dnSFkP==AUGy_8>=wkCx6!zo}yK=osy*`_(XCF
z@0vc}=;oL(_qHdL<lV!e3vavt0^>X5PQgsoH5=Dknm~JTJZ{D!w$Fb&>HUkK_=OM8
z5cO91vB38~wX$qt!aiKs<U`fdPG+VCZ#t~<3_MmPC*A)TkU%IsocGHU!f^g6QU{0w
z2uH^I5V61}<F2TDNtgTCOE=XSj_2#!UCah2AB_OJf+Y@$y2F8c?%zLyOx*nt&i_Wg
z1KK(-*IScmvioo&`WW+c2!~e<KFs^}RRj)O8BhdksD~mVQIa?D@8wfIi<;o2@9^s~
zz>2knYve77edfv2cS0*3mq8F8DVj*mm^IcK3SUy+ut2okv>leHfEJl1;+8L&jm{HC
zI?EVJjB<-{l|RANay({5c@oVz&e_UV-uyKp%JN723slzN;>a+Ss&><Y=VOw+T+2$n
z=H#K*=XXd>;LgrL$3!~MCNUF}K0HM%aV8IosSk1U9`FJL)@&pRR=HB<p))dDD7;h8
zAQa^{5_EfzjL|%I@+BATho1ZpoU&EwBE%%fFau=B)A>G>5Mk5Yu3)i>HWmT9X@$`3
zT#iZ&kc1ENao*NC4s*(UciiUvcmqCsQD#GPjxRDig7qJne_E;RLpnaqNqR5AVpWRc
z+NqW)XlqbC^-;8c!TMLVMD40grYE;HWVY+eJ4JSbG<ZCP6xUIyD;%!0a2hIqdoJ=S
ztS!zFV<s(io6Rlf_eG<((g}`>jO7wC+9WyE)-565asM6*9+{ba{heFzlmypjqRe6t
zfJj0Z>Zsf)jJU$%fNTEgO5P$;FEAymxM<n+eMKOlJJll)jsAPrg#z?y!;V66cOxlO
z>pXD0;*0r{G|jQ>AM7S^;t%r8jEAF@WUGEq_`MbjDJD^ivHvnOG6(^)c@0m6-MR=l
zl8m2Pa{e-Tu|m{cEz!`3=>a^3TW+WvQcoF7slK&{Z|weO0WtG!2Fi1<Ei2KA`^7Gn
z+4e8^ri`Oj;$wvXqvuR{n-u{Hr!|O$*}dgi>(rogS2xF>2n}00b>y+*J><sBJ9UX#
zV+M8P_THVQxxa{w^xLojs?^-e+VsGrf@#$Ww&KQB1zip~-q&+e2BV`uQ|B6rq3L;>
z#mu~$%CdWo6wjNCn5~>utxLj-&PScG%-&#mXChI{k$fc%5sj0~&$(RdHayM44oA+Y
zY1Ebn4{zz7N6YPvkAd^z60h@#<8c!ud~RYOr*bSalW1*8$bDM)w*2kSPi?_ZSaizb
z9|oZ35PQ6NqJC~#IN<j=vK!SE#r*0B`pvX|fAdoi+^E@J`{8b**#iYtm^0-EZJL!e
z+_{4B5PWXa<v$6ab$Lp1wd4OYiY65hcDs%P%`j1#rw_Vo^gD+JQYUjb5?^&E=as(c
zcoY1+P|@<KUg6D!O6ldHkQ$8w9UCb4`T#DR`{AohLeAyGQW(15RP<XJr{B8*usJ<I
z&0`Db*k7Q01bu7=K(UxSc6TX05jxcSnqh#pKPa~Qwk3dCA+c@?W(SDx5_SJ1+5i3Q
zo+7Yo^E&Q<_aI2;VfJ(ed<ZeW(`1b`n~x5}LqN`?s^I^P#IUGe#B*5SH<Hb04Mf<f
zhJi!<;d>AI%>%7X26Yl}SM6NQSIYJC|1K_O>_uwVdKOKm1SxltKaS^%_onwPe1S`?
z7dU*f*-Nils8}+uQ~vAUpyK(3+T)A(0J+7kQ1xTPj8ll>0t5!2lHEby{<_2c;@&PR
z|J<_ZlvT}O%$jI#<@jMftg!z2PyN3~B+U#3H9RjHJB$0-8PF3sFDDp5VzV!Ix0?J@
zoATx?2cO)=lL#Rd<Q?)gAkFgHcLNw%LE!YQO09(;uQ&Kj&yO-QZ^^+lD#UnE*X5ps
zUm(5%X0OtCAS1RyKO1uu`pbu`hJRnLcZiSXU&QtGgadHDAU=qD39|n)*E8EUddR`L
zAWM8l4Q$cwMhsEX?dEM-zP%9j4aE3?r7i#&mlMsv*LagdCa=g&-vDlKh_nT5bvN+i
z9U~uOxOX#E@1Rg(GWMVcrU{<RdP5Xx<+M9*e?FqA?yq%NDff*g3>0X<Dy=g;6CiX+
z!845Zo{P@9WwGoTfI?*Bn8j<#V|>5iCuHyDgYGqemI=Lp^91zVF$(}`Oo?w@S*J#F
z{;%j46W0Sy>3bbW3^nTog%JxUoe9gn@4V4CNRgJkfl+3WMmZQ~CU0=B09YOi)n>|W
zG`$ZR9=E6(lSmMN)8H(@gI|D+hNNcS{`OU5gbo<a4{c(Xo#qY__s<GM5J7}k@3Ia6
zJ1`lRM&`Y;TUM1Eq8PERcE8-v)dl*NpML@dcmp`70EsJSA2M|d)AZs|Am2^kxjF-o
zy$5HqRUCwb2H<VEQKs3J>IVUVnh?%R1ca2pq7G5`+3vRt{jJ<yyNB?u>r>?=M1M)N
zON`&9g~gCy5bCplhO}6iLC_--K{q~xWM5odm-Ra5AQC8F&j1~#&QV!h<cx0U`>3Ww
zjGZ0L=Weloc~@`bM+i2+3Rw`?r`B<IeSNupGU}Mmw(Lgc&qkg?_a>gu5Xd>!)6#jP
zKT^(4vivXSbhF?lP=^Wt@7AXV6d9i%60?82lidJx4DP^+`8TMOAj1gYPm`wLimb1B
zBd`)eh@ojhTnF0MC(Je?!$W!j{$t>oqlA$8bIQzs;YPY4EJCbe3eX22(lYydp`Q?s
z#?4AVotI#Or-XY39Dft4pCYw<W-W1^+MwrV<<({fC2+y!QvgbD^5_Q2qpw#Z)eY?5
zhNVeI5?UfrMrRIkIOkL?U2)8X5hy-q4|xAgC+iqOJkQZ&6$eEH;B?5Ag0ny`#~t)K
z_PQYD4JhrxSjd+WL_aNW4p*Q``>GDYBf4Z(-1(Rxdh-_A;=m)G0cK#Jl8%R}9bD&S
zIc^==n`CJ{hyi@G0ZI><#slX#V%u3^+C$F`4FNCrI|oMZ2-e>=TL=o}UrDZxpaYg}
zJ1u$b<qe{q5{66fu@7))#Ckeai_wwX1k|k`$4zqi&sH;w+<-*L@fXUrM(tZVr&{dI
zXG-Tl;t$#yT@}xZ^JaJ$B*CCS*XA?cTR!O{LQVBbZ<7C)yJAylQ&=ciyR4^{=Gy!O
z>qd@1`1E5UTWSSjLi0x=*BF)Kt1;Iyn4VDxUT@AA?#{OXRVMk-RD(B>JB#O&>A)k7
zx$9e;C8bA;l4g&?r0MhKOW?Q@97<(Wf*S<}ca~%ru12}09l^S8o$=E*mw<V=0{A#U
zi=P~JZW65erWv>VD<zG>aU!gtD0+oOw~BAx^pj`c|INY$lBA0)jp?vg^Xx=A4_}7M
zrF)!)mJUBt8}*~6mP<VFWpU95h-l#`P1UH;;;_HSH}80ofRa1qY$&exS(cO8ylFvo
z_@;F-01$U>IZXf{ghVsV%m$wjh1oc1$l>{)*Ts%)2yf#};QxMv)(C1@(xHvwVI52h
z^Fm|hYGL=|1N|jtY>rBmcTVK*GC<Pa^861m=<}Xg%u(zc8<E0bbD^oIu+G5?JC0(S
zcYYe0y%RDCu49jp^2M>Vp<#*2@6(<)A-O;fPMhHUq3OfS>f;lbSu-CqW+$jmp5y-Q
zW8|TGX2B@l`{<s@8#dr`Uo0muRcZ``YEsJYUmd&??I`e`NkIqglryWBRcN<0g5PJj
z+A07N)LN8vv^G_0>0X&VKAdnO&@5WpUZMz)*~XYZ$Rp+<aSfhvGdI<?i)`wGD$0E;
zx!Vpw&?8dStfR8!uJ^_lzju+0=tjzgD?6zx<1s5VDq=XY#oikn$X3O@7=bE4^uHA#
zJ^TUlgRu4ZjqF(DrRfdHZEMTw=^Roi_cuXOXG$CTa-}%b=IUeZ-*ja}z7+mvH$xt^
zXCflHuZ{v~R|`!j?U%Jf9bI064aEjSm|~Lg-Prox;-<v2p>Vn4?!&nvTEl`3XyD^a
zR0!@SF`HuMi8sED%J%6&<4(DLR+HtSW%L}e7*@YMJc)f&s{(#sF}Yd&?U}6z6LRQy
zHBHuFLi>6x@)v%IjYMrno6fe3zPBF<yQy;V{f2bdh7XH5lF~ztr;1&BEOvon)2Z+l
zdj#REN7daBVR!*&b#n9V_pv&&Eu=PeVqOQk2HJj_t^8a|816FPDH)0n(Io=HK{^VL
zKO|`e3d4pNIyMejNS?Y~IIL4f5qRL>)Tvv3p?Tu(XX?5Y+7l?TFQ7z!*y;m;>c2}V
zKq>PHC$w8}sPzze+sYruJFwUntq?ToPun>gtbwro3n<?J3o;Gbst=NyVcvT>HCA(=
z`2BvmoHRtD3SQ2G7J%|b8?O#QtqrB+>c-1WpyAh;eD(4ggz>MG!b_p9?Tj4Qa^J@*
z!Ix`)-yo{F0WIT*{gsUAFat^o$xy=QwaYy^pjE_82~HSaZNt|fHX0+?YT`K`>Z2jh
zStOc>9EE5*l~-h1Q3S;2J7G}@7;ZnCNzH#B0A3;)fJ~^I0#3tjsK)a^=h(F(c<sm$
zc?AT9E`Y^@pcLrRW-=xK%jRn!)Q<*s?+YhWqWQU1>hGivB^j<ha(iO`a;hctxy8&<
zV4_lFYL9>ix7ig)0ioU*<njvI+B>V6;m<OFu5uU1?A<`BD;2JZIt8NLS!BsH68DQ;
zpuprrNIU=<*F)g_9+@K*@ZnkhX}UjgEr|L%zU4t7IharOo@RbhaGA$4-UU+ikeo4m
z3kU`nAAsurO)yi{39#Zn7rfpV<oZJ=UMWdv-3n+3Z5UtWu-STk=ye`z!9Rpon!*&t
zf8q~vw(KZI%|d(Fci?^VstKSQ&Xhelj<L4@mrMcH2kKNpn-1c1oYsJb(tTGP2P<w5
zc!DP5#sC~~Pm*g8&oR{Hp9KiQlk1Q&0<fP_e5f*Kpe1<<`j37ua(@7zdj;Ir*Zc)9
z^w-lLGi9um0JXfAk-Wg^Vt^%CDn69J<x=<@L88yVuH9XXQlNGXkGoVUal+mNebN~h
zfNC0n5ypV+LF)aMkdF-Cp8v5mgIJW7Va;2dWXunb$UMCX3V~Vbj{4AoX)q}PF?5q)
z83R{&u0m>9p>ttz)sHq}w*#<g0blZj<ocuo2$_lBy|_mgKHqU&4>;O60m)>FW(Dr0
zXrN%@p%<%x$nnODB^Ho`t}ONz>41SRiG-KZ{bNUdqdhRd@dEu>@_3#pILBn3v(#Uf
z0e_ipH+f`BQKfhamLC{W3dkP=BmSz9b0-p!oT!hHLh?Xc|Jl#vHd4}y!fCu#t7R_*
zyArOD5*h#lIxpNI+*g<Z&9Z1Db6L*=4$oAXOVMFqUc9D9LtMgtJO`<(Yz=BSL%L4@
z|G^4}Io_F5bVcHy^6C$+wt<OnHdiO`$F?Hx-tEJZ64j9DP30uHb20z3oA|O>PTtr2
zM{>A}<bjQ|2Pc2k;2aSUJ%VBe)(A*m)dUj1@%20TD6WIdm{On#N%Q!h9T-qamxl>&
zYayk@H{z2%bO?IQ4=_ShB~<oDs1@T!IZ@P&uoSs6RLN&4yGDFI@Q2+!l^4K=))okh
zF1%b(c_owH?MU+2p!FDZ#WCw<TdT$RpA#o|YPk*pcjd__Z!{Hs2`Qefi^j`|#$=$m
zFaiTRko&PM%UK|N>_LPgK&~XGxKRnz7Dd_Glo6nKH)4J9SH?hu0Q6S<tJ(<4t0xe?
z?W7hVLc46b^ROp{{~Wu-;VwK4qeCU2x`i<Tf5w|Fq7|hloO!)v-?&ixlKd^L@24EU
zyhqZmBN|3?@wr?WD^Sb+%wI8;%><JvqiXCizkbXa##t^X(g2XJQQ=yQ5JgVzV7M-e
zt7q)l>4-{mZtXWv*oYRN@s|kK-P5N0Kg2`~++gh8O<DB^bF+DipnH6j=8Z051yRl=
zz*n<#QqLQ0K2$;E^~*tk;c>*{J?@g6s;Fs;({g(o<%k;^r*mmufDQ`7v*ZAr^*Ug%
z{)=k?l@Ue%8gxd)Ef5E1RVDQ)DhGCJrXiSBmMc$h6cius`p4h}i>Or3L$@98VCLF@
zctUC`${-{;l4wW4Lm*vFkNU0jFd;frjt-;nN=A=OviR|BI*kzWMJDYHG@0W(3z=>k
zuD|X+>NpKWmT-Z*;vus0`^@EgL{2To%E#9{2#F`L5PTz~=IZzr`c)#4Vig(@5#x^8
zsa=t#NnvyBdOL5#O6qSy0+=UN*r}QBxh6g3>gV~^!dapf@nN%)`X{};T3NP8qg?er
zQt}OI_>ni|n@~$WzXu(A+=v!0ua0APAj2X(YvEU*z7wd+cgD7k%8Jc{uQfiLlFP(o
z(b9Dr4<Ij4Qn+L(Y`2A_zPyA$d`F|`uUKqP)9FsWjmf-ya6f(rm(BJ?RlsBF&`03`
zHqTJlB=N9R`&b&RLKcWgF_i;&2?^*aX?{$aV1#p}I2*n-ns0-4E11|G1l=7b=BBK1
zPt!xTp4X}IZNh_|K+OGD>tKCD{9GgroNXCs>hYrBUJfRoPrbS&a}?k<gPnQrI<ly%
zV{LJjOOk$y8*Pxnr)WiW41F!p;QzXPMb7K)d9(z7l&J6OUjJC6(056!Qk0~;*fS_&
zLOKGR*H+C*(+4@AxO#Y}>cgETOm-itQ^qWe<JJ~dxkcxha~<F&pO&kKFBQF~hEQoB
zasL)_NTmfKim=hF^*qAYhCTf<nS*O$Z2U~g%U19xpUdLaB3M>)?-<EthHbcCjf7PX
zi+#bP11;R9-q@HmG-pK476r(0YX2i_U3t_*Ab0S<lVPq_9^Jh4eU|9qvd;RGvDuhh
z!?&Ef^!1A0bMtdtn-oZ%|7`6uu4o>8e;~U;m~Y01>AG$f(trQ<m;n`pQ_QXm^G-A#
zzf2*Wuv<!>8pNT6%Q?)Wi2i!L$AI0dCMB8WoRXlH@}8#CHWCg3CrGzHO!<>gx_$Rk
zm;h}>-IMa^U(duiDz`(9W*)hcHn_fZjC%R8m<Fk#=VSf<Pn#7cTC@;Eje))fTB3=X
zC5?B+KP1(5%5OdiJz9MDR+Le$(mt@B#1zza>C~pKx8I2R!1HCxz~i+y>~>^JqjIVa
z#YzIPboJvLk3*6?qos!Z76eq!tEH!;k~s&KAS~ks$K`TU7OJ2;!Ustx07F4%zf-|;
zp~XOyVc@;u1chXXSmvmvk)MRydi`cc4)v-w<=1BbSa<Qgz8RU7N+U<?ZzT&B&6MQ1
zl%N*HBTlM$s-9n5yB9|IiEOF+YLNrG`8}t96kr9pOeSJ)SwT^WG)cr`Q|ooA+~t-p
zEGA~9JT4rEd2#S(<z;dXlbJ17GH?j=b`S6T%}UawlfT7BA9(7RhYk;yOUs`rudEM8
z1?7qr(EL2B_$d|#DNj$~XLoMiH*D$%Hs<~BvoDO;4{XnRJF1*GV609D#-_^;GSjo}
zX*^_h>&i(Hn+XD!sQ;ND9&o+?vw#dLh0d2*j7Mk;iqL#6PSC7ByKN+}--#6-Pb)vY
zS_Q1qK0r}}BS8C95BNu?T_>jA^|i77!hxi!;W)W2PBKqmnsF@kmymjc7;pbM+%=<$
z;LdkZujeUQZAv_7A9grs4*`dA0h%&y5bp&NK7>cX(53V%PPq*_O}sj^SXjul0N-&w
zG1mXnJ(q^d766h~Fx=7$xW<7DaiH&Mt?wsXKLv|-k_6q|f(7IsQzQaeEYC*KPzJT8
z<j+JcVB9gyQ#Eo^14jtSBBv!qu7L$gYl}ala9DIEKXI(b%nKWh8E1wr_a@o@*7vsn
z>x_P;3si(=^Z_nX@LkvAEun+_hfEVPqdAHt2V0N>4Wuvdd{607NFW~Um%?LbdY{GQ
zE;9ZZXzBpmEdtwzAS(nIX%C-N2B_z9BV{YpV}CEpuq%Ku)Gqv~x&hXOU{Kg!c!0eB
z)jDE6;74(?uQ=WJIctWsvS0@G>o2$0TWa-p=ns)74U6~@cBM+QjhZSsYgCLAhHM!N
zUrH!JCuU00LurW>)d>Q=0B(WhL~hfxuAn*oYQ1G>ixst(dOQ^nftSax)D9lm_3>5G
z+9|l3xO*dnM+#v+!h(Ly$bI-MZZ%pR?RpT{ph2_B!FvRoH`*Bs&K-yJlw*@sjV)El
zf-?L@?`>P9^SXi`Y1TuDU?UI{J!92!q53mjmIszHOQ-)D&gh+>?C{VH><loMl%l)e
zGcQeoN+!qc>Ve<8Ir}CkI+?=xlBh3i-=797QN)cBzh~BMA6-ebtugF37actNCajE^
zbl*Fr$BDsLSV3W)7vB~4@#Tw{Tw)sUXeG)$w>=f!oM-A^^_qe3)4C8R7BUHkcAsF)
z)?My^*<GL)s|n@0vkgueROQsk)`En+plWpXk#FM!W(34~c0u`Mj=hiLx&T-Xj3(K;
zQcnkx6PYQBbZulSL(~!*GMmV+{Mvsm8&`Mld%qFgUY9}YrN`?}tIj#Q?lskG36eo#
zQ_ru(W?(SQbAOa1`y<FA=bxCG)`keSm4yVeG~l9yvCRM_8UVm<3!$8SU<w7$k?~+l
z*ePPuXCO~rgmjLt%dILbOWU5Lp7t0iuQN?Q*Ve9=sV}s5*Fq!qB<_|OSkIfTHP0?g
zUpUpypQuNQR6ujgsFFE#LVLxxKF7%zB@Fv%isfC@MjI27*{2y?KPA=HV%TO`B0OD>
zttI^qsR9AfO+&TJTKu2#UQ^T~>iIe$x4`>JuIMvJTMb6Rce3J^fbybg^a|kJAUWfM
zjw?ZDdjzmee)HkQF~he2=Aa<9G~vX+JX^UoIT4fSXh6NNJrc1|%#iY?maEZ53+Rve
zMk|*X{XXjxs@*l_`t_B-#%I@t#udjtoA#B@E8Iq|?j@tg)s5%hkH(!#wrxhP{y?e-
z`$hvZFps6nJX1!q;cTxN{aOhj260&sz&k}uI!}{0IH|5T!msw+OyFSm(>Xj$?V`2?
zB9z`KtC0n?KU_j>u#kNjAFZhW^ZjFdk2Wf8hblVu-yVt&=KD4YBh%QVGv;!x6wo7;
z9(gma_d3gvQbl0KFJY>i6<31YB-=Zd82LnmMl$Z3X1d*8Zh%5(te*S;)<LAUxj8mJ
z(AfbiIh4<UW&?3%=4_<;OWj^qexA`OOK#zmLXwkOTT)L4qo>eMc*i@m1{i_&*ZL9&
znw*He=nF-D#yoY4Af+dsWtDol!iw+ZRY&lJ>5h<#hf(ImBjJUoI+d8wgn>C9k^JGk
zG<g}YoOS3tqlLirvHD_uWnTqDTH~M`hB)OyRn$XB?EUH*RQPnwejfnk{$q<)4QOD2
zVx&JZ!!Kp>C7Z9V$%qR3Owoq*#~ObsxUcQvyWfyfk$P6}uTHSSzjv2liRFCe5$;z;
zCa}&_W{qYxFC_Oy6eej+M#+HB#%m@Eg6U#=<yAjos~pN?m>!wX@I0zhMEK{T^h~L~
zg5_4wiZgRsv!c@WvTkOA82_KIsRa*xpJ)cO5xoVQqRt(=zm`mx@Kjp#j2p%wV&?z)
zvORTP&lI34Vzyx)pejE*epX03uR{~i!^8YgmCQ4p+`FlswFpb<o>N$vb;;;6d@rZ5
zyy`$vvViI(Vy2(}J}oJf7ukr=Vm2@$z5+Tx_j!5ZnUO<y#h4kP*3#SWg8p`rq)e4C
zMHZ)l;G8lGjZwyEA@W0wlL6<7?ztzJLX-8iS1Nc(6l=t5aAkKA(7bXkNb`(hZKE^Z
zdQ&@$3|XMaJinhxjqXbtL{W&PoKP7CD}3PRo3`^Xk@!-6tmxmzl1W7x(Jg$Bh%)cN
zQc>m>*jmJL{Uk|_{|fX0BH_l8?ZGRol_l&das9+8!{%FN1&x3x2LgvHSUTA0nJ!XW
zF688yX7gZSQ*7MCWOyfnS4NUzB}*D53sDgviox?;tOk+}&bhCk6QPU@nT@ord1;%T
ztx1v`=-roqB3AWn{Lf;ZZ`p{6lo5?zsXZ4L*ks{qEf8vm6DBr#P08~bgATZm6HQH)
z`o;RI2KzS6m-;njO|Gp<G&g!b|ML^T$$yQ~Gy|2kK&!nBB*6JrhC&$0f9Mn(gQV73
z9DmB1^TjY*K99s){REz$z4X_gANerh6c!ZK5q6l4gS08wg03kVuS`8Yu<po`<;k#r
z;bAaw%+3&LdZF0qMY=`x9J!!4#T$M+33;ceW*9jm_J{s#X?C)3nla^a67LLLVUYIU
zb3Tj-*9FOR_U%B$uOwtM%qw*ci3~m#RSe276n*`nTurx;6oV<1CBPu)T=Eg0&)4sP
zRH)eGgV^CCYOha9^VyHR1_wS-yc%4xn0pjR?d8Cjph6CJnieOYte2MDf~Bk?b~?wq
zR})&~-LnzeWBbqB56)8pVylDW^nQwDA?NT3(8tdkkesV484Bbe)>3+kt!rbNml1^{
zHVp~oSwDQ@>AJxU&Qv;|mqU?}OcGLbnH(Z<eIlvw@Yx*3N2SngD!D*oqk95!2dD9v
zQs8kK<|5+b+s}@mq4c){QFLK(Hjv*F93SNU7*Pzb`_0|*!YSL7aWw0>f}blr_&V3K
zIYYzpJ1MI2)ZSNQXBw;wmsI>#B^W-8emd-(T?UR?C()m!DqN#A5;5+|TBG684_bBN
zg{6_3JwhGetbKkKCe~b+3r-RAu0|o!>O57?Q&PK(xybo^HX#+B(#|~C%Q5i>i1(oJ
z$2=bE`c`KM@W^<SV62;>!e{0Ve62xL!%bH<D|u$*Cqo)}hI;wcb%N=nW>Be*(4u+%
z>@j_j464E`Q_?;5v(*`5Y_zF%QhJsT^zD_h*fuMfs4D||ORkyNDtw1IiW%#%<Ud$y
zWg?{>BfL#~nrmIBh}9cFWRmf#<K`6R$$EUmjuAJ1A|p_75MlV<oag@hUI8U}V_6$2
z{WbW%bvB6ziZHHupj^A|1FEvEHK^Q$_Kf(i@Rae8gmWQIe8OdrV|U;`%ag~e%D{6>
zU4qU@w>w<@uxkMmb8i77#=efu@bhuZmyuwsrU`h@S<d`&ZEV~wb>}<kF%qx=V-l5z
z9jdM)kv#VZL?LY-;PeQpzN{1m#AXGz&1pKVWq+fOEm&G`*{mE5N@u{*QX?ud^C-`0
zJIZ*w#M{tEXP@l=8@%}aCD*u%R?$)`N9o^i>48Wqy*k#QV9Je%nZ_M$#81?^kkh=g
z_CNsQL%@l5uL03x|FzNUa7~FK2KwwC-iq8nKc&#(LWx4Lb+Uvy8pU5TLJy@qMr?c~
z7<xa6_O|gq+gKB67rOf8ez_0GranORA=8#iN2_779)l;_02sPraM*&rza8`j-L(;b
z1l4jw*#5}B5;>LG{Mv%P<(Y)12IVh^i4d*=%QJt>$IQ9(N0Xa(S2r9MLy%S2qm0xp
zhQo7v<TEYyqzn(%O-kyggr~UM!g0tjz`Cid$#3gCxlo$b&9lj6EVK{ezcGMVjzgd>
z2E)Deed#-Kv}=Pf+&URI@3+KJSY6ckQ}83Qkw56M%2g2pVMd#|+XG=ris|W-556#Q
z<7*$Nkww52H6MT}tD)MrpVNFkrHi{6*Mr5a;cBm~+n|;&U^dp7EFERD#|R2zeUl_x
z)1PKOY5w+_aSQsZaNeyNsFbM4^?LP0E@JLwJe#6u_$CU{xe5z5e+(8HZLhKNr|O&2
zXB;b(ZAyTX{O4LG_(v0(kwkc2Fi+^G*LXA{`$b0lW&lU4#eHiIa6uYBLtN=dpKTl%
zFEGC}O%L3HNk8N7rizeqjlG6*Dm+h57fCkJFrKKA3sJMqJ$Vg@VOwGz@f&albD34Y
zlgSmj1|AJw-||O+e~FX}B*y$!;<ChmXOMWp0`irN$sKg}_phy=OabT~{``F~W0{l^
zM)Yc?RE3f(`KzrU7|P;kf*8+M7gBadC*6x@k^jUOMTBC^wCAGwwMyw6PtsBL9gPQS
znUj&xWaZi#pWed_3oqChzUwN^XY|EU#Kh%ZqM|ripBQ+novx6aWD%6(gR2TSW#wB0
zo<)qxz`bw>t?c>o0521lVHLHdj|f`()YYbrfD;UK-S{#KcwBoh#Ph^5)`=+ysNi@?
z%TPup{<s4X#|&uk8rKYc?SK|SqA_W+uvOVAbte(u$>BV7HOHp3`xk4;VWx>M44MS4
zI&CoLkbes7k1lH&R-wV=l*5V|+<VcZiPBRZ`F=!(jCYy>GcCRAZdGhyq(%x7Bkd9%
z+e%8G6>+^Dz7c{hm}r99<u(jP)b3yn&VHbRi`Njh%%}p`I|b=`Em6_A;u<LQ*jEO&
z8i1w(+T6PhX7sx!xDvx$>vUQt21BI8&SmMs?Yu@hoWc}$agTpc{ypKU2zJ<-YE@K3
zVSkoNE#8zFk3S=kG@M8=TrP?c8vp0ewuKFS!^ZrwW7<OfWAkfU193`#uMlSzG!eD>
z(EA`3Olo&^6|#oL@k><fB$|Es26+9J)#e@0d5Fy$k@ojEBMeSrZf(Bbr3*p}nX{8>
zAGknXdy^%vCL;e%lqw5ke5v{Zv#%6X1F*<XBq+y<veY*SEUg$Zuj}QHIZjNoPvXI`
z9sPMqLYa}T&V@4eTJbvRPLoYC@-29i63UgJH*CdzyG`d|9!w?|%?s9kEgGIZ?7i)&
zEp-Mtk3vDUUH0Q{rB3At+z3fl2R!0Rx{eix<j0pfV~%M~-VMt4${w8LNqyBfV@Y#~
zur^@s@9+MQp+~1<tf`AO0OL{YB!4QVWE8!28_%UZ3X05`Z5W;h{mI~_cty-m$LGt|
z#!!#{#`KOxJ->y?8|GIVy3|StHkkP<;c5PqLc!P;TJXnypBMsj{3Nbi6bd40_gUsG
z5QR&&pR|<^x`wMAW2UI)6sGOaGJK`ba()+L`MVEl2SDBpyE0=CTH>E7(0kin-z+ua
zVd*NaNCB!O{0DajX1|uexW%sE?$r?>;rgz#CCL8+vFJQiQBd>V;Wt>T>txFl;9%2h
zKQ@Wk5|D#&_4_?@8^+hxWG6&J;4g^je0%e<NF$`Hbk|4bSuyPJSh0JUXVqIg>M4zc
zRSk67>Llk?+to}8)uF)%`t2jU%kI>Eh&Tp1<7==AJC$yVAzjxER1v`hSS-H58oN{s
zPL)kq4^Tg;U8^zWS#h=TSn`^Q^&;+bWU}USq5jsv_w_Dnr-}L-a6M(3kt&MExDcK?
zW21;?6?$ps1jdSKXQQc9dfOlcVwe<sT#ngzbvAqJ1%R0+O=_{uuK)H!E!88Q>MzUm
zcMDb04MfM#M1E)7y9cw)+$Gd}{o%DZ_h8f=Qyw0!&h$5g>)#{YfvCLW{EUdxD0&O5
z#$|EZ4X)|e({k4`+Y#b5i&6C{MtpY97%Azgdh5Or?^3kyfRo+dTUv{_svh^bMy;ga
zZWsI^PxKh=^Ml`lY@vQD7OJuUM-*LM)Q>otvN5AYAtUo5za`-I-bfNwB^w7sP*)?r
zI9vN_F!HKMZp(<T!q*RyglyKJObKJkvU1GA`0mincMX>2PE1T6h1u6p=s79n@6mb+
z5Kkr1@2Mjr4Kfi0mc9Bq&-yT&r}4!t{-J``tS;*UYZvo%LL}Ue=;e~Ol(oCejv!R?
z=kln2Y~QeHys2gYCPT-Gp1=@<`V_90lZfphe%NvtrzRMQV))AI;Cnp}W*nUbm^^cJ
zcf4={=G1eA8RBbaLom*lX0A48zG8pb1r4&{V4Z`Ej%yX7Z9=Ywp8JpI?7XEbB+30k
zcWlGEcJr}1#TKT=BiDyXUE<{P<dGe-|G{W|m}z?2cKG4xD-7>8dikgm;eYV-A}DC;
zdD5_uu7FPBgxCq&@VmOazMm;t$*G(8MK#*a^me+H+{Oi46=pW|76~aT5ZEacurINX
z_49HP^{=X`9Mol>B}QZ;rTGxMjeNZa_MU@B8^rJhw^djarIyfO@qnD0yoi^2jf%xl
zx+d#h=s7gC03JC~pP`)58@2mS!Y}h-v`)QRKyxpPF)STdOKEJR<SViR1^~XxlZJ{K
zFDe#1!Z`_Rn#ibmApAEnF~b)OQqgQ-z94RMez`o^g`Go=T#x_m0h-su`^L>GElcNd
zF9n0R^wW+G8b+>n9gjnp#hSs-)LKp#PyN37NtVfk5YM;?%O+(nI#+qt^-+Ub!Bt?F
zixPgm<3Jw(wo*{)LItTSohtYXq1?i9A7>o*MXul_-J?IXU|&4lYgL<fqR&NsL0A9`
zHgs{dM$fqbcxU0Y7NCM3$5y;vFuD~Znf-g5ZpmQ-k*emiqfUKHRq}KH?Ou??aPAIw
z)3Xr0qbNTtU5r!9#yeE|d%&qt`eR|1{Lif%7a#ZjDgENlfRFF`-Y%paG_6lT7uH{;
zjrCQm5yM%td-}wK37*uB6+Uzor9%W?&owRc(z|}KiqLQ$5twxjYjp#qpxDV2WUe>$
zQLE)%RgEjyndtIvA?P!_U+9sTJ!{C(3O|()!l_wjsxW|ms@b^`b%~wE?a(L)7}U@?
zSOf*|2l6$$QJrIO1Edcmj-d_EU^ZL5$(Ga^%;IK7#{>n*T=UQW$>5)w;P7U410xWf
zJ|x+MAx`ahMw38X|M2>7uJLNAWL`YQX+0lIkQ&snQ}xqNj+X_k`#DoJvN8TAE21Uy
zTRo^W;bsd<%h}Y{FNwS()FDIH@{O&fN^P%t+?0Vv%Nw>Q{xFK9t?*Tv*S<~kk9+$+
zxRC<Z0qvlZ?6?d~we0-(Ma*U6-N%VqI$j4@mq{>&vJFN~0r|8G2A_Q!v70|9^#ZYU
zb1h|P@r$@xB+UGa3AZ(k&j}od8lXvEwgHP}ur)JDlQcPv#Io$pM0`;P5bv&Ky2Ze2
zyJ3~;i}hF)HLaM<$+HEJA!KDE_UPs-M>Nf#`Tcnjnh{4C|5)4bb#jc#=p%<k#9xNt
zOhv!Su7J&5*y&p^_nGax_QuXP!RNl=nk1nT!iBc4!eyx~<vLTiaMbZln!}Yxcx<R`
zq}T`*tzgpKi6b#PPA9QEqng0{t0!R>-u;=kQF2AT?T*7GfVYWLe}fW>V`%x*k;(sR
znX?go9-8N62J}*?RRNb@mjDn*B1Nk;o(*@iG`jrOWeZG!%osxvpy3>zjW1Z@xV9WK
zF8iQguDR)#1$|`5hCcm|-qn@{H?RZL`Uf8vFlD0~C$Z;~eN)41bY&g|Xpga@Hs{(#
zHf08nk$LLg;UjCPv2O~9t=IP>@h@Av-D~E97-TFx_0Hnvr-A4o^{~iD_*bzx1$b<Y
z76|*tbu0X~{1e=vA=tkh)asdYa2obHn~w7srl0s_Ix?aHasVoF6%j0`g!0?-FuINa
zRRt*b@-%&!csJEW2(JV1;|LireCVS4GXp%BN^PDCoDGCK*#Rjh%MjNfI#y6(xXf?c
zCj0KMR($9?Y*E|@td{`7vlsS1?I7GIx!xra`t|~`z@X;zq(czB632q%YAe<T+WqaE
z2WQWl`G_Gy2g@63$w8nHXjv=?zGYly+Y2iZLl?LmcY$^gfhZ#SO;V(Lea*qV3l(8C
zK?mHm<l{*Lg=Gt8iEjj5)}`vK<~ukmWHzlBl8oOc%hL5T)2c7}7Vb~spB_5rzsjko
zjRl54z%<E+xH<vb=Eb_*PcR}s6VTL3`hw9{lyd!emYdM?;+7{%D@UYN6iUeEn>_(z
z;EbEByzxn)e((K%sA;^zW+Ok{qc+lr`{{Lu${+Kiu0be!O;RIJczD;r9{@>=2h~+p
zl)Tq63(%+r5NeI={&G7iCl72x$hXJ@wnQil-%sJtnKwfaVSOL6(qu-Rd!4+R#I&aq
zl>>BUccC5YR<zml9VhL>-^x!vRzYZ>d^7mi$(^pG-(%pVCPQ?-HgB}L`EN@qAta%p
zIzIGKm$t;oS0Dv27p8jbm8Ty5@x{@I_f2x<;kztxzbO1upf-xMVfppqL-#~fPz&ZS
z-Hfv4{tEAjt-^7F!zcN>GB8y0wfCwCp#J1h6*qVn6~17Tvj15{Rc9#%h^@SG^R11y
zqzue)zfcTZ*2KyD=j^<!psMWQCF}*tmS3tO$Kw@_K%C<}W&TwEog`zbg*dLq?DA9Q
z)v=%r8HQrSkGyvuN;IBteFqTmiFn>|xXU7iZ^*mkhdjZk=&#?TeLVWmrOP^#7Rll{
z2V^tlKrKq*xEOhl(7}W0g`c1MMfrVYy^1&TZv@MRq+1Hg=0eA;g4H<2W5K)qPkO<0
zl0smE!7*DlX)rwyV}K{=a8>v6`{?{<x5Gy;*A$Qb&v^P(%2{;o{YKyCj|IN6X()NK
z=<YA^je2t<+^@jwAQe`L>(uZLA1-Y7CFp})5=`&-jn;43(Vuk~TsO78@L<xVEBcX0
zT&!Z8M-lpwd}}h&GjXr+Hniv<C_mL+=YRX>6I{`qlfe~mN6&!9*UuvsJ!Yc^t&e8b
z4wZ8&2=ShY8f!H2MtJp663;cWaZSA7e!{Os6w#mEb4=c!wEgBTbkk~G(<?hykLWBw
z@Vj7z$`eqFNElpQ{hk@lxi5FOKOqV>kAXEgse)r42#zip;Gj-rSIJQNU#9B^7u-kH
zwtN%CY(@nasa-+LkK1Z%n=HyNHzB~0yZ6rAImQt6n1ic)>DM*Plnk5yj&yR3m9d3U
z-MY}rm8jc_z&HjD8(%XT38^bVmdkzLb57@t^C}eTXjpa@ThIl~(|w;jBge((D%@eO
z-ZCvJJ-9zIG21G!6$$SK6e}3N;)XxtWOr&avdEN{K(HU81EFdTa;V@441!yZA^%$A
zqh8;R;4p=Z<Q2?K{U=kuS%f($a{gk$^SoI_bu;;w6q)L82A$peZ$pVuC-3W6ZkjBE
za+6XqwKBC~g@+hFs5Z#wJ2eM?)$(J6lEffH>qQDP@|{)FBoe-)>2`zs9|jfWl=G!A
zX^XJ62h~p6zD*OX=+cXTk^CnB5>diE{r{*s%eXAo@9WbIlF}gEjWkGiqlC1ybax8U
z9n#Vv-AE(dDbgw3DIm!Ix{v4lp3n2b*Uml6T(kGuYkjvyb~!6dHDr*%6R@atpbkgh
zRJV3dSB}OTnaV0jaNIJ=IRXV7N#BNfd?BZg_%D<U0!O5zMc_NTYjRNC=%y4Oi4i;@
zKwp>b8Ou5q)a5&7>Av0uIw;~C7MQAwZnRN$g2X8)z^cfXDmP2#c73A}z7qOOvThJZ
zh(6=jN7>n(OH^?okBp0qR^FJjt5^V6Bn4BSUGh_tI^Ruz2|Ia_xa}ZKX<ya(L$JiH
zU_>ZF`Pv6gr-H*Od2?YyfiHUw&$6%8uQjC<Hf?ilC`))hIPtzc^J*%2yZ65rmIAs<
ztHi0A?}#W@nL)960X~e&WJzt<J>>nHV6jJ_P@9RlvyvTqfl-pf^QMq+Tiv%9L@`_C
z7n}>TJ-XNlH=5OKDNfp%SXm#;x~#95H_MNV9z%im3^?Uh(96D#-kr4pucBuQ@uG*x
zV%ht~7o;pxv%o6<_>mZ_iN){e;_~ZashcVzU}&BylT{${^vpPh6v0dW(8AMCL9-Gn
zFB`sK7*h$$2i20-+cGGkh~e~H%*(6VE8>LSmo>hONr2Kdd8U*l+wKzvZV{xfZh?a-
zty2tzm7A(G;(i=bWg$o6X%3#!*P(T=vfoBrL|7C4*6S4XK}Jdl;mZ9N4?&B!X<Mu#
zN;@jz0D;)$W<L@%iEAm5f0b%FSS^b=kdOR<KKk3#5%4#;+N~l)Ge}Zi-#2A%?{ohc
zyC4^xaP740vecQ<Ba?t_QP8jm$)OJ7Lkjxn?!kUVV2|pEBepj{+xW)($dvSTj#GO2
zlw^P{j#w$jmlC4c^|@RsHR{v<+Cv?fS*tyD<fId&jF3E!w&*OWcvxu}7*@9Z$?5o0
zvcg49X8YVH06vMXiw@&hUchs-Qa|g(RDAaFf(m*DRn?@9A)SDYC<I}VoZC|cJUP_r
zY<YvdR|)~+dDSrJpXP9npWo%mDbUsiJ`9V91SDjn;s!CsjjZFPopaL$L2vfIrjQOZ
zQcr)4^fO73JHGO*j<rQr4ma$5l{1-*)1S}(z9N^hIqJ31k$<i_-7CTq7Vf|Ngj4Ds
zE0h&hn=VX(Bbj)Wt<X}!DeAd_$$cW9{826+UnvzahO`8J?1xp5wlWwMfDKmH4>VUP
zaP+0LO2xjpM_+uV#?rnL;}5qmqTw1KVajyBNR(DRS8Fkjv8P1Zo|nYDW<0c|pdU-r
zpbI<r60sN(OwPEP%97-U;Ov9a6c~fXO7voo35)?DZ}Fmhz)pcv<R3|#VNG$jseDd-
zB_i)yOOI;iS|r21-QYVtsY1f|n?1(k8swKGHk6$#N^Qg5@&RBvvOAH}M6LS6vCvE^
z{!Jw<tY~{1$@EBVI#PObpGimpq80@RnM^)Qf6w^m7^U(3lfbsB#9^Pzfo%Kh|Ab<o
z>Kq8@wP*`du0RdIy2Xhvpr_Mim*`Dr@X17{>tEyxj{?(3c+u1V^xAmi<JhPUJvgiD
z&hmfReM*zod#%k~Q&K&>sTy)Evp-nAb_nMhqCJg65k{23moy;D-O%=jPfzfJtrZ17
z_xb35>bqrvC?Ti#7|%+^mF<jf`OoHv&FIXz-_2He4bk!+-y_=<rf8-QLWXbDak9ex
z$n7-HjEWy3D2Xlgddz4zCD?eegf>i@#dc5gn)8pKdJwury(U>jo^>0wt9g6k5w)3a
zt~0)GFZj?=`|GmGih{sH*Rd2SFC<Hla<EIa6b$qJYFdKGuZh0qwntX7BLAdOTAL$R
z@Dq04<aS+&ac(z$U)~w*qMQEuPj7;jK9IByrw6aN6>@T&%5>>pRuE1X=XgXG9g1QK
zt4UJMnuR3jR(c{Wwnx)`U*mpBg=CuY7W^6NJI$kHb6TM7t|Pdz{(B(hb4rHi;Jzgg
zh76m?DGDe#*lin5R23hhTaU#y{eM#gFGgnc+-mMhYWWtqPk2EAogvHunyX$UFf-?s
zj;D&M{<?gU=8q?DvTFZ)`T0pwLr}p_4o=Lo{3XwSDh_)rTzd3OBgI&n#R6B!{+sa0
zvhSLOCzHMOIKRA@4)3mAn3RO+X8!k($HA3MO%`lf&V<1i2N-dWktuOaM#=J|w<OU1
z^SF-MAah%dgz;nE*HudG%JDweZWWsu<_iAj2YjZz*p?N&1ifqaf1?oL*++R^VUG14
z;o0)^-CwHa!Y?s`q~j^blwu9)chJ;$)hpnlGbzvoDZyFUEsq+SU`aFj->8rto8J6-
zkYQLuTv>N^$u_<5A!Ct?L}>}()42J&=MXuC`C(6Z(M*PQ;;G>7>Cf3(U(xXtt+HHQ
ziB;&T@UfMOGud_0>(u1GXRTi{R4T?r%K<5Y2MKr1tYPw1s^!kOmf*#^)h@lB^HN?b
zMQ)W*UKK<64_qA95RHA#?|${5dF4dKb66w$@yJ53ZY*%ZmGp-$`mvvN*yg^9|NS>q
zHpLf&a5=v(#)%*gl}JiJm*K{F-ZKP@lo-#G?;A1_h^x^VJD;C!cb^{Do`fHMgg)Ku
ztwBK8sbo<_m2pRyX011~Rt38Pn`~7{qDtUVv;H>tvtAyGUEc*r2yZPZLYtJVC5k$o
zMo<ejzVxU5uhy4>nWfT|epKD%`(F9RR`a*amd$&<X2*+_R7+<2RnTXHeBTkEzjn;3
zD`Wx2Xg5m{oYHYb%jO#?o|12eFWqt&oC9bZ@Axtxzm~`Uk?cT_1tGhZ=JGygtEk58
zEoVFWq`NIU%-7Z;HC&y)a{8VQvT6cE@?LoYi>CRnH3TJGH8NnsQwZ23s`|xEZ0^^<
z4}}<<fa+`jOb#Hgo(=z88>&Ea3$w@g0EKk*t8hqvl2Lat%k5Ff=jcwq>++v<#v07v
zV9{VcE4R}|p46<g(o%u2=#HNtgR=RLJCXm^si}6z1Z>ZN2~z60PH)LMu!Xr*@pCY;
z0A~9RERd5(*~VCPoEZxs>|nN9_Q>Q_*D9Hzubto|Y47aNYHVb}&T;#?-0U$#?mWsd
z0elv`D=ssTxyTD|tpkOkf4^_+9LP|U4#&V--(M~6trDeEJZ*CTs>iBjXv?J%O)wOK
zI5e_QqJELOjHk2aLH!u8;(Z(4grj430lAqMX6Yoq6=0roNM+Rc-2)icHsoKi$Th>;
z3otBO1@Z%=Bil)g>Yg?v=hn>tr^6@$a#yVb3lw^ldqXGG6_^+LgS_D*@Y_E^)+9_x
z6FbhLoqn>LEi<gFFLjq_^d07k_xkMDY;*HdHlQ)uk_*y>dQt}MJN;RE`t1*aX+U7N
zpnXqwe1vqxkojftY9>iI-KT$UC%YVWfTd$Pj0uXMm-flZ(^=>%0<5B(X*q25+I?^2
zQU5`T&?{fMT3Cj_8ijxX3Q4<A%!2Wv@v!mJZQw{@9!VdigGdoza7W&A!sgM9TC!84
zl|b{~$l#QwOZ^fOd>j`%<tBmP`xTgLB-h*-1Sel%%Vu~z`IOo0|CECg?F8-8Dd+;<
z|482fjtaP>Dlq7o@Ls(v*#aupL?Pq+V*o6Mk;nolS{2Oy6aI{DWJPa{wqSl^)G5E6
zv6yf_jnAf4KaGDDKbA^~jD&Rt7`cAGm)8CO+R3<R-W+)6`o^cgupJNL{nuHD6*wpX
zdWQ;J>hueHLr~+T4E$S{C3Qt51<+{b2MGT9Wg)ahDEbvPHkc$}7IX$Ws|U~Zk8e#k
z861%czK&Sj1`^(R@5L+A{QZyjTHuZr5N9BmE`V|f0see`cL`(1?~ImE-<joEpPv7~
zk|Ciu7=fhxy%pQH5x)Xki|5kyjT!uZkDf0!RL3hoB8E7EOiH20p0-~7F#!=9^BH3$
z|EWj=NZ^%-d487AE9wR0&VxP^i+{513>M;xxq2yNoxK66OM6~9xW&Q|$d<f4VdVuX
z4upb?!J_zrnFi{gW9z%fb*aj>7eQ^ps(t4p!6HQ3kWYJ=Rk5;BT9IHXFZM!J@vv<4
zuRVkzup_YxJVB@!ap_|mk3e3*N{ROk{nC^_>p@{GcLUx-gc)>_AoMn{r~sGT21xp+
zt=Z-ue#?UM?Dw9DnZ^JW^zW;XZw(wtcyQ{){_yiKRV?qsH%Mmgy4XKgXW^dAaquqL
zaLxS!qcmG!RWksr&5<%M@n(X(HC-9|pVJolod1fVzI4#}J-`R!0_KHcyE%8@h#P-i
z2OJy7sEzjrWyH(?dsD?{yVX$T%aZ!X@afu6@$`ldGMrw)z*%rl7)4O2gyXy!`8mw>
z^*REs+~jRKNH_Cu<JRCq8l6Y*xTGNGa3vyTDL-}WE7{+#ocay){z++SgXgZ|&6<LA
z1W@OA3qWflSUj{Zv}F+hJeXHNUjjqt>Dd!pUmP9a<oNp*bjo&JFf83c_JV-K(JH&x
z1Tm`iB%xWVcND|u0f^R=BEbcH;5a7`TfJ#)VgcgZ*Rgs-5yw}JbrDkOTJlC8<)BDH
zM<5{-$if2=!2370dz)->UzdH&tjt#3Kot!Fo(^6&;E6)^cm#2*8abFChni-Mj_0DC
z=h61u?bou75NHqgtsGreJqQ(DST`+%ncEls?@POfj|>5ofcP)JbxX(`&=lW>v3qBx
z+8DXSBz4^b?2ft{;ff=K*9O5;>uDT+id`x`RxDU8CTGo9F@U++2lL}6z%3f3EZU0z
zIP@al`w`|kv{&N8vy`-lw_sYD4z_7<v3W0y%*&QL!EF1WXUx~+2b>-;y}kqie)zo~
z$S*FywXyOP3_k%<4{Yfv00DuRcO$%pd8&Zp{|Z(^YAApIby)KC!*2#;VqLv>9^=m0
zJw+y%HQPg&umi-_^B;b~o;*%B*jRjqOwl0cqZ=`D*9<u46UTl7hsAIIr(gcw!BbzG
z&!|jgLW`wVoOU4CCVE;bd8X9nwheT9BYZ2fUthG^<p=Qv77?hjBSWAtJNNP;`Iu8O
zW%&fdIn2SyyiK@v#Yq0I<08k9x+{jN5{hp;sB)})UmC}ecv~|4v%oq=c_+w&V{()m
z-X|X)X(0cMnHG6;SUB>eh7xvWdKh=_a0aS4WD!o2_Rwq%$c>9{0B=Us?cO98EPus)
ze5{Wt@O9-IFRbXuK%GM*4BM@yK_76gvA)13WqB8+FIlrWlw^1M%2!wgmFOAT86_;7
z&f(JkdvoKuAh7@*S6GyQ@h~!`LKneLvWUC;FahHyJ#y`AEs4=b*2o1)?NzS~c{&oH
z>JaOGfoM}3@YBfzRN!f98-q}t+3ItfbLY>$NmA_?KwSEl&{1_ymy1-%3dCe;u=@pL
zvMHGg^V#0Oa8X=~(Bg+E93kR1D9(z)`6JI2q{Z*|jL!dk6%473EZ1R8gn1l1xtDTY
zaOSh9kfr%S+}MqWHILH+HWr-_MXb}O)W(7sxjGBn>J_&bkk|`^$k)xWu$vHkX6&m!
z9yNNmpIV^~@!QtnMTOSwWUL&F@$q@J7CoOUXIuBQ#7p6Gm0h}MVYw3x8Z=r#@w)GL
zb`xfxRDzESM9j|MyyW<|3%gf_x$5kK?P{@AnDWrfh49BTb%as2n!$(Zmws_=_alAa
zmEZnARf=skEcYeXhRBJ~SHL4HbNm>usxwCkUfU5M4o;}$>kXxe9?_U7Sv=4$rrdV9
zRIln5Pu66)VA4yTC~9CCc}$%t)niAb#x#b^!mR^S;mbEjDg=TW5a;)Xp5c-3G*pcT
zSE?4G)o$58AL&U!YzRm6odyp1;B>*{&3Mhu;Od-ht*SXC4Z9BHq}NGM6w#oAer{)h
z&F`^^YIqCNj?A&Nd<nAW*ZQYk_^@}7G+=SWBKyIEnxH&?(bDU;_9RTLX9rFmjgV<b
zdFOj2t`?w!oOS>$_H}QPx{&T|H!8GTETl;Cv6)|iFr=RC*3qX@RS{^wl&AiriW0Tf
zPfPr_smcEYm8c}Tt9?e|+pVU~S#ARi85nbxC=@R^t2|&oCa7Hy3a8bS>*cd0CvoY*
zeBaqlTj@DkQA~g;JTg_y5CcK4i565(jQ4^)vPXTO^7(9*^;tlLz|Ag{&{4F>fgxBS
z5e{y3Gax2bB0hDrw2wBZXh7G#9S^RlGz>NhDOSlo(2tJkJb1%th6f4C)=uI8U@P*@
z%l|+|V0J~A8%OiKEdRZ$M%>r|21k6gU@b+@Ht%-56R|M*pXWx=xG4@b4Ye(c@5>y&
zn)c={$?RPAk~~RUPPB1*ysv&ak0sx5!{}^Y0x_0OJ`==gHM5uiyLOC2OCGb@R7(^n
zM#XmLmxtEJhV3Rw!dM|=2cv@?{+uOZlsUogn*d0~z;l5Clh0ct47|mo&?NG2f8~X%
zf2&vEd&*<MvEw4PYo;jkX)E+$Ddv3Q`tFcwCHz>=k~pgPuYaHg4$5pX8wI_%_r7dM
zY0Ex-4x}`v&FTK6^k*TJihRX`=S_s>!nCg5XIwx(zoDA==aJ<(TpZONhx-Gu1vsX+
zXHE=h>e@<a)xHnD6LY~wuvccQzX1hW8|W6!sBD;MuQF!?{Sb+h(Ti*u6liV6Fi&1n
zZM*{m!=*XoE(ZcqM3hevyW@j<<242`2oInMaJ6AHX=^?WdX*CKKZ8(yQotK|3uVD*
zq=cBjuB;Zj_8)|e9nqN>+}}<G=I(*rk>FP!1bgulDnkD_zm#d7M~&zEcgf2M|A(2l
z0hnajcGi-GOyvTRj7~rDt1Jw72QZY9&($Y+4d;)#@r{O5I(BX*AZiW-HCtd~Q%mk*
zbc>EAIX+^NaV`&se0$RfYxbBtsOJ`0TgtUmH}}KPRo1%AMAcUIrDwno7Wm%o*$CC%
zn{O9Oas{hm0e6i590X`|&?qS)7J&%qQy$E}65<H&oM2c#Y0+>Hr4Ri1lgc7JDiMSO
zb;8Kfr;s-;W3H!^9P?4y{aclJoXnqBOEht-Y;}+LlOvB1cT$LAO|ne2)Ntg?Akb1L
z`r7QQ);*m-d@U1f{i^;A$Sn)?p7jmxiWl`P#rPuZXI@O*4*WOOD7aL1pR6Qw{jZ9G
zxNBXZ&^y4mtUltiz&1uh*^k+v%H29U(yn_^jc%X+o(n7*qkx<z7A?(m5_t=c@ZT@q
zKT8rajZcG$?q8?k2}qv*`ktt!($cp=cnWRno2vK6T;uB9(S0<`5qK&T3S{#SaMML1
z#2rpg*YSnDcMd-cyjk4w-5uOOp=mB()tcOM;tr%^2rdS|@aFl4*U^Jw=igMa1(kBe
z#lAsaY#&Cmm+#^pCoIxgYJ6>@GuNm!P~>$90q*g3-YGSIt=n6QhWo`;Pp4>)rEC7T
zALo$m5@k0k@yj!Q=}#h1nNzQy&br_H%pnsB9m5<xxuJEDYO$lT=l~5z1+8)SoQ|=_
zU+<ds<<#p~!d(+p)jR)x$0iCeS2@u)XcMCo<v-8}zRlSI_Fmr#P5MasCLGF!rl|1=
zE9p(_T<gG>F+m@2{q^}Sk|p|V^kaAlfqGD`xfCEa{p2iJCy*a@EE6T1{-s$q23Wm5
z5yH^!;GGH3D!u8=*NL!I-=SVvrTyM`^Y7c}OAX!3!)lunmZGKVm1<4NJtvO(qtQox
zN0e)^!9p^fHb~us*1Q7F>4wMpmG6WrNz`-tN3aItIp!lMY)lw*!1V3ts~(6#rZ1ul
zinn~e?WJq}1V*2yjysq1RAdmq<Zkyx6lgy#M%eIe+ry8;V$*xm|3usS^sM?@#Ls0p
zyd9zBS>ppRj+fultuRx`!eC8y4<YU4TWTlm$FJ4a#e3X}d_D1Iet(^CW7tTi_`{#Y
zO#5?mP2f)PiPNTkoAEp~X55Q(&O0mV0Q+$2J7m?`pc*7y)|SZV+#tHe?jabK;K-iv
zgJ@{~5YMk*(VcCNMZI)2s8QTSOzRCuEJgMU61DfzHl&6sI*F9hG4OPykA-xDWA=SS
zN#DtEhVP7{^{h!F=A+oLgPL#@f+TEwsXj?5jxy<Y{auU664PmKKlNdMsdjAiZ(G_!
zjfq-4OJznT+rEBXBt!aXCTZb{?<+S2P*XoTcG~LqOtHV+CyG?JFj2*w_-zv>>eV^=
zv1)n7_W^8J>7{uDM4xz^j1UDxmAg<R-j0Wh5#EN3zAO`goKcQ`rqEbQ;&xV(5=`Nb
z-d;1=vqf(OUM+qfCeB7312}8?6(ZgC(pz-}78*FgK|kO<6xbVA>=|++Ims9t-g<hN
zc*+EX0I~{}RH@5j<324akt{ld5Q)ECY1dIa6sF!?&;+T2Zz);i#E?;50z&VyFVUO2
zsgg)7ujG<1w%k?c!g<D7Rz$&`3do~WK=-%|qarU-hZ#j!7Vi~qJE|SwiwLV3+=gZJ
z1^M?;mz7!UfNUu}%v6B4Hz@D_@Js{}`1i(9+Lf8dKf`kj(NMA&#;_;+ns#@LDSY0G
zbBtJV>OfkVRNZXzMCn5cSp8ncFGYzLv1itcn6{JUP-R|p|M2{tk(g=}<#(OKJ_OvE
z-~deR#y0v>!ec#t_z4tKN9$+~LX(dSxePB;f)_IuH4Ks^v!p}K&wdS8MoWOcZh)em
znNtn9Ymc1?wZ&m8ue7@KLuVI#@g<)_eJM-~D`lk*aF@Ie{%{MKvw$RXLwM08RmdR0
z1iT&|s2e>jHZknee)3ncd-pu13lkN@5O1N>k8x$2_?9jCmSQXbczpjIw!y$MQIM99
zEd-r(<Du=}ho{BdoA!4C#sH0-v#Nh((erUt7S4&hKL1&2Q>)6%6@NV)#&HSr;rl8~
zZ=7)a%W2c2Pv*$mmziMDNR_G%6eeX(Cn!uA+$DiPHP-!QSvX^#g%N{(uBy_P<*1X#
z;9|GKCP78_OnxV|17N3k;-hZ~;<aym>MTO%$Z7^1;2=KQg+GB-lZKK+LZSIK!#~8d
zG|WAHfW-!`aD&){s+6=)yrQnC=g{Z*%T%^Wpe1RB{coH_ttz%Ix)=S#w3gU!f)r|^
z2;2QEb%rScf%AlHOtf91E-0{Sw!}lZz8FU-Uk26;GN^^z^UF|nS8OAB_XI3={0`Rg
ztw!ht2L^{+(v$~*x4)Dkv<6*{e^PB|mhdOtI0jvBS=OMZ2XsWtHx3ZVwU-LGxdddC
z31~D?6>*SE>9&VU9B#or6Ed1w2hAma4}V(+Ky&i9CgohMY!rQa*#Vq{<b{tN$(}oZ
zkL)zYz~SG8JFM9BD;`)-D=WIc02Eb*YXUAu4gE)EQHo2^d84wby<3q*1*Aq=RC)EC
z)ItOwdc{~205EEdes)zxCkNqS;8(@5+<|mIVJ!YSSs28#0&#+2<)=0s<Mlqf7VjC|
z=<-UOv(qhb-T}srOJI^Y0d}Xa8ftgP%<WgxS^I*Wc&B~PDx3W8h?x+&1n@~8w;6k8
z5Y)-XE&6$ME`uF=Yxb85d?2z~0i2g1OcMVK1DU1=h30fgkpYP%ek#(tseD=@O`CXR
zMiD-Afi~W$sE^OmzjYoNyzzc<+V7J0UWF-|gu8GIeK-e<TXGUarL9KS#D35~g<lwL
zQ2Bwa3e^%4DVUc4Yq7!GzOMw45_QXpi?!DXSElA|P_iJaz(z;6V&1v}KQ}qZZ;xl0
zCy<}C<<6PA4AxX7{cdir@VHpzSjaIIoOJh)5?lIw-~G%lP9u&l*+bw6SrKtAxz_!;
zPgB$+&l##1Kgq$W_bE-<oNzNK<CxYzVo}Z-jZy0Q9Fpme@Ajg{Py-vtIQuZ&*Dc{6
z-wbvzEFYBRD{-f~>~K9jpwV8!4{Gv+y;QuczTSINF9ZA21C1zl@Cyuj^$AC6ly_SX
zMHL}!V14OC^|M9Ym0ZVW!^o~%KpTjJC(_2Z#Z-hqQ3c_Qa(#eKR|(VvnV&esirv;5
z69X1X&m5t;$A!NqOa|b5!g@*rv{aI6H`7u#6iq6Ot=oAb>gEDsy1ZL3qdshVgA?Em
za&MXIrGBno7dd6jIZY@{GnnlOuLwzsDsnivAm>miHqsz5`wxBI!=20NL(v{bHf}O#
zmjPwx>YoPmSi^4RK3$H8zRfoD)7d3bMUK0GgWQJm%Wc)uB0IWfAIVuFmsDdeqENq3
zcxrK7^=)=IB#!lf?ji*PlPY<IE&Z;NG_!&**Y;EJSAMsiBwOpkx;-UjHW03d{ytO^
zpdHo|-sOVFZzJ0)!RP_ZS+=^Te4NLpaFZdgnoc#WJu4<hw$>$z?SaD^7?_$2G8ERk
zq#>(GP;P30z;N+_e7r7XZ3MCy4s5}k{1dcscFXJOv2tiIfLClsNF<Y3EBnuh3zV0x
z238Y9eInaz!L?<&u_x+SmwXcBo(S)fJ=qCff7c6rOx08f)kbMVW%|G@Bx{Q?o85WW
zNvu~)Dxz6IMt^kIz;OzfZ{Cu|#r`bTi!ei(Zv)OsA>Tks?0c4DKOrAq`QqD>FPOo|
zo$}n3-wYu$%`3m5wJvIffb5{DEFLdF^a4npKw3}7Ggo7loeWlO<0VF`A<&Q?gUa<4
zh(^J^%KZY+(itBx1j4-Kbnn&7-^Y%nJ9P4_-(>CzStP*=CQglCbu_?yv}J-){q690
zFgkE%^rvfKMLtNlYKLpD;SOkKm~TE7>Pgx1LtVA>+==XsNe9nt?B?T+2<)cKUfXV`
zq=ARJYcvi@Sk6}6rG5`;)pry5<NFjFfwU1C=Z_<$q;uw_n*4i((J!YpIk7(MXnMi(
zdw`n`kYsd3-%}Xh;;K9#qeQzZplD+C<1Qxeg#O5vZYT?S6Y_Fc$2Cl%@#4FJ6c-mo
z<CJWazU+!F{QfS;_yj%5`i(vh(oK>oy1DX0w)&X`xA-9dS4rN81Utw2HjP~w$<Skn
z=2JBe7s2EQw058()6k`WI?c1ErcjiBsG3_fDNdLMzGz9E->xb_9V=f1={HCK*9puF
z()e#8FA9lg6XU_fyO<v|M=#`cs4hdQAmGFp5c^C$#uRLQWK>){25N5GBv54+2g<mk
z2`PW%{OQ2D2>t+QcsQpfPJcgk?aa{WlZ7Ekxgj%v`}57szj-0`9eI+llnp&((gUb3
zG~*G|AL(wWitHBObkyA>g+y(b6X)T=<--fj1H8r&exB4Z{3c!x8&(gPpGbxoG%qvp
z8APWd@iOD4hA7!LNEWI0!(`;6`Nu4~DK<Q?_j@yq!OwQTNUsOmPAh9K7-Jvv)z=Na
zCP__-UH-ag8f<ix17D?9>Uo`klvY4rT>jQS`a??mNe90<tnJv=ehHlHf#Vq1#-o@S
z+W<Aadl4ArwF2SD0a+*9TfZ(J$bz49Tdlglrw6_9WHPFzV_F%JJ)A0>-_Ti%nvN5v
zD;oNvVH`r;nu9QJJK=avWvK#Nd%Xh(94nt<Zw}g06f*W;HoI@RA=f@cj1x%~4RQEf
z(rCr0%WiWiW!wi<nikzl?E!J2rqlqHxO>xp@C#oayQ{{^hQmC)0kmZRv4h2O;Qoed
zWS5PKa&o_B`T`PtCouNr`Wvm3i()N%P^3eZ#LrbMPR+A>0dX1`ughmE*^9z{SdY?D
zh={P_DsQ8ctZ*5&=-0m1pt-vW<etziEqqIB+;r*I|2?arQb_o<V~;<vI*eX<`DgG7
zp@Xiv=?34!S<jsxJyHO~_<jmhx<xWg0nN0F!dzX#St~i7>ze1MiYiMHvL-}EHE*Qq
z+e|bCjSk0`+spzy0gl{O$e|qj^r1FRKu=8V-EEN|`tW*p&n_6v)KeOlojljIiI?@|
z%l)VPXHTTY<ZGSErz7z~kJQckDK3|Fp`hb5L=(*{jni5n>ShqKURyJC?kNrfXNQ3?
zotJ0}8Y#Yc6*JG7Ti1bOcXpw3aXm1ROcM{@fT`lQP;aYQir^S538k>Lp+~D9)7?=p
z6c{iSWtod;7^}97GKyhHu$ZgaDQ7NM`HqUI10(oOeGupaVEfC#9am}`+&rU=iA7UR
zn@5_4up-tKR`noFdry4k`@+~R7u_vF_U5ahjT-b^PC6A7#R1i>v~`=B1SK<ZeOs+m
z(vW<L6<f|O=>DLc-WAs<l&)V6>?@X2_3U4yBO7_Re6Pz4zhft6(xhnT>6>t|{nOl!
zawB&QY!utEqc}f{b+}J-M`3Dh|Kn05)4}9rHyK}r)J7KTBqVS0t}B5@yX#8%shZDo
z?J@Zgspu8xa9~6uPhcLoN=%pr{3xy${}iB@ZLdR=Q1<1Lpzr(O^Iz8sMC3vLkGjgQ
zBd`%yIfdx(o;3|Q`B>E>n*Gm55@iT8OL^R5z5S)E5{7(%x`b>U&&W(ZbWyObsDLE6
z*Ykr;P<kEZ(95`rceOsR_XZ&DYCesSw^C1Tb`yN7Z&{6G@Eb#JcG0T@SedrgXI@KY
zWH@cAvCOeD7Zb4@XgXW{k>a#Pz+^45)e+y(>%}uTZ-h%r`=<~BDyIX>Wck9Z`7L>b
zsgcdQpD7cTwuO!W2@vp69|69SS+&*&-s&|sHfAFn>3I5hVBF)<ty$}9c2n=oshC&t
zJ<r{hZk)^*4Dyp(DQIt-O$y(0!Ci|VqiIJt^o8eAAmOSl^UxJ}vBV+_CY^;ztoer6
zIB0=^XnWaaR?TR|$&}d!HH+Am!%CgA3wV$GAl%1mv)=4Ffok>U61syZJVHf8?)^!u
zk(h)4jt>%H4N^i@2Se#l+}|P6<Xh48EVfiu6=J;bktInko!C(!;9Gr>_|-UI$v3fF
zC_N5M>yu8fQW+0B@;8%}MulmD%9~M@f8?{3xBx6T-M!~G+neHuFw4r#Tb?jUtpzNv
zYgM)Vev(OeUZ`<HF*pHig)`2sfNaCNO%D{o7;SDe2cGS^EQ#r1&N#?)$PB$3y|*c5
zcqq;t-AHLEGATeeIHX&nD8x)*SQ?H48%`s1e2k?0glM0eGq|gN0+}r62SK6U6zWR2
zOWZcZTTfWX;g4XF2=%h}b(ba95@8>`sWPT4mUNT%$5rD{f_N%lci>WX#?QrMpoMyb
zK*67)X**-E?p$b%h-w5AS<(vq0T8LKy@BiTc6Itk{T(Q%NHN&ItXrH$bes5~g);SN
z46f3Ch}eTOmpM?cS`5vv+U0^l{$*`$mh*1?JVGffGCn?EIpwcea*_=@z-wbA$QPEf
zs%?50Tbib|*b<BlukaSJ^=^<zh_RSif=;10wwfLlq&cXtG9qWff>&WW(4{GP-g27B
z;ER6}39Yle;%#w!gHILct*AXvs@g|do=Z6>!U_A{8Y;F|=IO?=nd%+tn)mj!IE7_%
z^-^kZrwr#Oa(85Nd{?EDfO)phFP9b=12fwjhN+!DCPOt?b_!MWa%2%Bu=FP>|K?M@
zgzSnE=Cc{<#&?N(6j0m;+&--?8Ik~RHF&8CBI>mIC#xX`98=2;>8jGielG**6x?R0
zg&xT%U7-~~z*u;_Lz5OvN9^W0aA143={7-=wQVk0<-$p4%R}KDPJgIiRs8)KaV~Sf
zPY-tF&vZJFmPdmL`miu+;lwqv2jpYA<`lb#v=JLheZxgB+YGy0^5-FzW%%qiDBr<p
zELz*Uc=OPNo@NQ#pcR3iSpH~l;DqZOJwhz5k}rbS3D@`4=gL~5r5SYJN|=o7DsNSm
z3&OT-E8!q4)ic{mOSx`v<U9PZdHb>g$J9&nANTZub8KhbYh+#Kd}fJe+sG?ieFSC{
zzY8D@edagj_7*Kw&^<l=5}48La)Xv}xZoA)TtVjVOq}f>wElLc;7HVhCn4P7P;U-O
zbmUUAcw0xtQoBEST&n*(rDou5Ff`R{OAEoEFOo<S^#Zi5b;1yy%!?k0q?gRdZ%-3U
zyhV4{g|;l;KmR)dUP%$1pN%|+LDJmnB&&KDr)q?*kd^RCI<0`4*GGTjTqYv{JISn(
zK1M%X&1a^pBFz9#BQ*S4)$zM$RLdrd2_g~Q8|T>EyY`4C&Z)9Kvncwzw`Mlj>e4@D
zMyBAewlrCf<ubhDrD=h&&ECiqIZT{BETW`_buj`Pw;-go?oA&+6ZYn<4l?hI=B}r1
z*ivlM2O&Gd{IcuVUx8{^G@)IOdp&12k_582TfGvsE+<rGw!3kW_IdJm^3&s4kx|zb
z0SLVqr9FG)f)TA5WTH!RG(Jfi1f0hXim^R9Sok9Xnv+k!E^xzo2ao<E``>c|=aZ<|
zfpJ+s`=rA%#mxnQEBa5~oVzZj8Z1OHpGi3B&&5Vk>Km?~9&%#6;~2iJLTeLmQOCWU
zlI*4@uG+KxlP9!}s%XDy^Da&{tNa25G5~y_aY=wW6&`nsX*Y$(-wELepoJma5T}!L
z;d8DJXYl>$k_}uCueEhjJctiM+RRi>1K2Y7qOPYZUu1ZUQUX9-7S^Lg|8QG!_K+kd
zfkI6|ujcXiVNJ3PCBHOPdKUajP$|+?irxoeSWm<cUX$}mW^dsqnFw@?x?Iqd>U}1|
z>lsJ>4ydh?M4=~f-*UqqM7vlo_t;IA#31anjk~~<Bkr)`!8^nbB<wU>4&MG=Bafv0
z<a7Lq)vQ?Ui;k-b<oOi7=JzV&U~ehyoRP@NN4Vx3yP&`-=tEEy@yvJsZQg5^(1dDm
zi$yja^oD;C=RJvVsN>)^V8*c;lFF{AISB1nh{t>wN{uK%^>T~H^BtoKV<p20ox<!{
z_PJ+~M`D1ojkJG}7b#qx&oCk<t)o!Cytm=~M*$;~=+VWR`wg|yU)72TIan!+%|lcd
zlV8<|nv#?y{)8vGzl4&p;s;(@Jo#7{w?bQ^Tx+@|uo)F9`M&p)@>Ns#v7ulnOF_Iq
z4c!PWDAR3?=pgU#6Dbwr8fL)+<!r$DV`9Aox6X{G&b;LW6KRLd7e^=ZeY@{dF0{xz
zQ0I3UdVg%cc5lNBah0HMnIZ0i^%paRcIg{HyJKzh>YZ?uPtws?x~U&C{f*VyMOQs5
z%9kRun6Tht_9F0TspDUzbCL=>zy&N3B;s)&U#8o6jl^d#B&NB|8#NDmrJ-<ni<4X<
z^d+Xhz7a8c6xhC$z_f3bI&R?)(&un~csoA;t$7}s`Te<~MJ*ofGRH`cn`Mhygq$q%
zIf-op)+zkG#TcG_aENzO-+)rT&hj5E5B(4NTRU8HK_uR<Ei`-9Axlc#kxQ+UX<*d)
z2c5Zd4|Tn}k<}g7S}K;Je3Us!9;q(w3j~*&8tMLyZ@X@(p{^0mUh>GH8J&x1>migg
z6ovP@*s18IpCeN!jM+fU?F(z}XzlqL&9>D!kzJ71iM@H6Dl_#?`jWwdg^cMP&XaCt
zFC=D2nW8=7tUdwSoc<z_k37+za21>CR>zfz<%*|UKb(`n%vt|=T(*a_3R9AmB=&5(
zF7vd+1GX-1llG`HFZ=BSw9wY*bWx9ZUB5N@!uMdsww~*-|5NNK>cK#Fqmswc6jQg^
z%6}S2NW~>u2HTlQw7A)@PqM7$z2x2hSB#cWLw$na=as*2oNT_%f|zeIQ@_eNY+%ys
zdnvxF?HbV6%^^4;T<0u)Ut<D)mQ9peS^8jGj^@w?&B5;$TY8Q|-5BozOfQH@a_az8
zk89<3@VznywZl%XmsLHQFfmr-$#v=kzJ?+c7>*{a{Fx6s^d${G=Qz}Q{C$S?kY@w!
zvz(J(S=gP^F|0Q)apzm9AQFvTHm(kWXem(BZq}f*5GxsOycW+HBJwKYmRr-*HtMR2
zW8R4JIb`#L{8Mlwv^G5A&-2YeGGoGl;rBjQ%RF#Frc+J!>h-<n9E9!+$pUWU%6f_9
z3OGFDZr{sS7nL=rrt~3e&wCEvP4S%#{xrVQMOB=J%jIoF@|VXU%k_u1!>8$YK{Zg>
z@+uqUqJyY2tjNJd>fT1@&Cj?IUhhU3z;?`t&YXR$O1D^CH(a`zwfxrZ*Akp|eNzOk
z``3<pTAt=PRg-NJIsYLWF}u*f3Vh3U0xQzi-iE#vtA&f^JXNd^wggI&oHwBF;&TJF
zFr;WH9PFrxI7B_RX*K1I5h1O4^tpdy+w$&PS75KCSubUxtOJ-%C3K=Acr4l&5lR0^
z=a!FkD}Hg@b~%2N@XwZYD4Y+OmtV^3Q(+cVGG%?SUuo9LgG!K=J`koC5)p4Ss`78J
zCbC2z#<aCs%N9^Vcwg>YrT7tYv!@MVMwn(83=b|RUXdf=ya>tYeQRB@&4;i7KY)KF
z(S<qiNg!yJbsFk1(?XJve0xeZ@q&gK%LnvuTMiRunY%-`0-W6`^RIzIl!49mgk$c?
z^|=(loLlsvg$yLoXRrd67^)$}T`LIgNl`w_8?jUu=S*SUmTi>gGrgvplw_kPSa>Bs
zE0aEsx4ratFye@eCVGj0iSbBVY^+GWP73$E>|~~-_Dg%NIU=^2m6<yEfwe`t<r$`H
zk$F2DXZC&hZ{R7^BN9C@iQ)f9bpdvUS({L{vQh6ct90yd2}Xx`k~RLG9}q$Kb?JfS
z27!Z^s_vq5CQ~BBKyp1$Maou?h+eUipw&L@OIs$oVt*WjPi81uo=eBUo<Z8e;~d*w
zy*P5N)q?gUnr?z0l!l~j0&bSlj5GpOd7xTWYJ@!Drb2JwqX}y~u>Y#a3~{2p8chud
z_2{~;$(FY*9?V+x1?PFSRWg3b1XN1hYorI?e6krO<1%OXYU?$BgmE*z<EZIm!e`%F
z+7<?xvO`{|dPjHY(mqUFM?F)!RQ05*<Hn`&z)O?p6LnKWd?!W3_%UdYw)ucCx~DX8
zb~9v0^VGmMbfzAmlk_Wg&nVc-$X#dEXvN$5Jv~&E6x|<Tirx(HLpEVq;Nn-S7j=({
zhhFWOee<zfZ2X48bQ`0I09lgJNWWHMa`LJ1#_lOE!5_T0lbSHEF;x`yH1h)XW#N2(
z{y4orJ-4)U$QQ3rAp!~Nid3uuXZ>y-OUeYVpGjX{8u6uHe8C;+W&Q+-NnXH06?@pD
z9)DZehIqGx5Yr>u>34n|8vJSrxA|o%X_4^?jB=D<GhHWy*nT28AJxmw6>0aIRL^&d
zYV12tFD?LQ=RH_`20*;lU%OMgm_g90f_r7eSfr^#49VOK;#Up(cK3Z*tN$(43`QtD
z6E&KU?TC{>6J4Moy5d?im(G%AIz)97m50JGcl~IMT5l`=A}n)Ga9?W2(sK42Q6H{Z
zZbGF=Bfa-Y=oXG)v9tH$G<}|&1~~^aQR1&vh^)H$z?!(s*#A1Cr}$U<e=GnP2tyfS
z(>ddwrD@@(M=n*t6kF`{II0-{uGFt2YtUjF?>ySj+%LANe|-rd6%54~ZuC~aP2Y!)
zfg6$eJm2lYvk$nB&JpMn=I;Pm8fqW|l13C6Qx-htwrdq}FM4jQMTmfB?rp$PaNJtg
zo$+OxU`A8(pH5e?60U@ZmQJBNlPt>_pFj%_o*W<70DDzFHu>X0|L2$b(aVx)bP*}U
zm6auq&0dH0hulSQjUIhp7YieX@ZLHd%7D+<=|C0FM;_FE7{yChXydeLhL<sf7+mk}
z+@hCOq)<y(WPZXa`h(xIE)EUd-B2ooQ!j^^`K|A5?oqEQzDo7}G48ABI`O?QC5%#3
z7M0G})l1Xb^!JHf!1}!(8P?u2V^ur<T%^SumLrzx(|1?{-XL%B;~t9N>g<2R&qhJm
zIVPoi*?u>gV$(Q3=W?4l`gC)#Zo81Ytchbcs$d_R4fo?aCcmFxJ~ZE%b_4ZZ@__5;
z1BUB>9WgNzygdF7F!yBJeT1p(t^$KY5&w@gz`Ymda>U>z&n-<K?Hn@Q#+>=;KKoZ&
zaRt-m{6cJ9a97yogsR&jS2spX9sK2UE^kLuhEzY0sp2b3)|hNNE#4dIwP{W-7Y9pA
zsJ7g2us^DoTpLx!(q%dXbqTsLwB3!_@BHD8q5s^|z;*f88<z-I;w6s|#dAWip1!KQ
zna{Y#`$avdZVgf747%v*ozmyth^xWApq+$N$?>5tRS>nH>eX6xG=6vpJgES#T84EN
zCOxIGU`VKfK<6jq7GorYi+u9OJ%AM2!W}ON<oZyf#1o@P)F`HL@olQ$>g9^<tHZ07
z4oUaYw`9>pa__!pOvg>vN0#ghh6&q;p6jmNYjOx=7)0-2^;$?aO%3<!TzzC+@~qeh
zIQ@J-TU<1P{GOXdB^|sKV8GIKV8e|1!JXH^IX*Tb%8NB6XN0xY25et!w3(-=^Gcn@
zDZ<~d2!ebx>OVuvBmvitTFw<@IM^5$Q8i%f1*qdeCCykW52v3q1mE;|4Nn9&2L0)a
zHf}J3O*K$+dNWJCvg_OZ<mW}K@%~3DFYq72Y(e#6R%l;uf3K(04p(eqmYjDwsHajQ
zJ;WW4Lh9Ys?Zf&k(?Oo0tn=}v=kQCo^`g`#^nWH@VlWQ2qDywXA45j$$T2=^%74{a
zM8V?<w)>IspaBC+@V?TIXb?}d0bnzO%{Gjq!xAgwY6z!*@0QYv`r|m<J-L*Oxeney
z2uDpUll@r{Di^2YIL@w--#3@&SZ6s;R^`l%0*5fz)Zpc>V1r({y=4ZAYT`G~AVo!+
zZ#p3?2f#^sGXnM!`;ts^)0TB|By8xdzj&M$Jp2|TY4+a9O`T|#?l$nBx(8U>*aK=M
zdrMcN4%=s{ZHB-kpYodUBKg=QaO!I-yNALV6qf38jC80H#IuLPTZn`6ER@Z6U%?{N
z5y71(12-4K99{b}YY_Ls#nMSU@qo|pA-mSC`i-t<&ZaE^yEXRnI(%w`QfGq*8WUZj
z2d7M__r-y0QCU?coEi%crfMPG41RujrU&LKzCi|<9*q>Bz}YiIo|ht;V{NICDQ9_P
z8nwC8hIsnRu@9xZ;(1fKzP5&SLfbEl?gq#q*waoAO*4GKjF@_^Zfr*S#PBDlOvb21
z<sEc4hE_af!`Ns+u?RNP%b#C7!D;r4xwW6j`U)mu`)fUr?2(juSVBiI!yP|hjYzpX
zKvtL%3@}QJbZ`3I53PWK+w-uc=S|g^#zFlD<})d}=6`!Oxg@9&so)yUocUM74HFz#
z^^^*jTS;-unL>r0Wh0U{G98i1I9bn=KKG_j0}tWf^wM4@x2+R^Dn0%H4Eqp#gTW7(
zj2p=9V5q~d<%ErgXo%0AC6+an-byyYRE$4&xEhb|-I)w`Y+O^s?Okj<g)ImN<w8D^
zt!0fhIx({<t{Vt#-7|sTaRz^hNb9D=i7p6O01v_afwTEC3cQt#?RBQ*tf3F_0TZ*R
z-JXk$TwIXTV1WUe{sCamv8U!`=>iB+m6ddg%uTTjq-kNgXJ4I%tMKIokHhVy>1SS!
zQMq6vE-x@V9JwMwj^h`Z*?wkZ_U@4c%sx3^Ip+I!qlo7fF#8>Q${R^LKmpKuj_Diw
z?p>vMSc9Q+t>U$<HHl17AKd$-c!{`M$Iqu+F<vq{!N@v#&g7A1S+CsLUbwO}MOinM
z8TT*m<VCV#&uGxbUY_>_5~|@YC&HEg6xiNnjLK&-yP=dk4}(vHI3xhNFA%QFu85v7
zoiySubNC*3#VZYBl~HS+p@dmr<nx2?gd)+XvH$YZ{JWwYPn|vhZ=$^Jc(}^U*0HcX
z34SQ=T@YokYeI^ul#5c9B}CTxmOfqr1qX3z<I2o+UkKB4vXc0md%8IM-tXSAO1R{-
zsjW9ZNKAVjWwm9l<=ty{1(^~vJVY8k<Y9%$l@e!&x!!@L2?#USeajQrT>K7`?>d|I
zzZTC~v@jl<W`k?Ks)f5*aW`~yP4k=oMo`Rmt+pag=FD11zyhS=fF1Uh^C@vwcMV#%
z#v8c>w7HRlp+=>AaH6T?pABfdd=~K;grCN_T7dUw!xf|Zs9{-+fC1iw@Y#Uzu&Qt-
zWe(Ge)WX1lZ@_XXE{rZAlC^ME%D$R8J4WM0cc73c*}Gw^89KZ1y5CiS+{h7U%tbhT
zqYYpOOmukXQ;%Hp2~)zOeV&IsTAnwBChH2R@d?W15U5Fdce{5Ild5Dx>JxIgu7o3C
zO18m&ZViKH>kvNk=n{D2hBzrTy!p6UQLQzbu83m0xP|*GKCZ6RB)RJ$l*DOJdxZM2
zUKYxw@xEUnkZl;^`@Xzy@P0OoRc->o;Oe>KUP6cdAOZdu<r?GD`Y!kNKt#?CWrYEm
za$H5;e0DYHTR!_J*;o2-dSnbPhs8!^XE(xy3(#h3KVD{<$@*Mv7(`2F_rFqpS;XYm
zgzzdqOI@!KEtHeAJxSJ=`<>!H-;gD2qo=%_^0yE$%vHi0>z5ym`&PcZ{65D16K|`f
z8bwHaMy`E{+$`l10A2kbp!hy(#{xO^;c^0Bx5rp|bO~3I_gxySI^*UQ479Uc)6IZF
zE@G2a|0k|M<Le*hqSZh0I=CMjkK`!jLg6^5mkO4w{Uk{P_+Y0&`p5TGqod64nhPqg
zqP2=rTnk2vd;!v-wmzgL(3qIhrxtyd|0k+2Ll2EQ*l4ci<&tDR89jc-b0L0ELm^LF
z(f39lZq?Dc&+5oXbHxyn+9CI06VtyP3Xi)5R<sY$2@JkqB|@#xkLc*|wv%2Du<+i5
zgxd77bqMM+dT9Tz@>IA4_n>F~-P<X;F<!?E60T*F0j_0*DH?aBmt6a(5}b$eSvdf&
zSRlIZ!)<)X&em{a*GpnJU=h*9ma5%SLqbh&8tdPH^*b33G9Xq=fHL{I5i8amFc^kW
zRaaj3sc&k;r8!v9;0Urtknn}M`dS^EFg@@x{92bGe2@JL3ff@w$XF1^Ysa=*AeBeZ
zg8>bX%savYW(^IxcoaR;3^u>45>cXb)*IpYoCxl^7kmFPI^2e<x^!JAGm=E^I4al6
z(2Lzxl=Ib9A|TKT1~b;tyhelT><RbKC0qrF6z5kauXsvUXe;Joh;c)rxS94MhZL=U
z|I;q-StIjt$34-#U}dJ#uV70`=wkc!hR`v<^-l`08ESpvxw&fCt36L-1sAN+B1;!a
zQH(K3k}U`#CGgR;lL+Au8_sUbyQj70=yXz|0hfD-D+5R9=r>?enE+=1WK~Pra{=7_
z5UsOi{@qO2V+ic0-R3u2?K>rl%*HfqgWZ~$Xce7<bdPTd@ZAwJL3qRop85IlF5Pd>
z;C7`Dgp5c(H)3PHSi9;{nVKwV33sRF|J$ogDFnRfgL#q4>Ia*sgUijTAwx#t>R9&T
zIy>5!{XU!4>=Zz@yx9Dx8f`bk04Vba$Q!W~Ge5IiQSmJf9rfR{itgMNfLk`+&8@f(
zxEQe?lm+=%-Af6#jF^H4!E!xDzWlKU-;EQ+FG;Pbo0P~7<)2@yl7{y7zwQk%eHKs(
zZ!M6RP<tfDyX^3xCZapT)kkqTJ<Ry3k$oj;{c1*qS7Lq4zTiq{tef^-pZyo!zb5al
zFzlSNV_NLWVS3p7{HA_c^67}_cl>8!r15f~4W(rT7KpTFG`d7sDWk*)(oh~3P6L$n
z_jBX$ec8z;U!g^JVK~V^kBA$`5O^u$?e=7uSZ<}l!E|@Pm)PKaVfDpjGgk3sS$VB|
za1Z_;r%klhfk0?0P$%P}b(=#OXg@cY-Tq{C+{eE2YvK8Om1<$041uv()2_SLHaqPr
z7W#1O(w5#soFv=lkl~ax8?^Gl++);;l2jBQ`Q4yQIwYAeNdx)pB^V9#%$e#3e1gCy
zgg$iRA`N*;t7h4TZ5i{naJI=PgWOWTcic#~Gz{m>lvB38$UCc=qeU~pa2b(kWv8v;
zchrsO0a?<xxVf+`_r3#b!`%s8HER^8Ey80kKdP&|sS@W~p=US{Cm)&f{rr*QS{}!Q
zy7|ny4}nAP)@bJ93Dm(xnhIUwhI%WB-+)ovW}-ww0c}z(vZi0e1J$<z$!~KEsqla-
zJe*4rgG&@<|8W|JJpL8y5_<+s)c9D}R4<@&61rm`GP!&Yqu0BK$JaXpRJB#C<nazQ
zli2+JqAHr7Bai^jL4|FM@QL*F>;))L<~N6uwG4)uPndYykGo|?nA6|yR<6;V;?Bsz
zYJc*}?0-f4to$q$uOyOt-L><L0WVLTxne!pgQsaC7~HTy6X>sBo+%JY9({kR;J<E)
zClf~3v-2^<!){C!P|rNz(&KD4;^h21@cAs(;q4N85*HhKwt-H)VztQit1qkN9P#(u
z{`mfJfa+Og>~aDJS;?Z}Qry*iTjc(7FT%?ojF^!J=`ryNJi4F}t25jh`t6VwkyRgV
zJrR*hrqTbbY{|`TUN{NVT4;rxcW7f8Nz@|F>S1WEf}t_p9x$CZg2hd#5zWh~1gX6!
zNjtj{1|=kwI_1Afhj$>!52F-WsJudQm??Fv?dit^GFA>+qWiQ~RH~4W$xrP^W<HlC
z-J-`-muVXNl^f|Caj)2-{rE{$3R{Y7{&D!EjjsiCsu$ll$Q8qAW3GX4nHpkVf&1^%
zMAC15ZtOo!M4_@^`vKyGCo70RYsIZQf$ZvLhqu#H53T4cCN)YzEsk@ad1dy_TvVl(
z-64+}A!#f6Cn-_qs50BJ%<hhxhAMuQu3qydu=x&+rzM5<4NyebduX7h)KT5&ld)#v
zhA~#Ws!;Q;ZGXDth;=pAuKE4u$FKselX*e)Ft5BwcVp=rCD)fz1UXX4)$+?S8C3_u
z!&e0mY+pg7%WE_#Bi#F*A}5N%yCPVcCHqd{CB^{GFBdp3^58qbUnTNkFl9m<xm0l6
z9Oj{@qU5HP8EM{<&o(+$v`=&wvSY|_ty~WOp3))u<idk+d_Uv|N~qIsl2K9G7pb{<
zerZcKb?CnZLj5A?0JHnk2lV-ir4FLa<Wk7ZH<#-o7eU2ufaR`}*LotFJ9LVbyl!+H
z%i?lh=6>=xB0t)rWNiaQo9P)w_O-W$zFNLudoVM-j2zA*qZCRR1S<-p@B@_zgs$V>
z8$OXap=2$6*EhOvZtbhlRiI~ce$3~p04>8PlBw-6Kd(-u0g1L$1Qp8%ku-~o#38f5
ztEI1hleX{))OkVOslPCAyqn&qGj{mBl_B@7H~BihvO-t*)*rWsW5>kD=;LM6dGQt4
zI3-;$zHB4Fz^OtDBAang%&4>d?c)2+8(00lm{o}KtgzN0zI37{mr+zoQMs{Chv!&#
zXh@TzT}L@n*_Kf}NjGRRI9UggDsj=7M35gq#6Mv?)F?g>V>dz1#RyB`^A$8O01W;-
z<+d2gno|g=viLoMYsXYxoiow?8kZTL5oF_55^d-ohWlXsl8+7ZKYr;4YwbR)u$f;s
zCam{d4ypJ}8@Dj4`0Xl>Pqz*o#2c2E*|ryT03c(VT+nw43bs2(VW3FcE2RB3ge8iu
zFQSC>Y@p4@Y7O#@?!MW1@t%C|7S1v%7pti8-AlC3MF$7R;l71aQI^^T8J1rLx}^eM
z3<7+Fdy;aWeHS^IV9Ors^K-V&2Pgltv$Qzp^2Kkjl%A*WPvX2Z@V)*Hwt#hPMVBu_
zAjL*rOB&09?E6_-KZkIA!Ilu15J({P$*os$s7P-2HvyEUq3`vdEWSHRN+g}Knvn>^
zV)n?cmMy8TReMHS{`?@($so}|if^9VmV4`dFU$wk(ENWyopW$xU9`nxJClh$vCWAw
zu|2UTb|$uMb7D_yCllMYZNHnZ-mCXls=88L-M8;O`<%Vk`mIXm`uqPj;?l_wX1KxT
z)ehLg;A;cg?mZ&~R=q8fN-5%Vdo}A>aa?$jUQ9yRNYirdXP6m*spde$pm^g|Y_kHR
z2z}eMWtSfJ$i}Rz+KYd1hBVr#Ry!azY*J4luI7{NzRf9%YX__%uAP=Z(|J=*jpmr$
zp%XfugIxd0`xE14t+w#}eIOBN0x8mps-$2aCt!tNa`QC8L~HkqC8_o?Y(o?%*5Zo?
zrJ@Z=NQ`^eOHUQJwNec1W&lTs2njHs3u${yeWis=CJCZY(>A~}vc4vF`Y6}h-wF6_
zeGH<wccRbhM3L&1oM9+Gw9o0wk5)ZokVT|1j|w(SkN>y<(h8R1n_BOyaNX54xkcl1
zAvm8D_OI!#DS~&}Iio1iQ~k#=?SCelqxyb00Lz3Xz0a~$;(46$8d&UN;ps2r@3%2w
ztU)XY;e0PGu%-NU^m8kFgra}{E1{p$=_6_MYer>E2d5cQ9s4xqbOMTGa!p+o1+Goy
z`sA0%GO^OcAnKX=x{~0WYgsVh;?7TiyxLQ?nC)2>(0bE)d}g^?dT00@(icd5nr#CN
zcI|g;ya0Y3SW1(6LgQO{6MzWn7VP;l_1D{7LGzrI@FmnKOlktq7pPso&~$GZ0o|VM
zbCSjCQ0Uw8H@^5pfXgZ6dDXiP#C=H%K6O0`?_3d*=Cv<tS2RkkNi=+JXw}I!<vOlq
zvUw;FAaX$&Lq&e?HuEKZ(rf*W&$?RX>k1_J?`qybOu#)!?kYWui$|y)fJGM<KKsT%
zBzb37kIsu4xd}AR@}kyPMP0SQR9~!cG-d-M6^Xgv_iywonJ;kF-rx%e_l3_J41Kgw
z(e)t$QFDhIh_OFQ9s69Et~Sq&S8f4<(%ItKxz#tu(o|yG>Kd+<?L@-1_GY1+>t0m3
z3irEte%=2}xaJ8UK01OrW=)`B;o4n46vW$AydKQ+27K8^SF%tP%;#)bVGM?uB1y(d
z-DQBy0P~P;-!Xq5ktUZEJD&O5+ovl#fwpT^`8lJSN(T_2ea2ceh1g1CLf8&KRHKUq
zyFRh2lc{?>?NL0v$UyT@r*FUzTBO5?s3&JdsY+{RdfgjD4@_4m3E}OtnU@Ef?z(hb
z57THF!X9;>1L{m;?h<5%YL9rdLwDGK2dRub*iGit*1-asSeV0~1TTJ2iU%O^>kOVB
z;F)bme`GwTjj(;EKh0%G6m{UqK>+D;@`!zPhN2>BifOyZeVpq+{q2*NoLTaR?9HOk
z>g?)MGoMW&MV%cy-{_fk1Hhr=SqXyexFKOzGlnI!Ile*BUjXL>-j19D>r0yvbb1cq
zOUxo2v9|_hRtOzn(D?o>7=CO$$>G0-&UYGLZdO9GZ-&mKVa?(;Cv=2vr-W&4FD&1A
zKHE<A9Xtzxz+P+-mEF(7VRZBS8t`?_+0iNC83f{>y$H4O(&_($o&j7nYI1hC<l;HK
z4qF;V)IDTwKr$dfR_Vw3<Jh9}X>gxaiPx|4IdC2`nU?1fq0t)=-n%V6f5e;^R$*5f
zU&iqF5D_sPUGG5c#+bQbcKeWrNnzyrFNV1i7`^>wgq5!QpM=}*WT0Y9Gd5}tW&7O+
zV+_z)?umsmE<s0vCKG6%_2NL-Ut|(39WVZjmm$d8r_2)K6$sd)fyw8uwoTnj<a0yn
zy&+h^MLGBk-JU>k_Jq<LC%*qV&X{6NZLmXxNm@9=hZxViDvlw5u72P*afth44f-yh
znGQA3_VcDEw8yV(F&|yP&vhrd1Lhws<`)4MG|H$h4#AXc?P;$5W54@08+VPr3jPCQ
zx{~%^FkRgE*9V!1rw9f(wj_I1Y^n!s-R!D7W>&1ss%B^}{~TxI)%+pvp@`CgL%PL{
z#78WCojVxaDpRVj1xn(&ytc~y<Wd2KMk_n=l<BV|31D|J34MSKh|LdRC)z%n!NBJt
z9dPj<!Kb(dY&k1p0^UhbUcS?3Fd0dKPLzEFd|O0Z?IFFzZ@i#E&+JNyKXom^QBqO6
zN)t8_T3!QaeHH^Ha!(4Iz6-^zB$uauH8qhIw_<4m^rxSbXxzwG)s361hw{I})CDeJ
zAD_7NYg1!UvzN0*hNt*Al$NW{maD0CDUoV~=IC?4F0xIkL<vdie*z{zGTc`%-6S=N
zt_u09>Zd-nA2%O8tK^#Fjf~5xjwqGum|InD@L)HzX%Z=JB*DkR&h&9!7XVD{200O$
z;eFc3vMl?a{2ca#?f|BpBYspG#>+$@)Gb2F#-Q_`nk{rT*b6EmS=#y>!K=!(pN1Qn
z6@pnrZ}*A>XAPK5(d|MSjnd+H7YIvJ#26^{d$95dLbg`>CXy+hu}_bsR3ugO+&KLc
zk#@_o_FTE(x;L0!6N0kq>jd=19y6ctkA1H;5u(Rb--Rb*pf6g(m*g5X{&jDPp1mMf
z>KPz^g3bGydxyRbe$_CFm3S?5j?*(+L-4k&?~ZM6>mRk089zY}k|<}@_@q8*CxV==
z?9wYP6&)btIM~2TSH+7K22DYIK_$h<Fe(}I(^AQ^Q(=wtW-3Ppd(Wi;rM>{aHN4)1
z%(Be9)Q$cK-$Dq>(V4~irv=dfECQ3IRJGq9y#RLSqAA%l_=sr#w_lbiD+P&ZOf<#B
zLv3>MfDRZM;t+X&UfT7aH46a@`@P`0)rV%pgI-HD^=alf-d*G1LVr}31TyR(qR&#a
zu(Rn&(*S3G=3C-}6<!%Do!A@fJBqkchUDIQzKi23!VTm8#7+eDR+r3&fJ78w0vjQg
z!qXehmeZxD36awX<h$Rt!^;g*3g{sTwY|s{%Y8aB7GjkYPq76ne}uwx$nN7*wAXAv
z5>;kh{+B=JRqMOX2|w19hsDiL^!bNPzG-}J;zQALsI|LMXq~T2Z(M#!o04;cNHw*_
z{Dy~P$q<kI@hOAcyQ9`i^DsgraIm{%pYERgMUmeG-|M<pse|$XKt#ELVd@zQ_>7&H
zKn2%xWV3HYrYtzJr9<)soi!YR<D?+krvX2o#G|wU7D3m?-H{3)%hi=6Z-5w{iDGJh
z@eX^>;N^a)9<n{L8Fdy5_Qct@lBw0V4#ZHk&5-BlIc6%(2D}ZnxcracNSu02q+XPm
z@Xuhcc5O_iC>?Io6bPasGg-Divuq1LZvcc)eCqFITox)sJzd|C5-<G=jY+zcu<72v
zfEw%^gq!S~?xbo^qQq`F_40BFprJgbqz3+qV2Zd&G2Z@cDt+tvib3}+6_`Ot-TskS
z<%qx#IhPv}Lt>KFc<8OO0j;<(YND_E6y+0UI)UU{e&+|6S_xf1@A244bk4RRH=a3H
zr&wv8W+xETGhT+h=3p?JcZGdHJ{WCTvoOTdIH^y=n^I?o2OQJ^7DuoQq2v<eRvL3_
z;78vuh*d3BUpc{dq+1Y{VW43mS-Uh+NpCetkh8IZ?0G0m(67bXShRZRkb2H@TJ4W-
z+|^z)8iQXh$sh@4rvn(|{&g2VV?XwS%wQMEM2-2sP5mut{d~aR$9IBzP+&TQG2V|~
zLA4tHmP~C}B|}*zDc5><pRsp^*2tvS3E2`6G);XQ5Rzp`km|JgdrOQ(tFNRM((YA6
z3CbgTJYHXN@L;$s7s9qM5s{Rf#Q2aucH~AHNtnAt<S5(xA`>T}H|%w4u9W`U0lQ$-
zw*qNp2X%7wkaAs0eFnZ=A^vJ5SDVaq8+NC%G*i$dt-^WzxbNJuP$5W>E&mbt<<M&#
zSL!L&vv&V$zRQD&MGCYqGO7Fqa0wa&$>x*0S#aE+gwaj5+T=5)*&{%R<pAUt;{)IV
zN#ARzFKN)<C5iaDSUI?msQrq3kATw?t0}MHcX0>s^P{5d%5t^C#Jujs*~5cRmTwSU
zYKwoZ8X91SXYr(O3#hldzcVX(HRXN7{))i$QZtnzn(&Gunh@!@{@*yQ{TbU9-ub;5
zf)%fFYt-bO)5-!~ju|-7Gs?bzo20T-;$8^em)g3}4;XeOR5E!_vV$NsF#Zl%Hv9S&
ztm3b&&69MWqs|>u=!L99VMy7g=PTqG2d9^ORf}#m45$M_-tOerK{*9<iV_^LOdwS*
zBU22z#J-bT+eD;MH|aQer^rnHYW`jv*32vJSC$uh%YF98Q#Mg+ceK?Ca}7INw<Fj7
zfbQ!y<{3z;qZ+!Ru?UCI3!Mg8EoNJVJm&#cQMiIGeEodJ@G_F&A`rvIp|m!-EJ+H?
zY5=pz;rs?0r*->y$%M(Q7)pyVFl#d<<uJM=G8H!k^WU%FXMw2p1ATy0Imt{hZzxxH
z@Xxg>Vk+%{%HbM$py0cL4GY)rcUuPFT3pUdU74~uBso?g*)9gv(`Yy=k~}_0-)v`%
zWNJlhYG(6XMj`goz4>HoDcMq(XRn9v0&8V@+ja7iMKi6^-GH4%6~eJz4Zj{tsA;&k
zPw0zpr@oXX3fCe;{)<xb{Q<Q2bNr3VTzl2t1tF178?oA4&0aQuJTqChJI!m<<1P?J
zWo!)pDJbq(|9$Pm9NpAD$k#C@V@$v?6)qks)NzvI8kW}hkyQrr<s9|>YgiwnUNHDu
z<;W$WrR2rSxjk>oNk=QOFUoUeG(OHLJkIMYK3(0U5RpxUPQ~p$-RSX8RZU$P-oYjd
zh7zu7{+fnWtTl6}`V~yy0qS7XWf#FwRnweIiCmTyNutk-%K(bQ9K?TbNt!S2VADYv
zWDg#k8{fTRnf+_8;k*Lg4M`X`&0awgKo-$Ns{;NEjq=~q6X%DB(w}w!e>C*yTOaWr
zefi#O{)i_KLZ{ROE>%?|jl;+(M^4dhtO<$4o=_DL-la8GWbb8$TVCtA_qki8i?@x{
zghs|#H;lbmyyfg9en#(6w4cFG#Z3DebK61V24`}~gOLBuC*0u>TIxUsIX5H3K#T?8
zOq?|RMX_0fvDA%H5qs*8k7b@F3bin%E%<^{<3?!vhl9kAE#K}IilCQ-Jd!1lYu75q
z9d|8wQQ&-N*tOtiWi4hF5}LBJvz`m49?@J6>j^=s*JE{cJ8t2D$83=h3^lbjkg{cJ
zXiKkU3X}c=Kwk~RMzYXY>D3Po!A?OT?zG>eEwq{3BAu_9d0c-|4|3K8juR-bWQYc2
zQhonKMkiggU5C&PU|Q@9%>0A>3uIEb20-+LLYC>7-5zwH-7-J*ZHJ0c(bxzUh7C9?
z%HwXKo3+g$Q1Z_2F+Lprby3EURNzx?uN&1nr{_OM1A-ESz-sYJUP!ludtgC-Tpxf2
zwvOzoY*)d1fo)^Y(I#NhF+%d=4;UhX7}O1BHy#NdLS{L9_Bk>1wB=TOoK%OX%q9|4
za(Qx)T}1}%UTT=l68%-l5W6zn?V7+*;V+jL*7^3ot&&BO{46+&)sx`}csx6DrL<-?
zg^nv6zVj3#aq?_b{+=QD-OFk8__8K2pXvp*^j{-N!axUWZ?yz-YjQRT-Ihaw$-mwE
z;{fvPzeWHM22ke#2L5#VM8&M6PK4yI8PUAgKRS*Y5^U|5%JG1)z-);^Z+10RBVMc2
zmf^{q>RgJHCI4M<0w?4A6;|kC|6<P~%&V`GpmU>PR9qE2x%4TDA4!l2>pqk;HIy;m
zO`4!NDT{M$Ia7<2Mb_M={59gt)9X4j^rU5uha>TMn7B)!dW42HgOpwasS{R6csM`5
z{E*8vCQR&$(v?~t>6k9+30pac!ccY^@=;a7^CX)0z<ZF)svx!!BvaASV|`@QB?kjj
z2AA!x<)s46(^*u|_;XTF&xDW>j+VQ5tqJ}Qj~jGVXKTg;qI_E211j~sLow4aRxD#K
zPE9V1*qF|E-OR9_AEc=JUt#mG_L2sz>uRNVKl2^!q3YlAS-ib-?}L|E&P*p$?a?;U
z8hiJyxT@JKgCQ$*0tJ`9jC3dxlfSr4t6zLFQr{>+y(6ID!3#teJnxqV-;<PZFfEe1
zg)X-Nl%F}2k`B}BQh88d{Rja@5|~fckWcrxyg|ZNy%1Qr5MUl>5I~MHg!y*qk~{&L
zxfjxoaHv+&t%(FF8$G3bxaR|hARR<aO&<W;{TYTQ?KSDaI!7$790qmcvQVv803S9u
z7p+UYvico%)cFs6Tacc_du7PthQw?4k94ux;-zEanNX<r_oC6#BC#KtVZMv6Q+_)r
z{+-7l&L&6^zfF+dA6p-4Q^dw;@xJ2SEjQ{F`}CZ_<>57l$N0b|TD{DYFz90~)B~gQ
zdn!d_;&e-OwSto+-w2Y71*;6i2(xta$wxNX=gML8*S=DAfGhaSW5DtXyEsTy*^}@F
z`YTRX`82lr&%ly&lt~?XV)Q)F!#BeSQL_AB_*9_)m>Aq9=erO#n~)k~-Bg|}`>2&5
zTCvIF=75P@@;}Ws4I<Yan1ACiBnaVl!A`3vhOUquslH$OKv(D*7JIZ1p4w{A?E(Xj
z5ND33d4v;j9u^IelU$+LBJK3)^?V!>MxU|)B0Bw*ADJ0!q>+*uirmxdGo^Q>XKb_z
za5pLuHJ0Z^e`EY04#t}nAIrQgd_Lg@WEkLBPcN(#L6C2Bpu@Ik&<5V3wk(sD3-_}=
zhTfy(&&~snfX_};fb>Uey0-;}2WdPt6K1xfuQZRaHhA=?^lkDY9n~W@f#(;T7(5T6
zD5eg`w~;*9@`qMYrG;Y4@Cslfn8zf*fk^D+NhcE81?hz_^X^1m28!z1O>uu#8f$gA
z8Q1XYX=l{KEWH%08k?HS&q2zql$x!(5;Vk`%IdX3$(~65v4s==MCGeN25MNq#QoY2
zKU$!&X};q}9LJ3qjVVh-jO{(eO6Pfk{IwdYWN!swAU8n};_t(JT>TZK7B7`4tdCBi
z+NL`vncJS?e5PFCosP9VuTeFk6_k{~fixq7cFA>~Ic4+kWptKX4RR#pB!XaUaj`8t
z&JGbr7S)Vv?Xl@hbY|w<kG1T&$gQ@#9k~(Rz)stp%1&(Ru6zy7gsvR;rrwa%*+LM!
z7hLBQ)O<rT-|I3(HbamqWC-#TO|rFx{oUz$H@{*~QoYRgYi&wem^n<)6b~FMZ9g2y
zjU&!(+tI4y{uhplqJ09Uu*0e=A|%mkn!PFO&<5|;`DJ^2)&xx~-=N6kAh}Lcw9BcL
zmI|$OlSZ-m;Zk=lF>4139!YKdUp1jUT{i5-A!h<fRiBWtp+vy3(Ve6kYg@!39N#3B
z>;WrkhUMZP-iXFb8p*s)Qk>jVo2Z{zl$(lHyBZlM)H|jndZ{4#Gmrq$Ouw;(L}=_s
zKC+`uk;ETa`QI3@qXPMsM_LB~{LumdR|md}x?dJ-qjPfW_*rHQxC-}4hJD8wx4$&O
zJ7l#2r`EhHWdT<5_bj@u{*6v52-b@UcR+TN={F>73gl>OF`eE;S%CT%ya#;^XUN-m
zhR;-TsGf@OJrWJ#^)9bto__VdKZh9a_*07fNQ(fD<DBBFzab*hg@~%K^fRACWU|RC
z#<Gb*EE0!}zIzQTQi|0tY@k@;C!<(C8iaqIg1mev90CG(EeLZjdl&78$HiN_OUrY|
z!dlhK-MY%#lXAA!!)%FzSL5+)v(w3Xk%L>w)D7Ot0^R7B-Xo%NJqgRJ2(!n@38#gn
z-~l5o8Hl|&Iyw7?q13b0+`X~^>s=&tP=zLri3Xdg(yZ@{d}wpt@aFD0&r4BVf<$`5
zvcuB3p`v+km~tvZS-;D-HP~*zC4LH?gOb$R$C+b2*}t+|J%^+|jX)fH8Bd}Zm2^9X
zf$*)xEeU_}V3~_y)N4D?_tD7yisYta^19e;ed)Tn*NFVO*wn}+dLN8JHYx5Hx5inC
zB13zlr6mflRnmvKh;G0%ZOs*a;*~xG-l{a8-i`rc+Zw;L7goKDF0HDi<dSNCVqnwQ
zh#;4`#WaWxu28OpipwZ+%*SYB_n@pfOc{;zZ6aZ+r9JjgsP+5T!tn9_lO7ia$7g16
z%1PC2WJI{kG5maI7t5(yo6fpzB}TE6sL{+^(n-`saR})&jRDy<DSOct(La-0pjRnH
z)-J6p4#pW`R59}u+<msO?O58wTZ&m3$G^ovz)U$5IoSISJMs;t#(T0>e)=}43vOD>
zsp2u%VhXrYGB`g}4sAJN?yLqElvdDw<6-^7l0sLhB!+OHHlLW$A!<CZe0)Im=CMRA
zzbHfL=-^~Y`Q@M6-Mag22-xv0s>zqP85TS<yz9#7e6X1t2#M+wdRA_AiwWd#bvw+a
zlh@#q^f|{^x3R(%R;zC<HMeBhrhGT4=ieH)PK&s~@O|GziC7Hxcqv-08!Eu}h&>2g
zk*$)D6mrf{>?w~I!zeI%=Wn6}04H&5i?L>RWzMQJt+b00J(7T_NUF8y@iZ|s^p|Sv
z?>Yk*%vt@Ejwr2IUEB6@i)T^Qqc;4j#N}6nL!9~@L>Q0enOj*bIjE{>{f7kjS;H5=
z!+;2v>|XO4k>DCjb75c8NWh98b#^}InbEyNTj}GlSfjv!q2qRyA-){j%;E^a4fYb~
zvz4l`0wK8n%_ZgOw%FBxoJ~_kD7hh=m+I)ZI1x<7+K)8|OHgDuKPMX-ym+K7Qcw1L
zExNA>@O^X#T04!^zX+r1RqBAP@oVTn3tUIR9-`0+fug|;IKPGYQfndrc%DJtvhtnY
z{TC|Sp~h?P-l#~F?^IU%qH|8ZWVQ8);B<j8lWY9ecq;*?MZ(*0L9YIpZudUZNNlyS
zbq2-*e8?^=@Cj$%{7@M)f+x`fuU4dRv!{iFA*<fqePXMjmD-id+XMTTIS$##3>DHl
z90ZC5`<-uw_W0;XU!ZZ8Q%-0I`OLTX1<lUKglao>VSR+DhH-b@8@3;Jx>zSrEbV>+
zB;xGsF!}9#${uo?Nw+Z<{2Q&+dk|3*zuk&rbdihhW0i8A$=sSSu#=m*e^?eBxh5QG
zyAsKa_{2t-IV0#LBX@FSLv+2ZnO#f2?1%NtWlkK$H&ck@k$w}6^Ixpe3Agb{1oy_P
z?xJ~<6p3Avl-kl{ZD*%UB2UnQ(TE;{kvhy#b^ZHSBb55&ioD;|ZP|Ws%+v7WulRv;
zgR4JECO+rc*`1d*WKT2;lFxSO>5Xqtdym=4iSBDT!}~3Tw;NwnD`{LAi;v5l=m`SE
zVWy*8iwSf}7d+xWz*iCP1cOd~h|mJDEL>sbtj@%xDrc|nwkCTpD-ql;8W7}xA8q&7
zO=#iXh*(ck8gopY=cDarBD1k(rvPem>(Wd#+`hLnux8l788@pzwTK5yP_N*Jw(GC8
zLy^ulvy2oGMC6;p<JhG?$z9!lmweBWCR+X3{!v-}_Sr}^`+FeFSuNxg?tR!k#07yz
zwAruGxpDtDxqb8mLTC17M9DGg+Fi+AG5qPP_*syg5j$7w?PS8{$T#R^(4>ae&OmMq
zVT6G1E<U23L=v=&WheC4csFQf>ZFaB)`_{k8ow^`Fe|_V>zyqraFP}adR>PbO(&XR
zWEqZNPBFot(v+u}wF`}uo^mf~8*uxVuaoHOQ48q&Hq7Llo+C)BfBng_$+ZNZkMb-t
zHajX{ei)U<2&a7dZEuCMe&Zo5MB>yVbsC^<>4F<1YQkbL()n*POVp-ceos_cw7U`_
zn0krM4u(@+>sqIT@y(+1-ok3;pt-m`u{0&QrGc6uFOi7Zq3M~964Hap-u-YnNVCPN
z@$fr~azBZrNl3+Trg0$dg=gEw<X2yN{mP%qz4bYZsu&}6+DxZmTryZ|+`W7q^=OUF
zy49A(Q>uGXb}JYT---?Xl=DfKuE{z=AVGtUKvYbW$nuGtHT2SkyCJ8U3cq8~i3Er8
zX<cP_R|X#i&)CUI)9{ENbH`sQ*apR1o0ZhdVJc_ECFKr>cPSx&GgWaCPm7Gmk6|20
z&;Io5<t{LI4F9(TL20GGs5+|htvezR>$T@Gq@qF#j<Qb)K}APogwb}qA_WCH*nbcn
zm@w*Qsw#MMLg3|`iR-_$O$qaD5_4=Yzx)mfT@GACnC6N@I#E`kA{o#`X}|L1n8!3x
z70hWtQE>7sGpH~Ag9T+!ITx^a^=#c6Lq6@e5)k)G^vSlVIiI`i!GyEuxnp-Jqe8R?
zc2%c<vF^;eie=m&1vVR3WlyvY0iA8yfD^N3j&Rk66Sv{cp`tf612o<)Os5nXYR}xd
zM_#NRp~9dag*e`#!j?*SMUp8%J%B83;R>swC-WTUlm~1enVzmb&NO(~QftGbbkVur
z;T^KpicU<jZ8ge%*}3tT1=R%CiwjU?Sz%7`N`krk1;-PS$qWnQ$MD`fcoE1Ppl)qb
z7>#=vt2+J(H1xz}&j)+Z6C?B?#p}-G25a7YsVPD8nz@|5;;pKg_BMc(pk^WUZcZJK
zv?h5*z!|T=lFZ=Y##3c>g)i*w-YRt9c$$oaCKCR_3H?hrpDRuh?4t77|Ag)5puWYF
zSmP2(2c$|6GM!jav%v^Y*Q+Hf-tuy}^Abi-?viNG*c&f`f~~5>c=goq&yQRq!3jsb
zqWzyQm)Wf5?5xgQ?52}r^+cu$rh$RdBR&L396Z=@UjWR7%Bmj{#sd|qiac^v(-FcA
z4V#62+k7rltwGNuwGx7{T6lUbcyCX=DrdjdgD11<DLS>bw76b2su@QJQoi0T6z&sU
zZERw9?pgcQs>jS&ui_eFZ#IWIjD~`2`I=6460~OrXIW8{fPYtMY>VuWF0!YRtEHd?
zUp5BQD72wwU~bBsdIDoesuZs1cx|D4g=!?%;J^p0aKZ*>I|a3W#SRGXB>@sr#*S@8
znpCIVQCiQ?-uh;$5_-^<ZO80))1@bGCy?@&^@$SJ8NGLJPCT%-$gPHd)n*G3rtdZi
z6Olptyf4?;iyFy`dy7A3O%)DOgB*;Ujj$dMM!}nL8gMOZ@8{pt@J(XcMx>e-*@>-8
z)n}c>)uG5#eG1ZK(djJ`9rH+SJx~}sC=I(st(jz9+BaX}gA^Fa1NE`K5D4oAyYJNK
z-MV-`xbR=0^Kan}13HGI2hWWM4gTehid+TFOmE*pHcje+HfvmRUZlaj{Ht?=f#nY;
z2<IEzf*n>~$3KoPvy4`CQQloMJ}FF`JhlvmSXhI{*9Md|J44vQnfQ+!LS-5>hI^5z
zrm@5<V>t(mLL8iBq2E#PgMFO;+F`jL5ma_i8*cucnn<){Lm}~IMoH7*w(xd`dgZyl
zVNAf0L6^G4yUS*4*ZrvbaeAu5w}>R{V%TR@g~sM=WTw2v_zO(CmxYu9L52q0zZbS4
z9BrO<%UMi4NY9|ASuA_)r4Dsn6c$&us@uEHz>H+y5_u%&*G~Jxh(qB~mJ7r^JZL6=
zmE0$_rq;=-vyXllwL}+~LVr^8FE}1OjzKb$<-T$)(AsrwX?t%NlY84@es_WpgHY90
z8XVOfG^QYw-1q7Pbx46Vv6LE*O59(KKPGeb@}ln4Uo<O7fu6#xc_fEIOm$7GwJOk_
z*%eW~&Rru*vZky)JykbfzYfUl8=sKd<3PZz_;N$!MuB(V4{1h*NJhB)or@Eqa=J~_
zM%=Bu9`>mMA-n!Bc2d6>#T*4fyfAZm+}<zCiOGz$sd;Ti6dKCN%U!FzLL&z5#=M{?
zc4&RDHIs%dv!2OfrmQtAD)7?y+VJUnd1lEqHrhG){OO>%@g!{?=7i$Sq~20K9!pzw
zBeOKK0H?pCwzDuP|N82^1{UFo{f_)kiowi4p8)~MHojeF%vljE@;^t_)pO^@)|t%n
z-Bw7P?>VY1i|E0?Q^Gxwg;9%cs{e)k&rxT`wMREqm%b;a?X=w_`n^Z%Uj}^hx5_3{
zSm>gUPy(Dw-jLMyFA)j;KE@O6jcTVbU%<9CV_UR$T$+0E?;ptGe~|=WoOiyhblyFI
zGCc!B))#T!ZPZ$;RP>H|<T1%CG{)h^=6hb@iV9Io+fx*EHbugD+n|SwG#g^X5$2gS
zWe>NNcJkoz>;!3~<Ss*E^DWfLLQNXRNr$(^I>!@Eh%od>f1OfH2`3G;Z;DbKQxgxY
zDCA>SXq;Thy>rh`L8^uoG6u;<&2=U17*N>V&p}Ag`8VYGUw;ngU8BE?LPbTyFa3E!
zEByWy^6Qh>8;0J})X~|dd$RTkEK+<g;@!S76W(gYT3g9`Cuub%g!IXL7UX2!fsC*v
zLr+6lZ_(Sn&3j?RamVxv0Q=v`zJ$#tisQ?%>n@gDIhviwB!f_moJI8apfqGAAP>$-
z2fl`ye#-X7sGZZV_(go|f%g9N(u5psw15mVynBnStv@>gACW%x$(}?=Zqu3M!6LGf
z<iX)q=l>WX65!1hE$l-)6}K;a$gvE_t_u*^aR0(_FwvSdDIb*L8i>QL+{le11?xPl
zx+0nkJ$=W-piTdF3NsRg5FD}saV8UV>mSt>OQNTt{nH(W`ls`f@iJP@h*fyw-u~u5
zf7U^va91ve-8YzWoRSmn)S!H0llrukFiPBnFBQUaSj)AG4f@4O;b;xq^~L(f)1Ati
z!JLqsmux3QF)bAM!4lD$_%RKnq9H%;j$_EE_newIf=X4asHjq{^bfh`(E1Cj=Q{T^
zvSpKt8zgT+ck!`%HvUo#>C;F`;L|tEW%V~C<ie4z>cg&z8_=kef8DDdqu{P9B6rdn
zH&9ZC`s!aRTJ>-8+NFimb`>SX@d#Y;!k_=q-^%Uj^Ur7O9Uz5i{|r&)JR^wV(mov$
z(wqB{IcP)is4t0|)j3Yy(2s4c`1g(YUxQIf<KF~*$_y51TV!u0EM;_9^!GXBAu0v%
zDN6Z0GArP}ay7M>N3}GD8NZBUmmZ;P*<h2Sk-wG^#wq1N<tgM0OD+E~dJ(Sw|F=?}
zA@1SVw{3#9!BxODNdLN`6s}5}B>mWEfVH?^Aziv#Z5(0{-)5ZhBgOXoDP~bJAV=yT
zbaZRB0bwiq$BzotS>O$OHNs`yE|H%?tbX<pJPVWB!%BD+q$0VuC+tdpqcyhyU7V#J
zAKH@E%hbVg3r9e-q2K{Cg#_i9g*@zdyS{Ji^xSCg(*UhmX;y={$10gf)A|j0oTsJj
z*d2)J3fp@gu3gq5O7k@cF=eYiiVlk%Cyjqu$J;0M;IB{vF<yN>n8Uf2Hs@<>%!XqI
zX4Sp71`WZhu`gcrS;ee<XnsI{i5u5VLxIJ%z=6jeHUD;#l@dHf^48{XA%A&P?>e_I
zLiTZ;bOF_bYuiOoQKC=t&1Ra5Py15GY##NhVKSD=bYyT<T&Bx=B*z-(*#M29R$8_7
zwSdP^3f(zMet1g4*+_0-a$QHhf&*0bAxq`yEn)*#Z<3_-7>Mb*y8Te0L2_s>d$$9&
z8p8=p1{%bu<UqMyvTk*hE-(YUcm^fB1pU-9jIOZ{CYNvuKQJk#I2vrA0_(~Gqj&q1
z$9(QU;IaPvMCyG_Y1~t8nD@P@_x*gQ`-$f##0!XvNNo(dxEsfYszLN#hVyUPMV4WO
z<NUS@paI%|77>B%2Xu;4PY?w^E2D*hvZZ=gS|=YC*W!H!t1_tr%3oZ(>dt4js*deD
zwbi{!mn%vu8&02ov8TfcwnfH=LXP&(;*oQ^rOXR^Q$8|wgOMu2iI~(AuHsdlL6~LF
z4kRR2$Fnxc3kT)Tlu8FD`(}<WOC|i$wwG75$Yky34o74_b$^g@=_9T_MT9f6ar^_d
zDEKr6u5VUwbnvTvD~zLUzrL!5_lB78ni%}WBxq<WLVx{9g;0@Oz#rLD=Vf`J1-s)k
zD|t*ksGH*i#9vi0|6p>MlEw6%h9ZevSjpju__%uDvD~N<&G=`N8j#1~1$n7Y_{xYY
zKw9KT|1LO3ml0IMIE$mtl-O}sVT1qN1=FF_A;-J=Zwwh$8V<=!MDiX!GEVq3sXF3W
zT0a%Mm}Kl@o8et(uw&!xeDL?U(YRgsvatRPwtWa$VJzcfqV5m;6Pn<OIW-+b65Ua(
zzNooGDP{aVv6|S8Er43i<2!ioHjm254RH%39AEtYxbc3M8#!24d~1q+Y5L^TewLTF
zN?-^K*sH2b=6&+kMt~TA&gZgDw@*ry9qh@R8z8;;WivWOwXVgt2XjiIm@SiyNaQbU
z)7Wl*Xf<qeo#`^(cd5kxrd0J@ruKcWMDEG-<1~A0pYGh7rIV=D%7F7ba|%DuAb9J9
z>J|5R6sNRLe*s2Y`)Xc$YC0d!3+WM^FRh&`QUv4V?YjjGOkD(k`rv@^I0sf@ow^B{
z+dd?1cI+DmO-4q>PEOLpAmHWme)H!$^!M6?_980NFgAh3^LnzBrlNw%4LY2+p|>Rz
znB4xH34cs8yrDgSZb9>If_B_c2gAyux1~NB^6eYKP+1Aj>en|x8*m}fw&#SpHgx&c
zzV!0H_8z?MI-EOQxOeM2KkX;CCM>~0&!v`2xq@yi2Z@T?9Z$TiPFz(0IYn=44DBy0
z9f#PfG<Z3;$7-E8X3;=2&D31$e4pUm4@#Azj>N5I4>33(To#CTe+Mr9gxB8q#9D!y
z_6Bzb4&Q}kX(y1o@d)IcNF~piwgp|DwA~oIKX^<$gtT;A({xT-$c?sL`oXWEcbq#_
z&8RRYG!QeQ)aSnPDS%TNl1zO3n3F>h4i{_W8U3A13Ds9Y-;xOb3?y#7S+{lGX?5O_
z@?Vp#1g_f-lTxeN6nj`dS_#GP0a*z>!MwY~B$}Do-j74suM!+K`u;w-vA=IKFI*48
zy+8U1pK}S1PyQK6ZV8IK2km<14TzZlv?C)6BwFFYoL**i#;$G<4t2h_8UDBI_Zjcu
z={OXjC>fGpc~we}bg^xFV>Z912l<C=yFl>lJ{Uf(8F0T`07=?>r@efqsS_UL_<ug{
z#fg4Q-PdCA?_tH}*}N|2JvaATV@aVwZ`Gewit(4%*@BmhBWl0b4u{tc&|plL>$qh6
z3`#~s7oLWBR{J>Cg`J6{LJBs#%De-Xp!LDAq{<0%pmM=#A<N>tK619(J-0Bp*zn0y
zc0*QhK<DJy9q(1WhO31YkES7IriJ6a(<r=s=Rn9)CJaPORg&bj0lxTiF2HXeD+vUx
zxRm?2mGi!M@RExOqZ{4*RKCAHe7tX@2Uiz(aPHYu-lt(!Er$sN<FL5}LKe_Mc$oYj
z3!u3NE3aa*7i}&DLUEg7B)D5}?Q+s~;P?7(TbPQ_Z^4TqEI{GqKU|3L_s=$B*?Awo
zc#XOfqTj*vzQX)C@qVj<W3m`-fA@Zk{+O~U4g5`RYDqSIj|qLv24P}>yelQS+wIGj
z>kqD(;dqu_<y7R02oug(oM$oC_sl_#LALc6{k9n0{OtP0Lg@8)T%_3TfM??=*?TV;
zlA{~6mi|{{Kren7lBUEQ;g@sJV6;FYE5_F(Ek~b()~_MVya>ikNVb5YM1i0;Z;Lk0
z(PuX7HKd>jc1PT6SKO!20B#TA)#l@@v;DE3;r%mrE1g$aEd(13U=};9Vdo{HP!^;I
z>LUl)=#o_^%c8TxbwtVsWPthqHtb=im<UdBa%@yoE%n)P=s{36R{lMy(JHA<O^k=s
zF#3YkbLW&eq)@r7qNaUU1}sI^=8nYp)AQ3mznDhys5i?Dn<zOIs4m~LpKn>R#j5^*
z!3ihmrAi3^%_VvEMfM=lbpe<(W&O_Pg_o;_DDXzFg0!X>+q=Iwdui5f1C?S29NsUH
zKNxK{FKY;2YWxr~h~{H(-OmFpsJV{ldY^#1i`o4cCW98j@xAIXtg3oSlv#kb#P-X4
z=i`~&+pOGM#>7(wAez=8yh0_s64kvG)x9Ruz0QID9+F-^y&JV)gON!q)!IM-@7rEp
zax%uQ9TKMtR1Y}CHJLQjiW`klcH=abTXV`L4z?Gs;UbT)@wb#gUa=Vctm=DnNn46i
zqsQE;n+X;}Sjzr+?)ID+>KtEvHzHrk+VY8HO?tMHRgV!y^z;gq&*ESZTkJ$c7S}A&
zWAkOp8;AwE8QXXrOA7fC7f(iF`C@M+8MTjfZfvWcH#A+${xLNcg%mvzaS13@LWQOQ
zzVwsXBW2@(Z)A^|%h|rd!#3gD6k!F)%xL)6iE>}izA!3}l=9H2zuj<?Z&yG-IHpH8
zW{N-hZ;<2c*Ld&i&zA@oJhm{sav$Zii^Iao8Cs-?gv`je3Gt9xx<B7cq3dpY=TOfy
zob6)cxnkol*wrB*g9Ycm1Yc8bS^@#1<W<>xsRZKDKkho;lf!BLnN&r6GqY6^Mv}_?
zX7&R7RJIGOG}}3PJiOf&S95oBmDb=lv=s3SmSn0FHLLWUz&!`9qupcpRS@}I!y9X7
z!7*C<4o;;My!glFry)iyvH{`K1?%ks|FaFc(5>~mtfk9A&O~jGA6f4i2TGhl^=<f5
zKHqUZRwejh;r2IAkf4OY3FrGlt^iz?dfIHB6Eq8a+JPtnvr?24Me^e{*K@HH7@&FK
zPIKpr*z4qH_vdu@7(aDJ0UkTJ935R3Nb#c2v!FBZTAs-=N%;Vl*ihqAxtBUQ_qj5i
zes#5jc>bsOJ?@|_T51f_lL78dG|$C#_T<D=`?T5<SUFdrvlzRq(a(zqnY`x{0l%=;
z6);5MIZdI@xIH^uqNVF$v38N(ag_e=BVyt)LXk7&pG%!%BZAQ{vbgT;?Z10fAJ0{H
zS__#Qu8X|{g$^w<g~XIUf9lNML86A=l969Fp7-uaI;I3bervleV2He7@^h8N<o;?7
z6T}N;+i`x<LEk8W&C}y(7Zg-i*$VYl%=g69Yv0t>9AEm9N!X-she&UUE+Vs&)Rq&*
z732jdv`^M{zs+^lep==+`2&mQN8Qt!YpfPGt1~CI;V~qGt8WEE5SEPxC(ga<I1yNx
z*m0^2h|x0R5=@||6nB}=sVIY6tU#Z#eg`T=ZhwBEq7uApcbJ(3#eAoe=T<mX0N&Iq
z|NG!aO5JB!Z@e~TXZx;>`K7>fPR&!rM73VTQmlvnS^VRlk*;q@XqZPR9!pq4vq<GO
zkh!96cd3+h{Y6O=EaB&ZXQ~ZcFe{zw-_-8If=>0myuv0fg_1*=6nY5Mh_Eg_V|oa`
zE>X&-&#J~Ok!}G=pC?@dkK%P)<TcDow~5{tZof&?K>8CysnrL$N70rMJKZ|dZ(uK3
z7YSla5I9X*poOITIfHvFpLnJX1QqPK;EXxy2@RqPnfCp#PYmoQp%&b|ct5`|sD35N
z!zfqUw~KbigBEuv+gT+97o?DU!Ti|PZF)etT0!gO?&NiuORu!QtI**+l8`e1yA810
zMlKS$r>0KuDc9BqOrtVjCOwI@3GsVkYplzTB1knS>2mg6v+mkIljGj4gV%4?AKIRl
zIaV~bN}D~VKYzsO88Q?FSVGY=rd5<n^{zQ_pTMmi7Z}QK8FX1_KC{PBm*G9XmX>V(
zgjB*TfL2pM<Qz&)n%{=nIoRcAP5l99eCqrFieSC&OMSo(h1T1NEw8;@@xLRx1nbJC
zFinc`%=7|AddG|0Tr{iwHB$r_`2LYDZ8P`|LqDms7uw7?foQ>r#~1I9i}1U#@Q=@J
zVPE|-_!j#VlYDr<M-RiRABFkH{>Cfr0?xlF!41Yk={l@iVRz|7XwY9FE>64wHA-fO
zxG#{RLlWLnGpu&~^<^}9h`-fVt&aI0p+5wwD^LD{;kbdJ`0R)UW<@X;6T?E!X}djq
z(3g3QFWEavZw7=;e|+w7mn(<}k_9kK1785s_?u136*-65ED<(L5Ct3SEw<cyMC-=R
zd9`9O9S8e8(ba+8M1>wU+Tn3EI=-(`1W2$sYiei&>P+fj3R9B1V3R((YlQ%I3Kjy#
z6z}sF@M#$ydkEF#w>Ykd+N4Q8b3y-g^OHY%Jw`+bZ0PDdjXtKTU;U4}g}#pHnPApS
zqs37Po*LXGqsWz2fGvxiVz+l#JCuGaZ2U#pASDbHgd)yrh)s(Ig*cu`I!ML!s2In~
ztC|}@_L356HIerKdH&DHZ&M<{WVsgpo~Cn~hX28VU#*Sa3+)$7Q9`(ELXJL(nS4z-
z3K{Q-#sr#2icEjixEqPwX0Z2Sb-?STIB&*Q(oz#FclrVsykKed1v}<O(AI7YIUkDz
z{FKO$5J|em*a=s^vmNn+{`y#j!mMmZG$#s~&L1DLY41nmkKA{?gR(3l)yWnMP-x#|
z0x;+W$XaTJIAZJL0DNnH5ff|ogjU!(Y0Br(yuT<iywDQ${~QNNnh#KOX75y_ysJaX
zOt-P!c3|p%b`d+H28LX&a-jW%4gP)?@?#QoHKFBK-o(WF>4bAD*<Z(r9B?cg?%MEi
z^*SE0An@EYlcr5+=$v#&WrZ|i(f|zYF@%q5WEYJ!2q=1dpE-nYVNS+P-P@INCp|xV
z)~FyUS$pY*v}!SX%`+S3xWSNKvLyNLRtH*xQ7w*<b>H`ND>sl>mYVgq<_~=k8hwH=
z+!#$)an88GP$JHm94V&01yl(14n|Jq^KC1%uu{H{ct0`xgbx44<F^Xa968|t&K)AT
zcH*W?cI?Db#Q=Nz8+>$JNGGC9%Wpx9nJ8hI7zyd?_jm>Vms)-c`Bos&$^FMLh;MRE
zb@AFBOAI1hejzVKqN7dYo>&s-`FOy!P1|)OZ_(u%<J1z?331Wl_vc!&M$mIVB6i+V
zoK_lS;c~uwhLZQTY)^urQURSxZLWv1VZmOs@9yiUoIx<^G*3|%k4Q5#jX#yDOOi@?
zD^QIj*#si+FA~F+pW+S8MAeR1iK~y(xB<cPIRWu6)=s#QQ%k0?6w)#DI1TaQ@6rg3
zt0Xah`EdT=9naf`k7fCw8<bvlE$3mf(?~qK=^#4||FHl`X&Yj)`R!(5|5vdx0P74r
zP=;VDMDl(_uQupUWmr(^5|gF9rD&CKLdFQ-O9VVo6~V@zsk;4g2DHQAg7OBUTy&aG
z!b<8mwwEp=bmS)bo=MrFG3RT6L<4P%06n^@Wq;YCd>n~JpJVOYx*wxJUBTKHsNfX6
zae{_C9lOBl=onP8czO@(eKVLZTfVW91<t4^J}4)<r)aWAXqB-m)mtwt9BZ`8o0}Q-
z2VZ63^lY{Lz)`*_oB|O#j#odk9^Ji23Z?7L*f?|P99FMmL7;hIa-ndL%3+XT)UYK-
z52QOUUOFbI|4yQOXGRz~PIEpWRa8^oC#4kLw?-dRQdq!XZaF8|{Au4Pwf@ra@O+J6
z-u9cmg&fP;Y%^IbKRAMppc+j40cBQG6*C<N34!Ua0cU0?GtHrb?Ih@KUzAXguU<oS
zJm}-OEpNs2O1};f254*I45+f>$gT-islojG<yu-B9RHKK-#C3963ADF6d~&^rs$_b
zDf;#RH}1|oO4TOMyj*%FWnA7qTdwl@GBQ~waB!4lEo2wM5$Eqqhp30OtSkjBA`&ez
zgO(iYhx2c%FV-G}W#0=owf-$&SRMFOr)O+=>3J9iSWJgE!7$Ow)Qt$hl^g6^|AyLe
z?7QlQaS_yVnYC2zAzXi1e`tPLV=(?91}6)(KW#yYv8H`l!&`(}G4j^x{cy4B<tm)a
zn%{fe3gN&i?#o5DJ=NMYUt?!{aH;15yMUNc^^{fx$}HYzam0w?Y+gqn0&P<%6`|tN
zsOs__)fFn%<=@2cHqF46j>XK>_oX-WfYB}xt>a&OG4DG6)V=dwG#41I@9BNmfX0Xn
z!BKLaaJazNGl;MIAXlk>Xl@?KHQ~Fy#z^}jNvKNoP-#w&1Y*K5rDI9MlHhLj<6C<N
zH)=ny&xi@VD#z~h`A@nPB7F3Lexngdc|RDQvAItmnj569H8)ZHJ>Jsz5g<ZVn0k`}
z0x?r$e*N62iwrHOZcC)gB~<}emWa)&btV%qER*EZBL;)6+-DMdr}NOZz_W2T?ylo3
zxA;rcVaTpWe{qVhj}4qIq(+Bw6XpJCIaEdZ-8dm$&y$fCE_s=X7^Oz`7v!`q#OZL$
zn%hU`!~2EXLF*&K{q$=6e3Cc~eftlZWZGmgdQG$xa+e>nA{w|}oBjl5gJ8Vho#n&j
z$~9*RY_Uk<K%s|98-z22u<61siB69L1p7f2CDmt~_v4RBVS{K-;1X|*U|g|hlciZj
z^d(z{(R%%ZG|@Ew;v9PkLwA*b#FHM4mM0xD#5hH7Dc?r-bo*-eRz+p<9FTV#I6xTm
z%E6EX9Z(zMN}0SR;gOxBbgHbe{!yT!?~xHw?bd$7wZXeDkWUmvxhV(vX-zB=JRG=b
zILKGT4<gunxLROjBQiwz!W4Z+CfWE`NCK2n0Vm8nrw}8}&hVvoo{HwA6n#74$w}U(
zi_X#?wWRDPP82c}c8_>}kQ$C7)^ndBBPh&8ZSLMcceFw}7;n&c6?pzt8I3qi0P*4G
z4vJm+3eI7p!brZ<CP@5&qxc^<qss*8fImMm_Z%Xqc+i}1>k*{Qc-W3PFaMBui6Ef@
z6RmL^FtMb528Twqicv|iY$g~8dQt19Aw<*Y9$su7i%my&WI|sRQm3?j&vY(J!^@0W
z<~bLy0~e%`F2LdroH&85lxO>)WQ^5(9ml}Eg#?`z&#Beq8l=%;$4P64R4yI%d<+be
zgV94NnOxqezN;kyqwFY057LOCi3YsF4%!i8b}Bz_cE3#-Na{Unyp6>4>0ZJH2J@S|
z%4%UiOqxxYI{MzGK**1gB(sJ`#XtSBSA!P1FPde>s*ezG<U%6_<U5~;gtP{W)Hnh3
zj8uHO2-tq!B@X>h#8iNP<jdpt!p4QCmXj)U&ldNouzf58Py)nXFqBu=d{YsOnGjng
zcdjqTIS11)3ygZ`^fWcUid)dkWqK**NMp>;DQVG{#Xj0MUfTUYn3B|og`a5c!wC-W
zPfht(Ou$vJLQrN}C;gyW#ff%)waj<zzHoMYCX9bI9?}QH^C`h_(w4D3(t8%Rt^R9<
zI5(inLHA6cmue}PiT8@ep4m4|%O=83MJv4Rd}511lC~)#KdMq&ZRJwMLw26N<-Ap^
z{cShy_w44F+R6Dcq+hLdnX9+|zcP2{JeF1behRN`ik1+@x}()%{ibMGo%fcJTC6Ji
z@AU-=u^ArKCv98FSsvj6eKtEW|8$5G(MpgJ4ziJT1O!JHK{I<)5LQ{_hz$)KsFncB
zZQ#+7eJd+>k+|HsFamZqc5d#}*0F1<pxRVC+n7SimpKDW;-Jpv$@5>0Zv!De-elC;
zLlvHV_PZ$YI41u!zdtt~Jg+~`V}7O=*CPjPxkhkufTx*wp@2YQ=F+(=s5F_Dxs<C{
zH{qT4-XZ4SZM?5|mkBfPvQ>GF9e8&i@IU7hWRCPHHVir(&z<)r$fNl@r^st%=Qh}%
zoV%qB%G>TsxMF}yGWa2QEnfd1f;7pqB?Gkb1NXS@a}gSdq1V3P1Ino3hi<EXX%1T^
zJ;YDAg~d1PNQeR$^Rd6|m*c>%*8k3?I5>MUvhmw9Uo?lzU2&2|X}Z+v*wb=W4qTXL
z`0enf!hEH5<+;$hK?cGsY<psYM5EI~`lb#+rOB)hvn&!>2yx@v44p2qaGY;DLwgsf
zdl@(k`IVetCe(aC(#9=6XO*#<Mw<@36lI1JH5pzbpi^=bpPWEHqdULS_?uEVYy>4J
z=$gp!5`tc1S@ZBW<@u>>KGsoYt;8zX6pJmraa?_@1{Fq#F-l*^r#zGyianVyOH`EA
z%X3_UXF=owVq1u@ZQ1a0f<E|)$(Y-qf@FDL!{dSqA(rAd>T|8lE{zyuX&Ei%qBa!=
zPS+5*_<*B*O;gx_pd3Ooa3o9}O1Li~Zn`q9JI6NzZBp2wi*r^sTv~Vu7Vt>&1<jlO
zTeDt0=s_3joCO%1O;K5!N{Mbg%r0z|O<JhIerir@TQDkHg_YD<ivlXIWJnGS(2I%!
z-XIB9XuTK<WD`6^+EbUd7?mCS#DtG13__EGd-8#l*MaW>P*EV5-Xf4>HTfrpX-ThE
zFoj2DShI;rTEZ|;#~?wW>t*0g&PLk`A^br!cEkvX3dy#B%IAFVTsq(dfdUR$9f79)
zmIU8N;JRcm1M6Jhf-2|3Oq2s8M1F#84x*1yOUA38=6&@$%k8ORFL_DIAL1ZzTb=58
z@3=LkBi}!NFhE(~=s;Ro>GR%ga3gqrg_x^R`1+6Q{S+3E{Y{!i!)T(-uz&JR_wq^C
zcEWv4>GsId)}JD?8O(&)H`UgGXZIKv^LJa$;1Ms0W^)j$=|+fDRqmk)a>$Et=`%!F
zEgdZcwT7u#9cSDCPqV1Rh5e7e?#OQ<y2hwl<&wPTcF~=0XEuW<q?%ii7p+sCkU0p>
zNJ_NUZ6~m=K~ehBOI1A{c@bY0v>9+-M-e$<EY(^L1%)+Pf9PKNVfrZVRs^KN_21q;
zrFXvLMw54)3oBw}6g0%<k=ik5Km5)fbV8Obl$iA&^>RuPvjqlxgP5RY;DrQ+lU)({
z|Ne)mua2tfi?&uorMpA8q;yGlH%K=~NJ@A2rMu<QNP~2DcXxMpm-rq0z4ylV595x(
zWpMUBJJy<O&bcs%+0DbHnvM}7wk!nZj;o0oq0^R@`~=rcds%4$;`;Xb61Eje3a3j-
z<V~0v(~`f+vPdx+*>%bUKWIg<7p}99BwMJ&be|5=MXtU$)j7kZnh7_A_(DHX)jScs
zba@lATzxclhMA5=z=2ElRqn9nyQ)p(<hBl|zCx5k^-qy$T1sucruFP5D48<d$c$!&
zm4`K9-VtvBuPk+*hFN=FtkC0rvM)AJ6e*XW{SJrS99rFNa-5y2S!6WHO0}sk+6kZc
zcEm~R$iy!kl-KrP>^|S)q5Xj{Tb{z&o=1T`ammx-Ih}Le+SVp9b~CqSr0FNR1S~-8
z%gk;*l91mGA6)V*zV{gRgN67Zc=~kOpoo1NeeXz><0>T6D<l^5HYSw`TWx&^->;tv
z?9wq^ocQQ3pf{d_QgpvU0@wXVfJ(wwK)$pJ)k@WT<Gf#-=Q1yq{dBniTDMb&1M+={
zEkHeV<2{F5*drgp?{_dh$t<_J#V=ibr5d1P3Jr9GeCn<Ze~mCCLk>$Ilfe<<GX@_?
za|vuJOrAe&XVcx=`C{PI$Z?Q4vLta+)X#?SdFnlJw{9%}5F<Qq58A`bdW7RlQ!SIz
zjZ(bx(RX4D=J>ZTT0ZN>GWHF@^wIiASS>=&Cx?u<Z&7?%S#F?L{ROtB(<lisfRY>z
zp(*4WlTCIy{0VE6^*z`cCDL89G&@g+0^j<JARi7(ced8Or^>)miY|11RGR~4!(jkE
zp*ubvdiS?e&7;IzOlFsB4>m4oc0w^b|1ARp<2<!q%l(ge(r4xdU{G+seo|Q{1P*D_
zWBpyUwi3$wFS}R1JU&P{A;eqp)X!jBXni@3#=>87zJaRg@ld{<XpB4N3JFNP@QwMA
z?I}X-2$vCS_Ypic7Oe8$>7BHeu$+K$y})b94Vxf-OU#IQtIyTd%lrH&$7D<yRjyjd
zgo$S0M^<i8cabW_r^1q+gNK?VinimS*5S&O386@#LYK(^;i>b#_e6iY)V_Os9|&To
zE@spmAir9EjB#5eM@0QTIRE)fd5|f1;W#p)_F9s&Rx)89_9KL|nCy|1Y{w57dBpeI
z2nWyc$inubEGBwUso>hn$=Ya?%Mxpm7!9Wa#mWuojX4HtQ?`d4UY=-v?Bti-&jae@
zNJ^1<gB!OT!FB<}rz6l<i&95LA|+@dKfzI-Z}t8H1&;eZ{#6#N{P?|iZBfD8r;iFq
zh=ixfXoYc6{AeiMQt|SU66l<wdBXW9-#OAH3aEQ}qak!J5?xfLPqo%hwH{w6pe~|w
z;7F)yX;2Zv^yu2%gWj@$V&Zt}-acWvD5&Ssk3%e&SQ=``%L4>mr)?zuLP9YIyZj|M
zKakV^>Kccka@;ZH=30(}VS0auv_P}Nj2Z^hCKOYkh3!1VS7?Rd(v%l5%{s_`>}o1<
zHHco=)bZKfkd+n3PwXpgW8uzM-F(JzZ5?tK#ZO#8g=iphYFUo=O!D~Q7<Y`HmJ{xf
zM$Ljf0;rQ;&#mAC&zRn+sCe##D!b%s3D52vlRo&jEp?NErpBf))ZwH<Mx`ESsgI~k
zhRt-#*p1IV9jy#dP0>m|?Rj6V@!hdLc5P#^I97b$8~{2ZSg;iVM9`w@KYR$#p$&9C
z8v1r@=Co3zSZxdajd?`xD)(`A(fkBg%ss6|XiCYdxzsjPIWUA#V^X?;&86eja<Jxk
z=*!!Chll?J$4;S9Xx98Nz?v10L-VurBp@*&$+t1RVMwoKEUKOGiA~gZC!lq0>~?Zz
z(fcidPF@PL^9T!ct#0>yZqL@%G#A2H;*-CTfZ1aM=jYT87sZ1MSMg5crxOE$ptEVE
zkKW9tmu6`?FjN!;lwRUk=#8LT-fJ*6&)+6x$`^`Unhq*gRO6^q7}M8e8(GcFFeT4x
z8~B_{kkLp^#jJ%o!a1ydKA*rG8m5QD{s7|z`%9A5tz;r$Yr!w&Hq(WR4&>Ol*Ou<n
zemTx$RU}4{R(j7Xs4Hr>T(gMOy^zNr)R3@n5ZP`kEWogXdu>uUBm0n>%Zu+O`f00w
ziLiF=5wE5$YnoMkxQ9<cR%jB*5|PiC(i1P;PQwb#DZ1HIg@N-IOi5#&)m)HdnSjK{
zN!dO$e<Q9j{p5@u=I`^`p1sl32{hy&2nF)HNh6-}$k@0!5J~Ag?a_qIY($ZpnE{n;
z8ml96e2*k+^kkz4y|=kB&aR0G-lH1TtSpY^yRa;n51pX86>4xwJBipO4>r6!)I#}&
zq;Nax)rspybTnz7FWmzEl<vMddq>AEO5{x3VE32zhV;H=2AEE27KdB{?oo{z>(%dm
z@NsLCV}tNib?3;b5oKhNk<&UEORP8q91=vkct6ww91nw_u9vbxt;?}-Q^!$J$qW&B
z8N&%huzG02_x9Aw!D5ikpOv%L4n;>UkD5tkB5JDqtSqKA;xAVU<R3S1hth+@sIdIz
zE+HMCC=U}(6b!9y66f4T1#(jGX<>yYpsXyXdVJ~ui%p;u+p$`tXtJkb>^XUg-O)&|
zfH;Etft;@3j`apA7rhVqZP&srr<L~CRM3>u*|>74l4x~c#V<L`iOXbDkFyOu08u*t
zmDWB-hR%sHqgFqY7fs^?Gr>6*U59L2y3CWWR~#q9(i^yYC8Eyv{A{GsTbpEv3rfCg
zKtu@33hj;M`YFqU-?t?&7?6u{)z3dHrieGA*F!LvQ0>Z;3qfJR1&dHp(VB^VUb80=
zQPlCe=P;ei9Rkvp`RrWWhdo%0cuKp_+Fv&FiWXxJKAka}?O_p&!)#KbxVthoD6Zws
zs6m}Z$S}u)=c=utS~pj)T7_!Z#mAZxHVW7v1R{Jwuw}5qW$$Ta?TVwRTPzVdDyj-s
zlq~XhPL&6BzA$dk6tl=WNmu)Hcq;+()Q?j2FitJZu4tY>TtPbFg9YX@>{8HWow-~C
zcyU?(fMpgB!wh#^k*ik%0YQ?ANN%hpE;V3&iFtS-=r8GB^c4gNgu6}2bE2OQ1U%4h
zS7stbvZ`PNiSkr-@K@Arx3q41g-bp<FIFD(h~rhJ(QZu_o-9daw$gmMdvgjw9Qag7
z6qoL_Z4Qvvl&aJCvz^7>yYaD(4HsT6T(}{GO-lU;Hb^c|_|fx~{0PNH-P9TDrwj%_
zuqOmd6whMI7VJ`#Vo-h<*wz7l{R=iFW(p-0TFo_K)M6OUsRw1(yu}DhVUnU$1}C(1
zP>rnUAa)!j)kxVjK~7oI=<_HFo$$W7XXinAa-BIj!qd!K3b?N_%%GYbtwLoUEv7F!
zGfhR40vKJ*4sK5s?Tm)GMg@tWYr)~LzySk$`;nw)hTVZ*w6mo+KQD2u?Cnid!m?0R
zwgqUJe{iiJb3yfec<VW{c(v}lTep9~pZZuX;rdC2QhW6sGA!TV_EQ_*($0uukfVH)
ziq>1Rr`p8Jg3UQ{=%Z9JBK+Yd5^=wFdXgKRJA9U&JPr4bcHwWyb&?O^DXtx>f9>I#
z&>dlnPv^UrwK*iNa%x^OObOImO$g+wH%eA@&Q@fJ7N8FXJ?+@(XwOgQ2d!|}4%)`2
z)8W}dRwDS^t!(!~`$QtrFi!{wed$mcE6I>Jq4`AWh0h&9SXTzRF@562Ki*s|HqP;j
zoEW5@ESb;D7nRbvepp};?wq<}i3BL8kK?wz)!fZa$3KGF%ef<**VDVob5>J#j{Ch}
z5OT!a8-i112PP!@d@<eqH!*Om478ji=ji;0uRj_0>9$y<8vY^F(n!%?%U~me@foHN
zW+$@bO7JJdO`KlvoQNzP#zi`vwYrnK!z1*ig1X?r{eDnvnxRKnn>MTzxCJ-6G0;bS
zY0)RW^8<rTD=<i`ukC*I#MNL_`vTy6G7<w)jmR1<4R8P&VcEXmd~#8Fs|pW))jMHo
zRm$ELhw_AH6`V<<qT{(Q9@(~;&zt0_I>|aO+!GvcKxRo|$j26Vt<@QIG+(4~u}so>
ze7};D`$nipTv4z2&v2`zkaDFGIukUbsvjm;Njs)jw<=gb{wkZ{LL8GjL$HffHNjX*
zs-vyr)8qP@flIqm8O-|D^`bN8J^Io~AeDMJ+8N3jYS^@W7y@=^=ECpL11x@v3St|0
zdRceFhE=jDZPlgbrq_~@#${&XCa1sH;DTbQ<Pj&$3xN91is)e+pE6-tPX*jkSpys}
zAV(Fzh(g^P8NtlC!k~Dp%rtw^wJw1_MhQ$nLtv^BDvPlS`211gee8{t81~$=3fG%D
zUtiM=ltk7T9XGAV6dw42Tf|7Aw%Wf$q$n&%vFSw6%u@SvV3#HJQZ$W{8mkMsF=SY9
z+L$3WRK>HGQB`R^QGEK*wL5W}P=6r?L~RKJc~{U3N9mxUfI!nRi4a+&p3OCZ%E1S-
zpD`m!Ng5n0Cwt0<9GTydi_0&2U~I=JzfhUdK$NEQO0;!MbHd|m@?o`}O&3AAz2Hi}
zeknJxc9}%XkkNdaiMXq<I7yOfimlpvDfgst3#;ZGW8!xgAlyyXTAAUdg2B;SJ%gB9
zaG2;bwK(IfWN(fj2O$NUj2q?G)^6Ay=DLS>G^XET0tniT{&#czi@e_U;RN;;7DM79
ztMMk2`2y~^8jo^L&K-gW%*VCJ`QQb2uS;7P3U_g0R(TGj@O8wyH-XfarQ=VmC~Wjn
zEQ-c147Bi;j8Pb}dFEdTewkFTQr-oTX_$ZK)D|UI3AI;T*-#epm(jBDU-i=$w(E-S
zoKBF&|M4UeQ<&cqTH9dRUCdn5xDgqv1nw<R!I8=^FOHyNgR86m62X;r3`4*K)p*pP
zHCm%k@3*hqZBy@RGgnA*8yHlrZxeB4$UUg(S-S^oGR6%>_gc;7(%r>xYFSE8<Ev9J
zXdY(&Jy%GLi?>SN_0^E?(AdrTlXXvdVFK<^b1#R+Btca5!!~`?C=p3>usJ4;;hyX#
zJp#=dXETp3uTI-HP<!+zwfyV+^}gflO=h{dklIw7M8fXu#JEn!V2LvwRmYXeU!7A9
z#a#w0E))oHT~uB4p8X<=nF6GsM-kUAyYa_rp9bQRj4-Si_z|SPSQl+9be9hol;RYf
zMj~)@g;$w1#}WE&`L&M=2EzK?xc;;hCi(~ym9&{vOvDqV$}EVWITG|BlA+?Vn|J+}
z(D$ux8mVK;;Fqnw_dMS&i`{1)<gmP2<2{bTtXg*OVmAb!0AmdcUR#c7dB~~8m6DIY
z?umHJwR!g;N4HJn{kGS!6Anz>rw^TF2y3X>#P1S&`o_6ud65vteL}jU|4dRc!wyqs
z+({pUhM2^x+*6l?cB7Y0MjWA4MSO%4MJU9ycU+0Mbm;}9-Px|+Uc@6=hG|?zQP>so
zYCSS?ny6K`RHdOpJ4H^v?T~%S;vN|5&*)B>XGBR{IlZmB8cN%5(b2?|)${_P!H}Sp
z>08@HmM3O*$aAi4AUUHHr+p`|qq;Fsn_|`Z6Z%bcsC0DT<PH5F!ZVjX#}Az(+UEql
zb0=2LU5M=WW-W0XU+OuM5@3%IB4C-n`Ld)~9RtD6-A^A%gucHhv~L=w($1>e2zT%C
zS6O*9fUXtODEPs))hZz!F3LPCglthSA$;iRicdy5fn_HhpmN1z5&_gzg-!x*AyUVy
z2&$>EtFkI%3IbmrCgVfikPQhEM~)KCvvdc7Ya<;^lX?I;iLwz?BlrfDga-pS_Zbn_
z>uZ@1({_p;>zui%5eiGV3B~w^ytGHpJav&ES;BG}J=S2jKH1hK?j=ugNm{YpwsaBL
zcQv_D!n{SI%qm^Z#5dKqJ(|Y6Z+uKfg5)p!POSwSQ$8Mdvm}oziDwFaGeovf;vm1l
zf7@8fFnPTU%gN@`V-(w9GRDFb^(F|tG5sfej|XvZ!7Tz5Xa1XyyYNkdAU(oRob(DC
zA+8r?*<PfsZ(lsztRf8QJL}hp1ggQwIVI+%IM|Qx4BD<g*SXr|ueu-*8WZU+Ft{{9
zSR#GMt6uwooR&o_J7%t9GI0Ebx_B0>XGon6YP-NP5anm&n6$6cc8VHGu8q`bIV}XF
zjjWDo!_X9I`(9meW@$7gWh5QBHJdJ4`K9lQ@S(Yo)>qzP%)A}F7f?Tjw13f%GVw5#
ziQZtj!7Y~=lpo*t1Rj8`EGu?{Ls_@qblR>ew5$a4e%d`&{3H*x=JjXE+Hs=X*(cx^
z&%3W$Lkc~NYI9Rnv6<tfRCjNzz5A9{-c9Em$D8OFnrR7d<OM<HuTwjs&kDT?F5z+C
z49uCI2dJHLY(u4M5R8^=a3d{84l4$?qZuWpp9B*6Mz;Ti3ATvq7aY!w_>0)0F>wDq
zmo`P-1><FS?0NY2pT*w`ZFjE%2~~ZmMPhSI&N>L?VA~<kO1J5y@gx6!2edhR*28Pj
z`sG!GO9QkBeeE^>3WnXm0-}mjI0;X{2*m5})7l!+aOvWqoS_rpQyZ$Ur~ByA#WBpQ
zs2F$(0r5;yn`9Umr@$Ub%zIszj&ly_=G6Gh_{_q50%Sap!Aok}Ln?OLeUApJnqLPM
zGmRQM6*(uSX7Wp@TJxsHnA<GTO9xRAH%;?QDbQVK&+~N8>y+L5irszY1+ZVc2HIa6
zhh<i&u4Oze?|F^Pd5kQr-qvnfzt2WSZ(p6g5dSvAdb4FMIady7%=iE2u~-5Hdg@EC
zJ}wzuCe}6Y03wANH%s=g^7$hY=eDO6&%{H(7ruC;_>~oEY18Mw2EWF)cO#i`Ey@4)
zivMqsT8q@f+MkuRg=%9@UIxFg+|o)tz|<O=izctFkM}fY%$t)OcFhy%6re#boh1K_
zmw$-w-Yj~a(fc!V=XG}HCi&-89!B&y9<-U8Uxk<OkM#apEH3p}s`JFZDInab$g(fU
z1$1&K`!Q~3u>UiauS0++Lz|;&YQKE+KK}?1arrL+&&+71))`~+rM=Pk=VgFNEdSgs
zuMOC2GPrcWA3Ht(Bv83wfP>8c96-tQHs439`?(Y#&8g1z+{wF5#<gxsw~COROlLd;
zl(Kn%;Cc2udiD*)&wF&wV^qzfM-Aq}@(Z?NSZU2oY*O>~QQOPWnL|FeC_1F=0>2GZ
z5AbmR+QIKByLFR(+Q916c&7I15Aq03W1PN!U28%7&l@4@H8X||#Mjlq0#Vc0Ob6uu
z|9IX&gUR=qU~?=D9x550Dqm$Ih4QY4eO}9bF8~Lufp;I->)b!EljtFa=s~*eT3W6J
z&;`7VBmWuy$h$Ad;Jz?4UZm>s$Gz>&J>#)3gZj%WH%`*x9O~|sa;x|FOV16kRPvs4
z_Dl+NMHq7(wTIqZJ4ammHlZVmZ*2GO8~}~@;P(=@=~lIrf>vJnAg|2ms%Yzl7kprh
zH1TQ%vVZElj_SN#IpXj5*5BC}v#x~Ye!PbLiEaaG3e_SVzbmwkkmaBHzcm)w&Lk#K
zPfNb;1+<={#;T)wURb;MkyY;USloJ8O#AIAhP&a$5t3L<bXy*zF;M2Yr;D$mQ}X;;
z_;QMWW$D%6{Cy>=Ch2)Kn7#mVhiVdf67MbiwbwR~>Ue)&37PCI2eL)NJ;m9&_@3+U
zh+O)(S2q$<P4t}9BoIvkcY)=mXZiN!l@afCf$goZQoS?Xa_Y$7Zni8vue`-b5gyu)
zaXpPXFOd1W(E8g%;lKU*-#(E;f!cC=L|M}0s7sD;=@<TF(6QtW@jLK-8T;F?oPOin
zyhSLdKe~sS=EwnmF@|VuV}L$FdMt9MkCkCkVM`9xvja5pzCs_-N_{msv@GW0Ob$Hr
zyKh?Fq0c@~wZ?UV#F=TNL5+G%v3thdxGz~BM^p^T30LsYozJ@)I*%Ob{dEgGEDGjt
zh$_qhsIwSQyowB;fzb1RcQIh~C?qg#!3pWz)UjBCaUb+VyLgN2;xj^b>-VJkIF<hL
z#odrh|Dss$k2w$4kcs}qGo{>#Sq0$^?wAW0xj~%*TUQH}68b9b%wvz}xr6B1tziE-
z>hFD&CAdN&O0VgrYRBt=PM^yw&}r^@g6L0-nu5xJ&fA+_I{K;|?~9gS&NN4G_Fb6<
ziWsRxH$X^pbqi8FYbm<R`S09&L;)J6JRTonr7=UJ1q64VxL<$^)OcPSV=ex*+zKst
zd}VBATu$e^$SV_^?Z{;)Rg+j%XnQ>Ku3hmaj#)Z9RPjHtBzxQerv<}vCxcPSGeC3C
ztFd>VvSj*u0$`><x4QvoUE3baIc@t9fR#Ak$Fs|{kS|Bew$o|#MONfo&U2H}E1Qr(
z^}0}mDC|@U63blT!PM()jImfH7pILngI=Fe2|qoef$rNPe%PoS8yT1>e#we?IK%zl
z`Mutcx<0?PR7HgCxlQf(o(n1rMmagGeENiS9QnB&`5EBqE72^l<va;xBOdYfm~z(#
zI)yD~9|G}9E3$WY7tmS<dk>K61JaPFtIfh2>r`DpW5L&tmBjFv%`kaI7w|;fh_j;D
z*a$Rbi4cupH}>zONPHy{^S|l<07Xad9T7T)nY549QP{UX)_-rUU)wr(&6;@5ZhgAr
zp#Cn?Q0OudrYrK2^XaAlq*&MCxePh|ky2%9#=uzxGwj`Ak@RLi(hBiL88)l-ld*MT
z!0g{#w3Jq@ARtNm@17zR_nAtRLIeqUu#!|OX*a5itTtX3O@>6aqmmF4-(uQt{C+7s
zIvlpE)}dFePgttSzB3q-a+;?#UP{a6U-I^wqN<jlNL%Krqh@R5u)qix-uAd~gQmX2
zGllN8sVV8u@~M;G4f5I#3J#?h6+?X5V$E0eN>k=caccKo%=i3>51SD(4UzcM9O27B
z>$d1_*u(=X4r6Wocu!nD`?Hb0>`KxKCGPqMugcg|*S4fv!zx6f`|{e$^Wr{pob}+}
zFihV1ihQCKwdrZD3l5-6>I=$gusHH)W@jIDw?9zqAu@UU%W4Z&NMKCnD=7cA-peHp
ziMD9=*UkC7pR5qPUqa5!Aw<-KgQGZ6Six&Xh$CD;Hn^E4*;Z-*px+ZUEqMdFnD)B)
zlNN<oF!Z%p^jHs$5?Mju8WrhK9Vy-4r65BOTsMD#;=`>1gs5iV>H?MbN$B*&ayWR>
zrzxIIKizO{<?^(~phPh^%Tpbs)!GyuO@B5tRN%LhssbDg{sj!}Trk~zvC;d!Q9F=Q
zB)2KKeH3T9aGONo(aunwm_}mZ&Bm%xp=qw4*Nud}{=x!%iJA{?j_x>IJZ|ntTIof)
zCQFBowFycH;K`}<+b>LYCrof!#<I#ZD)eG#?>&d=Ji_Ehrb{~&2WCVlM>znN&!v^G
zM9?fe>g963-JjUer1*IqSifiJ{XFP1`+0T>b8#cS=v3s%gdHOTvFa(|(LA0>O)%Gy
zvUHM;rM_0hcL{qslU$VtMFW!3(sU&)CJd9IuYufnlXi)i=z)YEDh4SLN`bEG`qDQ=
zmHl65x)zwL4{LTf;zF~6A5sv(zb>UUgKOOqayL~G69Np>7r&;7VhGbT;2i$2B`HcM
z4VG-^DoR)C`HC2r#i0eJmYE`_c3kRAtQrOD%L<c}CJ-UUscAW?LsZaMx20KA`hOpi
zH{ejrkhW<ScGZGZLbJAy`^&4dN{jNFFU^q@5N9;HNra^qFALHR6~IwDaa9H+dl7%a
z!^m=j`{eAi&+GLj8aPN{b%toR$x!hJO058Q1Ohs!g+H64_D7=!w{}X<Z895wHX7~?
zGQRVel+amZ4;owUkVb`s9WFy~Ts)le0MXvN$-loZPOv`_c(DrAdr1b+$xo*j8X&Bg
zY`FWmG++|u908$b=MPmIZ?n$&dw>>t%T`x&N*}Bi8|I%?rns*nwMhL%yQC6RcnI+4
z*`Y%npenrl6`OQft%8NSSY^be-G?&stKaOzoLnBKbCx-vj0d3*vr(=lkd6@C#grSL
zvY*%`YKVOUkpLvvh$)cLMwL}GY4=f81aj)1@<>X>hjOF4<umr;U{#FW&EJ=a>gE#W
zzYJK@G*#-lYlq`H6J1H@YHCf?c~WDktwTt^<W8GreO{(W-Yzelw6iPiv|Uw!j;lc<
zZ)Wm3B1X%n6Oj<nWpWGL9R&vs=N1Ieue_opRxs6FZ%dmfh;EjHiKI^6Z31#7V|?Qx
zP`9cjZf;L)|CG44?^FnJdEW}ddp*ZO6oM8d?Y7Y%iFJA-`6AZzfMeFA&+HxRAfsgY
zUY*#@n?*V$3m!R9<A9+l6cVbmR?}|PR~wF*P`bEFNJRSyuIA{unr^-(38L&z`()Lk
z(|^AXND7d2Wo;o99%{(J1LWy=q9QNZuOAH1vrFeEr57j=kfQL{1II-Ea)rb8twpom
z7@ggs!TFNFHR>SFKsm*jV$K&Ocuw0_gp`DvLT%7y;N}m8?dq`Gd&6ykDlWP@ZKLRS
zX8EA^rD9!_TQQ=bv$rc6#X3k}T5?Hk5Kv9~@>VUnoLh6UJvbl?x0s%pJZ8Tvhg=X$
ze~sPpBr_2RLz<+-gWDTp9u;S9svKC+-+ZX@(`HY&UL>0eckOYDYKC$a+26Ky9nxYf
zwjeZ}-+u8rOIl&hBS4R$qAW$nCn?3?CAUfAM_gvVC>bl4T-HUNRWtO2r8!#l<1ejl
z#VeLfu5HTSn)=pnZbMk|i^ku(+`QyN&)}d{pGzjVJ7$nmhGxe$Jlo#3p_De57I^5p
z_t?SxoCCCpLl2Ui`~&Z#h3xG*aVtuw4Y=W-8vX@%(O*|waKcCCt^158J`TpIvemE(
z%_Y`V$At~|;T)GOP7vDwCg<Jp;n~~9VYR8*G&bVFi*aBd&*ii9zIrIPBQf2%cEdbS
zcemjOyqvoQts-n)#urwi6p;308ANI?gPqhaS<)O6%xt3Ir=V(*gxUqJ`ux?Lwo(=c
zOB2_4O69tQ0*eg6y!zy{f1Ek$+6g>-vHX#jj{Jx9fhu?ybG5vBJUL_Wp1-3??zjm=
z?ke6Mrmy>*0URGq`o*Zs`_EcZdKI#c#Z9_Wku}9_4~mYpBr*{_;3_=HvW4dPca}?R
zc8J$*I8i7+VAo?E$9-*3jkdny_>+u}uC42HE@U`2*?;wi8b6?Ia)AzqXWL(@Ixu`8
zOd#lOcIoloO`(d8&bij$2**oxuyR3ILid(zeESsIPV2IwmePck2+GA1V#XvN+a`0e
zes{+u5`HY4XOdcmR&TPxD=lU4Xc`yOY%$^%_+LcD2$Nh&Udj90`|Jt)oBYzOM%QX!
zS&LuJcu|g+9y@1F%-+^j$Uai;(DtE2yMX>CnIxKB%JZ!!=AUrZtj=3VUqGM0t!3Yf
z*ugFKkxUwIIO`4J+YyIAIuT=vAk5$~>=dw`MiC;5h68)WPFA@R){14a!ccAkr%d|!
zN}2Q2()+zA8X0Vas+`)zb6!K2ukGsTzuiUc^Rwr}ac~!v+>Hywv|#<MUV6QIqIb8e
zV!@zBOko!au!t96IFq}%iaSlTf%h_&gp%FmV%!xSk}hu0UotV`HkzqE6&<%`4+jy(
zgQ9uI$H+$0AwFrppSdE5tXH##GnVsW#}`BpLR4JGUxn7;FNqiWv{_&Oo9#1po|^Wk
zq$>5s;q5dkhXo9d64-|cv+F9rT&!+~j+d{8iy-~h?#E=+ME(v{DHgo@K#J(Py2v5=
zHOs|kS`z>9Tvi#|D@2yw{17_dCLUjR%N>W{+v9mecFvR;+B#|73KHt9=7BLPW|jcs
zil^sZEmwzBG(+2>ZRH_Gm(X2FRrk;@K|ST#_v&27wM!*pKcy1{M{5zP#PDIMQa;B~
z%rYAZMhMn%T5%sXkakgnG15yxzdpeBWR?$xB{P+n^k=6VD&c>vMkr0Mx&+MOz9%0p
z7;xuH_qR8HlgJg#AjH+=Ej}RceiZ&DI98iLc(WX%KM@^|p&ERML$?l`Vd*TeLkJy!
zen^ulX){M;4{NA_A}GdR+;kKbc4S9UbZ=p6uLJ`b%o)2w7t;l}lZ;Y8k@zAY?pn)0
za+C-^9B`PMYZD>K*DUFx2CA%1(v4-VE?3A8K5YOq#UXYWhMn_4ALdH6(e9E~X+YL%
zfQ6O9N#5#!Hz4Kmr$yO>h;heoSf}$;?y|n-+}972*~}`HGd9%n6U;xdIpYGWO~6I!
z-6FTDJ3Y0ST`zcwr5J*~<_4L2r-i|jjNOSl_B>i(*=tA^X8r3oMQE^ER9q`8d%l`h
zBc$$I^%Hb=7QJuoF(2i4UaRzo+$+P;+$~T*qR!{YZoWtfe0Lgrg(|}_<orF1Gl-Og
z2<poA7mtWsjV<Q@YI-wk(G-wszk#l7P2}k!>q3c$1|Vd$;{886AUdcVb%*}%)Sjvv
zk_o`9__YrNPh<a1l>5!tFhfzjYF!(b6ERFym7@%=2?J_kr9(G~)%#Fu0*26Pq)?$(
zn$!)K(i6Ne+vbJzC)MSQQc(@c$RP%H8Syoh_RTVf-(b+`C#MazadzdN_HH~!R8uSb
zo?&zdz-&Q$H;Sh6!+qA?SR|2=z0E-SKCkw6ELvif%;Sf}SL4V%fiXHn-l07NI+z|t
zjMIX>{%GpAC^`~}^C~z<ozaTl))4h4z~>J#4P5JQ$<TzxF6qkp#k9C}V|F#JsMU<{
zh3`YPF-rTox(9+s*Z!0_ubrpkiyTTp5?qkIsE&3PMq;rAsuYQ%++*TWFybasQT9Iq
zv((NEeOMJPhiBV^=BC=|>tg9H)0Y-hg#ANZVb@`kmefX(^rpumF@&z6uv&~%XgX3a
z-E4-3nq2XB%`J-11JD<G6`Z16H)ypYt*fVIV`?`eg@bkVIgckF2uS*IUaXx+kWAt(
zn8D`^%rXJ-^Mbwe-H8ZSvy=#q^%64g%d+-982sBKcwpMifnF&Tb)(Pf<$)k_?st56
zv6`0X<Tf!dR|c*L7+u-vGa@cnxD-O2Y}?#Wt+gOS{Hm9RoOEvH<CSRM8weD&x|xo^
zsQY&9S`p_Q=z?xJ(nkyJ=bW4C{yk#83o=zjD`Z%^aN)*0;>-bn=*sB;GkAxghTSfP
zD{{0Qu=!<5xcya4tnh|pPNLA|Cu~{8%8l0DiTi*c&OK!Ov!{s1+PRnTG>9m~q~kC=
zdJw&yQhuGD{pqJa%MFQQ+4%avQUO3fHiUAf;-@lblpP!=;g^NHmuQLrL{4A<G22Uy
zk6kH51uL}v>^Nr+xRG?fU3PzA^MQ(YRr+;;M;|`A-fKPNBQ#pciLXbaGKvG8qGn$E
z9Pm;)7nj_~w2Cc+tGi6s+8&m>FmKn*<-6R6hFLjIJpFCCYXfWzoRgZC11UL;B;4)$
z(0!+>@>)q-aS;ZXOg!OZKt{KsI43aZ_KJxk(5E~?Xp7W8&G1u2f@hc|r_&d>q)jzr
zVB!<0^LP+ONfIyPt+bd#?p1-j!py27fttafQ)x$32FGrn*1%o1mcPmHhVjQQu>@??
z9%}gpj~;N*X286Z*Su;B5kYM%@KsusZwX-KWw<NW;yCR?%nIePMElO@E>>2JmPf!x
zL3DtT{W-#ul$U#lVAQIw4>@H|A@#zlTutihC`ck%gOcGnOdU>$;T2N7_3;52XA}kB
zzQU6ejUQqAO5x5xGHpjXr-K9C5d`drO?*IQ8CO$D@BNz0^JQ+p)&h&+E}Q-s>Fq*e
zlMp%V5qf<T%gy=y5>#qOZ&Y&U$dn=?PbXEJ2WBak{OdyCgX$#)4MfZ$2;^vS(C<iU
zerT$iul!>5ylb_bDd&*xZb!O8<*!*ZvBFl-+NmS-`ZtJBy@d~RohghhV%KcXeSa}b
zI*X0ye7ZvTIbY135s~qHKNVUB*}R{EiF{M6&74NbC7+wlsaqC3Byn68nj2cdew={o
zojfqlg2b|xzfnFAF|e)jNl3TLv6HdT4_)|PD|=qCo=bR9HfL!YnxY2rSJ`Fl1m{+7
z=np`<GUOxYirO<f7;b>h8MX)(q_45l<TAm=Ra|A$gjgO~_ViE}uCifsK~zp{6dr2H
z5aKj{iMeCR{@|cQu(U`204d}sM5~=_APb<Yp};(vCb1td>%r23lQ!;F1=S|X(NM+Y
zs}8U~V&q9#)gmg21nwd+ku73*zz-rS4iK{%3uhu~nN*NRwNK^-E9L-2+{kti@wevG
zNZG;CCXN6n<g$=U(UoEXf__RlyQpbr&w)+UZAr{}N{`>7&H?khL9j?@=tflZ9gXGf
zD^+Aj!Jj@*R{T<uR@0|^&4^*4Sc|_y<8r_lPT|O`!%SiSuNS~qdak5!!7XJSQu;!%
zO`MD|1Bd)@rGb)^T&bT0U<$bFuKz>x2%w=-H#_}J*J=A@`bQ_17lz201Y_?ru~ulD
z#v0w*QQ?9SL{83}mo^v_ggS%axQ#(gI9{#!p0p`}h#}G+%(NJDvsRc9F2Q#@CN=Du
z3n+C)jvf^Wa8_(&KeF~B5%B5Q(J*S=tj@nE4*`aA6kt%TbjJtbsX?)gdK>N_FaF=b
zvs%3PCv5$RSt^Abi=BvWRiLZ#ss<1D?*&dvt@$=lZdSn-4ogrr&0Fb}8%4MlLgyf$
zvyS^JceA8HFs@q;MOOmrV5Kho7jX<aw%_&5LRSix#I=pk0L$^U$nXkU67T^K;hOhw
zpm@MwoXE*$Q1@w9<!XIRi|R6s)9<gsHpFh{+q@kjbT_c9*)$bTza5h1Q8{{Hwwulr
z7$>R}qO-`@ohpwIEK4GaH@YY;p?Ac-B9fCU2JY7ArgzM2T^cUGZ#4f0U9X3ejFaAU
z)t`ry(%8r!^OgLKXd5JJjz+6gYmdPcgk*n|Bm*_$I5kBv6os;37{j8V4$w*pqsJ|N
zHnF1)I%1*4<fhVYOSmDIQC?iycr-6z(6|06Z(jPl_YsYPdS+w%pii8@uB%&Mybkq)
zgi}@UXu@Z;9swNEb5LscPRJa36D7U1|K%5j2&e4-t8DZk0y~YU1GCVDD@!aJrNKpq
zmIN9#PujrQR`UG?XKI0HH4{%;+r5Er)3^u+iTwR1_r(lLZYX$npmM82^3~S$@Zco2
zf_Ue4CYVvIe5Z_6&~?+Ux*{6%jaH%|q$h+wM7^6n;qdr%k=sEtht=3G*tP1qO`-&6
z{J4rU?x32k6F=<R%&ygy=ZGcyoJ4Jen{#(ZZjRa=_AQ>HxJ)o%T%??2hfalju+%Lb
zprX&m#)Afr32Fm{e?5$TgA_9wMO~^IeDu9qV_zv+>6uwjC9mlm$HF4a9+?V_4FRF^
zIve3Erg_Ive*N)XLX+TrkKd<Gp%QA{*J@IA<KGP^gP<Wf#)~Ut=ks%0kkm~qC4T*r
z>QG;<we(}HZW<_49n-gdU8_%Qd)DE2w`&6@5jjIjWW8kELkYbq@v#k9P2bi~7=>iz
z^FU}3+_3uXs;jj|KX#SjD1)Tox#i)B-JtrnZ9EZR<NFIOeu~FXK|W6*BjftYZtX=c
zosL-g3pDTqC|5Kobn{b~kAZQ&keI=oShsRMgBR+L`8;&R2v`DGZ%=i<1hD2tDCR5N
zli`h`^n^rDImLi>zW0;Km4*thG(;N!tlyuE)j{ZBQP$u^-7!YxDZqs93t4R49QxQT
z!l>9-IpXt(_3<w^oNdW41a#ruaQl}FYMJTea}EfjjOId&zCo}s8o2F2CKErOvQ5p1
z?^f^6?1F7CQRiv*rUsRK8JTSZ!P2pq0}8KCqQ^wE?j*2>03S?9_*;v^3QRg{YkVKC
zmovbUF0BF=dzy>H1RFUCE3t^v{ilm2Lt}zY&~O(eWrKOy?#-OunO}Cp@*RF$d56AE
zyc_{0RBG>-0@cKl&U;@w?_lWD%K&Z-=Onf1$Y{mb`4t!RWo3l;Kc~-uc}b7mAyRmR
z$$>{#6)Vglbq2{y>;!SK1E5~Xf$ee}<DD%y&v<J*+<pUf(J{#t<)pJz5Y2wb&NoEd
zh-J7jp>H3-D>$7HRXoi`z)^G+i1a2Y9VH~sW*U*qm?esWn{}Aw^U0MyCQ^=+DVVK=
z0PlC*29+@Y(L}%TT<+Ds<X~K)Z3`&OkGZcZB|8K3gA?0Rjz@O7YWz+(lLrrSl13V2
zTRT_dN8};2s-p?KU#45<Fs^``ub-`o$n$zp3*d6t^mtgTBRD$Sn7&lH6g42ao#LL$
z*Ay-O_*?u?9PTe(gh@N)7+wS9u4JAtCeTS(;U_~=8+MdtVRHM*I!1jC;xtIdM&EV5
zTz2NirXxc0VSE;6I-?8{lbjSA(q8G0_;3Suq&);#i+4Z{^}(A|KWDc0Kz2Gn->$xJ
zrDF9%S93x{EMOj1Qzs9W!CR3VbG?eA#t+iM$xi2sw)&cek~OE1W!qFhBL<VtAgelP
zZz@&rDOj96d^lK^Pp$m#84z7W;=FywWB9CNh_)9pxAt;dM;h)nu?iGeoeq8_!96^G
zb><OpMe9~woRi?s;<=96MJEZeMDp$0n;@AhI(V%IL0uP@HA;okv)Q!ppxIWO+5~ZD
z_Uvav(NJhtP;2Rw=z5vLTub~J&*VD?ZxU}yUgT<%k%S!?cDGWQtI=<uw&KSLk?T+O
z)V0o2;8IDamadZ31>r{;YYX6D>GoAC=vrB{9&qLZV3oz+yMQccu)ca>JGWl!0tdHV
z@n<lwUyQPHu4&T|J#WqX#e}ymtGY@NV8OpkSktI4VTH*k`v|}@h$#gMK{QZh4Mmf>
ze@xLzuZdcyokJSY+Zg(ONJ*UQWVycIsZzNPqwgd=a7Qy6m@*BxYX**3j&vBDtyo{{
z=BqcebL61R=s%4|WpSx)-QKT-@iX^~CtbS(atJt9RLiAGG=oR~hzDJOB4}xgWf*&Q
z!qV3D>}l0z2Y=ON2mg1|3m3aGL8S?Cw*5eYoqWO@Uauy{%edXg4G<}CuLys5KL5ET
z%&N-imn5@Jzo$hmQd&b9>0A(f6_MRrBxu2-<&cDbXsM0)U}i8;e&YG@MfjWgv^g?)
zQ088wY)STQq|j||Cb9$uPGhtz&DJVS3pua2wdBY49Cw(GmGuGicuMOnJD!C8@a^s5
z`FYpZKw}FmK#>%1yzJ+)!sV`N8F*|82mkgY6t!}1?hk@%P3%3nx1^L^@K?04uYr9T
zCUG=|f>mYJ`qE8Op3yv-+du4ew1ek#UK%aRTEKm(rW?zI@oxx00z^(UBi`>u7Z!T<
z|3)GL7c@Ux(&kr;kSFv?QnCPjTHF@J_eXyB-TG7m8pFLd@AXMss04AM1n}U+3a|b!
z<{(p7L8xo*OY~vA9L61szT?2{ee&pxK3rnqC_c~-&M^=b{{<_2pdo@sh_vUnt31fA
zlEE@vh*KGw?tgDdw=z9`3m%>cPX9$`VEhEp&)x(0v1gMIZuzxw+(}`SL?f$avwOy{
z^!0Q)LZ2|~7|PFM_Bbt_g^cU*F!AEQQkXgSU4i(*h8iU}A1^O%!t6dWPDM+<vBz<s
z7YCJxh(w^8X&3N1D`t$zX@`X7L-`*VRV)`Y5b1ic{W`h`m%D@VY5Vh77dHAE=!@AD
z8?fbf30Nb;jz65}lMv}mt;uD0ol2nY$2`V#dQV66iBe#DsYscQ7Lx=V*ZBNw*Ex;X
zROIoqwQ8ShZ2C$&kxU?8f}lVyy8VL?t3u@gNFc=v=-&N=XiOO{>pjLl6XF&P8dc=i
z6yWKQ6=f-sM!ALAV}ex>G(^X$$>onOQKXck0veagTG3iMHLw>|<cQP9oA<kSE^co6
z$z!LYUkf+l>&L|HW;TSd3ri|apr#(3p_6Axw%%o+rdRvc(1!OaW%D?*Iw~;{AKCVV
zbx!x8mqw6IeJ=$)Z3&!CN3S8cSJ!Z4H~mx~gI#k5^8;G(iM|n@Cj_QjhvIj8T36}3
zPI&sxEt}uif{TfN20TupIzuZC-312P7tIYyRLE48eS>iltJ-Yf(DxC??yqViqLA(#
zBzwU=3+^QYU1q@mK0*OUn%70^h*IDhU>FjF%AvVj8o@NXGEg_}{@QyM=#<HA9hc%K
zu2{xpLgKThAe8@=FLCVC<R^wtYnPSA)B6M{AJY12as`(>cR=$j(xq>3o7J>!dw?!?
zTO0XfLT_eu#?ww6@|(%11&})h^1vd+>2lDo>lZcYpg*3L0bS^u+6MR75UWvwCt54F
zjA9tKghU^A@OsrL-vug?t<-lDDJ<mT=HT;beN?RkfT}m2h&FtwT44asq@N5E6;SKO
zM2kkw_}o*j@}P>)LzF!t>7-*fpxx9MufgE#D6-+0gqe|!Hb;-GP^Kf9V2NjrfAX5F
z+w`Ia%MUfa!H!pPx@a_7nd?nH{@%8<a!R3#t*!aEqkoQVMP|c|L~8EA$Hgt<Do4mE
zPt{x?|7oBW!Q!$TxeGnq+Ve<j?K+R$)u_WzSl$$=*TaSKEmz;Dd!IG70s8mHjSA6B
zAdtJABv9%xj=XHXsyqe((ISI(h5PJ^1=0I#p{{#uBvIPWJ)UGJ;Wn-?5RjiJ($;rP
zuz>}xa$#f7*W_9u46Cv7_y#(N^4P{F94R>(eMjNxScAwRPy?5Mgekq+Cano!wGI@!
zoE_DiAA3^w4(y06T5>a4+$%#2IB@6jp)`os!vd0Lr|I79-6&l&3ZRcXs3VLzIh|#h
zi(<ayKR*~WEoC77Jy<vU1Go!_><K8*)5{kniMZa(#xZQpO3T4PE#17t83@l^nW6^2
zOQ0loBHHY>9g|W0x=kYLrIBvQ0#nfx->%^ESr<d_k`@+gD>hl|nEJ!%elW7{hQu{(
z<{KoF=noI5EBZN{%0X{DM3T?swCUwTcvcBgEkG3{oY?`t4K(-utH2f$qx{yj7isY_
zcb#``?a9=}<rEroU*7tZ?ZhXgIP{G6urn6+^^>=Xl2{t`=DDj>%9pgR5WeE`?k~Y9
z0w>lNgbobHcGFhI$JPV>FwPuE=$ZD%MY*adM-Kz9FNE@Lkx*%5=zutuBPrl4gb+Q)
zWP(@0-L8xif}j+mS5~8YY77JFBiD6+u%-))bCCujVPDzg4Jv|*{Hkz@R1*T|b`r#o
z*)YT6AmcOIWclTzToA@>3}}c!Yt?`~JcdMCCk6SJI^9tXlL~@Z$t@4|Cu;an)M-JA
zMmrJt|1uvGl=Z>Qkzu6fl!kUV+uO>pIaI6_&M)|er*=tA^K<$PA4|>Q*6Wqkm_c^)
zyOc>m5ACQ)?z=W8HFJ$|8+BDZ0A0n(m|9?OyJ{WHaECVy3EC~!i>qv_qLw5*KGgR2
zWiIq-bJcWNjh5cVZzX!Qv@T2s3R}g_k?}pNi`~)tAJw|EDM4pHEmj;>cT?J@nPucG
z?UOap!6^kZ)5#L1GxIJ-rhI=)VcvB!$6;=ii+-TMjK@aJytfK~K%`K)E3%24{uP^Z
zgov;FZhp#qT)>GdSkY_}wp#mHabws9xTjtG?OIB?`u3+_nlIQ1SF{8&qdIPh$V}Nu
z`PMDl{;k^+CN_(qIbi^w&hEr(CYaOnH@r4|o|p5?e?2V@Es8>BFSSyiB62CKYGW^!
zgJNROqvC#6ip+;XT;wIhr`{ot?j4~bA3GzEWHJTJ<P}$PS?_ZVmxl2Oa@LY%lR3s^
zz<g<pea*8{M179p>IJ=`_V(V;QNpVq-6#b@Qj>3DrRif#Fga`Bz69-e96a#fw~-%2
zyBdFTq4Dgs<Cz>`NA#W@U3=bTHwlC1KYI}wymVdWQ)BQIN+938$yN#gm{{@?Jv;+>
z-DbT@6j)LDSgn*!dueftBd4(Ywo(LGbqUD{dw6N{<<ZMhs;7*u<3HnRKVcQbC?NpG
zj|h&nbiZqhdPvF@)XR*#;+UVIBq%6dX|wr-(E_z(4AO2cL#Juz$FxP2k7@|T>HvWG
z^}I$8dgJ~1A|T~`$+S^|PQMkAgz7?rc+-4f`9mk{?(`-s>UU<Y);c@X<};jAZh7d$
zGufs%Ks;f@73{lEC@-Npt`Q^=ZUy(vj)3o|D66s>Js-_imiI<kb3TGA1SbBSsKm3i
zqW10NJm6V_%urO+X|5aoB;C&^a$eW?IgAGV3~Se4xX`j}(EusXqD9-aTGli@So>>a
z0U5os>S#f(a>%YF%|{dIq~(7+BP61?NO5u7e;;JhSx|us=Al1FC~`s*Yzf-bJY`CE
zIxpV(NVxS)tESk<+x1ZGq=@cv;`u5Z$5|qejHmj0%ouIsoeg`Zm@^?l#Yf(^jhGHq
z0?+)Mim+$hxPrirHrW=+aADcnP44(kDQ;sPJFXr2aqj@Fl2mAq*0^x<+XnaF<OEn~
z-)%MYVxK@){HPQqyB`X%!=I`0LzEPJ{_X@?n7HC6QGU+P#qestxCvDkT^jw5geJ!Y
zWjpa~!s+olW08oYZjj!F9e({HMJPiUb^Q_|qe{}Fj}>G0@RjeBr%*}s7+yZtvY%k?
zl5}C3@yCJdk|TSw34p0;2mtWf+r}m&NlDe8pE>szrZiqpB9oiJ43DM<KC%9|dh19A
zPX2Tk!kB1YS2$Pu9Rec$^ysvtaN+nkv63o~YM+^|XpcTgj6{C~bZP&D_U0wrNR@ed
z=Vu@B>?n~;jQ64HIvJx2|K?kc9IA9|&y3TliKHrt7vC`8jK|!mXBi*|w9JV58z#Xa
ze#r-nYACy3E>ZY6uCK<}aVOZ^T?iIu6>0Xf?b&gRp$AP!BuYaJ{+yS9%8zWeOWsbO
z@KV9VV~df6M3H;(Y*fC{*(Be&$)@hqcVUc*bhsZ4|Mv5@L%)uYz_JX5P~!SRq51o)
zQ{q3p_^2C1B&QGXzeD=t;$&KtTXD3ahUBH#kKA6*k3Klx`ehDR04dXvA~=qtlHyQr
zWpDe~ZLKA!_iQlCM)Z-!P;G;1mjsN}Co-maFTS)HYfY;uC~guaJ@VdMxd&|~ge45v
zg=5FR`2T<@YmH6t72yY6B^$`fcl&xy2OgZsVRz8&uM)KL$%?RKqPhdIPDkKEMqvdl
zIyE|+Pjo_-N`+_QB!VHz1FZX>I61Cg(-a_uNAz`Z=L{m~;}bg;?zduSI$?tF3}BV_
z^kJ_acEUHybM=nrz3WD4V(=gZSDy!{2j<8B4&By5wi^>5dRfaIFU!;t87qo_6<V35
z1_2x?XaH5ZOSb`|1hbnk`n^j)(Fn&CzKfXGX{J{p$Y|FQjbU@<LhDjZbgJN);0zkV
z9^%{~8?9qV$;gtMl|6Y)Es=aVTwNO+d@Ch%N@!u1MTfPk$!}e(b@8gcD5R{ZCVSWw
zAz{15bI{B+I@Nr?YYyq{Y`|I}>%U6D<C=z!p(d{dfK^x^`pS4Si9ALCbukgL7_{#B
zl3lb9slzN$Q-mVaXQ!m;nSdW$Dy1Ej;;9MXd`Ri4#{X{{M`HMLIJwRmxLDg(wH%*d
z8{V6z^H|$lJ}*0(pQv73UKKUbM1KjYHL`loo1rm`g&ik~pLtfBHR{cVI&av<llslE
zcnk{aV)~Ly*DHKA2$2OQ5aO;~3h|fIO>YdY@=>X)_&4_i-q3?7CKu$=D>Y1F$|J>L
zxhD~V60}kYCx05M=C>PgiO0Db4?|ww?{{7=`lo*^U4rL%k#ItcdYxk;*yEN7bpJQv
z1Ug&w!hH!jO-sB?*p)Pkz)OW#LT?W5Y~H{vlzNAiS26WQiVHg3Mv|fhTL-3S5DW1*
z)F@jMihs3oZEf|JLjon*b?*mU4;avpxj#e#Nw=qXFksj5A5Qv8waSO~g<Gh?qMshs
zy)CI6sv5M)4^?rpWqT1h38^IvJrLa^aSWdSaBy|PmMNWWUwA8gyZ;=2ot(p#10Win
z@7<pMMFal>yuOF(w36(n=fbWuF$^bn(hEloqrtEYn=6fc(hfiK`d;N;Tfa?159J_z
za3lIQKR1W*SDmZ*YtmELQoT|RWE}tdWKJRi<&oliKf-Xm85qvejSa(-zG5!+k<|Io
z3lL$CoA6Um2amaDlU<8m6K0(vxC;P>k^7rcwf_RJ|00xX&^Y)QYw^TxfTXc~MgO*I
zT21>$c#NBa*CJQPMO8i!*`ED$h<Lq^{^vgfUe;)pdtG|B?dr1PTB>88u3LL{_REGf
zel!4Q-9?de>i@7I|HY-Kz5&N{T6!YZ6Z_X7;@5orO#}P?$4mWhH1n)be-L+cglB2~
zKQ8=#+*+i!uWuWwzvUH;`rncNdz|(%R2(m^;(SXK_2ryeM0@_3*LUv*{YXEf<Q-DS
zG#)-q8EAQa@UOubFKP6^ciYf|n8(CuyFS-5cj-v4S1_2v9cH@<Ic)U$7-U;ZTZXF-
zGt);@s$JbiXMK=Z1^gIAht`zQe9YadN_eq!mA3xwz6T3su~}t&(+KxO-#1*z(Q_@)
z@+Jd~-bWATgGNSd{`mD)WV?o_kJfn$8<FyDOKnwX$3o4Lwfe4-r>}s?XIdF_&L8k;
zmdM;=(9r`MW}W%0rN@dKk~6$qW6&uCr`&v&G^I8>jxN<ad`rJs?gzA7%_d22{0yYd
z`@N3akz*gs{JET8T=z1#t-H9@Tv3l$;tL#uZ)Vi>I=LRzR$e~{QB@&Uc+vd7^4|Hq
zlAv1~4t8wYwr$(ColNXxCN?Lw?TKyMwr$QN^X@t4eAoM)=U@2tFMVBIy{oIMt5&aC
zweI`Alv|VRURL_ANV(>!2i^>@j5bgYm?6F;+8e$L`A5xrLj1<l>8KSLm^+qfSjw(6
zo3dOn3FF54*z;=QV>e;UoIOFOwm{~&;t!9Br$c;n3!8h0y<|14n{f?!Yvb(~!m>DT
z<`yN~UUmsE-bP15NOBy?cd#i74%p>GF3tK$(4y6PCu5SeT7i6B;Z80;^v$(Rk*=mC
ziE=}YkCUaMvvrU_j|ULeZDZK1J6Fjp?t3AOkkJG@t+^Q)^0LIjawNPwHjAmL&Ms0!
zlxqrjqD!I%|McPak(0|hYk#B{$v<oaB$t*SE(SD&Y%%$zMhJe8a=}C{DX?6dZ31`G
zJRPPcP`g?lCkG2h-dnS|^k%hbzxjuin0o59_22@#sAfj)ef3}dlN?4CdhPDSzg>mv
zYbSq>m9%=UX{zQGtzhtDG)mLo`|BdYP~2Q!4ptBCCaP;+=T*mdGp--ZTQh~(Xx2CB
zyt6OJ*I0sPbbLsIEF+sH+_z})?k4jm2eQiXORcvkgQ_3qrZY*2>UK61NrS^bmvN#R
zBV?k1*=P=rsRbp`WeoN<Ks4F#Pg8Aj(jQe1!z<B^iV6|HA}V_xV4xcDUS9UXDKQ<I
zK1`-l!*D3108uY2l`$+e9a!e9_^&Ktt!AQXo&X1vr&Yy_#&xn+!!`O?12w~4TfCWr
zL^muow_ah0Q9vCV2F<ZvU7d-47#f~!fPu^E+R~;hQ~pnm)dJ28v>;lQD|bWpqdYP<
zw_hsapv~o@mICGR^}fiYOnPlfi;7$P5><dbZ%A5J*6{Bv1^ICIglJFsWFGn=Up`=X
zEL2#u>99m?^#mCOn<_*v>>KkB*!US)ZJPpf&Q(HwC5{bL-Q9*Fk6g?K2_MHd`tGO3
z8D__#M|f#=u|wv2o`Hgnrw*kjP<SLZP;ry^rEtMUg^jz(M~P-;E9_Qpm~np-U)rZA
z_;h#E9~2@IZl+He<%z%;rwHLK|Dg>~qC+C=WvTe!3wNQMjqG_|&%mn+j<Ktcw?qpJ
zm<jM<Y~8%gjZXyBub1vQD~u*h7!R>Zkbkgs_LY;Vr7MgwsHEWetyy)>ivf9z{q@85
z71U8n<qs%74B!q46TJZ>lUiF?0{IG}!IWu@uYAoHYuHd(r8+;z*m2Ewa0l5w07AYm
z3PVqv`1tQO+w0Y)=gst_90~t8{#!OsX6@?g%Fzn=d)*r9gF8CbI-s3WKhYKo;%)<a
z_vT@`A1FUlg?aCikrrMwoD$FwD&V0a?@3iE>NvUv7CEe;voG)K%e4~wTpJ;>9D3@}
zs~MJ2$T)HvN;GC#?CC1~rnol!o-XN%Ro&%4sI{Ou&x#U=pa5`OMr%C!kD_9g*(u9y
zrXhb)h|#i$!Q715VLuGKaJl<T#ED<Q3eO1$IOZN%&3S(dDf|qxB;HvvTaY(VQ8jTb
zeOMvPyRT*95y&$Q7FfQ;Kl0R)V@qL^9nPy&pSAM0DQ$fkNVD?kZ2CsN5H?Z<pK$IT
z+e)CI{e^O)kxsfoeG7Jgc7Zk6zQUN8>k~Z^!d}Eb{h~nJZru2UgVS>t$^_IH7cx%#
zhfxtc6G3_~xJ&kN7mH((m}x(eM9wW=HnH8VuwGp-Vlwa<S&-8|KwSj_G&Wl=gK1{+
z-+=1fA3)^<)#cTVYu!$LhqOq#z~-7zN2nJxCH`|QpmzNS?`r$Z!Em&gJ-F}t#4%N#
z-viHSwphS~H!y5J-+@pl>Zj&zvkFxC0YZ_k9sl7)>fB1`nzP%_%f7Cbj6N^4xH{%z
z8NJ8lTIwqK?XvQ<^7T60&i|!a<alh@%oj@3#OY-GxsU%rzL59zR7T*rr9$<g6GI#T
zl#hO&HOhUFGgRQ>sX16Qdal%Z{vB3xQCq_=qC`8TBzS!EFB4<azHyq+_sY9{(!r>c
z`|j@`(U;Rmh84G}-{<A6gRcGXNC>ml-|MSIMWO2$wwbvoy`6{DH+5WDJxr>{lf2$P
zenPMFusSvyQ}T|y(8?fMXZ60+dgK>o(UQ|!_3R~4J2u7$fAZ_$d#^0_&?+=_ZA8i6
zQ6)|oSI`FjD$UhX#k8wbz*tDXIP3;o59njq(+BU(<W?gIs#*MAH}3xf0T0)b<)C?j
zf)YZ5i)j;$`f!$6)K~63UqhiLIUBBkhjCS2_WYb_ja(J_JO~=I(43*>2i^ECiqeE?
zsmsF9!za;L`*0@3-C2b-W^cdAnA0_k3Abz-K|&Vbw)w(<`7N;Yz$|Xiit`P9ytAVa
z28{(i^9<U4nFIA#)|?ascpoAW2&!=xlX7OY4a&T8#E9mv39CZ>u_lAWTF0_)=M_pO
zEG!Vsu=nCYkr`2E@sV}1Cri@$3^M+K7!G@X+uN*x_!Yj}@2{PBHVi)i|DvBS+n&1u
zKy~IlZFRwtjf@go87^g9A1OlbBggNLcxq$YHy&KI4l#5;-yo*`vHJ<}pNGq(GPZAD
ze+#EK&AT*WV;H?&GmAeS1B;b=T`CjQxV{`G+U)&$t_vsn{+8DBR6^jf78pK}%>T+W
z+r3=lIDFtWY3Fw)MpS8Z!K~L}k0bNVqcN-BL|}8c`0M$qm+-4&u=l08uHy3{?Y~G`
z9qj3>QW$^F{t`pv8H!RY`S-PM_7|XA?pst6`g{fsK$^1b0X3lX1<~JPDuD_`!kzCx
zz+tJ^Um#kmW%FBS-shE7YsYd8be|S+N-Nc$Js`MkA4UVR_AXHhzx}tnr_pSjF`!0L
z{pj1hv4G{L(-}~M4}G4oa`+dK$D7H&q2JxHv0VECgb^n5Uwg6mDRS?Jh#7gG8GU`E
zRp}qo!tT6{aP9b9)_}_^|9EN46Zm>NZ0G=LYj^>(i%KxszzjE2A{AgkqlR``F5E~s
z^C<Ds{}IpD$VEB>L0=!;xBj<lrfcxOvJv$pj3NqW^f~H=tvUiYdeT`}%$W5)kC}eF
z?|AmUF4y@l5A~C#nDp$|mR~02{ULwCzLqn%P(|`x8?4@e@-WMx8!{?V<i8&(ivK>E
zVU^oI@GltO12h&CNu)NN-J}1YivMPtT_^byS(0zYq`svX4PGw#36PAx-SX=ItM|^X
zG2Uy4>;^big1b3urpP8!yZ;%e-@AXmCbW87HXT|Wb~S&w%c2XY-~Q8?A^JR%vNX??
ze*^DVShmV}h9aB+5?cE=#=rpS)_k`x>bxZKLWzmHYJV(d?L3tUee&?T*XskjRcJ2&
z99^HvJ1kV4g#9rP>3hIund7Eo?`I$sq4fu6uM`$BbDW~U@lxH7!^;rA1GGG9>J?6s
zqvCGM!NWMdneC&tzhA$F-ujWgnIwDYw69~1G^Pw54&5j!bxYWYZfw|LoXLMxoj5cc
zV}5|3a0Hb;vtY2K&+@(UYf1_xTR^TD4*#rT#dsJhcv60?quI}oHy~tcSsre?UGuS{
z*NHVjjaSKL^8LNJoBL|aTx7!#ao`f;Dl(0?wFk)#V+Ck71oSJToiI_`21<5Umb0Uw
zK_@#HHP8fD+d1wcF3g8$^?ocDAHax&jcE02S8aQ2Uaz$FaJcrBB^wzbyj3q3A(}z}
ztypV4*hssj%lSW*pPS}Y!!A9rYK+#@^uDbXN54Zn$y<UOC#aLFaU~zt{ns_u_=rp~
z4LPERgz$8fW`p{dcOZannCZK-N#wJAreQujl+dg3ydCEg5EE63c&uY<kfJ;TL|2-a
zJ9#i^aPlgTuh4=rI;{KNb~}1*ZgxLeFfAXY)^L75Varp=iCom21&#&p>uvATob|dh
z<v<VD+-Gbe0k>OOt)8DEEc2+-hRoJ~?!twdZ2f`5cLJpqk$txdC@JKBA8mAn0ke(8
zWH=S^<I0R^5pMom#YR2~zMKN^%=hLErDvusnTf8)ERb0^g(H_r&r;MR-EzJ@nVpP<
z11Jp>=Ay%fa9AwQ$Zkvf0~bU2l(FzIgT#AAY`EGA{462)pNZAPBH&K^8CUxLZ+e0M
zwa=<)WzlFxg;Z!Fo$^ie@uAbr+*m`9YdqG}BlK7^Avbr@o<HS3<+;*Zs*;494&XS-
zK#!+qaMhxS9QqhDTiFBaxzMQV02-_xNm*NfpYjt~yMHWQzlrx)-W+4*As0m~N#q==
z-2bC!pZ)&Q=(+uz>3i$%|04Cl)%!KAmiMxx>hYb&|FxyHTj4UDjE8>hpY;RIl~;cL
zPr6YI)+O<Zf?4~^%v7Nq%l;F%^dYS7Gl?LY*0x~=@n5!WWvoyTLMePEJ&SPgZMvoR
z_8S}wKYR>uHHM=Bb*HX*zq8MUF)SDC^bc`qIWh}vfs@+%1gMnuBeD7OWiwvz(QVxu
zg)0dT2?|H}pB>5^t#Dg2QCkor{FCqwS0<@n<CbZ^&77>3MT3&e(5)+P7;=;Y=fgW@
zjN>%LGP-pZ#`Vjj_AOP<{vG)7)>oHyb}sz46~X^(EqYvZ3#iY1X)4S3cwM(5N+h09
z$_YkyJGZ+p`kVcjcX{nC&{)>WTE&lgfvbOBfdqmhnEI?=<|;2%4dK1u6A}CX9G3S>
zYD(QeFz0Ik6n}t!!8&|YVqXqx7lfe1q1Ws_sIqv!S#InY9pXPz!hp=<$GdQGjh6Qo
za?}1V*&>uqzaIuRdwx+1QR<21<PS_5VTlrBkmPFR6L8eC9a0=jrkYgI3z2=~?r7uV
z`BsCckf!`nKlNGdB<!cW5NEK)PM9`>iz2m`;wTc?LS1Uo3bAqqP=!z%FRYwh|D!D^
z&lJ5Q$GF$^rajFp&n9DGG1}y@(%uF}#JL7ol97cW;&NfPX!BCVvT6arAu~SHc7}s|
z>e0EV3P5jZw^j^CP$8IMm!%XL5VY&_1Lz4EE|&{$_D%SmRCX`_hU|6UK#{^PxJ6$Q
z)^b1$T2$nV^=^(bjyTlt*Q1LXnp!TX0C3kYt?;|W44@gt(LuYF2WwxU7$4tR2a3JB
z`#|r_6yE?-2q-3cOeyTnVg0|^*$h`{)Wr!4Qmz=tx7-LTi9)JisjWstS82H!EgJzf
zt<o3`WcR2s__eY-2dd?{Kr9TOIE$Y-8F}LFwezrVy?orBnZnDgZNF1;LI6aCXyhHL
z?->`1SO(Tc$S^DtwvRg-A?<rqMb0&>(P!6wlq?Ml00IMJb}XERi?pl{P)bZ97KjJP
zQKPn8^7OuSPp@3C{@%ksFd>DCjYh$gd9L)L&ygmPFCi(IBz8X`Vdan#U6)0(7dsgn
znvkfh{b{op;>WHc?i1_B9C_SY4%;Y`4u)dVIgaI{<UOHw<mxO4H0q|8xG*awYZHwl
z9gq+Ctz;&NW=rAWwul%uE%wG%;wFV1s<xQ!8YdF)oOO15h@~(yEO6rwXM!xxp1r5r
zp52=Up`4;PQ(;$YH=Va}>3sLKo;{Zijz6*je9oKCYxNxVybN3paLWSsWvFt7g4N-S
zlGcYZ$V;gtf^r$1Np_xVJ-2}z!uw^CgJNB>YCy`t3Z~5!zC=ha$>-<r-tV20-$u!h
zo)d5&G^W@0bi06ryIdB&O)V&PW+6mxeNbVid*5hH-W}W*lpw^EfCO~#4nFt@JkW(6
z+qU}UD!qDCb4@@knt8}fpMUC94@;iCGdG+9(0(`Gr@E6Ff?lSE5@-i;c!H)!D3AyJ
zy$2X|bNIg?V#b0Xb08l;AXuiA?^`Aoqi_`>e0?q$aM2cudamG8b@GrL<UUccdP8zP
ziGWVfoE5;kJFICiXeHWo2QrJeu%_vs$a)v(A(0XIGmtp}^SQ9vD$l<qzm9KrW~OuM
zpO?7%vC=9I`n;=Sl_SAd1KNf%^Be}kEuJQ~6t`M2f$I*06Duu!dmbl~vTVsem|<aR
zO=~>xYmhXtECB0xON{pur(<p~7suhfXfX6z!DaZJKCDdenm=zIcqb)rp<-`GZN#95
z)(ow=aKTX+Q8|*Ic#eUXQa1XbX^AY)ay6prpB>m|d?M2*8?@Vi>kJ!2=lwouuv4|s
zl)4OmZY_;WJ&A%oyK<4R($k1fS?9u8BZ=WcKRq~y_qRDJ15}DLQ8;2l)i4d`J@5C`
zsmGdgxHg0O5b=AQXV;)42_I<^ytBgc=4@LcTSICKmmb*mX-d`Uh654LVE2RH2aQoI
zh}D6614In%5qA-(Ng(;gb#a0pozljRGcBjxHlu*42g@c7!BIotSY*LMG!_%#^v!4M
zWAkmHq$da1M72Gt5Ab7ZBmHnIv4SA>sc{?`q)Sc$B+V)OXi-?5lDFrD4jHgxdS-6o
z!33iqC5y{v-Zo`K<Z-Ic1+JYQ>BTPBUXXx9o=?-trfA|GLl9<n69wJ>gbwMQX$t&m
z3j*!!6oVcsiOc$7{eTvylrYfn!%-3Cce$+Z&cq{TZmY6jg=s?AOcHWzg-E@oJ7>fM
z=I%twA9%jsm8qPh@Om>D!U4jivXKtod~RIx@WkM&u%W9iO~Lbiu;r`B8c1p~aIocJ
zYfjm43%j$ZMc+1z60Yp%q9DLOU2KY&o^%AYEeHn$E_<+*Y8i*XHySr0vX(wP*TLz{
zJKLL>-p`_Fr4g<^%bVWC6BwH&gs=18kOBQs(1jI43&N&W|7_`CDn6UlprCc$IcjLA
zpUu^T;*>*GtH4AGzIk(iZG3Hoxr3>x0YjjhoY@yE7F?`UQ6-3VgcT>x$63+)K}Rq%
z*yc@yn0j?3xe9VFZ<QtD1#U#ch!iU}7Uwtug1wBix*?j<RqyH7t0~;XILl9L^g9$R
zIGlub+DDAj8jxRxC$P5F^H}C7#HcT5^qet1W@`5>FqvC|D?#TKTGW(OPeveCbF8G<
zy;6b?iFG_z9C1ZXBGz60__(EZ?Xrj<&@g8PPHek~88E+K!JX){pDg+r(5|`8@mll*
z8>K}u-1U6pu&M?yqB+4g^J5K%VzD)TsticEJj%jL8h`1oc3>{6Fddg3^Sjfz>lWrS
zaDE>LE_v^`llm%Mw$2kIF|f>^{~9OhN)K@3b~CU)u%wA`zOIzO@oa-PWGWXsNKb2Z
zv1$!*>>)o_nnLmfm+r(;C}!E9KAiA)=~gr<m@2xPRy8A6V`dkS)L^TuY|)s1Gcbo-
z_V33DKYpArA0Iyib5Vj&sZ2@i)kLTW3Y5-MWt*TRCC#g&Nq3mky9JMBUI}NPApP!e
zU#+~SW@mpaj_iNR&?ZZDUfLs7e2fM@!=BfBAfMahrve)m56ttD+pSIm{F&1p)oddj
z2p7XJOBFzUrnL}C>qZ;N-*M_|cOmvDj%MC2A$?mjb8k)H#Gd#44P21S4&kU4MA9wk
zZG4wjr!qLkM>)<@Kmwf*Mi9lJhAGp`A2J3y(zV~G8b+19c2d@JXj-15VjQmztaWYR
zF4q=M!6V^VH<}jM`-e|6)|EBxfIeL5TLg<K+2oZlRYlc&_ifMb4UqwT34VUihkpyA
z5d-TsUD0te6q{gm;<@T0))ed%%fNc1Y?3(cOZA{LILvyyky7uF*MLSbH^kMCqc97)
z*lWdv!!1J=Waz$Sr-Z@%!@lAL<e0vZPKu6kl`nX|E)A9B<q^=T@=0+U8bbC6I{zbp
zLh-g$t(|(YBUXaKTu%Xz(Lr$7PX?|(ISC1hu>QKFg~AWQ+yBC}8-t4gL|=~^+$S*0
zkSgLY{coi;F@T~bbC#dy`3hr=MyKdTTh`AfdWq#4p}!LEO1Ijv%=6204SCtfaGbd?
zq3XzE8#|)ig4|GITZJQg_*2btl)a(+8<#0VuRX5<%M6BHYukFtV^C?`h^Px_cxA~e
z#cEm1cz(Ltwz?Q3FjD+JTU^Z}xL84Y5CV?#>g*#~0T^I%rffL`Fs|&c!h7!Vmp3NZ
zkdNhv8JY<{hRl7)p<{LD)YnN9b3Z^5_7A0#IusX7__ffVg1u8-Qg0c<nT7$EmVs!_
zcG_f^4#z?`F6tBY#eO|f=5-A1;tJx=F_^)cbxQ(^bJBL-G+`dwL@Dd|Wu;1KN%M5D
z&W>Yr!I@uUT-?|~lZ<b0^uI@f5>La!gzM?frEvd5kr(CJ===rmyq1<<k({Z?!Ey0W
z$$*z^7}{spj9}Bge^^u1QrnZFlYzu~s6kO4Njw-;LRQ>d2NKrbXg-drhogaQZ?(tl
zg~7i`>ttzlosfQEpAehn!a3{_wX;h0q1Amkmp~f^pd4^N(fw_adI;1dO;ZR|Oj#Qx
z2co&CibG7i6|&DF(VxTauDczivxLNSU2VByU(Kc{JlqLx(2dNsD^hj0ys7-qdGe9-
zS&Pv06wf?{$;EANLu`)i7c~@z-2r8zR5h3kA6l^vSXvnr$x()VQRFkhD4UT#z^7}|
zB(_zg2q=P34R9_32^*?7bvEeYm(9ut%uN?Xh}B8i1HY7odYUPo3hH>5K6FAtGu(p`
z?+d7U1W%LU;yN~+Tw6lU_gFTeG0s?morY>IVvW8DJoB9;LcxGL@1}6<49~PLDay{7
zTsTlocRcL{Beg(9bE7wFtj@X8UTj62jZC;|ef-MivrgKk>;c7gP32oWdZf)`z=2DJ
z;=IfeRVPC{PzYn2Gz#l`vR15VP@hLoHCOWbgXWjvAl%Fw(D%Xrf;ZWBzWO4<wnx^u
zv^;H%x-*fHK?%Svqk&jeUk!kyOBHt-%|^84+guSXR7djJ3%=8B@Utaq(*?ml$W$;O
zTlhvHYTQ^!0s3R+7Jsd0u1277-blbMrw@N!qJq;w_|VyO6Usa#?1)qoGs@37Q`j*>
zBsTz=1S-elT+p>-Fgva?Q|IkcTZFQ9V)QUS>VsP@)3_Wx(YAw?1V*r95Pqhl0aSvG
zM@xl?D-y5BlUvcGuisj+X$axC!jD{&DxMKf9Oy`>B>p=6^NL@lUp+{$M55GbD2lec
zOet<KV*lqScG@g4DanuRlk~U|5r&!sl|=c-OhiEjofHOvXV~E*@c?XSRWdBz9Ue2R
zoYNG>eUjmx6y++v4Q0spC=60MKetP&RF$(C6K;bDDpRr{EW9A^u|mvORS_aMc!9fe
z$>ya$#L+j&w+`rOXj(@hQ<@NesN$e=IIna-OV?%S%cAI<m0MoRRZu38&R<j~hRn=J
zD&w?NT1qJr#_cyL2r@)gP^G1cx+?80z%XT9qzSpjEBXbnC3VCGE}&<+xGb^sHoY?)
z`+f&VKBgeZX5X*s)YRx3h}qJN1eM&OQFCHqd)g|OO(73DaR`R?`^BnDc};3|TeF9=
z35bbZR2fh9xJp(e;S!KF=+$JT{?>+R&5A_n+Jj(XUfqbajIL3#|7EAU7l}7M4vy^W
zO}Upz1k(&}TMf9Uy&3WdmA;{c$bY8lt7u&glG5-hv1N?5C~hLf4EIpKK21lOy19U*
z<ks$o6d)&4TTaO@g>s>_mYEvI)Hg@az<oYIM!3h8C*380`WZ4GLc%px^cKF~H?V;f
zN2+3$Jb(aH$=b)?sx&6C-QnT@P=lnDZ&(khxNn@<R|S-45!Pf3(nBYP+^6MB=mQi{
z#g4d1jfRSi>F>th0E#UUh#TIXqPqMNf{5_w-;gzFLX!vWB25ZOQ^GXEr3)I!?tMH%
zG#_ty@Zlk-rV`-4jhsJVOrF+dJ3gTVbffwp3$g0`GrTUmn6))}!LIPJ8!u-c4ouxO
z7}C@5xOMFPIqD_GOiD|FGg7Vz5dQvn91}5Bb8b|<_xKIhW{d(d@0}wZBR0hCK$}H^
z^ad`%ylgt&4o8Q8QD9`SIeJNpD&K2M*CZGIFi_KzEo;?mY7^2`VnR~$DgFxha`Uv%
zw9)<e;6!DcgHAfMY#klf!y_=2i7C^Wx>0CSf!Chf-ZCjIuL<)mhQdHFx4v|s+596d
zM*M-hX)Ktp(h0LvIo>hAnm)3i!G0DCXY`IIc%&8GWF6f(4K--q2enZ{o+x?PXa2lo
zXDNq&Va4E}%6F6?7IVz22IW6YUR?R+)r4nO4YNx7ZQM#mQuLAqMW6(|PL!5{L23Xa
z`%us;X6k84FM_gIZ^^W{9dAv#NXk7RT1<ZMD*v41leW0uOJdz=65EtBQR0LeFYo3L
z|8CtKabkhVoDOWHsaP5lSM8Zwc~F!-VU^$ggp1MM5FNg9%LFb&qJ+54Uc!%-tKCWU
z>nd?ME5#HR<PO#K78v`SI~2?m>qiU|k)x)uCcv^+sIdqRHG~eb*JV`LSiBU!fyiD8
zbKkra5rUG(baj}F-uzILVf}PC%s6OVjeGm2zU8W>`tcs1cO7~x+kkJ9qxV<`PnGRC
z4kUek6117(*QZ76Yov_79-6*43LL0ob3*Y6+DH-FX}0|)F(xX@s%B6mOaK{~pIhpC
zZe4{<2Wso%ES(<3_dB`UPIf@XaKL#B?=-F{_)N#;+hKfJxnf(Ucp1#fR1rAN7=78(
zOnI~{>z?Vu(wKtPlgYsA@FE?6gtjf2Akrc9Zuc)LUThUAvx8!RR|!m~q3i1743T8I
zu6Lo-9aO|#AvXn=fw^><oqb}XBlNMe#Cn2MtcoKs%_0R9v3aT4O|5QD6RAoi`jz1D
zqo!iX_z-1;FtxA-c*p+14?bQ|7_rcDv}@Ejl>UOMnbZ(p1)<OI9r|zRWM~BJ9z%`i
zjlT?W;OK*slDvGXd1(l=j0rQ(Z+;%$)QB@jX3sgn$Exw5O3IeJYHkoy1b{QIplW!{
zbO*Qa$4p3(E<7_9*vOZI1U9C%VMKimWWcywtg3=;mRBvP<M=#Otc%kH*^?^34@M0J
zj3m@0=78Y;1ywj|daw?S3{$f%F?RuUuCEtPmX1anz7<A)?w%1v^o<S&iO5Gk8AQ;!
z&ed*|>tf0Nsu;tv9$5RnHPSI=211A*gjZ9~Gme8iun5TLuUR0Ti)4Ma^ykaRNrNi6
zNZ>&vo;g$58fu{4q-kxRc3@5(JeDx_`K74c;YO2#a>OdMaSihssY^RH1Se3}ELFHu
zH3jZ{1QjbQ#zh_!M!w~?$6aa%z5@A+mX=5Z&9EDjZQ=K>-H+Xqz)549cVi5-A8w1n
zxfoTYF@C>k7uHZm2(j3`tWVOA`!;DgzpRhiMlmc(&LLTQKl*wf&SHYoGbxajw#^bC
zh~g8im9$y_iM6j!OLXW-XdnoWm8%&BgxEF5U%3rW@W`$|r=F0xzT-Pb8#XwXqMJh1
z3@H)<slRknd2n8Sru7V&Za|~AD&q+5Pt5WAou!&g1<BpfYXk<rQR4#SBNHi;u<s$_
zrABrCJQNWn7W0yV{t?np<JgI3GUCY(qMoVPBs7&dj|u?A7!7PHNZg9Q2ttO*o6D89
z+n``_9+%y7FfMjamnXa7FDC9IiSiP!kRCrrG?Pf|Q!8Re28|Y@Ed?#XhQtMMC(KbY
zNoULe>H5Cg!ha*tt0?ZIoh?VNq!db3JOiVYmDw|P-^XA{2%Hh}He)w+dCc1Fl3tB3
zEboXwHhuldkRl*C>WfH+A0h!5(C1w9fM}YFx{8uFzl9RnwUl>X5x%;ZR*hd3xh}Va
z-P4fPl#(=~XQ)Iq!2~K(=_P$1*ASk$jWDrhAzmN`3q;B?Wi#;0$+&Ch?KJ@?uGR=D
z99C~4&Hsi7S9K;Lza-F4iHmZgo)DW?MWNhBn38}S%H7Y;>MH&#h3_4L$a~iZ4vc_T
zt9X1Ot9f<RPy;TgTlVnZVPX~x`Jcl)D!Q`HKk-4cq?6>9L^68FWUse1e8m%`F9q?`
zvO%Vo{KZ*mE273GR{plS1Cz>whwn&Q3NBVv5O{x@{j#1XWuv&XNwg0R!jcdb$<$sG
ze_Y1@nH)Cvas%aWvC>MSx5Fg@5cRbYZORFSv9JU!Hh9KpG&9PLyMG_(R2|aup++ML
zmUA0fW8plnzo)o3@0$X;nZshNYWX7ZF(PVkCt*LE{L)4fNDM=<?<<ee_8IDuAH=%~
zNBvPGg*Wt0(%=Py*@m&ZE*@l3IGEXngS9FjB0XqGQ9Ru<$l7vIv|vpRZZJkyG32bD
z*zWEzyM4(E3m1Y)YrQn6c}<^G5=Uv{OHlaoB_Y=_>PFrH{wd)C&MgTz4RolN9wR!^
zn@V8TtB6>1y{VX_89%4arvw`*QHx!pu?1!{u0&$30UV~huE+yXGj;19Dp3hS=|wan
z(_LJSxqVQ+XUX<&2hHyNqetIx7uW^qCYknaZD{lMh&<C1cuxAA1e!PCOD^46LWlah
zDkPJzRB-&>!S3{-QBy5FuvjOtsoJOu)>>?4Hd{!~IVRAywhSV+$LUY3W3VCm3IZ_|
zeQ%pR1JE?YJ1;1MRkMkD^@v55CaiXXyv<#kM9`{HUpsaTEpE$+YD#Ja30%VZZW7~#
zp=Wu#8q*b!;sb&R4`hQ?zoREZlL(M*I)@V!VCjmhKPGxZjal0!?xDrPHe6QGf@k)p
z{MVAo>>RRJv}Ya6DO#Q{V63qFJ=Rh1CEaXYK<{)5{i7xcpHrB=^jo%=K5`nZYfKx*
z@XmCe)Nsz<!@4tm!j!dKu~^eW*@mo@P((}TZ|J9Wd<@3jV}Zzumck6T+(fhoOSso>
zt^zu$nug9FAL&2|@MTiaC4vkS`=+Wi*2VNgLAUH(Q566hc#4_T9PQWyGQE0BHxw+z
z=w+@kw1N<2_`69`=e=^}*;ofpVSF_<I^0R{e4rehwdPM3z5|*mdkWVI2PTU;5F`W>
zk7@jXZJV+jQ+8V~x>NOzw4?>QPDPZWUmJ1l2}oAZGU4U$XjWK=m~8O1J!-U&<4rA3
z2rAKjjFeQ`qc|OuR6MfdO`=H=slUeX#a(M|nDr0CU#g~}=3q(wUNbgsf4y$yfxs$q
zkYuiKnO7#0l~rk~l6yP?2^=9yT(*W)_bs`)GcYmMHB%h5-OC>@6?xAOisA(`U)yIH
zUQCLSz3kkrnjx(&#4UrCzcGM4!POv_2F%ff1!X%nAP9!!y5MreHC<4WTmAjC=TPOO
z1@55s>gh;l?z30wS83`p>Qc^|e1}omJ_#d}F%Q5n)y~lIzR)EhtTO<i(eQ*l6g*3$
zrP^7;g`Q&ZR$f2=7x~_{gpCV#(&*%$z4Wjsl&Vb{O8wlUHq1$(FeSW57M3eBDNXJg
zDiAWHU)WTdg;-^e>g^B&tsJk3LoCd4B;B?s6z^P6%+ux2LHi*3NVbd3v;Uha+vS4h
zdtTq%9Yg6h57lT0CkD%bBYsmF2nhPKjJSyU8P%x3mh;6#j2vDVW{KhZQ0z=2LrLuo
zS4JV0Xr*TE&l|*cl;GT|&nKgtnXLkI(inogrkjq<T9fec@luvX#6>3ErZFjBOF9W;
zEkVMfTInSzKX}`Ciz{t{diw1W`uE_$a$_nbhP4L~%=itAzh|Rf1}T!!*!#pz@j<p$
zc{%1uX16|RIi4)pR4W`Q>xYBnK}$f{1cVt&c@`A3h99-624@Bv*2lBopk*TD`S_^V
zbg{U7;Z3AfOK2%b8rS_)P<LV0tlf_^4E`FvIO>v^Blmu)TGQ`y()H5JkgGyYf>g5Z
z4I*HyItfESS+UU)M?qQK(LA_4N~eU|SbTD0;5!>aahT20U^F+qEJ`NRlPi!xvY%n|
zTvw$h>v(1KuhZdLdpwSP-p#JPHz$c0fM>|98xo#to=A62o(^U|hd0VQrOKPW^V|Pk
z#s~QoIi^g`{`cdw*YC_GO>9FO*_`Kb)rzobSERs_WPbnxZraVhGYGLF)9t8J8Y772
zlEwUH2mR<UMM~qb6P+Kp1wKcw(-9(fa@xYdAE8o6)fnQ-cO-D#H97MFT5S$^I712n
zZs$~T!5lhNwARrP21mBm@UxEAJuQv0WTb-YgYW$9m`D>==dl+DnI>X|1gU}d6u<pd
zwzB010csz%Rv8ETK$|YjXSdSK&!gDe>IVl{QlZ*EDUXz3D8pCCrl*JdAn@qh)q&`7
zr?y*0&76q9o01s77#6_bJ53lT!I%XnvbZ*W(5=SQHk8DndscMy>rDD_RF_S07lWUw
z^&1?;1ygLRN>|TZm|3H%l^FL7w}OnzX_!BC>{m(?c84)^UrozaW!rg<s92<9B}Re?
zg$QqD2@#d7g2vKVq;|jxG6&oRJs$1>-S;}y2$n=+ghXe(NKlIE2&_Nbc5-$o0N(jH
zl9o_-@@=pY7qE10z;PvnMWBlOn)^iW(6|cYmJd%!&C}8DIiqLCAB+j1LG%(@5gB%b
zdu?Ixw<wphB(d?i=!$g3DKvOZdK=jO$_Ir;h}=l=&Uy+B9+`#lNMV`|5twrsX}oP+
z-{O|0CPs5hZOvL;w_^#-FdrzrcT^v7v^#dvYJ*tl_hym?!Ka9T=&O1`Ep07cm&VQ7
zPMt6bAs@O3(yRV*6YG5uv_d%ADv@~fJ~;siylX0M2DCkKM9PHt#EACLJPVKv1J-Hz
z10|JEsm7w~@8QYN!UFJIb%o0+G>H8x28#6zfNkUmEyBkQ019+UJ`BFB<q+%g9_lyd
zCwSEWZFRlwMe5!Y6ByeT%)&!g?p`Z;Wv@D~RyRUANnxGbz;aM9&O9r}Y~0DFxEmH_
zd<d)PP+ij$MVoe=Ai;R=D>HzR7pmro`lDy>XHMXVhQ(8xkt9(WXp-w+-`bFFZbZ!0
zgshpA{Z2i{Np`~H1m&i+Ba33EgII!;5igpe+O<T}QOWJ?;~{KkCNdud_L99Ac3s@T
z%IZ`F^yiDOX2EM#Us0u|lqO6J%IM$Quit&I4gS>cRWA!`&UgSo)(!c!s9a5UCxfT>
ztGW?GS?THdr8SP!k-P%)Vg2r`SjPH_xtEEzMnWCl#SKE+@~Df5ab2P6R{y}68&O}N
zf7YTwXu3U>!-DoR$WG-Jw7hEf-3r?GalR#Z4!_eC##S3$txM9CT4!!_)7Ah{a$};u
zqHNvtmHT!~sAgacG*<Lj8}sBlq(m$(VZ*jt*i40N$-#o+*P?VFp%>)Kk0;tsv-Liz
zvI1-kj<Tua4-^sJ5I|t*J!WPxn-21nzt6zi2;Gk>Sqo0H2M$1qU1DCs5>!gEE9yoe
zWDLlkb<rmhi}*_oPgxgjbmmbA93QDQNniTKXa!q9(<<}>DnC0|ikCf_iQJb|p8Dg}
z^rQ+-ImWr7L7}=MYUcM-D7_?11b=7D>Q3=LnIKWL1dYiruJBVT$WmKMIZUmxtj1ql
z+e%%jV4&6p@=L6oYx!I(-edcMnS#JNZ=4Ir2h%<bJ|+Y&U~^uok+F+}mA2W@o#3qX
zK#PZgP<&gEG^=+xoog%`aX>eJ;?avLDk%CB$$IAlhp9m)K}H8<VundY$O2<f#(PmS
z;3X$Lycv`-7^YCJ&M+ThZ4<&@gsB$fJ+P_CSy#DMSU=Kej3i%4$BzdCZWw_hdIohJ
z%8xc4`I@RSg7ay=R%p`3<EJ*%T~lXlYGIspH>W<FUYN(FR4qD-`yREV%FutY5U~zs
zAY;h@rd!1?OJW?YQyXP<JX>IeHs5$_OoXm)5c|NhxT)`lXDCquGYmJRZtifdv4qVI
zT?i$x;QfHUeC^Y3KBrkjChzOBz9iwesnu^A#^<A@$8^v_BoTkq1lZB+FJ5<s(~ZQA
zbkKnyk!Jy+-WT-Z3pRegAdLq>C__@W0~!PYUhv7iEZn0`b|G|1H^>a;0+POGl$ax7
zSHA2Y#w%}``T~@p9Q8dI{UU=)WP3StN?H8yf(Y!dWR~#ra)+~0_s1~)aUE;;&tcdq
zOf@f2{De59n$f%CfA@BwM}L8@{2Ae(cx`ihEkMj0k>Zw(B#o3oVGT2fC#{azU1;BC
z(A6dEwEMJK!mE~S<8XK0;t&xm_qJgnM~N06tNsZx%9+`Ez@)f6l*}TRF>B=Bb8UTJ
zeb^bjkNWpyxDx9JmSC1ca|U-bM{s;Hwu#`S^wJCwbml^cU_J<121QXPKr^FLwK#9|
zlRtom`bwT8S4)w!-=uf*cC<FEf!5wvFmogl%Y`#I!cZk8-BdSouSgYRx#jR`Alnf^
zjPaLdWe`;RqlRZo0^WF6*V6I8N~RNV-?5U|FjCzkZz&7W@51spviGt4q$quE^m0Hv
zcK(G`=4AxOivKyTMA4jdQB|Q)P_N6YL0dbqnJ&7o&-nz8tDbt2M#_dbVK;(Uh=txl
z%)3xk*7rb$iZ)tXqG81Pm}cQ6upLYV%UP``2)z$?6BR;XEI^eggS{w9gKlCqAS|oJ
z4wzo)TEZ?ON|Sa^2X8L7TR5QXf-Bl7-!ES2Zh{AYEF->t<BwzD3@uyOEg8_FlYt1x
zXWG~gGK-eEe&%!yE?d=}lq619D6&Kzq+98KrG|{r2u}in_tc(1HzV<5tP*MMG!Lg^
zP%Z70owf-Po+IY!TdoXJtO~ImFfNtyZB<~C$_IJCOKj<MXP?@#ZI1wNF(OYvDL7Qv
zOKo2O@Gvx@0+SUKL^u3|BDS63OQsQA9(i<leK4T`p#$3&Mu5xI1OtUXChURx{_cs@
zM`3IL&$2h|SE`IbKmwd8Y#Ag=Y89*3WG^TL6G-fze3bGNDvZCSu6&Xu9d+G<7{bD@
zUW75KQ@bP3tRnbCy2=B(Sb}~*PM9UfT7Q{3glSlZ(tlEdM_=rUzIK9nRmLSS4iPqx
zV&H<^jsIChiwZGGhQ?7&bz8l#)>3DmxINM*!maU;oL3DjA&oqN`dc0iPh!$UVQ6E_
z4jStYhqS;M#8zU7&|~kEUNU9twmxt0l4qez0ncai5EZr6O%bf-T3FnJg&JT)An0RY
zB;H%)zZCWUI8G#Os=>;a=`<ZYvQ6i|e?!Y~{Y>?C#$i_j6bqp66KBn@_6rhxTXJji
zCq~jJzz`kKv`z-{9<!2iu(}430TH1(y;u%6Gfx|EzbKz5z}X`DN(^7~ZQf%h!A+o|
z=FlrT<V5fg6HCW8v_qVq*_+djF$i{C<Oq(ir&9GCD$<?@pu^)re>D&s9Ywm*cg10Y
zJpkFF+wc$xyls;HUeE=cm3rYv&U-I(R4gDbEH5AjNT+~U0hVTUXo!<|)JtmQBtlwe
z>4PkhYB&q#+D>L0m0QEYCCAQlS^mZOLl8lHu3LzI@{Hl26H;Lx!4qN-&9*Ch)jE`b
ze~5^cXR`6MxSiAC*)G|fc*#NW5S}wySm4CUM+p+Aw=a0{QL7R;TSkVIMl5X{tl#0!
zspoS1;4ka>d8gSV9|XT}U6o0A$~=a{54oAqtl~~;esE78@#K~0zQ<!&%t?ZJ2nfdi
zxz^_{ou=--k=0HD6d6_)TN)5)71<|e(Ifhr%Xfco8z&WZG(dSZozu_HZ(Xu<Pmfq2
ziI^Wv!cM5+cch?%HZ`q{gZt&grEz*kD8$4MnOw7fA&%|8r^17DM3O~v@-x0P1;;f*
zrGnO$p?{((frOCi>)Qxq0SQSNEVm%z0a(=A$3SJ9-L{1EOi6JE<w4hb*MnIzKC#gb
zUi*9>H6LC^Fcp`<W4kh8(WI>bH=q0AT?2#jXer%Sqm6F_%g$}0P@5EZvuDEXJe>pj
zYqJe=xo0-F%gE+TCXF2p*tPcQ5~n8RVId^Z9&{cFnpZ8Aqg};)$3<1<9og|$U3@S=
zovuDu>t5S^P0{gbJ|HHfg`f#VJctIDdxv*A51L8yLCUUoxf~~xmR#^~N{o<wN0l6&
zss%a*aaOr3!HGCt6Mo~^xj7{^>6<N&x{mO+$NrcaQWeT&_l(-A(uWx=Cg5{3$bL03
zB_G*WAji1U-s?hSuI}4r>w?<yGaymzQ7UqMOy`L13JPB~)y@G|6%SiN?i^N*$*QaJ
zouh#CuSUM@7d6XeSiY9TF`LFVJ*cBa+`xACgxYcT!Pc{{Ts<UzN;b}2oUuRN!Xb{!
ziZ9)%d;r~?+b78EfCHz%0vFnQfEY^AIf^6P>`5l6_H5Xcq~i0-d~#rRtT+YEnSu$o
zV{Rh=M~x`IiH1KDdT7a^Cwvmm77d~9JQMrKx4O>ObZ`OC3OXxd;F>B4uSdUC0jb!c
zpiVVEIBx@yT0314NxZl9HY6v#GcK0v(<TU>nX2Khfp)zqy{d)ka@WKV*}lkB-OsHs
zjo%eD8u_X_Jk$y<s0oMYz^L<UNV8tc!QF1yiql=8SOxC$)eOF+DCaW5>G^_M@17GE
z0j7qBfCa-A1+o&g1vuLkLcFfc6jp~Ii~AR)su1hXmEnYpl15FrFM_hLN%x^xo}ZSe
zY*ohLm9{U+4DOe5lc>eJ<y4+o9|UH?@)Hzx_i6ckebL<^M2=Q{fz9FU<_(H;37cw6
zY^={|C~34EE9S7K^UE@<3PV3%0lyO_=C)r8YhtK8FaKuA&jr7iW2-?<rZRlDR!XOn
zrL)K}pZZfrcDfwfjyxS>53i8qlE!Ki8ER`$jVZw1RLkqa60k#l?8xBCC#<W^Njcr*
zLxr=MD(vIV+G>uMG{QeQZHjzV)S=5_>jUY{ec8abI<wlmN6Z4AC(EOV)=9ejSByuI
zO~dc!a-}2LeC6n#hQGbdo5xn0$Efl4oBBzxxZdhsij;vENG~F~5p)dNQg+5WU>!n4
zY(N>@SNYi9|3|ZXP3<HnWvtmHr<1gm@2(<lgRczSSjXBQ*1M(0C%I5-(I7*xxWJW8
zG(#&*(gbLGAL#mGc+rgzg}PI@Xr7?jik(}nO16RqXB*Ss-z>dPh!LbEsv{j{Lhpe7
z?Nm%sJ?WQoWQGmwEXRFDpyh^4n9k#THjM*Li!J#qy-gEKoK;L?q{sBnS>8#r6je-G
zgH5Kh4oL_7$qp|&#a{WovPs?GDZJ<r$FVnC9S2@^unP@i)Q$@?h2k>90&Gg_#*}UW
zZlPN92*rAWLDctbXmT(t9l!wbn<kAQV&_;38y+Va*olg3f8S~bcRS*mJ%1()+NW^p
zj0pZDo~6B;Ab8!eXqpvu-=6o%TXn%DpP&qzu;+!d*#4rkKKKk?w55BcBL8Y%7c#BD
zGmSrXae#&FivgLHzhAG7aCUs8FsW0bRNqT$e1pEnCgVfwG-MnZ@+V_8wvqoj%c~Jz
zqb!oFfiOE7?XUZ)2g%%Go8N_ALU<^7b5Unt8m;jp%fzmC>FnUbknwi0N<Xk|C<R>u
zDAB{bNvx{6&nsH3<>kQT<hMP~UT9eJ1raj+qyd})fOJKy#=R#wZrdzE$Sk+$=N_?=
zQgFTM?@{LNDh&#_MfQHcS5-|1W{K%+V$~NCqMenh@Gq}#u<1SBhESN3eBxA`7)2U@
z8W_zBwqNmkK@Kp=XeZvwco94ELID=pCn-u?IQU|pbAsKL^S;T1SAp~^^MC&F*9Kr;
z+abQV{kIJ7=KvM(Vtugy&l{JaxXByaVL4#FJlP_!R}z3PBC<~&+ML~=((S?EzuUwA
z94WD=mIp0oNeTA<UGv|UVzGiPf?Z+#qSx{N@4x;}4gU^L(8XmtWQjk}0{^E8|8r;$
z5%#%ot>98k{om)s^QDLdo(UuWWc_Qy|5MQv1z<rfPp&)h*Z)3G4kiG0Uhyh@wEG_w
z88eb3<bLR1#3obxS8x5_;xiO!rEV$GzyJ9ht}ua5OQ))i#q|I0jsM*)9@tm`)OW7`
zs3=_y_;iA4K4ca$z}NAAZy*iu0XFtFajf@0D(cf01wLK-5+04*fArKGSwgN)y!e;G
ze^hjW3VgcRk{xNC|9u10Ktf<+eaRmj|Dz)2G-PQdf5vCYiJ$+`ga5yM6<<W5jE-Ba
TK}@h9z@LnSqIj*SanSz(S8fza


From b75d40b8cb8e699cc0f825be2b699f0d2f574614 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 20 Jun 2014 23:12:26 -0400
Subject: [PATCH 114/248] conclusion upd.

---
 tutorials/multiprocessing_intro.ipynb | 17 ++++++++++++++---
 1 file changed, 14 insertions(+), 3 deletions(-)

diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index 5f536e7..a3b0916 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:4b992169e84879b172c1ddfadf3f353a3812b16f9d4e448fc4aa443cbd3db9da"
+  "signature": "sha256:a96ed2f762cf56d93a4e5345428c7db5ec576916158ce54446dfdf837ec7e505"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -1129,9 +1129,20 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel. However, on my particular machine it 6 processes didn't lead to a further performance improvement, which makes sense for a 4-core CPU. \n",
-      "This even lead to a slightly worse performance, because of an additional overhead for communicating between the additional processes."
+      "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel. However, on my particular machine, the submission of 6 parallel 6 processes doesn't lead to a further performance improvement, which makes sense for a 4-core CPU.  \n",
+      "We also notice that there was a significant performance increase when we were using 3 instead of only 2 processes in parallel. However, the performance increase was less significant when we moved up to 4 parallel processes, respectively.  \n",
+      "This can be attributed to the fact that in this case, the CPU consists of only 4 cores, and system processes, such as the operating system, are also running in the background. Thus, the fourth core simply does not have enough capacity left to further increase the performance of the fourth process to a large extend. And we also have to keep in mind that every additional process comes with an additional overhead for inter-process communication.  \n",
+      "\n",
+      "Also, an improvement due to parallel processing only makes sense if our tasks are \"CPU-bound\" where the majority of the task is spent in the CPU in contrast to I/O bound tasks, i.e., tasks that are processing data from a disk. "
      ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}

From 018a0b281e5d517d48e3bff0ea7ee538a45a7377 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 22 Jun 2014 12:39:16 -0400
Subject: [PATCH 115/248] note about numpy in-place ops

---
 Images/literature.png                 | Bin 0 -> 6689 bytes
 Images/literature_small.png           | Bin 0 -> 4482 bytes
 tutorials/matrix_cheatsheet.md        |  65 +++++++++++++++++---------
 tutorials/matrix_cheatsheet_only.html |  28 ++++++-----
 4 files changed, 61 insertions(+), 32 deletions(-)
 create mode 100644 Images/literature.png
 create mode 100644 Images/literature_small.png

diff --git a/Images/literature.png b/Images/literature.png
new file mode 100644
index 0000000000000000000000000000000000000000..25a54470c48a8ef8b34ffaa6b20b740d72de110b
GIT binary patch
literal 6689
zcmV++8s6oJP)<h;3K|Lk000e1NJLTq002z@002Y?0ssI2OJpxE000U>X+uL$Nkc;*
zP;zf(X>4Tx07wm;mUmQB*%pV-y*Itk5+Wca^cs2zAksTX6$DX<Nq|rShJ+?|L<L3^
z5h+$=RKNj8hazJ|6bplbV%G`s5KzX!QA9=M-HdAq@2xfS-kSZ#S>M^`x7XQc?|s+0
z08spb1j2M!0f022SQPH-!CVp(%f$Br7!UytSOLJ{W@ZFO_(THK{JlMynW#v{v-a*T
zfMmPdEWc1DbJqWVks>!kBnAKqMb$PuekK>?0+ds;#ThdH1j_W4DKdsJG8Ul;qO2n0
z#IJ1jr{*iW$(WZW<e?f_&KbNko{YOt-kK%hql^ThT$m-`XQO-vWxZ5MngHeZDAUvU
zoJ;^P6q#Sl=O&?Si84hL8SaVl0ssh<#5ufj4vYCYXr2Igrf1}e1c^yvrV-beY31n1
zX8Q57Q~6>sE0n`c;fQ!l&-AnmjxZO1uWyz`0VP>&nP`#itsL#`S=Q!g`M=rU9)45(
zJ;-|dRq-b5&z?byo>|{)?5r=n76A4nTALlSzLiw~v~31J<>9PP?;rs31pu_(obw)r
zY+jPY;tVGXi|p)da{-@gE-UCa`=5eu%D;v=_nFJ?`&K)q7e9d`Nfk3?MdhZarb|T3
z%nS~f&t(1g5dY)AIcd$w!z`Siz!&j_=v7hZlnI21XuE|xfmo0(WD10T)!}~_HYW!e
zew}L+XmwuzeT6wtxJd`dZ#@7*BLgIEKY9Xv>st^p3dp{^Xswa2bB{85{^$B13tWnB
z;Y>jyQ|9&zk7RNsqAVGs--K+z0uqo1bf5|}fi5rtEMN^BfHQCd-XH*kfJhJnmIE$G
z0%<@5vOzxB0181d*a3EfYH$G5fqKvcPJ%XY23!PJzzuK<41h;K3WmW;Fah3yX$XSw
z5EY_9s*o0>51B&N5F1(uc|$=^I1~fLLy3?Ol0f;;Ca4%HgQ}rJP(Ab`bQ-z{U4#0d
z2hboi2K@njgb|nm(_szR0JebHusa+GN5aeCM0gdP2N%HG;Yzp`J`T6S7vUT504#-H
z!jlL<$Or?`Mpy_N@kBz9SR?@vA#0H$qyni$nvf2p8@Y{0k#Xb$28W?xm>3qu8RLgp
zjNxKdVb)?wFx8l2m{v>|<~C*!GlBVnrDD~wrdTJeKXwT=5u1%I#8zOBU|X=4u>;s)
z>^mF|$G{ol9B_WP7+f-LHLe7=57&&lfa}8z;U@8Tyei%l?}87(bMRt(A-)QK9Dg3)
zj~~XrCy)tR1Z#p1A(kK{Y$Q|=8VKhI{e%(1G*N-5Pjn)N5P8I0VkxnX*g?EW941ba
z6iJ387g8iCnY4jaNopcpCOsy-A(P2EWJhusSwLP-t|XrzUnLKcKTwn?CKOLf97RIe
zPB}`sKzTrUL#0v;sBY9)s+hW+T2H-1eM)^VN0T#`^Oxhvt&^*fYnAJldnHel*Ozyf
zUoM{~Um<@={-*r60#U(0!Bc^wuvVc);k3d%g-J!4qLpHZVwz%!VuRu}#Ze`^l7W)9
z5>Kf>>9Eozr6C$Z)1`URxU@~QI@)F0FdauXr2Es8>BaOP=)Lp_WhG@><tXJG<r?L)
z%2EcxFktvIQW>R;lZ?BJkMlI<xzFRz+cvLhUjMu)mH8@eDtwh9m1dOzm5-`SRd3Z4
z)t#zss!!A~Y9?x7YT0W0)h?@z&!^9Kp3j|MH2>uMhw8ApiF&yDYW2hFJ?fJhni{?u
z85&g@mo&yT8JcdI$(rSw=QPK(Xj%)k1X|@<=e1rim6`6$RAwc!i#egKuI;BS(LSWz
zt39n_sIypSqfWEV6J3%nTQ@<sT(?tqLQhLCSTA3%QSYHXQJ<}!q`ybMTYt*H&>-4i
zi$R;gsG*9XzhRzXqv2yCs*$VFDx+GXJH|L;wsDH_KI2;^u!)^Xl1YupO;gy^-c(?^
z&$Q1BYvyPsG^;hc$D**@Sy`+`)}T4VJji^bd7Jqw3q6Zii=7tT7GEswEK@D(EFW1Z
zSp`^awCb?>!`j4}Yh7b~$A)U-W3$et-R8BesV(1jzwLcHnq9En7Q0Tn&-M=XBKs!$
zF$X<|c!#|X_t<oHD7%Dx)e-CH;keH6jN=C<dnd8eNvGePS<WfW4bGzr3>WYh)GZit
z(Q)Cp9CDE^WG;+fcyOWARoj*0TI>4EP1lX*cEoMO-Pk?Z{kZ!p4@(b`M~lalr<3Oz
z&kJ6Nm#<fmSFg8{_hRpA@25UGK8Ze!J`=unzN>vN_+kA5{dW4@^Vjg_`q%qU1ULk&
z3Fr!>1V#i_2R;ij2@(Z$1jE4r!MlPVFVbHmT+|i<Li|H^g**v03|$raa~LixG^{4<
zdAL=0et35TEn-DPL&UpCkI2%<M~jUXOBQ!V$w$RS)kjT5dqtN;OP5$IS+nFuj9QE!
zracxP8x?ybc5<or(%nmk<Lu%J<L)jqT$Z!!+H$q!smsr<kYB-BaVj1gA06Ki|A`aA
zspU+r^k2Dm<pkH0yNCOd=f*4NjqzRhW&Du@mxQu}(L|TTU5R5!u1OV1;{s1XwcvHK
zU-E(Esg#hEqbW0~(W%X8gtYjy(?TU-im)qPGd(B0FT*sWFhjb^Y1Qsk6QV%TkxVFa
zS!TPKj{Z#bNQ@+#C4*TDvud*5XGdk9%2CV_=Je#6<ZjCy$@9tkel=z_cXemJcK(L^
z!8Pt{4y}dOu3X!>PIq0wy5aS{>yK?9ZAjVh%SOwMWgFjair&;wpi!{CU}&@N=Eg#~
zLQ&zpEzVmGY{hI9Z0+4<v#n~|mm*%#^<vB7isDZt+>-0xS$$Xe-OToc?Y*V;rTcf_
zb_jRe-RZjXSeas3UfIyD;9afd%<`i0x4T#DzE)vdabOQ=k7SRuGN`h>O0Q~1)u-yD
z>VX=Mn&!Rgd$;YK+Q-}1zu#?t(*cbG#Ronf6db&N$oEidtwC+YVcg-Y!_VuY>bk#Y
ze_ww@?MU&F&qswvrN_dLb=5o6*Egs)ls3YRlE$&)amR1{;Ppd$6RYV^Go!iq1UMl%
z@#4q$AMc(FJlT1QeX8jv{h#)>&{~RGq1N2iiMFIRX?sk2-|2wUogK~{EkB$8eDsX=
znVPf8XG_nK&J~=SIiGia@<PUi@r#KUhdNhuKDxBz(w(lbuHMUmm#<#&xpJx7z5D!C
zm#b&4IbAz_oqfIShW(A!9=o2FU+jKq>9y}|z3FhX{g&gcj=lwb=lWgyFW&aLedUh-
zof`v-2Kw$UzI*>(+&$@i-u=-BsSjR1%z8NeX#HdC<Dw@DPb!|OKdt@M_}6Bsz4Yv$
z*I>`Hh-Z(6xI-`hmHDqv!v)W&&nrf>M(RhcN6(D;jNN*%^u_SYjF;2ng}*8Ow)d6M
ztDk;%`@Lsk$;9w$(d(H%O5UixIr`T2ZRcd@<kNR)@201U-mAVp_JRGO`(yOSk?HJD
z_)nFejX!sM3H<VSCT(Ws-}i*``!YINegFUf32;bRa{vGf6951U69E94oEQKA4~9uZ
zK~!i3?V2rg<46+6wf6!90t5mK3<LrM0t5mC1{e$o7+|o#;6T8DfCB*s0uBT$2n-ky
zFu-7ep}_)!1qKHU1_%TQ3=jw~Fc1h3@Vj@vs%o|6k7S%l=H9;B?dR9-kLs%WS5<%7
zKP~^W4E_Y^Z+`-e0`V`L|6h(i_}{^)E0Og$50<qQtSY4-2qVc4!T@y+qTsM2)i+H)
z{n&K~Fb0f*z~d8sI|nMr!!QUt&IGT<{KIfA3Pp$y_W<?`4ZgL{>!7|hPW)Dt!DIPa
zcpIL_!=3aszJ1gAInNt}uQUGkXkz|hDpNP^ZFla|@<#qNgmtYZ-)|B=6RR7}T%!!c
zv4`<K*xEF&_9uY~$feFhX5zl-MU&PuRRQxNIIINiOVaCbg0L!D;_xJCD<|qi4()u<
z6{GlMjan75hJeH9YKRfj6$IA4sAOAuNesUtZp&#<&rJm<-jvwR9Z{p@CP64$jyJgV
z+K^33&qxbVvcFfq3n#W@RY1A6PU||X)%GH@NN&ana*fiaVfS|w2c2wp*Ltfr<9hQm
zH9(e=K$aZUOad*Gs^_%f1WyLRMpwH<m?=-o4WzO4qc*f+ICGUn_$b-v*{=R=yXm<W
zKz<#&gPWNgVO|?XZacxd9d&mm8v+^0=kTPAjSM7R2%<{9`e=2LuN@(Gig?+5#Xyq#
zBxgDm>~oJ<bPDAt!`bbSLmUNFtBqF9cN(1Jv|H|((_*k`L8w(ty5&fht~vnfx6<D8
z6V~F%1{Hj<E7gr8U?PME)x4UQ^SP)})pZm`olX?R1Q@_@RHuH(>$;jhRN-M59fsjy
z6n0`6%i3maJ%1}am74-d|M}C>-Z+bqSk98yfU1=RUKl)9byb#M=d;<Ap6I9(cRQin
zMQT!>7ZlDvUNB^MH-sQffi0<gz#Mh8sA(aE-Bj2<R=Z7UV=VXJiI?NEcF#t6Kb)+t
zY?Y2R_ohgcsOJE$DX<*OtKw@hud3>yOiof#e0mmiGZ{hbVFJ)T&d0wDszp`KtNvM*
z4zAbkU)8+6A5DIr6!%3(Qk*1lryGYL!HC4ag$ZFL=58!50yIGQJ~@BJrF-DyB=B`2
z43VVv8;|a=J;LyqiVX1^<<3zQp7aCOdJ>bzZk1Ber0dPi&+*yWS^AGrz=g)VL1)`7
zRaxJUi=rqWO2g2W)qB^Al1!{jlG)40aE3uA3A#OL-<9^jnGfYy2gpZp*zMij_)9$-
zU4`%^u~}nmOBma0+@8q*wyv-<9bWbN7qY*D(~|X4mT9S~Bg-0@tLz-hiYTZaiTwOx
zSPef$S(Y)YnbIqe3}jS#ZY;NR^m;aN=uwc>Ly>+Qvw&Ql2G{3V$h^m6xQJd(VT6|1
zbxDZoQbap?J9W+cQVsnQir99qDI!qe$ub5!^)uF722nSRmUY}aFYbG@Jm}xduFt}&
z>l<f2)F7BcLK;l~zau@ne=>||iPSK<yStk&%9{^Cb<pX5I1`Q}?>zxY(ustA)=L9u
zrCDVgDve%gB(-}Dm9?95j5z=x<3fvBmlZJ|cj7@BTu<uBc;vPsvmG3``aY4K#rZH(
zke%6dGW-(6M<IJdmUMzRb1Is0<UX~hCBTfp%&e$F!)EmDUe7@aYBlS1?Zk-8`Sjgc
zIWPO~Q<^V7&#L*n7U{awfws-u4Rl{m<3u_3Uy00m`rD{n)R+CZTvW+%EZU1IaK|U=
zRpG|eqD%<sER_b0A|ci?^JgR3aM!D>9D%a3viM&?N;PDW%TCkqAi|~PA{dXyw8{c(
zP_gn8>1mue6TV@@W=N34NBA?6ZKF5Pu!J`zXhK)GqfmpHcAN<eph}M-+%~qYH;@T&
z+X@XUXT(MpV3u`un&tV-Tzc~xN0CyHLV`cP2d8bAICC;V)xl}V;fCoU9z&kmaNVDR
zXw8|Xx<_k{RVtT(Tt29%O9+%Mp>>l?5H;&vR#DPpd&M@%k+^jF`;@Yy_QcI-|NVqN
zt3u-bAn?uvAvl>H?#}6?w*6_!>0%3egGcUs(kSXBQxJq&Z|BmWdi#M&A{j7b$*I{_
zs>S@~V@4Y;Ex#7hXG}ctdf^G24|y1d^<#bU%b18gKMgsqa>|h#kn<#AwlO~~*mQm(
z!<=cl`g~os)XZKM*jAQSGnte$d^Cc*(;llN>(RrB*<3mrk5N_iX&fsf*U}R>S;r58
z$z)P2gnjSqj46Q?WqtQsB|QQ_bK$AV*%EIeOm8!@<<@RAD^v{CrE5iFCNqaC0MTL%
zJVxh)$W9K+n@L@kr9?qny2)ztT{vql7;<pE9?mdWU!BR6U}DH9@h2Y@jIOYD8mn?*
z1v+ivk)osxcgYOLKrI^KLyI(PYC&3=6+V9DtCN#t{!nt>B-}{j$pnQ-Hhw$bg|jMQ
zh%-%EmZmtf_rB*WYFZjpQ_lH8$t|jFT|t^5G_fmpDWyZJeQSJVn6z4>H!D@MDb8O8
z+-ZQyMd#{63@t7#%96984BVA=;p7~<z?nI#S^qR*8j$4vAp&4zyimn<XYU8|l59mQ
z=h(ht`3kdAnl=Xh8Gnj7OqLE1@Va9yYtHn=eV)DR5p*PRjGga|^LoAuXHbuS8#6Q6
zi_?=tcri0cVp07_1++RJoQkT7(5Mxx5nvXIh7c#aWR}v35g6L0+xZiOf@;;es?yHg
zJ;}JZRgcwW<5Jy5TlB5s4xDwF-;PU*dnQ2nP$#E7x8oz#CW*JPsUq;yODJdFLx$~*
z455X51x6_?2tATD0^qfb=+tfaQ-xR)XQLu7vUi!elvx_c@n%Qbfm1HK3n3kxaq=j#
zG?t6CCDXML;|#n4phTvz*V~YIJdik_tGlGCqb!#wT?c8hY?jKJq#(_d%Q{I@Jr4yJ
z{e&K7X+FLcnys{nlRNI!FvmPuH;9iDxX22GW=;<Y7-gak^pI#m32xZjMWN*0U}WsK
z*5YA9X<#gv=`|W5V3n_go-XqCf@}!kX_~l+P$Q88%<SK@Uglgn_+`A)rDm=b{L!Z?
zHJUh+a|Vwf@>=6V(eMa4=N<^Th_UK$T`bElll(rXg_8<z3*<ys6h&#8#f1HFf5ilP
zOGimLx{#NY=INjr9>v|56dk8>k0;3j&ZxN?=tF=r!%4ylL_4$eVpy_v;!=69SWoNk
z3HQVF{RRAR=Jn<Ia5TQWI2U2az#srK8=FHGIoBdKXyCEFGm|?<F+Y&#V@ePmMy!6c
zR}ekSIca2P=R8&Ap9bmASG*^bqs!5+LlzQj!6Oi>I%XpwUDz%vZseRaS?USS1k92C
z1d3?P#ChEZ$YC`8EV0T1+Td!)jALafzD(Wgl=387^XKsPh9>0oP7@T2yHRvH(4REr
zost72sw2DjTH>zyAVd{?7V2br5JO#P<mJlJVU(V*<fik*yvXy(<ox>P#;h>=G~oOV
zc-}gpmFGZ`il!=We#!xEET@dk?+bf~h>oJ<7}RmdKz<z*Ss|f|$wJ;ZPD9>wfI9wk
z)&JK>g1<>Cf6itzb|j3^ICBJGj#mIPNL+zv(cM%sd7&}KqG)3R*g_9wELRF4MkfM+
zR22E_OWr>_0}pqg!QTfUqmknqm)mI>^x|Mv23ahsb&f(fAYDe<<b*Tl9d$L=!-%Iq
zU5BEO+kc1W4yG9P>0b>AU2*B;uKirJ!g>86!WhUP&?R8HG4FR&Eu!QdS?3$F>SfKz
z!$flU0z8U}c2A`2B<}Tkbn=RK@p*ocRsDV+Zo|<Ci3aS6q0(%a0V5|C^*@d1P<}0?
zTg&3`#w&7zSp;|I@%io4yVT({XR^$qFaG2fn<S|O)T4wGkSrjXHrTS53#$CV*<NKm
zm*FKM>U=PO2y7fAS;@Kau}gmwq~{Bhlr4aFZ%ofBbrhjer`s_C@Z)X51kEgTap`n)
z+v!~xfy0S2*?j235{4Fbf<}C0lrG<<@s!ULU^4nA028<62EonkkXW@L`{bqxOC8T~
zYYGKKDd5E)q_(!=((9ouNQ(1*7^W8+gWtT$DYE}Qv$YE5T!!p4^5%j(LtK*odKJFE
zrbQRWD6w;sQD7Jhyt7QBb$J?JXh!T9Xn00>Fv@2eWIlE_+KsJePC*mG`B1Cvi=5OZ
z_Cu^)x;Y7UGsW4JmnBX@EI;!K77_!@Y-m{1SZu8zV_>S=3HIStSJ!$rRp_Pcx;#w~
z)(66Y<(EkTBm?XV*qdi3nPW>wX&k%=g1jlqwz*O6L}7Z8GA%u34;aNkVSAXKXUw0d
zAH`vpmsxfKMWc2DwN{LeYZrvT(v3_0Hep8-XJ(@j<V`y*vhI&gYO$c6ghXAMDa!&b
z@|0UM)-4OR6+6+k3L1r#;c2@p1#91#ha6p$;c8#eU&Lw!sCg9UpD)->*s0fnI!qgl
zi195}*r>8-dsReNx^W}FCUiT<v$Qe9svC_gt(+;650}lL?e7)}IyrTRQ48l5eO-*_
z`9xR95Z1C^ixXTfEgJPr3wkr(*L$@J7>wZUh2EA-9XW4sE^l!*SFhwEMdpsg2&X@`
z?s#z9@YwhJIh?}d>!h5GrGPLE(Z&@bTfu7>^EfDmu(E4GT}O<kwJdxj89-M6<@Q)@
zaqY-eKEfnUGx?qK<>^~E;rQjYEQ$t|3nkM`LRF`k+1{1c8X3P**W9A3mUDYEq=^M}
znzN}{wh0gc$=eOT(|uRkynpUsa(XFqed(eUEpdo!gHjMn3t9@)MJHE~UU9h4n4is3
zQWLk!B4k69rs055lwm7Z5rE7F-whHS_VA~u8r0Jpj<Q-@#jvZs)mJk${2VdH%*m*C
z=})TPCv9Jxa5t#VZz2t^%7Ur>wA^%%vN&8qK>VX+p>NuSOd~?pHg%n&F-n)~Z0BDJ
zL3U+)Uz5K8r-;diWx@YpSo$<drHQhJq5%e*Q(cj_$PBZgZE_thVXwq%IoA|!zvuF=
zgSSX8#3^@|D#&km>}Wjcb@hcd!Uh1p=i7fVW$#fx%M^78&9_>0X9k&#$Gx`z=y<#s
zryHmHjfR{HPFbkTlfK{rn-LAoTBxtJYw)WNkF(dTH%`5+ukXcajP6HeJ~gzeXyd&n
zLa~WYL1@5?4gQqB3h{S<xMwYIp1%&KslzYA<&?+4){?VR&jwrLa0joO)!iPO%@0j)
zfwNJNU#p^r5sz{9sL33bDQ8QO=I(!dV2V0bXVAQXG~<8d_gmupk$S$C_a5h=*NXT<
rjQ<PgAHof9ro><Vd^1!2AsGD^d5+R<tw?#@00000NkvXXu0mjfO>^VI

literal 0
HcmV?d00001

diff --git a/Images/literature_small.png b/Images/literature_small.png
new file mode 100644
index 0000000000000000000000000000000000000000..2ffcedfdf530467dc1b5dc0efbd63aeee62c5ba0
GIT binary patch
literal 4482
zcmV-|5q<87P)<h;3K|Lk000e1NJLTq0015U000^Y1^@s6prP!v000U>X+uL$Nkc;*
zP;zf(X>4Tx07wm;mUmQB*%pV-y*Itk5+Wca^cs2zAksTX6$DX<Nq|rShJ+?|L<L3^
z5h+$=RKNj8hazJ|6bplbV%G`s5KzX!QA9=M-HdAq@2xfS-kSZ#S>M^`x7XQc?|s+0
z08spb1j2M!0f022SQPH-!CVp(%f$Br7!UytSOLJ{W@ZFO_(THK{JlMynW#v{v-a*T
zfMmPdEWc1DbJqWVks>!kBnAKqMb$PuekK>?0+ds;#ThdH1j_W4DKdsJG8Ul;qO2n0
z#IJ1jr{*iW$(WZW<e?f_&KbNko{YOt-kK%hql^ThT$m-`XQO-vWxZ5MngHeZDAUvU
zoJ;^P6q#Sl=O&?Si84hL8SaVl0ssh<#5ufj4vYCYXr2Igrf1}e1c^yvrV-beY31n1
zX8Q57Q~6>sE0n`c;fQ!l&-AnmjxZO1uWyz`0VP>&nP`#itsL#`S=Q!g`M=rU9)45(
zJ;-|dRq-b5&z?byo>|{)?5r=n76A4nTALlSzLiw~v~31J<>9PP?;rs31pu_(obw)r
zY+jPY;tVGXi|p)da{-@gE-UCa`=5eu%D;v=_nFJ?`&K)q7e9d`Nfk3?MdhZarb|T3
z%nS~f&t(1g5dY)AIcd$w!z`Siz!&j_=v7hZlnI21XuE|xfmo0(WD10T)!}~_HYW!e
zew}L+XmwuzeT6wtxJd`dZ#@7*BLgIEKY9Xv>st^p3dp{^Xswa2bB{85{^$B13tWnB
z;Y>jyQ|9&zk7RNsqAVGs--K+z0uqo1bf5|}fi5rtEMN^BfHQCd-XH*kfJhJnmIE$G
z0%<@5vOzxB0181d*a3EfYH$G5fqKvcPJ%XY23!PJzzuK<41h;K3WmW;Fah3yX$XSw
z5EY_9s*o0>51B&N5F1(uc|$=^I1~fLLy3?Ol0f;;Ca4%HgQ}rJP(Ab`bQ-z{U4#0d
z2hboi2K@njgb|nm(_szR0JebHusa+GN5aeCM0gdP2N%HG;Yzp`J`T6S7vUT504#-H
z!jlL<$Or?`Mpy_N@kBz9SR?@vA#0H$qyni$nvf2p8@Y{0k#Xb$28W?xm>3qu8RLgp
zjNxKdVb)?wFx8l2m{v>|<~C*!GlBVnrDD~wrdTJeKXwT=5u1%I#8zOBU|X=4u>;s)
z>^mF|$G{ol9B_WP7+f-LHLe7=57&&lfa}8z;U@8Tyei%l?}87(bMRt(A-)QK9Dg3)
zj~~XrCy)tR1Z#p1A(kK{Y$Q|=8VKhI{e%(1G*N-5Pjn)N5P8I0VkxnX*g?EW941ba
z6iJ387g8iCnY4jaNopcpCOsy-A(P2EWJhusSwLP-t|XrzUnLKcKTwn?CKOLf97RIe
zPB}`sKzTrUL#0v;sBY9)s+hW+T2H-1eM)^VN0T#`^Oxhvt&^*fYnAJldnHel*Ozyf
zUoM{~Um<@={-*r60#U(0!Bc^wuvVc);k3d%g-J!4qLpHZVwz%!VuRu}#Ze`^l7W)9
z5>Kf>>9Eozr6C$Z)1`URxU@~QI@)F0FdauXr2Es8>BaOP=)Lp_WhG@><tXJG<r?L)
z%2EcxFktvIQW>R;lZ?BJkMlI<xzFRz+cvLhUjMu)mH8@eDtwh9m1dOzm5-`SRd3Z4
z)t#zss!!A~Y9?x7YT0W0)h?@z&!^9Kp3j|MH2>uMhw8ApiF&yDYW2hFJ?fJhni{?u
z85&g@mo&yT8JcdI$(rSw=QPK(Xj%)k1X|@<=e1rim6`6$RAwc!i#egKuI;BS(LSWz
zt39n_sIypSqfWEV6J3%nTQ@<sT(?tqLQhLCSTA3%QSYHXQJ<}!q`ybMTYt*H&>-4i
zi$R;gsG*9XzhRzXqv2yCs*$VFDx+GXJH|L;wsDH_KI2;^u!)^Xl1YupO;gy^-c(?^
z&$Q1BYvyPsG^;hc$D**@Sy`+`)}T4VJji^bd7Jqw3q6Zii=7tT7GEswEK@D(EFW1Z
zSp`^awCb?>!`j4}Yh7b~$A)U-W3$et-R8BesV(1jzwLcHnq9En7Q0Tn&-M=XBKs!$
zF$X<|c!#|X_t<oHD7%Dx)e-CH;keH6jN=C<dnd8eNvGePS<WfW4bGzr3>WYh)GZit
z(Q)Cp9CDE^WG;+fcyOWARoj*0TI>4EP1lX*cEoMO-Pk?Z{kZ!p4@(b`M~lalr<3Oz
z&kJ6Nm#<fmSFg8{_hRpA@25UGK8Ze!J`=unzN>vN_+kA5{dW4@^Vjg_`q%qU1ULk&
z3Fr!>1V#i_2R;ij2@(Z$1jE4r!MlPVFVbHmT+|i<Li|H^g**v03|$raa~LixG^{4<
zdAL=0et35TEn-DPL&UpCkI2%<M~jUXOBQ!V$w$RS)kjT5dqtN;OP5$IS+nFuj9QE!
zracxP8x?ybc5<or(%nmk<Lu%J<L)jqT$Z!!+H$q!smsr<kYB-BaVj1gA06Ki|A`aA
zspU+r^k2Dm<pkH0yNCOd=f*4NjqzRhW&Du@mxQu}(L|TTU5R5!u1OV1;{s1XwcvHK
zU-E(Esg#hEqbW0~(W%X8gtYjy(?TU-im)qPGd(B0FT*sWFhjb^Y1Qsk6QV%TkxVFa
zS!TPKj{Z#bNQ@+#C4*TDvud*5XGdk9%2CV_=Je#6<ZjCy$@9tkel=z_cXemJcK(L^
z!8Pt{4y}dOu3X!>PIq0wy5aS{>yK?9ZAjVh%SOwMWgFjair&;wpi!{CU}&@N=Eg#~
zLQ&zpEzVmGY{hI9Z0+4<v#n~|mm*%#^<vB7isDZt+>-0xS$$Xe-OToc?Y*V;rTcf_
zb_jRe-RZjXSeas3UfIyD;9afd%<`i0x4T#DzE)vdabOQ=k7SRuGN`h>O0Q~1)u-yD
z>VX=Mn&!Rgd$;YK+Q-}1zu#?t(*cbG#Ronf6db&N$oEidtwC+YVcg-Y!_VuY>bk#Y
ze_ww@?MU&F&qswvrN_dLb=5o6*Egs)ls3YRlE$&)amR1{;Ppd$6RYV^Go!iq1UMl%
z@#4q$AMc(FJlT1QeX8jv{h#)>&{~RGq1N2iiMFIRX?sk2-|2wUogK~{EkB$8eDsX=
znVPf8XG_nK&J~=SIiGia@<PUi@r#KUhdNhuKDxBz(w(lbuHMUmm#<#&xpJx7z5D!C
zm#b&4IbAz_oqfIShW(A!9=o2FU+jKq>9y}|z3FhX{g&gcj=lwb=lWgyFW&aLedUh-
zof`v-2Kw$UzI*>(+&$@i-u=-BsSjR1%z8NeX#HdC<Dw@DPb!|OKdt@M_}6Bsz4Yv$
z*I>`Hh-Z(6xI-`hmHDqv!v)W&&nrf>M(RhcN6(D;jNN*%^u_SYjF;2ng}*8Ow)d6M
ztDk;%`@Lsk$;9w$(d(H%O5UixIr`T2ZRcd@<kNR)@201U-mAVp_JRGO`(yOSk?HJD
z_)nFejX!sM3H<VSCT(Ws-}i*``!YINegFUf32;bRa{vGf6951U69E94oEQKA2Iomc
zK~zW$jaFN4R8<uI&Smb?DKpb4Z9$+QQi0G=MY*J?pa_}>Dv3{Od@=FGnD_&XFUA+6
z@x@DGjF<RgjL}#D@fM?1Ap$`|3bdAM9q5#S&diyaGw0&B_Bj=V*0|F-=XCG2_P5rz
zE_-W^=lJ+P!>5==K~3jFesc`p1(d7^&pph!mu6N%yYx`hYXMFTXjyzW3F6TXt{D*^
zng+}S|2KkpS<_}W;J<;za@j;&wWoo~UqJ05L92jAP><e04CA`U83c`IfwqM}dVXM#
z?*!|7UOTk8sTOK!ms1DIe*#xd0WQ51nuTsf`HVLqD~e`%DKhCEps<oGWN6!s;IjaS
z9%%HSdI2aN1R5pydQ5pBPzpxa;0T~K&iVglRSb@{12+u<i9FdM_%*c80xY{3@TX2f
zoA^mttC?}7BiqkF`JNZ<Lulo_505eRJ<7;iye30hoS?nifY`i$fnNtqCDQAcPQaY_
zjvxj%)8H|d&8CfpWuet_nY13-+tY9y4|UGzhK^VwiDWVX!!T)w7(+eRg=bfw6$XIC
z^jwlH99Mx=A~B}b2^iy_;c~f-%5((=lcwPsaLhI&I~F6I>w-<dKM#C`e9s~jmh{1{
zT|uQd4$qoGD$;~c<F#l4-T4ed(SqYL41K#9_pX1u4zrq`N>;i}80UY+f#HJ~IaR@q
zhjuVLH(_2ZjaXZj6%$vUNa;B4J;=}Rrl(P6c^-wH9yF?#nf%O)`ehs&8OQ#8`|;5B
z&B!J_IOQ|Y1UN{FuoDoM5ra`4#rfk0@X`zKV%6$C6c#T*DiuQ_5rf8<I?XzhyrzB$
znyfRDZo`7pvPr9><TBYD+Pk|ku(l85qht7Z=tp>5*Q_F`X8DErzzhhy)#)uR{Eq!U
z3}gM)eyqIpX7~cqa^SXFjH^Q(oq4A*d>)h32^tjRtA(ZII4)eyx2!s1@hEog+KI3B
z?8V5~X&6qGA(pzQ%K|Cd%G>58oH%v>pTF}X?z?vj5~&0ei@C`VsAN&(h@I5pPgRD{
zMkbUe+)d-8$SOlz8_SmVVB>~$_;lz4CT0^l*Qu?BPI2Zg!+W`iFNQwCy8D-7>GCC<
zk<t(hdCz1cznSH}(x8YQ3GI*uL0QTLiOUd>-nRC6*tq_79Qgz|JX)d?G&~%-I3WLy
zjGe*v?_I#AO@qi}(s1pjvfhlCFyl!W(YPubGZuqs>U5MmaU(<4A_>~S`FI*;EUrpQ
zc}BLDF6+g5CiM5e9AR#-O6aJpnI`Mbv#S^y+KbH;ShZ#~H;Xe_*hCn+TBVBVi77N1
z4d|p#Pi2u`*o!#p*7J2to}WNta!gUKaj#UuKq9ImVlW|@3x&=OY})!XUVG+k9Nqmm
z1_rkWz!Ih-hYw-&-8b>bbC02EyC{yHM8$H@poO(^33VP0cD+Q&2x1Wp6AK4fk~cHU
zhfzHG1<IwvaI|?ym<F<)w<41*Aj$GeCgW(&7tqz2$L1%p`279Xv8sP9Vww3ctJN!b
z?QI61ZFRa<!*}~WWoG)YtO_ocCkgJM>19y0Y}j@SsdNfk27X4icnI}o1N(j%#mUne
z;_)itJQ?$uVYH`dfRt6bjUh@Rkxar*4dBH$_F(XVz1aHjlfc>IzxfX`FYe@TLl*wp
zB^`cW6uz7(bkXk)f4zM7=YzTaMV<az&))4n|LA~{S=U9q0{qp~@1-tJW#->}wRRi7
zw^48Vf<X7>$Kbb0W4_jER{iNezJygei_x)DxNv+QyHqbLq^`<BP8}H<U-Bg-b8hkq
z&P@~%jYP4iD~EiJbSW5@>#PwTDsJT%QtZ(y*4{-qNk%1rnCT<8=0RvKj}zUuxg+~A
z+g<USS5dok4rZ#89jBq{UGhMNRDkpfX)3l&9Zu+m!DhkJO9Xfmk#h5DXVJFcHgs%w
z3elW!CXM6jaDDc2$8Awonjg<1zMZLY6u*82R)vQ}asfT1lGHNSgF*<+pyyoR94%3e
zL1zi_Bx+3}vwSBy?$`}InFAf8%#7)36Yfd9;0D5NgFhCz3Fp!o6o2{<(`UYABIj76
zZHjiGSy7-M<sl7&?Nshy&NL5{Rp{v7h4d}!d2wK{-InSFl)P@xlpC%-VhzDP+d^gZ
zSCmJ-Mq}y(d$UCvrGo{&D<2phBbG&?V+Gom-idVY07I0Qp2xbD8^_g7bsh37z#<WX
z%jRBl8rD=1PU#eEs|?-)6mfAj3B8TSVs0T~*-o;;k`Yw_l%d!OK4*L0=<$pc!PTJm
zVz(F(&II$<%qboS7_gw8|H#bs5ncplS)Q?&8}hQFV1gJ2vdRcyW|p;CcDrHfZ?z2<
Uf%CU0hX4Qo07*qoM6N<$g0rirNB{r;

literal 0
HcmV?d00001

diff --git a/tutorials/matrix_cheatsheet.md b/tutorials/matrix_cheatsheet.md
index 24bfd84..347c164 100644
--- a/tutorials/matrix_cheatsheet.md
+++ b/tutorials/matrix_cheatsheet.md
@@ -235,6 +235,8 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 <br>
 <br>
 
+
+
 <table cellpadding="2" cellspacing="0" style="page-break-before: always">
 	<col width="383">
 	<col width="387">
@@ -243,7 +245,7 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 	<col width="511">
 	<col width="690">
 	<tr>
-		<td height="57" bgcolor="#0066cc" style="border: none; padding: 0in">
+		<td height="59" bgcolor="#0066cc" style="border: none; padding: 0in">
 			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
 		</td>
 		<td bgcolor="#0066cc" style="border: none; padding: 0in">
@@ -264,9 +266,9 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="70" bgcolor="#ffffff" style="border: none; padding: 0in" id="creating">
+		<td colspan="6" height="70" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>CREATING
-			MATRICES </b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+			MATRICES</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
@@ -313,10 +315,15 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 			3</font></font></p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			a = np.array([[1],[2],[3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			a<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp;
-			&nbsp; &nbsp; [3]])</font></font></p>
+			<p align="left"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
+			<font face="Andale Mono"><font size="1" style="font-size: 8pt">a</font></font><font color="#2fff12">
+			</font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">=
+			np.array([1,2,3]).reshape(1,3)</font></font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
+			<font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape<br>(1,
+			3)</font></font></font></p>
+			<p align="left"><br>
+			</p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -345,6 +352,14 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b<br>array([1, 2, 3])</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			note that numpy doesn't have <br># explicit “row-vectors”, but
+			1-D <br># arrays</font></font></p>
+			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12">
+			</font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape</font></font></font></font></font></p>
+			<p style="margin-bottom: 0in"><font face="Andale Mono"><font size="1" style="font-size: 8pt">(3,)</font></font></p>
+			<p align="left"><br>
+			</p>
 		</td>
 		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -434,7 +449,7 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			np.ones([3,2])<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
+			np.ones((3,2))<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
 			[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.]])</font></font></p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
@@ -519,9 +534,9 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in" id='accessing'>
+		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ACCESSING
-			MATRIX ELEMENTS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+			MATRIX ELEMENTS</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
@@ -714,9 +729,9 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in" id="manipulating">
+		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>MANIPULATING
-			SHAPE AND DIMENSIONS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+			SHAPE AND DIMENSIONS</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
@@ -772,10 +787,10 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 			A = np.array([[1,2,3],[4,5,6],[7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [7, 8, 9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			total_elements = A.shape[0] * A.shape[1]<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
-			B = A.reshape(1, total_elements)&nbsp;<br><br># or
-			A.reshape(1,9)<br># Alternative: A.shape = (1,9)&nbsp;<br>#
-			to&nbsp;change the array in place<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			total_elements = np.prod(A.shape)</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			B = A.reshape(1, total_elements)&nbsp;<br><br># alternative
+			shortcut:<br># A.reshape(1,-1)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			B<br>array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])</font></font></p>
 		</td>
 		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
@@ -885,9 +900,9 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="114" bgcolor="#ffffff" style="border: none; padding: 0in" id='basic_ops'>
+		<td colspan="6" height="114" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>BASIC
-			MATRIX OPERATIONS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+			MATRIX OPERATIONS</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
@@ -912,6 +927,9 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 			A + 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A - 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A / 2</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			Note that NumPy was optimized for<br># in-place assignments<br>#
+			e.g., A += A instead of <br># A = A + A</font></font></p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -1025,6 +1043,9 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 			A + A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A - A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A / A</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			Note that NumPy was optimized for<br># in-place assignments<br>#
+			e.g., A += A instead of <br># A = A + A</font></font></p>
 		</td>
 		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -1232,9 +1253,9 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="202" bgcolor="#ffffff" style="border: none; padding: 0in" id='advanced_ops'>
+		<td colspan="6" height="202" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ADVANCED
-			MATRIX OPERATIONS</b></font></font><br>[<a href='#cheat_overview'>back to cheat sheet overview</a>]</p>
+			MATRIX OPERATIONS</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
@@ -1328,7 +1349,7 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="539" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td height="541" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
 			a Gaussian dataset:<br><br>creating random vectors from the
 			multivariate normal<br>distribution given mean and covariance
@@ -1395,6 +1416,8 @@ Bezanson, J., Karpinski, S., Shah, V.B. and Edelman, A. (2012), “Julia: A fast
 		</td>
 	</tr>
 </table>
+
+
 <br>
 
 <br><br><font size=0.6>
diff --git a/tutorials/matrix_cheatsheet_only.html b/tutorials/matrix_cheatsheet_only.html
index 659d121..7624ea4 100644
--- a/tutorials/matrix_cheatsheet_only.html
+++ b/tutorials/matrix_cheatsheet_only.html
@@ -5,7 +5,7 @@
 	<title></title>
 	<meta name="generator" content="LibreOffice 4.2.4.2 (MacOSX)">
 	<meta name="created" content="0;0">
-	<meta name="changed" content="20140616;230046743144000">
+	<meta name="changed" content="20140622;123733939958000">
 	<style type="text/css">
 	<!--
 		td p { color: #000000; font-family: "Verdana"; font-size: 12pt }
@@ -22,7 +22,7 @@
 	<col width="511">
 	<col width="690">
 	<tr>
-		<td height="58" bgcolor="#0066cc" style="border: none; padding: 0in">
+		<td height="59" bgcolor="#0066cc" style="border: none; padding: 0in">
 			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
 		</td>
 		<td bgcolor="#0066cc" style="border: none; padding: 0in">
@@ -92,12 +92,12 @@
 			3</font></font></p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font face="Andale Mono"><font size="1" style="font-size: 8pt">
-			</font></font><font face="Andale Mono"><font size="1" style="font-size: 8pt">a</font></font><font color="#2fff12"><font face="AndaleMono, serif"><font size="4" style="font-size: 14pt">
-			</font></font></font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">=
+			<p align="left"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
+			<font face="Andale Mono"><font size="1" style="font-size: 8pt">a</font></font><font color="#2fff12">
+			</font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">=
 			np.array([1,2,3]).reshape(1,3)</font></font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font face="Andale Mono"><font size="1" style="font-size: 8pt">
-			</font></font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape<br>(1,
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
+			<font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape<br>(1,
 			3)</font></font></font></p>
 			<p align="left"><br>
 			</p>
@@ -132,10 +132,10 @@
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
 			note that numpy doesn't have <br># explicit “row-vectors”, but
 			1-D <br># arrays</font></font></p>
-			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12"><font face="AndaleMono, serif"><font size="4" style="font-size: 14pt"><b>
-			</b></font></font></font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape</font></font></font></font></font></p>
+			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12">
+			</font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape</font></font></font></font></font></p>
 			<p style="margin-bottom: 0in"><font face="Andale Mono"><font size="1" style="font-size: 8pt">(3,)</font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><br>
+			<p align="left"><br>
 			</p>
 		</td>
 		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
@@ -704,6 +704,9 @@
 			A + 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A - 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A / 2</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			Note that NumPy was optimized for<br># in-place assignments<br>#
+			e.g., A += A instead of <br># A = A + A</font></font></p>
 		</td>
 		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -817,6 +820,9 @@
 			A + A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A - A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A / A</font></font></p>
+			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			Note that NumPy was optimized for<br># in-place assignments<br>#
+			e.g., A += A instead of <br># A = A + A</font></font></p>
 		</td>
 		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -1120,7 +1126,7 @@
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="540" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td height="541" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
 			a Gaussian dataset:<br><br>creating random vectors from the
 			multivariate normal<br>distribution given mean and covariance

From 226b921be68dabfc047e6cdabec18dac1591c7ed Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 25 Jun 2014 18:06:16 -0400
Subject: [PATCH 116/248] data entry tutorial

---
 tutorials/python_data_entry_point.ipynb | 994 ++++++++++++++++++++++++
 1 file changed, 994 insertions(+)
 create mode 100644 tutorials/python_data_entry_point.ipynb

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
new file mode 100644
index 0000000..b0dea44
--- /dev/null
+++ b/tutorials/python_data_entry_point.ipynb
@@ -0,0 +1,994 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:ac00f18b1255fe2fd96fea78f54cf237c8ebc58fc5f36c9d734b1056494a81a8"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "\n",
+      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/python_data_entry_point.ipynbc?reate=1)  \n",
+      "\n",
+      "- [Link to this IPython notebook on Github](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/python_data_entry_point.ipynb)  \n",
+      "\n",
+      "- [Link to the GitHub Repository pattern_classification](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import time\n",
+      "import platform\n",
+      "print('Last updated: %s' %time.strftime('%d/%m/%Y'))\n",
+      "print('Created using Python', platform.python_version())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Last updated: 25/06/2014\n",
+        "Created using Python 3.4.1\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Entry point: Data  "
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "- Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various other data analyses.  "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Sections"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Installing Python packages](#Installing-Python-packages)\n",
+      "\n",
+      "- [About the dataset](#About-the-dataset)\n",
+      "\n",
+      "- [Downloading and saving CSV data files from the web](#Downloading-and-savin-CSV-data-files-from-the-web)\n",
+      "\n",
+      "- [Reading in a dataset from a CSV file](#Reading-in-a-dataset-from-a-CSV-file)\n",
+      "\n",
+      "- [Visualizating of a data](#Visualizating-of-a-data)\n",
+      "\n",
+      "    - [Histograms](#Histograms)\n",
+      "\n",
+      "    - [Scatterplots](#Scatterplots)\n",
+      "\n",
+      "- [Splitting into training and test dataset](#Splitting-into-training-and-test-dataset)\n",
+      "\n",
+      "- [Feature Scaling](#Feature-Scaling)\n",
+      "\n",
+      "- [Saving the processed datasets](#Saving-the-processed-datasets)\n",
+      "\n",
+      "    - [Pickle](#Pickle)\n",
+      "\n",
+      "    - [Comma Separated Values (CSV)](#Comma-Separated-Values)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Installing Python packages"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "**In this section want to recommend a way for installing the required Python-packages packages if you have not done so, yet. Otherwise you can skip this part.**\n",
+      "\n",
+      "The packages we will be using in this tutorial are:\n",
+      "\n",
+      "- [NumPy](http://www.numpy.org)\n",
+      "- [SciPy](http://www.scipy.org)\n",
+      "- [matplotlib](http://matplotlib.org)\n",
+      "- [scikit-learn](http://scikit-learn.org/stable/)\n",
+      "\n",
+      "Although they can be installed step-by-step \"manually\", but I highly recommend you to take a look at the [Anaconda](https://store.continuum.io/cshop/anaconda/) Python distribution for scientific computing.\n",
+      "\n",
+      "Anaconda is distributed by Continuum Analytics, but it is completely free and includes more than 195+ packages for science and data analysis as of today.\n",
+      "The installation procedure is nicely summarized here: http://docs.continuum.io/anaconda/install.html\n",
+      "\n",
+      "If this is too much, the [Miniconda](http://conda.pydata.org/miniconda.html) might be right for you. Miniconda is basically just a Python distribution with the Conda package manager, which let's us install a list of Python packages into a specified `conda` environment from the Shell terminal, e.g.,\n",
+      "\n",
+      "<pre>$[bash]> conda create -n myenv python=3\n",
+      "$[bash]> source activate myenv\n",
+      "$[bash]> conda install -n myenv numpy scipy matplotlib scikit-learn</pre>\n",
+      "\n",
+      "When we start \"python\" in your current shell session now, it will use the Python distribution in the virtual environment \"myenv\" that we have just created. To un-attach the virtual environment, you can just use\n",
+      "<pre>$[bash]> source deactivate myenv</pre>\n",
+      "\n",
+      "**Note:** environments will be created in ROOT_DIR/envs by default, you can use the `-p` instead of the `-n` flag in the conda commands above in order to specify a custom path.\n",
+      "\n",
+      "**I find this procedure very convenient, especially if you are working with different distributions and versions of Python with different modules and packages installed and it is extremely useful for testing your own modules.**"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "About the dataset"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "For the following tutorial, we will be working with the free \"Wine\" Dataset that is deposited on the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Wine).\n",
+      "\n",
+      "<br>\n",
+      "\n",
+      "<font size=\"1\">\n",
+      "**Reference:**\n",
+      "Forina, M. et al, PARVUS - An Extendible Package for Data\n",
+      "Exploration, Classification and Correlation. Institute of Pharmaceutical\n",
+      "and Food Analysis and Technologies, Via Brigata Salerno, \n",
+      "16147 Genoa, Italy.</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The Wine dataset consists of 3 different classes where each row correspond to a particular wine sample.\n",
+      "\n",
+      "The class labels (1, 2, 3) are listed in the first column, and the columns 2-14 correspond to the following 13 attributes (features):\n",
+      "\n",
+      "1) Alcohol  \n",
+      "2) Malic acid  \n",
+      "3) Ash  \n",
+      "4) Alcalinity of ash    \n",
+      "5) Magnesium  \n",
+      "6) Total phenols  \n",
+      "7) Flavanoids  \n",
+      "8) Nonflavanoid phenols  \n",
+      "9) Proanthocyanins  \n",
+      "10) Color intensity  \n",
+      "11) Hue  \n",
+      "12) OD280/OD315 of diluted wines  \n",
+      "13) Proline     \n",
+      "\n",
+      "An excerpt from the wine_data.csv dataset:\n",
+      "    \n",
+      "<pre>1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065\n",
+      "1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050\n",
+      "[...]\n",
+      "2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520\n",
+      "2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680\n",
+      "[...]\n",
+      "3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630\n",
+      "3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530</pre>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Downloading and saving CSV data files from the web"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Usually, we have our data stored locally on our disk in as a common text (or CSV) file with comma-, tab-, or whitespace-separated rows. Below is just an example for how you can CSV datafile from a HTML website directly into Python and optionally save it locally."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import csv\n",
+      "import urllib\n",
+      "\n",
+      "url = 'https://raw.githubusercontent.com/rasbt/pattern_classification/master/data/wine_data.csv'\n",
+      "csv_cont = urllib.request.urlopen(url)\n",
+      "csv_cont = csv_cont.read() #.decode('utf-8')\n",
+      "\n",
+      "# Optional: saving the data to your local drive\n",
+      "with open('./wine_data.csv', 'wb') as out:\n",
+      "    out.write(csv_cont)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 100
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Note:** If you'd rather like to work with the data directly in `str`ing format, you could just apply the `.decode('utf-8')` method to the data that was read in byte-format by default.\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Reading in a dataset from a CSV file"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Since it is quite typical to have the input data stored locally, as mentioned above, we will use the [`numpy.loadtxt`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html) function  now to read in the data from the CSV file.  \n",
+      "(alternatively [`np.genfromtxt()`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.genfromtxt.html) could be used in similar way, it provides some additional options)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "# reading in all data into a NumPy array\n",
+      "all_data = np.loadtxt(open(\"./wine_data.csv\",\"r\"),\n",
+      "        delimiter=\",\", \n",
+      "        skiprows=0, \n",
+      "        dtype=np.float64\n",
+      "        )\n",
+      "\n",
+      "# load class labels from column 1\n",
+      "y_wine = all_data[:,0]\n",
+      "\n",
+      "# conversion of the class labels to integer-type array\n",
+      "y_wine = y_wine.astype(np.int64, copy=False)\n",
+      "\n",
+      "# load the 14 features\n",
+      "X_wine = all_data[:,1:]\n",
+      "\n",
+      "# printing some general information about the data\n",
+      "print('\\ntotal number of samples (rows):', X_wine.shape[0])\n",
+      "print('total number of features (columns):', X_wine.shape[1])\n",
+      "\n",
+      "# printing the 1st wine sample\n",
+      "float_formatter = lambda x: '{:.2f}'.format(x)\n",
+      "np.set_printoptions(formatter={'float_kind':float_formatter})\n",
+      "print('\\n1st sample (i.e., 1st row):\\nClass label: {:d}\\n{:}\\n'\n",
+      "          .format(int(y_wine[0]), X_wine[0]))\n",
+      "\n",
+      "# printing the rel.frequency of the class labels\n",
+      "print('Class label frequencies')\n",
+      "print('Class 1 samples: {:.2%}'.format(list(y_wine).count(1)/y_wine.shape[0]))\n",
+      "print('Class 2 samples: {:.2%}'.format(list(y_wine).count(2)/y_wine.shape[0]))\n",
+      "print('Class 3 samples: {:.2%}'.format(list(y_wine).count(3)/y_wine.shape[0]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "total number of samples (rows): 178\n",
+        "total number of features (columns): 13\n",
+        "\n",
+        "1st sample (i.e., 1st row):\n",
+        "Class label: 1\n",
+        "[14.23 1.71 2.43 15.60 127.00 2.80 3.06 0.28 2.29 5.64 1.04 3.92 1065.00]\n",
+        "\n",
+        "Class label frequencies\n",
+        "Class 1 samples: 33.15%\n",
+        "Class 2 samples: 39.89%\n",
+        "Class 3 samples: 26.97%\n"
+       ]
+      }
+     ],
+     "prompt_number": 97
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Visualizating of a data"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "There are endless way to visualize datasets for get an initial idea of how the data looks like. The most common ones are probably histograms and scatter plots."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Histograms"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Histograms are a useful data to explore the distribution of each feature across the different classes. This could provide us with intuitive insights which features have a good and not-so-good inter-class separation. Below, we will plot a sample histogram for the \"Alcohol content\" feature for the three wine classes."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 53
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "from math import floor, ceil # for rounding up and down\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "# bin width of the histogram in steps of 0.1\n",
+      "bins = np.arange(floor(min(X_wine[:,0])), ceil(max(X_wine[:,0])), 0.1)\n",
+      "\n",
+      "# get the max count for a particular bin for all classes combined\n",
+      "max_bin = max(np.histogram(X_wine[:,0], bins=bins)[0])\n",
+      "\n",
+      "# the order of the colors for each histogram\n",
+      "colors = ('blue', 'red', 'green')\n",
+      "\n",
+      "for label,color in zip(\n",
+      "        range(1,4), colors):\n",
+      "\n",
+      "    mean = np.mean(X_wine[:,0][y_wine == label]) # class sample mean\n",
+      "    stdev = np.std(X_wine[:,0][y_wine == label]) # class standard deviation\n",
+      "    plt.hist(X_wine[:,0][y_wine == label], \n",
+      "             bins=bins, \n",
+      "             alpha=0.3, # opacity level\n",
+      "             label='class {} ($\\mu={:.2f}$, $\\sigma={:.2f}$)'.format(label, mean, stdev), \n",
+      "             color=color)\n",
+      "\n",
+      "plt.ylim([0, max_bin*1.3])\n",
+      "plt.title('Wine data set - Distribution of alocohol contents')\n",
+      "plt.xlabel('alcohol by volume', fontsize=14)\n",
+      "plt.ylabel('count', fontsize=14)\n",
+      "plt.legend(loc='upper right')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxAAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x142bdd240>"
+       ]
+      }
+     ],
+     "prompt_number": 95
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Scatterplots"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Scatter plots are useful for visualizing features in more than just one dimension, for example to get a feeling for the correlation between particular features.  \n",
+      "Unfortunately, we can't plot all 13 features here at once, since the visual cortex of us humans is limited to a maximum of three dimensions."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Below, we will create an example 2D-Scatter plot from the features \"Alcohol content\" and \"Malic acid content\".  \n",
+      "Additionally, we will use the [`scipy.stats.pearsonr`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html) function to calculate a Pearson correlation coefficient between these two features.\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from scipy.stats import pearsonr\n",
+      "\n",
+      "\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for label,marker,color in zip(\n",
+      "        range(1,4),('x', 'o', '^'),('blue', 'red', 'green')):\n",
+      "\n",
+      "    # Calculate Pearson correlation coefficient\n",
+      "    R = pearsonr(X_wine[:,0][y_wine == label], X_wine[:,1][y_wine == label])\n",
+      "    plt.scatter(x=X_wine[:,0][y_wine == label], # x-axis: feat. from col. 1\n",
+      "                y=X_wine[:,1][y_wine == label], # y-axis: feat. from col. 2\n",
+      "                marker=marker, # data point symbol for the scatter plot\n",
+      "                color=color,\n",
+      "                alpha=0.7, \n",
+      "                label='class {:}, R={:.2f}'.format(label, R[0]) # label for the legend\n",
+      "                )\n",
+      "    \n",
+      "\n",
+      "plt.title('Wine Dataset')\n",
+      "plt.xlabel('alcohol by volume in percent')\n",
+      "plt.ylabel('malic acid in g/l')\n",
+      "plt.legend(loc='upper right')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nah7OJ7wrYQAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x16dacf6a0>"
+       ]
+      }
+     ],
+     "prompt_number": 98
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If we want to pack 3 different features into one scatter plot at once, we can also do the same thing in 3D:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from mpl_toolkits.mplot3d import Axes3D\n",
+      "\n",
+      "fig = plt.figure(figsize=(8,8))\n",
+      "ax = fig.add_subplot(111, projection='3d')\n",
+      "   \n",
+      "for label,marker,color in zip(\n",
+      "        range(1,4),('x', 'o', '^'),('blue','red','green')):\n",
+      "    \n",
+      "    ax.scatter(X_wine[:,0][y_wine == label], \n",
+      "               X_wine[:,1][y_wine == label], \n",
+      "               X_wine[:,2][y_wine == label],  \n",
+      "               marker=marker, \n",
+      "               color=color, \n",
+      "               s=40, \n",
+      "               alpha=0.7,\n",
+      "               label='class {}'.format(label))\n",
+      "\n",
+      "ax.set_xlabel('alcohol by volume in percent')\n",
+      "ax.set_ylabel('malic acid in g/l')\n",
+      "ax.set_zlabel('ash content in g/l')\n",
+      "\n",
+      "plt.title('Wine dataset')\n",
+      "     \n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmUVOWd//++t/a9u4GmbZoGBAkIyiIKImvUqLjGNWZQ\nJzHKccwYRyff4zITz/klmWRmgsZ8Z8YlUb/GmBhHR8ERTVBBFkUkCCJEoJutm26g1+rabtVdnt8f\n5XO5VV1VXcutqltVz+scztGq6lvPvXXv834+n+ezcIQQAgaDwWAwGBnhyz0ABoPBYDAqASaYDAaD\nwWBkARNMBoPBYDCygAkmg8FgMBhZwASTwWAwGIwsYILJYDAYDEYWMMFkMBgMBiMLmGAyGAwGg5EF\nTDAZDAaDwcgCJpgMBoPBYGQBE0wGg8FgMLKACSaDwWAwGFnABJPBYDAYjCxggslgMBgMRhYwwWQw\nGAwGIwuYYDIYDAaDkQVMMBkMBoPByAImmAwGg8FgZAETTAaDwWAwsoAJJoPBYDAYWcAEk8FgMBiM\nLGCCyWAwGAxGFjDBZDAYDAYjC5hgMhgMBoORBUwwGQwGg8HIAiaYDAaDwWBkARNMBoPBYDCygAkm\ng5ElmzdvxrRp00ryXcuWLcNzzz1Xku9iMBjZwQSTUbP87Gc/w4oVKxJeO+uss1K+9uqrr2Lx4sX4\n8ssvSzI2juPAcVxWn504cSI++OCDIo+odN/DYBgVJpiMmmXp0qX46KOPQAgBAHR3d0OSJOzatQuK\noqivtbe3Y8mSJeUcakY4jlPPoRq+h8EwKkwwGTXLvHnzIIoidu3aBSDucl2+fDmmTp2a8NqUKVPQ\n1NSEjRs3Yvz48erfT5w4EatXr8asWbNQV1eHb33rW4hGo+r7//u//4vZs2ejvr4eF110Efbs2ZN2\nLOvXr8e0adNQV1eHv//7vwchRBWn9vZ2fP3rX8fo0aMxZswYrFy5En6/HwBw22234dixY7j66qvh\n8Xjwi1/8AgBw00034YwzzkBdXR2WLl2Kffv2qd+1bt06zJgxA16vFy0tLVi9evWIY073PQxGTUEY\njBpm+fLl5IknniCEEHLvvfeS559/njz66KMJr915552EEEI2bNhAWlpa1L+dOHEimT9/Punu7ib9\n/f1k+vTp5OmnnyaEELJz507S2NhItm/fThRFIS+++CKZOHEiiUajw8bQ09NDPB4Pef3114kkSeSJ\nJ54gZrOZPPfcc4QQQtra2sh7771HYrEY6enpIUuWLCH3339/wjjef//9hGO+8MILJBgMklgsRu6/\n/34ye/Zs9b2mpiayZcsWQgghg4ODZOfOnRnHHIvF0n4Pg1FLMAuTUdMsXboUmzZtAgBs2bIFS5Ys\nweLFi9XXNm/ejKVLl6b9+/vuuw9NTU2or6/H1VdfrVqmzz77LFatWoXzzz8fHMfh9ttvh81mw7Zt\n24YdY926dZg5cyauv/56mEwm3H///WhqalLfnzx5Mi6++GJYLBaMHj0a//AP/4APP/ww43n97d/+\nLVwuFywWCx577DHs3r0bgUAAAGC1WrF3714MDQ3B5/Nhzpw5OY+ZwahFmGAyapolS5Zgy5YtGBgY\nQE9PDyZPnowLL7wQH330EQYGBrB3796M+5daYXM4HAgGgwCAo0ePYvXq1aivr1f/dXZ2oru7e9gx\nurq60NLSkvCa1vV78uRJfOtb30JLSwt8Ph9uu+029PX1pR2Toih46KGHMGXKFPh8PkyaNAkcx6G3\ntxcA8Prrr2PdunWYOHEili1bpgpiujF3dXVlcSUZjOqHCSajJiGEIBaLYfr06fD7/XjmmWdw0UUX\nAQC8Xi+am5vx7LPPorm5GRMmTMj6uDSytbW1FY8++igGBgbUf8FgELfccsuwv2lubkZHR0fC2LT/\n/8gjj8BkMuGLL76A3+/HSy+9pAYlab+T8vLLL2Pt2rV4//334ff7cfjw4YQ90Xnz5uHNN99ET08P\nrrvuOtx8881ZjTnbqF0Go1phgsmoORRFQTQahSiKsNlsmD17Np544gmcd955GBgYgCAIWLhwIR5/\n/PGM7thUUFG666678PTTT2P79u0ghCAUCuHtt99WLVAtV155Jfbu3Ys33ngDkiThV7/6FU6cOKG+\nHwwG4XK54PV6cfz4cfz7v/97wt+PHTsW7e3tCZ+32WxoaGhAKBTCI488or4niiJefvll+P1+mEwm\neDwemEymrMac/D0MRq3BBJNRMxBCIEkSBgYGQAgBz/PgeR6LFy9Gb28vFixYAFEUIQgC5s2bh97e\nXlxwwQUQBAGSJIEQktHK0uZOnnfeefj1r3+N73//+2hoaMBZZ52F3/72tyn/btSoUfjv//5vPPTQ\nQxg9ejTa2tqwaNEi9f3HHnsMO3fuhM/nw9VXX40bbrghYRwPP/wwfvKTn6C+vh6PP/44br/9dkyY\nMAHjxo3DzJkzceGFFyZ8/ne/+x0mTZoEn8+HZ599Fi+//HJWY07+Hgaj1uAIYYlVjOqHumAVRcHg\n4CDq6+vV13g+vm6UJAmiKMLhcKh/o3VlAoDJZILFYoHZbIbJZGJuSgajhjCXewAMRrFRFAWxWGyY\nhUgF0mw2q6KpJbnaDiEEiqJAEAT1NSagDEbtwASTUbVQF6wkSeA4LkEUI5EIotEoeJ6HIAiqi5YQ\nAlmWwfP8MPFLJ6CRSER9nQkog1G9MJcsoypRFAWiKEJRlAShk2UZfr8fZrMZTqcTkiSB53n185Ik\nqcfgeT5B+EYSP+q+pd8JMAFlMKoJZmEyqgpqIYqiCCDRKozFYgiFQgAAl8uVIF48z8NkMkFRFDgc\nDiiKAlmWIcsyYrEYgLj40X+pBJS+Ri3ZVBao2WxW/zEBZTAqCyaYjKoh2QVLxYgQgnA4DFEU4fF4\nEAgERhQqGkFrsVgAQDcBpWLOBJTBqDyYYDKqgnQuWEmSEAqFYDKZ4PV6Uwb3aEm3Q6EVUOp6zSSg\nmYKIRhJQi8WSIMQMBsMYMMFkVDTJoqMVo2g0ikgkAqfTCavVmlJ8tK9lK05a4SuGgIbDYfXvqIDS\nSF4moAxG+WCCyahYtLmVWqtSURSEw2HIsgyv16tWskn+W73ER28BBU5btLIsq0UT6D4rE1AGozww\nwWRUJMm5lVoXbDAYhMVigdfrHdGqLAapBJTugUqShGg0Co7jchJQ4LQFSiN5OY5L2ANlAspgFBcm\nmIyKIl1uJSEEgiBAEAS4XC5YrdYyj/Q0WnEEkFFAzWZz2n3UVAJKiy/Q95mAMhjFgwkmo2LI5IKl\nBcLTuWCNRCYBpYFL0WgUsixnDP5hAspglBYmmIyKQJIkhMNhxGIxuN3uYbmVNpsNDoejIgUhWUDD\n4TDM5vijSYvB0/3LXAVUFEUmoAyGTjDBZBgarQuWwnEcCCGIRCKqgNJ8yVyOO1KKSbmgrmYqmskW\naC4CqrW20wkoTWNhAspgZIYJJsOwZCpvFwwGwfN8VrmVyVSaKIzkwmUCymCUBiaYDMORXN6OCiLH\ncZBlGUNDQ3A4HLDZbLpP6JVQWrlUAkoIgcVigdVqZQLKYIAJJsNg0ElbluVh5e0ikQgURYHX61Xd\nlXpSqWKQSkC1OaCKouQloIIgJBSF4DhOzQE1m81ZFaRnMKoJJpgMwzBSbiWd9IshltWENrgHyF9A\nAQzbS43FYohGo+p7TEAZtQSbeRhlJ1Nupba8Hc/ziEQiun93tU/y2QhochGFbF24VEC1FijdA2UC\nyqg2mGAyykqm3MpQKKS6YE0mk1oirlBolC3971ojk4BGo1FVQOliIt2iQiug9HrGYjG1FCC1TpmA\nMqoFJpiMskGtymQXrCiKCIVCsFgsCTmXjOKQTkCj0ahaCGEkC5T+fy4CatS0HgYjHUwwGSWnEsvb\n1RJUQCVJUveMU1mgTEAZtQYTTEZJSZdbqS1v5/P52ORpILJ14TIBZVQ7TDAZJSFdbiVwuryd3W6H\n3W5P64LV7j0yykeygNIcUPr7EkLyElC6n60VUBqFywSUYQSYYDKKTrp0EUIIwuEwRFHMq7xdvmQS\nXibIp8k2gpj27qS/XyYBTZd+kqoOLo2SFgQBoijCbrczAWWUFSaYjKJCO28MDg6ivr5et/J2xYAF\nF+lDJgGlaUHJOaCZBJTubVMBTZUHygSUUQqYYDKKQnJgj/b1WCyGcDicc3k75pKtTDIJKHW/jiSg\n9DgUrQVKBdRkMqn7n0xAGcWACSZDd9LlVlIXrCRJ8Hg8rGJPjaIVUCp8mQQUGG79p3LhKooCQRDU\n/9cGEFFXMINRCGzGYuiKdt8qeVIbGhqCxWKBz+djkxcDwGnhG0lAaY3hdJZjJgGl9yK1QKkLl92D\njFxhgsnQhZFyKwGoUbDlhrl2jUsqAZVlWS0En8oCzVVAKUxAGbnCBJNRMJlyK2l5OwAFFyJgQld7\nUPHkOA52uz2hlZkkSWodWyagjFLABJORN8m5lcnl7YLBIGw2G9xuNwYHB8s5VEaVkKkXKBNQRrFh\ngsnIi5H6Vkaj0YTcSmYd5ga7VtmRjYBm2ws0lYBGIpGEIgtMQGsbJpiMnElXiECWZYRCIXAcV9Hl\n7crd8otNxInk8ntkElBRFCEIQk4Cqt2LpwIai8VgtVrVKFwmoLUDE0xG1mhdsNrJBMi+vF0haK3Y\nQo6fztplE171obeAchynNuBmFmjtwQSTkRUj5VaKopgxt5K5ZBlGQC8BpYvFVBYo/Tytt8sEtHpg\ngskYkXQuWEmSEAqFYDKZDFPejsHIhVwFNN09nsqFq/XGAExAqwEmmIy0ZMqtjEajiEQicDqdsFqt\nFfvwV+q4S0G593LLQSoB1RZRoClS0Wg05z3QVAKqLeVXa9e6EmGCyUgJLZoeCATg9XoTcivD4TBk\nWYbX61UnlpHQyyVLj8MmF0YpSG5lpi0gTwU03yAiGslL0QYRpWqJxig/TDAZCWhXwoqiqHuWQNwF\nGwwGYbFYEkS0EmH7qZWDkRZIVPhsNhuA1BboSL1AtcehJAtocik/JqDGgAkmQyXZBWsymdTanoIg\nQBAEuFyugiv2lBs28TD0ItkC1QpoNBrVVUC1e6BMQMsDE0wGgNSBPdQKCwQCAJCTCzYZvV2yemEk\n64VR+RRTQCVJSqiqxQS09DDBrHEy5VbSh9NsNsPhcLAHklHz5LrAKraACoIARVHUQgpMQIsLE8wa\nJlNuJa1oAqBqxVIURUQiETVog/ZMZFYno1gUQ0Dpf4uiyCzQIsMEs0bJVN4uGAyC53l4vV7diqYb\nqXCBdnVutVrV/49GowDi0Y8sV45RCpIFlAbaUfcrISRrAdVul9Baz8kCStNYmIDmBxPMGiNdbiUQ\nzy0Lh8NwOByw2WwJe5lGebgKFV7aiowQAq/XqwZVUOGktXCTk9bZKr18GOn+KzY8z4Pn+QQBpRZo\nKgFN9ywwAS0OTDBriEwu2FAoBEmSMpa3q3REUUQoFErIm5NlWZ106DWxWCzgeT5vdxmjeim1eFMB\npV1/Ugkoz/MQRVH1iORrgWrr4LJ7OzXVOTMyhpEcYZecW2k2m+Hz+YY9JEaNbs2F5LQYrUhmItN+\nkyAI6mpf675lkwyjmCQLKL0PaR4ogGFFFLIV0Fgspm5L0O+g9z+7t+Mwwaxykl2wWqtSW96OJmJX\nG4qiIBQKQVEUNS2GVmrJFa2A2my2hNV+qsmK1dZlFBu6rUJzo0e6JwsVUOrCrVUBZYJZxdD9umQX\nbCoRqRRysVS1lYncbvewsPxC0a72aYEHbeNiKrCsViijWFCXLCXdPZnrok4roPRZoQKq3bqoNQFl\nglmFaHMrASQ8FHQfL5WIpKISXbJa6zlVZaJiPNjaWqF0ssqnbZSRMEpUs5GopACkVPdkPgJKzzeV\ngNJj0EClahdQJphVBu1PSQhR9x7o69VU3i4dNIAp1+LwyccoFLpCT24bJUlSQtFuowdZGGFMlSRS\npSaXa5NOQOmWDbUeCxFQQRAgyzJsNtuwKNxqoDrOggHgdIeRaDSq7lnS1wOBAERRhM/ny0ksjZQ/\nORKSJMHv94PjuLzFslgTM52IbDYbnE6numih1nAoFFKLRdCJjMEoJtr9T4fDAZfLBbvdDp7nIUkS\nwuEwQqEQBEFQt3bSHSedCzcUCuGee+7BoUOHSnZexYRZmFVAusAe4PRNa7fbYbfby7ZSL6ZrtxID\nmNJF4EqSpLrMtGks1bJCr3Sq2dpN5xXJdV8+WUB7enoq4pnMBiaYFU6q3EqO49TAHlEU4Xa71TD0\nfL/DqOjhgjUCWgGl+50cx7EIXEZGiingmQQ03b58qrkiHA7D6XQWZYylhj11FYy2+HJyFCwVUa/X\nW5BY6vkw6i28sizD7/cDyK2TSiW4mWkUot1uh9PphMPhSHCVhcNh1fVu9HNhVAdUQLUuXFoRjAYT\nUtetNu+bBt9liyAImD9/PmbPno2zzz4bDz/88LDPbNy4ET6fD3PmzMGcOXPwk5/8RLfzzASzMCuQ\nTLmVNPTbbDZnFQVbKvQWXlrGr1JcsIVAf2O691wNEbi5wBYExiSVBUorYomiiFtuuQX9/f1wOBx4\n9913sXTpUvh8vhGPa7fbsWHDBjidTkiShEWLFmHLli1YtGhRwueWLl2KtWvXFuXc0sEszAqDWo+p\nxJIGjtjtdt0mTSNaY3S/0uPxVL1YpiLVSp+KKd2zpgFE2VY1MjpGWQAYbQ/TSOOh8xFtB/jqq6/i\n5z//OWKxGH75y19i3LhxuOCCC/Dkk0+OeCzqwqX3cENDw7DPlOO+ZhZmhZApt1KboO/z+RCLxdTP\nVRM0+IDn+ZRl/PLBiAuCXMlUwi+5Bi4rc8YoFXa7HRdddBEcDgfef/99RKNRfPLJJwgGgyP+raIo\nmDt3Ltrb23HPPffg7LPPTnif4zh89NFHmDVrFsaNG4df/OIXwz5TDJhgVgC0ULIsy8OsSppbWSzX\npFEKF1AXLM/z6r4JIzXJAqotl0bLArIAourASBYmMLzyEIXjONjtdixdujSr4/A8j127dsHv9+Oy\nyy7Dxo0bsWzZMvX9uXPnoqOjA06nE++88w6uu+46HDhwQK/TSD+uon8DoyBobmWyWCqKgmAwiFgs\nBq/XmyCW1WA1UbSuZr06qYx0farl2lFooro2gIh2atHm2rEAIkYxKETQfT4frrzySuzYsSPhdY/H\no7ptr7jiCoiiiP7+/oLGmQ1MMA0KtSq1xY/pjSeKIvx+P0wmU8WkUuQj4rIsY2hoSI32LUXbMSOt\n1ouBttKL3W5PSFankY40ApcVUBiO0Sw6o5Hq+uR6D/X29qqN6yORCNavX485c+YkfObkyZPqcbdv\n3w5CSMp9Tr1hLlkDkqlvZSQSQTQazZhbqaeFWS5rlQavaJtZM/QnU64dEJ+wyl3CL52br9aphMWM\nKIo5p7V1d3fjjjvugKIoUBQFt912Gy6++GI888wzAIBVq1bhtddew1NPPQWz2Qyn04lXXnmlGMMf\nBkcq4arXENrGsFqxlGUZoVAIHMfB5XJlnEBEUUQkEoHX6y14POFwGBzHweFwFHQcmu7i8Xgyfo4u\nCmKxGNxu9zCrMhQKwWQywW636zIWbTcXIC4QtA9guRAEASaTqaD8WT0IBoNwOBwJQUTlaKJtlOth\ntLHQ7Qq3213uoaiEw2HYbDZ1ATYwMIB77rkHb7/9dplHpg/MwjQIybmVWkHMtbydUS3MkY6jXRR4\nvd60wQNsjVc6qCCmKuFHF3bJpdKq2RvAXLK5EQ6HC15sGwkmmAaAlrGTZTlBEGnnEVEUdQt4KRcj\nTTKlrnnLRDc/MkXgshJ+pcWI4p08JlrfuVqo3Bm4CtDmVtJJh95skiSp7sd01lY69LbCiiku2e7L\n6onRJplKJlXD4uR2UayJdu0SDodzKotndJhglonk3Eqe59WqLNrOG1artayTjF7fnUrEaWoMEA8f\nZ9ZIZVPrJfwYwy3Maiq8DjDBLAu0vF1yYI9enTcqYZ9PFEUEg8GcXbCVcG6MOOkicLVNtLMJIDKS\n65GNJTeYYDLyJlNgDy1YbLPZ4PV6DfMgFMO9S6sTlcoFyzAGqQQ0XQm/cqWwMPKn2lt7AUwwS0am\n3EoqICaTSRd/vxGtMNqjMxAIACi/Czbd9THitatWMgUQ0VrIJpNJzcdjJGJUCzM56IftYTJyIl1u\npXYPz+l0qlV9jIReAkLLrtFOBoU86IWOR/vdRpxwahVtABEwPAJXFMVhReQZxiYUCjELk5Ed2eRW\n2mw2OByOorRhMsIKVGtBcxxX8MNT7vNhlA4qoJIkwWw2q4FxNAK3HAFERnimjEqqaxOJRDB69Ogy\njUh/mGAWCbonma68Ha1kU4w9PKM80DS/lBACj8ejumMZjFzJVMIvOQKXiqtRnoNiUQnizfIwGRlJ\n7luZXN4uGAyC5/lhuZXF2DvT44HKd1y0Rydtcsz2BRn5kuo+ZgFExiOdhcn2MBkpSXbBam8e2s+x\nVMXEyzU5aPNIXS5XQk6eHrCgHEYqMjXRFgQBhBDV+qyFEn5Gge1hMlIyUm6lJEkZy9sVy8IslFzG\nRV2wtB2XNo/USEKXPJZKcG2VGqP8VvmiFVCbzaZbCT8j3StGGguQ3sJkgslQ0bpgkwN7qFvSbDbD\n5/OV9OYu9YNEz9ViscDtdhvqQWbkT7X8jqlK+GkDiFgJv+LAXLIMlUy5ldrydjabbcRjGdXCzOY7\ncj1XBqOc0GdVK6CVWMLPaF6AVBYmqyXLABB3Pw4NDaluH21uZTq3ZDbo5WYpZg1YSi6l/LSLiULG\nVmzXrpFcx4zSkE0JPyqg9H2jCKhRxpEOVumnxtEG9kiSlLD6FEURoVAoL7dkMW78Yk78WheskUr5\nMaqPUgtUughcSZIAxEWAReAOh0XJMhJI5YKl+yE0OV8bGZor9HhGtzBpxG+lumCZFcnIBe3+piRJ\ncLlcCU20gfL0ACWEGL7DjyzLeTeRMCJMMLMgU26lkeqjJlOMPdFsIn7ToeeCgMEoF5lq4NZyE+10\nz3Y1Pe9MMEcgU24lTSVxOBw5tahKh56Wj94WJi26YDKZSh7xm2o8eh6PwciGdIIwUhPtSgggKgbV\nuDhmgpmBTOXtwuEwJElSK9kYET2FZWhoqGRFF0oFDdCSZVl1uVXjQ84oHXSeKEUTbaPdq+lcxEYa\nY6EwwUxBptxKbXk7m82mq7vFaBYmdcEC0KXurZH2Dgkh8Pv9sFqtagsp7f40ABbQwSiYTCX8cmmi\nXakY5XnXCyaYSaTLrQSGl7ejJbf0Qm9BKeRYWhcsgJz3K4tJIedF80YBwOVywWQyQRRFdTEgCIL6\nOfrf5QroYMQxmiVVCJlK+CXXwKXpaunO3Wj3SPLvZLTx6YFxZkEDkE95O6PeFIVMMLT1GF0Y0EAG\nI1DIeWnzRgHAarWq/609PnWpJZdUK9V+VLWIQ7VRDOHOFEAUiUQAZF6wGfleEUWxIqPoM8EEE5n7\nVmrzDZODXYrx8JTTwqR7s6IoJiwM9IpuLadLVmsxezweDA4Opvxc8hiTAzpS7UfR/c9qc6cxSs9I\nTbQBqNanURfrlGorvA4wwcxY3o7mVqbLNzTSnlwyuU7cmVqPVTrJFnO+ZNqP0rrTtALKYBRCpgUb\nnaOMUgM3eVFNt6+qiZoWTG3icXJuZbbl7aphD5MKit1u1yU9ppjkco20zbq1FrOegVXZ5ONpW0pV\nCtW0b6gX5V4cJy/YaL9Z7aLfSE20q63KD1Cjgpkpt1IURQSDQdhsthHL2xm5nF02wqIVlExRsHoJ\neSktckVREAwGASBls25A/wkwG/etUSazSqHcIpWMkX4zKqA8z6vCWc4m2qksTOaSrXAy5VZGIhFE\no9GsUyiMnkSfaWxUUDiOqzoXLN13pjmy5ZjksnXfZhMNyTCWUBmFZIHKFIFbjibabA+zgiGEQBRF\niKKoWgIUWZYRCoXAcVzO5e2M6pLN9CBQKzpbF6yR92qTEQRBdQXlW9O3GKSazCRJShkNydyhDD3Q\n3nN6NtFOR/J9W23No4EaEUwqlqFQCISQBL96Ift3lTap5WNF64kewpvuGNrqS/m0VSs1HMfBYrGk\nLKcmy7J6nkYI5mDEMdJCJp/nqNRNtKutFyZQA4JJE9Vp2Saad5cuhSIXiuGSLdYepnZPL58i8Ua2\nMLUpI7m2GjPCBJhcTo0WTOA4riaqwTDyJ9/7gN5zejXRpvMD28OscOiNQf/RlXwoFFIn2Grav0uF\nNpApnz09I0/OySkjuYzVqIsArYAmB3OUs/oQo3pJteee3EQ710UbszArFK21JcsyAoEAnE4nrFZr\nQWJQDAtTURRdjxWJRCAIQllcsKnGpNf10ubJ5nNu6X53PX8Dvci0F1UMV5qRMJIb1EgU+7rkErRG\nP5M8HkEQMHbs2KKNsRzUxNKUToLRaBSyLMPr9erSdaMYwTB6Cgp1r/h8voLE0khBP3QswWBQl3Or\nRKgbzW63w+VyqXvvdJ8+HA4jFotBlmXD/G7VQC2LN12U2Ww2OJ1OuFwumM1mKIqiBtppAyuB3AsX\nCIKA+fPnY/bs2Tj77LPx8MMPp/zcfffdh7POOguzZs3CZ599psv5ZUtNWJiiKCIQCAxbEemFXg+S\nXg+jKIoIh8MAAI/HU1UPuSRJAOKi4XQ6q+rc8oFVH2KUA23QGhCfc+gi7emnn8ZvfvMbTJs2DZIk\nYcGCBWhsbBzxmHa7HRs2bIDT6YQkSVi0aBG2bNmCRYsWqZ9Zt24d2tracPDgQXzyySe45557sG3b\ntqKdZzI18fTQ8nZ6l2kyWi1ZGgVLU0b0cs8ZpXBBNBpVA5dcLlfNi2Uqki0Bp9MJs9mspq+EQiEI\nggBJkpj1WcEYzdqle5p2ux333XcfXnnlFXg8Hnz44YeYOnUqzj33XDzwwAPDmh0kQ4OEqPg2NDQk\nvL927VrccccdAID58+djcHAQJ0+eLM5JpaAmBNPj8aj7lXpPEkZxV9Io2FgsBq/Xq0a+VQO0y0gk\nEoHb7VZf0+O41Y7Wfet0OmG328HzfIL7lm5V1ML1YBQHrYDzPI+ZM2fC5/PhP//zP9Hb24tf//rX\nmDp16ohUqJ0WAAAgAElEQVTePUVRMHv2bIwdOxbLly/H2WefnfD+8ePHMX78ePX/W1pa0NnZqf8J\npaEmXLL0hzSKuKUj3/FpO6rQcn7UdVnpFKsikZFW56Uil0AOnucN/ayUA6NZdUaHppWYzWbMnz8f\n8+fPH/FveJ7Hrl274Pf7cdlll2Hjxo1YtmxZwmeS78tS/iY1YWEWk3KKMI0UpVG/WjdlMXM6S3Uc\nURTh9/vVhQDbf9OXVIEcFotFDZAD4tsZtJRkOWAilZpKuC6FFF/3+Xy48sorsWPHjoTXx40bh46O\nDvX/Ozs7MW7cuILGmQs1NQMZ3SWby7GomzIajcLr9RqqDFyh0IVAMBiE2+0uWz3YWoMKqN1uV6PI\nTSYTJElCOBxW3bds/5ORTCoBzzUPs7e3V+1TG4lEsH79esyZMyfhM9dccw1++9vfAgC2bduGurq6\nkqau1IRLlqIttG7ECThbwdS6YNNVtjGihQl85U45cgT8n/8MrrMTZMYMKJdeCny1uU8XAjT9J9We\nBx2PXr+hEe+FckOLJ2jL99H8z1qtPkSrhTGyI9dast3d3bjjjjugKAoURcFtt92Giy++GM888wwA\nYNWqVVixYgXWrVuHKVOmwOVy4YUXXijW8FNSE4KZXNFf78m2VKttWuaP3oiFNEMuBxzHgd++HebH\nH4+/4HCA27UL/Nq1kP7t3yA3NSEQCGRcCOhJqvvA6Pvc5UK7/8mqD5Ufoy36U42H9ufMlnPOOQc7\nd+4c9vqqVasS/v8//uM/8hukDrC7ukBKZcklu2BHEku9J35djiWKsP/XfwE+H9DcDNTXA+PGAeEw\n8NxzGBoaUpPxiz0ZGGmyqUS0+58ulwsOhwMmkwmyLCMcDqv3KnPf1i7US1FN1JxgVqIFIUkS/H4/\nAOTciUOvYB094I8cAUIhQLOvQQBI9fXAxx/D81XaA6PyYNWHSovRLcxq/Y1r1iWr57GLaWFGo1E1\nPDsXF6yRHiYKZ7EAmvMjhECUJEBRYLXZgCxL3FXioqeW0LP6kJGEwUhjqQSq8XrVhGBqMbJgUujx\nCm0/phd6FSRXJkyA0tgI08AAlLo6iKIIE8/D3NcH5RvfAMq071WND7aR0BaPB5C2kTEVUPZbVB61\nEhBV/WdYQdCJQpZlDA0NgRACn8+Xt1gazhLjeYR/8APIigLlyBFYTp2C+cQJYMIEKLfdVrZhsQm6\ntLDqQ4Vj9EVetQooszANdjwACAQCefV3LBZ6nqM4cSL8jz8OzxdfAD09kCdPBpk3DyhxHqne0dKM\n/MjGfQvEa4vWSvpKJZL8LEWj0arKDafUhGBWwh4mIUTtMOJyuXS52YxkYWrTDzzjxoFraUG+Tl4j\nnRdDX5Ldt7RdlKIoatuocqWvsAVW9tC4i2qjJgRTixEnW1mWEQwG1dWz3u3Hyk0sFkMoFFLLrrFJ\nh5EtNDXBbreDEKJaoJIkIRqNguf5BAGtlXvLaC7P5MWEEQWTFt+g1y2fe6XmBFNvCg2IoWJCXbA0\nfUSvsZWzLRctcScIAtxut1p4wcgYcUFVDKJSFDaz8QtfaCdiKp50D5RVHzIuuVb5KQUffvghtmzZ\ngtGjR8Nms8HhcMBut8PhcIAQgnPOOWfEurQ1J5hGccnS3pWxWCwhCrZaJmxFURAKhdTAJZ7n1YhI\no1FrrrZBYRC7T+7GhS0XwmpK7fqvhHtwpOpDhBA18lYP962RronR7tnk8YRCIcMJJu2d6ff70dfX\np/aGBeJF3B977DGMGzcuY4WimhDMYu5h5oPWBatny6pkymVhamvdOp1O3ROa9Tgv7TGMNPGUgraB\nNvRH+9E51Ikz689M+7lKuy7a/U+bzZaQvhKNRtX3C3HfVto1KRdGdMlOnDgRN9xwA6ZNm4ampqa0\nn8s0H9eEYCZTTguTumDtdrtaDaWQ4xmNTIUW2GRTfgaFQfSEe9DsakbbYBtavC1prcxUiLKIz099\njrlNcw3/e/I8n9J9K4oiBEEAz/MJxROMfj5GJtnCLKS1V7H44osv8Nprr2H06NE4++yzMW/ePLS2\ntqK+vh4ejycrw6VmBLOYaQTZCJzWBet2u2FJU9XG0F1GRnjfKIUWGOlpG2iD3WyHiTeBgIxoZSaz\nr28f3jjwBkY5R2Gib2LxBqozuVYfMnodVKO5ZJMxooW5cuVKrFy5EidOnMBbb72F1atXIxKJYN68\nebj88ssxZ84ceDyejMcwTphViSjGHuZIKIqCQCAASZLg9XrTiqVRGekcFUXB0NAQFEWB1+tNK5al\nFPBSkm4oBhoigNPWpccanxTqrHVoG2xDTM5ub1mURXx47EN4bB58ePRDQ/0GuaItHu90OuF0OmE2\nmyHLMiKRCMLhMARBSCgeb3SRKhep7gMjCiYhBKIooqmpCXfddRfWrFmDP//5z7jkkkvwxz/+Eeed\ndx527dqV8Rg1ZwaUOuhHFEUEg8G0Lthijq8U7t1cz08PjDRpxWLAj39sw6pVMbS0nL7W69eb0NXF\n4447xDKOLhFqXVJytTL39e1DIBZAq7cVR4eO4ujQ0aJbmaUSqWzct0A8/qCW0ldyITmtxOv1lnE0\nqbFYLPj4448hyzLsdjvcbjfOOeccLFq0SF1EZaJmLMxiVtJPdTzqgg0Gg3C73XA4HBX7kKUS3ko/\nP70WE1Yr8I1vSPjpT23o7Iyf//r1JqxZY8bll0v5j29wEPy2bTC99x74AwcAKf9jAYAgCQjEAojK\nUfRGetV/hBCcCJ0Y8e+pdTnGMQYA4LP6Kt7KTAd131qtVjgcDrhcLnU/nsYg0O2VcpXvM7q1KwiC\n4fYwafrfAw88gKVLl+Kb3/wmli9fjvHjx6OpqQnLli3Dxx9/nPEYNWlhFvt4iqIgGAwCgJpSkcvx\njG5hEkIQDAZVF2y2hRYqPaApHYsXywCAn/7UhgULZHz6KY9//ucYxo7N71z59nbY16wBbzIBFgu4\n7duhtLRAuvlmIM/2Z3azHctal4Fg+Jg4jPxMUOuy3lsPAKiz15XMyiw32mIiDocDANTiCeWuPmQE\nUok3zS03EvR3ueqqq/DII4/g6quvBgBs2LAB77zzDhYtWoSHH34Yr7zyCs4444zUxyjZaA1CsV2y\noijC7/fDbDZnHXmVjNFERXuOsizD7/erKTHlqEo07DeUZSASyWvTMBaLIRKJqMW+gfyu/+LFMhob\nFbzzjgnf/a6Yt1giFoP17beh1NeDjBsH0tgIZcIEcMePg0/RjT4XOI4Dz/HD/o20iBRlERuPbgQA\nnAqfUv9JslS1VmYy2hQk6rqjvT9p82xJkhAOh9Xi8bXcPNuIUbKU559/Huedd576/8uXL8e6detw\nxRVXIBaLZXTL1qSFWQyXrLaqTSG1YPW0gPU+V+qOyrU3Z9GQJPDr1oH785/BhcMgzc1QbrwRZPbs\nEf+UEKK61GjJPm2xb1EUc7IW1q83oa+Pw403Svj1r6149NFowp5mtnBdXeBiMZCk1TlpbIRp1y4o\nCxfmfMxCUYiCWY2zICrD92O1e6K1Srr9z2JWHzKSSzbVWIwomHSM3/jGN/Bf//VfuOaaa1BXV4ct\nW7agvr5eXfRkamJfM4JZrD1MetxgMAhCSMFWlxHdlnRBEA6HC0oZ0fvc+FdeAb9+PUhzM8ioUcDQ\nEEy//CXkf/xHkJkz0/4dIQSSJIHjOHg8HvW/AahRkrkku9M9S+qGHTuW4Kc/teUtmkbDZrZh2YRl\nZfluoz0LI1Hq6kNGxYiVfii//OUv8cMf/hC33347BEHA+eefj5dffhmxWAxPPPEE3G532r+tGcGk\n6L0qo248nueHVbUpN3oIFC1xB6CoVYlyZmAA/IYNIK2tAF2g+HwgigLuzTfTCqYsywgEAuA4Djab\nbdj5JBf7TmctmM1mcBwHUeSwbZspYc+S7ml++KEJf/M3uQXrkOZmwGKJu5g1OWFcTw/kxYtzOla1\nYKRnKlcyVR+ipSJzrT5ktEVEOgvTqIJps9nwq1/9KuV7ixYtyvi3NSmYerXjikajiEQiAKCbWBZa\nzF1PaENfq9UKSZIMI5Ycx4E7dQqE50+LJaWuDvyRI1AIAZJ+D22he1mWs0rxSWUtSJKk/u4mkwkP\nPyx95VU4fTwqmjljtSJ21VWwr1kDbmgIsFrBRSJQxo2Dotl3YZQWvVygelYfMvJCIhKJZLTUyg2N\nbtYW9M+GmhNMSiEPALW6aJTo0NCQofYUKPmKr3YxQPdjqTupkHPUtXCBzweOhvRrxxQMgowZk/Ba\nctcUi8WiWs25oLUWqJuaRkrqWWpNmTwZwne/C9vhw+ACAZDWVihnnhm3PEuE0ayYaqRaqg+li5I1\nqoUJIO9ts5oRTG2LoELQFhZ3u93qTWz0VJBsIYQgFApBluWyRcFmAxk7Fsrs2eB37QJpaQF4Pl5F\noLcXyqpVpz+nSYHJNcUnE/R3p8FdqSa7QvaqSH09lLFjdRlrvhhxcq5mtAsyACndt5Wy7ykIguHS\nSrQMDQ1BFEVYLBaYzWZYLJasKrDVjGBqyaeurNbqKnaUaLnEl3ZRMZlM8Hq9hp8wlTvvBF58Efxf\n/hJ3z5rNUG69FeTCCwGU9nwyTXa13Oi4WiiHBynZfUs9GtJXRSzC4bAh7ql018aIwi6KIrZu3Yr3\n3nsPsVgMHMdBkiSMGzcODzzwwIi/c00LZraMZHXpbWGWg+RG1um6qBTqkgUKn3zU6+1yQfm7v4My\nMAAEAkBjo5rYT0v2pTsfOo5ikWqvSpKktMFDyeMjhKA/0o8GR0PRxlgJGHGroxxoPRpmsxnhcBg2\nmy3lPWWU7ivl/n4t9D46cOAA7rrrLnznO9/B1KlTIYoiwuEwxowZk9VxakYwtT9eLgKndcGms1KM\n6pLN5ljZdlExNPX18X9I9ASM1BUm3et6C2mqvSpJktQUFgAJe1VAvEDAjp4duHTipXBbjRs8UQqM\nNPEaAUJIgseCvlau6kPJixpqDRuRUCiEBQsW4JFHHkn5/kj3Ws0IZj5k6u2YjFFvkEzQ4CWaP5rp\noSr33mo2VMr+K8dx6p5JqkhJAPj81OcIi2Ec6D+AuU1zyzxihtFJtyUgSVLZtgSMuNCxWCzw+/14\n4403MGPGDLVesNfrzWrPtSYFc6TJn068tB3XSBOvUavzZDoWtZzpDVOqm1sP1y4wfIFSyH5lOR/s\nVNbnsf5jGBAGMMYxBn89+Ve0ulrhs/sqJuCjGjGSazib+aGU1YeoxavFKNeKQn8/Qgja2trw4IMP\nYtSoUQCAnp4eXHvttXjyySfVbjTpYIKZhHbi9fl8Wf3wlWB9aaGWcy4l/Ix0jsljKUeLsWKyt28v\n6hx1cDlcCJMwDg8dxkzzTBY8xFDJdUFYyupDRlpcUOg5zZ07F/v27QNwuj8mregFjJxuUjOCmc0e\nJhWSTIEixaaYFiYtbyeKoqFdltmSKl801783GqfCp+CP+tHkbgIAjHKMwrHAMUwfMx0uuytl8JA2\n95PBGIliVB/SIgiCMWpNa9i1axcIIRgzZgw2bNiAxsZGtXWb1WpFc3NzVoUWakYwM6EVknxqpRo1\n6EcLtZx5ns/aci4GelZaCofDWbvNizUOPSGEYM+pPbCZbIjJMRAuPj4CgoP9BzGnaU7a4CE9Jjoj\nYkRrpdzofU2yqT5EF2ap3LfJ46HeKyPR1dUFjuPg9/vx85//HKNGjVLzszs7O/Hggw/i4YcfhiRJ\nrFtJMtrJMh8XbKbjGQk6Lj1clkY7R9qGpxLyRbMlKkdhMVlgM9sgExk8iVuMPpsPETky7PMjBQ8Z\nLc2gkqkV4c62+pA2JSoZ6qUzCoQQrFixQv3/vXv3pv3sSMZSTQvmSLmH5RybHtDw7mAwWLkpI0lI\nkqSuemmlpUIxymRoN9uxtHUpotEoAOTk1so00dHI25EmOqMiKzLMtTlVlZ1MBTloSpQ2zYXjODWz\nwChwXLxEKM/zkGVZ/W/6HpB9kYWa2fRIniBisZjarqrQQJFiWF96dBmJRCIghMDn8xUslnqdYyHH\niUajCAQCajmrSpr0Sw2d6Gw227Amx6FQSG1yTItQG5XB6CA2d26GpOTW9aWaKecCj7pu7XY7nE6n\nGmEvyzLa29uxaNEiPPPMM+r9lS0dHR1Yvnw5ZsyYgZkzZ6bsJrJx40b4fD7MmTMHc+bMwU9+8pOc\nxg3EF40WiyXB85LL3n/NLdu0fQ71aldFVzB6oMeDQFNG6Kqw0oNBkoOVRFFU26oVetxk9KpGZDTS\nVR5KVeSb5/mii6hCFPDcyPflgf4D6A51ozPQiYm+iUUdUyaMvKgoF9oKVXa7Ha2trfjZz36GN998\nE1u2bMGYMWOwcOFCXHrppbjtttvQ2NiY9lgWiwVPPPEEZs+ejWAwiPPOOw+XXnoppk+fnvC5pUuX\nYu3atTmPlT7Phw8fRn19Perq6lRLUxAEWK3WrObJyp5Jc0QURQwNDakuhkoXklRQK4xGgFU6iqIg\nEAionWGo26fQCczIYphubKIItLcPf6+3l0NfX+5pBjabDU6nE06nE2azWXWz0RzkYlVskRUZT+18\nCu0D7Rk/NyAM4EToBFrcLdjXu6/sVqaR7xkjYLVasXjxYlx22WW46667cPToUdx9991oa2sbsTtQ\nU1MTZs+eDQBwu92YPn06urq6hn0u3/uR/nZPPvkk9uzZA+C0IfGjH/0IGzZsyOr41acYaaAtntxu\nd87pByOht0s2n+PRYguRSAQej0f3sO5yuGQlSVIXOG63uygLHLpKrgQLguOAnh4eX3xx+jr09nLY\nvZsHz+c//mQ3G92ioJWgaOlEvdy3e3v3Yvep3VjXvi7j8fb37YfdbIfFZEFMjqEz0Fnwd1cDRvN+\nJI+HNqior6/H9ddfj6eeegqTJk3K+nhHjhzBZ599hvnz5ye8znEcPvroI8yaNQsrVqxQ8ymzoa2t\nDe+99x42b96MrVu34osvvsDOnTvh9/uxZ88e1WM10v1dMy5Z6oKlwT7lFjg9j6coCoLBYFo3s9Ee\nsGyIRqOIdHXBs2sXLEeOAC0tUJYsAcrc8qoY9PXFLcSpUxPd+rt385gyRQF1FJjNwLx5MnbsMOGL\nL3iMHUvw+ec85s6VaSndtOzv2w+P1YNmT3PGz2mDh3ieh9VqTVujNJ99ZFmR8Xb72xjvGY9Dg4fQ\nPtiOKfVThn1uQBhAd7AbddY6AEC9vR77evehxdMCM18z01ZFUkhaSTAYxI033ognn3xyWF7k3Llz\n0dHRAafTiXfeeQfXXXcdDhw4kNVxu7q68Oyzz6KjowPPP/88XnjhBcRiMQQCAVx77bWqdTvS/Vwz\nFiZQnS4VURTh9/vV/pxasTRqyb5M0P3K6KFDaPj5z2F7/XVwX34Jfu1amB96CNyXXxZ9DKUmEgFe\ne82ML788/dt98okZ69ebkRw3QUWzo4PDjh085swZWSwFScD27u34pPsTKCS3vXYaPKQN8uB5Pu/g\nob29e9ET6oHX5oXX6sU77e+k/Lv9ffvhtJyOtLSarBBlsWxWppEWnUYbSzL5RsmKoogbbrgBK1eu\nxHXXXTfsfY/Hox73iiuugCiK6O/vz+rYS5Yswauvvoo//OEPOHDgAPbv34/Dhw+jt7cXzz33nLq/\nyoqvp6DcFqEex8u2yo1etVv1ItO5UUsZAOrWrgUniiCtrQAAMmoU4PeDf+YZcD/7mbFcqAMD4Nva\ngIEB4IwzoEyeDOQwYbS0ENx2m4iXXrLg+usl9PSY8dFHZtx9dwwNKbp7DQ5y4HlAUYCuLh4NDZlF\n8GD/QQDAkDCEjqEOTPBNyOn0KNR9nW/TbGpd1tvjCt/gaEhpZYbEEPoifZCJjKHoECyiBbyJhwIF\nRwaPlDX4h5GaZJfsuHHjcvp7QgjuvPNOnH322bj//vtTfubkyZNobGwEx3HYvn07CCFoSPWApDm+\nLMtYsGAB3nzzTfT29qoLQY7jcPXVV2cl8kwwDXi8kShHV45in6O2jZqT58Hv2QPS0pL4IZ8PXEcH\nuO5uYPTogr5Ptz3Z48dhfucdELMZxG4H19EB/vPPIV11FeDzZX2c8ePjovn001bIMo/77w+joWH4\nIojuWV5wgQyPB6p7dubM1KIpSAI+P/U5RjlGISpHsfPkToz3js8qQnUkMuXopap7S61LrWBTK/P7\n531fnXRdFhcumXgJACAcCcNmtan3uB7jZuhHqsV4PoULtm7dit/97nc499xzMWfOHADAv/zLv+DY\nsWMAgFWrVuG1117DU089BbPZDKfTiVdeeSWn7zCbzbj77ruxd+9eTJkyBWazGZIkoa+vD5dccgkT\nzHQYPcgj2+Lw2VS5Mfq5AqebV6tt1KJRgOcBSQLX2QnuyBFAkkCamgCHAzBKDVxZhmnTJih1dapF\nSbxecL294HfuhLJ8efxcCFEbW2fi+PHTYnDqFI+mpsT3I5H4vqZ2z5LuaR45wmHixOG/88H+g1Cg\nwMybYebN6A50F2RlZmKkDhkfHPogHrwz1Alwp+/NI/4j6Ax0Yrx3vHosmzketCabZNjMtpIsCisF\nI3mMUkF70ebCokWLRkzNu/fee3HvvfcWMjR8+umnapRsPtSUYBbrJiuVKBmxMlGuaK8VIaebVyfU\n8LXZoMybB/7ll8GFw3ExsljAtbeDeDwgNpsxFgGDgzgVOAHrGeOhtSVJQwP4ffvARaPgjx0DCIHS\n0gJ54UKk23Dcts2ELVtMePDBKAYHJbz8sg1WK8G0aacnEYcDWLRIhjYAmu5pproVtNYlxWv36mpl\npiNVh4y/mfk3CEVDakSi1voc60odzGUUcUjVwoqR3sI0UqUf4PTcf9ZZZ2HNmjU499xzYbfbYbPZ\nYLFY4PF4sjpOTQkmxegu2VRdRlIKSwnHpvc50pJ9hKRuXk0uvBDcs8/GLc1oFERRAJcLaGmBecsW\n4NJLdRtLMtnu+yoc8LK4A6MDJ7CybunpN2IxmPbuheJ0gpxxRvyYfX0wv/UWpBtvHLa/uW8fjy1b\nTPjud+N7lm63gm9/W8Af/+jC3/6tiObm09c9VbZQutuhfbAd/pgfMkks8hASQ+gKdqHF05L6D4sA\nx3EY4x6DMe4xan4nLRwvyzKiQjShcIIRRJKRH0YUTEpDQwMefPBBLFy4UDU6CCF49tlns7rnmGAa\nEO34tIEwelUmKjeKoqiRvU6nM/WN6veDnHMOiM0GbmgIcLniLllBAL97d1EFM1sOkl4ctwno8u9H\nt+scnGGJByDw+/eD+HwgY8ac/nBDA9DVBf7wYSgzZiQc56yzFNx1Vyxhy7OlRcGqVSIaGvK/T5vd\nzTi38VyEoiFMH5NYMaXOVpf3cQsll+AhIz+n5cIoVjeQOkqWBiIake985zu47777cOrUKcRiMUiS\nBEVRsr6eNSmYFL1uvGIJMA2EsVqtas3GfNDLwtSj/J+iKIjFYmqCfFpcrvhe5bhxINqIu4EBYMrw\nvL1cKfQ3U4iCdw69A9+ZMyEe/Cs+OL4JK+0LwCkKYLeDpApKcjiAnh4cGjyEOlsdGhxxgbVYUscH\njRpV2O/ms/mw4egGDAgDWDphKezmkfdRs6Wtvw12i10XKzVd8JAkxSv7RCKRqmtbVk2kcskaVTDP\nP/98bN26FQCwYsUKNS8/WyrfXMkB+sMW64HTsyemKIoIBAJq6bJ8x2yUyYW6lSVJgs1myyyWAMi5\n54K4XIDff/rFWAxcJAL5618vu+VxsP8guoPd8NWNRcOci7BrnAnHZ50J6YorIF15ZdyFnIwgIFbv\nxdOfPY03D7xZ9DHu692HzqFOhMUwtndtT3hPIQp+u+e36BjqyPm4giTgQP+BopWro4FDNNKSWqJ0\nD59uTyiKUrL7wGhWnVHGkgpa6cdoCIKAH//4x3jggQdw9913AwA++OADXHvttQCym79rSjC16GkV\n6nnz0v6VtNC4HmX8yi0uNA0mFovBarVmF/HockH54Q/j/93REU8n6emBfPvtIEkFmUsNtS6pW5M3\nW2BraMT79i6QceNAJkyIW8h9faf/aHAQsFqxzTWAoegQdpzYga7A8FqZeo7xjQNvwGfzYYxjDNYc\nXANBEtT32wbasOPEDqw/vD7l32e6Z475jwEcEJNj6AoW7xyA4XVvXS4XzGaz2o0nHA5DEAS19i2j\ntKQSb0EQDNUPk3LixAm8//77+OyzzzD2q4phM2bMQG9vLwAmmBkxYuCPLMsYGhoCEO+FqEcovV5i\nnu/5ac+J7sFmexwyZQrkJ56A8tBDkB94ANL//b8gl12W8xj05mD/QRzzH4PFZEFYDCMshuG2uLHz\n5E50B7sBmy2ehzlqFLiuLnBdXYDbjcgV38BbHX9Go7MRVpMV7x56Vz1m+0A73m5/W7cxUuvSZ/PB\nYXEgJIZUK1MhCt5tfxfN7mYcHDiIY0PHUh4j1b0jSALaBtpQZ6uDz+bDl31fFrUoevKEzHHcsLq3\nPM9DFEW18pCedW8Z+WHEWAtJkjB69GgcPHhQbXd45MiRnKzhmtrDTH7wjCSYoigiGAzC4XBAluWy\nC50eaM8p7zQYiwXk7LP1H5yGXK9RX6QPrb7WYa+3eFrQH+nHGe4zgLq6uGiGw/E8TJcLn3RsQSAW\nQL23HnazHTtO7MDlZ16OJncT3m57Gx2BDpzfdD7qLIUF5GitS3rNqZV5QfMFODZ0DN2hbkzwTYCo\niHjv8Hv47qzvZnVsal2aeBNMMMEf9aMr2IVW7/DrUWy0qStA5qbZqSoP5YKRxNdILtlUYzHStdJS\nX1+PxYsX4+mnn4Yoili3bh1eeOEF3HTTTVkfo6YE04jQLiq0k4rFYlEbPxuJXERFW7aPnlPy+6Ua\nSybyPcbCloVY2LIwuw9/tXqNyTG81fYWnGYndnTvwNymuaqVuWj8InSHuuGyuPBhx4e49sxr8xoX\n5a+9f8X+vv2ot9dDCJ52w/ZGerHt+DZ8fupz1NnjojzaMRptA204NnRsRNFTrUv7aUGnVmazuxlm\nwmgn86kAACAASURBVAGhUDyKKckld3jwMMZ7xxe1cLo2eMhmsyUED2krD9HWfrmKjlFEysgYbd6i\nEEIwatQo3HrrrWrt2GeeeQYrV67ETTfdlHWebc0KphEsTNo+iRACn8+n/mB6RaTmO65CGKlsn1FS\neko9+W3v2o4ToRPwC34cGzoGr82LMc4x2Nq5FV2BLtTZ6+CxevDZyc9wYdOFGOMYM/JB02A32/HN\nr30z5XuBWEC1LoH4dXBanFlZmd3BbgiygAFhIOH1qBxF76E9GHe0H5AkQFFAGhuhzJwJ2Gzoi/Rh\nw9ENWNa6DGfWn5n3eeVKrk2zKwEjPDtaUlmYNG3ISHAch4MHD2LTpk147LHH1Ne7u7vx+uuv44Yb\nbsjKcmeCqSO5HC+hdmoBUbClIpvrlWvZPqNSjHsjJsdg5a04GTqJFm8LesI9OGfMObCZbDgeOo5Z\njbMAAFbeis0dm3H91OsBWQZisXhZvRyu5eT6yZhcPznle8/tfg6iIuJ44Lj6ml/wIySGcCJ4Ak3u\nppR/BwBnuM9IsC4pXP8APDv3gIwee7qKQn8/+F27oMyfj90nd8NqtmLnyZ1o9bWWpT1XKvctLZxA\n0woqKXXFyOMzmqhHIhEMDQ3hjTfewAcffIBbbrkF3d3daG1txbvvvos//elPuOGGG9RFVCZqSjCL\n6WvP5QaORqNqNYxUjZ6LUVWn2ND9SrvNBvsI+5VGe6BKwayxs/CHfX/AaOdoLBm/BF2BLixqWYSP\nj38Ml/V0zlqjqxG7T36GZd12tO45FK9FW1cH+etfhzJtWtbfJysytndtx4JxCxJ+i2vOugaXTjpd\n9KE72I3/2f8/uG7qdRjtzFzQ3m62p8zl5I+3AZ66xJJD9fXgenrQdypeJ7bZ04zuYDeO+Y9lbWUW\n8z6hwUPJdW9FUYQgCMP2Po20b2gkkl2ZiqIYzloPBAJYs2YNXnvtNfT29uKRRx5BMBgEz/M4cuQI\nli1blvWxakowteh982fbkiscDkMUxZxL3BUyrmIjCAKE7m743noL1i1bAEWBsnAhlJUrga/6zOk5\nHqO4dbX09XHDCg1IEjA0FC/ys/HoRnQFu+C1edET6sEoxyj8ft/vQUDgtroRiAXUv4sca8enxzvQ\nesbFgNUKBIMwv/oqxG9/GyTLog27Tu7Cc58/h3pHPaaNOi20Y5yJrt7NHZthN9vRPtCO+c3zkw+T\nFVw4DJKqZh/P4/MTu+C0xvdx6+31eVmZxb6HswkeohapxWJhwpkBI6aUeDweXH755RAEAf39/bj1\n1ltx8uRJSJKESZMmYdKkSQCQVVZCTQtmKV2ytMQdx3EjlrgzooWZakx0ASAFg2hYvRr8iRPA2LEA\nx4H/+GPwf/0rpMcfB7IsbFxq6PnQHL58XXGxGPDTn1pxzTUSli2Tvzom8NRTFrhcwLW3nsCfDv8J\nLosLbosbncFOzHHNgaRI+PqEr2PGGE2pvHAY/OYX4R4zLi6WAOB2gygKTBs3QspCMGVFxtq2tbCZ\nbVhzcA2+1vC1lOfVMdSBw4OHMbluMg4NHkJnoDOvyj1KQwO4U6cSyxURgt7YIDrEAJq9EwHELdQB\nYSAnK7McpAoeCofDqvu20OChQjCapZs8HiPWkXU4HGhtbcV9992HYDCI9vZ2jB07Fk6nEzzP5zRm\nY9nORabYaSXpEEVRrZ3qdruzclkYsaiCFkVREAgEoCgKfAcOgD9+HBg3Lu6WM5mA5magrw/8pk3x\nP4hEwG3dCvP//A9MH38MCELmLygy9PcPhUIQBEGtIiMIglocPFusVuD//J8YXlkzhLfe86tiGY1y\nuO02EVs6tuB48DhkImMwOogBYQBf9HwBADg4cBBTG6aq/77GN2IqGYUmS1JXE68X/MmT8a7RGrYd\n34aj/qMJr+06uQu94V60eltx1H8U+/v3pxz3po5N8Fg94DgObqsbm45tyvqctZCJE8FJEvBVzWOI\nIrieHuzyhiFZeAwKg+o/nuPxl5N/KWrupt5QUbTZbAnN2qPRqFp5SBRF3QL1Khnaps9I0N9l+/bt\nuO+++3D33Xfj5ptvxooVKzBjxgz85je/AQC1i04matbCBPTfw0xlgWVKr8h0LD3R28KkAUu0xB1/\n4EA8lSAZhwPYtw847zyYfvpTcL29AMfBLEkwrV0L+Z/+aZjLNtuxFArdt1IUBS6XSxVJmoYgCEJO\nlkRTE4Hv8sfx8w95/PHF/w+zZxH84AcxDEl96Ap24RtnXI8TJ3lMPlOGpEjwR/24esrVaP9rHbq6\nOLUjCXG7wRMyTBgRCoHU18e7t3xFMBbEM7uewZl1Z+KfFv4TOI5Trct6R1xwvTZvSiuTWpc0WrbB\n3pC/lenxQF6wAPzBg+D6+gCLBcr06WhwnAGHHE346MmTHIhigqzIqltWloG//IXHOecoydkohoFa\nUunq3tLC8fT9YgUPGd3CjEQihnPJ0n3VF198ES6XC5988on6nnZxzFyyKdC2biqmS3ak9IpsxqgH\nej9cNGBJu9JGU1N81hv+YaC5GfxzzwGBAMiECSBfdQcw9feDf/55KA89lNc4Crk+siwjFArFLSu3\nW3XJ0jQEURTVc0vVQcNsNg+7rgf7D+JA4DP4XTyCtgOYPftMWCzA0b649WexEXR1AaLI4WvTzPDZ\nfNi3x4ET+yZgySzx9IG8XkgzZ8L0+edAa2vcWo/FwJ06Ben66xO+84MjH0BURHzZ9yX29+/HtFHT\nTluXXxVWqLfXq1amdi9zU8cmmHkzwmJYfc3Mm7Hp2CZ8e8a3c7+oPh+UefPiQv+VqM9K8bGAF9i4\n0YzOehmTJxPIMrBliwk2W1Y9tg3HSE2zk4OHjCR2xcCInUqoR2/SpEnwfbVtIEmSOifnEqRUc4JJ\n0TPXkR6PQtMrzGazIdIr9Eryp/U7kxcAyoUXgv/97+OF0uk+VjAI8DyUuXNhfvttkPHjAXy1GACA\nxkbwe/ZAGRoCvN6Cx5ct1PVqs9kgimLa34YGgqTqoJEqCf73e1/B8WN2OCzAlOtfxNq3fgKzGVi0\n5FxVqG6eCvzhDxY0n1LgdAJ79jpxx+3isE4l4qWXgphMMH3xBTgAxGqFtGJFPK/xK4KxINa0rUGj\noxGBWAD//eV/4+EFD2Nt21pIioSuQBds5nggjkzkBCszJsfAc/ywqNjRztHq+3kzwuTj8QDLlknY\nuNEMRZHR1cXDZgPmzx/eBNtogV0joQ0eok2z6T0jivFFkTb3s9zzgh4kW5jBYNBwLll6H9XX1+Ol\nl15CR0cHZs6cqXr8Fi5cqNaWHYmaFsxiuGTphOxwOEbsyFGKsenxUNKgByBNT86GBsj//M8wPf44\ncPyr/D63G/JDD6V3udJxSRIQCoE7cCDeKLqlBWjRv7FxsnucdoTJlmRLQuuG+2vPAbz56WdwK+Mw\nc4aCQ5Fd+Md79+KP/xkXuGXL4o+Z0wfceTvwi1/Erdcf/CCWsq0XrFaIl10G/pJLwEUiIF4vYLEg\nEgHWrjXjhhskfHDkA0TlKGxmGwKDNnx0aj/2TtmLaaOmQZAEDMWGMKthFkSJw0A/hwleO2Qiw8yZ\nYTVZcaHz23B6ovifY8/jpmk3YZRjVMIQolI0xcD0weMBFi+W8Kc/xa/LzTdLadNMjSAq+T6LWvct\ndf1R8aQu/1ybZhvNJZuMETuV0OtFCMH06dPh9/uxbt06xGIxHD58GKtXr8bYsWOzSompWcHUG9pl\nJBaLlSxlJFsKEV9tgQVJktLeUGTaNEhPPQUcOQJOUUAmTozvaxISty4HBuL5FZT+fpBJk8D19ID/\nf/8vHmrKcfHPL1gA5YYbRrRWsp081GheSVKtY21x7lSVSjJds+R9rP89thY+lxXTWmSAEJhgwp+6\nf4cf/vBH6OxM3Nvdvduk1iH49FMTLr44blmtP7wek+omYUq9JgrW6QTRTD4WC9DdzePfn4zh4Nfi\n1mVfH4ddu02YeLYTa9vW4vtzv49ToVNoIk24eOLFqOcmYO1aM2Y0KTDzcY/KoUMcNm0yo+n8bfjo\n+EcYZR+Fm6Yn1tPMtlRYPshy/Do0NBBEIhwOHeIweXL8ekfECAgInBZjTbpAYeJN9z+zaZpdSZWH\nkjGqS1aWZXzve9/D9773PXR1dYHnedTX1yfkwWdzzSvzVykAetPracXRpsiKosDr9RYslkaxMKPR\nKAKBABwOR3arRpMJmDwZ5KyzTgcBcRyUu+4CJ4rgOjuBgQHwx4+DkyTIt94K/re/jbtkJ0yI79mN\nHw9u61Zwu3frck7aaN5c95Kz4WD/QXx2aiemT6gHbwbAxwNoPjv1Gfq4L3DOOQEIggBRFPHRRzz+\n8hceq1bFcO+9MbS383j/fRN6Qr347ORn2HRsE2QlfaSe2Qx8//sxHObex1/29+LIyUF8tKcHrdOP\nw+0VsfPETqxtWwuryYo6ex22dm6Fx0NwzTUSdu/msWcPr4rlpZeHsbXvf3Fm3ZnYenwr+iJ9ab83\nW2JyDFs6tkAh6bc6BAF46SUzLBbgkktkLF8uYd8+E7Zt49HZyWHnyZ3DendWI3TRRduWOZ1OdSEX\nDocRDocRjUYN37YsedFqxChZmjK2b98+3H///bj11ltx7bXX4qabbsLmzZtzOlbNCSZFL1GSJAlD\nQ0MJ+1lGGRsl12NRiywSicDj8aSsRpTT8aZMgfSv/wrl6qtBJk9GdMUKSP/6r+AIic+g2hUpzwMN\nDeA+/rig7wRO/zZms1l1w+rN/v79cFlc8Ef9GBQG4Y/6ERAD8Ng8OBY+hkjECVE04cgRBR9/LOPG\nG4fgcMTQ16dg5cq4aL6x41O4LC4MCANoH2jP+H1mM3D7jXUY030rej75Br49fymuPWcZvu6bixXh\n8Ti6eS0aj/bAK1twKnwqXrfWC1xzjYTNm014910zrrxSQof8FwxFh+CxemDiTPjgyAcFX4t9Pfvw\n7qF30dbflvYzR47wUBTqIou7Z6dPl/HnP5sxJPbjqP8ouoJdugh4JUHd/Xa7HS6XS+3uk9w0O5vU\nh1KRal4xooVJY1V+9KMfoaGhAa+//jo+/vhj3HnnnXj00UfR3h5/5rKZJ43jNywxhYqSdk+MpiXk\nsidWKnIVCW1B+FT7lXnvoTQ2QrnllrjbOhKB3euN73dyHNDbC+7LL+NuW58v7sJ1u3P/Dg10oklX\nflAvrppyFa6actWw10VZhMVkwTvvmHDwoBV33y3i7/6OwGKxYPNmDhs2mPCDHwRx6bXH8dqhL9Dq\nGI+IFMGWzi0Y/7Xx4Ln0C6+G4EWY5F8OjgNs+xSsbNkF+4bX8b4tCsVigWn/QeDwEfgWzMXWzq1o\n9bait/f08Tq6RLztf1sN+hnrGov3j76PCb4JuKD5gryuQ0yOYXPnZoxxjsHGjo2Y0jAl5Tl87WsK\npk5V8OmnPD75xIQJExQcOMDj3ntj+DKyDzaTDTzHY8+pPVgyfkleY9GbUu8bZgoekqR4/qogCIYJ\nHjJ6Wgkd39GjR7F69WqMHh2/76+99lr827/9W06LkJq1MAuBpoxEo1F4vV5YrdayF3PX4zjUIuN5\nHh6PJ0Es9XootdeJtLYCvb3gP/gA6OuLVwAYHAS/YYPaFiub42ghhCASiSAUCsHtdo8slrIM/vPP\nYXnpJZhfeQXcwYN5nxslEAtg9fbVOPX/s3fe4XFUZ9v/nZntq14sW7IsN9wwbuACbmDs2IQaSiih\nhBJIaKFD3pAvkOQlvIQQiAkJvZhQAoTugOPeG8bGNu5FlmxJVl9Jq92dcr4/RrNayZIl2TLeRL6v\nS5et1eyZM/U591Pup+4gA8ftwMj4hhdesCTVVq1ys2SJl5//HFJTvWys+gqX4iQSieCUTsrqythZ\nsfOQY7Ndtd9+q/DnP7u4++4Izz0XwonG2oe/oCzVz9eJdSR7U4mkJKLpYXw791BUW8TSTQUsXuzg\nsst0rr5a49OvvmZvUU00TqgqKmXBMl7f+Dr1Wv0RHfO3pd8S1IJk+DKorK9slWUKYTkRxowxKS0V\nrFqlMnasieKvJL86n1RPKimeFA7UHqAsWHbcjUE8wHbfejwenE5n1JMVj02zg8EgCUe52O1s2PfQ\nhAkTePHFF1myZAnffvstn3zyCR6Ph5SUlCbbHQ5djmEebQyztY4c8RJ3PNKxvitG1gRpaQjDAE0D\nt9v6NxJBdutmZc0ahhUXbSc6UvsqhEBqGsrf/oaybh2m14swTdRFi9DPPht50UVHfFjLCpaxtXwr\nc/bMwev00mO4jrG5H7/4RSI+H9xzT4T0dElZsIKd1TvJScpBEQqmaZLqSWVp4VJ6JVh1lA6Hg22V\n29hSvoUZPS/lL39xcuedEYYMsdxMt11ayDcLwyzd4SZtsA+NBgWdRC8c2Ier31C+WFzJhaMNMjIk\nQoDW+wtKtmisCu0nM1OiiyCBcIA6rY61xWuZmDuxQ8drs0ubsaZ4Ug7LMsESMFBVcLkk27cr6DkW\nu7TvV6/Dy6bSTYzLGnckl+C/Fvb5aSl5yFap+q6Sh1pi3vEojWfP8amnnuLGG2/krrvuQtM0vF5v\ntDdme9HlDKaNIzFwsSUj7jY6chwtYgUWjhaHO06bkbUnu7cz5wRATQ0kJmJOmGAlBEUiyL59ITsb\nUVxssc523szNtXrbtVrctAmxfj1mXp51joQAw8D9738THj2auuwM5u6Zyzn9zsGlutp3SJEa5ufP\nZ1D6IBbkL2BM9hhSPamEU7YBp+FwSPx+63qsKlpFxIxQXFeMrjc2+whGghTVF5Ej+hLR6/lyx5dU\nRao4rdtpPPZYLklJCmAdn8PjYPRpOlqPHrhcMU2tIxGEUkX4jOupPhnKyiQ7dwr695fcOOoaxno0\nXC7o0wcWFSxkcPpgvE4vOyp2cFr30xC0/xrb7DLNm0ZtpJYEVwKFgUJ2VuxkQPqAQ7YvLhasW6dw\n+uk6qakwb3k1S7/ey7ihGWiGFdbwO/0U1hRSnlQedy/geEJLurct1Qt/V23L4tElayM/P59XXnml\nyWfhcMfKp7qsS7YjBrO5m8/j8XS4FOF44XAPiJSS2traaLnFd9U9JXqe3G6kokBKCnLECOSYMVa2\nrKpa4gbtfPCONLnHuW6dFSuN3b5h3+q2bXxd/DVritawtXxru49vWcEyDAycipPKsOVmLN6dwb/W\nbOOeB6s45RSTF15w8m3JLr4u+porBl/Bac6rKPryGs7NvYIrBl/B1SdfTZLI5qmnEpi/sZAQIdK8\naSwtXIrDESIYDBIKhdB1HTMzE5GViau2osk8RHExxujRCAEpKdCvn8Uud+xQ0Ev7MChzAN879SSS\nPUkIBAPTB9IrqRdCiA4dr2ZoLCpYRMSMsKNiB/P2zmNb+TbqjXoWFiw8JGM2HIavv1YYN84gLc06\n9f1PriLRkczuggghPRT9SXInEYgE2j2XY4V4qn1say528pDX640mDwGHJA91hmhLS3Ox65zjEQ88\n8AAHDx6MSmJWVFRw7733dui93eUYZvML3NYNGJsEk5yc3KqL41gJIXQGWhrHMAxqamo61MC60xcF\nbjdy/HiUxYutRB/bvb1/P+appx62y0lzoYgjciWrqpWq2XxsKaklwpqizeQl57G0cCmD0ge1yTJt\ndpnly6IqXEWCM4E1u3fTraiW731PUiG2c8klw3n/fZXfvrWA9MGl7AvsY9LwyZTucfDGXyX33aPj\nkhp/mull6LAIW8PzSU3MINHtY19gH5VGJdkJ2ezbZ5KVpREyDELnnIP//fdR8vOtej/A7NsX44xG\nxikE9O0rWbtWAIJTTzURAtYWr8XvbMxqzPBmsOHgBnon9CZBbfvFJ4Rgcq/JmNJk1YFVBCIB0r3p\nTMydiENxHMJU3W6YNs1o0jqzd0oe93w/D9Ns6oG3axRP4MgQmzwETZtm19dbserO1r2tq6uLuyxZ\nsBbVGzZsaOJ+TUtLY+HChR067i7NMNvC4ZJgjjU6yzi1dJyRSIRAIBBNYT+eq2fzooswTzkFUVAA\nhYWwbx/mSSdhXn75Yb9nZynX1dUdcemLduqploRf7HnWNBCCrzI0JJJEVyJ1kbp2sS6bXapC5UDt\nARJcCfg8KtmnfUWvzFS2lm+lXg8ycup2fNl7GJDen4X7FlKvB7noIh1x0r+4/fcb+d3vfJx8ss7J\nZ25mw7ZaVi5OwjCtjiLL9i/j3/928tJLfhwOi0U4c3LYesUMZk/Iofbss6m98kpCl1+OdDUaeClh\n1y5BSgqkpkp27xYU15awu3I3IT1EcW0xxbXFlAXLqApXsb1ie7vOoUNxMCJrBHnJeRjSYGJPy1Dm\nJOYwrNuwFu+tlhwZQnQoXH0CRwAhRLR0xefzRT1ldvJQbOlKe949rTHMeHShRyIRevTowapVq6iq\nqqK2tpYNGzZ0WI2tyzHMWBwuJmeLjLeXucSrSxYaGaaU0mr2HAp1qHtKZ+KQ8+T1Yt56K2ZhIaK8\nHJmSYrllDwNbZiwSiRyW9bc1D23wYOSECYilSxGKYnEh06T8wnNYE9lJVoKlL5npy2Tp3oUMDiXi\nTElvUfvWMA1WHliJaZpsr9xOSW0JqqIiXZJddRvZX9MXiWRv1V6e/GA5aTmJuB0u9HqdNUVrSA6f\nzDdl31Bq7EBUDeXMKSE+2LeQM0ensmY5zJ+nctaUNOas3YN/WzGP3JPZoA0hUFSFfxcvpFAt5NSR\nPyDRkYim64TC4YbED5W9e53U1wtKSgTTppns3i04sM/L1N7T2L5dweuR9MprvC4eOvYiWVe8Dq/D\niyIUfA4fa4rWtFhu0xHEkys0XtBZCkwtsc/mTbPtv7fUbKA1xKtL1uPxcMstt/CLX/yCGTNmUFdX\nx7x587j33ns7NM4Jg9lCS65gMIimaR2SuItXl2ysjqKdQXo0RuaYLQp69rR0ZFtCKIQoLER6vZjd\nu1PT0Hexvb1FW4WiYF53Hca4cciNGxEuF+awYXwV2gYHwalYCwrft9upXD2HPSWfMLwuAWPSJCK3\n3NJEcEFVVO4afReGNAjrYeq0OsBK9HWqCileK3W9uLYYd/c9bFrTl2SXQbf0bnyyaRE7lwaokl5O\nGhRhsHsdv52ZQcaUavxeB/1HVfHVVyrPvy1weyW3XraWtLRzovveVbmL/TX7cakuVhxYETVU9kuw\nqspybXbrZvL660mUlytcfrnB3r3JHNyRwqevuLj33giD0hvjWqEO9Cstry9nW8U2eiZY1y/Nm0Z+\ndT4ldSVk+dsnah3P6AqGu73JQ7Gt7lo6L+FwuLGLURxBURSuvPJKBg4cyJw5c0hLS+Pll19m4MCB\nHRqnyxnMtuKVNTU1KIrSssh4OxCvD5etRnRMu6ccPIjYuRP8fuTJJ7fse+sgxL//jfraa6BpSF1H\n790b9113EWpPh5PCQpTFixF79yLT0pDjxyOHDm2a5CMEnHQSeu/eKIpCSA+xfs16NFPjQO0BlL17\nURcvxEjwsizXZGhFJuqCBbjq6oj86ldNdpfiSTlkCh9+6EDT4LLLdEDy6jevosn6aFurPn08rNxZ\ni6nM4ez+4xkxKkz+wSWcKn/C4n/cySOPhElMgr7lKp9ucpLtNZl+UkynGGkyZ88ckt3JJLoSWXlg\nJWfknEGaNy36EszIcJCebrHyX/6ynt/9zoOmmQwZovPii37uvTfEoEESOpAZG4t1xetwKS4MaUDD\nesqlujqFZZ7A8UFLbcts42m3LWuNcMSrDq6UklGjRjFq1KgjHqPLGcxYxF5wTdOora3F4/G0mAXb\nnrGO1dyOBrYyiMvlOqLjatecTBPlpZdQPv7YqkqXEtLT0R95xKpbaM8YLe1v/XrU556DrCwMp9Pq\nU1lYiOfJJwk/8sjhv7xvH+qrryJ9PmRmJgSDKO+9hxkIIMePb/VrLtXFBf0vQCiWsXG99WtEfT9Q\nE3CgoKgOZHY26tq1Futto7PKjBk6zz3n4r33HEw5r4TPts5la2EJ55zioarCyedLVDw9DxDRTFwu\nQX2dmznzYIhnO6HKM9ACYdauV1i/wsGf/jfCBx84ePUFwW23RXA6G9ml3QhaFSrL9y/nvP7nEQ5b\nSTb2eRdCkJGh8MtfGtx2m5/PPpP88pe19O4dIRhs2nqqvQs/wzQIhAO4VBc1kZro507FSUgPEdbD\n0TZjJ3D0OB4L8ubuW5t9apoWTYr84IMPyMzM7HCmfUFBAddeey0HDx5ECMHNN9/MnXfeech2d955\nJ//617/w+Xy89tprjBw58oiOw47PCiGOqD9plzeYdo/HzojrdXqd4lEgVroPOGpjeTiIBQtQ/vlP\nyMlpzNwoL8fxyCPoL7/cMaZZUYGyaBHs2IGyZg1SVTEcDgxdx+VyoWRlwd69qFu2wNixrQ6jzJ+P\n9PsbO6QkJiI9HpQFCzBGjWq1W7EiFHom9kRVVSs9f28VMisLdAUMA1FWYtWHVlejfvop+tVXHzab\n1+uFW2+N8NxzLp7+az0lmpNMbza1W88gL3ABQwYeZNam10mUPVlWo7LuK0j1Z5DPMv7+tyGsW+1n\n0SIH990XJi0NbrpJ46WXnPzlLy5+dmsoyi5tZPmyWHlgJafnnMHrf+3O6NEGEyc2Sn8tXqzy2WcO\nwDKgW7d6GTbMgZSNL0HbHRv7Umnt3lEVlUsGXdLq8f83IF6e6XiBzT5t5ul0OgkGgzz99NOsW7eO\nadOmMWPGDKZPn87QoUMPe+6cTid/+tOfGDFiBLW1tZx66qlMmzaNwYMHR7eZPXs2O3fuZMeOHaxa\ntYqf/exnrFy58ojmfrTNF+KTO3+HsDPDkpKSjksSTGs4GobZXLqvM+OhLY2jfvihVewXezOmp0N5\nOWLTpvbvYN8+HPfcg/LWWygNogJiwwbM8nJcqoq6fTtizhyUdetIeOQRS0KvJZgmIj8fUlObft7Q\nbozyRmHvts6L2bu3JbAgJWLvXigqAkVBqCrKvn0433gDGnqFtgavF847T2dR1Vs4hJMBOel8E7CL\nugAAIABJREFUFXkHE52hk7eSlmFQqZWwv2Y/hTX7qdLLGDuhmlJ9Dz16yKixBOsU33STxujRBgV1\ne9hWvo3KUCWFgUIKA4UU1RYRCAdYc2A111yjMWeOgyVLrOuyeLHKW285KCoSPPpomBdfrGfNGpV/\n/MOJoli6pXb9nv1CrK+v/4/pnNEVEE/G23a/qqrKT3/6U7744guGDx/OHXfcwe7du7nwwgt59NFH\nDztG9+7dGTFiBGDlJAwePJgDBw402eaTTz7huuuuA2Ds2LFUVVVRUlLS4fmWlZWxfPly1q5dy5Yt\nW9i9ezfV1dUdGqPLMUz7ZrMD2g6Hg8TExE65CTtbHu9IxrLjsMc8XhmLqipLB7Yl1NQ0+TU2Can5\n3NRXXrH6YvbsaSWs9OiBun8/7q1bLYO8bx94vUiPB+ly4XzySUyf71AXq6JYrC8Uaip+IKWVhdPO\nulMA7aqrcP/mN5QURMgI16AmeBGBAOaAAQRyBrHgm7Wcsm4+PSe0HqvLzxf8eVYBos8Skquy2fGt\nSq/+BYSrv2Tx38/hoanDeeUVF7s2K/Tqb9JLBLmlbC65f3wNh8uDMX06xrhx0f6gqgrjxxtUhdIZ\nkD4AwzSY0ntKk32melLJTJTceWeEP//ZxZIlKnuqdrMm5a/MuuO3DB5sjfX//l+Y3/zGTWam5Oyz\njeg1sksQVFWNuuDsgvfm2ZPH6h6LJ+NwAu2Dw+Hgwgsv5MILL+xwQ4q9e/fy9ddfM7aZ52j//v3k\n5uZGf+/ZsyeFhYVkZbWdUGbfQ7t37+bPf/4z69ato76+Hk3TKC4u5pJLLuEvf/lL1Ba0eXztPpr/\nItjF7qqqdqrE3fEuLWktDnvYeUmJ2LwZsXQpGAby9NORI0a02bw5FuaoUSgLF0L37o0fGgaYJrJf\nv9a/aBiIDRsQmzeDx4P46ivo1w/TNNE0DUffvojSUqisRFRWIhMSLCOYno7MzEQGg6hvvIHeQkzS\nHD8e5fPPLYF3+1iKiqxenbGNrBuwfbtC//5Nvce6DrtTxjH4gQdw/fJJaorqSMwyEcOGUT3sDOYs\nMfhiyFa+3RbgnvHntngf5ecLnn/ehXPMG6QbDtKzYOs2iR5IZ2XoTSY6p/OXP2YxdqxOTrqCw4xw\n346H8MxZh6OPC4cwcSxYgHbeeWj33095hUJ6unUtDWlQGarElCaucHcG9sw8ZP+ZmZKRIw3mzVMJ\njHweU/6LsoSpwFkAJCdbRrO19U5s/Kp554xjVfx+Av+ZsBdTNoQQ7c6Yra2t5dJLL+WZZ55psSyl\n+furvfeZbTDnzZvHli1bWLx48SFzBtode+1yBtNu0JqYmEg4HI5bF1NHjW8oFIq2Gmt3WreUKC+/\njPLJJ5a7UgiYMwdz4kTMe+45xGi2NifzsstQli+H4mLLGIXDUFGBecEFkJ3d2oRR//d/Ed98Y+1H\n1xEbN2IoCoZp4lQUlIwM5JgxiOXLkXV1lqhAXh6yd28wTUhMROzb16JIuxw9GrOqCmXVKhACaRiW\nMb7wwpZOA19/rbJihcI112hWbHvnHlY+vwOvEsG8aRC+Zx6h5M//ZGltf0YMUli7TCE07FtcnjB7\nRDXbK7YzMP3QFPX8fIVJF27nqT0LSPWkUmcESOsOmzYrpPYsYW/5HLr3OJ9FRV/y0R8nEHp/EYkL\n1hHJ7Ma2gyqDBpkomDg//xztnHP5y4cjGTPGYMYMg+WFy3EqLjZsUHlmw0qeu+38Q/a/eLHK+vUK\nF92wlZ/NW0pmSjee//p5JuVOQlWsc5acfMjXWkVs+YEdw2qNfcZrtmRHEU9MN97mEnuNg8HgEenI\naprGJZdcwtVXX81FLTQ9yMnJoaCgIPp7YWEhOTk57RrbNE0URSEtLY0xY8ZE92cv7jp6j3Y5g+lw\nOEhOTkYI0ekG83gwTLtu1NaDbSmo3dq8xI4dKJ9+2jRZR0pLqm7SJEvbtT3IzUV/6imUd99F+eor\nSE7GuPZa5Pe+1+LmQgjE558j1q4FVUUcPIh0OJBCoC5YgJKdjVAU2LEDmZaGOW0aYs8ey/g2zFOY\nJgSDyG7dWpaIURTk9OkYp59uxSz9/laF3IWAyy7TeO89F7NmObky7V/sf24OfVWTvie7EW+swhww\ngOwhSZTvCrJ4cTJ9h4RYkLyebkEPtVl5fLLjE+5Lu++Ql9mkSQbfHKxmVP2oBgMDa7aoTB5ukpzc\nk249g4zIXcsflv2dFz5L5NFdc6nPdOHJlhiGbFizKIRCIJav4PbbT+bpp10E9DK2pqynYHMvqIWM\n09ZRGhxHpq+RZS5erDJ3rsqdd2o8vfllemQJAkUp7DCLWFywmLPyzmrf9W0FJ9jnCcTiSDqVSCm5\n8cYbGTJkCHfddVeL21xwwQU8++yzXHHFFaxcuZKUlJR2uWMBVqxYwRdffIEQgnXr1nHHHXdwxhln\nRO/HESNGcNJJJ7V7vl3OYELTFl+dPe53GcM8kg4dTfaxevWhmmRCWO7RpUsPMZiHnVOvXpj33097\nJZ3V2bMRBw+ClEiPB7O2FqWmxjqGSMSKPZomoqQE48ILUdauRVm2DNmjhzXfSARRXo7RllJHUlKL\nyjw27ONRFLj8cp0PZxZT/OQrZDqq6dZDQWwE2cCaKyacx8ElW+gtClheuIfatFoyhowlNaMbe6v3\ntsoyh3UbxrBuw6K/B09vbPdpmAaPrXiM04akEzI/onSNil4B9WGVfn2tuVVXC6pLBMVbPGyY7eDu\nuyPc+udV5Id99E5TmDLFoCLiYWnhUn4w4AcNxwUVFYI779SoVneytHApWUkZpHtM9pf7D2GZANXh\nalYUrmBGvxntuobN0RXZZ1dGc7Z7JAxz2bJlvPnmmwwbNixaKvLYY4+xb98+AG655Ra+//3vM3v2\nbPr374/f7+fVV19t9/imaVJdXU1ycjLDhg2joqKCzz//HIfDQX5+Pj/5yU846aSTMAyjXRm0XdZg\n2hc7Xl2ybUHXdWpra3G73W2WjLR6nIrStIi/6Zc6aaatDF9UZIkRJCdjmiYiEkE4nUgaGksnJkJC\nAlJVEbt2Ydx1F9LttkpOhCXprV1/PUorLLZdc2h2jKYJeWs/IqVuP8HuvZDJgABRVYVeE2bRgTIy\n/+c2BvfYyTNznqKkfCrdEr14gSRXUqssszliF+EbSzdSGiwlLzmPfdX72H3REEavXcqWCsm2bQo9\nsiWlB3Syk+DFmjP58RQDw11OXdJXeIp6kZUlcTogU81kffF6JvScQKYvEyHgoousGtynF72KIhQU\noeByQ5/sBPbX7OeznZ9x4YBGF/UXu7/gnW/f4aS0k8j2tOJK78C5bc4+j7Xwd1dAPLlkm8MOCXUE\nEyZMaFfnlGefffaI5jR58mQmT558yOcVFRWkxeQytLfcpEsv8+JVzq6tscLhMDU1Nfh8Prxe7xE/\nQHLMGMtKNIgbANbvoRBy0qQjGrM9EEJgpqYiG16gQggU+xgUBdmjB/Lkk5F5eVa9ZDgMfj/mffeh\nz5qF/swz1P7xj+jnnNNphl3XYdYsJzlF6+jW14vDAQUFClKCTEykakc5w/sHGHO6YKXYj8wOkpIb\nYNnGEopqiwgbYb4t/7bdouVgsctPd35Kmsd6cDN8GXzizUdePJVB6SX4AsXML9uNx13Kp7k/48e/\nzaVHD8lf3i5AN2D02XvZuK+QhesL+ebgN+yo3MHOip1N9lEQKGDBvgWY0qQsWBb9KQ+V89Tqp6L3\nWFWois93fY7f5eef2/7ZKec0Fq0Jf8e2nbIL4W3Ei3GIl3nEG1pimPEmvG5n6T799NO88847gNVI\n+vTTT+fWW2+luLi4Q+N1SYZp4z/NYMbq3LYWr+zIvGS/fpiXXILywQeNblnDwJw6FdmCkkZnHp92\n6qk4tmxBralBuN2WodY0yMhoLAWRElFbixmTZi4OHkR58038+fkoqooYOxbzyisP63ZtC1LCW285\n8bp08k5yIHapZKfqFB10UliokNvTICtVI3Vqfwwsw3bJQKtYX9MgtnzX62y/SyqWXQL4nD7KgmUs\nu2oG6X0uYPGLi3jzpM9ID1/Jr++8kh49TGbNcuKrHM17t56CxwOVp8FTzyjUq68yvG8pOYlNkyHS\nvek8NvkxJI3XbcueatZU/wu/x8me6j30TenLh5u/pLrGZGBWNl+XfM2e6j0MyDi0+fPRYMMGhT59\nTJKSGtlnJOJi2zbBsGGRqPsWiLp1T6AR8X4+4tFg2gZ92bJl3HDDDZSWlvLVV1/x7rvvMnPmTObO\nncvVV18dTQ5qC13aYP4noXm8slNiQEJgXnMNcuxYxIoVoOvIsWMP1VvtRNixrbrx40nets3KXi0q\nsoxkdjaiuNjKei0vt+odhw9Hnnaa9eXCQtQnn0T6/Zg5OZbbb+1aKCnB/OUvj7g/lKIIJk+OkJen\nICuHIEP1qAcOkOODSFBHlBjI7O4YDTHdoZlDGZo5FLAaKAshcCgde5RMafLpzk8JG2H21+yPfq6Z\nGq+t+hfKosfwXfMtVTuGUBrezDN/q+f3j3jIzTX54Q8NPB7rWDPS4aLrt/HOV7Vk+jNZsX8FuUm5\n0ReFz+ljcq+mLqmywKdkVg2hu9/Fon2LUMOpfLRlNr3SuyGEwO1w89HOj3gg44EjOp+tITFRMn++\ngylTdJKSrM5q8+c7GDTIUouJ1S21k4ds8QTbfXsi9tn5uRdHipYYZrz1wrTnl5CQQGFhIfPmzWP4\n8OGMGDGCcDhM4mFUulpClzSYxyqGeawYph2vtJVYjkTnttV5CYEcOBDZDtX+oz0+KSW1DZ1GPKNG\nQXU1zJ5t1UYCDBiAcfrploC7pmGecQZy7NioKIIybx5SUSwRg0gEVBXZsydi717Ejh3IQYOOaE5C\nCPLyGqpbzj0XddcuDF1HLSrCLTQwJMYpp1j7bPZC+GjHR6hC5eKBF3d4v5NyJxHSm3YF2btXYfYq\nN1PPKOT1/FVceFYPdpcWsXvRPB555GIefTTcRNVPN3U2BZYxclAqPqePgkABhTWF5Cbl0hJKg6Xs\nqvmWAbk5lB10sL0mn+17/47XZ+DzWFQ505vJhoMb2F21u1NZZt++EjCYP9/B2LEGa9ZYZTMDBjS6\nYVvSLVVVNdpM2laWORH7jD/EI8O0F1iXX345b7/9Ntu3b2fmzJmA9QymtVCTfTh0SYNpI54Npo2O\n9uWMVxiGQW1tbZQlKHbZx5gxVvNolwvZt6/1bytjiD17WtRtFUJAaSl00GC29LKVffoQuvRSvM89\nBykpmKmpmMOGIZOScMyejX7FFVEmWxYsY/WB1QghmNxrMune9HbvW1XUQ5gfQHU6TOyu8+hH/2Dk\ncEFKksIp/m64XHO4KG0S8+encPXVjTHnXZW7CEQCUQOZ5E5ieeFyfjj4hy0e38r9K3E73Hg9CklJ\nktJiD0uq3qF/eh4ldY1yY0EtyIfbP+TBjAfbfUztQd++kro6kwULVAYMaGosW4Id+2zeNSM28/ZY\ns8946cARb7HU5vOJx+bR9jt5xowZTJ06NSpQUFNTw29/+1uyG+rE23t9u7TBhPiOC9hZhR3py9kS\n2mXIq6sRixahrF0LLhfmhAmW5FxMgM4Wq+8omisQBQKBxj+mpiKba762Atmrl1UK43QipLTYJg0d\npdLbb6zaglpVhXbWWVaNZwxEYSGiqCjaoWTBvgU4FAcSyaJ9izrEMqU81OstpSUiUCOLSR2xhLyU\nXgA4VScpySZ67jx+NKUxq1U3dZYVLmtiqJPdyZaubDOWaUqTkroSvin9hm7+blQE6imvFKSlKaTU\n5DAoeSS56RnR7SORSLSBdmeithb27FFITZUUFioMGGC2Gn5uSd2ledNj+xk5wT6PP4LBIJmZh6pN\nHW8IIaiqqmL16tXs2bMn2lDA5/NxwQUXdGisLmkwj2Ud5pEYlOawO6hIKY+42XOHUFeHOnOmVeCf\nmQmGgfLBB8gdOzBvuumo4pk2Q45VIDpSJi7T0lBXrADTxOlyYfbpYwms9+zZLpdyeyECAWQL3UxM\nIaiuKSWJnlF2mZOYAxJWHljZbpZZUiJ47z0HN9ygRUtMpIR333XQr5/JFtd8gnodFaGK6HdURWXO\nnjmcmXdmtDtJUW2R1ahas7Jco/OUJlvKtzQxmP/a9S+2lm8lJzGHYFBSWaGQ1c3E7U7g4oQZpARH\nMj13BCkNLT2DwWCnezRiY5YDBpjs3i2axDRbQlvlUsebfXZVtPT8xiPDtFnwM888w+bNm/n44485\n99xz2bBhA0IIpkyZ0qIUX2vokgbTRjy6ZO14pZ0l2BkPelvzEmvWIEpLrTIOG717IzZuhN274XB6\nsK3ATtaIRCJHzZDtOarvvYc5bJjlmg0EUDdsQI4bh3HXXW0n/AQCUFZm/T8j47BZtUZuLo6NG5tu\nIyU7jIO8X/QJt/cfGGWXqlBBWG3B2ssyu3WT5OZK/vY3Fz/9aQSv1zKWZWWCH/zApGxfJmflnnWI\nxKEqVHSz0R3bM7En1w+7vsV9uNTG7wbCAebunYtu6vzo5B8RPNiDzCGSQECQk2PdF3V1liE3DMkR\nqJu1C6tXN41Z2jHNFStUpk83Dv/lNtBS7NMwjEPYpy2a0JHFcry4QuNlHrGI96Qf+5y98847bNmy\nhSlTpvDee++hqiqXXHJJh9t9nTCYceSStWvSfD4fDoeDmmadPo4VxJYtyOaCokJYbawKC6MC6u09\nX3Zyj5Sy0zJ6lQ8/tBR3kpORvXqhVVcjFAWn3Z4nFLLaga1ZY7mUzzwTbNa5YwfK+vVRF66QEnPo\n0KZi8TEwBg9G3bLFkuzz+SyBhUA1X2ZWUmwqzM+f38guG9Dd171dLHPFCpX0dMm55+p8/rmDv/7V\nRVKSZO1ahT/8IYzDAVN7T8UwDDyt9Oy0IYQgwdX26njxvsWWQLvDxZd7vuS6U66jqEjw5JMubr5Z\nY/BgE7/fWh888YSbSy7RGDq0zWE7jEmTjENao/btK+nV6+iMZUuw4+TN2Wc4HD7BPjsBLRnveEz6\nseeYmJhIIBBAVVXmzZvHuHHj2Lx58wmD2VHEA8NsiY0ZRue9RNqcV0oK7NnT0sQOyQptC3Zyj8Ph\nwNdKG60OnyfTRBQUWApA1gCIhnnJmhrE7t0oH3yAmDcPEQyCaaI++yzG5Zdj/vSnlrHMzIyyUGkY\nKBs3oni9yISEQ+eYmIg2YQKuV15B3bQJVJWtI3PZ38dJn9R+fLj9Q/xOPwdqmvbt06XO5tLNTOrV\nuuhDdrbJG284qaoSXHGFxrx5Klu3qvz4xxqbNqmsWGFpv3YWAuEA8/LnkeXPQlVU1hatZXqf6fTo\n0Z2f/Uzjr391cvPNGpmZkj/8wcX3vqczZozZVovPI0JrToajdD60iWPJPk+gEUei9HOsYV/LH/7w\nh+i6zi233MITTzyBy+Vi1KhRJ8pK2oPmMczOdHV01GC2xsa+S/ZrjhtnxQYjkca+ljU14PEgYzqf\ntzUnO7nH6/UeWdu06mqUhQsRX3+NTE1FTp2KHDLEUv/p1s0KgsXe4KaJMAzLpbxwobU/mzWGQqjv\nvw9pacgY0XbAKkdxOlGKiy3h+eaor8fz7rvgcGBOm4aJ5Mu6+aSt13Cn96OHvweTek3ijJwzDvmq\n13F4f2ZenuTaazVmznTywANuPB4YOdJk1SqVFSvgoYciHTtnbWBJwRJMaeJUreStWJY5YIDJz36m\n8X//Z13zq67Soj0x/5vRFvuMleyLJ/YZTy7Z/xSGaeO+++4D4NJLL2XcuHFUVlZyyimndHic+Lkb\njgOORdJPR2AYBtXV1SiKQmJi4jF7ONs0vn36YF5+uVWasW+f9aNpGDff3G6GGQ6Hqa2txe/3t6lt\n2yIqKlB/9SuUd9+FkhLEhg2ov/0tYvZsAMyLLkKUlVkyeWD10ty/H3P0aMRXXyEiEUhIsIx+TY2l\nHKSqKHPmtLw/ISyBhIa5h8PhqDSbumULorISsrJAUdgpKtnvN0mJKCgFBWQlZLHqwCoUoZDgSmjy\nEytm3hp69ZL07Gldj2BQkJNjsm+fQlqaJDW18TppWnSKTWCfgrZgxy6z/I3Zrt183VhbtJbiWksS\nLC2tcX/Z2fETnoDvJoPdZp9utxufz4fP50NVVXRdJxgMEgwGMQwD0zTjKnwTj4jHGGZL6Nmz5xEZ\nS+iiDDMWsSIGnTVWe2DHK71eb4uxqu86virHj8cYMcLqL+lwWD0nYzXfWvtejDu5Tbk+w0Ds2oWj\nqsoSK4hJalE+/xxRXt7odgWkpqG+/Tb6+PHICRMwgkHL9RqJIAwDfcIEHNdei3r77dYXiooQVVXR\nrF4JkJGBiESsc2lfYykRkQhGt26EQiGMoiJ8c+eirl2L4fUik5IwFcVqgI3kC7aTggdcBlRV41bd\naIbGV8VfMTF3YgfPNBQUCNatU7nwQp1VKxX2zN7Bb05egaOshi1/6oc2aiQjzvIxf77KgQNWj875\n81VOPdUgGBS89JKTe++NHFYNcNEilZq0TYSNcLS+UtehqEjQLVtnbfFaxqWczx/+4OKqqzRyc2XU\nPTt48NFnencWvmtG1RL7tHVuI5HIcWWf8cQwW0I8umQ7G13eYHY22jJyUkpCoRChUIiEhAScbRil\nznhI2m18/f4mLti2xulQcs+uXTieegrKy/EaBsLphBtvRJ59tjX2qlXIjIym32k4N2LnTuRpp1lC\nB2eeCWVl1DudyIQEHD6fJRS/eDEiFGrUoTUMRH09UtOQ/ftbYzT8TdTXY/bti5Gaijh4kNQnn0QG\nApCZiRKJIFasQBoGke7dKRVBypU6IopBwKhFelMxAwVIKdlStqXDBjMUgs8/d3DJJTqZmRJjwTJO\nrlnMpjXpTJzqpZ/YzJJnt/GV/kPOPsfDyy8r3Huvm5wcSV6eyaxZLi67TGtiLGtr4W9/c/GTn0RI\nToZ//MOBYcAXr03kjCknc+mlOgX7BL9+xM0Pf6gzfaLOornJPL7ExTnn6FE37NVXa/zudy5+9asI\nMeuWLgubfSqKEg0x2JJ9XT322dJ76YTB/C9F7IXubDm7w0FKSV1dHYZhtFlfGe8PX3uSe6IIBnE8\n9pjF+Hr2xNQ0FE3D8be/YWRnW0ba47GsSXNI2ZTput1W3LG+3vobYP7gB6hPPGElAIHFJE3TyqpN\nSECmpyNzchCFhQDo2dnUeL0IKUlYutQSeG/QphVuN8bJJ+NYuBB3QQE9+vbll+aZyNIypK5TP+Vu\nlPT0KNPoKObNc5CdLcnMlHzy93p+PXwJ4fRs5i92sXC5IDvbS6/c/Xz+7DeI9GlkZkq2b1cIBk0e\neMDDAw+EGT68KQP0+6F3b5PHH3fz0ENhRo0y+MUv3NTXC3Zv6sbeIRoPPeTm7LMNrroogpRQst+J\nrsPEiZaxrKqCDz90ctZZOikpJ1yPLaE5+4xNHIqNfTocjrh/fo8FwuFwmwTgPx1d0mDG4rtqyRUr\nDdfeZs+d5S7urGO0x4lN7mmr9AFArF9vxRUbaIsASwLP40F8+SVy8GDMadNQX3kF6fc3uk4DAfD5\nDq8RKyXU1CD79LHGLyiwmlJnZyMHDkTU1FhMMy8PmZWFruvU1NTgcbuJRCKo33zT2JNTCOtcu90Y\nffuiuN2ohYWogJmbi3bppbizulvF8eHGrhodUZaZPl1H1+Htt53cc80BkpeBmaVwycU6Bw8KvvzS\ngbNHOoMc23nmme/jdkO/fgbffKOSng5jxx7qLhUCLrnEqs98/HE3Y8YYJCRAWppJ9+4mt9/uYfJk\ng/vuiyCEtf1Pf6rx8stOZs50ce21Gs884+L00w3OPdca51hkyf6noqVnUIjGZtlAE8H4Y8U+48kl\n29pc4ilJ6ljghMHs5FhhS2MddfZoHMHumtIed7INEQhE2WATeL2I0lIA5JQpmJs3o6xZY73RpQSv\nF+P++y1W2XxMIZBlZaivvorYvRvKyhA1NcghQ5D9+1tjaBrU1kal7Oy4sd/nw1VXhxYOo6UkMTf0\nDdOUoQgEppRI0wS3m/AVV2D2729lxjXI9zlobD1lvyRtZZn2MAyHA955x0F2tklWLxdymfV5cbHC\n+vUKV1+tsXp+iMJIIvv2CWprBT6fwhVXWKpAs2Y5ufZa7RCdBttoLlig8txzTs4+22DsWJ2HH/aQ\nnm6d+wcecPPww2HS0iyh+Rtv1HjqKRcPPeTm/PP1qLE8gY7jBPuMf69YZ6BLGszmF/ZYuWSllITD\nYerr6ztkYGLH60xmeDSwY6+2XF9H3JEyL6/RCMa6wwMBzClTrF+cTsy770bu2GGpCyUkIIcPb1Fs\nvWFCeP76Vzh40DKICQmIxYstdSKXC5mcjKisxPzRj5B+P+FQiPr6epLy83G9/jqiqIhEw2BZL8Gz\n/s2kyXTGkYseDkNlJSIpCW3QIPB40KVE0fXoSxGa1va5XK4WGUZsYXzsvXHhhTrPP+8CM4dz09Mp\n+baS1dsymTzZwKGYJGvlzKu9jAEDTDZvVpFSMmaMydixBi+/7OSNN1o2mvPnq7hccP75Ou+95+DN\nNx1MnWoQCgm2brWycHVd0JAORSAAFRXWvHbtUppUFcUD4olRdQTtYZ+t3Rv/KWh+baSUXSKL+L+b\nP7cDnXmzxtZ12vHKcDhMUlLSf7Rv32aVttuyo7E7OXAgctgwRH6+FXu00jUhIQFz2rTGDYVADhiA\nnDEDOWGCZQQXLUK9914cP/4xypNPwt691qb5+Sj79ll1l0JAairm5MlWrHL7dnA4MH70I4zzziMY\nDBIOh0kuL8f9f/8HtbWY2dmY2d15R27Ab6rMCq/GmDsH56ef4l6zBpGTg0fXcblc0RefYRhomhYt\nP4nVDbbZhdfrjerm2ouMYDBIKBSK9ndMTIRbbomwcbOTmfsvZfWmBM4ekE9GaD9Fa4pYqE8k5Yz+\nFBYqnHSSyTnnGLzyioPiYsGNN2okJ8tDwr3z5ql89pmDhx6KcPnlOqGQwDAgOVkSDEqVS3D2AAAg\nAElEQVRcLsm0aTqvvebENGHXLsETT7gZP97gxRdDJCVJZs500dC/+T/WWB0LHO25aM+9oWlauxIG\n4/2axPv8jhbqI4888sjxnsTxgK2ko2ladMXXGaivr8ftdlNTU3PU9ZV2EP1o4wJSSiKRSLvijc1h\nGAY1NTU4HA68Xi+apnV8HCGQY8aA04mybRsEAlYz5p//HJp1BImF8s47qC+9ZMU8fT7Erl0o8+dj\njhyJWVWFuno1SmynE3te+/cjEhIQ33yDvmoV+oABJGRn43j7bSgqQqalIYVgtesgcxNKyKtzUOgK\n08uVSY/+o4gMGYIoKkJZuxZj4kRUt/uQWJRdlxdrNGMFMex7yja4dmeNcDjM6tWCoiJB796SZesS\n8Z4+jBGX9OKzHYPQJ0xkZeVA/AmS88+3NGd9PonLJUhKgj59JIMHm02YYG0tvPmmi3vvjZCVJXn3\nXScXXKAzeLDkk08clJYq/OpXYdatU7n99ghbtyrccIOXadMMLr9cRwhLPOHjjx1s2KAwYYKJpmk4\nnc7j+gLUdT0uxAMikQgul6vTSs+a3xtA9N6wF1X2trH7NAzjiBasxwK6rh8yl7feeosbbrjhOM7q\n2KNLumRjcSzqHQOBQLSV1dE+ZMfTzdE8ueeoirc9HszLLsO87DKCDRklh1UFqaxE+ec/oWfPRu20\n7t2hpATlH/+Aa6+1XLyGAapKGUG21eUzcdnX1mdbtiANA+fOnbgqKzH+9jfE7t2YDWnvJiZveraR\nIt3IUCUpJrw1WGNsuDsuFKTPh8zPR1uxgsiYMTidzqibzeFwNDGY9uLLfqHFum7BYhi2kLqUkoED\nTR57zElVFdxxR4DZs73c96fenH66wYaVKiNHRhDC5KKLFAwD3nrLyYgRBuvXKwwfbtBc9jchAX7z\nmzD2Lq+/XqOiwuoE8uCDERYudPDnP7v4wx/CBIOCjz92cPLJJj16NBp725178cUn4pjfJVqLfYYa\nXAi2278zmzEcCxiGEbdz60x0SYN5rFbN9k3e3uzRttBZ8zySRUGoIeZ3JLHXzpiPyM+3/tOc+aen\no2zYgExPJ3LmmfjmzYP6ev7p2cR8/0GG1ErS0nMxfD4rUzEUgpUr0Zcvx8jNRV27Fun3s9pRQokI\n0lP3IzSNFH8K+aKWNUoR48wchKIgPB58paU4/f4oAwgGg1GjabeWcjqdUcMZ+9KTUkazZ2NjnwcP\nOnG7VbKzoarKg9cL1dUmXm+I224zcLtt95sXh8OSrJMSHA6D1m6J2HdVRYXgiSesGsspUwymTjV4\n+GE3P/+5h6FDTa68UiMnx+Tppy0j7nTCokUO7r473JltRU+gg4iNfbrd7qhkn33vQWMz6+Md+2xu\nvO2mEf/t+O9fErSBzkqIqaurIxQKYffoi5e5dRSxx9I89vqdzsfvbzmzNhTCpliRSy9FpqdTXLSD\nRanVOCR8NlBYrb9M03qgfT6kpiFWrcL4/vdB1zECVcxyb0UzdcojlZSmuSl1aRhI3nRuwcBiXtIw\noEePqHRaQkJC9JzYZUI1NTWEQqFoJqTL5cLlcuF0OlFVNWpINU2LSqwVFwtuu03jjjs05s51UV7u\nZOZMHdN04/WqSGlimgbBYJBIJIIQBg6HbNVYNsf27QozZljGEqwk49tvj2CalqxeSookJQXuuivC\nxx87ef99J3fddcJYtoTj6eGxPRN27NM2UC3FxY834rEX5rFAl2SY0LTG8WhuODshRghBcnIygUAg\nLm7gWLT3GGOPpbPacrWGNhMc+vVD9uyJOHiwMc5pGFBainHLLRZrKyxEhMN8fP5JqIqHbjuLmdOn\nmnMPmGTU1EB6OtI0EQ0yd/Tvj/bAA2gvPc/IEpUhjiRkbi/MU4ahzp8HtQ68nkQMaaJWVILfj3nq\nqU3mJYSIGkWbTeq6Hm34Hcs+m3fHsBOFJk7UCYcFb77pZeBAk4MHBZs3q1x8sQE40TQrRmQb5lj3\nnJ1deTh2ccYZTQVoi4sF77zj5Pe/t9y2s2Y5ueYajS1bFFwu6zqsXq1yzjnxJbweT0kux3sedjzT\nDg3Y7FPTNEKh0Heeedv82sSz8HpnossaTBtCiCaJGx2B3ezZ7XZH45XflRBCZ8NO7rFXtId74I72\nRdau7yoKxgMP4Hj8cSgsjKr3mN//PnL6dEv6rqiIYrWehco+ehh+1MRkRFWAz/PCXLcraPX4rKtD\n+v3IsWOtzMRBg9D+93Gura9H8XqjbFX0vQLHyy8j9hwEipA9e6L/5CeQmGhl5C5dCpqGOXq01UGl\n4VrbBtKO8dpZtPX19aiqGn3B2Uzd1HWC5XXMeieRrB4655+vUVUlePVVD6YpGD++MeHDHts2zrqu\nR2s+29vPsaRE8PrrTs47T+fkk+37XOfRR134/fDrX4dRVaLu2eZGMxiEsjJBr15N78NduwS5uTKu\nylC6EprHxVuKfbZncdVZOGEwT+CwsONZdpp4PKO9bbl8Ph/uFkQCYsf5TpGdjf7004iGzFqZl9fY\nvsswkImJfOwrQDEkKgKRnk5WoIo5vWo5t0AnPRSyMmKHDMEYPZr6+npM08SfmIjSLHNGDhiA9vvf\nI0pKkEJYnUqEQP3HP3C88EKje3jWLIxzzkG/556mgUOIao663e6ogdN1nbq6OoSUeOfMwfPppwT3\n1XGZJ4Pu91+O4ZxEerrJ9deHePNNN4MGhfD7jSbXy16IxTJbXdfb1c8xIUHygx/oDBjQuChUVave\nMtYNe9ddEZ5+2kVurmTo0MZt9+5VmDnTxX33henXz5rT5s3WZw8+GKZPn/jypnRFtBb7PJbs8wTD\n7GI4UpeslJJgMIimaS125/hPYphHK6xwpOjQcSlKq4Lw+3OS+SLpIAkRhVJvCGFKZM90qgMan07I\n4rrwIIyRI9Gvuoo6KVGEwO/3t/7CUBRkjx6N88zPx/HCC5YovH1uTBN19mzM8eMxTz/9sMdoJwR5\nPB6Ut9+2Oq9kZuIf1I3E+iCOZ55GSBN16lR69ICbbjLx+RTC4QiKoqBpViPpujqFpCTB6tVOUlOt\nLFt77FBI8tlnKtOm1eN0Hqoq4/eLJsYS4KSTTB5/PExCQuNnKSlw//0Rmr/zhgwxuemmCE8+6ea+\n+8KEQoKZM138/OeRLmEs48kt3N65HA/22RWE16ELG0wbHXl5tyfG911I7R3NWLHiCsFgEF3X227L\nFYPO0rc9WpimiYZk2sQbcCxehLJxO1RXW3/r25/MK68jcvrVGC4XwWAQZ8PquyPzVpYvt2KfsQsJ\nRUG63ahffnlYgxkLUV+P8+OPkbm51qJESkxHIjog33iD2jFjcLhcvPOOl+7dI0yf7sTttl54c+cK\ntm4V/OQnITIyJLNmebj4YpOhQy39h1decZGRIUlKclNfb52Xl192cPnlQTyeRlWZlStdaJrC2Web\nCEETY2mjtffdqaeaQIT/9/8s78PDD0fiqgXYCbSO1lz7Nvu0y1aOln12lSzZEwaznQbOjle2J8bX\nmXM7FuM0N/zH2/i1C7t2ocyejdi/n0ifPoQnTSK7RzY3T74b9eMCKI8gU1MtdaDianhhHvUDzqEu\nKQmPx3NkbnNNO8TtClif2ZI4DSgrg+bdyXTdEhVIrS5t2nVFCBRVRUlJQRQW4jEMIqbJhRdW89pr\nPhwOmD7dZMkSFxs3qtx0UwS320XfvibXXBPif/7Hi2HA+PEGOTkml11msHOn4LHH3PzoRxo5OYIX\nX0zi9tvDuFwGixcLvvxS4dZb6wiHlQ6JxduIrZKyE4VO4D8Lsa59aGSftgGF9rPP5ovmE1myJxCF\nHa9sK8YH8euStccyTbPdyT3Hak7RMerrERs3IrZsQSYnI0eOhLy8Q7dftQr1qaeQTie614vYtYuU\nxYupuf9+yyrt3Yvs2zc6NpmZyMJCzE8+wXfzzUes4mSedhq8/rrFMm3DKSWivh7d1sDFmsKnn6oM\nGyYb2Jj12ccfq6SmSqacmtJEZCGKUMgqe/F40DWNzEwvt90meOEFlfnzIT1d46ab6vB6VYSwXLAD\nB8Kvf61z9dUe9uxxMH9+NVu2CB5/3IfbbdK3r06fPgJw8OyzbkaNMliyxMG994ZJT3c1EYtvb+KQ\nHbN8+OEIwSBR96wd0zxWiAdPRjxlvHf2+Yhln7acZ0vs0xaMP9y+T8Qw/8sRK2PW2kMhpaS+vp5I\nJEJiYmK7XrzHo3ayvdA0rd2G/5ijrg7l7bcRBw8ik5IQe/agrFyJedFFyHHjGrfTNNSXXkKmpaE1\nuJRcycnI0lI877xjSe41KO3Y594wTaTXi3fvXvSjkDyUgwdjnHsu6mefId1uyx1bV09o6EiUSZOi\n2zkcMHmywfz5KvX1CqecYjJ3rorXC2eeaYKSTGD0WSSt+DcyJ8cymrqOUlxM6KqrqNc0fD4fDoeD\n5GQYNEiltFRh4ECF1FQTw7AK162MbieLFvm44gqD9993cP75SeTlSbxekwcfrCc310TTJOedZ7Bk\niZdPPnFw//2RhsqcRrH4thKHbGzfrkRjlo1uWCum+YtfhA/Jnv1vxPE22t8FDsc+6+vrgabssyWG\n2d1OyPsvxgnhglYMnO22tGN8naU12xlz6yjsMYLBIAkJCcffWBoGrg8/RFm50nJ7ut3QvTsyOxvl\ns88sP6aNAweQNTVEGh5kl8tladOmp6Nu347m9UJD7MV+yKVp4tQ0ZG7u0c1TCPS770Z77DHk6acj\nR4yg8Ib/YdbwJ6isa3Tx5udbMnTTphl88YXKLbe4EAJmzDBQFNi2TfCq82aCE6chSkoQRUWI0lLq\nL7qImu99Dyn9rFhhjTdvnsK2bYK779ZYs0bl/fe9eL2+Bk1iL6+95iYpKcxtt1Xw6KM1FBQobNig\n8sADJgMHuqL6pMuWqbjdJqecovH3vysEAmazQxPRhCS/3x+9J2xvin0us7IM7r033CRmeeqpJnfc\nESEz87/fWMYTvkvGbbNPj8eDz+ezEtcaEtHq6uoAmjQiCAaDeL3edo9/ww03kJWVxSmnnNLi3xcu\nXEhycjIjR45k5MiR/O53v+uU4zpadFmGaaMlo2TXJDqdTnw+X4du0nhjmLZyj9UlI/GoM2GP+vhC\nIVzPP494911wuRClpYj16zEnTLBKRkwTUVAQzYw1HA6kpqEADqezsU2YriNcLupPOQU1KQmlqAgz\nLQ2hKDjCYaShY37/+0d1rA0HjDluHGYD680CRn8r+OgjhYsuMggEBP/+t8q55xpkZloZqNXVVm/L\n6dMNCgutv196pcTR/VYiVVdBRQX1iYnoHg8Jfj81NQrz56ssXargdMLNN+vs3SsIh2HrVoU5cyTf\n+57J/PluuneHSy8V7Nzp4N13HYwZo7Fli8pDDwlefjlESoqDFStczJ3r4J57IqSlGXz4ocLMmU5u\nvTVEQgLR5I6WWpUB0RegJX4epmdPlUikqWtuyJATST9dBa3dH6Zp8vbbb/Pkk08ybNgwpJTMmDGj\nXa7Z66+/njvuuINrr7221W0mT57MJ5980mnH0Rno8gyzOSKRCIFAICpHdTxifJ01lh2vtHUf48G1\nJJYsQdm6FZmeDklJVmNmj8dim3qD8HfDg6lpGgGvFwYMwFFVZTHLBrUcUVICZ55JQo8eGL/9LUZ2\nNmpxMaKoCE2L8Icr+/CNP3hMFi9DhkjGjjWZNcvBxx/bxlLy8ccqmZnwu99p6LrkF79w8vHHKpde\nqkfLR2VyMnVZWZg+HwkJCSiKQnIy3HWXxq5dCnl5kr17BW+84eDuu3UefFDDXrjPmGHwwx8avPii\nyh13uPF6Ff74R5OVK8M4nQpTpyaybVuQf//b4Kc/rSE93cDhULnsMoWhQwUrVngOkevTdf0Q4Q7b\nkNrs09bKra+vj7ZKixdJtmONeIijxhvs8+HxeLjuuut4++23cblcfPTRR2RlZTFjxgyee+65w44x\nceJEUmM7DbWAeLy/uizDbB7DtPvThcPhdscrjzWOxmA2z+oNBALHfU4AyrJlGJmZllrr9u1WBb3b\nbUnKFBSA34/My4u6BhMSE1EuuADxP/8Da9eC34/o3h05dCjGFVeg6zrB1FTcf/gDSkUFMhxmk7ua\nlSsfp3TzO9zvvR+X0xVV3Omsl19sCYbTaRlLr7fRDXvppQaPP+5EiMYe2PbKXFGUQxKukpPhj3+M\n8OCDLubNU3noIY3eva3zPGmS2bAfa9u8PEldnWDSJIPevSVFRYJBgwQul2TTpmR+/WsNXbc0Rw3D\nwOFwcO65zob4U+ti8cAhC6vmiSH29zqaOHQCR4fYll/HG7GLCEVRGDZsGBkZGTz88MP069ePefPm\nsXPnzqPahxCC5cuXM3z4cHJycnjyyScZMmRIZ0z/qHD8rUKcoLa2FinlUWuoxoNLNhKJROui7NhU\nPMwLsFypqsr/Z+/Lw6Mos69PVS9Jd7oTFiEQQEFEQQZZFEUcWWWHJDAiIaAjiBPEEQUVdRh0cBvw\nUxlFB5dB+TEKAkmAEDCjsgRRAUFZFFAEBIzskKQ76bWqvj86b1Fdqd6ruqpJn+fxebRNqt+uVL/n\nvfeeey7TogVQXQ2cPu1ToNpsoGw2MH/5C2rrNmSr1QrDrl2g33gDXFYWuCZNQFdWgqMoeCdNgjsl\nBc662onBYPD9DMdh3Tfz0MLaAmecZ3DCcQI3pN4A98WL8OzbB311Nei2bUH/4Q+gw6jl7txJw+0G\n/vjHy1HYgQMUFizQ4+mnvXC5gJISHTp2ZNGrF8fXLL/+Wod//tON77/X4ZNP9Bg71gWKqoXBYAjY\nD3r8+OXXfvyRRtu20t6uw4ax6NnThcWL9Vi6VIejR2mMGeNF164cGAbQ63XQ6y+nzy4rHx08wQkP\nEGRUGfG7Jc8JGdkklbqN1HEoGiSju8QBaSvJyMjAmDFjYr5ejx49cPLkSZjNZnz66afIzc3Fzz//\nLMNKY0ODJ0ySjqIoChaLJeYvqJopWRIlO51OzUTJYrC33gqqrMwn8unWDbh0CTh3zteqMX06HA4H\nvBcuIL1VK9AMA7pOIcvVRWQsAJw7B+6//4VzxgykpaX5mS4cunAIv1z6BdekXwOaorHm8BrMufo+\nWBa+BdTUgKEowOOBJysL9qlToW/cmB/SLfW379yZxfLlvvvYsyeL1at1+PRTHf70Jy/fVvH77xy+\n/FKHG2/04uzZupplXRq2fXsG5eUcli5lMXFiCqxW6X7Qfft8adinn/agcWMO//qXL5wcMUKaNK+6\nChg9msG77+rRogWH7t19axGf9Yjri9AsnqilOY7zm/Op0+nAMAzvgyuMPknNU3iYFLoZkejT6/Wq\n5meaRHwgdZAhgkK5YCVpGQDDhg3DtGnTcPHiRTRp0kS294gG2ttR4wSKovhIDEDE4h6tgYh7GIZR\n1LIv1utw/fsDe/eCPn4caNoUcLlA6fVgOnQAu2gRUnQ6WA0GsLffDu4Pf/CZp2dk+P1tvI0agT5w\nABaDAbTgc3IchzU/r4HVaAVFUWiU0ggnqo7jpyWv4A+0BbjmGr5on3riBHSbNsExZgxPHuLIC/Cl\nXseP92L5cj2qq4Evv6Rx7bUsRozwHbSOH6dw8iSFAQMYpKUB6emcX83S4/Gge3cHGjVKg8Ui7ab0\n++8+svzrX718Gvaxxzz4178MaNqUQ69e9QU2p04By5bpkZPD4JtvaHz2GY3Bg4MLcYTpVQB82wBJ\nf5P6pl6v51PGwkkroeZ8kuhTys9U2NOXSKlbrUS5WllHIMhtXHDmzBk0b94cFEVh586d4DhOdbIE\nGjBhulwu1NTUwGKx8CpSOaBGhJlQzj1WK7wzZsD59dcwHD8ONGkC5tQpeA8fBnX11TAYjQDHgd66\nFR6HAzTLQvhpPLW10J04Af2JE6BffBFs375g+/QBUlP9okvAd//SvToUur7DjU1y/BRuXFYWDDt2\nAPn5AEXVIw8heaal0Rg1yoslS/S4+moON93E4rPPaHTsyGHLFhrDhjE8QQpb0VwuF1wuF9LS0tC1\na2DrwZYtOTzzjMdvHmVGBjBjhgdSc8hPnQLefdeAnBwvunfn0K0bi0WLfF/lUKQphJDgiEk8TdN8\n/VsYfUqlbgFfrZxEnkIilPIzFfb0kcgzGX0mHgJFmJF4yY4fPx7l5eU4f/482rRpg7lz5/JuQwUF\nBSgsLMSiRYug1+thNpvxySefyPoZogXFaaKwFX8Q4QJN06iqqkJaWposKUyy8WSIpmEoda1wLfvI\nBhhrD6Yc1yFtO40aNYLn4kVQc+dCd8010NdtsCzLgnO5QFVVgfJ4QJ06BbZZM3icThi//x70uXPg\nrr8eXOfOwNmz4Dp3hnf6dCzcswjf/v4tzAZy0uWA33+HZ/8e/P1ke1zfqL1v4onR6GtfqaiAZ+HC\nenlM4aR73yavQ3GxGddeS+HECR2uv57DwYM0GMaXFs3K8v8KkdS41+v1G/wbDVgW2LqVRq9eLE+e\nn39Ow2DgYDJRuO02H3FVVwNFRTrk5zOI9E/j9XpRW1vLWwiKLdPEcz7JMyaMPoXbCMluSH1uoXCI\nKHQDCYfsdntUSnU5QQZ/p0qdXOIIUifWgpsOeS6EfZdDhw7Fl19+mVDZg2jQYCNMYToJ0KaEOVSE\nKSXuSSS4XC44z55FI6MRdN1GTf6B0QjK5YJ36lTQr70G7tdfYbx0Cbpz58C1bu2rfxqNQNu2oA8c\nAHXwIO7pdA+Gt7/ce0lv3AjdV2dAHWmENp5a0Cf2AceOgenTB9SlS2B79JD0ihXW/Ww2DmvW0OjY\n0Y1bbnGia1fgP/+xoLoa6NiRwt69FDIzOd7xjrhDsSwbM1kCvrZTu53CkiV63H+/F6mpQI8eLD74\nQI++fS9Hk+npwKRJkQ+AJrM7iXDK957hz/kURp/kH6K4JaKhQKlbo9HoR55EOJRoaduGBqkIM5R1\n3pWCBk2YUv8ux3WVTslG0wKjag3z/HnQ69aB3r4dnMEA9OsH9vbb4QBgveYa0Ckp4FwucHXtDhRF\ngbLbwTVvDlfLlnA+9xwsR4+CXrECTMuWQIcOPibxLQgwGkH//DMyu3RBZlqm7/ULF2As/Rpci46A\nsQXoPXsAne913c6dYG66CUx2dsilHzyoQ5cuQO/eBgAGnDvHolEjwGr1YPDgGnz7rQnr1+swbBig\n11O+2ZdUiDFiEYCifMKf9et1WLJEj+xsBh9/rEPfvixuvTU28wC32w2n08nb8gVC0DmfdeRqMBj4\nSJFEnGLylBIOkf8mwiFSJ3W5XAB8hyo1hUNarx1qBVoMOJRA8hgHbZkNhAIR97jdbtUs+yJCdTV0\nL74IurwcXEYGYDSCKyxE2rvvIt1igc5kAjNkCLiTJ8HZbKAAXyr2wgXUDhwIp8MBy/nz0Ot04K67\nzjeXSrSBcQzju7YA9JEj4ABf82KzZj4R0dVXg8vMBJeeDu/f/gY0axZy+bfeyqJ3bx8xORzA1q0G\n3H03hUcf1SEry4KhQzmwLINt25x8r6vc0T4hTZOJw9tv63HbbbGTpcvlgtPpjLgUQZSxJpMJVquV\nF8s5nb7PX1NTA6/X62e9ZzQaecIjKl232+1XCyXXJkOQSepRaMdGfJ3FRgsNAVoibvFaGgpZAg04\nwhRCMz2KIojXRZx7dDpdxOIetT4j9eWXoC5e9JEVx8HNsqBat4bh0CHofvoJ7I03grn9dlCpqdBt\n3gy6ogJs69awZ2fDm56Oxm+8AbqiAqAoUDYbqIoKsBkZl50DamsBmgbbrZv/G+v1oADwn9hiAXfD\nDUB1NbhGjXw5zAhhMgHjx1+uEfoMCIwYPpyBzebh63sOh6Ney0asm93Fi8DZsxT0euDAARq33spK\nCoJCgQwN93g8vNNQtJCyTLtc9w2v5xNAnQWfv+qWIJRwSK6ezyRiQzIl28Cg5QiTbBZ2ux0pKSlI\nTU1V7eGM9PNR+/eDS0/31cHcbt8Gq9fDS9PAsWPwXn+97+d69ADTowe8nG+wNTgOjRYtAnX2LHD1\n1QDq/kY1NaD27gXXurVvLUYjvAUF9YZRsh07gktN9Rm5k/4wjgN14QKY0aOj/vzi4JEYAqSnm/18\neoOpbiMlqQsXwNcse/Zk+fQsqWmGC7nrq2KE6vkU3gMhyRLyFKZwxWuTchwi95e0wghHUckFLR6k\nk1APDZYwlaphEsiRQiG/73a7edl2VIOQoWIUfdVV4H76Ce7UVBj0euj0ep95OsfBW1fnE6oua2pq\nfGKTqirQv/wCrnVr4YcAd9NNwPnz8D74IKDXg+vQAZCakmA2w/vww9AvXAhcvMi3pjC9e/uM3mVA\nsBqgVE8iaeonwpZghgkELAt89JHer2ZJaprFxT5VbDjgyEEEiLi+ynHAmTP+LTMAUFXl011J3f5Q\nPZ9C1S05aAhJ01vnK+zxeMIyi5cSDhHVrVzfQzWh5ZQsyRA0BDRYwhRC7hqmXBCO5dKKc0+k98rZ\nuzf0n38OY6NGoFNTAY4DW1kJmM2obt8eeoeDjwocDgcvLqHOngVHUfXqlTAaAa8XXJcul81VA4Dr\n0gWeV18FvW8f4HCAu/Za36BpGSzbXC4X3G53PachKUTitiN+fmgaeOABL4QmKqSmWee5ERLBPGzD\nwdmzwMsvG/DQQ1506uT721dWAq+8YsCwYQzuvDN0TVF4gBAOKiZzPsWRp8vl4jMpJHUbqXAo6TgU\nHxClfkOA+juwiiCbv9zRl/C60YKIewDIQpbCjSceIOk/d1YWMh56CLqlS8FdvOgLVywWcLNmwdKy\nJbxeLy8AIZsgwzDQ1Y36wokTgNkMNGniY4/ff/c5BNXUAI0ahV5IerpsESX5XKTHMpoaYLDUYqDU\nrZTjGEVJvy6GX9QeZSo/MxOYNs2Lf/9bj4ce8qJlSw6vvGJAr15sWGRZf+3+lnrC2iR5RlNSUmA0\nGuv1fAYyixdGn+T+Cv1uhY5DSbP42ECmHxHI7fKjZTRowhRCCcKMFkJxTyIW0zukk0wAACAASURB\nVDmO8zOzp/r1g6dnT7CHD4PT64HrrgNlMICq67kkNTWO43xRh9OJ1LVroT9xArrjxwG93keyej3o\nS5fAdOkCwxNPgO3XD0xenu//k5FfLAsuM9P3mgKfi6Q15fIdDpS6FYtmokktMgyDmpoaPmqPBR07\ncpg2zYv5831R/ejRDLKzI+/7FENIcDRN81kGhmFQXV0dsOdTbBZPSFNMnhRF+QmHIjWLF5ODWtBS\nSlaM2traJGE2JGjpQfR6vbDZbEhNTUVqaiqqqqpkua6cfZjBIlUh2RNS4TgOTEoKuM6d+U0sUKRm\nMBhAffMN9GVl8HbpAm+rVtAdPw79sWOgGQbeUaNANW0KMAx0X3wBzmIB17MndIWFPrsbigJMJjC9\neoE+cgTUL7+Ay8oCe9ddvraUKBFrWjMcxJK6FUPs3iMHWrS4/Px06CBvtoLYCFosFj4tG6jnU2gW\nL+z5FNZApVK34sg2XsKhKw1i8k4SZgOB0inZSEHSckJxj1ZbXqRAbPqESl6y6QGXlY+hIjXd//4H\nNGkCfUoK0LIluKZNwV26BNbrhcflAjwe6GgadMuW0K1fD+7gQXBpaUCbNr4LnDwJ45NPgrvhBnDN\nm4P64Qfovv0W3oICsLfeGvHnYhgGtbXBR3PJjWhStwRS7j2xgtQsR49mcP31LJ+eJTXNaBGszUVI\ncELHIal7IBQOiVO3oczihb8nFg7xzlMqQ+sRZiQ+somMBk2YBEq1gkTys6QpWylxj9JOP1I2fSRl\nFlAJG6CmRl26BGG/BOXxgKYocHo9jAAYmvYRCAD9mTPgmjYFlZkJiuMAigL944++eidN+/o109LA\n1dZCt2wZ2O7dQ4qFhFAiUosUkaRuyYYfyr0nElRVga9ZkjQsqWlOm+ZFx47RPVeReO7K2fMZyCxe\nSjgkJt2kcCgZYSYB+WuYkbxvsOHViRBhOp1OOBwOWCwWv3SXmCyFNTWhoEMMtksX6L75BlzLlr4X\nUlPB0TTgdgMZGfzGxdntQEoKPGYzvHVWajqOQ+rp0+CsVp81D4HZ7GsvOXPGv1UlCJSI1ORAoNSt\n3W4HAL/ISI7N3WgEhg9n/IZok5pmtIGFsCc0mnpwrD2fQuGQ2CxeGN0zDMMfPIjLUFI45I8kYTYw\nyH1iDJfkiBmBsN4nBbkIU+4Ik6RWPR4PP4NT6CEqFFJEQj7siBGgv/0W1KlT4OpmZnLp6aDcbr6H\nEzYb6AsXwNx9Nww//QR9aqpv43S7wVIUuNpasK1bgyLrAHwiozCjROFoLi33mJHIy+PxgKIomEwm\nsCwri2ECgckEP7IkiCWyjLYnVAqx9HyGYxZP0rNC1W0kwiE5oBXxEVA/wkyqZBsIyB9djShOLO4J\n9EWT6wso9xdZKjImJ33y5Sb31e12R5Qm5Fq2hPe553yG7Xv3+mZozp4NUBR0GzaAOnHC9zN//SvY\nbt2gW7oU1MmTQMuWvnRcZibokyfBNWsGhmXh8XqhP3cOaNcOTJMmCEZ/sbaNxBvCSM1qtfJ/ZyVU\nt3Ktl8zdVEo8FUnPZyizePG+kBQO1UeyhtnAEG/Rj5S4Jx5rk/M61dXV/HBXXglbR5ahlLBhvUdW\nFpiCAogbF9i+fQGGAXQ63oCAGT8e9Ndfg969G2BZMHl5oH/4Abpjx6CjaYDjwDRvjpp774Wntrbe\npkk2NqWt4+RGqEhNTtWtHCBKY51OFzd7x2A9nyR1K5y2YqibmCO26xMeAiMVDpH7e6UQqFQNs3Hj\nxiquKH5IEqYCCERy8RD3BFuTHCAnapPJFJMSNmpQVP0eS7MZ7F13gb3rrsvrzMkBdeSIzzEoPR1c\nx44w6fVIDbJpkk1O7aHF4SDSNpdYVLdyrbempiauSmMxhPcg1JxPYU+owWDwEw5JpW4BaeGQ2Cw+\nWuGQllWypNTSEJAkTMQnJRtK3BOPtcV6HbfbDYfDwW/SQHAlrE6nUyztFhIUBe666+r1XkptmuRz\nAZfHSWl5iHGs5KO0YYIYchooyIlgcz6FqlhSpwTqm8WTUWZiAZDUAYVhmIQXDkntIbW1tbCEYzt1\nBaBBE6ZSNUzx9Yi4R5jCjDdieU/SK0eK+06nM2YlrFZAaqykbcTj8URlkh4vKEE+SqZuyXrVbMsJ\nByR1S9M03G43f29D9XwKFbfB7PoI+UYrHNKaUj7ZVpKEbGkPIWESub/JZIo6GlAzwiSpVa/Xi/T0\ndP4asSphw4LX6/OMTUtTxOpOar1SxEE8fUktTK1evHj0hMqZutVCD2skIOs1mUx+6w0WgYuFQ+LU\nbaSOQySylTKL18KBDai/jqRKtoFBqQcxEnFPMMjVDhLN+5LePqvVCpqm4fV6+dqP8AtN2jBkaZhn\nWdCbNvkcf1wuIDUVzLBhYPv1i3nSCEGw0VxA4HqX0+kEwzB+UVdYKTWHA/TOnaB++gnIzARzxx31\nZngGAyF3OQ0JQiGW1K1We1gDQUiW4vXK2fMZjnBIbBZP2rW0AKl1OByOpEq2IUB8epOzsO7xeOB2\nu/n+xGihVoQplUYmzfAGg4FPy5JIhGEY2dow6C++gL64GGyrVr6JzS4XdCtX+kaDDRgQ07VJejnc\n0VxA8E1NLBaRvN7FizA8+yyoU6d8LgBuN3QrVsAzeza4zp1Dvr9WekIDEYcwAifPQ7DDiNYQjCzF\niLbnUxh9kn0mkHBIaBZPrk/uaSzCIaWQbCtpgJCLmEhNjGVZZGRkxEwgxOIrVkTy5SI9osI0Mqm7\nkOZ44LLHKlmfw+GIXWnpdkP3v/+Bbd3aRy4AkJICLisLuk8/BXvnnRFZ2wlBVMqxkruYOIL1+VEU\nBd3y5T6HIeJ1CwDV1TD8619wL1oUMN1MyN3j8ahOlmJIReCk9ktELaTGp2VRS6yReyQ9n9GYxZN7\nTDyaiUhNLeGQVFCRrGE2QMhBmCQqIw+6FjeKUFE08YQVppEDKWFJT53FYvHbLMKKugKhutpngSdO\nYaekAE4nYLcDUfR8KdXmErLPT6dDRnk5uObN/X8xPR2oqAB17Bi4Dh0k1xuuz6raIBE4iYTS0tJ4\nAtWKYYIU5E5zR9rzCdQ3iyftWVJm8ULHoUA9n4Q843mPkynZJCKGUNxDURTcbrcs15Ur8g31BSIb\ntNPp5HtEQylhjUYjH4FSFBVR1BUQVqsvghSTptvtI80o5OvxGM0FBKh7ut1g6+p+NC736vG12AD9\nuqQ+JmsPq0IQRsLCyD1U6lZNNxxSw1Yqco+k5zOUWTypYQrvVSCzeKfTCQABhUOxQurALfTbvdLR\nMD5lAEjVMKOB2Hzc4/FopkgvhtQDL1TCkjRyKE/YYMrHUCftoBtmSgqYgQOhLy31pWXryJOuqIA3\nNzfidKwao7kIaJpGSmoq0LcvUrZtA9OiBViOA+PxgKqpAW02w3v11aAFfxOhdZxaLUiRgBy0GIaR\njIQDEUe8DBOkoDRZSiFYzyfgf4iQcg4CwPsFB+v5FIqzxMIhpe6xlk0V5EaDJkwhoiFMQjRC83Et\nrCvYtcRgWZZPI6enp/PvJ7YDA6JTwoo3TCmRhHjDZIcOhReA7osvfDZ4ej282dlgBw2K6PNqpa2B\nyc8HffAgdL//Djo1FZTbDY6iUDNzJpxuN+B282k6p9PJz3/U+iYkthIMx20olHhK6dStFgRUwgNl\nqEMERVHweDwwmUyg68baBVPdktekhENXuuNQPJAkzDpESkzEbUXKuUduIwSlrsUwDGw2GwwGQ9ie\nsLFuNFJtCuI0lcFgAEaM8FndVVf76n0RNulrqq2hWTN4Xn0V9NdfgzpwAGjZEkyfPjC2bAlDXSQv\ndBsiG6hW6+CAPBNHpMRTgaIuOTZpp9MpOahaTQQ6RAgJjhymSJ0ymFl8MOGQsOeTCIdiNYsX7hMN\nAUnCjAJSRCOE0lGhHNcS1lxT64Y1C5WwYk9YIuaQc6MJS23auLHvJBzBdbUQRdSDxQJ28GBg8GC/\nl8mzQqIIvV4vj3hKQQjTxnLVhCOJuiJ9BsVqY62QpRTId4JYNJJ7EajnU2gWL6xlkmuF6vmMVDiU\njDAbMKKpYUoRjZJQIloVGyoEEvcIxTJK19Ok6p6ENMK1ZxNGwlqKIoIhlNtQ1OIphUAyKyTFrsQa\n5EzdJpLamIB8zrS0NL/SRzQ9n+T3wjWLj0Y4pFW9hhJo0IQpRDjEJBb3xHIttUBO2tEoYeOFUM3h\nQk9PoVgmkUZzAeG5DYVqUxDWupSGWhNHok3dCgVJiaA2BoK3uoTq+RS2rAQyiw8ndSs+rAmFQ+K+\nWo/Ho37JI45o8IRJyC0YyUUq7pE7JSuXoQJJ/QkHPkspYbUiliEIVffU6/XweDwJM5oLiDxtrLba\nVCsTR8JN3ep0On6oc6I8E5H0hYbb8yk2ixfb9RHvWnHqlrSJAf7CIVKy4TgOBw8eRKtWrSLqwZw8\neTLWr1+P5s2bY//+/ZI/M336dHz66acwm81YsmQJunfvHvb1lYb2j+Eqg2VZ2Gw2sCwbsRJWK1Em\n+QwAYDabebIkXx4hWbrdbt65QwtkKQaJNtLS0pCeng6DwcBvjCzLwuVy8RuBFkGiHrfbDYvFEnVd\nkrQpWCwW/j54vV7Y7XbY7XY+spLjPggnjmhpPBdJ3aampsJiscBqtfJtXXa7nRdOkTSllhGLiQI5\nTKWmpsJqtcJisfCHSJvNBrvdzhu7k9YTo9HIHyyEo8eIV3Sg65MDqtfrxaOPPoqbb74Zx44dw7Jl\ny3Dx4sWQa500aRLKysoC/v8NGzbgl19+weHDh/Hee+/hoYceiuheKI0kYdZBKpJjGAbV1dW8m00k\nMyyVXFckIJ9BGHkEU8KSqCcRGpEJQaampiI9PR0mk4nPBthsNjgcDk31xJK0sdz1NBINmM1mWK1W\npKam8sKcWO8DSX2KJ3hoEaQeB/iyEmazWbb7oCTkdhwSHypJZkZ4H0hWyWg08pkkYQmEmCyQgygB\niUTT0tJQXl6OFStWoEmTJli5ciXatWuHP/7xjzh48GDAtd15551oHMSpq6SkBH/+858BALfddhsq\nKytx5syZmO+JXND+rqgwAqVkYxX3CK8rx/qigfgz2Gy2gErYRKv/SYllxPJ5YapOqu4ZT8jRhhEO\nQqVuI5myosaElFhA7jFFUbxITQnVrZxQ+h5Hoj4Wz/kUt62QzJTw2U1LS0OXLl2waNEiOJ1OlJeX\nIysrK+r1VlRUoI3Ac7l169b47bffkJmZGfU15YT2vwVxAiEmoiINR9yjZZAvBPkM5LOR4bi8SYBA\nCZsotZ5wxDLBVJbC2k48Nst4WfOJEY7aNFDLSqh7rDUEa3XRgmGCFIRq2Hi0DIXq+ST3QdibKez5\nJOlaYdtKTU0Nb7yempqKIUOGxLxOcYCgpT1J+9+EOEJoEaelsVxkbeE8OCRadLvd9ZSwRqMRbrcb\ndrud/+IQAo23EjYakMNMJKO5gAD9npWVYH75BdDrQXfsCIPZ7HPiqagAl5YGrlMnWYZWx6MNI1yE\nO2WFNLZrqo81CISDAMK5x/E2TJCCGvZ8YgQa1yalwqZpGi6XiydSwBd1Hj58WNaUaatWrXDy5En+\nv3/77Te0atVKtuvHiiRhCkAk18QiLhbIRZiRrIOcshmGqaeEJa4eJNokKk3Ad9IFoLlpEkKQg0Cs\no7koikLKV18h7f/+DxzDACwLxmSCp3lzUEePgtLpoKMocM2agXn8cXAxfFm1oiyVQqC+V2GDvFgQ\npkXEeiBR0jAhELRAlmIEauUiIkCidUhNTeV/5siRI3j//fcxf/582daRnZ2Nt956C3l5edi+fTsa\nNWqkmXQskCRMUBTFb2yAvKOf5EI49VDiCUvTtKQnrLDHkgy3Jl9Y8WZJNhCtDKkV1v9i/ftQhw9D\n/5//+MZt1ZGY/sABGLduhXfECDBGI7wsC+r8eVCvvgrPvHnQG40Rv6fWWnOCgaTqiLm32WzmVZPi\nBnkt1bfl7guNR+pWi2QpBXIfjEYjvzfSNI3S0lLMnTsX/fr1w5dffonly5eja9euYV93/PjxKC8v\nx/nz59GmTRvMnTuXP7AXFBRg+PDh2LBhA6677jqkpaXhww8/VOTzRQuK05pkLM6ora1FdXU1UlJS\n4Ha70ahRI1muW11dLZuP6aVLl4IOoyZWfUajka/fBFLCEuMCs9lc7wsrFMuQekW8m+PFkLv+p3vv\nPei++QZcixb8a/SWLYDNBq5HD37IM8dx4E6cQM1TT8F19dURkYamfGzDgFD0RdqOhP+PPA9er5e3\nT1M7G0HIkhhrKA1h6laYkYkkdZsoZEkgPKgSERXDMPjss8+wcOFCnDlzBmfPnsXQoUMxcuRI5OXl\naeKArSQafITJsiyf4pNrhiUQP/MCooQ1m838xkG+3AAiUsJKnbBJNKqGslCJ0VzUhQvgRKpnyu0G\nR9NAXYoaqJPP6/UwAUhJT+dJQ+i5KUUaiSiWCTZxhLSsiOtcwmyE0vU+MdRIdceaur0SyBIAzp49\ni1deeQXvvPMOunfvjoqKCmzYsAHbt2/H+PHj1VxyXKD9b7TCICOntNzcHIgwpZSwoTxhI1HCimf4\nEdIQTxZRgjyVSmmyXbpA/+OP4ASZBLZZM9DHjoEVZhc8HoCiwF1zTUDSqKmp8av9kDRmIm6K4Y7n\nCqd1R8kDldBEQa1Ud6SpW2Ivl4jPhZAsT58+jQkTJmDhwoW8+06rVq3w4IMPqrbWeKPBEyZ5GORW\ntcp9PSGESlii5g3mCStHlCYmDSmFpVxpOiVTmmyfPuA+/xxURQW4q67yzds0mcA1aQLK5QLncAAO\nB6jqanjz8nyjxQQI1OcoVBYmglgmWBtGOIilZSVaaIEspRBKdctxHFJTUzVV/w0EsrcA9SPLCRMm\nYMGCBbj11lvVXKKqaPA1TKIG4zgOly5dQuPGjWXZ6GpqaniZe6wQ1kPJRidMJQciy3gIT4QRF7mP\nsYiG4jKa68IF6Nat89UyU1LADhgApls36DZvBr1/P7irrgI7ZAjYHj2AEOsXpjRNJhO/WXq9Xs00\nx4uhdKuL8EBFTDJinbJCnuVEqQsD4J2zjEYj792q1WcCuPwscxznR5YXLlxAXl4e5s2bhzvvvFPl\nVaqLJGHWESYAXLx4UdOEqdPpYLPZoNPp+BRaICWsGrW0WERDxJovkcYwBUpdkf9H7oPH41GtOV6M\neE8cERp3E1u6SIVkiUiW5OAnbIESRuFer1czzwQQuJZ96dIljBs3Di+++CL69eun2vq0ggZPmGSD\nB0KrUSMB6V0ymUwxX8tms0Gv1/On1XCUsFqopZF76/F4gp6ug6k0tYpI1LtihSVJ68a7dUcLfaHC\nAxWJwoOpjxPNng+QJksxAqlu1WjnCkSWlZWVyMvLw7PPPou77rorbuvRMpKEKSDMyspKWK1WWUhG\nGHnEiqqqKp5MwlXCao14SMQlPF0T8nQ4HH7+n1pHLFGaMAqPNuKKBlrsCw3VskIEZolGluSwGu73\nT5yZYRgmLgIq8t5kso2QLKurq5GXl4enn34aQ4cOVez9Ew2J8RQmIOQS/ZCHmYxWCqWEpShKk56w\ngURDTqeTr3Elglgm1ihNKJaJl7OMVlOaoVpWiFgmEZSlQHRkCUQmoJLz+xGILO12O/Lz8/HEE08k\nyVKEJGEKIHfvpHiuXCQQKmHJiTuUElYLfqXhgKIoPoJISUnhZ/dp1WmIQIkoLZzWnViUpomS0hSq\nj10uF5xOJ4xGI3+oilfEFS2cTic8Ho8s9fdgqlu50vmByLKmpgb5+fl45JFHMHLkyJg+x5UI7X6D\n4gThAxcvs4FQ4DgOdrsdHMchPT2dV64FU8Jq0a80EAKN5hKmpqQMoNUkz3gQj1QUHstGmWgmCoB/\n/S+eLSuxQE6yFCOQYQIhu2gOEoHI0uFwYOLEiSgoKMDo0aNl/RxXChp8DZOrG3kF+MQ1ZBp5rCAW\ndBaLJaLfY1m2nhKW9HMZ6zxNxUpYraXagiGSTTxc0ZDSUNulJRqlaVzac2RGOClNJVpWYoGSZBkK\nUqrbUKlboT2mcM1OpxMTJ07Evffe2yAce6JFkjAVIky32w2XywWr1Rr273i9XtjtdqSkpPCpVXIa\nFNb6SAqTPPSJsCHGqt4NtDkoaQgeyntXLQQ7SFAUJbkhahmBNvFwfi/WlpV4r1nJ9YSjxJYieJfL\nhfvuuw9jx47Fvffeq6kyiNbQ4AkTAD/mym6388rHWEGiknSRU0ywnyfDWKWUsMT4mHi7Aj4ZutFo\n1FytTwxSjyUpoFg3l0BRhpz9bInSFyo+SJDPnihpWDnvc6QtK7GsWUtkKUYg1S05YAjbXdxuNyZN\nmoSRI0di8uTJmt5HtIAkYeIyYcppNkDqLeEQptPphMPhCOkJS1x+AJ8HrvBkrYYJdjgI1twv1/XJ\nQUKufjayZo7jNKk4lgJZM8uy0Ol0mkhXhoKwliZ3G1SolpVYxDJaJkspsCwLh8PBH74PHTqErVu3\nYsiQIfh//+//YdCgQSgoKNDc86FFaP8IGgeQ1Ge8RT9kk/N4PCE9YaXszMi/k7SUy+WCw+HQjFBG\n7tFcUgjH2zWSe0EOJVptz5GC8FBC5oUK05VaE1CRNQebkhIrQrWsRHPATJSsgxgejwcsy8JqtfLP\n9a+//oqcnBywLIs2bdpg8+bNuPPOOxNGC6EWkhEmfGkJsunI5c5D1I0ZGRmS/1+ohA3XEzYcJaxU\nWkoNoQzpVyTzCtXYoCMVDSntsaoECMGHOpQIa31qe5oqTZah3jsak4BEJUspIRXDMJg2bRq6d++O\n/v37o7S0FOvWrYPdbsePP/4o69/jp59+Ql5eHv/fR48exQsvvIDp06fL9h7xRJIwcZkwhcbDsYIM\ndZYaSE2UsHq9nk9TBvKEjWVyRyB3HSWFMoA2XWVCiYaUmL2pNKKN4NUQUBEonaKPFMGUpkTkFagN\nQ+uQsuhjGAbTp09Hp06d8OSTT/p9lpqaGqSlpSm2HpZl0apVK+zcuRNt6ga1JxqSKVkBYjUbEF9L\n6iwSSAkr5QlLlLbRKmEDueuIhyDLqf6UfTQXy4I6fRrgOHAtWgBRrlWqGVw4noxlWT6CT4QNMZZo\nONS9UKruqTWyBMIzCSBZH5LuTgQEMn+fOXMmrrvuunpkCUBRsgSAL774Au3bt09YsgSShAlAmRqm\nFIgSNi0tjY+8gnnCihVtsUCoJJXaGORQmcrd+0edOAHdxx+DunjRR5jp6WAmTADXvn1s1xV8XlLX\n0uv1cLvd8Hg8qhijRwI5TdTFzwWp9ZFsi1xisnBTx2pCbBLAMAyfOqYoCg6HQzM14GAgB20xWc6a\nNQtZWVn429/+psr6P/nkE+Tn58f9feVEMiWLy0XxaM0GpMBxvvmaTZo04ZV1DocDVqvVz9VGSgkb\nz1O4cJOMdp6lIvWd6moY5s8HZzQCJK1ts4Gy2eCZNQto2jTmtxCbKKhljB4J4jlxRFz3JPfBYDBE\ndC8SgSzFENdZxapbpVpWYoWUyQbLspg9ezbS0tLw0ksvqXL/3W43WrVqhQMHDqBZs2Zxf3+5kIww\nBVAiwhRKuqNRwioNoco0Gms68cYi1+ZB798PuFxA8+aXX7Ragepq0N9/DzbGcUNS0TBFhWeMHilh\nyIV414bJvUhJSQloTxeKMBJVSCUWJVEUJen5Ky5vqDk8IBBZ/uMf/4DBYMCLL76o2to+/fRT3Hzz\nzQlNlkCSMP0gd1sJ4DNDoCgKVqtVNiWsUghFGOIIQ9EWjAsXAIkaKJeSAurcuagvK+yjC5XuFhqj\nR0sYckH22nCECKfuKSaMeA+rlgPhKHiVaFmJFVJkyXEcXnrpJXg8HixYsEDVSHj58uVXhOVekjBx\nmdzkJEyGYQD4NhryxVNCCaskQhEGy7LQ6/WKpNm4a64BysvrvU45HGDbto3umjFEw8EIQ+kIQ2sT\nRwLVPYWEodPp+IkjchiBxAPRtLsEytCID5lKtu8EIsv58+ejqqoKb731lqpkWVNTgy+++ALvv/++\namuQC+p/+65AeL1e2Gw2UBSluBI2XhASBhEL0TTN13/ljra4jh3BZWaC+u03nzqWokCdOQOucWOw\nXbtGfj1BbTjWaDgQYcg5folAbeP3UJAiDLfbDYfDAQD8f6uVxg4XwrayaJ8PYYYGiM+UFTK5REyW\nCxYswKlTp/Duu++qXmNNS0vD+fPnVV2DXEiKfgC+gTlY72S4ECpha2tr+VSslBI2ERuhxdGwor6u\n1dXQ/e9/oHftAjgObPfuYIYOBRo3jugy8RKdyC0aSsSJI0JRksFg8Hs2tCqUEfdgK/F8CJXpwczR\nIwH5LorJcuHChTh06BAWL16cMM9NoiBJmLisBmRZFlVVVWgc4YYMXCZAp9PJK2Grqqr4DVpNJaxc\nCDWaS+zrKlu0RXpjo9hk1RSdBHIaChVtJaJfKXCZLKVESYoerGKAGt9FoW1htAcrqTQ9x3F49913\n8f3332PJkiVJslQAScLEZcIUtoJEAvKl83q9fuKe6upq6HQ6fqIIkLiqwUhHc0lFW7GaokcKYbRD\nZomqBbGjTKBoK1EzD0S0Fk4dXnywAmI3zI8GWjm4RjplJRBZLl68GF9//TU++ugjTdS6r0QkCRP1\nCbNx48YR2YwRJazQ+FpIFmRagk6ng9vt5k/giUKWcozmEvZ6ErGQkv2NWrTnIxBHW0Q0pNfr4XK5\nYqqjqYFIyFIMsbdrPJ4N8r5aIEsxxP2eYkEZsXAUk+XSpUuxadMmLFu2TFPCwSsNScLEZcIEgIsX\nL4ZNmKTmaTAYgnrCkgiNjBGjaVoT6ahQUGpTiTZVGS60qjqWgjDaInNOjUajpp2GhJBbwRuPmZZa\nJUsxpExFOI7zs3DkOA7Lli3Dhg0bsGLFCs0dDq80JAkTl7+kAHDp0iVkZGSE/HJ6PB7Y7XaYTCZe\nNh+obYSQpdlshk6nU6bOJzPiMZqLvI+cwpBQdVYtQtjPmpKSElNtK55QWZJBkwAAIABJREFUut0l\nVLQVzf1IFLIUg7TtGAwGsCyLhx56CB6PB+3bt8eBAwdQUlKiWv92Q0KSMOFPmJWVlbBarUHrdKTH\nSsoTVizuCVaP0kKdTwpqjeaSSlVGIsNPRFVpsJq2XNZ0SiDeBxM56p6ELCmKShiLPuByylt4r8+c\nOYO3334bJSUlOH36NLp3747s7GyMHj0a1157rexrqKysxJQpU/jxXx988AF69eol+/toHYlxBNcI\nCAG6XK6IPGEDTTkQO+uIja/ViC7UrP2J+xvDNYgXHkzkMquPB0I54QSzplOzRUON3lBhv2c0g8IT\nnSxNJpPfwWTHjh3Yv38/9uzZA4qisGnTJpSUlGD9+vV45JFHZF/Ho48+iuHDh6OwsJD/TjZEJCNM\n+EeYVVVVSEtLq3dqJmkzhmFC2tyRjVCn00X95RRGnvESQmi19idVyxGSp9PpBMuyMJvNCUOWsZio\nBxINxaMmrsUoPtTQ9EQ0fwcCi6nWr1+Pd999F2vWrJFlUEQoVFVVoXv37jh69Kji76V1JCNMwO8L\nJGWPJ1TCpqen+ylhxWQpVyuD2JZOaRNwLW6EBIHsx4SjlxKlRQcI3q8YDuLpNCSEVp8R4XdFWPcU\nWjiSLE4iPSNSZPnZZ59h0aJFcSNLADh27BiaNWuGSZMmYe/evbj55pvxxhtvwGw2x+X9tYTEOI7H\nGULCZBgG1dXV0Ov1fm0jDMPUI0uyaZlMJllrf2RDsFgssFqt/BzH6upq1NTUwO12Rz34mrSNuN1u\nWCwWTW2EUiBpbHIY0ev1MBqNcLvdqK6uRm1tLdxut+xTZ+QCSWeZTCZZUt7kHphMJlitVj6Ccjgc\nsNlscDgcfH08WpCUdyI8I8QY3Ww2w2q18gI8ckghk4O0+nwAlw9UYrLctGkTFixYgNWrVyM9PT1u\n6/F6vfjuu+8wbdo0fPfdd0hLS8O8efPi9v5aQjLCFEFIctEqYZUUQYhNwEnaNpoJGrGYkauJQLU/\nYRqb1PmEqTm1oXTKW1znI6KhQNNmwkGiug6RmiUpiwCIuO6pBgKR5datWzF//nyUlJQgIyMjrmtq\n3bo1WrdujZ49ewIA7r777iRhNmRIpWTJJmOxWPgHl2VZMAwDiqIkPWHjLTghp+lAEzSCkaewlSGR\nmuSD1f6kUnNqjeMSQ412l1hFQ4nqOkSebXEaNpxZp2oeroSpeiFZfvXVV3jhhRdQUlISlW1nrGjR\nogXatGmDn3/+Gddffz2++OILdO7cOe7r0AKSop86EFMBu93O1yYtFktYSlitObOE8u0kG0oi2fMB\n0St4g4lk4pFe1FrtL5z7Ic4+JMozEogsQ/0OuRcej0eRqSKhEKiuvWPHDsyePRtr165Vdfjy3r17\nMWXKFLjdbrRv3x4ffvhh3CNdLSBJmHUgdcCqqipwHMebFwRTwsajsT9WSPWvcRzHzynU6rrFkCud\nKWUQL9wc5b4fpPanFbIUI9AUDVJ2CNQSpUVEQ5ZS15B7qkgoBCLLXbt24amnnsLq1avRokUL2d83\niciRJMw6OJ1O2Gw2sCzLiwZCKWHj3dgfK8icQr1eD5ZlNWOUEApKpTOlpkbIdT8SMZ1J7gfJmggP\nE1qq80lBDrKUumasU0VCgSjwxWS5Z88ezJw5E8XFxcjKyor5fZKQB0nChO+Lce7cOb/IxWQySYp7\nSKSjRVPvYBCTTiCXIS1tjqSWHK8ITS6DeGE6M5F6Q8W2ccK6p9achoSI1wSgQB7I0dY9CVmK6/H7\n9+/H9OnTUVRUhNatW8v5EZKIEUnCrIPD4eCb4BmG4YUjwi9CvJSwciJc0hGepONllBAMakdoscyy\nTOTaX6ASg9yev3JBrXF54vsRad0zEFkeOHAA06ZNw8qVK9G2bVsFP0ES0SBJmHUgROFwOOByuZCa\nmsqThXDzJgbqiQCyeUc6mktMFvGOLLRGOpHMskxEY+9InXC0IJIBQlsLxguBRHaB6uJk3aSkQ3Do\n0CEUFBTgk08+Qfv27eP9MZIIA0nCrIPb7YbX65VMyzEMAwAJU4sC5Nu8pchCSfm9sN1Fi6Qj3BzF\nZEGyFFoWgYlBxGvR1v6kRDJKiqiE69YCWYoRqu5Jnm8xWR4+fBhTpkzBsmXL0KFDBxU/QRLBkCTM\nOuzevRsdOnTw+5IThSMAP0GIVmp8gaCUglfY26hEWk6t9Fq0EJMnIQuj0ajpOacEct/vQCIquevi\nWiVLKYgnzgDgnZnId+bo0aOYNGkS/vvf/6Jjx45qLjeJEEgSJnzp2KlTp2Lfvn244447kJOTA7PZ\njAkTJqCoqAjXXXedH1loWQARLwVvoEgrWvIk6sxE2ASFEJKOXq+PaztCLIgH6Sgxnky4buK+lQgg\nNUuSsl62bBlWrlyJ/v37o6SkBMuXL2+wZgCJhCRhCuDxeLB161YsWLAAW7ZswdixY5Gfn49evXr5\n1WYCpSnVJk+1RnPFagwQy+QONRFo3VpXIAcSnCj9nsEmioR7jUQlS/G6a2trsXr1arz33ns4evQo\nsrKykJOTg5ycHNxyyy2KPCNt27ZFeno6f7jduXOn7O9xpSNJmCIsXrwYs2fPxvLly0FRFAoLC7Fj\nxw706NEDubm5uOOOO/wUslJpSjUstrQymiuQMUCg0VOBRhhpHZEcTrSkQNbC4SQa0VAgoYzWESiS\nP3XqFPLz87Fo0SJ07doVO3bswNq1a7F//36sX79ekeeiXbt22L17N5o0aSL7tRsKkoQpwJEjRzBy\n5EisXbsW119/Pf86wzDYvn07CgsLsW3bNtx0003IyclBnz59/DZLtchTa9ZrBFIuQ0JjAI/HE3d/\nVTkQC8nLEWlFi1jHiimBcERDiUqWHMfBbrfXqxGfPn0a+fn5ePPNN3HrrbfGbT3t2rXDrl270LRp\n07i955WGJGGKQDaxQGBZFrt27UJhYSG2bNmCjh07Ijc3F/3796+XlpOzxicFtXsVI4E4TUnGkZHN\nW+00ZbggkbwcJC8+YClpEK9Wuj4SBFKYer1e3soxURDIeejcuXPIy8vD66+/jttvvz2ua7r22muR\nkZEBnU6HgoICPPjgg3F9/ysBScKMASzLYu/evVi1ahU2bdqEdu3aITc3F3fddRc/UghQhjwT2U2G\nGCkYDAZ+rqjaRgnhQMmJI0oaxCdq2tvj8aC2tpZ/HhLhGQECk+WFCxcwbtw4zJ8/H3feeWfc13Xq\n1Cm0bNkS586dw6BBg7Bw4UJV1pHISBKmTOA4Dj/88AMKCwvx+eefo1WrVsjJycGQIUOQlpbm93Pi\njTFS8tR6r2IgBCJ5KaMErW2M8Ux7y2kQn6hkKRYmqZnKjgSByPLSpUsYN24cXnjhBfTv31/lVQJz\n586FxWLB448/rvZSEgpJwlQAHMfhp59+QmFhIcrKytCsWTPk5ORg6NChfpPSA9VvgkUVidarSBCu\nkUKgjVEtBTKJiNUaoByLQbyc6eN4IpQwSStOQ1LrknJMqqysRF5eHubMmYNBgwapsrba2lowDAOr\n1YqamhoMHjwYzz33HAYPHqzKehIVScJUGBzH4ciRIygqKsL69euRkZGB7OxsjBgxAo0aNfL7uVDq\nUi0oHKNBpNZrBPF2GRJDazViqXaVQGnKRCfLcGutajkNSa1D6hmvrq5GXl4ennrqKQwbNiwua5HC\nsWPHMHr0aAC+rMOECRPwzDPPqLaeREWSMOMIjuNw/PhxFBUVYd26dTCbzRg1ahRGjhyJJk2a+A2m\nFpMnTdPwer0wmUyaFW1IQa6IOFBUoZT5t9b8bKUQyPOXRMVaU02HQqwqXuH3xuv1xm18HcmeUBTl\nR5Z2ux15eXmYMWMGRo0apch7JxFfJAlTJXAch4qKChQXF6OkpAQ0TWPUqFEYNWoUmjVr5keedrvd\nbyanlh1khBBGxHIqYQPVgeVKySWiiTqJxl0uF1iWhU6ng9Fo1FyNLxDkbnkRRuOxjmsL9T5SZFlT\nU4Px48dj2rRpGDNmjCzvlYT6SBKmBsBxHM6cOYPVq1djzZo18Hg8PHl+8MEH+OGHH/Dxxx+DpmlJ\nBxktkme82hgiNUoI53rRpI+1AOH4OWE6W+loPFbEoz9UCdFQoIOVw+FAfn4+pkyZgrFjx8r2GZJQ\nH0nC1Bg4jsP58+dRVFSEefPmgaZpTJ48GXfffTfatGnjF3lqlTzVch2KVV2qlGl9POB0OiWFSYFG\nT0V7oJAbapgpyCEaCkSWTqcTEydOxMSJE5Gfn6/YZ0hCHSQJU4Ow2+0YN24cWJbFO++8g82bN6O4\nuBiVlZUYOnQocnJy0LZtW7/NTkgUanqXKtmrGAkiPVAksvo4XBWv1IFCzfS+FpyHojlQBCJLl8uF\nP//5z7j77rtx7733JswzlET4SBKmBvHss8+ioqIC77zzjl+EVlVVhXXr1qG4uBhnz57FoEGDkJOT\ngw4dOvh9OcWOOvFq+NaqRR9Q/0AhNaMw0Uy9Y1Hxqm0QT8hSS/2h4YiGiBiM4zg/snS73Zg0aRJG\njhyJyZMnX5FkuXfvXphMJj/b0IaGJGFqEB6PJ+SmZbPZsGHDBhQWFqKiogIDBgxAbm4uOnXqFJA8\nlTIF0Fr7RSiI7wng87hNpDQsuecMw8ii4o2nQbwWyVKMQKIhYukovOcejwdTpkzBgAEDMHXq1IR5\nhiKBw+HAE088gYqKCrzyyisNljSThHkFoLa2FmVlZSgsLMTRo0fRr18/jB49Gp07d/YjLyXIM1Et\n+gCfMIlElizLgmEY1Y0SwoHSLS/i50TO/tdEdR4i81oJYe7ZswdHjx7F0KFD8fe//x29e/fGX//6\nV80+M7Hg+PHjmDdvHhYsWICXX34ZP//8M5577jl06tRJ7aXFHUnCvMLgdDrx2WefoaioCAcPHkSf\nPn2Qm5uLbt261SPPWGd6JmL7BYHUxq22UUI4CJQSVApS94Tcl0jvSaKSpfiAAgDbtm3D22+/jU2b\nNiEzMxMPP/wwRo8ejXbt2qm8WmXw8MMPw2KxYP78+Xj88cdRUVGBOXPm4MYbb0yo732sSBLmFQy3\n241NmzZh1apV2L9/P3r37o2cnBz07NkzZvJMZEVpOC448ZwkEi7UPqDE0v+ayGQplfpmGAbTp09H\n+/bt0a1bN6xduxYlJSW47rrrsG3bNsX+NgzD4JZbbkHr1q2xbt06Rd5DCJZlQdM0bDYb5s+fj4kT\nJ6Jjx4549NFHcerUKTz77LPo3LlzQn3/Y0GSMBsIvF4vysvLsWrVKuzevRu33XYbsrOzcfvtt/tt\nduHM9Aw0FDcRQFS8kQiT5DDMjxWBGuTVQjAfZLG69EojS5ZlMWPGDFxzzTWYPXu2H4kePnwYHTt2\nVGxNr7/+Onbv3g2bzYaSkhLF3ofjOL+/ocPhwJtvvgmPx4O///3vAIBZs2Zhz549WLBgATp37qzY\nWrSEJGE2QDAMgy+//BJFRUXYvn07evTogdzcXNxxxx1+EZfUWDK9Xg+Xy4XU1NSE8rMF5FHxBmtD\nUEoZrHUzhWDqUpLOvJLIctasWbjqqqswd+7cuP4tfvvtN9x///2YPXs2Xn/9dcUiTCFZrlq1Cunp\n6RgyZAjOnz+PMWPGYOLEifjLX/4CAHj++ecxZcoUZGVlKbIWraHBEObkyZOxfv16NG/eHPv37wcA\nXLx4EePGjcPx48fRtm1brFy50s8QvSGAYRhs374dhYWF2LZtG2666Sbk5OSgT58+fr1xZANxu90A\noIkUZbhQauKI3C5DUiCpb/G4KK1C3K5C1KXEpk/r6weCk+Xs2bNhNpvx8ssvx/2zjB07Fn/7299Q\nXV2NV199VXHC3LlzJ1588UWsX78ea9euxciRI/H9999j+fLlmDZtGtq2bcv/DkndXum48j9hHSZN\nmoSysjK/1+bNm4dBgwbh559/xsCBAzFv3jyVVqcedDod7rjjDixYsAA7duxAQUEBtm3bhsGDB2Pq\n1KkoKyuDy+VCSUkJJk+eDLPZjPT0dKSkpIBhGNjtdtjtdt7DVGsgm58S47lI47/JZILVauWHhtfW\n1sJms8HhcPARVzQgqe9EIUvAd09IJoLjOJhMJj4rUV1djdraWrjd7qjvidIghyuv1+tXJ2ZZFnPn\nzoVer8dLL70U979FaWkpmjdvju7duyt+7yiKwtq1a3HffffhkUcewZw5czB+/HgUFxeje/fuAIBf\nfvkFAPjvfEMgS6ABRZgA8Ouvv2LUqFF8hNmxY0eUl5cjMzMTp0+fRr9+/XDo0CGVV6kNsCyLffv2\nYdWqVVi5ciUuXbqE2bNn47777uOJAVAnRRku1Jo4IodtYSLXiUnNUiyqSgQVstThiuM4vPTSS7Db\n7fjXv/6lylr/9re/4b///S/0ej2cTieqq6vxpz/9CUuXLlXk/V555RUYDAbMmDEDALBmzRpMmDAB\nn376KSwWC8aNG4cvv/wSmZmZCfVsxgptPKUq4cyZM8jMzAQAZGZm4syZMyqvSDugaRpdu3ZFWloa\nvF4vPvzwQ1RWVmLUqFG47777UFxcjJqaGp4gzWYzH2WRmpvNZuNTW/E+lxGRDMdxcR/PRaKs1NRU\nWK1WfvN1Op2w2Wyora3liVQKQrJMlMiSwOPxSJIl4HumjEYj0tLSkJ6eDoPBAK/XC5vNpoksRSCy\nfOWVV3Dp0iXVyBIAXn75ZZw8eRLHjh3DJ598ggEDBihGlgCg1+uxe/du/r9zc3MxbNgw5OXlQa/X\nY/369WjRokVCPZtyoEETphBkdFYSl/H+++9jxYoV+OqrrzBq1CjMnTsXX331FV588UX8+uuvGD16\nNCZMmICVK1eiurpaMkVJyNNut8eNPMl7UhSlif5QnU6HlJQUWCwWWCwW6HS6gClKkuY2Go0JZdMH\nRDa0mqIoGI1GyRS/GgetQGS5YMECVFRU4O2339ZMFAxA8Wd62rRp2LdvHx544AFUV1ejpKQEV111\nFQoKClBWVpZ0+mkIkErJbtmyBS1atMCpU6fQv3//ZEpWgJqaGng8noBCKI7jcOTIERQVFWH9+vXI\nyMhAdnY2RowY4fc7YhUlAMVMvxNJJCNOUep0OjAMk5AK5EjIMhhinTgTDVwuF9xudz2yXLhwIQ4d\nOoTFixerXl5QCkJFLPl3j8cDg8EAt9uN3NxcNGvWDLt27cKaNWtQWlqKo0ePYuHChSqvXB00aMKc\nNWsWmjZtiqeeegrz5s1DZWVlgxT+yAGO43D8+HEUFRWhtLQUJpMJo0aNwsiRI9GkSZO4jCVL9Lof\naR0hytJoHXXiDbnIUgxCnuRQocQIu0Bk+e677+K7777DkiVLVJ26oySIsvXw4cMwGo3IyMjgD7pu\ntxtGo5HP1jAMgy1btuC5557DmjVr/BSyDQkNhjDHjx+P8vJynD9/HpmZmXj++eeRk5ODe+65BydO\nnGiwbSVKgOM4VFRUoLi4GCUlJaBpmh+I3axZM0XIkxh6p6SkJFx0Jm7sl+p/1WoLj1JkKQVhlkIO\ng3jSl2uxWPzIcvHixfj666/x0UcfXbFkyTAMdDoddu3ahXvuuQft2rVDr169MHz4cNxxxx0AwAuz\nAODcuXNYvHgxxowZ02DTsUADIswk1AHHcThz5gxWr16NNWvWwOPxYOTIkcjJyaknGoh2pqcW5ipG\ni1AuOFIuQ8J0tpogZKnGOLdABvHheiEHIsulS5di48aNWL58eUIZLUSDgwcP4o033sD999+PJk2a\n8Gp40octhpBAGyqShJlE3MBxHM6fP481a9ZgzZo1qK2txbBhw5CdnY02bdpENdMzUW3XgMijs3gY\nJYQLNclSjEgN4ok9opgsly1bhvXr12PlypUJd/AKB7W1tXj33XcxY8YMsCyLmTNnYvHixTh27Biu\nuuoq/PjjjygpKUFFRQVyc3Nx1113qb1kzSFJmEmohosXL6KkpATFxcWorKzE0KFDkZOTg7Zt24ZF\nnsSUoCGQpRhS5KmUkEqMaPx444VA6WwiGgq09hUrVqC4uBirVq1KOHVyuHA4HPj888/Rv39/WK1W\nOJ1OjB07Fm63G+vXr4der8eBAwdQWFiInJwcdO3aVe0law5JwowzpCz6Vq1ahX/84x84dOgQvv32\nW/To0UPlVcYfVVVVKC0tRVFREc6cOYPBgwcjJycHHTp0kCRPt9sNlmX5lo1EsV0D5CccJYVUYmiZ\nLMUQG8ST18gBi9yX4uJifPzxxyguLvYz5biSQGqWAJCdnQ2DwYCioiK43W5MnToVZ8+exapVq2Ay\nmWCz2WC1WlVesTaRJMw448svv4TFYsF9993HE+ahQ4dA0zQKCgrw2muvNUjCFMJms2HDhg0oLCxE\nRUUFBgwYgNzcXHTq1AkURWHJkiXo1q0bOnfuzE9XiXamZ7wRD8KRWxxDkEhkKYbL5YLT6eTNEqZO\nnYqsrCxcc801+OKLL7B27VqYzWa1l6kIxJNHXC4XRowYgaysLCxduhRerxcPPPAAjhw5gi1btoCm\nac2Jy7SCJGGqAHF7C0H//v2ThClCbW0tysrKUFhYiKNHjyIrKwu7du3C2rVrccMNN/A/J8dAbKUh\nx7SUSCEWx5B7Eil5JjJZitfOcRz27t2LDz74ACUlJaAoCrm5uRg9ejQGDBigWP3S6XSib9++fCtL\nTk4O/vnPfyryXgQ//vgjf7AU9lgyDIPBgwejVatWWLp0KRiGwY4dO9C7d29F15PoSB4jktA0zGYz\nxowZg48//hh33nkndu3ahf79+6OgoADPPvssvvvuO76fTGy75vF4UF1djZqaGj6FqxaEqsx4Eg5N\n07zLkNVq5et4wvsS6sycyGTp8XjqrZ2iKJw7dw5HjhzBL7/8gq+++godOnTA888/j9mzZyu2ltTU\nVGzevBl79uzBvn37sHnzZmzbtk2x91u9ejV69eqFr776ChRF8al6YpKxceNGnDx5EiNHjoROp+PJ\nMhlDBUbD1ggnkRBgWRbTpk3Dd999h++//x5NmzaF2+3Gpk2bsGTJEuzduxe9e/dGbm4uevbsyZMn\nabwmUafD4Yh7T6NwtJhQlakGCHmmpKT4ReQOhyOgEXqik6WUknfz5s14/fXXsXbtWqSnpyM9PR1P\nPPEEnnjiCcUPVSTt63a7wTAMmjRposj7MAyD0aNH4+WXX8bUqVPx1ltvoW/fvuA4Dnq9ns/CbN68\nGaWlpX6/q5WMjBaRjDCT0DxomsYtt9yCjRs3omnTpgAAo9GIoUOH4j//+Q+++eYbZGdnY8WKFejf\nvz9mzZqFbdu2gWGYgJ6l8TD8VnK0WKwIxwjd6XQmPFmazWa/tW/duhXz5s3D6tWrJU1KlP4bsSyL\nbt26ITMzE/3798eNN94o+3uQCPLMmTM4cuQImjRpgiFDhqCsrIyPNAlpAsDIkSMBJCPLcKCdb3AS\nAJIPbSBMmTIloHJPr9dj4MCBeOedd7B9+3aMHTsW69atw4ABAzBjxgyUl5f7jR6Lx0xP8RBiLZGl\nGFKHCrfbDZfLBYqi4PV6wTCM2ssMG4Fadr766iu88MILWL16tWKRXSjQNI09e/bgt99+w9atW7Fl\nyxbZ34PMH83Pz0eLFi1QXl6Ot956C/fffz9KS0v9SFOIZGQZGrp//OMf/1B7EQ0J48ePx7PPPouT\nJ0/ivffeQ6NGjXD8+HF+kHVRURE2btyIiRMnqr3UhARN02jbti2GDx+OBx54AE2bNkVpaSlefPFF\n7N69G0ajEa1bt+b78gwGA4xGI2iahtfr5SNClmVBUVRURCeew6llshSDEKTX64XFYoFerwfDMHA6\nnXy9k0z20eIGS1LvYrLcsWMH5syZgzVr1qBZs2YqrtCH1NRUnDp1CufOnZNNaPPaa6+Boii0adMG\ner2enzJ09dVXo0ePHtDpdJg8eTJ69OjhJ5hLInwkVbJJNAiwLItdu3ahsLAQW7ZsQceOHZGTk4MB\nAwb4ec8Gc9MJJy2p1tBquSBlGQeoM0UkUgQaXL1r1y489dRTWL16NVq0aKHa+s6fPw+9Xo9GjRrB\n4XBgyJAheO655zBw4MCYr+3xeHDs2DF06NABn3zyCcaPH4+ZM2eCpmm8+uqrAIDff/8dY8eOxYQJ\nEzBt2rSY37MhIkmYSTQ4sCyLffv2YdWqVdi4cSPatWvHW4EJG9cjtaIjQ6sBaGIOZ6SQmtwhhXga\nJYSLQGS5Z88ezJw5E8XFxcjKyor7uoTYv38//vznP4NlWbAsi3vvvRdPPvlkzNcVerzu3r0bjz32\nGCZNmoS8vDyMGDECN9xwA7p3746PPvoIkydPxqRJk2J+z4aKJGEm0aDBcRx++OEHFBYW4vPPP0dW\nVhZyc3MxZMgQpKWl+f1csAgLQEKTZbTipEDkGY5pvlwIRJb79+/HI488gqKiIrRp00bxdagBQpZu\ntxtbt27FgAEDsHXrVrzxxhvIzs5Gfn4+3nvvPVy4cAEmkwlPPfUUgMujvZKIDEnCbKCQsuh78skn\nUVpaCqPRiPbt2+PDDz9ERkaGyiuNHziOw08//YTCwkKUlZWhWbNmyMnJwdChQ5Genu73c2KSAHz1\nU7PZnHAbkZxK3nBN8+VCIPP9AwcO4KGHHsKqVauu+NmNDMNg+PDhuOGGG/Dmm2/C5XJhx44dWLhw\nIf74xz/i0UcfrffziaZ61gqShNlAIWXR9/nnn2PgwIGgaRpPP/00ADTYgdocx+HIkSMoKirC+vXr\nkZGRgezsbIwYMcKvHaGqqgoA/KZeqJ2ejARKtr3EOoIrFAKR5aFDh1BQUIBPPvkE7du3j/l9tI6n\nn34adrsdb731Fv+ay+XCzp078fzzz2PmzJkYNmwYgPo2eUlEhiRhNmAEsugDfC4hRUVF+Oijj1RY\nmbbAcRyOHz+OoqIilJaWIjU1FdnZ2ejduzfuvfdePPLII8jPzwdFUZIzPbVInkJDhXgoeQNZF4qN\nEsIFmYEqJsvDhw9jypQp+Pjjj6/YQcdi0nvqqafQvXt35OXloaYJHVBIAAAgAElEQVSmBmlpabwF\n3rFjx9CuXTsVV3tlIbFyR0nEDR988AGGDx+u9jI0AYqi0LZtWzz++OPYtGkT3n//fZw/fx59+/ZF\ny5Yt4XA4cO7cOXAcB51Oh9TUVFitVp6InE4nbDYbamtr/VK4aiHeZAlIGyV4PJ6oDCQCkeXRo0cx\nZcoULF269IolS2LGIUTnzp3x+uuv4+DBg3zdPTc3F1u3buXJUk1byCsJSWu8JOrhpZdegtFoRH5+\nvtpL0RwoigLLsli6dCmeeOIJPPjggyguLkZBQQE8Hg9GjhyJnJwctGjRAjqdjh8/RtKTLpernhVd\nPCNPNchSDGKUQKwLSeTpcrlA03RQ68JAZHnixAlMmjQJH374ITp16hTPjxM3CGuPzzzzDCiKwtCh\nQzFy5EjU1tYiLy8PBQUF+PTTT9G6dWv06dOH/91Eq6trFcmUbAOGVEp2yZIleP/997Fx48YrdpBu\nrBg5ciTuuusuPPbYY/xrHMfhwoULWLNmDVavXo3a2loMGzYM2dnZaNOmTcCB2LFMEIkUxH3I6/Vq\n0lBBSJ5CZyaS0iZkmZqa6jdRpKKiAhMmTMD777/fIIYe33PPPbjuuuuQlpaGjz76CI899hiys7Ox\ne/du/Pzzz9Dr9Zg+fTqApMBHbiQJswFDTJhlZWV4/PHHUV5ejquuukrl1WkXTqcz5GHi4sWLKCkp\nQXFxMSorKzFkyBDk5OSgXbt29cgzHmPJtE6WYojbeMhrxDye3JtTp04hPz8fixYtahBj8YqLi7Fz\n507MmzcPOTk5MJlMcLlc6NOnD+6//340btyY/9kkWcqPJGE2UIwfPx7l5eU4f/48MjMzMXfuXPzz\nn/+E2+3mfTZvv/12/Pvf/1Z5pYmPqqoqlJaWoqioCGfPnsWgQYOQk5ODDh06xIU8xb62WhIfhQOv\n14uamhro9XqwLIslS5bg119/5efHvvnmm7jtttvUXmbcUFNTg0WLFuHcuXOYP38+nnnmGZSWluK1\n117D4MGD1V7eFY0kYSaRRBxht9uxYcMGFBYW4rfffsOAAQOQm5uLTp06+REZGUsWq6o00cmSZVnY\n7XY+DctxHA4dOoSPPvoIq1atgtPpxD333IMxY8agb9++fnVNOXHy5Encd999OHv2LCiKwl/+8hc+\n7akUxGpYodnA3LlzcfToUfzf//0fHn30UTRq1Ahz585VdD1JJAkzriATH0iaJNkT1bBRW1uLsrIy\nFBYW4ujRo+jXrx9yc3Pxhz/8oZ6PKyFOj8cT9kzPK4UsSRqW4MKFCxg3bhzmzZuHFi1a8C1QTqcT\n+/btU2Qtp0+fxunTp9GtWzfY7XbcfPPNWLNmjWICI2E61eFw8JaNZM84duwYHnjgAdTU1KBFixZY\nu3YtgKSDj9JIEmYc4HA4YDQaJesJp06dQsuWLVVYVRJagsvlwmeffYbCwkIcPHgQffr0QW5uLrp1\n61aPPAlxBiPPK5UsL126hHHjxuGFF15A//79/X7HZrMFHAEnN3Jzc/HII4/IYpwuhpD0Zs6ciTvu\nuAMjRoyoVze32WzYtWsXfx+SNUvlkSTMOKCsrAxz5sxBy5Yt8de//hUDBw6ETqfD999/j1GjRuHX\nX3+tN5suiYYLt9uNTZs2obCwEHv37kXv3r2Rm5uLnj17BiRPr9fLt2SQeYiJOjGFZVnU1NTAaDT6\nkWVVVRXGjRuHOXPmYNCgQaqt79dff0Xfvn3x448/wmKxKPY+Tz/9NPbv34+SkpJ6RCjOTiXJMj5I\nxu5xQO/evbFlyxbMnj0bH3/8MU6ePAnAp3gbPnw49Ho938zOMExCDeuNFZMnT0ZmZia6dOnCvzZn\nzhx07doV3bp1w8CBA/n71VBgNBoxdOhQ/Oc//8E333yD7OxsrFixAv3798esWbOwbds2voGdDMS2\nWq1+A7E9Hg8vkkkkBCLL6upqjB8/Hs8884yqZGm323H33XfjjTfekJ0sd+7cyVstXrx4EQcPHsTC\nhQuh0+ngdrsB+DIRQP1hz0myjA+ShKkwdu/ejYcffhhDhgzB//73P3zxxRc8OZaWlmL06NEAfK0K\ntbW1fLM7AcdxqjvDKIlJkyahrKzM77VZs2Zh79692LNnD3Jzcxu0mEGv12PgwIF45513sH37dowd\nOxbr1q3DgAEDMGPGDJSXl/M9ixRFYcWKFaAoCmazGRzHoaamBjabjU/PahmELA0Ggx9Z2u125Ofn\n4/HHH+c9UdWAx+PBn/70J0ycOBG5ubmyXvvChQvYvn07UlJSYLfb0aRJE3Ach0uXLgEA33e6ceNG\nVFdXy/reSYSPJGEqiMrKSixYsAAdO3ZEWVkZjh8/jrZt26J58+b46aefUFtbi4EDB+L333/HU/+/\nvXuPi7pKHzj+YRgcENBVBBLBRMzEEKSyNMB7qQmiRqKgqOulsPBCW2imoSlavszcvFR4SVNLV+EF\nFpGSVGReWe8iXoBArEhAGUAuM5zfH+x8FxTbX7vCcDnvf4JxnO+Z8Msz55znPE9EBD4+Pvj5+bFv\n3z6qqqpqdbevqqpq9L/w/hs+Pj61zo4BtfahiouL5ZnQfzE1NWXAgAF8+OGHHD16lJCQEKVg/qxZ\nsxg/fjy7d+9W9jQtLCywtrbGwsKizuDZmD6I1QyWNffqSkpKCA4O5tVXX8XPz89o4xNCMG3aNHr2\n7FmrYMWDcPr0aWxsbJg9ezZHjx7ltddeo6SkhMGDBxMQEMDVq1cpLS1l4cKFfPjhh/W6DCz9Mblx\nVo8sLCwoKipi4MCBWFlZYWpqSp8+fbC0tGTXrl0MGTIErVbLBx98QEFBAampqcTExPDzzz+jUqm4\nffs2SUlJ9O/fH1tbW+V1V61aRe/evRk4cGC9pdEb28KFC/nss89o3bo1R48eNfZwGh1TU1O8vLzw\n8vKioqICX19fsrKy6NChA/PmzcPf35/Bgwej0WiU3pTm5uZKMYCSkpJ7enoaa6/zfsHyzp07TJw4\nkRkzZjB27FijjM3g8OHD7NixA3d3dzw9PQFYsWIFw4cP/59e99atW7z33nu0a9eOdevWYWVlhYWF\nBcuXL2fZsmXo9XqmTJmCg4MDpaWl7Nu3D5VKJTPsjUQm/dSjqqoq3nrrLeLj4/Hw8GDv3r1s376d\nwMBA+vbty/Lly6mqquLQoUNMnDiRxx57TPm7iYmJ7Nmzh99++42cnBzGjh1LZGQkWq2WGTNmEBAQ\nQEBAgDJLaMo3zx91TVm5ciXp6els3brVCCNr/CorKwkODkar1RIbG0urVq04e/Ys//jHP/j2229x\ndnZm9OjRDB06VDmaAPdv/NzQnVXuXoY1XLesrIxJkyYRFBREcHBwg4zFGPR6PWfPnuWjjz7CysqK\nVatWcenSJTZt2oRarSYyMlL5gGNjY4NGo1HO5UoNTy7J1iOVSkVUVBTnz58nLCyMtWvX4u3tzZUr\nV0hPT8fLywtLS0tOnDhBjx49gH9v6i9duhRPT0+++uorYmNjycjIoLCwkJMnT6LRaCgpKeHXX39V\nlmwNDEu5zUVQUBAnTpww9jAarYKCAmxsbIiNjcXc3ByVSkXv3r1Zvnw5R44cYeHChaSlpeHn50dI\nSAgxMTHK7NLQWcXKykrJpr1z5w5arZY7d+6g0+nq9d+SYZlYrVbXCpbl5eVMmTKFcePGNdsGAIZk\nLFNTU9zd3QkLC6OwsJB58+bx6KOPMnPmTExMTJg9ezZFRUU4ODgoRfxlsDQeGTDrUc19x759+/Ly\nyy/TqVMnzMzMiIqKwtzcnHbt2qFWq7l58yYAGo2G27dvk56eTlhYGDqdDhcXF44cOUJxcTFpaWlc\nu3aNU6dO0bdvX+UTqIFKpcLExKRJB80rV64oX8fFxSlLYNK97O3t2bhxY521bU1MTHBzcyMyMpLD\nhw+zbNkysrKyGDNmDMHBwezevZuioqJawdPQlqy+g2fNYGlubq4Ey4qKCqZNm8aoUaMICQlp0isn\n91PznOX169fRarW4ubmxYMECiouLmTt3Li4uLkyePBlXV1dsbGyUvyuLEhiXXJJtIFVVVffMBqG6\nTub69evZsGEDLi4urFy5krKyMhYvXqxkj545cwZfX19ycnKYNGkS7u7uvP766xQVFfH8888TExOD\nnZ0dBw4c4OrVqwwaNKhWBZLVq1djaWnJyy+/3KDv+f+jrpq2CQkJpKenY2pqiouLCxs3bsTOzs7Y\nQ202hBBcu3aNffv2kZCQQJs2bRg1ahQjR47kL3/5S63n6vV65aynYXbzv3ZWMQRLQ5A2vE5lZSXT\np09n0KBBhIaGNstgWdPq1atJTEzEyckJFxcXFi5cSGZmJu+++y5arZatW7cqpRBlBZ/GQQZMI7jf\nIePU1FQcHR2xt7dn5syZtG/fnkGDBrFt2zY8PDx44YUXWLp0KXPnzuXxxx/nxIkTjBkzhuvXr7N1\n61a2b9+Oh4cHycnJLFmyhNGjR6PX6xk6dCihoaGMGzdO2f+QB50lqA5e2dnZ7Nu3j/3792Nubs6o\nUaPw9fWlffv2921L9t8Gz/sFS51Ox0svvUS/fv0ICwtr9sFy8+bNbNu2jZiYGF5//XW++eYb/P39\n2bhxIxkZGWzatImIiAjatm1r7KFKNciAaWSG5Iu7g9f58+fZsmUL586d45VXXmH06NHK7HPmzJk4\nODgwc+ZM2rRpw6xZs1i5ciXDhw9n7NixJCcnExkZyffff8+5c+eYOHEiqampSoEEExMTLl26xNtv\nv82iRYtwc3Mz0ruXGhMhBLm5ucTExLB//35MTEzw8/PDz88PW1vb/7mnpyFYqlQqLCwslOfp9Xpm\nzZpF7969CQ8Pb5bBsmZWa2VlJV999RVeXl7s3LmT5ORk/v73vzN8+HC8vLzYtGmTMqOUM8vGRe4e\nG5lh/+hubm5uvP/++8r3lZWV5OXl4enpqSxPJiQk8PXXX3P27Fns7OyUFkfZ2dk8+uijAHz99de4\nubmhVquV2WVVVRUZGRmUlpbKYCkpTExMcHR0ZPbs2YSFhZGXl0dMTAwvvfQSlZWV+Pr64u/vz0MP\nPYRKpVLqvBqCZ0VFBaWlpXW2JfujYDlnzhwee+yxFhMsTU1NGT16NAUFBaSkpLBixQoefvhhBg4c\nyPnz58nPz1f2LWWwbFzkT6ORqpkwZEj5f//99wkICECtVpOWlsZvv/1Gr169sLa25tSpU3Tq1Amo\nXtp1c3OjsLCQ77//XqkmZFBWVsbJkycpKSnh3Xff5ciRIw3+/hqjusr0GaxevRqVSkVBQYERRtbw\nTExMsLe3JzQ0VDni1KZNG2bPns3IkSNZt24d2dnZCCGU4GlpaYm1tTVmZmZUVlZSVFRESUkJ5eXl\ndQbLqqoqwsPDcXZ2JiIiolkGS/j3ka+PPvqICRMm8Oqrr3LmzBnat2+PmZkZ//znP1m9ejU3b94k\nPj4eGxubJlfSsKWQS7JNRM2lGcP+Y25uLp06dSI7O5vQ0FDc3d1xcnLigw8+IDk5Wal7aTiKYvik\nm5OTg4+PDy+++CJdu3YlOjqaLVu24OzsTEFBAc7OzrU+FbeUZaGUlBSsrKwICQmpdSY0JyeHGTNm\nkJ6eTmpqqtJgu6UqLCwkLi6O2NhYCgsLGTZsGP7+/jg7O9/T07OiooLy8nKEEKjVaq5cuYKDgwMd\nOnTgjTfeoEOHDixZsqRZBsua91Bubi6TJ09m3rx5XLx4kc8//5wvvvhC2T821I3t1auXLErQmAmp\nSaqqqhJCCKHT6YQQQmRmZor58+eLxYsXi9OnTwshhIiKihKBgYG1nldVVSUSEhLEkCFDRGVlpRBC\niJCQELFp0yaRmZkp/P39xYULF4QQQty5c+cPr90cZWZmCjc3t1qPBQQEiDNnzoguXbqI/Px8I42s\ncbp165bYsWOHGDNmjPDy8hKRkZHi1KlTori4WBQUFIiXX35ZpKenC61WKwoLC0V4eLiwtrYW7u7u\n4rnnnhM3btxokHFOnTpV2NnZ3fOzrS8175GUlBSxYcMG8d577ymPrV27Vjz55JPi1KlTQoh/32uG\n+1RqnOQeZhNl+ARq2P/s0qULK1asqPWcnJwcfH19AZTEosrKSpKTk3n00UeV85+enp4UFhbi6OjI\nxYsXlf3P9evXk52dzeLFi2udBWtJn37j4uJwdHTE3d3d2ENplNq2bUtwcDDBwcEUFxeTkJBAVFQU\n2dnZmJiYYG1tja2tLSqVilatWimzyevXr6NSqejZsyfu7u5KCbz6MnXqVMLCwggJCam3a9Tl4MGD\nvPLKKzg6OlJZWYm3tzdPPfUUs2fPVjKDDx06pFRhkpnrjZsMmM2EocJPzRtuw4YNyteG6iDZ2dnE\nxsYyefJkANLS0sjMzMTb25tbt27x7LPPcvPmTdLS0oiJiSE6OrpWsExMTKRbt25069atgd6Z8ZSW\nlhIVFcXBgweVx4TcwbgvKysrxo0bx9ixYxk3bhzXr1/H1taWESNGMGDAAMaMGUN8fDzl5eXs2rUL\nlUpFWVkZSUlJZGVl1evYfHx86v0aNZmYmJCSksLatWtJTEyka9eu/O1vf2PPnj0IIXj66acJDw8n\nJCQES0vLBhuX9L+RAbOZuHuP0fCL3TAbNPxXo9EwYsQIjhw5wrRp08jMzMTX15dBgwbRoUMHVCoV\nH3/8MVqtlsDAQHr27KkkIKhUKi5dusSXX37JunXr0Ov15OXlYW9vX+v6hl6NTX3f89q1a2RlZeHh\n4QFUV2V54oknOH78uCykcB86nY5JkyZRVlZGSkoKGo2G8vJyDhw4wIoVK9BqtSQlJSn/NszNzZVV\nkOYmJyeHhIQEJkyYQNeuXVm0aBErVqxgy5YtCCHw8vKiQ4cOcs+yKTHmerBkPDdv3hQfffSR2Llz\nZ63H+/XrJ7p06SLi4+OFVqsVQlTvxxj2VubNmyfmz58vhBAiKSlJTJs2TURHRwshhLhw4YIoKyu7\n51qrVq1qMnszde1hGsg9zP+ssrJSvPfee/fd/zamP/rZ1pfo6Gjh6uoqEhIShBBCFBUViYiICHHt\n2rUGHYf0YMiA2cLo9Xqh1+trPWZIUMjLyxMzZswQwcHBtR6vaf78+WLbtm1CCCGGDBkitmzZIn7/\n/XcRGRkphg0bJpycnMRbb70lbt++LYQQIi4uTrRp00YIUZ3QcPe1G5Px48eLjh07ilatWglHR0ex\nZcuWWn/u7OwsA2YTZoyAKYQQO3fuFL179xZ79+4VQgjlHmjOyXPNlVySbWEMS2HiXxWGDMXaP/vs\nMw4cOICDgwPh4eEAtSoQiX8tGz311FMcOHCAVq1aIYRg6tSpfPrpp6SmprJz505sbGwYPHgwvr6+\nPP3003z66afK692d0NDYjqt8/vnnf/jnGRkZDTQSqTkJCgpCCMHixYvx8fFRcgLkMmzT03h+W0kN\nylBhyMTEBJ1Ox+XLl7GysmLBggVKq7GaAc6wj5mdnc22bdu4cOECy5Yto7S0lLS0NMaPH4+NjQ1l\nZWW0bduWkpISAJKTk5k6dSo5OTksXLiQ33//XXnNu/c9Jam+TJgwgWeeeYbLly/j5OTU4P1Vg4OD\nOXToEHZ2djITtgmTM0wJtVrNO++8o3wv6khCMNzk7dq1Q6PRMGDAAPr164dOp+Pq1as88cQTAFy8\neJHu3btjZmbGjz/+iI2NDZ07d2bv3r0cP34cW1tboPrIio+Pj3Jcw/D69+vqIkn/i/+0etAQ7O3t\ngbrvL6lpkDNM6R5/dDOHhIRw8+ZNhg4dClQH20ceeYSLFy8ihGDVqlVUVVXh4+PD5s2bGTduHADH\njx/H29sbqD7KkpKSQm5uLvn5+fj5+fHTTz/xyy+/KEvEUB08ZYkwqbmRwbLpkgFT+lMMZflqBjJD\n2binnnoKe3t7XnvtNaC68PuUKVMoLCzk6tWrDB48GKgOnvb29jzzzDN8+eWXnDx5km+//Za+ffvy\n9ttvU1xcrBxsr7lsK6qT1Br2DRtRXbVtIyMjcXR0xNPTE09PT6VnqiRJ9U8uyUp/imHptGYgc3Fx\nUZa8SktLad26NT/88ANFRUV0796d8+fPc+7cOXx8fIDqhtidO3embdu2fPbZZ8ydO5eIiAi8vb1Z\nsGABlpaWfPLJJwwYMIDVq1crTY1rdr4Q/yr63ZzVVZ3GxMSE8PBwJZFKkqSG07x/40gNomZnldat\nW6PX6+nfvz9nz55VnmNra8snn3zCtm3b2L9/P3369KGgoIBr164RGhoKQF5eHhYWFkyaNImrV69y\n/vx5MjMzger+oNu3b+fixYvNoijC/4ePjw/t2rW75/GWNMuWpMak+f/WkeqdSqWqlfln+Lpbt25U\nVVXh5ubGmjVrSEtL45tvvuHJJ5/E09OTPXv20KtXL9q0aUN+fj6ZmZkMHDiQjh07cvv2bTIzM3F1\ndeWHH35g8+bN3Lhxg+nTp/PGG29QVlZ2zzh27drFmjVrGux9G8uHH36Ih4cH06ZN49atW8YejiS1\nGDJgSvXKMBN8+umnWbNmjRLUrK2t+e677xg+fDgAmZmZ3LhxQ8ma/eKLL+jfvz+3bt1i7dq1JCQk\nMGjQIOLj48nOzub27dv3XCswMJDx48cD1bMwvV7f7JKGQkNDyczM5PTp03Ts2FHZL5Ykqf7JPUyp\nQdQsDv/QQw8B1UHRsJSbl5endE4BiI2NZeLEiVy5cgVHR0eGDBnCBx98wMmTJ7G2tiY3Nxd7e3ul\n+EFeXh6BgYF8/fXX6HQ61Gp1szzvVrOG7fTp0/Hz8zPiaCSpZZEBU2oQdRWHNxRPAHj++ed54okn\nsLe3p7S0lGPHjhEdHU1lZSUnTpxg7dq1zJo1C71ez6VLl3BwcACqs3ZVKhX79u3D1NQUc3NzduzY\nQXR0NBMnTsTNzY1+/fo1+PutL7/88gsdO3YEqj9U1MyglSSpfsklWcko6jqLZm9vj16vp3Xr1pw5\ncwYnJyc6deqEp6cns2fPJiUlheLiYlxdXZVkGEMg3rt3L0FBQQDs2LEDjUaDhYUFISEhtdqc3a0x\nn/U0VKdJT0/HycmJLVu2EBERgbu7Ox4eHnz//fctYs9WkhoLEyFT7qRGyrDceuPGDT7++GMOHjyI\np6cnUVFRtG3bVpml3rp1C09PT44dO4ZKpaJ///7ExMTQo0cPoqKi0Ol0vPnmm6jVauU1CwsL68xA\nlRqnxMRE5s6di16vZ/r06URERBh7SFILJJdkpUbLMHt0cHBgyZIlLFmyBK1Wi7W1NVC9HKtWq9m1\naxeurq7Y2dmxd+9e7Ozs6NGjB1qtFpVKhZmZ2T1LwitXrqS0tBQ7Ozt69+6t7AUagnBdDbkl49Dr\n9bz66qskJSXRqVMn+vTpw6hRo3B1dTX20KQWRi7JSo2e4ZynEEIJllBdlg/gwoULjBw5EoDdu3fz\n3HPPAdWZt7///jsPP/wwKpVK2e8sKSnh+vXrpKWl0b59e5YtW8bu3btr1fi8+6iMZDzHjx+nW7du\ndOnSBTMzM8aPH09cXJyxhyW1QHKGKTV6/6lIwfr166mqqqK8vByNRsPYsWMBOHfuHDqdDg8PD+Df\n7coOHz6MRqMhIiKCZ599lkceeYQlS5YQGBhIdnY269atIy8vD19fX1544YVa+61y5tnwcnNzcXJy\nUr53dHTk2LFjRhyR1FLJGabUpNUsk6fRaNixYwc9evRQup5YW1vTvXt34N8FFU6ePEm7du2UJb3T\np08zYMAATp8+zdKlS3n88ccJDQ0lNjZWqdX666+/otPpas08DWc9W0oaQF21baG6kIKrqytubm71\nsrcoi5VLjYWcYUpN2t2zP0NrMJVKRVBQEBUVFZiZmSnJPqWlpVy4cIGCggIcHR0BOHDgABEREWza\ntInTp09z5swZ5s6di6mpKT/99BPDhw9n+fLlnDlzhocffpigoCBGjBhR61hMS2jZVFdt2+TkZOLj\n4zl79ixmZma1+p0+KJ06dSInJ0f5PicnR/nZSVJDkjNMqdmo2RrMMPNs1apVreccP34ctVrNQw89\nxIsvvkh4eDgFBQV4e3tTUVHBihUr2Lp1K5cuXSIjI4POnTuTlZVFUlISw4cPx9/fn4qKCs6dO8ec\nOXP45JNPKC4uxsTERJlp1txzbU7qqm27ceNGFixYgJmZGYDS7/RBevLJJ7ly5QpZWVlUVFSwe/du\nRo0a9cCvI0n/iQyYUrN0dxNqw9cJCQl07tyZpUuXMnLkSLp168b+/fuxsLCgW7duJCYm4ubmxjvv\nvMOPP/7IxIkTuXTpEs7OzkRERBAQEEBWVhZvvvkmXbt25dtvvyUiIoJffvkFExMT7ty5oyzb1rx+\ncwueBleuXOGHH36gb9++DBw4kJMnTz7wa6jVatatW8ewYcPo2bMngYGBMkNWMgq5JCu1CIbg5e3t\njZmZGU5OTkyZMqXWc4YOHUp4eDj+/v706dOHCRMmYGtrS2pqKu7u7piamnLq1CmSk5OZP38+Xl5e\nzJkzh/z8fGxsbPjiiy/Ys2cPpaWlvPjii0yePFnp6WmYgRr6iTaX5VudTkdhYSFHjx7lxIkTjBs3\njoyMjAd+nREjRjBixIgH/rqS9GfIGabUoowaNUr5xWuoY2vw+OOPk5SUxJQpU/j555+pqKigoKCA\nlJQUpfn1tWvXcHBwwNXVlfLycvR6PTY2NsTGxrJo0SJiYmKIiori2LFj3LhxA5VKRXx8vLJsq1ar\nlWCp1WrRarUN+z/gAXN0dFSykvv06YNKpSI/P9/Io5Kk+iEDptRi3X00pKqqCrVazZgxY4iOjsbV\n1RVLS0uGDh3KoEGDAOjVqxfZ2dkIIdBoNJiamnLnzh3i4+MRQuDj48PmzZspKCjgyy+/5OrVq8yZ\nM4e3336boUOHsmHDBqUUX1paGnFxcZSUlDT4e39QRo8ezaFDhwC4fPkyFRUV2NjYGHlUklQ/ZMCU\npH8xnPesmbBja2vL66+/riS1PPLII3Tt2pWAgADWrl1LVg3eB0UAAAHmSURBVFYWFhYWHDlyhKNH\njyp7oGq1moEDB/Ldd9/RvXt3AgICCA8PJzExkfz8fE6cOMG8efM4cuQIlpaWRnvPf4ahtu3ly5dx\ncnJi69at/PWvfyUjI4NevXoxYcIEtm/fbuxhSlK9kbVkJekPGI6j3O2bb77hxx9/ZMCAAQwePJip\nU6cSFBTEsGHDaj1v5MiRvPTSS4wcORJTU1OGDBnCokWLaNWqFSEhIej1evz8/Jg/f77SgUWSpMZJ\nBkxJ+hPud94yKSmJiIgINBoNzz33HFOnTqW0tJTw8HDWr19P165dyc3NpX///ly8eJGUlBT2799P\nZGQkZWVltGnTpsnMNCWppZJZspL0JxiCZc0iCVCdYZuamsrhw4e5fPkyDg4OrFq1Cjs7O6Xpc1xc\nHB4eHmg0Go4ePYqVlZXsmCJJTYgMmJL0X7h7mdZwXMTLywsvLy8AAgMDKSkpoXXr1gBs2rSJOXPm\ncPv2ba5fv46/vz9QfTTDUEhekqTGS96lkvQAGDJua+55uri4KH+u0+kIDAzkhRdeQKPRkJ6eTkVF\nBYAMlpLURMg9TEmqJ3Xtdwoh0Ol0rFmzhoMHDxIWFibLvElSEyEDpiQ1AMNtdncANbQkkySp8ZMB\nU5IamKFEnlyKlaSm5f8A5/W2rh+LSFkAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x106d84a20>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Splitting into training and test dataset "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "It is a typical procedure for machine learning and pattern classification tasks to split one dataset into two: a training dataset and a test dataset.  \n",
+      "The training dataset is henceforth used to train our algorithms or classifier, and the test dataset is a way to validate the outcome quite objectively before we apply it to \"new, real world data\".\n",
+      "\n",
+      "Here, we will split the dataset randomly so that 70% of the total dataset will become our training dataset, and 30% will become our test dataset, respectively."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.cross_validation import train_test_split\n",
+      "from sklearn import preprocessing\n",
+      "\n",
+      "X_train, X_test, y_train, y_test = train_test_split(X_wine, y_wine,\n",
+      "     test_size=0.30, random_state=123)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 102
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Note that since this a random assignment, the original relative frequencies for each class label are not maintained."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Class label frequencies')\n",
+      "        \n",
+      "print('\\nTraining Dataset:')    \n",
+      "for l in range(1,4):\n",
+      "    print('Class {:} samples: {:.2%}'.format(l, list(y_train).count(l)/y_train.shape[0]))\n",
+      "    \n",
+      "print('\\nTest Dataset:')     \n",
+      "for l in range(1,4):\n",
+      "    print('Class {:} samples: {:.2%}'.format(l, list(y_test).count(l)/y_test.shape[0]))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Class label frequencies\n",
+        "\n",
+        "Training Dataset:\n",
+        "Class 1 samples: 36.29%\n",
+        "Class 2 samples: 42.74%\n",
+        "Class 3 samples: 20.97%\n",
+        "\n",
+        "Test Dataset:\n",
+        "Class 1 samples: 25.93%\n",
+        "Class 2 samples: 33.33%\n",
+        "Class 3 samples: 40.74%\n"
+       ]
+      }
+     ],
+     "prompt_number": 106
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Feature Scaling"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another popular procedure is to standardize the data prior to fitting the model and other analyses so that the features will have the properties of a standard normal distribution with   \n",
+      "\n",
+      "$\\mu = 0$ and $\\sigma = 1$\n",
+      "\n",
+      "where $\\mu$ is the mean (average) and $\\sigma$ is the standard deviation from the mean, so that the standard scores of the samples are calculated as follows:\n",
+      "\n",
+      "\\begin{equation} z = \\frac{x - \\mu}{\\sigma}\\end{equation} "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "std_scale = preprocessing.StandardScaler().fit(X_train)\n",
+      "X_train = std_scale.transform(X_train)\n",
+      "X_test = std_scale.transform(X_test)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 107
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\n",
+      "f, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(10,5))\n",
+      "\n",
+      "for a,x_dat, y_lab in zip(ax, (X_train, X_test), (y_train, y_test)):\n",
+      "\n",
+      "    for label,marker,color in zip(\n",
+      "        range(1,4),('x', 'o', '^'),('blue','red','green')):\n",
+      "\n",
+      "        a.scatter(x=x_dat[:,0][y_lab == label], \n",
+      "                y=x_dat[:,1][y_lab == label], \n",
+      "                marker=marker, \n",
+      "                color=color,   \n",
+      "                alpha=0.7,   \n",
+      "                label='class {}'.format(label)\n",
+      "                )\n",
+      "\n",
+      "    a.legend(loc='upper right')\n",
+      "\n",
+      "ax[0].set_title('Training Dataset')\n",
+      "ax[1].set_title('Test Dataset')\n",
+      "f.text(0.5, 0.04, 'malic acid (standardized)', ha='center', va='center')\n",
+      "f.text(0.08, 0.5, 'alcohol (standardized)', ha='center', va='center', rotation='vertical')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x132013390>"
+       ]
+      }
+     ],
+     "prompt_number": 108
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Saving the processed datasets"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Pickle"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The in-built [`pickle`](https://docs.python.org/3.4/library/pickle.html) module is a convenient tool in Python's standard library to save Python objects in byte format. This allows us, for example, to save our NumPy arrays and load them in a later or different Python session to continue working with our data, e.g., to train a classifier."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# export objects via pickle\n",
+      "\n",
+      "import pickle\n",
+      "\n",
+      "pickle_out = open('standardized_data.pkl', 'wb')\n",
+      "pickle.dump([X_train, X_test, y_train, y_test], pickle_out)\n",
+      "pickle_out.close()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 113
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# import objects via pickle\n",
+      "\n",
+      "my_object_file = open('standardized_data.pkl', 'rb')\n",
+      "X_train, X_test, y_train, y_test = pickle.load(my_object_file)\n",
+      "my_object_file.close()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 114
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Comma-Separated-Values"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "And it is also always a good idea to save your data in common text formats, such as the CSV format that we started with. But first, let us add back the class labels to the front column of the test and training data sets."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "training_data = np.hstack((y_train.reshape(y_train.shape[0], 1), X_train))\n",
+      "test_data = np.hstack((y_test.reshape(y_test.shape[0], 1), X_test))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 137
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, we can save our test and training datasets as 2 separate CSV files using the [`numpy.savetxt`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.savetxt.html) function."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.savetxt('./training_set.csv', training_data, delimiter=',')\n",
+      "np.savetxt('./test_set.csv', test_data, delimiter=',')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 138
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 139232134e64c42acac8e5bcc93a893094d6046a Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 25 Jun 2014 18:07:49 -0400
Subject: [PATCH 117/248] data entry tutorial

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index db04777..a4bb604 100755
--- a/README.md
+++ b/README.md
@@ -38,6 +38,8 @@
 
 - Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1)]
 
+- Entry point: Data - using sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb?create=1)]
+
 
 <br>
 

From 559d05f69d8c81a09ac14cf45e1b9ad51ce5275c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 25 Jun 2014 21:52:15 -0400
Subject: [PATCH 118/248] added classification examples via scikit

---
 Images/mda_overview.png                 | Bin 0 -> 79543 bytes
 tutorials/python_data_entry_point.ipynb | 573 ++++++++++++++++++++++--
 tutorials/standardized_data.pkl         | Bin 0 -> 20263 bytes
 tutorials/test_set.csv                  |  54 +++
 tutorials/training_set.csv              | 124 +++++
 tutorials/wine_data.csv                 | 178 ++++++++
 6 files changed, 901 insertions(+), 28 deletions(-)
 create mode 100644 Images/mda_overview.png
 create mode 100644 tutorials/standardized_data.pkl
 create mode 100644 tutorials/test_set.csv
 create mode 100644 tutorials/training_set.csv
 create mode 100644 tutorials/wine_data.csv

diff --git a/Images/mda_overview.png b/Images/mda_overview.png
new file mode 100644
index 0000000000000000000000000000000000000000..f70be342f6514eeaac80424efebb54fda1b55ea2
GIT binary patch
literal 79543
zcmeFYg;!k7(l-hr!C^>%;4?S`NN^t@1cEyW9wc~>!I{C`g1Zh7EVu*<3{KGC5G=u6
zX9m}NJm>xHd(K(+{t0*1+H3aiuBzT$UAwxvyMB=xYVt(*)c6<}7(|K+vRW7zj}RCb
zn0~m}4<)89gJus8k+w228j3P9^cpS@D_eU@3=DSi$^;2zENPOk<<ArBc$CaG_z6$m
zavg0%zg4Lp#7U2M8-f4H^Lut=eB8PV!!_1{eLaZ-*%^0)(DT%f`Jwc<UCZ>3A2F_G
zx5;Qdp~3Okp7*qtvch)Ch^k8ajWI+%HSnr`e(RUeiNI<ofGOgmil&_xSCW2FJn)xa
zaA%V-i{Gr;^}g?Jp0hK5jf>Dz!5_eIKTC*ld1(NTLQrOPspZ<U+OK<IiattQf+{&D
zOwOHofM>#@6twpn+27`WXyZ}t&p%$le|F<aJm&OD1ktJgk^>f{`1JEAC?X*LU1I2F
z1lc#;zalBh?n6^T#xHYq=JlA4OL$|+5>0Vs<Q_l!SllRo^(eBe9dhtIiuJRmrC{pU
z)U%dSDn@19Y5j!I4bhB#6x<n5U+2LcGLcrL)ApD#>GiUi?r;u<=f!JeX}M|+R?p|o
z!*5lTQLdnoH;Z?(Esgx3y7ea}v^#%mDt-y9h+>P$r=Iy*>gU*b<$9fKTw^kO+OFg)
zBa$A*2G2=B-xULcfb^dW6C*R5@?oUlw(s=Z^i);E%pnf1%q$=uEnh($oF1q#Fu+i;
zhg%0rH#2&ugT14x7*vAsUld{w_x}`gGt&Qy#LZ5EQBPHaUIyY~NiX<H;1v&}BtAVo
zJ=n#<N=!>u?*FnsJV`LxxVbrradUfmdcN}He+6-|=H?X@73JpP<L2Xg`9Sf~73Sz>
z27T%1%Je@%{vSEAmagV5woY!g5J&od<eGhixVuR(GXB%h|Nj2xJuRWO|8B|A_5XJ3
zp@ZE2)Nu2@;^F>Z*$=GXe@ewPY@wF+da||-mX59uZAkL+@(Y9i#qj^C`gfE6!K(jn
zRv|v#|K$9Sn*YlQ=KiM#|IwrWQP;mpAM_=O59a<~-Am%{2sk{9)N2eyS?PCB%!3@9
zJkVQIzc7OvNqjN=<(D~4`-qPWd3j&wS|_>Y#OI#$1~Envz5P`4@hzFgyT?y7$uw^{
zBi_6rYD<eJart|tvUD-mbbO?1Jjj`$xO6;pG&<_%>FMF&aqK8(nH=;e@HIUN?&JUc
zvyJNfX=_l$uJd1|ujyBCL%L{)82*#=wW$FU3HEV))e^hWe^XN;Cb0ik;=kk<3qCSK
zs_pN^bU*p8iU*+%|IPdV3;qApX=g=bKweo{uL-w+d0%v|Np-#3u2x)biKa2F_2|KJ
zA0*L}*(Y;|e%=K}u6`B!-gs?s-}%eQ@%vHo7x2BpWxrZRGl4(DQL%PkGWb=R8onwu
zWbu8Nk6l<x*=>F`CG^9#RX5d40I`7H*=`?_m~c5|I*EGb$+c(21+8F*syDjCTK*Z~
zv1-$YlSTx|%2G8vbruWV-o1{@DxujwQFBQ)+dR`7)<Scha8?grI!iR-K3+2aydBl;
z0BfQ_2N!1^eb8L!dB?pCt=~qih|V~}InZ&9N2#q{!$1A*Z?9}uyF+7^bnrvuU1#3w
zs(!N?QQEI+b!-{)Jz77T(9weq5qMo~rj-zIBY5ska{sC{EqRrn%$o)iO%{Oeb_95M
z;*UH#ZrnF!-l5Wry-H6EY>QI%>H)qxGE+mG)!EOk+y4+lKI)#B#y8&_wN6$0ot2+%
zOZ^pnX;9DJwk>h22!M8Cfk$U_O>(Lb=%LdHc`##TZ6Ea!`LVmH?MQPt#;VU@!)!5U
zRWqZ+?JB^e>gs}i4WDK+R{ThT!o<h<iz=jUkd>2*nA4?;yJhE#+w2F=88HCYc%Y$u
z?OOwfhMBNkylZ~1r@rhGr2;!L3yD<|?)9}`IF6OnhV*5oT~&tAZzmZ*!sx&kBJuaM
zr?i&wuUfa^-bVe9_N%S4HnYh7tG@TTA5yAXk6KRia3t6(8-5wAXB*ZWKf!EB+pq4B
zoOoqD2G2S2UcNa`tdC^^3NQP7TciW{tYGDYAI&~px0qxMpbMf483`SwdjCg%ZSzfW
zyaY(QcxF`mI67l)YWcJA>6g6_QnjxtUEW@(gz;joyJ^C2d*5>J?B(IbU(5edTZ((0
zoblSA<`w1KOabuhtC`Oj_~d65*u~FbJDk=f;$+EfN|lrUpxpoO&9eLSch_yNbSk8u
zx@9F(^i(AJYU!eV`|9t5?)9s#;<Rgv2@MyP5?Pe@gd$cx#R(;xh~@v;xVZZ$dOI!@
z|I+3oNgf^eBxCGUNMmn&`S1P3tK@~KSfSD?V`LD8K=IW?oZmZ&jfeNI@c83C+XuBY
zHiCQ`5t_*l2O4D5iZCx{nmIGJ1s!o+NnCaTjug2mxcU#!G|QJ^!>1P=fB(Rge(?^|
zQ=Y{H?5&hY-IdOcK>TkExAQ&z5XhkNAmdwpSHmV=e1DEZ<$E(IAHN&xr}NC7{qy&3
z`EN|i(e?89r%Ee<-U?%2=!noFmqY!8-0uBFhd*EN*k7apF9H9Pou%O1y94XpP;MWy
z>rNcW6*$hMVd;m*SiKTN+qwRJd_5u$MhbFKXST)pnZ?NnI$9-%ZHq5w7~k#b-q-_-
zkv5E*sE)sP+!;^zmPjo^0jF8Zx5Y2?)h~LpGKTx5?op?8X>DA0>!801!b{KsZ*3M*
zw6xfHuMZ(pW!(_Rs4D!B*MhEsnna`R+oQS%i}K2%&pdLiYSHsOoB3|^s_^_%u&mhi
zPQjEk4&8=t!cX!>qsNgdw3w$PA@B{_YUW-JP0~Leiw<}nuoSnIq{`w*!{BCbiJl#6
z2`3S&6RcY2`g(ZXEH1-6=FGy~mSEekYqdjZV{?8nFaYe4nERdp(XPP;1e($MAq`~q
zOxEMy*3dFMqW<f45FRsGV?cOSW-mp^cof1!Pbc`?*Yyq1U<$(#_yi=vE4vu6=}Wz<
z+m24Lfd8tWHTH(ROJ>ARE)pZ`BY4%^N&a{3$7kQcG=(iZXY<wiDYX*;${G8VPQ4#U
ziSs%0sq6jf<sHT}7s=hiB=E~%>_r}Caq!aZmT+rrEZ3eM@cVGwnu*7}<?i_+M19Qv
z{$yVfL$L_yId7R3(pCAAII%RcZR1U>pQWbXU2#f9f#UMJ=YN8ap(q{TrgI@IR^UU1
z)NIpl7v<~orj8urlgwE{g6BYY-$lR7iH$$%293+<CCnKmKW-R?m8FM4m@^Vyq%t~p
zAFvCmN)1QnmqK|Fqhk9|AJ$h<+DhC@J7NnAi+}}O->CeEBi{paP?kLlyTpMRz;3!E
zadYSi!DO}not<^0S`i`jfEpOpYhk<|yZB<k;>B?isE}#XfzuT@M%r-OS+48H@_C~0
zB`jsOU^S;C6e65Gg!ukE&To=~Xm@e)N+$hz``!8CjfDbf+b^$rjwb4|#oQT@gK&~7
zk1xg>dn~?&8FIt^0x(Y{<vANm8r5qxTRa+0gCpRQkyErf*^+R_mFfk_?OC<I>UOrP
z+V!x>eWIG^W^zbZKTwORmoh!g$VIWT8KXPZ<HPEjv*|GvO6t5euBhYg5V9-2e5H2%
z;IFgnGdK>Lmi^iRk6Av;KbARvu;$v&+8*?nKijKJZr3P);g|vS2f;ncF2D4xVeOAO
z+T{<{ae_exkhZhif!B|@H&zFH4~I{1gQW<ShP36Q&F+(RE>7GwMSUW2OITvD+xYza
zi2Uy}Cod8`M>(a~D$(39&M|QNsH=)wHP|DHY3XhQ?{A}9NGWah#T#T$JZ2)pICIk)
zS7q3&nYNF0`dzfF<77>TCh+Z?;#RbKb}av+?rc0<xe`PjRNoonbyUyyquRPYxZz?V
zA9J<{%c@1^oW$POg0LN{)1}f9_#UVBL3s~<`TOdBtP3wZc)@B^8=Y;<slNkrV{b{u
zld{cQzB}@a+<s2~<KnZVpMw02JodfIAGtY&OO6kN)fA$OlY4%kw=~bmfgE<mYK$IJ
zn5wpde({YlF5gANLkRl8h{*8n6gk*4Prdf9(!)WK)DHF!Q<dY#-3xH#4+@jmxz-+N
zql?~i-Cgma6jVTm(~8lq=$_<+dzBN40!TI7uKr#QwS~JY3EOZ<rD8YQWicErP)Rc3
z%;obf0S}*AE*PJTUY)u~G60S7iZ`DC*}Z?70}B?<o|*%&X3rfjpHb7yS~5Q(i6RZA
z;66-bxhS-&`p)Wm<eIB|{4fjHnPXaU4X*}v25jW>ytwZHNP*ife{i2$3UbiF<41WB
zgITA9&jtn?$OHgc4o!bN;AxX0!NgL%1=7+6sjt$eT2NZoU?A2LEc&eLR@hQ9u_WdE
zirkfsFk~dGQMPOov?iD^N!rI8jPt0u>D<7s#QoQYe&?U-0w|l75`nUC8pr0YeVU#)
z9EvlZ$h9t+?e0R-<wlV;>4ind);~k8Nhd7oRHQK4&4guhEo|axOaSF1Yl;a_-FBGC
z*<vO2@Xc95NkSq6U@!I>N0^ZMC8qn3E~VWw9H|AO!~V_Uy)^gm78803zGrECZ+Z`t
z<K&}qouva_YE%%Lks$M4T}QoVidNi<X!+vEqm&;QhzS8TvQ_O*RxL1keODANxZrv7
z;H=_$W!_f2F{AB3#XwZl{m@Age-U@CYRjhX7;Cf=LuGW>qG95F0!uc)LK?*^E$RiK
zO&m}kK_At1?RCo9clr;0=w`}oXxsO2)RUt<c~yIKz3?BmByp7pd9nLEaP0C9N`7}B
zHxdu<+Zmz&a$L<VdFxrD)cG_U`K9cLoSQ;%bEJn-Dn1a^kffPeB5e|ccjb{t73XkN
zIs&6lt11eQ5A_J;cUX@R25O}uXs=+SSg@6@W_BP<T<0pq*v#m`50u-Jn@da;MC-0q
zx<x<TG2$s!Rf^+)U!J?XqaN&}d_6z$_mtre8)UOuV6ED&yOgx=xAE|iZKX#t;1CyR
z#D8DfF+o8tf93LSDP*%b7*EJxiTK2=K&(k6jX*#DAy|1mV`p+>5H>XgA37@-Igt2?
z|AA`mOoq1*wr{w+=lrvLqkl?Wsx5UEjPEx_Xf7prR;pfEK?-Yr@y>r72beAMiyjPw
z?H_L>sh{(pLzzq5SB9d^<E-iGs3|GG#!B5^?um}U)N+0it_~%i#k=`UpbNnE9gb)1
z7j4yEJv2)z2;;Ld-S@edy=-LwKV)zU)tUR=!z>&(^AL9}UFVZLivJvb(*F1E*c&Y5
zLclb1`g>clNo*wmi$lUklb;5xdDUEB^kWFI<qrF)S9U&cox2C{zYOmG)$^#avb#FL
zWVZ{gY;vvDFr_9wvzvQb$o4+>c7u)6VlS<CR?PlK58g4fUz|g`UGUgi>Slgsw*9=}
zQ&-TF(acLFrtF_~CgCsqe_#q{t-O1PjO95EuPfJIWP8X!jo~E{hqft)x|Q~m@B6v0
zGSOxB<oRB^$|eTGO0A8Gx<7hE*QyI9x8*}oPAlP3x2t<EA+XOUYnWf|E|!;h-+%x6
z;O=3Rb#G4T7ALj09Tw@!f&r4vnp<G?OP@XM+_D}1JpypHZ8=j)W$cD#+XzYn;9$OA
z*;Hkp+txJGcCpm@CFV6pPke|o?-p*r38yhCs%7*cun>k!mB~niguM#2M^?cf6zk=4
zJ^fUkc6HmT;yM8*=xt-v`+2xg2Sn~0XiQ|C5RmFPdd830IQx5jzTH^r3#pRU;_D`c
z5~47_-@X@r%0c(Ya6iYsNYJ5v3_cykiw8HlYvAnAS!r)4TWJ_d0FVN?{^U&|+JPIP
zehi-%d*6e}@D+7kM`d6;z{T<d=asAE67Uaqp}Vi7BU!@L*QX^?A0$@s)W1%RX7HC>
zuTga9#nI(V3Wq9sI9+!E{CH$9(zT1fn0z69kNqP&ZIp`i!P)2P3YnE|eldc0O=WS!
zM7rNv$}~xdjP}$mU;k<{;yPvbQlnh_6+8r2bZ*;zS_j&+Gxk-7jBQTu+K3O>FFNj#
z_3BdhDffNM`U!(-e;;pXRIS~LRH^su6WLMszfKCD*Xfl5ZdjIoCwI`=tR3`!>usMj
z%t}?2WeXvJDSFxwrc{8VV;!3Y%}175%V*5O>2%_bIjZ^h(MkP9aUa<>)>-cKJZ2s|
zv`IBv!~3F{r&-aw2Q1t$#g{aVMCrj<3aIa+V-8;=3h4rtA0~M8rz#0Ib>B49iQOsw
zp=u+E?Za~8!~BU}ifVwwOAm^RL;aT>&x`#gwgEclE)1B{7RUvlVOn4iiMsQ5zuDHM
z&Cn;0@l{tD$YxLA7YGKkM!_#z>uf&&w>F4#`fRDn!Gu1HO_)nef{Y(!AZ_mbX^Aev
z?2TMwTiNh&fm5C_hqUug{ts>Fl<Ep1^62TNwnK+1dzaueY?-CR1fTdo?`rQq`(;yv
z!lQ=_V2}upq(;^6_3Fb?hiEB@6cq$h9C$KiIj!T?5@P>*eNaJyuJl$gf)4{3B81|h
zFD0!3BRjG75rP`{x)u<oAJTZot;g-7!P1yN@F9y{TI!PUvkL3?jmdizA#9L8Jk^OM
z-|j+Wx_b>F*>jMNyFI-4$%5vh0IJSN<+k>lKgU(W=7tX0+_$|`U`h8IX1YFXk&U_T
za+*jGT_$EkBW5F5f6O9mFoDpRl{7?CcT4lc6`R0`(SI;n+8o|yV5OD}Q0M3gmcllk
zME9E(-)PMh7POH7uxLYLJ_@QEhdNhAra?)iY;H*~wtuf!)&(<M#D{}>_hYa7cSuM0
zgC900ABT5k*in7`LugFM_O-&~jLw_5`)Uvt2v&J?A`oSFGbvH!R^lM)iW9o=Erk%U
z9U~6&>exXt&F{pWl3$vwOkUW`o>~%$9(CNGRagHU7K-+_MJmm%j?X=Owh}Lsq|Z{u
ze75-g0Psy3Z)U*7$o?*Be3&vlSzir%F-+Ht>$TE41)U#R914IHbxTDso-lC!uoB8o
zC7@$q%$dwgTOiV&<m<v=JAF=SsTxALZBg;{zB5fgq=H9IWIIL~=5>TB@p!*g?$ya7
zYlXJt%XgzzPcU>3U{Gpym`ANC0AGn%XKQ#tpMvNrc%u|4)yUQcFj#YtdD+yhP79{;
z0Yyg%YI5!94H5r)pY2F2rBQ~pqvrM4hKgQNoe)$i`%rY%uZ?H@QGLFHvMoYdw0+9g
zl0H`jzCkZMf|rJZem6cTZ8$g;q}u%?FLgDj{@a$dJ(~ZD`Q4Sa)O6e0YDj)eaIS;=
z*-bQ6=2!NdSd|(}b>6OGu0rbyUhe7=>o;8XB{5xDfGQZvVq)@GAto7sQ~rf1jKE$$
z?;~q!INo@+Zpq0*K$?N-)W+(W?YMWhmJW3?i3-$b!Nx4C_nTOir+W?&L=@6acaf`Y
zvl)dEaPdMj6brcUWZ--z)Y$w2U~w%27%idEB%?SmFIyw*JRmO|@(@P(P1_Bwbmc~C
ziD@Y!-oy-I!q%UJp%KHEux2p;U09^1B0J~C)_VmdwhA5tJaem3OkL;~cDx=MIksT3
zuv)}ss-c&NT#F7?1Quj+lFDJQ@tpWYWi$?1^icwF-Db`Zu*R)DlSko~C2!bYj@#d^
zV`pxSaetT?l6ZPoQZ9&?!kbxmzm*^;rL^9y#gqi{5W1<-qEG7^<R#Gwk|cbc(nDE|
z@)=0<+Mj^lS?)FK-%s;iJ$%mOY=}ga%u-!e=2NqTQ*txHuutD6?ItC9A!wx1av4!X
zb(KC*fbew~6L!x?aJhZ2CfKd;I@rMk*?gXgV||ej=hItlSwFn9+u-m5fNf;)+B5Ag
zAv!(#X{<BB=ItV(F;yd)(5t%(bS>ybd|VlAv&m_Mn>MRw8`*=O&AQvx?xD5$SeceN
z`3aT*EJ{kQWkF%euGvW|{<%b%WL5o+S~Oob;QF_8%b|R?6JZ_*7iZhZ<e6<dRbU16
z{4<zDqk^Qx`xb`i!$)lR*BMSRo=R+O)w&*9kTUT~5>q`}7Bz(Jm<x5Ozd{LzMEcre
z1{1=nX8>9v1L>5i6r(x%ITk3JgZ`4ly^8D#gHh*!bDXzP!;FsM4Cm*>IHtoWL_)r<
zmUt&S+e`OKUOBK0(^`y7G!%WQ<hRjlD^2MubMYgXSH)a{)Gz{(A{EAK>+GlDFUq@%
zZP#6^>mn|@yJ_0>xe6r<9yjwEsW3L@1T^faIp2t?eGS*e67IwhBJa!_0?DOX0UL7*
z5)<DQl$c`{gKM%c_>{G*Cp1^K^URoSO69|fe(%4lz$SRvV;hN>jnaTq>J3z~JRPK$
zMAs-`k`T*5r4;3x6*eat5Yd?`5hbkn(AO64o?+i75&-Q#(*y1;kS-vWpxU?+Sy=-p
zdz;l1HFI*)nlRmJV7`hUpORvKZZgh=zDKR^Y=fpUe65<ekISUDP*FJnCZVW9^%otM
zq;?gBv-DwO$oguaFi<9p>5x@@__cgXv86F;G>#*j(9J0%{yq95vg}W(>a*~AQ}&NH
zm@vD^e4YjZ2k`_%2qyrhBA}uCDw%!hMKrY&k-<H#hCH9uSCa)HUjt*ABSm>eI_00g
z*CFk@DrZl<vx1gsO1sL1Fx8=UqU0ZPAT%0a)%wDI6h{6qJ=ho-Z~P-&J6AXBG=fM9
z-+B!npc9asDx`Yf(sxNujKw8uP{d7S+?@arZTc43F@(uInxaM;MJs<AK&UnI1a%^b
z?Vq}r5H9viiVlxk(JXU(%S+AjJ?$=x7UMetnmkA<V=UV|RN_79keGY3(%&rYmAdv;
zsT$E0Btj`05B+8tcas^Pa-FD%u}b-d#4>2w$`ZLFc^Ew?OiWN2z#tRJU)P-`1(jaw
z`owvGa{Ro)knz3WA}o?w?NQPC!!B&vv>{n%c_d})Y8)vZW7|C$XpGBQV00VC85`S8
zdG{G;<(<(7T8r0lZnnw-A+9)yjA5gKn9P!On^#jfg%Gq)O|affKIE&UqWm@3A3VP?
zqIoMiB>m$P<~eXW+1LRac$L11Cx_(EhuHF9^$jK5(zPPq{N#fD<*yFyi2Frd2Jy4v
zM+D~BW2f8O1((!z&BiSdj$ogu(QHZ7-H}NBow9Q5uKWU~1_aFg90%#}|5yct>6M^)
zgv^=P3W>HG$9`118v7M&_U{eKxV>u^Yh*rI-014_+^Fr_x=<+Li^Ks0zeY4X(lXw9
ze{Pr6a9!+TX8}(oT0?G8*KHDY98=5#d?I?VaLS|{Ug<6gWvR*oVHS5RHEPWYud*<&
z_cv7(uX`RL8KdjHY(9tItT^y5UQ5(Ou--R<)9lCW>e>wN8v)(%Jz^~#QKN+J)#hs_
z)_r*$6Fms5!SfARy1QPYsB<i#sqG@+ByT_MI-rt7A)N>VI3`vBIwN7FDEx*A)&!nJ
zK%v!A-LlX<1+R^*e0=Tpdnb~M2=zN{K$N&CfL*$tJi=)W1-CPlL*ryMS6`O-A2A49
zD9ywbfsHtGVu8GVsp5~U1TK5zwiGK)wul7dsjS`qZfAyDyi42T{|zEa@y$Ii2Cpc$
z217e3Cxf{f4-BP@?0`J87h6T>@I8CMml;i?>)VK1xnaP1AZ!rvw}5+Gb8mYR1P<w)
z3sNf?^XS2V?})6G#eGg&xZ{VT2xob{+?m%<9EwAxi<DufzGu8cy`Yw#ti^+HZ-tYs
z5xW=jZA}RjZNb1Gd-SoqMPO4+SQ8b~AZye8g2*YYtu5!4#DxI3f=KZ%XXR-J)ez7z
zEDCfclNDtDWwRgLwl@?l1CS|^*=U>I3%+IGkH2O2Ep^*bJfUV=km2s7$+6M3;<?tV
z6ni?#K_D`c!QZc6HtJ3W-0_S+D*`87N3g<#6kUG9Oxg#c-gijjFFs}@+GQaCI7?6V
z4CoBW8`#qUmx7tIOvt1XN3!Ye<I5b(oO9ItLNw!In?A_Q;c{eMCQ5DAktbM`a|||y
zdHqJvb$za*PjZk!Ik}EsC4daCP&gL09pK-pMrfV+cYav`#h5gop-;FD1DzsxsrX8Y
z7d0)ts`4cc^7RrnX3rF2Nk1VUafI^3rFJZnz8+!95J4vc)sGgqM#*tQv5a1*ya19L
z^hH)J#=euiFTs}earY8?#*CFY+AZq5TL`8iQp8^R^%{%XA@np6F2XLqc%kjjs_(*-
z1m(dDn06xTx6<*;ZPIz&@?h73VF`!1=ZkBKS5yM_ecN(u_&I?uAQjAcNA?;0DQ$am
zRD!n=!b#L<8Bu4z_i5NyLu}xQ5VK*o%l)3l+St=N%UcWYqr^j9l`Txqx1m~j>(a>g
zM%b*_SY*U!N|f$Hn%e_G`7uT%IUbo(L`0yY1V9L;B8^-==gZyBC^y0*CI+(t^HHqz
zoEwggZZcg5T`a-Xkol!53va*ulHT{m-6QMA>h}Ek0U@;}b8%4DN>@5^w%B0`%<Cs&
zC{&!kPYy3Oodu9-wkUhCN5m01w4(u{w-;cRtoM%4{k3akHej>(0!%A`XInQa+DCP4
z5IXH>N$+WAf-i4!2ub>2sOBlTL%czs4Ypl8)pQedQRMd82m>uhLbz7jI-_y6Fi+#i
zQ(B14aX;YP?{V*tD<qK|DXSfAZ*S#qq;^>Ls2li&u*8CvCSu^<9GHQIP*+R{YCDE%
z>&dy|V>3$R&YLC6F`D?i6suSKuW!5kdOwfNn>T+#(9^b4I&N*A3DY~NqeLlejmUtp
z88hM8JX;^s8V{>}rSSzpTQQD-B@P<KDKb9p%<A1RPCRC;tP?PiPh@|VU3&V)Dzf=#
zVgEK~yz#izZ?>%uvs;n7z-YHiifu_H0851wILQ#a(HVwmw@AGr2Z9I$_U5`qs~P4y
ztB9v$H8WC;Z*9(0im5wxf{ZCEd`ezl-1ho8Wu}MEn%sRV=Le<<FmB8WniGX2REoWC
zB{}DSz~QdZX4m1BAK6auy?M-^fhzO;2fu3->n>qgah>GX)PB)7|6#-I^NwLl2$X6>
z6pXP5h99V6M|MxaY$CkQo{?jSQ1;zoeq?(V52M`(z{nXgO~uP+W_V#v#&-23&p|wl
zW^F^>M=U1USQ|KM6YUpIV2=N3pFWX+n}**mS<*#mFqttB({^RdZjN41u7;Hkn{-Kv
z(D>edo*}>4I_rXN%vE355~w;06W#eq+2lqt>hPjRJQ_j27a$6<kd;K}oin#^T8iN-
zfgnQ3+g%M{qMr*d09ZjoN+Qf$0(gnKX*`P*TUZRx_-&vN&7a+Q@uCr6O-2W{dbOr@
z7R!iUg0bDZA<lc%v#hk-x;0HKZL1~P4Es-8M4&YsRlmny*&1*Piz>gA?}$^U+vk>2
z$YLMcC>MG32LJ{Kq=m~4OYg=zD3LtlJbflMNv{as;fnHd>?lK-Q~H4K6wkMk4AP(*
z#8QPxD$#U101I=K*W0Uu=I`+n9S=IIi0dlxMtd5eEceLM{QYrhN~cM87^uWVn$jSC
z1O0g|oR{cXEKC$^ij=<~S8oxG(}ruaQPMKf;*eBVFsgvb;h+R6^NnY&HUVsEgt&1m
zn~9G5?}Sj=w1_@~_dNxX3?WS2jYKUI9DtG<*>)jDVi9wvRj$Bfb#3bCp+rZ-uADy+
z@NAHBDH(J@Nnp9N)rfyJ(QiZW>10TzZk*eBa%IrMCm<b+3ViMjotk=<=5x9KONji{
zy5`*|6@?TsxCuEJ+<-3kEK^<r$BsI~&8EMhRlx@UH^RAL;ulk{m1qq$g(b&WXhf-Z
zTVjJsHDcF9WZOQ@x&Qk8tz$o)AoK-c7&bQ6FcrHL{wOGnK<Vj|viT&A_&23%it0EP
zecj^hwsSDVGYsk~qkT7OD`9)>PCZ7GXwF6PT{w0!i$yh|D>puVu*krRZl*v&K4v@+
zWN&&PMVK;KR>oW>8nZggpl{XS(w5|XhR8GQrSr!%+@W)FekO^eASl1GHPu^V=OW!~
z^w(RBigVLob*I8P{iQbywH4i>?!nzQYo*>;LJ>Tj>$a;AI4YzCU`Vti?lGxDpa@G>
zl2^&gHJC;#ljP=l&e(=Dj#VGn^T^1$O9Ii_nsouy_LeUl(nEuG8_4WwOpdmD1?$X_
z0Svh~TjjPSXM?GXjqWvcAs%n&@W-;%v(;Zc=6OCL4^2dFjHfyPiU8x(WC?nll9sKM
zwzktp$m!RUY=n8}sM6{I!*UUdjO`5dXH((yYj#(LaxXS(!siiV!e<}UMJ}22#}6Ap
z6K%HmC8`C&$QlM3xdv?GAGYG%X?_rIrKGm!6F4x|%P*phXn85Kk2xK#DYdvC3HRmr
zm|bPcw5dhS1k5z%vnEN3onu-|vxDAAsoJ{5wU0dx_3Hd8qjrf3Khpfe;a9?hPzXn<
z=Bx=?)6geTFa06R=hhdsU<qnex)pjRwX_`Vo9A5iQu2Zqt*j-!((|rSYR=uf^rTtt
zueP5KU^>jzcwjN?#b(~!1Y5?b8Jp>G1UvuYl#Pk0(ybQukN1@8sf528o0WPr&)>wB
zW=qUT#Y1m?ti(aTxs&z-MV#Y!L!3b}@38IQYTIk2etCEl^4_lo4sldnlI7&l3=-*I
zE0uvis0N+i5E%;s39-nu**H>w-=14VxKSn(DPb1`2O65<(H9jZ^7clseJl>@;;hph
zynK-oG7-u)3sli?@|C5ESA*M}e_ct)(=PwAKVa_j=x+iEbV}hx8np>2XtlVDZA^V`
z5>IboEpDC@KWp%jh-o&OMci9&*!nmJsX+<r3YVz%0f!WM))dN^>M3>Akzp(L8TbXu
zUQkZYjhd>HwR)ym=CU5QBKQX)yDfP%B?XteNH+@UN`t?gvWR1W{JE5QMOMhobRYme
zsJ>bQw{|U}^pfRx)49^67V>%Q)3+Eb^H2|!j2)To0`m1u2AhouibwS_gvQvw)cm(l
z55fE{cGC{_Ldh@CKe6@t3i6}7I2*${PbzV3I+wf7J@~qM(^-hsuv8ySh*RQFu0c(e
z-^>n0biukdXwCDJe(q^gw%XIJ@%JC(Zqm|)9_2aoV119{SD!ePI9PrwG}#HVFWTps
z*59nf5%YB!J;(5l`wAhCZ&F!2V!t`)vQ`_a+oO(SoBli@2d1J_nhlF_$|shc{E)e`
z?>CijI*lp9#}Jl0t6(J7oyY{2xF5fW9<*<hHmTDZfW``|U$-a<4F)|`_MT{j7dI#B
zV}At7pW}>`^H^za2{A^W2gi~%Mh#1BnjeY%IWHKBsi+W|)Sb^%{9Y?V>5YWf(c#FX
zi*a0i&PTs?gjERIzRNBy=<_OKi;Xr_jXL2$#naY%URNy$o8yt%(<()0TyX?Vx{#^Q
zD@;DESDfVEG6$D;Y}lp<Zx+zK`Aze3g!X0dM4>LGz|<Ef0gk(;RU*_im<itO{O7fA
zdU0fd2CozFZBUv9*ih<>b8b|85;oo%vVofLRYa65?|6P@L|D+90+bmoe>0C=#?KJ6
zHPs_572{8<oZr7=OHqFz%;i&y<=#yXti28G8mpi)pMoRKV=kvl_f%j4bP0F|`d$|E
z@s=c)d?Xse1F!p2X4{en;Y}Wm2jAX0o6fBsnsr)eFy>sc`@0K7zY}AE1!_)#ax%Si
zN<zb(UO&G_J{oV-*xFDx>~ReR*OwSO#oMntXG{6F)3kC;F@i`jqY15!*&VJ4WTkL3
zy>M#;xIQ*W12Sqhlq6(6eT$ksWDkyPmod0^i^Iv>IYwH+V4XTTXo*my9NQXIcap4s
z4=;ndd4_OHs!zQG?)~YA+MV2HmqPcP<(HA?tk(?J!R-|y_PVX3Itk4F8U9lZlVuPd
zY1!GqEVZi%cQ7qyqFG*G`}F(XDG7(EZj6BBToVhamg-I0p#?c4tE8TJ^db!*xa^ef
zV&ZRa0xb)-+so_RnfFxdiCxc9C%jv9ywDo}nM|+l*2sMC76}bi@$S#D?e^ttlap!+
zpJP){{~Yc2;AuYVWAOsC4ZxC?W+it04~S7@9k&Zg#fxW}o5e)Demt0#%J|qFz7(Ch
z3cOIN)|P~Y`~}QDKbjZI#U2XcVfD*W+`*T-z{4L)%^QtaX7x<hqQtKb9xFKCeMDy7
zQvF4JlF4*3;~(M0d1?%)JV@S^2)r9tTRb57vAZpjk6rz^2=#{cL<&Q@=!K)-V*w60
zb~%gq%^UfTW0bqSp9R?cB|=z$Pz>A7PqIJtYy#Lpgv?@OH{pkG2il`Y!~9dHuFS2%
ztf@CE`(~Ac-!Bc{DD=n#63*p4R>VvuQk`XgifD*G3S^6+?Mdw$5}e!g|4EAqtN9uJ
zOvbt4sZS}JSj>`JJ|<}>fyvr{P2Xqx;}o|iTp0@LV2gYeXgZR3eXu{ir)Is>-Fv(6
zb3zjv86d<t58)QlF*=xgEhoLI81&UMwSaP`H|g@4i5SqZ+F;7Z{G<EMSkdx8(60%K
zE;EZSaC3=HFq8!a^ojYS;@0OF@)~jE8}DCJvY&o{HR!d)%l9*#qGbMBj^`hn@q7&6
zo44j0MDt@c6Ovy4wm-3%bliy}xg&ow_C@=m#y9#riq(Id)R!Yi>F>vV%ENdtPkhGX
z^*cMJ2K5Ero3VKpW;R^k=f=IOv}*$%K-liWr3>ha%4U}nTD#~*r<NotL_E>YOGrNx
z4Bug^v|us1q+bT%=#o*{c`-yomjt?WuvAq}+OtkB&zssehx-x<j(GsBC`6#s%c;Fg
zMf`OJZ&i8aWKcoYxmh*XekKh}+4qe{g>ivabs#7jIbHU4QBMwhje~8Hq*f4vvX5``
zY_1Ytz&$IFYM@(S1eBYbCV8sCK-0c6!e_lu+|)uD*1IkmHhhoAk0FzSC{h!;hLI#Z
zqIbob^StcyxrB<WqqdOx55TovZ)LAZfdoRe2D@v-=sf<U?9gXP$nliw!E$QC2=gO=
zq#2rrvmLsd=|Rs{*e&NuYv5Y2T2DKrC5pjk+DD_F|Aw768S};hxrZvFfLj#>^Ll^b
zuied@7asQIVj7=g5PVJuQW%zlcJt&Ne)DbDO&T45tKMtSo)e44afhm}%it+W0<tZn
zC__l(#IY#rrpCLH3dH!mZL`3<TqAk$oq}<>X;g6%BBqFQzH!Fj)VGDED{S!YH0jVb
z85M<T24*b&58zu8h-v`H%$}tNpbt&on`N2@z^SqIQ(vR*In_h=Gd*Nz=+)f~7HF{W
zl+E0f28VdrQmZ;uywA`zj@xAf==ZSJqFu5osz$^O2gr8dvlJQs65rRK+AbO({HY1j
z-mwtIfP3>q`K$r}Uuoc0y0n~k=+ODDFpb~FceB47!<ReaH`8CMQ{w@PgUC}J<j~3r
zP_}~!#2S9Sq}@^CH_`}doUYjmluS_zl`;&#Em5r{!c6Qspm-^@5h@*{S>-d`xXN;u
zf6vWjFgMe*%S|apIJZ)Ij|*w=cIxJge3d}lqwtvX=Gj90d#Oowx(w45pB~^+cm9__
zVMLY!EQmm4eWPm^gMlKw$8e5+e!gF4DB`o30_CA}_1NTobLYdprXXjQi^mS$U)GH4
z@8Jo-&OOYE*w6VkH!atKoQF7=!%S)LWN7gzD|ehGQ!F)oHIF}qD=CX(0@^SGc!`3-
z*PJ#o6+y<<I)IHfNKz#tjz8LKvQfd}%TL(%%_7UX^kr_CbEVUbP2{ke)EjG85bUg^
zj_KP%+tAzjU94zZ%5Qv`!ydhY&FDTR))6o5Coaee;<Mgw^gCI_^}R{8O5%laW&V5#
zAvMc8G7j$HL?v_>iOZz1r~T<%6m@EwS9<HE9bZ2xJ^JhK-s0;_s72ecgDz<PCAts5
zNbfFftchcE3W3~P%gI}Q%e;n<v`gUZTlm$F?wCX{#5lMjq1Ukj&I9WHI=1jW>dX^?
zf~v`Fz7#p<%&2^x6ESbeU-S~0LxoP-N<hbNs7#{E`GED@@r~k1_MobSm^!4Wx&m0V
zX)quhWvkRylXXJ^MJp;zqMwk*S|MTS&~)Lx<E#s}0c({=i<l-pKv4MN4Wq<|5)i6B
zqqt;sU&<<;Nb#d`_B~9)f{Kz72`v@~@0EVut?GQ`J3~%kHK!}|9pz;B=G1AX)rqmP
z&ErQ88gvr%3v|tH;xnontzhk4$DvIK<C7A$TwnD8wo*KMUaxwY$w-|_2*X{s+>75c
zeCqD-%`2{?8tbXrhooQNsWY4x$+1k_MZ!f}&Jot|mw@N$d!cFPq`%%RW$R|g0Uk|<
z5>2|(W$+qyK;eF0B<+H&?*tXvuURH%Gwf?zfIcG97FdwGQv2%+)uTP1uda6)39Hwm
zIx~CTFhfo8Uup159t_(N8KDi9v*fq_dvS<7cyr^%zI;1#Nn^1GJM5&Ok`?9?%aYOG
z%!f7FvpUkxY#)DalSuyIt=?5FxvzV495|20^^26k@y_NQ2M%aIFjw{34h}Pr6ju@P
zx5_3?V2Ml1Ex+B0Kyo7zo+Fg%BIWL0Y!;&i;0Z{dkyc~>4SDcSCcWKZ&!SlAIP`a8
z$;d_7?nzpV(T-kO5M^Koa_nk{3Fs4n`7Bk{#DMg9_p;9N9zZ>k4b5{Om_+62H(e6?
zBa3hM8+u2juR5F3`V{s2<d@gDM6gis;Y)Y8)#;z|@U#rkvj){}jw}HvI19X4aYuXa
zgCe6EZWQK1uMnkta2UuIlaUmX5m-yF-SrvC5@YtCA+_AZ(%NuPi)9dc1h!9Uy2nVf
zC!6GG>yxU{rD3I$y+?*z9JL-BFXAQUh+H>EiZb5~7yc$S+eXft@o(7W^v>Ztq+uLx
ziwr+DsIusufy-F+#S6B#TaHJEW#Gdx!4R-nl>`G+4EdTQjw<u~ibs2u2rKjK88NDc
zn%^<vPV;<*JGa4g<lrr;`8h9BFbHUVxNP~C1Fkyh#*|_RY~qvT2Mtgw(mnR$0@&sn
z((ypwh}0PrAjN<FdW+vv9^|vqN2oIV7pgE<-h-xx2I#j7$DER=r|^|qK)8VJKoJXU
zz$l_TPQrm}vxh>f-B0rtSNEm$s_GYD2*WV&XS_bpBGnPQ{CJ{gzZ(k*b_)18&`mI)
zFZryC<{({v?Ma>^a$%7z+Ls33rMA9Dd&9@)Q5@##L8!&~m=z4cO){K`2#XLXGX(C?
z(}MLmSRe~@XNulDmIuS8;6e=d?k*<6dz&%Z<8q7Lr+x@pTJFS!Xsb3@%dOE_)#ZjN
zsOy_d+xUgZTFOVfj0{O-6X?DEQ!^Hft9jz?q#fuP@|WFiipw_~z!;gbOIzy8G~QA9
zuuj!;F_x!G?`y)5_p4X=W23?V`VgGw-02D}Vc}KuyOg7=Om}$8Lfta`ppfmb%8yWQ
zW}G{$_EUa^VK{hG&6^2+9WP%4+Qn?^j;PcoQLf^uUVqA%vj)q%+gVbsB+6Xx-8Azi
zyQZTXP$C7?)*1Xr<?n`SW3&Y6=GRJKX!$2Bz0)7(9vGVof@7Mv(UqH5;IC3`WMDPI
zq?13_Lt%q<fW>Z1p`8Xy!vqVJj|fu&977(cM4C>8l1jGOxy<}(%xwJK$c|oW)QAlV
ztf1buk`NpbjELU|!tu^t?NMTZ_lq_@>cl6z&hm8ncB@YFWm%^8{ZjJxMIoFR-R|j(
zIY4>Yj)RWsM9E5AJ2UV){H2o*2^L*;(n7`Mmf17_UCW1&iY#th7kuL1$JWOL<gspe
zbFHyyW>5cz^<ueK*PaSMrHkY_(I7YUoQv&C6`83GTxI-PIUqBvdQ!V!Zg)|oR;Sdt
zf3On=)`-gnJN0q!IV7gMbK~VK_L?!`J`6zYsGkBBc+5#1GG}D%v2@;)`k#t=Uv{N5
zvOQ96WF6Ouf2uAFAg&Wr%hB-Akwgtmv7S2PH!xk+Tx8C?o(y8R3vdp)Ua`c8W`pKE
zB#BKS=mMqI8TojYc={G)rx%69aK{7yl^@l|!{@PQ-u>lJACo8j_&pwP|B9#FRA{7P
zFcpsjs{o$Et|s+p)Ar%a<|54jq-!bndF&M-u2j?L<`tja(-$x0!JvU|HVV~*%k5W@
zB_c}NK$JbnGIea)Ahn)^ouhL34O9(9JcLznBbK?k{VmJLN}Kpq0TF8vyI*BJ^dx<z
z8*B3JcEI}i6}BklZk&rA&Z$80C5QHD14Vkh5(GPKj9$>2MFQ@=Kb}Iw^??^ji0$7?
z3Lpm(XcIPo8$&j7w^GuwTmprSS@$jOzbhknZswZ?D$PaYwPi(b46OF>uR;`o0Du(S
zPMg?$sZqVOn;@wF3-jR530B;D33u&8cLPcPX!!z)<9DNmKB}6!Lb#$*+HX>baDhbn
z$N-J!3aC5A);>ek#mtAL9L}a?k+DON#<0?#)zB?TStA<cZBow9QPi7=P7@&BWGyvD
ziVM3RZU1vjCn2m+cOnP4&}STN3qV9OFv;=9H@s56pSWLtjyux@m8iCai&*KsgMLRM
z@^CH@(5YOako!R`n(0q*>6iNvCGjcDG(T@698-)nY0+GRgwISE-Uto7HtA2Aj_e&{
z?Z3Z*@T2z{RhPR6zDnAM`B-9`SSAH40mY$<9xu3?*1EKPP(F(RM)Th=1x>E6vz9Jd
zkHet_^gqVNcEwLwU{81Xt)g%AEaZOFxcL<f$DyT7GV|Y6t8_0Cf@VVvQid&GjM)<G
z;PRX9yW~^+#MDsELo|2I$#{zwTW+d3Pr#xjzQ<}&%J<>)(eVrI`;4SlAQ`vP*@@ul
zh@ojZ<`2bAN2`a^d)OjK`Nf1;vba_U{?dA-l6o<%ek<hr3U>B3)vdPb4d0MUB^hsi
z`KaL-Le;|NdaS+|u4U7=4t#sQGUX$My0@&xS8ZURWkH96W2_IhKG3p~nc(v?scYJD
z-f0QT=OE<G*b|EB`7PMic12>{r}(s(8QMM4B@Lid2WxhUifjAy)nt?X$9MjpGHMBP
zehj^Z|Kh&B&5-utTybHk6TsL5&v)=rYdsaPGe3{P`6&kfW_dnzAm>dv*qZ%TpNW$3
zjTE1L5fKX9Nu%$@fOh!IovoxF@<Bh0PYRl58;NBy*neQaZ*zplWTs@y62~X8IV)l1
zvd~oSU1apIw{B$hvkWjaz(uRomH=|uZyzeXe%M4oKV3$stSS2}5ZAr#ov08=8^bze
z>%a+vHn;%@J0A^Q-NXR>k<C2-W9Z{|uZG5dS-Je`etW@8Gdn;KM&k3rdLG^xL{4V4
z8b7J6Hkla9$MKu#$5=B{o{);%w6s1Xr@K^Up@97u=Qn2$Ik!}*ZRUa}X)fP~QGTg#
z(5~xVYU<@42+HwZZn6}hthONOfBM3V4L0oK(q4b%=az#mpe<xkkEqV7Z13ZP7y|pT
zyl)4m9*)M*L2qAqj|z1FKz&NFB<XIG%Ta8<UD~f;NwGsGZ^Hw^kMd>eo5Z-Yph74n
zm0i(rHh;!LEH>iD%XNP4CYzW9dz5RyYgJn`0vvSLNm?;;?j|L*wL8=@Nhx;=Hiox@
zAyMJ9_1>CQY5J?3cB5r@ps*p-2u#xvshuIfN`k{NthY2)TOipQ;}2Vc{iV<SP0RrF
zFT+W3X70!1g~~J9aNDAz?4wL{G_<qHH;vi2T2^u8{!DsmKT;L(T#tOjs5X!4E!R$8
ztEUq$It-IHG&quRKsOHZEnO@Q72G&%N>{0wt-tg{=7Mp@b?B+^lceh!?^BmSiZ>%u
zX`7G3)mo~w{c^g8d@`$A2WKiB7toN_WoDU<3$el9FDeWr9r%<>&K^z^Ra;tgfjMZK
zq&R^)Bn4=xx#wJ<ai;c8>w+b?YQCzE=ILb(g(2bGtCAY<t8G<F?sX1TKl`NIdJR&O
zqGPg>nCi*c4G}Z!YW%4291JSOe3vEIwd2*sZp)96tF7JUWypdWYV|{y@Sow$2^cr3
zWpF7*KBIjZt^Dg&4{guk504wRxhcL}j=6rRp5aOJSIcVcEVGdUE?VtqNz$Q)@=DNk
zMk7`$Z-z<;XYWK}L66FUSr5qNjtYn%9MYs?#nA7x4s`-W>&EcL+1p-D_2D+Yw(hK>
zw2R)Pg2~Mbu^(gWDYo9KX(u+~+%VE=-TMN?MOMJY>Y`$p-^pR0IxLlMlP~&7T%tcW
z_(Z_+yXzGi>aaa@{A|%&%KO}Yxz)i?)@Rv4JM#hLbj>e(K)zL}l@mRnC3JblWRl^>
zHC6Uo)$(9_DTE4fhfC#*^&e<Uy@{vssD3OvFz!H)xsRB)sc9$CyKdM{boe;bH1?PX
znfaad_$VsOk`lpb5F-!!H?OG@BYiN%Dy-ls#Gv!ifw3FPi32enm=gT);ptImS1aSR
zOET}jk($L%9(_j*^?X8LPc8ARRg+sB3swJJpYgfQQv~`OVDNOm3F#7lv%;wVpFm8~
z(AV5y_T0{_O81*N5K6=_4Qhb<-!dNs^1j9hem<$m1+%Hh#pzFW6a)3F<cv1ZSQ7jj
zO!@%*RQS{3PecFTpwGvTv?&95pg5dyn*lD0riguaMl@|TpK3{6G=&_?a%Y7sa}`!#
z?OXcmxw$o}-$!rh7k;(rsib_Ho8)=_iDQHCzgnPt`e=1kaPCs=0=Au#_K=0`KfNpt
zdYb9Wnkfu{cgP<<;Pa5lKPlPx8%AudSg#+lSkv4l-cqPqq1aOF>qXlg{EohF6JOom
z6h7k%!FyaR^5`E_*7eQu-y@Us%(b_8C^n41M&1nW)Ew7Q(W)M{*A)XUA-EOe*wxhh
z*xOl4r^Tm@O>H9`8jn)(X&9;`Sj1ODNGGS@=k;zQGqcd!%>--ppaxlv-0Nfn0>>%K
zp;7Ul1mI#>QG=)^BS!-Mh2J+1n7GrH*|t}IoO*}?7hR+u9JVHjhLrf}xXhF*{3N^H
zX5!0^Ij7QQoSEo;6D{HOm+7ihF8#IKgS(&BlKf%cpOmBIOhbjBPhQ1bMsZdt=o-U{
z9ABmtBqJw3?Yay?m@~cxbL-X6``)gD)Jt;xyf|r$p>)&Yj~)<X<}E8h1YYVSybm^(
z$NgC60XkXVM{ReS!pc}Zhr7}i`l{`8@^sX*o&R6ROItbWMkV9}4vF)R%kWE1AwBJi
z({FuB##C}^x?|DU5H|wd7PMY56Jp~-@$T8elGe87tdZ-8+gc=Bi>~JbNbk(s0hWvw
z__5DIoPjv7<W_zFVK}rr<jXJfssm`d)T>?GJ`w?jpm%4pH*AE*53tT6QP(XI9eg__
zsZ4}?QlnyOZL{vs<FVqF>XUA!nhMxeC7(nvQd6ET1xbdSm;V}LlF|4Q&R+{!+jhQI
z<(8yn<8XK-la2G}O`d(+@3F;}wOs{0Zc<btgbltn;;bho=KJgTr1|Qq#*KB`rgQ_Z
z!+2QwH=nPREgPc*dW%o#{ubyGNmgYS>)?#n!+>h2_I-JXI2qmLje|w67c>gk{i<+#
zNaQd12cG%0avB0xjaCBYSU4(yE{@)sPGfVYNIywV>Eu$3VUp?)-e1LXrNV}w26|Rz
zlS|eVSU+s{GnfU7h4tNB_De~|2_0m+siK_#Z{W9J>(0Appzfx*j|6|$5ig0&?zGrM
zS7w-%o4}p81`qhXySYf5vta9@LPtyH?2bk;NQ=o9@2}aM1p~uY4=U_M9qo)}9C+LV
zmP|bA;UHU&vm!EZEeTL)_rsyJ+9|e|;g7;pcY(@qo0<zLkPNq;Vf>dz^YXOLs*E@*
zBBuA-a^pfNAktbJ&ZIiq%h@rT%673>qv=)jl6Qy2VNhmPEUKw0D>h1dOWi0`ut@t>
zEj=;T={~bBPR(*Uy=jP}9C|(!M*x-BRa8gcI7m6vc_!)JF03`6-nWP}GVy#0){R+d
zTy#9?8$4UEt3D)pLtY%_@c<e4h4o_Di}jUrugu3i-~|P@@xe{?G>@*KLqj3<SEy1+
z*mw*#N^v;Xg0uREwmeh`9!fxQ?XVG#`4xdjPy3z0Dq!HZY++zL=F**pzDLo>rYYnl
zAX(bY7(B0GXaL~yQ~IvJ`H7%sjs<u|gvYklv{`x6YgXR>!Sm0iZ5Jy<-4YHg-INC#
z28I7&l`I4|>)c={BWhckei_)!I2fUn{|{kb8Bo=?ysdzMbV$RYK}t&c5Q3C+mmEO4
zk?xj~?(Xi+LpLZO9n#&6H1FmY_kZuF_v=1;pEYaN%&dBznSh}To1tz}hsv`d1~=ud
zUS_#rehHUl&%47%UFtu|5&pq~f24-GahIA{m^7gSwB)sFbZ21MbOCFTp%k`A+2L-N
z)cV?3wFO5pr3juUZk<z%fjd}qx9X=fw^}-T^QDY=uWY%!e{%uce!(_d`+yJLU6yYU
zi2p-0&)uBvw$m!>N-meMA7gdXB(Lq+0b{XJ(iXfFhrl+<XWZ*CE=<_XBQ94PR^y*=
zzw**^1@S9WE0YVD><$xslUZ9N7Q%{&J|4-1Ux?ONU^D+ppZxM0__Lo3wt%H&Arzfd
z;_U9*LnLkPUA0?d!UC*iBZi!<U3tCa9l#Kj9$Lp>E<}JsscqYBXn(N0oE!D5uYO>N
zV{%YKAhDM>gypIGtdUbF0AQw7ui$~C8~k)y9v3;Jmo{rB;>jsi&4cUey)$1OtisxF
zrz$*E4x9QxBIAcm+v!tu1*wWj3OtO5&-5CzWsg5)0GZ;+Ig#U0&%FX5Kkr3~pcS<N
zU#UJ5oC6m!KmSbQn|b2K)3*D`0g+v3*lUpGznt9ri^(wh7pM6d^q=>Bn&mHQ$P_^K
z6r>u_FixY6_f#f$0-R3u?zp{OzYq~1m|8k?yhKp0*QuWeF#Q1!Uza(D)DTGBZ5*yw
z1J#_Epe0*$ZMtehRhQFRYU0FSKRN(}^{LZ1;|T@myY_qAgkI3jIa}+>E9OFh(_lH}
zXD&(!KwBS+QH+?RvlaR6HR`scqz~Mr#xbF)02JvbF!WvlGC5=Id7d%^+9;kVZ-2a<
z`jGMDo~D*m55;BP>jJ44Su6+{`gw~hC64b9LOafZ&)R(V)(sQ1w;K7^2}TPI=YgrC
zHEdz2oEJp9W_*`M!tTcVyW5lt2-N%ht*t-1j+55Po>>X10HARL<EF<?qF(&R-C#E$
zAn!3Pc=a`;T<Tp@&&`hbu0>MW?SQy|;-9i-;t`I6+{3JpeCso#!SnRH&igA;=V0{1
z4S+-~b_ww0vqw@~YZ0(ZN-EmsYgB$K!V{@OkJ~Qtn9C-t>gSB6@Y=6?_tw5Mt6p%!
z!)Rj;EF|*d=(PT6JuX{#V|hz*>@4muZ(5*YWY5doJM-&GR}~f(R^(PaeHS1-ThE+E
zxNlIJw?(-9UN7!O<0}QWv5y$(pM#ir*8#q^>17vQTczxu6$HV?zgRkPfI}Fg7rK%T
zUB33XeWp<vS~s2EyFNBK&6XaZicBF0ocJJlync7zdR13ZCd<2<i=<&kFl2Z3Fj*j=
zQb2k5+^v~j%SK<Gb#DT@kvm}i(XJEcRL!Z`Iqn*xG!*QL(vopSOS=TfP_5DFO70|w
zhn(fxW1<5C&JF9~e69re4dh`@p|55cV<l&8+%HRXwA0+r1~~*mM!CPWo=rEjO|2gT
zF%P1;rwaYq==2bTHY$1c6`jxjlz$XEB`fC9u(YEdM(qHpuObj2|BkGGKzEg|8x^>7
zcBA!rJ34CJBPR}*D0v~H434`7Xy3C4O2Cn)c)^4xKqNn7(JKGbcM9lcu;Q7criuV^
z&Lm2C=3zd7NIr_u;uH+!R4)S*Xg6ZQ&F!BnH)jd$2Yyd>8F!!CBNTpiAQffJm-K=U
zTt?j>i<ow8J$W${ZcFkCh*$AGW+SOQaX)Q?w+ztVxO^UW-(=7^JqO(Xa0;*O<P}$B
z`ppmHY3Po!wR1PoJz<`-@e{y(Q?oqZpuOXN1LF^l{<D)4aaHLuxA8*6@8a#5Q|rFA
z$m2i?e8ewF0*idLVTSap6KzWt3UqAb6G|pi3+8AB{J}M00GLRMWM0vFRSdBF)#T)o
zSdCVhdac~jz@dZiF|cPYy$q05d~X#2iLAtPsLJ-_OL%tEjHml;H!bqAdF(%|0q1`)
zDGp;_<K?o^1(zoYb!OZSth095X0&@;-FPPia<;Y0e6(*bf*d;NgRJhl7~HVMN&ajy
zd%5(!KvsavpFQW9K)NT>>%2MbCq3RR)Q_dP8PC63a<guK0(kwEw}6VJCdn;Bf{Cir
zT(*)Nv<e^+5fqC6?FyVasLW%k{5bGbD9%5$&RUgq%t|g#)PeYpRYj9V5dZf;$3~=p
zoaTHP%LLr`w}=d(OdzEL9SUJ8K`rSm@?j#U<WoGWHzk?%=%)pP6Z!)ndxFa%%~Zcl
zKw(5w2F+!z0<S%PbJEG8({k%H@<1kM`aXT0NW%gVB^b@NNN(F>9S8#@Ia%+S#!!ND
zY%{St;4JXnAOOSK4H7FJU^<8gE5+so@XgPwP}kL5ecMv`P;%M7;zyzJ3z2RU`$bm;
zu!^t$=_9iK{IH7dg->A(p`k&|t&7Vcca`OY5lbhg9w@(W2YlLvw-0;Qg$9k=C{a16
zlKK533yshBlVAQ|YrfnZ=};G2s5fOejE#Z&H<K9dKZz$nwiaGjH5pCrv0(tB2wu(2
zs{dGDdiZN5Tc@~T7Q{d<GW=o|9J+i07;L*ngZL(C{}E#7z)D`{3tq1)v%Gl|6-Sf{
z|6kW9_iT-3@fv-dCIQO3?+Mpp(#3M_J_S~HVo-ceS6VBqqO%5vP@L)-iSoWNl07l7
z@dS*Q=c8;a2~xb^c+omN6Uld3zn7VFlwyjq&atc=tYik#=8gfsP<7l0Cu1Z=z#cBL
z;xWQ~Ehuy(0oZAvbo<2U$NOzTH+)d2d$vN~L><c};W|=VKe1h??>?m%+K^*T80Q%`
zzB#pmTJACss?8!Aui3wT$ijF`J9aMsHTL+qcC<(|#%2taa^EQEy%ibHWSmDNc)ud?
z5;MV7e;i(wP0<eIESi2hS<oyl3bC7F03n}!JI+tB+JmJC!Kx?^`rzLgU7ywbb|T0A
z6gPp2F^MIlVBCys)*wcvcm}$~Q5X0Tyc38bG0=~M1PAE~t;P~dNeI+54bFKPa{GcZ
zZ##&wv?KCST<}&wdlWS2xq@oszs?gFTcOh<M>gjDq6Ddy;38f16;2qASQwtOA^B~s
zy_D%macdP8j2sHO%OS9nwx`xllmJtsi-}7*Q`I4<@6liKK|`ol!5s|E0BNO;u)u|I
z(03%8I8-FByhCK57_>ToqTreL@Nn#PtS4=-odRI@`{-3MB!?BK?t9c?bcwa7(~Kax
zZe%kq_lMSA=epzH1Bqd+U52&x;`&n&VjRzm9ZDzdW>NiF$+rhMEI-Jy#3_W+_i64*
zY<8-LVEbGU)=&|BWpRI<LrfhtI+5{c&-Do>zt&dupnl)#a<-g%qsWFBRQ;OD+DdCx
zeJISydF-)nK6ycKjkJH{*}SF>-dP8^h-`hX!AAMQ@&0H(H6(|1)x%eL^)2a9PT|K~
z%Fkg*Ba+1NrJ|ok6_7CUsFRH$%Hv}Lx$jqz^-k6}46ovdw(vF4T?RzJX;8(J(P8qu
zm(~t@Y~fI=vw_#Jp?u?}b7E(a0#C!oB^FK9DvfVW0UtHTSuj-L=Ay2kA>5b<j#U*S
z4nb2S;OQTmjwX|mq8O*=|5IzxXZwbVICoaP3yZF7yyP))^Xk)EJfm$H__k}dxlFW^
zwQco*oSta1+_Pv|+hI2bKlMYU5aKrJ5nDwNKGYc_9mq#AbEu$q_NJ=qMO49=8gZHO
z03TzL!AZ4dme1#19C1S<oDZ<CZ#uL<m%p(qpuw=FhQ5Q;xwaX+J4`^P5dC`r!Hu%}
zrs?9>u&B9b*P{1uFn)-I+Ld`Bo;}so;4m{eV)FXwxdkh5I!zgpIlJ?eSFYPGks)9I
zdT^jZK(4_T#X#evudwtJYziNZF4}@5ne@>Y7M0P)ix_*9miaBmIut$EqDXP_1^VHz
zqZx_G``A;NRw&m)?ws8<%0#%fu+=s&9eeJ8d4I>SO3l$$V_$#(8q4l<7K?8N{ZJuc
zWNaWfzm;hHfpJ2gG*W!`O(yjBwj8gfdmK6&cDTYjuzy22Yjm)mF%Csp*9ME;D~z=^
z=bUqOoTbH7+{h#SOkWN(KPVGc`uc5}H8as&o=6-7{<Z|5e$V658NxFgOwVT5zLU7O
z|ET+sS$1m6Qc1Y&^#Xklmr0G}g6+B8f|P+nbhLx*vO!w^ssPcNWrPBoekYgV_X3VY
zgQanU8iVW`090|UPss;oy<ht1z-;d989`d&9AUiFW#E<@?J{sLg4I)%l-XY+7VUhQ
zxHcV3Bs(x+v^xlH9|S8opbXKusK~OP#L&f)f2}orc!}=pz09?#9q7cX;1U-YzWjLA
z5JR_T5Yh#4eQ-7Yz%$U#o6t_1fKFfmPy-zjoiWi}&tfuCEl3r3&ju(TULtfA)p$y}
zOZGSY40ymX&mT*R+Ysj5w7jbZhdO~oEZVr3KeE9NRj6#&;+$>|TprBT#JFs=hH^@{
z+yk4(zHY|+gL^5ddSz}ZRCn?e>bt0`Txg@u56&1_IhP?NaH@G1UBvjF!xtlaSK`G#
z$af!%&Z}>!4R@%RqhbZoX=XXS4Pwd&`ML(R45Yq!YPKOLvW;>5TtC{St{JymhCejG
z&biHEOFWg;WvQ1GWs{4kE*_R4AUi*zS#XTD$h=_t#q1(K0k(i_;kE9312I>K51|EY
z^`7x=<Pg=uZX5=DXaAmogZ1(}z@>=*dk3$!2+BW^@BCt(W@1Urax;@sHdl0;1%7PA
zQt&bIdXMt=hKlm}fq7!Boy6fJN`?Wv`h%|JBL?QF^IbU4xABgS7YVW#=<MZC^hl5*
z&s*4wQe%FGx%iOV5A^MGn+QB)DUG)Z)e;FOk)?;daz;W^=q%X3LiY^%Y^$jsYdhhI
zPza1A83=x1N0!mtDhPHB60;1d;*b!XkTdCZ4M8&nAX#b_Pi@hBUVAa8jgq5utrw|7
zbnrtNtm)|Ag}M|>i;RaX7Z}`26_O$qxKA<ORlLfxqG~@Q11W=Wmh5Y{ZQZ4_EXn!U
ziqd&L^`&LB9H<!YNYBH&M79t0238ifRJX<y%$M@KPt6$T9K`+;K*5sT@V@ZFF9kA=
zp!?5$l(59PAs5JZdXb-v(;V*YYPW*VhrU%<qDyePDc8;qYz7cgW$5SCd@=Zh+clLr
zt!3S6z)tOTh@D1~GYNJayyCs_mLgo+GL>8wgUxR=u-G>IxqjaJKD{G0)CHe}sG7jh
zv!%dpFvPcdF1Nj9=87wcm8;|k%p#n0l5KH*KP@-2l_+IJLx|7#_GPHSx$Xs(c!9)P
zm~D=X#1<Rs@yWMIPHu5{6v)EeCX%n4qFAa(^Hy5#*!|5pw!^r89dL{LZFh?GYD~Bb
zKQN(7K1iA(ku0&GI|hmU4Ye1=U<b!nd|hXBO1O+v|G>lJ;^itS!lM``B1|!X<W!Dc
zKLmJ3t))*nw)@FBXWN603uloQz2xXu!7sTI-+WM<_S6&X3U{q7Lxn_iMGvOBXhWBO
zJ!KNJ>veg+JHo>Oq;9{p59;z{FJR1=-EkA^Llj;WdEJUS$}Ag6j}Z~YLQd^-%d!eA
z(aWuFSglKbcf6?fXavRYGgzG<NnHdS{=7iPp6xb!SK+K%IJ*~Ipt6uZnrS>{w_`%n
z6^pFMi$b8)cjb0p8<!QXOS>g`A%$XbCWN3QkVoxxbc#?UH+)@dY<}O?$JJg*s=d&i
z?<pnL-sKQmB`M&t7%AAOz{!L9W8q*h^5rA9)qK#!4v&BadK|Cn?wHC($~H5PQA&3w
z=%eanv-EHI)>+kP1FiAjFZ#(;?~1T5owLurqzKio%_S;UuQKWp8+G}BdUgU0FxF$e
zl_NY^uVK+|VPjsTx=cFBTR%^yaWFe@?{UNt;t^_Ph>f`33q%o8%)I|d*vdrLM2l}-
zV##jPE7tht)iwo(Cs-N3$=R5k5O--SO1^-t@Pipm@^A%IS>Pv&L;Hfl(Cgz}G`@os
z^|hPsO!-9KAf^}|KJb=qh;O9D5CRX2v*C`(^lz$~@e8|yNzh8pEf(l^jB(eJY?Q^Q
zqj(cD!Nmbq1CeCaENI?9D(2|r7mThB7<PlIGK9gai9G!X{%M_tDSud`SKka4bbpV*
z>p%sZ156`isD7Vb>`VzX&%6<APZ;iaktHAmsfC57U*L?UH3b`w9eynS4h}!5dzkfX
zku+M=*Iki29F3RYM}?(?@X%C+lz`t7_0`Yo?53$gxyxq}3(+g*#dW%M&CDvOKiM+I
z{uwqu-EO<Ze^`hQd58cfjct|$_E3}b6k|U8y3)EOb$zw|?J>MfVL2vT)w5oW<5#~0
zu<@94^fAy%9^Xo_PG@1vV~8DLH@~UkkV7YzTS(su$8eMS0Bo((?o(eg>qn=e40MpX
zMxB0WMo#-4m|8`5T+!3}HZBIfDLBWKj5zw&5ngY^brHm7;N<A*Q4D0Y7`D&V9+p9-
zb&sdgJO_*p&6567UccUch-|t63twvcVTHnF{@S5M&aKnC?`G-{R`tXc*n#dbnr`Dm
z2Nu3teoJjSI;2taU;f?^gaV;XP{`Upo0WncT9;ov$<?2IROM3ci5Y=zC7U;%)yz~}
zM^_wXnOWqT?fmfFiNyb*n4IhoV$IwiHFDd-$DDKI!1rmF{LU(0mz<ZjFR05F#0Fvs
z;B|nn>_i?8B^A(@TpW^Iq8?o7g*8VPs2%bz#lM}@uec;nHQ{4)S)(?tRBWjW#Ly<L
zZx=G-0^FBTu4>-<14>q`ydu7(vtIBMyq2L!j(g>904jJT(BiG^!a|&doE;3Fgry5`
zZY_0HTxH-^j6ED|>E1GBsid9hOfIu@pS@YIQQ=zKph~sSux<F(a6S1k(8=$m1|&D0
zaqS8z#QT=~Fsk0)=dDNyD%N;md&1#6xf}s8x4J;i2PsYG0CH#=qqTl1vD5FbuPK8q
zW!CzmF1Up(&|Rw$WId)L1%~jN$lQ!NNEnEN3#!S8N=%<O82#kds`WNKLOayNY*&nu
z`x^_Cd&Wtv`_*%>=2leMcS%7tKbWieE8D(>;V{O*!eV!$2nSR%GcZ->?4PVvU&!$i
z*!q7M&-*lCA4}PDH96fm;G159FsMBm=ipr~KDrt^>-F?tK4%W^M?0?4rLo#<bYE+%
z6;PU7<q_s|skm(6)swF4E1(G@z9HIB<+>;qEfe$VAThdm^%JCcl+E&U10@T+UqjmA
zfN-IKk@gRmQ}y|YthUKetS;xsas4p^jlx0wPx`*-sEHmTpRJoV<E>6+MP8Qsn=mdC
zP7Zux;Ve>y=Y<(I@~Sb!s@JcK&97vm<z?}Y`UrmGszs2uV!&PB%W#f=$Wfg@Wf=6*
zwBm5@E!S?8c7P&RQyxYKtjH9|%H|jWgaEHpA5fX_{*OsC!tRMXiwP^M5FPp~E)tYW
zv%G+KUNzHQxxTZi5MqWU_pO0sRpc{R9Odjl%YdwdA<R~pBnASSVzWNAYN<HM&$(W|
z>EE9S_UAtnK9gOQG4?)~e3TI3e|LN}Qv}B3bit5c=v5lR<vLWch>?GgS{Km*A5B;h
z2&&W71d1$j=l*2t8ynq7qb`SfMF+a^id*Ytp7n=dujAT&5vqQQo)UE{oWGP6Sn61Y
z)a`ne60Z*SywzuBSfJWD2N!(sQfnG$AgXe}SdWk62Tu)j*&)Y<vBJTo%gy;=dc;ks
zx$O)!kn6qYEE|ZVWs`VbOn3QITKS6;j9(#+cLhrN@&DLz(5(=(ySGE<G2Iwgg$Cz3
zov{pl_X*o3p^RlBja94n@54CBmUX2QPZwX{i2;BJ52Z{QhM)EN9R(yTW&b=U_jE**
zM)Kpbwy<{%U1T5O?oNPD4jJ+e?ez6Eqr5)R#l5REasFX5H~Jk$Xm``4{xmV@Zm)DB
z>@-9#;!g);drE<rAe_xVF-eZoY;Q~vJfF`JZ_6Nv!t<u2RfVc5R6?tg%b6RDZ`>pD
z(rzOH2R>B$D!F=`jM?`jcHBBS30qu5sNFIYYWfGiWnfr{I!E56#AC&h!2An~H*i(O
zremZ6F%<Z1%#xMCrjZG7@O((#$6u;%k<Ku{!(8y)7V|S5KhzXWn&sfDFt+6EU&_lV
zxgY7h&^xFOjLn(1BG9>09Te%|Nte8ox@Ej$s@skZ<k8sd82j?7Pk?x3Ng>qYam*>1
zk<HT-rMt%5+T#?Vi4}zv;zn-Zv+|uWRunz8y7lEcA&)c2M}-_zYQ%<IF1^R<9ao-k
z@Bhk_tp4+0q@`5Fp(E_pdyK5@v9I{e_}7F8Z^~V#;*mBBY)Ggh%6aJDp|S5J2P2ob
z^8^cG+ocPRLAJoM?c2J8e&HL#xCHarXQpetxEzJVI>~E&tyLdZmipWJ1D1mt3xBaO
z^dbjVXNm~Fecg2vBc9;Q=uq_|yS69pm+y9165H@BK9h5(0{AT#zhyFT;Aueapz3Cw
z$y}#<f8tG$d)9QYv*LDXvuLW0M4KwOR+4(dAHQ?zX&z9-ojz#$*r`91PHQlp{$+|M
zn~Z1%Bjo#s5RY1UW?R3Pxd^ng(=V!W5qDSL+29E>9WF405v!fy#rM#y7mcK@zV*Vd
zC6DJOP&PduQewjx5n`XtZ3w-8I35N;z1xR|nko&LiT#L|zD}Vsuw1ZT9h-R%Z+?py
zpXg6=He<F<N4SA217=R<FK~KPRrldDSc*hg!p-$+!nc0H^ZAoSGP_|+`2P4ohNMaY
znw<&|{9@^gvB6T@PqKBE2A%5zRLM9D(Bg6G3-h~^4-cyNyWHa^fe8_koFxk2!f)^1
zcx`5ostCmIYZOK6Au<nkM_IlBAD~EDcd3aLU!8pdovO6=Vfu_1%GqH{M#NWBAUNwq
zPAroPa4@cQ>IXF-DU2GDNTBWVXft>$D4d?CvrnIIk*0h3oUu`m*4nIUmrL=Lp#>Gt
zn%t11SxN?Q+;xy2ZSTf0^Tsj}N!y>8iRj|LZ+mtwH5?g)UuV8Y3ho^EKVb}OZOY(3
z5|7uUdUL%I>4VTia{2+P7kn7?TVA5CarNQvJ89`@`@kXI)NkL=ZYtkk{Hn^bo+aJ%
zcBY?%{5X=*N5SYJ54ESK{i80t04#B^VEv(3Jkukk-4SbDN@tEiORR{mday9k)!@ld
zRo{{h{){qz)9k@YJ7*kczTQT8-YK-IfBe(FH)72wg9u_5Ph2<iX_F#pWcci>4F1{5
z!)h5EPu%HwC1jM^dgQy^BjP&x=nL3)vHip6l{+5T@BshK!*Y-Eq@cWrVUWhpCr^te
z&3`p-WqwIRV}|lH4sxddV&zp$1^cB+ga;lCmNYV)^Tg{O+?PRZ;%-VQ8-)6ja_jq+
zXrQLe0!xC=mazt4;-75XR1bqZw@B#a9!=v8Shqr}xq*>#@P*(`|J6>>V=`u2AQ3`O
zXGb@Cp`n%W>}p^fXk)_#7SV+&@R|Z0PA4i|Yo$&dKW_jwD*n2;@2$voJg{|M;op)W
z^hZWMiz_EXiJ*o2ewhN5p!p^R+C<gqy6zV0KV`=f#@*Af4GAn)X#?N{2I$%8Z}J;?
zKdBCu-^qwWbV6PZ?{AvG4h7b(&_lTMSwaZzk~b{YNdAaLJTHS(l6gPG;49aL1_J;y
zy@9F=Isrye)_tSt0a$4a_kjp*#+jNQqe@lw5Z;z}*<g{JwK`^eN{d%3tN2H!;?M`n
zb3T8kU}_c)v7=urhVpi(lJDZ2<`%QsLvq%~#thYvCKSH*sQrmT(BA^?sl(j(4~M~*
z=?c9H^Y;=rWW4D9RTdc<S7_3p`>1rZJ`sEjb({9G7&odrg|@DMJVFU~9<@|rxub@t
zHHK7;cW#&Xznx$B&oy1!<q9Yy>iT_{vMiEtcgPLh7=-ind8v;aJ&xxYEhUwGaIW7Z
zWr?9d2IY``XK-Q`kvzmK!0VbFK+2N0xOsY!%zXt@ZRpG0<?9nYv&8aM1YSZa8CZp_
z8TKAZM5{!}_c1!K6?yMa*5YlJU0+CnraOu9{_r!%wTdU<5YjLYlw1&ND2ypGxyAfK
zFD4qq>=z;ve-S}jfuS!N(t61Fp+wL&_qfWnzp(?i{v~_y8K)@mp$wi*D?0HgrI@4=
zX*Vm|0>u2)poyql<x@tU^HqE_smJ?-z-#;-{Ca1xJSpcCm|M8+w87%gPq(jbDA{Aa
zqCg;ZqUjq2ZLgMTvPp$4P<4C#ulMW*zKJK7xywK9F+lR*(=8rqBHlHIt7wk@a?()H
zhjx7Zm@z8J8)Lirq0a|~&U)lDvc?*Bd?ETR?Z-z9*KnMyYP6MpPLvDg$@1+0dfwGn
zo;&2KskOx=t`unXa0^rKjlM{c!BP=Cba~=OkahKDn1h=?b$hVrY2jN&eGS1evIps{
z2tS^P(JWd2aQ1JF!9L9dXMjI%Z=kkkK4Rei0znQ*b0_LCEAW5WN-7c4gBk?VD6Dl4
zc#`>?%B@8+J1Yq?+)cQh)5+kqV~LRh^USUoPs680&>vi@M+aP8p(-aTuxqI!gKg9T
zP$9zjd=qtlJ)z7Wwi-JZkwg2yHes!{l{Ru`kL9hogv)?DRyaVLorqI=S4my)vsEOz
zVQ3{7!7Gxe&0W{qL*%5#q-U%nGUq-vf|6YV-162eXk_4w!?f9<wDSN;>Bj-qK+9qL
ze9n@%aeU?Nv-X-nFJi{uRS)?kNbJoHT0P3Q1Xz8wbXV}JJl{z7v~c=@Smqk@H{nZh
zZ{4ZU@AiWHXt5$N{h0a)oO6O)I{YnZq<^TleSD{ii|_vSdu+|n91W4*LS6!7EhL(I
zU{JST=%ZHuaL*9jCBo8MD|jgyhXc|4ij};rB7Id3d2ciQu4X3dDI6DJGXy9NkYo!1
zad3aEf+U%<ZMhH9ADbqLzYx~pPg`$V(-mQfdXS{ghuOyh(gEFeBTGL`wMgzR+im?I
zi2mycoh8WomID4&0Huew(<GZh7seW)NHDd>Q59C-$9Aed@~iUVH0+T?;X6^%GbF~_
z#9K5E6l+#jeEvYRm1HIyiwJ#WTx(I?wZVAMMGwgBU=<DL7G?p{86R06x%HLKCFTX;
zE_XKHPED*_AN7N;r(Ao$B<(}?HKqVG9=ZEX>i|8jwTJGwv<rdG=1S)0*uc9rDM3;3
z(|BKIuN#If<=N#+@EHcFK0a;F;}-GDV{qBY1{3EF*;NOdhLItTKK2jEKzAr7>KxfF
zQXg8M+1?v|@uW@|m&Bk-5;*?hZnuX$p21*NhSa%{0d<MF+5Ap*SOqWew%>@MFKoDU
zHJME8TT*OFdwN^!2wua|pQJ}P<<e*@8-t`Cl4+6fO%kwz_LOXTc7-011r}klw?qO5
zK1SbEFNNkaK^QC@w>0LFaW39_a@!i;P}e(+?SAT(mA-UKT-z}b`(Sv-$<W4Z6$Sta
zsPBG(rQ%#x_l|9}Snt_A>+O~1V#+{)qwou*QNNn2#SW_z$4i3r12!VTP4aC<7|QN;
zizp4Zd^0@_1TKc`HN3!Gqa%P<Aimn|O3mQUU0>Fp*~U=`0Hb9nZStKM7235t_GDUW
z1Bub6GQ3}5E0sSEiYnp8(J3cjYYYo2DaN@7&<p6lE^<%R0pMkEt+VYwI#C<<jscX0
zA&_se1(tAZn*_8oNzSY*LNKo1X!u<X!KV?m)41SrOT|^T{g~0_iX4Ilh?f!Ll*_DQ
zlfE%FWfhU)^sP|@Ehm8Fkv?@s+FXoH<>oY*s+Knr_-fYYvM4KErLx6Z{KLCMI*ONQ
z9?n7EZ@Cg2OZw1-vN`m|^5OL$_(wbz!+9dwV2X7~O152kK0d?zz*dT->YR|0*HVIw
zmQ`8140AetM)}W)jSMADbQPwYuEv*3wHi|#;^(d^^|p!$>=AN=+9~8ZbGlm|s=KD&
z-Sa2m-LrY=+RE4Q5n~Yx!!jFgEShBK18XJSUa1+vOD#U)=(ywS<4e6qlN8K3|Cz%Z
zO6noq+)48-eOfj4GrA3vFKKg6=mGf|8EJ+ym;OY~ClJZ%hsH89R7qpEEA5K03UVM9
zGRV{7rv1x3dx7lFnNUVjKg;h6ENE+^zlh1=2-+QZZ%bvI8TxGO`Eswj<T7x@w#nID
z$3lCP17%P?3nt|lO5CR6;b(6T@rq`gHn|`!)mvs)Tf)R0s+0@f4bSRh@63ZD-O~p=
z?Jaed?1;KkKh#-p>N&OYjUJ!`$swjF9IhJe-0{a@^V5;^DX6olru4VL1g6F^|FMXh
zVy!e*HUn*qow6>`n5Q#FjG|gFSfJUzjSbu<PLo;EwUSNLJG~dXe^0^U*P<E~6M!Mk
zPHj|p=DQ{{p6}GF7Z5Qca}56jIg2~(h@*FKU9R^WD(x)ZiOvjLGi9YE?MlZC*68cL
z3*k~?zk-ZAhUlj?ZS}$u{U{`d`fX4S4HvkryYx^BC7sT%`RG>AJ8${pz2=$YWY@d_
z^6OdGM<$**EX~`cZU0PH-@}<Zt942B99ex?gua%P7qMDLPCE3uZZmF%vU<UL^VF32
z_KjV^1&GHJ#s|d*TK0=}$GVmss$WZY*$;-qyU&_}DwYM{Yrw4pmP_Khl#J{X;<Z2D
zMX2Rd>Rb?eq}V%$@9G<Z+kf?=&zhvXy6$KQv%ML!b{2?2uzsg+ro=PZuA$v4HCQrr
zF@|#!E$A-E;1Rb?sd47^F#RE2^*%Y=1-vQ@-jhhz=+#TjeV1bK+MY~rmb3}Yd??0k
zt;nWc&O|iqVm?E=PwD;{<r_1yl2>Sn>NC<O^g~CB_(+7X@`2*1O4kjpkRKeuCpx-N
z2`IL!8OQ0pY0GK61#!xrY)CzxK-yX%3a?|xLcB5aVI}smmEHinLsu5wP0Qv49klEq
z%>cVy_UrsLy(KZM4-K4&(mjVv<@w`mZ2c~T%X32{6^gz!lPcoUc(zM5Dlrl4w7{BB
z7CKQjTKA1>u(*-Ud5-c&JuHf$4EXGxZ*Zzd$23l^;38yUcQ#&Nw1r8Qwyo8F(`Dn7
zj_5GI=y2n7&HVrqn5&nn@buq`bHEWwFPYdHil>65iDOk~y2I=H7nA2J7bbQCSl*tl
zw{?jrLdHY3O<XuOndf2=zS=Wa+~wD{E@2A-Fz|`Y0!OjW<D1nsWhW44$00erHKn!4
z?X>==&z}Tw#p5tH87bs8O8}N~?=F>>Qgb67fb`3(SF5L6D=Dkbvus}Oi?~}$XwY}w
z;T0U+{N<6i-Ar{lFtH0Mu&2&A(*%EN`+eM(Z~`WQO46nqLI5n_PnWkIv>*VJNt<-e
z9bH2ARPW~EdWMO<IO70PJ&Q)8l1VQQRf8~CX$ZuXg_6#!M|9-AcUC#DLX}4Sbh^&{
z#>eU!HEeNV%K3!R4gzwCb*b0>LxKFY&yR-9I?nk0P!rxwoE$I`5Dff%&M9QUwOR${
z-IQ@4<vw=B{gTAZRZ93Z_2sb0uAAgcY{vK>+k<#<+Q*z<J_FNC$B>N(ZhgOwYA4-L
z)QT0X|0{7bLHx26RV@9NDdhLtrS~6n6^~ZaXlBJ&|5KO7)b9oSJCM{;@JPRD{j#T<
zSES0H0+|mvWD{5bYPrC$PAuy`?^cPEg)UU}zvq5O3im~-`>d14_VvH$<a{uUvoovf
z4Kfm3J{C;OZB|_ooWTI$zmk+9BHFtSn2Wykiv4OvfdN`7(y%6X_-_ohk5B|qb!UN6
z9+T<6k=H`H!0YhGlcf!jy#Mn&Qyi{q<q)$l2>0JM{t|_S0~68D$!uCng5-amk5K|O
zd9DzZKmIRbJ24q(l#gSdDHrYEp8VC_9ZaD2ZgBM3{|n}pmH`?~OTNTaMEH+Zo=g5*
z1PS3o`C)K={m)ale46J%KtElPi~bMK{@iERk7OuIAfFJE|3b(E(kfv;tK6vd+vNW~
z=La-k9U&xI``pz3iCGj3G^$jyB)s>(&q06&8pJaxH2>%Aj>+dn|Nm;&JtLD>ncBdl
zp?V%$$q1m0n6?n%j(^%9ei^z*z;3kBum=81mhk66!%Ro~2d17VjW{sd3e>V02Wp(q
z#F&sv0YXTBdDdD_9QME3d{1ff`)hw*%?R%>Y{EP3d1AVwze*^vJgf27n}3v;4+nF0
z1OYIPrlb6Sr29c`7R^94kMSQ#>`Di_X;>=w!tgNI0l*j7@E!)le=CLs3r<$x#z4;T
z;+J><0ATMQ?Oq{y9UVo3q5c&EFpS=!{kNun=|u_mrHfNV+wGqHNhiY0W+I=Hl_u2r
ztFw#w$2BUTYstXt4X@*X(DAoj(P46L#wv~9Y-eO}E?Cq6l~0KB0DvAN6dF^VFI*_3
zYfBE;=|*uxB+cK0lqn>%v{+K!OiyAvKn`-fJ8YP#pwVZxc6LD?TVn&>2_OSLz~Ek^
z`1^g`Y6_dZqJsSM5k6*1;DC4+z`cBeCWoE_1q6N{Q#u`Nrj^#=*}j6o{@#J2gm&7L
z99-r%!2eHU$S^1>YM;M~YSx-4Gy{Jzp{ivW_go{!9)Nc?uC_eh?iZkP{jNk#Kzz1z
z92_4F2D8qb-~U*e62i;2(!MtB9f0F|MJaaF(czuZFZY+C(lCOUP}9bOEB~<oNk4A^
z7M}#XH4-SsAN{KFte_0w7n2Wwb>Zr+IsR>Q9UH<gn3*T-CtC-pze->OI*fS*7&ony
zj6d=J8uz*{FoNqa<}9fHXW792B{DucriK~C#(!Bn@Ejixuq6^ab-%wy@!$XIFnDf%
zP!XG4{BHq(SD!!ZXna<3V+W!1|9T(|^0Q~gCnNIwugCU1OWFTwTK7MZ>+jLmc(#Vo
z<e-Y1i`VKO(f><SUEgPS8%5#<O0JBDaV_l>bzL7~LxVv7C3goLAcZx0=KkGbg0`c^
zJOGXwPcly5EdnCgOK8x4#cQ7qIH1g!H9AhFbuFpa{VC`UKoH6{EV-H|^Vk_7<uo9?
zp#1bt*9tKULq!Y)JsBH!)4RT}G%{ioqKH0d1(<+7kPnBuNTkYXKZi&~Cub*C=k3vq
z3^X9&w>^-wpDWF#>-BCZ`^ZVG-ihN&7T<q#BNpl%`WgSn%BbhPy3Hj2H|)?;qW5oJ
zN~@cQU0D-kyFkHj3|NCX@&?v>Z<K{~r<JiBDGYkQBkOJa<)z=5>QDHg&lur(tCquY
zP4lu%2*kyQR=96~{T~x|CHJn<B&sm7W2)Qd!z-}ATccmJRf{`<^5ykP(YFY8s}d<`
zI^@Mpf2PiJP)k|u=TK$Mu}muCG+9IC+Rw-Pa+uet-s}G@o}{Gq<cMz~P0C8HfvJs-
zsdjzw4<jezG3yW2PbH<bpENzDlT8m*rlrh(Xfl-5pCvoEI&a)Jq9qeP$E&RLlcg^M
z5ZgiOU7&1%&NG&H0_t)OU>t-FHx~hB4WCU==06rBl<5Q)e?yu;(30D1sSt<zTQRT1
zN^bX-k9G1Of6fYY3?SDVsSV2Fl;&-I66`*jk4mI0(I$1)|Co)zTx@x-#J|PXUK+aC
z68}4?ylMT+ezEJ-?p8}VtKXmcX78>vqTw-3+}dUW=ze+g*Lj;Tnejezfew__y5HB@
zMi=o_33<WGgU^wkWKR_Ge406J<f+np)Vx0qPh_zI&oX!gpdf33YE0Lh2n78kv!e3<
zI>1<nkX7Vb@5Az?O7n%$`PMsD_G!lIxS!u!LLPWFRg<{UG%b;t9-%44_Q83rqs4ST
zA=YB9V;-mX<=g=k(8}2Bkrr#IgvHxs`^`hV0>1d)X6o;NT4m+Ys_CXH_F27BczLg!
zKBty9+d(wf^Xta*%`Kxib+)rif5utT=;gXMR|c<0dUqfhpA4}~Rn?)%6HhDQGHJ%v
z1J#wD`7!ygka2TcujUyJfm#WsK&2^Z;8IXrv%2TK)PFj`f&}M2P{q3aj!5xcqmk+D
za#&nOg~bAns-AJ$H@yqA`P#X`6KS>TMh4bqt&IgsqMjx-mECNjVjw56aV`^QqG{g}
z8vtizN$s9&qolkz%KQFvr=3jOcK^m|XCzrqto$J_X}E(5!j+t4G*6|^fnf2AA9=#d
zT7XO2^X}WO&mR{9*D>CRTFHT8y$eU!uhIS$Ebz?e8C9fs6=k8?;gLtX?sGCvGC-s+
zCTjSs?Yf)iX$LSmWcdzjC#n(s+Rxz8n+Wj!4OBu1aQNST+@H0-YEa$%{Yq*A6*<Ah
zR;>=RnbWpq!N}nq$bqFTR0rB-tfP@PY~4i5uQ!pj+h!hFve1+6l;(_-F)(I`+#QBo
zA+x<~<JkOI{>o~jym_1uEuW~#Pv47K^ym7hf!Fa5jn^W#zC=%77AW6IMa=7_)IRS0
zu~Qvt$7{nhzM<@Ze$|W#o&Wx>`Ryq$E8_Y^7c#T7IEh27&hQH>cFp6aZBT^MpGAxG
zPjzZ)e;$kg#M?hT@F4SE{5BVCc{^GXpFgq9EYraFsa|d}HS#y<d(Ast9!7e`6~9&F
z<&tBG-O29>`QLx8Bhz`cCw2)oRq-rCn~ilIO6(`H%_q!^cD@u=hjoU{D8xoxoFpZw
z+O{<?wh5$pOjV5cay<+;Jqo(byu??js%u}AS-=oR5)FBeiP4Efe|6->$(ZT8XpC2-
ze|*SKbai$yCwj2?;OOy%SserJ)h>rWRqip(+?m(%!BXYx6a%Fl_Q*xIZ2--EzSwHl
z?sk;KPBC9*nS--8S1dzTIfxAj?72E#CY4Hf*V|9(g`(kx^Th&))T|q7h98Y^w74L)
z2JbIOD#Cx(AgaSL!r;FAE+m48UoBOyA>%g0h3tFH!ECWqJG>}_NkCy=n>GJ)Gyyyq
zhi*gG!-@CpgrRJ3Xn9t$LLz^_D9FJ6cIcS3Y=xmFZ9|rG*-}QBTF#QXiJLES_fMDg
zsn+8RVgQU-M7`DM`!9l;VL&Dv=Wmhvn<QLT#BEQHZoi$^!|-`*sQ^GdAFM6}0y=8_
z19AVe=w!4^XYF_pDB3}xi&+UsIgjx&@Zgp}^`PYDgC-(@B>A51EUxNepK_dgQzd=q
zTrG~?y~G*=1+*#}5_k||A*+3gvlV|Y*5LPEQ*Y+6OFpW8cYXM3gZ6$nWY9gauqwBq
zigd*ljN95Xoddt0X{uqg={c4qf?&~lIgQV5gyJs1D=Dg^p+P{;zz|k9Ggz|>sLA^;
zc}&dVh^w_kGv+I{0;L;Sn+SvK%S$%i=Q%#*nu|o98VCNlI04~TF@C?dLMe(r2!OqI
z=*3eXGC{J{i@bEurtVKp**j%R?gsmt7A>28_uye=Npf77I`x`~6R1xMz?@}dgxczz
zID?JqYJrM2-PdPrCtb+!I3(DwRl(Z(F$*W?y`ZYUht6l3oGhD8QG*19hzq`VI?m8Z
zJ9=>zRH$7i*n*f?O(l%Qg<H*FX60@qX;n~mjVm>@J*M5=B0FfM%_x%pV2GkaBUJ;U
zD3!Bj6yEo8i=l<u%E{ZqYkYmK7PAVJTIk*_$Os1*vTNs~f?WOfgvBK#W#610o<L>r
ze<?&a=3__XqRD7JBbpxT3|KwGVCJO;uCe~E;g99rRPKh;TttqioD*Y4`X}rn$z3Di
zx!Xw{?0Ew{h$brnJF@5d;-HK9TD`x_7GZ05qJW6uXr<FnW?B?a+kdXcIEA}qXe^f_
z3X<}dp+1j5Wj#h>I$l?5;powA^}+OUibMrX&4XCR(v2wQcnK7hgU1RE`O{-h>RM4>
zpsInLh=grHq}(wydN)xG++Db_(?Y(}z_Z_xK#<~SOTl1riby-Sw@_!UmI^GfX}<WK
zxm_J-5Lx}8Gj3;kc8=#Z$28$Sf;NXLbJZ)ByN$YL1H|4M22L8(XW*A`Ef9&ttFi7=
zx;E55S!yhwe%Ia{?I#Vb+0Uvt2uQ4rUdRwticE_33*Xf)6Fi2uiRL6iwq~5B=#-^`
zZ{mAPXEB%g!S6@CNc7L}hi6;=+ar-NBeRqZ8ld!{bi*4?Ll3w^nkiGc1%VCS^Z}B0
zrlte_?_!pQmgS>G983IEPz0|?U{XuB99apw;q&rXau}$e2IueGI-LrUY!tyymL{%N
zZ?_$^cdIn8AU@`jht0RabB=$t)Uoy5Tg1wXdX8y58_P&z8TAdChJDQLd}ZuoFZ+&b
z*2YdddEvoz_Xz!M-1kqVWbt%$eI=+L+SN%uTHzFB<+@T+>fL`q<GJmRuUXhf^Z6Nn
zQqC7-DfN%$DtRbY8kq)|9Y09+#OaBgE?DlkOBna=qzy`1FfdSD0a=<*r!??x!X4}l
zHx(Hf(|in^_FOY^r<L0&AE6N0;*@XDGtvCUiHQ8Y`E|<(Z)&JzL{Xf{Utxg}CWQwk
zJH+;N;YP^C1b&K&)SVThq^mgxCzBJSZ~4Pz3I+K<ZDDt5woa&dN1qW>TI(KOK+ZYQ
zOaSVzg26-joWoo*HFO`#av}boSYv^U$W8opD+J@y&JfwtY*4Gcw$rk_H797Hx#rM)
zW(tT3g~y70uhX^g!{+5126z|kYCo&9A%A<^{5+mej<Wyy=awUQZ9Y;MFVy`9^JBNN
z)9u7(je7Tej7t9ohwHid{{)gkKksg)@ATb?T5mYX+f$*uuVy-BXJbB^NsK4=WJ-&@
z9%pLk9}^`pXQYm*d#YkXAuXGvINA3r3}zWh*3wW4xS?(*YM<@1Um}aZbh;rjzga!o
z!<xtl`&MZk>0B>QC*`b#m|AA^kE(EeoDsR-y<k5Vq1p4PA+0#j9HtyZqIFq$IAqum
z;02Yhx%<aR0x05`GhB*4kH}HH$4*N5<(FL9Q3GYA<0bbUslGvRPG!6ELAw2l@AgeZ
zGh-D9KCa9lTv~k`q(PI^X&8p}s?6r(teB{vDW31P40;|c#C#mXe+P>TCY;*I&;|ed
zCkkX<ZkK&xf-_;YIP_gPX`!QTwGNeLr1ZGqD7(9&6M=L5&yOK}DqkK9s{Pwd^2!Ut
z{gt&6l-Y$XwEO6r6S3P3sKdlZh>m;UZQq-1Y7bFIN|iLmvmZi<h`ufhMOsu5hP98H
zRH<wI`ezCOY=smioWqGThZt@UE$sNH*&rBgL%9bBCEIq!&!<38c!VI3jEfreXq<xT
z;hGu=@(BM(-qNK)Vq&A3JQ@FJ_}SlHK=OEnX*xpNR)J1U6!mt8aE*Fd+iThgVLskn
zycB9B`x!{x6cuO>vieM<NijTaN&pjoQjfx=HT)kb9566GyrE{)<*w+>)6*xhOm|4Y
zBDOtXF*YoXQY)Afb2N!7d!6|u!f!R51#>PTG>VHkB>e`zEvuzl3F2pCf0_sXt<qA%
zW%K)QE<B0tE+a)_OC=r5k2goE$_ch-zjZK|7q@)obQ7i*UhZZ5n1xC)Y-OrV2^r)Y
z)g(lDmUE?X<^+^UbsO1NTdMsN;B*hkS0X!ryY;EhRi*JYG?P?C=(JJa6iouh4cMai
z9!#lOw+5f;=Oif<cWYYQ>Bec>gLRod4ZN85c<?$YRS%Xgo@GGhh_+W&UBc3I!Lu_p
zh#{;P531cF)l?NAjq|Zxhh?yBWTiDdUb~5{b2YvB9oED^ly?+=VijBPliu<l10lkK
zLBnBK$LjjtIN)EJ{ra3eS~hAiHydZb^-!<zd%QSec{ZKDugTLAyF|*Vz906FwRNE|
zFo=pdIWW8-BYeXNBL0hCP<M(lgZxkso#IcYR8;MkT{Bw}9SJJ6>_!eR6?~4135+PG
z+XeNyc#vIbdWcqw|FkwbZE@KYJdx6*jE<J<BjtK{tSt!R;!WoL7XMVDXQANm&~~SF
z0<jg!4X2>dYwHvdvsapb2kMTkkPfWqMWle!=mMd&+(>GTrI>d7Lu=@mG|PFZW^g4#
zDB3aD=YU~EXy_n=5{87`R_TS3K~RZy?!zf8IK$nyYsS;tx<y-_)xzt|!!~6u^p-*q
z_b0_Kt55Q|8ce~^YOH~61E@pXSyQp51_$kYS;YVfMU4uFgvX(svk_xvUK;M#&8Qt3
z4<4LBQn^*n^D&I1KaDZ+kQ$^L9QTz<(=_riUni=4X|9FHe}bGk948W7kDIUidV3*v
zbFrqKGr5kP`I-7uN7h@{E9{MQM~rHAJNbzmQa0;0L_@U)Y{S5GG&Q?vY8qC%2|}%^
zZNzKAWDyOm&ck&$4z{n$V-E`FzjP2C=ds=~6~za|MU%vSwfn$2)+$ot0;w6va(9Z`
z7^z%}=9OY7FM(nWR<uRLHo2V0G?VyzXpOqRVLnN>B{du2eZg^OL5jW3{lSuMzL&*t
zn0_nUIMB0Cji)oYUY+4Ng5s>q75^uKVsK#@$6nS1eKj{kHB>#vde!Kgs$0L{)8Qo`
zG$Qzj#!MY_fSX}M_Zg&#=K?0T-K!-=`bEFXVW1W>BdvY@X~}S)RtjbDfDec8#wc*I
z0_Py-H|Kl-eJQRer(zy;AhA;bR=^ScTCo{O_DqvmlJ-5u$3{CeckFk8f7aZh9H;aP
zc^+TnhI*m-OkDXqb?Fm0h2K_vt{Fk*qbjLW)zRA22|lPD=^2-WXf13f3F`dcDn=L6
zgbpU$$DW6h-K08s{b-KSg(BU3>M+|o_ntaHIz*nPOdZb2<*RTx!q)|T{0hx#BBhQE
z>Q9aua?RZ2L#nZ+)`$7R%eL<(<>&4P&GwWYJbQ<wK6<37H^29Hb)wvD4PB-GEI~bo
zn%>Oio{;v!z_32(Amq0hd5D=Tq{a$0I5nuAPXD#_!iq`WKZ}kIqabgQhhh^UZDESS
zL9@#nRpH8k-G;tEv}1u_G_{OxAt@l?UrNW6he_hUPJN_S=msd)bGt32{P{w`s!`4^
zRsHLhH8u9gebM7VgqD_zDlxXkh*7!B%sLoprCVgga#`Fi)s1_jos!GRpW}x_88pX@
ztK2nh7Qd^FW$0ItKOQwntqiWOVZvlmv*mGKo;5~C=`<nw$cOE6v9H??7}eoC$5WhW
z)$qS8dG;eFBHxA{$(5Wc%egh>>Wh$8Elr%NBTg+tEGSEo?VirW*kkj#yyFAhn+Wcd
z#|AI)Uv?Q3n{^3ek2!b*><K6(<ncvz%1_|l;qOjpGOezf{ew!cF_+@v_RMqLTQyH}
zX;TZ%%W~reK9zhA#i`YxioACAOo1J(<q23JObDxe)0j~HaU8Gx#3wuH9l2tF6}hC&
zJ<{<`3nq9A<659GiL7yzALQ)*^50wlJDSd(88gCmZLT;~XYrL8QE@ZLQ%a^rO6-_`
zbtiext^H-)x^^NRQ;H0#4BpXsqxa@PD|NzG^M&+I@IUFyF_n{RRk4LHRYl~l#}nT2
zoJluEOHSUuR7T@}@v6c-Fa$O{f+ZUsKmW_+bl<TGf}}c5OT*xG>o4y1kUuTSEH&j{
zp2mB#J+Zd)HTx1~h|PsEZZDH-bmW^*rpQTQ)mY%aX5+j9GgZ+sd!Zb!a&@wMpb01K
z+AuMHoc}3Dc{%B}N}q2!?>{ObRE#Ldv{$Z@C((K(04JL_dc;GCF{}5&y&=u-#8E@S
z<!muNYR4qw;tnkUw)X6uWNkwI=l^!&=f$V~<^4g;=*emz=`>81`*tFmNG%2r9cQ~|
z6LZ@&%(DA=7#*ROI05QZ2{Wam&O&%<Mf)S6j^~YGnT0sS;^y{{+rjU1I;WuE@(yq?
zchZ9`m)V%8beWXOcG;l6*=LpVDa?IfFHgC{Mw$8P@qPM}%e$>oWmy<F(cWnhEYgas
zWh)<Mtc?0z`W>h4JTDKvL(zQ$-a)M%LOLC-r^rS5i6l%M!lc@GUN^$ehN1y0W|Dd}
zD?Qb(Bp_@FJ#{{_`V{jjaS{(P({g=xVU5yb^Qp1^u{`aRMTy(mR&T1VYr5Bb8HOqO
zEipT-Ur=ERHL<_BQA-RY@uW((Px}>@Wp+@7h<g0`Sc8kgg^j7kNyh(fouJV59Nxdh
zrl|2FZDNsr7b_R?<I`{4q|ErV*G?MLp>oIFGugM<>$5c4)Nm3~SSj*&YBvrZzh;^>
zNUS9NQk+PnoVkwAt=+7FskIi^J-;tkuY&pZ?VDH#VpeI^tzCGFp~9E(HhnsW`Z{Le
zB4tg@kT?d^7WTU;QdxdXY0G_P)qtTskX?OR{qupX1-lIqrJ_smaqL%v-OT#5jmgWq
z2WQ}3LP1648x>X-EIJtDaK4VcH<fKZcrl(>-0Gm7vL(wJp=S9kUw8c8Yvsyx`ODs#
z#BRK8NWi?=cX#muMX$tM>T0-(m>G(j+c2BxH)<*Bmh`ysQx~UiUY5%RsYAX~I5*0R
z9w{tPZl9Lmi<P|TyrLWP#ZK$H`Y;_cGv+G2Y0w!)UEUYw8&hfY<SW~M5Gwu73Um6n
zD+T44Ip){R0J+JZb~TqT`>HXBqUHkyS|Xwj{yRVp?J1uDm)X;)Sa#RWkS%8F(&cxg
zXu?`nFPC8x$ZNYDPts4A8`~a0ZBH$)Irz|ndcw3Guq!4!{rGygxhosU+g4l<hYK2t
z39}$tWV0<L<h`iVmU}OJl-`7l?+N!2g@Zrxa=%xZ0$B^dn737!w4rEq|Fr_Ns7K5#
zDA1p)HoRVbdPLALML;F=$D|Y^V`BQL%jU4B41IdMD=aPTArB<>1x^!(BRlu@%-n%n
zo$JdP9af83me|<XOuOxoI&NP7E1>)~`(}Z3k}tJXylw>l6>vL3tb6A9cw-zCDVxHH
zN}FoqOP>4xh<eMQxVmU-Hz5RfcWK<+T^e_STL=!p-Q67m1b26LcMa}N<L(Y$zvrA=
z_kUM))vmqPoO?WDjtL&U$4h3^v%Gr(Cr=<9Q`Mcsjn>t%c7S(?|K+kJ0t=Wr1oi`{
zQu6W~=O1@-igAP5ZP9ofaNnSj*u7t#_F`F#M^Ffy=anTC<4q`zKVY#rP)adTH|>WB
zC$GT=aS-GU=0QiRjdU>r51PiNri%$qs_xOq&+KieqA2x|K7rKtO&0A&uSD?y0JX_|
zS}XRMMO@|J2ruRt+*vnC`LL_9IGD7=Uyq0^&Pd|jd;7;ICN1W*2nSBUmB4C%)w=zN
zMM`j8paj|B#oSKXc#Wy6E;vQ|mJZ&AKA=pNE28X!FVEqP@W}3*M|Y7M)4!@S&hy5n
z<oR_=srFHs+)0@*&wv;CW{)AiyY6FkrpLm~NzV0T(R@>%20Km3DFnk6Z?LTKfO0Wc
zO<UbEk=u7tPJ2#)e(K<705N$R6yIUSvCc=2jDo&#wN~~?b{@x50A|RQY0&|Dc^AVv
zKjht+Y2yF2Rt+frAdvC8$7$tIpZmx2A-;n<82I5>)SLYq0`HY2&TyKQ=DzH>MYw)H
zt?r&+X@wf$t#~^-`~9C57DCQz$qlJi^<R_;LkKc)4BJ0`bZ~|W;iktagX$R=$oQ@P
zmuw>Ux@6!h;Bj~4@Ayw(>F9cdEO!3n8k~KVb`Fb9nFCh-T8{D}<vjlheRhmy^Sjr@
z)9upt`CJcT8_nbaaroV8NZZ$}n=?r(syl8XYLOztgl;F;z)D=fvnMzTiN*WHsVf}$
z=jZ#Qq=?8@@RECau`C0(o$qrJM#Kufx8pa=`A=Fu)b*nLQ!{7UjSAEwMZGx|f8rB3
za%cUT!+S^DHgiB__Gt{Et__gFNx|f-`i9s_#9l^snuwA@$P5Sk^g%AY+>nnvSNL-b
z+)1C?!5^@`_1iT+h1nCl728C>h;EHS-@lMNz$_*`EO=LPK53<HInaV^y9>LNK*dyR
zKhgG{Q9z%3auKH{z%6xSvy_O0lYF6XPr>Zt`!IA9eE;5c$OLqI&D?%pyO&B+c=)#$
zE)YL&rGHq~B|};5W7_Evex_pkOpE=36C79y^j0h&)EVlisA2y91(=eOlAiq(gBb8u
z;w6+(RRx~!92^`&W2>dARJ!0StfQ5Bs%QnT`-$$34t}F^OhbOKMlP2L1{fQ0Vp>+G
zR12sxN&79Hj~Aw|`LTkmgP>n_$Au|)vS(lxX4r)+`1G@6gT-hFXZ-eNe>~b*4Qnx#
z%?vNH#wlrEW6@O0aD0566#fRhhdx5T(?bOGkQ+)AFwz0zwAJNZyP&RH8hL@z+S(c-
z*ZsKZ*#eFMo2V7^e!<yggm0!_F&d8HAYYylV<bLF-#bw1j*WkZasR9FMg>=n)nmX$
zux34zW!Pm74d48=Hf+nh+!52YIqaid9?by_zm>S){h4jmcO(oRU`}3^MsQG6{rg}o
zwqLb{U)0N)en<Q_XiZjQtxPZw<)SH(6ZNQFpsdYyaABo|3_IK{ykHV@+v?>G<LT9L
zkeO1ex2$>dtN%J9|Dv!!GU}G9F<fC251;l3AS$6OAXyq@tI7ynirfkHXy)4|bnP)u
z&;jqIksURi)fw-yi)gn$b1#*=6>=4wLd^8<j`u*!WH+-;{G_b;jc_EE%e>->#o^pE
z9e~pK+*~S0H}Su>tUAn5W2Z_|H(aSj=@5tE_3q#@c$DsHUR4n{vSbY02N#SD3PY-|
z)rFnt_%zk})FAokS1+NMo=1|8s)=H0w_=7hoG4m=*D4+@RcEgxd$rOu*lumgF(>Li
z!(n4$8iLjLI1^2B4P$K^WLhR3z`UgxuQV}5tM<rX@8NA~6NT&=pq7x=E_%fr4K>+I
z0Nj3?r1H{aOp_&)+YTf4(HMN{?EfjMdLp>6Gk~;$GCWGYm$l}8-Q}8X`D?793@`4s
zXP@<gS3LEy(t_M#OCxeU0>4e86`T>uBz8i_l=pI<3*)qCI~uRw?ULc0Rc&X<ns8LV
zX8$Hg-gU0zaV{{@^6uaSx-7fu@P)9zMO7gfY*CLmamdqnQ8d8k3Mt!D6s@r$Hr98o
zqEYFaHfKbY7W->t&aj~BJ6`55^KxBO++ef7fvTs)NmnNO<k6i`2V6-{UB+%@oF}Mf
zvW(f^QusCjS#K9Rb+u(L$nVP_!X3#yvZ_Z6%~6On+Q+C7yu4QjXO*X86n>tjO6lvh
zki^(0|17`2A>V#Hr0gG&hBi{!+nZlYd0u-!_7m3jmZhHKf3;5r2!Q2Hr5f6b+AN4o
z%lWeV)5qxRf|V~tJP=hVUQ<g4NDqWcGS2-ko@D}z3BryI>OWaOYAt0G*Tgv~WM7e#
z&$ha|?Oo%TD)$oU>S*c8yOP9}_RaXfSLP}u2tb+w2h@+dtOkwLm3ti@qc~dAg>y#|
zME1zWGBhpsfy%4G+$1DIYVG(uQUm3^!i>w`B5o-DYP?XqsX_mGJ*%8%(7`kcQ%84L
zJT?UHf~D-!0cIrv^q2oa(3iK=6ozI_CCy3ww!wi<>4uP&{a`h2K5G59y=6*GRT6He
ztO<|~N(!2&XGJDtD*En&r1Z4P{?vpUAROy6B6vv+iW|YqRK!jv2co0cNA60a(bc8%
z8o@aXp9P=f$4Kn7z#{A?je9Zi#@&Ue&Xru2&P$WqH;SGVzq!$smi<DW6DBzJl1p7E
zL|=~hrKPV2XW;kOP6R5x%am#;7#;!N+{xaaBCbZN*Z_T^4KUx#$I$ab)40KBw(*Cw
z<Zp?eFs_Bj(NfhXH_aFN>Om+e*qraNs%k9mdcZ<{h1u+JXki8Oz3&5~fk}`f{|dvO
z$rjP35XtZ7<kP+6OCl1N)N%XPgq71bWFXNx%CuJ^DsBEp-r-b+6xRgT8<``_Zpr|G
zhh?J>JD1bNNffzwc`Z+lR@sjxKvUhUCE9YLqM!wRRkrU352bvXF@4p&p~}kAmK&ka
z+f^XxXspl&cMQ(~j;dT8;dm)!4uftBifz|3<-t^@l`a|-0*+q{As?PAL@P##d@F&R
zhe?8khF}+5Ddi2AO1Gz(Be0oH^c-gRQRxAbO};1V?nj<k&ZfV3fg{4i?)TGjN3B+|
zR+1aFK`W#kfO^aNT2NfNJFvbs?}QHH6}hLTO25FdFa8O(bpR@bAX7%_2`#dFTw$bb
zWVxw)NH^vN|3cDJfm+u4g9bzJ6_?g!L+ApnC)y<guwRGr&=hDp|4yCug#Pv|q-Sr>
zebo$7UEqeYta**@1<#;>4om8||8pf%+2>wJ*=dw){Q?I2no)2^<<I&JjHSmN7l<Vb
z>7+ZXi?XVb7q~!M9LzSNHmvR3hq*7BIFV5$ql~JFw$);s@~#F7{^Qy$@=_wZoz?uu
z?WQ*=mpY;Q3Guj}DfsZ;cVz@EZcf}44yklz*iGL~soint@jLb=gXsD2>>j>kWdK}i
z@d(m&n9GmkswCkSsQno!#9H~~^Cqz#w9TgeS(!R8so*4oBoL*7FUg|_WAA#q86O!P
zWkK(P41RMrvCia<uN8KG13ULwjg=X{V(Yu*)`roR1xSG+nYuJj6ShZ^5rV()y`I%0
zT6}=hm#lPAFbYMa6URYD<>bFA!fhRB{tJ<uDOM!Tuph!b`0HfXmpyvOHL_Fz_BoYZ
z4=;#wQN%Co#R_f8Riw+H(e=?`L!)kj3+?KiGU#Pm44K1N9hCr7US5I(I7^@tGB~!s
z8Kp}9kf|D}%~R!0K3ncOZmG4yjWoq!gX`v6F9`HxZaT-}Df@@WF*>M5I%<4gAsLMK
zgC_&DTmArWOP(yROsw<D3jeEQwi5<LF+lXQpH!Qb=eoie#Qg+)@f)i=j?)GTnsMvU
z{LeX81m^DgiF6%CHP>_@cEoU$q+dKyl(Uvkf%GUf&w<wy6f&1G2_7IPL3`qZF8sBT
zeKb>~JCL=$4bOm|OB6{SpI5&-Dv=6!LWj=uPg#BNxgF_jowzCr=_<>Yj=vs@CFJr)
zLFV(9fJ1_?xQebJ8J+4x%fuzgNz(RSfpA;#3(xP2j3ODl*Q(@O*Srv9$P(OvjFR&F
zsKAPj3^%DhtG#Pps-p95J+BvqJ6`M8MJ}{(JdksJ!h*P<L!0|?Qchxbyi<Rf1^Db%
zh(Ts7-7#3t;u;u#-vqauwUeCj+-)%(FE^)`eEW9{fXz&-!_LQ*|J?5FwQY^TXv^l|
z1PtsscE<JaKEv<bM1FY@`7<nD*cW`P=rlwdS?Y-hl3h}rx>Q5a_@UsGGe2_DU9@<=
z2y2yr8JQ1o(%;~;K0C?cp{5nP@d)Y$vU;WPR1kdJ%_2Va%fC=5Emr7Mt{C9)Byv6|
zj7>TRL$W-$*`#r%0AOW~HCm#&bZq#|B(<_0(6^OpQ~P><|980qF~#OZ$}f4tHl@of
z8RMY^r!kKdVP5EYXiR$wnrqJBn9)FoV!x4Ow6mYh6}F>U2%7$1;OQd(l2HIc?US2e
z=K&$XtYWC#H?mP)iCA7j?!y#MrMew^WCfy%MpoJTGqr@MRzqA!^53eAGne>N<F2-Z
z12n>=548%eq1d}dm%MeTaK>;~6S@tB)_>x$zdYgBc!onoZ|5eHQKu%O+a&CE1K%|Q
zP^b}XQgwxw<!Oo|XgE36S}{oL_;JgZ%LY2|=q9J&?S+@yn&&>#R@I+a!To$`%Vq3@
zPd!Gp*(dYm8PFand$3)4yySSJA$UU<GZ1DCFxF^(Lu3Ui<dU`}O}c#}J|Jw~ph71z
zemss<t{o<&<~bqwqvy!QD&?VS_AdUZg~ggUpgn!N7`MSYiR${Ukl($)8=A)#&?Bdt
zFAEmkN#SHWb`cMF2AU`}<+HaPK0?H0@!|?TED{biqU1BXBNT3(h>GriBtG!Z9ZcW<
zG#v2G>0QJV%o!92c=wprZl_x(Z<pbKMgoTUv@$;La|SU~Q)wevkP`WA`y(Jr)!}d2
zTFsvgmSDHpoGJ!H<_9oOi+7F*<7%86>2OwJLelT((bZ_@f|atKg8)CNtlMHiuEz7Y
zzK}7V7M^_!Q#r%llW~`a%$un$RYlvvKcHbx@aGP$fquNoKA*`i=dem*rY)GgP*tv(
z8wH-?u3KyU4YYP3c{}pE1I3NEt&!F>J`NY?k~{1+W_dP5#<eg_x=;<Z4QnIXnDeew
z6l9P28a*(!RGb4wM}Q0HyJH3(7kW4<pWu_qrhKb!@VK)}aXC#>X2eIX)>xKyFjZZ+
zwLJsyHU&KP!q3+)1&(y8KtzqO{3KN~fhzW6aoMu=oFLMni;%~uLurrJKMo<-(%n-b
zk74QYZ>wgAI=|9q6@5C0-^2clXNzoCyrT-*;TPz=;_$N_kYyCX`r0?B5b5C=zsq}G
zh>;6i)07Pp!r3L1K6XjlVTwz;ceLH#DR-OfFBS--FCu{@)G9*V${+trwkcso9v4Wf
z8BnQDYbX;2wSVdWiWyEvqD7KBC<<?-U8+e|ywRU2s`t)BhPO-30Taw$W2Td78>Q55
zDSyY$j+iywCYRt1m?OmO$Fex}kJ4P7*A!n5TGu)^QwWfZ7&3aq|EM7VXXP|SviR%W
z?O;ATKaa(Am?vl1;U&>O{s3B<TbCjHnbpK+58|q#KNgt~)pOkOy|;ARe8IIq?ur2^
z`UE|W=%Bo;HI)cjL1qqFs3oaPN|U#PX@|(F&{4hTg~*j2CsY^y)V6rDOaZYo$9g?B
zP?z%LliFmrXprap_%Y7w)vBcH(CLYZhe+))CiJz?cDpmMKo@;QJOcfPbnfVHZ9W*y
zzIGS8MwS0zV#4VpBzdc=4Y~1ptt_A<raq%?eJlF*aaqdUJAcVNoUZR!Y}2CX@>dm$
zqO+9ubQ$5F(GH@AZ@?}+@)i$o{WTHiAIM+eQZb#yEYSDSPyw>6_oE&!9!?!5><Kq1
zfDs&{DobMCj%yfReP)y_On7NO;Btmf1y+Skq>lRZ?8Yjnyp%a8C93&)0P0&iV!-9(
z>Gnrf!Q-D-6oN)NujcGXB`yU<wvW2#$P&jN@3U)h^W^hUuY7MtvEOkDN$1<?ypZ*S
zdrG{?$lDYOk7C*;+j3njO4?4@e-PKllAihRh;1Lz+L8`^Ihdn_ul=d|9uZMdLBpj3
zixs<AXAn+;a(|;xP{LsJe`xL$&tDTeAh~G+i&cBINi~*zas3hKl9I&GL)NVuScu7F
zWO7WN!SWMB;+*<*d`QHFkX0phq};2GzT~n9iJ^mXR8M$OWQHtp*jm!OV1W0fAe-yH
z{AulY1h<}w*F!;MXFrx~Z`?J`eu;y+^L-1(5-k~kG2jW2I|b;tG2)bltO<U<`i}E$
zX`4I+3f7W4xKx%q7#y2SF-PF-HYwgTR6dAZ;xi+oQ~xifWW`%*AOXLzEA+)V^fOHr
zP|%ngZbsg*V1&flZM_$fcPnjn-jlUPS_8d*txI)BBE~U)xxk3X0LkjZWX*_LgQfq<
z_J3Xs|HcTvTIR_0c-;w_G?6+_3arF4Mh5q<RJH<e!<P-$aeC0y^79Fwmi$EKW77^e
z2j#yNdsU1(KS=n2?n5R$n&=iYlU|=%ZpFT9IrsAx#l4p=SW7Rji0V-SJ&vN-A!C^j
z!aH<Zj>j9gN9<KXv!CwYWj^lN^CdDw#;LFgEP8IvlvsWLJaL5qe<$8Im40<>a0^xJ
zj-xDLjGTQZImUiDBl2PETeJT5nP{}*er@Gu>rKfTUx|D1t+6bOI&!|;hQ1av9ox{_
zcn2mhTC4%+@;zWG_{Lbo_DlO}lM&~<uTXfmycP6US+^ymD)qAeQ?gL}!@8@&<X{XY
ze2E|5`SIY#^?`;fk=#~2usEH!LqHco8#3e|%Nu;75@*)a413c$tE@hgk(TFQAVE78
zv4l$YNCGFfTV>;Dp>Ly(K4dgys-@wCqfT>vQt-9<N%#r)7P{yY;x+iD+lXOB2wV&e
zA0K=<ma*vZ?CFQUhyBTWUvJa|_ZQd+a)lYnKh;E1YVSCwMafoBH>|j|$|*s`^bm_E
z_8Nk(NBa6-E@8lrL9RJud^qRjN?_af9M6p#mHB?uwS<Y!NI`~dlG91bdfx0D@mSSB
zqN8n+q#ZDFV}%@49v&P$uSC4rwA{>yXzibuG-C+~=+_5FU7hOBcwIMzfS<;+DCC5d
zh5j;t1+L`tWuo}%xQ=c_@<CGKNLIIbTpwpsvJbqr{dy%jIFA>HlEosZx^C-cyb_Nc
z;X9L$e+<=m;5yhmeuw85<*Oe(Yjr7d9B*cC7&;U*&_vBFEnH4Jv-6oHa_^U^)Vg;;
zIV(oJ5EwOj*>hel{QPxTxl|&|h%o*SO@i*8<imhx+{b`7b^9G<f%h8@@1_qeoK40^
zFpY=!TjVKvZwWzkFBE)&Z(DCfGsVPCW?h7W(qy4?E4gHai*xgWAVbMchTIPU4y0ad
zPcpe|J&j<Y-MQBn4g|#^b2zx~iv)RE%}L3ZG)OfS{R(Jb7sbMzq~qAf`|wFtDq+=0
zS2FYH0(l-74~4-pB-mXNnNzt1fMlQMA<JhxT-2SN(wyyaTL--VrU2KLoPC&ig@nD}
z&YugvJGgIc-jv@k#@_ar$@fGvnq`IVMv~v6<IW_s(nhLCJI?ZP|Lyh;ibxkMas9T%
z5d0JZWz`9_m@|x3wL=dM8(1b^2di_+8ejb)yV1M<@x1N*X=uF84o|Ah;k||od>y?c
zJM75c<~v3JchD*=9hG6~st)eDJ3Dm-t;D#Gx{BEw!^&Qum8%N}7zah1sqexPhWiY*
ztXS3>dxvcy{3pir9lvRNmOe`v2u1^S85G2s&;q%L;%4}pa=!yCk%)#qYV8@YD+JN@
zdTNgP=xYn=QI)Md!gyQYT4eA>TS%K{qdnT-QkP;FNgEfRhIEp~72{+Pbl`3GxEt@x
z$zoL*FfC5CAWr~2vRhGKUtAJks)lI1BWq3;a$;@s#(376OIV{i>X?)N1l893^)2ty
z9Jnl8|Hp)!UdT_!GgTU6jK_n9A)kP~mYit&pE1q9G18A-B?dlF`^9ocvgNdR?`pWL
zSV`-dTf)X2ziPw4SY!W3fH>*Fs@l|0!a(_iZq*RR;?IE^14iE0BKlO>Vx$}P4+<u|
zFN*epdMIf#?hyOCpt7Ipk{FxMECHV9AsOjNVh{C?B&QYq+d|Lu7<%0P>I!J|!zm3c
z8yQ9oP|$qV;R$>n*6JRPswP`V?YT!3L*F-TglJ`N)_eTQe%}jgaz`q%|5&tO+-%;x
z92*-ay0Oa`n#duVTtb%oAioc<q)a=u)uTKHj#puIND(`eCirCFhU-nVbY29zUuC&c
z^%uPlYzYGnd45e|J}+g<gZ*EPS4{#%>HyR*!n%n<BFE0tdyU#yb)<wfhap>fD)-9m
zf>1a#eTJFQehJ|U1n3{(Gl(kHQ>)jLYOfgBbv#6_S8Z|Q6ytLV8e75~SQc+l7OSX6
z|MNgO#f8N+6szi{M`hHs8{s~=UMaj<K%!4&u97m_s=7^kAItUGRTA-fT^*YvOme9F
zsGAbeu6NBGq+K7c7_7E<p=gZ6y1<l>m}0`97&>egKwmx#$i)l;63Hu^**9K4guVQD
z5uMJ~`&*_B|JT%JgRh^kPC_k5K10{?W?lwJ1r8Qc%oa&Cv)!5IGVS7&7WOV0t>!$N
z=w0UzQ6&puJ}PUyfL_Wa>Y}Iiy5E$ofa>cd>Yk^bgCaCp2Ta=NC%oPAUP08iMU)QS
ze%Q~YvoOkrf>1seVYfBHZGjI1m~Ll6Q9XxkLQiO2$4HcudLW3#%vsgG*{OYYgI0<j
z+!ziGmNce<5j&QMPB#+7R13N%Q{uils3|U4N6TDR%Hf@0i=$tQ3qhfcBTEOb+6kQ<
zx&JLDDOUpUO|*bVU|UwgC%ARN!3KzP{KnzBn{cS(V&~}XnnF6L(D_maP&q69?>^Or
z1ke>-0}j6HVpa_NlkM(>hX07>DXhNrf6zs+aD|;(&lh@O6)&s$^?Yh`W~?DFvs@VJ
z7|l>?lo*pLuJN_gM=_Hp=>mFB$z!8aSK>*xYIR>^$1$7qasFFe?OvBA#|s*oelc1%
zM;PrO8n@X`HQBq?1)H%9i!1sMxGDyVFPhwh7Vw|qgH3$B=u#PHX)e_CXn69T7zEZD
zi=dIFI}0yJ#2TrAU$*3wAE=m>`&GcZgk6q){Gh|7?ukj#n!PVCG#aT%|JLDC`a4OG
z{q>-(O9Memy$tb-@>-ljpMW--M$%97A{51nJL#||IJyKohLD~1W8y0px9=89(_@?A
z<Xkk-wE?!0Kb34--aKF|v{|%TfFEKFZt3&vck=L!6qf%v0<hrPXH+164I!CT1VPkA
zu8(o#_DCk2#;y}M<vV^f81ttf$F9@68J@1j$lpI2#ad*{#A+;JMw8+F9_K0g<3KUY
z10)6ARO(;;#(n{{#3__lC)`DKhW?d%HT(|^!+Z@r8Y?T;m{m>r^ozyr@Y_-s=soqt
z;dRgIo$-$C+gURu6`&Ax0s=UbtZGJEP3v6dMw{EV9;n^!p?}=3O~t7-D++eLl6{_+
z!k9RlYBKi`<K}U5(?Wb>nf1fkOU}WZ2Emj5Dzj(V^R>;nU5=*~FrvSC6oAr-)s1;D
zJBtDhcZ(cv?@-QbRw|8R91(ocyE*0fmb*IhW#UsI>lu?fLW;_4?V0oY;*%0fD`|7s
zl>`u)AV9;QEoe@jG*}i2oP`b5<!ZwWsbS1@o@}=LVht5=nefx5x46{&Z2bkWdtnuj
ziZ*HuS9`Ca-g~(xA33_lh*|0+T~d`731omeP<lg7wy(7*@ud1e*euk65P$Gt#Q64@
z<=+$6W#_1p*(>C8-q!jD*d|YZpI=j2i5ix5--KngZ;b@Q4LOU8Zfvh763|}p$M@}^
z#fF|lnm{A*zPMLD-}z29Jz-|nxa_7M)v)5*Z^_cXkgta(h7&XhMt0cPF9%0zR!$YM
zNOkcE4|8B}VGcfVLF{G$GZg_APsSf~Pd@vEnuE<;=k~-g7cLk`7hA<HzgCO3`;;P;
zo!;VtHG*bveJx{$ZS4Hu9U)SEuNW!u0*yEp#G6h&bk!MUe#v8M<7^ZLOJ(1{(z^2e
z?P}v8pCE5(IBI%5`9xXH!v>3xBs8)1-r21<@J*+oOD((oDgOk6{)iGk>_#*|^ZP>A
z*`x*uYeTJ-vp8=gnT))hVLnL(l{XO~a8U^bag&&NeNev3&ndOO<_>YWar8zX3%`F|
z#XU2MFh}8f;~#UAF1LDaSDmoOwtKiIl%?FCQi7K9r_BHkSuB*a*<s8k1N-8ivpXS2
zFTJ53huNh3swG<-Z3Zey)wznh@XG2Z34=M)n-MOa$5A%TpeNpqa0NY{9p7H=5#~Tu
z(C0y7F<a0Z9<CyO<h<&N;W}icpU0(F6Z%mN?fSxQTOUJ(r1y2js23~WIr^dJLet-6
zNlM2(;t;g^(!Xd=GX46GI;>S#nV%`m23x?8cf%j6|D;TcKf<~VDQ00!O<vPXusY#M
zqWH;Bu*@l52lU&=emnnlqJh>owV^j67Y)go^1=C^m@_FRj3kcqRa2K0IeXF<-Jr51
zz}EgLLcj|h>4Y6E)J6d2{W*@N2z5s`Jp^9NyCC39S9C9Ij7JuhuOMoRQW0%JSf+y8
zl#(Gk77066;>6-G=cuC{Wnx170Gf&PbT(1RW$)XSEeB-Q)W-E%De`uV{V>3GHAA~m
ze{`gmF8d9+IpzZ~&}Ac#4g5#{{FzJhaE=wTqwgmCsaxI&e>|SYB4u~PoYm$lv0UIi
zg??t*t|p8y7`@XLU^%l_KWF;$q0Qt9x;xib=}_^mHoINWPT3ZzuXy0snYUTkCtHbB
zyfMSt{#8F$T8g=PKlO6E^!j?PknX2s(ALoD$|lP9+j^uRKX*U|F{NUOMVd8wy(A_p
z`&0a2|KHECLdl)JCGaQn;Zm~x)sl+W%}Gj&(QeXFyZpZ)hR%~_%F1|t&wrh$RluaH
zPH$+@yW{)Wg<ifBg>6hm#SXd+#tC#4Yqsd$BbB5lvvJeN6{yM&N`zn;sPbFrh6T)5
zwwwy0IUFjKs63Qk7~gTMs`M{Mu{CVq@cKEBxSjD5S~+hsoA!|_x(B1Kl*E@JHkzIC
zt%?YNi<A%Q4@KdAFo9vRA|^zFnU7dqnm)+MYE${nSAeq&4m>g_gh3Ypeq}W30Fk~1
zSOZJEJv4=D%J{(p+(+o4Gm4P^P<E>saR`BSNVzAXXsFdck^S!)xu7dHQYKXcAjM|J
z4aZH(M&)Vp2or+|{VH0UeIZG82TZk)J>AshGyUwE87Zh*+}TAVh)Pbf7(SHb@xpN@
zA3o?Y1itoUz&FQy9tflH91Sn70v8y;!+SmOoky9W2S`QWS=pBU=Z~BUEdxNhJDhgt
zW5IZDOolN{I8Zc9yJ*^#hf_^WZQ*6GEM~;ovJ9dDm{5_n;6)9ik$5F>L2w|<x`DVp
z543K7LY0yQ`rWekY3c1y`6JBgk%ZsyH`E+?9UWrj6}A?oC8zua?M%|pXiE&RxgFWj
zPwE6qKXzEJOSL(V4vi(Ss^+te-p@X}yWNf=+kY@n;Xj7N?Ntzx)w?7{x8V>mtQQl%
zp~r~*6jsbHzkHdC8={ES+n+SxVZaJpbvctL|F6s~pxC@XO#IBZx#>$@>)YLpv&Y=l
zVP``bO(-vcT~%Hg3FTb$WqnbKW<R0Y=+;E(yJFBgO43_`sQKV%!~>%UT}T4ojgFha
z7S}Ou`Z70swS|0^oQ*y1x~(jn#c}*ha@tQZEhjF$x>Gwh!M$dCfN4XW`k6cR73dYr
z+?2FopOa7Obd;TD-L(C9zQ**c`-l)xb~As~!X>Qn+J_7JhPpM^k>qYZ+_BYVNZnFP
zHci^hHYOXvZ1=jueNflF?PE-7f+RZp=^gWFGxN1sK079tE91K_5VwPTxY*#GgxC4h
z-!h@O;SOUWjTg~X&yBok)rC61DYk^3X>c@UE7IC!6H$#mu@6WTadXSOGpih>yNFb6
zN}2of`Bzl;4Q&*Wz^dktdq463KA$(Yx2t~SqxDv{o7-FaWy8>&lk#dx{}?3{WDTd+
zqsGsj{LeSPi*LOB8W&<%l?8{Jb^wRAgB;UqIH#6-t+$sCc3l=b*)wh`<b~pOxKGY+
zc2oC8)4o!~XqT2%0T}`Nk?Ok#^^_#Vj_RPV4An4H`cCW*g1@niDi>_))6O?Pm*Bkm
z+5WDvppUED-AJ-C7_>mFWb$tBEp-jR8pZY`>o}9;YDE1!oZzE)uH@JD4R~)7-|yUW
zBO~u!h_Za($ygu)3wAk}Vcv4ny(_ItaBS(TME)bymFN4~;ptx>4#HEH>KPr0`$Hy~
z@)YSmLBw=cRbf>~W$Ph6-j*Hl>4Ca^UHSt{W&|Zo6SF@FaO*k!=S<pvqH5B?jI5H6
zXY1E@ahzJvgAK5++5LaJ$n2K4k<5n&EARf4TgjD|4SE)Ks$;Byf-ITy@FBYSlM2b}
zM3c1ET{K=%F{VvBqLt2380t!>D+2?zF2y361r9zwjI4beJUqwXNSs%^exjN@Kt&|a
z?QmU6GK>DU=G~9*B)f6$;!z)S(CKjC@yUAIT4nWl`G&t~hOwvPfwm*$`@Bk!O~lan
zYY?uUV)g)Kb2O34Xtlwj(ZS>KVzO346d=8?POlR_7@7Q6i0ZTARH8nfMCH;K^I}j|
z$?IZE>G>%%q$G`X>)roEZGR}3^oDO=#-st`UwZ4v8;^qwd?}Y}#<k!;bhXCSJ-KQP
z?@iB7mnYIrf(2uS7;>L<L0K90RIxV{lD68ovqdzWeVU*&`VEP{G-vY-Tc@!9$v@~<
z_zv}Lg3g^MijjO1bO?s(BIFr=!!ANBC#~UB?`5m>cH-c-n2@d|&P5bFjQLdztp5Xn
zi{d?`jH<~#(YCj?0snk4AEpF|hR+K$W>F)nV8#!?-J>46ZiBZkO;X^G*I0c~PH&fS
zruOe@-cU13F6318m^oK(?Wkj;z_eyUkB2R!NK?gpOec7Zx!v+V@kx<4zZ5*Ivcny1
zJpzWg4I`=_I?wq?UdOrOfm_=Vx77m?ov{NouS~rgTc0qWCayhPc7I{vQ)ft_;XY|y
zzujm$oi}n+d4am6imZZtzTI4(Z@S|DH8-KpuX9LmIS5BSW;z4cbN&%|_#DZ9n-avs
zfU-+)LIcA7{{B!NevV&827!cz&N&`xp5pq4<XNy``$YhG%s3o;@_>@jv2G##z)LO*
zbUyvLokGCGz$72CcpX>L1+s%b%+JWmcCwLClUY8ggw$3br;Yoy40(7^T@_`P^Z6R)
z*Ub*&qn|x21?o?BkyJtLB^I?@h=kPTLFiwSjJ5O|>mUVamev@f+peO<(HOS4pKoZ6
zP4m)8-&3M(t5(Y>hT{PTP-PC2Qd8C=y;Zns)-#Y`k384cIL;J68@qF_y&UsWl{)vU
zk;#ah)C^>ecHUZtm0MuIKip!BZ*F6=BoC*uzHBk=3R_&%CY7vM{`XOSVKEg=gCC+D
z0*@l|^LLU}hvXUT6Qu3w)xafEDr<*`qkg=iU}ozV<Rjra9hL{Y3Oq+1QSIc1Yu7l(
z3z&w>dWHzamZ<8J_Fn_Id)L!y^paeG3L74{ADAWKPnAUXvE?<F$_-HtO7L?%M_P3V
zD^%zHG9k=5&Z`_SP9x$MYM(=#doF(6MQFjE#O7^E{hG(5b(&hYK(u;3SFuB)ji}$4
zO1{9L{WUt#qB@IB1cbFq6!iGRoJ!$tBeF3R<Isj<Pw)t7EJ4YmMMd2odg7H2gq?kP
z3$u|bi>Mbmc1<aA$Kj6>K<SyNmH^UPlAh|}on)qzL*U)G8XstTL{~Ye^Tk=juAIT5
zsOUbq3Y%0rR!nFT0_r~33I_I)O4aU)tG>o-z{c$^^D^4j>F|P5iCreb;@y<@qN9H%
z9@HuU^1M)KiICxb{=Fvk#HR4WF)6O+lONtc_Z#X|&c1j-Ga_31z(w<Mbva_gD!Vus
zJXmB5t3QewK@Y<xVrZW|V(N~iY<ZN(F9fy>Vd;gZ3#$l;JKT=>3*Cq<chP0f|L>_2
z?jdC$o^t;VKen0Y{79iYmF`oFAb?@pWvn4=@%wi@X?$RHWI5N5M}n}tVnM%bx{Tnt
z4m-zPk-;oUz|$^v#`D(XG)a8B9x`t-BXrW!<D{9<eqC_m;dxM`&G1*Ue*k3!eAjP?
zCsh*)FkRQ^{b~1<{!`*;45@_(;Rn8F$Ve|X^FQM0BL>pK5aQGQraa@4m%r$(0C?iN
zXt$o_!;(jv*9=C5SmnE8_oy|x5N20TOU*u#2b+8nfQ!;ZP{t_+b}ea%|9&Ys^EsIN
zClLV}IBw#d(b%FfPE1~8^0M9r_<F{-@?P?FP}g1PT8=(XJFNgJRNj#hmlu0u;(AJ*
z!!FeB_p}7uHP~KjcYSQ1Uf(sfx1|Py!P7vUNE^ZGzfa@|6DU6e{;cT-ey&6+dv7y~
zxy%u{!)H+mN*o31pb*G4G0{cbnLE#VbIDBJeY1j(uUxd4L-`4NS!7S7vkrggG3%^7
z+G{PLkK;R|sFA3GI8z9c4?Y?S0LAIITHZ|^%noQXW_jBrUeh*_wp*hvOQG87;+ld!
zqZE^M{Z7*And|=L;;hONChWVib)ri`%TL0haG}GES?c9sB=j-ETI^r=vVoR>H>Oeq
z-kR@x=aYNu9Po-kxb`I9p<{UA-(7KHOm59;-<f$`Poo6`U4CJ9ty1|%M;9qIPEJ+G
z-pHhC+x3af;v5M~mz*V6my%}B!~pqzjeud~VQX%ECCX2a_Ur=WydAsw5}aF1SsSC<
zOzYV6#7@nb$>Ok<$8;P*ytfsg9&*_fFWROBTb$4H`xSV$L}l~!D;TtWglmvYQ21Ib
zO1O<rl^kTJL`U<!DxEb9H;w3YsGQ_Or$Aiv+ZmT@=-%eMw0zsvPl#=pyMX=>z{*O6
zj~A~y{rm@nNnpR-Eg2j=R+a6V#NB?L)4>E6d~nTKBbtkr_(k8Fn0?`-?c8`)U6GB~
zzKg_fJ=4A%Lg;>dlVRlCm8m1!xx=;df@+wM=dYC<&U5xUwLu22I&U#{0dD?`!2X0u
zN4U=(G=(38f4nFq5T*bCa?^$jPAPs)^oa?am5Q!3<>76!ze%N5)Kiu;<eh+`5a95a
z-=-hse@64+zre=t&OMw6{UuL<$FG62D7WW<k<>#{hd1FsWrcM%zF9-D#8k<uy>Lip
zB{RuuvmDjm=SX0`8(SreJ>pbhaFCj;u~Df2){n{{V&o7Aum`=|`(4ghwZaU@UJ-fo
zVf$_uoN82oAW+1fNOC`FmC<}vb@P@#zw+Ig+=cr{*u|;qwL1MJ|9uxXlS}BOkrCGL
za31IK(0}k%68`6p%YV?YJRHqxfWHOp|INQ--GemybdcjF4Y_!S{@Pd06CWhxkkW4l
zsUdFP%q@H=>TXO19aZBZn~v<H;Ib?*%i~0I$`v+X)g}ZKSTU=(doQKn(kwhtm5<5P
z*KkMqXZB?bNSs1gnUge(f`y1M5)#&5n@=r#G(%m|Z6MCag4%wun}(M{-@UM)NyuUn
zhSZsB#X!XHBKKfLVLiO@jO&W763rg1VO7!W`8&D!5wxS~T}zwN=?F_WE3Bz)y7J{~
zwYps`glM@YY4-@o+$OP%?E{s0h@mx1PnV>mxHIR7sOEd;h?S)`w)jocVspag7f{ss
zBo>+~X@ntnyMSnGDyl2A!U4RqaTqI$L#ukpR;^oIn^&-!8jS<Df|(Nup2Q^gJ8M-n
z4Q3^F=|-F4I~D@gOEmSMGMPh<PZQ)<eFjcz1(?6r?5jKOPI^yHjH8U+cR(i&mdVzm
zB|J(3cHRB*Cv64~5v#fo$}c}uV8wpnzerpDG|jYx`m8JqL*U2jn2<Brbii@jSXWA$
z`5LW?SI~%;o-D>$iU?iZ7?O+m8&xwD<|tu$SxNd-6UZK*%EnmlTyorIE#p)6Vuc2e
zHGR_~9XuZY`jjM<dCw+_XO}7&5niEjdC1yb?Y|R=uc*v(kpSV;@nLS<dCfe2d&ZpL
zm%jJw0)2_(u(ljvL=I~_7g5S{^3R^@HJC5{wTHW9oSU=QJ^_39SWY7Hh&`+N20j_*
zeQz{z?4;X=n1!(wZM_0SxgzgCfq%1wfq)3d84(v$MJu){WFR_wYmj^5&xU#-z7)(>
z72^2-sHfFl_o?Z=!Cu_5BsOU(Anv9YF-f{&W!Jt5a^3NLB7g*c&z*xI)Z1Y7nOh`B
z)m%~-#`9&wo8)Dzv&?elB48S&-Mms)$M>7K@KP0#3a0AgTCj*$zgvSxA(Me#G$wD?
zVgi(vAgWc(CHu!u@C+o`lH;Ktc)6nVv+VSB(cVl<pbs_UX_0B267!Sm!$5odEhzd-
zyft-!VE1R@^(eVh$Bwz1`aW3&m7O!?j5z-fukTbfrO8*d|Gl^+k$#)yPtPhqI?HhK
z74ZZ~CF6a0w}#w7_Wg3Rcs5gNmCp=QDU(IIN3z?d@okzhTRe@vi@a>hFvHO@R{;pz
zW`mdtF5=+!5|q}@E~3q1Vv&WISEW*O{$+7!Hr9o*-gdI{{loaz?LzVvUC>THcAWbH
zZV4`DfX`sV%a~A;|6J9FBQ=-i-!0&`eZ1GnFc1Y;k4651$T|P_3nBVm5uNA>nJtOM
z?$VH1v&a0$)s`I|&KShzZM8pLbzB7J9#L+Wk_|2Ppw!DF75FDFN8U=#A2To+&R=E~
zy0D2*Ef^SQGG1fu^vI48^I@1S!w33`nk0J!B^m^Z@H4~_rP^%nQ-(+2Rg;61#Irlt
zHwOP|duK`vu)mMi(vP%6-?)zn*l1vMJi|jok*!=Gpl_$XAqG%62q)B86&%5gedV7n
zq~cw7g_6{)x^Y}>9C^C<W~}GK>h*~uE1Eeg{LTTnXj*cqEo@|Bvdj-tK&8_hqHT8;
z0`vpF`19!~Z0H76Iq9AeNRnx%IkjRfBO}<ZM`-E<&Flx?+aAPwgJ_Q2&QV9+mvFBx
zT_9KGTVM;h1i})GrXpkX640!FB4(skH~%cvufX%B5!KzG-<yJuf0#+U^5Q})m%1B!
z;DfROV#RrC20EO;VWl#xy}|^ww&^U&hvv;Cq)NTqNd7VKp^H!ii8C?q&}T2YL2A?1
zmSTJeCuoD_P-cLTOrx1&!q6Qo9P{sOmes9hP`Bie4{NEue7UC8=g$W<X*;7HJ)epS
z?CG6<%d=@ZU@&8rQaC6i9H_Z!9+6-aPNIA4USwa7L`4nsh4#p(&IKWJFPxs4yLuVa
zyqx>fsA*+g>!|RytnW$N7ofnNk~Jt2n-Ktjt+Fr|-j@|}sXD4JC)AngUq{!HRs<3l
zy_)LVovw{^p8f`{^P}Y|bZ+ZkA;RHG3^niBwU~%TaN$^dDV7yG&Mls)OAncd9L|~B
z)slMIW%v=(i3cp!k7Q;bHiZ!Bc!_rUG04N1*6sBAa|1@@VcInO%P=fz+XB|+Bw8>B
zZ>C%TRi7+Xc@a7^E|*cC`r}Dp0KS1(aUyi?yQ@(CS-`a>$BAgmKDo7STN;TxUWFyM
z=FEA{8Hz&Q%Y#6H&l@SNq7bo(XSAOVzeeJO@wps(aNzH)=$j2Bk&{|>0KXd_!^m3x
zT^JjRLb0vgci61ASbV9r*dvBqE%!u|v?CEh8U-!n;Rj>1&9LBRh4_Q>xCy8S9ML{S
zu4eNmR_u4jKplJgh{|SiPDrVN7M^hDAH-#sD83312xhahK0Qk4Jo%M2MJK*NZ;~)f
z#7SxSr3`D35R)ymX>rELwstVwhQVb4j9&F5VZ_OHqJi!Ky2HjxA`*du*)digkD{Ym
z&3(6P`~_YwJTk+bVPl!0{rz?<h{!1&SNZ5HGDrl7pQ(G>-j57HP?vr9P?haoyDrBP
zIryS%iIzu#%6+h2|D*I;0Z95z$!N%2nJpZ<R5DN{$Q9M~jkxVtmx37K{_3_pL5*Px
zx6@?XTN^kOpEpaKC=vtCncBFnCRIe&L-t&`DZblOX(!(z3{TriyGUk7&6^PcA5No)
zNKOlN@z5xp`4jIRO^$SpJ0Iz1rCsP0xpA{hx=9bit#%mc^C(gyqi`ax(=t_??{!d+
zqP1I>D)1)5zoeWg<RVmA7P;Wz_b*hxct=hqvTB!z4ye-r1RWCo5bJ214_u_*D12A-
zyblGXN@35?FPLP7`|N1OoSZVO+~2Cpf{`Gg5mZaR(bxaqjRLm$4m$<L9mQ=V4eq=7
zYQthBdroH^jAJOE%mEAicC2*ItaKck@OKm9MrD)p`fptU+<rzQg6(9tEBM>*)^GvZ
zk^C2tA}8bi{BlD2lf*}N$K(yKPa;ne1e4(~LV(vvD(>{Zf}I_?4JC>*EK{j*^-YE~
zHw+N?_z-Q%cHR&JLvLHlFVl-JX(gQG9=jaTR9HEK3;WgL4+XFz9mk?S);}QCH}3_B
zXL(l6O3ruQ-;0Z=Hp)n~wKsJ2<hz<SeHDd|g$#zr%CjT}@6o3}k|X2aZv;%M#0L7-
z3V!|I+wDubzeqf?TM$xmzrLr&`LxB@k^%@`Yk?G6{5}Sw6i>qy^D?pShNj7>$lB6J
zuq9np=@<6))yY_xz;Q5i<w~S@^=k6I4n+sLaoXiBPGIM*kx+)$C@LSefAsyKnm*#K
z)33e47m(Ub2ETk(GXJR3Y-@skLNR}_F36lL+prDKVSzJ!5e@<|W2JF68M6%ub^5+B
z;Bs{{tqx*I&xKRa=fY+pl?9Wc9PaodEeIn)mJ9kHFPq9zkq^Na_FQ`RYyQ3Z^t-KQ
zWv8`R>JED}(Nw}2`+3GIg?G-20=CL8e=;QM6cS~g^24dO)2`65zh+-JLeSd!z#k*8
z2V@l6>_&r)$?R9`-q70l#bp2k{{KeQ{o7#7qoxJchL<*3Nz%ykYH_KGU-h+2(`D!h
zD8p|+OaYHm*VGg|3_QEUUULh`-#_0_3<aNxeXl7eJsX6F;EA#53?^%JxizDAU#Vuz
z9yQrwuww}#>#QzzNrqrJhw#$<^jRD*Gjrb(@M|Kf46FXI|3MalEGPP88dk+ajnZcc
zHsV_0dgq)a3^cw?i%v2Q%hKN&*F@*4%`LbzC3z0KT&6CK#5?GegBLB3dkLEHr6qf|
zrvoQ7r;D?@-&9|1e$c1DoAN8fn~cTB+P+}M_OO}3N;?NUqK=Y%eVot>xT7QHU_~G;
zmRl-QX}Tsz==zICI)ms`uC7O<@!2VTPGDk%yb|3c(&Nr(g>Rug<^dF5N@Uxd*n#^c
z#0XECFH3xPy!rj#2HWOQQ$JDY&(KaJ;Fh(kMD^FTxu0=zF)cAZPz3q>h2?&;6x11a
zgl}Rk;$%sTEzcaFP4}|!#hht=1!AZC1zJJ1E;xEUX8c><H-!4jE{Mb#e{Y)J%8h2x
zL4}MQyT^L-$CdGh<fmuL7`6x<H!Sw0e#-ysHH;8n8H{@>$p4UL?`S9KMhJ~Zav1ij
z6|7P|ruH_!Lj?|vL=CYhSi(7N;n4|YL<<))NKf!brRzke5qa0%uqYJG4lqm5k|bK=
zkZ}x*+U`88P<PxZFB)>Qc3M)AI?q7pc&!goA~98do0yyP1dx$3l>OFlUf~|a=Nm9b
zi}<e7^}3}DPOV4~(I(i(CO9-MUrQ@b?!`H%l-~p)IB5PtVJfvg*-jM<m3`|%;ztv`
zNA>KfccSQ65`tYitVdb4W;TLyRMGy{hF6=A-yG~UcjUff<>tL`rRPaYrWcoyWk=YT
zd2i>?;Sb2dv%i({=to%ENn=B{$gd8(T;M~wSoTNeV(NK6K`!BO;$9<HfffCuK=3EX
zIzUa%{dI;N;xz1-y)ukph8OY|Qw%O0H;;9z`fu1?S+0<;eCL7Ss4<vK&%54vTR~P|
zOY3o;wO!F6x-&L&&w9aU?ZyX(9Q&Ejva-*Z5#J|`Al5qW@W4RoO4Nvsnwtk#z_%7n
zs%nR0v>e3?uFLMI>aFy!<1JsjuO)1@SkIBQOItCLmxA9=L1T9NcgL0MujUFhBT;fB
zqRWM^|6dlsbk1S`hJ#nFw$hS%EzIvS7~^8I{D2h8?wH5jV4><9t9h5@Y<2041TNRv
zbZMXEj30B3RtW|#J_#)mcVp1((}kgiT#SO6?9%q^O{KaKDMJtfxsg&c2|1x?@xV?A
z#lL4Vj$^9s!$+i@<gs-;zY@QAx@S;z$CD?ma-oO1?XFTZa|NYzmvJDEC(!N<hll<h
zfw#cspSq+af4@l~ZU1n!6)suALTwHx8uAq+pFb1CaXYsxh;Bo-7?^1ML&^aC7<{?t
zn3J@+t!O70wz@0cq-0Qe;lk0eBoN-Pmdz%M6M6Px5M6b(h+vT8GRk_i+`tJ|C|#{{
z+G0#B>8G8GHAK3ZLCaAPF`*txMc*a%mKN78hxTkhOsTQI2g!af5shO_^yN|+1lV{(
z*;RnH7w1W4&IB!{^HiMCZtuWxWBUkW^Rj`y+?}1gD9=U$_k&{CVGVat+I8iPyUMEH
zffZ*$PPPRm4P?sIJ5O>N&<nP|epcnnWMp!Nk|^MFjst3HNw%@B4Oi<QMM_{h{SVEE
zBcmp|ud1^zgllbm3F-ssJ0f1t?A@0&Ulu4mBtn}XO3&KEd;>%$$*B<8r+(&NE3$ni
zWps?_lV#(b0Yy2wB!)FrUGJY(r1rT($rS~*4Jwh8TN}>@N{8rIU_jmJaaNLEsEw`1
z<Q>`zOejbwb78jj=OWf4Y4YP0pX;b0#zp$q2%?}O!Gw*Tsc-TvH486GrL99T<-Ag}
z<Aou_{O}fgS?q_34gueAIZ7I4TXHItOLGwnhZZgZbRL5~BUrA8+#`ADK46`6RswTp
z%z;&r)?Oknm=<-~+Lf|mZw{Qz`!M1X4arN*97}8vctuW~7Tc+H7=M)q1_|?GS$HYS
zO*|PZ-Ihl^Oa`G{x_Hr7Ha`FQUR@#-6xf^orVGzQ)jbSkI5o9^nmPJfPe>?!s>3P=
z&Fwdw%(bAqd>W?Q0_s|t(2w(26o&>`hg_(-{<aDfG+!Y9pX#Cs3Glx{UO&N!BD~?*
zK-b9geE90-b%5`-tJ76<c<0kh+F9$&8F+rA6h9X1usS-aCZ-SA;6z5fm$8(KjIojk
z8tQ#9+QD<bH=b|W5cctQr|p)|?`XTe0Yo2%y=0~qVIHa<0-Ml2*^`a2uvcUi#|pIT
zbh)M(@g3{|O`cl*q@|2gs`iJNz&wh-&-meu_y-wr1wvRP`-8D4>z!16ofhOCd+;qL
z#}uXd=;;z5ILVN3M<ym=T9csH_Na+J;2|kCM?sCM{8+oQ=V&q+oS-MWc!krd=w5v2
z9L}-2pyp^lUE*JEsi}q{$X!B=NIQ=vf-Pk-?v*EDRV@1A4Ot$occ7;B3)`Eraxa{=
zt@x*{^iP%x2wGZShT<5S`7&g|;+y-w$>kM#_j1wZxbiWEhHNV&oBygVz7&i6>(`Jk
zSFh^J?)I`~r=8G=QhOe+S?eW>pKH~b&pDlU?9!>~sEuguXsfh9U4}%BD`a5JI-4~z
z&~2K-ACY;*Z!?J=;ET-e3mQ#|GGLg+-$hKPAOmuV5S+M-;##ve(<G|vqjraIYj32<
zUs{wp@BP~LqtShC8V7Odc~7E|ub9%uWJwnL#V<jkI*tv!Q^K6MVm;@oXINRQ&~HY~
z{}y}O9RsX5GfheIZa}TuzQKH+YUCfRD@*ac8WwbX>J7MZp$fPm%cOB)Z>m=3!i!V9
zeYT-|yjBGtwlZ;#p1F&8L|cUg`s$kGrSe{OG>7|nUjEb@D`~s6Btvib;rXv4*?MwE
ze(`ybog^wWki5geOzK+vO)LZ2AIft142vXg<X`^xU)D*D(SB@QmOallhF=_IH>fAm
zLnW(l!h!Y0q~T|i{4|kwvu>+K<suS=iHz;|dlzY8`BQCp$olSpF0YiBdvK4%Q?XFM
z#mh`}$RIup%_K2KFqFb>HRSk(YG%sk!o}F@b_?d%8j|EV^G`+;tHhuG$J944*41@e
zx3SH}_DOSM+eVX|*tTukHk-ytW1CIVq_N%D)_0!gz2AH9AJ}{CHRqmVOsugyt~MAh
z`=U{|vPJ|4T^0R|tdKYEKA9Yt%;^T7BTWU^!q60AB&Vcr6;QG02}G=#fQPXDi1)T#
zS^3~CS;FeDoI2kA;={}ZP7iU4KWyJms~bU&kZ>{=jqcO#5K#5HircDqf3X$2<w?l5
zSdXBll{fOG381?%>+P}%C2PGtL3T!xpVIDy<8-!^``T?4Ii^Cl3*0^o*%}%~L+2$1
z%L<7Nk7tYSl~4g#%Syif6+*04As4Nt!#fm3{q~$|N_{8>XYZT)b9&5-uGR-(ainM9
z;r*A3^b*Q98lsr22?^3rfv$5Zr2Mk*&|$1?7EBi7bCsHe1?=wdnvxG&cc?HsQUBG-
zt*m!U3Qh;JG9*@bDss|F)|1vU1r~vhK*M0P<2mH+7iRHRZ^hQbLv}hv-^C)#GieWs
zRgs0VW(m^YjVlXhTAtNHLg_(ac)E+K7R$MfGW9Q9j-(x|IEo)otp1OatsZ0c8X;eM
z@%y=T+jpBIHwjKGcyKMb?sHC9c48Q=M%l|Vqz#F6f7%Lr%`<DIs&05=5iu_j(>Z?>
z0N6(j*Vbda?M*t@b|hda*J+dSXG^PE;h5K)p#{pRo~@GBb07GjRxcb$hXro|6mm$h
zahAWeV{X-bB)R}G_@^nj^)^x!!^xCQLvp*r+Vzs)tc4lkAQruLy5`K=aBYT{h3$do
zr)&?g|Fa#qFDW;S>zrnIMSrI_$SD)xNf6&g$+<B#GRHSA&GVHu2+5=RxSW1MMXD&9
z*Y9h?hcm0Mw1y+yy&B~{lkmAbMdI6~xQ}aXsk`fH90#!?Gfg~D)TK@8<5|~*_%~)n
zqyIKJM$CYMal}Il^Ka7+z1R0BW0Q5qxr&WHEouup1hy=OzbqLjsFG39=>}EokXh)W
z6z@jgwqGuBpu%r=OW2JP^TK}2y~Aws=KNgfqVV<3CcW$=p*NBMS26k@9@!Hk?R1dF
z#C{i$IP8~%$B@{cMOm@X53XaOm!rBl2r;X5KPQk)nPnO(cp=&@LwARd6dx2AqJQkR
zLXd-d6QZ&R<|QWVD&xdfOb{mU9JX?B78%jAe6NG>R*eV)Ic!^IJ}0EW242G+Rr>*4
z=FQkHi5v+A3mOR?xQsFVF~Urk(^duumxbCQOkbHSTngsa$jUoveA!wZw0piC6V{lp
zM@n19@-HrvTUn$cU*wkb<ZG+zt}_LHUq$&QdQXGCA9X->DhdYebmVQ%_Lhr@9A%{C
z<caOoHlNn?%RcBuj@b(aU!nFypM{aYk(lq%`>ag85F7f$&7&4mjMY>&AZ>sI2Lv5x
zMRGdJq#eivj=anLVhs-SzAqP*j~R!y5}}v18#e9t619+JgJbR}`ivOIdQRgk&rVKm
zt@uc$?v;NubMM-5xZ|olY*D;B>aBGPuG?`tGK|Y<2!IGraFt?~0i!d~y}SGQ_q&Ow
zE~Vf00>~2@Wxz6&$3<*8)Smq1)i@z{0mU>tSEk4to=^WHCP_L<b!vV^?A_`D;x096
zxm*hODW7~d#0@VFeCi-3DBaJHi>+qXZTubSgdJjD+fIJMQZ5&YbN=qryoKV&z8r>M
zX){+3d1uzoeN?rtYxu}G2LkAcwbIswdAj7tj+th2q!*mqF)mQ<UkoD5MHGqp<IkP8
zb5L9?w?c&J?7r@60B-gumevNJD^FCqO=MbfCL=Z_)%^JCJAobuL1vcwlYa<R#U5Aw
z+{0-i+OHb7t5fmd_Nxsq<&J4~T;nBN<h<(2MqjhMluy?O)WhS>RSPfXtYMEwBeLu#
zyDNdT%&J<imyw;IGt?9`2|NKzU5D!t;WYx#`dx2~6#HUT{a-BlwVtZDLp1o!i|qu~
z)$Q3og|_&^Jrvo`#F@05!oX+?UbD60ql?s*Y1W}AXi3Z4A(e`Udc5aN{55_BU)*`?
zt5p);yLcfG?w;2giH`p$W}gJ(LyZhXg2)6uaPB#Mn)Z5A#;_9rIj=Z$)PA7m4xfg)
z>30g_MoC?H<j;!Ok>L}z=8pMYjW6?o6EjlqiNsoBg(j?1c+sgLrjDsW?$z#~xAuFX
znKw8AIv%tKxlP>No_5HOS08j7oTyX!tbwx70@k3h{%pJ9?dM`G=NH+4M<xC4>EE<o
z*Zd`2uBbeDK&bOqPg);e1o{>dVyceFajJ8d$nQt%B+#4+Vg^0vqiyeSCh?ouHJ{9j
z99}WuT-F<ujfH}l>Th`RGYL6Y$wh*t@{@ct=lIX)vgP>o$RpVpvW^bQO6=*#A=cn3
zVzA<#)IeHZ@^2Y7$+#VxIkscb^0V@u@PA9gM{i5+PX&vSGqKL}tN1$Bt)RXXLR6>O
zLRV2ineB&v<rmx;-qMk6Z%&)a4Gi7%4>R;8im=poUY0q)uGWk>w0naU@QRatqA%cL
z-H)bu2>m4LMdc`Q>{vWK;Mj2<8D7gvv^i70IFO0gkXQD~otnJW>Lu?i#cUcrsdXV>
zlHg+X>+zH5hzCv2F#E3gb?O&?r@I={-C-doUA@ln$5REr6MUVg(U0F*FYdcW3jkJv
zTK+3VEJp2unbgFq;4{2@<Dq+^uUMrm{9!R2H6jbEiUsR?AEHy-lD+;r__Vx_lv*j=
zt@GB6_kX_Xquwqz%wFE*tB8tQpDZCq_nO58=-^Cp^?y>)-nR?xx#LAizj%hKS-+Fj
z;duN;ICF+3(7O>*QM$PGJbh@efFN%($KG-DIejwx+@|K&jF-rCVz@CI*YG!6_7KAw
zHwg^>UaRJH+;2Yzo%Bdy1%25l90}bupq17eIq6XZKId(wJE`Cpr<1UK31n)Y;p)40
zDYg)vZ9828sI=HBSc^q%5VK{JSo8Uw91gt{&QDkBOU4dQSSw|E!S2k*rfbw@xruu;
z&hgxQ*&b`LtR@4uvbBPnZJ~J=k0G8<-Ur30k~|M`gohs&90_xthdP#QZ$r7ZLL49O
zhhFg9-O+vBy>|+dYlL8{&sF2cEA)^<O+K!<r{1=$b6V#d_=IFHa6dTViMo9VbmKPA
zEqfFW_TW)~@W?;0BqWXj4B}I)Ersj+i-o6U>Gju<nGV>K5+n7i<hBQDS~~mEn7=n1
zjcJ+aqY`x6S-@8Fj-hnKV`{&R-GOxKIdhi;SdVgY1N|J7k+no-SEwjJb0zp8U3;UA
z%03Uy7VbA(qHezC`awaC4`fm;C=?}no4Jj}+gdFNIn9>?&LJ!IV1oq&IMNvjx;CoI
zYy*G#HcI=o5QjQ;ydsX+v!iEjq(7GZRneI!)hP$WX?lsL<X%mxtwjjtuy`P12#R`8
zyw;lXV*o1Xq_h<B*9jzN^WD*9>~aGBq~}QhaM>NK-yt=IZ2%s^Q>LSj+lVH*Y+t^O
z#0uXPhEu#8Kqd@JtCMvNRdyw_iQ2m97(15JZresiJXdpT9(Z*$E97A95=^r@%!V|l
z<>fys<J>~3^Cl9gyw$MT=NqvZGSS4If028|W~=RH%KZvHkwJ|#|IF}LM;jU}aW2%O
ztJ^r`TA3}0A+5jBzwB|?23z+F09&xwOH>zxdd}+qN`h<WU>L3UR$TA4*qOjbnNqrX
z0Ug7QZ2o>tBw^pdPmC}4MKFda@`tRQ$h-j{T5|v_REygTW1}U_Ud(l72vgp3$elv$
z0rBI-HaO_zuYcL1z98Lf&}~6!rX5mNf%>=w()1=1CyauU_bDXO>W9)79=wH^Yc@L1
zqqR-t(JzgDc`-@-5G4)?046PAjd1A<)-%b*95b#|9BYM@XsHAFR%6a#L7Nd{>-)?i
ztK^s-C(}_^7(3Xd6%4n3MA9sfKAQ(3tbcd?74@myYu52G;5V0P??;O}ae(r-Wr7bg
z>Mjj0*w2;_GRn#+MdvHt7g5lQaSb(H(KR()MVjw5zD(A_iyH*rX~7En!o8Fs2?-qg
z{@uLJ1C(Wxl*uO4+_=;?2VL6!_6OY;x$fs(%+Ze?&~z27j{B1NTf6&MU4GlEgNlvy
z<i&@Y1>R$6erb@cB~QN)<!CvtYWk5!yIwyH`aeQP)p%hKN#;cFyy%Ht7eA0622Sid
zcDK5Y&9x;2?TvwmR7|Ds*Nojo=L2DjFV}dLJ+}ET-3Y=vMMo$cuhQQGyA0KGbSYot
zrxXm|+^qppWZ$Z;q>kX1fdHy+lm#DJiyo&ulH6sYIDrl}OPb!pFz8irO~7c#9+&O=
ztDOT_0I{$JAA<e?nK4-1I<ysLK~6-coW9T}MJsbtRz@c)hHMOlDstHJb0C^OHxsRJ
za&tt$>(7`4#`Mj3b07sIY~l68$lLqY$LI8PyJ3NmNih=ijy7Fy<Kg{>h|r&e*xEkB
z53bJJ7oCLj@AayKQSs?5vWiuL&M184x$&w9-9s#*k~{Qculs~o;-gc6$wY?>5Vn2(
ze(57QDCi^K-ogwAUQ5l!^vhjPrkLixr%5{buYE9ZTsTL)4ni7fgq$425*E0jZ}?m0
zIU3`2tm>)g_biou)N5ZCJCJZ#A*p{n(k;~J7_EKL%jJZf=y}5<>Aw8rhprJf$-E&t
zMdvTPKjtqw_RXF2>=`ubdE8upIG9h+>lg2<kp&hr+YcJr`mpA>i^oucFhtwB$TC`Z
zegqz+e16%v-L@Z-e^N0qn;c})?~c^yIkf+MvD3S$&j@P`TW~p%rt!TwP_QWze=IhL
znHxD(HBNLpnWV@Eto~QZ%(!amZ_{}Qt%jUWy2B=i$Xt6#UNRBSiywNpj%=@v!sp~m
z+xhyTL??lg*>kP<&C&|Ca@_4eHsA8io#MCs^~*p4VSd!<U`@mKNS)h$m-5{q7QgpH
zDeiX&E{!S{L=lJPyvJ!jq4BuGfhLPKxNS82>kjG9Oo+Oa`d6eFdLKFmN$EVZ^m?Gk
zlC}@=**9irpFsKffe(u1`doZ?PhgxF%%pT3VgIz8Dw}w~bQ-%D-RBR}z6;~$Co5Er
z@n8JaaDBM&&$EuhLEWoyiAU|t>Dfy77jz00s;ImJno2}Yl35ukL3BF@X!!}0FN6M^
zt?o&-3S&FJevPCBQY5qxb9sUVt@kw79GB_Js&q}hafI*5Mpya9EPn-7H2tY)PnRPw
zCcGCfY@AEzgE<EPg+x$Z1{IkU7(22}0#-i8_&P44rq55k^#|^gKa5sH7|^MFb6nK?
ztDdm!*Zuhw-WHu7J+~!yvR~<8;t1o{QSt)$K*8@<-ZesZ6Au*b8~jU*0v(~{y0Ep3
zxu?;JVhdE|eaUH&JCNq1p~33MJWXlSE}4Jx0P2?8KZN0F14q%m3Upr+91M9M3e1_X
zefT?W7B!k=Rr5Q3#JB7m_7^+uWdQzC2QNKo$M1s(m$ZkZ1!X_C>gDyb^Ul;IWW?+Y
zOr<I4waXtZT3R|t`Rlcx1eSg5aCnAdl@z;2gqlobjM0{BroC^XXQcM7JEI|(t<zvD
z-agDV0T5yjr_MoZX}_s{Z5CcT<Bbvg%8|hLGk^5>cxb>bdyA$1?yW?>OU&}=YIbVR
z*sS<9lE8FkQ-YF+8}{6oI<unxi?d^8DpB^x@Yqn_VkHB{bob`uwszO51ttG8%#OVu
zdg=$yP(J0LstSX@A1SY=vG=mw>bv0391%KMK@cZ7o;D4pzdQK^bnS!DJayYmY9ltI
zn{_I^pd9MFHmL{Xs(`1W^mKm+e>|HVGS*;IYX0l!;04F-ukr-f-_lDi2jWt+%tO-g
zYmg6r9a~SF>PMrn4btD@QzKRP9`K9)vY!lO>wTR;aO(!@X7~qa1+`L+@^v@^dS8H$
z1Dh9~^GHgKX}ZZVL=kFt<{<DTi;FY<k6dc%Hhn~m@dJMb%!|xF$UNTawG;Cfv4igg
zzKBy$8{C9+ZDH0=2wPEk=ToYtnD(w~ENI$)XEidXm_Nzft~I9QwX0}=p_-|HUqa+!
zJ}z#T@2wgfx%#uid)c+?<D>aVh12CrY$ip*THiCs2D$P`+%f89!gE<HhdJg~vK;tk
zEl<Pz6;2GeXM^~TCO`^X3oWrELyS`=CwhdIZ_P^2H96kk1<Yk`FJteJkx$FWpM``U
zk~X_zVM)Ja_~T-*I#{=Zzd*8&x*oA-eMt7i29lSr$v;mnFWAW$*qTTKp`E6f$sf}C
z49-y8ENJ9(_jvxy;=0irBoaWcDHjyoKVwyD+MIgVv6=$WX4=Jy22LC;UaNZ9tygGQ
zO9(;RR~FiWhShzlnXWSN@sG9K27awiV7nf}(1lb!mUQ0);m_3V$AD-Bo)8}C8D`|w
zC9<#fC>)USLwK3Fr`9~r8%Q>nVM7d#nux&U@YQi-3<-sb;0^K*t(!UjCC;#1Uf3~0
zMLypwYZ_TS++4_3S|qV7!s2$uO^u}9g8&|%8Achg17nn_=Qi_!U~uR~SH!?{4|eHn
zrZ;y_?I5R!_V^wnR_R>)<)-l5tWLQ=nSIK-=a`c_6GfFg+=mv1)OULsA^1O%0Y&K_
zPk+k%@0D^mx6L~1Z6NfX_QllN^}!eN3Pu<sq7D_Vc9`t=K7Y*47k1|q`IDG?73y0{
zmu^CQNs_8;0o{H6Zm-`Jd9pcX*==<f$Vb-@PeT*ayF~bRjrCg@S~68Ce*Z)trR8P(
zQ}?Jcpf`ao{t1zOVf;Z@2W~R8yw5}%Rf-_DAtA(EibRd*Cqa_gyNQZAQ5|CE69{-|
zA6-9dCqFZYlR3tg-OwM^8Xy<$+@~0US+DIoObmRqFUq&%UreWVVh>+dZ>iN<<7^+r
zOsMMZR#LIbIlus`2OK0>54oJuKJhXnZ!V_;rD6-!^-b@^{>Eb+9IfDHn{Nih(&$k`
zX#UYRLs`i8gZyEx9}juZ8SpmUjpDBB#3$f!q<p22@M7!vw2mTDJFwvChIqM@b2AjX
zZ8+KefjoM9DCP6P4nmJmEgUPkZ$MC*-C<6++6kU*R5y55J~YV3WahB@;<^5^^T(Q4
zd^&I-F()lBk@o{zKU!12LoH3^fEFRjD`+Qr2XuaK`OS{@vaGX3(-cEYCz~K9jQrS-
zssKt$ETBfor+q*i8U@db4}kZA?Nu%>nm$RvVt*vs`cW*S=4CWjtKmg}$@SQj&c{^b
zZ!PxCfb@fUb1Cu)o6zchSqp}w%3lBS%By*~3FcY8tVUahpRRvHd;spPx1;<X7tuc9
z8_!@4*Ns^Jsy>Bs8i5;08%5__7`f&BnZw&-^rO@Sku+DTna=ZRnw{_t>qYO=6h<rR
z#%ITe?#K|%!SZz1or0_bs3&=c?rpmY-yte$y=x=8UtBc5=sJ?ilo9Q*@;qKrKknU`
zIU<_^h%C8!ekWv83UWPrd_!6%#ugnOE%tx1fi9Z#P;%H~+?Di(dvi2(&FlDZ3<71^
zru)W~wFq0J=|!3!E|;37PeAuD_j@)^4%fX+dH1@`S&sU>3O^on$2CZMM=qs`Q|=`u
zn!j>fdkT6uw?|Fa_RO&&?J4SrkW-J5<0Lh9mQ0;;4IbNs)SO<y7Fr0e=n7w8b-17(
zC1XeYky<M-X|I4v5n_eUDCZ1}v(Zei=i=RWTWchh2RxVW-Zg0}U2Pw6IUkzf|HSf6
z)8mkBCu1evDu+k}oMx?XJ({dA?V<?d!l+Bo8>Xvs+95Mlp>{~MqdB`ay#{>P8T;BG
zMkfajt|y$zU@};wPTn*6{VeOTr_>NhcO-$oU>2LL{|I@BAX1*PR{u>+neT(1gqj;0
zWo{zyZ0j{<aDW#2GXfN@8H#z>751pDC$gVgUT8J0NqKh`H`udoAE0*Dn4IrU@vYK3
zVnH`*T}%Ubo$URGzbaolG5}#i9b$FO#&)EwXf)ascxV@0)(&5f;rH>?&0M%hnMWR=
zvYi+?hG{}NL}so>7VNw0a5yy5%MkD2wyv)Omr(@&5LSJ2iZjmF@xF}L8nkksY{~(1
zlpd|nL)=Dg(Wv;2xKq<cP%-_6nRf<TwAKD}8R1_eiZk+^U1bphNYwsk`Szr<{c?zd
zjVL4KZM~N-T&{9g5mRft>}CNE9Fo}NdQrUg|Cp}NJ#Vc!wrstJ;%k!ob&+7F6z}CW
zI&9S7kIKX`{hkmACDVV^avqYm^39BKO+sn-<x;CgYB*%ndgI-noktffKN^)R4J^NC
zpC=FE|9-xrxKTQXSRc<8Tk#^fGFtvXxR;%_^-yUW>z8bl5E%dDoZN0KzwG3RFv7ig
ztp2@wIw>u<Offr729$@7UvX(tTBw5qknz#P5dqTB4{Bv!Q`YF@6ZX`wLjH+qO>I}3
zM9bIXK7Tc>?!lM@mOR^5vsN!YjqxYqYg!C6ds`!|N3ngz?o6a!33`3>-oB`>8V|o%
z_?_7Wof(??2ghU+5_>zlmDvG*CuC5JkZo7x>dR$B>4OLNCpnzo6Fi)339&D``;KUb
zQ~P`!B%QB#fVK<dnB30tOs^PLL6628C69ZihPn{k%=BMkcX7qa4!`_O(!%|AqN$8D
z#=-X|qz-AhSpG0=DorjX2>uYQ#`1serQG2T{LKA+uZ8?p9?>oeW@GAPy^;%9U2S8!
zjtlpT1{*D)$NmRYABN}!ott~;R+9&xOoHq*6@fm>RC+N!)%#F3jN1t}1KSElltC|N
zb8DERPK)K()k}iwd!o1>s#7ei^BxT<=pQFmd(&A#$APQGmlrpay-f#?XA5fjD<U6O
z&9zOC)ooY-{By8I)tS{~AsFD>qQv#O=HS#62>~@Qvt!YHAx|E#_hL_}G-CpZd^^WD
zv*}U4Qchx+8Hlhoa!k0gPY_|dBCW-SV%}cU2>?!pX47zmaU4;5zfO#&e+HVx+t%_|
zT~v#Lar?x<R=uW~0K_-}<GdwW*#-;&b!3@FnD#CUm+PDHM0YTAZ#>?dH^giDQ1!H@
zZB*MK=>Q(dDcsB_Y>?DXXJMBfA!LhpO`nVxuc4_)4(&Hd4ex-U-LCu|X9QwRGF)kr
za-D>@{yz4w*y);6V{l4>;%4^wAv`Q=RN%L!yJ?(#2P5@^r4ocpfi1k|@9e~kVAYW+
ziJg{;$J=;+gXIItwfnxIR-bs2HkMd^x*5?|k2y@A?`XQ>#p0D#dPkjHZ^bkn1Tr5_
zTtH?noMoV@{LxQ}MCX$u;HVY846|Rq+?*6-uzq2vf8Mg>b)*h1{*LmTeOSKyD4k7u
zPWGBSOzpmr^PL21_U9htSQss4o0Ki<&*C+0W(mR>!h==J;?y9s4Hw)(^suL~A0E^p
zbZ&IL{6@CZQlr9pG7n!Fihpa16vRDMQGqO1($Kc-Be&f%Qw=NRY()`E)(-_|yZvKJ
zH4dib<>dOw%yr^|#UD4gUo<-(cK9fCVm*-6mfBfn8eSfM9m?iNUOv1Gn!7*rEHKg=
z!qiKRB)$$Tj`AA(Tw^KUjz8=Xxw{3=*9{&?y=f8)&PV9>Zx`UA0`>jiKp$<3HaB+b
z*;``c>Gor@*|=kHYq9b*Uk2%i$6EZPx_e|}sfW+J!<t;EX6~V?{>sP6jzofsn)hw#
zWHe{=KCdGhj~12%0)gH2XV$oSk*CU<^J<=Jn~K9Qt=YBA0}L6t8JeuJ_k1t}7k;0|
zrgf~Vy5eSyuwKnSkGEu#o+JH2^Xh9#AYW?!+@>(+-|W{Toer-Di}{<umtgnei%xDr
zb1)PBnw@m%lV-vvn>>!5gFU)$SEEfa3%bvj!y8PxQn$sxB!mrGZl>3_OU}Ci7!a4p
z#@PkE%n$P)eZn+$j`k6_vKN7@?}3mCZ@lMiw_-kz<CwFXJh4ORbNTa(xQsWaDrf}t
z3<4cGqyw66%Uqt^Kj;&$E=@1v&SQgQP51Maum7IyO(?rQI*w*EGUp5t$VuAq&{HRN
z<`OKD?T$AlK8@>mnCg!&f+c!N_|aQLa3;`%H#80Eb0)fabYiYm-t^xr_SQ5BZZk0c
zT0p%PEmG`I<9nm>$s9aQr$f`_4YEda8akKWVqSOH#?@FTD)^jE)3}_@Y4f9@RcET`
zfkixeSN-`omHCY{)b0rYbL``=yXW(rSyJaf2c*#?coeS}TuAHW6U+(IzljT|bdLBE
zl(Rf5KMK2<wR~T)K=2EBHGE@^bnM^_pOi^v5B}tj`(-8blfU9=qxu+WQ&V>FNNS+~
z<3bmio|z2S-tC0=-{I|=9un&sspNZp33XRlA;&@_rQ~Hi0gru_$924B0Im&ebsgk=
zWK*lhLA6<%oO|uNgKf?2%Wa+`<PJw3&4hv5ouEjOKY$4fxOqNY%$)WJFV%DF(z?JX
zI?MYo@#QMK@bZ;+YwTzzKfL8L_Cz$Qx!F>4bkE#g)A!Vl>1VWK0WfDfMswhKExv@^
zLmC3CzXv{5P7hu0yj}IpcGW#ZEIpO3yi-c5>z**E62CJlo@lMJ%L7OS@EOp14m9@k
z=z}You?%jitP{L$Oh;2ckBFD+FyUh3OOFgOeECaok`uQ=QmVYe9w-bvAY-YygQ=Vc
zq~yP<Z|ps?8Sm>hTrp&RmgBjELzvMrO+qfmGd2$99y750IJRjr;=F*2mB@3toxr13
zgqQ>d&&s@1Tk54{(Yrv<dx)CPTd_Q?7VSIMYr6j`IXT@Fg})CcdEMP}#&!0E+m%#H
zMnyy}X?&TiS@8rtgh8QK!RESE)URx~jyVoD8Scf+UG51o4|_L!AEeqD{+iavQs<bI
zKNk9w^*lbU^IG`h8JYdgRc)N!hCHwBwv@94mcHZmt7_T2fx(w}B%uT<u(lIBVoeac
zRL293ntT>%@P>(5xKHNqLLC^)8O6#x13X!O!Om<^iGUW<a$QAJmvnFu$)6hSFG1P8
z;HUg5f4upMmYwYj2}7HF;=g%hMG7!=Ln3v$YX+InX7kij9LjT*65Y=#c(B!N2n!VM
z3jhbEM{H<jA0}D&_|T3A1Cw0eRvnbiC65(k@&j6&ftyBB$9A)s_Eo`=4>iW?SPPyJ
z0sK{iyMz2a9uVE)C|Vh<HP6Hf%V5`(G8v?~%*6HyIV2y~Oz=+nt~du)Upm28+eHQJ
z1sVkH+R4e@w(l=yNGz7~xnztXOmS;|n_D;<UB)Itw&QhH#kk_w+w!IZ+&I3#3j_JD
z{M9ZnYm$`~5rITy>6WxOnXvl9g6h|Y>$d!b!(f+(X^8Y5c&E{46zCe+3d8rttLL>o
z=fz_cAFP1`&5g-~_j}b>Z7DT!g<zjaERC~$J$0~xu1Nkf&G1bQbjyMv*orzo;IyT6
z%RMXLKjA+@nx?P%pmp^6oCOJJFkS0Qc%5ncGbSNO|Cwqg3Tl$CE6G3pwtK1ZL9T?_
zbtr-_-m`TsYDj|5dNmcqz<lv=)*XRMjn5;N@H`l#T|bgZ>+`IthD3mvUZvCKd2C4A
zeI~7D&pOOVVvj_&P=IbFZegDA+A`)!D!ripH9bRVPJ)zJoLt;c#;0A=Gdv@8j2tBy
z*ATtss@5kaMYSfxd7h_jlybaDQT^j(&$}c>5EiUOof_S!(*ak|Cfp4*zd?U1vno;M
zI>C0Pa$@If$ibYe_m(}vL|*YYw@t*xzC8^DdH$8PlEEbn#@bo@h#C~n&m^JtkH9LA
zW1^DMx(w8m6zAuhpU7eZPy%#APRrk^a8$0!3nJ1;nw{rJM*ViEDPNnbs6X0cInI%H
zI0=~3HU`Y}LLtR&Yjm!O0BzR-r}cWX)mre(yzrh#;+yL+xEGS+{it&5rH7a1BRmV$
z=DsfwM6Sm)H;LZ2CasdhQONE9q2=Wka^rjHQLk-*&u!x4I~wf@<yeocx}4^}cEz0*
z3&81!3wmA8M30R#*1@ThARu#=fZud}UNb8%M$8tKO$}ybHulH7kLR++c!a;c3{2Gi
zn8wg$q=S<e+>t$zQPw)&o0QGQuEmkj$q{Zg*Bu}3&vPfe?Lcx5{A0IyGjoEsqkL|x
zqbrbESa1Y_IEOrATOK-OKoB`)zBgjdgum#k<R;lutAD?DzX>Q!v#Q%{D?eYfRla#)
z{ZFgh?KB~dol`6c890PcKsA7tp}dARIXsm$&-3|v-BMx?M@Vu}E$sy&UkBX9t`{%*
z8M=NH|9}8ZuZ49QQ&afO$CIn=-^z?ES|rdqTBnh}9p9J?H4+S|Xni(6qTXBMBsf}}
z4wDsgHJ147R$`X7Q6ff28@yEGZHVrcQOAqHoEf-ApeWusi8^LE(4f~!07}n^^FRTx
zx+0YCCUDdZxM`5>2jePrE$Y#7UFbVJ97pd2n-|>{oh0yGFt*B=rojk=Ay@%<|H{<Y
zA*BdY`a;}DHk+5P|81KI24^L3p8>7O)R3~OdobVZ_MUD~%R3?<!kXJ%^1qPRAp7+H
z@v=>r<zgyEZzFdDCD6%<)wafF;(v)@5PdYD{S)q9SSZ9;E&OKn$jvSo+H^gOZ6LJZ
z_P!sMov-^wHxzS*&yBVpA9W%r!;>`KgB&?B8R&hWN87Un_t+k_#opa@M`Q0)n}gA^
zmVq=%NVagy_7pZZH0>vwC0C73E>>`VvkzzbV>v`3z!!rzB^9X2v^JFxU(YeY#&#v>
zAH23(_UYz5yu@^q{BEIA$!S3J`!Vn2+BRoaJ=Sr;p{;51eYom@!(<XSAvfuyvU*Gj
z4Y9;;$l4;GQ>-3lTMF!w(iSZpGz?YNuO_SLwCPh_Tbm?g7o*anM_#3mM<_T~J}eyi
zPOKbf?mvz)-hQzCA?Y04#WIDLUh+6@GW!&$ktX0~TY+Ki0>MG#)U3%87bNN9&rMTm
z%j#SnN-p|3EfIAAfP6Vsl4Xa#{ii8lE7$vhxB6k6DnKvcU)Nk)NHBjaPuwWi`B!4D
zN*(eGndW4eA~E-6jXf3~LKW9(GK&<>on~OrH9Ig{GAQB_?uP@Yq`%RYM2)1C0fX6N
zDX090uUm*>RmW5klb^8+s{-N4`MvtY56AV8LxbdCTSeA}IZ7tdf}j@YORc0oT1*@^
z_lCK;VZfZt8aN?$u-mj=0zCIpqSi3WGD$41sy}Gr>*Fpr`NwtrX|LlJlaqP~DUMfT
zt3WhaK2hhs@TDE^^B*3JAqsycD;f5`eVbt)2)t(DE#4YKP4qqoNTn<f$(ZYg7g0<l
z8yf}g@2!*VfwF7=s7T;@H(bXWlwQ=v3yc7X;+g5N&;PLXEFsH?<?MC(qSDM_yx~OE
zMI(<Za;64gSP(!+pw$^iZTB8Gen=-(p1L^=yuCADlfkSE($As|EK^Cirn8dqp5Fc7
zSA?xDBCO+JGiMZd^X)?K2y-l<rxNIZw`Ud#iqL(y>gcaGVoIvX4Zl8740jh|Zi7q^
z{%xoy^m=!r?Hqn!!|aR~EtT#M{=;2o`zIv-gooXe8u)Ye;7s6Y)r+i5lP!k&mUnhq
zf+QZV!=dX<+Hbrg=2=PUMUQk#X>Nu(w&>|3`Wr4Lt<00Et0vz0b4;By2-1oLcqJ>~
zeIqM7rBe8O$lXdkBlO$w979j`0Ly<;nrtN!Ub#0-E)jSb6$b^O9UKY-*>v$Ax$0Xc
zJ0{RLcDImRwc%+$Q=w6RUOl{>a>}yoGGWY&X#UD-dGc39u-zag;fSE|EQUi_$(#Yh
z*@XKzFq6XcQ^w>E1D|AyPZ7Zj8}DDFcN$N)9f9M_JYTRiav{J<AA8AwFz4lCGi0se
z3H{U-kuov@K;@6Suh>mz5kl4M(O==ehjPjpB*t`ukIQRPSl7(WtJ^*I!(n8egiMfD
zpO)K+uh?o7?;U$5)ZJ?D5qI!jF0J}$KxwHEAa>fOFXM5Q%1;HSlOJ7$E8WhVm~Ck{
z%bD+fXOz53JRG1YT4CH>EeS|_<+6zB9%L4@z=@!3JGN6pA`H~IVwhUh3F!wt^v^XZ
zMm)`{dU``wxJr5L7NBG*a{=46e`u<=zg!$eaMH?D|9J+tSis<|1!c1-YJEgv+F;X(
zhaift_gm}El2L!v{XUJ(h<huJto>PD@R#s%v59)|-Ofs#x(<PfM5x&_Z8pp`&*({7
zr$#;paw;n-%&hA6FDdb%;ziQ!`bEg#Liy8#kD12-5WW*ehp|Ik6*awZbqCq5b!r5?
zhc>kJZszsFP~83l2J?8yO|fvA;O-2h@0y9-U^=l-yBt8z=cah+=YyjI_8Yj1qyT(q
zt$5rW3jI#Vr^Q%p^kia)3{@+~o_xP-&2H4<a@h4sy}Zgd%5y=hhC&kFp!<M%MeyAP
z@+o6|_G>=lU3RMt%d83e&!h=E(X03Js0rcDOaP4r%u=cS0~81Gi~Ernq$@d6*cQ)1
z%QQ*lSkGkY+}}%<osf;GEUK*FMtdM;EEs45Fe1=C-%Fp)s_YQqZB9hGt9!*xe6k;c
z?9vaBu>x_NQg9!T$=AT|mgE6g#2Yt$&%0l*NKz<w%7I!a1ax1r&{J#DCVyFfITyZ5
zssFxVn!-nydc-u8pN{;1bQFADsgM@^QwaVG$t;$o-ezl&RwpZZ@wGcpma=lmHcIP+
zM5Ax@Ip#wx=uBu~g3-XN{mUH1JDnB<ZwYSF)OVxDxXZO?C=Nwlk+k>}1vVNIasLkJ
z3S-li8on#;B4%M@;c?dp;*Exz?(cpFte`X}z8tezDVlLNLA-fIU!)dB`J=7&q(VNP
zB(6BCS|k;MIEYNt2B%&rE}bi2VG98^1@#VUCfa&>Chll70B+V3`rwJZ1_5e-SiNd7
zXSoka$hzwNjxhO;ndJeI7tb4_T;1_r<$v5v0ZML>+SFa%-5}=i#;<!goYGSYm#I%D
zXvtHiU@7*11r;k9D;_^`8843$9dV(@csMMB<1riUU0=_A$xko-FR?lEmrfz)J`^ow
z5u|&kfj}z?@l!LZ{mkbDJWr8H$zt(5a0zpi)s{{2z3eeK0UoAKkJujn1mGTq{c2Qa
zGzf*BgbC5Fq#lCa15)%;%=~eJ#I^r$WZ^_7JtnzE%!luJX>#~vZfwBMMG>^dq7k<u
z9Vfd#`5H{7o<Jn2!$&Wnkm4mx1>sr{Z#zFzFA>rxr?OSh*c#Myu%5h#t$>fUB`=kL
zUOyDUuu9oI=1b*=X@@U7Z$vVbI8?kJ)ZcW9Z_|T~CI3%s2E&*_FG67<@Ymvj3^UHe
z+DSE!q<sbxmNxY8okKx7I2l0u<y(#&-!*GWUw`~dQ|A^MBd4JuLAko@`PYkTzI66W
zKm6@#>V7b+d8{}|ZYmj}Q#JA8#n#gCki}<Lc&rH<n9}P%B~*}jlk}$S!A&K}MyON}
zzoj{05acPq!e+0B(<qs_h4<rTfhh6uuKnm89M(sqa(=g!4P9lO&iVCwKw`m(9lT}t
zy!bWl@+guk9uHL$nV(KqvVNy9dwFxHQ$?F^-Q#2~TzjKW3$PPVN5w&}Znmkny8nii
z4X7%YFAVBxQ^<M%Uv=4<&xsbO9Tq5Mt2LF7SiAK&&FCLXftjm?v)X|4)cb0|iP~Em
z;hx*=B@X+SPcq&M#<4admG&=wygbCy3EG2wh)gJ_6^}o#PrKV+ti^xa^p#a~bU^E)
zs@C$j!qwD9Xihz8H&4?^Jzg-3cHKY>R%^wbesn<7JxmENU)^k#zKFu9W`OEfS#`@u
zlM&$|j~3|GPZtHj1dcZFp1fBe(ifAaavLwWJM;QyZD?40+RxCt!a{>qB&BF^(a*XB
zcwzgon2Fqz5%OC8tHl?bMvJ;(0+ym*8z#gmQaA5JYQ+w)#D_>+EiHEE4E!U+wfD-c
zzGZ%Ju6nT-&LHKz#6ZJJlycN6N!uUWX;ZX)oz9A9Cx|th)-=U5dEXk@6u-~GO!V(F
z;Zm;_zUq08#dxM_jG<lwF0r)}9hQFLy-Tey6}!m#5uzR{aZ(QZuMZ_s_tr%ErcA;_
z=Dv)jBvf{4(#@$lZD`+Dp5ANm)@_;)ak)}3brhS{IKiY5$?DRSGKn)Ex^LD@OaW&A
z#0(t1=4%8<ha3f#`|Rs@TyIO?0ld<_CWKM1jUz}pZ*%8RS_b{r5A7p1(0|Rf%r1_#
z8{L!jV*MHf;;|$~yzd=e@uQ3d9-g?GK&`?d4bmidHV4zHPNsQlBSWMjXC{MhLC)~$
zs08speygJQSRnHi(-hg9+PRRWDc7sm-%lb#5%2gU9sxftOZ*gA5wwQvAnROOz(0qw
zXfJ}oM!^N8@8Z`?=A1NjM+obxiD;>}C$SjgI<7$@o#`d)4xBLTuAg;rllc+0&cm79
zUR^o)U8%Z+89gyMiEVBSvD4>ny3`d*O*GU9o8K;*x25Y>nybdmeOewhC(ffi*u|cn
zOn_&5p%)bXQ%IQ&r|y|RIk5^xO7tEx?rVEKY-w4X2q|n+%Mb@R_5{6^K5n=CF2oA`
zPAbSr%Iecy?_*eI@|(5}@xXD5P9+}vG)prFTFG;Y#j&3j&h<|@{=H4ltb6NTX_*8|
z>7=TCY)YJZUzZHarBZZ<8nO!+DncC~*rUK-`&*<pLazd;7Lqr@djJYOzroUO$1`R%
z5ot31Nk!ASZmov=XAAP<=`|15(KR{fq3pV$DE$iNFs5$_pgK7_FrSBD$r>-w>`E;;
z*0Q2XcgT!VeB+LtzfL3vvRbwU7j`xsu{&+Y1PbgJ1|6gs>?={#4Lnt(c6vxe&yi{R
zsTgvHszHHk6Q~mXbMXWx_J4n+PcCUk&9iT3yC?Y05|evvvKVom1#2WY9|T5xcm`&m
zlsJ|-!?QIPLi|-x){V?*(;(lkIjNT}(_r8^)3WJ&)}`!x?8F~EV0@<-1$MDhs3N0x
z<XF2q`O`G04ki(O-&oLWFZ~x#?I2PNo6GkC>MbF2|I#BKTt4^0J1s3Xsr_K-I%1^~
zZ1=xQPo*hGbt+OcfR@3qvc#V(?h>v0ewNCD#(Td+psV`)Mfvz@EWnaw5NgCy!S_vM
zk?v5Us-3*7qvUp2K}BJ6k}UA_Pi!H#&MJMZ6b!Phe1VD=P>aTr)V4QZ`I=rDb>yh0
z`LvFKRG@*o0YcmEdQ+^feOv2;3Bg2REvj&JOwC}nv4PVDC`C9not+lgk9Z+kqsI3#
zULnPGz)_;-OiUlgMJ#T<4xulT<)67(cAau7;MnNkeiFUIeWTQH_#2pBhpOGYi5%Bv
zPkJ>1mJVe4`b$pfE`w<uFKMWO7sQCwM(>u*=ltcLv6sJ81|6ITo1JQ~ZUkF{(?HW@
z3{TrMSTmherntmC?Bl!sJ^YC2qhZ=S=VVidbR>n7e2RW%hpGxY9<K^^JytEy9A?AU
znisVy04QpIC|XHH*QBc65v9!oE#a$SXIT*XSm2|beiKso)#wf(=x;uEKb&sI-%x!y
z)}k;ADsZC1G%dH)(hC|`p?=)LaCTY_S>Nj1wcHzZ>U;IX6#(iVTo56qb78z#SR%7a
zUHna`80Tx#RU$3o{1_K8Q93>E5JZ^jBI>@CL+`llK&Db`!^H95l!wg$lui^t-j4$L
zvl>@InRd$}mGbFF_D?Jiw(o+A#dC0?)If0IMx2sEcHhkvcIm*>eIIIRLV0F5I)1<b
zUuuDwKvS6#a)sl$u42|dW@k|V|20`N_2=p4(~cKB8`4+5Jh=6D?ny=UJ|vwh{6obf
zQ+-KztbD9SLcRq~=kJWHo4A#n%mZ1o`wc&x3(>0q=-q^;2e?0GuMJ28c@YnUt!pH4
z5)IaF9|><|NKEvk9f2xZ5W9c&4DbKu|GP8D({e%=QNbM_Ekg?B--5BWq+=v^#!aQX
z&y-uk9UUVfOge$M-ykK&$+6c>$F`K1I;$#7M!(INFj$U>wBq=}J$yP+^Y_9YlkFKw
zt2dHAC}>KUL=y=PUf9bQl)A79X>cKC3|J)(FdZN?qnSn@0vX+|i~G2NuXu{>o0k9h
zr`)Ctr}=9LkidCQ(ZO9GJ2}<r*NAE@>$5~U2XZ>sGbTwmi2zlo?lh#EXx%5@!XF52
z*jb{uq^GX8kGsS=eomdN$-LV04@bRU5fRS9OOFv=rufZi42J-&@`YWqA9Sth(u4H`
zepA%Heq_Xb(55}fL@G+^skrZz-(oR%x;M&w-3Ap}PaIte@d`nL1M7e^k~C`bPdybl
zT^J0F*N`0RxS*59P+E^GZO~F7mZ#)-OlK4jOV<H)P4`*q0{_R~eci^YruRPoMBkO}
z?_Aq*SR{UNkK1|x0A}*G#~xFPtsqs*6kF>m@<y6StA#izjoZxW-q9+D^q?IZv-k4s
zT-njT0C<YLya;rt643{h1TnAB$S@4;X!i;HHm_>M>iW~@k|-<fK&l!8VM(55N!Q$W
zU<%!|zKOp<Cq<`#zoQH9L@~42`Gfx(5cb;4re=c{@~Ih47IdWg&b_bS$to=76t%`>
zLdFglC~2&+(W7@^N?h^;6L6HnAHR5rSdxZ6+M(iN$=yC0(NcpY`)GU(`jSb=%?|)T
z?EwTe*9(esC^De}8QjljYdRsfZHGnI-!YXvp6@OXOo}dZRui?`pe;YTmCNu5X#6^X
z*<PdM?S&1H>ydxbbA1Fs=T0Id<wvj8sYQh;?fU<wu_sVfI_xU)@0abu=^<i{e}AQB
z`&*+~ROL3;{`6$WX`vuN#3^3@%3ft3ji(ZXtR-aG%|RbxtBAy6u|uhW0kFbgWGF5i
z67974Xi}>fWtNv`uWd06-n(e$eRFH<gzg;4g)G6+QbN8z?9e}yro00Q3scsL79q!R
z5V|itFSfr@Kb(^tu{%btRfd7;qQ;1+8#gYB$**3vXI1IirewW}W?AXfDNY73Bpn=j
z)=MD$BOIJFDM1j0eR>g@bDYqwJ`t}*4LryiW&f?cf(#2h)3n!yu56m;*bop@DMRSB
z<!dL7H3C-(SFiW#f|E95bd}5Jqsz1d7&lK!mr|GUZ7xP^-P1SBaKdL^ZG)}o8JfR6
zY`~x#o8bFOw!ll4BAFxuE%@D9MOkoR_cf-en}9SD=6H((frh$6YD~gIHGKvC;3fSn
zfwGdW(IVlYOTb~*rm~5?95OJ%?mTmtO&S~|pgB2E>22~dAQLuPOrePaYJLjWhLvoJ
zFo&@o+W*8#H*nkImM<u=<bSsHQ!SI*tW(p~JCXr1xJ_dQMZhL63(P4$gWEjd_n^Jx
zi&%(4eC|u5G>U^n=dwkD)|+sxO$>JZg7LFf09|Xdn}?h2v0h*swAl^A`grr1+B}w<
zt2+C!Ts|c=9LS9sd<rJZX3X3c;I{DpJeP+7JS|`g?oe6CrkgsnQQ9R&@UkKUzUj&a
zW%e^H;c5W)y46|8WH)k1($6t=HVmpd8N=ri>jQ;v4#HL_qx{-2ys7fhpA=$_0mUYe
zjoD3N>ET-4!>a%L29D1?&L$&v#($dPi~zi2WS9j%zbYYLf76B5*AWVlNfSU&0X1`0
ziBpOEh-x*QG1oA&a!^ZqnJDQ^Z6Lxfbx5(jW`-s#`jz#ZF@M<0rq#*abAG=wTwugm
zl3bYhuTT8TywTvlZy}?rl@x)D0W?sk%D4;qKQ%>t0W>OtUhtlrxkwt0pMBu3E+e(%
zbefgag?TX|dtNOBB!_2DT26T;WD?wWrAK`Z+*l@}W)x%s6bJ1WFr5>eU$}RYm5y8P
zIev2?=qltX)UR?Pnjt@RH(;Pa;$OAG{Kq)|?x<K$z|uV1PuM_!<@>zDxG9w$>$<u#
zJ=yq7c2eM=d7jvLnhMsWADm;Tbt11Zqfb|-z**F%PrzqsF<~{B{%q2TGbMfrN~Hqt
zp&wwPOLQ+Xyl*ZwWNNwPt0=L6_&|OaXRYk?_GWa3t&hAbqugWE<lQFb(BSjp_V;Me
zAk!@-aE#W)HYY+&n^pOL4Z#18iC?knjN%w}Zd38$aBMFf`NJ<mD`-W-M}iw7Rkyuw
zZTAQTu|4CGx7U86Z;<g8T{es;t(zNU$ii}d4h9$R#%Ki$!W@mIy7ts4eePg(-XDlL
z)?eXd?s*gje`s0~Xu1R$rFFOGI92JVK^AxLCXm$-fd?5llp*=6{&C*_cwAuJKQ<x&
zO<-n7-Y5MEAcRe`zYSDf<hVfZj@U=w9CynJYUM_yI4Mdt5rzXvpKU8D&-<h4nC>|B
z_L(FiNYqD3^RN(Smk|bqyy;8M$}$(c&QR2QK8U_iT>kh00YKJ9Gz9|u>E`aqWz)I{
ze-8z|w<#EpHHox%o^5J{*@0=mzyH!da}i_^#{;Wqnd)nb+0^3pX>uLxl#7A%^&U=T
zL6^ah#GoParx$1`?|Tk4MLcAEMbevPUbL2XRF9#ag~>8Bzu~*`0-jjh^w20L6G3O#
z9Xhvoe)!1gcLLDP+{yP8=#5<vwRX!qf8aTy&88H3ySL5mU;vL%%X_~|?f-1YKZ_w3
z6=Ld4++I4>2^(e8>9M;m$k1Z>imf4)8ToxL23~s8KXQ$Acwo`{D}tRD(G|f0=|hHY
zB10oJ7z0VxJoyLjgykxkI0~a8N}DucO85EvlCPH6Db@FrjwH#gN1;q~ys{Y0lw)Hh
zr-MY-h`IPdsUtl}@;!1OVoi0~3Hhr^ucqTLmoWCE|1JvcKkXIZnoUfq1{-BMJ~0hs
z%ZBa|d;FWF)5EG{_GlvhD1+gA{G}Z)acKHG$yyi8XhRQVz{rO90(Qs5r{;<6^MwIH
zq98|73-akQ6j{%dx~T@El(kDznRln4zZS4jrdwb5<|7y$e-R!AeX$Xnb}{%4_e_jm
zRV7$w%e1h;!Ro8=Uk6SRf&)WN*r$r7BBe*=Pj0Dn<FsCax@YVs;!4C}<X%KZ^h}tW
z%itzuU-mpSiepmgCnk~(f*-`X3@`0t0z7b%{m$Z6RO;ILPV@-o8#Yy4SUo?Fc}&d0
z<X>{>adb&?t|I@Yo%YS};d+f1@G;m~SG89|tKNM3KP!L)uYluIS+$$?>Y>o{brfQ6
zy0vcvuCG-Zp`F^DO29aqA^F$D490%v%2kM#F7EZAV;NOz%HR~~hlp0F#C8F$PCE}V
zIgRKUw=pB!u0|rflwI+7x%5n{SgJ)h-PIW>b;%o*|MSBC`Q+*%n^kdjd}pt#)CfI=
zP4VrCW#li&YAIke87u`gop(#H!k-oVt{P@!x=S|V*Mm(mMbhq(!;he5RdBE~l<du_
z65(Vk`y%^a7_smLL!x%Rqb3#7xHCEb?{MY?4`nrgKs!*OO4_>ijsOl&(e2weBD!`T
zt>bUP8V=y6G%=5pV_wiWm_4JiL(H-5hrw=73+ZO~@6YsG1U&QfGbd7r-(sdz@#1Ho
z!fV||xT&~6<14`2;j3!8G}N%=FCk%Bc-a$R`p3QqI#vVz4*zfV`M`|^EQfTqa`fG1
zVlXR$|HteFP4Es?5TZIcdi`42O$W75JP4o9n2Jv28VJfg<l@{7!u1-2MB`9p-!HdY
zFi1>d;jdCt6*e{(U%pign!<HH35Gl_ni$_8n2@gE<d_B;r7!HOIfDLX9SEVKZ?`pk
z7ynU(W**Fx>`iW>)C@Z5_{Z}Lgdqgnk}wvkwHFNt`Tq?PaMa+@`3#8^8&$pr-tvUK
zwI<#>*8+=Z)XM0$e)r<ZWm&^<sh`j7dqZr)5T~2Jiv1D5S@yM7A8z<h$0h?i?Y=C=
z%?JRUP+m$l&BB_FCl$1>Y{mx6LHU2mm<v!lrk`^pi9Mm#+g(}3wWIB|7eyz)$Vuu3
z2PK`kRN-R&y#hN~J?0?d)4sI@E{TlTWesyGRY;46GCjl7?6o%8aq8|LJX$?lFTZB;
z`2%bkb)$Bvl5qxQy`xAJlPE>In#Y|o8t%!p-WW{6jI>OcLjoV2mVfDPwR|taYDfrF
zjzd3uwQdf0ms5mb$-y5yu2|O8sr5UH_8sX7NC2U4w95Y8W%|v<{jzZU?CXV#^wuZV
z?65Tbo>6S;YPdBI?h~EMptMyi$J{zosN$~>8g5I*_xxOOcK^D^N_0Sad^sgouIu^K
zYNd!)4S27W<-y*fmY-Lh+L-o@!YH_^8mk7YPusSZ<6z;Zye-Jk2CIRRvn@1VUUlfN
z<39mgXb|rEOe~(vFDZBz1hD%WpRy4UX-UQ`L>M%S$IWx=%!ybxvGYPCKB{B(m=#k;
z+Wmjh1hy>c0T^^*pH%VCs>mY#Uv*#o5M|T-El8)xQc?mpCEXoLOQY0ExpXWYi<E!}
zk}61wlz_@Expc<@(xEGz(%lX3#f?7S=lK&}egtObTr+3p#OE_}^u5%VZ?Qj1`)dW{
z@n31mr)RV%=i&dyN?ofp<eI~3B>4<%_#cCDo!z^!Uny)i=IoFDN3dRtwBlrM`tQvC
zb*);i)yK)|zdX2WVG{8GVRFlIz5eTTd3hHv4H&NYojvCNjAnGb9*KR4X#M}}-s>)$
zfZ<xN1(rV}-~L|e>wiu2`{U20vSo51M0!;%IfB11%V(|Oai`fT6<yHjQ~d1uJ0}r$
zfezB#nBko>wUf@uqN$3&B)n{?Kq5gvrFB{5n0sNe0jJ5*>qk0aE+eWU!8ew{6Z79@
z@6)v!*(16a9F!e2v$4R+O=|~pd1=lqDvSZIfI7an{I=@FfGX?H1@&jg1uIg)H=;|j
z0ZvG-c#&CL$LDX~zJ*XU_I4`A0~3m;)6?G3(YoQCV_0Oywh>Ju#Bb4bt0z^2@NA>f
zoM*NGJypEzV0AMu%S~8x?qomT@o9dv0RP}{O5!#S$x;YsmXl))#U)kt^74w|vnDrQ
ze$IM_3=>pxK7}x;zROU(^H^KE!=>@UW>-s76GbdG@e-&KeNRF{qJ2_X$wjf)k?m&s
z?#yHTXrXP#*U->tlRaAxotQ9j8ZA{qIa99-J6PfAH0^$GIXzg@)zj1C8h*#3!py>Q
zd)46yVN~GX1-Mndy_zJC-$so=Fe%h0ep_4i<nz~)&ua1f9*Y|5B^?ayrQPZNt~uyQ
zYN-;i>CIQ!iC{X+LPe0NC3hpZF}NTrkiBlcMvIZ+=T+;a-TVQko_jxnbB#$^DQe$Y
z7ggW*F!E01I)pAx8FGjKc4iA~7h_`@paQo#3#`_21p@4r4MlS-=(n4d15_N_Wu#L$
z_-+IPCh~A7wSaPRS%Mu*elvcAEavLaoq#1p$In=)|5)(UNb;D)e-RG3Y0QD~W#%Uz
z(DWgu!(dEve4f6+zuFe;%`#&y2%n7n74ko7pCG?>SEqTr#$Uwef0Wr9*bbHH+Ak@K
zuv(bpr%l%yyFHu7XMg9o{cY~ahAFA8U@JL3h9*&X`ABRePR!^SXKRa}82$RM?%H8s
z0QKK)ePYwcgg$JF9PR3QEPZvp$MN8SASCI`95#y$H?6I(aS)6&rETO{&9na7FarB}
zlg@J6(@Ti}b4*T7P8?plS<_wDxYJH+gF#SxjFm(;Rs39mr`shTCqL$|`*G)fCNJ@+
zz7A%Id=vWz&tt9^{W3@QB_wFNllVG(m(pz$Da?v3FdEKRxvD;D?Jp1B4!T$~x6IN9
ztNi_Z=p*^1StxlAIO47y69!qWiGB>BCf$qWtW162nfhrFNKzpQAr)_1GcNtBx;kkS
z?@?=Nxxl*7JipM>{ff8sZ2!uFWX3QRD5IFgu|`l%-KPew4nTsNfa>S5`UM6|va$?I
zpZs{2S7*Fy$*8Epi`u%*4t|->|9GM;GAB+smgDvGdeJ<UF@cgWs;a7A>}y0Eeyk6~
zL;!0=tNSrE<*pq)CR$G_=+fW+cs9K1aLjRMe^}e{;`BfqDABH&1$;GKS9iDDOfW7p
z4^J<V*^@QrwU4B|A{F@CN1sEbEYIlL-8W7qJ*rJ?h;Ys}0Ld6yUlSMDzbBZ!2O^vN
zJ4}Q$2A6kKRFoS~k9ihe*7qndP*$3O)VX?rlFqdW<9M@n>upNP8=1qAoW)0l5V75*
zM;_4un1@H8GoC@pHpyVVhGR@}HVyvXs?*g1x3^Wf#Ei1i=roG*1H)$8@_c2kJS+$D
zFNKAog@km)Hhg2C`ZQ4}<|N&V!*%@Ay~V{v$m)-;?F-HA1?lmDdYI{Hw=$x)ao#)2
zpT_`kF;wm4v_wsIz~#x3_<C{cK)Ivg9x~P|b6W<ew<2ISaNkQkYFkaWnf7^RYWiI}
zD5gQ%m``Gdip4(eysYd4!b3vGjZ?eJ$M+5JMpRHq=7AE0)r+Y{Un|gMx5+!Zx&+0{
zZ{ra0<3x!#qLa~m=~7f3^gbrNnklj+jweaLHtnWyAbFhGi-#3uLJ2<dNYhs&)WZ}|
zZMrl&<~Rvw43vNT_;Dwt*}af?6Zw!H=<mZ@AASbjDn&=(AIjjAyn1Rq*1XVgZ>Ob+
z6Iw`#LtH*O0h3y2m*Q13DVq4Q|5Vi>MF8jW>y=akY_EB3uVo>EoC~4e!1uG<rkjb8
zMAEDdCQw*4N3g#!{)<R)@y!Uda?*9oKL7dd@9)b~^-@0e0BTK9M~3-z6u`RJnzRzK
z=FK(6(vXRa-2nd?szuwiz+T}-IKWaa@m0$39_cq-ooW(OazE8pC055{a*Vz&WvwJq
z$rn%ZGz7}lG%{o-t8y<mOE>KVFw(;-QFtP^S?|zeJzZAr@ErgvieX%8alF_dhH#WG
z)PIH%%q|p7iqG+g_0g@jY1OAw54OH(HvOnNC0UkWBzz$uYIQ-BTW9yi(mCrk)HdCK
z61MZ>kyst1px*Te_75I;5gjRYwPtw$(y3-{d$3E)ZGGu%W~5NX!s$|&=sRN=#^^XR
zt88zE{E%;5pb>q})^1S#i1%x+v>=qD`c+Jf{+et1h@(cWjBH%-ZqU{8iwf&*N+Np6
zAICr?EK%ao(NT=ouj}sd5yf;~0C$mo8#!p*SeAicd~TDCW<T}%<7pSOI)mlbVi2x(
zkDJBOrB?LDf?|;q?hyIKZXB~jq7ol#5=H5n*ctzo3h5&6?o6m2qYbG$<nRZ~itP=I
zFp|oDHHnh9rK4cU^7*X4f9*<8XA~|@9(^?a<Y8OH`J3fWgM=&i7{Rs^^iKB8)otw+
zs-`Sf#^W&*K{NB_(`{T_TP>xf{l$)#_{ZZU9&h76@|9GyTTWzWGYj1r<t5c-^oYEQ
zb-Ud}scZ=BTGJ&3$DErGURP^|lq{ftpdNzSpW7>k9DsJzy<o~d?CqXD7ONIiIKL%O
zk#uR<z!QEv2SHx%XgwMaW<D@8YLV*({qW}1Sl)UBi7+ETQD7TAD<v>T_>szi{3Ex-
z<l-LeU^8R{{PTKAtL>H9Fh9TFNJg7QLJ+pYH(Mn*Y-27*e{ZLyRBF37PmG^w>CU>?
z%+u%%0em%pvc0o5UaU>~bQ!I}?tb=kTRAp&@$-$i^vN$NLYg(X$L3l~iOu}W+M^5&
zVLPRGtpRPTe)C8e5fz4sdFssmU<JL&v@K<o+i|!vUBX(^S4-1m;ejw=$L7RHF`IZq
zzhMpX8#R~MWpoBXLo@r@RVa#?$d8q@Ln3Dh^0X4ay|>MU!i9TjnaKvl>$u++N#)k<
zgPZNrr6mp<IH8{QAP@w|RO_!D;EyVq1QBKC*Y`tyn%tPZ%+R};99DSV#xsSbj~Qjn
zW}8urumM)GDro=6-Fm0c52;00_b<D)YTP~>!LI~FFzU<BRp9;3uH)acvj*ON;qpnE
z9iNs%)lX-zs>xKju9H)b{){wx7WLXaCicSMN6_W?c$HhQ(YN74l*IUS<Wx$i2KGvS
z4<G)Oy<HGs6UVRU?1*oC-}}n=I_G*E?%#N6r=C5))M|s|9V4b{RHQ4`Xf6`XDuRi#
z-d;>=GteDriR4A)o{rVeDXmL7;^c<`p<C394_J}&ZaT@;zLoUhMvb;>hnVe~GzF$U
z#=Xx~uhyL=Xkpx`Vc8V5gy_Im5`>430_PM>_&1&RyYE59>{h4m8uiF4C|>*O99q2^
zf80hc46qzx`@*#vcd33uIhAOv5&D9zecxND+%MG2`q(oUm%Y!&@@-Z6)B{LTnGRMZ
zm)?@-$ZoCOg>98r^Vcuih3#;GE0MsWOLvae(9M6C^<jj2B@myT*F|ovhPW(KDPd7Q
z$>B`CIlbq#%^?k8O7^>WcSl~NQ8a6aaQ(I5=`puZg(V4(TYboaevRj&vL4w?NuFtn
zQFaoM3LhK$MA$(rQJXE((#Z$0v;ps5u31syM4B>`G0VR9I_y`LXOMNXzO%M`=&`V9
z!S8Gg<8J`(J<R-K2dceH0U0=%M2}N_;#?XJE>oF?w(*W<L&V-szwcbk4G}Aq77@FV
zVz~{AV-UVP>HCuK>h2ovQ7@=q#jsI1V_8JDTCZ6IC2PXuO#O4t`^98Jm8g5`49Mj7
zLTd!?ywd&@pY#3knLO<HofDU>WS4j4SSqcCT`r?usb4!7jt>Q$;Fp-TE=PBiea1(h
zYRgb(&vJ{H;8N5PmkqM~jVoH%ER^^VIpk;6+u9}j?h+yDiaM9f!zsGdx^@*l78G{%
z2c7Mf=K5)0J9wuD!`uSKhAvvRzN!mI2)hvV(fF~u-({OC+|p{>jgiCS3U?#73o?Lo
z6LX9iCQ8ThMVU3>D|~u8N}ZSbO&6(>bEf3y5tX;AzpXqxI#6@t!pVo<mmgFg=2hCN
zYTz}gR;O!ti%qwAJQ6eXK=$;I(Xqe1)R}0Hs@Cm0gwL6Jv&F3Sf-CGK4*Am@{NjS^
zt?vFO*;SjTF9K0@N@AH+*#lMh$jaPbiTyM*pTNEtmt*tN8nmv(7*^bky339e2DW?#
zU4WVAvVFV;T++NwU<xQTMNG-Z<de9~Ql@ktil!7CeJz@>;Uj*H(L1`hQF?nY@p2Z`
zo8jkL+*9s%ek@+~Sqt5p)yqY3OEmB7A3Cw$ZvJ}PL9ojX++0~t?AdS58w%ZA!%>4`
zKBg-U(wFmI%PGQ?;ZUh3i3VDNXj{h<sD~NY!U$_@-hdZQ@HYkmJkiHZ5#T|4e>wMX
zaqf5eqr4_F53fwvIYP$g)v73J8<QgRkLn$`Ige&XC+DoI&1*=JefP_C`WVw`qsyOK
zY)CBYz&S24w6Au3_Pgn#zuqQsU^fUX>86-p4D{nMNUV=)jPuo89HX~e{TBM;kwn=b
znF>$cpc?~{@{cuph_AD*r<%tm?uzbn8{wP`E<lwBqxJpZh*W26XcqjAPiRl)cBdrc
zW%(02@ut`;5gRMKd}!b=oFa{b4-qUX=fo_6N9_}g{cU%R(_=d9pwI#fo?7dltgz)Y
z$DOIyxUNHQarwV)nQKtMHig?r0GFf0qY#;QGya_%EZ_v3renY4&GsoV(%(AojOi_v
zOp`&6Y3B6vc0*Ep{Ab$J3+j@k`odb<j~Nfa8B^#&l_w`sZ2>O}5*G6Jo4&IYzU(-|
zT?`14iF_bR{R?wVd;F;^kA&6{KG>z8jsoUBNDm9x*bsMUiV?S1e#7=Gi2mtL#VfaG
z`TEV{=(cj{Kie_^3$Emv^ia^FwMu;Aq<|_ea8|VJpY7!3K?A~`H0y+@v+a6jWKrVJ
zADh(kE%@YC65{xv+KNCM6{)M8C^N>_J~ZL+6#;k6UHpNk4P7fW(hTgm>k(`H-J@Rg
zd2ri?M$-OscN&rnKh{_Vx7cIx4jO^Nz}k&-Y<uM$Adbh-jQ<<s*pXm3D#@7O6q|~U
zylLdwpSj4m2pw53S^Pb2mHIL@&^efvif?!3H5qTu2<McM|H|SWO-N-P5611){b1br
z8Ck>GQGLVtP;;|AMiOK`n=_W!x-ZNJ)eVD<`W`#>6S|#yQ(+P7Jy`)goPZ{N_*5>c
zfWZC}kJ_@!KUWJ*p<ZhGPCi$xo_aejaK1S&672!a6t${G%_;BXar^okQQ>NlH%=<<
zSQX3G55mmstPjox+h;SO+RfYhlRHC^wYpCmrd$@vyM#kN<S!(n$K8E6WiKwgh&1SV
z5+^<0(IruSiR#M3n{yt9@As5*I`jNB6;c>$W$HmwLX{q~CDQ>g+c)rs6OWr$TNVUW
zH}O+gAsJAUPOaH!ybU>3#8<ZAxWvoS{UsULK(&#{uISX&f_^+)WP$(=JaWrgfQp$T
z)7T7@uK9c@ys#2Yq?wLbmMshstLcwJYyB}z{J|><rs5{tg?kML7N+=!sKB5xRnZj8
zwC981lv(-9xRu$){mN{^myJ!T7sNk;{EH?_o=la%R+1!+5msT}(?iFZY2aT>^^S=v
zI%PVG>*zHOp%PMx|BUDOLILK44;SI&-;D488y38{SMvilBM{i_Dl;x#Em!XD8hs8a
z-%RZ4Au8(5regQ<$WG!GY`)VBVb-5~vA5ITzozuqU-FQ_6%+lGyIG3s%5u)#_1Wj!
zxa|QYo0@J}Bew&}aIw4IP=Md%4Z7_{or~qp7Y`=>N1MDbYa^qYY3kFf)}%-cK*v0Z
z`-m9D;!!xq_AKDlwI6xj=lZ_YcSr3x{hJ!mRF%w9OTYDKnVR&Nq`>++>V#A&|GX>R
z1#@k>uyXAT=Oyto+fw~z^AWPv@pO!UUWemn=k^;nZqT*s(W*P*{xFh7xqABpH_lEM
zX&_gTmx7(zFgy-8^!<0pc&T_~Dk_75-z5GXXRzP6CCaN>^6O=eb)d;i?}z?~aGWIi
zyo8dW2H*0sy-mS`{DSGWnr|`GlU}y*DF<s%v%b^^;Op#LqEObsO~NKvOQi{H%)Spi
zaTNNo>P&@?@@mSRi*F&L_v~wt^@CCrgNgLO8?s5uUwZZzj^RF&3}^c_1TOGu%ITu3
zA%ELCv`>!l^!=;4T8&fm?$Y*;NRMK_B=TbD<%rEf-ka=rAgi}5%?JMDNGxMvamZZ^
zCt!rTP_ZYfGk!WjH&TbC_cECfDX-()y*Tm{>pT7tGi;SP7FLU5k&wliqzSn-5YUWI
zbw~%J9-80X?kZK$S>Y5b$Tm)TmAT9W1tZQ!SFdIp7)elXqc8X^_kXIYcM1zBBHNwr
zFzIWsC?x<QM9lr@`J3H*rP5Tx7rnaBMiXiZQ^iRNsDrHC66NZA)F-m-dBu|AaWp|X
znfz&^iM6(aNgJnA*W_8ZOXrD_uA-DG!W^x0o_oZ4?Am%eQ^$H+`}tyJY&h7uhD&E)
zNYDSsC!bl)p4lcgCDhrz-b<-mhpAmvnxam?JsX+&$8xygQ!_p2m9jz-`j*~r`><8}
za1H)!pBDQ;9IhwaHy@8AaHq-_doE}%4YzE{%aS(5<tX**&T{&Oa0j9)*7`j<-~05X
z!T^`Gl=Ob9FDziCF(&sHubNX5!=hiW9ELKXfH@M!&wu~Q%L3KT=mxl;1a|Cw3;|T9
z*2Dt;c3}WMLSUH~J{~#Mt^7MF9KjWA7KlO#W*wC!@nZRN7~fTM)T>^z{UrOg()X2p
z<nm&8?JmpZR-sg7Q=VzQRxT-&;kq)W%5*+hCEa@vQ=(;eehy1I0}$T_>+m8}d$0BK
z2U%5{tj-S-6sl@-I9KEr8jj{jysHLZ3g@d+I(Pw$aVaj4B&T+Z8(y`VK6ab9iDbWn
zi5)#9?44<2|KzV^ZM?x=X#ucxLK-KwGSPJ|UVN%e>TtDCletEMr%@D@WApJ;)6d~e
z&M)LU7yO?{xUO|L*IkB_#%ag2{$K*w6mAoPm^_srBoK7L?7vG(?{#zUYm8xZ5ZB)$
zKhse7$im=k{$cy;>v0yo20wcdA6ngKW~Q&%sit4|6EL=&u$VU`VBq51(WUf~x1ir3
zykp}BKymmQIe)<)J55Ys1>MXc+JlXZiQ^?(27lwl)@s2hg&HDBu5a5}$a@^`4BbAE
zqING;D7JH)47;U(8=EJl^R91+LM)d%cVjm3(FZdH<y-S5s9zBKm8)Xgqo}uJ8GO+M
zI-66`f=9|FFc-zIexY@N?|)GyRH3#Ek3Y&d@d8hj=)L@jrlIX2xfp&9?CTBwd_CIi
zKcQdPtqV5<VaqpWK8E9-n6)H70bz{gPaM1?yPtUWj%>R8Y?$9NjnnLYy1OJ_sbR4@
zBOX5$xfI)y+u*(Zu-waIMCX+D)5T6;kGUVuU8P=|e~47TCWgKXOd_(+rpkVsnz_-a
zCK`IXlX!z1UF(FJjRK&u2Oo0IAlHff5!oQH-yioN6U#0o*EbP8MNp#be3#4RBb@f(
zkzP_AM}+Ba_}ScXHA#4^(<oy2sStk*FFo?m-F`-Mfi{d-sZqg9OFW(Tgqu(0V^E_8
zl#&kar?@RU36#5gy6p|$`--@M_=8E5@6&Pd%<C<K*IaQG2CY0o5cQf$HYS(D7!I26
zR;fGmx4onHz99Y_c_bPyA5}Epp*E+0Ir4*TCFIG}9bFK?whvv;(;iAm5+-X+X;IXr
zpA%;G{>_12BQPrLI=>e3b1DtT^KYj3&xZbfJ@Z!VCTRu#_v_%k>$i(+rRK-X{zg#1
ze|ri7N50f3{;x%Vc=i{6$t)uSlEtHaRTYN+g>j@u1GA{*-BMotXB2UazWy<A7OQWX
zuVv^e`h6X^AmCkJrE?lfy4>EHDqZOK-MYzp6A&Kl^h9oSUo!enZ;~Py1T?0YV!P%T
z3-^5U1`~3?u$&bqJJXx}m8pgaVne?Gkcn5#8KITFJ!=QM2!oF^{i6M1)Bfa*m#2=J
zCS_!*zn(HQyFmj*iQ{DU2NaCT9X`BS+<`fak<qbNO*M}LWnxE=_VHx3a_8H(8G_E;
z;z{R6_M3Nn`HzJvp?Fg+M?2|fP&0p0Ez6@{nzZK<u<=FPz2YtJAlJ+0m5|Cxr_qq!
zG%=w!_{|;uvR5a+F={imDn4C;o?VTJi#Eg@t>kCS<!1y6nuPYNDeTaGtL*Jx?B73t
zj>DQyk|ToUL`?(iH%ew|M$f6QMr|{c+}uh|E@h7<Jc8*CVgPknJ^BL=L0;eD+ebQs
z32nE%6*}Omu96o?(rN9HIA^1a)t)PQ2?+`3Z}8)OCE>D+NmrRxG60{vfgINA?CV=(
zGvPJVlydr5V;{PAak^HtvlVnT{&{04m#{sK(WOEGK{YRcmDCKST@QRl)gTlTV|g?#
zIeKv@v{y1tOkZX#VCH%$YU;``arrx5d%J~@FWCy@T|R%(pO2`Nv01afi+zdJG0Xc*
zwh};sVp>`rIin@nMdi}ai{m==8*a22D>RS3cPJ8_m#nO+cUyYxRW1l#wuBMt8XMa?
z->pJC=L)Vuq}?!q0bRfU0(ccn2DN;3c_hbe-asd{RVO4UB=is?IU^%3hE^<ua;)6(
z7i6N!P1yqcP=040MP;5`<+oYQ?(X5CX{p}kaJuSP^c;X@M*v9c-TU_;RpM6*gNbTu
ztM6IWqgBtAbK;`;#~h352P$362MDU2Mg=X6&liA-rcD^Q)MTg^{FA@G#|yasR(+C)
zAe1}5Jlo1Egzq8khxs9Gcr3AY)>bqb0f#iF`zxUVN0U_c_V%!)bf0Yi5_k^a0%B0&
zzNno>0Mqf@nxc22RXVsvu;2kbS3qjLy|8xE0j48df_U@hjmX@1MZER!+)1z4bn>aj
zM{zPWHMP7=LP~D_gRZWw7W1o9!`}-FDWc~)9gJVTd}*rPYKXeLIGg7SIw2ey9@e~<
zxYxy1^wJGh*coCy@Tk`ZY(Ofxe822^l`VJXP}H*i04v97dfcTEI`+`5aid~9b$78p
z!=-O;e`VM)8DZWy)rR=?t%bLCJtX)xZtCs{dli&1@HlIBtjJuhRYXLD0v{h=WT_T_
z7<)1fEPS2V8%d?(%mWTS%$5S_V2kyUlGltpn@(cgOTE!D;c(o>oC+B@SL2i4ZCgx7
zduI|hJ=9Mc5SF^SWN5gtWdq+tN7_dQnHR}aJe74cmXhr(e2KN=&b2A0#RwW2nnkIu
zG)`y7k;eeH=9%$w;ti4oJc7uD$3g8}v%;pML+8KUtW2b9r9(7;32P-5qb@1Gx;&!|
zhVW$fOYcRKMT6pirEdk6(-~lZni`sW#E>OOE6*Ock!u6+Pc<bzxHndEm80%>E=u`h
z>^%{JtK$wijgx3`M>O~OQR9^erp-dvhh!n!STg4)`C1|dqy2JM6Tf|ce1B$UX48+P
z4QffxFJwO2G6x_I4w~g}+Z&bBb3jpdu}5o>T!r#pUgczsAS6g0G`+XjE3{*59{IFF
zf}I`rm#Gi^gmWd?B)}Z_Otu7?<*VO+Z_h|xe>CN1gtU97rd=V*&lf2WIlvfnP6qt4
zNJ4Sv5-1N30klnkIb*|;280CA7kZ9;Qki`nmJwrfoXFBB-kQz`u~NUQi=$p*T+~SU
zFS96IM*ji2oLh|-hvP9iQY)Y1qrWW7A0HpXSY}4eD$>O8DyLo)kY&R=;<aV%SG#Cq
z)%of{0O%vs5;u)e5!?(8tCsQEG#Sogi4|{|_`H4olNdNw%hWt}zq8YFef1)=Fh(Xb
zxOXxs-x=ScXjG}gnDMpoJHB{cwbv&VbB5nP`<j^6p^w7%J;g}_o0##{Nq>-r|5~E_
z^Iq}uo-MlWoMP*lUJ{p)W7^{-xvMv3^)FJ=xEPjtNp3f?OK14+)B=YBfEPMjXFa<y
zBzJPL;4``UNsyP>Vb7ahV(fOF`C5j3rif28120f>fB^*-)JPS^pANdJCo@MT44tZU
zQ)A~*=z{?Msv#1NrXU1a*ZUo{11GX`mxT#fi&NNcWzr@MsASYm<N>hNw?qvLz$!Fa
z(mRSQ5H^iu3+^%`T<_NKTvq@QNEvCP7aer^J-_Ox!Os!vK8me7e<6M@7X2hr8I0tN
zw2P_V?c$2DV<!ctaoX80$ZXZ^MK8Rw9@tfHR%8I>qaXFuA(nGd?<uw2#pk1iqUE`{
zxP&Mn6-BoFpI%E~v!)C=_-<A&%@wzjSduML2PD`NTL*AH2q&U}yfXS#XsQ{AL<`cd
zDLaFFrENidM4>puf*~2>J)s-9Up}Ke0o{Z@Z{ND5bnMnaZ`;P|qGyB_alfq~z(B_#
z8FYDKI{}-I$#Wub6rA(n94Y_Zbeptoc`!#)4;_QH$2lb%-y&#yQup<F{R%<z1$^(P
zp*b-);(JDqkz6S&ErI3ceAH5ye{~$?GaPhqu>UAdpq#N-MXxM0Q~%@ilPPh22E+mp
zsK0X3JFVorl=t2$I{+C6^49z@=Gkv=@zkPaUZT0pX4o~7?XVv3m>=qjVv{nWhELVq
z9=%g5>D~u<B4gd;P#{D&5h%{zUsD&gF(Z%BQ<suYF%7Z=<$$t!T5DAKg9x-~m))B?
zy5Zy~!uh=t5ym~a!x76o?nqkMM@tQX7iTnCZO5Qozx9&v?~#Rc6=v|;d)fTi@&wMb
zVXP?e?$5=g!M^Wq->&3rU&V<uA%W+h65`IZr8b0F=3qI^6=R~s{)uXiA}EI2C-J1+
z`O6DAL13i}-$NSDE%fAss*b0S4*VIH<TNK1Cv{4qligPvi%-(FL?A@;5nx%+>+`-=
zsRe~KUF){BF!G0l{Gm`5Q<IA6K)=@LlO4aZv?@&gV$I+E#Y&3YVH|t8E`0s0ap^Bs
zzqW^{uZc9W)TKb`IWrl3f0J2;EgXf4iFwM*`a~I6d=e7OV$E3i1gz%bVt95+OVSDd
zTXu7}oC|!ps|h;wv3L_%F${5b>0HlL+I@aO9k(JPtO2-ltxo#@SbU8lE7D*PP@Dz`
z8aYxjUJcg=&%K&^*-W4=dzxRIC0+qZ>fi*~v*xJI<hbKRJ|uXysJ-Usnt5zg0tx%x
z5y=5wTJCuKu+`IlP)|j-OXb49txx`)@@%VQ2?KXHB!M|1FoOsQAL0o;m?a|P4&9-#
zv;I8I+g$=(bW2o;5=B|Nj@x;yv&()Hjs5<)k8vEQc04feXCiabMlQ<#aD6nhGbcF*
zH5B>OJy#^PCAFc22+nw0iGQv8MVWIM-zVs*=9O7@DzpqU2C|v>%(?mEXxil!e^_2Q
zTrd_Yge{6_+Q+uJ|77ZZaXtJBz|dWs*_IRK)ww1mIB<NmV4sYx4;LcP_V504P6(&o
z7APtHnny5E$L~@@RZJqStS)+cl5#nux!*|+#nq2($~J<t_&trO#%-i&i=+E~B_VHB
z(|X<(-*D*$Jbs~>7Q3dSSQFW{C#CR<V5;sNEl)Rc6I>vv#OI>AE@8Deu_*DQ_`}Pg
zk*-I7;??_)@kgR05O3<5_H;#2<QMnt)pBMsbu#gCYj0t*U+8lFNPZ28nhQHyDcx?T
zbxA%(;so+Fz(E4i${Ae%gj}^qR)%Wc<rIcRha_AUA%%w=I!ienp!A%|93vdg9LLM?
z-Oa8<AU+VYKl}>V<J4sB=Y@5<kA>bAWZ>x&xSj5o2JkhWmwt~z8xV*Q@DZ>is+B0c
zQ!AL3-dW0xu$5YNZj#&<kVYhlx~4%bGNo`C>xKB`xGFf@Ih#1Zh`0F%sdMRpU*Y5@
zX8t><JRUd|;^RKl=0uv&NoW!i_K9P+^L0oZ<SqKyX8wn9u)Z-afnKC{X{PIEZNWI9
z3-{_li;K=^^iu-E#D^uFK9TaLIzq&Infw)EgohvBl8s*h)P=McL-}b-5pLgKN&A}F
z?JP+^8sL092<gjDRdU7YpkH;*pPMvX9ux)9xOIuX$L?Mt$BVR%GyvzGT>SQVxw5RH
z87u8`?u3%28L4s4WcD58d1Z2Zk^6HLJS4#px0C|Zt(3@=<C_Fi!}>OkLcNB7MN$v6
zGIYN9^2gX@Vr9qOLfzu~4Z@=v)f()gV!YD3KLqabslncUC#cf0H!`Tg2z`am;|y{G
zF@acYKl$dB#Oiqx#ar%^THnjdm*Y`lK;7xK8U@*b9(wv@lComs@MM9Q&<^TiIFa{d
zA^A-Z<7kzre{e+vbaYA!qr0iF?_mEkNqG!0MnZ;$y1a``z*wB;%l96>>)Jhi!=Go)
zW&36(8l$mOo`b|JJ2yG*`^sH;bs?VbaMIgAL*C9X1E<JGV0);=Xirq46FPk^mY0M(
z+?gCHzo#6UofZdZa$pIl6=eTWHriT{7{wT1kwP{6mgU0o`zN>WNnZJ=<V|9$i}SsH
z+K0Wy44v<wd9csLmTA5Ut+Z5A2?JCqn`GC}f6GT9ssVz>84jNuPiuB@ngd;u83Vp?
z?jyAKC6+!~cG*sWYNQ$cmwItfQvEb1C9&L5VozN9f`*a74duu}A??MEEv%H`ZyJs~
z61#N`2k2*uLfxfjz6ve0yx=b@<b8tcM?DQsmfv%YcYE{85tup;Q-myKh*5z~tP~xK
zpj_k49^G4n^Y+{110T@)fBAsA+D!x);OCs}u`-87mP|QeQ8~L3I^TgBq|j(h+SSV$
zm?#g55!YfzS5&E#_^|3?VnhlNL1hMGvg!M1Y>6nKZOl3kITAzCe>8OcICBvL6-&s=
zy6A~|6c@7Z5_I+q0jE_brxW-AYqno?4u7wr;C64!yfXS!RxCkO@66aH!1rfmQC8K#
z&)TxhJt;gdG}^KDPll8YZ{P(=KI$KYxqqDo;eCV^osMFOI#ZLyJWdeFl+8zrK^-3{
zaZ+cYfl}qD-10@yM8^4Fz42bzO0kLNU~w8dx7>wyOyZ606)D=TXI1oc(~H*g$vvT$
zSiiXJDo35Ti)yT4^`026G*NcjG0LuWGf+yM%6jg8v{0295T+gp#m)5F&|XTfZrYlp
zRZiM`Jgw;MYsz@Ni?7xFMf6iZ<=L@WlBMg|3^a|c3b?mBU?iQ^s7XbaG~Rr+Q6cxM
z@4}F$F8%D>aqZtP5>RU**`3RAlbXpF;oAf``kQDsq8Aett{&&*KT|H|2c8GL^F{aq
zassIp<_NaCW>cp2$D%cF@&uQPnT1C1vlDInY$p}?PqD6Td#?ofGQ2P93T4Ii%r^-J
zJ3dTgv7YeQfJsz=CgZA~de}FF$FrYeKfKF@&D5)rNPo>DtVc|llv6q!_H_SAN?bEN
zlUXzOw%gag4oCjYbp31lUR^VtITD0pZ%pAIXtL*;MOBus#Z~x!Nf6+|AprneW$6rw
z-}s(?QXT^8Ko32iBoS5r(**|*roVoqHvc#K{_kjQ+&~Wkeyhm2|LG#@4lo=6Ys?>7
l#?9aElmR{be<tuHW3cpxMmfTR)?2`jnv#}c;UkNX{|9`&G{XP@

literal 0
HcmV?d00001

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index b0dea44..bf2ac77 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ac00f18b1255fe2fd96fea78f54cf237c8ebc58fc5f36c9d734b1056494a81a8"
+  "signature": "sha256:11c7df5b48317dee4e78f1592431823f89b38ea262acb74b420b17dca4440d63"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -42,7 +42,7 @@
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -116,6 +116,16 @@
       "\n",
       "- [Feature Scaling](#Feature-Scaling)\n",
       "\n",
+      "- [Linear Transformation: Principal Component Analysis (PCA)](#PCA)\n",
+      "\n",
+      "- [Linear Transformation: Multiple Discrciminant Analysis (MDA)](#MDA)\n",
+      "\n",
+      "- [Simple Supervised Classification](#Simple-Supervised-Classification)\n",
+      "\n",
+      "    - [Linear Discriminant Analysis as simple linear classifier](#Linear-Discriminant-Analysis-as-simple-linear-classifier)\n",
+      "    \n",
+      "    - [Classification Stochastic Gradient Descent (SGD)](#SGD)\n",
+      "\n",
       "- [Saving the processed datasets](#Saving-the-processed-datasets)\n",
       "\n",
       "    - [Pickle](#Pickle)\n",
@@ -206,7 +216,8 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "For the following tutorial, we will be working with the free \"Wine\" Dataset that is deposited on the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Wine).\n",
+      "For the following tutorial, we will be working with the free \"Wine\" Dataset that is deposited on the UCI machine learning repository  \n",
+      "(http://archive.ics.uci.edu/ml/datasets/Wine).\n",
       "\n",
       "<br>\n",
       "\n",
@@ -308,7 +319,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 100
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -408,7 +419,7 @@
        ]
       }
      ],
-     "prompt_number": 97
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -471,7 +482,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 53
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -516,13 +527,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxAAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZmb2v/Uv93HY7aGU4uwAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x142bdd240>"
+        "<matplotlib.figure.Figure at 0x1056b1b00>"
        ]
       }
      ],
-     "prompt_number": 95
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -569,8 +580,6 @@
      "input": [
       "from scipy.stats import pearsonr\n",
       "\n",
-      "\n",
-      "\n",
       "plt.figure(figsize=(10,8))\n",
       "\n",
       "for label,marker,color in zip(\n",
@@ -600,13 +609,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nah7OJ7wrYQAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERERERERERERERERERKSW+H/CqQ+gYvnblgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x16dacf6a0>"
+        "<matplotlib.figure.Figure at 0x105c1c358>"
        ]
       }
      ],
-     "prompt_number": 98
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -658,13 +667,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU5IamKFEnlyKlaSm5f8A5/W2rh+LSFkAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZFLfDZEkSZKkxkKFnA6RpEbn/wFnNYMMVynu2gAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x106d84a20>"
+        "<matplotlib.figure.Figure at 0x106fa3ba8>"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -712,7 +721,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 102
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -756,7 +765,7 @@
        ]
       }
      ],
-     "prompt_number": 106
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -805,13 +814,12 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 107
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "\n",
       "f, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(10,5))\n",
       "\n",
       "for a,x_dat, y_lab in zip(ax, (X_train, X_test), (y_train, y_test)):\n",
@@ -842,13 +850,522 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgghhBBCCCGEEEIIIYSoIf4fkAk01RhtGrsAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x1070e5c88>"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id=\"PCA\"></a>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Linear Transformation: Principal Component Analysis (PCA)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The main purposes of a principal component analysis are the analysis of data to identify patterns and finding patterns to reduce the dimensions of the dataset with minimal loss of information.\n",
+      "\n",
+      "Here, our desired outcome of the principal component analysis is to project a feature space (our dataset consisting of n x d-dimensional samples) onto a smaller subspace that represents our data \"well\". A possible application would be a pattern classification task, where we want to reduce the computational costs and the error of parameter estimation by reducing the number of dimensions of our feature space by extracting a subspace that describes our data \"best\".\n",
+      "\n",
+      "If you are interested in the Principal Component Analysis in more detail, I have outlined the procedure in a separate article  \n",
+      "[\"Implementing a Principal Component Analysis (PCA) in Python step by step](http://sebastianraschka.com/Articles/2014_pca_step_by_step.html)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here, we will use the [`sklearn.decomposition.PCA`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html) to transform our training data onto 2 dimensional subspace:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.decomposition import PCA\n",
+      "sklearn_pca = PCA(n_components=2)\n",
+      "sklearn_transf = sklearn_pca.fit_transform(X_train)\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for label,marker,color in zip(\n",
+      "        range(1,4),('x', 'o', '^'),('blue', 'red', 'green')):\n",
+      "\n",
+      "\n",
+      "    plt.scatter(x=sklearn_transf[:,0][y_train == label],\n",
+      "                y=sklearn_transf[:,1][y_train == label], \n",
+      "                marker=marker, \n",
+      "                color=color,\n",
+      "                alpha=0.7, \n",
+      "                label='class {}'.format(label)\n",
+      "                )\n",
+      "\n",
+      "plt.xlabel('vector 1')\n",
+      "plt.ylabel('vector 2')\n",
+      "\n",
+      "plt.legend()\n",
+      "plt.title('Most significant singular vectors after linear transformation via PCA')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAH4CAYAAACbjOPoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x132013390>"
+        "<matplotlib.figure.Figure at 0x1070e5668>"
        ]
       }
      ],
-     "prompt_number": 108
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='MDA'></a>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Linear Transformation: Multiple Discrciminant Analysis (MDA)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The main purposes of a Multiple Discriminant Analysis is to analyze the data to identify patterns to project it onto a subspace that yields a better separation of the classes. Also, the dimensionality of the dataset shall be reduced with minimal loss of information.\n",
+      "\n",
+      "**The approach is very similar to a Principal Component Analysis (PCA), but in addition to finding the component axes that maximize the variance of our data, we are additionally interested in the axes that maximize the separation of our classes (e.g., in a supervised pattern classification problem)**\n",
+      "\n",
+      "Here, our desired outcome of the multiple discriminant analysis is to project a feature space (our dataset consisting of n d-dimensional samples) onto a smaller subspace that represents our data \"well\" and has a good class separation. A possible application would be a pattern classification task, where we want to reduce the computational costs and the error of parameter estimation by reducing the number of dimensions of our feature space by extracting a subspace that describes our data \"best\"."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 4,
+     "metadata": {},
+     "source": [
+      "Principal Component Analysis (PCA) Vs. Multiple Discriminant Analysis (MDA)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Both Multiple Discriminant Analysis (MDA) and Principal Component Analysis (PCA) are linear transformation methods and closely related to each other. In PCA, we are interested to find the directions (components) that maximize the variance in our dataset, where in MDA, we are additionally interested to find the directions that maximize the separation (or discrimination) between different classes (for example, in pattern classification problems where our dataset consists of multiple classes. In contrast two PCA, which ignores the class labels).\n",
+      "\n",
+      "**In other words, via PCA, we are projecting the entire set of data (without class labels) onto a different subspace, and in MDA, we are trying to determine a suitable subspace to distinguish between patterns that belong to different classes. Or, roughly speaking in PCA we are trying to find the axes with maximum variances where the data is most spread (within a class, since PCA treats the whole data set as one class), and in MDA we are additionally maximizing the spread between classes.**\n",
+      "\n",
+      "In typical pattern recognition problems, a PCA is often followed by an MDA."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](../Images/mda_overview.png)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If you are interested, you can find more information about the MDA in my IPython notebook   \n",
+      "[Stepping through a Multiple Discriminant Analysis - using Python's NumPy and matplotlib](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/multiple_discriminant_analysis.ipynb?create=1)."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Like we did in the PCA section above, we will use a `scikit-learn` funcion, [`sklearn.lda.LDA`](http://scikit-learn.org/stable/modules/generated/sklearn.lda.LDA.html) in order to transform our training data onto 2 dimensional subspace, where MDA is basically the more generalized form of an LDA (Linear Discriminant Analysis):"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.lda import LDA\n",
+      "sklearn_lda = LDA(n_components=2)\n",
+      "sklearn_transf = sklearn_lda.fit_transform(X_train, y_train)\n",
+      "\n",
+      "plt.figure(figsize=(10,8))\n",
+      "\n",
+      "for label,marker,color in zip(\n",
+      "        range(1,4),('x', 'o', '^'),('blue', 'red', 'green')):\n",
+      "\n",
+      "\n",
+      "    plt.scatter(x=sklearn_transf[:,0][y_train == label],\n",
+      "                y=sklearn_transf[:,1][y_train == label], \n",
+      "                marker=marker, \n",
+      "                color=color,\n",
+      "                alpha=0.7, \n",
+      "                label='class {}'.format(label)\n",
+      "                )\n",
+      "\n",
+      "plt.xlabel('vector 1')\n",
+      "plt.ylabel('vector 2')\n",
+      "\n",
+      "plt.legend()\n",
+      "plt.title('Most significant singular vectors after linear transformation via LDA')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH4CAYAAAD+YRGXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x107e3b208>"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Simple Supervised Classification"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Linear Discriminant Analysis as simple linear classifier"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The LDA that we've just used in the section above can also be used as a simple linear classifier."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# fit model\n",
+      "lda_clf = LDA()\n",
+      "lda_clf.fit(X_train, y_train)\n",
+      "LDA(n_components=None, priors=None)\n",
+      "\n",
+      "# prediction\n",
+      "print('1st sample from test dataset classified as:', lda_clf.predict(X_test[0,:]))\n",
+      "print('actual class label:', y_test[0])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1st sample from test dataset classified as: [3]\n",
+        "actual class label: 3\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another handy subpackage of sklearn is `metrics`. The [`metrics.accuracy_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html), for example, is quite useful to evaluate how many samples can be classified correctly:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn import metrics\n",
+      "pred_train = lda_clf.predict(X_train)\n",
+      "\n",
+      "print('Prediction accuracy for the training dataset')\n",
+      "print('{:.2%}'.format(metrics.accuracy_score(y_train, pred_train)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Prediction accuracy for the training dataset\n",
+        "100.00%\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To verify that over model was not overfitted to the training dataset, let us evaluate the classifier's accuracy on the test dataset:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pred_test = lda_clf.predict(X_test)\n",
+      "\n",
+      "print('Prediction accuracy for the test dataset')\n",
+      "print('{:.2%}'.format(metrics.accuracy_score(y_test, pred_test)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Prediction accuracy for the test dataset\n",
+        "98.15%\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "**Confusion Matrix**  \n",
+      "As we can see above, there was a very low misclassification rate when we'd apply the classifier on the test data set. A confusion matrix can tell us in more detail which particular classes could not classified correctly.\n",
+      "\n",
+      "|              |         |                | **predicted class** |               |\n",
+      "|--------------|---------|----------------|-----------------|---------------|\n",
+      "|              |         | class 1        | class 2         | class 3       |\n",
+      "|              | class 1 | True positives |                 |               |\n",
+      "| **actual class** | class 2 |                | True positives  |               |\n",
+      "|              | class 3 |                |                 | True positive |"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('Confusion Matrix of the LDA-classifier')\n",
+      "print(metrics.confusion_matrix(y_test, lda_clf.predict(X_test)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Confusion Matrix of the LDA-classifier\n",
+        "[[14  0  0]\n",
+        " [ 1 17  0]\n",
+        " [ 0  0 22]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As we can see, one sample from class 2 was incorrectly labeled as class 1, from the perspective of class 1, this would be 1 \"False Negative\" or a \"False Postive\" from the perspective of class 2, respectively"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='SGD'></a>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Classification Stochastic Gradient Descent (SGD)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let us now compare the classification accuracy of the LDA classifier with a simple classification (we also use the probably not ideal default settings here) via stochastic gradient descent, an algorithm that minimizes a linear objective function.  \n",
+      "More information about the `sklearn.linear_model.SGDClassifier` can be found [here](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from sklearn.linear_model import SGDClassifier\n",
+      "\n",
+      "sdg_clf = SGDClassifier()\n",
+      "sdg_clf.fit(X_train, y_train)\n",
+      "\n",
+      "pred_train = sdg_clf.predict(X_train)\n",
+      "pred_test = sdg_clf.predict(X_test)\n",
+      "\n",
+      "print('\\nPrediction accuracy for the training dataset')\n",
+      "print('{:.2%}\\n'.format(metrics.accuracy_score(y_train, pred_train)))\n",
+      "\n",
+      "print('Prediction accuracy for the test dataset')\n",
+      "print('{:.2%}\\n'.format(metrics.accuracy_score(y_test, pred_test)))\n",
+      "\n",
+      "print('Confusion Matrix of the SGD-classifier')\n",
+      "print(metrics.confusion_matrix(y_test, sdg_clf.predict(X_test)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Prediction accuracy for the training dataset\n",
+        "99.19%\n",
+        "\n",
+        "Prediction accuracy for the test dataset\n",
+        "100.00%\n",
+        "\n",
+        "Confusion Matrix of the SGD-classifier\n",
+        "[[14  0  0]\n",
+        " [ 0 18  0]\n",
+        " [ 0  0 22]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Quite impressively, we achieved a 100% prediction accuracy on the test dataset without any additional efforts of tweaking any parameters and settings."
+     ]
     },
     {
      "cell_type": "markdown",
@@ -910,7 +1427,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 113
+     "prompt_number": 19
     },
     {
      "cell_type": "code",
@@ -925,7 +1442,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 114
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -966,7 +1483,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 137
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -985,7 +1502,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 138
+     "prompt_number": 22
     }
    ],
    "metadata": {}
diff --git a/tutorials/standardized_data.pkl b/tutorials/standardized_data.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..b81fb23611e60d8c1b95cd9bd42f541a726a2887
GIT binary patch
literal 20263
zcmbV!c{o*H`@ShtQYwX#Bqd3cOvO5;6qVAXG%AH8B87@dqJ%V3BvLYzGAkC!JkRqy
z9J6yA$2|Oe-nVCcugiOV{`vW%b?tNZIjghxTF-Mo_kBOxiT$({i_`^+8&@suWG`I1
zZY+EChPjRD`Rmuu+wq>gZhYaIg|*G~8y9SNtyuqi7O%y{|GsO*rpxlsid}=%$cp2U
z)jWMGPAOeh-P^hgT&%eMo_f*7&eE9IiravVg@xskq7{!Wi!SRUD_(u8`BJ*<2CTEM
z?y}-LaNvN>%<SKP{?TQzv07l{rhaZ43rktc<DqSJ{Yb<%{k5@c9e$@|+$dL8hN|ZM
zuvZ!z!yn_`@VxiwLhFaOZ&<gTo#Ot`o65Fr0_j+596oR&7k{xYRf_sjgXHh6lcX8;
zAqAyP@mYphNcA&s+=`PuC}1jIZmSFvMa3C2pLKVl6qDaczq@+zIydem185WniOQ*G
z1eD{oc_PW8;??*Z3-55@&Pr_2aqG~Zhm{!SabKItVV*FKZz-yT*_SrqSkb)!6XAnM
z+<vh3l0^wtn=fYd_)`_e(I#O6stmkCG_uWt?+3=pn`@hSTXDICXtESf8>$TZ@!QFW
zhABLvn%gaF&_$gi(p9F-C{#??4i#i09%E0>&En12<m_d(G2a0kvR6~&kaYuQxQ{Gf
ztJ#a#86$^7YzML4l1B#3Z@TeI1I;I8@-&?3r0)Juu??#*=uYhuUN|K0P@K1RFRoOY
zxv4fhi7vSJtY;jfq493>-TvQ)$aQmenqMkwSb!xO=KBhb&{3z}^cNam>FjymcuP!a
zSZ{pulFOTi(Bu6LVy7=g<EhJwbOJ8XknF)P=Gis%*hpc@{UYZmg5UGoe%PJuNHxN;
z4UbKxN^s&jp8A5x4*X%BVpZ^rAL4z{SQT)g2I<`EZ8<nKgkl8PdiU%bN79ousfxNi
zs6(JsbK+Gy-YY@vV2Ng;vR8G-MiS{roFhK_^Y#RE?dFXylqWQl^|hi(r)3Z^gpTj;
z@vp~*C$1>l`Z5vE)>}FTC(2N@Y6yR0=m63x!RDdgzaw#D&l>&0F_a>AgQj`E5WRKe
z(2)FEh6;yy_t4^I5ZoV8&wk58Aw%d_!fpHg>#6ARhP`W)(_0Xa{XEA&*Ge=Rf1-Cu
zK^dCbdvM0Ku>{|I&HUsVU4}ONcrviYtqT3<Q9ktbO(ec2xY1KJk&2!~Wu5$>-Gw5p
zQdaSIb)z1!8j-ks2KLQv<MuTjL|fl$c|>h)K^orO_2c{dk&EQ~k?9j7IHWM3LdQM;
zZGLcbh1{7?^tQr#UaD#U&Xx#!tJOPzy!tO)5j#?hURd97_|DgkRXk3`oQkf;%-Dh%
z_P8NzeMah0es(=sZ}zVgb}IVh;F+8E=X+kxC+nK3*y4JUs}w&la$hUK*GGO&t@ihv
zg0vwd)-$cU)w&4VwQ?6aUt}O1$uJ~ws0mMM{@L^?qz<bsK=ZGhAHd6F8KRVRML1XD
z$d>%ejc8aiCRBW94qCa(CH{3*2ObX_P`#GXie|*BkMEF7A@js0uAeATU?N??gnh5%
zd(ilq!K*SCno;B?7UaZHjUPv--s!tlg5dY8I`>)f$H+T!-HthoUQT)2OXf*f{nIPs
zPBpo1aDRMw&Q~Sa4I=vbN?Yb=Bhp$?5gqb$1fBcDC^_8JfOFHIsNLRNiL^?09hAp(
z-1A5z{c+kfSqC4KT8=yG4<NS8y~itVHe(jgp`;HZgD9=uD>nOM34(bF#B)U6q)npq
z)uu5g*Y=~UT;YkkuhpS-veWUyPNQhE+PYUXhh`KgFMdR5{}2)tVt@GkMLKHI3&k<c
z-T012ewf+A5iIw4g0;-J1Ks-SE>)^ri^JT<)Z%Pt=;?gpP>zW~%q%F_d2G`dmKo_z
zxbE^CH+rUdD@cu?voTBdnwX5E`a?g0em|~8oM$fxj?qI%9Oc%2V<{5UMdD6%kVx-_
z165?7THPpL87Q2D&g$y1roXMlJ9D*pm7eya`|DD>cD)@y-odYTUNIWOyUb<wOfjm_
zxcBK)j-#xUosELtd+(JZX<2DWPtO9hp*}t4K1(02N*l}kG4u<6FcKSB*v>-XJ39T7
z^CbO$;^^y)JCTpw^YP~E9|bnAt;ec~9(Q9r+Q|KBm7Ray>TERjPx(OU*6+iH;a$I&
zt_)<D>n}2-IfM*X6_-tTGLVbIZY7BmA^6IS{xdg=A><a(dY)x(6SBVO*Wq<13S*9a
z)9bF#P<L*+(i@Fr1aXwQ^@$al8N}Cwy%l@>`BFk$Z-`J5=cPz;?a`~{?#2SEwOdP{
zH{)l{(ZXEM$1$vftbTuio(|TOSd(+n^&U)|`m!4@b+1IVV>#(!yBPTFNwavfMO1v`
znWQc?J`bCOXEUlNy0FqzbL8i?V3L0%MA+0)B*%~qzof<{+aVmLu<n=p(td0aS@2SY
zXAqy;$NIF`dk6;=F|W(4uEwe>dw7hm_2Jv~3v&2R%#i$35osc4=Q56b^FOGI6f@AF
zCiAI_hqK5&h4mI>khmx{Wf%n&HaB&;(2*x=CzrzNHZ1j3VR;D;4e8z;{%~+b5ZR}C
zr@wh_&!Llby(TA~)sZ=jxVy)=%?4{RUqjWr`5m<+zOBC#XmzZr2kF*g{%$mabawpK
zkymGu`@`mVq5IaBR<v;I#i%DjT?nq*9p|;3jf%y{AWiAc>wXrB+yjyDXxb=ZNU5#-
z?p=aUn2x{x*gTA2y#*W@t>UH*lYKfo(Jt|kgN~ND<#^@BrC^vRb5D6oh;}nN>|I8e
z*;$W9YAS;5C%tebe?@Vn*BE+kw)ZRjbqEsI)h)kiIEqYvkL+EOmWZsreu_8S)P+LT
z!>bo8??*{*DN5t?VH_sCzbfWP7l!qAL2#ktMT<VH5G-Ew>f8(()4DhDL}3zVj;uE2
zyf%&74<zb+TGK}IcB|9L+C;&A40&O==+#Xv?^3+cY1@cf#4oIHIAU4j%2cvX0Uv~2
zHFf6?>_RF{E(6A8=@|0dFSBzuY#%k@Mfob4-W~<mZhKkB%oQdMZd)i)lt;tw&jxpS
z<RuXt0{OIKL&F2zi7~|3zo}(WoH=^$t8I`HI*H%0+){XT#U05|e;;qz(S;?PeuQk9
zNI;S$oNDbR?MU;2yW8!f)yOfq->E&f5ufe2AX69Fh0CQw{C*h@;_#a6rpxQ87~*xa
zv{A)b*+wk%Q@@Vmb0<DAy3{8B_9$vt@uysVa0&~4xL~<9y&R1%NOEIQjzAG>L>Yy5
zMzKTAxA4aHK76J<>drBzZseh&lk7XqO@Z}B@`4&y|JR^0+^jHMdQgRiXO<?49%~=N
zOKQ_s_|3*eFV*;G^B2-_rg_m*y?qQc=2}*GOmzhBPXG95%OWNg<JWyMasCUIi(d4!
zkrGI7h?HdP4?~?EY_YEGeuerVxj(%QZ2M)k6OrHI<KZ)ln@L{Sx~xC%VRs{T4v{L}
zjaqOt-oz%1`q8$%|K!t;$aCR&p=jKN^duw&Ebo`010qtI209J6lh3q2?|BF2@sL?Y
z6>2AV6z2J&p?OtkAe`(|z=;{F<J-J0)Z^?O%e$L+3(&_1`&6ak-vsYOtc(a0{V;@&
z`B`(x*wInD<<A4>+!+6Z6H`TYQSZHM!tC+hkDpaHVOVc~uW!4&z4YqPINl$&rgC&;
zI@a5HIO%svGivg0T$T2x1&?yYo-MVh#(?7??f`!C@rb)wcaM$}8u!G;MULQgJ501H
zJ*x4Tv0GwoB^|}9=yBy}zs5gJDok$nQ?cOwlbwoxs_@97PiI}1RbXuaL8*;fx^eS7
zmoqyn7`X7as;RERG?HKZDnhm(11;#^92I`3AH5gO)z+P15*(lK(MZj8R}KDbe)^Dg
zQY*pNPj*QtxzK31$lsxDu}l|^m3O-!n3968Xz)^-<(hGfzpuUibT?*ScKPkhkr6Tv
z;6%s^%HPd5sP}|oJ-PPV%0WZeC1CxPT~0&D^@?T(YcL)E@jB{!iav<k9wxJ1>zl+P
zrwr!n$B&Y{;2+wx|3@ed@7v2ljZ~&0$P0ku0ar~%{xi=wj=k_jz_fn?OAM$rU8t@@
zP?rQZzcAK2l8?%GPX6{PVPcCE&j>$O27VN!S9^@N6U)1rCr>ES5ab`5(S%e6Pd>>D
z5O*?%u7xe<qoJDDZAOc>(9!zmjw%acXgDh)kXn7H3OTy7=UvPgAaxdNxw3E0ERM0U
z@V|C{OE3D7$^Lb9SsHrAwaa0p3j;M~997wHshz|f$Uj+y>(f$WhVaQ>-(NR&jT5}n
zthG;mhQAFbM&>2|8L7qUkFq;k1jliOD7Uc6wPCy|<LbQFygc-2iBprgKMlEUwfCEJ
z;h_AKxoNt5uo6RFfOTO0*f5h>$da<c<waVG-88vwBQM0N4sV&nkmoLQYBEFaO^|r4
zrDnQhe+UgX9%6g;C?FMkXsZ2b&T2wXm%uuJ`*SO2WyZBQ1}@GFEtyQ4Ao1<_h+O0i
ziE#|^TEx$C%AdU#H;i;?USHgV<<>Z=FS*}<S$x%KuYQgZd=2x|#BAUD?&UOs`vY|e
zqf<W1ZhkMBr%p=tLG9QyTKxNX(k**F3gn;W@~79?cxZV4Q1W}$+!D+;a7N_Bd<M2x
ze0jh8SvScGZ#OJ(t~MG$ug9m>TfSpSfqH>f;LMWuq=oFCy}!yOe3BcmS8>jb8om(>
z`E<f0@eij!7c#xb@xnHsnp`)g`2pJfwqYbXBp*&q9YlA6xaU21o`zu^?0zPzVoOOz
zi^ODOgr1b3FSct<M=iTZy<i?7_vqDwTrBIu;xSoQj1r~X4r%%`(48+aGdlLc=-xXs
zyTr3hH0tMHIxN#ca23fvfAa$T9{K4t5%wLWq+aM=#V;!`nu0x-aCAJKe+PL{qL})n
zz3A(wpO2q1^AO+zz=@nb^wIBX1B5>~e?qO$-6I37X)OE3raFq3O-zs7QK`UXcWZSW
z-P17ODu@RS;aLmOo>biY=RllId^?(_@s74LdJw@r74A6`lyZ0+B|A4<OD-zHdincC
z6)Ia1{2ss|(q-SwGE3Pgiu+<xEQf}X{P#5B6T-dt)aKUeh&ux)Z*4}v4z?i#{6X@o
z?$0qCgGfHz_%(MZE5$5zk@t%mbnN139kt|43kH0B$g8BkQj3AaZp%5EKWWEXj>i1t
zdeDP6sH<)GdZ2;q)7%nOr*6I9_+`=aqi0zM@l~IQhU&~Qv|e6W;gMkr)=c=kQ0ZP5
zmKCemsHR23eg|a&@{)S6pa%22?&1-We}w3(zfCP@#>M+T3W@&th;DN%s_Ius#87Vo
z-f?!`v@SrIM);7lmVu3N=bKRAj9BKSZ%hn%u0i+eohjXR!XrUm*ywF2rsL9&ufE!$
z62f2-d=2p$@{hOB(vihstpvvdP6QkR>%f*hWUWwEGuc0U%La5E*e4Om3-{*h(%xra
z!}{gHb!z1Z;`Mdcl{@S<H6Vdj`njMJLpbk%abb9GIcB#z@<1mz9kq{TdOIr);S~bM
zkMA8HLN4dtF8X;j8ykuXt@tEbgxZ?d#mxMO#!zS7lsWJ06xN3PWq52Nyt@f627Jg|
z+*`0LQ$=V)3fgKj9CYz>AF2?&Bi*Otf`(7a+FP8e#ejDJhrIq^Wt!^WM0l63?!gOI
zd;199DRd8SSB>sKXG-iw?^^pH$lLTtxhrZn2T*z3v-?gG)kxXGf7wU10c3o$?&ZpD
z&6w`Cp!MDR0dz^tQ}@y=KL-4pr1ijb&f-k8CW_hqX0Vs!AE>vx4}O@&_lnV;(=MMH
zj2P%}?xK#5TWDx>erVjK@=>IEQu1q<Ung1D{$IK6e7`kez#$@+9g1(&w;;efvcA&?
z<qf_N9RE>e)5~v0UvW!Z&%DfxAq;$##s&SwyR>W3;jt%Axt0tOyaPPNg5qw^KK4co
z_XpM+#GTF)J<b=u)sVR3(T`QS+VW9_R}k;%!UlXLRk6$cat~tHx?|N<-;O3;Chd=O
zOu^SUzFB6Z4Wrj9kov-*77YABaDL394JzFje$Q^PtS6tRN09UKTQBBcnI`K1_z*5X
zQ{8=+#}V+B5Z_oIG-(At;iL>NX5(D3tQP@~1iTCIJArDdREZoa;VpS0cf634Y$yC1
z_gOWK^?{`b?$2)Z<hqQBF2v57p2w40j-*lpmPa~Ip!Z|n6$L*Jp!@lum(ISJAh?Pv
zg;V*7RT_eMfDbYFRu#o9Jxp*S#GS6BY_ZL6CrO?4gq>-pHOrf^;u(z<qutp1Xr2V^
z<Os6bhR^HSHj;g+EpoW_i*7Flo{z;l{AJC3CW3Vhd=;$gP_4+6yA`eI&1oy0)Y@e1
z(th&GmY_cL=TL{~@`g5)aYOhAzibQgLod4C)psGD`MztePtlO7ZRmlMR%57rl9RbY
zYa9uj)`<7mI)d-3I!@kJYQ=!7fFEUTw^qFLeGHXna1NyBj^l+NOTN3Wn!%BLJEqk+
zx@PgufA=ZepY*mDy+X2dyyM+-eScxr6o{kuJ|28LPqr0#ca$EJP@3gK{_=^=EH|f=
zz1S$aU%1W$&gKWmKgk{6H!Y5-Kzh3D0|tr=5)al2Rm7EdrDN;W7FGv0@=^eYd}^Hk
z<>ti+BrU9}xZz4YN~7z3Ja5s0e(X^;3VT398~i_Md{J*gSrZG5%NebBN+bKX@>(jA
z7}j{f_O25Fe$!zdl5aQtgQTkz_R0m!qX6!OcrATg*28_4*DXGq8x`i*hj-NfdVj}b
z3~SMMuaNR&rG&X$EBXC79hcSpj9L*tga9X=^9`B0#5#dTqK!=!n#AKC_w+q+4$au=
z`sSaNySuS`;fg=rSI2PWE)8Gj$P5he8uG%t5>0({q8kIR40RUdIpF!A&N`q|Bbhwh
zf<GxA8*k7r!PT)G&s{{mBZ$|q4gkMtsk(|SygY@)Y9{ydrqT&Mh}PgZ^DcJ;1Ah%T
z-ck43Y9~B}lbB^PiBhc?_-oAzErS-1X6yD$F{O%kzj4jG-3mHgjU+GJTeVlt!L$Jt
z*PM`D|Er0_1K^b<lg?>h{Z))Fi}T@>&NeJOKJV4(fd+I;?66c|Mm`p}yQ^^9#%$DH
zY}9i-qlVO{n>-!wpWV!n0(Cv`y$}yz|CGGH*w~}oj+2H>u5?NllYHuXGfj456Af{%
z5V&_Mq8CA30(pCrvd-xz0}KS~4dSTJ`NC7tzWu19Uf9XktrGz+2K8xcZAawQ+z!0f
zz?go&YXZefo0ZTrJCNt=H7m0^XxQsff9sdX9(1^85tsP8ChV|OI@on*DW0(Aur<}}
zA@Klo48A1(Ta#^FWF83%vwb@3!C2;=@~>a-s!-EsJ|2O*91QCk@@f1Ij?UM-wb)gy
zB5Ku6#{ck0Qt^dsOH=BQm05F`-t7VGdq8MgX2t-9eX6)AdHV;a5rRjVqQkVR*WHBY
zgL>gO`|0xK+eR_)2fGC~-;jShjv<c1brUW7dS%9`2+ghQU60r}cJSAbJO}a3T`SM;
zrg|BM`UmD=y~y!)=w=7*TcWjo+v*{_eT@_2^LR6Yd4L~X_xjO&hFl8<UfI0A$kz6F
z4hDV{cv#?-0Ve`)sok#J;p<e6p{{pTZ;5MI&6Yyyg}-^u&mzDo%W-!7|J8pI6xUI|
z>atT3CfhDQXriN;GtZvt&+0_5u7_4vANstHo#JMDJ$-A!ID&bAM`~W2p>wNsnCKXA
z-A;DXT%%ZP5d0p{G1S@8%ffagBj8;?&${w+Piy{yaRhjjHbPTW(P7}ZybD}6;Q6T1
z?5);6Q^-7T#tkdRTB@-3kkCH!PjswV**LZ5=oH$2)@xBSqYV{AUT4!X7{P%wXWNS(
zYYBdX{L_7j7A$?TA441k-hb;mcFQxA9>T-M?cV6T`bh;2z{i|;dZRI{>#Eu<OkS5R
z)P4AoO1K#ljo**db?IeKfqe>i2l!FYH52a48?jiJjqd9BMQ|?-M8Ll_Y<saw-?bej
zYStzdbuqDrbBl4t1zrm1jceYV5!d3QV^t#?UTbeAy2+*W{`}qvWcF>r-aQLP5#Vd6
zw?{<OT1>asVV}VQ^MCZRO$XWToUxlE{EpNP`Ouu9DHN&g-ZFD`45O~*n*AE1B#u6}
zkT5m-(?@V3=vf&8Pk!zDSxa<`PL5Z;%#YLfoG62ng+54lvCQ7ZwcF2@<2sh0Or}E*
zrp0O4_?#=o!2279Hw*vTQGr%GUVb(I2$kqrP}c+Q1)d!4Pvy}3;j*3GWF5dfp#MBG
zbiiiH!|2V99^SFxQuJb}EN3Fu1Ok54z>4$6t69Db`0E4fFNbx`^0&bEKIWIAJhZ4q
zcKes!)>Y^teB!eAHU=vDViD-uzqHCizwT>8ihV(KA+M&1KKI8m^L=J}H(Go9ZG2A<
zmDHzf_k?zb$aWGu>Ji?0&&GZn>8QVxUBEU@bmyD|MdckjR3zASBEzbn3qkz@>i~3G
z-!o(F0jDRh(ur%w-*=ahdK>WQnqw(%0}o}+#$%rS=f>#-R{<Y?rSrbDMOE+r=uAd$
zGH)kTQnBcZ#L4P;G#od<$y%pQ$BSu&8)yrt_||)u`4Z=-2>7=Zfgwin>9cxx+^u3w
zz77n0;(?Wl7hcBpAddTw7flV&G1bzeFO}Mh*XU#{MeA89(H?$T&l6j5@;>3)0<Rdj
zxvlZn*i9N{@~tzA4eQ6pWS1!qh_j|ZUC-T~S@Nu!kMbtXBfFEk6YF`&s<SWZ!v=Ro
zXFQKhV42I&J9e!aL(C?-B_4<SF|31m;*?c+p)F{^jiQYKjY)Xw<sh%g-E!=*a;N-c
zZz|D$=H><CRl5}=tVVF}#_;D}uO|q;?&w#zM>#cx)lSqI^4?@6Jkr>%E#8^WN3gU|
z`z5Zy9$c}*P5I2zY%H_YG&_O6gWw(D-{86#bf5WZ7%_&q8b#%o2@hc4DFE--ic|Af
z1!p471(wflNzgEDaK5MYxk)U~`tar5WgU3A@1Dk>tL}I#=@`q-mkfN#W<!s+9$N~m
z1Bj!%A;;?v2(%Kt5$1vY1N<B4C9z+HM2`6uVV1X#cgAMYu?*{`GRNmFC@mp3-@&&U
z178K}VD+HK2kJ>GV*8JN>Vu%3gZ$&J9I)1JCKEf;R;_=rrvkspIN{&f)I$6eVz2l-
z7rN1q<&D+S52PkBE##bO06h-_PYym5z=_Mp?LV9H(h=kZzz2XwQ&#6Woc`EJ>c+YC
zDXatF{lOOi*A4j5htuqO3J!F_hk))3_a|bsTRrT42L|1F`Q7OH)YMMAVd&bKz|=tu
zadZdwWo@cbC-EP^JdhVc`rD8D+$u+M^C!IyeX1gP8|vTzDQA`tzdqutsee^7!XNqz
z!9KkglbS4--GdDx(y19kSxD4u=CZz81MzLZb(>+(4)@qrATIT9Yxa-7BXxb3P)akm
z-E2Mb=Gr<No&g*###*&6g-Z0`#f6>~nF0gYdEZ7E`|?_xhd=bQ#<pQoS9T4xrXpPD
zu+`%-qlxGXpkr+Kc<xfpPzR2E9Iy$C&mh2wkQczWp}5(BrPi^B@M2Z#WLWOVjo^|`
z^nE#}rqK)OR;wiET=IKX9!YI{p2ENp*?txpYr2U)1aL3ZbMSjEyft37v9=%ARO{KC
zJ~fQ?`Pm-~J^g?8CsXuU!LuVh2=W}PH>iIg{{Zg-@lD7}NcylaE9Lz~j_Znx8939-
z?1|Yj7K-Hcm;3y)3(%ChsKg(k*}VOicR8Y0!KHAl1cAN)^&IetpwB&vpVceG%kYgv
zzR`eY4hrDWxjbLU^|o6wvVCabOlxYF8<W&?HtDD4C9lpwfJ30310Jaga~9+#&hnc)
zsV$0pWB3XGD{F&RHVWwIyOdTcRtWT?ihU8U4XbHHmoegI924%z#NbEj5wygbLNf@~
z0q{t0-B_+(40o0p$Bu75d8%nm61@@f&(_|%oo^8p0gkVFVtVas`4G{691Gl)Y+}05
z(xsBccSib<SG&)zjHjJM-v<7zGt(zsnX3!mSRG|xojgu-ruC01>Yj#=;v#8TPL7+^
z#3uwe1nRjz4c|1g#Y<4uRMm(5y;J1(K)ePWy^MXKuAAK;uHKO@Ipj7<_+E%RF+ZqL
zt&i9!z*B$^#qpM+u187%0^Vis?w1vP;bX{P$+<7J9PMb$``(2+mrdgcGcmi4<{82#
zF3h1CmVWF<QTd|%S5GrA@V(%B0(`K}=#Obe6$2F*I%!E*JVNxFd!s((jo`@up0<R<
zS>0u-!|AAIAA)ra>j3ySGyfaQmbOO`KCw&C=cjYR5Q2RQI$+E5*aJmktq6P@pl3;k
z^dxR7`Guf91swx$73ebUi>rJZVh0I+gZ<-U=44TtS&Bf%@bYV78{S1l$38EWiR~Lg
z;CBIj2jUy(+u#>dZ7VxjFFb(WiEZ(!C}}6xEp_;)(UH$1D32wV=`qzzbadaNmrnhx
zXhPmvVTrb>bW|CWxI3kQPIx}pr+<e2@eMW+{5Dk(V)k-%5Bhfe>%`OiS^vggza`wC
zWebj436)L|9_fu;(6_2tf8*B}+Si^}qv-D4RPnKhN(8<dz@xw`Lq6qhD?H0|C`C!D
z<#y{E_2Q0gQEO!-ClL5*-bDP|emS#?=-V6aqo1zvG~#>WDUL~deWD#bWhh2(5Gq3|
zPN(@di!w>QAin9>wqW%tw3bIiyY?mn+bpTEc{j}@`U2$b(uAw^ZmY%#4v95a8<zOa
zAoEZu<xRn|y|_4yZ|0B2AYKq~#m&xP7(X??*{)E>#HT4id`1==6#X66r7NDc<LN!V
zrvBEYn0u375mhD-H6J&nG@fRmKt2Ut8FWDKaUG9(mh?_~7_C%2=<{r{kod)5pT3Qi
zULLcrmG~T(J{cX4&Wz&JW}hE&{9WkB;g?&}y{S0((mm(3sVtOL+<J8@Lr1ZP)v=;j
z%p^LX6xVtar`~*|AoHu=Mq+^AM2Mr1Pv`Qmz`p_S@)T8G=?k5$tN-fTLjA+hO|w3M
z&&8q^_KAgfbLaUJl+SH=@txP<Rqry;Tt6(tQNRa+yrVVc%hCu>9vBf(^F6r}N4d9$
zc5i3m2L~-<R|}RBy~IJ^Dw4s+#KIbO%h?Zhp{IhT;%-%yr2c_A3-Bn^+u-X2UjX=o
z7F^dVWUBq2zZdEfL5}a=28ufntJBHks-2M-cqGs<2FGa2tR?v<jxR!euZVI|fRERF
zC@?PJ@qauLuYTpr`@StGDzfS5Ges)iBeK0@$*ED)7_(TMvVnn|j;s2Q9qB;x-#=$-
zV5bv3nA^wm;L?FkqSr&*iRXCsegW64FY~Xz3h;r0!;eR0l6}bHd#PPQTq9D_ckp|m
zQbhcGFc0vs;D=p2zNhQ`@-&ivATQKAOdsT*q~WCE-xtrtj1t`$@b%WdjY*NA(}agL
zw!IR%w_pUDyu7z(`^h5YQ!)IgREi}<wcmN?hCU{#8$m~hbujL|l^V;PkK*2U92VW0
zN_6Lt0F6xx;wmw(%$GGK{5=F;!~PkNeAtF>3}D{uOP7i+&hlanCdKQ`e`4SdfTwtS
z+TVavkDcPA&X+f&G>G}>{l7J>8KlkveF1nE;HyB_1by3(Z#wU3Tq%;T_W0<rw-77U
zm+m<2GDLJ*;H$u|JY!pZ^7@lO;)@0z_IUB+Ch?{^;!8{OS9nx;pa^LgT}WwOGDi4#
z*)}e-lI6UV8U5SuEmNr&eh<_?O-r`#sx+@7elg&yfOi4E3*a4y*MJi@zO$mso*W=P
zVpwlb|3JL}>i~S6;1dG<6yn>*T^cTt;b{o+9O&DiV=TgsyKkqD5WfrHDySD=9`GMP
z+yQ(Ie3hug@=tOCE!gR5?%+UHA7*gYXP<jY#h^ES`OI6)`?wVYF9vb6s-y9Ustz3i
zue_-^)h+NUljyWESMmZwH>II5?g#eO!yO284Dbo98x+kmR~;n&(XXL5n)8>_iGB)k
z6xOxlFAiI?8&k+q{Mou|r)PDqzk0oZq^_DfM+*X<!(4p<d>d66W85zaVo=uPCxy&W
zI_}o8sGRU+OWBnn<up<@gu#CR{vEd~O=3rX&`BJ9aO>;yPqY0BiszN&`<baEZ(m(>
zeEax!DpEhMJhHo_6IT@oxGO7mlKKbqg}Ljk<cp!pahW#a69T;+>bYt5X#K<C^;k?d
zFp?U{z_6|l?LT_Y<0k`4?3Dhw({PgbVSyKe`vdq5<^g>h>h13bE#|A0jw07{A)BAS
zq+zJDpzi~4uNA+I)j}f^R+Im9vMe&0ZDSG8w_5y98;kruZ7fSn=DVrW1X)<lb<gwi
z%W5XZSrCqaIDs1pg@y0T=Y!?Klcez5QyTm@`p7U5a2^G?<hmT`AcP<o%K%7xU_C8=
z=rfHF08lVPU<ZN|gd!+lILA{zneeftJXR@M@Yb+%R`~fHo+>niG>&U*#b1Uo{P65k
z2NVj`+VC~*OU`RXxhTSiB1#1}4iaGnh*Z~-Z%Ga8J-8(@{#4qcaZ)IuY*ef=D@=ef
z281Hhwmg=fa>Hb!0T3^@xi(X{x)p&D3xGHfoZ`k6-=^-&64Kw5bUsA)kiw#E$4{dQ
z?IAq(My77gkuhz{CIToJmmtA|U<eznUOwabK;saGz|N6=#lpm=ofKqc_UkIAB(7th
zaQ?IUrv~wXtcwp%@yr^!e*flt(9ukUV`xBt1P|8@0u%_;AQbIXo#yv{NhkAwQK5^Y
zD#MqDn-FMTjdAOw)(v6rq^?*a-x{p6s&raPxgK$i2|M1kA3+ds!9V~7GZ-R3Xaz$N
zG;Wkvpg1SCG7JeG1P^GafWWT7y3dGzGaCg8Oh~-&dw|e@LKY0VP+(d)X}@_P+Ju2<
zIDXK1d~v}H_7R?7V7*^B`d1uR^|mII>v3ql$7~=4U<8d0(8vYC7Z@giZ~`DPHm^|o
z`YsO2SkOBki{K{G=m3Q|5KaL_e244LG^6}@zuYtXYlx8^_9<Mqm#<e=NTehaW7%Mp
z!*k<PZRpFWjfa555C)?H5DoXg-tRk@HjLwfdfk0?w-DnJ7-JwnfdR)-e^W}g9219k
z#?<&;9w)%aggdwxU#gpJ2&=I-eKm-{U}(h6d!*{}7^WE<+4$o}1zr}uFf>V{8kyC8
zc(tv!1g&}gPea9b0>I$@fPnxEhLCuHum$4<5OtSc%yT&7T#VWM=e>4u|AC}pc9~o3
z?n9}0uF2Iw4G0Y3a|xPgi9_3OT$>e2{sKLbZ8%`DU^5}Q%bz`JWI4h@fqB4~`Eyxe
z^??_)_>{k(l)FPO0s;*JJH*jyd!LV?v+EENuRUew8=ISL2#i=jGypLS@eS7XE>8Ui
z&Sypt6n>|^+ZL>%HW9$T_FMX)167^~gjOKX%()uRq}KnRp%oJE+7nv6D#|^`TD$St
znj6hTSW&j_-x24`MuEh;Osz^OB)Jq@=)V>h`80~kq;~~1JZGUmVGcwC5ceR21E2)L
z2?Qx<42XO&%zAtyjTkebp$~`?Fc3h32jfx#=l#1MTUv;r71mqu%5mpaSqxHGY&Er}
z<(`N^(4YuJ-2=(c+h-QCP|ol<TiV_lL4i3=0W*^96acR9d(u=NK8;;;hs5hQ778B%
z!!Xj$=qoS|93p}t2$RwQpUhSWGcXu**XIXi1sy5F)f(el$E^Ah02at|(1>!&GtBPN
z+EHRyspPDf9GFN%-)subN1Vte#xhHO+aLQ}XB*T066*Wcvfn)6H9-n80D3>|8^j0d
zSSb(>*tVEdW_z^Z(`O%ZtY~N^*NxSCMVn+?GX{VR1_Cg&g2BVNH`UOxtq?D2RIpCj
z)Q+GL67CQDo~!Zy2t}_*K7}|MX6g0A`}`<g{L0e#@G3uI{Cg~UXkUyG6>D^_{lipl
z!buk@)7D7j;_%f&cgoJOQo!&v7l1(=H4nz(!3tv-3^){)ZH=Mb!^C+0l|zYt$BAjY
zZfV2C<@$pd3K(OPkN1N*OY!=qa|x_xW*g@IHl{&5fV>Uy4U91WT+_yGyH;K5z&SNp
zy)VCTQ25o#d^8TQrsS8l3U=+BAo;X6*}63BK*6lx<?$N_^?n3I9n1s!6aYWq5NH@P
z5xb#SJITQJAAec1lU76;)0i}A{tWpp{NmjJPl0R;2IGZb;5O-r?hztLLH>dJ6R-Bv
zChKD-TA<W)JA=0lLH>bwFtjdUzR-F)X{>>Umra)Xj$;A67#jMdzKQS&9$=;XK78-m
zs#)S63@dQmz!354Pq&jBlR=C=+V7%nYi}9A6Eb%tohC++dd|wEh?PwI`lo#<FM~!J
zx%lF{wGFOPNn-%u5H}GCu|?N7C?J3W;RN{ya3VA$fgu9siJESF|3hciFncEa?28Rm
zNan+DcH6)XT>g+Nc7~-L1ECIupRkdY(7Xo%6fi^pK?8)~t$;^Q&QEYrVE=%Tu{0~;
z{O?tt@mj6qVT0gil(u1m&Qwz`f;b8Va}>2c@{>RthJp+V=F7)7@qgRUK^kw5DJ+S8
z{O&s{iBI9a5!FHpGHARtuzh`VZ2L3<<192_L!JXX3WOl!AMU}tXOzW5gct_nEHorR
z{((YPFzeZOJ7YEqtZUDs)%F5kTCn8_>ywV#I#6e<MP+7EHD2}Ccn*ZdlT_EoUqT8=
zK?Znd8}(a?AhRAr{xJ_&da71@7>yhCb0({I5dr{=vpyRmuA2SqB1AV3b%1xk;0}cn
z5GS{swzFo{%{EB=HJ&R>1ohj+=i+E}$AEd9bkaziExX}is$3s2NP)2o3UkOmu-;&w
zf|2nlYqD_M?HmL+5ePy5Pj2ni$z}LU(Jy7bBQv;OMAX-feHd9&O<cv-P7~u22p$Ft
zKfnIY&O|wn3N5RbG?0Qhi5@sl_#7Vv1VfldueLesws<arcwHm0Id<|<G2W@G{(ajU
z8o@h&Lm)3ed;_5fjM}~2oB5k&3E)+J7hP^7vr$akyZux>=@<l@c~@OqvP*LDrB@lb
ze_Uqp2f32N458l$9$)}C5$de{Wl@)?t9lWvYuG=Xi?6?A881g5=z=gQtNLi!mtY!c
zsPJ~D4AVwi5D0nD2x(bid`PKr7(qM$qMO?*;>cR_2_h6}Bz&D(Ix~aM2b_x8Jg<c0
z9}p%%@JLqlI&mYOm2y=@{j|uPX7T_7cpwAJ1A+%MV8eO?gVbX6<th$O;}O(Z(AWu$
zLQro5esd2v$W`>J978^Z2N(n@-z~iQqyvLs2=xybFN9jQFjFI%F*FW>k^ZN7_u2U=
zJp@;2zt~pMQaEd@_}fqc^&B*&fpB~-t2xIf{}0Iv(6|BX4I1FI9^_w`Ue}3OwNte>
ziw$DxL2Dm5p$f$5dP4pF`95N7hP(|1-IEbU)d36Jh;a#oR@gtDmt6Kvn2w^A=@VS~
zK~orrRM<aW&o<MnlN0cn3)KZ1{?JLi0Ch0rQ!sddKmh9+1W=Q*y?5J6Qc&dM#@L)o
zLu6e8aqq>fa1EQSM_?U9EwD(oXG|ezgaiQx>R_l(!GI&6$i7qQNfT*&g7^ma2l5X*
zU;-K{zz_j^$cA7+fz3fvL@<PgWxyeCGw<z~$nQo|Dei0C`(_^$@E6BdT<I6>WsV@I
ze*%{qdQ})qVK6o`=|4^<*z!_TczNHd>2OmTtQcz}GMNbC&bx)?w4=k^6u?y=cmQDw
z>tJ&9W*^mQ1SS7Derw927kBNq)r+2OFo!(%{GRry?QbTK;r=&!mwfq!AihEV@zj{;
zqRu|p1M0c2%AP$7pXL%m5b_TgXR{0Fhk0fiP`|jT;PXBf3J8W^AYk8BrH8}VQ$Q#J
zfnce<Pk6h*I9b;qOwO&dXfx|<`dAZ)A)KX~k9+a&2|PZd>f4a?2ZJz~d4wq%CfAK%
z|9}Ap8ik;-6Y4oIDgZGIjVQoZfsq~>Dga;4jiVqKqAO>&+447$Mid~_O@rCLtt@CG
zdCs-fPU?Z(IC)?O@JP{xi_X%F(+H6Yc@E-Ruq{)6U(g>6Mn<UTpw5Cu)tMEl36Gqa
z2n;ya=-v;#7N%jlBlfOpIiq;_SLIhqOB3+@opNEGX5$Ff8^nVfv`*F<N*Ok+-qvxJ
z=NAGa39N(W_%}7RTYE9KIKu5X?1sRg%XO{BX69%w{xHq1dupHq4R0@hr`Etunadx*
zJdn3JACy_{4<9D=g43HK<x%BXK5UDX=LMf4!XpU=(zi4Y^^nGO;E{wLwlA<>H2Ocr
z3&6d=BNZkV_e-*m5F<U{(b%aKuG2lk=<?pz+19SZ*q}*GVo0SQw*+kcKB8TUR@mD`
zt6r(c6~VYg!*mcsUI6?i5t4P)?^-a1`ZPM>&5GQ}91Ma7;3}GqB%9=H<2y9GfFT?j
zxvZAjSP2qhc-cS1@N#`Cq5nMJ=%JOc$$uYcWU6Q-^4|v<t<bkxIr~5(lmC5`k?8EB
zjGEY4SpIp`(LaCxJP7HZzqup4|8FCl|8MWlPVt{Vcz^Esa6I=sI3GrMAI_gU!uxPO
zjC0R}V>lm1I3JGxZG_Lm`7q9X9ghEPgwM?#=bk_JefWRD>vNCiz7F5_Z|A}L|Mt1L
z@56a>Ux#Cu2ae(Vxg)$kcZAQw2=Bw^;rRb|gzx>gaqc{DJa>fo;d|yD&wU*}59h)0
z-1FwX565#y`25`0=N?<Fvau2)5o7f~5kp+xYR!M7q=!}#CM(_4BWJ&B?tf$MF?=n&
z4%5RijC0R}*I|Uu!3eLz=U{~Q;TX>QxAEW3gV*8v{%wTsh4cUI7(Nf@!3gKW2<O2F
b=fN?Ib6<yJ7~%V1gx9Us+E_^%8O#1Z4m3YE

literal 0
HcmV?d00001

diff --git a/tutorials/test_set.csv b/tutorials/test_set.csv
new file mode 100644
index 0000000..34928b9
--- /dev/null
+++ b/tutorials/test_set.csv
@@ -0,0 +1,54 @@
+3.000000000000000000e+00,5.557391941977805061e-01,1.238550434380931708e+00,-5.214359630530969181e-01,1.440900834400055952e-01,-8.135811375106852816e-01,-1.108104519644248498e+00,-1.756306948722317518e+00,1.945738962393688265e+00,-1.395679990380090274e+00,3.179531790963357474e-01,-6.978181815257052945e-01,-1.202178738239686906e+00,-5.377053749563008855e-01
+2.000000000000000000e+00,-8.806943312902405507e-01,-1.040848934513725732e+00,-1.900449979236804865e-01,-9.806418628362864043e-01,9.844444918418605861e-02,-4.406380376353093431e-01,-1.109628967894998430e+00,2.361008551613684059e+00,-2.256058981699655241e+00,-7.611823339265566535e-01,1.256044077739537190e+00,-1.416442100938596793e+00,-2.326243111513074946e-01
+3.000000000000000000e+00,-6.330333786198930612e-01,2.996159586299703914e+00,1.135518862593988709e+00,1.911525999017035904e+00,-2.523346226215336663e-01,-8.442224221058305389e-01,-1.629091608231697297e+00,2.361008551613684059e+00,-9.929493986985916676e-01,7.091097155584770960e-03,-6.534122210878593462e-01,-1.387873652578742290e+00,-7.360080664295466368e-01
+2.000000000000000000e+00,-1.487463665332594553e+00,5.977554310772129620e-01,-3.741510896622448068e-01,-3.379378935355480951e-01,8.701584071567695089e-01,-9.528797563863556919e-01,-8.657995652879766357e-01,6.999301947337017715e-01,-1.267538438481431662e+00,-1.036517320788364671e+00,-3.172877495800926345e-02,-2.022830456447746450e-01,-7.817702260002956649e-01
+2.000000000000000000e+00,-1.190270522128176189e+00,-3.725912882112759061e-01,-4.276012453282835440e-02,-1.772619012103635316e-01,-1.304671838038692799e+00,-1.139149472295827081e+00,-6.749765545520465260e-01,1.447415455329694067e+00,-4.428855480368826952e-03,-1.147539492910061520e+00,5.011427502961482983e-01,-5.593886501429575775e-01,-8.427864387612943320e-01
+3.000000000000000000e+00,5.433561465642633426e-01,-5.190587175378403861e-01,1.135518862593988709e+00,1.108146037391112948e+00,-7.434253231495413505e-01,4.130981602831020894e-01,-1.099027689520780227e+00,1.447415455329694067e+00,1.185456983578603740e+00,3.004689744441396915e+00,-1.763561232034019932e+00,-1.259315634959396135e+00,-4.156729494343035514e-01
+1.000000000000000000e+00,4.566748131296410329e-01,1.467405792703688538e+00,-7.791844914870873495e-01,-8.085630581525254934e-02,-6.732695087883974194e-01,1.802610153962628525e-01,5.759742936057181684e-01,-6.289324907702836809e-01,-2.973238312487311297e-01,-3.126527585549017663e-01,-2.537585771472410934e-01,4.833597149917366331e-01,8.504134653564191515e-01
+3.000000000000000000e+00,-9.797587123583800794e-01,2.272976653999792429e+00,7.673066791168583478e-01,6.261180604155592855e-01,-7.434253231495413505e-01,-1.480643951463191721e+00,-1.766908227096535944e+00,1.530469373173693137e+00,-1.542127478264271323e+00,-5.036108946042202605e-01,-9.642539441527840927e-01,-1.987811068135689530e+00,-1.105918856293101188e-01
+3.000000000000000000e+00,5.062070036637096315e-01,1.339246792042944945e+00,9.882339892031365425e-01,1.268822029716297539e+00,8.000025927956255778e-01,-9.839247090379342753e-01,-1.289850700256710558e+00,6.999301947337017715e-01,-3.339357032197764474e-01,2.529514847760534924e+00,-5.201943397743199471e-01,-1.573568566917797229e+00,-1.868621515805584665e-01
+2.000000000000000000e+00,-9.302265218243114253e-01,5.428301450797512473e-01,-4.109723080099570436e-01,-3.379378935355480951e-01,-8.135811375106852816e-01,1.802610153962628525e-01,1.201193235143292598e-01,-7.950403264582820428e-01,6.362789040129241958e-01,-1.258561665031758148e+00,8.119844733610726006e-01,9.118864403895559079e-01,-1.422440459990781836e+00
+3.000000000000000000e+00,-5.711181404523050231e-01,-8.669188621884306167e-01,-8.528269281825133774e-01,3.047660757651901586e-01,2.387560779064739624e-01,-1.325419188205298582e+00,-1.650294164980134148e+00,1.530469373173693137e+00,-4.437713191329124562e-01,1.161721687221230814e+00,-1.719155271596173540e+00,-1.587852791097724481e+00,-3.546567366733048843e-01
+3.000000000000000000e+00,3.947595749620530503e-01,2.956663580911739198e-01,6.770353051031155478e-02,3.047660757651901586e-01,-7.434253231495413505e-01,-1.449598998811613138e+00,-1.735104391973880889e+00,2.016066876697071852e-01,-1.835022454032633643e+00,2.735443102476567967e-01,-1.186283746342016387e+00,-2.737041665444109872e-01,7.245675265368593798e-02
+3.000000000000000000e+00,9.148475755697846878e-01,4.787506447493790618e-01,-1.164025612282544170e-01,9.474700450659284678e-01,-6.732695087883974194e-01,-1.527211380440559374e+00,-1.544281381237950557e+00,5.338223590457034096e-01,-1.121090950597250391e+00,2.041017290425069408e+00,-1.186283746342016387e+00,-1.402157876758669541e+00,-4.309270026245532459e-01
+1.000000000000000000e+00,8.900814803027503608e-01,-6.472177181985840910e-01,1.356446172680266793e+00,1.440900834400055952e-01,2.273274694379648242e+00,9.563848316857274101e-01,6.395819638510282790e-01,1.281307619641695705e+00,-5.353009990605254176e-01,1.847265725502997280e-01,1.256044077739537190e+00,4.833597149917366331e-01,1.460575592966406155e+00
+1.000000000000000000e+00,6.671866228994370873e-01,-5.282129318707506904e-01,1.045247488580237777e-01,1.554928957985841215e-02,4.492235209899058113e-01,8.166825447536233407e-01,1.116639490690853442e+00,-1.044202079990279364e+00,5.630551600708334492e-01,8.508596052804802135e-01,5.455487107339948016e-01,3.119490248326086679e-01,2.314802571620387717e+00
+3.000000000000000000e+00,8.518338412411752270e-02,-3.085117878809041647e-01,1.540552264418831196e+00,2.072201991342220495e+00,1.150781664601345344e+00,-2.078008927484694124e-01,-9.082046787848500058e-01,-7.119864086142828619e-01,-1.508763433645497753e-01,9.396773429778373377e-01,-1.585937390282634141e+00,-1.902105723056125575e+00,-1.010581023854040694e+00
+2.000000000000000000e+00,-1.190270522128176189e+00,-1.013386291514995152e+00,6.568430240737184178e-01,1.590174014366666722e+00,-1.515139281122124704e+00,-5.182504192642555241e-01,-5.901663275582996748e-01,4.507684412017047282e-01,-4.437713191329124562e-01,-1.236357230607418645e+00,1.522479840366615989e+00,8.340143795377198688e-02,-3.851648430538042178e-01
+2.000000000000000000e+00,-7.692469025885839695e-01,2.007504438345394160e+00,1.466909827723405169e+00,2.393553975992589677e+00,1.686002635453300036e-01,-2.078008927484694124e-01,-7.096016976290977010e-03,6.999301947337017715e-01,1.237126964182898581e-01,-1.289647873225833186e+00,-2.093526167093946455e-01,6.547704051508645984e-01,-1.193629662137036807e+00
+1.000000000000000000e+00,1.038678051904960764e+00,-3.176660022138144135e-01,9.514127708554226404e-01,-1.301993847486655476e+00,9.844444918418605861e-02,1.422059121459405828e+00,1.508886790536932310e+00,-1.459471669210274936e+00,1.054067604327671853e-01,1.980492332049030579e-01,-3.869764584607804925e-01,1.283276269067666675e+00,1.033462103639415153e+00
+2.000000000000000000e+00,-1.722741570369424879e+00,-5.190587175378403861e-01,1.061876425898562681e+00,2.232877983667405086e+00,-8.135811375106852816e-01,-6.424302298705699688e-01,-5.583624924356446195e-01,4.507684412017047282e-01,-5.536069350460480765e-01,-1.058721755212703952e+00,1.744509642555848394e+00,7.833284227702103086e-01,-5.926199664411997414e-01
+3.000000000000000000e+00,-2.491589019808523320e-01,6.618349314075846479e-01,1.025055207550848779e+00,1.590174014366666722e+00,9.844444918418605861e-02,-2.153117683899845661e-02,-1.629091608231697297e+00,1.530469373173693137e+00,-1.523821542278748664e+00,-2.843599792335822080e-02,-3.425704980229340446e-01,-7.307993403020855983e-01,-5.071972685758016075e-01
+3.000000000000000000e+00,-2.120097590803008691e-01,2.172280296337779859e+00,5.463793690305801531e-01,7.867940527407438767e-01,-9.538927662329731438e-01,-9.839247090379342753e-01,-1.576085216360605834e+00,1.032145866109698051e+00,-1.432291862351135592e+00,1.183926121645570539e+00,-1.896779113347558887e+00,-1.145041841519977677e+00,-4.004188962440538568e-01
+3.000000000000000000e+00,-3.110741401484403146e-01,3.963627157531867673e-01,5.463793690305801531e-01,9.474700450659284678e-01,8.701584071567695089e-01,-1.325419188205298582e+00,-8.233944517911032657e-01,-8.780942443022812238e-01,-7.000544229302294585e-01,2.582805490378949465e+00,-2.163214875974637685e+00,-1.702126584537143161e+00,-8.427864387612943320e-01
+3.000000000000000000e+00,-2.367758543473351684e-01,9.456155757278031926e-01,9.145915525077105146e-01,9.474700450659284678e-01,4.492235209899058113e-01,-1.061537090666880845e+00,-1.629091608231697297e+00,2.111846798081686405e+00,-1.249232502495909003e+00,2.646625364779212619e-01,-4.313824188986269959e-01,-7.879362370217949385e-01,-5.682134813368002746e-01
+2.000000000000000000e+00,-4.225215688500969513e-01,1.494868435702419562e+00,2.518096222488758751e-01,1.268822029716297539e+00,9.844444918418605861e-02,7.701151157762557986e-01,4.381576747408793548e-01,6.999301947337017715e-01,5.630551600708334492e-01,-1.072044415867307698e+00,9.896083151124585031e-01,6.690546293307916281e-01,-8.946502196081432823e-01
+3.000000000000000000e+00,4.442917654961238694e-01,2.163126082004868778e+00,1.945585666243673684e+00,1.911525999017035904e+00,8.701584071567695089e-01,-5.492953719158341075e-01,-1.247445586759837077e+00,-6.289324907702836809e-01,-9.746434627130690087e-01,1.561401506859339205e+00,-1.319501627655555342e+00,-1.059336496440413944e+00,-3.851648430538042178e-01
+3.000000000000000000e+00,-7.320977596880324789e-01,7.259144317379564448e-01,1.135518862593988709e+00,2.554229968317774269e+00,-1.821788082603897352e-01,-6.734751825221485522e-01,-1.650294164980134148e+00,2.361008551613684059e+00,-9.197256547565014762e-01,1.117312818372552252e+00,-1.319501627655555342e+00,-1.330736755859032838e+00,3.775378164586793983e-01
+2.000000000000000000e+00,-7.073316644209959314e-01,-6.563719325314943953e-01,-4.846147447053830715e-01,-1.658590888517895781e-02,4.377949125213967285e+00,2.578733970252090058e-01,1.413218802627659587e-01,-2.136629015502882756e-01,2.997744646145346081e+00,-1.058721755212703952e+00,8.563904337989191040e-01,-4.515657966557431319e-02,5.514340228275256273e-01
+1.000000000000000000e+00,9.024645279362675243e-01,-3.085117878809041647e-01,1.282803735984840765e+00,-6.271546797208799040e-01,1.080625850240201302e+00,1.033997213314673536e+00,6.925883557221198528e-01,3.677145233577055472e-01,6.879488846172185368e-02,5.844063921884077306e-01,7.231725524853797049e-01,3.833701457322456485e-01,1.887689082293396936e+00
+1.000000000000000000e+00,1.311105099842342581e+00,-7.050221522523687778e-02,-1.164025612282544170e-01,-3.379378935355480951e-01,3.790677066287618246e-01,9.563848316857274101e-01,1.254456109555691867e+00,-1.044202079990279364e+00,1.350210407448307448e+00,3.401576135206750284e-01,1.256044077739537190e+00,7.261915260505009684e-01,2.299548518430138078e+00
+1.000000000000000000e+00,-2.367758543473351684e-01,-5.831382178682121831e-01,6.936642424214323199e-01,-4.022082904656216762e-01,-3.224904369826776529e-01,2.268284443736304223e-01,2.473346640049494949e-01,-7.119864086142828619e-01,-3.339357032197764474e-01,-4.680837995252772332e-01,5.455487107339948016e-01,1.383265838327157660e+00,7.893972525954204844e-01
+1.000000000000000000e+00,1.088509832301457966e-02,-5.373671462036609947e-01,8.041278974645705846e-01,-3.379378935355480951e-01,-1.120229938992457902e-01,1.802610153962628525e-01,3.109423342502596332e-01,-4.628246550822858185e-01,-3.705475751908218207e-01,-3.259754192095054570e-01,6.787665920475342007e-01,-2.165672698247022854e-01,1.063970210019914653e+00
+3.000000000000000000e+00,-6.577994738869272773e-01,-9.126899338529820271e-01,-3.005086529668187789e-01,-4.986138858607326307e-01,-1.024048580594117075e+00,-5.182504192642555241e-01,-1.650294164980134148e+00,2.111846798081686405e+00,-7.183603589157521174e-01,2.069310069746389813e-01,-9.642539441527840927e-01,-1.644989687817433932e+00,-3.241486302928054952e-01
+2.000000000000000000e+00,-1.958019475406255205e+00,1.375863649374586162e+00,-1.883821041918477546e+00,1.440900834400055952e-01,5.193793353510497424e-01,1.313401787178881008e+00,4.699615098635346877e-01,-8.780942443022812238e-01,3.546922725711026292e+00,-9.254951486666679328e-01,-9.642539441527840927e-01,2.119594555731176555e-01,-5.926199664411997414e-01
+3.000000000000000000e+00,-2.739249972478866590e-01,1.037157719056905902e+00,-1.164025612282544170e-01,1.440900834400055952e-01,-8.135811375106852816e-01,-1.325419188205298582e+00,-1.565483937986387186e+00,4.507684412017047282e-01,-1.230926566510386344e+00,2.342997598596084252e+00,-1.097471825466323381e+00,-1.302168307499178113e+00,-2.173702579610578278e-01
+2.000000000000000000e+00,-1.970402523039774589e+00,1.491989287646094120e-01,3.254520589443019585e-01,3.047660757651901586e-01,-2.523346226215336663e-01,8.787724500567805075e-01,6.925883557221198528e-01,-2.136629015502882756e-01,3.433839282445618757e-01,-7.700641076962924103e-01,-7.422241419635517978e-01,1.040444458008902284e+00,-4.004188962440538568e-01
+3.000000000000000000e+00,5.928883370983319967e-01,8.540734323987003718e-01,1.430088609375692821e+00,1.429498022041482130e+00,-1.821788082603897352e-01,-1.216761853924773540e+00,-1.713901835225444259e+00,1.281307619641695705e+00,-1.999775877902337795e+00,-2.815665503608269504e-01,-3.425704980229340446e-01,-8.593573579214313085e-01,-7.207540132392969978e-01
+1.000000000000000000e+00,2.215067577089114614e+00,-5.465213605365711880e-01,-5.950783997485230570e-01,-1.623345832137024658e+00,-1.821788082603897352e-01,7.235476867988874794e-01,8.940126448322682773e-01,-4.628246550822858185e-01,6.179729680274015369e-01,9.590883485294224298e-02,5.011427502961482983e-01,2.690963522928269680e-01,8.809215717369185406e-01
+2.000000000000000000e+00,5.557391941977805061e-01,-5.282129318707506904e-01,-3.373298713145309602e-01,1.590174014366666722e+00,-8.837369518718292127e-01,-7.045201351737271356e-01,-3.145330898286227694e-01,-6.289324907702836809e-01,-1.121090950597250391e+00,-5.524606503377667011e-01,5.708314591768366697e-02,1.691067830333353450e-01,-8.671929238656937988e-01
+2.000000000000000000e+00,-1.190270522128176189e+00,-7.753767188593280180e-01,6.200218057260061810e-01,1.108146037391112948e+00,-1.094204394955261117e+00,3.510082549799448670e-01,1.625244370112026715e-01,6.999301947337017715e-01,-1.102785014611727732e+00,-9.254951486666679328e-01,-1.649466562715481699e-01,7.547599744103555830e-01,-1.132613449376038028e+00
+3.000000000000000000e+00,3.452273844279844517e-01,9.181529327290725018e-01,1.781671855534498472e-01,7.867940527407438767e-01,-5.329578800661094462e-01,-6.269077535447806770e-01,-1.459471154244204039e+00,8.660380304217001335e-01,-7.183603589157521174e-01,1.525874411780396178e+00,-1.852373152909712495e+00,-1.487863221838233274e+00,-3.241486302928054952e-01
+3.000000000000000000e+00,-1.005623303786420397e-01,1.110391433720188115e+00,6.770353051031155478e-02,-1.772619012103635316e-01,4.492235209899058113e-01,-1.465121475137402429e+00,-1.523078824489514149e+00,4.507684412017047282e-01,-1.285844374466954321e+00,1.314359299318852425e-01,-1.275095667217708950e+00,-1.302168307499178113e+00,-2.478783643415571614e-01
+1.000000000000000000e+00,2.956951938939136326e-01,-5.465213605365711880e-01,1.871943229548247656e+00,-1.141317855161470884e+00,7.298467784344815357e-01,4.130981602831020894e-01,5.759742936057181684e-01,-4.755506586228989974e-02,-5.169950630750027587e-01,-1.705443782391301843e-01,5.455487107339948016e-01,1.691067830333353450e-01,3.775378164586793983e-01
+1.000000000000000000e+00,1.261572909308273926e+00,-5.556755748694814923e-01,-1.900449979236804865e-01,-9.806418628362864043e-01,1.852339808212784655e+00,1.033997213314673536e+00,1.095436933942416813e+00,-8.780942443022812238e-01,8.376441998536735545e-01,2.957487446719964663e-01,5.455487107339948016e-01,1.497539631766576118e+00,7.245675265368593798e-02
+2.000000000000000000e+00,-1.834188999071081572e+00,-1.711985728872501555e-01,3.307970745109054977e+00,3.036257945293327598e+00,1.361249107684777027e+00,1.313401787178881008e+00,3.120281103418120594e+00,1.032145866109698051e+00,4.166076721866525667e-01,4.511797856423717668e-01,-1.649466562715481699e-01,1.468971183406721615e+00,-8.885485983320433601e-01
+1.000000000000000000e+00,1.026295004271441380e+00,-3.085117878809041647e-01,1.724658356157395600e+00,1.119548849749682357e-01,5.193793353510497424e-01,9.563848316857274101e-01,8.834113664580501846e-01,2.016066876697071852e-01,2.152423763459032080e-01,-2.149532470878087187e-01,1.256044077739537190e+00,1.054728682188829314e+00,4.843161887904270935e-01
+1.000000000000000000e+00,2.116003196020975530e+00,-4.641334315403787270e-01,2.149884039011636938e-01,-2.458860992227984266e+00,-6.031136944272533773e-01,1.189221976572566675e+00,1.646703409401770735e+00,6.999301947337017715e-01,2.137365654825782002e+00,1.847265725502997280e-01,1.256044077739537190e+00,9.768566213369900275e-02,1.201256688732161626e+00
+2.000000000000000000e+00,-1.735124618002942043e+00,-1.620443585543396847e-01,4.727369323351540698e-01,8.189292512057811946e-01,-1.094204394955261117e+00,-5.958628008932020936e-01,-4.735522654418980459e-01,1.115199783953697343e+00,-5.353009990605254176e-01,-9.699040175153467169e-01,1.458950667933766043e-01,1.262541104935536729e-01,-2.326243111513074946e-01
+1.000000000000000000e+00,5.681222418312976696e-01,-3.908997168770964592e-01,1.025055207550848779e+00,3.047660757651901586e-01,-2.523346226215336663e-01,3.044408260025766033e-01,5.017653449861895210e-01,-5.458785729262844999e-01,3.218301649067650816e-02,-6.501601618048599152e-01,6.787665920475342007e-01,1.654666097745776554e+00,2.707594441269316476e-01
+2.000000000000000000e+00,-1.190270522128176189e+00,-1.437359298885191317e-01,-2.325675662091033935e+00,-4.986138858607326307e-01,-1.821788082603897352e-01,-1.612334637711018426e-01,3.530909652058243642e-02,-7.119864086142828619e-01,-4.437713191329124562e-01,-7.478596732719532403e-01,1.344855998615230197e+00,4.262228182720272929e-01,-1.410999920098094662e-01
+2.000000000000000000e+00,-1.549378903500182370e+00,-9.796485822396792942e-02,1.651015919461969350e+00,3.036257945293327598e+00,-5.329578800661094462e-01,-3.164582270289948984e-01,1.095180451401111393e-01,1.945738962393688265e+00,2.152423763459032080e-01,-8.810862798179891486e-01,1.267718547983720349e-02,-3.165568390841932978e-01,-8.854977876939934545e-01
+3.000000000000000000e+00,7.167188134335057415e-01,2.483523583656729095e+00,6.770353051031155478e-02,3.047660757651901586e-01,-5.329578800661094462e-01,-5.182504192642555241e-01,-1.417066040747330558e+00,1.032145866109698051e+00,-1.139396886582773050e+00,-2.593621159364872808e-01,-2.537585771472410934e-01,-8.736415821013588934e-01,-6.292296940977989417e-01
+3.000000000000000000e+00,-9.797587123583800794e-01,1.504022650035329534e+00,-4.846147447053830715e-01,-1.772619012103635316e-01,8.701584071567695089e-01,-1.480643951463191721e+00,-1.438268597495767187e+00,-4.628246550822858185e-01,-9.197256547565014762e-01,1.432615787198171287e+00,-1.408313548531248349e+00,-9.450627030009952634e-01,3.012675505074310367e-01
+2.000000000000000000e+00,-4.596707117506483864e-01,-1.150699506508649161e+00,-3.373298713145309602e-01,-3.379378935355480951e-01,-4.186717953810183829e-02,-2.078008927484694124e-01,-2.085203060864394553e-01,-3.797707372382866375e-01,-3.339357032197764474e-01,-1.049839981442968195e+00,1.167232156863844406e+00,7.119073018705739386e-01,-9.343107579027923881e-01
diff --git a/tutorials/training_set.csv b/tutorials/training_set.csv
new file mode 100644
index 0000000..80c551d
--- /dev/null
+++ b/tutorials/training_set.csv
@@ -0,0 +1,124 @@
+1.000000000000000000e+00,9.643797661038555624e-01,-4.458250028745581739e-01,3.254520589443019585e-01,-1.623345832137024658e+00,8.000025927956255778e-01,2.399975129984130984e+00,1.699709801272862641e+00,-2.136629015502882756e-01,4.166076721866525667e-01,9.174729085534980566e-01,1.903010272312230799e-01,8.547495436698466786e-01,1.323289114254158960e+00
+2.000000000000000000e+00,7.786340516010937796e-01,-9.035357195200717229e-01,-1.515608858441348072e+00,-3.379378935355480951e-01,-3.926462513438215840e-01,-3.630256560063624960e-01,-3.675394816997145098e-01,-2.136629015502882756e-01,-1.670269030162930157e+00,-5.258153290285596526e-01,1.167232156863844406e+00,-2.879883907243386276e-01,-3.851648430538042178e-01
+2.000000000000000000e+00,-1.202653569761693353e+00,-7.050221522523687778e-02,-5.950783997485230570e-01,6.261180604155592855e-01,-1.024048580594117075e+00,4.130981602831020894e-01,5.441704584830628910e-01,2.016066876697071852e-01,-5.353009990605254176e-01,-9.876675650548181196e-01,-4.757883793364734437e-01,8.833179920297012933e-01,-1.153969123842387701e+00
+2.000000000000000000e+00,-8.311621407561718966e-01,-1.013386291514995152e+00,-6.318996180962351827e-01,-1.658590888517895781e-02,-8.837369518718292127e-01,1.810121029604138121e+00,1.021227985322888498e+00,-1.293363833522276796e+00,4.166076721866525667e-01,-2.371576815121479997e-01,1.122826196425997791e+00,2.976648006526816381e-01,-1.025835077044290333e+00
+1.000000000000000000e+00,1.261572909308273926e+00,-7.965642955814736248e-02,1.025055207550848779e+00,-4.664786873956953128e-01,1.501560736407065111e+00,4.130981602831020894e-01,3.957525612440059848e-01,-2.967168193942874566e-01,-7.183603589157521174e-01,2.929553157992404247e-02,4.123308294204554025e-01,1.311844717427521179e+00,1.643624231249402046e+00
+1.000000000000000000e+00,8.529323374021988702e-01,-6.746803611973148929e-01,1.356446172680266793e+00,1.108146037391112948e+00,9.844444918418605861e-02,1.033997213314673536e+00,1.180247160936163553e+00,-4.628246550822858185e-01,1.350210407448307448e+00,3.179531790963357474e-01,9.896083151124585031e-01,6.911721377384434650e-02,1.613116124868902768e+00
+1.000000000000000000e+00,2.461630033598449785e-01,3.231290010899045551e-01,1.982406884591387586e+00,6.261180604155592855e-01,1.291093293323633207e+00,7.235476867988874794e-01,5.865755719799363721e-01,3.677145233577055472e-01,3.250779922590392168e-01,-2.948892110154302526e-01,3.235189085447624513e-01,3.833701457322456485e-01,-6.482972605856110460e-02
+1.000000000000000000e+00,7.414849087005422890e-01,7.899939320683285748e-01,8.409491158122843757e-01,-1.237723450556581950e+00,1.150781664601345344e+00,5.683229235409951174e-01,9.470190367033598511e-01,-1.459471669210274936e+00,3.218301649067650816e-02,5.149996600426331311e-02,-3.172877495800926345e-02,9.975917854691199738e-01,2.707594441269316476e-01
+2.000000000000000000e+00,-6.577994738869272773e-01,-4.641334315403787270e-01,-1.294681548355069767e+00,4.654420680903747498e-01,-1.024048580594117075e+00,-2.078008927484694124e-01,-2.297228628348761681e-01,-2.136629015502882756e-01,-2.973238312487311297e-01,-9.077316011271964191e-01,3.235189085447624513e-01,1.297560493247593483e+00,-2.570307962557069614e-01
+1.000000000000000000e+00,1.212040718774203052e+00,-5.099045032049300818e-01,-4.477935263576708902e-01,-9.806418628362864043e-01,-2.523346226215336663e-01,4.907105419120482703e-01,2.049295505080760971e-01,-7.119864086142828619e-01,6.179729680274015369e-01,-1.261355093904512337e-01,3.235189085447624513e-01,1.326128941607448208e+00,8.504134653564191515e-01
+1.000000000000000000e+00,5.433561465642633426e-01,-3.908997168770964592e-01,2.886308405965897217e-01,4.654420680903747498e-01,2.828863482304210669e-02,5.683229235409951174e-01,8.940126448322682773e-01,-7.119864086142828619e-01,3.983017362011299078e-01,5.149996600426331311e-02,3.235189085447624513e-01,1.154718251448320965e+00,4.995702419806767325e-01
+1.000000000000000000e+00,1.472084719078069925e+00,-4.824418602061992800e-01,3.622732772920158051e-01,-1.109182656696434011e+00,1.922495622573928697e+00,7.235476867988874794e-01,9.788228718260151284e-01,-5.458785729262844999e-01,1.185456983578603740e+00,2.913078577871283104e-01,3.235189085447624513e-01,1.797508339545049960e+00,9.419377844979172076e-01
+3.000000000000000000e+00,4.442917654961238694e-01,1.531485293034060557e+00,5.463793690305801531e-01,1.268822029716297539e+00,1.686002635453300036e-01,-8.286999457800412472e-01,-1.470072432618422242e+00,6.999301947337017715e-01,-4.254653831473897974e-01,1.028495080675194684e+00,-1.186283746342016387e+00,-1.573568566917797229e+00,-1.906756648781209040e-02
+1.000000000000000000e+00,1.447318623811035598e+00,1.650490079361894180e+00,3.990944956397280419e-01,-4.872110735021632777e-02,8.000025927956255778e-01,8.011600684278343820e-01,5.441704584830628910e-01,-3.797707372382866375e-01,-7.183603589157521174e-01,1.136723823924137428e-01,-4.313824188986269959e-01,9.547391129293382184e-01,9.876999440686662357e-01
+2.000000000000000000e+00,-1.586528046400733860e+00,4.055169300860972381e-01,2.166512976329951989e+00,3.047660757651901586e-01,2.387560779064739624e-01,-9.063123274089878167e-01,-1.131088007184745114e-01,2.111846798081686405e+00,-1.084479078626205073e+00,-5.258153290285596526e-01,1.167232156863844406e+00,-2.308514940046293151e-01,-4.553334877289527127e-01
+1.000000000000000000e+00,6.424205276324028713e-01,-4.824418602061992800e-01,-7.958134288054057037e-02,-9.163714659062127676e-01,1.220937478962489164e+00,1.266834358201512911e+00,1.222652274433037034e+00,-4.755506586228989974e-02,1.276986663506216813e+00,5.089113151456540196e-01,-7.613473539585573213e-02,1.026160233828974588e+00,1.182189122244349522e-01
+2.000000000000000000e+00,-8.311621407561718966e-01,-1.187316363840290157e+00,-3.577597085913275077e+00,-2.715942579948279700e+00,-8.135811375106852816e-01,-5.492953719158341075e-01,-1.660895443354352574e+00,-5.458785729262844999e-01,-2.237753045714132583e+00,-1.347379402729115494e+00,3.679248689826089547e-01,-1.202178738239686906e+00,-7.207540132392969978e-01
+3.000000000000000000e+00,-2.243928067138180049e-01,-8.119935761909689020e-01,-4.276012453282835440e-02,-3.379378935355480951e-01,1.571716550768208931e+00,-1.278851759227930707e+00,-9.400085139075050611e-01,-1.127255997834278656e+00,-1.285844374466954321e+00,-3.925887224825236888e-01,-9.198479837149375893e-01,-1.959242619775834804e+00,-3.851648430538042178e-01
+1.000000000000000000e+00,1.657830433580831819e+00,1.238550434380931708e+00,-1.900449979236804865e-01,-9.806418628362864043e-01,1.686002635453300036e-01,1.422059121459405828e+00,1.095436933942416813e+00,-6.289324907702836809e-01,1.002397623723377151e+00,-3.731777169309376946e-02,3.235189085447624513e-01,1.111865578908538543e+00,9.419377844979172076e-01
+1.000000000000000000e+00,1.434935576177516214e+00,-4.366707885416478696e-01,4.359157139874418885e-01,-1.237723450556581950e+00,8.701584071567695089e-01,1.453104074110984412e+00,1.328665058175220626e+00,-4.755506586228989974e-02,5.996670320418788780e-01,7.842463020074620372e-01,3.679248689826089547e-01,2.690963522928269680e-01,2.116499880147141965e+00
+1.000000000000000000e+00,1.558766052512692291e+00,-2.902033592150836117e-01,1.430088609375692821e+00,3.047660757651901586e-01,1.431404922045921069e+00,7.235476867988874794e-01,1.063633098819761980e+00,-1.306089837062890946e-01,5.996670320418788780e-01,5.399975233397292795e-01,4.567367898583018504e-01,-1.658813130571964653e-02,1.597862071678653129e+00
+3.000000000000000000e+00,4.803424122356605291e-02,1.522331078701150142e+00,1.045247488580237777e-01,7.867940527407438767e-01,9.403142215179134400e-01,-1.434076522485823846e+00,-7.915906166684484324e-01,-4.755506586228989974e-02,-9.197256547565014762e-01,1.961081326497447597e+00,-1.763561232034019932e+00,-1.902105723056125575e+00,-6.292296940977989417e-01
+2.000000000000000000e+00,-8.311621407561718966e-01,-9.401525768517128290e-01,8.409491158122843757e-01,-3.058026950705107216e-01,-1.120229938992457902e-01,1.336935864188945888e-01,5.441704584830628910e-01,2.016066876697071852e-01,8.010323278826282367e-01,-1.705443782391301843e-01,9.896083151124585031e-01,-5.165359776031759331e-01,-2.387259324274073613e-01
+2.000000000000000000e+00,-1.338867093730384150e+00,-1.050003148846635925e+00,-1.164025612282544170e-01,6.261180604155592855e-01,9.844444918418605861e-02,1.623851313694666398e+00,3.505261397927611402e-03,-1.791687340586271437e+00,1.387708050515384234e-02,-7.878276552357639240e-01,1.014891063555301426e-01,6.690546293307916281e-01,3.958426802869789984e-01
+2.000000000000000000e+00,-4.968198546511998770e-01,-8.028393618580585978e-01,-1.147396674964217711e+00,-7.235602751159908586e-01,2.828863482304210669e-02,-4.872054666126768852e-01,-7.703880599200116919e-01,1.530469373173693137e+00,-1.871634326003678961e+00,3.401576135206750284e-01,5.708314591768366697e-02,-1.530715894378015252e+00,-9.343107579027923881e-01
+3.000000000000000000e+00,8.281662421351645431e-01,3.124318586960447952e+00,4.359157139874418885e-01,4.654420680903747498e-01,-3.224904369826776529e-01,-1.014969661689512970e+00,-1.618490329857479315e+00,1.447415455329694067e+00,-1.066173142640682414e+00,1.206130556069909820e+00,-1.452719508969094742e+00,-1.316452531679105586e+00,-4.957567286831143089e-02
+1.000000000000000000e+00,3.565119359004889632e-02,-6.746803611973148929e-01,-8.528269281825133774e-01,-1.141317855161470884e+00,-1.120229938992457902e-01,1.026486337673159777e-01,5.335691801088447983e-01,-5.458785729262844999e-01,-4.986891270894800443e-01,-5.702241978772382147e-01,9.452023546746119997e-01,4.054876541398967638e-02,8.046513057856702344e-01
+1.000000000000000000e+00,3.204612891609501246e-01,-4.732876458732889757e-01,-7.055420547916613216e-01,-6.592898781859172219e-01,-3.926462513438215840e-01,1.026486337673159777e-01,5.651165326901914226e-02,-6.289324907702836809e-01,-5.353009990605254176e-01,-4.592020257555414764e-01,2.347069876690695278e-01,1.548225588534083430e-01,1.613116124868902768e+00
+2.000000000000000000e+00,-1.289334903196315496e+00,1.092083005054367728e+00,-1.221039111659643739e+00,-1.658590888517895781e-02,-8.837369518718292127e-01,-5.182504192642555241e-01,-5.265586573129897863e-01,2.016066876697071852e-01,4.166076721866525667e-01,-1.644918824015262793e+00,-1.649466562715481699e-01,5.547808358913729476e-01,-5.865183451650999302e-01
+2.000000000000000000e+00,-3.977554735830604038e-01,-4.458250028745581739e-01,-1.900449979236804865e-01,1.108146037391112948e+00,-1.094204394955261117e+00,-1.480643951463191721e+00,-3.993433168223695651e-01,1.115199783953697343e+00,-2.273479146589149624e-02,-7.478596732719532403e-01,-3.869764584607804925e-01,-3.451252874440479679e-01,-8.183799536568948652e-01
+2.000000000000000000e+00,-1.239802712662246842e+00,1.888499652017560981e+00,1.781671855534498472e-01,9.474700450659284678e-01,-1.374827652399836841e+00,-3.630256560063624960e-01,-4.099445951965879353e-01,6.168762768897025905e-01,-5.353009990605254176e-01,-1.058721755212703952e+00,-7.866301024013981902e-01,-1.308619247451383028e-01,-5.377053749563008855e-01
+3.000000000000000000e+00,-8.435451883896890601e-01,1.458251578370778345e+00,1.781671855534498472e-01,6.261180604155592855e-01,-8.135811375106852816e-01,-5.257612949057706431e-02,-1.289850700256710558e+00,1.281307619641695705e+00,-1.102785014611727732e+00,1.183926121645570539e+00,-1.807967192471865880e+00,-1.545000118557942503e+00,-7.207540132392969978e-01
+3.000000000000000000e+00,-1.041673950525968007e+00,7.259144317379564448e-01,-4.276012453282835440e-02,-1.658590888517895781e-02,-2.523346226215336663e-01,-1.682436143698452291e+00,-1.745705670348099314e+00,4.507684412017047282e-01,-1.670269030162930157e+00,2.291354413989782624e-01,-1.363907588093401735e+00,-1.187894514059759654e+00,-7.512621196197962758e-01
+1.000000000000000000e+00,1.013911956637924217e+00,-8.119935761909689020e-01,-2.268662162713926955e-01,-9.806418628362864043e-01,-1.120229938992457902e-01,1.002952260663094952e+00,1.074234377193980183e+00,-1.044202079990279364e+00,3.799958002156071935e-01,9.929679855962517676e-01,1.903010272312230799e-01,1.268992044887738979e+00,8.809215717369185406e-01
+3.000000000000000000e+00,1.470986222917055053e-01,1.220242005715111100e+00,-6.687208364439490849e-01,6.261180604155592855e-01,1.686002635453300036e-01,-1.294374235553719998e+00,-1.682098000102789204e+00,6.999301947337017715e-01,-6.268306789881387120e-01,-4.369975913312021953e-01,-1.630343350720480533e+00,-1.402157876758669541e+00,2.249972845561826473e-01
+3.000000000000000000e+00,1.038678051904960764e+00,2.565911512652921278e+00,-3.741510896622448068e-01,3.047660757651901586e-01,-1.374827652399836841e+00,-2.101543004494763167e+00,-1.904724845961374369e+00,4.507684412017047282e-01,-1.761798710090543230e+00,-3.731777169309376946e-02,-1.719155271596173540e+00,-1.902105723056125575e+00,-1.041089130234540194e+00
+3.000000000000000000e+00,-1.377114732791957230e-01,5.245217164139306387e-01,1.356446172680266793e+00,6.261180604155592855e-01,-2.523346226215336663e-01,-1.232284330250562832e+00,-1.735104391973880889e+00,1.530469373173693137e+00,-1.633657158191884617e+00,-1.705443782391301843e-01,-8.754420232770911969e-01,-5.022517534232482372e-01,-4.766891621953022184e-01
+3.000000000000000000e+00,1.608298243046760945e+00,-5.099045032049300818e-01,1.356446172680266793e+00,1.911525999017035904e+00,-1.120229938992457902e-01,7.235476867988874794e-01,-8.764008436621949505e-01,1.530469373173693137e+00,1.936000358985032754e+00,3.559800605049880495e+00,-1.763561232034019932e+00,-1.002199599720704493e+00,-2.936405239123061617e-01
+2.000000000000000000e+00,-1.190270522128176189e+00,-8.303020048567895106e-01,-1.164025612282544170e-01,1.461633220506519448e+00,-2.076385796011276152e+00,-2.078008927484694124e-01,-5.795650491840813601e-01,6.168762768897025905e-01,-4.803831911039578295e-01,-1.440638027311340608e+00,4.567367898583018504e-01,7.833284227702103086e-01,-4.004188962440538568e-01
+1.000000000000000000e+00,-1.377114732791957230e-01,1.430788935372047321e+00,1.172340080941700835e+00,-1.451267027453261582e-01,1.686002635453300036e-01,1.181711100931056302e-01,2.897397775018228927e-01,-7.950403264582820428e-01,6.179729680274015369e-01,-2.149532470878087187e-01,2.791129481069160034e-01,1.226139372347957224e+00,4.194864627318659750e-02
+1.000000000000000000e+00,1.335871195109376908e+00,-6.929887898631355014e-01,-4.276012453282835440e-02,-7.235602751159908586e-01,-3.224904369826776529e-01,-2.078008927484694124e-01,3.109423342502596332e-01,-7.119864086142828619e-01,-1.325704073790270887e-01,7.091097155584770960e-03,9.007963942367656074e-01,2.262436797530446575e-01,1.597862071678653129e+00
+2.000000000000000000e+00,-8.311621407561718966e-01,-9.767694341833539351e-01,-1.515608858441348072e+00,1.762252819050429686e-01,-1.515139281122124704e+00,-3.475031796805735373e-01,-1.449126358411293447e-01,-6.289324907702836809e-01,-1.102785014611727732e+00,-1.350172831601871848e-01,6.787665920475342007e-01,1.169002475628247995e+00,-7.512621196197962758e-01
+1.000000000000000000e+00,1.645447385947312435e+00,-2.627407162163527543e-01,6.200218057260061810e-01,-7.235602751159908586e-01,9.403142215179134400e-01,2.353407701006763553e+00,1.434677841917403995e+00,-8.780942443022812238e-01,9.840916877378549366e-01,1.250539424918588161e+00,-4.757883793364734437e-01,1.126149803088466239e+00,2.208024199288639799e+00
+2.000000000000000000e+00,-1.091206141060036661e+00,-5.739840035353018788e-01,-7.958134288054057037e-02,1.204551632786224014e+00,-6.732695087883974194e-01,-8.597448984316198306e-01,-4.735522654418980459e-01,6.999301947337017715e-01,-1.508763433645497753e-01,-1.125335058485722017e+00,1.611291761242308995e+00,-5.736728743228852734e-01,-7.970242791905453039e-01
+2.000000000000000000e+00,-1.016907855258931459e+00,-8.669188621884306167e-01,-1.441966421745921823e+00,-1.658590888517895781e-02,-5.329578800661094462e-01,4.055872846415876226e-02,-1.025075223442559191e-01,3.677145233577055472e-01,8.010323278826282367e-01,-1.014312886364025168e+00,-4.757883793364734437e-01,5.119281633515913033e-01,-1.355322625953683247e+00
+2.000000000000000000e+00,-1.289334903196315496e+00,-6.655261468644046996e-01,3.254520589443019585e-01,9.474700450659284678e-01,-9.538927662329731438e-01,-1.371986617182666679e+00,-9.400085139075050611e-01,1.281307619641695705e+00,-2.273479146589149624e-02,-6.146330667259169989e-01,3.679248689826089547e-01,-1.658813130571964653e-02,-9.343107579027923881e-01
+2.000000000000000000e+00,-8.311621407561718966e-01,-5.556755748694814923e-01,-1.164025612282544170e-01,1.750850006691851313e+00,-8.135811375106852816e-01,-1.767559400968908290e-01,3.321448909986963183e-01,4.507684412017047282e-01,4.715254801432201548e-01,-1.271884325686361672e+00,-3.425704980229340446e-01,1.691067830333353450e-01,-1.263798306812185190e+00
+1.000000000000000000e+00,1.311105099842342581e+00,-1.986612158859807908e-01,2.518096222488758751e-01,-8.085630581525254934e-02,2.387560779064739624e-01,6.459353051699413539e-01,8.304049745869581667e-01,-2.136629015502882756e-01,1.350210407448307448e+00,5.399975233397292795e-01,4.567367898583018504e-01,1.262541104935536729e-01,9.266837313076675686e-01
+1.000000000000000000e+00,1.088509832301457966e-02,-4.275165742087375653e-01,-8.528269281825133774e-01,-6.592898781859172219e-01,5.193793353510497424e-01,1.033997213314673536e+00,9.152152015807050178e-01,-5.458785729262844999e-01,7.095026479550144982e-01,2.485464469505626467e-02,-3.869764584607804925e-01,9.833075612891929440e-01,3.927918696489290373e-01
+1.000000000000000000e+00,9.767628137373727260e-01,-5.373671462036609947e-01,9.882339892031365425e-01,-5.950194812558435853e-01,-3.926462513438215840e-01,1.802610153962628525e-01,9.046139232064869251e-01,-1.044202079990279364e+00,1.185456983578603740e+00,2.735443102476567967e-01,1.211638117301690798e+00,1.011876009649047559e+00,1.552099912107903990e+00
+2.000000000000000000e+00,-7.692469025885839695e-01,-5.739840035353018788e-01,-5.214359630530969181e-01,1.108146037391112948e+00,5.895351497121936735e-01,-5.182504192642555241e-01,-4.950113047316438697e-02,-4.755506586228989974e-02,-5.934666343693642543e-02,-1.298529646995568942e+00,4.123308294204554025e-01,4.262228182720272929e-01,-1.254645874898035585e+00
+2.000000000000000000e+00,-3.977554735830604038e-01,-3.908997168770964592e-01,-4.846147447053830715e-01,-8.085630581525254934e-02,-9.538927662329731438e-01,-2.078008927484694124e-01,4.169551179924426698e-01,-7.119864086142828619e-01,2.335483123314258669e-01,-4.814064601798809795e-01,8.563904337989191040e-01,6.833388535107193240e-01,-1.288967494576097328e-01
+1.000000000000000000e+00,1.422552528543999051e+00,-5.922924322011223763e-01,5.463793690305801531e-01,-8.199658705111018131e-01,5.895351497121936735e-01,1.499671503088352065e+00,1.901134090383011177e+00,-2.136629015502882756e-01,3.983017362011299078e-01,1.650219244556696330e+00,1.167232156863844406e+00,2.262436797530446575e-01,2.818186326898626692e+00
+1.000000000000000000e+00,8.776984326692331972e-01,-4.641334315403787270e-01,2.886308405965897217e-01,-9.806418628362864043e-01,-7.434253231495413505e-01,4.130981602831020894e-01,6.607845205994645754e-01,-4.628246550822858185e-01,3.067720562735165579e-01,2.735443102476567967e-01,8.119844733610726006e-01,3.405174731924633380e-01,1.719894497200650463e+00
+3.000000000000000000e+00,9.891458613708897785e-01,1.723723794025176392e+00,1.781671855534498472e-01,1.440900834400055952e-01,-7.434253231495413505e-01,-8.286999457800412472e-01,-1.385262205624675502e+00,1.115199783953697343e+00,-1.508763433645497753e-01,1.787886737987600450e+00,-1.763561232034019932e+00,-1.459294773478378771e+00,-8.427864387612943320e-01
+1.000000000000000000e+00,1.459701671444552762e+00,-4.915960745391095288e-01,-1.164025612282544170e-01,-8.842362674411753387e-01,1.291093293323633207e+00,1.344446739830459592e+00,9.152152015807050178e-01,-7.119864086142828619e-01,7.095026479550144982e-01,6.199334872673507579e-01,-1.205406958337021939e-01,9.261706645694836038e-01,6.521107738831735112e-01
+3.000000000000000000e+00,5.681222418312976696e-01,8.083023607341491834e-01,1.061876425898562681e+00,1.590174014366666722e+00,1.641872365129352973e+00,-1.449598998811613138e+00,-6.007676059325179896e-01,-1.044202079990279364e+00,-7.183603589157521174e-01,1.605810375708017768e+00,-1.674749311158327147e+00,-1.944958395595907552e+00,-7.817702260002956649e-01
+1.000000000000000000e+00,1.088509832301457966e-02,-4.641334315403787270e-01,-1.073754238268791683e+00,-2.137509007577615083e+00,-5.329578800661094462e-01,5.993678761925737009e-01,1.201449717684600405e+00,-1.459471669210274936e+00,2.320425014681008591e+00,9.840862118265162328e-01,6.787665920475342007e-01,3.548016973723909784e-01,1.201256688732161626e+00
+1.000000000000000000e+00,1.088509832301457966e-02,-1.711985728872501555e-01,3.271149526761342852e+00,1.911525999017035904e+00,1.712028179490496793e+00,4.596655892604696314e-01,5.759742936057181684e-01,1.032145866109698051e+00,5.081373521142654726e-01,-6.235148404956527557e-01,7.231725524853797049e-01,7.690441985902832789e-01,2.249972845561826473e-01
+3.000000000000000000e+00,1.594816699252226688e-01,2.703224727646575509e+00,-4.276012453282835440e-02,9.474700450659284678e-01,-4.628020657049655151e-01,-9.218348037347771085e-01,-1.597287773109042464e+00,2.194900715925685919e+00,-1.691822793500724342e-01,1.294948293767267167e+00,-1.630343350720480533e+00,-1.687842360357215910e+00,-9.533783243906045202e-02
+2.000000000000000000e+00,-5.463520451852706961e-01,-8.211477905238792063e-01,-1.589251295136774100e+00,-1.772619012103635316e-01,-8.135811375106852816e-01,-1.371986617182666679e+00,-8.233944517911032657e-01,-4.628246550822858185e-01,-5.353009990605254176e-01,-1.125335058485722017e+00,3.235189085447624513e-01,1.548225588534083430e-01,-5.926199664411997414e-01
+3.000000000000000000e+00,-2.863080448814038226e-01,6.681099976841710364e-02,2.518096222488758751e-01,1.590174014366666722e+00,-1.120229938992457902e-01,-1.837660906956345430e+00,-1.109628967894998430e+00,-6.289324907702836809e-01,-1.487209670307703568e+00,3.179531790963357474e-01,-1.363907588093401735e+00,-1.859253050516343597e+00,-5.987215877172996636e-01
+1.000000000000000000e+00,2.461630033598449785e-01,1.595564793364432354e+00,-1.532237795759666399e-01,-4.986138858607326307e-01,2.387560779064739624e-01,4.751880655862593117e-01,5.229679017346261505e-01,-2.136629015502882756e-01,3.218301649067650816e-02,-2.771256634759587389e-01,-6.534122210878593462e-01,4.833597149917366331e-01,-2.326243111513074946e-01
+1.000000000000000000e+00,1.966308128257741317e-01,-4.183623598758273165e-01,-7.055420547916613216e-01,-2.523131389158058457e+00,2.828863482304210669e-02,4.907105419120482703e-01,6.607845205994645754e-01,-7.119864086142828619e-01,-6.634425509591841408e-01,-2.682438897062232042e-01,3.679248689826089547e-01,1.054728682188829314e+00,8.961756249271681796e-01
+2.000000000000000000e+00,1.013911956637924217e+00,-6.655261468644046996e-01,1.245982517637126863e+00,1.911525999017035904e+00,-9.538927662329731438e-01,9.563848316857274101e-01,7.667973043416480561e-01,-1.127255997834278656e+00,4.166076721866525667e-01,-7.123325781930102130e-01,1.744509642555848394e+00,7.119073018705739386e-01,-1.056343183424789833e+00
+1.000000000000000000e+00,8.653153850357160337e-01,-5.190587175378403861e-01,-3.005086529668187789e-01,-8.521010689761391310e-01,1.291093293323633207e+00,4.130981602831020894e-01,8.092024178385215372e-01,-1.127255997834278656e+00,-4.104072745141375961e-02,3.845664823693535350e-01,-2.093526167093946455e-01,7.690441985902832789e-01,9.266837313076675686e-01
+3.000000000000000000e+00,-4.225215688500969513e-01,1.201933577049290491e+00,1.045247488580237777e-01,7.867940527407438767e-01,4.492235209899058113e-01,-9.839247090379342753e-01,-9.930149057785968569e-01,-1.459471669210274936e+00,-1.468903734322180910e+00,7.091097155584770960e-03,-8.310360628392446936e-01,-1.959242619775834804e+00,-4.766891621953022184e-01
+2.000000000000000000e+00,-1.512229760599628881e+00,-4.732876458732889757e-01,-1.662893731832200128e+00,1.440900834400055952e-01,-9.538927662329731438e-01,2.578733970252090058e-01,-5.265586573129897863e-01,2.016066876697071852e-01,-4.071594471618671385e-01,-1.298529646995568942e+00,-1.205406958337021939e-01,-3.165568390841932978e-01,-1.041089130234540194e+00
+2.000000000000000000e+00,-1.512229760599628881e+00,-7.021430041960458057e-01,-1.257860330007356753e+00,5.618476634854857599e-01,-9.538927662329731438e-01,-5.492953719158341075e-01,-5.689637708098629343e-01,-3.797707372382866375e-01,-2.057941513211177798e-01,-1.347379402729115494e+00,-7.613473539585573213e-02,9.547391129293382184e-01,-7.970242791905453039e-01
+1.000000000000000000e+00,1.657830433580831819e+00,-3.359744308796348000e-01,1.781671855534498472e-01,-2.266049801437763023e+00,1.686002635453300036e-01,1.499671503088352065e+00,1.593697017530679050e+00,-4.628246550822858185e-01,2.411954694608621441e+00,1.117312818372552252e+00,1.034014275550305006e+00,4.833597149917366331e-01,2.412428512037985584e+00
+1.000000000000000000e+00,1.670213481214348983e+00,-3.359744308796348000e-01,4.359157139874418885e-01,-1.430534641346803193e+00,-2.523346226215336663e-01,2.578733970252090058e-01,4.063538396182245771e-01,-3.797707372382866375e-01,6.179729680274015369e-01,1.181132692772815240e-01,2.347069876690695278e-01,1.311844717427521179e+00,1.628370178059152407e+00
+2.000000000000000000e+00,-1.289334903196315496e+00,-1.205624792506110765e+00,-1.221039111659643739e+00,-1.658590888517895781e-02,-9.538927662329731438e-01,1.336935864188945888e-01,1.307206018885473664e-01,-3.797707372382866375e-01,-3.888535111763444796e-01,-1.103130624061382736e+00,1.833321563431540291e+00,6.547704051508645984e-01,-1.459050187647381147e+00
+1.000000000000000000e+00,4.319087178626067614e-01,-4.275165742087375653e-01,1.061876425898562681e+00,-9.485066643712489753e-01,-4.628020657049655151e-01,8.011600684278343820e-01,8.516075313353949072e-01,-4.755506586228989974e-02,-3.522416392052991063e-01,-8.172664054177269932e-02,-2.093526167093946455e-01,7.976126469501378935e-01,1.338543167444408599e+00
+2.000000000000000000e+00,-2.490490523647506116e+00,-6.655261468644046996e-01,-4.846147447053830715e-01,7.867940527407438767e-01,-1.024048580594117075e+00,1.957834917220518112e-01,3.530909652058243642e-02,1.447415455329694067e+00,6.728907759839690694e-01,-1.369583837153454997e+00,3.298718257880472571e+00,2.976648006526816381e-01,-1.065495615338939661e+00
+3.000000000000000000e+00,1.348254242742896292e+00,1.705415365359355340e+00,1.503731046071119071e+00,1.750850006691851313e+00,-2.523346226215336663e-01,-4.406380376353093431e-01,-1.459471154244204039e+00,1.779631126705690791e+00,-5.353009990605254176e-01,1.872263588800089806e+00,-1.585937390282634141e+00,-1.516431670198087778e+00,-5.987215877172996636e-01
+2.000000000000000000e+00,-9.302265218243114253e-01,-5.739840035353018788e-01,-4.477935263576708902e-01,4.333068696253373764e-01,2.387560779064739624e-01,-1.915273288585291667e+00,-1.183837916514526967e+00,2.016066876697071852e-01,-3.339357032197764474e-01,-8.588818453936499786e-01,-2.715209613223796836e-01,-1.202178738239686906e+00,3.470297100781800093e-01
+1.000000000000000000e+00,7.291018610670251254e-01,-3.908997168770964592e-01,1.356446172680266793e+00,-5.950194812558435853e-01,8.701584071567695089e-01,8.011600684278343820e-01,8.198036962127400740e-01,-3.797707372382866375e-01,-3.339357032197764474e-01,1.028495080675194684e+00,1.389261959053076589e+00,3.119490248326086679e-01,1.689386390820151185e+00
+1.000000000000000000e+00,8.281662421351645431e-01,-3.451286452125450488e-01,1.045247488580237777e-01,-7.878306720460643842e-01,9.844444918418605861e-02,4.286206366088910480e-01,7.879998610900847966e-01,-6.289324907702836809e-01,8.710082444724451256e-02,-5.258153290285596526e-01,6.343606316096876974e-01,1.911782132984468419e+00,8.504134653564191515e-01
+1.000000000000000000e+00,8.405492897686817066e-01,-7.387598615276869118e-01,6.200218057260061810e-01,-7.556954735810281765e-01,5.895351497121936735e-01,1.654896266346244982e+00,1.625500852653333883e+00,-1.293363833522276796e+00,7.278085839405374902e-01,8.064507364318013183e-01,-3.425704980229340446e-01,2.976648006526816381e-01,1.613116124868902768e+00
+2.000000000000000000e+00,-1.710358522735907716e+00,-3.268202165467247178e-01,-1.515608858441348072e+00,-9.806418628362864043e-01,-1.821788082603897352e-01,-1.123626995970037790e+00,-6.007676059325179896e-01,-4.755506586228989974e-02,-9.014197187709788173e-01,-5.258153290285596526e-01,1.167232156863844406e+00,-7.450835644820126280e-01,-1.001428591939890866e+00
+2.000000000000000000e+00,-1.078823093426519497e+00,-7.204514328618663033e-01,7.304854607691444457e-01,-1.658590888517895781e-02,3.089118922676178935e-01,-6.889976588479378439e-01,-4.099445951965879353e-01,8.660380304217001335e-01,-1.121090950597250391e+00,-9.032907142423284297e-01,2.144163286496465481e+00,-6.165255468626669177e-01,-1.224137768517536085e+00
+2.000000000000000000e+00,-1.029290902892448623e+00,-9.584610055175334375e-01,-2.141569570352468865e+00,-7.235602751159908586e-01,3.606235167241383266e+00,-7.510875641510947887e-01,-9.082046787848500058e-01,-1.708633422742272145e+00,1.569881639274579577e+00,-9.476995830910072138e-01,1.389261959053076589e+00,5.833492842512276733e-01,-1.166935069054099855e-01
+3.000000000000000000e+00,-3.358402354154746416e-01,1.400447144316993575e-01,-1.900449979236804865e-01,1.440900834400055952e-01,-9.538927662329731438e-01,-1.465121475137402429e+00,-1.724503113599662463e+00,1.115199783953697343e+00,-1.835022454032633643e+00,2.183125226652152140e+00,-1.763561232034019932e+00,-1.473578997658306244e+00,-8.732945451417937210e-01
+3.000000000000000000e+00,-1.996267114467815129e-01,6.892975744063156718e-01,2.518096222488758751e-01,3.047660757651901586e-01,3.089118922676178935e-01,-1.604823762069505833e+00,-9.718123490301601164e-01,-8.780942443022812238e-01,-1.487209670307703568e+00,1.847265725502997280e-01,-1.008659904590630596e+00,-1.773547705436779864e+00,-6.902459068587976088e-01
+2.000000000000000000e+00,-1.388099694402193489e-02,-1.223933221171931374e+00,-2.288854443743320921e+00,-9.806418628362864043e-01,-9.538927662329731438e-01,-5.958628008932020936e-01,-1.131088007184745114e-01,-8.780942443022812238e-01,-3.339357032197764474e-01,-1.705443782391301843e-01,9.896083151124585031e-01,-2.594199423644839575e-01,-1.111257774909688578e+00
+1.000000000000000000e+00,5.805052894648148332e-01,-4.000539312100067635e-01,1.172340080941700835e+00,-1.658590888517895781e-02,7.298467784344815357e-01,2.503625213836980362e-02,4.169551179924426698e-01,-4.628246550822858185e-01,-1.874882153355951209e-01,-3.481798536338447381e-01,5.899546711718413050e-01,2.976648006526816381e-01,1.033462103639415153e+00
+2.000000000000000000e+00,-1.759890713269978590e+00,-2.261238588847116482e-01,-1.900449979236804865e-01,-3.379378935355480951e-01,-1.120229938992457902e-01,1.065042165966252119e+00,1.307206018885473664e-01,-1.459471669210274936e+00,-5.353009990605254176e-01,-7.700641076962924103e-01,8.563904337989191040e-01,4.262228182720272929e-01,-1.254645874898035585e+00
+2.000000000000000000e+00,-3.358402354154746416e-01,1.092083005054367728e+00,-1.294681548355069767e+00,-9.806418628362864043e-01,-1.374827652399836841e+00,-1.092582043318459428e+00,-9.400085139075050611e-01,6.999301947337017715e-01,-1.487209670307703568e+00,-7.034508044232744561e-01,-1.186283746342016387e+00,-7.736520128418672426e-01,-1.172273987670687356e+00
+2.000000000000000000e+00,-9.302265218243114253e-01,-7.570682901935074094e-01,-1.294681548355069767e+00,-9.806418628362864043e-01,-1.024048580594117075e+00,3.354857786541551867e-01,3.851512828697878366e-01,-4.628246550822858185e-01,2.335483123314258669e-01,-9.254951486666679328e-01,1.167232156863844406e+00,1.119698863136266570e-01,-1.001428591939890866e+00
+2.000000000000000000e+00,-8.683112836567233872e-01,1.949700004291610167e-01,4.727369323351540698e-01,6.261180604155592855e-01,-1.120229938992457902e-01,3.510082549799448670e-01,-2.829857372472768459e-02,-4.755506586228989974e-02,-6.085247430026160531e-01,-9.699040175153467169e-01,-7.422241419635517978e-01,1.026160233828974588e+00,-9.709204855593915884e-01
+2.000000000000000000e+00,-4.349046164836141148e-01,-6.472177181985840910e-01,-2.636874346191065421e-01,5.297124650204483309e-01,-1.374827652399836841e+00,-1.480643951463191721e+00,-7.173816680489198960e-01,1.945738962393688265e+00,-4.104072745141375961e-02,-8.588818453936499786e-01,-3.172877495800926345e-02,-8.593573579214313085e-01,-7.970242791905453039e-01
+2.000000000000000000e+00,-1.450314522432043063e+00,1.897653866350470953e+00,2.149884039011636938e-01,6.261180604155592855e-01,-1.234516023677548979e+00,8.166825447536233407e-01,9.470190367033598511e-01,-1.127255997834278656e+00,2.320425014681008591e+00,-9.699040175153467169e-01,-9.642539441527840927e-01,1.397550062507085133e+00,-1.147867502566287889e+00
+2.000000000000000000e+00,-9.302265218243114253e-01,8.540734323987003718e-01,-4.477935263576708902e-01,-3.379378935355480951e-01,-8.135811375106852816e-01,8.011600684278343820e-01,9.046139232064869251e-01,8.660380304217001335e-01,2.137365654825782002e+00,-1.191948361758740083e+00,2.010945405182926304e+00,2.405279039329723256e-01,-1.068546425976989456e+00
+2.000000000000000000e+00,-7.692469025885839695e-01,2.865121437582633934e-01,-2.268662162713926955e-01,9.474700450659284678e-01,-6.732695087883974194e-01,-1.014969661689512970e+00,-3.145330898286227694e-01,2.610170305145681713e+00,-4.071594471618671385e-01,-1.014312886364025168e+00,-4.757883793364734437e-01,9.118864403895559079e-01,-1.346170194039533419e+00
+3.000000000000000000e+00,-5.463520451852706961e-01,2.041242147620710989e-01,-4.846147447053830715e-01,-1.772619012103635316e-01,-3.926462513438215840e-01,-1.108104519644248498e+00,-1.565483937986387186e+00,2.361008551613684059e+00,-1.285844374466954321e+00,9.396773429778373377e-01,-1.053065865028476988e+00,-1.545000118557942503e+00,-1.868621515805584665e-01
+3.000000000000000000e+00,8.529323374021988702e-01,1.943424938015023029e+00,-2.636874346191065421e-01,1.108146037391112948e+00,-8.135811375106852816e-01,-1.635868714721084416e+00,-1.766908227096535944e+00,1.447415455329694067e+00,-9.014197187709788173e-01,7.265147725041797289e-01,-8.310360628392446936e-01,-1.302168307499178113e+00,-7.207540132392969978e-01
+3.000000000000000000e+00,8.034001468681281066e-01,9.364613613948926663e-01,7.673066791168583478e-01,3.047660757651901586e-01,5.193793353510497424e-01,-7.821325168026733721e-01,-1.671496721728570778e+00,1.281307619641695705e+00,-1.542127478264271323e+00,3.978891430239572813e-01,-3.172877495800926345e-02,-1.202178738239686906e+00,-2.326243111513074946e-01
+3.000000000000000000e+00,2.833121462603964691e-01,1.870191223351740817e+00,-2.636874346191065421e-01,3.047660757651901586e-01,1.431404922045921069e+00,-1.154671948621616373e+00,-1.533680102863732353e+00,6.999301947337017715e-01,-5.353009990605254176e-01,2.316352277286876760e+00,-1.674749311158327147e+00,-1.573568566917797229e+00,2.402513377464323419e-01
+1.000000000000000000e+00,1.459701671444552762e+00,1.604719007697342770e+00,6.568430240737184178e-01,-1.880427419857320093e+00,1.992651436935072740e+00,1.033997213314673536e+00,9.576203150775784989e-01,-1.210309915678277504e+00,8.010323278826282367e-01,5.149996600426331311e-02,-3.425704980229340446e-01,1.240423596527884254e+00,1.144053989268725355e-02
+2.000000000000000000e+00,-1.487463665332594553e+00,-1.233087435504841345e+00,9.145915525077105146e-01,-3.379378935355480951e-01,-3.926462513438215840e-01,-2.078008927484694124e-01,7.771421001745584811e-02,-1.044202079990279364e+00,1.295292599491739916e+00,-8.588818453936499786e-01,-7.866301024013981902e-01,5.976335084311552581e-01,-7.207540132392969978e-01
+1.000000000000000000e+00,1.470986222917055053e-01,1.125820714329683059e-01,1.245982517637126863e+00,-1.451267027453261582e-01,9.844444918418605861e-02,7.235476867988874794e-01,1.169645882561945571e+00,-3.797707372382866375e-01,2.137365654825782002e+00,3.090714053265997685e-01,2.791129481069160034e-01,7.261915260505009684e-01,1.308035061063909321e+00
+1.000000000000000000e+00,6.424205276324028713e-01,-4.641334315403787270e-01,4.727369323351540698e-01,4.654420680903747498e-01,1.150781664601345344e+00,9.719073080115163688e-01,6.819870773479013160e-01,-1.210309915678277504e+00,1.478351959346966504e+00,5.622019577640684496e-01,5.708314591768366697e-02,5.262123875315183330e-01,1.109732369590663570e+00
+1.000000000000000000e+00,1.112976337706063523e+00,-4.641334315403787270e-01,-2.268662162713926955e-01,-5.307490843257700597e-01,5.895351497121936735e-01,8.477274974052019241e-01,1.487684233788495680e+00,-2.136629015502882756e-01,8.010323278826282367e-01,1.739036982254054120e+00,6.787665920475342007e-01,6.262019567910099838e-01,1.536845858917654351e+00
+2.000000000000000000e+00,3.699934796950187232e-01,-1.187316363840290157e+00,1.045247488580237777e-01,-6.592898781859172219e-01,7.298467784344815357e-01,3.044408260025766033e-01,-8.870021220364133763e-01,1.696577208861691499e+00,-2.237753045714132583e+00,-8.055912027752354376e-01,2.347069876690695278e-01,-1.045052272260486470e+00,-1.906756648781209040e-02
+3.000000000000000000e+00,6.671866228994370873e-01,3.139747867569944728e-01,1.319624954332552891e+00,1.750850006691851313e+00,3.790677066287618246e-01,-1.216761853924773540e+00,-1.374660927250457298e+00,3.677145233577055472e-01,-1.874882153355951209e-01,1.632455697017225260e+00,-1.008659904590630596e+00,-1.230747186599541632e+00,-1.906756648781209040e-02
+2.000000000000000000e+00,-8.806943312902405507e-01,-1.141545292175738746e+00,-1.405145203398208809e+00,-1.366264244416729001e+00,2.553897951824224410e+00,-6.734751825221485522e-01,-3.039318114544043992e-01,3.549885198170882328e-02,2.045835974898168264e+00,-7.034508044232744561e-01,4.123308294204554025e-01,-5.022517534232482372e-01,-1.906756648781209040e-02
+3.000000000000000000e+00,1.385403385643447782e+00,2.498952864266222873e-01,5.463793690305801531e-01,3.047660757651901586e-01,-6.031136944272533773e-01,-1.014969661689512970e+00,-1.523078824489514149e+00,7.829841125777009525e-01,-7.366662949012747763e-01,2.094307933043483505e+00,-1.541531429844787748e+00,-1.359305204218887564e+00,-2.936405239123061617e-01
+3.000000000000000000e+00,2.337799557263278150e-01,9.730782187265337724e-01,-1.900449979236804865e-01,-1.772619012103635316e-01,-1.120229938992457902e-01,-8.286999457800412472e-01,-1.385262205624675502e+00,2.194900715925685919e+00,4.166076721866525667e-01,2.458460657602648869e+00,-1.807967192471865880e+00,-1.644989687817433932e+00,-2.478783643415571614e-01
+2.000000000000000000e+00,-6.454164262534102248e-01,1.766615717633404636e-01,-5.950783997485230570e-01,6.261180604155592855e-01,-8.135811375106852816e-01,3.354857786541551867e-01,1.413218802627659587e-01,-7.119864086142828619e-01,-7.732781668723200941e-01,-1.325174968304776213e+00,-2.981645375850875967e-01,1.691067830333353450e-01,-1.315662087659034141e+00
+1.000000000000000000e+00,7.910170992346109431e-01,-3.725912882112759061e-01,1.045247488580237777e-01,-5.950194812558435853e-01,3.089118922676178935e-01,1.336935864188945888e-01,5.865755719799363721e-01,6.168762768897025905e-01,5.996670320418788780e-01,-5.080517814890882500e-01,1.167232156863844406e+00,2.976648006526816381e-01,7.131269866441721783e-01
+2.000000000000000000e+00,-6.341318747809056300e-02,-5.190587175378403861e-01,9.882339892031365425e-01,3.518285922268881372e+00,2.764365394907656093e+00,1.499671503088352065e+00,7.986011394643033334e-01,-1.127255997834278656e+00,5.813610960563562191e-01,-7.256552388476137372e-01,1.522479840366615989e+00,1.197570923988102498e+00,6.978729334539224283e-01
+1.000000000000000000e+00,1.063444147171994869e+00,-5.099045032049300818e-01,-7.791844914870873495e-01,-9.806418628362864043e-01,9.844444918418605861e-02,1.189221976572566675e+00,1.328665058175220626e+00,-1.127255997834278656e+00,9.108679437957641900e-01,4.955886544910502733e-01,-2.537585771472410934e-01,9.547391129293382184e-01,6.978729334539224283e-01
+3.000000000000000000e+00,1.594816699252226688e-01,3.231290010899045551e-01,1.413459672057376104e-01,3.047660757651901586e-01,1.431404922045921069e+00,-1.061537090666880845e+00,-1.544281381237950557e+00,1.530469373173693137e+00,-3.339357032197764474e-01,1.916672457648769035e+00,-1.630343350720480533e+00,-1.487863221838233274e+00,2.555053909366820086e-01
+2.000000000000000000e+00,-2.019934713573843243e+00,-1.370400650498495576e+00,6.200218057260061810e-01,6.261180604155592855e-01,-8.135811375106852816e-01,2.268284443736304223e-01,-1.343113574669112242e-01,6.168762768897025905e-01,-3.705475751908218207e-01,-8.455591847390462323e-01,5.899546711718413050e-01,-5.022517534232482372e-01,-9.831237281115913218e-01
+2.000000000000000000e+00,-1.524612808233146044e+00,-1.071190725568779978e-01,1.503731046071119071e+00,7.867940527407438767e-01,2.413586323101936326e+00,-1.139149472295827081e+00,-1.215641751637182022e+00,-1.708633422742272145e+00,-1.508763433645497753e-01,-1.103130624061382736e+00,-7.613473539585573213e-02,-5.736728743228852734e-01,-4.004188962440538568e-01
+1.000000000000000000e+00,3.080782415274307406e-01,-2.444322875505322012e-01,1.282803735984840765e+00,-7.235602751159908586e-01,1.686002635453300036e-01,1.033997213314673536e+00,1.159044604187726923e+00,-2.967168193942874566e-01,3.218301649067650816e-02,4.511797856423717668e-01,4.567367898583018504e-01,2.548121281128993276e-01,1.567353965298153851e+00
+2.000000000000000000e+00,1.088509832301457966e-02,3.261631801954101739e+00,-7.423632731393751127e-01,7.867940527407438767e-01,-9.538927662329731438e-01,4.441431129346806728e-01,5.441704584830628910e-01,-3.797707372382866375e-01,6.728907759839690694e-01,-1.058721755212703952e+00,-1.053065865028476988e+00,6.262019567910099838e-01,-1.147867502566287889e+00
+2.000000000000000000e+00,-9.797587123583800794e-01,-4.641334315403787270e-01,-7.791844914870873495e-01,-1.658590888517895781e-02,-1.374827652399836841e+00,-1.061537090666880845e+00,-1.131088007184745114e-01,2.016066876697071852e-01,-2.273479146589149624e-02,-7.034508044232744561e-01,1.458950667933766043e-01,7.261915260505009684e-01,-7.512621196197962758e-01
+2.000000000000000000e+00,1.088509832301457966e-02,1.485714221369509147e+00,-4.276012453282835440e-02,1.108146037391112948e+00,-1.024048580594117075e+00,-1.061537090666880845e+00,-5.795650491840813601e-01,2.194900715925685919e+00,-4.104072745141375961e-02,-8.172664054177269932e-02,-5.646003002121663394e-01,-9.307784788210682336e-01,-7.360080664295466368e-01
+1.000000000000000000e+00,9.767628137373727260e-01,-6.106008608669429849e-01,1.061876425898562681e+00,3.047660757651901586e-01,1.080625850240201302e+00,9.563848316857274101e-01,1.339266336549438607e+00,4.507684412017047282e-01,1.420186324038125170e-01,7.176329987344437500e-01,7.231725524853797049e-01,-1.308619247451383028e-01,1.140240475971162848e+00
+2.000000000000000000e+00,-8.311621407561718966e-01,-1.068311577512456534e+00,-8.528269281825133774e-01,-1.772619012103635316e-01,-8.135811375106852816e-01,1.841165982255716704e+00,1.710311079647080845e+00,-8.780942443022812238e-01,5.630551600708334492e-01,-2.149532470878087187e-01,3.235189085447624513e-01,1.548225588534083430e-01,-2.936405239123061617e-01
+2.000000000000000000e+00,8.518338412411752270e-02,-1.123236863509918138e+00,-2.325675662091033935e+00,-1.301993847486655476e+00,-1.515139281122124704e+00,1.002952260663094952e+00,1.106038212316635461e+00,-7.119864086142828619e-01,1.167151047593080859e+00,1.403177037016207773e-01,6.787665920475342007e-01,7.404757502304285532e-01,-7.756686047241958537e-01
+2.000000000000000000e+00,-7.568638549550668060e-01,-6.472177181985840910e-01,-1.532237795759666399e-01,7.867940527407438767e-01,-9.538927662329731438e-01,6.304128288441523953e-01,1.074234377193980183e+00,3.677145233577055472e-01,2.335483123314258669e-01,-4.636429126404094658e-01,-1.230689706779862780e+00,2.548121281128993276e-01,-1.233290200431685912e+00
+2.000000000000000000e+00,-1.772273760903495754e+00,-8.119935761909689020e-01,1.356446172680266793e+00,3.047660757651901586e-01,-3.926462513438215840e-01,6.304128288441523953e-01,8.304049745869581667e-01,-4.628246550822858185e-01,1.551575703289057140e+00,-1.036517320788364671e+00,-3.172877495800926345e-02,8.547495436698466786e-01,-2.326243111513074946e-01
diff --git a/tutorials/wine_data.csv b/tutorials/wine_data.csv
new file mode 100644
index 0000000..a0b3962
--- /dev/null
+++ b/tutorials/wine_data.csv
@@ -0,0 +1,178 @@
+1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
+1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
+1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
+1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
+1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
+1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
+1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
+1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
+1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
+1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
+1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
+1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
+1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
+1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
+1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
+1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
+1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
+1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
+1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
+1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
+1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
+1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
+1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
+1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
+1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
+1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
+1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
+1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
+1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
+1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
+1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
+1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
+1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
+1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
+1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
+1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
+1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
+1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
+1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
+1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
+1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
+1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
+1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
+1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
+1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
+1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
+1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
+1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
+1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
+1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
+1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
+1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
+1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
+1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
+1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
+1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
+1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
+1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
+1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
+2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
+2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
+2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
+2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
+2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
+2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
+2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
+2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
+2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
+2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
+2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
+2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
+2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
+2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
+2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
+2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
+2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
+2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
+2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
+2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
+2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
+2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
+2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
+2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
+2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
+2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
+2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
+2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
+2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
+2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
+2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
+2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
+2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
+2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
+2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
+2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
+2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
+2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
+2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
+2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
+2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
+2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
+2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
+2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
+2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
+2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
+2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
+2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
+2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
+2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
+2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
+2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
+2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
+2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
+2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
+2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
+2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
+2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
+2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
+2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
+2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
+2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
+2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
+2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
+2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
+2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
+2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
+2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
+2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
+2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
+2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
+3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
+3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
+3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
+3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
+3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
+3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
+3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
+3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
+3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
+3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
+3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
+3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
+3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
+3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
+3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
+3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
+3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
+3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
+3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
+3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
+3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
+3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
+3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
+3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
+3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
+3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
+3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
+3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
+3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
+3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
+3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
+3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
+3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
+3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
+3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
+3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
+3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
+3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
+3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
+3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
+3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
+3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
+3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
+3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
+3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
+3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
+3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
+3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560

From 2a823d8d8f4300820b83ba72b0b5a8174f20dd82 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 26 Jun 2014 00:24:05 -0400
Subject: [PATCH 119/248] typo and formatting fixes

---
 tutorials/python_data_entry_point.ipynb | 48 ++++++++++++++++++-------
 1 file changed, 36 insertions(+), 12 deletions(-)

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index bf2ac77..4d603be 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:11c7df5b48317dee4e78f1592431823f89b38ea262acb74b420b17dca4440d63"
+  "signature": "sha256:b7a43c691f0f722f1924e6a62b540eb713077a2506223698ab1699c92608edbc"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -106,7 +106,7 @@
       "\n",
       "- [Reading in a dataset from a CSV file](#Reading-in-a-dataset-from-a-CSV-file)\n",
       "\n",
-      "- [Visualizating of a data](#Visualizating-of-a-data)\n",
+      "- [Visualizating of a dataset](#Visualizating-of-a-data)\n",
       "\n",
       "    - [Histograms](#Histograms)\n",
       "\n",
@@ -434,7 +434,7 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Visualizating of a data"
+      "Visualizating of a dataset"
      ]
     },
     {
@@ -595,7 +595,6 @@
       "                label='class {:}, R={:.2f}'.format(label, R[0]) # label for the legend\n",
       "                )\n",
       "    \n",
-      "\n",
       "plt.title('Wine Dataset')\n",
       "plt.xlabel('alcohol by volume in percent')\n",
       "plt.ylabel('malic acid in g/l')\n",
@@ -920,7 +919,6 @@
       "for label,marker,color in zip(\n",
       "        range(1,4),('x', 'o', '^'),('blue', 'red', 'green')):\n",
       "\n",
-      "\n",
       "    plt.scatter(x=sklearn_transf[:,0][y_train == label],\n",
       "                y=sklearn_transf[:,1][y_train == label], \n",
       "                marker=marker, \n",
@@ -971,7 +969,7 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Linear Transformation: Multiple Discrciminant Analysis (MDA)"
+      "Linear Transformation: Multiple Discriminant Analysis (MDA)"
      ]
     },
     {
@@ -1241,12 +1239,38 @@
       "**Confusion Matrix**  \n",
       "As we can see above, there was a very low misclassification rate when we'd apply the classifier on the test data set. A confusion matrix can tell us in more detail which particular classes could not classified correctly.\n",
       "\n",
-      "|              |         |                | **predicted class** |               |\n",
-      "|--------------|---------|----------------|-----------------|---------------|\n",
-      "|              |         | class 1        | class 2         | class 3       |\n",
-      "|              | class 1 | True positives |                 |               |\n",
-      "| **actual class** | class 2 |                | True positives  |               |\n",
-      "|              | class 3 |                |                 | True positive |"
+      "<table cellspacing=\"0\" border=\"0\">\n",
+      "\t<colgroup width=\"60\"></colgroup>\n",
+      "\t<colgroup span=\"4\" width=\"82\"></colgroup>\n",
+      "\t<tr>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" colspan=2 rowspan=2 height=\"44\" align=\"center\" bgcolor=\"#FFFFFF\"><b><font face=\"Helvetica\" size=4><br></font></b></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" colspan=3 align=\"center\" bgcolor=\"#FFFFFF\"><b><font face=\"Helvetica\" size=4>predicted class</font></b></td>\n",
+      "\t\t</tr>\n",
+      "\t<tr>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#EEEEEE\"><font face=\"Helvetica\" size=4>class 1</font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#EEEEEE\"><font face=\"Helvetica\" size=4>class 2</font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#EEEEEE\"><font face=\"Helvetica\" size=4>class 3</font></td>\n",
+      "\t</tr>\n",
+      "\t<tr>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" rowspan=3 height=\"116\" align=\"center\" bgcolor=\"#F6F6F6\"><b><font face=\"Helvetica\" size=4>actual class</font></b></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#EEEEEE\"><font face=\"Helvetica\" size=4>class 1</font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#99FFCC\"><font face=\"Helvetica\" size=4>True positives</font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#F6F6F6\"><font face=\"Helvetica\" size=4><br></font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#F6F6F6\"><font face=\"Helvetica\" size=4><br></font></td>\n",
+      "\t</tr>\n",
+      "\t<tr>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#EEEEEE\"><font face=\"Helvetica\" size=4>class 2</font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#FFFFFF\"><font face=\"Helvetica\" size=4><br></font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#99FFCC\"><font face=\"Helvetica\" size=4>True positives</font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#FFFFFF\"><font face=\"Helvetica\" size=4><br></font></td>\n",
+      "\t</tr>\n",
+      "\t<tr>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#EEEEEE\"><font face=\"Helvetica\" size=4>class 3</font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#F6F6F6\"><font face=\"Helvetica\" size=4><br></font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#F6F6F6\"><font face=\"Helvetica\" size=4><br></font></td>\n",
+      "\t\t<td style=\"border-top: 1px solid #c1c1c1; border-bottom: 1px solid #c1c1c1; border-left: 1px solid #c1c1c1; border-right: 1px solid #c1c1c1\" align=\"left\" bgcolor=\"#99FFCC\"><font face=\"Helvetica\" size=4>True positives</font></td>\n",
+      "\t</tr>\n",
+      "</table>"
      ]
     },
     {

From e5b55f445db8c26118b9f5e5578ef0107e9788cf Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 26 Jun 2014 08:58:21 -0400
Subject: [PATCH 120/248] changed histogram binsize

---
 tutorials/python_data_entry_point.ipynb | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index 4d603be..a210d3a 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b7a43c691f0f722f1924e6a62b540eb713077a2506223698ab1699c92608edbc"
+  "signature": "sha256:0eb0e38d29bc75ef86bf3edae32f4b70c1c1495f608d2ccc8cedf2712fd23043"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -37,7 +37,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: 25/06/2014\n",
+        "Last updated: 26/06/2014\n",
         "Created using Python 3.4.1\n"
        ]
       }
@@ -493,8 +493,8 @@
       "\n",
       "plt.figure(figsize=(10,8))\n",
       "\n",
-      "# bin width of the histogram in steps of 0.1\n",
-      "bins = np.arange(floor(min(X_wine[:,0])), ceil(max(X_wine[:,0])), 0.1)\n",
+      "# bin width of the histogram in steps of 0.15\n",
+      "bins = np.arange(floor(min(X_wine[:,0])), ceil(max(X_wine[:,0])), 0.15)\n",
       "\n",
       "# get the max count for a particular bin for all classes combined\n",
       "max_bin = max(np.histogram(X_wine[:,0], bins=bins)[0])\n",
@@ -527,13 +527,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZmb2v/Uv93HY7aGU4uwAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrA8xusMnb/cPHAAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1056b1b00>"
+        "<matplotlib.figure.Figure at 0x10569da58>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",

From 9d2e49f72d97b58bbe7fcac087237db810f182f2 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 26 Jun 2014 10:41:36 -0400
Subject: [PATCH 121/248] fixed links to data entry nb

---
 tutorials/python_data_entry_point.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index a210d3a..da6ce33 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0eb0e38d29bc75ef86bf3edae32f4b70c1c1495f608d2ccc8cedf2712fd23043"
+  "signature": "sha256:061fb3d28e8de33facea5faf69a6445f8ef172c670fe07d9afe2b7d8d29d6631"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -14,9 +14,9 @@
      "source": [
       "[Sebastian Raschka](http://sebastianraschka.com)  \n",
       "\n",
-      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/python_data_entry_point.ipynbc?reate=1)  \n",
+      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb?create=1) \n",
       "\n",
-      "- [Link to this IPython notebook on Github](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/python_data_entry_point.ipynb)  \n",
+      "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb)  \n",
       "\n",
       "- [Link to the GitHub Repository pattern_classification](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/python_howtos/)"
      ]

From 660ffd0a721118e3768c791570c46c519d2f44da Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 26 Jun 2014 17:41:00 -0400
Subject: [PATCH 122/248] short section on PCA feat. extr.

---
 tutorials/python_data_entry_point.ipynb | 118 +++++++++++++++++++-----
 1 file changed, 93 insertions(+), 25 deletions(-)

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index da6ce33..2c35622 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:061fb3d28e8de33facea5faf69a6445f8ef172c670fe07d9afe2b7d8d29d6631"
+  "signature": "sha256:ea0d498feb91238c4ac1c1b582ca9e09d18639ea850f6cd2cbd12081785e39e5"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -419,7 +419,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -482,7 +482,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -527,13 +527,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xt4lPWd///nhFPKSWJSQKOAgCfwANIKiJVYrKgUraxo\n8YBs3Srqr3zXw5dK6SrUA1irq19166pVCtha4roIS5VWDtJYuxRQlIMgUAggtSEYOQiJQH5/3JNh\nEmbCBCbkJnk+risXc58+877vucO88vnc9wxIkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkuqRbwEf\nH6Xnmg/ccpSeK+yWARelqa0bgNlx0/uBzmlqG2AH0CmN7aXia8BMoAT43WFsn+5jUNUk4MHD3HY+\n/h5IhyWjrguQjsAY4PdV5n2SZN61wJ+AM45CXQDl0Z9UrAe+XXulJHWkb+ydom3siP78nSBoXFJl\nvbOABSm2daj/k14BBtawzmTmc3B4aEXwehxN1wBtgeOB647yc6eiJudyOrc9HJ1I7TxK1QiC/zek\no86ApmPZO8AFQCQ6fQLQGOjBgXP7BKALhw4IdamcA/twtKXjeY8jCDbnAH8E/hu4uRbqaXSYbSZz\nNINDdToCqwmChdKjrn6fJElAU2AX0DM6fS3wEkHPyHlx81ZHH+cBG+O2Xw/cAywlGF56FWgWt/y7\nwAfA58C7wNnV1PIdguHTEuBpKvfOdAHmAluBImAqQagBmALsA74k6IW6Nzo/H9gSbe8doFs1zz0C\nWAtsB9YB18ct+wGwAtgGvAV0iM5fQBAIdkafd2g17SfTicS9FfcQ9KZVWM+BHsLzgUXAF9F1fhGd\nX8iB3rjtQJ/ofr0LPEFw7B7k4B6N/cCPCPa/CPg5B96cxxEc36r1NgIeBvYCu6PP+f/i2qvoVTwO\nmAz8I7oPY+PaHgEUAI8RHNt1wGUkdybBOfE5wZDv4Oj88UApUBat458TbHs+8F50208Jzq8mVY5B\nKjUD/JDgfNgOLOfA706y+gBeBp4B/ie63V+o3PN6AfBXgnN1IdA3btk8gnMwkQzgJ8CaaLuLgJNS\naHM+8DOC47+dYMg7O7os/jzaAfSOzk/2e0B0/dsI/p/4PLqvFcdkN8F5siO6LcAVBMduO7CJ4HyX\nJFUxF/jX6ONnCN7gHqoy78Xo4zwqB7S/EbzZtAeyCP4Dvy26rCfwGfBNgje44dH1myaoIYfgP+sh\nBG/+/wp8xYE3pi7AAII31RyCwPXvVeqoOsQ5AmgR3ebfgfcT7z4tCMLOqdHpdhwIc1cRDO+eTvBm\nOJYg8FRI1xBn1YDWOTr/9Oh0/P69R3AdGUBzDryBdkzQ1giC43hndH4miQPaHKANcDKwigPB+AES\nB7SK50gUHuKPyWSC3sAW0fpWxa0/giBU3UJwfowENpNYE4IQch9BD+/FBOfLaXF1Tk6yLQR/bJwf\nrbsjwXn6fw6j5qEEgaJXdLoLQVA5VH2TCALyNwjO76nAb6PLjicINTdE6/s+QZDJii6vLqD9X+BD\nDpy7Z0fbO1Sb8wnO664E58Q8YEJ0WaLzKJXfgxlAa4Jz6B8cGEa/mYOHOLcA/aKPj+NAyJUkxXkA\neD36+AOCN52BcfOWAjdFH+dxcECL7216FPhl9PEvCf5Kj/cxiS92Hw78ucq8jSR/Y/oesKRKHdVd\ng9aG4E2kVYJlLQjezIYQXGwe780qNWQQ9DieHJ2urYCWGZ1f0esRv3/vEPRs5aTQ1ghgQ5X1RnBw\nQLs0bvp24O3o43EcOqBVvQat4pg0IujZir9m8dboNhV1fBK3rHl027Yc7FsEb+rxfkNw7iaq81D+\nlQPnd01qnk3Q21jT+iYBz8ctuxxYGX18E8EfOfH+zIEh7uoC2sdU7qmrkEqbP4lbdjvBuQ6Jz6NU\nfg8uiFv+O+DH0ccjODigbSA4rq0T1C6ljdeg6Vi3ALiQ4K/rrxMMdb1H8B9uFtCd6q8/ix+K2w20\njD7uSDB08Xncz0kE17RVdSJBz0S8+CDYjmD4dBNBb9cUDgzJJJIBTCTo1fiCIOCUc3CogeCN5jqC\nHpxPCYahKnquOgJPxdVfHJ2fW81zx1vOgaGifodYN15F+9sSLLuFoGdmJcHQ1aBDtLXxEMurrlNI\n8HqkKtl1aDkEPUvxAbGQyscu/tz5MvpvSw52IgfvxwZSfx1OI3hdtxCcDw+T+Pw5VM0nEfx+pFpf\nxXEsJ+hNrhD/e3Ji9DmSbVudk6up51BtJvu9TSSV34Oqr2WLatr7J4JhzvUEvXl9qllXOmwGNB3r\n/kIwzPBDDgxbbCcIK7dG/63aC1OdijfsQoI3wqy4n5Yk/hiETznw1zgEQ17x048QXGd2VrTWm6j8\nu1c1JNwAXEkwLHoccEq0zWQXPv+BoBepPUGvxAtx+3BrlX1owcG9E8l0J+i1a0XlIaFDuZrgDX1V\ngmVrCHotv07QY/kaQc9fsqCUyoX8Hao8rhhq3EXQs1WhfQ3a3kowvNqpSttVg3gqKs6P+NevYw3a\n+iXBsGZXgvNhLIn/7z5UzRujbaRaX7Ih23ibo+vGS3XbZPUcSZuJXtMj+T1I1N4igl7wrwPTgWkp\ntCPVmAFNx7rdBP9h3k3lnrKC6Lx3athexZvUCwS9UudH57Ug6O1J9Jf6LIIwczXBNTyjqBwGWhKE\nhe0Ef7X/3yrbf0YwNBu/filBD1QLgoCXTFuCa2xaELw57yIIgwDPEQwFVVyTdhyVbwao+ryHq+KY\ntQP+P+B+go9ASeRGgjc2CHqDygmGmIqi/x5OPfdy4Bq0URwI0e8TDEmfTLDvVWuqbv/3EbzxPkzw\nenQE7iK4/qqm/kLQKzOaoIcrj+AGlFdT3L4lQS/mlwTDl7cfZs0vEhyr8whes64EAe5Q9VV3R+Sb\nBD18wwjO/euiNf5P3DrJtn+R4MaPrtF1ziG4/uz3R9BmovPoUL8HVcX/MfQZQc9jxU0ZTQj+gDqO\n4Hjv4MDvmySpiooeqh5x84ZG5/0wbl4elYdOql77VfVi7YEEw3AVd8/9juRDKQMJeowq7uKMv/am\nG0GI3EFw7dndVeq4kqCX7/PoshYEf5lvj9Z4U3RfEl0v1p5gmKUkuv1cKl+DdCPBhdhfRJ/zxbhl\nt0X363OCz+KqqU4cuGNuJ8Gb2f9Q+ZowqHycp0TX2wF8RLDvFcYTXKC9jeDmgZs5eHi66rz9BKFw\nLUEP0mNU/sPzGYL9Ww38C8FxrFjeh+A12wY8GddexXFuE633HwTH7qcceONOVFuy1wiCc2A+weu0\njCBUVzjUTQLfIhgS3hF9zvFVnjv+eaurGYLX/ONoWx8C56ZQ38tUvh4zj8rnbz+C87uE4M7L+Ou5\nDnUX51iCO2C3A//LgWHMmrRZ9bWoOI8+J/gDC6r/Paj6usXvbxOCc7o42mYTglC6LdrW/1apTZIk\nSZIkSZIkSZIkSZIkqc4dE99Xdu6555YvXbq0rsuQJElKxVIq37hWY8fEx2wsXbqU8vJyf6r8PPDA\nA3VeQxh/PC4eE4+Lx8Xj4jGpyx8O3CF92I6JgCZJktSQGNAkSZJCxoB2DMvLy6vrEkLJ43Iwj0li\nHpfEPC6JeVwO5jGpPcfETQJAeXRMV5IkKdQikQgcYcZqnJ5SJEkNyfHHH8/nn39e12VIdSorK4tt\n27bVStv2oEmSaiwSieD/y2rokv0epKMHzWvQJEmSQsaAJkmSFDIGNEmSpJAxoEmSJIWMAU2SJClk\nDGiSJEkhY0CTJDUonTp1Ys6cOXVdRlJjxozhqaeequsyBPTu3ZsVK1bUyXP7QbWSpLTIz59NcXFZ\nrbWfnd2UoUMHHnE7kUik4nOqat0zzzzDpEmTWLZsGcOGDePll1+udv2ioiKmTJnC2rVra62mDz74\ngKlTp/KLX/wiNu+NN95g586drF27lpycHO64445K2+zfv5+srCwyMg7063znO99h2rRpsemFCxcy\nZ84cxowZU2u1JzN9+nRWrFhBRkYGubm53HTTTdWuX7XWnTt38vOf/5yTTz6Z7du3c/fddxOJRLj3\n3nu5//77ee21147GblRiQJMkpUVxcRm5uYNrrf3Nm2fWWtu1JTc3l3/7t39j9uzZ7N69+5DrT5o0\niUGDBtGsWbNaqeeJJ56goKCA4447LjavpKSE6667jpKSEpo1a0ZOTg6DBg2iY8eOsXU2bNjAL3/5\nSy644AIikQjTp0/n0ksvjS3fv38/999/PxdccEGt1F2dL774ggcffJDFixcD0LdvXy6//HJycnIS\nrp+o1lGjRvHAAw/QsWNHunfvzjXXXEPHjh0ZPHgwI0eO5LPPPqNdu3ZHZX8qOMQpSaqXNm7cyJAh\nQ2jbti05OTmMGjXqoHUmTpxI165dad26Nd27d2f69OmVlj/66KOcdNJJtG7dmjPOOIO5c+dWO7+q\nq6++mquuuors7OyUan7rrbfo379/Dfc0dXfffTdXXXVVpXlt2rRh8eLFZGZmEolE2Lt370Gfjt+s\nWTO+973v0alTJ1q3bk2TJk0488wzY8vz8/O55JJL6uTbJRYsWEC3bt1i0+eeey7z5s1Lun7VWtet\nW8enn34aC6R/+MMfYo8zMzPp1asXs2fPrsU9SMweNElSvbNv3z6++93vcskll/DKK6+QkZER62GJ\n17VrVwoKCmjfvj3Tpk3jxhtvZM2aNbRv355Vq1bx7LPPsmjRItq3b09hYSF79+5NOr86qQaXjz76\niNNPP71G+7pu3TpeeOGFpMv79OlTKZQlqqV79+4AFBQUkJeXR6dOnSotP/HEE2OP//M//5O77ror\nNl1UVESjRo34+te/zq5du2pUe3VS3a9NmzbRpk2b2Pw2bdrwySefJNwmUa1z586lTZs2TJkyhZKS\nElq1asWIESNi25x55pksXbo0PTtVAwY0SVK9s3DhQrZs2cJjjz0Wu24q0fDbNddcE3t87bXXMmHC\nBBYuXMiVV15Jo0aNKC0tZfny5WRnZ9OhQwcA1qxZk3B+dVK95q0iIFTYt28f/fv3p6CgAIBbbrmF\nMWPG0LVr19g6nTt3ZsKECSm1X10tr7/+Ovn5+Tz++ONJt922bRtbt26tNAT7+uuvc+uttzJ58uSU\nawBYvXo1P/3pTykqKmLRokXk5eUxaNAgRo4cCaS+XyUlJWRmZsammzZtys6dOxOum6jWzz77jGXL\nlvHqq68C8K1vfYt+/fpx6qmnAtCqVSu2bNlSo31LB4c4JUn1zsaNG+nYsWOli9oTmTx5Mj179iQr\nK4usrCyWLVtGcXExEPSuPfnkk4wbN4527doxbNgwtmzZknR+dVLtQcvKymLHjh2x6ffeey823FZe\nXs57771XKZwdjmS1DBkyhBdeeIHLL7+c9evXJ1znd7/7XaWhzb/85S/07t076ZeGJ7Nt2zZGjhzJ\n5MmTmTdvHgMGDGDq1KmxcFYTrVq1qvTcu3fv5vjjjz9ovWS1tm7dmrPPPjs23aFDB/7whz/Eprdv\n305WVlaN6zpS9qBJkuqdk08+mcLCQvbt20ejRo0SrrNhwwZuvfVW5s6dS9++fYlEIvTs2bPSm/ew\nYcMYNmwYO3bs4LbbbuPHP/4xkydPTjo/mVR70M455xxWrVpFr169gOCatIEDgztX33///UpBokJN\nhzir1jJr1iweeeQR3n33XVq2bEnbtm157bXXuPfeew9qa968eQwfPjw2/de//pUvv/yS2bNn8+67\n77J7925mzJjBlVdeWe1+Pvvss9x5552xnq/S0lKaN29+WPvVpUsXFi1aFJu/detWzjvvvIPWT1Tr\nG2+8Qffu3fnTn/4UWy8jI4P9+/fHpleuXFlpn48WA5okqd7p3bs3J5xwAvfddx/jx48nIyODJUuW\nVBrm3LVrF5FIhJycHPbv38/kyZNZtmxZbPnq1avZtGkT/fr1o1mzZmRmZlJeXp50fiL79u3jq6++\nYu/evezbt4/S0lIaN26cNDReccUVvPPOO1x//fUAzJ49m+9///tAEKQGDBhwUACq6RBn1VobNWpE\nXl5ebNnGjRs555xzAFi7di2dO3eOhbpPPvmEr33ta7Ftf/SjH8Uejxs3jkgkEqttxIgRRCKRhB8t\nsmPHjtiF/cuXL6d79+40adKk0jqp7tdFF13E6NGjY9NLlizh0UcfPaj+RLVeddVV7Nmzh5/85Cex\nZWvXrmXcuHEA7NmzhyVLljBlypRD1pFuBjRJUlpkZzet1Y/CyM5umvK6GRkZzJw5k1GjRtGhQwci\nkQg33HBDpYDWrVs37rnnHvr27UtGRgbDhw/nwgsvjC0vLS1lzJgxrFy5kiZNmtCvXz+ef/55ioqK\nEs5P5MEHH+RnP/tZbHrq1KmMGzeO+++/P+H6w4cPp0ePHuzZs4cdO3ZQWFjIjBkzKCwspHnz5hQV\nFdG5c+eUj0NVzzzzDNOmTWPjxo2MHz+eu+66i8suu4x169bx9NNPs2HDBsaOHRv7CI2hQ4fyq1/9\nip49ewJw/PHHk5ube1C706ZNY8aMGUQikdjHVGzcuDEWNKu6/fbbmTFjBitWrGDTpk1MnDjxsPep\nRYsWjB49moceeoj9+/czevRo2rZtm7D+ZLVWvCb79+/nzjvvpEuXLgDMnDmTiy++mPbt2x92fYfr\n6HxS35Err4tbdyVJidX0miOlbuzYsbRt25bs7GxWrlzJww8/XNcl1VhZWRk9e/bkww8/TNpbeCzo\n06cPL730UqWP8YiX7Pcg2uN4RBnLgCZJqjEDWu0bNWoUN998c+x6NIWPAc2AJkmhYkCTajeg+TEb\nkiRJIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNA\nkyQ1KJ06dWLOnDl1XUZSY8aM4amnnqrrMgT07t2bFStW1MlzN66TZ5Uk1Tuz8/MpKy6utfabZmcz\ncOjQI24nEolUfBVPrSorK+P2229nzpw5bNu2jS5dujBhwgQuu+yypNsUFRUxZcoU1q5dW2t1ffDB\nB0ydOpVf/OIXsXm/+c1v2LJlCwsXLuTqq6/m+9///kHbJVtn5syZbNq0iT179tCxY0eGDBlSa7Un\nM336dFasWEFGRga5ubncdNNNCdfr0qULmzZtok2bNjz22GMMHz4cgDfeeIOdO3eydu1acnJyuOOO\nOwC49957uf/++3nttdeO2r5UMKBJktKirLiYwbm5tdb+zM2ba63t2rB37146dOjAggUL6NChA7Nm\nzeLaa6/lo48+omPHjgm3mTRpEoMGDaJZs2a1UtMTTzxBQUEBxx13XGzemjVrKC4u5p577mHr1q2c\neuqp9O7dm1NOOeWQ6zRu3JhVq1Zx7733AvAv//IvXHrppbRs2bJW6k/kiy++4MEHH2Tx4sUA9O3b\nl8svv5ycnJyD1r3vvvsYOHAgJ554Io0bBxGopKSE6667jpKSEpo1a0ZOTg6DBg2iY8eODB48mJEj\nR/LZZ5/Rrl27o7ZP4BCnJKme2rhxI0OGDKFt27bk5OQwatSog9aZOHEiXbt2pXXr1nTv3p3p06dX\nWv7oo49y0kkn0bp1a8444wzmzp1b7fx4zZs354EHHqBDhw4ADBo0iFNOOYUlS5Ykrfmtt96if//+\nR7Lb1br77ru56qqrKs1bvnw5P//5zwHIycmha9eusbBzqHW2bt3K22+/TVlZGQAtWrSgadOmtVZ/\nIgsWLKBbt26x6XPPPZd58+YlXLdp06Z06NAhFs4A2rRpw+LFi8nMzCQSibB3797YF6BnZmbSq1cv\nZs+eXbs7kYA9aJKkemffvn1897vf5ZJLLuGVV14hIyPjoNAB0LVrVwoKCmjfvj3Tpk3jxhtvZM2a\nNbRv355Vq1bx7LPPsmjRItq3b09hYSF79+5NOv9QPvvsM1avXk337t2TrvPRRx9x+umn12hf161b\nxwsvvJB0eZ8+fSqFsorwUeGKK67gzTffjC3bsmULXbt2TWmdHj16sH//fr75zW9y6623cumll6Yt\noKW6XxVDlhXatGnDJ598knCbv/71r5SWlrJ9+3ZOO+00rrzySoDYa1JQUEBeXh6dOnWKbXPmmWey\ndOnSNOxRzRjQJEn1zsKFC9myZQuPPfYYGRnBYNEFF1xw0HrXXHNN7PG1117LhAkTWLhwIVdeeSWN\nGjWitLSU5cuXk52dHesJW7NmTcL51fnqq6+44YYbGDFiBKeddlrS9UpKSmjVqlVset++ffTv35+C\nggIAbrnlFsaMGVMpQHXu3JkJEyYcsoYKVa+/a9KkCWeddRYAs2bN4hvf+AY9evRIeZ377ruPCRMm\ncO+99/Lkk0+mXMfq1av56U9/SlFREYsWLSIvL49BgwYxcuTIGu1XSUkJmZmZsemmTZuyc+fOhOsO\nGDCAq6++GoAePXpw0UUXxcLd66+/Tn5+Po8//nilbVq1asWWLVtS3q90cYhTklTvbNy4kY4dO8bC\nWTKTJ0+mZ8+eZGVlkZWVxbJlyyiO3ujQtWtXnnzyScaNG0e7du0YNmxYrOco0fxk9u/fz0033URm\nZibPPPNMtfVkZWWxY8eO2PR7770Xu16tvLyc995776DerZqq2oNWoaSkhEmTJjF16tSk21ZdZ/Xq\n1cyfP58//vGPzJw5k4ceeog///nPh6xh27ZtjBw5ksmTJzNv3jwGDBjA1KlTY+GsJlq1alVpn3bv\n3s3xxx+fcN34nsSsrCzmz58fmx4yZAgvvPACl19+OevXr4/N3759O1lZWTWu60jZgyZJqndOPvlk\nCgsL2bdvH40aNUq4zoYNG7j11luZO3cuffv2JRKJ0LNnz0pv9sOGDWPYsGHs2LGD2267jR//+MdM\nnjw56fyqysvLueWWWygqKuL3v/990loqnHPOOaxatYpevXoBwTVpAwcOBOD999/n7LPPPmibmg5x\nJrqDtby8nIkTJ/Liiy/SsmVLNmzYcNCNDFXXWb9+PTNnzmRo9M7aSy65hF//+tcUFBQk7K2M9+yz\nz3LnnXfGer5KS0tp3rz5Ye1Xly5dWLRoUWz+1q1bOe+88w5af+rUqcyYMYNp06YBsGvXLho3bsys\nWbN45JFHePfdd2nZsiVt27bltddei934sHLlytjdnkeTAU2SVO/07t2bE044gfvuu4/x48eTkZHB\nkiVLKgWHXbt2EYlEyMnJYf/+/UyePJlly5bFlq9evZpNmzbRr18/mjVrRmZmJuXl5UnnJ3L77bfz\n8ccf8/bbb6d0Z+YVV1zBO++8w/XXXw/A7NmzYx9nMWvWLAYMGMCMGTNi105BzYc4E9X69NNPM3To\nUPbs2cPChQvZvXs3HTt2ZO3atXTu3JlIJJJwnVNOOYVly5bFgmNpaSl9+vQBYMSIEUQiEV5++eWD\nnm/Hjh2xC/uXL19O9+7dadKkSaV1Ut2viy66iNGjR8emlyxZwqOPPgpQqf5OnTrFeui+/PJLioqK\n+Pa3v82CBQvIy8uLHZuNGzdyzjnnALBnzx6WLFnClClTDllHuhnQJElp0TQ7u1Y/CqNpdnbK62Zk\nZDBz5kxGjRpFhw4diEQi3HDDDZUCWrdu3bjnnnvo27cvGRkZDB8+nAsvvDC2vLS0lDFjxrBy5Uqa\nNGlCv379eP755ykqKko4v6oNGzbw/PPPk5mZSfv27WPzn3/+eYYNG5aw7uHDh9OjRw/27NnDjh07\nKCwsZMaMGRQWFtK8eXOKioro3LlzysehqmeeeYZp06axceNGxo8fz1133cWHH37IXXfdFQtukUiE\nwsJCAIYOHcqvfvUrdu3alXCd/v3789RTT/HII4/QokUL2rRpw8033wzApk2bku7n7bffzowZM1ix\nYgWbNm1i4sSJh71PLVq0YPTo0Tz00EPs37+f0aNH07Zt20r19+zZkwsvvJBXXnmFJ598kg0bNvDq\nq6/SvHlzLrvsMtatW8fTTz/Nhg0bGDt2LJdeeikQfMbbxRdfXOn1O1pq/5P60qM82V8nkqSjLxKJ\nJO010pEZO3Ysbdu2JTs7m5UrV/Lwww/XdUk1VlZWRs+ePfnwww8POawbZn369OGll16q9DEe8ZL9\nHkSHkY8oYxnQJEk1ZkCrfaNGjeLmm2+OXY+m8DGgGdAkKVQMaFLtBjQ/ZkOSJClkDGiSJEkhY0CT\nJEkKGQOaJElSyBjQJEmSQsaAJkmSFDIGNEmSpJAxoEmSJIWMAU2SJClkDGiSpAalU6dOzJkzp67L\nSGrMmDE89dRTdV2GgN69e7NixYo6ee7GdfKskqR6J/+NfIp3Ftda+9ktsxl61dAjbicSiVR8FU+t\nu/HGG5kzZw67du0iJyeHW265hbFjxyZdv6ioiClTprB27dpaq+mDDz5g6tSp/OIXv4jNe+ONN9i5\ncydr164lJyeHO+64I+Vtu3TpwqZNm2jTpg2PPfYYw4cPr7Xak5k+fTorVqwgIyOD3NxcbrrppoTr\nJav1N7/5DVu2bGHhwoVcffXVfP/73wfg3nvv5f777+e11147avtSwYAmSUqL4p3F5H4jt9ba37xo\nc621XVvGjBnDiy++SGZmJqtWraJ///706tWLyy67LOH6kyZNYtCgQTRr1qxW6nniiScoKCjguOOO\ni80rKSnhuuuuo6SkhGbNmpGTk8OgQYPo2LHjIbcFuO+++xg4cCAnnngijRsf/VjxxRdf8OCDD7J4\n8WIA+vbty+WXX05OTs5B6yaqdc2aNRQXF3PPPfewdetWTj31VHr37s0pp5zC4MGDGTlyJJ999hnt\n2rU7qvvlEKckqV7auHEjQ4YMoW3btuTk5DBq1KiD1pk4cSJdu3aldevWdO/enenTp1da/uijj3LS\nSSfRunVrzjjjDObOnVvt/Kq6d+9OZmZmbLpx48a0bds2ac1vvfUW/fv3P5zdTcndd9/NVVddVWle\nmzZtWLx4MZmZmUQiEfbu3ZvwC8ATbQvQtGlTOnToUCfhDGDBggV069YtNn3uuecyb968hOsmqnX5\n8uX8/Oc/ByAnJ4euXbvGwl5mZia9evVi9uzZtbgHidmDJkmqd/bt28d3v/tdLrnkEl555RUyMjJi\nb7rxunarePzEAAAgAElEQVTtSkFBAe3bt2fatGnceOONrFmzhvbt27Nq1SqeffZZFi1aRPv27Sks\nLGTv3r1J5ydzxx138Otf/5rS0lKeeeYZzjvvvKTrfvTRR5x++uk12td169bxwgsvJF3ep0+fSsEq\nUfjq3r07AAUFBeTl5dGpU6eEbSXa9q9//SulpaVs376d0047jSuvvLJG9SeT6n5VDFlWaNOmDZ98\n8knCbRLVesUVV/Dmm28Cwf5t2bKFrl27xrY588wzWbp0aVr2qSYMaJKkemfhwoVs2bKFxx57jIyM\nYLDoggsuOGi9a665Jvb42muvZcKECSxcuJArr7ySRo0aUVpayvLly8nOzqZDhw5AMCSWaH4y//Ef\n/8Gzzz7LO++8wzXXXMN5553H+eefn3DdkpISWrVqFZvet28f/fv3p6CgAIBbbrmFMWPGVAoQnTt3\nZsKECSkeGZJef/f666+Tn5/P448/XqNtBwwYwNVXXw1Ajx49uOiiiyoFpmRWr17NT3/6U4qKili0\naBF5eXkMGjSIkSNHAqnvV0lJSaVeyqZNm7Jz586E6yar9ayzzgJg1qxZfOMb36BHjx6xbVq1asWW\nLVsOWUe6OcQpSap3Nm7cSMeOHWPhLJnJkyfTs2dPsrKyyMrKYtmyZRQXBzc6dO3alSeffJJx48bR\nrl07hg0bFutdSTS/OpFIhLy8PIYOHcpvf/vbpOtlZWWxY8eO2PR7770XuxasvLyc9957r1I4OxyJ\nesEAhgwZwgsvvMDll1/O+vXrU942vncuKyuL+fPnH7KGbdu2MXLkSCZPnsy8efMYMGAAU6dOjYWz\nmmjVqlWlunbv3s3xxx+fcN3qai0pKWHSpElMnTq10jbbt28nKyurxnUdKXvQJEn1zsknn0xhYSH7\n9u2jUaNGCdfZsGEDt956K3PnzqVv375EIhF69uxZ6c1+2LBhDBs2jB07dnDbbbfx4x//mMmTJyed\nfyhfffUV2dnZSZefc845rFq1il69egHBNWkDBw4E4P333+fss88+aJuaDnFW7QWbNWsWjzzyCO++\n+y4tW7akbdu2vPbaa9x7770HtVV126lTpzJjxgymTZsGwK5du1K6Fu3ZZ5/lzjvvjPV8lZaW0rx5\n88Pary5durBo0aLY/K1btyYcRq6u1vLyciZOnMiLL75Iy5Yt2bBhQywYr1y5sk7uTDWgSZLqnd69\ne3PCCSdw3333MX78eDIyMliyZEmlYc5du3YRiUTIyclh//79TJ48mWXLlsWWr169mk2bNtGvXz+a\nNWtGZmYm5eXlSedXVVRUxJw5cxg8eDCZmZm8/fbb5Ofn8/bbbyet+4orruCdd97h+uuvB2D27Nmx\nj3yYNWsWAwYMYMaMGZWu86rpEGfVWhs1akReXl5s2caNGznnnHMAWLt2LZ07d44Fs6rbdurUKdbr\n9eWXX1JUVMS3v/1tAEaMGEEkEuHll18+qIYdO3bELuxfvnw53bt3p0mTJpXWSXW/LrroIkaPHh2b\nXrJkCY8++uhB9VdX69NPP83QoUPZs2cPCxcuZPfu3XTs2JE9e/awZMkSpkyZcsg60s2AJklKi+yW\n2bX6URjZLZP3PFWVkZHBzJkzGTVqFB06dCASiXDDDTdUCmjdunXjnnvuoW/fvmRkZDB8+HAuvPDC\n2PLS0lLGjBnDypUradKkCf369eP555+nqKgo4fyqIpEIzz33HLfffjvl5eWcdtppTJkyhW9+85tJ\n6x4+fDg9evRgz5497Nixg8LCQmbMmEFhYSHNmzenqKiIzp07p3wcqnrmmWeYNm0aGzduZPz48dx1\n111cdtllrFu3jqeffpoNGzYwduxYLr30UgCGDh3Kr371K3r27Jlw2wsvvJBXXnmFJ598kg0bNvDq\nq6/GesI2bdrEsGHDEtZx++23M2PGDFasWMGmTZuYOHHiYe9TixYtGD16NA899BD79+9n9OjRsTtl\n4+tPVmtBQQF33XVXLHxGIhEKCwsBmDlzJhdffDHt27c/7PoO19H5pL4jV55szFySdPRFIpGk1zLp\nyIwdO5a2bduSnZ3NypUrefjhh+u6pBorKyujZ8+efPjhh0mHmI8Fffr04aWXXqr0MR7xkv0eRHsc\njyhjGdAkSTVmQKt9o0aN4uabb45dj6bwMaAZ0CQpVAxoUu0GND9mQ5IkKWQMaJIkSSFjQJMkSQoZ\nA5okSVLIHM2A9hLwGfBR3LxxwCbg/ejPZUexHkmSpFA6mh9U+zLwNBD/XRjlwBPRH0nSMSIrKyvp\nl25LDUVtfkfn0QxofwI6JZjvb7gkHWO2bdtW1yVI9VoYrkH7EbAU+BXQpo5rkSRJqnN1/V2cvwR+\nFn38IPA4cEuiFceNGxd7nJeXF/tiV0mSpLo0f/585s+fn9Y2j/bwYidgJnB2DZf5TQKSJOmYUB++\nSeCEuMdXU/kOT0mSpAbpaA5x/hboD+QAG4EHgDygB8HdnH8DbjuK9UiSJIXSsXIHpUOckiTpmFAf\nhjglSZJUhQFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSF\njAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQp\nZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJ\nIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNAkyRJ\nChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJ\nUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJ\nkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJ\nkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJ\nkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJ\nkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFN\nkiQpZAxokiRJIWNAkyRJChkDmiRJUsg0rusCJFU2Oz+fsuLitLbZNDubgUOHprVNSVLtMaBJIVNW\nXMzg3Ny0tjlz8+a0tidJql0OcUqSJIWMAU2SJClkDGiSJEkhY0CTJEkKGQOaJElSyBjQJEmSQsaA\nJkmSFDIGNEmSpJAxoEmSJIWMAU2SJClkDGiSJEkhY0CTJEkKGQOaJElSyBjQJEmSQsaAJkmSFDIG\nNEmSpJAxoEmSJIWMAU2SJClkjmZAewn4DPgobt7xwB+B1cAfgDZHsR5JkqRQOpoB7WXgsirz7iMI\naKcBc6LTkiRJDdrRDGh/Aj6vMu9K4NfRx78GvncU65EkSQqlur4GrR3BsCfRf9vVYS2SJEmhUNcB\nLV559EeSJKlBa1zHz/8Z0B74O3AC8I9kK44bNy72OC8vj7y8vFouTTq02fn5lBUXp7XN5YsXMzg3\nN61tSmGWnz+b4uKytLaZnd2UoUMHprVNKZn58+czf/78tLZZ1wFtBnAz8Gj03+nJVowPaFJYlBUX\npz1MLV2wIK3tSWFXXFxGbu7gtLa5efPMtLYnVadqx9H48eOPuM2jOcT5W+DPwOnARuCfgYnAdwg+\nZuPb0WlJkqQG7Wj2oA1LMv+So1iDJElS6KXag9YhybqR6DJJkiSlSaoBbT2Qk2B+NvC3tFUjSZKk\nI74GrQWwJx2FSJIkKXCoa9Cejnv8CPBllW3PB5amuyhJkqSG7FAB7ey4x2cC8R9UUwYsBn6R7qIk\nSZIaskMFtLzov5OAUcD22ixGkiRJqX/MxojaLEKSJEkHpBrQvgb8H2AA0JbKNxeUA+ekuS5JkqQG\nK9WA9ixwNZBP8G0A8V9q7hecS5IkpVGqAe17wLXAH2uxFkmSJJH656B9CRTWZiGSJEkKpBrQHgPu\nJvhqJ0mSJNWiVIc4LwG+BVwGrAD2Elx7Fon+e2WtVCdJktQApRrQioHpSZZ5k4AkSVIa+TlokiRJ\nIXOkX5YuSZKkNEu1B+2jBPPir0Hzg2olSZLSJNWA9l9VppsAPYALgP9Ia0WSJEkNXKoBbVyS+aOB\nDukpRZIkSXDk16C9DtyYjkIkSZIUONKA9i2CbxmQJElSmqQ6xDmTAzcFEP33BKAnML4W6pIkSWqw\navJBtfEBbT+wDBgD/KEW6pIkSWqw/KBaSZKkkEk1oFXoDHQj6E1bCaxLe0WSJEkNXKoBrTXwEjCE\nYHgTghsM/gv4AbAj/aVJkiQ1TKnexfkUcDZwMdA8+vNtgm8QeKp2SpMkSWqYUg1oVwI/BN4ByqI/\n86PzvlcrlUmSJDVQqQa0rxHcyVnVNiAzfeVIkiQp1YD2Z+BBoEXcvJbAz6LLJEmSlCap3iRwFzAb\n2AwsJfg8tLMJvkVgYO2UJkmS1DClGtA+Ak4FrgfOjM6bDLwC7K6FuiRJkhqsVAPaI8AG4D+rzB8J\n5AL/ls6iJEmSGrJUr0G7CViSYP4S4Ob0lSNJkqRUA9rXga0J5hcD7dJXjiRJklINaBuB/gnmfwvY\nlL5yJEmSlOo1aM8B/w40BeZE510CTAAerYW6JEmSGqxUA9rjQA7B1zo1i84rjU7/vBbqkiRJarBS\nDWgAY4CHgW7R6ZX4JemSJElpV5OABrATWFgbhUiSJCmQ6k0CkiRJOkoMaJIkSSFjQJMkSQoZA5ok\nSVLIGNAkSZJCpqZ3cUoJzc7Pp6y4OK1tNs3OZuDQoWltM911Ll+8mMG5uWlrr6HLfyOf4p3pPY+y\nW2Yz9Kr0nkeSVNsMaEqLsuLitAeVmZs3p7U9SH+dSxcsSFtbguKdxeR+I73n0eZF6T+PJKm2OcQp\nSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMgY0\nSZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMgY0SZKkkDGg\nSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQqZxnVdgCRJDVV+/myKi8vS1l52dlOGDh2YtvZUdwxokiTV\nkeLiMnJzB6etvc2bZ6atLdUthzglSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFj\nQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQoZ\nA5okSVLIGNAkSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQqZxnVdgKRj\n0+z8fMqKi9Pa5vLVi8n9Rm5a21R65efPpri4LK1tLl68nNzcwWltszY05H3X0WdAk3RYyoqLGZyb\n3jD15vsL0tqe0q+4uCztgWLBgqVpba+2NOR919HnEKckSVLIGNAkSZJCxoAmSZIUMgY0SZKkkDGg\nSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwB\nTZIkKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAkSZJCpnFdFxC1HtgO7AO+As6v02okSZLqUFgCWjmQ\nB2yr4zokSZLqXJiGOCN1XYAkSVIYhCWglQNvA4uAH9ZxLZIkSXUqLEOc/YAtwNeBPwIfA3+KX2Hc\nuHGxx3l5eeTl5R296uqZ2fn5lBUXp7XN5YsXMzg3N61tfrh4MTz3XFrbrI06FW6LFy/nuR0z09rm\n6vV/4bSzT05vmx+v5rQzTgt9m4uX/Y3c3MFpbbM2LF78Ybr/+2Dx4uXHxL7r6Js/fz7z589Pa5th\nCWhbov8WAf9NcJNA0oCmI1NWXJz2kLJ0wYK0tgdQvmvXMVGnwm3Xrn1pf1Nd8P6bXPyNPultc+EC\nLv7GxeFvc8GHaW2vtuzaVZ7+133B0rS2p/qjasfR+PHjj7jNMAxxNgdaRR+3AC4FPqq7ciRJkupW\nGHrQ2hH0mkFQzyvAH+quHEmSpLoVhoD2N6BHXRchSZIUFmEY4pQkSVIcA5okSVLIGNAkSZJCxoAm\nSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMgY0\nSZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMo3rugBJte83\nM/J58/0FaW2zZONnDL79X9PaZm34R+Ealr75XFrbXLFkIW++lZ3WNpcsWUGbtxaGvs3Cwk/T2h5A\n4d+X8WZBel+jwr8vS2t7x4rFiz/kufQeSrKzmzJ06MD0NqpDMqBJDcCuPdu5/sIL0trmCy9/ktb2\nak1pKZdm56a1yfzdZWQff35a29y9+41jos3S0qVpbQ+glC/JPiu9r1HpX79Ma3vHil27ysnNHZzW\nNjdvnpnW9pQahzglSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQoZA5ok\nSVLIGNAkSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAk\nSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIkSSHTuK4LkHRs+rRkG88VvJnWNtd8WpjW\n9pR+20o+5c2C59Lb5vZP09qeVB8Y0CQdlrKMveSelZ3WNks/Kk1re0q/vRllZJ+Vm94255SltT2p\nPnCIU5IkKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwBTZIk\nKWQMaJIkSSFjQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMgY0SZKkkDGgSZIkhYwBTZIkKWQMaJIk\nSSFjQJMkSQoZA5okSVLIGNAkSZJCxoAmSZIUMgY0SZKkkGlc1wXUJ7Pz8ykrLk5rmytWr6bbaael\ntc3lixczODc3rW02VMv+XshzBW+mtc3ff7yYnII2aW3z0+3b0tpebSne9gVvvrUwrW2u27ye11en\n9zUqKfsire1JUlUGtDQqKy5Oe/BZumABgy++OO1tKj2+pJTcs7LT2ubOOXvS3mbZnL1pba+27N1b\nTvbx56e1zTJe4uunp/d47vvg2Dieko5dDnFKkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY\n0CRJkkLGgCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsgY0CRJkkLG\ngCZJkhQyBjRJkqSQMaBJkiSFjAFNkiQpZAxokiRJIWNAkyRJChkDmiRJUsg0rusCUvX555+ntb1W\nrVrRuPExs/uSJKkBOWYSyopp09LW1u7SUjpefjmnnnpq2trUsWHZ3wt5ruDNtLX36fZtaWtLx46v\nynbzt8XpO48Avtz2aYNts+zLL9LaHsCWzct44T+vT2ubH65eRJuTc9La5uKPf0+bgvS1me72AL7a\n9TdgcFrbzM+fTXFxWVrbXL16Baed1i2tbWZnN2Xo0IFpbTNVx0xA63fiiWlra/XmzZSXl6etPR07\nvqSU3LOy09Ze2Zy9aWtLx46M/fvp2Tp95xHAnH17G2yb8/fVxu9RKf3P65LWFpd88g7ZZ+Wmtc09\nc3amtc10twewtuDDtLYHUFxcRm5uekPfggVLufji9La5efPMtLZXE16DJkmSFDIGNEmSpJAxoEmS\nJIWMAU2SJClkDGiSJEkhY0CTJEkKGQOaJElSyBjQJEmSQsaAJkmSFDIGNEmSpJAxoEmSJIWMAU2S\nJClkDGiSJEkhY0CTJEkKGQOaJElSyBjQJEmSQsaAJkmSFDIGNEmSpJAxoEmSJIVMWALaZcDHwCfA\nj+u4lmPG/I8+qusSQsnjcrCdO/bUdQmhtGb9+rouIZQ8LomVflla1yWEzvz58+u6hHorDAGtEfAM\nQUjrBgwDzqzTio4RBpHEPC4H22VAS2jthg11XUIoeVwSM6AdzIBWe8IQ0M4H1gDrga+AV4Gr6rIg\nSZKkutS4rgsAcoGNcdObgN5VV3ooPz9tT1i6dy83ffvbaWtPkiQpnSJ1XQDwTwTDmz+MTt9IENB+\nFLfOGqDLUa5LkiTpcKwFuh5JA2HoQdsMnBw3fTJBL1q8I9pJSZIk1UxjgqTZCWgKfIA3CUiSJNW5\ny4FVBEOZY+q4FkmSJEmSJCk8XgI+A+I/uGoosBzYB5xXzbb1+cNtj+S4rAc+BN4HFtZSfXUh0TF5\nDFgJLAVeB45Lsm1DO1dSPS7rqZ/nCiQ+Lg8SHJMPgDlUvvY1XkM7X1I9Luupn+dLomNS4R5gP3B8\nkm0b2rlS4VDHZT3181yBxMdlHMG18+9Hfy5Lsu0xdb58C+hJ5R09AzgNmEfyINKIYDi0E9CE+nfd\n2uEeF4C/kfyX5liW6Jh8hwOf5Tcx+lNVQzxXUjkuUH/PFUh8XFrFPf4R8GKC7Rri+ZLKcYH6e74k\nOiYQBNW3SL7fDfFcgUMfFw6x7FiX6Lg8ANx9iO1qfL7U9QfV/gn4vMq8j4HVh9iuvn+47eEelwph\n+PiUdEt0TP5I8FccwP8CJyXYriGeK6kclwr18VyBxMdlR9zjlsDWBNs1xPMlleNSoT6eL4mOCcAT\nwOhqtmuI5woc+rhUqI/nCiQ/Lofa3xqfL3Ud0A5Xog+3za2jWsKmHHgbWMSBz5ZrCH4A/D7B/IZ+\nriQ7LtAwz5WHgULgZhL3LDbU8+VQxwUa1vlyFcFr/2E16zTEcyWV4wIN61yp8COCSwV+BbRJsLzG\n58uxGtDK67qAEOtH0P16OXAnQXdsfTcWKAN+k2BZQz5Xqjsu0HDPlQ7AJODfEyxvqOfLoY4LNJzz\npTnwE4JhqwqJekca2rmS6nGBhnOuVPglcArQA9gCPJ5gnRqfL8dqQEvlw20bqi3Rf4uA/yboVq3P\nRgBXADckWd5Qz5URVH9coOGdK/F+A3wzwfyGer5USHZcoOGcL10IrhNaSnAt1UnAYqBtlfUa2rmS\n6nGBhnOuVPgHQQArJ7iGM9H+HpPnSycS3yUyD+iVZJuG8OG2naj5cWnOgQt+WwDvApemvbK604nK\nx+Qygjtbc6rZpiGeK6kcl/p+rsDBx+XUuMc/AqYk2KYhni+pHJf6fr50IvH/t5D8gveGeK7ES3Zc\n6vu5AgcflxPiHt9F4lGLY+58+S3wKcEwzEaC62W+F328G/g78GZ03ROBWXHb1ucPtz3c49KZ4EX/\nAFhG/TouiY7JJ8AGDtza/B/RdRv6uZLKcanP5wokPi6vEfyn+gHwXxz4y7+hny+pHJf6fL5UHJNS\ngmPyz1WWr+NAEGmI50pNj0t9Plcg8e/QZILr8pYC04F20XUb0vkiSZIkSZIkSZIkSZIkSZIkSZIk\nSZIkSZIkSVJt2Q8MCVl740j+IZoQfCDkfuC8I3ye2jAJmFnXRUgKr2P1q54k6VhW8bUwkpSQAU2S\njr4Iyb9oWpIMaJK4DPgTsA0oBt4CzjjENicCrwBbgV0EXymVF7f8NoKvMykl+Oqpf0nQRjaQD+wk\n+I66ql/sfjbwNvBltK6Xgdap7VIlpwMFBF+TthL4TnR+JFrjPVXWP5VgaLRHgrZOiy47q8r8Wwm+\nGLpRdPoi4H858NVsTwBNqqlxPvB0lXmTqDwMOp/ga7seJzge/wBGAZnAc0AJwVd8DavSTi7wKsHr\nuw34H6BrNbVICgEDmqTmBAHim0B/4AuCYJAsULQA3gE6AFcB3YEH4pZfTRA2nogue4ogWHy3Sjv3\nA/8NnAP8DngJODnuOWYD26N1XQ1cEF2npn4OPAmcC/wReIMgYJYDL3Lwdwz+gCBwfpCgrdXAXzk4\nTN4Q3Yd9BIHoTWAxQci7hSA0TaimxkRDnonm3UDw+pwPTIzu1xvAcoJr7X5NcIwqvguwOTCPIORe\nBPQBthAE369VU48kSQqZFsBeoF/cvPiL+n9IEJyOJ7F3CYJPvJcJeuni23s4broRQU/c9XHPURKt\npUL/6Hado9PjSO0mgfgvJY4QfFnxg9Hp9gRfetw7ro7NwB3VtPsjYH3cdAeCYNYnOv1w9Dni3Qzs\nIejtgoN7x+YB/6/KNlXXmU9wbOP9g+DLmSs0Jui1rHitfkAQKuM1Iuj5HIqk0LIHTVIX4DcEw31f\nEAzJZXCgN6uqnsBSguGyRM7g4CDxLtCtyrwP4x7vIxgibBudPjP6HLvi1nmPIHBVbedQ3ot7XE4w\n9FjRxt8Jhvx+EJ2+DMgiGL5N5ncEPXDfik4PA9YBf4mr/S9VtnkXaMqRDS2WU/mYQRDQ4kPqXuBz\nDhzHXsApwI64nxKgDQeCrqQQalzXBUiqc/8DFBJcR7WZICytIAgUyRzOBe5Vh+u+SrA8/o/GZM9x\npHc/Rqq08SJBQP1XgqD2OkFQTeYfBEOlNxD0Ct5A5UBXTs1r359gm0RDzImOWXXHMYNgqPa6BG19\nnqQWSSFgD5rUsGUTXET/CDCXYGiuNdX/8baE4Lqx7CTLVwIXVpl3IcF1UqlaQXCTQMu4eRcQ/J+1\nsgbtAPSNexwhuH4rvo2Ka91uJ7hOLpXr3KYSDBH2IrhhYGrcspUEw53xgetCgqHUtUnaKyLolYt3\nLkceRhcT9NoVE/Tyxf8Y0CRJCqkMgh6hVwjeyPsDCwnCxPC49eKvQWtOMBxaQBA8OgNXcuAuzqui\n27T4JV4AAAGISURBVN9BcEfkj6LTg5K0V+FvwN3Rx18j6M17nSAAXUQQHvPj1h9HategbQD+iSCI\nPkVwwXzVMDSO4NqtZAGqqq8RhLoPOHg480SCO1N/STDcOYjgwvzH4taZROXry26N1jU4WucTBEOR\nVa9Bq3qn5zKCmy3ibeHANXRfAz6ObnsRwXDnRcAv8E5OSZJC7WKCoLOb4Bqn/7+dO8alIIrCAPwX\norMDEbtQaVmDTqLQWILECnRqYgtKa6DS60VCKyrFPxPjee8pn/B93eTe3LlTzck95569tFZpUYCW\nfLZueE3rxO7TH//oOG2v8Z4WqR/NvPOnAC1pYDa22XhJT7Y2JuNn+V6TNbWdpmsP0hqwsc3G/py5\nW8OeTpesN+t6WP9kzthuGri9pXVu5/masrxKcjN5XktykZ6kPaffNjtn3kWChywP0JLWo10meRr2\n85imdRedgAIA/Ao7aS3X5qo3AgDw362nQdltejsTAIAVO0xbU9ylaVsAAAAAAAAAAAAAAAAAAAD+\nrA8xusMnb/cPHAAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIjIw7BBIyIiIvIw/wc8hBWSvYAm0wAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x10569da58>"
+        "<matplotlib.figure.Figure at 0x1063ddac8>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -608,13 +608,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERERERERERERERERERKSW+H/CqQ+gYvnblgAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nah7OJ7wrYQAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x105c1c358>"
+        "<matplotlib.figure.Figure at 0x106804780>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -666,13 +666,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmUVOWd//++t/a9u4GmbZoGBAkIyiIKImvUqLjGNWZQ\nJzHKccwYRyff4zITz/klmWRmgsZ8Z8YlUb/GmBhHR8ERTVBBFkUkCCJEoJutm26g1+rabtVdnt8f\n5XO5VV1VXcutqltVz+ucPgdqufXcqnuf9/P5PJ8FYDAYDAaDwWAwGAwGg8FgMBgMBoPBYDAYDAaD\nwWAwGAwGg8FgMKoNLtOThBBSqoEwGAwGg2EEOI5LqY18qQfCYDAYDEYlwgSTwWAwGIwsYILJYDAY\nDEYWMMFkMBgMBiMLmGAyGAwGg5EFTDAZDAaDwcgCJpgMBoPBYGQBE0wGg8FgMLKACSaDwWAwGFnA\nBJPBYDAYjCxggslgMBgMRhYwwWQwGAwGIwuYYDIYDAaDkQVMMBkMBoPByAImmAwGg8FgZAETTAaD\nwWAwsoAJJoPBYDAYWcAEk8FgMBiMLGCCyWAwGAxGFjDBZDAYDAYjC5hgMhgMBoORBUwwGYws2bx5\nM6ZNm1aSz1q2bBmee+65knwWg8HIDiaYjJrlZz/7GVasWJHw2FlnnZXysVdffRWLFy/Gl19+WZKx\ncRwHjuOyeu3EiRPxwQcfFHlEpfscBsOoMMFk1CxLly7FRx99BEIIAKC7uxuSJGHXrl1QFEV9rL29\nHUuWLCnnUDPCcZx6DtXwOQyGUWGCyahZ5s2bB1EUsWvXLgBxl+vy5csxderUhMemTJmCpqYmbNy4\nEePHj1ffP3HiRKxevRqzZs1CXV0dvvWtbyEajarP/+///i9mz56N+vp6XHTRRdizZ0/asaxfvx7T\npk1DXV0d/v7v/x6EEFWc2tvb8fWvfx2jR4/GmDFjsHLlSvj9fgDAbbfdhmPHjuHqq6+Gx+PBL37x\nCwDATTfdhDPOOAN1dXVYunQp9u3bp37WunXrMGPGDHi9XrS0tGD16tUjjjnd5zAYjK8gDEaVs3z5\ncvLEE08QQgi59957yfPPP08effTRhMfuvPNOQgghGzZsIC0tLep7J06cSObPn0+6u7tJf38/mT59\nOnn66acJIYTs3LmTNDY2ku3btxNFUciLL75IJk6cSKLR6LAx9PT0EI/HQ15//XUiSRJ54okniNls\nJs899xwhhJC2tjby3nvvkVgsRnp6esiSJUvI/fffnzCO999/P+GYL7zwAgkGgyQWi5H777+fzJ49\nW32uqamJbNmyhRBCyODgINm5c2fGMcdisbSfw2BUI+k0kVmYjJpm6dKl2LRpEwBgy5YtWLJkCRYv\nXqw+tnnzZixdujTt+++77z40NTWhvr4eV199tWqZPvvss1i1ahXOP/98cByH22+/HTabDdu2bRt2\njHXr1mHmzJm4/vrrYTKZcP/996OpqUl9fvLkybj44othsVgwevRo/MM//AM+/PDDjOf1t3/7t3C5\nXLBYLHjsscewe/duBAIBAIDVasXevXsxNDQEn8+HOXPm5DxmBqMWYYLJqGmWLFmCLVu2YGBgAD09\nPZg8eTIuvPBCfPTRRxgYGMDevXsz7l9qhc3hcCAYDAIAjh49itWrV6O+vl796+zsRHd397BjdHV1\noaWlJeExrev35MmT+Na3voWWlhb4fD7cdttt6OvrSzsmRVHw0EMPYcqUKfD5fJg0aRI4jkNvby8A\n4PXXX8e6deswceJELFu2TBXEdGPu6urK4ptkMKofJpiMmoQQglgshunTp8Pv9+OZZ57BRRddBADw\ner1obm7Gs88+i+bmZkyYMCHr49LI1tbWVjz66KMYGBhQ/4LBIG655ZZh72lubkZHR0fC2LT/f+SR\nR2AymfDFF1/A7/fjpZdeUoOStJ9Jefnll7F27Vq8//778Pv9OHz4cMKe6Lx58/Dmm2+ip6cH1113\nHW6++easxpxt1C6DUa0wwWTUHIqiIBqNQhRF2Gw2zJ49G0888QTOO+88DAwMQBAELFy4EI8//nhG\nd2wqqCjdddddePrpp7F9+3YQQhAKhfD222+rFqiWK6+8Env37sUbb7wBSZLwq1/9CidOnFCfDwaD\ncLlc8Hq9OH78OP793/894f1jx45Fe3t7wuttNhsaGhoQCoXwyCOPqM+JooiXX34Zfr8fJpMJHo8H\nJpMpqzEnfw6DUWswwWTUDIQQSJKEgYEBEELA8zx4nsfixYvR29uLBQsWQBRFCIKAefPmobe3Fxdc\ncAEEQYAkSSCEZLSytLmT5513Hn7961/j+9//PhoaGnDWWWfht7/9bcr3jRo1Cv/93/+Nhx56CKNH\nj0ZbWxsWLVqkPv/YY49h586d8Pl8uPrqq3HDDTckjOPhhx/GT37yE9TX1+Pxxx/H7bffjgkTJmDc\nuHGYOXMmLrzwwoTX/+53v8OkSZPg8/nw7LPP4uWXX85qzMmfw2DUGhl9LJmihRiMSoK6YBVFweDg\nIOrr69XHeD6+bpQkCaIowuFwqO/RujIBwGQywWKxwGw2w2QyMTclg1GFcGlubHOpB8JglBpFURCL\nxYZZiFQgzWazKppakqvtEEKgKAoEQVAfYwLKYNQOTDAZVQt1wUqSBI7jEkQxEokgGo2C53kIgqC6\naAkhkGUZPM8PE790AhqJRNTHmYAyGNULc8kyqhJFUSCKIhRFSRA6WZbh9/thNpvhdDohSRJ4nldf\nL0mSegye5xOEbyTxo+5b+pkAE1AGoxJhLllGTUAtRFEUASRahbFYDKFQCADgcrkSxIvneZhMJiiK\nAofDAUVRIMsyZFlGLBYDEBc/+pdKQOlj1JJNZYGazWb1jwkog1FZMMFkVA3JLlgqRoQQhMNhiKII\nj8eDQCAwolDRCFqLxQIAugkoFXMmoAxG5cEEk1EVpHPBSpKEUCgEk8kEr9ebMrhHS7pdCK2AUtdr\nJgHNFEQ0koBaLJYEIWYwGMaACSajokkWHa0YRaNRRCIROJ1OWK3WlOKjfSxbcdIKXzEENBwOq++j\nAkojeZmAMhjlgwkmo2LR5lZqrUpFURAOhyHLMrxer1rJJvm9eomP3gIKnLZoZVlWiybQfVYmoAxG\neWCCyahIknMrtS7YYDAIi8UCr9c7olVZDFIJKN0DlSQJ0WgUHMflJKDAaQuURvJyHJewB8oElMEo\nLkwwGRVFutxKQggEQYAgCHC5XLBarWUe6Wm04gggo4Cazea0+6ipBJQWX6DPMwFlMIoHE0xGxZDJ\nBUsLhKdzwRqJTAJKA5ei0ShkWc4Y/MMElMEoLUwwGRWBJEkIh8OIxWJwu93DcittNhscDkdFCkKy\ngIbDYZjN8VuTFoOn+5e5CqgoikxAGQydYILJMDRaFyyF4zgQQhCJRFQBpfmSuRx3pBSTckFdzVQ0\nky3QXARUa22nE1CaxsIElMHIDBNMhmHJVN4uGAyC5/msciuTqTRRGMmFywSUwSgNTDAZhiO5vB0V\nRI7jIMsyhoaG4HA4YLPZdJ/QK6F8cqkElBACi8UCq9XKBJTBABNMhsGgk7Ysy8PK20UiESiKAq/X\nq7or9aRSxSCVgGpzQBVFyUtABUFIKArBcZyaA2o2m7MqSM9gVBNMMBmGYaTcSjrpF0MsqwltcA+Q\nv4ACGLaXGovFEI1G1eeYgDJqCTbzMMpOptxKbXk7nucRiUR0/+xqn+SzEdDkIgrZunCpgGotULoH\nygSUUW0wwWSUlUy5laFQSHXBmkwmtURcodAoW/rvWiOTgEajUVVA6WIi3aJCK6D0+4zFYmopQGqd\nMgFlVAtMMBllg1qVyS5YURQRCoVgsVgSci4ZxSGdgEajUbUQwkgWKP1/LgJq1LQeBiMdTDAZJacS\ny9vVElRAJUlS94xTWaBMQBm1BhNMRklJl1upLW/n8/nY5GkgsnXhMgFlVDtMMBklIV1uJXC6vJ3d\nbofdbk/rgtXuPTLKR7KA0hxQ+vsSQvISULqfrRVQGoXLBJRhBJhgMopOunQRQgjC4TBEUcyrvF2+\nZBJeJsinyTaCmPbupL9fJgFNl36Sqg4ujZIWBAGiKMJutzMBZZQVJpiMokI7bwwODqK+vl638nbF\ngAUX6UMmAaVpQck5oJkElO5tUwFNlQfKBJRRCphgMopCcmCP9vFYLIZwOJxzeTvmkq1MMgkodb+O\nJKD0OBStBUoF1GQyqfufTEAZxYAJJkN30uVWUhesJEnweDysYk+NohVQKnyZBBQYbv2ncuEqigJB\nENT/awOIqCuYwSgENmMxdEW7b5U8qQ0NDcFiscDn87HJiwHgtPCNJKC0xnA6yzGTgNJrkVqg1IXL\nrkFGrjDBZOjCSLmVANQo2HLDXLvGJZWAyrKsFoJPZYHmKqAUJqCMXGGCySiYTLmVtLwdgIILETCh\nqz2oeHIcB7vdntDKTJIktY4tE1BGKWCCycib5NzK5PJ2wWAQNpsNbrcbg4OD5Rwqo0rI1AuUCSij\n2DDBZOTFSH0ro9FoQm4lsw5zg31X2ZGNgGbbCzSVgEYikYQiC0xAaxsmmIycSVeIQJZlhEIhcBxX\n0eXtyt3yi03EieTye2QSUFEUIQhCTgKq3YunAhqLxWC1WtUoXCagtQMTTEbWaF2w2skEyL68XSFo\nrdhCjp/O2mUTXvWht4ByHKc24GYWaO3BBJORFSPlVoqimDG3krlkGUZALwGli8VUFih9Pa23ywS0\nemCCyRiRdC5YSZIQCoVgMpkMU96OwciFXAU03TWeyoWr9cYATECrASaYjLRkyq2MRqOIRCJwOp2w\nWq0Ve/NX6rhLQbn3cstBKgHVFlGgKVLRaDTnPdBUAqot5Vdr33UlwgSTkRJaND0QCMDr9SbkVobD\nYciyDK/Xq04sI6GXS5Yeh00ujFKQ3MpMW0CeCmi+QUQ0kpeiDSJK1RKNUX6YYDIS0K6EFUVR9yyB\nuAs2GAzCYrEkiGglwvZTKwcjLZCo8NlsNgCpLdCReoFqj0NJFtDkUn5MQI0BE0yGSrIL1mQyqbU9\nBUGAIAhwuVwFV+wpN2ziYehFsgWqFdBoNKqrgGr3QJmAlgcmmAwAqQN7qBUWCAQAICcXbDJ6u2T1\nwkjWC6PyKaaASpKUUFWLCWjpYYJZ42TKraQ3p9lshsPhYDcko+bJdYFVbAEVBAGKoqiFFJiAFhcm\nmDVMptxKWtEEQNWKpSiKiEQiatAG7ZnIrE5GsSiGgNJ/i6LILNAiwwSzRslU3i4YDILneXi9Xt2K\nphupcIF2dW61WtX/R6NRAPHoR5YrxygFyQJKA+2o+5UQkrWAardLaK3nZAGlaSxMQPODCWaNkS63\nEojnloXDYTgcDthstoS9TKPcXIUKL21FRgiB1+tVgyqocNJauMlJ62yVXj6MdP0VG57nwfN8goBS\nCzSVgKa7F5iAFgcmmDVEJhdsKBSCJEkZy9tVOqIoIhQKJeTNybKsTjr0O7FYLOB5Pm93GaN6KbV4\nUwGlXX9SCSjP8xBFUfWI5GuBauvgsms7NdU5MzKGkRxhl5xbaTab4fP5ht0kRo1uzYXktBitSGYi\n036TIAjqal/rvmWTDKOYJAsovQ5pHiiAYUUUshXQWCymbkvQz6DXP7u24zDBrHKSXbBaq1Jb3o4m\nYlcbiqIgFApBURQ1LYZWaskVrYDabLaE1X6qyYrV1mUUG7qtQnOjR7omCxVQ6sKtVQFlglnF0P26\nZBdsKhGpFHKxVLWVidxu97Cw/ELRrvZpgQdt42IqsKxWKKNYUJcsJd01meuiTiug9F6hAqrduqg1\nAWWCWYVocysBJNwUdB8vlYikohJdslrrOVVlomLc2NpaoXSyyqdtlJEwSlSzkaikAKRU12Q+AkrP\nN5WA0mPQQKVqF1AmmFUG7U9JCFH3Hujj1VTeLh00gCnX4vDJxygUukJPbhslSVJC0W6jB1kYYUyV\nJFKlJpfvJp2A0i0baj0WIqCCIECWZdhstmFRuNVAdZwFA8DpDiPRaFTds6SPBwIBiKIIn8+Xk1ga\nKX9yJCRJgt/vB8dxeYtlsSZmOhHZbDY4nU510UKt4VAopBaLoBMZg1FMtPufDocDLpcLdrsdPM9D\nkiSEw2GEQiEIgqBu7aQ7TjoXbigUwj333INDhw6V7LyKCbMwq4B0gT3A6YvWbrfDbreXbaVeTNdu\nJQYwpYvAlSRJdZlp01iqZYVe6VSztZvOK5LrvnyygPb09FTEPZkNTDArnFS5lRzHqYE9oijC7Xar\nYej5foZR0cMFawS0Akr3OzmOYxG4jIwUU8AzCWi6fflUc0U4HIbT6SzKGEsNu+sqGG3x5eQoWCqi\nXq+3ILHU82bUW3hlWYbf7weQWyeVSnAz0yhEu90Op9MJh8OR4CoLh8Oq693o58KoDqiAal24tCIY\nDSakrltt3jcNvssWQRAwf/58zJ49G2effTYefvjhYa/ZuHEjfD4f5syZgzlz5uAnP/mJbueZCWZh\nViCZcitp6LfZbM4qCrZU6C28tIxfpbhgC4H+xnTvuRoicHOBLQiMSSoLlFbEEkURt9xyC/r7++Fw\nOPDuu+9i6dKl8Pl8Ix7Xbrdjw4YNcDqdkCQJixYtwpYtW7Bo0aKE1y1duhRr164tyrmlg1mYFQa1\nHlOJJQ0csdvtuk2aRrTG6H6lx+OperFMRaqVPhVTumdNA4iyrWpkdIyyADDaHqaRxkPnI9oO8NVX\nX8XPf/5zxGIx/PKXv8S4ceNwwQUX4MknnxzxWNSFS6/hhoaGYa8px3XNLMwKIVNupTZB3+fzIRaL\nqa+rJmjwAc/zKcv45YMRFwS5kqmEX3INXFbmjFEq7HY7LrroIjgcDrz//vuIRqP45JNPEAwGR3yv\noiiYO3cu2tvbcc899+Dss89OeJ7jOHz00UeYNWsWxo0bh1/84hfDXlMMmGBWALRQsizLw6xKmltZ\nLNekUQoXUBcsz/PqvgkjNckCqi2XRssCsgCi6sBIFiYwvPIQheM42O12LF26NKvj8DyPXbt2we/3\n47LLLsPGjRuxbNky9fm5c+eio6MDTqcT77zzDq677jocOHBAr9NIP66ifwKjIGhuZbJYKoqCYDCI\nWCwGr9ebIJbVYDVRtK5mvTqpjPT9VMt3R6GJ6toAItqpRZtrxwKIGMWgEEH3+Xy48sorsWPHjoTH\nPR6P6ra94oorIIoi+vv7CxpnNjDBNCjUqtQWP6YXniiK8Pv9MJlMFZNKkY+Iy7KMoaEhNdq3FG3H\njLRaLwbaSi92uz0hWZ1GOtIIXFZAYThGs+iMRqrvJ9drqLe3V21cH4lEsH79esyZMyfhNSdPnlSP\nu337dhBCUu5z6g1zyRqQTH0rI5EIotFoxtxKPS3MclmrNHhF28yaoT+Zcu2A+IRV7hJ+6dx8tU4l\nLGZEUcw5ra27uxt33HEHFEWBoii47bbbcPHFF+OZZ54BAKxatQqvvfYannrqKZjNZjidTrzyyivF\nGP4wMl75pBJ+kSpD2xhWK5ayLCMUCoHjOLhcrowTiCiKiEQi8Hq9BY8nHA6D4zg4HI6CjkPTXTwe\nT8bX0UVBLBaD2+0eZlWGQiGYTCbY7XZdxqLt5gLEBYL2ASwXgiDAZDIVlD+rB8FgEA6HIyGIqBxN\ntI3yfRhtLHS7wu12l3soKuFwGDabTV2ADQwM4J577sHbb79d5pHlBpfmomYWpkFIzq3UCmKu5e2M\namGOdBztosDr9aYNHmDruNJBBTFVCT+6sEsulVbN3gDmks2NcDhc8GLbSDDBNAC0jJ0sywmCSDuP\niKKoW8BLuRhpkil1zVsmuvmRKQKXlfArLUYU7+Qx0frO1ULlzsBVgDa3kk469GKTJEl1P6azttKh\ntxVWTHHJdl9WT4w2yVQyqRoWJ7eLYk20a5dwOJxTWTyjwwSzTCTnVvI8r1Zl0XbesFqtZZ1k9Prs\nVCJOU2OAePg4s0Yqm1ov4ccYbmFWU+F1gAlmWaDl7ZIDe/TqvFEJ+3yiKCIYDObsgq2Ec2PESReB\nq22inU0AkZFcj2wsucEEk5E3mQJ7aMFim80Gr9drmBuhGO5dWp2oVC5YhjFIJaDpSviVK4WFkT/V\n3toLYIJZMjLlVlIBMZlMuvj7jWiF0R6dgUAAQPldsOm+HyN+d9VKpgAiWgvZZDKp+XiMRIxqYSYH\n/bA9TEZOpMut1O7hOZ1OtaqPkdBLQGjZNdrJoJAbvdDxaD/biBNOraINIAKGR+CKojisiDzD2IRC\nIWZhMrIjm9xKm80Gh8NRlDZMRliBai1ojuMKvnnKfT6M0kEFVJIkmM1mNTCORuCWI4DICPeUUUn1\n3UQiEYwePbpMI9IfJphFgu5JpitvRyvZFGMPzyg3NM0vJYTA4/Go7lgGI1cylfBLjsCl4mqU+6BY\nVIJ4szxMRkaS+1Yml7cLBoPgeX5YbmUx9s70uKHyHRft0UmbHLN9QUa+pLqOWQCR8UhnYbI9TEZK\nkl2w2ouH9nMsVTHxck0O2jxSl8uVkJOnBywoh5GKTE20BUEAIUS1PmuhhJ9RYHuYjJSMlFspSVLG\n8nbFsjALJZdxURcsbcelzSM1ktAlj6USXFulxii/Vb5oBdRms+lWws9I14qRxgKktzCZYDJUtC7Y\n5MAe6pY0m83w+XwlvbhLfSPRc7VYLHC73Ya6kRn5Uy2/Y6oSftoAIlbCrzgwlyxDJVNupba8nc1m\nG/FYRrUws/mMXM+VwSgn9F7VCmgllvAzmhcglYXJaskyAMTdj0NDQ6rbR5tbmc4tmQ16uVmKWQOW\nkkspP+1iopCxFdu1ayTXMaM0ZFPCjwoofd4oAmqUcaSDVfqpcbSBPZIkJaw+RVFEKBTKyy1ZjAu/\nmBO/1gVrpFJ+jOqj1AKVLgJXkiQAcRFgEbjDYVGyjARSuWDpfghNztdGhuYKPZ7RLUwa8VupLlhm\nRTJyQbu/KUkSXC5XQhNtoDw9QAkhhu/wI8ty3k0kjAgTzCzIlFtppPqoyRRjTzSbiN906LkgYDDK\nRaYauLXcRDvdvV1N9zsTzBHIlFtJU0kcDkdOLarSoaflo7eFSYsumEymkkf8phqPnsdjMLIhnSCM\n1ES7EgKIikE1Lo6ZYGYgU3m7cDgMSZLUSjZGRE9hGRoaKlnRhVJBA7RkWVZdbtV4kzNKB50nStFE\n22jXajoXsZHGWChMMFOQKbdSW97OZrPp6m4xmoVJXbAAdKl7a6S9Q0II/H4/rFar2kJKuz8NgAV0\nMAomUwm/XJpoVypGud/1gglmEulyK4Hh5e1oyS290FtQCjmW1gULIOf9ymJSyHnRvFEAcLlcMJlM\nEEVRXQwIgqC+jv67XAEdjDhGs6QKIVMJv+QauDRdLd25G+0aSf6djDY+PTDOLGgA8ilvZ9SLopAJ\nhrYeowsDGshgBAo5L23eKABYrVb139rjU5dackm1Uu1HVYs4VBvFEO5MAUSRSARA5gWbka8VURQr\nMoo+E0wwkblvpTbfMDnYpRg3TzktTLo3K4piwsJAr+jWcrpktRazx+PB4OBgytcljzE5oCPVfhTd\n/6w2dxqj9IzURBuAan0adbFOqbbC6wATzIzl7WhuZbp8QyPtySWT68SdqfVYpZNsMedLpv0orTtN\nK6AMRiFkWrDROcooNXCTF9V0+6qaqGnB1CYeJ+dWZlverhr2MKmg2O12XdJjikku35G2WbfWYtYz\nsCqbfDxtS6lKoZr2DfWi3Ivj5AUb7TerXfQbqYl2tVX5AWpUMDPlVoqiiGAwCJvNNmJ5OyOXs8tG\nWLSCkikKVi8hL6VFrigKgsEgAKRs1g3oPwFm4741ymRWKZRbpJIx0m9GBZTneVU4y9lEO5WFyVyy\nFU6m3MpIJIJoNJp1CoXRk+gzjY0KCsdxVeeCpfvONEe2HJNctu7bbKIhGcYSKqOQLFCZInDL0USb\n7WFWMIQQiKIIURRVS4AiyzJCoRA4jsu5vJ1RXbKZbgRqRWfrgjXyXm0ygiCorqB8a/oWg1STmSRJ\nKaMhmTuUoQfaa07PJtrpSL5uq615NFAjgknFMhQKgRCS4FcvZP+u0ia1fKxoPdFDeNMdQ1t9KZ+2\naqWG4zhYLJaU5dRkWVbP0wjBHIw4RlrI5HMflbqJdrX1wgRqQDBpojot20Tz7tKlUORCMVyyxdrD\n1O7p5VMk3sgWpjZlJNdWY0aYAJPLqdGCCRzH1UQ1GEb+5Hsd0GtOrybadH5ge5gVDr0w6B9dyYdC\nIXWCrab9u1RoA5ny2dMz8uScnDKSy1iNugjQCmhyMEc5qw8xqpdUe+7JTbRzXbQxC7NC0Vpbsiwj\nEAjA6XTCarUWJAbFsDAVRdH1WJFIBIIglMUFm2pMen1f2jzZfM4t3e+u52+gF5n2oorhSjMSRnKD\nGolify+5BK3R1ySPRxAEjB07tmhjLAc1sTSlk2A0GoUsy/B6vbp03ShGMIyegkLdKz6fryCxNFLQ\nDx1LMBjU5dwqEepGs9vtcLlc6t473acPh8OIxWKQZdkwv1s1UMviTRdlNpsNTqcTLpcLZrMZiqKo\ngXbawEog98IFgiBg/vz5mD17Ns4++2w8/PDDKV9333334ayzzsKsWbPw2Wef6XJ+2VITFqYoiggE\nAsNWRHqh142k180oiiLC4TAAwOPxVNVNLkkSgLhoOJ3Oqjq3fGDVhxjlQBu0BsTnHLpIe/rpp/Gb\n3/wG06ZNgyRJWLBgARobG0c8pt1ux4YNG+B0OiFJEhYtWoQtW7Zg0aJF6mvWrVuHtrY2HDx4EJ98\n8gnuuecebNu2rWjnmUxN3D20vJ3eZZqMVkuWRsHSlBG93HNGKVwQjUbVwCWXy1XzYpmKZEvA6XTC\nbDar6SuhUAiCIECSJGZ9VjBGs3bpnqbdbsd9992HV155BR6PBx9++CGmTp2Kc889Fw888MCwZgfJ\n0CAhKr4NDQ0Jz69duxZ33HEHAGD+/PkYHBzEyZMni3NSKagJwfR4POp+pd6ThFHclTQKNhaLwev1\nqpFv1QDtMhKJROB2u9XH9DhutaN13zqdTtjtdvA8n+C+pVsVtfB9MIqDVsB5nsfMmTPh8/nwn//5\nn+jt7cU9iSOEAAAgAElEQVSvf/1rTJ06dUTvnqIomD17NsaOHYvly5fj7LPPTnj++PHjGD9+vPr/\nlpYWdHZ26n9CaagJlyz9IY0ibunId3zajiq0nB91XVY6xapIZKTVeanIJZCD53lD3yvlwGhWndGh\naSVmsxnz58/H/PnzR3wPz/PYtWsX/H4/LrvsMmzcuBHLli1LeE3ydVnK36QmLMxiUk4RppGiNOpX\n66YsZk5nqY4jiiL8fr+6EGD7b/qSKpDDYrGoAXJAfDuDlpIsB0ykUlMJ30shxdd9Ph+uvPJK7Nix\nI+HxcePGoaOjQ/1/Z2cnxo0bV9A4c6GmZiCju2RzORZ1U0ajUXi9XkOVgSsUuhAIBoNwu91lqwdb\na1ABtdvtahS5yWSCJEkIh8Oq+5btfzKSSSXgueZh9vb2qn1qI5EI1q9fjzlz5iS85pprrsFvf/tb\nAMC2bdtQV1dX0tSVmnDJUrSF1o04AWcrmFoXbLrKNka0MIGv3ClHjoD/85/BdXaCzJgB5dJLga82\n9+lCgKb/pNrzoOPR6zc04rVQbmjxBG35Ppr/WavVh2i1MEZ25FpLtru7G3fccQcURYGiKLjttttw\n8cUX45lnngEArFq1CitWrMC6deswZcoUuFwuvPDCC8UafkpqQjCTK/rrPdmWarVNy/zRC7GQZsjl\ngOM48Nu3w/z44/EHHA5wu3aBX7sW0r/9G+SmJgQCgYwLAT1JdR0YfZ+7XGj3P1n1ofJjtEV/qvHQ\n/pzZcs4552Dnzp3DHl+1alXC///jP/4jv0HqALuqC6RUllyyC3YksdR74tflWKII+3/9F+DzAc3N\nQH09MG4cEA4Dzz2HoaEhNRm/2JOBkSabSkS7/+lyueBwOGAymSDLMsLhsHqtMvdt7UK9FNVEzQlm\nJVoQkiTB7/cDQM6dOPQK1tED/sgRIBQCNPsaBIBUXw98/DE8X6U9MCoPVn2otBjdwqzW37hmXbJ6\nHruYFmY0GlXDs3NxwRrpZqJwFgugOT9CCERJAhQFVpsNyLLEXSUuemoJPasPGUkYjDSWSqAav6+a\nEEwtRhZMCj1eoe3H9EKvguTKhAlQGhthGhiAUlcHURRh4nmY+/qgfOMbQJn2varxxjYS2uLxANI2\nMqYCyn6LyqNWAqKq/wwrCDpRyLKMoaEhEELg8/nyFkvDWWI8j/APfgBZUaAcOQLLqVMwnzgBTJgA\n5bbbyjYsNkGXFlZ9qHCMvsirVgFlFqbBjgcAgUAgr/6OxULPcxQnToT/8cfh+eILoKcH8uTJIPPm\nASXOI9U7WpqRH9m4b4F4bdFaSV+pRJLvpWg0WlW54ZSaEMxK2MMkhKgdRlwuly4Xm5EsTG36gWfc\nOHAtLcjXyWuk82LoS7L7lraLUhRFbRtVrvQVtsDKHhp3UW3UhGBqMeJkK8sygsGgunrWu/1YuYnF\nYgiFQmrZNTbpMLKFpibY7XYQQlQLVJIkRKNR8DyfIKC1cm0ZzeWZvJgwomDS4hv0e8vnWqk5wdSb\nQgNiqJhQFyxNH9FrbOVsy0VL3AmCALfbrRZeMDJGXFAVg6gUhc1s/MIX2omYiifdA2XVh4xLrlV+\nSsGHH36ILVu2YPTo0bDZbHA4HLDb7XA4HCCE4JxzzhmxLm3NCaZRXLK0d2UsFkuIgq2WCVtRFIRC\nITVwied5NSLSaNSaq21QGMTuk7txYcuFsJpSu/4r4RocqfoQIUSNvNXDfWuk78Ro12zyeEKhkOEE\nk/bO9Pv96OvrU3vDAvEi7o899hjGjRuX0QCqCcEs5h5mPmhdsHq2rEqmXBamttat0+nUPaFZj/PS\nHsNIE08paBtoQ3+0H51DnTiz/sy0r6u070W7/2mz2RLSV6LRqPp8Ie7bSvtOyoURXbITJ07EDTfc\ngGnTpqGpqSnt6zLNxzUhmMmU08KkLli73a5WQynkeEYjU6EFNtmUn0FhED3hHjS7mtE22IYWb0ta\nKzMVoizi81OfY27TXMP/njzPp3TfiqIIQRDA83xC8QSjn4+RSbYwC2ntVSy++OILvPbaaxg9ejTO\nPvtszJs3D62traivr4fH48nKcKkZwSxmGkE2Aqd1wbrdbljSVLUxdJeREZ43SqEFRnraBtpgN9th\n4k0gICNamcns69uHNw68gVHOUZjom1i8gepMrtWHjF4H1Wgu2WSMaGGuXLkSK1euxIkTJ/DWW29h\n9erViEQimDdvHi6//HLMmTMHHo8n4zGME2ZVIoqxhzkSiqIgEAhAkiR4vd60YmlURjpHRVEwNDQE\nRVHg9XrTimUpBbyUpBuKgYYI4LR16bHGJ4U6ax3aBtsQk7PbWxZlER8e+xAemwcfHv3QUL9BrmiL\nxzudTjidTpjNZsiyjEgkgnA4DEEQEorHG12kykWq68CIgkkIgSiKaGpqwl133YU1a9bgz3/+My65\n5BL88Y9/xHnnnYddu3ZlPEbNmQGlDvoRRRHBYDCtC7aY4yuFezfX89MDI01asRjw4x/bsGpVDC0t\np7/r9etN6OricccdYhlHlwi1Lim5Wpn7+vYhEAug1duKo0NHcXToaNGtzFKJVDbuWyAef1BL6Su5\nkJxW4vV6yzia1FgsFnz88ceQZRl2ux1utxvnnHMOFi1apC6iMlEzFmYxK+mnOh51wQaDQbjdbjgc\njoq9yVIJb6Wfn16LCasV+MY3JPz0pzZ0dsbPf/16E9asMePyy6X8xzc4CH7bNpjeew/8gQOAlP+x\nAECQBARiAUTlKHojveofIQQnQidGfD+1Lsc4xgAAfFZfxVuZ6aDuW6vVCofDAZfLpe7H0xgEur1S\nrvJ9Rrd2BUEw3B4mjX594IEHsHTpUnzzm9/E8uXLMX78eDQ1NWHZsmX4+OOPMx6jJi3MYh9PURQE\ng0EAUFMqcjme0S1MQgiCwaDqgs220EKlBzSlY/FiGQDw05/asGCBjE8/5fHP/xzD2LH5nSvf3g77\nmjXgTSbAYgG3fTuUlhZIN98M5Nn+zG62Y1nrMhAMHxOHke8Jal3We+sBAHX2upJZmeVGW0zE4XAA\ngFo8odzVh4xAKvGmueVGgv4uV111FR555BFcffXVAIANGzbgnXfewaJFi/Dwww/jlVdeSX+MkozU\nQBTbJSuKIvx+P8xmc9aRV8kYTVS05yjLMvx+v5oSU46qRMN+Q1kGIpG8Ng1jsRgikYha7BvI7/tf\nvFhGY6OCd94x4bvfFfMWS8RisL79NpT6epBx40AaG6FMmADu+HHwKbrR5wLHceA5ftjfSItIURax\n8ehGAMCp8Cn1T5KlqrUyk9GmIFHXHe39SZtnS5KEcDisFo+v5ebZRoySpTz//PM477zz1P8vX74c\n69atwxVXXIFYLJbRLVuTFmYxXLLaqjaF1ILV0wLW+1ypOyrX3pxFQ5LAr1sH7s9/BhcOgzQ3Q7nx\nRpDZs0d8KyFEdanRkn3aYt+iKOZkLaxfb0JfH4cbb5Tw619b8eij0YQ9zWzhurrAxWIgSatz0tgI\n065dUBYuzPmYhaIQBbMaZ0FUhu/HavdEa5V0+5/FrD5kJJdsqrEYUTDpGL/xjW/gv/7rv3DNNdeg\nrq4OW7ZsQX19vbroydTEvmYEs1h7mPS4wWAQhJCCrS4jui3pgiAcDheUMqL3ufGvvAJ+/XqQ5maQ\nUaOAoSGYfvlLyP/4jyAzZ6Z9HyEEkiSB4zh4PB713wDUKMlckt3pniV1w44dS/DTn9ryFk2jYTPb\nsGzCsrJ8ttHuhZEodfUho2LESj+UX/7yl/jhD3+I22+/HYIg4Pzzz8fLL7+MWCyGJ554Am63O+17\na0YwKXqvyqgbj+f5YVVtyo0eAkVL3AEoalWinBkYAL9hA0hrK0AXKD4fiKKAe/PNtIIpyzICgQA4\njoPNZht2PsnFvtNZC2azGRzHQRQ5bNtmStizpHuaH35owt/8TW7BOqS5GbBY4i5mTU4Y19MDefHi\nnI5VLRjpnsqVTNWHaKnIXKsPGW0Rkc7CNKpg2mw2/OpXv0r53KJFizK+tyYFU692XNFoFJFIBAB0\nE8tCi7nrCW3oa7VaIUmSYcSS4zhwp06B8PxpsaTU1YE/cgQKIUDS76EtdC/LclYpPqmsBUmS1N/d\nZDLh4Yelr7wKp49HRTNnrFbErroK9jVrwA0NAVYruEgEyrhxUDT7LozSopcLVM/qQ0ZeSEQikYyW\nWrmh0c3agv7ZUHOCSSnkBqBWF40SHRoaMtSeAiVf8dUuBuh+LHUnFXKOuhYu8PnA0ZB+7ZiCQZAx\nYxIeS+6aYrFYVKs5F7TWAnVT00hJPUutKZMnQ/jud2E7fBhcIADS2grlzDPjlmeJMJoVU41US/Wh\ndFGyRrUwAeS9bVYzgqltEVQI2sLibrdbvYiNngqSLYQQhEIhyLJctijYbCBjx0KZPRv8rl0gLS0A\nz8erCPT2Qlm16vTrNCkwuab4ZIL+7jS4K9VkV8heFamvhzJ2rC5jzRcjTs7VjHZBBiCl+7ZS9j0F\nQTBcWomWoaEhiKIIi8UCs9kMi8WSVQW2mhFMLfnUldVaXcWOEi2X+NIuKiaTCV6v1/ATpnLnncCL\nL4L/y1/i7lmzGcqtt4JceCGA0p5PpsmulhsdVwvl8CAlu2+pR0P6qohFOBw2xDWV7rsxorCLooit\nW7fivffeQywWA8dxkCQJ48aNwwMPPDDi71zTgpktI1ldeluY5SC5kXW6LiqFumSBwicf9ft2uaD8\n3d9BGRgAAgGgsVFN7Kcl+9KdDx1HsUi1VyVJUtrgoeTxEULQH+lHg6OhaGOsBIy41VEOtB4Ns9mM\ncDgMm82W8poySveVcn++FnodHThwAHfddRe+853vYOrUqRBFEeFwGGPGjMnqODUjmNofLxeB07pg\n01kpRnXJZnOsbLuoGJr6+vgfEj0BI3WFSfe43kKaaq9KkiQ1hQVAwl4VEC8QsKNnBy6deCncVuMG\nT5QCI028RoAQkuCxoI+Vq/pQ8qKGWsNGJBQKYcGCBXjkkUdSPj/StVYzgpkPmXo7JmPUCyQTNHiJ\n5o9muqnKvbeaDZWy/8pxnLpnkipSEgA+P/U5wmIYB/oPYG7T3DKPmGF00m0JSJJUti0BIy50LBYL\n/H4/3njjDcyYMUOtF+z1erPac61JwRxp8qcTL23HNdLEa9TqPJmORS1nesGU6uLWw7ULDF+gFLJf\nWc4bO5X1eaz/GAaEAYxxjMFfT/4Vra5W+Oy+ign4qEaM5BrOZn4oZfUhavFqMcp3RaG/HyEEbW1t\nePDBBzFq1CgAQE9PD6699lo8+eSTajeadDDBTEI78fp8vqx++EqwvrRQyzmXEn5GOsfksZSjxVgx\n2du3F3WOOrgcLoRJGIeHDmOmeSYLHmKo5LogLGX1ISMtLij0nObOnYt9+/YBON0fk1b0AkZON6kZ\nwcxmD5MKSaZAkWJTTAuTlrcTRdHQLstsSZUvmuv7jcap8Cn4o340uZsAAKMco3AscAzTx0yHy+5K\nGTykzf1kMEaiGNWHtAiCYIxa0xp27doFQgjGjBmDDRs2oLGxUW3dZrVa0dzcnFWhhZoRzExohSSf\nWqlGDfrRQi1nnueztpyLgZ6VlsLhcNZu82KNQ08IIdhzag9sJhticgyEi4+PgOBg/0HMaZqTNnhI\nj4nOiBjRWik3en8n2VQfoguzVO7b5PFQ75WR6OrqAsdx8Pv9+PnPf45Ro0ap+dmdnZ148MEH8fDD\nD0OSJNatJBntZJmPCzbT8YwEHZceLkujnSNtw1MJ+aLZEpWjsJgssJltkIkMnsQtRp/Nh4gcGfb6\nkYKHjJZmUMnUinBnW31ImxKVDPXSGQVCCFasWKH+f+/evWlfO5KxVNOCOVLuYTnHpgc0vDsYDFZu\nykgSkiSpq15aaalQjDIZ2s12LG1dimg0CgA5ubUyTXQ08nakic6oyIoMc21OVWUnU0EOmhKlTXPh\nOE7NLDAKHBcvEcrzPGRZVv9NnwOyL7JQM5seyRNELBZT21UVGihSDOtLjy4jkUgEhBD4fL6CxVKv\ncyzkONFoFIFAQC1nVUmTfqmhE53NZhvW5DgUCqlNjmkRaqMyGB3E5s7NkJTcur5UM+Vc4FHXrd1u\nh9PpVCPsZVlGe3s7Fi1ahGeeeUa9vrKlo6MDy5cvx4wZMzBz5syU3UQ2btwIn8+HOXPmYM6cOfjJ\nT36S07iB+KLRYrEkeF5y2fuvuWWbts+hXu2q6ApGD/S4EWjKCF0VVnowSHKwkiiKalu1Qo+bjF7V\niIxGuspDqYp88zxfdBFViAKeG/m6PNB/AN2hbnQGOjHRN7GoY8qEkRcV5UJbocput6O1tRU/+9nP\n8Oabb2LLli0YM2YMFi5ciEsvvRS33XYbGhsb0x7LYrHgiSeewOzZsxEMBnHeeefh0ksvxfTp0xNe\nt3TpUqxduzbnsdL7+fDhw6ivr0ddXZ1qaQqCAKvVmtU8WdkzaY6IooihoSHVxVDpQpIKaoXRCLBK\nR1EUBAIBtTMMdfsUOoEZWQzTjU0Ugfb24c/19nLo68s9zcBms8HpdMLpdMJsNqtuNpqDXKyKLbIi\n46mdT6F9oD3j6waEAZwInUCLuwX7eveV3co08jVjBKxWKxYvXozLLrsMd911F44ePYq7774bbW1t\nI3YHampqwuzZswEAbrcb06dPR1dX17DX5Xs90t/uySefxJ49ewCcNiR+9KMfYcOGDVkdv/oUIw20\nxZPb7c45/WAk9HbJ5nM8WmwhEonA4/HoHtZdDpesJEnqAsftdhdlgUNXyZVgQXAc0NPD44svTn8P\nvb0cdu/mwfP5jz/ZzUa3KGglKFo6US/37d7evdh9ajfWta/LeLz9ffthN9thMVkQk2PoDHQW/NnV\ngNG8H8njoQ0q6uvrcf311+Opp57CpEmTsj7ekSNH8Nlnn2H+/PkJj3Mch48++gizZs3CihUr1HzK\nbGhra8N7772HzZs3Y+vWrfjiiy+wc+dO+P1+7NmzR/VYjXR914xLlrpgabBPuQVOz+MpioJgMJjW\nzWy0GywbotEoIl1d8OzaBcuRI0BLC5QlS4Ayt7wqBn19cQtx6tREt/7u3TymTFFAHQVmMzBvnowd\nO0z44gseY8cSfP45j7lzZVpKNy37+/bDY/Wg2dOc8XXa4CGe52G1WtPWKM1nH1lWZLzd/jbGe8bj\n0OAhtA+2Y0r9lGGvGxAG0B3sRp21DgBQb6/Hvt59aPG0wMzXzLRVkRSSVhIMBnHjjTfiySefHJYX\nOXfuXHR0dMDpdOKdd97BddddhwMHDmR13K6uLjz77LPo6OjA888/jxdeeAGxWAyBQADXXnutat2O\ndD3XjIUJVKdLRRRF+P1+tT+nViyNWrIvE3S/MnroEBp+/nPYXn8d3Jdfgl+7FuaHHgL35ZdFH0Op\niUSA114z48svT/92n3xixvr1ZiTHTVDR7OjgsGMHjzlzRhZLQRKwvXs7Pun+BArJba+dBg9pgzx4\nns87eGhv7170hHrgtXnhtXrxTvs7Kd+3v28/nJbTkZZWkxWiLJbNyjTSotNoY0km3yhZURRxww03\nYOXKlbjuuuuGPe/xeNTjXnHFFRBFEf39/Vkde8mSJXj11Vfxhz/8AQcOHMD+/ftx+PBh9Pb24rnn\nnlP3V1nx9RSU2yLU43jZVrnRq3arXmQ6N2opA0Dd2rXgRBGktRUAQEaNAvx+8M88A+5nPzOWC3Vg\nAHxbGzAwAJxxBpTJk4EcJoyWFoLbbhPx0ksWXH+9hJ4eMz76yIy7746hIUV3r8FBDjwPKArQ1cWj\noSGzCB7sPwgAGBKG0DHUgQm+CTmdHoW6r/Ntmk2ty3p7XOEbHA0prcyQGEJfpA8ykTEUHYJFtIA3\n8VCg4MjgkbIG/zBSk+ySHTduXE7vJ4TgzjvvxNlnn437778/5WtOnjyJxsZGcByH7du3gxCChlQ3\nSJrjy7KMBQsW4M0330Rvb6+6EOQ4DldffXVWIs8E04DHG4lydOUo9jlq26g5eR78nj0gLS2JL/L5\nwHV0gOvuBkaPLujzdNuTPX4c5nfeATGbQex2cB0d4D//HNJVVwE+X9bHGT8+LppPP22FLPO4//4w\nGhqGL4LonuUFF8jweKC6Z2fOTC2agiTg81OfY5RjFKJyFDtP7sR47/isIlRHIlOOXqq6t9S61Ao2\ntTK/f9731UnXZXHhkomXAADCkTBsVpt6jesxboZ+pFqM51O4YOvWrfjd736Hc889F3PmzAEA/Mu/\n/AuOHTsGAFi1ahVee+01PPXUUzCbzXA6nXjllVdy+gyz2Yy7774be/fuxZQpU2A2myFJEvr6+nDJ\nJZcwwUyH0YM8si0On02VG6OfK3C6ebXaRi0aBXgekCRwnZ3gjhwBJAmkqQlwOACj1MCVZZg2bYJS\nV6dalMTrBdfbC37nTijLl8fPhRC1sXUmjh8/LQanTvFoakp8PhKJ72tq9yzpnuaRIxwmThz+Ox/s\nPwgFCsy8GWbejO5Ad0FWZiZG6pDxwaEP4sE7Q50Ad/raPOI/gs5AJ8Z7x6vHspnjQWuySYbNbCvJ\norBSMJLHKBW0F20uLFq0aMTUvHvvvRf33ntvIUPDp59+qkbJ5kNNCWaxLrJSiZIRKxPliva7IuR0\n8+qEGr42G5R588C//DK4cDguRhYLuPZ2EI8HxGYzxiJgcBCnAidgPWM8tLYkaWgAv28fuGgU/LFj\nACFQWlogL1yIdBuO27aZsGWLCQ8+GMXgoISXX7bBaiWYNu30JOJwAIsWydAGQNM9zVSXgta6pHjt\nXl2tzHSk6pDxNzP/BqFoSI1I1FqfY12pg7mMIg6pWlgx0luYRqr0A5ye+8866yysWbMG5557Lux2\nO2w2GywWCzweT1bHqSnBpBjdJZuqy0hKYSnh2PQ+R1qyj5DUzavJhReCe/bZuKUZjYIoCuByAS0t\nMG/ZAlx6qW5jSSbbfV+FA14Wd2B04ARW1i09/UQsBtPevVCcTpAzzogfs68P5rfegnTjjcP2N/ft\n47Fliwnf/W58z9LtVvDtbwv44x9d+Nu/FdHcfPp7T5UtlO5yaB9shz/mh0wSizyExBC6gl1o8bSk\nfmMR4DgOY9xjMMY9Rs3vpIXjZVlGVIgmFE4wgkgy8sOIgklpaGjAgw8+iIULF6pGByEEzz77bFbX\nHBNMA6IdnzYQRq/KROVGURQ1stfpdKa+UP1+kHPOAbHZwA0NAS5X3CUrCOB37y6qYGbLQdKL4zYB\nXf796HadgzMs8QAEfv9+EJ8PZMyY0y9uaAC6usAfPgxlxoyE45x1loK77oolbHm2tChYtUpEQ0P+\n12mzuxnnNp6LUDSE6WMSK6bU2eryPm6h5BI8ZOT7tFwYxeoGUkfJ0kBEI/Kd73wH9913H06dOoVY\nLAZJkqAoStbfZ00KJkWvC69YAkwDYaxWq1qzMR/0sjD1KP+nKApisZiaIJ8Wlyu+VzluHIg24m5g\nAJgyPG8vVwr9zRSi4J1D78B35kyIB/+KD45vwkr7AnCKAtjtIKmCkhwOoKcHhwYPoc5WhwZHXGAt\nltTxQaNGFfa7+Ww+bDi6AQPCAJZOWAq7eeR91Gxp62+D3WLXxUpNFzwkSfHKPpFIpOrallUTqVyy\nRhXM888/H1u3bgUArFixQs3Lz5bKN1dygP6wxbrh9OyJKYoiAoGAWros3zEbZXKhbmVJkmCz2TKL\nJQBy7rkgLhfg959+MBYDF4lA/vrXy255HOw/iO5gN3x1Y9Ew5yLsGmfC8VlnQrriCkhXXhl3IScj\nCIjVe/H0Z0/jzQNvFn2M+3r3oXOoE2ExjO1d2xOeU4iC3+75LTqGOnI+riAJONB/oGjl6mjgEI20\npJYo3cOn2xOKopTsOjCaVWeUsaSCVvoxGoIg4Mc//jEeeOAB3H333QCADz74ANdeey2A7ObvmhJM\nLXpahXpevLR/JS00rkcZv3KLC02DicVisFqt2UU8ulxQfvjD+L87OuLpJD09kG+/HSSpIHOpodYl\ndWvyZgtsDY14394FMm4cyIQJcQu5r+/0mwYHAasV21wDGIoOYceJHegKDK+VqecY3zjwBnw2H8Y4\nxmDNwTUQJEF9vm2gDTtO7MD6w+tTvj/TNXPMfwzggJgcQ1eweOcADK9763K5YDab1W484XAYgiCo\ntW8ZpSWVeAuCYKh+mJQTJ07g/fffx2effYaxX1UMmzFjBnp7ewEwwcyIEQN/ZFnG0NAQgHgvRD1C\n6fUS83zPT3tOdA822+OQKVMgP/EElIcegvzAA5D+7/8FueyynMegNwf7D+KY/xgsJgvCYhhhMQy3\nxY2dJ3eiO9gN2GzxPMxRo8B1dYHr6gLcbkSu+Abe6vgzGp2NsJqsePfQu+ox2wfa8Xb727qNkVqX\nPpsPDosDITGkWpkKUfBu+7todjfj4MBBHBs6lvIYqa4dQRLQNtCGOlsdfDYfvuz7sqhF0ZMnZI7j\nhtW95XkeoiiqlYf0rHvLyA8jxlpIkoTRo0fj4MGDarvDI0eO5GQN19QeZvKNZyTBFEURwWAQDocD\nsiyXXej0QHtOeafBWCwgZ5+t/+A05Pod9UX60OprHfZ4i6cF/ZF+nOE+A6iri4tmOBzPw3S58EnH\nFgRiAdR762E327HjxA5cfublaHI34e22t9ER6MD5TeejzlJYQI7WuqTfObUyL2i+AMeGjqE71I0J\nvgkQFRHvHX4P35313ayOTa1LE2+CCSb4o350BbvQ6h3+fRQbbeoKkLlpdqrKQ7lgJPE1kks21ViM\n9F1pqa+vx+LFi/H0009DFEWsW7cOL7zwAm666aasj1FTgmlEaBcV2knFYrGojZ+NRC6ioi3bR88p\n+flSjSUT+R5jYctCLGxZmN2Lv1q9xuQY3mp7C06zEzu6d2Bu01zVylw0fhG6Q91wWVz4sONDXHvm\ntXmNi/LX3r9if99+1NvrIQRPu2F7I73YdnwbPj/1OerscVEe7RiNtoE2HBs6NqLoqdal/bSgUyuz\n2dAFM4IAACAASURBVN0MM+GAUCgexZTkkjs8eBjjveOLWjhdGzxks9kSgoe0lYdoa79cRccoImVk\njDZvUQghGDVqFG699Va1duwzzzyDlStX4qabbso6z7ZmBdMIFiZtn0QIgc/nU38wvSJS8x1XIYxU\nts8oKT2lnvy2d23HidAJ+AU/jg0dg9fmxRjnGGzt3IquQBfq7HXwWD347ORnuLDpQoxxjBn5oGmw\nm+345te+mfK5QCygWpdA/HtwWpxZWZndwW4IsoABYSDh8agcRe+hPRh3tB+QJEBRQBobocycCdhs\n6Iv0YcPRDVjWugxn1p+Z93nlSq5NsysBI9w7WlJZmDRtyEhwHIeDBw9i06ZNeOyxx9THu7u78frr\nr+OGG27IynJngqkjuRwvoXZqAVGwpSKb7yvXsn1GpRjXRkyOwcpbcTJ0Ei3eFvSEe3DOmHNgM9lw\nPHQcsxpnAQCsvBWbOzbj+qnXA7IMxGLxsno5fJeT6ydjcv3klM89t/s5iIqI44Hj6mN+wY+QGMKJ\n4Ak0uZtSvg8AznCfkWBdUrj+AXh27gEZPfZ0FYX+fvC7dkGZPx+7T+6G1WzFzpM70eprLUt7rlTu\nW1o4gaYVVFLqipHHZzRRj0QiGBoawhtvvIEPPvgAt9xyC7q7u9Ha2op3330Xf/rTn3DDDTeoi6hM\n1JRgFtPXnssFHI1G1WoYqRo9F6OqTrGh+5V2mw32EfYrjXZDlYJZY2fhD/v+gNHO0Vgyfgm6Al1Y\n1LIIHx//GC7r6Zy1Rlcjdp/8DMu67Wjdcyhei7auDvLXvw5l2rSsP09WZGzv2o4F4xYk/BbXnHUN\nLp10uuhDd7Ab/7P/f3Dd1Osw2pm5oL3dbE+Zy8kfbwM8dYklh+rrwfX0oO9UvE5ss6cZ3cFuHPMf\ny9rKLOZ1QoOHkuveiqIIQRCG7X0aad/QSCS7MhVFMZy1HggEsGbNGrz22mvo7e3FI488gmAwCJ7n\nceTIESxbtizrY9WUYGrR++LPtiVXOByGKIo5l7grZFzFRhAECN3d8L31FqxbtgCKAmXhQigrVwJf\n9ZnTczxGcetq6evjhhUakCRgaChe5Gfj0Y3oCnbBa/OiJ9SDUY5R+P2+34OAwG11IxALqO+LHGvH\np8c70HrGxYDVCgSDML/6KsRvfxsky6INu07uwnOfP4d6Rz2mjTottGOcia7ezR2bYTfb0T7QjvnN\n85MPkxVcOAySqmYfz+PzE7vgtMb3cevt9XlZmcW+hrMJHqIWqcViYcKZASOmlHg8Hlx++eUQBAH9\n/f249dZbcfLkSUiShEmTJmHSpEkAkFVWQk0LZildsrTEHcdxI5a4M6KFmWpMdAEgBYNoWL0a/IkT\nwNixAMeB//hj8H/9K6THHweyLGxcauj50By+fF1xsRjw059acc01EpYtk786JvDUUxa4XMC1t57A\nnw7/CS6LC26LG53BTsxxzYGkSPj6hK9jxhhNqbxwGPzmF+EeMy4ulgDgdoMoCkwbN0LKQjBlRcba\ntrWwmW1Yc3ANvtbwtZTn1THUgcODhzG5bjIODR5CZ6Azr8o9SkMDuFOnEssVEYLe2CA6xACavRMB\nxC3UAWEgJyuzHKQKHgqHw6r7ttDgoUIwmqWbPB4j1pF1OBxobW3Ffffdh2AwiPb2dowdOxZOpxM8\nz+c0ZmPZzkWm2Gkl6RBFUa2d6na7s3JZGLGoghZFURAIBKAoCnwHDoA/fhwYNy7uljOZgOZmoK8P\n/KZN8TdEIuC2boX5f/4Hpo8/BgQh8wcUGfr7h0IhCIKgVpERBEEtDp4tVivwf/5PDK+sGcJb7/lV\nsYxGOdx2m4gtHVtwPHgcMpExGB3EgDCAL3q+AAAcHDiIqQ1T1b+v8Y2YSkahyZLU1cTrBX/yZLxr\ntIZtx7fhqP9owmO7Tu5Cb7gXrd5WHPUfxf7+/SnHvaljEzxWDziOg9vqxqZjm7I+Zy1k4kRwkgR8\nVfMYogiupwe7vGFIFh6DwqD6x3M8/nLyL0XN3dQbKoo2my2hWXs0GlUrD4miqFugXiVD2/QZCfq7\nbN++Hffddx/uvvtu3HzzzVixYgVmzJiB3/zmNwCgdtHJRM1amID+e5ipLLBM6RWZjqUneluYNGCJ\nlrjjDxyIpxIk43AA+/YB550H009/Cq63F+A4mCUJprVrIf/TPw1z2WY7lkKh+1aKosDlcqkiSdMQ\nBEHIyZJoaiLwXf44fv4hjz+++P9h9iyCH/wghiGpD13BLnzjjOtx4iSPyWfKkBQJ/qgfV0+5Gu1/\nrUNXF6d2JCFuN3hChgkjQiGQ+vp495avCMaCeGbXMziz7kz808J/AsdxqnVZ74gLrtfmTWllUuuS\nRss22BvytzI9HsgLFoA/eBBcXx9gsUCZPh0NjjPgkKMJLz15kgNRTJAVWXXLyjLwl7/wOOccJTkb\nxTBQSypd3VtaOJ4+X6zgIaNbmJFIxHAuWbqv+uKLL8LlcuGTTz5Rn9MujplLNgXa1k3FdMmOlF6R\nzRj1QO+biwYsaVfaaGqKz3rDXww0N4N/7jkgEACZMAHkq+4Apv5+8M8/D+Whh/IaRyHfjyzLCIVC\nccvK7VZdsjQNQRRF9dxSddAwm83DvteD/QdxIPAZ/C4eQdsBzJ59JiwW4Ghf3Pqz2Ai6ugBR5PC1\naWb4bD7s2+PAiX0TsGSWePpAXi+kmTNh+vxzoLU1bq3HYuBOnYJ0/fUJn/nBkQ8gKiK+7PsS+/v3\nY9qoaaety68KK9Tb61UrU7uXualjE8y8GWExrD5m5s3YdGwTvj3j27l/qT4flHnz4kL/lajPSvGy\ngBfYuNGMznoZkycTyDKwZYsJNltWPbYNx0hNs5ODh4wkdsXAiJ1KqEdv0qRJ8H21bSBJkjon5xKk\nVHOCSdEz15Eej0LTK8xmsyHSK/RK8qf1O5MXAMqFF4L//e/jhdLpPlYwCPA8lLlzYX77bZDx4wF8\ntRgAgMZG8Hv2QBkaArzegseXLdT1arPZIIpi2t+GBoKk6qCRKgn+93tfwfFjdjgswJTrX8Tat34C\nsxlYtORcVahungr84Q8WNJ9S4HQCe/Y6ccft4rBOJeKll4KYTDB98QU4AMRqhbRiRTyv8SuCsSDW\ntK1Bo6MRgVgA//3lf+PhBQ9jbdtaSIqErkAXbOZ4II5M5AQrMybHwHP8sKjY0c7R6vN5M8Lk4/EA\ny5ZJ2LjRDEWR0dXFw2YD5s8f3gTbaIFdI6ENHqJNs+k1I4rxRZE297Pc84IeJFuYwWDQcC5Zeh3V\n19fjpZdeQkdHB2bOnKl6/BYuXKjWlh2JmhbMYrhk6YTscDhG7MhRirHpcVPSoAcgTU/OhgbI//zP\nMD3+OHD8q/w+txvyQw+ld7nScUkSEAqBO3Ag3ii6pQVo0b+xcbJ7nHaEyZZkS0LrhvtrzwG8+eln\ncCvjMHOGgkORXfjHe/fij/8ZF7hly+K3mdMH3Hk78ItfxK3XH/wglrKtF6xWiJddBv6SS8BFIiBe\nL2CxIBIB1q4144YbJHxw5ANE5ShsZhsCgzZ8dGo/9k7Zi2mjpkGQBAzFhjCrYRZEicNAP4cJXjtk\nIsPMmWE1WXGh89tweqL4n2PP46ZpN2GUY1TCEKJSNMXA9MHjARYvlvCnP8W/l5tvltKmmRpBVPK9\nF7XuW+r6o+JJXf65Ns02mks2GSN2KqHfFyEE06dPh9/vx7p16xCLxXD48GGsXr0aY8eOzSolpmYF\nU29ol5FYLFaylJFsKUR8tQUWJElKe0GRadMgPfUUcOQIOEUBmTgxvq9JSNy6HBiI51dQ+vtBJk0C\n19MD/v/9v3ioKcfFX79gAZQbbhjRWsl28lCjeSVJtY61xblTVSrJ9J0l72P977G18LmsmNYiA4TA\nBBP+1P07/PCHP0JnZ+Le7u7dJrUOwaefmnDxxXHLav3h9ZhUNwlT6jVRsE4niGbysViA7m4e//5k\nDAe/Frcu+/o47NptwsSznVjbthbfn/t9nAqdQhNpwsUTL0Y9NwFr15oxo0mBmY97VA4d4rBpkxlN\n52/DR8c/wij7KNw0PbGeZralwvJBluPfQ0MDQSTC4dAhDpMnx7/viBgBAYHTYqxJFyhMvOn+ZzZN\nsyup8lAyRnXJyrKM733ve/je976Hrq4u8DyP+vr6hDz4bL7zyvxVCoBe9HpacbQpsqIo8Hq9BYul\nUSzMaDSKQCAAh8OR3arRZAImTwY566zTQUAcB+Wuu8CJIrjOTmBgAPzx4+AkCfKtt4L/7W/jLtkJ\nE+J7duPHg9u6Fdzu3bqckzaaN9e95Gw42H8Qn53aiekT6sGbAfDxAJrPTn2GPu4LnHNOAIIgQBRF\nfPQRj7/8hceqVTHce28M7e083n/fhJ5QLz47+Rk2HdsEWUkfqWc2A9//fgyHuffxl/29OHJyEB/t\n6UHr9ONwe0XsPLETa9vWwmqyos5eh62dW+HxEFxzjYTdu3ns2cOrYnnp5WFs7ftfnFl3JrYe34q+\nSF/az82WmBzDlo4tUEj6rQ5BAF56yQyLBbjkEhnLl0vYt8+Ebdt4dHZy2Hly57DendUIXXTRtmVO\np1NdyIXDYYTDYUSjUcO3LUtetBoxSpamjO3btw/3338/br31Vlx77bW46aabsHnz5pyOVXOCSdFL\nlCRJwtDQUMJ+llHGRsn1WNQii0Qi8Hg8KasR5XS8KVMg/eu/Qrn6apDJkxFdsQLSv/4rOELiM6h2\nRcrzQEMDuI8/LugzgdO/jdlsVt2werO/fz9cFhf8UT8GhUH4o34ExAA8Ng+OhY8hEnFCFE04ckTB\nxx/LuPHGITgcMfT1KVi5Mi6ab+z4FC6LCwPCANoH2jN+ntkM3H5jHcZ034qeT76Bb89fimvPWYav\n++ZiRXg8jm5ei8ajPfDKFpwKn4rXrfUC11wjYfNmE95914wrr5TQIf8FQ9EheKwemDgTPjjyQcHf\nxb6efXj30Lto629L+5ojR3goCnWRxd2z06fL+POfzRgS+3HUfxRdwS5dBLySoO5+u90Ol8uldvdJ\nbpqdTepDqUg1rxjRwqSxKj/60Y/Q0NCA119/HR9//DHuvPNOPProo2hvj99z2cyTxvEblphCRUm7\nJ0bTEnLZEysVuYqEtiB8qv3KvPdQGhuh3HJL3G0dicDu9cb3OzkO6O0F9+WXcbetzxd34brduX+G\nBjrRpCs/qBdXTbkKV025atjjoizCYrLgnXdMOHjQirvvFvF3f0dgsViweTOHDRtM+MEPgrj02uN4\n7dAXaHWMR0SKYEvnFoz/2njwXPqFV0PwIkzyLwfHAbZ9Cla27IJ9w+t43xaFYrHAtP8gcPgIfAvm\nYmvnVrR6W9Hbe/p4HV0i3va/rQb9jHWNxftH38cE3wRc0HxBXt9DTI5hc+dmjHGOwcaOjZjSMCXl\nOXztawqmTlXw6ac8PvnEhAkTFBw4wOPee2P4MrIPNpMNPMdjz6k9WDJ+SV5j0ZtS7xtmCh6SpHj+\nqiAIhgkeMnpaCR3f0aNHsXr1aoweHb/ur732Wvzbv/1bTouQmrUwC4GmjESjUXi9Xlit1rIXc9fj\nONQi43keHo8nQSz1uim13xNpbQV6e8F/8AHQ1xevADA4CH7DBrUtVjbH0UIIQSQSQSgUgtvtHlks\nZRn855/D8tJLML/yCriDB/M+N0ogFsDq7av/f/bOOzyqMu3/n+ec6ZNeCCSELk2kKUVpiiBYcRXr\nWtay6trWrrvr+1vd9XV91XVVXNeyVlzLqmtnFekdQQQB6SUkkIT0STKZmVOe3x8nZ1JISAJBRsP3\nunJBJmee85x6P9+7fG/2V++n3+htGGnf89JLlqTaypVuFi/28tvfQnKyl/Xl3+JSnEQiEZzSSXF1\nMdtLtx9wbLar9ocfFJ591sWdd0Z4/vkQTjRWP/glxcl+vouvJtGbTCQpHk0P49u+i/yqfJZsyGXR\nIgcXXaRzxRUan337HbvzK6NxQlVRKQ4W88b6N6jRag7pmH8o+oGgFiTNl0ZZTVmzLFMIy4kwcqRJ\nUZFg5UqVUaNMFH8ZORU5JHuSSfIksa9qH8XB4qNuDGIBtvvW4/HgdDqjnqxYbJodDAaJO8zFbnvD\nvofGjh3Lyy+/zOLFi/nhhx/49NNP8Xg8JCUlNdjuYOhwDPNwY5jNdeSIlbjjoY71YzGyBkhJQRgG\naBq43da/kQiyUycra9YwrLhoK9GW2lchBFLTUF54AWXNGkyvF2GaqAsXop9+OvL88w/5sJbmLmVz\nyWZm75qN1+mlyxAdY2Nvfve7eHw+uOuuCKmpkuJgKdsrtpOVkIUiFEzTJNmTzJK8JXSLs+ooHQ4H\nW8q2sKlkE1O7Tufvf3dy++0RBg603Ey3TM/j+wVhlmxzkzLAh0atgk68F/btwdV7EF8uKmPaCIO0\nNIkQoPX4ksJNGitDe0lPl+giSCAcoFqrZnXBasZlj2vT8drs0masSZ6kg7JMsAQMVBVcLsnWrQp6\nlsUu7fvV6/CyoWgDozNGH8ol+NnCPj9NJQ/ZKlU/VvJQU8w7FqXx7Dk+9dRTXHfdddxxxx1omobX\n6432xmwtOpzBtHEoBq5+yYi7hY4ch4v6AguHi4Mdp83IWpPd255zAqCyEuLjMceOtRKCIhFkr16Q\nmYkoKLBYZytv5sZava1aLW7YgFi7FrN7d+scCQGGgfvrrwmPGEF1Zhpzds3hzN5n4lJdrTukSCXz\ncubRP7U/83PmMzJzJMmeZMJJW4CTcDgkfr91PVbmryRiRiioLkDX65p9BCNB8mvyyRK9iOg1fLXt\nK8oj5ZzU6SQefTSbhAQFsI7P4XEw4iQdrUsXXK56Ta0jEYRSTviUa6g4HoqLJdu3C/r0kVw3/EpG\neTRcLujZExbmLmBA6gC8Ti/bSrdxUueTELT+GtvsMsWbQlWkijhXHHmBPLaXbqdvat8Dti8oEKxZ\no3DyyTrJyTB3WQVLvtvN6EFpaIYV1vA7/eRV5lGSUBJzL+BYQlO6t03VC/9Ybcti0SVrIycnh1df\nfbXBZ+Fw28qnOqxLti0Gs7Gbz+PxtLkU4WjhYA+IlJKqqqpoucWP1T0lep7cbqSiQFIScuhQ5MiR\nVrasqlriBq188A41uce5Zo0VK62/fe2+1S1b+K7gO1blr2JzyeZWH9/S3KUYGDgVJ2Vhy81YsDON\n/67awl33l3PCCSYvveTkh8IdfJf/HZcOuJSTnJeT/9WVnJ19KZcOuJQrjr+CBJHJU0/FMW99HiFC\npHhTWJK3BIcjRDAYJBQKoes6Zno6IiMdV1Vpg3mIggKMESMQApKSoHdvi11u26agF/Wkf3pfzjjx\nOBI9CQgE/VL70S2hG0KINh2vZmgszF1IxIywrXQbc3fPZUvJFmqMGhbkLjggYzYchu++Uxg92iAl\nxTr1fY4vJ96RyM7cCCE9FP1JcCcQiARaPZcjhViqfWxpLnbykNfrjSYPAQckD7WHaEtTc7HrnGMR\n9913H/v3749KYpaWlnL33Xe36b3d4Rhm4wvc0g1YPwkmMTGxWRfHkRJCaA80NY5hGFRWVrapgXW7\nLwrcbuSYMSiLFlmJPrZ7e+9ezBNPPGiXk8ZCEYfkSlZVK1Wz8dhSUkWEVfkb6Z7YnSV5S+if2r9F\nlmmzywxfBuXhcuKccazauZNO+VWccYakVGzlwguH8MEHKn9+ez6pA4rYE9jD+CETKNrl4M1/SO65\nS8clNf42w8ugwRE2h+eRHJ9GvNvHnsAeyowyMuMy2bPHJCNDI2QYhM48E/8HH6Dk5Fj1foDZqxfG\nKXWMUwjo1UuyerUABCeeaCIErC5Yjd9Zl9WY5k1j3f519IjrQZza8otPCMGEbhMwpcnKfSsJRAKk\nelMZlz0Oh+I4gKm63TB5stGgdWaPpO7cdVZ3TLOhB96uUTyGQ0P95CFo2DS7psaKVbe37m11dXXM\nZcmCtahet25dA/drSkoKCxYsaNNxd2iG2RIOlgRzpNFexqmp44xEIgQCgWgK+9FcPZvnn495wgmI\n3FzIy4M9ezCPOw7zkksO+j07S7m6uvqQS1+0E0+0JPzqn2dNAyH4Nk1DIol3xVMdqW4V67LZpSpU\n9lXtI84Vh8+jknnSt3RLT2ZzyWZq9CDDJm3Fl7mLvql9WLBnATV6kPPP1xHH/Zdb/7KeRx7xcfzx\nOsefupF1W6pYsSgBw7Q6iizdu5Svv3byz3/6cTgsFuHMymLzpVOZNTaLqtNPp+qyywhdcgnSVWfg\npYQdOwRJSZCcLNm5U1BQVcjOsp2E9BAFVQUUVBVQHCymPFzO1tKtrTqHDsXB0IyhdE/sjiENxnW1\nDGVWfBaDOw1u8t5qypEhRJvC1cdwCBBCREtXfD5f1FNmJw/VL11pzbunOYYZiy70SCRCly5dWLly\nJeXl5VRVVbFu3bo2q7F1OIZZHweLydki461lLrHqkoU6himltJo9h0Jt6p7SnjjgPHm9mDffjJmX\nhygpQSYlWW7Zg8CWGYtEIgdl/S3NQxswADl2LGLJEoSiWFzINCmZdiarItvJiLP0JdN96SzZvYAB\noXicSalNat8apsGKfSswTZOtZVsprCpEVVSkS7Kjej17K3shkewu382THy4jJSset8OFXqOzKn8V\nieHj+b74e4qMbYjyQZw6McSHexZw6ohkVi2DeXNVTpuYwuzVu/BvKeChu9JrtSEEiqrwdcEC8tQ8\nThz2C+Id8Wi6Tigcrk38UNm920lNjaCwUDB5ssnOnYJ9e7xM6jGZrVsVvB5Jt+5118VD214kawrW\n4HV4UYSCz+FjVf6qJstt2oJYcoXGCtpLgakp9tm4abb996aaDTSHWHXJejwebrzxRn73u98xdepU\nqqurmTt3LnfffXebxjlmMJtoyRUMBtE0rU0Sd7Hqkq2vo2hnkB6OkTlii4KuXS0d2aYQCiHy8pBe\nL2bnzlTW9l1sbW/RZqEomFdfjTF6NHL9eoTLhTl4MN+GtsB+cCrWgsL3w1bKvpnNrsJPGVIdhzF+\nPJEbb2wguKAqKneMuANDGoT1MNVaNWAl+jpVhSSvlbpeUFWAu/MuNqzqRaLLoFNqJz7dsJDtSwKU\nSy/H9Y8wwL2GP89II21iBX6vgz7Dy/n2W5UX3xG4vZKbL1pNSsqZ0X3vKNvB3sq9uFQXy/ctjxoq\n+yVYXm65Njt1MnnjjQRKShQuucRg9+5E9m9L4rNXXdx9d4T+qXVxrVAb+pWW1JSwpXQLXeOs65fi\nTSGnIofC6kIy/K0TtY5ldATD3drkofqt7po6L+FwuK6LUQxBURQuu+wy+vXrx+zZs0lJSeGVV16h\nX79+bRqnwxnMluKVlZWVKIrStMh4KxCrD5etRnREu6fs34/Yvh38fuTxxzfte2sjxNdfo77+Omga\nUtfRe/TAfccdhFrT4SQvD2XRIsTu3ciUFOSYMchBgxom+QgBxx2H3qMHiqIQ0kOsXbUWzdTYV7UP\nZfdu1EULMOK8LM02GVSajjp/Pq7qaiL/8z8NdpfkSTpgCh995EDT4KKLdEDy2vevocmaaFurnj09\nrNhehanM5vQ+Yxg6PEzO/sWcKH/Non/fzkMPhYlPgF4lKp9tcJLpNZlyXL1OMdJk9q7ZJLoTiXfF\ns2LfCk7JOoUUb0r0JZiW5iA11WLlf/hDDY884kHTTAYO1Hn5ZT933x2if38JbciMrY81BWtwKS4M\naUDtesqlutqFZR7D0UFTbcts42m3LWuOcMSqDq6UkuHDhzN8+PBDHqPDGcz6qH/BNU2jqqoKj8fT\nZBZsa8Y6UnM7HNjKIC6X65COq1VzMk2Uf/4T5ZNPrKp0KSE1Ff2hh6y6hdaM0dT+1q5Fff55yMjA\ncDqtPpV5eXiefJLwQw8d/Mt79qC+9hrS50Omp0MwiPL++5iBAHLMmGa/5lJdnNfnPIRiGRvX239E\n1PQGNQ4HCorqQGZmoq5ebbHeFjqrTJ2q8/zzLt5/38HEcwr5fPMcNucVcuYJHspLnXyxWMXTdR8R\nzcTlEtRUu5k9FwZ6thIqOwUtEGb1WoW1yx387X8jfPihg9deEtxySwSns45d2o2gVaGybO8yzulz\nDuGwlWRjn3chBGlpCn/4g8Ett/j5/HPJH/5QRY8eEYLBhq2nWrvwM0yDQDiAS3VRGamMfu5UnIT0\nEGE9HG0zdgyHj6OxIG/svrXZp6Zp0aTIDz/8kPT09DZn2ufm5nLVVVexf/9+hBDccMMN3H777Qds\nd/vtt/Pf//4Xn8/H66+/zrBhww7pOOz4rBDikPqTdniDafd4bI+4XrvXKR4G6kv3AYdtLA8GMX8+\nyn/+A1lZdZkbJSU4HnoI/ZVX2sY0S0tRFi6EbdtQVq1CqiqGw4Gh67hcLpSMDNi9G3XTJhg1qtlh\nlHnzkH5/XYeU+Hikx4Myfz7G8OHNditWhELX+K6oqmql5+8uR2ZkgK6AYSCKC6360IoK1M8+Q7/i\nioNm83q9cPPNEZ5/3sXT/6ihUHOS7s2kavMpdA+cx8B++5m54Q3iZVeWVqqs+RaS/WnksJR/vTCQ\nNd/4WbjQwT33hElJgeuv1/jnP538/e8ufnNzKMoubWT4MlixbwUnZ53CG//ozIgRBuPG1Ul/LVqk\n8vnnDsAyoJs3exk82IGUdS9B2x1b/6XS3L2jKioX9r+w2eP/OSBWnulYgc0+bebpdDoJBoM8/fTT\nrFmzhsmTJzN16lSmTJnCoEGDDnrunE4nf/vb3xg6dChVVVWceOKJTJ48mQEDBkS3mTVrFtu3b2fb\ntm2sXLmS3/zmN6xYseKQ5n64zRdikzv/iLAzwxISEo5KEkxzOByG2Vi6rz3joU2No370kVXsV/9m\nTE2FkhLEhg2t38GePTjuugvl7bdRakUFxLp1mCUluFQVdetWxOzZKGvWEPfQQ5aEXlMwTURODiQn\nN/y8tt0YJXXC3i2dF7NHD0tgQUrE7t2Qnw+KglBVlD17cL75JtT2Cm0OXi+cc47OwvK3cQgnYxy3\nwQAAIABJREFUfbNS+TbyLiY6gyZsJiXNoEwrZG/lXvIq91KuFzNqbAVF+i66dJFRYwnWKb7+eo0R\nIwxyq3expWQLZaEy8gJ55AXyyK/KJxAOsGrfN1x5pcbs2Q4WL7auy6JFKm+/7SA/X/Dww2FefrmG\nVatU/v1vJ4pi6Zba9Xv2C7GmpuYn0zmjIyCWjLftflVVlZtuuokvv/ySIUOGcNttt7Fz506mTZvG\nww8/fNAxOnfuzNChQwErJ2HAgAHs27evwTaffvopV199NQCjRo2ivLycwsLCNs+3uLiYZcuWsXr1\najZt2sTOnTupqKho0xgdjmHaN5sd0HY4HMTHx7fLTdje8niHMpYdhz3i8cr6KC+3dGCbQmVlg1/r\nJyE1npv66qtWX8yuXa2ElS5dUPfuxb15s2WQ9+wBrxfp8SBdLpxPPonp8x3oYlUUi/WFQg3FD6S0\nsnBaWXcKoF1+Oe4//YnC3Ahp4UrUOC8iEMDs25dAVn/mf7+aE9bMo+vY5mN1OTmCZ2fmInouJrE8\nk20/qHTrk0u44isW/etMHpg0hFdfdbFjo0K3PibdRJAbi+eQ/dfXcbg8GFOmYIweHe0PqqowZoxB\neSiVvql9MUyDiT0mNthnsieZ9HjJ7bdHePZZF4sXq+wq38mqpH8w87Y/M2CANdb/+39h/vQnN+np\nktNPN6LXyC5BUFU16oKzC94bZ08eqXsslozDMbQODoeDadOmMW3atDY3pNi9ezffffcdoxp5jvbu\n3Ut2dnb0965du5KXl0dGRssJZfY9tHPnTp599lnWrFlDTU0NmqZRUFDAhRdeyN///veoLWjx+Fp9\nND8j2MXuqqq2q8Td0S4taS4Oe9B5SYnYuBGxZAkYBvLkk5FDh7bYvLk+zOHDURYsgM6d6z40DDBN\nZO/ezX/RMBDr1iE2bgSPB/Htt9C7N6Zpomkajl69EEVFUFaGKCtDxsVZRjA1FZmejgwGUd98E72J\nmKQ5ZgzKF19YAu/2seTnW7066zeyrsXWrQp9+jT0Hus67EwazYD77sP1hyepzK8mPsNEDB5MxeBT\nmL3Y4MuBm/lhS4C7xpzd5H2UkyN48UUXzpFvkmo4SM2AzVskeiCVFaG3GOecwt//msGoUTpZqQoO\nM8I92x7AM3sNjp4uHMLEMX8+2jnnoN17LyWlCqmp1rU0pEFZqAxTmrjCnenXNf2A/aenS4YNM5g7\nVyUw7EVM+V+K4yYBpwGQmGgZzebWO/XjV407Zxyp4vdj+GnCXkzZEEK0OmO2qqqK6dOn88wzzzRZ\nltL4/dXa+8w2mHPnzmXTpk0sWrTogDkDrY69djiDaTdojY+PJxwOx6yLqa3GNxQKRVuNtTqtW0qU\nV15B+fRTy10pBMyejTluHOZddx1gNJubk3nRRSjLlkFBgWWMwmEoLcU87zzIzGxuwqj/+7+I77+3\n9qPriPXrMRQFwzRxKgpKWhpy5EjEsmXI6mpLVKB7d2SPHmCaEB+P2LOnSZF2OWIEZnk5ysqVIATS\nMCxjPG1aU6eB775TWb5c4corNSu2vX0XK17chleJYF7fH98zD1H47H9YUtWHof0VVi9VCA3+AZcn\nzC5RwdbSrfRLPTBFPSdHYfy0rTy1az7JnmSqjQApnWHDRoXkroXsLplN5y7nsjD/Kz7+61hCHywk\nfv4aIumd2LJfpX9/EwUT5xdfoJ15Nn//aBgjRxpMnWqwLG8ZTsXFunUqz6xbwfO3nHvA/hctUlm7\nVuH8azfzm7lLSE/qxIvfvcj47PGoinXOEhMP+FqzqF9+YMewmmOfsZot2VbEEtONtbnUv8bBYPCQ\ndGQ1TePCCy/kiiuu4Pwmmh5kZWWRm5sb/T0vL4+srKxWjW2aJoqikJKSwsiRI6P7sxd3bb1HO5zB\ndDgcJCYmIoRod4N5NBimXTdq68E2FdRubl5i2zaUzz5rmKwjpSVVN368pe3aGmRnoz/1FMp776F8\n+y0kJmJcdRXyjDOa3FwIgfjiC8Tq1aCqiP37kQ4HUgjU+fNRMjMRigLbtiFTUjAnT0bs2mUZ39p5\nCtOEYBDZqVPTEjGKgpwyBePkk62Ypd/frJC7EHDRRRrvv+9i5kwnl6X8l73Pz6aXatLreDfizZWY\nffuSOTCBkh1BFi1KpNfAEPMT19Ip6KEqozufbvuUe1LuOeBlNn68wff7KxheM7zWwMCqTSoThpgk\nJnalU9cgQ7NX88TSf/HS5/E8vGMONekuPJkSw5C1axaFUAjEsuXceuvxPP20i4BezOakteRu7AZV\nkHbSGoqCo0n31bHMRYtU5sxRuf12jac3vkKXDEEgP4ltZj6LchdxWvfTWnd9m8Ex9nkM9XEonUqk\nlFx33XUMHDiQO+64o8ltzjvvPJ577jkuvfRSVqxYQVJSUqvcsQDLly/nyy+/RAjBmjVruO222zjl\nlFOi9+PQoUM57rjjWj3fDmcwoWGLr/Ye98eMYR5Kh44G+/jmmwM1yYSw3KNLlhxgMA86p27dMO+9\nl9ZKOquzZiH27wcpkR4PZlUVSmWldQyRiBV7NE1EYSHGtGkoq1ejLF2K7NLFmm8kgigpwWhJqSMh\noUllHhv28SgKXHKJzkczCih48lXSHRV06qIg1oOsZc2lY89h/+JN9BC5LMvbRVVKFWkDR5Gc1ond\nFbubZZmDOw1mcKfB0d+DJ9e1+zRMg0eXP8pJA1MJmR9TtEpFL4WasErvXtbcKioEFYWCgk0e1s1y\ncOedEW5+diU5YR89UhQmTjQojXhYkreEX/T9Re1xQWmp4PbbNSrU7SzJW0JGQhqpHpO9Jf4DWCZA\nRbiC5XnLmdp7aquuYWN0RPbZkdGY7R4Kw1y6dClvvfUWgwcPjpaKPProo+zZsweAG2+8kbPOOotZ\ns2bRp08f/H4/r732WqvHN02TiooKEhMTGTx4MKWlpXzxxRc4HA5ycnL49a9/zXHHHYdhGK3KoO2w\nBtO+2LHqkm0Juq5TVVWF2+1usWSk2eNUlIZF/A2/1E4zbWb4/HxLjCAxEdM0EZEIwulEUttYOj4e\n4uKQqorYsQPjjjuQbrdVciIsSW/tmmtQmmGxrZpDo2M0Tei++mOSqvcS7NwNmQgIEOXl6JVhFu4r\nJv33tzCgy3aemf0UhSWT6BTvxQskuBKaZZmNUX8Rvr5oPUXBIrondmdPxR52nj+QEauXsKlUsmWL\nQpdMSdE+ncwEeLnyVH410cBwl1Cd8C2e/G5kZEicDkhX01lbsJaxXceS7ktHCDj/fKsG9+mFr6EI\nBUUouNzQMzOOvZV7+Xz750zrW+ei/nLnl7z7w7scl3IcmZ5mXOltOLeN2eeRFv7uCIgll2xj2CGh\ntmDs2LGt6pzy3HPPHdKcJkyYwIQJEw74vLS0lJR6uQytLTfp0Mu8WJWza2mscDhMZWUlPp8Pr9d7\nyA+QHDnSshK14gaA9XsohBw//pDGbA2EEJjJycjaF6gQAsU+BkVBdumCPP54ZPfuVr1kOAx+P+Y9\n96DPnIn+zDNU/fWv6Gee2W6GXddh5kwnWflr6NTLi8MBubkKUoKMj6d8WwlD+gQYebJghdiLzAyS\nlB1g6fpC8qvyCRthfij5odWi5WCxy8+2f0aKx3pw03xpfOrNQV4wif6phfgCBcwr3onHXcRn2b/h\nV3/OpksXyd/fyUU3YMTpu1m/J48Fa/P4fv/3bCvbxvbS7Q32kRvIZf6e+ZjSpDhYHP0pCZXw1DdP\nRe+x8lA5X+z4Ar/Lz3+2/Kddzml9NCf8Xb/tlF0IbyNWjEOszCPW0BTDjDXhdTtL9+mnn+bdd98F\nrEbSJ598MjfffDMFBQVtGq9DMkwbPzWDWV/ntrl4ZVvmJXv3xrzwQpQPP6xzyxoG5qRJyCaUNNrz\n+LQTT8SxaRNqZSXC7bYMtaZBWlpdKYiUiKoqzHpp5mL/fpS33sKfk4OiqohRozAvu+ygbteWICW8\n/bYTr0un+3EOxA6VzGSd/P1O8vIUsrsaZCRrJE/qg4Fl2C7sZxXraxrUL9/1OlvvkqrPLgF8Th/F\nwWKWXj6V1J7nsejlhbx13Oekhi/jj7dfRpcuJjNnOvGVjeD9m0/A44Gyk+CpZxRq1NcY0quIrPiG\nyRCp3lQenfAokrrrtmlXBasq/ovf42RXxS56JfXio41fUVFp0i8jk+8Kv2NXxS76ph3Y/PlwsG6d\nQs+eJgkJdewzEnGxZYtg8OBI1H0LRN26x1CHWD8fsWgwbYO+dOlSrr32WoqKivj222957733mDFj\nBnPmzOGKK66IJge1hA5tMH9KaByvbJcYkBCYV16JHDUKsXw56Dpy1KgD9VbbEXZsq3rMGBK3bLGy\nV/PzLSOZmYkoKLCyXktKrHrHIUOQJ51kfTkvD/XJJ5F+P2ZWluX2W70aCgsx//CHQ+4PpSiCCRMi\ndO+uIMsGIkM1qPv2keWDSFBHFBrIzM4YtTHdQemDGJQ+CLAaKAshcChte5RMafLZ9s8IG2H2Vu6N\nfq6ZGq+v/C/KwkfxXfkD5dsGUhTeyDMv1PCXhzxkZ5tcfLGBx2Mda1oqnH/NFt79top0fzrL9y4n\nOyE7+qLwOX1M6NbQJVUc+Iz08oF09rtYuGchajiZjzfNoltqJ4QQuB1uPt7+Mfel3XdI57M5xMdL\n5s1zMHGiTkKC1Vlt3jwH/ftbajH1dUvt5CFbPMF23x6LfbZ/7sWhoimGGWu9MO35xcXFkZeXx9y5\ncxkyZAhDhw4lHA4TfxCVrqbQIQ3mkYphHimGaccrbSWWQ9G5bXZeQiD79UO2QrX/cI9PSklVbacR\nz/DhUFEBs2ZZtZEAfftinHyyJeCuaZinnIIcNSoqiqDMnYtUFEvEIBIBVUV27YrYvRuxbRuyf/9D\nmpMQgu7da6tbzj4bdccODF1Hzc/HLTQwJMYJJ1j7bPRC+Hjbx6hC5YJ+F7R5v+OzxxPSG3YF2b1b\nYdZKN5NOyeONnJVMO60LO4vy2blwLg89dAEPPxxuoOqnmzobAksZ1j8Zn9NHbiCXvMo8shOyaQpF\nwSJ2VP5A3+wsivc72FqZw9bd/8LrM/B5LKqc7k1n3f517Czf2a4ss1cvCRjMm+dg1CiDVausspm+\nfevcsE3plqqqGm0mbSvLHIt9xh5ikWHaC6xLLrmEd955h61btzJjxgzAegZTmqjJPhg6pMG0EcsG\n00Zb+3LGKgzDoKqqKsoSFLvsY+RIq3m0y4Xs1cv6t5kxxK5dTeq2CiGgqAjaaDCbetnKnj0JTZ+O\n9/nnISkJMzkZc/BgZEICjlmz0C+9NMpki4PFfLPvG4QQTOg2gVRvaqv3rSrqAcwPoCIVxnXWefjj\nfzNsiCApQeEEfydcrtmcnzKeefOSuOKKupjzjrIdBCKBqIFMcCewLG8ZFw+4uMnjW7F3BW6HG69H\nISFBUlTgYXH5u/RJ7U5hdZ3cWFAL8tHWj7g/7f5WH1Nr0KuXpLraZP58lb59GxrLpmDHPht3zaif\neXuk2WesdOCItVhq4/nEYvNo+508depUJk2aFBUoqKys5M9//jOZtXXirb2+HdpgQmzHBeyswrb0\n5WwKrTLkFRWIhQtRVq8Glwtz7FhLcq5egM4Wq28rGisQBQKBuj8mJyMba742A9mtm1UK43QipLTY\nJrUdpVJbb6xaglpejnbaaVaNZz2IvDxEfn60Q8n8PfNxKA4kkoV7FraJZUp5oNdbSktEoFIWkDx0\nMd2TugHgVJ0kJZro2XP55cS6rFbd1Fmat7SBoU50J1q6so1YpilNCqsL+b7oezr5O1EaqKGkTJCS\nopBUmUX/xGFkp6ZFt49EItEG2u2JqirYtUshOVmSl6fQt6/ZbPi5KXWXxk2P7WfkGPs8+ggGg6Sn\nH6g2dbQhhKC8vJxvvvmGXbt2RRsK+Hw+zjvvvDaN1SEN5pGswzwUg9IYdgcVKeUhN3tuE6qrUWfM\nsAr809PBMFA+/BC5bRvm9dcfVjzTZsj1FYgOlYnLlBTU5cvBNHG6XJg9e1oC6127tsql3FqIQADZ\nRDcTUwgqKotIoGuUXWbFZ4GEFftWtJplFhYK3n/fwbXXatESEynhvfcc9O5tssk1j6BeTWmoNPod\nVVGZvWs2p3Y/NdqdJL8q32pUrVlZrtF5SpNNJZsaGMz/7vgvm0s2kxWfRTAoKStVyOhk4nbHcUHc\nVJKCw5iSPZSk2paewWCw3T0a9WOWffua7NwpGsQ0m0JL5VJHm312VDT1/MYiw7RZ8DPPPMPGjRv5\n5JNPOPvss1m3bh1CCCZOnNikFF9z6JAG00YsumTteKWdJdgeD3pL8xKrViGKiqwyDhs9eiDWr4ed\nO+FgerDNwE7WiEQih82Q7Tmq77+POXiw5ZoNBFDXrUOOHo1xxx0tJ/wEAlBcbP0/Le2gWbVGdjaO\n9esbbiMl24z9fJD/Kbf26Rdll6pQQVhtwVrLMjt1kmRnS154wcVNN0Xwei1jWVws+MUvTIr3pHNa\n9mkHSByqQkU369yxXeO7cs3ga5rch0ut+24gHGDO7jnops4vj/8lwf1dSB8oCQQEWVnWfVFdbRly\nw5AcgrpZq/DNNw1jlnZMc/lylSlTjIN/uQU0Ffs0DOMA9mmLJrRlsRwrrtBYmUd9xHrSj33O3n33\nXTZt2sTEiRN5//33UVWVCy+8sM3tvo4ZzBhyydo1aT6fD4fDQWWjTh9HCmLTJmRjQVEhrDZWeXlR\nAfXWni87uUdK2W4ZvcpHH1mKO4mJyG7d0CoqEIqC027PEwpZ7cBWrbJcyqeeCjbr3LYNZe3aqAtX\nSIk5aFBDsfh6MAYMQN20yZLs8/ksgYVABV+ll1FgKszLmVfHLmvR2de5VSxz+XKV1FTJ2WfrfPGF\ng3/8w0VCgmT1aoUnngjjcMCkHpMwDANPMz07bQghiHO1vDpetGeRJdDucPHVrq+4+oSryc8XPPmk\nixtu0BgwwMTvt9YHjz/u5sILNQYNanHYNmP8eOOA1qi9ekm6dTs8Y9kU7Dh5Y/YZDoePsc92QFPG\nOxaTfuw5xsfHEwgEUFWVuXPnMnr0aDZu3HjMYLYVscAwm2JjhtF+L5EW55WUBLt2NTWxA7JCW4Kd\n3ONwOPA100arzefJNBG5uZYCkDUAonZesrISsXMnyocfIubORQSDYJqozz2HccklmDfdZBnL9PQo\nC5WGgbJ+PYrXi4yLO3CO8fFoY8fievVV1A0bQFXZPCybvT2d9EzuzUdbP8Lv9LOvsmHfPl3qbCza\nyPhuzYs+ZGaavPmmk/JywaWXasydq7J5s8qvfqWxYYPK8uWW9mt7IRAOMDdnLhn+DFRFZXX+aqb0\nnEKXLp35zW80/vEPJzfcoJGeLnniCRdnnKEzcqTZUovPQ0JzTobDdD60iCPJPo+hDoei9HOkYV/L\niy++GF3XufHGG3n88cdxuVwMHz78WFlJa9A4htmero62Gszm2NiPyX7N0aOt2GAkUtfXsrISPB5k\nvc7nLc3JTu7xer2H1jatogJlwQLEd98hk5ORkyYhBw601H86dbKCYPVvcNNEGIblUl6wwNqfzRpD\nIdQPPoCUFGQ90XbAKkdxOlEKCizh+caoqcHz3nvgcGBOnoyJ5KvqeaSs1XCn9qaLvwvju43nlKxT\nDviq13Fwf2b37pKrrtKYMcPJffe58Xhg2DCTlStVli+HBx6ItO2ctYDFuYsxpYlTtZK36rPMvn1N\nfvMbjf/7P+uaX365Fu2J+XNGS+yzvmRfLLHPWHLJ/lQYpo177rkHgOnTpzN69GjKyso44YQT2jxO\n7NwNRwFHIumnLTAMg4qKChRFIT4+/og9nC0a3549MS+5xCrN2LPH+tE0jBtuaDXDDIfDVFVV4ff7\nW9S2bRKlpaj/8z8o770HhYWIdetQ//xnxKxZAJjnn48oLrZk8sDqpbl3L+aIEYhvv0VEIhAXZxn9\nykpLOUhVUWbPbnp/QlgCCbVzD4fDUWk2ddMmRFkZZGSAorBdlLHXb5IUUVByc8mIy2DlvpUoQiHO\nFdfgp76YeXPo1k3Stat1PYJBQVaWyZ49CikpkuTkuuukadEpNoB9ClqCHbvM8Ndlu3bydWJ1/moK\nqixJsJSUuv1lZsZOeAJ+nAx2m3263W58Ph8+nw9VVdF1nWAwSDAYxDAMTNOMqfBNLCIWY5hNoWvX\nrodkLKGDMsz6qC9i0F5jtQZ2vNLr9TYZq/qx46tyzBiMoUOt/pIOh9Vzsr7mW3Pfq+dOblGuzzAQ\nO3bgKC+3xArqJbUoX3yBKCmpc7sCUtNQ33kHfcwY5NixGMGg5XqNRBCGgT52LI6rrkK99VbrC/n5\niPLyaFavBEhLQ0Qi1rm0r7GUiEgEo1MnQqEQRn4+vjlzUFevxvB6kQkJmIpiNcBG8iVbScIDLgPK\nK3CrbjRD49uCbxmXPa6NZxpycwVr1qhMm6azcoXCrlnb+NPxy3EUV7Lpb73Rhg9j6Gk+5s1T2bfP\n6tE5b57KiScaBIOCf/7Tyd13Rw6qBrhwoUplygbCRjhaX6nrkJ8v6JSps7pgNaOTzuWJJ1xcfrlG\ndraMumcHDDj8TO/2wo/NqJpin7bObSQSOarsM5YYZlOIRZdse6PDG8z2RktGTkpJKBQiFAoRFxeH\nswWj1B4PSauNr9/fwAXb0jhtSu7ZsQPHU09BSQlew0A4nXDddcjTT7fGXrkSmZbW8Du150Zs3448\n6SRL6ODUU6G4mBqnExkXh8Pns4TiFy1ChEJ1OrSGgaipQWoask8fa4zav4maGsxevTCSkxH795P8\n5JPIQADS01EiEcTy5UjDINK5M0UiSIlSTUQxCBhVSG8yZiAXKSWbije12WCGQvDFFw4uvFAnPV1i\nzF/K8ZWL2LAqlXGTvPQWG1n83Ba+1S/m9DM9vPKKwt13u8nKknTvbjJzpouLLtIaGMuqKnjhBRe/\n/nWExET4978dGAZ8+fo4Tpl4PNOn6+TuEfzxITcXX6wzZZzOwjmJPLbYxZln6lE37BVXaDzyiIv/\n+Z8I9dYtHRY2+1QUJRpisCX7Onrss6n30jGD+TNF/Qvd3nJ2B4OUkurqagzDaLG+MtYfvtYk90QR\nDOJ49FGL8XXtiqlpKJqG44UXMDIzLSPt8VjWpDGkbMh03W4r7lhTY/0NMH/xC9THH7cSgMBikqZp\nZdXGxSFTU5FZWYi8PAD0zEwqvV6ElMQtWWIJvNdq0wq3G+P443EsWIA7N5cuvXrxB/NUZFExUtep\nmXgnSmpqlGm0FXPnOsjMlKSnSz79Vw1/HLKYcGom8xa5WLBMkJnppVv2Xr547ntE6mTS0yVbtyoE\ngyb33efhvvvCDBnSkAH6/dCjh8ljj7l54IEww4cb/O53bmpqBDs3dGL3QI0HHnBz+ukGl58fQUoo\n3OtE12HcOMtYlpfDRx85Oe00naSkY67HptCYfdZPHKof+3Q4HDH//B4JhMPhFgnATx0d0mDWx4/V\nkqu+NFxrmz23l7u4vY7RHqd+ck9LpQ8AYu1aK65YS1sEWBJ4Hg/iq6+QAwZgTp6M+uqrSL+/znUa\nCIDPd3CNWCmhshLZs6c1fm6u1ZQ6MxPZrx+istJimt27IzMy0HWdyspKPG43kUgE9fvv63pyCmGd\na7cbo1cvFLcbNS8PFTCzs9GmT8ed0dkqjg/XddVoi7LMlCk6ug7vvOPkriv3kbgUzAyFCy/Q2b9f\n8NVXDpxdUunv2Mozz5yF2w29ext8/71KaiqMGnWgu1QIuPBCqz7zscfcjBxpEBcHKSkmnTub3Hqr\nhwkTDO65J4IQ1vY33aTxyitOZsxwcdVVGs884+Lkkw3OPtsa50hkyf5U0dQzKERds2yggWD8kWKf\nseSSbW4usZQkdSRwzGC2c6ywqbEOO3s0hmB3TWmNO9mGCASibLABvF5EUREAcuJEzI0bUVatst7o\nUoLXi3HvvRarbDymEMjiYtTXXkPs3AnFxYjKSuTAgcg+fawxNA2qqqJSdnbc2O/z4aquRguH0ZIS\nmBP6nsnKIAQCU0qkaYLbTfjSSzH79LEy42rl+xzUtZ6yX5K2skxrGIbDAe++6yAz0ySjmwu51Pq8\noEBh7VqFK67Q+GZeiLxIPHv2CKqqBD6fwqWXWqpAM2c6ueoq7QCdBttozp+v8vzzTk4/3WDUKJ0H\nH/SQmmqd+/vuc/Pgg2FSUiyh+euu03jqKRcPPODm3HP1qLE8hrbjGPuMfa9Ye6BDGszGF/ZIuWSl\nlITDYWpqatpkYOqP157M8HBgx15tub62uCNl9+51RrC+OzwQwJw40frF6cS8807ktm2WulBcHHLI\nkCbF1msnhOcf/4D9+y2DGBeHWLTIUidyuZCJiYiyMsxf/hLp9xMOhaipqSEhJwfXG28g8vOJNwyW\ndhM8599IikxlNNno4TCUlSESEtD69wePB11KFF2PvhShYW2fy+VqkmHUL4yvf29Mm6bz4osuMLM4\nOzWVwh/K+GZLOhMmGDgUk0SthLlVF9G3r8nGjSpSSkaONBk1yuCVV5y8+WbTRnPePBWXC849V+f9\n9x289ZaDSZMMQiHB5s1WFq6uC2rToQgEoLTUmteOHUqDqqJYQCwxqragNeyzuXvjp4LG10ZK2SGy\niH/e/LkVaM+btX5dpx2vDIfDJCQk/KR9+zartN2WbY3dyX79kIMHI3JyrNijla4JcXGYkyfXbSgE\nsm9f5NSpyLFjLSO4cCHq3Xfj+NWvUJ58EnbvtjbNyUHZs8equxQCkpMxJ0ywYpVbt4LDgfHLX2Kc\ncw7BYJBwOExiSQnu//s/qKrCzMzEzOzMu3IdflNlZvgbjDmzcX72Ge5VqxBZWXh0HZfLFX3xGYaB\npmnR8pP6usE2u/B6vVHdXHuREQwGCYVC0f6O8fFw440R1m90MmPvdL7ZEMfpfXNIC+0lf1U+C/Rx\nJJ3Sh7w8heOOMznzTINXX3VQUCC47jqNxER5QLh37lyVzz938MADES65RCcUEhgGJCYOCHCYAAAg\nAElEQVRKgkGJyyWZPFnn9dedmCbs2CF4/HE3Y8YYvPxyiIQEyYwZLmr7N/9kjdWRwOGei9bcG5qm\ntSphMNavSazP73BxzGAegfIN0zSjHTlaLLU4CGKBYRqGQSAQQFGUlpN7moOiYNxzD+YllyDCYZTS\nUoxRo9AfeeSgXUaUd99F/dvfEKWl4POhrF6N4/e/h507EZWVRANyNpKSkN26ITUNUVKC8q9/Yd57\nL+TlkZCQgOPLL5EOBzIhASkEqz0l7E9Q6BbxUuDSWNkvDm3CBGrOOANz1y6URx9F1jYvdrvdUeOp\nKErU5aZpWrROz4bNMNxuN36/H6/Xi6IoaJpGdXU1S5ZofPedyZgxGjvKUsk/61c4rr2M/6jT2X3O\njeT0HE9CItx6q8bUqQZZWSY9e0p27lRwOOCCC/QG5bFVVTB/vmUsMzIkn3/u4E9/CnPHHRrz56ts\n26Zy++0Rtm9XuOmmCN9/r/DLX3rp08fk7LP1qHu2sFDwt7/9dBd2PwU0dW/YdZ/V1dUEg0EikQiG\nYcQ0Y2survtzR4d0ydbHkTCYgUAg2srqcG+io/nQNE7uOazibY8H86KLMC+6iGBtRslBVUHKylD+\n8x/o2rVOO61zZygsRPn3v+GqqywXr2GAqlJMkC3VOYxb+p312aZNSMPAuX07rrIyjBdeQOzciVlr\naUxM3vJsIUm6kaEykkx4e4DGqHBnXChInw+Zk4O2fDmRkSNxOp1RN5vD4YieC9vdBtbiQgjRwHUL\nFsOwhdSllPTrZ/Loo07Ky+G22wLMmuXlnr/14OSTDdatUBk2LIIQJuefr2AY8PbbToYONVi7VmHI\nEIPGsr9xcfCnP4Wxd3nNNRqlpVYnkPvvj7BggYNnn3XxxBNhgkHBJ584OP54ky5d6oy87c694IJj\nccwfE83FPkO1LgTb7d+ezRiOBAzDiNm5tSc6pME8Uish+yZvbfZoS2iveR7KoiBUG/M7lNhre8xH\n5ORY/2ksNJqairJuHTI1lcipp+KbOxdqaviPZwPz/PsZWCVJSc3G8PmsTMVQCFasQF+2DCM7G3X1\naqTfzzeOQgpFkK66H6FpJPmTyBFVrFLyGW1mIRQF4fHgKyrC6fdH40/BYDBqNO3WUk6nM2o467/0\npJTR7Nn6sc/9+5243SqZmVBe7sHrhYoKE683xC23GLjd9grei8NhSdZJCQ6HQXO3RP13VWmp4PHH\nrRrLiRMNJk0yePBBN7/9rYdBg0wuu0wjK8vk6actI+50wsKFDu68M9yebUWPoY2oH/t0u91RyT77\n3oO6ZtZHO/bZ2HjbTSN+7vj5LwlaQHslxFRXVxMKhbB79MXK3NqK+sfSOPb6o87H7286szYUwqZY\nkenTkampFORvY2FyBQ4Jn/cTVusv07QeaJ/PctGuXIlx1lmg6xiBcma6N6OZOiWRMopS3BS5NAwk\nbzk3YWAxL2kY0KVLVDotLi4uek7sMqHKykpCoVA0E9LlcuFyuXA6naiqGjWk9V23BQWCW27RuO02\njTlzXJSUOJkxQ8c03Xi9KlKamKYRdc8JYeBwyGaNZWNs3aowdaplLMFKMr711gimacnqJSVJkpLg\njjsifPKJkw8+cHLHHceMZVM4mh4e2zNhxz5tA9VUXPxoIxZ7YR4JdEiGCQ1rHA/nhrMTYoQQJCYm\nEggEYuIGro/WHmP9Y2mvtlzNocUEh969kV27Ivbvh06drA8NA4qKMG680WJteXmIcJhPzj0OVfHQ\naXsBs3tWcPY+k7TKSkhNRZomolbmjj590O67D+2fLzKsUGWgIwGZ3Q3zhMGo8+ZClQOvJx5Dmqil\nZeD3Y554YoN5CSGiRtFmk7quRxt+12efjbtj2IlC48bphMOCt97y0q+fyf79go0bVS64wACcaJrV\nF9U2zPXdc3Z25cHYxSmnNBSgLSgQvPuuk7/8xXLbzpzp5MorNTZtUnC5rOvwzTcqZ54ZW8LrsZTk\ncrTnYdcI26EBm31qmkYoFPrRM28bX5tYFl5vT3RYg2lDCNEgYaMtsJs9u93uaLzyxxJCaG8YhkFl\nZWV0RXuwB+5wX2St+q6iYNx3H47HHoO8vKh6j3nWWcgpUyzpu/x8CtQaFih76GL4UeMTEeUBvuge\n5uodQavHZ3U10u9HjhplZSb274/2v49xVU0NitcbZaui16U4XnkFsWs/kI/s2hX917+G+HgrI3fJ\nEtA0zBEjrA4qtdfaNpB2jNfOoq2pqUFV1egLzmbqpq4TLKlm5rvxZHTROfdcjfJywWuveTBNwZgx\nMnqO7LFt46zrerTms7X9HAsLBW+84eScc3SOP96+z3UeftiF3w9//GMYVSXqnm1sNINBKC4WdOvW\n8D7csUOQnS1jqgylI6FxXLyp2GdrFlfthWMG8xgOCjueZaeJxzJa25bL5/PhbkIkoP44PyoyM9Gf\nfhqxZQsEAlY9p92+yzCQ8fF84stFMSQqApGaSkagnNndqjg7Vyc1FEKmpCAHDsQYMYKamhpM08Qf\nH4/SKHNG9u2L9pe/IAoLkUJYnUqEQP33v3G89FKde3jmTIwzz0S/666GgUOIao663e6ogbOzH4WU\neGfPxvPZZwT3VHORJ43O916C4RxPaqrJNdeEeOstN/37h/D7G2ZI2gux+sxW1/VW9XOMi5P84hc6\nffvWLQpV1aq3rO+GveOOCE8/7SI7WzJoUN22u3crzJjh4p57wvTubc1p40brs/vvD9OzZ2x5Uzoi\nmot9Hkn2eYxhdjAcqktWSkkwGETTtCZLRn5KDPNwhRUOFW06LkVpVhB+b1YiXybsJy6iUOQNIUyJ\n7JpKRUDjs7EZXB3ujzFsGPrll1MtJYoQ+P3+5l8YioLs0qVunjk5OF56yRKFt8+NaaLOmoU5Zgzm\nyScf9BjthCCPx4PyzjtW55X0dPz9OxFfE8TxzNMIaaJOmkSXLnD99SY+n0I4HImWoQBUVyskJAi+\n+cZJcrKVZWuPHQpJPv9cZfLkGpzOA1Vl/H7RwFgCHHecyWOPhYmLq/ssKQnuvTdC43fewIEm118f\n4ckn3dxzT5hQSDBjhovf/jbSIYxlLLmFWzuXo8E+O4LwOnRgg2mjLS/v1sT4fgypvcMZq764QjAY\nRNf1NtWKtmc7tMOBaZpoSCaPuxbHooUo67dCRYX1t159SL/saiInX4HhchEMBnHWrr7bMm9l2TIr\n9ll/IaEoSLcb9auvDmow60PU1OD85BNkdra1KJES0xGPDsg336Rq5EgcLhfvvuulc+cIU6Y4cbut\nF96cOYLNmwW//nWItDTJzJkeLrjAZNAgS//h1VddpKVJEhLc1NRY5+WVVxxcckkQj6dOVWbFChea\npnD66SZC0MBY2mjufXfiiSYQ4f/9P8v78OCDkZhqAXYMzaM5177NPu2ylcNlnx0lS/aYwWylgbPj\nla2J8bXn3I7EOI0N/9E2fq3Cjh0os2Yh9u4l0rMn4fHjyeySyQ0T7kT9JBdKIsjkZEsdqKACXppL\nTd8zqU5IwOPxHJrbXNMOcLsC1me2JE4tiouhcXcyXbdEBZIrihp2XRECRVVRkpIQeXl4DIOIaTJt\nWgWvv+7D4YApU0wWL3axfr3K9ddHcLtd9OplcuWVIX7/ey+GAWPGWKIGF11ksH274NFH3fzylxpZ\nWYKXX07g1lvDuFwGixYJvvpK4eabqwmHlTaJxduoXyVlJwodw08L9V37UMc+bQMKrWefjRfNx7Jk\njyEKO17ZUowPYtcla49lmmark3uO1JyiY9TUINavR2zahExMRA4bBt27H7j9ypWoTz2FdDrRvV7E\njh0kLVpE5b33WlZp925kr17RsUlPR+blYX76Kb4bbohK27UV5kknwRtvWCzTNpxSImpq0G0NXKwp\nfPaZyuDBspaNWZ998olKcrJk4olJDUQWogiFrLIXjwdd00hP93LLLYKXXlKZNw9SUzWuv74ar1dF\nCMsF268f/PGPOldc4WHXLgfz5lWwaZPgscd8uN0mvXrp9OwpAAfPPedm+HCDxYsd3H13mNRUVwOx\n+NYmDtkxywcfjBAMEnXP2jHNI4VY8GTEUsZ7e5+P+uzTlvNsin3agvEH2/exGObPHPbFP5gBkFJS\nU1NDJBIhPj6+VS/eo1E72VpomtZqw3/EUV2N8s47iP37kQkJiF27UFaswDz/fOTo0XXbaRrqP/+J\nTElBq3UpuRITkUVFeN59FzlypGWIqDv3hmkivV68u3ejH6KxBJADBmCcfTbq558j3W7LHVtdQ2jQ\nMJTx46PbORwwYYLBvHkqNTUKJ5xgMmeOitcLp55qgpJIYMRpJCz/GpmVZRlNXUcpKCB0+eXUaBo+\nnw+Hw0FiIvTvr1JUpNCvn0JysolhWIXrVka3k4ULfVx6qcEHHzg499wEuneXeL0m999fQ3a2iaZJ\nzjnHYPFiL59+6uDeeyO1lTl1YvEtJQ7Z2LpVicYs69ywVkzzd78LH5A9+3PE0TbaPwYOxj5ramqA\nhuyzKYbZ2U7I+xnjmHBBMwbOdlvaMb5DZSlHYm5thT1GMBgkLi7u6BtLw8D10UcoK1ZYbk+3Gzp3\nRmZmonz+ueXHtLFvH7Kykkjtg+xyuSyR9tRU1K1b0bxeqI292A+5NE2cmobMzj68eQqBfuedaI8+\nijz5ZOTQoeRd+3tmDnmcsuo6F29OjiVDN3mywZdfqtx4owshYOpUA0WBLVsErzlvIDhuMqKwEJGf\njygqoub886k84wyk9LN8uTXe3LkKW7YI7rxTY9UqlQ8+8OL1+oiPj0dRvLz+upuEhDC33FLKww9X\nkpursG6dyn33mfTr54rq3S5dquJ2m5xwgsa//qUQCJiNDk1EE5L8fn/0nrC9Kfa5zMgwuPvucIOY\n5Yknmtx2W4T09J+/sYwl/JiM22afHo8Hn89nJa7V00MGGjQiCAaDeL3eVo9/7bXXkpGRwQknnNDk\n3xcsWEBiYiLDhg1j2LBhPPLII+1yXIeLDsswbTRllOyaRKfT2WbB8VhjmLZyj9UlI/6wM2EP+/hC\nIVwvvoh47z1wuRBFRYi1azHHjrVKRkwTkZsbzYw1HA6kpqEADqezrk2YriNcLmpOOAE1IQElPx8z\nJQWhKDjCYaShY5511mEda+0BY44ejVnLejOAET8IPv5Y4fzzDQIBwddfq5x9tkF6upWBWlFh9bac\nMsUgL8/6+/TLJI7ONxMpvxxKS6mJj0f3eIjz+6msVJg3T2XJEgWnE264QWf3bkE4DJs3K8yeLTnj\nDJN589x07gzTpwu2b3fw3nsORo7U2LRJ5YEHBK+8EiIpycHy5S7mzHFw110RUlIMPvpIYcYMJzff\nHCIujmhyR1OtyoDoC1DXdVQ1TNeuKpFIQ9fcwIHHkn46Cpq7P0zT5J133uHJJ59k8ODBSCmZOnVq\nq1yz11xzDbfddhtXXXVVs9tMmDCBTz/9tN2Ooz3Q4RlmY0QiEQKBQFSO6mjE+NprLDteaes+xoJr\nSSxejLJ5MzI1FRISrMbMHo/FNvVa4e/aB1PTNAJeL/Tti6O83GKWtWo5orAQTj2VuC5dMP78Z4zM\nTNSCAkR+PpoW4YnLevK9P3hEFi8DB0pGjTKZOdPBJ5/YxlLyyScq6enwyCMaui753e+cfPKJyvTp\nerR8VCYmUp2RgenzERcXh6IoJCbCHXdo7Nih0L27ZPduwZtvOrjzTp3779ewF+5TpxpcfLHByy+r\n3HabG69X4a9/NVmxIozTqTBpUjxbtgT5+muDm26qJDXVwOFQuegihUGDBMuXew6Q69N1/QDhDtuQ\n2uzT1sqtqamJtkqLFUm2I41YiKPGGuzz4fF4uPrqq3nnnXdwuVx8/PHHZGRkMHXqVJ5//vmDjjFu\n3DiSa5uyN4dYvL86LMNsHMO0+9OFw+FWxyuPNA7HYDbO6rXbjR3NOQEoS5dipKdbaq1bt1oV9G63\nJSmTmwt+P7J796hrMC4+HuW88xC//z2sXg1+P6JzZ+SgQRiXXoqu6wSTk3E/8QRKaSkyHGaDu4IV\nKx6jaOO73Ou9F5fTFVXcaa+XX/0SDKfTMpZeb50bdvp0g8cecyJEXQ9se2WuKMoBCVeJifDXv0a4\n/34Xc+eqPPCARo8e1nkeP96s3Y+1bffukupqwfjxBj16SPLzBf37C1wuyYYNifzxjxq6bmmOGoaB\nw+Hg7LOdtfGn5sXigQMWVo0TQ+zvtTVx6BgOD/YzFwvGu/4iQlEUBg8eTFpaGg8++CC9e/dm7ty5\nbN++/bD2IYRg2bJlDBkyhKysLJ588kkGDhzYHtM/LBx9qxAjqKqqQkp52BqqseCSjUQi0booOzYV\nC/MCLFeqqmJ07gyBABQUWBmolZWIykqMG24gWPtCjo+Px7l69f9n78vDoyizr0/1lnSnO4EgBAIo\niCjIIIuiiMMuOySBEQkBGUGcICoKIqiMOrgN+KmMooPLoPwYBYEkQNgyLmyiAoKyKKAICBjZIUl3\n0mtVfX80b1Fdqeq1qqua9Hken0cjVL1dqX7Pe+8991zo3nwTbHY22MxM6CoqwFIUfOPHw5OSAtfl\n2onRaPT/GZbFmm/noLGtMc64zuCE8wRuSr0JnosX4d23D4aqKuhatIDuT3+CLoxa7s6dOng8wJ//\nfCUKO3CAwrx5Bjz1lA9uN1BaqkebNgy6dmW5muU33+jxz3968MMPenz6qQEjR7pBUTUwGo2S/aDH\nj1/52U8/6dCihbi366BBDLp0cWPhQgMWL9bj6FEdRozwoUMHFjQNGAx6GAxX0mdXlI9OjuD4Bwgy\nqoz43ZL3hIxsEkvdRuo4FA2S0V3igLSVZGRkYMSIETFfr3Pnzjh58iQsFgs2bNiAvLw8/PLLLzKs\nNDbUecIk6SiKomC1WmP+gqqZkiVRssvl0kyULARz++2gysr8Ip+OHYFLl4Bz5/ytGlOmwOl0wnfh\nAtKbNoWOpqG7rJBlL0dkDACcOwf2v/+Fa+pUpKWlBZguHLpwCL9e+hXXpV8HHaXDqsOr8Oy142Cd\n/zZQXQ2aogCvF97sbDgmTYKhfn0YjUbJDb5dOwZLl/qfY5cuDFau1GPDBj3+8hcf11bxxx8svvpK\nj5tv9uHs2cs1y8tp2FataGzZwmLxYgZjx6bAZhPvB923z5+GfeopL+rXZ/Gvf/nDySFDxEnzmmuA\n4cNpvPeeAY0bs+jUyb8W4VmPuL7wzeKJWppl2YA5n3q9HjRNcz64/OiT1Dz5h0m+mxGJPn0+n2p+\npknEB2IHGSIolAs2kpYBMGjQIEyePBkXL15EZmambPeIBtrbUeMEiqK4SAxAxOIerYGIe2iaVtSy\nL9brsL17A3v3Qnf8ONCgAeB2gzIYQLduDWbBAqTo9bAZjWDuvBPsn/7kN0/PyAj43fjq1YPuwAFY\njUboeJ+TZVms+mUVbCYbKIpCvZR6OFF5HD8vehV/0lmB667jivapJ05Av3EjnCNGcOQhjLwAf+p1\n9Ggfli41oKoK+OorHa6/nsGQIf6D1vHjFE6epNCnD420NCA9nQ2oWXq9XnTq5ES9emmwWsXdlP74\nw0+Wjzzi49Kwjz/uxb/+ZUSDBiy6dq0tsDl1CliyxIDcXBrffqvDZ5/p0L9/cCEOP70KgGsbIOlv\nUt80GAxcypg/aSXUnE8SfYr5mfJ7+hIpdauVKFcr65CC3MYFZ86cQaNGjUBRFHbu3AmWZVUnS6AO\nE6bb7UZ1dTWsViunIpUDakSYCeXcY7PBN3UqXN98A+Px40BmJuhTp+A7fBjUtdfCaDIBLAvd1q3w\nOp3QMQz4n8ZbUwP9iRMwnDgB3UsvgenZE0yPHkBqakB0CfifX7pPjyL397g5MzdA4cZmZ8O4YwdQ\nUABQVC3y4JNnWpoOw4b5sGiRAddey+KWWxh89pkObdqw2LxZh0GDaI4g+a1obrcbbrcbaWlp6NBB\n2nqwSRMWTz/tDZhHmZEBTJ3qhdgc8lOngPfeMyI314dOnVh07MhgwQL/VzkUafLBJzhiEq/T6bj6\nNz/6FEvdAv5aOYk8+UQo5mfK7+kjkWcy+kw8SEWYkXjJjh49Glu2bMH58+fRvHlzzJ49m3MbKiws\nRFFRERYsWACDwQCLxYJPP/1U1s8QLYK+qawmil7KgAgXdDodKisrkZaWJksKk2w8GYJpGEpdK1zL\nPrIBxtqDKcd1SNtOvXr14L14EdTs2dBfdx0MlzdYhmHAut2gKitBeb2gTp0C07AhvC4XTD/8AN25\nc2BvvBFsu3bA2bNg27WDb8oUzN+zAN/98R0sRnLSZYE//oB3/x78/WQr3FivlX/iicnkb18pL4d3\n/vxaeUz+pHv/Jq9HSYkF119P4cQJPW68kcXBgzrQtD8tmp0d+DUhqXGfzxcw+DcaMAywdasOXbsy\nHHl+/rkORiMLs5nCHXf4iauqCigu1qOggEakvxqfz4eamhrOQlBomSac80neMX70yd8qSHZD7HPz\nhUNEoSslHHI4HFEp1eUEGfydKnZyiSNInVgLbjrkveD3XQ4cOBBfffVVQmUPgoGSeOnqbITJTycB\n2pQwh4owxcQ9iQS32w3X2bOoZzJBd3mjJv/AZALldsM3aRJ0r78O9rffYLp0Cfpz58A2a+avf5pM\nQIsW0B04AOrgQdzb9l4MbnWl91L35ZfQf30G1JF6aO6tge7EPuDYMdA9eoC6dAlM586iXrH8up/d\nzmLVKh3atPHgtttc6NAB+M9/rKiqAtq0obB3L4WsLJZzvCPuUAzDxEyWgL/t1OGgsGiRAfff70Nq\nKtC5M4MPPzSgZ88r0WR6OjB+fOQDoMnsTiKc8t8z/Dmf/OiT/EMUt0Q0JJW6NZlMAeRJhEOJlrat\naxCLMENZ510tqNOEKfbvclxX6ZRsNC0wqtYwz5+Hbs0a6LZvB2s0Ar16gbnzTjgB2K67DrqUFLBu\nN9jL7Q4URYFyOMA2agR3kyZwPf88rEePQrdsGegmTYDWrf1M4l8QYDJB98svyGrfHllpWf6fX7gA\n09pvwDZuA5gaQ7dnD6D3/1y/cyfoW24BnZMTcukHD+rRvj3QrZsRgBHnzjGoVw+w2bzo378a331n\nxrp1egwaBBgMlH/2JRVijFgEoCi/8GfdOj0WLTIgJ4fGJ5/o0bMng9tvj808wOPxwOVycbZ8Ugg6\n5/MyuRqNRi5SJBGnkDzFhEPkv4lwiNRJ3W43AP+hSk3hkNZrh1qBFgMOJZA8xkFbZgOhQMQ9Ho9H\nNcu+iFBVBf1LL0G3ZQvYjAzAZAJbVIS0995DutUKvdkMesAAsCdPgrXbQQH+VOyFC6jp2xcupxPW\n8+dh0OvB3nCDfy6VYANjadp/bR50R46ABfzNiw0b+kVE114LNisLbHo6fM88AzRsGHL5t9/OoFs3\nPzE5ncDWrUbccw+Fxx7TIzvbioEDWTAMjW3bXFyvq9zRPiFNs5nFO+8YcMcdsZOl2+2Gy+WKuBRB\nlLFmsxk2m40Ty7lc/s9fXV0Nn88XYL1nMpk4wiMqXY/HE1ALJdcmQ5BJ6pFvx0Z8nYVGC3UBWiJu\n4VrqClkCdTjC5EMzPYoCCNdFnHv0en3E4h61PiP11VegLl70kxXLwsMwoJo1g/HQIeh//hnMzTeD\nvvNOUKmp0G/aBF15OZhmzeDIyYEvPR3133wTuvJygKJA2e2gysvBZGRccQ6oqQF0OjAdOwbe2GAA\nBYD7xFYr2JtuAqqqwNar589hRgizGRg9+kqN0G9AYMLgwTTsdi9X33M6nbVaNmLd7C5eBM6epWAw\nAAcO6HD77YyoICgUyNBwr9fLOQ1FCzHLtCt13/B6PgFctuALVN0ShBIOydXzmURsSKZk6xi0HGGS\nzcLhcCAlJQWpqamqvZyRfj5q/36w6en+OpjH499gDQb4dDrg2DH4brzR/+c6dwbduTN8rH+wNVgW\n9RYsAHX2LHDttQAu/46qq0Ht3Qu2WTP/Wkwm+AoLaw2jZNq0AZua6jdyJ/1hLAvqwgXQw4dH/fmF\nwSMxBEhPtwT49AZT3UZKUhcugKtZdunCcOlZUtMMF3LXV4UI1fPJfwZ8kiXkyU/hCtcm5jhEni9p\nheGPopILWjxIJ6Ee6ixhKlXDJJAjhUL+vsfj4WTbUQ1ChopR9DXXgP35Z3hSU2E0GKA3GPzm6SwL\n3+U6H191WV1d7RebVFZC9+uvYJs1438IsLfcApw/D9+DDwIGA9jWrQGxKQkWC3wPPwzD/PnAxYuc\nHJzu1s1v9C4DgtUAxXoSSVM/EbYEM0wgYBjg448NATVLUtMsKfGrYsMBSw4iQMT1VZYFzpwJbJkB\ngMpKv+5K7PGH6vnkq27JQYNPmr7LvsJerzcss3gx4RBR3cr1PVQTWk7JkgxBXUCdJUw+5K5hygX+\nWC6tOPdE+qxc3brB8PnnMNWrB11qKsCyYCoqAIsFVa1aweB0clGB0+nkxCXU2bNgKapWvRImE+Dz\ngW3f/oq5qgTY9u3hfe016PbtA5xOsNdf7x80LYNlm9vthsfjqeU0JIZI3HaE749OBzzwgA98ExVS\n07zsuRESwTxsw8HZs8Arrxjx0EM+tG3r/91XVACvvmrEoEE0uncPXVPkHyD4g4rJnE9h5Ol2u7lM\nCkndRiocSjoOxQdEqV8XoP4OrCLI5i939MW/brQg4h4AspAlf+OJB0j6z5OdjYyHHoJ+8WKwFy/6\nwxWrFeyMGbA2aQKfz8cJQMgmSNM09JdHfeHECcBiATIz/ezxxx9+h6DqaqBevdALSU+XLaIkn4v0\nWEZTAwyWWpRK3Yo5jlGU+M+FCIjao0zlZ2UBkyf78O9/G/DQQz40acLi1VeN6NqVCYssa6890FKP\nX5sk72hKSgpMJlOtnk8ps3h+9EmeL9/vlu84lDSLjw1k+hGB3C4/WkadJkw+lCDMaMEX9yRiMZ1l\n2QAze6pXL3i7dAFz+DBYgwG44QZQRiOoyz2XpKbGsqw/6nC5kLp6NQwnTkB//Cn8LeMAACAASURB\nVDhgMPhJ1mCA7tIl0O3bwzh9OphevUDn5/v/Pxn5xTBgs7L8P1Pgc5G0ply+w1KpW6FoJprUIk3T\nqK6u5qL2WNCmDYvJk32YO9cf1Q8fTiMnJ/K+TyH4BKfT6bgsA03TqKqqkuz5FJrFE9IUkidFUQHC\noUjN4oXkoBa0lJIVoqamJkmYdQlaehF9Ph/sdjtSU1ORmpqKyspKWa4rZx9msEiVT/aEVFiWBZ2S\nArZdO24Tk4rUjEYjqG+/haGsDL727eFr2hT648dhOHYMOpqGb9gwUA0aADQN/RdfgLVawXbpAn1R\nkd/uhqIAsxl0167QHTkC6tdfwWZng7n7bn9bSpSINa0ZDmJJ3QohdO+RA40bX3l/WreWN1tBbASt\nViuXlpXq+eSbxfN7Pvk1ULHUrTCyjZdw6GqDkLyThFlHoHRKNlKQtBxf3KPVlhcxEJs+vpKXbHrA\nFeVjqEhN/7//AZmZMKSkAE2agG3QAOylS2B8PnjdbsDrhV6ng65JE+jXrQN78CDYtDSgeXP/BU6e\nhOnJJ8HedBPYRo1A/fgj9N99B19hIZjbb4/4c9E0jZqa4KO55EY0qVsCMfeeWEFqlsOH07jxRoZL\nz5KaZrQI1ubCJzi+45DYM+ALh4Sp21Bm8fy/JxQOcc5TKkPrEWYkPrKJjDpNmARKtYJE8mdJU7ZS\n4h6lnX7EbPpIykxSCStRU6MuXQK/X4LyeqGjKLAGA0wAaJ3OTyAADGfOgG3QAFRWFiiWBSgKup9+\n8tc7dTp/v2ZaGtiaGuiXLAHTqVNIsRAfSkRqkSKS1C3Z8EO590SCykpwNUuShiU1zcmTfWjTJrr3\nKhLPXTl7PqXM4sWEQ0LSTQqHkhFmEpC/hhnJfYMNr06ECNPlcsHpdMJqtQaku4Rkya+p8QUdQjDt\n20P/7bdgmzTx/yA1FaxOB3g8QEYGt3GxDgeQkgKvxQLfZSs1Pcsi9fRpsDab35qHwGLxt5ecORPY\nqhIESkRqckAqdetwOAAgIDKSY3M3mYDBg+mAIdqkphltYMHvCY2mHhxrzydfOCQ0i+dH9zRNcwcP\n4jKUFA4FIkmYdQxynxjDJTliRsCv94lBLsKUO8IkqVWv18vN4OR7iPKFFJGQDzNkCHTffQfq1Cmw\nl2dmsunpoDwerocTdjt0Fy6AvuceGH/+GYbUVP/G6fGAoSiwNTVgmjUDRdYB+EVGYUaJ/NFcWu4x\nI5GX1+sFRVEwm81gGEYWwwQCsxkBZEkQS2QZbU+oGGLp+QzHLJ6kZ/mq20iEQ3JAK+IjoHaEmVTJ\n1hGQX7oaUZxQ3CP1RZPrCyj3F1ksMiYnffLlJs/V4/FElCZkmzSB7/nn/Ybte/f6Z2jOmgVQFPTr\n14M6ccL/Zx55BEzHjtAvXgzq5EmgSRN/Oi4rC7qTJ8E2bAiaYeD1+WA4dw5o2RJ0ZiaC0V+sbSPx\nBj9Ss9ls3O9ZCdWtXOslczeVEk9F0vMZyixeuC8khUO1kaxh1jHEW/QjJu6Jx9rkvE5VVRU33JVT\nwl4my1BK2LDukZ0NurAQwsYFpmdPgKYBvZ4zIKBHj4bum2+g270bYBjQ+fnQ/fgj9MeOQa/TASwL\nulEjVN93H7w1NbU2TbKxKW0dJzdCRWpyqm7lAFEa6/X6uNk7Buv5JKlb/rQV4+WJOUK7Pv4hMFLh\nEHm+VwuBitUw69evr+KK4ockYSoAKZKLh7gn2JrkADlRm83mmJSwUYOiavdYWixg7r4bzN13X1ln\nbi6oI0f8jkHp6WDbtIHZYEBqkE2TbHJqDy0OB5G2ucSiupVrvdXV1XFVGgvBfwah5nzye0KNRmOA\ncEgsdQuIC4eEZvHRCoe0rJIlpZa6gCRhIj4p2VDinnisLdbreDweOJ1ObpMGgith9Xq9Ymm3kKAo\nsDfcUKv3UmzTJJ8LuDJOSstDjGMlH6UNE4SQ00BBTgSb88lXxZI6JVDbLJ6MMhMKgMQOKDRNJ7xw\nSGwPqampgTUc26mrAHWaMJWqYQqvR8Q9/BRmvBHLPUmvHCnuu1yumJWwWgGpsZK2Ea/XG5VJeryg\nBPkombol61WzLScckNStTqeDx+Phnm2onk++4jaYXR8h32iFQ1pTyifbSpKQLe3BJ0wi9zebzVFH\nA2pGmCS16vP5kJ6ezl0jViVsWPD5/J6xaWmKWN2JrVeMOIinL6mFqdWLF4+eUDlTt1roYY0EZL1m\nszlgvcEicKFwSJi6jdRxiES2YmbxWjiwAbXXkVTJ1jEo9SJGIu4JBrnaQaK5L+nts9ls0Ol08Pl8\nXO2H/4UmbRiyNMwzDHQbN/odf9xuIDUV9KBBYHr1innSCEGw0VyAdL3L5XKBpumAqCuslJrTCd3O\nnaB+/hnIygJ91121ZngGAyF3OQ0JQiGW1K1We1ilwCdL4Xrl7PkMRzgkNIsn7VpagNg6nE5nUiVb\nFyA8vclZWPd6vfB4PFx/YrRQK8IUSyOTZnij0cilZUkkQtO0bG0Yui++gKGkBEzTpv6JzW439MuX\n+0eD9ekT07VJejnc0VxA8E1NKBYRvd7FizA+9xyoU6f8LgAeD/TLlsE7axbYdu1C3l8rPaFSxMGP\nwMn7EOwwojUEI0shou355EefZJ+REg7xzeLJ9ckzjUU4pBSSbSV1EHIRE6mJMQyDjIyMmAmEWHzF\niki+XKRHlJ9GJnUX0hwPXPFYJetzOp2xKy09Huj/9z8wzZr5yQUAUlLAZmdDv2EDmO7dI7K244Oo\nlGMldyFxBOvzoygK+qVL/Q5DxOsWAKqqYPzXv+BZsEAy3UzI3ev1qk6WQohF4KT2S0QtpManZVFL\nrJF7JD2f0ZjFk2dMPJqJSE0t4ZBYUJGsYdZByEGYJCojL7oWN4pQUTTxhOWnkaWUsKSnzmq1BmwW\nYUVdUqiq8lvgCVPYKSmAywU4HEAUPV9KtbmE7PPT65GxZQvYRo0C/2J6OlBeDurYMbCtW4uuN1yf\nVbVBInASCaWlpXEEqhXDBDHIneaOtOcTqG0WT9qzxMzi+Y5DUj2fhDzj+YyTKdkkIgZf3ENRFDwe\njyzXlSvyDfUFIhu0y+XiekRDKWFNJhMXgVIUFVHUJQmbzR9BCknT4/GTZhTy9XiM5gIk6p4eD5jL\ndT8drvTqcbVYiX5dUh+TtYdVIfAjYX7kHip1q6YbDqlhKxW5R9LzGcosntQw+c9Kyize5XIBgKRw\nKFaIHbj5frtXO+rGp5SAWA0zGgjNx71er2aK9EKIvfB8JSxJI4fyhA2mfAx10g66YaakgO7bF4a1\na/1p2cvkqSsvhy8vL+J0rBqjuQh0Oh1SUlOBnj2Rsm0b6MaNwbAsaK8XVHU1dBYLfNdeCx3vd8K3\njlOrBSkSkIMWTdOikbAUccTLMEEMSpOlGIL1fAKBhwgx5yAAnF9wsJ5PvjhLKBxS6hlr2VRBbtRp\nwuQjGsIkRMM3H9fCuoJdSwiGYbg0cnp6Onc/oR0YEJ0SVrhhiokkhBsmM3AgfAD0X3zht8EzGODL\nyQHTr19En1crbQ10QQF0Bw9C/8cf0KWmgvJ4wFIUqqdNg8vjATweLk3ncrm4+Y9a34SEVoLhuA2F\nEk8pnbrVgoCKf6AMdYigKAperxdmsxm6y2Ptgqluyc/EhENXu+NQPJAkzMuIlJiI24qYc4/cRghK\nXYumadjtdhiNxrA9YWPdaMTaFIRpKqPRCAwZ4re6q6ry1/sibNLXVFtDw4bwvvYadN98A+rAAaBJ\nE9A9esDUpAmMlyN5vtsQ2UC1WgcH5Jk4Iiaekoq65NikXS6X6KBqNSF1iOATHDlMkTplMLP4YMIh\nfs8nEQ7FahbP3yfqApKEGQXEiIYPpaNCOa7Fr7mmXh7WzFfCCj1hiZhDzo0mLLVp/fr+k3AE19VC\nFFELViuY/v2B/v0DfkzeFRJFGAwGecRTCoKfNparJhxJ1BXpOyhUG2uFLMVAvhPEopE8C6meT75Z\nPL+WSa4VquczUuFQMsKsw4imhilGNEpCiWhVaKggJe7hi2WUrqeJ1T0JaYRrz8aPhLUURQRDKLeh\nqMVTCoFkVkiKXYk1yJm6TSS1MQH5nGlpaQGlj2h6PsnfC9csPhrhkFb1GkqgThMmH+EQk1DcE8u1\n1AI5aUejhI0XQjWH8z09+WKZRBrNBYTnNhSqTYFf61Iaak0ciTZ1yxckJYLaGAje6hKq55PfsiJl\nFh9O6lZ4WOMLh4R9tV6vV/2SRxxR5wmTkFswkotU3CN3SlYuQwWS+uMPfBZTwmpFLEMQqu5pMBjg\n9XoTZjQXEHnaWG21qVYmjoSbutXr9dxQ50R5JyLpCw2351NoFi+06yPetcLULWkTAwKFQ6Rkw7Is\nDh48iKZNm0bUgzlhwgSsW7cOjRo1wv79+0X/zJQpU7BhwwZYLBYsWrQInTp1Cvv6SkP7x3CVwTAM\n7HY7GIaJWAmrlSiTfAYAsFgsHFmSLw+fLD0eD+fcoQWyFIJEG2lpaUhPT4fRaOQ2RoZh4Ha7uY1A\niyBRj8fjgdVqjbouSdoUrFYr9xx8Ph8cDgccDgcXWcnxHPgTR7Q0noukblNTU2G1WmGz2bi2LofD\nwQmnSJpSy4jFRIEcplJTU2Gz2WC1WrlDpN1uh8Ph4IzdSeuJyWTiDhb80WPEK1rq+uSA6vP58Nhj\nj+HWW2/FsWPHsGTJEly8eDHkWsePH4+ysjLJ/79+/Xr8+uuvOHz4MN5//3089NBDET0LpZEkzMsQ\ni+RomkZVVRXnZhPJDEsl1xUJyGfgRx7BlLAk6kmERmRCkKmpqUhPT4fZbOayAXa7HU6nU1M9sSRt\nLHc9jUQDFosFNpsNqampnDAn1udAUp/CCR5aBKnHAf6shMVike05KAm5HYeEh0qSmeE/B5JVMplM\nXCaJXwIhJgvkIEpAItG0tDRs2bIFy5YtQ2ZmJpYvX46WLVviz3/+Mw4ePCi5tu7du6N+EKeu0tJS\n/PWvfwUA3HHHHaioqMCZM2difiZyQfu7osKQSsnGKu7hX1eO9UUD4Wew2+2SSthEq/+JiWWE8nl+\nqk6s7hlPyNGGEQ5CpW4jmbKixoSUWECeMUVRnEhNCdWtnFD6GUeiPhbO+RS2rZDMFP/dTUtLQ/v2\n7bFgwQK4XC5s2bIF2dnZUa+3vLwczXmey82aNcPvv/+OrKysqK8pJ7T/LYgTCDERFWk44h4tg3wh\nyGcgn40Mx+VMAnhK2ESp9YQjlgmmsuTXduKxWcbLmk+IcNSmUi0roZ6x1hCs1UULhgli4Kth49Ey\nFKrnkzwHfm8mv+eTpGv5bSvV1dWc8XpqaioGDBgQ8zqFAYKW9iTtfxPiCL5FnJbGcpG1hfPikGjR\n4/HUUsKaTCZ4PB44HA7ui0MINN5K2GhADjORjOYCJPo9KypA//orYDBA16YNjBaL34mnvBxsWhrY\ntm1lGVodjzaMcBHulBXS2K6pPtYg4A8CCOcZx9swQQxq2PMJITWuTUyFrdPp4Ha7OSIF/FHn4cOH\nZU2ZNm3aFCdPnuT++/fff0fTpk1lu36sSBImD0RyTSziYoFchBnJOsgpm6bpWkpY4upBok2i0gT8\nJ10AmpsmwQc5CMQ6mouiKKR8/TXS/u//wNI0wDCgzWZ4GzUCdfQoKL0eeooC27Ah6CeeABvDl1Ur\nylIxSPW98hvkhYIwLSLWA4mShglS0AJZCiHVykVEgETrkJqayv2ZI0eO4IMPPsDcuXNlW0dOTg7e\nfvtt5OfnY/v27ahXr55m0rFAkjBBURS3sQHyjn6SC+HUQ4knrE6nE/WE5fdYkuHW5Asr3CzJBqKV\nIbX8+l+svx/q8GEY/vMf/7ityyRmOHAApq1b4RsyBLTJBB/DgDp/HtRrr8E7Zw4MJlPE99Raa04w\nkFQdMfe2WCycalLYIK+l+rbcfaHxSN1qkSzFQJ6DyWTi9kadToe1a9di9uzZ6NWrF7766issXboU\nHTp0CPu6o0ePxpYtW3D+/Hk0b94cs2fP5g7shYWFGDx4MNavX48bbrgBaWlp+OijjxT5fNEi6G+c\n1ZqcTAHU1NSgqqoKKSkp8Hg8qFevnizXraqqks3H9NKlS0GHUROrPpPJxNVvpJSwxLjAYrHU+sLy\nxTKkXhHv5ngh5K7/6d9/H/pvvwXbuDH3M93mzYDdDrZzZ27IM8uyYE+cQPXMmXBfe21EpKEpH9sw\nwBd9kbYj/v8j74PP5+Ps09TORhCyJMYaSoOfuuVnZCJJ3SYKWRLwD6pEREXTND777DPMnz8fZ86c\nwdmzZzFw4EAMHToU+fn5mjhgywFK4oPU+QiTYRguxSfXDEsgfuYFRAlrsVi4jYN8uQFEpIQVO2GT\naFQNZaESo7moCxfAClTPlMcDVqcDLqeogcvyeYMBZgAp6ekcafA9N8VIIxHFMsEmjpCWFWGdi5+N\nULreJ4Qaqe5YU7dXA1kCwNmzZ/Hqq6/i3XffRadOnVBeXo7169dj+/btGD16tJpLjgu0/41WGGTk\nlJabm6UIU0wJG8oTNhIlrHCGHyEN4WQRJchTqZQm0749DD/9BJaXSWAaNoTu2DEw/OyC1wtQFNjr\nrpMkjerq6oDaD0ljJuKmGO54rnBad5Q8UPFNFNRKdUeauiX2con4XvDJ8vTp0xgzZgzmz5/Pue80\nbdoUDz74oGprjTfqPGGSl0FuVavc1+ODr4Qlat5gnrByRGlC0hBTWMqVplMypcn06AH2889BlZeD\nveYa/7xNsxlsZiYotxus0wk4naCqquDLz/ePFuNBqs+RryxMBLFMsDaMcBBLy0q00AJZiiGU6pZl\nWaSmpmqq/isFsrcAtSPLMWPGYN68ebj99tvVXKKqqPM1TKIGY1kWly5dQv369WXZ6KqrqzmZe6zg\n10PJRsdPJUuRZTyEJ/yIizzHWERDcRnNdeEC9GvW+GuZKSlg+vQB3bEj9Js2Qbd/P9hrrgEzYACY\nzp2BEOvnpzTNZjO3Wfp8Ps00xwuhdKsL/0BFTDJinbJC3uVEqQsD4JyzTCYT592q1XcCuPIusywb\nQJYXLlxAfn4+5syZg+7du6u8yvhAqoaZJMzLhAkAFy9e1DRh6vV62O126PV6LoUmpYRVo5YWi2iI\nWPMl0hgmqdQV+X/kOXi9XtWa44WI98QRvnE3saWLVEiWiGRJDn78Fih+FO7z+TTzTgDStexLly5h\n1KhReOmll9CrVy/V1hdvJAlTAmSDB0KrUSMB6V0ym80xX8tut8NgMHCn1XCUsFqopZFn6/V6g56u\ng6k0tYpI1LtChSVJ68a7dUcLfaH8AxWJwoOpjxPNng8QJ0shpFS3arRzSZFlRUUF8vPz8dxzz+Hu\nu++O23q0gCRhSoBPmBUVFbDZbLKQDD/yiBWVlZUcmYSrhNUa8ZCIi3+6JuTpdDoD/D+1jliiNH4U\nHm3EFQ202BcaqmWFCMwSjSzJYTXc758wM0PTdFwEVOTeZLINnyyrqqqQn5+Pp556CgMHDlTs/lpF\nsq0kzpBL9ENeZjJaKZQSlqIoTXrCSomGXC4XV+NKBLFMrFEaXywTL2cZraY0Q7WsELFMIihLgejI\nEohMQCXn90OKLB0OBwoKCjB9+vQ6SZbBkCRMHuTunRTOlYsEfCUsOXGHUsJqwa80HFAUxUUQKSkp\n3Ow+rToNESgRpYXTuhOL0jRRUpp89bHb7YbL5YLJZOIOVfGKuKKFy+WC1+uVpf4eTHUrVzpfiiyr\nq6tRUFCARx99FEOHDo3pc1yN0O43KE7gv3DxMhsIBZZl4XA4wLIs0tPTOeVaMCWsFv1KpSA1mouf\nmhIzgFaTPONBPGJReCwbZaKZKACB9b94tqzEAjnJUggpwwRCdtEcJKTI0ul0YuzYsSgsLMTw4cNl\n/RxXC+p8DZO9PPIK8ItryDTyWEEs6KxWa0R/j2GYWkpY0s9luuxpKlTCai3VFgyRbOLhioaUhtou\nLdEoTePSniMzwklpKtGyEguUJMtQEFPdhkrd8u0x+Wt2uVwYO3Ys7rvvvjrh2BMKSdGPBJQiTI/H\nA7fbDZvNFvbf8fl8cDgcSElJ4VKr5DTIr/WRFCZ56RNhQ4xVvSu1OShpCB7Ke1ctBDtIUBQluiFq\nGVKbeDh/L9aWlXivWcn1hKPEFiN4t9uNcePGYeTIkbjvvvs0VQZRC0nCDAIy5srhcHDKx1hBopJ0\ngVNMsD9PhrGKKWGpy8bHxNsV8MvQTSaT5mp9QpB6LEkBxbq5SEUZcvazJUpfqPAgQT57oqRh5XzO\nkbasxLJmLZGlEFKqW3LA4Le7eDwejB8/HkOHDsWECRM0vY/EE0nCDAJCmHKaDZB6SziE6XK54HQ6\nQ3rCEpcfwO+Byz9Zq2GCHQ6CNffLdX1ykJCrn42smWVZTSqOxUDWzDAM9Hq9JtKVocCvpcndBhWq\nZSUWsYyWyVIMDMPA6XRyh+9Dhw5h69atGDBgAP7f//t/6NevHwoLCzX3fqiJZFtJEJDUZ7xFP2ST\n83q9IT1hxezMyL+TtJTb7YbT6dSMUEbu0VxiCMfbNZJnQQ4lWm3PEQP/UELmhfLTlVoTUJE1B5uS\nEitCtaxEc8BMlKyDEF6vFwzDwGazce/1b7/9htzcXDAMg+bNm2PTpk3o3r17wmgh1EIywoQ/LUE2\nHbnceYi6MSMjQ/T/85Ww4XrChqOEFUtLqSGUIf2KZF6hGht0pKIhpT1WlQAh+FCHEn6tT21PU6XJ\nMtS9ozEJSFSyFBNS0TSNyZMno1OnTujduzfWrl2LNWvWwOFw4KeffpL19/Hzzz8jPz+f+++jR4/i\nxRdfxJQpU2S7hxJIpmSDgBAm33g4VpChzmIDqYkS1mAwcGlKKU/YWCZ3SLnrKCmUAbTpKhNKNKTE\n7E2lEW0Er4aAikDpFH2kCKY0JSIvqTYMrUPMoo+maUyZMgVt27bFk08+GfBZqqurkZaWpth6GIZB\n06ZNsXPnTjS/PKhdq0imZMNArGYDwmuJnTeklLBinrBEaRutElbKXUc4BFlO9afso7kYBtTp0wDL\ngm3cGIhyrWLN4PzxZAzDcBF8ImyIsUTDoZ6FUnVPrZElEJ5JAMn6kHR3IkDK/H3atGm44YYbapEl\nAEXJEgC++OILtGrVSvNkGQxJwoQyNUwxECVsWloaF3kF84QVKtpiAV9JKrYxyKEylbv3jzpxAvpP\nPgF18aKfMNPTQY8ZA7ZVq9iuy/u8pK5lMBjg8Xjg9XpVMUaPBHKaqAvfC1LrI9kWucRk4aaO1YTQ\nJICmaS51TFEUnE6nZmrAwUAO2kKynDFjBrKzs/HMM8+osv5PP/0UBQUFcb+vnEimZHGlKB6t2YAY\nWNY/XzMzM5NT1jmdTthstgBXGzElbDxP4fxNMtp5lorUd6qqYJw7F6zJBJC0tt0Oym6Hd8YMoEGD\nmG8hNFFQyxg9EsRz4oiw7kmeg9FojOhZJAJZCiGsswpVt0q1rMQKMZMNhmEwa9YspKWl4eWXX1bl\n+Xs8HjRt2hQHDhxAw4YN437/SJFMyYYBJSJMvqQ7GiWs0uCrTKOxphNuLHJtHrr9+wG3G2jU6MoP\nbTagqgq6H34AE+O4IbFomKLCM0aPlDDkQrxrw+RZpKSkSNrThSKMRBVSCUVJFEWJev4KyxtqDg+Q\nIst//OMfMBqNeOmll1Rb24YNG3DrrbcmBFkGQ5IweZC7rQTwmyFQFAWbzSabElYphCIMYYShaAvG\nhQuASA2UTUkBde5c1Jfl99GFSnfzjdGjJQy5IHttOEKEU/cUEka8h1XLgXAUvEq0rMQKMbJkWRYv\nv/wyvF4v5s2bp2okvHTp0qvCci9JmLhCbnISJk3TAPwbDfniKaGEVRKhCINhGBgMBkXSbOx11wFb\nttT6OeV0gmnRIrprxhANByMMpSMMrU0ckap78glDr9dzE0fkMAKJB6Jpd5HK0AgPmUq270iR5dy5\nc1FZWYm3335bVbKsrq7GF198gQ8++EC1NcgF9b99VyF8Ph/sdjsoilJcCRsv8AmDiIV0Oh1X/5U7\n2mLbtAGblQXq99/96liKAnXmDNj69cF06BD59Xi14VijYSnCkHP8EoHaxu+hIEYYHo8HTqcTALj/\nViuNHS74bWXRvh/8DA0QnykrZHKJkCznzZuHU6dO4b333lO9xpqWlobz58+ruga5kBT9AFwDc7De\nyXDBV8LW1NRwqVgxJWwiNkILo2FFfV2rqqD/3/+g27ULYFkwnTqBHjgQqF8/osvES3Qit2goESeO\n8EVJRqMx4N3QqlBG2IOtxPvBV6YHM0ePBOS7KCTL+fPn49ChQ1i4cGHCvDdaQ9K4IAiIGpBhGFRW\nVqJ+hBsycIUAXS4Xp4StrKzkNmg1lbByIdRoLqGvq2zRFumNjWKTVVN0IuU0FCraSkS/UuAKWYqJ\nkhQ9WMUANb6LfNvCaA9WYml6lmXx3nvv4YcffsCiRYuSZBkDkoQZBOTl5beCRALypfP5fAHinqqq\nKuj1em6iCJC4qsFIR3OJRVuxmqJHCn60Q2aJqgWho4xUtJWomQciWgunDi88WAGxG+ZHA60cXCOd\nsiJFlgsXLsQ333yDjz/+WBO17kRGkjCDQEiY9evXj8hmjChh+cbXfLIg0xL0ej08Hg93Ak8UspRj\nNBe/15OIhZTsb9SiPR+BMNoioiGDwQC32x1THU0NREKWQgi9XePxbpD7aoEshRD2ewoFZcTCUUiW\nixcvxsaNG7FkyRJNCQcTFUnCDAJCmABw8eLFsAmT1DyNRmNQT1gSoZExYjqdThPpqFBQalOJNlUZ\nLrSqOhYDP9oic05NJpOmnYb4kFvBG4+ZllolSyHETEVYlg2wcGRZFkuWLMH69euxbNkyzR0OExVJ\nwgwC8iUFgEuXLiEjIyPkl9Pr9cLhcMBsNnOyeam2EUKWFosFer1emTqfzIjHaC5yHzmFIaHqrFoE\nv581JSUlptpWPKF0u0uoaCua55EoZCkEadsxGo1gGAYPPfQQvF4vWrVqhOtllgAAIABJREFUhQMH\nDqC0tFS1/u2rEUnCDAI+YVZUVMBmswWt05EeKzFPWKG4J1g9Sgt1PjGoNZpLLFUZiQw/EVWlwWra\nclnTKYF4H0zkqHsSsqQoKmEs+oArKW/+sz5z5gzeeecdlJaW4vTp0+jUqRNycnIwfPhwXH/99bKv\noaKiAhMnTuTGf3344Yfo2rWr7PfRCpLWeDKAEKDb7Y7IE1ZqyoHQWUdofK1GdKFm7U/Y3xiuQTz/\nYCKXWX08EMoJJ5g1nZotGmr0hvL7PaMZFJ7oZGk2mwMOJjt27MD+/fuxZ88eUBSFjRs3orS0FOvW\nrcOjjz4q+zoee+wxDB48GEVFRdx3si4iGWEiMMKsrKxEWlparVMzSZvRNB3S5o5shHq9PuovJz/y\njJcQQqu1P7FaDp88XS4XGIaBxWJJGLKMxURdSjQUj5q4FqP4UEPTE9H8HZAWU61btw7vvfceVq1a\nJcugiFCorKxEp06dcPToUcXvpRUkI8wg4D8bMXs8vhI2PT09QAkrJEu5WhmEtnRKm4BrcSMkkLIf\n449eSpQWHSB4v2I4iKfTEB9afUf43xV+3ZNv4UiyOIn0joiR5WeffYYFCxbEjSwB4NixY2jYsCHG\njx+PvXv34tZbb8Wbb74Ji8USl/trCYlxHI8z+IRJ0zSqqqpgMBgC2kZomq5FlmTTMpvNstb+yIZg\ntVphs9m4OY5VVVWorq6Gx+OJevA1aRvxeDywWq2a2gjFQNLY5DBiMBhgMpng8XhQVVWFmpoaeDwe\n2afOyAWSzjKbzbKkvMkzMJvNsNlsXATldDpht9vhdDq5+ni0ICnvRHhHKMpvjG6xWGCz2TgBHjmk\nkMlBWn0/gCsHKiFZbty4EfPmzcPKlSuRnp4et/X4fD58//33mDx5Mr7//nukpaVhzpw5cbu/lpCM\nMAXgk1y0SlglRRBCE3CSto1mgkYsZuRqQqr2x09jkzofPzWnNpROeQvrfEQ0JDVtJhwkqusQqVmS\nsgiAiOueakCKLLdu3Yq5c+eitLQUGRkZcV1Ts2bN0KxZM3Tp0gUAcM899yQJsy5DLCVLNhmr1cq9\nuAzDgKZpUBQl6gkbb8EJOU1LTdAIRp78VoZEapIPVvsTS82pNY5LCDXaXWIVDSWq6xB5t4Vp2HBm\nnap5uOKn6vlk+fXXX+PFF19EaWlpVLadsaJx48Zo3rw5fvnlF9x444344osv0K5du7ivQwtIin4u\ng5gKOBwOrjZptVrDUsJqzZkllG8n2VASyZ4PiF7BG0wkE4/0otZqf+E8D2H2IVHeESmyDPV3yLPw\ner2KTBUJBam69o4dOzBr1iysXr1a1eHLe/fuxcSJE+HxeNCqVSt89NFHcY9044lkH2YIkDpgZWUl\nWJblzAuCKWHj0dgfK8T611iW5eYUanXdQsiVzhQziOdvjnI/D1L70wpZCiE1RYOUHaRaorSIaMhS\n7BpyTxUJBSmy3LVrF2bOnImVK1eicePGst83CWkkCTMEXC4X7HY7GIbhRAOhlLDxbuyPFWROocFg\nAMMwmjFKCAWl0pliUyPkeh6JmM4kz4NkTfiHCS3V+cQgB1mKXTPWqSKhQBT4QrLcs2cPpk2bhpKS\nEmRnZ8d8nyQiQ5Iwg4BlWZw7dy4gcjGbzaLiHhLpaNHUOxiEpCPlMqSlzZHUkuMVocllEM9PZyZS\nb6jQNo5f99Sa0xAf8ZoAJOWBHG3dk5ClsB6/f/9+TJkyBcXFxWjWrJmcHyGJMJEkzBBwOp1cEzxN\n05xwhP9FiJcSVk6ESzr8k3S8jBKCQe0ILZZZlolc+5MqMcjt+SsX1BqXJ3wekdY9pcjywIEDmDx5\nMpYvX44WLVoo+AmSCIYkYYYAIQqn0wm3243U1FSOLPibNzFQTwSQzTvS0VxCsoh3ZKE10olklmUi\nGntH6oSjBZEMENpaMF6QEtlJ1cXJuklJh+DQoUMoLCzEp59+ilatWsX7YyTBQ5IwQ8Dj8cDn84mm\n5WiaBoCEqUUB8m3eYmShpPye3+6iRdLhb45CsiBZCi2LwIQg4rVoa39iIhklRVT8dWuBLIUIVfck\n77eQLA8fPoyJEydiyZIlaN26tYqfIAkgSZghsXv3brRu3TrgS04UjgACBCFaqfFJQSkFL7+3UYm0\nnFrptWghJE9CFiaTSdNzTgnkft5SIiq56+JaJUsxCCfOAOCcmch35ujRoxg/fjz++9//ok2bNmou\nN4nLSBJmEHi9XkyaNAn79u3DXXfdhdzcXFgsFowZMwbFxcW44YYbAshCywKIeCl4pSKtaMmTqDMT\nYRPkg086BoMhru0IsSAepKPEeDL+uon7ViKA1CxJynrJkiVYvnw5evfujdLSUixdurTOmgFoEUnC\nDANerxdbt27FvHnzsHnzZowcORIFBQXo2rVrQG1GKk2pNnmqNZorVmOAWCZ3qAmpdWtdgSwlOFH6\nnsEmioR7jUQlS+G6a2pqsHLlSrz//vs4evQosrOzkZubi9zcXNx2222KvCMtWrRAeno6d7jduXOn\n7Pe4WpAkzDCxcOFCzJo1C0uXLgVFUSgqKsKOHTvQuXNn5OXl4a677gpQyIqlKdWw2NLKaC4pYwCp\n0VNSI4y0jkgOJ1pSIGvhcBKNaEhKKKN1SEXyp06dQkFBARYsWIAOHTpgx44dWL16Nfbv349169Yp\n8l60bNkSu3fvRmZmpuzXvtqQJMwwcOTIEQwdOhSrV6/GjTfeyP2cpmls374dRUVF2LZtG2655Rbk\n5uaiR48eAZulWuSpNes1AjGXIb4xgNfrjbu/qhyIheTliLSiRaxjxZRAOKKhRCVLlmXhcDhq1YhP\nnz6NgoICvPXWW7j99tvjtp6WLVti165daNCgQdzumahIEmaYIJuYFBiGwa5du1BUVITNmzejTZs2\nyMvLQ+/evWul5eSs8YlB7V7FSCBMU5JxZGTzVjtNGS5IJC8HyQsPWEoaxKuVro8EUgpTn8/HWTkm\nCqSch86dO4f8/Hy88cYbuPPOO+O6puuvvx4ZGRnQ6/UoLCzEgw8+GNf7JxKShKkAGIbB3r17sWLF\nCmzcuBEtW7ZEXl4e7r77bm6kEKAMeSaymwwxUjAajdxcUbWNEsKBkhNHlDSIT9S0t9frRU1NDfc+\nJMI7AkiT5YULFzBq1CjMnTsX3bt3j/u6Tp06hSZNmuDcuXPo168f5s+fr8o6EgFJwlQYLMvixx9/\nRFFRET7//HM0bdoUubm5GDBgANLS0gL+nHBjjJQ8td6rKAUpkhczStDaxhjPtLecBvGJSpZCYZKa\nqexIIEWWly5dwqhRo/Diiy+id+/eKq8SmD17NqxWK5544gm1l6JJJAkzjmBZFj///DOKiopQVlaG\nhg0bIjc3FwMHDgyYlC5VvwkWVSRaryJBuEYKUhujWgpkEhGrNUA5FoN4OdPH8UQoYZJWnIbE1iXm\nmFRRUYH8/Hw8++yz6Nevnyprq6mpAU3TsNlsqK6uRv/+/fH888+jf//+qqxH60gSpkpgWRZHjhxB\ncXEx1q1bh4yMDOTk5GDIkCGoV69ewJ8LpS7VgsIxGkRqvUYQb5chIbRWIxZrV5FKUyY6WYZba1XL\naUhsHWLveFVVFfLz8zFz5kwMGjQoLmsRw7FjxzB8+HAA/qzDmDFj8PTTT6u2Hq0jSZgaAMuyOH78\nOIqLi7FmzRpYLBYMGzYMQ4cORWZmZsBgaiF56nQ6+Hw+mM1mzYo2xCBXRCwVVShl/q01P1sxSHn+\nkqhYa6rpUIhVxcv/3vh8vriNryPZE4qiAsjS4XAgPz8fU6dOxbBhwxS5dxLKIEmYGgPLsigvL0dJ\nSQlKS0uh0+kwbNgwDBs2DA0bNgwgT4fDETCTU8sOMnzwI2I5lbBSdWC5UnKJaKJOonG32w2GYaDX\n62EymTRX45OC3C0v/Gg81nFtoe4jRpbV1dUYPXo0Jk+ejBEjRshyryTihyRhahgsy+LMmTNYuXIl\nVq1aBa/Xy5Hnhx9+iB9//BGffPIJdDqdqIOMFskzXm0MkRolhHO9aNLHWgB//Bw/na10NB4r4tEf\nqoRoSOpg5XQ6UVBQgIkTJ2LkyJGyfYYk4ockYSYIWJbF+fPnUVxcjDlz5kCn02HChAm455570Lx5\n84DIU6vkqZbrUKzqUqVM6+MBl8slKkySGj0V7YFCbqhhpiCHaEiKLF0uF8aOHYuxY8eioKBAsc+Q\nhLJIEmYCweFwYNSoUWAYBu+++y42bdqEkpISVFRUYODAgcjNzUWLFi0CNjs+UajpXapkr2IkiPRA\nkcjq43BVvGIHCjXT+1pwHormQCFFlm63G3/9619xzz334L777kuYdyiJ2kgSZgLhueeeQ3l5Od59\n992ACK2yshJr1qxBSUkJzp49i379+iE3NxetW7cO+HIKHXXi1fCtVYs+oPaBQmxGYaKZesei4lXb\nIJ6QpZb6Q8MRDRExGMuyAWTp8Xgwfvx4DB06FBMmTLgqyXLv3r0wm80BtqFXK5KEmUDwer0hNy27\n3Y7169ejqKgI5eXl6NOnD/Ly8tC2bVtJ8lTKFEBr7RehIHwmgN/jNpHSsOSZ0zQti4o3ngbxWiRL\nIaREQ8TSkf/MvV4vJk6ciD59+mDSpEkJ8w5FAqfTienTp6O8vByvvvrqVU+aScK8ilFTU4OysjIU\nFRXh6NGj6NWrF4YPH4527doFkJcS5JmoFn2AX5hEIkuGYUDTtOpGCeFA6ZYX4XsiZ/9rojoPkXmt\nhDD37NmDo0ePYuDAgfj73/+Obt264ZFHHtHsOxMLjh8/jjlz5mDevHl45ZVX8Msvv+D5559H27Zt\n1V6aYkgSZh2By+XCZ599huLiYhw8eBA9evRAXl4eOnbsWIs8Y53pmYjtFwRiG7faRgnhQColqBTE\nngl5LpE+k0QlS+EBBQC2bduGd955Bxs3bkRWVhYefvhhDB8+HC1btlR5tcrg4YcfhtVqxdy5c/HE\nE0+gvLwczz77LG6++eaE+t6HiyRh1kF4PB5s3LgRK1aswP79+9GtWzfk5uaiS5cuMZNnIitKw3HB\nieckkXCh9gEllv7XRCZLsdQ3TdOYMmUKWrVqhY4dO2L16tUoLS3FDTfcgG3btin2u6FpGrfddhua\nNWuGNWvWKHIPPhiGgU6ng91ux9y5czF27Fi0adMGjz32GE6dOoXnnnsO7dq1S6jvfzhIEmYdh8/n\nw5YtW7BixQrs3r0bd9xxB3JycnDnnXcGbHbhzPSUGoqbCCAq3kiESXIY5scKqQZ5tRDMB1moLr3a\nyJJhGEydOhXXXXcdZs2aFUCihw8fRps2bRRb0xtvvIHdu3fDbrejtLRUsfuwLBvwO3Q6nXjrrbfg\n9Xrx97//HQAwY8YM7NmzB/PmzUO7du0UW4saSBJmEhxomsZXX32F4uJibN++HZ07d0ZeXh7uuuuu\ngIhLbCyZwWCA2+1GampqQvnZAvKoeIO1ISilDNa6mUIwdSlJZ15NZDljxgxcc801mD17dlx/F7//\n/jvuv/9+zJo1C2+88YZiESafLFesWIH09HQMGDAA58+fx4gRIzB27Fj87W9/AwC88MILmDhxIrKz\nsxVZi1qo84Q5YcIErFu3Do0aNcL+/fsBABcvXsSoUaNw/PhxtGjRAsuXLw8wRK8LoGka27dvR1FR\nEbZt24ZbbrkFubm56NGjR0BvHNlAPB4PAGgiRRkulJo4IrfLkBhI6ls4LkqrELarEHUpsenT+vqB\n4GQ5a9YsWCwWvPLKK3H/LCNHjsQzzzyDqqoqvPbaa4oT5s6dO/HSSy9h3bp1WL16NYYOHYoffvgB\nS5cuxeTJk9GiRQvu75DU7dUCKcK8ej5hCIwfPx5lZWUBP5szZw769euHX375BX379sWcOXNUWp16\n0Ov1uOuuuzBv3jzs2LEDhYWF2LZtG/r3749JkyahrKwMbrcbpaWlmDBhAiwWC9LT05GSkgKapuFw\nOOBwODgPU62BbH5KjOcijf9msxk2m40bGl5TUwO73Q6n08lFXNGApL4ThSwB/zMhmQiWZWE2m7ms\nRFVVFWpqauDxeKJ+JkqDHK58Pl9AnZhhGMyePRsGgwEvv/xy3H8Xa9euRaNGjdCpUyfFnx1FUVi9\nejXGjRuHRx99FM8++yxGjx6NkpISdOrUCQDw66+/AgD3nb+ayDIY6kyECQC//fYbhg0bxkWYbdq0\nwZYtW5CVlYXTp0+jV69eOHTokMqr1AYYhsG+ffuwYsUKLF++HJcuXcKsWbMwbtw4jhgAdVKU4UKt\niSNy2BYmcp2Y1CyFoqpEUCGLHa5YlsXLL78Mh8OBf/3rX6qs9ZlnnsF///tfGAwGuFwuVFVV4S9/\n+QsWL16syP1effVVGI1GTJ06FQCwatUqjBkzBhs2bIDVasWoUaPw1VdfISsrK6HezXBR5yNMMZw5\ncwZZWVkAgKysLJw5c0blFWkHOp0OHTp0QFpaGnw+Hz766CNUVFRg2LBhGDduHEpKSlBdXc0RpMVi\n4aIsUnOz2+1caiveZy8ikmFZNu7juUiUlZqaCpvNxm2+LpcLdrsdNTU1HJGKgU+WiRJZEni9XlGy\nBPzvlMlkQlpaGtLT02E0GuHz+WC32zWRpZAiy1dffRWXLl1SjSwB4JVXXsHJkydx7NgxfPrpp+jT\np49iZAkABoMBu3fv5v47Ly8PgwYNQn5+PgwGA9atW4fGjRsn1LspB+o0YfJBRmclcQUffPABli1b\nhq+//hrDhg3D7Nmz8fXXX+Oll17Cb7/9huHDh2PMmDFYvnw5qqqqRFOUhDwdDkfcyJPck6IoTfSH\n6vV6pKSkwGq1wmq1Qq/XS6YoSZrbZDIllE0fENnQaoqiYDKZRFP8ahy0pMhy3rx5KC8vxzvvvKOZ\nKBiA4u/05MmTsW/fPjzwwAOoqqpCaWkprrnmGhQWFqKsrOyqd/qRQp1PyW7evBmNGzfGqVOn0Lt3\n72RKlofq6mp4vV5JIRTLsjhy5AiKi4uxbt06ZGRkICcnB0OGDAn4O0IVJQDFTL8TSSQjTFHq9XrQ\nNJ2QCuRIyDIYYp04Ew3cbjc8Hk8tspw/fz4OHTqEhQsXql5eUAp8RSz5d6/XC6PRCI/Hg7y8PDRs\n2BC7du3CqlWrsHbtWhw9ehTz589XeeXKos6rZIHahDljxgw0aNAAM2fOxJw5c1BRUVEnhT9ygGVZ\nHD9+HMXFxVi7di3MZjOGDRuGoUOHIjMzMy5jyRK97kdaR4iyNFpHnXhDLrIUgpAnOVQoMcJOiizf\ne+89fP/991i0aJGqU3eUBFG2Hj58GCaTCRkZGdxB1+PxwGQycdkamqaxefNmPP/881i1alWAQvZq\nRJ0nzNGjR2PLli04f/48srKy8MILLyA3Nxf33nsvTpw4UWfbSpQAy7IoLy9HSUkJSktLodPpuIHY\nDRs2VIQ8iaF3SkpKwkVnwsZ+sf5XrbbwKEWWYuBnKeQwiCd9uVarNYAsFy5ciG+++QYff/zxVUuW\nNE1Dr9dj165duPfee9GyZUt07doVgwcPxl133QUAnDALAM6dO4eFCxdixIgRdSIdW+cJMwl1wLIs\nzpw5g5UrV2LVqlXwer0YOnQocnNza4kGop3pqYW5itEilAuOmMsQP52tJghZqjHOTcogPlwvZCmy\nXLx4Mb788kssXbo0oYwWosHBgwfx5ptv4v7770dmZianhid92ELwCfRqR5Iwk1AdLMvi/PnzWLVq\nFVatWoWamhoMGjQIOTk5aN68eVQzPRPVdg2IPDqLh1FCuFCTLIWI1CCe2CMKyXLJkiVYt24dli9f\nnnAHr3BQU1OD9957D1OnTgXDMJg2bRoWLlyIY8eO4ZprrsFPP/2E0tJSlJeXIy8vD3fffbfaS1YN\nScJMQnO4ePEiSktLUVJSgoqKCgwcOBC5ublo0aJFWORJTAnqAlkKIUaeSgmphIjGjzdekEpnE9GQ\n1NqXLVuGkpISrFixIuHUyeHC6XTi888/R+/evWGz2eByuTBy5Eh4PB6sW7cOBoMBBw4cQFFREXJz\nc9GhQwe1l6wakoSpEYhZ9K1YsQL/+Mc/cOjQIXz33Xfo3LmzyquMPyorK7F27VoUFxfjzJkz6N+/\nP3Jzc9G6dWtR8vR4PGAYhmvZSBTbNUB+wlFSSCWElslSCKFBPPkZOWCR51JSUoJPPvkEJSUlAaYc\nVxNIzRIAcnJyYDQaUVxcDI/Hg0mTJuHs2bNYsWIFzGYz7HY7bDabyitWF0nC1Ai++uorWK1WjBs3\njiPMQ4cOQafTobCwEK+//nqdJEw+7HY71q9fj6KiIpSXl6NPnz7Iy8tD27ZtQVEUFi1ahI4dO6Jd\nu3bcdJVoZ3rGG/EgHLnFMQSJRJZCuN1uuFwuzixh0qRJyM7OxnXXXYcvvvgCq1evhsViUXuZikA4\necTtdmPIkCHIzs7G4sWL4fP58MADD+DIkSPYvHkzdDqd5sRl8UaSMDUEYXsLQe/evZOEKUBNTQ3K\nyspQVFSEo0ePIjs7G7t27cLq1atx0003cX9OjoHYSkOOaSmRQiiOIc8kUvJMZLIUrp1lWezduxcf\nfvghSktLQVEU8vLyMHz4cPTp00ex+qXL5ULPnj25Vpbc3Fz885//VOReBD/99BN3sOT3WNI0jf79\n+6Np06ZYvHgxaJrGjh070K1bN0XXkyhIWuMlkZCwWCwYMWIEPvnkE3Tv3h27du1C7969UVhYiOee\new7ff/89108mtF3zer2oqqpCdXU1l8JVC3xVZjwJR6fTcS5DNpuNq+Pxn0uoc3Eik6XX6621doqi\ncO7cORw5cgS//vorvv76a7Ru3RovvPACZs2apdhaUlNTsWnTJuzZswf79u3Dpk2bsG3bNsXut3Ll\nSnTt2hVff/01KIriUvXEJOPLL7/EyZMnMXToUOj1eo4sk3GSNOqGRjiJhAbDMJg8eTK+//57/PDD\nD2jQoAE8Hg82btyIRYsWYe/evejWrRvy8vLQpUsXjjxJ4zWJOp1OZ9x7GvmjxfiqTDVAyDMlJSUg\nInc6nZJG6IlOlmJK3k2bNuGNN97A6tWrkZ6ejvT0dEyfPh3Tp09X/FBF0r4ejwc0TSMzM1OR+9A0\njeHDh+OVV17BpEmT8Pbbb6Nnz55gWRYGg4HLwmzatAlr164N+LtaychoEckIMwnNQ6fT4bbbbsOX\nX36JBg0aAABMJhMGDhyI//znP/j222+Rk5ODZcuWoXfv3pgxYwa2bdsGmqYlPUvjYfit5GixWBGO\nEbrL5Up4srRYLAFr37p1K+bMmYOVK1eKmpQo/TtiGAYdO3ZEVlYWevfujZtvvln2e5AI8syZMzhy\n5AgyMzMxYMAAlJWVcZEmIU0AGDp0KIBkZBkOtPMNTgJA8qWVwsSJEyWVewaDAX379sW7776L7du3\nY+TIkVizZg369OmDqVOnYsuWLQGjx+Ix01M4hFhLZCmE2KHC4/HA7XaDoij4fD7QNK32MsOGVMvO\n119/jRdffBErV65ULLILBZ1Ohz179uD333/H1q1bsXnzZtnvQeaPFhQUoHHjxtiyZQvefvtt3H//\n/Vi7dm0AafKRjCxDIyn6iTOEFn2zZ89GZmYmHn30UZw/fx4ZGRno1KkTNmzYoPZSEx40TWP79u0o\nLi7Gtm3b0L59e87FhC/skNtNR605nHKBL04iKW21jRLChdQszh07dmDWrFlYtWoVGjVqpOIKr+DF\nF1+E2WzG9OnTZbne66+/jm7duuHOO+8EAPztb3/DuHHj8Oc//xkAMG/ePEyfPh2rVq3CsGHDZLnn\n1YqkSjaJOg2GYbBr1y4UFRVh8+bNaNOmDXJzc9GnT58A79lgbjrhkOfVQpbCeqsaU0QihRRZ7tq1\nCzNnzsTKlSvRuHFj1dZ3/vx5GAwG1KtXD06nEwMGDMDzzz+Pvn37xnxtr9eLY8eOoXXr1vj0008x\nevRoTJs2DTqdDq+99hoA4I8//sDIkSMxZswYTJ48OeZ7Xs1IEmYSSVwGwzDYt28fVqxYgS+//BIt\nW7bkrMD4jeuRWtGRodUANDGHM1KITe4QQzyNEsKFFFnu2bMH06ZNQ0lJCbKzs+O+Lj7279+Pv/71\nr2AYBgzD4L777sOTTz4Z83X5Hq+7d+/G448/jvHjxyM/Px9DhgzBTTfdhE6dOuHjjz/GhAkTMH78\n+JjvebUjSZhJJCEClmXx448/oqioCJ9//jmys7ORl5eHAQMGIC0tLeDPBYuwACQ0WUYrTpIiz3BM\n8+WCFFnu378fjz76KIqLi9G8eXPF16EGCFl6PB5s3boVffr0wdatW/Hmm28iJycHBQUFeP/993Hh\nwgWYzWbMnDkTwJXRXkmII0mYSQRAzKLvySefxNq1a2EymdCqVSt89NFHyMjIUHml8QPLsvj5559R\nVFSEsrIyNGzYELm5uRg4cCDS09MD/pyQJAC/oMNisSTcRiSnkjdc03y5IGW+f+DAATz00ENYsWLF\nVT+7kaZpDB48GDfddBPeeustuN1u7NixA/Pnz8ef//xnPPbYY7X+fKKpnuONJGEmEQAxi77PP/8c\nffv2hU6nw1NPPQUAdXagNsuyOHLkCIqLi7Fu3TpkZGQgJycHQ4YMCWhHqKysBICAqRdqpycjgZJt\nL7GO4AoFKbI8dOgQCgsL8emnn6JVq1Yx30freOqpp+BwOPD2229zP3O73di5cydeeOEFTJs2DYMG\nDQJQ2yYvCXEkCTOJWpCy6AP8LiHFxcX4+OOPVViZtsCyLI4fP47i4mKsXbsWqampyMnJQbdu3XDf\nfffh0UcfRUFBASiKEp3pqUXy5BsqxKPtRcq6UGiUEC7IDFQhWR4+fBgTJ07EJ598ctUOOhaS3syZ\nM9GpUyfk5+ejuroaaWlpnAXesWPH0LJlSxVXm5hIWuMlERE+/PBDDB48WO1laAIURaFFixZ44okn\nsHHjRnzwwQc4f/48evbsiSZNmsDpdOLcuXNgWRZ6vR6pqamw2WxdQm3+AAAgAElEQVQcEblcLtjt\ndtTU1ASkcNVCvMkSEDdK8Hq9URlISJHl0aNHMXHiRCxevPiqJUtixsFHu3bt8MYbb+DgwYNc3T0v\nLw9bt27lyFJNW8irCUlrvCRq4eWXX4bJZEJBQYHaS9EcKIoCwzBYvHgxpk+fjgcffBAlJSUoLCyE\n1+vF0KFDkZubi8aNG0Ov13Pjx0h60u1217Kii2fkqQZZCkGMEoh1IYk83W43dDpdUOtCKbI8ceIE\nxo8fj48++ght27aN58eJG/i1x6effhoURWHgwIEYOnQoampqkJ+fj8LCQmzYsAHNmjVDjx49uL+b\naHV1rSKZkq3DEEvJLlq0CB988AG+/PLLq3aQbqwYOnQo7r77bjz++OPcz1iWxYULF7Bq1SqsXLkS\nNTU1GDRoEHJyctC8eXPJgdixTBCJFMR9yOfzadJ9SGggIeyBJWSZmpoaYDxRXl6OMWPG4IMPPqgT\nQ4/vvfde3HDDDUhLS8PHH3+Mxx9/HDk5Odi9ezd++eUXGAwGTJkyBUBS4BMtkjXMJGpBSJhlZWV4\n4oknsGXLFlxzzTUqr067cLlcIQ8TFy9eRGlpKUpKSlBRUYEBAwYgNzcXLVu2rEWe8RhLpnWyFELY\nxkN+RszjybM5deoUCgoKsGDBgjoxFq+kpAQ7d+7EnDlzkJubC7PZDLfbjR49euD+++9H/fr1uT+b\nJMvokSTMJAIgZtH3z3/+Ex6Ph/PZvPPOO/Hvf/9b5ZUmPiorK7F27VoUFxfj7Nmz6NevH3Jzc9G6\ndeu4kKfQ11ZL4qNw4PP5UF1dDYPBAIZhsGjRIvz222/c/Ni33noLd9xxh9rLjBuqq6uxYMECnDt3\nDnPnzsXTTz+NtWvX4vXXX0f//v3VXt5VgSRhJpGEBuBwOLB+/XoUFRXh999/R58+fZCXl4e2bdsG\nEBnfwzUWVWmikyXDMHA4HFwalmVZHDp0CB9//DFWrFgBl8uFe++9FyNGjEDPnj0D6ppy4uTJkxg3\nbhzOnj0LiqLwt7/9jUt7KgWhGpZvNjB79mwcPXoU//d//4fHHnsM9erVw+zZsxVdT11CkjA1ADLx\ngaRJkj1RdRs1NTUoKytDUVERjh49il69eiEvLw9/+tOfavm4EuL0er1hz/S8WsiSpGEJLly4gFGj\nRmHOnDlo3Lgx1wLlcrmwb98+RdZy+vRpnD59Gh07doTD4cCtt96KVatWKSYw4qdTnU4nZ9lI9oxj\nx47hgQceQHV1NRo3bozVq1cDSDr4yIUkYaoIp9MJk8kkWk84deoUmjRposKqktAS3G43PvvsMxQV\nFeHgwYPo0aMH8vLy0LFjx1rkSYgzGHlerWR56dIljBo1Ci+++CJ69+4d8HfsdrvkCDi5kZeXh0cf\nfVQW43Qh+KQ3bdo03HXXXRgyZEiturndbseuXbu455CsWcqHJGGqiLKyMjz77LNo0qQJHnnkEfTt\n2xd6vR4//PADhg0bht9++63WbLok6i48Hg82btyIoqIi7N27F926dUNeXh66dOkiSZ5kLBlJ25K+\nxkQly+rqaphMpgCyrKysxKhRo/Dss8+iX79+qq3vt99+Q8+ePfHTTz/BarUqdp+nnnoK+/fvR2lp\naS0iFGankmQpL5LGBSqiW7du2Lx5M2bNmoVPPvkEJ0+eBOBXvA0ePBgGg4FrZqdpOqGG9caKCRMm\nICsrC+3bt+d+9uyzz6JDhw7o2LEj+vbtyz2vugKTyYSBAwfiP//5D7799lvk5ORg2bJl6N27N2bM\nmIFt27ZxDexkILbNZgsYiO31ejmRTCJBiiyrqqowevRoPP3006qSpcPhwD333IM333xTdrLcuXMn\nZ7V48eJFHDx4EPPnz4der4fH4wHgz0QAtYc9J8kyPkgSpsLYvXs3Hn74YQwYMAD/+9//8MUXX3Dk\nuHbtWgwfPhyAv1WhpqaGa3YnYFlWdWcYJTF+/HiUlZUF/GzGjBnYu3cv9uzZg7y8vDotZjAYDOjb\nty/effddbN++HSNHjsSaNWvQp08fTJ06FVu2bOF6FimKwrJly0BRFCwWC1iWRXV1Nex2O5ee1TII\nWRqNxgCydDgcKCgowBNPPMF5oqoBr9eLv/zlLxg7dizy8vJkvfaFCxewfft2pKSkwOFwIDMzEyzL\n4tKlSwDA9Z1++eWXqKqqkvXeSYSPJGEqiIqKCsybNw9t2rRBWVkZjh8/jhYtWqBRo0b4+eefUVNT\ng759++KPP/7AzJkz0b17dwwbNgzFxcVgGIZLuxB3Ga1veNGge/fuAb1jAALqUA6HI9kTehl6vR49\ne/bE/PnzsX37dowbN44zzJ88eTLy8/OxbNkyrqZpNpths9lgNptFyVNLBzE+WfJrddXV1RgzZgwe\neeQRDBs2TLX1sSyL/9/enYdFWe6PH3/PMAgI6FEEkkVFTMUQJLM0wJVSE0WNREFRj0tRudExNNPQ\nFC0vK06mFi5paulRuMRCMpKKzJXjLqKyBGpFAsoAsg337w+aJ1D0fPslDMv9ui6vZobHee4Jn/k8\n9/b5TJs2jR49etRIWPEwnD59GisrK2bPns3Ro0d57bXXKCoqYvDgwfj7+3P16lWKi4tZtGgRH374\nYZ0OA0sPJifO6pCZmRkFBQUMHDgQCwsLjIyM6NOnD+bm5uzcuZMhQ4ag1Wr54IMPyMvLIzk5mejo\naH7++WfUajW3b98mISGB/v37Y21trbzv6tWr6dWrFwMHDqyzZfSGtmjRIj777DNatmzJ0aNHDd2c\nBsfIyAhPT088PT0pKyvD19eXzMxM2rVrx7x58/Dz82Pw4MGYmJgotSlNTU2VZABFRUX31PQ01Fzn\n/YLlnTt3mDhxIjNmzGDs2LEGaZve4cOH2b59O25ubnh4eACwcuVKhg0b9rfe99atW7z77ru0adOG\ntWvXYmFhgZmZGStWrGD58uXodDqmTJmCnZ0dxcXF7N27F7VaLVfYG4hc9FOHKisrefPNN4mNjcXd\n3Z09e/awbds2AgIC6Nu3LytWrKCyspJDhw4xceJEHnvsMeXvxsfHs3v3bn777Teys7MZO3Ys4eHh\naLVaZsyYgb+/P/7+/kovoTFfPA+qmrJq1SpSU1PZsmWLAVrW8JWXlxMUFIRWqyUmJoYWLVpw9uxZ\n/vOf//Dtt9/i5OTE6NGj8fHxUbYmwP0LP9d3ZZW7h2H15y0pKWHSpEkEBgYSFBRUL20xBJ1Ox9mz\nZ9mwYQMWFhasXr2aS5cusXHjRjQaDeHh4coNjpWVFSYmJsq+XKnuyEU/BqBWq4mIiOD8+fPMmjWL\nyMhIvLy8uHLlCqmpqXh6emJubs6JEyfo3r078Oek/rJly/Dw8OCrr74iJiaG9PR08vPzOXnyJCYm\nJhQVFfHrr78qQ7Z6+qHcpiIwMJATJ04YuhkNVl5eHlZWVsTExGBqaoparaZXr16sWLGCI0eOsGjR\nIlJSUhg5ciTBwcFER0crvUt9ZRULCwtlNe2dO3fQarXcuXOHioqKOv23pB8m1mg0NYJlaWkpU6ZM\nYdy4cU22AIB+MZaRkRFubm7MmjWL/Px85s2bR7du3Zg5cyYqlYrZs2dTUFCAnZ2dksRfBkvDkQGz\nDlWfd+zbty8vvfQS9vb2GBsbExERgampKW3atEGj0XDz5k0ATExMuH37NqmpqcyaNYuKigqcnZ05\ncuQIhYWFpKSkkJaWxqlTp+jbt69yB6qnVqtRqVSNOmheuXJFebxv3z5lCEy6l62tLevXr681t61K\npcLV1ZXw8HAOHz7M8uXLyczMZMyYMQQFBbFr1y4KCgpqBE99WbK6Dp7Vg6WpqakSLMvKypg2bRqj\nRo0iODi4UY+c3E/1fZbXrl1Dq9Xi6urKwoULKSwsZO7cuTg7OzN58mRcXFywsrJS/q5MSmBYcki2\nnlRWVt7TG4SqPJkfffQR69atw9nZmVWrVlFSUsKSJUuU1aNnzpzB19eX7OxsJk2ahJubG/Pnz6eg\noIDnnnuO6OhobGxsOHjwIFevXmXQoEE1MpCsWbMGc3NzXnrppXr9zP8XteW0jYuLIzU1FSMjI5yd\nnVm/fj02NjaGbmqTIYQgLS2NvXv3EhcXR6tWrRg1ahQjRozgH//4R41jdTqdstdT37v5u5VV9MFS\nH6T171NeXs706dMZNGgQISEhTTJYVrdmzRri4+NxdHTE2dmZRYsWkZGRwTvvvINWq2XLli1KKkSZ\nwad+ycQFDcj9NhknJyfj4OCAra0tM2fOpG3btgwaNIitW7fi7u7O888/z7Jly5g7dy6PP/44J06c\nYMyYMVy7do0tW7awbds23N3dSUxMZOnSpYwePRqdToePjw8hISGMGzdOmf+QG50lqApeWVlZ7N27\nl/3792NqasqoUaPw9fWlbdu29y1L9v8bPO8XLCsqKnjxxRfp168fs2bNavLBctOmTWzdupXo6Gjm\nz5/P119/jZ+fH+vXryc9PZ2NGzcSFhZG69atDd3UZkkGzAZKv/ji7uB1/vx5Nm/ezLlz53jllVcY\nPXq00vucOXMmdnZ2zJw5k1atWvHyyy+zatUqhg0bxtixY0lMTCQ8PJzvv/+ec+fOMXHiRJKTk5UE\nCSqVikuXLvHWW2+xePFiXF1dDfTppYZECMH169eJjo5m//79qFQqRo4cyciRI7G2tv7bNT31wVKt\nVmNmZqYcp9PpePnll+nVqxehoaFNMlhWX9VaXl7OV199haenJzt27CAxMZF///vfDBs2DE9PTzZu\n3Kj0KGXP0jDuFzDl7LGB6eeP7ubq6sp7772nPC8vLycnJwcPDw9leDIuLo4DBw5w9uxZbGxslBJH\nWVlZdOvWDYADBw7g6uqKRqNRepeVlZWkp6dTXFwsg6WkUKlUODg4MHv2bGbNmkVOTg7R0dG8+OKL\nlJeX4+vri5+fH4888ghqtVrJ86oPnmVlZRQXF9daluxBwXLOnDk89thjzSZYGhkZMXr0aPLy8khK\nSmLlypV07NiRgQMHcv78eXJzc5V5SxksGxb522igqi8Y0i/5f++99/D390ej0ZCSksJvv/1Gz549\nsbS05NSpU9jb2wNVQ7uurq7k5+fz/fffK9mE9EpKSjh58iRFRUW88847HDlypN4/X0NUW5o+vTVr\n1qBWq8nLyzNAy+qfSqXC1taWkJAQZYtTq1atmD17NiNGjGDt2rVkZWUhhFCCp7m5OZaWlhgbG1Ne\nXk5BQQFFRUWUlpbWGiwrKysJDQ3FycmJsLCwJhks4c8tXxs2bGDChAm8+uqrnDlzhrZt22JsbMx/\n//tf1qxZw82bN4mNjcXKyqrRpTRsLuSQbCNRfWhGP/94/fp17O3tycrKIiQkBDc3NxwdHfnggw9I\nTExU8l7qt6Lo73Szs7Px9vbmhRdeoHPnzkRFRbF582acnJzIy8vDycmpxl1xcxkWSkpKwsLCguDg\n4Bp7QrOzs5kxYwapqakkJycrBbabq/z8fPbt20dMTAz5+fkMHToUPz8/nJyc7qnpWVZWRmlpKUII\nNBoNV65cwc7Ojnbt2vH666/Trl07li5d2iSDZfVr6Pr160yePJl58+Zx8eJFPv/8c7744gtl/lif\nN7Znz54yKUEDcL8h2QcSUoNVWVkphBCioqJCCCFERkaGWLBggViyZIk4ffq0EEKIiIgIERAQUOO4\nyspKERcXJ4YMGSLKy8uFEEIEBweLjRs3ioyMDOHn5ycuXLgghBDizp07Dzx3U5SRkSFcXV1rvObv\n7y/OnDkjOnXqJHJzcw3Usobp1q1bYvv27WLMmDHC09NThIeHi1OnTonCwkKRl5cnXnrpJZGamiq0\nWq3Iz88XoaGhwtLSUri5uYlnn31W3Lhxo17aOXXqVGFjY3PP77auVL9GkpKSxLp168S7776rvBYZ\nGSmeeOIJcerUKSHEn9ea/jqVDOt+MVHOYTZS+hsg/fxnp06dWLlyZY1jsrOz8fX1BVAWFpWXl5OY\nmEi3bt2U/Z8eHh7k5+fj4ODAxYsXlfnPjz76iKysLJYsWVJjL1hzuvvdt28fDg4OuLm5GbopDVLr\n1q0JCgoiKCiIwsJC4uLiiIiIICsrC5VKhaWlJdbW1qjValq0aKH0Jq9du4ZaraZHjx64ubkpKfDq\nytSpU5k1axbBwcF1do7afPPNN7zyyis4ODhQXl6Ol5cXTz75JLNnz1ZWBh86dEjJwiRXrjdsMmA2\nEfoMP9UvuHXr1imP9dlBsrKyiImJYfLkyQCkpKSQkZGBl5cXt27d4plnnuHmzZukpKQQHR1NVFRU\njWAZHx9Ply5d6NKlSz19MsMpLi4mIiKCb775RnntATefzZ6FhQXjxo1j7NixjBs3jmvXrmFtbc3w\n4cMZMGAAY8aMITY2ltLSUnbu3IlaraakpISEhAQyMzPrtG3e3t51fo7qVCoVSUlJREZGEh8fT+fO\nnfnXv/7F7t27EULw1FNPERoaSnBwMObm5vXWLunvkQGzibh7jlH/xa7vDer/a2JiwvDhwzly5AjT\npk0jIyMDX19fBg0aRLt27VCr1Xz88cdotVoCAgLo0aOHsgBBrVZz6dIlvvzyS9auXYtOpyMnJwdb\nW9sa59fXamzs855paWlkZmbi7u4OVGVl6d27N8ePH5eJFO6joqKCSZMmUVJSQlJSEiYmJpSWlnLw\n4EFWrlyJVqslISFB+bdhamqqjII0NdnZ2cTFxTFhwgQ6d+7M4sWLWblyJZs3b0YIgaenJ+3atZNz\nlk2FIceQpbp18+ZNsWHDBrFjx44ar/fr10906tRJxMbGCq1WK4Somo/Rz63MmzdPLFiwQAghREJC\ngpg2bZqIiooSQghx4cIFUVJScs+5Vq9e3WjmZmqbw9STc5j/W3l5uXj33XfvO/9tSA/63daVqKgo\n4eLiIuLi4oQQQhQUFIiwsDCRlpZWr+2Q/hoZMCUhhBA6nU7odLoar+kXKOTk5IgZM2aIoKCgGq9X\nt2DBArF161YhhBBDhgwRmzdvFr///rsIDw8XQ4cOFY6OjuLNN98Ut2/fFkIIsW/fPtGqVSshRNWC\nhrvP3ZCMHz9etG/fXrRo0UI4ODiIzZs31/i5k5OTDJiNmCECphBC7NixQ/Tq1Uvs2bNHCCGUa6Ap\nL55r7O4XE+WQbDOjHwoTf2QY0idr/+yzzzh48CB2dnaEhoYC1MhAJP4YNnryySc5ePAgLVq0QAjB\n1KlT+fTTT0lOTmbHjh1YWVkxePBgfH19eeqpp/j000+V97t7QUND267y+eefP/Dn6enp9dQSqSkJ\nDAxECMGSJUvw9vZW1gTIYdjGp+F8W0n1Sp9hSKVSUVFRweXLl7GwsGDhwoVKqbHqAU4/j5mVlcXW\nrVu5cOECy5cvp7i4mJSUFMaPH4+VlRUlJSW0bt2aoqIiABITE5k6dSrZ2dksWrSI33//XXnPu+c9\nJamuTJgwgaeffprLly/j6OhY7/VVg4KCOHToEDY2NnIlbCMme5gSGo2Gt99+W3kualmEoL/I27Rp\ng4mJCQMGDKBfv35UVFRw9epVevfuDcDFixfp2rUrxsbG/Pjjj1hZWdGhQwf27NnD8ePHsba2Bqq2\nrHh7eyvbNfTvf7+qLpL0d/yv0YP6YGtrC9R+fUmNg+xhSvd40MUcHBzMzZs38fHxAaqC7aOPPsrF\nixcRQrB69WoqKyvx9vZm06ZNjBs3DoDjx4/j5eUFVG1lSUpK4vr16+Tm5jJy5Eh++uknfvnlF2WI\nGKqCp0wRJjU1Mlg2XjJgSn+JPi1f9UCmTxv35JNPYmtry2uvvQZUJX6fMmUK+fn5XL16lcGDBwNV\nwdPW1pann36aL7/8kpMnT/Ltt9/St29f3nrrLQoLC5WN7dWHbYUQzWofZG25bcPDw3FwcMDDwwMP\nDw+lZqokSXVPDslKf4l+6LR6IHN2dlaGvIqLi2nZsiU//PADBQUFdO3alfPnz3Pu3Dm8vb2BqoLY\nHTp0oHXr1nz22WfMnTuXsLAwvLy8WLhwIebm5nzyyScMGDCANWvWKEWNq1e+EH8k/W7KastOo1Kp\nCA0NVRZSSZJUf5r2N45UL6pXVmnZsiU6nY7+/ftz9uxZ5Rhra2s++eQTtm7dyv79++nTpw95eXmk\npaUREhICQE5ODmZmZkyaNImrV69y/vx5MjIygKr6oNu2bePixYtNIinC/4W3tzdt2rS55/Xm1MuW\npIak6X/rSHVOrVbXWPmnf9ylSxcqKytxdXXl/fffJyUlha+//ponnngCDw8Pdu/eTc+ePWnVqhW5\nublkZGQwcOBA2rdvz+3bt8nIyMDFxYUffviBTZs2cePGDaZPn87rr79OSUnJPe3YuXMn77//fr19\nbkP58MMPcXd3Z9q0ady6dcvQzZGkZkMGTKlO6XuCTz31FO+//74S1CwtLfnuu+8YNmwYABkZGdy4\ncUNZNfvFF1/Qv39/bt26RWRkJHFxcQwaNIjY2FiysrK4ffv2PecKCAhg/PjxQFUvTKfTNblFQyEh\nIWRkZHD69Gnat2+vzBdLklT35BymVC+qJ4d/5JFHgKqgqB/KzcnJUSqnAMTExDBx4kSuXLmCg4MD\nQ4YM4YMPPuDkyZNYWlpy/fp1bG1tleQHOTk5BAQEcODAASoqKtBoNE1yv1v1HLbTp09n5MiRBmyN\nJDUvMmBK9aK25PD65AkAzz33HL1798bW1pbi4mKOHTtGVFQU5eXlnDhxgsjISF5++WV0Oh2XLl3C\nzs4OqFq1q1ar2bt3L0ZGRpiamrJ9+3aioqKYOHEirq6u9OvXr94/b1355ZdfaN++PVB1U1F9Ba0k\nSXVLDslKBlHbXjRbW1t0Oh0tW7bkzJkzODo6Ym9vj4eHB7NnzyYpKYnCwkJcXFyUxTD6QLxnzx4C\nAwMB2L59OyYmJpiZmREcHFyjzNndGvJeT312mtTUVBwdHdm8eTNhYWG4ubnh7u7O999/3yzmbCWp\noXjgDtoHJaGVpLqmH269ceMGH3/8Md988w0eHh5ERETQunVrpZd669YtPDw8OHbsGGq1mv79+xMd\nHU337t2JiIigoqKCN954A41Go7xnfn5+rStQpYYpPj6euXPnotPpmD59OmFhYYZuktSEqe6TXUIO\nyUoNlr73aGdnx9KlS1m6dClarRZLS0ugajhWo9Gwc+dOXFxcsLGxYc+ePdjY2NC9e3e0Wi1qtRpj\nY+N7hoRXrVpFcXExNjY29OrVS5kL1Afh2gpyS4ah0+l49dVXSUhIwN7enj59+jBq1ChcXFwM3TSp\nmZFDslKDp9/nKYRQgiVUpeUDuHDhAiNGjABg165dPPvss0DVytvff/+djh07olarlfnOoqIirl27\nRkpKCm3btmX58uXs2rWrRo7Pu7fKSIZz/PhxunTpQqdOnTA2Nmb8+PHs27fP0M2SmiHZw5QavP+V\npOCjjz6isrKS0tJSTExMGDt2LADnzp2joqICd3d34M9yZYcPH8bExISwsDCeeeYZHn30UZYuXUpA\nQABZWVmsXbuWnJwcfH19ef7552vMt8qeZ/27fv06jo6OynMHBweOHTtmwBZJzZXsYUqNWvU0eSYm\nJmzfvp3u3bsrVU8sLS3p2rUr8GdChZMnT9KmTRtlSO/06dMMGDCA06dPs2zZMh5//HFCQkKIiYlR\ncrX++uuvVFRU1Oh56vd6Npep/tpy20JVIgUXFxdcXV3rZG5RJiuXGgrZw5Qatbt7f/rSYGq1msDA\nQMrKyjA2NlYW+xQXF3PhwgXy8vJwcHAA4ODBg4SFhbFx40ZOnz7NmTNnmDt3LkZGRvz0008MGzaM\nFStWcObMGTp27EhgYCDDhw+vsS2mOZRsqi23bWJiIrGxsZw9exZjY+Ma9U4fFnt7e7Kzs5Xn2dnZ\nyu9OkuqT7GFKTUb10mD6nmeLFi1qHHP8+HE0Gg2PPPIIL7zwAqGhoeTl5eHl5UVZWRkrV65ky5Yt\nXLp0ifT0dDp06EBmZiYJCQkMGzYMPz8/ysrKOHfuHHPmzOGTTz6hsLAQlUql9DSrz7k2JbXltl2/\nfj0LFy7E2NgYQKl3+jA98cQTXLlyhczMTMrKyti1axejRo166OeRpP9FBkypSbq7CLX+cVxcHB06\ndGDZsmWMGDGCLl26sH//fszMzOjSpQvx8fG4urry9ttv8+OPPzJx4kQuXbqEk5MTYWFh+Pv7k5mZ\nyRtvvEHnzp359ttvCQsL45dffkGlUnHnzh1l2Lb6+Zta8NS7cuUKP/zwA3379mXgwIGcPHnyoZ9D\no9Gwdu1ahg4dSo8ePQgICJArZCWDkEOyUrOgD15eXl4YGxvj6OjIlClTahzj4+NDaGgofn5+9OnT\nhwkTJmBtbU1ycjJubm4YGRlx6tQpEhMTWbBgAZ6ensyZM4fc3FysrKz44osv2L17N8XFxbzwwgtM\nnjxZqemp74Hq64k2leHbiooK8vPzOXr0KCdOnGDcuHGkp6c/9PMMHz6c4cOHP/T3laS/QvYwpWZl\n1KhRyhevPo+t3uOPP05CQgJTpkzh559/pqysjLy8PJKSkpTi12lpadjZ2eHi4kJpaSk6nQ4rKyti\nYmJYvHgx0dHRREREcOzYMW7cuIFarSY2NlYZttVoNEqw1Gq1aLXa+v0f8JA5ODgoq5L79OmDWq0m\nNzfXwK2SpLohA6bUbN29NaSyshKNRsOYMWOIiorCxcUFc3NzfHx8GDRoEAA9e/YkKysLIQQmJiYY\nGRlx584dYmNjEULg7e3Npk2byMvL48svv+Tq1avMmTOHt956Cx8fH9atW6ek4ktJSWHfvn0UFRXV\n+2d/WEaPHs2hQ4cAuHz5MmVlZVhZWRm4VZJUN2TAlKQ/6Pd7Vl+wY21tzfz585VFLY8++iidO3fG\n39+fyMhIMjMzMTMz48iRIxw9elSZA9VoNAwcOJDvvvuOrl274u/vT2hoKPHx8eTm5nLixAnmzZvH\nkSNHMDc3N9hn/iv0uW0vX76Mo6MjW7Zs4Z///Cfp6en07G0pmCkAAAFASURBVNmTCRMmsG3bNkM3\nU5LqjMwlK0kPoN+Ocrevv/6aH3/8kQEDBjB48GCmTp1KYGAgQ4cOrXHciBEjePHFFxkxYgRGRkYM\nGTKExYsX06JFC4KDg9HpdIwcOZIFCxYoFVgkSTKs++WSlSTpr7nfheQDJAM/AeFAR8AFOAB0/uMY\neyANMPnj+EigDdAeaBzdTElqxuQqWUn6a/SjLuo/HuufJwC9AU+gK3ADmA/k/PEHwA84A5QCfYFC\nIL9eWi1JkiRJDURtyWWdATf+XCvwX2Ay0BrYAOj3ScgbV0lqBOSFKkkPh36PihrQV6ROq/ZzDbAL\n2EtVD7MboE9DVFEfDZQk6e+RJRck6eGqvlCu+nxnJXAYKKcqqLYDJgC3gdR6a50kSZIkNXAqal8w\nZFLfDZEkSZKkxkKFnA6RpEbn/wFnNYMMVynu2gAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU5IamKFEnlyKlaSm5f8A5/W2rh+LSFkAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x106fa3ba8>"
+        "<matplotlib.figure.Figure at 0x107c42940>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -720,7 +720,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -764,7 +764,7 @@
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -813,7 +813,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "code",
@@ -849,13 +849,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4VFX6wPHvpPcCIYB0QVkBC4rBZXFF1y42LCigItZV\nkbWjq9j3p6K7qygCghQREFFBBUFAEFmQ0KuQEEpCIJCQXibTzu+PMyEJJGGSzMydSd7P88yTuSX3\nvhOSl/eec+65IIQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGE1wUaHYDw\nS4uAAGCbm/cVQgghhGjSioEi58sBlFZZvtvAuBpqAPpzVHyGDOAroE89jvEa8IW7AzPwPEIIzd35\nbiXwQB3bO1M9H2UBPwBX1uMcw4HfGhBbfXnrPKKeAowOQHhNFBDtfB0EBlZZnl1lvyDvh9ZgmVR+\nhkuA3ehEc4WRQQkhDOdqvnOVcnG/WOc5zgOWAt8B9zXgfEKIZmI/lUXLAOAQ8DxwBJgOxAE/AseA\nXPSVXrsq37+SyqvF4cBqYKxz333AtQ3ctwuwCihEJ7NPqL1laQC6Vexk44D1VZY/BNKBAmAD0N+5\n/lqgHLCgr2g3O9ffD+xyxpAGPFzlWAnon0secNwZq8m57QzgG/TPbB8w8jTnEUJ4R9V8FwCMBvYC\nOehW9XjntjBgpnN9HpAMJAJvAzagDP03/FEN5+iMbiE7uZHjGXRrWYWKcxcCO4FbnOvPcR7f5jxH\nrnP9DeicUYDOY69WOVZt8YIuDKcAh9H5/U1nbLWdRwhhkJMLMivwf0Aw+o+8BXCr830UMBd9pVdh\nBTDC+X44uth4AF2cPIpuuWrIvmuB99CtdH9BJ6EZtXyGAdRckF0B2IFw5/JQdMINAJ5GF50hzm2v\n1nD869GFIcBfgRLgAufy/wGfosdeBjpjxHnsjcDLzti7oIu5q+s4jxDCO6rmu1HAGvQFVDAwAZjl\n3PYI8D0675mA3ujWLqiex2rSmZoLsjOd67s7l28H2jjf34nuWm3tXL6PU7sSLwN6Ot+fiy7ubnYh\n3u/QuSocaAWso/LisqbzCB8gXZYCdMJ4FV2YmdFXTd853xcD/0InhtocRF+NKXTh0ZbKKzVX9+2I\nHv81Bn319j90sjHVfJhaHXZ+T5xz+Uv01aMD+DcQSmVyNNVw/EXoBA66BexndGEGuphsi06+dmeM\nABejW8/ecsa+H5gM3FXHeYQQ3vcI+sLpMDrfvY4ukgLRf98tgbPQ+WkzuhWpQkP+hg87v7Zwfp1H\nZYvZXCAV6FvH8X9Ft6QBbAfmUJmLa4u3NXAd8BS6NSwb+C/V85HwQVKQCdB/sJYqyxHAROAAupXq\nV3QTeG1/yFWb5EudX6Pque8Z6ELQXGV7TS1gp9MOnZzyncvPorsg89GFWSy6eKrNdcDv6C7JPHSL\nWUvntrHo7oaf0S1gLzjXd3LGn1fl9SK1F6VCCGN0Rl9sVvyd7kJfRCWih0csQRc9mcC7VB9T6+o4\nsqoqhnpUdA3eiy6cKs7fi8r8UpO+6Na5Y+gc9kiV/WuLtxO69e9IlfNMQLeUCR8mBZmAUxPNM8DZ\nQBK6gLkMz7fyHEFfRYZXWdexAce5Fd19WAZcCjwH3IFuMYtHF5gVn+Pkzx2KHgf2HjpBx6NbzCr2\nL0YXeF2Bm9BdoFegx3bsd+5f8YpBDyQG3TonhDBeOnpcZ9W/1Qh0/rEBb6C7CPuh/37vdX5fQ4ox\n0PnoKLAHXShNAh5H57p4YAe15yPQ3anzgfboHDaByv+3a4s3HT1utWWVzxiL7vJszGcRHiYFmahJ\nFLqgKUAnjlfr3t0tDqIH3b+Gvrr7MzrBuJI8TOgr0VfR49Necq6PRietHPS4sTHoQqlCFvqKuSIh\nhjhfOegi6joqx4HhjKebc/9CdLelHT2Ytgh9Y0Q4uvujF5VTcBw96TxCCGNMQA/BqLjYa4W+uAI9\nLvVc9N9vEbpL0+7cdhR9IXY6FX/jrYEn0DnnRee6SHQ+y0H/33s/Ok9UOIouvIKrrItCt3BZ0BfI\nQ6jMibXFm4Vuxf83OgcGOGOvGHpR03mED5CCTMCpRc9/0YVFDnoA7E817FP1e0/e1tB9h6ILsePo\nu4K+onpX6snfdwaV8/4ko68ULwOWOfdZ7HyloLtfy9BXjxW+dn49ji4Gi4An0WM7ctHzFS2osn83\n9N2fReifyyfo7lwHuli7AH2HZTb6Srii+Dv5PEIIY3yIHpv6M/qiai260AE92P5r9IXoLvQd4l9U\n+b7b0Xnhv3UcPx/dkr4N3RJ3OzDNuW0X8IHznFnoYmx1le9djh4vloXuogR4DN0KVgi8gs6JFeqK\n9170xeUuZ8xfU3kzQU3nEaJWgeh+9h+MDkQY6iu80zonhBBCGMpXW8hGoSt76etuXvqgm9YD0N2F\nN6HHTwghhBDCy9qju5wuR1rImpuB6C7FEvSs+zLDtRBCCGGQr9ET3F2GFGRCCCGEaAZ87bmFA9GD\nDDej7yCpUdeuXVVaWpq3YhJCGC8NfVOF35P8JUSzdNoc5mtjyPqhxw3tRz8A9gpqeORMWloaSimf\nf7366quGxyBxSqxNIU5cm3LAL0j+ar6xSpzNM06lXMthvlaQvQR0QD8L8C7gFyon5hNCCCGEaJJ8\nrSA7mdxlKYQQQogmz9fGkFX1q/PltwYMGGB0CC6RON3PX2L1lziF9/nT74a/xCpxupe/xOkqf32U\ni3L2yQohmgGTyQT+m69OJvlLiGbGlRzmyy1kQvi8Fi1akJeXZ3QYTUZ8fDy5ublGhyFEsyE5zL0a\nk8P89YpTrjCFTzCZTMjvovvU9vOUFjIhPENymHs1Jof5+qB+IYQQQogmTwoyIYQQQgiDSUEmhBBC\nCGEwKciEEEIIIQwmBZkQzdC0adO49NJLjQ5DCCHqranmLynIhBAe9/HHH9OnTx/CwsK4//77jQ5H\nCCFc5q38JQWZEAbIzYWZM8Hh0Ms7dsDy5cbG5Ent2rXjlVdeYcSIEUaHIoRoJKVg1izIztbLJSUw\ndSpYrcbG5Sneyl9SkAnhAWvWwB9/6PdKwXffQdW5F8PDYedO+Phj2L4d3nkHEhKqH6OsrO5lV2Rk\nZDBo0CASExNJSEhg5MiRNe43atQoOnbsSGxsLH369GH16tUntiUnJ9OnTx9iY2Np06YNzzzzDABm\ns5lhw4aRkJBAfHw8SUlJHDt2rMbj33rrrdx88820bNmy/h9CCOFVubk6Z1VMp7VrF6xdW7ndZILI\nSHjpJTh4EF57DcrLIajKVPMOh15Xoays8niuam75SwoyITwgNBTeflsXZdOmwa+/Vk9W4eEwZgws\nXaqT2rPPwvnnVz/G//0fzJ+v32/dCiNHgtnsegx2u52BAwfSpUsXDh48SGZmJnfffXeN+yYlJbF1\n61by8vIYMmQId9xxBxaLBdDJ7qmnnqKgoIB9+/YxePBgAKZPn05hYSGHDh0iNzeXiRMnEh4eXmdM\nMgGlEL4vOBhWrIAZM3Qx9q9/QVhY9X1uvhn+9jd44gmIiYFHHtGFWoVff9U5rqwMiovhn/+E3393\nPYbmmL+kIBPCAy66CJ56Cp5/Hr79Ft58E6Kjq++TlqYLN4BVqyq7Lys8+SQsWgSvvw5jx8I//nFq\nUqxLcnIyR44cYezYsYSHhxMaGkq/fv1q3Hfo0KHEx8cTEBDA008/TXl5OXv27AEgJCSE1NRUcnJy\niIiIICkp6cT648ePk5qaislkonfv3kSf/CFPYjI1lcn2hWi6oqP1BeW8efDCC/DMM9C7d/V9Skpg\n40b9Pj0dcnKqb7/sMujQAZ57Tr969IBLLnE9huaYv6QgE8IDlIJt2/T7wEA4dKj69sOHdTflK6/A\n3Llw5Ah8+WX1fRISYPBg2LABzjkHevWqXwwZGRl06tSJgIDT/5m///779OjRg7i4OOLj4ykoKCDH\nmWGnTJlCSkoK55xzDklJSSxcuBCAe+65h2uuuYa77rqLdu3a8cILL2Cz2eo8j7SQCeEfMjKgInVs\n21a9u1Ep3YLftSt8/z0MHAgvv1x9DFlAANx3n+7SPHQIhg6t3oJ2+vNL/vIXSghfUNvv4ty5So0a\npVRhoVIbNig1dKhSGRmV2x0OpQ4dqlwuLVUqO7v6MbZs0d+3cqVSDz2k1Hff1S+2NWvWqMTERGWz\n2U7ZNnXqVNW/f3+llFKrVq1SiYmJaseOHSe2x8fHq+XLl5/yffPmzVNhYWGqtLS02voDBw6oHj16\nqClTptQZ08svv6yGDx9e6/bafp5AU8qEdf6MhPCmmn4fDx7UuWfTJp3DRo5Uat686vscOqTzWIWq\n+U0ppYqKlHrqKaUmTVLqo4+Uev55nedc5Y/5S6nG5TBpIRPCA/7618puyosugjfegDPOqNxuMkG7\ndpXL4eGnDupfsQJGj9ZN///6F2zZUr8xZH379qVt27aMHj2a0tJSzGYza9asOWW/oqIigoKCSEhI\nwGKx8MYbb1BYWHhi+8yZM8l23k4VGxuLyWQiICCAFStWsH37dux2O9HR0QQHBxMYGFhjLHa7HbPZ\njM1mw263U15ejt1ud/3DCCG8pn17nbN6967svuzfv/o+7dpVb/Fq37769u3boWdPePBBePxx6NwZ\ndu92PQbJX/6jzgpVCG/x9d/F9PR0dcstt6iWLVuqhIQENWrUKKWUUtOmTVOXXnqpUkopu92uRowY\noWJiYlTbtm3Ve++9p7p06XLiCnPYsGEqMTFRRUVFqV69eqkFCxYopZSaPXu26t69u4qMjFStW7dW\no0aNUna7vcY4Xn31VWUymaq9Xn/99VP2q+3nibSQCeERvvz76G/5S6nG5TB/HWHr/HxCGMtkMsm4\nAjeq7efpHEzrr/nqZJK/hM+QHOZejclh0mUphBBCCGEwKciEEEIIIQwmBZkQQgghhMGkIBNCCCGE\nMJgUZEIIIYQQBpOCTAghhBDCYFKQCSGEEEIYTAoyIYQQQgiDSUEm/IPdrp9Qe+xY9afcigaZNm0a\nl156qdFhCCFEvTXV/CUFmfB9+flw//1w220wcKB+sJrDYXRUwkUWi4UHHniAzp07ExMTQ+/evVm8\neLHRYQkhxGl5M39JQSZ83wcfwB9/QKtW+vXdd/DTT0ZH1Xjr1uki86qr4N13obzc6Ig8wmaz0bFj\nR1atWkVhYSFvvfUWd955JwcPHjQ6NCFEQ+XkwFNPwZVXwgMPwIEDRkfkEd7MX1KQCd+3axfExIDJ\nBAEBEBgIe/YYHVXdbDaYMAFuvhnuuQc2bqy+PTUVRo3SSc1kgrlz4b//PfU4mZmwenWDP29GRgaD\nBg0iMTGRhIQERo4cWeN+o0aNomPHjsTGxtKnTx9Wr159YltycjJ9+vQhNjaWNm3a8MwzzwBgNpsZ\nNmwYCQkJxMfHk5SUxLFjx045dkREBK+++iodO3YE4IYbbqBLly5s2rSpQZ9JCOEF69fDsGFwyy0w\ncaLOaRUcDvjHP3RuCgzUOfrRR6G4uPoxSkshOVkfy2yudwjNLX9JQSZ8X7duUFSk3zscOjF06WJs\nTKczYQJ89plOUAcOwBNP6CKswsaNYLXqQjM0FBISYOnS6sdYuRJuvx2efVYnxvHj6xWC3W5n4MCB\ndOnShYMHD5KZmcndd99d475JSUls3bqVvLw8hgwZwh133IHFYgF0snvqqacoKChg3759DB48GIDp\n06dTWFjIoUOHyM3NZeLEiYSHh582rqNHj5KSkkLPnj3r9XmEEF6SkgJPPgkHD+rcO3EiTJ5cuT0n\nR+ez1q0hJETnr4IC2Lu3cp/cXJ23nnxS57/hw6Gw0OUQmmP+koJM+L5nn4WOHSE7W7+uvBJuvNHo\nqOr244/QogVEREBcHFgsuouyQmRk9f3LyyE6unLZaoVXXtHf36qVTnjTplUv6k4jOTmZI0eOMHbs\nWMLDwwkNDaVfv3417jt06FDi4+MJCAjg6aefpry8nD3OVrmQkBBSU1PJyckhIiKCpKSkE+uPHz9O\namoqJpOJ3r17E131M9TAarUydOhQhg8fztlnn+3yZxFCeNHatTpnxcXpHNSihc5pFcLD9c1Vdrte\ndjj0+4iIyn0mToSMDJ2/EhN1sTZtmsshNMf8JQWZ8H2tWsGXX+o/5jlz4J13ICjI6KjqFhGhi6oK\nSukkVuGKK3TL3+HD+lVWBs6mdEBflZrNlQkuKEh3DeTkuBxCRkYGnTp1IiDg9H/m77//Pj169CAu\nLo74+HgKCgrIcZ5rypQppKSkcM4555CUlMTChQsBuOeee7jmmmu46667aNeuHS+88AK2qt0aJ3E4\nHNxzzz2EhYXx8ccfu/w5hBBeVlFwVbBaqxdb0dHw4IP6AvnwYcjKgquvhrPOqtzn4EEIC6tcDg3V\nd8q7SPKX/1BCGCWvLE/9a9W/lNVuVbX+Lq5cqdTFFyt14YX6ddNNSuXnV9+nuFip+fOVmjlTqd27\nq2+z2/X3XHKJUgMHKnX55Ur9+c9KHTnicpxr1qxRiYmJymaznbJt6tSpqn///koppVatWqUSExPV\njh07TmyPj49Xy5cvP+X75s2bp8LCwlRpaWm19QcOHFA9evRQU6ZMqTEWh8Ohhg8frq644gplNptr\njbm2nyfQlOY6qfXzC+FtNf4+5uXpvNO7t85fSUlKrVpVfR+HQ6m1a5WaMUOpZct0zqpq8mT9/ddf\nr9R11+njzJnjclz+mL+UalwOkxYyIeppzo45TN0yleX7lte+02WX6TEXjzyiW76mT4fY2Or7REbq\nQf9Dh0L37tW3BQToQf6tW8PRo7o7YOxYaNPG5Tj79u1L27ZtGT16NKWlpZjNZtasWXPKfkVFRQQF\nBZGQkIDFYuGNN96gsMpYj5kzZ5KdnQ1AbGwsJpOJgIAAVqxYwfbt27Hb7URHRxMcHExgYGCNsfz9\n739n9+7dfP/994SGhrr8GYQQBoiLgxkzdO565BGYMgVOnvfLZIJLLtE3Lf3tbzpnVXXvvXpoydGj\nuiXt9tv1y0WSv/xHnRWqEJ6SV5an+k3up/pN6adu+PKG2lvI3MXhUKqgQKkarhJdkZ6erm655RbV\nsmVLlZCQoEaNGqWUUmratGnq0ksvVUopZbfb1YgRI1RMTIxq27ateu+991SXLl1OXGEOGzZMJSYm\nqqioKNWrVy+1YMECpZRSs2fPVt27d1eRkZGqdevWatSoUcp+8lWy0lefJpNJhYeHq6ioqBOvWbNm\nnbJvbT9PpIVMCI/w+O9jSYlSJ7VIucrf8pdSjcthJo+nHs9wfj4hvGvChgl8vvlzWke1Jqs4iw0P\nb0B+F93HZDLV+PM0mUzgv/nqZJK/hM+o7W9ONExjcph0WQrhonxzPlO3TMWhHBwvPY7FbjE6JCGE\nEE2EL96qFgb8CoQCIcAC4EVDIxICsNgtXNftOqz2yrsnt7HNwIiEEEI0Fb7aBRABlKILxtXAs86v\nFaTJX/gEae53L+myFN70U+pPmG1mbj3nVqNDMYzkMPdqTA7zxRYy0MUY6BayQCDXwFiEEEI0MWXW\nMt5b8x52h50rz7yS6NC6JwUVwtN8dQxZALAFOAqsAHYZG44QQoim5Ps931NsKcZsM/PNH98YHY4Q\nPttC5gAuAGKBJcAAYGXVHV577bUT7wcMGMCAAQO8FZsQwsNWrlzJypUrjQ7DYyR/GavMWsaEjROI\nDdXzUn2++XNuO+c2aSUTbtOQHOYPYzJeAcqA96uskzEYwifI+Av3kjFkwhvm7pzLmBVjiAuLA/Qd\n1KP7j2b4BcONDcwAksPcq6mNIUsAbEA+EA5cBbxuaERCCCGajFYRrbj3/HurrTsj+gyDohFC88Ur\nznOB6ehxZAHAF8DYk/aRK0zhE/z16nLatGlMmTKF3377zehQqpEWMiG8yx9zmK/mL2h6E8NuBy5E\njyE7j1OLMSGEnxk2bBht27YlJiaGM888k7ffftvokIQQwiXeyl++WJAJ0WxsPLyR11c2/R75F198\nkf3791NYWMhPP/3EuHHjWLx4sdFhCSEawWwz8+RPT3K0+KjRoXiUt/KXFGRCeNB/1v6HzUc217jN\noRx8sPYD5u2ax45jO2o9xt7cvTiUo0Hnz8jIYNCgQSQmJpKQkMDIkSNr3G/UqFF07NiR2NhY+vTp\nw+rVlfMwJycn06dPH2JjY2nTpg3PPPMMAGazmWHDhpGQkEB8fDxJSUkcO3asxuP37NmTsLCwE8tB\nQUEkJiY26DMJIbxj4+GNfPj7h7Vu/zHlRxalLmLG1hm17pNTmkNeWV6Dzt/c8pcUZEJ4SFpuGtO2\nTOP9Ne/XOKZg3aF1pB5PJSokigkbJtR4jMNFh7nvu/tYnb66xu11sdvtDBw4kC5dunDw4EEyMzO5\n++67a9w3KSmJrVu3kpeXx5AhQ7jjjjuwWPSzOkeNGsVTTz1FQUEB+/btY/DgwQBMnz6dwsJCDh06\nRG5uLhMnTiQ8PLzWeB577DEiIyPp2bMnL7/8MhdeeGG9P5MQwjscysHYNWOZumUq+/P2n7LdbDMz\nYcME2sW045s/vqmxlUwpxUvLX+KNX9+o9/mbY/6SgkwID5m4cSLhweGk5KawLnNdtW0O5WBc8jhC\ng0JpEdGCdYfW1dhKNnXzVHLNuXz4+4f1biVLTk7myJEjjB07lvDwcEJDQ+nXr1+N+w4dOpT4+HgC\nAgJ4+umnKS8vZ8+ePQCEhISQmppKTk4OERERJCUlnVh//PhxUlNTMZlM9O7dm+jo2udxGj9+PMXF\nxSxbtoyXX36Z5OTken0eIYT3rM1YS1puGqGBoXy26bNTtv+Y8iMF5QXEhMZgV/YaW8m2Ht3KpiOb\nWJ2+mj05e+p1/uaYv6QgE8ID0nLTWHlgJS0jWhIaGMq4deOqtZKtz1zPpiObKLOVcbT4KAXlBUzc\nMLHaMQ4XHeb7Pd/TOa4z6QXp9W4ly8jIoFOnTgQEnP7P/P3336dHjx7ExcURHx9PQUEBOTk5AEyZ\nMoWUlBTOOecckpKSWLhwIQD33HMP11xzDXfddRft2rXjhRdewGaz1Xkek8nEgAEDuOOOO5g9e3a9\nPo8QwjsqLhjDgsNoGdGSpfuWVmsls9gtjF8/nlJrKUeKjmC1W5m9YzbZJdkn9lFKMX79eEICQwgK\nDKq1F6A2zTF/+eI8ZELUi81ho9RaSkxojNGhnDBp0yQKygtOLG/O2kxyZjJ92/cFICEigWf7PVvt\nexIjq49JmLp5KgpFUEAQ4cHhfPj7h/Tv2J8Ak2vXUR06dCA9PR273U5gYGCt+/3222+MHTuWX375\nhZ49ewLQokWLEwVkt27dmDVrFgDffPMNt99+O7m5uYSHhzNmzBjGjBnDwYMHuf766+nevTsjRow4\nbWxWq5WWLVu69DmEEN71+6Hf2Zq1lejQaMpt5RSaC5m8aTJv/63y7sL7L7ifcnv5iWUTJoIDg08s\nbz26lc1HNtM6qjUKdaKVrHtCd5diaI75Swoy4fdmbJ3B8n3L+WLQFy4XK57Wp22fUwqsqgVj1xZd\n6dqia63fX24rZ+m+pSilOFasB5pmFGawO2c3PVr1cCmGvn370rZtW0aPHs3rr79OQEAAmzZtOqXZ\nv6ioiKCgIBISErBYLLzzzjsUFhae2D5z5kyuueYaWrVqRWysftRMQEAAK1asICEhgR49ehAdHU1w\ncHCNiTM7O5vly5dz4403EhYWxrJly/j6669ZtmyZS59DCOFdsaGxPHjRg9XWdYzteOJ9SGAI95x/\nT53H+DHlR6wO64lWM5vDxqLURS4XZJK//IcSQimlCs2Fqv/n/dUFn16gfjv4m9fP78nfxfyyfJVd\nkn3ilVOSoxwOR72OkZ6erm655RbVsmVLlZCQoEaNGqWUUmratGnq0ksvVUopZbfb1YgRI1RMTIxq\n27ateu+991SXLl3U8uXLlVJKDRs2TCUmJqqoqCjVq1cvtWDBAqWUUrNnz1bdu3dXkZGRqnXr1mrU\nqFHKbrefEkN2dra67LLLVFxcnIqNjVUXX3zxiWOcrLafJ+BfM1fWrV7/hkJ4kqd+H8usZdXyV3ZJ\ntiq3ldfrGP6Wv5RqXA7z15mvnZ9PNHefb/6cT9d/SlhwGG2j2jLn9jlebSXzx1mufZnM1C+Ed0kO\nc6+mNlO/EC4pKi9i6papxIfHExsay/68/azJWGN0WEIIIUS9SUEm/NbSfUspNBdSbCkmtywXi8PC\nzG0zjQ5LCCGEqDd/7QKQJn9BUXkRGYUZ1dbFhcVxRvQZXotBmvvdS7oshfAuyWHu1Zgc5q8JThKa\n8AmSzNxLCjIhvEtymHvJGDIhhBBCCD8mBZkQQgghhMFkYlghGiE+Pr6iKVq4QXx8vNEhCNHkKKVq\nzVOSw9yrMTnMX/8VZAyG8EnpBekMnDWQsKAwAkwBFFmKuKD1BcwcNFOSXiPIGDIhGmbuzrnsy9vH\n6P6jjQ6lWXMlh0kLmRBudlevu3Aox4nl2NBYKcaEEF5XYinhk/WfUGopZdh5w2gf097okEQd/PV/\nCbnCFKIZkRYyIepv5raZfLjuQwII4PqzrufVAa8aHVKzJXdZCiGEEM1QiaWEzzZ9RlxYHC0jWrIw\ndSGHCg8ZHZaogxRkQgghRBPzQ8oPZBVlkVeWx7GSY+Sb85m+ZbrRYYk6+GsXgDT5C9GMSJelEPWz\nJ2cPu7J3VVvXIbYDfc7oY1BEzZvM1C+EaBKkIBNC+DMZQyaEEEII4QekIBNCCCGEMJgUZEIIIfyW\nUop8c77RYQjRaFKQCSGE8FvrMtdx+9zbpSgTfk8KMiGEEH5JKcW4dePIKMxg7s65RocjRKNIQSaE\nEMIvrctcR0puCp3jOjNj6wwKzAVGhyREg0lBJoQQwu9UtI6FBoYSEhiCxW7hq51fGR2WEA0mBZkQ\nQgi/s+PYDv7I+QO7w87x0uM4lIM5O+Zgc9i8Hsv6zPXklOZ47XxWu9Vr5xLe468TLcrEikI0IzIx\nrDiZ1W4l5XgKisqfZVhQGF3ju1b8vnhFUXkR1395PVeeeaVXHt6dW5bLffPv48NrP+TM+DM9fj7h\nHjIxrBDugLjLAAAgAElEQVRCiCYpODCYnok96ZXY68SrW4tuXi3GAObtmkeJtYSFqQvJKMjw+Plm\nb5/NruxdfLbxM4+fS3iXFGRCCCFEAxSVF/H5ls9JiEjAhInPN3/u0fPlluXy5fYvOTP+TJbvX86+\nvH0ePZ/wLinIhBBCiAaYt2seJZYSAgMCiQ2L5YeUHzzaSjZ7+2ysDithQWGYMEkrWRMjBZkQQgjR\nANuObSM6NJpSaynl9nKiQ6LZlb3LI+cqsZQwe8ds7A472SXZOJSDJWlLyCzM9Mj5hPf56yBZGRQr\nRDMig/pFc2d32FmXua7aHZYmk4mkdkmEBYUZGJlwhSs5zBcTXAdgBpAIKGAS8NFJ+0hCE6IZkYJM\nCOHP/LUga+N8bQGigI3ALcAfVfaRhCZEMyIFmWiwnTvhgw8gJwcGDIDHH4fQUKOjEs2MKzks6DTH\nSATuAP4KdEa3WB0EVgFfA8caG2QNspwvgGJ0IXYG1QsyIYQQom6ZmfDoo+BwQHg4fPkllJbCyy8b\nHZkQp6hrUP8UYC66lWoCcB9wPzARiHZum+zh+DoDvYF1Hj6PEEKIpmbTJjCboUULXZAlJsKiRUZH\nJUSN6moh+wjYWsP6P4BfgHeA8zwRlFMUMA8YhW4pq+a111478X7AgAEMGDDAg6EIUV2+OZ83f32T\nNy5/g8iQSKPDaXJWrlzJypUrjQ7DYyR/eUloKFTtHrZadWEmhIc1JIf56piMYOBH4CfgvzVslzEY\nwlATN0xk7JqxvD7gdYaeN9TocJo8GUMmGqSsDO6/H1JSIMDZIfTqq3DjjcbGJZqdxg7q317HNoXn\nWsdMwHTgOPBUbedvtgltxw74/HOdaG65Ba6+Gk5+VIjZrPdTCnr1kitCN8s353PDrBsICgjChImF\nQxZKK5mHSUEmGqykBH74AfLzoU8f/RLCyxo7qL/iEuIx59cvnAfzdHPAX4BhwDZgs3Pdi8BiD5/X\n96WkwEMP6fdBQbB+vW6CHziwcp+CAnj4YThwQC936gSTJkFcnNfDNZLVbmX94fX069DP7cf+asdX\nWOwWWoS3IKs4i/m750srmRC+KjIS7rrL6CiEOK26BvUfcL6uBp5Ht5htA15wrvOU1c64LkAP6O+N\nFGPakiVgsUBCgi6wIiNhzpzq+3z+OaSl6cGriYmwbx981vwer7EkbQkjF40k9XiqW49bWF7I1C1T\nsdqtZBVnUW4rZ9LGSZRZy9x6HiGEEM3L6aa9AN0q1h9dKIFuwWoqXQf+JTCwevekUnpdVenpEFZl\n1uawML2uGbHarXyS/AkWh4VJGycx9uqxbj3+feffh9VROVt2WFAYCumCEkII0XCuFGQjgKlArHM5\nHz39hfC2G27QLWJZWboQs9thxIjq+1x4Ifz6K8Q6/7nKyuCii7wfq4GWpC0hpzSHjrEdWXlgJanH\nUzmr5VluOXZMaAx/v/jvbjmWEEIIUaE+LV2xzv3zPRRLfTTfQbH79+uirKxMF2h9+1bfbrPB22/D\njz/qFrQbbtCTIAYHGxOvl1ntVm6afRNltjIiQyLJKcnhr53+6vZWMuFdMqhfCOHP3PXopDbA20A7\n4FqgB/Bn9MSxRpGEdjolJfprZPO6+y/leAoPfv8g5bbyE+taRrTku8HfERokj0vxV1KQCSH8mbsK\nssXoLst/oqe6CEbf/dirkfE1hiQ0IZoRKciEEP7MlRxW112WFRKArwC7c9kK2BoVmRBNVGZhJvP/\nmG90GEIIIfyMKwVZMdCyyvIlQIFnwhHCv41fP57Xf32dzMLMen9vblkub/z6BjaHXO8IIURz40pB\n9gzwA3AmsAY9QeyTngxKCH+0P28/S/ctJTAgkGlbptX7+2dum8nMbTNZvm+5+4MTQgjh01wpyDYC\nf0XPP/YI0JOaHzouRLM2edNkTJhoFdmKBXsW1KuVLLcsl9k7ZtMqshXjksdhtlRvJbNJo5kQQjRp\nrhRk+4CHgB3o2fot6Ad/CyGc9uftZ2HqQsKCwzDbzJRZy+rVSjZz20zsDjstwltwtOQoI95azi+/\n6G179sDIkVBeXvcxhBBC+C9XJoa1AgOAJOBRoBw9BYYQwimnNIfzW5+PQzkAaB/dHovd4tL35pbl\n8sXWL1AockpzKLeVc7TTOKZO/xspKUGsXg2jRkFIiCc/gRBCCCO5chv5ZvTzJJ8HbgPuBOY71xlF\nbhsXp0hNhagoaNtWLycnw3nnVX+SlC86VnKMyZsmV3scU2lBJEe+e5Ljx0K47DJo3x5iYqo/R745\nkWkvhBD+zF3zkFUUZABXAp8ALYBWjQmukSShiVMsXQqzZ+sHFezYAbNmwf/9H7RpY3Rk9bdrFwwb\nBmedBZmZ0LIlTJ+unynfHElBJoTwZ67kMFe6LMdUeb8MuBq4r+FhCeEZV12lH+/58MN6ecIE/yzG\nAL77DsaP18VleDi0a6e/CiGEaJrqGtR/jvPrYeDCKq+WwEIPxyVEjZRSpBek17o9MLDyfUAtv91p\nuWnM2THHzZG510svwb59uquyb19ISABTU2kfEkIIcYq6CrKnnV8/qOH1vofjEqJGW7K2cPc3d3Mg\n/8Ap25Yv192UEybA44/DP/8JR4+eeoz//P4f3ln9ToMmb/WWlBRYs0Z3ub7+OkRHww8/GB2VEL7H\nYrewPnO9505gt4PD4bnjC+Hkr9fcMgajudm+HbV8OQ9avua3kCPcee4Q3vrbW9V22bNHD+pv57wH\n+Pff4fzzq3f17Ty2k+HzhwNwy59u4Z9//aeXPkD9WSyVd1ba7aAUBLkyyKAJkjFkojbz/5jP26vf\n5ps7v6FjbEf3Hdhigffeg++/183tDz8M998vTdWiQRr7LMvbgEF1vEQTl1GQ4fLUDR61bh08+CBb\nvp/EtsOb6ba/kJ/3LDyllax798piDOCSS04dd/Xphk8JCggiITKh3pO3elvVaS4CA5tvMSZEbSx2\nC+M3jKfMWsbkTZPde/DPP4dvv9XjBeLi4JNP9J1DBsrP1+mwwp49cOCAYeEIN6urILvR+XoAmAIM\ndb4mAyM8H5owUqm1lAe+f4C5O+caHQp8+ikqJJiPexQTHBRKoMWKKb+AyRvrl4B3HtvJrwd/JSgw\niBJLCSWWkgY94kgI4RsWpSwiryyPjrEdWbx3cZ3jS+tt9Wo910xgIAQH61dysvuO3wCFhbouXLVK\nF2NvvgnHjxsaknCjugqy4cD9QAjQA91idhv60UkyRWUTt2D3ArKKs5i8aTIllhKvnPPjj/VcYgC5\nufDOO7rXgLIyDkbZ2BFehN0E2SE2lMPOioMrKDC7/pz7MlsZf+34V85NPJdeib24rPNlRARHeObD\nCCE8qqJ1LDo0msAAfTePW1vJ2raFsrLKZavV8Nu2O3bURdjYsfDss/CPf8BFFxkaknAjVzpBOgBZ\nVZaPAm7sqBe+ptRayqSNk0iMTKSgvIDvdn/HsPOGefy8F18Mb7yhHxM0dSpcfrmz2+7WW+k09j3m\nbzsHh80KNjt88D7B5/QiNizW5eP3OaMPfc7o47kPIITwmpTjKRSVF1FuLyffnA9AcmYyNoeNoAA3\n9O+PHAlbtlTeGdS1Kwwe3PjjNpLZXPm+tNS4OIT7uTI68WPgbGCWc//BQCow0oNxnY4MivWg2dtn\n8+/f/02bqDaYbWasdisLhywkMiTS4+devFg3yffrBy++6FzpcMDXX8P8+XpQ2N//rqs30WzIoH4D\n5OTAb7/p93/5CyQmGhuPEXJzYfNm3V158cWGTwaYlgavvqpbxlq1gldegUcf1flS+DZ3zdQPehD/\npc73q4DvGh6WW/hHQvNTg78ezN7cvQQHBgNgtVt558p3uKrrVR49b26unqoiKgqysmDMGD1TvRBS\nkHlZZiYMH67/KEEPap86VfeZCcOUlMD+/dCrl14+eFD3IlQ8Lk74LncUZEHADuBPborJXXw/ofmx\nwvJCyqxl1dYlRCScGKfhKf/8p56m4s479Z1EkybBp5/KQ7WFFGRe969/6Rbp1q318tGjcP318Npr\nhoYlqtt5bCfrMtcxorfcZ+fr3PHoJBuwB+gEHHRPWMLXxYTGEBMa4/Xzjh6tJ0AFPTt9jx5SjAlh\niNxc3U1XISQE8vKMi0ecQinF2DVj2Zq1lau7Xk37mPZGhyQaqa67LCu0AHYCvwA/OF/fezIo0TxV\nFGO1LQshvOTyy/Xo8bIy/bW0FK64wuioRBWbjmxiZ/ZOggODmbJpitHhCDdwpQtgQC3rV7ovjHrz\n/SZ/IYTbSJellykFs2fDtGn6/T336JfMUu8TlFLcv+B+UnNTiQ2NJac0h28HfyutZD7MnYP6fY3v\nJzQhhNtIQSZEpY2HN3Lv/HuJDdXT/uSZ87ijxx28cfkbBkcmatPYRydV+DOwHigGrIADKGxscEK4\ny9HiozS3/+AcDj1nW8VjU/bv18vyDGQhmr4AUwC3dL+FAZ0HMKDzAPq07cNPqT9hc9iMDk00gisF\n2cfAEPTcY2HoRymN92RQomnZkrUFh/JMpZBblstd39zF/zL+55Hj+6qAAD2kZ8wYWLFCz010xRV6\nvRCiaevdtjdv/+1t/vW3f/HWFW9Rai2lxFrCsrRlRocmGsHV9J0KBAJ2YCpwrcciEk3Knpw9PPzD\nw6zNWOuR48/aPovDRYcZt26cx4o+X9W/P1x9Nfz73/pr//5GRySE8LbfDv5GekE6iZGJfLz+Y2kl\n82OuFGQlQCiwFXgPeJqmM5ZDeNiEDRMothQzLtn9BVNuWS6zts+ic1xn9uXtY03GGrce39ft3w8/\n/6wLsZ9/ruy+FEI0Dw7l4KN1HxEeHE50aDRHS45KK5kfc6Ugu9e53xNAKdAe/ZBxIeq0J2cPq9NX\n0yW+C2m5aW5vJZu1fRY2h42QwBDCgsOaVSuZwwH/+Q88/DC88IL++u9/yxgyIZqT5MxkUnJTsNgt\nZJdkY7FZmLzZjQ9YF17lry1dcpeSH3hq8VP8nvk7CREJ5JvzaRfdjlm3zSLA1PiBTjaHjau/uJqi\n8qITDxK22C1MvWUq57U+r9HH9wfl5RAaWvtyUyJ3WQp/U2YtIzzYs8++zDfns+3otmrrokOi6d22\nt0fPK+qvsdNebK9jmwKM/F9PElpNyspgzx4ICoI//Ul/NUhheSE3zr6REkvJiXUhgcHMuuAtOhOn\nH1IZ07inAWQWZmK2mU8sm0wmOsV28vgjnoT3SUEm/MmRoiPcN/8+Jt04ic5xnY0OR/iAxhZknZ1f\nH3N+/cK5/1Dn8guNiK2xJKGdLDtb91sdOaL7rXr3hg8/hLAww0Ky2C3YHXa94HBgevttwn5aqgvF\nqCiYMAG6djUsPuE/pCAT/uSd1e/w2abPuLvn3bz1t7eMDkf4gMbOQ3bA+boaeB7dYrYNXYhd7Y4A\nhRt9+CFkZkKrVpCYCBs2wLx5hoYUEhhCeHC4fq1dT9jCJTq2hAQoLpYHFQshmpwjRUeYv3s+3Vp0\n4+d9P3Mg/4DRIQk/4cpgHhNQ9Yb6v9B0rlSbjv37ITJSvzeZ9IOBD/rQ8+CzsvQjWComyoqNhfR0\nY2MSQogG2pu7ly+3fXnK+ulbp+NQDkICQzBhYvJGGWQvXONKQTYCPRHsQedrvHOdp3wOHKXuMWzi\nZBdcAIWFuuix28FqhV69jI6qUrduuhizWnWMublw7rlGRyWEEA3y77X/ZuyasRwqPHRiXb45nwV7\nFqCUIqc0B4ViSdoSsoqzDIxU+Iv6tHTFOr8WeCKQKi5FP6ZpBlDb/9gyBuNkJSV6/oN16/TybbfB\n88/71tTt06bBp5/q9926wX//q7tYhTgNGUMmfMmOYzsYPn84JpOJG8++kTGXjQHA7rCzJWtLtclZ\nA0wB9G7b+8Td4KJ5ctfDxcPQ8451Bip+oxTgyaeYdgZ+QAqy+lEK8vL0oPlG3sHoMSUl+m7QFi08\nUixmFmayYM8CHrv4sdPvLPyGFGTClzy28DE2H9lMfHg8x0uP883gb2gf097osIQPc9fDxRcAN6Ef\nLF7sfJXU+R3CGCaTLnR8tRgDPc4tIcFjLXeTNk7i4+SP2X7Ucz3ezWXyWSHEqXYc28FvB38jMCCQ\nIksR2aXZfJL8idFhiSbAlTbUdsA1ng6kvl6rcofegAEDGDBggGGxCN+QXpDOT3t/Iiokik83fMr4\nG8a7/Rx7cvbw8i8vM/3W6UQER7j9+EJbuXIlK1euNDoMj5H85b/KbeVc0eUKFIpyWzl7j+9lz/E9\nHj1nvjmfuLA4j55DuFdDcpgrXQCTgI/RU154S2eky1LU06srXmXx3sW0imzF0ZKjfH7T55zb2r03\nDoxaPIpFKYt4/fLXGXLuELceW9ROuiyFL5qwYQITN0wkLDiMH+7+gYSIBLefIzkzmZeWv8S8O+dJ\nUebH3NVleSmwEUhB3/lYMR+ZED4jvSCdBXsWEBAQQL45n1JrKePXu7eF7I/sP1iTvoaOcR35bONn\nlFpL3Xp8IYT/yDfn88XWL0iMSsTusDNz20y3n0Mpxbh140gvSGf2jtluP77wLa4UZNcBZ6Eng73R\n+brJgzHNBtYAZwMZwP0ePJeo4HDAgQOQmqqnpvAz5bZyrup6Ff069KNv+75c2+1aOsV1cus5Jmyc\nQGBAIBHBERRbi5m/e75bjy+E8B9zdsyh3F5OUEAQcWFxzNkxh5zSHLeeY/3h9ezO2U2X+C58sfUL\n8s35de5fYC6odoen8C/16QJIRN9xWcHIWT2lyd+drFZ46SX49Vc92L5LFxg/HuLjjY7MZ6TlpjHo\nq0GEBIUQQABmu5lWEa1YPGyx3M7uBdJlKXzNAwseYGf2zhPLgQGBvHn5m1zR5Qq3HF8pxb3f3cv+\n/P3Eh8eTVZzFiN4j+Hufv9e4v0M5GPbtMC7vfDkPXfSQW2IQ7uOuaS9uAj4AzgCOAZ2AP4CejYyv\nMSShudPcufDuu9Cmjb5T8+hRuOGGZvdoI6vdSnBgcI3bii3FrM1Yi6Ly9y40MJRLO11KgMmH5npr\noqQgE83NzmM7uee7ewgNDMVkMmGxW4gJjWHJsCU15qnfDv7Gkz89SWRIJIuGLiIm1Ifvtm+GXMlh\nrlzavwX8GVgK9AYuB+5pbHDCh6Sl6bnLKqaiiIqCPZ69a8jX7Mvbx6jFo5hxywziwytbBu12/WOJ\nConiqq5XYbdDYKCBgQohmoXuCd354tYvTrkIrKlF3qEcfLTuI6JDoymzlfHVjq+klcwPuXJpbwVy\nnPsGAiuAPp4MSnjZ2WeDzabHkSmlH/zdo4fRUXnVpI2T2JW965SBs5Mnw+zZ+seSnQ1PPKEbEIUQ\nwpOCAoLomdiTXom9TrzOanlWRUtLNavTV3Mg/wAxoTHEh8Uzfet0CssLDYhaNIYrBVkeEA38BnwJ\nfISeHFY0FTffDNdeqyuO7GxdjI0caXRUXrMvbx+/7P+FM+PPZOa2meSV5Z3YNngwrFkDH32kh9ld\ney20bm1gsEIIcZJZ22dhdVjJLcul2FJMUXkRP6f9bHRYop5cGZMRCZjRxdtQIAZdmB33YFynI2Mw\nGkkpRVZxFm2j21asgCNH9AD/9u2bVb/c6GWjWXlgJa0iW5FVnMX9F9xf7dFLqanw9NMQEQFz5uhh\ndsK7ZAyZcIec0hxiQmMICQwxOhS3yirOOuUOzA4xHYgMiTQoInEyd81DNgawo7sup6FbyJ5vZGzC\nYGsPreWub+6qvE3bZIIzzoBOnZpVMXao8BA/p/2M3WEnuyQbu8PO7O2zT8wxlp0N770Hd9yhn4Ne\n0X0phPAvdoedR398lGlbphkditu1iWrDnxL+VO0lxZj/ceWKczN6MH9V26l9Fn1vkCvMRnAoB0O+\nGcLmrM08etGjPPXnp4wOiR3HdhAcEEz3hO5ePa/ZZiY5M5mqv0/BgcH0bdeXwIBApkzRj968+WbI\nz4e33oLnnpNuS2+TFjLRWMvSlvHc0ueIDIlk4ZCFxIbFGh2SaEYaO+3F34HHgK5AWpX10cD/0N2X\nRpGE1ghrMtbw5E9PkhCRQIG5gB+GeOaRH66yO+wM+moQYUFhzL59tk9NI6FU9S7Kk5eFd0hBJhrD\n7rBz29zbyDPnUWot5aELH+Lhix5u9HFtDhvZJdmVQz+EqEVjuyxnoWfl/x4YSOUs/RdibDEmGqHi\n9ujw4HCCA4OxKztfbP3C0JhW7F/B4aLD7Mvbx5qMNYbGcrKTiy8pxoTwPyv2ryCzMJOY0BhahLdg\nxtYZFJgLGn3cb3Z9w/AFwymzlrkhStHc1VWQFQAHgJeBo873XYBhgDzh1E/tyt7F3ty9WO1Wckpz\nUEqxYM8CLHaLIfHYHXbGJY8jIiSCsOAwxq0bh0M5DIlFCNE0zd01F5uykVOaQ2F5IcWWYpbtW9ao\nY5ZZy5iwcQKHCw/zQ8oPbopUNGeuXO9vQc871hlYBCxAz9J/vefCOi1p8m8gh3KQXlD9qVchgSGc\nEX2GIfFUjOtoFdkKgOySbMZdP47+HfsbEo/wTdJl2QSUlMDu3RAcrKfWCfLeI8eOlx6nyFJUbV2b\nqDaEBYXV8h2n99WOr3h/7fvEhsailOLHIT8SHhze2FBFE+WumfoVYAMGAeOcr82NDU4YI8AUQOe4\nzkaHccKe43toHVU5Qr51VGtSjqdIQSZEU3L0KDz0EBw7piegvuACPblfWMMLovpoGdGSlhEt3Xa8\nitax2NBYwoPDySrK4oeUH7iz551uO4dofly54lwHfAi8hB5Dth/YAfTyYFyn0zyvMIVopqSFzM+N\nHg2//KJvT66Y8/CZZ2DIEKMja5Dl+5bz3NLnCA0MBcDisHBm3Jl8fefXBkcmfJW7WshGAI8Ab6OL\nsS6AsaPAhRBC+I+DByHSOS+WyaS7K9PT6/6e+igogAkTYO9e6NkTHn5Yz+TsIZd1vowFdy2otk7m\n/RKN5UpBthN4ssryfuBdz4QjmjuL3cLoZaN5rt9zciu5EE1F797w1Ve6KHM49LNzz3XTVJZWKzz+\nuB6fFhkJmzbpwuyjjyDAM1PoBAUE0SG2g0eOLZqvun5bFwJ3ADVdZkQAg9GD/IVwm8V7F/PDnh+a\n5GzaQjRbjz8O/frpsWTZ2bqr8rrr3HPstDT9fLM2bSAmBtq2heRkfS4h/EhdBdn96Nn4N6Bn5v8Z\nWOp8vxE4B7jP0wGK5sNit/DJ+k84I+YM5u+Zz5GiI0aHJIRwh8hI3WK1dCmsWKHHj7mr9SogoPrz\nzCree6h1rKkpthTz7v/exWq3Gh1Ks1fXb+wx9HMsewBXAa+g5yS7Cl2MvQZkezg+0Yws3ruY3NJc\nYkJjUEpJK5kQTYnJBHFxEBXl3uN27QoXXqhvFDh+HLKy4IorIDHRvedpor7941umbJrCkrQlRofS\n7PnrXUvN7y6lJs7msHH9l9eTWZRJeFA4dmUHBYuHLa42LYZonuQuS1GnsjKYNUt3XfbqBYMH6/nO\nRJ2KLcXc8OUNWB1W4sLiWHDXAoID5efmCe66y1IYQSnYvl1f8XXrBh2a9gBSpRRDzh2C2WY+sS7Q\nFCjJQQhxeuHh8MADRkfhd77941tKbaW0iWpDVlEWS9KWMPDsgUaH1Wz56xVn077CVAreeQe+/RYC\nA/W6d9+Fyy4zNi4hDCItZMJXWO1W/rH4Hzz3l+d8apLt+iq2FHPtzGsx28yEBoVSai2lTWQbvr/7\ne7kQ9gBpIfNXO3boYiwxUQ9MLS2FMWP0YFgZqCqEEIZZkraEn9N+Jj4snrf+9pbR4TSY2WZmQOcB\n1Z5jHBEcgcVukYLMIHVVa9vr2KaA89wcS3007SvMX3+FF16AVvr5jiilB6quWuXRyQ6F8FXSQiZ8\ngdVu5abZN1FqK6XMWsbcO+b6dSuZ8J7GtpDd6NZohOu6ddN3JJWU6AIsOxvOOkuKsVpkFWeRUZBB\nl9CLCQurvIkrPR3at294o6LFbiEkMMR9gQoh/NqStCXklObQJroNZquZyRsn+3UrmfAtdf1XdaDK\nqww9J1kvoNS5TnhKu3Ywdqye0TorC848Ez74wOiofNZ/f/8vz/z8DIuWlTBmDBQX6/shXnwRDh1q\n2DGLLcXcPvd2dhzb4d5ghRB+ye6w80nyJxRbi8kszKTcXs6CPQtIL3DjI6BOI6s4y2vnEt7nyhiy\nO4GxwK/O5Y+B5wB5iqon9e+vH8ZbXq7vIDKKUvD11/DNNxAWBo88omfc9hH78vbxy/5fsDvscOG3\n9Ci+h7vv1vdCvPkmdOzYsOPO2zWPP3L+YPz68Yy/Ybx7gxZC+B2TycSDFz5Y7U5wk8lEZLB3nmG5\n89hOHvnxEWYOmindpE2UK2MytgFXoieKBWgFLEfGkDUPX3+t7/iMidHPn7NY4LPP4Dwj//krjV42\nmhX7VxATFoPFZuHd8xfx9qs6Qc6e3bA5KIvKi7hh1g2EBoWSX5bP5zd/zrmt3fTcPRfYHDZAPy9P\naDKGTDR3Tyx6giVpSxjcY7B0k/ohV3KYK6NrTFSfkf/46Q4qmpD58yE6Wlc2cXG6G3XpUqOjAnTr\n2NK0pcSGxRJoCuRYQSFPTfqWt9+Gm2/mRPdlfX3zxzeYbWbCgsIICgzi0w2fuj/4Ory16i3eX/O+\nV88phPBdO4/tZN2hdXRr0Y2f9/3MgfwDRockPMCVgmwxsAQYjn6+5SLgJw/GJHxJRIRuGavgcNR5\nc4E3r/x3Z+8mPjwem8OGxW4hIiCeHpft5Lzz9ByRl1wCZvPpj1OV1W7li61fYHPYyCnJwaEcrMlY\nQ8rxFM98iJOkF6SzKHUR3+3+Tp7lKYQA4NMNnxIUEERQQBAmk4nJGycbHZLwAFdaukzAIKA/erqL\n34DvPBmUC6TJ35PWroUNGyAhQT8h4NlndVGmFMTHwxdfQNu2p3zbjmM7+M/a/zDxxol+293mUA42\nHt5Iub282vqL2l5EeLDnx/KNWTGGxXsXA3Drn27lxUtf9Pg5/YF0WYrm6lDhIW776jZMJhMmTDiU\ngzz0LSQAACAASURBVICAABYOWUiL8BZGhydc5EoO89cEJwnNU77+Wj8VwGQCux3+9Cd47jn43//0\noP4bbtDFWEoKTJump+YYOBCuvJLHFj3Osn3L+Ojaj7j2rGuN/iR+J70gndvn3k5CRAIKRW5ZLvMH\nz6dt9KnFb3MjBZlorpRSHCw4iEM5TqwLCgiiQ0yHir8L4QfcNVP/bcA7QOsqB1NATGOCEz5IKRg3\nDlq0gNBQvS41FfLz4YknKvc7cABGjACrVT/A93//Y3vebtYXrOeM6DP4eP3HXNn1ylpbyZRSkkhq\nMHfnXEqsJQSa9eOyii3FzNs1j5F9RxocmWh2lIKNG/W8MR06wEUXGR1Rs2UymeSuymbClYLsPWAg\n8IeHYxFGU0pPs1H11kSTSa+r6uef9eOc2rXTy0VFfPq/Dwk6rxXRodFkFWexLG1Zja1kqw6uYs6O\nOXxy/SdSlJ3kzp538pcOf6m2rkNs036ovPBRn3wC06dXLj/0EDz8sHHxCNEMuDKoPwspxpqHgAC4\n/no4elQXXDk5egB/797V9zOZ9Mvpj/ASfo3Mxmq3klWcRam1lPEbxp8ywN/usPOftf/ht4O/kZyZ\n3Ohw16yBoiL93uGA5cv1V3/VMbYjf+7w52qv9jHtjQ5LNDdHjsCMGXoMaZs2+uuUKTofCCE8pq4W\nstucXzcAXwHzgYqnkCrgWw/GJYwyerSec2zVKv24pqefhtatq+9zzTV6YP/RoxAURKKy8navUXDh\nhSd2iQiOOKUFbNXBVRwqPERMWAwfJ39MUrukRrWSpabCV1/pCWBnzICMDPjLX/RQNyFEAxUW6ouz\nIOd/D0FB+gKssFAXZ0IIj6jrf8Np6MKrYr+TR6He74mAXCSDYo22bx/MnKkH9V93HQwYUOfudoed\n2+feTq45l+iQaDKLMrm71908/5fnGxyCUvq+gm+/1cNcPvjA2IcaCM+RQf1eVFYGgwZBQYGeezAv\nD1q21E/rqBhbKoSol8YO6h/uzmBEE3PmmXrmVRetyVjD7uO7iQqJosxaxpGiI7yz+h0GnTOIbi26\nNSgEpXQ9CPqG0KrTpQkhGig8HMaPh5dfhr174eyz4a23pBgTwsNcueLsAHyEnocMYBUwCmjgY5td\nci3wXyAQmAy8e9J2377CFKc4UnSEjUc2AvrRRK//+jpKKW7qfhNjrx7boGN++qm+4fO113TX5ebN\n+ilP0krW9EgLmRDCn7lrHrJlwJfATOfyUOfrqsYEV4dAYA/6+ZmZwHrgbqrfWCAJzY99tO4jZm6b\nSavIVhwrOcbs22Y3qJVs61Z98R4erlvLkpMhKana/QaiiZCCTAjhz9z1LMtWwFTA6nxNAxIbGVtd\nkoC9wAHn+eYAN3vwfMKLcstymbltJmFBYZhtZmx2GxM3TGzQsc4/v7I1zGSCvn2lGBNCCOGfXJmH\n7DhwDzALXd3dBXjy/ud2QEaV5UNAXw+eT3jR4aLDdInrgs2hB3zFhcZRYi0xOCohhBDCWK4UZP/f\n3nlHR1F3b/xJD0kInSCgoCIiVbCA+AooFgQbdhQVRRQFRRFFBTEvCPhTBBSxvihHqXZALChVLDQp\nhi6KAimEhPSySXZ/fzw7ziambJLdzG7yfM7Zk5kts3cnu3fu99b7AMwBMNO5/xO8W2Hpli8/Njb2\nn+1+/fqhXwVVfnWS9HQgPh5o1sxnytU7N++MpbcstVoM4eOsW7cO69ats1oMryH9JWoLRUVAEIeL\nwOFgL0hjvy5TFR3miwGeXgBiwcR+AHgGgB3FE/uVg1ERmzdzKHhBAX8lTz8N3HCD1VIJJz/9BJx2\nGtC6Nf89K1cC/furIKEslEMmhO9x7BhHH8fGAo0asT1lURFwr5VNsXwUT+WQfQCgoct+IwDvVV2s\nCtkK4CwAbQGEArgNwHIvvp/vc/Qoe34tWsRmrBVhswFPPcVlStOmbPQ6fTq9ZcInyM1lV4GjR9kE\nffVqKjIhahVbtgDXXceOzU89ZY7WELWCVq34r50wAXjtNWDrVuCmmyp+nSgdd0KWXQGkueyfBNCj\njOd6gkIAowF8C1ZczkNdHt30++8c5J2Vxf158zhjrnU5I3VOnuQVv7mz9iIsjJ23ExKAli29L7Oo\nkP79+fehh+gVe++94iNEhfB7/v4bGDMGCAnhotBYdbzyitWSCQ9y6630Fxw9Crz+Ov/Vomq44yEL\nANDYZb8xaCh5k68BnA2gHYDpXn4v32bePA73btWKt4wMYOHC8l/TqBEQGcnnAkBeHv8aw8CF5Tgc\nwJ9/cttuB9LSyn++EH5HXBy7NUdHc/xSixbADz/wy+8OJ04w1eLmmxkT04/E53A4GKY8/XTg+uvZ\nBzI11Wqp/Bd3DLJXAPwMYAqAF5zbVevkKSpPRgYQGmruBwebhlZZhIYCM2fSK5aczHb2zz9PheiH\npKbS6Wfw99/+35V/4UJg925g8WJ6ySZO1Oxm4QdkZwMffwy8+y6wYwewfz8Npp49mTjkmhYRFcUr\ntmGA5ebSOHOnN43NBowcCaxZQwXw5Zf0timu71PExwO7dnGQw/33c4LeihVWS+W/uJsk2wnAZWAF\n5BoAe7wmkXvUnaTY5cuB//6XM+UcDhpjL70EXHZZxa/NyQESE808Mj/lq6+oj6dO5ceZOpX25Vln\nWS1Z1Tl8mP+W8HDazXv3AuecQ29ZsDuJBHUMJfX7ADk5wLBhTKMICDCNrYgIoEEDGk6tWgEffcT8\n1cJC4NFHWWAUEMDb9OlmvL489u3jezVrxn2Hg4vLTz8tP11D1Dh2O3VYWfuCVHeWpWuYMgHsQwbQ\nKGsMQI5JT2Ao5rJWjddeS0W4cCG/5WPGuGeMAVSUZ5zhGTktZOBA2qF338392Fj/NsYAoG1b/l28\nmJ6xUaPYpeS554CxY2vFv03UNjZsAP74w0x9SEwEDh4ELrmE+82aMZEoOZne+OBgZnqvX88vd6dO\nwNlnu/deoaG8shtXd6Ofgmu0QPgEJY0vGWNVpzyD7FeU3xPsdA/LUrew2+n2/+ADbg8ZAowe/e9v\nc0AAcPvtvNVhunUzU+fcMVYcjuI2bsl9X+GGG+gAnTqV7v++fWWMCR/FyEU1qFePIUSjEZXNxh+Z\na3VKcLB7HrGStG3LH8OaNaa3bfBg02MmRC3EBy9RbuGfLn9Xli3jlbhZMyqx48dZFl7HDa/S2LuX\nBsvjj3NBvmED9xs1Kv35hYXMyXroIaBNG+YWf/YZvU++aJQlJAAPPMDtZcvcW2E6HPycISHct9m4\n7YufzxMoZOkD/P03F44BAYy1p6QAMTF08RpfvMceA+680zPvV1jIhKRDh4AOHegql/tF+CmeGi4O\nsPfYWQDCXe7bUDWxPIJ/KjRXnnoK+PFHoLEzMpyWBnTtCsyda61cPsi2bfx73nn8u3w5h4iXV6Ow\nYQPwv//Rvl24EBg/nqfX1zh5kj18LryQaTOtWjF8WdF1Z+1aOg8mTqSDNTaW3raLLqoRsWscGWQ+\nwq5dbFuRmsoM7tGjmdyfmMhSO1/8kQnhA1Q3h8xgBIBHAZwKYDvYSf9nMMlfVJWYGLazMMjPN/uG\niWIYhpjBdddV/Jo+fegZe/NN4MEHffc6sWYNZb39dhahTZ8O/PUXr22uZGYC77xDr19EBNCkCXOr\nJ0ygI+Gss1joJoRX6dqVfRBd0RevSmTbshEZGmmpDK6pHMa2v6R71Ebc8f+OAXAhgMMALgXQHUC6\nF2WqG9xzD108iYmMWYWG0k2SrUHbniAujg7IK69k0ddff1ktUenceKMZpa5Xj1HsksYYwLZyERGs\nLt28meNKxoxh14FDh4DhwxXNEcJf2J6wHTd9dBPS86y7lH73HfDWW/SwFxSwdcXXX7MPudG9ZOFC\net/9vc2Qv+COCs8DkOvcDgewD2zaKqpD06YchRQbS29ZTg6vxrfdphFH1aSwEHjjDYYpH3mE/XHe\neMP9fpTlkZxsbhuV+NWh5MqzrJVoYCA9fTk5wJQp/EzPPsuvTt++wKRJ7KG5x+qGNEKIcnE4HHh9\n8+s4dPIQlu5eapkcF1/MotnXXwemTWMOav/+rKEYOJAFsvPmMb3ZNZgjvIc7jsjPAdwHesr6g6OT\nggEM9KJcFeG/ORgl+egjujtOOcVM7r/kEmDGDKsl82sKCsyE99L2K4vDwWOMGgXccgsV14IF9FBN\nnVozLv24OIY0o6OZcpiXx/cfP55fl6Ag5kCfeqr3ZalplENWR8nMZNFA8+Z0E9cCtidsx4gVI9C4\nXmPkFORg5R0r0SC8gSWypKcDQ4dy+/PPWRRrtwPnn8/B4X370iirX98S8WoVnhouPhg0wmIBPAfg\nfwBuqKZswuDIEV5JjSt6ZKTvxteqyrFjwMaNwIEDNfaWJY2vsoyx3Fz39j/7jAbY009zVEjXrsz/\neuaZmjHGjH7A48ez7qNPHybw33kn60McjtprjIk6yrp1wIABwB13AFdfzVi9n2N4x0KDQhEWHAZb\nka3aXrL8fBpRBiV1WFkUFACzZwPduwPt27MLk93O3ogNGrDebPduzYOvSSqbdbIOwHIANs+LUkfp\n0oV9fAoLeVVNTwd6eHN2ew2zdi1Hq4wbx6XYW29ZLdE/5OWxSOzTT2lcff45PVBffklXflIS8PDD\nrPa//HIaYhMm0F4ODGTP3ppaOUZHA3Pm0BA0wpd33gls3crHs7OBTZtqRhYhvE5qKmPy4eFM7wgK\nAp58kjF7P2Z/yn78mvgrHA4HTuScgN1hx+LfFsNWVPVL6qJF1A12O7NdRo3iGrgiVq8GwsKY7jBl\nCnXeN98A77/PLiO//MJ6jbvvllFWU/hrCKD2uPztdv6aFizgfs+eDGHWBve8zcapAuHhzFgvLGTv\nosWLgTPPdPswCQn0QhltLvbuZfPUsDDuf/ghV3g9e1Jfv/oqDZbGjYsfZ/9+4LTTKAoA7NxJ42bS\nJI4yatkSuPVWFgNMm8brwNq1VFCtWzNMuHEjldNXX9FjNWQICwc8jev4EbvdnDzj+vjdd1PuxYvp\nwXv3XVaVGo3TaxMKWdYx4uKAESP4IzRITgaWLDHHXPghtiIb4o7HwfX/HxYchk7NOhnf8UqTlwdM\nnsx1fVISF2pXXFHx64zhB0FB3C8sZMhy+3agXTsuNu12Gma9eqloqLp4sg+Zr1H7FFp2Nn3IDRrU\nnhrjlBTgqqvoQ8/IoCUUHc2YWyVK5Vev5ipw6lSu/GbN4orOqEb8/XfWQ9x3Hw2lM86gQVZSgbz9\nNleBsbGsJvr2W+ZevfoqoyPZ2dT/779vXgfsdlZBZmUxgT4tjX979aLS+/Zb5pRt3mw2JD98mI7O\nbt2qdtp27WJq4cSJLL6dM4cG59VXF3/egQNsit6yJZXrr79yHmZERNXe15eRQVbHOHGCLuioKC7o\ncnIYm/v229qxWPUwf/zBqmvA/ebSombxVA6ZqAkiIzlAvDRjzOFgp9OFC+mi8ZQydzh4zCuv5G3B\nAveOnZrKjtz9+nE5VlZuWMOG7Hx6+DAVakICNUcl+63Vq0dDY8QItn3o3r34SLt27RjNmDmTi+gH\nH6TuTnWZtnr8OGcVt25NL9gXX9DA+/57hiT79mUo8uBBGmcAPWF33cW8rG7d6JmbPZsOzEWL+HHu\nu4/XjsmTaUQdPkyPW2YmV6zz5tEWBfi8+fMrPsWdO9MgnDyZBmNSEnDppf9+Xvv2NMYAfm3OO692\nGmOiDtK0KX/sWVn84eTl0W0tY+xfxMdzgfrAA8yAMcKXdYnUVC6ki4q4HxfH8Ku/IYPM13E4gJdf\n5sTpV16hITRrlmeOvXIlrRhjiO+sWbyvInnGjmVcLzycFsjIkTS8SpKVRQUaFcVfSmgo444pKZUS\ns0ULhg5TU1kDsW8fHW0GOTlm2LKwENiyhbenn6Yuj49nMnxcnGnAhIXRVb9zJzBoEI+7ZAmNv3Hj\nmCA/ahSnBDz3HI23adOAH36gE3P4cE4C+PNP/luGDOFY0kceoeH4n/9wlRoSQk/Xn38y/yw6umIH\naGAg21p8/DH/HePH8/VPPKE2daIOMWAAEzrnzePfPn2slsgn2biRdQ/XXsvFYFYW21vWJaKiqCNn\nzWKE4cUX2bjA35BB5uskJACffEKvUqtW/LtkCd0m1WX1alogrrfVq8t/TXo6S29iYmhtNG5Mi6g0\nL1lwMC2fHj0Y4+vVi4kJwe4MiAA/+5o1yP9xK0KC7AgNpQ2Yn188t3fePIYpX36Z4cg33gA6dWKI\n79576TG74w56wlat4kqqa1f+aMePp3Nw+nTq+3fe4UebPp0fc8YMOvoCA5kON3QobddrrwUuuAB4\n9FGmtFxxhWloJSTQoLPb6WGLieH79+7N+ZuGPWq383klmy7a7cxPczhML9wNN/DfYzgIUnNTMe2H\naSi0q2OjsJjMTHrwN250v8TPXZo0ATp2LHtwrcCttwJtexxEXmEewsO58DMWnnWF0FAufH/6iZ//\niSeqnjJiJTLIfJ3sbFoDRuZlUBD3PeEqadyYifcGNlvFiq9ePb5/QQH3De9aaaGEyEgmWB0/TkMu\nMZHKtVOnimX79Ve+9tln0falhzD+5NNoUN+OVq040MA1ZDl8uJkz1q4dDbImTYqnqXXvzvumTmU0\nZNQoGmDBwfxITZrweR07cmX1xx987IIL/i3axo38wW/axI8+bx4T7MeOZUhy1Sp6z155hSHQRYt4\nurZtA44epcI4cYLhz02bTDe7QVwc7/viC8q6ahWNuhdeMJ+zYNcCzN8xH+v+XFfxuRTCWyQl0T38\n5JP8Adx1F3/r4h8cDi4GDfLzPbOeNsj8eR0emH4RlgzpAjzzDHZvysKXX5qPf/EF0y1qOwcOUGcH\nBdGvUFKv+gMyyHyd006jVyw5mQbT8eO0GFq3rv6xhw1jDC0+nrfoaLqUANNiePZZhguMxKewMLqF\nUlOZYZ+YyASnjh1Lf49x4xjzGzCAPSbmznWvQ2tsLH9ZzZohPSwGrQ6swaJRP2LGDCbQu3bIj4go\nnsQaGcmPM2ECRb3vPn6Mjh3NZP3AQIYqXUVxOExjaeRIHvfRR03b08DocH3gAA2lPn2Y4rJ+PU/h\n4ME8/qFDZnreli1strhrF0/XvfeyGOG558xqUYOuXZkTEhTEz9GiBY3GnTv5eEpOChbHLUazyGaY\ns3kOiux+qHlE7eDNN6mTmjfnj+HwYbNiXACgrnr2WeqA/HzmhpaXGZKayvQ5o9XExo1c9JXK4cP4\nZMZwpCIX752WgszVX6Pth5OxbBmwfDlb+nz9tefHJOfkUJcZHD3qXqsNb5GUxIjHhAnM5T15knrY\nCN3u3WtGHXwZf61aqltVSseOsYzw4EE2iJk0qWoB8kOHaGQlJdGKeOABs5fDgQPAjh20JK6+Gli6\nlN/mkBA+v1kzerYefJCup23bKE/z5syINzx4nqJXL3rwgoIY0ktKRPDzE4Hrr/9HnPIqiX77jeIb\n5d8rVvDUnXVW2a9xOGgzXn01cM01VKCzZ9PrVdKGPHiQDgGAHrC0NNZH/Pgj8xmMBq6HD1OGJ56g\nvPHxPLVr19I4fOWVf7fnABiqvOYavnb4cPO9liwB5sW9hgW7FiAmKgaJmYmY3n86Lj/zcjdPrH+i\nKksfZcQI9pNp4Ow0n5LCFcfUqdbKVQNkZzOFwlB9GRnFc1td2b+fIbW8PKY+jBlTtv5yOKhzdu7k\nOnbRorJn3GYu+wiDVg9HWHAY0oMK8XDSaRi2vx6OL/8Fw+/nz2X+fDMC4CkOH+ZicvRoZtJMnMiF\nr1Vpfg4H9b1xWbTZ2Apo1So6befNow7u3t0a+QC1vRCuJCcz2SAvj+6W9HROtp4wgUudESMYBwwK\nogFot9OlFB9P6yMggK4bm40Nr7p29a68o0cznhcTQ5kzMpi5f7Z3x6jm5pp9ykrbB7hyfe455ig4\nHDx9U6awf8/LLzPqGxHBItQbb+S1qWFD4PHHOTcuMZGvX7GCA35nzjQjvg4HT2+fPlx1nn8+FfKl\nl/LUn9o+FVd9eBUQAIQEhiDLloW2Ddvi89s+R1Cgh41iH0IGmY/y3ntcecTE8Mt7/Di/3DfU/mEu\n777LhdjYsazQjo2lF8bol+hKfj7zT/PyeHouvLD8YzscVMlJSdQfZanb9xc/hTc3zUVMQBTyAoqQ\n7yjEyn09sOr+NVi4kM8ZNgy47rrqfNLScV2UPvaY2fbHl5g5k4vfhx/+d9ugmkZtL4TJtm0sv2na\nlFf/mBj6tO12LiPsdloN9etz2ZeWxtclJjIwHxjIx+12WhHeZvJk4Nxz+f75+VwietkYA/5tfJXc\nB+hM7N6diu7eeynmqFFUnLNmMcl/61YqqNBQ2ryGMrjiCjNMefPNtDtdW1UEBDBK/dJLjEpPnMjT\n3qEDDcCCogJc0/4aDDhzAPqf3h/Xn309Lj71YhQ5FLYUFnDXXVx1JCfTO3bPPd65+vsg99xDlTp6\nNH/TI0eWbYxNnsyinpdf5tDuLVvKP/aPP3IB1r07bd6yOuV/HfgHiiLrIcmRhXR7LnJQgCWXD8Kq\nVTQY33qLCz9vTPFw1Y1RUZ4/fnXZu5epyBddxIp1f6g89dcVZ+1ZYdYUa9YwjmZojPx8un82bOAK\nd/5809+bnEz3TMuWzEbNyOBjTZrQTz9qFJccFZGZyYQvu53Z8Q0bVl7u/HzGC3280+G8ecwLMZrT\nGt1Kqqqoli5lKk6/flyF1pZewVVFHjIfp6CAX1J3K6itxDj3HvhR7dvHeoaQEF70S8vcSEujbhgy\nhGps/36q1bK66aemMrw2aRIXd/PnUwUbjV9dySvMQ0FuNvDjRiAjE+h4DkLP6oGcnIB/6rNSU7nO\nLpl2kZZmqmRjap+7KvrYMS4077qLC8jJk9nypyLPX01hs9FQfughGrVffcWWRdOmWadLFbJ0h5QU\n/lpycxknOucczxzXCoyupqGhTD6IiTEfy83lku7336k0i4qoSW6/nb+uu+/mL9RQqpMmAT//zJK/\nDRvoaweocWbMYP5ZeaSk0H1kLEuaNOFSrxrNYRwO88fkul3TlHxvY3/nTmDK09m4qmAF7r8xFQEX\nXFB6mWYFpKXRM5aRwX/F1Kn+2VPHk8ggE9XGZuMqacUKuqhHj2YldxUxpn4MH871blQUF0+eSKfN\ny2OgAqB+yc839z2BzUZj5d57WaS0eDHb/Lib+peeDuzZQ+8TwEtPYGClJuJ5HddzWNp+TSODrCJO\nnKCJn5TEK2pICP3JvmLmV4Zdu+gzz8/nL7hJE3Yqdb2SZ2Yy0/HECX5G16GH8fEMYdpsjLUZrSkO\nHWI+SFERrYNGjfgeq1eX3zV71iwmPxkeuaQkGomTJ1fp433/PRNJhw+nKDNmMOm9c+cqHa7KFBXR\nVr3/fibZ7t9PZTZmDDD5mVwM/2k4Io/uQ9PmgagfBVpWFeTTGOXZQUH8140bx677Q4ZwUsznn9OJ\n6Q/OB28hg0xUmzlz6G5q3pzN/06epL7v3btKh/v0U6rX3r2pNufO5eAST1c0egtjqkhkJHXPCy9U\nLYgh3MMdHVaHVTzoGUtKYpkIQNfE66/TkPEljh/nvJ4DB5hH9fTTxYfuAiw/B8yOgAkJ1BijR5vP\nqV+fXjJjWrUrLVvSoCtJWhqXfq7vd/y42YW/LJKSivvIw8P5uirSqxfdzm+/bTZW7dChyoerMkFB\nwMCBLEu/4w6GFR9/nHp+UKOf0Cn0IHK7t8KBg0CnFrkImT27QoNs+XKuMI1O/BkZzEsLCKANe/75\nddsYE8IjrF/PatDgYDMvdvPmKhtkN91kboeGUg/4E23a8HLyyy9U/TLGrMe3E3O8TU5O8dykkBDf\nm01jszFf64cfGHZcv545XCWbY+XkFDeAAgNpNLmSnc08sp492api+fKK3//MM+neT0uj+yY5mQZs\nSYOwJL1705NWUMDVaHY25wlVkagoruZWrqQCGT/eOiOld2/mds2dC1x/Pb1Zo0YB/f+Tj4DAAERE\nAF27ACERofyfVeANGTSI/77nnweeeebfkfOKTrUQwg2aNSs+SaCw0PP9IPwEh4Oe/YQEVogvXcru\nR8Ja6rZBdskldHmkp/OKmJ7OmTi+xN9/M8E+JoZlLS1asMb66NHiz7vmGhpghovF4fh31uiMGYz9\nNW9OI2vKFPYeA6icdu1iNaarUdqwIb2GjRoxH6xdO7r5K0qUuOYa1m2npzM0MHQoY3BVpLCQBlCX\nLgwVujOk2xusXUvDcPVq9hOaNo1GYmgoENCjOz2BKSkIzM/l+Ro4sMJkt9BQOjJ37uS/+447lMQv\nhMcZO5Z6LzHR1GV1oD1HaRQU8BS88AK98ZMnM11YWIu/qn3P5WD8/DMNjpwclmvfc49vVfQdPcqy\ncqMTqt1OL9WyZcXzw+x2LnM++YSespEj6cZx5cor+ddoDR8fz+SnW29lS3qjMWzz5qyZdi0KMN6j\nsufG4eCtmuf0q6/Y52v8eCZnTprEOoRzz63WYStNVhY9WJdeShv1778ZuZ0502lE7d3Lbq/JyTT4\nH3nk3634S5CRwYqlc8/lkPOICIYvPd1r159RDlkdxOFgSDE+nvG1Hj2qf8ykJC46w8KYke7ac0YI\nL6Kk/tqAw8EeXMuXmwaZ0dC1sm6UoUOZydmokTm5+r//pRdr5kwaeAEBzPW67DJO2K4KmZlM9DK8\neh7AGJlphCkLCtybwOQNUlNptwOskQCqJ8tXX/F0DR3Kz/XSSyz+qoG2a36DDLI6yMyZjKsB1FcP\nPcSqHiH8EBlkvkxWFrv27d/PK+/IkWU3rTKat/7xB3O6rriiah6nuDgqNZuNx+zalZO4Z87kBFqj\nPCgri6FRQxm6g8PBpjzffMMZQsHBXH3Onl1hV/8VB1ageURz9Gzds9zn+QKFhayvyM3l7fTTmeIX\nGMjT+sMPtGUDAljMevgwk/LLo2RbJCtbevgqMsjqGEeOMGu+aVO6igsLuRL65hv802BLCD9CmtH9\nBwAAFFBJREFUVZa+SlERQ4U7dzLetX07jZm33y49ThUYyHK7yrJqFQ2uvDx61e6/n5NXd+2i56pX\nLyYwdenCUGdREd8rM5O5T+5idEFdtMjsc9alC483diyn25bhQsrMz8T/bfw/NK7XGJ/d9hmCA337\nK7l2Lf/GxtKbFRvLf99557GGYdkyRpkHDeJA4UGDKjbIShpfMsZEnSczk3rE0IfBwfxhZGbKIBO1\nFh9KlqpDHDlCb1WLFpxG26IFp2EfOeK599iyhRZBejpXl2+/zR4Np5wCXHUVE6FycthnrG9fJtwn\nJzPHondv9zrxG+zcyTbVkZE0vIKCaGDWr88EqdTUMl/68Z6PkV+Yj4SsBKz9c60HPrh3ufxys8Kz\nXj02UjzvPD5Wvz6TZL/9lg0XBw5kFaYQopK0acMf1IkTXNglJ1NPerFDcnpeuteOLYQ7yCCzgsDA\nf5cIeiDxvRjr1/N4UVG0HKKj6e43+OYbWgxDh7Ky9NJL6f757jvg1VfL7zFWkuRkvpeRIBsQwPhd\nZibfu4wVbWZ+Jt7f8T4a1WuEiJAIzNk8B4X2wmp8aO9TcjpMydYbRjQYoC2syJQQVSAykr0VTz+d\nC7oOHVh85aXE0fS8dNzy8S3YdNQLQx+FcBMZZFbQujUrfBISmM2dkECvVOvWnnuP6GizBTzAeFqD\nBtxOSmIyf2Sk2eRq3DiGLxs2rHzMzJiX4XBwOy+PitNuZ4Z6aGipL/t4z8dIyUlBTkEOiuxFOHTy\nENb8saaSH7SGKCxkCHjxYnoESyEtjU7J229nGt22bZVLwxNCuHDGGUyD+OUX9rnxpH4EYHfYYXdw\n9bR091L8lf4XXtv0GpTfJ6zCtxN2aiuBgcy5WryYob0OHczJs57ixhs5c+fYMRpY4eHsXgrwPsAc\n7FW/PisrU1KqFhI44wwaeFOm8L0uuwx47DGWqZfT/rlecD1cdeZVxe4L8MUEqsJCfp5ffqGRGRTE\nkUgl4pHR0RzxaeSMvfCCeaqFEL7F/238P0SGRuKebvfgg50foE2DNjiQcgCbj232iwIjUfvwwauf\nW6hKyR1SUxmGzM+nB65tW96fkAAMHkwLIiyMVZUOB5OfjJ5Zhw9z9ltSEvPN7ruv4tb4hYU8VoMG\ntSszfdMmdm6NieHnys/n59y40bd61tViVGUpPMmxjGMYvHQwggKDcEvHW7AkbgliomJwMvck2jRo\ngwU3LvDNxaHwW1RlWddp3Lj4wDWDZs2Abt3ooQsM5Cik9983jbETJ9jvJyOD9+3Zw3jcU0+V/37B\nwbVzIFp2Ns+ToaBDQ5ksVlhYZjhWCOG7zN8xHwBQZC/Cm1veRHR4NFJyUuCAA3tO7EHc8Th0ieli\nrZCizuFrBtktAGIBdABwAYBfLZWmtvLBB8DWrYyt5eYy58t1NubWrWxBf+IEPWf16nEKwJNP1i7P\nl7t06kTD9ORJ5t2lpJgtQ4QQfsWxjGNYtn8ZmkQ0gcPuQEFRAWZfNRuN6pnFR+2btLdQQlFX8bV4\ny28ABgPYYLUgtZrVqxmuNJL6Q0LY0dQgPp43I6ySlVW3k6FiYtjPrXVresb69+cQSyGEd9m9m5Xf\n+/d77JBL4pYg05aJkzknkZaXhryiPOxJ3oPOzTv/cwsN0mJL1Dy+5iHbZ7UAdYLGjdn135gMUFTE\n+wyionhfXh49YnY7Dbi6TOfOwJIlVkshRN3h3XeBd94xR8Y9+STn7lZEYSFTLBo0KLVNxrVnX4tz\nWxQfgnt6o9M9JbUQVcbXDDJRWRwOYM0ariIbNGBfsVNPLf81o0cDI0YwuR8AWrYEbr7ZfDwtzewr\nZgwHt9nqZrhSCFHzxMfTIGvalLmpNhvwyitsam207ymNPXuAxx9nekFEBPDii0wvcKF9k/YKSQqf\nxAqD7DsALUq5/1kAK9w9SGxs7D/b/fr1Q79+/aorl3+yfDnbTYSGcmX43Xfs3dOitFPs5OyzmdC/\neTOVXZ8+xZVcixZshXHyJPeDg1kIoCGLooZYt24d1q1bZ7UYXkP6qwJOnmR7GaOyOzSUi0TD81Ua\nNhtH0uXlMc0gK4tetWXLikcAhKgBqqLDfPXquhbAEyg7qV9l4wbXX8+O+EZn/WPH2OT1jjuqfsxf\nfgGuvNLsuWWzcUD4JnWxFtagthd1jMxM6raCAhpgqan8u2xZ2cU0R4/S09+smXlfSgrw1lvUX0JY\niDs6zNeS+l2pLcrXu5T0WgUEVH9eT2oqw5hhYTxe06b0vvnKRaSggLNA4+JoLAohahf163NUUoMG\nTK1o0YL75VU2N2pEL1puLvdtNubCGtNIhPBxfC2HbDCA1wA0BbASwHYAV1sqka9z550cT5SfT0Ml\nKgqoTvjDZgP27qV3rEcPs+eWrxg+2dmcOLB3L/fbtePMu7pedCBEbaNjR+DLL6l73GkxExkJxMYC\nzz/PcKXdDowdy8VlVbHb1fxZ1Bj+6oWSy9/A4aDS+vZbGiXDh5uzJStLXh7w0EPArl3An39SEbZt\nyxFL48cDt9ziUdGrxBtvAPPmmSOeEhM5PHLcOGvlEl5FIUvhNvHxwJEj9Kq1aVO1Y2zdCjz3HHsx\ndusGTJ9ePBQqRCVxR4f5q4KTQvMGy5dzJuUpp9DVv28fw5azZwMDBlgtHRk3Dvj5ZzNJNy2N+SFz\n55b9msJCVl8VFXFuaL16NSOr8BgyyESNkZDAXLSQEEYcjh+n3vjgA6slE36MRifVNZKSgC1bWJl0\n8cXMw6gMaWn8GxDAWZYpKdx+8UU2Re3c2eMiV5rOndnmw27nfk5O+Qm7OTls87F7N/dbt2ZvoyZN\nvC+rEML/2L+/eG/G5s25oMvJYSsNIbyEDDJ/IjcXSE6mMWFUVRr88QfDlcag8NatgfnzKzdbsls3\n5kskJtLtD5jesgkTWOFkBdu3A+vX08AcOJAK8/vv+dillwLDhpX92sWLgZ07+TkCAjgSau5cYNKk\nGhFdCOFnNGxInWfkj+XlMW0jPNxqyUQtRwaZv7BtG/DEE1QOwcHA1KlA377m43Pm0GAz+o8dOQJ8\n9BHwwAPuv0e3bkyKfeYZKqRTTgHOOIOGjDFKqab7kK1daw41LyoCPvkE+PBD9hey22mclifTX38x\nIdh4TkQE8+OEEKI0unZlisY331BvBASw16OS+4WXkUHmD+Tm0hgDmFiakwM8+yywYoXpVk9OLr6C\nCw5myLGyDBpEI+zuu3nsoCDmUHTqZE1T2DfeoBFlVFEeOwasWuV+n7WuXYGVK2nMBQbSg9i9u/fk\nFUL4N4GBzKW95hrq0PbtWc0thJeRye8PJCezrYWRE2aMNDLCigBbXWRksPVFXh4T2Xv3rtr7nXMO\nqyrT05mXdtpp1g3Tzs8vPo8uIKByLTgGDwZuvJHnMCmJUwlGjPC8nEKI2kNgINCzJ1MkZIyJGsJf\nq5bqVpVSdjZnuIWF0RjLy6OnZ/lysxS7sBB47TXgs89owIwcyUG81fFq5ebyvRs3ts5d/957zPmK\njqYhZrczZFlZJZmZSS9ZgwYa/+SHqMpSCOHPqO1FbWL9eoYpjWHfEycyvFjbsduBBQuAr79mCfqo\nUcC551otlahhZJAJIfwZGWS1jdRUhiljYtSkUNQpZJAJIfwZGWRWsHEjGwgWFQFDhgCXX261REL4\nPTLIhBD+jAyymmbLFuDhh82h3Lm5wMsvs1eWEKLKyCATQvgz7ugwVVl6ki++YLuJRo3YXDA8nEn2\nQgghhBDlIIPMk4SHM1RpUFREb5kQQgghRDnIIPMkt9/OrvAJCRw/BLDBqhBCCCFEOfhrTobv5mD8\n/jtnPhYWAtddxyarQohqoRwyIYQ/o6R+IUStQAaZEMKfUVK/EEIIIYQfIINMCCGEEMJiZJAJIYQQ\nQliMDDIhhBBCCIuRQSaEEEIIYTEyyIQQQgghLEYGmRBCCCGExcggE0IIIYSwGBlkQgghhBAWI4NM\nCCGEEMJiZJAJIYQQQliMDDIhhBBCCIuRQSaEEEIIYTEyyIQQQgghLEYGmRBCCCGExcggE0IIIYSw\nGBlkQgghhBAWI4NMCCGEEMJiZJAJIYQQQliMDDIhhBBCCIuRQSaEEEIIYTG+ZpC9DGAvgJ0APgPQ\nwFpxqse6deusFsEtJKfn8RdZ/UVOUfP403fDX2SVnJ7FX+R0F18zyFYB6ASgG4ADAJ6xVpzq4S9f\nFsnpefxFVn+RU9Q8/vTd8BdZJadn8Rc53cXXDLLvANid25sAtLZQFiGEEEKIGsHXDDJX7gPwldVC\nCCGEEEJ4mwAL3vM7AC1Kuf9ZACuc2xMA9ABwUxnH+B3AmZ4XTQjhoxwC0M5qITyE9JcQdQ+/1GHD\nAPwIINxiOYQQQggh6iQDAOwG0NRqQYQQQgghagorQpblcRBAKIBU5/7PAB62ThwhhBBCCCGEEEII\nIXyUKWDz2B0AVgM41VpxysVfmt3eAoaLi8CCCl9jAIB9oBd1vMWylMd7AJIA/Ga1IBVwKoC14P88\nDsCj1opTJuFgC5wdAPYAmG6tOB7DX3SY9JdnkP7yLNJfPkR9l+1HAPzPKkHc4AqY7UVedN58kQ4A\n2oNfcl9TaEFgZVpbACHgl/scKwUqh0sAdIfvK7QWAM51bkcB2A/fPacRzr/BAH4B8B8LZfEU/qLD\npL+qj/SX56mV+suX+5CVR6bLdhSAE1YJ4gb+0ux2HzgdwRe5EFRohwEUAFgC4HorBSqHHwCctFoI\nN0gELwwAkAV6QVpaJ0655Dj/hoIXt9Rynusv+IsOk/6qPtJfnqdW6i9/NcgAYCqAvwHcA99dtZVE\nzW6rRisAR1z2jzrvE56hLbgq3mSxHGURCCrfJNADssdacTyGv+kw6a+qIf3lXdqilugvXzbIvgPd\npiVv1zofnwDgNADzAcyyQD5XKpIVoLw2AItqXDoTd+T0RRxWC1CLiQLwCYAx4ErTF7GD4YnWAPoA\n6GepNO7jLzpM+su7SH95j1qlv4JrSKCqcIWbz1sE61dtFck6DMBAAP29L0q5uHtOfY1jKJ70fCq4\nyhTVIwTApwAWAPjCYlncIR3ASgDnA1hnrShu4S86TPrLu0h/eQfpLx/hLJftRwB8aJUgbuBvzW7X\nAjjPaiFKEAyOnWgLxuF9OSkWoJy+nhQbAOADWO9droimABo6t+sB2ADrDQNP4C86TPqr+kh/eR7p\nLx/iE/ALswO0kJtbK065HATwF4Dtztsb1opTJoPBPIdcMGHya2vF+RdXg5U0vwN4xmJZymMxgHgA\n+eD5vNdaccrkP6ArfQfM7+YASyUqnS4AfgXl3AXgSWvF8Rj+osOkvzyD9Jdnkf4SQgghhBBCCCGE\nEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC\nCCGEEEIIIYQQQgghhBBCCCGEqBP0A7DCuX0tgPFeep/zALxaxmOHATQu47HvAdQv57iPAahXdbEq\nJYs7DAMwx7n9IIC7qikPYMoUBmADgEAPHFMI4WWCrRZACOG3rIBpnHmabc5baTjKuP8yAPsBZJZz\n3DEAPgSQW3XR3JKlLAIB2Mt47O1qymJgyJQP4AcANwD4zEPHFkJ4Ca2chKhbtAWwD8D7oPGyEMCV\nAH4EcADABc7nXQjgJwC/Oh9rX8qxhsH07sQA+BzADuftolKe/waALQDiAMS63H+B8z12ANgEIArF\nPXFNAKxyvu5dAAFlfLY7ACxzbkcCWOk85m8AbgXwCICWANYCWO183ptlyHTYub8NwC4AZ7shy+cA\ntjofG+FyfxaAGTDPy73gud8EoLfL82IBPAHgFADbXW6FAE4F0AzAJwA2O2/Ga8uTaTmAIRBCCCGE\nT9EWQAGATuCFeyuAec7HrgONCoBhvyDn9uWgIQAUN5SGwTTIlgJ41LkdCCC6lPdu5PwbBBpFXQCE\nAjgEhigBGmNBJd7nNQATndsDQQ9TaWHCvS733wTgHZfHjDDmnyVeW1Kmzi7PG+Xcfgg0dCqSxThW\nPdAINPbtAG52bp8C4C/QiAoBsNF5TAB4HjTIXBkFYIlzexGAi53bpwHY44ZMYQCOQQjh8yhkKUTd\n408Au53bu8G8K4AelrbO7YYAPgDQDgyBhVRwzEsBDHVu2wFklPKc20DPUTBomHR03p8AMzyZVcrr\nLgEw2Ln9FYCTZcjQEkCqc3sX6JV6EcCXoOFTGqXJFOd8zAjz/QrgRjdkGQOGBwF6tM4CPVlFAD51\n3t8TNPxSnPtLUbr3EaDxdT9MI+xyAOe4PF4f9ASWJ1M+aCCHA8gr432EED6ADDIh6h75Ltt2ADaX\nbUMnTAHDeoMBtAGwzo3jlhVKBIDTQe/P+QDSwZBpONzPwSrv2KVxEEB3AIMAvAB+liluymRgnKci\nFNeVpcnSD0B/AL1Aw2ety7HyYH5OR4nXl/W5TgHwP7BwIsfluT1h/r9cKe/8BKDyuW5CiBpGOWRC\niNKIBhDv3L7XjeevBkN7AMN/JUOW0QCyQc9ZDICrQSNhP2h8nO98nmuo1GADmB8G5+saoXTiYYbq\nTgENoYWgp6y78/5MF9lKk6kiypIlGvRM5QHoABpmpbEZQF+nnCEAboFpLBlGVTCAjwE8BeB3l9eu\nghkWBoBuFcgEMGRZhOJGuBDCB5FBJkTdo6S3xFHK9ksApoPhuqAynuNw2R4Dhi13gXlprqE1ANgJ\nJqjvA40kI4RYAIYN54BJ79/C9JwZx/4vgD5gKHEwmINVGhthFiV0AZPmtwOYBHrJAOaVfQMakGXJ\nVBJ3ZPkGNKT2gOft5xKvN0gAk/d/dr7f7hLPc4DJ+ucBmAwzsb8FaIyd75R7N9gmozyZABqirrII\nIYQQQniVfmDVpDCZBjO/TAghhBCiRqioMWxdwmgMW9n8OyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ\nQgghhBBCCCGEEEIIIYSoIf4fkAk01RhtGrsAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1070e5c88>"
+        "<matplotlib.figure.Figure at 0x107d70e80>"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -911,7 +911,7 @@
      "collapsed": false,
      "input": [
       "from sklearn.decomposition import PCA\n",
-      "sklearn_pca = PCA(n_components=2)\n",
+      "sklearn_pca = PCA(n_components=2) # number of components to keep\n",
       "sklearn_transf = sklearn_pca.fit_transform(X_train)\n",
       "\n",
       "plt.figure(figsize=(10,8))\n",
@@ -941,13 +941,66 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAH4CAYAAACbjOPoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAH4CAYAAACbjOPoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nR5G8l9dhbL8AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1070e5668>"
+        "<matplotlib.figure.Figure at 0x107d70908>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 4,
+     "metadata": {},
+     "source": [
+      "PCA for feature extraction"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As mentioned in the short introduction above (and in more detail in my separate [PCA article](http://sebastianraschka.com/Articles/2014_pca_step_by_step.html)), PCA is commonly used in the field of pattern classification for feature selection (or dimensionality reduction).  \n",
+      "By default, the transformed data will be ordered by the components with the maximum variance (in descending order).  \n",
+      "\n",
+      "In the example above, I only kept the top 2 components (the 2 components with the maximum variance along the axes): The sample space of projected onto a 2-dimensional subspace, which was basically sufficient for plotting the data onto a 2D scatter plot.\n",
+      "\n",
+      "However, if we want to use PCA for feature selection, we probably don't want to reduce the dimensionality that drastically. By default, the `PCA` function (`PCA(n_components=None)`) keeps all the components in ranked order. So we could basically either set the number `n_components` to a smaller size then the input dataset, or we could extract the top **n** components later from the returned NumPy array.\n",
+      "\n",
+      "To get an idea about how well each components (relatively) \"explains\" the variance, we can use `explained_variance_ratio_` instant method, which also confirms that the components are ordered from most explanatory to least explanatory (the ratios sum up to 1.0)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sklearn_pca = PCA(n_components=None)\n",
+      "sklearn_transf = sklearn_pca.fit_transform(X_train)\n",
+      "sklearn_pca.explained_variance_ratio_"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 27,
+       "text": [
+        "array([0.36, 0.21, 0.10, 0.08, 0.06, 0.05, 0.04, 0.03, 0.02, 0.02, 0.01,\n",
+        "       0.01, 0.01])"
+       ]
+      }
+     ],
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -1067,13 +1120,28 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH4CAYAAAD+YRGXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH4CAYAAAD+YRGXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNCIiIiKF+X/RNt9EvCI3jgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x107e3b208>"
+        "<matplotlib.figure.Figure at 0x107d5c3c8>"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "heading",
+     "level": 4,
+     "metadata": {},
+     "source": [
+      "MDA for feature extraction"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If we want to use MDA for projecting our data onto a smaller subspace (i.e., for dimensionality reduction), we can directly set the number of components to keep via `LDA(n_components=...)`; this is analogous to the [PCA function](#PCA-for-feature-extraction), which we have seen above.\n"
+     ]
     },
     {
      "cell_type": "markdown",

From b792f8b03ed0549cc36926722994cddde2572f32 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 26 Jun 2014 17:54:14 -0400
Subject: [PATCH 123/248] namedtuple example

---
 howtos_as_py_files/namedtuple_example.py | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 howtos_as_py_files/namedtuple_example.py

diff --git a/howtos_as_py_files/namedtuple_example.py b/howtos_as_py_files/namedtuple_example.py
new file mode 100644
index 0000000..0546d85
--- /dev/null
+++ b/howtos_as_py_files/namedtuple_example.py
@@ -0,0 +1,5 @@
+from collections import namedtuple
+
+my_namedtuple = namedtuple('field_name', ['x', 'y', 'z', 'bla', 'blub'])
+p = my_namedtuple(1, 2, 3, 4, 5)
+print(p.x, p.y, p.z)

From d732c994b15df8d25e12f924ff47b85b30f06e96 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 26 Jun 2014 22:20:15 -0400
Subject: [PATCH 124/248] modified pickle section

---
 tutorials/classifiers.pkl               | Bin 0 -> 2453 bytes
 tutorials/python_data_entry_point.ipynb | 160 ++++++++++++++++--------
 2 files changed, 110 insertions(+), 50 deletions(-)
 create mode 100644 tutorials/classifiers.pkl

diff --git a/tutorials/classifiers.pkl b/tutorials/classifiers.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..1272af5379477528ccd2e994c314d3b43328e07a
GIT binary patch
literal 2453
zcmaJ@eN+_J6<@v-SCJ2iU*HGCDnvj81Qx+JxEKtpsH@Rc0+X5DS$1)m**h}}LJTnx
z5e!wTL?ubJCpI4@610j2(aa!5Jlasj21LUVG#WvgqoM{{deWKM#h$h&eShrR_vXF(
ze)rzr@4mOiBO6p<MsA~xB3L@gW+K$f(o)sn7XCK4zX={;hS4gO%FbFDmczl*NIUZF
zMNvkErK0j3Hr`6GEK#J!S<1-J9M3w8yc)cmtJJi~`4vX#RNG**){O-3?J!0U>M)&~
z&SM*lbuBdUMRrOJ;|%W7S>{CW(W!K9+hM#Oe8Y4e1~+Lo8T>LcGc$gXn)0Jl@!(HZ
zXeTHA_#xM-=|EW<YwO!L+(aE4<Uf}X1n@9{G>nn<az?^trE?|xffg@|0_Fvagk+)~
zCdo*Abnay^+2UKFy|U(T-Kw=6?ZR!Zl7b1vJxFu3v~um?Mzp^H`!GMC6RF;7G%Seu
zTDa;PlMYqgsDFRmiqz&F6z2DNQh89j5Leprr!L>~LgUn+Cw<dDMd6TCKdGw|H5^?P
zXbNu?qBNg;R;l~Dpkg21@oi}p9{pqIS1Y{Qg?r}IDe8(gVa?*ddOsU#Lesb2HGQ@1
zqR>Ehd5lVXfV|pf$J89}MdX8$8jtPggoJ@zMFWv-LJqb1T;1+|;nmJVzndGeSJ<3=
z=nqSi`-Sxxm4Ed0ypLcC4^v5lT8e^|<|)=l*?Am-M#2y*hhdr?rn?d{Lk_~^i%`CD
z-P~X?!F(|F^qH~ZRX@*xWJ9C~GkFLh4WlGu`4mA*@eCa?^rCENmL7hi7}EKa!EDLY
z;zTKRLTC<c`uG959;%-@hq)kJE%UQ{LEk_ol|S?C(;tK}m(SDc8Qti+-(Il~RrjK;
z-eV(%r~eII`cxA%IIdgRV5D0f4|Sua+4@sk?bpzEFMmArgP|SyYd4jx@4tuqZho0G
zzxEMYd~qOohiAV~+-gWEU)>>eUc&}UD{l!^Cn}4N?D!T@roBgP!CgYJzmKhVs7dGy
zNGMtL*-0TPHmqlf>I>x6x&I@#i=PPRH)6%1uk;BYT+E!iZ`yU#ec`hD-APSo%Dt9?
zgO`64;`5r#N!!~Hih5do?Ce9-S{P9nQq_iD8|@z3n|K%X?ag_sYS|HVp?q>d%g#qa
zZk=}~yYiV(5EnNzYTbPlJVT?qQrssb?wQ$<LOe#LgEyn!efm^jh&v0G&mR=#1%-!2
zW_JoOhleoI;4MYlXd^g|lHwgc67QGfc+b^Cge!587N>Zui4f%!&pbUuOX9KouWaVa
zvXM0<H(7CV^Kf=5h~XiYG<ZovBSV>Sh#NU_fqZ1V9uk}>mL!uXvn9G@{nr=Xi5(*r
zqWHD~{Act{=j2Juoa?A>?ZRnUKJH@pn!)--jR9DYXGV@=w+aId4@sn9oOA+>8<~7N
zLsK-*!NN?1S<X@e$(ag<O(enMkTQZ{k&FT90h2NKY=cx8fklDdeK*<ZLdzLvU+Ws*
zaP{@B{-o>s3D@|jYkb7jZ*ldPoZq*M*gbXz7W1Gb9pt~OrInVdCZ5ljC|eZAGe!%+
z@m3?A%MvE5<dk}KdYV?woz-lmST&?cMRm!W@N$@8qJ$BrHrWZ<M450Uzjz^K&9(55
zKH}_B+1Xe0km1U)P7$ZmxrgY#dsxQ9a?;=<&6}+}{#@;lS#Ehwn-#Ljm3mm^GO4#j
z|35CQ=3x!#ya1R&ip6<&H63+3PgZJ5A;IS398a)3tgTQIM%e5Y0@kIYsqwcT1ZyLe
zJpYG(hD^mGXK|R#HVW2PD8mAZB^i!_4LX%V0~*g`NDi`eZkFfO>NOdCwjOd^=y4?=
zIhl0Tlmx995zlYdH-?KHx3Y$3m97-wbsh-P@|?e<yxXV;ld@YbQIxb_DN!jw!vhO1
zrC_~r$4!P96~?u<Y^bSE#2jaH?(EvWM0^4AD*tlC9rFw;KYtGmAnx<ioma+du;I?y
zpXyJ}#7?(-<Uf=hD2}Q7`THBk0z_iZa`j8?<HS9eeIDO`U4x;xf|!H<T!IN3C$x1`
zFBW4nu%pG@v&9mOz*>5=VqWWEwjn(QJ85h?aQ4d(anbExQp)zt!WP~Ne*4_-=ZKqp
zu9o3FA>yQ%!s?quI2KUbkT<VsnfO!R%wf-O=3#NBC%3O@22pV7iLSs7tw=_HpB2zC
z2OALIc~tkoH0(}<2T%Sv1S8)woH}r7BG$Ki#oj}spP?O7VveU7p9safnjI_hHDaTq
zIW>Ntx7f4g=94!LhKqfS@1j^vgJlICFx8xh5n0pw)jQ9=D25&^AAjp{hzMpLa-Aj0
zHXmmR-pYU_9W}=)&EP39h2n{5u%<gXqf#k1ymSNQ{mMf^zN!gHDH|=mTDkJ6R4Z#R
zu$4i+9%zLql%2EM7#f&#)KDY0_Z7}7&ll;Emr+n)SC%q%-kML?0Gulrj>F9k+9;u<
zf&CQ`=bCV{Bx3ba$(Hma;nLAVC-!Z?>%pPCMYK@xd|GkRCYgpph5r_LA8b-+&r0fM
m6n#jLik!5!Q#4`Yi=bGEge?Z%aM9f=-OjLC=j9-&sDA;7_lHja

literal 0
HcmV?d00001

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index 2c35622..4b3463e 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ea0d498feb91238c4ac1c1b582ca9e09d18639ea850f6cd2cbd12081785e39e5"
+  "signature": "sha256:cfb34d1c50e9095a633d85dd5229c48fbfb6c528e4e69f20b2d25a017f7cfdb7"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -419,7 +419,7 @@
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -482,7 +482,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 4
     },
     {
      "cell_type": "code",
@@ -527,13 +527,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIjIw7BBIyIiIvIw/wc8hBWSvYAm0wAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrA8xusMnb/cPHAAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1063ddac8>"
+        "<matplotlib.figure.Figure at 0x1056b3b00>"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -608,13 +608,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nah7OJ7wrYQAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERERERERERERERERERKSW+H/CqQ+gYvnblgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x106804780>"
+        "<matplotlib.figure.Figure at 0x1059cba58>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -666,13 +666,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmUVOWd//++t/a9u4GmbZoGBAkIyiIKImvUqLjGNWZQ\nJzHKccwYRyff4zITz/klmWRmgsZ8Z8YlUb/GmBhHR8ERTVBBFkUkCCJEoJutm26g1+rabtVdnt8f\n5XO5VV1VXcutqltVz+scztGq6lvPvXXv834+n+ezcIQQAgaDwWAwGBnhyz0ABoPBYDAqASaYDAaD\nwWBkARNMBoPBYDCygAkmg8FgMBhZwASTwWAwGIwsYILJYDAYDEYWMMFkMBgMBiMLmGAyGAwGg5EF\nTDAZDAaDwcgCJpgMBoPBYGQBE0wGg8FgMLKACSaDwWAwGFnABJPBYDAYjCxggslgMBgMRhYwwWQw\nGAwGIwuYYDIYDAaDkQVMMBkMBoPByAImmAwGg8FgZAETTAaDwWAwsoAJJoPBYDAYWcAEk8FgMBiM\nLGCCyWAwGAxGFjDBZDAYDAYjC5hgMhgMBoORBUwwGQwGg8HIAiaYDAaDwWBkARNMBoPBYDCygAkm\ng5ElmzdvxrRp00ryXcuWLcNzzz1Xku9iMBjZwQSTUbP87Gc/w4oVKxJeO+uss1K+9uqrr2Lx4sX4\n8ssvSzI2juPAcVxWn504cSI++OCDIo+odN/DYBgVJpiMmmXp0qX46KOPQAgBAHR3d0OSJOzatQuK\noqivtbe3Y8mSJeUcakY4jlPPoRq+h8EwKkwwGTXLvHnzIIoidu3aBSDucl2+fDmmTp2a8NqUKVPQ\n1NSEjRs3Yvz48erfT5w4EatXr8asWbNQV1eHb33rW4hGo+r7//u//4vZs2ejvr4eF110Efbs2ZN2\nLOvXr8e0adNQV1eHv//7vwchRBWn9vZ2fP3rX8fo0aMxZswYrFy5En6/HwBw22234dixY7j66qvh\n8Xjwi1/8AgBw00034YwzzkBdXR2WLl2Kffv2qd+1bt06zJgxA16vFy0tLVi9evWIY073PQxGTUEY\njBpm+fLl5IknniCEEHLvvfeS559/njz66KMJr915552EEEI2bNhAWlpa1L+dOHEimT9/Punu7ib9\n/f1k+vTp5OmnnyaEELJz507S2NhItm/fThRFIS+++CKZOHEiiUajw8bQ09NDPB4Pef3114kkSeSJ\nJ54gZrOZPPfcc4QQQtra2sh7771HYrEY6enpIUuWLCH3339/wjjef//9hGO+8MILJBgMklgsRu6/\n/34ye/Zs9b2mpiayZcsWQgghg4ODZOfOnRnHHIvF0n4Pg1FLMAuTUdMsXboUmzZtAgBs2bIFS5Ys\nweLFi9XXNm/ejKVLl6b9+/vuuw9NTU2or6/H1VdfrVqmzz77LFatWoXzzz8fHMfh9ttvh81mw7Zt\n24YdY926dZg5cyauv/56mEwm3H///WhqalLfnzx5Mi6++GJYLBaMHj0a//AP/4APP/ww43n97d/+\nLVwuFywWCx577DHs3r0bgUAAAGC1WrF3714MDQ3B5/Nhzpw5OY+ZwahFmGAyapolS5Zgy5YtGBgY\nQE9PDyZPnowLL7wQH330EQYGBrB3796M+5daYXM4HAgGgwCAo0ePYvXq1aivr1f/dXZ2oru7e9gx\nurq60NLSkvCa1vV78uRJfOtb30JLSwt8Ph9uu+029PX1pR2Toih46KGHMGXKFPh8PkyaNAkcx6G3\ntxcA8Prrr2PdunWYOHEili1bpgpiujF3dXVlcSUZjOqHCSajJiGEIBaLYfr06fD7/XjmmWdw0UUX\nAQC8Xi+am5vx7LPPorm5GRMmTMj6uDSytbW1FY8++igGBgbUf8FgELfccsuwv2lubkZHR0fC2LT/\n/8gjj8BkMuGLL76A3+/HSy+9pAYlab+T8vLLL2Pt2rV4//334ff7cfjw4YQ90Xnz5uHNN99ET08P\nrrvuOtx8881ZjTnbqF0Go1phgsmoORRFQTQahSiKsNlsmD17Np544gmcd955GBgYgCAIWLhwIR5/\n/PGM7thUUFG666678PTTT2P79u0ghCAUCuHtt99WLVAtV155Jfbu3Ys33ngDkiThV7/6FU6cOKG+\nHwwG4XK54PV6cfz4cfz7v/97wt+PHTsW7e3tCZ+32WxoaGhAKBTCI488or4niiJefvll+P1+mEwm\neDwemEymrMac/D0MRq3BBJNRMxBCIEkSBgYGQAgBz/PgeR6LFy9Gb28vFixYAFEUIQgC5s2bh97e\nXlxwwQUQBAGSJIEQktHK0uZOnnfeefj1r3+N73//+2hoaMBZZ52F3/72tyn/btSoUfjv//5vPPTQ\nQxg9ejTa2tqwaNEi9f3HHnsMO3fuhM/nw9VXX40bbrghYRwPP/wwfvKTn6C+vh6PP/44br/9dkyY\nMAHjxo3DzJkzceGFFyZ8/ne/+x0mTZoEn8+HZ599Fi+//HJWY07+Hgaj1uAIYYlVjOqHumAVRcHg\n4CDq6+vV13g+vm6UJAmiKMLhcKh/o3VlAoDJZILFYoHZbIbJZGJuSgajhjCXewAMRrFRFAWxWGyY\nhUgF0mw2q6KpJbnaDiEEiqJAEAT1NSagDEbtwASTUbVQF6wkSeA4LkEUI5EIotEoeJ6HIAiqi5YQ\nAlmWwfP8MPFLJ6CRSER9nQkog1G9MJcsoypRFAWiKEJRlAShk2UZfr8fZrMZTqcTkiSB53n185Ik\nqcfgeT5B+EYSP+q+pd8JMAFlMKoJZmEyqgpqIYqiCCDRKozFYgiFQgAAl8uVIF48z8NkMkFRFDgc\nDiiKAlmWIcsyYrEYgLj40X+pBJS+Ri3ZVBao2WxW/zEBZTAqCyaYjKoh2QVLxYgQgnA4DFEU4fF4\nEAgERhQqGkFrsVgAQDcBpWLOBJTBqDyYYDKqgnQuWEmSEAqFYDKZ4PV6Uwb3aEm3Q6EVUOp6zSSg\nmYKIRhJQi8WSIMQMBsMYMMFkVDTJoqMVo2g0ikgkAqfTCavVmlJ8tK9lK05a4SuGgIbDYfXvqIDS\nSF4moAxG+WCCyahYtLmVWqtSURSEw2HIsgyv16tWskn+W73ER28BBU5btLIsq0UT6D4rE1AGozww\nwWRUJMm5lVoXbDAYhMVigdfrHdGqLAapBJTugUqShGg0Co7jchJQ4LQFSiN5OY5L2ANlAspgFBcm\nmIyKIl1uJSEEgiBAEAS4XC5YrdYyj/Q0WnEEkFFAzWZz2n3UVAJKiy/Q95mAMhjFgwkmo2LI5IKl\nBcLTuWCNRCYBpYFL0WgUsixnDP5hAspglBYmmIyKQJIkhMNhxGIxuN3uYbmVNpsNDoejIgUhWUDD\n4TDM5vijSYvB0/3LXAVUFEUmoAyGTjDBZBgarQuWwnEcCCGIRCKqgNJ8yVyOO1KKSbmgrmYqmskW\naC4CqrW20wkoTWNhAspgZIYJJsOwZCpvFwwGwfN8VrmVyVSaKIzkwmUCymCUBiaYDMORXN6OCiLH\ncZBlGUNDQ3A4HLDZbLpP6JVQWrlUAkoIgcVigdVqZQLKYIAJJsNg0ElbluVh5e0ikQgURYHX61Xd\nlXpSqWKQSkC1OaCKouQloIIgJBSF4DhOzQE1m81ZFaRnMKoJJpgMwzBSbiWd9IshltWENrgHyF9A\nAQzbS43FYohGo+p7TEAZtQSbeRhlJ1Nupba8Hc/ziEQiun93tU/y2QhochGFbF24VEC1FijdA2UC\nyqg2mGAyykqm3MpQKKS6YE0mk1oirlBolC3971ojk4BGo1FVQOliIt2iQiug9HrGYjG1FCC1TpmA\nMqoFJpiMskGtymQXrCiKCIVCsFgsCTmXjOKQTkCj0ahaCGEkC5T+fy4CatS0HgYjHUwwGSWnEsvb\n1RJUQCVJUveMU1mgTEAZtQYTTEZJSZdbqS1v5/P52ORpILJ14TIBZVQ7TDAZJSFdbiVwuryd3W6H\n3W5P64LV7j0yykeygNIcUPr7EkLyElC6n60VUBqFywSUYQSYYDKKTrp0EUIIwuEwRFHMq7xdvmQS\nXibIp8k2gpj27qS/XyYBTZd+kqoOLo2SFgQBoijCbrczAWWUFSaYjKJCO28MDg6ivr5et/J2xYAF\nF+lDJgGlaUHJOaCZBJTubVMBTZUHygSUUQqYYDKKQnJgj/b1WCyGcDicc3k75pKtTDIJKHW/jiSg\n9DgUrQVKBdRkMqn7n0xAGcWACSZDd9LlVlIXrCRJ8Hg8rGJPjaIVUCp8mQQUGG79p3LhKooCQRDU\n/9cGEFFXMINRCGzGYuiKdt8qeVIbGhqCxWKBz+djkxcDwGnhG0lAaY3hdJZjJgGl9yK1QKkLl92D\njFxhgsnQhZFyKwGoUbDlhrl2jUsqAZVlWS0En8oCzVVAKUxAGbnCBJNRMJlyK2l5OwAFFyJgQld7\nUPHkOA52uz2hlZkkSWodWyagjFLABJORN8m5lcnl7YLBIGw2G9xuNwYHB8s5VEaVkKkXKBNQRrFh\ngsnIi5H6Vkaj0YTcSmYd5ga7VtmRjYBm2ws0lYBGIpGEIgtMQGsbJpiMnElXiECWZYRCIXAcV9Hl\n7crd8otNxInk8ntkElBRFCEIQk4Cqt2LpwIai8VgtVrVKFwmoLUDE0xG1mhdsNrJBMi+vF0haK3Y\nQo6fztplE171obeAchynNuBmFmjtwQSTkRUj5VaKopgxt5K5ZBlGQC8BpYvFVBYo/Tytt8sEtHpg\ngskYkXQuWEmSEAqFYDKZDFPejsHIhVwFNN09nsqFq/XGAExAqwEmmIy0ZMqtjEajiEQicDqdsFqt\nFfvwV+q4S0G593LLQSoB1RZRoClS0Wg05z3QVAKqLeVXa9e6EmGCyUgJLZoeCATg9XoTcivD4TBk\nWYbX61UnlpHQyyVLj8MmF0YpSG5lpi0gTwU03yAiGslL0QYRpWqJxig/TDAZCWhXwoqiqHuWQNwF\nGwwGYbFYEkS0EmH7qZWDkRZIVPhsNhuA1BboSL1AtcehJAtocik/JqDGgAkmQyXZBWsymdTanoIg\nQBAEuFyugiv2lBs28TD0ItkC1QpoNBrVVUC1e6BMQMsDE0wGgNSBPdQKCwQCAJCTCzYZvV2yemEk\n64VR+RRTQCVJSqiqxQS09DDBrHEy5VbSh9NsNsPhcLAHklHz5LrAKraACoIARVHUQgpMQIsLE8wa\nJlNuJa1oAqBqxVIURUQiETVog/ZMZFYno1gUQ0Dpf4uiyCzQIsMEs0bJVN4uGAyC53l4vV7diqYb\nqXCBdnVutVrV/49GowDi0Y8sV45RCpIFlAbaUfcrISRrAdVul9Baz8kCStNYmIDmBxPMGiNdbiUQ\nzy0Lh8NwOByw2WwJe5lGebgKFV7aiowQAq/XqwZVUOGktXCTk9bZKr18GOn+KzY8z4Pn+QQBpRZo\nKgFN9ywwAS0OTDBriEwu2FAoBEmSMpa3q3REUUQoFErIm5NlWZ106DWxWCzgeT5vdxmjeim1eFMB\npV1/Ugkoz/MQRVH1iORrgWrr4LJ7OzXVOTMyhpEcYZecW2k2m+Hz+YY9JEaNbs2F5LQYrUhmItN+\nkyAI6mpf675lkwyjmCQLKL0PaR4ogGFFFLIV0Fgspm5L0O+g9z+7t+Mwwaxykl2wWqtSW96OJmJX\nG4qiIBQKQVEUNS2GVmrJFa2A2my2hNV+qsmK1dZlFBu6rUJzo0e6JwsVUOrCrVUBZYJZxdD9umQX\nbCoRqRRysVS1lYncbvewsPxC0a72aYEHbeNiKrCsViijWFCXLCXdPZnrok4roPRZoQKq3bqoNQFl\nglmFaHMrASQ8FHQfL5WIpKISXbJa6zlVZaJiPNjaWqF0ssqnbZSRMEpUs5GopACkVPdkPgJKzzeV\ngNJj0EClahdQJphVBu1PSQhR9x7o69VU3i4dNIAp1+LwyccoFLpCT24bJUlSQtFuowdZGGFMlSRS\npSaXa5NOQOmWDbUeCxFQQRAgyzJsNtuwKNxqoDrOggHgdIeRaDSq7lnS1wOBAERRhM/ny0ksjZQ/\nORKSJMHv94PjuLzFslgTM52IbDYbnE6numih1nAoFFKLRdCJjMEoJtr9T4fDAZfLBbvdDp7nIUkS\nwuEwQqEQBEFQt3bSHSedCzcUCuGee+7BoUOHSnZexYRZmFVAusAe4PRNa7fbYbfby7ZSL6ZrtxID\nmNJF4EqSpLrMtGks1bJCr3Sq2dpN5xXJdV8+WUB7enoq4pnMBiaYFU6q3EqO49TAHlEU4Xa71TD0\nfL/DqOjhgjUCWgGl+50cx7EIXEZGiingmQQ03b58qrkiHA7D6XQWZYylhj11FYy2+HJyFCwVUa/X\nW5BY6vkw6i28sizD7/cDyK2TSiW4mWkUot1uh9PphMPhSHCVhcNh1fVu9HNhVAdUQLUuXFoRjAYT\nUtetNu+bBt9liyAImD9/PmbPno2zzz4bDz/88LDPbNy4ET6fD3PmzMGcOXPwk5/8RLfzzASzMCuQ\nTLmVNPTbbDZnFQVbKvQWXlrGr1JcsIVAf2O691wNEbi5wBYExiSVBUorYomiiFtuuQX9/f1wOBx4\n9913sXTpUvh8vhGPa7fbsWHDBjidTkiShEWLFmHLli1YtGhRwueWLl2KtWvXFuXc0sEszAqDWo+p\nxJIGjtjtdt0mTSNaY3S/0uPxVL1YpiLVSp+KKd2zpgFE2VY1MjpGWQAYbQ/TSOOh8xFtB/jqq6/i\n5z//OWKxGH75y19i3LhxuOCCC/Dkk0+OeCzqwqX3cENDw7DPlOO+ZhZmhZApt1KboO/z+RCLxdTP\nVRM0+IDn+ZRl/PLBiAuCXMlUwi+5Bi4rc8YoFXa7HRdddBEcDgfef/99RKNRfPLJJwgGgyP+raIo\nmDt3Ltrb23HPPffg7LPPTnif4zh89NFHmDVrFsaNG4df/OIXwz5TDJhgVgC0ULIsy8OsSppbWSzX\npFEKF1AXLM/z6r4JIzXJAqotl0bLArIAourASBYmMLzyEIXjONjtdixdujSr4/A8j127dsHv9+Oy\nyy7Dxo0bsWzZMvX9uXPnoqOjA06nE++88w6uu+46HDhwQK/TSD+uon8DoyBobmWyWCqKgmAwiFgs\nBq/XmyCW1WA1UbSuZr06qYx0farl2lFooro2gIh2atHm2rEAIkYxKETQfT4frrzySuzYsSPhdY/H\no7ptr7jiCoiiiP7+/oLGmQ1MMA0KtSq1xY/pjSeKIvx+P0wmU8WkUuQj4rIsY2hoSI32LUXbMSOt\n1ouBttKL3W5PSFankY40ApcVUBiO0Sw6o5Hq+uR6D/X29qqN6yORCNavX485c+YkfObkyZPqcbdv\n3w5CSMp9Tr1hLlkDkqlvZSQSQTQazZhbqaeFWS5rlQavaJtZM/QnU64dEJ+wyl3CL52br9aphMWM\nKIo5p7V1d3fjjjvugKIoUBQFt912Gy6++GI888wzAIBVq1bhtddew1NPPQWz2Qyn04lXXnmlGMMf\nBkcq4arXENrGsFqxlGUZoVAIHMfB5XJlnEBEUUQkEoHX6y14POFwGBzHweFwFHQcmu7i8Xgyfo4u\nCmKxGNxu9zCrMhQKwWQywW636zIWbTcXIC4QtA9guRAEASaTqaD8WT0IBoNwOBwJQUTlaKJtlOth\ntLHQ7Qq3213uoaiEw2HYbDZ1ATYwMIB77rkHb7/9dplHpg/MwjQIybmVWkHMtbydUS3MkY6jXRR4\nvd60wQNsjVc6qCCmKuFHF3bJpdKq2RvAXLK5EQ6HC15sGwkmmAaAlrGTZTlBEGnnEVEUdQt4KRcj\nTTKlrnnLRDc/MkXgshJ+pcWI4p08JlrfuVqo3Bm4CtDmVtJJh95skiSp7sd01lY69LbCiiku2e7L\n6onRJplKJlXD4uR2UayJdu0SDodzKotndJhglonk3Eqe59WqLNrOG1artayTjF7fnUrEaWoMEA8f\nZ9ZIZVPrJfwYwy3Maiq8DjDBLAu0vF1yYI9enTcqYZ9PFEUEg8GcXbCVcG6MOOkicLVNtLMJIDKS\n65GNJTeYYDLyJlNgDy1YbLPZ4PV6DfMgFMO9S6sTlcoFyzAGqQQ0XQm/cqWwMPKn2lt7AUwwS0am\n3EoqICaTSRd/vxGtMNqjMxAIACi/Czbd9THitatWMgUQ0VrIJpNJzcdjJGJUCzM56IftYTJyIl1u\npXYPz+l0qlV9jIReAkLLrtFOBoU86IWOR/vdRpxwahVtABEwPAJXFMVhReQZxiYUCjELk5Ed2eRW\n2mw2OByOorRhMsIKVGtBcxxX8MNT7vNhlA4qoJIkwWw2q4FxNAK3HAFERnimjEqqaxOJRDB69Ogy\njUh/mGAWCbonma68Ha1kU4w9PKM80DS/lBACj8ejumMZjFzJVMIvOQKXiqtRnoNiUQnizfIwGRlJ\n7luZXN4uGAyC5/lhuZXF2DvT44HKd1y0Rydtcsz2BRn5kuo+ZgFExiOdhcn2MBkpSXbBam8e2s+x\nVMXEyzU5aPNIXS5XQk6eHrCgHEYqMjXRFgQBhBDV+qyFEn5Gge1hMlIyUm6lJEkZy9sVy8IslFzG\nRV2wtB2XNo/USEKXPJZKcG2VGqP8VvmiFVCbzaZbCT8j3StGGguQ3sJkgslQ0bpgkwN7qFvSbDbD\n5/OV9OYu9YNEz9ViscDtdhvqQWbkT7X8jqlK+GkDiFgJv+LAXLIMlUy5ldrydjabbcRjGdXCzOY7\ncj1XBqOc0GdVK6CVWMLPaF6AVBYmqyXLABB3Pw4NDaluH21uZTq3ZDbo5WYpZg1YSi6l/LSLiULG\nVmzXrpFcx4zSkE0JPyqg9H2jCKhRxpEOVumnxtEG9kiSlLD6FEURoVAoL7dkMW78Yk78WheskUr5\nMaqPUgtUughcSZIAxEWAReAOh0XJMhJI5YKl+yE0OV8bGZor9HhGtzBpxG+lumCZFcnIBe3+piRJ\ncLlcCU20gfL0ACWEGL7DjyzLeTeRMCJMMLMgU26lkeqjJlOMPdFsIn7ToeeCgMEoF5lq4NZyE+10\nz3Y1Pe9MMEcgU24lTSVxOBw5tahKh56Wj94WJi26YDKZSh7xm2o8eh6PwciGdIIwUhPtSgggKgbV\nuDhmgpmBTOXtwuEwJElSK9kYET2FZWhoqGRFF0oFDdCSZVl1uVXjQ84oHXSeKEUTbaPdq+lcxEYa\nY6EwwUxBptxKbXk7m82mq7vFaBYmdcEC0KXurZH2Dgkh8Pv9sFqtagsp7f40ABbQwSiYTCX8cmmi\nXakY5XnXCyaYSaTLrQSGl7ejJbf0Qm9BKeRYWhcsgJz3K4tJIedF80YBwOVywWQyQRRFdTEgCIL6\nOfrf5QroYMQxmiVVCJlK+CXXwKXpaunO3Wj3SPLvZLTx6YFxZkEDkE95O6PeFIVMMLT1GF0Y0EAG\nI1DIeWnzRgHAarWq/609PnWpJZdUK9V+VLWIQ7VRDOHOFEAUiUQAZF6wGfleEUWxIqPoM8EEE5n7\nVmrzDZODXYrx8JTTwqR7s6IoJiwM9IpuLadLVmsxezweDA4Opvxc8hiTAzpS7UfR/c9qc6cxSs9I\nTbQBqNanURfrlGorvA4wwcxY3o7mVqbLNzTSnlwyuU7cmVqPVTrJFnO+ZNqP0rrTtALKYBRCpgUb\nnaOMUgM3eVFNt6+qiZoWTG3icXJuZbbl7aphD5MKit1u1yU9ppjkco20zbq1FrOegVXZ5ONpW0pV\nCtW0b6gX5V4cJy/YaL9Z7aLfSE20q63KD1Cjgpkpt1IURQSDQdhsthHL2xm5nF02wqIVlExRsHoJ\neSktckVREAwGASBls25A/wkwG/etUSazSqHcIpWMkX4zKqA8z6vCWc4m2qksTOaSrXAy5VZGIhFE\no9GsUyiMnkSfaWxUUDiOqzoXLN13pjmy5ZjksnXfZhMNyTCWUBmFZIHKFIFbjibabA+zgiGEQBRF\niKKoWgIUWZYRCoXAcVzO5e2M6pLN9CBQKzpbF6yR92qTEQRBdQXlW9O3GKSazCRJShkNydyhDD3Q\n3nN6NtFOR/J9W23No4EaEUwqlqFQCISQBL96Ift3lTap5WNF64kewpvuGNrqS/m0VSs1HMfBYrGk\nLKcmy7J6nkYI5mDEMdJCJp/nqNRNtKutFyZQA4JJE9Vp2Saad5cuhSIXiuGSLdYepnZPL58i8Ua2\nMLUpI7m2GjPCBJhcTo0WTOA4riaqwTDyJ9/7gN5zejXRpvMD28OscOiNQf/RlXwoFFIn2Grav0uF\nNpApnz09I0/OySkjuYzVqIsArYAmB3OUs/oQo3pJteee3EQ710UbszArFK21JcsyAoEAnE4nrFZr\nQWJQDAtTURRdjxWJRCAIQllcsKnGpNf10ubJ5nNu6X53PX8Dvci0F1UMV5qRMJIb1EgU+7rkErRG\nP5M8HkEQMHbs2KKNsRzUxNKUToLRaBSyLMPr9erSdaMYwTB6Cgp1r/h8voLE0khBP3QswWBQl3Or\nRKgbzW63w+VyqXvvdJ8+HA4jFotBlmXD/G7VQC2LN12U2Ww2OJ1OuFwumM1mKIqiBtppAyuB3AsX\nCIKA+fPnY/bs2Tj77LPx8MMPp/zcfffdh7POOguzZs3CZ599psv5ZUtNWJiiKCIQCAxbEemFXg+S\nXg+jKIoIh8MAAI/HU1UPuSRJAOKi4XQ6q+rc8oFVH2KUA23QGhCfc+gi7emnn8ZvfvMbTJs2DZIk\nYcGCBWhsbBzxmHa7HRs2bIDT6YQkSVi0aBG2bNmCRYsWqZ9Zt24d2tracPDgQXzyySe45557sG3b\ntqKdZzI18fTQ8nZ6l2kyWi1ZGgVLU0b0cs8ZpXBBNBpVA5dcLlfNi2Uqki0Bp9MJs9mspq+EQiEI\nggBJkpj1WcEYzdqle5p2ux333XcfXnnlFXg8Hnz44YeYOnUqzj33XDzwwAPDmh0kQ4OEqPg2NDQk\nvL927VrccccdAID58+djcHAQJ0+eLM5JpaAmBNPj8aj7lXpPEkZxV9Io2FgsBq/Xq0a+VQO0y0gk\nEoHb7VZf0+O41Y7Wfet0OmG328HzfIL7lm5V1ML1YBQHrYDzPI+ZM2fC5/PhP//zP9Hb24tf//rX\nmDp16ohUqJ0WAAAgAElEQVTePUVRMHv2bIwdOxbLly/H2WefnfD+8ePHMX78ePX/W1pa0NnZqf8J\npaEmXLL0hzSKuKUj3/FpO6rQcn7UdVnpFKsikZFW56Uil0AOnucN/ayUA6NZdUaHppWYzWbMnz8f\n8+fPH/FveJ7Hrl274Pf7cdlll2Hjxo1YtmxZwmeS78tS/iY1YWEWk3KKMI0UpVG/WjdlMXM6S3Uc\nURTh9/vVhQDbf9OXVIEcFotFDZAD4tsZtJRkOWAilZpKuC6FFF/3+Xy48sorsWPHjoTXx40bh46O\nDvX/Ozs7MW7cuILGmQs1NQMZ3SWby7GomzIajcLr9RqqDFyh0IVAMBiE2+0uWz3YWoMKqN1uV6PI\nTSYTJElCOBxW3bds/5ORTCoBzzUPs7e3V+1TG4lEsH79esyZMyfhM9dccw1++9vfAgC2bduGurq6\nkqau1IRLlqIttG7ECThbwdS6YNNVtjGihQl85U45cgT8n/8MrrMTZMYMKJdeCny1uU8XAjT9J9We\nBx2PXr+hEe+FckOLJ2jL99H8z1qtPkSrhTGyI9dast3d3bjjjjugKAoURcFtt92Giy++GM888wwA\nYNWqVVixYgXWrVuHKVOmwOVy4YUXXijW8FNSE4KZXNFf78m2VKttWuaP3oiFNEMuBxzHgd++HebH\nH4+/4HCA27UL/Nq1kP7t3yA3NSEQCGRcCOhJqvvA6Pvc5UK7/8mqD5Ufoy36U42H9ufMlnPOOQc7\nd+4c9vqqVasS/v8//uM/8hukDrC7ukBKZcklu2BHEku9J35djiWKsP/XfwE+H9DcDNTXA+PGAeEw\n8NxzGBoaUpPxiz0ZGGmyqUS0+58ulwsOhwMmkwmyLCMcDqv3KnPf1i7US1FN1JxgVqIFIUkS/H4/\nAOTciUOvYB094I8cAUIhQLOvQQBI9fXAxx/D81XaA6PyYNWHSovRLcxq/Y1r1iWr57GLaWFGo1E1\nPDsXF6yRHiYKZ7EAmvMjhECUJEBRYLXZgCxL3FXioqeW0LP6kJGEwUhjqQSq8XrVhGBqMbJgUujx\nCm0/phd6FSRXJkyA0tgI08AAlLo6iKIIE8/D3NcH5RvfAMq071WND7aR0BaPB5C2kTEVUPZbVB61\nEhBV/WdYQdCJQpZlDA0NgRACn8+Xt1gazhLjeYR/8APIigLlyBFYTp2C+cQJYMIEKLfdVrZhsQm6\ntLDqQ4Vj9EVetQooszANdjwACAQCefV3LBZ6nqM4cSL8jz8OzxdfAD09kCdPBpk3DyhxHqne0dKM\n/MjGfQvEa4vWSvpKJZL8LEWj0arKDafUhGBWwh4mIUTtMOJyuXS52YxkYWrTDzzjxoFraUG+Tl4j\nnRdDX5Ldt7RdlKIoatuocqWvsAVW9tC4i2qjJgRTixEnW1mWEQwG1dWz3u3Hyk0sFkMoFFLLrrFJ\nh5EtNDXBbreDEKJaoJIkIRqNguf5BAGtlXvLaC7P5MWEEQWTFt+g1y2fe6XmBFNvCg2IoWJCXbA0\nfUSvsZWzLRctcScIAtxut1p4wcgYcUFVDKJSFDaz8QtfaCdiKp50D5RVHzIuuVb5KQUffvghtmzZ\ngtGjR8Nms8HhcMBut8PhcIAQgnPOOWfEurQ1J5hGccnS3pWxWCwhCrZaJmxFURAKhdTAJZ7n1YhI\no1FrrrZBYRC7T+7GhS0XwmpK7fqvhHtwpOpDhBA18lYP962RronR7tnk8YRCIcMJJu2d6ff70dfX\np/aGBeJF3B977DGMGzcuY4WimhDMYu5h5oPWBatny6pkymVhamvdOp1O3ROa9Tgv7TGMNPGUgraB\nNvRH+9E51Ikz689M+7lKuy7a/U+bzZaQvhKNRtX3C3HfVto1KRdGdMlOnDgRN9xwA6ZNm4ampqa0\nn8s0H9eEYCZTTguTumDtdrtaDaWQ4xmNTIUW2GRTfgaFQfSEe9DsakbbYBtavC1prcxUiLKIz099\njrlNcw3/e/I8n9J9K4oiBEEAz/MJxROMfj5GJtnCLKS1V7H44osv8Nprr2H06NE4++yzMW/ePLS2\ntqK+vh4ejycrw6VmBLOYaQTZCJzWBet2u2FJU9XG0F1GRnjfKIUWGOlpG2iD3WyHiTeBgIxoZSaz\nr28f3jjwBkY5R2Gib2LxBqozuVYfMnodVKO5ZJMxooW5cuVKrFy5EidOnMBbb72F1atXIxKJYN68\nebj88ssxZ84ceDyejMcwTphViSjGHuZIKIqCQCAASZLg9XrTiqVRGekcFUXB0NAQFEWB1+tNK5al\nFPBSkm4oBhoigNPWpccanxTqrHVoG2xDTM5ub1mURXx47EN4bB58ePRDQ/0GuaItHu90OuF0OmE2\nmyHLMiKRCMLhMARBSCgeb3SRKhep7gMjCiYhBKIooqmpCXfddRfWrFmDP//5z7jkkkvwxz/+Eeed\ndx527dqV8Rg1ZwaUOuhHFEUEg8G0Lthijq8U7t1cz08PjDRpxWLAj39sw6pVMbS0nL7W69eb0NXF\n4447xDKOLhFqXVJytTL39e1DIBZAq7cVR4eO4ujQ0aJbmaUSqWzct0A8/qCW0ldyITmtxOv1lnE0\nqbFYLPj4448hyzLsdjvcbjfOOeccLFq0SF1EZaJmLMxiVtJPdTzqgg0Gg3C73XA4HBX7kKUS3ko/\nP70WE1Yr8I1vSPjpT23o7Iyf//r1JqxZY8bll0v5j29wEPy2bTC99x74AwcAKf9jAYAgCQjEAojK\nUfRGetV/hBCcCJ0Y8e+pdTnGMQYA4LP6Kt7KTAd131qtVjgcDrhcLnU/nsYg0O2VcpXvM7q1KwiC\n4fYwafrfAw88gKVLl+Kb3/wmli9fjvHjx6OpqQnLli3Dxx9/nPEYNWlhFvt4iqIgGAwCgJpSkcvx\njG5hEkIQDAZVF2y2hRYqPaApHYsXywCAn/7UhgULZHz6KY9//ucYxo7N71z59nbY16wBbzIBFgu4\n7duhtLRAuvlmIM/2Z3azHctal4Fg+Jg4jPxMUOuy3lsPAKiz15XMyiw32mIiDocDANTiCeWuPmQE\nUok3zS03EvR3ueqqq/DII4/g6quvBgBs2LAB77zzDhYtWoSHH34Yr7zyCs4444zUxyjZaA1CsV2y\noijC7/fDbDZnHXmVjNFERXuOsizD7/erKTHlqEo07DeUZSASyWvTMBaLIRKJqMW+gfyu/+LFMhob\nFbzzjgnf/a6Yt1giFoP17beh1NeDjBsH0tgIZcIEcMePg0/RjT4XOI4Dz/HD/o20iBRlERuPbgQA\nnAqfUv9JslS1VmYy2hQk6rqjvT9p82xJkhAOh9Xi8bXcPNuIUbKU559/Huedd576/8uXL8e6detw\nxRVXIBaLZXTL1qSFWQyXrLaqTSG1YPW0gPU+V+qOyrU3Z9GQJPDr1oH785/BhcMgzc1QbrwRZPbs\nEf+UEKK61GjJPm2xb1EUc7IW1q83oa+Pw403Svj1r6149NFowp5mtnBdXeBiMZCk1TlpbIRp1y4o\nCxfmfMxCUYiCWY2zICrD92O1e6K1Srr9z2JWHzKSSzbVWIwomHSM3/jGN/Bf//VfuOaaa1BXV4ct\nW7agvr5eXfRkamJfM4JZrD1MetxgMAhCSMFWlxHdlnRBEA6HC0oZ0fvc+FdeAb9+PUhzM8ioUcDQ\nEEy//CXkf/xHkJkz0/4dIQSSJIHjOHg8HvW/AahRkrkku9M9S+qGHTuW4Kc/teUtmkbDZrZh2YRl\nZfluoz0LI1Hq6kNGxYiVfii//OUv8cMf/hC33347BEHA+eefj5dffhmxWAxPPPEE3G532r+tGcGk\n6L0qo248nueHVbUpN3oIFC1xB6CoVYlyZmAA/IYNIK2tAF2g+HwgigLuzTfTCqYsywgEAuA4Djab\nbdj5JBf7TmctmM1mcBwHUeSwbZspYc+S7ml++KEJf/M3uQXrkOZmwGKJu5g1OWFcTw/kxYtzOla1\nYKRnKlcyVR+ipSJzrT5ktEVEOgvTqIJps9nwq1/9KuV7ixYtyvi3NSmYerXjikajiEQiAKCbWBZa\nzF1PaENfq9UKSZIMI5Ycx4E7dQqE50+LJaWuDvyRI1AIAZJ+D22he1mWs0rxSWUtSJKk/u4mkwkP\nPyx95VU4fTwqmjljtSJ21VWwr1kDbmgIsFrBRSJQxo2Dotl3YZQWvVygelYfMvJCIhKJZLTUyg2N\nbtYW9M+GmhNMSiEPALW6aJTo0NCQofYUKPmKr3YxQPdjqTupkHPUtXCBzweOhvRrxxQMgowZk/Ba\nctcUi8WiWs25oLUWqJuaRkrqWWpNmTwZwne/C9vhw+ACAZDWVihnnhm3PEuE0ayYaqRaqg+li5I1\nqoUJIO9ts5oRTG2LoELQFhZ3u93qTWz0VJBsIYQgFApBluWyRcFmAxk7Fsrs2eB37QJpaQF4Pl5F\noLcXyqpVpz+nSYHJNcUnE/R3p8FdqSa7QvaqSH09lLFjdRlrvhhxcq5mtAsyACndt5Wy7ykIguHS\nSrQMDQ1BFEVYLBaYzWZYLJasKrDVjGBqyaeurNbqKnaUaLnEl3ZRMZlM8Hq9hp8wlTvvBF58Efxf\n/hJ3z5rNUG69FeTCCwGU9nwyTXa13Oi4WiiHBynZfUs9GtJXRSzC4bAh7ql018aIwi6KIrZu3Yr3\n3nsPsVgMHMdBkiSMGzcODzzwwIi/c00LZraMZHXpbWGWg+RG1um6qBTqkgUKn3zU6+1yQfm7v4My\nMAAEAkBjo5rYT0v2pTsfOo5ikWqvSpKktMFDyeMjhKA/0o8GR0PRxlgJGHGroxxoPRpmsxnhcBg2\nmy3lPWWU7ivl/n4t9D46cOAA7rrrLnznO9/B1KlTIYoiwuEwxowZk9VxakYwtT9eLgKndcGms1KM\n6pLN5ljZdlExNPX18X9I9ASM1BUm3et6C2mqvSpJktQUFgAJe1VAvEDAjp4duHTipXBbjRs8UQqM\nNPEaAUJIgseCvlau6kPJixpqDRuRUCiEBQsW4JFHHkn5/kj3Ws0IZj5k6u2YjFFvkEzQ4CWaP5rp\noSr33mo2VMr+K8dx6p5JqkhJAPj81OcIi2Ec6D+AuU1zyzxihtFJtyUgSVLZtgSMuNCxWCzw+/14\n4403MGPGDLVesNfrzWrPtSYFc6TJn068tB3XSBOvUavzZDoWtZzpDVOqm1sP1y4wfIFSyH5lOR/s\nVNbnsf5jGBAGMMYxBn89+Ve0ulrhs/sqJuCjGjGSazib+aGU1YeoxavFKNeKQn8/Qgja2trw4IMP\nYtSoUQCAnp4eXHvttXjyySfVbjTpYIKZhHbi9fl8Wf3wlWB9aaGWcy4l/Ix0jsljKUeLsWKyt28v\n6hx1cDlcCJMwDg8dxkzzTBY8xFDJdUFYyupDRlpcUOg5zZ07F/v27QNwuj8mregFjJxuUjOCmc0e\nJhWSTIEixaaYFiYtbyeKoqFdltmSKl801783GqfCp+CP+tHkbgIAjHKMwrHAMUwfMx0uuytl8JA2\n95PBGIliVB/SIgiCMWpNa9i1axcIIRgzZgw2bNiAxsZGtXWb1WpFc3NzVoUWakYwM6EVknxqpRo1\n6EcLtZx5ns/aci4GelZaCofDWbvNizUOPSGEYM+pPbCZbIjJMRAuPj4CgoP9BzGnaU7a4CE9Jjoj\nYkRrpdzofU2yqT5EF2ap3LfJ46HeKyPR1dUFjuPg9/vx85//HKNGjVLzszs7O/Hggw/i4YcfhiRJ\nrFtJMtrJMh8XbKbjGQk6Lj1clkY7R9qGpxLyRbMlKkdhMVlgM9sgExk8iVuMPpsPETky7PMjBQ8Z\nLc2gkqkV4c62+pA2JSoZ6qUzCoQQrFixQv3/vXv3pv3sSMZSTQvmSLmH5RybHtDw7mAwWLkpI0lI\nkqSuemmlpUIxymRoN9uxtHUpotEoAOTk1so00dHI25EmOqMiKzLMtTlVlZ1MBTloSpQ2zYXjODWz\nwChwXLxEKM/zkGVZ/W/6HpB9kYWa2fRIniBisZjarqrQQJFiWF96dBmJRCIghMDn8xUslnqdYyHH\niUajCAQCajmrSpr0Sw2d6Gw227Amx6FQSG1yTItQG5XB6CA2d26GpOTW9aWaKecCj7pu7XY7nE6n\nGmEvyzLa29uxaNEiPPPMM+r9lS0dHR1Yvnw5ZsyYgZkzZ6bsJrJx40b4fD7MmTMHc+bMwU9+8pOc\nxg3EF40WiyXB85LL3n/NLdu0fQ71aldFVzB6oMeDQFNG6Kqw0oNBkoOVRFFU26oVetxk9KpGZDTS\nVR5KVeSb5/mii6hCFPDcyPflgf4D6A51ozPQiYm+iUUdUyaMvKgoF9oKVXa7Ha2trfjZz36GN998\nE1u2bMGYMWOwcOFCXHrppbjtttvQ2NiY9lgWiwVPPPEEZs+ejWAwiPPOOw+XXnoppk+fnvC5pUuX\nYu3atTmPlT7Phw8fRn19Perq6lRLUxAEWK3WrObJyp5Jc0QURQwNDakuhkoXklRQK4xGgFU6iqIg\nEAionWGo26fQCczIYphubKIItLcPf6+3l0NfX+5pBjabDU6nE06nE2azWXWz0RzkYlVskRUZT+18\nCu0D7Rk/NyAM4EToBFrcLdjXu6/sVqaR7xkjYLVasXjxYlx22WW46667cPToUdx9991oa2sbsTtQ\nU1MTZs+eDQBwu92YPn06urq6hn0u3/uR/nZPPvkk9uzZA+C0IfGjH/0IGzZsyOr41acYaaAtntxu\nd87pByOht0s2n+PRYguRSAQej0f3sO5yuGQlSVIXOG63uygLHLpKrgQLguOAnh4eX3xx+jr09nLY\nvZsHz+c//mQ3G92ioJWgaOlEvdy3e3v3Yvep3VjXvi7j8fb37YfdbIfFZEFMjqEz0Fnwd1cDRvN+\nJI+HNqior6/H9ddfj6eeegqTJk3K+nhHjhzBZ599hvnz5ye8znEcPvroI8yaNQsrVqxQ8ymzoa2t\nDe+99x42b96MrVu34osvvsDOnTvh9/uxZ88e1WM10v1dMy5Z6oKlwT7lFjg9j6coCoLBYFo3s9Ee\nsGyIRqOIdHXBs2sXLEeOAC0tUJYsAcrc8qoY9PXFLcSpUxPd+rt385gyRQF1FJjNwLx5MnbsMOGL\nL3iMHUvw+ec85s6VaSndtOzv2w+P1YNmT3PGz2mDh3ieh9VqTVujNJ99ZFmR8Xb72xjvGY9Dg4fQ\nPtiOKfVThn1uQBhAd7AbddY6AEC9vR77evehxdMCM18z01ZFUkhaSTAYxI033ognn3xyWF7k3Llz\n0dHRAafTiXfeeQfXXXcdDhw4kNVxu7q68Oyzz6KjowPPP/88XnjhBcRiMQQCAVx77bWqdTvS/Vwz\nFiZQnS4VURTh9/vV/pxasTRqyb5M0P3K6KFDaPj5z2F7/XVwX34Jfu1amB96CNyXXxZ9DKUmEgFe\ne82ML788/dt98okZ69ebkRw3QUWzo4PDjh085swZWSwFScD27u34pPsTKCS3vXYaPKQN8uB5Pu/g\nob29e9ET6oHX5oXX6sU77e+k/Lv9ffvhtJyOtLSarBBlsWxWppEWnUYbSzL5RsmKoogbbrgBK1eu\nxHXXXTfsfY/Hox73iiuugCiK6O/vz+rYS5Yswauvvoo//OEPOHDgAPbv34/Dhw+jt7cXzz33nLq/\nyoqvp6DcFqEex8u2yo1etVv1ItO5UUsZAOrWrgUniiCtrQAAMmoU4PeDf+YZcD/7mbFcqAMD4Nva\ngIEB4IwzoEyeDOQwYbS0ENx2m4iXXrLg+usl9PSY8dFHZtx9dwwNKbp7DQ5y4HlAUYCuLh4NDZlF\n8GD/QQDAkDCEjqEOTPBNyOn0KNR9nW/TbGpd1tvjCt/gaEhpZYbEEPoifZCJjKHoECyiBbyJhwIF\nRwaPlDX4h5GaZJfsuHHjcvp7QgjuvPNOnH322bj//vtTfubkyZNobGwEx3HYvn07CCFoSPWApDm+\nLMtYsGAB3nzzTfT29qoLQY7jcPXVV2cl8kwwDXi8kShHV45in6O2jZqT58Hv2QPS0pL4IZ8PXEcH\nuO5uYPTogr5Ptz3Z48dhfucdELMZxG4H19EB/vPPIV11FeDzZX2c8ePjovn001bIMo/77w+joWH4\nIojuWV5wgQyPB6p7dubM1KIpSAI+P/U5RjlGISpHsfPkToz3js8qQnUkMuXopap7S61LrWBTK/P7\n531fnXRdFhcumXgJACAcCcNmtan3uB7jZuhHqsV4PoULtm7dit/97nc499xzMWfOHADAv/zLv+DY\nsWMAgFWrVuG1117DU089BbPZDKfTiVdeeSWn7zCbzbj77ruxd+9eTJkyBWazGZIkoa+vD5dccgkT\nzHQYPcgj2+Lw2VS5Mfq5AqebV6tt1KJRgOcBSQLX2QnuyBFAkkCamgCHAzBKDVxZhmnTJih1dapF\nSbxecL294HfuhLJ8efxcCFEbW2fi+PHTYnDqFI+mpsT3I5H4vqZ2z5LuaR45wmHixOG/88H+g1Cg\nwMybYebN6A50F2RlZmKkDhkfHPogHrwz1Alwp+/NI/4j6Ax0Yrx3vHosmzketCabZNjMtpIsCisF\nI3mMUkF70ebCokWLRkzNu/fee3HvvfcWMjR8+umnapRsPtSUYBbrJiuVKBmxMlGuaK8VIaebVyfU\n8LXZoMybB/7ll8GFw3ExsljAtbeDeDwgNpsxFgGDgzgVOAHrGeOhtSVJQwP4ffvARaPgjx0DCIHS\n0gJ54UKk23Dcts2ELVtMePDBKAYHJbz8sg1WK8G0aacnEYcDWLRIhjYAmu5pproVtNYlxWv36mpl\npiNVh4y/mfk3CEVDakSi1voc60odzGUUcUjVwoqR3sI0UqUf4PTcf9ZZZ2HNmjU499xzYbfbYbPZ\nYLFY4PF4sjpOTQkmxegu2VRdRlIKSwnHpvc50pJ9hKRuXk0uvBDcs8/GLc1oFERRAJcLaGmBecsW\n4NJLdRtLMtnu+yoc8LK4A6MDJ7CybunpN2IxmPbuheJ0gpxxRvyYfX0wv/UWpBtvHLa/uW8fjy1b\nTPjud+N7lm63gm9/W8Af/+jC3/6tiObm09c9VbZQutuhfbAd/pgfMkks8hASQ+gKdqHF05L6D4sA\nx3EY4x6DMe4xan4nLRwvyzKiQjShcIIRRJKRH0YUTEpDQwMefPBBLFy4UDU6CCF49tlns7rnmGAa\nEO34tIEwelUmKjeKoqiRvU6nM/WN6veDnHMOiM0GbmgIcLniLllBAL97d1EFM1sOkl4ctwno8u9H\nt+scnGGJByDw+/eD+HwgY8ac/nBDA9DVBf7wYSgzZiQc56yzFNx1Vyxhy7OlRcGqVSIaGvK/T5vd\nzTi38VyEoiFMH5NYMaXOVpf3cQsll+AhIz+n5cIoVjeQOkqWBiIake985zu47777cOrUKcRiMUiS\nBEVRsr6eNSmYFL1uvGIJMA2EsVqtas3GfNDLwtSj/J+iKIjFYmqCfFpcrvhe5bhxINqIu4EBYMrw\nvL1cKfQ3U4iCdw69A9+ZMyEe/Cs+OL4JK+0LwCkKYLeDpApKcjiAnh4cGjyEOlsdGhxxgbVYUscH\njRpV2O/ms/mw4egGDAgDWDphKezmkfdRs6Wtvw12i10XKzVd8JAkxSv7RCKRqmtbVk2kcskaVTDP\nP/98bN26FQCwYsUKNS8/WyrfXMkB+sMW64HTsyemKIoIBAJq6bJ8x2yUyYW6lSVJgs1myyyWAMi5\n54K4XIDff/rFWAxcJAL5618vu+VxsP8guoPd8NWNRcOci7BrnAnHZ50J6YorIF15ZdyFnIwgIFbv\nxdOfPY03D7xZ9DHu692HzqFOhMUwtndtT3hPIQp+u+e36BjqyPm4giTgQP+BopWro4FDNNKSWqJ0\nD59uTyiKUrL7wGhWnVHGkgpa6cdoCIKAH//4x3jggQdw9913AwA++OADXHvttQCym79rSjC16GkV\n6nnz0v6VtNC4HmX8yi0uNA0mFovBarVmF/HockH54Q/j/93REU8n6emBfPvtIEkFmUsNtS6pW5M3\nW2BraMT79i6QceNAJkyIW8h9faf/aHAQsFqxzTWAoegQdpzYga7A8FqZeo7xjQNvwGfzYYxjDNYc\nXANBEtT32wbasOPEDqw/vD7l32e6Z475jwEcEJNj6AoW7xyA4XVvXS4XzGaz2o0nHA5DEAS19i2j\ntKQSb0EQDNUPk3LixAm8//77+OyzzzD2q4phM2bMQG9vLwAmmBkxYuCPLMsYGhoCEO+FqEcovV5i\nnu/5ac+J7sFmexwyZQrkJ56A8tBDkB94ANL//b8gl12W8xj05mD/QRzzH4PFZEFYDCMshuG2uLHz\n5E50B7sBmy2ehzlqFLiuLnBdXYDbjcgV38BbHX9Go7MRVpMV7x56Vz1m+0A73m5/W7cxUuvSZ/PB\nYXEgJIZUK1MhCt5tfxfN7mYcHDiIY0PHUh4j1b0jSALaBtpQZ6uDz+bDl31fFrUoevKEzHHcsLq3\nPM9DFEW18pCedW8Z+WHEWAtJkjB69GgcPHhQbXd45MiRnKzhmtrDTH7wjCSYoigiGAzC4XBAluWy\nC50eaM8p7zQYiwXk7LP1H5yGXK9RX6QPrb7WYa+3eFrQH+nHGe4zgLq6uGiGw/E8TJcLn3RsQSAW\nQL23HnazHTtO7MDlZ16OJncT3m57Gx2BDpzfdD7qLIUF5GitS3rNqZV5QfMFODZ0DN2hbkzwTYCo\niHjv8Hv47qzvZnVsal2aeBNMMMEf9aMr2IVW7/DrUWy0qStA5qbZqSoP5YKRxNdILtlUYzHStdJS\nX1+PxYsX4+mnn4Yoili3bh1eeOEF3HTTTVkfo6YE04jQLiq0k4rFYlEbPxuJXERFW7aPnlPy+6Ua\nSybyPcbCloVY2LIwuw9/tXqNyTG81fYWnGYndnTvwNymuaqVuWj8InSHuuGyuPBhx4e49sxr8xoX\n5a+9f8X+vv2ot9dDCJ52w/ZGerHt+DZ8fupz1NnjojzaMRptA204NnRsRNFTrUv7aUGnVmazuxlm\nwmgn86kAACAASURBVAGhUDyKKckld3jwMMZ7xxe1cLo2eMhmsyUED2krD9HWfrmKjlFEysgYbd6i\nEEIwatQo3HrrrWrt2GeeeQYrV67ETTfdlHWebc0KphEsTNo+iRACn8+n/mB6RaTmO65CGKlsn1FS\neko9+W3v2o4ToRPwC34cGzoGr82LMc4x2Nq5FV2BLtTZ6+CxevDZyc9wYdOFGOMYM/JB02A32/HN\nr30z5XuBWEC1LoH4dXBanFlZmd3BbgiygAFhIOH1qBxF76E9GHe0H5AkQFFAGhuhzJwJ2Gzoi/Rh\nw9ENWNa6DGfWn5n3eeVKrk2zKwEjPDtaUlmYNG3ISHAch4MHD2LTpk147LHH1Ne7u7vx+uuv44Yb\nbsjKcmeCqSO5HC+hdmoBUbClIpvrlWvZPqNSjHsjJsdg5a04GTqJFm8LesI9OGfMObCZbDgeOo5Z\njbMAAFbeis0dm3H91OsBWQZisXhZvRyu5eT6yZhcPznle8/tfg6iIuJ44Lj6ml/wIySGcCJ4Ak3u\nppR/BwBnuM9IsC4pXP8APDv3gIwee7qKQn8/+F27oMyfj90nd8NqtmLnyZ1o9bWWpT1XKvctLZxA\n0woqKXXFyOMzmqhHIhEMDQ3hjTfewAcffIBbbrkF3d3daG1txbvvvos//elPuOGGG9RFVCZqSjCL\n6WvP5QaORqNqNYxUjZ6LUVWn2ND9SrvNBvsI+5VGe6BKwayxs/CHfX/AaOdoLBm/BF2BLixqWYSP\nj38Ml/V0zlqjqxG7T36GZd12tO45FK9FW1cH+etfhzJtWtbfJysytndtx4JxCxJ+i2vOugaXTjpd\n9KE72I3/2f8/uG7qdRjtzFzQ3m62p8zl5I+3AZ66xJJD9fXgenrQdypeJ7bZ04zuYDeO+Y9lbWUW\n8z6hwUPJdW9FUYQgCMP2Po20b2gkkl2ZiqIYzloPBAJYs2YNXnvtNfT29uKRRx5BMBgEz/M4cuQI\nli1blvWxakowteh982fbkiscDkMUxZxL3BUyrmIjCAKE7m743noL1i1bAEWBsnAhlJUrga/6zOk5\nHqO4dbX09XHDCg1IEjA0FC/ys/HoRnQFu+C1edET6sEoxyj8ft/vQUDgtroRiAXUv4sca8enxzvQ\nesbFgNUKBIMwv/oqxG9/GyTLog27Tu7Cc58/h3pHPaaNOi20Y5yJrt7NHZthN9vRPtCO+c3zkw+T\nFVw4DJKqZh/P4/MTu+C0xvdx6+31eVmZxb6HswkeohapxWJhwpkBI6aUeDweXH755RAEAf39/bj1\n1ltx8uRJSJKESZMmYdKkSQCQVVZCTQtmKV2ytMQdx3EjlrgzooWZakx0ASAFg2hYvRr8iRPA2LEA\nx4H/+GPwf/0rpMcfB7IsbFxq6PnQHL58XXGxGPDTn1pxzTUSli2Tvzom8NRTFrhcwLW3nsCfDv8J\nLosLbosbncFOzHHNgaRI+PqEr2PGGE2pvHAY/OYX4R4zLi6WAOB2gygKTBs3QspCMGVFxtq2tbCZ\nbVhzcA2+1vC1lOfVMdSBw4OHMbluMg4NHkJnoDOvyj1KQwO4U6cSyxURgt7YIDrEAJq9EwHELdQB\nYSAnK7McpAoeCofDqvu20OChQjCapZs8HiPWkXU4HGhtbcV9992HYDCI9vZ2jB07Fk6nEzzP5zRm\nY9nORabYaSXpEEVRrZ3qdruzclkYsaiCFkVREAgEoCgKfAcOgD9+HBg3Lu6WM5mA5magrw/8pk3x\nP4hEwG3dCvP//A9MH38MCELmLygy9PcPhUIQBEGtIiMIglocPFusVuD//J8YXlkzhLfe86tiGY1y\nuO02EVs6tuB48DhkImMwOogBYQBf9HwBADg4cBBTG6aq/77GN2IqGYUmS1JXE68X/MmT8a7RGrYd\n34aj/qMJr+06uQu94V60eltx1H8U+/v3pxz3po5N8Fg94DgObqsbm45tyvqctZCJE8FJEvBVzWOI\nIrieHuzyhiFZeAwKg+o/nuPxl5N/KWrupt5QUbTZbAnN2qPRqFp5SBRF3QL1Khnaps9I0N9l+/bt\nuO+++3D33Xfj5ptvxooVKzBjxgz85je/AQC1i04matbCBPTfw0xlgWVKr8h0LD3R28KkAUu0xB1/\n4EA8lSAZhwPYtw847zyYfvpTcL29AMfBLEkwrV0L+Z/+aZjLNtuxFArdt1IUBS6XSxVJmoYgCEJO\nlkRTE4Hv8sfx8w95/PHF/w+zZxH84AcxDEl96Ap24RtnXI8TJ3lMPlOGpEjwR/24esrVaP9rHbq6\nOLUjCXG7wRMyTBgRCoHU18e7t3xFMBbEM7uewZl1Z+KfFv4TOI5Trct6R1xwvTZvSiuTWpc0WrbB\n3pC/lenxQF6wAPzBg+D6+gCLBcr06WhwnAGHHE346MmTHIhigqzIqltWloG//IXHOecoydkohoFa\nUunq3tLC8fT9YgUPGd3CjEQihnPJ0n3VF198ES6XC5988on6nnZxzFyyKdC2biqmS3ak9IpsxqgH\nej9cNGBJu9JGU1N81hv+YaC5GfxzzwGBAMiECSBfdQcw9feDf/55KA89lNc4Crk+siwjFArFLSu3\nW3XJ0jQEURTVc0vVQcNsNg+7rgf7D+JA4DP4XTyCtgOYPftMWCzA0b649WexEXR1AaLI4WvTzPDZ\nfNi3x4ET+yZgySzx9IG8XkgzZ8L0+edAa2vcWo/FwJ06Ben66xO+84MjH0BURHzZ9yX29+/HtFHT\nTluXXxVWqLfXq1amdi9zU8cmmHkzwmJYfc3Mm7Hp2CZ8e8a3c7+oPh+UefPiQv+VqM9K8bGAF9i4\n0YzOehmTJxPIMrBliwk2W1Y9tg3HSE2zk4OHjCR2xcCInUqoR2/SpEnwfbVtIEmSOifnEqRUc4JJ\n0TPXkR6PQtMrzGazIdIr9Eryp/U7kxcAyoUXgv/97+OF0uk+VjAI8DyUuXNhfvttkPHjAXy1GACA\nxkbwe/ZAGRoCvN6Cx5ct1PVqs9kgimLa34YGgqTqoJEqCf73e1/B8WN2OCzAlOtfxNq3fgKzGVi0\n5FxVqG6eCvzhDxY0n1LgdAJ79jpxx+3isE4l4qWXgphMMH3xBTgAxGqFtGJFPK/xK4KxINa0rUGj\noxGBWAD//eV/4+EFD2Nt21pIioSuQBds5nggjkzkBCszJsfAc/ywqNjRztHq+3kzwuTj8QDLlknY\nuNEMRZHR1cXDZgPmzx/eBNtogV0joQ0eok2z6T0jivFFkTb3s9zzgh4kW5jBYNBwLll6H9XX1+Ol\nl15CR0cHZs6cqXr8Fi5cqNaWHYmaFsxiuGTphOxwOEbsyFGKsenxUNKgByBNT86GBsj//M8wPf44\ncPyr/D63G/JDD6V3udJxSRIQCoE7cCDeKLqlBWjRv7FxsnucdoTJlmRLQuuG+2vPAbz56WdwK+Mw\nc4aCQ5Fd+Md79+KP/xkXuGXL4o+Z0wfceTvwi1/Erdcf/CCWsq0XrFaIl10G/pJLwEUiIF4vYLEg\nEgHWrjXjhhskfHDkA0TlKGxmGwKDNnx0aj/2TtmLaaOmQZAEDMWGMKthFkSJw0A/hwleO2Qiw8yZ\nYTVZcaHz23B6ovifY8/jpmk3YZRjVMIQolI0xcD0weMBFi+W8Kc/xa/LzTdLadNMjSAq+T6LWvct\ndf1R8aQu/1ybZhvNJZuMETuV0OtFCMH06dPh9/uxbt06xGIxHD58GKtXr8bYsWOzSompWcHUG9pl\nJBaLlSxlJFsKEV9tgQVJktLeUGTaNEhPPQUcOQJOUUAmTozvaxISty4HBuL5FZT+fpBJk8D19ID/\nf/8vHmrKcfHPL1gA5YYbRrRWsp081GheSVKtY21x7lSVSjJds+R9rP89thY+lxXTWmSAEJhgwp+6\nf4cf/vBH6OxM3Nvdvduk1iH49FMTLr44blmtP7wek+omYUq9JgrW6QTRTD4WC9DdzePfn4zh4Nfi\n1mVfH4ddu02YeLYTa9vW4vtzv49ToVNoIk24eOLFqOcmYO1aM2Y0KTDzcY/KoUMcNm0yo+n8bfjo\n+EcYZR+Fm6Yn1tPMtlRYPshy/Do0NBBEIhwOHeIweXL8ekfECAgInBZjTbpAYeJN9z+zaZpdSZWH\nkjGqS1aWZXzve9/D9773PXR1dYHnedTX1yfkwWdzzSvzVykAetPracXRpsiKosDr9RYslkaxMKPR\nKAKBABwOR3arRpMJmDwZ5KyzTgcBcRyUu+4CJ4rgOjuBgQHwx4+DkyTIt94K/re/jbtkJ0yI79mN\nHw9u61Zwu3frck7aaN5c95Kz4WD/QXx2aiemT6gHbwbAxwNoPjv1Gfq4L3DOOQEIggBRFPHRRzz+\n8hceq1bFcO+9MbS383j/fRN6Qr347ORn2HRsE2QlfaSe2Qx8//sxHObex1/29+LIyUF8tKcHrdOP\nw+0VsfPETqxtWwuryYo6ex22dm6Fx0NwzTUSdu/msWcPr4rlpZeHsbXvf3Fm3ZnYenwr+iJ9ab83\nW2JyDFs6tkAh6bc6BAF46SUzLBbgkktkLF8uYd8+E7Zt49HZyWHnyZ3DendWI3TRRduWOZ1OdSEX\nDocRDocRjUYN37YsedFqxChZmjK2b98+3H///bj11ltx7bXX4qabbsLmzZtzOlbNCSZFL1GSJAlD\nQ0MJ+1lGGRsl12NRiywSicDj8aSsRpTT8aZMgfSv/wrl6qtBJk9GdMUKSP/6r+AIic+g2hUpzwMN\nDeA+/rig7wRO/zZms1l1w+rN/v79cFlc8Ef9GBQG4Y/6ERAD8Ng8OBY+hkjECVE04cgRBR9/LOPG\nG4fgcMTQ16dg5cq4aL6x41O4LC4MCANoH2jP+H1mM3D7jXUY030rej75Br49fymuPWcZvu6bixXh\n8Ti6eS0aj/bAK1twKnwqXrfWC1xzjYTNm014910zrrxSQof8FwxFh+CxemDiTPjgyAcFX4t9Pfvw\n7qF30dbflvYzR47wUBTqIou7Z6dPl/HnP5sxJPbjqP8ouoJdugh4JUHd/Xa7HS6XS+3uk9w0O5vU\nh1KRal4xooVJY1V+9KMfoaGhAa+//jo+/vhj3HnnnXj00UfR3h5/5rKZJ43jNywxhYqSdk+MpiXk\nsidWKnIVCW1B+FT7lXnvoTQ2QrnllrjbOhKB3euN73dyHNDbC+7LL+NuW58v7sJ1u3P/Dg10oklX\nflAvrppyFa6actWw10VZhMVkwTvvmHDwoBV33y3i7/6OwGKxYPNmDhs2mPCDHwRx6bXH8dqhL9Dq\nGI+IFMGWzi0Y/7Xx4Ln0C6+G4EWY5F8OjgNs+xSsbNkF+4bX8b4tCsVigWn/QeDwEfgWzMXWzq1o\n9bait/f08Tq6RLztf1sN+hnrGov3j76PCb4JuKD5gryuQ0yOYXPnZoxxjsHGjo2Y0jAl5Tl87WsK\npk5V8OmnPD75xIQJExQcOMDj3ntj+DKyDzaTDTzHY8+pPVgyfkleY9GbUu8bZgoekqR4/qogCIYJ\nHjJ6Wgkd39GjR7F69WqMHh2/76+99lr827/9W06LkJq1MAuBpoxEo1F4vV5YrdayF3PX4zjUIuN5\nHh6PJ0Es9XootdeJtLYCvb3gP/gA6OuLVwAYHAS/YYPaFiub42ghhCASiSAUCsHtdo8slrIM/vPP\nYXnpJZhfeQXcwYN5nxslEAtg9fbVOPX/s3fe4XFUZ9v/nZntq14sW7IsN9wwbuACbmDs2IQaSiih\nhBJIaKFD3pAvkOQlvIQQiAkJvZhQAoTugOPeG8bGNu5FlmxJVl9Jq92dcr4/RrNayZIl2TLeRL6v\nS5et1eyZM/U591Pup+4gA8ftwMj4hhdesCTVVq1ys2SJl5//HFJTvWys+gqX4iQSieCUTsrqythZ\nsfOQY7Ndtd9+q/DnP7u4++4Izz0XwonG2oe/oCzVz9eJdSR7U4mkJKLpYXw791BUW8TSTQUsXuzg\nsst0rr5a49OvvmZvUU00TqgqKmXBMl7f+Dr1Wv0RHfO3pd8S1IJk+DKorK9slWUKYTkRxowxKS0V\nrFqlMnasieKvJL86n1RPKimeFA7UHqAsWHbcjUE8wHbfejwenE5n1JMVj02zg8EgCUe52O1s2PfQ\nhAkTePHFF1myZAnffvstn3zyCR6Ph5SUlCbbHQ5djmEebQyztY4c8RJ3PNKxvitG1gRpaQjDAE0D\nt9v6NxJBdutmZc0ahhUXbSc6UvsqhEBqGsrf/oaybh2m14swTdRFi9DPPht50UVHfFjLCpaxtXwr\nc/bMwev00mO4jrG5H7/4RSI+H9xzT4T0dElZsIKd1TvJScpBEQqmaZLqSWVp4VJ6JVh1lA6Hg22V\n29hSvoUZPS/lL39xcuedEYYMsdxMt11ayDcLwyzd4SZtsA+NBgWdRC8c2Ier31C+WFzJhaMNMjIk\nQoDW+wtKtmisCu0nM1OiiyCBcIA6rY61xWuZmDuxQ8drs0ubsaZ4Ug7LMsESMFBVcLkk27cr6DkW\nu7TvV6/Dy6bSTYzLGnckl+C/Fvb5aSl5yFap+q6Sh1pi3vEojWfP8amnnuLGG2/krrvuQtM0vF5v\ntDdme9HlDKaNIzFwsSUj7jY6chwtYgUWjhaHO06bkbUnu7cz5wRATQ0kJmJOmGAlBEUiyL59ITsb\nUVxssc523szNtXrbtVrctAmxfj1mXp51joQAw8D9738THj2auuwM5u6Zyzn9zsGlutp3SJEa5ufP\nZ1D6IBbkL2BM9hhSPamEU7YBp+FwSPx+63qsKlpFxIxQXFeMrjc2+whGghTVF5Ej+hLR6/lyx5dU\nRao4rdtpPPZYLklJCmAdn8PjYPRpOlqPHrhcMU2tIxGEUkX4jOupPhnKyiQ7dwr695fcOOoaxno0\nXC7o0wcWFSxkcPpgvE4vOyp2cFr30xC0/xrb7DLNm0ZtpJYEVwKFgUJ2VuxkQPqAQ7YvLhasW6dw\n+uk6qakwb3k1S7/ey7ihGWiGFdbwO/0U1hRSnlQedy/geEJLurct1Qt/V23L4tElayM/P59XXnml\nyWfhcMfKp7qsS7YjBrO5m8/j8XS4FOF44XAPiJSS2traaLnFd9U9JXqe3G6kokBKCnLECOSYMVa2\nrKpa4gbtfPCONLnHuW6dFSuN3b5h3+q2bXxd/DVritawtXxru49vWcEyDAycipPKsOVmLN6dwb/W\nbOOeB6s45RSTF15w8m3JLr4u+porBl/Bac6rKPryGs7NvYIrBl/B1SdfTZLI5qmnEpi/sZAQIdK8\naSwtXIrDESIYDBIKhdB1HTMzE5GViau2osk8RHExxujRCAEpKdCvn8Uud+xQ0Ev7MChzAN879SSS\nPUkIBAPTB9IrqRdCiA4dr2ZoLCpYRMSMsKNiB/P2zmNb+TbqjXoWFiw8JGM2HIavv1YYN84gLc06\n9f1PriLRkczuggghPRT9SXInEYgE2j2XY4V4qn1say528pDX640mDwGHJA91hmhLS3Ox65zjEQ88\n8AAHDx6MSmJWVFRw7733dui93eUYZvML3NYNGJsEk5yc3KqL41gJIXQGWhrHMAxqamo61MC60xcF\nbjdy/HiUxYutRB/bvb1/P+appx62y0lzoYgjciWrqpWq2XxsKaklwpqizeQl57G0cCmD0ge1yTJt\ndpnly6IqXEWCM4E1u3fTraiW731PUiG2c8klw3n/fZXfvrWA9MGl7AvsY9LwyZTucfDGXyX33aPj\nkhp/mull6LAIW8PzSU3MINHtY19gH5VGJdkJ2ezbZ5KVpREyDELnnIP//fdR8vOtej/A7NsX44xG\nxikE9O0rWbtWAIJTTzURAtYWr8XvbMxqzPBmsOHgBnon9CZBbfvFJ4Rgcq/JmNJk1YFVBCIB0r3p\nTMydiENxHMJU3W6YNs1o0jqzd0oe93w/D9Ns6oG3axRP4MgQmzwETZtm19dbserO1r2tq6uLuyxZ\nsBbVGzZsaOJ+TUtLY+HChR067i7NMNvC4ZJgjjU6yzi1dJyRSIRAIBBNYT+eq2fzooswTzkFUVAA\nhYWwbx/mSSdhXn75Yb9nZynX1dUdcemLduqploRf7HnWNBCCrzI0JJJEVyJ1kbp2sS6bXapC5UDt\nARJcCfg8KtmnfUWvzFS2lm+lXg8ycup2fNl7GJDen4X7FlKvB7noIh1x0r+4/fcb+d3vfJx8ss7J\nZ25mw7ZaVi5OwjCtjiLL9i/j3/928tJLfhwOi0U4c3LYesUMZk/Iofbss6m98kpCl1+OdDUaeClh\n1y5BSgqkpkp27xYU15awu3I3IT1EcW0xxbXFlAXLqApXsb1ie7vOoUNxMCJrBHnJeRjSYGJPy1Dm\nJOYwrNuwFu+tlhwZQnQoXH0CRwAhRLR0xefzRT1ldvJQbOlKe949rTHMeHShRyIRevTowapVq6iq\nqqK2tpYNGzZ0WI2tyzHMWBwuJmeLjLeXucSrSxYaGaaU0mr2HAp1qHtKZ+KQ8+T1Yt56K2ZhIaK8\nHJmSYrllDwNbZiwSiRyW9bc1D23wYOSECYilSxGKYnEh06T8wnNYE9lJVoKlL5npy2Tp3oUMDiXi\nTElvUfvWMA1WHliJaZpsr9xOSW0JqqIiXZJddRvZX9MXiWRv1V6e/GA5aTmJuB0u9HqdNUVrSA6f\nzDdl31Bq7EBUDeXMKSE+2LeQM0ensmY5zJ+nctaUNOas3YN/WzGP3JPZoA0hUFSFfxcvpFAt5NSR\nPyDRkYim64TC4YbED5W9e53U1wtKSgTTppns3i04sM/L1N7T2L5dweuR9MprvC4eOvYiWVe8Dq/D\niyIUfA4fa4rWtFhu0xHEkys0XtBZCkwtsc/mTbPtv7fUbKA1xKtL1uPxcMstt/CLX/yCGTNmUFdX\nx7x587j33ns7NM4Jg9lCS65gMIimaR2SuItXl2ysjqKdQXo0RuaYLQp69rR0ZFtCKIQoLER6vZjd\nu1PT0Hexvb1FW4WiYF53Hca4cciNGxEuF+awYXwV2gYHwalYCwrft9upXD2HPSWfMLwuAWPSJCK3\n3NJEcEFVVO4afReGNAjrYeq0OsBK9HWqCileK3W9uLYYd/c9bFrTl2SXQbf0bnyyaRE7lwaokl5O\nGhRhsHsdv52ZQcaUavxeB/1HVfHVVyrPvy1weyW3XraWtLRzovveVbmL/TX7cakuVhxYETVU9kuw\nqspybXbrZvL660mUlytcfrnB3r3JHNyRwqevuLj33giD0hvjWqEO9Cstry9nW8U2eiZY1y/Nm0Z+\ndT4ldSVk+dsnah3P6AqGu73JQ7Gt7lo6L+FwuLGLURxBURSuvPJKBg4cyJw5c0hLS+Pll19m4MCB\nHRqnyxnMtuKVNTU1KIrSssh4OxCvD5etRnRMu6ccPIjYuRP8fuTJJ7fse+sgxL//jfraa6BpSF1H\n790b9113EWpPh5PCQpTFixF79yLT0pDjxyOHDm2a5CMEnHQSeu/eKIpCSA+xfs16NFPjQO0BlL17\nURcvxEjwsizXZGhFJuqCBbjq6oj86ldNdpfiSTlkCh9+6EDT4LLLdEDy6jevosn6aFurPn08rNxZ\ni6nM4ez+4xkxKkz+wSWcKn/C4n/cySOPhElMgr7lKp9ucpLtNZl+UkynGGkyZ88ckt3JJLoSWXlg\nJWfknEGaNy36EszIcJCebrHyX/6ynt/9zoOmmQwZovPii37uvTfEoEESOpAZG4t1xetwKS4MaUDD\nesqlujqFZZ7A8UFLbcts42m3LWuNcMSrDq6UklGjRjFq1KgjHqPLGcxYxF5wTdOora3F4/G0mAXb\nnrGO1dyOBrYyiMvlOqLjatecTBPlpZdQPv7YqkqXEtLT0R95xKpbaM8YLe1v/XrU556DrCwMp9Pq\nU1lYiOfJJwk/8sjhv7xvH+qrryJ9PmRmJgSDKO+9hxkIIMePb/VrLtXFBf0vQCiWsXG99WtEfT9Q\nE3CgoKgOZHY26tq1Futto7PKjBk6zz3n4r33HEw5r4TPts5la2EJ55zioarCyedLVDw9DxDRTFwu\nQX2dmznzYIhnO6HKM9ACYdauV1i/wsGf/jfCBx84ePUFwW23RXA6G9ml3QhaFSrL9y/nvP7nEQ5b\nSTb2eRdCkJGh8MtfGtx2m5/PPpP88pe19O4dIRhs2nqqvQs/wzQIhAO4VBc1kZro507FSUgPEdbD\n0TZjJ3D0OB4L8ubuW5t9apoWTYr84IMPyMzM7HCmfUFBAddeey0HDx5ECMHNN9/MnXfeech2d955\nJ//617/w+Xy89tprjBw58oiOw47PCiGOqD9plzeYdo/HzojrdXqd4lEgVroPOGpjeTiIBQtQ/vlP\nyMlpzNwoL8fxyCPoL7/cMaZZUYGyaBHs2IGyZg1SVTEcDgxdx+VyoWRlwd69qFu2wNixrQ6jzJ+P\n9PsbO6QkJiI9HpQFCzBGjWq1W7EiFHom9kRVVSs9f28VMisLdAUMA1FWYtWHVlejfvop+tVXHzab\n1+uFW2+N8NxzLp7+az0lmpNMbza1W88gL3ABQwYeZNam10mUPVlWo7LuK0j1Z5DPMv7+tyGsW+1n\n0SIH990XJi0NbrpJ46WXnPzlLy5+dmsoyi5tZPmyWHlgJafnnMHrf+3O6NEGEyc2Sn8tXqzy2WcO\nwDKgW7d6GTbMgZSNL0HbHRv7Umnt3lEVlUsGXdLq8f83IF6e6XiBzT5t5ul0OgkGgzz99NOsW7eO\nadOmMWPGDKZPn87QoUMPe+6cTid/+tOfGDFiBLW1tZx66qlMmzaNwYMHR7eZPXs2O3fuZMeOHaxa\ntYqf/exnrFy58ojmfrTNF+KTO3+HsDPDkpKSjksSTGs4GobZXLqvM+OhLY2jfvihVewXezOmp0N5\nOWLTpvbvYN8+HPfcg/LWWygNogJiwwbM8nJcqoq6fTtizhyUdetIeOQRS0KvJZgmIj8fUlObft7Q\nbozyRmHvts6L2bu3JbAgJWLvXigqAkVBqCrKvn0433gDGnqFtgavF847T2dR1Vs4hJMBOel8E7CL\nugAAIABJREFUFXkHE52hk7eSlmFQqZWwv2Y/hTX7qdLLGDuhmlJ9Dz16yKixBOsU33STxujRBgV1\ne9hWvo3KUCWFgUIKA4UU1RYRCAdYc2A111yjMWeOgyVLrOuyeLHKW285KCoSPPpomBdfrGfNGpV/\n/MOJoli6pXb9nv1CrK+v/4/pnNEVEE/G23a/qqrKT3/6U7744guGDx/OHXfcwe7du7nwwgt59NFH\nDztG9+7dGTFiBGDlJAwePJgDBw402eaTTz7huuuuA2Ds2LFUVVVRUlLS4fmWlZWxfPly1q5dy5Yt\nW9i9ezfV1dUdGqPLMUz7ZrMD2g6Hg8TExE65CTtbHu9IxrLjsMc8XhmLqipLB7Yl1NQ0+TU2Can5\n3NRXXrH6YvbsaSWs9OiBun8/7q1bLYO8bx94vUiPB+ly4XzySUyf71AXq6JYrC8Uaip+IKWVhdPO\nulMA7aqrcP/mN5QURMgI16AmeBGBAOaAAQRyBrHgm7Wcsm4+PSe0HqvLzxf8eVYBos8Skquy2fGt\nSq/+BYSrv2Tx38/hoanDeeUVF7s2K/Tqb9JLBLmlbC65f3wNh8uDMX06xrhx0f6gqgrjxxtUhdIZ\nkD4AwzSY0ntKk32melLJTJTceWeEP//ZxZIlKnuqdrMm5a/MuuO3DB5sjfX//l+Y3/zGTWam5Oyz\njeg1sksQVFWNuuDsgvfm2ZPH6h6LJ+NwAu2Dw+Hgwgsv5MILL+xwQ4q9e/fy9ddfM7aZ52j//v3k\n5uZGf+/ZsyeFhYVkZbWdUGbfQ7t37+bPf/4z69ato76+Hk3TKC4u5pJLLuEvf/lL1Ba0eXztPpr/\nItjF7qqqdqrE3fEuLWktDnvYeUmJ2LwZsXQpGAby9NORI0a02bw5FuaoUSgLF0L37o0fGgaYJrJf\nv9a/aBiIDRsQmzeDx4P46ivo1w/TNNE0DUffvojSUqisRFRWIhMSLCOYno7MzEQGg6hvvIHeQkzS\nHD8e5fPPLYF3+1iKiqxenbGNrBuwfbtC//5Nvce6DrtTxjH4gQdw/fJJaorqSMwyEcOGUT3sDOYs\nMfhiyFa+3RbgnvHntngf5ecLnn/ehXPMG6QbDtKzYOs2iR5IZ2XoTSY6p/OXP2YxdqxOTrqCw4xw\n346H8MxZh6OPC4cwcSxYgHbeeWj33095hUJ6unUtDWlQGarElCaucHcG9sw8ZP+ZmZKRIw3mzVMJ\njHweU/6LsoSpwFkAJCdbRrO19U5s/Kp554xjVfx+Av+ZsBdTNoQQ7c6Yra2t5dJLL+WZZ55psSyl\n+furvfeZbTDnzZvHli1bWLx48SFzBtode+1yBtNu0JqYmEg4HI5bF1NHjW8oFIq2Gmt3WreUKC+/\njPLJJ5a7UgiYMwdz4kTMe+45xGi2NifzsstQli+H4mLLGIXDUFGBecEFkJ3d2oRR//d/Ed98Y+1H\n1xEbN2IoCoZp4lQUlIwM5JgxiOXLkXV1lqhAXh6yd28wTUhMROzb16JIuxw9GrOqCmXVKhACaRiW\nMb7wwpZOA19/rbJihcI112hWbHvnHlY+vwOvEsG8aRC+Zx6h5M//ZGltf0YMUli7TCE07FtcnjB7\nRDXbK7YzMP3QFPX8fIVJF27nqT0LSPWkUmcESOsOmzYrpPYsYW/5HLr3OJ9FRV/y0R8nEHp/EYkL\n1hHJ7Ma2gyqDBpkomDg//xztnHP5y4cjGTPGYMYMg+WFy3EqLjZsUHlmw0qeu+38Q/a/eLHK+vUK\nF92wlZ/NW0pmSjee//p5JuVOQlWsc5acfMjXWkVs+YEdw2qNfcZrtmRHEU9MN97mEnuNg8HgEenI\naprGJZdcwtVXX81FLTQ9yMnJoaCgIPp7YWEhOTk57RrbNE0URSEtLY0xY8ZE92cv7jp6j3Y5g+lw\nOEhOTkYI0ekG83gwTLtu1NaDbSmo3dq8xI4dKJ9+2jRZR0pLqm7SJEvbtT3IzUV/6imUd99F+eor\nSE7GuPZa5Pe+1+LmQgjE558j1q4FVUUcPIh0OJBCoC5YgJKdjVAU2LEDmZaGOW0aYs8ey/g2zFOY\nJgSDyG7dWpaIURTk9OkYp59uxSz9/laF3IWAyy7TeO89F7NmObky7V/sf24OfVWTvie7EW+swhww\ngOwhSZTvCrJ4cTJ9h4RYkLyebkEPtVl5fLLjE+5Lu++Ql9mkSQbfHKxmVP2oBgMDa7aoTB5ukpzc\nk249g4zIXcsflv2dFz5L5NFdc6nPdOHJlhiGbFizKIRCIJav4PbbT+bpp10E9DK2pqynYHMvqIWM\n09ZRGhxHpq+RZS5erDJ3rsqdd2o8vfllemQJAkUp7DCLWFywmLPyzmrf9W0FJ9jnCcTiSDqVSCm5\n8cYbGTJkCHfddVeL21xwwQU8++yzXHHFFaxcuZKUlJR2uWMBVqxYwRdffIEQgnXr1nHHHXdwxhln\nRO/HESNGcNJJJ7V7vl3OYELTFl+dPe53GcM8kg4dTfaxevWhmmRCWO7RpUsPMZiHnVOvXpj33097\nJZ3V2bMRBw+ClEiPB7O2FqWmxjqGSMSKPZomoqQE48ILUdauRVm2DNmjhzXfSARRXo7RllJHUlKL\nyjw27ONRFLj8cp0PZxZT/OQrZDqq6dZDQWwE2cCaKyacx8ElW+gtClheuIfatFoyhowlNaMbe6v3\ntsoyh3UbxrBuw6K/B09vbPdpmAaPrXiM04akEzI/onSNil4B9WGVfn2tuVVXC6pLBMVbPGyY7eDu\nuyPc+udV5Id99E5TmDLFoCLiYWnhUn4w4AcNxwUVFYI779SoVneytHApWUkZpHtM9pf7D2GZANXh\nalYUrmBGvxntuobN0RXZZ1dGc7Z7JAxz2bJlvPnmmwwbNixaKvLYY4+xb98+AG655Ra+//3vM3v2\nbPr374/f7+fVV19t9/imaVJdXU1ycjLDhg2joqKCzz//HIfDQX5+Pj/5yU846aSTMAyjXRm0XdZg\n2hc7Xl2ybUHXdWpra3G73W2WjLR6nIrStIi/6Zc6aaatDF9UZIkRJCdjmiYiEkE4nUgaGksnJkJC\nAlJVEbt2Ydx1F9LttkpOhCXprV1/PUorLLZdc2h2jKYJeWs/IqVuP8HuvZDJgABRVYVeE2bRgTIy\n/+c2BvfYyTNznqKkfCrdEr14gSRXUqssszliF+EbSzdSGiwlLzmPfdX72H3REEavXcqWCsm2bQo9\nsiWlB3Syk+DFmjP58RQDw11OXdJXeIp6kZUlcTogU81kffF6JvScQKYvEyHgoousGtynF72KIhQU\noeByQ5/sBPbX7OeznZ9x4YBGF/UXu7/gnW/f4aS0k8j2tOJK78C5bc4+j7Xwd1dAPLlkm8MOCXUE\nEyZMaFfnlGefffaI5jR58mQmT558yOcVFRWkxeQytLfcpEsv8+JVzq6tscLhMDU1Nfh8Prxe7xE/\nQHLMGMtKNIgbANbvoRBy0qQjGrM9EEJgpqYiG16gQggU+xgUBdmjB/Lkk5F5eVa9ZDgMfj/mffeh\nz5qF/swz1P7xj+jnnNNphl3XYdYsJzlF6+jW14vDAQUFClKCTEykakc5w/sHGHO6YKXYj8wOkpIb\nYNnGEopqiwgbYb4t/7bdouVgsctPd35Kmsd6cDN8GXzizUdePJVB6SX4AsXML9uNx13Kp7k/48e/\nzaVHD8lf3i5AN2D02XvZuK+QhesL+ebgN+yo3MHOip1N9lEQKGDBvgWY0qQsWBb9KQ+V89Tqp6L3\nWFWois93fY7f5eef2/7ZKec0Fq0Jf8e2nbIL4W3Ei3GIl3nEG1pimPEmvG5n6T799NO88847gNVI\n+vTTT+fWW2+luLi4Q+N1SYZp4z/NYMbq3LYWr+zIvGS/fpiXXILywQeNblnDwJw6FdmCkkZnHp92\n6qk4tmxBralBuN2WodY0yMhoLAWRElFbixmTZi4OHkR58038+fkoqooYOxbzyisP63ZtC1LCW285\n8bp08k5yIHapZKfqFB10UliokNvTICtVI3Vqfwwsw3bJQKtYX9MgtnzX62y/SyqWXQL4nD7KgmUs\nu2oG6X0uYPGLi3jzpM9ID1/Jr++8kh49TGbNcuKrHM17t56CxwOVp8FTzyjUq68yvG8pOYlNkyHS\nvek8NvkxJI3XbcueatZU/wu/x8me6j30TenLh5u/pLrGZGBWNl+XfM2e6j0MyDi0+fPRYMMGhT59\nTJKSGtlnJOJi2zbBsGGRqPsWiLp1T6AR8X4+4tFg2gZ92bJl3HDDDZSWlvLVV1/x7rvvMnPmTObO\nncvVV18dTQ5qC13aYP4noXm8slNiQEJgXnMNcuxYxIoVoOvIsWMP1VvtRNixrbrx40nets3KXi0q\nsoxkdjaiuNjKei0vt+odhw9Hnnaa9eXCQtQnn0T6/Zg5OZbbb+1aKCnB/OUvj7g/lKIIJk+OkJen\nICuHIEP1qAcOkOODSFBHlBjI7O4YDTHdoZlDGZo5FLAaKAshcCgde5RMafLpzk8JG2H21+yPfq6Z\nGq+t+hfKosfwXfMtVTuGUBrezDN/q+f3j3jIzTX54Q8NPB7rWDPS4aLrt/HOV7Vk+jNZsX8FuUm5\n0ReFz+ljcq+mLqmywKdkVg2hu9/Fon2LUMOpfLRlNr3SuyGEwO1w89HOj3gg44EjOp+tITFRMn++\ngylTdJKSrM5q8+c7GDTIUouJ1S21k4ds8QTbfXsi9tn5uRdHipYYZrz1wrTnl5CQQGFhIfPmzWP4\n8OGMGDGCcDhM4mFUulpClzSYxyqGeawYph2vtJVYjkTnttV5CYEcOBDZDtX+oz0+KSW1DZ1GPKNG\nQXU1zJ5t1UYCDBiAcfrploC7pmGecQZy7NioKIIybx5SUSwRg0gEVBXZsydi717Ejh3IQYOOaE5C\nCPLyGqpbzj0XddcuDF1HLSrCLTQwJMYpp1j7bPZC+GjHR6hC5eKBF3d4v5NyJxHSm3YF2btXYfYq\nN1PPKOT1/FVceFYPdpcWsXvRPB555GIefTTcRNVPN3U2BZYxclAqPqePgkABhTWF5Cbl0hJKg6Xs\nqvmWAbk5lB10sL0mn+17/47XZ+DzWFQ505vJhoMb2F21u1NZZt++EjCYP9/B2LEGa9ZYZTMDBjS6\nYVvSLVVVNdpM2laWORH7jD/EI8O0F1iXX345b7/9Ntu3b2fmzJmA9QymtVCTfTh0SYNpI54Npo2O\n9uWMVxiGQW1tbZQlKHbZx5gxVvNolwvZt6/1bytjiD17WtRtFUJAaSl00GC29LKVffoQuvRSvM89\nBykpmKmpmMOGIZOScMyejX7FFVEmWxYsY/WB1QghmNxrMune9HbvW1XUQ5gfQHU6TOyu8+hH/2Dk\ncEFKksIp/m64XHO4KG0S8+encPXVjTHnXZW7CEQCUQOZ5E5ieeFyfjj4hy0e38r9K3E73Hg9CklJ\nktJiD0uq3qF/eh4ldY1yY0EtyIfbP+TBjAfbfUztQd++kro6kwULVAYMaGosW4Id+2zeNSM28/ZY\ns8946cARb7HU5vOJx+bR9jt5xowZTJ06NSpQUFNTw29/+1uyG+rE23t9u7TBhPiOC9hZhR3py9kS\n2mXIq6sRixahrF0LLhfmhAmW5FxMgM4Wq+8omisQBQKBxj+mpiKba762Atmrl1UK43QipLTYJg0d\npdLbb6zaglpVhXbWWVaNZwxEYSGiqCjaoWTBvgU4FAcSyaJ9izrEMqU81OstpSUiUCOLSR2xhLyU\nXgA4VScpySZ67jx+NKUxq1U3dZYVLmtiqJPdyZaubDOWaUqTkroSvin9hm7+blQE6imvFKSlKaTU\n5DAoeSS56RnR7SORSLSBdmeithb27FFITZUUFioMGGC2Gn5uSd2ledNj+xk5wT6PP4LBIJmZh6pN\nHW8IIaiqqmL16tXs2bMn2lDA5/NxwQUXdGisLmkwj2Ud5pEYlOawO6hIKY+42XOHUFeHOnOmVeCf\nmQmGgfLBB8gdOzBvuumo4pk2Q45VIDpSJi7T0lBXrADTxOlyYfbpYwms9+zZLpdyeyECAWQL3UxM\nIaiuKSWJnlF2mZOYAxJWHljZbpZZUiJ47z0HN9ygRUtMpIR333XQr5/JFtd8gnodFaGK6HdURWXO\nnjmcmXdmtDtJUW2R1ahas7Jco/OUJlvKtzQxmP/a9S+2lm8lJzGHYFBSWaGQ1c3E7U7g4oQZpARH\nMj13BCkNLT2DwWCnezRiY5YDBpjs3i2axDRbQlvlUsebfXZVtPT8xiPDtFnwM888w+bNm/n44485\n99xz2bBhA0IIpkyZ0qIUX2vokgbTRjy6ZO14pZ0l2BkPelvzEmvWIEpLrTIOG717IzZuhN274XB6\nsK3ATtaIRCJHzZDtOarvvYc5bJjlmg0EUDdsQI4bh3HXXW0n/AQCUFZm/T8j47BZtUZuLo6NG5tu\nIyU7jIO8X/QJt/cfGGWXqlBBWG3B2ssyu3WT5OZK/vY3Fz/9aQSv1zKWZWWCH/zApGxfJmflnnWI\nxKEqVHSz0R3bM7En1w+7vsV9uNTG7wbCAebunYtu6vzo5B8RPNiDzCGSQECQk2PdF3V1liE3DMkR\nqJu1C6tXN41Z2jHNFStUpk83Dv/lNtBS7NMwjEPYpy2a0JHFcry4QuNlHrGI96Qf+5y98847bNmy\nhSlTpvDee++hqiqXXHJJh9t9nTCYceSStWvSfD4fDoeDmmadPo4VxJYtyOaCokJYbawKC6MC6u09\nX3Zyj5Sy0zJ6lQ8/tBR3kpORvXqhVVcjFAWn3Z4nFLLaga1ZY7mUzzwTbNa5YwfK+vVRF66QEnPo\n0KZi8TEwBg9G3bLFkuzz+SyBhUA1X2ZWUmwqzM+f38guG9Dd171dLHPFCpX0dMm55+p8/rmDv/7V\nRVKSZO1ahT/8IYzDAVN7T8UwDDyt9Oy0IYQgwdX26njxvsWWQLvDxZd7vuS6U66jqEjw5JMubr5Z\nY/BgE7/fWh888YSbSy7RGDq0zWE7jEmTjENao/btK+nV6+iMZUuw4+TN2Wc4HD7BPjsBLRnveEz6\nseeYmJhIIBBAVVXmzZvHuHHj2Lx58wmD2VHEA8NsiY0ZRue9RNqcV0oK7NnT0sQOyQptC3Zyj8Ph\nwNdKG60OnyfTRBQUWApA1gCIhnnJmhrE7t0oH3yAmDcPEQyCaaI++yzG5Zdj/vSnlrHMzIyyUGkY\nKBs3oni9yISEQ+eYmIg2YQKuV15B3bQJVJWtI3PZ38dJn9R+fLj9Q/xOPwdqmvbt06XO5tLNTOrV\nuuhDdrbJG284qaoSXHGFxrx5Klu3qvz4xxqbNqmsWGFpv3YWAuEA8/LnkeXPQlVU1hatZXqf6fTo\n0Z2f/Uzjr391cvPNGpmZkj/8wcX3vqczZozZVovPI0JrToajdD60iWPJPk+gEUei9HOsYV/LH/7w\nh+i6zi233MITTzyBy+Vi1KhRJ8pK2oPmMczOdHV01GC2xsa+S/ZrjhtnxQYjkca+ljU14PEgYzqf\ntzUnO7nH6/UeWdu06mqUhQsRX3+NTE1FTp2KHDLEUv/p1s0KgsXe4KaJMAzLpbxwobU/mzWGQqjv\nvw9pacgY0XbAKkdxOlGKiy3h+eaor8fz7rvgcGBOm4aJ5Mu6+aSt13Cn96OHvweTek3ijJwzDvmq\n13F4f2ZenuTaazVmznTywANuPB4YOdJk1SqVFSvgoYciHTtnbWBJwRJMaeJUreStWJY5YIDJz36m\n8X//Z13zq67Soj0x/5vRFvuMleyLJ/YZTy7Z/xSGaeO+++4D4NJLL2XcuHFUVlZyyimndHic+Lkb\njgOORdJPR2AYBtXV1SiKQmJi4jF7ONs0vn36YF5+uVWasW+f9aNpGDff3G6GGQ6Hqa2txe/3t6lt\n2yIqKlB/9SuUd9+FkhLEhg2ov/0tYvZsAMyLLkKUlVkyeWD10ty/H3P0aMRXXyEiEUhIsIx+TY2l\nHKSqKHPmtLw/ISyBhIa5h8PhqDSbumULorISsrJAUdgpKtnvN0mJKCgFBWQlZLHqwCoUoZDgSmjy\nEytm3hp69ZL07Gldj2BQkJNjsm+fQlqaJDW18TppWnSKTWCfgrZgxy6z/I3Zrt183VhbtJbiWksS\nLC2tcX/Z2fETnoDvJoPdZp9utxufz4fP50NVVXRdJxgMEgwGMQwD0zTjKnwTj4jHGGZL6Nmz5xEZ\nS+iiDDMWsSIGnTVWe2DHK71eb4uxqu86virHj8cYMcLqL+lwWD0nYzXfWvtejDu5Tbk+w0Ds2oWj\nqsoSK4hJalE+/xxRXt7odgWkpqG+/Tb6+PHICRMwgkHL9RqJIAwDfcIEHNdei3r77dYXiooQVVXR\nrF4JkJGBiESsc2lfYykRkQhGt26EQiGMoiJ8c+eirl2L4fUik5IwFcVqgI3kC7aTggdcBlRV41bd\naIbGV8VfMTF3YgfPNBQUCNatU7nwQp1VKxX2zN7Bb05egaOshi1/6oc2aiQjzvIxf77KgQNWj875\n81VOPdUgGBS89JKTe++NHFYNcNEilZq0TYSNcLS+UtehqEjQLVtnbfFaxqWczx/+4OKqqzRyc2XU\nPTt48NFnencWvmtG1RL7tHVuI5HIcWWf8cQwW0I8umQ7G13eYHY22jJyUkpCoRChUIiEhAScbRil\nznhI2m18/f4mLti2xulQcs+uXTieegrKy/EaBsLphBtvRJ59tjX2qlXIjIym32k4N2LnTuRpp1lC\nB2eeCWVl1DudyIQEHD6fJRS/eDEiFGrUoTUMRH09UtOQ/ftbYzT8TdTXY/bti5Gaijh4kNQnn0QG\nApCZiRKJIFasQBoGke7dKRVBypU6IopBwKhFelMxAwVIKdlStqXDBjMUgs8/d3DJJTqZmRJjwTJO\nrlnMpjXpTJzqpZ/YzJJnt/GV/kPOPsfDyy8r3Huvm5wcSV6eyaxZLi67TGtiLGtr4W9/c/GTn0RI\nToZ//MOBYcAXr03kjCknc+mlOgX7BL9+xM0Pf6gzfaLOornJPL7ExTnn6FE37NVXa/zudy5+9asI\nMeuWLgubfSqKEg0x2JJ9XT322dJ76YTB/C9F7IXubDm7w0FKSV1dHYZhtFlfGe8PX3uSe6IIBnE8\n9pjF+Hr2xNQ0FE3D8be/YWRnW0ba47GsSXNI2ZTput1W3LG+3vobYP7gB6hPPGElAIHFJE3TyqpN\nSECmpyNzchCFhQDo2dnUeL0IKUlYutQSeG/QphVuN8bJJ+NYuBB3QQE9+vbll+aZyNIypK5TP+Vu\nlPT0KNPoKObNc5CdLcnMlHzy93p+PXwJ4fRs5i92sXC5IDvbS6/c/Xz+7DeI9GlkZkq2b1cIBk0e\neMDDAw+EGT68KQP0+6F3b5PHH3fz0ENhRo0y+MUv3NTXC3Zv6sbeIRoPPeTm7LMNrroogpRQst+J\nrsPEiZaxrKqCDz90ctZZOikpJ1yPLaE5+4xNHIqNfTocjrh/fo8FwuFwmwTgPx1d0mDG4rtqyRUr\nDdfeZs+d5S7urGO0x4lN7mmr9AFArF9vxRUbaIsASwLP40F8+SVy8GDMadNQX3kF6fc3uk4DAfD5\nDq8RKyXU1CD79LHGLyiwmlJnZyMHDkTU1FhMMy8PmZWFruvU1NTgcbuJRCKo33zT2JNTCOtcu90Y\nffuiuN2ohYWogJmbi3bppbizulvF8eHGrhodUZaZPl1H1+Htt53cc80BkpeBmaVwycU6Bw8KvvzS\ngbNHOoMc23nmme/jdkO/fgbffKOSng5jxx7qLhUCLrnEqs98/HE3Y8YYJCRAWppJ9+4mt9/uYfJk\ng/vuiyCEtf1Pf6rx8stOZs50ce21Gs884+L00w3OPdca51hkyf6noqVnUIjGZtlAE8H4Y8U+48kl\n29pc4ilJ6ljghMHs5FhhS2MddfZoHMHumtIed7INEQhE2WATeL2I0lIA5JQpmJs3o6xZY73RpQSv\nF+P++y1W2XxMIZBlZaivvorYvRvKyhA1NcghQ5D9+1tjaBrU1kal7Oy4sd/nw1VXhxYOo6UkMTf0\nDdOUoQgEppRI0wS3m/AVV2D2729lxjXI9zlobD1lvyRtZZn2MAyHA955x0F2tklWLxdymfV5cbHC\n+vUKV1+tsXp+iMJIIvv2CWprBT6fwhVXWKpAs2Y5ufZa7RCdBttoLlig8txzTs4+22DsWJ2HH/aQ\nnm6d+wcecPPww2HS0iyh+Rtv1HjqKRcPPeTm/PP1qLE8gY7jBPuMf69YZ6BLGszmF/ZYuWSllITD\nYerr6ztkYGLH60xmeDSwY6+2XF9H3JEyL6/RCMa6wwMBzClTrF+cTsy770bu2GGpCyUkIIcPb1Fs\nvWFCeP76Vzh40DKICQmIxYstdSKXC5mcjKisxPzRj5B+P+FQiPr6epLy83G9/jqiqIhEw2BZL8Gz\n/s2kyXTGkYseDkNlJSIpCW3QIPB40KVE0fXoSxGa1va5XK4WGUZsYXzsvXHhhTrPP+8CM4dz09Mp\n+baS1dsymTzZwKGYJGvlzKu9jAEDTDZvVpFSMmaMydixBi+/7OSNN1o2mvPnq7hccP75Ou+95+DN\nNx1MnWoQCgm2brWycHVd0JAORSAAFRXWvHbtUppUFcUD4olRdQTtYZ+t3Rv/KWh+baSUXSKL+L+b\nP7cDnXmzxtZ12vHKcDhMUlLSf7Rv32aVttuyo7E7OXAgctgwRH6+FXu00jUhIQFz2rTGDYVADhiA\nnDEDOWGCZQQXLUK9914cP/4xypNPwt691qb5+Sj79ll1l0JAairm5MlWrHL7dnA4MH70I4zzziMY\nDBIOh0kuL8f9f/8HtbWY2dmY2d15R27Ab6rMCq/GmDsH56ef4l6zBpGTg0fXcblc0RefYRhomhYt\nP4nVDbbZhdfrjerm2ouMYDBIKBSK9ndMTIRbbomwcbOTmfsvZfWmBM4ekE9GaD9Fa4pYqE8k5Yz+\nFBYqnHSSyTnnGLzyioPiYsGNN2okJ8tDwr3z5ql89pmDhx6KcPnlOqGQwDAgOVkSDEqVS3D2AAAg\nAElEQVRcLsm0aTqvvebENGHXLsETT7gZP97gxRdDJCVJZs500dC/+T/WWB0LHO25aM+9oWlauxIG\n4/2axPv8jhbqI4888sjxnsTxgK2ko2ladMXXGaivr8ftdlNTU3PU9ZV2EP1o4wJSSiKRSLvijc1h\nGAY1NTU4HA68Xi+apnV8HCGQY8aA04mybRsEAlYz5p//HJp1BImF8s47qC+9ZMU8fT7Erl0o8+dj\njhyJWVWFuno1SmynE3te+/cjEhIQ33yDvmoV+oABJGRn43j7bSgqQqalIYVgtesgcxNKyKtzUOgK\n08uVSY/+o4gMGYIoKkJZuxZj4kRUt/uQWJRdlxdrNGMFMex7yja4dmeNcDjM6tWCoiJB796SZesS\n8Z4+jBGX9OKzHYPQJ0xkZeVA/AmS88+3NGd9PonLJUhKgj59JIMHm02YYG0tvPmmi3vvjZCVJXn3\nXScXXKAzeLDkk08clJYq/OpXYdatU7n99ghbtyrccIOXadMMLr9cRwhLPOHjjx1s2KAwYYKJpmk4\nnc7j+gLUdT0uxAMikQgul6vTSs+a3xtA9N6wF1X2trH7NAzjiBasxwK6rh8yl7feeosbbrjhOM7q\n2KNLumRjcSzqHQOBQLSV1dE+ZMfTzdE8ueeoirc9HszLLsO87DKCDRklh1UFqaxE+ec/oWfPRu20\n7t2hpATlH/+Aa6+1XLyGAapKGUG21eUzcdnX1mdbtiANA+fOnbgqKzH+9jfE7t2YDWnvJiZveraR\nIt3IUCUpJrw1WGNsuDsuFKTPh8zPR1uxgsiYMTidzqibzeFwNDGY9uLLfqHFum7BYhi2kLqUkoED\nTR57zElVFdxxR4DZs73c96fenH66wYaVKiNHRhDC5KKLFAwD3nrLyYgRBuvXKwwfbtBc9jchAX7z\nmzD2Lq+/XqOiwuoE8uCDERYudPDnP7v4wx/CBIOCjz92cPLJJj16NBp725178cUn4pjfJVqLfYYa\nXAi2278zmzEcCxiGEbdz60x0SYN5rFbN9k3e3uzRttBZ8zySRUGoIeZ3JLHXzpiPyM+3/tOc+aen\no2zYgExPJ3LmmfjmzYP6ev7p2cR8/0GG1ErS0nMxfD4rUzEUgpUr0Zcvx8jNRV27Fun3s9pRQokI\n0lP3IzSNFH8K+aKWNUoR48wchKIgPB58paU4/f4oAwgGg1GjabeWcjqdUcMZ+9KTUkazZ2NjnwcP\nOnG7VbKzoarKg9cL1dUmXm+I224zcLtt95sXh8OSrJMSHA6D1m6J2HdVRYXgiSesGsspUwymTjV4\n+GE3P/+5h6FDTa68UiMnx+Tppy0j7nTCokUO7r473JltRU+gg4iNfbrd7qhkn33vQWMz6+Md+2xu\nvO2mEf/t+O9fErSBzkqIqaurIxQKYffoi5e5dRSxx9I89vqdzsfvbzmzNhTCpliRSy9FpqdTXLSD\nRanVOCR8NlBYrb9M03qgfT6kpiFWrcL4/vdB1zECVcxyb0UzdcojlZSmuSl1aRhI3nRuwcBiXtIw\noEePqHRaQkJC9JzYZUI1NTWEQqFoJqTL5cLlcuF0OlFVNWpINU2LSqwVFwtuu03jjjs05s51UV7u\nZOZMHdN04/WqSGlimgbBYJBIJIIQBg6HbNVYNsf27QozZljGEqwk49tvj2CalqxeSookJQXuuivC\nxx87ef99J3fddcJYtoTj6eGxPRN27NM2UC3FxY834rEX5rFAl2SY0LTG8WhuODshRghBcnIygUAg\nLm7gWLT3GGOPpbPacrWGNhMc+vVD9uyJOHiwMc5pGFBainHLLRZrKyxEhMN8fP5JqIqHbjuLmdOn\nmnMPmGTU1EB6OtI0EQ0yd/Tvj/bAA2gvPc/IEpUhjiRkbi/MU4ahzp8HtQ68nkQMaaJWVILfj3nq\nqU3mJYSIGkWbTeq6Hm34Hcs+m3fHsBOFJk7UCYcFb77pZeBAk4MHBZs3q1x8sQE40TQrRmQb5lj3\nnJ1deTh2ccYZTQVoi4sF77zj5Pe/t9y2s2Y5ueYajS1bFFwu6zqsXq1yzjnxJbweT0kux3sedjzT\nDg3Y7FPTNEKh0Heeedv82sSz8HpnossaTBtCiCaJGx2B3ezZ7XZH45XflRBCZ8NO7rFXtId74I72\nRdau7yoKxgMP4Hj8cSgsjKr3mN//PnL6dEv6rqiIYrWehco+ehh+1MRkRFWAz/PCXLcraPX4rKtD\n+v3IsWOtzMRBg9D+93Gura9H8XqjbFX0vQLHyy8j9hwEipA9e6L/5CeQmGhl5C5dCpqGOXq01UGl\n4VrbBtKO8dpZtPX19aiqGn3B2Uzd1HWC5XXMeieRrB4655+vUVUlePVVD6YpGD++MeHDHts2zrqu\nR2s+29vPsaRE8PrrTs47T+fkk+37XOfRR134/fDrX4dRVaLu2eZGMxiEsjJBr15N78NduwS5uTKu\nylC6EprHxVuKfbZncdVZOGEwT+CwsONZdpp4PKO9bbl8Ph/uFkQCYsf5TpGdjf7004iGzFqZl9fY\nvsswkImJfOwrQDEkKgKRnk5WoIo5vWo5t0AnPRSyMmKHDMEYPZr6+npM08SfmIjSLHNGDhiA9vvf\nI0pKkEJYnUqEQP3HP3C88EKje3jWLIxzzkG/556mgUOIao663e6ogdN1nbq6OoSUeOfMwfPppwT3\n1XGZJ4Pu91+O4ZxEerrJ9deHePNNN4MGhfD7jSbXy16IxTJbXdfb1c8xIUHygx/oDBjQuChUVave\nMtYNe9ddEZ5+2kVurmTo0MZt9+5VmDnTxX33henXz5rT5s3WZw8+GKZPn/jypnRFtBb7PJbs8wTD\n7GI4UpeslJJgMIimaS125/hPYphHK6xwpOjQcSlKq4Lw+3OS+SLpIAkRhVJvCGFKZM90qgMan07I\n4rrwIIyRI9Gvuoo6KVGEwO/3t/7CUBRkjx6N88zPx/HCC5YovH1uTBN19mzM8eMxTz/9sMdoJwR5\nPB6Ut9+2Oq9kZuIf1I3E+iCOZ55GSBN16lR69ICbbjLx+RTC4QiKoqBpViPpujqFpCTB6tVOUlOt\nLFt77FBI8tlnKtOm1eN0Hqoq4/eLJsYS4KSTTB5/PExCQuNnKSlw//0Rmr/zhgwxuemmCE8+6ea+\n+8KEQoKZM138/OeRLmEs48kt3N65HA/22RWE16ELG0wbHXl5tyfG911I7R3NWLHiCsFgEF3X227L\nFYPO0rc9WpimiYZk2sQbcCxehLJxO1RXW3/r25/MK68jcvrVGC4XwWAQZ8PquyPzVpYvt2KfsQsJ\nRUG63ahffnlYgxkLUV+P8+OPkbm51qJESkxHIjog33iD2jFjcLhcvPOOl+7dI0yf7sTttl54c+cK\ntm4V/OQnITIyJLNmebj4YpOhQy39h1decZGRIUlKclNfb52Xl192cPnlQTyeRlWZlStdaJrC2Web\nCEETY2mjtffdqaeaQIT/9/8s78PDD0fiqgXYCbSO1lz7Nvu0y1aOln12lSzZEwaznQbOjle2J8bX\nmXM7FuM0N/zH2/i1C7t2ocyejdi/n0ifPoQnTSK7RzY3T74b9eMCKI8gU1MtdaDianhhHvUDzqEu\nKQmPx3NkbnNNO8TtClif2ZI4DSgrg+bdyXTdEhVIrS5t2nVFCBRVRUlJQRQW4jEMIqbJhRdW89pr\nPhwOmD7dZMkSFxs3qtx0UwS320XfvibXXBPif/7Hi2HA+PEGOTkml11msHOn4LHH3PzoRxo5OYIX\nX0zi9tvDuFwGixcLvvxS4dZb6wiHlQ6JxduIrZKyE4VO4D8Lsa59aGSftgGF9rPP5ovmE1myJxCF\nHa9sK8YH8euStccyTbPdyT3Hak7RMerrERs3IrZsQSYnI0eOhLy8Q7dftQr1qaeQTie614vYtYuU\nxYupuf9+yyrt3Yvs2zc6NpmZyMJCzE8+wXfzzUes4mSedhq8/rrFMm3DKSWivh7d1sDFmsKnn6oM\nGyYb2Jj12ccfq6SmSqacmtJEZCGKUMgqe/F40DWNzEwvt90meOEFlfnzIT1d46ab6vB6VYSwXLAD\nB8Kvf61z9dUe9uxxMH9+NVu2CB5/3IfbbdK3r06fPgJw8OyzbkaNMliyxMG994ZJT3c1EYtvb+KQ\nHbN8+OEIwSBR96wd0zxWiAdPRjxlvHf2+Yhln7acZ0vs0xaMP9y+T8Qw/8sRK2PW2kMhpaS+vp5I\nJEJiYmK7XrzHo3ayvdA0rd2G/5ijrg7l7bcRBw8ik5IQe/agrFyJedFFyHHjGrfTNNSXXkKmpaE1\nuJRcycnI0lI877xjSe41KO3Y594wTaTXi3fvXvSjkDyUgwdjnHsu6mefId1uyx1bV09o6EiUSZOi\n2zkcMHmywfz5KvX1CqecYjJ3rorXC2eeaYKSTGD0WSSt+DcyJ8cymrqOUlxM6KqrqNc0fD4fDoeD\n5GQYNEiltFRh4ECF1FQTw7AK162MbieLFvm44gqD9993cP75SeTlSbxekwcfrCc310TTJOedZ7Bk\niZdPPnFw//2RhsqcRrH4thKHbGzfrkRjlo1uWCum+YtfhA/Jnv1vxPE22t8FDsc+6+vrgabssyWG\n2d1OyPsvxgnhglYMnO22tGN8naU12xlz6yjsMYLBIAkJCcffWBoGrg8/RFm50nJ7ut3QvTsyOxvl\ns88sP6aNAweQNTVEGh5kl8tladOmp6Nu347m9UJD7MV+yKVp4tQ0ZG7u0c1TCPS770Z77DHk6acj\nR4yg8Ib/YdbwJ6isa3Tx5udbMnTTphl88YXKLbe4EAJmzDBQFNi2TfCq82aCE6chSkoQRUWI0lLq\nL7qImu99Dyn9rFhhjTdvnsK2bYK779ZYs0bl/fe9eL2+Bk1iL6+95iYpKcxtt1Xw6KM1FBQobNig\n8sADJgMHuqL6pMuWqbjdJqecovH3vysEAmazQxPRhCS/3x+9J2xvin0us7IM7r033CRmeeqpJnfc\nESEz87/fWMYTvkvGbbNPj8eDz+ezEtcaEtHq6uoAmjQiCAaDeL3edo9/ww03kJWVxSmnnNLi3xcu\nXEhycjIjR45k5MiR/O53v+uU4zpadFmGaaMlo2TXJDqdTnw+X4du0nhjmLZyj9UlI/GoM2GP+vhC\nIVzPP494911wuRClpYj16zEnTLBKRkwTUVAQzYw1HA6kpqEADqezsU2YriNcLupPOQU1KQmlqAgz\nLQ2hKDjCYaShY37/+0d1rA0HjDluHGYD680CRn8r+OgjhYsuMggEBP/+t8q55xpkZloZqNXVVm/L\n6dMNCgutv196pcTR/VYiVVdBRQX1iYnoHg8Jfj81NQrz56ssXargdMLNN+vs3SsIh2HrVoU5cyTf\n+57J/PluuneHSy8V7Nzp4N13HYwZo7Fli8pDDwlefjlESoqDFStczJ3r4J57IqSlGXz4ocLMmU5u\nvTVEQgLR5I6WWpUB0RegJX4epmdPlUikqWtuyJATST9dBa3dH6Zp8vbbb/Pkk08ybNgwpJTMmDGj\nXa7Z66+/njvuuINrr7221W0mT57MJ5980mnH0Rno8gyzOSKRCIFAICpHdTxifJ01lh2vtHUf48G1\nJJYsQdm6FZmeDklJVmNmj8dim3qD8HfDg6lpGgGvFwYMwFFVZTHLBrUcUVICZ55JQo8eGL/9LUZ2\nNmpxMaKoCE2L8Icr+/CNP3hMFi9DhkjGjjWZNcvBxx/bxlLy8ccqmZnwu99p6LrkF79w8vHHKpde\nqkfLR2VyMnVZWZg+HwkJCSiKQnIy3HWXxq5dCnl5kr17BW+84eDuu3UefFDDXrjPmGHwwx8avPii\nyh13uPF6Ff74R5OVK8M4nQpTpyaybVuQf//b4Kc/rSE93cDhULnsMoWhQwUrVngOkevTdf0Q4Q7b\nkNrs09bKra+vj7ZKixdJtmONeIijxhvs8+HxeLjuuut4++23cblcfPTRR2RlZTFjxgyee+65w44x\nceJEUmM7DbWAeLy/uizDbB7DtPvThcPhdscrjzWOxmA2z+oNBALHfU4AyrJlGJmZllrr9u1WBb3b\nbUnKFBSA34/My4u6BhMSE1EuuADxP/8Da9eC34/o3h05dCjGFVeg6zrB1FTcf/gDSkUFMhxmk7ua\nlSsfp3TzO9zvvR+X0xVV3Omsl19sCYbTaRlLr7fRDXvppQaPP+5EiMYe2PbKXFGUQxKukpPhj3+M\n8OCDLubNU3noIY3eva3zPGmS2bAfa9u8PEldnWDSJIPevSVFRYJBgwQul2TTpmR+/WsNXbc0Rw3D\nwOFwcO65zob4U+ti8cAhC6vmiSH29zqaOHQCR4fYll/HG7GLCEVRGDZsGBkZGTz88MP069ePefPm\nsXPnzqPahxCC5cuXM3z4cHJycnjyyScZMmRIZ0z/qHD8rUKcoLa2FinlUWuoxoNLNhKJROui7NhU\nPMwLsFypqsr/Z+/Lw6Mos69PVS9Jd7oTFiEQQEFEQQZZFEUcWWWHJDAiIaAjiBPEEQUVdRh0cBvw\nUxlFB5dB+TEKAkmAEDCjsgRRAUFZFFAEBIzskKQ76bWqvj86b1Fdqd6ruqpJn+fxebRNqt+uVL/n\nvfeeey7TogVQXQ2cPu1ToNpsoGw2MH/5C2rrNmSr1QrDrl2g33gDXFYWuCZNQFdWgqMoeCdNgjsl\nBc662onBYPD9DMdh3Tfz0MLaAmecZ3DCcQI3pN4A98WL8OzbB311Nei2bUH/4Q+gw6jl7txJw+0G\n/vjHy1HYgQMUFizQ4+mnvXC5gJISHTp2ZNGrF8fXLL/+Wod//tON77/X4ZNP9Bg71gWKqoXBYAjY\nD3r8+OXXfvyRRtu20t6uw4ax6NnThcWL9Vi6VIejR2mMGeNF164cGAbQ63XQ6y+nzy4rHx08wQkP\nEGRUGfG7Jc8JGdkklbqN1HEoGiSju8QBaSvJyMjAmDFjYr5ejx49cPLkSZjNZnz66afIzc3Fzz//\nLMNKY0ODJ0ySjqIoChaLJeYvqJopWRIlO51OzUTJYrC33gqqrMwn8unWDbh0CTh3zteqMX06HA4H\nvBcuIL1VK9AMA7pOIcvVRWQsAJw7B+6//4VzxgykpaX5mS4cunAIv1z6BdekXwOaorHm8BrMufo+\nWBa+BdTUgKEowOOBJysL9qlToW/cmB/SLfW379yZxfLlvvvYsyeL1at1+PRTHf70Jy/fVvH77xy+\n/FKHG2/04uzZupplXRq2fXsG5eUcli5lMXFiCqxW6X7Qfft8adinn/agcWMO//qXL5wcMUKaNK+6\nChg9msG77+rRogWH7t19axGf9Yjri9AsnqilOY7zm/Op0+nAMAzvgyuMPknNU3iYFLoZkejT6/Wq\n5meaRHwgdZAhgkK5YCVpGQDDhg3DtGnTcPHiRTRp0kS294gG2ttR4wSKovhIDEDE4h6tgYh7GIZR\n1LIv1utw/fsDe/eCPn4caNoUcLlA6fVgOnQAu2gRUnQ6WA0GsLffDu4Pf/CZp2dk+P1tvI0agT5w\nABaDAbTgc3IchzU/r4HVaAVFUWiU0ggnqo7jpyWv4A+0BbjmGr5on3riBHSbNsExZgxPHuLIC/Cl\nXseP92L5cj2qq4Evv6Rx7bUsRozwHbSOH6dw8iSFAQMYpKUB6emcX83S4/Gge3cHGjVKg8Ui7ab0\n++8+svzrX718Gvaxxzz4178MaNqUQ69e9QU2p04By5bpkZPD4JtvaHz2GY3Bg4MLcYTpVQB82wBJ\nf5P6pl6v51PGwkkroeZ8kuhTys9U2NOXSKlbrUS5WllHIMhtXHDmzBk0b94cFEVh586d4DhOdbIE\nGjBhulwu1NTUwGKx8CpSOaBGhJlQzj1WK7wzZsD59dcwHD8ONGkC5tQpeA8fBnX11TAYjQDHgd66\nFR6HAzTLQvhpPLW10J04Af2JE6BffBFs375g+/QBUlP9okvAd//SvToUur7DjU1y/BRuXFYWDDt2\nAPn5AEXVIw8heaal0Rg1yoslS/S4+moON93E4rPPaHTsyGHLFhrDhjE8QQpb0VwuF1wuF9LS0tC1\na2DrwZYtOTzzjMdvHmVGBjBjhgdSc8hPnQLefdeAnBwvunfn0K0bi0WLfF/lUKQphJDgiEk8TdN8\n/VsYfUqlbgFfrZxEnkIilPIzFfb0kcgzGX0mHgJFmJF4yY4fPx7l5eU4f/482rRpg7lz5/JuQwUF\nBSgsLMSiRYug1+thNpvxySefyPoZogXFaaKwFX8Q4QJN06iqqkJaWposKUyy8WSIpmEoda1wLfvI\nBhhrD6Yc1yFtO40aNYLn4kVQc+dCd8010NdtsCzLgnO5QFVVgfJ4QJ06BbZZM3icThi//x70uXPg\nrr8eXOfOwNmz4Dp3hnf6dCzcswjf/v4tzAZy0uWA33+HZ/8e/P1ke1zfqL1v4onR6GtfqaiAZ+HC\nenlM4aR73yavQ3GxGddeS+HECR2uv57DwYM0GMaXFs3K8v8KkdS41+v1G/wbDVgW2LqVRq9eLE+e\nn39Ow2DgYDJRuO02H3FVVwNFRTrk5zOI9E/j9XpRW1vLWwiKLdPEcz7JMyaMPoXbCMluSH1uoXCI\nKHQDCYfsdntUSnU5QQZ/p0qdXOIIUifWgpsOeS6EfZdDhw7Fl19+mVDZg2jQYCNMYToJ0KaEOVSE\nKSXuSSS4XC44z55FI6MRdN1GTf6B0QjK5YJ36lTQr70G7tdfYbx0Cbpz58C1bu2rfxqNQNu2oA8c\nAHXwIO7pdA+Gt7/ce0lv3AjdV2dAHWmENp5a0Cf2AceOgenTB9SlS2B79JD0ihXW/Ww2DmvW0OjY\n0Y1bbnGia1fgP/+xoLoa6NiRwt69FDIzOd7xjrhDsSwbM1kCvrZTu53CkiV63H+/F6mpQI8eLD74\nQI++fS9Hk+npwKRJkQ+AJrM7iXDK957hz/kURp/kH6K4JaKhQKlbo9HoR55EOJRoaduGBqkIM5R1\n3pWCBk2YUv8ux3WVTslG0wKjag3z/HnQ69aB3r4dnMEA9OsH9vbb4QBgveYa0Ckp4FwucHXtDhRF\ngbLbwTVvDlfLlnA+9xwsR4+CXrECTMuWQIcOPibxLQgwGkH//DMyu3RBZlqm7/ULF2As/Rpci46A\nsQXoPXsAne913c6dYG66CUx2dsilHzyoQ5cuQO/eBgAGnDvHolEjwGr1YPDgGnz7rQnr1+swbBig\n11O+2ZdUiDFiEYCifMKf9et1WLJEj+xsBh9/rEPfvixuvTU28wC32w2n08nb8gVC0DmfdeRqMBj4\nSJFEnGLylBIOkf8mwiFSJ3W5XAB8hyo1hUNarx1qBVoMOJRA8hgHbZkNhAIR97jdbtUs+yJCdTV0\nL74IurwcXEYGYDSCKyxE2rvvIt1igc5kAjNkCLiTJ8HZbKAAXyr2wgXUDhwIp8MBy/nz0Ot04K67\nzjeXSrSBcQzju7YA9JEj4ABf82KzZj4R0dVXg8vMBJeeDu/f/gY0axZy+bfeyqJ3bx8xORzA1q0G\n3H03hUcf1SEry4KhQzmwLINt25x8r6vc0T4hTZOJw9tv63HbbbGTpcvlgtPpjLgUQZSxJpMJVquV\nF8s5nb7PX1NTA6/X62e9ZzQaecIjKl232+1XCyXXJkOQSepRaMdGfJ3FRgsNAVoibvFaGgpZAg04\nwhRCMz2KIojXRZx7dDpdxOIetT4j9eWXoC5e9JEVx8HNsqBat4bh0CHofvoJ7I03grn9dlCpqdBt\n3gy6ogJs69awZ2fDm56Oxm+8AbqiAqAoUDYbqIoKsBkZl50DamsBmgbbrZv/G+v1oADwn9hiAXfD\nDUB1NbhGjXw5zAhhMgHjx1+uEfoMCIwYPpyBzebh63sOh6Ney0asm93Fi8DZsxT0euDAARq33spK\nCoJCgQwN93g8vNNQtJCyTLtc9w2v5xNAnQWfv+qWIJRwSK6ezyRiQzIl28Cg5QiTbBZ2ux0pKSlI\nTU1V7eGM9PNR+/eDS0/31cHcbt8Gq9fDS9PAsWPwXn+97+d69ADTowe8nG+wNTgOjRYtAnX2LHD1\n1QDq/kY1NaD27gXXurVvLUYjvAUF9YZRsh07gktN9Rm5k/4wjgN14QKY0aOj/vzi4JEYAqSnm/18\neoOpbiMlqQsXwNcse/Zk+fQsqWmGC7nrq2KE6vkU3gMhyRLyFKZwxWuTchwi95e0wghHUckFLR6k\nk1APDZYwlaphEsiRQiG/73a7edl2VIOQoWIUfdVV4H76Ce7UVBj0euj0ep95OsfBW1fnE6oua2pq\nfGKTqirQv/wCrnVr4YcAd9NNwPnz8D74IKDXg+vQAZCakmA2w/vww9AvXAhcvMi3pjC9e/uM3mVA\nsBqgVE8iaeonwpZghgkELAt89JHer2ZJaprFxT5VbDjgyEEEiLi+ynHAmTP+LTMAUFXl011J3f5Q\nPZ9C1S05aAhJ01vnK+zxeMIyi5cSDhHVrVzfQzWh5ZQsyRA0BDRYwhRC7hqmXBCO5dKKc0+k98rZ\nuzf0n38OY6NGoFNTAY4DW1kJmM2obt8eeoeDjwocDgcvLqHOngVHUfXqlTAaAa8XXJcul81VA4Dr\n0gWeV18FvW8f4HCAu/Za36BpGSzbXC4X3G53PachKUTitiN+fmgaeOABL4QmKqSmWee5ERLBPGzD\nwdmzwMsvG/DQQ1506uT721dWAq+8YsCwYQzuvDN0TVF4gBAOKiZzPsWRp8vl4jMpJHUbqXAo6TgU\nHxClfkOA+juwiiCbv9zRl/C60YKIewDIQpbCjSceIOk/d1YWMh56CLqlS8FdvOgLVywWcLNmwdKy\nJbxeLy8AIZsgwzDQ1Y36wokTgNkMNGniY4/ff/c5BNXUAI0ahV5IerpsESX5XKTHMpoaYLDUYqDU\nrZTjGEVJvy6GX9QeZSo/MxOYNs2Lf/9bj4ce8qJlSw6vvGJAr15sWGRZf+3+lnrC2iR5RlNSUmA0\nGuv1fAYyixdGn+T+Cv1uhY5DSbP42ECmHxHI7fKjZTRowhRCCcKMFkJxTyIW0zukk0wAACAASURB\nVDmO8zOzp/r1g6dnT7CHD4PT64HrrgNlMICq67kkNTWO43xRh9OJ1LVroT9xArrjxwG93keyej3o\nS5fAdOkCwxNPgO3XD0xenu//k5FfLAsuM9P3mgKfi6Q15fIdDpS6FYtmokktMgyDmpoaPmqPBR07\ncpg2zYv5831R/ejRDLKzI+/7FENIcDRN81kGhmFQXV0dsOdTbBZPSFNMnhRF+QmHIjWLF5ODWtBS\nSlaM2traJGE2JGjpQfR6vbDZbEhNTUVqaiqqqqpkua6cfZjBIlUh2RNS4TgOTEoKuM6d+U0sUKRm\nMBhAffMN9GVl8HbpAm+rVtAdPw79sWOgGQbeUaNANW0KMAx0X3wBzmIB17MndIWFPrsbigJMJjC9\neoE+cgTUL7+Ay8oCe9ddvraUKBFrWjMcxJK6FUPs3iMHWrS4/Px06CBvtoLYCFosFj4tG6jnU2gW\nL+z5FNZApVK34sg2XsKhKw1i8k4SZgOB0inZSEHSckJxj1ZbXqRAbPqESl6y6QGXlY+hIjXd//4H\nNGkCfUoK0LIluKZNwV26BNbrhcflAjwe6GgadMuW0K1fD+7gQXBpaUCbNr4LnDwJ45NPgrvhBnDN\nm4P64Qfovv0W3oICsLfeGvHnYhgGtbXBR3PJjWhStwRS7j2xgtQsR49mcP31LJ+eJTXNaBGszUVI\ncELHIal7IBQOiVO3oczihb8nFg7xzlMqQ+sRZiQ+somMBk2YBEq1gkTys6QpWylxj9JOP1I2fSRl\nFlAJG6CmRl26BGG/BOXxgKYocHo9jAAYmvYRCAD9mTPgmjYFlZkJiuMAigL944++eidN+/o109LA\n1dZCt2wZ2O7dQ4qFhFAiUosUkaRuyYYfyr0nElRVga9ZkjQsqWlOm+ZFx47RPVeReO7K2fMZyCxe\nSjgkJt2kcCgZYSYB+WuYkbxvsOHViRBhOp1OOBwOWCwWv3SXmCyFNTWhoEMMtksX6L75BlzLlr4X\nUlPB0TTgdgMZGfzGxdntQEoKPGYzvHVWajqOQ+rp0+CsVp81D4HZ7GsvOXPGv1UlCJSI1ORAoNSt\n3W4HAL/ISI7N3WgEhg9n/IZok5pmtIGFsCc0mnpwrD2fQuGQ2CxeGN0zDMMfPIjLUFI45I8kYTYw\nyH1iDJfkiBmBsN4nBbkIU+4Ik6RWPR4PP4NT6CEqFFJEQj7siBGgv/0W1KlT4OpmZnLp6aDcbr6H\nEzYb6AsXwNx9Nww//QR9aqpv43S7wVIUuNpasK1bgyLrAHwiozCjROFoLi33mJHIy+PxgKIomEwm\nsCwri2ECgckEP7IkiCWyjLYnVAqx9HyGYxZP0rNC1W0kwiE5oBXxEVA/wkyqZBsIyB9djShOLO4J\n9EWT6wso9xdZKjImJ33y5Sb31e12R5Qm5Fq2hPe553yG7Xv3+mZozp4NUBR0GzaAOnHC9zN//SvY\nbt2gW7oU1MmTQMuWvnRcZibokyfBNWsGhmXh8XqhP3cOaNcOTJMmCEZ/sbaNxBvCSM1qtfJ/ZyVU\nt3Ktl8zdVEo8FUnPZyizePG+kBQO1UeyhtnAEG/Rj5S4Jx5rk/M61dXV/HBXXglbR5ahlLBhvUdW\nFpiCAogbF9i+fQGGAXQ63oCAGT8e9Ndfg969G2BZMHl5oH/4Abpjx6CjaYDjwDRvjpp774Wntrbe\npkk2NqWt4+RGqEhNTtWtHCBKY51OFzd7x2A9nyR1K5y2YqibmCO26xMeAiMVDpH7e6UQqFQNs3Hj\nxiquKH5IEqYCCERy8RD3BFuTHCAnapPJFJMSNmpQVP0eS7MZ7F13gb3rrsvrzMkBdeSIzzEoPR1c\nx44w6fVIDbJpkk1O7aHF4SDSNpdYVLdyrbempiauSmMxhPcg1JxPYU+owWDwEw5JpW4BaeGQ2Cw+\nWuGQllWypNTSEJAkTMQnJRtK3BOPtcV6HbfbDYfDwW/SQHAlrE6nUyztFhIUBe666+r1XkptmuRz\nAZfHSWl5iHGs5KO0YYIYchooyIlgcz6FqlhSpwTqm8WTUWZiAZDUAYVhmIQXDkntIbW1tbCEYzt1\nBaBBE6ZSNUzx9Yi4R5jCjDdieU/SK0eK+06nM2YlrFZAaqykbcTj8URlkh4vKEE+SqZuyXrVbMsJ\nByR1S9M03G43f29D9XwKFbfB7PoI+UYrHNKaUj7ZVpKEbGkPIWESub/JZIo6GlAzwiSpVa/Xi/T0\ndP4asSphw4LX6/OMTUtTxOpOar1SxEE8fUktTK1evHj0hMqZutVCD2skIOs1mUx+6w0WgYuFQ+LU\nbaSOQySylTKL18KBDai/jqRKtoFBqQcxEnFPMMjVDhLN+5LePqvVCpqm4fV6+dqP8AtN2jBkaZhn\nWdCbNvkcf1wuIDUVzLBhYPv1i3nSCEGw0VxA4HqX0+kEwzB+UVdYKTWHA/TOnaB++gnIzARzxx31\nZngGAyF3OQ0JQiGW1K1We1gDQUiW4vXK2fMZjnBIbBZP2rW0AKl1OByOpEq2IUB8epOzsO7xeOB2\nu/n+xGihVoQplUYmzfAGg4FPy5JIhGEY2dow6C++gL64GGyrVr6JzS4XdCtX+kaDDRgQ07VJejnc\n0VxA8E1NLBaRvN7FizA8+yyoU6d8LgBuN3QrVsAzeza4zp1Dvr9WekIDEYcwAifPQ7DDiNYQjCzF\niLbnUxh9kn0mkHBIaBZPrk/uaSzCIaWQbCtpgJCLmEhNjGVZZGRkxEwgxOIrVkTy5SI9osI0Mqm7\nkOZ44LLHKlmfw+GIXWnpdkP3v/+Bbd3aRy4AkJICLisLuk8/BXvnnRFZ2wlBVMqxkruYOIL1+VEU\nBd3y5T6HIeJ1CwDV1TD8619wL1oUMN1MyN3j8ahOlmJIReCk9ktELaTGp2VRS6yReyQ9n9GYxZN7\nTDyaiUhNLeGQVFCRrGE2QMhBmCQqIw+6FjeKUFE08YQVppEDKWFJT53FYvHbLMKKugKhutpngSdO\nYaekAE4nYLcDUfR8KdXmErLPT6dDRnk5uObN/X8xPR2oqAB17Bi4Dh0k1xuuz6raIBE4iYTS0tJ4\nAtWKYYIU5E5zR9rzCdQ3iyftWVJm8ULHoUA9n4Q843mPkynZJCKGUNxDURTcbrcs15Ur8g31BSIb\ntNPp5HtEQylhjUYjH4FSFBVR1BUQVqsvghSTptvtI80o5OvxGM0FBKh7ut1g6+p+NC736vG12AD9\nuqQ+JmsPq0IQRsLCyD1U6lZNNxxSw1Yqco+k5zOUWTypYQrvVSCzeKfTCQABhUOxQurALfTbvdLR\nMD5lAEjVMKOB2Hzc4/FopkgvhtQDL1TCkjRyKE/YYMrHUCftoBtmSgqYgQOhLy31pWXryJOuqIA3\nNzfidKwao7kIaJpGSmoq0LcvUrZtA9OiBViOA+PxgKqpAW02w3v11aAFfxOhdZxaLUiRgBy0GIaR\njIQDEUe8DBOkoDRZSiFYzyfgf4iQcg4CwPsFB+v5FIqzxMIhpe6xlk0V5EaDJkwhoiFMQjRC83Et\nrCvYtcRgWZZPI6enp/PvJ7YDA6JTwoo3TCmRhHjDZIcOhReA7osvfDZ4ej282dlgBw2K6PNqpa2B\nyc8HffAgdL//Djo1FZTbDY6iUDNzJpxuN+B282k6p9PJz3/U+iYkthIMx20olHhK6dStFgRUwgNl\nqEMERVHweDwwmUyg68baBVPdktekhENXuuNQPJAkzDpESkzEbUXKuUduIwSlrsUwDGw2GwwGQ9ie\nsLFuNFJtCuI0lcFgAEaM8FndVVf76n0RNulrqq2hWTN4Xn0V9NdfgzpwAGjZEkyfPjC2bAlDXSQv\ndBsiG6hW6+CAPBNHpMRTgaIuOTZpp9MpOahaTQQ6RAgJjhymSJ0ymFl8MOGQsOeTCIdiNYsX7hMN\nAUnCjAJSRCOE0lGhHNcS1lxT64Y1C5WwYk9YIuaQc6MJS23auLHvJBzBdbUQRdSDxQJ28GBg8GC/\nl8mzQqIIvV4vj3hKQQjTxnLVhCOJuiJ9BsVqY62QpRTId4JYNJJ7EajnU2gWL6xlkmuF6vmMVDiU\njDAbMKKpYUoRjZJQIloVGyoEEvcIxTJK19Ok6p6ENMK1ZxNGwlqKIoIhlNtQ1OIphUAyKyTFrsQa\n5EzdJpLamIB8zrS0NL/SRzQ9n+T3wjWLj0Y4pFW9hhJo0IQpRDjEJBb3xHIttUBO2tEoYeOFUM3h\nQk9PoVgmkUZzAeG5DYVqUxDWupSGWhNHok3dCgVJiaA2BoK3uoTq+RS2rAQyiw8ndSs+rAmFQ+K+\nWo/Ho37JI45o8IRJyC0YyUUq7pE7JSuXoQJJ/QkHPkspYbUiliEIVffU6/XweDwJM5oLiDxtrLba\nVCsTR8JN3ep0On6oc6I8E5H0hYbb8yk2ixfb9RHvWnHqlrSJAf7CIVKy4TgOBw8eRKtWrSLqwZw8\neTLWr1+P5s2bY//+/ZI/M336dHz66acwm81YsmQJunfvHvb1lYb2j+Eqg2VZ2Gw2sCwbsRJWK1Em\n+QwAYDabebIkXx4hWbrdbt65QwtkKQaJNtLS0pCeng6DwcBvjCzLwuVy8RuBFkGiHrfbDYvFEnVd\nkrQpWCwW/j54vV7Y7XbY7XY+spLjPggnjmhpPBdJ3aampsJiscBqtfJtXXa7nRdOkTSllhGLiQI5\nTKWmpsJqtcJisfCHSJvNBrvdzhu7k9YTo9HIHyyEo8eIV3Sg65MDqtfrxaOPPoqbb74Zx44dw7Jl\ny3Dx4sWQa500aRLKysoC/v8NGzbgl19+weHDh/Hee+/hoYceiuheKI0kYdZBKpJjGAbV1dW8m00k\nMyyVXFckIJ9BGHkEU8KSqCcRGpEJQaampiI9PR0mk4nPBthsNjgcDk31xJK0sdz1NBINmM1mWK1W\npKam8sKcWO8DSX2KJ3hoEaQeB/iyEmazWbb7oCTkdhwSHypJZkZ4H0hWyWg08pkkYQmEmCyQgygB\niUTT0tJQXl6OFStWoEmTJli5ciXatWuHP/7xjzh48GDAtd15551oHMSpq6SkBH/+858BALfddhsq\nKytx5syZmO+JXND+rqgwAqVkYxX3CK8rx/qigfgz2Gy2gErYRKv/SYllxPJ5YapOqu4ZT8jRhhEO\nQqVuI5myosaElFhA7jFFUbxITQnVrZxQ+h5Hoj4Wz/kUt62QzJTw2U1LS0OXLl2waNEiOJ1OlJeX\nIysrK+r1VlRUoI3Ac7l169b47bffkJmZGfU15YT2vwVxAiEmoiINR9yjZZAvBPkM5LOR4bi8SYBA\nCZsotZ5wxDLBVJbC2k48Nst4WfOJEY7aNFDLSqh7rDUEa3XRgmGCFIRq2Hi0DIXq+ST3QdibKez5\nJOlaYdtKTU0Nb7yempqKIUOGxLxOcYCgpT1J+9+EOEJoEaelsVxkbeE8OCRadLvd9ZSwRqMRbrcb\ndrud/+IQAo23EjYakMNMJKO5gAD9npWVYH75BdDrQXfsCIPZ7HPiqagAl5YGrlMnWYZWx6MNI1yE\nO2WFNLZrqo81CISDAMK5x/E2TJCCGvZ8YgQa1yalwqZpGi6XiydSwBd1Hj58WNaUaatWrXDy5En+\nv3/77Te0atVKtuvHiiRhCkAk18QiLhbIRZiRrIOcshmGqaeEJa4eJNokKk3Ad9IFoLlpEkKQg0Cs\no7koikLKV18h7f/+DxzDACwLxmSCp3lzUEePgtLpoKMocM2agXn8cXAxfFm1oiyVQqC+V2GDvFgQ\npkXEeiBR0jAhELRAlmIEauUiIkCidUhNTeV/5siRI3j//fcxf/582daRnZ2Nt956C3l5edi+fTsa\nNWqkmXQskCRMUBTFb2yAvKOf5EI49VDiCUvTtKQnrLDHkgy3Jl9Y8WZJNhCtDKkV1v9i/ftQhw9D\n/5//+MZt1ZGY/sABGLduhXfECDBGI7wsC+r8eVCvvgrPvHnQG40Rv6fWWnOCgaTqiLm32WzmVZPi\nBnkt1bfl7guNR+pWi2QpBXIfjEYjvzfSNI3S0lLMnTsX/fr1w5dffonly5eja9euYV93/PjxKC8v\nx/nz59GmTRvMnTuXP7AXFBRg+PDh2LBhA6677jqkpaXhww8/VOTzRQuK05pkLM6ora1FdXU1UlJS\n4Ha70ahRI1muW11dLZuP6aVLl4IOoyZWfUajka/fBFLCEuMCs9lc7wsrFMuQekW8m+PFkLv+p3vv\nPei++QZcixb8a/SWLYDNBq5HD37IM8dx4E6cQM1TT8F19dURkYamfGzDgFD0RdqOhP+PPA9er5e3\nT1M7G0HIkhhrKA1h6laYkYkkdZsoZEkgPKgSERXDMPjss8+wcOFCnDlzBmfPnsXQoUMxcuRI5OXl\naeKArSQafITJsiyf4pNrhiUQP/MCooQ1m838xkG+3AAiUsJKnbBJNKqGslCJ0VzUhQvgRKpnyu0G\nR9NAXYoaqJPP6/UwAUhJT+dJQ+i5KUUaiSiWCTZxhLSsiOtcwmyE0vU+MdRIdceaur0SyBIAzp49\ni1deeQXvvPMOunfvjoqKCmzYsAHbt2/H+PHj1VxyXKD9b7TCICOntNzcHIgwpZSwoTxhI1HCimf4\nEdIQTxZRgjyVSmmyXbpA/+OP4ASZBLZZM9DHjoEVZhc8HoCiwF1zTUDSqKmp8av9kDRmIm6K4Y7n\nCqd1R8kDldBEQa1Ud6SpW2Ivl4jPhZAsT58+jQkTJmDhwoW8+06rVq3w4IMPqrbWeKPBEyZ5GORW\ntcp9PSGESlii5g3mCStHlCYmDSmFpVxpOiVTmmyfPuA+/xxURQW4q67yzds0mcA1aQLK5QLncAAO\nB6jqanjz8nyjxQQI1OcoVBYmglgmWBtGOIilZSVaaIEspRBKdctxHFJTUzVV/w0EsrcA9SPLCRMm\nYMGCBbj11lvVXKKqaPA1TKIG4zgOly5dQuPGjWXZ6GpqaniZe6wQ1kPJRidMJQciy3gIT4QRF7mP\nsYiG4jKa68IF6Nat89UyU1LADhgApls36DZvBr1/P7irrgI7ZAjYHj2AEOsXpjRNJhO/WXq9Xs00\nx4uhdKuL8EBFTDJinbJCnuVEqQsD4J2zjEYj792q1WcCuPwscxznR5YXLlxAXl4e5s2bhzvvvFPl\nVaqLJGHWESYAXLx4UdOEqdPpYLPZoNPp+BRaICWsGrW0WERDxJovkcYwBUpdkf9H7oPH41GtOV6M\neE8cERp3E1u6SIVkiUiW5OAnbIESRuFer1czzwQQuJZ96dIljBs3Di+++CL69eun2vq0ggZPmGSD\nB0KrUSMB6V0ymUwxX8tms0Gv1/On1XCUsFqopZF76/F4gp6ug6k0tYpI1LtihSVJ68a7dUcLfaHC\nAxWJwoOpjxPNng+QJksxAqlu1WjnCkSWlZWVyMvLw7PPPou77rorbuvRMpKEKSDMyspKWK1WWUhG\nGHnEiqqqKp5MwlXCao14SMQlPF0T8nQ4HH7+n1pHLFGaMAqPNuKKBlrsCw3VskIEZolGluSwGu73\nT5yZYRgmLgIq8t5kso2QLKurq5GXl4enn34aQ4cOVez9Ew2J8RQmIOQS/ZCHmYxWCqWEpShKk56w\ngURDTqeTr3Elglgm1ihNKJaJl7OMVlOaoVpWiFgmEZSlQHRkCUQmoJLz+xGILO12O/Lz8/HEE08k\nyVKEJGEKIHfvpHiuXCQQKmHJiTuUElYLfqXhgKIoPoJISUnhZ/dp1WmIQIkoLZzWnViUpomS0hSq\nj10uF5xOJ4xGI3+oilfEFS2cTic8Ho8s9fdgqlu50vmByLKmpgb5+fl45JFHMHLkyJg+x5UI7X6D\n4gThAxcvs4FQ4DgOdrsdHMchPT2dV64FU8Jq0a80EAKN5hKmpqQMoNUkz3gQj1QUHstGmWgmCoB/\n/S+eLSuxQE6yFCOQYQIhu2gOEoHI0uFwYOLEiSgoKMDo0aNl/RxXChp8DZOrG3kF+MQ1ZBp5rCAW\ndBaLJaLfY1m2nhKW9HMZ6zxNxUpYraXagiGSTTxc0ZDSUNulJRqlaVzac2RGOClNJVpWYoGSZBkK\nUqrbUKlboT2mcM1OpxMTJ07Evffe2yAce6JFkjAVIky32w2XywWr1Rr273i9XtjtdqSkpPCpVXIa\nFNb6SAqTPPSJsCHGqt4NtDkoaQgeyntXLQQ7SFAUJbkhahmBNvFwfi/WlpV4r1nJ9YSjxJYieJfL\nhfvuuw9jx47Fvffeq6kyiNbQ4AkTAD/mym6388rHWEGiknSRU0ywnyfDWKWUsMT4mHi7Aj4ZutFo\n1FytTwxSjyUpoFg3l0BRhpz9bInSFyo+SJDPnihpWDnvc6QtK7GsWUtkKUYg1S05YAjbXdxuNyZN\nmoSRI0di8uTJmt5HtIAkYeIyYcppNkDqLeEQptPphMPhCOkJS1x+AJ8HrvBkrYYJdjgI1twv1/XJ\nQUKufjayZo7jNKk4lgJZM8uy0Ol0mkhXhoKwliZ3G1SolpVYxDJaJkspsCwLh8PBH74PHTqErVu3\nYsiQIfh//+//YdCgQSgoKNDc86FFaP8IGgeQ1Ge8RT9kk/N4PCE9YaXszMi/k7SUy+WCw+HQjFBG\n7tFcUgjH2zWSe0EOJVptz5GC8FBC5oUK05VaE1CRNQebkhIrQrWsRHPATJSsgxgejwcsy8JqtfLP\n9a+//oqcnBywLIs2bdpg8+bNuPPOOxNGC6EWkhEmfGkJsunI5c5D1I0ZGRmS/1+ohA3XEzYcJaxU\nWkoNoQzpVyTzCtXYoCMVDSntsaoECMGHOpQIa31qe5oqTZah3jsak4BEJUspIRXDMJg2bRq6d++O\n/v37o7S0FOvWrYPdbsePP/4o69/jp59+Ql5eHv/fR48exQsvvIDp06fL9h7xRJIwcZkwhcbDsYIM\ndZYaSE2UsHq9nk9TBvKEjWVyRyB3HSWFMoA2XWVCiYaUmL2pNKKN4NUQUBEonaKPFMGUpkTkFagN\nQ+uQsuhjGAbTp09Hp06d8OSTT/p9lpqaGqSlpSm2HpZl0apVK+zcuRNt6ga1JxqSKVkBYjUbEF9L\n6iwSSAkr5QlLlLbRKmEDueuIhyDLqf6UfTQXy4I6fRrgOHAtWgBRrlWqGVw4noxlWT6CT4QNMZZo\nONS9UKruqTWyBMIzCSBZH5LuTgQEMn+fOXMmrrvuunpkCUBRsgSAL774Au3bt09YsgSShAlAmRqm\nFIgSNi0tjY+8gnnCihVtsUCoJJXaGORQmcrd+0edOAHdxx+DunjRR5jp6WAmTADXvn1s1xV8XlLX\n0uv1cLvd8Hg8qhijRwI5TdTFzwWp9ZFsi1xisnBTx2pCbBLAMAyfOqYoCg6HQzM14GAgB20xWc6a\nNQtZWVn429/+psr6P/nkE+Tn58f9feVEMiWLy0XxaM0GpMBxvvmaTZo04ZV1DocDVqvVz9VGSgkb\nz1O4cJOMdp6lIvWd6moY5s8HZzQCJK1ts4Gy2eCZNQto2jTmtxCbKKhljB4J4jlxRFz3JPfBYDBE\ndC8SgSzFENdZxapbpVpWYoWUyQbLspg9ezbS0tLw0ksvqXL/3W43WrVqhQMHDqBZs2Zxf3+5kIww\nBVAiwhRKuqNRwioNoco0Gms68cYi1+ZB798PuFxA8+aXX7Ragepq0N9/DzbGcUNS0TBFhWeMHilh\nyIV414bJvUhJSQloTxeKMBJVSCUWJVEUJen5Ky5vqDk8IBBZ/uMf/4DBYMCLL76o2to+/fRT3Hzz\nzQlNlkCSMP0gd1sJ4DNDoCgKVqtVNiWsUghFGOIIQ9EWjAsXAIkaKJeSAurcuagvK+yjC5XuFhqj\nR0sYckH22nCECKfuKSaMeA+rlgPhKHiVaFmJFVJkyXEcXnrpJXg8HixYsEDVSHj58uVXhOVekjBx\nmdzkJEyGYQD4NhryxVNCCaskQhEGy7LQ6/WKpNm4a64BysvrvU45HGDbto3umjFEw8EIQ+kIQ2sT\nRwLVPYWEodPp+IkjchiBxAPRtLsEytCID5lKtu8EIsv58+ejqqoKb731lqpkWVNTgy+++ALvv/++\namuQC+p/+65AeL1e2Gw2UBSluBI2XhASBhEL0TTN13/ljra4jh3BZWaC+u03nzqWokCdOQOucWOw\nXbtGfj1BbTjWaDgQYcg5folAbeP3UJAiDLfbDYfDAQD8f6uVxg4XwrayaJ8PYYYGiM+UFTK5REyW\nCxYswKlTp/Duu++qXmNNS0vD+fPnVV2DXEiKfgC+gTlY72S4ECpha2tr+VSslBI2ERuhxdGwor6u\n1dXQ/e9/oHftAjgObPfuYIYOBRo3jugy8RKdyC0aSsSJI0JRksFg8Hs2tCqUEfdgK/F8CJXpwczR\nIwH5LorJcuHChTh06BAWL16cMM9NoiBJmLisBmRZFlVVVWgc4YYMXCZAp9PJK2Grqqr4DVpNJaxc\nCDWaS+zrKlu0RXpjo9hk1RSdBHIaChVtJaJfKXCZLKVESYoerGKAGt9FoW1htAcrqTQ9x3F49913\n8f3332PJkiVJslQAScLEZcIUtoJEAvKl83q9fuKe6upq6HQ6fqIIkLiqwUhHc0lFW7GaokcKYbRD\nZomqBbGjTKBoK1EzD0S0Fk4dXnywAmI3zI8GWjm4RjplJRBZLl68GF9//TU++ugjTdS6r0QkCRP1\nCbNx48YR2YwRJazQ+FpIFmRagk6ng9vt5k/giUKWcozmEvZ6ErGQkv2NWrTnIxBHW0Q0pNfr4XK5\nYqqjqYFIyFIMsbdrPJ4N8r5aIEsxxP2eYkEZsXAUk+XSpUuxadMmLFu2TFPCwSsNScLEZcIEgIsX\nL4ZNmKTmaTAYgnrCkgiNjBGjaVoT6ahQUGpTiTZVGS60qjqWgjDaInNOjUajpp2GhJBbwRuPmZZa\nJUsxpExFOI7zs3DkOA7Lli3Dhg0bsGLFCs0dDq80JAkTl7+kAHDp0iVkZGSE/HJ6PB7Y7XaYTCZe\nNh+obYSQpdlshk6nU6bOJzPiMZqLvI+cwpBQdVYtQtjPmpKSElNtK55QWZJBkwAAIABJREFUut0l\nVLQVzf1IFLIUg7TtGAwGsCyLhx56CB6PB+3bt8eBAwdQUlKiWv92Q0KSMOFPmJWVlbBarUHrdKTH\nSsoTVizuCVaP0kKdTwpqjeaSSlVGIsNPRFVpsJq2XNZ0SiDeBxM56p6ELCmKShiLPuByylt4r8+c\nOYO3334bJSUlOH36NLp3747s7GyMHj0a1157rexrqKysxJQpU/jxXx988AF69eol+/toHYlxBNcI\nCAG6XK6IPGEDTTkQO+uIja/ViC7UrP2J+xvDNYgXHkzkMquPB0I54QSzplOzRUON3lBhv2c0g8IT\nnSxNJpPfwWTHjh3Yv38/9uzZA4qisGnTJpSUlGD9+vV45JFHZF/Ho48+iuHDh6OwsJD/TjZEJCNM\n+EeYVVVVSEtLq3dqJmkzhmFC2tyRjVCn00X95RRGnvESQmi19idVyxGSp9PpBMuyMJvNCUOWsZio\nBxINxaMmrsUoPtTQ9EQ0fwcCi6nWr1+Pd999F2vWrJFlUEQoVFVVoXv37jh69Kji76V1JCNMwO8L\nJGWPJ1TCpqen+ylhxWQpVyuD2JZOaRNwLW6EBIHsx4SjlxKlRQcI3q8YDuLpNCSEVp8R4XdFWPcU\nWjiSLE4iPSNSZPnZZ59h0aJFcSNLADh27BiaNWuGSZMmYe/evbj55pvxxhtvwGw2x+X9tYTEOI7H\nGULCZBgG1dXV0Ov1fm0jDMPUI0uyaZlMJllrf2RDsFgssFqt/BzH6upq1NTUwO12Rz34mrSNuN1u\nWCwWTW2EUiBpbHIY0ev1MBqNcLvdqK6uRm1tLdxut+xTZ+QCSWeZTCZZUt7kHphMJlitVj6Ccjgc\nsNlscDgcfH08WpCUdyI8I8QY3Ww2w2q18gI8ckghk4O0+nwAlw9UYrLctGkTFixYgNWrVyM9PT1u\n6/F6vfjuu+8wbdo0fPfdd0hLS8O8efPi9v5aQjLCFEFIctEqYZUUQYhNwEnaNpoJGrGYkauJQLU/\nYRqb1PmEqTm1oXTKW1znI6KhQNNmwkGiug6RmiUpiwCIuO6pBgKR5datWzF//nyUlJQgIyMjrmtq\n3bo1WrdujZ49ewIA7r777iRhNmRIpWTJJmOxWPgHl2VZMAwDiqIkPWHjLTghp+lAEzSCkaewlSGR\nmuSD1f6kUnNqjeMSQ412l1hFQ4nqOkSebXEaNpxZp2oeroSpeiFZfvXVV3jhhRdQUlISlW1nrGjR\nogXatGmDn3/+Gddffz2++OILdO7cOe7r0AKSop86EFMBu93O1yYtFktYSlitObOE8u0kG0oi2fMB\n0St4g4lk4pFe1FrtL5z7Ic4+JMozEogsQ/0OuRcej0eRqSKhEKiuvWPHDsyePRtr165Vdfjy3r17\nMWXKFLjdbrRv3x4ffvhh3CNdLSBJmHUgdcCqqipwHMebFwRTwsajsT9WSPWvcRzHzynU6rrFkCud\nKWUQL9wc5b4fpPanFbIUI9AUDVJ2CNQSpUVEQ5ZS15B7qkgoBCLLXbt24amnnsLq1avRokUL2d83\niciRJMw6OJ1O2Gw2sCzLiwZCKWHj3dgfK8icQr1eD5ZlNWOUEApKpTOlpkbIdT8SMZ1J7gfJmggP\nE1qq80lBDrKUumasU0VCgSjwxWS5Z88ezJw5E8XFxcjKyor5fZKQB0nChO+Lce7cOb/IxWQySYp7\nSKSjRVPvYBCTTiCXIS1tjqSWHK8ITS6DeGE6M5F6Q8W2ccK6p9achoSI1wSgQB7I0dY9CVmK6/H7\n9+/H9OnTUVRUhNatW8v5EZKIEUnCrIPD4eCb4BmG4YUjwi9CvJSwciJc0hGepONllBAMakdoscyy\nTOTaX6ASg9yev3JBrXF54vsRad0zEFkeOHAA06ZNw8qVK9G2bVsFP0ES0SBJmHUgROFwOOByuZCa\nmsqThXDzJgbqiQCyeUc6mktMFvGOLLRGOpHMskxEY+9InXC0IJIBQlsLxguBRHaB6uJk3aSkQ3Do\n0CEUFBTgk08+Qfv27eP9MZIIA0nCrIPb7YbX65VMyzEMAwAJU4sC5Nu8pchCSfm9sN1Fi6Qj3BzF\nZEGyFFoWgYlBxGvR1v6kRDJKiqiE69YCWYoRqu5Jnm8xWR4+fBhTpkzBsmXL0KFDBxU/QRLBkCTM\nOuzevRsdOnTw+5IThSMAP0GIVmp8gaCUglfY26hEWk6t9Fq0EJMnIQuj0ajpOacEct/vQCIquevi\nWiVLKYgnzgDgnZnId+bo0aOYNGkS/vvf/6Jjx45qLjeJEEgSJnzp2KlTp2Lfvn244447kJOTA7PZ\njAkTJqCoqAjXXXedH1loWQARLwVvoEgrWvIk6sxE2ASFEJKOXq+PaztCLIgH6Sgxnky4buK+lQgg\nNUuSsl62bBlWrlyJ/v37o6SkBMuXL2+wZgCJhCRhCuDxeLB161YsWLAAW7ZswdixY5Gfn49evXr5\n1WYCpSnVJk+1RnPFagwQy+QONRFo3VpXIAcSnCj9nsEmioR7jUQlS/G6a2trsXr1arz33ns4evQo\nsrKykJOTg5ycHNxyyy2KPCNt27ZFeno6f7jduXOn7O9xpSNJmCIsXrwYs2fPxvLly0FRFAoLC7Fj\nxw706NEDubm5uOOOO/wUslJpSjUstrQymiuQMUCg0VOBRhhpHZEcTrSkQNbC4SQa0VAgoYzWESiS\nP3XqFPLz87Fo0SJ07doVO3bswNq1a7F//36sX79ekeeiXbt22L17N5o0aSL7tRsKkoQpwJEjRzBy\n5EisXbsW119/Pf86wzDYvn07CgsLsW3bNtx0003IyclBnz59/DZLtchTa9ZrBFIuQ0JjAI/HE3d/\nVTkQC8nLEWlFi1jHiimBcERDiUqWHMfBbrfXqxGfPn0a+fn5ePPNN3HrrbfGbT3t2rXDrl270LRp\n07i955WGJGGKQDaxQGBZFrt27UJhYSG2bNmCjh07Ijc3F/3796+XlpOzxicFtXsVI4E4TUnGkZHN\nW+00ZbggkbwcJC8+YClpEK9Wuj4SBFKYer1e3soxURDIeejcuXPIy8vD66+/jttvvz2ua7r22muR\nkZEBnU6HgoICPPjgg3F9/ysBScKMASzLYu/evVi1ahU2bdqEdu3aITc3F3fddRc/UghQhjwT2U2G\nGCkYDAZ+rqjaRgnhQMmJI0oaxCdq2tvj8aC2tpZ/HhLhGQECk+WFCxcwbtw4zJ8/H3feeWfc13Xq\n1Cm0bNkS586dw6BBg7Bw4UJV1pHISBKmTOA4Dj/88AMKCwvx+eefo1WrVsjJycGQIUOQlpbm93Pi\njTFS8tR6r2IgBCJ5KaMErW2M8Ux7y2kQn6hkKRYmqZnKjgSByPLSpUsYN24cXnjhBfTv31/lVQJz\n586FxWLB448/rvZSEgpJwlQAHMfhp59+QmFhIcrKytCsWTPk5ORg6NChfpPSA9VvgkUVidarSBCu\nkUKgjVEtBTKJiNUaoByLQbyc6eN4IpQwSStOQ1LrknJMqqysRF5eHubMmYNBgwapsrba2lowDAOr\n1YqamhoMHjwYzz33HAYPHqzKehIVScJUGBzH4ciRIygqKsL69euRkZGB7OxsjBgxAo0aNfL7uVDq\nUi0oHKNBpNZrBPF2GRJDazViqXaVQGnKRCfLcGutajkNSa1D6hmvrq5GXl4ennrqKQwbNiwua5HC\nsWPHMHr0aAC+rMOECRPwzDPPqLaeREWSMOMIjuNw/PhxFBUVYd26dTCbzRg1ahRGjhyJJk2a+A2m\nFpMnTdPwer0wmUyaFW1IQa6IOFBUoZT5t9b8bKUQyPOXRMVaU02HQqwqXuH3xuv1xm18HcmeUBTl\nR5Z2ux15eXmYMWMGRo0apch7JxFfJAlTJXAch4qKChQXF6OkpAQ0TWPUqFEYNWoUmjVr5keedrvd\nbyanlh1khBBGxHIqYQPVgeVKySWiiTqJxl0uF1iWhU6ng9Fo1FyNLxDkbnkRRuOxjmsL9T5SZFlT\nU4Px48dj2rRpGDNmjCzvlYT6SBKmBsBxHM6cOYPVq1djzZo18Hg8PHl+8MEH+OGHH/Dxxx+DpmlJ\nBxktkme82hgiNUoI53rRpI+1AOH4OWE6W+loPFbEoz9UCdFQoIOVw+FAfn4+pkyZgrFjx8r2GZJQ\nH0nC1Bg4jsP58+dRVFSEefPmgaZpTJ48GXfffTfatGnjF3lqlTzVch2KVV2qlGl9POB0OiWFSYFG\nT0V7oJAbapgpyCEaCkSWTqcTEydOxMSJE5Gfn6/YZ0hCHSQJU4Ow2+0YN24cWJbFO++8g82bN6O4\nuBiVlZUYOnQocnJy0LZtW7/NTkgUanqXKtmrGAkiPVAksvo4XBWv1IFCzfS+FpyHojlQBCJLl8uF\nP//5z7j77rtx7733JswzlET4SBKmBvHss8+ioqIC77zzjl+EVlVVhXXr1qG4uBhnz57FoEGDkJOT\ngw4dOvh9OcWOOvFq+NaqRR9Q/0AhNaMw0Uy9Y1Hxqm0QT8hSS/2h4YiGiBiM4zg/snS73Zg0aRJG\njhyJyZMnX5FkuXfvXphMJj/b0IaGJGFqEB6PJ+SmZbPZsGHDBhQWFqKiogIDBgxAbm4uOnXqFJA8\nlTIF0Fr7RSiI7wng87hNpDQsuecMw8ii4o2nQbwWyVKMQKIhYukovOcejwdTpkzBgAEDMHXq1IR5\nhiKBw+HAE088gYqKCrzyyisNljSThHkFoLa2FmVlZSgsLMTRo0fRr18/jB49Gp07d/YjLyXIM1Et\n+gCfMIlElizLgmEY1Y0SwoHSLS/i50TO/tdEdR4i81oJYe7ZswdHjx7F0KFD8fe//x29e/fGX//6\nV80+M7Hg+PHjmDdvHhYsWICXX34ZP//8M5577jl06tRJ7aXFHUnCvMLgdDrx2WefoaioCAcPHkSf\nPn2Qm5uLbt261SPPWGd6JmL7BYHUxq22UUI4CJQSVApS94Tcl0jvSaKSpfiAAgDbtm3D22+/jU2b\nNiEzMxMPP/wwRo8ejXbt2qm8WmXw8MMPw2KxYP78+Xj88cdRUVGBOXPm4MYbb0yo732sSBLmFQy3\n241NmzZh1apV2L9/P3r37o2cnBz07NkzZvJMZEVpOC448ZwkEi7UPqDE0v+ayGQplfpmGAbTp09H\n+/bt0a1bN6xduxYlJSW47rrrsG3bNsX+NgzD4JZbbkHr1q2xbt06Rd5DCJZlQdM0bDYb5s+fj4kT\nJ6Jjx4549NFHcerUKTz77LPo3LlzQn3/Y0GSMBsIvF4vysvLsWrVKuzevRu33XYbsrOzcfvtt/tt\nduHM9Aw0FDcRQFS8kQiT5DDMjxWBGuTVQjAfZLG69EojS5ZlMWPGDFxzzTWYPXu2H4kePnwYHTt2\nVGxNr7/+Onbv3g2bzYaSkhLF3ofjOL+/ocPhwJtvvgmPx4O///3vAIBZs2Zhz549WLBgATp37qzY\nWrSEJGE2QDAMgy+//BJFRUXYvn07evTogdzcXNxxxx1+EZfUWDK9Xg+Xy4XU1NSE8rMF5FHxBmtD\nUEoZrHUzhWDqUpLOvJLIctasWbjqqqswd+7cuP4tfvvtN9x///2YPXs2Xn/9dcUiTCFZrlq1Cunp\n6RgyZAjOnz+PMWPGYOLEifjLX/4CAHj++ecxZcoUZGVlKbIWraHBEObkyZOxfv16NG/eHPv37wcA\nXLx4EePGjcPx48fRtm1brFy50s8QvSGAYRhs374dhYWF2LZtG2666Sbk5OSgT58+fr1xZANxu90A\noIkUZbhQauKI3C5DUiCpb/G4KK1C3K5C1KXEpk/r6weCk+Xs2bNhNpvx8ssvx/2zjB07Fn/7299Q\nXV2NV199VXHC3LlzJ1588UWsX78ea9euxciRI/H9999j+fLlmDZtGtq2bcv/DkndXum48j9hHSZN\nmoSysjK/1+bNm4dBgwbh559/xsCBAzFv3jyVVqcedDod7rjjDixYsAA7duxAQUEBtm3bhsGDB2Pq\n1KkoKyuDy+VCSUkJJk+eDLPZjPT0dKSkpIBhGNjtdtjtdt7DVGsgm58S47lI47/JZILVauWHhtfW\n1sJms8HhcPARVzQgqe9EIUvAd09IJoLjOJhMJj4rUV1djdraWrjd7qjvidIghyuv1+tXJ2ZZFnPn\nzoVer8dLL70U979FaWkpmjdvju7duyt+7yiKwtq1a3HffffhkUcewZw5czB+/HgUFxeje/fuAIBf\nfvkFAPjvfEMgS6ABRZgA8Ouvv2LUqFF8hNmxY0eUl5cjMzMTp0+fRr9+/XDo0CGVV6kNsCyLffv2\nYdWqVVi5ciUuXbqE2bNn47777uOJAVAnRRku1Jo4IodtYSLXiUnNUiyqSgQVstThiuM4vPTSS7Db\n7fjXv/6lylr/9re/4b///S/0ej2cTieqq6vxpz/9CUuXLlXk/V555RUYDAbMmDEDALBmzRpMmDAB\nn376KSwWC8aNG4cvv/wSmZmZCfVsxgptPKUq4cyZM8jMzAQAZGZm4syZMyqvSDugaRpdu3ZFWloa\nvF4vPvzwQ1RWVmLUqFG47777UFxcjJqaGp4gzWYzH2WRmpvNZuNTW/E+lxGRDMdxcR/PRaKs1NRU\nWK1WfvN1Op2w2Wyora3liVQKQrJMlMiSwOPxSJIl4HumjEYj0tLSkJ6eDoPBAK/XC5vNpoksRSCy\nfOWVV3Dp0iXVyBIAXn75ZZw8eRLHjh3DJ598ggEDBihGlgCg1+uxe/du/r9zc3MxbNgw5OXlQa/X\nY/369WjRokVCPZtyoEETphBkdFYSl/H+++9jxYoV+OqrrzBq1CjMnTsXX331FV588UX8+uuvGD16\nNCZMmICVK1eiurpaMkVJyNNut8eNPMl7UhSlif5QnU6HlJQUWCwWWCwW6HS6gClKkuY2Go0JZdMH\nRDa0mqIoGI1GyRS/GgetQGS5YMECVFRU4O2339ZMFAxA8Wd62rRp2LdvHx544AFUV1ejpKQEV111\nFQoKClBWVpZ0+mkIkErJbtmyBS1atMCpU6fQv3//ZEpWgJqaGng8noBCKI7jcOTIERQVFWH9+vXI\nyMhAdnY2RowY4fc7YhUlAMVMvxNJJCNOUep0OjAMk5AK5EjIMhhinTgTDVwuF9xudz2yXLhwIQ4d\nOoTFixerXl5QCkJFLPl3j8cDg8EAt9uN3NxcNGvWDLt27cKaNWtQWlqKo0ePYuHChSqvXB00aMKc\nNWsWmjZtiqeeegrz5s1DZWVlgxT+yAGO43D8+HEUFRWhtLQUJpMJo0aNwsiRI9GkSZO4jCVL9Lof\naR0hytJoHXXiDbnIUgxCnuRQocQIu0Bk+e677+K7777DkiVLVJ26oySIsvXw4cMwGo3IyMjgD7pu\ntxtGo5HP1jAMgy1btuC5557DmjVr/BSyDQkNhjDHjx+P8vJynD9/HpmZmXj++eeRk5ODe+65BydO\nnGiwbSVKgOM4VFRUoLi4GCUlJaBpmh+I3axZM0XIkxh6p6SkJFx0Jm7sl+p/1WoLj1JkKQVhlkIO\ng3jSl2uxWPzIcvHixfj666/x0UcfXbFkyTAMdDoddu3ahXvuuQft2rVDr169MHz4cNxxxx0AwAuz\nAODcuXNYvHgxxowZ02DTsUADIswk1AHHcThz5gxWr16NNWvWwOPxYOTIkcjJyaknGoh2pqcW5ipG\ni1AuOFIuQ8J0tpogZKnGOLdABvHheiEHIsulS5di48aNWL58eUIZLUSDgwcP4o033sD999+PJk2a\n8Gp40octhpBAGyqShJlE3MBxHM6fP481a9ZgzZo1qK2txbBhw5CdnY02bdpENdMzUW3XgMijs3gY\nJYQLNclSjEgN4ok9opgsly1bhvXr12PlypUJd/AKB7W1tXj33XcxY8YMsCyLmTNnYvHixTh27Biu\nuuoq/PjjjygpKUFFRQVyc3Nx1113qb1kzSFJmEmohosXL6KkpATFxcWorKzE0KFDkZOTg7Zt24ZF\nnsSUoCGQpRhS5KmUkEqMaPx444VA6WwiGgq09hUrVqC4uBirVq1KOHVyuHA4HPj888/Rv39/WK1W\nOJ1OjB07Fm63G+vXr4der8eBAwdQWFiInJwcdO3aVe0law5JwowzpCz6Vq1ahX/84x84dOgQvv32\nW/To0UPlVcYfVVVVKC0tRVFREc6cOYPBgwcjJycHHTp0kCRPt9sNlmX5lo1EsV0D5CccJYVUYmiZ\nLMUQG8ST18gBi9yX4uJifPzxxyguLvYz5biSQGqWAJCdnQ2DwYCioiK43W5MnToVZ8+exapVq2Ay\nmWCz2WC1WlVesTaRJMw448svv4TFYsF9993HE+ahQ4dA0zQKCgrw2muvNUjCFMJms2HDhg0oLCxE\nRUUFBgwYgNzcXHTq1AkURWHJkiXo1q0bOnfuzE9XiXamZ7wRD8KRWxxDkEhkKYbL5YLT6eTNEqZO\nnYqsrCxcc801+OKLL7B27VqYzWa1l6kIxJNHXC4XRowYgaysLCxduhRerxcPPPAAjhw5gi1btoCm\nac2Jy7SCJGGqAHF7C0H//v2ThClCbW0tysrKUFhYiKNHjyIrKwu7du3C2rVrccMNN/A/J8dAbKUh\nx7SUSCEWx5B7Eil5JjJZitfOcRz27t2LDz74ACUlJaAoCrm5uRg9ejQGDBigWP3S6XSib9++fCtL\nTk4O/vnPfyryXgQ//vgjf7AU9lgyDIPBgwejVatWWLp0KRiGwY4dO9C7d29F15PoSB4jktA0zGYz\nxowZg48//hh33nkndu3ahf79+6OgoADPPvssvvvuO76fTGy75vF4UF1djZqaGj6FqxaEqsx4Eg5N\n07zLkNVq5et4wvsS6sycyGTp8XjqrZ2iKJw7dw5HjhzBL7/8gq+++godOnTA888/j9mzZyu2ltTU\nVGzevBl79uzBvn37sHnzZmzbtk2x91u9ejV69eqFr776ChRF8al6YpKxceNGnDx5EiNHjoROp+PJ\nMhlDBUbD1ggnkRBgWRbTpk3Dd999h++//x5NmzaF2+3Gpk2bsGTJEuzduxe9e/dGbm4uevbsyZMn\nabwmUafD4Yh7T6NwtJhQlakGCHmmpKT4ReQOhyOgEXqik6WUknfz5s14/fXXsXbtWqSnpyM9PR1P\nPPEEnnjiCcUPVSTt63a7wTAMmjRposj7MAyD0aNH4+WXX8bUqVPx1ltvoW/fvuA4Dnq9ns/CbN68\nGaWlpX6/q5WMjBaRjDCT0DxomsYtt9yCjRs3omnTpgAAo9GIoUOH4j//+Q+++eYbZGdnY8WKFejf\nvz9mzZqFbdu2gWGYgJ6l8TD8VnK0WKwIxwjd6XQmPFmazWa/tW/duhXz5s3D6tWrJU1KlP4bsSyL\nbt26ITMzE/3798eNN94o+3uQCPLMmTM4cuQImjRpgiFDhqCsrIyPNAlpAsDIkSMBJCPLcKCdb3AS\nAJIPbSBMmTIloHJPr9dj4MCBeOedd7B9+3aMHTsW69atw4ABAzBjxgyUl5f7jR6Lx0xP8RBiLZGl\nGFKHCrfbDZfLBYqi4PV6wTCM2ssMG4Fadr766iu88MILWL16tWKRXSjQNI09e/bgt99+w9atW7Fl\nyxbZ34PMH83Pz0eLFi1QXl6Ot956C/fffz9KS0v9SFOIZGQZGrp//OMf/1B7EQ0J48ePx7PPPouT\nJ0/ivffeQ6NGjXD8+HF+kHVRURE2btyIiRMnqr3UhARN02jbti2GDx+OBx54AE2bNkVpaSlefPFF\n7N69G0ajEa1bt+b78gwGA4xGI2iahtfr5SNClmVBUVRURCeew6llshSDEKTX64XFYoFerwfDMHA6\nnXy9k0z20eIGS1LvYrLcsWMH5syZgzVr1qBZs2YqrtCH1NRUnDp1CufOnZNNaPPaa6+Boii0adMG\ner2enzJ09dVXo0ePHtDpdJg8eTJ69OjhJ5hLInwkVbJJNAiwLItdu3ahsLAQW7ZsQceOHZGTk4MB\nAwb4ec8Gc9MJJy2p1tBquSBlGQeoM0UkUgQaXL1r1y489dRTWL16NVq0aKHa+s6fPw+9Xo9GjRrB\n4XBgyJAheO655zBw4MCYr+3xeHDs2DF06NABn3zyCcaPH4+ZM2eCpmm8+uqrAIDff/8dY8eOxYQJ\nEzBt2rSY37MhIkmYSTQ4sCyLffv2YdWqVdi4cSPatWvHW4EJG9cjtaIjQ6sBaGIOZ6SQmtwhhXga\nJYSLQGS5Z88ezJw5E8XFxcjKyor7uoTYv38//vznP4NlWbAsi3vvvRdPPvlkzNcVerzu3r0bjz32\nGCZNmoS8vDyMGDECN9xwA7p3746PPvoIkydPxqRJk2J+z4aKJGEm0aDBcRx++OEHFBYW4vPPP0dW\nVhZyc3MxZMgQpKWl+f1csAgLQEKTZbTipEDkGY5pvlwIRJb79+/HI488gqKiIrRp00bxdagBQpZu\ntxtbt27FgAEDsHXrVrzxxhvIzs5Gfn4+3nvvPVy4cAEmkwlPPfUUgMujvZKIDEnCbKCQsuh78skn\nUVpaCqPRiPbt2+PDDz9ERkaGyiuNHziOw08//YTCwkKUlZWhWbNmyMnJwdChQ5Genu73c2KSAHz1\nU7PZnHAbkZxK3nBN8+VCIPP9AwcO4KGHHsKqVauu+NmNDMNg+PDhuOGGG/Dmm2/C5XJhx44dWLhw\nIf74xz/i0UcfrffziaZ61gqShNlAIWXR9/nnn2PgwIGgaRpPP/00ADTYgdocx+HIkSMoKirC+vXr\nkZGRgezsbIwYMcKvHaGqqgoA/KZeqJ2ejARKtr3EOoIrFAKR5aFDh1BQUIBPPvkE7du3j/l9tI6n\nn34adrsdb731Fv+ay+XCzp078fzzz2PmzJkYNmwYgPo2eUlEhiRhNmAEsugDfC4hRUVF+Oijj1RY\nmbbAcRyOHz+OoqIilJaWIjU1FdnZ2ejduzfuvfdePPLII8jPzwdFUZIzPbVInkJDhXgoeQNZF4qN\nEsIFmYEqJsvDhw9jypQp+Pjjj6/YQcdi0nvqqafQvXt35OXloaYJHVBIAAAgAElEQVSmBmlpabwF\n3rFjx9CuXTsVV3tlIbFyR0nEDR988AGGDx+u9jI0AYqi0LZtWzz++OPYtGkT3n//fZw/fx59+/ZF\ny5Yt4XA4cO7cOXAcB51Oh9TUVFitVp6InE4nbDYbamtr/VK4aiHeZAlIGyV4PJ6oDCQCkeXRo0cx\nZcoULF269IolS2LGIUTnzp3x+uuv4+DBg3zdPTc3F1u3buXJUk1byCsJSWu8JOrhpZdegtFoRH5+\nvtpL0RwoigLLsli6dCmeeOIJPPjggyguLkZBQQE8Hg9GjhyJnJwctGjRAjqdjh8/RtKTLpernhVd\nPCNPNchSDGKUQKwLSeTpcrlA03RQ68JAZHnixAlMmjQJH374ITp16hTPjxM3CGuPzzzzDCiKwtCh\nQzFy5EjU1tYiLy8PBQUF+PTTT9G6dWv06dOH/91Eq6trFcmUbAOGVEp2yZIleP/997Fx48YrdpBu\nrBg5ciTuuusuPPbYY/xrHMfhwoULWLNmDVavXo3a2loMGzYM2dnZaNOmTcCB2LFMEIkUxH3I6/Vq\n0lBBSJ5CZyaS0iZkmZqa6jdRpKKiAhMmTMD777/fIIYe33PPPbjuuuuQlpaGjz76CI899hiys7Ox\ne/du/Pzzz9Dr9Zg+fTqApMBHbiQJswFDTJhlZWV4/PHHUV5ejquuukrl1WkXTqcz5GHi4sWLKCkp\nQXFxMSorKzFkyBDk5OSgXbt29cgzHmPJtE6WYojbeMhrxDye3JtTp04hPz8fixYtahBj8YqLi7Fz\n507MmzcPOTk5MJlMcLlc6NOnD+6//340btyY/9kkWcqPJGE2UIwfPx7l5eU4f/48MjMzMXfuXPzz\nn/+E2+3mfTZvv/12/Pvf/1Z5pYmPqqoqlJaWoqioCGfPnsWgQYOQk5ODDh06xIU8xb62WhIfhQOv\n14uamhro9XqwLIslS5bg119/5efHvvnmm7jtttvUXmbcUFNTg0WLFuHcuXOYP38+nnnmGZSWluK1\n117D4MGD1V7eFY0kYSaRRBxht9uxYcMGFBYW4rfffsOAAQOQm5uLTp06+REZGUsWq6o00cmSZVnY\n7XY+DctxHA4dOoSPPvoIq1atgtPpxD333IMxY8agb9++fnVNOXHy5Encd999OHv2LCiKwl/+8hc+\n7akUxGpYodnA3LlzcfToUfzf//0fHn30UTRq1Ahz585VdD1JJAkzriATH0iaJNkT1bBRW1uLsrIy\nFBYW4ujRo+jXrx9yc3Pxhz/8oZ6PKyFOj8cT9kzPK4UsSRqW4MKFCxg3bhzmzZuHFi1a8C1QTqcT\n+/btU2Qtp0+fxunTp9GtWzfY7XbcfPPNWLNmjWICI2E61eFw8JaNZM84duwYHnjgAdTU1KBFixZY\nu3YtgKSDj9JIEmYc4HA4YDQaJesJp06dQsuWLVVYVRJagsvlwmeffYbCwkIcPHgQffr0QW5uLrp1\n61aPPAlxBiPPK5UsL126hHHjxuGFF15A//79/X7HZrMFHAEnN3Jzc/HII4/IYpwuhpD0Zs6ciTvu\nuAMjRoyoVze32WzYtWsXfx+SNUvlkSTMOKCsrAxz5sxBy5Yt8de//hUDBw6ETqfD999/j1GjRuHX\nX3+tN5suiYYLt9uNTZs2obCwEHv37kXv3r2Rm5uLnj17BiRPr9fLt2SQeYiJOjGFZVnU1NTAaDT6\nkWVVVRXGjRuHOXPmYNCgQaqt79dff0Xfvn3x448/wmKxKPY+Tz/9NPbv34+SkpJ6RCjOTiXJMj5I\nxu5xQO/evbFlyxbMnj0bH3/8MU6ePAnAp3gbPnw49Ho938zOMExCDeuNFZMnT0ZmZia6dOnCvzZn\nzhx07doV3bp1w8CBA/n71VBgNBoxdOhQ/Oc//8E333yD7OxsrFixAv3798esWbOwbds2voGdDMS2\nWq1+A7E9Hg8vkkkkBCLL6upqjB8/Hs8884yqZGm323H33XfjjTfekJ0sd+7cyVstXrx4EQcPHsTC\nhQuh0+ngdrsB+DIRQP1hz0myjA+ShKkwdu/ejYcffhhDhgzB//73P3zxxRc8OZaWlmL06NEAfK0K\ntbW1fLM7AcdxqjvDKIlJkyahrKzM77VZs2Zh79692LNnD3Jzcxu0mEGv12PgwIF45513sH37dowd\nOxbr1q3DgAEDMGPGDJSXl/M9ixRFYcWKFaAoCmazGRzHoaamBjabjU/PahmELA0Ggx9Z2u125Ofn\n4/HHH+c9UdWAx+PBn/70J0ycOBG5ubmyXvvChQvYvn07UlJSYLfb0aRJE3Ach0uXLgEA33e6ceNG\nVFdXy/reSYSPJGEqiMrKSixYsAAdO3ZEWVkZjh8/jrZt26J58+b46aefUFtbi4EDB+L333/HU/+/\nvXuPi7pKHzj+YRgcENBVBBLBRMzEEKSyNMB7qQmiRqKgqOulsPBCW2imoSlavszcvFR4SVNLV+EF\nFpGSVGReWe8iXoBArEhAGUAuM5zfH+x8FxTbX7vCcDnvf4JxnO+Z8Msz55znPE9EBD4+Pvj5+bFv\n3z6qqqpqdbevqqpq9L/w/hs+Pj61zo4BtfahiouL5ZnQfzE1NWXAgAF8+OGHHD16lJCQEKVg/qxZ\nsxg/fjy7d+9W9jQtLCywtrbGwsKizuDZmD6I1QyWNffqSkpKCA4O5tVXX8XPz89o4xNCMG3aNHr2\n7FmrYMWDcPr0aWxsbJg9ezZHjx7ltddeo6SkhMGDBxMQEMDVq1cpLS1l4cKFfPjhh/W6DCz9Mblx\nVo8sLCwoKipi4MCBWFlZYWpqSp8+fbC0tGTXrl0MGTIErVbLBx98QEFBAampqcTExPDzzz+jUqm4\nffs2SUlJ9O/fH1tbW+V1V61aRe/evRk4cGC9pdEb28KFC/nss89o3bo1R48eNfZwGh1TU1O8vLzw\n8vKioqICX19fsrKy6NChA/PmzcPf35/Bgwej0WiU3pTm5uZKMYCSkpJ7enoaa6/zfsHyzp07TJw4\nkRkzZjB27FijjM3g8OHD7NixA3d3dzw9PQFYsWIFw4cP/59e99atW7z33nu0a9eOdevWYWVlhYWF\nBcuXL2fZsmXo9XqmTJmCg4MDpaWl7Nu3D5VKJTPsjUQm/dSjqqoq3nrrLeLj4/Hw8GDv3r1s376d\nwMBA+vbty/Lly6mqquLQoUNMnDiRxx57TPm7iYmJ7Nmzh99++42cnBzGjh1LZGQkWq2WGTNmEBAQ\nQEBAgDJLaMo3zx91TVm5ciXp6els3brVCCNr/CorKwkODkar1RIbG0urVq04e/Ys//jHP/j2229x\ndnZm9OjRDB06VDmaAPdv/NzQnVXuXoY1XLesrIxJkyYRFBREcHBwg4zFGPR6PWfPnuWjjz7CysqK\nVatWcenSJTZt2oRarSYyMlL5gGNjY4NGo1HO5UoNTy7J1iOVSkVUVBTnz58nLCyMtWvX4u3tzZUr\nV0hPT8fLywtLS0tOnDhBjx49gH9v6i9duhRPT0+++uorYmNjycjIoLCwkJMnT6LRaCgpKeHXX39V\nlmwNDEu5zUVQUBAnTpww9jAarYKCAmxsbIiNjcXc3ByVSkXv3r1Zvnw5R44cYeHChaSlpeHn50dI\nSAgxMTHK7NLQWcXKykrJpr1z5w5arZY7d+6g0+nq9d+SYZlYrVbXCpbl5eVMmTKFcePGNdsGAIZk\nLFNTU9zd3QkLC6OwsJB58+bx6KOPMnPmTExMTJg9ezZFRUU4ODgoRfxlsDQeGTDrUc19x759+/Ly\nyy/TqVMnzMzMiIqKwtzcnHbt2qFWq7l58yYAGo2G27dvk56eTlhYGDqdDhcXF44cOUJxcTFpaWlc\nu3aNU6dO0bdvX+UTqIFKpcLExKRJB80rV64oX8fFxSlLYNK97O3t2bhxY521bU1MTHBzcyMyMpLD\nhw+zbNkysrKyGDNmDMHBwezevZuioqJawdPQlqy+g2fNYGlubq4Ey4qKCqZNm8aoUaMICQlp0isn\n91PznOX169fRarW4ubmxYMECiouLmTt3Li4uLkyePBlXV1dsbGyUvyuLEhiXXJJtIFVVVffMBqG6\nTub69evZsGEDLi4urFy5krKyMhYvXqxkj545cwZfX19ycnKYNGkS7u7uvP766xQVFfH8888TExOD\nnZ0dBw4c4OrVqwwaNKhWBZLVq1djaWnJyy+/3KDv+f+jrpq2CQkJpKenY2pqiouLCxs3bsTOzs7Y\nQ202hBBcu3aNffv2kZCQQJs2bRg1ahQjR47kL3/5S63n6vV65aynYXbzv3ZWMQRLQ5A2vE5lZSXT\np09n0KBBhIaGNstgWdPq1atJTEzEyckJFxcXFi5cSGZmJu+++y5arZatW7cqpRBlBZ/GQQZMI7jf\nIePU1FQcHR2xt7dn5syZtG/fnkGDBrFt2zY8PDx44YUXWLp0KXPnzuXxxx/nxIkTjBkzhuvXr7N1\n61a2b9+Oh4cHycnJLFmyhNGjR6PX6xk6dCihoaGMGzdO2f+QB50lqA5e2dnZ7Nu3j/3792Nubs6o\nUaPw9fWlffv2921L9t8Gz/sFS51Ox0svvUS/fv0ICwtr9sFy8+bNbNu2jZiYGF5//XW++eYb/P39\n2bhxIxkZGWzatImIiAjatm1r7KFKNciAaWSG5Iu7g9f58+fZsmUL586d45VXXmH06NHK7HPmzJk4\nODgwc+ZM2rRpw6xZs1i5ciXDhw9n7NixJCcnExkZyffff8+5c+eYOHEiqampSoEEExMTLl26xNtv\nv82iRYtwc3Mz0ruXGhMhBLm5ucTExLB//35MTEzw8/PDz88PW1vb/7mnpyFYqlQqLCwslOfp9Xpm\nzZpF7969CQ8Pb5bBsmZWa2VlJV999RVeXl7s3LmT5ORk/v73vzN8+HC8vLzYtGmTMqOUM8vGRe4e\nG5lh/+hubm5uvP/++8r3lZWV5OXl4enpqSxPJiQk8PXXX3P27Fns7OyUFkfZ2dk8+uijAHz99de4\nubmhVquV2WVVVRUZGRmUlpbKYCkpTExMcHR0ZPbs2YSFhZGXl0dMTAwvvfQSlZWV+Pr64u/vz0MP\nPYRKpVLqvBqCZ0VFBaWlpXW2JfujYDlnzhwee+yxFhMsTU1NGT16NAUFBaSkpLBixQoefvhhBg4c\nyPnz58nPz1f2LWWwbFzkT6ORqpkwZEj5f//99wkICECtVpOWlsZvv/1Gr169sLa25tSpU3Tq1Amo\nXtp1c3OjsLCQ77//XqkmZFBWVsbJkycpKSnh3Xff5ciRIw3+/hqjusr0GaxevRqVSkVBQYERRtbw\nTExMsLe3JzQ0VDni1KZNG2bPns3IkSNZt24d2dnZCCGU4GlpaYm1tTVmZmZUVlZSVFRESUkJ5eXl\ndQbLqqoqwsPDcXZ2JiIiolkGS/j3ka+PPvqICRMm8Oqrr3LmzBnat2+PmZkZ//znP1m9ejU3b94k\nPj4eGxubJlfSsKWQS7JNRM2lGcP+Y25uLp06dSI7O5vQ0FDc3d1xcnLigw8+IDk5Wal7aTiKYvik\nm5OTg4+PDy+++CJdu3YlOjqaLVu24OzsTEFBAc7OzrU+FbeUZaGUlBSsrKwICQmpdSY0JyeHGTNm\nkJ6eTmpqqtJgu6UqLCwkLi6O2NhYCgsLGTZsGP7+/jg7O9/T07OiooLy8nKEEKjVaq5cuYKDgwMd\nOnTgjTfeoEOHDixZsqRZBsua91Bubi6TJ09m3rx5XLx4kc8//5wvvvhC2T821I3t1auXLErQmAmp\nSaqqqhJCCKHT6YQQQmRmZor58+eLxYsXi9OnTwshhIiKihKBgYG1nldVVSUSEhLEkCFDRGVlpRBC\niJCQELFp0yaRmZkp/P39xYULF4QQQty5c+cPr90cZWZmCjc3t1qPBQQEiDNnzoguXbqI/Px8I42s\ncbp165bYsWOHGDNmjPDy8hKRkZHi1KlTori4WBQUFIiXX35ZpKenC61WKwoLC0V4eLiwtrYW7u7u\n4rnnnhM3btxokHFOnTpV2NnZ3fOzrS8175GUlBSxYcMG8d577ymPrV27Vjz55JPi1KlTQoh/32uG\n+1RqnOQeZhNl+ARq2P/s0qULK1asqPWcnJwcfH19AZTEosrKSpKTk3n00UeV85+enp4UFhbi6OjI\nxYsXlf3P9evXk52dzeLFi2udBWtJn37j4uJwdHTE3d3d2ENplNq2bUtwcDDBwcEUFxeTkJBAVFQU\n2dnZmJiYYG1tja2tLSqVilatWimzyevXr6NSqejZsyfu7u5KCbz6MnXqVMLCwggJCam3a9Tl4MGD\nvPLKKzg6OlJZWYm3tzdPPfUUs2fPVjKDDx06pFRhkpnrjZsMmM2EocJPzRtuw4YNyteG6iDZ2dnE\nxsYyefJkANLS0sjMzMTb25tbt27x7LPPcvPmTdLS0oiJiSE6OrpWsExMTKRbt25069atgd6Z8ZSW\nlhIVFcXBgweVx4TcwbgvKysrxo0bx9ixYxk3bhzXr1/H1taWESNGMGDAAMaMGUN8fDzl5eXs2rUL\nlUpFWVkZSUlJZGVl1evYfHx86v0aNZmYmJCSksLatWtJTEyka9eu/O1vf2PPnj0IIXj66acJDw8n\nJCQES0vLBhuX9L+RAbOZuHuP0fCL3TAbNPxXo9EwYsQIjhw5wrRp08jMzMTX15dBgwbRoUMHVCoV\nH3/8MVqtlsDAQHr27KkkIKhUKi5dusSXX37JunXr0Ov15OXlYW9vX+v6hl6NTX3f89q1a2RlZeHh\n4QFUV2V54oknOH78uCykcB86nY5JkyZRVlZGSkoKGo2G8vJyDhw4wIoVK9BqtSQlJSn/NszNzZVV\nkOYmJyeHhIQEJkyYQNeuXVm0aBErVqxgy5YtCCHw8vKiQ4cOcs+yKTHmerBkPDdv3hQfffSR2Llz\nZ63H+/XrJ7p06SLi4+OFVqsVQlTvxxj2VubNmyfmz58vhBAiKSlJTJs2TURHRwshhLhw4YIoKyu7\n51qrVq1qMnszde1hGsg9zP+ssrJSvPfee/fd/zamP/rZ1pfo6Gjh6uoqEhIShBBCFBUViYiICHHt\n2rUGHYf0YMiA2cLo9Xqh1+trPWZIUMjLyxMzZswQwcHBtR6vaf78+WLbtm1CCCGGDBkitmzZIn7/\n/XcRGRkphg0bJpycnMRbb70lbt++LYQQIi4uTrRp00YIUZ3QcPe1G5Px48eLjh07ilatWglHR0ex\nZcuWWn/u7OwsA2YTZoyAKYQQO3fuFL179xZ79+4VQgjlHmjOyXPNlVySbWEMS2HiXxWGDMXaP/vs\nMw4cOICDgwPh4eEAtSoQiX8tGz311FMcOHCAVq1aIYRg6tSpfPrpp6SmprJz505sbGwYPHgwvr6+\nPP3003z66afK692d0NDYjqt8/vnnf/jnGRkZDTQSqTkJCgpCCMHixYvx8fFRcgLkMmzT03h+W0kN\nylBhyMTEBJ1Ox+XLl7GysmLBggVKq7GaAc6wj5mdnc22bdu4cOECy5Yto7S0lLS0NMaPH4+NjQ1l\nZWW0bduWkpISAJKTk5k6dSo5OTksXLiQ33//XXnNu/c9Jam+TJgwgWeeeYbLly/j5OTU4P1Vg4OD\nOXToEHZ2djITtgmTM0wJtVrNO++8o3wv6khCMNzk7dq1Q6PRMGDAAPr164dOp+Pq1as88cQTAFy8\neJHu3btjZmbGjz/+iI2NDZ07d2bv3r0cP34cW1tboPrIio+Pj3Jcw/D69+vqIkn/i/+0etAQ7O3t\ngbrvL6lpkDNM6R5/dDOHhIRw8+ZNhg4dClQH20ceeYSLFy8ihGDVqlVUVVXh4+PD5s2bGTduHADH\njx/H29sbqD7KkpKSQm5uLvn5+fj5+fHTTz/xyy+/KEvEUB08ZYkwqbmRwbLpkgFT+lMMZflqBjJD\n2binnnoKe3t7XnvtNaC68PuUKVMoLCzk6tWrDB48GKgOnvb29jzzzDN8+eWXnDx5km+//Za+ffvy\n9ttvU1xcrBxsr7lsK6qT1Br2DRtRXbVtIyMjcXR0xNPTE09PT6VnqiRJ9U8uyUp/imHptGYgc3Fx\nUZa8SktLad26NT/88ANFRUV0796d8+fPc+7cOXx8fIDqhtidO3embdu2fPbZZ8ydO5eIiAi8vb1Z\nsGABlpaWfPLJJwwYMIDVq1crTY1rdr4Q/yr63ZzVVZ3GxMSE8PBwJZFKkqSG07x/40gNomZnldat\nW6PX6+nfvz9nz55VnmNra8snn3zCtm3b2L9/P3369KGgoIBr164RGhoKQF5eHhYWFkyaNImrV69y\n/vx5MjMzger+oNu3b+fixYvNoijC/4ePjw/t2rW75/GWNMuWpMak+f/WkeqdSqWqlfln+Lpbt25U\nVVXh5ubGmjVrSEtL45tvvuHJJ5/E09OTPXv20KtXL9q0aUN+fj6ZmZkMHDiQjh07cvv2bTIzM3F1\ndeWHH35g8+bN3Lhxg+nTp/PGG29QVlZ2zzh27drFmjVrGux9G8uHH36Ih4cH06ZN49atW8YejiS1\nGDJgSvXKMBN8+umnWbNmjRLUrK2t+e677xg+fDgAmZmZ3LhxQ8ma/eKLL+jfvz+3bt1i7dq1JCQk\nMGjQIOLj48nOzub27dv3XCswMJDx48cD1bMwvV7f7JKGQkNDyczM5PTp03Ts2FHZL5Ykqf7JPUyp\nQdQsDv/QQw8B1UHRsJSbl5endE4BiI2NZeLEiVy5cgVHR0eGDBnCBx98wMmTJ7G2tiY3Nxd7e3ul\n+EFeXh6BgYF8/fXX6HQ61Gp1szzvVrOG7fTp0/Hz8zPiaCSpZZEBU2oQdRWHNxRPAHj++ed54okn\nsLe3p7S0lGPHjhEdHU1lZSUnTpxg7dq1zJo1C71ez6VLl3BwcACqs3ZVKhX79u3D1NQUc3NzduzY\nQXR0NBMnTsTNzY1+/fo1+PutL7/88gsdO3YEqj9U1MyglSSpfsklWcko6jqLZm9vj16vp3Xr1pw5\ncwYnJyc6deqEp6cns2fPJiUlheLiYlxdXZVkGEMg3rt3L0FBQQDs2LEDjUaDhYUFISEhtdqc3a0x\nn/U0VKdJT0/HycmJLVu2EBERgbu7Ox4eHnz//fctYs9WkhoLEyFT7qRGyrDceuPGDT7++GMOHjyI\np6cnUVFRtG3bVpml3rp1C09PT44dO4ZKpaJ///7ExMTQo0cPoqKi0Ol0vPnmm6jVauU1CwsL68xA\nlRqnxMRE5s6di16vZ/r06URERBh7SFILJJdkpUbLMHt0cHBgyZIlLFmyBK1Wi7W1NVC9HKtWq9m1\naxeurq7Y2dmxd+9e7Ozs6NGjB1qtFpVKhZmZ2T1LwitXrqS0tBQ7Ozt69+6t7AUagnBdDbkl49Dr\n9bz66qskJSXRqVMn+vTpw6hRo3B1dTX20KQWRi7JSo2e4ZynEEIJllBdlg/gwoULjBw5EoDdu3fz\n3HPPAdWZt7///jsPP/wwKpVK2e8sKSnh+vXrpKWl0b59e5YtW8bu3btr1fi8+6iMZDzHjx+nW7du\ndOnSBTMzM8aPH09cXJyxhyW1QHKGKTV6/6lIwfr166mqqqK8vByNRsPYsWMBOHfuHDqdDg8PD+Df\n7coOHz6MRqMhIiKCZ599lkceeYQlS5YQGBhIdnY269atIy8vD19fX1544YVa+61y5tnwcnNzcXJy\nUr53dHTk2LFjRhyR1FLJGabUpNUsk6fRaNixYwc9evRQup5YW1vTvXt34N8FFU6ePEm7du2UJb3T\np08zYMAATp8+zdKlS3n88ccJDQ0lNjZWqdX666+/otPpas08DWc9W0oaQF21baG6kIKrqytubm71\nsrcoi5VLjYWcYUpN2t2zP0NrMJVKRVBQEBUVFZiZmSnJPqWlpVy4cIGCggIcHR0BOHDgABEREWza\ntInTp09z5swZ5s6di6mpKT/99BPDhw9n+fLlnDlzhocffpigoCBGjBhR61hMS2jZVFdt2+TkZOLj\n4zl79ixmZma1+p0+KJ06dSInJ0f5PicnR/nZSVJDkjNMqdmo2RrMMPNs1apVreccP34ctVrNQw89\nxIsvvkh4eDgFBQV4e3tTUVHBihUr2Lp1K5cuXSIjI4POnTuTlZVFUlISw4cPx9/fn4qKCs6dO8ec\nOXP45JNPKC4uxsTERJlp1txzbU7qqm27ceNGFixYgJmZGYDS7/RBevLJJ7ly5QpZWVlUVFSwe/du\nRo0a9cCvI0n/iQyYUrN0dxNqw9cJCQl07tyZpUuXMnLkSLp168b+/fuxsLCgW7duJCYm4ubmxjvv\nvMOPP/7IxIkTuXTpEs7OzkRERBAQEEBWVhZvvvkmXbt25dtvvyUiIoJffvkFExMT7ty5oyzb1rx+\ncwueBleuXOGHH36gb9++DBw4kJMnTz7wa6jVatatW8ewYcPo2bMngYGBMkNWMgq5JCu1CIbg5e3t\njZmZGU5OTkyZMqXWc4YOHUp4eDj+/v706dOHCRMmYGtrS2pqKu7u7piamnLq1CmSk5OZP38+Xl5e\nzJkzh/z8fGxsbPjiiy/Ys2cPpaWlvPjii0yePFnp6WmYgRr6iTaX5VudTkdhYSFHjx7lxIkTjBs3\njoyMjAd+nREjRjBixIgH/rqS9GfIGabUoowaNUr5xWuoY2vw+OOPk5SUxJQpU/j555+pqKigoKCA\nlJQUpfn1tWvXcHBwwNXVlfLycvR6PTY2NsTGxrJo0SJiYmKIiori2LFj3LhxA5VKRXx8vLJsq1ar\nlWCp1WrRarUN+z/gAXN0dFSykvv06YNKpSI/P9/Io5Kk+iEDptRi3X00pKqqCrVazZgxY4iOjsbV\n1RVLS0uGDh3KoEGDAOjVqxfZ2dkIIdBoNJiamnLnzh3i4+MRQuDj48PmzZspKCjgyy+/5OrVq8yZ\nM4e3336boUOHsmHDBqUUX1paGnFxcZSUlDT4e39QRo8ezaFDhwC4fPkyFRUV2NjYGHlUklQ/ZMCU\npH8xnPesmbBja2vL66+/riS1PPLII3Tt2pWAgADWrl1LVg3eB0UAAAHmSURBVFYWFhYWHDlyhKNH\njyp7oGq1moEDB/Ldd9/RvXt3AgICCA8PJzExkfz8fE6cOMG8efM4cuQIlpaWRnvPf4ahtu3ly5dx\ncnJi69at/PWvfyUjI4NevXoxYcIEtm/fbuxhSlK9kbVkJekPGI6j3O2bb77hxx9/ZMCAAQwePJip\nU6cSFBTEsGHDaj1v5MiRvPTSS4wcORJTU1OGDBnCokWLaNWqFSEhIej1evz8/Jg/f77SgUWSpMZJ\nBkxJ+hPud94yKSmJiIgINBoNzz33HFOnTqW0tJTw8HDWr19P165dyc3NpX///ly8eJGUlBT2799P\nZGQkZWVltGnTpsnMNCWppZJZspL0JxiCZc0iCVCdYZuamsrhw4e5fPkyDg4OrFq1Cjs7O6Xpc1xc\nHB4eHmg0Go4ePYqVlZXsmCJJTYgMmJL0X7h7mdZwXMTLywsvLy8AAgMDKSkpoXXr1gBs2rSJOXPm\ncPv2ba5fv46/vz9QfTTDUEhekqTGS96lkvQAGDJua+55uri4KH+u0+kIDAzkhRdeQKPRkJ6eTkVF\nBYAMlpLURMg9TEmqJ3Xtdwoh0Ol0rFmzhoMHDxIWFibLvElSEyEDpiQ1AMNtdncANbQkkySp8ZMB\nU5IamKFEnlyKlaSm5f8A5/W2rh+LSFkAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZFLfDZEkSZKkxkKFnA6RpEbn/wFnNYMMVynu2gAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x107c42940>"
+        "<matplotlib.figure.Figure at 0x106e96b00>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -720,7 +720,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -764,7 +764,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -813,7 +813,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 9
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
@@ -849,13 +849,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgghhBBCCCGEEEIIIYSoIf4fkAk01RhtGrsAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x107d70e80>"
+        "<matplotlib.figure.Figure at 0x106fe0c50>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -941,13 +941,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAH4CAYAAACbjOPoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nR5G8l9dhbL8AAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAH4CAYAAACbjOPoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x107d70908>"
+        "<matplotlib.figure.Figure at 0x106fe0780>"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -993,14 +993,14 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 27,
+       "prompt_number": 13,
        "text": [
         "array([0.36, 0.21, 0.10, 0.08, 0.06, 0.05, 0.04, 0.03, 0.02, 0.02, 0.01,\n",
         "       0.01, 0.01])"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -1090,7 +1090,7 @@
      "input": [
       "from sklearn.lda import LDA\n",
       "sklearn_lda = LDA(n_components=2)\n",
-      "sklearn_transf = sklearn_lda.fit_transform(X_train, y_train)\n",
+      "transf_lda = sklearn_lda.fit_transform(X_train, y_train)\n",
       "\n",
       "plt.figure(figsize=(10,8))\n",
       "\n",
@@ -1098,8 +1098,8 @@
       "        range(1,4),('x', 'o', '^'),('blue', 'red', 'green')):\n",
       "\n",
       "\n",
-      "    plt.scatter(x=sklearn_transf[:,0][y_train == label],\n",
-      "                y=sklearn_transf[:,1][y_train == label], \n",
+      "    plt.scatter(x=transf_lda[:,0][y_train == label],\n",
+      "                y=transf_lda[:,1][y_train == label], \n",
       "                marker=marker, \n",
       "                color=color,\n",
       "                alpha=0.7, \n",
@@ -1120,13 +1120,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH4CAYAAAD+YRGXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNCIiIiKF+X/RNt9EvCI3jgAAAABJRU5ErkJggg==\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH4CAYAAAD+YRGXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x107d5c3c8>"
+        "<matplotlib.figure.Figure at 0x107d32e80>"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 14
     },
     {
      "cell_type": "heading",
@@ -1229,7 +1229,7 @@
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -1243,10 +1243,10 @@
      "collapsed": false,
      "input": [
       "from sklearn import metrics\n",
-      "pred_train = lda_clf.predict(X_train)\n",
+      "pred_train_lda = lda_clf.predict(X_train)\n",
       "\n",
       "print('Prediction accuracy for the training dataset')\n",
-      "print('{:.2%}'.format(metrics.accuracy_score(y_train, pred_train)))"
+      "print('{:.2%}'.format(metrics.accuracy_score(y_train, pred_train_lda)))"
      ],
      "language": "python",
      "metadata": {},
@@ -1260,7 +1260,7 @@
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -1273,10 +1273,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "pred_test = lda_clf.predict(X_test)\n",
+      "pred_test_lda = lda_clf.predict(X_test)\n",
       "\n",
       "print('Prediction accuracy for the test dataset')\n",
-      "print('{:.2%}'.format(metrics.accuracy_score(y_test, pred_test)))"
+      "print('{:.2%}'.format(metrics.accuracy_score(y_test, pred_test_lda)))"
      ],
      "language": "python",
      "metadata": {},
@@ -1290,7 +1290,7 @@
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -1362,7 +1362,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -1414,20 +1414,20 @@
      "input": [
       "from sklearn.linear_model import SGDClassifier\n",
       "\n",
-      "sdg_clf = SGDClassifier()\n",
-      "sdg_clf.fit(X_train, y_train)\n",
+      "sgd_clf = SGDClassifier()\n",
+      "sgd_clf.fit(X_train, y_train)\n",
       "\n",
-      "pred_train = sdg_clf.predict(X_train)\n",
-      "pred_test = sdg_clf.predict(X_test)\n",
+      "pred_train_sgd = sgd_clf.predict(X_train)\n",
+      "pred_test_sgd = sgd_clf.predict(X_test)\n",
       "\n",
       "print('\\nPrediction accuracy for the training dataset')\n",
-      "print('{:.2%}\\n'.format(metrics.accuracy_score(y_train, pred_train)))\n",
+      "print('{:.2%}\\n'.format(metrics.accuracy_score(y_train, pred_train_sgd)))\n",
       "\n",
       "print('Prediction accuracy for the test dataset')\n",
-      "print('{:.2%}\\n'.format(metrics.accuracy_score(y_test, pred_test)))\n",
+      "print('{:.2%}\\n'.format(metrics.accuracy_score(y_test, pred_test_sgd)))\n",
       "\n",
       "print('Confusion Matrix of the SGD-classifier')\n",
-      "print(metrics.confusion_matrix(y_test, sdg_clf.predict(X_test)))"
+      "print(metrics.confusion_matrix(y_test, sgd_clf.predict(X_test)))"
      ],
      "language": "python",
      "metadata": {},
@@ -1450,7 +1450,7 @@
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -1459,6 +1459,44 @@
       "Quite impressively, we achieved a 100% prediction accuracy on the test dataset without any additional efforts of tweaking any parameters and settings."
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Decision Regions"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sgd_clf2 = SGDClassifier()\n",
+      "sgd_clf2.fit(X_train[:, :2], y_train)\n",
+      "\n",
+      "x_min = X_test[:, 0].min()  \n",
+      "x_max = X_test[:, 0].max() \n",
+      "y_min = X_test[:, 1].min() \n",
+      "y_max = X_test[:, 1].max() \n",
+      "\n",
+      "step = 0.01\n",
+      "X, Y = np.meshgrid(np.arange(x_min, x_max, step), np.arange(y_min, y_max, step))\n",
+      "\n",
+      "Z = sgd_clf2.predict(np.c_[X.ravel(), Y.ravel()])\n",
+      "Z = Z.reshape(X.shape)\n",
+      "\n",
+      "# Plots decision regions\n",
+      "plt.contourf(X, Y, Z)\n",
+      "\n",
+      "\n",
+      "# Plots samples from training data set\n",
+      "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train)\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -1501,7 +1539,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The in-built [`pickle`](https://docs.python.org/3.4/library/pickle.html) module is a convenient tool in Python's standard library to save Python objects in byte format. This allows us, for example, to save our NumPy arrays and load them in a later or different Python session to continue working with our data, e.g., to train a classifier."
+      "The in-built [`pickle`](https://docs.python.org/3.4/library/pickle.html) module is a convenient tool in Python's standard library to save Python objects in byte format. This allows us, for example, to save our NumPy arrays and classifiers so that we can load them in a later or different Python session to continue working with our data, e.g., to train a classifier."
      ]
     },
     {
@@ -1514,12 +1552,16 @@
       "\n",
       "pickle_out = open('standardized_data.pkl', 'wb')\n",
       "pickle.dump([X_train, X_test, y_train, y_test], pickle_out)\n",
+      "pickle_out.close()\n",
+      "\n",
+      "pickle_out = open('classifiers.pkl', 'wb')\n",
+      "pickle.dump([lda_clf, sgd_clf], pickle_out)\n",
       "pickle_out.close()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 19
+     "prompt_number": 24
     },
     {
      "cell_type": "code",
@@ -1529,12 +1571,30 @@
       "\n",
       "my_object_file = open('standardized_data.pkl', 'rb')\n",
       "X_train, X_test, y_train, y_test = pickle.load(my_object_file)\n",
-      "my_object_file.close()"
+      "my_object_file.close()\n",
+      "\n",
+      "my_object_file = open('classifiers.pkl', 'rb')\n",
+      "lda_clf, sgd_clf = pickle.load(my_object_file)\n",
+      "my_object_file.close()\n",
+      "\n",
+      "print('Confusion Matrix of the SGD-classifier')\n",
+      "print(metrics.confusion_matrix(y_test, sgd_clf.predict(X_test)))"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Confusion Matrix of the SGD-classifier\n",
+        "[[14  0  0]\n",
+        " [ 0 18  0]\n",
+        " [ 0  0 22]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",

From 132a2e97adf8ea7f55a51c76b8365f201b0ed0db Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 27 Jun 2014 11:31:13 -0400
Subject: [PATCH 125/248] ipynb tut 1st commit

---
 README.md                            |   1 +
 tutorials/awesome_things_ipynb.ipynb | 873 +++++++++++++++++++++++++++
 2 files changed, 874 insertions(+)
 create mode 100644 tutorials/awesome_things_ipynb.ipynb

diff --git a/README.md b/README.md
index a4bb604..c128a20 100755
--- a/README.md
+++ b/README.md
@@ -40,6 +40,7 @@
 
 - Entry point: Data - using sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb?create=1)]
 
+- Awesome things that you can do in IPython Notebooks (in progress) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb)]
 
 <br>
 
diff --git a/tutorials/awesome_things_ipynb.ipynb b/tutorials/awesome_things_ipynb.ipynb
new file mode 100644
index 0000000..c4a0e80
--- /dev/null
+++ b/tutorials/awesome_things_ipynb.ipynb
@@ -0,0 +1,873 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:68e419b336c43b3a5f99d948a5148ad6a7da83f9796fdc45c9132c236a5a43bc"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "\n",
+      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/awesome_things_ipynb?create=1) \n",
+      "\n",
+      "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/awesome_things_ipynb.ipynb)  \n",
+      "\n",
+      "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference/)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import time\n",
+      "import platform\n",
+      "print('Last updated: %s' %time.strftime('%d/%m/%Y'))\n",
+      "print('Created using Python', platform.python_version())"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Last updated: 27/06/2014\n",
+        "Created using Python 3.4.1\n"
+       ]
+      }
+     ],
+     "prompt_number": 37
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Awesome things that you can do in IPython Notebooks (in progress)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Writing local files"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file hello.py\n",
+      "def func_inside_script(x, y):\n",
+      "    return x + y\n",
+      "print('Hello World')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Writing hello.py\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Running Python scripts"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "We can run Python scripts in IPython via the %run magic command. For example, the Python script that we created in the [Writing local files](#Writing-local-files) section."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%run hello.py"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Hello World\n"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "func_inside_script(1, 2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 16,
+       "text": [
+        "3"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Benchmarking"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%timeit [x**2 for x in range(100)] "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "10000 loops, best of 3: 38.8 \u00b5s per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%timeit -r 5 -n 100 [x**2 for x in range(100)] "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "100 loops, best of 5: 39 \u00b5s per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Using system shell commands"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "By prepending a \"`!`\" we can conveniently execute most of the system shell commands, below are just a few examples."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "my_dir = 'new_dir'\n",
+      "!mkdir $my_dir\n",
+      "!pwd\n",
+      "!touch $my_dir'/some.txt'\n",
+      "!ls -l './new_dir'\n",
+      "!ls -l $my_dir | wc -l"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "/Users/sebastian/Desktop\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "total 0\r\n",
+        "-rw-r--r--  1 sebastian  staff  0 Jun 27 10:11 some.txt\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "       2\r\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Debugging"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%debug \n",
+      "def some_func():\n",
+      "    var = 'hello world'\n",
+      "    for i in range(5):\n",
+      "        print(i)\n",
+      "    i / 0\n",
+      "    return 'finished'"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "> \u001b[0;32m<ipython-input-1-3d5f00f75cf4>\u001b[0m(5)\u001b[0;36msome_func\u001b[0;34m()\u001b[0m\n",
+        "\u001b[0;32m      4 \u001b[0;31m        \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0m\u001b[0;32m----> 5 \u001b[0;31m    \u001b[0mi\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0m\u001b[0;32m      6 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0;34m'finished'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0m\n"
+       ]
+      },
+      {
+       "name": "stdout",
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "ipdb> var\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "'hello world'\n"
+       ]
+      }
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Inline Plotting with matplotlib"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 33
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "from matplotlib import pyplot as plt\n",
+      "import math\n",
+      "\n",
+      "def pdf(x, mu=0, sigma=1):\n",
+      "    \"\"\"Calculates the normal distribution's probability density \n",
+      "        function (PDF).  \n",
+      "        \n",
+      "    \"\"\"\n",
+      "    term1 = 1.0 / ( math.sqrt(2*np.pi) * sigma )\n",
+      "    term2 = np.exp( -0.5 * ( (x-mu)/sigma )**2 )\n",
+      "    return term1 * term2\n",
+      "\n",
+      "\n",
+      "x = np.arange(0, 100, 0.05)\n",
+      "\n",
+      "pdf1 = pdf(x, mu=5, sigma=2.5**0.5)\n",
+      "pdf2 = pdf(x, mu=10, sigma=6**0.5)\n",
+      "\n",
+      "plt.plot(x, pdf1)\n",
+      "plt.plot(x, pdf2)\n",
+      "plt.title('Probability Density Functions')\n",
+      "plt.ylabel('p(x)')\n",
+      "plt.xlabel('random variable x')\n",
+      "plt.legend(['pdf1 ~ N($\\mu=5$, $\\sigma=2.5$)', 'pdf2 ~ N($\\mu=10$, $\\sigma=6$)'], loc='upper right')\n",
+      "plt.ylim([0,0.5])\n",
+      "plt.xlim([0,20])\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEZCAYAAACXRVJOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLCAYY4wB4C+6RXPdrA827gAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x107e67080>"
+       ]
+      }
+     ],
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "C-extensions via the Cython magic"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Cython (see [Cython's C-extensions for Python](http://cython.org)) is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.\n",
+      "Since we are working in an IPython notebook here, we can make use of the very convenient IPython magic: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.\n",
+      "Also, we are adding C type declarations; those type declarations are not necessary for using Cython, however, it will improve the performance of our code significantly."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext cythonmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%cython\n",
+      "import numpy as np\n",
+      "cimport numpy as np\n",
+      "cimport cython\n",
+      "@cython.boundscheck(False) \n",
+      "@cython.wraparound(False)\n",
+      "@cython.cdivision(True)\n",
+      "cpdef cython_lstsqr(x_ary, y_ary):\n",
+      "    \"\"\" Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    cdef double x_avg, y_avg, var_x, cov_xy,\\\n",
+      "         slope, y_interc, temp\n",
+      "    cdef double[:] x = x_ary # memoryview\n",
+      "    cdef double[:] y = y_ary\n",
+      "    cdef unsigned long N, i\n",
+      "    \n",
+      "    N = x.shape[0]\n",
+      "    x_avg = 0\n",
+      "    y_avg = 0\n",
+      "    for i in range(N):\n",
+      "        x_avg += x[i]\n",
+      "        y_avg += y[i]\n",
+      "    x_avg = x_avg/N\n",
+      "    y_avg = y_avg/N\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    for i in range(N):\n",
+      "        temp = (x[i] - x_avg)\n",
+      "        var_x += temp**2\n",
+      "        cov_xy += temp*(y[i] - y_avg)\n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "    return (slope, y_interc)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "building '_cython_magic_cf6c91cb1e11de8d2dbe7d9178e469df' extension\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "C compiler: /usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/sebastian/miniconda3/envs/py34/include -arch x86_64\n",
+        "\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "compile options: '-I/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include -I/Users/sebastian/miniconda3/envs/py34/include/python3.4m -c'\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "clang: /Users/sebastian/.ipython/cython/_cython_magic_cf6c91cb1e11de8d2dbe7d9178e469df.c\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "/usr/bin/clang -bundle -undefined dynamic_lookup -L/Users/sebastian/miniconda3/envs/py34/lib -arch x86_64 /Users/sebastian/.ipython/cython/Users/sebastian/.ipython/cython/_cython_magic_cf6c91cb1e11de8d2dbe7d9178e469df.o -L/Users/sebastian/miniconda3/envs/py34/lib -o /Users/sebastian/.ipython/cython/_cython_magic_cf6c91cb1e11de8d2dbe7d9178e469df.so\n"
+       ]
+      }
+     ],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "x_ary = np.array([x_i*np.random.randint(8,12)/10 for x_i in range(100)])\n",
+      "y_ary = np.array([y_i*np.random.randint(10,14)/10 for y_i in range(100)])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cython_lstsqr(x_ary, y_ary)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 32,
+       "text": [
+        "(1.1399825800539194, 2.0824398156005444)"
+       ]
+      }
+     ],
+     "prompt_number": 32
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Running Fortran Code"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "There is also a convenient IPython magic command for compiling Fortran code. The Fortran magic uses NumPy's [F2PY](http://www.f2py.com) module for compiling and running the Fortran code. For more information, please see the ['Fortran magic's documentation'](http://nbviewer.ipython.org/github/mgaitan/fortran_magic/blob/master/documentation.ipynb)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%install_ext https://raw.github.com/mgaitan/fortran_magic/master/fortranmagic.py"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Installed fortranmagic.py. To use it, type:\n",
+        "  %load_ext fortranmagic\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext fortranmagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "javascript": [
+        "$.getScript(\"https://raw.github.com/marijnh/CodeMirror/master/mode/fortran/fortran.js\", function () {\n",
+        "IPython.config.cell_magic_highlight['magic_fortran'] = {'reg':[/^%%fortran/]};});\n"
+       ],
+       "metadata": {},
+       "output_type": "display_data"
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%fortran\n",
+      "SUBROUTINE fortran_lstsqr(ary_x, ary_y, slope, y_interc)\n",
+      "    ! Computes the least-squares solution to a linear matrix equation. \"\"\"\n",
+      "    IMPLICIT NONE\n",
+      "    REAL(8), INTENT(in), DIMENSION(:) :: ary_x, ary_y\n",
+      "    REAL(8), INTENT(out) :: slope, y_interc\n",
+      "    REAL(8) :: x_avg, y_avg, var_x, cov_xy, temp\n",
+      "    INTEGER(8) :: N, i\n",
+      "    \n",
+      "    N = SIZE(ary_x)\n",
+      "\n",
+      "    x_avg = SUM(ary_x) / N\n",
+      "    y_avg = SUM(ary_y) / N\n",
+      "    var_x = 0\n",
+      "    cov_xy = 0\n",
+      "    \n",
+      "    DO i = 1, N\n",
+      "        temp = ary_x(i) - x_avg\n",
+      "        var_x = var_x + temp**2\n",
+      "        cov_xy = cov_xy + (temp*(ary_y(i) - y_avg))\n",
+      "    END DO\n",
+      "    \n",
+      "    slope = cov_xy / var_x\n",
+      "    y_interc = y_avg - slope*x_avg\n",
+      "\n",
+      "END SUBROUTINE fortran_lstsqr"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\tBuilding module \"_fortran_magic_a044885f2b0c0feac78a230b6b714e2b\"...\n",
+        "\t\tConstructing wrapper function \"fortran_lstsqr\"...\n",
+        "\t\t  slope,y_interc = fortran_lstsqr(ary_x,ary_y)\n",
+        "\tWrote C/API module \"_fortran_magic_a044885f2b0c0feac78a230b6b714e2b\" to file \"/var/folders/bq/_946cdn92t7bqzz5frpfpw7r0000gp/T/tmp3y_jxtl_/src.macosx-10.5-x86_64-3.4/_fortran_magic_a044885f2b0c0feac78a230b6b714e2bmodule.c\"\n",
+        "\tFortran 77 wrappers are saved to \"/var/folders/bq/_946cdn92t7bqzz5frpfpw7r0000gp/T/tmp3y_jxtl_/src.macosx-10.5-x86_64-3.4/_fortran_magic_a044885f2b0c0feac78a230b6b714e2b-f2pywrappers.f\"\n"
+       ]
+      }
+     ],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "x_ary = np.array([x_i*np.random.randint(8,12)/10 for x_i in range(100)])\n",
+      "y_ary = np.array([y_i*np.random.randint(10,14)/10 for y_i in range(100)])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "fortran_lstsqr(x_ary, y_ary)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 25,
+       "text": [
+        "(1.1313508052697814, 3.681685640167956)"
+       ]
+      }
+     ],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Running code from other interpreters: Ruby, Perl, and Bash"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To use any interpreter that is installed on your system:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%script perl\n",
+      "print 'Hello, World!';"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Hello, World!"
+       ]
+      }
+     ],
+     "prompt_number": 44
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Or use the magic command for the respective interpreter directly:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%perl\n",
+      "print 'Hello, World!';"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Hello, World!"
+       ]
+      }
+     ],
+     "prompt_number": 45
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%ruby\n",
+      "puts \"Hello, World!\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Hello, World!\n"
+       ]
+      }
+     ],
+     "prompt_number": 46
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%bash\n",
+      "echo \"Hello World!\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Hello World!\n"
+       ]
+      }
+     ],
+     "prompt_number": 47
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%script R --no-save\n",
+      "cat(\"Goodbye, World!\\n\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "R version 3.0.2 (2013-09-25) -- \"Frisbee Sailing\"\n",
+        "Copyright (C) 2013 The R Foundation for Statistical Computing\n",
+        "Platform: x86_64-apple-darwin10.8.0 (64-bit)\n",
+        "\n",
+        "R is free software and comes with ABSOLUTELY NO WARRANTY.\n",
+        "You are welcome to redistribute it under certain conditions.\n",
+        "Type 'license()' or 'licence()' for distribution details.\n",
+        "\n",
+        "  Natural language support but running in an English locale\n",
+        "\n",
+        "R is a collaborative project with many contributors.\n",
+        "Type 'contributors()' for more information and\n",
+        "'citation()' on how to cite R or R packages in publications.\n",
+        "\n",
+        "Type 'demo()' for some demos, 'help()' for on-line help, or\n",
+        "'help.start()' for an HTML browser interface to help.\n",
+        "Type 'q()' to quit R.\n",
+        "\n",
+        "> cat(\"Goodbye, World!\\n\")\n",
+        "Goodbye, World!\n",
+        "> \n"
+       ]
+      }
+     ],
+     "prompt_number": 55
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From c07530130fbcf66cdfef542a3fa01af3baf25f38 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 27 Jun 2014 11:32:22 -0400
Subject: [PATCH 126/248] ipynb tut 1st commit

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c128a20..c9e9b15 100755
--- a/README.md
+++ b/README.md
@@ -40,7 +40,7 @@
 
 - Entry point: Data - using sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb?create=1)]
 
-- Awesome things that you can do in IPython Notebooks (in progress) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb)]
+- Awesome things that you can do in IPython Notebooks (in progress) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/awesome_things_ipynb.ipynb)]
 
 <br>
 

From 2c30cac81f98580913a7d4feec047c842a7af37f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 00:46:45 -0400
Subject: [PATCH 127/248] datemagic

---
 ipython_magic/datemagic.py | 72 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 ipython_magic/datemagic.py

diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
new file mode 100644
index 0000000..4e6244c
--- /dev/null
+++ b/ipython_magic/datemagic.py
@@ -0,0 +1,72 @@
+""" 
+IPython magic function for printing basic information, such as the current date, time,
+Python, and IPython version.
+
+Installation: 
+    %install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/datemagic.py
+
+Usage:
+    %load_ext datemagic
+    
+    
+    %date
+
+    optional arguments:
+     -d, --date      prints date (default)
+     -t, --time      print current time
+     -s, --datetime  print current time
+     -p, --python    prints Python version
+     -i, --ipython   prints IPython version
+    
+"""
+
+import platform
+from time import strftime
+from platform import python_version
+
+import IPython
+from IPython.core.magic import Magics, magics_class, line_magic
+from IPython.core.magic_arguments import argument, magic_arguments, parse_argstring
+
+@magics_class
+class DateMagic(Magics):
+    """ 
+    IPython magic function for printing the current date, time, Python,
+    and IPython version.
+    
+    """
+    @magic_arguments()
+    @argument('-d', '--date', action='store_true', help='prints date (default)')
+    @argument('-t', '--time', action='store_true', help='print current time')
+    @argument('-s', '--datetime', action='store_true', help='print current time')
+    @argument('-p', '--python', action='store_true', help='prints Python version')
+    @argument('-i', '--ipython', action='store_true', help='prints IPython version')
+    @line_magic
+    def date(self, line):
+        """ 
+        IPython magic function for printing the current date, time, Python,
+        and IPython version.
+    
+        """
+        args = parse_argstring(self.date, line)
+        out = ''
+        if args.date:
+            out += strftime('%d/%m/%Y')
+        if args.time:
+            if out:
+                out += ' '
+            out += strftime('%H:%M:%S')
+        if args.python:
+            if out:
+                out += '\n'
+            out += 'Python %s' %python_version()
+        if args.ipython:
+            if out:
+                out += '\n'
+            out += 'IPython %s' %IPython.__version__
+        if not out:
+            out += strftime('%d/%m/%Y')
+        print(out)
+
+def load_ipython_extension(ipython):
+    ipython.register_magics(DateMagic)

From 99b3c1a861b6d194740ebc4f046cf96cf6402339 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 01:15:40 -0400
Subject: [PATCH 128/248] datemagic

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index c9e9b15..0b71806 100755
--- a/README.md
+++ b/README.md
@@ -97,6 +97,8 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 ###// Useful scripts and snippets
 
+- [IPython magic function %date](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/datemagic.ipynb) - for printing date stamps and/or the current Python/IPython version
+
 - [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to .py files.
 
 - convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)]

From 6fae18bfb5a2b53d6fc2c4f3fa8b57e5262bd7e2 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 01:16:59 -0400
Subject: [PATCH 129/248] datemagic

---
 ipython_magic/datemagic.py | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
index 4e6244c..ff3180b 100644
--- a/ipython_magic/datemagic.py
+++ b/ipython_magic/datemagic.py
@@ -1,3 +1,5 @@
+__version__ == '1.0.0'
+
 """ 
 IPython magic function for printing basic information, such as the current date, time,
 Python, and IPython version.
@@ -23,6 +25,7 @@
 import platform
 from time import strftime
 from platform import python_version
+from pkg_resources import get_distribution
 
 import IPython
 from IPython.core.magic import Magics, magics_class, line_magic
@@ -37,10 +40,13 @@ class DateMagic(Magics):
     """
     @magic_arguments()
     @argument('-d', '--date', action='store_true', help='prints date (default)')
+    @argument('-dd', '--dateday', action='store_true', help='prints date with abbrv. day and month names')
     @argument('-t', '--time', action='store_true', help='print current time')
     @argument('-s', '--datetime', action='store_true', help='print current time')
-    @argument('-p', '--python', action='store_true', help='prints Python version')
+    @argument('-z', '--timezone', action='store_true', help='prints time zone')
+    @argument('-y', '--python', action='store_true', help='prints Python version')
     @argument('-i', '--ipython', action='store_true', help='prints IPython version')
+    @argument('-p', '--packages', action=str, help='prints versions of Python modules and packages')
     @line_magic
     def date(self, line):
         """ 
@@ -52,10 +58,16 @@ def date(self, line):
         out = ''
         if args.date:
             out += strftime('%d/%m/%Y')
+        elif arts.dateday:
+            out += strftime('%a %b %M %Y')
         if args.time:
             if out:
                 out += ' '
             out += strftime('%H:%M:%S')
+        if args.timezone:
+            if out:
+                out += ' '
+            out += strftime('%Z')
         if args.python:
             if out:
                 out += '\n'
@@ -64,6 +76,10 @@ def date(self, line):
             if out:
                 out += '\n'
             out += 'IPython %s' %IPython.__version__
+        if args.packages:
+            packages = args.packages.split(',') 
+            for p in packages:
+                out += '\n%s' %get_distribution(p).version
         if not out:
             out += strftime('%d/%m/%Y')
         print(out)

From b7550e37f8ced739f0e91f5836611b5353d487dd Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 01:22:48 -0400
Subject: [PATCH 130/248] datemagic

---
 ipython_magic/datemagic.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
index ff3180b..2fc99f2 100644
--- a/ipython_magic/datemagic.py
+++ b/ipython_magic/datemagic.py
@@ -1,12 +1,17 @@
-__version__ == '1.0.0'
-
 """ 
+Sebastian Raschka 2014
+
+datemagic.py
+version 1.0.0
+
+
 IPython magic function for printing basic information, such as the current date, time,
 Python, and IPython version.
 
 Installation: 
     %install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/datemagic.py
 
+
 Usage:
     %load_ext datemagic
     
@@ -20,6 +25,8 @@
      -p, --python    prints Python version
      -i, --ipython   prints IPython version
     
+
+
 """
 
 import platform

From 692e1f99f5070c998b93503ea5967b5132e094e7 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 01:27:19 -0400
Subject: [PATCH 131/248] datemagic

---
 ipython_magic/datemagic.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
index 2fc99f2..493ac54 100644
--- a/ipython_magic/datemagic.py
+++ b/ipython_magic/datemagic.py
@@ -54,7 +54,6 @@ class DateMagic(Magics):
     @argument('-y', '--python', action='store_true', help='prints Python version')
     @argument('-i', '--ipython', action='store_true', help='prints IPython version')
     @argument('-p', '--packages', action=str, help='prints versions of Python modules and packages')
-    @line_magic
     def date(self, line):
         """ 
         IPython magic function for printing the current date, time, Python,

From 94019bc82f3e79f084db4749b7efb7179283c94e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 01:29:04 -0400
Subject: [PATCH 132/248] datemagic

---
 ipython_magic/datemagic.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
index 493ac54..c2c77a7 100644
--- a/ipython_magic/datemagic.py
+++ b/ipython_magic/datemagic.py
@@ -53,7 +53,7 @@ class DateMagic(Magics):
     @argument('-z', '--timezone', action='store_true', help='prints time zone')
     @argument('-y', '--python', action='store_true', help='prints Python version')
     @argument('-i', '--ipython', action='store_true', help='prints IPython version')
-    @argument('-p', '--packages', action=str, help='prints versions of Python modules and packages')
+    @argument('-p', '--packages', type=str, help='prints versions of Python modules and packages')
     def date(self, line):
         """ 
         IPython magic function for printing the current date, time, Python,

From a83847c6608313a707d2ae8562cd982941c981a5 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 01:33:53 -0400
Subject: [PATCH 133/248] datemagic

---
 ipython_magic/datemagic.ipynb | 349 ++++++++++++++++++++++++++++++++++
 ipython_magic/datemagic.py    |   1 +
 2 files changed, 350 insertions(+)
 create mode 100644 ipython_magic/datemagic.ipynb

diff --git a/ipython_magic/datemagic.ipynb b/ipython_magic/datemagic.ipynb
new file mode 100644
index 0000000..0fb86bc
--- /dev/null
+++ b/ipython_magic/datemagic.ipynb
@@ -0,0 +1,349 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:13f411c2769ead4dd7d65e26a464ccd775c9a1aab115536fb6be57d952325013"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "IPython magic function documentation - `%date`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Installation"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "You can install the magic function directly from my GitHub repository via:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/datemagic.py"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Installed datemagic.py. To use it, type:\n",
+        "  %load_ext datemagic\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Loading the `%date` magic"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "To load the `date` magic, execute the following line in your IPython notebook or current IPython shell"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext datemagic"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "TypeError",
+       "evalue": "'option_strings' is an invalid keyword argument for this function",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-2-3c30dab41681>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext datemagic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m                     \u001b[0m__import__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m             \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_load_ipython_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mmagics_class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mDateMagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMagics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m     \"\"\" \n\u001b[1;32m     44\u001b[0m     \u001b[0mIPython\u001b[0m \u001b[0mmagic\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprinting\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPython\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36mDateMagic\u001b[0;34m()\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0margument\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-i'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'--ipython'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'store_true'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhelp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'prints IPython version'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0margument\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'--packages'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhelp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'prints versions of Python modules and packages'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mline_magic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \"\"\" \n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;31m# This should be the first decorator in the list of decorators, thus the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;31m# last to execute. Build the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstruct_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36mconstruct_parser\u001b[0;34m(magic_func)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdeco\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmagic_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorators\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36madd_to_parser\u001b[0;34m(self, parser, group)\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m         \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_method_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/argparse.py\u001b[0m in \u001b[0;36madd_argument\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1322\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'unknown action \"%s\"'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maction_class\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1324\u001b[0;31m         \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m         \u001b[0;31m# raise an error if the action type is not callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mTypeError\u001b[0m: 'option_strings' is an invalid keyword argument for this function"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Usage"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In order to display the available `magic` arguments, type:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%date?"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<pre>%date [-d] [-t] [-s] [-p] [-i]\n",
+      "\n",
+      " \n",
+      "IPython magic function for printing the current date, time, Python,\n",
+      "and IPython version.\n",
+      "\n",
+      "optional arguments:\n",
+      "  -d, --date      prints date (default)\n",
+      "  -t, --time      print current time\n",
+      "  -s, --datetime  print current time\n",
+      "  -p, --python    prints Python version\n",
+      "  -i, --ipython   prints IPython version\n",
+      "</pre>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Examples"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%date "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "28/06/2014\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%date -t"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "01:18:28\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%date -dt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "28/06/2014 01:18:33\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%date -p"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Python 3.3.5\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%date -i"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "IPython 2.1.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%date -d -i -z -p"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stderr",
+       "text": [
+        "UsageError: unrecognized arguments: -z"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
index c2c77a7..d563c8c 100644
--- a/ipython_magic/datemagic.py
+++ b/ipython_magic/datemagic.py
@@ -54,6 +54,7 @@ class DateMagic(Magics):
     @argument('-y', '--python', action='store_true', help='prints Python version')
     @argument('-i', '--ipython', action='store_true', help='prints IPython version')
     @argument('-p', '--packages', type=str, help='prints versions of Python modules and packages')
+    @line_magic
     def date(self, line):
         """ 
         IPython magic function for printing the current date, time, Python,

From a9cbbc64d45b6e055b472be7e8573df07b6f52a8 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 01:35:21 -0400
Subject: [PATCH 134/248] datemagic

---
 ipython_magic/datemagic.ipynb | 47 ++++++++++++-----------------------
 ipython_magic/datemagic.py    | 24 +++++++++++-------
 2 files changed, 31 insertions(+), 40 deletions(-)

diff --git a/ipython_magic/datemagic.ipynb b/ipython_magic/datemagic.ipynb
index 0fb86bc..f6aa94b 100644
--- a/ipython_magic/datemagic.ipynb
+++ b/ipython_magic/datemagic.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:13f411c2769ead4dd7d65e26a464ccd775c9a1aab115536fb6be57d952325013"
+  "signature": "sha256:bbbd486d20e7851cbefc2cd9d352354732ba5184d9a235eb50e6022152985218"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -90,30 +90,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "ename": "TypeError",
-       "evalue": "'option_strings' is an invalid keyword argument for this function",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-2-3c30dab41681>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext datemagic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m                     \u001b[0m__import__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m             \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_load_ipython_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mmagics_class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mDateMagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMagics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m     \"\"\" \n\u001b[1;32m     44\u001b[0m     \u001b[0mIPython\u001b[0m \u001b[0mmagic\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprinting\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPython\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36mDateMagic\u001b[0;34m()\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0margument\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-i'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'--ipython'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'store_true'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhelp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'prints IPython version'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0margument\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'-p'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'--packages'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhelp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'prints versions of Python modules and packages'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mline_magic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \"\"\" \n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;31m# This should be the first decorator in the list of decorators, thus the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;31m# last to execute. Build the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstruct_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36mconstruct_parser\u001b[0;34m(magic_func)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdeco\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmagic_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorators\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36madd_to_parser\u001b[0;34m(self, parser, group)\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m         \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_method_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/argparse.py\u001b[0m in \u001b[0;36madd_argument\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1322\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1323\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'unknown action \"%s\"'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maction_class\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1324\u001b[0;31m         \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1326\u001b[0m         \u001b[0;31m# raise an error if the action type is not callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mTypeError\u001b[0m: 'option_strings' is an invalid keyword argument for this function"
-       ]
-      }
-     ],
+     "outputs": [],
      "prompt_number": 2
     },
     {
@@ -148,7 +125,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -195,14 +172,22 @@
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "28/06/2014\n"
+       "ename": "NameError",
+       "evalue": "name 'arts' is not defined",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-4-85a08300c34a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36mdate\u001b[0;34m(self, line)\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36mdate\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%d/%m/%Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0marts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdateday\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%a %b %M %Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mNameError\u001b[0m: name 'arts' is not defined"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
index d563c8c..1c114be 100644
--- a/ipython_magic/datemagic.py
+++ b/ipython_magic/datemagic.py
@@ -16,17 +16,23 @@
     %load_ext datemagic
     
     
-    %date
+  %date [-d] [-dd] [-t] [-s] [-z] [-y] [-i] [-p PACKAGES]
+
+ 
+    IPython magic function for printing the current date, time, Python,
+    and IPython version.
 
     optional arguments:
-     -d, --date      prints date (default)
-     -t, --time      print current time
-     -s, --datetime  print current time
-     -p, --python    prints Python version
-     -i, --ipython   prints IPython version
+      -d, --date            prints date (default)
+      -dd, --dateday        prints date with abbrv. day and month names
+      -t, --time            print current time
+      -s, --datetime        print current time
+      -z, --timezone        prints time zone
+      -y, --python          prints Python version
+      -i, --ipython         prints IPython version
+      -p PACKAGES, --packages PACKAGES
+                        prints versions of Python modules and packages
     
-
-
 """
 
 import platform
@@ -65,7 +71,7 @@ def date(self, line):
         out = ''
         if args.date:
             out += strftime('%d/%m/%Y')
-        elif arts.dateday:
+        elif args.dateday:
             out += strftime('%a %b %M %Y')
         if args.time:
             if out:

From e2457c2d5b82c4fa44fc782a3220ecf937c61fe8 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 02:41:42 -0400
Subject: [PATCH 135/248] datemagic

---
 ipython_magic/datemagic.ipynb |  47 ++++++++++++++-
 ipython_magic/datemagic.py    | 104 ++++++++++++++++++++++------------
 2 files changed, 112 insertions(+), 39 deletions(-)

diff --git a/ipython_magic/datemagic.ipynb b/ipython_magic/datemagic.ipynb
index f6aa94b..8eecae9 100644
--- a/ipython_magic/datemagic.ipynb
+++ b/ipython_magic/datemagic.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:bbbd486d20e7851cbefc2cd9d352354732ba5184d9a235eb50e6022152985218"
+  "signature": "sha256:7dc125262acee5bca3388c30b7a0647023fef2622e7167d0700b17b8e0f6e327"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -59,6 +59,26 @@
      ],
      "prompt_number": 1
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%install_ext ./datemagic.py"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Installed datemagic.py. To use it, type:\n",
+        "  %load_ext datemagic\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -90,7 +110,30 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [],
+     "outputs": [
+      {
+       "ename": "ValueError",
+       "evalue": "'store_true' is not callable",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-2-3c30dab41681>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext datemagic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m                     \u001b[0m__import__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m             \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_load_ipython_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mmagics_class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mWaterMark\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMagics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m     \"\"\" \n\u001b[1;32m     49\u001b[0m     \u001b[0mIPython\u001b[0m \u001b[0mmagic\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprinting\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPython\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36mWaterMark\u001b[0;34m()\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mline_magic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwatermark\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \"\"\" \n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;31m# This should be the first decorator in the list of decorators, thus the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;31m# last to execute. Build the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstruct_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36mconstruct_parser\u001b[0;34m(magic_func)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdeco\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmagic_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorators\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36madd_to_parser\u001b[0;34m(self, parser, group)\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m         \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_method_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/argparse.py\u001b[0m in \u001b[0;36madd_argument\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1327\u001b[0m         \u001b[0mtype_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_registry_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1328\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%r is not callable'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtype_func\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m         \u001b[0;31m# raise an error if the metavar does not match the type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        "\u001b[0;31mValueError\u001b[0m: 'store_true' is not callable"
+       ]
+      }
+     ],
      "prompt_number": 2
     },
     {
diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
index 1c114be..be5e517 100644
--- a/ipython_magic/datemagic.py
+++ b/ipython_magic/datemagic.py
@@ -36,66 +36,96 @@
 """
 
 import platform
-from time import strftime
-from platform import python_version
 from pkg_resources import get_distribution
+from multiprocessing import cpu_count
 
 import IPython
 from IPython.core.magic import Magics, magics_class, line_magic
 from IPython.core.magic_arguments import argument, magic_arguments, parse_argstring
 
 @magics_class
-class DateMagic(Magics):
+class WaterMark(Magics):
     """ 
     IPython magic function for printing the current date, time, Python,
     and IPython version.
     
     """
     @magic_arguments()
-    @argument('-d', '--date', action='store_true', help='prints date (default)')
-    @argument('-dd', '--dateday', action='store_true', help='prints date with abbrv. day and month names')
-    @argument('-t', '--time', action='store_true', help='print current time')
-    @argument('-s', '--datetime', action='store_true', help='print current time')
-    @argument('-z', '--timezone', action='store_true', help='prints time zone')
-    @argument('-y', '--python', action='store_true', help='prints Python version')
-    @argument('-i', '--ipython', action='store_true', help='prints IPython version')
+    @argument('-d', '--date', action='store_true', help='prints current date')
+    @argument('-n', '--datename', action='store_true', help='prints date with abbrv. day and month names')
+    @argument('-t', '--datetime', action='store_true', help='prints currenttime')
+    @argument('-u', '--custom_time', type=str, help='prints a valid strftime() string')
+    @argument('-v', '--python', action='store_true', help='prints Python and IPython version')
     @argument('-p', '--packages', type=str, help='prints versions of Python modules and packages')
-    @line_magic
-    def date(self, line):
+    @argument('-m', '--machine', type='store_true', help='prints system and machine info')
+    def watermark(self, line):
         """ 
         IPython magic function for printing the current date, time, Python,
         and IPython version.
     
         """
         args = parse_argstring(self.date, line)
-        out = ''
-        if args.date:
-            out += strftime('%d/%m/%Y')
-        elif args.dateday:
-            out += strftime('%a %b %M %Y')
-        if args.time:
-            if out:
-                out += ' '
-            out += strftime('%H:%M:%S')
-        if args.timezone:
-            if out:
-                out += ' '
-            out += strftime('%Z')
-        if args.python:
-            if out:
-                out += '\n'
-            out += 'Python %s' %python_version()
-        if args.ipython:
-            if out:
-                out += '\n'
-            out += 'IPython %s' %IPython.__version__
-        if args.packages:
+
+
+        if not any(vars(args).values()):
+             print_customtime('%d/%m/%Y')
+             print_pyver(args)
+             print_sysinfo()
+
+
+        def print_customtime(self, ctime):
+            print(strftime(ctime))
+  
+        def print_pack(self):
+            out = ''
             packages = args.packages.split(',') 
             for p in packages:
                 out += '\n%s' %get_distribution(p).version
-        if not out:
-            out += strftime('%d/%m/%Y')
-        print(out)
+            print(out)
+            
+
+        def print_pyver(self, args):
+            out = ''
+            if args.python:
+                if out:
+                    out += '\n'
+                out += 'Python %s' %python_version()
+            if args.ipython:
+                if out:
+                    out += '\n'
+                out += 'IPython %s' %IPython.__version__
+            print(out)
+
+
+        def print_datetime(self, args):
+            out = ''
+            if args.date:
+                out += strftime('%d/%m/%Y')
+            elif args.dateday:
+                out += strftime('%a %b %M %Y')
+            if args.time:
+                if out:
+                    out += ' '
+                out += strftime('%H:%M:%S')
+            if args.timezone:
+                if out:
+                    out += ' '
+                out += strftime('%Z')
+            if args.custom_time:
+                if out:
+                    out += ' '
+                out += strftime(args.custom_time)
+            print(out)
+
+        def print_sysinfo(self):
+             print('compiler        : %s' %platform.python_compiler())
+             print('system     : %s' %platform.system())
+             print('release    : %s' %platform.release())
+             print('machine    : %s' %platform.machine())
+             print('processor  : %s' %platform.processor())
+             print('CPU count  : %s' %cpu_count())
+             print('interpreter: %s' %platform.architecture()[0])
+
 
 def load_ipython_extension(ipython):
     ipython.register_magics(DateMagic)

From e1364a11a994f59e7a51c97a5a7527e5b8c5d635 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 03:05:21 -0400
Subject: [PATCH 136/248] rem

---
 ipython_magic/datemagic.ipynb |  61 ++++++----------
 ipython_magic/datemagic.py    | 131 ++++++++++++++--------------------
 2 files changed, 72 insertions(+), 120 deletions(-)

diff --git a/ipython_magic/datemagic.ipynb b/ipython_magic/datemagic.ipynb
index 8eecae9..fde6d92 100644
--- a/ipython_magic/datemagic.ipynb
+++ b/ipython_magic/datemagic.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7dc125262acee5bca3388c30b7a0647023fef2622e7167d0700b17b8e0f6e327"
+  "signature": "sha256:b0db931ae8afaadadc3173ffe809324b1b9396a4360f574e697b83ce32103015"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -43,7 +43,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/datemagic.py"
+      "#%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/datemagic.py"
      ],
      "language": "python",
      "metadata": {},
@@ -110,30 +110,7 @@
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "ename": "ValueError",
-       "evalue": "'store_true' is not callable",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-2-3c30dab41681>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext datemagic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     96\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m                     \u001b[0m__import__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     99\u001b[0m             \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmodule_str\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_load_ipython_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mmagics_class\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mWaterMark\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMagics\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m     \"\"\" \n\u001b[1;32m     49\u001b[0m     \u001b[0mIPython\u001b[0m \u001b[0mmagic\u001b[0m \u001b[0mfunction\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mprinting\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPython\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36mWaterMark\u001b[0;34m()\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m     \u001b[0;34m@\u001b[0m\u001b[0mline_magic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwatermark\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m         \"\"\" \n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, func)\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;31m# This should be the first decorator in the list of decorators, thus the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;31m# last to execute. Build the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 209\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconstruct_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    210\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36mconstruct_parser\u001b[0;34m(magic_func)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdeco\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmagic_func\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecorators\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_to_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0mgroup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic_arguments.py\u001b[0m in \u001b[0;36madd_to_parser\u001b[0;34m(self, parser, group)\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m         \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparser\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_method_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/argparse.py\u001b[0m in \u001b[0;36madd_argument\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1327\u001b[0m         \u001b[0mtype_func\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_registry_get\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'type'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1328\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype_func\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%r is not callable'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtype_func\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1330\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m         \u001b[0;31m# raise an error if the metavar does not match the type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mValueError\u001b[0m: 'store_true' is not callable"
-       ]
-      }
-     ],
+     "outputs": [],
      "prompt_number": 2
     },
     {
@@ -163,7 +140,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%date?"
+      "%watermark?"
      ],
      "language": "python",
      "metadata": {},
@@ -209,28 +186,30 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%date "
+      "%watermark"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "ename": "NameError",
-       "evalue": "name 'arts' is not defined",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-4-85a08300c34a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mmagic\u001b[0;34m(self, arg_s)\u001b[0m\n\u001b[1;32m   2203\u001b[0m         \u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg_s\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2204\u001b[0m         \u001b[0mmagic_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmagic_name\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefilter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mESC_MAGIC\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2205\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmagic_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2206\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2207\u001b[0m     \u001b[0;31m#-------------------------------------------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line)\u001b[0m\n\u001b[1;32m   2124\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2125\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2126\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2127\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36mdate\u001b[0;34m(self, line)\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    191\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 193\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m/Users/sebastian/.ipython/extensions/datemagic.py\u001b[0m in \u001b[0;36mdate\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%d/%m/%Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0;32melif\u001b[0m \u001b[0marts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdateday\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%a %b %M %Y'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mNameError\u001b[0m: name 'arts' is not defined"
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "28/06/2014 02:56:36\n",
+        "Python 3.4.1\n",
+        "IPython 2.1.0\n",
+        "\n",
+        "compile    : GCC 4.2.1 (Apple Inc. build 5577)\n",
+        "system     : Darwin\n",
+        "release    : 13.2.0\n",
+        "machine    : x86_64\n",
+        "processor  : i386\n",
+        "CPU count  : 2\n",
+        "interpreter: 64bit\n"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
index be5e517..e84b816 100644
--- a/ipython_magic/datemagic.py
+++ b/ipython_magic/datemagic.py
@@ -1,7 +1,7 @@
 """ 
 Sebastian Raschka 2014
 
-datemagic.py
+watermark.py
 version 1.0.0
 
 
@@ -9,33 +9,11 @@
 Python, and IPython version.
 
 Installation: 
-    %install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/datemagic.py
-
-
-Usage:
-    %load_ext datemagic
-    
-    
-  %date [-d] [-dd] [-t] [-s] [-z] [-y] [-i] [-p PACKAGES]
-
- 
-    IPython magic function for printing the current date, time, Python,
-    and IPython version.
-
-    optional arguments:
-      -d, --date            prints date (default)
-      -dd, --dateday        prints date with abbrv. day and month names
-      -t, --time            print current time
-      -s, --datetime        print current time
-      -z, --timezone        prints time zone
-      -y, --python          prints Python version
-      -i, --ipython         prints IPython version
-      -p PACKAGES, --packages PACKAGES
-                        prints versions of Python modules and packages
     
 """
 
 import platform
+from time import strftime
 from pkg_resources import get_distribution
 from multiprocessing import cpu_count
 
@@ -57,75 +35,70 @@ class WaterMark(Magics):
     @argument('-u', '--custom_time', type=str, help='prints a valid strftime() string')
     @argument('-v', '--python', action='store_true', help='prints Python and IPython version')
     @argument('-p', '--packages', type=str, help='prints versions of Python modules and packages')
-    @argument('-m', '--machine', type='store_true', help='prints system and machine info')
+    @argument('-m', '--machine', action='store_true', help='prints system and machine info')
+    @line_magic
     def watermark(self, line):
         """ 
         IPython magic function for printing the current date, time, Python,
         and IPython version.
     
         """
-        args = parse_argstring(self.date, line)
+        args = parse_argstring(self.watermark, line)
 
 
         if not any(vars(args).values()):
-             print_customtime('%d/%m/%Y')
-             print_pyver(args)
-             print_sysinfo()
+             self._print_customtime('%d/%m/%Y %H:%M:%S')
+             self._print_pyver(args)
+             self._print_sysinfo()
 
 
-        def print_customtime(self, ctime):
-            print(strftime(ctime))
+    def _print_customtime(self, ctime):
+        print(strftime(ctime))
   
-        def print_pack(self):
-            out = ''
-            packages = args.packages.split(',') 
-            for p in packages:
-                out += '\n%s' %get_distribution(p).version
-            print(out)
+    def _print_pack(self):
+        out = ''
+        packages = args.packages.split(',') 
+        for p in packages:
+            out += '\n%s' %get_distribution(p).version
+        print(out)
             
 
-        def print_pyver(self, args):
-            out = ''
-            if args.python:
-                if out:
-                    out += '\n'
-                out += 'Python %s' %python_version()
-            if args.ipython:
-                if out:
-                    out += '\n'
-                out += 'IPython %s' %IPython.__version__
-            print(out)
-
-
-        def print_datetime(self, args):
-            out = ''
-            if args.date:
-                out += strftime('%d/%m/%Y')
-            elif args.dateday:
-                out += strftime('%a %b %M %Y')
-            if args.time:
-                if out:
-                    out += ' '
-                out += strftime('%H:%M:%S')
-            if args.timezone:
-                if out:
-                    out += ' '
-                out += strftime('%Z')
-            if args.custom_time:
-                if out:
-                    out += ' '
-                out += strftime(args.custom_time)
-            print(out)
-
-        def print_sysinfo(self):
-             print('compiler        : %s' %platform.python_compiler())
-             print('system     : %s' %platform.system())
-             print('release    : %s' %platform.release())
-             print('machine    : %s' %platform.machine())
-             print('processor  : %s' %platform.processor())
-             print('CPU count  : %s' %cpu_count())
-             print('interpreter: %s' %platform.architecture()[0])
+    def _print_pyver(self, args):
+        print('\nPython %s\nIPython %s' %(
+                platform.python_version(), 
+                IPython.__version__)
+             )
+
+
+    def _print_datetime(self, args):
+        out = ''
+        if args.date:
+            out += strftime('%d/%m/%Y')
+        elif args.dateday:
+            out += strftime('%a %b %M %Y')
+        if args.time:
+            if out:
+                out += ' '
+            out += strftime('%H:%M:%S')
+        if args.timezone:
+            if out:
+                out += ' '
+            out += strftime('%Z')
+        if args.custom_time:
+            if out:
+                out += ' '
+            out += strftime(args.custom_time)
+        print(out)
+
+    def _print_sysinfo(self):
+         print('\ncompile    : %s' %platform.python_compiler())
+         print('system     : %s' %platform.system())
+         print('release    : %s' %platform.release())
+         print('machine    : %s' %platform.machine())
+         print('processor  : %s' %platform.processor())
+         print('CPU count  : %s' %cpu_count())
+         print('interpreter: %s' %platform.architecture()[0])
 
 
 def load_ipython_extension(ipython):
-    ipython.register_magics(DateMagic)
+    ipython.register_magics(WaterMark)

From b49d098bc2ac48f533f631f1ecc6256cb6d9003c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 14:06:33 -0400
Subject: [PATCH 137/248] watermark

---
 README.md                                     |   2 +-
 ipython_magic/datemagic.py                    | 104 ------------
 .../{datemagic.ipynb => watermark.ipynb}      | 148 ++++++++++--------
 ipython_magic/watermark.py                    | 137 ++++++++++++++++
 4 files changed, 223 insertions(+), 168 deletions(-)
 delete mode 100644 ipython_magic/datemagic.py
 rename ipython_magic/{datemagic.ipynb => watermark.ipynb} (59%)
 create mode 100644 ipython_magic/watermark.py

diff --git a/README.md b/README.md
index 0b71806..5c70dcb 100755
--- a/README.md
+++ b/README.md
@@ -97,7 +97,7 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 ###// Useful scripts and snippets
 
-- [IPython magic function %date](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/datemagic.ipynb) - for printing date stamps and/or the current Python/IPython version
+- [IPython magic function %watermark](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) - for printing date- and time-stamps and various system info
 
 - [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to .py files.
 
diff --git a/ipython_magic/datemagic.py b/ipython_magic/datemagic.py
deleted file mode 100644
index e84b816..0000000
--- a/ipython_magic/datemagic.py
+++ /dev/null
@@ -1,104 +0,0 @@
-""" 
-Sebastian Raschka 2014
-
-watermark.py
-version 1.0.0
-
-
-IPython magic function for printing basic information, such as the current date, time,
-Python, and IPython version.
-
-Installation: 
-    
-"""
-
-import platform
-from time import strftime
-from pkg_resources import get_distribution
-from multiprocessing import cpu_count
-
-import IPython
-from IPython.core.magic import Magics, magics_class, line_magic
-from IPython.core.magic_arguments import argument, magic_arguments, parse_argstring
-
-@magics_class
-class WaterMark(Magics):
-    """ 
-    IPython magic function for printing the current date, time, Python,
-    and IPython version.
-    
-    """
-    @magic_arguments()
-    @argument('-d', '--date', action='store_true', help='prints current date')
-    @argument('-n', '--datename', action='store_true', help='prints date with abbrv. day and month names')
-    @argument('-t', '--datetime', action='store_true', help='prints currenttime')
-    @argument('-u', '--custom_time', type=str, help='prints a valid strftime() string')
-    @argument('-v', '--python', action='store_true', help='prints Python and IPython version')
-    @argument('-p', '--packages', type=str, help='prints versions of Python modules and packages')
-    @argument('-m', '--machine', action='store_true', help='prints system and machine info')
-    @line_magic
-    def watermark(self, line):
-        """ 
-        IPython magic function for printing the current date, time, Python,
-        and IPython version.
-    
-        """
-        args = parse_argstring(self.watermark, line)
-
-
-        if not any(vars(args).values()):
-             self._print_customtime('%d/%m/%Y %H:%M:%S')
-             self._print_pyver(args)
-             self._print_sysinfo()
-
-
-    def _print_customtime(self, ctime):
-        print(strftime(ctime))
-  
-    def _print_pack(self):
-        out = ''
-        packages = args.packages.split(',') 
-        for p in packages:
-            out += '\n%s' %get_distribution(p).version
-        print(out)
-            
-
-    def _print_pyver(self, args):
-        print('\nPython %s\nIPython %s' %(
-                platform.python_version(), 
-                IPython.__version__)
-             )
-
-
-    def _print_datetime(self, args):
-        out = ''
-        if args.date:
-            out += strftime('%d/%m/%Y')
-        elif args.dateday:
-            out += strftime('%a %b %M %Y')
-        if args.time:
-            if out:
-                out += ' '
-            out += strftime('%H:%M:%S')
-        if args.timezone:
-            if out:
-                out += ' '
-            out += strftime('%Z')
-        if args.custom_time:
-            if out:
-                out += ' '
-            out += strftime(args.custom_time)
-        print(out)
-
-    def _print_sysinfo(self):
-         print('\ncompile    : %s' %platform.python_compiler())
-         print('system     : %s' %platform.system())
-         print('release    : %s' %platform.release())
-         print('machine    : %s' %platform.machine())
-         print('processor  : %s' %platform.processor())
-         print('CPU count  : %s' %cpu_count())
-         print('interpreter: %s' %platform.architecture()[0])
-
-
-def load_ipython_extension(ipython):
-    ipython.register_magics(WaterMark)
diff --git a/ipython_magic/datemagic.ipynb b/ipython_magic/watermark.ipynb
similarity index 59%
rename from ipython_magic/datemagic.ipynb
rename to ipython_magic/watermark.ipynb
index fde6d92..bc2b250 100644
--- a/ipython_magic/datemagic.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,19 +1,41 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b0db931ae8afaadadc3173ffe809324b1b9396a4360f574e697b83ce32103015"
+  "signature": "sha256:6a672b3872feb4ac09bbf846694bc0d4a04b3d7914e4afec8b2bd6398a5a57bd"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
  "worksheets": [
   {
    "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "\n",
+      "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference/)\n",
+      "\n",
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+      "<hr>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 1,
      "metadata": {},
      "source": [
-      "IPython magic function documentation - `%date`"
+      "IPython magic function documentation - `%watermark`"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I wrote this simple `watermark` IPython magic function to conveniently add date- and time-stamps to my IPython notebooks. Also, I often want to document various system information, e.g., for my [Python benchmarks](https://github.com/rasbt/One-Python-benchmark-per-day) series.\n"
      ]
     },
     {
@@ -36,34 +58,14 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "You can install the magic function directly from my GitHub repository via:"
+      "The `watermark` line magic can be directly installed from my GitHub repository via"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/datemagic.py"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Installed datemagic.py. To use it, type:\n",
-        "  %load_ext datemagic\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%install_ext ./datemagic.py"
+      "%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/watermark.py"
      ],
      "language": "python",
      "metadata": {},
@@ -92,7 +94,7 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Loading the `%date` magic"
+      "Loading the `%watermark` magic"
      ]
     },
     {
@@ -106,7 +108,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%load_ext datemagic"
+      "%load_ext watermark"
      ],
      "language": "python",
      "metadata": {},
@@ -133,7 +135,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In order to display the available `magic` arguments, type:"
+      "In order to display the optional `watermark` arguments, type"
      ]
     },
     {
@@ -151,18 +153,26 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<pre>%date [-d] [-t] [-s] [-p] [-i]\n",
+      "<pre>%watermark [-d] [-n] [-t] [-z] [-u] [-c CUSTOM_TIME] [-v] [-p PACKAGES]\n",
+      "                 [-m]\n",
       "\n",
       " \n",
-      "IPython magic function for printing the current date, time, Python,\n",
-      "and IPython version.\n",
+      "IPython magic function to print date/time stamps \n",
+      "and various system information.\n",
       "\n",
       "optional arguments:\n",
-      "  -d, --date      prints date (default)\n",
-      "  -t, --time      print current time\n",
-      "  -s, --datetime  print current time\n",
-      "  -p, --python    prints Python version\n",
-      "  -i, --ipython   prints IPython version\n",
+      "  -d, --date            prints current date\n",
+      "  -n, --datename        prints date with abbrv. day and month names\n",
+      "  -t, --time            prints current time\n",
+      "  -z, --timezone        appends the local time zone\n",
+      "  -u, --updated         appends a string \"Last updated: \"\n",
+      "  -c CUSTOM_TIME, --custom_time CUSTOM_TIME\n",
+      "                        prints a valid strftime() string\n",
+      "  -v, --python          prints Python and IPython version\n",
+      "  -p PACKAGES, --packages PACKAGES\n",
+      "                        prints versions of specified Python modules and\n",
+      "                        packages\n",
+      "  -m, --machine         prints system and machine info\n",
       "</pre>"
      ]
     },
@@ -195,21 +205,22 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 02:56:36\n",
+        "28/06/2014 14:03:45\n",
+        "\n",
         "Python 3.4.1\n",
         "IPython 2.1.0\n",
         "\n",
-        "compile    : GCC 4.2.1 (Apple Inc. build 5577)\n",
+        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
         "system     : Darwin\n",
         "release    : 13.2.0\n",
         "machine    : x86_64\n",
         "processor  : i386\n",
-        "CPU count  : 2\n",
+        "CPU cores  : 2\n",
         "interpreter: 64bit\n"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -222,7 +233,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%date -t"
+      "%watermark -d -t"
      ],
      "language": "python",
      "metadata": {},
@@ -231,11 +242,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "01:18:28\n"
+        "28/06/2014 14:03:49 \n"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -248,7 +259,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%date -dt"
+      "%watermark -u -n -t -z"
      ],
      "language": "python",
      "metadata": {},
@@ -257,11 +268,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 01:18:33\n"
+        "Last updated: Sat Jun 03 2014 14:03:50 EDT\n"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -274,7 +285,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%date -p"
+      "%watermark -v"
      ],
      "language": "python",
      "metadata": {},
@@ -283,11 +294,12 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Python 3.3.5\n"
+        "Python 3.4.1\n",
+        "IPython 2.1.0\n"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -300,7 +312,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%date -i"
+      "%watermark -m"
      ],
      "language": "python",
      "metadata": {},
@@ -309,11 +321,17 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "IPython 2.1.0\n"
+        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
+        "system     : Darwin\n",
+        "release    : 13.2.0\n",
+        "machine    : x86_64\n",
+        "processor  : i386\n",
+        "CPU cores  : 2\n",
+        "interpreter: 64bit\n"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -326,28 +344,32 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%date -d -i -z -p"
+      "%watermark -v -m -p numpy,scipy"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
-       "stream": "stderr",
+       "stream": "stdout",
        "text": [
-        "UsageError: unrecognized arguments: -z"
+        "Python 3.4.1\n",
+        "IPython 2.1.0\n",
+        "\n",
+        "numpy 1.8.1\n",
+        "scipy 0.14.0\n",
+        "\n",
+        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
+        "system     : Darwin\n",
+        "release    : 13.2.0\n",
+        "machine    : x86_64\n",
+        "processor  : i386\n",
+        "CPU cores  : 2\n",
+        "interpreter: 64bit\n"
        ]
       }
      ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "prompt_number": 9
     }
    ],
    "metadata": {}
diff --git a/ipython_magic/watermark.py b/ipython_magic/watermark.py
new file mode 100644
index 0000000..d54a074
--- /dev/null
+++ b/ipython_magic/watermark.py
@@ -0,0 +1,137 @@
+""" 
+Sebastian Raschka 2014
+
+watermark.py
+version 1.0.0
+
+
+IPython magic function to print date/time stamps and various system information.
+
+Installation: 
+
+    %install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/watermark.py
+    
+Usage:
+
+    %load_ext watermark
+    
+    %watermark
+    
+    optional arguments:
+      -d, --date            prints current date
+      -n, --datename        prints date with abbrv. day and month names
+      -t, --time            prints current time
+      -z, --timezone        appends the local time zone
+      -u, --updated         appends a string "Last updated: "
+      -c CUSTOM_TIME, --custom_time CUSTOM_TIME
+                        prints a valid strftime() string
+      -v, --python          prints Python and IPython version
+      -p PACKAGES, --packages PACKAGES
+                        prints versions of specified Python modules and
+                        packages
+      -m, --machine         prints system and machine info
+    
+Examples:
+
+    %watermark -d -t
+    
+"""
+import platform
+from time import strftime
+from pkg_resources import get_distribution
+from multiprocessing import cpu_count
+
+import IPython
+from IPython.core.magic import Magics, magics_class, line_magic
+from IPython.core.magic_arguments import argument, magic_arguments, parse_argstring
+
+@magics_class
+class WaterMark(Magics):
+    """ 
+    IPython magic function to print date/time stamps 
+    and various system information.
+    
+    """
+    @magic_arguments()
+    @argument('-d', '--date', action='store_true', help='prints current date')
+    @argument('-n', '--datename', action='store_true', help='prints date with abbrv. day and month names')
+    @argument('-t', '--time', action='store_true', help='prints current time')
+    @argument('-z', '--timezone', action='store_true', help='appends the local time zone')
+    @argument('-u', '--updated', action='store_true', help='appends a string "Last updated: "')    
+    @argument('-c', '--custom_time', type=str, help='prints a valid strftime() string')
+    @argument('-v', '--python', action='store_true', help='prints Python and IPython version')
+    @argument('-p', '--packages', type=str, help='prints versions of specified Python modules and packages')
+    @argument('-m', '--machine', action='store_true', help='prints system and machine info')
+    @line_magic
+    def watermark(self, line):
+        """ 
+        IPython magic function to print date/time stamps 
+        and various system information.
+    
+        """
+        self.out = ''
+        args = parse_argstring(self.watermark, line)
+
+        if not any(vars(args).values()):
+            self.out += strftime('%d/%m/%Y %H:%M:%S')
+            self._get_pyversions()
+            self._get_sysinfo()  
+            
+        else:
+            if args.updated:
+                self.out += 'Last updated: '
+            if args.custom_time:
+                self.out += '%s ' %strfime(args.custom_time)
+            if args.date:
+                self.out += '%s ' %strftime('%d/%m/%Y')
+            elif args.datename:
+                self.out += '%s ' %strftime('%a %b %M %Y')
+            if args.time:
+                self.out += '%s ' %strftime('%H:%M:%S')
+            if args.timezone:
+                self.out += strftime('%Z')
+            if args.python:
+                self._get_pyversions()
+            if args.packages:
+                self._get_packages(args.packages)
+            if args.machine:
+                self._get_sysinfo()
+                
+        print(self.out)
+
+  
+    def _get_packages(self, pkgs):
+        if self.out:
+            self.out += '\n'
+        packages = pkgs.split(',') 
+        for p in packages:
+            self.out += '\n%s %s' %(p, get_distribution(p).version)
+            
+            
+    def _get_pyversions(self):
+        if self.out:
+            self.out += '\n\n'
+        self.out += 'Python %s\nIPython %s' %(
+                platform.python_version(), 
+                IPython.__version__
+                )
+
+        
+    def _get_sysinfo(self):
+        if self.out:
+            self.out += '\n\n'
+        self.out += 'compiler   : %s\nsystem     : %s\n'\
+        'release    : %s\nmachine    : %s\n'\
+        'processor  : %s\nCPU cores  : %s\ninterpreter: %s'%(
+        platform.python_compiler(),
+        platform.system(),
+        platform.release(),
+        platform.machine(),
+        platform.processor(),
+        cpu_count(),
+        platform.architecture()[0]
+        )
+
+
+def load_ipython_extension(ipython):
+    ipython.register_magics(WaterMark)
\ No newline at end of file

From 4fe9ae0c15f8b04e93c068b9bf3b910fbc2fed50 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 14:08:21 -0400
Subject: [PATCH 138/248] watermark

---
 ipython_magic/watermark.ipynb | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index bc2b250..7e137e4 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6a672b3872feb4ac09bbf846694bc0d4a04b3d7914e4afec8b2bd6398a5a57bd"
+  "signature": "sha256:fca7570e5ddd2ad9995dcf8f622de267860c7dac6cb0b275efffc900a181ebd7"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -74,8 +74,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Installed datemagic.py. To use it, type:\n",
-        "  %load_ext datemagic\n"
+        "Installed watermark.py. To use it, type:\n",
+        "  %load_ext watermark\n"
        ]
       }
      ],
@@ -205,7 +205,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 14:03:45\n",
+        "28/06/2014 14:08:08\n",
         "\n",
         "Python 3.4.1\n",
         "IPython 2.1.0\n",
@@ -242,7 +242,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 14:03:49 \n"
+        "28/06/2014 14:08:09 \n"
        ]
       }
      ],
@@ -268,7 +268,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: Sat Jun 03 2014 14:03:50 EDT\n"
+        "Last updated: Sat Jun 08 2014 14:08:09 EDT\n"
        ]
       }
      ],

From 8ecab48e20c5898a83ba86389353fb8d0e910f72 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 15:32:39 -0400
Subject: [PATCH 139/248] python implementation added

---
 ipython_magic/watermark.ipynb | 25 +++++++++++++++++++++++--
 ipython_magic/watermark.py    | 11 +++++++----
 2 files changed, 30 insertions(+), 6 deletions(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index 7e137e4..542227e 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:fca7570e5ddd2ad9995dcf8f622de267860c7dac6cb0b275efffc900a181ebd7"
+  "signature": "sha256:b293f22eb716f74ec27869a5122812437e0a7f4743ddfe8c06a94c39f31c7d3e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -81,6 +81,26 @@
      ],
      "prompt_number": 1
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%install_ext ./watermark.py"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Installed watermark.py. To use it, type:\n",
+        "  %load_ext watermark\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -205,9 +225,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 14:08:08\n",
+        "28/06/2014 15:29:14\n",
         "\n",
         "Python 3.4.1\n",
+        "Python implementationCPython\n",
         "IPython 2.1.0\n",
         "\n",
         "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
diff --git a/ipython_magic/watermark.py b/ipython_magic/watermark.py
index d54a074..aaff64e 100644
--- a/ipython_magic/watermark.py
+++ b/ipython_magic/watermark.py
@@ -2,7 +2,7 @@
 Sebastian Raschka 2014
 
 watermark.py
-version 1.0.0
+version 1.0.1
 
 
 IPython magic function to print date/time stamps and various system information.
@@ -50,7 +50,7 @@ class WaterMark(Magics):
     """ 
     IPython magic function to print date/time stamps 
     and various system information.
-    
+
     """
     @magic_arguments()
     @argument('-d', '--date', action='store_true', help='prints current date')
@@ -68,6 +68,8 @@ def watermark(self, line):
         IPython magic function to print date/time stamps 
         and various system information.
     
+        watermark version 1.0.1
+    
         """
         self.out = ''
         args = parse_argstring(self.watermark, line)
@@ -111,7 +113,8 @@ def _get_packages(self, pkgs):
     def _get_pyversions(self):
         if self.out:
             self.out += '\n\n'
-        self.out += 'Python %s\nIPython %s' %(
+        self.out += '%s %s\nIPython %s' %(
+                platform.python_implementation(),
                 platform.python_version(), 
                 IPython.__version__
                 )
@@ -134,4 +137,4 @@ def _get_sysinfo(self):
 
 
 def load_ipython_extension(ipython):
-    ipython.register_magics(WaterMark)
\ No newline at end of file
+    ipython.register_magics(WaterMark)

From 5739472a489e89bdc90ecf9edfccdd2cc9ec4988 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 16:17:42 -0400
Subject: [PATCH 140/248] author info  added

---
 ipython_magic/watermark.ipynb | 62 +++++++++++++++++++++++++++++------
 ipython_magic/watermark.py    |  7 ++--
 2 files changed, 57 insertions(+), 12 deletions(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index 542227e..811d566 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b293f22eb716f74ec27869a5122812437e0a7f4743ddfe8c06a94c39f31c7d3e"
+  "signature": "sha256:0b017c7b1bd605534beb62287fd7916407f3782284c44ee02093a73e4a9896a8"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -173,14 +173,20 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<pre>%watermark [-d] [-n] [-t] [-z] [-u] [-c CUSTOM_TIME] [-v] [-p PACKAGES]\n",
-      "                 [-m]\n",
+      "<pre>%watermark [-a AUTHOR] [-e AUTHOR_EMAIL] [-d] [-n] [-t] [-z] [-u]\n",
+      "                 [-c CUSTOM_TIME] [-v] [-p PACKAGES] [-m]\n",
       "\n",
       " \n",
       "IPython magic function to print date/time stamps \n",
       "and various system information.\n",
       "\n",
+      "watermark version 1.0.2\n",
+      "\n",
       "optional arguments:\n",
+      "  -a AUTHOR, --author AUTHOR\n",
+      "                        prints author name\n",
+      "  -e AUTHOR_EMAIL, --author_email AUTHOR_EMAIL\n",
+      "                        prints author name and link to email address\n",
       "  -d, --date            prints current date\n",
       "  -n, --datename        prints date with abbrv. day and month names\n",
       "  -t, --time            prints current time\n",
@@ -225,10 +231,9 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 15:29:14\n",
+        "28/06/2014 16:16:36\n",
         "\n",
-        "Python 3.4.1\n",
-        "Python implementationCPython\n",
+        "CPython 3.4.1\n",
         "IPython 2.1.0\n",
         "\n",
         "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
@@ -263,7 +268,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 14:08:09 \n"
+        "28/06/2014 16:16:36 \n"
        ]
       }
      ],
@@ -289,7 +294,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: Sat Jun 08 2014 14:08:09 EDT\n"
+        "Last updated: Sat Jun 16 2014 16:16:37 EDT\n"
        ]
       }
      ],
@@ -315,7 +320,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Python 3.4.1\n",
+        "CPython 3.4.1\n",
         "IPython 2.1.0\n"
        ]
       }
@@ -374,7 +379,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Python 3.4.1\n",
+        "CPython 3.4.1\n",
         "IPython 2.1.0\n",
         "\n",
         "numpy 1.8.1\n",
@@ -391,6 +396,43 @@
       }
      ],
      "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -a \"John Doe\" -d -v -m "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "John Doe 28/06/2014 \n",
+        "\n",
+        "CPython 3.4.1\n",
+        "IPython 2.1.0\n",
+        "\n",
+        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
+        "system     : Darwin\n",
+        "release    : 13.2.0\n",
+        "machine    : x86_64\n",
+        "processor  : i386\n",
+        "CPU cores  : 2\n",
+        "interpreter: 64bit\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
     }
    ],
    "metadata": {}
diff --git a/ipython_magic/watermark.py b/ipython_magic/watermark.py
index aaff64e..9b5705c 100644
--- a/ipython_magic/watermark.py
+++ b/ipython_magic/watermark.py
@@ -2,7 +2,7 @@
 Sebastian Raschka 2014
 
 watermark.py
-version 1.0.1
+version 1.0.2
 
 
 IPython magic function to print date/time stamps and various system information.
@@ -53,6 +53,7 @@ class WaterMark(Magics):
 
     """
     @magic_arguments()
+    @argument('-a', '--author', type=str, help='prints author name')
     @argument('-d', '--date', action='store_true', help='prints current date')
     @argument('-n', '--datename', action='store_true', help='prints date with abbrv. day and month names')
     @argument('-t', '--time', action='store_true', help='prints current time')
@@ -68,7 +69,7 @@ def watermark(self, line):
         IPython magic function to print date/time stamps 
         and various system information.
     
-        watermark version 1.0.1
+        watermark version 1.0.2
     
         """
         self.out = ''
@@ -80,6 +81,8 @@ def watermark(self, line):
             self._get_sysinfo()  
             
         else:
+            if args.author:
+                self.out += '% s ' %args.author.strip('\'"') 
             if args.updated:
                 self.out += 'Last updated: '
             if args.custom_time:

From 54e530cf5b586024353a631b6e2d3163d9aec284 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 16:35:39 -0400
Subject: [PATCH 141/248] prints host name via -h

---
 ipython_magic/watermark.ipynb | 12 +++++++++--
 ipython_magic/watermark.py    | 39 ++++++++++++++++++++++++-----------
 2 files changed, 37 insertions(+), 14 deletions(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index 811d566..ff43f6e 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0b017c7b1bd605534beb62287fd7916407f3782284c44ee02093a73e4a9896a8"
+  "signature": "sha256:9c5a9f291a08a3d3ea3459178cdaf482808d62014c15f3542116e57632436f7e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -198,6 +198,7 @@
       "  -p PACKAGES, --packages PACKAGES\n",
       "                        prints versions of specified Python modules and\n",
       "                        packages\n",
+      "  -h, --hostname        prints the host name\n",
       "  -m, --machine         prints system and machine info\n",
       "</pre>"
      ]
@@ -432,7 +433,14 @@
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
     }
    ],
    "metadata": {}
diff --git a/ipython_magic/watermark.py b/ipython_magic/watermark.py
index 9b5705c..b8736bc 100644
--- a/ipython_magic/watermark.py
+++ b/ipython_magic/watermark.py
@@ -2,7 +2,7 @@
 Sebastian Raschka 2014
 
 watermark.py
-version 1.0.2
+version 1.0.3
 
 
 IPython magic function to print date/time stamps and various system information.
@@ -18,19 +18,25 @@
     %watermark
     
     optional arguments:
-      -d, --date            prints current date
-      -n, --datename        prints date with abbrv. day and month names
-      -t, --time            prints current time
-      -z, --timezone        appends the local time zone
-      -u, --updated         appends a string "Last updated: "
-      -c CUSTOM_TIME, --custom_time CUSTOM_TIME
+    -a AUTHOR, --author AUTHOR
+                        prints author name
+    -e AUTHOR_EMAIL, --author_email AUTHOR_EMAIL
+                        prints author name and link to email address
+    -d, --date            prints current date
+    -n, --datename        prints date with abbrv. day and month names
+    -t, --time            prints current time
+    -z, --timezone        appends the local time zone
+    -u, --updated         appends a string "Last updated: "
+    -c CUSTOM_TIME, --custom_time CUSTOM_TIME
                         prints a valid strftime() string
-      -v, --python          prints Python and IPython version
-      -p PACKAGES, --packages PACKAGES
+    -v, --python          prints Python and IPython version
+    -p PACKAGES, --packages PACKAGES
                         prints versions of specified Python modules and
                         packages
-      -m, --machine         prints system and machine info
-    
+    -h, --hostname        prints the host name
+    -m, --machine         prints system and machine info 
+
+
 Examples:
 
     %watermark -d -t
@@ -38,6 +44,7 @@
 """
 import platform
 from time import strftime
+from socket import gethostname
 from pkg_resources import get_distribution
 from multiprocessing import cpu_count
 
@@ -62,6 +69,7 @@ class WaterMark(Magics):
     @argument('-c', '--custom_time', type=str, help='prints a valid strftime() string')
     @argument('-v', '--python', action='store_true', help='prints Python and IPython version')
     @argument('-p', '--packages', type=str, help='prints versions of specified Python modules and packages')
+    @argument('-h', '--hostname', action='store_true', help='prints the host name')
     @argument('-m', '--machine', action='store_true', help='prints system and machine info')
     @line_magic
     def watermark(self, line):
@@ -69,7 +77,7 @@ def watermark(self, line):
         IPython magic function to print date/time stamps 
         and various system information.
     
-        watermark version 1.0.2
+        watermark version 1.0.3
     
         """
         self.out = ''
@@ -101,6 +109,13 @@ def watermark(self, line):
                 self._get_packages(args.packages)
             if args.machine:
                 self._get_sysinfo()
+            if args.hostname:
+                space = ''
+                if args.machine:
+                    space = '  '
+                self.out += '\nhost name%s: %s' %(space, gethostname())
+
+
                 
         print(self.out)
 

From fbe7759fd7e0298bf0bd05ea4aac01b87aa8ed25 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 28 Jun 2014 17:59:40 -0400
Subject: [PATCH 142/248] updated install info

---
 ipython_magic/watermark.ipynb | 30 +++++-------------------------
 1 file changed, 5 insertions(+), 25 deletions(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index ff43f6e..273a006 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9c5a9f291a08a3d3ea3459178cdaf482808d62014c15f3542116e57632436f7e"
+  "signature": "sha256:72529e8db6a991fa9a1ce25da1f89e9aa64dd9fdffdcdbef3ae0f18098f5ab0a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -81,26 +81,6 @@
      ],
      "prompt_number": 1
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%install_ext ./watermark.py"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Installed watermark.py. To use it, type:\n",
-        "  %load_ext watermark\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -232,7 +212,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 16:16:36\n",
+        "28/06/2014 16:35:11\n",
         "\n",
         "CPython 3.4.1\n",
         "IPython 2.1.0\n",
@@ -269,7 +249,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 16:16:36 \n"
+        "28/06/2014 16:35:12 \n"
        ]
       }
      ],
@@ -295,7 +275,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: Sat Jun 16 2014 16:16:37 EDT\n"
+        "Last updated: Sat Jun 35 2014 16:35:12 EDT\n"
        ]
       }
      ],
@@ -433,7 +413,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",

From 1bbf720e690626013f2d84d3abc41cd50f790cbf Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 29 Jun 2014 01:22:49 -0400
Subject: [PATCH 143/248] Git commit hash arg

---
 ipython_magic/watermark.ipynb | 58 +++++++++++++++++++++++------------
 ipython_magic/watermark.py    | 52 +++++++++++++++++++------------
 2 files changed, 72 insertions(+), 38 deletions(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index 273a006..b6715e3 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:72529e8db6a991fa9a1ce25da1f89e9aa64dd9fdffdcdbef3ae0f18098f5ab0a"
+  "signature": "sha256:f60eef12fdb981a16a1ac0578a3dcf7377d991866b8528653a69fd33bf2ba8f9"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -81,6 +81,26 @@
      ],
      "prompt_number": 1
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%install_ext ./watermark.py"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Installed watermark.py. To use it, type:\n",
+        "  %load_ext watermark\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -153,20 +173,18 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "<pre>%watermark [-a AUTHOR] [-e AUTHOR_EMAIL] [-d] [-n] [-t] [-z] [-u]\n",
-      "                 [-c CUSTOM_TIME] [-v] [-p PACKAGES] [-m]\n",
+      "<pre>  %watermark [-a AUTHOR] [-d] [-n] [-t] [-z] [-u] [-c CUSTOM_TIME] [-v]\n",
+      "                 [-p PACKAGES] [-h] [-m] [-g]\n",
       "\n",
       " \n",
       "IPython magic function to print date/time stamps \n",
       "and various system information.\n",
       "\n",
-      "watermark version 1.0.2\n",
+      "watermark version 1.1.0\n",
       "\n",
       "optional arguments:\n",
       "  -a AUTHOR, --author AUTHOR\n",
       "                        prints author name\n",
-      "  -e AUTHOR_EMAIL, --author_email AUTHOR_EMAIL\n",
-      "                        prints author name and link to email address\n",
       "  -d, --date            prints current date\n",
       "  -n, --datename        prints date with abbrv. day and month names\n",
       "  -t, --time            prints current time\n",
@@ -180,6 +198,7 @@
       "                        packages\n",
       "  -h, --hostname        prints the host name\n",
       "  -m, --machine         prints system and machine info\n",
+      "  -g, --githash         prints current Git commit hash\n",
       "</pre>"
      ]
     },
@@ -212,7 +231,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 16:35:11\n",
+        "29/06/2014 01:19:10\n",
         "\n",
         "CPython 3.4.1\n",
         "IPython 2.1.0\n",
@@ -227,7 +246,7 @@
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -249,11 +268,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "28/06/2014 16:35:12 \n"
+        "29/06/2014 01:19:11 \n"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -275,11 +294,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: Sat Jun 35 2014 16:35:12 EDT\n"
+        "Last updated: Sun Jun 19 2014 01:19:12 EDT\n"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -306,7 +325,7 @@
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -338,7 +357,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -376,7 +395,7 @@
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -389,7 +408,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%watermark -a \"John Doe\" -d -v -m "
+      "%watermark -a \"John Doe\" -d -v -m -g"
      ],
      "language": "python",
      "metadata": {},
@@ -398,7 +417,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "John Doe 28/06/2014 \n",
+        "John Doe 29/06/2014 01:20:48 EDT\n",
         "\n",
         "CPython 3.4.1\n",
         "IPython 2.1.0\n",
@@ -409,11 +428,12 @@
         "machine    : x86_64\n",
         "processor  : i386\n",
         "CPU cores  : 2\n",
-        "interpreter: 64bit\n"
+        "interpreter: 64bit\n",
+        "Git hash   : fbe7759fd7e0298bf0bd05ea4aac01b87aa8ed25\n"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
diff --git a/ipython_magic/watermark.py b/ipython_magic/watermark.py
index b8736bc..35349a9 100644
--- a/ipython_magic/watermark.py
+++ b/ipython_magic/watermark.py
@@ -2,39 +2,39 @@
 Sebastian Raschka 2014
 
 watermark.py
-version 1.0.3
+version 1.1.0
 
 
 IPython magic function to print date/time stamps and various system information.
 
 Installation: 
 
-    %install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/watermark.py
+  %install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/watermark.py
     
 Usage:
 
-    %load_ext watermark
+  %load_ext watermark
     
-    %watermark
+  %watermark
     
-    optional arguments:
-    -a AUTHOR, --author AUTHOR
+optional arguments:
+
+  -a AUTHOR, --author AUTHOR
                         prints author name
-    -e AUTHOR_EMAIL, --author_email AUTHOR_EMAIL
-                        prints author name and link to email address
-    -d, --date            prints current date
-    -n, --datename        prints date with abbrv. day and month names
-    -t, --time            prints current time
-    -z, --timezone        appends the local time zone
-    -u, --updated         appends a string "Last updated: "
-    -c CUSTOM_TIME, --custom_time CUSTOM_TIME
+  -d, --date            prints current date
+  -n, --datename        prints date with abbrv. day and month names
+  -t, --time            prints current time
+  -z, --timezone        appends the local time zone
+  -u, --updated         appends a string "Last updated: "
+  -c CUSTOM_TIME, --custom_time CUSTOM_TIME
                         prints a valid strftime() string
-    -v, --python          prints Python and IPython version
-    -p PACKAGES, --packages PACKAGES
+  -v, --python          prints Python and IPython version
+  -p PACKAGES, --packages PACKAGES
                         prints versions of specified Python modules and
                         packages
-    -h, --hostname        prints the host name
-    -m, --machine         prints system and machine info 
+  -h, --hostname        prints the host name
+  -m, --machine         prints system and machine info
+  -g, --githash         prints current Git commit hash
 
 
 Examples:
@@ -43,6 +43,7 @@
     
 """
 import platform
+import subprocess
 from time import strftime
 from socket import gethostname
 from pkg_resources import get_distribution
@@ -71,13 +72,14 @@ class WaterMark(Magics):
     @argument('-p', '--packages', type=str, help='prints versions of specified Python modules and packages')
     @argument('-h', '--hostname', action='store_true', help='prints the host name')
     @argument('-m', '--machine', action='store_true', help='prints system and machine info')
+    @argument('-g', '--githash', action='store_true', help='prints current Git commit hash')
     @line_magic
     def watermark(self, line):
         """ 
         IPython magic function to print date/time stamps 
         and various system information.
     
-        watermark version 1.0.3
+        watermark version 1.1.0
     
         """
         self.out = ''
@@ -114,6 +116,9 @@ def watermark(self, line):
                 if args.machine:
                     space = '  '
                 self.out += '\nhost name%s: %s' %(space, gethostname())
+            if args.githash:
+                self._get_commit_hash(bool(args.machine))
+               
 
 
                 
@@ -154,5 +159,14 @@ def _get_sysinfo(self):
         )
 
 
+    def _get_commit_hash(self, machine):
+        process = subprocess.Popen(['git', 'rev-parse', 'HEAD'], shell=False, stdout=subprocess.PIPE)
+        git_head_hash = process.communicate()[0].strip()
+        space = ''
+        if machine:
+            space = '   '
+        self.out += '\nGit hash%s: %s' %(space, git_head_hash.decode("utf-8")) 
+
+
 def load_ipython_extension(ipython):
     ipython.register_magics(WaterMark)

From e5c221a3bfdae5d5a700e972635b198256c15507 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 29 Jun 2014 01:37:27 -0400
Subject: [PATCH 144/248] Git commit hash arg

---
 ipython_magic/watermark.ipynb | 22 +---------------------
 1 file changed, 1 insertion(+), 21 deletions(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index b6715e3..3c2433e 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f60eef12fdb981a16a1ac0578a3dcf7377d991866b8528653a69fd33bf2ba8f9"
+  "signature": "sha256:ae521d31a2ed8eb2f249825915f64611628365cb0ced53ef18be691238c0018e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -81,26 +81,6 @@
      ],
      "prompt_number": 1
     },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%install_ext ./watermark.py"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Installed watermark.py. To use it, type:\n",
-        "  %load_ext watermark\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
     {
      "cell_type": "markdown",
      "metadata": {},

From d0ded09a55b0b59303a2c7bf263d1825a02d6a26 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 29 Jun 2014 22:21:05 -0400
Subject: [PATCH 145/248] scikit learn

---
 README.md                            |   4 +-
 tutorials/awesome_things_ipynb.ipynb | 113 +++++++++++++++++++++++----
 tutorials/hello_world.py             |   3 +
 3 files changed, 103 insertions(+), 17 deletions(-)
 create mode 100644 tutorials/hello_world.py

diff --git a/README.md b/README.md
index 5c70dcb..ee43906 100755
--- a/README.md
+++ b/README.md
@@ -149,4 +149,6 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 - [Cython](http://cython.org) - C-extensions for Python, an optimizing static compiler to combine Python and C code
 
-- [Numba](http://numba.pydata.org) - an just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
\ No newline at end of file
+- [Numba](http://numba.pydata.org) - an just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
+
+- [scikit-learn](http://scikit-learn.org/stable/) - a powerful machine learning library for Python and tools for efficient data mining and analysis
\ No newline at end of file
diff --git a/tutorials/awesome_things_ipynb.ipynb b/tutorials/awesome_things_ipynb.ipynb
index c4a0e80..8d08bba 100644
--- a/tutorials/awesome_things_ipynb.ipynb
+++ b/tutorials/awesome_things_ipynb.ipynb
@@ -299,7 +299,6 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "%debug \n",
       "def some_func():\n",
       "    var = 'hello world'\n",
       "    for i in range(5):\n",
@@ -309,6 +308,18 @@
      ],
      "language": "python",
      "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%debug\n",
+      "some_func()"
+     ],
+     "language": "python",
+     "metadata": {},
      "outputs": [
       {
        "output_type": "stream",
@@ -320,21 +331,6 @@
         "\u001b[0m\u001b[0;32m      6 \u001b[0;31m    \u001b[0;32mreturn\u001b[0m \u001b[0;34m'finished'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
         "\u001b[0m\n"
        ]
-      },
-      {
-       "name": "stdout",
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ipdb> var\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "'hello world'\n"
-       ]
       }
      ]
     },
@@ -858,6 +854,91 @@
      ],
      "prompt_number": 55
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def hello_world():\n",
+      "    \"\"\"This is a hello world example function.\"\"\"\n",
+      "    print('Hello, World!')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pdoc hello_world"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pdef hello_world"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        " \u001b[0mhello_world\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+        " "
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%psource math.mean()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Object `math.mean()` not found.\n"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from math import sqrt"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "ImportError",
+       "evalue": "cannot import name 'mean'",
+       "output_type": "pyerr",
+       "traceback": [
+        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+        "\u001b[0;32m<ipython-input-16-fdd1a06c836a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmath\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+        "\u001b[0;31mImportError\u001b[0m: cannot import name 'mean'"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
     {
      "cell_type": "code",
      "collapsed": false,
diff --git a/tutorials/hello_world.py b/tutorials/hello_world.py
new file mode 100644
index 0000000..498b2df
--- /dev/null
+++ b/tutorials/hello_world.py
@@ -0,0 +1,3 @@
+def hello_world():
+    """This is a hello world example function."""
+    print('Hello, World!')
\ No newline at end of file

From e48fa3cd35b2564cf8949f47a1c42df38269e766 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 2 Jul 2014 07:39:26 -0400
Subject: [PATCH 146/248] upd. reference

---
 tutorials/python_data_entry_point.ipynb | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index 4b3463e..a68c9a4 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:cfb34d1c50e9095a633d85dd5229c48fbfb6c528e4e69f20b2d25a017f7cfdb7"
+  "signature": "sha256:6e3f719b1daa1babf21267ceb967f34d2d592ae35badaa05a16c0cfd6bc5acdb"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -222,11 +222,15 @@
       "<br>\n",
       "\n",
       "<font size=\"1\">\n",
-      "**Reference:**\n",
+      "**Reference:**  \n",
       "Forina, M. et al, PARVUS - An Extendible Package for Data\n",
       "Exploration, Classification and Correlation. Institute of Pharmaceutical\n",
       "and Food Analysis and Technologies, Via Brigata Salerno, \n",
-      "16147 Genoa, Italy.</font>"
+      "16147 Genoa, Italy.\n",
+      "\n",
+      "Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
+      "\n",
+      "</font>"
      ]
     },
     {

From 79830528ae71f26a5f6de80ceff42a3274524c00 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 2 Jul 2014 10:31:44 -0400
Subject: [PATCH 147/248] updated lda section

---
 Images/lda_overview.png                 | Bin 0 -> 84427 bytes
 Images/mda_overview.png                 | Bin 79543 -> 0 bytes
 tutorials/python_data_entry_point.ipynb |  30 ++++++++++++------------
 3 files changed, 15 insertions(+), 15 deletions(-)
 create mode 100644 Images/lda_overview.png
 delete mode 100644 Images/mda_overview.png

diff --git a/Images/lda_overview.png b/Images/lda_overview.png
new file mode 100644
index 0000000000000000000000000000000000000000..88563665e829882dcf89e8b6c419edd54358b7e7
GIT binary patch
literal 84427
zcmb@tV_;^@vIZL4wr$(?#J2s$cHY>wZ6_1kPG*A1#Ky$d+<a#r?0fg$yMC=zT~%FO
z-BsQFbgw97C22%BJU9>#5JXuS2~`jf2n!GpFl899FG}T=5+eu*+@y`TxU#IcIEk_g
zz|zM4t6T=LF5OdIZ4!I@xc43^7)VEXPt^;F0+Xi<Q56!G2!SRC6HH<%jiqHQiHZs;
zxhNSOVh3u@7)gVLH5v|SkSHl?fDO;39)PU9m%sh;C$Qh!%2Mcx-}CZ!7Q5>lWYF(n
z)EG5mI*_Vj1=-93YBOgA2Jj~wV^GW&d(imdSrlJjI8<;T<JA?Iwa<0>OKU?Q=i4?o
zoIn5+HV6#{Zv3%@lNzcJt3ju!EENb1Xz_X748}KC)FC%mG?JmWa$e_jR9}&Vg2QQc
zE>si4$XO5&FSZDBS%@Hx=#y9mN3y+$Sm`yn5%g#jOR|eGOeZfGD{=v0PELnxWHNh#
zzsSN-e~po-4UbZWWm9i13=^{^hPH+foZ+l~&dmR9kyT70JLXh#OH5}-e2yUPrE{^=
zn6wFI!y08q$XX7Yh(FRMc}U1La}39JNpErWCb$~lO{bFfnPHU<S=i<pQt%idkY5@y
z|CFSKuecglKmZfc8+%76xaJiUJ(<qmClHmmC$NGo!J2cHa{0~>P7s`-kRdZ{&Ppxg
zXW^?bD`NTC%an2FU#B1n{F^(s@B22xYJ@=>Bb|Fndq=L6)w#`nbEo!h?jxo7-QKaB
zLadzH8La&3JhqpIUEPCf0RK6N<iIRQ#yGTx$gcDOdNAwV{$LAqCymOG5|s*$4VP#_
zewTfXtbWulJT9$`5M=qv@fTE3$CKq2dTH$6`m!pJfp_%I<%B*vXGRbLV(1{bk9u5G
zkit_?r})t{xn|3cq6&Tl5-@KOXj>8Z6Hqhbc!?treGvF49uQy<3<`KUI1@^cIteKK
zAYLQtL8w?G;spd|AszvU`wk2bl<uI76OlgH>Hwk>v;ah?F+>l@{*GWKkOT%=g#@n@
z%0?V25!W1?m6Uq|8ZKn05Qhdy4k(d`p$qO10#}4q3HA&5r+B|eMd957W+ieLGNP#2
z0{#hmY{(=F;R+DZL_QPNcOcjaq6a|<xV%figS-WoG0b^w@*PqL*(mgCAoLpE3k*`3
zB9%=J+9KYxxJQ|^Qd=ENE<`tiP9&|!R#~yqL!J8!%NFGU?EyAUa(MxvmG>9qpM-b`
z*TvHl_Y*QV$S$PKkV~;`G3280#cdXhSg0|C{a6z-EHgA#de)arHU-8biX*ZkBuj`p
zqL@)Sv$)21?Ld13F4%57J>X`j!<dKhTjTH=ECEtQIQwwto|qGXJ{1#j2fincCz2;R
zcgR~L^={h@mzR+rgCTH!G-DV4+Ukb;X8t+_f^INqM*)WP8m0zj6c`Hh7;+ziF-fiz
z;UMcmBaf6Fi5qb-MZY0)4th?PAUjRYm5GrNTY^&MJ*7Eivx&Tqs+HlQ^i9f0E<30@
z5I;b=^}p4F3p155Q8A^>O07%vnSz`$o08_u&J82bi>K}|tXHj<ye$7s?;X+;+!OKc
z@<Q;!_OgGwdT68yt1e!RohEJ|U00f4{HR8aVGXweM+#R3CkclLr-dPf=^Py%Efg&<
zDliHjjg1juLz7K38#7ksP)t~AP#U+;<VMbylP|$sN?i1`G@gc$c9B|~+MGt0Hb;X<
zSCWdDwmwBWH99q(dPNtgxu|}lyjx?hVx!twbt~$gPbZ(&pj-H>)<^oG1y6idno-B0
zTes#HxqE9@)hpRU+L`Z9=IZ`j>4Ian1pWr&ttM||4r4^MIF`7dc+ea^98?@_93LE`
z+1;GX)_p%zS4?dvZPIM>e&Q{c+jQH&x5>B7+X!3HunOSN#=1yho9Ro7j~hu>w-{Au
zS*fndaI11_b?7`L5ve3pWfW>vuU2u_H`Ec=_Dl7v{0#;j46-3%S+iJhxE@@XVEN_L
zVp6kPnN*FsK{H;tXg!c=df}MssBh-C0bw`9A<MOH<}!@I)y9pPshMra9Xd-e1GMy=
zc|9^a;-AHyZOGaNC~-Zp%(DSFh?|w$VVWn}tlPXSssOllotw=Yw>fFO8qq&RYa&*n
zS3p-fSN^O3>=hiobER>0IMBBDwVyj|*c)wRt>654{I%Iqu@1cfXuoRxd$w~z;%x1L
z<NCuh*0t8<!fGk2Xol>_f;u*(V%eu+S*akGFqb_qf%osB>1Fc8(hghKfHi2~;2`91
zeOEof>PLO3BSHsHhreU!CEq2%6W$XwgCJId#<b6o$zIZd^V)t|MOwwkUFk72vsFWz
zA>kGcK==ym%8*#HK&Ak$V7#xZFRCxDZ$IEMpyq?`o%J*LbK~P}J=>nCJ717!2d9y%
zkUo(Bhe#XsnPP%N2JHtC0yH$l6%3KKb)#}?BPRr~QQQM40LKZZi2UJ>>Y;K{JSF*U
zxOR92=pL~=qy^*#_9tA&(!_h=*bvahUBo6Q=;CP*Kal?7@1pQ=YC7C&i)k3G9EUOc
zW$N6d($v<3F7KCqq0iR*Bf#c!se3v$-GaHhS>NA#>Xx1UK+8#!AYph!a-4I-e;4Rv
z%Y%M%@m&4vT#xOE{REjXN`Dx4WAH-xZUtKpO9@*NRUgHVHV$za!Zs*hl+h^m%r_}q
z#dj+1B6C}GP?dU;dZg}WtA9qDR7|7uBdt6wJ#U4Y3;j3-dO2^|vphi_jIs?^d8|3M
zU4buo=UyhROjstO`FAs2i%@;6uD5_5sb1=4%aq&Cbd7lzKi2@mIKx?k%}yz+*)dLW
zJHeGpBj~yqqt#ABfdAHO5@G=U85bHi72ut5m9B&0X|w!9@sD@5H~wvS;PqP(s}Yk?
z@^=5ah3iAY32~V4qhr%%v+r~VnBm}0v6`XgA!f<n#Wlh@f7L&if2;uOdPk8)eLGA#
zqS`k%FZ+7yC5k92%)O+q<I`28Y(!7H^XNScX<O)Pwj_U3ZgOf9sc*IG1wOTJ8qq;u
zdeRBf=hSdix2yZF4R5%*GTQ;v+AQ5o9*+Ho{GT4*{s5|2Yiap|1QXqNP6sPPH-*^)
z>l-v07;8U#WZbM9nQH56wwn6-L`#akqdKN?#S<F(JddGXkvk|JDZb^T<-jL|lq{9Z
z2S&XeUN#Mw<&9^J=P2RiR`SX6>nwn^)!O0$Fk6w@M_V8A;COznUoHjQYQ-2K=dJJ>
zcuSos-01AB_q3=TyUZ!NMe-Zwe-jY?-C2CM8#apV=*f6?){EnHb4$4`kKHBMHE-b2
z>-QRcm!_tbrKQ)vt8aJObjACU^8ujtCG^p&=Qjj&@4A)r0z|jhb1u2(w{QF0Pr2S6
z+xX$H?l<Fld>aC7{_Y5F^)9~AJ{bv5_^`Y<4TD$~?w~%XteeG+bN1)FZwZ{Rcv(*P
zRvybfr$h|@-o*|yf4{MyxS~C@S!e%a^-#i9QZeo~?kHe%>vs_LJbEzwE{yqE>tpeL
zHeyuU?=yy-kkFIflkcN;uleUsk*CKMAGH`YQU5o8;g_PFl3(Lp-)6pT7L*7J=jgvc
zK9#-KOs+lxg14c{RV&Iz2w<(Kuti82a}z-Za>PNDO%wHZp+F=$*L%Fiz_Mo#QNus$
zmu~}a>@NibgAP!3K;VNS5kY`%Aj)y~;W8Q`wlwgIIw0+BX&NkgY-silmM~;(WL2{3
zKYr*@5RYVmT(<?<+BUn}p4l-bp9_E#?tj<|2{|YVB7n%7UQHl$tvp(qt}H)23ZJZj
z(Q}ie;DdmG=G&-iyJ;&b@R<P|m`uz8rWQ<I4o+W-UJwugFTSr=2Mae75-$gPM^`>C
zL9+ki;QM<2hs;by@?R`&c7kNuipnJ702d1qP9_c}7BV3?5)u*t7jsKKRSBv87XSJZ
zB(rvNbK+xW_Vn~*@?>WMxL7f>^78UBv#>F<u`zyeFuHm>x|w(}I=YhoOUeJ}k+5(z
zbFp!9vjI4g{G->z6yWYANJjQgMgRN!%T5a~oBylH(e=NZ_0>S;e@d8HnOK<rSNE5w
zz&}(zWg9OGdu<6D2Mb5nuR4TSS=l%R{!8FLivF+4e-qXDKT%FzwttuWx03&s6kz_R
z1^?Eff4S?ws9*jPf)il=U*8MCz3U#`fPjdA$V!N+dx2i$dG7$kKmJ}>QcF}~Sjfhd
zs+K0p&^yY~Vlpnt(dt?_6hZ2U;iHy?c|^(3*{Bp@ESkbbF+6pBfBGcy_V?xN&*F7G
z`UohPf4d6^6q<h`E||~dWpBv3%jIqGc3`(U+8d3rI-!OD2BDyUiHX3#fRd8@>jM_7
zC7HnD|GxjXa3ByRv9HwozuEqm9E37KPU26S5aSZ|AG($@IH3Qt25*Q`=0j(d`u}gu
zVj{1}NB^Nij3goGzD$5=3iJPM=a&I8MoR?$AM3xU2dIgCspFEJ|7PKTGo8w3gZ~FL
zSO9D(<Mum^*1!4SznT1HUH{!P|Dq;Ji@d55VIRW%ugCsJWht`_(trBx_RGwv{bK$9
z@$dg|1B31<Xa7S7P=pBdRNh-ZmHuD7@qgU^IG^|b59c2pv_#}|8xMwbU-fmD&wh;)
zhL#yNPD;ILecwNARt5f#Mt@&d@c}(I>3WVj-iK*gdJb(rwT%4p+*iC`mkat1lI7b~
ztr?xh2|XDMdwA$pcF84DJN3Ui?XLDcwrzhtb_9O0rE8nQ5*;7{yu`IHR`dXeE=Mz2
z^!49OGlk(!Hvs*4pY!>GzG0Y}IANca$mkIRy-!=y8=xN+83yeso(_9mbW>_*!TeV}
zu8YNzyCs8|8AbswEEN?TYCfOuFPs{dv)OIGU8aAg)-t=U8hCEH&I{VSBpsIRft(sE
zMiA&7dM~|gUIp~PgbK5ft@Qu>0Gu$i{ZKW4jR<{`!;k_6Ex@0^k-;N$gN;u4+^}ot
zJu5f>MfMu7%yD?E*6KYg3`Od?ouc>sBXB;2DoxO|8;*~qYaZu4DQ|e2*5DTg*{1bs
z_{kjj0uS%>3r-hTJ`DSu#rbZQ8_@Ps(<DX@(CaR<eHdDnVaPW_oOJJVUMGD_d=n`h
zD--x5E=qjIcl#}0@kdF5kk6hjss-r+5X7S?Q6t&VERHi9ZuOwRYcC3QaEMZq9jkd<
z@X~p$MM>~MT|F<G3eJM)@AJ4-4*%cxJNP!Ln{=K5iV)!_ML^>q6qiav-|LxDSMcNp
zOyA2a;1XCEselWC66N4{(LBwRC!h}f>YuLoP5(%dd-J}f;Nt?`=y|B`?eeWsTJY=-
zAn>jK^PyqtNQwVX>%*#1;Jn~d&sT55X|Mz<yL@%`Z_^k?rtXVovB{x2H6`9KR5|#a
zxuJ%%Bjo(M{HLDdT-P~PK4kwBaqe|<^jyo(6imo}4f_)mPz6f{^ZqZJ;sqbqGI}p8
zV3Mfw+>$q1d>l@Y1D}T*0zb~}8PkjG``G@KM+&JS#=aTB5hu{}4I>K^$nWA><)R){
z*rXSG_MRIs`=84HeeCEj^UQGxc;6@f8m(i}gzoBU!D64-{m-N2-C=r)8oGBXcskCs
z;qtH4!Y{B=88J*<Fm(-W#c(jebEwZ3R>E&h3H}$Z1#kMtsnpp!8d?UD^DiHlp8Yu|
z?Gi>XIADRFcLh<IUPI{lL7a8S3UKgnDroCGy+XPwnEy0vz)i}S;*lAV2jpVX`=Q3@
zV}x4Bd6sk4o~-R|jz92J-pIB~Z^Ei+-7G;ca$&eciw0}c6k27FHOzw>u+`(ZNBei<
zdKBBpe(?_Aw~t?Nn_BVC&HoA5NRJ$k0i5W<xJ3+*Mi97+Vrrr2zGScVl=D{)7v8^Z
z{|&h6eJZCMIwyWx8%HXUZ6k%a6bJEKZpnYB{fdOA&!_%CK?(_0j9eLs(p>MOtPRI*
z2SL#^3^4Qvkm6KW(mZ>^?C$~5%_uUcIDo&&!pL;Xc1CZy{jq2fL_XGmQ^|`aqCT7F
zhQ~K6s5cjflhg&dvmFY1AAJvX08F@h=ku$;zuiAgUA5HQAJsrH`UAiEK+z(V`CnNp
zB?QE9)}eiUEMBbYV&Gfgt6F;q1{`*4l^EkD8W?&ohzT<lu+hSp>?EcfC6fn2YPJ9V
zHlyD!O+yp5fIw@Wb5-B<<uJo_Mm!U6+A_y?4n$X4b6+(m(aSvFdfR%E%JP`R?><b|
z+l%wQO(~c`3$wwkKxsn!`+h83atqAqKUD<0?i2sjc1_1_3bfyiqmjsH6>~C9<((*l
zcDXmtAt;sn`LFpT7z~WX8|fJq-U)|6e(ft<f3^O3)arlz8FBd*)&_Sf#*+W@^@zKF
z@{DU@F)(b!UH+eWf<+Mp$G|^o8J_4abKE>)mw)>O$*SnNlg97}TKHMClzA7==Ui`l
ziuR}B82Y^L*4^3`dj5IW%TbPNhQ|$+|J%m)V{-aVPxpR2Ka#CVfX_<_@kiwF)bcw^
z1P<)VG?F|1HvLRhwu!hL{C~|61EnM)_SD_U&5A~W9}g0O$|MH)Nc<O#o1WYLb!{tC
znh5Mor0tHXl#g%s%ijVI>5XtT)ph?hrTK^%Gwk2we>taR>AEQYM5Sp%lDATD-Cx35
zV*7cFTg+mT{uY)h<Zuo0fH$A#c_-AauQ3T~U+}^N*_ABaIM0mQF||tmUy+=N1S4=A
zjC|mvwUT|0F6(!{H%$ELgb1^?`97mh|82p&>o*NW1C&A<D%jWDh4JO4zp6#czkj(c
zEAn2vMF_u}Xvcc-63FdpB?#RnB>*8AW|0cMM#!>FK<02dHAeM$eK*q##nGRP{`bS>
z{Kr-ncDa@xH=}DbDGBz4geu%K)bU{CrkU&^HYm?u!#sihs(r)J_r8R<cToy|_x^nU
zJ4~((!yf{rNDWNR2U28jn4HrnAOdvF?ae9dxQ$ahZiHMDdTgN_LK6Y{_CZ`N#fEyU
zP>D+@2yCl`&s;+2g=k6D6z~X~q^QDPtNSbOM>dJ}1}>%KkDM<oKk5is%^-u^WQ&>q
zX<LO77usDa8g3CrdDchh-V)8($I7(#D-7>@kc*a`h6cxBK0%2ZVP!$Ujrb-~r=?2A
z3_*nvd@S_T>4(1`#_VVEV`{bk(_Kh0{TD=usSOKJEr{>lS9E_iQW!NF-$avv4}*P_
z={Q6Y0VA%92a}91oXoa3_cltjUq4Uu7*AY{`49?-7tx*Qyuhy5Y8$AV8^L&}CD!`n
zg9HmULfD-Kq#E4L@XI7M1wAqble9UA1GW=d_KDwjpjXkGlI^j+?}We`e-en#d&9cr
zKg3@xcFZN(i~H4Pkq|x$D=*LvnDbtqo|8)~G%{>)Q)x9tZoq*h4Q1)A=eJtAI3_|~
zG|HR))X=i<APritz|%XogeMVRD+D=0lV=z_X-O5n%bLGnW*M(+TDhLp?}N#9SZo4w
zN<WPnp3;@3AzO;pi2x1=SowL2lpf|&0Ix++e2qyEAX>tlOi|3h?yBa6#_(>|WI%;%
z%5Or6?nz`=YfWO(Z1PbtgEHWcpoW8C$PUSgUnZu`J}^QL6<?X(dUYxCUB=gxQ-xS!
zH|(0$X&FV3s$()}DT*n_Wt<nBir_l>UVk=}$2ISsg%ZCTry^&-`sz{T^iKb_5KjBZ
z1q^|G=5$ajMyW`aN?i^Sm-sQ2=qZQ_Aq;t9mN+giu;<?(0Dq;5aY0+MdS2@LU@q<^
zrvd(cJRLi<M%QiEP&Pt9dX=SV%E=~$77h%Cj!;^5FG^EYOal5p9u@VWphm`m%8%AM
zj7tV4<LVT_IcvwKN+!9}q^*PMtlmN?AlPOi%DeLC!(Aj=8G<7D2_V;>a?M$<qpWOM
z0tF*Rm;-L$ufhxR_qJ<B7viz9JrH(-P;{`o8uVn0lh<r2@|I-n%vBh^Vu!f}Sc>80
zbY8t`k?(uFeweALGN(<>l6&=&jZ{yCY$XV=TFy?aQmBuu6W1UufhqiXlVBxI7cX!-
z!Pb_4In)}ZG%knP=9oe7Ar}ONl?67N3SJs!LfbPprcfO2#cfj8IMS+_4IUasYBlTE
zsJ7+Uo>JBZH92_t6s^@mMo5?+f^~{urY=kDTqX-){;TK8|H|wWL&g~?{f4zmf)!4U
zvOAm%q_7NIW}OksZOB!B3uZh_fhg`<FlJ<9=@Ri+vGSN>5!DUiWMW^%?twO3Ee?G8
zVMjm%Rvdg1RSmWYhu^f`mMIFS`^8uCtR$U;rB4;b4fr@WnlHAgGqY8oCkqJ)MAw{g
zi04$Yd&moA`bf^`p?C|zVN*WVUl~#b%!epxSR+v;qC0-z5(c96r120j^;{2$JzQhW
zMBAinO2X1IF!(`IGZ-l?J1)~MWct4(Mrn9%`)Y01<f&Y)I9)zgFk3v}DjD4fS%sn~
zU$%gytlmPrazq2}#pxcv)+KCdISwJ-lzBE7Hsu&MfL3J06&%9^+U?lwz%W~5K;&&F
z0-&fCEHz~)7iFEw1k~@6mJ;odgv?CXiLVF8GWVJQZEeH;$J8rYZG`6*Js$1rjNMfh
z#lq|wGKzc2syxlWlX!D$$O9PU{Gm<vbVowV$W2A(Bt)QDd#qL84kR;+EerpAEZT)g
z<eo<%v9naVhJBOD=rx)SoqC6MYa$-&udr{?mNAetwHjG-`9o-`NZYeAx_gq>@t~Yk
zO)@`3*u+n0pD3b>EJ`ywW)fwzj+en`^J;HK`Tzc?5aO&W&TW!1cDtuEo^gyhT!>&B
zqR#g!{-*K`2U>;`cShF2_QBJ8S-f6p*(>y)ReEH~HCc{%RvCeFP%8qM^TdXCjCTgB
z%<#*Hi7fg@{tBY&bC<t0Oa!A7E^Va@kSQ?2{Y}2bXK1h|<vw?mLs!Ja^U7<Sf9ihG
zfo9(g!Rp9iWZ}BQ*c3o^xGgH`VXh&T?;OIDs;!!LqbsD}8inx^tNTodF<`D}&L$zT
z7PKk>aZHWK>ZAzC@Rz;dMd3NK^whTW$f0A?!%KkffwJ|xa%mBAB-vtebOIQ;joP?&
zDFPXRf5&eZc{dd2YUbTRc&#R?+IXJ5$Z{;-sqtleI2kug(zr#~2q@^nESz_a(~!Oq
z4x|q+WwXL4GA@)0W&lkAwZk9k70_1a2AK?1RRm{BjGpSEKj6kmVZ{qLO1P4yseSkT
zMNt&G_^}J+k{G!OiPS7;?+FsCw9hINdvz)vf;#-Wu}yjk+oD}TuA#=fZ!Zy!LN`(0
z-np;dc+kmS8F)DiRx$)i^Yf<cLH6&zk%xo@wLxP?xaYqZv<I0^dj<r51o=-oZUv1u
z&6!-Vsx~ry4Qqc`%nXe|I5Xjaru|Zo;D?+9R9~J!p|=ZkLHC9XYta=fh6ixsXArW;
zx1<wTQ5Xg@p@rP3%rfNTuu&0cR<JAAc7&`?PICyP8D1OKRBj|M4q8yDQ0s@UZ5Os0
zF6E0MyZ)_O!E5TdrPB-*H0lxGIkGNRJBNM8r!vnaj<uNDMU`lBU69SUDPEMDC@WCv
z3CetNGC#hI^vwEp^t@AGFiJ~Emi#F`s!(k<(OPI-(~iiUHq0}X@5wIsryGb&55)cn
zz5f|yxxx7vC^UgF2@7=3o$%k6`RV!kZnKC1nNxWc<IdTE<^#B$&mmp!&s$V6e3<7V
zE1wXpfNPVGyYCob6Ok8U?+GcRCrI1FPmXb92D|as-6li4LB5&8-8~7GKg0KLD2)yn
zJ3>&$hO;`>C>2qFIhd_%k^CiNEy0u%rzkVS$q@XKTg_WTfp7#=7lx#9F=KHjYyBd_
zxTy5BwK)&d(xp51;^<B|i|A|fP{OSqI&a#Ms7%f+IJ7?|zV->s8}zFgB9l-^Dwx}a
zHQGC{zhLC+Be`hL+y**tz7}}{OZ(+knq*x-+Nl9^>jkXl51XGEI3$k`yb=o%!mJ3L
zk~Bi}R8#$!12~Guj0V@tsJCau6ETMOWcnVpFmA{pC&<6uDYD!Z>C`HjJ5NIJ5Znsh
z52#BjDNni~{_bJ-<2W#mL^ticTMb7`X6Yo2RtupoN7bM(Y@gKB>eNms8?RfCn!OBH
zf-1jTINSfsaN_!|2TO>56GYnnSs1}Gsbk(NnIDGTU)3=Y{3*3FE$QW|$^wlr$U;v{
zSo)e+Ai<FZ<t6R!c=3Hdz7fo~q%#a5ia{c=8gl?8q0-W3D<o1qFk}DB2+SG%gOFc7
zlfXtB7&{q*y1q`V%Gnk#{OJXlKxgtn(>%z^w?^czwaH-X6UI4Gtz4>RzCl{!gIIQx
zB1WZ%oO?|`Lf^cV|6U<XzKmCX(J=%OyYU@rFD8p=fo>QqJ)9VqGq>@xS#or(_Xujv
zb&i+1OD`~ref5{vleO+eQMmNl4`dWOpDu7@fd!c+8FT&te8=1nje<jJJ*X`!3GEt~
zp1E|BU#?pvnBpH3>CXG;R%Pb!iKl9A`*GZkbZRL^%Kmj-^77@wjX-Y5${kIy-VW*g
z%uoAsd(Kv{%s6LwtLenaDO+oHC&ewgSV*bptaS<L7d4RJq@|4JzICc{lO6IayuVGS
zwt<4ipf<EK9UNR?gV6ED_9fwA^&)W~TJ$RvRXorWXYxe$q4GUdm~^`63<ONE;bExj
zbs&LJQm}w9o6PDr+?}=EI>Y72U-j-BNv0YPJANxd5PkOaGU+ScwMvS+A+`KW-^r6L
zy|d5d$7~kab4+tk<7)2`r>1I09JMo0oLc;;(0A7kph-ptva#%bW;8PE(Yqa84<mo0
zO$^mNpAWm+p5i{0$APO88N>}y-eva~M&zE66RKw}s=`x-$mE0+-+OD8(ot<m5TSx^
zS`>|#1lv+)ow71u=WC7R-FP^w#G$W^3S_vnE?ViW4UCdO>7F*K<tk5<`0-uh+*Ozn
zYpoT8mnM4aD>rX2zU8YT1RNr1-_JA9vSlG}u^JZJvu0n*pcmjP>eI*<CGyt4qMbE^
zAH$Z`U#o~S_heMXdLf<usMsVqm~En6u8C+_fbaQ2gdV{{DY}+2YR#-A!sZ6_1-1s}
z)@0zjpXXwJ-1q4)KJIiCg{-u^qG92kYkO~F9EsD1|77{2Y7x(<&hkM8yLH<0#{tlC
zl<h%#y~AWk;Cs<zxQJHk!GjYgmZFBgpGX_rsE$yK8g`9_l>mxqE2C0h><g-IzOwLz
zf&Ix^A92mUBcRto`ZK-qFyrGN)vmj#Q$K8ca!WM?0oVD^Y0pp?%{-=<UC}}5Pb=Yd
zRwgZHB-Y#tOYq4kP5d2~vNmMPf*g{Yg&5$K;k5}mj;b;PQ#{Wh8@&P{)>+qYFE_)b
zyO42RkcnE?HVjj{Qs8z64nu<Ng6;e#(=2}SaghB+7bj;lMZ7ZY_$j-Q1V)#ECqA|u
zy1y}p!(kMg&HmsBR(4t&DdCjWcO67{l|E>1eLlb=rERgY6DMy|$_@!#;()4ZHS+nG
z$(gWplj&T(67gtAtb=y#A@(BR?T=LnATZ~i(o>4mrt|~W^*2iFKI=?J>$&`jcmlqh
ze6m?l=txCs81abkxd2r^LteJRUv&^T#=9bA$hcpIaK8Q4d^OCdGnTz%zlufT^uE{5
zBE<}H^Q#yj{r=kOPM4wp7>tu;Qfezhdw{@i<B3jw5vu5lNcJt#js45&Mnl7qxLak%
zD5y&p1@OCf*Ex-ABtCvJ0*dI+huN(iICMk`hw5SE1FZt1%i+zuglF;);c2U4d^^#f
zUqDOpV+1Q3L}HFz{v0N<t;NIF$v@5l+fxxE89K=jd1-I;q)Eulk$A9j%F6Bc&OfXz
zH8C3F;uiF*TYn}VcE8Q~IoeKhpJHCr8e(%!D$FtX=G(vuheLRJxY2i+>tze7SMNwt
z!e<Dc$~U%y(RN!j)%F-w%_h^Dz~jWVFWqEKz9lm<_|w8q|ClDA^@sgBiW%J!`ZSfM
z8ORS<q2(KezFCy@E}o1su%_vGc$p8}l0kh!JF;4)@*7TKV4|T@TgY(Q)T6IcMViB<
z&+1BuOla1_2`Vmc$sk(Tm?CZYIXMDh#<-_vO}GEzB{Nev=aMo-0c^mp%C0M&NRo=C
zJy7x3N<5d3EUE*|@&d2j{Lyh*q^JWqpF<v;w;1U}AohjFH1{2tGC`x2x|a~7`}%z?
z69T`&CCN;a!~mV#R=@Z_FA*LUv8r_U6%8UMgeSJVJ4OwsL<*L<`4TgiP9^Om*_rh)
zVi5p0id^%(WA>Ng10}PFpQd&bZJf!X%&t)Ml<{bz9)Ual=9FQ;V@pBX?YdfXqtDuA
zb`h}eeDa6wA;tP<X;w=uH2E{OIL|N1XK@Uv6Z1T$UFI{c*W}{?3FGAe8`!yDw=z>F
zD?1rj3>b^B8vLb#Pu9My<=^M&=5v3KeBm`p+`=3W5ZxJ@w}kE{vI*P+ovCMuCbIGo
zptYw4F4`*3(|E&;mhjqC4!aswM}<Zf{F;?^9X#{dnlku$%o*HuL2PpmOSjq*3KK!t
z!{)m~notJt9YQt&?iWk4^vU8d-8xB4i$1L)?WGpQ>I2p??3ht1Nb-0=2Rn^$cgq~p
zq#(Cu?1nxacLZD3RI>Cs%NaXHHVr(c6JuR9q;`K|{|ltNcEYHbsfm_2$uI$%j<oyC
zC$8r-FQ-OM$`S;hq-~{wS#M^0xWXft_sncv>HvF~iT1)rSYxTNu7l=I5Sh%UN_&I|
z{;96H<`QTZvC;1LX7?-lct=<?ZzuV1?2n@dc1m2B+MY2&g4K1G3Rv8<o7uaI#G<t4
z2O4#cbeNZADhrpqWwgjgVWV!7bByTV8QvnZ(Nf?{h$fXUn4Egvp^yu!BxI)V(FGY#
zV%+)S<~>>{gCokV%F9y25b)69VknozAOZ76I#!-o+uKzD`%;Jr_Wdr=w?Bt!ZL8%A
z5UY5}-^T~i84;A?wZM=ce^+(*k*oflbQWI@okna7YF#DVnU`Tee~1IX%h`-Vxv7V<
zgv*gvtvozmMUZ)0BE`?NwwRRO&?NHo@my3joGn!qS=rP;^+#j7JS@>G;<GF}Q4-7E
zH?j53>wR__G{tlhnO{fd4G(-;DdxpvW@RF})$to|ZMQY;yYh@K{VQ}E3_LBihgPx!
z=>Vf+0E_1Ab?TqU$g9#RzMJA9k5m18#6lC$dl7tQrEr+1=P(OCh1Iu=$bed#RULC5
zAZ$%7qRvVzA?nk22?p#%#_rU>f5kKB-(=kO;h&&bixAzi?j|NnsM}IQ*M!H0$s~fn
z399D0m&nB)yDCLKy~S;v%x}7dJ4P;5@NZP7t&Vo__|L;$&nvn(6Nr^UAtfN&XXzz0
z!$OU4J2S@-TGTUEvYe_gg(d-{h_vuAGL!^Q%o<QQIRn{JDqTn-;)j*bV#1vk*<961
zx?k9v?{)e2^H#WMR&o0_)xT&Tt$Mj+i7T^5D6p0qC^E%hmqv1jU6rK~t05X)#*$&s
zJ@>P{X#mPuXRlB}^1u?^Ems^#UN`oO_H#*-LPRK2J4m5$6%qy`nQkxw@5Xg{iyuzC
zSk=-UzrXQ6e8>wGbHZZi+n<@p@*_~pkj@@Nx^#6N)XC%_<dUK<N_Tdw6|odd1RFB4
znmjlz?;Q1&fLXNAD1+E=(n;aec&l8in@f|@0WvbRn_tpTyQx(KSoX_S8pJm99ZgIr
zjxq}vg~PgfvI5d|i!wLGcAJ)^*C1j|GM8;o_`i>eIuO|WRL|=*@t98Vv!OeExx6*S
z5J-VA)QS19fCkeO8_ruWY1*wYKxg7M$OPC2w2-pjIc3cO*K(&6&+1VsMMw^9PN68M
zhpXK3+aXNVMmnNU*E}{HN=$*!zCDU=HAiTWv7U_XVP)=%t}JL3FPyv*(3v-=uK4F*
z^xcd!obGk8=km3p4{h&>uzM?Kb~>_8$FA?X76x2kH-pFl?PVi&-pBq|5!<$MBGD*r
zm!`<JL)FwuIZWpeM{A4UC;AHI6eNztV$LVmOA=b<<F_=N1|OP&_KKUzgmp!RS3cf|
zO)N9VRPEo=2cw;ohi3T3r*g<eqBxqmIRRxqZv}rUji3uXd0oNm+`qe!z3n~AkARq9
zGpSv7>!|+zW9Fz_!=T|)@({HcH(nD6WoR~M=C0MOVcz&5AVD4W_!}?bAk0^;6dvbi
z2?(r9vmFk-#X-_l)u|)AbODbw8B&35*WpzZ?1|!Nu^;w*iC_0fLCk0j2N!b4hqf{-
z-T{ozorG15TR!ac{$gXWGZ#1<a#HbSjJ3nGM?>?02*jfarjE|E3iQ||ESk&{9Y@<l
zwT(D>lW!!-nXyUw$)l6sJ-3pWbbu`xXRaTPzoK~9i)Ll~Nl-6J1=AQ^ZMy<KQKzzD
z2k;9Y9~gqLipdTimf-b(-F7`4gfF#2>}V$SUN4(Ez0cc0WjRKt`zC{P%khdoZuKM`
zmyuV<!yb5DnyZO$cZ{i2&=u7e<8P1bNSAWt%cJHmt~4TfchS^qSW9J+cYJeBX*{8O
ztYgG;$-6PNlZ$muL?j$}=Z)a!T+0cJ@hz2YU(9FY&mizqK0}Ppy)(~0wgyEc9x)Xk
z%H^1~wHq__NJR|@@fuDCTz%8+>nA8)=AFptN~$YWKpgyTW9$CB=Oz<9e(L9#N^0B1
zUvWC7eNaMUwh!>ZHAcBazaQV_lmS0}cMOEh9ZK?q=j(B|A5esC4lfhKx6`g!B%|a+
z<%L-RPktDMk~bHmO~);M&bBwZkKW-Hsx^<8A0pq8eqRvbNshcqL^0%i7Q=i<e`ea7
z;B|>Zv~=PVU{~CXp|fP(&4fU|Wkg+GJFUBNoJpr%PA4M+|M)Rio@^o%&&;IeF@Gtv
zw;IL8rAD(6VBdmX&`v=U&jM?VfJ}#AqonV1{ety~=u31HmwPsyh)N0$GyJ1+IG=_Q
z;tbX4N<zC#)@X`OK*vAIwce|L$Ba}*%wxB>?(?oujw_C2A_mI$;I~$3)(_$II*lw?
z=@8MTg28s1-~@jaygHCOnpd2@j3Lc~Pjk+{(RuJzX}x8c2_#Zkuevs}KP=5Q>wC&9
znt4r*TXgrnx<?qNol7j|c?I3Su;8L}N1BFqKa3nQ(6X$T^9|-h770?m=G^6Ct*YaD
z7F04*;np#pKdfu1iN&RUkI0(ei-`nRZQ>Ku;1r+P9Ocs2dM#BNjU;@zU;>@6kB2b1
z*f(z;G4LQCt)zyTwPwkh$z@0SQGD+}%S-o2!q#q=^F-f>OlSjH_u+HF4KI0avvy{d
zyT?l&9zG}~?*!-|XzMTvytQZDCR;Keo>&<I2iHIK@;13}AXtKv9xI1LZVmz!AxFw5
z*2n*Yk$@Nht&X>p>yrKbv^-~x>Gy%uxO>zpjt&sZ?op86f5nTcF$|`oSw7WCuU+<j
z7h|^BSBx!Dj0Yx7E%f)wsN8(QiAOnc2n^mPi^kL7an2yT#08=_%1l0t7`*_$c(`lB
zWY6AkDG&vR4J;sDY|T;AXefAq+HuG<U8&!|?xkvDsxLd+U9il&h4(~;z##8K0O}An
zj}jr=Gfwd?v$Yw+mJJqXFa-*7F72oXPy?nn{A&Vv8maF3fwbovE$4Pv@+b=<fR_m5
z)YMvSl4HAU*zh4Y5(61#n7XARU#+k0F3(9fAIVeDKHWj9vUx@0w_+~&P(m6iI#-0C
zaa4^xli&L%MMSNOwfV;kh@R_kyYBJ?$OO(h&Xt@v3nBSslyz##H=*^Wp{bzWP-1P;
zJv`0F(dreZGDrb|QlXvMND{F(+Emi@PeX#9_~%+DB_Oj>U}+&Zr{-NEhTw&tBk-F+
z0nyii-nZ0OrM}l=Sa}zUR)Fi1pQUVB>~zpeyhg)ZIt7GWe=9_b@YF7vTE`z1SwI<Y
zCdfR`K7v@*vYuJSzOEx4vW^!PM&ku4p$cZYNx;NNr2=rQ$)yB@L|i%68YSR@2PlY0
zhK}wr|2-!<L?ya}B`X2~SQubNus1$s9*~*eK713TeU$6ma!|O5NfxzR#9~JjhX|<4
zuY{pw%DEWV#)Zw?g$CaP(MLM4pf0PbqA9R1+m{yJy)7y1t(Z@nH3{UpA+2@t$j~OS
zCwm_qKNq(=`dQmXCa;zyW^4`ftn}14T5FJytRtk;zp?-UuNnA?_6vIk6msl!QV3Bj
z%zN=!8SN7hhr9uQ#k1vbHCF~p2#_sRegMw-o;U%kBdr>tlU0^Q8RT&Oj<U>)O?o@f
z{hUcuk=4G(>*qbWB&hRR8x2{V<biZ$?kjh{>Y9yHdMEgh7#79i5-=lfNlu;5PNV%>
zdq=x&;r*pZu?enwx+XawFj;)%n<--##L%g<kVslyk#>qaU5$jrWC&(6gzyTLaa1Fi
zU_iJ=!&wvO@p2!CSi6~GQ}c(Su(^S&X~aK$842B#A}OXSAa?9Z4li$abaEzNX8>SK
z6y!n`O{`<)WJZp~>G<{|n%M2=WhSlsZnjrJF@)3J_C((C>Xf!R7?brJcS0<pYpCMs
z**QZOc*Y80E!WfPkq=rA6~8*O!f1sSWJUZZT<fyyygpMU32I7%?kZppy`WJID>642
zFw_VWH|&_5Z*mB+xUc|18)XK!IC#e|YiwdFk1SjXidV$hgNNhRY3^<UYo4OfO5K>^
z!A~vro6wmb%X?aC_v`jccR;t+aUQBD=>k32vx#aqk#59IN2Klw7j~LWFA&uPxmpK;
zT-EP+FqKl_qQ@GCRzE&U&cj#s1oU?%v3Zj!szZ!0fli8iK4c0O(%rQyeL!V0ey82*
zKG%iNbACkE4#{mp;wlGT+G)cv3U2A03NanCGsWI-_P<0vwDM3Mg8aIPFEfQK8Z3^Q
zswLVqygW#CKjFn^z-)^`8TL}TX$sePG$rbr=Uj}T_9*43(v60zCnfbo29RGaCf(pg
zpL!%m2p_?!RWO;d?gU_>u18N3Fp4928O2qVk&eSFmh&Ze1qCJ5xqW#t=X@uGaI*Yz
zN!yyq{r5U;tM^{kijwd?me3Sle>j8ohg+&QC&-&yb0<Ej_`6j<+drP{X7_pf*(&Zf
zB6OjH&sxeE>c-EB&zoHE>@0z84=2~z*yG#ZvH~zG<eYLnb#$7syVDpTBMn$T;&{xC
z()_&<8<38rFsg?yJE!-bb+u^(CKwTdom{a5G&2;r)*)1{)izjIVAgkkP*RPjQkt^q
z6iuI-ek3AR+}TRP6aq_^(KbmwV2Fr}4;BYehsO@KZ)z)~oRa*@bN#Wu`!rIfrIp<m
zoo&#iN@xtTCv?Li0m!J@xo~Vw_#O$V1Ys5CEXj517I6D5da|c!;wRwMshpxR&4`PN
zsXK@G5>&Q1JEpMsX~Tae&|EHm6DB;|*3WqEj+}~loJ-p{(|5TRy|JEjfev@YmISMf
zr0%+3lK&H$Yn@}9mM^>Zi&$OLuLt9NvL6Us=8)(2bLOKM1@{o*)@_=_%KSP#decrU
zTN<|nSFpPW+g0rw9HwRIT(11Bgb=_H+?bE3@U${cxwt(O?uqam)JXbU?$MvGmT;Un
z(60gFrM|e1&;DDr^EZvhEDPj*Xocv}SXWUP1T)=VGe8@cOEooN|3iGd?(MJ3L2C$7
z%7_MH0=CU!obF!FFTl`^Ehq_sc!2F6k;BBcB^6N81z0czU042yA1trtjI<Lag0O|8
zvoxFI&HtPmtVUkf^*57FMl0TTkEQ!C+^?W(d!CfsEhM|4COE+`zaS+WJQgwYHc*j~
zl<LTk6Sr^%nW1@X`#&{m6X!`MaT&%9lVYlf?V;yUv86;hGGuZ<xYi4crQhbsBMH%n
z>xuY=WRL{znWAu2jw(@v$op11hRu!wfA9Hb=|){Qa}b9982??;c@eWrN}GKGa4hWY
zC|*<xuBv4Qg-9x)CD0anK8f=e<L{M6x>*=esOVU_q9ui^s3G*hgKE5Gep>_~16h_D
zXM(obxQMUKNOLs(fgduMosTikNyQz&Nqm3(jr*ST{7v<^!AC8Fp#Z1d1+-f}sG!PZ
z0yn^%jEM^VYD{6{Ub9<SmH9VInA7Fm-4(VT@ftj6(8x}h=9>Mn3jaq9q`z@y8LxAe
zka;bi;_i{jd73zuriw0%0t6WF)GU>>nvpVoeMqPReRJv-G{k+{4$;$}JObT(pUe@r
zzgG6S+j7atF0F5yQynG~lL>x9Lf1NSC*)7Z_7)s)1`f(`gF10F=@&f4JC=~`ihs96
z;=}tCM9;5hWP-I0w6ya*GI7X500-Y=7Wo;Pt9JR0Cu|rCaW^w=KurzA;ywc0RxH)(
z*Utn)M-`mmp?i?oXeYWJ@M;EXSu_Q67y|Y2P?l420P2mRD?MRuxsEDnMQo-hWbWEX
zCVGs0&24R7oi_^l*KdQ@XJL#gEDU7SYZXY|-YPZ4(d+UQaDZQ+6%@=#gt4O6Ca(@l
z476htVu0`X$$Lx3P_Z>GG^j%U6&CHCLTnYcpOia05DK|s+Bf9Z3!_oBX5we@{FgZS
zrqYoZ&?UUv2zJhnZPQ8l^Xs)2Mz`|v*X26D#Zrk4o1N27`m6yo=rN5Po@5b+<1~oy
zB&jh8CT7CqKY%T<y8Ot~c1yUG^1w_+C<e(o{h9fb)}UW2<-=e^$U5F*NuyE`7)u=S
za#XM{7g9SsD$F9nuruNgP?Ua(5EtvXXof)$gTddYufv8epT4f};MGv8Wt%Bf;Sa{=
zhrhzbHs-kDMZ_f&K@3O!kT6mjNmO(zkPPHN9gvw{OnUGqU*oU&1H@RfL0^w$z=7(A
z{>^~z6t?D|beo7|^9;hep+bd~_`?`GZ5K@m4U(C}l|%tZKwF1-cz<7SLTZayOa2-#
zSsjN3+A>sN>6`wpnLWWq?{Uz7#M!|6xMrr4mxQ#=pRF0|Vw_oiOoA3<>(=prrUjLa
zpA3a0;<0pl8nZ$tke3#9(P&L25lW8lWIptT{2r%2P=z2+7S!x9u|uEhsWpM#-5f0v
zN3ta87BKPC)Dap(<AvLMkef}H*Jc0tUF)e>kCrWdw~N4+E>H?Dz)K*t`V0D^y*<cM
z@lV~)IuPv}j}$0+kLe6MUd=RH5eY;rpeSxxSP<SLd8w*|dVUJ;Qo^<uZaK=(Mldu{
z<nu_8GS^-JcMeJO>Xuur0M^Xa=+^rYH@T8{^oxE;7YX#QK7<Rx+?69Y8K)zx2s2V=
z(y{B(_A5Sy#PTTenr*ZK&H3YNmXuGkebHaM>Pt#|Qsx!Xs^xWwp;-lydK6mQh3Q!0
zDq{+1kJP?>xOV2cY_lea^7v%M42(J{`=1c;vGl3j$edliy5&yvj2BiM&ibl8*r<bZ
ziMtQtunO=%-Wi+c81Z0Dlr2g`W|jy{(2w>O7CIEeF72QY)T7l>liHQI3vo#dB;Es*
zD|VvNlX`=_igrS`lQC`AWwySaL7k=UgX=jx`k8e5V3y?s>cMriqZAK#l(*Dkz0)#h
z!MR7$dcI!06tnG-9e|oi@#jV70(H(5@WsiAeqI&tsCzehBArDz$I{@2bT~GdaUYa|
zB%QHvrYblHkoHKsXa}a}3nSUJ-KG_yNmQXGv$Cv%Pq37XO+(Q<1jqjYr?I5>*vZ8=
zc~o}qm>GIBdGqo4EHM@4$!M?x@`*$iT!vXfAbf(H#nS_M6>`1=-NUW3g^wjA(|CyN
z5jTRhRsC&0yHWZmcVGnz70o^nFHC2rSTpy}KbKAu9LqB1Hk!K2JawjPwFuY~w-jAx
zI-u|t8571uT_leaE~PSvOeT`?Hk+5DP!Wp#y4?uT=(+Izx#+;ATI7>xAkM2eNJN|k
zP2RRTOSM3$;5gV3wS}KH2w`73MTXUu=v_-e9auOg=~gU{q}Vj%#@VIe9mPLQ(<+xx
zX`bW~Uy0Qg0=la3h|{+oLbMsjgiD^Ps%s^({tahSvXxyHM2E~ruq0W355UcfqPj>2
z2;((gAVL@Ib!Xd0ZOSvyER!mh?@m3Mk!cApU@v+_OBn8)jO_(o9XC4LBT+ID^{}!g
z53x2<m}+&(PB9pE;xDJPbCHrWDjs&s9~973Cef*6ct-RXn*z<O>Vx3y2yGs#h@H6^
zG(2}^=rPDQ%G!xL94$xgNwmjc`VlRVs130^)QI*sm8fXa2GqrvCLj}SEUgs*`TUpA
zE!^QUTRQlJhq;GG*AUk1{M<5X&!Pm-5BqH=G|co+?~xJ&s<ld6RCh|#W+?-OGtzIT
zC^5QGz~Wq&s(d>U_PVUf-h|%`KE&^tQcomH4Lo@9M@FQNHCiLt2BTUkv5{3hE)v+r
zyO&Xob)ep1Q6kSJUX5gskxATenYf6-np`?2HV;TXrPaRW8B#ZDxwl@N+3@crrooe1
ze3`#Ih(pmPc$!zSU8;W(Y9efqktFua=M}u@6<W?-UKTxMbiFb-TnAPm`)ITmzAe-$
zz=*mI%n&c`Hq-k@F}<kpxzRdIxH_R^r6tm4Fs)elM~U8xFet3a^eS=5k*ID=os7#^
zK2X~3nl{^rL@HMk@#vTy|4xf=7`hWwX<%kGbOe(^ae=c5`HtqxDvBUU$*7znzLN`I
zY)@fRJ!ChUW2gMQo~a|^M~w;N3a5@PdiH9@MN2WUwL*3rioU-p5zP&@TAU{FVMlkQ
zi6a(rW6X_7?y|<k|NPtf%n&`S{N1LA`R3Y*vV~wR@%|oLg93iLD1s<^Nwj$$ONl*a
zg(VlwW_Kl5<qPt6V@e{a*!yEbh*$vjIsIm)DHslAvWit#+Xq2^lj#y0uLF(@7aD`n
z_)F7Fbq)7h$;(=1@%N!8!EH1rSNj6Dfhn&wg|qph9>P{RSs@M7MObE?2?uvkS#ko7
z9t#@*OQv&_S_I`J^Pu$ae?Q;4IVq>lNe`(_MG<K!+7=qySz|6ZO(tjxJj&Ui9Y#o|
zn08TUW#reXvhOM3x|wEeGY%&tSUW++vu))iJ+ejPmM80ufjd^d#s4G?S8DuAp-+!X
zqoIh{Nh)~2<8QDpSYy||ydP{gMYmO<-7h<8e#C+MSx^;9OaPy)EDsBu+m#wTT2roW
z2DEV@#~oZ0h1w7Acd*QqCiGtp?-fPF0tt!s<I!p{993HY+0Z!n?bkhYe=nO~KVJ!A
zPn6;^Q-93JWn8=D?8n64cqdcs)jB(Fn@FP*A4kfO2?Lu9Wyn=PyD#L7LhyPP!)T~P
z&LW3>Mv2S{C~D<wGQO6=hl|0p*U&;UWP2yY4@g&(gDETM2(AGLfRsMq_OL@(<RXGx
zX&@P0qs9!Uw&~}xV~Sb>vr1;xP9L)&@W(rxW+oM3O|t>XMgdF88n0H?iSU`U7dD6r
zsXpHj@@Y}j7rfO~uS1#(g4CrclgN>dXH_JkwP4@N2!Xt{o{9l}00sYDnlwD}VRQb#
zoRC_dB^!T&rRT^}Z1f21;_+49C_23115I*WyOUJ=E(6yHX4)BES*Duvo#DdT1JTSI
zW?H>mTG6O@gQ0+l8ypn?AAFkoHd0QdAk;UR#)(V`yoL0S>XzUK7SIj74K5-v;EY@p
zXMI1731<=Gh$5^rF3#-C16R#tg*gtp+UKy^NN7>dAJw$(khu}N-QRFs0Y(Hg1vme6
zJlK60NFK8kLD9=zm$xqu#$ktch{FVfkKTy>E)Rg8k36!(c!vL0#FPXCPMCq4t$IC&
z@w^%pF8fUHBdo&)3Aqk{LW7Xx<n)^A=-!&<)11gN1XPZH>es^6enY?we0hxkNQyig
zd%{8ZR}YMP`+=?WVxqC=7i4WXbP$43Vs&Sej3q`cl_Uf)KMJ|}D7sZLvmQ31^$$-7
zD0D-UKY-Q2J|Ts<q0s8mheK+MoA^rw3!!feYhr=HBn_<j?J*-o6EwF)h&5dl;|w@x
zqvq<;LyHKwqb5puk2^Ppg1-Z?*$*Nbv8nMXQRa*tNeS2Z70})Y@pD<SdGKVi{2TC0
zO03iBOGR_3tRs(LibXYGJjaGy$ni~OBJRVm4JV$cdm?CzbH!(UpuvFX-De@HN$#q+
zoSKv+iDw-ut{&?c309u#*@wCWunG|i74pxAFtM#XOm_8?&_Ao07g4xIt{)^M#;&F?
zLa2}suR!eRzkXTd>ZJ0A4T;>j%r5jjF^ffw!3Z7FhZ_~kr_reXv+lc;Q8~ngfA#;c
z_m)9*EnD9&5C{u*cXxM!J6Slv-Q5Z9?(P<xV8MbUNYDg#77(1^5Zn{so$P(~d7hI~
z_uH+yb*tWw3wm|;9Nj&-*X;5855|Vf1RM#ZK0H&6YjK^!+Cb%3Ke^vw`qg3}1aBAI
zSO6w`+XOSABw|PGHW7ia%>^bPNs&K%$4wsGKR}5pj;k2k!-WVncpS%!F%pK&84)Ok
zsgRVXcp6mrE(ngNoG+IeskANJhqw!y-6V{=02^87z7uEl)f>&7j<+c~y0m)Uo}X_s
zkx|>|C<+{-`RH>WIm`H~+7M)yu<_@leG)EL$`3N$2}98lP3o8TtzgV%?%_;}j?QLQ
zgIHOUtyl6?Aen^`bEST(FPPw`9oE_%k<D?1gnDszl0CAB`Mdnb^UkMKxvWLZH(@x(
z2i9DZvYH3;T%yS&lPzQdH>kGV2(^WrOQo8p!5)$Ah{^tfZOXiy>@*sKJ*4)$1@#CJ
z-MVQ;7H@elHsHzFH=PZ4(@jb;ENjm_1q2aZG;`S^oiP=o%YRk%Hb|g?DH6j)@!YoH
zW)ezjZWJ6aj%zD#Q2hA%U~%;tI~q!F<b2~uGq^X%74t<+`949il(+w=gcL0F%f@<h
zdaXHUD2bQMjeOLY<rpnNos)X);aCjV%94ns1hTDe%_&t3L2z{K1Kmpl$6t$<cuK`E
z&py}V_bfK5A}Dp}`6D!XF&-)K2IkQR^T!eu$)i_IWm>~!kP_;Z&`!R)S$0#-wd4VA
z(^VBmB4O9@sYkmimsIvNjEWIuR#lW^3qruX+Vz8YgyC`ziBb4Vvq`*CsPJHNXF?wJ
z{f3Vj(USG2Fq=Mt=Yv!u*uPmv84r!?KX4W*x^O7<U>&)i8pQ6WQob~1<PolS^S7bR
zm&+w~MJ+~#v7V*hq4(^RZhhIYrO`i&@`d)p_le&eIqbB^_*r+!S&}4z4j51Tj{Wpx
zhpM;fXM2u5(Kl;1;4se(2FGfo3nZIa$E9BhC#20qmDFen)1dlpqI8DknAfh+V^Ebj
zu)VFnyk|@^UTDI4o|Q&T-3Fe0Z}{COd&(1D4kHt$Gk%c%wq3x*K9Bw-J{o9g00}wQ
z?5<}yYAcwmJAd7<>kF(`@8B(X*Zc4^w=a6qmc0_Q1!LMgmQh%Cv!_&?pDZz=`a5yY
z)djTnDDlWr!Wje8;%Sws3#`0fR+{W>p%(XWqCEI_mR?iuahc61IB&Jjo&(ti>9RX%
zK@KWTD(|C@1*#Kw3w;~)o;X=cEXwp61M08dR#XO`f^@hNO?Isk@020*FvqX;_|Rkc
z``+{s$a93A1QC)-<|c!b<;p5NO?V<f{`s=kQQjpl$B+Eh^{$S@j0GudOjg{(S?pE!
z&!oE-fR{?&b_iLQ1|5!M-ct-RhSmk~p=PM5oabY|bP^pN;PPH9sa*=FW(cl@-630%
z-j2f8eM8>41-CJ({JB5&E80sKtgqh(j!BsNH>$>%zE+3L4*G_f!Iw0OI?=$3?VFac
zVXt3ze6M-+^(Tko`E3y$-A}8m7c-$7V&4MXk#~lw6(cuVi|mAJc;pD@;X885Vx$p0
zP?$mAey{UN$y|Qy>Wf;xyCn8NB$8r)b56tcK@DWGR=<2VFxX|;v)ATSf2zONH(266
zVSsL9vgJz))%&rQyyiPE{i$~cik}pJdjJ8+CbH^^yc?>?Yk^UdoTAa0q&m-jjz(=U
zvZl(&1&&4!DeBRVw`Zm#CMuvIE>&2Zkf8~f7%h}!w&t_&hf3^*UdB=sCYfM_QMUdV
zjGmXe-HenPZqd4f$5eib3Zezc5CZH~#r_xNpTV+1Y$~Uj7|f~L@n(ZrUe}wa-_JKs
zuQ`O*1LAx?`FP>IlDI|XL@^FclHj2Iofr@it~Vn#tTN4K$lC7}X1McmnvKp@0#bED
zUEb0n7o0qqX)7owZlN86UK^CV9m$vUPPr1nR@MAJS=a6)jCFIQSS0C-(eH<m%kMaB
z9b*CmE8kL2CS(cn`syN8|ADpQ%fkMi^bm6WvG62+bP=ee3{n0AKMi&f4^r;IbQott
zS#<D$ozIL)bYLv`%?!Y*jrH{E|5-YY9>)6hk@)5}c}pGx^Sb7rX_lEHxsi5N@8bYA
zcc@53;*avsFnRbUOY-~yp;SA=!aKi_6G}J9QDV{)otbB{TRh9y6qkzTL;psP{?+w3
z8kjtDudf7F3wQvCiAVNpIAAG<i}bbtb65U5#uBl(J$!rOHkYztMa8c>uhhs6UEF5^
z=zoCN2dbdqu=4(!!g$zkkc%>0CEuM~_&lg_I#vPn<>lAd>%Se9HX-7Z{@pD8m1vW1
zr2p4<fr=DFVCcbqM+Inu=LqHHtEY>sU7x{nhOK1&wziH3+hC&{AG0bt%>UhHmJI0B
zW$WGJWrZK}Y5=|JjlYIFaJS@F=g~CATmAd}19wPGrfhoGERrtcpS*3iyM)-TutLQY
zr0Gx6cM=I01Rrs*#eYtMAoMIHfc4E$Dyg;llT|Ln1DNNlkc{C!>tfFbxL)pj8NCTc
z*#9J@#n^z#4&YeQ|Evq}Amsiwg7GZ{8vkh_P_Q9T+5gkS+)uj`ItW+)30-%RllxcV
zeSD+&XI=YY-Ff?$^#&q`P5~FW&p?TaAHYGLCPfOBf~PxYasYx+$0}dC6Gv0qq`J1{
zAiui}atp*XIPd0ruAT(k`0jAM6ZkN}9`Voxz^5PDHW39M7ru#HZveO_zDV$AW`LiB
zr96G6PFH<T4BpOZyt|*RP%`GCWPy!H=mr4wFK6*ilSg?1Zic5Jc*vdpJp4AGQS&3Z
z+A|kNFCZ-R*Qk|9A6B0Gw0xb{02A$v>W8j-TP!^Q2$FpS(6D#Q<H${K3v#Ri0Xoco
zzbq5AX~kw00yOpjV4uk7&!|gl$2Q9hF^X^(U>6LSg?dK-(_v{@*zC9%k9FKv1I6*c
zAmOn}sc&KcF!ZNa$8Bac@1*lj5TFNsT)}1{{>KV=<^toNhco`AZwKj>-r7CxxMM(=
zNP^bHoZjE`8~~ylAEA5o5v>NzHvBqnPc)dA+lZDC`;~bwb?cJoyL`Rmq`Vz6qz8yI
z{H+B5ppZ?<m@6839*Y*T6<h|wxWOK4cjKOg*DbA`wM;q1GYBxSV&DCI7y3vc2;vxj
zVRz3x*$1JE-+vQ*bm_c;*c}XwW^*~*>DgA6<qXpYzNQ2QeZs{5!vBj1lc#~h)tr{-
zk2tQ+<%~W6Zr}ak=HdM2-vqjG3?;iAggIJI-Q8Y~6ny5z0=3`r=QNtZ1+euQkG-q)
z|5)?DhmX$_432!|4{so0{GD=g_mM`V*4U^+$F8=S^s*41z%NtZbqKHPG+gxe1{wf?
zj>Ry27;JUnU3Tu9?>v6%7emo|Q=aR%*(8l;;64R_a-MRvE#GYuz51yP@24|G9`=<J
zU-KG_zY~2LF?!@&BfLNK{{nFCxrtvzE{o9oN6ko^o+IwR{61fd@v}eJ6@B{T?;3*(
z(5%0#V#BkA(nuPfg5ZUWSKiKF>CGGZ{seG#K31FLx_%##XaJ&(NApPm_1pnh72l{L
z*H3FFo{@JQ=b$qHI)=Nn$?23oto!l^@Y9zcD}-6>X<k18lrfbR0MV}+hobcAA>lIL
zUeyJX?97&WQx;UKq*w_(T+U30{IYj$JNrsRaLfbI{=uN^Jq!dHX)Vy}kngny?LFi3
zDHo*7sj54Y@EHj*J$Bw9iPX~^T^WNNg{gT8^#J+k!vBpn*It6IQ`-(^>zT7Y$1$e8
zK;c`4B1i5|efQg(*XKOB8gdX0Gm0W81MSm1$~l7lI-=E&R0IIOoW5Hky7y~btdA0Z
z4d304U+?q+$mg~Dxj|9Dq<m|BTrB5t{Z^^#`7lGzwo2O6j|7-z$gdaVS_W@%GQ}FE
zUo&|J@N&r2dD-2Po~3|UhqQ{~Z;Pd4@%072nytEyu()@BYu$N!O}YvI%2oy5v{vjP
za{y#MVF&uM3D2`rPq7|865S5muePr}uv(>dj{*G(biPGY4@Z@Zq+p$&GoWB^1fA{#
z09dtY(HEd#))v6+^Rf*J9@aGVp}u18Yd_#kd;TG$yP>#MwlkgKs?A5n8wY_%lswaI
zi<M|boA9CGAQDRspSuyGu3^S%Y{vUq;Ca?63Vayz&=LLONFRp~QIoBNeh{n#pyeOy
zNIoOrHXtAl{NuoGRm-Z6-HEay%(7VGTrh_Oy{SI=)iS_y0x{ApY&nYVjhrtOkj1xK
zzdW$`mr%m$NNI}$M79-A?JLDgVA8qjCE`Pzg?&~p3bb%7$romPu-_IYdPs60;8P3=
zar?#b7u(ZoP^zoKX-@cHG?N-M#!*Vw3qrwEzwjix?S>~TU9xXN0qeaH=`ff0zRRr>
zm|S;+Ta9D%*m1iV>ut1$3684`qUMjHtoh)611c1{JPYO1(RzNLTy?=|4=58Xj#Kmm
z0#UVBv{}N$o)B&@{}6`vAd(f<p6+u4x6WQI0-PA@s{jPgHP<-zEaspm&2Ma{C$w|4
z*u;QVaXVKRfP?w_G*Z;wJpf>@!|qC--Roj)V!&mIKBY6Ciyn2yg#WRdA&K+#vfBhr
zhIVh2Y~b2uIUkOoeI26TSfx&Hb3&Bv9cGb0F_t0e+gVjGTc<l#I3O>80YT3=VR*>f
z6{?8lysPt>tCq0!5Fui!wlH+=fn!cxJQzh3`7oTh(|e$}KLLzPeJ?W!=QAL{RM*j1
z>1g)|z~Le#aWAKJ7Z3~a@z+lmkEqzU`C*#7S>Iij(w)hlOSrKC8V)sD7dvAm_@7~`
zDyg~|Ng)pa%x2N5{j&Ery|7mu@2Zy>q0eb3nSGj08Tx#OmPttZk4Z^1X{4C@q|VYM
zxR)WQ5!|T*0%Z|9nv?j+A64$Ab%!xvO5&hmna^Er1Hx^*e!N;eLye;imt_}*hbvTh
z`C{z(lA@WLnzVdMdlhitkB3H2jOZJ<b@+r5+8hccy!BPZO4w24zn-HC=-+E(i9Um>
zxb$yUi;Jid_=~&!*dvRcW>w7M5QlM!2c~4n;;-s~qpRsgJ*V|+ICTuz21K0q;jQDM
zPkx$1y*^=gm>P^Vm}S1Yi5-q>KZ_|M-Dnk!yf(sD5fRe#h6|;_vuy}dncB)`nIrWq
z(AhP0<)!eVny3MBg%jHpv<A%*sWN{RrAW}|UZUoEHj4f>YdAV-sr5_dBlvM_F9#)&
zJ99#bNQ9q=Q6je?qsC5aU`y8yKpTU(W9!sfL0{ylFeo7bc?=CgO!^b$#n@{hcIH+M
zGLHq5@NeHnOq}4x7X46=v<|@5IvD(brUV#v9Ppvi^fSvlxlYH45o{wp9QpBjLY7$k
zeN&n2os;k<)Fly8VASK-$((T5Xb0M`YLS)JF85vpx-8NJ+u&Nb%k@vpqDA2;k|-m>
z;uiith}QPdK<91H*{P!+l2Flp0i=4pLe>5;j+Va3<;ZIr?Uk$4hwCLafU>y;ov?$%
zJdT$?wf}5c0#q?1WWIfadxvnG_VM#}*ddmJP^0x_5!>78Y84|1Lk^mZE)IGx{X1cR
z!b4t(z%NDOh4WQg)%PSjb2N?eAG0&OD8}mqMcUrZ7HZML`HEk?%?^T%!6kvJRtXK+
zdEPW=7t+~aWl*5IJR=i(wGok>fivk3X){GDZ$O`oRsi*V`FQIAm>wMF#s8!CJUrs7
zTJL=nDA;7s$t1=>_saUALiC{$oYZ#6x8kl2qha`PpmjkQAr&=cJ>Bj#Hbu}O;L}Se
zwFmFW-G4t>8S--avLpS@+0qG=WFo?S`AmKJYA&D)YGjjd&~406c+c!Ut8xgHyHOMN
z1`MaHNDcd#-eZD8(O8c#UXEy^pJ09ck`y>84xj#s(!s>x(kkp1Pp#}_lX35%jc_A8
zap}tVRhji$<786_&em)w(j>UoK|zSK+?|sC2=7WK)D)1j)^%F4L<hTB$r?pud<<qL
znZAg^z65ZxtJ4_H(}k;Ka>-w;`!`)TowPaK!W)UF`o77|=j%DhzH!sr!RF<nVV*3C
z=BV}q^o6KJO=TA7Rr*D}BWbwg@WFHX)gTD0ti?zY!c9ZVdag}25YM*{37Ii=*e&pN
z1fja#0U`{fFW@s^nLLHf>pHJv6g=q-x;`4;DZP?Ra2cBY($DgyKZmTqA5WO$S6S`4
zwA2H;f)}Me`c7<p3Ofi_H}O5}222Zs%LFvd;m_xDl^s%3#*Juy6621Mgwl%dNhiZ-
zO5f}~aHQCul~cJ1dV%V!ioZhDK~&zbU{cy3<C4=i7Cn3cBSfwo7T^e%CB&>7&{`2z
z64u&TP6<Yh=hOqOMh*9=jX|L5eosb<NZFge3A#bOs7f~}=ZWU;64^Lz4aeFDHP+SV
zaL1&i(3xM$`3*L>8os2##iym_%Bv8|EsHd<(zbWr%3*|i-I2Ai!XCdDv}M)z?TzSm
znz|QCA52@whU&GZ+R?*FV4xSD0+#eqj4-RaTsW$O6k--~f~H-pBUTEArj{-!juwWw
z(9xu4CuD-+i0<V3**CJ2BJmnW#n*B=Mw^Q6Kgew{W6c&~%1pkHepgg@ts6vqn*xt*
zj1(J&Xef!xz)kOqGSyzZ-Dlk^TmzLN$=K6`d-CP!vBeMhmeTB9bHrrYDOffJzJ`d^
zN*4>$xhGq)BQKmt45a~D6DuTi<EkG^+5M1|wdB+>t7V)#yh*;tU8&~dN1_JK3}mCL
z_aXi7!l&r__TsQdz5$vWJ#r~U$dX^Jzxznh<!W{pK&S>$S3iv4(@w&K?<PgPtUfZ5
zXmz<^M#9=~D=L+8!YIR+;;VM}w4D)`1C9Uq)*m5z@>+J1X*p5V>Q)z<_9c;G2X+((
z1xDE`DPtA?p;5H@(kKmYybV*{B<(%<ZqMFRAvPHph`keG!7}^W%;SpM>M3n}UhiO9
zIqI1bX4t`8A8hKJPq-NFkb-6$iIgT3X-@ty&wWa8sQ(89VowWxeiJXl1ry5UxPkQq
zqzhq#$;=J~iXN`duOssCI+HXp+Vf39O7Zupm9kyL6`RM+uN2r8bGpA;jy<fyi&`_<
z->2Y&a#+k8RF9P7F?}UmmO{Df9%d&_Ryo#~%+a1xD2<@=2~bARH-St7RPT%~2F(~^
z#aPk!;^)D8-BXe&*s696oAdg+dvhlO`_^>|=0h;aH87hF1!_u8<Brc{rW|y~IT8pN
zj@-g0;f{DT<Gv`W#lmZtig)uaa!1{kDNgrpWcyd-Fftl}kjbDkjBi|zfWx*Ti5U4s
z{X+TG`x>?enFXc@@&bdvTATjYsSG+Q^(v(gJ^FU;#w+^<Bf%saw2h|BhpHUhe7=(V
z&Z#WXj9Cm0s*<Ep^luC)-EvdZTc`s7F_VS?xLETH&UVc>RI0bjF5iHKKJ-YoraSYI
znUI$LLra|LfSoR6!yu9@ppD5UN_o+v!8&)e&nL~czyyam3nECVl6A6Q04YITDtkc-
z!r6iyPP?K(cDQ!eCPf;d|6Gj3-iG^oQF!A%MU{$`fqUJD0}~nDv<U~0*Ac;8Rr@UY
z?5OaI0F@+FMOy@tc;bA;E-GK@VjXhOlWCvKebOFec_3%xFnJZljUA+#vIZzajj)R<
zw2RW^S01@@dnhaC_<?}8xFp?-Do~LQ>^E;Oo*udH5^9!?^va0!Ijg^*6tfj=tuHhy
zkv~~%z{%T%Sj)Iapbv37BuEYaaCv>AxpP^?mQKJOpQAQx*2u#D^F?ggUGte6ZiaJp
z+c#2Qe0?1{tJgxqF7YrMU4?ggEN=Nz#PE4i?nyhfL}}5E(wRcZI^iyb(yd3+^K`SW
z<{^WY1R4y_gU&dc$(|##62*$Kf*fzJ!XM%6_O6)OBq{blCi8<KCL`9$W=bde&Uo)x
zI0FX@`VO(*X%D3OS+dEbYg)p17TpU_U2t-{0<<{fJ~`P}@(tl{h+I*qwAQd?HxQ2%
zU0oH?L1?ba%MH#Q!1gnWCq4;vP5p6R<uuN<`2MD4#d=dTs*9S9>Aq4pKU2sts+9`8
zT7s-F6|8BRpa}z7hmMcj>!)+7(WM8u`ofw`16{g`rougExEWJ%WVS|h15bB!pj%qY
z=K6>sK=^0hEa{H#=ZdTMeIEPWLHC#e5>4|Z%x5{bSQc0vuOeP(OfMv<81){e^APas
zY=$b#&@O-FO5ei`M!hV9_$PHGSI!L{P`WZkZ1OZ}B}e6Gf_^{TT~>&$yvZ7}O3*6Z
zIvsr(H{%snQ!9b*Qr0!gNPXuahMiviLAT5IqHonFUHp<}v-!vyqp}1FxkCAp#lpIb
zRt(oEYmwn><yE*LmI#6EZ*bT}%k<6dj{Qf9SK7#75^k$u7yUUNotjzXvtv1ZXE-~u
zHkKv(0_<z<!hMQPQlQO5#`4U(*HR{$lQo|Y53wxN2AGRAA(?_l>4#wJ{Y4zXBYX<h
zh?gXw3l_!O>J^7{IEKXdz7~?kxUy^1gfb2_i^Ig`SVw!R!Hgh?tXz97F$Fs@tO_G@
z7_^&whP2d*^D8AyXSO|X+wsq<N*$7V2b~RfB#ws$v-@=$)wOBx%;b9UREYpnQ(yLU
zOhIU#nXtIS>F7Odag+udEHV+#sIVsITkJydY}G{2PeRMh1XQI)#dh1;g|!@u{DG*F
z^cp`8W!gx$g_RmtqPN{1Ka9O%Wcp*SK9as&V68=a&vtjNbvhZL(ng)ls#nzY#tkuQ
z;3pSs<pEC1d-pZa@7?uv@c;$S>(Mj@;3xruZ>Lz}#CSr(@lw{}^o{ZD?ru9wG0X-H
zyzL^*y?+*2d2$#(GY%SN9a9%=KVds~G9jO8D8k8;Dh%gv=F|mC4N2p7GIWJXoSq>k
zT!nsvU*@{*jKXP4Uu7Q`R|jP*E^;Q=-t1B7euZ0HYcWnvM1)KT$0m5(O{lR!Y|($M
z-%56X{jq2<MgC)}O@wOj9;V567{$e=Z;<vq$FZ_p1cTTSh{VM=g+UhmI$S<XTuHX=
zc=GS1mMTYO-QI(&`AKqVJ5xz!#iIqRbj4cl=`A4QXh=W6uv36qnf!2fozOw!sfzWl
z^2uIY{LOmm40}TY*ZQig5x$PxauXO@9oHDGw#PD>bShX{3}}hc)3-Cht$;w6<$G4o
zml<{g#^>89QwB!ElG*%>KQgz=mTO#3xnZU1QWTw&*a{3iT4hGp=v8PC!{npUL!jXT
z0`O&Y%ahdC&|T&s=rMLuNw<>yIGj3-dUq59>q4hwm|e0=l-8O2nYNnfvo?IDu6f3<
zhxtWYSk|~QGR}yJba=1PP4=>FiY<k6qC92_X1D4miQPsy(##un#D3&3KI;v}V^sO3
zT1So)X^528_9AM}V5Qqg8!OpGyFwdsCE7YL+9dcM`-r_!mlt&kQVdol2yqZvrTv_4
z_`TxAAm2_3Zas0${3%8UQ=052D>|kRM7#{Hnc3eoi_2qx(=EzwilT-fbVan6oye}!
zy<R-_LO=J);I2}Z=5W&HgKQ$5+<fcL7RW(+&<QE4%IRBHUCzo)+3uUmAP6q6sOZa*
zNgwyvHvTsxm)_S~>`NSeGH^%J%?vlP;RDNC1eUyIOh$<I@Rg<zDXv^mj7~ylKkqt>
zsy!BQp5v)8k5w)V#iKdra`RSl3sahFY&LzRTf2cbXX=#08tz&kWCCc9(HY7B3a+=!
zE#sKc+u=Bn4z=3tPN+Ju2R)S?-LSpfkBTatIvgG8^uZgPcI_X@0}<WAPV`pKaVf`&
zV*;Xs=R8?_Ed6_lC=W<q)!el=&Nb|F^YklGSE2W8Gt+>3xRP0;#wjyM$A4togq(ij
z=E{{6g6kCqVuO`+p~&QN7Sq&8b?P*lNEM~pSAz>M{aRzZV5{mEmwFL(tDAblvKaC{
z+Luw4k57akGEuV3zW)Y?Kv!B0tUlxeqsF9`;hhJmV90qGPiuw^Bb41~Dn-t)mq^~o
z_0R?@-RGA~B1ll@lpp9sJNuDta`45Sdu6-T#*>lx7l){I5!O_JBWG@I0w1psst6Eu
z_pmquDfwthCZKJ!0GidEl8~aeOeD_m6gAJ#3#SnlzI~As8!hLkq~+FV60*|jp#b+y
z0v9q5;6f-=v=P+jspjC6eL7fPoIS=bU0NN9dl|JqaldA+OlYo5VAz&L5P*!BMCt4_
zXf3KfUTeaIb(4eY*BjGe5VHc7!{%S3e;7ehW58=s10$V&*f@H9A^eR`pTf~p?Tp`#
zZ89flMJJBn>~4W?s@)mmBjlh2iVzj!)0=*ac)J`dHeG*HRVHTmoxt}-rG>_a*c?fg
z+tJqF%OZuXP9m}gp=bx7@qiPE-jxkEV#fn+!?bR7-_`pH54~3zEbFhf8F6%}HdPZo
zNXqm(>_}zrm$7M7hNa?aDv|wVXsFGc2=5ttu$ZEWsheF)(Gq#W5BXLyHH=^lek;2J
z9|DEjJnBAGA&>8{celq6>#l4N*<w=Skd#~@WSjQ%^-`QZ;#-mit9oNjmt_nn^ws$3
zBWIe(R6P#*^VnlcwzDR^NAyb?=D<afS*!0aHod#Aq}t>ZrMak78&BZ-=YvU-E#e5W
zaFC9=?Q+(MNO%kyIip0Zd#`2Ck%vkeDhReqQzRZrYpMCY>e$;L+VhAHF8)I-&f&e=
zdb}F^L#f84!<8`NO7j+=`vS&o*8LCY_~^l?MoEu8y=vo-#itKjT2o}3c^hUHwo;c3
zE7%s%rbioFx9}l7>@cokTt=4FE`AN?n_0z(k!|&~joGlOq`so6iwl~9Z{SSuxWm*5
z<zJ*D5QLKGcu_=km9+Ap5bSQq>qH$2ppB+7c7xkJs`OwPYAVa$)`fSOQ|y6sZ)JqO
zFhEy{)V#;q>+aJ@d}-gh?=pP%f$i$!<gI2bt-p;kLdCMfO6_0A{nf8IU%goDVwy9=
z-+omakB}G-YuhW1Z44>=6sISz1C5O%1m@3CL=*RZ@2Q-gi!Z{z{dj9ofbE1iZsf{t
zfF7d)(xxjP*Hka<^pQ+!<$)z=AhkNtNMo;nhP0cAkV62?uFW(4B|_0IvZn+8CSxJm
zra;6|CuxK<?X8!qpw-xN6tlvu*Y<COJC0P04yv?56YF9Lg;~rfm<w7eVek(6N;$(k
zdwx}hp}&_c?87VVQdN+K%_5kWh!}URI9E#`fTIZ$dqu2>+n3mrM&{oQNg(ajR2U7h
z(4%%N1G9b#bD+kRQ>Z6E%-S~&+ieieC3jecdy!+mhe@Pp6XkD2{JxK70x6P@R(Up$
zN6IRPkaJv8niu1gYH9{;Cv`5<Zw5@mgH|JEv<X{h&%!TyOeK@C#y$_6u*XES?8*<p
zI`4C<fHxSxg|?z1(-*qu19Z;n&yQGe(LyZCX_8);4K0qy7p7Uh4aujfpjlFp{h-AX
zk!v(c;A&KOCR6aub|i8_$y5&cJt{}Osn_pkLDLMf?Ax%T*O2cqIQSuP+R<DwMlBOw
zF+M2qpnc@?AbXtpVB@*V8Qpk(Tq!O}*Y4<Fi+vs}@sh}E<h-hz_r>k~bM+OtC`o)U
zOtiR)uP(?LaSdFTJ<?{rS+qWT^L^1n`oJQt+nK9_F4QR^^QtiSG+dY!N@F$<x34P?
z@-)2ZuJ-Wa3L!=hLl^}D#cGBtzjpOrw20&^?ix`L?Gh3TKEe$Qm`GGe4z|9B8Y$U8
z3iot_s;g@(T9$Y?q+-NJ`sTJMuihdmp4+dY^sS`pCI1vn3PRCip24_D0rwViaGlQS
z4>6&F{I(b4t8%U3XuaNh5v@3HhsK){IFT}mXKX!Z6nD0;R-k5yf$9^jV}q1&sWPZl
z;xjtzOVWwkFFcy^ZN~bett<}AD(O$?<oJdtfI+Ag)(R$k9t7L(dsWD1uKXc!ghJS4
zg1LhAP(6Ct`5S4ru6CBb68Nl-o{7h02ui(Yi7e*Tac^nYj$U0(N_xY7ltK-+Gl*v&
zZ-@v+Q^C5JJZ$nTrn~uWt-P(1y)cs)rJVQT`b)uq8h@TwCuE>}&<84$`ZD3FuWR03
zYV=s{GkRR)0ETKe%<=xq_V>@1eVE8&j%3$3#yLNmMbprJP0GHlQjn^<Ka8H2K->t0
zr5`osDSUwvjH!z3BmgZGirjgli12)~bsC~gW{)=`brU&bHySJ{>CC7g;mi`yk`@V$
zKq}m3J=5$u>>@j%t-vbcX?Wu@4!uDaR=hE=S;HVw_4T?m$x8~$aWqk(MNB>1%$35F
zjB~?c_Z6(4PQnZ!q7@L=lgo<0wEoQ?jl*d$1Z~l}|G*wa+E>#cU3#pZjhJ_8Vuugz
zwyi3!Hr?{G!h|cq$^dxC#~f}weo)ok%Tk-p=lT3oB3s>Y*%>&xbRQnEXmJUT7Wn?C
zBIYWxdtna@`zxn0EIa~>NWh?hR`AnH?3R_>{czgno0vW_0mdAX?0`Z=lw=o%KEich
zbAd0YXP=vQ7j{qautMVenFN_?nvdJCjq|@|zgX%$6K?BOd7AwgjuX_Rll<c>|329+
z00iqeUCz<VB6Hit0A{{eptVDlet)7DNF&>%l{acfhG2|3&t|3ro5d)VMIL!p%GzTN
zW>g2oA;AQ=Uj-?}<?L=-)2L^lvnGn%TVJ2L54ySxxCLQ}`h=7q47?DIOhhs{`QgjI
zr@HkqN8os7g>=lt8(o}NjGh0u?Z;$4KX!tHD!Z+p5s&hB-IsH*@BDLMI>RHh(`=$t
zyhCoB6E<R8D4j{cCF*b6(3={ubktR-I$vZs$LBZ~oMyNLsAohaNu?2v`*7?0jF>pK
zN&GGKt5%d}UX=B_`kOSqs{ToI{7b)%4R`E`I{y@8Cw?Q}Zjv{e8D8OxuVlhf{6#`o
zC{Jbm*w|6=ag8)NS_5eE8|Y=SpfNw)%?OBgo-b>Yj%ljHwExr-GdrsnemhNy4mLkT
z{ZTLo!5$BYil$ji*reyr)J_^Dir7!9gKuF~x?+=7Y|R~xv#isfwMKkx<CDj|MgvCE
zP#&R|1;ydPkaUr6!_zqqizFr_NV&g&6gM_<gXTjx+mkuc09j-KkMAtdzWpodzW6Hx
zno+xCJSkp6jwbX)Pq?4S(uq$oMTsb8*y;5!yqpP5<y{-G`n~JNuKIBljU=Eh4EP{@
z!2Q-JD#5pb{y|g+;hY)|fBoJtvk+Xuoy-}>($h|p?eovCbU<;DUHzy^NRc#ZpLXO9
zjec+9@dQagL};RkE*H!a%Mkl-7OluIRZgAE=935?IIUkD`SDoFvIm84X?QFN@NhuV
zHSpPBNbaT_Ez;$gA>UpUD=oda&rJArC@PNU;o1$noE7F<(P1Y&>|~kN8Qx*vz<oKJ
z^ZtG~lX6MZf5-ZI_*>>)=CD=Inp`2RXbqPM4xPpmEBWQZ8*y!9c?K3(8CYoE416?-
zh}wdpjr$!qisN|#t-~Lux{DZ!Y+gM`w+gm)cQEt4V-ikxCTq+yJtSLt7R7aklrO}3
z65FkdCEp;ygBK!UT^=v7Po21HDMXu!Dk&}nyI$A~za*wzu8L1HEokr=mt+bu8~x@<
zT&n2CD4CNzCf>N~PU%>LFU4!O`#X<=fAq?_@%z~3kG1Oh&TosHHnjI07Q<iCLb%if
zeiX4SG9fziSK6&tdp{)`M8S&H(5=SFiJ}P!pLW0anZ#Z5I{h8!{r>b;hRN<rUxl9}
zJM^S2dFQiN2Ah+lObXp%7A#$*<FvW~U1$}Jo2A%?KcMdlDus>5C=j<JIc3QX7v<^l
zrR4Lxt|>62VkAzpI<fcOC`Mw3$8#<Tgs_cf7hVFEOl&n(zpZUu)x9{%k~`}^Z(1O&
z+f?6vUy@)eDBh2x7eZjF9xi~jGF^s_4+o3h?_k;#-;`q3BpLcsxP!c(1ZUyRr8^1h
z_JT+f{4K^TUUQ1!XL6!S!TxkH=*RBR(&k=b{@OsESCp8zcCVYL?z_evO%F`fInJjf
zg<ih02hsFXV|fT`1Sp`jB+?>Zm}I(cOqM^3d#96fwMb-*Fg@JX#WUyE37FyLchJQb
zL(Q8v=l;a(;@LrHbvD!V`Kg`hz)et2-_NlW#z}_}d^VXW)P`%P$)wHCZF@=Fa)!?(
z5rv>UZt}TAq(K1F1a#Q-6V1lKy&tK5E<K4waOl-gu>A*OO^@972(NHMeuUcgJKF2g
z-eV~y6lg9EQ{z%8VDO}%8t-q&9YYH0+jK!9b_<T}KOwqZ`cP;Q4qt^g!m#KO+%1uq
zVSlKO8X`jI0xa&AX}arNrR`}nSlM$rO;dw@qJ#<<)3J)ecqSt-J4SM;PU`lZw-w$c
zOP(S`>W8;B+AM&4G26oB?#uZ)8T4cTj(QqnUC$W;A7{!F0fjoyVoKccX6>#Z!}U!p
zAXQx*-NuBI^#uGX>iTvi4WCJX@lk5|mth;jlvHV@UzQU}5uG!lQPw*}KAtVJetG}J
z7**O|NTYgD)au%oXFiJ<yQ)`vDI4rN(Q)P`{3gMM0)hCp_|yA5!a6@Ke<2JWxv%4H
z(4|ud^<xn-gmGu&GV53*fM9@0d-p9xZ^Ftf#}!+u+A?uu2_LpN@q(NTQ_6}m`cwap
zlO^!);{_V8=}=U`C`T&oi|A+(d;C*bCEO4gB<i?nQz|5WXTwhea^2-fRwj7#VXcV4
zuqF-S)2I5a4;R~4npot!ojH_a(MVG;%d&h69pFL+9D&Se<(O<pzxd}oPpv{rHaLT~
zsSfHU*7p>E8u;g7vN6Lm_qaOgrggp+wnQA#?6MI`E0Bp|t7dyW%$TMVj_l!gJUM;|
zt&M%S(YJQ480*&1N&>}b3R-$>hz}b-*(0LiQsG)vI6rHANuyFWlVd!>q^6D&7M=VS
zHcx0Zvb$eH=LOZv`e_72=e$@P7C*YFnG|iOe{aH6YS1o8!cvXMT(|o!P(%adzID+~
zK@un8$Cga&V`>zOcp_tin#9f#;rB|75@4_ze--+vkosGO^1Y)k#pRv6bQU7g;THat
z;~*#W8VgFGEqTgDNu}K%OhU|Lx^ArMU|@YjE>#d-7L4Y0C&|E33?hfVIlBnrGaQ5*
z>>%z^iPqArjEqK`n3&j8<BCQIkX-}<fXfBvlI06D-XF`;^+9DE$hq%gv`sc$W?;6u
z1qD!Mz9L7{u}CI;SlJ%T-#Z3w)T!FT&2d*=v&H-Oh0N%wCcGL04J0_a*ff&wmP)rq
zJza-NHMq%o9<rHi2AuPcFv<?`%!L~*{7^5Lam|To-U&Q*d&ec1g_euB#A}hjWy^@Z
zvxI;+8MYF6mtiT)j)w&oMPL7;V#aify#~TjtimrrrB^<$kZ64E%!WP7iNR-d7SNEv
zhqL3~F!AaZ-1A!*spNK15~C$~HQsg9M8ey?Y^v7L*P@8zeS#{}BG?F!49sUSW~Kc+
z<`{O_e8osiVDE(W18iTq8;Rh?As)km680^uwU@i9E_oA<4I8N%5C@?}g6;yDD+GP<
z`<O4yzJx`2_WQlv^@HU|R(4#8b#c2hBUiCmsM)8)<9ixQ(9-99{aAeXFNX+dBw+&$
zeXAEGu;24@f3lN?dzyF#a6+$zB05Ri@CjhV;I34C>aQOVv^aH5k}QhxEo!JQ9bi_1
z6<oMQoW^@K{QJ{FY5p|DH^I<{*?x-gG}+Xl3|Ja8u%(oW>5M7yq%ACl4dNk2+&puf
zNF(e{+3dl5iQ`fk9dYnrD0+12^{|@)?sw-t6k$4rTYQeg1vb1@#2MH~UwgThtS^Z>
z=O~AkGk$l&5kbNRi!j5)=)q53$oID>I!48j_DqeH-9lfnrS#)HiR3d$k^mvWNPp*-
zzV+=L$*pnwzmVb%w*C4k5%ZMN#AxbgC{(AXwE!NPhS%qQeov*$*=Y7jyMa<HspA34
zV$hYDc@}CdCK<{@;-f<re-_afS*fB5@H|@d&8a3OBA{}oVhR3`JGQ>xZ(E}4HAB6Z
zl8c~og~aJOI{^V(+T8wx3p+Z>!Jam8c6j0lYAPa_xAV&MRyr-c$-oj>PK;TJO~Hrh
zJHI-_YDT<QYwbJ$Btm}L!-~h<MXSEV`7UnkN8p$^B1_yu`_r=o7BlN)%tAE`I~9Jn
z1;>n=0F|#zEXHdvDugw>8$=nD{gvBq72^mg*&|pwC4AzG)3|494r!6RQE^A^8|_MO
zm-RPFVV+4PcniO5=WL?b1rE_Dn!m<SechHp=jV0gNSJ<WOTEx78Mz{@;4I}eKTVIo
z+HyWFqf2~p{u~s-(z-ki;n5W9y;Y~=5nzt8j+TLUBCVF!j*Spon&|G<?lVC%fd;E1
z`^A9*U5*=$BK(kHofCgi_1&Sgrlny}tY*~<gZpp<grgk!nfgHCXuu1$Vq`d#muhNT
z-FGX2sQOcz__ea@M;;om2vxFux?f1&F0l$XXrqy4XipxcrszO2>ASo5L(>*xyJEVD
zV`>yA(%b^4k_?(q012F4&gg8j61E;%i7%oeSPJ^ukcI?)G}92%-kw)zg{-g7QGYbk
zK~1ReR^!khe6$-_F<A8??cG-zXqe1ow^;%&eKYd1jzJSFja`?)>Jt!nUud$&vSv)8
zFGjsShG!|kX52$>7N#8bJW3&)@e%Jj)(Q4s#5d|j`tGWH2jZhX*3*SnM4w%lY?|1C
zIJ?$Qnzct3qrZK}jQ~;BV95siPQJMG_Kc$I`9#R8`b9*wx{%GKwa@TvHQN+lO~(eu
zeLX5ZoTf8#_|)wtA>x_*Oq3~wtSL4W?mSaz5~H6=mgu;j8s&39OaxT9V`fYCS7#i2
zHU-iT$4xn~m=sF^If6!$neQ8cNW51JtAuS#U;#2AWIPNAwwvdvgpLVfJ-RUq3Q@S(
z#iJmPxe7OmKCD*|$H6DL*U&AY5jcI^4ai*(l@!(<+&xUrmo{{$6QhE=l4{%BtZ={$
zaIafUQIt(k;Nthe11t}G+n_#x=kRutFU%<QP(CS2molP^L{kV~<D(mka{4$WBh-i`
zl|Ts@udYC-k((MF`XCDn-$?q26RP+0j=sPAuWKZ4dXZ?8#L)w>Z`!fR6pED>vW|0^
z^h2eHQ{C@g7Q|t^_1C481DKN`8H7l=$1+g%&}U*Ie}te@3>Qq)9)U-gNQ57yj(9zY
zo2W)H*@LF9B-_H|!!2uJ?jI=}C>{GuXVi^gpwYi_V}79ApkI&ei_p@Rl|<H?0aOi|
zw3(dq>NR)oG5Z_7n%pDeC7wbzNSUJajuU#6q;g{fJ=W8`^K+$9<RvnO0abjy$xwiR
zjXWE$G1oUdb*lPkeRSUe+?#p(fyVBAiZEyFy8(JZ!UAKE%6s%)31>u`VsVM&-u)!t
z$1t`pIno@E;UbP4hQGCsE2&pUk?_!DD@>lOsD_P#m8!7GTsG||<|pFpbWR!EncQE2
z^yvFw&4})fUZVIJd^-%<`H$}kLNC05IGV-BB>5V`cHG=zw|qIp5C@6l9=Tk}#lBZ>
z`ncJ_-rTaU;ZHoPho)__yQC&ueAa7_YA2L%KMbm=%`jP_ZX7EfVjv~i>DS0kUec<h
zOJ4c_5f}>^?Mb~e+l-$*6ZZ){>ST<Hg*NVQZ~SyTZ8N$v2WyaqWNh}q804t|0;!}@
zCE9M$llDaU7)+98wk@nQ3=^d`o7RRSL#D56QU<4T_%~3rc<T5*Xd^S$Yx%)2oOBcP
z@~T`?-oElM!iV0r3EA1>6?JM&Dims_7kiRy62|EC${Vy|lNuZws`N&5YG86sIxR$)
zK~N-&dX&3f)l^2n?U>voo!dSWxFGyR<}%IQgKiueEL$?seOA&Ns*i~joS8T=c&>ko
z$#Kn|rj)esLQ}kr%Y$&j{x$oY0}|E*4&hznwn<Lg51V}WQrz;_1qozJ4#NE$dM-%{
zq>&V%Fu{e3G!@bO_Xz6wd?DlkJ5a*r<5(`F<Jt!yP$rmCs-~l6xix{Wqx8JMUQKdR
z8jUP43HiG{g<-TC?@!k|H|NZBq|Z)I0D@2|bFr12(JXU)wA{9Zkn%;t2r2~D^mjcy
zS9X&|4yX4T1{pN2nmZI%hawK{3T<?KblQ1%_a1o=Tx1z;1`OJkzlr7#&?X5*6qWAz
zw1{39zMW$#PcWmk)ZiuTdQY3Sls3QD6*{GfVsex`{K5vpM9)6#)5JOPu_=*6Y|xj6
zK13ijt8Ks$Xy7L0nmt$2YM}%TBf#0XYKG=^3kZ45*Pw2cvgO1uwvPIsEBR4EFYC#g
z{+GKVcQ7il4q0$i*`%h)qp6MU&-&oFu73Jg{x3ts9EErX?~w(;2#JZL(l;Fnb}&ET
zGVnjhA`@Hfmc}B9KZe%HuD<J8;!&%2?~n}BZlb(cXGU}vxvaaU_G0H+ob1`!ikuuA
zH4oCxSKL1zuRt(FSD4ts+uEqt6sJdtWmw^Z@jn3jw^qdbN1StNl$fSbPXFV38w&PK
zP~11~O)Avl&wr*@>rRsryPr-Y))Y?rGk%)nIZFBky7V8Q1}K;z8pwXeAW-8&|L+L$
ze?b+3Vn8A?c=DLAKkHJ*1Y)t(UA3=N{-ck;2lpVLGE^~agg@&lr-pGW-%{T%`VR)7
z5tQeDy|{Hzd3<DG0gwym6on^qGC_N4f3O136sLh8dQxR|@&EeA|L?P<{ohn|<N%oR
z|52*C|7-mJGJYXkFxP}oQ(2hfU+hUaJ-{NB?{yzE{Ihc1!@AS83<ZGXG>;xq%I1Il
z`3Eq9WiEs)SN{-0-KQAOd7ki?wY%3g_-b+gBAHSXx<?0M@sxe&W}*M>AdCUknc9y%
zFNv6e1b^2@i_dBJ0132LIS+5@{;C@*Ea*2F3$|wPAG9e85d;X|>uk#3GU3g<!B-Uy
z1ss$xCHa$DVo+t<znoRDO=!@o%%kXS(;Qx^I(UF_S`P#bez_E7PWvB(O9FQJlX<o)
zNBBDMD>1B_TU_k1%!YH)N8z8|byA=UWY#q5D|80Gp9cI}oG8pWTl^FL0m47lP=JC-
zb{o=7inW1B!j%WUCqoE*mnq}pH7oE>C8;4|kWc2q$+0$SGE(utQ?Tm*J>k|FtTy<k
zNJ;E-k(KmV8}~>>VJraRfk96Sw1E-Bd9Lzr6UT}5d@@g@!JFYntGj1~4HI($jqcaB
z{VRXhGeoCO5)9-QyFY50xUK+F7qYPUAAj2Qp|udI{mX?LBT|t+O=FYl3+Mb}BvkY)
z0k?8hoenP-_${fw_6BxMkoD8FHcF=8e-UfwK;lbosrtG9+FZ=Sf#Gi;WPSTzkQW62
z0@*|vS2O(c82{`TUlD;l#EeJ(pypqF`S*;FeBQ&*do<PmDf*A8<8Xkf_|>*;R_D*k
zBBFryDMD(?|0((p6o}6RU~%zt&XrAnR)&lLv@Z#(r-<<HhW{=CJinX~VfBWZ)t~eo
zJ|)n81gItFe<sl1?HSiRLs^=AW#j)$(SMSLvP%K&qhQ$c(EL4!|L#S%_A_Hw69LZu
zFZwR(xqZq7+t+{Wb-@il{?h+<2gb|z9gw=O&rX5mzpTIxJnPGd`h<V4bqJ2ue+?on
z*|QM|LqpL-ym~_5ev_#A+RFUd?O=?oPhMx6!O>A-NHA^bybH#m;YfJn1;W41N7#%!
zSKjI|?)`&%1t*Ie18F=VctglkT(k#Kz})DQ1Qw-<al9UaMRdmVif2dHBKo%-|6)fA
zkzlus4qyf-Q9N077(QjiSnrqrJ*(n~pV#Yh(tAnZ8^Dm?WzrJc1BJk@9E$v#w*Q+R
z1q!%7dm0kxMKM~}zv>~JRQp%Q;wXW7uDBU5j<a13e-C&rcV_kf_Qhq>=l-$!t0iV4
zTfl6*hG2x5!)=kqs-}KxG(9u>(`;S=sM`4nwA}2x>VV{!?Ri3Sfw%f;ef16rk4?Yr
z)^KpFK)`CeMgTlv=&>&_!op{D_lw2*iN=a;E|9BAKpJB*SFqs)5#WJ1+&J=`dl9gf
zTHgN;5hy8c+|$YB*k*Qi)yj6i+c8iY`{gOWsd*HWOH<DgLdZ0#?{Jq5rr)fH(k0_N
zG5ol`bNAF{^LX{7>-=K{>7=T({xZ)(OUL#Kp82Bt7hC6jo(}j8M6IDR{@LU5cj@e=
zG}GbDz;3cW`#xYzO)>yx;OY5feHM4<Wb}Jp54VW<K^euCaV1aGZYK`@1H0SLbgLr%
zhc%tBvGBJQanF#YV@}{ov*+XXyNfy_p^_()Y#z63+YzB#@sZljVb1D_ydo2Z5|M$E
z*G4(r(5pZaMrEP1{_>Xqj)?auE2mranVbZgk3$7!<a9%u=TuzvWAol{F5b%ArsC`B
z4A%@j*PZ+7aecco9cz{QtC;T2*yB$-<JFHoOYd-*^VaNEB<?ISwM~BFkOpsZ+m+`9
z!R3{ov+RShcs|4JrtmNT7ro+apa1=c)0q<dXADKO1_3jaZ^{wwS%j^=mT2YzNf6a?
zY@rQ5dQGi4e%Y*AdUQT1ZnrzU)OM^~9I0uaHk-`ZvOB=|RH>qWKP?r%36Pbc!&|e@
zxt+l8e2+gB3`N=j$f4qwH~$>MftOVPJq^G8eF@j-JW=}McwcmQM826;Q{QLDY(DEM
zXnV`XHu&JH_x$4-WNq}z&3J~Z!Lp&rP&%)+_mA{UA2GasDKq<L?^9YdZOTPmI2ZS~
zwcO)_GT6~+j*t1~SvrodO{`yUK#ACI>kxh5hz-5E1Y>B|pMDr|y1PQT_#p43Xgw~O
zVPw^rPFg)fPK&*jOv&nRBiC0nU@0e7P4Kt3-6Mo%F?lcl<+I0zFjBirO9JV)Jz<-%
z#cF0I*_o%v`S^>v_M@HH<Lr8<j;FeYtq`+I()QtUCYEMfS6Kpebih+&iwo~$nE?##
z!86OX(FvUWt>Sqg3jk!-J9_^SH`k};&A*QRGKj(Z9bpZ6`_n{^ZXf!#5<pecC0g&A
zAFEus9;}G@p-+@s4bSJ7p7@7CIo%*WOb&yp=&4I6%_6X{mk(vggFpu02N@!Vk3eqE
zjtvy2`XyO1e;~s4VLz6wi(g$==kL)5_SWt<QtQXzTmjdu;d~QB9>|6<G{RyI?G}%l
zbRu5YNBgzh8!Go>qJgd>27G1oC<42pwo5EuIvskxkTwmc^85J>)>eNqc|LuRK9j~v
zz}qh@#AzTMujisgrbFWpZAG5z@bV|OzUkIgA115i+WKBZ6H9d;L89Fp_J2=qIG7nX
zgm$x@&3C^Yrt&?NyBe{TQl6d2qNJ5u(CX#y74H)gyfyd74&P(^%5O$4l^VQ;Z!Z8>
zA*|M^W+e#hnQBy7PQFV^&k5#$>)>60@kFdxA_=(NP@Q^C;+YGiOErlkDgJ%5eW`EX
zsPYfu{}n0NrY&q^=+k;#YKuvwkZ^{9>&^@$r*k=FTTT7+*uit{yqaj@i`7&JhF^=t
z4)aa_TtUwo#!>EPZhl}%5^{6^oH4)%%{=E)1`^4304X0c^v?tH_Gi_Vg-<#zq9%V0
zYjsijS7L~-+y)*Qz<Xh9;H?;{$(Mz~;KryizY&iy{T2d;BmM9b4nuXHQW{pZ!P8#5
zfyq=H8cD<F1c5s2vc=zXM>I9M&)l;qdnJEm)bZHQZ(}U?=D7W2l(xz0*nbxTCJc%~
z4Ac(?qZrRd0ex~pti08th4>^14F^&wr#8b<3!?W-vuD2Na$RBy4^%INf*wvQ1S76t
z>!=XID87Q{U0;C`{|w|yy*^%v)le{P3{!GgYUu=^S=o3(oJlAHYFH4oL=ZYTEGQUX
zQtZwgA?Uk)ReGfGAbuQ&v*g>>W!2wK8?R|nX+}qnTRw<BzT$i-nNM@2&I;3?dC<uR
zk4BouX)z3>emcGVk+{3N8@q&p&u%<fDE{+%GnSTG_H)gDH(*K_jMzR+1%~NBZ9SME
zMvGEkCY$>Z6E7X;t=)QV74uNYi~HfDul=xRx)3rV^d|UmLxPj^lGG8PO)?FA_JNI+
zOxTCJkGnDBeV}Y*#{bm3B?dK(s@+t&#Vu5)6T;kM5_LOpX7W~#mtLfi`s><GOAsdy
z<b#T-@_S|~pxeJgqq9=_HnRAfNLhg`3w?>>%;Kr`e^+Qq0DUC=%M&%UP|==50~I%B
zcY&Ib6|Eli{e}RLh&jigxf(NOSA{5%rFCct3WRQS{$l)QE$|6o@FZq?thqNivHL`S
zpY{=OS=Q*ajw{<$QT}5EP_VX4kcEa_qE{6TEeR`zDs$WSG_pveF&&)tX0P}G4H^u1
zX`a4ol~*IYjKegpw8XGcY~Cf23LP}yjE=|qLxa!m7$2@q)&haIg|T>SxVG=bK<Ix@
z6Lgrf;BE*^`6Ol^M^2urWuSbAYPqmQF8Bzp;TQBswMdqFGn>Z)-NXQhdk$ryAAgob
z67l5WX01-|TYfyrD@bdK?LV^tT7tYBgI{-uls?|Ov7t6dp09caYg5YPciWY<<56mS
z=eC|2%Klg!0mlQ9#P|#7?|}@LiQ-C1N@{9qG7+Ulq2(>!(tGu4%70HT=x_@3Z;9;I
zLd>G*U_vx#5yFKu8*ABoaiT(bd;O5FeK{dU%L+o)PbTW+T`a7#c1lMZ1VlGM9W}8~
z+dt+8sIr=_e=dQk!9Ch#Eq|?vzn&Q#1(rOu{+b?3GQ=xbJXnAAi#G>b27LCCuC@6;
zS^#0S15f?%I=v6Q<qEG}L*Tny+%@iiF;jaKrbI*d*W`zm5DS3yz~k6%!8dH#iXCiV
z#I?)zJ2ZR0W4t~7+DZpmV7ah5yrsi$r08X?>I)hwF+ces&qzP2{~6aPaurLn8|(qz
z=F`}b3^RozM^mJ7_j=RJHSYoU?c>HVk3^pGipl<bx~|!NQ7CTze_2MNaEKyEp(F5O
z(`}xz6z{9GCTSn%5*Q*bBlIYe@PDZK%CNYWZQDSA;O;KL8wl=BAh^4`ySoM_cw@oc
zCAhm=a0@O$gS)(yea^o3zQ6tT*HTq$${1sg`ogrPSV#7{Kks5D=2LpOW}Qw81cd)?
zxC877gBcx><9)~XuC{4$K_xWrA7iB}%BN-FizqbpKM_DJ7d&mv**F)4&0Pa*!vB6_
z04i5tz(8r_m`pnct4e7jwucULvW<naHnQ3Yraal}sP`!6P|UZ_&%ZLeS;;#waY<UG
z{{2o=NXM_;U}IwWlh67=ZP3U~RtFav<T7nz9G*+5ICls>iG;*YuaT#LtJA8c<iIke
z-F<EU-{TMQfJRedb|LTAHu}S(CaJW3Xiz4~7W#9e&z>+%mxM&wt$G{XUXwI7MfAIi
zLfH2*`~PQ)@L&V@ZJ5FfOQgAK4C_{n$oioI!+?|6n`Vi4W2=-HllwH2Q{JC~9}<rL
zKhH#n5FcrrGWL5Gl-BP=ezgn4Q;Mr)FM4Iwmmcg0r(us(bOiz%PF&wiiG73_8;R%}
za2^+wV-nCvI}3K(L_7Z*f`N|<;DhzRou3pb{E(s~kTL6{V}CU&-@aeLz?I5wsWDhV
zA6zH{A6slVG4py!)l^V?*gxAYFj(s?<wNWChsaaze^*cmt=o&ZP1|Zm+_IQqRQ7S-
zlYOj!sa;tnjjN8$v$=|Gs{SfF#J>}9w8@D#L%(zlb@u1cd*oP#61)LnW1BH;i;1Y<
z2)0aGRDCO+yL7Yv*&H@SC{&#l?Y!&F1nP3#ktBOu<-AlDI1@Pcs_oVYaBdTfH-bAB
zt;;qnBT?BJ(TU4;{!7m&7mj>v?CQtyp!<Y(&{+Q`0tldSsSFAkX+MPJTl+JcN<E)_
z1}NPrVQ`taERrES7A&(WEpxw1{bSCoF^ZwM<MdYqW_BdWZmwp{RLG0M`{r0Sg#SHr
z=dthl%5E{~*#@_dYg1X+wXd5SuW437WWXLzZOJ%BMjHDLdyE^0oSEs(8`+#$c9a)|
z9<3Q{IRED(#7Ud@ID({uHgaT4?>a`y=B<*o2FfZrm{1t%KH`F!YCpqcjMCTa8@#FY
z^igS)xPTKB^eWcex20>HiiiIVM!*NcLI=K3N{x#ne8Q;El=8JtNH?t2MfsBG`774w
zA?zK|d!3cynmz3>`a78O#t5C!QgT$I9W@O7$e1TFJ@kL$Z3xgo7@*`yNq{a2F(6T_
zl1<Mom>$AVQ8(K#RchxWdvcf5rVDO$%KlVgEnhNJl|5D|bvU5{N9(({aMu4G(SUK-
zlN8#jyhd#nvskth%|FN44^vYa9k=hIEEJZ8;ObO<n3X=~at>Sq%ba$bGP-HFq`Zr`
zt9F_=AV-KhQb|Q)w8??49~qb-#`ypJIt^XA{uGhlzkhu-WHHmVU9Vv?9yHZUf-|9g
zd00*K^K4pPjrF*LztmAq_){KA4fgNulqs6ufU>+1bFSD4s`APw;b<!C*PH%r$F><T
zk;|?2yD4UW8m;RlSC#n3uE&x_oE@UA*TqcKy;A#{Wcr8yjZSl)-?41F(u?rVTG#~J
zi<wteO39>o)P<ZT;2H;2Zp`OuRi6$Ou)DkQqEPEuc|e+8nl!m*dbthfH{;F6!>7H2
zTZT)fn{t9ZuIv4T`cTCoF|+$1i6U6Y?KN)w>4l^`@lTeC4^y*la7T2w{IBTpZaQiR
z41(f19oQ06IFhkFS;EyVF*_?{NWO1>SY%`VDRP>{NX1DEhhw!{7a4XMr6a~R<h>vC
z){J7gTTC8NR(eIlK^G>a9!HClRfO|!cLsd>4I9gkdVAJGHQq7Q<uTOxF@Goz*(b)2
z$cXA^R>#)SWh_<K@8H!3S)VV>d)qlbxl`q9-UN-vWPtgQA2)z|rhJoUw?5&Ot^0*q
z##15)&<<V;OoWXXMMjIk0#yzg<ie^=w17=}vGBov6PqkbAg*r+QMr-&f!A(_E~Kf^
zHC4kHM1#E;*n~p*@=+_cA(yD3v}nb>`GzgRfStqgm(I01)Q7l{`oq3znLqx}#*n5W
zCbV0JT6HS?-a*CQ=4qkZ<JZ>Qe3IF8`NAxeOpr;j)J$0>|E$S>QXpoakX*}ii#ZS^
z!2IX<Ma7QGJ;~kaMp2-iD+;WbG=#7{p2&L#w%ril>-k&l7AR?1Nza-4<xnR7NJ7nC
zng?b`Y0;X2Q`c>2M{jFOqkogMP~zwAJw;(*Ui4XWM6*f8F?;HAwL_VBTuLX7+(%S2
zosS;T4Ll0d;AsUk+5V)F=M^LNK2jw4+hKIUc}VGRGm9>?+gWVs7(iG@^1@I3-?EQ9
zr2D6oPe<~GTV?nA-wEsQj`53SL2smQ9%wZd?&Xmnv;gs-@}WuzJhgp9Nw~hyb*Q8f
z?sJ`MiC6Ob+MBJ_J=s1Cz8~H;rnlr<N$^sl0rv?rXRr^V{~M^c$Q{@qHO-CK+w-XX
zH!-f-F|T@Wc<Ej!AiiM^#cDbivMI|ERA|n;skZHa(^b<p9_Q4V<r(dbkqKL<h%wy4
znFji8$*KQO3ZsLRi!IYQLyb%YU08!d8f2Iy(k|)AE~v0%T2@+cCDMMqeG_*=BC95<
z?G84+7s5SQWceJ<0z?lQO8IqOntb+cX_g?#qW_K3ITvV~_W|vCpJZlYs~@DoqG*-3
zFYk7VSe+KQ1T2&Ba$!2YvSfjLgq%NdI&Sl+HVk-+g|Z7iHTn(U|9Om?958{i2HRkq
zp^piQ1+j4RZzd)s;LQAr^r7CCjvq=D|DOULHjsuqPM?+&Z`wkt$k$rXIIbF>SU#=d
zKv&;wDoXLYQM2gY2r@8+Y<Qt0DvRG)H7S_IXZX$UWnp(Ht4cHtmt$+0GiFRpXh5#Z
zOt~)<vT^$DrMenFOXC$H(lb3TA%H`qMZ;nYp^)Gvf)6Ov$zz#g{;73@ls|VXI}ZzZ
z+dQ8-wU}@cbmz<-Mo-)Aq<#qjeMpt_(RX4k6KP4j2*6E@Q+l40_v0*tHl_15iI<v9
zed>nsmXB2(VQM7q{(y4K*g%3ZEwoL^!8<#jPmgbXM=T(mW;Kk?k@lH?wyNRyS`NK(
zesSDa>qC*LvpzdtAWO>>SEi(VdA0ZO4G>C08E1$hm_Fkh*2(`pCqlEI4F;p}0K_ll
z<mzoFR!~mx#btS7!Mr9fuh)X@Y^gTC6J~3!xS-$}aH-nTfNimlc)4%m&DN{GiTzqm
z&p~+8%;z*@B616A|Ay+H$V<S)7j|^JK(v<U#C^Ol|GkCqQB?b7Zc({Vuvv4@@kvjG
z*S7WhY3Gd|J4Z^{3~>jqo)MFY1~+cHoOPy0wzGo0Ofi^6RgI#qKap~3#I}52UWr}$
zHr=MC*v3o?2u$<N+2^W#?@jAuNDxR=v>v)dR(~y6sei6nS9+o$C4HOt7BtHS^=}wQ
zWrw=<dWqp&U#ie#)f7LODI9BcJ%&=|Tyqc|12cS`wZLw~eEZRIpDnvLby}js<(_<8
zO~~CjDeeq;6OEN?n{tH5&}`NBP4EXb(nP9zKBuV@H_L%uHoIks;entM2%qyU>!v?0
z+FZh0kt(h*nw#PW6*Butsj4f2L1hk4q|b;OD1f?0w@f-sX()PeQ4G9GF5S+WBa>xU
z{OCIFy3QR>D*dc6zyi6q=v{^g2ZU6z9`VfoJ|F3bkZ^EtK%ZncTNDFg6|WyX6+pw*
zsJBpTvioIHOIQ&<@!9`M_oUIe-fEuPc6*mzSh&F{LVq;j0Y7ZM-O!R~R7DglL*V~&
ziGfz1A<l^;vFke!8Yiqfv6QK@KxvK75FMSn_F2}`a%>T%wH-Oa{y`Sq%0+=XNs_vj
zisc&ePYZQo;GlCd7FkmmIu<z&;qq20F7tGj|E#c35*nA!{kgvXi)pRK<MrWehqrt6
z&q}r9Si#qDj);he_b`ZDSL3W5_=LP}Cx8{i;M97D_ubL_*m2q7;n93Kb4ZVYMx)Jg
zm2Ml4>rsearB(xaIuW0TLZw=h^&;@MWj#GTDa4~UnNHU`2;#HL%E|!2?J>ZoEF8-5
zk#9Y7AAAq4;OnFWo7yhb7`{B-l$MtIKHUzW203=VQ-6Cn$QBJl&IIh@0GBa1T%4n^
zv9Tumjc<ht+D&$*xJCM1e)usk6y)1UNlE?gV*Kqvj!+Go!MKqW65p0=8UeHfD=m;-
zy-JLHqdV|DGeUJPWfqt_5=H$~*cdzyT+Q))Strq?JL@~MzY4d8NjmK<jpxbEcp61?
zo7z<pTygXJa8Mwq$6O)>f-=^Rbl_noBHK7yXJ;LW&y>#dTC`#4>oEE-dh2Q%3M0{-
zU9g)}1!%$RkX$~Ip-L)aRbsMA_zjYC+Cl}U10Id#%os>M^*>W34N`WSBlTq}Zf45l
zpD*J3=$2t|0*BwgA;)Al)c<Mf!fm0<Ceqn0rgN~^hXCi|a6~qrKR8A8Zl^22y;*pM
z;GLmZf4~GThZ2KY4znunyypW+z{hp~TY0)YCE&Cplt~7IBMCf`nVL5t5uxiYwxgJW
zkUJia%87X0LVR<VucicJM*$4g{Cu-V@Z}ogh^+N|GZ-byece+!h4ELT?dsd>onTTd
zG5@H0An|*-En<HyB_$T%xG)<Jo~*TCoc6psn9QWdm4GmIJzcp0u0vY=VU9BH_In!Q
z`NIu2H}@3Z72^F8fD(p4hGWqE_0n3}YBSbL(REuMj;9EO!$lkIE^BQ~?}Gm^TP)uN
zNXxdkONd|wXaF#S-=BRcTRY17s`L9dRt=6)`8KWxfq((&9uc&O+X`-=Mr!&t>B~$b
zk(Sj$W<1#dsTp)nY%P_vjnYpoRrI#Wbz?J$oF{W-H=gPm1WK0C>ct;NT7gi?kwM}>
z=n^g!wM(<n+uUmE+9b#ro>1{7y$g93y(i|(f(Dz8o(#lr3dEd{Kx|jPI_7Hqw*$~b
z)u=Op!-W<h_kk^}sX5P!V)S~x7!o$~cmn*+Ux2gb3S10wRh$EFfs)=M5wcLB`QGas
zuuBWhJ4e#<+)D<m2q{=uSp&T906)VqrY=Of-lhitZmViNqU*Op<wCc5y!)fCrZ)H?
z9$;+VBf*9E-WQ6&UatASy#OdT()SCZJ-|uN>Q^g!IJSsTGdy+(7fPO9P7v2ukG}hh
z9sh?t98`|)y>oz?(g|?GBjR#+r^qx-aGeQ&O+nI~U}h$<??Tz*W@q%Ispv-fv4ELk
zhJEJ<=j-#;)Yg%<eaApHqfYZVAmo{tn&M28>0E$Zw0UI0n=(7jS1g~j#gJaHgD5I;
z=!BZ`991DOeRRho|Cj@sta@LNNa^>;XId7+Qu!2EZlZci988%&xemK&m4$iHoHxEY
z7ddy%N3^0g;Y+?XkAg_QczKDUdPP;1@X=9@7y|kg<cw*Su1zytTXdrIy<OgFv?*@?
zpPn@!0j*~B^xltYH^bE2d`DZr3binBHAg;&IeZ>gS?Ek>wb3@dX>gG=5>kpZj#4FP
z1h!t{_ns=(+w&E4U5K>0jwAG!u^?z_wNQg=z#TA#S~`bvivMoy$CLp5Z^nT7O_x9G
z9Xb4Z;P})#Ai@PjUzotDuQu5Wx*n;J^#e3-kO7}&uWD-0#r3s|#udw+D|};UftbfY
zBBc(rg+X$-GMUsM=b%O`|845@#fo(`^q%OFY)Smz_77-tzYglKWZkSyYNo3w_jj>L
zUo(uGkzG+*j2gbgX8+{3dkJ*7ySiW*Q!JwHJpZ^r6K-%J>*RHTku4bwijZ|ovYbJu
zB1@~jW*ejPd{(B7r|kJM>;Q*dfVmsZ6fuHc9hwq<RGE6vmRyO+gf%NJ*Nn+ps^K1T
zuT-%ie3|`Ez51CV;F+hIonzQmT25}g&J^Jp##iJ#Odm%@MJ2>z^7-Kk?bDX?{Pa6^
zb$#E4Ma1~pebpUTI*=s#Q5X`zVC?WBA`y>@Msx_f9Il+q$TSRn=_a)r4IK78<UP8Y
z2AOEJk_#!*H%??;2i!nfN2YPK2an=RxAf+jrs!|{-nUTdnEF9`*bG`W>+NaPP>TJd
z^tlis4w{;p+q>dhN}Ijl?Rj=Zq~zJ3X{O4LgVHQ|g~6^~kn+FYRA1JSmNCLi$2$iz
zSUlGb5$q<?x%%rh)h66iD2%sGSB{)s87<1BXZ71>wV9{7ie0U?Y#*JQtSeO8iEKYB
zK1R8F((cb6ZaOR3?SMahzV6<pEFNN9L`^pys!an`%q#X`(C%B_5-r#-Gb}p97!}Vb
zMpDYeu&^O%kpNwaIth!)KZVviGSc^udL|J~u*h*48DlAA9s!%<+5WE)7AW`>ij|&M
z`_Giqu|YwQY`2&14Y8~7tCcr|(Rd+|Fw!Gzdogsdu24JimS8_(hcNm;Sh|j-Fx{!v
zi-KU|*+8B_m?1a^7j+67v};0TS$|Y(3EJ&Knq#V0x65ymb3ab%*DZSm``y?PP>*;N
z=2i_#KHNgbRL<19LenUZ>@r!4vay@c8s02+(|L}t-V*m6^|3a4#TA;{Z7zUHRVQcT
zz=zCdn(iX6!uD|%vZwXt<DCojH}3WBE(EZ){C%%6LC?Wb^oIz<N)XbvHg4N+=!}jo
zhZCbjIv+Kwf-QeR;<>}w`>61rZt*;U<`t-t#ZP&jWYphQ>a|;7V(N%$HsB>JTWNVm
zcV-+QP;>5;-Z}h6|8ITK3`=UYYYkbOR$tFHM^O^wDyH%LUXj;gbPLl6aaeDsO=t(%
zk(lkaE&wF(&!WM6>Bg|Y#qX<|h7KucUYdfmR*AI4+92*E^MJP7aW%lgz(zAQG0DMm
z+J)MgK@e^s`&D5tZ4=&7?L(kGRd(7aP;6Q{JC63;dU7nZYbEuvH!F!(<#MvxcqaNY
zp;!hejKsj-Grh3nwa5qL`fRO+6CO6Qf1+D?49NSbBQC=TzxKH67kPme;tJ#WG=uQ<
zM&|4z%|ZNRZ<{}ul=;n?kK!cF-eqh{@5ymLpo3V8Gj(3p=6FmZNKle2FW*;fy&uRQ
zvS<;pn>NDyu-7c>BguUi#7Fiwsgok3Ej2q*?Dh^|ybp$4<^ue^6yQ#IK^o~Srjev0
zF&z=4AjWzL)(RYSxd+IE?RKknx!l+T)%PvjyN#Zzm&KX9?t|!5G)m#sBFre5lT8RS
zM{zn~K75YGbEGk-?4*HT;zTQCe6Sp;Kd!tyv)*BGcSEQ;ez_6`C{7p%#+<F=&}P}1
zy~+V=4Xl*lJ(sM(PnNzrl+7V{U!Ui**;A#ceI0{XCR%xXUnhJ_;7m#_24ihRtZKNP
zY_mHz!5Ai`;z(1{%uE1y^XztACX>y11f^M~*mtEgzJq>MKorg-X^m@C4zIZ*O~$iT
zo#A!>;Xh9&`|7CrIeA0ckG`Spc~y~wy9nNuGPzxtyUR}5Q~|)k;o%vrWd76kq7p)q
zzKe6gcnq+VN&N&N9Uz3F0B8HYUp1thMo0?c1iH@2hqC`7%kR<BF%F*oYv=tof+Nnx
zCE(zSJYbtktn(Wn6i9g8DikOf*{l~%8dvQy_&iyZ4nw@cl}PJd0aI6ab{_~Uzi?jR
zVBwMvL_tQx+tNN00EHBAEn*`@&lZ5rQ$-#P<da~)P59zQgAsa$=C^nb`ot!d%7CXG
zEiU2@QmMh?_sWim(P6G!8eNzyT&^^;jmdD<U0-QEN6-<q!q$6_u60Z#KN2x2P^p>M
zAmcHZ!UM=g9X<n&Cc0!8LQ=#}0tQjL1fvhOB2;NgCvA8*3rv!hczYa!cbzhR7OfWq
z_QE(5KaC}0$EEyis>DW3yq<pSekdmJ+pHrW2&1BAO8ar;Vv$tZi)S$gbs?nk4H+_F
zMmv88K1Srf3+WFlBt^j66lAvPEx1783lu5}k-?~hg32^IhF$7k%tU`8^X&qZF0^-G
zSq~sa4;tX3$$>~}%gz1pliMaAYA+Z?@EH}~Wl4zkom!5D0)vFUm{|_*2RNr`+!DXV
zMd&s%U7w2q(NIJ@b}LLZ@|VZsYK6hS0u*R8zFdkgbAWY>_tgX^#bd~6M$HYwFo6b%
z`0i@Ae1A#}JOUwNE!7DPbfMEdhz80?322c9O>()(RPvaTuGc*eh=v~_vH^=xOg2Ph
zQVHf=A~00;0CahqmppfIJ%o{A)2#rdXeQoB1O0X`g)SokuUxNU(U72}pFO88G`he%
zqRL4PSh5HJaeSY+Q?U6T^2?3onb`TVSn!bT99=5+<^#9t?mFr`24g3cr!JKGc4!kB
z&BK0B4KdZAz0zT_R4}SS7RNBXK~Re8MTz*$8fvJdfC;+xQWGBbP|jx^_rA6>e*^LJ
zNGR(R(J``CX#HvT`LZd@_L*|<PfEUQFT$Vg;;XuqTT*Gs9dqkZdR0@b$1aX$Mtsbr
zZ+}#2OqPDBC4O5yNJpk5-F#z6LFbENB?+Ny@zbXtTsE=f`3Jr_hpo_RfbSvyGDY_1
z+eQtnbh3CXG13SZ1i8%-{H62nX)+rrgh8hf%9Mb}9R3o7DTj0OE8reV=tGet7;Rib
zd&-YvhWQ$pP!!vcUtkx61CIjiNtKZ^wt69KjOgnjaDp142GOk)gosIv6mX%DDSXcW
zkO1d<$a_*&T-~_%fDq0`5lGMX%Dr}qOB-tKr0_k=Ctnaqa^8QF+@kY?H@01=hojrd
z`DWlq8U|}3jI9G(XIP8fzp%xDm<2QzOYxFR+HjiTdwY91nL)Ie(?roK3L-fAm)d&T
zH9i;XI1VF+Cn`Gk*T1t`(J~4AobfSYYhEbF7lKx53N~6ebsjd-Dc2nRMsHcC1j*i}
z9o{r-e>P<{nmUOs)a<F8t|~W51~yyVlE|f>*B3=f`#11AAkfUh)rF|THR;)soHC3Z
zx?`vFhoOooX*VYsANMr{vpAZ-6_iIueOf(ho@73P?;;|oIEiN`UDct!T{pn7LgfYs
z&(FxKJYeH(L**f3p8o`H4z$gOglKC&p9duy$Ue`P{oAxv>BU?HO+VJgKHoif(W{TL
z!Ltz&5=)_cR84${o!2Cqkoc3|_;H=fET5#3G9XQRVRSNo5WM2D-E&{q)^wQocJ9U5
zcc5DVprhYaa;PbN%dpZXlZfoEsVvLl`nJ!8p7$njVBSKv<G4{uhS^*wDDunW+>nTP
zlz*|!01zxGEwY<WL%S(6u`I>$x3|uG7k<IKiG_n*wg${POj(pZyz*Y+$8Aa`075MG
zqf2|dfqVF{;hWa$sNxBXCo*ojl<Dq4RCDfsE}E5`zoKRtiE!w>{>ho1r!b8EC{m4r
zeqckM!fXDzDJ^T+98XD{BvtH`yTfmzBpAWOUb-rqNco>`6&5)VV9DX|+=r*f#>Ww>
zihH%*w40qSvc5hWp@(%u4b46W$vM`YqQ`|A7^>nLp;$6)XqSh&k7~B#>k&PCI2pUX
zzJYJcc4}4LiknS0Kp^1=UqmQIN4ObTC`E{>J^PY}7BL}|RuQ`%u`6BGp|!`;)Yi|&
z$MlpV1C}`3yyr2GbiEbi1xWawx`fiAO_SOGkDd}r6nY1V*ejBMPK`(eTf%NYrgmS1
z|FgF!uS;O*g^-Ej%qdGni)AalY6U^w(qm7F;*oG}8k>471Wm6l%by~jlUo>^^(>6)
zfl7ms^c#c|4L6#-WJO5jbtAiQ)cDE90WV>Y3G7toO5%TDieW-~7{4sJb1*EOR%1rH
zRd}b0D8Rc`EYW2bRc(}&LhUEd-r@-t_bmVk)xBZm{0>HcfUk?_y~;|E1XBQeD<&0W
zy&tX2yF$0vh+X^A#9X)k?s;u+H&81_xlNi{-Rd}TuVUfIlQ?><Oz;PxiYdQRoM>RR
z@!W+F`oMDjB@D`S%>zMj*fp#bhErNYc)=m*%^<|%5?SUE(1U`-*oMwrx0<DEcSU2t
z+G}$WRk%spsn(Dz#eF%S<_tFG)|}fS&5&!#!XegCgvMr%<1*^YXoJziheI`Cu+Z*3
z0PE3*;-$P%fc@j_hMG*fz<r6=rJXk_7`{+6otVDkmSX=>XQWcFHuRcp7K^NWnoeyi
zNtp6&*K?c0N=bxB_NfjD7x(d*#djB!(#;_>%4>;~GCxM~){)K^SMg6Kr6nWgr{qj#
zxIzu|uAxax(Gw}3hNCEi!8xJk-uG@5rbfyM&AIxoLS9LdGJjw6w}`F*!;$X*Ockh#
zv~l9b{X>Zy2k=ynz)<4-4-<^}wR>^~=?{5bzf&MiQ@N_+&+DzsnW1ZV^16bOLnwzL
zIu?2d$mpf}l(nk4JE}5zX&-)OyGM4z0i%t_ckEjja{4=DKEKRY8mja@m4aD1o>~qo
z<>Nw!B)~%8le;g!LKYYC9AjE5^4zj1<MD|XSX3vxMRH_9^<OdFFSONglHubig?V&9
zZ|_D6^j~8)I+f^>Tjr}Vr*^850PeD_n2qA!)e~xHvzcAx-S^PotZ``5O2U*SSzzAz
z_G2dwWEeC*`ALmwU^{H!bTrjp`LYS&N0IKy=QJTOOI9c2=|CI$>;0F<@iGcD{Yw>T
zcOeVfBAKEozd7QanRAnm(lvucg(3-otlI9`lz}XsT1^f&Cv?p!`hg=d&NsXW>d<xl
zO_(%^#kTbBQ`77{wQVlGqRPHWXgN31ijX=FBO8j4L81SI$N<!EK1lDe#`fABTAV&#
z1h=*yQBhg=K1#Rz2S*`ciVBRd9W_|03Pkw}_r)D{8T3~9v<9T1lHP}B{pq9$KYTf1
zbAHg`IYd!Q<EI4AA4ySx8t*!F@~YF|jsmI2DpH#1-afQttPZn>h_+cuT<$olvXc{6
z*%fQ)rD6xgck8+wH#Se5o~?>|lcq>Y8vU5@WqMfH<P_=hE7#{;+K7;~5B2Zt__$Dz
zRyR39460>jkD`IZ)Q_ZTH~IcAs}s%v`83wlRcpNOZ$kT{;P*)2sR*_7&s%}}3!&{3
z*+>usc}&qs4=3or`N1;u`>9V7tw|je1$N4~3~r<|audUS#>h=~dSM8({SV^M#Ef8@
zU$-5dk)FAl*msxGPSf49wV7gdA%h;t3fV+r<-KImpO7Q?YWq~|#DD|JPJYkE_Al~H
z$_m*g#o5{E8ydBRecjBDM=JfY%F$IVA0AJcmKhbzT~7bICdKk4HeJb%z^Gc(mX`Ca
zqrY4!x7d|d`8MQg=0Jx)1V|@Du;$aGF#QAjjhE!VAhaJ^XkhsQBDq3QFc(-p2eCZh
z=}D@R0(#Ms0?Gaez+>!A<og<GI?TU60~H-|>DWwjl3MV<Z_t0p$$O^8tknG&e0TTL
z`kshel%@PxZh<sddcUhn#@{Zw?Q>XO+F0r}xED$oT$j~8UgK}9X<E9s-4^1thiyfJ
zu_kJi)p`EOh&ftZ8(#)L>l6ERLyIchWu@JZFiZlpVRXWsMG*I^@-au(6E__Zrykio
z;T`ldIYsWz3hC&lG&=tv%>Q3I69|I}04*J@wkvgO@}70Oj?N~f^2ZCtPlpZoX?OFM
z-N_~Tjm`<!!i0mm{^fYF0{;37b5S8Qi9%q5;pTip+j`Q$C=2D7L9g|O==3L^u+?#s
zdN-FdoRt6$P323%tj)xWGKNko4<Pa>P#sEb<Ob%{>mkSLS~9ZB`UAT*@g(<y>c5yR
z2~>@N<vO6c7;8FLu3Md=q4l}lXI+Uswh#BN3!z;Ne$LZ(8wxs#Iunx!WQiC_0Ep<q
z9qg}fThk;fWk$IAyjI1s?k=(Rz836j>4hze<(i?DaAZf3PQF-IAGMIdZZI6j20?Fm
z*-5|g*FKna%$-Ax_M4bCd)>kE*?re_Mb}pvB%<_fr}_0^54hV14i-Q3KZjdL0E%?#
zW;LEtknlBJb>4^Fd_Fmu0x2tg=P5&OEa5b{q?g2S6+YFM(l1r=U235AN(KrwgExZh
z8UruOAXML$Eln^eneQ@7ir~x&dUk)KCVhC}?x?QTUN}Se9Lj{&cwrv5++t8^al{<{
zCKu}nmXGf--t+VhIQ}yX?)IgJseZkxheR)pYs!t3#dMfj8re2PZlS7&H!@>3U3~-A
zrU%!0wuw9g$0tS>dRwob!*fd$%Kxo(g!sq;UW5eKt_V2__o(+S3X)R3oqk_`&+RcX
z$Tii2u_2L>p{$5-^zJ>V?HJkx!gPj+Hvp2lZc(4O4vGc*JR<Xj>0{L+6c~CGAf|wB
ztwiS4O8D^pP7(H@L=<eb09u~vKwJ<?^BCY0HqqHey&O#WPPV%<&!ttprqi~7PK_=<
zK|>->=C0g`0|%dm(jxqyK1&Fe4H^nr{Y)4+O9)JOJ1Y%;pzo_yJ#xUwP#5BTis<x$
z{&w-sl&<SgQeKM6f0XKD@-Gg~=VA1U9;b`l>hIK<fh;u2E4{q&snk@`07#JwGZ{;;
zEuTVhy7$yRe||)_U+5=5O|z5Rqp^WHncmTL)P^sAlWzRA=(Ds?f-kk{^KXKQc%Kcg
zLw31XQ7>aFJnp~gm)mB<$%bO{vV_oXVW4D<{jJ85_^zpD|4*)3{>=O{tA#36CVsKY
zKOzuRStzw#-@Az(cf5sQ;S2N9)=T)9t3ld>j8^s(&#|8ta&)`3-+T79=fCw;YNk83
z6xF37FqF>YYr8I7OLt&iiMYYF4Uz9KT0c^L>3DCd#A@gI;5d7$`U$p9h361q!2o`l
zYf;f2l=i5)uS_v|rl(=9yz+*5RlhexZ<j_ihbkAFZT~E+iORX(^oG9JZ;EFEhmVz>
zhwf^ce3Q#8vQI~4?X_b(iz6Mg@v8N*W>REZK3%jSW`KzF-QpIIh{@Jlf+)$~0seCa
z<(XwxXVlh?j!(oZzcl#h=d~S*p*na*a-=v)6OoSCew0a?_I~*qu>we|0tjIZv@>Gw
z7}u1ZQS74)(MP37PLiC?`b2Lm_o<SCsRw69ehzLT17vu^8@r5?*UXgt)g?-Njtrc?
zFNEG$nW~%Su+oH%E_J5kNy90fanRkk=jg6upt7evyazF4G{ngTZveCfDoKh)ejvLe
z7eSZ)w`@q$?GbdVW*BkN;Y7+#Ocs|*w~w740X+l*4C*^!x`tX)wZ^qcwI$WfFNQow
z!O6&t=*(ATTFPFPbiXPl<y4x!sA0InW9S|I#m6BbKhV4RH6MIM_dXRbmop!#OSqtr
zvv+*KYc0wNrrz9Cb%1H{14<(<_I8PNV|J~ORG>3<8&%=kE5}B;o?9x<y*;&D$h(f1
z>Wu+KQ2&tz-<K<-PD4q6r%^PqScn;^hIMOA2zeummafg_5vV9*vh(>9m=vh4GO|hQ
zpqTg=>QHrzepovTh@OxAGMT{_eMg<H`-Ox_$^wB*s|&A6(J2oOC3w;7GdA{{RE169
zFMY6Mf4A`OQrEIp-7=ne;I{M^aYQW^8CW(lsBhY_e*V8-Dk-f)xYgD5$2BBkPgACU
zsP;RbW4^*ZSxGn`q_XVmSnIt}#9@|iMPOYK7H8>RBc|};?Dte_ve@au)dBKG(0d6u
zK&*tH(!rahhLcIRYJQlaW9Plx{oxbmYx}6@mQeB(w&9BI2NjsiV&kW7Y)mIrU9CxF
zs{5U@X*ustI8D4hbx_tz6}RnbDRd_3vdc)3OzeAYBnQqC6~+C%*eXB1VmGc~+o95$
z)KoCK=`+8UcBk+Zl&`YST10Sujiz$_2$fC@xfFg|{9XQ)`_{SAh>OJs*I9G9;}rdg
z->H_2wYKz4SU5y*DSvxmIrQa?hxZOcIxHCDGSU;ST!86q<Em%n+SXrWQ@FCum8hhW
z@vBW{icyw*wRnZ2w7jVOur$i*r(kGJDUI`Qj?3_;`?`%oXV}UBiCr*sQ0B67^nhU;
z#1fG&(;D2Ofp%b*W(&tu*Kd4Ms+qrpy6(ci4FGSET>WeS1$_RgY-ira4@+BNU^pX&
zXkU!(<fO>QZO9RZK<9fxX-^q7&p(7VY(PMt@zcxi#jbt7Q#0$P*ZwGy4AXbkT^IXR
z3{1ZZY)830AnoomNKUrwe66-v>G=*T+vm<bMpb<GxZJN2gBlL!U!gP~<u`>*n_w?d
zEGoJI$@~|u2`#RhPe=#1mNssu>%qT`o==&cF=;U5!E<T^bPXSzjDwhZR28VyV<(44
zMe$lPWtx<0+U)L`Sovi7#uEwRzrg~6SlZrqi4PWJ0e40@k`nZ(#;P{3)dcx_Mevw*
z>S)e6N!<KVt9UhLqPHbTa_@m>U&w|uAZ4^X_T_SCE0*ERsmD0%1yOr`A_bT&2%qHn
zA@FQ(6tuO(7;^hzBXNH6PrYy&WE|y7&qP(?B<3H%F#^R^hLr@q=})hi@rmQhi<eIz
z)lBBy3_JCz3<qc7Gr#1a*-oAdAJ_TM^n#MC>7!@f1Jx+4&oAkb<mLyRo$45BphjfE
zz2gA;F;pv)q@hGieW_0rK1pm_mQW^8qXoa5QR6AIK$=4vTkYrVs8rkYpWFV#9V8__
zNHwiqJuUiIy}F9NZb2rG{W{uJ^JNlnA67j$blIXJcY;LOU^-CJtk-H7GQK}0S+K$0
zC27}5@Ef%@kg}I&pa^+asUL}2vu=i!^?Z72CzF}7ywqoU@>5EDy`fC8n{=S$Y+foc
zOK8J8?;Qt{Fl;{}q;{mbzxVlzk$2W+?bv*0*>(|x?PO?`Xny!6?E@N2OXkKcC*cHY
zH)4yy%DM6&E2qYXbp^LKYokHPx+G@jkk{e$yfo=wr)?Lv7;1jewkeH`yxuEk#?TTd
zo=7VzIi5C;W47XJhpMs4afKD7ee(;W+oc;kZPyr_Uwo}~E@wE&TOr|9bihO`WxC04
zgxXYYo$F4YvE<XHyItknPrr4frw^k`fEkH(?3qSzWbFNnp0_2AG45JJZ#TMLrYoMZ
z1i{X0<?ds*6UFU3Rmx3Ta(k|-qop~aLqJZuHJJInSTDh?h&{9?+E*+ix~iA28YX>E
zFdno5B^2<N)7Wh<*bR&Meifa^0A=MghWCk*50uYU@hnQlS#8}oXkJ_X{679O(6lLV
z{f)_n>V<u(ZP0?hvZBi(30jLX1zW>%U9Iv>FX$f7Lib9$NTT`ljZ#nG4gg59x3U%y
zk3r$Dq{GeW@(kR=nZkcv<nZUR)6=)NVu;AC9Kf@2W#N7ZX#C8TmUpBa-S0{%U~0zF
zpZrH-3s9CwIt;wn0xusj@LPA&7sIz_$K)4<H`t%wN1ZKTKGV&A3uke3lF>M$#L!ea
zAdzqJPFUFwNO%G(F7mPcu@0MfyR8=ganVA_Dv~eb3M933O1amq_+=->q8{6-IcpL1
zk11X_CAvaT_@}Y<Le;;?$xvY-pprOtnWgWVo3KCC%7H&k`3{VaiV|BVD(E4(XdmK}
zO(&rRJpWqdHS4O`V-5r{XZ4RpW+>}2$sW0HVFb&>d;w$n>E~Vd%H-2#y#jcJdQSZP
zQ<!D-TDcJ}vu0hUtKxD86M}~4S{1iFpFWAE2&<0Pr&ky@jMj5#?YDT1m42f)oi<A6
zeOOT>42;CWyfi_Ha8M6KOZP6P$6dNsM{(&$Bd<j5w9o<eYYAT_FLzkBzWt5?V@+kc
z`jlrhJ3%emaVdB9yad<)Jq6WEq*3qFeoNy@=95ohANNDp0hRYkz1aKk=wnVhlm(!h
zbNU5tRXYUX{w=W(wG8W3FUMLw6=BU+d7Qe`C;Zay4Pg50x><r~Hdp%CFv}rTB&)J7
z5e!sW0sc_W!yC0c(l-vQ&Zoc6CLZe(cWk@f;)6?6FDWYKMyt3muNT)yshF(FN<pLP
zUJ(NBAC4-5?G$kKQ{5}Y@k~m0FXUApF@ysIJ0CXpXx+nGADIL`9cT8auWx(D$RF(F
z%K+pb`hrgs$6w6~GhU_7>X@qYh$T-VA;Rhy5#{kWrj*~RmH1f$R3%lsp-8B^Bp>nN
zre)e}r$)War}(x7>pq@wy)Jt*wcq+U#w68kwQUHcm0`<Vu9!#Q;JNGZ4Lj;;1Z@Uu
z=^M_a7kG%+l7|5*`C&tCpy(jsa3xgTg#%v^GaBEf)J*TI6$D>$L1$h7_Q4DZ7Tk$^
z9tl_?LJzRJsw7^1SH^OK(yw-N4Z7O2Qm=9&u{A~rZAT7C_viwOMx!Che2FIlCU7@X
z(L^nF+!9Vf{8erCM>lt}qD+A-WNP_l^Bs5I5_!|&7ANeZ?7yz{*n2p#1biO>Va%bm
z*!{<FrUwp2XUs~$*P-*<#i)#ny$lj!Zm+clk&c|uMepovI4^c=FUeZM=2{0;o!^|#
z-w*8t=&Iti8hKTy{0X2I)tTlQ7wr^|$6~acKk=7TKEn=Q;0yaz50jHAM7_hNCCh<g
zZ%LhXg5AOv$*_>EDz@jfb<UF^>z2?3Mr=d_4AbOqJ3huyCUha3!iYaG;3p=i@_9;L
zg-y<IHGh@Nie8|^5WtoI-)lNpd2A6a2y>>tY1phk^U}Z*Skvp|qSBb&u4H~l$SP6o
zyV}r8z<bjbE!h*h65)MLpiC{R7D!e67=#xsX+^;H7b&zDE)jJ>eW$#lI1E}nUe}%b
zIE)@kr}9%c?YxzZhjf+(XrrdXVV~39jP2$HxVr0F6SgCkj}8%-4t@I4yk_80j*7&%
z$z$22EfM}G@K!u$Tk1;ctz;P2f@x{83)u$KDX+kSq$&YZADjXUTomO9x(sRT@5&e=
z60^_>`Z}r?z8^PcP77SD=18i{3{t>1PxPl?I$#WcZG~+Hh8FOP>WjnSY>i`DZ;ei(
zqgaS8G`=(MbF@eU1*|Zc!amLG)_Isu%F(FxwM=!`6M+S^u>$U8O#XwZ5~|0$s9JZD
zZL39S0ia|sI?%2^{wRRd2!iEn)^?+N+JGDugtE?cTrID~EbXD7{pt5+p;it@N_2L%
zRx8ZP!eQa6gn(B$Vk(86>Vzcjb|f5dd2s|0T1N05LQFl*Yq0=Q%Fj^T#@4mU+~Gqd
z=*i%oD2NV_%;q?vY1F+Tg$==wtW|NSs!|XW<xJ&NE{U{KDoU$j45P2@LKCw!!t1?4
zB*7&I4RehiAa{z4K2mmhp+IV)LlH0SnptD7kBkY1e+KBnld)631xQItpF>NYa1>~+
zkNcs*>nP%WlMkQ(S{)}nhph7BQia;9+2%Z{ulyz$7|&W3({<C*8O|;@%XreUNmG35
z=j2VDINupuROEz}YUH@j21}^{0Be?jAb56^!Q6lSU|nrGr0x&o8s+Z-dtOHWAc+99
z!@o?t7*b&6KzUPgMUTU99UXm}wZn%*5tsA4(17UolCUPj%=mpKlkJfk=8_71eOgo}
ziuB>poZi;oKh1QHi8olR3Ul3P3%zH%YE8QDD_=^&PKMMJ!Lv@23y+>^RZ>3d*eOei
zkCWdc&z=z_=hM90aj<goHGzw1@J4w!gk=BroiEuUjB?^4uU}%{f{u<!YN3Ag%kApt
zlK!d&(JCH!uFt|vUit9#69)?FYR9h+i^V)jSTjA*NgqsbNEjdkQ7v4cq$<|zf(b79
z8cEl~*;^OU+&2wqmA@dQkt4Gr0e_GHqHXnY;C1mqv2wm|21(aWkbaZ?#ex^tcxU7W
zt1Hmbnr*2F+tU$Jb9)t_Uzw?vV05T)e;!@$;P^z!T7Jc^hGfizNcWuA4qM;;y{0Y$
zzK3~Jq>|l}m1mx44v_hX+|oTa2Z(~V$ZXQ}A4m@aEgv_yDcvt}`QsA#?971K06-{T
z$MVbERg_>+ms94|fQ6$Fs|hE?(Q>qW$0gk2#r?Pc8?WQz)ky5bO`Z%u3}R#rQoh*x
zzbtapy-8q>54RFaEJ(`pNpUK}7E48&N{mzv62oNF+X<d=YoQs_Yv#jf=LsXp8nzG{
zC^~1r09bb5JJ<k_@F4^eU)J1q?^o8aqRjNtYxfi>zYxvu!?G4!^K{)r@qQ!q51ihM
zfdGbuinmC_yUTZ~!=(Jd-LAK&9$Es~Yo4MGs%{P_E2_O-Rcy=ER?AXd?3$FTMN_&q
zew8Jv7e*JWyC;XWuC8IfiZbTgx6|Il8`Iv&sjX&CBi0KzW?O&J?<6*XV}mh)DF$1j
zw8Gq05v}z7UbJ_-*~(e(dO_tV<OlB%Ws3fl6eu?(TDQ$Tr+oT@)W_tX_xo_E(`p@q
zuD`7&(+)|qSA)hmLyX^1UUqf(wYlaO95L~7{IJ&x(5#e^5NKA@JQz|OixuJrFHLo#
zo2iq%_k7O-NjhEGBL1*Z2zf7`0yHW@(L!>2>jxZykwmTvnrfHlSeY=o-l2RO3IqAW
zdTN&x<6CMk%|i@NNqa269oy7i&PQ4WJ9n@KbKeIR%6{p)iUEzV8x6bFeAo<Uy!64(
zBz}r)|08ny(Lv%AtP4vXL7SKg()>ek+V17yi@jzy^+*4$<#7?I-GvK^xW)|5(qnsA
z$yPs9)??qb@3PBrRGI1-J0Ri0WQ4mp8_R_DGAg>k>eqCm`XVP$Su8#vHP3@;01~KT
zkPsW<bsZ;(agCUSip1wP*xzh)HqGC%|DrTPgruMDHa=W2gfXMFRhk!hU5*=uriPQ!
z1I0Jq{2w>k6sCdcu!S5|UAI;~pOL=S6p{xzCubaGswdmU^iGXY2{2@0Va^Mha#7+c
zx!@P~dzxsm7?2?-c)`Q(7){t)j%Zgrp!4fU49zk^_`Al;7tYuoy%RAo<hIK;c_+L9
z*=&R}-QT_mM9~e*<L2Z&Pd+{UHVy*O68;(P+E(X!SJzUU$n>BMI0K`8;dm1Z<@?2}
zK9$v-j%XFi-f@-IDxNHCRu{4rcX@ZuKbiZYI7iF_Sr;;wYLP%`r_;cPs0KxhB`OTm
z+B7AK+0&@Q)kglm{!G#q+rRZ}h4p`?shs_bxm|<=3?(L)#3s~h^KI?(;sks_^U&82
zDfu}z<Vbp0H0vR)8M3Jqz?dV`_+&5qKKd(l!A=ka#LPM5lI3QCxNX~=85M19qoz~d
z&3Z<DTqv~%mL=}jz!FqkK)W4ykz0OA8t{DCAi-zt#P24R4oj?<qpt8dgw|uT<O5lb
z4HSs+u>*X7j8}6h?imJX1|l!6{1i^>t*>rJ0JZ2jN0-~&ylMJ2g&z}iiI_lEbeNuT
z+|TrC+6oGmt{cmVYE%=1c@2x*^3;P>`Xzk1&8>@;BP;2N1W_#b5sd5$rE!D>Glf$M
zBx8k1w!Ojrl=RDAu`_G2s<mB24hO0;oWau&f-=G_(>?iFAps(2HC$Ln8sjZklQ_OR
zjmd{o8{ce$JIs*O5}Du3`m$X0ZqVgpv|op_9Q0P`@4OVLQf0H9ZEyQ?0TDmC?EE6g
zaNc}5_F7i+xj_9r(0CoDn91W2gG>{cJE2m@A3y5@`B$+~pZmM~xOD1MS$opo3_<g)
z$2U{-(NGU}McriW7<<?QUlG^zn)OT;O+WHUdo2~Ce<dU4Vf;u3h5X|v_qcWEEH>fw
zjf5?&{PQXQU;eB$SUWny|86$GFu~8~*B!)3abbPgjw%T3_w&DdoD2+_{F;pG`W=jH
zI@!u&IA4r6^$q)NV1IaKb}{YkfzEhv4;W1dcE#v@M@u5V*nASv<xf*2!>7q&uUS<|
zC--2aS7UZu@6MfoBUoydmPC2gpA<nY9U7eBI)p;~b}p64h?F@@P&E4))Cp8-GO*U*
z6O=Xb3e$x6zaxwJ1``Rk+Z_wkXO+5Rn4d>6xp4^|xu$P;{zjtrA2Nr-g-WpuDW|qr
z1eal1zM)spUTsLv9Bd@9dp6^kLTdIRwA8DgNxwc@MsNUBNOn7$@g(~o!`QKlVQT$X
zcMk8T_-N>+aFTA<6)4Z3CBjo(f{$i%-%%BK08%R97#UAg7yXZ&ck9(hJ3-~El|KN?
zpGaxb5aOi{OtWm>@rrn9TohF6oVNA`38g{Rg-AW69RNKe416w$m+?h(VOBIp4XFZE
zASidnY26j8X&iafkIRr}BM>p&i!<!T7Sti0>94b7E(tK&V}cGzmJ?KmFvr$upN=U}
zS#|iy&Pl=kTycq9SA~y{qZ%Y+!2Z@mq>^{eiP^c-7q>K}YI+VEyV5a!kass}O27Cm
z_21=vzoxI@dnOm*4S$T_JhvbfGZxS6y$knujsacW7gD)(Cu|NmN@tp)Qjt;FBn%)Y
zk^<fhr0ljLP!XXQiEyWeBqbbf@XGOj$n+d7XjCKnJ4oW)6gZ&Vsf33i@@b;T3|rtP
zQ7COjzm?VZ_KO(U0N$k3<T9nAhcHOf5l22M5yAWgq);^XS$?yUgk0L<eVxkfIWL{8
z146=h?u=Zmy2x?~JDe#Vg3T3ZQIl{0YZ%>L2N>u`TxsQThxZp-)y-c@D!>4=p7$iC
zMhO_UcWbz>G)P+EsL@E@q><WDb6!%~-$l%oCU92Axlby%EIG{O?;3m=9g;8fVaEC4
zR@}sx{=%C0eUjf8P!|HgF~IPY)+>?V1Cx!U;RUzNcpbnG#`(qy^UstFK$u_)JWymL
zkm0mPz}%cG2YKI*u(K`yrBz!{JuK$;+Do7F_1D%!-3w(_gGcxDeBA&nw2z?mbVzi{
zbXtQx-ranyjd=F1z|3+qYR*cP%S19jQCIOh|EZDh8FD8KQwJeSJf$J5Z4Vhpbp}jW
zS=s}8G!0BG+}V}eW}=f{VLqe5htawJp-16KR{Tv|vT5k@X6Kx}`l~6%?La{O7`trF
zWJ8*4juZ^-wJ(7bYse1$Ri=EOYzeVZ`fl8B0;8FRoG2y=Xa;Gvs?Wn>bZ87HRvILd
z8?;9L%*iPf)q`c6(#hPZ)1jro9%DwMyL=thma8NsDVd-PV?%0T&nrrgAJ>V%VE{_f
zR3_c|&)&+h!&Z!A>`Y$;d!5FXkvt6<E_P{Vucc$2-uBRbxN=vUtSsh!2Dg7&#5`#5
zs3{)Qi}8}ziV9M1sx5l!FYQPV^(y17X?fTQo+Fm4>z!}By1-e0CeYlLAa<$apb+Nh
z45=J!ObICgqtT%|F8x{C7fr`w-ElCg<)9oLifZ63GVrgJoz4z34gf*)juI5B>ODch
zx7Haye$T9kM2Z3R1a<sLrrZ%UXy5^zvfJ&|laK>vv1MGG)-xawS%g#$wZ=obZh#B>
zEAPKv03+>rf=J3s6^ZkB8qSvyCMW%v$s9KM;Wu2TfE*^jQT@ult-%y}V%@7B5LwDJ
z1JaR~`6m@SY9^0L!8-cA#I)RHHc8paIFt1fB+e~`nNJt%!$P0vcCax3+Rr!KktruQ
z>@>L%tAD1d3}{#`*>-{shuaL0;IQ(Gq~!aqT6J&N)w$$O^OYR6g=*+r2Zo1CW%@8;
zRZmGfKMn?u0-z{GPB&H-GrWa}hyJxSG;w2P_l84kn)Dt^^_yaxKP(=G@FoNqfkcX5
zRimzf6!6M&MRPvs`Masg1D)*=i<ojiOx#?URHwOslqAcO)@PwqglcYEgH;`IpTJ^A
zcR^{o7N&6vi7ro%>}6!_O{upjtnH^W;Dji)Q*SEUHYd$2f5cyk=JDzDv3wAn1K)gB
zzqzk#xLGCYrZK<i=Z%Oc4&W6c7I1yB+iD3HI{jnVYD6gp5LkeS{9Vg6Udc>ilmkJA
z-@bSEUH^$P%Z&3!jfxIK3<B6*2y3tuwVxW-F^=%7Fo=cG6c#FlEjRzSIn)mh%uGbV
z&~yi^equJO$Goa=`FFoyKCgu?x#kV`3Ouc&3`LF^S-=~y^Bo7oCol5B9006Wft*A^
z=)u`^$9d=CeSbSJJ4PX#1{$3uNP@>~_-oBT0`-Mt>O%r5{rP9XdL0&~Iw*F&B28ze
zfp$Pdbot1#ulJ`gbf~}tYeI*PX1}n5nWKOC@|O-WF!u+%XO}4}UI6Oj*8)!P47&a`
zRzgBCf86n+At0wsp4hrma!8Kz=W^+{$FZGMOy<2#cE!xUin7XALD#p!T-q_27BwB$
z597C12X&mbtNr-XW;D=5qER-ACD6q4SI1j@xle7q$TE+GXYWD&7GKyWcPmSEJoo}$
zo1Mb=M;pK``c}SrZc_sNw}Zrkh-4ap^{^nLdg(+Ow58t+)j*O5vDmHM|9CxbG<eyq
z+j)!?i)>7BX%<Su?fALiF7`M5hJ6+I4h;w|16i0OCQdpSD`qJ-C4&&h8rna6`jD7l
zu66x&%UkfN%lg|cwVa=tD$({nVUfFlbgO&a>)pKGq@~kU!k|Dho2OrMQiLH!mM`l=
z;9={;cK|91(9qw0FHbe;Nog{ggo+rjom&dxg)E4r<}G+d+ODSd-|BZ#dP6cUa$9vY
z(`PQ;Xf^IDvosF9^s{z-JbMYfX>-_Z5v~3rV^`EYQXt67v-O8*D>bD%I43nM#UGlf
z0^>|QfHtX<-SS0ZilN#Fc`n44$K{aw%~=lNcZg9IbMEGZu7?oJC-k{O%zQ5m>I6&_
zcve8jOoISXE1;LId9$F3wwy11%Z=cVj`ai^EI_PsDU#Z3Infj`w$7M#pO%78jQLln
zq3t@6|1AqA!~k1Yh5*bxNx0iKl~7do#oZ+K$8o^)c%*mQqK`T6of2>`!lSJ#u&R*x
z`(xgx$WP6<jDR1A9&ZyG9>CwbcP`aqqPp0b9wAMSmsT@VP|#WfK!5o<S_b!QC#RdJ
z|Hs~2MOD?d4Wo*nAkqrb-6ajuEiAelK{}<oyI}!~?(PO@q#LBWySrmge4h7v|L;H8
z$NON9F~=G(Va@xV*L`)4E8MN9;6neFKT6E6<y1XLy6AK(C3JHdz)|&{t$d%XMz*w%
zHK+ZVVaD5j+bi3OX#&rm1&gMtyV1pdllG_H?x`U&D`%0C>XzikJ)g>Dh8pT8m`Oh@
z(f^c^7oXv?F5yjk9O=~KLVRIBG#w9?-|o-WpsbL(P|3~Yyi!b3Qk-v(s(@L(7we|N
zOzyUJd!(r$%C|ee7mGZ!&aOwaab~r*^~Z*gMq2hfG)(Xj!x_2Rau2+eOMEsKMuv-(
zHC;^{_Y<9-cgO*}WJZO8(xzg+^7ohYIA#1{4<2-qiHc7+!`E>Mj>q-Ou>AcIZd7+*
zi^IIyTwec)0<UnPrc6W5R;uw@X3RHl5}le4?IjgN#u%Gv%<4RrkgssD5}u!g;U&>*
z{&1SZ0_Yx6wMKUrYl^njBnL_`;N3g=BP;;{5)n7Ccw`o|oNot1d1z7R#a;p{|Kkq@
z;9$2<@(fL>^St3KUSg-U-pqZUk;daRWJ#XNuIbv*-27JSppO57nq{*mVdF!qi6an9
z($emoF1j-UkDkS*T~)vMT@b#D8=;xKhP#VP7ICFinaSrSOecVbZrQNPi-kytPoy&M
zx(Wu^CM@p{@aSyvwx3bYvWQaXb6jxpW@ptVh#9mf7!A#9Jk$xLHBm%2_Ur;_{fmNR
zpodZ2e!a%?x8vKO+g>mYP`w}^k?@Of={7ln#!nECW0oQC(Fo!aldFb{+3rAVhp|JY
zrw)<#$gFXDWjNBY5rsAEq$AT(&i4BW$F!DzO0$l0R0=4mzg~6bKXt)8Vc6=T3ovR4
z849^lW}bkp1PI>47E|6_j=Pn8p}8JP)sfK8S)Vx4T7S3WQY&X$SOT1w<Cn*Vv>Uk{
z{8f|`JGFY_#xBL%>k9obH&l5tRziOB&2~v0Fsb_mS_~Yg$BJ8-8ZEKvS^&UG9fCD+
zSqi`Op$>j9cd1d5q!)AY#J<g2XvGQR!o}k8hFvkPUW=TMqtWT5Yktc-V7BUTz)k>)
zS5{LR$XilRoy{y+=Ey_^)$TP4UH;8DfrY&=w)*Y1Xb@ksFvW}&#?8Hs8FreNr`!O+
z{|RrN55(Og9ilndQN7U^=3)AlDx*m~b*2b`3ET5)9G4qF>{XSiE??Tb!qcu-0P=lk
zyt&Hmz&u&?lW1JB&z*0Y|2xyQ4wn47M@SKWj0u%$PfeVEvVTt!%PwjlO%RI30^?~;
zcNedL2z^xrHIft1;>jhSC9wrVW7CC_yHT-N6yht!VrgUjs(-@4k@*I!h27~RdXoGe
z*nBz#w3OsNPwDo9?GM%_xuOIE<72X%rt3Ym?d|zMMnCr~0?Jgg?AAy^sCX&|GQ8t=
zBwc8il>E1y@scz`xaApyH}rDpaz6W7%PJ~WdhX}rSX3Q{G-b~=C1tu=Zf>MgKZd}k
z<M<~i8x+xM<_}lOi@CU{_jl%y;yeki);4A-pm9p~Z|+)=E|VNyBp0X<)qBzpeFeLH
z#+Vf>v#*xC8M)cFHA|*A*~9g?PWy9Sp|>|>E8Dh9#Ybly`SPI&qzenopK@bVOY_90
z*~pzmU}BEAS2W*Eve50Au@DmIrI?tQ86ot4HB1QYYq1tPq0ZlA+avA)_l!&_D0x}_
zEEda=Qn@MBj<WjQ65|c%CXDO9^k7XH^9iANt`#vUqk%;I!zSI&MU(ls!=04y?HLmM
z{Nyn2OwbSkUralHnb-wwZMjO-hQ&AgUTV@2qczs6;n_O3?q@o=r(6kCOFjN0FF)zf
zQ^%&|*P^tT($cMEsFt5th@n45ylVNt?YNbUm+<H)ExC1>pgOLELnmU9IwTxJ8FP<O
zPyd+Vh$inw^idn??U)Zqjb1@DPIIKca`R8Dq+Yiu*l57^3}SM*x2qLB%1`Cy*Xk!B
z0xcwx&I{J5k}u-(=!<9EpdKaYBHlga#H(j~6Mav6u*{-Y(2!|+oZEUbBB#1HEuXz{
ziK$d{8N>n_p_?6j&fFh8YYX?tl1TW#`E=GO2&;E$>*VtIl|Bbm&v-9BBgfCl$aQxJ
zDR`RkHmpRf0@Dn;Dz#G5yDM}K4XR(bEuCd)>NY6DymU)*0vl0ZUwTf<)o9LADw~gb
z+A_x(Vv-9W(^8TF3jC1R2e4xl>9>ICQvLT4j6mYHpBn_)>s_%vK*Ec>&;EgGl5B@w
zI67&hfEsC<&>17vXzBqgND{@U1)DEi41byv$d7&RA3}4mhl6CVz7}z9uzY<a?@)4Q
zAnV4o@9?MQS0_AI_1E2)fNeKqxBi%$^Tt_uItiC`e)@js${3c*%woz8JJIWjZ4Xap
z2Gxe6mV!Fo)8ul<?#3{8W_0MgYody0XHV5}QXo(p$PA?-&XCz*&X#=UDbmFqUemBv
zTwHKFB^m8*wWY}XaVnzProw>t{V8daEtAD<2mKegRyw)*ebx2-I>E=-hJ_S&dK19R
zwGUYattt?WW1C<CJr+jUx+2k63V-(93{aVghsXojL-CB5t!lZ}FbMQXQAbCA68=rN
zWhha1mO=tGO^-MDZC4}C#3%vH1hS#~H+DzEjhEHp3p%}}eta7x6*H3<%Jh7-8zlN#
z&jNNcq{8{NtZqpd4cz7NNWI7O9k1M2lC-NSN2ZuSvrJoeO+>`rb?-x$w=H{zqn6_V
zq&3bhoE5h*mF4kJKd3KCqjkqxk9TbPr!AX*5uUrt>>~YX`MI8$c1H|V8J;$WbM8u2
zj5mgA!&8f~YuUOek|Z|MMJDzUN^L=`E^RWaVck@%DSAPk8hf~`HULLhG%w8@Y>dN1
z)9ab<0s1VTHh(ae!sG85?#;>(bMds}5G5aZBWaaowpU;VwX`0bD{wx$^pRh`A^<ra
z=a$k@Cp2~bam$}^tzzpYqjedWx(^16@+$0>pO9S;--Z~<@g%4MCj6AwbV!L2Jfgld
z=8?sp7RP?J;-GQmn0=$v?4xBK@1h^iU$3Gk6+g|WyOu^e^ynGfSct<%S)GGIXr#k`
zFp<d8AA50yx?hS+dsa2?pE~Q_Yvn4kCgEL8qc#fG+11@I9JL7Uu$rCQ?sk&JyV1jx
z_A%G&#BzW6iQRxD`BE1Gcc|htbMlkm6EqZ(mMNt6@%()4c71dueAhB<EQ@t}37#iz
zJcI3XSFCKVL}Dcr*T@BnlLMFjn9@gU7Jy3c+UEz33P@#j$=}566N6kaWXMXCl4dJ4
zU%@kKVJ+|{iNKLFLWu+IRY(aVE&H5)+=nuXq8xwhAel4Y-s5U^(Z4>JUYg+m`LJw+
zZCVy{wX<$GSq~6*#YyaY3_2RbV2y}0%l>V1q`q!qC;n%=m4Q-~aGA_p*5dq~i!qLp
zQVGgS;|OC=c)RuNW0{(N7@QOAa`=sr-SE{p;zXUk5ev}#ZLJDdZJBJo=!c2&gK6cs
z@=7Q14R&VREEpsQB-`F#II`!6dbHeke_Eb2><OXCND#>iGtkA0p2{@ZVpKMjvZLDF
zb{N-w&^GAoMfLXt#D<<&T$Ny4)glH(lQ0vIs9dKdF8RJ-Q`{?P4jHN<h_9&g3VFyh
zX(Gr$JF9|(<VojSLgUcaEkDWg<6EoEfubVTEf);|u&tRCQ>Ip)=vf%}F&Tuyqg(U!
zuY)PuYQTCh2T?V#o(2cV+e$6A?~-rT357F(h`FqW`zoeo$Vot`M0v>QRlOfCe0$GM
z6n>D2WDrw8%ORX*(c(;WewvS81Iae9RGe84@5z<0@6@_bw^>n`Yd;Q%p5uw|TQW<l
zR`(kr65*$#WJ$0j^%T%r3qcO1_7=F4y!g)TQ{<Q)jz<OvbL6~e^d*}=`~8#cGC5(z
z%Lf_LYGK>GMk=K9NME~h5@?PQ0Q0-8QldyCM6SK50USME--uT(X5zM7n=($y#0|~U
z#Xd*rBiO4Wb<b{xhT7cKf_NJ3E{LQm&R~nms|Ju9p*x9Wzm(jvuFw8)jp_UIBhcwl
z`m<jM5ckiii_sU#N7o;20!J_^h9)FEEQb88<vhzJ*T)nv+c+DyLs=}&>{o4RSiv|A
zR6q=UWuN-BbqKb(8~GQeW*FH^Ry^69I6gcl4gc2#0Al+BDt(*f=Gx~)(|QCj6$e?{
z%~XElJE95VWUifizgk&t8rMy1)OHu?-;cQbE_wyiYV7zm#r^a_^vk$5k2?dOwg%(T
zov6BEsE4XNJxZno5I%e}4q2X@dV5yi>~Dl_`&1S8`YTXM<n@^k5I35$t5>RSOTxv*
zMoFe}7vwG8MUzrC?G2p<^hY()jz?)(BgNJn3sL>tA+NI^>f?3gL@$bXyQ{A;3560Q
z_92-fT9wc~Kvx2woMbN(Zf=b@KNMNj8|;{k0%FV-XnrN1E=UB$7upLJD$bk=y^)(p
zSGwcF6M@-1F$s9iqrd2Mcy4?T^oV`q{(b>&(*xq(WHiMciB(2(>5fg6YD24!#e*9A
zB;2hphwkkL%eC^D)_=Hx`vqq)`e{orom$reD&`SPT0M;!YIubo-hMUN+K_zp3Y`XH
zQPq8($q4YvGN@YD?8`Bqld}?Xc)~1$Fj1ruPH>}M8epcQptM=k5`PU%n7kh9acOpx
zZIs>ZM%yQ`yYn7Api~b*<i2cVvOaqo#(o_cAfl^ku<@+m%{V*4j*t1q5g}k(d^4ZY
zS{ULR<ii-d^o%8S+K(hSGu%vr8bvxYA|7hSh{x+^`(wwn0a_waif}Q0(UuIGnhD56
zrFNjIbYB}ey~|2{#p!PLxKG}_K7}oTvD_}kgELcG+jR5!csw<%XrWZi0CfhWsed|N
z+Wf}la*JkSF^=+ff4edlQF*@bje4ZDA`m7JOm`kdXjdZx6tUK+J%HV<Yi&pH{nn{E
zP&rRpJ`&Ks{Dc%tDc`+-9qAtc)EvDh?BRZ|4F$bj@X~gvpFiDhtS>Ivc0IaTJHh&D
zKd!b_tY~dxx_a(xaB%k>wS&~-q^e2m9pg8S=-^~>S%O!9y1Mj${rnygDX|*kcx{@o
zkDU@|ujk@nJ><5jl3@HO=S~|PDtMBc`3mE$I1|ci*tIDX4Svs0CAmNh&ab+%XI86i
zLm9?-%|flun9Fu=sm}CRN3HoaycKK7O={Y>xx5xW)2$-q`9Lz$n4{kG!k(m74CQVS
zOaZ<G!;x0>(^`t$0kjcuBx&{P#7TDxP#R?v$p}LRDtD6G)*=X_c)0JWd%dtiA4DnN
zK6m^|tyfPhPnK{b`!`C#YrvZAWPqB5D+#D;UG+L*lAME<kk6{oGZ><>ElztVI#(e@
z>p?>@F^M93<=y7fcv(Pt-MmNoj2u&R$$}1zcOtrM{S>BkhID!PJ_GM6>zpin4K#L)
zNEB?G9Z85=W>iDc@{u<C6W4HtoPoAjx}mx3QU>vTt<KxLG`E)DF+ckg++?>f6=cS#
zTFvo(h)+}&$1@IL&C1_DDa@Q^8z?WZb5r;%g=vNl7GSuD1-MWGnd!_!jnf|62w+lX
zt|n9Bw4geVfxZ5*_UDGFdtO^yoRr}$I%0FdBI?HddSoKdPD)BPS*-$nzbz2c7l-B3
zcQm`M?{l^o|6FV&J`%4z^z^#lww!r1?KIa18GYlkT~uNz9|Wj0tA+1|HBEr-YWmWH
z-ivzheF$~w4(^{D0muz`q(ZKzsJu#*8JOr>aO-k}ub?QqZPl7SzDnzE0Q#0zL@M|H
z>O!|auXvkU*=~|EY>4E&tm$lXfAI;AyEmF*m&wZhNS*7DlfJ*2?Rdw>=_oi`0@{1Q
zTsqETNE+1q$9nfM|9Z|)EL|d9Z|dCgY+N=6EC3mm{T@U!V5WBI^Sv;81-A2zHjW2E
zHi696{w)ir`w{y_o@KqyamxVkHtta8j1mRyfIlYZBM^>~AoKYz{lYu1$Raf;Qeqk!
z)7JU0EPX#zUqVnh_Ok-YA|yM|wN6aq?k#H$46Lr#yNU<F?gLX6(%aQ^-=mqBDG=a#
z0}98Wy;^?yBVU{2-FAW6e<tLj^^*$d_rfe{?@#7zx;H2*>^`d;=JEJaH44hFcqrCx
zeW^oH@(rg^^$L!RmK-$`ddg+y$N6E~FN36QlkL^ab&}&TE>3q@YS&XrxvTNsdfR5W
zhZxH*jN1FDNHc&@(Y%Qup{i<Ncu6@$>kmz&09&FM2f);1+4l{XC5?fWFwFagtuYcr
zs@9yi>Q8EK*&P3bNM^<~T^lS`Z7(a*N`Ey!97*$dI3PA!Cq0p22oA{r(zNBah%Cm5
z65JKL9Ay%ajrL&m64kjDerWA?M!$xB3eC-SPnW*=d4)ebSud2JFl3raQ|Td8++k6Z
z#vugcX%L!FMoVd1&1;ZlDYCCK8V?<6-u?ED!%F2#;F>)IT0QtN`TnvD>3v0tCqQAC
zpzqjuKJ}sXDS`cm&e#pdQcOW6m2vxcP?a`=>vQ85i$Iu0Vym;Nmrjp;)F+kc+7)EC
zM`F21EoX&s`yX59tP_Yh8HxDr0dJ@QfxLG-ON+=m6O1lX<W6;`UU>`4Hhgxo0vaFf
znGmHJV2X+2@Rv}jGPcRUCPHEJy~}22gqE|0fUR!{UOLlz#l{!~i*)|_dFZC@)Y}_G
zU+uX~@8$<kqw7hcjqp(WP&MZ6uBGvLIq@`3b4AnCxzfg)9e@6gE8(>Dzvx=v4A8Q_
zM|(rpsz1pV0%_rW&e(3*JL`bgf<h8oZk{mvv%a$_Ci10ZxJANiJIO!^>4=Y$9_DRO
zDJqmC%Il>0yQ%Pmd+OTr=sKWMiQZ+!8&S3HVgl1{VV<HM5r(H7N@8eJ_OYCkNN(RG
zPt&~Qdm_s<uBHoD&v;X_rQ0Y=Xiw7}w)(TFE9csu-C>gsz1qv>j{*pl==9vQx(OjX
zi1nYhgR_>W4gz;<XnWtEhlGSK(~6Qy&np3S1Qok1+SovR?C3k)9xld*a&C9&NTq$|
z>E|XYtEZ%VUB>Z33<(3ZY3t9CIlg(?iY}k0jPjkgF?0O}8$5c|xLPki*=b~;=@anL
zm&97(V|9c`_F$2LPT(W@vL|dGfCMcnG;mUeKRJkf(#TGEfg@)+eSmbh<CjZK|7QEL
zWSH8c0;C`G#Gi?xnkYsN`fRa|pS&YHFF(99we9T-3`tM4JVDc`BgZH^bUCGnK{~#}
z=-(|-b~_M;{1l^kj%0ac468wnl?!Jruy#L=VkG*j$i?%u6@z|VhhiG)U92{<B-HA&
z;n(f$6vFXxT))$Z#X_rspA|Esh`~&dV4C`V27;^<a_PbOGpn@%U56sm2{BZ?Yao9l
z)!P!2nWMQ5+V56A_vrgDz#K+B%uz%K(r^S^)k*%sR_@@?=y*3Lq74I|xK^2cT6IhD
zl9Xb=OU0?${!`U}qYAx)-}BCykk7RbJ~G$Z`B!KkttCMv2`+qz()2MHJ0q1m2R^$a
z!#}{6hm0YGA+e_L>lqT$lS5QazqVZE$8r8zxL_JZ<(Tggl%b=y2j`PmlG`?KGnzI>
za_}Osq2VS_yndBwyI5~GERWQG&%P^sr+L_LSGal_1GiTmn`QU~juQ3JJX)YN4>cpx
zz?Uyt@RhdU@y=N%jCq+~F*;e<AC+-Nt@1ss_y_e_jQIseq@@kyf`p&@ADxFL+EOBT
zgoe=Om{+2!2OPspab51=`k5^Z)_$qJ=(2smy<idl#^Ya?SPc4%t~C-b39t1owozMV
zKT8smHoYi*$rjW=L+`l6Lr1Qaishs%jzxA0`jQPrlYS1k5co4b5R&)2Li)T?Loe^U
z;5nP#i#lidWJB5U6aLNDw2tN}@6xaC?@8WMhFJ1C^)zV5eQ11oDmqF)MB&m~(2-XI
z9Pu)_?S9xaf|kM#l<#T_SZo?gM*o(C0g1lMCWo8k%TRxsY~{^URRq?wF=y9Xml~bk
zXLlE2$y}UTGIyJ*|1r(=bU=1d5K?noba*hcF<tH-HQAHNZCbxu;A;9D8AWrd1!NlC
zPDV`NA&s37i9&3#nop>4uxKWztzT+4aBAPz92lgZwlNc&A!9D@N9JI$Gy!MGbI&N;
z4L@vBt_P>-Xcu6hT0*erIWF&fvCgKu*7b&0>kH3u+c0NtE6YWLi>m*<Jyh-n2qF{F
zF<Hx`9eH-1&5XGeEruBg7-qhF62zl~H3MkPS_g~CF0np>d9;1KiB^NBpRCVW@lCCX
zkjYZ0xV@en&(dIcdDY2^HUod(RN$od=d-XyTN)CEGltbe#Kekm8Gf4q-ey%I&>E#D
zBgY8mJC>U-glwH(kFH>aAzz@ur$E?Ejm}@2Uw_OcaBQfJo|RWFYoFU}&@eI-Bgc3e
zm+a8*nY8pFK7?j67O@9wYA~;KbcS|Qnfq*FO3;J;9_NvW{CNcgsfKySP5X7i4#L@S
zR9DOp#X_DD3X6J_f=EK?#o47(rQp0^5vH_Xt)6k+rhLY0>3-m6;YR;)+E=NYny<KL
za%_2IbR|m*z*nK~rl)(-S1o2%)1{Hcg-wG>Zu^3d7mss(bl6$RqVX5%&gs6ha--}>
zoV!x2N+8$InUvw=5=?a_%SV-x?Zzs8i7X&BsTHryXxaT<USIt?Aw%~eaR1?|2Taoj
zzEpK|)&!F&AB2O(O_-yJ0Q(8(`MrUUF>Ctiteiusi#2zSrX#!MK3@Ok*;TliKLJRX
zDZ*m;#9>L#We);;uk@<o54Vd3mf^H6I|%#f*j?ASdX%Pn!a$kY>4c?IKS>J}T^z>d
zvBq`4udV8;w+7Xb{O$RI4Y{HqU68I(!Ar*-1Y((88W)f02e(fPJnQVmkl&y59hU7%
zPraoG_cACqP`af>*Q5i~Yiy;36wXM#rq39>rM)IQYOVVvYd5}YRXlhv^){9(s?Eo3
z=2eZX<wKOq`vFiwClu1K*EW{N=F}I_Kt{~!ZvK+8l^|^eOE?5F1R%|7?C1N&SX4Y#
z`?|flM~l3*b4DW31lK+w=9PcTy;xmK)j2!R-9jou7EU9HSvoQw!l}(;U~w53(EbGL
ztNYzZl=i9+DTn;>#d6LK^9-@_Vl<~MSZo|O;La<yU65QHQno|zW29U~L2rM2An%cv
z3G<`1o#0RZ(U9x1-W)Wqa$#6;sDc~d&5aU8+Ee}-D4i1Vq|{k$?8e{72If?B0;Oav
z&pEdpWvgZ+6!P;MQYqtl^QzP;LIrL<u%ku$ZwZ6b-X@)K_mhJ>v8`>8M()#}g{L!U
zvL1W@gV~LSfhysQg_-<hi@<y}1&*3`3nxG5rL{BQ0V*nWM2`K#%ZN511bJFsa%$_5
zO$OZPg|>S`*3p|woZX9`$0d$mzj-ThZv;n5qA-7f1s)Y3P#6d8_%4OOfA?|ekt{V)
ziYs*=G2XbmliIXxSe$Qmxd7@a4`Ljis?LUNjt4pCei(+3Amc-u8O;0nSbko<{q`qC
zZ+m3c>AI=1c+ZdY=w|0Td-G0IpO_R=@_Sup5n*LR9VXTqa>~sX54M!52?|KLAslai
zpcAD7*I4RZVxMVBD?2k*D&vD7L9e5gF%v1V<lf!^rm-w!-22rt<sa`#ytx^r)gE0g
zZWYvU1lc%=p<q&-Ak!0};$P9E^8KRH-!W55jA&B@XkxXm@*lcP*dd|}($5-CP9iAe
zMMPe{#Gb#L46j@F%8&AbQ^`4$Ha3=?RvVfl=xxcrC7;smq7Lhh%}1Dbphh<mPEg_a
zo6djEY39zTIR=S2e5(la{nIy#z<+X`fY$dst?3?4Pc7&O*?5Iisdtu_H}$yt@NG{q
z=JaZRb6^BM4A2Y6Mk1eDio0#BKjjd$NYv=-u~!n)4SQoDuQ+od5WGu_++JV^y7tsx
zNW<14)f)XSWU8Y@tt$q&9!=bjQpgFnpzXs2lyniS%(~<f1;197J@IVoYc&zPM*V{5
zHB$F@?IU^a+ic-bHwNZ-=g`+^03I@cbEvm8<8A*c;5<v0RvLa}M{`fMT`I5Hfk;_@
zY8O33=zyS036v_We7Od59RKo{h#JpwXUMFomzD}`4OLfvnuip&SziZwWOL^{Etsj&
z^j&@r)}NJ!Dj?1iawYr}r)rPx;{T3l?d{`Ci{?fu^IHZApE2SV+<2BkQMJ~<kgD{z
zz{FU#EPkX}Xjqy>{m@uPexFaD=;hgU;SSpe6zmUeX1YF(*1F3-jtc7TsCsihTDPb4
zK=ZHD!bQG<gGOPmT)Dt}e4?lY&zsGkpBd0EsoIXP9eu6SLC1x@q<EOu`pO9=JZuga
zJi5y!P1{^@$UV?U)unXWjob2{tG@o)04e^&j@;|4k~PC!<3KIqf8M)HNv=SXLE!7A
z(1}`jwyn=b4LRxMw$u371s1Nf6{Gx+H$8|Z2n8gE_?F3zyO=x(e-e3=IY`%gFHfUG
zQf^4O-o*h%8Ybm^>ar_-;Mr_cX2HscG)T5Dh#WW)EdSyK{f$5u3awEhjb-O;$Vb?#
z?-h^@+r%ne>CI)2z~^R(lgS_SMx6_pTICmI^lTqeLrwJiNJ2<SrjU}<uHrB<m-01q
zu0K@ap;P3}j8%a%SD?W6`a2=6hWzg^D6kstMHo_@`zVM%Wu)-5?$+UU^LP^J_D#@L
zOjH|+F;3#UHZ{eG`Fa?^G}oKu)mY`p8{zewSqh?cNMOcO)4Rh}L03h+f{WzCh8EeB
zY_Q$YXyhn=JE&VAA?dPTm(n7{-d}539G6D<t~)Kp`;)^x={UDXZ_7QiBP8;jZh`I9
zyt_*!qlazT{f@z=fDTn$8cP_h@|y59sd5499%*dbUe4SlX^TmmYzm066P84tM7MyU
zO|VbWlA?pRnkaz)l3s~yztCVFW>)y?o6GrAQ+-c%D?2#_ce47|xb}=rE)qT{6y!G6
zpoBN2pJ3$2c^K4#wV#I2qBUz!KI2=V2)TW$*zfJK_0+pN4B61vUKLb(QxOwKq`$Z2
z8O&u^r)MhO`2&j$rYibt&V1EcE~nK3%3cmTo`!tNe0A>3%rL2w;~Z(i93iBzUm$B0
zMtBeyzau&OVN>{Q1b^PXxK81KA3?kix+3PZ|FAdV>~es8Sxl8AdH=UlU>KH^$QI#i
zR01!1w9g4_1%_(h;TuVu#>Tam6^@>DK*_EW)tyk!=~qKj)2)TC`)oT*mp-C=mpscc
zh+R4<EKDVB+sz~A#kL>+lzVAu+>1)yTz(qCV0CA@kSa%y>2NSt>pF>-Hht2P^SJmy
zp3#pUB2z<8IzH*bOy`}s+6RVv$6OB|aL6WLrO5Qy9-QDnz)zW=SFtg)r{TIyPEktn
z=Stt8a)MZ1J8j<_dk6=Br9aV6xnp?WW~prlrFn>QoYTVTh3uaNm5&Cyy365aYTrl3
zc!$}PkIg^ynp7&pZ9n{g;5_jUqj$j{tnVe~mLbJi6f_s(!qeah^Dq>=zyEaxZtTo9
zW}VZ53MF6C(nZ;dyUsGIJ<G^kdfpVV^xfL~rLxc8Jx>ZrblyGZXmzjODo;53+}B)J
zrsY}o$OZFeGiEfTx(;v9(U@+m88gL%JT3LAwdc)rm~P*A$)a|u<gW2=aL}kyyZ{ui
ze_4`~jbKhC$YbdH{XNmRxCPta{8Q_1p#}J>FNmMd^Jhg>KoFti7Uu}d8Xj~F9OM<9
z6TQA;o8r_6_sIwzDvDmm&J+Y%_S_`l0PXHatqK#tu3#BcG!lK4VRn&jrhb+2R=t=g
zle!dY`<`vo5Tz!Gs6f)6EJ>zFyvnHv6#R{EC5<#`2yG^fJ))W=3h<xcwb4arAYb#u
zKZ3-cn#f+G$^^B+H4kH~c11@+W)6#%)K-J;XV%<ohLmt6oDh*guqQwFT}W+~=kcHT
zQS`Wi1=CF8-ZNg@Na6_Kk`#yZ8H4td$B2Wo1`8EP(|tU+6b`jBm9*s6mo;%kL8pi~
zJ>UCm|6C*%HlqaRyfF<wKm-?9bgojs|Jh_y?=N%Yf*Jv<GKTH6xAydkc;<Zbgq{;k
zHx*y~eH#$OxQj`z$Qr+-erT-yNo2sBX<zq^^wqM#Tj35)f`<<92o(Hz<|C6#*}QIv
zfx6DYuyIYX(bJ%Y@x0XdQlSx&z)!d4@pnPTG~W0pT5-_qyyKhYwlfq58hbx+8%kGR
z^;Mhn9oQhHxII-@a?k5rU@8xCoCmi-aSXWwB0bGRAXQgic<M(SWe2aXvB<MVRVfj>
zpE2tT3;G<>X63i7LW&()H&?4#Jg?ln3x?j@At-kol*TXQxkGK8#QTUK4UynU$}+eU
z7Ej`IFHzqH?_g!}bZ%T^WE*NpH*O*Jc<>?;nJv{4AC5TWJYM1{lFA3uhEbn-_wGMH
z?&FxT2%PHU-iEZUYTQ{=X*tl{Hj!23IPH{hnY4I{)yIkH!3E|FSOn>!(N;I{s$#BW
z*!N=^AHNGX=+PpBb}_P{P16V&C&Qhc)?J;(U_{XZ<Wj!$3LtIwn(VM^(^zx9xL6Ys
z+0_2h1G#7a7L5ny?P%|*w@`O4lg~}l*k0qIU?E5smQ`4&uHt>6Yk=;)F1MXj%??OM
zv?x_;EOj-O$t<jsn_ap9S;SBk{t;i)-)2sB4>dy6rMw(*NE)F2sqS*<SGu0)S}x0G
zpm&rlHEbbU2KiJd>WRjDIn!Od70wx#9Sq($UN)+I)3@A0r5ML{pFbo7J$+=mD75WR
z7O*AZb{y5j(X%p6bRkPk**?JR+Kxgr0=KQ{c2%&w^=w@IV|O|uhcUI8=l=J(#F9}d
z2hf-nHai&pDEgWa1_l$UMl^x3G9*D)49<5NAW1CbHo9D<J1zV32`-_;s83OE54+2Q
zcxDZftuO@Nj8>bj+{je;+Bzm=(m||~!NXmsUzdn$(z*>9ZmXDzp6si5i9eN)^PX0o
zFj98OE&3VelS3GB>py}BEQ|5)fHR}N&H8pozumw8p#8i=xQ*H=msj62Q(H>Yda;_A
zMK22_{<u;xXjtZOiKps%|NKyJ$fHAh>@SG3?|ur}Ix|fz;NFm^-<oj1A5L~avoP&R
z)3EQLwswxkIAP?r9xq;?<2KOZV0(@rHg~DLF%!$FYnuF`o2c=oQ*Q2E#A@x{FKQ|}
zcNwYUUcdA4ysETc?^&Q>9A!iX=O24V%>6(1urByC)~(p=2fERK{xT_|bbt@V9GI6R
zX1;%x7RJ4cF)Oi8;pVK4Bb~?m;r1C3tjVGt7fbhn-0<UhyvK5|odci%@vcMP=PkpH
z058}vOD<B8kzUxniJ!XK3n#u&G7LA`ntlG-q|QmjZ1jln9eO$Busr51E0jLBlU+Z#
zbRUwRN_$C-UQ&%~Tr%F1aF^5d4bE^IVp;$l7|=3k;3_sG2TGEyKsPmg9B;I8@3oVi
zh8D?^vaF}L1Al$DpWa?@OFYmAcY(=U4JZ4Q2VgiIWqFUF9j;&V>%!Ufqnw+BCr{?8
zRH`FydFdAT2>xCJ&YlFB&g%#hwTW(Zi`AM38kx;k1?q=Tu+OyckC~PwZasSWF;jda
zVhFN{pRU-tSGc&%1-&rLkNH@m^G%JC`HS7J(mm^Iv#d{v4*OPHiOoClCyaf%eQ~K(
z+u;%1euDEyHZ~jw)E>Rf=Xy4RB`1st0}h5f9>k0SG@q7(Oahpd^WBX5;Fo?kaQ1C~
z(#p;5!iNXoH<sGSbGeCLUDlvv1B?}fm{2t@-uZ0_NNWm`1?0ZV38gK!m6i^aK1vTi
zhd_U8<Abigg&qI!O@IB?7x%e%VsHf(OW@>U_pvy9N@_AVUFlPZLL)W$!NZ{Cn6pap
zIY!>nT_ijE47a`B5VwbHSu|`aPyN6jdl1-JvfLmRR))R+n)R2OIgc6S_^O0{H{U}n
zzBERn22IL}&hgzY?nh9jfuA0jBn^rGR~7|C2(41jyR2{HQWvQTOINIVGHUrI(S}BO
z&CZu#0Q?HhNPsUX;)`U%*aU1QkF6_*Kjdv)9j#f&VTyS<i_rcR-J(W)Z8P`bH}~_Y
z!-DC>A9VAk?_Di1Z_)shNz}xm_8|S>4@`kul3S-x3fh;MW$>_Z_Y_y!a&->ZS6w(<
zt7}X@N%58#2@THe)-Zb4Od^O3b`u?`&C;d1xj^wfST1W*6?{7_$)0mSPk(a?7ZLA5
z)=JLAyO&B@UlK?UHqVvS2*P0CK__|9h7gfIPxOVhW#i`aO$%3nbF@UzK(FS=e|!T^
z7GTbjRrne?f)zZ@F49ksWZB*foHXQJ1v%Fg%U}a)_T0~@%vju5cV&gTaskFk94jpX
zJ{aGCWo^5OE)@C>_`Oy)zzFL>AoU5(@yEJ{eB#Ia>Tast{0ci~WmXhkMa5{oC1c%A
zq|TpVk%oa6j--KG*Zc)hzkh^MDYciWyGe#xy*OuBJQ;B0MeqB{>{85Xe~bY-q11E~
zdBh(9SX>{<d8Sd2YUPI^p7?hYA}9sjie1~h=vaaiVm3*c+Zw3c?WsA{*Lc5mSz*b(
z<%T{lm3??dwkmt`8ZWgK<vGSBCBfYVH6|W_edi<<{1N;K!7bz0+aZIhMt;mE{sbna
zY4#LW`mR8H!wRTwdofSIMCgY;i6~!HD)#gF{*Bxwu_Hf?epW2y{g{Gwh5vaR)W8YW
zh$=X16_A^JjI+GAtT=NzTf0dd%v}&X0|SF`!q<DGxQOB;WQ1daR+7vPVpLFE8<3|>
zr;0E16uMf_{_iV%y}h_W*RHZ$*N>xtPACdRG?mgcFjq8(?k542`&zJJ@p*$~k1PUA
ztLGB;_OnDUCRd~<xUJmP#-upnhuqxwsi|wr#SqKYHt2b9RZ4wDkL1$*=M#9p94=oQ
z9h6C{%IE~&(9jbe2zjk4=HzGmn5Brl9|ZTh+hr7%Q^_v~qVUzjSTo47>nPt`OD-@c
z*T}K|1Sw;i(GTwQRYf&DRw~hh;7PrNj#ANJ?nlsBYR<;3rQ!3VD~E3CnFg7@s(C32
z-@^%yl9|>T(#!Tf`+3gc>o9D{$l|kCBNx@JuaH~tXcua@U>rWRZ%51(=g$7Fq0<5;
zJ0yH>-5L^px==7GGyv7G{V}kY*s)dslh!xGBxox`hdz?}mdbQbea30SpnZcg(DFN@
zI$siV1vswPT4V=^!RZ|H-J}$s0R6+)16hk~KQ{+Op|Le^Uf*=#J#e?oVy!@bdI$~p
zOYP+SdC%JW*QXQ^-Pft+0P#lhF+nqevsY8r_2_)FM%LHQNr6ex1f~j1S)V4e4Z2X(
zPA(7dXe)f@{5L~$RD$<@b>%VwVsEW<FV1EX;c0|bh<4%)*LN8qSOPrfVekp#qy%T)
zT4t*-)bRF@>s`a%Zk%sw6*RzV%hHsGo4COypY%zTHM^syl~V0MI~<@BMpbbKg|rh<
zVfzyLiVv22TFI_oOoze`0kx|$+_Mm0r(%EV3F1L@CUR=p^LT0E=!X=?uD;t`thZV^
zSAs<p>Mq|r_U>{32mR4mHERnxKaN?T6ll;ttkK>Fl0ASUiO#8}P_Ryi1Dz^aYDG}g
z#<DBgf#SM9si5Yy9#8@xSU{G!B$hymX>uR0OKNi2&#aXbT$i#Fizd4WtQ)~7TfRR(
zDx=oa$La`GvT^Fu6y1{4m1qJcQ=4-?6mr%+WR2gI?om9WMMQK15E~2wRp`xLx>OPC
zEH$bZ7Fk@$x}a5TtfyO?Ug=C}!468lNZ~t@?@<zH?`g!8+|;K16>nUBi#M?{VxCk8
zSBCZ{ZG71pPE%0tK3O>Z2zu*!oXD2oT;N3aR{Zitf>bsv)G)n8v}+<pI_=PP6L!0<
z40`fyMGV{a&H|_O-Y>m&yoFO3be!wo_^5!>h2XUtYTIyH5O?S`63d{!A*B|tHUR`M
zsB04u(23x%A>UcUqsO+3q>6|L+AuYsM=unibvm;SG{=;mCu+JiRFip*$$W<goNrKb
z^xg;N%#NE2o8lH>WV>FOiH5dKLvB7uRXAiLLP%fMLX}D31FBg)N)c=)s9QplG>ojU
zBqXaXamEi-iEYSNo2IWmg=ktpcXH;BPXPAsfyDQLKjeJPAKh}Xrd8xX^&dH`wVkzg
z4;);@D7}8zT#J#ds#cn72RIi+G?edk@(M3`_A%!+lFl1XWWC`Wv-)S*4wPO=I5TFJ
zPTtuQN=d5f&(}tCC(8&px}01N-Xxgc``;_i>`2KwGMnG{n8qZBUZ?;0R#2yk{!TOo
z+diaF<YL~WyTycOYB6Eo^Z{Ibz1EuN{y@S7qJXYYaG%XOo2gNhWEu(BO1j5>*l9Cx
z?jiw12{bXmGgJN;innt|eNlhw&!kc${Yn;RNF^8{;~<l%smqQN0vS}km&pyEF=3fU
z+Oz%U&)rBITVBiqCqr+{Oh@H5Ly$dkU&Lkm#m@DkexDvA%oLk$gh5Z2+%k^M0?z&)
ztM+2RNz?WVTHy|MoGFr<<q1Z;=_Yb=6$!Em*q;3H?{MdG3&nCS8$GC>{_J1p&xz~B
z&eMkboaL@<<o5<w#SFuJZmTb_@vdMgt4vLb%VdHo{f#zI>W274YDC&+S%~la?HxA=
z2J_VBt*$L{75d8=0)9FvN>t_`K@ZL=`NOe{W;9uv)x|!se*AIc!bNa)-F2@qh@1WS
zXE14V80h0+hE_1q;AaIz*)pHwmIZ{ZVD2INC5_6G*^6(eZnQXGCgq$t=i5?qLNT_#
z@$5+RevJOHc}=<8JU9IH%5X$Fw)>0ojXPcw(F+2tqqI$p8#kBiBI4FtS*N_NfbP@d
zN|dc+?}%ced;Tf3A1>!6n@>hfX{qsqLOC@-G0{bJA@1ocRz{4Lsu_#0x+zZ|*GRbi
zgz3bFZ%j9jIT5%X7tx@>L$ev3=RHRJtENOV#$(cGKOEt=KKIeVyJ2yc%i)y^`z1CQ
z$F@|`J_V1HQ~p)_In@NXG{ZnVyc31f0BFEa{npkCjqHqgm+j(D_r%|Wb==2Is}dTw
z!VAmI#`;`7JdEeO0+eqq$2S$QGW}TD(_vPq_Hq^z{)MHWIf?w^S*|~ho`n8sqV@-O
zIF+0@Szyk>{Ro(=G}N%HE(YBW8YXyq(H$yta&sub-BZt7#rbEh&7XIemJ9n25A}gb
zGxQLwH(W6b7b1Ph*F+GB8y;)jjIMUx;=mpsm=`h4Ck>74Z(-&zkWaVuxf@f|HsF+-
zW(FBs5c`6ejLbKq!?@h15{a>g&Im7)lVJ3=tytC{b|J55l_`@Uc?#2N;%Z)Ff5`(k
zx@d0~W1W||Bu3)C6FhHO6Q}uH$d>0KY9@HP7hTGa0I5s?JZ(IH)ujAfVHYhGQ4D9J
zh#8|L!_0M{FDIV6fxq;j%G#HzUH0>-v&Hv#<oK``_^n0om6@Hdy6M`m3&k<-2J%dm
z8aJF+?V9Mp%iT-txGLllE4vf{9vi7W#^&g%q$|m)u6X2htzfvTd=|C+;0g~o{^)*s
zHx<eRaEp8WpQ?|@pd7X)CEl%_@ax*M!6a(KK@o0Au<^Xfs;a>IPPx+({F@ZxsG2oU
zpY0`HSS9}-z}e#aCUL>9qrD(mml8*OQ1FEnza{mI4$r-+4BavFuM<s5BQb5OVaW&}
z<tUZTK7^G>wzd8F#LVura=81Ado|8Z1st#)iXY2{iLn}|E0u-zoy#grL?-Neu|?Ug
z4@*N}VGtSz(Bk#$<#-;pF>8xLZGI4`lkb>zEBRwh<Invnj=>{m=*s<c6_di*Z5#JT
z>mePcO$M-fb(z)*Xz1+YhZuRahBij7WNf4LYb4s&x?cfRCkn3jnTxFV7E}@uJ7B6N
ze{kPCxGjH(4Jwvzn{|7%9@JP|yi@6wUgVkX4vw8KWV?H$6$$`Xi8$*-6$9cCJ@>jt
zINPo~S@tjIu{Lp?nfN7<Ryysc1tsJfxV2;_eg+=F@)FNc=2m1oJjm~m^tc?pU^_x!
zHe_^iMx3{~#z1j_WEx>ZaeW^_jawB)p)3OEb@bC~7M^z7$=|S{1vW`jad)h7_gu0m
zX~fBbX=XNQI85tbg}zW+rp^s1n${^>1|MNcvQ}k@s%bw2guoW-MzIs{1(~hz+ZJ6<
zD%}^5&_2yn9PGELmU*@Pe7bmES_Uj#`bN;bsY$(WkNIMy0G9|htggju<2>4~coQVa
zcp1$XQIwg>9cG5dWKi<&X_9-gSi`n``7+XEUS@f&<<S#y-D@SzU6p;bdqFAuWI&>t
zqLMUC{-bv|k#^pcRk8j`L`(UHm|6H9WUi<Rjo*?(=ZFCG10&BvEbCN$eV`tU_3V(;
zjAfTTX#hk`c#0NTT-C<F(ou_xHQJ{sX+n~Wlu%rbHVvxcby;wh_Hk>RyqC6z^M!U)
zJVKGG$yJ@6!c;^ClK`fDB}nfUjYn+wB#HLZ^<miLxs=L7)tqxUs_t8E^2Y*D;RMG}
za^|16K=V$uZ9BzA>fn9*kJE?6MidA*rJt7~R4es3&a9&w3G#i$6H&DYw}Nc|s{{+7
z91EYdi{<IqE<H}Kf&_|d0+(5HY2YJEUp6vGcjU!S5Zo-m5F??&$dC!|Kx2G?(P;os
zr0zGqu!!={_M(%y6=y#zQ#>r2Tj%rzByzt-^zAdjOdW`kQ9o71gG<QrG{tL@kVz>z
z2(uI?^M%S5wy%{Mu9O=3O}Q0Ss+5mQ&TqN#T16Sfe&VPOPLY7D!K9>J5QxQyc$NMa
zA;-*C2ExZYt@r$yQr-{rx*ypF_W0>J&^;1emNCKGo_|$szym$9-4ZGlWrKhMY#NO7
zL`ViU{+ZXt7~0F(1^#O#;Xs+V`eNo?SH}QW$sf3%?S?CCd`N~2j-1zLIh)Xah&|**
zIC=~7Ib|aWvnc&riPrN|iur^TSgF8W4Oz3<ic(_9>LMDYWgP^(F1f~Z2ByE31fU1M
z`1oEu0<cWR(jL}v*p0*f(dyCH;Fh940`Zp>eVgGMM|nAJXo|=?t+&6EX?CC_?xAzE
z1j2tG<{x*}?Ust?v)`Ted7?qQ&P#9#p2msNd9;wqE^6|xlKtg{O1$S+xL+39iG6hm
z79q~+4v++#41HglJJF^Ce@G;WRe;c3;;r;_Buc)ozr1fc%gfiQkKW7^zgVu{;J$F_
zW^dTa3};NDtbg4xJ&@dqAqt}{Kj90ViGZg4tLwdpZ-UndFH{c8IVZU~h%d2hHOlTW
zLFSqDCSW`14LRsvaR!Kt7vnAp@CRVJRLkR{hC6?;$G<S=M(p`KJE;-h*GqASN3JIN
zLR9};djL0<3n)^m$(<{RQ_u?|X6(j@XNulYmP27I0({H=*#I}%O5K(-UP`U`fVc%G
zl8ul^x=o_3qa}QooZf{>@ZWX+uD<32fV>;8=RmK4CzIFv`&W|X1>U%2GNCkD(-&j?
z-|GiTx)$S<BaC`d6p88(Sp+?M$~6jF8&;Fm;*jdcOcXRd{{Q)3)IegWhqsF}KE@D<
zK${Xd{Omid=*EoM{vk#Eq1>Xl9hE2Zs#x{9VrFG@wtEWi&LhmRRX3WCAe8hUDF4S(
zKmcL+<13j}$z~xTgX$ep!7j&`8DPrWd$u!r+zGI!Y)AZHUfDpBi^Sf2#9Kv+`Rd~F
z<XtD5@WWqvm8uf&9O^<3Eh<hei2@ldU&Qmj;P!(1X<>Bp`#90UKaO&b*SKu?P~}n(
zFpl1d0UE)9hn7)6QUB9?{p)uzcf63X!@lL_AbZBNMj&=V06DYZMo*=ipNcK7BKB_+
zbr8Zg<@bM&!>Nn@;)r>%iT_@miLWl@ibc~q4%d6<=%{|hQyC7H;yc3@%Zrl{LP%Oi
zwtqsFPAYjRb2_%_G`-mJ)K63JoyM)B6d@9oDgwH<!bnq6+>M99i347X+Qg0V<k!_C
z^NB8l%>M-tfmbIP!R;g0p+bl<A2`t!!{pVn{Nb}s2;1GFQT|Z#{zUet<&3R6S<xvR
zYmqDBxc3zbjN7==k(7TdD>ku8)Rn$3rsNa?W0IsAzBi2Nzk0>BE+)jU@O85YqYIBJ
zFeh=n%&1<&RQgOlM^=+oiYjM{;1u)a1>+SSVTrY0b~4HVcoxFmemg`;qGxla-cyWh
zdEZB3a?P1M7edPLO}vjpjtCtzQ^MR-mGdPU{)E~2dG-S!Of%r}A5jd9*qKuS{6B*G
z$*q<A;nb7M&L4Hp=xV<7=#3X;IxUF}5C=3mPz8(j%Cp-Z=OEo;Bk=1U{z4-@r}<=%
z7_n!XqXPbY=KY_O1<p5}6lzbRQr#qMw-99~l}^9VMo;EU8egrh3~d0m;xqkYbATZ6
zR>4%s8>;TqoMQ4aZ-m@#;p}1IW9klGoX8r#B-;Synz+q$3xjt^c&4vWqW_CHp3pIX
zWgEjB>c*XEb0I1Z?f8Z5t>e#2EJQxQPoZ`;hrhxPrjl-^qj(TZf-uvyF|C{B;E+$R
z)QE*({)>EnQ7{o!$irW<x`OBx=_nI*;u~i^=BE1Nqx$P2!1FR^Yu~D366&w)w4|AV
za;{bheU|S~>*UO9x{Pz)l8#NgsSju)|G_ih2OK`nQ#{1BjHNKvBz<n4aSZIHnj;Em
z_is<DLkAuj$T$enC{M3Z&dcNa^lb9qNlt{G$DNhoVgP{#%%_Zl!siL8Buo=Hq7{UF
zvu?(wQBjGPkmYMs*l15yd>tLE4zs_EkipBSftZ;!WylY;%~;@_L&WfD4UeIJpKcci
z=$%l9>4#^6CJDers0Uj^xJSwEQLr`Ib}9}ie8^AyjtLsv0h7445;BdmQoE6<5HUaW
z;2Z+Rrhn|!f0ztx8o-HGtM+81Xj_x#HICFPJEjS3u;Do&u3HWyY-k8&8LWps9*QY<
zhS9TQr%LU`zbnpq{0XB4nC5FsuJmlr^0s>;08a{k%Alqqdxx@xH^$4(BW0kgA7DWg
z<p(qX;t>M8)|goW%VUOSc~e~*3#Km{C53|JO%C&ykp<e6qT@{Hn8_FL@)5rd<8A+z
z@`7Bp!h9i@3JrTdy+8X%a^kfb%d2L7BkPpIk2gIM&xs*8W@{AtFOOCOc;#4d_10nN
zp00n|=Cxy-MBvUS?{spUKpJ?<@-y~-`KlK*LEEJx7J_v#I#V(AX~_(3fEHPZ-d?KC
zQ4+BZLMqUQ`$bFmMj#8<xkpSR>r}N2ywV*JUvu^!$Ig=u1H;(oxI?Wv<*d}g#n5rG
z%VET1X8mcp8Q(cK-N(P@W(vjd06WftfdxpB$j>jX8LaIY_nC<XN#0LR114EN>7qrN
z1QNY43P24LOykLE-SUQlJ1=<&p3oB{$nz0jSX{arUZ^159AL=iOu^#e@g6gLRD=(2
zrnXik)PFpF@6wErC=m6Z{k>@4jm4n`#5j=Yp>>~7(CG<gkpNs0L<JyXG-G?FBfrgB
zLU(0OdA++l9-|9{HKCq!IDQF=W!1$D{e^lJwCx$nRI$0lUm(>0LbO2gSAn~x+l&Is
zO-5p^@{L`7;)-OuY?X?0Tpu4IR&$b>|I1$I2-1rp%2~}ef4m@XAnWVB93wRI3?5wf
znYQWbiov@2h3#NF3fQ7qU(c1m4@(?F2h}xvk$o>f;)7x!O78&lu&D1<z%N1H09!Uo
zcKR>?!BwKM=uw^8wXyI)&BEMUq~A*Xrc6FKCYEOXj}iL}z{>s)O7s=<bOuZs*<NCJ
zs8&3T-!NYTX(e-N+>P?qusnpDdG|Pz2_na$p^6R|!4e#OsHIt+v5Noa10p>WfdiVz
zF3MMbb{5ky<KsLmIag-BDZ-M7MR>xBD_A<b_HUktBPS-E&n78?W5AX>%vA`8F+kFw
zPFi94Uz^!Udr@(I=+QJ!(0ojGR6m9*o<)0xdlxgxcY+)AY=#ae+d8BmGK8x4k1O||
z_wfAlmp*i@(&Lzky8hzcUzYWPgnVq!gAEnF{{L&oz}GxmfbZ;@av34~uMI?cy1(2v
zq%nf~zwZD4|Nj>t{~v}A3A$HiW+nnQD~VFttojQ<{k^hYw7|K*KU%1P7*NQdH}4RW
zSxnIt<(ocG-t+&<0IdMxp+tNat4Z`a3;D#k`Fv|A%Y?Dt@sHHMkNL(2un{=+odZU=
zosd_G(JkFRQ~!ff6adOlem?;NEYBV~KT)?Ql{Wf66juF>@9h4OmS@9f2PKk((mA$*
z3^mXO3Oz(07)e}ZIC2C`#!YsJU3}5h|6ZePFUm=GfU_9!s2N!}EIUrP!?N4;PF6%X
z&<T+GAIb+l-x32%_$~IAL=aRE->at_VEnX4SMaBgpr9`c2gLs(?@j_Bo}BS;=R}}R
zC02mh#0jkCYBEUmsPwR~|J5#aL2X#`TeE*9G%S>YxGUNV<@~*DU${zuk3jS{f(lp}
zO42FEs=9x-@SWmC)y1O20zsV$dx}%GbVoP;BZLXO*h$(RL00Vf2dHqLz4>HiW!>Dk
ziT$y4%Z*6>-5CiD!0UW*o$ZtVUFqK?(-B@$Zr&2`{2z9~3uL~jyc_`M#R2>Orb{5l
zD?e`TRcFut0Op?`;6T8M>I9_XlK%&=|A5!X2hee`J#XUw_p$^!0MjNt7_ItiK>f#X
z0Ul)z*e)h-kn#WLK>s=Q|Cj9p;^x)bUw;12iZ7pDzNCE3G3A0I;s4ijs`)*g3lR8R
zCH^akU-lna4!jkr{m!fvY9PYPB)HxDtZ-n~yBw>>W~{Q7`|W1`N3H)p$`cRvn|rG^
zlKtr07xyyZihU!wWFqtbQ`c8URk=lLv+3>z>F!SHltvKgPHClc(;HAi=@vw!ySovQ
zmX0kYE#2|Go^!-=&%K|6AC8y7in(S#bFJyNIck&buj58S1a!Opy3!kceY%rOg*~JC
z535lD%nr!Lpd!HL5K7qg`@8E;nt2;v(>;$qGnNCb;7>Nk)WuFGUz6~zQS2`?zFKT@
z2igy_v9TrKdH)t2V7YU6fThe5HUO+2*RO`?K-c5bxAe+tVyD`h`HWd!(&0S5XS+am
z-^)=MtXrT#ca!_h*~x_ZmsqcVjY9fs6k?%tKoV?eT*l;xT7mUw$f6N(s3<An79Rf?
z$rkn8|B)TY@dWoNrs!@3z|NNt+88SShwb1(egSh36b}I<02>v#+RRf{QpyRltb}JW
zsB>sM=t2X^3D}eN92c7sD;{`UkxB=C(<F+(yG|JvMb#AOe+@LdZ@IhL`Pp{8k0pL#
zx;<I=UTAxJyYCIz`9``kCNUS)3Gi6%Zy8aPgoD+=m@~!ub#gg$&_l0bk)4tWHPO9r
zGN*xoffV6a8{_%P)z#IB-Z~|^u7|xuCa%4Jq$;ufs;mO|1Hua{CntA$dg_0D_NF?Z
zSIe`3f-_70oj*0m0=ok<e|&sAg2D%R!`NfiidO!0hcUyf+owWcSg6p)ehOc7^>w#j
zMLWNKByVtLD$Ke&g5YevriDdc>=^l-Kzx|R`{G-vK;76EyVWCimm`5~M}w?t1zN<n
zL%bt57pt)}vJ`4IBG<dsIPkEYhkdU}EK-ceH2xthBmq3V(@R38tr~x%)k=Q)b{jJL
z%Y?q|$d!u*I#UY6zl_5>K0d}VCXe80H01ciuxk};@C5Uc%4QYeA93yB1{E<ZVsfmC
zZF<<+5~I-31X;ugGw|~hq^GS7n*d!mAy-t(4`I<tWQX9gA1}}mGlxQtjnHAgHz;&r
z8ef)yp&qDO?HrZ|e7iJ84MW3)(a{{ga3SoYK`|?la^UN0MR1t~Z49Yn3<0R<qi#Ib
zi)0_>e`~wRUydlD-V|9oJUM1R&{Z{B25q%I+UF<9Ip>yts4eBc6pSP+E;w)t=;q!J
zBEwg%eB^(XZ2l#?^pE7hpwMxE_D#m`XC~S|+;iuK{o;YYey&k&j5mru0WHvfB&NaF
z>6DhcJ};Vu-4O?a5132!sy8lwWZP6kz>W6o_mMV#oaOm5ZB2SdJ5i%x@->qCTLyex
z0CB9!eA-;BI0-w?>R*1dA#4SybyjOwPLXK8a<)2czP83BOBJ>A+P`q~)yeI$_iXo^
zbhvYwh-N&?`ZlYFt!5_gi5&4%<w%3G7haU?A%1~`cI>}c{&)m_W%rHZzDO*~hR_#r
zO=;Cvw~t8o=qXc%zO25^PM}5DkK#AR*~{9iXA2$;t-U2tNb|*liIYENyP8^+50}%)
z?iNKYxI!l&3z4ZY<Z|-H6p}e50V4_q#&_Q5(*f@&xz#vGc&@?y)PmYd)QTsGfN{7Y
z#nS&Pi~fsRqo^RZKHq>y-&2af&EOXC+3`@Y3y-H7+O+q$l8o!|r@r+-=h`3L{Km<i
zf!P#~`<8f%9hK5qp0{6Lzq)8`Uu!GQvV#NQ)liQ?we1?tH}_cV`lqu~f^Ex(@B4q^
z2+_UsKI>FpqxZCnRQuZ+B`?+XC}3?(D*F&4%>6zyx=~Ennf4mnbk9zK<-tX9A(h|&
zA8W+NYIvwai#&5l@KRQ*TT^?dfmKV_Q>f~-_JQ@`o&qD}c6*tNm^=g+`O3<KH#$eF
zeNoQ>-Z=*vy?GAQrvS?$O%>(lY_uQE3vF`uCTm-~yZbSJXYjK$QB<7n8j9zWxy+xh
zq4qrt2QWeapkn)wU6ce%J`1@op!wNV+P2>8p9M{ADWsAieQql58u!gkxgp=@i-$L*
z$tK6FguCGB;M?IxR*#luaSw~N*5{f_+`c>aN|Y@(4-wI1G<`fFPZYJ&KYzEE^Z}C2
zW!t%)A8l-M@0DMyQrB)X2Y%;cGRkNH$>>5`1p*(Q{h6_szx8Sm1FWw5*%y!H;L@dh
zQU86oFs)^ByJ0@xD6t^Ai8S-&M<$JH2ws<k<9gkt?@+&viEt7Z4<`wt7S#GC`<$~s
z#-kk*wAjp-adxMBYpJhwxATs5KTg<9X+J=^{8)ZX4N7UV@C&g?e{ZSqpjK7fo$gom
z+QQ~rlszsiJ4Z!DsZz(uQK(Y^s9+=DdyzYmakyK=7M4t@!dbQM*P@fwVXf-i6Gpag
z21a!~tIiJNmfMNqn@8{K>Gd)r4@8{G46kRZ3=G@CVH|y@D#L!f{^=L07qt+Xj68}y
zt*}ksjX{F(;C0xhONR8{&-(mqF-fmmw9uL;jp%Q;oYPxRzilke74Kl={p>a!@8Oth
z!soTq#_aPm(7>1wF65hSGFG$LY})vl5Q%U<fD~YHwpLDQy-@=a@MR>Il1a4F1CMg|
zzT#CZc+5L~l`v!ZBlg5eFppW_j)sKQ@D@}zdzO4A%0>MkaPgMUdM9#`)R0L7=9BlU
zn!xO8=_pqf%{n-oCDsPZL+{n>x3`bzL33r}?(51pNL|*LtS*fdo3+#7<5!EZY|V{x
zMnS=abtlE)%jxM0jjf4X?Y|NVB<3j(-dLO@9XvL2nO4-vD@{qBXM8D14(Hz?^J`w{
z_ux^Y`oQ%$JpyDR+xir1_3DT~CPzBUbk+%8f5{^DSI=fSfvKzb*W0O~4T~$b9Oac0
zEJo|G9IcHl?}--`tLRLGZ=4W2dCwqqFZ@MwpVaANQU1v~f+<kQ=jLC2lTDv#*V5i<
zKSd+D;qu|DffM-Vdvor$I;VrPnJmY1>mKE3YV5G#rPV|snzu|T_dKddP`qgbZX#%S
ztBi>dD%R##CiHolW%kZ_U?F%?ys7#vqFk(`!(CFYjfDMQ2=QO+93+6OvSrtZsN79T
zw6gc<h?ZR1j{FHjM)gUD=l*Fvs4|q}&Mw$dds?@TEM>;(O8B-s3AQf9W6~j<5;16C
zo|t;|ai6gAy`C%M6~eo(495r|&K=USt10TrwA4U30t$c9KTa^<3=sxJim^?r)^(l-
zXB&OH6E<mxvR~=^vUYq;nf24Su9!Les@J0Aop0Y$w#a&O!ymNPm<>9?Ib5k8Q%?St
zTdD*Y*CPzL-mfp_ca9%7Z!aygK1W#{p>R2^$Z(b4MRs%|0-R`%@8;IOB^8thlx)zm
zX;%vj`=3ej_%Bs7Uv=cAq0dw&2b{7kOiiMZSMjyBKV!@k5(=F2hAR(2LOk0oWA(9|
zRrMuPayK{Vv2JNR{QPV^TJCV-%8&;Ds3FDXQgFXws8Q1~?cWmxVd4-^EWW%I|E&Gg
zzN_o8jBjcZ&|Rm!j}<~L(eZLlqZLALHZGNkDI7R@y26#a()1{ntrf=%SA;Kso4uvQ
zZR{p98yPQVpWEU^J)Xr2OtR@1AoTYrnZ^Ar5RyYEulKDRU5hYq)(*7v?XRw}ICk$N
z8=v72Ypfng)KzRjW;Bhy{)nAQ*2NVgO%&Yfk`mPY0CuE5ZO$^e1&=J#9nfsPM2|K_
zn=0O+aNO43U$gMd!jrL>sp<QbQbHWwcu+RxDyEjnU`t%zRDpymlNryCj<3JIusK%0
z-3?#rR!{48TNg^F<KG9q(UV$IW{H09>J*GRz#?POxp96qky=N}_o$bFDyc_PndEGj
z_u7c0H%%|`4p8S?fv;_;AGVZ$g8tJ%*jf9X%*Lww5`qE7#>s_Q^?3xl>U<IhrTrI`
zsX|@c?(iJse!?vFGKuB0k3u=jgS17~?#G@1O}V>_W3Vf_LXL$m>iU}86@bvpQ${-E
zVH12*E(?-74Z$BHM_17nMT^n#u0frn*;n~?^h;)~>=tO|l-T884@HXlQ{k%RBON$P
zE)~4}p&y>h+yH2ypYQ5K*JPoQI&mETIJWh~025#bqG(if<bKB{rgX5+UstaeAU@`G
zr<e#?ZZ?vi8CW~Kq)h!;=87QXsT9v4q!>G&By9rAMkL$NqT-85@y(|w%itEq{8fbY
zgaN9WL0bKWGlz*7`pzz!P~|yWtDd4S9~Iz`fh+|cVu2h5SZ0qcPA!MJzS+^F);pVk
zv1|0DpXU-y9j!0bI|#y8J<HQ<mFu>A=(Ao<YB`crBQ8?Wyml_rKVJIyBT?PmYwy$-
zC&%3Wo6k%*Q{WHGmSO9<ol&*44?u#Az80lA;5QqCZgDpgt=e2v-bHVGd9&m!xaC7T
z!5b|mu|HqOXVKtpn$^khvXvL{Ge??pU3-0vWsuV{x}sK(S6n@N=J<8)FGW+E6ke#*
zVH#*%RAsvU;QpZ~LI>KWn8nhqW!3N2mUP}vyisxHIQOe6douiJarxyowna$~nol0)
zP5^3`1f(Z<T3q{SJu6d)n$z<9$%1-wJANOzp3Bvw6XRwNSK50qVC8$M)p8Fj_s3~)
zD*@g+Gc`;9LN+s?{c~(F5&-7UTiROe8lh{_$8w0Xb(6bX*}TxS-4A|`i_WLSo3F&{
z%b7jos#7iAUaZ&Ex3Tyr@QJ-Y)?_>pkzlZ{(98s^@c3uRQS{%_1f}38Rv`-T2$QZ3
zIhLPvf5GR~O0)O6)A>0eAsCsDV>A?)k+VFJl*TI|^wmza)n)X}RG*APbdg|N>tP|3
zoV(SeVds^%^S6VHLz}Z^nvWY5hciH+7j;WK3j1dgJf;Dz`(@p9uGQ<WZ6i}V^k>~w
zM#k_xc$nn@!s8|dGbPBUrU%&+cK+&HuPQftVnrqHtVRSywnmS}<;u%^Rfs152jeoN
zOyL7k=8QbAxf~Gv>#WUk{FXc*-KVl=C7i21v34=ro3;GKyglAdrtoLb&jlb~U15H(
zA+zTawiT`aVM=AoXJNC2IrqeV=dQNd!{7tIT<HQ(95GVbwCs!u-On1oeoOtzVS@qX
zQLTV(Jm{Ct<}zyg;J<@K;IUcDZLB5ZBy(?k<H@APC+!#)^vz`a^YU2x?b3Wx#J=J%
znm;+YFS!fcPFTR07J_cpp5CJpMZLH*HoR7km8ihy6)gO+-tDAbC-TfbihwK<(2eo(
z(RB}Y^OI7CQ+!#vd9|QOszBm(;0#fV!8dt>B0rmatODup%)6<gU$a(UvsQ)%2(x`A
z!fexI^9pfBvj46x{a)&-t_TAN8^$!=(q9gmd48DX<+n{xB@PBAp$?Cff0)EpHb*(_
z@*+sJ_{dmq+tt@%!(3l?vW5zqo1)38=SN1Vz0$~MqZk62>&}nf;(<fM?QgL{NRxQQ
z85^tRoypR<VS_Gi=6PYg=M;adkQ^;oMy$GzqOf%Y(-#tYl+`3RU8Sn3v7EN!&LZpI
zxT|A09^-L5nyf7LnC~(&aVx1BP`=mUF~O&Z7v(<E*X5xA$lbg8sY?Ap(jHMkYn~Kf
zIf`JMl@er;K1VlHiF-lz1$IxLZX(6$@UfY4trfYZOA~QfY+cLB(Uhm@CJkFtwe?O`
z6AgY{`lFC41qEHS=w9X^ue;;u32KY_2ZgH5=%Ov59Pq-lU8DBP$=M)r51~m->E}iO
z7!>ZMa28Dt?)y3F#Ar2B9k08^a^1AKm{UNyz_IwlkTgTA&$`ZjlqQ`E8<1Q$3_{h1
zuxk_oGb|+HEBh{bE3bCxEorHd3flQrzAM6^gh90dIiUJ!!_T5~>x&HuNHLDvtOHmt
zH4S6d%v=0bF7A1PF7DA1ng`K-3lD&KWp{j#R$k5kTlDYow$`y>TkBV6ABo)0t$?Mw
zKi60^_aAT2lWa8%M|gUibvTC9@4qW8AKW4}1eE@QW;F?h9{qe=MhL%cyee@tzyp<i
z(NlkTKmocCuiBp&(EPc+v4vSb6Y=Z4_9T9yvqqM%81pdEDoPfFCE#hf-pW;fW;QV5
zD7f0ze73FE3otR%J&o3TRn&j7OusW?r6}m11t}-_x%7g|f8;Oo&Gm-LybPRa;p8!n
zg-<K$tOvxI$(FY00x8{i)VjhyV0lGsIkHZELv{S4W}P`3EXs+3ZAsAk+6ON1q2hXl
zU&n)he(6}?Jj=mu9^KwDS>5EyO+or7nK=4zEl9lZ9ASl&EK1;cS>nXEW-t5Iz^E4i
z3b!sU7+)!hjb%z^8vw81wWvqULrPN89TtIPL&leXkILupIaY>5k-+7l{z!9inl_2~
ziIpgxId|6U8m`j&PYa({&RSJRK5wxY`PH3%{poq5t}R|V|3uB8NC~4-Uj|TlJSPWZ
zUJs}C4P}VfsaBBXqw4w{W!<NSXcVML@#Y4*Z<ND^O`AC%Vf>5EHPkW4mv<gF_?V6X
z`sa%>QK8KoU>L4kj?r@O#=IjkX6%!E94oE=uhBa`zq;vQAevxR7Tf=*i>~S9`Hf<^
z`*^X-_nXA?CXcsYX$WEE8y~ek2W}f$y-c<UOQdv2i)dAOq1)2r@R`+~l92y=gq-_T
z2mjjy-Ll90pZe@GCzTIHs&<Kf>r}}}<c!BNY5T~NI)<zFmi{L;2lHoF``1%p9qSic
zX$*^e-}WmQ_D^m%yLjJ`u1@Y1E!MeA6M>jCZ)f#uzo$Mszfu7$7-{(!^IcdMH!@?J
zYtNfmxk>}7TMhB;;%{ET)rbX8Fl36?*tstRKKIhujrnNFQhK_4SzBCp7+NqyH?eDX
z#;33nMUQS?#J&m%p6Im=Xt?ZMX4O$}wN&2r5UX4fNG_*vw-!F)p!D&r`bN@wrT6Rx
zaA~93xvgR!q&-(1W{l`a3y$YwpH<VI^7*myf~H=|^VOr})7s(^4pWz^Z*E`sd<*(?
za+g6e-@I(=-|_6uaslDh;lVhW#KKALG*Ot>{<E^E{^Sw4%ulm`Jv5)$BZ-k@y`c`$
zLDqF@-mBB0IL7%+z+V|V<P`csE-n3*?jJ8s*h^Ut@3q&+;%tVMVit(td)-Is%idT8
z87b>jZ}k<{1$nEI<>O{F_B!nCvgztwMfYR1rw3ZB4<=f*pQur0SdTzbR6^?S4hdd)
zPB}?o2vk4H$`7s)$C>U6j+BvnD#~WPl4Gg<P#mz5CFz}czZ|`w?7=7ZxmuAyc0|;U
zOPaUFfdBI5F7R&CxT3bOsF-o?<H-kI5hw;rIcI?PVmEL#hoDgBvxoUlJ+0;}Y$*ps
zpwfLZO7wXmr~I?GpZ&(5>ES<OByzZy;v~@kk<y3hwtV1wT-%@yn;o&^Brno4M|09R
zRwMqgr0lsiN6eACl{;G_ErK60RatyYIC&BEFkXQ?ZL{~**Vo7i)0?)aA80ccfp~0K
zX>dN9`8BI-w?RWQvs+K9e|gH}X$luAHyR2&5ZE4yJ+RKNOUyHR>%MM5WW`6apXrM2
zrfhdYDF{OyJCf)Q$wV_>EZpheBx#XmPeGQ;KFY_EnKnYH`fThSkv*my{9X4XfG{&k
z<#STP#I>;i-w&vWI>ue`sdjX<2&a>)(Y)7uf^M{|CqG4G)r4<`x8X=?DSw|0oeLEZ
zm%(`-#QXsnx@e)Nm&<zV{;wJfXpwkScg^}Vm2ud(&_?<$K8JZ>Q963BM>{%SlC6Rj
zrYa9F6*9!2+XI7D+FgJbt~B%BrvKsCawVbXRUN|B^Q`IQs(VGQwr!V5GL8bf!7J)s
z@B+`u0$jgJC~bR&CEtP#WvZ~+yaw0_Bhz~@JG#;Z0P9&B3HeuHM-D$MNBMoei5O-1
zu}H~B{AT7}hVS(g9QiY80R_+|?#0MukI#1YgCSfvrQg2Te8rJWX|5L(uQi>v5t<N~
zO-pl7MY*xDyJ0VbYuCzr6Zzx~bCA7#RH{;;aF&YwI{<7+hot<4%Rk7OB9#<M4QHHk
zu(Wyo!>0cJE$&<352P_gMgJ<vd{FLm*oXi@xu}2x{wK}-PqJJ37jXv6YzqD}P8SeT
z;3LvmKZN3cbA+PeFAzz1cbD%!<NPWy=mPh!{h9Hvlpx9zKo?O*WM)33h5jA~`4@;p
z9G4RI=X`&ypNbkFFa26Y^~8V1F$3IcR5;w~|FL)=bxa5BDwN#ABke!qpaaV^lQjRU
zrv6{N97+d(NO9f6YX8nZ17n5)AQCgW;?Q4r$^T3p7YFPL_vOs2-5=@uHChlCAS7X+
zNWDKN@)wlxzlblw5SNwJ=6Ct>D|&{2<D8M_V!r$<n@sm90D>x%|60P!16%yO+(RTY
zK9S$85O`t5?`+m47p(y3_$-4ZX1Td<>(KIS^=yXx54AU=IwlLXkT6K`3Enxi9LlTo
z{}|3>eL*JZWT>yNul*vXS}9nvSMTRcrVuS~gf?)4`wv8`iS2N#Uz}=#W&crDRCILd
zcXVuD)+pb$5%yLRn`xca_#Jo&*x8n&tnFVnsenJ+jP8heBpPsc`Geh{4xLh*yetL4
zah3z`{d*udRf&pH;fbuz&-Ug$cV{YRXJ?OSU4s+`Oa;>W0m#ei0Gd+(U^4ADM_;O}
zzEoJ+-dr3qGc!w(B-8`z4TUREPUc|MDY-bCcdo9g!W7x0!cREX*3ofVY9aaPwA^+Z
zK^Y))x%Tc6CQ?>Xa`HWZc<oG=M}Uwh{VK-yLS#!|hDJt`S!e4U=Wi~LSy@=*-;jn4
zy=-d0M!dh;DHj$Nh8Zt45R7m0I$T+7_GDya%zF<sWrZ@cRqB@lACm3C&%B!Kr_17L
z6}nzFFLF?P`}VC=zZR9*%F4<l@RmOy&uA?kE&P?{)2By={ggj8HoA&NpV@3~bcLab
zQDD8G5OD+E;c#{3PJ@%tJsne^q=xf(L#fO0qcd!J=N3RcG*KMnG4d2*ld_|nJoe@Y
zikS}U$X^X{ui)%T;b^11`>3A1RH(%mPl2lRA#XPr0nHR^iKAiTpld)C3cI!Wh(&lU
zPDa|dU?_42zzuU)T54K-&&9Vt<yj6(&AQBwjvj0kWIN5(pl~krMB>RTkI)0}x83&8
z((`Fz=?6W@6!i>$LVTVxI9;L#2YpKidTv|m_3qbsn01GLSMM{ggij=>_j-1KInXyf
zHMLUv{G+5tF2$voRJdF;;r9`Ya!T~!fdSh(eTKSpaJIxB(HMmdQlRw5_gNjFjcwVj
z`of@+!sUIm20%^#B%k&2>}UHu#Wq6qyfSC&2>>yIb$#|~r`HNnMsB}j1xh0Bt_cHB
z*?aO~p;dIcL6WI5qxts0`ye<ZW4AFm0FZN#k^*f&uzBOuUs9|iY<C*xMRT?hAN$_)
zsiCX9y8n59hgEGHzXyB-J9M)VzS5XvoAddR>1j&LP`lZiz^uj9*{l7m?5qb*-S6Ui
zL@#@b3{V3g$des#wMAs?Wmaz7n$tj)u{NZ^e|vQrqA$6l+lwR=D|VLR<um?-u`{5f
z6W(YNtJ|q*i=lCM`R+_m#f+Mfqj3#aF_>B=+*FA@i^Hf1KMlh{Q#i|9nP9gaKs(K1
z2LRv@Q5!9vW%^?actN_h!*{>&2ZjI1SjGKzad{GNgJGG(-C-<t=#Pw7!$YaO+~iOy
zJYN7S);tcpJEkPYYWW00#kRmWrI`jl5e`NA2>qlmfb}~02EW$K{}EV{H|Gn@ZR3wD
zJmT_(6;!4-A3l6IU-DIdjEWF;qCnvdzfDx5iG_$+DoGW269;Mt^9D#l;YL){y5F?Z
zlV=?$aeFe6nwkm)59bb3i1|oLLo$fPiJ6mw34@0KJh=Prq(DN-a?YU+K!)`KqC8RZ
zP{iQgjUt!^MG>kWJ_5QPQpTi>NZk9~xOdQD$Dv7U<{=BgF&O*C^}94II>m$1@ML0>
zk-QHOKs{?{oGnzz&y1<&0V!rGXaSfmWpi~gN$VGSn&r}kvO<`Pc?RXg?+!Xgnp$zz
z8b<jL7q&ter>POp6D@#}GB?zT{=jgNPhiM;4i^Qi-@H7}_St4u8MJDxdG$lhZ{i*R
zU<XK`$4^vM!!~f3Q#j2@QL|$kdfD*aDXVbzvTq!ILe+YvN6>-sOjj3qM4r_L1<~{=
zrXb`Ke6>1$<L%jeFMCNZJbtyjp1x$1X;evu=iPbxy&P%=Rxaczr<4G+*;`1ZhjtTN
zkp}|rYM`YBSL%L$dme3YlO^a(u|Q=j{XvrK=CnL8F`MOd9AHUE)j`LAdL<@y653)g
zDHH<3N`*@@Rca7{e__+Zj@kt@>APN6>OtTmW9jEJTA(2-0U-bK9(Fz$c=i^t6~ZzB
zrffjPX)H){nptB7(b;r(NjaDfv-3a&Wj{W}0<&-Tn?|Ci<j5Qj0}r}QnYxThJsBj4
z+Y3UX0AD1qqQjo{kvW?Na26{7ulC7nh|)*|<4JxSNDLv+zXxgu*5(q3?yr~cVNf~P
z;vq#`Z5pC00*ahcnYk|;g)(@t=?v<f2$)?^nb#+tJvRz-ic;9b?1^Lx%6Z16d%zG`
zpc{eWiaESO*oBqV-A@^4xG<c;y$T@UMAH`-?npXE%aDv1G>D@g26X|L%UEq4&13U@
zg!ui<@c;}KH60CKQ3eU8x3O{7%_98iTNQ?2wC#N4oJ!<>OdK6N$ghy{N=_Wts?`n9
z87l%11cvApQD-I+CQ5Hywkjz^sDU4~^+B!3MrX<xi~U8v#9ffEJ#eh7RZtfV!p=!-
zElW+T6Wl17t<NM-<}RYkqtXVU(@8CyMm57PJ}46=GkMxN17HHT(a^4G5E9N~WvOSG
z-W#b$+CqwmOsBXl##M|;lVAcT#ZdTO;8cc^Y%Xcwk~o(DagzGV?;DMb7Y7+sa6v~^
zfM<)iRmu!^NSTGp8_gJ1wk72ausY1mrBPW46N5p!gvT62cX|C7&~-EjJ}9Kmrg#-Y
zwVMG^NW7$$9*uhJj|CexdjlxR4?|cWnD6YNtg>jKK}XCtK~?p#sWJ9Ucn}gRnow?X
zerXEUQ`{5kC1YDtE9;hd-2|qmqlM3nz2yR;zIl4tgy!6SF}cG%*|@*`8Azli2M#9f
z|H4Vsg8=T3V0a20ej26z8uo7R!1MhOXIbS3Y|alij<9s}OrM=i?d+IIVVNuuX}sP9
zzW}qmyH%W2mU5YM_wDG?Xz9QCmj@^XW8enGM9l>SMrQ=u)gS46p!8gDJ>dLK+Ftnr
zQ{rWSA(YI&WVcI}d|G!gG+!@HeGpx@qM!w%wXcg;1ut<+7YWix>qkn1gXjNYfP`zy
zp;u9ujH6;3T^!_v2>lcvSTTt@4qJpp0u>9ndP>;IKSe1`22YQ;X#OGw!ClI_<4JHE
zvd#b&<_!M_Hi!X}jFpn|H1?-W2J2%;GW$^nBVv4reNOl_a+k_J4hPg(O8q+pe(Lv)
z;2{m2m_p$e*7n;oGF&s_4@3<64dG4Tgs`Vw@}$UgjmS%Q-su>)-_wJ`@ztq$18xo#
zY>5}7eLl;rp*dXP7Goq5nnS)T2Mx&xAnSX*gD&CZf$<Gx$_Drch_HD#okxi~ap3I_
z4Tc#waw@5tdUTi?JP+z*ET#7bZi8Vv-zo|@R#4%|LCb<x2(EWbaVK%wIKjg>wYPvS
z49D)`&(O}n;QUl2I%5uxg6bH8{^`u*{)(t2Ay|UYxNAD19ZfU#NU$Xs``bz^f)bXh
zg*$6P$|s~QiU0{VAz7S*&gIN1IYW!(1Y#+x%$681;9SFx>W2Q~6t85%WU#xaLiDPt
z>Ic0U+p7CpG%oR{nSx2U7&5<#6Oe?~gW4;j*lN7uf>8%^_#_{9eWX&M=bj*_j}z1-
zr!PBP7d!jTXaAu>N>A8&FJ;tX1C=r42nlFR>lKF=%u?fSg~4tFhw!m?lwFFXLrDM*
zRZ76X60He?rMM-GaT_ouLEbLnlG`2M;hxd*<Y;NU9o&bKK7|*-?xs89n`fS3pp~Pa
z`5G4di99_gConiJJcibTRjR@)o>-ewJL0J-GkZTjOMmh$#1Gq&4pvSQkrvJp&fHXz
zP0BsT=#$oFc8*dH`%O{<n6Y5C;C-aaj#(g%NLXz!k)aD{lEIilc)tDq);TUz3uh2@
zhv5B-0|rO|Hp_uo7rHDVNG$)UoF`Ro@Kx}l(kBuzX;(JMUhC5O<p47OAeYwlBm*ku
zb@@ahFA$XW3L1hH2id5wKqanB!nJsVk1f7eGa(zmhdLnjM!HIRISTz|5eJAp9=-3c
z`p0%**TYlkbIlSgTe6BWCxj>l!DuW+$qu<5W(sCJ{wgj<VTs+jsUblQJ2>e2=I0Uh
zw_x1FdJWe>14Ef~Gh?O^x3QWG5E^V{QIQ^PBIr;yAO&MF?1|$hmLKc@+#68+YeVk~
zB1w>fl&v0#+{Lrn1iYO?almN?97P&ic<XP#aWP`?<Y~DahMgfZSE)1OGoyA11mRb%
z#^Jrh)beu!iBs<Cc264GMZOgtf6qr%3`Gxm4xfv5Rg{>sSAkt70BO>ot^B5116x1|
z59f0Zc-AuBYH55ZWz_kg6;ilRG)pvp{Ip<GEXX=cerVd5UV}Lm7gYA`tCjVj6e%K8
zGM&`6(<y@i&gKE2gnOT-CX5SRmnQ-t>Okl{TN9iS(Hzl-0nMWdJQOD;K2+}TE9$0-
zBMqnL-XMjk>!0nh_%-w`A8m;+I{icQ3s!1-wD_(7zb;fDDp^X8teo~&m_s6%^bbNE
zbioKVqC~DFm{c!$@=lBE9?xKH9p5kql0vj1=m%@g{w@Qg^{S^w=Br{5Bho9K%lL+l
z1ra9tO&!_WrwlX*#YMr^2QR~uUgHZ4%({SlD>~(k&FCqlA{Sqch;>cCe#d!1L1@0{
z(v30bBVGQzI0&yqPv^aLD4{bif^S^t2&f6+4yl0?%8o<!&XhL;OA4s=HbsVGihd^a
z(en9QTeQpf(LF-axgDLs>@qIKpE)@3?Rr@p2BmXgdq!pY<?3{fWGW>&&4e?mqz7<I
zbn)`3EGm}J)p0Dblu)8quoYxa)b!w|Z84j(g8PtZk-x}c4BpI$M=*s!sjxtnSUv9t
z;C_&Gu_H&|u5ox2N@bg>KJuWUhSS-P)G@{Vz<ff@lQZoF4?WOA5cYt|^%%g%!>N;=
zk)pVV{zL<GR43VYTj?IOe8(exLXnj)c%+s!-E|1?OjAy>DLT35kte(@zUb)M(-m0r
zC$<|oZcBB^fVT0C1RhoSJI<s7N|DIB<k9>ZIXsDv5M7tn3@kfJpPHJ^z1d<rE2qV(
zQ-?;sQo&olX#i(~w2WQN(Kk*~db?O@Y&tw14ViHJCTU4?V&7m;_pZ4O+5T);#gx@S
ze(5?uf;~ubC1vaz?xtc>=}eKXzV>(pwhf^{)_NrCjd%48@_YP)udOcfX7t1v=!2<Z
zgkZC6^q&y}Si(Ulm4-UrAYSYi5U;f`&Q2lKBWjkl1RV~j44lnuBTfRIn6wI7zKNRg
zBGNQ=QWGe?3fV)@>>TYwn4+u{PLSkqNX{Mv8-^Z??^T>519gbc1_~RSX|jScaNUAv
zX7K14gHZ^pzw3nO!6RrLGJtsr)d%IMlAyjp8d9i_xgeEgek|u|!<-xk8>R@4jD^?R
zI1e$q`SBA-<Pq6rL!m($`0`yAA%Y~%F@~rC?D#lQIOe#)8kboE44q>UZOcIH@>0r>
zidPo1hajpu6QMrwh<UQ@lTIpAnI`*!67eG@nv_u40lJc9nS!-mFGblKbYw-g>je9b
zU==q01ZVFt9N24<+nC9&-&#o$_D}(41!RtRhw3oWfr!2dA374A(Lr>aTdg5`fk|<N
zGzTZj&1E0K3S%ECYYL|V;s*5=Dy8(9>EUcNr`TBJG6(*6V@KZwL&dw@&xS|#hAPwr
zUPHNAOEwSN&K6-1<5-bRQu?u#%DUMz<r__jDV|PkCPJ^cie1!W)08&}qckZWyG{KH
zW7jP|5gI5+!i-^t>URiG4;dcvOX%2i@`Ij}`Ely<hL@h@vJ6>qJN7>1?x~S97`z&z
z86J@nq;`N3f<0k1@j8&o$gJv;$~p-XEpX8f@zRhu(1h?@1aM#_a*W`|VQ83W;_-a2
z=1sX!B|+J^@}T&(c!e?~v93wVHq4<4?iac+h+69_UKsnW=f*=9I2mxRIHRIN(;&)K
zWU<#{$vg3!RBS=WU2xTYHE8ar<;Xf-!H*NLCZA(7g@zz`glZwFd=lcrIC5!(n4%LK
z4rq^whCT&5B45Wy#iwf-gL!A9JEcQ9gF=E`dZ`$YYNB|^%5bX4o8Mw*k+CjR@L{%w
zFLYtkF3>EgG<?85!gM`=y5jnQaY%9FuQbl444mZ<9Y$ewFZ!vj4+}ulp$aRtNURN~
z2Xht(w2u|N_Hf7%)DJ_D<2KQZxwt~Zh=sheu^$^f#<+c>bTAC<#fvqibLgOAY3J`C
zLv!=WKTL*5JQK<rwMuxqJT&?u6;~Xj%=+a?cX^obLke&u7REXNTVh5G>rD^|HADQx
zAm#ExcQ>uC92a*lAJMV%CMpC~WtvMfNmG*8bR4k&ug$Rk1jqMr4)z+q{y;dsiv0JI
zjuRr2J&P*VHv9-18F-p^#kfQ%cQiVnHp7Sp>1f^Oh+!~{Ffk1TB&NJ=;R>NJR#HQx
zl^dvEzEfqy;Z?sH(pg7O(UV?+(4@4RFGcoh$KgSg!!NlvI+Y?}?Y|eofCdn~$VN@p
z%^#~|Qe$x~D&QD)6HkH%#|WXO!7*xP0<n}pOJpx>OmZ_6H`vJp2Lg_SURSg)+?q~=
zXt+4BAU<q*EG1lnI;#1cC$f$Q<E9`oS<I>3&b+Wl@s{A18t`s-ku&M=_b3~)x>E>W
zVdh<?;+SF*Ly`3%FR0_V<SRg!<l30X#7aZMA-l577g+Hi3)Ckl?_u*%p)MPe0+J@6
zm)J}vk7nL~;+7vJ(AAG*e|ki<!t4`KkKEc_j4VjygVf40gm#4yfi5JYokKf-<&WSR
zHmzbAQaEbbMF~m9D;Mm8<;1-S@QMfSI)t3V%S*p9pM<7`)Iv~#Z1HR#r5@g)ErxZ6
zz3+$m^!mcSlO!&Mvcip_A`+Ez_dF*2X~z6WB)FV7v>ct}z!UH+eZV1tl%+YMM}ti{
z`%uCKQ(l&YbDj4r*x~X|t-}|>4mBpdc&Sz2pHw{69tOv(wh~-7yI^RUeYi;=D#f!M
zvtRJx7Jq<YA-=E|G?ZF=4af;%y?i)$_UoSWg9R^4ET)i|Vf6@l!=b3)b2#dvJl%qz
zqApoV7W{V(aI-2}Pb8ILt=Y*e3?8C;L>lm^9Iy3ITpn!4OD|$$|I=p<Nn9G7b9@8_
z={-&50<U=BfXZxGT<BdsJh_`k>OY=s7Y*2-R#!Fx;kghQzWqo~7rR$dMGkApQPW6h
zz78^e(DGNOuCdljzBu3>6B?{xYypB*-R~-yK6<bs9OMKXl0ubHVNWx30nc)JUXUHo
z<&l;{AB<BU*8QsD9$vHBAWDwH^(!I^#0zmI1Fyz?Q1Wa-8&@M80D9*_^?sYZf0C?V
zO%#{=x0(tu9{))(e^LdCjU2m5vj1nMy5kd&Vty_1vL^k1ewX}p>+S`f^<TI79;$04
zVda5q5SEkid5aIU`};pH0DSamI@ivBE^BbH0N8n64Ef%F^6&ru)dRH{?rXM!e-h5W
zHU^#pRyoNqboHPj{?2^A2A+TU{E1ef`9I$$8GwQ$0kf&U9$fmfQd4bUQ}U05=l%mu
z<ovIyqQCR)c>p-Uvpq28^gq+@pug`2rkwtV#Kga@_y3=yhu(vZ6=?#u3#)WtfPYWq
L)MU$~O@sd*)Xw*9

literal 0
HcmV?d00001

diff --git a/Images/mda_overview.png b/Images/mda_overview.png
deleted file mode 100644
index f70be342f6514eeaac80424efebb54fda1b55ea2..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 79543
zcmeFYg;!k7(l-hr!C^>%;4?S`NN^t@1cEyW9wc~>!I{C`g1Zh7EVu*<3{KGC5G=u6
zX9m}NJm>xHd(K(+{t0*1+H3aiuBzT$UAwxvyMB=xYVt(*)c6<}7(|K+vRW7zj}RCb
zn0~m}4<)89gJus8k+w228j3P9^cpS@D_eU@3=DSi$^;2zENPOk<<ArBc$CaG_z6$m
zavg0%zg4Lp#7U2M8-f4H^Lut=eB8PV!!_1{eLaZ-*%^0)(DT%f`Jwc<UCZ>3A2F_G
zx5;Qdp~3Okp7*qtvch)Ch^k8ajWI+%HSnr`e(RUeiNI<ofGOgmil&_xSCW2FJn)xa
zaA%V-i{Gr;^}g?Jp0hK5jf>Dz!5_eIKTC*ld1(NTLQrOPspZ<U+OK<IiattQf+{&D
zOwOHofM>#@6twpn+27`WXyZ}t&p%$le|F<aJm&OD1ktJgk^>f{`1JEAC?X*LU1I2F
z1lc#;zalBh?n6^T#xHYq=JlA4OL$|+5>0Vs<Q_l!SllRo^(eBe9dhtIiuJRmrC{pU
z)U%dSDn@19Y5j!I4bhB#6x<n5U+2LcGLcrL)ApD#>GiUi?r;u<=f!JeX}M|+R?p|o
z!*5lTQLdnoH;Z?(Esgx3y7ea}v^#%mDt-y9h+>P$r=Iy*>gU*b<$9fKTw^kO+OFg)
zBa$A*2G2=B-xULcfb^dW6C*R5@?oUlw(s=Z^i);E%pnf1%q$=uEnh($oF1q#Fu+i;
zhg%0rH#2&ugT14x7*vAsUld{w_x}`gGt&Qy#LZ5EQBPHaUIyY~NiX<H;1v&}BtAVo
zJ=n#<N=!>u?*FnsJV`LxxVbrradUfmdcN}He+6-|=H?X@73JpP<L2Xg`9Sf~73Sz>
z27T%1%Je@%{vSEAmagV5woY!g5J&od<eGhixVuR(GXB%h|Nj2xJuRWO|8B|A_5XJ3
zp@ZE2)Nu2@;^F>Z*$=GXe@ewPY@wF+da||-mX59uZAkL+@(Y9i#qj^C`gfE6!K(jn
zRv|v#|K$9Sn*YlQ=KiM#|IwrWQP;mpAM_=O59a<~-Am%{2sk{9)N2eyS?PCB%!3@9
zJkVQIzc7OvNqjN=<(D~4`-qPWd3j&wS|_>Y#OI#$1~Envz5P`4@hzFgyT?y7$uw^{
zBi_6rYD<eJart|tvUD-mbbO?1Jjj`$xO6;pG&<_%>FMF&aqK8(nH=;e@HIUN?&JUc
zvyJNfX=_l$uJd1|ujyBCL%L{)82*#=wW$FU3HEV))e^hWe^XN;Cb0ik;=kk<3qCSK
zs_pN^bU*p8iU*+%|IPdV3;qApX=g=bKweo{uL-w+d0%v|Np-#3u2x)biKa2F_2|KJ
zA0*L}*(Y;|e%=K}u6`B!-gs?s-}%eQ@%vHo7x2BpWxrZRGl4(DQL%PkGWb=R8onwu
zWbu8Nk6l<x*=>F`CG^9#RX5d40I`7H*=`?_m~c5|I*EGb$+c(21+8F*syDjCTK*Z~
zv1-$YlSTx|%2G8vbruWV-o1{@DxujwQFBQ)+dR`7)<Scha8?grI!iR-K3+2aydBl;
z0BfQ_2N!1^eb8L!dB?pCt=~qih|V~}InZ&9N2#q{!$1A*Z?9}uyF+7^bnrvuU1#3w
zs(!N?QQEI+b!-{)Jz77T(9weq5qMo~rj-zIBY5ska{sC{EqRrn%$o)iO%{Oeb_95M
z;*UH#ZrnF!-l5Wry-H6EY>QI%>H)qxGE+mG)!EOk+y4+lKI)#B#y8&_wN6$0ot2+%
zOZ^pnX;9DJwk>h22!M8Cfk$U_O>(Lb=%LdHc`##TZ6Ea!`LVmH?MQPt#;VU@!)!5U
zRWqZ+?JB^e>gs}i4WDK+R{ThT!o<h<iz=jUkd>2*nA4?;yJhE#+w2F=88HCYc%Y$u
z?OOwfhMBNkylZ~1r@rhGr2;!L3yD<|?)9}`IF6OnhV*5oT~&tAZzmZ*!sx&kBJuaM
zr?i&wuUfa^-bVe9_N%S4HnYh7tG@TTA5yAXk6KRia3t6(8-5wAXB*ZWKf!EB+pq4B
zoOoqD2G2S2UcNa`tdC^^3NQP7TciW{tYGDYAI&~px0qxMpbMf483`SwdjCg%ZSzfW
zyaY(QcxF`mI67l)YWcJA>6g6_QnjxtUEW@(gz;joyJ^C2d*5>J?B(IbU(5edTZ((0
zoblSA<`w1KOabuhtC`Oj_~d65*u~FbJDk=f;$+EfN|lrUpxpoO&9eLSch_yNbSk8u
zx@9F(^i(AJYU!eV`|9t5?)9s#;<Rgv2@MyP5?Pe@gd$cx#R(;xh~@v;xVZZ$dOI!@
z|I+3oNgf^eBxCGUNMmn&`S1P3tK@~KSfSD?V`LD8K=IW?oZmZ&jfeNI@c83C+XuBY
zHiCQ`5t_*l2O4D5iZCx{nmIGJ1s!o+NnCaTjug2mxcU#!G|QJ^!>1P=fB(Rge(?^|
zQ=Y{H?5&hY-IdOcK>TkExAQ&z5XhkNAmdwpSHmV=e1DEZ<$E(IAHN&xr}NC7{qy&3
z`EN|i(e?89r%Ee<-U?%2=!noFmqY!8-0uBFhd*EN*k7apF9H9Pou%O1y94XpP;MWy
z>rNcW6*$hMVd;m*SiKTN+qwRJd_5u$MhbFKXST)pnZ?NnI$9-%ZHq5w7~k#b-q-_-
zkv5E*sE)sP+!;^zmPjo^0jF8Zx5Y2?)h~LpGKTx5?op?8X>DA0>!801!b{KsZ*3M*
zw6xfHuMZ(pW!(_Rs4D!B*MhEsnna`R+oQS%i}K2%&pdLiYSHsOoB3|^s_^_%u&mhi
zPQjEk4&8=t!cX!>qsNgdw3w$PA@B{_YUW-JP0~Leiw<}nuoSnIq{`w*!{BCbiJl#6
z2`3S&6RcY2`g(ZXEH1-6=FGy~mSEekYqdjZV{?8nFaYe4nERdp(XPP;1e($MAq`~q
zOxEMy*3dFMqW<f45FRsGV?cOSW-mp^cof1!Pbc`?*Yyq1U<$(#_yi=vE4vu6=}Wz<
z+m24Lfd8tWHTH(ROJ>ARE)pZ`BY4%^N&a{3$7kQcG=(iZXY<wiDYX*;${G8VPQ4#U
ziSs%0sq6jf<sHT}7s=hiB=E~%>_r}Caq!aZmT+rrEZ3eM@cVGwnu*7}<?i_+M19Qv
z{$yVfL$L_yId7R3(pCAAII%RcZR1U>pQWbXU2#f9f#UMJ=YN8ap(q{TrgI@IR^UU1
z)NIpl7v<~orj8urlgwE{g6BYY-$lR7iH$$%293+<CCnKmKW-R?m8FM4m@^Vyq%t~p
zAFvCmN)1QnmqK|Fqhk9|AJ$h<+DhC@J7NnAi+}}O->CeEBi{paP?kLlyTpMRz;3!E
zadYSi!DO}not<^0S`i`jfEpOpYhk<|yZB<k;>B?isE}#XfzuT@M%r-OS+48H@_C~0
zB`jsOU^S;C6e65Gg!ukE&To=~Xm@e)N+$hz``!8CjfDbf+b^$rjwb4|#oQT@gK&~7
zk1xg>dn~?&8FIt^0x(Y{<vANm8r5qxTRa+0gCpRQkyErf*^+R_mFfk_?OC<I>UOrP
z+V!x>eWIG^W^zbZKTwORmoh!g$VIWT8KXPZ<HPEjv*|GvO6t5euBhYg5V9-2e5H2%
z;IFgnGdK>Lmi^iRk6Av;KbARvu;$v&+8*?nKijKJZr3P);g|vS2f;ncF2D4xVeOAO
z+T{<{ae_exkhZhif!B|@H&zFH4~I{1gQW<ShP36Q&F+(RE>7GwMSUW2OITvD+xYza
zi2Uy}Cod8`M>(a~D$(39&M|QNsH=)wHP|DHY3XhQ?{A}9NGWah#T#T$JZ2)pICIk)
zS7q3&nYNF0`dzfF<77>TCh+Z?;#RbKb}av+?rc0<xe`PjRNoonbyUyyquRPYxZz?V
zA9J<{%c@1^oW$POg0LN{)1}f9_#UVBL3s~<`TOdBtP3wZc)@B^8=Y;<slNkrV{b{u
zld{cQzB}@a+<s2~<KnZVpMw02JodfIAGtY&OO6kN)fA$OlY4%kw=~bmfgE<mYK$IJ
zn5wpde({YlF5gANLkRl8h{*8n6gk*4Prdf9(!)WK)DHF!Q<dY#-3xH#4+@jmxz-+N
zql?~i-Cgma6jVTm(~8lq=$_<+dzBN40!TI7uKr#QwS~JY3EOZ<rD8YQWicErP)Rc3
z%;obf0S}*AE*PJTUY)u~G60S7iZ`DC*}Z?70}B?<o|*%&X3rfjpHb7yS~5Q(i6RZA
z;66-bxhS-&`p)Wm<eIB|{4fjHnPXaU4X*}v25jW>ytwZHNP*ife{i2$3UbiF<41WB
zgITA9&jtn?$OHgc4o!bN;AxX0!NgL%1=7+6sjt$eT2NZoU?A2LEc&eLR@hQ9u_WdE
zirkfsFk~dGQMPOov?iD^N!rI8jPt0u>D<7s#QoQYe&?U-0w|l75`nUC8pr0YeVU#)
z9EvlZ$h9t+?e0R-<wlV;>4ind);~k8Nhd7oRHQK4&4guhEo|axOaSF1Yl;a_-FBGC
z*<vO2@Xc95NkSq6U@!I>N0^ZMC8qn3E~VWw9H|AO!~V_Uy)^gm78803zGrECZ+Z`t
z<K&}qouva_YE%%Lks$M4T}QoVidNi<X!+vEqm&;QhzS8TvQ_O*RxL1keODANxZrv7
z;H=_$W!_f2F{AB3#XwZl{m@Age-U@CYRjhX7;Cf=LuGW>qG95F0!uc)LK?*^E$RiK
zO&m}kK_At1?RCo9clr;0=w`}oXxsO2)RUt<c~yIKz3?BmByp7pd9nLEaP0C9N`7}B
zHxdu<+Zmz&a$L<VdFxrD)cG_U`K9cLoSQ;%bEJn-Dn1a^kffPeB5e|ccjb{t73XkN
zIs&6lt11eQ5A_J;cUX@R25O}uXs=+SSg@6@W_BP<T<0pq*v#m`50u-Jn@da;MC-0q
zx<x<TG2$s!Rf^+)U!J?XqaN&}d_6z$_mtre8)UOuV6ED&yOgx=xAE|iZKX#t;1CyR
z#D8DfF+o8tf93LSDP*%b7*EJxiTK2=K&(k6jX*#DAy|1mV`p+>5H>XgA37@-Igt2?
z|AA`mOoq1*wr{w+=lrvLqkl?Wsx5UEjPEx_Xf7prR;pfEK?-Yr@y>r72beAMiyjPw
z?H_L>sh{(pLzzq5SB9d^<E-iGs3|GG#!B5^?um}U)N+0it_~%i#k=`UpbNnE9gb)1
z7j4yEJv2)z2;;Ld-S@edy=-LwKV)zU)tUR=!z>&(^AL9}UFVZLivJvb(*F1E*c&Y5
zLclb1`g>clNo*wmi$lUklb;5xdDUEB^kWFI<qrF)S9U&cox2C{zYOmG)$^#avb#FL
zWVZ{gY;vvDFr_9wvzvQb$o4+>c7u)6VlS<CR?PlK58g4fUz|g`UGUgi>Slgsw*9=}
zQ&-TF(acLFrtF_~CgCsqe_#q{t-O1PjO95EuPfJIWP8X!jo~E{hqft)x|Q~m@B6v0
zGSOxB<oRB^$|eTGO0A8Gx<7hE*QyI9x8*}oPAlP3x2t<EA+XOUYnWf|E|!;h-+%x6
z;O=3Rb#G4T7ALj09Tw@!f&r4vnp<G?OP@XM+_D}1JpypHZ8=j)W$cD#+XzYn;9$OA
z*;Hkp+txJGcCpm@CFV6pPke|o?-p*r38yhCs%7*cun>k!mB~niguM#2M^?cf6zk=4
zJ^fUkc6HmT;yM8*=xt-v`+2xg2Sn~0XiQ|C5RmFPdd830IQx5jzTH^r3#pRU;_D`c
z5~47_-@X@r%0c(Ya6iYsNYJ5v3_cykiw8HlYvAnAS!r)4TWJ_d0FVN?{^U&|+JPIP
zehi-%d*6e}@D+7kM`d6;z{T<d=asAE67Uaqp}Vi7BU!@L*QX^?A0$@s)W1%RX7HC>
zuTga9#nI(V3Wq9sI9+!E{CH$9(zT1fn0z69kNqP&ZIp`i!P)2P3YnE|eldc0O=WS!
zM7rNv$}~xdjP}$mU;k<{;yPvbQlnh_6+8r2bZ*;zS_j&+Gxk-7jBQTu+K3O>FFNj#
z_3BdhDffNM`U!(-e;;pXRIS~LRH^su6WLMszfKCD*Xfl5ZdjIoCwI`=tR3`!>usMj
z%t}?2WeXvJDSFxwrc{8VV;!3Y%}175%V*5O>2%_bIjZ^h(MkP9aUa<>)>-cKJZ2s|
zv`IBv!~3F{r&-aw2Q1t$#g{aVMCrj<3aIa+V-8;=3h4rtA0~M8rz#0Ib>B49iQOsw
zp=u+E?Za~8!~BU}ifVwwOAm^RL;aT>&x`#gwgEclE)1B{7RUvlVOn4iiMsQ5zuDHM
z&Cn;0@l{tD$YxLA7YGKkM!_#z>uf&&w>F4#`fRDn!Gu1HO_)nef{Y(!AZ_mbX^Aev
z?2TMwTiNh&fm5C_hqUug{ts>Fl<Ep1^62TNwnK+1dzaueY?-CR1fTdo?`rQq`(;yv
z!lQ=_V2}upq(;^6_3Fb?hiEB@6cq$h9C$KiIj!T?5@P>*eNaJyuJl$gf)4{3B81|h
zFD0!3BRjG75rP`{x)u<oAJTZot;g-7!P1yN@F9y{TI!PUvkL3?jmdizA#9L8Jk^OM
z-|j+Wx_b>F*>jMNyFI-4$%5vh0IJSN<+k>lKgU(W=7tX0+_$|`U`h8IX1YFXk&U_T
za+*jGT_$EkBW5F5f6O9mFoDpRl{7?CcT4lc6`R0`(SI;n+8o|yV5OD}Q0M3gmcllk
zME9E(-)PMh7POH7uxLYLJ_@QEhdNhAra?)iY;H*~wtuf!)&(<M#D{}>_hYa7cSuM0
zgC900ABT5k*in7`LugFM_O-&~jLw_5`)Uvt2v&J?A`oSFGbvH!R^lM)iW9o=Erk%U
z9U~6&>exXt&F{pWl3$vwOkUW`o>~%$9(CNGRagHU7K-+_MJmm%j?X=Owh}Lsq|Z{u
ze75-g0Psy3Z)U*7$o?*Be3&vlSzir%F-+Ht>$TE41)U#R914IHbxTDso-lC!uoB8o
zC7@$q%$dwgTOiV&<m<v=JAF=SsTxALZBg;{zB5fgq=H9IWIIL~=5>TB@p!*g?$ya7
zYlXJt%XgzzPcU>3U{Gpym`ANC0AGn%XKQ#tpMvNrc%u|4)yUQcFj#YtdD+yhP79{;
z0Yyg%YI5!94H5r)pY2F2rBQ~pqvrM4hKgQNoe)$i`%rY%uZ?H@QGLFHvMoYdw0+9g
zl0H`jzCkZMf|rJZem6cTZ8$g;q}u%?FLgDj{@a$dJ(~ZD`Q4Sa)O6e0YDj)eaIS;=
z*-bQ6=2!NdSd|(}b>6OGu0rbyUhe7=>o;8XB{5xDfGQZvVq)@GAto7sQ~rf1jKE$$
z?;~q!INo@+Zpq0*K$?N-)W+(W?YMWhmJW3?i3-$b!Nx4C_nTOir+W?&L=@6acaf`Y
zvl)dEaPdMj6brcUWZ--z)Y$w2U~w%27%idEB%?SmFIyw*JRmO|@(@P(P1_Bwbmc~C
ziD@Y!-oy-I!q%UJp%KHEux2p;U09^1B0J~C)_VmdwhA5tJaem3OkL;~cDx=MIksT3
zuv)}ss-c&NT#F7?1Quj+lFDJQ@tpWYWi$?1^icwF-Db`Zu*R)DlSko~C2!bYj@#d^
zV`pxSaetT?l6ZPoQZ9&?!kbxmzm*^;rL^9y#gqi{5W1<-qEG7^<R#Gwk|cbc(nDE|
z@)=0<+Mj^lS?)FK-%s;iJ$%mOY=}ga%u-!e=2NqTQ*txHuutD6?ItC9A!wx1av4!X
zb(KC*fbew~6L!x?aJhZ2CfKd;I@rMk*?gXgV||ej=hItlSwFn9+u-m5fNf;)+B5Ag
zAv!(#X{<BB=ItV(F;yd)(5t%(bS>ybd|VlAv&m_Mn>MRw8`*=O&AQvx?xD5$SeceN
z`3aT*EJ{kQWkF%euGvW|{<%b%WL5o+S~Oob;QF_8%b|R?6JZ_*7iZhZ<e6<dRbU16
z{4<zDqk^Qx`xb`i!$)lR*BMSRo=R+O)w&*9kTUT~5>q`}7Bz(Jm<x5Ozd{LzMEcre
z1{1=nX8>9v1L>5i6r(x%ITk3JgZ`4ly^8D#gHh*!bDXzP!;FsM4Cm*>IHtoWL_)r<
zmUt&S+e`OKUOBK0(^`y7G!%WQ<hRjlD^2MubMYgXSH)a{)Gz{(A{EAK>+GlDFUq@%
zZP#6^>mn|@yJ_0>xe6r<9yjwEsW3L@1T^faIp2t?eGS*e67IwhBJa!_0?DOX0UL7*
z5)<DQl$c`{gKM%c_>{G*Cp1^K^URoSO69|fe(%4lz$SRvV;hN>jnaTq>J3z~JRPK$
zMAs-`k`T*5r4;3x6*eat5Yd?`5hbkn(AO64o?+i75&-Q#(*y1;kS-vWpxU?+Sy=-p
zdz;l1HFI*)nlRmJV7`hUpORvKZZgh=zDKR^Y=fpUe65<ekISUDP*FJnCZVW9^%otM
zq;?gBv-DwO$oguaFi<9p>5x@@__cgXv86F;G>#*j(9J0%{yq95vg}W(>a*~AQ}&NH
zm@vD^e4YjZ2k`_%2qyrhBA}uCDw%!hMKrY&k-<H#hCH9uSCa)HUjt*ABSm>eI_00g
z*CFk@DrZl<vx1gsO1sL1Fx8=UqU0ZPAT%0a)%wDI6h{6qJ=ho-Z~P-&J6AXBG=fM9
z-+B!npc9asDx`Yf(sxNujKw8uP{d7S+?@arZTc43F@(uInxaM;MJs<AK&UnI1a%^b
z?Vq}r5H9viiVlxk(JXU(%S+AjJ?$=x7UMetnmkA<V=UV|RN_79keGY3(%&rYmAdv;
zsT$E0Btj`05B+8tcas^Pa-FD%u}b-d#4>2w$`ZLFc^Ew?OiWN2z#tRJU)P-`1(jaw
z`owvGa{Ro)knz3WA}o?w?NQPC!!B&vv>{n%c_d})Y8)vZW7|C$XpGBQV00VC85`S8
zdG{G;<(<(7T8r0lZnnw-A+9)yjA5gKn9P!On^#jfg%Gq)O|affKIE&UqWm@3A3VP?
zqIoMiB>m$P<~eXW+1LRac$L11Cx_(EhuHF9^$jK5(zPPq{N#fD<*yFyi2Frd2Jy4v
zM+D~BW2f8O1((!z&BiSdj$ogu(QHZ7-H}NBow9Q5uKWU~1_aFg90%#}|5yct>6M^)
zgv^=P3W>HG$9`118v7M&_U{eKxV>u^Yh*rI-014_+^Fr_x=<+Li^Ks0zeY4X(lXw9
ze{Pr6a9!+TX8}(oT0?G8*KHDY98=5#d?I?VaLS|{Ug<6gWvR*oVHS5RHEPWYud*<&
z_cv7(uX`RL8KdjHY(9tItT^y5UQ5(Ou--R<)9lCW>e>wN8v)(%Jz^~#QKN+J)#hs_
z)_r*$6Fms5!SfARy1QPYsB<i#sqG@+ByT_MI-rt7A)N>VI3`vBIwN7FDEx*A)&!nJ
zK%v!A-LlX<1+R^*e0=Tpdnb~M2=zN{K$N&CfL*$tJi=)W1-CPlL*ryMS6`O-A2A49
zD9ywbfsHtGVu8GVsp5~U1TK5zwiGK)wul7dsjS`qZfAyDyi42T{|zEa@y$Ii2Cpc$
z217e3Cxf{f4-BP@?0`J87h6T>@I8CMml;i?>)VK1xnaP1AZ!rvw}5+Gb8mYR1P<w)
z3sNf?^XS2V?})6G#eGg&xZ{VT2xob{+?m%<9EwAxi<DufzGu8cy`Yw#ti^+HZ-tYs
z5xW=jZA}RjZNb1Gd-SoqMPO4+SQ8b~AZye8g2*YYtu5!4#DxI3f=KZ%XXR-J)ez7z
zEDCfclNDtDWwRgLwl@?l1CS|^*=U>I3%+IGkH2O2Ep^*bJfUV=km2s7$+6M3;<?tV
z6ni?#K_D`c!QZc6HtJ3W-0_S+D*`87N3g<#6kUG9Oxg#c-gijjFFs}@+GQaCI7?6V
z4CoBW8`#qUmx7tIOvt1XN3!Ye<I5b(oO9ItLNw!In?A_Q;c{eMCQ5DAktbM`a|||y
zdHqJvb$za*PjZk!Ik}EsC4daCP&gL09pK-pMrfV+cYav`#h5gop-;FD1DzsxsrX8Y
z7d0)ts`4cc^7RrnX3rF2Nk1VUafI^3rFJZnz8+!95J4vc)sGgqM#*tQv5a1*ya19L
z^hH)J#=euiFTs}earY8?#*CFY+AZq5TL`8iQp8^R^%{%XA@np6F2XLqc%kjjs_(*-
z1m(dDn06xTx6<*;ZPIz&@?h73VF`!1=ZkBKS5yM_ecN(u_&I?uAQjAcNA?;0DQ$am
zRD!n=!b#L<8Bu4z_i5NyLu}xQ5VK*o%l)3l+St=N%UcWYqr^j9l`Txqx1m~j>(a>g
zM%b*_SY*U!N|f$Hn%e_G`7uT%IUbo(L`0yY1V9L;B8^-==gZyBC^y0*CI+(t^HHqz
zoEwggZZcg5T`a-Xkol!53va*ulHT{m-6QMA>h}Ek0U@;}b8%4DN>@5^w%B0`%<Cs&
zC{&!kPYy3Oodu9-wkUhCN5m01w4(u{w-;cRtoM%4{k3akHej>(0!%A`XInQa+DCP4
z5IXH>N$+WAf-i4!2ub>2sOBlTL%czs4Ypl8)pQedQRMd82m>uhLbz7jI-_y6Fi+#i
zQ(B14aX;YP?{V*tD<qK|DXSfAZ*S#qq;^>Ls2li&u*8CvCSu^<9GHQIP*+R{YCDE%
z>&dy|V>3$R&YLC6F`D?i6suSKuW!5kdOwfNn>T+#(9^b4I&N*A3DY~NqeLlejmUtp
z88hM8JX;^s8V{>}rSSzpTQQD-B@P<KDKb9p%<A1RPCRC;tP?PiPh@|VU3&V)Dzf=#
zVgEK~yz#izZ?>%uvs;n7z-YHiifu_H0851wILQ#a(HVwmw@AGr2Z9I$_U5`qs~P4y
ztB9v$H8WC;Z*9(0im5wxf{ZCEd`ezl-1ho8Wu}MEn%sRV=Le<<FmB8WniGX2REoWC
zB{}DSz~QdZX4m1BAK6auy?M-^fhzO;2fu3->n>qgah>GX)PB)7|6#-I^NwLl2$X6>
z6pXP5h99V6M|MxaY$CkQo{?jSQ1;zoeq?(V52M`(z{nXgO~uP+W_V#v#&-23&p|wl
zW^F^>M=U1USQ|KM6YUpIV2=N3pFWX+n}**mS<*#mFqttB({^RdZjN41u7;Hkn{-Kv
z(D>edo*}>4I_rXN%vE355~w;06W#eq+2lqt>hPjRJQ_j27a$6<kd;K}oin#^T8iN-
zfgnQ3+g%M{qMr*d09ZjoN+Qf$0(gnKX*`P*TUZRx_-&vN&7a+Q@uCr6O-2W{dbOr@
z7R!iUg0bDZA<lc%v#hk-x;0HKZL1~P4Es-8M4&YsRlmny*&1*Piz>gA?}$^U+vk>2
z$YLMcC>MG32LJ{Kq=m~4OYg=zD3LtlJbflMNv{as;fnHd>?lK-Q~H4K6wkMk4AP(*
z#8QPxD$#U101I=K*W0Uu=I`+n9S=IIi0dlxMtd5eEceLM{QYrhN~cM87^uWVn$jSC
z1O0g|oR{cXEKC$^ij=<~S8oxG(}ruaQPMKf;*eBVFsgvb;h+R6^NnY&HUVsEgt&1m
zn~9G5?}Sj=w1_@~_dNxX3?WS2jYKUI9DtG<*>)jDVi9wvRj$Bfb#3bCp+rZ-uADy+
z@NAHBDH(J@Nnp9N)rfyJ(QiZW>10TzZk*eBa%IrMCm<b+3ViMjotk=<=5x9KONji{
zy5`*|6@?TsxCuEJ+<-3kEK^<r$BsI~&8EMhRlx@UH^RAL;ulk{m1qq$g(b&WXhf-Z
zTVjJsHDcF9WZOQ@x&Qk8tz$o)AoK-c7&bQ6FcrHL{wOGnK<Vj|viT&A_&23%it0EP
zecj^hwsSDVGYsk~qkT7OD`9)>PCZ7GXwF6PT{w0!i$yh|D>puVu*krRZl*v&K4v@+
zWN&&PMVK;KR>oW>8nZggpl{XS(w5|XhR8GQrSr!%+@W)FekO^eASl1GHPu^V=OW!~
z^w(RBigVLob*I8P{iQbywH4i>?!nzQYo*>;LJ>Tj>$a;AI4YzCU`Vti?lGxDpa@G>
zl2^&gHJC;#ljP=l&e(=Dj#VGn^T^1$O9Ii_nsouy_LeUl(nEuG8_4WwOpdmD1?$X_
z0Svh~TjjPSXM?GXjqWvcAs%n&@W-;%v(;Zc=6OCL4^2dFjHfyPiU8x(WC?nll9sKM
zwzktp$m!RUY=n8}sM6{I!*UUdjO`5dXH((yYj#(LaxXS(!siiV!e<}UMJ}22#}6Ap
z6K%HmC8`C&$QlM3xdv?GAGYG%X?_rIrKGm!6F4x|%P*phXn85Kk2xK#DYdvC3HRmr
zm|bPcw5dhS1k5z%vnEN3onu-|vxDAAsoJ{5wU0dx_3Hd8qjrf3Khpfe;a9?hPzXn<
z=Bx=?)6geTFa06R=hhdsU<qnex)pjRwX_`Vo9A5iQu2Zqt*j-!((|rSYR=uf^rTtt
zueP5KU^>jzcwjN?#b(~!1Y5?b8Jp>G1UvuYl#Pk0(ybQukN1@8sf528o0WPr&)>wB
zW=qUT#Y1m?ti(aTxs&z-MV#Y!L!3b}@38IQYTIk2etCEl^4_lo4sldnlI7&l3=-*I
zE0uvis0N+i5E%;s39-nu**H>w-=14VxKSn(DPb1`2O65<(H9jZ^7clseJl>@;;hph
zynK-oG7-u)3sli?@|C5ESA*M}e_ct)(=PwAKVa_j=x+iEbV}hx8np>2XtlVDZA^V`
z5>IboEpDC@KWp%jh-o&OMci9&*!nmJsX+<r3YVz%0f!WM))dN^>M3>Akzp(L8TbXu
zUQkZYjhd>HwR)ym=CU5QBKQX)yDfP%B?XteNH+@UN`t?gvWR1W{JE5QMOMhobRYme
zsJ>bQw{|U}^pfRx)49^67V>%Q)3+Eb^H2|!j2)To0`m1u2AhouibwS_gvQvw)cm(l
z55fE{cGC{_Ldh@CKe6@t3i6}7I2*${PbzV3I+wf7J@~qM(^-hsuv8ySh*RQFu0c(e
z-^>n0biukdXwCDJe(q^gw%XIJ@%JC(Zqm|)9_2aoV119{SD!ePI9PrwG}#HVFWTps
z*59nf5%YB!J;(5l`wAhCZ&F!2V!t`)vQ`_a+oO(SoBli@2d1J_nhlF_$|shc{E)e`
z?>CijI*lp9#}Jl0t6(J7oyY{2xF5fW9<*<hHmTDZfW``|U$-a<4F)|`_MT{j7dI#B
zV}At7pW}>`^H^za2{A^W2gi~%Mh#1BnjeY%IWHKBsi+W|)Sb^%{9Y?V>5YWf(c#FX
zi*a0i&PTs?gjERIzRNBy=<_OKi;Xr_jXL2$#naY%URNy$o8yt%(<()0TyX?Vx{#^Q
zD@;DESDfVEG6$D;Y}lp<Zx+zK`Aze3g!X0dM4>LGz|<Ef0gk(;RU*_im<itO{O7fA
zdU0fd2CozFZBUv9*ih<>b8b|85;oo%vVofLRYa65?|6P@L|D+90+bmoe>0C=#?KJ6
zHPs_572{8<oZr7=OHqFz%;i&y<=#yXti28G8mpi)pMoRKV=kvl_f%j4bP0F|`d$|E
z@s=c)d?Xse1F!p2X4{en;Y}Wm2jAX0o6fBsnsr)eFy>sc`@0K7zY}AE1!_)#ax%Si
zN<zb(UO&G_J{oV-*xFDx>~ReR*OwSO#oMntXG{6F)3kC;F@i`jqY15!*&VJ4WTkL3
zy>M#;xIQ*W12Sqhlq6(6eT$ksWDkyPmod0^i^Iv>IYwH+V4XTTXo*my9NQXIcap4s
z4=;ndd4_OHs!zQG?)~YA+MV2HmqPcP<(HA?tk(?J!R-|y_PVX3Itk4F8U9lZlVuPd
zY1!GqEVZi%cQ7qyqFG*G`}F(XDG7(EZj6BBToVhamg-I0p#?c4tE8TJ^db!*xa^ef
zV&ZRa0xb)-+so_RnfFxdiCxc9C%jv9ywDo}nM|+l*2sMC76}bi@$S#D?e^ttlap!+
zpJP){{~Yc2;AuYVWAOsC4ZxC?W+it04~S7@9k&Zg#fxW}o5e)Demt0#%J|qFz7(Ch
z3cOIN)|P~Y`~}QDKbjZI#U2XcVfD*W+`*T-z{4L)%^QtaX7x<hqQtKb9xFKCeMDy7
zQvF4JlF4*3;~(M0d1?%)JV@S^2)r9tTRb57vAZpjk6rz^2=#{cL<&Q@=!K)-V*w60
zb~%gq%^UfTW0bqSp9R?cB|=z$Pz>A7PqIJtYy#Lpgv?@OH{pkG2il`Y!~9dHuFS2%
ztf@CE`(~Ac-!Bc{DD=n#63*p4R>VvuQk`XgifD*G3S^6+?Mdw$5}e!g|4EAqtN9uJ
zOvbt4sZS}JSj>`JJ|<}>fyvr{P2Xqx;}o|iTp0@LV2gYeXgZR3eXu{ir)Is>-Fv(6
zb3zjv86d<t58)QlF*=xgEhoLI81&UMwSaP`H|g@4i5SqZ+F;7Z{G<EMSkdx8(60%K
zE;EZSaC3=HFq8!a^ojYS;@0OF@)~jE8}DCJvY&o{HR!d)%l9*#qGbMBj^`hn@q7&6
zo44j0MDt@c6Ovy4wm-3%bliy}xg&ow_C@=m#y9#riq(Id)R!Yi>F>vV%ENdtPkhGX
z^*cMJ2K5Ero3VKpW;R^k=f=IOv}*$%K-liWr3>ha%4U}nTD#~*r<NotL_E>YOGrNx
z4Bug^v|us1q+bT%=#o*{c`-yomjt?WuvAq}+OtkB&zssehx-x<j(GsBC`6#s%c;Fg
zMf`OJZ&i8aWKcoYxmh*XekKh}+4qe{g>ivabs#7jIbHU4QBMwhje~8Hq*f4vvX5``
zY_1Ytz&$IFYM@(S1eBYbCV8sCK-0c6!e_lu+|)uD*1IkmHhhoAk0FzSC{h!;hLI#Z
zqIbob^StcyxrB<WqqdOx55TovZ)LAZfdoRe2D@v-=sf<U?9gXP$nliw!E$QC2=gO=
zq#2rrvmLsd=|Rs{*e&NuYv5Y2T2DKrC5pjk+DD_F|Aw768S};hxrZvFfLj#>^Ll^b
zuied@7asQIVj7=g5PVJuQW%zlcJt&Ne)DbDO&T45tKMtSo)e44afhm}%it+W0<tZn
zC__l(#IY#rrpCLH3dH!mZL`3<TqAk$oq}<>X;g6%BBqFQzH!Fj)VGDED{S!YH0jVb
z85M<T24*b&58zu8h-v`H%$}tNpbt&on`N2@z^SqIQ(vR*In_h=Gd*Nz=+)f~7HF{W
zl+E0f28VdrQmZ;uywA`zj@xAf==ZSJqFu5osz$^O2gr8dvlJQs65rRK+AbO({HY1j
z-mwtIfP3>q`K$r}Uuoc0y0n~k=+ODDFpb~FceB47!<ReaH`8CMQ{w@PgUC}J<j~3r
zP_}~!#2S9Sq}@^CH_`}doUYjmluS_zl`;&#Em5r{!c6Qspm-^@5h@*{S>-d`xXN;u
zf6vWjFgMe*%S|apIJZ)Ij|*w=cIxJge3d}lqwtvX=Gj90d#Oowx(w45pB~^+cm9__
zVMLY!EQmm4eWPm^gMlKw$8e5+e!gF4DB`o30_CA}_1NTobLYdprXXjQi^mS$U)GH4
z@8Jo-&OOYE*w6VkH!atKoQF7=!%S)LWN7gzD|ehGQ!F)oHIF}qD=CX(0@^SGc!`3-
z*PJ#o6+y<<I)IHfNKz#tjz8LKvQfd}%TL(%%_7UX^kr_CbEVUbP2{ke)EjG85bUg^
zj_KP%+tAzjU94zZ%5Qv`!ydhY&FDTR))6o5Coaee;<Mgw^gCI_^}R{8O5%laW&V5#
zAvMc8G7j$HL?v_>iOZz1r~T<%6m@EwS9<HE9bZ2xJ^JhK-s0;_s72ecgDz<PCAts5
zNbfFftchcE3W3~P%gI}Q%e;n<v`gUZTlm$F?wCX{#5lMjq1Ukj&I9WHI=1jW>dX^?
zf~v`Fz7#p<%&2^x6ESbeU-S~0LxoP-N<hbNs7#{E`GED@@r~k1_MobSm^!4Wx&m0V
zX)quhWvkRylXXJ^MJp;zqMwk*S|MTS&~)Lx<E#s}0c({=i<l-pKv4MN4Wq<|5)i6B
zqqt;sU&<<;Nb#d`_B~9)f{Kz72`v@~@0EVut?GQ`J3~%kHK!}|9pz;B=G1AX)rqmP
z&ErQ88gvr%3v|tH;xnontzhk4$DvIK<C7A$TwnD8wo*KMUaxwY$w-|_2*X{s+>75c
zeCqD-%`2{?8tbXrhooQNsWY4x$+1k_MZ!f}&Jot|mw@N$d!cFPq`%%RW$R|g0Uk|<
z5>2|(W$+qyK;eF0B<+H&?*tXvuURH%Gwf?zfIcG97FdwGQv2%+)uTP1uda6)39Hwm
zIx~CTFhfo8Uup159t_(N8KDi9v*fq_dvS<7cyr^%zI;1#Nn^1GJM5&Ok`?9?%aYOG
z%!f7FvpUkxY#)DalSuyIt=?5FxvzV495|20^^26k@y_NQ2M%aIFjw{34h}Pr6ju@P
zx5_3?V2Ml1Ex+B0Kyo7zo+Fg%BIWL0Y!;&i;0Z{dkyc~>4SDcSCcWKZ&!SlAIP`a8
z$;d_7?nzpV(T-kO5M^Koa_nk{3Fs4n`7Bk{#DMg9_p;9N9zZ>k4b5{Om_+62H(e6?
zBa3hM8+u2juR5F3`V{s2<d@gDM6gis;Y)Y8)#;z|@U#rkvj){}jw}HvI19X4aYuXa
zgCe6EZWQK1uMnkta2UuIlaUmX5m-yF-SrvC5@YtCA+_AZ(%NuPi)9dc1h!9Uy2nVf
zC!6GG>yxU{rD3I$y+?*z9JL-BFXAQUh+H>EiZb5~7yc$S+eXft@o(7W^v>Ztq+uLx
ziwr+DsIusufy-F+#S6B#TaHJEW#Gdx!4R-nl>`G+4EdTQjw<u~ibs2u2rKjK88NDc
zn%^<vPV;<*JGa4g<lrr;`8h9BFbHUVxNP~C1Fkyh#*|_RY~qvT2Mtgw(mnR$0@&sn
z((ypwh}0PrAjN<FdW+vv9^|vqN2oIV7pgE<-h-xx2I#j7$DER=r|^|qK)8VJKoJXU
zz$l_TPQrm}vxh>f-B0rtSNEm$s_GYD2*WV&XS_bpBGnPQ{CJ{gzZ(k*b_)18&`mI)
zFZryC<{({v?Ma>^a$%7z+Ls33rMA9Dd&9@)Q5@##L8!&~m=z4cO){K`2#XLXGX(C?
z(}MLmSRe~@XNulDmIuS8;6e=d?k*<6dz&%Z<8q7Lr+x@pTJFS!Xsb3@%dOE_)#ZjN
zsOy_d+xUgZTFOVfj0{O-6X?DEQ!^Hft9jz?q#fuP@|WFiipw_~z!;gbOIzy8G~QA9
zuuj!;F_x!G?`y)5_p4X=W23?V`VgGw-02D}Vc}KuyOg7=Om}$8Lfta`ppfmb%8yWQ
zW}G{$_EUa^VK{hG&6^2+9WP%4+Qn?^j;PcoQLf^uUVqA%vj)q%+gVbsB+6Xx-8Azi
zyQZTXP$C7?)*1Xr<?n`SW3&Y6=GRJKX!$2Bz0)7(9vGVof@7Mv(UqH5;IC3`WMDPI
zq?13_Lt%q<fW>Z1p`8Xy!vqVJj|fu&977(cM4C>8l1jGOxy<}(%xwJK$c|oW)QAlV
ztf1buk`NpbjELU|!tu^t?NMTZ_lq_@>cl6z&hm8ncB@YFWm%^8{ZjJxMIoFR-R|j(
zIY4>Yj)RWsM9E5AJ2UV){H2o*2^L*;(n7`Mmf17_UCW1&iY#th7kuL1$JWOL<gspe
zbFHyyW>5cz^<ueK*PaSMrHkY_(I7YUoQv&C6`83GTxI-PIUqBvdQ!V!Zg)|oR;Sdt
zf3On=)`-gnJN0q!IV7gMbK~VK_L?!`J`6zYsGkBBc+5#1GG}D%v2@;)`k#t=Uv{N5
zvOQ96WF6Ouf2uAFAg&Wr%hB-Akwgtmv7S2PH!xk+Tx8C?o(y8R3vdp)Ua`c8W`pKE
zB#BKS=mMqI8TojYc={G)rx%69aK{7yl^@l|!{@PQ-u>lJACo8j_&pwP|B9#FRA{7P
zFcpsjs{o$Et|s+p)Ar%a<|54jq-!bndF&M-u2j?L<`tja(-$x0!JvU|HVV~*%k5W@
zB_c}NK$JbnGIea)Ahn)^ouhL34O9(9JcLznBbK?k{VmJLN}Kpq0TF8vyI*BJ^dx<z
z8*B3JcEI}i6}BklZk&rA&Z$80C5QHD14Vkh5(GPKj9$>2MFQ@=Kb}Iw^??^ji0$7?
z3Lpm(XcIPo8$&j7w^GuwTmprSS@$jOzbhknZswZ?D$PaYwPi(b46OF>uR;`o0Du(S
zPMg?$sZqVOn;@wF3-jR530B;D33u&8cLPcPX!!z)<9DNmKB}6!Lb#$*+HX>baDhbn
z$N-J!3aC5A);>ek#mtAL9L}a?k+DON#<0?#)zB?TStA<cZBow9QPi7=P7@&BWGyvD
ziVM3RZU1vjCn2m+cOnP4&}STN3qV9OFv;=9H@s56pSWLtjyux@m8iCai&*KsgMLRM
z@^CH@(5YOako!R`n(0q*>6iNvCGjcDG(T@698-)nY0+GRgwISE-Uto7HtA2Aj_e&{
z?Z3Z*@T2z{RhPR6zDnAM`B-9`SSAH40mY$<9xu3?*1EKPP(F(RM)Th=1x>E6vz9Jd
zkHet_^gqVNcEwLwU{81Xt)g%AEaZOFxcL<f$DyT7GV|Y6t8_0Cf@VVvQid&GjM)<G
z;PRX9yW~^+#MDsELo|2I$#{zwTW+d3Pr#xjzQ<}&%J<>)(eVrI`;4SlAQ`vP*@@ul
zh@ojZ<`2bAN2`a^d)OjK`Nf1;vba_U{?dA-l6o<%ek<hr3U>B3)vdPb4d0MUB^hsi
z`KaL-Le;|NdaS+|u4U7=4t#sQGUX$My0@&xS8ZURWkH96W2_IhKG3p~nc(v?scYJD
z-f0QT=OE<G*b|EB`7PMic12>{r}(s(8QMM4B@Lid2WxhUifjAy)nt?X$9MjpGHMBP
zehj^Z|Kh&B&5-utTybHk6TsL5&v)=rYdsaPGe3{P`6&kfW_dnzAm>dv*qZ%TpNW$3
zjTE1L5fKX9Nu%$@fOh!IovoxF@<Bh0PYRl58;NBy*neQaZ*zplWTs@y62~X8IV)l1
zvd~oSU1apIw{B$hvkWjaz(uRomH=|uZyzeXe%M4oKV3$stSS2}5ZAr#ov08=8^bze
z>%a+vHn;%@J0A^Q-NXR>k<C2-W9Z{|uZG5dS-Je`etW@8Gdn;KM&k3rdLG^xL{4V4
z8b7J6Hkla9$MKu#$5=B{o{);%w6s1Xr@K^Up@97u=Qn2$Ik!}*ZRUa}X)fP~QGTg#
z(5~xVYU<@42+HwZZn6}hthONOfBM3V4L0oK(q4b%=az#mpe<xkkEqV7Z13ZP7y|pT
zyl)4m9*)M*L2qAqj|z1FKz&NFB<XIG%Ta8<UD~f;NwGsGZ^Hw^kMd>eo5Z-Yph74n
zm0i(rHh;!LEH>iD%XNP4CYzW9dz5RyYgJn`0vvSLNm?;;?j|L*wL8=@Nhx;=Hiox@
zAyMJ9_1>CQY5J?3cB5r@ps*p-2u#xvshuIfN`k{NthY2)TOipQ;}2Vc{iV<SP0RrF
zFT+W3X70!1g~~J9aNDAz?4wL{G_<qHH;vi2T2^u8{!DsmKT;L(T#tOjs5X!4E!R$8
ztEUq$It-IHG&quRKsOHZEnO@Q72G&%N>{0wt-tg{=7Mp@b?B+^lceh!?^BmSiZ>%u
zX`7G3)mo~w{c^g8d@`$A2WKiB7toN_WoDU<3$el9FDeWr9r%<>&K^z^Ra;tgfjMZK
zq&R^)Bn4=xx#wJ<ai;c8>w+b?YQCzE=ILb(g(2bGtCAY<t8G<F?sX1TKl`NIdJR&O
zqGPg>nCi*c4G}Z!YW%4291JSOe3vEIwd2*sZp)96tF7JUWypdWYV|{y@Sow$2^cr3
zWpF7*KBIjZt^Dg&4{guk504wRxhcL}j=6rRp5aOJSIcVcEVGdUE?VtqNz$Q)@=DNk
zMk7`$Z-z<;XYWK}L66FUSr5qNjtYn%9MYs?#nA7x4s`-W>&EcL+1p-D_2D+Yw(hK>
zw2R)Pg2~Mbu^(gWDYo9KX(u+~+%VE=-TMN?MOMJY>Y`$p-^pR0IxLlMlP~&7T%tcW
z_(Z_+yXzGi>aaa@{A|%&%KO}Yxz)i?)@Rv4JM#hLbj>e(K)zL}l@mRnC3JblWRl^>
zHC6Uo)$(9_DTE4fhfC#*^&e<Uy@{vssD3OvFz!H)xsRB)sc9$CyKdM{boe;bH1?PX
znfaad_$VsOk`lpb5F-!!H?OG@BYiN%Dy-ls#Gv!ifw3FPi32enm=gT);ptImS1aSR
zOET}jk($L%9(_j*^?X8LPc8ARRg+sB3swJJpYgfQQv~`OVDNOm3F#7lv%;wVpFm8~
z(AV5y_T0{_O81*N5K6=_4Qhb<-!dNs^1j9hem<$m1+%Hh#pzFW6a)3F<cv1ZSQ7jj
zO!@%*RQS{3PecFTpwGvTv?&95pg5dyn*lD0riguaMl@|TpK3{6G=&_?a%Y7sa}`!#
z?OXcmxw$o}-$!rh7k;(rsib_Ho8)=_iDQHCzgnPt`e=1kaPCs=0=Au#_K=0`KfNpt
zdYb9Wnkfu{cgP<<;Pa5lKPlPx8%AudSg#+lSkv4l-cqPqq1aOF>qXlg{EohF6JOom
z6h7k%!FyaR^5`E_*7eQu-y@Us%(b_8C^n41M&1nW)Ew7Q(W)M{*A)XUA-EOe*wxhh
z*xOl4r^Tm@O>H9`8jn)(X&9;`Sj1ODNGGS@=k;zQGqcd!%>--ppaxlv-0Nfn0>>%K
zp;7Ul1mI#>QG=)^BS!-Mh2J+1n7GrH*|t}IoO*}?7hR+u9JVHjhLrf}xXhF*{3N^H
zX5!0^Ij7QQoSEo;6D{HOm+7ihF8#IKgS(&BlKf%cpOmBIOhbjBPhQ1bMsZdt=o-U{
z9ABmtBqJw3?Yay?m@~cxbL-X6``)gD)Jt;xyf|r$p>)&Yj~)<X<}E8h1YYVSybm^(
z$NgC60XkXVM{ReS!pc}Zhr7}i`l{`8@^sX*o&R6ROItbWMkV9}4vF)R%kWE1AwBJi
z({FuB##C}^x?|DU5H|wd7PMY56Jp~-@$T8elGe87tdZ-8+gc=Bi>~JbNbk(s0hWvw
z__5DIoPjv7<W_zFVK}rr<jXJfssm`d)T>?GJ`w?jpm%4pH*AE*53tT6QP(XI9eg__
zsZ4}?QlnyOZL{vs<FVqF>XUA!nhMxeC7(nvQd6ET1xbdSm;V}LlF|4Q&R+{!+jhQI
z<(8yn<8XK-la2G}O`d(+@3F;}wOs{0Zc<btgbltn;;bho=KJgTr1|Qq#*KB`rgQ_Z
z!+2QwH=nPREgPc*dW%o#{ubyGNmgYS>)?#n!+>h2_I-JXI2qmLje|w67c>gk{i<+#
zNaQd12cG%0avB0xjaCBYSU4(yE{@)sPGfVYNIywV>Eu$3VUp?)-e1LXrNV}w26|Rz
zlS|eVSU+s{GnfU7h4tNB_De~|2_0m+siK_#Z{W9J>(0Appzfx*j|6|$5ig0&?zGrM
zS7w-%o4}p81`qhXySYf5vta9@LPtyH?2bk;NQ=o9@2}aM1p~uY4=U_M9qo)}9C+LV
zmP|bA;UHU&vm!EZEeTL)_rsyJ+9|e|;g7;pcY(@qo0<zLkPNq;Vf>dz^YXOLs*E@*
zBBuA-a^pfNAktbJ&ZIiq%h@rT%673>qv=)jl6Qy2VNhmPEUKw0D>h1dOWi0`ut@t>
zEj=;T={~bBPR(*Uy=jP}9C|(!M*x-BRa8gcI7m6vc_!)JF03`6-nWP}GVy#0){R+d
zTy#9?8$4UEt3D)pLtY%_@c<e4h4o_Di}jUrugu3i-~|P@@xe{?G>@*KLqj3<SEy1+
z*mw*#N^v;Xg0uREwmeh`9!fxQ?XVG#`4xdjPy3z0Dq!HZY++zL=F**pzDLo>rYYnl
zAX(bY7(B0GXaL~yQ~IvJ`H7%sjs<u|gvYklv{`x6YgXR>!Sm0iZ5Jy<-4YHg-INC#
z28I7&l`I4|>)c={BWhckei_)!I2fUn{|{kb8Bo=?ysdzMbV$RYK}t&c5Q3C+mmEO4
zk?xj~?(Xi+LpLZO9n#&6H1FmY_kZuF_v=1;pEYaN%&dBznSh}To1tz}hsv`d1~=ud
zUS_#rehHUl&%47%UFtu|5&pq~f24-GahIA{m^7gSwB)sFbZ21MbOCFTp%k`A+2L-N
z)cV?3wFO5pr3juUZk<z%fjd}qx9X=fw^}-T^QDY=uWY%!e{%uce!(_d`+yJLU6yYU
zi2p-0&)uBvw$m!>N-meMA7gdXB(Lq+0b{XJ(iXfFhrl+<XWZ*CE=<_XBQ94PR^y*=
zzw**^1@S9WE0YVD><$xslUZ9N7Q%{&J|4-1Ux?ONU^D+ppZxM0__Lo3wt%H&Arzfd
z;_U9*LnLkPUA0?d!UC*iBZi!<U3tCa9l#Kj9$Lp>E<}JsscqYBXn(N0oE!D5uYO>N
zV{%YKAhDM>gypIGtdUbF0AQw7ui$~C8~k)y9v3;Jmo{rB;>jsi&4cUey)$1OtisxF
zrz$*E4x9QxBIAcm+v!tu1*wWj3OtO5&-5CzWsg5)0GZ;+Ig#U0&%FX5Kkr3~pcS<N
zU#UJ5oC6m!KmSbQn|b2K)3*D`0g+v3*lUpGznt9ri^(wh7pM6d^q=>Bn&mHQ$P_^K
z6r>u_FixY6_f#f$0-R3u?zp{OzYq~1m|8k?yhKp0*QuWeF#Q1!Uza(D)DTGBZ5*yw
z1J#_Epe0*$ZMtehRhQFRYU0FSKRN(}^{LZ1;|T@myY_qAgkI3jIa}+>E9OFh(_lH}
zXD&(!KwBS+QH+?RvlaR6HR`scqz~Mr#xbF)02JvbF!WvlGC5=Id7d%^+9;kVZ-2a<
z`jGMDo~D*m55;BP>jJ44Su6+{`gw~hC64b9LOafZ&)R(V)(sQ1w;K7^2}TPI=YgrC
zHEdz2oEJp9W_*`M!tTcVyW5lt2-N%ht*t-1j+55Po>>X10HARL<EF<?qF(&R-C#E$
zAn!3Pc=a`;T<Tp@&&`hbu0>MW?SQy|;-9i-;t`I6+{3JpeCso#!SnRH&igA;=V0{1
z4S+-~b_ww0vqw@~YZ0(ZN-EmsYgB$K!V{@OkJ~Qtn9C-t>gSB6@Y=6?_tw5Mt6p%!
z!)Rj;EF|*d=(PT6JuX{#V|hz*>@4muZ(5*YWY5doJM-&GR}~f(R^(PaeHS1-ThE+E
zxNlIJw?(-9UN7!O<0}QWv5y$(pM#ir*8#q^>17vQTczxu6$HV?zgRkPfI}Fg7rK%T
zUB33XeWp<vS~s2EyFNBK&6XaZicBF0ocJJlync7zdR13ZCd<2<i=<&kFl2Z3Fj*j=
zQb2k5+^v~j%SK<Gb#DT@kvm}i(XJEcRL!Z`Iqn*xG!*QL(vopSOS=TfP_5DFO70|w
zhn(fxW1<5C&JF9~e69re4dh`@p|55cV<l&8+%HRXwA0+r1~~*mM!CPWo=rEjO|2gT
zF%P1;rwaYq==2bTHY$1c6`jxjlz$XEB`fC9u(YEdM(qHpuObj2|BkGGKzEg|8x^>7
zcBA!rJ34CJBPR}*D0v~H434`7Xy3C4O2Cn)c)^4xKqNn7(JKGbcM9lcu;Q7criuV^
z&Lm2C=3zd7NIr_u;uH+!R4)S*Xg6ZQ&F!BnH)jd$2Yyd>8F!!CBNTpiAQffJm-K=U
zTt?j>i<ow8J$W${ZcFkCh*$AGW+SOQaX)Q?w+ztVxO^UW-(=7^JqO(Xa0;*O<P}$B
z`ppmHY3Po!wR1PoJz<`-@e{y(Q?oqZpuOXN1LF^l{<D)4aaHLuxA8*6@8a#5Q|rFA
z$m2i?e8ewF0*idLVTSap6KzWt3UqAb6G|pi3+8AB{J}M00GLRMWM0vFRSdBF)#T)o
zSdCVhdac~jz@dZiF|cPYy$q05d~X#2iLAtPsLJ-_OL%tEjHml;H!bqAdF(%|0q1`)
zDGp;_<K?o^1(zoYb!OZSth095X0&@;-FPPia<;Y0e6(*bf*d;NgRJhl7~HVMN&ajy
zd%5(!KvsavpFQW9K)NT>>%2MbCq3RR)Q_dP8PC63a<guK0(kwEw}6VJCdn;Bf{Cir
zT(*)Nv<e^+5fqC6?FyVasLW%k{5bGbD9%5$&RUgq%t|g#)PeYpRYj9V5dZf;$3~=p
zoaTHP%LLr`w}=d(OdzEL9SUJ8K`rSm@?j#U<WoGWHzk?%=%)pP6Z!)ndxFa%%~Zcl
zKw(5w2F+!z0<S%PbJEG8({k%H@<1kM`aXT0NW%gVB^b@NNN(F>9S8#@Ia%+S#!!ND
zY%{St;4JXnAOOSK4H7FJU^<8gE5+so@XgPwP}kL5ecMv`P;%M7;zyzJ3z2RU`$bm;
zu!^t$=_9iK{IH7dg->A(p`k&|t&7Vcca`OY5lbhg9w@(W2YlLvw-0;Qg$9k=C{a16
zlKK533yshBlVAQ|YrfnZ=};G2s5fOejE#Z&H<K9dKZz$nwiaGjH5pCrv0(tB2wu(2
zs{dGDdiZN5Tc@~T7Q{d<GW=o|9J+i07;L*ngZL(C{}E#7z)D`{3tq1)v%Gl|6-Sf{
z|6kW9_iT-3@fv-dCIQO3?+Mpp(#3M_J_S~HVo-ceS6VBqqO%5vP@L)-iSoWNl07l7
z@dS*Q=c8;a2~xb^c+omN6Uld3zn7VFlwyjq&atc=tYik#=8gfsP<7l0Cu1Z=z#cBL
z;xWQ~Ehuy(0oZAvbo<2U$NOzTH+)d2d$vN~L><c};W|=VKe1h??>?m%+K^*T80Q%`
zzB#pmTJACss?8!Aui3wT$ijF`J9aMsHTL+qcC<(|#%2taa^EQEy%ibHWSmDNc)ud?
z5;MV7e;i(wP0<eIESi2hS<oyl3bC7F03n}!JI+tB+JmJC!Kx?^`rzLgU7ywbb|T0A
z6gPp2F^MIlVBCys)*wcvcm}$~Q5X0Tyc38bG0=~M1PAE~t;P~dNeI+54bFKPa{GcZ
zZ##&wv?KCST<}&wdlWS2xq@oszs?gFTcOh<M>gjDq6Ddy;38f16;2qASQwtOA^B~s
zy_D%macdP8j2sHO%OS9nwx`xllmJtsi-}7*Q`I4<@6liKK|`ol!5s|E0BNO;u)u|I
z(03%8I8-FByhCK57_>ToqTreL@Nn#PtS4=-odRI@`{-3MB!?BK?t9c?bcwa7(~Kax
zZe%kq_lMSA=epzH1Bqd+U52&x;`&n&VjRzm9ZDzdW>NiF$+rhMEI-Jy#3_W+_i64*
zY<8-LVEbGU)=&|BWpRI<LrfhtI+5{c&-Do>zt&dupnl)#a<-g%qsWFBRQ;OD+DdCx
zeJISydF-)nK6ycKjkJH{*}SF>-dP8^h-`hX!AAMQ@&0H(H6(|1)x%eL^)2a9PT|K~
z%Fkg*Ba+1NrJ|ok6_7CUsFRH$%Hv}Lx$jqz^-k6}46ovdw(vF4T?RzJX;8(J(P8qu
zm(~t@Y~fI=vw_#Jp?u?}b7E(a0#C!oB^FK9DvfVW0UtHTSuj-L=Ay2kA>5b<j#U*S
z4nb2S;OQTmjwX|mq8O*=|5IzxXZwbVICoaP3yZF7yyP))^Xk)EJfm$H__k}dxlFW^
zwQco*oSta1+_Pv|+hI2bKlMYU5aKrJ5nDwNKGYc_9mq#AbEu$q_NJ=qMO49=8gZHO
z03TzL!AZ4dme1#19C1S<oDZ<CZ#uL<m%p(qpuw=FhQ5Q;xwaX+J4`^P5dC`r!Hu%}
zrs?9>u&B9b*P{1uFn)-I+Ld`Bo;}so;4m{eV)FXwxdkh5I!zgpIlJ?eSFYPGks)9I
zdT^jZK(4_T#X#evudwtJYziNZF4}@5ne@>Y7M0P)ix_*9miaBmIut$EqDXP_1^VHz
zqZx_G``A;NRw&m)?ws8<%0#%fu+=s&9eeJ8d4I>SO3l$$V_$#(8q4l<7K?8N{ZJuc
zWNaWfzm;hHfpJ2gG*W!`O(yjBwj8gfdmK6&cDTYjuzy22Yjm)mF%Csp*9ME;D~z=^
z=bUqOoTbH7+{h#SOkWN(KPVGc`uc5}H8as&o=6-7{<Z|5e$V658NxFgOwVT5zLU7O
z|ET+sS$1m6Qc1Y&^#Xklmr0G}g6+B8f|P+nbhLx*vO!w^ssPcNWrPBoekYgV_X3VY
zgQanU8iVW`090|UPss;oy<ht1z-;d989`d&9AUiFW#E<@?J{sLg4I)%l-XY+7VUhQ
zxHcV3Bs(x+v^xlH9|S8opbXKusK~OP#L&f)f2}orc!}=pz09?#9q7cX;1U-YzWjLA
z5JR_T5Yh#4eQ-7Yz%$U#o6t_1fKFfmPy-zjoiWi}&tfuCEl3r3&ju(TULtfA)p$y}
zOZGSY40ymX&mT*R+Ysj5w7jbZhdO~oEZVr3KeE9NRj6#&;+$>|TprBT#JFs=hH^@{
z+yk4(zHY|+gL^5ddSz}ZRCn?e>bt0`Txg@u56&1_IhP?NaH@G1UBvjF!xtlaSK`G#
z$af!%&Z}>!4R@%RqhbZoX=XXS4Pwd&`ML(R45Yq!YPKOLvW;>5TtC{St{JymhCejG
z&biHEOFWg;WvQ1GWs{4kE*_R4AUi*zS#XTD$h=_t#q1(K0k(i_;kE9312I>K51|EY
z^`7x=<Pg=uZX5=DXaAmogZ1(}z@>=*dk3$!2+BW^@BCt(W@1Urax;@sHdl0;1%7PA
zQt&bIdXMt=hKlm}fq7!Boy6fJN`?Wv`h%|JBL?QF^IbU4xABgS7YVW#=<MZC^hl5*
z&s*4wQe%FGx%iOV5A^MGn+QB)DUG)Z)e;FOk)?;daz;W^=q%X3LiY^%Y^$jsYdhhI
zPza1A83=x1N0!mtDhPHB60;1d;*b!XkTdCZ4M8&nAX#b_Pi@hBUVAa8jgq5utrw|7
zbnrtNtm)|Ag}M|>i;RaX7Z}`26_O$qxKA<ORlLfxqG~@Q11W=Wmh5Y{ZQZ4_EXn!U
ziqd&L^`&LB9H<!YNYBH&M79t0238ifRJX<y%$M@KPt6$T9K`+;K*5sT@V@ZFF9kA=
zp!?5$l(59PAs5JZdXb-v(;V*YYPW*VhrU%<qDyePDc8;qYz7cgW$5SCd@=Zh+clLr
zt!3S6z)tOTh@D1~GYNJayyCs_mLgo+GL>8wgUxR=u-G>IxqjaJKD{G0)CHe}sG7jh
zv!%dpFvPcdF1Nj9=87wcm8;|k%p#n0l5KH*KP@-2l_+IJLx|7#_GPHSx$Xs(c!9)P
zm~D=X#1<Rs@yWMIPHu5{6v)EeCX%n4qFAa(^Hy5#*!|5pw!^r89dL{LZFh?GYD~Bb
zKQN(7K1iA(ku0&GI|hmU4Ye1=U<b!nd|hXBO1O+v|G>lJ;^itS!lM``B1|!X<W!Dc
zKLmJ3t))*nw)@FBXWN603uloQz2xXu!7sTI-+WM<_S6&X3U{q7Lxn_iMGvOBXhWBO
zJ!KNJ>veg+JHo>Oq;9{p59;z{FJR1=-EkA^Llj;WdEJUS$}Ag6j}Z~YLQd^-%d!eA
z(aWuFSglKbcf6?fXavRYGgzG<NnHdS{=7iPp6xb!SK+K%IJ*~Ipt6uZnrS>{w_`%n
z6^pFMi$b8)cjb0p8<!QXOS>g`A%$XbCWN3QkVoxxbc#?UH+)@dY<}O?$JJg*s=d&i
z?<pnL-sKQmB`M&t7%AAOz{!L9W8q*h^5rA9)qK#!4v&BadK|Cn?wHC($~H5PQA&3w
z=%eanv-EHI)>+kP1FiAjFZ#(;?~1T5owLurqzKio%_S;UuQKWp8+G}BdUgU0FxF$e
zl_NY^uVK+|VPjsTx=cFBTR%^yaWFe@?{UNt;t^_Ph>f`33q%o8%)I|d*vdrLM2l}-
zV##jPE7tht)iwo(Cs-N3$=R5k5O--SO1^-t@Pipm@^A%IS>Pv&L;Hfl(Cgz}G`@os
z^|hPsO!-9KAf^}|KJb=qh;O9D5CRX2v*C`(^lz$~@e8|yNzh8pEf(l^jB(eJY?Q^Q
zqj(cD!Nmbq1CeCaENI?9D(2|r7mThB7<PlIGK9gai9G!X{%M_tDSud`SKka4bbpV*
z>p%sZ156`isD7Vb>`VzX&%6<APZ;iaktHAmsfC57U*L?UH3b`w9eynS4h}!5dzkfX
zku+M=*Iki29F3RYM}?(?@X%C+lz`t7_0`Yo?53$gxyxq}3(+g*#dW%M&CDvOKiM+I
z{uwqu-EO<Ze^`hQd58cfjct|$_E3}b6k|U8y3)EOb$zw|?J>MfVL2vT)w5oW<5#~0
zu<@94^fAy%9^Xo_PG@1vV~8DLH@~UkkV7YzTS(su$8eMS0Bo((?o(eg>qn=e40MpX
zMxB0WMo#-4m|8`5T+!3}HZBIfDLBWKj5zw&5ngY^brHm7;N<A*Q4D0Y7`D&V9+p9-
zb&sdgJO_*p&6567UccUch-|t63twvcVTHnF{@S5M&aKnC?`G-{R`tXc*n#dbnr`Dm
z2Nu3teoJjSI;2taU;f?^gaV;XP{`Upo0WncT9;ov$<?2IROM3ci5Y=zC7U;%)yz~}
zM^_wXnOWqT?fmfFiNyb*n4IhoV$IwiHFDd-$DDKI!1rmF{LU(0mz<ZjFR05F#0Fvs
z;B|nn>_i?8B^A(@TpW^Iq8?o7g*8VPs2%bz#lM}@uec;nHQ{4)S)(?tRBWjW#Ly<L
zZx=G-0^FBTu4>-<14>q`ydu7(vtIBMyq2L!j(g>904jJT(BiG^!a|&doE;3Fgry5`
zZY_0HTxH-^j6ED|>E1GBsid9hOfIu@pS@YIQQ=zKph~sSux<F(a6S1k(8=$m1|&D0
zaqS8z#QT=~Fsk0)=dDNyD%N;md&1#6xf}s8x4J;i2PsYG0CH#=qqTl1vD5FbuPK8q
zW!CzmF1Up(&|Rw$WId)L1%~jN$lQ!NNEnEN3#!S8N=%<O82#kds`WNKLOayNY*&nu
z`x^_Cd&Wtv`_*%>=2leMcS%7tKbWieE8D(>;V{O*!eV!$2nSR%GcZ->?4PVvU&!$i
z*!q7M&-*lCA4}PDH96fm;G159FsMBm=ipr~KDrt^>-F?tK4%W^M?0?4rLo#<bYE+%
z6;PU7<q_s|skm(6)swF4E1(G@z9HIB<+>;qEfe$VAThdm^%JCcl+E&U10@T+UqjmA
zfN-IKk@gRmQ}y|YthUKetS;xsas4p^jlx0wPx`*-sEHmTpRJoV<E>6+MP8Qsn=mdC
zP7Zux;Ve>y=Y<(I@~Sb!s@JcK&97vm<z?}Y`UrmGszs2uV!&PB%W#f=$Wfg@Wf=6*
zwBm5@E!S?8c7P&RQyxYKtjH9|%H|jWgaEHpA5fX_{*OsC!tRMXiwP^M5FPp~E)tYW
zv%G+KUNzHQxxTZi5MqWU_pO0sRpc{R9Odjl%YdwdA<R~pBnASSVzWNAYN<HM&$(W|
z>EE9S_UAtnK9gOQG4?)~e3TI3e|LN}Qv}B3bit5c=v5lR<vLWch>?GgS{Km*A5B;h
z2&&W71d1$j=l*2t8ynq7qb`SfMF+a^id*Ytp7n=dujAT&5vqQQo)UE{oWGP6Sn61Y
z)a`ne60Z*SywzuBSfJWD2N!(sQfnG$AgXe}SdWk62Tu)j*&)Y<vBJTo%gy;=dc;ks
zx$O)!kn6qYEE|ZVWs`VbOn3QITKS6;j9(#+cLhrN@&DLz(5(=(ySGE<G2Iwgg$Cz3
zov{pl_X*o3p^RlBja94n@54CBmUX2QPZwX{i2;BJ52Z{QhM)EN9R(yTW&b=U_jE**
zM)Kpbwy<{%U1T5O?oNPD4jJ+e?ez6Eqr5)R#l5REasFX5H~Jk$Xm``4{xmV@Zm)DB
z>@-9#;!g);drE<rAe_xVF-eZoY;Q~vJfF`JZ_6Nv!t<u2RfVc5R6?tg%b6RDZ`>pD
z(rzOH2R>B$D!F=`jM?`jcHBBS30qu5sNFIYYWfGiWnfr{I!E56#AC&h!2An~H*i(O
zremZ6F%<Z1%#xMCrjZG7@O((#$6u;%k<Ku{!(8y)7V|S5KhzXWn&sfDFt+6EU&_lV
zxgY7h&^xFOjLn(1BG9>09Te%|Nte8ox@Ej$s@skZ<k8sd82j?7Pk?x3Ng>qYam*>1
zk<HT-rMt%5+T#?Vi4}zv;zn-Zv+|uWRunz8y7lEcA&)c2M}-_zYQ%<IF1^R<9ao-k
z@Bhk_tp4+0q@`5Fp(E_pdyK5@v9I{e_}7F8Z^~V#;*mBBY)Ggh%6aJDp|S5J2P2ob
z^8^cG+ocPRLAJoM?c2J8e&HL#xCHarXQpetxEzJVI>~E&tyLdZmipWJ1D1mt3xBaO
z^dbjVXNm~Fecg2vBc9;Q=uq_|yS69pm+y9165H@BK9h5(0{AT#zhyFT;Aueapz3Cw
z$y}#<f8tG$d)9QYv*LDXvuLW0M4KwOR+4(dAHQ?zX&z9-ojz#$*r`91PHQlp{$+|M
zn~Z1%Bjo#s5RY1UW?R3Pxd^ng(=V!W5qDSL+29E>9WF405v!fy#rM#y7mcK@zV*Vd
zC6DJOP&PduQewjx5n`XtZ3w-8I35N;z1xR|nko&LiT#L|zD}Vsuw1ZT9h-R%Z+?py
zpXg6=He<F<N4SA217=R<FK~KPRrldDSc*hg!p-$+!nc0H^ZAoSGP_|+`2P4ohNMaY
znw<&|{9@^gvB6T@PqKBE2A%5zRLM9D(Bg6G3-h~^4-cyNyWHa^fe8_koFxk2!f)^1
zcx`5ostCmIYZOK6Au<nkM_IlBAD~EDcd3aLU!8pdovO6=Vfu_1%GqH{M#NWBAUNwq
zPAroPa4@cQ>IXF-DU2GDNTBWVXft>$D4d?CvrnIIk*0h3oUu`m*4nIUmrL=Lp#>Gt
zn%t11SxN?Q+;xy2ZSTf0^Tsj}N!y>8iRj|LZ+mtwH5?g)UuV8Y3ho^EKVb}OZOY(3
z5|7uUdUL%I>4VTia{2+P7kn7?TVA5CarNQvJ89`@`@kXI)NkL=ZYtkk{Hn^bo+aJ%
zcBY?%{5X=*N5SYJ54ESK{i80t04#B^VEv(3Jkukk-4SbDN@tEiORR{mday9k)!@ld
zRo{{h{){qz)9k@YJ7*kczTQT8-YK-IfBe(FH)72wg9u_5Ph2<iX_F#pWcci>4F1{5
z!)h5EPu%HwC1jM^dgQy^BjP&x=nL3)vHip6l{+5T@BshK!*Y-Eq@cWrVUWhpCr^te
z&3`p-WqwIRV}|lH4sxddV&zp$1^cB+ga;lCmNYV)^Tg{O+?PRZ;%-VQ8-)6ja_jq+
zXrQLe0!xC=mazt4;-75XR1bqZw@B#a9!=v8Shqr}xq*>#@P*(`|J6>>V=`u2AQ3`O
zXGb@Cp`n%W>}p^fXk)_#7SV+&@R|Z0PA4i|Yo$&dKW_jwD*n2;@2$voJg{|M;op)W
z^hZWMiz_EXiJ*o2ewhN5p!p^R+C<gqy6zV0KV`=f#@*Af4GAn)X#?N{2I$%8Z}J;?
zKdBCu-^qwWbV6PZ?{AvG4h7b(&_lTMSwaZzk~b{YNdAaLJTHS(l6gPG;49aL1_J;y
zy@9F=Isrye)_tSt0a$4a_kjp*#+jNQqe@lw5Z;z}*<g{JwK`^eN{d%3tN2H!;?M`n
zb3T8kU}_c)v7=urhVpi(lJDZ2<`%QsLvq%~#thYvCKSH*sQrmT(BA^?sl(j(4~M~*
z=?c9H^Y;=rWW4D9RTdc<S7_3p`>1rZJ`sEjb({9G7&odrg|@DMJVFU~9<@|rxub@t
zHHK7;cW#&Xznx$B&oy1!<q9Yy>iT_{vMiEtcgPLh7=-ind8v;aJ&xxYEhUwGaIW7Z
zWr?9d2IY``XK-Q`kvzmK!0VbFK+2N0xOsY!%zXt@ZRpG0<?9nYv&8aM1YSZa8CZp_
z8TKAZM5{!}_c1!K6?yMa*5YlJU0+CnraOu9{_r!%wTdU<5YjLYlw1&ND2ypGxyAfK
zFD4qq>=z;ve-S}jfuS!N(t61Fp+wL&_qfWnzp(?i{v~_y8K)@mp$wi*D?0HgrI@4=
zX*Vm|0>u2)poyql<x@tU^HqE_smJ?-z-#;-{Ca1xJSpcCm|M8+w87%gPq(jbDA{Aa
zqCg;ZqUjq2ZLgMTvPp$4P<4C#ulMW*zKJK7xywK9F+lR*(=8rqBHlHIt7wk@a?()H
zhjx7Zm@z8J8)Lirq0a|~&U)lDvc?*Bd?ETR?Z-z9*KnMyYP6MpPLvDg$@1+0dfwGn
zo;&2KskOx=t`unXa0^rKjlM{c!BP=Cba~=OkahKDn1h=?b$hVrY2jN&eGS1evIps{
z2tS^P(JWd2aQ1JF!9L9dXMjI%Z=kkkK4Rei0znQ*b0_LCEAW5WN-7c4gBk?VD6Dl4
zc#`>?%B@8+J1Yq?+)cQh)5+kqV~LRh^USUoPs680&>vi@M+aP8p(-aTuxqI!gKg9T
zP$9zjd=qtlJ)z7Wwi-JZkwg2yHes!{l{Ru`kL9hogv)?DRyaVLorqI=S4my)vsEOz
zVQ3{7!7Gxe&0W{qL*%5#q-U%nGUq-vf|6YV-162eXk_4w!?f9<wDSN;>Bj-qK+9qL
ze9n@%aeU?Nv-X-nFJi{uRS)?kNbJoHT0P3Q1Xz8wbXV}JJl{z7v~c=@Smqk@H{nZh
zZ{4ZU@AiWHXt5$N{h0a)oO6O)I{YnZq<^TleSD{ii|_vSdu+|n91W4*LS6!7EhL(I
zU{JST=%ZHuaL*9jCBo8MD|jgyhXc|4ij};rB7Id3d2ciQu4X3dDI6DJGXy9NkYo!1
zad3aEf+U%<ZMhH9ADbqLzYx~pPg`$V(-mQfdXS{ghuOyh(gEFeBTGL`wMgzR+im?I
zi2mycoh8WomID4&0Huew(<GZh7seW)NHDd>Q59C-$9Aed@~iUVH0+T?;X6^%GbF~_
z#9K5E6l+#jeEvYRm1HIyiwJ#WTx(I?wZVAMMGwgBU=<DL7G?p{86R06x%HLKCFTX;
zE_XKHPED*_AN7N;r(Ao$B<(}?HKqVG9=ZEX>i|8jwTJGwv<rdG=1S)0*uc9rDM3;3
z(|BKIuN#If<=N#+@EHcFK0a;F;}-GDV{qBY1{3EF*;NOdhLItTKK2jEKzAr7>KxfF
zQXg8M+1?v|@uW@|m&Bk-5;*?hZnuX$p21*NhSa%{0d<MF+5Ap*SOqWew%>@MFKoDU
zHJME8TT*OFdwN^!2wua|pQJ}P<<e*@8-t`Cl4+6fO%kwz_LOXTc7-011r}klw?qO5
zK1SbEFNNkaK^QC@w>0LFaW39_a@!i;P}e(+?SAT(mA-UKT-z}b`(Sv-$<W4Z6$Sta
zsPBG(rQ%#x_l|9}Snt_A>+O~1V#+{)qwou*QNNn2#SW_z$4i3r12!VTP4aC<7|QN;
zizp4Zd^0@_1TKc`HN3!Gqa%P<Aimn|O3mQUU0>Fp*~U=`0Hb9nZStKM7235t_GDUW
z1Bub6GQ3}5E0sSEiYnp8(J3cjYYYo2DaN@7&<p6lE^<%R0pMkEt+VYwI#C<<jscX0
zA&_se1(tAZn*_8oNzSY*LNKo1X!u<X!KV?m)41SrOT|^T{g~0_iX4Ilh?f!Ll*_DQ
zlfE%FWfhU)^sP|@Ehm8Fkv?@s+FXoH<>oY*s+Knr_-fYYvM4KErLx6Z{KLCMI*ONQ
z9?n7EZ@Cg2OZw1-vN`m|^5OL$_(wbz!+9dwV2X7~O152kK0d?zz*dT->YR|0*HVIw
zmQ`8140AetM)}W)jSMADbQPwYuEv*3wHi|#;^(d^^|p!$>=AN=+9~8ZbGlm|s=KD&
z-Sa2m-LrY=+RE4Q5n~Yx!!jFgEShBK18XJSUa1+vOD#U)=(ywS<4e6qlN8K3|Cz%Z
zO6noq+)48-eOfj4GrA3vFKKg6=mGf|8EJ+ym;OY~ClJZ%hsH89R7qpEEA5K03UVM9
zGRV{7rv1x3dx7lFnNUVjKg;h6ENE+^zlh1=2-+QZZ%bvI8TxGO`Eswj<T7x@w#nID
z$3lCP17%P?3nt|lO5CR6;b(6T@rq`gHn|`!)mvs)Tf)R0s+0@f4bSRh@63ZD-O~p=
z?Jaed?1;KkKh#-p>N&OYjUJ!`$swjF9IhJe-0{a@^V5;^DX6olru4VL1g6F^|FMXh
zVy!e*HUn*qow6>`n5Q#FjG|gFSfJUzjSbu<PLo;EwUSNLJG~dXe^0^U*P<E~6M!Mk
zPHj|p=DQ{{p6}GF7Z5Qca}56jIg2~(h@*FKU9R^WD(x)ZiOvjLGi9YE?MlZC*68cL
z3*k~?zk-ZAhUlj?ZS}$u{U{`d`fX4S4HvkryYx^BC7sT%`RG>AJ8${pz2=$YWY@d_
z^6OdGM<$**EX~`cZU0PH-@}<Zt942B99ex?gua%P7qMDLPCE3uZZmF%vU<UL^VF32
z_KjV^1&GHJ#s|d*TK0=}$GVmss$WZY*$;-qyU&_}DwYM{Yrw4pmP_Khl#J{X;<Z2D
zMX2Rd>Rb?eq}V%$@9G<Z+kf?=&zhvXy6$KQv%ML!b{2?2uzsg+ro=PZuA$v4HCQrr
zF@|#!E$A-E;1Rb?sd47^F#RE2^*%Y=1-vQ@-jhhz=+#TjeV1bK+MY~rmb3}Yd??0k
zt;nWc&O|iqVm?E=PwD;{<r_1yl2>Sn>NC<O^g~CB_(+7X@`2*1O4kjpkRKeuCpx-N
z2`IL!8OQ0pY0GK61#!xrY)CzxK-yX%3a?|xLcB5aVI}smmEHinLsu5wP0Qv49klEq
z%>cVy_UrsLy(KZM4-K4&(mjVv<@w`mZ2c~T%X32{6^gz!lPcoUc(zM5Dlrl4w7{BB
z7CKQjTKA1>u(*-Ud5-c&JuHf$4EXGxZ*Zzd$23l^;38yUcQ#&Nw1r8Qwyo8F(`Dn7
zj_5GI=y2n7&HVrqn5&nn@buq`bHEWwFPYdHil>65iDOk~y2I=H7nA2J7bbQCSl*tl
zw{?jrLdHY3O<XuOndf2=zS=Wa+~wD{E@2A-Fz|`Y0!OjW<D1nsWhW44$00erHKn!4
z?X>==&z}Tw#p5tH87bs8O8}N~?=F>>Qgb67fb`3(SF5L6D=Dkbvus}Oi?~}$XwY}w
z;T0U+{N<6i-Ar{lFtH0Mu&2&A(*%EN`+eM(Z~`WQO46nqLI5n_PnWkIv>*VJNt<-e
z9bH2ARPW~EdWMO<IO70PJ&Q)8l1VQQRf8~CX$ZuXg_6#!M|9-AcUC#DLX}4Sbh^&{
z#>eU!HEeNV%K3!R4gzwCb*b0>LxKFY&yR-9I?nk0P!rxwoE$I`5Dff%&M9QUwOR${
z-IQ@4<vw=B{gTAZRZ93Z_2sb0uAAgcY{vK>+k<#<+Q*z<J_FNC$B>N(ZhgOwYA4-L
z)QT0X|0{7bLHx26RV@9NDdhLtrS~6n6^~ZaXlBJ&|5KO7)b9oSJCM{;@JPRD{j#T<
zSES0H0+|mvWD{5bYPrC$PAuy`?^cPEg)UU}zvq5O3im~-`>d14_VvH$<a{uUvoovf
z4Kfm3J{C;OZB|_ooWTI$zmk+9BHFtSn2Wykiv4OvfdN`7(y%6X_-_ohk5B|qb!UN6
z9+T<6k=H`H!0YhGlcf!jy#Mn&Qyi{q<q)$l2>0JM{t|_S0~68D$!uCng5-amk5K|O
zd9DzZKmIRbJ24q(l#gSdDHrYEp8VC_9ZaD2ZgBM3{|n}pmH`?~OTNTaMEH+Zo=g5*
z1PS3o`C)K={m)ale46J%KtElPi~bMK{@iERk7OuIAfFJE|3b(E(kfv;tK6vd+vNW~
z=La-k9U&xI``pz3iCGj3G^$jyB)s>(&q06&8pJaxH2>%Aj>+dn|Nm;&JtLD>ncBdl
zp?V%$$q1m0n6?n%j(^%9ei^z*z;3kBum=81mhk66!%Ro~2d17VjW{sd3e>V02Wp(q
z#F&sv0YXTBdDdD_9QME3d{1ff`)hw*%?R%>Y{EP3d1AVwze*^vJgf27n}3v;4+nF0
z1OYIPrlb6Sr29c`7R^94kMSQ#>`Di_X;>=w!tgNI0l*j7@E!)le=CLs3r<$x#z4;T
z;+J><0ATMQ?Oq{y9UVo3q5c&EFpS=!{kNun=|u_mrHfNV+wGqHNhiY0W+I=Hl_u2r
ztFw#w$2BUTYstXt4X@*X(DAoj(P46L#wv~9Y-eO}E?Cq6l~0KB0DvAN6dF^VFI*_3
zYfBE;=|*uxB+cK0lqn>%v{+K!OiyAvKn`-fJ8YP#pwVZxc6LD?TVn&>2_OSLz~Ek^
z`1^g`Y6_dZqJsSM5k6*1;DC4+z`cBeCWoE_1q6N{Q#u`Nrj^#=*}j6o{@#J2gm&7L
z99-r%!2eHU$S^1>YM;M~YSx-4Gy{Jzp{ivW_go{!9)Nc?uC_eh?iZkP{jNk#Kzz1z
z92_4F2D8qb-~U*e62i;2(!MtB9f0F|MJaaF(czuZFZY+C(lCOUP}9bOEB~<oNk4A^
z7M}#XH4-SsAN{KFte_0w7n2Wwb>Zr+IsR>Q9UH<gn3*T-CtC-pze->OI*fS*7&ony
zj6d=J8uz*{FoNqa<}9fHXW792B{DucriK~C#(!Bn@Ejixuq6^ab-%wy@!$XIFnDf%
zP!XG4{BHq(SD!!ZXna<3V+W!1|9T(|^0Q~gCnNIwugCU1OWFTwTK7MZ>+jLmc(#Vo
z<e-Y1i`VKO(f><SUEgPS8%5#<O0JBDaV_l>bzL7~LxVv7C3goLAcZx0=KkGbg0`c^
zJOGXwPcly5EdnCgOK8x4#cQ7qIH1g!H9AhFbuFpa{VC`UKoH6{EV-H|^Vk_7<uo9?
zp#1bt*9tKULq!Y)JsBH!)4RT}G%{ioqKH0d1(<+7kPnBuNTkYXKZi&~Cub*C=k3vq
z3^X9&w>^-wpDWF#>-BCZ`^ZVG-ihN&7T<q#BNpl%`WgSn%BbhPy3Hj2H|)?;qW5oJ
zN~@cQU0D-kyFkHj3|NCX@&?v>Z<K{~r<JiBDGYkQBkOJa<)z=5>QDHg&lur(tCquY
zP4lu%2*kyQR=96~{T~x|CHJn<B&sm7W2)Qd!z-}ATccmJRf{`<^5ykP(YFY8s}d<`
zI^@Mpf2PiJP)k|u=TK$Mu}muCG+9IC+Rw-Pa+uet-s}G@o}{Gq<cMz~P0C8HfvJs-
zsdjzw4<jezG3yW2PbH<bpENzDlT8m*rlrh(Xfl-5pCvoEI&a)Jq9qeP$E&RLlcg^M
z5ZgiOU7&1%&NG&H0_t)OU>t-FHx~hB4WCU==06rBl<5Q)e?yu;(30D1sSt<zTQRT1
zN^bX-k9G1Of6fYY3?SDVsSV2Fl;&-I66`*jk4mI0(I$1)|Co)zTx@x-#J|PXUK+aC
z68}4?ylMT+ezEJ-?p8}VtKXmcX78>vqTw-3+}dUW=ze+g*Lj;Tnejezfew__y5HB@
zMi=o_33<WGgU^wkWKR_Ge406J<f+np)Vx0qPh_zI&oX!gpdf33YE0Lh2n78kv!e3<
zI>1<nkX7Vb@5Az?O7n%$`PMsD_G!lIxS!u!LLPWFRg<{UG%b;t9-%44_Q83rqs4ST
zA=YB9V;-mX<=g=k(8}2Bkrr#IgvHxs`^`hV0>1d)X6o;NT4m+Ys_CXH_F27BczLg!
zKBty9+d(wf^Xta*%`Kxib+)rif5utT=;gXMR|c<0dUqfhpA4}~Rn?)%6HhDQGHJ%v
z1J#wD`7!ygka2TcujUyJfm#WsK&2^Z;8IXrv%2TK)PFj`f&}M2P{q3aj!5xcqmk+D
za#&nOg~bAns-AJ$H@yqA`P#X`6KS>TMh4bqt&IgsqMjx-mECNjVjw56aV`^QqG{g}
z8vtizN$s9&qolkz%KQFvr=3jOcK^m|XCzrqto$J_X}E(5!j+t4G*6|^fnf2AA9=#d
zT7XO2^X}WO&mR{9*D>CRTFHT8y$eU!uhIS$Ebz?e8C9fs6=k8?;gLtX?sGCvGC-s+
zCTjSs?Yf)iX$LSmWcdzjC#n(s+Rxz8n+Wj!4OBu1aQNST+@H0-YEa$%{Yq*A6*<Ah
zR;>=RnbWpq!N}nq$bqFTR0rB-tfP@PY~4i5uQ!pj+h!hFve1+6l;(_-F)(I`+#QBo
zA+x<~<JkOI{>o~jym_1uEuW~#Pv47K^ym7hf!Fa5jn^W#zC=%77AW6IMa=7_)IRS0
zu~Qvt$7{nhzM<@Ze$|W#o&Wx>`Ryq$E8_Y^7c#T7IEh27&hQH>cFp6aZBT^MpGAxG
zPjzZ)e;$kg#M?hT@F4SE{5BVCc{^GXpFgq9EYraFsa|d}HS#y<d(Ast9!7e`6~9&F
z<&tBG-O29>`QLx8Bhz`cCw2)oRq-rCn~ilIO6(`H%_q!^cD@u=hjoU{D8xoxoFpZw
z+O{<?wh5$pOjV5cay<+;Jqo(byu??js%u}AS-=oR5)FBeiP4Efe|6->$(ZT8XpC2-
ze|*SKbai$yCwj2?;OOy%SserJ)h>rWRqip(+?m(%!BXYx6a%Fl_Q*xIZ2--EzSwHl
z?sk;KPBC9*nS--8S1dzTIfxAj?72E#CY4Hf*V|9(g`(kx^Th&))T|q7h98Y^w74L)
z2JbIOD#Cx(AgaSL!r;FAE+m48UoBOyA>%g0h3tFH!ECWqJG>}_NkCy=n>GJ)Gyyyq
zhi*gG!-@CpgrRJ3Xn9t$LLz^_D9FJ6cIcS3Y=xmFZ9|rG*-}QBTF#QXiJLES_fMDg
zsn+8RVgQU-M7`DM`!9l;VL&Dv=Wmhvn<QLT#BEQHZoi$^!|-`*sQ^GdAFM6}0y=8_
z19AVe=w!4^XYF_pDB3}xi&+UsIgjx&@Zgp}^`PYDgC-(@B>A51EUxNepK_dgQzd=q
zTrG~?y~G*=1+*#}5_k||A*+3gvlV|Y*5LPEQ*Y+6OFpW8cYXM3gZ6$nWY9gauqwBq
zigd*ljN95Xoddt0X{uqg={c4qf?&~lIgQV5gyJs1D=Dg^p+P{;zz|k9Ggz|>sLA^;
zc}&dVh^w_kGv+I{0;L;Sn+SvK%S$%i=Q%#*nu|o98VCNlI04~TF@C?dLMe(r2!OqI
z=*3eXGC{J{i@bEurtVKp**j%R?gsmt7A>28_uye=Npf77I`x`~6R1xMz?@}dgxczz
zID?JqYJrM2-PdPrCtb+!I3(DwRl(Z(F$*W?y`ZYUht6l3oGhD8QG*19hzq`VI?m8Z
zJ9=>zRH$7i*n*f?O(l%Qg<H*FX60@qX;n~mjVm>@J*M5=B0FfM%_x%pV2GkaBUJ;U
zD3!Bj6yEo8i=l<u%E{ZqYkYmK7PAVJTIk*_$Os1*vTNs~f?WOfgvBK#W#610o<L>r
ze<?&a=3__XqRD7JBbpxT3|KwGVCJO;uCe~E;g99rRPKh;TttqioD*Y4`X}rn$z3Di
zx!Xw{?0Ew{h$brnJF@5d;-HK9TD`x_7GZ05qJW6uXr<FnW?B?a+kdXcIEA}qXe^f_
z3X<}dp+1j5Wj#h>I$l?5;powA^}+OUibMrX&4XCR(v2wQcnK7hgU1RE`O{-h>RM4>
zpsInLh=grHq}(wydN)xG++Db_(?Y(}z_Z_xK#<~SOTl1riby-Sw@_!UmI^GfX}<WK
zxm_J-5Lx}8Gj3;kc8=#Z$28$Sf;NXLbJZ)ByN$YL1H|4M22L8(XW*A`Ef9&ttFi7=
zx;E55S!yhwe%Ia{?I#Vb+0Uvt2uQ4rUdRwticE_33*Xf)6Fi2uiRL6iwq~5B=#-^`
zZ{mAPXEB%g!S6@CNc7L}hi6;=+ar-NBeRqZ8ld!{bi*4?Ll3w^nkiGc1%VCS^Z}B0
zrlte_?_!pQmgS>G983IEPz0|?U{XuB99apw;q&rXau}$e2IueGI-LrUY!tyymL{%N
zZ?_$^cdIn8AU@`jht0RabB=$t)Uoy5Tg1wXdX8y58_P&z8TAdChJDQLd}ZuoFZ+&b
z*2YdddEvoz_Xz!M-1kqVWbt%$eI=+L+SN%uTHzFB<+@T+>fL`q<GJmRuUXhf^Z6Nn
zQqC7-DfN%$DtRbY8kq)|9Y09+#OaBgE?DlkOBna=qzy`1FfdSD0a=<*r!??x!X4}l
zHx(Hf(|in^_FOY^r<L0&AE6N0;*@XDGtvCUiHQ8Y`E|<(Z)&JzL{Xf{Utxg}CWQwk
zJH+;N;YP^C1b&K&)SVThq^mgxCzBJSZ~4Pz3I+K<ZDDt5woa&dN1qW>TI(KOK+ZYQ
zOaSVzg26-joWoo*HFO`#av}boSYv^U$W8opD+J@y&JfwtY*4Gcw$rk_H797Hx#rM)
zW(tT3g~y70uhX^g!{+5126z|kYCo&9A%A<^{5+mej<Wyy=awUQZ9Y;MFVy`9^JBNN
z)9u7(je7Tej7t9ohwHid{{)gkKksg)@ATb?T5mYX+f$*uuVy-BXJbB^NsK4=WJ-&@
z9%pLk9}^`pXQYm*d#YkXAuXGvINA3r3}zWh*3wW4xS?(*YM<@1Um}aZbh;rjzga!o
z!<xtl`&MZk>0B>QC*`b#m|AA^kE(EeoDsR-y<k5Vq1p4PA+0#j9HtyZqIFq$IAqum
z;02Yhx%<aR0x05`GhB*4kH}HH$4*N5<(FL9Q3GYA<0bbUslGvRPG!6ELAw2l@AgeZ
zGh-D9KCa9lTv~k`q(PI^X&8p}s?6r(teB{vDW31P40;|c#C#mXe+P>TCY;*I&;|ed
zCkkX<ZkK&xf-_;YIP_gPX`!QTwGNeLr1ZGqD7(9&6M=L5&yOK}DqkK9s{Pwd^2!Ut
z{gt&6l-Y$XwEO6r6S3P3sKdlZh>m;UZQq-1Y7bFIN|iLmvmZi<h`ufhMOsu5hP98H
zRH<wI`ezCOY=smioWqGThZt@UE$sNH*&rBgL%9bBCEIq!&!<38c!VI3jEfreXq<xT
z;hGu=@(BM(-qNK)Vq&A3JQ@FJ_}SlHK=OEnX*xpNR)J1U6!mt8aE*Fd+iThgVLskn
zycB9B`x!{x6cuO>vieM<NijTaN&pjoQjfx=HT)kb9566GyrE{)<*w+>)6*xhOm|4Y
zBDOtXF*YoXQY)Afb2N!7d!6|u!f!R51#>PTG>VHkB>e`zEvuzl3F2pCf0_sXt<qA%
zW%K)QE<B0tE+a)_OC=r5k2goE$_ch-zjZK|7q@)obQ7i*UhZZ5n1xC)Y-OrV2^r)Y
z)g(lDmUE?X<^+^UbsO1NTdMsN;B*hkS0X!ryY;EhRi*JYG?P?C=(JJa6iouh4cMai
z9!#lOw+5f;=Oif<cWYYQ>Bec>gLRod4ZN85c<?$YRS%Xgo@GGhh_+W&UBc3I!Lu_p
zh#{;P531cF)l?NAjq|Zxhh?yBWTiDdUb~5{b2YvB9oED^ly?+=VijBPliu<l10lkK
zLBnBK$LjjtIN)EJ{ra3eS~hAiHydZb^-!<zd%QSec{ZKDugTLAyF|*Vz906FwRNE|
zFo=pdIWW8-BYeXNBL0hCP<M(lgZxkso#IcYR8;MkT{Bw}9SJJ6>_!eR6?~4135+PG
z+XeNyc#vIbdWcqw|FkwbZE@KYJdx6*jE<J<BjtK{tSt!R;!WoL7XMVDXQANm&~~SF
z0<jg!4X2>dYwHvdvsapb2kMTkkPfWqMWle!=mMd&+(>GTrI>d7Lu=@mG|PFZW^g4#
zDB3aD=YU~EXy_n=5{87`R_TS3K~RZy?!zf8IK$nyYsS;tx<y-_)xzt|!!~6u^p-*q
z_b0_Kt55Q|8ce~^YOH~61E@pXSyQp51_$kYS;YVfMU4uFgvX(svk_xvUK;M#&8Qt3
z4<4LBQn^*n^D&I1KaDZ+kQ$^L9QTz<(=_riUni=4X|9FHe}bGk948W7kDIUidV3*v
zbFrqKGr5kP`I-7uN7h@{E9{MQM~rHAJNbzmQa0;0L_@U)Y{S5GG&Q?vY8qC%2|}%^
zZNzKAWDyOm&ck&$4z{n$V-E`FzjP2C=ds=~6~za|MU%vSwfn$2)+$ot0;w6va(9Z`
z7^z%}=9OY7FM(nWR<uRLHo2V0G?VyzXpOqRVLnN>B{du2eZg^OL5jW3{lSuMzL&*t
zn0_nUIMB0Cji)oYUY+4Ng5s>q75^uKVsK#@$6nS1eKj{kHB>#vde!Kgs$0L{)8Qo`
zG$Qzj#!MY_fSX}M_Zg&#=K?0T-K!-=`bEFXVW1W>BdvY@X~}S)RtjbDfDec8#wc*I
z0_Py-H|Kl-eJQRer(zy;AhA;bR=^ScTCo{O_DqvmlJ-5u$3{CeckFk8f7aZh9H;aP
zc^+TnhI*m-OkDXqb?Fm0h2K_vt{Fk*qbjLW)zRA22|lPD=^2-WXf13f3F`dcDn=L6
zgbpU$$DW6h-K08s{b-KSg(BU3>M+|o_ntaHIz*nPOdZb2<*RTx!q)|T{0hx#BBhQE
z>Q9aua?RZ2L#nZ+)`$7R%eL<(<>&4P&GwWYJbQ<wK6<37H^29Hb)wvD4PB-GEI~bo
zn%>Oio{;v!z_32(Amq0hd5D=Tq{a$0I5nuAPXD#_!iq`WKZ}kIqabgQhhh^UZDESS
zL9@#nRpH8k-G;tEv}1u_G_{OxAt@l?UrNW6he_hUPJN_S=msd)bGt32{P{w`s!`4^
zRsHLhH8u9gebM7VgqD_zDlxXkh*7!B%sLoprCVgga#`Fi)s1_jos!GRpW}x_88pX@
ztK2nh7Qd^FW$0ItKOQwntqiWOVZvlmv*mGKo;5~C=`<nw$cOE6v9H??7}eoC$5WhW
z)$qS8dG;eFBHxA{$(5Wc%egh>>Wh$8Elr%NBTg+tEGSEo?VirW*kkj#yyFAhn+Wcd
z#|AI)Uv?Q3n{^3ek2!b*><K6(<ncvz%1_|l;qOjpGOezf{ew!cF_+@v_RMqLTQyH}
zX;TZ%%W~reK9zhA#i`YxioACAOo1J(<q23JObDxe)0j~HaU8Gx#3wuH9l2tF6}hC&
zJ<{<`3nq9A<659GiL7yzALQ)*^50wlJDSd(88gCmZLT;~XYrL8QE@ZLQ%a^rO6-_`
zbtiext^H-)x^^NRQ;H0#4BpXsqxa@PD|NzG^M&+I@IUFyF_n{RRk4LHRYl~l#}nT2
zoJluEOHSUuR7T@}@v6c-Fa$O{f+ZUsKmW_+bl<TGf}}c5OT*xG>o4y1kUuTSEH&j{
zp2mB#J+Zd)HTx1~h|PsEZZDH-bmW^*rpQTQ)mY%aX5+j9GgZ+sd!Zb!a&@wMpb01K
z+AuMHoc}3Dc{%B}N}q2!?>{ObRE#Ldv{$Z@C((K(04JL_dc;GCF{}5&y&=u-#8E@S
z<!muNYR4qw;tnkUw)X6uWNkwI=l^!&=f$V~<^4g;=*emz=`>81`*tFmNG%2r9cQ~|
z6LZ@&%(DA=7#*ROI05QZ2{Wam&O&%<Mf)S6j^~YGnT0sS;^y{{+rjU1I;WuE@(yq?
zchZ9`m)V%8beWXOcG;l6*=LpVDa?IfFHgC{Mw$8P@qPM}%e$>oWmy<F(cWnhEYgas
zWh)<Mtc?0z`W>h4JTDKvL(zQ$-a)M%LOLC-r^rS5i6l%M!lc@GUN^$ehN1y0W|Dd}
zD?Qb(Bp_@FJ#{{_`V{jjaS{(P({g=xVU5yb^Qp1^u{`aRMTy(mR&T1VYr5Bb8HOqO
zEipT-Ur=ERHL<_BQA-RY@uW((Px}>@Wp+@7h<g0`Sc8kgg^j7kNyh(fouJV59Nxdh
zrl|2FZDNsr7b_R?<I`{4q|ErV*G?MLp>oIFGugM<>$5c4)Nm3~SSj*&YBvrZzh;^>
zNUS9NQk+PnoVkwAt=+7FskIi^J-;tkuY&pZ?VDH#VpeI^tzCGFp~9E(HhnsW`Z{Le
zB4tg@kT?d^7WTU;QdxdXY0G_P)qtTskX?OR{qupX1-lIqrJ_smaqL%v-OT#5jmgWq
z2WQ}3LP1648x>X-EIJtDaK4VcH<fKZcrl(>-0Gm7vL(wJp=S9kUw8c8Yvsyx`ODs#
z#BRK8NWi?=cX#muMX$tM>T0-(m>G(j+c2BxH)<*Bmh`ysQx~UiUY5%RsYAX~I5*0R
z9w{tPZl9Lmi<P|TyrLWP#ZK$H`Y;_cGv+G2Y0w!)UEUYw8&hfY<SW~M5Gwu73Um6n
zD+T44Ip){R0J+JZb~TqT`>HXBqUHkyS|Xwj{yRVp?J1uDm)X;)Sa#RWkS%8F(&cxg
zXu?`nFPC8x$ZNYDPts4A8`~a0ZBH$)Irz|ndcw3Guq!4!{rGygxhosU+g4l<hYK2t
z39}$tWV0<L<h`iVmU}OJl-`7l?+N!2g@Zrxa=%xZ0$B^dn737!w4rEq|Fr_Ns7K5#
zDA1p)HoRVbdPLALML;F=$D|Y^V`BQL%jU4B41IdMD=aPTArB<>1x^!(BRlu@%-n%n
zo$JdP9af83me|<XOuOxoI&NP7E1>)~`(}Z3k}tJXylw>l6>vL3tb6A9cw-zCDVxHH
zN}FoqOP>4xh<eMQxVmU-Hz5RfcWK<+T^e_STL=!p-Q67m1b26LcMa}N<L(Y$zvrA=
z_kUM))vmqPoO?WDjtL&U$4h3^v%Gr(Cr=<9Q`Mcsjn>t%c7S(?|K+kJ0t=Wr1oi`{
zQu6W~=O1@-igAP5ZP9ofaNnSj*u7t#_F`F#M^Ffy=anTC<4q`zKVY#rP)adTH|>WB
zC$GT=aS-GU=0QiRjdU>r51PiNri%$qs_xOq&+KieqA2x|K7rKtO&0A&uSD?y0JX_|
zS}XRMMO@|J2ruRt+*vnC`LL_9IGD7=Uyq0^&Pd|jd;7;ICN1W*2nSBUmB4C%)w=zN
zMM`j8paj|B#oSKXc#Wy6E;vQ|mJZ&AKA=pNE28X!FVEqP@W}3*M|Y7M)4!@S&hy5n
z<oR_=srFHs+)0@*&wv;CW{)AiyY6FkrpLm~NzV0T(R@>%20Km3DFnk6Z?LTKfO0Wc
zO<UbEk=u7tPJ2#)e(K<705N$R6yIUSvCc=2jDo&#wN~~?b{@x50A|RQY0&|Dc^AVv
zKjht+Y2yF2Rt+frAdvC8$7$tIpZmx2A-;n<82I5>)SLYq0`HY2&TyKQ=DzH>MYw)H
zt?r&+X@wf$t#~^-`~9C57DCQz$qlJi^<R_;LkKc)4BJ0`bZ~|W;iktagX$R=$oQ@P
zmuw>Ux@6!h;Bj~4@Ayw(>F9cdEO!3n8k~KVb`Fb9nFCh-T8{D}<vjlheRhmy^Sjr@
z)9upt`CJcT8_nbaaroV8NZZ$}n=?r(syl8XYLOztgl;F;z)D=fvnMzTiN*WHsVf}$
z=jZ#Qq=?8@@RECau`C0(o$qrJM#Kufx8pa=`A=Fu)b*nLQ!{7UjSAEwMZGx|f8rB3
za%cUT!+S^DHgiB__Gt{Et__gFNx|f-`i9s_#9l^snuwA@$P5Sk^g%AY+>nnvSNL-b
z+)1C?!5^@`_1iT+h1nCl728C>h;EHS-@lMNz$_*`EO=LPK53<HInaV^y9>LNK*dyR
zKhgG{Q9z%3auKH{z%6xSvy_O0lYF6XPr>Zt`!IA9eE;5c$OLqI&D?%pyO&B+c=)#$
zE)YL&rGHq~B|};5W7_Evex_pkOpE=36C79y^j0h&)EVlisA2y91(=eOlAiq(gBb8u
z;w6+(RRx~!92^`&W2>dARJ!0StfQ5Bs%QnT`-$$34t}F^OhbOKMlP2L1{fQ0Vp>+G
zR12sxN&79Hj~Aw|`LTkmgP>n_$Au|)vS(lxX4r)+`1G@6gT-hFXZ-eNe>~b*4Qnx#
z%?vNH#wlrEW6@O0aD0566#fRhhdx5T(?bOGkQ+)AFwz0zwAJNZyP&RH8hL@z+S(c-
z*ZsKZ*#eFMo2V7^e!<yggm0!_F&d8HAYYylV<bLF-#bw1j*WkZasR9FMg>=n)nmX$
zux34zW!Pm74d48=Hf+nh+!52YIqaid9?by_zm>S){h4jmcO(oRU`}3^MsQG6{rg}o
zwqLb{U)0N)en<Q_XiZjQtxPZw<)SH(6ZNQFpsdYyaABo|3_IK{ykHV@+v?>G<LT9L
zkeO1ex2$>dtN%J9|Dv!!GU}G9F<fC251;l3AS$6OAXyq@tI7ynirfkHXy)4|bnP)u
z&;jqIksURi)fw-yi)gn$b1#*=6>=4wLd^8<j`u*!WH+-;{G_b;jc_EE%e>->#o^pE
z9e~pK+*~S0H}Su>tUAn5W2Z_|H(aSj=@5tE_3q#@c$DsHUR4n{vSbY02N#SD3PY-|
z)rFnt_%zk})FAokS1+NMo=1|8s)=H0w_=7hoG4m=*D4+@RcEgxd$rOu*lumgF(>Li
z!(n4$8iLjLI1^2B4P$K^WLhR3z`UgxuQV}5tM<rX@8NA~6NT&=pq7x=E_%fr4K>+I
z0Nj3?r1H{aOp_&)+YTf4(HMN{?EfjMdLp>6Gk~;$GCWGYm$l}8-Q}8X`D?793@`4s
zXP@<gS3LEy(t_M#OCxeU0>4e86`T>uBz8i_l=pI<3*)qCI~uRw?ULc0Rc&X<ns8LV
zX8$Hg-gU0zaV{{@^6uaSx-7fu@P)9zMO7gfY*CLmamdqnQ8d8k3Mt!D6s@r$Hr98o
zqEYFaHfKbY7W->t&aj~BJ6`55^KxBO++ef7fvTs)NmnNO<k6i`2V6-{UB+%@oF}Mf
zvW(f^QusCjS#K9Rb+u(L$nVP_!X3#yvZ_Z6%~6On+Q+C7yu4QjXO*X86n>tjO6lvh
zki^(0|17`2A>V#Hr0gG&hBi{!+nZlYd0u-!_7m3jmZhHKf3;5r2!Q2Hr5f6b+AN4o
z%lWeV)5qxRf|V~tJP=hVUQ<g4NDqWcGS2-ko@D}z3BryI>OWaOYAt0G*Tgv~WM7e#
z&$ha|?Oo%TD)$oU>S*c8yOP9}_RaXfSLP}u2tb+w2h@+dtOkwLm3ti@qc~dAg>y#|
zME1zWGBhpsfy%4G+$1DIYVG(uQUm3^!i>w`B5o-DYP?XqsX_mGJ*%8%(7`kcQ%84L
zJT?UHf~D-!0cIrv^q2oa(3iK=6ozI_CCy3ww!wi<>4uP&{a`h2K5G59y=6*GRT6He
ztO<|~N(!2&XGJDtD*En&r1Z4P{?vpUAROy6B6vv+iW|YqRK!jv2co0cNA60a(bc8%
z8o@aXp9P=f$4Kn7z#{A?je9Zi#@&Ue&Xru2&P$WqH;SGVzq!$smi<DW6DBzJl1p7E
zL|=~hrKPV2XW;kOP6R5x%am#;7#;!N+{xaaBCbZN*Z_T^4KUx#$I$ab)40KBw(*Cw
z<Zp?eFs_Bj(NfhXH_aFN>Om+e*qraNs%k9mdcZ<{h1u+JXki8Oz3&5~fk}`f{|dvO
z$rjP35XtZ7<kP+6OCl1N)N%XPgq71bWFXNx%CuJ^DsBEp-r-b+6xRgT8<``_Zpr|G
zhh?J>JD1bNNffzwc`Z+lR@sjxKvUhUCE9YLqM!wRRkrU352bvXF@4p&p~}kAmK&ka
z+f^XxXspl&cMQ(~j;dT8;dm)!4uftBifz|3<-t^@l`a|-0*+q{As?PAL@P##d@F&R
zhe?8khF}+5Ddi2AO1Gz(Be0oH^c-gRQRxAbO};1V?nj<k&ZfV3fg{4i?)TGjN3B+|
zR+1aFK`W#kfO^aNT2NfNJFvbs?}QHH6}hLTO25FdFa8O(bpR@bAX7%_2`#dFTw$bb
zWVxw)NH^vN|3cDJfm+u4g9bzJ6_?g!L+ApnC)y<guwRGr&=hDp|4yCug#Pv|q-Sr>
zebo$7UEqeYta**@1<#;>4om8||8pf%+2>wJ*=dw){Q?I2no)2^<<I&JjHSmN7l<Vb
z>7+ZXi?XVb7q~!M9LzSNHmvR3hq*7BIFV5$ql~JFw$);s@~#F7{^Qy$@=_wZoz?uu
z?WQ*=mpY;Q3Guj}DfsZ;cVz@EZcf}44yklz*iGL~soint@jLb=gXsD2>>j>kWdK}i
z@d(m&n9GmkswCkSsQno!#9H~~^Cqz#w9TgeS(!R8so*4oBoL*7FUg|_WAA#q86O!P
zWkK(P41RMrvCia<uN8KG13ULwjg=X{V(Yu*)`roR1xSG+nYuJj6ShZ^5rV()y`I%0
zT6}=hm#lPAFbYMa6URYD<>bFA!fhRB{tJ<uDOM!Tuph!b`0HfXmpyvOHL_Fz_BoYZ
z4=;#wQN%Co#R_f8Riw+H(e=?`L!)kj3+?KiGU#Pm44K1N9hCr7US5I(I7^@tGB~!s
z8Kp}9kf|D}%~R!0K3ncOZmG4yjWoq!gX`v6F9`HxZaT-}Df@@WF*>M5I%<4gAsLMK
zgC_&DTmArWOP(yROsw<D3jeEQwi5<LF+lXQpH!Qb=eoie#Qg+)@f)i=j?)GTnsMvU
z{LeX81m^DgiF6%CHP>_@cEoU$q+dKyl(Uvkf%GUf&w<wy6f&1G2_7IPL3`qZF8sBT
zeKb>~JCL=$4bOm|OB6{SpI5&-Dv=6!LWj=uPg#BNxgF_jowzCr=_<>Yj=vs@CFJr)
zLFV(9fJ1_?xQebJ8J+4x%fuzgNz(RSfpA;#3(xP2j3ODl*Q(@O*Srv9$P(OvjFR&F
zsKAPj3^%DhtG#Pps-p95J+BvqJ6`M8MJ}{(JdksJ!h*P<L!0|?Qchxbyi<Rf1^Db%
zh(Ts7-7#3t;u;u#-vqauwUeCj+-)%(FE^)`eEW9{fXz&-!_LQ*|J?5FwQY^TXv^l|
z1PtsscE<JaKEv<bM1FY@`7<nD*cW`P=rlwdS?Y-hl3h}rx>Q5a_@UsGGe2_DU9@<=
z2y2yr8JQ1o(%;~;K0C?cp{5nP@d)Y$vU;WPR1kdJ%_2Va%fC=5Emr7Mt{C9)Byv6|
zj7>TRL$W-$*`#r%0AOW~HCm#&bZq#|B(<_0(6^OpQ~P><|980qF~#OZ$}f4tHl@of
z8RMY^r!kKdVP5EYXiR$wnrqJBn9)FoV!x4Ow6mYh6}F>U2%7$1;OQd(l2HIc?US2e
z=K&$XtYWC#H?mP)iCA7j?!y#MrMew^WCfy%MpoJTGqr@MRzqA!^53eAGne>N<F2-Z
z12n>=548%eq1d}dm%MeTaK>;~6S@tB)_>x$zdYgBc!onoZ|5eHQKu%O+a&CE1K%|Q
zP^b}XQgwxw<!Oo|XgE36S}{oL_;JgZ%LY2|=q9J&?S+@yn&&>#R@I+a!To$`%Vq3@
zPd!Gp*(dYm8PFand$3)4yySSJA$UU<GZ1DCFxF^(Lu3Ui<dU`}O}c#}J|Jw~ph71z
zemss<t{o<&<~bqwqvy!QD&?VS_AdUZg~ggUpgn!N7`MSYiR${Ukl($)8=A)#&?Bdt
zFAEmkN#SHWb`cMF2AU`}<+HaPK0?H0@!|?TED{biqU1BXBNT3(h>GriBtG!Z9ZcW<
zG#v2G>0QJV%o!92c=wprZl_x(Z<pbKMgoTUv@$;La|SU~Q)wevkP`WA`y(Jr)!}d2
zTFsvgmSDHpoGJ!H<_9oOi+7F*<7%86>2OwJLelT((bZ_@f|atKg8)CNtlMHiuEz7Y
zzK}7V7M^_!Q#r%llW~`a%$un$RYlvvKcHbx@aGP$fquNoKA*`i=dem*rY)GgP*tv(
z8wH-?u3KyU4YYP3c{}pE1I3NEt&!F>J`NY?k~{1+W_dP5#<eg_x=;<Z4QnIXnDeew
z6l9P28a*(!RGb4wM}Q0HyJH3(7kW4<pWu_qrhKb!@VK)}aXC#>X2eIX)>xKyFjZZ+
zwLJsyHU&KP!q3+)1&(y8KtzqO{3KN~fhzW6aoMu=oFLMni;%~uLurrJKMo<-(%n-b
zk74QYZ>wgAI=|9q6@5C0-^2clXNzoCyrT-*;TPz=;_$N_kYyCX`r0?B5b5C=zsq}G
zh>;6i)07Pp!r3L1K6XjlVTwz;ceLH#DR-OfFBS--FCu{@)G9*V${+trwkcso9v4Wf
z8BnQDYbX;2wSVdWiWyEvqD7KBC<<?-U8+e|ywRU2s`t)BhPO-30Taw$W2Td78>Q55
zDSyY$j+iywCYRt1m?OmO$Fex}kJ4P7*A!n5TGu)^QwWfZ7&3aq|EM7VXXP|SviR%W
z?O;ATKaa(Am?vl1;U&>O{s3B<TbCjHnbpK+58|q#KNgt~)pOkOy|;ARe8IIq?ur2^
z`UE|W=%Bo;HI)cjL1qqFs3oaPN|U#PX@|(F&{4hTg~*j2CsY^y)V6rDOaZYo$9g?B
zP?z%LliFmrXprap_%Y7w)vBcH(CLYZhe+))CiJz?cDpmMKo@;QJOcfPbnfVHZ9W*y
zzIGS8MwS0zV#4VpBzdc=4Y~1ptt_A<raq%?eJlF*aaqdUJAcVNoUZR!Y}2CX@>dm$
zqO+9ubQ$5F(GH@AZ@?}+@)i$o{WTHiAIM+eQZb#yEYSDSPyw>6_oE&!9!?!5><Kq1
zfDs&{DobMCj%yfReP)y_On7NO;Btmf1y+Skq>lRZ?8Yjnyp%a8C93&)0P0&iV!-9(
z>Gnrf!Q-D-6oN)NujcGXB`yU<wvW2#$P&jN@3U)h^W^hUuY7MtvEOkDN$1<?ypZ*S
zdrG{?$lDYOk7C*;+j3njO4?4@e-PKllAihRh;1Lz+L8`^Ihdn_ul=d|9uZMdLBpj3
zixs<AXAn+;a(|;xP{LsJe`xL$&tDTeAh~G+i&cBINi~*zas3hKl9I&GL)NVuScu7F
zWO7WN!SWMB;+*<*d`QHFkX0phq};2GzT~n9iJ^mXR8M$OWQHtp*jm!OV1W0fAe-yH
z{AulY1h<}w*F!;MXFrx~Z`?J`eu;y+^L-1(5-k~kG2jW2I|b;tG2)bltO<U<`i}E$
zX`4I+3f7W4xKx%q7#y2SF-PF-HYwgTR6dAZ;xi+oQ~xifWW`%*AOXLzEA+)V^fOHr
zP|%ngZbsg*V1&flZM_$fcPnjn-jlUPS_8d*txI)BBE~U)xxk3X0LkjZWX*_LgQfq<
z_J3Xs|HcTvTIR_0c-;w_G?6+_3arF4Mh5q<RJH<e!<P-$aeC0y^79Fwmi$EKW77^e
z2j#yNdsU1(KS=n2?n5R$n&=iYlU|=%ZpFT9IrsAx#l4p=SW7Rji0V-SJ&vN-A!C^j
z!aH<Zj>j9gN9<KXv!CwYWj^lN^CdDw#;LFgEP8IvlvsWLJaL5qe<$8Im40<>a0^xJ
zj-xDLjGTQZImUiDBl2PETeJT5nP{}*er@Gu>rKfTUx|D1t+6bOI&!|;hQ1av9ox{_
zcn2mhTC4%+@;zWG_{Lbo_DlO}lM&~<uTXfmycP6US+^ymD)qAeQ?gL}!@8@&<X{XY
ze2E|5`SIY#^?`;fk=#~2usEH!LqHco8#3e|%Nu;75@*)a413c$tE@hgk(TFQAVE78
zv4l$YNCGFfTV>;Dp>Ly(K4dgys-@wCqfT>vQt-9<N%#r)7P{yY;x+iD+lXOB2wV&e
zA0K=<ma*vZ?CFQUhyBTWUvJa|_ZQd+a)lYnKh;E1YVSCwMafoBH>|j|$|*s`^bm_E
z_8Nk(NBa6-E@8lrL9RJud^qRjN?_af9M6p#mHB?uwS<Y!NI`~dlG91bdfx0D@mSSB
zqN8n+q#ZDFV}%@49v&P$uSC4rwA{>yXzibuG-C+~=+_5FU7hOBcwIMzfS<;+DCC5d
zh5j;t1+L`tWuo}%xQ=c_@<CGKNLIIbTpwpsvJbqr{dy%jIFA>HlEosZx^C-cyb_Nc
z;X9L$e+<=m;5yhmeuw85<*Oe(Yjr7d9B*cC7&;U*&_vBFEnH4Jv-6oHa_^U^)Vg;;
zIV(oJ5EwOj*>hel{QPxTxl|&|h%o*SO@i*8<imhx+{b`7b^9G<f%h8@@1_qeoK40^
zFpY=!TjVKvZwWzkFBE)&Z(DCfGsVPCW?h7W(qy4?E4gHai*xgWAVbMchTIPU4y0ad
zPcpe|J&j<Y-MQBn4g|#^b2zx~iv)RE%}L3ZG)OfS{R(Jb7sbMzq~qAf`|wFtDq+=0
zS2FYH0(l-74~4-pB-mXNnNzt1fMlQMA<JhxT-2SN(wyyaTL--VrU2KLoPC&ig@nD}
z&YugvJGgIc-jv@k#@_ar$@fGvnq`IVMv~v6<IW_s(nhLCJI?ZP|Lyh;ibxkMas9T%
z5d0JZWz`9_m@|x3wL=dM8(1b^2di_+8ejb)yV1M<@x1N*X=uF84o|Ah;k||od>y?c
zJM75c<~v3JchD*=9hG6~st)eDJ3Dm-t;D#Gx{BEw!^&Qum8%N}7zah1sqexPhWiY*
ztXS3>dxvcy{3pir9lvRNmOe`v2u1^S85G2s&;q%L;%4}pa=!yCk%)#qYV8@YD+JN@
zdTNgP=xYn=QI)Md!gyQYT4eA>TS%K{qdnT-QkP;FNgEfRhIEp~72{+Pbl`3GxEt@x
z$zoL*FfC5CAWr~2vRhGKUtAJks)lI1BWq3;a$;@s#(376OIV{i>X?)N1l893^)2ty
z9Jnl8|Hp)!UdT_!GgTU6jK_n9A)kP~mYit&pE1q9G18A-B?dlF`^9ocvgNdR?`pWL
zSV`-dTf)X2ziPw4SY!W3fH>*Fs@l|0!a(_iZq*RR;?IE^14iE0BKlO>Vx$}P4+<u|
zFN*epdMIf#?hyOCpt7Ipk{FxMECHV9AsOjNVh{C?B&QYq+d|Lu7<%0P>I!J|!zm3c
z8yQ9oP|$qV;R$>n*6JRPswP`V?YT!3L*F-TglJ`N)_eTQe%}jgaz`q%|5&tO+-%;x
z92*-ay0Oa`n#duVTtb%oAioc<q)a=u)uTKHj#puIND(`eCirCFhU-nVbY29zUuC&c
z^%uPlYzYGnd45e|J}+g<gZ*EPS4{#%>HyR*!n%n<BFE0tdyU#yb)<wfhap>fD)-9m
zf>1a#eTJFQehJ|U1n3{(Gl(kHQ>)jLYOfgBbv#6_S8Z|Q6ytLV8e75~SQc+l7OSX6
z|MNgO#f8N+6szi{M`hHs8{s~=UMaj<K%!4&u97m_s=7^kAItUGRTA-fT^*YvOme9F
zsGAbeu6NBGq+K7c7_7E<p=gZ6y1<l>m}0`97&>egKwmx#$i)l;63Hu^**9K4guVQD
z5uMJ~`&*_B|JT%JgRh^kPC_k5K10{?W?lwJ1r8Qc%oa&Cv)!5IGVS7&7WOV0t>!$N
z=w0UzQ6&puJ}PUyfL_Wa>Y}Iiy5E$ofa>cd>Yk^bgCaCp2Ta=NC%oPAUP08iMU)QS
ze%Q~YvoOkrf>1seVYfBHZGjI1m~Ll6Q9XxkLQiO2$4HcudLW3#%vsgG*{OYYgI0<j
z+!ziGmNce<5j&QMPB#+7R13N%Q{uils3|U4N6TDR%Hf@0i=$tQ3qhfcBTEOb+6kQ<
zx&JLDDOUpUO|*bVU|UwgC%ARN!3KzP{KnzBn{cS(V&~}XnnF6L(D_maP&q69?>^Or
z1ke>-0}j6HVpa_NlkM(>hX07>DXhNrf6zs+aD|;(&lh@O6)&s$^?Yh`W~?DFvs@VJ
z7|l>?lo*pLuJN_gM=_Hp=>mFB$z!8aSK>*xYIR>^$1$7qasFFe?OvBA#|s*oelc1%
zM;PrO8n@X`HQBq?1)H%9i!1sMxGDyVFPhwh7Vw|qgH3$B=u#PHX)e_CXn69T7zEZD
zi=dIFI}0yJ#2TrAU$*3wAE=m>`&GcZgk6q){Gh|7?ukj#n!PVCG#aT%|JLDC`a4OG
z{q>-(O9Memy$tb-@>-ljpMW--M$%97A{51nJL#||IJyKohLD~1W8y0px9=89(_@?A
z<Xkk-wE?!0Kb34--aKF|v{|%TfFEKFZt3&vck=L!6qf%v0<hrPXH+164I!CT1VPkA
zu8(o#_DCk2#;y}M<vV^f81ttf$F9@68J@1j$lpI2#ad*{#A+;JMw8+F9_K0g<3KUY
z10)6ARO(;;#(n{{#3__lC)`DKhW?d%HT(|^!+Z@r8Y?T;m{m>r^ozyr@Y_-s=soqt
z;dRgIo$-$C+gURu6`&Ax0s=UbtZGJEP3v6dMw{EV9;n^!p?}=3O~t7-D++eLl6{_+
z!k9RlYBKi`<K}U5(?Wb>nf1fkOU}WZ2Emj5Dzj(V^R>;nU5=*~FrvSC6oAr-)s1;D
zJBtDhcZ(cv?@-QbRw|8R91(ocyE*0fmb*IhW#UsI>lu?fLW;_4?V0oY;*%0fD`|7s
zl>`u)AV9;QEoe@jG*}i2oP`b5<!ZwWsbS1@o@}=LVht5=nefx5x46{&Z2bkWdtnuj
ziZ*HuS9`Ca-g~(xA33_lh*|0+T~d`731omeP<lg7wy(7*@ud1e*euk65P$Gt#Q64@
z<=+$6W#_1p*(>C8-q!jD*d|YZpI=j2i5ix5--KngZ;b@Q4LOU8Zfvh763|}p$M@}^
z#fF|lnm{A*zPMLD-}z29Jz-|nxa_7M)v)5*Z^_cXkgta(h7&XhMt0cPF9%0zR!$YM
zNOkcE4|8B}VGcfVLF{G$GZg_APsSf~Pd@vEnuE<;=k~-g7cLk`7hA<HzgCO3`;;P;
zo!;VtHG*bveJx{$ZS4Hu9U)SEuNW!u0*yEp#G6h&bk!MUe#v8M<7^ZLOJ(1{(z^2e
z?P}v8pCE5(IBI%5`9xXH!v>3xBs8)1-r21<@J*+oOD((oDgOk6{)iGk>_#*|^ZP>A
z*`x*uYeTJ-vp8=gnT))hVLnL(l{XO~a8U^bag&&NeNev3&ndOO<_>YWar8zX3%`F|
z#XU2MFh}8f;~#UAF1LDaSDmoOwtKiIl%?FCQi7K9r_BHkSuB*a*<s8k1N-8ivpXS2
zFTJ53huNh3swG<-Z3Zey)wznh@XG2Z34=M)n-MOa$5A%TpeNpqa0NY{9p7H=5#~Tu
z(C0y7F<a0Z9<CyO<h<&N;W}icpU0(F6Z%mN?fSxQTOUJ(r1y2js23~WIr^dJLet-6
zNlM2(;t;g^(!Xd=GX46GI;>S#nV%`m23x?8cf%j6|D;TcKf<~VDQ00!O<vPXusY#M
zqWH;Bu*@l52lU&=emnnlqJh>owV^j67Y)go^1=C^m@_FRj3kcqRa2K0IeXF<-Jr51
zz}EgLLcj|h>4Y6E)J6d2{W*@N2z5s`Jp^9NyCC39S9C9Ij7JuhuOMoRQW0%JSf+y8
zl#(Gk77066;>6-G=cuC{Wnx170Gf&PbT(1RW$)XSEeB-Q)W-E%De`uV{V>3GHAA~m
ze{`gmF8d9+IpzZ~&}Ac#4g5#{{FzJhaE=wTqwgmCsaxI&e>|SYB4u~PoYm$lv0UIi
zg??t*t|p8y7`@XLU^%l_KWF;$q0Qt9x;xib=}_^mHoINWPT3ZzuXy0snYUTkCtHbB
zyfMSt{#8F$T8g=PKlO6E^!j?PknX2s(ALoD$|lP9+j^uRKX*U|F{NUOMVd8wy(A_p
z`&0a2|KHECLdl)JCGaQn;Zm~x)sl+W%}Gj&(QeXFyZpZ)hR%~_%F1|t&wrh$RluaH
zPH$+@yW{)Wg<ifBg>6hm#SXd+#tC#4Yqsd$BbB5lvvJeN6{yM&N`zn;sPbFrh6T)5
zwwwy0IUFjKs63Qk7~gTMs`M{Mu{CVq@cKEBxSjD5S~+hsoA!|_x(B1Kl*E@JHkzIC
zt%?YNi<A%Q4@KdAFo9vRA|^zFnU7dqnm)+MYE${nSAeq&4m>g_gh3Ypeq}W30Fk~1
zSOZJEJv4=D%J{(p+(+o4Gm4P^P<E>saR`BSNVzAXXsFdck^S!)xu7dHQYKXcAjM|J
z4aZH(M&)Vp2or+|{VH0UeIZG82TZk)J>AshGyUwE87Zh*+}TAVh)Pbf7(SHb@xpN@
zA3o?Y1itoUz&FQy9tflH91Sn70v8y;!+SmOoky9W2S`QWS=pBU=Z~BUEdxNhJDhgt
zW5IZDOolN{I8Zc9yJ*^#hf_^WZQ*6GEM~;ovJ9dDm{5_n;6)9ik$5F>L2w|<x`DVp
z543K7LY0yQ`rWekY3c1y`6JBgk%ZsyH`E+?9UWrj6}A?oC8zua?M%|pXiE&RxgFWj
zPwE6qKXzEJOSL(V4vi(Ss^+te-p@X}yWNf=+kY@n;Xj7N?Ntzx)w?7{x8V>mtQQl%
zp~r~*6jsbHzkHdC8={ES+n+SxVZaJpbvctL|F6s~pxC@XO#IBZx#>$@>)YLpv&Y=l
zVP``bO(-vcT~%Hg3FTb$WqnbKW<R0Y=+;E(yJFBgO43_`sQKV%!~>%UT}T4ojgFha
z7S}Ou`Z70swS|0^oQ*y1x~(jn#c}*ha@tQZEhjF$x>Gwh!M$dCfN4XW`k6cR73dYr
z+?2FopOa7Obd;TD-L(C9zQ**c`-l)xb~As~!X>Qn+J_7JhPpM^k>qYZ+_BYVNZnFP
zHci^hHYOXvZ1=jueNflF?PE-7f+RZp=^gWFGxN1sK079tE91K_5VwPTxY*#GgxC4h
z-!h@O;SOUWjTg~X&yBok)rC61DYk^3X>c@UE7IC!6H$#mu@6WTadXSOGpih>yNFb6
zN}2of`Bzl;4Q&*Wz^dktdq463KA$(Yx2t~SqxDv{o7-FaWy8>&lk#dx{}?3{WDTd+
zqsGsj{LeSPi*LOB8W&<%l?8{Jb^wRAgB;UqIH#6-t+$sCc3l=b*)wh`<b~pOxKGY+
zc2oC8)4o!~XqT2%0T}`Nk?Ok#^^_#Vj_RPV4An4H`cCW*g1@niDi>_))6O?Pm*Bkm
z+5WDvppUED-AJ-C7_>mFWb$tBEp-jR8pZY`>o}9;YDE1!oZzE)uH@JD4R~)7-|yUW
zBO~u!h_Za($ygu)3wAk}Vcv4ny(_ItaBS(TME)bymFN4~;ptx>4#HEH>KPr0`$Hy~
z@)YSmLBw=cRbf>~W$Ph6-j*Hl>4Ca^UHSt{W&|Zo6SF@FaO*k!=S<pvqH5B?jI5H6
zXY1E@ahzJvgAK5++5LaJ$n2K4k<5n&EARf4TgjD|4SE)Ks$;Byf-ITy@FBYSlM2b}
zM3c1ET{K=%F{VvBqLt2380t!>D+2?zF2y361r9zwjI4beJUqwXNSs%^exjN@Kt&|a
z?QmU6GK>DU=G~9*B)f6$;!z)S(CKjC@yUAIT4nWl`G&t~hOwvPfwm*$`@Bk!O~lan
zYY?uUV)g)Kb2O34Xtlwj(ZS>KVzO346d=8?POlR_7@7Q6i0ZTARH8nfMCH;K^I}j|
z$?IZE>G>%%q$G`X>)roEZGR}3^oDO=#-st`UwZ4v8;^qwd?}Y}#<k!;bhXCSJ-KQP
z?@iB7mnYIrf(2uS7;>L<L0K90RIxV{lD68ovqdzWeVU*&`VEP{G-vY-Tc@!9$v@~<
z_zv}Lg3g^MijjO1bO?s(BIFr=!!ANBC#~UB?`5m>cH-c-n2@d|&P5bFjQLdztp5Xn
zi{d?`jH<~#(YCj?0snk4AEpF|hR+K$W>F)nV8#!?-J>46ZiBZkO;X^G*I0c~PH&fS
zruOe@-cU13F6318m^oK(?Wkj;z_eyUkB2R!NK?gpOec7Zx!v+V@kx<4zZ5*Ivcny1
zJpzWg4I`=_I?wq?UdOrOfm_=Vx77m?ov{NouS~rgTc0qWCayhPc7I{vQ)ft_;XY|y
zzujm$oi}n+d4am6imZZtzTI4(Z@S|DH8-KpuX9LmIS5BSW;z4cbN&%|_#DZ9n-avs
zfU-+)LIcA7{{B!NevV&827!cz&N&`xp5pq4<XNy``$YhG%s3o;@_>@jv2G##z)LO*
zbUyvLokGCGz$72CcpX>L1+s%b%+JWmcCwLClUY8ggw$3br;Yoy40(7^T@_`P^Z6R)
z*Ub*&qn|x21?o?BkyJtLB^I?@h=kPTLFiwSjJ5O|>mUVamev@f+peO<(HOS4pKoZ6
zP4m)8-&3M(t5(Y>hT{PTP-PC2Qd8C=y;Zns)-#Y`k384cIL;J68@qF_y&UsWl{)vU
zk;#ah)C^>ecHUZtm0MuIKip!BZ*F6=BoC*uzHBk=3R_&%CY7vM{`XOSVKEg=gCC+D
z0*@l|^LLU}hvXUT6Qu3w)xafEDr<*`qkg=iU}ozV<Rjra9hL{Y3Oq+1QSIc1Yu7l(
z3z&w>dWHzamZ<8J_Fn_Id)L!y^paeG3L74{ADAWKPnAUXvE?<F$_-HtO7L?%M_P3V
zD^%zHG9k=5&Z`_SP9x$MYM(=#doF(6MQFjE#O7^E{hG(5b(&hYK(u;3SFuB)ji}$4
zO1{9L{WUt#qB@IB1cbFq6!iGRoJ!$tBeF3R<Isj<Pw)t7EJ4YmMMd2odg7H2gq?kP
z3$u|bi>Mbmc1<aA$Kj6>K<SyNmH^UPlAh|}on)qzL*U)G8XstTL{~Ye^Tk=juAIT5
zsOUbq3Y%0rR!nFT0_r~33I_I)O4aU)tG>o-z{c$^^D^4j>F|P5iCreb;@y<@qN9H%
z9@HuU^1M)KiICxb{=Fvk#HR4WF)6O+lONtc_Z#X|&c1j-Ga_31z(w<Mbva_gD!Vus
zJXmB5t3QewK@Y<xVrZW|V(N~iY<ZN(F9fy>Vd;gZ3#$l;JKT=>3*Cq<chP0f|L>_2
z?jdC$o^t;VKen0Y{79iYmF`oFAb?@pWvn4=@%wi@X?$RHWI5N5M}n}tVnM%bx{Tnt
z4m-zPk-;oUz|$^v#`D(XG)a8B9x`t-BXrW!<D{9<eqC_m;dxM`&G1*Ue*k3!eAjP?
zCsh*)FkRQ^{b~1<{!`*;45@_(;Rn8F$Ve|X^FQM0BL>pK5aQGQraa@4m%r$(0C?iN
zXt$o_!;(jv*9=C5SmnE8_oy|x5N20TOU*u#2b+8nfQ!;ZP{t_+b}ea%|9&Ys^EsIN
zClLV}IBw#d(b%FfPE1~8^0M9r_<F{-@?P?FP}g1PT8=(XJFNgJRNj#hmlu0u;(AJ*
z!!FeB_p}7uHP~KjcYSQ1Uf(sfx1|Py!P7vUNE^ZGzfa@|6DU6e{;cT-ey&6+dv7y~
zxy%u{!)H+mN*o31pb*G4G0{cbnLE#VbIDBJeY1j(uUxd4L-`4NS!7S7vkrggG3%^7
z+G{PLkK;R|sFA3GI8z9c4?Y?S0LAIITHZ|^%noQXW_jBrUeh*_wp*hvOQG87;+ld!
zqZE^M{Z7*And|=L;;hONChWVib)ri`%TL0haG}GES?c9sB=j-ETI^r=vVoR>H>Oeq
z-kR@x=aYNu9Po-kxb`I9p<{UA-(7KHOm59;-<f$`Poo6`U4CJ9ty1|%M;9qIPEJ+G
z-pHhC+x3af;v5M~mz*V6my%}B!~pqzjeud~VQX%ECCX2a_Ur=WydAsw5}aF1SsSC<
zOzYV6#7@nb$>Ok<$8;P*ytfsg9&*_fFWROBTb$4H`xSV$L}l~!D;TtWglmvYQ21Ib
zO1O<rl^kTJL`U<!DxEb9H;w3YsGQ_Or$Aiv+ZmT@=-%eMw0zsvPl#=pyMX=>z{*O6
zj~A~y{rm@nNnpR-Eg2j=R+a6V#NB?L)4>E6d~nTKBbtkr_(k8Fn0?`-?c8`)U6GB~
zzKg_fJ=4A%Lg;>dlVRlCm8m1!xx=;df@+wM=dYC<&U5xUwLu22I&U#{0dD?`!2X0u
zN4U=(G=(38f4nFq5T*bCa?^$jPAPs)^oa?am5Q!3<>76!ze%N5)Kiu;<eh+`5a95a
z-=-hse@64+zre=t&OMw6{UuL<$FG62D7WW<k<>#{hd1FsWrcM%zF9-D#8k<uy>Lip
zB{RuuvmDjm=SX0`8(SreJ>pbhaFCj;u~Df2){n{{V&o7Aum`=|`(4ghwZaU@UJ-fo
zVf$_uoN82oAW+1fNOC`FmC<}vb@P@#zw+Ig+=cr{*u|;qwL1MJ|9uxXlS}BOkrCGL
za31IK(0}k%68`6p%YV?YJRHqxfWHOp|INQ--GemybdcjF4Y_!S{@Pd06CWhxkkW4l
zsUdFP%q@H=>TXO19aZBZn~v<H;Ib?*%i~0I$`v+X)g}ZKSTU=(doQKn(kwhtm5<5P
z*KkMqXZB?bNSs1gnUge(f`y1M5)#&5n@=r#G(%m|Z6MCag4%wun}(M{-@UM)NyuUn
zhSZsB#X!XHBKKfLVLiO@jO&W763rg1VO7!W`8&D!5wxS~T}zwN=?F_WE3Bz)y7J{~
zwYps`glM@YY4-@o+$OP%?E{s0h@mx1PnV>mxHIR7sOEd;h?S)`w)jocVspag7f{ss
zBo>+~X@ntnyMSnGDyl2A!U4RqaTqI$L#ukpR;^oIn^&-!8jS<Df|(Nup2Q^gJ8M-n
z4Q3^F=|-F4I~D@gOEmSMGMPh<PZQ)<eFjcz1(?6r?5jKOPI^yHjH8U+cR(i&mdVzm
zB|J(3cHRB*Cv64~5v#fo$}c}uV8wpnzerpDG|jYx`m8JqL*U2jn2<Brbii@jSXWA$
z`5LW?SI~%;o-D>$iU?iZ7?O+m8&xwD<|tu$SxNd-6UZK*%EnmlTyorIE#p)6Vuc2e
zHGR_~9XuZY`jjM<dCw+_XO}7&5niEjdC1yb?Y|R=uc*v(kpSV;@nLS<dCfe2d&ZpL
zm%jJw0)2_(u(ljvL=I~_7g5S{^3R^@HJC5{wTHW9oSU=QJ^_39SWY7Hh&`+N20j_*
zeQz{z?4;X=n1!(wZM_0SxgzgCfq%1wfq)3d84(v$MJu){WFR_wYmj^5&xU#-z7)(>
z72^2-sHfFl_o?Z=!Cu_5BsOU(Anv9YF-f{&W!Jt5a^3NLB7g*c&z*xI)Z1Y7nOh`B
z)m%~-#`9&wo8)Dzv&?elB48S&-Mms)$M>7K@KP0#3a0AgTCj*$zgvSxA(Me#G$wD?
zVgi(vAgWc(CHu!u@C+o`lH;Ktc)6nVv+VSB(cVl<pbs_UX_0B267!Sm!$5odEhzd-
zyft-!VE1R@^(eVh$Bwz1`aW3&m7O!?j5z-fukTbfrO8*d|Gl^+k$#)yPtPhqI?HhK
z74ZZ~CF6a0w}#w7_Wg3Rcs5gNmCp=QDU(IIN3z?d@okzhTRe@vi@a>hFvHO@R{;pz
zW`mdtF5=+!5|q}@E~3q1Vv&WISEW*O{$+7!Hr9o*-gdI{{loaz?LzVvUC>THcAWbH
zZV4`DfX`sV%a~A;|6J9FBQ=-i-!0&`eZ1GnFc1Y;k4651$T|P_3nBVm5uNA>nJtOM
z?$VH1v&a0$)s`I|&KShzZM8pLbzB7J9#L+Wk_|2Ppw!DF75FDFN8U=#A2To+&R=E~
zy0D2*Ef^SQGG1fu^vI48^I@1S!w33`nk0J!B^m^Z@H4~_rP^%nQ-(+2Rg;61#Irlt
zHwOP|duK`vu)mMi(vP%6-?)zn*l1vMJi|jok*!=Gpl_$XAqG%62q)B86&%5gedV7n
zq~cw7g_6{)x^Y}>9C^C<W~}GK>h*~uE1Eeg{LTTnXj*cqEo@|Bvdj-tK&8_hqHT8;
z0`vpF`19!~Z0H76Iq9AeNRnx%IkjRfBO}<ZM`-E<&Flx?+aAPwgJ_Q2&QV9+mvFBx
zT_9KGTVM;h1i})GrXpkX640!FB4(skH~%cvufX%B5!KzG-<yJuf0#+U^5Q})m%1B!
z;DfROV#RrC20EO;VWl#xy}|^ww&^U&hvv;Cq)NTqNd7VKp^H!ii8C?q&}T2YL2A?1
zmSTJeCuoD_P-cLTOrx1&!q6Qo9P{sOmes9hP`Bie4{NEue7UC8=g$W<X*;7HJ)epS
z?CG6<%d=@ZU@&8rQaC6i9H_Z!9+6-aPNIA4USwa7L`4nsh4#p(&IKWJFPxs4yLuVa
zyqx>fsA*+g>!|RytnW$N7ofnNk~Jt2n-Ktjt+Fr|-j@|}sXD4JC)AngUq{!HRs<3l
zy_)LVovw{^p8f`{^P}Y|bZ+ZkA;RHG3^niBwU~%TaN$^dDV7yG&Mls)OAncd9L|~B
z)slMIW%v=(i3cp!k7Q;bHiZ!Bc!_rUG04N1*6sBAa|1@@VcInO%P=fz+XB|+Bw8>B
zZ>C%TRi7+Xc@a7^E|*cC`r}Dp0KS1(aUyi?yQ@(CS-`a>$BAgmKDo7STN;TxUWFyM
z=FEA{8Hz&Q%Y#6H&l@SNq7bo(XSAOVzeeJO@wps(aNzH)=$j2Bk&{|>0KXd_!^m3x
zT^JjRLb0vgci61ASbV9r*dvBqE%!u|v?CEh8U-!n;Rj>1&9LBRh4_Q>xCy8S9ML{S
zu4eNmR_u4jKplJgh{|SiPDrVN7M^hDAH-#sD83312xhahK0Qk4Jo%M2MJK*NZ;~)f
z#7SxSr3`D35R)ymX>rELwstVwhQVb4j9&F5VZ_OHqJi!Ky2HjxA`*du*)digkD{Ym
z&3(6P`~_YwJTk+bVPl!0{rz?<h{!1&SNZ5HGDrl7pQ(G>-j57HP?vr9P?haoyDrBP
zIryS%iIzu#%6+h2|D*I;0Z95z$!N%2nJpZ<R5DN{$Q9M~jkxVtmx37K{_3_pL5*Px
zx6@?XTN^kOpEpaKC=vtCncBFnCRIe&L-t&`DZblOX(!(z3{TriyGUk7&6^PcA5No)
zNKOlN@z5xp`4jIRO^$SpJ0Iz1rCsP0xpA{hx=9bit#%mc^C(gyqi`ax(=t_??{!d+
zqP1I>D)1)5zoeWg<RVmA7P;Wz_b*hxct=hqvTB!z4ye-r1RWCo5bJ214_u_*D12A-
zyblGXN@35?FPLP7`|N1OoSZVO+~2Cpf{`Gg5mZaR(bxaqjRLm$4m$<L9mQ=V4eq=7
zYQthBdroH^jAJOE%mEAicC2*ItaKck@OKm9MrD)p`fptU+<rzQg6(9tEBM>*)^GvZ
zk^C2tA}8bi{BlD2lf*}N$K(yKPa;ne1e4(~LV(vvD(>{Zf}I_?4JC>*EK{j*^-YE~
zHw+N?_z-Q%cHR&JLvLHlFVl-JX(gQG9=jaTR9HEK3;WgL4+XFz9mk?S);}QCH}3_B
zXL(l6O3ruQ-;0Z=Hp)n~wKsJ2<hz<SeHDd|g$#zr%CjT}@6o3}k|X2aZv;%M#0L7-
z3V!|I+wDubzeqf?TM$xmzrLr&`LxB@k^%@`Yk?G6{5}Sw6i>qy^D?pShNj7>$lB6J
zuq9np=@<6))yY_xz;Q5i<w~S@^=k6I4n+sLaoXiBPGIM*kx+)$C@LSefAsyKnm*#K
z)33e47m(Ub2ETk(GXJR3Y-@skLNR}_F36lL+prDKVSzJ!5e@<|W2JF68M6%ub^5+B
z;Bs{{tqx*I&xKRa=fY+pl?9Wc9PaodEeIn)mJ9kHFPq9zkq^Na_FQ`RYyQ3Z^t-KQ
zWv8`R>JED}(Nw}2`+3GIg?G-20=CL8e=;QM6cS~g^24dO)2`65zh+-JLeSd!z#k*8
z2V@l6>_&r)$?R9`-q70l#bp2k{{KeQ{o7#7qoxJchL<*3Nz%ykYH_KGU-h+2(`D!h
zD8p|+OaYHm*VGg|3_QEUUULh`-#_0_3<aNxeXl7eJsX6F;EA#53?^%JxizDAU#Vuz
z9yQrwuww}#>#QzzNrqrJhw#$<^jRD*Gjrb(@M|Kf46FXI|3MalEGPP88dk+ajnZcc
zHsV_0dgq)a3^cw?i%v2Q%hKN&*F@*4%`LbzC3z0KT&6CK#5?GegBLB3dkLEHr6qf|
zrvoQ7r;D?@-&9|1e$c1DoAN8fn~cTB+P+}M_OO}3N;?NUqK=Y%eVot>xT7QHU_~G;
zmRl-QX}Tsz==zICI)ms`uC7O<@!2VTPGDk%yb|3c(&Nr(g>Rug<^dF5N@Uxd*n#^c
z#0XECFH3xPy!rj#2HWOQQ$JDY&(KaJ;Fh(kMD^FTxu0=zF)cAZPz3q>h2?&;6x11a
zgl}Rk;$%sTEzcaFP4}|!#hht=1!AZC1zJJ1E;xEUX8c><H-!4jE{Mb#e{Y)J%8h2x
zL4}MQyT^L-$CdGh<fmuL7`6x<H!Sw0e#-ysHH;8n8H{@>$p4UL?`S9KMhJ~Zav1ij
z6|7P|ruH_!Lj?|vL=CYhSi(7N;n4|YL<<))NKf!brRzke5qa0%uqYJG4lqm5k|bK=
zkZ}x*+U`88P<PxZFB)>Qc3M)AI?q7pc&!goA~98do0yyP1dx$3l>OFlUf~|a=Nm9b
zi}<e7^}3}DPOV4~(I(i(CO9-MUrQ@b?!`H%l-~p)IB5PtVJfvg*-jM<m3`|%;ztv`
zNA>KfccSQ65`tYitVdb4W;TLyRMGy{hF6=A-yG~UcjUff<>tL`rRPaYrWcoyWk=YT
zd2i>?;Sb2dv%i({=to%ENn=B{$gd8(T;M~wSoTNeV(NK6K`!BO;$9<HfffCuK=3EX
zIzUa%{dI;N;xz1-y)ukph8OY|Qw%O0H;;9z`fu1?S+0<;eCL7Ss4<vK&%54vTR~P|
zOY3o;wO!F6x-&L&&w9aU?ZyX(9Q&Ejva-*Z5#J|`Al5qW@W4RoO4Nvsnwtk#z_%7n
zs%nR0v>e3?uFLMI>aFy!<1JsjuO)1@SkIBQOItCLmxA9=L1T9NcgL0MujUFhBT;fB
zqRWM^|6dlsbk1S`hJ#nFw$hS%EzIvS7~^8I{D2h8?wH5jV4><9t9h5@Y<2041TNRv
zbZMXEj30B3RtW|#J_#)mcVp1((}kgiT#SO6?9%q^O{KaKDMJtfxsg&c2|1x?@xV?A
z#lL4Vj$^9s!$+i@<gs-;zY@QAx@S;z$CD?ma-oO1?XFTZa|NYzmvJDEC(!N<hll<h
zfw#cspSq+af4@l~ZU1n!6)suALTwHx8uAq+pFb1CaXYsxh;Bo-7?^1ML&^aC7<{?t
zn3J@+t!O70wz@0cq-0Qe;lk0eBoN-Pmdz%M6M6Px5M6b(h+vT8GRk_i+`tJ|C|#{{
z+G0#B>8G8GHAK3ZLCaAPF`*txMc*a%mKN78hxTkhOsTQI2g!af5shO_^yN|+1lV{(
z*;RnH7w1W4&IB!{^HiMCZtuWxWBUkW^Rj`y+?}1gD9=U$_k&{CVGVat+I8iPyUMEH
zffZ*$PPPRm4P?sIJ5O>N&<nP|epcnnWMp!Nk|^MFjst3HNw%@B4Oi<QMM_{h{SVEE
zBcmp|ud1^zgllbm3F-ssJ0f1t?A@0&Ulu4mBtn}XO3&KEd;>%$$*B<8r+(&NE3$ni
zWps?_lV#(b0Yy2wB!)FrUGJY(r1rT($rS~*4Jwh8TN}>@N{8rIU_jmJaaNLEsEw`1
z<Q>`zOejbwb78jj=OWf4Y4YP0pX;b0#zp$q2%?}O!Gw*Tsc-TvH486GrL99T<-Ag}
z<Aou_{O}fgS?q_34gueAIZ7I4TXHItOLGwnhZZgZbRL5~BUrA8+#`ADK46`6RswTp
z%z;&r)?Oknm=<-~+Lf|mZw{Qz`!M1X4arN*97}8vctuW~7Tc+H7=M)q1_|?GS$HYS
zO*|PZ-Ihl^Oa`G{x_Hr7Ha`FQUR@#-6xf^orVGzQ)jbSkI5o9^nmPJfPe>?!s>3P=
z&Fwdw%(bAqd>W?Q0_s|t(2w(26o&>`hg_(-{<aDfG+!Y9pX#Cs3Glx{UO&N!BD~?*
zK-b9geE90-b%5`-tJ76<c<0kh+F9$&8F+rA6h9X1usS-aCZ-SA;6z5fm$8(KjIojk
z8tQ#9+QD<bH=b|W5cctQr|p)|?`XTe0Yo2%y=0~qVIHa<0-Ml2*^`a2uvcUi#|pIT
zbh)M(@g3{|O`cl*q@|2gs`iJNz&wh-&-meu_y-wr1wvRP`-8D4>z!16ofhOCd+;qL
z#}uXd=;;z5ILVN3M<ym=T9csH_Na+J;2|kCM?sCM{8+oQ=V&q+oS-MWc!krd=w5v2
z9L}-2pyp^lUE*JEsi}q{$X!B=NIQ=vf-Pk-?v*EDRV@1A4Ot$occ7;B3)`Eraxa{=
zt@x*{^iP%x2wGZShT<5S`7&g|;+y-w$>kM#_j1wZxbiWEhHNV&oBygVz7&i6>(`Jk
zSFh^J?)I`~r=8G=QhOe+S?eW>pKH~b&pDlU?9!>~sEuguXsfh9U4}%BD`a5JI-4~z
z&~2K-ACY;*Z!?J=;ET-e3mQ#|GGLg+-$hKPAOmuV5S+M-;##ve(<G|vqjraIYj32<
zUs{wp@BP~LqtShC8V7Odc~7E|ub9%uWJwnL#V<jkI*tv!Q^K6MVm;@oXINRQ&~HY~
z{}y}O9RsX5GfheIZa}TuzQKH+YUCfRD@*ac8WwbX>J7MZp$fPm%cOB)Z>m=3!i!V9
zeYT-|yjBGtwlZ;#p1F&8L|cUg`s$kGrSe{OG>7|nUjEb@D`~s6Btvib;rXv4*?MwE
ze(`ybog^wWki5geOzK+vO)LZ2AIft142vXg<X`^xU)D*D(SB@QmOallhF=_IH>fAm
zLnW(l!h!Y0q~T|i{4|kwvu>+K<suS=iHz;|dlzY8`BQCp$olSpF0YiBdvK4%Q?XFM
z#mh`}$RIup%_K2KFqFb>HRSk(YG%sk!o}F@b_?d%8j|EV^G`+;tHhuG$J944*41@e
zx3SH}_DOSM+eVX|*tTukHk-ytW1CIVq_N%D)_0!gz2AH9AJ}{CHRqmVOsugyt~MAh
z`=U{|vPJ|4T^0R|tdKYEKA9Yt%;^T7BTWU^!q60AB&Vcr6;QG02}G=#fQPXDi1)T#
zS^3~CS;FeDoI2kA;={}ZP7iU4KWyJms~bU&kZ>{=jqcO#5K#5HircDqf3X$2<w?l5
zSdXBll{fOG381?%>+P}%C2PGtL3T!xpVIDy<8-!^``T?4Ii^Cl3*0^o*%}%~L+2$1
z%L<7Nk7tYSl~4g#%Syif6+*04As4Nt!#fm3{q~$|N_{8>XYZT)b9&5-uGR-(ainM9
z;r*A3^b*Q98lsr22?^3rfv$5Zr2Mk*&|$1?7EBi7bCsHe1?=wdnvxG&cc?HsQUBG-
zt*m!U3Qh;JG9*@bDss|F)|1vU1r~vhK*M0P<2mH+7iRHRZ^hQbLv}hv-^C)#GieWs
zRgs0VW(m^YjVlXhTAtNHLg_(ac)E+K7R$MfGW9Q9j-(x|IEo)otp1OatsZ0c8X;eM
z@%y=T+jpBIHwjKGcyKMb?sHC9c48Q=M%l|Vqz#F6f7%Lr%`<DIs&05=5iu_j(>Z?>
z0N6(j*Vbda?M*t@b|hda*J+dSXG^PE;h5K)p#{pRo~@GBb07GjRxcb$hXro|6mm$h
zahAWeV{X-bB)R}G_@^nj^)^x!!^xCQLvp*r+Vzs)tc4lkAQruLy5`K=aBYT{h3$do
zr)&?g|Fa#qFDW;S>zrnIMSrI_$SD)xNf6&g$+<B#GRHSA&GVHu2+5=RxSW1MMXD&9
z*Y9h?hcm0Mw1y+yy&B~{lkmAbMdI6~xQ}aXsk`fH90#!?Gfg~D)TK@8<5|~*_%~)n
zqyIKJM$CYMal}Il^Ka7+z1R0BW0Q5qxr&WHEouup1hy=OzbqLjsFG39=>}EokXh)W
z6z@jgwqGuBpu%r=OW2JP^TK}2y~Aws=KNgfqVV<3CcW$=p*NBMS26k@9@!Hk?R1dF
z#C{i$IP8~%$B@{cMOm@X53XaOm!rBl2r;X5KPQk)nPnO(cp=&@LwARd6dx2AqJQkR
zLXd-d6QZ&R<|QWVD&xdfOb{mU9JX?B78%jAe6NG>R*eV)Ic!^IJ}0EW242G+Rr>*4
z=FQkHi5v+A3mOR?xQsFVF~Urk(^duumxbCQOkbHSTngsa$jUoveA!wZw0piC6V{lp
zM@n19@-HrvTUn$cU*wkb<ZG+zt}_LHUq$&QdQXGCA9X->DhdYebmVQ%_Lhr@9A%{C
z<caOoHlNn?%RcBuj@b(aU!nFypM{aYk(lq%`>ag85F7f$&7&4mjMY>&AZ>sI2Lv5x
zMRGdJq#eivj=anLVhs-SzAqP*j~R!y5}}v18#e9t619+JgJbR}`ivOIdQRgk&rVKm
zt@uc$?v;NubMM-5xZ|olY*D;B>aBGPuG?`tGK|Y<2!IGraFt?~0i!d~y}SGQ_q&Ow
zE~Vf00>~2@Wxz6&$3<*8)Smq1)i@z{0mU>tSEk4to=^WHCP_L<b!vV^?A_`D;x096
zxm*hODW7~d#0@VFeCi-3DBaJHi>+qXZTubSgdJjD+fIJMQZ5&YbN=qryoKV&z8r>M
zX){+3d1uzoeN?rtYxu}G2LkAcwbIswdAj7tj+th2q!*mqF)mQ<UkoD5MHGqp<IkP8
zb5L9?w?c&J?7r@60B-gumevNJD^FCqO=MbfCL=Z_)%^JCJAobuL1vcwlYa<R#U5Aw
z+{0-i+OHb7t5fmd_Nxsq<&J4~T;nBN<h<(2MqjhMluy?O)WhS>RSPfXtYMEwBeLu#
zyDNdT%&J<imyw;IGt?9`2|NKzU5D!t;WYx#`dx2~6#HUT{a-BlwVtZDLp1o!i|qu~
z)$Q3og|_&^Jrvo`#F@05!oX+?UbD60ql?s*Y1W}AXi3Z4A(e`Udc5aN{55_BU)*`?
zt5p);yLcfG?w;2giH`p$W}gJ(LyZhXg2)6uaPB#Mn)Z5A#;_9rIj=Z$)PA7m4xfg)
z>30g_MoC?H<j;!Ok>L}z=8pMYjW6?o6EjlqiNsoBg(j?1c+sgLrjDsW?$z#~xAuFX
znKw8AIv%tKxlP>No_5HOS08j7oTyX!tbwx70@k3h{%pJ9?dM`G=NH+4M<xC4>EE<o
z*Zd`2uBbeDK&bOqPg);e1o{>dVyceFajJ8d$nQt%B+#4+Vg^0vqiyeSCh?ouHJ{9j
z99}WuT-F<ujfH}l>Th`RGYL6Y$wh*t@{@ct=lIX)vgP>o$RpVpvW^bQO6=*#A=cn3
zVzA<#)IeHZ@^2Y7$+#VxIkscb^0V@u@PA9gM{i5+PX&vSGqKL}tN1$Bt)RXXLR6>O
zLRV2ineB&v<rmx;-qMk6Z%&)a4Gi7%4>R;8im=poUY0q)uGWk>w0naU@QRatqA%cL
z-H)bu2>m4LMdc`Q>{vWK;Mj2<8D7gvv^i70IFO0gkXQD~otnJW>Lu?i#cUcrsdXV>
zlHg+X>+zH5hzCv2F#E3gb?O&?r@I={-C-doUA@ln$5REr6MUVg(U0F*FYdcW3jkJv
zTK+3VEJp2unbgFq;4{2@<Dq+^uUMrm{9!R2H6jbEiUsR?AEHy-lD+;r__Vx_lv*j=
zt@GB6_kX_Xquwqz%wFE*tB8tQpDZCq_nO58=-^Cp^?y>)-nR?xx#LAizj%hKS-+Fj
z;duN;ICF+3(7O>*QM$PGJbh@efFN%($KG-DIejwx+@|K&jF-rCVz@CI*YG!6_7KAw
zHwg^>UaRJH+;2Yzo%Bdy1%25l90}bupq17eIq6XZKId(wJE`Cpr<1UK31n)Y;p)40
zDYg)vZ9828sI=HBSc^q%5VK{JSo8Uw91gt{&QDkBOU4dQSSw|E!S2k*rfbw@xruu;
z&hgxQ*&b`LtR@4uvbBPnZJ~J=k0G8<-Ur30k~|M`gohs&90_xthdP#QZ$r7ZLL49O
zhhFg9-O+vBy>|+dYlL8{&sF2cEA)^<O+K!<r{1=$b6V#d_=IFHa6dTViMo9VbmKPA
zEqfFW_TW)~@W?;0BqWXj4B}I)Ersj+i-o6U>Gju<nGV>K5+n7i<hBQDS~~mEn7=n1
zjcJ+aqY`x6S-@8Fj-hnKV`{&R-GOxKIdhi;SdVgY1N|J7k+no-SEwjJb0zp8U3;UA
z%03Uy7VbA(qHezC`awaC4`fm;C=?}no4Jj}+gdFNIn9>?&LJ!IV1oq&IMNvjx;CoI
zYy*G#HcI=o5QjQ;ydsX+v!iEjq(7GZRneI!)hP$WX?lsL<X%mxtwjjtuy`P12#R`8
zyw;lXV*o1Xq_h<B*9jzN^WD*9>~aGBq~}QhaM>NK-yt=IZ2%s^Q>LSj+lVH*Y+t^O
z#0uXPhEu#8Kqd@JtCMvNRdyw_iQ2m97(15JZresiJXdpT9(Z*$E97A95=^r@%!V|l
z<>fys<J>~3^Cl9gyw$MT=NqvZGSS4If028|W~=RH%KZvHkwJ|#|IF}LM;jU}aW2%O
ztJ^r`TA3}0A+5jBzwB|?23z+F09&xwOH>zxdd}+qN`h<WU>L3UR$TA4*qOjbnNqrX
z0Ug7QZ2o>tBw^pdPmC}4MKFda@`tRQ$h-j{T5|v_REygTW1}U_Ud(l72vgp3$elv$
z0rBI-HaO_zuYcL1z98Lf&}~6!rX5mNf%>=w()1=1CyauU_bDXO>W9)79=wH^Yc@L1
zqqR-t(JzgDc`-@-5G4)?046PAjd1A<)-%b*95b#|9BYM@XsHAFR%6a#L7Nd{>-)?i
ztK^s-C(}_^7(3Xd6%4n3MA9sfKAQ(3tbcd?74@myYu52G;5V0P??;O}ae(r-Wr7bg
z>Mjj0*w2;_GRn#+MdvHt7g5lQaSb(H(KR()MVjw5zD(A_iyH*rX~7En!o8Fs2?-qg
z{@uLJ1C(Wxl*uO4+_=;?2VL6!_6OY;x$fs(%+Ze?&~z27j{B1NTf6&MU4GlEgNlvy
z<i&@Y1>R$6erb@cB~QN)<!CvtYWk5!yIwyH`aeQP)p%hKN#;cFyy%Ht7eA0622Sid
zcDK5Y&9x;2?TvwmR7|Ds*Nojo=L2DjFV}dLJ+}ET-3Y=vMMo$cuhQQGyA0KGbSYot
zrxXm|+^qppWZ$Z;q>kX1fdHy+lm#DJiyo&ulH6sYIDrl}OPb!pFz8irO~7c#9+&O=
ztDOT_0I{$JAA<e?nK4-1I<ysLK~6-coW9T}MJsbtRz@c)hHMOlDstHJb0C^OHxsRJ
za&tt$>(7`4#`Mj3b07sIY~l68$lLqY$LI8PyJ3NmNih=ijy7Fy<Kg{>h|r&e*xEkB
z53bJJ7oCLj@AayKQSs?5vWiuL&M184x$&w9-9s#*k~{Qculs~o;-gc6$wY?>5Vn2(
ze(57QDCi^K-ogwAUQ5l!^vhjPrkLixr%5{buYE9ZTsTL)4ni7fgq$425*E0jZ}?m0
zIU3`2tm>)g_biou)N5ZCJCJZ#A*p{n(k;~J7_EKL%jJZf=y}5<>Aw8rhprJf$-E&t
zMdvTPKjtqw_RXF2>=`ubdE8upIG9h+>lg2<kp&hr+YcJr`mpA>i^oucFhtwB$TC`Z
zegqz+e16%v-L@Z-e^N0qn;c})?~c^yIkf+MvD3S$&j@P`TW~p%rt!TwP_QWze=IhL
znHxD(HBNLpnWV@Eto~QZ%(!amZ_{}Qt%jUWy2B=i$Xt6#UNRBSiywNpj%=@v!sp~m
z+xhyTL??lg*>kP<&C&|Ca@_4eHsA8io#MCs^~*p4VSd!<U`@mKNS)h$m-5{q7QgpH
zDeiX&E{!S{L=lJPyvJ!jq4BuGfhLPKxNS82>kjG9Oo+Oa`d6eFdLKFmN$EVZ^m?Gk
zlC}@=**9irpFsKffe(u1`doZ?PhgxF%%pT3VgIz8Dw}w~bQ-%D-RBR}z6;~$Co5Er
z@n8JaaDBM&&$EuhLEWoyiAU|t>Dfy77jz00s;ImJno2}Yl35ukL3BF@X!!}0FN6M^
zt?o&-3S&FJevPCBQY5qxb9sUVt@kw79GB_Js&q}hafI*5Mpya9EPn-7H2tY)PnRPw
zCcGCfY@AEzgE<EPg+x$Z1{IkU7(22}0#-i8_&P44rq55k^#|^gKa5sH7|^MFb6nK?
ztDdm!*Zuhw-WHu7J+~!yvR~<8;t1o{QSt)$K*8@<-ZesZ6Au*b8~jU*0v(~{y0Ep3
zxu?;JVhdE|eaUH&JCNq1p~33MJWXlSE}4Jx0P2?8KZN0F14q%m3Upr+91M9M3e1_X
zefT?W7B!k=Rr5Q3#JB7m_7^+uWdQzC2QNKo$M1s(m$ZkZ1!X_C>gDyb^Ul;IWW?+Y
zOr<I4waXtZT3R|t`Rlcx1eSg5aCnAdl@z;2gqlobjM0{BroC^XXQcM7JEI|(t<zvD
z-agDV0T5yjr_MoZX}_s{Z5CcT<Bbvg%8|hLGk^5>cxb>bdyA$1?yW?>OU&}=YIbVR
z*sS<9lE8FkQ-YF+8}{6oI<unxi?d^8DpB^x@Yqn_VkHB{bob`uwszO51ttG8%#OVu
zdg=$yP(J0LstSX@A1SY=vG=mw>bv0391%KMK@cZ7o;D4pzdQK^bnS!DJayYmY9ltI
zn{_I^pd9MFHmL{Xs(`1W^mKm+e>|HVGS*;IYX0l!;04F-ukr-f-_lDi2jWt+%tO-g
zYmg6r9a~SF>PMrn4btD@QzKRP9`K9)vY!lO>wTR;aO(!@X7~qa1+`L+@^v@^dS8H$
z1Dh9~^GHgKX}ZZVL=kFt<{<DTi;FY<k6dc%Hhn~m@dJMb%!|xF$UNTawG;Cfv4igg
zzKBy$8{C9+ZDH0=2wPEk=ToYtnD(w~ENI$)XEidXm_Nzft~I9QwX0}=p_-|HUqa+!
zJ}z#T@2wgfx%#uid)c+?<D>aVh12CrY$ip*THiCs2D$P`+%f89!gE<HhdJg~vK;tk
zEl<Pz6;2GeXM^~TCO`^X3oWrELyS`=CwhdIZ_P^2H96kk1<Yk`FJteJkx$FWpM``U
zk~X_zVM)Ja_~T-*I#{=Zzd*8&x*oA-eMt7i29lSr$v;mnFWAW$*qTTKp`E6f$sf}C
z49-y8ENJ9(_jvxy;=0irBoaWcDHjyoKVwyD+MIgVv6=$WX4=Jy22LC;UaNZ9tygGQ
zO9(;RR~FiWhShzlnXWSN@sG9K27awiV7nf}(1lb!mUQ0);m_3V$AD-Bo)8}C8D`|w
zC9<#fC>)USLwK3Fr`9~r8%Q>nVM7d#nux&U@YQi-3<-sb;0^K*t(!UjCC;#1Uf3~0
zMLypwYZ_TS++4_3S|qV7!s2$uO^u}9g8&|%8Achg17nn_=Qi_!U~uR~SH!?{4|eHn
zrZ;y_?I5R!_V^wnR_R>)<)-l5tWLQ=nSIK-=a`c_6GfFg+=mv1)OULsA^1O%0Y&K_
zPk+k%@0D^mx6L~1Z6NfX_QllN^}!eN3Pu<sq7D_Vc9`t=K7Y*47k1|q`IDG?73y0{
zmu^CQNs_8;0o{H6Zm-`Jd9pcX*==<f$Vb-@PeT*ayF~bRjrCg@S~68Ce*Z)trR8P(
zQ}?Jcpf`ao{t1zOVf;Z@2W~R8yw5}%Rf-_DAtA(EibRd*Cqa_gyNQZAQ5|CE69{-|
zA6-9dCqFZYlR3tg-OwM^8Xy<$+@~0US+DIoObmRqFUq&%UreWVVh>+dZ>iN<<7^+r
zOsMMZR#LIbIlus`2OK0>54oJuKJhXnZ!V_;rD6-!^-b@^{>Eb+9IfDHn{Nih(&$k`
zX#UYRLs`i8gZyEx9}juZ8SpmUjpDBB#3$f!q<p22@M7!vw2mTDJFwvChIqM@b2AjX
zZ8+KefjoM9DCP6P4nmJmEgUPkZ$MC*-C<6++6kU*R5y55J~YV3WahB@;<^5^^T(Q4
zd^&I-F()lBk@o{zKU!12LoH3^fEFRjD`+Qr2XuaK`OS{@vaGX3(-cEYCz~K9jQrS-
zssKt$ETBfor+q*i8U@db4}kZA?Nu%>nm$RvVt*vs`cW*S=4CWjtKmg}$@SQj&c{^b
zZ!PxCfb@fUb1Cu)o6zchSqp}w%3lBS%By*~3FcY8tVUahpRRvHd;spPx1;<X7tuc9
z8_!@4*Ns^Jsy>Bs8i5;08%5__7`f&BnZw&-^rO@Sku+DTna=ZRnw{_t>qYO=6h<rR
z#%ITe?#K|%!SZz1or0_bs3&=c?rpmY-yte$y=x=8UtBc5=sJ?ilo9Q*@;qKrKknU`
zIU<_^h%C8!ekWv83UWPrd_!6%#ugnOE%tx1fi9Z#P;%H~+?Di(dvi2(&FlDZ3<71^
zru)W~wFq0J=|!3!E|;37PeAuD_j@)^4%fX+dH1@`S&sU>3O^on$2CZMM=qs`Q|=`u
zn!j>fdkT6uw?|Fa_RO&&?J4SrkW-J5<0Lh9mQ0;;4IbNs)SO<y7Fr0e=n7w8b-17(
zC1XeYky<M-X|I4v5n_eUDCZ1}v(Zei=i=RWTWchh2RxVW-Zg0}U2Pw6IUkzf|HSf6
z)8mkBCu1evDu+k}oMx?XJ({dA?V<?d!l+Bo8>Xvs+95Mlp>{~MqdB`ay#{>P8T;BG
zMkfajt|y$zU@};wPTn*6{VeOTr_>NhcO-$oU>2LL{|I@BAX1*PR{u>+neT(1gqj;0
zWo{zyZ0j{<aDW#2GXfN@8H#z>751pDC$gVgUT8J0NqKh`H`udoAE0*Dn4IrU@vYK3
zVnH`*T}%Ubo$URGzbaolG5}#i9b$FO#&)EwXf)ascxV@0)(&5f;rH>?&0M%hnMWR=
zvYi+?hG{}NL}so>7VNw0a5yy5%MkD2wyv)Omr(@&5LSJ2iZjmF@xF}L8nkksY{~(1
zlpd|nL)=Dg(Wv;2xKq<cP%-_6nRf<TwAKD}8R1_eiZk+^U1bphNYwsk`Szr<{c?zd
zjVL4KZM~N-T&{9g5mRft>}CNE9Fo}NdQrUg|Cp}NJ#Vc!wrstJ;%k!ob&+7F6z}CW
zI&9S7kIKX`{hkmACDVV^avqYm^39BKO+sn-<x;CgYB*%ndgI-noktffKN^)R4J^NC
zpC=FE|9-xrxKTQXSRc<8Tk#^fGFtvXxR;%_^-yUW>z8bl5E%dDoZN0KzwG3RFv7ig
ztp2@wIw>u<Offr729$@7UvX(tTBw5qknz#P5dqTB4{Bv!Q`YF@6ZX`wLjH+qO>I}3
zM9bIXK7Tc>?!lM@mOR^5vsN!YjqxYqYg!C6ds`!|N3ngz?o6a!33`3>-oB`>8V|o%
z_?_7Wof(??2ghU+5_>zlmDvG*CuC5JkZo7x>dR$B>4OLNCpnzo6Fi)339&D``;KUb
zQ~P`!B%QB#fVK<dnB30tOs^PLL6628C69ZihPn{k%=BMkcX7qa4!`_O(!%|AqN$8D
z#=-X|qz-AhSpG0=DorjX2>uYQ#`1serQG2T{LKA+uZ8?p9?>oeW@GAPy^;%9U2S8!
zjtlpT1{*D)$NmRYABN}!ott~;R+9&xOoHq*6@fm>RC+N!)%#F3jN1t}1KSElltC|N
zb8DERPK)K()k}iwd!o1>s#7ei^BxT<=pQFmd(&A#$APQGmlrpay-f#?XA5fjD<U6O
z&9zOC)ooY-{By8I)tS{~AsFD>qQv#O=HS#62>~@Qvt!YHAx|E#_hL_}G-CpZd^^WD
zv*}U4Qchx+8Hlhoa!k0gPY_|dBCW-SV%}cU2>?!pX47zmaU4;5zfO#&e+HVx+t%_|
zT~v#Lar?x<R=uW~0K_-}<GdwW*#-;&b!3@FnD#CUm+PDHM0YTAZ#>?dH^giDQ1!H@
zZB*MK=>Q(dDcsB_Y>?DXXJMBfA!LhpO`nVxuc4_)4(&Hd4ex-U-LCu|X9QwRGF)kr
za-D>@{yz4w*y);6V{l4>;%4^wAv`Q=RN%L!yJ?(#2P5@^r4ocpfi1k|@9e~kVAYW+
ziJg{;$J=;+gXIItwfnxIR-bs2HkMd^x*5?|k2y@A?`XQ>#p0D#dPkjHZ^bkn1Tr5_
zTtH?noMoV@{LxQ}MCX$u;HVY846|Rq+?*6-uzq2vf8Mg>b)*h1{*LmTeOSKyD4k7u
zPWGBSOzpmr^PL21_U9htSQss4o0Ki<&*C+0W(mR>!h==J;?y9s4Hw)(^suL~A0E^p
zbZ&IL{6@CZQlr9pG7n!Fihpa16vRDMQGqO1($Kc-Be&f%Qw=NRY()`E)(-_|yZvKJ
zH4dib<>dOw%yr^|#UD4gUo<-(cK9fCVm*-6mfBfn8eSfM9m?iNUOv1Gn!7*rEHKg=
z!qiKRB)$$Tj`AA(Tw^KUjz8=Xxw{3=*9{&?y=f8)&PV9>Zx`UA0`>jiKp$<3HaB+b
z*;``c>Gor@*|=kHYq9b*Uk2%i$6EZPx_e|}sfW+J!<t;EX6~V?{>sP6jzofsn)hw#
zWHe{=KCdGhj~12%0)gH2XV$oSk*CU<^J<=Jn~K9Qt=YBA0}L6t8JeuJ_k1t}7k;0|
zrgf~Vy5eSyuwKnSkGEu#o+JH2^Xh9#AYW?!+@>(+-|W{Toer-Di}{<umtgnei%xDr
zb1)PBnw@m%lV-vvn>>!5gFU)$SEEfa3%bvj!y8PxQn$sxB!mrGZl>3_OU}Ci7!a4p
z#@PkE%n$P)eZn+$j`k6_vKN7@?}3mCZ@lMiw_-kz<CwFXJh4ORbNTa(xQsWaDrf}t
z3<4cGqyw66%Uqt^Kj;&$E=@1v&SQgQP51Maum7IyO(?rQI*w*EGUp5t$VuAq&{HRN
z<`OKD?T$AlK8@>mnCg!&f+c!N_|aQLa3;`%H#80Eb0)fabYiYm-t^xr_SQ5BZZk0c
zT0p%PEmG`I<9nm>$s9aQr$f`_4YEda8akKWVqSOH#?@FTD)^jE)3}_@Y4f9@RcET`
zfkixeSN-`omHCY{)b0rYbL``=yXW(rSyJaf2c*#?coeS}TuAHW6U+(IzljT|bdLBE
zl(Rf5KMK2<wR~T)K=2EBHGE@^bnM^_pOi^v5B}tj`(-8blfU9=qxu+WQ&V>FNNS+~
z<3bmio|z2S-tC0=-{I|=9un&sspNZp33XRlA;&@_rQ~Hi0gru_$924B0Im&ebsgk=
zWK*lhLA6<%oO|uNgKf?2%Wa+`<PJw3&4hv5ouEjOKY$4fxOqNY%$)WJFV%DF(z?JX
zI?MYo@#QMK@bZ;+YwTzzKfL8L_Cz$Qx!F>4bkE#g)A!Vl>1VWK0WfDfMswhKExv@^
zLmC3CzXv{5P7hu0yj}IpcGW#ZEIpO3yi-c5>z**E62CJlo@lMJ%L7OS@EOp14m9@k
z=z}You?%jitP{L$Oh;2ckBFD+FyUh3OOFgOeECaok`uQ=QmVYe9w-bvAY-YygQ=Vc
zq~yP<Z|ps?8Sm>hTrp&RmgBjELzvMrO+qfmGd2$99y750IJRjr;=F*2mB@3toxr13
zgqQ>d&&s@1Tk54{(Yrv<dx)CPTd_Q?7VSIMYr6j`IXT@Fg})CcdEMP}#&!0E+m%#H
zMnyy}X?&TiS@8rtgh8QK!RESE)URx~jyVoD8Scf+UG51o4|_L!AEeqD{+iavQs<bI
zKNk9w^*lbU^IG`h8JYdgRc)N!hCHwBwv@94mcHZmt7_T2fx(w}B%uT<u(lIBVoeac
zRL293ntT>%@P>(5xKHNqLLC^)8O6#x13X!O!Om<^iGUW<a$QAJmvnFu$)6hSFG1P8
z;HUg5f4upMmYwYj2}7HF;=g%hMG7!=Ln3v$YX+InX7kij9LjT*65Y=#c(B!N2n!VM
z3jhbEM{H<jA0}D&_|T3A1Cw0eRvnbiC65(k@&j6&ftyBB$9A)s_Eo`=4>iW?SPPyJ
z0sK{iyMz2a9uVE)C|Vh<HP6Hf%V5`(G8v?~%*6HyIV2y~Oz=+nt~du)Upm28+eHQJ
z1sVkH+R4e@w(l=yNGz7~xnztXOmS;|n_D;<UB)Itw&QhH#kk_w+w!IZ+&I3#3j_JD
z{M9ZnYm$`~5rITy>6WxOnXvl9g6h|Y>$d!b!(f+(X^8Y5c&E{46zCe+3d8rttLL>o
z=fz_cAFP1`&5g-~_j}b>Z7DT!g<zjaERC~$J$0~xu1Nkf&G1bQbjyMv*orzo;IyT6
z%RMXLKjA+@nx?P%pmp^6oCOJJFkS0Qc%5ncGbSNO|Cwqg3Tl$CE6G3pwtK1ZL9T?_
zbtr-_-m`TsYDj|5dNmcqz<lv=)*XRMjn5;N@H`l#T|bgZ>+`IthD3mvUZvCKd2C4A
zeI~7D&pOOVVvj_&P=IbFZegDA+A`)!D!ripH9bRVPJ)zJoLt;c#;0A=Gdv@8j2tBy
z*ATtss@5kaMYSfxd7h_jlybaDQT^j(&$}c>5EiUOof_S!(*ak|Cfp4*zd?U1vno;M
zI>C0Pa$@If$ibYe_m(}vL|*YYw@t*xzC8^DdH$8PlEEbn#@bo@h#C~n&m^JtkH9LA
zW1^DMx(w8m6zAuhpU7eZPy%#APRrk^a8$0!3nJ1;nw{rJM*ViEDPNnbs6X0cInI%H
zI0=~3HU`Y}LLtR&Yjm!O0BzR-r}cWX)mre(yzrh#;+yL+xEGS+{it&5rH7a1BRmV$
z=DsfwM6Sm)H;LZ2CasdhQONE9q2=Wka^rjHQLk-*&u!x4I~wf@<yeocx}4^}cEz0*
z3&81!3wmA8M30R#*1@ThARu#=fZud}UNb8%M$8tKO$}ybHulH7kLR++c!a;c3{2Gi
zn8wg$q=S<e+>t$zQPw)&o0QGQuEmkj$q{Zg*Bu}3&vPfe?Lcx5{A0IyGjoEsqkL|x
zqbrbESa1Y_IEOrATOK-OKoB`)zBgjdgum#k<R;lutAD?DzX>Q!v#Q%{D?eYfRla#)
z{ZFgh?KB~dol`6c890PcKsA7tp}dARIXsm$&-3|v-BMx?M@Vu}E$sy&UkBX9t`{%*
z8M=NH|9}8ZuZ49QQ&afO$CIn=-^z?ES|rdqTBnh}9p9J?H4+S|Xni(6qTXBMBsf}}
z4wDsgHJ147R$`X7Q6ff28@yEGZHVrcQOAqHoEf-ApeWusi8^LE(4f~!07}n^^FRTx
zx+0YCCUDdZxM`5>2jePrE$Y#7UFbVJ97pd2n-|>{oh0yGFt*B=rojk=Ay@%<|H{<Y
zA*BdY`a;}DHk+5P|81KI24^L3p8>7O)R3~OdobVZ_MUD~%R3?<!kXJ%^1qPRAp7+H
z@v=>r<zgyEZzFdDCD6%<)wafF;(v)@5PdYD{S)q9SSZ9;E&OKn$jvSo+H^gOZ6LJZ
z_P!sMov-^wHxzS*&yBVpA9W%r!;>`KgB&?B8R&hWN87Un_t+k_#opa@M`Q0)n}gA^
zmVq=%NVagy_7pZZH0>vwC0C73E>>`VvkzzbV>v`3z!!rzB^9X2v^JFxU(YeY#&#v>
zAH23(_UYz5yu@^q{BEIA$!S3J`!Vn2+BRoaJ=Sr;p{;51eYom@!(<XSAvfuyvU*Gj
z4Y9;;$l4;GQ>-3lTMF!w(iSZpGz?YNuO_SLwCPh_Tbm?g7o*anM_#3mM<_T~J}eyi
zPOKbf?mvz)-hQzCA?Y04#WIDLUh+6@GW!&$ktX0~TY+Ki0>MG#)U3%87bNN9&rMTm
z%j#SnN-p|3EfIAAfP6Vsl4Xa#{ii8lE7$vhxB6k6DnKvcU)Nk)NHBjaPuwWi`B!4D
zN*(eGndW4eA~E-6jXf3~LKW9(GK&<>on~OrH9Ig{GAQB_?uP@Yq`%RYM2)1C0fX6N
zDX090uUm*>RmW5klb^8+s{-N4`MvtY56AV8LxbdCTSeA}IZ7tdf}j@YORc0oT1*@^
z_lCK;VZfZt8aN?$u-mj=0zCIpqSi3WGD$41sy}Gr>*Fpr`NwtrX|LlJlaqP~DUMfT
zt3WhaK2hhs@TDE^^B*3JAqsycD;f5`eVbt)2)t(DE#4YKP4qqoNTn<f$(ZYg7g0<l
z8yf}g@2!*VfwF7=s7T;@H(bXWlwQ=v3yc7X;+g5N&;PLXEFsH?<?MC(qSDM_yx~OE
zMI(<Za;64gSP(!+pw$^iZTB8Gen=-(p1L^=yuCADlfkSE($As|EK^Cirn8dqp5Fc7
zSA?xDBCO+JGiMZd^X)?K2y-l<rxNIZw`Ud#iqL(y>gcaGVoIvX4Zl8740jh|Zi7q^
z{%xoy^m=!r?Hqn!!|aR~EtT#M{=;2o`zIv-gooXe8u)Ye;7s6Y)r+i5lP!k&mUnhq
zf+QZV!=dX<+Hbrg=2=PUMUQk#X>Nu(w&>|3`Wr4Lt<00Et0vz0b4;By2-1oLcqJ>~
zeIqM7rBe8O$lXdkBlO$w979j`0Ly<;nrtN!Ub#0-E)jSb6$b^O9UKY-*>v$Ax$0Xc
zJ0{RLcDImRwc%+$Q=w6RUOl{>a>}yoGGWY&X#UD-dGc39u-zag;fSE|EQUi_$(#Yh
z*@XKzFq6XcQ^w>E1D|AyPZ7Zj8}DDFcN$N)9f9M_JYTRiav{J<AA8AwFz4lCGi0se
z3H{U-kuov@K;@6Suh>mz5kl4M(O==ehjPjpB*t`ukIQRPSl7(WtJ^*I!(n8egiMfD
zpO)K+uh?o7?;U$5)ZJ?D5qI!jF0J}$KxwHEAa>fOFXM5Q%1;HSlOJ7$E8WhVm~Ck{
z%bD+fXOz53JRG1YT4CH>EeS|_<+6zB9%L4@z=@!3JGN6pA`H~IVwhUh3F!wt^v^XZ
zMm)`{dU``wxJr5L7NBG*a{=46e`u<=zg!$eaMH?D|9J+tSis<|1!c1-YJEgv+F;X(
zhaift_gm}El2L!v{XUJ(h<huJto>PD@R#s%v59)|-Ofs#x(<PfM5x&_Z8pp`&*({7
zr$#;paw;n-%&hA6FDdb%;ziQ!`bEg#Liy8#kD12-5WW*ehp|Ik6*awZbqCq5b!r5?
zhc>kJZszsFP~83l2J?8yO|fvA;O-2h@0y9-U^=l-yBt8z=cah+=YyjI_8Yj1qyT(q
zt$5rW3jI#Vr^Q%p^kia)3{@+~o_xP-&2H4<a@h4sy}Zgd%5y=hhC&kFp!<M%MeyAP
z@+o6|_G>=lU3RMt%d83e&!h=E(X03Js0rcDOaP4r%u=cS0~81Gi~Ernq$@d6*cQ)1
z%QQ*lSkGkY+}}%<osf;GEUK*FMtdM;EEs45Fe1=C-%Fp)s_YQqZB9hGt9!*xe6k;c
z?9vaBu>x_NQg9!T$=AT|mgE6g#2Yt$&%0l*NKz<w%7I!a1ax1r&{J#DCVyFfITyZ5
zssFxVn!-nydc-u8pN{;1bQFADsgM@^QwaVG$t;$o-ezl&RwpZZ@wGcpma=lmHcIP+
zM5Ax@Ip#wx=uBu~g3-XN{mUH1JDnB<ZwYSF)OVxDxXZO?C=Nwlk+k>}1vVNIasLkJ
z3S-li8on#;B4%M@;c?dp;*Exz?(cpFte`X}z8tezDVlLNLA-fIU!)dB`J=7&q(VNP
zB(6BCS|k;MIEYNt2B%&rE}bi2VG98^1@#VUCfa&>Chll70B+V3`rwJZ1_5e-SiNd7
zXSoka$hzwNjxhO;ndJeI7tb4_T;1_r<$v5v0ZML>+SFa%-5}=i#;<!goYGSYm#I%D
zXvtHiU@7*11r;k9D;_^`8843$9dV(@csMMB<1riUU0=_A$xko-FR?lEmrfz)J`^ow
z5u|&kfj}z?@l!LZ{mkbDJWr8H$zt(5a0zpi)s{{2z3eeK0UoAKkJujn1mGTq{c2Qa
zGzf*BgbC5Fq#lCa15)%;%=~eJ#I^r$WZ^_7JtnzE%!luJX>#~vZfwBMMG>^dq7k<u
z9Vfd#`5H{7o<Jn2!$&Wnkm4mx1>sr{Z#zFzFA>rxr?OSh*c#Myu%5h#t$>fUB`=kL
zUOyDUuu9oI=1b*=X@@U7Z$vVbI8?kJ)ZcW9Z_|T~CI3%s2E&*_FG67<@Ymvj3^UHe
z+DSE!q<sbxmNxY8okKx7I2l0u<y(#&-!*GWUw`~dQ|A^MBd4JuLAko@`PYkTzI66W
zKm6@#>V7b+d8{}|ZYmj}Q#JA8#n#gCki}<Lc&rH<n9}P%B~*}jlk}$S!A&K}MyON}
zzoj{05acPq!e+0B(<qs_h4<rTfhh6uuKnm89M(sqa(=g!4P9lO&iVCwKw`m(9lT}t
zy!bWl@+guk9uHL$nV(KqvVNy9dwFxHQ$?F^-Q#2~TzjKW3$PPVN5w&}Znmkny8nii
z4X7%YFAVBxQ^<M%Uv=4<&xsbO9Tq5Mt2LF7SiAK&&FCLXftjm?v)X|4)cb0|iP~Em
z;hx*=B@X+SPcq&M#<4admG&=wygbCy3EG2wh)gJ_6^}o#PrKV+ti^xa^p#a~bU^E)
zs@C$j!qwD9Xihz8H&4?^Jzg-3cHKY>R%^wbesn<7JxmENU)^k#zKFu9W`OEfS#`@u
zlM&$|j~3|GPZtHj1dcZFp1fBe(ifAaavLwWJM;QyZD?40+RxCt!a{>qB&BF^(a*XB
zcwzgon2Fqz5%OC8tHl?bMvJ;(0+ym*8z#gmQaA5JYQ+w)#D_>+EiHEE4E!U+wfD-c
zzGZ%Ju6nT-&LHKz#6ZJJlycN6N!uUWX;ZX)oz9A9Cx|th)-=U5dEXk@6u-~GO!V(F
z;Zm;_zUq08#dxM_jG<lwF0r)}9hQFLy-Tey6}!m#5uzR{aZ(QZuMZ_s_tr%ErcA;_
z=Dv)jBvf{4(#@$lZD`+Dp5ANm)@_;)ak)}3brhS{IKiY5$?DRSGKn)Ex^LD@OaW&A
z#0(t1=4%8<ha3f#`|Rs@TyIO?0ld<_CWKM1jUz}pZ*%8RS_b{r5A7p1(0|Rf%r1_#
z8{L!jV*MHf;;|$~yzd=e@uQ3d9-g?GK&`?d4bmidHV4zHPNsQlBSWMjXC{MhLC)~$
zs08speygJQSRnHi(-hg9+PRRWDc7sm-%lb#5%2gU9sxftOZ*gA5wwQvAnROOz(0qw
zXfJ}oM!^N8@8Z`?=A1NjM+obxiD;>}C$SjgI<7$@o#`d)4xBLTuAg;rllc+0&cm79
zUR^o)U8%Z+89gyMiEVBSvD4>ny3`d*O*GU9o8K;*x25Y>nybdmeOewhC(ffi*u|cn
zOn_&5p%)bXQ%IQ&r|y|RIk5^xO7tEx?rVEKY-w4X2q|n+%Mb@R_5{6^K5n=CF2oA`
zPAbSr%Iecy?_*eI@|(5}@xXD5P9+}vG)prFTFG;Y#j&3j&h<|@{=H4ltb6NTX_*8|
z>7=TCY)YJZUzZHarBZZ<8nO!+DncC~*rUK-`&*<pLazd;7Lqr@djJYOzroUO$1`R%
z5ot31Nk!ASZmov=XAAP<=`|15(KR{fq3pV$DE$iNFs5$_pgK7_FrSBD$r>-w>`E;;
z*0Q2XcgT!VeB+LtzfL3vvRbwU7j`xsu{&+Y1PbgJ1|6gs>?={#4Lnt(c6vxe&yi{R
zsTgvHszHHk6Q~mXbMXWx_J4n+PcCUk&9iT3yC?Y05|evvvKVom1#2WY9|T5xcm`&m
zlsJ|-!?QIPLi|-x){V?*(;(lkIjNT}(_r8^)3WJ&)}`!x?8F~EV0@<-1$MDhs3N0x
z<XF2q`O`G04ki(O-&oLWFZ~x#?I2PNo6GkC>MbF2|I#BKTt4^0J1s3Xsr_K-I%1^~
zZ1=xQPo*hGbt+OcfR@3qvc#V(?h>v0ewNCD#(Td+psV`)Mfvz@EWnaw5NgCy!S_vM
zk?v5Us-3*7qvUp2K}BJ6k}UA_Pi!H#&MJMZ6b!Phe1VD=P>aTr)V4QZ`I=rDb>yh0
z`LvFKRG@*o0YcmEdQ+^feOv2;3Bg2REvj&JOwC}nv4PVDC`C9not+lgk9Z+kqsI3#
zULnPGz)_;-OiUlgMJ#T<4xulT<)67(cAau7;MnNkeiFUIeWTQH_#2pBhpOGYi5%Bv
zPkJ>1mJVe4`b$pfE`w<uFKMWO7sQCwM(>u*=ltcLv6sJ81|6ITo1JQ~ZUkF{(?HW@
z3{TrMSTmherntmC?Bl!sJ^YC2qhZ=S=VVidbR>n7e2RW%hpGxY9<K^^JytEy9A?AU
znisVy04QpIC|XHH*QBc65v9!oE#a$SXIT*XSm2|beiKso)#wf(=x;uEKb&sI-%x!y
z)}k;ADsZC1G%dH)(hC|`p?=)LaCTY_S>Nj1wcHzZ>U;IX6#(iVTo56qb78z#SR%7a
zUHna`80Tx#RU$3o{1_K8Q93>E5JZ^jBI>@CL+`llK&Db`!^H95l!wg$lui^t-j4$L
zvl>@InRd$}mGbFF_D?Jiw(o+A#dC0?)If0IMx2sEcHhkvcIm*>eIIIRLV0F5I)1<b
zUuuDwKvS6#a)sl$u42|dW@k|V|20`N_2=p4(~cKB8`4+5Jh=6D?ny=UJ|vwh{6obf
zQ+-KztbD9SLcRq~=kJWHo4A#n%mZ1o`wc&x3(>0q=-q^;2e?0GuMJ28c@YnUt!pH4
z5)IaF9|><|NKEvk9f2xZ5W9c&4DbKu|GP8D({e%=QNbM_Ekg?B--5BWq+=v^#!aQX
z&y-uk9UUVfOge$M-ykK&$+6c>$F`K1I;$#7M!(INFj$U>wBq=}J$yP+^Y_9YlkFKw
zt2dHAC}>KUL=y=PUf9bQl)A79X>cKC3|J)(FdZN?qnSn@0vX+|i~G2NuXu{>o0k9h
zr`)Ctr}=9LkidCQ(ZO9GJ2}<r*NAE@>$5~U2XZ>sGbTwmi2zlo?lh#EXx%5@!XF52
z*jb{uq^GX8kGsS=eomdN$-LV04@bRU5fRS9OOFv=rufZi42J-&@`YWqA9Sth(u4H`
zepA%Heq_Xb(55}fL@G+^skrZz-(oR%x;M&w-3Ap}PaIte@d`nL1M7e^k~C`bPdybl
zT^J0F*N`0RxS*59P+E^GZO~F7mZ#)-OlK4jOV<H)P4`*q0{_R~eci^YruRPoMBkO}
z?_Aq*SR{UNkK1|x0A}*G#~xFPtsqs*6kF>m@<y6StA#izjoZxW-q9+D^q?IZv-k4s
zT-njT0C<YLya;rt643{h1TnAB$S@4;X!i;HHm_>M>iW~@k|-<fK&l!8VM(55N!Q$W
zU<%!|zKOp<Cq<`#zoQH9L@~42`Gfx(5cb;4re=c{@~Ih47IdWg&b_bS$to=76t%`>
zLdFglC~2&+(W7@^N?h^;6L6HnAHR5rSdxZ6+M(iN$=yC0(NcpY`)GU(`jSb=%?|)T
z?EwTe*9(esC^De}8QjljYdRsfZHGnI-!YXvp6@OXOo}dZRui?`pe;YTmCNu5X#6^X
z*<PdM?S&1H>ydxbbA1Fs=T0Id<wvj8sYQh;?fU<wu_sVfI_xU)@0abu=^<i{e}AQB
z`&*+~ROL3;{`6$WX`vuN#3^3@%3ft3ji(ZXtR-aG%|RbxtBAy6u|uhW0kFbgWGF5i
z67974Xi}>fWtNv`uWd06-n(e$eRFH<gzg;4g)G6+QbN8z?9e}yro00Q3scsL79q!R
z5V|itFSfr@Kb(^tu{%btRfd7;qQ;1+8#gYB$**3vXI1IirewW}W?AXfDNY73Bpn=j
z)=MD$BOIJFDM1j0eR>g@bDYqwJ`t}*4LryiW&f?cf(#2h)3n!yu56m;*bop@DMRSB
z<!dL7H3C-(SFiW#f|E95bd}5Jqsz1d7&lK!mr|GUZ7xP^-P1SBaKdL^ZG)}o8JfR6
zY`~x#o8bFOw!ll4BAFxuE%@D9MOkoR_cf-en}9SD=6H((frh$6YD~gIHGKvC;3fSn
zfwGdW(IVlYOTb~*rm~5?95OJ%?mTmtO&S~|pgB2E>22~dAQLuPOrePaYJLjWhLvoJ
zFo&@o+W*8#H*nkImM<u=<bSsHQ!SI*tW(p~JCXr1xJ_dQMZhL63(P4$gWEjd_n^Jx
zi&%(4eC|u5G>U^n=dwkD)|+sxO$>JZg7LFf09|Xdn}?h2v0h*swAl^A`grr1+B}w<
zt2+C!Ts|c=9LS9sd<rJZX3X3c;I{DpJeP+7JS|`g?oe6CrkgsnQQ9R&@UkKUzUj&a
zW%e^H;c5W)y46|8WH)k1($6t=HVmpd8N=ri>jQ;v4#HL_qx{-2ys7fhpA=$_0mUYe
zjoD3N>ET-4!>a%L29D1?&L$&v#($dPi~zi2WS9j%zbYYLf76B5*AWVlNfSU&0X1`0
ziBpOEh-x*QG1oA&a!^ZqnJDQ^Z6Lxfbx5(jW`-s#`jz#ZF@M<0rq#*abAG=wTwugm
zl3bYhuTT8TywTvlZy}?rl@x)D0W?sk%D4;qKQ%>t0W>OtUhtlrxkwt0pMBu3E+e(%
zbefgag?TX|dtNOBB!_2DT26T;WD?wWrAK`Z+*l@}W)x%s6bJ1WFr5>eU$}RYm5y8P
zIev2?=qltX)UR?Pnjt@RH(;Pa;$OAG{Kq)|?x<K$z|uV1PuM_!<@>zDxG9w$>$<u#
zJ=yq7c2eM=d7jvLnhMsWADm;Tbt11Zqfb|-z**F%PrzqsF<~{B{%q2TGbMfrN~Hqt
zp&wwPOLQ+Xyl*ZwWNNwPt0=L6_&|OaXRYk?_GWa3t&hAbqugWE<lQFb(BSjp_V;Me
zAk!@-aE#W)HYY+&n^pOL4Z#18iC?knjN%w}Zd38$aBMFf`NJ<mD`-W-M}iw7Rkyuw
zZTAQTu|4CGx7U86Z;<g8T{es;t(zNU$ii}d4h9$R#%Ki$!W@mIy7ts4eePg(-XDlL
z)?eXd?s*gje`s0~Xu1R$rFFOGI92JVK^AxLCXm$-fd?5llp*=6{&C*_cwAuJKQ<x&
zO<-n7-Y5MEAcRe`zYSDf<hVfZj@U=w9CynJYUM_yI4Mdt5rzXvpKU8D&-<h4nC>|B
z_L(FiNYqD3^RN(Smk|bqyy;8M$}$(c&QR2QK8U_iT>kh00YKJ9Gz9|u>E`aqWz)I{
ze-8z|w<#EpHHox%o^5J{*@0=mzyH!da}i_^#{;Wqnd)nb+0^3pX>uLxl#7A%^&U=T
zL6^ah#GoParx$1`?|Tk4MLcAEMbevPUbL2XRF9#ag~>8Bzu~*`0-jjh^w20L6G3O#
z9Xhvoe)!1gcLLDP+{yP8=#5<vwRX!qf8aTy&88H3ySL5mU;vL%%X_~|?f-1YKZ_w3
z6=Ld4++I4>2^(e8>9M;m$k1Z>imf4)8ToxL23~s8KXQ$Acwo`{D}tRD(G|f0=|hHY
zB10oJ7z0VxJoyLjgykxkI0~a8N}DucO85EvlCPH6Db@FrjwH#gN1;q~ys{Y0lw)Hh
zr-MY-h`IPdsUtl}@;!1OVoi0~3Hhr^ucqTLmoWCE|1JvcKkXIZnoUfq1{-BMJ~0hs
z%ZBa|d;FWF)5EG{_GlvhD1+gA{G}Z)acKHG$yyi8XhRQVz{rO90(Qs5r{;<6^MwIH
zq98|73-akQ6j{%dx~T@El(kDznRln4zZS4jrdwb5<|7y$e-R!AeX$Xnb}{%4_e_jm
zRV7$w%e1h;!Ro8=Uk6SRf&)WN*r$r7BBe*=Pj0Dn<FsCax@YVs;!4C}<X%KZ^h}tW
z%itzuU-mpSiepmgCnk~(f*-`X3@`0t0z7b%{m$Z6RO;ILPV@-o8#Yy4SUo?Fc}&d0
z<X>{>adb&?t|I@Yo%YS};d+f1@G;m~SG89|tKNM3KP!L)uYluIS+$$?>Y>o{brfQ6
zy0vcvuCG-Zp`F^DO29aqA^F$D490%v%2kM#F7EZAV;NOz%HR~~hlp0F#C8F$PCE}V
zIgRKUw=pB!u0|rflwI+7x%5n{SgJ)h-PIW>b;%o*|MSBC`Q+*%n^kdjd}pt#)CfI=
zP4VrCW#li&YAIke87u`gop(#H!k-oVt{P@!x=S|V*Mm(mMbhq(!;he5RdBE~l<du_
z65(Vk`y%^a7_smLL!x%Rqb3#7xHCEb?{MY?4`nrgKs!*OO4_>ijsOl&(e2weBD!`T
zt>bUP8V=y6G%=5pV_wiWm_4JiL(H-5hrw=73+ZO~@6YsG1U&QfGbd7r-(sdz@#1Ho
z!fV||xT&~6<14`2;j3!8G}N%=FCk%Bc-a$R`p3QqI#vVz4*zfV`M`|^EQfTqa`fG1
zVlXR$|HteFP4Es?5TZIcdi`42O$W75JP4o9n2Jv28VJfg<l@{7!u1-2MB`9p-!HdY
zFi1>d;jdCt6*e{(U%pign!<HH35Gl_ni$_8n2@gE<d_B;r7!HOIfDLX9SEVKZ?`pk
z7ynU(W**Fx>`iW>)C@Z5_{Z}Lgdqgnk}wvkwHFNt`Tq?PaMa+@`3#8^8&$pr-tvUK
zwI<#>*8+=Z)XM0$e)r<ZWm&^<sh`j7dqZr)5T~2Jiv1D5S@yM7A8z<h$0h?i?Y=C=
z%?JRUP+m$l&BB_FCl$1>Y{mx6LHU2mm<v!lrk`^pi9Mm#+g(}3wWIB|7eyz)$Vuu3
z2PK`kRN-R&y#hN~J?0?d)4sI@E{TlTWesyGRY;46GCjl7?6o%8aq8|LJX$?lFTZB;
z`2%bkb)$Bvl5qxQy`xAJlPE>In#Y|o8t%!p-WW{6jI>OcLjoV2mVfDPwR|taYDfrF
zjzd3uwQdf0ms5mb$-y5yu2|O8sr5UH_8sX7NC2U4w95Y8W%|v<{jzZU?CXV#^wuZV
z?65Tbo>6S;YPdBI?h~EMptMyi$J{zosN$~>8g5I*_xxOOcK^D^N_0Sad^sgouIu^K
zYNd!)4S27W<-y*fmY-Lh+L-o@!YH_^8mk7YPusSZ<6z;Zye-Jk2CIRRvn@1VUUlfN
z<39mgXb|rEOe~(vFDZBz1hD%WpRy4UX-UQ`L>M%S$IWx=%!ybxvGYPCKB{B(m=#k;
z+Wmjh1hy>c0T^^*pH%VCs>mY#Uv*#o5M|T-El8)xQc?mpCEXoLOQY0ExpXWYi<E!}
zk}61wlz_@Expc<@(xEGz(%lX3#f?7S=lK&}egtObTr+3p#OE_}^u5%VZ?Qj1`)dW{
z@n31mr)RV%=i&dyN?ofp<eI~3B>4<%_#cCDo!z^!Uny)i=IoFDN3dRtwBlrM`tQvC
zb*);i)yK)|zdX2WVG{8GVRFlIz5eTTd3hHv4H&NYojvCNjAnGb9*KR4X#M}}-s>)$
zfZ<xN1(rV}-~L|e>wiu2`{U20vSo51M0!;%IfB11%V(|Oai`fT6<yHjQ~d1uJ0}r$
zfezB#nBko>wUf@uqN$3&B)n{?Kq5gvrFB{5n0sNe0jJ5*>qk0aE+eWU!8ew{6Z79@
z@6)v!*(16a9F!e2v$4R+O=|~pd1=lqDvSZIfI7an{I=@FfGX?H1@&jg1uIg)H=;|j
z0ZvG-c#&CL$LDX~zJ*XU_I4`A0~3m;)6?G3(YoQCV_0Oywh>Ju#Bb4bt0z^2@NA>f
zoM*NGJypEzV0AMu%S~8x?qomT@o9dv0RP}{O5!#S$x;YsmXl))#U)kt^74w|vnDrQ
ze$IM_3=>pxK7}x;zROU(^H^KE!=>@UW>-s76GbdG@e-&KeNRF{qJ2_X$wjf)k?m&s
z?#yHTXrXP#*U->tlRaAxotQ9j8ZA{qIa99-J6PfAH0^$GIXzg@)zj1C8h*#3!py>Q
zd)46yVN~GX1-Mndy_zJC-$so=Fe%h0ep_4i<nz~)&ua1f9*Y|5B^?ayrQPZNt~uyQ
zYN-;i>CIQ!iC{X+LPe0NC3hpZF}NTrkiBlcMvIZ+=T+;a-TVQko_jxnbB#$^DQe$Y
z7ggW*F!E01I)pAx8FGjKc4iA~7h_`@paQo#3#`_21p@4r4MlS-=(n4d15_N_Wu#L$
z_-+IPCh~A7wSaPRS%Mu*elvcAEavLaoq#1p$In=)|5)(UNb;D)e-RG3Y0QD~W#%Uz
z(DWgu!(dEve4f6+zuFe;%`#&y2%n7n74ko7pCG?>SEqTr#$Uwef0Wr9*bbHH+Ak@K
zuv(bpr%l%yyFHu7XMg9o{cY~ahAFA8U@JL3h9*&X`ABRePR!^SXKRa}82$RM?%H8s
z0QKK)ePYwcgg$JF9PR3QEPZvp$MN8SASCI`95#y$H?6I(aS)6&rETO{&9na7FarB}
zlg@J6(@Ti}b4*T7P8?plS<_wDxYJH+gF#SxjFm(;Rs39mr`shTCqL$|`*G)fCNJ@+
zz7A%Id=vWz&tt9^{W3@QB_wFNllVG(m(pz$Da?v3FdEKRxvD;D?Jp1B4!T$~x6IN9
ztNi_Z=p*^1StxlAIO47y69!qWiGB>BCf$qWtW162nfhrFNKzpQAr)_1GcNtBx;kkS
z?@?=Nxxl*7JipM>{ff8sZ2!uFWX3QRD5IFgu|`l%-KPew4nTsNfa>S5`UM6|va$?I
zpZs{2S7*Fy$*8Epi`u%*4t|->|9GM;GAB+smgDvGdeJ<UF@cgWs;a7A>}y0Eeyk6~
zL;!0=tNSrE<*pq)CR$G_=+fW+cs9K1aLjRMe^}e{;`BfqDABH&1$;GKS9iDDOfW7p
z4^J<V*^@QrwU4B|A{F@CN1sEbEYIlL-8W7qJ*rJ?h;Ys}0Ld6yUlSMDzbBZ!2O^vN
zJ4}Q$2A6kKRFoS~k9ihe*7qndP*$3O)VX?rlFqdW<9M@n>upNP8=1qAoW)0l5V75*
zM;_4un1@H8GoC@pHpyVVhGR@}HVyvXs?*g1x3^Wf#Ei1i=roG*1H)$8@_c2kJS+$D
zFNKAog@km)Hhg2C`ZQ4}<|N&V!*%@Ay~V{v$m)-;?F-HA1?lmDdYI{Hw=$x)ao#)2
zpT_`kF;wm4v_wsIz~#x3_<C{cK)Ivg9x~P|b6W<ew<2ISaNkQkYFkaWnf7^RYWiI}
zD5gQ%m``Gdip4(eysYd4!b3vGjZ?eJ$M+5JMpRHq=7AE0)r+Y{Un|gMx5+!Zx&+0{
zZ{ra0<3x!#qLa~m=~7f3^gbrNnklj+jweaLHtnWyAbFhGi-#3uLJ2<dNYhs&)WZ}|
zZMrl&<~Rvw43vNT_;Dwt*}af?6Zw!H=<mZ@AASbjDn&=(AIjjAyn1Rq*1XVgZ>Ob+
z6Iw`#LtH*O0h3y2m*Q13DVq4Q|5Vi>MF8jW>y=akY_EB3uVo>EoC~4e!1uG<rkjb8
zMAEDdCQw*4N3g#!{)<R)@y!Uda?*9oKL7dd@9)b~^-@0e0BTK9M~3-z6u`RJnzRzK
z=FK(6(vXRa-2nd?szuwiz+T}-IKWaa@m0$39_cq-ooW(OazE8pC055{a*Vz&WvwJq
z$rn%ZGz7}lG%{o-t8y<mOE>KVFw(;-QFtP^S?|zeJzZAr@ErgvieX%8alF_dhH#WG
z)PIH%%q|p7iqG+g_0g@jY1OAw54OH(HvOnNC0UkWBzz$uYIQ-BTW9yi(mCrk)HdCK
z61MZ>kyst1px*Te_75I;5gjRYwPtw$(y3-{d$3E)ZGGu%W~5NX!s$|&=sRN=#^^XR
zt88zE{E%;5pb>q})^1S#i1%x+v>=qD`c+Jf{+et1h@(cWjBH%-ZqU{8iwf&*N+Np6
zAICr?EK%ao(NT=ouj}sd5yf;~0C$mo8#!p*SeAicd~TDCW<T}%<7pSOI)mlbVi2x(
zkDJBOrB?LDf?|;q?hyIKZXB~jq7ol#5=H5n*ctzo3h5&6?o6m2qYbG$<nRZ~itP=I
zFp|oDHHnh9rK4cU^7*X4f9*<8XA~|@9(^?a<Y8OH`J3fWgM=&i7{Rs^^iKB8)otw+
zs-`Sf#^W&*K{NB_(`{T_TP>xf{l$)#_{ZZU9&h76@|9GyTTWzWGYj1r<t5c-^oYEQ
zb-Ud}scZ=BTGJ&3$DErGURP^|lq{ftpdNzSpW7>k9DsJzy<o~d?CqXD7ONIiIKL%O
zk#uR<z!QEv2SHx%XgwMaW<D@8YLV*({qW}1Sl)UBi7+ETQD7TAD<v>T_>szi{3Ex-
z<l-LeU^8R{{PTKAtL>H9Fh9TFNJg7QLJ+pYH(Mn*Y-27*e{ZLyRBF37PmG^w>CU>?
z%+u%%0em%pvc0o5UaU>~bQ!I}?tb=kTRAp&@$-$i^vN$NLYg(X$L3l~iOu}W+M^5&
zVLPRGtpRPTe)C8e5fz4sdFssmU<JL&v@K<o+i|!vUBX(^S4-1m;ejw=$L7RHF`IZq
zzhMpX8#R~MWpoBXLo@r@RVa#?$d8q@Ln3Dh^0X4ay|>MU!i9TjnaKvl>$u++N#)k<
zgPZNrr6mp<IH8{QAP@w|RO_!D;EyVq1QBKC*Y`tyn%tPZ%+R};99DSV#xsSbj~Qjn
zW}8urumM)GDro=6-Fm0c52;00_b<D)YTP~>!LI~FFzU<BRp9;3uH)acvj*ON;qpnE
z9iNs%)lX-zs>xKju9H)b{){wx7WLXaCicSMN6_W?c$HhQ(YN74l*IUS<Wx$i2KGvS
z4<G)Oy<HGs6UVRU?1*oC-}}n=I_G*E?%#N6r=C5))M|s|9V4b{RHQ4`Xf6`XDuRi#
z-d;>=GteDriR4A)o{rVeDXmL7;^c<`p<C394_J}&ZaT@;zLoUhMvb;>hnVe~GzF$U
z#=Xx~uhyL=Xkpx`Vc8V5gy_Im5`>430_PM>_&1&RyYE59>{h4m8uiF4C|>*O99q2^
zf80hc46qzx`@*#vcd33uIhAOv5&D9zecxND+%MG2`q(oUm%Y!&@@-Z6)B{LTnGRMZ
zm)?@-$ZoCOg>98r^Vcuih3#;GE0MsWOLvae(9M6C^<jj2B@myT*F|ovhPW(KDPd7Q
z$>B`CIlbq#%^?k8O7^>WcSl~NQ8a6aaQ(I5=`puZg(V4(TYboaevRj&vL4w?NuFtn
zQFaoM3LhK$MA$(rQJXE((#Z$0v;ps5u31syM4B>`G0VR9I_y`LXOMNXzO%M`=&`V9
z!S8Gg<8J`(J<R-K2dceH0U0=%M2}N_;#?XJE>oF?w(*W<L&V-szwcbk4G}Aq77@FV
zVz~{AV-UVP>HCuK>h2ovQ7@=q#jsI1V_8JDTCZ6IC2PXuO#O4t`^98Jm8g5`49Mj7
zLTd!?ywd&@pY#3knLO<HofDU>WS4j4SSqcCT`r?usb4!7jt>Q$;Fp-TE=PBiea1(h
zYRgb(&vJ{H;8N5PmkqM~jVoH%ER^^VIpk;6+u9}j?h+yDiaM9f!zsGdx^@*l78G{%
z2c7Mf=K5)0J9wuD!`uSKhAvvRzN!mI2)hvV(fF~u-({OC+|p{>jgiCS3U?#73o?Lo
z6LX9iCQ8ThMVU3>D|~u8N}ZSbO&6(>bEf3y5tX;AzpXqxI#6@t!pVo<mmgFg=2hCN
zYTz}gR;O!ti%qwAJQ6eXK=$;I(Xqe1)R}0Hs@Cm0gwL6Jv&F3Sf-CGK4*Am@{NjS^
zt?vFO*;SjTF9K0@N@AH+*#lMh$jaPbiTyM*pTNEtmt*tN8nmv(7*^bky339e2DW?#
zU4WVAvVFV;T++NwU<xQTMNG-Z<de9~Ql@ktil!7CeJz@>;Uj*H(L1`hQF?nY@p2Z`
zo8jkL+*9s%ek@+~Sqt5p)yqY3OEmB7A3Cw$ZvJ}PL9ojX++0~t?AdS58w%ZA!%>4`
zKBg-U(wFmI%PGQ?;ZUh3i3VDNXj{h<sD~NY!U$_@-hdZQ@HYkmJkiHZ5#T|4e>wMX
zaqf5eqr4_F53fwvIYP$g)v73J8<QgRkLn$`Ige&XC+DoI&1*=JefP_C`WVw`qsyOK
zY)CBYz&S24w6Au3_Pgn#zuqQsU^fUX>86-p4D{nMNUV=)jPuo89HX~e{TBM;kwn=b
znF>$cpc?~{@{cuph_AD*r<%tm?uzbn8{wP`E<lwBqxJpZh*W26XcqjAPiRl)cBdrc
zW%(02@ut`;5gRMKd}!b=oFa{b4-qUX=fo_6N9_}g{cU%R(_=d9pwI#fo?7dltgz)Y
z$DOIyxUNHQarwV)nQKtMHig?r0GFf0qY#;QGya_%EZ_v3renY4&GsoV(%(AojOi_v
zOp`&6Y3B6vc0*Ep{Ab$J3+j@k`odb<j~Nfa8B^#&l_w`sZ2>O}5*G6Jo4&IYzU(-|
zT?`14iF_bR{R?wVd;F;^kA&6{KG>z8jsoUBNDm9x*bsMUiV?S1e#7=Gi2mtL#VfaG
z`TEV{=(cj{Kie_^3$Emv^ia^FwMu;Aq<|_ea8|VJpY7!3K?A~`H0y+@v+a6jWKrVJ
zADh(kE%@YC65{xv+KNCM6{)M8C^N>_J~ZL+6#;k6UHpNk4P7fW(hTgm>k(`H-J@Rg
zd2ri?M$-OscN&rnKh{_Vx7cIx4jO^Nz}k&-Y<uM$Adbh-jQ<<s*pXm3D#@7O6q|~U
zylLdwpSj4m2pw53S^Pb2mHIL@&^efvif?!3H5qTu2<McM|H|SWO-N-P5611){b1br
z8Ck>GQGLVtP;;|AMiOK`n=_W!x-ZNJ)eVD<`W`#>6S|#yQ(+P7Jy`)goPZ{N_*5>c
zfWZC}kJ_@!KUWJ*p<ZhGPCi$xo_aejaK1S&672!a6t${G%_;BXar^okQQ>NlH%=<<
zSQX3G55mmstPjox+h;SO+RfYhlRHC^wYpCmrd$@vyM#kN<S!(n$K8E6WiKwgh&1SV
z5+^<0(IruSiR#M3n{yt9@As5*I`jNB6;c>$W$HmwLX{q~CDQ>g+c)rs6OWr$TNVUW
zH}O+gAsJAUPOaH!ybU>3#8<ZAxWvoS{UsULK(&#{uISX&f_^+)WP$(=JaWrgfQp$T
z)7T7@uK9c@ys#2Yq?wLbmMshstLcwJYyB}z{J|><rs5{tg?kML7N+=!sKB5xRnZj8
zwC981lv(-9xRu$){mN{^myJ!T7sNk;{EH?_o=la%R+1!+5msT}(?iFZY2aT>^^S=v
zI%PVG>*zHOp%PMx|BUDOLILK44;SI&-;D488y38{SMvilBM{i_Dl;x#Em!XD8hs8a
z-%RZ4Au8(5regQ<$WG!GY`)VBVb-5~vA5ITzozuqU-FQ_6%+lGyIG3s%5u)#_1Wj!
zxa|QYo0@J}Bew&}aIw4IP=Md%4Z7_{or~qp7Y`=>N1MDbYa^qYY3kFf)}%-cK*v0Z
z`-m9D;!!xq_AKDlwI6xj=lZ_YcSr3x{hJ!mRF%w9OTYDKnVR&Nq`>++>V#A&|GX>R
z1#@k>uyXAT=Oyto+fw~z^AWPv@pO!UUWemn=k^;nZqT*s(W*P*{xFh7xqABpH_lEM
zX&_gTmx7(zFgy-8^!<0pc&T_~Dk_75-z5GXXRzP6CCaN>^6O=eb)d;i?}z?~aGWIi
zyo8dW2H*0sy-mS`{DSGWnr|`GlU}y*DF<s%v%b^^;Op#LqEObsO~NKvOQi{H%)Spi
zaTNNo>P&@?@@mSRi*F&L_v~wt^@CCrgNgLO8?s5uUwZZzj^RF&3}^c_1TOGu%ITu3
zA%ELCv`>!l^!=;4T8&fm?$Y*;NRMK_B=TbD<%rEf-ka=rAgi}5%?JMDNGxMvamZZ^
zCt!rTP_ZYfGk!WjH&TbC_cECfDX-()y*Tm{>pT7tGi;SP7FLU5k&wliqzSn-5YUWI
zbw~%J9-80X?kZK$S>Y5b$Tm)TmAT9W1tZQ!SFdIp7)elXqc8X^_kXIYcM1zBBHNwr
zFzIWsC?x<QM9lr@`J3H*rP5Tx7rnaBMiXiZQ^iRNsDrHC66NZA)F-m-dBu|AaWp|X
znfz&^iM6(aNgJnA*W_8ZOXrD_uA-DG!W^x0o_oZ4?Am%eQ^$H+`}tyJY&h7uhD&E)
zNYDSsC!bl)p4lcgCDhrz-b<-mhpAmvnxam?JsX+&$8xygQ!_p2m9jz-`j*~r`><8}
za1H)!pBDQ;9IhwaHy@8AaHq-_doE}%4YzE{%aS(5<tX**&T{&Oa0j9)*7`j<-~05X
z!T^`Gl=Ob9FDziCF(&sHubNX5!=hiW9ELKXfH@M!&wu~Q%L3KT=mxl;1a|Cw3;|T9
z*2Dt;c3}WMLSUH~J{~#Mt^7MF9KjWA7KlO#W*wC!@nZRN7~fTM)T>^z{UrOg()X2p
z<nm&8?JmpZR-sg7Q=VzQRxT-&;kq)W%5*+hCEa@vQ=(;eehy1I0}$T_>+m8}d$0BK
z2U%5{tj-S-6sl@-I9KEr8jj{jysHLZ3g@d+I(Pw$aVaj4B&T+Z8(y`VK6ab9iDbWn
zi5)#9?44<2|KzV^ZM?x=X#ucxLK-KwGSPJ|UVN%e>TtDCletEMr%@D@WApJ;)6d~e
z&M)LU7yO?{xUO|L*IkB_#%ag2{$K*w6mAoPm^_srBoK7L?7vG(?{#zUYm8xZ5ZB)$
zKhse7$im=k{$cy;>v0yo20wcdA6ngKW~Q&%sit4|6EL=&u$VU`VBq51(WUf~x1ir3
zykp}BKymmQIe)<)J55Ys1>MXc+JlXZiQ^?(27lwl)@s2hg&HDBu5a5}$a@^`4BbAE
zqING;D7JH)47;U(8=EJl^R91+LM)d%cVjm3(FZdH<y-S5s9zBKm8)Xgqo}uJ8GO+M
zI-66`f=9|FFc-zIexY@N?|)GyRH3#Ek3Y&d@d8hj=)L@jrlIX2xfp&9?CTBwd_CIi
zKcQdPtqV5<VaqpWK8E9-n6)H70bz{gPaM1?yPtUWj%>R8Y?$9NjnnLYy1OJ_sbR4@
zBOX5$xfI)y+u*(Zu-waIMCX+D)5T6;kGUVuU8P=|e~47TCWgKXOd_(+rpkVsnz_-a
zCK`IXlX!z1UF(FJjRK&u2Oo0IAlHff5!oQH-yioN6U#0o*EbP8MNp#be3#4RBb@f(
zkzP_AM}+Ba_}ScXHA#4^(<oy2sStk*FFo?m-F`-Mfi{d-sZqg9OFW(Tgqu(0V^E_8
zl#&kar?@RU36#5gy6p|$`--@M_=8E5@6&Pd%<C<K*IaQG2CY0o5cQf$HYS(D7!I26
zR;fGmx4onHz99Y_c_bPyA5}Epp*E+0Ir4*TCFIG}9bFK?whvv;(;iAm5+-X+X;IXr
zpA%;G{>_12BQPrLI=>e3b1DtT^KYj3&xZbfJ@Z!VCTRu#_v_%k>$i(+rRK-X{zg#1
ze|ri7N50f3{;x%Vc=i{6$t)uSlEtHaRTYN+g>j@u1GA{*-BMotXB2UazWy<A7OQWX
zuVv^e`h6X^AmCkJrE?lfy4>EHDqZOK-MYzp6A&Kl^h9oSUo!enZ;~Py1T?0YV!P%T
z3-^5U1`~3?u$&bqJJXx}m8pgaVne?Gkcn5#8KITFJ!=QM2!oF^{i6M1)Bfa*m#2=J
zCS_!*zn(HQyFmj*iQ{DU2NaCT9X`BS+<`fak<qbNO*M}LWnxE=_VHx3a_8H(8G_E;
z;z{R6_M3Nn`HzJvp?Fg+M?2|fP&0p0Ez6@{nzZK<u<=FPz2YtJAlJ+0m5|Cxr_qq!
zG%=w!_{|;uvR5a+F={imDn4C;o?VTJi#Eg@t>kCS<!1y6nuPYNDeTaGtL*Jx?B73t
zj>DQyk|ToUL`?(iH%ew|M$f6QMr|{c+}uh|E@h7<Jc8*CVgPknJ^BL=L0;eD+ebQs
z32nE%6*}Omu96o?(rN9HIA^1a)t)PQ2?+`3Z}8)OCE>D+NmrRxG60{vfgINA?CV=(
zGvPJVlydr5V;{PAak^HtvlVnT{&{04m#{sK(WOEGK{YRcmDCKST@QRl)gTlTV|g?#
zIeKv@v{y1tOkZX#VCH%$YU;``arrx5d%J~@FWCy@T|R%(pO2`Nv01afi+zdJG0Xc*
zwh};sVp>`rIin@nMdi}ai{m==8*a22D>RS3cPJ8_m#nO+cUyYxRW1l#wuBMt8XMa?
z->pJC=L)Vuq}?!q0bRfU0(ccn2DN;3c_hbe-asd{RVO4UB=is?IU^%3hE^<ua;)6(
z7i6N!P1yqcP=040MP;5`<+oYQ?(X5CX{p}kaJuSP^c;X@M*v9c-TU_;RpM6*gNbTu
ztM6IWqgBtAbK;`;#~h352P$362MDU2Mg=X6&liA-rcD^Q)MTg^{FA@G#|yasR(+C)
zAe1}5Jlo1Egzq8khxs9Gcr3AY)>bqb0f#iF`zxUVN0U_c_V%!)bf0Yi5_k^a0%B0&
zzNno>0Mqf@nxc22RXVsvu;2kbS3qjLy|8xE0j48df_U@hjmX@1MZER!+)1z4bn>aj
zM{zPWHMP7=LP~D_gRZWw7W1o9!`}-FDWc~)9gJVTd}*rPYKXeLIGg7SIw2ey9@e~<
zxYxy1^wJGh*coCy@Tk`ZY(Ofxe822^l`VJXP}H*i04v97dfcTEI`+`5aid~9b$78p
z!=-O;e`VM)8DZWy)rR=?t%bLCJtX)xZtCs{dli&1@HlIBtjJuhRYXLD0v{h=WT_T_
z7<)1fEPS2V8%d?(%mWTS%$5S_V2kyUlGltpn@(cgOTE!D;c(o>oC+B@SL2i4ZCgx7
zduI|hJ=9Mc5SF^SWN5gtWdq+tN7_dQnHR}aJe74cmXhr(e2KN=&b2A0#RwW2nnkIu
zG)`y7k;eeH=9%$w;ti4oJc7uD$3g8}v%;pML+8KUtW2b9r9(7;32P-5qb@1Gx;&!|
zhVW$fOYcRKMT6pirEdk6(-~lZni`sW#E>OOE6*Ock!u6+Pc<bzxHndEm80%>E=u`h
z>^%{JtK$wijgx3`M>O~OQR9^erp-dvhh!n!STg4)`C1|dqy2JM6Tf|ce1B$UX48+P
z4QffxFJwO2G6x_I4w~g}+Z&bBb3jpdu}5o>T!r#pUgczsAS6g0G`+XjE3{*59{IFF
zf}I`rm#Gi^gmWd?B)}Z_Otu7?<*VO+Z_h|xe>CN1gtU97rd=V*&lf2WIlvfnP6qt4
zNJ4Sv5-1N30klnkIb*|;280CA7kZ9;Qki`nmJwrfoXFBB-kQz`u~NUQi=$p*T+~SU
zFS96IM*ji2oLh|-hvP9iQY)Y1qrWW7A0HpXSY}4eD$>O8DyLo)kY&R=;<aV%SG#Cq
z)%of{0O%vs5;u)e5!?(8tCsQEG#Sogi4|{|_`H4olNdNw%hWt}zq8YFef1)=Fh(Xb
zxOXxs-x=ScXjG}gnDMpoJHB{cwbv&VbB5nP`<j^6p^w7%J;g}_o0##{Nq>-r|5~E_
z^Iq}uo-MlWoMP*lUJ{p)W7^{-xvMv3^)FJ=xEPjtNp3f?OK14+)B=YBfEPMjXFa<y
zBzJPL;4``UNsyP>Vb7ahV(fOF`C5j3rif28120f>fB^*-)JPS^pANdJCo@MT44tZU
zQ)A~*=z{?Msv#1NrXU1a*ZUo{11GX`mxT#fi&NNcWzr@MsASYm<N>hNw?qvLz$!Fa
z(mRSQ5H^iu3+^%`T<_NKTvq@QNEvCP7aer^J-_Ox!Os!vK8me7e<6M@7X2hr8I0tN
zw2P_V?c$2DV<!ctaoX80$ZXZ^MK8Rw9@tfHR%8I>qaXFuA(nGd?<uw2#pk1iqUE`{
zxP&Mn6-BoFpI%E~v!)C=_-<A&%@wzjSduML2PD`NTL*AH2q&U}yfXS#XsQ{AL<`cd
zDLaFFrENidM4>puf*~2>J)s-9Up}Ke0o{Z@Z{ND5bnMnaZ`;P|qGyB_alfq~z(B_#
z8FYDKI{}-I$#Wub6rA(n94Y_Zbeptoc`!#)4;_QH$2lb%-y&#yQup<F{R%<z1$^(P
zp*b-);(JDqkz6S&ErI3ceAH5ye{~$?GaPhqu>UAdpq#N-MXxM0Q~%@ilPPh22E+mp
zsK0X3JFVorl=t2$I{+C6^49z@=Gkv=@zkPaUZT0pX4o~7?XVv3m>=qjVv{nWhELVq
z9=%g5>D~u<B4gd;P#{D&5h%{zUsD&gF(Z%BQ<suYF%7Z=<$$t!T5DAKg9x-~m))B?
zy5Zy~!uh=t5ym~a!x76o?nqkMM@tQX7iTnCZO5Qozx9&v?~#Rc6=v|;d)fTi@&wMb
zVXP?e?$5=g!M^Wq->&3rU&V<uA%W+h65`IZr8b0F=3qI^6=R~s{)uXiA}EI2C-J1+
z`O6DAL13i}-$NSDE%fAss*b0S4*VIH<TNK1Cv{4qligPvi%-(FL?A@;5nx%+>+`-=
zsRe~KUF){BF!G0l{Gm`5Q<IA6K)=@LlO4aZv?@&gV$I+E#Y&3YVH|t8E`0s0ap^Bs
zzqW^{uZc9W)TKb`IWrl3f0J2;EgXf4iFwM*`a~I6d=e7OV$E3i1gz%bVt95+OVSDd
zTXu7}oC|!ps|h;wv3L_%F${5b>0HlL+I@aO9k(JPtO2-ltxo#@SbU8lE7D*PP@Dz`
z8aYxjUJcg=&%K&^*-W4=dzxRIC0+qZ>fi*~v*xJI<hbKRJ|uXysJ-Usnt5zg0tx%x
z5y=5wTJCuKu+`IlP)|j-OXb49txx`)@@%VQ2?KXHB!M|1FoOsQAL0o;m?a|P4&9-#
zv;I8I+g$=(bW2o;5=B|Nj@x;yv&()Hjs5<)k8vEQc04feXCiabMlQ<#aD6nhGbcF*
zH5B>OJy#^PCAFc22+nw0iGQv8MVWIM-zVs*=9O7@DzpqU2C|v>%(?mEXxil!e^_2Q
zTrd_Yge{6_+Q+uJ|77ZZaXtJBz|dWs*_IRK)ww1mIB<NmV4sYx4;LcP_V504P6(&o
z7APtHnny5E$L~@@RZJqStS)+cl5#nux!*|+#nq2($~J<t_&trO#%-i&i=+E~B_VHB
z(|X<(-*D*$Jbs~>7Q3dSSQFW{C#CR<V5;sNEl)Rc6I>vv#OI>AE@8Deu_*DQ_`}Pg
zk*-I7;??_)@kgR05O3<5_H;#2<QMnt)pBMsbu#gCYj0t*U+8lFNPZ28nhQHyDcx?T
zbxA%(;so+Fz(E4i${Ae%gj}^qR)%Wc<rIcRha_AUA%%w=I!ienp!A%|93vdg9LLM?
z-Oa8<AU+VYKl}>V<J4sB=Y@5<kA>bAWZ>x&xSj5o2JkhWmwt~z8xV*Q@DZ>is+B0c
zQ!AL3-dW0xu$5YNZj#&<kVYhlx~4%bGNo`C>xKB`xGFf@Ih#1Zh`0F%sdMRpU*Y5@
zX8t><JRUd|;^RKl=0uv&NoW!i_K9P+^L0oZ<SqKyX8wn9u)Z-afnKC{X{PIEZNWI9
z3-{_li;K=^^iu-E#D^uFK9TaLIzq&Infw)EgohvBl8s*h)P=McL-}b-5pLgKN&A}F
z?JP+^8sL092<gjDRdU7YpkH;*pPMvX9ux)9xOIuX$L?Mt$BVR%GyvzGT>SQVxw5RH
z87u8`?u3%28L4s4WcD58d1Z2Zk^6HLJS4#px0C|Zt(3@=<C_Fi!}>OkLcNB7MN$v6
zGIYN9^2gX@Vr9qOLfzu~4Z@=v)f()gV!YD3KLqabslncUC#cf0H!`Tg2z`am;|y{G
zF@acYKl$dB#Oiqx#ar%^THnjdm*Y`lK;7xK8U@*b9(wv@lComs@MM9Q&<^TiIFa{d
zA^A-Z<7kzre{e+vbaYA!qr0iF?_mEkNqG!0MnZ;$y1a``z*wB;%l96>>)Jhi!=Go)
zW&36(8l$mOo`b|JJ2yG*`^sH;bs?VbaMIgAL*C9X1E<JGV0);=Xirq46FPk^mY0M(
z+?gCHzo#6UofZdZa$pIl6=eTWHriT{7{wT1kwP{6mgU0o`zN>WNnZJ=<V|9$i}SsH
z+K0Wy44v<wd9csLmTA5Ut+Z5A2?JCqn`GC}f6GT9ssVz>84jNuPiuB@ngd;u83Vp?
z?jyAKC6+!~cG*sWYNQ$cmwItfQvEb1C9&L5VozN9f`*a74duu}A??MEEv%H`ZyJs~
z61#N`2k2*uLfxfjz6ve0yx=b@<b8tcM?DQsmfv%YcYE{85tup;Q-myKh*5z~tP~xK
zpj_k49^G4n^Y+{110T@)fBAsA+D!x);OCs}u`-87mP|QeQ8~L3I^TgBq|j(h+SSV$
zm?#g55!YfzS5&E#_^|3?VnhlNL1hMGvg!M1Y>6nKZOl3kITAzCe>8OcICBvL6-&s=
zy6A~|6c@7Z5_I+q0jE_brxW-AYqno?4u7wr;C64!yfXS!RxCkO@66aH!1rfmQC8K#
z&)TxhJt;gdG}^KDPll8YZ{P(=KI$KYxqqDo;eCV^osMFOI#ZLyJWdeFl+8zrK^-3{
zaZ+cYfl}qD-10@yM8^4Fz42bzO0kLNU~w8dx7>wyOyZ606)D=TXI1oc(~H*g$vvT$
zSiiXJDo35Ti)yT4^`026G*NcjG0LuWGf+yM%6jg8v{0295T+gp#m)5F&|XTfZrYlp
zRZiM`Jgw;MYsz@Ni?7xFMf6iZ<=L@WlBMg|3^a|c3b?mBU?iQ^s7XbaG~Rr+Q6cxM
z@4}F$F8%D>aqZtP5>RU**`3RAlbXpF;oAf``kQDsq8Aett{&&*KT|H|2c8GL^F{aq
zassIp<_NaCW>cp2$D%cF@&uQPnT1C1vlDInY$p}?PqD6Td#?ofGQ2P93T4Ii%r^-J
zJ3dTgv7YeQfJsz=CgZA~de}FF$FrYeKfKF@&D5)rNPo>DtVc|llv6q!_H_SAN?bEN
zlUXzOw%gag4oCjYbp31lUR^VtITD0pZ%pAIXtL*;MOBus#Z~x!Nf6+|AprneW$6rw
z-}s(?QXT^8Ko32iBoS5r(**|*roVoqHvc#K{_kjQ+&~Wkeyhm2|LG#@4lo=6Ys?>7
l#?9aElmR{be<tuHW3cpxMmfTR)?2`jnv#}c;UkNX{|9`&G{XP@

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index a68c9a4..ed6987a 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:6e3f719b1daa1babf21267ceb967f34d2d592ae35badaa05a16c0cfd6bc5acdb"
+  "signature": "sha256:bd98b97f9830126c7a9d419b19f563a8b78d66b25dba8c380e54754b84335a50"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -118,7 +118,7 @@
       "\n",
       "- [Linear Transformation: Principal Component Analysis (PCA)](#PCA)\n",
       "\n",
-      "- [Linear Transformation: Multiple Discrciminant Analysis (MDA)](#MDA)\n",
+      "- [Linear Transformation: Linear Discrciminant Analysis (MDA)](#MDA)\n",
       "\n",
       "- [Simple Supervised Classification](#Simple-Supervised-Classification)\n",
       "\n",
@@ -1026,7 +1026,7 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Linear Transformation: Multiple Discriminant Analysis (MDA)"
+      "Linear Transformation: Linear Discriminant Analysis (MDA)"
      ]
     },
     {
@@ -1040,11 +1040,11 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "The main purposes of a Multiple Discriminant Analysis is to analyze the data to identify patterns to project it onto a subspace that yields a better separation of the classes. Also, the dimensionality of the dataset shall be reduced with minimal loss of information.\n",
+      "The main purposes of a Linear Discriminant Analysis (LDA) is to analyze the data to identify patterns to project it onto a subspace that yields a better separation of the classes. Also, the dimensionality of the dataset shall be reduced with minimal loss of information.\n",
       "\n",
       "**The approach is very similar to a Principal Component Analysis (PCA), but in addition to finding the component axes that maximize the variance of our data, we are additionally interested in the axes that maximize the separation of our classes (e.g., in a supervised pattern classification problem)**\n",
       "\n",
-      "Here, our desired outcome of the multiple discriminant analysis is to project a feature space (our dataset consisting of n d-dimensional samples) onto a smaller subspace that represents our data \"well\" and has a good class separation. A possible application would be a pattern classification task, where we want to reduce the computational costs and the error of parameter estimation by reducing the number of dimensions of our feature space by extracting a subspace that describes our data \"best\"."
+      "Here, our desired outcome of the Linear discriminant analysis is to project a feature space (our dataset consisting of n d-dimensional samples) onto a smaller subspace that represents our data \"well\" and has a good class separation. A possible application would be a pattern classification task, where we want to reduce the computational costs and the error of parameter estimation by reducing the number of dimensions of our feature space by extracting a subspace that describes our data \"best\"."
      ]
     },
     {
@@ -1052,40 +1052,40 @@
      "level": 4,
      "metadata": {},
      "source": [
-      "Principal Component Analysis (PCA) Vs. Multiple Discriminant Analysis (MDA)"
+      "Principal Component Analysis (PCA) Vs. Linear Discriminant Analysis (LDA)"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Both Multiple Discriminant Analysis (MDA) and Principal Component Analysis (PCA) are linear transformation methods and closely related to each other. In PCA, we are interested to find the directions (components) that maximize the variance in our dataset, where in MDA, we are additionally interested to find the directions that maximize the separation (or discrimination) between different classes (for example, in pattern classification problems where our dataset consists of multiple classes. In contrast two PCA, which ignores the class labels).\n",
+      "Both Linear Discriminant Analysis and Principal Component Analysis are linear transformation methods and closely related to each other. In PCA, we are interested to find the directions (components) that maximize the variance in our dataset, where in LDA, we are additionally interested to find the directions that maximize the separation (or discrimination) between different classes (for example, in pattern classification problems where our dataset consists of multiple classes. In contrast two PCA, which ignores the class labels).\n",
       "\n",
-      "**In other words, via PCA, we are projecting the entire set of data (without class labels) onto a different subspace, and in MDA, we are trying to determine a suitable subspace to distinguish between patterns that belong to different classes. Or, roughly speaking in PCA we are trying to find the axes with maximum variances where the data is most spread (within a class, since PCA treats the whole data set as one class), and in MDA we are additionally maximizing the spread between classes.**\n",
+      "**In other words, via PCA, we are projecting the entire set of data (without class labels) onto a different subspace, and in LDA, we are trying to determine a suitable subspace to distinguish between patterns that belong to different classes. Or, roughly speaking in PCA we are trying to find the axes with maximum variances where the data is most spread (within a class, since PCA treats the whole data set as one class), and in LDA we are additionally maximizing the spread between classes.**\n",
       "\n",
-      "In typical pattern recognition problems, a PCA is often followed by an MDA."
+      "In typical pattern recognition problems, a PCA is often followed by an LDA."
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "![](../Images/mda_overview.png)"
+      "![](../Images/lda_overview.png)"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "If you are interested, you can find more information about the MDA in my IPython notebook   \n",
-      "[Stepping through a Multiple Discriminant Analysis - using Python's NumPy and matplotlib](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/multiple_discriminant_analysis.ipynb?create=1)."
+      "If you are interested, you can find more information about the LDA in my IPython notebook   \n",
+      "[Stepping through a Linear Discriminant Analysis - using Python's NumPy and matplotlib](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/minear_discriminant_analysis.ipynb?create=1)."
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Like we did in the PCA section above, we will use a `scikit-learn` funcion, [`sklearn.lda.LDA`](http://scikit-learn.org/stable/modules/generated/sklearn.lda.LDA.html) in order to transform our training data onto 2 dimensional subspace, where MDA is basically the more generalized form of an LDA (Linear Discriminant Analysis):"
+      "Like we did in the PCA section above, we will use a `scikit-learn` funcion, [`sklearn.lda.LDA`](http://scikit-learn.org/stable/modules/generated/sklearn.lda.LDA.html) in order to transform our training data onto 2 dimensional subspace:"
      ]
     },
     {
@@ -1137,14 +1137,14 @@
      "level": 4,
      "metadata": {},
      "source": [
-      "MDA for feature extraction"
+      "LDA for feature extraction"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "If we want to use MDA for projecting our data onto a smaller subspace (i.e., for dimensionality reduction), we can directly set the number of components to keep via `LDA(n_components=...)`; this is analogous to the [PCA function](#PCA-for-feature-extraction), which we have seen above.\n"
+      "If we want to use LDA for projecting our data onto a smaller subspace (i.e., for dimensionality reduction), we can directly set the number of components to keep via `LDA(n_components=...)`; this is analogous to the [PCA function](#PCA-for-feature-extraction), which we have seen above.\n"
      ]
     },
     {

From 9db24a8d8311820bc51ed273e071098fd74697a5 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 2 Jul 2014 10:32:15 -0400
Subject: [PATCH 148/248] updated lda section

---
 tutorials/python_data_entry_point.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index ed6987a..e64201d 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:bd98b97f9830126c7a9d419b19f563a8b78d66b25dba8c380e54754b84335a50"
+  "signature": "sha256:27a1e02eab6a2a036c525e3b099008ab58937df3266f55f5072b9a68b1693306"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -118,7 +118,7 @@
       "\n",
       "- [Linear Transformation: Principal Component Analysis (PCA)](#PCA)\n",
       "\n",
-      "- [Linear Transformation: Linear Discrciminant Analysis (MDA)](#MDA)\n",
+      "- [Linear Transformation: Linear Discrciminant Analysis (LDA)](#MDA)\n",
       "\n",
       "- [Simple Supervised Classification](#Simple-Supervised-Classification)\n",
       "\n",

From 94d3ea9488aa55f49455cf7ca6c136f4a5cf0a66 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 4 Jul 2014 21:13:11 -0400
Subject: [PATCH 149/248] fixed typos, added watermark info

---
 tutorials/python_data_entry_point.ipynb    | 38 +++++++++++---
 tutorials/scope_resolution_legb_rule.ipynb | 61 +++++++++++++++++-----
 2 files changed, 79 insertions(+), 20 deletions(-)

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index e64201d..e2f6532 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:27a1e02eab6a2a036c525e3b099008ab58937df3266f55f5072b9a68b1693306"
+  "signature": "sha256:7417613f49b14e98fba46fa1e285f4e3d46728b4798e853cfb103caef077b452"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -25,10 +25,18 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "import time\n",
-      "import platform\n",
-      "print('Last updated: %s' %time.strftime('%d/%m/%Y'))\n",
-      "print('Created using Python', platform.python_version())"
+      "%load_ext watermark"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -a 'Sebastian Raschka' -v -d -p numpy,scipy,matplotlib,scikit-learn"
      ],
      "language": "python",
      "metadata": {},
@@ -37,12 +45,26 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: 26/06/2014\n",
-        "Created using Python 3.4.1\n"
+        "Sebastian Raschka 04/07/2014 \n",
+        "\n",
+        "CPython 3.4.1\n",
+        "IPython 2.1.0\n",
+        "\n",
+        "numpy 1.8.1\n",
+        "scipy 0.14.0\n",
+        "matplotlib 1.3.1\n",
+        "scikit-learn 0.15.0b1\n"
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
+     ]
     },
     {
      "cell_type": "markdown",
diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index b2af45c..fa6bd76 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:134ba1fb10441f1756c0ab8a2ecef528f9c1cfd7543d8df415e2ae855f6891f9"
+  "signature": "sha256:b33e0c5563d80d68580ea6ce62ae2703856ccde40aec8ff9fb1364ac70d70521"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,12 +13,49 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
-      "last updated: 04/28/2014\n",
       "\n",
       "- [Link to the containing GitHub Repository](https://github.com/rasbt/python_reference)\n",
-      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb)\n",
-      "\n",
-      "Note: The code in this IPython notebook was executed in Python 3.4.0"
+      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb)\n"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -a 'Sebastian Raschka' -v -d"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Sebastian Raschka 04/07/2014 \n",
+        "\n",
+        "CPython 3.3.5\n",
+        "IPython 2.1.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
      ]
     },
     {
@@ -26,9 +63,9 @@
      "metadata": {},
      "source": [
       "<hr>\n",
-      "I am really looking forward to your comments and suggestions to improve and extend this tutorial! Just send me a quick note   \n",
-      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
-      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
       "<hr>"
      ]
     },
@@ -207,7 +244,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "So, how does Python now which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section."
+      "So, how does Python know which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section."
      ]
     },
     {
@@ -274,7 +311,7 @@
       "- *Global* refers to the uppermost level of the executing script itself, and  \n",
       "- *Built-in* are special names that Python reserves for itself.  \n",
       "\n",
-      "So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the global namespaces (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).\n",
+      "So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the built-in namespace (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).\n",
       "\n",
       "**Note**:  \n",
       "Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:"
@@ -635,7 +672,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitely access a variable from the outer (enclosed) scope in order to modify its value.  \n",
+      "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitly access a variable from the outer (enclosed) scope in order to modify its value.  \n",
       "Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet)."
      ]
     },
@@ -1020,7 +1057,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "**This also applies if we explicitely defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:"
+      "**This also applies if we explicitly defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:"
      ]
     },
     {

From 318b8ff9ab3cf636b6168e9e8ef0912604759493 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 5 Jul 2014 23:31:43 -0400
Subject: [PATCH 150/248] book reviews

---
 README.md                    |   1 +
 other/python_book_reviews.md | 122 +++++++++++++++++++++++++++++++++++
 2 files changed, 123 insertions(+)
 create mode 100644 other/python_book_reviews.md

diff --git a/README.md b/README.md
index ee43906..0995fe8 100755
--- a/README.md
+++ b/README.md
@@ -92,6 +92,7 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 - Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia [[Markdown](./tutorials/matrix_cheatsheet.md)]
 
+- [Python Book Reviews](./other/python_book_reviews.md)
 
 <br>
 
diff --git a/other/python_book_reviews.md b/other/python_book_reviews.md
new file mode 100644
index 0000000..bc1105f
--- /dev/null
+++ b/other/python_book_reviews.md
@@ -0,0 +1,122 @@
+
+<a id="table-of-contents"></a>
+
+# Python Book Reviews
+
+- [Matplotlib Plotting Cookbook](#Matplotlib-Plotting-Cookbook)
+- [Python High Performance Programming](#Python-High-Performance-Programming)
+- [Learning Ipython for Interactive Computing and Data Visualization](#Learning-Ipython-for-Interactive-Computing-and-Data-Visualization)
+- [The Practice of Computing Using Python (2nd Edition)](#The-Practice-of-Computing-Using-Python-(2nd-Edition))
+
+
+<div style="height:50px;"></div>
+
+**Where are the links?**  
+
+I decided to **not** post any links to any online shop here - I don't want to advertise anything but merely want to leave my brief thoughts in hope that it might be helpful to one or the other.
+
+
+**About the rating scale/review scores**  
+
+Most popular review sites provide some sort of rating, e.g., 7/10, 90/100, 3 starts out of 5 etc.  
+I have to admit that I am not a big fan of those review scores - and you won't find them here. Based on my experience, review scores are just kindling all sorts of arguments, destructive debates, and hate-mails. Let's be honest, every opinion is subjective, and I think that boiling it down to a final score is just an annoyance for everyone.
+
+<div style="height:100px;"></div>
+
+
+<hr>
+
+
+<a id="Matplotlib-Plotting-Cookbook"></a>
+### Matplotlib Plotting Cookbook 
+[[back to top](#table-of-contents)]
+***by Alexandre Devert***
+
+<font size='0.7em'>
+Paperback: 222 pages  
+Release Date: March 2014  
+ISBN: 1849513260  
+ISBN 13: 9781849513265</font>
+
+**A good alternative to the official matplotlib documentation**
+
+
+
+As a frequent matplotlib user I have to say that this book really fulfills it's promise as a cookbook by covering the most common use cases, and it is a pretty good and thorough introduction for beginners too (Python beginners as well as matplotlib beginners). However, the problem of this book is that there is the (free) matplotlib gallery (http://matplotlib.org/gallery.html) which also has plenty of very good examples, and I can imagine that it is a tough job as an author to add additional value to that.
+
+
+What this book does very well is introducing matplotlib quite gently in the first chapter, which makes it quite attractive for Python & matplotlib beginners. But also here, we have the alternative free user guide available online http://matplotlib.org/contents.html
+
+My main point of criticism why I find the matplotlib.org resources more accessible might be that they are actually in color: the plot and the code syntax. Unfortunately, the book only uses colors throughout the first chapter (and very very rarely for a handful of other plots later on), so that the largest portion of the plots are in gray-scale - also no syntax highlighting throughout this book. Since I have the ebook version, I do not fully understand why there is no coloring throughout the other chapters (especially the 2nd chapter, which is called "Chapter 2: Customizing Colors and Styles").
+
+But overall, it covers matplotlib pretty well, and I'd recommend it as an alternative to the resources matplotlib.org.
+
+But to it's defense, my hard copy of the "Gnuplot in Action" is also presented in gray-scales, and the "R Graphic's cookbook" also only makes use of colors rather sparingly. However, I think, nowadays in 2014 I'd at least expect the ebook to be in color - especially if you want to make it more attractive than the freely available online resources.
+
+
+Not a real point of criticism but more like a suggestion for future editions: as big fan of it, I was actually looking for this section that mentions how to use it in IPython notebooks (%pylab inline vs. matplotlib inline), and maybe also plotly for additional value :)
+
+<hr>
+
+<a id="Python-High-Performance-Programming"></a>
+### Python High Performance Programming
+[[back to top](#table-of-contents)]
+
+***by Gabriele Lanaro***
+
+<font size='0.7em'>
+Paperback: 108 pages  
+Release Date: December 2013  
+ISBN: 1783288450  
+ISBN 13: 9781783288458</font>
+
+**Really recommended book for Python beginners**
+
+A really nice read! It covered 4 important topics: how to profile & benchmark Python code, NumPy, C-extensions via Cython, and parallel programming. However, I found it a little bit too brief on all of the topics, a little bit more depth would have been nice.
+Also, I missed a few parts, like general Python tricks for better performance (e.g., in-place operators for mutable types and many many others that I started to create benchmarks for here: https://github.com/rasbt/One-Python-benchmark-per-day) 
+And another thing that I think would be worth adding in a future addition would be the JIT (just-in-time) compilers, such as parakeet or Numba, especially since Numexpr was briefly mentioned in the NumPy section.
+
+But overall I think it is a very recommended read for Python beginners!
+
+<hr>
+
+<a id="Learning-Ipython-for-Interactive-Computing-and-Data-Visualization"></a>
+###Learning Ipython for Interactive Computing and Data Visualization
+[[back to top](#table-of-contents)]
+  
+***by Cyrille Rossant***
+
+
+<font size='0.7em'>
+Paperback: 138 pages  
+Release Date: April 2013  
+ISBN: 1782169938  
+ISBN 13: 9781782169932</font>
+
+
+
+**A short intro book that I would recommend to everyone to get a taste of IPython's greatest features**
+
+It's a brief but good book that provides a good introduction to the IPython environment. I think the high-performance chapter that explained the usage of NumPy among others was a little bit redundant, since it is a general Python topic and is not necessarily specific to IPython. And on the other hand, the chapters on customizing IPython and especially writing own IPython magic extensions were way too brief - when I wrote my own extensions, I needed to look more closely at the IPython extension source code to be able to handle this task.
+But still, this is a nice book that I would recommend to people who are fairly new to Python and people who want to get a taste of IPython!
+
+<hr>
+
+<a id="The-Practice-of-Computing-Using-Python-(2nd-Edition)"></a>
+###The Practice of Computing Using Python (2nd Edition)
+[[back to top](#table-of-contents)]
+
+***by William F. Punch and Richard Enbody***
+
+<font size='0.7em'>
+Paperback: 792 pages  
+Release Date: February 25, 2012  
+ISBN-10: 013280557X  
+ISBN-13: 978-0132805575</font>
+
+
+**A great first Python book**
+
+This was actually my first Python book. It is not meant to be a thorough coverage of all the greatest Python features and capabilities, but it provides a great introduction to computing and programming in general by using the Python language.  
+It is maybe a little bit to trivial for programmers who just want to pick up the syntax Python language, but I would really recommend this book as a first introduction to people who have never programmed before - I think that Python is a very nice language to pick up this valuable skill.  
+I am a big fan of books that contains self-assessments: from short exercises up to bigger project assignments, and this book comes with a huge abundance of valuable material, which is a big bonus point.
\ No newline at end of file

From c807c4e2b114ed6a9e050000d2a64ea6c63e91c6 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 5 Jul 2014 23:34:30 -0400
Subject: [PATCH 151/248] book reviews

---
 other/python_book_reviews.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/other/python_book_reviews.md b/other/python_book_reviews.md
index bc1105f..c4772f3 100644
--- a/other/python_book_reviews.md
+++ b/other/python_book_reviews.md
@@ -3,10 +3,10 @@
 
 # Python Book Reviews
 
-- [Matplotlib Plotting Cookbook](#Matplotlib-Plotting-Cookbook)
-- [Python High Performance Programming](#Python-High-Performance-Programming)
-- [Learning Ipython for Interactive Computing and Data Visualization](#Learning-Ipython-for-Interactive-Computing-and-Data-Visualization)
-- [The Practice of Computing Using Python (2nd Edition)](#The-Practice-of-Computing-Using-Python-(2nd-Edition))
+- [Matplotlib Plotting Cookbook](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#Matplotlib-Plotting-Cookbook)
+- [Python High Performance Programming](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#Python-High-Performance-Programming)
+- [Learning Ipython for Interactive Computing and Data Visualization](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#Learning-Ipython-for-Interactive-Computing-and-Data-Visualization)
+- [The Practice of Computing Using Python (2nd Edition)](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#The-Practice-of-Computing-Using-Python-(2nd-Edition))
 
 
 <div style="height:50px;"></div>
@@ -29,7 +29,7 @@ I have to admit that I am not a big fan of those review scores - and you won't f
 
 <a id="Matplotlib-Plotting-Cookbook"></a>
 ### Matplotlib Plotting Cookbook 
-[[back to top](#table-of-contents)]
+[[back to top](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#python-book-reviews)]
 ***by Alexandre Devert***
 
 <font size='0.7em'>

From 7a55543f9b0f14e986f8a98907e3587a0d654465 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 5 Jul 2014 23:35:32 -0400
Subject: [PATCH 152/248] book reviews

---
 other/python_book_reviews.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/other/python_book_reviews.md b/other/python_book_reviews.md
index c4772f3..bc1105f 100644
--- a/other/python_book_reviews.md
+++ b/other/python_book_reviews.md
@@ -3,10 +3,10 @@
 
 # Python Book Reviews
 
-- [Matplotlib Plotting Cookbook](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#Matplotlib-Plotting-Cookbook)
-- [Python High Performance Programming](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#Python-High-Performance-Programming)
-- [Learning Ipython for Interactive Computing and Data Visualization](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#Learning-Ipython-for-Interactive-Computing-and-Data-Visualization)
-- [The Practice of Computing Using Python (2nd Edition)](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#The-Practice-of-Computing-Using-Python-(2nd-Edition))
+- [Matplotlib Plotting Cookbook](#Matplotlib-Plotting-Cookbook)
+- [Python High Performance Programming](#Python-High-Performance-Programming)
+- [Learning Ipython for Interactive Computing and Data Visualization](#Learning-Ipython-for-Interactive-Computing-and-Data-Visualization)
+- [The Practice of Computing Using Python (2nd Edition)](#The-Practice-of-Computing-Using-Python-(2nd-Edition))
 
 
 <div style="height:50px;"></div>
@@ -29,7 +29,7 @@ I have to admit that I am not a big fan of those review scores - and you won't f
 
 <a id="Matplotlib-Plotting-Cookbook"></a>
 ### Matplotlib Plotting Cookbook 
-[[back to top](https://github.com/rasbt/python_reference/blob/master/other/python_book_reviews.md#python-book-reviews)]
+[[back to top](#table-of-contents)]
 ***by Alexandre Devert***
 
 <font size='0.7em'>

From 2273c0446768b91344c9370d7cd2eccc283f396b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 10:49:05 -0400
Subject: [PATCH 153/248] regex collection

---
 README.md                    |   4 +-
 tutorials/useful_regex.ipynb | 826 +++++++++++++++++++++++++++++++++++
 2 files changed, 829 insertions(+), 1 deletion(-)
 create mode 100644 tutorials/useful_regex.ipynb

diff --git a/README.md b/README.md
index 0995fe8..99b97ec 100755
--- a/README.md
+++ b/README.md
@@ -42,6 +42,8 @@
 
 - Awesome things that you can do in IPython Notebooks (in progress) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/awesome_things_ipynb.ipynb)]
 
+- A collection of useful regular expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/useful_regex.ipynb)]
+
 <br>
 
 
@@ -92,7 +94,7 @@ GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Py
 
 - Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia [[Markdown](./tutorials/matrix_cheatsheet.md)]
 
-- [Python Book Reviews](./other/python_book_reviews.md)
+- Python Book Reviews [[Markdown](./other/python_book_reviews.md)]
 
 <br>
 
diff --git a/tutorials/useful_regex.ipynb b/tutorials/useful_regex.ipynb
new file mode 100644
index 0000000..1f4c880
--- /dev/null
+++ b/tutorials/useful_regex.ipynb
@@ -0,0 +1,826 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:9fd7d5201ce5b97fadad65f2c30cfec993fc83907e04418b032bd1bbdac05ff4"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+      "\n",
+      "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/useful_regex.ipynb)  "
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -d -v -u -t -z"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Last updated: 06/07/2014 10:07:02 EDT\n",
+        "\n",
+        "CPython 3.4.1\n",
+        "IPython 2.1.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<hr>\n",
+      "I would be happy to hear your comments and suggestions.  \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "A collection of useful regular expressions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Sections"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [About the `re` module](#About-the-re-module)\n",
+      "- [Identify files via file extensions](#Identify-files-via-file-extensions)\n",
+      "- [Username validation](#Username-validation)\n",
+      "- [Checking for valid email addresses](#Checking-for-valid-email-addresses)\n",
+      "- [Check for a valid URL](#Check-for-a-valid-URL)\n",
+      "- [Checking for integers](#Checking-for-integers)\n",
+      "- [Validating dates](#Validating-dates)\n",
+      "- [Time](#Time)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "About the `re` module"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The purpose of this IPython notebook is not to rewrite a detailed tutorial about regular expressions or the in-built Python `re` module, but to collect some useful regular expressions for copy&paste purposes."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The complete documentation of the Python `re` module can be found here [https://docs.python.org/3.4/howto/regex.html](https://docs.python.org/3.4/howto/regex.html). Below, I just want to list the most important methods for convenience:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- `re.match()`  : Determine if the RE matches at the beginning of the string.\n",
+      "- `re.search()` : Scan through a string, looking for any location where this RE matches.\n",
+      "- `re.findall()` : Find all substrings where the RE matches, and returns them as a list.\n",
+      "- `re.finditer()` : Find all substrings where the RE matches, and returns them as an iterator."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If you are using the same regular expression multiple times, it is recommended to compile it for improved performance.\n",
+      "\n",
+      "    compiled_re = re.compile(r'some_regexpr')    \n",
+      "    for word in text:\n",
+      "        match = comp.search(compiled_re))\n",
+      "        # do something with the match\n",
+      "    \n",
+      "**E.g., if we want to check if a string ends with a substring:**"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import re\n",
+      "\n",
+      "needle = 'needlers'\n",
+      "\n",
+      "# Python approach\n",
+      "print(bool(any([needle.endswith(e) for e in ('ly', 'ed', 'ing', 'ers')])))\n",
+      "\n",
+      "# On-the-fly Regular expression in Python\n",
+      "print(bool(re.search(r'(?:ly|ed|ing|ers)$', needle)))\n",
+      "\n",
+      "# Compiled Regular expression in Python\n",
+      "comp = re.compile(r'(?:ly|ed|ing|ers)$') \n",
+      "print(bool(comp.search(needle)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "True\n",
+        "True\n",
+        "True\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%timeit -n 10000 -r 50 bool(any([needle.endswith(e) for e in ('ly', 'ed', 'ing', 'ers')]))\n",
+      "%timeit -n 10000 -r 50 bool(re.search(r'(?:ly|ed|ing|ers)$', needle))\n",
+      "%timeit -n 10000 -r 50 bool(comp.search(needle))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "10000 loops, best of 50: 2.74 \u00b5s per loop\n",
+        "10000 loops, best of 50: 2.93 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "10000 loops, best of 50: 1.28 \u00b5s per loop"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Identify files via file extensions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A regular expression to check for file extensions."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = r'(?i)(\\w+)\\.(jpeg|jpg|png|gif|tif|svg)$'\n",
+      "\n",
+      "# remove `(?i)` to make regexpr case-sensitive\n",
+      "\n",
+      "str_true = ('test.gif', \n",
+      "            'image.jpeg', \n",
+      "            'image.jpg',\n",
+      "            'image.TIF'\n",
+      "            )\n",
+      "\n",
+      "str_false = ('test.pdf',\n",
+      "             'test.gif.pdf',\n",
+      "             )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Username validation"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Checking for a valid user name that has a certain minimum and maximum length.\n",
+      "\n",
+      "Allowed characters:\n",
+      "- letters (upper- and lower-case)\n",
+      "- numbers\n",
+      "- dashes\n",
+      "- underscores"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "min_len = 5 # minimum length for a valid username\n",
+      "max_len = 15 # maximum length for a valid username\n",
+      "\n",
+      "pattern = r\"^(?i)[a-z0-9_-]{%s,%s}$\" %(min_len, max_len)\n",
+      "\n",
+      "# remove `(?i)` to only allow lower-case letters\n",
+      "\n",
+      "\n",
+      "\n",
+      "str_true = ('user123', '123_user', 'Username')\n",
+      "            \n",
+      "str_false = ('user', 'username1234_is-way-too-long', 'user$34354')\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Checking for valid email addresses"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A regular expression that captures most email addresses."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = r\"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+$)\"\n",
+      "\n",
+      "str_true = ('test@mail.com',)\n",
+      "            \n",
+      "str_false = ('testmail.com', '@testmail.com', 'test@mailcom')\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">source: [http://stackoverflow.com/questions/201323/using-a-regular-expression-to-validate-an-email-address](http://stackoverflow.com/questions/201323/using-a-regular-expression-to-validate-an-email-address)</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Check for a valid URL"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Checks for an URL if a string ...\n",
+      "\n",
+      "- starts with `https://`, or `http://`, or `www.`\n",
+      "- or ends with a dot extension"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = '^(https?:\\/\\/)?([\\da-z\\.-]+)\\.([a-z\\.]{2,6})([\\/\\w \\.-]*)*\\/?$'\n",
+      "\n",
+      "str_true = ('https://github.com', \n",
+      "            'http://github.com',\n",
+      "            'www.github.com',\n",
+      "            'github.com',\n",
+      "            'test.de',\n",
+      "            'https://github.com/rasbt',\n",
+      "            'test.jpeg' # !!! \n",
+      "            )\n",
+      "            \n",
+      "str_false = ('testmailcom', 'http:testmailcom', )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">source: [http://code.tutsplus.com/tutorials/8-regular-expressions-you-should-know--net-6149](http://code.tutsplus.com/tutorials/8-regular-expressions-you-should-know--net-6149)</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Checking for integers"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Positive integers"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = '^\\d+$'\n",
+      "\n",
+      "str_true = ('123', '1', )\n",
+      "            \n",
+      "str_false = ('abc', '1.1', )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Negative integers"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = '^-\\d+$'\n",
+      "\n",
+      "str_true = ('-123', '-1', )\n",
+      "            \n",
+      "str_false = ('123', '-abc', '-1.1', )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "All integers"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = '^-{0,1}\\d+$'\n",
+      "\n",
+      "str_true = ('-123', '-1', '1', '123',)\n",
+      "            \n",
+      "str_false = ('123.0', '-abc', '-1.1', )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Positive numbers"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = '^\\d*\\.{0,1}\\d+$'\n",
+      "\n",
+      "str_true = ('1', '123', '1.234', )\n",
+      "            \n",
+      "str_false = ('-abc', '-123', '-123.0')\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Negative numbers"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = '^-\\d*\\.{0,1}\\d+$'\n",
+      "\n",
+      "str_true = ('-1', '-123', '-123.0', )\n",
+      "            \n",
+      "str_false = ('-abc', '1', '123', '1.234', )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "All numbers"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = '^-{0,1}\\d*\\.{0,1}\\d+$'\n",
+      "\n",
+      "str_true = ('1', '123', '1.234', '-123', '-123.0')\n",
+      "            \n",
+      "str_false = ('-abc')\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">source: [http://stackoverflow.com/questions/1449817/what-are-some-of-the-most-useful-regular-expressions-for-programmers](http://stackoverflow.com/questions/1449817/what-are-some-of-the-most-useful-regular-expressions-for-programmers)</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Validating dates"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Validates dates in `mm/dd/yyyy` format."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = '^(0[1-9]|1[0-2])\\/(0[1-9]|1\\d|2\\d|3[01])\\/(19|20)\\d{2}$'\n",
+      "\n",
+      "str_true = ('01/08/2014', '12/30/2014', )\n",
+      "            \n",
+      "str_false = ('22/08/2014', '-123', '1/8/2014', '1/08/2014', '01/8/2014')\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Time"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "12-Hour format"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = r'^(1[012]|[1-9]):[0-5][0-9](\\s)?(?i)(am|pm)$'\n",
+      "\n",
+      "str_true = ('2:00pm', '7:30 AM', '12:05 am', )\n",
+      "            \n",
+      "str_false = ('22:00pm', '14:00', '3:12', '03:12pm', )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "24-Hour format"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = r'^([0-1]{1}[0-9]{1}|20|21|22|23):[0-5]{1}[0-9]{1}$'\n",
+      "\n",
+      "str_true = ('14:00', '00:30', )\n",
+      "            \n",
+      "str_false = ('22:00pm', '4:00', )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 33
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 6e5414c44db1cd5f549e46b47b5880325a6ba166 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 11:09:31 -0400
Subject: [PATCH 154/248] link fix and benchmarks

---
 README.md | 38 ++++++++++++++++++++++++--------------
 1 file changed, 24 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index 99b97ec..969f614 100755
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@
 
 - Key differences between Python 2.x and Python 3.x [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1)]
 
-- A thorough guide to SQLite database operations in Python [[Markdown](./sqlite3_howto/README.md)]  
+- A thorough guide to SQLite database operations in Python [[Markdown](./tutorials/sqlite3_howto/README.md)]  
 
 - Unit testing in Python - Why we want to make it a habit [[Markdown](./tutorials/unit_testing.md)]  
 
@@ -69,28 +69,38 @@
 
 - Sequential Selection Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
+- Counting points inside a hypercube [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/geometry/points_in_hybercube.ipynb)]
 
 
 <br>
 ###// Benchmarks
 
-*For more recent benchmarks, please also see my separate   
-GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
-
-- Python benchmarks via `timeit` [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/timeit_tests.ipynb?create=1)]  
-
-- Least squares fit method for linear regression sped up via Cython [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/cython_least_squares.ipynb?create=1)]   
-
-- Benchmarks of different palindrome functions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/palindrome_timeit.ipynb?create=1)]    
-
-
-<br>
-
+*The benchmark category was moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
+
+- **1** - Reversing strings [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day1_string_reverse.ipynb)]
+- **2** - Calculating sample means [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day2_mean_values.ipynb)]
+- **3** - 6 different ways to count elements using a dict [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day3_dictionary_counting.ipynb)]
+- **4** - Python vs. Cython vs. Numba [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_python_cython_numba.ipynb)]
+- **4.2** - (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)]
+- **5** - Comparing 9 ways for flattening lists of sublists [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day5_flattening_lists.ipynb)]
+- **6** - Determining if a string is a number [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day6_string_is_number.ipynb)]
+- **7** - Speeding up NumPy array expressions with Numexpr [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_numpy_numexpr.ipynb)]
+- **7.2** - Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)] 
+- **8** - Calculating square roots and exponents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day8_sqrt_and_exp.ipynb)] 
+- **9** - The most Pythonic way to check if a string ends with a particular substring [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day9_string_endswith.ipynb)]
+- **10** - Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)]
+- **11** - The `deque` container data type [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day11_deque_container.ipynb)]
+- **12** - Lightning fast insertion into sorted lists via the `bisect` module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day12_insert_into_sorted_list.ipynb)]
+- **13** - Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)]
+- **14** - Python's and NumPy's in-place operator functions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day14_inplace_operators.ipynb)]
+- **15** - Array indexing in NumPy: Extracting rows and columns [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day15_array_indexing_numpy.ipynb)] 
+- **16** - Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]
+- **17** - Stacking NumPy arrays [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day17_numpy_stacking.ipynb)]
 
 
 ###// Other 
 
-- Happy Mother's Day [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)]
+- Happy Mother's [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)]
 
 - Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia [[Markdown](./tutorials/matrix_cheatsheet.md)]
 

From 76da7ee6e0418587f54c58a06bc6181773131ce3 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 12:36:55 -0400
Subject: [PATCH 155/248] html tag regex

---
 tutorials/useful_regex.ipynb | 68 +++++++++++++++++++++++++++++-------
 1 file changed, 56 insertions(+), 12 deletions(-)

diff --git a/tutorials/useful_regex.ipynb b/tutorials/useful_regex.ipynb
index 1f4c880..91e7dc7 100644
--- a/tutorials/useful_regex.ipynb
+++ b/tutorials/useful_regex.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:9fd7d5201ce5b97fadad65f2c30cfec993fc83907e04418b032bd1bbdac05ff4"
+  "signature": "sha256:8f1ee7a7bfaeca0ee3e12b2387445faa10632d57277d59af6dbfdca9732e5910"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -41,7 +41,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: 06/07/2014 10:07:02 EDT\n",
+        "Last updated: 06/07/2014 12:24:58 EDT\n",
         "\n",
         "CPython 3.4.1\n",
         "IPython 2.1.0\n"
@@ -264,7 +264,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "A regular expression to check for file extensions."
+      "A regular expression to check for file extensions.  \n",
+      "\n",
+      "Note: This approach is not recommended for thorough limitation of file types (parse the file header instead). However, this regex is still a useful alternative to e.g., a Python's `endswith` approach for quick pre-filtering for certain files of interest."
      ]
     },
     {
@@ -746,14 +748,6 @@
       "<br>"
      ]
     },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Time"
-     ]
-    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -810,6 +804,49 @@
       "\n",
       "for t in str_true:\n",
       "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Checking for HTML tags"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = r\"\"\"</?\\w+((\\s+\\w+(\\s*=\\s*(?:\".*?\"|'.*?'|[^'\">\\s]+))?)+\\s*|\\s*)/?>\"\"\"\n",
+      "\n",
+      "str_true = ('<a>', '<a href=\"something\">', '</a>', '<img src>')\n",
+      "            \n",
+      "str_false = ('a>', '<a ', '< a >')\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
       "\n",
       "for f in str_false:\n",
       "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
@@ -817,7 +854,14 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 33
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">source: [http://haacked.com/archive/2004/10/25/usingregularexpressionstomatchhtml.aspx/](http://haacked.com/archive/2004/10/25/usingregularexpressionstomatchhtml.aspx/)</font>"
+     ]
     }
    ],
    "metadata": {}

From 2689e95b3644b03a0b20b051d85cee81c3f38fe5 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 12:40:03 -0400
Subject: [PATCH 156/248] html tag regex

---
 tutorials/useful_regex.ipynb | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tutorials/useful_regex.ipynb b/tutorials/useful_regex.ipynb
index 91e7dc7..4918422 100644
--- a/tutorials/useful_regex.ipynb
+++ b/tutorials/useful_regex.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8f1ee7a7bfaeca0ee3e12b2387445faa10632d57277d59af6dbfdca9732e5910"
+  "signature": "sha256:95c2fd6c2ae1aca2d5dd07df567ff9d9ca66d642afe68338cc4e18e61798958f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -103,7 +103,8 @@
       "- [Check for a valid URL](#Check-for-a-valid-URL)\n",
       "- [Checking for integers](#Checking-for-integers)\n",
       "- [Validating dates](#Validating-dates)\n",
-      "- [Time](#Time)"
+      "- [Time](#Time)\n",
+      "- [Checking for HTML tags](#Checking-for-HTML-tags)"
      ]
     },
     {

From 6d42aa1ac38d096b97796fe9f4301db615e21d7c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 12:44:45 -0400
Subject: [PATCH 157/248] checking for numbers

---
 tutorials/useful_regex.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tutorials/useful_regex.ipynb b/tutorials/useful_regex.ipynb
index 4918422..14a9594 100644
--- a/tutorials/useful_regex.ipynb
+++ b/tutorials/useful_regex.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:95c2fd6c2ae1aca2d5dd07df567ff9d9ca66d642afe68338cc4e18e61798958f"
+  "signature": "sha256:2e496899fa42e9e6ec0548cb798a7ca3f66221e0e43f94315ad4cbfed2e8fd54"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -101,7 +101,7 @@
       "- [Username validation](#Username-validation)\n",
       "- [Checking for valid email addresses](#Checking-for-valid-email-addresses)\n",
       "- [Check for a valid URL](#Check-for-a-valid-URL)\n",
-      "- [Checking for integers](#Checking-for-integers)\n",
+      "- [Checking for numbers](#Checking-for-numbers)\n",
       "- [Validating dates](#Validating-dates)\n",
       "- [Time](#Time)\n",
       "- [Checking for HTML tags](#Checking-for-HTML-tags)"
@@ -499,7 +499,7 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Checking for integers"
+      "Checking for numbers"
      ]
     },
     {

From 350171ba957ca026db2a4803aa5e36b0aedc1f2e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 13:32:10 -0400
Subject: [PATCH 158/248] chtml parsing warrning

---
 tutorials/useful_regex.ipynb | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/tutorials/useful_regex.ipynb b/tutorials/useful_regex.ipynb
index 14a9594..96bf73c 100644
--- a/tutorials/useful_regex.ipynb
+++ b/tutorials/useful_regex.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2e496899fa42e9e6ec0548cb798a7ca3f66221e0e43f94315ad4cbfed2e8fd54"
+  "signature": "sha256:295b519fef2aba4116d805cb3c00fc582bdc1a0b7da29b1ae36415f606592010"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -836,6 +836,14 @@
       "[[back to top](#Sections)]"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Also this regex is only recommended for \"filtering\" purposes and not a ultimate way to parse HTML. For more information see this excellent discussion on StackOverflow:  \n",
+      "[http://stackoverflow.com/questions/1732348/regex-match-open-tags-except-xhtml-self-contained-tags/](http://stackoverflow.com/questions/1732348/regex-match-open-tags-except-xhtml-self-contained-tags/) "
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,

From d490411a5fb7a7d6d77f07a5d9a7d6a791eaf52f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 22:35:00 -0400
Subject: [PATCH 159/248] examples for cython

---
 tutorials/cython_bubblesort_nomagic.c   | 19147 ++++++++++++++++++++++
 tutorials/cython_bubblesort_nomagic.pyx |    21 +
 tutorials/running_cython.ipynb          |   529 +-
 tutorials/setup.py                      |    14 +
 4 files changed, 19597 insertions(+), 114 deletions(-)
 create mode 100644 tutorials/cython_bubblesort_nomagic.c
 create mode 100644 tutorials/cython_bubblesort_nomagic.pyx
 create mode 100644 tutorials/setup.py

diff --git a/tutorials/cython_bubblesort_nomagic.c b/tutorials/cython_bubblesort_nomagic.c
new file mode 100644
index 0000000..028e020
--- /dev/null
+++ b/tutorials/cython_bubblesort_nomagic.c
@@ -0,0 +1,19147 @@
+/* Generated by Cython 0.20.2 on Sun Jul  6 22:33:02 2014 */
+
+#define PY_SSIZE_T_CLEAN
+#ifndef CYTHON_USE_PYLONG_INTERNALS
+#ifdef PYLONG_BITS_IN_DIGIT
+#define CYTHON_USE_PYLONG_INTERNALS 0
+#else
+#include "pyconfig.h"
+#ifdef PYLONG_BITS_IN_DIGIT
+#define CYTHON_USE_PYLONG_INTERNALS 1
+#else
+#define CYTHON_USE_PYLONG_INTERNALS 0
+#endif
+#endif
+#endif
+#include "Python.h"
+#ifndef Py_PYTHON_H
+    #error Python headers needed to compile C extensions, please install development version of Python.
+#elif PY_VERSION_HEX < 0x02040000
+    #error Cython requires Python 2.4+.
+#else
+#define CYTHON_ABI "0_20_2"
+#include <stddef.h> /* For offsetof */
+#ifndef offsetof
+#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
+#endif
+#if !defined(WIN32) && !defined(MS_WINDOWS)
+  #ifndef __stdcall
+    #define __stdcall
+  #endif
+  #ifndef __cdecl
+    #define __cdecl
+  #endif
+  #ifndef __fastcall
+    #define __fastcall
+  #endif
+#endif
+#ifndef DL_IMPORT
+  #define DL_IMPORT(t) t
+#endif
+#ifndef DL_EXPORT
+  #define DL_EXPORT(t) t
+#endif
+#ifndef PY_LONG_LONG
+  #define PY_LONG_LONG LONG_LONG
+#endif
+#ifndef Py_HUGE_VAL
+  #define Py_HUGE_VAL HUGE_VAL
+#endif
+#ifdef PYPY_VERSION
+#define CYTHON_COMPILING_IN_PYPY 1
+#define CYTHON_COMPILING_IN_CPYTHON 0
+#else
+#define CYTHON_COMPILING_IN_PYPY 0
+#define CYTHON_COMPILING_IN_CPYTHON 1
+#endif
+#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600
+#define Py_OptimizeFlag 0
+#endif
+#if PY_VERSION_HEX < 0x02050000
+  typedef int Py_ssize_t;
+  #define PY_SSIZE_T_MAX INT_MAX
+  #define PY_SSIZE_T_MIN INT_MIN
+  #define PY_FORMAT_SIZE_T ""
+  #define CYTHON_FORMAT_SSIZE_T ""
+  #define PyInt_FromSsize_t(z) PyInt_FromLong(z)
+  #define PyInt_AsSsize_t(o)   __Pyx_PyInt_As_int(o)
+  #define PyNumber_Index(o)    ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
+                                (PyErr_Format(PyExc_TypeError, \
+                                              "expected index value, got %.200s", Py_TYPE(o)->tp_name), \
+                                 (PyObject*)0))
+  #define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \
+                                  !PyComplex_Check(o))
+  #define PyIndex_Check __Pyx_PyIndex_Check
+  #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
+  #define __PYX_BUILD_PY_SSIZE_T "i"
+#else
+  #define __PYX_BUILD_PY_SSIZE_T "n"
+  #define CYTHON_FORMAT_SSIZE_T "z"
+  #define __Pyx_PyIndex_Check PyIndex_Check
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
+  #define Py_TYPE(ob)   (((PyObject*)(ob))->ob_type)
+  #define Py_SIZE(ob)   (((PyVarObject*)(ob))->ob_size)
+  #define PyVarObject_HEAD_INIT(type, size) \
+          PyObject_HEAD_INIT(type) size,
+  #define PyType_Modified(t)
+  typedef struct {
+     void *buf;
+     PyObject *obj;
+     Py_ssize_t len;
+     Py_ssize_t itemsize;
+     int readonly;
+     int ndim;
+     char *format;
+     Py_ssize_t *shape;
+     Py_ssize_t *strides;
+     Py_ssize_t *suboffsets;
+     void *internal;
+  } Py_buffer;
+  #define PyBUF_SIMPLE 0
+  #define PyBUF_WRITABLE 0x0001
+  #define PyBUF_FORMAT 0x0004
+  #define PyBUF_ND 0x0008
+  #define PyBUF_STRIDES (0x0010 | PyBUF_ND)
+  #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES)
+  #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
+  #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
+  #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
+  #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE)
+  #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE)
+  typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
+  typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
+#endif
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
+          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyClass_Type
+#else
+  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
+          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyType_Type
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define Py_TPFLAGS_CHECKTYPES 0
+  #define Py_TPFLAGS_HAVE_INDEX 0
+#endif
+#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
+  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define Py_TPFLAGS_HAVE_VERSION_TAG 0
+#endif
+#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT)
+  #define Py_TPFLAGS_IS_ABSTRACT 0
+#endif
+#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE)
+  #define Py_TPFLAGS_HAVE_FINALIZE 0
+#endif
+#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
+  #define CYTHON_PEP393_ENABLED 1
+  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ? \
+                                              0 : _PyUnicode_Ready((PyObject *)(op)))
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
+  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
+  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
+  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
+#else
+  #define CYTHON_PEP393_ENABLED 0
+  #define __Pyx_PyUnicode_READY(op)       (0)
+  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
+  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
+  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
+  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
+#endif
+#if CYTHON_COMPILING_IN_PYPY
+  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
+#else
+  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
+  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \
+      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
+#endif
+#define __Pyx_PyString_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
+#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
+#else
+  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBaseString_Type            PyUnicode_Type
+  #define PyStringObject               PyUnicodeObject
+  #define PyString_Type                PyUnicode_Type
+  #define PyString_Check               PyUnicode_Check
+  #define PyString_CheckExact          PyUnicode_CheckExact
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define PyBytesObject                PyStringObject
+  #define PyBytes_Type                 PyString_Type
+  #define PyBytes_Check                PyString_Check
+  #define PyBytes_CheckExact           PyString_CheckExact
+  #define PyBytes_FromString           PyString_FromString
+  #define PyBytes_FromStringAndSize    PyString_FromStringAndSize
+  #define PyBytes_FromFormat           PyString_FromFormat
+  #define PyBytes_DecodeEscape         PyString_DecodeEscape
+  #define PyBytes_AsString             PyString_AsString
+  #define PyBytes_AsStringAndSize      PyString_AsStringAndSize
+  #define PyBytes_Size                 PyString_Size
+  #define PyBytes_AS_STRING            PyString_AS_STRING
+  #define PyBytes_GET_SIZE             PyString_GET_SIZE
+  #define PyBytes_Repr                 PyString_Repr
+  #define PyBytes_Concat               PyString_Concat
+  #define PyBytes_ConcatAndDel         PyString_ConcatAndDel
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
+  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
+#else
+  #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \
+                                         PyString_Check(obj) || PyUnicode_Check(obj))
+  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define PySet_Check(obj)             PyObject_TypeCheck(obj, &PySet_Type)
+  #define PyFrozenSet_Check(obj)       PyObject_TypeCheck(obj, &PyFrozenSet_Type)
+#endif
+#ifndef PySet_CheckExact
+  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
+#endif
+#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
+#if PY_MAJOR_VERSION >= 3
+  #define PyIntObject                  PyLongObject
+  #define PyInt_Type                   PyLong_Type
+  #define PyInt_Check(op)              PyLong_Check(op)
+  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
+  #define PyInt_FromString             PyLong_FromString
+  #define PyInt_FromUnicode            PyLong_FromUnicode
+  #define PyInt_FromLong               PyLong_FromLong
+  #define PyInt_FromSize_t             PyLong_FromSize_t
+  #define PyInt_FromSsize_t            PyLong_FromSsize_t
+  #define PyInt_AsLong                 PyLong_AsLong
+  #define PyInt_AS_LONG                PyLong_AS_LONG
+  #define PyInt_AsSsize_t              PyLong_AsSsize_t
+  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
+  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+  #define PyNumber_Int                 PyNumber_Long
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBoolObject                 PyLongObject
+#endif
+#if PY_VERSION_HEX < 0x030200A4
+  typedef long Py_hash_t;
+  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
+#else
+  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
+  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
+#endif
+#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
+  #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
+  #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
+  #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b)
+#else
+  #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0)))
+  #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1)))
+  #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1)))
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
+#endif
+#if PY_VERSION_HEX < 0x02050000
+  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),((char *)(n)))
+  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
+  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),((char *)(n)))
+#else
+  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),(n))
+  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a))
+  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),(n))
+#endif
+#if PY_VERSION_HEX < 0x02050000
+  #define __Pyx_NAMESTR(n) ((char *)(n))
+  #define __Pyx_DOCSTR(n)  ((char *)(n))
+#else
+  #define __Pyx_NAMESTR(n) (n)
+  #define __Pyx_DOCSTR(n)  (n)
+#endif
+#ifndef CYTHON_INLINE
+  #if defined(__GNUC__)
+    #define CYTHON_INLINE __inline__
+  #elif defined(_MSC_VER)
+    #define CYTHON_INLINE __inline
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_INLINE inline
+  #else
+    #define CYTHON_INLINE
+  #endif
+#endif
+#ifndef CYTHON_RESTRICT
+  #if defined(__GNUC__)
+    #define CYTHON_RESTRICT __restrict__
+  #elif defined(_MSC_VER) && _MSC_VER >= 1400
+    #define CYTHON_RESTRICT __restrict
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_RESTRICT restrict
+  #else
+    #define CYTHON_RESTRICT
+  #endif
+#endif
+#ifdef NAN
+#define __PYX_NAN() ((float) NAN)
+#else
+static CYTHON_INLINE float __PYX_NAN() {
+  /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and
+   a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is
+   a quiet NaN. */
+  float value;
+  memset(&value, 0xFF, sizeof(value));
+  return value;
+}
+#endif
+#ifdef __cplusplus
+template<typename T>
+void __Pyx_call_destructor(T* x) {
+    x->~T();
+}
+#endif
+
+
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
+#else
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
+#endif
+
+#ifndef __PYX_EXTERN_C
+  #ifdef __cplusplus
+    #define __PYX_EXTERN_C extern "C"
+  #else
+    #define __PYX_EXTERN_C extern
+  #endif
+#endif
+
+#if defined(WIN32) || defined(MS_WINDOWS)
+#define _USE_MATH_DEFINES
+#endif
+#include <math.h>
+#define __PYX_HAVE__cython_bubblesort_nomagic
+#define __PYX_HAVE_API__cython_bubblesort_nomagic
+#include "string.h"
+#include "stdio.h"
+#include "stdlib.h"
+#include "numpy/arrayobject.h"
+#include "numpy/ufuncobject.h"
+#include "pythread.h"
+#include "pystate.h"
+#ifdef _OPENMP
+#include <omp.h>
+#endif /* _OPENMP */
+
+#ifdef PYREX_WITHOUT_ASSERTIONS
+#define CYTHON_WITHOUT_ASSERTIONS
+#endif
+
+#ifndef CYTHON_UNUSED
+# if defined(__GNUC__)
+#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
+#     define CYTHON_UNUSED __attribute__ ((__unused__))
+#   else
+#     define CYTHON_UNUSED
+#   endif
+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
+#   define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+#   define CYTHON_UNUSED
+# endif
+#endif
+typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;
+                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
+
+#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
+#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
+#define __PYX_DEFAULT_STRING_ENCODING ""
+#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
+#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (    \
+    (sizeof(type) < sizeof(Py_ssize_t))  ||             \
+    (sizeof(type) > sizeof(Py_ssize_t) &&               \
+          likely(v < (type)PY_SSIZE_T_MAX ||            \
+                 v == (type)PY_SSIZE_T_MAX)  &&         \
+          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||       \
+                                v == (type)PY_SSIZE_T_MIN)))  ||  \
+    (sizeof(type) == sizeof(Py_ssize_t) &&              \
+          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||        \
+                               v == (type)PY_SSIZE_T_MAX)))  )
+static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
+static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
+#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
+#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
+#define __Pyx_PyBytes_FromString        PyBytes_FromString
+#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
+#if PY_MAJOR_VERSION < 3
+    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#else
+    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
+    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
+#endif
+#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
+#define __Pyx_PyObject_FromUString(s)  __Pyx_PyObject_FromString((const char*)s)
+#define __Pyx_PyBytes_FromUString(s)   __Pyx_PyBytes_FromString((const char*)s)
+#define __Pyx_PyByteArray_FromUString(s)   __Pyx_PyByteArray_FromString((const char*)s)
+#define __Pyx_PyStr_FromUString(s)     __Pyx_PyStr_FromString((const char*)s)
+#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((const char*)s)
+#if PY_MAJOR_VERSION < 3
+static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
+{
+    const Py_UNICODE *u_end = u;
+    while (*u_end++) ;
+    return (size_t)(u_end - u - 1);
+}
+#else
+#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
+#endif
+#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
+#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
+#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
+#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None)
+#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
+#if CYTHON_COMPILING_IN_CPYTHON
+#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
+#else
+#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
+#endif
+#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+static int __Pyx_sys_getdefaultencoding_not_ascii;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys;
+    PyObject* default_encoding = NULL;
+    PyObject* ascii_chars_u = NULL;
+    PyObject* ascii_chars_b = NULL;
+    const char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (!sys) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+    Py_DECREF(sys);
+    if (!default_encoding) goto bad;
+    default_encoding_c = PyBytes_AsString(default_encoding);
+    if (!default_encoding_c) goto bad;
+    if (strcmp(default_encoding_c, "ascii") == 0) {
+        __Pyx_sys_getdefaultencoding_not_ascii = 0;
+    } else {
+        char ascii_chars[128];
+        int c;
+        for (c = 0; c < 128; c++) {
+            ascii_chars[c] = c;
+        }
+        __Pyx_sys_getdefaultencoding_not_ascii = 1;
+        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
+        if (!ascii_chars_u) goto bad;
+        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
+        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
+            PyErr_Format(
+                PyExc_ValueError,
+                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
+                default_encoding_c);
+            goto bad;
+        }
+        Py_DECREF(ascii_chars_u);
+        Py_DECREF(ascii_chars_b);
+    }
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(default_encoding);
+    Py_XDECREF(ascii_chars_u);
+    Py_XDECREF(ascii_chars_b);
+    return -1;
+}
+#endif
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
+#else
+#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
+#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+static char* __PYX_DEFAULT_STRING_ENCODING;
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
+    PyObject* sys;
+    PyObject* default_encoding = NULL;
+    char* default_encoding_c;
+    sys = PyImport_ImportModule("sys");
+    if (!sys) goto bad;
+    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+    Py_DECREF(sys);
+    if (!default_encoding) goto bad;
+    default_encoding_c = PyBytes_AsString(default_encoding);
+    if (!default_encoding_c) goto bad;
+    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
+    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
+    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
+    Py_DECREF(default_encoding);
+    return 0;
+bad:
+    Py_XDECREF(default_encoding);
+    return -1;
+}
+#endif
+#endif
+
+
+/* Test for GCC > 2.95 */
+#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
+  #define likely(x)   __builtin_expect(!!(x), 1)
+  #define unlikely(x) __builtin_expect(!!(x), 0)
+#else /* !__GNUC__ or GCC < 2.95 */
+  #define likely(x)   (x)
+  #define unlikely(x) (x)
+#endif /* __GNUC__ */
+
+static PyObject *__pyx_m;
+static PyObject *__pyx_d;
+static PyObject *__pyx_b;
+static PyObject *__pyx_empty_tuple;
+static PyObject *__pyx_empty_bytes;
+static int __pyx_lineno;
+static int __pyx_clineno = 0;
+static const char * __pyx_cfilenm= __FILE__;
+static const char *__pyx_filename;
+
+#if !defined(CYTHON_CCOMPLEX)
+  #if defined(__cplusplus)
+    #define CYTHON_CCOMPLEX 1
+  #elif defined(_Complex_I)
+    #define CYTHON_CCOMPLEX 1
+  #else
+    #define CYTHON_CCOMPLEX 0
+  #endif
+#endif
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    #include <complex>
+  #else
+    #include <complex.h>
+  #endif
+#endif
+#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
+  #undef _Complex_I
+  #define _Complex_I 1.0fj
+#endif
+
+
+static const char *__pyx_f[] = {
+  "cython_bubblesort_nomagic.pyx",
+  "__init__.pxd",
+  "stringsource",
+  "type.pxd",
+};
+#define IS_UNSIGNED(type) (((type) -1) > 0)
+struct __Pyx_StructField_;
+#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
+typedef struct {
+  const char* name; /* for error messages only */
+  struct __Pyx_StructField_* fields;
+  size_t size;     /* sizeof(type) */
+  size_t arraysize[8]; /* length of array in each dimension */
+  int ndim;
+  char typegroup; /* _R_eal, _C_omplex, Signed _I_nt, _U_nsigned int, _S_truct, _P_ointer, _O_bject, c_H_ar */
+  char is_unsigned;
+  int flags;
+} __Pyx_TypeInfo;
+typedef struct __Pyx_StructField_ {
+  __Pyx_TypeInfo* type;
+  const char* name;
+  size_t offset;
+} __Pyx_StructField;
+typedef struct {
+  __Pyx_StructField* field;
+  size_t parent_offset;
+} __Pyx_BufFmt_StackElem;
+typedef struct {
+  __Pyx_StructField root;
+  __Pyx_BufFmt_StackElem* head;
+  size_t fmt_offset;
+  size_t new_count, enc_count;
+  size_t struct_alignment;
+  int is_complex;
+  char enc_type;
+  char new_packmode;
+  char enc_packmode;
+  char is_valid_array;
+} __Pyx_BufFmt_Context;
+
+struct __pyx_memoryview_obj;
+typedef struct {
+  struct __pyx_memoryview_obj *memview;
+  char *data;
+  Py_ssize_t shape[8];
+  Py_ssize_t strides[8];
+  Py_ssize_t suboffsets[8];
+} __Pyx_memviewslice;
+
+#include <pythread.h>
+#ifndef CYTHON_ATOMICS
+    #define CYTHON_ATOMICS 1
+#endif
+#define __pyx_atomic_int_type int
+#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||           \
+                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) && \
+                    !defined(__i386__)
+    #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1)
+    #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1)
+    #ifdef __PYX_DEBUG_ATOMICS
+        #warning "Using GNU atomics"
+    #endif
+#elif CYTHON_ATOMICS && MSC_VER
+    #include <Windows.h>
+    #define __pyx_atomic_int_type LONG
+    #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value)
+    #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value)
+    #ifdef __PYX_DEBUG_ATOMICS
+        #warning "Using MSVC atomics"
+    #endif
+#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0
+    #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value)
+    #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value)
+    #ifdef __PYX_DEBUG_ATOMICS
+        #warning "Using Intel atomics"
+    #endif
+#else
+    #undef CYTHON_ATOMICS
+    #define CYTHON_ATOMICS 0
+    #ifdef __PYX_DEBUG_ATOMICS
+        #warning "Not using atomics"
+    #endif
+#endif
+typedef volatile __pyx_atomic_int_type __pyx_atomic_int;
+#if CYTHON_ATOMICS
+    #define __pyx_add_acquisition_count(memview) \
+             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
+    #define __pyx_sub_acquisition_count(memview) \
+            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
+#else
+    #define __pyx_add_acquisition_count(memview) \
+            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
+    #define __pyx_sub_acquisition_count(memview) \
+            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
+#endif
+
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":723
+ * # in Cython to enable them only on the right systems.
+ * 
+ * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t
+ */
+typedef npy_int8 __pyx_t_5numpy_int8_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":724
+ * 
+ * ctypedef npy_int8       int8_t
+ * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int32      int32_t
+ * ctypedef npy_int64      int64_t
+ */
+typedef npy_int16 __pyx_t_5numpy_int16_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":725
+ * ctypedef npy_int8       int8_t
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_int64      int64_t
+ * #ctypedef npy_int96      int96_t
+ */
+typedef npy_int32 __pyx_t_5numpy_int32_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":726
+ * ctypedef npy_int16      int16_t
+ * ctypedef npy_int32      int32_t
+ * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_int96      int96_t
+ * #ctypedef npy_int128     int128_t
+ */
+typedef npy_int64 __pyx_t_5numpy_int64_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":730
+ * #ctypedef npy_int128     int128_t
+ * 
+ * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t
+ */
+typedef npy_uint8 __pyx_t_5numpy_uint8_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":731
+ * 
+ * ctypedef npy_uint8      uint8_t
+ * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint32     uint32_t
+ * ctypedef npy_uint64     uint64_t
+ */
+typedef npy_uint16 __pyx_t_5numpy_uint16_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":732
+ * ctypedef npy_uint8      uint8_t
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uint64     uint64_t
+ * #ctypedef npy_uint96     uint96_t
+ */
+typedef npy_uint32 __pyx_t_5numpy_uint32_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":733
+ * ctypedef npy_uint16     uint16_t
+ * ctypedef npy_uint32     uint32_t
+ * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_uint96     uint96_t
+ * #ctypedef npy_uint128    uint128_t
+ */
+typedef npy_uint64 __pyx_t_5numpy_uint64_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":737
+ * #ctypedef npy_uint128    uint128_t
+ * 
+ * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_float64    float64_t
+ * #ctypedef npy_float80    float80_t
+ */
+typedef npy_float32 __pyx_t_5numpy_float32_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":738
+ * 
+ * ctypedef npy_float32    float32_t
+ * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
+ * #ctypedef npy_float80    float80_t
+ * #ctypedef npy_float128   float128_t
+ */
+typedef npy_float64 __pyx_t_5numpy_float64_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":747
+ * # The int types are mapped a bit surprising --
+ * # numpy.int corresponds to 'l' and numpy.long to 'q'
+ * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longlong   long_t
+ * ctypedef npy_longlong   longlong_t
+ */
+typedef npy_long __pyx_t_5numpy_int_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":748
+ * # numpy.int corresponds to 'l' and numpy.long to 'q'
+ * ctypedef npy_long       int_t
+ * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longlong   longlong_t
+ * 
+ */
+typedef npy_longlong __pyx_t_5numpy_long_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":749
+ * ctypedef npy_long       int_t
+ * ctypedef npy_longlong   long_t
+ * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_ulong      uint_t
+ */
+typedef npy_longlong __pyx_t_5numpy_longlong_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":751
+ * ctypedef npy_longlong   longlong_t
+ * 
+ * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_ulonglong  ulong_t
+ * ctypedef npy_ulonglong  ulonglong_t
+ */
+typedef npy_ulong __pyx_t_5numpy_uint_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":752
+ * 
+ * ctypedef npy_ulong      uint_t
+ * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_ulonglong  ulonglong_t
+ * 
+ */
+typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":753
+ * ctypedef npy_ulong      uint_t
+ * ctypedef npy_ulonglong  ulong_t
+ * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_intp       intp_t
+ */
+typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":755
+ * ctypedef npy_ulonglong  ulonglong_t
+ * 
+ * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_uintp      uintp_t
+ * 
+ */
+typedef npy_intp __pyx_t_5numpy_intp_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":756
+ * 
+ * ctypedef npy_intp       intp_t
+ * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_double     float_t
+ */
+typedef npy_uintp __pyx_t_5numpy_uintp_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":758
+ * ctypedef npy_uintp      uintp_t
+ * 
+ * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_double     double_t
+ * ctypedef npy_longdouble longdouble_t
+ */
+typedef npy_double __pyx_t_5numpy_float_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":759
+ * 
+ * ctypedef npy_double     float_t
+ * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_longdouble longdouble_t
+ * 
+ */
+typedef npy_double __pyx_t_5numpy_double_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":760
+ * ctypedef npy_double     float_t
+ * ctypedef npy_double     double_t
+ * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_cfloat      cfloat_t
+ */
+typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    typedef ::std::complex< float > __pyx_t_float_complex;
+  #else
+    typedef float _Complex __pyx_t_float_complex;
+  #endif
+#else
+    typedef struct { float real, imag; } __pyx_t_float_complex;
+#endif
+
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    typedef ::std::complex< double > __pyx_t_double_complex;
+  #else
+    typedef double _Complex __pyx_t_double_complex;
+  #endif
+#else
+    typedef struct { double real, imag; } __pyx_t_double_complex;
+#endif
+
+
+/*--- Type declarations ---*/
+struct __pyx_array_obj;
+struct __pyx_MemviewEnum_obj;
+struct __pyx_memoryview_obj;
+struct __pyx_memoryviewslice_obj;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":762
+ * ctypedef npy_longdouble longdouble_t
+ * 
+ * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_cdouble     cdouble_t
+ * ctypedef npy_clongdouble clongdouble_t
+ */
+typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":763
+ * 
+ * ctypedef npy_cfloat      cfloat_t
+ * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
+ * ctypedef npy_clongdouble clongdouble_t
+ * 
+ */
+typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":764
+ * ctypedef npy_cfloat      cfloat_t
+ * ctypedef npy_cdouble     cdouble_t
+ * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
+ * 
+ * ctypedef npy_cdouble     complex_t
+ */
+typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":766
+ * ctypedef npy_clongdouble clongdouble_t
+ * 
+ * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline object PyArray_MultiIterNew1(a):
+ */
+typedef npy_cdouble __pyx_t_5numpy_complex_t;
+
+/* "View.MemoryView":99
+ * 
+ * @cname("__pyx_array")
+ * cdef class array:             # <<<<<<<<<<<<<<
+ * 
+ *     cdef:
+ */
+struct __pyx_array_obj {
+  PyObject_HEAD
+  char *data;
+  Py_ssize_t len;
+  char *format;
+  int ndim;
+  Py_ssize_t *_shape;
+  Py_ssize_t *_strides;
+  Py_ssize_t itemsize;
+  PyObject *mode;
+  PyObject *_format;
+  void (*callback_free_data)(void *);
+  int free_data;
+  int dtype_is_object;
+};
+
+
+/* "View.MemoryView":269
+ * 
+ * @cname('__pyx_MemviewEnum')
+ * cdef class Enum(object):             # <<<<<<<<<<<<<<
+ *     cdef object name
+ *     def __init__(self, name):
+ */
+struct __pyx_MemviewEnum_obj {
+  PyObject_HEAD
+  PyObject *name;
+};
+
+
+/* "View.MemoryView":302
+ * 
+ * @cname('__pyx_memoryview')
+ * cdef class memoryview(object):             # <<<<<<<<<<<<<<
+ * 
+ *     cdef object obj
+ */
+struct __pyx_memoryview_obj {
+  PyObject_HEAD
+  struct __pyx_vtabstruct_memoryview *__pyx_vtab;
+  PyObject *obj;
+  PyObject *_size;
+  PyObject *_array_interface;
+  PyThread_type_lock lock;
+  __pyx_atomic_int acquisition_count[2];
+  __pyx_atomic_int *acquisition_count_aligned_p;
+  Py_buffer view;
+  int flags;
+  int dtype_is_object;
+  __Pyx_TypeInfo *typeinfo;
+};
+
+
+/* "View.MemoryView":922
+ * 
+ * @cname('__pyx_memoryviewslice')
+ * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
+ *     "Internal class for passing memoryview slices to Python"
+ * 
+ */
+struct __pyx_memoryviewslice_obj {
+  struct __pyx_memoryview_obj __pyx_base;
+  __Pyx_memviewslice from_slice;
+  PyObject *from_object;
+  PyObject *(*to_object_func)(char *);
+  int (*to_dtype_func)(char *, PyObject *);
+};
+
+
+
+/* "View.MemoryView":302
+ * 
+ * @cname('__pyx_memoryview')
+ * cdef class memoryview(object):             # <<<<<<<<<<<<<<
+ * 
+ *     cdef object obj
+ */
+
+struct __pyx_vtabstruct_memoryview {
+  char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *);
+  PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *);
+  PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
+  PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *);
+  PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
+  PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *);
+  PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *);
+};
+static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview;
+
+
+/* "View.MemoryView":922
+ * 
+ * @cname('__pyx_memoryviewslice')
+ * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
+ *     "Internal class for passing memoryview slices to Python"
+ * 
+ */
+
+struct __pyx_vtabstruct__memoryviewslice {
+  struct __pyx_vtabstruct_memoryview __pyx_base;
+};
+static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice;
+#ifndef CYTHON_REFNANNY
+  #define CYTHON_REFNANNY 0
+#endif
+#if CYTHON_REFNANNY
+  typedef struct {
+    void (*INCREF)(void*, PyObject*, int);
+    void (*DECREF)(void*, PyObject*, int);
+    void (*GOTREF)(void*, PyObject*, int);
+    void (*GIVEREF)(void*, PyObject*, int);
+    void* (*SetupContext)(const char*, int, const char*);
+    void (*FinishContext)(void**);
+  } __Pyx_RefNannyAPIStruct;
+  static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
+  static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/
+  #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;
+#ifdef WITH_THREAD
+  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
+          if (acquire_gil) { \
+              PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \
+              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
+              PyGILState_Release(__pyx_gilstate_save); \
+          } else { \
+              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
+          }
+#else
+  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
+          __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
+#endif
+  #define __Pyx_RefNannyFinishContext() \
+          __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
+  #define __Pyx_INCREF(r)  __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_DECREF(r)  __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_GOTREF(r)  __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+  #define __Pyx_XINCREF(r)  do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0)
+  #define __Pyx_XDECREF(r)  do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0)
+  #define __Pyx_XGOTREF(r)  do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0)
+  #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0)
+#else
+  #define __Pyx_RefNannyDeclarations
+  #define __Pyx_RefNannySetupContext(name, acquire_gil)
+  #define __Pyx_RefNannyFinishContext()
+  #define __Pyx_INCREF(r) Py_INCREF(r)
+  #define __Pyx_DECREF(r) Py_DECREF(r)
+  #define __Pyx_GOTREF(r)
+  #define __Pyx_GIVEREF(r)
+  #define __Pyx_XINCREF(r) Py_XINCREF(r)
+  #define __Pyx_XDECREF(r) Py_XDECREF(r)
+  #define __Pyx_XGOTREF(r)
+  #define __Pyx_XGIVEREF(r)
+#endif /* CYTHON_REFNANNY */
+#define __Pyx_XDECREF_SET(r, v) do {                            \
+        PyObject *tmp = (PyObject *) r;                         \
+        r = v; __Pyx_XDECREF(tmp);                              \
+    } while (0)
+#define __Pyx_DECREF_SET(r, v) do {                             \
+        PyObject *tmp = (PyObject *) r;                         \
+        r = v; __Pyx_DECREF(tmp);                               \
+    } while (0)
+#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
+#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
+    PyTypeObject* tp = Py_TYPE(obj);
+    if (likely(tp->tp_getattro))
+        return tp->tp_getattro(obj, attr_name);
+#if PY_MAJOR_VERSION < 3
+    if (likely(tp->tp_getattr))
+        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
+#endif
+    return PyObject_GetAttr(obj, attr_name);
+}
+#else
+#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
+#endif
+
+static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/
+
+static CYTHON_INLINE int  __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj,
+    __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
+static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
+
+#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d
+#define __Pyx_MEMVIEW_DIRECT   1
+#define __Pyx_MEMVIEW_PTR      2
+#define __Pyx_MEMVIEW_FULL     4
+#define __Pyx_MEMVIEW_CONTIG   8
+#define __Pyx_MEMVIEW_STRIDED  16
+#define __Pyx_MEMVIEW_FOLLOW   32
+#define __Pyx_IS_C_CONTIG 1
+#define __Pyx_IS_F_CONTIG 2
+static int __Pyx_init_memviewslice(
+                struct __pyx_memoryview_obj *memview,
+                int ndim,
+                __Pyx_memviewslice *memviewslice,
+                int memview_is_new_reference);
+static CYTHON_INLINE int __pyx_add_acquisition_count_locked(
+    __pyx_atomic_int *acquisition_count, PyThread_type_lock lock);
+static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(
+    __pyx_atomic_int *acquisition_count, PyThread_type_lock lock);
+#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p)
+#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview))
+#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__)
+#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__)
+static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int);
+static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int);
+
+static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+
+static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
+    const char *name, int exact); /*proto*/
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/
+#else
+#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
+#endif
+
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/
+
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
+
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
+
+static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
+
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
+
+static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
+    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
+
+static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/
+
+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
+    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
+    const char* function_name); /*proto*/
+
+#include <string.h>
+
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
+
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
+
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
+#else
+#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
+#endif
+
+static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); /* proto */
+
+#ifndef __PYX_FORCE_INIT_THREADS
+  #define __PYX_FORCE_INIT_THREADS 0
+#endif
+
+#define UNARY_NEG_WOULD_OVERFLOW(x)            (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x)))
+
+static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
+static PyObject *get_memview(PyObject *__pyx_v_self); /*proto*/
+static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); /*proto*/
+
+static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
+         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
+         const char* encoding, const char* errors,
+         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
+
+static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+
+static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+
+#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) : \
+    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) : \
+               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
+#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
+    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck);
+#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
+    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck);
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
+                                                     int is_list, int wraparound, int boundscheck);
+
+static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
+static PyObject *__pyx_memoryview_transpose(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview__get__base(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_shape(PyObject *__pyx_v_self); /*proto*/
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
+    PyListObject* L = (PyListObject*) list;
+    Py_ssize_t len = Py_SIZE(list);
+    if (likely(L->allocated > len)) {
+        Py_INCREF(x);
+        PyList_SET_ITEM(list, len, x);
+        Py_SIZE(list) = len+1;
+        return 0;
+    }
+    return PyList_Append(list, x);
+}
+#else
+#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
+#endif
+
+static PyObject *__pyx_memoryview_get_strides(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_suboffsets(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_ndim(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_itemsize(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_nbytes(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_size(PyObject *__pyx_v_self); /*proto*/
+static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject* none = _PyList_Extend((PyListObject*)L, v);
+    if (unlikely(!none))
+        return -1;
+    Py_DECREF(none);
+    return 0;
+#else
+    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
+#endif
+}
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
+    PyListObject* L = (PyListObject*) list;
+    Py_ssize_t len = Py_SIZE(list);
+    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
+        Py_INCREF(x);
+        PyList_SET_ITEM(list, len, x);
+        Py_SIZE(list) = len+1;
+        return 0;
+    }
+    return PyList_Append(list, x);
+}
+#else
+#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
+#endif
+
+static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
+
+static CYTHON_INLINE long __Pyx_div_long(long, long); /* proto */
+
+static PyObject *__pyx_memoryviewslice__get__base(PyObject *__pyx_v_self); /*proto*/
+static void __Pyx_WriteUnraisable(const char *name, int clineno,
+                                  int lineno, const char *filename,
+                                  int full_traceback); /*proto*/
+
+static int __Pyx_SetVtable(PyObject *dict, void *vtable); /*proto*/
+
+typedef struct {
+  Py_ssize_t shape, strides, suboffsets;
+} __Pyx_Buf_DimInfo;
+typedef struct {
+  size_t refcount;
+  Py_buffer pybuffer;
+} __Pyx_Buffer;
+typedef struct {
+  __Pyx_Buffer *rcbuffer;
+  char *data;
+  __Pyx_Buf_DimInfo diminfo[8];
+} __Pyx_LocalBuf_ND;
+
+#if PY_MAJOR_VERSION < 3
+    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
+    static void __Pyx_ReleaseBuffer(Py_buffer *view);
+#else
+    #define __Pyx_GetBuffer PyObject_GetBuffer
+    #define __Pyx_ReleaseBuffer PyBuffer_Release
+#endif
+
+
+static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0};
+static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1};
+
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_long(unsigned long value);
+
+static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *);
+
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    #define __Pyx_CREAL(z) ((z).real())
+    #define __Pyx_CIMAG(z) ((z).imag())
+  #else
+    #define __Pyx_CREAL(z) (__real__(z))
+    #define __Pyx_CIMAG(z) (__imag__(z))
+  #endif
+#else
+    #define __Pyx_CREAL(z) ((z).real)
+    #define __Pyx_CIMAG(z) ((z).imag)
+#endif
+#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX
+    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
+    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
+#else
+    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
+    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
+#endif
+
+static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
+
+#if CYTHON_CCOMPLEX
+    #define __Pyx_c_eqf(a, b)   ((a)==(b))
+    #define __Pyx_c_sumf(a, b)  ((a)+(b))
+    #define __Pyx_c_difff(a, b) ((a)-(b))
+    #define __Pyx_c_prodf(a, b) ((a)*(b))
+    #define __Pyx_c_quotf(a, b) ((a)/(b))
+    #define __Pyx_c_negf(a)     (-(a))
+  #ifdef __cplusplus
+    #define __Pyx_c_is_zerof(z) ((z)==(float)0)
+    #define __Pyx_c_conjf(z)    (::std::conj(z))
+    #if 1
+        #define __Pyx_c_absf(z)     (::std::abs(z))
+        #define __Pyx_c_powf(a, b)  (::std::pow(a, b))
+    #endif
+  #else
+    #define __Pyx_c_is_zerof(z) ((z)==0)
+    #define __Pyx_c_conjf(z)    (conjf(z))
+    #if 1
+        #define __Pyx_c_absf(z)     (cabsf(z))
+        #define __Pyx_c_powf(a, b)  (cpowf(a, b))
+    #endif
+ #endif
+#else
+    static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex);
+    static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex);
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex);
+    #if 1
+        static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex);
+        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex);
+    #endif
+#endif
+
+static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
+
+#if CYTHON_CCOMPLEX
+    #define __Pyx_c_eq(a, b)   ((a)==(b))
+    #define __Pyx_c_sum(a, b)  ((a)+(b))
+    #define __Pyx_c_diff(a, b) ((a)-(b))
+    #define __Pyx_c_prod(a, b) ((a)*(b))
+    #define __Pyx_c_quot(a, b) ((a)/(b))
+    #define __Pyx_c_neg(a)     (-(a))
+  #ifdef __cplusplus
+    #define __Pyx_c_is_zero(z) ((z)==(double)0)
+    #define __Pyx_c_conj(z)    (::std::conj(z))
+    #if 1
+        #define __Pyx_c_abs(z)     (::std::abs(z))
+        #define __Pyx_c_pow(a, b)  (::std::pow(a, b))
+    #endif
+  #else
+    #define __Pyx_c_is_zero(z) ((z)==0)
+    #define __Pyx_c_conj(z)    (conj(z))
+    #if 1
+        #define __Pyx_c_abs(z)     (cabs(z))
+        #define __Pyx_c_pow(a, b)  (cpow(a, b))
+    #endif
+ #endif
+#else
+    static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex);
+    static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex);
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex);
+    #if 1
+        static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex);
+        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex);
+    #endif
+#endif
+
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
+
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
+
+static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs,
+                                        char order, int ndim);
+
+static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
+                                __Pyx_memviewslice *slice2,
+                                int ndim, size_t itemsize);
+
+static __Pyx_memviewslice
+__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
+                                 const char *mode, int ndim,
+                                 size_t sizeof_dtype, int contig_flag,
+                                 int dtype_is_object);
+
+static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);
+
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/
+
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
+
+static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
+
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
+
+static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
+
+static int __Pyx_ValidateAndInit_memviewslice(
+                int *axes_specs,
+                int c_or_f_flag,
+                int buf_flags,
+                int ndim,
+                __Pyx_TypeInfo *dtype,
+                __Pyx_BufFmt_StackElem stack[],
+                __Pyx_memviewslice *memviewslice,
+                PyObject *original_obj);
+
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *);
+
+static int __Pyx_check_binary_version(void);
+
+#if !defined(__Pyx_PyIdentifier_FromString)
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
+#else
+  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
+#endif
+#endif
+
+static PyObject *__Pyx_ImportModule(const char *name); /*proto*/
+
+static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);  /*proto*/
+
+typedef struct {
+    int code_line;
+    PyCodeObject* code_object;
+} __Pyx_CodeObjectCacheEntry;
+struct __Pyx_CodeObjectCache {
+    int count;
+    int max_count;
+    __Pyx_CodeObjectCacheEntry* entries;
+};
+static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
+static PyCodeObject *__pyx_find_code_object(int code_line);
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
+
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename); /*proto*/
+
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
+
+
+/* Module declarations from 'cpython.buffer' */
+
+/* Module declarations from 'cpython.ref' */
+
+/* Module declarations from 'libc.string' */
+
+/* Module declarations from 'libc.stdio' */
+
+/* Module declarations from 'cpython.object' */
+
+/* Module declarations from '__builtin__' */
+
+/* Module declarations from 'cpython.type' */
+static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
+
+/* Module declarations from 'libc.stdlib' */
+
+/* Module declarations from 'numpy' */
+
+/* Module declarations from 'numpy' */
+static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
+static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
+static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
+static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
+static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
+static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
+
+/* Module declarations from 'cython.view' */
+
+/* Module declarations from 'cython' */
+
+/* Module declarations from 'cython_bubblesort_nomagic' */
+static PyTypeObject *__pyx_array_type = 0;
+static PyTypeObject *__pyx_MemviewEnum_type = 0;
+static PyTypeObject *__pyx_memoryview_type = 0;
+static PyTypeObject *__pyx_memoryviewslice_type = 0;
+static PyObject *generic = 0;
+static PyObject *strided = 0;
+static PyObject *indirect = 0;
+static PyObject *contiguous = 0;
+static PyObject *indirect_contiguous = 0;
+static PyObject *__pyx_f_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(PyArrayObject *, int __pyx_skip_dispatch); /*proto*/
+static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
+static void *__pyx_align_pointer(void *, size_t); /*proto*/
+static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
+static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
+static PyObject *_unellipsify(PyObject *, int); /*proto*/
+static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
+static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
+static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
+static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
+static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
+static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
+static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
+static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
+static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
+static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
+static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
+static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
+static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
+static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
+static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
+static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
+static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
+static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
+static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/
+static int __pyx_memoryview_err(PyObject *, char *); /*proto*/
+static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
+static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
+static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
+static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
+static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
+static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
+static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
+static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 };
+#define __Pyx_MODULE_NAME "cython_bubblesort_nomagic"
+int __pyx_module_is_main_cython_bubblesort_nomagic = 0;
+
+/* Implementation of 'cython_bubblesort_nomagic' */
+static PyObject *__pyx_builtin_xrange;
+static PyObject *__pyx_builtin_ValueError;
+static PyObject *__pyx_builtin_range;
+static PyObject *__pyx_builtin_RuntimeError;
+static PyObject *__pyx_builtin_MemoryError;
+static PyObject *__pyx_builtin_enumerate;
+static PyObject *__pyx_builtin_Ellipsis;
+static PyObject *__pyx_builtin_TypeError;
+static PyObject *__pyx_builtin_id;
+static PyObject *__pyx_builtin_IndexError;
+static PyObject *__pyx_pf_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_inp_ary); /* proto */
+static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
+static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */
+static int __pyx_array_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
+static int __pyx_array_getbuffer_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
+static void __pyx_array_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
+static PyObject *get_memview_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_array_MemoryView_5array_6__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
+static PyObject *__pyx_array_MemoryView_5array_8__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
+static int __pyx_array_MemoryView_5array_10__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
+static int __pyx_MemviewEnum_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
+static PyObject *__pyx_MemviewEnum_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
+static int __pyx_memoryview_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
+static void __pyx_memoryview_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
+static int __pyx_memoryview_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
+static int __pyx_memoryview_getbuffer_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
+static PyObject *__pyx_memoryview_transpose_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview__get__base_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_get_shape_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_get_strides_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_get_suboffsets_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_get_ndim_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_get_itemsize_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_get_nbytes_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_get_size_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static Py_ssize_t __pyx_memoryview_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
+static void __pyx_memoryviewslice_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_memoryviewslice__get__base_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
+static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+static char __pyx_k_B[] = "B";
+static char __pyx_k_H[] = "H";
+static char __pyx_k_I[] = "I";
+static char __pyx_k_L[] = "L";
+static char __pyx_k_O[] = "O";
+static char __pyx_k_Q[] = "Q";
+static char __pyx_k_b[] = "b";
+static char __pyx_k_c[] = "c";
+static char __pyx_k_d[] = "d";
+static char __pyx_k_f[] = "f";
+static char __pyx_k_g[] = "g";
+static char __pyx_k_h[] = "h";
+static char __pyx_k_i[] = "i";
+static char __pyx_k_l[] = "l";
+static char __pyx_k_q[] = "q";
+static char __pyx_k_Zd[] = "Zd";
+static char __pyx_k_Zf[] = "Zf";
+static char __pyx_k_Zg[] = "Zg";
+static char __pyx_k_id[] = "id";
+static char __pyx_k_obj[] = "obj";
+static char __pyx_k_base[] = "base";
+static char __pyx_k_main[] = "__main__";
+static char __pyx_k_mode[] = "mode";
+static char __pyx_k_name[] = "name";
+static char __pyx_k_ndim[] = "ndim";
+static char __pyx_k_pack[] = "pack";
+static char __pyx_k_size[] = "size";
+static char __pyx_k_step[] = "step";
+static char __pyx_k_stop[] = "stop";
+static char __pyx_k_test[] = "__test__";
+static char __pyx_k_class[] = "__class__";
+static char __pyx_k_error[] = "error";
+static char __pyx_k_flags[] = "flags";
+static char __pyx_k_range[] = "range";
+static char __pyx_k_shape[] = "shape";
+static char __pyx_k_start[] = "start";
+static char __pyx_k_format[] = "format";
+static char __pyx_k_import[] = "__import__";
+static char __pyx_k_name_2[] = "__name__";
+static char __pyx_k_struct[] = "struct";
+static char __pyx_k_unpack[] = "unpack";
+static char __pyx_k_xrange[] = "xrange";
+static char __pyx_k_fortran[] = "fortran";
+static char __pyx_k_memview[] = "memview";
+static char __pyx_k_Ellipsis[] = "Ellipsis";
+static char __pyx_k_itemsize[] = "itemsize";
+static char __pyx_k_TypeError[] = "TypeError";
+static char __pyx_k_enumerate[] = "enumerate";
+static char __pyx_k_IndexError[] = "IndexError";
+static char __pyx_k_ValueError[] = "ValueError";
+static char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
+static char __pyx_k_MemoryError[] = "MemoryError";
+static char __pyx_k_RuntimeError[] = "RuntimeError";
+static char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
+static char __pyx_k_allocate_buffer[] = "allocate_buffer";
+static char __pyx_k_dtype_is_object[] = "dtype_is_object";
+static char __pyx_k_pyx_releasebuffer[] = "__pyx_releasebuffer";
+static char __pyx_k_strided_and_direct[] = "<strided and direct>";
+static char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
+static char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
+static char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
+static char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
+static char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
+static char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'";
+static char __pyx_k_getbuffer_obj_view_flags[] = "getbuffer(obj, view, flags)";
+static char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct";
+static char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
+static char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)";
+static char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)";
+static char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
+static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
+static char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
+static char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
+static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
+static char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced";
+static char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
+static char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
+static char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions";
+static char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
+static char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd";
+static char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
+static char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
+static char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported";
+static char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)";
+static char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
+static char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
+static char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous";
+static char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
+static char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short.";
+static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
+static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
+static PyObject *__pyx_kp_s_Cannot_index_with_type_s;
+static PyObject *__pyx_n_s_Ellipsis;
+static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
+static PyObject *__pyx_kp_u_Format_string_allocated_too_shor;
+static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;
+static PyObject *__pyx_n_s_IndexError;
+static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
+static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;
+static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;
+static PyObject *__pyx_n_s_MemoryError;
+static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
+static PyObject *__pyx_kp_s_MemoryView_of_r_object;
+static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;
+static PyObject *__pyx_n_b_O;
+static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
+static PyObject *__pyx_n_s_RuntimeError;
+static PyObject *__pyx_n_s_TypeError;
+static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
+static PyObject *__pyx_n_s_ValueError;
+static PyObject *__pyx_n_s_allocate_buffer;
+static PyObject *__pyx_n_s_base;
+static PyObject *__pyx_n_s_c;
+static PyObject *__pyx_n_u_c;
+static PyObject *__pyx_n_s_class;
+static PyObject *__pyx_kp_s_contiguous_and_direct;
+static PyObject *__pyx_kp_s_contiguous_and_indirect;
+static PyObject *__pyx_n_s_dtype_is_object;
+static PyObject *__pyx_n_s_enumerate;
+static PyObject *__pyx_n_s_error;
+static PyObject *__pyx_n_s_flags;
+static PyObject *__pyx_n_s_format;
+static PyObject *__pyx_n_s_fortran;
+static PyObject *__pyx_n_u_fortran;
+static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
+static PyObject *__pyx_n_s_id;
+static PyObject *__pyx_n_s_import;
+static PyObject *__pyx_n_s_itemsize;
+static PyObject *__pyx_kp_s_itemsize_0_for_cython_array;
+static PyObject *__pyx_n_s_main;
+static PyObject *__pyx_n_s_memview;
+static PyObject *__pyx_n_s_mode;
+static PyObject *__pyx_n_s_name;
+static PyObject *__pyx_n_s_name_2;
+static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;
+static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;
+static PyObject *__pyx_n_s_ndim;
+static PyObject *__pyx_n_s_obj;
+static PyObject *__pyx_n_s_pack;
+static PyObject *__pyx_n_s_pyx_getbuffer;
+static PyObject *__pyx_n_s_pyx_releasebuffer;
+static PyObject *__pyx_n_s_pyx_vtable;
+static PyObject *__pyx_n_s_range;
+static PyObject *__pyx_n_s_shape;
+static PyObject *__pyx_n_s_size;
+static PyObject *__pyx_n_s_start;
+static PyObject *__pyx_n_s_step;
+static PyObject *__pyx_n_s_stop;
+static PyObject *__pyx_kp_s_strided_and_direct;
+static PyObject *__pyx_kp_s_strided_and_direct_or_indirect;
+static PyObject *__pyx_kp_s_strided_and_indirect;
+static PyObject *__pyx_n_s_struct;
+static PyObject *__pyx_n_s_test;
+static PyObject *__pyx_kp_s_unable_to_allocate_array_data;
+static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;
+static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
+static PyObject *__pyx_n_s_unpack;
+static PyObject *__pyx_n_s_xrange;
+static PyObject *__pyx_int_0;
+static PyObject *__pyx_int_1;
+static PyObject *__pyx_int_neg_1;
+static PyObject *__pyx_tuple_;
+static PyObject *__pyx_tuple__2;
+static PyObject *__pyx_tuple__3;
+static PyObject *__pyx_tuple__4;
+static PyObject *__pyx_tuple__5;
+static PyObject *__pyx_tuple__6;
+static PyObject *__pyx_tuple__7;
+static PyObject *__pyx_tuple__8;
+static PyObject *__pyx_tuple__9;
+static PyObject *__pyx_tuple__10;
+static PyObject *__pyx_tuple__11;
+static PyObject *__pyx_tuple__12;
+static PyObject *__pyx_tuple__13;
+static PyObject *__pyx_tuple__14;
+static PyObject *__pyx_tuple__15;
+static PyObject *__pyx_tuple__16;
+static PyObject *__pyx_tuple__17;
+static PyObject *__pyx_tuple__18;
+static PyObject *__pyx_tuple__19;
+static PyObject *__pyx_tuple__20;
+static PyObject *__pyx_tuple__21;
+static PyObject *__pyx_tuple__22;
+
+/* "cython_bubblesort_nomagic.pyx":6
+ * @cython.boundscheck(False)
+ * @cython.wraparound(False)
+ * cpdef cython_bubblesort_nomagic(np.ndarray[long, ndim=1] inp_ary):             # <<<<<<<<<<<<<<
+ *     """ The Cython implementation of Bubblesort with NumPy memoryview."""
+ *     cdef unsigned long length, i, swapped, ele, temp
+ */
+
+static PyObject *__pyx_pw_25cython_bubblesort_nomagic_1cython_bubblesort_nomagic(PyObject *__pyx_self, PyObject *__pyx_v_inp_ary); /*proto*/
+static PyObject *__pyx_f_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(PyArrayObject *__pyx_v_inp_ary, CYTHON_UNUSED int __pyx_skip_dispatch) {
+  unsigned long __pyx_v_length;
+  unsigned long __pyx_v_i;
+  unsigned long __pyx_v_swapped;
+  unsigned long __pyx_v_ele;
+  unsigned long __pyx_v_temp;
+  __Pyx_memviewslice __pyx_v_np_ary = { 0, 0, { 0 }, { 0 }, { 0 } };
+  __Pyx_LocalBuf_ND __pyx_pybuffernd_inp_ary;
+  __Pyx_Buffer __pyx_pybuffer_inp_ary;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  __Pyx_memviewslice __pyx_t_1 = { 0, 0, { 0 }, { 0 }, { 0 } };
+  unsigned long __pyx_t_2;
+  unsigned long __pyx_t_3;
+  int __pyx_t_4;
+  unsigned long __pyx_t_5;
+  unsigned long __pyx_t_6;
+  unsigned long __pyx_t_7;
+  unsigned long __pyx_t_8;
+  unsigned long __pyx_t_9;
+  unsigned long __pyx_t_10;
+  unsigned long __pyx_t_11;
+  unsigned long __pyx_t_12;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("cython_bubblesort_nomagic", 0);
+  __pyx_pybuffer_inp_ary.pybuffer.buf = NULL;
+  __pyx_pybuffer_inp_ary.refcount = 0;
+  __pyx_pybuffernd_inp_ary.data = NULL;
+  __pyx_pybuffernd_inp_ary.rcbuffer = &__pyx_pybuffer_inp_ary;
+  {
+    __Pyx_BufFmt_StackElem __pyx_stack[1];
+    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer, (PyObject*)__pyx_v_inp_ary, &__Pyx_TypeInfo_long, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __pyx_pybuffernd_inp_ary.diminfo[0].strides = __pyx_pybuffernd_inp_ary.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_inp_ary.diminfo[0].shape = __pyx_pybuffernd_inp_ary.rcbuffer->pybuffer.shape[0];
+
+  /* "cython_bubblesort_nomagic.pyx":9
+ *     """ The Cython implementation of Bubblesort with NumPy memoryview."""
+ *     cdef unsigned long length, i, swapped, ele, temp
+ *     cdef long[:] np_ary = inp_ary             # <<<<<<<<<<<<<<
+ *     length = np_ary.shape[0]
+ *     swapped = 1
+ */
+  __pyx_t_1 = __Pyx_PyObject_to_MemoryviewSlice_ds_long(((PyObject *)__pyx_v_inp_ary));
+  if (unlikely(!__pyx_t_1.memview)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_v_np_ary = __pyx_t_1;
+  __pyx_t_1.memview = NULL;
+  __pyx_t_1.data = NULL;
+
+  /* "cython_bubblesort_nomagic.pyx":10
+ *     cdef unsigned long length, i, swapped, ele, temp
+ *     cdef long[:] np_ary = inp_ary
+ *     length = np_ary.shape[0]             # <<<<<<<<<<<<<<
+ *     swapped = 1
+ *     for i in xrange(0, length):
+ */
+  __pyx_v_length = (__pyx_v_np_ary.shape[0]);
+
+  /* "cython_bubblesort_nomagic.pyx":11
+ *     cdef long[:] np_ary = inp_ary
+ *     length = np_ary.shape[0]
+ *     swapped = 1             # <<<<<<<<<<<<<<
+ *     for i in xrange(0, length):
+ *         if swapped:
+ */
+  __pyx_v_swapped = 1;
+
+  /* "cython_bubblesort_nomagic.pyx":12
+ *     length = np_ary.shape[0]
+ *     swapped = 1
+ *     for i in xrange(0, length):             # <<<<<<<<<<<<<<
+ *         if swapped:
+ *             swapped = 0
+ */
+  __pyx_t_2 = __pyx_v_length;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+    __pyx_v_i = __pyx_t_3;
+
+    /* "cython_bubblesort_nomagic.pyx":13
+ *     swapped = 1
+ *     for i in xrange(0, length):
+ *         if swapped:             # <<<<<<<<<<<<<<
+ *             swapped = 0
+ *             for ele in xrange(0, length-i-1):
+ */
+    __pyx_t_4 = (__pyx_v_swapped != 0);
+    if (__pyx_t_4) {
+
+      /* "cython_bubblesort_nomagic.pyx":14
+ *     for i in xrange(0, length):
+ *         if swapped:
+ *             swapped = 0             # <<<<<<<<<<<<<<
+ *             for ele in xrange(0, length-i-1):
+ *                 if np_ary[ele] > np_ary[ele + 1]:
+ */
+      __pyx_v_swapped = 0;
+
+      /* "cython_bubblesort_nomagic.pyx":15
+ *         if swapped:
+ *             swapped = 0
+ *             for ele in xrange(0, length-i-1):             # <<<<<<<<<<<<<<
+ *                 if np_ary[ele] > np_ary[ele + 1]:
+ *                     temp = np_ary[ele + 1]
+ */
+      __pyx_t_5 = ((__pyx_v_length - __pyx_v_i) - 1);
+      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+        __pyx_v_ele = __pyx_t_6;
+
+        /* "cython_bubblesort_nomagic.pyx":16
+ *             swapped = 0
+ *             for ele in xrange(0, length-i-1):
+ *                 if np_ary[ele] > np_ary[ele + 1]:             # <<<<<<<<<<<<<<
+ *                     temp = np_ary[ele + 1]
+ *                     np_ary[ele + 1] = np_ary[ele]
+ */
+        __pyx_t_7 = __pyx_v_ele;
+        __pyx_t_8 = (__pyx_v_ele + 1);
+        __pyx_t_4 = (((*((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_7 * __pyx_v_np_ary.strides[0]) ))) > (*((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_8 * __pyx_v_np_ary.strides[0]) )))) != 0);
+        if (__pyx_t_4) {
+
+          /* "cython_bubblesort_nomagic.pyx":17
+ *             for ele in xrange(0, length-i-1):
+ *                 if np_ary[ele] > np_ary[ele + 1]:
+ *                     temp = np_ary[ele + 1]             # <<<<<<<<<<<<<<
+ *                     np_ary[ele + 1] = np_ary[ele]
+ *                     np_ary[ele] = temp
+ */
+          __pyx_t_9 = (__pyx_v_ele + 1);
+          __pyx_v_temp = (*((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_9 * __pyx_v_np_ary.strides[0]) )));
+
+          /* "cython_bubblesort_nomagic.pyx":18
+ *                 if np_ary[ele] > np_ary[ele + 1]:
+ *                     temp = np_ary[ele + 1]
+ *                     np_ary[ele + 1] = np_ary[ele]             # <<<<<<<<<<<<<<
+ *                     np_ary[ele] = temp
+ *                     swapped = 1
+ */
+          __pyx_t_10 = __pyx_v_ele;
+          __pyx_t_11 = (__pyx_v_ele + 1);
+          *((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_11 * __pyx_v_np_ary.strides[0]) )) = (*((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_10 * __pyx_v_np_ary.strides[0]) )));
+
+          /* "cython_bubblesort_nomagic.pyx":19
+ *                     temp = np_ary[ele + 1]
+ *                     np_ary[ele + 1] = np_ary[ele]
+ *                     np_ary[ele] = temp             # <<<<<<<<<<<<<<
+ *                     swapped = 1
+ *     return inp_ary
+ */
+          __pyx_t_12 = __pyx_v_ele;
+          *((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_12 * __pyx_v_np_ary.strides[0]) )) = __pyx_v_temp;
+
+          /* "cython_bubblesort_nomagic.pyx":20
+ *                     np_ary[ele + 1] = np_ary[ele]
+ *                     np_ary[ele] = temp
+ *                     swapped = 1             # <<<<<<<<<<<<<<
+ *     return inp_ary
+ */
+          __pyx_v_swapped = 1;
+          goto __pyx_L8;
+        }
+        __pyx_L8:;
+      }
+      goto __pyx_L5;
+    }
+    __pyx_L5:;
+  }
+
+  /* "cython_bubblesort_nomagic.pyx":21
+ *                     np_ary[ele] = temp
+ *                     swapped = 1
+ *     return inp_ary             # <<<<<<<<<<<<<<
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(((PyObject *)__pyx_v_inp_ary));
+  __pyx_r = ((PyObject *)__pyx_v_inp_ary);
+  goto __pyx_L0;
+
+  /* "cython_bubblesort_nomagic.pyx":6
+ * @cython.boundscheck(False)
+ * @cython.wraparound(False)
+ * cpdef cython_bubblesort_nomagic(np.ndarray[long, ndim=1] inp_ary):             # <<<<<<<<<<<<<<
+ *     """ The Cython implementation of Bubblesort with NumPy memoryview."""
+ *     cdef unsigned long length, i, swapped, ele, temp
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __PYX_XDEC_MEMVIEW(&__pyx_t_1, 1);
+  { PyObject *__pyx_type, *__pyx_value, *__pyx_tb;
+    __Pyx_ErrFetch(&__pyx_type, &__pyx_value, &__pyx_tb);
+    __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer);
+  __Pyx_ErrRestore(__pyx_type, __pyx_value, __pyx_tb);}
+  __Pyx_AddTraceback("cython_bubblesort_nomagic.cython_bubblesort_nomagic", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  goto __pyx_L2;
+  __pyx_L0:;
+  __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer);
+  __pyx_L2:;
+  __PYX_XDEC_MEMVIEW(&__pyx_v_np_ary, 1);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* Python wrapper */
+static PyObject *__pyx_pw_25cython_bubblesort_nomagic_1cython_bubblesort_nomagic(PyObject *__pyx_self, PyObject *__pyx_v_inp_ary); /*proto*/
+static char __pyx_doc_25cython_bubblesort_nomagic_cython_bubblesort_nomagic[] = " The Cython implementation of Bubblesort with NumPy memoryview.";
+static PyObject *__pyx_pw_25cython_bubblesort_nomagic_1cython_bubblesort_nomagic(PyObject *__pyx_self, PyObject *__pyx_v_inp_ary) {
+  CYTHON_UNUSED int __pyx_lineno = 0;
+  CYTHON_UNUSED const char *__pyx_filename = NULL;
+  CYTHON_UNUSED int __pyx_clineno = 0;
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("cython_bubblesort_nomagic (wrapper)", 0);
+  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_inp_ary), __pyx_ptype_5numpy_ndarray, 1, "inp_ary", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_r = __pyx_pf_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(__pyx_self, ((PyArrayObject *)__pyx_v_inp_ary));
+
+  /* function exit code */
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_pf_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_inp_ary) {
+  __Pyx_LocalBuf_ND __pyx_pybuffernd_inp_ary;
+  __Pyx_Buffer __pyx_pybuffer_inp_ary;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("cython_bubblesort_nomagic", 0);
+  __pyx_pybuffer_inp_ary.pybuffer.buf = NULL;
+  __pyx_pybuffer_inp_ary.refcount = 0;
+  __pyx_pybuffernd_inp_ary.data = NULL;
+  __pyx_pybuffernd_inp_ary.rcbuffer = &__pyx_pybuffer_inp_ary;
+  {
+    __Pyx_BufFmt_StackElem __pyx_stack[1];
+    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer, (PyObject*)__pyx_v_inp_ary, &__Pyx_TypeInfo_long, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __pyx_pybuffernd_inp_ary.diminfo[0].strides = __pyx_pybuffernd_inp_ary.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_inp_ary.diminfo[0].shape = __pyx_pybuffernd_inp_ary.rcbuffer->pybuffer.shape[0];
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __pyx_f_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(__pyx_v_inp_ary, 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  { PyObject *__pyx_type, *__pyx_value, *__pyx_tb;
+    __Pyx_ErrFetch(&__pyx_type, &__pyx_value, &__pyx_tb);
+    __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer);
+  __Pyx_ErrRestore(__pyx_type, __pyx_value, __pyx_tb);}
+  __Pyx_AddTraceback("cython_bubblesort_nomagic.cython_bubblesort_nomagic", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  goto __pyx_L2;
+  __pyx_L0:;
+  __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer);
+  __pyx_L2:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":194
+ *         # experimental exception made for __getbuffer__ and __releasebuffer__
+ *         # -- the details of this may change.
+ *         def __getbuffer__(ndarray self, Py_buffer* info, int flags):             # <<<<<<<<<<<<<<
+ *             # This implementation of getbuffer is geared towards Cython
+ *             # requirements, and does not yet fullfill the PEP.
+ */
+
+/* Python wrapper */
+static CYTHON_UNUSED int __pyx_pw_5numpy_7ndarray_1__getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
+static CYTHON_UNUSED int __pyx_pw_5numpy_7ndarray_1__getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0);
+  __pyx_r = __pyx_pf_5numpy_7ndarray___getbuffer__(((PyArrayObject *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
+  int __pyx_v_copy_shape;
+  int __pyx_v_i;
+  int __pyx_v_ndim;
+  int __pyx_v_endian_detector;
+  int __pyx_v_little_endian;
+  int __pyx_v_t;
+  char *__pyx_v_f;
+  PyArray_Descr *__pyx_v_descr = 0;
+  int __pyx_v_offset;
+  int __pyx_v_hasfields;
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  PyObject *__pyx_t_4 = NULL;
+  int __pyx_t_5;
+  int __pyx_t_6;
+  int __pyx_t_7;
+  PyObject *__pyx_t_8 = NULL;
+  char *__pyx_t_9;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__getbuffer__", 0);
+  if (__pyx_v_info != NULL) {
+    __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
+    __Pyx_GIVEREF(__pyx_v_info->obj);
+  }
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":200
+ *             # of flags
+ * 
+ *             if info == NULL: return             # <<<<<<<<<<<<<<
+ * 
+ *             cdef int copy_shape, i, ndim
+ */
+  __pyx_t_1 = ((__pyx_v_info == NULL) != 0);
+  if (__pyx_t_1) {
+    __pyx_r = 0;
+    goto __pyx_L0;
+  }
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":203
+ * 
+ *             cdef int copy_shape, i, ndim
+ *             cdef int endian_detector = 1             # <<<<<<<<<<<<<<
+ *             cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)
+ * 
+ */
+  __pyx_v_endian_detector = 1;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":204
+ *             cdef int copy_shape, i, ndim
+ *             cdef int endian_detector = 1
+ *             cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)             # <<<<<<<<<<<<<<
+ * 
+ *             ndim = PyArray_NDIM(self)
+ */
+  __pyx_v_little_endian = ((((char *)(&__pyx_v_endian_detector))[0]) != 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":206
+ *             cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)
+ * 
+ *             ndim = PyArray_NDIM(self)             # <<<<<<<<<<<<<<
+ * 
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
+ */
+  __pyx_v_ndim = PyArray_NDIM(__pyx_v_self);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":208
+ *             ndim = PyArray_NDIM(self)
+ * 
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):             # <<<<<<<<<<<<<<
+ *                 copy_shape = 1
+ *             else:
+ */
+  __pyx_t_1 = (((sizeof(npy_intp)) != (sizeof(Py_ssize_t))) != 0);
+  if (__pyx_t_1) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":209
+ * 
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
+ *                 copy_shape = 1             # <<<<<<<<<<<<<<
+ *             else:
+ *                 copy_shape = 0
+ */
+    __pyx_v_copy_shape = 1;
+    goto __pyx_L4;
+  }
+  /*else*/ {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":211
+ *                 copy_shape = 1
+ *             else:
+ *                 copy_shape = 0             # <<<<<<<<<<<<<<
+ * 
+ *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
+ */
+    __pyx_v_copy_shape = 0;
+  }
+  __pyx_L4:;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":213
+ *                 copy_shape = 0
+ * 
+ *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)             # <<<<<<<<<<<<<<
+ *                 and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)):
+ *                 raise ValueError(u"ndarray is not C contiguous")
+ */
+  __pyx_t_1 = (((__pyx_v_flags & PyBUF_C_CONTIGUOUS) == PyBUF_C_CONTIGUOUS) != 0);
+  if (__pyx_t_1) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":214
+ * 
+ *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
+ *                 and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)):             # <<<<<<<<<<<<<<
+ *                 raise ValueError(u"ndarray is not C contiguous")
+ * 
+ */
+    __pyx_t_2 = ((!(PyArray_CHKFLAGS(__pyx_v_self, NPY_C_CONTIGUOUS) != 0)) != 0);
+    __pyx_t_3 = __pyx_t_2;
+  } else {
+    __pyx_t_3 = __pyx_t_1;
+  }
+  if (__pyx_t_3) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":215
+ *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
+ *                 and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)):
+ *                 raise ValueError(u"ndarray is not C contiguous")             # <<<<<<<<<<<<<<
+ * 
+ *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
+ */
+    __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __Pyx_Raise(__pyx_t_4, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    {__pyx_filename = __pyx_f[1]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":217
+ *                 raise ValueError(u"ndarray is not C contiguous")
+ * 
+ *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)             # <<<<<<<<<<<<<<
+ *                 and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)):
+ *                 raise ValueError(u"ndarray is not Fortran contiguous")
+ */
+  __pyx_t_3 = (((__pyx_v_flags & PyBUF_F_CONTIGUOUS) == PyBUF_F_CONTIGUOUS) != 0);
+  if (__pyx_t_3) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":218
+ * 
+ *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
+ *                 and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)):             # <<<<<<<<<<<<<<
+ *                 raise ValueError(u"ndarray is not Fortran contiguous")
+ * 
+ */
+    __pyx_t_1 = ((!(PyArray_CHKFLAGS(__pyx_v_self, NPY_F_CONTIGUOUS) != 0)) != 0);
+    __pyx_t_2 = __pyx_t_1;
+  } else {
+    __pyx_t_2 = __pyx_t_3;
+  }
+  if (__pyx_t_2) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":219
+ *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
+ *                 and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)):
+ *                 raise ValueError(u"ndarray is not Fortran contiguous")             # <<<<<<<<<<<<<<
+ * 
+ *             info.buf = PyArray_DATA(self)
+ */
+    __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __Pyx_Raise(__pyx_t_4, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    {__pyx_filename = __pyx_f[1]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":221
+ *                 raise ValueError(u"ndarray is not Fortran contiguous")
+ * 
+ *             info.buf = PyArray_DATA(self)             # <<<<<<<<<<<<<<
+ *             info.ndim = ndim
+ *             if copy_shape:
+ */
+  __pyx_v_info->buf = PyArray_DATA(__pyx_v_self);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":222
+ * 
+ *             info.buf = PyArray_DATA(self)
+ *             info.ndim = ndim             # <<<<<<<<<<<<<<
+ *             if copy_shape:
+ *                 # Allocate new buffer for strides and shape info.
+ */
+  __pyx_v_info->ndim = __pyx_v_ndim;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":223
+ *             info.buf = PyArray_DATA(self)
+ *             info.ndim = ndim
+ *             if copy_shape:             # <<<<<<<<<<<<<<
+ *                 # Allocate new buffer for strides and shape info.
+ *                 # This is allocated as one block, strides first.
+ */
+  __pyx_t_2 = (__pyx_v_copy_shape != 0);
+  if (__pyx_t_2) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":226
+ *                 # Allocate new buffer for strides and shape info.
+ *                 # This is allocated as one block, strides first.
+ *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)             # <<<<<<<<<<<<<<
+ *                 info.shape = info.strides + ndim
+ *                 for i in range(ndim):
+ */
+    __pyx_v_info->strides = ((Py_ssize_t *)malloc((((sizeof(Py_ssize_t)) * ((size_t)__pyx_v_ndim)) * 2)));
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":227
+ *                 # This is allocated as one block, strides first.
+ *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)
+ *                 info.shape = info.strides + ndim             # <<<<<<<<<<<<<<
+ *                 for i in range(ndim):
+ *                     info.strides[i] = PyArray_STRIDES(self)[i]
+ */
+    __pyx_v_info->shape = (__pyx_v_info->strides + __pyx_v_ndim);
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":228
+ *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)
+ *                 info.shape = info.strides + ndim
+ *                 for i in range(ndim):             # <<<<<<<<<<<<<<
+ *                     info.strides[i] = PyArray_STRIDES(self)[i]
+ *                     info.shape[i] = PyArray_DIMS(self)[i]
+ */
+    __pyx_t_5 = __pyx_v_ndim;
+    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+      __pyx_v_i = __pyx_t_6;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":229
+ *                 info.shape = info.strides + ndim
+ *                 for i in range(ndim):
+ *                     info.strides[i] = PyArray_STRIDES(self)[i]             # <<<<<<<<<<<<<<
+ *                     info.shape[i] = PyArray_DIMS(self)[i]
+ *             else:
+ */
+      (__pyx_v_info->strides[__pyx_v_i]) = (PyArray_STRIDES(__pyx_v_self)[__pyx_v_i]);
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":230
+ *                 for i in range(ndim):
+ *                     info.strides[i] = PyArray_STRIDES(self)[i]
+ *                     info.shape[i] = PyArray_DIMS(self)[i]             # <<<<<<<<<<<<<<
+ *             else:
+ *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
+ */
+      (__pyx_v_info->shape[__pyx_v_i]) = (PyArray_DIMS(__pyx_v_self)[__pyx_v_i]);
+    }
+    goto __pyx_L7;
+  }
+  /*else*/ {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":232
+ *                     info.shape[i] = PyArray_DIMS(self)[i]
+ *             else:
+ *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)             # <<<<<<<<<<<<<<
+ *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)
+ *             info.suboffsets = NULL
+ */
+    __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(__pyx_v_self));
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":233
+ *             else:
+ *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
+ *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)             # <<<<<<<<<<<<<<
+ *             info.suboffsets = NULL
+ *             info.itemsize = PyArray_ITEMSIZE(self)
+ */
+    __pyx_v_info->shape = ((Py_ssize_t *)PyArray_DIMS(__pyx_v_self));
+  }
+  __pyx_L7:;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":234
+ *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
+ *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)
+ *             info.suboffsets = NULL             # <<<<<<<<<<<<<<
+ *             info.itemsize = PyArray_ITEMSIZE(self)
+ *             info.readonly = not PyArray_ISWRITEABLE(self)
+ */
+  __pyx_v_info->suboffsets = NULL;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":235
+ *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)
+ *             info.suboffsets = NULL
+ *             info.itemsize = PyArray_ITEMSIZE(self)             # <<<<<<<<<<<<<<
+ *             info.readonly = not PyArray_ISWRITEABLE(self)
+ * 
+ */
+  __pyx_v_info->itemsize = PyArray_ITEMSIZE(__pyx_v_self);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":236
+ *             info.suboffsets = NULL
+ *             info.itemsize = PyArray_ITEMSIZE(self)
+ *             info.readonly = not PyArray_ISWRITEABLE(self)             # <<<<<<<<<<<<<<
+ * 
+ *             cdef int t
+ */
+  __pyx_v_info->readonly = (!(PyArray_ISWRITEABLE(__pyx_v_self) != 0));
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":239
+ * 
+ *             cdef int t
+ *             cdef char* f = NULL             # <<<<<<<<<<<<<<
+ *             cdef dtype descr = self.descr
+ *             cdef list stack
+ */
+  __pyx_v_f = NULL;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":240
+ *             cdef int t
+ *             cdef char* f = NULL
+ *             cdef dtype descr = self.descr             # <<<<<<<<<<<<<<
+ *             cdef list stack
+ *             cdef int offset
+ */
+  __pyx_t_4 = ((PyObject *)__pyx_v_self->descr);
+  __Pyx_INCREF(__pyx_t_4);
+  __pyx_v_descr = ((PyArray_Descr *)__pyx_t_4);
+  __pyx_t_4 = 0;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":244
+ *             cdef int offset
+ * 
+ *             cdef bint hasfields = PyDataType_HASFIELDS(descr)             # <<<<<<<<<<<<<<
+ * 
+ *             if not hasfields and not copy_shape:
+ */
+  __pyx_v_hasfields = PyDataType_HASFIELDS(__pyx_v_descr);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":246
+ *             cdef bint hasfields = PyDataType_HASFIELDS(descr)
+ * 
+ *             if not hasfields and not copy_shape:             # <<<<<<<<<<<<<<
+ *                 # do not call releasebuffer
+ *                 info.obj = None
+ */
+  __pyx_t_2 = ((!(__pyx_v_hasfields != 0)) != 0);
+  if (__pyx_t_2) {
+    __pyx_t_3 = ((!(__pyx_v_copy_shape != 0)) != 0);
+    __pyx_t_1 = __pyx_t_3;
+  } else {
+    __pyx_t_1 = __pyx_t_2;
+  }
+  if (__pyx_t_1) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":248
+ *             if not hasfields and not copy_shape:
+ *                 # do not call releasebuffer
+ *                 info.obj = None             # <<<<<<<<<<<<<<
+ *             else:
+ *                 # need to call releasebuffer
+ */
+    __Pyx_INCREF(Py_None);
+    __Pyx_GIVEREF(Py_None);
+    __Pyx_GOTREF(__pyx_v_info->obj);
+    __Pyx_DECREF(__pyx_v_info->obj);
+    __pyx_v_info->obj = Py_None;
+    goto __pyx_L10;
+  }
+  /*else*/ {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":251
+ *             else:
+ *                 # need to call releasebuffer
+ *                 info.obj = self             # <<<<<<<<<<<<<<
+ * 
+ *             if not hasfields:
+ */
+    __Pyx_INCREF(((PyObject *)__pyx_v_self));
+    __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
+    __Pyx_GOTREF(__pyx_v_info->obj);
+    __Pyx_DECREF(__pyx_v_info->obj);
+    __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
+  }
+  __pyx_L10:;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":253
+ *                 info.obj = self
+ * 
+ *             if not hasfields:             # <<<<<<<<<<<<<<
+ *                 t = descr.type_num
+ *                 if ((descr.byteorder == c'>' and little_endian) or
+ */
+  __pyx_t_1 = ((!(__pyx_v_hasfields != 0)) != 0);
+  if (__pyx_t_1) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":254
+ * 
+ *             if not hasfields:
+ *                 t = descr.type_num             # <<<<<<<<<<<<<<
+ *                 if ((descr.byteorder == c'>' and little_endian) or
+ *                     (descr.byteorder == c'<' and not little_endian)):
+ */
+    __pyx_t_5 = __pyx_v_descr->type_num;
+    __pyx_v_t = __pyx_t_5;
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":255
+ *             if not hasfields:
+ *                 t = descr.type_num
+ *                 if ((descr.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<
+ *                     (descr.byteorder == c'<' and not little_endian)):
+ *                     raise ValueError(u"Non-native byte order not supported")
+ */
+    __pyx_t_1 = ((__pyx_v_descr->byteorder == '>') != 0);
+    if (__pyx_t_1) {
+      __pyx_t_2 = (__pyx_v_little_endian != 0);
+    } else {
+      __pyx_t_2 = __pyx_t_1;
+    }
+    if (!__pyx_t_2) {
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":256
+ *                 t = descr.type_num
+ *                 if ((descr.byteorder == c'>' and little_endian) or
+ *                     (descr.byteorder == c'<' and not little_endian)):             # <<<<<<<<<<<<<<
+ *                     raise ValueError(u"Non-native byte order not supported")
+ *                 if   t == NPY_BYTE:        f = "b"
+ */
+      __pyx_t_1 = ((__pyx_v_descr->byteorder == '<') != 0);
+      if (__pyx_t_1) {
+        __pyx_t_3 = ((!(__pyx_v_little_endian != 0)) != 0);
+        __pyx_t_7 = __pyx_t_3;
+      } else {
+        __pyx_t_7 = __pyx_t_1;
+      }
+      __pyx_t_1 = __pyx_t_7;
+    } else {
+      __pyx_t_1 = __pyx_t_2;
+    }
+    if (__pyx_t_1) {
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":257
+ *                 if ((descr.byteorder == c'>' and little_endian) or
+ *                     (descr.byteorder == c'<' and not little_endian)):
+ *                     raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
+ *                 if   t == NPY_BYTE:        f = "b"
+ *                 elif t == NPY_UBYTE:       f = "B"
+ */
+      __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      {__pyx_filename = __pyx_f[1]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":274
+ *                 elif t == NPY_CDOUBLE:     f = "Zd"
+ *                 elif t == NPY_CLONGDOUBLE: f = "Zg"
+ *                 elif t == NPY_OBJECT:      f = "O"             # <<<<<<<<<<<<<<
+ *                 else:
+ *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
+ */
+    switch (__pyx_v_t) {
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":258
+ *                     (descr.byteorder == c'<' and not little_endian)):
+ *                     raise ValueError(u"Non-native byte order not supported")
+ *                 if   t == NPY_BYTE:        f = "b"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_UBYTE:       f = "B"
+ *                 elif t == NPY_SHORT:       f = "h"
+ */
+      case NPY_BYTE:
+      __pyx_v_f = __pyx_k_b;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":259
+ *                     raise ValueError(u"Non-native byte order not supported")
+ *                 if   t == NPY_BYTE:        f = "b"
+ *                 elif t == NPY_UBYTE:       f = "B"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_SHORT:       f = "h"
+ *                 elif t == NPY_USHORT:      f = "H"
+ */
+      case NPY_UBYTE:
+      __pyx_v_f = __pyx_k_B;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":260
+ *                 if   t == NPY_BYTE:        f = "b"
+ *                 elif t == NPY_UBYTE:       f = "B"
+ *                 elif t == NPY_SHORT:       f = "h"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_USHORT:      f = "H"
+ *                 elif t == NPY_INT:         f = "i"
+ */
+      case NPY_SHORT:
+      __pyx_v_f = __pyx_k_h;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":261
+ *                 elif t == NPY_UBYTE:       f = "B"
+ *                 elif t == NPY_SHORT:       f = "h"
+ *                 elif t == NPY_USHORT:      f = "H"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_INT:         f = "i"
+ *                 elif t == NPY_UINT:        f = "I"
+ */
+      case NPY_USHORT:
+      __pyx_v_f = __pyx_k_H;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":262
+ *                 elif t == NPY_SHORT:       f = "h"
+ *                 elif t == NPY_USHORT:      f = "H"
+ *                 elif t == NPY_INT:         f = "i"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_UINT:        f = "I"
+ *                 elif t == NPY_LONG:        f = "l"
+ */
+      case NPY_INT:
+      __pyx_v_f = __pyx_k_i;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":263
+ *                 elif t == NPY_USHORT:      f = "H"
+ *                 elif t == NPY_INT:         f = "i"
+ *                 elif t == NPY_UINT:        f = "I"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_LONG:        f = "l"
+ *                 elif t == NPY_ULONG:       f = "L"
+ */
+      case NPY_UINT:
+      __pyx_v_f = __pyx_k_I;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":264
+ *                 elif t == NPY_INT:         f = "i"
+ *                 elif t == NPY_UINT:        f = "I"
+ *                 elif t == NPY_LONG:        f = "l"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_ULONG:       f = "L"
+ *                 elif t == NPY_LONGLONG:    f = "q"
+ */
+      case NPY_LONG:
+      __pyx_v_f = __pyx_k_l;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":265
+ *                 elif t == NPY_UINT:        f = "I"
+ *                 elif t == NPY_LONG:        f = "l"
+ *                 elif t == NPY_ULONG:       f = "L"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_LONGLONG:    f = "q"
+ *                 elif t == NPY_ULONGLONG:   f = "Q"
+ */
+      case NPY_ULONG:
+      __pyx_v_f = __pyx_k_L;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":266
+ *                 elif t == NPY_LONG:        f = "l"
+ *                 elif t == NPY_ULONG:       f = "L"
+ *                 elif t == NPY_LONGLONG:    f = "q"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_ULONGLONG:   f = "Q"
+ *                 elif t == NPY_FLOAT:       f = "f"
+ */
+      case NPY_LONGLONG:
+      __pyx_v_f = __pyx_k_q;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":267
+ *                 elif t == NPY_ULONG:       f = "L"
+ *                 elif t == NPY_LONGLONG:    f = "q"
+ *                 elif t == NPY_ULONGLONG:   f = "Q"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_FLOAT:       f = "f"
+ *                 elif t == NPY_DOUBLE:      f = "d"
+ */
+      case NPY_ULONGLONG:
+      __pyx_v_f = __pyx_k_Q;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":268
+ *                 elif t == NPY_LONGLONG:    f = "q"
+ *                 elif t == NPY_ULONGLONG:   f = "Q"
+ *                 elif t == NPY_FLOAT:       f = "f"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_DOUBLE:      f = "d"
+ *                 elif t == NPY_LONGDOUBLE:  f = "g"
+ */
+      case NPY_FLOAT:
+      __pyx_v_f = __pyx_k_f;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":269
+ *                 elif t == NPY_ULONGLONG:   f = "Q"
+ *                 elif t == NPY_FLOAT:       f = "f"
+ *                 elif t == NPY_DOUBLE:      f = "d"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_LONGDOUBLE:  f = "g"
+ *                 elif t == NPY_CFLOAT:      f = "Zf"
+ */
+      case NPY_DOUBLE:
+      __pyx_v_f = __pyx_k_d;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":270
+ *                 elif t == NPY_FLOAT:       f = "f"
+ *                 elif t == NPY_DOUBLE:      f = "d"
+ *                 elif t == NPY_LONGDOUBLE:  f = "g"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_CFLOAT:      f = "Zf"
+ *                 elif t == NPY_CDOUBLE:     f = "Zd"
+ */
+      case NPY_LONGDOUBLE:
+      __pyx_v_f = __pyx_k_g;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":271
+ *                 elif t == NPY_DOUBLE:      f = "d"
+ *                 elif t == NPY_LONGDOUBLE:  f = "g"
+ *                 elif t == NPY_CFLOAT:      f = "Zf"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_CDOUBLE:     f = "Zd"
+ *                 elif t == NPY_CLONGDOUBLE: f = "Zg"
+ */
+      case NPY_CFLOAT:
+      __pyx_v_f = __pyx_k_Zf;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":272
+ *                 elif t == NPY_LONGDOUBLE:  f = "g"
+ *                 elif t == NPY_CFLOAT:      f = "Zf"
+ *                 elif t == NPY_CDOUBLE:     f = "Zd"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_CLONGDOUBLE: f = "Zg"
+ *                 elif t == NPY_OBJECT:      f = "O"
+ */
+      case NPY_CDOUBLE:
+      __pyx_v_f = __pyx_k_Zd;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":273
+ *                 elif t == NPY_CFLOAT:      f = "Zf"
+ *                 elif t == NPY_CDOUBLE:     f = "Zd"
+ *                 elif t == NPY_CLONGDOUBLE: f = "Zg"             # <<<<<<<<<<<<<<
+ *                 elif t == NPY_OBJECT:      f = "O"
+ *                 else:
+ */
+      case NPY_CLONGDOUBLE:
+      __pyx_v_f = __pyx_k_Zg;
+      break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":274
+ *                 elif t == NPY_CDOUBLE:     f = "Zd"
+ *                 elif t == NPY_CLONGDOUBLE: f = "Zg"
+ *                 elif t == NPY_OBJECT:      f = "O"             # <<<<<<<<<<<<<<
+ *                 else:
+ *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
+ */
+      case NPY_OBJECT:
+      __pyx_v_f = __pyx_k_O;
+      break;
+      default:
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":276
+ *                 elif t == NPY_OBJECT:      f = "O"
+ *                 else:
+ *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)             # <<<<<<<<<<<<<<
+ *                 info.format = f
+ *                 return
+ */
+      __pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_t); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_8 = PyUnicode_Format(__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_t_4); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_8);
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_8);
+      __Pyx_GIVEREF(__pyx_t_8);
+      __pyx_t_8 = 0;
+      __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_8);
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __Pyx_Raise(__pyx_t_8, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
+      {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      break;
+    }
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":277
+ *                 else:
+ *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
+ *                 info.format = f             # <<<<<<<<<<<<<<
+ *                 return
+ *             else:
+ */
+    __pyx_v_info->format = __pyx_v_f;
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":278
+ *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
+ *                 info.format = f
+ *                 return             # <<<<<<<<<<<<<<
+ *             else:
+ *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
+ */
+    __pyx_r = 0;
+    goto __pyx_L0;
+  }
+  /*else*/ {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":280
+ *                 return
+ *             else:
+ *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)             # <<<<<<<<<<<<<<
+ *                 info.format[0] = c'^' # Native data types, manual alignment
+ *                 offset = 0
+ */
+    __pyx_v_info->format = ((char *)malloc(255));
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":281
+ *             else:
+ *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
+ *                 info.format[0] = c'^' # Native data types, manual alignment             # <<<<<<<<<<<<<<
+ *                 offset = 0
+ *                 f = _util_dtypestring(descr, info.format + 1,
+ */
+    (__pyx_v_info->format[0]) = '^';
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":282
+ *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
+ *                 info.format[0] = c'^' # Native data types, manual alignment
+ *                 offset = 0             # <<<<<<<<<<<<<<
+ *                 f = _util_dtypestring(descr, info.format + 1,
+ *                                       info.format + _buffer_format_string_len,
+ */
+    __pyx_v_offset = 0;
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":283
+ *                 info.format[0] = c'^' # Native data types, manual alignment
+ *                 offset = 0
+ *                 f = _util_dtypestring(descr, info.format + 1,             # <<<<<<<<<<<<<<
+ *                                       info.format + _buffer_format_string_len,
+ *                                       &offset)
+ */
+    __pyx_t_9 = __pyx_f_5numpy__util_dtypestring(__pyx_v_descr, (__pyx_v_info->format + 1), (__pyx_v_info->format + 255), (&__pyx_v_offset)); if (unlikely(__pyx_t_9 == NULL)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_v_f = __pyx_t_9;
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":286
+ *                                       info.format + _buffer_format_string_len,
+ *                                       &offset)
+ *                 f[0] = c'\0' # Terminate format string             # <<<<<<<<<<<<<<
+ * 
+ *         def __releasebuffer__(ndarray self, Py_buffer* info):
+ */
+    (__pyx_v_f[0]) = '\x00';
+  }
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":194
+ *         # experimental exception made for __getbuffer__ and __releasebuffer__
+ *         # -- the details of this may change.
+ *         def __getbuffer__(ndarray self, Py_buffer* info, int flags):             # <<<<<<<<<<<<<<
+ *             # This implementation of getbuffer is geared towards Cython
+ *             # requirements, and does not yet fullfill the PEP.
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_XDECREF(__pyx_t_8);
+  __Pyx_AddTraceback("numpy.ndarray.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  if (__pyx_v_info != NULL && __pyx_v_info->obj != NULL) {
+    __Pyx_GOTREF(__pyx_v_info->obj);
+    __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = NULL;
+  }
+  goto __pyx_L2;
+  __pyx_L0:;
+  if (__pyx_v_info != NULL && __pyx_v_info->obj == Py_None) {
+    __Pyx_GOTREF(Py_None);
+    __Pyx_DECREF(Py_None); __pyx_v_info->obj = NULL;
+  }
+  __pyx_L2:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_descr);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":288
+ *                 f[0] = c'\0' # Terminate format string
+ * 
+ *         def __releasebuffer__(ndarray self, Py_buffer* info):             # <<<<<<<<<<<<<<
+ *             if PyArray_HASFIELDS(self):
+ *                 stdlib.free(info.format)
+ */
+
+/* Python wrapper */
+static CYTHON_UNUSED void __pyx_pw_5numpy_7ndarray_3__releasebuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info); /*proto*/
+static CYTHON_UNUSED void __pyx_pw_5numpy_7ndarray_3__releasebuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__releasebuffer__ (wrapper)", 0);
+  __pyx_pf_5numpy_7ndarray_2__releasebuffer__(((PyArrayObject *)__pyx_v_self), ((Py_buffer *)__pyx_v_info));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info) {
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  __Pyx_RefNannySetupContext("__releasebuffer__", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":289
+ * 
+ *         def __releasebuffer__(ndarray self, Py_buffer* info):
+ *             if PyArray_HASFIELDS(self):             # <<<<<<<<<<<<<<
+ *                 stdlib.free(info.format)
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
+ */
+  __pyx_t_1 = (PyArray_HASFIELDS(__pyx_v_self) != 0);
+  if (__pyx_t_1) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":290
+ *         def __releasebuffer__(ndarray self, Py_buffer* info):
+ *             if PyArray_HASFIELDS(self):
+ *                 stdlib.free(info.format)             # <<<<<<<<<<<<<<
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
+ *                 stdlib.free(info.strides)
+ */
+    free(__pyx_v_info->format);
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":291
+ *             if PyArray_HASFIELDS(self):
+ *                 stdlib.free(info.format)
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):             # <<<<<<<<<<<<<<
+ *                 stdlib.free(info.strides)
+ *                 # info.shape was stored after info.strides in the same block
+ */
+  __pyx_t_1 = (((sizeof(npy_intp)) != (sizeof(Py_ssize_t))) != 0);
+  if (__pyx_t_1) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":292
+ *                 stdlib.free(info.format)
+ *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
+ *                 stdlib.free(info.strides)             # <<<<<<<<<<<<<<
+ *                 # info.shape was stored after info.strides in the same block
+ * 
+ */
+    free(__pyx_v_info->strides);
+    goto __pyx_L4;
+  }
+  __pyx_L4:;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":288
+ *                 f[0] = c'\0' # Terminate format string
+ * 
+ *         def __releasebuffer__(ndarray self, Py_buffer* info):             # <<<<<<<<<<<<<<
+ *             if PyArray_HASFIELDS(self):
+ *                 stdlib.free(info.format)
+ */
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":768
+ * ctypedef npy_cdouble     complex_t
+ * 
+ * cdef inline object PyArray_MultiIterNew1(a):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(1, <void*>a)
+ * 
+ */
+
+static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__pyx_v_a) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("PyArray_MultiIterNew1", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":769
+ * 
+ * cdef inline object PyArray_MultiIterNew1(a):
+ *     return PyArray_MultiIterNew(1, <void*>a)             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline object PyArray_MultiIterNew2(a, b):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = PyArray_MultiIterNew(1, ((void *)__pyx_v_a)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 769; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":768
+ * ctypedef npy_cdouble     complex_t
+ * 
+ * cdef inline object PyArray_MultiIterNew1(a):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(1, <void*>a)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew1", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":771
+ *     return PyArray_MultiIterNew(1, <void*>a)
+ * 
+ * cdef inline object PyArray_MultiIterNew2(a, b):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
+ * 
+ */
+
+static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__pyx_v_a, PyObject *__pyx_v_b) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("PyArray_MultiIterNew2", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":772
+ * 
+ * cdef inline object PyArray_MultiIterNew2(a, b):
+ *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline object PyArray_MultiIterNew3(a, b, c):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = PyArray_MultiIterNew(2, ((void *)__pyx_v_a), ((void *)__pyx_v_b)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 772; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":771
+ *     return PyArray_MultiIterNew(1, <void*>a)
+ * 
+ * cdef inline object PyArray_MultiIterNew2(a, b):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew2", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":774
+ *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
+ * 
+ * cdef inline object PyArray_MultiIterNew3(a, b, c):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
+ * 
+ */
+
+static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("PyArray_MultiIterNew3", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":775
+ * 
+ * cdef inline object PyArray_MultiIterNew3(a, b, c):
+ *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline object PyArray_MultiIterNew4(a, b, c, d):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = PyArray_MultiIterNew(3, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 775; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":774
+ *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
+ * 
+ * cdef inline object PyArray_MultiIterNew3(a, b, c):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew3", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":777
+ *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
+ * 
+ * cdef inline object PyArray_MultiIterNew4(a, b, c, d):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
+ * 
+ */
+
+static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("PyArray_MultiIterNew4", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":778
+ * 
+ * cdef inline object PyArray_MultiIterNew4(a, b, c, d):
+ *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = PyArray_MultiIterNew(4, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 778; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":777
+ *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
+ * 
+ * cdef inline object PyArray_MultiIterNew4(a, b, c, d):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew4", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":780
+ *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
+ * 
+ * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
+ * 
+ */
+
+static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d, PyObject *__pyx_v_e) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("PyArray_MultiIterNew5", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":781
+ * 
+ * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
+ *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = PyArray_MultiIterNew(5, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d), ((void *)__pyx_v_e)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 781; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":780
+ *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
+ * 
+ * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):             # <<<<<<<<<<<<<<
+ *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew5", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":783
+ *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
+ * 
+ * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL:             # <<<<<<<<<<<<<<
+ *     # Recursive utility function used in __getbuffer__ to get format
+ *     # string. The new location in the format string is returned.
+ */
+
+static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *__pyx_v_descr, char *__pyx_v_f, char *__pyx_v_end, int *__pyx_v_offset) {
+  PyArray_Descr *__pyx_v_child = 0;
+  int __pyx_v_endian_detector;
+  int __pyx_v_little_endian;
+  PyObject *__pyx_v_fields = 0;
+  PyObject *__pyx_v_childname = NULL;
+  PyObject *__pyx_v_new_offset = NULL;
+  PyObject *__pyx_v_t = NULL;
+  char *__pyx_r;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  Py_ssize_t __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  int __pyx_t_5;
+  int __pyx_t_6;
+  int __pyx_t_7;
+  int __pyx_t_8;
+  int __pyx_t_9;
+  long __pyx_t_10;
+  char *__pyx_t_11;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("_util_dtypestring", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":790
+ *     cdef int delta_offset
+ *     cdef tuple i
+ *     cdef int endian_detector = 1             # <<<<<<<<<<<<<<
+ *     cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)
+ *     cdef tuple fields
+ */
+  __pyx_v_endian_detector = 1;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":791
+ *     cdef tuple i
+ *     cdef int endian_detector = 1
+ *     cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)             # <<<<<<<<<<<<<<
+ *     cdef tuple fields
+ * 
+ */
+  __pyx_v_little_endian = ((((char *)(&__pyx_v_endian_detector))[0]) != 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":794
+ *     cdef tuple fields
+ * 
+ *     for childname in descr.names:             # <<<<<<<<<<<<<<
+ *         fields = descr.fields[childname]
+ *         child, new_offset = fields
+ */
+  if (unlikely(__pyx_v_descr->names == Py_None)) {
+    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
+    {__pyx_filename = __pyx_f[1]; __pyx_lineno = 794; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __pyx_t_1 = __pyx_v_descr->names; __Pyx_INCREF(__pyx_t_1); __pyx_t_2 = 0;
+  for (;;) {
+    if (__pyx_t_2 >= PyTuple_GET_SIZE(__pyx_t_1)) break;
+    #if CYTHON_COMPILING_IN_CPYTHON
+    __pyx_t_3 = PyTuple_GET_ITEM(__pyx_t_1, __pyx_t_2); __Pyx_INCREF(__pyx_t_3); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 794; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    #else
+    __pyx_t_3 = PySequence_ITEM(__pyx_t_1, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 794; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    #endif
+    __Pyx_XDECREF_SET(__pyx_v_childname, __pyx_t_3);
+    __pyx_t_3 = 0;
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":795
+ * 
+ *     for childname in descr.names:
+ *         fields = descr.fields[childname]             # <<<<<<<<<<<<<<
+ *         child, new_offset = fields
+ * 
+ */
+    __pyx_t_3 = PyObject_GetItem(__pyx_v_descr->fields, __pyx_v_childname); if (unlikely(__pyx_t_3 == NULL)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 795; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+    __Pyx_GOTREF(__pyx_t_3);
+    if (!(likely(PyTuple_CheckExact(__pyx_t_3))||((__pyx_t_3) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_t_3)->tp_name), 0))) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 795; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_XDECREF_SET(__pyx_v_fields, ((PyObject*)__pyx_t_3));
+    __pyx_t_3 = 0;
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":796
+ *     for childname in descr.names:
+ *         fields = descr.fields[childname]
+ *         child, new_offset = fields             # <<<<<<<<<<<<<<
+ * 
+ *         if (end - f) - <int>(new_offset - offset[0]) < 15:
+ */
+    if (likely(__pyx_v_fields != Py_None)) {
+      PyObject* sequence = __pyx_v_fields;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      Py_ssize_t size = Py_SIZE(sequence);
+      #else
+      Py_ssize_t size = PySequence_Size(sequence);
+      #endif
+      if (unlikely(size != 2)) {
+        if (size > 2) __Pyx_RaiseTooManyValuesError(2);
+        else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
+        {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); 
+      __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); 
+      __Pyx_INCREF(__pyx_t_3);
+      __Pyx_INCREF(__pyx_t_4);
+      #else
+      __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      #endif
+    } else {
+      __Pyx_RaiseNoneNotIterableError(); {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_ptype_5numpy_dtype))))) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_XDECREF_SET(__pyx_v_child, ((PyArray_Descr *)__pyx_t_3));
+    __pyx_t_3 = 0;
+    __Pyx_XDECREF_SET(__pyx_v_new_offset, __pyx_t_4);
+    __pyx_t_4 = 0;
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":798
+ *         child, new_offset = fields
+ * 
+ *         if (end - f) - <int>(new_offset - offset[0]) < 15:             # <<<<<<<<<<<<<<
+ *             raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd")
+ * 
+ */
+    __pyx_t_4 = __Pyx_PyInt_From_int((__pyx_v_offset[0])); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 798; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_3 = PyNumber_Subtract(__pyx_v_new_offset, __pyx_t_4); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 798; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    __pyx_t_5 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 798; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __pyx_t_6 = ((((__pyx_v_end - __pyx_v_f) - ((int)__pyx_t_5)) < 15) != 0);
+    if (__pyx_t_6) {
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":799
+ * 
+ *         if (end - f) - <int>(new_offset - offset[0]) < 15:
+ *             raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd")             # <<<<<<<<<<<<<<
+ * 
+ *         if ((child.byteorder == c'>' and little_endian) or
+ */
+      __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      {__pyx_filename = __pyx_f[1]; __pyx_lineno = 799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":801
+ *             raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd")
+ * 
+ *         if ((child.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<
+ *             (child.byteorder == c'<' and not little_endian)):
+ *             raise ValueError(u"Non-native byte order not supported")
+ */
+    __pyx_t_6 = ((__pyx_v_child->byteorder == '>') != 0);
+    if (__pyx_t_6) {
+      __pyx_t_7 = (__pyx_v_little_endian != 0);
+    } else {
+      __pyx_t_7 = __pyx_t_6;
+    }
+    if (!__pyx_t_7) {
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":802
+ * 
+ *         if ((child.byteorder == c'>' and little_endian) or
+ *             (child.byteorder == c'<' and not little_endian)):             # <<<<<<<<<<<<<<
+ *             raise ValueError(u"Non-native byte order not supported")
+ *             # One could encode it in the format string and have Cython
+ */
+      __pyx_t_6 = ((__pyx_v_child->byteorder == '<') != 0);
+      if (__pyx_t_6) {
+        __pyx_t_8 = ((!(__pyx_v_little_endian != 0)) != 0);
+        __pyx_t_9 = __pyx_t_8;
+      } else {
+        __pyx_t_9 = __pyx_t_6;
+      }
+      __pyx_t_6 = __pyx_t_9;
+    } else {
+      __pyx_t_6 = __pyx_t_7;
+    }
+    if (__pyx_t_6) {
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":803
+ *         if ((child.byteorder == c'>' and little_endian) or
+ *             (child.byteorder == c'<' and not little_endian)):
+ *             raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
+ *             # One could encode it in the format string and have Cython
+ *             # complain instead, BUT: < and > in format strings also imply
+ */
+      __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__5, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      {__pyx_filename = __pyx_f[1]; __pyx_lineno = 803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":813
+ * 
+ *         # Output padding bytes
+ *         while offset[0] < new_offset:             # <<<<<<<<<<<<<<
+ *             f[0] = 120 # "x"; pad byte
+ *             f += 1
+ */
+    while (1) {
+      __pyx_t_3 = __Pyx_PyInt_From_int((__pyx_v_offset[0])); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 813; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_t_3, __pyx_v_new_offset, Py_LT); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 813; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 813; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (!__pyx_t_6) break;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":814
+ *         # Output padding bytes
+ *         while offset[0] < new_offset:
+ *             f[0] = 120 # "x"; pad byte             # <<<<<<<<<<<<<<
+ *             f += 1
+ *             offset[0] += 1
+ */
+      (__pyx_v_f[0]) = 120;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":815
+ *         while offset[0] < new_offset:
+ *             f[0] = 120 # "x"; pad byte
+ *             f += 1             # <<<<<<<<<<<<<<
+ *             offset[0] += 1
+ * 
+ */
+      __pyx_v_f = (__pyx_v_f + 1);
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":816
+ *             f[0] = 120 # "x"; pad byte
+ *             f += 1
+ *             offset[0] += 1             # <<<<<<<<<<<<<<
+ * 
+ *         offset[0] += child.itemsize
+ */
+      __pyx_t_10 = 0;
+      (__pyx_v_offset[__pyx_t_10]) = ((__pyx_v_offset[__pyx_t_10]) + 1);
+    }
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":818
+ *             offset[0] += 1
+ * 
+ *         offset[0] += child.itemsize             # <<<<<<<<<<<<<<
+ * 
+ *         if not PyDataType_HASFIELDS(child):
+ */
+    __pyx_t_10 = 0;
+    (__pyx_v_offset[__pyx_t_10]) = ((__pyx_v_offset[__pyx_t_10]) + __pyx_v_child->elsize);
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":820
+ *         offset[0] += child.itemsize
+ * 
+ *         if not PyDataType_HASFIELDS(child):             # <<<<<<<<<<<<<<
+ *             t = child.type_num
+ *             if end - f < 5:
+ */
+    __pyx_t_6 = ((!(PyDataType_HASFIELDS(__pyx_v_child) != 0)) != 0);
+    if (__pyx_t_6) {
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":821
+ * 
+ *         if not PyDataType_HASFIELDS(child):
+ *             t = child.type_num             # <<<<<<<<<<<<<<
+ *             if end - f < 5:
+ *                 raise RuntimeError(u"Format string allocated too short.")
+ */
+      __pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_child->type_num); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 821; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __Pyx_XDECREF_SET(__pyx_v_t, __pyx_t_4);
+      __pyx_t_4 = 0;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":822
+ *         if not PyDataType_HASFIELDS(child):
+ *             t = child.type_num
+ *             if end - f < 5:             # <<<<<<<<<<<<<<
+ *                 raise RuntimeError(u"Format string allocated too short.")
+ * 
+ */
+      __pyx_t_6 = (((__pyx_v_end - __pyx_v_f) < 5) != 0);
+      if (__pyx_t_6) {
+
+        /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":823
+ *             t = child.type_num
+ *             if end - f < 5:
+ *                 raise RuntimeError(u"Format string allocated too short.")             # <<<<<<<<<<<<<<
+ * 
+ *             # Until ticket #99 is fixed, use integers to avoid warnings
+ */
+        __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_4);
+        __Pyx_Raise(__pyx_t_4, 0, 0, 0);
+        __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+        {__pyx_filename = __pyx_f[1]; __pyx_lineno = 823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":826
+ * 
+ *             # Until ticket #99 is fixed, use integers to avoid warnings
+ *             if   t == NPY_BYTE:        f[0] =  98 #"b"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_UBYTE:       f[0] =  66 #"B"
+ *             elif t == NPY_SHORT:       f[0] = 104 #"h"
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_BYTE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 826; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 826; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 826; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 98;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":827
+ *             # Until ticket #99 is fixed, use integers to avoid warnings
+ *             if   t == NPY_BYTE:        f[0] =  98 #"b"
+ *             elif t == NPY_UBYTE:       f[0] =  66 #"B"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_SHORT:       f[0] = 104 #"h"
+ *             elif t == NPY_USHORT:      f[0] =  72 #"H"
+ */
+      __pyx_t_3 = PyInt_FromLong(NPY_UBYTE); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 827; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 827; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 827; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 66;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":828
+ *             if   t == NPY_BYTE:        f[0] =  98 #"b"
+ *             elif t == NPY_UBYTE:       f[0] =  66 #"B"
+ *             elif t == NPY_SHORT:       f[0] = 104 #"h"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_USHORT:      f[0] =  72 #"H"
+ *             elif t == NPY_INT:         f[0] = 105 #"i"
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_SHORT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 828; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 828; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 828; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 104;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":829
+ *             elif t == NPY_UBYTE:       f[0] =  66 #"B"
+ *             elif t == NPY_SHORT:       f[0] = 104 #"h"
+ *             elif t == NPY_USHORT:      f[0] =  72 #"H"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_INT:         f[0] = 105 #"i"
+ *             elif t == NPY_UINT:        f[0] =  73 #"I"
+ */
+      __pyx_t_3 = PyInt_FromLong(NPY_USHORT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 829; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 829; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 829; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 72;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":830
+ *             elif t == NPY_SHORT:       f[0] = 104 #"h"
+ *             elif t == NPY_USHORT:      f[0] =  72 #"H"
+ *             elif t == NPY_INT:         f[0] = 105 #"i"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_UINT:        f[0] =  73 #"I"
+ *             elif t == NPY_LONG:        f[0] = 108 #"l"
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_INT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 830; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 830; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 830; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 105;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":831
+ *             elif t == NPY_USHORT:      f[0] =  72 #"H"
+ *             elif t == NPY_INT:         f[0] = 105 #"i"
+ *             elif t == NPY_UINT:        f[0] =  73 #"I"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_LONG:        f[0] = 108 #"l"
+ *             elif t == NPY_ULONG:       f[0] = 76  #"L"
+ */
+      __pyx_t_3 = PyInt_FromLong(NPY_UINT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 831; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 831; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 831; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 73;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":832
+ *             elif t == NPY_INT:         f[0] = 105 #"i"
+ *             elif t == NPY_UINT:        f[0] =  73 #"I"
+ *             elif t == NPY_LONG:        f[0] = 108 #"l"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_ULONG:       f[0] = 76  #"L"
+ *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_LONG); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 832; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 832; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 832; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 108;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":833
+ *             elif t == NPY_UINT:        f[0] =  73 #"I"
+ *             elif t == NPY_LONG:        f[0] = 108 #"l"
+ *             elif t == NPY_ULONG:       f[0] = 76  #"L"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"
+ *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"
+ */
+      __pyx_t_3 = PyInt_FromLong(NPY_ULONG); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 833; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 833; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 833; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 76;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":834
+ *             elif t == NPY_LONG:        f[0] = 108 #"l"
+ *             elif t == NPY_ULONG:       f[0] = 76  #"L"
+ *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"
+ *             elif t == NPY_FLOAT:       f[0] = 102 #"f"
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_LONGLONG); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 113;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":835
+ *             elif t == NPY_ULONG:       f[0] = 76  #"L"
+ *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"
+ *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_FLOAT:       f[0] = 102 #"f"
+ *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"
+ */
+      __pyx_t_3 = PyInt_FromLong(NPY_ULONGLONG); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 835; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 835; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 835; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 81;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":836
+ *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"
+ *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"
+ *             elif t == NPY_FLOAT:       f[0] = 102 #"f"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"
+ *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_FLOAT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 102;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":837
+ *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"
+ *             elif t == NPY_FLOAT:       f[0] = 102 #"f"
+ *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"
+ *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf
+ */
+      __pyx_t_3 = PyInt_FromLong(NPY_DOUBLE); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 837; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 837; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 837; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 100;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":838
+ *             elif t == NPY_FLOAT:       f[0] = 102 #"f"
+ *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"
+ *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"             # <<<<<<<<<<<<<<
+ *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf
+ *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_LONGDOUBLE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 838; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 838; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 838; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 103;
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":839
+ *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"
+ *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"
+ *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf             # <<<<<<<<<<<<<<
+ *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd
+ *             elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg
+ */
+      __pyx_t_3 = PyInt_FromLong(NPY_CFLOAT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 839; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 839; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 839; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 90;
+        (__pyx_v_f[1]) = 102;
+        __pyx_v_f = (__pyx_v_f + 1);
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":840
+ *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"
+ *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf
+ *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd             # <<<<<<<<<<<<<<
+ *             elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg
+ *             elif t == NPY_OBJECT:      f[0] = 79 #"O"
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_CDOUBLE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 90;
+        (__pyx_v_f[1]) = 100;
+        __pyx_v_f = (__pyx_v_f + 1);
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":841
+ *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf
+ *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd
+ *             elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg             # <<<<<<<<<<<<<<
+ *             elif t == NPY_OBJECT:      f[0] = 79 #"O"
+ *             else:
+ */
+      __pyx_t_3 = PyInt_FromLong(NPY_CLONGDOUBLE); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 90;
+        (__pyx_v_f[1]) = 103;
+        __pyx_v_f = (__pyx_v_f + 1);
+        goto __pyx_L11;
+      }
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":842
+ *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd
+ *             elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg
+ *             elif t == NPY_OBJECT:      f[0] = 79 #"O"             # <<<<<<<<<<<<<<
+ *             else:
+ *                 raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
+ */
+      __pyx_t_4 = PyInt_FromLong(NPY_OBJECT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 842; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 842; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 842; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      if (__pyx_t_6) {
+        (__pyx_v_f[0]) = 79;
+        goto __pyx_L11;
+      }
+      /*else*/ {
+
+        /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":844
+ *             elif t == NPY_OBJECT:      f[0] = 79 #"O"
+ *             else:
+ *                 raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)             # <<<<<<<<<<<<<<
+ *             f += 1
+ *         else:
+ */
+        __pyx_t_3 = PyUnicode_Format(__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_v_t); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_3);
+        __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_4);
+        PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
+        __Pyx_GIVEREF(__pyx_t_3);
+        __pyx_t_3 = 0;
+        __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_3);
+        __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+        __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+        __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+        {__pyx_filename = __pyx_f[1]; __pyx_lineno = 844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+      __pyx_L11:;
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":845
+ *             else:
+ *                 raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
+ *             f += 1             # <<<<<<<<<<<<<<
+ *         else:
+ *             # Cython ignores struct boundary information ("T{...}"),
+ */
+      __pyx_v_f = (__pyx_v_f + 1);
+      goto __pyx_L9;
+    }
+    /*else*/ {
+
+      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":849
+ *             # Cython ignores struct boundary information ("T{...}"),
+ *             # so don't output it
+ *             f = _util_dtypestring(child, f, end, offset)             # <<<<<<<<<<<<<<
+ *     return f
+ * 
+ */
+      __pyx_t_11 = __pyx_f_5numpy__util_dtypestring(__pyx_v_child, __pyx_v_f, __pyx_v_end, __pyx_v_offset); if (unlikely(__pyx_t_11 == NULL)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_v_f = __pyx_t_11;
+    }
+    __pyx_L9:;
+  }
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":850
+ *             # so don't output it
+ *             f = _util_dtypestring(child, f, end, offset)
+ *     return f             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_r = __pyx_v_f;
+  goto __pyx_L0;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":783
+ *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
+ * 
+ * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL:             # <<<<<<<<<<<<<<
+ *     # Recursive utility function used in __getbuffer__ to get format
+ *     # string. The new location in the format string is returned.
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_AddTraceback("numpy._util_dtypestring", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_child);
+  __Pyx_XDECREF(__pyx_v_fields);
+  __Pyx_XDECREF(__pyx_v_childname);
+  __Pyx_XDECREF(__pyx_v_new_offset);
+  __Pyx_XDECREF(__pyx_v_t);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":966
+ * 
+ * 
+ * cdef inline void set_array_base(ndarray arr, object base):             # <<<<<<<<<<<<<<
+ *      cdef PyObject* baseptr
+ *      if base is None:
+ */
+
+static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_arr, PyObject *__pyx_v_base) {
+  PyObject *__pyx_v_baseptr;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  __Pyx_RefNannySetupContext("set_array_base", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":968
+ * cdef inline void set_array_base(ndarray arr, object base):
+ *      cdef PyObject* baseptr
+ *      if base is None:             # <<<<<<<<<<<<<<
+ *          baseptr = NULL
+ *      else:
+ */
+  __pyx_t_1 = (__pyx_v_base == Py_None);
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":969
+ *      cdef PyObject* baseptr
+ *      if base is None:
+ *          baseptr = NULL             # <<<<<<<<<<<<<<
+ *      else:
+ *          Py_INCREF(base) # important to do this before decref below!
+ */
+    __pyx_v_baseptr = NULL;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":971
+ *          baseptr = NULL
+ *      else:
+ *          Py_INCREF(base) # important to do this before decref below!             # <<<<<<<<<<<<<<
+ *          baseptr = <PyObject*>base
+ *      Py_XDECREF(arr.base)
+ */
+    Py_INCREF(__pyx_v_base);
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":972
+ *      else:
+ *          Py_INCREF(base) # important to do this before decref below!
+ *          baseptr = <PyObject*>base             # <<<<<<<<<<<<<<
+ *      Py_XDECREF(arr.base)
+ *      arr.base = baseptr
+ */
+    __pyx_v_baseptr = ((PyObject *)__pyx_v_base);
+  }
+  __pyx_L3:;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":973
+ *          Py_INCREF(base) # important to do this before decref below!
+ *          baseptr = <PyObject*>base
+ *      Py_XDECREF(arr.base)             # <<<<<<<<<<<<<<
+ *      arr.base = baseptr
+ * 
+ */
+  Py_XDECREF(__pyx_v_arr->base);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":974
+ *          baseptr = <PyObject*>base
+ *      Py_XDECREF(arr.base)
+ *      arr.base = baseptr             # <<<<<<<<<<<<<<
+ * 
+ * cdef inline object get_array_base(ndarray arr):
+ */
+  __pyx_v_arr->base = __pyx_v_baseptr;
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":966
+ * 
+ * 
+ * cdef inline void set_array_base(ndarray arr, object base):             # <<<<<<<<<<<<<<
+ *      cdef PyObject* baseptr
+ *      if base is None:
+ */
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":976
+ *      arr.base = baseptr
+ * 
+ * cdef inline object get_array_base(ndarray arr):             # <<<<<<<<<<<<<<
+ *     if arr.base is NULL:
+ *         return None
+ */
+
+static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__pyx_v_arr) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  __Pyx_RefNannySetupContext("get_array_base", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":977
+ * 
+ * cdef inline object get_array_base(ndarray arr):
+ *     if arr.base is NULL:             # <<<<<<<<<<<<<<
+ *         return None
+ *     else:
+ */
+  __pyx_t_1 = ((__pyx_v_arr->base == NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":978
+ * cdef inline object get_array_base(ndarray arr):
+ *     if arr.base is NULL:
+ *         return None             # <<<<<<<<<<<<<<
+ *     else:
+ *         return <object>arr.base
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __Pyx_INCREF(Py_None);
+    __pyx_r = Py_None;
+    goto __pyx_L0;
+  }
+  /*else*/ {
+
+    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":980
+ *         return None
+ *     else:
+ *         return <object>arr.base             # <<<<<<<<<<<<<<
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __Pyx_INCREF(((PyObject *)__pyx_v_arr->base));
+    __pyx_r = ((PyObject *)__pyx_v_arr->base);
+    goto __pyx_L0;
+  }
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":976
+ *      arr.base = baseptr
+ * 
+ * cdef inline object get_array_base(ndarray arr):             # <<<<<<<<<<<<<<
+ *     if arr.base is NULL:
+ *         return None
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":116
+ *         cdef bint dtype_is_object
+ * 
+ *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,             # <<<<<<<<<<<<<<
+ *                   mode="c", bint allocate_buffer=True):
+ * 
+ */
+
+/* Python wrapper */
+static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+  PyObject *__pyx_v_shape = 0;
+  Py_ssize_t __pyx_v_itemsize;
+  PyObject *__pyx_v_format = 0;
+  PyObject *__pyx_v_mode = 0;
+  int __pyx_v_allocate_buffer;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0);
+  {
+    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_shape,&__pyx_n_s_itemsize,&__pyx_n_s_format,&__pyx_n_s_mode,&__pyx_n_s_allocate_buffer,0};
+    PyObject* values[5] = {0,0,0,0,0};
+    values[3] = ((PyObject *)__pyx_n_s_c);
+    if (unlikely(__pyx_kwds)) {
+      Py_ssize_t kw_args;
+      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
+      switch (pos_args) {
+        case  5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4);
+        case  4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
+        case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
+        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
+        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+        case  0: break;
+        default: goto __pyx_L5_argtuple_error;
+      }
+      kw_args = PyDict_Size(__pyx_kwds);
+      switch (pos_args) {
+        case  0:
+        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_shape)) != 0)) kw_args--;
+        else goto __pyx_L5_argtuple_error;
+        case  1:
+        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_itemsize)) != 0)) kw_args--;
+        else {
+          __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 1); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        }
+        case  2:
+        if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_format)) != 0)) kw_args--;
+        else {
+          __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 2); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        }
+        case  3:
+        if (kw_args > 0) {
+          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s_mode);
+          if (value) { values[3] = value; kw_args--; }
+        }
+        case  4:
+        if (kw_args > 0) {
+          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s_allocate_buffer);
+          if (value) { values[4] = value; kw_args--; }
+        }
+      }
+      if (unlikely(kw_args > 0)) {
+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      }
+    } else {
+      switch (PyTuple_GET_SIZE(__pyx_args)) {
+        case  5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4);
+        case  4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
+        case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
+        values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
+        values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+        break;
+        default: goto __pyx_L5_argtuple_error;
+      }
+    }
+    __pyx_v_shape = ((PyObject*)values[0]);
+    __pyx_v_itemsize = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_itemsize == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+    __pyx_v_format = values[2];
+    __pyx_v_mode = values[3];
+    if (values[4]) {
+      __pyx_v_allocate_buffer = __Pyx_PyObject_IsTrue(values[4]); if (unlikely((__pyx_v_allocate_buffer == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 117; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+    } else {
+
+      /* "View.MemoryView":117
+ * 
+ *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,
+ *                   mode="c", bint allocate_buffer=True):             # <<<<<<<<<<<<<<
+ * 
+ *         cdef int idx
+ */
+      __pyx_v_allocate_buffer = ((int)1);
+    }
+  }
+  goto __pyx_L4_argument_unpacking_done;
+  __pyx_L5_argtuple_error:;
+  __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __Pyx_RefNannyFinishContext();
+  return -1;
+  __pyx_L4_argument_unpacking_done:;
+  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_shape), (&PyTuple_Type), 1, "shape", 1))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (unlikely(((PyObject *)__pyx_v_format) == Py_None)) {
+    PyErr_Format(PyExc_TypeError, "Argument '%.200s' must not be None", "format"); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __pyx_r = __pyx_array_MemoryView_5array___cinit__(((struct __pyx_array_obj *)__pyx_v_self), __pyx_v_shape, __pyx_v_itemsize, __pyx_v_format, __pyx_v_mode, __pyx_v_allocate_buffer);
+
+  /* "View.MemoryView":116
+ *         cdef bint dtype_is_object
+ * 
+ *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,             # <<<<<<<<<<<<<<
+ *                   mode="c", bint allocate_buffer=True):
+ * 
+ */
+
+  /* function exit code */
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __pyx_r = -1;
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static int __pyx_array_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer) {
+  int __pyx_v_idx;
+  Py_ssize_t __pyx_v_i;
+  Py_ssize_t __pyx_v_dim;
+  PyObject **__pyx_v_p;
+  char __pyx_v_order;
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  Py_ssize_t __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_t_4;
+  char *__pyx_t_5;
+  int __pyx_t_6;
+  PyObject *__pyx_t_7 = NULL;
+  Py_ssize_t __pyx_t_8;
+  PyObject *__pyx_t_9 = NULL;
+  PyObject *__pyx_t_10 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__cinit__", 0);
+  __Pyx_INCREF(__pyx_v_format);
+
+  /* "View.MemoryView":123
+ *         cdef PyObject **p
+ * 
+ *         self.ndim = <int> len(shape)             # <<<<<<<<<<<<<<
+ *         self.itemsize = itemsize
+ * 
+ */
+  if (unlikely(__pyx_v_shape == Py_None)) {
+    PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()");
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 123; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __pyx_t_1 = PyTuple_GET_SIZE(__pyx_v_shape); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 123; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_v_self->ndim = ((int)__pyx_t_1);
+
+  /* "View.MemoryView":124
+ * 
+ *         self.ndim = <int> len(shape)
+ *         self.itemsize = itemsize             # <<<<<<<<<<<<<<
+ * 
+ *         if not self.ndim:
+ */
+  __pyx_v_self->itemsize = __pyx_v_itemsize;
+
+  /* "View.MemoryView":126
+ *         self.itemsize = itemsize
+ * 
+ *         if not self.ndim:             # <<<<<<<<<<<<<<
+ *             raise ValueError("Empty shape tuple for cython.array")
+ * 
+ */
+  __pyx_t_2 = ((!(__pyx_v_self->ndim != 0)) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":127
+ * 
+ *         if not self.ndim:
+ *             raise ValueError("Empty shape tuple for cython.array")             # <<<<<<<<<<<<<<
+ * 
+ *         if itemsize <= 0:
+ */
+    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "View.MemoryView":129
+ *             raise ValueError("Empty shape tuple for cython.array")
+ * 
+ *         if itemsize <= 0:             # <<<<<<<<<<<<<<
+ *             raise ValueError("itemsize <= 0 for cython.array")
+ * 
+ */
+  __pyx_t_2 = ((__pyx_v_itemsize <= 0) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":130
+ * 
+ *         if itemsize <= 0:
+ *             raise ValueError("itemsize <= 0 for cython.array")             # <<<<<<<<<<<<<<
+ * 
+ *         if isinstance(format, unicode):
+ */
+    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "View.MemoryView":132
+ *             raise ValueError("itemsize <= 0 for cython.array")
+ * 
+ *         if isinstance(format, unicode):             # <<<<<<<<<<<<<<
+ *             format = (<unicode>format).encode('ASCII')
+ *         self._format = format  # keep a reference to the byte string
+ */
+  __pyx_t_2 = PyUnicode_Check(__pyx_v_format); 
+  __pyx_t_4 = (__pyx_t_2 != 0);
+  if (__pyx_t_4) {
+
+    /* "View.MemoryView":133
+ * 
+ *         if isinstance(format, unicode):
+ *             format = (<unicode>format).encode('ASCII')             # <<<<<<<<<<<<<<
+ *         self._format = format  # keep a reference to the byte string
+ *         self.format = self._format
+ */
+    if (unlikely(__pyx_v_format == Py_None)) {
+      PyErr_Format(PyExc_AttributeError, "'NoneType' object has no attribute '%s'", "encode");
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 133; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+    __pyx_t_3 = PyUnicode_AsASCIIString(((PyObject*)__pyx_v_format)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 133; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_DECREF_SET(__pyx_v_format, __pyx_t_3);
+    __pyx_t_3 = 0;
+    goto __pyx_L5;
+  }
+  __pyx_L5:;
+
+  /* "View.MemoryView":134
+ *         if isinstance(format, unicode):
+ *             format = (<unicode>format).encode('ASCII')
+ *         self._format = format  # keep a reference to the byte string             # <<<<<<<<<<<<<<
+ *         self.format = self._format
+ * 
+ */
+  if (!(likely(PyBytes_CheckExact(__pyx_v_format))||((__pyx_v_format) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_v_format)->tp_name), 0))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = __pyx_v_format;
+  __Pyx_INCREF(__pyx_t_3);
+  __Pyx_GIVEREF(__pyx_t_3);
+  __Pyx_GOTREF(__pyx_v_self->_format);
+  __Pyx_DECREF(__pyx_v_self->_format);
+  __pyx_v_self->_format = ((PyObject*)__pyx_t_3);
+  __pyx_t_3 = 0;
+
+  /* "View.MemoryView":135
+ *             format = (<unicode>format).encode('ASCII')
+ *         self._format = format  # keep a reference to the byte string
+ *         self.format = self._format             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_5 = __Pyx_PyObject_AsString(__pyx_v_self->_format); if (unlikely((!__pyx_t_5) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 135; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_v_self->format = __pyx_t_5;
+
+  /* "View.MemoryView":138
+ * 
+ * 
+ *         self._shape = <Py_ssize_t *> PyMem_Malloc(sizeof(Py_ssize_t)*self.ndim*2)             # <<<<<<<<<<<<<<
+ *         self._strides = self._shape + self.ndim
+ * 
+ */
+  __pyx_v_self->_shape = ((Py_ssize_t *)PyMem_Malloc((((sizeof(Py_ssize_t)) * __pyx_v_self->ndim) * 2)));
+
+  /* "View.MemoryView":139
+ * 
+ *         self._shape = <Py_ssize_t *> PyMem_Malloc(sizeof(Py_ssize_t)*self.ndim*2)
+ *         self._strides = self._shape + self.ndim             # <<<<<<<<<<<<<<
+ * 
+ *         if not self._shape:
+ */
+  __pyx_v_self->_strides = (__pyx_v_self->_shape + __pyx_v_self->ndim);
+
+  /* "View.MemoryView":141
+ *         self._strides = self._shape + self.ndim
+ * 
+ *         if not self._shape:             # <<<<<<<<<<<<<<
+ *             raise MemoryError("unable to allocate shape and strides.")
+ * 
+ */
+  __pyx_t_4 = ((!(__pyx_v_self->_shape != 0)) != 0);
+  if (__pyx_t_4) {
+
+    /* "View.MemoryView":142
+ * 
+ *         if not self._shape:
+ *             raise MemoryError("unable to allocate shape and strides.")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__9, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "View.MemoryView":145
+ * 
+ * 
+ *         for idx, dim in enumerate(shape):             # <<<<<<<<<<<<<<
+ *             if dim <= 0:
+ *                 raise ValueError("Invalid shape in axis %d: %d." % (idx, dim))
+ */
+  __pyx_t_6 = 0;
+  __pyx_t_3 = __pyx_v_shape; __Pyx_INCREF(__pyx_t_3); __pyx_t_1 = 0;
+  for (;;) {
+    if (__pyx_t_1 >= PyTuple_GET_SIZE(__pyx_t_3)) break;
+    #if CYTHON_COMPILING_IN_CPYTHON
+    __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_1); __Pyx_INCREF(__pyx_t_7); __pyx_t_1++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    #else
+    __pyx_t_7 = PySequence_ITEM(__pyx_t_3, __pyx_t_1); __pyx_t_1++; if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    #endif
+    __pyx_t_8 = __Pyx_PyIndex_AsSsize_t(__pyx_t_7); if (unlikely((__pyx_t_8 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
+    __pyx_v_dim = __pyx_t_8;
+    __pyx_v_idx = __pyx_t_6;
+    __pyx_t_6 = (__pyx_t_6 + 1);
+
+    /* "View.MemoryView":146
+ * 
+ *         for idx, dim in enumerate(shape):
+ *             if dim <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("Invalid shape in axis %d: %d." % (idx, dim))
+ *             self._shape[idx] = dim
+ */
+    __pyx_t_4 = ((__pyx_v_dim <= 0) != 0);
+    if (__pyx_t_4) {
+
+      /* "View.MemoryView":147
+ *         for idx, dim in enumerate(shape):
+ *             if dim <= 0:
+ *                 raise ValueError("Invalid shape in axis %d: %d." % (idx, dim))             # <<<<<<<<<<<<<<
+ *             self._shape[idx] = dim
+ * 
+ */
+      __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_idx); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_7);
+      __pyx_t_9 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_9);
+      __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_10);
+      PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_7);
+      __Pyx_GIVEREF(__pyx_t_7);
+      PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_9);
+      __Pyx_GIVEREF(__pyx_t_9);
+      __pyx_t_7 = 0;
+      __pyx_t_9 = 0;
+      __pyx_t_9 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_t_10); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_9);
+      __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
+      __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_10);
+      PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_9);
+      __Pyx_GIVEREF(__pyx_t_9);
+      __pyx_t_9 = 0;
+      __pyx_t_9 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_10, NULL); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_9);
+      __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
+      __Pyx_Raise(__pyx_t_9, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+
+    /* "View.MemoryView":148
+ *             if dim <= 0:
+ *                 raise ValueError("Invalid shape in axis %d: %d." % (idx, dim))
+ *             self._shape[idx] = dim             # <<<<<<<<<<<<<<
+ * 
+ *         cdef char order
+ */
+    (__pyx_v_self->_shape[__pyx_v_idx]) = __pyx_v_dim;
+  }
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+
+  /* "View.MemoryView":151
+ * 
+ *         cdef char order
+ *         if mode == 'fortran':             # <<<<<<<<<<<<<<
+ *             order = b'F'
+ *             self.mode = u'fortran'
+ */
+  __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_fortran, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (__pyx_t_4) {
+
+    /* "View.MemoryView":152
+ *         cdef char order
+ *         if mode == 'fortran':
+ *             order = b'F'             # <<<<<<<<<<<<<<
+ *             self.mode = u'fortran'
+ *         elif mode == 'c':
+ */
+    __pyx_v_order = 'F';
+
+    /* "View.MemoryView":153
+ *         if mode == 'fortran':
+ *             order = b'F'
+ *             self.mode = u'fortran'             # <<<<<<<<<<<<<<
+ *         elif mode == 'c':
+ *             order = b'C'
+ */
+    __Pyx_INCREF(__pyx_n_u_fortran);
+    __Pyx_GIVEREF(__pyx_n_u_fortran);
+    __Pyx_GOTREF(__pyx_v_self->mode);
+    __Pyx_DECREF(__pyx_v_self->mode);
+    __pyx_v_self->mode = __pyx_n_u_fortran;
+    goto __pyx_L10;
+  }
+
+  /* "View.MemoryView":154
+ *             order = b'F'
+ *             self.mode = u'fortran'
+ *         elif mode == 'c':             # <<<<<<<<<<<<<<
+ *             order = b'C'
+ *             self.mode = u'c'
+ */
+  __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_c, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (__pyx_t_4) {
+
+    /* "View.MemoryView":155
+ *             self.mode = u'fortran'
+ *         elif mode == 'c':
+ *             order = b'C'             # <<<<<<<<<<<<<<
+ *             self.mode = u'c'
+ *         else:
+ */
+    __pyx_v_order = 'C';
+
+    /* "View.MemoryView":156
+ *         elif mode == 'c':
+ *             order = b'C'
+ *             self.mode = u'c'             # <<<<<<<<<<<<<<
+ *         else:
+ *             raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode)
+ */
+    __Pyx_INCREF(__pyx_n_u_c);
+    __Pyx_GIVEREF(__pyx_n_u_c);
+    __Pyx_GOTREF(__pyx_v_self->mode);
+    __Pyx_DECREF(__pyx_v_self->mode);
+    __pyx_v_self->mode = __pyx_n_u_c;
+    goto __pyx_L10;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":158
+ *             self.mode = u'c'
+ *         else:
+ *             raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode)             # <<<<<<<<<<<<<<
+ * 
+ *         self.len = fill_contig_strides_array(self._shape, self._strides,
+ */
+    __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_v_mode); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_9);
+    PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_3);
+    __Pyx_GIVEREF(__pyx_t_3);
+    __pyx_t_3 = 0;
+    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_9, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __pyx_L10:;
+
+  /* "View.MemoryView":160
+ *             raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode)
+ * 
+ *         self.len = fill_contig_strides_array(self._shape, self._strides,             # <<<<<<<<<<<<<<
+ *                                              itemsize, self.ndim, order)
+ * 
+ */
+  __pyx_v_self->len = __pyx_fill_contig_strides_array(__pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_itemsize, __pyx_v_self->ndim, __pyx_v_order);
+
+  /* "View.MemoryView":163
+ *                                              itemsize, self.ndim, order)
+ * 
+ *         self.free_data = allocate_buffer             # <<<<<<<<<<<<<<
+ *         self.dtype_is_object = format == b'O'
+ *         if allocate_buffer:
+ */
+  __pyx_v_self->free_data = __pyx_v_allocate_buffer;
+
+  /* "View.MemoryView":164
+ * 
+ *         self.free_data = allocate_buffer
+ *         self.dtype_is_object = format == b'O'             # <<<<<<<<<<<<<<
+ *         if allocate_buffer:
+ * 
+ */
+  __pyx_t_3 = PyObject_RichCompare(__pyx_v_format, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 164; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 164; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  __pyx_v_self->dtype_is_object = __pyx_t_4;
+
+  /* "View.MemoryView":165
+ *         self.free_data = allocate_buffer
+ *         self.dtype_is_object = format == b'O'
+ *         if allocate_buffer:             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_4 = (__pyx_v_allocate_buffer != 0);
+  if (__pyx_t_4) {
+
+    /* "View.MemoryView":168
+ * 
+ * 
+ *             self.data = <char *>malloc(self.len)             # <<<<<<<<<<<<<<
+ *             if not self.data:
+ *                 raise MemoryError("unable to allocate array data.")
+ */
+    __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len));
+
+    /* "View.MemoryView":169
+ * 
+ *             self.data = <char *>malloc(self.len)
+ *             if not self.data:             # <<<<<<<<<<<<<<
+ *                 raise MemoryError("unable to allocate array data.")
+ * 
+ */
+    __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0);
+    if (__pyx_t_4) {
+
+      /* "View.MemoryView":170
+ *             self.data = <char *>malloc(self.len)
+ *             if not self.data:
+ *                 raise MemoryError("unable to allocate array data.")             # <<<<<<<<<<<<<<
+ * 
+ *             if self.dtype_is_object:
+ */
+      __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__10, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+
+    /* "View.MemoryView":172
+ *                 raise MemoryError("unable to allocate array data.")
+ * 
+ *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
+ *                 p = <PyObject **> self.data
+ *                 for i in range(self.len / itemsize):
+ */
+    __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0);
+    if (__pyx_t_4) {
+
+      /* "View.MemoryView":173
+ * 
+ *             if self.dtype_is_object:
+ *                 p = <PyObject **> self.data             # <<<<<<<<<<<<<<
+ *                 for i in range(self.len / itemsize):
+ *                     p[i] = Py_None
+ */
+      __pyx_v_p = ((PyObject **)__pyx_v_self->data);
+
+      /* "View.MemoryView":174
+ *             if self.dtype_is_object:
+ *                 p = <PyObject **> self.data
+ *                 for i in range(self.len / itemsize):             # <<<<<<<<<<<<<<
+ *                     p[i] = Py_None
+ *                     Py_INCREF(Py_None)
+ */
+      if (unlikely(__pyx_v_itemsize == 0)) {
+        #ifdef WITH_THREAD
+        PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+        #endif
+        PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero");
+        #ifdef WITH_THREAD
+        PyGILState_Release(__pyx_gilstate_save);
+        #endif
+        {__pyx_filename = __pyx_f[2]; __pyx_lineno = 174; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+      else if (sizeof(Py_ssize_t) == sizeof(long) && unlikely(__pyx_v_itemsize == -1)  && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_self->len))) {
+        #ifdef WITH_THREAD
+        PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+        #endif
+        PyErr_SetString(PyExc_OverflowError, "value too large to perform division");
+        #ifdef WITH_THREAD
+        PyGILState_Release(__pyx_gilstate_save);
+        #endif
+        {__pyx_filename = __pyx_f[2]; __pyx_lineno = 174; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+      __pyx_t_1 = __Pyx_div_Py_ssize_t(__pyx_v_self->len, __pyx_v_itemsize);
+      for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_1; __pyx_t_8+=1) {
+        __pyx_v_i = __pyx_t_8;
+
+        /* "View.MemoryView":175
+ *                 p = <PyObject **> self.data
+ *                 for i in range(self.len / itemsize):
+ *                     p[i] = Py_None             # <<<<<<<<<<<<<<
+ *                     Py_INCREF(Py_None)
+ * 
+ */
+        (__pyx_v_p[__pyx_v_i]) = Py_None;
+
+        /* "View.MemoryView":176
+ *                 for i in range(self.len / itemsize):
+ *                     p[i] = Py_None
+ *                     Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
+ * 
+ *     @cname('getbuffer')
+ */
+        Py_INCREF(Py_None);
+      }
+      goto __pyx_L13;
+    }
+    __pyx_L13:;
+    goto __pyx_L11;
+  }
+  __pyx_L11:;
+
+  /* "View.MemoryView":116
+ *         cdef bint dtype_is_object
+ * 
+ *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,             # <<<<<<<<<<<<<<
+ *                   mode="c", bint allocate_buffer=True):
+ * 
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_7);
+  __Pyx_XDECREF(__pyx_t_9);
+  __Pyx_XDECREF(__pyx_t_10);
+  __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_format);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":179
+ * 
+ *     @cname('getbuffer')
+ *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
+ *         cdef int bufmode = -1
+ *         if self.mode == u"c":
+ */
+
+/* Python wrapper */
+static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
+static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0);
+  __pyx_r = __pyx_array_getbuffer_MemoryView_5array_2__getbuffer__(((struct __pyx_array_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static int __pyx_array_getbuffer_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
+  int __pyx_v_bufmode;
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  char *__pyx_t_4;
+  Py_ssize_t __pyx_t_5;
+  int __pyx_t_6;
+  Py_ssize_t *__pyx_t_7;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__getbuffer__", 0);
+  if (__pyx_v_info != NULL) {
+    __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
+    __Pyx_GIVEREF(__pyx_v_info->obj);
+  }
+
+  /* "View.MemoryView":180
+ *     @cname('getbuffer')
+ *     def __getbuffer__(self, Py_buffer *info, int flags):
+ *         cdef int bufmode = -1             # <<<<<<<<<<<<<<
+ *         if self.mode == u"c":
+ *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ */
+  __pyx_v_bufmode = -1;
+
+  /* "View.MemoryView":181
+ *     def __getbuffer__(self, Py_buffer *info, int flags):
+ *         cdef int bufmode = -1
+ *         if self.mode == u"c":             # <<<<<<<<<<<<<<
+ *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ *         elif self.mode == u"fortran":
+ */
+  __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 181; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":182
+ *         cdef int bufmode = -1
+ *         if self.mode == u"c":
+ *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS             # <<<<<<<<<<<<<<
+ *         elif self.mode == u"fortran":
+ *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ */
+    __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS);
+    goto __pyx_L3;
+  }
+
+  /* "View.MemoryView":183
+ *         if self.mode == u"c":
+ *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ *         elif self.mode == u"fortran":             # <<<<<<<<<<<<<<
+ *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ *         if not (flags & bufmode):
+ */
+  __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 183; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = (__pyx_t_2 != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":184
+ *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ *         elif self.mode == u"fortran":
+ *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS             # <<<<<<<<<<<<<<
+ *         if not (flags & bufmode):
+ *             raise ValueError("Can only create a buffer that is contiguous in memory.")
+ */
+    __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS);
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":185
+ *         elif self.mode == u"fortran":
+ *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ *         if not (flags & bufmode):             # <<<<<<<<<<<<<<
+ *             raise ValueError("Can only create a buffer that is contiguous in memory.")
+ *         info.buf = self.data
+ */
+  __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":186
+ *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ *         if not (flags & bufmode):
+ *             raise ValueError("Can only create a buffer that is contiguous in memory.")             # <<<<<<<<<<<<<<
+ *         info.buf = self.data
+ *         info.len = self.len
+ */
+    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "View.MemoryView":187
+ *         if not (flags & bufmode):
+ *             raise ValueError("Can only create a buffer that is contiguous in memory.")
+ *         info.buf = self.data             # <<<<<<<<<<<<<<
+ *         info.len = self.len
+ *         info.ndim = self.ndim
+ */
+  __pyx_t_4 = __pyx_v_self->data;
+  __pyx_v_info->buf = __pyx_t_4;
+
+  /* "View.MemoryView":188
+ *             raise ValueError("Can only create a buffer that is contiguous in memory.")
+ *         info.buf = self.data
+ *         info.len = self.len             # <<<<<<<<<<<<<<
+ *         info.ndim = self.ndim
+ *         info.shape = self._shape
+ */
+  __pyx_t_5 = __pyx_v_self->len;
+  __pyx_v_info->len = __pyx_t_5;
+
+  /* "View.MemoryView":189
+ *         info.buf = self.data
+ *         info.len = self.len
+ *         info.ndim = self.ndim             # <<<<<<<<<<<<<<
+ *         info.shape = self._shape
+ *         info.strides = self._strides
+ */
+  __pyx_t_6 = __pyx_v_self->ndim;
+  __pyx_v_info->ndim = __pyx_t_6;
+
+  /* "View.MemoryView":190
+ *         info.len = self.len
+ *         info.ndim = self.ndim
+ *         info.shape = self._shape             # <<<<<<<<<<<<<<
+ *         info.strides = self._strides
+ *         info.suboffsets = NULL
+ */
+  __pyx_t_7 = __pyx_v_self->_shape;
+  __pyx_v_info->shape = __pyx_t_7;
+
+  /* "View.MemoryView":191
+ *         info.ndim = self.ndim
+ *         info.shape = self._shape
+ *         info.strides = self._strides             # <<<<<<<<<<<<<<
+ *         info.suboffsets = NULL
+ *         info.itemsize = self.itemsize
+ */
+  __pyx_t_7 = __pyx_v_self->_strides;
+  __pyx_v_info->strides = __pyx_t_7;
+
+  /* "View.MemoryView":192
+ *         info.shape = self._shape
+ *         info.strides = self._strides
+ *         info.suboffsets = NULL             # <<<<<<<<<<<<<<
+ *         info.itemsize = self.itemsize
+ *         info.readonly = 0
+ */
+  __pyx_v_info->suboffsets = NULL;
+
+  /* "View.MemoryView":193
+ *         info.strides = self._strides
+ *         info.suboffsets = NULL
+ *         info.itemsize = self.itemsize             # <<<<<<<<<<<<<<
+ *         info.readonly = 0
+ * 
+ */
+  __pyx_t_5 = __pyx_v_self->itemsize;
+  __pyx_v_info->itemsize = __pyx_t_5;
+
+  /* "View.MemoryView":194
+ *         info.suboffsets = NULL
+ *         info.itemsize = self.itemsize
+ *         info.readonly = 0             # <<<<<<<<<<<<<<
+ * 
+ *         if flags & PyBUF_FORMAT:
+ */
+  __pyx_v_info->readonly = 0;
+
+  /* "View.MemoryView":196
+ *         info.readonly = 0
+ * 
+ *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
+ *             info.format = self.format
+ *         else:
+ */
+  __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":197
+ * 
+ *         if flags & PyBUF_FORMAT:
+ *             info.format = self.format             # <<<<<<<<<<<<<<
+ *         else:
+ *             info.format = NULL
+ */
+    __pyx_t_4 = __pyx_v_self->format;
+    __pyx_v_info->format = __pyx_t_4;
+    goto __pyx_L5;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":199
+ *             info.format = self.format
+ *         else:
+ *             info.format = NULL             # <<<<<<<<<<<<<<
+ * 
+ *         info.obj = self
+ */
+    __pyx_v_info->format = NULL;
+  }
+  __pyx_L5:;
+
+  /* "View.MemoryView":201
+ *             info.format = NULL
+ * 
+ *         info.obj = self             # <<<<<<<<<<<<<<
+ * 
+ *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")
+ */
+  __Pyx_INCREF(((PyObject *)__pyx_v_self));
+  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
+  __Pyx_GOTREF(__pyx_v_info->obj);
+  __Pyx_DECREF(__pyx_v_info->obj);
+  __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
+
+  /* "View.MemoryView":179
+ * 
+ *     @cname('getbuffer')
+ *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
+ *         cdef int bufmode = -1
+ *         if self.mode == u"c":
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView.array.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  if (__pyx_v_info != NULL && __pyx_v_info->obj != NULL) {
+    __Pyx_GOTREF(__pyx_v_info->obj);
+    __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = NULL;
+  }
+  goto __pyx_L2;
+  __pyx_L0:;
+  if (__pyx_v_info != NULL && __pyx_v_info->obj == Py_None) {
+    __Pyx_GOTREF(Py_None);
+    __Pyx_DECREF(Py_None); __pyx_v_info->obj = NULL;
+  }
+  __pyx_L2:;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":205
+ *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")
+ * 
+ *     def __dealloc__(array self):             # <<<<<<<<<<<<<<
+ *         if self.callback_free_data != NULL:
+ *             self.callback_free_data(self.data)
+ */
+
+/* Python wrapper */
+static void __pyx_array___dealloc__(PyObject *__pyx_v_self); /*proto*/
+static void __pyx_array___dealloc__(PyObject *__pyx_v_self) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0);
+  __pyx_array_MemoryView_5array_4__dealloc__(((struct __pyx_array_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+static void __pyx_array_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self) {
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  __Pyx_RefNannySetupContext("__dealloc__", 0);
+
+  /* "View.MemoryView":206
+ * 
+ *     def __dealloc__(array self):
+ *         if self.callback_free_data != NULL:             # <<<<<<<<<<<<<<
+ *             self.callback_free_data(self.data)
+ *         elif self.free_data:
+ */
+  __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":207
+ *     def __dealloc__(array self):
+ *         if self.callback_free_data != NULL:
+ *             self.callback_free_data(self.data)             # <<<<<<<<<<<<<<
+ *         elif self.free_data:
+ *             if self.dtype_is_object:
+ */
+    __pyx_v_self->callback_free_data(__pyx_v_self->data);
+    goto __pyx_L3;
+  }
+
+  /* "View.MemoryView":208
+ *         if self.callback_free_data != NULL:
+ *             self.callback_free_data(self.data)
+ *         elif self.free_data:             # <<<<<<<<<<<<<<
+ *             if self.dtype_is_object:
+ *                 refcount_objects_in_slice(self.data, self._shape,
+ */
+  __pyx_t_1 = (__pyx_v_self->free_data != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":209
+ *             self.callback_free_data(self.data)
+ *         elif self.free_data:
+ *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
+ *                 refcount_objects_in_slice(self.data, self._shape,
+ *                                           self._strides, self.ndim, False)
+ */
+    __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0);
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":210
+ *         elif self.free_data:
+ *             if self.dtype_is_object:
+ *                 refcount_objects_in_slice(self.data, self._shape,             # <<<<<<<<<<<<<<
+ *                                           self._strides, self.ndim, False)
+ *             free(self.data)
+ */
+      __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0);
+      goto __pyx_L4;
+    }
+    __pyx_L4:;
+
+    /* "View.MemoryView":212
+ *                 refcount_objects_in_slice(self.data, self._shape,
+ *                                           self._strides, self.ndim, False)
+ *             free(self.data)             # <<<<<<<<<<<<<<
+ *         PyMem_Free(self._shape)
+ * 
+ */
+    free(__pyx_v_self->data);
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":213
+ *                                           self._strides, self.ndim, False)
+ *             free(self.data)
+ *         PyMem_Free(self._shape)             # <<<<<<<<<<<<<<
+ * 
+ *     property memview:
+ */
+  PyMem_Free(__pyx_v_self->_shape);
+
+  /* "View.MemoryView":205
+ *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")
+ * 
+ *     def __dealloc__(array self):             # <<<<<<<<<<<<<<
+ *         if self.callback_free_data != NULL:
+ *             self.callback_free_data(self.data)
+ */
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+/* "View.MemoryView":217
+ *     property memview:
+ *         @cname('get_memview')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ * 
+ *             flags =  PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE
+ */
+
+/* Python wrapper */
+static PyObject *get_memview(PyObject *__pyx_v_self); /*proto*/
+static PyObject *get_memview(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = get_memview_MemoryView_5array_7memview___get__(((struct __pyx_array_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *get_memview_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self) {
+  int __pyx_v_flags;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":219
+ *         def __get__(self):
+ * 
+ *             flags =  PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE             # <<<<<<<<<<<<<<
+ *             return  memoryview(self, flags, self.dtype_is_object)
+ * 
+ */
+  __pyx_v_flags = ((PyBUF_ANY_CONTIGUOUS | PyBUF_FORMAT) | PyBUF_WRITABLE);
+
+  /* "View.MemoryView":220
+ * 
+ *             flags =  PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE
+ *             return  memoryview(self, flags, self.dtype_is_object)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_INCREF(((PyObject *)__pyx_v_self));
+  PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_self));
+  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
+  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  __pyx_t_1 = 0;
+  __pyx_t_2 = 0;
+  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_memoryview_type)), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":217
+ *     property memview:
+ *         @cname('get_memview')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ * 
+ *             flags =  PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView.array.memview.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":223
+ * 
+ * 
+ *     def __getattr__(self, attr):             # <<<<<<<<<<<<<<
+ *         return getattr(self.memview, attr)
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr); /*proto*/
+static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__getattr__ (wrapper)", 0);
+  __pyx_r = __pyx_array_MemoryView_5array_6__getattr__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_attr));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_array_MemoryView_5array_6__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__getattr__", 0);
+
+  /* "View.MemoryView":224
+ * 
+ *     def __getattr__(self, attr):
+ *         return getattr(self.memview, attr)             # <<<<<<<<<<<<<<
+ * 
+ *     def __getitem__(self, item):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = __Pyx_GetAttr(__pyx_t_1, __pyx_v_attr); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":223
+ * 
+ * 
+ *     def __getattr__(self, attr):             # <<<<<<<<<<<<<<
+ *         return getattr(self.memview, attr)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_AddTraceback("View.MemoryView.array.__getattr__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":226
+ *         return getattr(self.memview, attr)
+ * 
+ *     def __getitem__(self, item):             # <<<<<<<<<<<<<<
+ *         return self.memview[item]
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item); /*proto*/
+static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0);
+  __pyx_r = __pyx_array_MemoryView_5array_8__getitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_array_MemoryView_5array_8__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__getitem__", 0);
+
+  /* "View.MemoryView":227
+ * 
+ *     def __getitem__(self, item):
+ *         return self.memview[item]             # <<<<<<<<<<<<<<
+ * 
+ *     def __setitem__(self, item, value):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = PyObject_GetItem(__pyx_t_1, __pyx_v_item); if (unlikely(__pyx_t_2 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 227; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":226
+ *         return getattr(self.memview, attr)
+ * 
+ *     def __getitem__(self, item):             # <<<<<<<<<<<<<<
+ *         return self.memview[item]
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_AddTraceback("View.MemoryView.array.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":229
+ *         return self.memview[item]
+ * 
+ *     def __setitem__(self, item, value):             # <<<<<<<<<<<<<<
+ *         self.memview[item] = value
+ * 
+ */
+
+/* Python wrapper */
+static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /*proto*/
+static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0);
+  __pyx_r = __pyx_array_MemoryView_5array_10__setitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item), ((PyObject *)__pyx_v_value));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static int __pyx_array_MemoryView_5array_10__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__setitem__", 0);
+
+  /* "View.MemoryView":230
+ * 
+ *     def __setitem__(self, item, value):
+ *         self.memview[item] = value             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (unlikely(PyObject_SetItem(__pyx_t_1, __pyx_v_item, __pyx_v_value) < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+
+  /* "View.MemoryView":229
+ *         return self.memview[item]
+ * 
+ *     def __setitem__(self, item, value):             # <<<<<<<<<<<<<<
+ *         self.memview[item] = value
+ * 
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("View.MemoryView.array.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":234
+ * 
+ * @cname("__pyx_array_new")
+ * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format,             # <<<<<<<<<<<<<<
+ *                           char *mode, char *buf):
+ *     cdef array result
+ */
+
+static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, char *__pyx_v_format, char *__pyx_v_mode, char *__pyx_v_buf) {
+  struct __pyx_array_obj *__pyx_v_result = 0;
+  struct __pyx_array_obj *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  PyObject *__pyx_t_5 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("array_cwrapper", 0);
+
+  /* "View.MemoryView":238
+ *     cdef array result
+ * 
+ *     if buf == NULL:             # <<<<<<<<<<<<<<
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'))
+ *     else:
+ */
+  __pyx_t_1 = ((__pyx_v_buf == NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":239
+ * 
+ *     if buf == NULL:
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'))             # <<<<<<<<<<<<<<
+ *     else:
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'),
+ */
+    __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __pyx_t_3 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_t_4 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_5 = PyTuple_New(4); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    __Pyx_INCREF(__pyx_v_shape);
+    PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_shape);
+    __Pyx_GIVEREF(__pyx_v_shape);
+    PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2);
+    __Pyx_GIVEREF(__pyx_t_2);
+    PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_t_3);
+    __Pyx_GIVEREF(__pyx_t_3);
+    PyTuple_SET_ITEM(__pyx_t_5, 3, __pyx_t_4);
+    __Pyx_GIVEREF(__pyx_t_4);
+    __pyx_t_2 = 0;
+    __pyx_t_3 = 0;
+    __pyx_t_4 = 0;
+    __pyx_t_4 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_array_type)), __pyx_t_5, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+    __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_4);
+    __pyx_t_4 = 0;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":241
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'))
+ *     else:
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'),             # <<<<<<<<<<<<<<
+ *                        allocate_buffer=False)
+ *         result.data = buf
+ */
+    __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_t_2 = PyTuple_New(4); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_INCREF(__pyx_v_shape);
+    PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape);
+    __Pyx_GIVEREF(__pyx_v_shape);
+    PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_4);
+    __Pyx_GIVEREF(__pyx_t_4);
+    PyTuple_SET_ITEM(__pyx_t_2, 2, __pyx_t_5);
+    __Pyx_GIVEREF(__pyx_t_5);
+    PyTuple_SET_ITEM(__pyx_t_2, 3, __pyx_t_3);
+    __Pyx_GIVEREF(__pyx_t_3);
+    __pyx_t_4 = 0;
+    __pyx_t_5 = 0;
+    __pyx_t_3 = 0;
+    __pyx_t_3 = PyDict_New(); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+
+    /* "View.MemoryView":242
+ *     else:
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'),
+ *                        allocate_buffer=False)             # <<<<<<<<<<<<<<
+ *         result.data = buf
+ * 
+ */
+    if (PyDict_SetItem(__pyx_t_3, __pyx_n_s_allocate_buffer, Py_False) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+    /* "View.MemoryView":241
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'))
+ *     else:
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'),             # <<<<<<<<<<<<<<
+ *                        allocate_buffer=False)
+ *         result.data = buf
+ */
+    __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_array_type)), __pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_5);
+    __pyx_t_5 = 0;
+
+    /* "View.MemoryView":243
+ *         result = array(shape, itemsize, format, mode.decode('ASCII'),
+ *                        allocate_buffer=False)
+ *         result.data = buf             # <<<<<<<<<<<<<<
+ * 
+ *     return result
+ */
+    __pyx_v_result->data = __pyx_v_buf;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":245
+ *         result.data = buf
+ * 
+ *     return result             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __Pyx_XDECREF(((PyObject *)__pyx_r));
+  __Pyx_INCREF(((PyObject *)__pyx_v_result));
+  __pyx_r = __pyx_v_result;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":234
+ * 
+ * @cname("__pyx_array_new")
+ * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format,             # <<<<<<<<<<<<<<
+ *                           char *mode, char *buf):
+ *     cdef array result
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_AddTraceback("View.MemoryView.array_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_result);
+  __Pyx_XGIVEREF((PyObject *)__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":271
+ * cdef class Enum(object):
+ *     cdef object name
+ *     def __init__(self, name):             # <<<<<<<<<<<<<<
+ *         self.name = name
+ *     def __repr__(self):
+ */
+
+/* Python wrapper */
+static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+  PyObject *__pyx_v_name = 0;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__init__ (wrapper)", 0);
+  {
+    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_name,0};
+    PyObject* values[1] = {0};
+    if (unlikely(__pyx_kwds)) {
+      Py_ssize_t kw_args;
+      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
+      switch (pos_args) {
+        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+        case  0: break;
+        default: goto __pyx_L5_argtuple_error;
+      }
+      kw_args = PyDict_Size(__pyx_kwds);
+      switch (pos_args) {
+        case  0:
+        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_name)) != 0)) kw_args--;
+        else goto __pyx_L5_argtuple_error;
+      }
+      if (unlikely(kw_args > 0)) {
+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 271; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      }
+    } else if (PyTuple_GET_SIZE(__pyx_args) != 1) {
+      goto __pyx_L5_argtuple_error;
+    } else {
+      values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+    }
+    __pyx_v_name = values[0];
+  }
+  goto __pyx_L4_argument_unpacking_done;
+  __pyx_L5_argtuple_error:;
+  __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 271; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("View.MemoryView.Enum.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __Pyx_RefNannyFinishContext();
+  return -1;
+  __pyx_L4_argument_unpacking_done:;
+  __pyx_r = __pyx_MemviewEnum_MemoryView_4Enum___init__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), __pyx_v_name);
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static int __pyx_MemviewEnum_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__init__", 0);
+
+  /* "View.MemoryView":272
+ *     cdef object name
+ *     def __init__(self, name):
+ *         self.name = name             # <<<<<<<<<<<<<<
+ *     def __repr__(self):
+ *         return self.name
+ */
+  __Pyx_INCREF(__pyx_v_name);
+  __Pyx_GIVEREF(__pyx_v_name);
+  __Pyx_GOTREF(__pyx_v_self->name);
+  __Pyx_DECREF(__pyx_v_self->name);
+  __pyx_v_self->name = __pyx_v_name;
+
+  /* "View.MemoryView":271
+ * cdef class Enum(object):
+ *     cdef object name
+ *     def __init__(self, name):             # <<<<<<<<<<<<<<
+ *         self.name = name
+ *     def __repr__(self):
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":273
+ *     def __init__(self, name):
+ *         self.name = name
+ *     def __repr__(self):             # <<<<<<<<<<<<<<
+ *         return self.name
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0);
+  __pyx_r = __pyx_MemviewEnum_MemoryView_4Enum_2__repr__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_MemviewEnum_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__repr__", 0);
+
+  /* "View.MemoryView":274
+ *         self.name = name
+ *     def __repr__(self):
+ *         return self.name             # <<<<<<<<<<<<<<
+ * 
+ * cdef generic = Enum("<strided and direct or indirect>")
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(__pyx_v_self->name);
+  __pyx_r = __pyx_v_self->name;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":273
+ *     def __init__(self, name):
+ *         self.name = name
+ *     def __repr__(self):             # <<<<<<<<<<<<<<
+ *         return self.name
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":288
+ * 
+ * @cname('__pyx_align_pointer')
+ * cdef void *align_pointer(void *memory, size_t alignment) nogil:             # <<<<<<<<<<<<<<
+ *     "Align pointer memory on a given boundary"
+ *     cdef Py_intptr_t aligned_p = <Py_intptr_t> memory
+ */
+
+static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) {
+  Py_intptr_t __pyx_v_aligned_p;
+  size_t __pyx_v_offset;
+  void *__pyx_r;
+  int __pyx_t_1;
+
+  /* "View.MemoryView":290
+ * cdef void *align_pointer(void *memory, size_t alignment) nogil:
+ *     "Align pointer memory on a given boundary"
+ *     cdef Py_intptr_t aligned_p = <Py_intptr_t> memory             # <<<<<<<<<<<<<<
+ *     cdef size_t offset
+ * 
+ */
+  __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory);
+
+  /* "View.MemoryView":294
+ * 
+ *     with cython.cdivision(True):
+ *         offset = aligned_p % alignment             # <<<<<<<<<<<<<<
+ * 
+ *     if offset > 0:
+ */
+  __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment);
+
+  /* "View.MemoryView":296
+ *         offset = aligned_p % alignment
+ * 
+ *     if offset > 0:             # <<<<<<<<<<<<<<
+ *         aligned_p += alignment - offset
+ * 
+ */
+  __pyx_t_1 = ((__pyx_v_offset > 0) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":297
+ * 
+ *     if offset > 0:
+ *         aligned_p += alignment - offset             # <<<<<<<<<<<<<<
+ * 
+ *     return <void *> aligned_p
+ */
+    __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset));
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":299
+ *         aligned_p += alignment - offset
+ * 
+ *     return <void *> aligned_p             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview')
+ */
+  __pyx_r = ((void *)__pyx_v_aligned_p);
+  goto __pyx_L0;
+
+  /* "View.MemoryView":288
+ * 
+ * @cname('__pyx_align_pointer')
+ * cdef void *align_pointer(void *memory, size_t alignment) nogil:             # <<<<<<<<<<<<<<
+ *     "Align pointer memory on a given boundary"
+ *     cdef Py_intptr_t aligned_p = <Py_intptr_t> memory
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":317
+ *     cdef __Pyx_TypeInfo *typeinfo
+ * 
+ *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):             # <<<<<<<<<<<<<<
+ *         self.obj = obj
+ *         self.flags = flags
+ */
+
+/* Python wrapper */
+static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+  PyObject *__pyx_v_obj = 0;
+  int __pyx_v_flags;
+  int __pyx_v_dtype_is_object;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0);
+  {
+    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_obj,&__pyx_n_s_flags,&__pyx_n_s_dtype_is_object,0};
+    PyObject* values[3] = {0,0,0};
+    if (unlikely(__pyx_kwds)) {
+      Py_ssize_t kw_args;
+      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
+      switch (pos_args) {
+        case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
+        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
+        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+        case  0: break;
+        default: goto __pyx_L5_argtuple_error;
+      }
+      kw_args = PyDict_Size(__pyx_kwds);
+      switch (pos_args) {
+        case  0:
+        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_obj)) != 0)) kw_args--;
+        else goto __pyx_L5_argtuple_error;
+        case  1:
+        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--;
+        else {
+          __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        }
+        case  2:
+        if (kw_args > 0) {
+          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s_dtype_is_object);
+          if (value) { values[2] = value; kw_args--; }
+        }
+      }
+      if (unlikely(kw_args > 0)) {
+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      }
+    } else {
+      switch (PyTuple_GET_SIZE(__pyx_args)) {
+        case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
+        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
+        values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
+        break;
+        default: goto __pyx_L5_argtuple_error;
+      }
+    }
+    __pyx_v_obj = values[0];
+    __pyx_v_flags = __Pyx_PyInt_As_int(values[1]); if (unlikely((__pyx_v_flags == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+    if (values[2]) {
+      __pyx_v_dtype_is_object = __Pyx_PyObject_IsTrue(values[2]); if (unlikely((__pyx_v_dtype_is_object == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+    } else {
+      __pyx_v_dtype_is_object = ((int)0);
+    }
+  }
+  goto __pyx_L4_argument_unpacking_done;
+  __pyx_L5_argtuple_error:;
+  __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __Pyx_RefNannyFinishContext();
+  return -1;
+  __pyx_L4_argument_unpacking_done:;
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview___cinit__(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_obj, __pyx_v_flags, __pyx_v_dtype_is_object);
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static int __pyx_memoryview_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  PyObject *__pyx_t_5 = NULL;
+  PyObject *__pyx_t_6 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__cinit__", 0);
+
+  /* "View.MemoryView":318
+ * 
+ *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):
+ *         self.obj = obj             # <<<<<<<<<<<<<<
+ *         self.flags = flags
+ *         if type(self) is memoryview or obj is not None:
+ */
+  __Pyx_INCREF(__pyx_v_obj);
+  __Pyx_GIVEREF(__pyx_v_obj);
+  __Pyx_GOTREF(__pyx_v_self->obj);
+  __Pyx_DECREF(__pyx_v_self->obj);
+  __pyx_v_self->obj = __pyx_v_obj;
+
+  /* "View.MemoryView":319
+ *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):
+ *         self.obj = obj
+ *         self.flags = flags             # <<<<<<<<<<<<<<
+ *         if type(self) is memoryview or obj is not None:
+ *             __Pyx_GetBuffer(obj, &self.view, flags)
+ */
+  __pyx_v_self->flags = __pyx_v_flags;
+
+  /* "View.MemoryView":320
+ *         self.obj = obj
+ *         self.flags = flags
+ *         if type(self) is memoryview or obj is not None:             # <<<<<<<<<<<<<<
+ *             __Pyx_GetBuffer(obj, &self.view, flags)
+ *             if <PyObject *> self.view.obj == NULL:
+ */
+  __pyx_t_1 = (((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self))) == ((PyObject *)((PyObject *)__pyx_memoryview_type)));
+  if (!(__pyx_t_1 != 0)) {
+    __pyx_t_2 = (__pyx_v_obj != Py_None);
+    __pyx_t_3 = (__pyx_t_2 != 0);
+  } else {
+    __pyx_t_3 = (__pyx_t_1 != 0);
+  }
+  if (__pyx_t_3) {
+
+    /* "View.MemoryView":321
+ *         self.flags = flags
+ *         if type(self) is memoryview or obj is not None:
+ *             __Pyx_GetBuffer(obj, &self.view, flags)             # <<<<<<<<<<<<<<
+ *             if <PyObject *> self.view.obj == NULL:
+ *                 (<__pyx_buffer *> &self.view).obj = Py_None
+ */
+    __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 321; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+    /* "View.MemoryView":322
+ *         if type(self) is memoryview or obj is not None:
+ *             __Pyx_GetBuffer(obj, &self.view, flags)
+ *             if <PyObject *> self.view.obj == NULL:             # <<<<<<<<<<<<<<
+ *                 (<__pyx_buffer *> &self.view).obj = Py_None
+ *                 Py_INCREF(Py_None)
+ */
+    __pyx_t_3 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0);
+    if (__pyx_t_3) {
+
+      /* "View.MemoryView":323
+ *             __Pyx_GetBuffer(obj, &self.view, flags)
+ *             if <PyObject *> self.view.obj == NULL:
+ *                 (<__pyx_buffer *> &self.view).obj = Py_None             # <<<<<<<<<<<<<<
+ *                 Py_INCREF(Py_None)
+ * 
+ */
+      ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None;
+
+      /* "View.MemoryView":324
+ *             if <PyObject *> self.view.obj == NULL:
+ *                 (<__pyx_buffer *> &self.view).obj = Py_None
+ *                 Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
+ * 
+ *         self.lock = PyThread_allocate_lock()
+ */
+      Py_INCREF(Py_None);
+      goto __pyx_L4;
+    }
+    __pyx_L4:;
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":326
+ *                 Py_INCREF(Py_None)
+ * 
+ *         self.lock = PyThread_allocate_lock()             # <<<<<<<<<<<<<<
+ *         if self.lock == NULL:
+ *             raise MemoryError
+ */
+  __pyx_v_self->lock = PyThread_allocate_lock();
+
+  /* "View.MemoryView":327
+ * 
+ *         self.lock = PyThread_allocate_lock()
+ *         if self.lock == NULL:             # <<<<<<<<<<<<<<
+ *             raise MemoryError
+ * 
+ */
+  __pyx_t_3 = ((__pyx_v_self->lock == NULL) != 0);
+  if (__pyx_t_3) {
+
+    /* "View.MemoryView":328
+ *         self.lock = PyThread_allocate_lock()
+ *         if self.lock == NULL:
+ *             raise MemoryError             # <<<<<<<<<<<<<<
+ * 
+ *         if flags & PyBUF_FORMAT:
+ */
+    PyErr_NoMemory(); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 328; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "View.MemoryView":330
+ *             raise MemoryError
+ * 
+ *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
+ *             self.dtype_is_object = self.view.format == b'O'
+ *         else:
+ */
+  __pyx_t_3 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
+  if (__pyx_t_3) {
+
+    /* "View.MemoryView":331
+ * 
+ *         if flags & PyBUF_FORMAT:
+ *             self.dtype_is_object = self.view.format == b'O'             # <<<<<<<<<<<<<<
+ *         else:
+ *             self.dtype_is_object = dtype_is_object
+ */
+    __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    __pyx_t_6 = PyObject_RichCompare(__pyx_t_5, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+    __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely((__pyx_t_3 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+    __pyx_v_self->dtype_is_object = __pyx_t_3;
+    goto __pyx_L6;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":333
+ *             self.dtype_is_object = self.view.format == b'O'
+ *         else:
+ *             self.dtype_is_object = dtype_is_object             # <<<<<<<<<<<<<<
+ * 
+ *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(
+ */
+    __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object;
+  }
+  __pyx_L6:;
+
+  /* "View.MemoryView":335
+ *             self.dtype_is_object = dtype_is_object
+ * 
+ *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(             # <<<<<<<<<<<<<<
+ *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))
+ *         self.typeinfo = NULL
+ */
+  __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int))));
+
+  /* "View.MemoryView":337
+ *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(
+ *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))
+ *         self.typeinfo = NULL             # <<<<<<<<<<<<<<
+ * 
+ *     def __dealloc__(memoryview self):
+ */
+  __pyx_v_self->typeinfo = NULL;
+
+  /* "View.MemoryView":317
+ *     cdef __Pyx_TypeInfo *typeinfo
+ * 
+ *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):             # <<<<<<<<<<<<<<
+ *         self.obj = obj
+ *         self.flags = flags
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_XDECREF(__pyx_t_6);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":339
+ *         self.typeinfo = NULL
+ * 
+ *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<
+ *         if self.obj is not None:
+ *             __Pyx_ReleaseBuffer(&self.view)
+ */
+
+/* Python wrapper */
+static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/
+static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0);
+  __pyx_memoryview_MemoryView_10memoryview_2__dealloc__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+static void __pyx_memoryview_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  __Pyx_RefNannySetupContext("__dealloc__", 0);
+
+  /* "View.MemoryView":340
+ * 
+ *     def __dealloc__(memoryview self):
+ *         if self.obj is not None:             # <<<<<<<<<<<<<<
+ *             __Pyx_ReleaseBuffer(&self.view)
+ * 
+ */
+  __pyx_t_1 = (__pyx_v_self->obj != Py_None);
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":341
+ *     def __dealloc__(memoryview self):
+ *         if self.obj is not None:
+ *             __Pyx_ReleaseBuffer(&self.view)             # <<<<<<<<<<<<<<
+ * 
+ *         if self.lock != NULL:
+ */
+    __Pyx_ReleaseBuffer((&__pyx_v_self->view));
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":343
+ *             __Pyx_ReleaseBuffer(&self.view)
+ * 
+ *         if self.lock != NULL:             # <<<<<<<<<<<<<<
+ *             PyThread_free_lock(self.lock)
+ * 
+ */
+  __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":344
+ * 
+ *         if self.lock != NULL:
+ *             PyThread_free_lock(self.lock)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef char *get_item_pointer(memoryview self, object index) except NULL:
+ */
+    PyThread_free_lock(__pyx_v_self->lock);
+    goto __pyx_L4;
+  }
+  __pyx_L4:;
+
+  /* "View.MemoryView":339
+ *         self.typeinfo = NULL
+ * 
+ *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<
+ *         if self.obj is not None:
+ *             __Pyx_ReleaseBuffer(&self.view)
+ */
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+/* "View.MemoryView":346
+ *             PyThread_free_lock(self.lock)
+ * 
+ *     cdef char *get_item_pointer(memoryview self, object index) except NULL:             # <<<<<<<<<<<<<<
+ *         cdef Py_ssize_t dim
+ *         cdef char *itemp = <char *> self.view.buf
+ */
+
+static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) {
+  Py_ssize_t __pyx_v_dim;
+  char *__pyx_v_itemp;
+  PyObject *__pyx_v_idx = NULL;
+  char *__pyx_r;
+  __Pyx_RefNannyDeclarations
+  Py_ssize_t __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  Py_ssize_t __pyx_t_3;
+  PyObject *(*__pyx_t_4)(PyObject *);
+  PyObject *__pyx_t_5 = NULL;
+  Py_ssize_t __pyx_t_6;
+  char *__pyx_t_7;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("get_item_pointer", 0);
+
+  /* "View.MemoryView":348
+ *     cdef char *get_item_pointer(memoryview self, object index) except NULL:
+ *         cdef Py_ssize_t dim
+ *         cdef char *itemp = <char *> self.view.buf             # <<<<<<<<<<<<<<
+ * 
+ *         for dim, idx in enumerate(index):
+ */
+  __pyx_v_itemp = ((char *)__pyx_v_self->view.buf);
+
+  /* "View.MemoryView":350
+ *         cdef char *itemp = <char *> self.view.buf
+ * 
+ *         for dim, idx in enumerate(index):             # <<<<<<<<<<<<<<
+ *             itemp = pybuffer_index(&self.view, itemp, idx, dim)
+ * 
+ */
+  __pyx_t_1 = 0;
+  if (PyList_CheckExact(__pyx_v_index) || PyTuple_CheckExact(__pyx_v_index)) {
+    __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0;
+    __pyx_t_4 = NULL;
+  } else {
+    __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext;
+  }
+  for (;;) {
+    if (!__pyx_t_4 && PyList_CheckExact(__pyx_t_2)) {
+      if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else if (!__pyx_t_4 && PyTuple_CheckExact(__pyx_t_2)) {
+      if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else {
+      __pyx_t_5 = __pyx_t_4(__pyx_t_2);
+      if (unlikely(!__pyx_t_5)) {
+        PyObject* exc_type = PyErr_Occurred();
+        if (exc_type) {
+          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+          else {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        }
+        break;
+      }
+      __Pyx_GOTREF(__pyx_t_5);
+    }
+    __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_5);
+    __pyx_t_5 = 0;
+    __pyx_v_dim = __pyx_t_1;
+    __pyx_t_1 = (__pyx_t_1 + 1);
+
+    /* "View.MemoryView":351
+ * 
+ *         for dim, idx in enumerate(index):
+ *             itemp = pybuffer_index(&self.view, itemp, idx, dim)             # <<<<<<<<<<<<<<
+ * 
+ *         return itemp
+ */
+    __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_v_itemp = __pyx_t_7;
+  }
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+
+  /* "View.MemoryView":353
+ *             itemp = pybuffer_index(&self.view, itemp, idx, dim)
+ * 
+ *         return itemp             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_r = __pyx_v_itemp;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":346
+ *             PyThread_free_lock(self.lock)
+ * 
+ *     cdef char *get_item_pointer(memoryview self, object index) except NULL:             # <<<<<<<<<<<<<<
+ *         cdef Py_ssize_t dim
+ *         cdef char *itemp = <char *> self.view.buf
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.get_item_pointer", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_idx);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":356
+ * 
+ * 
+ *     def __getitem__(memoryview self, object index):             # <<<<<<<<<<<<<<
+ *         if index is Ellipsis:
+ *             return self
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index); /*proto*/
+static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_4__getitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) {
+  PyObject *__pyx_v_have_slices = NULL;
+  PyObject *__pyx_v_indices = NULL;
+  char *__pyx_v_itemp;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  PyObject *__pyx_t_5 = NULL;
+  char *__pyx_t_6;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__getitem__", 0);
+
+  /* "View.MemoryView":357
+ * 
+ *     def __getitem__(memoryview self, object index):
+ *         if index is Ellipsis:             # <<<<<<<<<<<<<<
+ *             return self
+ * 
+ */
+  __pyx_t_1 = (__pyx_v_index == __pyx_builtin_Ellipsis);
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":358
+ *     def __getitem__(memoryview self, object index):
+ *         if index is Ellipsis:
+ *             return self             # <<<<<<<<<<<<<<
+ * 
+ *         have_slices, indices = _unellipsify(index, self.view.ndim)
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __Pyx_INCREF(((PyObject *)__pyx_v_self));
+    __pyx_r = ((PyObject *)__pyx_v_self);
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":360
+ *             return self
+ * 
+ *         have_slices, indices = _unellipsify(index, self.view.ndim)             # <<<<<<<<<<<<<<
+ * 
+ *         cdef char *itemp
+ */
+  __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  if (likely(__pyx_t_3 != Py_None)) {
+    PyObject* sequence = __pyx_t_3;
+    #if CYTHON_COMPILING_IN_CPYTHON
+    Py_ssize_t size = Py_SIZE(sequence);
+    #else
+    Py_ssize_t size = PySequence_Size(sequence);
+    #endif
+    if (unlikely(size != 2)) {
+      if (size > 2) __Pyx_RaiseTooManyValuesError(2);
+      else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+    #if CYTHON_COMPILING_IN_CPYTHON
+    __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0); 
+    __pyx_t_5 = PyTuple_GET_ITEM(sequence, 1); 
+    __Pyx_INCREF(__pyx_t_4);
+    __Pyx_INCREF(__pyx_t_5);
+    #else
+    __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_5 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    #endif
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  } else {
+    __Pyx_RaiseNoneNotIterableError(); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __pyx_v_have_slices = __pyx_t_4;
+  __pyx_t_4 = 0;
+  __pyx_v_indices = __pyx_t_5;
+  __pyx_t_5 = 0;
+
+  /* "View.MemoryView":363
+ * 
+ *         cdef char *itemp
+ *         if have_slices:             # <<<<<<<<<<<<<<
+ *             return memview_slice(self, indices)
+ *         else:
+ */
+  __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 363; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":364
+ *         cdef char *itemp
+ *         if have_slices:
+ *             return memview_slice(self, indices)             # <<<<<<<<<<<<<<
+ *         else:
+ *             itemp = self.get_item_pointer(indices)
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 364; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_r = __pyx_t_3;
+    __pyx_t_3 = 0;
+    goto __pyx_L0;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":366
+ *             return memview_slice(self, indices)
+ *         else:
+ *             itemp = self.get_item_pointer(indices)             # <<<<<<<<<<<<<<
+ *             return self.convert_item_to_object(itemp)
+ * 
+ */
+    __pyx_t_6 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_indices); if (unlikely(__pyx_t_6 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 366; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_v_itemp = __pyx_t_6;
+
+    /* "View.MemoryView":367
+ *         else:
+ *             itemp = self.get_item_pointer(indices)
+ *             return self.convert_item_to_object(itemp)             # <<<<<<<<<<<<<<
+ * 
+ *     def __setitem__(memoryview self, object index, object value):
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->convert_item_to_object(__pyx_v_self, __pyx_v_itemp); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 367; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_r = __pyx_t_3;
+    __pyx_t_3 = 0;
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":356
+ * 
+ * 
+ *     def __getitem__(memoryview self, object index):             # <<<<<<<<<<<<<<
+ *         if index is Ellipsis:
+ *             return self
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_have_slices);
+  __Pyx_XDECREF(__pyx_v_indices);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":369
+ *             return self.convert_item_to_object(itemp)
+ * 
+ *     def __setitem__(memoryview self, object index, object value):             # <<<<<<<<<<<<<<
+ *         have_slices, index = _unellipsify(index, self.view.ndim)
+ * 
+ */
+
+/* Python wrapper */
+static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /*proto*/
+static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_6__setitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index), ((PyObject *)__pyx_v_value));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static int __pyx_memoryview_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) {
+  PyObject *__pyx_v_have_slices = NULL;
+  PyObject *__pyx_v_obj = NULL;
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_t_4;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__setitem__", 0);
+  __Pyx_INCREF(__pyx_v_index);
+
+  /* "View.MemoryView":370
+ * 
+ *     def __setitem__(memoryview self, object index, object value):
+ *         have_slices, index = _unellipsify(index, self.view.ndim)             # <<<<<<<<<<<<<<
+ * 
+ *         if have_slices:
+ */
+  __pyx_t_1 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (likely(__pyx_t_1 != Py_None)) {
+    PyObject* sequence = __pyx_t_1;
+    #if CYTHON_COMPILING_IN_CPYTHON
+    Py_ssize_t size = Py_SIZE(sequence);
+    #else
+    Py_ssize_t size = PySequence_Size(sequence);
+    #endif
+    if (unlikely(size != 2)) {
+      if (size > 2) __Pyx_RaiseTooManyValuesError(2);
+      else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+    #if CYTHON_COMPILING_IN_CPYTHON
+    __pyx_t_2 = PyTuple_GET_ITEM(sequence, 0); 
+    __pyx_t_3 = PyTuple_GET_ITEM(sequence, 1); 
+    __Pyx_INCREF(__pyx_t_2);
+    __Pyx_INCREF(__pyx_t_3);
+    #else
+    __pyx_t_2 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __pyx_t_3 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    #endif
+    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  } else {
+    __Pyx_RaiseNoneNotIterableError(); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __pyx_v_have_slices = __pyx_t_2;
+  __pyx_t_2 = 0;
+  __Pyx_DECREF_SET(__pyx_v_index, __pyx_t_3);
+  __pyx_t_3 = 0;
+
+  /* "View.MemoryView":372
+ *         have_slices, index = _unellipsify(index, self.view.ndim)
+ * 
+ *         if have_slices:             # <<<<<<<<<<<<<<
+ *             obj = self.is_slice(value)
+ *             if obj:
+ */
+  __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_4 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 372; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (__pyx_t_4) {
+
+    /* "View.MemoryView":373
+ * 
+ *         if have_slices:
+ *             obj = self.is_slice(value)             # <<<<<<<<<<<<<<
+ *             if obj:
+ *                 self.setitem_slice_assignment(self[index], obj)
+ */
+    __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->is_slice(__pyx_v_self, __pyx_v_value); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 373; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_1);
+    __pyx_v_obj = __pyx_t_1;
+    __pyx_t_1 = 0;
+
+    /* "View.MemoryView":374
+ *         if have_slices:
+ *             obj = self.is_slice(value)
+ *             if obj:             # <<<<<<<<<<<<<<
+ *                 self.setitem_slice_assignment(self[index], obj)
+ *             else:
+ */
+    __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_v_obj); if (unlikely(__pyx_t_4 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 374; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    if (__pyx_t_4) {
+
+      /* "View.MemoryView":375
+ *             obj = self.is_slice(value)
+ *             if obj:
+ *                 self.setitem_slice_assignment(self[index], obj)             # <<<<<<<<<<<<<<
+ *             else:
+ *                 self.setitem_slice_assign_scalar(self[index], value)
+ */
+      __pyx_t_1 = PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(__pyx_t_1 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 375; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+      __Pyx_GOTREF(__pyx_t_1);
+      __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assignment(__pyx_v_self, __pyx_t_1, __pyx_v_obj); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 375; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      goto __pyx_L4;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":377
+ *                 self.setitem_slice_assignment(self[index], obj)
+ *             else:
+ *                 self.setitem_slice_assign_scalar(self[index], value)             # <<<<<<<<<<<<<<
+ *         else:
+ *             self.setitem_indexed(index, value)
+ */
+      __pyx_t_3 = PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(__pyx_t_3 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 377; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+      __Pyx_GOTREF(__pyx_t_3);
+      if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 377; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assign_scalar(__pyx_v_self, ((struct __pyx_memoryview_obj *)__pyx_t_3), __pyx_v_value); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 377; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_1);
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+    }
+    __pyx_L4:;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":379
+ *                 self.setitem_slice_assign_scalar(self[index], value)
+ *         else:
+ *             self.setitem_indexed(index, value)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef is_slice(self, obj):
+ */
+    __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_indexed(__pyx_v_self, __pyx_v_index, __pyx_v_value); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 379; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_1);
+    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":369
+ *             return self.convert_item_to_object(itemp)
+ * 
+ *     def __setitem__(memoryview self, object index, object value):             # <<<<<<<<<<<<<<
+ *         have_slices, index = _unellipsify(index, self.view.ndim)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_have_slices);
+  __Pyx_XDECREF(__pyx_v_obj);
+  __Pyx_XDECREF(__pyx_v_index);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":381
+ *             self.setitem_indexed(index, value)
+ * 
+ *     cdef is_slice(self, obj):             # <<<<<<<<<<<<<<
+ *         if not isinstance(obj, memoryview):
+ *             try:
+ */
+
+static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  PyObject *__pyx_t_5 = NULL;
+  PyObject *__pyx_t_6 = NULL;
+  PyObject *__pyx_t_7 = NULL;
+  PyObject *__pyx_t_8 = NULL;
+  int __pyx_t_9;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("is_slice", 0);
+  __Pyx_INCREF(__pyx_v_obj);
+
+  /* "View.MemoryView":382
+ * 
+ *     cdef is_slice(self, obj):
+ *         if not isinstance(obj, memoryview):             # <<<<<<<<<<<<<<
+ *             try:
+ *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
+ */
+  __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_obj, ((PyObject *)__pyx_memoryview_type)); 
+  __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":383
+ *     cdef is_slice(self, obj):
+ *         if not isinstance(obj, memoryview):
+ *             try:             # <<<<<<<<<<<<<<
+ *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
+ *                                  self.dtype_is_object)
+ */
+    {
+      __Pyx_ExceptionSave(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5);
+      __Pyx_XGOTREF(__pyx_t_3);
+      __Pyx_XGOTREF(__pyx_t_4);
+      __Pyx_XGOTREF(__pyx_t_5);
+      /*try:*/ {
+
+        /* "View.MemoryView":384
+ *         if not isinstance(obj, memoryview):
+ *             try:
+ *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,             # <<<<<<<<<<<<<<
+ *                                  self.dtype_is_object)
+ *             except TypeError:
+ */
+        __pyx_t_6 = __Pyx_PyInt_From_int((__pyx_v_self->flags | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 384; __pyx_clineno = __LINE__; goto __pyx_L4_error;}
+        __Pyx_GOTREF(__pyx_t_6);
+
+        /* "View.MemoryView":385
+ *             try:
+ *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
+ *                                  self.dtype_is_object)             # <<<<<<<<<<<<<<
+ *             except TypeError:
+ *                 return None
+ */
+        __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 385; __pyx_clineno = __LINE__; goto __pyx_L4_error;}
+        __Pyx_GOTREF(__pyx_t_7);
+
+        /* "View.MemoryView":384
+ *         if not isinstance(obj, memoryview):
+ *             try:
+ *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,             # <<<<<<<<<<<<<<
+ *                                  self.dtype_is_object)
+ *             except TypeError:
+ */
+        __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 384; __pyx_clineno = __LINE__; goto __pyx_L4_error;}
+        __Pyx_GOTREF(__pyx_t_8);
+        __Pyx_INCREF(__pyx_v_obj);
+        PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_v_obj);
+        __Pyx_GIVEREF(__pyx_v_obj);
+        PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_6);
+        __Pyx_GIVEREF(__pyx_t_6);
+        PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7);
+        __Pyx_GIVEREF(__pyx_t_7);
+        __pyx_t_6 = 0;
+        __pyx_t_7 = 0;
+        __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_memoryview_type)), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 384; __pyx_clineno = __LINE__; goto __pyx_L4_error;}
+        __Pyx_GOTREF(__pyx_t_7);
+        __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
+        __Pyx_DECREF_SET(__pyx_v_obj, __pyx_t_7);
+        __pyx_t_7 = 0;
+      }
+      __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
+      __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
+      goto __pyx_L11_try_end;
+      __pyx_L4_error:;
+      __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
+      __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0;
+      __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0;
+
+      /* "View.MemoryView":386
+ *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
+ *                                  self.dtype_is_object)
+ *             except TypeError:             # <<<<<<<<<<<<<<
+ *                 return None
+ * 
+ */
+      __pyx_t_9 = PyErr_ExceptionMatches(__pyx_builtin_TypeError);
+      if (__pyx_t_9) {
+        __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
+        if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 386; __pyx_clineno = __LINE__; goto __pyx_L6_except_error;}
+        __Pyx_GOTREF(__pyx_t_7);
+        __Pyx_GOTREF(__pyx_t_8);
+        __Pyx_GOTREF(__pyx_t_6);
+
+        /* "View.MemoryView":387
+ *                                  self.dtype_is_object)
+ *             except TypeError:
+ *                 return None             # <<<<<<<<<<<<<<
+ * 
+ *         return obj
+ */
+        __Pyx_XDECREF(__pyx_r);
+        __Pyx_INCREF(Py_None);
+        __pyx_r = Py_None;
+        __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+        __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
+        __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
+        goto __pyx_L7_except_return;
+        __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
+        __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
+        __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+        goto __pyx_L5_exception_handled;
+      }
+      goto __pyx_L6_except_error;
+      __pyx_L6_except_error:;
+      __Pyx_XGIVEREF(__pyx_t_3);
+      __Pyx_XGIVEREF(__pyx_t_4);
+      __Pyx_XGIVEREF(__pyx_t_5);
+      __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5);
+      goto __pyx_L1_error;
+      __pyx_L7_except_return:;
+      __Pyx_XGIVEREF(__pyx_t_3);
+      __Pyx_XGIVEREF(__pyx_t_4);
+      __Pyx_XGIVEREF(__pyx_t_5);
+      __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5);
+      goto __pyx_L0;
+      __pyx_L5_exception_handled:;
+      __Pyx_XGIVEREF(__pyx_t_3);
+      __Pyx_XGIVEREF(__pyx_t_4);
+      __Pyx_XGIVEREF(__pyx_t_5);
+      __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5);
+      __pyx_L11_try_end:;
+    }
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":389
+ *                 return None
+ * 
+ *         return obj             # <<<<<<<<<<<<<<
+ * 
+ *     cdef setitem_slice_assignment(self, dst, src):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(__pyx_v_obj);
+  __pyx_r = __pyx_v_obj;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":381
+ *             self.setitem_indexed(index, value)
+ * 
+ *     cdef is_slice(self, obj):             # <<<<<<<<<<<<<<
+ *         if not isinstance(obj, memoryview):
+ *             try:
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_6);
+  __Pyx_XDECREF(__pyx_t_7);
+  __Pyx_XDECREF(__pyx_t_8);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_obj);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":391
+ *         return obj
+ * 
+ *     cdef setitem_slice_assignment(self, dst, src):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice dst_slice
+ *         cdef __Pyx_memviewslice src_slice
+ */
+
+static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src) {
+  __Pyx_memviewslice __pyx_v_dst_slice;
+  __Pyx_memviewslice __pyx_v_src_slice;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("setitem_slice_assignment", 0);
+
+  /* "View.MemoryView":395
+ *         cdef __Pyx_memviewslice src_slice
+ * 
+ *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],             # <<<<<<<<<<<<<<
+ *                                  get_slice_from_memview(dst, &dst_slice)[0],
+ *                                  src.ndim, dst.ndim, self.dtype_is_object)
+ */
+  if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 395; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+  /* "View.MemoryView":396
+ * 
+ *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],
+ *                                  get_slice_from_memview(dst, &dst_slice)[0],             # <<<<<<<<<<<<<<
+ *                                  src.ndim, dst.ndim, self.dtype_is_object)
+ * 
+ */
+  if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 396; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+  /* "View.MemoryView":397
+ *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],
+ *                                  get_slice_from_memview(dst, &dst_slice)[0],
+ *                                  src.ndim, dst.ndim, self.dtype_is_object)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):
+ */
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_2 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_3 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_3 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+
+  /* "View.MemoryView":395
+ *         cdef __Pyx_memviewslice src_slice
+ * 
+ *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],             # <<<<<<<<<<<<<<
+ *                                  get_slice_from_memview(dst, &dst_slice)[0],
+ *                                  src.ndim, dst.ndim, self.dtype_is_object)
+ */
+  __pyx_t_4 = __pyx_memoryview_copy_contents((__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice))[0]), (__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice))[0]), __pyx_t_2, __pyx_t_3, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 395; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+  /* "View.MemoryView":391
+ *         return obj
+ * 
+ *     cdef setitem_slice_assignment(self, dst, src):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice dst_slice
+ *         cdef __Pyx_memviewslice src_slice
+ */
+
+  /* function exit code */
+  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assignment", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":399
+ *                                  src.ndim, dst.ndim, self.dtype_is_object)
+ * 
+ *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):             # <<<<<<<<<<<<<<
+ *         cdef int array[128]
+ *         cdef void *tmp = NULL
+ */
+
+static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value) {
+  int __pyx_v_array[128];
+  void *__pyx_v_tmp;
+  void *__pyx_v_item;
+  __Pyx_memviewslice *__pyx_v_dst_slice;
+  __Pyx_memviewslice __pyx_v_tmp_slice;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  char const *__pyx_t_5;
+  PyObject *__pyx_t_6 = NULL;
+  PyObject *__pyx_t_7 = NULL;
+  PyObject *__pyx_t_8 = NULL;
+  PyObject *__pyx_t_9 = NULL;
+  PyObject *__pyx_t_10 = NULL;
+  PyObject *__pyx_t_11 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0);
+
+  /* "View.MemoryView":401
+ *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):
+ *         cdef int array[128]
+ *         cdef void *tmp = NULL             # <<<<<<<<<<<<<<
+ *         cdef void *item
+ * 
+ */
+  __pyx_v_tmp = NULL;
+
+  /* "View.MemoryView":406
+ *         cdef __Pyx_memviewslice *dst_slice
+ *         cdef __Pyx_memviewslice tmp_slice
+ *         dst_slice = get_slice_from_memview(dst, &tmp_slice)             # <<<<<<<<<<<<<<
+ * 
+ *         if <size_t>self.view.itemsize > sizeof(array):
+ */
+  __pyx_v_dst_slice = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_dst, (&__pyx_v_tmp_slice));
+
+  /* "View.MemoryView":408
+ *         dst_slice = get_slice_from_memview(dst, &tmp_slice)
+ * 
+ *         if <size_t>self.view.itemsize > sizeof(array):             # <<<<<<<<<<<<<<
+ *             tmp = PyMem_Malloc(self.view.itemsize)
+ *             if tmp == NULL:
+ */
+  __pyx_t_1 = ((((size_t)__pyx_v_self->view.itemsize) > (sizeof(__pyx_v_array))) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":409
+ * 
+ *         if <size_t>self.view.itemsize > sizeof(array):
+ *             tmp = PyMem_Malloc(self.view.itemsize)             # <<<<<<<<<<<<<<
+ *             if tmp == NULL:
+ *                 raise MemoryError
+ */
+    __pyx_v_tmp = PyMem_Malloc(__pyx_v_self->view.itemsize);
+
+    /* "View.MemoryView":410
+ *         if <size_t>self.view.itemsize > sizeof(array):
+ *             tmp = PyMem_Malloc(self.view.itemsize)
+ *             if tmp == NULL:             # <<<<<<<<<<<<<<
+ *                 raise MemoryError
+ *             item = tmp
+ */
+    __pyx_t_1 = ((__pyx_v_tmp == NULL) != 0);
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":411
+ *             tmp = PyMem_Malloc(self.view.itemsize)
+ *             if tmp == NULL:
+ *                 raise MemoryError             # <<<<<<<<<<<<<<
+ *             item = tmp
+ *         else:
+ */
+      PyErr_NoMemory(); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 411; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+
+    /* "View.MemoryView":412
+ *             if tmp == NULL:
+ *                 raise MemoryError
+ *             item = tmp             # <<<<<<<<<<<<<<
+ *         else:
+ *             item = <void *> array
+ */
+    __pyx_v_item = __pyx_v_tmp;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":414
+ *             item = tmp
+ *         else:
+ *             item = <void *> array             # <<<<<<<<<<<<<<
+ * 
+ *         try:
+ */
+    __pyx_v_item = ((void *)__pyx_v_array);
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":416
+ *             item = <void *> array
+ * 
+ *         try:             # <<<<<<<<<<<<<<
+ *             if self.dtype_is_object:
+ *                 (<PyObject **> item)[0] = <PyObject *> value
+ */
+  /*try:*/ {
+
+    /* "View.MemoryView":417
+ * 
+ *         try:
+ *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
+ *                 (<PyObject **> item)[0] = <PyObject *> value
+ *             else:
+ */
+    __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0);
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":418
+ *         try:
+ *             if self.dtype_is_object:
+ *                 (<PyObject **> item)[0] = <PyObject *> value             # <<<<<<<<<<<<<<
+ *             else:
+ *                 self.assign_item_from_object(<char *> item, value)
+ */
+      (((PyObject **)__pyx_v_item)[0]) = ((PyObject *)__pyx_v_value);
+      goto __pyx_L8;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":420
+ *                 (<PyObject **> item)[0] = <PyObject *> value
+ *             else:
+ *                 self.assign_item_from_object(<char *> item, value)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 420; __pyx_clineno = __LINE__; goto __pyx_L6_error;}
+      __Pyx_GOTREF(__pyx_t_2);
+      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    }
+    __pyx_L8:;
+
+    /* "View.MemoryView":424
+ * 
+ * 
+ *             if self.view.suboffsets != NULL:             # <<<<<<<<<<<<<<
+ *                 assert_direct_dimensions(self.view.suboffsets, self.view.ndim)
+ *             slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize,
+ */
+    __pyx_t_1 = ((__pyx_v_self->view.suboffsets != NULL) != 0);
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":425
+ * 
+ *             if self.view.suboffsets != NULL:
+ *                 assert_direct_dimensions(self.view.suboffsets, self.view.ndim)             # <<<<<<<<<<<<<<
+ *             slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize,
+ *                                 item, self.dtype_is_object)
+ */
+      __pyx_t_2 = assert_direct_dimensions(__pyx_v_self->view.suboffsets, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 425; __pyx_clineno = __LINE__; goto __pyx_L6_error;}
+      __Pyx_GOTREF(__pyx_t_2);
+      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+      goto __pyx_L9;
+    }
+    __pyx_L9:;
+
+    /* "View.MemoryView":426
+ *             if self.view.suboffsets != NULL:
+ *                 assert_direct_dimensions(self.view.suboffsets, self.view.ndim)
+ *             slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize,             # <<<<<<<<<<<<<<
+ *                                 item, self.dtype_is_object)
+ *         finally:
+ */
+    __pyx_memoryview_slice_assign_scalar(__pyx_v_dst_slice, __pyx_v_dst->view.ndim, __pyx_v_self->view.itemsize, __pyx_v_item, __pyx_v_self->dtype_is_object);
+  }
+
+  /* "View.MemoryView":429
+ *                                 item, self.dtype_is_object)
+ *         finally:
+ *             PyMem_Free(tmp)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef setitem_indexed(self, index, value):
+ */
+  /*finally:*/ {
+    /*normal exit:*/{
+      PyMem_Free(__pyx_v_tmp);
+      goto __pyx_L7;
+    }
+    /*exception exit:*/{
+      __pyx_L6_error:;
+      __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0;
+      __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0;
+      if (PY_MAJOR_VERSION >= 3) __Pyx_ExceptionSwap(&__pyx_t_9, &__pyx_t_10, &__pyx_t_11);
+      if ((PY_MAJOR_VERSION < 3) || unlikely(__Pyx_GetException(&__pyx_t_6, &__pyx_t_7, &__pyx_t_8) < 0)) __Pyx_ErrFetch(&__pyx_t_6, &__pyx_t_7, &__pyx_t_8);
+      __Pyx_XGOTREF(__pyx_t_6);
+      __Pyx_XGOTREF(__pyx_t_7);
+      __Pyx_XGOTREF(__pyx_t_8);
+      __Pyx_XGOTREF(__pyx_t_9);
+      __Pyx_XGOTREF(__pyx_t_10);
+      __Pyx_XGOTREF(__pyx_t_11);
+      __pyx_t_3 = __pyx_lineno; __pyx_t_4 = __pyx_clineno; __pyx_t_5 = __pyx_filename;
+      {
+        PyMem_Free(__pyx_v_tmp);
+      }
+      if (PY_MAJOR_VERSION >= 3) {
+        __Pyx_XGIVEREF(__pyx_t_9);
+        __Pyx_XGIVEREF(__pyx_t_10);
+        __Pyx_XGIVEREF(__pyx_t_11);
+        __Pyx_ExceptionReset(__pyx_t_9, __pyx_t_10, __pyx_t_11);
+      }
+      __Pyx_XGIVEREF(__pyx_t_6);
+      __Pyx_XGIVEREF(__pyx_t_7);
+      __Pyx_XGIVEREF(__pyx_t_8);
+      __Pyx_ErrRestore(__pyx_t_6, __pyx_t_7, __pyx_t_8);
+      __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0;
+      __pyx_lineno = __pyx_t_3; __pyx_clineno = __pyx_t_4; __pyx_filename = __pyx_t_5;
+      goto __pyx_L1_error;
+    }
+    __pyx_L7:;
+  }
+
+  /* "View.MemoryView":399
+ *                                  src.ndim, dst.ndim, self.dtype_is_object)
+ * 
+ *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):             # <<<<<<<<<<<<<<
+ *         cdef int array[128]
+ *         cdef void *tmp = NULL
+ */
+
+  /* function exit code */
+  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assign_scalar", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":431
+ *             PyMem_Free(tmp)
+ * 
+ *     cdef setitem_indexed(self, index, value):             # <<<<<<<<<<<<<<
+ *         cdef char *itemp = self.get_item_pointer(index)
+ *         self.assign_item_from_object(itemp, value)
+ */
+
+static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) {
+  char *__pyx_v_itemp;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  char *__pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("setitem_indexed", 0);
+
+  /* "View.MemoryView":432
+ * 
+ *     cdef setitem_indexed(self, index, value):
+ *         cdef char *itemp = self.get_item_pointer(index)             # <<<<<<<<<<<<<<
+ *         self.assign_item_from_object(itemp, value)
+ * 
+ */
+  __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_index); if (unlikely(__pyx_t_1 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 432; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_v_itemp = __pyx_t_1;
+
+  /* "View.MemoryView":433
+ *     cdef setitem_indexed(self, index, value):
+ *         cdef char *itemp = self.get_item_pointer(index)
+ *         self.assign_item_from_object(itemp, value)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef convert_item_to_object(self, char *itemp):
+ */
+  __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 433; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+
+  /* "View.MemoryView":431
+ *             PyMem_Free(tmp)
+ * 
+ *     cdef setitem_indexed(self, index, value):             # <<<<<<<<<<<<<<
+ *         cdef char *itemp = self.get_item_pointer(index)
+ *         self.assign_item_from_object(itemp, value)
+ */
+
+  /* function exit code */
+  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_indexed", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":435
+ *         self.assign_item_from_object(itemp, value)
+ * 
+ *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
+ *         """Only used if instantiated manually by the user, or if Cython doesn't
+ *         know how to convert the type"""
+ */
+
+static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp) {
+  PyObject *__pyx_v_struct = NULL;
+  PyObject *__pyx_v_bytesitem = 0;
+  PyObject *__pyx_v_result = NULL;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  PyObject *__pyx_t_5 = NULL;
+  PyObject *__pyx_t_6 = NULL;
+  size_t __pyx_t_7;
+  int __pyx_t_8;
+  int __pyx_t_9;
+  PyObject *__pyx_t_10 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("convert_item_to_object", 0);
+
+  /* "View.MemoryView":438
+ *         """Only used if instantiated manually by the user, or if Cython doesn't
+ *         know how to convert the type"""
+ *         import struct             # <<<<<<<<<<<<<<
+ *         cdef bytes bytesitem
+ * 
+ */
+  __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 438; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_v_struct = __pyx_t_1;
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":441
+ *         cdef bytes bytesitem
+ * 
+ *         bytesitem = itemp[:self.view.itemsize]             # <<<<<<<<<<<<<<
+ *         try:
+ *             result = struct.unpack(self.view.format, bytesitem)
+ */
+  __pyx_t_1 = __Pyx_PyBytes_FromStringAndSize(__pyx_v_itemp + 0, __pyx_v_self->view.itemsize - 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 441; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_v_bytesitem = ((PyObject*)__pyx_t_1);
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":442
+ * 
+ *         bytesitem = itemp[:self.view.itemsize]
+ *         try:             # <<<<<<<<<<<<<<
+ *             result = struct.unpack(self.view.format, bytesitem)
+ *         except struct.error:
+ */
+  {
+    __Pyx_ExceptionSave(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4);
+    __Pyx_XGOTREF(__pyx_t_2);
+    __Pyx_XGOTREF(__pyx_t_3);
+    __Pyx_XGOTREF(__pyx_t_4);
+    /*try:*/ {
+
+      /* "View.MemoryView":443
+ *         bytesitem = itemp[:self.view.itemsize]
+ *         try:
+ *             result = struct.unpack(self.view.format, bytesitem)             # <<<<<<<<<<<<<<
+ *         except struct.error:
+ *             raise ValueError("Unable to convert item to object")
+ */
+      __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_unpack); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 443; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      __Pyx_GOTREF(__pyx_t_1);
+      __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 443; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      __Pyx_GOTREF(__pyx_t_5);
+      __pyx_t_6 = PyTuple_New(2); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 443; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      __Pyx_GOTREF(__pyx_t_6);
+      PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_5);
+      __Pyx_GIVEREF(__pyx_t_5);
+      __Pyx_INCREF(__pyx_v_bytesitem);
+      PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_v_bytesitem);
+      __Pyx_GIVEREF(__pyx_v_bytesitem);
+      __pyx_t_5 = 0;
+      __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 443; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      __Pyx_GOTREF(__pyx_t_5);
+      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+      __pyx_v_result = __pyx_t_5;
+      __pyx_t_5 = 0;
+    }
+    /*else:*/ {
+
+      /* "View.MemoryView":447
+ *             raise ValueError("Unable to convert item to object")
+ *         else:
+ *             if len(self.view.format) == 1:             # <<<<<<<<<<<<<<
+ *                 return result[0]
+ *             return result
+ */
+      __pyx_t_7 = strlen(__pyx_v_self->view.format); 
+      __pyx_t_8 = ((__pyx_t_7 == 1) != 0);
+      if (__pyx_t_8) {
+
+        /* "View.MemoryView":448
+ *         else:
+ *             if len(self.view.format) == 1:
+ *                 return result[0]             # <<<<<<<<<<<<<<
+ *             return result
+ * 
+ */
+        __Pyx_XDECREF(__pyx_r);
+        __pyx_t_5 = __Pyx_GetItemInt(__pyx_v_result, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 1); if (unlikely(__pyx_t_5 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 448; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;};
+        __Pyx_GOTREF(__pyx_t_5);
+        __pyx_r = __pyx_t_5;
+        __pyx_t_5 = 0;
+        goto __pyx_L6_except_return;
+      }
+
+      /* "View.MemoryView":449
+ *             if len(self.view.format) == 1:
+ *                 return result[0]
+ *             return result             # <<<<<<<<<<<<<<
+ * 
+ *     cdef assign_item_from_object(self, char *itemp, object value):
+ */
+      __Pyx_XDECREF(__pyx_r);
+      __Pyx_INCREF(__pyx_v_result);
+      __pyx_r = __pyx_v_result;
+      goto __pyx_L6_except_return;
+    }
+    __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0;
+    __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
+    goto __pyx_L10_try_end;
+    __pyx_L3_error:;
+    __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0;
+    __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
+    __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
+
+    /* "View.MemoryView":444
+ *         try:
+ *             result = struct.unpack(self.view.format, bytesitem)
+ *         except struct.error:             # <<<<<<<<<<<<<<
+ *             raise ValueError("Unable to convert item to object")
+ *         else:
+ */
+    __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_error); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 444; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    __pyx_t_9 = PyErr_ExceptionMatches(__pyx_t_5);
+    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+    if (__pyx_t_9) {
+      __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
+      if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_1) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 444; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;}
+      __Pyx_GOTREF(__pyx_t_5);
+      __Pyx_GOTREF(__pyx_t_6);
+      __Pyx_GOTREF(__pyx_t_1);
+
+      /* "View.MemoryView":445
+ *             result = struct.unpack(self.view.format, bytesitem)
+ *         except struct.error:
+ *             raise ValueError("Unable to convert item to object")             # <<<<<<<<<<<<<<
+ *         else:
+ *             if len(self.view.format) == 1:
+ */
+      __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 445; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;}
+      __Pyx_GOTREF(__pyx_t_10);
+      __Pyx_Raise(__pyx_t_10, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 445; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;}
+      __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+      goto __pyx_L4_exception_handled;
+    }
+    goto __pyx_L5_except_error;
+    __pyx_L5_except_error:;
+    __Pyx_XGIVEREF(__pyx_t_2);
+    __Pyx_XGIVEREF(__pyx_t_3);
+    __Pyx_XGIVEREF(__pyx_t_4);
+    __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4);
+    goto __pyx_L1_error;
+    __pyx_L6_except_return:;
+    __Pyx_XGIVEREF(__pyx_t_2);
+    __Pyx_XGIVEREF(__pyx_t_3);
+    __Pyx_XGIVEREF(__pyx_t_4);
+    __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4);
+    goto __pyx_L0;
+    __pyx_L4_exception_handled:;
+    __Pyx_XGIVEREF(__pyx_t_2);
+    __Pyx_XGIVEREF(__pyx_t_3);
+    __Pyx_XGIVEREF(__pyx_t_4);
+    __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4);
+    __pyx_L10_try_end:;
+  }
+
+  /* "View.MemoryView":435
+ *         self.assign_item_from_object(itemp, value)
+ * 
+ *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
+ *         """Only used if instantiated manually by the user, or if Cython doesn't
+ *         know how to convert the type"""
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_XDECREF(__pyx_t_6);
+  __Pyx_XDECREF(__pyx_t_10);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_struct);
+  __Pyx_XDECREF(__pyx_v_bytesitem);
+  __Pyx_XDECREF(__pyx_v_result);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":451
+ *             return result
+ * 
+ *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
+ *         """Only used if instantiated manually by the user, or if Cython doesn't
+ *         know how to convert the type"""
+ */
+
+static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) {
+  PyObject *__pyx_v_struct = NULL;
+  char __pyx_v_c;
+  PyObject *__pyx_v_bytesvalue = 0;
+  Py_ssize_t __pyx_v_i;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  PyObject *__pyx_t_4 = NULL;
+  PyObject *__pyx_t_5 = NULL;
+  PyObject *__pyx_t_6 = NULL;
+  Py_ssize_t __pyx_t_7;
+  PyObject *__pyx_t_8 = NULL;
+  char *__pyx_t_9;
+  char *__pyx_t_10;
+  char *__pyx_t_11;
+  char *__pyx_t_12;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("assign_item_from_object", 0);
+
+  /* "View.MemoryView":454
+ *         """Only used if instantiated manually by the user, or if Cython doesn't
+ *         know how to convert the type"""
+ *         import struct             # <<<<<<<<<<<<<<
+ *         cdef char c
+ *         cdef bytes bytesvalue
+ */
+  __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 454; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_v_struct = __pyx_t_1;
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":459
+ *         cdef Py_ssize_t i
+ * 
+ *         if isinstance(value, tuple):             # <<<<<<<<<<<<<<
+ *             bytesvalue = struct.pack(self.view.format, *value)
+ *         else:
+ */
+  __pyx_t_2 = PyTuple_Check(__pyx_v_value); 
+  __pyx_t_3 = (__pyx_t_2 != 0);
+  if (__pyx_t_3) {
+
+    /* "View.MemoryView":460
+ * 
+ *         if isinstance(value, tuple):
+ *             bytesvalue = struct.pack(self.view.format, *value)             # <<<<<<<<<<<<<<
+ *         else:
+ *             bytesvalue = struct.pack(self.view.format, value)
+ */
+    __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_1);
+    __pyx_t_4 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4);
+    __Pyx_GIVEREF(__pyx_t_4);
+    __pyx_t_4 = 0;
+    __pyx_t_4 = PySequence_Tuple(__pyx_v_value); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_6 = PyNumber_Add(__pyx_t_5, __pyx_t_4); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_6);
+    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+    __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+    if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4);
+    __pyx_t_4 = 0;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":462
+ *             bytesvalue = struct.pack(self.view.format, *value)
+ *         else:
+ *             bytesvalue = struct.pack(self.view.format, value)             # <<<<<<<<<<<<<<
+ * 
+ *         for i, c in enumerate(bytesvalue):
+ */
+    __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_6 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_6);
+    __pyx_t_1 = PyTuple_New(2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_1);
+    PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_6);
+    __Pyx_GIVEREF(__pyx_t_6);
+    __Pyx_INCREF(__pyx_v_value);
+    PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_v_value);
+    __Pyx_GIVEREF(__pyx_v_value);
+    __pyx_t_6 = 0;
+    __pyx_t_6 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_1, NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_6);
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+    if (!(likely(PyBytes_CheckExact(__pyx_t_6))||((__pyx_t_6) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_6)->tp_name), 0))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_v_bytesvalue = ((PyObject*)__pyx_t_6);
+    __pyx_t_6 = 0;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":464
+ *             bytesvalue = struct.pack(self.view.format, value)
+ * 
+ *         for i, c in enumerate(bytesvalue):             # <<<<<<<<<<<<<<
+ *             itemp[i] = c
+ * 
+ */
+  __pyx_t_7 = 0;
+  if (unlikely(__pyx_v_bytesvalue == Py_None)) {
+    PyErr_SetString(PyExc_TypeError, "'NoneType' is not iterable");
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 464; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  __Pyx_INCREF(__pyx_v_bytesvalue);
+  __pyx_t_8 = __pyx_v_bytesvalue;
+  __pyx_t_10 = PyBytes_AS_STRING(__pyx_t_8);
+  __pyx_t_11 = (__pyx_t_10 + PyBytes_GET_SIZE(__pyx_t_8));
+  for (__pyx_t_12 = __pyx_t_10; __pyx_t_12 < __pyx_t_11; __pyx_t_12++) {
+    __pyx_t_9 = __pyx_t_12;
+    __pyx_v_c = (__pyx_t_9[0]);
+
+    /* "View.MemoryView":465
+ * 
+ *         for i, c in enumerate(bytesvalue):
+ *             itemp[i] = c             # <<<<<<<<<<<<<<
+ * 
+ *     @cname('getbuffer')
+ */
+    __pyx_v_i = __pyx_t_7;
+
+    /* "View.MemoryView":464
+ *             bytesvalue = struct.pack(self.view.format, value)
+ * 
+ *         for i, c in enumerate(bytesvalue):             # <<<<<<<<<<<<<<
+ *             itemp[i] = c
+ * 
+ */
+    __pyx_t_7 = (__pyx_t_7 + 1);
+
+    /* "View.MemoryView":465
+ * 
+ *         for i, c in enumerate(bytesvalue):
+ *             itemp[i] = c             # <<<<<<<<<<<<<<
+ * 
+ *     @cname('getbuffer')
+ */
+    (__pyx_v_itemp[__pyx_v_i]) = __pyx_v_c;
+  }
+  __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
+
+  /* "View.MemoryView":451
+ *             return result
+ * 
+ *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
+ *         """Only used if instantiated manually by the user, or if Cython doesn't
+ *         know how to convert the type"""
+ */
+
+  /* function exit code */
+  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_XDECREF(__pyx_t_6);
+  __Pyx_XDECREF(__pyx_t_8);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_struct);
+  __Pyx_XDECREF(__pyx_v_bytesvalue);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":468
+ * 
+ *     @cname('getbuffer')
+ *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
+ *         if flags & PyBUF_STRIDES:
+ *             info.shape = self.view.shape
+ */
+
+/* Python wrapper */
+static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
+static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_getbuffer_MemoryView_10memoryview_8__getbuffer__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static int __pyx_memoryview_getbuffer_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  Py_ssize_t *__pyx_t_2;
+  char *__pyx_t_3;
+  void *__pyx_t_4;
+  int __pyx_t_5;
+  Py_ssize_t __pyx_t_6;
+  __Pyx_RefNannySetupContext("__getbuffer__", 0);
+  if (__pyx_v_info != NULL) {
+    __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
+    __Pyx_GIVEREF(__pyx_v_info->obj);
+  }
+
+  /* "View.MemoryView":469
+ *     @cname('getbuffer')
+ *     def __getbuffer__(self, Py_buffer *info, int flags):
+ *         if flags & PyBUF_STRIDES:             # <<<<<<<<<<<<<<
+ *             info.shape = self.view.shape
+ *         else:
+ */
+  __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":470
+ *     def __getbuffer__(self, Py_buffer *info, int flags):
+ *         if flags & PyBUF_STRIDES:
+ *             info.shape = self.view.shape             # <<<<<<<<<<<<<<
+ *         else:
+ *             info.shape = NULL
+ */
+    __pyx_t_2 = __pyx_v_self->view.shape;
+    __pyx_v_info->shape = __pyx_t_2;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":472
+ *             info.shape = self.view.shape
+ *         else:
+ *             info.shape = NULL             # <<<<<<<<<<<<<<
+ * 
+ *         if flags & PyBUF_STRIDES:
+ */
+    __pyx_v_info->shape = NULL;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":474
+ *             info.shape = NULL
+ * 
+ *         if flags & PyBUF_STRIDES:             # <<<<<<<<<<<<<<
+ *             info.strides = self.view.strides
+ *         else:
+ */
+  __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":475
+ * 
+ *         if flags & PyBUF_STRIDES:
+ *             info.strides = self.view.strides             # <<<<<<<<<<<<<<
+ *         else:
+ *             info.strides = NULL
+ */
+    __pyx_t_2 = __pyx_v_self->view.strides;
+    __pyx_v_info->strides = __pyx_t_2;
+    goto __pyx_L4;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":477
+ *             info.strides = self.view.strides
+ *         else:
+ *             info.strides = NULL             # <<<<<<<<<<<<<<
+ * 
+ *         if flags & PyBUF_INDIRECT:
+ */
+    __pyx_v_info->strides = NULL;
+  }
+  __pyx_L4:;
+
+  /* "View.MemoryView":479
+ *             info.strides = NULL
+ * 
+ *         if flags & PyBUF_INDIRECT:             # <<<<<<<<<<<<<<
+ *             info.suboffsets = self.view.suboffsets
+ *         else:
+ */
+  __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":480
+ * 
+ *         if flags & PyBUF_INDIRECT:
+ *             info.suboffsets = self.view.suboffsets             # <<<<<<<<<<<<<<
+ *         else:
+ *             info.suboffsets = NULL
+ */
+    __pyx_t_2 = __pyx_v_self->view.suboffsets;
+    __pyx_v_info->suboffsets = __pyx_t_2;
+    goto __pyx_L5;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":482
+ *             info.suboffsets = self.view.suboffsets
+ *         else:
+ *             info.suboffsets = NULL             # <<<<<<<<<<<<<<
+ * 
+ *         if flags & PyBUF_FORMAT:
+ */
+    __pyx_v_info->suboffsets = NULL;
+  }
+  __pyx_L5:;
+
+  /* "View.MemoryView":484
+ *             info.suboffsets = NULL
+ * 
+ *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
+ *             info.format = self.view.format
+ *         else:
+ */
+  __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":485
+ * 
+ *         if flags & PyBUF_FORMAT:
+ *             info.format = self.view.format             # <<<<<<<<<<<<<<
+ *         else:
+ *             info.format = NULL
+ */
+    __pyx_t_3 = __pyx_v_self->view.format;
+    __pyx_v_info->format = __pyx_t_3;
+    goto __pyx_L6;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":487
+ *             info.format = self.view.format
+ *         else:
+ *             info.format = NULL             # <<<<<<<<<<<<<<
+ * 
+ *         info.buf = self.view.buf
+ */
+    __pyx_v_info->format = NULL;
+  }
+  __pyx_L6:;
+
+  /* "View.MemoryView":489
+ *             info.format = NULL
+ * 
+ *         info.buf = self.view.buf             # <<<<<<<<<<<<<<
+ *         info.ndim = self.view.ndim
+ *         info.itemsize = self.view.itemsize
+ */
+  __pyx_t_4 = __pyx_v_self->view.buf;
+  __pyx_v_info->buf = __pyx_t_4;
+
+  /* "View.MemoryView":490
+ * 
+ *         info.buf = self.view.buf
+ *         info.ndim = self.view.ndim             # <<<<<<<<<<<<<<
+ *         info.itemsize = self.view.itemsize
+ *         info.len = self.view.len
+ */
+  __pyx_t_5 = __pyx_v_self->view.ndim;
+  __pyx_v_info->ndim = __pyx_t_5;
+
+  /* "View.MemoryView":491
+ *         info.buf = self.view.buf
+ *         info.ndim = self.view.ndim
+ *         info.itemsize = self.view.itemsize             # <<<<<<<<<<<<<<
+ *         info.len = self.view.len
+ *         info.readonly = 0
+ */
+  __pyx_t_6 = __pyx_v_self->view.itemsize;
+  __pyx_v_info->itemsize = __pyx_t_6;
+
+  /* "View.MemoryView":492
+ *         info.ndim = self.view.ndim
+ *         info.itemsize = self.view.itemsize
+ *         info.len = self.view.len             # <<<<<<<<<<<<<<
+ *         info.readonly = 0
+ *         info.obj = self
+ */
+  __pyx_t_6 = __pyx_v_self->view.len;
+  __pyx_v_info->len = __pyx_t_6;
+
+  /* "View.MemoryView":493
+ *         info.itemsize = self.view.itemsize
+ *         info.len = self.view.len
+ *         info.readonly = 0             # <<<<<<<<<<<<<<
+ *         info.obj = self
+ * 
+ */
+  __pyx_v_info->readonly = 0;
+
+  /* "View.MemoryView":494
+ *         info.len = self.view.len
+ *         info.readonly = 0
+ *         info.obj = self             # <<<<<<<<<<<<<<
+ * 
+ *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")
+ */
+  __Pyx_INCREF(((PyObject *)__pyx_v_self));
+  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
+  __Pyx_GOTREF(__pyx_v_info->obj);
+  __Pyx_DECREF(__pyx_v_info->obj);
+  __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
+
+  /* "View.MemoryView":468
+ * 
+ *     @cname('getbuffer')
+ *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
+ *         if flags & PyBUF_STRIDES:
+ *             info.shape = self.view.shape
+ */
+
+  /* function exit code */
+  __pyx_r = 0;
+  if (__pyx_v_info != NULL && __pyx_v_info->obj == Py_None) {
+    __Pyx_GOTREF(Py_None);
+    __Pyx_DECREF(Py_None); __pyx_v_info->obj = NULL;
+  }
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":501
+ *     property T:
+ *         @cname('__pyx_memoryview_transpose')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             cdef _memoryviewslice result = memoryview_copy(self)
+ *             transpose_memslice(&result.from_slice)
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_transpose(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_transpose(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_transpose_MemoryView_10memoryview_1T___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_transpose_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  struct __pyx_memoryviewslice_obj *__pyx_v_result = 0;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_t_2;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":502
+ *         @cname('__pyx_memoryview_transpose')
+ *         def __get__(self):
+ *             cdef _memoryviewslice result = memoryview_copy(self)             # <<<<<<<<<<<<<<
+ *             transpose_memslice(&result.from_slice)
+ *             return result
+ */
+  __pyx_t_1 = __pyx_memoryview_copy_object(__pyx_v_self); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 502; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (!(likely(((__pyx_t_1) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_1, __pyx_memoryviewslice_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 502; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_1);
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":503
+ *         def __get__(self):
+ *             cdef _memoryviewslice result = memoryview_copy(self)
+ *             transpose_memslice(&result.from_slice)             # <<<<<<<<<<<<<<
+ *             return result
+ * 
+ */
+  __pyx_t_2 = __pyx_memslice_transpose((&__pyx_v_result->from_slice)); if (unlikely(__pyx_t_2 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 503; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+  /* "View.MemoryView":504
+ *             cdef _memoryviewslice result = memoryview_copy(self)
+ *             transpose_memslice(&result.from_slice)
+ *             return result             # <<<<<<<<<<<<<<
+ * 
+ *     property base:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(((PyObject *)__pyx_v_result));
+  __pyx_r = ((PyObject *)__pyx_v_result);
+  goto __pyx_L0;
+
+  /* "View.MemoryView":501
+ *     property T:
+ *         @cname('__pyx_memoryview_transpose')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             cdef _memoryviewslice result = memoryview_copy(self)
+ *             transpose_memslice(&result.from_slice)
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.T.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_result);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":508
+ *     property base:
+ *         @cname('__pyx_memoryview__get__base')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.obj
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview__get__base(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview__get__base(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview__get__base_MemoryView_10memoryview_4base___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview__get__base_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":509
+ *         @cname('__pyx_memoryview__get__base')
+ *         def __get__(self):
+ *             return self.obj             # <<<<<<<<<<<<<<
+ * 
+ *     property shape:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(__pyx_v_self->obj);
+  __pyx_r = __pyx_v_self->obj;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":508
+ *     property base:
+ *         @cname('__pyx_memoryview__get__base')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.obj
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":513
+ *     property shape:
+ *         @cname('__pyx_memoryview_get_shape')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return tuple([self.view.shape[i] for i in xrange(self.view.ndim)])
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_get_shape(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_shape(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_get_shape_MemoryView_10memoryview_5shape___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_get_shape_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  int __pyx_v_i;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  PyObject *__pyx_t_4 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":514
+ *         @cname('__pyx_memoryview_get_shape')
+ *         def __get__(self):
+ *             return tuple([self.view.shape[i] for i in xrange(self.view.ndim)])             # <<<<<<<<<<<<<<
+ * 
+ *     property strides:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = __pyx_v_self->view.ndim;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+    __pyx_v_i = __pyx_t_3;
+    __pyx_t_4 = PyInt_FromSsize_t((__pyx_v_self->view.shape[__pyx_v_i])); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    if (unlikely(__Pyx_ListComp_Append(__pyx_t_1, (PyObject*)__pyx_t_4))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  }
+  __pyx_t_4 = PyList_AsTuple(((PyObject*)__pyx_t_1)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_r = __pyx_t_4;
+  __pyx_t_4 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":513
+ *     property shape:
+ *         @cname('__pyx_memoryview_get_shape')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return tuple([self.view.shape[i] for i in xrange(self.view.ndim)])
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.shape.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":518
+ *     property strides:
+ *         @cname('__pyx_memoryview_get_strides')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             if self.view.strides == NULL:
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_get_strides(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_strides(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_get_strides_MemoryView_10memoryview_7strides___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_get_strides_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  int __pyx_v_i;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  PyObject *__pyx_t_5 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":519
+ *         @cname('__pyx_memoryview_get_strides')
+ *         def __get__(self):
+ *             if self.view.strides == NULL:             # <<<<<<<<<<<<<<
+ * 
+ *                 raise ValueError("Buffer view does not expose strides")
+ */
+  __pyx_t_1 = ((__pyx_v_self->view.strides == NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":521
+ *             if self.view.strides == NULL:
+ * 
+ *                 raise ValueError("Buffer view does not expose strides")             # <<<<<<<<<<<<<<
+ * 
+ *             return tuple([self.view.strides[i] for i in xrange(self.view.ndim)])
+ */
+    __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "View.MemoryView":523
+ *                 raise ValueError("Buffer view does not expose strides")
+ * 
+ *             return tuple([self.view.strides[i] for i in xrange(self.view.ndim)])             # <<<<<<<<<<<<<<
+ * 
+ *     property suboffsets:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = __pyx_v_self->view.ndim;
+  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+    __pyx_v_i = __pyx_t_4;
+    __pyx_t_5 = PyInt_FromSsize_t((__pyx_v_self->view.strides[__pyx_v_i])); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_5))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+  }
+  __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_5);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_r = __pyx_t_5;
+  __pyx_t_5 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":518
+ *     property strides:
+ *         @cname('__pyx_memoryview_get_strides')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             if self.view.strides == NULL:
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.strides.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":527
+ *     property suboffsets:
+ *         @cname('__pyx_memoryview_get_suboffsets')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             if self.view.suboffsets == NULL:
+ *                 return [-1] * self.view.ndim
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_get_suboffsets(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_suboffsets(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_get_suboffsets_MemoryView_10memoryview_10suboffsets___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_get_suboffsets_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  int __pyx_v_i;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  PyObject *__pyx_t_5 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":528
+ *         @cname('__pyx_memoryview_get_suboffsets')
+ *         def __get__(self):
+ *             if self.view.suboffsets == NULL:             # <<<<<<<<<<<<<<
+ *                 return [-1] * self.view.ndim
+ * 
+ */
+  __pyx_t_1 = ((__pyx_v_self->view.suboffsets == NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":529
+ *         def __get__(self):
+ *             if self.view.suboffsets == NULL:
+ *                 return [-1] * self.view.ndim             # <<<<<<<<<<<<<<
+ * 
+ *             return tuple([self.view.suboffsets[i] for i in xrange(self.view.ndim)])
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __pyx_t_2 = PyList_New(1 * ((__pyx_v_self->view.ndim<0) ? 0:__pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 529; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    { Py_ssize_t __pyx_temp;
+      for (__pyx_temp=0; __pyx_temp < __pyx_v_self->view.ndim; __pyx_temp++) {
+        __Pyx_INCREF(__pyx_int_neg_1);
+        PyList_SET_ITEM(__pyx_t_2, __pyx_temp, __pyx_int_neg_1);
+        __Pyx_GIVEREF(__pyx_int_neg_1);
+      }
+    }
+    __pyx_r = __pyx_t_2;
+    __pyx_t_2 = 0;
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":531
+ *                 return [-1] * self.view.ndim
+ * 
+ *             return tuple([self.view.suboffsets[i] for i in xrange(self.view.ndim)])             # <<<<<<<<<<<<<<
+ * 
+ *     property ndim:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = __pyx_v_self->view.ndim;
+  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
+    __pyx_v_i = __pyx_t_4;
+    __pyx_t_5 = PyInt_FromSsize_t((__pyx_v_self->view.suboffsets[__pyx_v_i])); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_5))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+  }
+  __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_5);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_r = __pyx_t_5;
+  __pyx_t_5 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":527
+ *     property suboffsets:
+ *         @cname('__pyx_memoryview_get_suboffsets')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             if self.view.suboffsets == NULL:
+ *                 return [-1] * self.view.ndim
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.suboffsets.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":535
+ *     property ndim:
+ *         @cname('__pyx_memoryview_get_ndim')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.view.ndim
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_get_ndim(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_ndim(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_get_ndim_MemoryView_10memoryview_4ndim___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_get_ndim_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":536
+ *         @cname('__pyx_memoryview_get_ndim')
+ *         def __get__(self):
+ *             return self.view.ndim             # <<<<<<<<<<<<<<
+ * 
+ *     property itemsize:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 536; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":535
+ *     property ndim:
+ *         @cname('__pyx_memoryview_get_ndim')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.view.ndim
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.ndim.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":540
+ *     property itemsize:
+ *         @cname('__pyx_memoryview_get_itemsize')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.view.itemsize
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_get_itemsize(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_itemsize(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_get_itemsize_MemoryView_10memoryview_8itemsize___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_get_itemsize_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":541
+ *         @cname('__pyx_memoryview_get_itemsize')
+ *         def __get__(self):
+ *             return self.view.itemsize             # <<<<<<<<<<<<<<
+ * 
+ *     property nbytes:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 541; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":540
+ *     property itemsize:
+ *         @cname('__pyx_memoryview_get_itemsize')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.view.itemsize
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.itemsize.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":545
+ *     property nbytes:
+ *         @cname('__pyx_memoryview_get_nbytes')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.size * self.view.itemsize
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_get_nbytes(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_nbytes(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_get_nbytes_MemoryView_10memoryview_6nbytes___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_get_nbytes_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":546
+ *         @cname('__pyx_memoryview_get_nbytes')
+ *         def __get__(self):
+ *             return self.size * self.view.itemsize             # <<<<<<<<<<<<<<
+ * 
+ *     property size:
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 546; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 546; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = PyNumber_Multiply(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 546; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_r = __pyx_t_3;
+  __pyx_t_3 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":545
+ *     property nbytes:
+ *         @cname('__pyx_memoryview_get_nbytes')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.size * self.view.itemsize
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.nbytes.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":550
+ *     property size:
+ *         @cname('__pyx_memoryview_get_size')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             if self._size is None:
+ *                 result = 1
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_get_size(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview_get_size(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_get_size_MemoryView_10memoryview_4size___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_get_size_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  PyObject *__pyx_v_result = NULL;
+  PyObject *__pyx_v_length = NULL;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  Py_ssize_t __pyx_t_5;
+  PyObject *(*__pyx_t_6)(PyObject *);
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":551
+ *         @cname('__pyx_memoryview_get_size')
+ *         def __get__(self):
+ *             if self._size is None:             # <<<<<<<<<<<<<<
+ *                 result = 1
+ * 
+ */
+  __pyx_t_1 = (__pyx_v_self->_size == Py_None);
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":552
+ *         def __get__(self):
+ *             if self._size is None:
+ *                 result = 1             # <<<<<<<<<<<<<<
+ * 
+ *                 for length in self.shape:
+ */
+    __Pyx_INCREF(__pyx_int_1);
+    __pyx_v_result = __pyx_int_1;
+
+    /* "View.MemoryView":554
+ *                 result = 1
+ * 
+ *                 for length in self.shape:             # <<<<<<<<<<<<<<
+ *                     result *= length
+ * 
+ */
+    __pyx_t_3 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_shape); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    if (PyList_CheckExact(__pyx_t_3) || PyTuple_CheckExact(__pyx_t_3)) {
+      __pyx_t_4 = __pyx_t_3; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0;
+      __pyx_t_6 = NULL;
+    } else {
+      __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext;
+    }
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    for (;;) {
+      if (!__pyx_t_6 && PyList_CheckExact(__pyx_t_4)) {
+        if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break;
+        #if CYTHON_COMPILING_IN_CPYTHON
+        __pyx_t_3 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_3); __pyx_t_5++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        #else
+        __pyx_t_3 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        #endif
+      } else if (!__pyx_t_6 && PyTuple_CheckExact(__pyx_t_4)) {
+        if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break;
+        #if CYTHON_COMPILING_IN_CPYTHON
+        __pyx_t_3 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_3); __pyx_t_5++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        #else
+        __pyx_t_3 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        #endif
+      } else {
+        __pyx_t_3 = __pyx_t_6(__pyx_t_4);
+        if (unlikely(!__pyx_t_3)) {
+          PyObject* exc_type = PyErr_Occurred();
+          if (exc_type) {
+            if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+            else {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+          }
+          break;
+        }
+        __Pyx_GOTREF(__pyx_t_3);
+      }
+      __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_3);
+      __pyx_t_3 = 0;
+
+      /* "View.MemoryView":555
+ * 
+ *                 for length in self.shape:
+ *                     result *= length             # <<<<<<<<<<<<<<
+ * 
+ *                 self._size = result
+ */
+      __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_v_result, __pyx_v_length); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 555; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_DECREF_SET(__pyx_v_result, __pyx_t_3);
+      __pyx_t_3 = 0;
+    }
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+
+    /* "View.MemoryView":557
+ *                     result *= length
+ * 
+ *                 self._size = result             # <<<<<<<<<<<<<<
+ * 
+ *             return self._size
+ */
+    __Pyx_INCREF(__pyx_v_result);
+    __Pyx_GIVEREF(__pyx_v_result);
+    __Pyx_GOTREF(__pyx_v_self->_size);
+    __Pyx_DECREF(__pyx_v_self->_size);
+    __pyx_v_self->_size = __pyx_v_result;
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":559
+ *                 self._size = result
+ * 
+ *             return self._size             # <<<<<<<<<<<<<<
+ * 
+ *     def __len__(self):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(__pyx_v_self->_size);
+  __pyx_r = __pyx_v_self->_size;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":550
+ *     property size:
+ *         @cname('__pyx_memoryview_get_size')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             if self._size is None:
+ *                 result = 1
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.size.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_result);
+  __Pyx_XDECREF(__pyx_v_length);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":561
+ *             return self._size
+ * 
+ *     def __len__(self):             # <<<<<<<<<<<<<<
+ *         if self.view.ndim >= 1:
+ *             return self.view.shape[0]
+ */
+
+/* Python wrapper */
+static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self); /*proto*/
+static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self) {
+  Py_ssize_t __pyx_r;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__len__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_10__len__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static Py_ssize_t __pyx_memoryview_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  Py_ssize_t __pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  __Pyx_RefNannySetupContext("__len__", 0);
+
+  /* "View.MemoryView":562
+ * 
+ *     def __len__(self):
+ *         if self.view.ndim >= 1:             # <<<<<<<<<<<<<<
+ *             return self.view.shape[0]
+ * 
+ */
+  __pyx_t_1 = ((__pyx_v_self->view.ndim >= 1) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":563
+ *     def __len__(self):
+ *         if self.view.ndim >= 1:
+ *             return self.view.shape[0]             # <<<<<<<<<<<<<<
+ * 
+ *         return 0
+ */
+    __pyx_r = (__pyx_v_self->view.shape[0]);
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":565
+ *             return self.view.shape[0]
+ * 
+ *         return 0             # <<<<<<<<<<<<<<
+ * 
+ *     def __repr__(self):
+ */
+  __pyx_r = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":561
+ *             return self._size
+ * 
+ *     def __len__(self):             # <<<<<<<<<<<<<<
+ *         if self.view.ndim >= 1:
+ *             return self.view.shape[0]
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":567
+ *         return 0
+ * 
+ *     def __repr__(self):             # <<<<<<<<<<<<<<
+ *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,
+ *                                                id(self))
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_12__repr__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__repr__", 0);
+
+  /* "View.MemoryView":568
+ * 
+ *     def __repr__(self):
+ *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,             # <<<<<<<<<<<<<<
+ *                                                id(self))
+ * 
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+
+  /* "View.MemoryView":569
+ *     def __repr__(self):
+ *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,
+ *                                                id(self))             # <<<<<<<<<<<<<<
+ * 
+ *     def __str__(self):
+ */
+  __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_INCREF(((PyObject *)__pyx_v_self));
+  PyTuple_SET_ITEM(__pyx_t_2, 0, ((PyObject *)__pyx_v_self));
+  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
+  __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_id, __pyx_t_2, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+
+  /* "View.MemoryView":568
+ * 
+ *     def __repr__(self):
+ *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,             # <<<<<<<<<<<<<<
+ *                                                id(self))
+ * 
+ */
+  __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_3);
+  __Pyx_GIVEREF(__pyx_t_3);
+  __pyx_t_1 = 0;
+  __pyx_t_3 = 0;
+  __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_t_2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_r = __pyx_t_3;
+  __pyx_t_3 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":567
+ *         return 0
+ * 
+ *     def __repr__(self):             # <<<<<<<<<<<<<<
+ *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,
+ *                                                id(self))
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.__repr__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":571
+ *                                                id(self))
+ * 
+ *     def __str__(self):             # <<<<<<<<<<<<<<
+ *         return "<MemoryView of %r object>" % (self.base.__class__.__name__,)
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__str__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_14__str__(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("__str__", 0);
+
+  /* "View.MemoryView":572
+ * 
+ *     def __str__(self):
+ *         return "<MemoryView of %r object>" % (self.base.__class__.__name__,)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  __pyx_t_1 = 0;
+  __pyx_t_1 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_object, __pyx_t_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":571
+ *                                                id(self))
+ * 
+ *     def __str__(self):             # <<<<<<<<<<<<<<
+ *         return "<MemoryView of %r object>" % (self.base.__class__.__name__,)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.__str__", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":575
+ * 
+ * 
+ *     def is_c_contig(self):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice *mslice
+ *         cdef __Pyx_memviewslice tmp
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
+static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("is_c_contig (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_16is_c_contig(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self) {
+  __Pyx_memviewslice *__pyx_v_mslice;
+  __Pyx_memviewslice __pyx_v_tmp;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("is_c_contig", 0);
+
+  /* "View.MemoryView":578
+ *         cdef __Pyx_memviewslice *mslice
+ *         cdef __Pyx_memviewslice tmp
+ *         mslice = get_slice_from_memview(self, &tmp)             # <<<<<<<<<<<<<<
+ *         return slice_is_contig(mslice, 'C', self.view.ndim)
+ * 
+ */
+  __pyx_v_mslice = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp));
+
+  /* "View.MemoryView":579
+ *         cdef __Pyx_memviewslice tmp
+ *         mslice = get_slice_from_memview(self, &tmp)
+ *         return slice_is_contig(mslice, 'C', self.view.ndim)             # <<<<<<<<<<<<<<
+ * 
+ *     def is_f_contig(self):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig(__pyx_v_mslice, 'C', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 579; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":575
+ * 
+ * 
+ *     def is_c_contig(self):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice *mslice
+ *         cdef __Pyx_memviewslice tmp
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.is_c_contig", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":581
+ *         return slice_is_contig(mslice, 'C', self.view.ndim)
+ * 
+ *     def is_f_contig(self):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice *mslice
+ *         cdef __Pyx_memviewslice tmp
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
+static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("is_f_contig (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_18is_f_contig(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self) {
+  __Pyx_memviewslice *__pyx_v_mslice;
+  __Pyx_memviewslice __pyx_v_tmp;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("is_f_contig", 0);
+
+  /* "View.MemoryView":584
+ *         cdef __Pyx_memviewslice *mslice
+ *         cdef __Pyx_memviewslice tmp
+ *         mslice = get_slice_from_memview(self, &tmp)             # <<<<<<<<<<<<<<
+ *         return slice_is_contig(mslice, 'F', self.view.ndim)
+ * 
+ */
+  __pyx_v_mslice = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp));
+
+  /* "View.MemoryView":585
+ *         cdef __Pyx_memviewslice tmp
+ *         mslice = get_slice_from_memview(self, &tmp)
+ *         return slice_is_contig(mslice, 'F', self.view.ndim)             # <<<<<<<<<<<<<<
+ * 
+ *     def copy(self):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig(__pyx_v_mslice, 'F', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 585; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":581
+ *         return slice_is_contig(mslice, 'C', self.view.ndim)
+ * 
+ *     def is_f_contig(self):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice *mslice
+ *         cdef __Pyx_memviewslice tmp
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.is_f_contig", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":587
+ *         return slice_is_contig(mslice, 'F', self.view.ndim)
+ * 
+ *     def copy(self):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice mslice
+ *         cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
+static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("copy (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_20copy(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self) {
+  __Pyx_memviewslice __pyx_v_mslice;
+  int __pyx_v_flags;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  __Pyx_memviewslice __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("copy", 0);
+
+  /* "View.MemoryView":589
+ *     def copy(self):
+ *         cdef __Pyx_memviewslice mslice
+ *         cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS             # <<<<<<<<<<<<<<
+ * 
+ *         slice_copy(self, &mslice)
+ */
+  __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_F_CONTIGUOUS));
+
+  /* "View.MemoryView":591
+ *         cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS
+ * 
+ *         slice_copy(self, &mslice)             # <<<<<<<<<<<<<<
+ *         mslice = slice_copy_contig(&mslice, "c", self.view.ndim,
+ *                                    self.view.itemsize,
+ */
+  __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_mslice));
+
+  /* "View.MemoryView":592
+ * 
+ *         slice_copy(self, &mslice)
+ *         mslice = slice_copy_contig(&mslice, "c", self.view.ndim,             # <<<<<<<<<<<<<<
+ *                                    self.view.itemsize,
+ *                                    flags|PyBUF_C_CONTIGUOUS,
+ */
+  __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_mslice), __pyx_k_c, __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_C_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 592; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_v_mslice = __pyx_t_1;
+
+  /* "View.MemoryView":597
+ *                                    self.dtype_is_object)
+ * 
+ *         return memoryview_copy_from_slice(self, &mslice)             # <<<<<<<<<<<<<<
+ * 
+ *     def copy_fortran(self):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_mslice)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 597; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":587
+ *         return slice_is_contig(mslice, 'F', self.view.ndim)
+ * 
+ *     def copy(self):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice mslice
+ *         cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.copy", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":599
+ *         return memoryview_copy_from_slice(self, &mslice)
+ * 
+ *     def copy_fortran(self):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice src, dst
+ *         cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
+static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("copy_fortran (wrapper)", 0);
+  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_22copy_fortran(((struct __pyx_memoryview_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryview_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self) {
+  __Pyx_memviewslice __pyx_v_src;
+  __Pyx_memviewslice __pyx_v_dst;
+  int __pyx_v_flags;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  __Pyx_memviewslice __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("copy_fortran", 0);
+
+  /* "View.MemoryView":601
+ *     def copy_fortran(self):
+ *         cdef __Pyx_memviewslice src, dst
+ *         cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS             # <<<<<<<<<<<<<<
+ * 
+ *         slice_copy(self, &src)
+ */
+  __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_C_CONTIGUOUS));
+
+  /* "View.MemoryView":603
+ *         cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS
+ * 
+ *         slice_copy(self, &src)             # <<<<<<<<<<<<<<
+ *         dst = slice_copy_contig(&src, "fortran", self.view.ndim,
+ *                                 self.view.itemsize,
+ */
+  __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_src));
+
+  /* "View.MemoryView":604
+ * 
+ *         slice_copy(self, &src)
+ *         dst = slice_copy_contig(&src, "fortran", self.view.ndim,             # <<<<<<<<<<<<<<
+ *                                 self.view.itemsize,
+ *                                 flags|PyBUF_F_CONTIGUOUS,
+ */
+  __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_src), __pyx_k_fortran, __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_F_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 604; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_v_dst = __pyx_t_1;
+
+  /* "View.MemoryView":609
+ *                                 self.dtype_is_object)
+ * 
+ *         return memoryview_copy_from_slice(self, &dst)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_dst)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 609; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":599
+ *         return memoryview_copy_from_slice(self, &mslice)
+ * 
+ *     def copy_fortran(self):             # <<<<<<<<<<<<<<
+ *         cdef __Pyx_memviewslice src, dst
+ *         cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_AddTraceback("View.MemoryView.memoryview.copy_fortran", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":613
+ * 
+ * @cname('__pyx_memoryview_new')
+ * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo):             # <<<<<<<<<<<<<<
+ *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
+ *     result.typeinfo = typeinfo
+ */
+
+static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, int __pyx_v_dtype_is_object, __Pyx_TypeInfo *__pyx_v_typeinfo) {
+  struct __pyx_memoryview_obj *__pyx_v_result = 0;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("memoryview_cwrapper", 0);
+
+  /* "View.MemoryView":614
+ * @cname('__pyx_memoryview_new')
+ * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo):
+ *     cdef memoryview result = memoryview(o, flags, dtype_is_object)             # <<<<<<<<<<<<<<
+ *     result.typeinfo = typeinfo
+ *     return result
+ */
+  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_INCREF(__pyx_v_o);
+  PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_o);
+  __Pyx_GIVEREF(__pyx_v_o);
+  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  __pyx_t_1 = 0;
+  __pyx_t_2 = 0;
+  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_memoryview_type)), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  __pyx_v_result = ((struct __pyx_memoryview_obj *)__pyx_t_2);
+  __pyx_t_2 = 0;
+
+  /* "View.MemoryView":615
+ * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo):
+ *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
+ *     result.typeinfo = typeinfo             # <<<<<<<<<<<<<<
+ *     return result
+ * 
+ */
+  __pyx_v_result->typeinfo = __pyx_v_typeinfo;
+
+  /* "View.MemoryView":616
+ *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
+ *     result.typeinfo = typeinfo
+ *     return result             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_check')
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(((PyObject *)__pyx_v_result));
+  __pyx_r = ((PyObject *)__pyx_v_result);
+  goto __pyx_L0;
+
+  /* "View.MemoryView":613
+ * 
+ * @cname('__pyx_memoryview_new')
+ * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo):             # <<<<<<<<<<<<<<
+ *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
+ *     result.typeinfo = typeinfo
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView.memoryview_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_result);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":619
+ * 
+ * @cname('__pyx_memoryview_check')
+ * cdef inline bint memoryview_check(object o):             # <<<<<<<<<<<<<<
+ *     return isinstance(o, memoryview)
+ * 
+ */
+
+static CYTHON_INLINE int __pyx_memoryview_check(PyObject *__pyx_v_o) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  __Pyx_RefNannySetupContext("memoryview_check", 0);
+
+  /* "View.MemoryView":620
+ * @cname('__pyx_memoryview_check')
+ * cdef inline bint memoryview_check(object o):
+ *     return isinstance(o, memoryview)             # <<<<<<<<<<<<<<
+ * 
+ * cdef tuple _unellipsify(object index, int ndim):
+ */
+  __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_o, ((PyObject *)__pyx_memoryview_type)); 
+  __pyx_r = __pyx_t_1;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":619
+ * 
+ * @cname('__pyx_memoryview_check')
+ * cdef inline bint memoryview_check(object o):             # <<<<<<<<<<<<<<
+ *     return isinstance(o, memoryview)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":622
+ *     return isinstance(o, memoryview)
+ * 
+ * cdef tuple _unellipsify(object index, int ndim):             # <<<<<<<<<<<<<<
+ *     """
+ *     Replace all ellipses with full slices and fill incomplete indices with
+ */
+
+static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
+  PyObject *__pyx_v_tup = NULL;
+  PyObject *__pyx_v_result = NULL;
+  int __pyx_v_have_slices;
+  int __pyx_v_seen_ellipsis;
+  CYTHON_UNUSED PyObject *__pyx_v_idx = NULL;
+  PyObject *__pyx_v_item = NULL;
+  Py_ssize_t __pyx_v_nslices;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  Py_ssize_t __pyx_t_5;
+  PyObject *(*__pyx_t_6)(PyObject *);
+  PyObject *__pyx_t_7 = NULL;
+  Py_ssize_t __pyx_t_8;
+  PyObject *__pyx_t_9 = NULL;
+  int __pyx_t_10;
+  int __pyx_t_11;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("_unellipsify", 0);
+
+  /* "View.MemoryView":627
+ *     full slices.
+ *     """
+ *     if not isinstance(index, tuple):             # <<<<<<<<<<<<<<
+ *         tup = (index,)
+ *     else:
+ */
+  __pyx_t_1 = PyTuple_Check(__pyx_v_index); 
+  __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":628
+ *     """
+ *     if not isinstance(index, tuple):
+ *         tup = (index,)             # <<<<<<<<<<<<<<
+ *     else:
+ *         tup = index
+ */
+    __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 628; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_INCREF(__pyx_v_index);
+    PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_index);
+    __Pyx_GIVEREF(__pyx_v_index);
+    __pyx_v_tup = __pyx_t_3;
+    __pyx_t_3 = 0;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":630
+ *         tup = (index,)
+ *     else:
+ *         tup = index             # <<<<<<<<<<<<<<
+ * 
+ *     result = []
+ */
+    __Pyx_INCREF(__pyx_v_index);
+    __pyx_v_tup = __pyx_v_index;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":632
+ *         tup = index
+ * 
+ *     result = []             # <<<<<<<<<<<<<<
+ *     have_slices = False
+ *     seen_ellipsis = False
+ */
+  __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 632; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __pyx_v_result = ((PyObject*)__pyx_t_3);
+  __pyx_t_3 = 0;
+
+  /* "View.MemoryView":633
+ * 
+ *     result = []
+ *     have_slices = False             # <<<<<<<<<<<<<<
+ *     seen_ellipsis = False
+ *     for idx, item in enumerate(tup):
+ */
+  __pyx_v_have_slices = 0;
+
+  /* "View.MemoryView":634
+ *     result = []
+ *     have_slices = False
+ *     seen_ellipsis = False             # <<<<<<<<<<<<<<
+ *     for idx, item in enumerate(tup):
+ *         if item is Ellipsis:
+ */
+  __pyx_v_seen_ellipsis = 0;
+
+  /* "View.MemoryView":635
+ *     have_slices = False
+ *     seen_ellipsis = False
+ *     for idx, item in enumerate(tup):             # <<<<<<<<<<<<<<
+ *         if item is Ellipsis:
+ *             if not seen_ellipsis:
+ */
+  __Pyx_INCREF(__pyx_int_0);
+  __pyx_t_3 = __pyx_int_0;
+  if (PyList_CheckExact(__pyx_v_tup) || PyTuple_CheckExact(__pyx_v_tup)) {
+    __pyx_t_4 = __pyx_v_tup; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0;
+    __pyx_t_6 = NULL;
+  } else {
+    __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_tup); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext;
+  }
+  for (;;) {
+    if (!__pyx_t_6 && PyList_CheckExact(__pyx_t_4)) {
+      if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_7 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else if (!__pyx_t_6 && PyTuple_CheckExact(__pyx_t_4)) {
+      if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else {
+      __pyx_t_7 = __pyx_t_6(__pyx_t_4);
+      if (unlikely(!__pyx_t_7)) {
+        PyObject* exc_type = PyErr_Occurred();
+        if (exc_type) {
+          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+          else {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        }
+        break;
+      }
+      __Pyx_GOTREF(__pyx_t_7);
+    }
+    __Pyx_XDECREF_SET(__pyx_v_item, __pyx_t_7);
+    __pyx_t_7 = 0;
+    __Pyx_INCREF(__pyx_t_3);
+    __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_3);
+    __pyx_t_7 = PyNumber_Add(__pyx_t_3, __pyx_int_1); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_7);
+    __Pyx_DECREF(__pyx_t_3);
+    __pyx_t_3 = __pyx_t_7;
+    __pyx_t_7 = 0;
+
+    /* "View.MemoryView":636
+ *     seen_ellipsis = False
+ *     for idx, item in enumerate(tup):
+ *         if item is Ellipsis:             # <<<<<<<<<<<<<<
+ *             if not seen_ellipsis:
+ *                 result.extend([slice(None)] * (ndim - len(tup) + 1))
+ */
+    __pyx_t_2 = (__pyx_v_item == __pyx_builtin_Ellipsis);
+    __pyx_t_1 = (__pyx_t_2 != 0);
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":637
+ *     for idx, item in enumerate(tup):
+ *         if item is Ellipsis:
+ *             if not seen_ellipsis:             # <<<<<<<<<<<<<<
+ *                 result.extend([slice(None)] * (ndim - len(tup) + 1))
+ *                 seen_ellipsis = True
+ */
+      __pyx_t_1 = ((!(__pyx_v_seen_ellipsis != 0)) != 0);
+      if (__pyx_t_1) {
+
+        /* "View.MemoryView":638
+ *         if item is Ellipsis:
+ *             if not seen_ellipsis:
+ *                 result.extend([slice(None)] * (ndim - len(tup) + 1))             # <<<<<<<<<<<<<<
+ *                 seen_ellipsis = True
+ *             else:
+ */
+        __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)(&PySlice_Type))), __pyx_tuple__14, NULL); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_7);
+        __pyx_t_8 = PyObject_Length(__pyx_v_tup); if (unlikely(__pyx_t_8 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_9 = PyList_New(1 * ((((__pyx_v_ndim - __pyx_t_8) + 1)<0) ? 0:((__pyx_v_ndim - __pyx_t_8) + 1))); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_9);
+        { Py_ssize_t __pyx_temp;
+          for (__pyx_temp=0; __pyx_temp < ((__pyx_v_ndim - __pyx_t_8) + 1); __pyx_temp++) {
+            __Pyx_INCREF(__pyx_t_7);
+            PyList_SET_ITEM(__pyx_t_9, __pyx_temp, __pyx_t_7);
+            __Pyx_GIVEREF(__pyx_t_7);
+          }
+        }
+        __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
+        __pyx_t_10 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_9); if (unlikely(__pyx_t_10 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+
+        /* "View.MemoryView":639
+ *             if not seen_ellipsis:
+ *                 result.extend([slice(None)] * (ndim - len(tup) + 1))
+ *                 seen_ellipsis = True             # <<<<<<<<<<<<<<
+ *             else:
+ *                 result.append(slice(None))
+ */
+        __pyx_v_seen_ellipsis = 1;
+        goto __pyx_L7;
+      }
+      /*else*/ {
+
+        /* "View.MemoryView":641
+ *                 seen_ellipsis = True
+ *             else:
+ *                 result.append(slice(None))             # <<<<<<<<<<<<<<
+ *             have_slices = True
+ *         else:
+ */
+        __pyx_t_9 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)(&PySlice_Type))), __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 641; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_9);
+        __pyx_t_10 = __Pyx_PyList_Append(__pyx_v_result, __pyx_t_9); if (unlikely(__pyx_t_10 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 641; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+      }
+      __pyx_L7:;
+
+      /* "View.MemoryView":642
+ *             else:
+ *                 result.append(slice(None))
+ *             have_slices = True             # <<<<<<<<<<<<<<
+ *         else:
+ *             if not isinstance(item, slice) and not PyIndex_Check(item):
+ */
+      __pyx_v_have_slices = 1;
+      goto __pyx_L6;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":644
+ *             have_slices = True
+ *         else:
+ *             if not isinstance(item, slice) and not PyIndex_Check(item):             # <<<<<<<<<<<<<<
+ *                 raise TypeError("Cannot index with type '%s'" % type(item))
+ * 
+ */
+      __pyx_t_1 = PySlice_Check(__pyx_v_item); 
+      __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
+      if (__pyx_t_2) {
+        __pyx_t_1 = ((!(__Pyx_PyIndex_Check(__pyx_v_item) != 0)) != 0);
+        __pyx_t_11 = __pyx_t_1;
+      } else {
+        __pyx_t_11 = __pyx_t_2;
+      }
+      if (__pyx_t_11) {
+
+        /* "View.MemoryView":645
+ *         else:
+ *             if not isinstance(item, slice) and not PyIndex_Check(item):
+ *                 raise TypeError("Cannot index with type '%s'" % type(item))             # <<<<<<<<<<<<<<
+ * 
+ *             have_slices = have_slices or isinstance(item, slice)
+ */
+        __pyx_t_9 = __Pyx_PyString_Format(__pyx_kp_s_Cannot_index_with_type_s, ((PyObject *)Py_TYPE(__pyx_v_item))); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_9);
+        __pyx_t_7 = PyTuple_New(1); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_7);
+        PyTuple_SET_ITEM(__pyx_t_7, 0, __pyx_t_9);
+        __Pyx_GIVEREF(__pyx_t_9);
+        __pyx_t_9 = 0;
+        __pyx_t_9 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_t_7, NULL); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __Pyx_GOTREF(__pyx_t_9);
+        __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
+        __Pyx_Raise(__pyx_t_9, 0, 0, 0);
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+        {__pyx_filename = __pyx_f[2]; __pyx_lineno = 645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+
+      /* "View.MemoryView":647
+ *                 raise TypeError("Cannot index with type '%s'" % type(item))
+ * 
+ *             have_slices = have_slices or isinstance(item, slice)             # <<<<<<<<<<<<<<
+ *             result.append(item)
+ * 
+ */
+      if (!__pyx_v_have_slices) {
+        __pyx_t_11 = PySlice_Check(__pyx_v_item); 
+        __pyx_t_2 = __pyx_t_11;
+      } else {
+        __pyx_t_2 = __pyx_v_have_slices;
+      }
+      __pyx_v_have_slices = __pyx_t_2;
+
+      /* "View.MemoryView":648
+ * 
+ *             have_slices = have_slices or isinstance(item, slice)
+ *             result.append(item)             # <<<<<<<<<<<<<<
+ * 
+ *     nslices = ndim - len(result)
+ */
+      __pyx_t_10 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_10 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 648; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+    __pyx_L6:;
+  }
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+
+  /* "View.MemoryView":650
+ *             result.append(item)
+ * 
+ *     nslices = ndim - len(result)             # <<<<<<<<<<<<<<
+ *     if nslices:
+ *         result.extend([slice(None)] * nslices)
+ */
+  __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 650; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_v_nslices = (__pyx_v_ndim - __pyx_t_5);
+
+  /* "View.MemoryView":651
+ * 
+ *     nslices = ndim - len(result)
+ *     if nslices:             # <<<<<<<<<<<<<<
+ *         result.extend([slice(None)] * nslices)
+ * 
+ */
+  __pyx_t_2 = (__pyx_v_nslices != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":652
+ *     nslices = ndim - len(result)
+ *     if nslices:
+ *         result.extend([slice(None)] * nslices)             # <<<<<<<<<<<<<<
+ * 
+ *     return have_slices or nslices, tuple(result)
+ */
+    __pyx_t_3 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)(&PySlice_Type))), __pyx_tuple__16, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 652; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_t_4 = PyList_New(1 * ((__pyx_v_nslices<0) ? 0:__pyx_v_nslices)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 652; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    { Py_ssize_t __pyx_temp;
+      for (__pyx_temp=0; __pyx_temp < __pyx_v_nslices; __pyx_temp++) {
+        __Pyx_INCREF(__pyx_t_3);
+        PyList_SET_ITEM(__pyx_t_4, __pyx_temp, __pyx_t_3);
+        __Pyx_GIVEREF(__pyx_t_3);
+      }
+    }
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __pyx_t_10 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_4); if (unlikely(__pyx_t_10 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 652; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    goto __pyx_L9;
+  }
+  __pyx_L9:;
+
+  /* "View.MemoryView":654
+ *         result.extend([slice(None)] * nslices)
+ * 
+ *     return have_slices or nslices, tuple(result)             # <<<<<<<<<<<<<<
+ * 
+ * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_4 = __Pyx_PyBool_FromLong(__pyx_v_have_slices); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (!__pyx_t_2) {
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_nslices); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_t_9 = __pyx_t_3;
+    __pyx_t_3 = 0;
+  } else {
+    __pyx_t_9 = __pyx_t_4;
+    __pyx_t_4 = 0;
+  }
+  __pyx_t_4 = PyList_AsTuple(__pyx_v_result); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_9);
+  __Pyx_GIVEREF(__pyx_t_9);
+  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_4);
+  __Pyx_GIVEREF(__pyx_t_4);
+  __pyx_t_9 = 0;
+  __pyx_t_4 = 0;
+  __pyx_r = ((PyObject*)__pyx_t_3);
+  __pyx_t_3 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":622
+ *     return isinstance(o, memoryview)
+ * 
+ * cdef tuple _unellipsify(object index, int ndim):             # <<<<<<<<<<<<<<
+ *     """
+ *     Replace all ellipses with full slices and fill incomplete indices with
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_XDECREF(__pyx_t_7);
+  __Pyx_XDECREF(__pyx_t_9);
+  __Pyx_AddTraceback("View.MemoryView._unellipsify", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF(__pyx_v_tup);
+  __Pyx_XDECREF(__pyx_v_result);
+  __Pyx_XDECREF(__pyx_v_idx);
+  __Pyx_XDECREF(__pyx_v_item);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":656
+ *     return have_slices or nslices, tuple(result)
+ * 
+ * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):             # <<<<<<<<<<<<<<
+ *     cdef int i
+ *     for i in range(ndim):
+ */
+
+static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __pyx_v_ndim) {
+  int __pyx_v_i;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  PyObject *__pyx_t_4 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("assert_direct_dimensions", 0);
+
+  /* "View.MemoryView":658
+ * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):
+ *     cdef int i
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         if suboffsets[i] >= 0:
+ *             raise ValueError("Indirect dimensions not supported")
+ */
+  __pyx_t_1 = __pyx_v_ndim;
+  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
+    __pyx_v_i = __pyx_t_2;
+
+    /* "View.MemoryView":659
+ *     cdef int i
+ *     for i in range(ndim):
+ *         if suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
+ *             raise ValueError("Indirect dimensions not supported")
+ * 
+ */
+    __pyx_t_3 = (((__pyx_v_suboffsets[__pyx_v_i]) >= 0) != 0);
+    if (__pyx_t_3) {
+
+      /* "View.MemoryView":660
+ *     for i in range(ndim):
+ *         if suboffsets[i] >= 0:
+ *             raise ValueError("Indirect dimensions not supported")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__17, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 660; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 660; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+  }
+
+  /* "View.MemoryView":656
+ *     return have_slices or nslices, tuple(result)
+ * 
+ * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):             # <<<<<<<<<<<<<<
+ *     cdef int i
+ *     for i in range(ndim):
+ */
+
+  /* function exit code */
+  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_AddTraceback("View.MemoryView.assert_direct_dimensions", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":667
+ * 
+ * @cname('__pyx_memview_slice')
+ * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
+ *     cdef int new_ndim = 0, suboffset_dim = -1, dim
+ *     cdef bint negative_step
+ */
+
+static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *__pyx_v_memview, PyObject *__pyx_v_indices) {
+  int __pyx_v_new_ndim;
+  int __pyx_v_suboffset_dim;
+  int __pyx_v_dim;
+  __Pyx_memviewslice __pyx_v_src;
+  __Pyx_memviewslice __pyx_v_dst;
+  __Pyx_memviewslice *__pyx_v_p_src;
+  struct __pyx_memoryviewslice_obj *__pyx_v_memviewsliceobj = 0;
+  __Pyx_memviewslice *__pyx_v_p_dst;
+  int *__pyx_v_p_suboffset_dim;
+  Py_ssize_t __pyx_v_start;
+  Py_ssize_t __pyx_v_stop;
+  Py_ssize_t __pyx_v_step;
+  int __pyx_v_have_start;
+  int __pyx_v_have_stop;
+  int __pyx_v_have_step;
+  PyObject *__pyx_v_index = NULL;
+  struct __pyx_memoryview_obj *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  struct __pyx_memoryview_obj *__pyx_t_4;
+  char *__pyx_t_5;
+  int __pyx_t_6;
+  Py_ssize_t __pyx_t_7;
+  PyObject *(*__pyx_t_8)(PyObject *);
+  PyObject *__pyx_t_9 = NULL;
+  Py_ssize_t __pyx_t_10;
+  int __pyx_t_11;
+  PyObject *__pyx_t_12 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("memview_slice", 0);
+
+  /* "View.MemoryView":668
+ * @cname('__pyx_memview_slice')
+ * cdef memoryview memview_slice(memoryview memview, object indices):
+ *     cdef int new_ndim = 0, suboffset_dim = -1, dim             # <<<<<<<<<<<<<<
+ *     cdef bint negative_step
+ *     cdef __Pyx_memviewslice src, dst
+ */
+  __pyx_v_new_ndim = 0;
+  __pyx_v_suboffset_dim = -1;
+
+  /* "View.MemoryView":675
+ * 
+ * 
+ *     memset(&dst, 0, sizeof(dst))             # <<<<<<<<<<<<<<
+ * 
+ *     cdef _memoryviewslice memviewsliceobj
+ */
+  memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst)));
+
+  /* "View.MemoryView":679
+ *     cdef _memoryviewslice memviewsliceobj
+ * 
+ *     assert memview.view.ndim > 0             # <<<<<<<<<<<<<<
+ * 
+ *     if isinstance(memview, _memoryviewslice):
+ */
+  #ifndef CYTHON_WITHOUT_ASSERTIONS
+  if (unlikely(!Py_OptimizeFlag)) {
+    if (unlikely(!((__pyx_v_memview->view.ndim > 0) != 0))) {
+      PyErr_SetNone(PyExc_AssertionError);
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 679; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+  }
+  #endif
+
+  /* "View.MemoryView":681
+ *     assert memview.view.ndim > 0
+ * 
+ *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
+ *         memviewsliceobj = memview
+ *         p_src = &memviewsliceobj.from_slice
+ */
+  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), ((PyObject *)__pyx_memoryviewslice_type)); 
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":682
+ * 
+ *     if isinstance(memview, _memoryviewslice):
+ *         memviewsliceobj = memview             # <<<<<<<<<<<<<<
+ *         p_src = &memviewsliceobj.from_slice
+ *     else:
+ */
+    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 682; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_3 = ((PyObject *)__pyx_v_memview);
+    __Pyx_INCREF(__pyx_t_3);
+    __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3);
+    __pyx_t_3 = 0;
+
+    /* "View.MemoryView":683
+ *     if isinstance(memview, _memoryviewslice):
+ *         memviewsliceobj = memview
+ *         p_src = &memviewsliceobj.from_slice             # <<<<<<<<<<<<<<
+ *     else:
+ *         slice_copy(memview, &src)
+ */
+    __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice);
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":685
+ *         p_src = &memviewsliceobj.from_slice
+ *     else:
+ *         slice_copy(memview, &src)             # <<<<<<<<<<<<<<
+ *         p_src = &src
+ * 
+ */
+    __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src));
+
+    /* "View.MemoryView":686
+ *     else:
+ *         slice_copy(memview, &src)
+ *         p_src = &src             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __pyx_v_p_src = (&__pyx_v_src);
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":692
+ * 
+ * 
+ *     dst.memview = p_src.memview             # <<<<<<<<<<<<<<
+ *     dst.data = p_src.data
+ * 
+ */
+  __pyx_t_4 = __pyx_v_p_src->memview;
+  __pyx_v_dst.memview = __pyx_t_4;
+
+  /* "View.MemoryView":693
+ * 
+ *     dst.memview = p_src.memview
+ *     dst.data = p_src.data             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_5 = __pyx_v_p_src->data;
+  __pyx_v_dst.data = __pyx_t_5;
+
+  /* "View.MemoryView":698
+ * 
+ * 
+ *     cdef __Pyx_memviewslice *p_dst = &dst             # <<<<<<<<<<<<<<
+ *     cdef int *p_suboffset_dim = &suboffset_dim
+ *     cdef Py_ssize_t start, stop, step
+ */
+  __pyx_v_p_dst = (&__pyx_v_dst);
+
+  /* "View.MemoryView":699
+ * 
+ *     cdef __Pyx_memviewslice *p_dst = &dst
+ *     cdef int *p_suboffset_dim = &suboffset_dim             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t start, stop, step
+ *     cdef bint have_start, have_stop, have_step
+ */
+  __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim);
+
+  /* "View.MemoryView":703
+ *     cdef bint have_start, have_stop, have_step
+ * 
+ *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<
+ *         if PyIndex_Check(index):
+ *             slice_memviewslice(
+ */
+  __pyx_t_6 = 0;
+  if (PyList_CheckExact(__pyx_v_indices) || PyTuple_CheckExact(__pyx_v_indices)) {
+    __pyx_t_3 = __pyx_v_indices; __Pyx_INCREF(__pyx_t_3); __pyx_t_7 = 0;
+    __pyx_t_8 = NULL;
+  } else {
+    __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext;
+  }
+  for (;;) {
+    if (!__pyx_t_8 && PyList_CheckExact(__pyx_t_3)) {
+      if (__pyx_t_7 >= PyList_GET_SIZE(__pyx_t_3)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else if (!__pyx_t_8 && PyTuple_CheckExact(__pyx_t_3)) {
+      if (__pyx_t_7 >= PyTuple_GET_SIZE(__pyx_t_3)) break;
+      #if CYTHON_COMPILING_IN_CPYTHON
+      __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #else
+      __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      #endif
+    } else {
+      __pyx_t_9 = __pyx_t_8(__pyx_t_3);
+      if (unlikely(!__pyx_t_9)) {
+        PyObject* exc_type = PyErr_Occurred();
+        if (exc_type) {
+          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
+          else {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        }
+        break;
+      }
+      __Pyx_GOTREF(__pyx_t_9);
+    }
+    __Pyx_XDECREF_SET(__pyx_v_index, __pyx_t_9);
+    __pyx_t_9 = 0;
+    __pyx_v_dim = __pyx_t_6;
+    __pyx_t_6 = (__pyx_t_6 + 1);
+
+    /* "View.MemoryView":704
+ * 
+ *     for dim, index in enumerate(indices):
+ *         if PyIndex_Check(index):             # <<<<<<<<<<<<<<
+ *             slice_memviewslice(
+ *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
+ */
+    __pyx_t_2 = (__Pyx_PyIndex_Check(__pyx_v_index) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":708
+ *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
+ *                 dim, new_ndim, p_suboffset_dim,
+ *                 index, 0, 0, # start, stop, step             # <<<<<<<<<<<<<<
+ *                 0, 0, 0, # have_{start,stop,step}
+ *                 False)
+ */
+      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 708; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+      /* "View.MemoryView":705
+ *     for dim, index in enumerate(indices):
+ *         if PyIndex_Check(index):
+ *             slice_memviewslice(             # <<<<<<<<<<<<<<
+ *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
+ *                 dim, new_ndim, p_suboffset_dim,
+ */
+      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 705; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
+    }
+
+    /* "View.MemoryView":711
+ *                 0, 0, 0, # have_{start,stop,step}
+ *                 False)
+ *         elif index is None:             # <<<<<<<<<<<<<<
+ *             p_dst.shape[new_ndim] = 1
+ *             p_dst.strides[new_ndim] = 0
+ */
+    __pyx_t_2 = (__pyx_v_index == Py_None);
+    __pyx_t_1 = (__pyx_t_2 != 0);
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":712
+ *                 False)
+ *         elif index is None:
+ *             p_dst.shape[new_ndim] = 1             # <<<<<<<<<<<<<<
+ *             p_dst.strides[new_ndim] = 0
+ *             p_dst.suboffsets[new_ndim] = -1
+ */
+      (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1;
+
+      /* "View.MemoryView":713
+ *         elif index is None:
+ *             p_dst.shape[new_ndim] = 1
+ *             p_dst.strides[new_ndim] = 0             # <<<<<<<<<<<<<<
+ *             p_dst.suboffsets[new_ndim] = -1
+ *             new_ndim += 1
+ */
+      (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0;
+
+      /* "View.MemoryView":714
+ *             p_dst.shape[new_ndim] = 1
+ *             p_dst.strides[new_ndim] = 0
+ *             p_dst.suboffsets[new_ndim] = -1             # <<<<<<<<<<<<<<
+ *             new_ndim += 1
+ *         else:
+ */
+      (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1;
+
+      /* "View.MemoryView":715
+ *             p_dst.strides[new_ndim] = 0
+ *             p_dst.suboffsets[new_ndim] = -1
+ *             new_ndim += 1             # <<<<<<<<<<<<<<
+ *         else:
+ *             start = index.start or 0
+ */
+      __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);
+      goto __pyx_L6;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":717
+ *             new_ndim += 1
+ *         else:
+ *             start = index.start or 0             # <<<<<<<<<<<<<<
+ *             stop = index.stop or 0
+ *             step = index.step or 0
+ */
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 717; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_9);
+      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 717; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      if (!__pyx_t_1) {
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+        __Pyx_INCREF(__pyx_int_0);
+        __pyx_t_12 = __pyx_int_0;
+      } else {
+        __pyx_t_12 = __pyx_t_9;
+        __pyx_t_9 = 0;
+      }
+      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_t_12); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 717; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
+      __pyx_v_start = __pyx_t_10;
+
+      /* "View.MemoryView":718
+ *         else:
+ *             start = index.start or 0
+ *             stop = index.stop or 0             # <<<<<<<<<<<<<<
+ *             step = index.step or 0
+ * 
+ */
+      __pyx_t_12 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 718; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_12);
+      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_12); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 718; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      if (!__pyx_t_1) {
+        __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
+        __Pyx_INCREF(__pyx_int_0);
+        __pyx_t_9 = __pyx_int_0;
+      } else {
+        __pyx_t_9 = __pyx_t_12;
+        __pyx_t_12 = 0;
+      }
+      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 718; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+      __pyx_v_stop = __pyx_t_10;
+
+      /* "View.MemoryView":719
+ *             start = index.start or 0
+ *             stop = index.stop or 0
+ *             step = index.step or 0             # <<<<<<<<<<<<<<
+ * 
+ *             have_start = index.start is not None
+ */
+      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 719; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_9);
+      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 719; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      if (!__pyx_t_1) {
+        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
+        __Pyx_INCREF(__pyx_int_0);
+        __pyx_t_12 = __pyx_int_0;
+      } else {
+        __pyx_t_12 = __pyx_t_9;
+        __pyx_t_9 = 0;
+      }
+      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_t_12); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 719; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
+      __pyx_v_step = __pyx_t_10;
+
+      /* "View.MemoryView":721
+ *             step = index.step or 0
+ * 
+ *             have_start = index.start is not None             # <<<<<<<<<<<<<<
+ *             have_stop = index.stop is not None
+ *             have_step = index.step is not None
+ */
+      __pyx_t_12 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 721; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_12);
+      __pyx_t_1 = (__pyx_t_12 != Py_None);
+      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
+      __pyx_v_have_start = __pyx_t_1;
+
+      /* "View.MemoryView":722
+ * 
+ *             have_start = index.start is not None
+ *             have_stop = index.stop is not None             # <<<<<<<<<<<<<<
+ *             have_step = index.step is not None
+ * 
+ */
+      __pyx_t_12 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 722; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_12);
+      __pyx_t_1 = (__pyx_t_12 != Py_None);
+      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
+      __pyx_v_have_stop = __pyx_t_1;
+
+      /* "View.MemoryView":723
+ *             have_start = index.start is not None
+ *             have_stop = index.stop is not None
+ *             have_step = index.step is not None             # <<<<<<<<<<<<<<
+ * 
+ *             slice_memviewslice(
+ */
+      __pyx_t_12 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 723; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_12);
+      __pyx_t_1 = (__pyx_t_12 != Py_None);
+      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
+      __pyx_v_have_step = __pyx_t_1;
+
+      /* "View.MemoryView":725
+ *             have_step = index.step is not None
+ * 
+ *             slice_memviewslice(             # <<<<<<<<<<<<<<
+ *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
+ *                 dim, new_ndim, p_suboffset_dim,
+ */
+      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 725; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+      /* "View.MemoryView":731
+ *                 have_start, have_stop, have_step,
+ *                 True)
+ *             new_ndim += 1             # <<<<<<<<<<<<<<
+ * 
+ *     if isinstance(memview, _memoryviewslice):
+ */
+      __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);
+    }
+    __pyx_L6:;
+  }
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+
+  /* "View.MemoryView":733
+ *             new_ndim += 1
+ * 
+ *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
+ *         return memoryview_fromslice(dst, new_ndim,
+ *                                     memviewsliceobj.to_object_func,
+ */
+  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), ((PyObject *)__pyx_memoryviewslice_type)); 
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":734
+ * 
+ *     if isinstance(memview, _memoryviewslice):
+ *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
+ *                                     memviewsliceobj.to_object_func,
+ *                                     memviewsliceobj.to_dtype_func,
+ */
+    __Pyx_XDECREF(((PyObject *)__pyx_r));
+
+    /* "View.MemoryView":735
+ *     if isinstance(memview, _memoryviewslice):
+ *         return memoryview_fromslice(dst, new_ndim,
+ *                                     memviewsliceobj.to_object_func,             # <<<<<<<<<<<<<<
+ *                                     memviewsliceobj.to_dtype_func,
+ *                                     memview.dtype_is_object)
+ */
+    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 735; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
+
+    /* "View.MemoryView":736
+ *         return memoryview_fromslice(dst, new_ndim,
+ *                                     memviewsliceobj.to_object_func,
+ *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<
+ *                                     memview.dtype_is_object)
+ *     else:
+ */
+    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 736; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
+
+    /* "View.MemoryView":734
+ * 
+ *     if isinstance(memview, _memoryviewslice):
+ *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
+ *                                     memviewsliceobj.to_object_func,
+ *                                     memviewsliceobj.to_dtype_func,
+ */
+    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 734; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 734; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);
+    __pyx_t_3 = 0;
+    goto __pyx_L0;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":739
+ *                                     memview.dtype_is_object)
+ *     else:
+ *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
+ *                                     memview.dtype_is_object)
+ * 
+ */
+    __Pyx_XDECREF(((PyObject *)__pyx_r));
+
+    /* "View.MemoryView":740
+ *     else:
+ *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,
+ *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 739; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+
+    /* "View.MemoryView":739
+ *                                     memview.dtype_is_object)
+ *     else:
+ *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
+ *                                     memview.dtype_is_object)
+ * 
+ */
+    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 739; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);
+    __pyx_t_3 = 0;
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":667
+ * 
+ * @cname('__pyx_memview_slice')
+ * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
+ *     cdef int new_ndim = 0, suboffset_dim = -1, dim
+ *     cdef bint negative_step
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_9);
+  __Pyx_XDECREF(__pyx_t_12);
+  __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj);
+  __Pyx_XDECREF(__pyx_v_index);
+  __Pyx_XGIVEREF((PyObject *)__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":764
+ * 
+ * @cname('__pyx_memoryview_slice_memviewslice')
+ * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
+ *         __Pyx_memviewslice *dst,
+ *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
+ */
+
+static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {
+  Py_ssize_t __pyx_v_new_shape;
+  int __pyx_v_negative_step;
+  int __pyx_r;
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+
+  /* "View.MemoryView":784
+ *     cdef bint negative_step
+ * 
+ *     if not is_slice:             # <<<<<<<<<<<<<<
+ * 
+ *         if start < 0:
+ */
+  __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":786
+ *     if not is_slice:
+ * 
+ *         if start < 0:             # <<<<<<<<<<<<<<
+ *             start += shape
+ *         if not 0 <= start < shape:
+ */
+    __pyx_t_1 = ((__pyx_v_start < 0) != 0);
+    if (__pyx_t_1) {
+
+      /* "View.MemoryView":787
+ * 
+ *         if start < 0:
+ *             start += shape             # <<<<<<<<<<<<<<
+ *         if not 0 <= start < shape:
+ *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
+ */
+      __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
+      goto __pyx_L4;
+    }
+    __pyx_L4:;
+
+    /* "View.MemoryView":788
+ *         if start < 0:
+ *             start += shape
+ *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
+ *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
+ *     else:
+ */
+    __pyx_t_1 = (0 <= __pyx_v_start);
+    if (__pyx_t_1) {
+      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);
+    }
+    __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":789
+ *             start += shape
+ *         if not 0 <= start < shape:
+ *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
+ *     else:
+ * 
+ */
+      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, __pyx_k_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 789; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L5;
+    }
+    __pyx_L5:;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":792
+ *     else:
+ * 
+ *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<
+ * 
+ *         if have_step and step == 0:
+ */
+    __pyx_t_2 = (__pyx_v_have_step != 0);
+    if (__pyx_t_2) {
+      __pyx_t_1 = (__pyx_v_step < 0);
+      __pyx_t_4 = __pyx_t_1;
+    } else {
+      __pyx_t_4 = __pyx_t_2;
+    }
+    __pyx_v_negative_step = __pyx_t_4;
+
+    /* "View.MemoryView":794
+ *         negative_step = have_step != 0 and step < 0
+ * 
+ *         if have_step and step == 0:             # <<<<<<<<<<<<<<
+ *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
+ * 
+ */
+    if ((__pyx_v_have_step != 0)) {
+      __pyx_t_4 = (__pyx_v_step == 0);
+      __pyx_t_2 = __pyx_t_4;
+    } else {
+      __pyx_t_2 = (__pyx_v_have_step != 0);
+    }
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":795
+ * 
+ *         if have_step and step == 0:
+ *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, __pyx_k_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 795; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
+    }
+    __pyx_L6:;
+
+    /* "View.MemoryView":798
+ * 
+ * 
+ *         if have_start:             # <<<<<<<<<<<<<<
+ *             if start < 0:
+ *                 start += shape
+ */
+    __pyx_t_2 = (__pyx_v_have_start != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":799
+ * 
+ *         if have_start:
+ *             if start < 0:             # <<<<<<<<<<<<<<
+ *                 start += shape
+ *                 if start < 0:
+ */
+      __pyx_t_2 = ((__pyx_v_start < 0) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":800
+ *         if have_start:
+ *             if start < 0:
+ *                 start += shape             # <<<<<<<<<<<<<<
+ *                 if start < 0:
+ *                     start = 0
+ */
+        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
+
+        /* "View.MemoryView":801
+ *             if start < 0:
+ *                 start += shape
+ *                 if start < 0:             # <<<<<<<<<<<<<<
+ *                     start = 0
+ *             elif start >= shape:
+ */
+        __pyx_t_2 = ((__pyx_v_start < 0) != 0);
+        if (__pyx_t_2) {
+
+          /* "View.MemoryView":802
+ *                 start += shape
+ *                 if start < 0:
+ *                     start = 0             # <<<<<<<<<<<<<<
+ *             elif start >= shape:
+ *                 if negative_step:
+ */
+          __pyx_v_start = 0;
+          goto __pyx_L9;
+        }
+        __pyx_L9:;
+        goto __pyx_L8;
+      }
+
+      /* "View.MemoryView":803
+ *                 if start < 0:
+ *                     start = 0
+ *             elif start >= shape:             # <<<<<<<<<<<<<<
+ *                 if negative_step:
+ *                     start = shape - 1
+ */
+      __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":804
+ *                     start = 0
+ *             elif start >= shape:
+ *                 if negative_step:             # <<<<<<<<<<<<<<
+ *                     start = shape - 1
+ *                 else:
+ */
+        __pyx_t_2 = (__pyx_v_negative_step != 0);
+        if (__pyx_t_2) {
+
+          /* "View.MemoryView":805
+ *             elif start >= shape:
+ *                 if negative_step:
+ *                     start = shape - 1             # <<<<<<<<<<<<<<
+ *                 else:
+ *                     start = shape
+ */
+          __pyx_v_start = (__pyx_v_shape - 1);
+          goto __pyx_L10;
+        }
+        /*else*/ {
+
+          /* "View.MemoryView":807
+ *                     start = shape - 1
+ *                 else:
+ *                     start = shape             # <<<<<<<<<<<<<<
+ *         else:
+ *             if negative_step:
+ */
+          __pyx_v_start = __pyx_v_shape;
+        }
+        __pyx_L10:;
+        goto __pyx_L8;
+      }
+      __pyx_L8:;
+      goto __pyx_L7;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":809
+ *                     start = shape
+ *         else:
+ *             if negative_step:             # <<<<<<<<<<<<<<
+ *                 start = shape - 1
+ *             else:
+ */
+      __pyx_t_2 = (__pyx_v_negative_step != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":810
+ *         else:
+ *             if negative_step:
+ *                 start = shape - 1             # <<<<<<<<<<<<<<
+ *             else:
+ *                 start = 0
+ */
+        __pyx_v_start = (__pyx_v_shape - 1);
+        goto __pyx_L11;
+      }
+      /*else*/ {
+
+        /* "View.MemoryView":812
+ *                 start = shape - 1
+ *             else:
+ *                 start = 0             # <<<<<<<<<<<<<<
+ * 
+ *         if have_stop:
+ */
+        __pyx_v_start = 0;
+      }
+      __pyx_L11:;
+    }
+    __pyx_L7:;
+
+    /* "View.MemoryView":814
+ *                 start = 0
+ * 
+ *         if have_stop:             # <<<<<<<<<<<<<<
+ *             if stop < 0:
+ *                 stop += shape
+ */
+    __pyx_t_2 = (__pyx_v_have_stop != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":815
+ * 
+ *         if have_stop:
+ *             if stop < 0:             # <<<<<<<<<<<<<<
+ *                 stop += shape
+ *                 if stop < 0:
+ */
+      __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":816
+ *         if have_stop:
+ *             if stop < 0:
+ *                 stop += shape             # <<<<<<<<<<<<<<
+ *                 if stop < 0:
+ *                     stop = 0
+ */
+        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);
+
+        /* "View.MemoryView":817
+ *             if stop < 0:
+ *                 stop += shape
+ *                 if stop < 0:             # <<<<<<<<<<<<<<
+ *                     stop = 0
+ *             elif stop > shape:
+ */
+        __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
+        if (__pyx_t_2) {
+
+          /* "View.MemoryView":818
+ *                 stop += shape
+ *                 if stop < 0:
+ *                     stop = 0             # <<<<<<<<<<<<<<
+ *             elif stop > shape:
+ *                 stop = shape
+ */
+          __pyx_v_stop = 0;
+          goto __pyx_L14;
+        }
+        __pyx_L14:;
+        goto __pyx_L13;
+      }
+
+      /* "View.MemoryView":819
+ *                 if stop < 0:
+ *                     stop = 0
+ *             elif stop > shape:             # <<<<<<<<<<<<<<
+ *                 stop = shape
+ *         else:
+ */
+      __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":820
+ *                     stop = 0
+ *             elif stop > shape:
+ *                 stop = shape             # <<<<<<<<<<<<<<
+ *         else:
+ *             if negative_step:
+ */
+        __pyx_v_stop = __pyx_v_shape;
+        goto __pyx_L13;
+      }
+      __pyx_L13:;
+      goto __pyx_L12;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":822
+ *                 stop = shape
+ *         else:
+ *             if negative_step:             # <<<<<<<<<<<<<<
+ *                 stop = -1
+ *             else:
+ */
+      __pyx_t_2 = (__pyx_v_negative_step != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":823
+ *         else:
+ *             if negative_step:
+ *                 stop = -1             # <<<<<<<<<<<<<<
+ *             else:
+ *                 stop = shape
+ */
+        __pyx_v_stop = -1;
+        goto __pyx_L15;
+      }
+      /*else*/ {
+
+        /* "View.MemoryView":825
+ *                 stop = -1
+ *             else:
+ *                 stop = shape             # <<<<<<<<<<<<<<
+ * 
+ *         if not have_step:
+ */
+        __pyx_v_stop = __pyx_v_shape;
+      }
+      __pyx_L15:;
+    }
+    __pyx_L12:;
+
+    /* "View.MemoryView":827
+ *                 stop = shape
+ * 
+ *         if not have_step:             # <<<<<<<<<<<<<<
+ *             step = 1
+ * 
+ */
+    __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":828
+ * 
+ *         if not have_step:
+ *             step = 1             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_v_step = 1;
+      goto __pyx_L16;
+    }
+    __pyx_L16:;
+
+    /* "View.MemoryView":832
+ * 
+ *         with cython.cdivision(True):
+ *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<
+ * 
+ *             if (stop - start) - step * new_shape:
+ */
+    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);
+
+    /* "View.MemoryView":834
+ *             new_shape = (stop - start) // step
+ * 
+ *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
+ *                 new_shape += 1
+ * 
+ */
+    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":835
+ * 
+ *             if (stop - start) - step * new_shape:
+ *                 new_shape += 1             # <<<<<<<<<<<<<<
+ * 
+ *         if new_shape < 0:
+ */
+      __pyx_v_new_shape = (__pyx_v_new_shape + 1);
+      goto __pyx_L17;
+    }
+    __pyx_L17:;
+
+    /* "View.MemoryView":837
+ *                 new_shape += 1
+ * 
+ *         if new_shape < 0:             # <<<<<<<<<<<<<<
+ *             new_shape = 0
+ * 
+ */
+    __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":838
+ * 
+ *         if new_shape < 0:
+ *             new_shape = 0             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_v_new_shape = 0;
+      goto __pyx_L18;
+    }
+    __pyx_L18:;
+
+    /* "View.MemoryView":841
+ * 
+ * 
+ *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<
+ *         dst.shape[new_ndim] = new_shape
+ *         dst.suboffsets[new_ndim] = suboffset
+ */
+    (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);
+
+    /* "View.MemoryView":842
+ * 
+ *         dst.strides[new_ndim] = stride * step
+ *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<
+ *         dst.suboffsets[new_ndim] = suboffset
+ * 
+ */
+    (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;
+
+    /* "View.MemoryView":843
+ *         dst.strides[new_ndim] = stride * step
+ *         dst.shape[new_ndim] = new_shape
+ *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":846
+ * 
+ * 
+ *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
+ *         dst.data += start * stride
+ *     else:
+ */
+  __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":847
+ * 
+ *     if suboffset_dim[0] < 0:
+ *         dst.data += start * stride             # <<<<<<<<<<<<<<
+ *     else:
+ *         dst.suboffsets[suboffset_dim[0]] += start * stride
+ */
+    __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));
+    goto __pyx_L19;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":849
+ *         dst.data += start * stride
+ *     else:
+ *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<
+ * 
+ *     if suboffset >= 0:
+ */
+    __pyx_t_3 = (__pyx_v_suboffset_dim[0]);
+    (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride));
+  }
+  __pyx_L19:;
+
+  /* "View.MemoryView":851
+ *         dst.suboffsets[suboffset_dim[0]] += start * stride
+ * 
+ *     if suboffset >= 0:             # <<<<<<<<<<<<<<
+ *         if not is_slice:
+ *             if new_ndim == 0:
+ */
+  __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":852
+ * 
+ *     if suboffset >= 0:
+ *         if not is_slice:             # <<<<<<<<<<<<<<
+ *             if new_ndim == 0:
+ *                 dst.data = (<char **> dst.data)[0] + suboffset
+ */
+    __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":853
+ *     if suboffset >= 0:
+ *         if not is_slice:
+ *             if new_ndim == 0:             # <<<<<<<<<<<<<<
+ *                 dst.data = (<char **> dst.data)[0] + suboffset
+ *             else:
+ */
+      __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":854
+ *         if not is_slice:
+ *             if new_ndim == 0:
+ *                 dst.data = (<char **> dst.data)[0] + suboffset             # <<<<<<<<<<<<<<
+ *             else:
+ *                 _err_dim(IndexError, "All dimensions preceding dimension %d "
+ */
+        __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset);
+        goto __pyx_L22;
+      }
+      /*else*/ {
+
+        /* "View.MemoryView":856
+ *                 dst.data = (<char **> dst.data)[0] + suboffset
+ *             else:
+ *                 _err_dim(IndexError, "All dimensions preceding dimension %d "             # <<<<<<<<<<<<<<
+ *                                      "must be indexed and not sliced", dim)
+ *         else:
+ */
+        __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, __pyx_k_All_dimensions_preceding_dimensi, __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 856; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+      __pyx_L22:;
+      goto __pyx_L21;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":859
+ *                                      "must be indexed and not sliced", dim)
+ *         else:
+ *             suboffset_dim[0] = new_ndim             # <<<<<<<<<<<<<<
+ * 
+ *     return 0
+ */
+      (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim;
+    }
+    __pyx_L21:;
+    goto __pyx_L20;
+  }
+  __pyx_L20:;
+
+  /* "View.MemoryView":861
+ *             suboffset_dim[0] = new_ndim
+ * 
+ *     return 0             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_r = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":764
+ * 
+ * @cname('__pyx_memoryview_slice_memviewslice')
+ * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
+ *         __Pyx_memviewslice *dst,
+ *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  {
+    #ifdef WITH_THREAD
+    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+    #endif
+    __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
+    #ifdef WITH_THREAD
+    PyGILState_Release(__pyx_gilstate_save);
+    #endif
+  }
+  __pyx_r = -1;
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":867
+ * 
+ * @cname('__pyx_pybuffer_index')
+ * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<
+ *                           Py_ssize_t dim) except NULL:
+ *     cdef Py_ssize_t shape, stride, suboffset = -1
+ */
+
+static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) {
+  Py_ssize_t __pyx_v_shape;
+  Py_ssize_t __pyx_v_stride;
+  Py_ssize_t __pyx_v_suboffset;
+  Py_ssize_t __pyx_v_itemsize;
+  char *__pyx_v_resultp;
+  char *__pyx_r;
+  __Pyx_RefNannyDeclarations
+  Py_ssize_t __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("pybuffer_index", 0);
+
+  /* "View.MemoryView":869
+ * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,
+ *                           Py_ssize_t dim) except NULL:
+ *     cdef Py_ssize_t shape, stride, suboffset = -1             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t itemsize = view.itemsize
+ *     cdef char *resultp
+ */
+  __pyx_v_suboffset = -1;
+
+  /* "View.MemoryView":870
+ *                           Py_ssize_t dim) except NULL:
+ *     cdef Py_ssize_t shape, stride, suboffset = -1
+ *     cdef Py_ssize_t itemsize = view.itemsize             # <<<<<<<<<<<<<<
+ *     cdef char *resultp
+ * 
+ */
+  __pyx_t_1 = __pyx_v_view->itemsize;
+  __pyx_v_itemsize = __pyx_t_1;
+
+  /* "View.MemoryView":873
+ *     cdef char *resultp
+ * 
+ *     if view.ndim == 0:             # <<<<<<<<<<<<<<
+ *         shape = view.len / itemsize
+ *         stride = itemsize
+ */
+  __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":874
+ * 
+ *     if view.ndim == 0:
+ *         shape = view.len / itemsize             # <<<<<<<<<<<<<<
+ *         stride = itemsize
+ *     else:
+ */
+    if (unlikely(__pyx_v_itemsize == 0)) {
+      #ifdef WITH_THREAD
+      PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+      #endif
+      PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero");
+      #ifdef WITH_THREAD
+      PyGILState_Release(__pyx_gilstate_save);
+      #endif
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 874; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+    else if (sizeof(Py_ssize_t) == sizeof(long) && unlikely(__pyx_v_itemsize == -1)  && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) {
+      #ifdef WITH_THREAD
+      PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+      #endif
+      PyErr_SetString(PyExc_OverflowError, "value too large to perform division");
+      #ifdef WITH_THREAD
+      PyGILState_Release(__pyx_gilstate_save);
+      #endif
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 874; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+    __pyx_v_shape = __Pyx_div_Py_ssize_t(__pyx_v_view->len, __pyx_v_itemsize);
+
+    /* "View.MemoryView":875
+ *     if view.ndim == 0:
+ *         shape = view.len / itemsize
+ *         stride = itemsize             # <<<<<<<<<<<<<<
+ *     else:
+ *         shape = view.shape[dim]
+ */
+    __pyx_v_stride = __pyx_v_itemsize;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":877
+ *         stride = itemsize
+ *     else:
+ *         shape = view.shape[dim]             # <<<<<<<<<<<<<<
+ *         stride = view.strides[dim]
+ *         if view.suboffsets != NULL:
+ */
+    __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]);
+
+    /* "View.MemoryView":878
+ *     else:
+ *         shape = view.shape[dim]
+ *         stride = view.strides[dim]             # <<<<<<<<<<<<<<
+ *         if view.suboffsets != NULL:
+ *             suboffset = view.suboffsets[dim]
+ */
+    __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]);
+
+    /* "View.MemoryView":879
+ *         shape = view.shape[dim]
+ *         stride = view.strides[dim]
+ *         if view.suboffsets != NULL:             # <<<<<<<<<<<<<<
+ *             suboffset = view.suboffsets[dim]
+ * 
+ */
+    __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":880
+ *         stride = view.strides[dim]
+ *         if view.suboffsets != NULL:
+ *             suboffset = view.suboffsets[dim]             # <<<<<<<<<<<<<<
+ * 
+ *     if index < 0:
+ */
+      __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]);
+      goto __pyx_L4;
+    }
+    __pyx_L4:;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":882
+ *             suboffset = view.suboffsets[dim]
+ * 
+ *     if index < 0:             # <<<<<<<<<<<<<<
+ *         index += view.shape[dim]
+ *         if index < 0:
+ */
+  __pyx_t_2 = ((__pyx_v_index < 0) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":883
+ * 
+ *     if index < 0:
+ *         index += view.shape[dim]             # <<<<<<<<<<<<<<
+ *         if index < 0:
+ *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
+ */
+    __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim]));
+
+    /* "View.MemoryView":884
+ *     if index < 0:
+ *         index += view.shape[dim]
+ *         if index < 0:             # <<<<<<<<<<<<<<
+ *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
+ * 
+ */
+    __pyx_t_2 = ((__pyx_v_index < 0) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":885
+ *         index += view.shape[dim]
+ *         if index < 0:
+ *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)             # <<<<<<<<<<<<<<
+ * 
+ *     if index >= shape:
+ */
+      __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_3);
+      PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4);
+      __Pyx_GIVEREF(__pyx_t_4);
+      __pyx_t_4 = 0;
+      __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_IndexError, __pyx_t_3, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __Pyx_GOTREF(__pyx_t_4);
+      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
+      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+    goto __pyx_L5;
+  }
+  __pyx_L5:;
+
+  /* "View.MemoryView":887
+ *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
+ * 
+ *     if index >= shape:             # <<<<<<<<<<<<<<
+ *         raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
+ * 
+ */
+  __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":888
+ * 
+ *     if index >= shape:
+ *         raise IndexError("Out of bounds on buffer access (axis %d)" % dim)             # <<<<<<<<<<<<<<
+ * 
+ *     resultp = bufp + index * stride
+ */
+    __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_4); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_4);
+    PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
+    __Pyx_GIVEREF(__pyx_t_3);
+    __pyx_t_3 = 0;
+    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_IndexError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "View.MemoryView":890
+ *         raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
+ * 
+ *     resultp = bufp + index * stride             # <<<<<<<<<<<<<<
+ *     if suboffset >= 0:
+ *         resultp = (<char **> resultp)[0] + suboffset
+ */
+  __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride));
+
+  /* "View.MemoryView":891
+ * 
+ *     resultp = bufp + index * stride
+ *     if suboffset >= 0:             # <<<<<<<<<<<<<<
+ *         resultp = (<char **> resultp)[0] + suboffset
+ * 
+ */
+  __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":892
+ *     resultp = bufp + index * stride
+ *     if suboffset >= 0:
+ *         resultp = (<char **> resultp)[0] + suboffset             # <<<<<<<<<<<<<<
+ * 
+ *     return resultp
+ */
+    __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset);
+    goto __pyx_L8;
+  }
+  __pyx_L8:;
+
+  /* "View.MemoryView":894
+ *         resultp = (<char **> resultp)[0] + suboffset
+ * 
+ *     return resultp             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_r = __pyx_v_resultp;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":867
+ * 
+ * @cname('__pyx_pybuffer_index')
+ * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<
+ *                           Py_ssize_t dim) except NULL:
+ *     cdef Py_ssize_t shape, stride, suboffset = -1
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_AddTraceback("View.MemoryView.pybuffer_index", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = NULL;
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":900
+ * 
+ * @cname('__pyx_memslice_transpose')
+ * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:             # <<<<<<<<<<<<<<
+ *     cdef int ndim = memslice.memview.view.ndim
+ * 
+ */
+
+static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
+  int __pyx_v_ndim;
+  Py_ssize_t *__pyx_v_shape;
+  Py_ssize_t *__pyx_v_strides;
+  int __pyx_v_i;
+  int __pyx_v_j;
+  int __pyx_r;
+  int __pyx_t_1;
+  Py_ssize_t *__pyx_t_2;
+  long __pyx_t_3;
+  Py_ssize_t __pyx_t_4;
+  Py_ssize_t __pyx_t_5;
+  int __pyx_t_6;
+  int __pyx_t_7;
+  int __pyx_t_8;
+  int __pyx_t_9;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+
+  /* "View.MemoryView":901
+ * @cname('__pyx_memslice_transpose')
+ * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:
+ *     cdef int ndim = memslice.memview.view.ndim             # <<<<<<<<<<<<<<
+ * 
+ *     cdef Py_ssize_t *shape = memslice.shape
+ */
+  __pyx_t_1 = __pyx_v_memslice->memview->view.ndim;
+  __pyx_v_ndim = __pyx_t_1;
+
+  /* "View.MemoryView":903
+ *     cdef int ndim = memslice.memview.view.ndim
+ * 
+ *     cdef Py_ssize_t *shape = memslice.shape             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t *strides = memslice.strides
+ * 
+ */
+  __pyx_t_2 = __pyx_v_memslice->shape;
+  __pyx_v_shape = __pyx_t_2;
+
+  /* "View.MemoryView":904
+ * 
+ *     cdef Py_ssize_t *shape = memslice.shape
+ *     cdef Py_ssize_t *strides = memslice.strides             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_2 = __pyx_v_memslice->strides;
+  __pyx_v_strides = __pyx_t_2;
+
+  /* "View.MemoryView":908
+ * 
+ *     cdef int i, j
+ *     for i in range(ndim / 2):             # <<<<<<<<<<<<<<
+ *         j = ndim - 1 - i
+ *         strides[i], strides[j] = strides[j], strides[i]
+ */
+  __pyx_t_3 = __Pyx_div_long(__pyx_v_ndim, 2);
+  for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_3; __pyx_t_1+=1) {
+    __pyx_v_i = __pyx_t_1;
+
+    /* "View.MemoryView":909
+ *     cdef int i, j
+ *     for i in range(ndim / 2):
+ *         j = ndim - 1 - i             # <<<<<<<<<<<<<<
+ *         strides[i], strides[j] = strides[j], strides[i]
+ *         shape[i], shape[j] = shape[j], shape[i]
+ */
+    __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i);
+
+    /* "View.MemoryView":910
+ *     for i in range(ndim / 2):
+ *         j = ndim - 1 - i
+ *         strides[i], strides[j] = strides[j], strides[i]             # <<<<<<<<<<<<<<
+ *         shape[i], shape[j] = shape[j], shape[i]
+ * 
+ */
+    __pyx_t_4 = (__pyx_v_strides[__pyx_v_j]);
+    __pyx_t_5 = (__pyx_v_strides[__pyx_v_i]);
+    (__pyx_v_strides[__pyx_v_i]) = __pyx_t_4;
+    (__pyx_v_strides[__pyx_v_j]) = __pyx_t_5;
+
+    /* "View.MemoryView":911
+ *         j = ndim - 1 - i
+ *         strides[i], strides[j] = strides[j], strides[i]
+ *         shape[i], shape[j] = shape[j], shape[i]             # <<<<<<<<<<<<<<
+ * 
+ *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:
+ */
+    __pyx_t_5 = (__pyx_v_shape[__pyx_v_j]);
+    __pyx_t_4 = (__pyx_v_shape[__pyx_v_i]);
+    (__pyx_v_shape[__pyx_v_i]) = __pyx_t_5;
+    (__pyx_v_shape[__pyx_v_j]) = __pyx_t_4;
+
+    /* "View.MemoryView":913
+ *         shape[i], shape[j] = shape[j], shape[i]
+ * 
+ *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:             # <<<<<<<<<<<<<<
+ *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")
+ * 
+ */
+    __pyx_t_6 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0);
+    if (!__pyx_t_6) {
+      __pyx_t_7 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0);
+      __pyx_t_8 = __pyx_t_7;
+    } else {
+      __pyx_t_8 = __pyx_t_6;
+    }
+    if (__pyx_t_8) {
+
+      /* "View.MemoryView":914
+ * 
+ *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:
+ *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")             # <<<<<<<<<<<<<<
+ * 
+ *     return 1
+ */
+      __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, __pyx_k_Cannot_transpose_memoryview_with); if (unlikely(__pyx_t_9 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 914; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L5;
+    }
+    __pyx_L5:;
+  }
+
+  /* "View.MemoryView":916
+ *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")
+ * 
+ *     return 1             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_r = 1;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":900
+ * 
+ * @cname('__pyx_memslice_transpose')
+ * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:             # <<<<<<<<<<<<<<
+ *     cdef int ndim = memslice.memview.view.ndim
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  {
+    #ifdef WITH_THREAD
+    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+    #endif
+    __Pyx_AddTraceback("View.MemoryView.transpose_memslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
+    #ifdef WITH_THREAD
+    PyGILState_Release(__pyx_gilstate_save);
+    #endif
+  }
+  __pyx_r = 0;
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":933
+ *     cdef int (*to_dtype_func)(char *, object) except 0
+ * 
+ *     def __dealloc__(self):             # <<<<<<<<<<<<<<
+ *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
+ * 
+ */
+
+/* Python wrapper */
+static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self); /*proto*/
+static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0);
+  __pyx_memoryviewslice_MemoryView_16_memoryviewslice___dealloc__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+static void __pyx_memoryviewslice_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__dealloc__", 0);
+
+  /* "View.MemoryView":934
+ * 
+ *     def __dealloc__(self):
+ *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef convert_item_to_object(self, char *itemp):
+ */
+  __PYX_XDEC_MEMVIEW((&__pyx_v_self->from_slice), 1);
+
+  /* "View.MemoryView":933
+ *     cdef int (*to_dtype_func)(char *, object) except 0
+ * 
+ *     def __dealloc__(self):             # <<<<<<<<<<<<<<
+ *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
+ * 
+ */
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+/* "View.MemoryView":936
+ *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
+ * 
+ *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
+ *         if self.to_object_func != NULL:
+ *             return self.to_object_func(itemp)
+ */
+
+static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("convert_item_to_object", 0);
+
+  /* "View.MemoryView":937
+ * 
+ *     cdef convert_item_to_object(self, char *itemp):
+ *         if self.to_object_func != NULL:             # <<<<<<<<<<<<<<
+ *             return self.to_object_func(itemp)
+ *         else:
+ */
+  __pyx_t_1 = ((__pyx_v_self->to_object_func != NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":938
+ *     cdef convert_item_to_object(self, char *itemp):
+ *         if self.to_object_func != NULL:
+ *             return self.to_object_func(itemp)             # <<<<<<<<<<<<<<
+ *         else:
+ *             return memoryview.convert_item_to_object(self, itemp)
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 938; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __pyx_r = __pyx_t_2;
+    __pyx_t_2 = 0;
+    goto __pyx_L0;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":940
+ *             return self.to_object_func(itemp)
+ *         else:
+ *             return memoryview.convert_item_to_object(self, itemp)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef assign_item_from_object(self, char *itemp, object value):
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __pyx_t_2 = __pyx_vtabptr_memoryview->convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 940; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __pyx_r = __pyx_t_2;
+    __pyx_t_2 = 0;
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":936
+ *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
+ * 
+ *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
+ *         if self.to_object_func != NULL:
+ *             return self.to_object_func(itemp)
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_AddTraceback("View.MemoryView._memoryviewslice.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":942
+ *             return memoryview.convert_item_to_object(self, itemp)
+ * 
+ *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
+ *         if self.to_dtype_func != NULL:
+ *             self.to_dtype_func(itemp, value)
+ */
+
+static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("assign_item_from_object", 0);
+
+  /* "View.MemoryView":943
+ * 
+ *     cdef assign_item_from_object(self, char *itemp, object value):
+ *         if self.to_dtype_func != NULL:             # <<<<<<<<<<<<<<
+ *             self.to_dtype_func(itemp, value)
+ *         else:
+ */
+  __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":944
+ *     cdef assign_item_from_object(self, char *itemp, object value):
+ *         if self.to_dtype_func != NULL:
+ *             self.to_dtype_func(itemp, value)             # <<<<<<<<<<<<<<
+ *         else:
+ *             memoryview.assign_item_from_object(self, itemp, value)
+ */
+    __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 944; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":946
+ *             self.to_dtype_func(itemp, value)
+ *         else:
+ *             memoryview.assign_item_from_object(self, itemp, value)             # <<<<<<<<<<<<<<
+ * 
+ *     property base:
+ */
+    __pyx_t_3 = __pyx_vtabptr_memoryview->assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 946; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":942
+ *             return memoryview.convert_item_to_object(self, itemp)
+ * 
+ *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
+ *         if self.to_dtype_func != NULL:
+ *             self.to_dtype_func(itemp, value)
+ */
+
+  /* function exit code */
+  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView._memoryviewslice.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":950
+ *     property base:
+ *         @cname('__pyx_memoryviewslice__get__base')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.from_object
+ * 
+ */
+
+/* Python wrapper */
+static PyObject *__pyx_memoryviewslice__get__base(PyObject *__pyx_v_self); /*proto*/
+static PyObject *__pyx_memoryviewslice__get__base(PyObject *__pyx_v_self) {
+  PyObject *__pyx_r = 0;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
+  __pyx_r = __pyx_memoryviewslice__get__base_MemoryView_16_memoryviewslice_4base___get__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self));
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+static PyObject *__pyx_memoryviewslice__get__base_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self) {
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__get__", 0);
+
+  /* "View.MemoryView":951
+ *         @cname('__pyx_memoryviewslice__get__base')
+ *         def __get__(self):
+ *             return self.from_object             # <<<<<<<<<<<<<<
+ * 
+ *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(__pyx_v_self->from_object);
+  __pyx_r = __pyx_v_self->from_object;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":950
+ *     property base:
+ *         @cname('__pyx_memoryviewslice__get__base')
+ *         def __get__(self):             # <<<<<<<<<<<<<<
+ *             return self.from_object
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":957
+ * 
+ * @cname('__pyx_memoryview_fromslice')
+ * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice,             # <<<<<<<<<<<<<<
+ *                           int ndim,
+ *                           object (*to_object_func)(char *),
+ */
+
+static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) {
+  struct __pyx_memoryviewslice_obj *__pyx_v_result = 0;
+  int __pyx_v_i;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  __Pyx_TypeInfo *__pyx_t_4;
+  Py_buffer __pyx_t_5;
+  Py_ssize_t __pyx_t_6;
+  int __pyx_t_7;
+  int __pyx_t_8;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("memoryview_fromslice", 0);
+
+  /* "View.MemoryView":966
+ *     cdef int i
+ * 
+ *     if <PyObject *> memviewslice.memview == Py_None:             # <<<<<<<<<<<<<<
+ *         return None
+ * 
+ */
+  __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":967
+ * 
+ *     if <PyObject *> memviewslice.memview == Py_None:
+ *         return None             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __Pyx_XDECREF(__pyx_r);
+    __Pyx_INCREF(Py_None);
+    __pyx_r = Py_None;
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":972
+ * 
+ * 
+ *     result = _memoryviewslice(None, 0, dtype_is_object)             # <<<<<<<<<<<<<<
+ * 
+ *     result.from_slice = memviewslice
+ */
+  __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 972; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 972; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_INCREF(Py_None);
+  PyTuple_SET_ITEM(__pyx_t_3, 0, Py_None);
+  __Pyx_GIVEREF(Py_None);
+  __Pyx_INCREF(__pyx_int_0);
+  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_0);
+  __Pyx_GIVEREF(__pyx_int_0);
+  PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  __pyx_t_2 = 0;
+  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_memoryviewslice_type)), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 972; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_2);
+  __pyx_t_2 = 0;
+
+  /* "View.MemoryView":974
+ *     result = _memoryviewslice(None, 0, dtype_is_object)
+ * 
+ *     result.from_slice = memviewslice             # <<<<<<<<<<<<<<
+ *     __PYX_INC_MEMVIEW(&memviewslice, 1)
+ * 
+ */
+  __pyx_v_result->from_slice = __pyx_v_memviewslice;
+
+  /* "View.MemoryView":975
+ * 
+ *     result.from_slice = memviewslice
+ *     __PYX_INC_MEMVIEW(&memviewslice, 1)             # <<<<<<<<<<<<<<
+ * 
+ *     result.from_object = (<memoryview> memviewslice.memview).base
+ */
+  __PYX_INC_MEMVIEW((&__pyx_v_memviewslice), 1);
+
+  /* "View.MemoryView":977
+ *     __PYX_INC_MEMVIEW(&memviewslice, 1)
+ * 
+ *     result.from_object = (<memoryview> memviewslice.memview).base             # <<<<<<<<<<<<<<
+ *     result.typeinfo = memviewslice.memview.typeinfo
+ * 
+ */
+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 977; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  __Pyx_GOTREF(__pyx_v_result->from_object);
+  __Pyx_DECREF(__pyx_v_result->from_object);
+  __pyx_v_result->from_object = __pyx_t_2;
+  __pyx_t_2 = 0;
+
+  /* "View.MemoryView":978
+ * 
+ *     result.from_object = (<memoryview> memviewslice.memview).base
+ *     result.typeinfo = memviewslice.memview.typeinfo             # <<<<<<<<<<<<<<
+ * 
+ *     result.view = memviewslice.memview.view
+ */
+  __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo;
+  __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4;
+
+  /* "View.MemoryView":980
+ *     result.typeinfo = memviewslice.memview.typeinfo
+ * 
+ *     result.view = memviewslice.memview.view             # <<<<<<<<<<<<<<
+ *     result.view.buf = <void *> memviewslice.data
+ *     result.view.ndim = ndim
+ */
+  __pyx_t_5 = __pyx_v_memviewslice.memview->view;
+  __pyx_v_result->__pyx_base.view = __pyx_t_5;
+
+  /* "View.MemoryView":981
+ * 
+ *     result.view = memviewslice.memview.view
+ *     result.view.buf = <void *> memviewslice.data             # <<<<<<<<<<<<<<
+ *     result.view.ndim = ndim
+ *     (<__pyx_buffer *> &result.view).obj = Py_None
+ */
+  __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data);
+
+  /* "View.MemoryView":982
+ *     result.view = memviewslice.memview.view
+ *     result.view.buf = <void *> memviewslice.data
+ *     result.view.ndim = ndim             # <<<<<<<<<<<<<<
+ *     (<__pyx_buffer *> &result.view).obj = Py_None
+ *     Py_INCREF(Py_None)
+ */
+  __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim;
+
+  /* "View.MemoryView":983
+ *     result.view.buf = <void *> memviewslice.data
+ *     result.view.ndim = ndim
+ *     (<__pyx_buffer *> &result.view).obj = Py_None             # <<<<<<<<<<<<<<
+ *     Py_INCREF(Py_None)
+ * 
+ */
+  ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None;
+
+  /* "View.MemoryView":984
+ *     result.view.ndim = ndim
+ *     (<__pyx_buffer *> &result.view).obj = Py_None
+ *     Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
+ * 
+ *     result.flags = PyBUF_RECORDS
+ */
+  Py_INCREF(Py_None);
+
+  /* "View.MemoryView":986
+ *     Py_INCREF(Py_None)
+ * 
+ *     result.flags = PyBUF_RECORDS             # <<<<<<<<<<<<<<
+ * 
+ *     result.view.shape = <Py_ssize_t *> result.from_slice.shape
+ */
+  __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS;
+
+  /* "View.MemoryView":988
+ *     result.flags = PyBUF_RECORDS
+ * 
+ *     result.view.shape = <Py_ssize_t *> result.from_slice.shape             # <<<<<<<<<<<<<<
+ *     result.view.strides = <Py_ssize_t *> result.from_slice.strides
+ *     result.view.suboffsets = <Py_ssize_t *> result.from_slice.suboffsets
+ */
+  __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape);
+
+  /* "View.MemoryView":989
+ * 
+ *     result.view.shape = <Py_ssize_t *> result.from_slice.shape
+ *     result.view.strides = <Py_ssize_t *> result.from_slice.strides             # <<<<<<<<<<<<<<
+ *     result.view.suboffsets = <Py_ssize_t *> result.from_slice.suboffsets
+ * 
+ */
+  __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides);
+
+  /* "View.MemoryView":990
+ *     result.view.shape = <Py_ssize_t *> result.from_slice.shape
+ *     result.view.strides = <Py_ssize_t *> result.from_slice.strides
+ *     result.view.suboffsets = <Py_ssize_t *> result.from_slice.suboffsets             # <<<<<<<<<<<<<<
+ * 
+ *     result.view.len = result.view.itemsize
+ */
+  __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets);
+
+  /* "View.MemoryView":992
+ *     result.view.suboffsets = <Py_ssize_t *> result.from_slice.suboffsets
+ * 
+ *     result.view.len = result.view.itemsize             # <<<<<<<<<<<<<<
+ *     for i in range(ndim):
+ *         result.view.len *= result.view.shape[i]
+ */
+  __pyx_t_6 = __pyx_v_result->__pyx_base.view.itemsize;
+  __pyx_v_result->__pyx_base.view.len = __pyx_t_6;
+
+  /* "View.MemoryView":993
+ * 
+ *     result.view.len = result.view.itemsize
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         result.view.len *= result.view.shape[i]
+ * 
+ */
+  __pyx_t_7 = __pyx_v_ndim;
+  for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) {
+    __pyx_v_i = __pyx_t_8;
+
+    /* "View.MemoryView":994
+ *     result.view.len = result.view.itemsize
+ *     for i in range(ndim):
+ *         result.view.len *= result.view.shape[i]             # <<<<<<<<<<<<<<
+ * 
+ *     result.to_object_func = to_object_func
+ */
+    __pyx_v_result->__pyx_base.view.len = (__pyx_v_result->__pyx_base.view.len * (__pyx_v_result->__pyx_base.view.shape[__pyx_v_i]));
+  }
+
+  /* "View.MemoryView":996
+ *         result.view.len *= result.view.shape[i]
+ * 
+ *     result.to_object_func = to_object_func             # <<<<<<<<<<<<<<
+ *     result.to_dtype_func = to_dtype_func
+ * 
+ */
+  __pyx_v_result->to_object_func = __pyx_v_to_object_func;
+
+  /* "View.MemoryView":997
+ * 
+ *     result.to_object_func = to_object_func
+ *     result.to_dtype_func = to_dtype_func             # <<<<<<<<<<<<<<
+ * 
+ *     return result
+ */
+  __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func;
+
+  /* "View.MemoryView":999
+ *     result.to_dtype_func = to_dtype_func
+ * 
+ *     return result             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_get_slice_from_memoryview')
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __Pyx_INCREF(((PyObject *)__pyx_v_result));
+  __pyx_r = ((PyObject *)__pyx_v_result);
+  goto __pyx_L0;
+
+  /* "View.MemoryView":957
+ * 
+ * @cname('__pyx_memoryview_fromslice')
+ * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice,             # <<<<<<<<<<<<<<
+ *                           int ndim,
+ *                           object (*to_object_func)(char *),
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView.memoryview_fromslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_result);
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1002
+ * 
+ * @cname('__pyx_memoryview_get_slice_from_memoryview')
+ * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview,             # <<<<<<<<<<<<<<
+ *                                                    __Pyx_memviewslice *mslice):
+ *     cdef _memoryviewslice obj
+ */
+
+static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_mslice) {
+  struct __pyx_memoryviewslice_obj *__pyx_v_obj = 0;
+  __Pyx_memviewslice *__pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("get_slice_from_memview", 0);
+
+  /* "View.MemoryView":1005
+ *                                                    __Pyx_memviewslice *mslice):
+ *     cdef _memoryviewslice obj
+ *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
+ *         obj = memview
+ *         return &obj.from_slice
+ */
+  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), ((PyObject *)__pyx_memoryviewslice_type)); 
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1006
+ *     cdef _memoryviewslice obj
+ *     if isinstance(memview, _memoryviewslice):
+ *         obj = memview             # <<<<<<<<<<<<<<
+ *         return &obj.from_slice
+ *     else:
+ */
+    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1006; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_3 = ((PyObject *)__pyx_v_memview);
+    __Pyx_INCREF(__pyx_t_3);
+    __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3);
+    __pyx_t_3 = 0;
+
+    /* "View.MemoryView":1007
+ *     if isinstance(memview, _memoryviewslice):
+ *         obj = memview
+ *         return &obj.from_slice             # <<<<<<<<<<<<<<
+ *     else:
+ *         slice_copy(memview, mslice)
+ */
+    __pyx_r = (&__pyx_v_obj->from_slice);
+    goto __pyx_L0;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1009
+ *         return &obj.from_slice
+ *     else:
+ *         slice_copy(memview, mslice)             # <<<<<<<<<<<<<<
+ *         return mslice
+ * 
+ */
+    __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice);
+
+    /* "View.MemoryView":1010
+ *     else:
+ *         slice_copy(memview, mslice)
+ *         return mslice             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_slice_copy')
+ */
+    __pyx_r = __pyx_v_mslice;
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":1002
+ * 
+ * @cname('__pyx_memoryview_get_slice_from_memoryview')
+ * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview,             # <<<<<<<<<<<<<<
+ *                                                    __Pyx_memviewslice *mslice):
+ *     cdef _memoryviewslice obj
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_WriteUnraisable("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename, 0);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XDECREF((PyObject *)__pyx_v_obj);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1013
+ * 
+ * @cname('__pyx_memoryview_slice_copy')
+ * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst):             # <<<<<<<<<<<<<<
+ *     cdef int dim
+ *     cdef (Py_ssize_t*) shape, strides, suboffsets
+ */
+
+static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_dst) {
+  int __pyx_v_dim;
+  Py_ssize_t *__pyx_v_shape;
+  Py_ssize_t *__pyx_v_strides;
+  Py_ssize_t *__pyx_v_suboffsets;
+  __Pyx_RefNannyDeclarations
+  Py_ssize_t *__pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  __Pyx_RefNannySetupContext("slice_copy", 0);
+
+  /* "View.MemoryView":1017
+ *     cdef (Py_ssize_t*) shape, strides, suboffsets
+ * 
+ *     shape = memview.view.shape             # <<<<<<<<<<<<<<
+ *     strides = memview.view.strides
+ *     suboffsets = memview.view.suboffsets
+ */
+  __pyx_t_1 = __pyx_v_memview->view.shape;
+  __pyx_v_shape = __pyx_t_1;
+
+  /* "View.MemoryView":1018
+ * 
+ *     shape = memview.view.shape
+ *     strides = memview.view.strides             # <<<<<<<<<<<<<<
+ *     suboffsets = memview.view.suboffsets
+ * 
+ */
+  __pyx_t_1 = __pyx_v_memview->view.strides;
+  __pyx_v_strides = __pyx_t_1;
+
+  /* "View.MemoryView":1019
+ *     shape = memview.view.shape
+ *     strides = memview.view.strides
+ *     suboffsets = memview.view.suboffsets             # <<<<<<<<<<<<<<
+ * 
+ *     dst.memview = <__pyx_memoryview *> memview
+ */
+  __pyx_t_1 = __pyx_v_memview->view.suboffsets;
+  __pyx_v_suboffsets = __pyx_t_1;
+
+  /* "View.MemoryView":1021
+ *     suboffsets = memview.view.suboffsets
+ * 
+ *     dst.memview = <__pyx_memoryview *> memview             # <<<<<<<<<<<<<<
+ *     dst.data = <char *> memview.view.buf
+ * 
+ */
+  __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview);
+
+  /* "View.MemoryView":1022
+ * 
+ *     dst.memview = <__pyx_memoryview *> memview
+ *     dst.data = <char *> memview.view.buf             # <<<<<<<<<<<<<<
+ * 
+ *     for dim in range(memview.view.ndim):
+ */
+  __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf);
+
+  /* "View.MemoryView":1024
+ *     dst.data = <char *> memview.view.buf
+ * 
+ *     for dim in range(memview.view.ndim):             # <<<<<<<<<<<<<<
+ *         dst.shape[dim] = shape[dim]
+ *         dst.strides[dim] = strides[dim]
+ */
+  __pyx_t_2 = __pyx_v_memview->view.ndim;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+    __pyx_v_dim = __pyx_t_3;
+
+    /* "View.MemoryView":1025
+ * 
+ *     for dim in range(memview.view.ndim):
+ *         dst.shape[dim] = shape[dim]             # <<<<<<<<<<<<<<
+ *         dst.strides[dim] = strides[dim]
+ *         if suboffsets == NULL:
+ */
+    (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]);
+
+    /* "View.MemoryView":1026
+ *     for dim in range(memview.view.ndim):
+ *         dst.shape[dim] = shape[dim]
+ *         dst.strides[dim] = strides[dim]             # <<<<<<<<<<<<<<
+ *         if suboffsets == NULL:
+ *             dst.suboffsets[dim] = -1
+ */
+    (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]);
+
+    /* "View.MemoryView":1027
+ *         dst.shape[dim] = shape[dim]
+ *         dst.strides[dim] = strides[dim]
+ *         if suboffsets == NULL:             # <<<<<<<<<<<<<<
+ *             dst.suboffsets[dim] = -1
+ *         else:
+ */
+    __pyx_t_4 = ((__pyx_v_suboffsets == NULL) != 0);
+    if (__pyx_t_4) {
+
+      /* "View.MemoryView":1028
+ *         dst.strides[dim] = strides[dim]
+ *         if suboffsets == NULL:
+ *             dst.suboffsets[dim] = -1             # <<<<<<<<<<<<<<
+ *         else:
+ *             dst.suboffsets[dim] = suboffsets[dim]
+ */
+      (__pyx_v_dst->suboffsets[__pyx_v_dim]) = -1;
+      goto __pyx_L5;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":1030
+ *             dst.suboffsets[dim] = -1
+ *         else:
+ *             dst.suboffsets[dim] = suboffsets[dim]             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_copy_object')
+ */
+      (__pyx_v_dst->suboffsets[__pyx_v_dim]) = (__pyx_v_suboffsets[__pyx_v_dim]);
+    }
+    __pyx_L5:;
+  }
+
+  /* "View.MemoryView":1013
+ * 
+ * @cname('__pyx_memoryview_slice_copy')
+ * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst):             # <<<<<<<<<<<<<<
+ *     cdef int dim
+ *     cdef (Py_ssize_t*) shape, strides, suboffsets
+ */
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+/* "View.MemoryView":1033
+ * 
+ * @cname('__pyx_memoryview_copy_object')
+ * cdef memoryview_copy(memoryview memview):             # <<<<<<<<<<<<<<
+ *     "Create a new memoryview object"
+ *     cdef __Pyx_memviewslice memviewslice
+ */
+
+static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx_v_memview) {
+  __Pyx_memviewslice __pyx_v_memviewslice;
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("memoryview_copy", 0);
+
+  /* "View.MemoryView":1036
+ *     "Create a new memoryview object"
+ *     cdef __Pyx_memviewslice memviewslice
+ *     slice_copy(memview, &memviewslice)             # <<<<<<<<<<<<<<
+ *     return memoryview_copy_from_slice(memview, &memviewslice)
+ * 
+ */
+  __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice));
+
+  /* "View.MemoryView":1037
+ *     cdef __Pyx_memviewslice memviewslice
+ *     slice_copy(memview, &memviewslice)
+ *     return memoryview_copy_from_slice(memview, &memviewslice)             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_copy_object_from_slice')
+ */
+  __Pyx_XDECREF(__pyx_r);
+  __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_r = __pyx_t_1;
+  __pyx_t_1 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1033
+ * 
+ * @cname('__pyx_memoryview_copy_object')
+ * cdef memoryview_copy(memoryview memview):             # <<<<<<<<<<<<<<
+ *     "Create a new memoryview object"
+ *     cdef __Pyx_memviewslice memviewslice
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_AddTraceback("View.MemoryView.memoryview_copy", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1040
+ * 
+ * @cname('__pyx_memoryview_copy_object_from_slice')
+ * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice):             # <<<<<<<<<<<<<<
+ *     """
+ *     Create a new memoryview object from a given memoryview object and slice.
+ */
+
+static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_memviewslice) {
+  PyObject *(*__pyx_v_to_object_func)(char *);
+  int (*__pyx_v_to_dtype_func)(char *, PyObject *);
+  PyObject *__pyx_r = NULL;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  int __pyx_t_2;
+  PyObject *(*__pyx_t_3)(char *);
+  int (*__pyx_t_4)(char *, PyObject *);
+  PyObject *__pyx_t_5 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0);
+
+  /* "View.MemoryView":1047
+ *     cdef int (*to_dtype_func)(char *, object) except 0
+ * 
+ *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
+ *         to_object_func = (<_memoryviewslice> memview).to_object_func
+ *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
+ */
+  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), ((PyObject *)__pyx_memoryviewslice_type)); 
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1048
+ * 
+ *     if isinstance(memview, _memoryviewslice):
+ *         to_object_func = (<_memoryviewslice> memview).to_object_func             # <<<<<<<<<<<<<<
+ *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
+ *     else:
+ */
+    __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func;
+    __pyx_v_to_object_func = __pyx_t_3;
+
+    /* "View.MemoryView":1049
+ *     if isinstance(memview, _memoryviewslice):
+ *         to_object_func = (<_memoryviewslice> memview).to_object_func
+ *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func             # <<<<<<<<<<<<<<
+ *     else:
+ *         to_object_func = NULL
+ */
+    __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func;
+    __pyx_v_to_dtype_func = __pyx_t_4;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1051
+ *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
+ *     else:
+ *         to_object_func = NULL             # <<<<<<<<<<<<<<
+ *         to_dtype_func = NULL
+ * 
+ */
+    __pyx_v_to_object_func = NULL;
+
+    /* "View.MemoryView":1052
+ *     else:
+ *         to_object_func = NULL
+ *         to_dtype_func = NULL             # <<<<<<<<<<<<<<
+ * 
+ *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,
+ */
+    __pyx_v_to_dtype_func = NULL;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1054
+ *         to_dtype_func = NULL
+ * 
+ *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,             # <<<<<<<<<<<<<<
+ *                                 to_object_func, to_dtype_func,
+ *                                 memview.dtype_is_object)
+ */
+  __Pyx_XDECREF(__pyx_r);
+
+  /* "View.MemoryView":1056
+ *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,
+ *                                 to_object_func, to_dtype_func,
+ *                                 memview.dtype_is_object)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1054; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_5);
+  __pyx_r = __pyx_t_5;
+  __pyx_t_5 = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1040
+ * 
+ * @cname('__pyx_memoryview_copy_object_from_slice')
+ * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice):             # <<<<<<<<<<<<<<
+ *     """
+ *     Create a new memoryview object from a given memoryview object and slice.
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = 0;
+  __pyx_L0:;
+  __Pyx_XGIVEREF(__pyx_r);
+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1062
+ * 
+ * 
+ * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<
+ *     if arg < 0:
+ *         return -arg
+ */
+
+static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
+  Py_ssize_t __pyx_r;
+  int __pyx_t_1;
+
+  /* "View.MemoryView":1063
+ * 
+ * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
+ *     if arg < 0:             # <<<<<<<<<<<<<<
+ *         return -arg
+ *     else:
+ */
+  __pyx_t_1 = ((__pyx_v_arg < 0) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":1064
+ * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
+ *     if arg < 0:
+ *         return -arg             # <<<<<<<<<<<<<<
+ *     else:
+ *         return arg
+ */
+    __pyx_r = (-__pyx_v_arg);
+    goto __pyx_L0;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1066
+ *         return -arg
+ *     else:
+ *         return arg             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_get_best_slice_order')
+ */
+    __pyx_r = __pyx_v_arg;
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":1062
+ * 
+ * 
+ * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<
+ *     if arg < 0:
+ *         return -arg
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1069
+ * 
+ * @cname('__pyx_get_best_slice_order')
+ * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
+ *     """
+ *     Figure out the best memory access order for a given slice.
+ */
+
+static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) {
+  int __pyx_v_i;
+  Py_ssize_t __pyx_v_c_stride;
+  Py_ssize_t __pyx_v_f_stride;
+  char __pyx_r;
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+
+  /* "View.MemoryView":1074
+ *     """
+ *     cdef int i
+ *     cdef Py_ssize_t c_stride = 0             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t f_stride = 0
+ * 
+ */
+  __pyx_v_c_stride = 0;
+
+  /* "View.MemoryView":1075
+ *     cdef int i
+ *     cdef Py_ssize_t c_stride = 0
+ *     cdef Py_ssize_t f_stride = 0             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(ndim - 1, -1, -1):
+ */
+  __pyx_v_f_stride = 0;
+
+  /* "View.MemoryView":1077
+ *     cdef Py_ssize_t f_stride = 0
+ * 
+ *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
+ *         if mslice.shape[i] > 1:
+ *             c_stride = mslice.strides[i]
+ */
+  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
+    __pyx_v_i = __pyx_t_1;
+
+    /* "View.MemoryView":1078
+ * 
+ *     for i in range(ndim - 1, -1, -1):
+ *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
+ *             c_stride = mslice.strides[i]
+ *             break
+ */
+    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1079
+ *     for i in range(ndim - 1, -1, -1):
+ *         if mslice.shape[i] > 1:
+ *             c_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
+ *             break
+ * 
+ */
+      __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
+
+      /* "View.MemoryView":1080
+ *         if mslice.shape[i] > 1:
+ *             c_stride = mslice.strides[i]
+ *             break             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(ndim):
+ */
+      goto __pyx_L4_break;
+    }
+  }
+  __pyx_L4_break:;
+
+  /* "View.MemoryView":1082
+ *             break
+ * 
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         if mslice.shape[i] > 1:
+ *             f_stride = mslice.strides[i]
+ */
+  __pyx_t_1 = __pyx_v_ndim;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) {
+    __pyx_v_i = __pyx_t_3;
+
+    /* "View.MemoryView":1083
+ * 
+ *     for i in range(ndim):
+ *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
+ *             f_stride = mslice.strides[i]
+ *             break
+ */
+    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1084
+ *     for i in range(ndim):
+ *         if mslice.shape[i] > 1:
+ *             f_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
+ *             break
+ * 
+ */
+      __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
+
+      /* "View.MemoryView":1085
+ *         if mslice.shape[i] > 1:
+ *             f_stride = mslice.strides[i]
+ *             break             # <<<<<<<<<<<<<<
+ * 
+ *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
+ */
+      goto __pyx_L7_break;
+    }
+  }
+  __pyx_L7_break:;
+
+  /* "View.MemoryView":1087
+ *             break
+ * 
+ *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
+ *         return 'C'
+ *     else:
+ */
+  __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1088
+ * 
+ *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
+ *         return 'C'             # <<<<<<<<<<<<<<
+ *     else:
+ *         return 'F'
+ */
+    __pyx_r = 'C';
+    goto __pyx_L0;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1090
+ *         return 'C'
+ *     else:
+ *         return 'F'             # <<<<<<<<<<<<<<
+ * 
+ * @cython.cdivision(True)
+ */
+    __pyx_r = 'F';
+    goto __pyx_L0;
+  }
+
+  /* "View.MemoryView":1069
+ * 
+ * @cname('__pyx_get_best_slice_order')
+ * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
+ *     """
+ *     Figure out the best memory access order for a given slice.
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1093
+ * 
+ * @cython.cdivision(True)
+ * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
+ *                                    char *dst_data, Py_ssize_t *dst_strides,
+ *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
+ */
+
+static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
+  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
+  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
+  Py_ssize_t __pyx_v_dst_extent;
+  Py_ssize_t __pyx_v_src_stride;
+  Py_ssize_t __pyx_v_dst_stride;
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  Py_ssize_t __pyx_t_5;
+  Py_ssize_t __pyx_t_6;
+
+  /* "View.MemoryView":1100
+ * 
+ *     cdef Py_ssize_t i
+ *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t dst_extent = dst_shape[0]
+ *     cdef Py_ssize_t src_stride = src_strides[0]
+ */
+  __pyx_v_src_extent = (__pyx_v_src_shape[0]);
+
+  /* "View.MemoryView":1101
+ *     cdef Py_ssize_t i
+ *     cdef Py_ssize_t src_extent = src_shape[0]
+ *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t src_stride = src_strides[0]
+ *     cdef Py_ssize_t dst_stride = dst_strides[0]
+ */
+  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
+
+  /* "View.MemoryView":1102
+ *     cdef Py_ssize_t src_extent = src_shape[0]
+ *     cdef Py_ssize_t dst_extent = dst_shape[0]
+ *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t dst_stride = dst_strides[0]
+ * 
+ */
+  __pyx_v_src_stride = (__pyx_v_src_strides[0]);
+
+  /* "View.MemoryView":1103
+ *     cdef Py_ssize_t dst_extent = dst_shape[0]
+ *     cdef Py_ssize_t src_stride = src_strides[0]
+ *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
+ * 
+ *     if ndim == 1:
+ */
+  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
+
+  /* "View.MemoryView":1105
+ *     cdef Py_ssize_t dst_stride = dst_strides[0]
+ * 
+ *     if ndim == 1:             # <<<<<<<<<<<<<<
+ *        if (src_stride > 0 and dst_stride > 0 and
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):
+ */
+  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":1106
+ * 
+ *     if ndim == 1:
+ *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)
+ */
+    __pyx_t_1 = ((__pyx_v_src_stride > 0) != 0);
+    if (__pyx_t_1) {
+      __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":1107
+ *     if ndim == 1:
+ *        if (src_stride > 0 and dst_stride > 0 and
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)
+ *        else:
+ */
+        __pyx_t_3 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
+        if (__pyx_t_3) {
+          __pyx_t_3 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
+        }
+        __pyx_t_4 = (__pyx_t_3 != 0);
+      } else {
+        __pyx_t_4 = __pyx_t_2;
+      }
+      __pyx_t_2 = __pyx_t_4;
+    } else {
+      __pyx_t_2 = __pyx_t_1;
+    }
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1108
+ *        if (src_stride > 0 and dst_stride > 0 and
+ *            <size_t> src_stride == itemsize == <size_t> dst_stride):
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
+ *        else:
+ *            for i in range(dst_extent):
+ */
+      memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent));
+      goto __pyx_L4;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":1110
+ *            memcpy(dst_data, src_data, itemsize * dst_extent)
+ *        else:
+ *            for i in range(dst_extent):             # <<<<<<<<<<<<<<
+ *                memcpy(dst_data, src_data, itemsize)
+ *                src_data += src_stride
+ */
+      __pyx_t_5 = __pyx_v_dst_extent;
+      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+        __pyx_v_i = __pyx_t_6;
+
+        /* "View.MemoryView":1111
+ *        else:
+ *            for i in range(dst_extent):
+ *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
+ *                src_data += src_stride
+ *                dst_data += dst_stride
+ */
+        memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize);
+
+        /* "View.MemoryView":1112
+ *            for i in range(dst_extent):
+ *                memcpy(dst_data, src_data, itemsize)
+ *                src_data += src_stride             # <<<<<<<<<<<<<<
+ *                dst_data += dst_stride
+ *     else:
+ */
+        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
+
+        /* "View.MemoryView":1113
+ *                memcpy(dst_data, src_data, itemsize)
+ *                src_data += src_stride
+ *                dst_data += dst_stride             # <<<<<<<<<<<<<<
+ *     else:
+ *         for i in range(dst_extent):
+ */
+        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
+      }
+    }
+    __pyx_L4:;
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1115
+ *                dst_data += dst_stride
+ *     else:
+ *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
+ *             _copy_strided_to_strided(src_data, src_strides + 1,
+ *                                      dst_data, dst_strides + 1,
+ */
+    __pyx_t_5 = __pyx_v_dst_extent;
+    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
+      __pyx_v_i = __pyx_t_6;
+
+      /* "View.MemoryView":1116
+ *     else:
+ *         for i in range(dst_extent):
+ *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
+ *                                      dst_data, dst_strides + 1,
+ *                                      src_shape + 1, dst_shape + 1,
+ */
+      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
+
+      /* "View.MemoryView":1120
+ *                                      src_shape + 1, dst_shape + 1,
+ *                                      ndim - 1, itemsize)
+ *             src_data += src_stride             # <<<<<<<<<<<<<<
+ *             dst_data += dst_stride
+ * 
+ */
+      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
+
+      /* "View.MemoryView":1121
+ *                                      ndim - 1, itemsize)
+ *             src_data += src_stride
+ *             dst_data += dst_stride             # <<<<<<<<<<<<<<
+ * 
+ * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
+ */
+      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
+    }
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1093
+ * 
+ * @cython.cdivision(True)
+ * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
+ *                                    char *dst_data, Py_ssize_t *dst_strides,
+ *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
+ */
+
+  /* function exit code */
+}
+
+/* "View.MemoryView":1123
+ *             dst_data += dst_stride
+ * 
+ * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
+ *                                   __Pyx_memviewslice *dst,
+ *                                   int ndim, size_t itemsize) nogil:
+ */
+
+static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
+
+  /* "View.MemoryView":1126
+ *                                   __Pyx_memviewslice *dst,
+ *                                   int ndim, size_t itemsize) nogil:
+ *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
+ *                              src.shape, dst.shape, ndim, itemsize)
+ * 
+ */
+  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
+
+  /* "View.MemoryView":1123
+ *             dst_data += dst_stride
+ * 
+ * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
+ *                                   __Pyx_memviewslice *dst,
+ *                                   int ndim, size_t itemsize) nogil:
+ */
+
+  /* function exit code */
+}
+
+/* "View.MemoryView":1130
+ * 
+ * @cname('__pyx_memoryview_slice_get_size')
+ * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
+ *     "Return the size of the memory occupied by the slice in number of bytes"
+ *     cdef int i
+ */
+
+static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
+  int __pyx_v_i;
+  Py_ssize_t __pyx_v_size;
+  Py_ssize_t __pyx_r;
+  Py_ssize_t __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+
+  /* "View.MemoryView":1133
+ *     "Return the size of the memory occupied by the slice in number of bytes"
+ *     cdef int i
+ *     cdef Py_ssize_t size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(ndim):
+ */
+  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
+  __pyx_v_size = __pyx_t_1;
+
+  /* "View.MemoryView":1135
+ *     cdef Py_ssize_t size = src.memview.view.itemsize
+ * 
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         size *= src.shape[i]
+ * 
+ */
+  __pyx_t_2 = __pyx_v_ndim;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+    __pyx_v_i = __pyx_t_3;
+
+    /* "View.MemoryView":1136
+ * 
+ *     for i in range(ndim):
+ *         size *= src.shape[i]             # <<<<<<<<<<<<<<
+ * 
+ *     return size
+ */
+    __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i]));
+  }
+
+  /* "View.MemoryView":1138
+ *         size *= src.shape[i]
+ * 
+ *     return size             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_fill_contig_strides_array')
+ */
+  __pyx_r = __pyx_v_size;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1130
+ * 
+ * @cname('__pyx_memoryview_slice_get_size')
+ * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
+ *     "Return the size of the memory occupied by the slice in number of bytes"
+ *     cdef int i
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1141
+ * 
+ * @cname('__pyx_fill_contig_strides_array')
+ * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
+ *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
+ *                 int ndim, char order) nogil:
+ */
+
+static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
+  int __pyx_v_idx;
+  Py_ssize_t __pyx_r;
+  int __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+
+  /* "View.MemoryView":1150
+ *     cdef int idx
+ * 
+ *     if order == 'F':             # <<<<<<<<<<<<<<
+ *         for idx in range(ndim):
+ *             strides[idx] = stride
+ */
+  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":1151
+ * 
+ *     if order == 'F':
+ *         for idx in range(ndim):             # <<<<<<<<<<<<<<
+ *             strides[idx] = stride
+ *             stride = stride * shape[idx]
+ */
+    __pyx_t_2 = __pyx_v_ndim;
+    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+      __pyx_v_idx = __pyx_t_3;
+
+      /* "View.MemoryView":1152
+ *     if order == 'F':
+ *         for idx in range(ndim):
+ *             strides[idx] = stride             # <<<<<<<<<<<<<<
+ *             stride = stride * shape[idx]
+ *     else:
+ */
+      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
+
+      /* "View.MemoryView":1153
+ *         for idx in range(ndim):
+ *             strides[idx] = stride
+ *             stride = stride * shape[idx]             # <<<<<<<<<<<<<<
+ *     else:
+ *         for idx in range(ndim - 1, -1, -1):
+ */
+      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
+    }
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1155
+ *             stride = stride * shape[idx]
+ *     else:
+ *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
+ *             strides[idx] = stride
+ *             stride = stride * shape[idx]
+ */
+    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
+      __pyx_v_idx = __pyx_t_2;
+
+      /* "View.MemoryView":1156
+ *     else:
+ *         for idx in range(ndim - 1, -1, -1):
+ *             strides[idx] = stride             # <<<<<<<<<<<<<<
+ *             stride = stride * shape[idx]
+ * 
+ */
+      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
+
+      /* "View.MemoryView":1157
+ *         for idx in range(ndim - 1, -1, -1):
+ *             strides[idx] = stride
+ *             stride = stride * shape[idx]             # <<<<<<<<<<<<<<
+ * 
+ *     return stride
+ */
+      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
+    }
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1159
+ *             stride = stride * shape[idx]
+ * 
+ *     return stride             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_copy_data_to_temp')
+ */
+  __pyx_r = __pyx_v_stride;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1141
+ * 
+ * @cname('__pyx_fill_contig_strides_array')
+ * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
+ *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
+ *                 int ndim, char order) nogil:
+ */
+
+  /* function exit code */
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1162
+ * 
+ * @cname('__pyx_memoryview_copy_data_to_temp')
+ * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
+ *                              __Pyx_memviewslice *tmpslice,
+ *                              char order,
+ */
+
+static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
+  int __pyx_v_i;
+  void *__pyx_v_result;
+  size_t __pyx_v_itemsize;
+  size_t __pyx_v_size;
+  void *__pyx_r;
+  Py_ssize_t __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  struct __pyx_memoryview_obj *__pyx_t_4;
+  int __pyx_t_5;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+
+  /* "View.MemoryView":1173
+ *     cdef void *result
+ * 
+ *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
+ *     cdef size_t size = slice_get_size(src, ndim)
+ * 
+ */
+  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
+  __pyx_v_itemsize = __pyx_t_1;
+
+  /* "View.MemoryView":1174
+ * 
+ *     cdef size_t itemsize = src.memview.view.itemsize
+ *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<
+ * 
+ *     result = malloc(size)
+ */
+  __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);
+
+  /* "View.MemoryView":1176
+ *     cdef size_t size = slice_get_size(src, ndim)
+ * 
+ *     result = malloc(size)             # <<<<<<<<<<<<<<
+ *     if not result:
+ *         _err(MemoryError, NULL)
+ */
+  __pyx_v_result = malloc(__pyx_v_size);
+
+  /* "View.MemoryView":1177
+ * 
+ *     result = malloc(size)
+ *     if not result:             # <<<<<<<<<<<<<<
+ *         _err(MemoryError, NULL)
+ * 
+ */
+  __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1178
+ *     result = malloc(size)
+ *     if not result:
+ *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1178; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1181
+ * 
+ * 
+ *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<
+ *     tmpslice.memview = src.memview
+ *     for i in range(ndim):
+ */
+  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);
+
+  /* "View.MemoryView":1182
+ * 
+ *     tmpslice.data = <char *> result
+ *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<
+ *     for i in range(ndim):
+ *         tmpslice.shape[i] = src.shape[i]
+ */
+  __pyx_t_4 = __pyx_v_src->memview;
+  __pyx_v_tmpslice->memview = __pyx_t_4;
+
+  /* "View.MemoryView":1183
+ *     tmpslice.data = <char *> result
+ *     tmpslice.memview = src.memview
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         tmpslice.shape[i] = src.shape[i]
+ *         tmpslice.suboffsets[i] = -1
+ */
+  __pyx_t_3 = __pyx_v_ndim;
+  for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) {
+    __pyx_v_i = __pyx_t_5;
+
+    /* "View.MemoryView":1184
+ *     tmpslice.memview = src.memview
+ *     for i in range(ndim):
+ *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<
+ *         tmpslice.suboffsets[i] = -1
+ * 
+ */
+    (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);
+
+    /* "View.MemoryView":1185
+ *     for i in range(ndim):
+ *         tmpslice.shape[i] = src.shape[i]
+ *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
+ * 
+ *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,
+ */
+    (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1;
+  }
+
+  /* "View.MemoryView":1187
+ *         tmpslice.suboffsets[i] = -1
+ * 
+ *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,             # <<<<<<<<<<<<<<
+ *                               ndim, order)
+ * 
+ */
+  __pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order);
+
+  /* "View.MemoryView":1191
+ * 
+ * 
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         if tmpslice.shape[i] == 1:
+ *             tmpslice.strides[i] = 0
+ */
+  __pyx_t_3 = __pyx_v_ndim;
+  for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) {
+    __pyx_v_i = __pyx_t_5;
+
+    /* "View.MemoryView":1192
+ * 
+ *     for i in range(ndim):
+ *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
+ *             tmpslice.strides[i] = 0
+ * 
+ */
+    __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1193
+ *     for i in range(ndim):
+ *         if tmpslice.shape[i] == 1:
+ *             tmpslice.strides[i] = 0             # <<<<<<<<<<<<<<
+ * 
+ *     if slice_is_contig(src, order, ndim):
+ */
+      (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0;
+      goto __pyx_L8;
+    }
+    __pyx_L8:;
+  }
+
+  /* "View.MemoryView":1195
+ *             tmpslice.strides[i] = 0
+ * 
+ *     if slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
+ *         memcpy(result, src.data, size)
+ *     else:
+ */
+  __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1196
+ * 
+ *     if slice_is_contig(src, order, ndim):
+ *         memcpy(result, src.data, size)             # <<<<<<<<<<<<<<
+ *     else:
+ *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
+ */
+    memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size);
+    goto __pyx_L9;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1198
+ *         memcpy(result, src.data, size)
+ *     else:
+ *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)             # <<<<<<<<<<<<<<
+ * 
+ *     return result
+ */
+    copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize);
+  }
+  __pyx_L9:;
+
+  /* "View.MemoryView":1200
+ *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
+ * 
+ *     return result             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_r = __pyx_v_result;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1162
+ * 
+ * @cname('__pyx_memoryview_copy_data_to_temp')
+ * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
+ *                              __Pyx_memviewslice *tmpslice,
+ *                              char order,
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  {
+    #ifdef WITH_THREAD
+    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+    #endif
+    __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, __pyx_filename);
+    #ifdef WITH_THREAD
+    PyGILState_Release(__pyx_gilstate_save);
+    #endif
+  }
+  __pyx_r = NULL;
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1205
+ * 
+ * @cname('__pyx_memoryview_err_extents')
+ * cdef int _err_extents(int i, Py_ssize_t extent1,             # <<<<<<<<<<<<<<
+ *                              Py_ssize_t extent2) except -1 with gil:
+ *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %
+ */
+
+static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent1, Py_ssize_t __pyx_v_extent2) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  #ifdef WITH_THREAD
+  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+  #endif
+  __Pyx_RefNannySetupContext("_err_extents", 0);
+
+  /* "View.MemoryView":1208
+ *                              Py_ssize_t extent2) except -1 with gil:
+ *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %
+ *                                                         (i, extent1, extent2))             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_err_dim')
+ */
+  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  PyTuple_SET_ITEM(__pyx_t_4, 2, __pyx_t_3);
+  __Pyx_GIVEREF(__pyx_t_3);
+  __pyx_t_1 = 0;
+  __pyx_t_2 = 0;
+  __pyx_t_3 = 0;
+
+  /* "View.MemoryView":1207
+ * cdef int _err_extents(int i, Py_ssize_t extent1,
+ *                              Py_ssize_t extent2) except -1 with gil:
+ *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %             # <<<<<<<<<<<<<<
+ *                                                         (i, extent1, extent2))
+ * 
+ */
+  __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1207; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1207; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
+  __Pyx_GIVEREF(__pyx_t_3);
+  __pyx_t_3 = 0;
+  __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1207; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1207; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+  /* "View.MemoryView":1205
+ * 
+ * @cname('__pyx_memoryview_err_extents')
+ * cdef int _err_extents(int i, Py_ssize_t extent1,             # <<<<<<<<<<<<<<
+ *                              Py_ssize_t extent2) except -1 with gil:
+ *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_4);
+  __Pyx_AddTraceback("View.MemoryView._err_extents", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  __Pyx_RefNannyFinishContext();
+  #ifdef WITH_THREAD
+  PyGILState_Release(__pyx_gilstate_save);
+  #endif
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1211
+ * 
+ * @cname('__pyx_memoryview_err_dim')
+ * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil:             # <<<<<<<<<<<<<<
+ *     raise error(msg.decode('ascii') % dim)
+ * 
+ */
+
+static int __pyx_memoryview_err_dim(PyObject *__pyx_v_error, char *__pyx_v_msg, int __pyx_v_dim) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  #ifdef WITH_THREAD
+  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+  #endif
+  __Pyx_RefNannySetupContext("_err_dim", 0);
+  __Pyx_INCREF(__pyx_v_error);
+
+  /* "View.MemoryView":1212
+ * @cname('__pyx_memoryview_err_dim')
+ * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil:
+ *     raise error(msg.decode('ascii') % dim)             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_err')
+ */
+  __pyx_t_1 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_dim); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = PyUnicode_Format(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_3);
+  __Pyx_GIVEREF(__pyx_t_3);
+  __pyx_t_3 = 0;
+  __pyx_t_3 = __Pyx_PyObject_Call(__pyx_v_error, __pyx_t_2, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __Pyx_Raise(__pyx_t_3, 0, 0, 0);
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+  /* "View.MemoryView":1211
+ * 
+ * @cname('__pyx_memoryview_err_dim')
+ * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil:             # <<<<<<<<<<<<<<
+ *     raise error(msg.decode('ascii') % dim)
+ * 
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView._err_dim", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  __Pyx_XDECREF(__pyx_v_error);
+  __Pyx_RefNannyFinishContext();
+  #ifdef WITH_THREAD
+  PyGILState_Release(__pyx_gilstate_save);
+  #endif
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1215
+ * 
+ * @cname('__pyx_memoryview_err')
+ * cdef int _err(object error, char *msg) except -1 with gil:             # <<<<<<<<<<<<<<
+ *     if msg != NULL:
+ *         raise error(msg.decode('ascii'))
+ */
+
+static int __pyx_memoryview_err(PyObject *__pyx_v_error, char *__pyx_v_msg) {
+  int __pyx_r;
+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  #ifdef WITH_THREAD
+  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+  #endif
+  __Pyx_RefNannySetupContext("_err", 0);
+  __Pyx_INCREF(__pyx_v_error);
+
+  /* "View.MemoryView":1216
+ * @cname('__pyx_memoryview_err')
+ * cdef int _err(object error, char *msg) except -1 with gil:
+ *     if msg != NULL:             # <<<<<<<<<<<<<<
+ *         raise error(msg.decode('ascii'))
+ *     else:
+ */
+  __pyx_t_1 = ((__pyx_v_msg != NULL) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":1217
+ * cdef int _err(object error, char *msg) except -1 with gil:
+ *     if msg != NULL:
+ *         raise error(msg.decode('ascii'))             # <<<<<<<<<<<<<<
+ *     else:
+ *         raise error
+ */
+    __pyx_t_2 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2);
+    __Pyx_GIVEREF(__pyx_t_2);
+    __pyx_t_2 = 0;
+    __pyx_t_2 = __Pyx_PyObject_Call(__pyx_v_error, __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __Pyx_Raise(__pyx_t_2, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1219
+ *         raise error(msg.decode('ascii'))
+ *     else:
+ *         raise error             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_copy_contents')
+ */
+    __Pyx_Raise(__pyx_v_error, 0, 0, 0);
+    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  }
+
+  /* "View.MemoryView":1215
+ * 
+ * @cname('__pyx_memoryview_err')
+ * cdef int _err(object error, char *msg) except -1 with gil:             # <<<<<<<<<<<<<<
+ *     if msg != NULL:
+ *         raise error(msg.decode('ascii'))
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_AddTraceback("View.MemoryView._err", __pyx_clineno, __pyx_lineno, __pyx_filename);
+  __pyx_r = -1;
+  __Pyx_XDECREF(__pyx_v_error);
+  __Pyx_RefNannyFinishContext();
+  #ifdef WITH_THREAD
+  PyGILState_Release(__pyx_gilstate_save);
+  #endif
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1222
+ * 
+ * @cname('__pyx_memoryview_copy_contents')
+ * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
+ *                                   __Pyx_memviewslice dst,
+ *                                   int src_ndim, int dst_ndim,
+ */
+
+static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) {
+  void *__pyx_v_tmpdata;
+  size_t __pyx_v_itemsize;
+  int __pyx_v_i;
+  char __pyx_v_order;
+  int __pyx_v_broadcasting;
+  int __pyx_v_direct_copy;
+  __Pyx_memviewslice __pyx_v_tmp;
+  int __pyx_v_ndim;
+  int __pyx_r;
+  Py_ssize_t __pyx_t_1;
+  int __pyx_t_2;
+  int __pyx_t_3;
+  int __pyx_t_4;
+  int __pyx_t_5;
+  void *__pyx_t_6;
+  int __pyx_t_7;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+
+  /* "View.MemoryView":1230
+ *     Check for overlapping memory and verify the shapes.
+ *     """
+ *     cdef void *tmpdata = NULL             # <<<<<<<<<<<<<<
+ *     cdef size_t itemsize = src.memview.view.itemsize
+ *     cdef int i
+ */
+  __pyx_v_tmpdata = NULL;
+
+  /* "View.MemoryView":1231
+ *     """
+ *     cdef void *tmpdata = NULL
+ *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
+ *     cdef int i
+ *     cdef char order = get_best_order(&src, src_ndim)
+ */
+  __pyx_t_1 = __pyx_v_src.memview->view.itemsize;
+  __pyx_v_itemsize = __pyx_t_1;
+
+  /* "View.MemoryView":1233
+ *     cdef size_t itemsize = src.memview.view.itemsize
+ *     cdef int i
+ *     cdef char order = get_best_order(&src, src_ndim)             # <<<<<<<<<<<<<<
+ *     cdef bint broadcasting = False
+ *     cdef bint direct_copy = False
+ */
+  __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim);
+
+  /* "View.MemoryView":1234
+ *     cdef int i
+ *     cdef char order = get_best_order(&src, src_ndim)
+ *     cdef bint broadcasting = False             # <<<<<<<<<<<<<<
+ *     cdef bint direct_copy = False
+ *     cdef __Pyx_memviewslice tmp
+ */
+  __pyx_v_broadcasting = 0;
+
+  /* "View.MemoryView":1235
+ *     cdef char order = get_best_order(&src, src_ndim)
+ *     cdef bint broadcasting = False
+ *     cdef bint direct_copy = False             # <<<<<<<<<<<<<<
+ *     cdef __Pyx_memviewslice tmp
+ * 
+ */
+  __pyx_v_direct_copy = 0;
+
+  /* "View.MemoryView":1238
+ *     cdef __Pyx_memviewslice tmp
+ * 
+ *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
+ *         broadcast_leading(&src, src_ndim, dst_ndim)
+ *     elif dst_ndim < src_ndim:
+ */
+  __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1239
+ * 
+ *     if src_ndim < dst_ndim:
+ *         broadcast_leading(&src, src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
+ *     elif dst_ndim < src_ndim:
+ *         broadcast_leading(&dst, dst_ndim, src_ndim)
+ */
+    __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim);
+    goto __pyx_L3;
+  }
+
+  /* "View.MemoryView":1240
+ *     if src_ndim < dst_ndim:
+ *         broadcast_leading(&src, src_ndim, dst_ndim)
+ *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
+ *         broadcast_leading(&dst, dst_ndim, src_ndim)
+ * 
+ */
+  __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1241
+ *         broadcast_leading(&src, src_ndim, dst_ndim)
+ *     elif dst_ndim < src_ndim:
+ *         broadcast_leading(&dst, dst_ndim, src_ndim)             # <<<<<<<<<<<<<<
+ * 
+ *     cdef int ndim = max(src_ndim, dst_ndim)
+ */
+    __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim);
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1243
+ *         broadcast_leading(&dst, dst_ndim, src_ndim)
+ * 
+ *     cdef int ndim = max(src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(ndim):
+ */
+  __pyx_t_3 = __pyx_v_dst_ndim;
+  __pyx_t_4 = __pyx_v_src_ndim;
+  if (((__pyx_t_3 > __pyx_t_4) != 0)) {
+    __pyx_t_5 = __pyx_t_3;
+  } else {
+    __pyx_t_5 = __pyx_t_4;
+  }
+  __pyx_v_ndim = __pyx_t_5;
+
+  /* "View.MemoryView":1245
+ *     cdef int ndim = max(src_ndim, dst_ndim)
+ * 
+ *     for i in range(ndim):             # <<<<<<<<<<<<<<
+ *         if src.shape[i] != dst.shape[i]:
+ *             if src.shape[i] == 1:
+ */
+  __pyx_t_5 = __pyx_v_ndim;
+  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_5; __pyx_t_3+=1) {
+    __pyx_v_i = __pyx_t_3;
+
+    /* "View.MemoryView":1246
+ * 
+ *     for i in range(ndim):
+ *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
+ *             if src.shape[i] == 1:
+ *                 broadcasting = True
+ */
+    __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1247
+ *     for i in range(ndim):
+ *         if src.shape[i] != dst.shape[i]:
+ *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
+ *                 broadcasting = True
+ *                 src.strides[i] = 0
+ */
+      __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0);
+      if (__pyx_t_2) {
+
+        /* "View.MemoryView":1248
+ *         if src.shape[i] != dst.shape[i]:
+ *             if src.shape[i] == 1:
+ *                 broadcasting = True             # <<<<<<<<<<<<<<
+ *                 src.strides[i] = 0
+ *             else:
+ */
+        __pyx_v_broadcasting = 1;
+
+        /* "View.MemoryView":1249
+ *             if src.shape[i] == 1:
+ *                 broadcasting = True
+ *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
+ *             else:
+ *                 _err_extents(i, dst.shape[i], src.shape[i])
+ */
+        (__pyx_v_src.strides[__pyx_v_i]) = 0;
+        goto __pyx_L7;
+      }
+      /*else*/ {
+
+        /* "View.MemoryView":1251
+ *                 src.strides[i] = 0
+ *             else:
+ *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
+ * 
+ *         if src.suboffsets[i] >= 0:
+ */
+        __pyx_t_4 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1251; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      }
+      __pyx_L7:;
+      goto __pyx_L6;
+    }
+    __pyx_L6:;
+
+    /* "View.MemoryView":1253
+ *                 _err_extents(i, dst.shape[i], src.shape[i])
+ * 
+ *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
+ *             _err_dim(ValueError, "Dimension %d is not direct", i)
+ * 
+ */
+    __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1254
+ * 
+ *         if src.suboffsets[i] >= 0:
+ *             _err_dim(ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
+ * 
+ *     if slices_overlap(&src, &dst, ndim, itemsize):
+ */
+      __pyx_t_4 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, __pyx_k_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1254; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L8;
+    }
+    __pyx_L8:;
+  }
+
+  /* "View.MemoryView":1256
+ *             _err_dim(ValueError, "Dimension %d is not direct", i)
+ * 
+ *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
+ * 
+ *         if not slice_is_contig(&src, order, ndim):
+ */
+  __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1258
+ *     if slices_overlap(&src, &dst, ndim, itemsize):
+ * 
+ *         if not slice_is_contig(&src, order, ndim):             # <<<<<<<<<<<<<<
+ *             order = get_best_order(&dst, ndim)
+ * 
+ */
+    __pyx_t_2 = ((!(__pyx_memviewslice_is_contig((&__pyx_v_src), __pyx_v_order, __pyx_v_ndim) != 0)) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1259
+ * 
+ *         if not slice_is_contig(&src, order, ndim):
+ *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
+ * 
+ *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
+ */
+      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
+      goto __pyx_L10;
+    }
+    __pyx_L10:;
+
+    /* "View.MemoryView":1261
+ *             order = get_best_order(&dst, ndim)
+ * 
+ *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
+ *         src = tmp
+ * 
+ */
+    __pyx_t_6 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_6 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1261; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_v_tmpdata = __pyx_t_6;
+
+    /* "View.MemoryView":1262
+ * 
+ *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
+ *         src = tmp             # <<<<<<<<<<<<<<
+ * 
+ *     if not broadcasting:
+ */
+    __pyx_v_src = __pyx_v_tmp;
+    goto __pyx_L9;
+  }
+  __pyx_L9:;
+
+  /* "View.MemoryView":1264
+ *         src = tmp
+ * 
+ *     if not broadcasting:             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
+  if (__pyx_t_2) {
+
+    /* "View.MemoryView":1267
+ * 
+ * 
+ *         if slice_is_contig(&src, 'C', ndim):             # <<<<<<<<<<<<<<
+ *             direct_copy = slice_is_contig(&dst, 'C', ndim)
+ *         elif slice_is_contig(&src, 'F', ndim):
+ */
+    __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'C', __pyx_v_ndim) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1268
+ * 
+ *         if slice_is_contig(&src, 'C', ndim):
+ *             direct_copy = slice_is_contig(&dst, 'C', ndim)             # <<<<<<<<<<<<<<
+ *         elif slice_is_contig(&src, 'F', ndim):
+ *             direct_copy = slice_is_contig(&dst, 'F', ndim)
+ */
+      __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'C', __pyx_v_ndim);
+      goto __pyx_L12;
+    }
+
+    /* "View.MemoryView":1269
+ *         if slice_is_contig(&src, 'C', ndim):
+ *             direct_copy = slice_is_contig(&dst, 'C', ndim)
+ *         elif slice_is_contig(&src, 'F', ndim):             # <<<<<<<<<<<<<<
+ *             direct_copy = slice_is_contig(&dst, 'F', ndim)
+ * 
+ */
+    __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'F', __pyx_v_ndim) != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1270
+ *             direct_copy = slice_is_contig(&dst, 'C', ndim)
+ *         elif slice_is_contig(&src, 'F', ndim):
+ *             direct_copy = slice_is_contig(&dst, 'F', ndim)             # <<<<<<<<<<<<<<
+ * 
+ *         if direct_copy:
+ */
+      __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'F', __pyx_v_ndim);
+      goto __pyx_L12;
+    }
+    __pyx_L12:;
+
+    /* "View.MemoryView":1272
+ *             direct_copy = slice_is_contig(&dst, 'F', ndim)
+ * 
+ *         if direct_copy:             # <<<<<<<<<<<<<<
+ * 
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)
+ */
+    __pyx_t_2 = (__pyx_v_direct_copy != 0);
+    if (__pyx_t_2) {
+
+      /* "View.MemoryView":1274
+ *         if direct_copy:
+ * 
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
+ *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)
+ */
+      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
+
+      /* "View.MemoryView":1275
+ * 
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)
+ *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)
+ *             free(tmpdata)
+ */
+      memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim));
+
+      /* "View.MemoryView":1276
+ *             refcount_copying(&dst, dtype_is_object, ndim, False)
+ *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
+ *             free(tmpdata)
+ *             return 0
+ */
+      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
+
+      /* "View.MemoryView":1277
+ *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)
+ *             free(tmpdata)             # <<<<<<<<<<<<<<
+ *             return 0
+ * 
+ */
+      free(__pyx_v_tmpdata);
+
+      /* "View.MemoryView":1278
+ *             refcount_copying(&dst, dtype_is_object, ndim, True)
+ *             free(tmpdata)
+ *             return 0             # <<<<<<<<<<<<<<
+ * 
+ *     if order == 'F' == get_best_order(&dst, ndim):
+ */
+      __pyx_r = 0;
+      goto __pyx_L0;
+    }
+    goto __pyx_L11;
+  }
+  __pyx_L11:;
+
+  /* "View.MemoryView":1280
+ *             return 0
+ * 
+ *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_2 = (__pyx_v_order == 'F');
+  if (__pyx_t_2) {
+    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
+  }
+  __pyx_t_7 = (__pyx_t_2 != 0);
+  if (__pyx_t_7) {
+
+    /* "View.MemoryView":1283
+ * 
+ * 
+ *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
+ *         transpose_memslice(&dst)
+ * 
+ */
+    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+    /* "View.MemoryView":1284
+ * 
+ *         transpose_memslice(&src)
+ *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
+ * 
+ *     refcount_copying(&dst, dtype_is_object, ndim, False)
+ */
+    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1284; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L14;
+  }
+  __pyx_L14:;
+
+  /* "View.MemoryView":1286
+ *         transpose_memslice(&dst)
+ * 
+ *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
+ *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
+ *     refcount_copying(&dst, dtype_is_object, ndim, True)
+ */
+  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
+
+  /* "View.MemoryView":1287
+ * 
+ *     refcount_copying(&dst, dtype_is_object, ndim, False)
+ *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
+ *     refcount_copying(&dst, dtype_is_object, ndim, True)
+ * 
+ */
+  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
+
+  /* "View.MemoryView":1288
+ *     refcount_copying(&dst, dtype_is_object, ndim, False)
+ *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
+ *     refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
+ * 
+ *     free(tmpdata)
+ */
+  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
+
+  /* "View.MemoryView":1290
+ *     refcount_copying(&dst, dtype_is_object, ndim, True)
+ * 
+ *     free(tmpdata)             # <<<<<<<<<<<<<<
+ *     return 0
+ * 
+ */
+  free(__pyx_v_tmpdata);
+
+  /* "View.MemoryView":1291
+ * 
+ *     free(tmpdata)
+ *     return 0             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_broadcast_leading')
+ */
+  __pyx_r = 0;
+  goto __pyx_L0;
+
+  /* "View.MemoryView":1222
+ * 
+ * @cname('__pyx_memoryview_copy_contents')
+ * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
+ *                                   __Pyx_memviewslice dst,
+ *                                   int src_ndim, int dst_ndim,
+ */
+
+  /* function exit code */
+  __pyx_L1_error:;
+  {
+    #ifdef WITH_THREAD
+    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+    #endif
+    __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
+    #ifdef WITH_THREAD
+    PyGILState_Release(__pyx_gilstate_save);
+    #endif
+  }
+  __pyx_r = -1;
+  __pyx_L0:;
+  return __pyx_r;
+}
+
+/* "View.MemoryView":1294
+ * 
+ * @cname('__pyx_memoryview_broadcast_leading')
+ * cdef void broadcast_leading(__Pyx_memviewslice *slice,             # <<<<<<<<<<<<<<
+ *                             int ndim,
+ *                             int ndim_other) nogil:
+ */
+
+static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_slice, int __pyx_v_ndim, int __pyx_v_ndim_other) {
+  int __pyx_v_i;
+  int __pyx_v_offset;
+  int __pyx_t_1;
+  int __pyx_t_2;
+
+  /* "View.MemoryView":1298
+ *                             int ndim_other) nogil:
+ *     cdef int i
+ *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(ndim - 1, -1, -1):
+ */
+  __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);
+
+  /* "View.MemoryView":1300
+ *     cdef int offset = ndim_other - ndim
+ * 
+ *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
+ *         slice.shape[i + offset] = slice.shape[i]
+ *         slice.strides[i + offset] = slice.strides[i]
+ */
+  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
+    __pyx_v_i = __pyx_t_1;
+
+    /* "View.MemoryView":1301
+ * 
+ *     for i in range(ndim - 1, -1, -1):
+ *         slice.shape[i + offset] = slice.shape[i]             # <<<<<<<<<<<<<<
+ *         slice.strides[i + offset] = slice.strides[i]
+ *         slice.suboffsets[i + offset] = slice.suboffsets[i]
+ */
+    (__pyx_v_slice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->shape[__pyx_v_i]);
+
+    /* "View.MemoryView":1302
+ *     for i in range(ndim - 1, -1, -1):
+ *         slice.shape[i + offset] = slice.shape[i]
+ *         slice.strides[i + offset] = slice.strides[i]             # <<<<<<<<<<<<<<
+ *         slice.suboffsets[i + offset] = slice.suboffsets[i]
+ * 
+ */
+    (__pyx_v_slice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->strides[__pyx_v_i]);
+
+    /* "View.MemoryView":1303
+ *         slice.shape[i + offset] = slice.shape[i]
+ *         slice.strides[i + offset] = slice.strides[i]
+ *         slice.suboffsets[i + offset] = slice.suboffsets[i]             # <<<<<<<<<<<<<<
+ * 
+ *     for i in range(offset):
+ */
+    (__pyx_v_slice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->suboffsets[__pyx_v_i]);
+  }
+
+  /* "View.MemoryView":1305
+ *         slice.suboffsets[i + offset] = slice.suboffsets[i]
+ * 
+ *     for i in range(offset):             # <<<<<<<<<<<<<<
+ *         slice.shape[i] = 1
+ *         slice.strides[i] = slice.strides[0]
+ */
+  __pyx_t_1 = __pyx_v_offset;
+  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
+    __pyx_v_i = __pyx_t_2;
+
+    /* "View.MemoryView":1306
+ * 
+ *     for i in range(offset):
+ *         slice.shape[i] = 1             # <<<<<<<<<<<<<<
+ *         slice.strides[i] = slice.strides[0]
+ *         slice.suboffsets[i] = -1
+ */
+    (__pyx_v_slice->shape[__pyx_v_i]) = 1;
+
+    /* "View.MemoryView":1307
+ *     for i in range(offset):
+ *         slice.shape[i] = 1
+ *         slice.strides[i] = slice.strides[0]             # <<<<<<<<<<<<<<
+ *         slice.suboffsets[i] = -1
+ * 
+ */
+    (__pyx_v_slice->strides[__pyx_v_i]) = (__pyx_v_slice->strides[0]);
+
+    /* "View.MemoryView":1308
+ *         slice.shape[i] = 1
+ *         slice.strides[i] = slice.strides[0]
+ *         slice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    (__pyx_v_slice->suboffsets[__pyx_v_i]) = -1;
+  }
+
+  /* "View.MemoryView":1294
+ * 
+ * @cname('__pyx_memoryview_broadcast_leading')
+ * cdef void broadcast_leading(__Pyx_memviewslice *slice,             # <<<<<<<<<<<<<<
+ *                             int ndim,
+ *                             int ndim_other) nogil:
+ */
+
+  /* function exit code */
+}
+
+/* "View.MemoryView":1316
+ * 
+ * @cname('__pyx_memoryview_refcount_copying')
+ * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
+ *                            int ndim, bint inc) nogil:
+ * 
+ */
+
+static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) {
+  int __pyx_t_1;
+
+  /* "View.MemoryView":1320
+ * 
+ * 
+ *     if dtype_is_object:             # <<<<<<<<<<<<<<
+ *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,
+ *                                            dst.strides, ndim, inc)
+ */
+  __pyx_t_1 = (__pyx_v_dtype_is_object != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":1321
+ * 
+ *     if dtype_is_object:
+ *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,             # <<<<<<<<<<<<<<
+ *                                            dst.strides, ndim, inc)
+ * 
+ */
+    __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc);
+    goto __pyx_L3;
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1316
+ * 
+ * @cname('__pyx_memoryview_refcount_copying')
+ * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
+ *                            int ndim, bint inc) nogil:
+ * 
+ */
+
+  /* function exit code */
+}
+
+/* "View.MemoryView":1325
+ * 
+ * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
+ * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
+ *                                              Py_ssize_t *strides, int ndim,
+ *                                              bint inc) with gil:
+ */
+
+static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {
+  __Pyx_RefNannyDeclarations
+  #ifdef WITH_THREAD
+  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
+  #endif
+  __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0);
+
+  /* "View.MemoryView":1328
+ *                                              Py_ssize_t *strides, int ndim,
+ *                                              bint inc) with gil:
+ *     refcount_objects_in_slice(data, shape, strides, ndim, inc)             # <<<<<<<<<<<<<<
+ * 
+ * @cname('__pyx_memoryview_refcount_objects_in_slice')
+ */
+  __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc);
+
+  /* "View.MemoryView":1325
+ * 
+ * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
+ * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
+ *                                              Py_ssize_t *strides, int ndim,
+ *                                              bint inc) with gil:
+ */
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+  #ifdef WITH_THREAD
+  PyGILState_Release(__pyx_gilstate_save);
+  #endif
+}
+
+/* "View.MemoryView":1331
+ * 
+ * @cname('__pyx_memoryview_refcount_objects_in_slice')
+ * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
+ *                                     Py_ssize_t *strides, int ndim, bint inc):
+ *     cdef Py_ssize_t i
+ */
+
+static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {
+  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
+  __Pyx_RefNannyDeclarations
+  Py_ssize_t __pyx_t_1;
+  Py_ssize_t __pyx_t_2;
+  int __pyx_t_3;
+  __Pyx_RefNannySetupContext("refcount_objects_in_slice", 0);
+
+  /* "View.MemoryView":1335
+ *     cdef Py_ssize_t i
+ * 
+ *     for i in range(shape[0]):             # <<<<<<<<<<<<<<
+ *         if ndim == 1:
+ *             if inc:
+ */
+  __pyx_t_1 = (__pyx_v_shape[0]);
+  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
+    __pyx_v_i = __pyx_t_2;
+
+    /* "View.MemoryView":1336
+ * 
+ *     for i in range(shape[0]):
+ *         if ndim == 1:             # <<<<<<<<<<<<<<
+ *             if inc:
+ *                 Py_INCREF((<PyObject **> data)[0])
+ */
+    __pyx_t_3 = ((__pyx_v_ndim == 1) != 0);
+    if (__pyx_t_3) {
+
+      /* "View.MemoryView":1337
+ *     for i in range(shape[0]):
+ *         if ndim == 1:
+ *             if inc:             # <<<<<<<<<<<<<<
+ *                 Py_INCREF((<PyObject **> data)[0])
+ *             else:
+ */
+      __pyx_t_3 = (__pyx_v_inc != 0);
+      if (__pyx_t_3) {
+
+        /* "View.MemoryView":1338
+ *         if ndim == 1:
+ *             if inc:
+ *                 Py_INCREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<
+ *             else:
+ *                 Py_DECREF((<PyObject **> data)[0])
+ */
+        Py_INCREF((((PyObject **)__pyx_v_data)[0]));
+        goto __pyx_L6;
+      }
+      /*else*/ {
+
+        /* "View.MemoryView":1340
+ *                 Py_INCREF((<PyObject **> data)[0])
+ *             else:
+ *                 Py_DECREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<
+ *         else:
+ *             refcount_objects_in_slice(data, shape + 1, strides + 1,
+ */
+        Py_DECREF((((PyObject **)__pyx_v_data)[0]));
+      }
+      __pyx_L6:;
+      goto __pyx_L5;
+    }
+    /*else*/ {
+
+      /* "View.MemoryView":1342
+ *                 Py_DECREF((<PyObject **> data)[0])
+ *         else:
+ *             refcount_objects_in_slice(data, shape + 1, strides + 1,             # <<<<<<<<<<<<<<
+ *                                       ndim - 1, inc)
+ * 
+ */
+      __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc);
+    }
+    __pyx_L5:;
+
+    /* "View.MemoryView":1345
+ *                                       ndim - 1, inc)
+ * 
+ *         data += strides[0]             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+    __pyx_v_data = (__pyx_v_data + (__pyx_v_strides[0]));
+  }
+
+  /* "View.MemoryView":1331
+ * 
+ * @cname('__pyx_memoryview_refcount_objects_in_slice')
+ * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
+ *                                     Py_ssize_t *strides, int ndim, bint inc):
+ *     cdef Py_ssize_t i
+ */
+
+  /* function exit code */
+  __Pyx_RefNannyFinishContext();
+}
+
+/* "View.MemoryView":1351
+ * 
+ * @cname('__pyx_memoryview_slice_assign_scalar')
+ * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim,             # <<<<<<<<<<<<<<
+ *                               size_t itemsize, void *item,
+ *                               bint dtype_is_object) nogil:
+ */
+
+static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) {
+
+  /* "View.MemoryView":1354
+ *                               size_t itemsize, void *item,
+ *                               bint dtype_is_object) nogil:
+ *     refcount_copying(dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
+ *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,
+ *                          itemsize, item)
+ */
+  __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
+
+  /* "View.MemoryView":1355
+ *                               bint dtype_is_object) nogil:
+ *     refcount_copying(dst, dtype_is_object, ndim, False)
+ *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,             # <<<<<<<<<<<<<<
+ *                          itemsize, item)
+ *     refcount_copying(dst, dtype_is_object, ndim, True)
+ */
+  __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item);
+
+  /* "View.MemoryView":1357
+ *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,
+ *                          itemsize, item)
+ *     refcount_copying(dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
+
+  /* "View.MemoryView":1351
+ * 
+ * @cname('__pyx_memoryview_slice_assign_scalar')
+ * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim,             # <<<<<<<<<<<<<<
+ *                               size_t itemsize, void *item,
+ *                               bint dtype_is_object) nogil:
+ */
+
+  /* function exit code */
+}
+
+/* "View.MemoryView":1361
+ * 
+ * @cname('__pyx_memoryview__slice_assign_scalar')
+ * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
+ *                               Py_ssize_t *strides, int ndim,
+ *                               size_t itemsize, void *item) nogil:
+ */
+
+static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) {
+  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
+  Py_ssize_t __pyx_v_stride;
+  Py_ssize_t __pyx_v_extent;
+  int __pyx_t_1;
+  Py_ssize_t __pyx_t_2;
+  Py_ssize_t __pyx_t_3;
+
+  /* "View.MemoryView":1365
+ *                               size_t itemsize, void *item) nogil:
+ *     cdef Py_ssize_t i
+ *     cdef Py_ssize_t stride = strides[0]             # <<<<<<<<<<<<<<
+ *     cdef Py_ssize_t extent = shape[0]
+ * 
+ */
+  __pyx_v_stride = (__pyx_v_strides[0]);
+
+  /* "View.MemoryView":1366
+ *     cdef Py_ssize_t i
+ *     cdef Py_ssize_t stride = strides[0]
+ *     cdef Py_ssize_t extent = shape[0]             # <<<<<<<<<<<<<<
+ * 
+ *     if ndim == 1:
+ */
+  __pyx_v_extent = (__pyx_v_shape[0]);
+
+  /* "View.MemoryView":1368
+ *     cdef Py_ssize_t extent = shape[0]
+ * 
+ *     if ndim == 1:             # <<<<<<<<<<<<<<
+ *         for i in range(extent):
+ *             memcpy(data, item, itemsize)
+ */
+  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
+  if (__pyx_t_1) {
+
+    /* "View.MemoryView":1369
+ * 
+ *     if ndim == 1:
+ *         for i in range(extent):             # <<<<<<<<<<<<<<
+ *             memcpy(data, item, itemsize)
+ *             data += stride
+ */
+    __pyx_t_2 = __pyx_v_extent;
+    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+      __pyx_v_i = __pyx_t_3;
+
+      /* "View.MemoryView":1370
+ *     if ndim == 1:
+ *         for i in range(extent):
+ *             memcpy(data, item, itemsize)             # <<<<<<<<<<<<<<
+ *             data += stride
+ *     else:
+ */
+      memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize);
+
+      /* "View.MemoryView":1371
+ *         for i in range(extent):
+ *             memcpy(data, item, itemsize)
+ *             data += stride             # <<<<<<<<<<<<<<
+ *     else:
+ *         for i in range(extent):
+ */
+      __pyx_v_data = (__pyx_v_data + __pyx_v_stride);
+    }
+    goto __pyx_L3;
+  }
+  /*else*/ {
+
+    /* "View.MemoryView":1373
+ *             data += stride
+ *     else:
+ *         for i in range(extent):             # <<<<<<<<<<<<<<
+ *             _slice_assign_scalar(data, shape + 1, strides + 1,
+ *                                 ndim - 1, itemsize, item)
+ */
+    __pyx_t_2 = __pyx_v_extent;
+    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
+      __pyx_v_i = __pyx_t_3;
+
+      /* "View.MemoryView":1374
+ *     else:
+ *         for i in range(extent):
+ *             _slice_assign_scalar(data, shape + 1, strides + 1,             # <<<<<<<<<<<<<<
+ *                                 ndim - 1, itemsize, item)
+ *             data += stride
+ */
+      __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item);
+
+      /* "View.MemoryView":1376
+ *             _slice_assign_scalar(data, shape + 1, strides + 1,
+ *                                 ndim - 1, itemsize, item)
+ *             data += stride             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+      __pyx_v_data = (__pyx_v_data + __pyx_v_stride);
+    }
+  }
+  __pyx_L3:;
+
+  /* "View.MemoryView":1361
+ * 
+ * @cname('__pyx_memoryview__slice_assign_scalar')
+ * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
+ *                               Py_ssize_t *strides, int ndim,
+ *                               size_t itemsize, void *item) nogil:
+ */
+
+  /* function exit code */
+}
+
+static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) {
+  struct __pyx_array_obj *p;
+  PyObject *o;
+  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
+    o = (*t->tp_alloc)(t, 0);
+  } else {
+    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
+  }
+  if (unlikely(!o)) return 0;
+  p = ((struct __pyx_array_obj *)o);
+  p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None);
+  p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None);
+  if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) {
+    Py_DECREF(o); o = 0;
+  }
+  return o;
+}
+
+static void __pyx_tp_dealloc_array(PyObject *o) {
+  struct __pyx_array_obj *p = (struct __pyx_array_obj *)o;
+  #if PY_VERSION_HEX >= 0x030400a1
+  if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) {
+    if (PyObject_CallFinalizerFromDealloc(o)) return;
+  }
+  #endif
+  {
+    PyObject *etype, *eval, *etb;
+    PyErr_Fetch(&etype, &eval, &etb);
+    ++Py_REFCNT(o);
+    __pyx_array___dealloc__(o);
+    --Py_REFCNT(o);
+    PyErr_Restore(etype, eval, etb);
+  }
+  Py_CLEAR(p->mode);
+  Py_CLEAR(p->_format);
+  (*Py_TYPE(o)->tp_free)(o);
+}
+static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) {
+  PyObject *r;
+  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
+  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
+  Py_DECREF(x);
+  return r;
+}
+
+static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) {
+  if (v) {
+    return __pyx_array___setitem__(o, i, v);
+  }
+  else {
+    PyErr_Format(PyExc_NotImplementedError,
+      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
+    return -1;
+  }
+}
+
+static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) {
+  PyObject *v = PyObject_GenericGetAttr(o, n);
+  if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) {
+    PyErr_Clear();
+    v = __pyx_array___getattr__(o, n);
+  }
+  return v;
+}
+
+static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) {
+  return get_memview(o);
+}
+
+static PyMethodDef __pyx_methods_array[] = {
+  {__Pyx_NAMESTR("__getattr__"), (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, __Pyx_DOCSTR(0)},
+  {0, 0, 0, 0}
+};
+
+static struct PyGetSetDef __pyx_getsets_array[] = {
+  {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, 0, 0},
+  {0, 0, 0, 0, 0}
+};
+
+static PySequenceMethods __pyx_tp_as_sequence_array = {
+  0, /*sq_length*/
+  0, /*sq_concat*/
+  0, /*sq_repeat*/
+  __pyx_sq_item_array, /*sq_item*/
+  0, /*sq_slice*/
+  0, /*sq_ass_item*/
+  0, /*sq_ass_slice*/
+  0, /*sq_contains*/
+  0, /*sq_inplace_concat*/
+  0, /*sq_inplace_repeat*/
+};
+
+static PyMappingMethods __pyx_tp_as_mapping_array = {
+  0, /*mp_length*/
+  __pyx_array___getitem__, /*mp_subscript*/
+  __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/
+};
+
+static PyBufferProcs __pyx_tp_as_buffer_array = {
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getreadbuffer*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getwritebuffer*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getsegcount*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getcharbuffer*/
+  #endif
+  #if PY_VERSION_HEX >= 0x02060000
+  __pyx_array_getbuffer, /*bf_getbuffer*/
+  #endif
+  #if PY_VERSION_HEX >= 0x02060000
+  0, /*bf_releasebuffer*/
+  #endif
+};
+
+static PyTypeObject __pyx_type___pyx_array = {
+  PyVarObject_HEAD_INIT(0, 0)
+  __Pyx_NAMESTR("cython_bubblesort_nomagic.array"), /*tp_name*/
+  sizeof(struct __pyx_array_obj), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc_array, /*tp_dealloc*/
+  0, /*tp_print*/
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
+  0, /*tp_compare*/
+  #else
+  0, /*reserved*/
+  #endif
+  0, /*tp_repr*/
+  0, /*tp_as_number*/
+  &__pyx_tp_as_sequence_array, /*tp_as_sequence*/
+  &__pyx_tp_as_mapping_array, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  0, /*tp_str*/
+  __pyx_tp_getattro_array, /*tp_getattro*/
+  0, /*tp_setattro*/
+  &__pyx_tp_as_buffer_array, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/
+  0, /*tp_doc*/
+  0, /*tp_traverse*/
+  0, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods_array, /*tp_methods*/
+  0, /*tp_members*/
+  __pyx_getsets_array, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  0, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new_array, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
+  0, /*tp_weaklist*/
+  0, /*tp_del*/
+  #if PY_VERSION_HEX >= 0x02060000
+  0, /*tp_version_tag*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
+};
+
+static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
+  struct __pyx_MemviewEnum_obj *p;
+  PyObject *o;
+  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
+    o = (*t->tp_alloc)(t, 0);
+  } else {
+    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
+  }
+  if (unlikely(!o)) return 0;
+  p = ((struct __pyx_MemviewEnum_obj *)o);
+  p->name = Py_None; Py_INCREF(Py_None);
+  return o;
+}
+
+static void __pyx_tp_dealloc_Enum(PyObject *o) {
+  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
+  #if PY_VERSION_HEX >= 0x030400a1
+  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
+    if (PyObject_CallFinalizerFromDealloc(o)) return;
+  }
+  #endif
+  PyObject_GC_UnTrack(o);
+  Py_CLEAR(p->name);
+  (*Py_TYPE(o)->tp_free)(o);
+}
+
+static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
+  int e;
+  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
+  if (p->name) {
+    e = (*v)(p->name, a); if (e) return e;
+  }
+  return 0;
+}
+
+static int __pyx_tp_clear_Enum(PyObject *o) {
+  PyObject* tmp;
+  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
+  tmp = ((PyObject*)p->name);
+  p->name = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  return 0;
+}
+
+static PyMethodDef __pyx_methods_Enum[] = {
+  {0, 0, 0, 0}
+};
+
+static PyTypeObject __pyx_type___pyx_MemviewEnum = {
+  PyVarObject_HEAD_INIT(0, 0)
+  __Pyx_NAMESTR("cython_bubblesort_nomagic.Enum"), /*tp_name*/
+  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc_Enum, /*tp_dealloc*/
+  0, /*tp_print*/
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
+  0, /*tp_compare*/
+  #else
+  0, /*reserved*/
+  #endif
+  __pyx_MemviewEnum___repr__, /*tp_repr*/
+  0, /*tp_as_number*/
+  0, /*tp_as_sequence*/
+  0, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  0, /*tp_str*/
+  0, /*tp_getattro*/
+  0, /*tp_setattro*/
+  0, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+  0, /*tp_doc*/
+  __pyx_tp_traverse_Enum, /*tp_traverse*/
+  __pyx_tp_clear_Enum, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods_Enum, /*tp_methods*/
+  0, /*tp_members*/
+  0, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  __pyx_MemviewEnum___init__, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new_Enum, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
+  0, /*tp_weaklist*/
+  0, /*tp_del*/
+  #if PY_VERSION_HEX >= 0x02060000
+  0, /*tp_version_tag*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
+};
+static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;
+
+static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
+  struct __pyx_memoryview_obj *p;
+  PyObject *o;
+  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
+    o = (*t->tp_alloc)(t, 0);
+  } else {
+    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
+  }
+  if (unlikely(!o)) return 0;
+  p = ((struct __pyx_memoryview_obj *)o);
+  p->__pyx_vtab = __pyx_vtabptr_memoryview;
+  p->obj = Py_None; Py_INCREF(Py_None);
+  p->_size = Py_None; Py_INCREF(Py_None);
+  p->_array_interface = Py_None; Py_INCREF(Py_None);
+  p->view.obj = NULL;
+  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) {
+    Py_DECREF(o); o = 0;
+  }
+  return o;
+}
+
+static void __pyx_tp_dealloc_memoryview(PyObject *o) {
+  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
+  #if PY_VERSION_HEX >= 0x030400a1
+  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
+    if (PyObject_CallFinalizerFromDealloc(o)) return;
+  }
+  #endif
+  PyObject_GC_UnTrack(o);
+  {
+    PyObject *etype, *eval, *etb;
+    PyErr_Fetch(&etype, &eval, &etb);
+    ++Py_REFCNT(o);
+    __pyx_memoryview___dealloc__(o);
+    --Py_REFCNT(o);
+    PyErr_Restore(etype, eval, etb);
+  }
+  Py_CLEAR(p->obj);
+  Py_CLEAR(p->_size);
+  Py_CLEAR(p->_array_interface);
+  (*Py_TYPE(o)->tp_free)(o);
+}
+
+static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
+  int e;
+  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
+  if (p->obj) {
+    e = (*v)(p->obj, a); if (e) return e;
+  }
+  if (p->_size) {
+    e = (*v)(p->_size, a); if (e) return e;
+  }
+  if (p->_array_interface) {
+    e = (*v)(p->_array_interface, a); if (e) return e;
+  }
+  if (p->view.obj) {
+    e = (*v)(p->view.obj, a); if (e) return e;
+  }
+  return 0;
+}
+
+static int __pyx_tp_clear_memoryview(PyObject *o) {
+  PyObject* tmp;
+  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
+  tmp = ((PyObject*)p->obj);
+  p->obj = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  tmp = ((PyObject*)p->_size);
+  p->_size = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  tmp = ((PyObject*)p->_array_interface);
+  p->_array_interface = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  Py_CLEAR(p->view.obj);
+  return 0;
+}
+static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
+  PyObject *r;
+  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
+  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
+  Py_DECREF(x);
+  return r;
+}
+
+static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
+  if (v) {
+    return __pyx_memoryview___setitem__(o, i, v);
+  }
+  else {
+    PyErr_Format(PyExc_NotImplementedError,
+      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
+    return -1;
+  }
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview_transpose(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview__get__base(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview_get_shape(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview_get_strides(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview_get_suboffsets(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview_get_ndim(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview_get_itemsize(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview_get_nbytes(o);
+}
+
+static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryview_get_size(o);
+}
+
+static PyMethodDef __pyx_methods_memoryview[] = {
+  {__Pyx_NAMESTR("is_c_contig"), (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, __Pyx_DOCSTR(0)},
+  {__Pyx_NAMESTR("is_f_contig"), (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, __Pyx_DOCSTR(0)},
+  {__Pyx_NAMESTR("copy"), (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, __Pyx_DOCSTR(0)},
+  {__Pyx_NAMESTR("copy_fortran"), (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, __Pyx_DOCSTR(0)},
+  {0, 0, 0, 0}
+};
+
+static struct PyGetSetDef __pyx_getsets_memoryview[] = {
+  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, 0, 0},
+  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, 0, 0},
+  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, 0, 0},
+  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, 0, 0},
+  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, 0, 0},
+  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, 0, 0},
+  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, 0, 0},
+  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, 0, 0},
+  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, 0, 0},
+  {0, 0, 0, 0, 0}
+};
+
+static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
+  __pyx_memoryview___len__, /*sq_length*/
+  0, /*sq_concat*/
+  0, /*sq_repeat*/
+  __pyx_sq_item_memoryview, /*sq_item*/
+  0, /*sq_slice*/
+  0, /*sq_ass_item*/
+  0, /*sq_ass_slice*/
+  0, /*sq_contains*/
+  0, /*sq_inplace_concat*/
+  0, /*sq_inplace_repeat*/
+};
+
+static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
+  __pyx_memoryview___len__, /*mp_length*/
+  __pyx_memoryview___getitem__, /*mp_subscript*/
+  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
+};
+
+static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getreadbuffer*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getwritebuffer*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getsegcount*/
+  #endif
+  #if PY_MAJOR_VERSION < 3
+  0, /*bf_getcharbuffer*/
+  #endif
+  #if PY_VERSION_HEX >= 0x02060000
+  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
+  #endif
+  #if PY_VERSION_HEX >= 0x02060000
+  0, /*bf_releasebuffer*/
+  #endif
+};
+
+static PyTypeObject __pyx_type___pyx_memoryview = {
+  PyVarObject_HEAD_INIT(0, 0)
+  __Pyx_NAMESTR("cython_bubblesort_nomagic.memoryview"), /*tp_name*/
+  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
+  0, /*tp_print*/
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
+  0, /*tp_compare*/
+  #else
+  0, /*reserved*/
+  #endif
+  __pyx_memoryview___repr__, /*tp_repr*/
+  0, /*tp_as_number*/
+  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
+  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  __pyx_memoryview___str__, /*tp_str*/
+  0, /*tp_getattro*/
+  0, /*tp_setattro*/
+  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+  0, /*tp_doc*/
+  __pyx_tp_traverse_memoryview, /*tp_traverse*/
+  __pyx_tp_clear_memoryview, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods_memoryview, /*tp_methods*/
+  0, /*tp_members*/
+  __pyx_getsets_memoryview, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  0, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new_memoryview, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
+  0, /*tp_weaklist*/
+  0, /*tp_del*/
+  #if PY_VERSION_HEX >= 0x02060000
+  0, /*tp_version_tag*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
+};
+static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
+
+static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
+  struct __pyx_memoryviewslice_obj *p;
+  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
+  if (unlikely(!o)) return 0;
+  p = ((struct __pyx_memoryviewslice_obj *)o);
+  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
+  p->from_object = Py_None; Py_INCREF(Py_None);
+  p->from_slice.memview = NULL;
+  return o;
+}
+
+static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
+  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
+  #if PY_VERSION_HEX >= 0x030400a1
+  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
+    if (PyObject_CallFinalizerFromDealloc(o)) return;
+  }
+  #endif
+  PyObject_GC_UnTrack(o);
+  {
+    PyObject *etype, *eval, *etb;
+    PyErr_Fetch(&etype, &eval, &etb);
+    ++Py_REFCNT(o);
+    __pyx_memoryviewslice___dealloc__(o);
+    --Py_REFCNT(o);
+    PyErr_Restore(etype, eval, etb);
+  }
+  Py_CLEAR(p->from_object);
+  PyObject_GC_Track(o);
+  __pyx_tp_dealloc_memoryview(o);
+}
+
+static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
+  int e;
+  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
+  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
+  if (p->from_object) {
+    e = (*v)(p->from_object, a); if (e) return e;
+  }
+  return 0;
+}
+
+static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
+  PyObject* tmp;
+  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
+  __pyx_tp_clear_memoryview(o);
+  tmp = ((PyObject*)p->from_object);
+  p->from_object = Py_None; Py_INCREF(Py_None);
+  Py_XDECREF(tmp);
+  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);
+  return 0;
+}
+
+static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {
+  return __pyx_memoryviewslice__get__base(o);
+}
+
+static PyMethodDef __pyx_methods__memoryviewslice[] = {
+  {0, 0, 0, 0}
+};
+
+static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {
+  {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, 0, 0},
+  {0, 0, 0, 0, 0}
+};
+
+static PyTypeObject __pyx_type___pyx_memoryviewslice = {
+  PyVarObject_HEAD_INIT(0, 0)
+  __Pyx_NAMESTR("cython_bubblesort_nomagic._memoryviewslice"), /*tp_name*/
+  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
+  0, /*tp_itemsize*/
+  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
+  0, /*tp_print*/
+  0, /*tp_getattr*/
+  0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
+  0, /*tp_compare*/
+  #else
+  0, /*reserved*/
+  #endif
+  #if CYTHON_COMPILING_IN_PYPY
+  __pyx_memoryview___repr__, /*tp_repr*/
+  #else
+  0, /*tp_repr*/
+  #endif
+  0, /*tp_as_number*/
+  0, /*tp_as_sequence*/
+  0, /*tp_as_mapping*/
+  0, /*tp_hash*/
+  0, /*tp_call*/
+  #if CYTHON_COMPILING_IN_PYPY
+  __pyx_memoryview___str__, /*tp_str*/
+  #else
+  0, /*tp_str*/
+  #endif
+  0, /*tp_getattro*/
+  0, /*tp_setattro*/
+  0, /*tp_as_buffer*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+  __Pyx_DOCSTR("Internal class for passing memoryview slices to Python"), /*tp_doc*/
+  __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/
+  __pyx_tp_clear__memoryviewslice, /*tp_clear*/
+  0, /*tp_richcompare*/
+  0, /*tp_weaklistoffset*/
+  0, /*tp_iter*/
+  0, /*tp_iternext*/
+  __pyx_methods__memoryviewslice, /*tp_methods*/
+  0, /*tp_members*/
+  __pyx_getsets__memoryviewslice, /*tp_getset*/
+  0, /*tp_base*/
+  0, /*tp_dict*/
+  0, /*tp_descr_get*/
+  0, /*tp_descr_set*/
+  0, /*tp_dictoffset*/
+  0, /*tp_init*/
+  0, /*tp_alloc*/
+  __pyx_tp_new__memoryviewslice, /*tp_new*/
+  0, /*tp_free*/
+  0, /*tp_is_gc*/
+  0, /*tp_bases*/
+  0, /*tp_mro*/
+  0, /*tp_cache*/
+  0, /*tp_subclasses*/
+  0, /*tp_weaklist*/
+  0, /*tp_del*/
+  #if PY_VERSION_HEX >= 0x02060000
+  0, /*tp_version_tag*/
+  #endif
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
+};
+
+static PyMethodDef __pyx_methods[] = {
+  {__Pyx_NAMESTR("cython_bubblesort_nomagic"), (PyCFunction)__pyx_pw_25cython_bubblesort_nomagic_1cython_bubblesort_nomagic, METH_O, __Pyx_DOCSTR(__pyx_doc_25cython_bubblesort_nomagic_cython_bubblesort_nomagic)},
+  {0, 0, 0, 0}
+};
+
+#if PY_MAJOR_VERSION >= 3
+static struct PyModuleDef __pyx_moduledef = {
+  #if PY_VERSION_HEX < 0x03020000
+    { PyObject_HEAD_INIT(NULL) NULL, 0, NULL },
+  #else
+    PyModuleDef_HEAD_INIT,
+  #endif
+    __Pyx_NAMESTR("cython_bubblesort_nomagic"),
+    0, /* m_doc */
+    -1, /* m_size */
+    __pyx_methods /* m_methods */,
+    NULL, /* m_reload */
+    NULL, /* m_traverse */
+    NULL, /* m_clear */
+    NULL /* m_free */
+};
+#endif
+
+static __Pyx_StringTabEntry __pyx_string_tab[] = {
+  {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0},
+  {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0},
+  {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0},
+  {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1},
+  {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0},
+  {&__pyx_kp_u_Format_string_allocated_too_shor, __pyx_k_Format_string_allocated_too_shor, sizeof(__pyx_k_Format_string_allocated_too_shor), 0, 1, 0, 0},
+  {&__pyx_kp_u_Format_string_allocated_too_shor_2, __pyx_k_Format_string_allocated_too_shor_2, sizeof(__pyx_k_Format_string_allocated_too_shor_2), 0, 1, 0, 0},
+  {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1},
+  {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0},
+  {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0},
+  {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0},
+  {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1},
+  {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0},
+  {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0},
+  {&__pyx_kp_u_Non_native_byte_order_not_suppor, __pyx_k_Non_native_byte_order_not_suppor, sizeof(__pyx_k_Non_native_byte_order_not_suppor), 0, 1, 0, 0},
+  {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1},
+  {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0},
+  {&__pyx_n_s_RuntimeError, __pyx_k_RuntimeError, sizeof(__pyx_k_RuntimeError), 0, 0, 1, 1},
+  {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1},
+  {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0},
+  {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
+  {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1},
+  {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1},
+  {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1},
+  {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1},
+  {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1},
+  {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0},
+  {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0},
+  {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1},
+  {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1},
+  {&__pyx_n_s_error, __pyx_k_error, sizeof(__pyx_k_error), 0, 0, 1, 1},
+  {&__pyx_n_s_flags, __pyx_k_flags, sizeof(__pyx_k_flags), 0, 0, 1, 1},
+  {&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1},
+  {&__pyx_n_s_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 0, 1, 1},
+  {&__pyx_n_u_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 1, 0, 1},
+  {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0},
+  {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1},
+  {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1},
+  {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1},
+  {&__pyx_kp_s_itemsize_0_for_cython_array, __pyx_k_itemsize_0_for_cython_array, sizeof(__pyx_k_itemsize_0_for_cython_array), 0, 0, 1, 0},
+  {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
+  {&__pyx_n_s_memview, __pyx_k_memview, sizeof(__pyx_k_memview), 0, 0, 1, 1},
+  {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1},
+  {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1},
+  {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1},
+  {&__pyx_kp_u_ndarray_is_not_C_contiguous, __pyx_k_ndarray_is_not_C_contiguous, sizeof(__pyx_k_ndarray_is_not_C_contiguous), 0, 1, 0, 0},
+  {&__pyx_kp_u_ndarray_is_not_Fortran_contiguou, __pyx_k_ndarray_is_not_Fortran_contiguou, sizeof(__pyx_k_ndarray_is_not_Fortran_contiguou), 0, 1, 0, 0},
+  {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1},
+  {&__pyx_n_s_obj, __pyx_k_obj, sizeof(__pyx_k_obj), 0, 0, 1, 1},
+  {&__pyx_n_s_pack, __pyx_k_pack, sizeof(__pyx_k_pack), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_getbuffer, __pyx_k_pyx_getbuffer, sizeof(__pyx_k_pyx_getbuffer), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_releasebuffer, __pyx_k_pyx_releasebuffer, sizeof(__pyx_k_pyx_releasebuffer), 0, 0, 1, 1},
+  {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1},
+  {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1},
+  {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1},
+  {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1},
+  {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1},
+  {&__pyx_n_s_step, __pyx_k_step, sizeof(__pyx_k_step), 0, 0, 1, 1},
+  {&__pyx_n_s_stop, __pyx_k_stop, sizeof(__pyx_k_stop), 0, 0, 1, 1},
+  {&__pyx_kp_s_strided_and_direct, __pyx_k_strided_and_direct, sizeof(__pyx_k_strided_and_direct), 0, 0, 1, 0},
+  {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0},
+  {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0},
+  {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1},
+  {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
+  {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0},
+  {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0},
+  {&__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_k_unknown_dtype_code_in_numpy_pxd, sizeof(__pyx_k_unknown_dtype_code_in_numpy_pxd), 0, 1, 0, 0},
+  {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1},
+  {&__pyx_n_s_xrange, __pyx_k_xrange, sizeof(__pyx_k_xrange), 0, 0, 1, 1},
+  {0, 0, 0, 0, 0, 0, 0}
+};
+static int __Pyx_InitCachedBuiltins(void) {
+  #if PY_MAJOR_VERSION >= 3
+  __pyx_builtin_xrange = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_xrange) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 12; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #else
+  __pyx_builtin_xrange = __Pyx_GetBuiltinName(__pyx_n_s_xrange); if (!__pyx_builtin_xrange) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 12; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 228; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_RuntimeError = __Pyx_GetBuiltinName(__pyx_n_s_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_MemoryError = __Pyx_GetBuiltinName(__pyx_n_s_MemoryError); if (!__pyx_builtin_MemoryError) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_Ellipsis = __Pyx_GetBuiltinName(__pyx_n_s_Ellipsis); if (!__pyx_builtin_Ellipsis) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 357; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 386; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_id = __Pyx_GetBuiltinName(__pyx_n_s_id); if (!__pyx_builtin_id) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 789; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  return 0;
+  __pyx_L1_error:;
+  return -1;
+}
+
+static int __Pyx_InitCachedConstants(void) {
+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":215
+ *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
+ *                 and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)):
+ *                 raise ValueError(u"ndarray is not C contiguous")             # <<<<<<<<<<<<<<
+ * 
+ *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
+ */
+  __pyx_tuple_ = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_C_contiguous); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple_);
+  __Pyx_GIVEREF(__pyx_tuple_);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":219
+ *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
+ *                 and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)):
+ *                 raise ValueError(u"ndarray is not Fortran contiguous")             # <<<<<<<<<<<<<<
+ * 
+ *             info.buf = PyArray_DATA(self)
+ */
+  __pyx_tuple__2 = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_Fortran_contiguou); if (unlikely(!__pyx_tuple__2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__2);
+  __Pyx_GIVEREF(__pyx_tuple__2);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":257
+ *                 if ((descr.byteorder == c'>' and little_endian) or
+ *                     (descr.byteorder == c'<' and not little_endian)):
+ *                     raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
+ *                 if   t == NPY_BYTE:        f = "b"
+ *                 elif t == NPY_UBYTE:       f = "B"
+ */
+  __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_u_Non_native_byte_order_not_suppor); if (unlikely(!__pyx_tuple__3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__3);
+  __Pyx_GIVEREF(__pyx_tuple__3);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":799
+ * 
+ *         if (end - f) - <int>(new_offset - offset[0]) < 15:
+ *             raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd")             # <<<<<<<<<<<<<<
+ * 
+ *         if ((child.byteorder == c'>' and little_endian) or
+ */
+  __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor); if (unlikely(!__pyx_tuple__4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__4);
+  __Pyx_GIVEREF(__pyx_tuple__4);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":803
+ *         if ((child.byteorder == c'>' and little_endian) or
+ *             (child.byteorder == c'<' and not little_endian)):
+ *             raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
+ *             # One could encode it in the format string and have Cython
+ *             # complain instead, BUT: < and > in format strings also imply
+ */
+  __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_u_Non_native_byte_order_not_suppor); if (unlikely(!__pyx_tuple__5)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__5);
+  __Pyx_GIVEREF(__pyx_tuple__5);
+
+  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":823
+ *             t = child.type_num
+ *             if end - f < 5:
+ *                 raise RuntimeError(u"Format string allocated too short.")             # <<<<<<<<<<<<<<
+ * 
+ *             # Until ticket #99 is fixed, use integers to avoid warnings
+ */
+  __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor_2); if (unlikely(!__pyx_tuple__6)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__6);
+  __Pyx_GIVEREF(__pyx_tuple__6);
+
+  /* "View.MemoryView":127
+ * 
+ *         if not self.ndim:
+ *             raise ValueError("Empty shape tuple for cython.array")             # <<<<<<<<<<<<<<
+ * 
+ *         if itemsize <= 0:
+ */
+  __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_s_Empty_shape_tuple_for_cython_arr); if (unlikely(!__pyx_tuple__7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__7);
+  __Pyx_GIVEREF(__pyx_tuple__7);
+
+  /* "View.MemoryView":130
+ * 
+ *         if itemsize <= 0:
+ *             raise ValueError("itemsize <= 0 for cython.array")             # <<<<<<<<<<<<<<
+ * 
+ *         if isinstance(format, unicode):
+ */
+  __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_itemsize_0_for_cython_array); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__8);
+  __Pyx_GIVEREF(__pyx_tuple__8);
+
+  /* "View.MemoryView":142
+ * 
+ *         if not self._shape:
+ *             raise MemoryError("unable to allocate shape and strides.")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_shape_and_str); if (unlikely(!__pyx_tuple__9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__9);
+  __Pyx_GIVEREF(__pyx_tuple__9);
+
+  /* "View.MemoryView":170
+ *             self.data = <char *>malloc(self.len)
+ *             if not self.data:
+ *                 raise MemoryError("unable to allocate array data.")             # <<<<<<<<<<<<<<
+ * 
+ *             if self.dtype_is_object:
+ */
+  __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__10)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__10);
+  __Pyx_GIVEREF(__pyx_tuple__10);
+
+  /* "View.MemoryView":186
+ *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
+ *         if not (flags & bufmode):
+ *             raise ValueError("Can only create a buffer that is contiguous in memory.")             # <<<<<<<<<<<<<<
+ *         info.buf = self.data
+ *         info.len = self.len
+ */
+  __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__11)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__11);
+  __Pyx_GIVEREF(__pyx_tuple__11);
+
+  /* "View.MemoryView":445
+ *             result = struct.unpack(self.view.format, bytesitem)
+ *         except struct.error:
+ *             raise ValueError("Unable to convert item to object")             # <<<<<<<<<<<<<<
+ *         else:
+ *             if len(self.view.format) == 1:
+ */
+  __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_Unable_to_convert_item_to_object); if (unlikely(!__pyx_tuple__12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 445; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__12);
+  __Pyx_GIVEREF(__pyx_tuple__12);
+
+  /* "View.MemoryView":521
+ *             if self.view.strides == NULL:
+ * 
+ *                 raise ValueError("Buffer view does not expose strides")             # <<<<<<<<<<<<<<
+ * 
+ *             return tuple([self.view.strides[i] for i in xrange(self.view.ndim)])
+ */
+  __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__13)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__13);
+  __Pyx_GIVEREF(__pyx_tuple__13);
+
+  /* "View.MemoryView":638
+ *         if item is Ellipsis:
+ *             if not seen_ellipsis:
+ *                 result.extend([slice(None)] * (ndim - len(tup) + 1))             # <<<<<<<<<<<<<<
+ *                 seen_ellipsis = True
+ *             else:
+ */
+  __pyx_tuple__14 = PyTuple_Pack(1, Py_None); if (unlikely(!__pyx_tuple__14)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__14);
+  __Pyx_GIVEREF(__pyx_tuple__14);
+
+  /* "View.MemoryView":641
+ *                 seen_ellipsis = True
+ *             else:
+ *                 result.append(slice(None))             # <<<<<<<<<<<<<<
+ *             have_slices = True
+ *         else:
+ */
+  __pyx_tuple__15 = PyTuple_Pack(1, Py_None); if (unlikely(!__pyx_tuple__15)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 641; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__15);
+  __Pyx_GIVEREF(__pyx_tuple__15);
+
+  /* "View.MemoryView":652
+ *     nslices = ndim - len(result)
+ *     if nslices:
+ *         result.extend([slice(None)] * nslices)             # <<<<<<<<<<<<<<
+ * 
+ *     return have_slices or nslices, tuple(result)
+ */
+  __pyx_tuple__16 = PyTuple_Pack(1, Py_None); if (unlikely(!__pyx_tuple__16)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 652; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__16);
+  __Pyx_GIVEREF(__pyx_tuple__16);
+
+  /* "View.MemoryView":660
+ *     for i in range(ndim):
+ *         if suboffsets[i] >= 0:
+ *             raise ValueError("Indirect dimensions not supported")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_tuple__17 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__17)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 660; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__17);
+  __Pyx_GIVEREF(__pyx_tuple__17);
+
+  /* "View.MemoryView":276
+ *         return self.name
+ * 
+ * cdef generic = Enum("<strided and direct or indirect>")             # <<<<<<<<<<<<<<
+ * cdef strided = Enum("<strided and direct>") # default
+ * cdef indirect = Enum("<strided and indirect>")
+ */
+  __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__18)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__18);
+  __Pyx_GIVEREF(__pyx_tuple__18);
+
+  /* "View.MemoryView":277
+ * 
+ * cdef generic = Enum("<strided and direct or indirect>")
+ * cdef strided = Enum("<strided and direct>") # default             # <<<<<<<<<<<<<<
+ * cdef indirect = Enum("<strided and indirect>")
+ * 
+ */
+  __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__19)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 277; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__19);
+  __Pyx_GIVEREF(__pyx_tuple__19);
+
+  /* "View.MemoryView":278
+ * cdef generic = Enum("<strided and direct or indirect>")
+ * cdef strided = Enum("<strided and direct>") # default
+ * cdef indirect = Enum("<strided and indirect>")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__20)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__20);
+  __Pyx_GIVEREF(__pyx_tuple__20);
+
+  /* "View.MemoryView":281
+ * 
+ * 
+ * cdef contiguous = Enum("<contiguous and direct>")             # <<<<<<<<<<<<<<
+ * cdef indirect_contiguous = Enum("<contiguous and indirect>")
+ * 
+ */
+  __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__21)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__21);
+  __Pyx_GIVEREF(__pyx_tuple__21);
+
+  /* "View.MemoryView":282
+ * 
+ * cdef contiguous = Enum("<contiguous and direct>")
+ * cdef indirect_contiguous = Enum("<contiguous and indirect>")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__22)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 282; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__22);
+  __Pyx_GIVEREF(__pyx_tuple__22);
+  __Pyx_RefNannyFinishContext();
+  return 0;
+  __pyx_L1_error:;
+  __Pyx_RefNannyFinishContext();
+  return -1;
+}
+
+static int __Pyx_InitGlobals(void) {
+  if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+  __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_int_neg_1 = PyInt_FromLong(-1); if (unlikely(!__pyx_int_neg_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  return 0;
+  __pyx_L1_error:;
+  return -1;
+}
+
+#if PY_MAJOR_VERSION < 3
+PyMODINIT_FUNC initcython_bubblesort_nomagic(void); /*proto*/
+PyMODINIT_FUNC initcython_bubblesort_nomagic(void)
+#else
+PyMODINIT_FUNC PyInit_cython_bubblesort_nomagic(void); /*proto*/
+PyMODINIT_FUNC PyInit_cython_bubblesort_nomagic(void)
+#endif
+{
+  PyObject *__pyx_t_1 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
+  __Pyx_RefNannyDeclarations
+  #if CYTHON_REFNANNY
+  __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny");
+  if (!__Pyx_RefNanny) {
+      PyErr_Clear();
+      __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny");
+      if (!__Pyx_RefNanny)
+          Py_FatalError("failed to import 'refnanny' module");
+  }
+  #endif
+  __Pyx_RefNannySetupContext("PyMODINIT_FUNC PyInit_cython_bubblesort_nomagic(void)", 0);
+  if ( __Pyx_check_binary_version() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #ifdef __Pyx_CyFunction_USED
+  if (__Pyx_CyFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  #ifdef __Pyx_FusedFunction_USED
+  if (__pyx_FusedFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  #ifdef __Pyx_Generator_USED
+  if (__pyx_Generator_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  /*--- Library function declarations ---*/
+  /*--- Threads initialization code ---*/
+  #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS
+  #ifdef WITH_THREAD /* Python build with threading support? */
+  PyEval_InitThreads();
+  #endif
+  #endif
+  /*--- Module creation code ---*/
+  #if PY_MAJOR_VERSION < 3
+  __pyx_m = Py_InitModule4(__Pyx_NAMESTR("cython_bubblesort_nomagic"), __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
+  #else
+  __pyx_m = PyModule_Create(&__pyx_moduledef);
+  #endif
+  if (unlikely(!__pyx_m)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  Py_INCREF(__pyx_d);
+  __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME)); if (unlikely(!__pyx_b)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #if CYTHON_COMPILING_IN_PYPY
+  Py_INCREF(__pyx_b);
+  #endif
+  if (__Pyx_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+  /*--- Initialize various global constants etc. ---*/
+  if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT)
+  if (__Pyx_init_sys_getdefaultencoding_params() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  #endif
+  if (__pyx_module_is_main_cython_bubblesort_nomagic) {
+    if (__Pyx_SetAttrString(__pyx_m, "__name__", __pyx_n_s_main) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+  }
+  #if PY_MAJOR_VERSION >= 3
+  {
+    PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    if (!PyDict_GetItemString(modules, "cython_bubblesort_nomagic")) {
+      if (unlikely(PyDict_SetItemString(modules, "cython_bubblesort_nomagic", __pyx_m) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
+  }
+  #endif
+  /*--- Builtin init code ---*/
+  if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  /*--- Constants init code ---*/
+  if (unlikely(__Pyx_InitCachedConstants() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  /*--- Global init code ---*/
+  generic = Py_None; Py_INCREF(Py_None);
+  strided = Py_None; Py_INCREF(Py_None);
+  indirect = Py_None; Py_INCREF(Py_None);
+  contiguous = Py_None; Py_INCREF(Py_None);
+  indirect_contiguous = Py_None; Py_INCREF(Py_None);
+  /*--- Variable export code ---*/
+  /*--- Function export code ---*/
+  /*--- Type init code ---*/
+  if (PyType_Ready(&__pyx_type___pyx_array) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 99; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_type___pyx_array.tp_print = 0;
+  __pyx_array_type = &__pyx_type___pyx_array;
+  if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_type___pyx_MemviewEnum.tp_print = 0;
+  __pyx_MemviewEnum_type = &__pyx_type___pyx_MemviewEnum;
+  __pyx_vtabptr_memoryview = &__pyx_vtable_memoryview;
+  __pyx_vtable_memoryview.get_item_pointer = (char *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_get_item_pointer;
+  __pyx_vtable_memoryview.is_slice = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_is_slice;
+  __pyx_vtable_memoryview.setitem_slice_assignment = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_slice_assignment;
+  __pyx_vtable_memoryview.setitem_slice_assign_scalar = (PyObject *(*)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_setitem_slice_assign_scalar;
+  __pyx_vtable_memoryview.setitem_indexed = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_indexed;
+  __pyx_vtable_memoryview.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryview_convert_item_to_object;
+  __pyx_vtable_memoryview.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryview_assign_item_from_object;
+  if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 302; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_type___pyx_memoryview.tp_print = 0;
+  if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 302; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_memoryview_type = &__pyx_type___pyx_memoryview;
+  __pyx_vtabptr__memoryviewslice = &__pyx_vtable__memoryviewslice;
+  __pyx_vtable__memoryviewslice.__pyx_base = *__pyx_vtabptr_memoryview;
+  __pyx_vtable__memoryviewslice.__pyx_base.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryviewslice_convert_item_to_object;
+  __pyx_vtable__memoryviewslice.__pyx_base.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryviewslice_assign_item_from_object;
+  __pyx_type___pyx_memoryviewslice.tp_base = __pyx_memoryview_type;
+  if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 922; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_type___pyx_memoryviewslice.tp_print = 0;
+  if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 922; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_memoryviewslice_type = &__pyx_type___pyx_memoryviewslice;
+  /*--- Type import code ---*/
+  __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "type", 
+  #if CYTHON_COMPILING_IN_PYPY
+  sizeof(PyTypeObject),
+  #else
+  sizeof(PyHeapTypeObject),
+  #endif
+  0); if (unlikely(!__pyx_ptype_7cpython_4type_type)) {__pyx_filename = __pyx_f[3]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_ptype_5numpy_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr), 0); if (unlikely(!__pyx_ptype_5numpy_dtype)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 155; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_ptype_5numpy_flatiter = __Pyx_ImportType("numpy", "flatiter", sizeof(PyArrayIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_flatiter)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 165; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_ptype_5numpy_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_broadcast)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 169; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_ptype_5numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject), 0); if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 178; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_ptype_5numpy_ufunc = __Pyx_ImportType("numpy", "ufunc", sizeof(PyUFuncObject), 0); if (unlikely(!__pyx_ptype_5numpy_ufunc)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 861; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  /*--- Variable import code ---*/
+  /*--- Function import code ---*/
+  /*--- Execution code ---*/
+
+  /* "cython_bubblesort_nomagic.pyx":2
+ * 
+ * cimport numpy as np             # <<<<<<<<<<<<<<
+ * cimport cython
+ * @cython.boundscheck(False)
+ */
+  __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+
+  /* "View.MemoryView":203
+ *         info.obj = self
+ * 
+ *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")             # <<<<<<<<<<<<<<
+ * 
+ *     def __dealloc__(array self):
+ */
+  __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_array_getbuffer)), __pyx_k_getbuffer_obj_view_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 203; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_array_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 203; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  PyType_Modified(__pyx_array_type);
+
+  /* "View.MemoryView":276
+ *         return self.name
+ * 
+ * cdef generic = Enum("<strided and direct or indirect>")             # <<<<<<<<<<<<<<
+ * cdef strided = Enum("<strided and direct>") # default
+ * cdef indirect = Enum("<strided and indirect>")
+ */
+  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__18, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_XGOTREF(generic);
+  __Pyx_DECREF_SET(generic, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":277
+ * 
+ * cdef generic = Enum("<strided and direct or indirect>")
+ * cdef strided = Enum("<strided and direct>") # default             # <<<<<<<<<<<<<<
+ * cdef indirect = Enum("<strided and indirect>")
+ * 
+ */
+  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__19, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 277; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_XGOTREF(strided);
+  __Pyx_DECREF_SET(strided, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":278
+ * cdef generic = Enum("<strided and direct or indirect>")
+ * cdef strided = Enum("<strided and direct>") # default
+ * cdef indirect = Enum("<strided and indirect>")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_XGOTREF(indirect);
+  __Pyx_DECREF_SET(indirect, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":281
+ * 
+ * 
+ * cdef contiguous = Enum("<contiguous and direct>")             # <<<<<<<<<<<<<<
+ * cdef indirect_contiguous = Enum("<contiguous and indirect>")
+ * 
+ */
+  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__21, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_XGOTREF(contiguous);
+  __Pyx_DECREF_SET(contiguous, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":282
+ * 
+ * cdef contiguous = Enum("<contiguous and direct>")
+ * cdef indirect_contiguous = Enum("<contiguous and indirect>")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__22, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 282; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __Pyx_XGOTREF(indirect_contiguous);
+  __Pyx_DECREF_SET(indirect_contiguous, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  __pyx_t_1 = 0;
+
+  /* "View.MemoryView":496
+ *         info.obj = self
+ * 
+ *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), __pyx_k_getbuffer_obj_view_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  PyType_Modified(__pyx_memoryview_type);
+
+  /* "View.MemoryView":953
+ *             return self.from_object
+ * 
+ *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
+  __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), __pyx_k_getbuffer_obj_view_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 953; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 953; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
+  PyType_Modified(__pyx_memoryviewslice_type);
+
+  /* "View.MemoryView":1361
+ * 
+ * @cname('__pyx_memoryview__slice_assign_scalar')
+ * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
+ *                               Py_ssize_t *strides, int ndim,
+ *                               size_t itemsize, void *item) nogil:
+ */
+  goto __pyx_L0;
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  if (__pyx_m) {
+    __Pyx_AddTraceback("init cython_bubblesort_nomagic", __pyx_clineno, __pyx_lineno, __pyx_filename);
+    Py_DECREF(__pyx_m); __pyx_m = 0;
+  } else if (!PyErr_Occurred()) {
+    PyErr_SetString(PyExc_ImportError, "init cython_bubblesort_nomagic");
+  }
+  __pyx_L0:;
+  __Pyx_RefNannyFinishContext();
+  #if PY_MAJOR_VERSION < 3
+  return;
+  #else
+  return __pyx_m;
+  #endif
+}
+
+/* Runtime support code */
+#if CYTHON_REFNANNY
+static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
+    PyObject *m = NULL, *p = NULL;
+    void *r = NULL;
+    m = PyImport_ImportModule((char *)modname);
+    if (!m) goto end;
+    p = PyObject_GetAttrString(m, (char *)"RefNannyAPI");
+    if (!p) goto end;
+    r = PyLong_AsVoidPtr(p);
+end:
+    Py_XDECREF(p);
+    Py_XDECREF(m);
+    return (__Pyx_RefNannyAPIStruct *)r;
+}
+#endif /* CYTHON_REFNANNY */
+
+static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
+    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
+    if (unlikely(!result)) {
+        PyErr_Format(PyExc_NameError,
+#if PY_MAJOR_VERSION >= 3
+            "name '%U' is not defined", name);
+#else
+            "name '%.200s' is not defined", PyString_AS_STRING(name));
+#endif
+    }
+    return result;
+}
+
+static CYTHON_INLINE int __Pyx_IsLittleEndian(void) {
+  unsigned int n = 1;
+  return *(unsigned char*)(&n) != 0;
+}
+static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
+                              __Pyx_BufFmt_StackElem* stack,
+                              __Pyx_TypeInfo* type) {
+  stack[0].field = &ctx->root;
+  stack[0].parent_offset = 0;
+  ctx->root.type = type;
+  ctx->root.name = "buffer dtype";
+  ctx->root.offset = 0;
+  ctx->head = stack;
+  ctx->head->field = &ctx->root;
+  ctx->fmt_offset = 0;
+  ctx->head->parent_offset = 0;
+  ctx->new_packmode = '@';
+  ctx->enc_packmode = '@';
+  ctx->new_count = 1;
+  ctx->enc_count = 0;
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  ctx->is_valid_array = 0;
+  ctx->struct_alignment = 0;
+  while (type->typegroup == 'S') {
+    ++ctx->head;
+    ctx->head->field = type->fields;
+    ctx->head->parent_offset = 0;
+    type = type->fields->type;
+  }
+}
+static int __Pyx_BufFmt_ParseNumber(const char** ts) {
+    int count;
+    const char* t = *ts;
+    if (*t < '0' || *t > '9') {
+      return -1;
+    } else {
+        count = *t++ - '0';
+        while (*t >= '0' && *t < '9') {
+            count *= 10;
+            count += *t++ - '0';
+        }
+    }
+    *ts = t;
+    return count;
+}
+static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
+    int number = __Pyx_BufFmt_ParseNumber(ts);
+    if (number == -1) /* First char was not a digit */
+        PyErr_Format(PyExc_ValueError,\
+                     "Does not understand character buffer dtype format string ('%c')", **ts);
+    return number;
+}
+static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
+  PyErr_Format(PyExc_ValueError,
+               "Unexpected format string character: '%c'", ch);
+}
+static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
+  switch (ch) {
+    case 'c': return "'char'";
+    case 'b': return "'signed char'";
+    case 'B': return "'unsigned char'";
+    case 'h': return "'short'";
+    case 'H': return "'unsigned short'";
+    case 'i': return "'int'";
+    case 'I': return "'unsigned int'";
+    case 'l': return "'long'";
+    case 'L': return "'unsigned long'";
+    case 'q': return "'long long'";
+    case 'Q': return "'unsigned long long'";
+    case 'f': return (is_complex ? "'complex float'" : "'float'");
+    case 'd': return (is_complex ? "'complex double'" : "'double'");
+    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
+    case 'T': return "a struct";
+    case 'O': return "Python object";
+    case 'P': return "a pointer";
+    case 's': case 'p': return "a string";
+    case 0: return "end";
+    default: return "unparseable format string";
+  }
+}
+static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return 2;
+    case 'i': case 'I': case 'l': case 'L': return 4;
+    case 'q': case 'Q': return 8;
+    case 'f': return (is_complex ? 8 : 4);
+    case 'd': return (is_complex ? 16 : 8);
+    case 'g': {
+      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
+      return 0;
+    }
+    case 'O': case 'P': return sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
+  switch (ch) {
+    case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(short);
+    case 'i': case 'I': return sizeof(int);
+    case 'l': case 'L': return sizeof(long);
+    #ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
+    #endif
+    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
+    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
+    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
+    case 'O': case 'P': return sizeof(void*);
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+  }
+}
+typedef struct { char c; short x; } __Pyx_st_short;
+typedef struct { char c; int x; } __Pyx_st_int;
+typedef struct { char c; long x; } __Pyx_st_long;
+typedef struct { char c; float x; } __Pyx_st_float;
+typedef struct { char c; double x; } __Pyx_st_double;
+typedef struct { char c; long double x; } __Pyx_st_longdouble;
+typedef struct { char c; void *x; } __Pyx_st_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+/* These are for computing the padding at the end of the struct to align
+   on the first member of the struct. This will probably the same as above,
+   but we don't have any guarantees.
+ */
+typedef struct { short x; char c; } __Pyx_pad_short;
+typedef struct { int x; char c; } __Pyx_pad_int;
+typedef struct { long x; char c; } __Pyx_pad_long;
+typedef struct { float x; char c; } __Pyx_pad_float;
+typedef struct { double x; char c; } __Pyx_pad_double;
+typedef struct { long double x; char c; } __Pyx_pad_longdouble;
+typedef struct { void *x; char c; } __Pyx_pad_void_p;
+#ifdef HAVE_LONG_LONG
+typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
+#endif
+static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
+  switch (ch) {
+    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
+    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
+    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
+    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
+#ifdef HAVE_LONG_LONG
+    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
+#endif
+    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
+    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
+    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
+    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
+    default:
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+}
+static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
+  switch (ch) {
+    case 'c':
+        return 'H';
+    case 'b': case 'h': case 'i':
+    case 'l': case 'q': case 's': case 'p':
+        return 'I';
+    case 'B': case 'H': case 'I': case 'L': case 'Q':
+        return 'U';
+    case 'f': case 'd': case 'g':
+        return (is_complex ? 'C' : 'R');
+    case 'O':
+        return 'O';
+    case 'P':
+        return 'P';
+    default: {
+      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
+      return 0;
+    }
+  }
+}
+static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
+  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
+    const char* expected;
+    const char* quote;
+    if (ctx->head == NULL) {
+      expected = "end";
+      quote = "";
+    } else {
+      expected = ctx->head->field->type->name;
+      quote = "'";
+    }
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected %s%s%s but got %s",
+                 quote, expected, quote,
+                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
+  } else {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_StructField* parent = (ctx->head - 1)->field;
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
+                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
+                 parent->type->name, field->name);
+  }
+}
+static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
+  char group;
+  size_t size, offset, arraysize = 1;
+  if (ctx->enc_type == 0) return 0;
+  if (ctx->head->field->type->arraysize[0]) {
+    int i, ndim = 0;
+    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
+        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
+        ndim = 1;
+        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
+            PyErr_Format(PyExc_ValueError,
+                         "Expected a dimension of size %zu, got %zu",
+                         ctx->head->field->type->arraysize[0], ctx->enc_count);
+            return -1;
+        }
+    }
+    if (!ctx->is_valid_array) {
+      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
+                   ctx->head->field->type->ndim, ndim);
+      return -1;
+    }
+    for (i = 0; i < ctx->head->field->type->ndim; i++) {
+      arraysize *= ctx->head->field->type->arraysize[i];
+    }
+    ctx->is_valid_array = 0;
+    ctx->enc_count = 1;
+  }
+  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
+  do {
+    __Pyx_StructField* field = ctx->head->field;
+    __Pyx_TypeInfo* type = field->type;
+    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
+      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
+    } else {
+      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
+    }
+    if (ctx->enc_packmode == '@') {
+      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
+      size_t align_mod_offset;
+      if (align_at == 0) return -1;
+      align_mod_offset = ctx->fmt_offset % align_at;
+      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
+      if (ctx->struct_alignment == 0)
+          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
+                                                                 ctx->is_complex);
+    }
+    if (type->size != size || type->typegroup != group) {
+      if (type->typegroup == 'C' && type->fields != NULL) {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        ++ctx->head;
+        ctx->head->field = type->fields;
+        ctx->head->parent_offset = parent_offset;
+        continue;
+      }
+      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
+      } else {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+      }
+    }
+    offset = ctx->head->parent_offset + field->offset;
+    if (ctx->fmt_offset != offset) {
+      PyErr_Format(PyExc_ValueError,
+                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
+                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
+      return -1;
+    }
+    ctx->fmt_offset += size;
+    if (arraysize)
+      ctx->fmt_offset += (arraysize - 1) * size;
+    --ctx->enc_count; /* Consume from buffer string */
+    while (1) {
+      if (field == &ctx->root) {
+        ctx->head = NULL;
+        if (ctx->enc_count != 0) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return -1;
+        }
+        break; /* breaks both loops as ctx->enc_count == 0 */
+      }
+      ctx->head->field = ++field;
+      if (field->type == NULL) {
+        --ctx->head;
+        field = ctx->head->field;
+        continue;
+      } else if (field->type->typegroup == 'S') {
+        size_t parent_offset = ctx->head->parent_offset + field->offset;
+        if (field->type->fields->type == NULL) continue; /* empty struct */
+        field = field->type->fields;
+        ++ctx->head;
+        ctx->head->field = field;
+        ctx->head->parent_offset = parent_offset;
+        break;
+      } else {
+        break;
+      }
+    }
+  } while (ctx->enc_count);
+  ctx->enc_type = 0;
+  ctx->is_complex = 0;
+  return 0;
+}
+static CYTHON_INLINE PyObject *
+__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
+{
+    const char *ts = *tsp;
+    int i = 0, number;
+    int ndim = ctx->head->field->type->ndim;
+;
+    ++ts;
+    if (ctx->new_count != 1) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Cannot handle repeated arrays in format string");
+        return NULL;
+    }
+    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+    while (*ts && *ts != ')') {
+        switch (*ts) {
+            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
+            default:  break;  /* not a 'break' in the loop */
+        }
+        number = __Pyx_BufFmt_ExpectNumber(&ts);
+        if (number == -1) return NULL;
+        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
+            return PyErr_Format(PyExc_ValueError,
+                        "Expected a dimension of size %zu, got %d",
+                        ctx->head->field->type->arraysize[i], number);
+        if (*ts != ',' && *ts != ')')
+            return PyErr_Format(PyExc_ValueError,
+                                "Expected a comma in format string, got '%c'", *ts);
+        if (*ts == ',') ts++;
+        i++;
+    }
+    if (i != ndim)
+        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
+                            ctx->head->field->type->ndim, i);
+    if (!*ts) {
+        PyErr_SetString(PyExc_ValueError,
+                        "Unexpected end of format string, expected ')'");
+        return NULL;
+    }
+    ctx->is_valid_array = 1;
+    ctx->new_count = 1;
+    *tsp = ++ts;
+    return Py_None;
+}
+static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
+  int got_Z = 0;
+  while (1) {
+    switch(*ts) {
+      case 0:
+        if (ctx->enc_type != 0 && ctx->head == NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        if (ctx->head != NULL) {
+          __Pyx_BufFmt_RaiseExpected(ctx);
+          return NULL;
+        }
+        return ts;
+      case ' ':
+      case '\r':
+      case '\n':
+        ++ts;
+        break;
+      case '<':
+        if (!__Pyx_IsLittleEndian()) {
+          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '>':
+      case '!':
+        if (__Pyx_IsLittleEndian()) {
+          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
+          return NULL;
+        }
+        ctx->new_packmode = '=';
+        ++ts;
+        break;
+      case '=':
+      case '@':
+      case '^':
+        ctx->new_packmode = *ts++;
+        break;
+      case 'T': /* substruct */
+        {
+          const char* ts_after_sub;
+          size_t i, struct_count = ctx->new_count;
+          size_t struct_alignment = ctx->struct_alignment;
+          ctx->new_count = 1;
+          ++ts;
+          if (*ts != '{') {
+            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
+            return NULL;
+          }
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0; /* Erase processed last struct element */
+          ctx->enc_count = 0;
+          ctx->struct_alignment = 0;
+          ++ts;
+          ts_after_sub = ts;
+          for (i = 0; i != struct_count; ++i) {
+            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
+            if (!ts_after_sub) return NULL;
+          }
+          ts = ts_after_sub;
+          if (struct_alignment) ctx->struct_alignment = struct_alignment;
+        }
+        break;
+      case '}': /* end of substruct; either repeat or move on */
+        {
+          size_t alignment = ctx->struct_alignment;
+          ++ts;
+          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+          ctx->enc_type = 0; /* Erase processed last struct element */
+          if (alignment && ctx->fmt_offset % alignment) {
+            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
+          }
+        }
+        return ts;
+      case 'x':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->fmt_offset += ctx->new_count;
+        ctx->new_count = 1;
+        ctx->enc_count = 0;
+        ctx->enc_type = 0;
+        ctx->enc_packmode = ctx->new_packmode;
+        ++ts;
+        break;
+      case 'Z':
+        got_Z = 1;
+        ++ts;
+        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
+          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
+          return NULL;
+        }
+      case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
+      case 'l': case 'L': case 'q': case 'Q':
+      case 'f': case 'd': case 'g':
+      case 'O': case 'p':
+        if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&
+            ctx->enc_packmode == ctx->new_packmode) {
+          ctx->enc_count += ctx->new_count;
+          ctx->new_count = 1;
+          got_Z = 0;
+          ++ts;
+          break;
+        }
+      case 's':
+        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
+        ctx->enc_count = ctx->new_count;
+        ctx->enc_packmode = ctx->new_packmode;
+        ctx->enc_type = *ts;
+        ctx->is_complex = got_Z;
+        ++ts;
+        ctx->new_count = 1;
+        got_Z = 0;
+        break;
+      case ':':
+        ++ts;
+        while(*ts != ':') ++ts;
+        ++ts;
+        break;
+      case '(':
+        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
+        break;
+      default:
+        {
+          int number = __Pyx_BufFmt_ExpectNumber(&ts);
+          if (number == -1) return NULL;
+          ctx->new_count = (size_t)number;
+        }
+    }
+  }
+}
+static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) {
+  buf->buf = NULL;
+  buf->obj = NULL;
+  buf->strides = __Pyx_zeros;
+  buf->shape = __Pyx_zeros;
+  buf->suboffsets = __Pyx_minusones;
+}
+static CYTHON_INLINE int __Pyx_GetBufferAndValidate(
+        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
+        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
+{
+  if (obj == Py_None || obj == NULL) {
+    __Pyx_ZeroBuffer(buf);
+    return 0;
+  }
+  buf->buf = NULL;
+  if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail;
+  if (buf->ndim != nd) {
+    PyErr_Format(PyExc_ValueError,
+                 "Buffer has wrong number of dimensions (expected %d, got %d)",
+                 nd, buf->ndim);
+    goto fail;
+  }
+  if (!cast) {
+    __Pyx_BufFmt_Context ctx;
+    __Pyx_BufFmt_Init(&ctx, stack, dtype);
+    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
+  }
+  if ((unsigned)buf->itemsize != dtype->size) {
+    PyErr_Format(PyExc_ValueError,
+      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
+      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
+      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
+    goto fail;
+  }
+  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
+  return 0;
+fail:;
+  __Pyx_ZeroBuffer(buf);
+  return -1;
+}
+static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
+  if (info->buf == NULL) return;
+  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
+  __Pyx_ReleaseBuffer(info);
+}
+
+static int
+__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
+                        int ndim,
+                        __Pyx_memviewslice *memviewslice,
+                        int memview_is_new_reference)
+{
+    __Pyx_RefNannyDeclarations
+    int i, retval=-1;
+    Py_buffer *buf = &memview->view;
+    __Pyx_RefNannySetupContext("init_memviewslice", 0);
+    if (!buf) {
+        PyErr_SetString(PyExc_ValueError,
+            "buf is NULL.");
+        goto fail;
+    } else if (memviewslice->memview || memviewslice->data) {
+        PyErr_SetString(PyExc_ValueError,
+            "memviewslice is already initialized!");
+        goto fail;
+    }
+    if (buf->strides) {
+        for (i = 0; i < ndim; i++) {
+            memviewslice->strides[i] = buf->strides[i];
+        }
+    } else {
+        Py_ssize_t stride = buf->itemsize;
+        for (i = ndim - 1; i >= 0; i--) {
+            memviewslice->strides[i] = stride;
+            stride *= buf->shape[i];
+        }
+    }
+    for (i = 0; i < ndim; i++) {
+        memviewslice->shape[i]   = buf->shape[i];
+        if (buf->suboffsets) {
+            memviewslice->suboffsets[i] = buf->suboffsets[i];
+        } else {
+            memviewslice->suboffsets[i] = -1;
+        }
+    }
+    memviewslice->memview = memview;
+    memviewslice->data = (char *)buf->buf;
+    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
+        Py_INCREF(memview);
+    }
+    retval = 0;
+    goto no_fail;
+fail:
+    memviewslice->memview = 0;
+    memviewslice->data = 0;
+    retval = -1;
+no_fail:
+    __Pyx_RefNannyFinishContext();
+    return retval;
+}
+static CYTHON_INLINE void __pyx_fatalerror(const char *fmt, ...) {
+    va_list vargs;
+    char msg[200];
+    va_start(vargs, fmt);
+#ifdef HAVE_STDARG_PROTOTYPES
+    va_start(vargs, fmt);
+#else
+    va_start(vargs);
+#endif
+    vsnprintf(msg, 200, fmt, vargs);
+    Py_FatalError(msg);
+    va_end(vargs);
+}
+static CYTHON_INLINE int
+__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
+                                   PyThread_type_lock lock)
+{
+    int result;
+    PyThread_acquire_lock(lock, 1);
+    result = (*acquisition_count)++;
+    PyThread_release_lock(lock);
+    return result;
+}
+static CYTHON_INLINE int
+__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
+                                   PyThread_type_lock lock)
+{
+    int result;
+    PyThread_acquire_lock(lock, 1);
+    result = (*acquisition_count)--;
+    PyThread_release_lock(lock);
+    return result;
+}
+static CYTHON_INLINE void
+__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
+{
+    int first_time;
+    struct __pyx_memoryview_obj *memview = memslice->memview;
+    if (!memview || (PyObject *) memview == Py_None)
+        return; /* allow uninitialized memoryview assignment */
+    if (__pyx_get_slice_count(memview) < 0)
+        __pyx_fatalerror("Acquisition count is %d (line %d)",
+                         __pyx_get_slice_count(memview), lineno);
+    first_time = __pyx_add_acquisition_count(memview) == 0;
+    if (first_time) {
+        if (have_gil) {
+            Py_INCREF((PyObject *) memview);
+        } else {
+            PyGILState_STATE _gilstate = PyGILState_Ensure();
+            Py_INCREF((PyObject *) memview);
+            PyGILState_Release(_gilstate);
+        }
+    }
+}
+static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,
+                                             int have_gil, int lineno) {
+    int last_time;
+    struct __pyx_memoryview_obj *memview = memslice->memview;
+    if (!memview ) {
+        return;
+    } else if ((PyObject *) memview == Py_None) {
+        memslice->memview = NULL;
+        return;
+    }
+    if (__pyx_get_slice_count(memview) <= 0)
+        __pyx_fatalerror("Acquisition count is %d (line %d)",
+                         __pyx_get_slice_count(memview), lineno);
+    last_time = __pyx_sub_acquisition_count(memview) == 1;
+    memslice->data = NULL;
+    if (last_time) {
+        if (have_gil) {
+            Py_CLEAR(memslice->memview);
+        } else {
+            PyGILState_STATE _gilstate = PyGILState_Ensure();
+            Py_CLEAR(memslice->memview);
+            PyGILState_Release(_gilstate);
+        }
+    } else {
+        memslice->memview = NULL;
+    }
+}
+
+static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    PyThreadState *tstate = PyThreadState_GET();
+    tmp_type = tstate->curexc_type;
+    tmp_value = tstate->curexc_value;
+    tmp_tb = tstate->curexc_traceback;
+    tstate->curexc_type = type;
+    tstate->curexc_value = value;
+    tstate->curexc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_Restore(type, value, tb);
+#endif
+}
+static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyThreadState *tstate = PyThreadState_GET();
+    *type = tstate->curexc_type;
+    *value = tstate->curexc_value;
+    *tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+#else
+    PyErr_Fetch(type, value, tb);
+#endif
+}
+
+static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) {
+    PyErr_Format(PyExc_TypeError,
+        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
+        name, type->tp_name, Py_TYPE(obj)->tp_name);
+}
+static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
+    const char *name, int exact)
+{
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    if (none_allowed && obj == Py_None) return 1;
+    else if (exact) {
+        if (likely(Py_TYPE(obj) == type)) return 1;
+        #if PY_MAJOR_VERSION == 2
+        else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
+        #endif
+    }
+    else {
+        if (likely(PyObject_TypeCheck(obj, type))) return 1;
+    }
+    __Pyx_RaiseArgumentTypeInvalid(name, obj, type);
+    return 0;
+}
+
+#if CYTHON_COMPILING_IN_CPYTHON
+static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
+    PyObject *result;
+    ternaryfunc call = func->ob_type->tp_call;
+    if (unlikely(!call))
+        return PyObject_Call(func, arg, kw);
+#if PY_VERSION_HEX >= 0x02060000
+    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
+        return NULL;
+#endif
+    result = (*call)(func, arg, kw);
+#if PY_VERSION_HEX >= 0x02060000
+    Py_LeaveRecursiveCall();
+#endif
+    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
+        PyErr_SetString(
+            PyExc_SystemError,
+            "NULL result without error in PyObject_Call");
+    }
+    return result;
+}
+#endif
+
+#if PY_MAJOR_VERSION < 3
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
+                        CYTHON_UNUSED PyObject *cause) {
+    Py_XINCREF(type);
+    if (!value || value == Py_None)
+        value = NULL;
+    else
+        Py_INCREF(value);
+    if (!tb || tb == Py_None)
+        tb = NULL;
+    else {
+        Py_INCREF(tb);
+        if (!PyTraceBack_Check(tb)) {
+            PyErr_SetString(PyExc_TypeError,
+                "raise: arg 3 must be a traceback or None");
+            goto raise_error;
+        }
+    }
+    #if PY_VERSION_HEX < 0x02050000
+    if (PyClass_Check(type)) {
+    #else
+    if (PyType_Check(type)) {
+    #endif
+#if CYTHON_COMPILING_IN_PYPY
+        if (!value) {
+            Py_INCREF(Py_None);
+            value = Py_None;
+        }
+#endif
+        PyErr_NormalizeException(&type, &value, &tb);
+    } else {
+        if (value) {
+            PyErr_SetString(PyExc_TypeError,
+                "instance exception may not have a separate value");
+            goto raise_error;
+        }
+        value = type;
+        #if PY_VERSION_HEX < 0x02050000
+        if (PyInstance_Check(type)) {
+            type = (PyObject*) ((PyInstanceObject*)type)->in_class;
+            Py_INCREF(type);
+        } else {
+            type = 0;
+            PyErr_SetString(PyExc_TypeError,
+                "raise: exception must be an old-style class or instance");
+            goto raise_error;
+        }
+        #else
+        type = (PyObject*) Py_TYPE(type);
+        Py_INCREF(type);
+        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
+            PyErr_SetString(PyExc_TypeError,
+                "raise: exception class must be a subclass of BaseException");
+            goto raise_error;
+        }
+        #endif
+    }
+    __Pyx_ErrRestore(type, value, tb);
+    return;
+raise_error:
+    Py_XDECREF(value);
+    Py_XDECREF(type);
+    Py_XDECREF(tb);
+    return;
+}
+#else /* Python 3+ */
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
+    PyObject* owned_instance = NULL;
+    if (tb == Py_None) {
+        tb = 0;
+    } else if (tb && !PyTraceBack_Check(tb)) {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: arg 3 must be a traceback or None");
+        goto bad;
+    }
+    if (value == Py_None)
+        value = 0;
+    if (PyExceptionInstance_Check(type)) {
+        if (value) {
+            PyErr_SetString(PyExc_TypeError,
+                "instance exception may not have a separate value");
+            goto bad;
+        }
+        value = type;
+        type = (PyObject*) Py_TYPE(value);
+    } else if (PyExceptionClass_Check(type)) {
+        PyObject *instance_class = NULL;
+        if (value && PyExceptionInstance_Check(value)) {
+            instance_class = (PyObject*) Py_TYPE(value);
+            if (instance_class != type) {
+                if (PyObject_IsSubclass(instance_class, type)) {
+                    type = instance_class;
+                } else {
+                    instance_class = NULL;
+                }
+            }
+        }
+        if (!instance_class) {
+            PyObject *args;
+            if (!value)
+                args = PyTuple_New(0);
+            else if (PyTuple_Check(value)) {
+                Py_INCREF(value);
+                args = value;
+            } else
+                args = PyTuple_Pack(1, value);
+            if (!args)
+                goto bad;
+            owned_instance = PyObject_Call(type, args, NULL);
+            Py_DECREF(args);
+            if (!owned_instance)
+                goto bad;
+            value = owned_instance;
+            if (!PyExceptionInstance_Check(value)) {
+                PyErr_Format(PyExc_TypeError,
+                             "calling %R should have returned an instance of "
+                             "BaseException, not %R",
+                             type, Py_TYPE(value));
+                goto bad;
+            }
+        }
+    } else {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: exception class must be a subclass of BaseException");
+        goto bad;
+    }
+#if PY_VERSION_HEX >= 0x03030000
+    if (cause) {
+#else
+    if (cause && cause != Py_None) {
+#endif
+        PyObject *fixed_cause;
+        if (cause == Py_None) {
+            fixed_cause = NULL;
+        } else if (PyExceptionClass_Check(cause)) {
+            fixed_cause = PyObject_CallObject(cause, NULL);
+            if (fixed_cause == NULL)
+                goto bad;
+        } else if (PyExceptionInstance_Check(cause)) {
+            fixed_cause = cause;
+            Py_INCREF(fixed_cause);
+        } else {
+            PyErr_SetString(PyExc_TypeError,
+                            "exception causes must derive from "
+                            "BaseException");
+            goto bad;
+        }
+        PyException_SetCause(value, fixed_cause);
+    }
+    PyErr_SetObject(type, value);
+    if (tb) {
+        PyThreadState *tstate = PyThreadState_GET();
+        PyObject* tmp_tb = tstate->curexc_traceback;
+        if (tb != tmp_tb) {
+            Py_INCREF(tb);
+            tstate->curexc_traceback = tb;
+            Py_XDECREF(tmp_tb);
+        }
+    }
+bad:
+    Py_XDECREF(owned_instance);
+    return;
+}
+#endif
+
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
+    PyErr_Format(PyExc_ValueError,
+                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
+}
+
+static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
+    PyErr_Format(PyExc_ValueError,
+                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
+                 index, (index == 1) ? "" : "s");
+}
+
+static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
+    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
+}
+
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    if (likely(PyObject_TypeCheck(obj, type)))
+        return 1;
+    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
+                 Py_TYPE(obj)->tp_name, type->tp_name);
+    return 0;
+}
+
+static void __Pyx_RaiseArgtupleInvalid(
+    const char* func_name,
+    int exact,
+    Py_ssize_t num_min,
+    Py_ssize_t num_max,
+    Py_ssize_t num_found)
+{
+    Py_ssize_t num_expected;
+    const char *more_or_less;
+    if (num_found < num_min) {
+        num_expected = num_min;
+        more_or_less = "at least";
+    } else {
+        num_expected = num_max;
+        more_or_less = "at most";
+    }
+    if (exact) {
+        more_or_less = "exactly";
+    }
+    PyErr_Format(PyExc_TypeError,
+                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                 func_name, more_or_less, num_expected,
+                 (num_expected == 1) ? "" : "s", num_found);
+}
+
+static void __Pyx_RaiseDoubleKeywordsError(
+    const char* func_name,
+    PyObject* kw_name)
+{
+    PyErr_Format(PyExc_TypeError,
+        #if PY_MAJOR_VERSION >= 3
+        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
+        #else
+        "%s() got multiple values for keyword argument '%s'", func_name,
+        PyString_AsString(kw_name));
+        #endif
+}
+
+static int __Pyx_ParseOptionalKeywords(
+    PyObject *kwds,
+    PyObject **argnames[],
+    PyObject *kwds2,
+    PyObject *values[],
+    Py_ssize_t num_pos_args,
+    const char* function_name)
+{
+    PyObject *key = 0, *value = 0;
+    Py_ssize_t pos = 0;
+    PyObject*** name;
+    PyObject*** first_kw_arg = argnames + num_pos_args;
+    while (PyDict_Next(kwds, &pos, &key, &value)) {
+        name = first_kw_arg;
+        while (*name && (**name != key)) name++;
+        if (*name) {
+            values[name-argnames] = value;
+            continue;
+        }
+        name = first_kw_arg;
+        #if PY_MAJOR_VERSION < 3
+        if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {
+            while (*name) {
+                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
+                        && _PyString_Eq(**name, key)) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    if ((**argname == key) || (
+                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
+                             && _PyString_Eq(**argname, key))) {
+                        goto arg_passed_twice;
+                    }
+                    argname++;
+                }
+            }
+        } else
+        #endif
+        if (likely(PyUnicode_Check(key))) {
+            while (*name) {
+                int cmp = (**name == key) ? 0 :
+                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
+                #endif
+                    PyUnicode_Compare(**name, key);
+                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                if (cmp == 0) {
+                    values[name-argnames] = value;
+                    break;
+                }
+                name++;
+            }
+            if (*name) continue;
+            else {
+                PyObject*** argname = argnames;
+                while (argname != first_kw_arg) {
+                    int cmp = (**argname == key) ? 0 :
+                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
+                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
+                    #endif
+                        PyUnicode_Compare(**argname, key);
+                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
+                    if (cmp == 0) goto arg_passed_twice;
+                    argname++;
+                }
+            }
+        } else
+            goto invalid_keyword_type;
+        if (kwds2) {
+            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
+        } else {
+            goto invalid_keyword;
+        }
+    }
+    return 0;
+arg_passed_twice:
+    __Pyx_RaiseDoubleKeywordsError(function_name, key);
+    goto bad;
+invalid_keyword_type:
+    PyErr_Format(PyExc_TypeError,
+        "%.200s() keywords must be strings", function_name);
+    goto bad;
+invalid_keyword:
+    PyErr_Format(PyExc_TypeError,
+    #if PY_MAJOR_VERSION < 3
+        "%.200s() got an unexpected keyword argument '%.200s'",
+        function_name, PyString_AsString(key));
+    #else
+        "%s() got an unexpected keyword argument '%U'",
+        function_name, key);
+    #endif
+bad:
+    return -1;
+}
+
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+    return PyObject_RichCompareBool(s1, s2, equals);
+#else
+    if (s1 == s2) {
+        return (equals == Py_EQ);
+    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
+        const char *ps1, *ps2;
+        Py_ssize_t length = PyBytes_GET_SIZE(s1);
+        if (length != PyBytes_GET_SIZE(s2))
+            return (equals == Py_NE);
+        ps1 = PyBytes_AS_STRING(s1);
+        ps2 = PyBytes_AS_STRING(s2);
+        if (ps1[0] != ps2[0]) {
+            return (equals == Py_NE);
+        } else if (length == 1) {
+            return (equals == Py_EQ);
+        } else {
+            int result = memcmp(ps1, ps2, (size_t)length);
+            return (equals == Py_EQ) ? (result == 0) : (result != 0);
+        }
+    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
+        return (equals == Py_NE);
+    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
+        return (equals == Py_NE);
+    } else {
+        int result;
+        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        if (!py_result)
+            return -1;
+        result = __Pyx_PyObject_IsTrue(py_result);
+        Py_DECREF(py_result);
+        return result;
+    }
+#endif
+}
+
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+    return PyObject_RichCompareBool(s1, s2, equals);
+#else
+#if PY_MAJOR_VERSION < 3
+    PyObject* owned_ref = NULL;
+#endif
+    int s1_is_unicode, s2_is_unicode;
+    if (s1 == s2) {
+        goto return_eq;
+    }
+    s1_is_unicode = PyUnicode_CheckExact(s1);
+    s2_is_unicode = PyUnicode_CheckExact(s2);
+#if PY_MAJOR_VERSION < 3
+    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
+        owned_ref = PyUnicode_FromObject(s2);
+        if (unlikely(!owned_ref))
+            return -1;
+        s2 = owned_ref;
+        s2_is_unicode = 1;
+    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
+        owned_ref = PyUnicode_FromObject(s1);
+        if (unlikely(!owned_ref))
+            return -1;
+        s1 = owned_ref;
+        s1_is_unicode = 1;
+    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
+        return __Pyx_PyBytes_Equals(s1, s2, equals);
+    }
+#endif
+    if (s1_is_unicode & s2_is_unicode) {
+        Py_ssize_t length;
+        int kind;
+        void *data1, *data2;
+        #if CYTHON_PEP393_ENABLED
+        if (unlikely(PyUnicode_READY(s1) < 0) || unlikely(PyUnicode_READY(s2) < 0))
+            return -1;
+        #endif
+        length = __Pyx_PyUnicode_GET_LENGTH(s1);
+        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
+            goto return_ne;
+        }
+        kind = __Pyx_PyUnicode_KIND(s1);
+        if (kind != __Pyx_PyUnicode_KIND(s2)) {
+            goto return_ne;
+        }
+        data1 = __Pyx_PyUnicode_DATA(s1);
+        data2 = __Pyx_PyUnicode_DATA(s2);
+        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
+            goto return_ne;
+        } else if (length == 1) {
+            goto return_eq;
+        } else {
+            int result = memcmp(data1, data2, (size_t)(length * kind));
+            #if PY_MAJOR_VERSION < 3
+            Py_XDECREF(owned_ref);
+            #endif
+            return (equals == Py_EQ) ? (result == 0) : (result != 0);
+        }
+    } else if ((s1 == Py_None) & s2_is_unicode) {
+        goto return_ne;
+    } else if ((s2 == Py_None) & s1_is_unicode) {
+        goto return_ne;
+    } else {
+        int result;
+        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        if (!py_result)
+            return -1;
+        result = __Pyx_PyObject_IsTrue(py_result);
+        Py_DECREF(py_result);
+        return result;
+    }
+return_eq:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(owned_ref);
+    #endif
+    return (equals == Py_EQ);
+return_ne:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(owned_ref);
+    #endif
+    return (equals == Py_NE);
+#endif
+}
+
+static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
+    Py_ssize_t q = a / b;
+    Py_ssize_t r = a - q*b;
+    q -= ((r != 0) & ((r ^ b) < 0));
+    return q;
+}
+
+static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
+#if CYTHON_COMPILING_IN_CPYTHON
+#if PY_MAJOR_VERSION >= 3
+    if (likely(PyUnicode_Check(n)))
+#else
+    if (likely(PyString_Check(n)))
+#endif
+        return __Pyx_PyObject_GetAttrStr(o, n);
+#endif
+    return PyObject_GetAttr(o, n);
+}
+
+static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
+         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
+         const char* encoding, const char* errors,
+         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
+    Py_ssize_t length;
+    if (unlikely((start < 0) | (stop < 0))) {
+        length = strlen(cstring);
+        if (start < 0) {
+            start += length;
+            if (start < 0)
+                start = 0;
+        }
+        if (stop < 0)
+            stop += length;
+    }
+    length = stop - start;
+    if (unlikely(length <= 0))
+        return PyUnicode_FromUnicode(NULL, 0);
+    cstring += start;
+    if (decode_func) {
+        return decode_func(cstring, length, errors);
+    } else {
+        return PyUnicode_Decode(cstring, length, encoding, errors);
+    }
+}
+
+static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyThreadState *tstate = PyThreadState_GET();
+    *type = tstate->exc_type;
+    *value = tstate->exc_value;
+    *tb = tstate->exc_traceback;
+    Py_XINCREF(*type);
+    Py_XINCREF(*value);
+    Py_XINCREF(*tb);
+#else
+    PyErr_GetExcInfo(type, value, tb);
+#endif
+}
+static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    PyThreadState *tstate = PyThreadState_GET();
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = type;
+    tstate->exc_value = value;
+    tstate->exc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_SetExcInfo(type, value, tb);
+#endif
+}
+
+static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
+    PyObject *local_type, *local_value, *local_tb;
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    PyThreadState *tstate = PyThreadState_GET();
+    local_type = tstate->curexc_type;
+    local_value = tstate->curexc_value;
+    local_tb = tstate->curexc_traceback;
+    tstate->curexc_type = 0;
+    tstate->curexc_value = 0;
+    tstate->curexc_traceback = 0;
+#else
+    PyErr_Fetch(&local_type, &local_value, &local_tb);
+#endif
+    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
+#if CYTHON_COMPILING_IN_CPYTHON
+    if (unlikely(tstate->curexc_type))
+#else
+    if (unlikely(PyErr_Occurred()))
+#endif
+        goto bad;
+    #if PY_MAJOR_VERSION >= 3
+    if (local_tb) {
+        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
+            goto bad;
+    }
+    #endif
+    Py_XINCREF(local_tb);
+    Py_XINCREF(local_type);
+    Py_XINCREF(local_value);
+    *type = local_type;
+    *value = local_value;
+    *tb = local_tb;
+#if CYTHON_COMPILING_IN_CPYTHON
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = local_type;
+    tstate->exc_value = local_value;
+    tstate->exc_traceback = local_tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_SetExcInfo(local_type, local_value, local_tb);
+#endif
+    return 0;
+bad:
+    *type = 0;
+    *value = 0;
+    *tb = 0;
+    Py_XDECREF(local_type);
+    Py_XDECREF(local_value);
+    Py_XDECREF(local_tb);
+    return -1;
+}
+
+static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyThreadState *tstate = PyThreadState_GET();
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = *type;
+    tstate->exc_value = *value;
+    tstate->exc_traceback = *tb;
+#else
+    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
+    PyErr_SetExcInfo(*type, *value, *tb);
+#endif
+    *type = tmp_type;
+    *value = tmp_value;
+    *tb = tmp_tb;
+}
+
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
+    PyObject *r;
+    if (!j) return NULL;
+    r = PyObject_GetItem(o, j);
+    Py_DECREF(j);
+    return r;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
+    if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
+        PyObject *r = PyList_GET_ITEM(o, i);
+        Py_INCREF(r);
+        return r;
+    }
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
+    if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
+        PyObject *r = PyTuple_GET_ITEM(o, i);
+        Py_INCREF(r);
+        return r;
+    }
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
+                                                     int is_list, int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    if (is_list || PyList_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
+            PyObject *r = PyList_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
+    }
+    else if (PyTuple_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
+        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
+            PyObject *r = PyTuple_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
+    } else {
+        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+        if (likely(m && m->sq_item)) {
+            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
+                Py_ssize_t l = m->sq_length(o);
+                if (likely(l >= 0)) {
+                    i += l;
+                } else {
+                    if (PyErr_ExceptionMatches(PyExc_OverflowError))
+                        PyErr_Clear();
+                    else
+                        return NULL;
+                }
+            }
+            return m->sq_item(o, i);
+        }
+    }
+#else
+    if (is_list || PySequence_Check(o)) {
+        return PySequence_GetItem(o, i);
+    }
+#endif
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+}
+
+static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
+    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
+}
+
+static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
+    long q = a / b;
+    long r = a - q*b;
+    q -= ((r != 0) & ((r ^ b) < 0));
+    return q;
+}
+
+static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno,
+                                  CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename,
+                                  int full_traceback) {
+    PyObject *old_exc, *old_val, *old_tb;
+    PyObject *ctx;
+    __Pyx_ErrFetch(&old_exc, &old_val, &old_tb);
+    if (full_traceback) {
+        Py_XINCREF(old_exc);
+        Py_XINCREF(old_val);
+        Py_XINCREF(old_tb);
+        __Pyx_ErrRestore(old_exc, old_val, old_tb);
+        PyErr_PrintEx(1);
+    }
+    #if PY_MAJOR_VERSION < 3
+    ctx = PyString_FromString(name);
+    #else
+    ctx = PyUnicode_FromString(name);
+    #endif
+    __Pyx_ErrRestore(old_exc, old_val, old_tb);
+    if (!ctx) {
+        PyErr_WriteUnraisable(Py_None);
+    } else {
+        PyErr_WriteUnraisable(ctx);
+        Py_DECREF(ctx);
+    }
+}
+
+static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
+#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0)
+    PyObject *ob = PyCapsule_New(vtable, 0, 0);
+#else
+    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
+#endif
+    if (!ob)
+        goto bad;
+    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
+        goto bad;
+    Py_DECREF(ob);
+    return 0;
+bad:
+    Py_XDECREF(ob);
+    return -1;
+}
+
+#if PY_MAJOR_VERSION < 3
+static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
+  #if PY_VERSION_HEX >= 0x02060000
+    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
+  #endif
+        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
+        if (PyObject_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
+        if (PyObject_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
+  #if PY_VERSION_HEX < 0x02060000
+    if (obj->ob_type->tp_dict) {
+        PyObject *getbuffer_cobj = PyObject_GetItem(
+            obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer);
+        if (getbuffer_cobj) {
+            getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj);
+            Py_DECREF(getbuffer_cobj);
+            if (!func)
+                goto fail;
+            return func(obj, view, flags);
+        } else {
+            PyErr_Clear();
+        }
+    }
+  #endif
+    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
+#if PY_VERSION_HEX < 0x02060000
+fail:
+#endif
+    return -1;
+}
+static void __Pyx_ReleaseBuffer(Py_buffer *view) {
+    PyObject *obj = view->obj;
+    if (!obj) return;
+  #if PY_VERSION_HEX >= 0x02060000
+    if (PyObject_CheckBuffer(obj)) {
+        PyBuffer_Release(view);
+        return;
+    }
+  #endif
+        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; }
+  #if PY_VERSION_HEX < 0x02060000
+    if (obj->ob_type->tp_dict) {
+        PyObject *releasebuffer_cobj = PyObject_GetItem(
+            obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer);
+        if (releasebuffer_cobj) {
+            releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj);
+            Py_DECREF(releasebuffer_cobj);
+            if (!func)
+                goto fail;
+            func(obj, view);
+            return;
+        } else {
+            PyErr_Clear();
+        }
+    }
+  #endif
+    goto nofail;
+#if PY_VERSION_HEX < 0x02060000
+fail:
+#endif
+    PyErr_WriteUnraisable(obj);
+nofail:
+    Py_DECREF(obj);
+    view->obj = NULL;
+}
+#endif /*  PY_MAJOR_VERSION < 3 */
+
+
+      static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_long(unsigned long value) {
+    const unsigned long neg_one = (unsigned long) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(unsigned long) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(unsigned long) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+        } else if (sizeof(unsigned long) <= sizeof(unsigned long long)) {
+            return PyLong_FromUnsignedLongLong((unsigned long long) value);
+        }
+    } else {
+        if (sizeof(unsigned long) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(unsigned long) <= sizeof(long long)) {
+            return PyLong_FromLongLong((long long) value);
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(unsigned long),
+                                     little, !is_unsigned);
+    }
+}
+
+#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func)             \
+    {                                                                     \
+        func_type value = func(x);                                        \
+        if (sizeof(target_type) < sizeof(func_type)) {                    \
+            if (unlikely(value != (func_type) (target_type) value)) {     \
+                func_type zero = 0;                                       \
+                PyErr_SetString(PyExc_OverflowError,                      \
+                    (is_unsigned && unlikely(value < zero)) ?             \
+                    "can't convert negative value to " #target_type :     \
+                    "value too large to convert to " #target_type);       \
+                return (target_type) -1;                                  \
+            }                                                             \
+        }                                                                 \
+        return (target_type) value;                                       \
+    }
+
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
+static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *x) {
+    const unsigned long neg_one = (unsigned long) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(unsigned long) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(unsigned long, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to unsigned long");
+                return (unsigned long) -1;
+            }
+            return (unsigned long) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(unsigned long)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return (unsigned long) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (unlikely(Py_SIZE(x) < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to unsigned long");
+                return (unsigned long) -1;
+            }
+            if (sizeof(unsigned long) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(unsigned long) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long long, PyLong_AsUnsignedLongLong)
+            }
+        } else {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(unsigned long)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return +(unsigned long) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(unsigned long) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (sizeof(unsigned long) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(unsigned long, long, PyLong_AsLong)
+            } else if (sizeof(unsigned long) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(unsigned long, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            unsigned long val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (unsigned long) -1;
+        }
+    } else {
+        unsigned long val;
+        PyObject *tmp = __Pyx_PyNumber_Int(x);
+        if (!tmp) return (unsigned long) -1;
+        val = __Pyx_PyInt_As_unsigned_long(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+}
+
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      return ::std::complex< float >(x, y);
+    }
+  #else
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      return x + y*(__pyx_t_float_complex)_Complex_I;
+    }
+  #endif
+#else
+    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
+      __pyx_t_float_complex z;
+      z.real = x;
+      z.imag = y;
+      return z;
+    }
+#endif
+
+#if CYTHON_CCOMPLEX
+#else
+    static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+       return (a.real == b.real) && (a.imag == b.imag);
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real + b.real;
+        z.imag = a.imag + b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real - b.real;
+        z.imag = a.imag - b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        z.real = a.real * b.real - a.imag * b.imag;
+        z.imag = a.real * b.imag + a.imag * b.real;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+        __pyx_t_float_complex z;
+        float denom = b.real * b.real + b.imag * b.imag;
+        z.real = (a.real * b.real + a.imag * b.imag) / denom;
+        z.imag = (a.imag * b.real - a.real * b.imag) / denom;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) {
+        __pyx_t_float_complex z;
+        z.real = -a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) {
+       return (a.real == 0) && (a.imag == 0);
+    }
+    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) {
+        __pyx_t_float_complex z;
+        z.real =  a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    #if 1
+        static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) {
+          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
+            return sqrtf(z.real*z.real + z.imag*z.imag);
+          #else
+            return hypotf(z.real, z.imag);
+          #endif
+        }
+        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
+            __pyx_t_float_complex z;
+            float r, lnr, theta, z_r, z_theta;
+            if (b.imag == 0 && b.real == (int)b.real) {
+                if (b.real < 0) {
+                    float denom = a.real * a.real + a.imag * a.imag;
+                    a.real = a.real / denom;
+                    a.imag = -a.imag / denom;
+                    b.real = -b.real;
+                }
+                switch ((int)b.real) {
+                    case 0:
+                        z.real = 1;
+                        z.imag = 0;
+                        return z;
+                    case 1:
+                        return a;
+                    case 2:
+                        z = __Pyx_c_prodf(a, a);
+                        return __Pyx_c_prodf(a, a);
+                    case 3:
+                        z = __Pyx_c_prodf(a, a);
+                        return __Pyx_c_prodf(z, a);
+                    case 4:
+                        z = __Pyx_c_prodf(a, a);
+                        return __Pyx_c_prodf(z, z);
+                }
+            }
+            if (a.imag == 0) {
+                if (a.real == 0) {
+                    return a;
+                }
+                r = a.real;
+                theta = 0;
+            } else {
+                r = __Pyx_c_absf(a);
+                theta = atan2f(a.imag, a.real);
+            }
+            lnr = logf(r);
+            z_r = expf(lnr * b.real - theta * b.imag);
+            z_theta = theta * b.real + lnr * b.imag;
+            z.real = z_r * cosf(z_theta);
+            z.imag = z_r * sinf(z_theta);
+            return z;
+        }
+    #endif
+#endif
+
+#if CYTHON_CCOMPLEX
+  #ifdef __cplusplus
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      return ::std::complex< double >(x, y);
+    }
+  #else
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      return x + y*(__pyx_t_double_complex)_Complex_I;
+    }
+  #endif
+#else
+    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
+      __pyx_t_double_complex z;
+      z.real = x;
+      z.imag = y;
+      return z;
+    }
+#endif
+
+#if CYTHON_CCOMPLEX
+#else
+    static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+       return (a.real == b.real) && (a.imag == b.imag);
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real + b.real;
+        z.imag = a.imag + b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real - b.real;
+        z.imag = a.imag - b.imag;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        z.real = a.real * b.real - a.imag * b.imag;
+        z.imag = a.real * b.imag + a.imag * b.real;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+        __pyx_t_double_complex z;
+        double denom = b.real * b.real + b.imag * b.imag;
+        z.real = (a.real * b.real + a.imag * b.imag) / denom;
+        z.imag = (a.imag * b.real - a.real * b.imag) / denom;
+        return z;
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) {
+        __pyx_t_double_complex z;
+        z.real = -a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) {
+       return (a.real == 0) && (a.imag == 0);
+    }
+    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) {
+        __pyx_t_double_complex z;
+        z.real =  a.real;
+        z.imag = -a.imag;
+        return z;
+    }
+    #if 1
+        static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) {
+          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
+            return sqrt(z.real*z.real + z.imag*z.imag);
+          #else
+            return hypot(z.real, z.imag);
+          #endif
+        }
+        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) {
+            __pyx_t_double_complex z;
+            double r, lnr, theta, z_r, z_theta;
+            if (b.imag == 0 && b.real == (int)b.real) {
+                if (b.real < 0) {
+                    double denom = a.real * a.real + a.imag * a.imag;
+                    a.real = a.real / denom;
+                    a.imag = -a.imag / denom;
+                    b.real = -b.real;
+                }
+                switch ((int)b.real) {
+                    case 0:
+                        z.real = 1;
+                        z.imag = 0;
+                        return z;
+                    case 1:
+                        return a;
+                    case 2:
+                        z = __Pyx_c_prod(a, a);
+                        return __Pyx_c_prod(a, a);
+                    case 3:
+                        z = __Pyx_c_prod(a, a);
+                        return __Pyx_c_prod(z, a);
+                    case 4:
+                        z = __Pyx_c_prod(a, a);
+                        return __Pyx_c_prod(z, z);
+                }
+            }
+            if (a.imag == 0) {
+                if (a.real == 0) {
+                    return a;
+                }
+                r = a.real;
+                theta = 0;
+            } else {
+                r = __Pyx_c_abs(a);
+                theta = atan2(a.imag, a.real);
+            }
+            lnr = log(r);
+            z_r = exp(lnr * b.real - theta * b.imag);
+            z_theta = theta * b.real + lnr * b.imag;
+            z.real = z_r * cos(z_theta);
+            z.imag = z_r * sin(z_theta);
+            return z;
+        }
+    #endif
+#endif
+
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
+    const int neg_one = (int) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(int) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(int) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+        } else if (sizeof(int) <= sizeof(unsigned long long)) {
+            return PyLong_FromUnsignedLongLong((unsigned long long) value);
+        }
+    } else {
+        if (sizeof(int) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(int) <= sizeof(long long)) {
+            return PyLong_FromLongLong((long long) value);
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(int),
+                                     little, !is_unsigned);
+    }
+}
+
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+    const int neg_one = (int) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(int) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to int");
+                return (int) -1;
+            }
+            return (int) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(int)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return (int) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (unlikely(Py_SIZE(x) < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to int");
+                return (int) -1;
+            }
+            if (sizeof(int) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(int) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong)
+            }
+        } else {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(int)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return +(int) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(int) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (sizeof(int) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong)
+            } else if (sizeof(int) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            int val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (int) -1;
+        }
+    } else {
+        int val;
+        PyObject *tmp = __Pyx_PyNumber_Int(x);
+        if (!tmp) return (int) -1;
+        val = __Pyx_PyInt_As_int(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+}
+
+static int
+__pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs,
+                             char order, int ndim)
+{
+    int i, index, step, start;
+    Py_ssize_t itemsize = mvs->memview->view.itemsize;
+    if (order == 'F') {
+        step = 1;
+        start = 0;
+    } else {
+        step = -1;
+        start = ndim - 1;
+    }
+    for (i = 0; i < ndim; i++) {
+        index = start + step * i;
+        if (mvs->suboffsets[index] >= 0 || mvs->strides[index] != itemsize)
+            return 0;
+        itemsize *= mvs->shape[index];
+    }
+    return 1;
+}
+
+static void
+__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
+                               void **out_start, void **out_end,
+                               int ndim, size_t itemsize)
+{
+    char *start, *end;
+    int i;
+    start = end = slice->data;
+    for (i = 0; i < ndim; i++) {
+        Py_ssize_t stride = slice->strides[i];
+        Py_ssize_t extent = slice->shape[i];
+        if (extent == 0) {
+            *out_start = *out_end = start;
+            return;
+        } else {
+            if (stride > 0)
+                end += stride * (extent - 1);
+            else
+                start += stride * (extent - 1);
+        }
+    }
+    *out_start = start;
+    *out_end = end + itemsize;
+}
+static int
+__pyx_slices_overlap(__Pyx_memviewslice *slice1,
+                     __Pyx_memviewslice *slice2,
+                     int ndim, size_t itemsize)
+{
+    void *start1, *end1, *start2, *end2;
+    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
+    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
+    return (start1 < end2) && (start2 < end1);
+}
+
+static __Pyx_memviewslice
+__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
+                                 const char *mode, int ndim,
+                                 size_t sizeof_dtype, int contig_flag,
+                                 int dtype_is_object)
+{
+    __Pyx_RefNannyDeclarations
+    int i;
+    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
+    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
+    Py_buffer *buf = &from_memview->view;
+    PyObject *shape_tuple = NULL;
+    PyObject *temp_int = NULL;
+    struct __pyx_array_obj *array_obj = NULL;
+    struct __pyx_memoryview_obj *memview_obj = NULL;
+    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
+    for (i = 0; i < ndim; i++) {
+        if (from_mvs->suboffsets[i] >= 0) {
+            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
+                                           "indirect dimensions (axis %d)", i);
+            goto fail;
+        }
+    }
+    shape_tuple = PyTuple_New(ndim);
+    if (unlikely(!shape_tuple)) {
+        goto fail;
+    }
+    __Pyx_GOTREF(shape_tuple);
+    for(i = 0; i < ndim; i++) {
+        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
+        if(unlikely(!temp_int)) {
+            goto fail;
+        } else {
+            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
+            temp_int = NULL;
+        }
+    }
+    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
+    if (unlikely(!array_obj)) {
+        goto fail;
+    }
+    __Pyx_GOTREF(array_obj);
+    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
+                                    (PyObject *) array_obj, contig_flag,
+                                    dtype_is_object,
+                                    from_mvs->memview->typeinfo);
+    if (unlikely(!memview_obj))
+        goto fail;
+    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
+        goto fail;
+    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
+                                                dtype_is_object) < 0))
+        goto fail;
+    goto no_fail;
+fail:
+    __Pyx_XDECREF(new_mvs.memview);
+    new_mvs.memview = NULL;
+    new_mvs.data = NULL;
+no_fail:
+    __Pyx_XDECREF(shape_tuple);
+    __Pyx_XDECREF(temp_int);
+    __Pyx_XDECREF(array_obj);
+    __Pyx_RefNannyFinishContext();
+    return new_mvs;
+}
+
+static CYTHON_INLINE PyObject *
+__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
+{
+    PyObject *cobj;
+#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 0)
+    cobj = PyCapsule_New(p, sig, NULL);
+#else
+    cobj = PyCObject_FromVoidPtr(p, NULL);
+#endif
+    return cobj;
+}
+
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
+    PyObject *empty_list = 0;
+    PyObject *module = 0;
+    PyObject *global_dict = 0;
+    PyObject *empty_dict = 0;
+    PyObject *list;
+    #if PY_VERSION_HEX < 0x03030000
+    PyObject *py_import;
+    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
+    if (!py_import)
+        goto bad;
+    #endif
+    if (from_list)
+        list = from_list;
+    else {
+        empty_list = PyList_New(0);
+        if (!empty_list)
+            goto bad;
+        list = empty_list;
+    }
+    global_dict = PyModule_GetDict(__pyx_m);
+    if (!global_dict)
+        goto bad;
+    empty_dict = PyDict_New();
+    if (!empty_dict)
+        goto bad;
+    #if PY_VERSION_HEX >= 0x02050000
+    {
+        #if PY_MAJOR_VERSION >= 3
+        if (level == -1) {
+            if (strchr(__Pyx_MODULE_NAME, '.')) {
+                #if PY_VERSION_HEX < 0x03030000
+                PyObject *py_level = PyInt_FromLong(1);
+                if (!py_level)
+                    goto bad;
+                module = PyObject_CallFunctionObjArgs(py_import,
+                    name, global_dict, empty_dict, list, py_level, NULL);
+                Py_DECREF(py_level);
+                #else
+                module = PyImport_ImportModuleLevelObject(
+                    name, global_dict, empty_dict, list, 1);
+                #endif
+                if (!module) {
+                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
+                        goto bad;
+                    PyErr_Clear();
+                }
+            }
+            level = 0; /* try absolute import on failure */
+        }
+        #endif
+        if (!module) {
+            #if PY_VERSION_HEX < 0x03030000
+            PyObject *py_level = PyInt_FromLong(level);
+            if (!py_level)
+                goto bad;
+            module = PyObject_CallFunctionObjArgs(py_import,
+                name, global_dict, empty_dict, list, py_level, NULL);
+            Py_DECREF(py_level);
+            #else
+            module = PyImport_ImportModuleLevelObject(
+                name, global_dict, empty_dict, list, level);
+            #endif
+        }
+    }
+    #else
+    if (level>0) {
+        PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4.");
+        goto bad;
+    }
+    module = PyObject_CallFunctionObjArgs(py_import,
+        name, global_dict, empty_dict, list, NULL);
+    #endif
+bad:
+    #if PY_VERSION_HEX < 0x03030000
+    Py_XDECREF(py_import);
+    #endif
+    Py_XDECREF(empty_list);
+    Py_XDECREF(empty_dict);
+    return module;
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+    const long neg_one = (long) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(long) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long long)) {
+            return PyLong_FromUnsignedLongLong((unsigned long long) value);
+        }
+    } else {
+        if (sizeof(long) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(long long)) {
+            return PyLong_FromLongLong((long long) value);
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(long),
+                                     little, !is_unsigned);
+    }
+}
+
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
+static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
+    const char neg_one = (char) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(char) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to char");
+                return (char) -1;
+            }
+            return (char) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(char)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return (char) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (unlikely(Py_SIZE(x) < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to char");
+                return (char) -1;
+            }
+            if (sizeof(char) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(char, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(char) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(char, unsigned long long, PyLong_AsUnsignedLongLong)
+            }
+        } else {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(char)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return +(char) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(char) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (sizeof(char) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(char, long, PyLong_AsLong)
+            } else if (sizeof(char) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(char, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            char val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (char) -1;
+        }
+    } else {
+        char val;
+        PyObject *tmp = __Pyx_PyNumber_Int(x);
+        if (!tmp) return (char) -1;
+        val = __Pyx_PyInt_As_char(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+}
+
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+    const long neg_one = (long) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+#if PY_MAJOR_VERSION < 3
+    if (likely(PyInt_Check(x))) {
+        if (sizeof(long) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to long");
+                return (long) -1;
+            }
+            return (long) val;
+        }
+    } else
+#endif
+    if (likely(PyLong_Check(x))) {
+        if (is_unsigned) {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(long)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return (long) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (unlikely(Py_SIZE(x) < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to long");
+                return (long) -1;
+            }
+            if (sizeof(long) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(long) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong)
+            }
+        } else {
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(long)) {
+                switch (Py_SIZE(x)) {
+                    case  0: return 0;
+                    case  1: return +(long) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(long) ((PyLongObject*)x)->ob_digit[0];
+                }
+            }
+ #endif
+#endif
+            if (sizeof(long) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong)
+            } else if (sizeof(long) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            long val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
+#endif
+            return (long) -1;
+        }
+    } else {
+        long val;
+        PyObject *tmp = __Pyx_PyNumber_Int(x);
+        if (!tmp) return (long) -1;
+        val = __Pyx_PyInt_As_long(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+}
+
+static int
+__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
+{
+    int i;
+    if (!a || !b)
+        return 0;
+    if (a == b)
+        return 1;
+    if (a->size != b->size || a->typegroup != b->typegroup ||
+            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
+        if (a->typegroup == 'H' || b->typegroup == 'H') {
+            return a->size == b->size;
+        } else {
+            return 0;
+        }
+    }
+    if (a->ndim) {
+        for (i = 0; i < a->ndim; i++)
+            if (a->arraysize[i] != b->arraysize[i])
+                return 0;
+    }
+    if (a->typegroup == 'S') {
+        if (a->flags != b->flags)
+            return 0;
+        if (a->fields || b->fields) {
+            if (!(a->fields && b->fields))
+                return 0;
+            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
+                __Pyx_StructField *field_a = a->fields + i;
+                __Pyx_StructField *field_b = b->fields + i;
+                if (field_a->offset != field_b->offset ||
+                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
+                    return 0;
+            }
+            return !a->fields[i].type && !b->fields[i].type;
+        }
+    }
+    return 1;
+}
+
+static int
+__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
+{
+    if (buf->shape[dim] <= 1)
+        return 1;
+    if (buf->strides) {
+        if (spec & __Pyx_MEMVIEW_CONTIG) {
+            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
+                if (buf->strides[dim] != sizeof(void *)) {
+                    PyErr_Format(PyExc_ValueError,
+                                 "Buffer is not indirectly contiguous "
+                                 "in dimension %d.", dim);
+                    goto fail;
+                }
+            } else if (buf->strides[dim] != buf->itemsize) {
+                PyErr_SetString(PyExc_ValueError,
+                                "Buffer and memoryview are not contiguous "
+                                "in the same dimension.");
+                goto fail;
+            }
+        }
+        if (spec & __Pyx_MEMVIEW_FOLLOW) {
+            Py_ssize_t stride = buf->strides[dim];
+            if (stride < 0)
+                stride = -stride;
+            if (stride < buf->itemsize) {
+                PyErr_SetString(PyExc_ValueError,
+                                "Buffer and memoryview are not contiguous "
+                                "in the same dimension.");
+                goto fail;
+            }
+        }
+    } else {
+        if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) {
+            PyErr_Format(PyExc_ValueError,
+                         "C-contiguous buffer is not contiguous in "
+                         "dimension %d", dim);
+            goto fail;
+        } else if (spec & (__Pyx_MEMVIEW_PTR)) {
+            PyErr_Format(PyExc_ValueError,
+                         "C-contiguous buffer is not indirect in "
+                         "dimension %d", dim);
+            goto fail;
+        } else if (buf->suboffsets) {
+            PyErr_SetString(PyExc_ValueError,
+                            "Buffer exposes suboffsets but no strides");
+            goto fail;
+        }
+    }
+    return 1;
+fail:
+    return 0;
+}
+static int
+__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)
+{
+    if (spec & __Pyx_MEMVIEW_DIRECT) {
+        if (buf->suboffsets && buf->suboffsets[dim] >= 0) {
+            PyErr_Format(PyExc_ValueError,
+                         "Buffer not compatible with direct access "
+                         "in dimension %d.", dim);
+            goto fail;
+        }
+    }
+    if (spec & __Pyx_MEMVIEW_PTR) {
+        if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) {
+            PyErr_Format(PyExc_ValueError,
+                         "Buffer is not indirectly accessible "
+                         "in dimension %d.", dim);
+            goto fail;
+        }
+    }
+    return 1;
+fail:
+    return 0;
+}
+static int
+__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
+{
+    int i;
+    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
+        Py_ssize_t stride = 1;
+        for (i = 0; i < ndim; i++) {
+            if (stride * buf->itemsize != buf->strides[i] &&
+                    buf->shape[i] > 1)
+            {
+                PyErr_SetString(PyExc_ValueError,
+                    "Buffer not fortran contiguous.");
+                goto fail;
+            }
+            stride = stride * buf->shape[i];
+        }
+    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
+        Py_ssize_t stride = 1;
+        for (i = ndim - 1; i >- 1; i--) {
+            if (stride * buf->itemsize != buf->strides[i] &&
+                    buf->shape[i] > 1) {
+                PyErr_SetString(PyExc_ValueError,
+                    "Buffer not C contiguous.");
+                goto fail;
+            }
+            stride = stride * buf->shape[i];
+        }
+    }
+    return 1;
+fail:
+    return 0;
+}
+static int __Pyx_ValidateAndInit_memviewslice(
+                int *axes_specs,
+                int c_or_f_flag,
+                int buf_flags,
+                int ndim,
+                __Pyx_TypeInfo *dtype,
+                __Pyx_BufFmt_StackElem stack[],
+                __Pyx_memviewslice *memviewslice,
+                PyObject *original_obj)
+{
+    struct __pyx_memoryview_obj *memview, *new_memview;
+    __Pyx_RefNannyDeclarations
+    Py_buffer *buf;
+    int i, spec = 0, retval = -1;
+    __Pyx_BufFmt_Context ctx;
+    int from_memoryview = __pyx_memoryview_check(original_obj);
+    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
+    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
+                                                            original_obj)->typeinfo)) {
+        memview = (struct __pyx_memoryview_obj *) original_obj;
+        new_memview = NULL;
+    } else {
+        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
+                                            original_obj, buf_flags, 0, dtype);
+        new_memview = memview;
+        if (unlikely(!memview))
+            goto fail;
+    }
+    buf = &memview->view;
+    if (buf->ndim != ndim) {
+        PyErr_Format(PyExc_ValueError,
+                "Buffer has wrong number of dimensions (expected %d, got %d)",
+                ndim, buf->ndim);
+        goto fail;
+    }
+    if (new_memview) {
+        __Pyx_BufFmt_Init(&ctx, stack, dtype);
+        if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
+    }
+    if ((unsigned) buf->itemsize != dtype->size) {
+        PyErr_Format(PyExc_ValueError,
+                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
+                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
+                     buf->itemsize,
+                     (buf->itemsize > 1) ? "s" : "",
+                     dtype->name,
+                     dtype->size,
+                     (dtype->size > 1) ? "s" : "");
+        goto fail;
+    }
+    for (i = 0; i < ndim; i++) {
+        spec = axes_specs[i];
+        if (!__pyx_check_strides(buf, i, ndim, spec))
+            goto fail;
+        if (!__pyx_check_suboffsets(buf, i, ndim, spec))
+            goto fail;
+    }
+    if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))
+        goto fail;
+    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
+                                         new_memview != NULL) == -1)) {
+        goto fail;
+    }
+    retval = 0;
+    goto no_fail;
+fail:
+    Py_XDECREF(new_memview);
+    retval = -1;
+no_fail:
+    __Pyx_RefNannyFinishContext();
+    return retval;
+}
+
+static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *obj) {
+    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
+    __Pyx_BufFmt_StackElem stack[1];
+    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
+    int retcode;
+    if (obj == Py_None) {
+        result.memview = (struct __pyx_memoryview_obj *) Py_None;
+        return result;
+    }
+    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
+                                                 PyBUF_RECORDS, 1,
+                                                 &__Pyx_TypeInfo_long, stack,
+                                                 &result, obj);
+    if (unlikely(retcode == -1))
+        goto __pyx_fail;
+    return result;
+__pyx_fail:
+    result.memview = NULL;
+    result.data = NULL;
+    return result;
+}
+
+static int __Pyx_check_binary_version(void) {
+    char ctversion[4], rtversion[4];
+    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
+    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
+    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
+        char message[200];
+        PyOS_snprintf(message, sizeof(message),
+                      "compiletime version %s of module '%.100s' "
+                      "does not match runtime version %s",
+                      ctversion, __Pyx_MODULE_NAME, rtversion);
+        #if PY_VERSION_HEX < 0x02050000
+        return PyErr_Warn(NULL, message);
+        #else
+        return PyErr_WarnEx(NULL, message, 1);
+        #endif
+    }
+    return 0;
+}
+
+#ifndef __PYX_HAVE_RT_ImportModule
+#define __PYX_HAVE_RT_ImportModule
+static PyObject *__Pyx_ImportModule(const char *name) {
+    PyObject *py_name = 0;
+    PyObject *py_module = 0;
+    py_name = __Pyx_PyIdentifier_FromString(name);
+    if (!py_name)
+        goto bad;
+    py_module = PyImport_Import(py_name);
+    Py_DECREF(py_name);
+    return py_module;
+bad:
+    Py_XDECREF(py_name);
+    return 0;
+}
+#endif
+
+#ifndef __PYX_HAVE_RT_ImportType
+#define __PYX_HAVE_RT_ImportType
+static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
+    size_t size, int strict)
+{
+    PyObject *py_module = 0;
+    PyObject *result = 0;
+    PyObject *py_name = 0;
+    char warning[200];
+    Py_ssize_t basicsize;
+#ifdef Py_LIMITED_API
+    PyObject *py_basicsize;
+#endif
+    py_module = __Pyx_ImportModule(module_name);
+    if (!py_module)
+        goto bad;
+    py_name = __Pyx_PyIdentifier_FromString(class_name);
+    if (!py_name)
+        goto bad;
+    result = PyObject_GetAttr(py_module, py_name);
+    Py_DECREF(py_name);
+    py_name = 0;
+    Py_DECREF(py_module);
+    py_module = 0;
+    if (!result)
+        goto bad;
+    if (!PyType_Check(result)) {
+        PyErr_Format(PyExc_TypeError,
+            "%.200s.%.200s is not a type object",
+            module_name, class_name);
+        goto bad;
+    }
+#ifndef Py_LIMITED_API
+    basicsize = ((PyTypeObject *)result)->tp_basicsize;
+#else
+    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
+    if (!py_basicsize)
+        goto bad;
+    basicsize = PyLong_AsSsize_t(py_basicsize);
+    Py_DECREF(py_basicsize);
+    py_basicsize = 0;
+    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
+        goto bad;
+#endif
+    if (!strict && (size_t)basicsize > size) {
+        PyOS_snprintf(warning, sizeof(warning),
+            "%s.%s size changed, may indicate binary incompatibility",
+            module_name, class_name);
+        #if PY_VERSION_HEX < 0x02050000
+        if (PyErr_Warn(NULL, warning) < 0) goto bad;
+        #else
+        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
+        #endif
+    }
+    else if ((size_t)basicsize != size) {
+        PyErr_Format(PyExc_ValueError,
+            "%.200s.%.200s has the wrong size, try recompiling",
+            module_name, class_name);
+        goto bad;
+    }
+    return (PyTypeObject *)result;
+bad:
+    Py_XDECREF(py_module);
+    Py_XDECREF(result);
+    return NULL;
+}
+#endif
+
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
+    int start = 0, mid = 0, end = count - 1;
+    if (end >= 0 && code_line > entries[end].code_line) {
+        return count;
+    }
+    while (start < end) {
+        mid = (start + end) / 2;
+        if (code_line < entries[mid].code_line) {
+            end = mid;
+        } else if (code_line > entries[mid].code_line) {
+             start = mid + 1;
+        } else {
+            return mid;
+        }
+    }
+    if (code_line <= entries[mid].code_line) {
+        return mid;
+    } else {
+        return mid + 1;
+    }
+}
+static PyCodeObject *__pyx_find_code_object(int code_line) {
+    PyCodeObject* code_object;
+    int pos;
+    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
+        return NULL;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
+        return NULL;
+    }
+    code_object = __pyx_code_cache.entries[pos].code_object;
+    Py_INCREF(code_object);
+    return code_object;
+}
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
+    int pos, i;
+    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
+    if (unlikely(!code_line)) {
+        return;
+    }
+    if (unlikely(!entries)) {
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (likely(entries)) {
+            __pyx_code_cache.entries = entries;
+            __pyx_code_cache.max_count = 64;
+            __pyx_code_cache.count = 1;
+            entries[0].code_line = code_line;
+            entries[0].code_object = code_object;
+            Py_INCREF(code_object);
+        }
+        return;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
+        PyCodeObject* tmp = entries[pos].code_object;
+        entries[pos].code_object = code_object;
+        Py_DECREF(tmp);
+        return;
+    }
+    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
+        int new_max = __pyx_code_cache.max_count + 64;
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
+            __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (unlikely(!entries)) {
+            return;
+        }
+        __pyx_code_cache.entries = entries;
+        __pyx_code_cache.max_count = new_max;
+    }
+    for (i=__pyx_code_cache.count; i>pos; i--) {
+        entries[i] = entries[i-1];
+    }
+    entries[pos].code_line = code_line;
+    entries[pos].code_object = code_object;
+    __pyx_code_cache.count++;
+    Py_INCREF(code_object);
+}
+
+#include "compile.h"
+#include "frameobject.h"
+#include "traceback.h"
+static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
+            const char *funcname, int c_line,
+            int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyObject *py_srcfile = 0;
+    PyObject *py_funcname = 0;
+    #if PY_MAJOR_VERSION < 3
+    py_srcfile = PyString_FromString(filename);
+    #else
+    py_srcfile = PyUnicode_FromString(filename);
+    #endif
+    if (!py_srcfile) goto bad;
+    if (c_line) {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #else
+        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
+        #endif
+    }
+    else {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromString(funcname);
+        #else
+        py_funcname = PyUnicode_FromString(funcname);
+        #endif
+    }
+    if (!py_funcname) goto bad;
+    py_code = __Pyx_PyCode_New(
+        0,            /*int argcount,*/
+        0,            /*int kwonlyargcount,*/
+        0,            /*int nlocals,*/
+        0,            /*int stacksize,*/
+        0,            /*int flags,*/
+        __pyx_empty_bytes, /*PyObject *code,*/
+        __pyx_empty_tuple, /*PyObject *consts,*/
+        __pyx_empty_tuple, /*PyObject *names,*/
+        __pyx_empty_tuple, /*PyObject *varnames,*/
+        __pyx_empty_tuple, /*PyObject *freevars,*/
+        __pyx_empty_tuple, /*PyObject *cellvars,*/
+        py_srcfile,   /*PyObject *filename,*/
+        py_funcname,  /*PyObject *name,*/
+        py_line,      /*int firstlineno,*/
+        __pyx_empty_bytes  /*PyObject *lnotab*/
+    );
+    Py_DECREF(py_srcfile);
+    Py_DECREF(py_funcname);
+    return py_code;
+bad:
+    Py_XDECREF(py_srcfile);
+    Py_XDECREF(py_funcname);
+    return NULL;
+}
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyObject *py_globals = 0;
+    PyFrameObject *py_frame = 0;
+    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
+    if (!py_code) {
+        py_code = __Pyx_CreateCodeObjectForTraceback(
+            funcname, c_line, py_line, filename);
+        if (!py_code) goto bad;
+        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
+    }
+    py_globals = PyModule_GetDict(__pyx_m);
+    if (!py_globals) goto bad;
+    py_frame = PyFrame_New(
+        PyThreadState_GET(), /*PyThreadState *tstate,*/
+        py_code,             /*PyCodeObject *code,*/
+        py_globals,          /*PyObject *globals,*/
+        0                    /*PyObject *locals*/
+    );
+    if (!py_frame) goto bad;
+    py_frame->f_lineno = py_line;
+    PyTraceBack_Here(py_frame);
+bad:
+    Py_XDECREF(py_code);
+    Py_XDECREF(py_frame);
+}
+
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+    while (t->p) {
+        #if PY_MAJOR_VERSION < 3
+        if (t->is_unicode) {
+            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
+        } else if (t->intern) {
+            *t->p = PyString_InternFromString(t->s);
+        } else {
+            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+        }
+        #else  /* Python 3+ has unicode identifiers */
+        if (t->is_unicode | t->is_str) {
+            if (t->intern) {
+                *t->p = PyUnicode_InternFromString(t->s);
+            } else if (t->encoding) {
+                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
+            } else {
+                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+            }
+        } else {
+            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
+        }
+        #endif
+        if (!*t->p)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
+    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
+}
+static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
+    Py_ssize_t ignore;
+    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
+}
+static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+    if (
+#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+            __Pyx_sys_getdefaultencoding_not_ascii &&
+#endif
+            PyUnicode_Check(o)) {
+#if PY_VERSION_HEX < 0x03030000
+        char* defenc_c;
+        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
+        if (!defenc) return NULL;
+        defenc_c = PyBytes_AS_STRING(defenc);
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+        {
+            char* end = defenc_c + PyBytes_GET_SIZE(defenc);
+            char* c;
+            for (c = defenc_c; c < end; c++) {
+                if ((unsigned char) (*c) >= 128) {
+                    PyUnicode_AsASCIIString(o);
+                    return NULL;
+                }
+            }
+        }
+#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/
+        *length = PyBytes_GET_SIZE(defenc);
+        return defenc_c;
+#else /* PY_VERSION_HEX < 0x03030000 */
+        if (PyUnicode_READY(o) == -1) return NULL;
+#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+        if (PyUnicode_IS_ASCII(o)) {
+            *length = PyUnicode_GET_LENGTH(o);
+            return PyUnicode_AsUTF8(o);
+        } else {
+            PyUnicode_AsASCIIString(o);
+            return NULL;
+        }
+#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
+        return PyUnicode_AsUTF8AndSize(o, length);
+#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
+#endif /* PY_VERSION_HEX < 0x03030000 */
+    } else
+#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII  || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */
+#if !CYTHON_COMPILING_IN_PYPY
+#if PY_VERSION_HEX >= 0x02060000
+    if (PyByteArray_Check(o)) {
+        *length = PyByteArray_GET_SIZE(o);
+        return PyByteArray_AS_STRING(o);
+    } else
+#endif
+#endif
+    {
+        char* result;
+        int r = PyBytes_AsStringAndSize(o, &result, length);
+        if (unlikely(r < 0)) {
+            return NULL;
+        } else {
+            return result;
+        }
+    }
+}
+static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+   int is_true = x == Py_True;
+   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
+   else return PyObject_IsTrue(x);
+}
+static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
+  PyNumberMethods *m;
+  const char *name = NULL;
+  PyObject *res = NULL;
+#if PY_MAJOR_VERSION < 3
+  if (PyInt_Check(x) || PyLong_Check(x))
+#else
+  if (PyLong_Check(x))
+#endif
+    return Py_INCREF(x), x;
+  m = Py_TYPE(x)->tp_as_number;
+#if PY_MAJOR_VERSION < 3
+  if (m && m->nb_int) {
+    name = "int";
+    res = PyNumber_Int(x);
+  }
+  else if (m && m->nb_long) {
+    name = "long";
+    res = PyNumber_Long(x);
+  }
+#else
+  if (m && m->nb_int) {
+    name = "int";
+    res = PyNumber_Long(x);
+  }
+#endif
+  if (res) {
+#if PY_MAJOR_VERSION < 3
+    if (!PyInt_Check(res) && !PyLong_Check(res)) {
+#else
+    if (!PyLong_Check(res)) {
+#endif
+      PyErr_Format(PyExc_TypeError,
+                   "__%.4s__ returned non-%.4s (type %.200s)",
+                   name, name, Py_TYPE(res)->tp_name);
+      Py_DECREF(res);
+      return NULL;
+    }
+  }
+  else if (!PyErr_Occurred()) {
+    PyErr_SetString(PyExc_TypeError,
+                    "an integer is required");
+  }
+  return res;
+}
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
+static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
+  Py_ssize_t ival;
+  PyObject *x;
+#if PY_MAJOR_VERSION < 3
+  if (likely(PyInt_CheckExact(b)))
+      return PyInt_AS_LONG(b);
+#endif
+  if (likely(PyLong_CheckExact(b))) {
+    #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+     #if CYTHON_USE_PYLONG_INTERNALS
+       switch (Py_SIZE(b)) {
+       case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0];
+       case  0: return 0;
+       case  1: return ((PyLongObject*)b)->ob_digit[0];
+       }
+     #endif
+    #endif
+  #if PY_VERSION_HEX < 0x02060000
+    return PyInt_AsSsize_t(b);
+  #else
+    return PyLong_AsSsize_t(b);
+  #endif
+  }
+  x = PyNumber_Index(b);
+  if (!x) return -1;
+  ival = PyInt_AsSsize_t(x);
+  Py_DECREF(x);
+  return ival;
+}
+static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
+#if PY_VERSION_HEX < 0x02050000
+   if (ival <= LONG_MAX)
+       return PyInt_FromLong((long)ival);
+   else {
+       unsigned char *bytes = (unsigned char *) &ival;
+       int one = 1; int little = (int)*(unsigned char*)&one;
+       return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0);
+   }
+#else
+   return PyInt_FromSize_t(ival);
+#endif
+}
+
+
+#endif /* Py_PYTHON_H */
diff --git a/tutorials/cython_bubblesort_nomagic.pyx b/tutorials/cython_bubblesort_nomagic.pyx
new file mode 100644
index 0000000..0d35766
--- /dev/null
+++ b/tutorials/cython_bubblesort_nomagic.pyx
@@ -0,0 +1,21 @@
+
+cimport numpy as np
+cimport cython
+@cython.boundscheck(False) 
+@cython.wraparound(False)
+cpdef cython_bubblesort_nomagic(np.ndarray[long, ndim=1] inp_ary):
+    """ The Cython implementation of Bubblesort with NumPy memoryview."""
+    cdef unsigned long length, i, swapped, ele, temp
+    cdef long[:] np_ary = inp_ary
+    length = np_ary.shape[0]
+    swapped = 1
+    for i in xrange(0, length):
+        if swapped: 
+            swapped = 0
+            for ele in xrange(0, length-i-1):
+                if np_ary[ele] > np_ary[ele + 1]:
+                    temp = np_ary[ele + 1]
+                    np_ary[ele + 1] = np_ary[ele]
+                    np_ary[ele] = temp
+                    swapped = 1
+    return inp_ary
\ No newline at end of file
diff --git a/tutorials/running_cython.ipynb b/tutorials/running_cython.ipynb
index 16285bf..5401d5d 100644
--- a/tutorials/running_cython.ipynb
+++ b/tutorials/running_cython.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0e619f8592165b10fa0b3558de3c622629bfb7aab1e9544b5bad64478cf35848"
+  "signature": "sha256:962f383af4f0226cdba8618e9034a3fda76689d31a32d165b2f89a6dc7ab7389"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,7 +13,58 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://sebastianraschka.com)  \n",
-      "last updated: 06/12/2014"
+      "\n",
+      "[Link to this IPython notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/running_cython.ipynb)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -d -m -v -p cython"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "06/07/2014 \n",
+        "\n",
+        "CPython 3.4.1\n",
+        "IPython 2.1.0\n",
+        "\n",
+        "cython 0.20.2\n",
+        "\n",
+        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
+        "system     : Darwin\n",
+        "release    : 13.2.0\n",
+        "machine    : x86_64\n",
+        "processor  : i386\n",
+        "CPU cores  : 2\n",
+        "interpreter: 64bit\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
      ]
     },
     {
@@ -55,7 +106,7 @@
     },
     {
      "cell_type": "heading",
-     "level": 3,
+     "level": 2,
      "metadata": {},
      "source": [
       "Bubblesort in regular (C)Python"
@@ -90,7 +141,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 1
+     "prompt_number": 3
     },
     {
      "cell_type": "code",
@@ -104,28 +155,36 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 4,
        "text": [
         "[1, 3, 5, 6, 6]"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
      "level": 2,
      "metadata": {},
      "source": [
-      "Implemented in Cython"
+      "Bubblesort implemented in Cython"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Maybe we can speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
-      "Since we are working in an IPython notebook here, we can make use of the very convenient *IPython magic*: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  "
+      "Next, we will speed things up a little bit via [Cython's C-extensions for Python](http://cython.org). Cython is basically a hybrid between C and Python and can be pictured as compiled Python code with type declarations.  \n",
+      "Since we are working in an IPython notebook here, we can make use of the very convenient ***IPython magic***: It will take care of the conversion to C code, the compilation, and eventually the loading of the function.  "
      ]
     },
     {
@@ -137,13 +196,21 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 3
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "First, we will take the initial Python code as is and use Cython for the compilation. Cython is capable of autoguessing types, however, we can make our code way more efficient by adding static types."
+      "First, we will take the initial Python code as is and use Cython for the compilation. Cython is capable of auto-guessing types, however, we can make our code way more efficient by adding static types."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 4,
+     "metadata": {},
+     "source": [
+      "Naive Cython implementation - auto-guessing types"
      ]
     },
     {
@@ -169,7 +236,15 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "heading",
+     "level": 4,
+     "metadata": {},
+     "source": [
+      "Cython with explicit type-declarations"
+     ]
     },
     {
      "cell_type": "code",
@@ -181,7 +256,7 @@
       "cimport cython\n",
       "@cython.boundscheck(False) \n",
       "@cython.wraparound(False)\n",
-      "cpdef cython_bubblesort_typed(inp_ary):\n",
+      "cpdef cython_bubblesort_typed(np.ndarray[long, ndim=1] inp_ary):\n",
       "    \"\"\" The Cython implementation of Bubblesort with NumPy memoryview.\"\"\"\n",
       "    cdef unsigned long length, i, swapped, ele, temp\n",
       "    cdef long[:] np_ary = inp_ary\n",
@@ -201,7 +276,15 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
     },
     {
      "cell_type": "heading",
@@ -227,7 +310,7 @@
       "import copy\n",
       "\n",
       "list_a = [random.randint(0,1000) for num in range(1000)]\n",
-      "list_b = copy.deepcopy(a_list)\n",
+      "list_b = copy.deepcopy(list_a)\n",
       "\n",
       "ary_a = np.asarray(list_a)\n",
       "ary_b = copy.deepcopy(ary_a)\n",
@@ -236,88 +319,96 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 11
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print('\\n(C)Python on list:')\n",
-      "%timeit python_bubblesort(list_a)\n",
+      "import timeit\n",
+      "\n",
+      "times = []\n",
+      "\n",
+      "times.append(min(timeit.Timer('python_bubblesort(list_a)', \n",
+      "            'from __main__ import python_bubblesort, list_a').repeat(repeat=3, number=1000)))\n",
       "\n",
-      "print('\\n(C)Python on numpy array:')\n",
-      "%timeit python_bubblesort(ary_a)\n",
+      "times.append(min(timeit.Timer('python_bubblesort(ary_a)', \n",
+      "            'from __main__ import python_bubblesort, ary_a').repeat(repeat=3, number=1000)))\n",
       "\n",
-      "print('\\nuntyped Cython on list:')\n",
-      "%timeit cython_bubblesort_untyped(list_b)\n",
+      "times.append(min(timeit.Timer('cython_bubblesort_untyped(list_b)', \n",
+      "            'from __main__ import cython_bubblesort_untyped, list_b').repeat(repeat=3, number=1000)))\n",
       "\n",
-      "print('\\nuntyped Cython on numpy array:')\n",
-      "%timeit cython_bubblesort_untyped(ary_b)\n",
+      "times.append(min(timeit.Timer('cython_bubblesort_untyped(ary_b)', \n",
+      "            'from __main__ import cython_bubblesort_untyped, ary_b').repeat(repeat=3, number=1000)))\n",
       "\n",
-      "print('\\ntyped Cython with memoryview on numpy array:')\n",
-      "%timeit cython_bubblesort_typed(ary_c)"
+      "times.append(min(timeit.Timer('cython_bubblesort_typed(ary_c)', \n",
+      "            'from __main__ import cython_bubblesort_typed, ary_c').repeat(repeat=3, number=1000)))\n",
+      "\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "import numpy as np\n",
+      "\n",
+      "bar_labels = ('(C)Python on list', \n",
+      "              '(C)Python on numpy array', \n",
+      "              'untyped Cython on list', \n",
+      "              'untyped Cython on numpy array', \n",
+      "              'typed Cython with memoryview on numpy array')\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "\n",
+      "# plot bars\n",
+      "y_pos = np.arange(len(times))\n",
+      "plt.yticks(y_pos, bar_labels, fontsize=14)\n",
+      "bars = plt.barh(y_pos, times,\n",
+      "         align='center', alpha=0.4, color='g')\n",
+      "\n",
+      "# annotation and labels\n",
+      "\n",
+      "for b,d in zip(bars, times):\n",
+      "    plt.text(max(times)+0.1, b.get_y() + b.get_height()/2.5, \n",
+      "             '{:.2} ms'.format(d),\n",
+      "             ha='center', va='bottom', fontsize=12)\n",
+      "\n",
+      "t = plt.title('Bubblesort on 1000 random integers', fontsize=18)\n",
+      "plt.ylim([-1,len(times)+0.5])\n",
+      "plt.vlines(min(times), -1, len(times)+0.5, linestyles='dashed')\n",
+      "plt.grid()\n",
+      "\n",
+      "plt.show()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "(C)Python on list:\n",
-        "1 loops, best of 3: 332 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "(C)Python on numpy array:\n",
-        "1 loops, best of 3: 839 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "untyped Cython on list:\n",
-        "10000 loops, best of 3: 183 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "untyped Cython on numpy array:\n",
-        "1 loops, best of 3: 666 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "typed Cython with memoryview on numpy array:\n",
-        "100000 loops, best of 3: 4.05 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA9sAAAHtCAYAAAAJNW1FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
        "text": [
-        "\n"
+        "<matplotlib.figure.Figure at 0x105dd0978>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -382,7 +473,11 @@
      "input": [
       "%%file cython_bubblesort_nomagic.pyx\n",
       "\n",
-      "cpdef cython_bubblesort_nomagic(inp_ary):\n",
+      "cimport numpy as np\n",
+      "cimport cython\n",
+      "@cython.boundscheck(False) \n",
+      "@cython.wraparound(False)\n",
+      "cpdef cython_bubblesort_nomagic(np.ndarray[long, ndim=1] inp_ary):\n",
       "    \"\"\" The Cython implementation of Bubblesort with NumPy memoryview.\"\"\"\n",
       "    cdef unsigned long length, i, swapped, ele, temp\n",
       "    cdef long[:] np_ary = inp_ary\n",
@@ -406,7 +501,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Overwriting cython_bubblesort_nomagic.pyx\n"
+        "Writing cython_bubblesort_nomagic.pyx\n"
        ]
       }
      ],
@@ -433,13 +528,18 @@
      "input": [
       "%%file setup.py\n",
       "\n",
+      "import numpy as np\n",
       "from distutils.core import setup\n",
       "from distutils.extension import Extension\n",
       "from Cython.Distutils import build_ext\n",
       "\n",
       "setup(\n",
       "    cmdclass = {'build_ext': build_ext},\n",
-      "    ext_modules = [Extension(\"cython_bubblesort_nomagic\", [\"cython_bubblesort_nomagic.pyx\"])]\n",
+      "    ext_modules = [\n",
+      "              Extension(\"cython_bubblesort_nomagic\",\n",
+      "                        [\"cython_bubblesort_nomagic.pyx\"],\n",
+      "                        include_dirs=[np.get_include()])\n",
+      "                    ]\n",
       ")"
      ],
      "language": "python",
@@ -449,7 +549,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Overwriting setup.py\n"
+        "Writing setup.py\n"
        ]
       }
      ],
@@ -498,72 +598,187 @@
        "stream": "stdout",
        "text": [
         "building 'cython_bubblesort_nomagic' extension\r\n",
-        "/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/sebastian/miniconda3/envs/py34/include -arch x86_64 -I/Users/sebastian/miniconda3/envs/py34/include/python3.4m -c cython_bubblesort_nomagic.c -o build/temp.macosx-10.5-x86_64-3.4/cython_bubblesort_nomagic.o\r\n"
+        "/usr/bin/clang -fno-strict-aliasing -Werror=declaration-after-statement -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/sebastian/miniconda3/envs/py34/include -arch x86_64 -I/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include -I/Users/sebastian/miniconda3/envs/py34/include/python3.4m -c cython_bubblesort_nomagic.c -o build/temp.macosx-10.5-x86_64-3.4/cython_bubblesort_nomagic.o\r\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "In file included from cython_bubblesort_nomagic.c:352:\r\n",
+        "In file included from /Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4:\r\n",
+        "In file included from /Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:17:\r\n",
+        "In file included from /Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/ndarraytypes.h:1761:\r\n",
+        "\u001b[1m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:15:2: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1m\r\n",
+        "      \"Using deprecated NumPy API, disable it by \"          \"#defining\r\n",
+        "      NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [-W#warnings]\u001b[0m\r\n",
+        "#warning \"Using deprecated NumPy API, disable it by \" \\\r\n",
+        "\u001b[0;1;32m ^\r\n",
+        "\u001b[0m"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "\u001b[1mcython_bubblesort_nomagic.c:16276:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "In file included from cython_bubblesort_nomagic.c:352:\r\n",
+        "In file included from /Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/arrayobject.h:4:\r\n",
+        "In file included from /Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/ndarrayobject.h:26:\r\n",
+        "\u001b[1m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/__multiarray_api.h:1629:1: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1m\r\n",
+        "      unused function '_import_array' [-Wunused-function]\u001b[0m\r\n",
+        "_import_array(void)\r\n",
+        "\u001b[0;1;32m^\r\n",
+        "\u001b[0mIn file included from cython_bubblesort_nomagic.c:353:\r\n",
+        "In file included from /Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/ufuncobject.h:327:\r\n",
+        "\u001b[1m/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/numpy/core/include/numpy/__ufunc_api.h:241:1: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1m\r\n",
+        "      unused function '_import_umath' [-Wunused-function]\u001b[0m\r\n",
+        "_import_umath(void)\r\n",
+        "\u001b[0;1;32m^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:18981:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
         "      '__Pyx_PyUnicode_FromString' [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) {\r\n",
+        "static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {\r\n",
         "\u001b[0;1;32m                               ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:16427:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:19132:33: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
         "      '__Pyx_PyInt_FromSize_t' [-Wunused-function]\u001b[0m\r\n",
         "static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\r\n",
         "\u001b[0;1;32m                                ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14058:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
-        "      '__Pyx_GetBufferAndValidate' [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE int __Pyx_GetBufferAndValidate(\r\n",
-        "\u001b[0;1;32m                         ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14092:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
-        "      '__Pyx_SafeReleaseBuffer' [-Wunused-function]\u001b[0m\r\n",
-        "static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {\r\n",
-        "\u001b[0;1;32m                          ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14165:1: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:16538:1: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
         "      '__pyx_add_acquisition_count_locked' [-Wunused-function]\u001b[0m\r\n",
         "__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,\r\n",
         "\u001b[0;1;32m^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14175:1: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:16548:1: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
         "      '__pyx_sub_acquisition_count_locked' [-Wunused-function]\u001b[0m\r\n",
         "__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,\r\n",
         "\u001b[0;1;32m^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14643:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17017:26: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
         "      '__Pyx_PyBytes_Equals' [-Wunused-function]\u001b[0m\r\n",
         "static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2...\r\n",
         "\u001b[0;1;32m                         ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14920:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
-        "      '__Pyx_GetItemInt_List_Fast' [-Wunused-function]\u001b[0m\r\n"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17298:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_GetItemInt_List_Fast' [-Wunused-function]\u001b[0m\r\n",
         "static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, P...\r\n",
         "\u001b[0;1;32m                               ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:14934:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17312:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
         "      '__Pyx_GetItemInt_Tuple_Fast' [-Wunused-function]\u001b[0m\r\n",
         "static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, ...\r\n",
         "\u001b[0;1;32m                               ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:15111:38: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17491:38: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
         "      '__Pyx_PyInt_From_unsigned_long' [-Wunused-function]\u001b[0m\r\n",
         "      static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_long(unsi...\r\n",
         "\u001b[0;1;32m                                     ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:15158:36: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17538:36: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction\r\n",
         "      '__Pyx_PyInt_As_unsigned_long' is not needed and will not be emitted\r\n",
         "      [-Wunneeded-internal-declaration]\u001b[0m\r\n",
         "static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *x) {\r\n",
         "\u001b[0;1;32m                                   ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:15627:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_char' is\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17644:48: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_t_float_complex_from_parts' [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_fro...\r\n",
+        "\u001b[0;1;32m                                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17654:30: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_eqf'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_...\r\n",
+        "\u001b[0;1;32m                             ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17657:48: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_sumf'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_floa...\r\n",
+        "\u001b[0;1;32m                                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17663:48: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_difff'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_flo...\r\n",
+        "\u001b[0;1;32m                                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17675:48: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_quotf'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_flo...\r\n",
+        "\u001b[0;1;32m                                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17682:48: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_negf'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_floa...\r\n",
+        "\u001b[0;1;32m                                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17688:30: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__Pyx_c_is_zerof' [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) {\r\n",
+        "\u001b[0;1;32m                             ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17691:48: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_conjf'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_flo...\r\n",
+        "\u001b[0;1;32m                                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17705:52: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_powf'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_...\r\n",
+        "\u001b[0;1;32m                                                   ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17764:49: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_t_double_complex_from_parts' [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_f...\r\n",
+        "\u001b[0;1;32m                                                ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17774:30: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_eq'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_...\r\n",
+        "\u001b[0;1;32m                             ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17777:49: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_sum'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_doub...\r\n",
+        "\u001b[0;1;32m                                                ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17783:49: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_diff'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_dou...\r\n",
+        "\u001b[0;1;32m                                                ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17795:49: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_quot'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_dou...\r\n",
+        "\u001b[0;1;32m                                                ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17802:49: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_neg'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_doub...\r\n",
+        "\u001b[0;1;32m                                                ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17808:30: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_is_zero'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) {\r\n",
+        "\u001b[0;1;32m                             ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17811:49: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_conj'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_dou...\r\n",
+        "\u001b[0;1;32m                                                ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:17825:53: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function '__Pyx_c_pow'\r\n",
+        "      [-Wunused-function]\u001b[0m\r\n",
+        "        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_...\r\n",
+        "\u001b[0;1;32m                                                    ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:18247:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_char' is\r\n",
         "      not needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
         "static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {\r\n",
         "\u001b[0;1;32m                          ^\r\n",
-        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:15727:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:18347:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mfunction '__Pyx_PyInt_As_long' is\r\n",
         "      not needed and will not be emitted [-Wunneeded-internal-declaration]\u001b[0m\r\n",
         "static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\r\n",
         "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:2955:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_f_5numpy_PyArray_MultiIterNew1' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyOb...\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:3005:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_f_5numpy_PyArray_MultiIterNew2' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyOb...\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:3055:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_f_5numpy_PyArray_MultiIterNew3' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyOb...\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:3105:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_f_5numpy_PyArray_MultiIterNew4' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyOb...\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:3155:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_f_5numpy_PyArray_MultiIterNew5' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyOb...\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:3909:27: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_f_5numpy_set_array_base' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *_...\r\n",
+        "\u001b[0;1;32m                          ^\r\n",
+        "\u001b[0m\u001b[1mcython_bubblesort_nomagic.c:3997:32: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1munused function\r\n",
+        "      '__pyx_f_5numpy_get_array_base' [-Wunused-function]\u001b[0m\r\n",
+        "static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObje...\r\n",
+        "\u001b[0;1;32m                               ^\r\n",
         "\u001b[0m"
        ]
       },
@@ -571,8 +786,8 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "13 warnings generated.\r\n",
-        "/usr/bin/clang -bundle -undefined dynamic_lookup -L/Users/sebastian/miniconda3/envs/py34/lib -arch x86_64 build/temp.macosx-10.5-x86_64-3.4/cython_bubblesort_nomagic.o -L/Users/sebastian/miniconda3/envs/py34/lib -o /Users/sebastian/Desktop/cython_bubblesort_nomagic.so\r\n"
+        "39 warnings generated.\r\n",
+        "/usr/bin/clang -bundle -undefined dynamic_lookup -L/Users/sebastian/miniconda3/envs/py34/lib -arch x86_64 build/temp.macosx-10.5-x86_64-3.4/cython_bubblesort_nomagic.o -L/Users/sebastian/miniconda3/envs/py34/lib -o /Users/sebastian/github/python_reference/tutorials/cython_bubblesort_nomagic.so\r\n"
        ]
       }
      ],
@@ -599,14 +814,100 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 20,
+       "prompt_number": 19,
        "text": [
         "array([1, 2, 4, 6, 6])"
        ]
       }
      ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Speed comparison"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from cython_bubblesort_nomagic import cython_bubblesort_nomagic\n",
+      "\n",
+      "list_a = [random.randint(0,100000) for num in range(100000)]\n",
+      "\n",
+      "ary_a = np.asarray(list_a)\n",
+      "ary_b = np.asarray(list_a)\n",
+      "\n",
+      "times = []\n",
+      "\n",
+      "times.append(min(timeit.Timer('cython_bubblesort_typed(ary_a)', \n",
+      "            'from __main__ import cython_bubblesort_typed, ary_a').repeat(repeat=3, number=1000)))\n",
+      "\n",
+      "times.append(min(timeit.Timer('cython_bubblesort_nomagic(ary_b)', \n",
+      "            'from __main__ import cython_bubblesort_nomagic, ary_b').repeat(repeat=3, number=1000)))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
      "prompt_number": 20
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "import numpy as np\n",
+      "\n",
+      "bar_labels = ('Cython with IPython magic', \n",
+      "              'Cython without IPython magic')\n",
+      "\n",
+      "fig = plt.figure(figsize=(10,8))\n",
+      "\n",
+      "# plot bars\n",
+      "y_pos = np.arange(len(times))\n",
+      "plt.yticks(y_pos, bar_labels, fontsize=14)\n",
+      "bars = plt.barh(y_pos, times,\n",
+      "         align='center', alpha=0.4, color='g')\n",
+      "\n",
+      "# annotation and labels\n",
+      "\n",
+      "for b,d in zip(bars, times):\n",
+      "    plt.text(max(times)+0.02, b.get_y() + b.get_height()/2.5, \n",
+      "             '{:.2} ms'.format(d),\n",
+      "             ha='center', va='bottom', fontsize=12)\n",
+      "\n",
+      "t = plt.title('Bubblesort on 100,000 random integers', fontsize=18)\n",
+      "plt.ylim([-1,len(times)+0.5])\n",
+      "plt.vlines(min(times), -1, len(times)+0.5, linestyles='dashed')\n",
+      "plt.grid()\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAx0AAAHtCAYAAAB8lBOjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XucXHV5+PHPlHCTbKKRW7GRYJGbaERStZqmK4WqFRGo\nYBEhEcQLYhVRqDd2twUs4uWnojWJXIVSa0UgaaTltoRYkVIQNWoQJAFERUUSQiBgkt8fzxl2djK7\nO2cns9/97nzer1demzlz5pzvzDOz+33OeZ4zIEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmS\nJEmSJKkD9AP3NbluN7ARmNvk+hcX60saWj/NfwbHWi/xGX5+4nFIbfdHqQcgSYl1E3/0a/89AdwL\nXAjsswX2samN65fddkqHAz2pBzGCvYFPAzcCjxLvh5HGfDxwJ7AO+BWwENhxiHV3Ay4FflOs/7/A\nm0uOcVvgH4mJ9JPAPcDHgElDrP8K4HpgDbAa+DYwcwttOxfj9XOyidbGNpVIXP5yi4xGkiS1TTcx\nsbwMeGvx70TgC8SkcDWtHYXsB35ecizHN7n+xeR1puNixv945wEbgBXERH0jcOYw659arHMj8A6g\nD3gM+BHwrLp1pxHvhTXERPEdwE3F4+eVGONVxWMWAicAXy1uX9Rg3VcSycPPgPcDHyASiTXA/i1u\nOxf9NP8ZHGtbAdu08PgZjPwelSRJ40A38Uf7gw3ue19x3wda2H4/Jh2Ti58XExP68ew5wJTi/wcy\n/IRuR+Bx4FagUrP80OJxH6lb/1PF8jfULPsj4HvAb4Edmhjf3xTbOK9u+aeL5X9et/w24ozNH9cs\n241Ipv+rxW03q0Jzz61d+hm/SUerZtDc2bjxZmvirJokSR2jm6GTjjcX972nwfqN+i4uZvMkoJ+Y\n8OwBXE1MAFcDVxbLGo1lLpHw3E2Ueq0ATmlyfxATzH8B7gfWA78A5gM71a03DfgcUUr2BDHxvR34\nUN16k4AzgB/XrHclmx8pn8HABOgtwP8RZ4suYuCIfv2/ZhKsw4HvAGuJswjLgMMarLey2M8+wH8S\nR/MfBb4B7NLEfurNYvik4x3F/cc2uO8eYHndsgeJmNZ7W7Gdo5oY02XFus+rW/4nxfIv1Szbk4Gz\nFvW+SiSAta9LmW0PpZuB9/B7iffMkwxMil9OvG/vJhK2NUQ8D2+wrYuLbU0h3s+/Jt5/y4rt1HsO\n8Vx/S7xXbiISx34aJx1l31cvAa4rxvww8dmZBGwPfIb4nD0B3EzzZZm9bN7TUV22F3AO8b55Evg+\n8Pqa9bpp/Jmq7195S/Hc1jCQJP9tg7FsBXwCWFU8j7uI92SjMULzv2eqj98P+GzxfP4AzCnufwPx\nmlVLDlcB3wRe2GCMyljuNZqStKXswEAd/vbEhPps4g/hNxusP1QddqPlk4mJz63APxCTiZOJ0pcD\niMlUrfcBuwJfISZDbyXKvaYR9fbDeT7wXeL3+wVEQvFCInF6DTGRXlOs+w3gL4iJww+I570fUR/+\n6ZptXk5MPv6bmHj+MTGh/G7x+O/XjeHwYhxfLv6tAX5PHNX/C2KSXfU/Izyfk4HzgZ8QpUsVohTp\nKuBdDJ5QbyImzDcRSdHVwEuL9aYArx1hX2X9WfHzuw3u+x7wd0SJ1TriNduNmNg3WhciNt9oYp8P\nEhO8Wg8CDxXbaHZ8JxCT8iWj2PZIPgA8F1hA9Lk8UCw/nHj//xsxudyRSFCuJJK3Kxps67+ISX5f\nsf4HiaRyDyJhgDhy/l/FGC8lPmsHEEnC7xpss+z76k+I9//XiRi9lihXq06mJxEJwk5E0n4VsC+t\n9WtcAjxFnCHblnhNryJev1VEQncqkfxcWfyDgdcE4Czgo0Qfz8eL8R5ZPIdTiM9n1fnFc7+x2OfO\nxO+G+xo8jzK/Z6ouJz4L5xXb+xXxu+Ya4vfPOcRBgucBfwX8KVEWKEnShNBN46OFG4m6/L2GWL/R\nEfqLaXymYyNxhK/W4cXyf2mw7dXEBLVqa2KS+BSDj0I32t/VxB/z3eqWHwg8zcAR56nFY89v8Dxq\nHVKsVz8ZfEmxvaU1y2YU664nGrLrNRrvcJ5DTKDuZqBEC6CLgb6EqTXLVxbbr2/MPp+BI8dljHSm\nYxFxtqBRmUi1lGrP4na1VOuTDdZ9FgN9RSN5jMZJBEQp1YM1t08rttso2aqWUr1jlNseSnex3d/S\nuJm+vs8FItn9KZufGbqYxu/R6hnId9YseyeNy4yqiUHtmY7Rvq/qzw7cXiz/Vt3yalnmXzOyXoY+\n03FN3brV9+M5NctmMPR79GXFfWc1uO9bxO+Z6vN/UbHukrr19ife4xvqxtjs75na53Mjm1/A6LPF\nfUNdeEETiFevkqQwHzi4+HcoUU60I3GEsJVGcoijev9ct+wqYtLTqKzkcuLIctXTDJRyvHGY/Uwl\nxn4NkaDsWPNvFXE0sjoReoJIDl4J7D7MNo8ofp5dt/wHxKR7NnFEu9Z/EiVhrTqEmKR+gcFHbx8r\nlk0m4lXrF8B/1C27qfi5J1tWdQK9vsF9T9atU2bdkfbZaBvV7Tyrbt0y+yyz7ZFcSiQe9dbV7e+5\nxFnGm4gzA5MbPOZzdbcbxfNwomTnM3Xr/gvxfqk1mvfVg2x+xvM7xc8v1i1f1mB8o/H5utu3E+Nt\ndrvHEr97LmXw74Idic9uF/H5h/i90WifPwKuZXDPUpnfM7X+H5sfdHi0+PlmrL6Z8Ew6JCn8jDgS\ndyNxtO88or57D+DcFrf9KFEeUu8nRE399g2WN1oXNu8DqbU3MTl4R7G/+n97ESUTEJOFDxBHMu8j\nJhdfAA6q2+YexFHORmP68RBjatS3MBrV7dYfAR9u341q96vlNfXJUauqE+hGZzq2q1unzLoj7XOo\nBtzt6rZRdp9ltj2Sod4DOxMlV78mJtC/Id6b7yImyM9u8Jj6mDaK5wuAXzI4iYB4n9c/fjTvq0bf\n8/H7Ie6rLm/1/dbovfxIie3uS/w++Cmb/y74KvF6V3t6qs+30cGC+liW+T0z3HYgzmLdSZR5/Y44\nYPE+PPMxIZlVStLQbiNKLV5Ts2y4Gu1JI9zfyJb8/oDq0civEfXgjTxR8//5RJnEG4ja6jcTdd5f\nB45pYRxlJqdb2nBXx6oMc99oPFRs83lsPkF8HnFU96GadavL61WX1fdSDLXPRtuobufBunXL7LPM\ntkfS6D1QIfoi9iGOet9OlPhsIPpL3krjg6FDfUa2dDyHM9z7aqj7Wh1fq9utEK/d64bZ1o+HWD7S\ndqH53zNVjd4TjxC9RH9BnIGaQ5zZ6iNKAG8dxfg0Tpl0SNLwJhFlCFWPFD+nNVj3BUNs4znEEcX6\nhvF9iSOD9X+g92uwjeqy4S79eQ8xydiWOGPTjF8RjaAXEBO+rxEJx6eJq0/9nLiqzX7ADxuMaRPN\nf9tz2S9Cu7f4uT8DJTW1+4a0l0K9DTgJeFWDcbySOGpcnWj9kpjgN7rsbLXE5fYm93ks0dhcmwRM\nJ5rVr6pbl2J8FzbY5yYixqPZ9mi8pPjXV/yr9c7NVy/l58SktYvB5VTbEp/L2mby8f6+KmO4z9Pd\nRD/PA8TZjuFUP8P7ED0ster7s0bze2Y4G4mrV91c3H4x8b78OANlX5oALK+SpKFVa79rJ2b3EbXj\nh9St+yoGJo+N/EPd7SOIMoRGE7ljGXzEeRviKjV/ABbXrVs76fgdURp2JPEt1PUqDL5CV32N/kYG\nEotqUlVtkq3/zon9ifKzZTS+OlAja4sxPKfJ9a8jLvH5PjZv+H0fMbm8rslttcPVRMJ4CoP/nr6R\nKFe5vG79K4gr8tROpLYinsvv2byJt5FqQ3/9d8dUb9fu814ikTmKzb+n4yjgBgaX/ZXZ9mhUj7bX\nzz32Jz4PjSbQzSapVxGv5Wl1y9/D4IMGMP7fV2VUy8kalVx9rfh5Do3ne7WXS15U/Hw/g8+kvJhI\nXEb7e2Ykjca9gughavb3hDLhmQ5JCgcycCnXbYmrubyTqAn/eM16a4mr6rwD+Ffi6NwLictt3gXM\nbLDt3xJ/oHerWf9k4ixDb4P17yauVvWVYn9vJa5c849sXoJTX2rxHiIRWEo0kH6fmHC8gEgSLim2\ns3cxliuJ2vbfE2de3k0c5b2l2N71wL8Tl399DlFzvStxydx1wN83GP9Qvls87svEpOVponxi5RDr\nrwZOJy7T+z3ida9e2vQFRB9AfZNwq6Yw8JyqV+b5SwbeA1czkJj9lvheg08Tr9O/EcniaUQPzP+r\n2/Y/E5P9fyWu2vMQcVbpQOL99Hjd+v1EuckM4rsQIF63xcRlY6cSr9+fE+VJX2PzSxC/nziafwvR\n8FwhJtaw+QS97LbL+jHxXjudSHjvJhLvdxIXJjiwwWOaLSW6qNjOmUTCV71k7puJ5Kt2vpPifdUu\nvyPOPPwd8TwfJt5Hi4iEs7f4933iMrm/JBLQA4nv/Kj28PyY6LV5J/Fevoq4/O/JwB3F+rWJR7O/\nZ0byVeIz89/Ee3x74ntFdii2K0nShPGXxBH+DQxcKvcPRELwHzSeCO3AwJeQPU5M3l9JTHzqa6dv\nIibxM4g/5KuLf99i83Ks7uLxxzPw5YBPEkf+3sfmGu0P4ujhp4rHPUEkFHcRtdLVLy2bRkx87yzu\nX1fs77Ns/kV6WxGTtOoXvVW/HPBFdevNYPhLzFaIBv0HiNe4+lxHUvslbmsZ+kvc7qNxuUd3iX3N\nYPBlk6uXC63+v9E25hKTrieI981XGfpI727EZOo3xfrVMxGN/B8x+Z1St3xb4J+I5/skMen8GBGn\nRl5JTCQfI3qUvk18f0kjZbddr5vhX+vnE0lsdXJ8K/Am4hKr9ZdlHer9DRGP+pKx5xCvffXLAW8k\nLhtb/QzWa/V91WjMMPLnYKRtDLXdocbyZ8TY17L55YEheiOuJRKUJ4krTP0nm5e0/VEx5lXFej8g\nkpnqN9LXv6eb+T0z0vM5gkjkHyj2+TARryMarCtJkqQt7DlEYlb/vRPSWFtEJKxj2bgvSZKkMXAU\nUUpXfzllqV22a7DsJUTye/UYj0UTkFmrJEmS3k2UxS0mStT2YaAE69VE6ZQkSZIkjdqfEb0fvyIu\noPFbog/tgJSD0sThmQ4pgZkzZ2666y4PGkmSpCzcxdAXoGiKSYeUxqZNm7bkF1FrrPT29tLb25t6\nGBol45c345ev1LGrVCr4d3f0KpUKtJg3+OWAklTCypUrUw9BLTB+eTN++TJ2MumQJEnShNbT49Wn\nUzPpkKQS5s2bl3oIaoHxy5vxy1fq2FmWl549HVIa9nRIkqQs2NMhSWOsv78/9RDUAuOXN+OXL2Mn\nkw5JkiRJbWV5lZSG5VWSJCkLlldJkiRJI7CRPD2TDkkqwbrkvBm/vBm/fKWOXV9fX9L9y6RDkiRJ\nUpvZ0yGlYU+HJEljpFKp4N/d0bOnQ5IkSdK4Z9IhSSWkrktWa4xf3oxfvoydTDokSZI0ofX09KQe\nQsezp0NKw54OSZKUBXs6JEmSJI17Jh2SVIJ1yXkzfnkzfvkydjLpkCRJktRW9nRIadjTIUmSsmBP\nhyRJkjSC3t7e1EPoeCYdklSCdcl5M355M375Sh27vr6+pPuXSYckSZKkNrOnQ0rDng5JksZIpVLB\nv7ujZ0+HJEmSpHHPpEOSSkhdl6zWGL+8Gb98GTuZdEiSJGlC6+npST2EjmdPh5SGPR2SJCkL9nRI\nkiRJGvdMOiSpBOuS82b88mb88mXsZNIhSZIkqa3s6ZDSsKdDkiRlwZ4OSZIkaQS9vb2ph9DxTDok\nqQTrkvNm/PJm/PKVOnZ9fX1J9y+TDkmSJEltZk+HlIY9HZIkjZFKpYJ/d0fPng5JkiRJ455JhySV\nkLouWa0xfnkzfvkydjLpkCRJ0oTW09OTeggdz54OKQ17OiRJUhbs6ZAkSZI07pl0SFIJ1iXnzfjl\nzfjly9jJpEOSJElSW9nTIaVhT4ckScqCPR2SJEnSCHp7e1MPoeOZdEhSCdYl58345c345St17Pr6\n+pLuXyYdkiRJktrMng4pDXs6JEkaI5VKBf/ujp49HZIkSZLGPZMOSSohdV2yWmP88mb88mXsZNIh\nSZKkCa2npyf1EDqePR1SGvZ0SJKkLNjTIUmSJGncM+mQpBKsS86b8cub8cuXsZNJhyRJkqS2sqdD\nSsOeDkmSlAV7OiRJkqQR9Pb2ph5CxzPpkKQSrEvOm/HLm/HLV+rY9fX1Jd2/TDokSZIktZk9HVIa\n9nRIkjRGKpUK/t0dPXs6JEmSJI17Jh2SVELqumS1xvjlzfjly9jJpEOSJEkTWk9PT+ohdDx7OqQ0\n7OmQJElZsKdDkiRJ0rhn0iFJJViXnDfjlzfjly9jJ5MOSZIkSW1lT4eUhj0dkiQpC/Z0SJIkSSPo\n7e1NPYSOZ9IhSSVYl5w345c345ev1LHr6+tLun+ZdEiSJElqM3s6pDTs6ZAkaYxUKhX8uzt69nRI\nkiRJGvdMOiSphNR1yWqN8cub8cuXsZNJhyRJkia0np6e1EPoePZ0SGnY0yFJkrJgT4ckSZKkcc+k\nQ5JKsC45b8Yvb8YvX8ZOJh2SJEmS2sqeDikNezokSVIW7OmQJEmSRtDb25t6CB3PpEOSSrAuOW/G\nL2/GL1+pY9fX15d0/zLpkCRJktRm9nRIadjTIUnSGKlUKvh3d/Ts6ZAkSZI07pl0SFIJqeuS1Rrj\nlzfjly9jJ5MOSZIkTWg9PT2ph9Dx7OmQ0rCnQ5IkZcGeDkmSJEnjnkmHJJVgXXLejF/ejF++jJ1M\nOiRJkiS1lT0dUhr2dEiSpCzY0yFJkiSNoLe3N/UQOp5JhySVYF1y3oxf3oxfvlLHrq+vL+n+ZdIh\nSZIkqc3s6ZDSsKdDkqQxUqlU8O/u6NnTIUmSJGncM+mQpBJS1yWrNcYvb8YvX8ZOJh2SJEma0Hp6\nelIPoePZ0yGlYU+HJEnKgj0dkiRJksY9kw5JKsG65LwZv7wZv3wZO5l0SJIkSWorezqkNOzpkCRJ\nWbCnQ5IkSRpBb29v6iF0PJMOSSrBuuS8Gb+8Gb98pY5dX19f0v3LpEOSJElSm9nTIaVhT4ckSWOk\nUqng393Rs6dDkiRJ0rhn0iFJJaSuS1ZrjF/ejF++jJ1MOiRJkjSh9fT0pB5Cx7OnQ0rDng5JkpQF\nezokSZIkjXsmHZJUgnXJeTN+eTN++TJ2MumQJEmS1Fb2dEhp2NMhSZKyYE+HJEmSNILe3t7UQ+h4\nJh2SVIJ1yXkzfnkzfvlKHbu+vr6k+5dJhyRJkqQ2s6dDSsOeDkmSxkilUsG/u6NnT4ckSZKkcc+k\nQ5JKSF2XrNYYv7wZv3wZO5l0SJIkaULr6elJPYSOZ0+HlIY9HZIkKQv2dEiSJEka90w6JKkE65Lz\nZvzyZvzyZexk0iFJkiSprezpkNKwp0OSJGXBng5JkiRpBL29vamH0PFMOiSpBOuS82b88mb88pU6\ndn19fUn3L5MOSZIkSW1mT4eUhj0dkiSNkUqlgn93R8+eDkmSJEnjnkmHJJWQui5ZrTF+eTN++TJ2\nMumQJEnShNbT05N6CB3Png4pDXs6JElSFuzpkCRJkjTumXRIUgnWJefN+OXN+OXL2MmkQ5IkSVJb\n2dMhpWFPhyRJysKW6OmYtGWGIqmsj33qY6mHIElSR1h63VLmHDJn0LKdp+zM+9/9/kQj6jwmHVIi\nu//V7qmHoFFYcfsK9p61d+phaJSMX96MX75Sx27ZGcs47p+PG7Rs1Q2rEo2mM9nTIUmSJKmtTDok\nqQSPsubN+OXN+OXL2MmkQ5IkSVJbmXRIUgkrbl+ReghqgfHLm/HLl7GTSYckSZImtENPOjT1EDqe\n39MhpbFp/u3zU49BkqSOteqGVZx9+tmph5GFLfE9HZ7pkCRJktRWJh2SVIJ1yXkzfnkzfvkydjLp\nkCRJktRWJh2SVILXms+b8cub8cuXsZNJhyRJkia0RfMXpR5CxzPpkKQSrEvOm/HLm/HLV+rYLV64\nOOn+ZdIhSZIkqc1MOiSpBOuS82b88mb88mXsZNIhSZIkqa1MOiSphNR1yWqN8cub8cuXsZNJhyRJ\nkia0Q086NPUQOl4l9QCkDrVp/u3zU49BkqSOteqGVZx9+tmph5GFSqUCLeYNnumQJEmS1FYmHZJU\ngnXJeTN+eTN++TJ2MumQJEmS1FYmHZJUgteaz5vxy5vxy5exk0mHJEmSJrRF8xelHkLHM+mQpBKs\nS86b8cub8ctX6tgtXrg46f5l0iFJkiSpzUw6JKkE65LzZvzyZvzyZexk0iFJkiSprUw6JKmE1HXJ\nao3xy5vxy5exk0mHJEmSJrRDTzo09RA6XiX1AKQOtWn+7fNTj0GSpI616oZVnH362amHkYVKpQIt\n5g2e6ZAkSZLUViYdklSCdcl5M355M375MnYy6ZAkSZLUViYdklSC15rPm/HLm/HLl7GTSYckSZIm\ntEXzF6UeQscz6ZCkEqxLzpvxy5vxy1fq2C1euDjp/mXSIUmSJKnNTDokqQTrkvNm/PJm/PJl7GTS\nIUmSJKmtTDokqYTUdclqjfHLm/HLl7GTSYckSZImtENPOjT1EDpeJfUApA61af7t81OPQZKkjrXq\nhlWcffrZqYeRhUqlAi3mDZ7pkCRJktRWqZOObmAjMC3xOJpxH/DBEdbpBX7Y/qGMS/3AF1MPIoF+\n4AupB6GxY11y3oxf3oxfvoydmk06dgE+D9wDPAk8CCwBXl9iX/3kPSmdBfxLze2NwJGJxjKj2P/L\nmli3fpwri2UbgceJJOmkEvvupnGiuKn412kOBz6SehCSJEnj2aQm1pkBfAdYDfwDcBeRrBxMTMJn\ntGls483vGixL3RMzmv1vAvqI2HUB84D5wKPAN9q874no0dQD0NjyWvN5M355M375MnZq5kzHl4kj\n27OA/wB+BqwAvgS8pFjnQmBRg23fD5wKXATMAd5bbGsD8PyadV8KfI848v6/wAF12zqSOCL/ZLHN\nj9bdvxL4GDF5Xg08AHxomOc0GXgaeEXNsgeAn9TcPhhYy0BitpKB8qqVxc9vFM/n53Xb/zvgXmAN\n8C3guTX3VYBPFPt7EvgBcFjN/TNofBaj9oxFdX//Wyy/cfOnOKzHgIeLMX6CiOmbgN2L7R1Yt/5J\nwG+AF9bs6zfFuhfWrLcVcE5x36+B8xicnDwHuAR4BFgHXAfsV3P/vGJsBwE/Il7/Gxk5sd0IvBu4\nhngPrSDOyDwf+O9iO3cw8H6FOFNzBRGHdcX+5tVtdwfg0mJMDxHvqcXE+7mqn8Fn8LYhXoOVRHzv\nBd43wvglSVIbLZpfP03VWBsp6ZgGvJZIMNY1uH9N8XMB8Dpg15r7DiHKsi4F3g98l5ig7gr8MVGi\nVXUOcDox0f4dcHnNfQcC/04kPPsTZ1s+ApxSN5ZTibMwBwDnAp8CXjnE81oL3E5MTAH2BKYSk9Rd\nimXdwP8Afyhu15YOzSp+vqN4Pn9Wc98M4ChiEv/XxXhqL43wAWLy+uHi+XwLuBKYOcRYG3l58fO1\nxf5bLfNaD2wLrCIm6SfU3X8CEcd7gb8tlu1X7Pv9xe0KcCzwFPDnRHw+ALylZjsXE6/VYcVzWAdc\nC2xXs862RIznFdt5NvCVJp7Dx4n3zUwitlcQ77cvEjH4JZHwVG1XrPeG4rl8nkhaD6pZ5zNEsnw4\nkYQeCMxm8HuhvqzsEuA44v24DzAX+H0T41cmrEvOm/HLm/HLV+rYLV64OOn+NXLSsScxmfzJCOvd\nCvyUmGBVnQBcTSQRa4jJ6DriCPvDxNHpqk8ANxNHqP+RmKztVtz3QeJoch/RU/KvwKeBM+rG8F/E\nWZmfA+cX6/7VMGPuB15T/L8bWAbcVresf4jH/rb4+WjxXGpLryYRE+YfEa/LgrpxfIg4A/BvxRh7\ngFsY/szMUPv/XbH/siU+1bMP1bHuD1xfLFsIHENM/gH2Jc4IXUDErDqBrsbxsZrtLiea6e8hzgLd\nxMBzfyHwRuCdxGv9I2JyPoVIVqomEWfEbifObn2ageRwOJcAXy/2fQ6RPC4mzsD9jEhCZzLQi/IQ\nkVT8gDgrsZBI/o4p7p8MvJ1Ihm8AfgycyOD3bb0XEknWiUQyubJ4rpc1MX5JkpS5Rx55hCOOOILJ\nkyczY8YMrrjiiiHXveSSS5g1axZTp05l+vTpnHHGGWzYsAGAp556ihNPPJEZM2YwZcoUDjjgAK69\n9tqxehptMVJPR5m6/YXAycRZhmnE0ezDm3zsD2r+/8vi587ExHAfYvJY6zvEZH0ycdZiU902KB67\n0zD7vJk4Gj+JmNTeBDyr+P/VxNmM05scf61VDJ6I/5J4LhAT7D8uxl9rGfA3o9jXaFSIMy+9RGLx\nFDEhX1Dcfw1xZutI4mzBCUTp249H2G6jGNQ+932JCft3a+5fQyQW+9YsW08kCbXb2IY44zFcclW7\n74eLnz9ssGxnorxrK+KMyluIBHfbYj83Fev9KbA1kYhWVcuwhnIA8RxvGmadZ1zUcxE77rYjANtP\n3p7pe09/pua1ekTI2+Pv9t6z9h5X4/G28euk28bP26O9XVV/f39/PwDd3d1b5PZRRx0FwMMPP8yd\nd97Ja1/7WtavX8+8efM2W/+JJ55g7ty57Lfffuy7774cdthhnHzyyRxzzDG8/OUv5/nPfz7nnnsu\nu+yyC48//jhHH300CxYsYNddd91i4x3qdvX/K1euZEsZKamYRtTnfxz4ZBPr/oIoQ3kZceR+95r7\nbyImgX9fs6ybqNnfkZgIQpQn/ZyY9N8B/B/wn8CZNY87mCgD6iJq+O8jymg+O8L+ak0u9vmXxBHy\nI4plC4jk6Wpiolstr6rfx0bgzcTR8apeovzoxTXL5hWP6yKSjkeJo/+1E9OziPK0WUSJ10qiDOn/\nivu3Jibj1f3NYPBrNJz6cd5HnC26gJhE/6rBYz5FTKBfR8T048BXi/u62Txm0Pj1vpiBBPSwYgzb\nEj09VcuIROTDDH6tqoba33DPcUciyegGlhbL9iESp/2Ln2cQSeXfF+NeS7zHdyJKrGYCdwIvYKCH\nB+Ks1M/l9wJUAAAgAElEQVQYKEGrfd5HE6/t9kTP0HD8ckBJksbIu2a9i/q/u1v6ywEff/xxpk2b\nxvLly9lzzz0BmDt3Lrvtthuf/ORI02j43Oc+x0033cQ111zT8P6ZM2fS29vLEUccsdl9F198MQsX\nLuQVr3gFF110Ec997nO59NJLWbFiBT09Paxfv57zzjuP448/HoAlS5bw4Q9/mAceeIApU6Zw6qmn\nctpppw05trH4csBHiLKlU4im2nrPrlv3SqK05O0Mrp+HOKLezNWy6v0EeHXdstlEA/Djo9he1Vpi\nUv9OIhm4gziiP50o96nt52jkaeJoeRlriDMws+uWzyZKkyCSPBgoL4NotK/1VPGz7P6rfkckLY0S\nDogE4zVEmdNkohSs1X3/hHi/vapm2RQGkoCxNps4q3M5cZbkPmDvmvvvJWL88pplzyLGO5TvE8/x\noGHWUeZS1yWrNcYvb8YvX50Qu7vvvptJkyY9k3BAJArLly8f5lEDbr75Zvbfv/E049e//jV33303\nL3rRi4Z8/G233cbMmTN55JFHOOaYYzj66KO54447uPfee7nssss45ZRTWLcuWrRPPPFEFixYwJo1\na1i+fDkHHdT+qUszV696L5HZ3E4cTd6bOGr8HqJxu9ZCYsL+EgZf1QjiaPHLibMfO9J8tvQZ4mxE\nD7BXsf0PEkfjh1NpYh/9wNuIo+GbiKsNfa9Y1j/CY1cSZ1x2Ja7K1KzziLNAf0c8n38kJsCfLu5/\ngugFOYNocH5VzX1VDxfrvY7oXZhaYv/NuJs4A/Epojdjbc19q4jX6lDirEA1GR3q9a4u+xlx9mg+\n8XxfTPQ6rCbODoy1FUT8Xk28n88nziBVx7uWeA+fSyQR+xHJWIXBjeO1z/tu4qIHXyXK0/YA/oJ4\nP0mSpEQOPenQtu9j7dq1TJkyZdCyrq4uHnvssSEeMeDCCy/kjjvu4EMf2rzF9+mnn+bYY49l3rx5\n7LXXXkNuY4899mDu3LlUKhWOPvpoHnroIc4880y23nprDjnkELbZZhvuueceALbZZhuWL1/OmjVr\nmDp1KgccUH/h2C2vmaTjPqJc6jpiAnYX0Vj7JuLqRLX6iTMQ/QwuSYGYOD9FHNX+NXFGARp/oVzt\nsjuJq0H9LVHGcg5RBvOlEcbdzJfV9ROvQX/dsq0YOek4jTgbcD8DZVBD7bN22ReIxONTxPN5EwOX\nBK6qlu78L/F9Gh+r294fiHKedxDlT98aYayjcSHR43BB3fJfEAng2cSZkurlYhs99/plbyd6JK4h\nkrvtiMRpfd1j6o3mSwdH2s5ZxVi+TfT3PEac9ahd50NEOdU1xHv+LiL5frJum7WPOZ5Ior5AnN25\niDijownCa83nzfjlzfjlK3Xs3viuN7Z9H5MnT2bNmjWDlq1evZqurq4hHhGuuuoqPvrRj/Ltb3+b\nadMGf/fyxo0bOe6449huu+04//zzh93OLrvs8sz/t99+ewB22mmnQcvWro3jyN/85jdZsmQJM2bM\noLu7m1tvvXXkJ9iiLf0Fb9sTl8I9hWhCVr7OIJKEfVIPZBzZljjTcy7wuRa3ZU+HJEkJjUVPx3HH\nHcf06dM555xzGj7m2muv5fjjj2fJkiXMmjVr0H2bNm3ihBNO4P7772fJkiVsu+22Q+774osv5oIL\nLuCWW24B4J577mGvvfZi48aBi25Onz6dr3/967zqVQOV7hs2bOCLX/win/3sZ7n//vuH3P5Y9HQ0\nq0JcFejjRHPyv2+h7Wrs7QC8iDiT8vnEY0ntpcBbiUtHH0D0Ke1AXHhAHaoT6pInMuOXN+OXr06I\n3Q477MCRRx7JmWeeybp161i2bBmLFi3iuOOOa7j+jTfeyLHHHsuVV165WcIB8J73vIef/vSnXHPN\nNcMmHGU9/fTTXH755axevZqtttqKrq4uttpqtG3CzdtSScfuRKnN24ij4xuGX13j2JeIcrFlRP9F\npzuVuMjADUQPyxziYgCSJEmDfPnLX+aJJ55g55135m1vextf+cpX2Hff+GaA+++/n66uLh58ML4f\n+6yzzuKxxx7j9a9/PV1dXXR1dfGGN7wBgFWrVrFgwQLuuusudt1112fuH+p7PyqVSvVsxKBlQ7ns\nssvYY489mDp1KgsWLODyyy8fct0tZUuXV0lqjuVVkiQltKXLqyay8VReJUmSJI1Li+YvSj2EjmfS\nIUkldEJd8kRm/PJm/PKVOnaLFy5Oun+ZdEiSJElqM5MOSSoh9bXm1Rrjlzfjly9jJ5MOSZIkSW1l\n0iFJJaSuS1ZrjF/ejF++jJ1MOiRJkjShHXrSoamH0PH8ng4pDb+nQ5KkhPyejub5PR2SJEmSxj2T\nDkkqwbrkvBm/vBm/fBk7mXRIkiRJaiuTDkkqwWvN58345c345cvYyaRDkiRJE9qi+YtSD6HjmXRI\nUgnWJefN+OXN+OUrdewWL1ycdP8y6ZAkSZLUZiYdklSCdcl5M355M375MnYy6ZAkSZLUViYdklRC\n6rpktcb45c345cvYyaRDkiRJE9qhJx2aeggdr5J6AFKH2jT/9vmpxyBJUsdadcMqzj797NTDyEKl\nUoEW8wbPdEiSJElqK5MOSSrBuuS8Gb+8Gb98GTuZdEiSJElqK5MOSSrBa83nzfjlzfjly9jJpEOS\nJEkT2qL5i1IPoeOZdEhSCdYl58345c345St17BYvXJx0/zLpkCRJktRmJh2SVIJ1yXkzfnkzfvky\ndjLpkCRJktRWJh2SVELqumS1xvjlzfjly9jJpEOSJEkT2qEnHZp6CB2vknoAUofaNP/2+anHIElS\nx1p1wyrOPv3s1MPIQqVSgRbzBs90SJIkSWorkw5JKsG65LwZv7wZv3wZO5l0SJIkSWorkw5JKsFr\nzefN+OXN+OXL2MmkQ5IkSRPaovmLUg+h45l0SFIJ1iXnzfjlzfjlK3XsFi9cnHT/MumQJEmS1GYm\nHZJUgnXJeTN+eTN++TJ2MumQJEmS1FYmHZJUQuq6ZLXG+OXN+OXL2GlS6gFInWrVDatSD0Gj8Ot7\nf812q7dLPQyNkvHLm/HLV+rYzT549mZ/d3eesnOi0XSmSuoBSB1q06ZNm1KPQZIkaUSVSgVazBss\nr5IkSZLUViYdklRCf39/6iGoBcYvb8YvX8ZOJh2SJEmS2sqeDikNezokSVIW7OmQJEmSRtDb25t6\nCB3PpEOSSrAuOW/GL2/GL1+pY9fX15d0/zLpkCRJktRm9nRIadjTIUnSGKlUKvh3d/Ts6ZAkSZI0\n7pl0SFIJqeuS1Rrjlzfjly9jJ5MOSZIkTWg9PT2ph9Dx7OmQ0rCnQ5IkZcGeDkmSJEnjnkmHJJVg\nXXLejF/ejF++jJ1MOiRJkiS1lT0dUhr2dEiSpCzY0yFJkiSNoLe3N/UQOp5JhySVYF1y3oxf3oxf\nvlLHrq+vL+n+ZdIhSZIkqc3s6ZDSsKdDkqQxUqlU8O/u6NnTIUmSJGncM+mQpBJS1yWrNcYvb8Yv\nX8ZOJh2SJEma0Hp6elIPoePZ0yGlsemj53409RgkSeoIO0/Zmfe/+/2ph5GtLdHTMWnLDEVSWbv/\n1e6phyBJUkdYdcOq1EPoeJZXSVIJK25fkXoIaoHxy5vxy5exk0mHJEmSpLYy6ZCkEvaetXfqIagF\nxi9vxi9fxk4mHZIkSZrQll63NPUQOp5JhySVYF1y3oxf3oxfvlLHbtn1y5LuXyYdkiRJktrMpEOS\nSrAuOW/GL2/GL1/GTiYdkiRJktrKpEOSSkhdl6zWGL+8Gb98GTuZdEiSJGlCm33w7NRD6HgmHZJU\ngnXJeTN+eTN++UoduzmHzEm6f5l0SJIkSWozkw5JKsG65LwZv7wZv3wZO5l0SJIkSWorkw5JKiF1\nXbJaY/zyZvzyZexk0iFJkqQJbel1S1MPoeOZdEhSCdYl58345c345St17JZdvyzp/mXSIUmSJKnN\nTDokqQTrkvNm/PJm/PJl7GTSIUmSJKmtTDokqYTUdclqjfHLm/HLl7GTSYckSZImtNkHz049hI5n\n0iFJJViXnDfjlzfjl6/UsZtzyJyk+5dJhyRJkqQ2M+mQpBKsS86b8cub8cuXsZNJhyRJkqS2MumQ\npBJS1yWrNcYvb8YvX8ZOJh2SJEma0JZetzT1EDqeSYcklWBdct6MX96MX75Sx27Z9cuS7l8mHZIk\nSZLazKRDkkqwLjlvxi9vxi9fxk4mHZIkSZLayqRDkkpIXZes1hi/vBm/fBk7mXRIkiRpQpt98OzU\nQ+h4Jh2SVIJ1yXkzfnkzfvlKHbs5h8xJun+ZdEiSJElqM5MOSSrBuuS8Gb+8Gb98GTuZdEiSJElq\nK5MOSSohdV2yWmP88mb88mXsZNIhSZKkCW3pdUtTD6HjmXRIUgnWJefN+OXN+OUrdeyWXb8s6f5l\n0iFJkiSpzUw6JKkE65LzZvzyZvzyZew0kZOObmAjMC3xOJpxH/DBEdbpBX7Y/qGMWj/wxdSDSKAf\n+ELqQUiSJI1n4yHp2AX4PHAP8CTwILAEeH2JbfST94R3FvAvNbc3AkduoW3Xb2tlsWwj8DiRyJxU\nYnvdNE7mNhX/Os3hwEdSD0JjJ3Vdslpj/PJm/PJl7DQp8f5nAN8BVgP/ANxFJEIHE5PwGakGNsZ+\n12BZpU372gT0Ea9vFzAPmA88CnyjxHbaNb7cPJp6AJIkaXizD56deggdL/WZji8TR81nAf8B/AxY\nAXwJeEmxzoXAorrH/RFwP3AqcBEwB3hvsa0NwPNr1n0p8D3iqP7/AgfUbetI4mj/k8U2P1p3/0rg\nY8TEfDXwAPChYZ7TZOBp4BU1yx4AflJz+2BgLQNJ30oGyqtWFj+/UTyfn9dt/++Ae4E1wLeA5w4z\nlqE8BjxcbOcTxOv+JmD3Yp8H1q1/EvAb4IXAjcWy3xTrXliz3lbAOcV9vwbOY3By8hzgEuARYB1w\nHbBfzf3zirEdBPyIeI1uZOTkcyPwbuAaIs4riDMyzwf+u9jOHQy8pyDO1FxBxGZdsb95ddvdAbi0\nGNNDRNwXE++5qn4Gn2XbhngNVhLvqXuB940wfmXEuuS8Gb+8Gb98pY7dnEPmJN2/0iYd04DXEgnG\nugb3ryl+LgBeB+xac98hRFnWpcD7ge8Sk99dgT8mSrSqzgFOB15GnFG4vOa+A4F/JxKe/YmzLR8B\nTqkby6nEWZgDgHOBTwGvHOJ5rQVuJya9AHsCU4kJ8C7Fsm7gf4A/FLdry5JmFT/fUTyfP6u5bwZw\nFJEg/HUxnrOHGEcZ64FtgVXEJP2EuvtPIF7re4G/LZbtV4zv/cXtCnAs8BTw58Rr+AHgLTXbuZh4\nPocBLyfifi2wXc062xJxmFds59nAV5p4Dh8nYjuTeP2vIN4TXyRep18SCU/VdsV6byiey+eJxPKg\nmnU+QyS0hxOJ4oHAbAbHq76s7BLgOOI9sw8wF/h9E+OXJEmasFImHXsSE9WfjLDercBPiclb1QnA\n1UQSsYaY6K4jjt4/TBz5rvoEcDNx9PsfiYngbsV9HySOVPcRPSX/CnwaOKNuDP9FnJX5OXB+se5f\nDTPmfuA1xf+7gWXAbXXL+od47G+Ln48Wz6W29GoSMRn/EfG6LBhhHEOpnn2obm9/4Ppi2ULgGGLy\nD7AvcdbmAuJ1rU6gq6/1YzXbXU40vN9DnKm5qWZ8LwTeCLyTeD1+REzOpxDJSu1zfC+REPyQiEd3\nE8/pEuDrxb7PIRK8xcRZsp8RieJMBnpRHiKSih8QZyUWAlcWzx3ijNXbiYT1BuDHwIkMfm/VeyGR\nZJ1InIVaWTzXy5oYvzJhXXLejF/ejF++OiV2jzzyCEcccQSTJ09mxowZXHHFFUOue8kllzBr1iym\nTp3K9OnTOeOMM9iwYcMz959//vnMmjWL7bbbjre//e1jMfy2StnTUaYnYCFwMnGWYRpxpPzwJh/7\ng5r//7L4uTMx6dyHmJjW+g7QQ0w61xJHsX9Qt85DwE7D7PNm4kj/JGLCfBPwrOL/VxNnM05vcvy1\nVjF4kv9L4rmUUSHOjvQSicVTxIR8QXH/NcTZpyOJswUnEOVpPx5hu41ep9rx7UtM2L9bc/8aIrHY\nt2bZeiJJqN3GNsQZj+H6J2r3/XDx84cNlu1MlHdtRZxReQuRhG5b7OemYr0/BbYmksWqahnWUA4g\nnuNNw6zzjIt6LmLH3XYEYPvJ2zN97+nPnH6u/nL2tre97W1ve3si3K5Ktf/tiqKK/v5+ALq7u9ty\n+6ijjgLg4Ycf5s477+S1r30t69evZ968eZut/8QTTzB37lz2228/9t13Xw477DBOPvlkjjnmGLq7\nu3ne857H4YcfzvOe97xnXr92j796u/r/lStXsqWkbAaeRtT+fxz4ZBPr/oIocXkZUVu/e839NxET\nzL+vWdZN9APsSEwyIcqTfk5M+u8A/g/4T+DMmscdTJQYdRH9AfcRJTqfHWF/tSYX+/xL4uj7EcWy\nBUTydDUxia6WV9XvYyPwZuLIe1UvUdr04ppl84rHdQ0xjkbbuo84o3MBMYn+VYPHfIqYQL+OeN0/\nDny1uK+bzV9XaPyaXMxAknhYMYZtib6bqmVEIvLhIZ7PUPsb7jnuSCQZ3cDSYtk+ROK0f/HzDCLx\n+/ti3GuJ9+FORInVTOBO4AUM9NkA3EIkRdUStNrnfTTx2m5P9PUMZ9P82+ePsIokSdoSVt2wirNP\n3xIV6UN7/PHHmTZtGsuXL2fPPfcEYO7cuey222588pMjTXXhc5/7HDfddBPXXHPNoOWf+MQnePDB\nB7nooouGeCRcfPHFLFy4kFe84hVcdNFFPPe5z+XSSy9lxYoV9PT0sH79es477zyOP/54AJYsWcKH\nP/xhHnjgAaZMmcKpp57KaaedNuT2K5UKtJg3pCyveoQoWzqFaNit9+y6da8kylbezuDafIij9aM5\na/MT4NV1y2YTzcWPj2J7VWuJhOadRPnQHcTZgulEKVFtP0cjTxNH4tvld0Ty1SjhgEgwXkOUOU0G\n/q3mvqeKn2XH9xPi/faqmmVTGEgCxtps4qzO5cRZkvuAvWvuv5eIw8trlj2LGO9Qvk88x4OGWUeS\nJI2xpdctHXmlFt19991MmjTpmYQDYObMmSxfvrypx998883sv//m04xNm5r7RoLbbruNmTNn8sgj\nj3DMMcdw9NFHc8cdd3Dvvfdy2WWXccopp7BuXbRRn3jiiSxYsIA1a9awfPlyDjqo/VOX1Fevei+R\nNd1OHKnemzgi/R6icbvWQmLC/hIGXzEJ4kj0y4mzHzvSfCb2GeJsRA+wV7H9DxJH+odTaWIf/cDb\niCPtm4grGX2vWNY/wmNXEmdcdiWu+DTW7ibOQHyK6M1YW3PfKuL5HEqcFagmjEO9JtVlPyPO8Mwn\nJvwvJnodVhNnB8baCuI1fjXxnjufOBNWHe9a4n12LpFE7EckYxUGN47XPu+7iQsTfJUoT9sD+Asi\n5pogOqUueaIyfnkzfvlKHbtl1y9r+z7Wrl3LlClTBi3r6uriscceG+IRAy688ELuuOMOPvShzS+Q\nWpxlGNEee+zB3LlzqVQqHH300Tz00EOceeaZbL311hxyyCFss8023HPPPQBss802LF++nDVr1jB1\n6lQOOKD+4q5bXuqk4z6iXOo6YnJ3F9G0+ybiyke1+okzEP0MLneBaDZ+ijhi/mvijAI0/rK62mV3\nEleD+luiROYcosTmSyOMu5kvwusnXt/+umVbMXLScRpxpuF+4ozJcPts1xfyXUj0OFxQt/wXRJJ2\nNnGmpHq52Ebjq1/2dqJH4hoiAduOKOFaX/eYeqN5jiNt56xiLN8menAeI8561K7zIaKc6hrifXkX\nkSA/WbfN2sccTyRRXyDO7lxEnNGRJEkT2OTJk1mzZs2gZatXr6ara7gqeLjqqqv46Ec/yre//W2m\nTav/7uXmz3Tssssuz/x/++23B2CnnXYatGzt2jiO/M1vfpMlS5YwY8YMuru7ufXWW5vaRyty+oK3\n7YlL4Z5CNDirvc4gkoR9Ug9kHNmWONNzLvC5FrdlT4ckSWPkXbPe1fTkfbQa9XQcd9xxTJ8+nXPO\nOafhY6699lqOP/54lixZwqxZsxqu02xPxwUXXMAtt9wCwD333MNee+3Fxo0DF92cPn06X//613nV\nqwYq3Tds2MAXv/hFPvvZz3L//fcPuf3cezqaVSGuOPRxovH539MOZ8LbAXgR0Rj9+cRjSe2lwFuJ\nyzsfQPQS7UBcHECSJOkZO+ywA0ceeSRnnnkm69atY9myZSxatIjjjjuu4fo33ngjxx57LFdeeWXD\nhGPDhg08+eST/OEPf2DDhg2sX79+0CV1R+vpp5/m8ssvZ/Xq1Wy11VZ0dXWx1VbtbCUOOSQduxNl\nPG8jjry3/mprOF8iSrqWEf0Xne5U4kIANxA9LHOISyarQ6WuS1ZrjF/ejF++OiV2X/7yl3niiSfY\neeededvb3sZXvvIV9t03vhng/vvvp6uriwcfjO+wPuuss3jsscd4/etfT1dXF11dXbzhDW94Zlv/\n9E//xLOe9SzOPfdcLrvsMrbffnvOPrvxFbgqlcpmvR/D9YJcdtll7LHHHkydOpUFCxZw+eWXD7nu\nlpJTeZU0kVhelakVt6945vrvyo/xy5vxy1fq2H3tH77GLdfdkmz/ueuU8ipJGjec8OTN+OXN+OUr\ndezmHDIn6f5l0iFJkiSpzUw6JKmETqlLnqiMX96MX76MnUw6JEmSJLWVSYcklZC6LlmtMX55M375\nMnYy6ZAkSdKEtvS6pamH0PFMOiSpBOuS82b88mb88pU6dsuuX5Z0/zLpkCRJktRmJh2SVIJ1yXkz\nfnkzfvkydjLpkCRJktRWJh2SVELqumS1xvjlzfjly9jJpEOSJEkT2uyDZ6ceQscz6ZCkEqxLzpvx\ny5vxy1fq2M05ZE7S/cukQ5IkSVKbmXRIUgnWJefN+OXN+OXL2MmkQ5IkSVJbmXRIUgmp65LVGuOX\nN+OXL2Mnkw5JkiRNaEuvW5p6CB3PpEOSSrAuOW/GL2/GL1+pY7fs+mVJ9y+TDkmSJEltZtIhSSVY\nl5w345c345cvYyeTDkmSJEltZdIhSSWkrktWa4xf3oxfvoydTDokSZI0oc0+eHbqIXQ8kw5JKsG6\n5LwZv7wZv3yljt2cQ+Yk3b9MOiRJkiS1mUmHJJVgXXLejF/ejF++jJ1MOiRJkiS1lUmHJJWQui5Z\nrTF+eTN++TJ2MumQJEnShLb0uqWph9DxTDokqQTrkvNm/PJm/PKVOnbLrl+WdP8y6ZAkSZLUZiYd\nklSCdcl5M355M375MnYy6ZAkSZLUViYdklRC6rpktcb45c345cvYyaRDkiRJE9rsg2enHkLHM+mQ\npBKsS86b8cub8ctX6tjNOWRO0v3LpEOSJElSm5l0SFIJ1iXnzfjlzfjly9jJpEOSJElSW5l0SFIJ\nqeuS1Rrjlzfjly9jJ5MOSZIkTWhLr1uaeggdb1LqAUidatUNq1IPQaOw6t5V7P6nu6cehkbJ+OXN\n+OUrdeyWXb8s2b4VKqkHIHWoTZs2bUo9Bo1Cf38/3d3dqYehUTJ+eTN++Uodu0qlgn93R69SqUCL\neYNJh5SGSYckSWPEpKM1WyLpsKdDkiRJUluZdEhSCf39/amHoBYYv7wZv3wZO5l0SJIkaULr6elJ\nPYSOZ0+HlIY9HZIkKQv2dEiSJEka90w6JKkE65LzZvzyZvzyZexk0iFJkiSprezpkNKwp0OSJGXB\nng5JkiRpBL29vamH0PFMOiSpBOuS82b88mb88pU6dn19fUn3L5MOSZIkSW1mT4eUhj0dkiSNkUql\ngn93R8+eDkmSJEnjnkmHJJWQui5ZrTF+eTN++TJ2MumQJEnShNbT05N6CB3Png4pDXs6JElSFuzp\nkCRJkjTumXRIUgnWJefN+OXN+OXL2MmkQ5IkSVJb2dMhpWFPhyRJyoI9HZIkSdIIent7Uw+h45l0\nSFIJ1iXnzfjlzfjlK3Xs+vr6ku5fJh2SJEmS2syeDikNezokSRojlUoF/+6Onj0dkiRJksY9kw5J\nKiF1XbJaY/zyZvzyZexk0iFJkqQJraenJ/UQOp49HVIa9nRIkqQs2NMhSZIkadwz6ZCkEqxLzpvx\ny5vxy5exk0mHJEmSpLayp0NKw54OSZKUBXs6JEmSpBH09vamHkLHM+mQpBKsS86b8cub8ctX6tj1\n9fUl3b9MOiRJkiS1mT0dUhr2dEiSNEYqlQr+3R09ezokSZIkjXsmHZJUQuq6ZLXG+OXN+OXL2Mmk\nQ5IkSRNaT09P6iF0PHs6pDTs6ZAkSVmwp0OSJEnSuGfSIUklWJecN+OXN+OXL2Mnkw5JkiRJbWVP\nh5SGPR2SJCkL9nRIkiRJI+jt7U09hI5n0iFJJViXnDfjlzfjl6/Usevr60u6f5l0SJIkSWozezqk\nNOzpkCRpjFQqFfy7O3r2dEiSJEka90w6JKmE1HXJao3xy5vxy5exk0mHJEmSJrSenp7UQ+h49nRI\nadjTIUmSsmBPhyRJkqRxz6RDkkqwLjlvxi9vxi9fxk4mHZIkSZLayp4OKQ17OiRJUhbs6ZAkSZJG\n0Nvbm3oIHc+kQ5JKsC45b8Yvb8YvX6lj19fXl3T/MumQJEmS1Gb2dEhp2NMhSdIYqVQq+Hd39Ozp\nkCRJkjTumXRIUgmp65LVGuOXN+OXL2Mnkw5JkiRNaD09PamH0PHs6ZDSsKdDkiRlwZ4OSZIkSeOe\nSYcklWBdct6MX96MX76MnUw6JEmSJLWVPR1SGvZ0SJKkLNjTIUmSJI2gt7c39RA6nkmHJJVgXXLe\njF/ejF++Useur68v6f5l0iFJpXz/+99PPQS1wPjlzfjly9jJpEOSSnj00UdTD0EtMH55M375MnYy\n6ZAkSZLUViYdklTCypUrUw9BLTB+eTN++TJ28pK5UhrfB2amHoQkSVIT7gJemnoQkiRJkiRJkiRJ\nkgteCbgAAAKlSURBVCRJkiRJkiRJkpTS64CfAj8DzhhinS8U998FHFDysWqvVuK3EvgBcCdwW/uG\nqGGMFL99gO8CTwKnlXys2quV2K3Ez15qI8XvWOJ35g+A7wAvKfFYtVcrsVuJnz0pia2Ae4AZwNbE\nFar2rVvnb4Alxf9fAdxa4rFqr1biB3AfMK29Q9QwmonfTsAs4CwGT1z9/KXVSuzAz15qzcTvz4Gp\nxf9fh3/7xotWYgclP3t+T4e05byc+PCuBJ4G/g14U906hwGXFP//HvBsYNcmH6v2Gm38dqm538uQ\np9NM/H4D3F7cX/axap9WYlflZy+dZuL3XWB18f/vAX9S4rFqn1ZiV9X0Z8+kQ9pyngc8UHP7wWJZ\nM+vs1sRj1V6txA9gE3A9MTE6qU1j1NCaiV87HqvWtfr6+9lLq2z8TmTgjLGfvbRaiR2U/OxNGsUA\nJTW2qcn1PCI3PrUav9nAQ0QZyHVEjewtW2Bcak6z8dvSj1XrWn39Xw38Ej97qZSJ32uAE4iYlX2s\ntrxWYgclP3ue6ZC2nF8A02tuTyeOGgy3zp8U6zTzWLXXaOP3i+L/DxU/fwN8izhtrbHTymfIz19a\nrb7+vyx++tlLo9n4vQRYSJSp/r7kY9UercQO/OxJyUwC7iUasrZh5EbkVzLQkNXMY9VercTvWUBX\n8f8diCt8/HUbx6rNlfkM9TK4GdnPX1qtxM7PXnrNxO/5RO/AK0fxWLVPK7Hzsycl9npgBfEB/Uix\n7F3Fv6rzi/vvAl42wmM1tkYbvxcQv6y/D/wI45fKSPHblahfXk0crbsfmDzMYzV2Rhs7P3vjw0jx\n+yrwO+LSqvWXV/Wzl9ZoY+dnT5IkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSZIkSWrg\n/wPnZgT7CL7wcwAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x105844390>"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
diff --git a/tutorials/setup.py b/tutorials/setup.py
new file mode 100644
index 0000000..c92789a
--- /dev/null
+++ b/tutorials/setup.py
@@ -0,0 +1,14 @@
+
+import numpy as np
+from distutils.core import setup
+from distutils.extension import Extension
+from Cython.Distutils import build_ext
+
+setup(
+    cmdclass = {'build_ext': build_ext},
+    ext_modules = [
+              Extension("cython_bubblesort_nomagic",
+                        ["cython_bubblesort_nomagic.pyx"],
+                        include_dirs=[np.get_include()])
+                    ]
+)
\ No newline at end of file

From 59851864c45aac55fb816d30832fd02281d75114 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 22:48:40 -0400
Subject: [PATCH 160/248] table of contents

---
 tutorials/running_cython.ipynb | 80 +++++++++++++++++++++++++++++++++-
 1 file changed, 79 insertions(+), 1 deletion(-)

diff --git a/tutorials/running_cython.ipynb b/tutorials/running_cython.ipynb
index 5401d5d..2c21a11 100644
--- a/tutorials/running_cython.ipynb
+++ b/tutorials/running_cython.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:962f383af4f0226cdba8618e9034a3fda76689d31a32d165b2f89a6dc7ab7389"
+  "signature": "sha256:c53d1aaf41825ecf8aae8e9e7691d07f984de379cd765b3cabd973cfb29cc420"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -104,6 +104,42 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Sections"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Bubblesort in regular (C)Python](#bubblesort-cpython)\n",
+      "- [Bubblesort implemented in Cython](#Bubblesort-implemented-in-Cython)\n",
+      "    - [Naive Cython implementation - auto-guessing types](#Naive-Cython-implementation---auto-guessing-types)\n",
+      "    - [Cython with explicit type-declarations](#Cython-with-explicit-type-declarations)\n",
+      "- [Speed comparison](#Speed-comparison)\n",
+      "- [How to use Cython without the IPython magic](#How-to-use-Cython-without-the-IPython-magic)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a name=\"introduction\"></a>\n",
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='bubblesort-cpython'></a>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 2,
@@ -112,6 +148,13 @@
       "Bubblesort in regular (C)Python"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -179,6 +222,13 @@
       "Bubblesort implemented in Cython"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -213,6 +263,13 @@
       "Naive Cython implementation - auto-guessing types"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -246,6 +303,13 @@
       "Cython with explicit type-declarations"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -294,6 +358,13 @@
       "Speed comparison"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
@@ -838,6 +909,13 @@
       "Speed comparison"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,

From c24f1c6532652a80ae367f5e93e82c4a00098da4 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 23:29:57 -0400
Subject: [PATCH 161/248] check for IP and MAC addresses

---
 Images/Ipv4_address.png      | Bin 0 -> 23459 bytes
 Images/Ipv6_address.png      | Bin 0 -> 53250 bytes
 Images/MACaddressV3.png      | Bin 0 -> 8125 bytes
 tutorials/useful_regex.ipynb | 196 ++++++++++++++++++++++++++++++++++-
 4 files changed, 193 insertions(+), 3 deletions(-)
 create mode 100644 Images/Ipv4_address.png
 create mode 100644 Images/Ipv6_address.png
 create mode 100644 Images/MACaddressV3.png

diff --git a/Images/Ipv4_address.png b/Images/Ipv4_address.png
new file mode 100644
index 0000000000000000000000000000000000000000..04028af3165c415ffb92f7ae5cf32f4402c64d93
GIT binary patch
literal 23459
zcmZU*1yq#Lw?0frmqEvXq983@LnsQ;3eqruNJ;k$Lx_|#NC`+uDcw1=v@kTtP($~?
z3^DXK{_g$Xd)NAAt(o<{=iR5@v(MSje)dGX(NduzXC}wP!=qAt{Za=H51$`*{hgE;
z_uJEvQj1H7uvb!gqpGCD@y6Z7#@^8y5AP{mS%RE8;q$v;OHpG_$rvBoktYzn;y+l6
zex*@6K=SR&t1sjqeVV>U#K*0=KfEB^bF97VM0+CiMS?!%eQqcRY3CBh9fC*5@2yHY
zL@XrUn}2<56>NxI(;~}L=kW&VCi(^Y{%%Z5V8oE2G@M`FYrJ*v<xey!hzCpu1a~xm
zo&?NjUF`aA=eW6vR=P{v6Yl|p2Uxp+N`8HM_WE;r#V_lX`&B=8!c_g#_{B9cF`RB4
zIW+err5RXn*1p&L?a?J;-2HoJnf(5x2Q}PPPzu&zB)|)aRJ|89!v5t`?z^v{>tASV
z3~r^8)x8EMBtQb$`hN{MQANVBv|lYqm6Y$?e_vRya!wFY+~%@JAITN<)>=H}XUa+Q
zFQ!N829rh!p=;7<eMebtG_}><LLp<R<@&969wjO+SsDyw;`y8@9{wuP%p~lN>e#O-
zXN>e<AAW(jo@uTZWv^Z(GH2bott_1uTb3r4QAs)Rw>HXj@Xhu;eRF~T*vEc3R~?p!
zn;SCM*G3+AcoYx*eev<qzcb=yD%1X*p{JpyhK!Yqlc41Xm-p6!U?*3cG#(xhEQ7mr
zvi7v(06RH4d&q$09{oo`26z20S?CeRe?&YT<Q^GnzTr@Eaku6W7ZekG_DG(bgM$O;
z{=r5@=cV$0XUE;iJ+kxkbd?bj^6~Kz^br+wakmu`mX?+ldL|+yA|im35b%IFds>18
zoIN=ItC0U!&P!_#D|dTWPkR?<j(_D^zIXBRlza5(Uq%1(`LA|bgYEyfCTEZT?iQ|t
zLjRr!2@5_G`k%6KS%LpjW!~6>tsM<t+B;c0d*JGjmz0tK{zv}*_vC+T{69I3{x_$X
z@U#D$^Zz{g@0>uPe;xQg9r~})`cEouxa7%!LjN=L^5ia4ax-{%ig>CopT7g+?=O&C
zIjQIg9a5d1(XvMp@UHcSfBDRzNA~1l=qTA8QlRnOS8q6KN7X;!>EtUBkVm|xAPxEP
znBy@m+4Bwy+Ab*hL+rasOMiNc_Ve<LCCh`6^HeD6Ht=@86u$Yr^(;x;q0FepW_a(f
zXetW}C57Q@i-$p>cj`ag5o{;2GmUSI?|4B#L6&rfp4cIml!A=^ai#U+%4dcqg~jwC
zf{!cXJGv|cJpU_C^gp4YcT=B7L+{YvH)JdgrN)WJccdhAq%)P)8MBS8x-H$F>J`h4
z`1g!$QfT@c9;8O8r^hr3@&(sdTK~loDjv4=I|<k=rn(#O#h&!G8q4HoaF^8Oy9l@m
z^v$drKR2;q(|_GMzb@Fostx@;P$Rj0)(`9I_FVzxZnZ`pZ117Y$OmM&7N4eHxL;n@
z*?7shRS=aqs4f|=r1v4xA%>WEif+Z6cJ0|kQzk%r!J$UCT*b7Zs_gL6M2gf*`|V{r
z`^~DtjTOy&1cRVF9BSJBBr;Ol)I1i7yu2^jlV~e~4G+YIBX6(UZzWSVZm;0CtTzX!
zn*$MRE&Hu(kL+C?&nVPHNTF#@E^6s!e<`vzM97P({Pv>amN&iqeAwk6!Ltc{pMiDo
zx-amm4{3h0ZXOx-TWNL)4n3gAEYxI8TXlHCX3Bf3w$3HLzRuJJxY%qf%{nU>hj+;)
z$x$EeEW6yEy6i^Iw84yS#GC&L1=Driz;DkLx>wn6vB)1$g1ptfPj;C4{n4S}z}rst
ztIop%pZ1o|x98cn5b%IHNCb!^(`L-V{^q9Eh79V4Vc-hwD=9jk<XNe*6t1_mY2y6W
zIK$zAH<;91f%O)(j4QcoJB<4GF!Z-RpiPG;_F|?TH1j*~v?g1?$ZwTp>8ER8&DF55
zC;QwQ*-gIs+%m;|q#{;K(7tfy@F=;$&l<d=n=KR1L+aES%m73>fq=l=T_yI5(Zidw
z_CRGqR`#o9lHHjJu4^4X-n`8|Ot``o5*aGwmy7uP`wX7@e3qdd+k?Cp2Y%_rngz`o
zy|>sCltq8kd5cbM_X8ski~9DT6=w2xF<jY{O*Bhcm+nWHggC!^*YGyqt(!vYCu+FX
zNZp`t)7I@^emOTTXH97TTw6VEwX|l0*Z98BzPkCvIP#Au+|$G}{B-$`$CF0SHxm;{
z*dg;9^eedZ=679HC3U&dqa}Hk(z~%)XKiP#-5%MO^GG=qL^~lo2=EBc@}435-2oGw
zGQ}Y=V`ui&KsIZv%+60_`|a7S{1xs^x%y+L_hWz^xD&%Mb|@K$kt7WDJBl&~{`?SM
zU3f+Z+0KTwxa~{4le=Cz&s;KUwMp7Uv|l6m40us3ZM!YSGg?`P8ED`cS`~KHgtFhp
zXm%U;hS9(<nJCtB$gl&o!d16|Y4v%%%Vj+|%Km0<rcEMIbA|8irIO3|%@O<U(cv#`
z|BrE3fyXL=Vy3R0M0C?bPJnEG7-i=r+tLwRcl*t5JALeSHZVbRJQ1TN*3BujWq-u_
zm(}BsaQCoFfXDNi%HK~);Tesi@0f(HA8<Pju9+1m?M|GWb#n8cb!Kk<UQgTfp0h6+
zc1=`W`HZMBw>$sMEib&K@x1H6ccz6poYnnxs44=(Q@L4izplNF_2^ezH;sZHb|1G|
zf*Rz7%+V;6myZL2g#TXgq6rFHY|#2q>T<6py#1o2ow?k1s)W8b@b)Z_9dx~8K%;Oy
zp<t$_Wfu*<#=Yan_UndQ{m;8Z9Z$`UA`QO3_2`pbbdx%rwF*aFwxL*@y-G}|qI<X%
z&Ij)IH*IFg0x!nmvdGT;QBZTpTN>zAci52l^Unrt=;UCvrqj9#xsrJfn(Hva3UCi!
z*uT2UKo`t2jlGBQ;lK}#gL>s(^Vi@0r1YFWAMp`fWoi~rpJ_et3SL~V?51W3SW^ke
z2aj&ut_a<{ms*V$4a+`H%Vr8#l?<?IyuC&#%p<9N7z56H0)4<kCW|`C=P3=ab=Rw-
zKANS=)+H0MNa!VE2~=yk|IvUCP~lm?M`e!fVhEdoZHRZLwqLEIL)N4(w(^8HyYFfy
zJdIV(17)56vDHs{7KUsH*lmauit(B$H?#ipqaP?}eZ7uTHHUc$7kS-6H!DKax0t{i
zI1+xd2M=4i8eU=@^@@YeTS=k&Im0s+j|;m|S0kt-M|n&doN}gR@gz(|Zs)CDN-X8A
z-J21it1cnh>__bvQxz#A&%8hU*hAgkpep>X&39Bu3aP>53TGY)25d%@mB$}qBr*X;
z^4LxqfHSz8WrTT_{RYWST@iS_8%4jZErkq0%FHy49Bu~QZ0ZT#v>g`+CHFlypTy6x
z+DUJKzquM0x*pFN2{H3K>X~9<HqJ$T;f!TGz%4z>C5+sXE@XRm5?chSWwI_Y5ozwC
zD}N(qe%3rAFn@f%eKXT%@o-`3dZF8`kIewf6f&W$;E!Fpy$ocaDddo%7zbYdg#&6;
zEG65HV!}y)%lHLR3?Q)O)Fzd)Yw+`yrXB%j;j6)lE44B04F%l30J8qd#?<9D`kaKW
z!T{I%C<ab;-{9<qwv(~~!nuf3g3)26nb>k%--LdpCW94jPCY|U@yGfUZjL^h{!<x4
zIlq-YM^?YJi550)zZz;cJ?q-QuXrN&6_(T?eyDwWto`JfGO{Yp-;s*q_duUTN|*Ug
zvxvo=3Sma`K2Gv-Q$0aW)lO~T(QjUiy*AX?tU&lq^GTVDS=gzwPm2i;g%40o)rgUw
zN|A6pUbMC=DmUd}c&}Q7*vh8D?YVU=B%bnc2y|NgsP9lO?za7A=9U?7*4Rfi(>z^g
z(@423=BM<5SmADW+U9n0uGKpzq2tpWeEB^~8or+HROF)q$<&_1ua^}!I~Dr=^9SAy
zC9Ywb+L3~O3wt(_d_6X`0jIWs-e7DuJLt%+W3%FVu0oGcS(Jv~@1rLrzDtaou4MEN
zVgRXmQ<nQ6Pq6RUsMuHN!Y)YM;EN3v8Q@IYcRVqYxVi8xVayGeWRt46#`+=eD4ypJ
zoTA;_0kx*dTa<2zTK&j-T!C1+IqgB^h69YlG>O+me0KmSpb^`1JoDBYu6e!8Q8KFU
z-?wu{jHEbZWdx`Qe|cD}^?!ju{@DHAyv4HL=4~LU<+01iTpEA7fft+iAQYQV^F=Ky
zKh<aay3dC6s(`k-KOPN^g)U;U^ibnFu>S8#FQzGZjmA_z3FbY#pww|WsZ0sf%z;vs
zrTzq#YvT9X`0qBI1Xwv}Se0OXo&Q8PGz~lTN^A=A<%us=F;)H*Wi5NZdrsv7?#7nd
z%9R`-T%8^1#N|;CIEx8fO5&AuQ?J5#qQWn%{#z@I@lG4h{A`GLJ*YnG-`T;}F<H9a
z(n7AGDYk03X3}5uk=F@>{Cq<My6Pmy%HgHeu3PVhCMh6;Ta>TQW0x)}cA6z_SpDZ^
zKHU;9@kq<3IT3-|YOYph!=kNrBX*gxhp{nb;j9@iqRVUn3Wq^7w6TR(dJ+Hxtcx^I
zQQqA-QcU7isb9n{<n=d)59A7fbj$Ls!f#fhSSuF5pRb8780wuS@#!k2pGK@#!&`AK
zz7rX|l>0?lzB~MjR9PAKvO{^)0mu)LaE}q`BCKO1$ZJ+6wTh|FD9a|k&nDj%ejRh+
z7~|}y>_R^;(^|!p>w9y(bbDO|sj?Cs(TnpeVnS9y&Ty?q93>fKi3}l@V9P{8j2Q{P
za%`4+ZNPo%f=&5ZV@2}vI2}Gx-GQL&+xr`XHnx?K2-Z7MM?DwaSw&qhqNe<h1y9hi
zJ}@iCOhjqcWZ>?o8fsbBxx!!=!<2!~V|pAB01uv)PKAxl0*e#&6nArMyuSIB)vzsL
zdnKpyns?kJg5@Pvfo;2HsA8-HIa6Jpq6cv=%fgm2bc;vM7oC{>{V}w*!e}{QaW64^
zYS0nQI#{s&BAtCasxDH{rF#^=cJLSSL8|B>p~7Ks@Oii*i~VMOdFOs-t7XBWxwef?
zb_;d-+i#kT0ee>Ld_w()L|aCGe6}C1uWui+etLNClZjA=!vx=L2hEit6?;Z!X_l;9
z2)bz;Pa#anr)kJ+2SnrW<*5d1yu@NEzyt`VVAT0mg0x)3X`C{^$xPt&jKj~LtV$|b
z5y(fWrZh#HN}{OGw)U^P5|fB?YEq4eyaR`q-}$Z+$mIc;1Kd9Oyj!$yk%S@N5{PvM
zt|h4GH9iAXxOT`AWrdOe^U`eX866E5DV3-kD0z}PqaI_xGCO>S8<Oo!uy7Lj!yxfV
zrJ}_QK&XLIe7~NM%dE$^DmPW(J-~4e$<$#o>=QNv;6Bl;!s1Vg+7USGg2WvY=S;oP
zxF*;R*y{^$R3?-wqEI+iQh0ZDNI<0$Tp=QG6z-x_w3T;p9+8cW@YPp2<Ymz8IFvX8
zb3nI$_zXWwPL1>iwUOO!<zD%>zS`J|25xh?-0z6X+Hm}{IL{mRhY)5YGa1a2xV+mv
zgAT~vs2u-h0@tNF<_o+|MwVp<IyBV{@1*P&ujqxDheRH_1YlUyo1+d33DzH8J<tNb
zWLXMaine``(t8;Bi21XKp-=ne0v{FUd*==W)A7AFWfyd&@Efv_og$!1V`J}zA|sFv
z&UmV3>m2;>M@I_(HySsWu7&u|m*0E4?NlD#eQd^?fGDlX@<R_O?Y4ew<j#83OmKY8
z<n2B@Y|m8w@#$j91#+lz^!otK%`(jhnCDKSf>=GEWlb^2i=LtG;a#Gbpgt%i^{dC)
z%K*RAnn2_3hi4yB4%=3@S;9q*>=_+M2V7U<#)l}v$t>WVxg#NjRKda+$DjFH!zJ6E
zV&plAlYjBh;@`(1ye11{+5N||ZL5f(PfYhiPX}jjwsZzH4R-(_uLsx3BOacUu>9D`
zB)n$NIXJji)CRkO)M9ciV6_}X??oy%_Qwm~@vPL=PG#&<EHvu>vgdgamWaO|>H`US
zrjT=kPEpvNvn$*X>{%@kV$TeavtwCn&#IgO(EkutHG=tD4P;-`7EMo$6MArR*JZ^D
zKl~P;d0%-=d-ZUB)|)4XGD#}u?KPr(KfvR0?59T-boK#*AId#9p5j?zDm5y1rVxh(
z%y@)3LBY%Yay=Fw#6C;#3j`+(j!qoi<^Po<=Jdt5SJPLn5q&iLMzuSy`Kq9oq{3CD
zlcx?sviMb+IP75F1I{*Anai9C0SNIh8hQ|wFD-3we{%I;1Y27?UH_Ow7~{Gf69l@n
ziLt?`IoZASpoX;`4qcAl)x@R~0$Vi?d1YM^`)5owSq`RuOMmZz0k{c!$0+C$;gs}Q
z{T-$`1A!LuiT56<dvH3<8e{D3U$2O|Y`uz(joM6ts6RM~hIn>8268&wkPh#t%B`@l
z`pnCbU_4W-o@&qWt4H)bVPpER9d5fUU45kC10_^0_v|!{PX%tM=b4ZHa06&~nK3&l
z1BA*v?SO$IQ#|R2--J2ZPEQTk7y|I)UT3_FYPMH`iTIAmu?3IN$cquMMI0+a=Aszz
zT+<aem8yxxFHUk`i(eA#xsU_=?pv5W)HR5ul-BMCx+q|$=#)_~LKODwm#xU#V3|fu
z><LxWa=G6+AIUI4<HUuGY%U|?_-htfhtLa=dt{8|Ftenr&ko;U_Y;Rros`Iv=lC*U
zK)l33E(y&o5#Dm1;2&>ZwkD^NxZ3movU{-Qx_DaOAiz0b2&yT-AM6e^cRO_b+uCXQ
z6m<N9VK~fcYr2Gq{D_e;0wFnvcSC6a`AQiX+xaZ^{YxG`WI-U0?)h2=7T?QFXZH;w
zou95Fd`Hu^bJ&&<LKWu397YUyB?j3YPLMhEraL5UUDsikA^ms0PIc=QS}hR%FuP6O
zzJAYcdHt)^&RuP0v8D@TT##Psc*FV6bW@4#iiC-xnnkF7>vOSaE(I!40O>V&h{=VA
zaL`0tIFpCKlbh}D-dvr7zWHG0j*!(4P!Li&?`PIwIKVyPVCx+eUL#_5p)%togMU&`
zis!fWP*zA~5<*GO9K*Pk_fV2E+9eTXXR-C8Xv{~XBQ8M|M0q&U-SUh6B<Dt>d^d@4
z5ccGq<dO5(7m(8N7NC2?0=MNz^3(frvS&@NG{dtuw#eR5cAovVe2I#<ms~(HPS|H>
zzr|nYr#p)1{3YC^qkML6P^?v~Gkej98EN-|LXl4?EEe#!H<^JWM(wR&LG=5<L?j7O
zIE_L?5iNQ(;<@sJS6E+)+p`pXDPtXg-*#Fui8gy5=BqrFsc(*Hds2<8=XRhwhipZz
z7YDa^H|^b`@5?W=x01q+bmAfy=$WV3iJlN1=jpX}1u?)>PLaBXJ9d%b8UtDL$&#V@
zKI@j_WM0w*Msh#78<+1S1VORe-(=LJyZTAxm392qI4Z!tl-wK-fMKcqKZ<x{_ym}E
zT*%j=K=$~H`6xu>GcQ_c^%;!PCa{FizN;O*Ow~5JIFkVMRFyl~C%6S4?5a0S)F#AK
z_vf`@pK_*3CjJtI%-lcyYf<*(p04R-*IwJ&+XJ=pQ^^2vSV6SuC6H`L+-+hr<ZR|?
zgR({f7yzS<WP5Tj(Gt$^d$|!cw1esi@+ZqkwUw+tzvU~)X2^bKFp61|KAM2SOM>KY
zKaQg`(;<xyQXJx3K(mX#)n$8x{SS^bQ4j>^J6QT|Yeu0_)TY0L;WvEUCwHg&G}8`N
z^Y<;8C5I73!`EWkU6;FR_P>kxuh!(HzAC`W<(B+!@>Q>B4OT9Js(j@%XRvps?1BqA
z@o3T89LYNq?ja{qmsokfbV)b$0&`x!$UafhwgqkI%~|99iK6V@Gq>@xqlgE@7n}1G
z3<~~Ph1rFw7d+1A1eldVFp!&Hs_4AO_Kt{VHq1XOq;nH++*l-a0mF<B4k?aRK(aod
zE2g}%siuwZRd&?e-2d{mJWw_2J`T$>C)>m1UtWBz>u5{4|Jo%T;3eQD<SOu3k$^<4
zfgUjZVhJbo+G6On1=7+ft=RZ(^M7!|;JQ&9m`HzrR^%TXD4?rH@q*!>oMk1w3=SB4
zL13axKoMMLUHtL+)<2k$pK|&I$^R?Kva()7Pk_|yKZs<f@z#HUNJl0zzsr<dPofu_
zWwrb53C0gE3%@Kf0M?9nmuxlzUF@^_HfvY(S0IsA1;@HqQJXQz9nBS<XO?TUPX~an
zjMRdP&M{R*uGrMB2Ge=kO=bCTR93~*XmES$a1Apo<KeLHq4Egk*je<jr9v!zf;QzN
z&4j1P5oCdC+Lz2a3w@G2Be@2#pNT4O?U5}Qt})Nq<g_gJ53W~9$~2o`+$XXY?HH!U
z)?OC)m(8yZFx+~pW;R?8zmvdVP1HbWWqI}O0ia)0<?xdrFSh$S_cMb2-aAq^Z_ejg
zll+(`*&;C~N%AtmbiXW%Y6wWErrc?|zxts@SQco*N1-D9mwkT9$VU-$nau7zYgxxx
z4C5)f@A$QS^n-_(Y|KjMn1O_GO?dLYJ=;&buhag~$yD=u>kC>IzxG{v@DZ$tsmXh9
zB(irg2U=Bbx4AuTZ@k-TQ(Ib33BA+2@>5nDSqJgI_UVs%D_W0ky`YR2@NgX7yGtbN
zc?y|V0ePEwZH4<Ck2|NwmOjeM|MkaY3(n>5EmtQ{OGWC0c~7D*8|AESH(0qbn)J)H
zUO=}m4^gGB=?cBNSpEF9o~$J{EWI%EY#yiYugWZEs@`M9AD1cw6!Wa7W11C6yP*gw
zPnz=(Z<REn7f`g+$S?5`IkdF*fUw@c(&<L|-oOJ>76=u4>@s=gK3-fE8&DXk)yO%c
zxaBkXZe_!)sIPAz1HF}M)o|fgro@OC`So>p@MpN@4Fg1Kx|Hcm4%}sqAolHjj?E@=
zJj(DoscgKoZ6MU#zb<X?<P9IbsPXZ6a8R1+>4I|0Nfp-^wc5G{>uHWmu`D4^eSYp+
z6g)_wI$^W>XznhzxxK%92dc9R+2=X<@IWs1csqH0iGP%~@-(Mmsj<wS4X?vt@9avM
z?}7?qy1#vpLp3V%3C-Uh_lH&I`N-d1A5nBP*JYe+e0RVl)|=pd#pv{?Cs80Z?eH@p
z1T%(%P3DE<;SgdYP4?~w@?RM5Hqd^H^UrO50zldIu4JBLA&~#X(cG6rol$etMFdl0
za9JO!pX)$fwNr+S9$u&j=0e=Hn0_GkBr`d!wwzji%E5G=I?|ClaPncyfybARB_VRu
z;!c;^ar>UBReIx;*(_ORvXBC5%H+O>NgUQw-CP$A&ewY#LmPAOwMnfXS#Kjwk4vd_
zds5A1LoC+poyF~l>yF>6`N*1D_GSbtBU~XVYqb+vC5?G;^1koRbiYlztgS1K_YVb?
z9jVRPzqZ<Cq|hAKa&zw8Jp5Z|ypy9(a#5ccVgsxvl(qT5z0YqEwNx4vUNG~~oo6V!
z2Ia1%s{6f(Bd#fehcR0=oaUQ{>qiPZy0?Ir8~h<h?~Ze{!1iip+~beSc5UvsyIUwy
zb8f{+XPn+ZyanHP3`lIiz#oA3Kz-%j#gR&6%UW6aOOytl(jVD=s^lDie@lmJGs0yS
zlWosG0jZ~4;|U<r0%1N8Gl%>!uoX5H5@V7#+za2h?_ju2`Q9hqhH86s25^MuJGzgb
zWx%{lq|(E>tVx;#RfM19HJ+>PhO9pQwp!PCgm;s4ub|?~sV%zIWs>>OCiJQK+Jmf(
zu?(q1%qRZEnXvhh7h<g5c)zR)0k_z`na7T<@b0<<%UnL?&;-lU@4pitl#La6h?K_U
zMjnZL$Bc)KiB7p1LYuydUM*9Dq+s!`k5gRxpiDNKu&(7~=Mww?#bd6SvfjSM@fK%d
zzL1v#kCsE=Eovn*iPOpNrP~FUHDnYY2FHyLFrWr&lyp~<rDdHL=apTZ!}vgtil#=p
z^9a!3-E|Hfk~m-4wY86yBS23Mu6f<ywsetcSxvB3>ru$}u`MukSs+)*@i}D_MAITR
zO4uqtNHPW<a9Xnql#_X2gzoxYMZ$V_P$@i5Vfv%!U?C}J-5jh5HMFi5$Z*7|&bHTF
zT|T_%BAj#K#n|E@B{lR;1l&Jq5sjBvOTBQXq2+o%%ocdPT{AQ7-=<dCa|`|ovipt$
z4%Sr-=~wO=9{$7fwXD3`jm~ZOJvF8ieu+Lf{8XhE$Mv^BHE->C+vNhPoKQEjuD>N(
z#k<VGtc2Q+j+&8+Tb(crXMN*oEwaAx=0fvX95rk|sTA}ugm$0Q3cq^ZdNM#(cew3T
zpy@Zbm3)(*sKpGr7*EwtYGNxQGcSlaDuR6VaVg(kUr!r{NrLs&5UHZA+D(v4TPkWA
zbfv^&f|j|_nTT<%Vq&e$Fr_wAzV|UVz85bLAKwlQ-G$_xf9v}`3(S!;&@W;YHC^;%
z+V~bl!FZq*^bj3}b&G?VzLG9u9L)xA(vR<4c{+BL{Urd7io$_48|~GCm5-*ED^jUE
zp?ulm9Jy~C-!(bBx}}9T)LJsv5W{%|db`Bq2WdAz+LA;Sjd1zPJ@U4t>jMJ{MY&@-
z4ws}wf{x?z4cFsYT5<bAtidIJyU3-xL57dZI8h*s4Dw(D1h#Xdgm5I5hrI)#&eR{~
zn`>`v>uPP8{Q8rRAzvKTHe=b?wqRUVjMf|H{=WFq2dqxwqXM&S{Eky8@tL2IqMRE(
zt-EF}mGN{ML+KkP!x$hdrb|EAp-Ig3E8&7G6)7qZGiNaFT({fIRQm2slAuc`=~KXq
zC8^LM&OgS*ID~=fGx;B-Z$7WrNht#!R3^>4lEMx{g-QqXBIk=L`dhEm13(=l3fN9x
zmYEdwNwv$?mvvkN-5w?!GTmoLn!v^|*#uOP{K+q)?+xX`yiFsI&W^am;$v0^gqqgA
zGV4S5h76T$)N{3OC<GfV8gEW26x4fvvO?ApluYJy`g-zGvzCLj7!S<B$k5>2?AWKx
zR#NuEW*r05pN;}n9%LJWlA-J4uasVylhj&^>fH#qK!(yA%^lAD4m=pBHZ#wve!TK$
z8VNfipNF?Er_Z!)$p=hF7F|E~(yXb7>Wq9Qu#^9do~XX=?a4)&dcaW+gF(e0U(Q+@
zuwc(OxW4cMcp_u*UG#-U_q|!UI|>X-d=9;l>J`9je5OZO$5)&ZaGc+zZ&D<DZP!h}
zM@a5dO_@*eWumq`#HR9;-u&U#%~$hPVfc@S#y7~RHcJY4H$un-&NxV7w&QO|>OdNz
zEyOsdc9ibPJwq8mH_@K|nh+JaRAEwEl81xBTMuhbJoHi%{;Kt_j@kWL^bOcjVlPHA
z0b9J;<~`S<x?55HsNAyLl99;G50+)tBA#6<@7XdMKKXBY?^X5bkF^!}P1j_Jb8=Rn
z4qKb_=@Soq)=NA&4mk+Ac3R`v#ALVIZTzjP`)pll^LuYobFd?-{&RrLZ$s$z%7e!q
z-{dE%)|C1x+x_;r2OLsch1)kqDOHg`6bw^~wGO05omk{r=uR4Co$edHk_=6MGUTtj
zqUS$?`OE-Y7Ll^)PieCEcuEJoIp2hD*~eI<aC-hBI7M^0e-xs(!KQwlxfjkZY$8?s
zt?(PP-##6MbJW2>IyKE}$TY_}gFB+l0$8GizdEyD+i%5e)u34jHEb1A!He|=>8JHR
z`k?XS8HvrNwUy;0giKwP7iDNs**9}cXx^_02}y~(y0Tdzz>993Y9lA`;1+|`nfn&X
z#Ulgp=R>vrI?gF_^149~pkFGDQmwEHGJl}ow5j>cdcWxsRz~T<>_K->SF7z)nl|Ra
z8h}Ms|M4nH_V|5IWwP=)Q`w^-rB%(2K5>bvLD|0yr0hU;hYm_^el;^b(tzDxBQm?C
zfA5GIy#fHW<esEGZUuWhEBfv}=-SY-P_|(XF2PD<E7=%*V*)z7@;J<SwU8i9Bcx9B
z!fIT=OPm(dE2QLq{&i+5q(x@<7~f3lWK!3bJ;LN^8W1e;FuS22`Byyt8HEGY!`8}Y
zF3Kv3gIX8<t;94!f1epNrP<CKe4p^+2|imZtCY+fd??D1H6JY^;+wWITMXZAY1NoW
zmAG#B;i!Xu|6{bGAv-9@<L2GFB;m2FpsNTIs`9mLkgl@p0Zloi@r;OF`lK5OKRj;i
zx~%<E=NoHUxLzI-Sh1~>*)pR#@}~QqR6MZgPjY!e%s&N~?0R<8%6f5l)0V2u-bRO-
znXzzs72$|C-0Ci*cLglSw#{G5X09o4b^d^DJA~1;BOzJQ8%LxxhE5j{DSAs5&LA&I
zLnwk!W1?bGR%1fwNpJEYf3gtLU-~kNPikgoZi09-=tqUnnPPH8dz&eISuv%(<@fq+
zkytr{|AhO-+d7anoX?@PdwRiQpN|m3`(BnTz-7HUzR&teef&VfhicsqRrXZV4aM)>
z8vctkv6s@(40~cqf%LshY6@GfdF{;YYvikq-h#e$@Bh)+Z_8~q@W`d7t0DC+!d}k@
zYrHL^O^?u1kVx))gk^lZbkA7I-u_Nc?dwrK)9km$+xo?S%2=}pJSYsxmK$K5#ZE(f
zd$*olWZaWK=JOGQu=14C$yPSz{@=S$g{ZK7i;v+(GD)5#JuP$-1&jNe&Pd@LcKus~
z-qbip^<VvJyFZT1c68733dj)9UAX{M;O!+}8-%6C6LDV1wDZ2A?9P%B_1d_8OPo^y
zCiW|djqch0gZ!cX-vI3J&r}Uxv*J%qSO%ln%YZrkIRomN1>;0#*)JBl&O#F*=266E
z0vAB+c$|sPj}~p>Fqu>L>i#iXX%1P(>2B()A=lX=8ELb=AEQSZa|a^?)s_9zc8{xP
z$@^gY>H<#14=v$}yHq=4hkZ1zLC?ctPAX$=yNXJZ|AvK>=j_|J^?jd~pzk)F9*3M8
z&OIYF+`k=mYl!_bT&qdwW!EN2B)uG3e&>e@WN~mtbKO!{=ltZwNaTS=b#pJ+$r@Sl
z5(<8txLqW)l3rr|)YNL(psOi)^@uU;*e}F~{k4jU_h*eqQHR=G{w=Ck-d}qP(UtGN
zk+$9T@*%nIUN-v9h6U2buUOAo{e1B14;`Ydf-pf3t+y<3N#T;9N+~4)O^c^{-Mm)P
z;e=+$>ar4w%IXQ>HbbA-*Uw!S3LE+oLZu-rKk?@T2^lZ)i|*Q}-#w_Hksn@ll>DLt
znXrP|v4hO#>tlrH@tc^L=iW|+_l5mAjX?{$pk26|Lby$eW^XC8N6aW+a@DU@#mT0P
zv0wtkE4=JU;*U+vnT^1FA2V2CbIm+=jb;nKl=^X$X{POp!jrF5+t<ruf~dl6;v>Tv
z+OPMauzfqZ-+N6Eqnb;uwJIl59J=Pyoo2{M&=u#TH`f~iJ)HL)^o`|D57&c_{B<BV
zrWHF2jCuMOX!q!WNq24L)6;lWO!-dJu>Dk>CUB;1zg!rqo^q8Dp66fm7*@N$+gneC
z1yp~bbax7CUBRheXspv_7_Zp*S?1@M;ei!rsh3~M|1nV1`kjL7n|1o1T4R3lmS&QR
z=1D_r@e#{h%0&=;E6n}K&3b8s27uCgS9<razwCHvqgI;}$LFESyNefuN{BX;C{uhe
zC(4ZJxL(TvG5EN8E43xW=jyV@wWVPX|FOB8<5;_<Wu>oW{dHw;JzR&ZmCD1098y<e
zR(62$`;*yMAEzGym3`Nt<NHAA3fg`K6*W2r*vbkNb$Jwv0|#noN6J_iF{kM>utISA
z#dDG=z3|@4=(bw_`p7AHxc-JeU>{k=7Nsv@r_@%ZS@1{ZtiI1Uv|+(;(|W1eGNacb
z!GiLQtZhX|8OhXZM9G}dt6A$RZ4(;Ap%D4@+$on31cxJ*l)qJ$t*JjRVS5<YHiEcX
zn5E|Wt3>Zq29lF4l=G!!a#lL|c4U{k#|b$4B6rMLT^T=3-PXES-^`EG89Q`;^ncGA
zb-^pQ^eTL(G#35mjFu%@RJ$D<VtVU6)-(Wamx%n2Zr?o-(br9TAu|$9AoQ2mM$qQm
z3;+@IP*#~M;T4ogyzoI()gu}u<#^8%+(G<b!J&vnY_t>0LYfZ1XWmVAFyjm8)!#WX
z@h4HIDYfPM7Qt%O4=8;;AS0I!9UJp3mAPd1C(8P}rvNt}#<b{)9b}naA8P2IR8rkY
zWOhE95F3y4-?Mt+M>iG==5^b=Y<A+f)T@f`<#CgxYFa~58A6@R&pAu<9wN9Rx^NRq
zbZu9(^rvyxT7qJ}P!gG}k##Xyl)|rtbAmfvYOXhAPUh%g3xg#RWNIT$jT5I}CCK`Q
zR;RIVuktfum%Sc+VNKVWUU^OOQ|A5<1RCpkzinde`JWHt%y!$#ECf?k`uqFnSVYsK
z>MsDPi_%QcJC*M9l*5drtKRyFIL($I^WD4Z!UB=U3$xxPc#RdO!>UU3S|4jE?8@~f
z9z;3%V_QDuen>!(HRJ&$$~qtIF_vv#dzXG8(*6%!uKJF|R3BW|9da*P-=(q8-g{2e
zh~Gk+jPbsJMZ=x?mH@|(G!D5T|B;PX6bfp+z)CMt<1A0;C3x|z0=(MPlvGW-sR43E
z(olVQg~5{hW6IUXV9YQcpFz~(TCz7_cnFzd8@QDcNY0~;T}0O?K$07bL8mE#jnKJ`
z0i*N+3%Tt%()m{YjRAKa=~8PfjtBRx^Cb<Y>jFEkPI7ToGR(rNZf4LWCtwS@fHZCv
z<h^{m!vm7KsFdFqa|S{D4db{WW}NMiBlVsqw>n3zCyl$7;a8gLU-luEnBT&hGc#vm
z&XEn;8Bzvbk6jLS3$IVM&xm}?&vfT8hXwO7jR#Y#^ee4(7|wh}_`8*!6M|F6jHuIO
z$o}_jrpFbv^>^&elMS$96;7gS$y^*R9o~HPnFGYmUC?<R6R;~ch;e>jG%Qjbb_j|0
z(@kgMbBjo2(ER(PwvVsO0E4ZQ&6na3x|<3kd$72wEZVlV@75BIVQ)W;3oYu)yzHkK
z!KLIymG(RV&8>4tjmZ<8d}co$A*}9;)5I2jD#tAtAJy=gN%Jn>w1R_z3<AHc&+YJu
zp+_eisE^hU*+BhnIKPEPBNR3m0~k<8UCQ*gx*qvoOpQ`X`mSZ}n7I}S`Zh=QpQd_m
zH9s8vW=fxq*7Gx25?<T|oiV$(MVUn7NEULv6*#vET~_G#n4lwTA?ROl|HJS}6pkLZ
z{*<ydk}3N*pL=SW<DXSz+d}cULJW8MhVtFu{Ho{JeieE5FmqGc=e9r`L8q^t+TQPa
z*IdZ1zHzdimj*i9-D|S2qhBY|wtr9WNbUefBNCvS(b8keKCLYpuAhs4bMudblIKn%
zyH|OHW4V=H-5B?b9BeKa`o{{2xY>o#G!T|0ME|4wsnff*<CssGWBf62E<Xx8@!QM;
z&!Vwha{Lyd`J-4LRu>%OZ^q~SIe;H{jzv6re#Pp$CD8Ye12bSe^UuzL4VfM2G&%{~
zPy6zF0yhH9D+XqAo6_(!|K}W_)(Z(LZ539x-{5H@TQb3KgPd?QaO*h@?>U>-<lCCe
z`^LdP{Wfv7KR5$1Unnp1ap<P^@Zq?eG|v$4eyP4Tv#kD0EuR*|LT^2{K2qaKcIkZN
zXc|}h?CA}=Ir?krJJXxVpalg-RS(<<1p;rcFnqk8=djIkoUQuL-178WCS;eIh*~ae
zpx^zPD3c$Kl%7dScIZm>3YVSF-Rba&`z!ZC1{;W@Y31$E9C)t`?`0?)$qV`RD*<%8
zK1rf?k-<2=;_H!}{w||zXQHjh1&tRqajV^NVu3jNi^M;-yFl}nq4&k>0Q4mFVTsx7
zIQQX>L3{J@^*`>I#%7nxc<EVkD2V>W=sz0Q$3Gzjxu)&|Pm+a_QKAuM`>XTyzh><y
z?wWMMwl!Uf#dvSnP~bl7QCy&3z9$O0bjb@lQS=)A#V#*or$Qfd)_!|EWT*Gx(u(mP
z7a|t!w;^iFm#$=B5-fW0exVqgd>Ntb_nu~+{k&hLk$)9Od^1U&qR$y|ecd1bV>@ae
z%I~R~p%z^w7?OJPB>G-Fz4-^E(!@WzTXg-OiJ;PZUXy+8C{Am?bW-j^m$E_it|uGk
zyDo5~?VLFk2Dja-RVSR^dZG|t?Qd&O!#}8!hV13G06tBk4t}I?)&v*m8KGv7A>M||
zqF_h=2&z5PV|XI|IG&Ka{4zexPv6SjpjvhRnr|{WlP-Y>L`m#ZXuW8Jb8QT%dc6z_
zRO*E4HS>B-IAu+X;<z1b?US}|->0D84SCDF)a`btpiUVKn{CSCc`XHs!!sdU=3eZ9
z*vN9g`H(rNSHmnXj{kj!E`vXWjPtDCxl)Gbc=s_=#U!V{)8%NK{2DjH+x52{33-lY
zZQfw<gXs-V<v}MFdVRlRYHHs;i@%ZrK;e-6r?3R!!oFb#-t&QZSksPF*3EHIs`FD!
zc)(V;4RKt8UW#{)+&qqiSaJH6sG+?EE)RX|!oG^5tqPSdZz<v?-0M|v=%=iw*-926
z&j?Fpv^iVzT<J=xRF?>C5(Oi3ks`|Ybe-6@KLLwl64;4VBH!ka?c~DVu+51;G0o?D
zY?G!dM<wRhA9=J8Rl-k88YXJ)T!>siTW23+9sYh?lC@NR-&beQWB>G_ASd&o(~NK*
zt8N_cHL+x4C!n>MxaYxMP&I{%Qkl`SyxxiY_#j;^=>poB8h{R*X%z3co*B68)_N4<
zQVI=RBg*z2H}bP}cs)q<=D=1`h<|iQarQW^%4A2Bx7Wl<qqQiAL{gixHe%NeGYvVl
zyR+1IOj^74WI{#ZVhl3RS)rJvAFVm4{l2e&4Z2rVqj@)Um()w@XfR~^6NEdL&%liX
z9xYhGn{&fhu<U*()t7uWTE_@2s)1u(>M1Xc6Ca+8r?%As2KtL6@!2N-sYvwfz#kR#
zHZ0$AFOMc#&}w=&@$Cd|liql7Hrpp(535<8zM*!`p!qGWDs{6m9Jcwp?95Bg!?3qL
zrqaeQJUvdhVN7XP`Xm2>@2OEG)C!DY&f!rt8|D@(+*t%OOFgaCGhDc=*+;>z=iyVx
zZ&^tE+vyGIuQQ^}=y~3fjhqm|#$<cM{6BrxLWpR0N@f$WAL&MoD$$hfhbH2VAQpU!
z%bM8ZIo~m4uVmX>B6UQ47gKr$h%e^IQFc!MZ;hi<qb`P+V01F9(yQ`L^K%S<SnBx)
zB5lzCx)g<yX8#`(E;KRiHhz2&4j!Ak0B3~e>f1EM0F@#lYMHGZ<;&!c)P1}ch@}JX
zC%1<cpc}MK^Vg#GQ!hoKKY<z#fh*i+<NXx(xm*V6<fmG0v2iZj3(iy*`XFCAAex}@
zM?>r4e(beA$n#a-eHF>ltfe{!gU@QtA7}wkW7~+i7+=TkKjb=Xt!GmWPEm4vaQ!8G
z-yc~aCE0ngGS!3BHxlB{YGaGPPH4gvfvpbR+i8pEkMj4E^^b0<3mR(&RY-gvrB0_M
zx2YqdmlSUa4O(~fc9-9uD}E5uP8q0KoY7<AfBCYB@$>xI@4r@Z8H*I$QkM6c;SP$?
z@8nD`xCKdgN!@2dT8k4U*N2B#fa+sJ48{bpA=Pm4PfPyP4R^z09p_LG9!1d`0`#4V
zXW8&IEDzj{G!EOzPU`ZHFe}@lJ*#uowJ<^HbNk2ev{5(ZnU|*##4oS_cXsN1z&!fB
zIpc}(igGhVjATX!{FQqK;E#@M!9^34ltX(_5}yWy1f_g7+Z|!Gn3XMJX|3JLKrQKs
z689-vnqP$Afm#>7gG;d2-DYHtEhK9v({`Ki@K{V4HJ+lQHGbw>w3wH-vYm81z{2cK
z6-x+dEhXlE>-%LostE-ZrQBjd_Y#tyExrtCGW3L8Lha=w8|IyYvW&Iw6J@6eymj3h
zscLEXKK{xLHayBld`u0L@jTzA3MD%1>{eBJ4c2a`%(AgRk?-7Px(32O0c3F`@W!Nx
z4Q-pVftsXJ<bgfc=5fGb4}#oT&UWXykf47@&7v&2LkaafvG3h9I;?(qyW4l+u6$X0
z!Af#sHLmwb4evn63E1F4_g6`&o?uAhK(K+DJihy?*{gCg1LE&wK6=?4mOCACu)~)O
z`MKPKYOsqfuQv$N;XyT%ge;P*3ry)jk*~^`PWu+#_-7Dmc?UrbYDdqQr`2`$i(Cz|
zH_x+^8rF4^2lES-ESP+xUp&Bf>VhhJuvE;bvQ^y(%ljrC%ZHFszMXry#9yJS_&q1l
zQX57hrwJp3KrN44fA_R}(nt$?znKL+p!CV;=Cwv1T>fYsmZow-L+EdVX#8@*qRBdZ
zl7xX@lsqy@HLEYw+FHdJ=los_gf*;BW+-M@{mT7r+w!JX^*rsX$X%!MTsu02WH0!G
zE86;DOI731jUpQe70F!4o?}yRt<!<^vtz=4{Oa&ig1m$4I?2c<k|!UYmILPId-&Tl
zYe&N3TVVr_s#V{6?K1f4sw8B%9{tkBUi~2PJp-4aUmhIhX)kUq&`M@TI){`48en6x
z&Eu<Kx_+K|5Kko;G+BHG+r10--?^{SgujGYI>i>t+(XKIlVAc}>@mekv@_Kf>^h-a
zz%#LPmi$W4re{$uLKZekuN&n^=GL&Ii;f1D-R!Nntw$kwD|gt0Dy`VfT`L1#mdh^V
z>5k?Cnb-?8+Cb`}6mGk+;e{GHE;~=N40kM3>@!{T=RsU_B_eYsG50_9Zje5qpJhy^
zDE^G{+mtoPyZU1*$T*kLhNQzW-79y;r9Vj^jp-&9ec$!m{}##pA$gLEzQlGWuTD}`
z39;iXb#>Ss<qwMQtq|_)W~@XHhV{9r*g3f5s>S<q#}EcIBGf<2+%rIpgDPm82t4d6
znxdTP=DD7P*T?zs7-f6_%$c;PWdmL@`SzTiI^-BKCm#7Df`9pn(JgT$Ms1uHR@+Tu
z5$>TM_mT=*?>@E87267{Xyp>}b0TO@YHM9D?$sc65`j@tWHGW>M$^6OWWkZ1{FyWn
z!#PH5PYtYcz_cZ;xy9$5t8of#Ti&dO&}8#t2ipBtic=4)le2(YINJF~4krRXJ0Xg&
zO&l<+XG4isp%?xGFxl!#WfC6B-9i)}0S;5FC<~ZdLwEe#{jx>P^i_cHR{(;gCEYe?
z;xGh1*JrTn0bn35h+B#3i|wPVG3Uf#y`?QqzD9&eT~`bZAcUB{HIdrPRrF~~dO6y3
zO1Syx`3R2d?nUlnBCpFvmHC6k6@OjSS$Gorp0uAZ#z~~*L%_UTTnP}}V9l*p5zANO
zuLB)23gZt;0|4eX>gO_GU3q{BeGo1RV7ebqVSo5zU8Z9@ZL6sEkiFfIW~7Y>x2W$O
z?(<r*tFNjiy*ejY&X@xz&I&280y^wh2uS}r*Ym3%IQv=<Ypjwz5<K!->vcm`<%-(b
z-4{~(CqDNgAu;BmB4XQ3U=iNPA3x*kd+n5EttUOcJu9T;LcjaKMXF~Cy?i71(ZpP&
zwn3sM>szkHho^j2xdu&e{{!8`81m7dJf&Gt4|7KMHO<9IkC=6#4|K|_Biz&&t*z!q
z`P+%77t@nIhoPQwT|6zV-8K^wv-arLtQLu;mFwvEn3Gg5kjWTuvl$qmU&SmO0P&-(
zY1J$4<*Ja1{P9-w#eR(A*zc`emf=GYHD*;P#vm~-mrl;9OmB)#JHB*M4?G_4$+|YV
zXgGYztXr>K>V_c|pRXq_hKWF~lj7u0pcmUk26<NR7<t2(gp$~58;q<Ksf<8}pBdgQ
z*vJ2CGC?MDj$~1$xADpGu?ESjX|7L9@-ctqEr7bP&ZD0ya>dNiVR`rzx<a0L0_RT&
z@qJ80-U7H=IklBIWOo0K{%Xw<`W_wa&Q#{fie0xv+cCa9fhK0m-<eC}U)Z%6Qzx0E
zS|u_-(A9&TTsC-Ph_hrk%=e%BUd%35E9re25gn1rqoi9cys1r`6w2Dpl-OE-2ZhRD
zE;#I5km?Gfxol~M+D&uev0GgVZLU&wGtUcrz%<TTEnqLdS<7%exo$#68!b}XS1j#~
zUSOwd<5pZmmRe(MARlw-R8lK{6=-u0AXq^>1+Y))X;B$Grx{vjAR!G9&q4llY)w*b
zp*<+*x)e!Kt9gh0h~^sBkxC&)VVbhJ<Lp7igKrXpHpv7?CSXGanZ0F6J_T5wwi`$R
zjIZack@5K=4_s5el3Dsx%Dw$v6B%55+rx|(cvP-lV8jcA7({9}6S|o_2o8Otao*!$
zS*14zi_GXV<o9!JDwjY3{ctD)RBZ;>+^yuwuV*O-vRAYNk-20Cq)V&|dSw=(w&RO7
ztAx0*7svj_M-#{Xb8D!I@$gM~KiT<;$D2HHt^B@CTGlc&LQKlB6UP%WTiHeSo5+Kc
zCT#`HsBC}_ygO+o#&^*nm@e!dD}F@^7-CXan+P~_ocF&`6YbmG9?CR0y;%-(`Q@&Z
z0d8+YUp5wesA7f<t!px2#`kEP!Kt`_jqfl_l2O(t-vhZ_d98FF1-(U)=CRG}RLS*n
zac68~y|hf4$mYN!H@<Z<!GNa5<gK}E*bp+TA>7{V*lpLXeTLXU?u+5F>3;=U>T?Dy
zR7mS5x+mEDi@U^8r7Zrjpyc0mObRTL(3*%0X{|%m^`|AC_zFLsFQjX+aCjKB|1?Ms
zV(2^l3UyJov-GQa_LpB^$p(WDi$IVyl|Jp{Chs}zZ<d68Y9!+QxZjI=Z36YRTjMM8
z@wEs&E+kDmJ)g~6yR|B_bI@U(^pnccPVxb#z<lHL9cl9Kjr)FeUGqK`9DXKZ+a@YA
znRoO*T&{GeVTo|DoNw<NCU>HnR8uxxXmeTKmcxb7owkDw2fvFqGKsOLOrtO_J6TP7
ziU3J1f)<7Q#<_lrufp&SFeVQXDY4UvN38H-+c2MGS9`G);MsQlh5v`;OHIE6u?qK+
znI-T1z$s!r&?}(8AZ#J`R)QZUqS2N;M6$#zI$Mq=4aD}Gd5it|h3N6Zs9Wd}t6SC9
ze3E7}Z`6GsX_9ST`Y?|Ex+d73c4;B(gSpFM^!Wp^Qk67aH0EzSGRMEbFJg%>c;5kH
z;)%UGcR;ZFipBVI#vdwe&5Kl4!Q^n1`E=gyYqP`B{;mV&Kr}bQLI1Cywi|{s7QP_s
z8Os98+0gMD4Gt<hxdHt!%M=02Jp*Sbwac#%cG5Rm%*Fe%g_qZ}HmfW3b-h7{Qj_?>
zxzzo6nejRvcEU*{Y5*B3sQq7UziCUhoC~SDK*7OT=KF@z7M2f{9XMx^JvdK-f|w=e
z*6eGh5OYM+EJHDyx_1Yq&mhQ@;r=ZDeM7g`U=Cn0h%s7e@g*cQ>qLvo@4;e`!re1n
zICTsOIoKD-?EVGl_^#nC`rF`&;(+DbkrxDyD}Qu;waZI-rxcU$qFZ_FIk6n;YZ)DS
z>2C@{H4X>RIr=jSGDlmo(vNJ@%48k-gBx1(mC#!r*(|_ol=H*0&^r<tcW{X9PAf6X
zcE;i2z+H$T%t0Fr;(m?QN*VrqmdPDN$GzZ_^_rel=wG0(O>##erc$CLhu%}*-+iyT
zg)U3!w%_nEWVNS3gb1pHQL{IQR2g=+Pl+-~OMDfHs{%xDDgJm8uP~-#e$XbvoGfPd
z`3#Wh=k!T@@Dpl?LvwragXS6XrJM1Z{`UBekB=+i#8F*B)_AxQsNOoH*~r-&k@>JM
zEujPgFB5%0Mh-D9Af~9w&^yyg1bhHJg^E!SQmA?BhYasx5pP%Kvp?}oB<0uIhAvxR
zDt|wA?lQ2Y)^VL%mbMp+XXq@Hhj0B!`zHADY3+;b#OO;;|0e%NcLRd$WTFObuQnVv
zfi`B#Q_U?_)`z#s$%vII7Vu0!QtT3>%UpDQn!WhtjH*cY$H0tlX+Kfhe3Zg&X^l;h
zVi9clZ2eQ-APvrplUZ39nA*~Sl>p`ZBXmaya7*5fZ103spSf6EM?3Ihf<&E$)v3Hn
zn)ha1C=gQCRk#%p#(J&I$l6gCXW|TgU!=Y?zMhZd|Dj=(EE&TmW<!QYxbpLIbaif%
zf=bS*g>2r5GC%VyapMXDXQ#&DeIm5~&Um{|p=fMT;6>33B<aa0YqJihJh}2(`u+UT
zsv_VBAMG9D+hq!sR^zU)nhL`5!!%{W66ZhVO=ka#C}j4##6Q0<Clb8DHTUs-!zs11
z(R5<mpE5Z8gOtLA{I}J+rz!$Y)ZAAh{=W{+#h>Z@kK-;y?wupK#3<zwnLBANHTQcd
z_gr#M%)Jd8l1+t?`~6NXQE_r<6k~GBEw{*h47qIPe(Cq!?=RS6kMC#S&)%>1>-iMt
z)DsGRDCCR+m@SVF7Lr^`dH`;U+4*s&R-nD#H>57Jyf|rsz8y0PVHLjZxNbXc=pSaU
zR8PZlV|K7-B<z|(YWD1(W&ER>(9|Q@jOYHOLd+PZefue{9l-LgumhBHS1<n4F#|*o
zgm6V_(a72Xo%CV?Lda%Cak1{d`Ci9OJKbV&3Ejx@{XZU>VeoeDyg|M37DaeK$sH%H
z6t^k26HvhStDRhj2Os_qboZJg&vx>`S+z8bR-=`G-N@qKpk2lZ+TvYR4!e~K#pg9e
z_W?9~@dlUM#P7RmUkvFEY5*p4^puvT)qfKg&a|IrwUS`$v(v6>o*R+@sQCVCQHSsS
zS=NU2Hv|i!C}mHoT?^-E`6(vZRILIht&1N&HA7L7F|-Gd-^u@bG7ro&b8}FK`0W>L
zGSF28zy-j^Msor1`G!qRS-M;ruyZ+gx2F=bS5D4F?oHy0LA&n^cbdmrd>cD&!R|w^
z99s$@sY6PNCPzS#DDdF@?9uz1nETqj9-7CKZ5J(F6g!Sm9S>9IfdtxygSZK_kW&6h
z6ZVO;>Z~IR%~I2O4u%EX>FvJiudsH|Qd@pLk#F$XoxFYNCb?k%5nV)QPcMn#Z=~Bl
zAje5guCo6r94KfZ-hyQkbu?k*b~bQzfHpPYQx$%-JYR#Ps&UwD#)&Te+;tV^w^mVZ
zsopp~6uw*KD5L=dgQ@oLN3=i_Z8v)~#<R~HvCT{zavy1J6+Gkx-XLiG0?CESLP=Pu
z5855K{;@0Jz5iI<_E_Zl8SHEsMzEWqq%F>V_V#8cpo*z;v?Fwo3_x@)-t4>KH1zkG
zdid|-V;TZV-UC*i69VBeZQ1g6&|CI2)Zfv+KO>GSbTFDwuD;>5d-66N=L(sB$-M@8
z{C+;(N25GXlUm3X-#<8tJj=Sz2k`2cA0%H6IPOPqje>G<;&qoD#s3HIi$a@z7#d_Z
z0$;f^+eGqqSc(O0=k4RlNl($9kj=(L!tzj@8FI}+j~eA((6#({45=WJT88YfA879`
z=U)%?g|;q8lD7atny~Er$bJf2Ns8^0+8+M;y_Lg^Eng}z4U%10xiaP-2PDCF)*NzA
zXi)L*6>Qx1dPE+N_TIdx*X42GefoeQOzHZcTWbM#ER@jbUKMbeQpP+lYM%G0_4V3A
zK@iVC4DXZS@#`OF?70R47w>-WvLoF+a+gKhO)p&L=?iP3b;trO7_#nH+*X_sghcFG
zMLe3xRmFq9g5XNDw*gvQgZwHQMumH!6Enl^6&GeDKUwhKJyht6-cOG>OlQOXzGN#Z
z9Il%Tx$%Z@WzI_|;)Gm>T;RA%TWAbG<Qw6klX|WEg92hjM9sfDns1mtMLF_i?iPd}
zAbhl%-6NUsnvr5uI<M3&i#}W$Zuv&{gn1gXfPJbL8R%9p89C*%ry&xTZGPesot<X=
z*qe+=@}>e%0mp-%1tWfndCP92I5udT=lArDjAT<_1!FCcBilGU(f9jg4PaND7QP@5
zqkcO=*N)19WFyf@aZWoYPeEONN>F>mAlP&gB|`Lg<Epp;pdD$o|2{qv_W@sN^W=>6
zh<#!AYg*jrZ(pnjgQjZPI6!8qYhLbx`YmNawslbKa|0)R13zU46DpU#eJ4zdo-Ol#
zpR)ljaaHqwC#0fzVpb4ve1}#J0La%7daQo~aj|e7fVNsfN8gxTsb(rlk~b9_GPyTP
zlzA?RGI^10ZCd=$f&Cn;?aGJ$8wY5au0zIQ`3$4%5Dc%GJhW18um*w$wy3m)B`wQg
z5q*8xBjzsBM5V3~^m85W11CQ|N$!oA=0dKSQZ0>mp_4xV@jZyHA+}}A`QoQ+E0b7`
z^JeGX^gR};>x`67D->U~?<N6|Dstzkuq>^er1ZR025u;l8xwZW=a|{crV;4kI*#7>
zkG+_q;rSBTcd0J2Uoxsi*;+ut&7=BL*-9H1%8$QrymjwCrNU=V_&q1kcIuuYnI-O2
zW7ggkpFEahE8GGPPg=m+biH%NWerU=0zYh^L4S{DH4V_~j(?Ac9}!Z^@z!&|vP8Ef
z{@IQ>KHfK|IO|U<aDMK<r`{!b4~m^~3;{o{mC+I$<+;+pwd9$$)GD~hUyD3S%EGIt
z%L$t3%`Si@?3of}{+1E39W5-4A6-7|S%;e8WC+bFDE8b}p-g#JQ_CYvkm-|U6=Bfc
z2MmWMnOjp%0lh6HZ2jb3n(!$}ueX4<@_6qT{Lcx*AeV`Ych-SU)`_;i8eCpjJI@@N
zN)J5a*VZSuuKUs5_)wX;IWn&WGnQz&Evnl$Oy~WP0qri3Q>gn5l0@+Ezdk2-K|!TB
zYW?>yA?XutRUS;-)ik3sVNbbQ!nD;dEL&R|MQJa&o1TALDo`Jd#>vO0UXALdXWQOx
z{<EAZ&o<8*aj<A}Q|g<91Rj!(kO!tXZv8G*$~At=Kw}!6*OO@I9p&YUUkvh;Iy5k2
zBDrPwPOzVFDc>&QnH$PB*Bzi-AWS4;t<vYKninPunG?%ZuoPR3wl6P*&2V}~0(+LA
z7&y7X<@#X5N5(d4CHolUUYhq$q-a*U)x^``>sy9yUVaeSTH;i8`#g94f%f~|Yke~Z
z=kwPC{E^Lrlb9x}<$xdqh4=>T7k5<;=AwJ)Z2FFXt|r_Q#<<V-y3E+^Tv=n;!Ad7M
z^?6f$ck=p7iAi2(@QT5l)@t`#X#2%)7Z0X821U5*D@8dh)xN&8@ytZ0HO_b%-}<E3
z{?>73srK96csdc4<<LySFn7`YescM9#=RW5I5~ghq~C>XtIv&yi_Q1U^2HiEB*j4m
z4GCjeWGcL5OqcwugdDA)_)-H=?sh(+$2i>YWtm3q2T+7(G*s=P+Vnm`9Sq#t73bpU
z;x3hm8<~u}AKiHiy6jf=0Udl<B&&R~56>9rf_`G-n~45m`NCETvRvHfUpOF}BKNDh
zzJ_Oj4KiiK(9&wlj)I4^1>&#F_>C5)ohA|9f-Do)k`Qz?+5GZm+yW|6Es!eID;>wf
zLZm;yxK!zXGb)b*ah~g!5|Gy3{!9^*!oOy`J3W(9IA2bNS-QmJ53Yi8QSFz3Ytb@^
z4%r_>ALb|G{D`S{N(_J3Pvre-)TNH1>i^G>VJz?|l!&uVbZ3_BXD6FTtA#X%XYO3f
zTCA}0R2^RFTJyiBtf#qRnA{&Qh_pi*Z@}2UAPMVXr%nmE2V?X$T3#|zP&}i~Tv1N{
zReHmxn<iau$c$X2%grBduepo9a}s7}o@92^i=2qtE%E4XcsX*f&NfV-6+XlNZ$VBO
zQ@E+?S7|l}UbT-3YpB~Yw}RC!0<9nERp%RbYYtn%18>%Uq(n9u=P6s0I)40&ieCx^
zzJ{L@{T(|7h&0%~I=FW~maS!CQth8jm0V1)2+Qk>m&XBOh7?)VZJGF?UuX42)`oPH
z`t!)CA2~AcDNPppHMi^ripdh~@AL&=qS0^s=jE-QY5RX0r%N4DeZnY~o`Lrp3*5!^
zokn3S&tJ%1hRA^F11;Y;&r#%UJ8EjrDH9XT@z$Gq*TtgJv#30+!vckKvsUCy*jbAt
zFSwU%csZ(c_y(%9>O{TO>xj6dVe(}-&^5z5A9bsDm0Yuj0+}FZP7Ap<&;Q9j_Zf#N
zr5rm%9N}P3CIy=;7fP{PTl=X;L_cZX1~9Q7-|*`LQWDle=_^LG^w`e%*nxa>uTI<_
zdAjzj{Mnt$`<#{EmA{2Z{wEA^;o-X8Bbpm{NI6n(DfHgl@LSt?3+Y{N#UPwtrH?x}
ztN)mLD>iSSiR9eWFdJ@0^PZ@EmE-<x;UIM2T>CsAlFjj8lf37~lFuxOt=<S1m}D^%
zw4HNgJ(4dqXd0omi9>?dHH5IAhD1y=s8vxZ4VQ$FCx!>x{ExHCV)FYoHeYK{_Y_Tg
z(>0xIcGmQ>)JfSNq#aR68_LG@iHA&5Jo6t|j?)_gcbJ%sCiZ{dx8=<6yU7U$iouWU
zq`ow0#4fn<k+v^{)hXN*aZLNNeAWp@`EQ(MdK~`7Fpa<Dd294ng2PE$P-1A2T?WA|
zvw>Ryc#XMK&`VDAQ95ch-5pbCig0Tby2-Zvw?tgMf^*=)Odd9`tVecbV6&XFy5BvX
zbWbW%ZS?p&7~Upr<!P+t=<evd@R<7t-=tARZnky#4ut&5>>a1RfZLt3{<65Lm_dHf
zkl@j^b^Fpx<_bo_cZSInVMx>4x@c(i0kh3U?n#T_1JicCyL!EHAuQ3)4gU^?1R5f0
zhL<teT-F_L-{_L0*M3^j&`W&hFcv3NK-{st4!CphG%JPLb$W#I+Kv$SoZEKD@b^Ej
zHamFp-_jiuEacU*Iam;|6w1OIa7`FS;X<Ge<9Yv>5!BXw!Nz;VI8zVw)|Q<>qhw(~
zNfefuPluYLz!i-LZR|OZLoEocf^O#p`J893QYSD%KenGbhB0vzbn;_c_wjEiDK}bF
z!~Ro<w73vI+z@T)Z5Hk|*r`?Y=6*dG3C~&hrsANe?{?J0E5=DZk&ELL9`aX(FSWD%
zEirXsbr6><EM2+kKO%)&!f>Her|&MsaZ%{jk~NI)I;j5arunA0XtP#nZdtE_%|x`x
zBy}Kw>oB!6d_d9>L|oH)Jm9mUT`3yW@g~Izr*@AqQ5a<f`mDO!n5_Bg*Fz?w8x;jP
zp^IAR$6rE5vHt{U#i)AmN$H`~fx1G?;wV;qZ$fSNJf~rmTnAo_m}1c*>MgE5?xhR^
zW}dZ^7Mf}$c%l+Hins|;d;O12;LlT<mML>Vn?;f%2c6ra-h>Ea&lq_w$iu{EY^OAJ
z?{W^cK6x!eexIGo<^Q?p+2Ry^SwaH#g(3Gv!gdjno(rxe<oGvM4^jkAK@Y`=&uL#h
zp8Wf(_SCi%&+Rhr_mH`tVwtPtxzACM1ne90o8}ZoFXWOhTw_K@N7b)OXFj!$T5_8J
zOgSo}c}K76zP2XlO<($%1a%d9JO9jx`MW%U9+P=+PQQ!cf$2gZPl73W$iUQ;XMu%A
z{f~O-mltYMlCag#=TqI&ZWoegc@nl=d!OzuJS<|zAJ;c|R0kD6B39!WT2F}J^qiaw
zZFhQIL!mp>9Ss?{K+$u>7od(#wGq0%40J-awDr_9S94vL%%4!6%rP2u-ylzI{^FYn
z8oc@R8KIE$RM+iUi{I}Cf_(AlN2*U3K+Y^rq(nUHQTxpL9^Uo6VdPEMvdPb#SK@i^
zbQ0dnVc(Yv+0{PwvSX}WDa#1<Des8e;H&8wfxIFEn{DzRgynXrN<S&y#HPy~r|UH|
z7B@bDwutHdXQwA<I((;<5W=+h|EW6l3qpzqvp1GOaE~N>`Mu%mqTtFi%igWeMAx#R
znEx8)A(h~B2VnX7l|H*P-E7@(cI@g6^W;F`cHd)fY7CvTg*mwW#~r8}{+a0k^|gg*
z#Eq4ZnI|#G#dXJ8Z-o+<JC@I$wV=A2g|N!X;3wGd!be%|;e^jH<mogUqd~HKHn03=
z&L_{H6F2K}gC62`y|U+pvYl&Bx~;f?stfz(7=L^}T|&QS+(uA|Se-W(96I#o=8dLz
zZ1Q;Q(bm=aq(^s(YKpSbl|FmLG@IRSGOU#jHq6^rVFVBWyVa=_-R?Nt-F5S|%;^$!
z>L9c$q;kj@_Pr=KRzsOlRk=3k8MNOL+W^Y;uR7U~`0_C~-{wd<Vm(&I0!TrbPmonr
z)7oX$uylt>Wa!tIS5u{YG&+;kApGi&tNIq&^_p;X@CznwJ^e7IcMt_V>q)a*z*<qq
zlDUo-KHFBy0{z>PwGHLGT@vc#+nOg2Wpi6|o0o+=D$Cm|8hHHB0qgL<jKp_Ky{h=X
z0L!BJMB!gUhfCs`O%RB~WzSG6tBChWKR?S1Z>xnUVUwBNz6gVa9X>S`Mbp*z#cY9d
zCZ|9s{8JB}R=`zN>oVLSDN5<)!EzGl(qRrsinOF;ZE#x3f&5yBEzg3h(BibfLLi(R
zil~FME*S}3rtEczKEUw*>d8Mc62MX1=>;qt7Iylg1D^a_+j-xADSWM>wi5cZRHs%w
zd(ahb;!aO2>NihUA<fx{;LOsgH9bn_wf+jm5RO-nsFOuuFu008yC|EJKG^L!TTo(t
z=mYT36^D}IS$N7yegum6Xpbfz=`mv#vVZcfCwV1F7gX`Zu#xdhxwP%tzC^M2=J8Ph
z>Go>})zV%WhT&{Y7W3Ae-7Yx&c;ET!?-6XT=Tk^rND@@ZUO{m2^DM2p$LY<qO0%93
zRTNok=H&9J-Db#7Xx7c9lON2AkU=Lto%(N?b@cNEIdN8`cMLM8$um6xO!7|FeIF!v
zYnM&vvO;G%EjDR3w+hP4(HOu=pJ>lVD~zL<nK%gDGfQj6dK|seZ|2#W_hj$Ib4FUK
z;zax-eYiX=WYmPF*JP(?Ig!{0r2`CP>FTKDIhYw6#7N0BJWD1vDa{g@ta4a(#?Lm#
zUm)(rACL`J=vp};EO%RWxn!u^veF2AZA0eDSNnOjy-jw$^9^IOHgjJu11`F?W>gse
z!<yf{61=M`996-ZduefF+5~UM6j-5FZ9GMVjYMP>Q%2bKgS9dxO}Xy-@l3*g|0<7v
z@gSg)o{@nsYH`XQdgWf)lS~hW1l2pI)R8L4Yt|H@PP3-rOyOZ}n#bmfJnvG$Pu%Z^
z8d!Z;-_BjeQVl?Xw$|@{b?a=~_d$A+U#zq&>;^k1Xvv%{JX^R|p&24P{o`(sT0=h6
zlHY|%o(_ZC-gV&oMcw>*nElm;ko3Hpd+t?6vFMwh^jP-#t3*Gb23W(6746UI6Ur=y
z#E%J%jn;(xca8@pQ0Pc^iwuIgEF4KeJ0$8WBIN}Z08Y#-^<#T9ZcVj*A((ab1a}UQ
zLZv&4i!&87pXj@ATHrjG5~2dyFKzFQKr#^8J7#42)P8H7m6Fmb$eHq=DJU#8%AINB
z!<fwxzrps8J}ydQdNSMe6o+z4+wda4nsJ+~NK2|}YUMm5j|qY?w2bOm;lz<{1fG_?
zhh;MUWh?NFF&6U;AG?1!2Peo}s3wQ7y3)+Y(4MJFGL-C$VrTyCL9us{*CwY8_z~0#
zetOx%ovV#W>uymjYa?cNS~gp}<a6kITuKHzJw1&f*U%09{V8&?9SYj?Njf#At;+SR
zo+3W2rAsW}t>hdN&#sHq_*rdoVBSs+<pE%QJ1cU1>hYl5Nnz8V_Z!^CS=<h+#is#E
zD@-Bq&5d^U%F=I|lv=cuMuE}hkH6On;>{g6&6a=#xmn?p`c++50TdLgJsid&Vk+*e
z3VV_F(eP-n^cgi)lzD^!r<1%<0d0ncq-hS8(k)*9s@4uZ+|~_t5c|!4Ex0CHp}s6V
zPU10U3n+I=4&>@Fg@PUsLRwLFf!$`ms@W^qd+8<4ZIxOH&fipk-Y_)amw4YZ3U4B%
znAx`c!>jSyw6Jf|)uAhqLDo3bP@SL4Ahh7m$zZ8HQ(M*zru#Mmd1#YEPMF7k_uZYp
zWDPa<`}kVqaS6sv4(SO$F4xFjT!=A8)uX%Y5ideL0!47K%Knal4nA*Nw-rcejpYZ5
zPP!Q8<^FP_NZbp>xOkNeT1&m^k75X}L>k?Zn4T2tAu6On*R+7~-=yahy3izNt40@$
z7DL}VN)gYeqWeg7>`(!U93~?n%DDdvLV<8ByHYBraFgE_&;M&QhQjVL)IQ>JF+~TB
zGsRQj`R(O&j-;Ok3s;3qhQAE6eA@n;=3EA)5`hHRX;ibAldEZ6mVG`&O8C@V9H-)-
z%AW!91S`Mh-F27Q0Z!nc<SM^wXuA|f4`WD}NA`4upk^rf%hivF9f>xVzh#q;CUf-L
z_a(aYsh^Ho_1+>86L#mPr>N>CMnqz#+@Cm!nGthh|0xdFl2b;OQr{0ckG*{ZEk5%K
zJv}qWx#}2}bBe_Fo&saN?P{oX!nnq7ss@!U;%71Fk%Jo2HX#8SpPkmiou_h|=+|=7
zx$=6bcA$)X*!XI8y*kO0aFZ3(IU|aS!FbKA?%@8YxAc~7jkGD4+(DY!bazD$C0|MF
zyRv7XiCuw<wQ&Oyie61@f?|l<&UUpA>$dj7hH-BO1_nBt%X*&Ml^kRg^Jab3Z%dDJ
zd8SV<X|c&CiG-_HpGhTQ?OE?1ulJfvm^S%_av2dLTVJRTRMl%ds|Z6dT1=x&>`y<V
ztL(QxdKBYY3&9bO)%11+@W!Qx{D=xb5wSDe&z7Awy12S^41C6ZP>nK@avizW2*#^t
z@TP~K24`vbtN(a;>-l3=Gs-!~d>Ln(e&5)qH(fx~!=r=L_R@IiqwqUK)X0_U_{vy`
zS>4pH_ldq@tzov)$DPSj5{K0fea-6XM^;iP;Dz*bz?U0LnGa=DpD^9*R7;Zf-M`Bq
zSM$j?gz*d4A0N-On6_W$WH75EZ9UwQ^o_VLM!vT3e>cYU4@v(X{LQ%)@`+?3!9p`O
N8|av7*W7b@@juUkx8VQ)

literal 0
HcmV?d00001

diff --git a/Images/Ipv6_address.png b/Images/Ipv6_address.png
new file mode 100644
index 0000000000000000000000000000000000000000..8929b651f48eb8916faa4d295e1cf7fa3edeb8b8
GIT binary patch
literal 53250
zcmZU)b95$6w?3RnCYadf9oxEN+xEn^J(<|HZQHgcwryMAywCIg&N*v+eXZ_Qd)G$q
zs_s9!uIf;ESur?hY-kV=5I6~OVMP#-Z!v${=TH!TubLFseSa09=0Za95<)_R@(#8p
z=2pfaAQWhou{=^>0*FCd;WHFJFiFgyW5GpeF83ouq#H&dQ$j>Spe<ZmGeTpc_Z)~`
z!OpE35Uo)k=tJ1hlMHeL388wo2)}~@b}~AJ6v1&IU5-{<P5De9>{7$3lGj1T(B?+y
zhF1<3+3r|&0uhKq45Sq-J!s-J3S;mWef)cxbxD1eWM9v`Pjc+-nQI)_kXQ%tgMEx`
zb<0bCF^K<8t1dO(MXoJ54U+JZq+ylLyd$>n$w5G7=fuGM+|Q_A8C3d#dA9O>8yfk|
z5q{c^j-#VTgO<uYOadu<iXh}yu5w)9K?rKS+80Nnl=Ijen=Wm(>WVt?RWV}}YTQpK
zA<^&121QL`FQB1iUAE`wVPxS7#;i#tNe^wMSO6)td5ze>ea_V3n=E^ThB_DefSKef
z)z0sLc>XPYwed_4*C+n#(sG$hu>SC#i~1_eFh_z(!L^U2wkBqRx;=0`+~cpBiba-f
zP6(h_(u23LMy91lw#TFV>o*ct^X*)zj`+WG^TSSD!x01o2IW8JH;}Xp%)c|0X|Am9
zq%I>3G_<v*(>Jm;Fs5^}w)<-h0>bSE{M)oPcG4$wv$nEv1iJA6{;>f5ZU0A24<P)<
z#L1Eepe`d%C}itkOvp;dLdO8$g(f5<<aRJJ0V)cM{@4Al!~-yMa<T)`)4RI5(z!Cz
z**cigGjeir(lap8GcnQrwV-u$w{g;UqqT7){x`_~iz968Xy{;W=VWecL-?P#`UbYn
zPCNj>e-i!A@$Y>ayP5xQCL729mi4zl`v3IMGtx28|4;0{uH6680_Dx!jIGp#&8>}X
z9RKFvWoKjN{>T3R*Ym#_|Bt7}|9Y~pGX7uB|I_ndPj34E6!3ov`ggSc(f%DSUTALm
z{~3B-=*{)!QxFh-5D8%cWw&qVI*>on2IsyC)u3qS<pr<s1j2p=7KMPL)>7~4eXso~
zz+dwe6o#rGFYiGwK$-g#uqh-iDCluDI_|^V-aIPh7LY`--yXPraWOSD_3qMi)me|S
z{}YJv?Z5D|6z=)NK9O<wAI-PWUn_noK|cZi!cGS&1#iT;*My7-iQ!*xA<jWafBzR%
zFyHFITIrT2gvmhvg$bm=>aWm$(c?_?(}i#eJUw+pxaVJ-^GoCWHx5z@-S-Lj{kEUX
zD5+2>{{k<>9PIx^S_<s>%7zc{sSWgPEF|%5K9KANRhoU#QTve|`ng49N7~*&#V^@X
zrLB@Rh;08so3vd<{8j0p0XVQ*(EMNza49hQfhKiANqBo(veWF}*}1`L3J8c@tRkef
zw0dy6;FiEg4kD?$U@356j|;V4G${$~A%&{xOiZZUuxc8=we#-GYV-Xe|E2ir;#S@x
zL7DSIs!W?P{mTd<FLGWJcu7KcbY4<x0#=uJ>0r*m9qqQdqM&Ip;>fIo<aLRQJjY~P
za`&@~2Ei5I+}wPG7SME>ac&eiH7YG8s!B)Bm>J4nN*F^9DWuF!(;7qzT~Jii=YZFG
z5)_TYDJ4tBRPR{jFEvU!b2~ECALl=RKY^^Dm6{Ybv3@3SEV)u%+j2>QyoVAaB+$wk
zk#B)=+u}zdG<74{R@3G((pRnL`@85VT5w5p^Cx19LYHbW9N_p5-Go*ha;r;~T?59*
z2ng`$^mjuE11=X9tQxzTk*NtFnMy>pif46w5wG*|7<wx$ky46O;l|z3cA6R8lVmtZ
zf9I-uhu;oav%$vS2MjXrcX0W6gp&Zqbh&uN$WRt7POZhLps=ciKRtOzSlB$AW&2O{
zKL03#$uTtbMp6(%zU;6pnKB)A6A=e?0-5i(NF({1{y0lHVCf8wbDYw+T)0GQ=s7!h
z(fS)*%*RW$xwyjiJS}l^&diiRRB8;`9s@q#i05sw^|h&|*R!!iCR#Gx@%SXn(1&t0
z{lVG7W1tt4F7BUHDxAnwv!8CXQEC7;hEzOH`2IH*{zS2(-SlRHU>+51fyBpEt@^xH
zoC`%+VAE$ka(Ge&e?m!UYPU~hf}tmaw+4tV!51_Ya4oU4DQ8(RCI>|}^eB4grIz65
z$!jJut0xZE4~Nna82?YV{b}Deg$4ODh<2OsD7Vj(Tn^qlZ)u+|Fej{gsMjrba(uUI
z{dwfx6mEB>vZjmBnd*q49^K08al@y4pUSmbgEe~t5%8b2CEOKE+gK4}O3wJ}F$Ak^
zptPN5+x&IxWF23;qMg^M*s9eO2eWl%E6=()VB@+*1^m;B?E2A~joyeI9&AkkgX)te
z99eS*z2%auEk}j=4`GEhX5$6-+B>O`bL!E;Vg1rGO4y;riP96pP;(wyX9ln1Y@ls-
z9F30Pq8(f?)3BbH_*Rxv2Y>44Hm>cQryvQpf?(5Yjp<jVc{-``Gs3~C)v85`L}aQ{
zH&=C8_xfDQt>%CgF`XE-dLqTA{snW=m6y_c=eqC<<q5-A<L@zX`6hQv<kUJ=h4kBc
zEp%&K!2V)mJ>^Q}s+_r>y7$Afi!#(2XaqJ|Aw!aEc?k`;)u*{(6ZMXQ@rRYa65`dJ
zn6O((>g2SrLD(mIVQjqujMAR3x3!McLJlgQ`NOmqHAXC!X|sV*wJ2un${As@@%l!}
zw?2jAq((2_R(9KyJ)qnk&~{Feyx6q9o|O+9Kct0ymIqO^R6g<aQ0+BddIvqCtaYg7
zxLIE?VlCx09cXtQ6xU#v>SjC5-Xmf^gzJws(SF3aXVsY1M08ADX;R;9IRQ0;P-Ut|
zC^93VA}b>)2BpT0_l-9{eTn$NC`#=FN50K~(ixnEQoL9(_TA02{LVDvn!8WZR#BqE
zU!$~ko%wVGmW^bd`orzfXhMdOFyV7nMh>8s3(<$lMImmKe!LhA%8n9G=UFy5!-F&F
z#AS_A$Z72~^|35TT^q46#krXS$~|>;1>SDolJmc>BB0!;=6${&5;DGygHsI}Gt?Nk
zO1aUu2Z+p$XE6~UvMj!2_p4=l|Ej&}2zJ&I&AZvgUsstk>cMRO69QzS`QD13s1O+>
zFbqE@-J1_cW3l<pPJU`#`ZPm~duepwsES?*r}&hS5K42~KN*=HlPKZikb2TkB@zYn
zQLefOhdc!vXZ~#uc%~`4>j?dHxII^xZsyIPF^A$M2*Sl?R4F}xp_fA4Y&|2&nKtuc
zd(_|%mrWS(L)I9#4!Dav2#4kLH!POz-&Ka6G(%Nr*A&T9n={r#6a8~T3<Vt>j4;Vh
zM^@N7nuuJ>l#r(OBQCEW(QTiEyKG+2z>Gv}Wah`#1&s!W>m_K}VoB&g$({I!i|CvQ
z9RD@p`#NQS$RLW*rrCVWH*Gxk06}vKy5095xO|@9&^j&i(wf=JRLVigXN8Q4i5VC`
zLK|qb%T;CE)XJkI57s%U=Mv4ok1*`VDbtzw++u5XKcRTAR7#=^3ieu^fGS`J{<NnI
zPTTC*hanLTBqmz=;cWiseK?|D0By=*HTxZHJ@Y_ZeR}ADhg#@@)vbktFxKkT?GLze
zsR9n%*^7MRUMxrTQTeL_F96+!#nODfhG;$0ee@XsK$+d%xeUwD{w62qS~hp0oXxy*
zegdA2tzx`dG0%a^?e?8<G_^#h8%dMJ9E8b}bN0xXQ%V#W6cm97%;vgbS`hj~fZV5m
z*F|w!JA=~$wzf32M6oj|A<}sF@weD>sXd1M{($-ZA13#WS2%@c(Svt33R*_=nIEOs
zH<e-!)9{=pqb-Q>A#Kjyh)7NlwUinqy6yO7YIM=_0pb}jp#k+`;LnTIFc^?y0T1M;
zg44Leqd7m9+tmcelPVQD<H$$lY9AZ!r>wEL$k<n7a_Kw6!ydTyc04`vOzZ1vD<)-X
zWlR&$9xL>q-{2AS@4vDJb%b9|C%{7_^B;r_by&$+zrpyterG*h94;sD3PK>4?Q3xr
z&D9wvd)|#^bzqirCIVk!C{iAC()X87m%<&8#i^|i5BD;u3%Ew4;e(v7nnPB}q)lP7
zr@&^j3Lxc@>yJH+@Q+7n`uxO7dvZrW_t<QArd6HkQn{kSdNy2be%`v9{<$8!wL9`+
zHubAcC1?c8>}s>IJMRH3KXy+6uXdO%NA{tM0~uZJ{=0$=@X-5-4d2(5Or}()=CeAR
z!F-cVxwsRR4b#Q+v0A7)>xr0S=68xx`}JY^^r#(%m}daqTA-R3cOXP0L2q()cl0J)
zdcO1BnjIP72UoJ(;92%ZSiBg*Pw{$DH=B2M<x+=&lwfxHp}T4ynB?0;q?%jL`7T3l
zCbJ)K0C2r-W?X8p-wK<gkkjlIzqXq36JKT+q&6p|R)l{Ab=Ap8LPPgx%{3eRw8c)Z
z&li2zT@gyTJ@K4>e%ZYe5@2$hdp|ke8i4E{iW(NX@(p<+>k5^<(Aq1EE+Ps{Qflcs
z0p?hOZ*`v7E+)|5ye`{gvcEF2+*C#uu0<(OuxGh1*I5BKpWT46_<SKdv}r$&ak2@4
zq!?2!E9z`_LeJ0hLpc(6r9P|rtCr4x>;{gEmarrr!^s&9_z|pt_<^Sfx7Tj<LC-e5
zs+9Du_$Q2}jJG%6)I0WI&zTIbKTNN75eI>5lt1p6kQuCR(^Fcn3BFtD`45q-6*DIh
z$4h6gh0nb$>K`i++wl_N9J$c*cc!V-SiSuKipK|_-I@Hnw%)|NTfn6p->EET*sTqo
zsg*;Je!VB|;4^1hZM5{)mFocOcBW?7sbpugIsf`oQA^*I-YhUC4hQ<~^M#uk<O|wN
z)oAZAhdXcf#xgZrT;7$b5O)zFAnQ|=h~|~PGsu;3Gd$~DcaWb(a|N+b>0Zw%H#=88
zq0Z=Fr&oK@q8X%d|8b`M+o1O_pD8tKSBD#o@RbRDBMb3uS`<1qovO<%mxuNzFDV_F
zvDE5&Y2IOGCRG=<57*5Z>2DO~+rj1Q@i(SuzOPMEgFNEF%Sh^hP8rfEgU=uk2@GQz
zbMKF%!*?f`u1pG(FGqD=-!DmmQNwk$?y!9FDX1@2-!ao|UV8wy!YqaRy)cVVhxlzW
z&S&UuG8#F=pSx$y&s+DiJX;EHW_vnbr9so-FpPGg1~;1y@uHZCl)XIUnXe9q=CI<k
zt!`hkyL&#FaTu)7-DyhO(IW}IX$&!hOSJJ1w$Mt;qfjat0wnz#PNt1Zm2Qc&(fZHW
z>FnqZG47Wez~CG*Ia#!am~3eL^ktlrCm_yfUEvFSH@#4+K#$(GMEJ0=f+#0B^znHz
zmaT@JMIe!Dq5OP;1*yfc6*l{dfTCY1UuYm5p{b|_-QY!4g)d1COK5r%z(658YnIi<
zn~iXt%q=RNMu%)Z%E1g2hrRvXvKM$o^6}D1P4S6u&qk%g3u&WHcrq-jL6u!97{d5b
zItA}0a=vy(pM~?<!GWVy^Vo3^9UAv(mLkdb<(YK!BbGw#aCYEXOf&4$uxnh844S7y
zVb<4}MxXZ0rs0_R)G^m0Uu5F#o)fG4Q=G%tTktjc3RrF=(M{N<xZ&jX>KH32qwqe6
zVxX=T(p&9Kq@LICEPvCB6Fth}sA>~$9%ZY7>EdRWz=4t|s86}uUu@pmxkn60OD+)a
zb{V+yn?98AK=M@mqb(<W$5oIe67_tLk6|{Nom3OC;NGds<GPOMw-yvWVuTjEMTbpk
z+n7E{N~0+Fm{a`pwkPhQBweNg&&j!Bw()|0%GcFP52LEj|Ac<cqNrA_L_q5SiuT4G
z0uNU<n$@7v-0qi3ZuThJkIOMyQ{H+!^C!=}zQ#9`e7649?usk?+PV7DOtHD`T%wHa
zA|yKNEP{RL>(?;BaUfNyfO;b{DglpNx*EWr40iO_xIZTIt%mYmVr>yz?G`-@-WyMN
z;V0zHB?Gdm!!9ETmuUoN4P>=)iQoPJ=5w`rNF(ZM)NK$cp0BhI-n4f;R2P~Ke`16Q
zcj8RPXy&q6E^plNW2Huj+kM}!EWDFY>zSyeR_wXVA*%P|(?B-s^s%F0h<kx>Algh`
zBsgE^2Z#Z+a=_N?R!nAMB<fhZl8kMdPq6svDeLLp#f{LPu0v?<hie^7<`ZYPSy<}1
z6+G+E-BgUtzKCc&vA4C${`k}eT=A)>;Gad*?NHYvN!o&5B^{o#bx2$mA=~@AQLhi)
z3-t=I+{cI#<z$V1Z(O@sHp+Y>tTt;|Ra?Z-rwz2lYOt3C4g?*`J=C80do^n{00q3W
zjT#TS2Ql5{76-aTQ&VDEPWTP%1u<C%4#m**xkBh6pIWRltq0NKMx`1HUj}Stmm)Z0
z7+mwwoCTffpKq7J^6g%9Q5Rbw&R<sGtBXSLrO2LE1iZR#sO6ye3C!P<s(-ucIOVj1
zRZH%Z>_n^i@(<4JUB69WjT9MQFMlUjuF!ytRj^-fG6i3M?fBK+&XQ9m{;*VuewfJ-
z;8ZMmontbR@8SLY>e5@gj@t^{)vaa2UaI`@%o_j+S+=5`e>gdlaf`!d#(=H*V1)Fi
z32*RXGnz$H<Ef~yMT4t3U;83RJR-8E4IBnZj3rg#ByMprF#VfL)<Q}7iYNBTiLjRJ
zjVfdF;esfnVA<F~sg;#_J?@%W>i(Xqu7H6kzP4lipVXKS(^ZahcXP~33mq}G<lnH2
z!LOm(K#y@HR$cPRM7-exy(T3)b9I;==2J+~PM&YBY~@;r0=$>YMn@)BL2uc({y)0m
z`~8%1OK`ej7HqGg+YUk~(vz1){-iJcrj}ui6+??Edg_`4=7EX~>+a{NqS$wpPtD>_
zt~Lj5Wp^8#e!?7!NY?eP&fO}hY9hnWPITI;(36pywe)G4$*|*HlnU(yHsvt)@;{{s
z%T=}u|5{Xu9Mf1B&+N;Ag>c^P8D<&@jZhv4I3J8h@Q&<_=KU^Zf{l9|FfyVx)VHC7
zwFuU)Hw^QQ>w2}`|9W-a@A>Gc+Rhct)@{)yGJA1CwAnLTAewdV=60We@#JAAj9#FO
zG2z=`AuNC}CNs?_p}9=R<AmM!LdP;Q+26|Nh=?oPiVfj1>rt6K5l;7!qRSu@)@FYh
z$=k&SA_FhMn|uy=z9(33s6U!p)@yF$GV{7yp}K#gU+I$lc84}Qsq^~Ml=ym{i`neC
z1oQS5^^Iz_r$qK$R@%pvCe5`S<pG-&6qI?Nftg0~0<X=pghwHjryEQ#3z*nxw#9-Z
zHTq?F_06rpmdgW!8c%jgZu=p0OVRT@h+yj-wUYY9j_IU@pmM39(Wk%@U7**Ky90HY
z4kNYWN-|Rh0uFP$RsKw*8r`lxMc~TaJbBU+J1VLHp`SJbW!1$jFtFZd=n}^Lc(bF8
zWN)uW&4KQq6)&_HJ{xkexvRYZEY`9@`Ol4jUrIOW4^z;k{3uAjRBJzS>)A`)4U`SY
z&R-&yY<p3Qa^r&R=Q4S@MEgOhI7)_u%21S1wR?v4_f<g!JDK$TrB3tmIQkAU*(39w
zc+%Bo>8{6qVyhtCU+-XLKxkN;kD}e}^N2CHk)I~#Leipi9qTQj#Q;}3{|thbGE*$^
zzR|Gc<loU^o6$eW(+Dm4S(GQkhncVwjOOkI<PUbV{r&06NLXg;^PP1dJ;_(k*+hN0
zPq*+R?oqL`TB!S7V{p7P=!+L4Jv=_`#o-GpUC+620qod#=Jg8gA$J0Hw`I@>^aNg9
zrpyq$Eg;q0tk6$R+JV;OfSJx*49zF(uo@+6M$1z*r*n5yKzu#p)7jFtWCUqfTB<v}
z7L;9JUeH*+B5i5%pczCsRq2^RuH8lt)<Pt@9gN5sq?S2ngaxyT+SH7n3EI)N?q+6F
z3(Pqi)jjU*dRb18tNQ>rqyf)D=oAam)zkAD)Cv!VGu5?I&?|H|r`gsQ7R{DBgujh#
z2wEwNAk0&;eFjYX@`zg+l{>XolVc1Rt7(~3hO0^#Uvi2d7%jpJIm#bW?)~qI0o0EZ
zThZZshdKJj<j870ezSuCdcU*|(ypBf;eR{D`Z5uzU{*z}Z(=@QE9kBaT6CmDnKPOH
z90MDvFoV6)f)nVvGQ6<8>gC~8hvztnAy<l1dSB29JhoI71h`u&=@qCNbAZ=(sbpGw
zyDKDjj+nfeEJxpsI?@&Fr^2^$x%-u@=5E1)H}g>S4b$0{N80yEEhBQ<{u?O~gZ60D
zdg+bw&*hn1DmEH~77o2xF-q(-oAy>kL#=Y|^$5{q_tthtAU1})?&@gsXlpFlun(PO
zumi8#Wn*;B&mpyt`-*TE8S|3~b-FY6r+nd7NKg1cJ80RUiU>@r9M30s3v&q^q~XDt
z!@Yo$&*v-?6_rwLB|`ubwg9!<{7Z{!N!X<;*d3Y7VDF#rX%p8J5oQjvh(flF^_Giy
zq&luMKN>e~Q|UF(Po+l+jcY?0=r7gFdIELh5~3Ka=k1|nr!s%JpFNf_w9S6PZ$6*O
zakN3bv-+~_UX=gbj;A+V8^wz|^irqUodP%Lot3#ZUx$eAdV_=M#*8G@VkeSB7wauD
z<5ikd3PO~B+*hjio2TR!jf`8#T4`;edUKd_83Mz@sN=zY=e%PicX-dEn?$0}W<B<h
z7p*Ldpx@4g5Xwod4K^hODo7;yKQGuWVW_`>AJVLmoxC6uG+<PFKcn1okIyNQb>V*j
zFTLNmXw*A?a5O?~eKt+xOb+C&DDl4$AIoGyA`lXn*o)1bI?U^plhHQErc2Rd7t8Az
z%<E<p)I2xfa<r%oJ3jd()sPzw*D^?qCudTd7(A{D9j`yQc&tX4JAMjcKY*^gYjZb(
z5%@Bu>uMh!PNlAv-QFgo%x?N!=dcCK%5&btiWd=fP^soNjr7FzuebQ!<)P@aq(pJI
zp>*4P)Ac#kU-1?d(;4`dw}WCj#@RdB55@}R2pl2H+=T@eic%APcq>eCFqR$(i1uk+
zi3Yiba&;M^X~jMJjFP>;gWKMh@Da_%=MC%+bB#X->%C}`6dZ(Hqj>H+X7<JlYeUQw
z4MVR+$LP1q21$M{ri-kkV(rfR(K<MW-=#Ks6pKs9VcaO_8_dqRjyZk3c{PPdN~5)U
zWUom|b}CF9ZF7EPYekK#Z+=(acQ-gmut!}|ItSuYDeeKx467>Ljx#9%8{5O4FJ3n3
zI=e4md94*9w1draCSekW^F_Eo9zyi$4Jy!Pn)sKVC!g90+x!%+3&RI#^R8F#$dFKI
zr0Pdn@_ycz7rELQ+fcWfa9r+CpXo>xvFV_Y1Ln<y8)h|Yw3Q%OMpH0h`c(RXdPB0X
zX+BITmdL@NUoa#pSt-|6^f!I`Ti?6MP&~ZyrMrnKBnmPVj_%nZz9VTgJzs3(<}HO2
zz%-JYzeThu&v&f9LSY8W(~Rc+9%9{1f3et0-m=e|qc*sLXl(s_O#uHLT~r{PjS-z>
zs&!5+8*uY24!8DtExC}EcG-!4H}|Z_z#8f7>sfQim&Mk<LT(gkc)(jFb(kMXGT1<|
zUoOP2F~OUU*CFHicF&cfs!6>$_*_AKp!$lB8k?6x*G5q(oQU^LkA3v2xRA!O>3Qcu
z?BH&Ej}M9O>$=s<V&1Qr?@J;hIurw@%SUF2kLeJlcGTBDh2!?yO9VVZ2rZ;sct6)X
z8T-<QE8nYN$Hy##LiZ$tJ#VN>%N$^!gg27ZJDoQ=M1dF|qhwXbifhPOGNc7YYydAq
z?@nwmHN~ln_r_rMx+>1cH3mrIWYNC+wVciu!eSs%+&oY`y+v#O_?<kgB)^O<&xhb<
zZbD<Xa>w6TyWU4GtnkbwyrBSQxA0qpoDLEqGVxua3NN`RtT{ZR{wg+>=v)7(4~*WO
zuXL<a%s4034+Zt6ZTf+I;9OcUv&W_<LE2L8q~-7asj1%^zSRQTD-5j|F=?^>Nt3HR
zoaCT>M+OI)rr}kt`9Vsci?-`9rfGC-D&`TvSKW{Y8@T42A-6fH!Xvd{&uhM1OcxpE
z^RZ%Y;4WsXRai{O21vNL0{Ije`?G0=jS!PF)d+=!xuD50mxC{%95Wt9*hzFRIGLBY
zzg82q6C$K3i~lYywAvuCK?KdVky$gSng@{Md&aPVy9e0N=+uHsI{1Nk-MfCcaHv-J
z=L%hk4eV5^H$T8$;ykjnHCBZaw=Wv5tjdIhO!6;rz4ef?Ld!0A&6Mg$%@@^QjLsa@
zY)^x8c#)wl;4~KV2-pS;7v-2<am8TUgL)!l7B;H*qz@ovdOQjxqdQZ&rnzJTf3@CW
z>~VKPD(1om@!Y|#vL7CVT0Os2_HFw2mS~n!DH<5gR9F(MZ?uvs(q|>|CB|5(ML~*)
zkGTxSh(Y9=kUj~vjlxbyr3(GR_9*jpx&GP{y3fK1HtN2TkD?k5RQh(k))C+I%{|-S
z7qkS2dseXKL!1HEjJ`m1yvj^#3GeIqWezr*4CQjc?(5?zSJfCnGhU6G9)h61Yyl@`
zZ(DqvqY9JZSa%?EH9Qk?dPdhD&tkqC&L46;>ew;(W;h$h{?Pzdo5yl@rZ_8>4G*;j
zZv1OAY)lyvA9I=Dz%j4JX%Jg@sBq>=Zf5&!hg@Na6u$fGlUxwl!S1N1*G!QJ!}qfh
zN=56jXU`w9XEH8gK?=iQJga}iu`(=6hYRp12$kk^8dMbNrO?^|34fs;960{5&xO;7
z-8@`B%`IyUB7-neI2dgBvP;S#c#)8A$_k71$g#>&;a4$0QSqb|EFTw`++^|0#?NVy
zFDRQS$oBVBs>q+72guT!HF<^<n;=Au_jo+t$hGdUTjRK;Z=qJgQ?3o^f7yv*`uMEB
zFyvh#Uko0^yACP#p=Nkgs?7cVr&An;FTk6;1kcgF04)N$<t@uoez+Z15TQbJBP!pH
z=9VK(jT*<9wHu65sZsg?I!Ll1SME*43qLc{`A8p8`$jb+Q|7d9BV7g=$8WK2fA)k<
z%+NiezZGR3k1X3riCzvV9?<s(Njj~lJW<@zhpmp;e+(LQ6~Y1KW=hFl{w$WB;ni>I
zE6rumMRnfh!K4fM@bz>s5tVW~4H5<~&YmHu*|;-zAkdhBglNJlP>-@2Iea>7aL#oz
zC<_H+uhr+Gx*U&+QA08&b`m~-J$6~N6^YtI(H}+v19dzXMkM$KA3VD;hcX>CevdU0
zHU{G`7uykSh-Iw!<vRYNAxu^;^;7W^x7^25@+&4|S1Tr3w)$%E<&?qO^ph#eIk(44
zQ?>7pT=2o%>A<UO12U37Z@1$g?uus%?-cUl8RMRm!#8*4?8owogP9Yjq{Gg0Ku@q#
z%X>YF(Dh=uZSGmTt>K7{6SvxuP93Z<0{BB>_DQ$)kDu*|)2qlZHB_2kg?M$DnuW2C
zQ|}yqYLrq5UW_-2IjqZH-a|CA2AJIJXY*w5{@j{xf;$Dl7mzozo>!b@`7AyRnPeo=
zrDa`VNB6xt;IlC(+u1Rt%N|O-TxXf9Dx!AfVQ`E)dtz5}dtl5scCyvfr@k)!Xjt)E
z2ks{0Nvv!e9Z+&nrKTk?fi}1jCC53&_0?J^fp|Q1BcPpN;x|XHE4<@eb~kF-EUN_H
z+8%pUx|>=3Mz|1_Ps6#qlpGc{Hg&oi^Qr`<;f*u`OlH%e)`g+2k9DI5Yyq{>3M`<D
zot!UHhsArWcVC}2E`(p+SSiRUQ0s`G4y^$kDa^|_ziUxZ?xahtlA>4q67p73DrEkl
z%Ey>MEilSxEX__${>!Y(Q~hPu#Y=lEm{|Ye)hXWoBd^dKDhd5V#``G>^qlicf~&@t
z{zIqpuZaDX$9%BTvD5w`%M+kMO&}GiYSN_tn}YX0^5gjO{GWfg_8etI2Cf)IzXc<U
ze^SanBK?)?^oELp{-xLpfB&WW6QC=|l>SXv7tLRb2leGMZmd%qn=GTkMjY0E!uLo0
zi*44~)Eg!F0`APEGDrJc_-<I2x<+-+ljdLtu6HfdANqduqp)z=EL(eRDY_~SC|mfK
zFrgYZ+_3oWmX_VtB(I9U4{<kZkL(ADQ*Sd5M?_#UxaqsDlfJRl9|r*Sa0g6rZCHoC
z?aVu6^{!hS;5~j>jTm8kvSCs7M387m&Xn3JY;p$Z9{S2@`&ov|^^x!KE>#Jf0M?Hu
z`o-xn#q6v3hqG?Y-b<|%n8C8G{Y1)$tfOJX3dx@6FUp)t?udLZWQl7cJTfru1l<Cq
zV?S)C!h-Fm4J-8NGTE?^HY%SF98c~xvbb3GS+SoH=ZUP_W_Zw+hjik!*H&ns@hEGF
zYu8nPp!N<Mlg~+4u8PUTm~P(R??#tRb|Tb9f6vAKT=rH~W!qQ?)=#)TO-IwVOU1&<
zsTLd?lJxH(kl2YH**7sIV&+mCk{~9bdl|bEiwPNuliF&gQ5Y6gjqy#I(DPm|t9j8d
zmUOs=ELO+2u?lyL2z}*9+#PxyK>#wMpPY=4RHQ`jI<Me{kLN9Od{XTkw3KDvo9LV$
zD`j$ule;&3Qbq8NidVHT#k`8YSSMT8_9Z$Pby)@QL#*H>*RxLH%6%*If+E+-JZuap
zd9laWNW~f<dR~?Tk!15S=9(U#Y@%|t$wD2H9w$&~7giOT0__@N{|Vc*1TeI30C&q(
z$Equ|2{itRr_L+jk`P;Jty!uYV(S56uHN!@s!z)HNYKM<WmU=Qpu9d|n*`NHiPnlc
zv<pm)BsB^<66o}J0jB4zqK=%Sw+Wz_UiENYsKZ_=rINpKy41&etP5nY#0hMO>m8JF
zG;-1{l%Tvyan>4WH;mS#;?Nf_f<(IMAOd|NmuhSSaJT&Rd|u#!rw4OHjy5qr&y`B_
zIy)u^>3P2SegNUlz-0R?1!+4t99UM_D@2KzRH2sJeTaNC-r1QbbFQ{`Fc-gEG*Vk;
z>%*a8`nJX~2nTAxhq_rx<TFTG%3yH9^^f=UxnhQ-mp0B0_mW9*j&X(`kJ+Ey$GTzS
zh^Kd&SN<;ZZn?SS0Sf6UdPZlyxj#l-Td%`5Z@A!`Dp!hg!dU5bBAz|rinTi2XPUjD
zSNX>id$Y~`4pXtrRY{L(>GZDmO3fFFGTbD%LrhAEOlT2*Zs>%e=l!54Q?naqKQve%
zT3LAd27ON{T%GW}4*7cL8NqJrc+^a7Nh-=`#ckQ#e^NHoZ|l_fKJ&PHM`aG93V`Fh
z<|0x*#xGt+o#o?&cv7XUysYs|tM$unY0=GffZw2xmSm^b_-)Opg3k`xMwgXw_h9Du
zPmPBUzUZx#jV6Tm^tkNc?n=GgxdAk{P9_we00*$QSC){H2#J$KqTZ#spXicBR!2Gj
za>g$I>Udw8Eg?9eK)AqUKhLpR0MqO0f~ni+75@Fcbk2^08-3xlaqu`<EXT(9wTfks
z37HdXl%vHI=a6>G??}%Zr}7kS=ma^}nE%_Qrb~ZKh(1tt;tgD?38HT$pDD{nAJwCw
ziu_<-_GO^z;rS)HFl2f}F#0okZ-wZoC-vUYJPt4=t`RLfklSYK8G-)-da6v8zP-E-
zTk-0Mj{ZXMtKO58o6!r^qw(S3cY(M{QIl~eOM-DyB<vc}UD3T}G-}-o7T;HdzdmD$
zW-;r|1K`x%lL9D3w)pu#jc&CHDaK9@>uG~}dHN^DNr|8YSnrEcG^HDo(g5q!`lbNu
zYXzcxVGJ)G$s3)jR16@b!B(+o95`0^68b31dU?^MIyew&sCb=vh;GQ?gK4UIhq+6t
z6ocSq*k8e5ypD3YClz@>8>&I^yqS|@MvBb{L~nlTm+B`FawiYeaj{@Il>Yd!77dZ>
z^)Q6VX@Z)zP-fI1=dRp+v=>e4Gq41Q!1n=wc-XDxpu(l6FO9?u<z&Ck$XLq(J$IZl
zU@%=(w&HPu$l8Rvo=urC(<p}Ck+LS)X|dc8oX}{&KIwx);C=zcZ!1^ovcZ^!FgT5#
zLlWGwwp~B5>3H9bFFDQ|dTBkee>QxTyGG!Kdm8JCvpw@rIlv%OA-5EonuP}(6pq)c
z@AKR~yU+~^`d;9BOAVOVI~6^fulJ)a31xI*(wcO9K)u*v3==CFt%9AIjyu6L;i3&U
z(Z+smeex3S?e;~5(QkUHu+UYz@iCfL!4}Wd+jp<772e<L*rmFCwdomWNx5bq27h3P
zf33>|8?C2qoQc2)RTJ6Dq1lH@^!dKfNPtz-{bhzk#NcApxgT`UqC5-?w>j<Ee)`|Q
zGCH52kJ9h-AqnW^qF)gBN|7Kri8WJsXJfMK?2Cs<9VGM)YL_6<?mMLh;H$p)vbp9*
zEbX`lZQAFG4LClrB{I>G^3>JUi%1G4q`Xb8F`Y-+`X-<3_D6+GlSd&%YO7#z-}UOL
zz8HsP4<iu0+SFN@cHL3Dw1=QzD3L}Mme`4o0RYsRan!WD+Fp-i-b}tZ?aQ7t>LlcY
z;rVaPVO`2gS#L(Q;&LH~hQUqpEL{lUhP_AtMk3h^jus*AKcKrSuD-GF4#@ojjvL~K
zekdYN+69<1xOtDnwqYr-VN+5VIBHYq2u~OK`wJqt9O*Ii!S~u1flkV=>H~R{)e=5_
zM}UD_g3Y+k4bwc*;`+D6k)taNTa4b|kHW9;molq56v08IOd={?mQ<roiY!}@nd2LK
zNma?t{b6>}55F9{+S8+(+3xY}@{4DSfx@9r<hl-Sm0-s>;(>5iWL@vAShvW$$I@A$
z>hKYkM3X_-#H*}1jDW2QgaTNts9-6d++g;xqiEkP3PHKcxX&lk>O}jb1gIMay<N{?
z8~_!sX2)vg`%@<Xprq2(WYi`+VgT9@d7*t9KjA9FAG6$iXnLa8br&Fw<?Yg4BJyA}
z&>Swzvcl7>($Dv)v+rPK6ZU+bx&~gxO)(`V5?(Fpk{dft4B(*CDcWdsN-NN7y^7%2
zW><Pm#`1E`j0;ffSnk_|sxJt&{d8r&xul*F844lL?T&v38%#A@5WUnYT+JA4mkkrb
zITFW0e<n!cB#Yt2lgJk1%OE!BlM5x=_ceNVB1+e-ySvi6l)L8{Tp3Znxm!EGil0_9
zbQPJAO8Mm}=g_uW5K6!4B|34lMqM`>Kd^qa84C?MIneco1<74mC8>SuSrvUbwy})=
z@4Bn%z6n_fl2Hjiq&>+h;5IKRoC^xX$wxuB@Gz_(g~9&wo$_YGlU_25m@l)suB!-s
zfTEy7y7qCpbazl~D4e*rOwn{m4{>_7`Lwkbh%eTJYA@`x`)>P{9Wq)}QG$eR4>>F(
z{k$?gq7_mZR0|3>uxN;3{CtDwD+z4ymCgv+-W^BM`>_25Th8oMcTNcZ_C=Hp*~Ow!
zU|-e%Be{h?9nP`fudU0Re|uCg6q2eZi)!n0?GGily%-}2*kUV{F5pvpzZt|#Rh#=H
z7I6!!pz1SldIk{7cDy;UV|@)J48{dN93<OEhZd86QurJ6`-_N=P~J=iV32$q)1x1Z
zyV-P~7F>GiBwsj5+$|;;hw1qJd^vaak41GC_2O_zcs@-g1tiw8t)H%-kVcPLXCcLo
zewUOj5|@}pFMgsynU+yJ;5(uwl{)&g4Iv2vvg?o(8iz4Mexe)dzUexw-Y%hzn!abi
z-MXK!6Cd1VJglFXhB@{M7<@Gu=aD75_P>d2qj4dE?XSbuUPfOHhA5y6)W%SZrqj=u
zK8C*zluzwlg^&Q?HVPpi<z&-{X%Y_m$!+3a-m)c70jz@DGI$Vn*-$bQsO`xziS}2M
z8ze8ZpOPzYeU-(+k^}ZTQeM=&OsXXE>7M#m4ia4VnJ0q1CL@XTq1MquJpRBFf-PU2
zj;Dia0G$pSGgEJqNZE|{k(%)lbuq<TYWq!zcsMuhaeo;oLIk?Kk!0+*GG^T8*J%Jd
zy_Oeq91a;&cRu5H>k3SFgtJAV?}H{G{yz1_>p0AdBVLQL-a@=`dbMJ_XWt^NOJM{P
zlhA7|KfG?<uhj+~iI47xnGL5ygUzl%U=cCqXaPht@`p3KqoDxSCBUIKn;<X!hDXNi
ze#ngwaQw)d=m)^9d-g!1FMgwluRGp8n{JuPVK#4&m^SrlV;8hpY$sR*e763@(8JD3
z=gH`HTsz_3eSY(U@qn1o<UM)UD+1fd{`XwF+88&1r_MsFb-0Qt$4fLEfSq`;Cedcs
z%b-cTkgX1XberRtH8vrhJPF%BiSHfMXp?|%OYL^mhfFkpKmqTqKmTv?_N3_;`#qD2
znCXUr%FC}?R$qhw@?U_p-vY#ByQ5v1+vlqI{y{vE_^9w{qHST)85~Vgyos*4a9X#V
zNsjT;kSFQgZwLfEP2Ts*%bAa&W*f7E-*h*Q-KU+tXTXTPQ1O;%H6wM5wT`n?d^7_4
zM9D^@;D#XkMGamPq8YtM;jz%M9H6?vW19VOWYlPDT%GwvPN!-uv7-V8Ml~#4qDVws
z*sd-pmU6Y^?u#{O5Ch!H7sq?nX>?7=lapY`H9c}4U`d9C$8t<I(RRdn#UKmc%CgD#
z{B2DP<G;wP3a_ukc>N@$d*(D#cW1*j?LYw2m;ErdJ|Xm5IFZg=auR}-#N}gid-Ef6
zDKCppsA_2BOEQCponmNsv@bPW?mYzA6AGC3r?Zkl3L-@(b*oR9Ob(zB&Q%DT_GQ}j
z_^0#c5w}@wuni=j!%<ZDXxeU})j3ult~Nu1($FzVPNTeE2B?s-=NiGGdLJRR{RBfh
z^)sSgbWF^gM2HxvU<h(z+8kYQTT?3muhy4C;ZMzlDY!=C-fV=7tQh!H{1c%%ZeQak
zC7T^MS>EsXc@o2$zi1~QWZ@;rkHVB6My)s6QOxc9Y&&1COGTutaZ^gomOVL(4`d^N
zQc_Tc412@T*TcX<zG6;C2l}a*PP$%_+Mm567clFA-bP2Bk5`G)^Mn42mD{?_SUlB3
zenM}xU&s1bM`qRgc1%V*Zf*#LaKa3OY_}8>uAe4+-v^BqU@ywec1SSRFiszg2WmTU
z7%gVmpSLHEYR>k-MXV&qI+l@FL^VA6D)W*%2)H{i-lD#5Bl0vpG$ebbL|w`<**y4V
zF1sFsJmPDUXWyA&{b-=sR}Bx+Ulwh<$ZyD~%fZc{DlxvOd~;FYjJcmrqZlPgy?{9(
zKNAt9G4W<bt@la&{Zv$axL&~JdXAZdPi$N0fXLZUAbQ|Q&V&E<hWL2J{foGrO%lju
zQ7$f|Od)33u>W`g+taJF4if8ipXua62VZsD<!DL>>T)5LQvoEY?`?(|#Qz#6F!iUC
z1nNBP_Z!otkzKy)bH9vz_xid?S8sX|=Y8m{9E;M1#mcx);i@$}5{TOYfxpmK)TApu
zuwSeM=Q*h22>C#DHCkdD9o9XCaBHRJ+LCGm$8Kw#o#o~t_bgmnKUhP`mQ_c5R?`PE
zSxe})loA(%G&|A-55bjgx7^ga=D-a|;}$Q&%fa)1XzFqxJBcdJDQj(-%TrxppeQ0a
zoo8qoPOI8iM9u^e0m)QmXAF3Eigv5b%tWEja4I>*#EzgBZr)98L46^2J~u0k)fF3N
z>@-kK>CALjO)>D&p}B0!C`60a87z-fM<FY{J%8%-u;Ic&=Qb)ZNv#sZ(orJ?KUxn)
z<;Xmn?dWoI?|OUjoVa5x)Ht(ue2o#Ouox5Z`tE6X$>!RAa1i7NKLQBYCp0xB{_Y0&
zUa~(I@JNW?+Z%y>Jlj@PFjH5{SmS#X^l<!jfpF~Z#lxG?itS6M8!o`J)-$JoH5(Ye
z>c*9uM&2lWPC;b*%nWhhowg_NYM6REQgU%o$6t>jL3S6@rA-nLOqFflCz1`Zf|D6w
zq-j~TbLtSPs=3%UY16L+_V-Ea_b<_FkK6BOE8N2?)3P&rqN~XMpvC2i4)hNvx3QE%
zaS~0}!gGj%KY`-L3~8L}67ZPx+&MgrV#7=8jYAG>38{}0T}w~;(l$cIJIvXETbQxJ
z#=}e^HxF+=-lbP7W%GHyR*_yq_Vdv()3#|D2;Z4a9=E*QpL@xYo*r1q?|pHhg$o-S
zjq8u(bLGZ}!9pVY0LtiA#Zi%P<LA0lGSU6rD~|WgjDUiXDAU6Y1IxECT=(kHdhtg!
z&kTY70avc^MHy3Sppi5bAMFQ=l7g#6#d4tN0Pz5&;%j{*L(?~x-)zp)F*ZR|xPUJj
zPK$ZxLnn@NIGh<TXD&qHgBXEb&lVKT3~4awO{omB+|J}T{0~q+MoY%sTpI=pTx7)s
zez-aXBhlYMSo?W-i1;|dZ|-HxOU9LulA*PxF<B`j^>fQ}8G^i8yVJxRQ+Q<$v%#*n
z`#E7Q*0Bjw2a5imRU{n^7Nc|JqTXKjr*q3*z2L4+W`zA__kto=^dD_j3-ak!6j8<{
z6l+SSOd+BtMFpWlgDLe;vZ~IxB{mZFricmg6fYL%jW+rieKCSyyONj6KdJ<MMhm{v
zi$W^)#DeDs3YBJk^=%=FDU_3Uz`8<00weRnQrROatVzfyQ8>*zSS6!q6o(ii1rlNf
z7fJE5!`i-)V<{4Gsq|bo^9|3j_`5J+D|&hws{_HNhRxDrV#kQ}DK|({*M30G%Al>M
zhB&F4Ipk4OWY8KlBaMV81*N9p2Hp`NHMp-q$sUv&K7WZCa5@wE4KzBan0hguDy=Wp
zapGT&GW(6=<geO+SqCf(56^uH+hl>U=D_0V4+Cd2*)2poNZ8J~gO|Eff45lU*MVtB
z;pD%G@`i5GdKb_!GL|XqzDK;qRp%d)k`gsRzj;Lmg&nt6p!x(#CFSMK#tIeNn{m5_
zN@_-*MgsSm(Z1yAw}tbmEaWMLAazvlwdJ%2^1!?I-molhjHm5U1yw>ULW$2P>;XS!
zg&2G<alLrF;dDQ4^@$S@OgMUUG2zM;`HPOEV69m+pF<LGRxD#%yZ~DGLTr#~^c@U4
zTHJS0B6wv#*D0f-)JK%D!@U+IoE*ql!+|{j>Bxz>c&2Sa<wUBO;2FrcQvZ>1GZr{-
zksQoG6GCboXF<iAcUTptneslJ9L3C{XOBlocg%raabh{NS$!lIO`l9&&RAJlRqCI*
z+r-d7sJO&95r1--$=Z8U#6pGDTchGJG|ABLTJw<uhnS6?gA3hal~Nss;}v$s?a(E&
zq$>(8^Cou~yw8}alBshws38Cm^^a2b7f?y2{6+yuRqpc><#^g;A$$9kHaGyZkk=qa
z-M2%hz{a}4(9n>u^MTS1gdtyM7~7*2r43RpG7}dk5E8L*JrZ{tw$YeG2%+|ObyA^%
z+;odzr-CGUT4sC~_cG!o{g*nj;aR~@Y{FxwufxuofdpnS0e`iOjZR$u;m<&sZ@tH}
zDWdG0+?vXHqcc42&ZOq@^|-VOiScvl7MvwLiD?tzItLaO(3LU*fx1Tv+o`c}5zEUQ
zf0(Ka3W{;wOC*)DJhn$9Rou%eWg>;3weiu+<5+ys7MG|IOqsOxCk(dtBikXle=m`H
z2XFD>_&?kg=cxq?aG<C9C4?Bdm8sCPc<#YiZ8YFUGB2~h>t8?9BgKjmtAUDztHx;g
z$r&bR#Qbu5H6+LJYGdv?pTb@DqaBV%^d<_BeH$Tc3(%iAjT}1uah=p=k2`s}Pf7TQ
zlQI&QV=^!z9n1vY{@b)~s7LOol1pJLNcWoJstVaGcOkx`jL<LOnS+GvP7vZGc3M4E
zP5$r8fBho2-1H7PFj&4q(TEndczoc<qqQ?v-PM0tsB}^wXVqne4y-aawPopmpgT?B
ztV~)g7niDRQ&4)ZdMv9XVrGp!r_gorcs0UdHH?dFa$KQS6z@E}boxt1)WxgNDaw=v
zD;A~64ZaJ6(|OWbR<5FLtLn0zXQchz${=udOQANZ#K>x=fH0^XStl+TA|{xJFO1kZ
zq0U;+hJRXje{=KO(;dH*N{x_@XUR^e@Kkr?FL{*ni=;}nZ3U-OS)q&8i@I+xb6mip
zFa^Vv)y>qj0O_%Iq$m+KveUnog}4}RAc{*srSCz0BOiNgg&JoL!oU_&i*$PR+J$nx
z)yzw0{Vc{~9fE?qp8Oqk3!-GV%;XXoK}LCfqlcH2G4@<zL1%#tI6JGjGZT0@khyV|
z7u0S836nyNg`Jxw=A7VKgDs30T1AW|(OgF7@yoP;)d(bWsO;Fv(@r}^>f{C5nUI@j
z7fD)B97Gp~9YrhZd!B2%M+h|hfbBS2p5k7VBC;4ZW6hkki!lN{i{)B|i{uFBjadw~
zoK1jnrz?LWwCKT~!YQZy2onJw?a4%`EcarOy}Mc=g?dpHnpG#^q6)?8u3i56A5{zu
z#loFU&}D}QvY8USQh{x;`^yyp;<yMabU&_6){(TOSB8+m2*{=76zZdsDsAFfdVYKd
z58R9Sa<D3JDZ26oo5_D7^y2eP3TuuQlMd5-TSA)@Pw}05ArHjsWp;JXPkZ`#&W%O7
z=dPnOBFc*M_59(#=F!lnzZwiBTRU80iXX$m=ljGJr6cd~!lESQ)`OG*tH$kyUV48p
z<?n(tYgQmTu2yCZfQ?M+n}!CE&s(i3-nuLrTf!cms$Gk^BN!iwtg%&!+4b<5X;(tD
z@!sL%@l7OvAs$xj#l{6WVe>bhI3cVqG9|4fG`+CEv+(Ci(5$*#1XfvFj&t^8E!^2{
z3r28HjqFExxLG+}H&Uzot_`4HlQEwlG4knRik70soo+KK9@@^wAV_d&FnG)L>N|C6
zNXLtgNR`D@SGKz^*c+bQF|L@s2}$+D=c%??AHd*^OdDx<8P6Ho{!^@}!eTKeIMj;i
zwIaHrQmqb`IKGm_D*hXQ;hvG$2avt!7usCd-gq;|Ek2-hamhN^nQQRBzf*8NfyJ`$
zP!ri}3N+4{V~8sVyUqcwqoQ@XQwBrE29U9z2UW6INWYoNq>XVDzdOW4mnfAw_;6y1
z76GhuB7v|{*oR&`=&Y8DJ#yhq4st}9a;yaNMg)`l=OvEYAId*`@4~Jt=U{2gVuq&#
znyMO<3>AKKnsc@|>!il*CTx0nWn_9*0?l-*iB?C+(@2$&s|YNl^&oY1+w|E3Vw{@j
zSDR+@Wex9R4oh?&WUYXxCLvc5MP1HDf}XVgwKUtR<!os)_;&0<5cnpJI>~J+w}L#g
zmBu<k?D_eNZ7O6NnhWKmO29voh+IHt(yL6(`QO3emkm`k-O|dxF*J@+2yjSoWMpSW
zI=*HRGwRAC^4HXM!>4(BYw$1Z8K?1@w>H5VLm!omiq1Rd%=d??w4SoeRtwNBJqvh6
zvI2Ny&K#H>?5yamF?reWE4|94fPdy!S2QTcT@&4Jh@FXZJ5C@tnJKZ;O`k_=`pE?k
zI?Lpkr7_Q8a5?1gAP=&$A~Wa7SUk^p8uDDp&&`?F^SLJrMp|33h%Ij4s&#&JgXG-p
z+3t+KdE(p*QA}>2U1l8^scT!cbb#@e$Q+H)qA!#!-=tx*9^%;w@jUK(<i-WhDzLBM
zYLAQ(Ay#5*a|atka+;t_1rCWLZ-SVD$-(o4fyEx&j0PLt3@A-+mkwtSdD=#ch+XdF
zvFU#te#CW|5C8pNWS;*-uj40coHz|6q?kt100boaMF04ej2JMmTp%1K1J2*k#hz7E
zpMQL@xj*?>?zApa$#|{XR!_O;8@#I|Rp*4HZbTC}#xy>JQnqFj>WsZd_c+p}O_@T4
zPBm!c#4iWd^q(VyWrFL!>h0``JK;r?{HS7?7vPdDCa=$Ha>b#zeicJ>$q{Q2IJH;S
zPta3p=x5=&=<;qY<d>gczw~B4k0{<b1(v5P_{5#wCQr<B(7ep)A>%?Tx(pz|r91V2
zwAu1xQ(3bxOGI8@wcW|GRBnN~KDf&lA#oObzjzU$;Ss_KoX)o%Up!uKs*I3YtkZ=`
zn$85e2(%wol5TDEhS{pd^;sN)`O7bbLm0C$B!z}+!`=xv2{W)cuICy<h0oRhe>9zA
zaAm>Twr4WYOl;e>GqG)BV(r+Tc*nMF+vbjwiEZ2ba^7>k>RLa#YSlv5vwA(<ecg8>
zVmO`8Di=xoF(ORHt#Ue@FNIlQw+afOq`d-!&C`KW*!Y$nZCG!ugExM^j<LFPskAjL
z<2FW8hfI-}FP719H!8LbQXEL6C0sGOcek6l>TM;k_-IxzH#~()$Pazo&ymhFy%*o5
zKsKf~4+Q+6o_6WO+EX;L7?tja&%<6SrdesW@NZhbh_|RSCP@#7k*I>=D{!=GIPq$a
z2rqVZTPifY1>?nYIiLA1%iSOx{awdhuCLEaG;P3Ui{^d0fG-+)gxsWffGW%$@`-9D
zH>I8_4H{wF$xg+`U+p*Q15<;}AU&P-gu+WL?D=ZmX}=B#?9cY&v_!D)QR#5N`b?L#
z(X6aNwxn|qR5gbAWq@w;s%A6&EJ0%FQ_&%1m;A;i%>GX=u96n5E3#tbHic*@_wsy;
zWy#o`#*aGMQIB7n$umqME>^zp3PaDA%M3$s)-)#%#kc!ZCu=7`M%pG{N||b;(*tjk
z_jH7y5hn}DTEnTL{X8qQ0NV-AffqbKVy+3!o)VFmhCy9a%_``nhvpQvS9v6Cb-O_N
zaZb~>_iP`-`%C?ix8~bt7w)VUm&wPQ)O*XdLtn#*w)>r1*ZJ6<8G<)TdQ7vy{(z{^
z(Eiu`IiF{c(022ax}a{fz^;wq!<e;F$4I^BJkz#|d?-rq;h_Fz2mRc*R;%=om5lXi
zUIRHfaPt3q0gQkok#N*#5gn5&kgl;#-00_uheN`cgpb~V3;hGz^|?1APV}AGbymCn
zUacC@Y&_AD52Z|p1FuHGqb;u?-84<-zX>1CSIE1v-qMbEuiWjAD!B8;OlmxLneEd(
zie-D6v!Bck1!IjfI(0u^i{nB*FWz_74bFq)yB9@N(&9i#eB87oTvD{DujLx878rPC
zWM-pi6bJzHXlJ^b+|=8}FklKPRW<48D0)a!HInPAIX2Yw;my6_aMQ$2bE!VnZyY$Z
z?K-!)fyHWg<dVk{Xy`SXXz4GF6D<(}pLU&y#<czQx`;Dwihb(Uge~!&A?~uXk4w{U
z_f}%NQ*I`s2QVb`{K@0PgEA4%#ql`iiknTkF3<1{*RpFMCk71yU~XoX#D8dKC(`yH
zl&<_xqODLnx{TK$PIAEyOctL)^AT7j!m;zdDbhv+;@hP>bLcx6cFoRdKma;2g;fvD
zHPxu)1{-o&gc_&&0-tF(!W#g)y0GLRX%zmh-+}C3egRNmkD;Ct!NtQ<IYu>M;#<M3
zN(W>(E@8*<*(ARD_9upQ5i+5?WY_PT;`IYj-HDy=&+<7^5|^BByjkaGeD+&gK7yGQ
zZfBLH#1ou?9-<v+$!`u^-gd<)T)XP_J3<u^wbM+xE{6}*$v3jPO`^2#&(=4uVV!2g
zCOTrgohb82dBZfDz`uA_Wb2DC>>y5N-deY@bkqeQz&Zh;9^SW^mt}-wV14hRTej6$
z+=Ngk%r2cvwwhP<^Y35M!Aos6p(!lBtw8pZ=xePc4JK=_Q3c_rOBhsJe(>$vCGb2&
zYvU0jU*Q8d!wFjgiEco?M8Mge#Pu({beos85MsM2e&&n*WuGC%SKeBW17obF)Tb!l
zhPWPKTNHg=emLll{d6ys%$owFTs)=SzS@AO_~p4YLeXi^C@WX7q9VI2#j4X$1U8zx
zadLon`e(17^5M2#E&0pCWwhlyMBMds$s#<49K@d=nTHdoaz|dnzs(l|4{4neSaw)l
z&UoM{FgO|wB>8{DCY}C;Z+6aa?y0_%RU1uVAf=I{5pT?;fd8`_<=sHCm3L!%7BIgA
z4cM%O>YGrG3UoX+{@Do-_wFFUo-3&_Az?cw731u3g_6^k`AdO!zCCBY+{!hQn=N`>
zU=plWshz7OI`szFC7GPLUbk++%my=CbU2#7hi|Z*B#Ah6oF5u#+)A0=U9x9{u4=$N
z(wW3@b-E1fD1Zj6_<VDbW3|e07%jXvI;3v2aN2;s5mohQ=lnAz2UzZcz{oT2uU8VC
zw_SmMyuS<xyKs;QSh`YPs{I7lDS>qhC1c<SAM5#v$Kxt$ZMzUYwvavcK$>U`LB0CD
z4AAJkG&`oIgHI=ZzEOu&tLZfGFQ}LSy<l1}*duZ^M>pIf4z8Q4Yt9!YE{J8#;-r^_
z`@;f?n<p61#xtXRoM`!!BmBy4mm8Z%MuLR%zOW5YAue063H$K&><>^^a#X4fk#p0H
zn95_$UxqbE5nNI!`3Jks!%9@X#qH`W%u)ywK78jNrE1eR*<XAl>twt3ubTfv<(ic1
zmh_`Gm*V=d=v-nP*?cu9X?=w?kF=pe02G}R0S>sz&u=BQV$(`H+HA(an~%whjr@hX
zz8jqhCEqjC`Rn3jmB6|DW>ow>iS5uwHR>(_(pGdlt!x>OZrhaHVAq2`qZJeCQY%os
zn`c{6w6~m_cbczjieU9eRDW+J@&zfDmqm7*eDgK3Bv_`0cDDh{dAsIx*)HDEj9<HS
zVfu*FP|^LVUD!rlwNgFP%VRgrehW|%zw_`9+3Y`0IPV6n!>EgXQ*NJhX>t4Z$BS_E
z`{PCW2-UdxB^ZsvW<`254s&;$3C0z)gnBZVRradknfkjEfq!iUt^_K1BzqkhgCM=$
z;RmDJsBe4IdeY4s-qUrxiM|%wmI*OxML|=bp(t73-K%3pHzmCD&2EM=L<z0nnE3BY
zE!O;X=jKgI^$1!VWkjpkJX#N9Ib`?F4NiEeDZy<GsLR!^6k24AD;%)_x&x0}r3!8;
zRY5-Quep})hd9F@mZ%-Dk!J#&7nZPF4y@cdo0Ydaj8!qB$^5_Obd)eE@~vN5`y|C9
z8n;)*Kg+1e(GWvRpO?m`q37L?Udb9(fb^j?NCFLO7B%(x!68g_6Y2)$0RgaenMHro
zZvU)@S&1<W?>cNltR;@i3}a0T8kB}OdlC6wm!T5>Lr9L_cG%`#|F^arRUo0}B7!~B
zEqy)veC$c|q2#L<)wmvmJwshB^pipTKoki0jXan4&~KcaEI3+ZQfoRoEa(1n*i?(e
zZ=%KUrvAVdWLpl;l3D5Hl+uN9P>iF|)GJ-7Q#|8(+R)TjvCD8tan{N~PH~zvxrp8(
zl7BnnnmXNb#OU1|Wz=29eBvRAyYI?&t~TRDcAv57ClO2v`*?HmnEI>k&&+uwWYj-{
zEe-R^nnti$N?1lL>DC#E<J6?jFz$6bbhz#fv-IbBJyw6>($+|jYnjATW~je{Ts6*`
zmr%d*hyrEABSb>f+oY&pw|SeDw`oQTZh=Z*;o5WpH|kb#ZL&S{4_*4U?+h<qY+s|E
zCHhsX8Q~SAQ0)>uq{){CxLax|qlr)Cci4BBGr7~!C!G~$40?5|4fWGxlb&C8M01@n
zncB-J;@Yc-b5=M?9;hQ@?ij<5mL~1Q>;J|Cdh9!E=rJrnV!8&;Fn|b$FN}?5%sCPK
z8FM)ydKKA3J9$L>uFZQ^WMN+oce5PzoO&cIxz`lO#LmP!VFJ@R=Ap1stJu)F{a3G4
zMu8oa($1Oe|0qCmFcY-zjhuRFjV$;7-{Xgp?aP^`U_OTpEB(Kyq4j_2faL1Z%>S~8
zrT;PZDTJ&XUHUI-2q5{Yo3wk$p8wYuNDA)D7l?|@Tz2TcsG$kYf9k9IR+eV}Wf3j^
z4}KK1VrKs@{J8pm@FTsxGSC0uN8$g#k8oP?wf}=3#s2zEHK^cz9z-<KyznS3*mLCT
zf09QgovX#`gS^X=0~Y!_{`0VFUeIsl-9d%Eq-O^Kxj>OSVQaPSKWd~m3%(sDV}3cN
znw6RN2)mjg$pxQkeZ{2r?Cy!y;7+>KqueUgc+fD|sHlPCG9MjO-UNPKEDG%|)+!|e
z`q=HFIwB(AJ3Bjjk4PFHQg_euT8)<L#IqPc_m4OCf`Wp*Q^igXXE#qz48#<^uXpfc
zjux<h3zITI2*g=qG8)QJEmt$2$%D{pwu$`v?S+-RHBXwFLwodC&ttnUNXdroGlR>~
zhF!5*DvP1}tu6-N_w&73rue>tn*8&7Y?AHu&LAe6B`UdmRz$L%$IsWtTM8NJsi?YR
zVIvy#DiI~6xN&h15y5zPXb&Cm+bZJ}H@!~FU)gl_L~|USuNO1YDU?F{=^Rfk`%6$S
zZmw7;Li(w9;;G67ltf2nM)6YgQh79%%)l^cv(eGof$`GRPnR<tOCo+XC(pnA{<t~Z
zU@?b?g+*0Je;llP3mO~i!?Ep%-Sk@nLrf)3gGLSi+jL%UHf8wsbUl*GP**p%JxJaq
zWE|>3xBzCs&tOzbBbi&BGY2PX)q0<agX*$ybJ~J4R+4~Xg>|oc8k?R@<G8S0NIL{q
z|9Gd8jd-zCo4;4c(=&?;WHq18xe`yX(SV8f3+?fZ{+pjqV!2SEdDL&tL9P0AgEpo5
zoHuI|7j792jkfPCioMb_ok`Koe~R}Pq-`sr7fKt9DSvipiB*Oil-^H!oLr@!$h{tD
z$?3tHB*(@QUAB?TUo+PPGPK3h88ngep!j{FezyTRc~esfO5I^Hs5|HhAB;`7y0qm{
zP*8vrdFNW%+w;x))s%j%Llf9=y7i|fSI^GPk<rlkT3S-i&dwT#^no<#>FGJ*R7;CY
z&NO{42JT^}wGg5X?G|o}w9{DRdXB#XhcsP*ZM*GQ=fx^8|BP*j?NEpni1DBw0Xn@#
zhKAG4w))G8N3tVVS9YZj#o0c;K2F!$Irv`BSg?xb-WOZUxt{neW=Y?!`VqEFU|ooa
zh;I0NKfKPC>!H?0$1`~_oKF`LGed2bNN0S#W<2aDo{sXPcb=|ypX&>2Y8a{3=S!6f
z-#R$hOvX@`{l4apYoDIo$Vo}JvVA}N%FAiMe*7rZSjlp^Sfhd>ToZgaTUO3Gqk(%)
zrQq>SMmQK0*+Lh|QL#=*Ne1R4RqY8Eo3<Q;ayy@l@Vx%!<-K`#3KN^1Q7IF-p`#+#
z%8I9uKLDNV?<Zb9USG$4Xx5vK(l&OHCHhKU&v-n&zDm1W&6g0F&(puNt|B_&YQAs)
zmI&+T@BVYfl>xwlM0;%XAs^%i555=j`}M5eKdIi>g-j7W4a&J|C(H8!sX<d8xh#cO
z2HXZ4-293R-|MOuxsrp7`A6+V0)fX7k$d3d>t%QHrR)`KpuF+*&D3}*%S^5?6j#}t
zG}3~_r!+rEm<$d-^%h`sG@a|<1R~M54BSU*G0Sp?l;?Zi^QAgKyIp9xIm$5e+8v7g
z;d}rM2bbt^^&T(J*Q@KgPUdd$Lc{!#3|&H$jI)G7F4ME!AF?k~aKx<rb$TKEm;c&x
zeNr59^sSlpJ{Ar`{&kkL#W=HcK=DuxtGo80fy}IvLM{)TM1tEg>eyd=DQ$TrKNhOA
z^X?C?e2+TWTZ4q7BlFgViE|yvR%Gv31$Cb)FYA?t?+l_xLsnN?y<4tSZl3{YEs6?K
zzrI=VY0>l9vpMWZyxo8plze<jW0I@nviTy#BC$ukGt(=#8w;U!`R%p@&X#J0yGBO$
zXDhWiDl}@r5^2=CEI@**NTBsjPuJz^>uX&ftVA9GpX4CZpJZC_ZD+0hTaR*YZ*L0K
zdYx}`t_x2e7$51iT{>{^W$&F{Bo#RIzQX*I!b!LPpeI?Sp2`d9+1lcIf|CO{Y}SCT
zo8A}%URQqHdjszy`{4&=cT_X*fsXTu*))GXKi(CisfoJbyzh<_pOtc>x~Y#peh|}b
zwmKf}@j(-K?!pJ^)8aB4OLX~s`k5~#6fOijTDLhJ`@h_u1~~7CwOTD!HAmp<`@XXc
zMd5`f(P_uP;q`l7c2aEqY$a!`icLX~xgrXPD`{CwaZV*&%nWRFz!j5Tefs)_W@-$C
zc^{UH91hG3Z6cLMU$7jrLXTY6qyl-E=-4>ycSKrEWz#2_5iIAW=&Iw4D;^=fu1~)2
zd;d>82)<p_=?`y1g%hQ~$@RMHW+JIf;g~c?uAXwHSe}5&_i!>3;w$5QK6z3?>oe9n
zfu3eA*Qhg=EIl+bRp5r^oR$}np^wcP3HzyP!{xEtXnAq2=XE{!wwu>H1|6`!7mHLK
z!2kJn>o6K}-<FzORS`zG3G%Sxy2|wZ6$lEDIW8j5t?!870D)|0rc#)`$ZhxNvIdaf
zHIwc!aSkX3-ZRH=FwDW(AJT%OQ#Xm8`V-!@N}Ft7%Ijx&$)66_9<vAAATf}Z3te^6
zCig|sjia{2L*7?kxh}<BGybM+X}#K{YSr8vcU4rujx>!oGECr03&q(yds8DD^pOpj
z^h8yW$GH)f33b}j$;zSA{03t+^k-L%4oVl@%VWyvKS=-w1{m3kGI^6qJDPZh7Q%Yy
z(V_OU4^8rq-N~Eusxu;mI1rTyLT0*tzS5<Djq{JzfBTE%Zsn);<)CU%ZwJO#0-zT1
z$S31!MXYdwH&XdCoT~9FHxRHO&Vd5IgF(14vMx+b9&Nuvq`a5Cn?+gQ1AL_#cI6~J
z>BnoSNAUY<i-(`-Z>CT;pOpi$-v8B1NJ0B4@dv32Iivx;dJYTlFdlFPHL=YKl5FRi
zW`@>B*s99uNS4#Zq!Dtq&LA4!v4V<(NLeyz!s!gZgl|A{7!*Go(VV@sE}X=@m)XsP
z;<pxI5@#n0JLNGqZ@W#_(l3L<BC21?F?GPd7no6(5B*If26m3lu?)`K?Jd0%%iksM
z1`pd`)<fD<*N)*lKZ5@HLq=cMWVLE=1R~RH@YI0G&RKF}nXu-aF?_AOI*m(nv^*b^
zGEBxcQg0O>GtceRT1@;3EKnN)9$th5jF3oqmd48nV5BpjNbK5#iybObPEJSKI4q#J
z7?TWnwI+2r-eP%CbM8A965JE{8gOyeAu<GLC9325M??JCx8!fYzasT$ZhP)}Hv+z9
zZ;mT#e7Ym0fF<aHs|dpw8}+K|lb|Q=R}(#+cynO8^B7#kh%=daGE%#>qJYoO&(20$
zR;cb);>g0!ZQofr%rf+IqBUm?t*6OB-VREX&q^zmOV7ypXH~jgPd(Uir;772{Ii$v
z5!+}(akO_9Hs9LRxO+t6w4-+^n^`zcZB9}IO&G5nGg@I=xu?htUmtjO*@%UhULN_2
zb@AD;f{y{CdrG$uiH*2uIenfmXB|~+7C0TcG7zZ7K1Y}H9Yit4J<N#dNtF?7+d#y2
z%kU!p)ZY_<=%#-HPr(>)OCw$!?8DTsZ2mK3s4!_hDDMBYSxE)kooKVrz)oor!xqWh
z1ybD5;5>9>o7BRWwdFZC8S9bq&HV7VSa4W+z*q`Bcgt7OHnEX4L(>@nv2w)6@7|xW
zHhApFc3k%fn=5pGKUpjfeaA`ub`NRrKvCT%4WD!#lZ6`%9MM45cg9@PI50LNT8s<d
z+asN35be$CQ$jI9@?nx<_mIh7naw*+h1<6oEvUB45;v4e%h*Q!)6ZGzwUf`VBV-)m
zku-W1lOF3|Z#GHDM;5SzNbrI}Do$^bBy3-MEg8nolE6G|K@G*>WWEXUuM-q7{8l@|
zM9LyweitEnH)uCNfx{aaob7KyL3T`OVmtsbKv6jAlp%H^;a(4tkQI^AN_vy!j?MIY
z3N$_`R$);q*L3yz_`p=5690Qp^k>Mddb?Wo)ws>*L)z{Wv`NIwC(6eXRpBZl6c&rH
zEoji;je_Qb^swJyKRmrrMv_1^9!>`iu*q{IQwb`K)8Mz_$DrkT2k^#79?ir>6uS8&
z)ylwLJpUURf~Fb8xu#F(_ELj1uZ(t1>d+)F*v1r!(+{_j74Iu2gTzn7LdsCXsV<@m
z41uThd)?n#tl-k0ZBS^HO#SIwMf)6eM6V91<sw^|eLJTYa42;{t;StU!$MCTUTG$l
z6H}#bsy&7(#m`e5hWREy5Xz$-9C_O{STc2JG<Y$0Rk=rO!r5v|^5%jo>>vZ}K;77r
za)>|R)BUW`un%tO#<jD{;Ih>-I`}!fq1vPF?D=V{YY~nr<tL=QwjR=t$p{e~f#qm$
zvpTc}g@Xbqv%F;I>YBU*e=n5S*1qp6cL6#*&1YO982VxJa@_uSK;BBRIU+g9C8>bi
zN&s>V&JEV4pCsoteB-Ikk8S3eBv^}@fRZDj_zn47HE}(OBi~=iio`Gf@x#U`YAiJ0
z)9&+DKhKuMwrto^d72oY_k;m2ewceT>u#)W5nj#b@l!Kb{A#^Ae7dHU03HH-r;>r9
z?>7l_d{>r#nXGnCF5{1I^oyZGCxJEL^>h%Q0Nc%u63v-rJG8!9RR2Pw)r|4<ZOYR&
zryKWy(L^2uP-0T>skQ#-`REpNp}F%6#dv~D=SGS6dR3m~&$%KThOk^;^{aUYcN+q{
ztyUCPcynJhQs!VhUO>DeyPLy&XN%nfVpGLts9?2M)FHt|;JLKv)??*N%1b|ZLg4uz
z&L&;&`;?oJkv9S$lLcz~wXfE2P>h$~6jAO{H(I#&8rY2(DE2$V@NC+rhJJEz=XlPJ
ze(w7}YvyZEUgpt#QHJ#To`(Ytg*@EMfa0Q|-7{AzF%gmC9y+QOA8AfuSn)ar2C!ub
zGd3yJ3flT6%1a4(cd#%JwV!80tpi|Xf2^rR%FJ9Barrp`XIrhOq$F1T1|-T&7?9pk
zGF*NP4-W&+WTrTZfO^hOPNu1=uM;)6-oL&*)|Io#Vgks?HF?p?lk6{UDJrGV>=+t~
z=y>H(vS!R$4U~EnfU==6QxSqr_laQn3W<4G`ct~x8Rsqwi%EZSbB0~Xv(KhvCX!sr
z_J_vZnEdfl7hF#Boq9{%b>NGzNJs+e^zm@W_ov%WjZUI#2D;-vUn@2=fN2JAn^o%E
zjD_TK28n8|D<98}s1FPbgq^+Jd=%a7A<8`m1#?@y<mGufw!s<DmusU9cFOU~#p{lz
z>aOXBx%$wr_VKbDL>$euBrdu0ss!R|Qf$hnHHzY{HtKAAc3Q@BIz0h3SS~RX>mR-$
z#j1%=?T5*WxrG)P`64nQnGKSxw*l#fi3eK$!ZA<E<H!M9;&G0B{*L60VI$Xw0(fz$
zEhwoL%(>b=7-l>NJpB<M<$kWv{5hl(Qo0?CS|$M2ZjL7!bbFVDyIH-!6Wu5eNdF*Q
ziOM8YVrCxL%tpY#)B6-0Ld&b5!`HdX7nsP}k&tMB=4w;TH`(Vysxe49;(4=1N=~6X
zko8b{$qtoTJc^B^(wA{-D|XvYhnQu!7<a)k?Tum~ewp`i|6G5wCZ=Ff?0961Jb3A#
zHk@@B_%Kn1$Pf$P%TThnSycM9C<Z==p%fBNrgv#W;iK!VP}J-?7~N+&;wEW3(nXf%
zJVhbm2Jl~EK2%P9*q$b}2lP+%o%Vi);zq@y&_N>F>evyA45>RS@E!4sfsaiH>>XuV
zJ~E~0`Ep%b;lj%aH+vEk{P6w=k(0Fq#k#ueK*&5F-~=B0GA`WTN#)=h*rJzh_%313
zr#6`p48dyYAb^hTN)Y<_n+JCChK7}Ha!PEvbxwon5rNbbZeadVOesyrXORzCwPVFe
z+j%UK{f2&Q@TR-SaOMMXDcLmFB3cpivrhrFKxB6@+gHq72wqlF+TipIp1@YVfSe-N
z!P@ukl>gg#+LL|zP^@)%&7XUC=#`<)mXsARJSZ#Glu$3uJl*0AOy(M>jPnLfiRs$R
zr~OAwyXzZKhbJcN-p1bIU43zsY%8ww=o{Jms`985Dt+7{Rt}`<NS|NwzWh+@IPPPq
zG0chc{u)GN<e0Z+pF#iHC-Za<<!VM{Axz+XY{(><tHU|;a3Of4%7J5qPiIuL**tj1
zjUijZuK0z3TlN+95y=i9LHF}ZUEO{v$(&;CTn$I2eFFM{;Y7yqk`HZ)%Tyz|hxOLE
zGE^138t@l_xQY_G2P}5fNl@&P`yQ?Jy?i^H_G&(RkbNDUD8Lxpzi;QNCuQ+^C*|YL
zf<`18re~nKIB&WVnb|S(>SLH7x6;>H_#8<hq7xXv!?9f)^gV^un!2-G2fi3&(aro(
z<a~PLws_eur6X6*$%CIAc@DA00l%B=o`O7^&|XmkhwdX0!5Ilo9nR%=kgK}5V7?XS
z-^BN0`ZmjRTOC59!4_W&77tz@!A7CU?JV**Wn&+v;zA<=*VdO*6kl9R@hAFZdH^I`
z=Ml93nA?CY47D>(5Zz{XG#}onGOvTuo^sgwYSUIMlWl)i2QRjhn2Q!B-p$_Mw*H~~
z+0k8<>>{J7W5$LP87FQPKm9p5JIiGx)wQ*)#e(0KxN68(b#00&DnJ8ae1TWUMyh`+
zDfff<8nvH<$jtKjf}IDPEv^`6Z%{@bI|d~eWmIjN0F@M^@NUzKq3U4_#-Nhr4h(X>
znJ;NDZhy%Xe(#c6<V>Np6{mVx*bv^(mVLWT$-ZE5`hqq1=Zm<(uEa0{t7Y&*)&;Vv
z4QNp9Uh&_cYLU8<P9T2QKVv-v>vf=3m$UuLl^!Z5+u+)umBMf-qAx>Qr2Mel537Q$
zv#=L9?xWQTiMy(se2q|SqM)-yc*T?TYJx__Wj=^DJ|e>uIt())g4jQzPjL|kn_P{D
ziE!re?>if3O%tDDe71aOG=}n4B*1D6K=LJWUv!xKrpIK;fwI0i37n<Q&3ofJW?#}?
zxG>K`_(XeDm{9zY_Mu$N@g=9Lq)7G>?Pa%ihcbC4rkjKl$KK&F!{IOdeh9n`S}n{s
zAI&<|7yGSqm~OV|Wi{NN@<<l36r#$)EzutubaUYda4`lioxFm4!x_s|*{{5>kt6YZ
z>Z7R!Ly8HR#7z}?<nS5uV!;PYQgHbTTLI<9D+F&v-rCd9*_mm6x}1&54M}&;VSl$c
z&riu93=d~FjXRo)%=1T|YWjC}KY!D_w*ay;Ycya$-9g7YyG$Q<7Y4BdCOpSvSQk0D
zfPc+n#E6Z-UdCj4sO3rYD2EIKphX8mjSM=UPp61&?Z>bj^DLw!;J^_glZ^r$)5B@v
zT?$E;$2C5k42VL+Ns7HtlQxb)6zD4EiYnjv86v~LL$BaO<ne`Nl1yOV4nVz^=&@sw
z(-+Oj1c;?sp?}aqI@?-2sma^((#q!dI|WmF!}3G6-LfDL1fxk0#yVnvHk4kVnyXQ?
zA8=7vqdLFKO{hr^Wc*|>PYE{XwBIQV!ZLhWl?T#CdzoWSSY?lok0N7C;-WsX;*m~@
zjWX4y+@5pJY}ZN-d3YQH{$kEH)Ti)%X-$q=<t^lt4rX_DEg(~93`r)#tMxoZd|bDa
z@|pjtRfY}DKN4Ut`0BHxAd9*av(U(AtaNGcK0R6O2q^`pd=7$k&a;~pI+U*W!zTu$
zyUm^LKp9g5@0T)2&&;A(ewJ|dn{Stc(`MvYwX2M;j!bWr)q^<Eyo5!xE9zo|`#VZ?
zYI0|(+4%I>3Rn#5cmUY?t6j6fBZT7cw=>dfxA+%B&^FA?dR+E!xHx|*{bm0{C91O)
zKb2BR%`8b&sHsio;IlMqG4&{F^#gz$-IkxC|6KE<^XPrd_2%P}BP4XOi*1zSnS4+z
z>=B9$9p%vK9j%aD&!Z$?!;4X7Nbx=2YuYyg8Z#I%O%IF=OQXw%rzxugh^O6bc<Vok
zeadAAYagl!<n2O0!4TX1c=YUDABC4)iGz(_%Twivjd%a)+i{Jr(8<$b<&j&I%yDu5
zeZ$wAJ5T9$OKqLN*7t$URK`PiXj}2^p4;+~SqgeIoaY`Z!qc;HIIA9`P~Sxe)5qgY
zILC+I&D|Xdu5r2^SpxkH$I#*1RN995iZo;pM<&#yQHvNJ<e!bE=h@pt@mHhWTs!wm
z)m3enr6!_`^USU7IWUF@Pl1>B)@MwO%w$A7>TH<VxEO_R^Ec(ua(j-%%-GMLH5?g4
zI7_XD<dD3au)5mOqIM4SgX|g|BMDU14h0&2t*s=srEGRKVUS{l)QF5#g)%uhi?ARf
z*Sg*||6~+AW>3A~W#TLpm}(G<-*pCe0@;)|1*MQD#74MOt3q76pCIQbqUN$nVOd24
zneTL;9P~JYI~HN^b(*Xj&gabNR!Sn{L6E7;^xlD0XPObu%3VFe{UO_f(Y^xM`<dbi
zHTHh4ogPj<S~$wTyQXoWnwjPFR6!2;@*~I%2BA)4HNNA381bysf@tvU!+4eAlG=6}
z<%@c?6)+t<7wE*l>4rR5{N%wAck)R&QP^UxSaktP*a<!QMQ;~x7Uj@GJ-EIOKhs)N
zagwr6zCr}xV@1J7cSph;s{r@D#tZ(F-XVVkqFt7J`LtdXH>s*W4?@Buea`2bg4}lc
zkDsjneRr}{_%T35)oKVx;ejcISB<Un1?$SHMTcmp-RixszXH{H+u1vc-#j}l@tBj`
zo6@arjl8+q^AhXEIWX-Z8fx7YyD(zne7xb#ncBo$|5%tD7<UdqM6SVu>btqxp5IS-
z9aK)~wum;E?B8q>)Xi>tx<Aidaw1J}qOgCusINxEPWE@6(-(49l2|N1_}d@SOw5yw
zjab@mZJuN)og{Y#093}N2`e!?s|3q41<8(%&b>*`)>v-9!nXS|;$tr0+eUbOdz}=G
z_vb^K>v}>I|56(Rf){g@;3B-hQ|gQ(bL9>UUKg;O-W)JEm$XT!7-6I69c=z)G=x(Y
zx{OZtZotnlJ?FsS{w#Jh2L7=UM^G7nNS-93C;2H#1~D=@IXv0JsWf@mS1*bpgTs${
zD$%X1ln}tB+&wUPdu`5=L%`>DW~C!RMXD<eNgqrg7RO4zRpj>mo=AEH{~6myRvD(8
ztY8^Zy!i&p##=rHa3y&9_Oj!<6eU$+tRE5cXCki`UbHTzvNYcS1JwgrT)usa6vyGq
zr*nuHN!)7bn)aawIU_71@O5=QAF7FD23LLC_Y^lnXBtQhl~0Cy-uNpM(;ME3Q_v8b
z72h9CmHRdL&I|B!a`-Jmi>*Szyk>-k+7LabRm_@uYrCqk=0<=v$z~<&mFGD>p=f*7
zB)MlamAXt~cn~_W6&9eU&P*VV98kO3Zue`W^f>gp?77_th(LP78y>&Fl?0tml!18~
z%}0mYeS{;?^R>UXsiRJAP(G43Bo*bbZ>ApqCYrV{>78`JJ*U2?rC`aARP5E0hlK#a
z$>Nw{j>e=PqRG2hYSVYT+zufuR%WU)jG~i)=Enylhc|MZ;xc}ceWc0hxIY}>meV&w
zM|^QSj9;RDWR5YmDv~QbotDnWyQ=xzV?z*Rl(f^_`w^^yumkY1A+APZfAPBTIlfg#
zO670{8JiRsWM+wT<>W>1A^)vT&K!<L`3yPCsubNb-B#HWOzonio9#NQa3+QNGA>7n
zb`mu~K-eQ&751sz%C<_%>xknS4H{{@t+pQ6oEb&)*~9R2v2&O{NvfS4n_m17&Uab9
zMkNi2HNUd%X@`pw0%J6B_;%mpGD13dUmOo+PioH8)>syRAI<j{LOCtpsGBL}X#){=
z`3(ci{u@p+AovkTWbS!sKAwpifx!2MYa7`LRlb<jC!g&lO2O`7bUtot_c=$3oi_Wc
z(i5&fdh>Ur{~QkE!Ska8;Fdlw%yK6YDPUAU_2=YY!F>H8^+REM{ox_=jVg{I-)FWJ
z_KKkS-rei%_~<_BcanLFWddb0Sz%@5;vjJhqQPTaA)(Xlqtg1N?ow2W0P!4a66^+v
z1!IV8H9R7pSKPTnHo@(|{3|>@G-1ys0edZz4i*oR3yX^YHS6~6)-Scn7QBvrsggZe
zgG(=t@+9F?Y3-AX2i-h1T*zq-|J7NiY^^Vd7m!Ezn91$0wcu>`Hu6)1b7Ang1_qkf
zIc>ai(r_-gvPP0mI9JZ#wyE-pU<Tn`4?6@Y`p*A;Z31(?jza6a1!~H75BMK_tUh1F
zz`JgGp7Sfc+tVn=m0))iB|s5k${Nexk3@Vsmwf|RV-YaoNP)OnPS!E<wJIz50=z&4
z10OS~3Jm-_OGq)jI*qsJ)1*G|zGuZ~bh}!=+(*4%yGXrheZJxtSmXDZ8=$;z3yuHD
znk@r^98j9Qd&R<$&F)2_KmK&!eEhhx7!K=0&P*9F#jh_l7_`N*qoYCkQ<T-!@)nRv
zzjB9S79TDuImR{99r^GM;YngHhE0>jJbio7z&0db1HIk9{o7=1LEf8M0)t}mGPST!
zwK+M3$+h(9rgC`yRwJ#B;?P2$c0wM**XK0}UCs*3@U5Ya7ps7D=rW;u=0-;=g<&$2
zo1WN^4JpS0GvH2&_Vb+P`t>YmXO$y()YjSa>_=>$mVCgG?XE2WmiZASe0F%OB&9uA
zq=Rf6X%jU8%)nh}^%!ThjFzE9p~Cpb3D)srPy6O*-S6(zzrDYs!+SVoIhB;?f0GMJ
zcN6mHW^nq*#Do^SWfkSqA%Tj!N#^bV@6ef^oie=cwkS*n3-l;y4ub?Jjokk_hIb9m
zgBy32J#m2Q<T4miaC#X?3>!Fwp~ZZu^^!xnzjt+0Z5|IPfCYfa;F6r*d63D%=e;`v
z{Gm0ltwl3+oQ^jszzF%oV@s+;Ads+1Tan!9<I^U*G^3uGMU)*2xuB>3X#66d`#C?)
z8Y}8EmPGQb6w!i+WrYYrA7#{B)^w63>;I-YSE?%(5}H3OAGRkW`?sJ(IwBfPafV5(
z)<#j$Z$w5O4ap#vn2$>sB3_-$2bC;{_(Q2eLJCCdDKADZX)F+*DekX_98^?TBux?(
z5g`*Bn??VW@dB$<U}jnnKLS#*D<X?c&m<!5>lcynsw0y9Q3t_A%zZ<Q)shfe)xe>X
z*wDW7MvLTj5Xgz+(5nCycKlj;mr46#Q?|Wo;#7eATl0>djLOeW_TL2+Z+J;KjN`}>
zg&Znoj<j5h2;{dkP6gEc@TC$7BuB}al31;bZ0TTZCStPR0XJy|6(PaYMkCQaiqM#*
zgkhNqFbO2;_;4^4sn<VrJ-6Ghr)Eo5e~}ex*{NBUDdG-~kJ1z=@>Fw~441i0P<2`<
z`msBxu;BHd`Z^Q!?T>Wn^&jl-Xlo}<{hIsp=0oJW$(Tlq5#9%#R;pp|3qDYcs=&vY
zMFb@WZ&$V#B{s3zgW+g@H$o3s{r9sU$|bllfQM@HQna0#e+yF*h+E#ev$`mjM;|(-
zhwp-5&|^VOYJ5Y^pH?z9Hu+svlGfmYGnzJwdJ@>zr9Aqy*NhN*VitL=oMiC%7Rveb
zq5&J1&p}AH&RCgusn~E6bt{-P&uZdvFrV<do>@O#Hi<z~iavbOijsX~Z@k{PXLMr_
zuX444&1==^pb%+K1`<w?=&;t%k&w?rC*1;VPNY{&&a8nkkMf7$;gr$gfdcHY6~)Xh
z__K@$P3cQP2($lwvx5T+Tpr;sqX#+2mYwL}?#KX^v}PqI<v)-y0|8x;V<$;>ZB-2n
zpCxdK^)&DGjs{G+5K)RHC#vB|*p^X~eYC3wYluOo5M~XKSU&Sx%fGoQf16iM)(1Bq
zSX?zXHj0zEepJ)GtFCTCRqfs^ufVfOtO1}2wX4w@3oZY-H<fWkkF5Yye(T>$k#e`T
z^e#@x!icffr}7$ES<4ekBd1H6U8zq~djN2CW?I~>`YGIk0Y4D9S>6dU%Zq3kFeWFl
z*g6}RsfE=W+mgK|yfe^3cREg=F#T^`S+T9nL;ik|DT&g-yL$TX)EMtkKGQE=@w9bM
z0ju}5aK5|G5L}!%jmWYcl0KR7-)ZWS42IF78OanHkM28k6qZ4Fv{d^eH+1R>KIH0!
z!MV&>-mIUe@2YTw{O8C;an{e;*`E<^{ZfiFRsl!;{FapVT{Y!OnO)WRtPH!hR6qIR
zVAx{SX!sH0N@tBb24a#Tc~)F6BDdu`LnZlr1LxY+4EDTr?TEQysJ274R!zUHbbV0x
zPlEr+X%3#C+58L~k&%*TSJ*WhTQBh<4bC3onTi;CWKkom#(l=#_8WfqBP)O^2aemD
z3X>dRL95Z3TG7XPC~EbMRs014S>9qY#$HoSB@6PovNySg5+e-1#2zQ<(vnPi4`+w<
zu2Q7Vtr97%nRpVaJw|wyF{$W1oITcDj9uhdmS5`~I9?X?^%Ip*<FAj&_%f`L+8`3P
zGLXW>+48lG98Rlrj71}$fFPDp=L>BCSiB+}!)_`+Uq4>|?aUvFdFm_(_JLiV+flhH
zzlw(Y(!^!qhU#Mghz+nYuqs7!{(<5*eIYKro@wIhU`&!!`Di3g(mFXKY2F=)*J8^a
z52j9DWwI?eAI$iptWOwrr9bOfL47yc6BhQMqn&Z=JS_8VJCjS<W0p=1QZtE$Hx84u
zJ24*+_>X;C?8iDr)8`%AI+pjQHjZw~W6Ory--pscGcT2OUQ}=6-Ba7DI_~+OI>7>5
zkbPmAIF`#y#m&2sKXL5576Gi~yM8tCt4!9%E=m>7Nk5lr<HB?-Vi!)`+uZXsSJ6p>
zD9mi7c>@zV?k1(=cu#U&&;|uy(L(+04MvxOg?36l_sp@W0>HELM1@d%hxVq1zV-+!
zxVycx`o<NtgGA%(6UYASP2lF)GEJFrw5?`(a1&=}M}PJA4sSL;UF8Ffs*q|z=BB=W
zkB5J=Xefm#_L{TZH{R=Fy%=gjf!k<@!PoNg+#yDg#K=WOl#Rb$KXLyz4#uvhQbhOd
zngTeP`DScB$+G*Q;qDNg+e>SRVocJvS8TZJ<#hScgai4M&^btm0{Pph;CbP%{Z{1K
zi#r@N$Hb)E<$%%RNOV`TbyJy9YG=Hl8L4S_KG_Dv>`v-cp-8M})yl}RDy^rRE5!}c
zoFENmBW6>2y+Fmy4}b7Jo!#GBV{th<yai?$bA1h2M{`0r5SD>AU%IHtC^eaS{C2@~
zdN(@(D6YdLW-IEbp@KZk;Vx#x^w&D&`^m+6NS%f<i7vIY9W~~@F#F`#;}DGIy45;E
zcAvC*%xc~T^u9RBpTZYw*Sc<}>H6326xD=;tXeOnQ6hYAAcCq(^%r%?{{+pM?TJbY
z&9lDrQXQj-Q{uD5x$-Mu+4Ei(-ps=md+IKe%Uegk3SGWPSr8LVys$WHqPy@-m9q-P
z<ne%2-w!?hGJS|`D*vN`=#McW0IiRtJpxn9ox6>|u=G~@=J3&NzYwED@i8D#+u7-I
z<HwpV9gJ*#D48S&vIde=dhh+Y>?#Xue?G%KY2=HI<(6?krasVF-0urw3p?mai&G7Y
zE&01-PeINvr~WT@3`707mKj$2lnoFfuSc$Zwa727y@Kwqd%rSqbrAmB{G<Vk^jqOM
z`mdUM+Udjn#pyg3G7S_Xd%Ib&au*j%sjO&|TQfs(Mn;b}V_p}1j?|<84|z9EY!Ud_
zSic**>F<%Ji==oZ3yvm*W)hz(A8-wiE*m8mMVdebG`0aMUnYUS507ghwf&F=Zv|Kl
z#G3KSHQ=#EWRQ4d;mEcTz9z7c@Yw#ZkZU0v`UD|W$Yc2$_4d^=^WEpapWbf|_ulnc
z&<WY&$7X_4S{A~M<}xJRcGE>E_ngSM;Dc#IiO$$W{-RsA`8jselFBAYs~P6W+-^fb
z^=5+EzbiO_Fk}kPiqGvguWH@wroEq@TZa`R|ID=6YGX^A<M}jVSJf2X6VkJt7~~k-
z*Cr*Q;FjntakaO`5#!X9pF~4l;QbA!;Mm<L{uJ3CyPxhXSEIN8*~$!%TH<oYTPkj6
zHQ$2rWEP46q$4-6V5*G>puK5ei6pJR{0<z(#|2LLyg>8eYo-3PUHeYZ$%!rB^^B(V
zY(6e`DM9^B@o^UE54gFLsCPh-$(U>uYIJkVdG_mHhfk|VtA}Z*#dosz<f=_dJ<NFl
zSkHIySOdls`ABX$Zlw-{qH$!$qFRUMdQPNVDj*AT{$QJ^FHmgAX%*O7-W*!sWc@F&
z)hFq=Y^2nVOf+aHac@kFCuqT{q2P}H-J&Yxe%JHv-DU?M6HGZVh3e?@G@U|FEk`o2
z$zm3!PS750%pp4vU9T2;rvn!dgYr5EgW$p#?Cm>SZFnHR@)GBlpM{bBgSGvpVd>To
z@R>$h)}?-ytJ<f$X(HMlt<n71L8?s!K{NK_m1^I>K(K2DQGlv+&gjP=@FLJRKV;w{
z->gunosmYeLjR>bmYzP_Jso~@FWfP1U^AXS>GI?en+m;Bk71!ohgc7XM~9dDLK-PE
zfv&$$8LL8<Q+J*tNzjwYcETk#r6{p?JCel&TYAwNl>y#{G`Ndh)3}a3E~8J^v+XTe
zJ)Y)ks+$ttT9^EVgR)#0rDy`32m?h0QBfy`!lg3HM58+|D4p9VOQX}U_G#z0>}d4H
zRG74qxHjp*=M_J4$RkF2b5*jHYgbB_nz9}GewgF%V<>{2+Y)dhoInP-SjZ3-lcpdt
zo8DDf!l@jwY+Op>xx$4?!DpZN7=as%8w~k-YbMY7%u(E4cPbS$Tr{Z(#%Knd&)Q-F
zG|to0LP&_9p56AFeFkipK%u4cbfFbR%k)UBC^_%RgiM4`&-U&JN|gC(DU4T{74u8P
zo;mpJi6I4ZuQ5|9B^~+ejo}lUc^G!$vWQ${Olqa>I+X`kLYw+xM4$wCOsYPgxe>>U
z58lcWT{3$%4D8Pky425=>7G>eLi8BG`dF|U|Kh#ygr!t8p&io^F0$mq`I$sLDsfp1
zdp!*`k{wyRyuQA^7DgWnIb5}Wg$gdeJy@*0q@7E(SlFJ+)wN*|!J~;o%M_{JOsNy+
zx5I3_=<zue5+v+QseBh?|7Fg6(eN+1qvTNOEfVRiYaC}XdTht=3|u760#q!iE^6d>
zG(NxoNQzU7n~Gq*Y2{2KNo6KuOk6B#P|uaLsfhyEXRxf|IwM%O-3NH0b&Fb)ZVA9$
z%wVMYAra&YawuO%auwK0W!d`oEy{&GP>jl<&QTIWE!jo7k_v^Aex&!A<xl}|D#3z)
z%$1aslk+!W=O%jofEC7xXurC;I+`7n!$wpLPatA0LtM#V5hiz@W?4^x9G!_!;GOZS
zqJcU8sR0k}Y~3<Xlq9=6$={+&1%K@MUAT)uT<CEIsihr}k_REslTo(HP87Vw-X8O|
zN|BA*nN4;}%6=^5d00Lja`7S*{bUFft26V6X4KB>$4s!mg9wq3<UTppSHgoGstjdQ
z^QV;1OO&;w1x<INKb_yoUgc3u@s}2qzG7?1bjHEI4aXLmpwbllM2F>jt$xK^ruoJF
zs)UEcw`z*(UvninHoEd@9vXOl?GS#<kbPW5!TsdfXj}`LB_DA-CY2b|sC8r6yY^-P
z8GpT!&a9T)o9<Q?*~Pc@ZuhFI*SmF^!*br;KboKWDwYW;b7eOJ5#k^mZC+ioOASGC
z)wYwwc{3d%1gXVd1Cv%nx-c-g;RMrLC)JlCgDSNq>bFLnMyGM1w0S5WWdqE5%Aq%`
zo9Y@<;h%|9VRNlaCI(_rA0;F?B!Bz`+#wBbgu4GBP5-a7V1tR#RFp%^ZXQD){ja2u
z<gg$*irksuegD7WXC)-SGT&X4vq&H@2a^!(N*8u=!({UwZ^6gfDdgTcT3LMcLe-*s
zYb?icm~lFPu2EgpJ%5ME4VDztBRk_)BG;5xp?9t;x@}uXP{|PM$)qas<4|~i-{rU?
zMNf_M_CpDc%V%m5`F79=2kit6(N>D5dm&W{g78?68;k?f4~P4QBAz^0rNZZavi_D7
zPR2FAt45>M;!uuYKm!}>=QP&Z7Vk&-UQ0i>`wi(Ax*NK-rt?KB_s`9tMB;FWt+zQJ
zH#C<GDKZ>t@2|(D{-k^8|6;uD-Z(4gMnM5zVD-tg`|?B@oAB4MEx4Ljuk5anS$AiY
zPGRyuD*WEeAKJkD@r4)z!^7k6n9e(nbtAFa_MRCm<Ap?dl+C0*&lUJS>GEC{oxndn
zq1UdkfR-iN#{y;Fe?TVWUxhPt^*|AL5n_$;M^b`BJ46v=i~s;yFfcGi&j$ctsYXy~
z_6s^qZN~rYks}#TUZU687Aq8GG@7rUrK;QJ^z)0O>~FGOz53$OrB_m!_@1@`8gyk{
zM-^I(T-Y<(bD4{8hNg<}8a;gLs~hmR<Kp53%(_6#2Eoj>8@y|dQ@_psm7!s?nhQW7
zIw#5mr5Y4UIdMj$?!J_slz2GP+~jpt>N|tmn1S1H{)qikLm9X?+wO8Mbh**FEqoZZ
zy1Gh2ODn@1GsXXzA;+HYSVOgTKI;}99&R73re3XC51H-#kXk#w+HA|fv*GjplIwnZ
z;4n1Ez-4<z``g!v9>PB$pw?oJ-0OKiInMM0iyeU3=6o6)g~!vkxv4+dB_k?Pgm+yz
zMl#nk&PYuywZKN74FQ8b!pr!Da}>gyELJ1Xlb8a>6`YC;X${}S#J4^pdnODBMUo8R
zzcBv(tL^^6mR>(Ow{Rfv+ms03$CHs{$FIlF$x&G;0q2CGOo~H;uMl{;XulK)nZoS$
z+Vp-jxcfq{EXr6QIQiPO|BYgYva~9?8>Yz|OUcTLDkvbjJ#c!zI4yIPn_qUl(p#6#
z3b_o?UN|_?tQw^DzhTs)1Qq6uCb;Ym?<2{#BYd?K&g<jl@K_?vKbpJCP5mUea6&Ey
zuREsju5mLhT)*4?FH<_*!s3toug{PWspS|pu$$SS58Uf+MqCM@k0m-BgTZhp0?X|n
z)hr@nXci>w+*TA3C`tuNW*APl?b3|$x=84HIicab86k0a7-Ud|va-O79mAM2k-`ZN
z`-0Z@$Bn-l%i*Dfnag+tamBBd;Y)HsH>g{Iz&t3rpV*&XRJyZSLvwAqQIws@^?rgg
zuHcsB@N{Kxw%(5Mb$7Muz57s#EP-OVOi~tuqCO9LdVXhPKZ`%gMGnfhCNGNbdg49)
zNG%qyfw$vuQ9S6}4#?4bFC}LYZu7%F6|g4jLSJDTxlM~%>`+h&ujEWjoA&yJo?qWD
z>Vxcfg%0Jy*9m;z{6g?OBwS?P0Qkx8w1c{#sdjoAyaR?+K`<C&NL_i$i;EZ}Vi7TU
zRbTPmx4PP{b67shYQji;G12m&Ofe;seXj+O>46PO%`e?u{uzFm@^yiwp1=20XcI~*
z8&v}xGIRefoxv|ez-_3181zU<T7b;#ak-r&m|@g79s75Y=%e4*ZPp~r_*A;AO^J&V
zdor*Xg)>+!kkzX7=ql4%j10s^$8;CcVexr2v>YXMV8{Ql{+4c2&&+opCcwf9MQQkP
z{c?|Ej{baTLdt(XudJjpP>-^ZS+k_XWt7%Xz2a*!<IEP^cge}mD{TN`ZED%AWBJ-&
z%2*danMAwt`rqhiDQ>=sebvKnG_mdz=rt<nH77{JU#QxDt|>F`f6$KY4E?9wc7rhV
zTIjHVV>qtZW%(wz8Vk`K?+>d=t*a%9Bvc;;rxa>uU7sJXN^kvQ5N9&GWp}jamS;~E
z2PL?3c-Yus0Z<5E>@(VIi$JHcI=b`qS-2TFpmM0Fc}z8-$itPTTK#luS;7Bw@2lnQ
zPUfiq%XOk3>2v+{rdeN$l6CIfG$-y0qs}X9y62t;2KQ!oeyebPpMhVUd0eIUl1>5-
z@`b>1wk%73fB*L3p{q6v61`iZe3zN%#h0L`XUxoHZMy~&_@#5?k6P68l%+0sPTgIn
zH$*H-%rs2#nv#i^wR8vPcfHVYpZX}W+3N9&OTDUyPYpdDFUVt+-Q5^}E%#1eoT%xF
zI;Bg-n@U2!<+Q`%(rJRjVNYNIWVtha1*u`0IGQBW<Z59D+WL#Ccn&f62MS~#!9j3;
zr0LDxsM~2}bAdyu_&#m4X*g}ucpFN_384xUh(xn#A&uX+Yg})xNcPKW)ZKmR*;E)T
zsl9%8Qf4q7AN`~MF3mL%fbR7L<a0S3)>VyMd&EQGy9v0otX-!?t<gx;=ds!(WxGF@
zUe@p<xTNITJ!kTHYfZ8~TVABtGxqOwlxj+KdfXS3Ebvym;J<Flnb>9_ygy%vZ1I~B
z(%PDGG4rrPnCz2AFKAXx<~OfaqI4=XZb$jyxz+?Zy=;GN)_MKaZxhkc$(}%_xI35)
z*nBB}{vUJS{9H*JZ9TD*iJggU+qP}n=ESyb+qP}<#C9@qzP#V9`#;?3{^4|WpK8?8
zz4u=0S$kn{I+r=XjbDZMpM%PzL!J1qn$=NU{aE?-9dDtwkK%d3QlIZXk#fVR)D$o;
zcYdE&OvKe5XLF_g!4AE>OgGch_Vb8~%gWLw@aQT_!Gx}r0bpWKoqE_rv@5-u`aRfX
zgf8d)!~quIZ%hzCl;gpk?D5Yxf9PSBk4|i7tGyw4w$0&y-ee+)t7blp5yUj7_wyN>
zR5puzGJDvg>pB3}?qQ&0W~;xBDVmJOHu29gP=>27^}gbT)S70rh~Q-#vI!B$k*>!P
z>bSJJnPGV4`%j2?vCQCmrlO=w2n1PyxkF?Bm@PS1bL`sh{Sk3F&d~~wd;D)NfTH_J
z_cX5HQVJ(lghIXx$Sq1xTPP4fcZ2)TIFWKr@U5@LR(xHxZzw#j?dHsbWN;ZL(DGSb
z&0iNHYsm>o^%bak3VJSGz5yoGWq(KQYi?UHvmCn_kQ<BPqp20y5?tzoKG+nUpO8ZD
zct}nG&ZyE;`AA*NWa7DYr;=R#nD=KTw@l^B8Zi8A4eUcQIu+D9V$f({i>>`tCB@*z
zT_^t}@RL2B&T;~OqXK_ZfB>W&j1D5cSM~e=>t?yh9lu+A4Z!c;E^4K4q1Z%GuX};_
zdtYMCsscoCxBMGa(704Pjt>vV*43$yjy}cTKv;22%&_D<w@mr8Q!2PsN=HrbkdO$~
zx*wj2rj*z2I*?id6m6Hv6dVOeW7j<3;vFl9RL&^AwOo!qC`SJs!-`xT4DZvfAl~iy
zC*S`(QyTaS1$DC#*x+HE<q8!l^eq%MUjqvDBqBw>wnD@<6WE|(RT{09Bu1kg|2iF>
zS~!DZVy)(_>+IuX%V^8Tioo?&yIf;JZ<7bpoaj33Y~0#Czq8`hs0-t@8`m4+sfSsQ
z{rI<fqtPM`db|9A7`1>^qG6`UptbsZAfx^Q-fQLMl*cSQE~!D0Bc=J`ngsPX109T3
zAT$>SCIE|Ym+HA>Ogdz(7@(`wYE_n2x*K(KTsxe1`<qteFXfxxbr{E(oops+v{o;E
zamUAr7ZTv(<Z&wkffDv*bIBiiV@%$#A7hSOJC0kMH&I<k{=||vKB;x&ee*Ts=h7I4
z{d30$BtBjaVgta@MY+XasmB_|U!*TWhK{E*!LGJd;DMz^Mwoj`JXA69O$p6^R-Gga
zF4wBt@(ZIWx>cx$?;lZ(TAWL69&Q1c1|#$WQ+ZL8TCknSE))2I-B0fK2lC^D>9-}M
zr~|m2<XC002lzlq*4f$Fho<f7G^4{r>TonQi9g$IC|0Y&R=v-=E5(Y@1vQ?-1GPxF
z)7tIcI9YBIk<%hOTU?S>3Q8)|cclg<@<5|P%bsC(e*%dVu}xPcQ|4HO22|{X2J8eE
z0rDct!tBd!*sk}I9jrbO8|5tO{P>N#0MP~HzpG8T{ca)DnmD#;@S9(KhP*k;jm_w}
zdPe6zv1if#tvjaAg@7ORB85{kn^sJ%h@N>yP@I(3qTvwnC>nH{Ik!k0jeDR(5QX>w
zZAknuIoHEj5;yXLIhOMETh_^Psp?)uULN1Pxzqio`(IR{y=D-lc)P8)sr6WZOw8Um
zmZV6xb%>F-fj^se%CU!<ae|<3+_mtW0&u`%PynpV#8Gyde7l+A5X=UrSV(Dqm_F?E
zc5;sio~+*r;!j!9Y79Pr6C)PRy|3(LTt5tjNVysmg~NfwWHyuZ_UpOvFrN72NR%TO
ziYo0jujof9s7X5Ai0H~qY0_#oi`UK{eWQZY)0>Es%JrQ&Bk9gMqBZ>ho?OLbFsiEA
zg(vhge^WebHRaZoh`Z5b6hno1A(f8iG1S7;EW=NvV#75T0Zgmc*Qb3QYWC106@php
ziDPFh$wlnv?RPI<#9sahPd`9&EhjK4A%FB^3MZZp&yOb25me<zZz`}tdY_XNnLxWZ
z|4Kvh$oJM};p=fI0}h6Suo78U69%pDlgm(wRY|8<WPOKCeP1#ywd<ttt@zxUXK{)W
z;SoclR#7Ru)W!T2)(fcGj%rd(O1Ucr+a>RyqBPSKBN@w(ne#Q?_xpa&#TSAl0<U5#
z$PS90je@I=Lf`d$ug-f=1PzCxn`H4TEV$7|lm9n|h!5(X<TjaAQRXZfSarB1Pyx=r
zsW0BqcDH2{^H6m!Q~4N8NTXWSSSClYW-%xl|056uD5&p@t2^MK(q_rwdC%=AD^?X^
zKp^msDUPpMEGV=FOcjHrhhyghm~G9vP(Dl4?T^{zrclIFNqt`c5CWs`SPuH$ZP(;k
zFOn<T0V_mM8_bGvJ;PzK9e_t$vn*xa$BSv9y%fgZ%fHqlf|8CK922X1yB4q@1xjKV
zPbjyh1N#}3RHIW()|vvg*Iv^7<J6^iA1<Shwl0<3C#R;P3ng9hC`Eirrs2V^N==8t
zxWtu22c9lc^?VVlj1*%IwxL`maS)Pwr>3O)nLIR|NPZG*C3tMSs(AOQQ?bplu%9C}
z=78k{MrbIZ4phuYMadtrkwI-KKnysQK}?Htu}I1hS=UFT6mL>u=8LqWT1z~3TWm7x
z`!h3m7hh2t3irfubPX(}58=DZ%MOZIm<`XD$^)w6qSWgLvR9(V%vhygUQcq1&ng3s
zWCx@T40R#arcyBn3q&G8uRv{vM4LG{6HBxHU^~{SUk(JrK$9Lk$qr1WQG6cj5!g>+
zhMnW5YuKBZ9_M2~(iA18Gupv^Q<k<m5YO}u{EbORb<V09ikQ-jCgdB5K_kMtQv}!@
zSnHvmRp6D2b61iqo;a>(HvLPBkGID);z6>hEG*b^m{MEDcCsE57n&Q7QjBHx{c!K^
z5Ub()nB_!vQb4~_A~R>2bCgaMICyk1HLT)wJDR33!>=eTti}VaZc0v8KNDyx?Ajbw
zFKJeE0j<JNE$N}oP=HP4`lYL1nl+%xvkRxTY*Ep*u%i_)V}jVUnpB@?ELy-Rjpj5@
z+YzvWUrz2;L@o~~IFDm!bRL{axq!=DS_Co|wRT4{(`;4m9XejBGGb8cyNDV>neROL
zN@4mPqUqu+IWr3@tfIYfERZ59g8F?;QPPk_iK9+;|MPq&M`W>YFz@zb;>98_l}i=S
z-mlx4X1SPjI4>v6mq*ys8544Nye3q(ORPwhySo<UmDb8Sj5GLj5ociop3SL7rQ2fe
zdJVgr9|Q6ZGLJT}HwLGfp?p*o;iI|U`w20^D#n@+R!ndSCo9*t%cEa+mFa@fM~k})
ziA||J^-HAQiN?)XolM+X4@O?uh9k|0t&@wRSk=i4C&W*i#wJ^0+7}s&y`>ovP(yv!
z9HY%h?Yizl92lsWexqe)(CQis!JE!l##ctnTI`&ym3Qq_t3~SLS0v2ulQDR|*slXs
zups=@M88WaBX3p$7-w580#Bacx9Tj2M8}1b1f749SjLQjRqp7{vvdaf;izF&=dY-t
z=z60{)<qZMD!+sIHpdc&<lSudBX89tHB<O1<<jA5(J2hJmYw~|x9=YnXt4!LtI5$+
zz1r8?)3aC~AqM1N8E1R0@0*0GC>{ajBqrxyaY7x1;hxXPz1<UjgAt0k2;*?<D?=??
z(gwF1-B%!7E;zG?nWz~9MCYcudJtpWU$J%VLWjfQ7c13Fu4Qi6Az6A4+z(8+@TBIY
zkPPm(`b4%>e-zpnT%E1!wZ|+bg4#i+78fN&zPoi0>oC9zDA91ch^R=k0?L}<t$QY9
zgWGlbdoZ1yLd!6q=pYY9CcQ)`5aIY9u%KKyeN9OE)XBHjQZI0CSVr9M__9Y0y8ctB
zPKKLFA0!vMo9!~_9zTVeVOA9uA+v&ICQx0b-Boz$u0H(-sgxK+#Cd3z+&sQ-LI6b?
z;8!R#G$f*CXag;Wg`kI>T~9#LyF&AAP)Qn&ZmN@mZ}s?a^;2!EpTV_q$CS9&l_d@P
zT7t2~RxFo9JL^VgMVGCW+$HU9J05jazLnfhb&%)=y$m7CVWj_*WJw|<t!BDUGf^8$
z+tchB`}UnXTCgAnzf_73vsKy$?!M%_eNOX3oq5Limgtp83rq>C<kc80Iin<Qdn8KO
zzl9RqmaP_JqGU#2wc%^LKI}RQvLTq<7M~f^DnYeRrqZES8FW8qIEjP*=$#G8957KG
z$z`z{NN+S<WAQv?ZUO^E8sl>(03=I?=&gCh&G^OnQ4`x~=DNXzDZSUABN>bl0}Khf
z>SD5?onXcqPvsbCe{cwNn;$87;?FcaIl01w?3fJHuKn!nm}1qU<F$G^JM2L#dv_4A
zVQFbN)HP-GGd7Mcisz3iu1T@Jf}fX%ziRCeTCLeqr4S>;4Z);zK%KX#HAK*MtU~F4
zCk{i56KlmhWW02ze$k=J@ionG>_V^&Ss52Uw>kg%j2zgA5ZbA5RL6VJWfBD;r@o#c
zV8bQzj%hjlcD%@&vh=^9o$K2y4N4v1UluV~8F174?Yl0&yp(!1wO&r20##}vZxRvD
zStrG!j>q#r>UAkSr~!VIA`(oIXc8Z}5KZ0<=@wMwq$oS8M3Aq8iJ%0laW>-)hs(J%
z7L=UUp;Rhs$VH>CJj0xGzgU|R`d5VEcZe8^po?gmA%2$<`3<13(@Kb+g&*(pZXKhf
zkXP4xOxD21hGHi>AA=|u*=|=r>@;OC!NDXOnG5y$TKIgT-BerI=TLr1eC<*jbFxf_
z3}aG`?{mq0rQ0Hls<fuJJsDD+8U*ke-X=&PRq*xsuIA2sQVlx0&|8gB!x(L<gG9$_
ztgRtECTmx;su}@JwzI5yg=1q`GZE@{=B|q?<s>?g5c1<^sI?+ilV~aS%appx&#;u~
zr(?2b<*ueuzeZ7Ew=1!Zvw2*XVkiV7YSKT@43PzE3*mV|X*~SDtVp2M9Vst}LMg^G
zIXqgrRGf^<3-<<4kMy!j>ElKz765{=3CJRGh;VQcj~`%d5RB7NEw~XhgC-iBS2gVp
zz&WnIU>beDb{rN4Ij4y|2P_hofLnBvBzAMX+pC8sQq}4vMJ0jkS!Pc?QH}^vt53ps
z)Z0K(3Qc06QYa8fxFr~_pkANt`%{UL<XCpvaw=(!K5$OT0LHCEcf22D!b`C@7Bw#K
zlZ0gzmk%zaziOaoFetKKDvIDMd>!9+8_i;ego8UPe|5kyTGccCR#Q`p!sF3w;OsFI
zyZ823_N&1KVj(1DQto<~;bJr_mP|fOer8Qj!^1qk-uNx=zOk{f|I^<9C|os~$n5-A
zz4xnQ`tRmXE2&#r_~ECR?{JPWs@8wmTy8(2<vfurr1T%)_V19!h`k$wHd?w%q!GS$
ze%l?xVjf~6XmrbQ+-g-7btLHgLbxJqfnKjKej#X3P|#o1ioh6mFdND;wwViRep5nb
zWT30RQMEm9724azC4!;Km?A8z>5n>?Tgp|-m1=Zx3IRiPJ-xj{fe^gTx^Xe(U@P!H
zr;3Wl>YkUdKw@kdPF2&CCa={7IJ;dzl9xzFLJPng7!P?qo%^@20a5tX@(}P8Ap)(?
z)nBaD6PXKS;jl0{nf?s_za5>tplvY~o)e9noK~O)K_oV|Pz`GXKhT14K!sgf&(hNz
z8w^n7W%zPf`qaB|k4Mu~jA~1O@$9{>rC1Z!=Rd69@^H0oH#>|?=fB2j^&);dmw3bB
zXMh>Z!*Fp1@awAZm`INU%esc$2Y)%qK$3$G_*>wnMHQZtM1pGo=bKWkCgtIsmJsR3
z!wMg5tgmbAB%Bp>C9LOOUcLyN;s_f$cJpz=7?jeRcr>0v*0Ry?bcOs~n4f<GKkI)|
z22^6S#w<S(^i8GBfCAgpP>KXoyB70I7!z`qtcD&<!`gT;A@?d+P$}tdlueWGbSPNt
z+MJP_=F_Xpx?#h!G4?_W4Ku4BR(#kVx?PtvrAf>U$dq|Xh8AthtoPd7TgEf}ei$hg
z)}%(86Z`XBV@t7xl5#$wUg>uJcGF}3Z<ujZ%3$It>3cmrwEbPaqX#E*zqksF1I%>B
zx~*@9IqGrF&lVFHZJa;JTX#GOm2+!-KHTeGfkpL6wpSMyD43#Jm<~P8VTpkTVw{DK
zTsk>9FJcTg+q2a8<viQBn-dtC&Gx-Qx8YADm<?PA4CU0a(!%Z+I;46QWTY6nHSSQd
zST(m=Ax8?s1J_D)9WHOS+nt`ivzT|FgVEHQb!s-&bbS~<VEV?xrS7^04v2NtWa_7>
zk1?o<oRgztkqVlZqtD7P-D(I~s9`-S*NE~oGlo%3DO3}3FhqERdXi4Fqzb=Y4{?1*
zF`u$H+JSipfkplklw&Ymdd37*biz?`NmGH>zw3dF91vh&V|CO~Lq-FGAad`Y!kUah
z*();Lu!6N&NerTr9W4bll0XbFsFkF2z;eA>LSGCoH}_L7ZU10iB-@lR!^Q&xkoiE-
zMPX=2SPUxe6;}Usk(fRm3IKy~mZmBCNSoA(Qi_gh#s0%=9#<vqjgQ2p_(fI;>rp(c
zi6+!oj9frpjF@$2PrSG-xBU47SVeW>R<ft$v}%du4uo=b|DZ8CD-yv(t4b<nqfwYd
z;;wY#$_}=u)2)kc9%8y93b^E>|GE&#^+I7W3;u}d3j|vXH_7*_M-_&U+H{|(OSePe
z<5Gedk}GP9gwFh(fs2EQT+5Xxoev+Lvn>xIm-ZkawNo99K~190J-IS)2g0$_AHA@3
zmaeTvK`o(1I*djkA|fg-ix{S&9xxou1YPhzf8)%w3S0_9ZgtXy5L5)W=Q88gARBOb
z`k|cigu0^)Y8fne)?@_x_LEL6^MBurNS!DKQX$)vkW>9OqW8Er)+lcSx|llHumm%j
zaKD?Wk3^$Mh<8u!ZEyt8$fS9krs-nm;7A%wxIDD(`X(akXlv)W%IyW)aXz%Ua?3E7
zl=M^0uh-Kinf^)en9AZ%GQ&ecg?R&Dh81(1j}c2S-;=w+9{>E6>T}sks&F&mOd)L^
zQiJ>7mXjh5|FZnt%3+Fhi^yULy$RuH>>s<Kq!v=T*2yXnvw?u{_v1{vsI&#q$T!(h
zG<8)pvH``eWUNx0qr0SLkw~=D<;v78gwX0BNHL%1{J$MBl><p4RL&Sk=GcyNuIqc*
zp39vV;<5;mkUt4tLiPWrmT*u}Nz@c7-8kh}L;qUtcHL(=<FVWC{rcgVieFSlAK&V_
z*7Ip*qSQ>(!yDQZzJ4eOM<o_hJWnu1NS$vLgY%P<(BVjQsYIP@4{g4f5{4x5$kwoQ
zCbJl3wj&z$M^DR$g{SRC^Nc&A2Q%I_Do##KMg&vpsMc%hp9C@6KTWWRO;m~5cB>of
zY`YERDal#d&Kh^_uB+aC=XA*y2Hdfbsk*fEhwAxnx&0R7&qet9W>`}CAJ(;v3ULl|
zOh@C=v&03|`Dgjz47L6J=Pa;`h7_q_HQ*_(&R#0E&DHLY+>GHY+ZM9DdwMQpBofOD
zf><0U7E8^UgXLb+^*H>kieo_%fE+a%KD0HTtutEgQ3KotX(AOZ@2du|E$ENSsewZo
zLK1H#7ZPub7fRU8cNHC`YBx3;=+C=F1D|z{M%5gGKgykrc^DJCj&5;;OUMQ*xXUk^
zSINF=&VFXvC150{bm;13Fha?`Gj>fb9MGqBpSU^)e<+<gzBuqNz71NGGf&svqkftT
zpunAbUU@KmhSbA0)lPJk-Bj4$C;t#$`uux;T=2CWzAXCj)VLR)_O#1o(8DtMzOf&t
z7wP?aQkW-4DGJ~2xl_73Q=B=XJg|;>hi@j040EoJ?&wClwXu%yDBr$*e!r7?q@`dN
zJEkLyGL%*m_D&k5{9}+C{77H?ATvujx)^CHc$`$yaNtPyQ!6n#w#do7@T1k^n@Xq+
zd-Qu``Z@CT4|3kGql2_U7aEFFgKJ|@YhEI?6oKl1q{CvP&5&qv$13RAi5l@}xc>WO
zD|<$EG?#O>ASDGx)hc!W&(VNAg+|(nHfU>|R_M)tcp%!`0G(>2n5)eImrOUJ<~h7C
zmx_adb|Lj1Q)I%#`}sCs+@eu%gYkP7c4V|z&nCeQUOt5qOnYun6|2{Q!FG(y)vAG*
zPcnU_a!yNCmtnv*cD?O|t}zPNKy3@zfE<K;MkZpmX4uGAb3BrZZixpiAyg0C#USYk
zQVN}3xKQbU{BkEPxAr9>sxwX}aDaSoJl)b`B&PA<I+1$2ArB#P!(3iI9ix#@z4sk-
z7EELyLiVI}YHrYSWGY~<{-I29Gj{=Lt=<x><%Sm)yY>9G;y7fgNW4n76_s}9Nnw2*
zZGC)Rz1Nm3xE4lDjB8-84iUs}ps$4a4@8TNsCEjA<!V(#LV)yu4_rbJ#JPJ10Szl`
zWlB|FdK`xP`*LVwH;bU`<=}H7x`m1d&svxv&TcC%S>eN6o-JJ1fc5207u4CXY}whF
ze^wHCm3BLH=&W#GWep$Y`jO#wMe>@@Ept}IilB=d#N~Dac&00kuzxkScUKeBY4e+W
zL?eV7KwlgS>wO-L3=C6wWU~HYPGZe#Nf@pI{mcQN-t54~ZS}7BW&PXRn@!bjEedpF
ze6ZJ+MjiFYaP=52Y}%!uiAO#yJ8JrZb#S8^yrPYR2(Zslp=RYkONi+U_h}$=)EqT=
zVNA|_@zXV~{uj1d?a&NJDeSJZKwpf@THQ|v%U~F9D-{uXKz#yz>?FAWh~AeetB(`%
zLKKFu(elJHyE7*a3oJ&J8iw+ns0#ww<z@#Owi^zAPBZrTN>xCgkyoALCo8eB67TtP
zP;>lb%KisHXAL<mEJS&?N+F=H@ZFw}9r5XGQ~4Krmle?rF-jrO^6SC}we@ZrqQr1b
zna(!uYkK%vlPwHRh3DYlGU6+y#9Ey%lcY}fo`kb8sczB9cA4rZI&Dk~B%jl7suf#7
zWk;ap8&2_P_@M;UAT@X3=JdC^>Bv#&0jj^Z6{044*V;icL8~z!WcnA!V6~LA>}^gU
zD|VZaRf=W<l~{suSEbCY_^j0!(I1us6!%rqOt*p&)F73Y?9_UqCj9Oyl`5$VO+v<d
z3YQ!x0dGsR%9#`wOpl$NnEsAW{$kjd5im~mrf-Hrqz^f~z)YlmaJ|?63e&}~Qjn0>
z>a5`%d9)1mLp9TS<2aYAD;?PT;S2H3{Y{)q0f+HA>TOpzqA_?OiJ54Cy?CLtMteAL
zSlsY}I-RvTs$WvaGlB=Y2nG2>E?8ErhT91m<TcCipPF8VUj?Y_brxW<#hr#jakT2(
zUhuMb8!^RR0}P&zMC?tjK=6Nf!qy|%^ydl-<D7i4S+97)hMi9GM0&;KSI-GkYIpsc
zBTWZJYGK}(okr>WXp%gn!XQTyg1wG-grOo%qcRE}VP0%E1Yt24uvF_q>s(KOsIz#`
zzj&_25+}~o>l~3+XtP7RGOq0PQa93C-8o$Cv}1@QXfZgN$c{h1u=ioi6*O$2Q)u6j
z!`n9u1lKXgUoH|~$LNZgq#Fw8pZ<Zhxvi0-g#LEo`7Qizu%<r?ZCD_%fp#GR=E2_e
zBzOL|A*=`KgTn^ey8}+(0*m9trC(ISNpX#UdczCy9;R4?-Om%f1vfhBMl#~Q&Zb{7
zNVIg~1dLBcxY)FNetQ-kCbf2Wh9&ZMxTbrX9n4fNQ*PR<s5N(D%&6FIEQ{aA-`gec
zH)`CFAu*rS6nyTNU#eGdi56FyyfFQ*I~WXQL?zO1VCl}xJ_If=iDtDz{&ivkiufg;
z^_U2;c!6czQgu)2uvbC^b!I1&S&&jG7AVIi2J5j8@ma$s8z+E&<9vB4-v+k=LDaP@
zFyA5Be&~;u=OJKHYDPD2CXf@gApnp~13*?gJ7OJXCHyF$UID>ycEW?*llhlZip}6B
zkr=_*9HTX#2>Y6b!Q_?+WVB@buJ+4=a2xYg`1R9Oz5P1YrDi7vdJr4l*(%b*2@?1_
zo8jOYN)#ps=w`}He|_OQgV(SvH@78m!9VJuYo~dvD{X}`nj!Ej5-iz?b}av(yh5=$
zqg4&elBPDue6dv{Gx?xr?X*B0E>*zgu=A^ST%x}SAGM%liL0;^BHWVd96m9pgx8Fu
z%s`YJdfr^bxs)ep$yYn<Ke#`^U3<7|ZCt-1Po@PHc@g?oS+OQpZALeS8~=9T&X;<a
zP-{0Jy|Yq}wp$}`OPVyLtrYz@q@q#*6W*<zhC1j!dE;;hy3?!&^vA{szok#FHQeB%
zO=pL)f`k;PN|38suLUo|d<6&%+MX`2VfKD?t1sZ9V}+SWA&hw?wCC65#R=PN6n=TR
z5y{8+o5Q$+;%s%#|J#wZZ)z5>kcABx(4L#<#nNoH4POjp+n;@%Ki%j+v}Ch{?aW$q
zw;RBFyqtqhu2>Oy;N)=s>Mk~4Y@pfF-kR$cg6raOpvB?A8VnV!&gNtpp<_BZ@OCz7
zdajOGvC-O}>v35^B+hfH3@O_zf|68>1sq5KozCZjZB@P(#;alJy8DrEHd#bU(&C-&
z^ib}+7~*wXMQC2ro-ULV_<k7i5+-oZPn0rlG+BjjZ>ky0Z!kMs&i{JKU>23#!W{a|
zXT8`CQ+z_m^(#IzVz(zZ0z@pPKUmdtmGCXY)jDuFS6W0m9PXU$R<Mw@+Tpa8gqe~z
zwuC0LuTny(D4R3f<pI4wqEywsT9nA-f*KTDH$ZH=rLEOWZ2i@-`TpL{zXt@Xzkdcx
z!63iRr974sZ^nGJhG4oXMhTMZ5pND;hIQljYP{(2Cpf%+V<Ar*q8QOXL#!u{e1XYh
z_Ii?ELxpqgYI@A&XQC$_bAi#woKL4<Ze%t*Wi-=Yp8sy3$+UcD`r-${%r}=H;Ix><
z6o+)B-_Dq?EmmQdy|MZ>DWDH{cf!0loBSK4RGKUggAz{$>MvLSI2;a;7}~m;uRh@^
zJDoV4fyID^6i6H#mmRKSJUI&ePDu(phYJI^-3HRc|M3E#NLscEppv0C-BwFw?qnCa
z)1I!EqT|56Iz2s#?7nXd_OD=hNGOC!)WjS&Z_lSpSe>+}s5cPkq@?OCmW=f{|JM7k
zN8#CE6En$}fAu8ZD@It!lX`Elcc~7rt=_&pH+xVhbair9aTE=8^<DGbi~2C#_1}iY
zFh&A@4HD2{K2+I9{|CslC?y70xnSm}IWg~gn+JC;WY(edD3F37>-x9@etBsLDy@Xs
zXs!uV@<*@pI@7L#+xuAiz2hTa8^Kf$@9Jv4+f3!NE5NI<*`6o%?O!I?DC5cv^K&a?
zWiQ1}wd;Q;T3M>#h?*sJ;pR%!#E^(A%a<*;kh<R&tg{G%7BTl`@Pvt$Z)R|@4K|`)
zAtOs2E409D%BC+jo3UV27mo{Qu=`pqkPpf5`ptH6NxL-r1}k|#DoGB8|EU!!X$?#k
zGj_Dx^;zZ43N2?$?_FI>+LQqGyxZ^=EOyTow1iRz(qyI@4Ifieg802CWSq^H+J}#z
zTy;2$fXI;vWlHoP%JGRg!y^EfnVv=o@7uS2B^lwv1}wN4o1Kh7D`{uNdV9HXxEv|i
z(N4=6-d`C=5h>#pr_8ClyCwYXMkN#R9deElKJK4M;S_KT;B5R%Wi&Axoh@cQkCB}f
z>`87UkfAz6F+NZv$mSohP@Gj4EJ(*4SFKA$dJ~MW3La#XsWw=k;Du~U0;AF*lSLwN
zR$LEWf<;m1n#3R>PMDaOB%SvsnwL;s^s7(ikt~}zIXS}56;0eSuEyD>KOU|xJ3IV^
z&V`+>s=&}aITIY&-Cb0hSB9Yq_FC84QXCU!I8VNqfm~orHE*7JOjk#lZ==S3vovgW
zbeO_Yv9E1>BHt><l>LF!p$IWp6QsI`W_cCsiZxyGk<vWy1Di$2P0S$yu|bGY@Q#=E
z#i(LUw>L3a_!6WB9XmEOnxhrF@LwioXUiOkI>)<HkIulgNTU_2iSP2|T1e^O`Bg#=
zXzuY#BVJlF9}`($#R-7`1mt)FOIVs8N?)}!H;O?yj^ZXm8LzL;APGEl@fsW-NV<(_
zerZ9RK|vt(u5p;FM4nMGz^g@u^TOp8>~o{6FUIs`5vHp7vL;Ae8cQ+Z%T~5E{T^MY
zE{Crp@+^0U7eQC#Fn8gX=ix$01hW#tB`@KWS1tmOGw1_LJ;VtWaKHsBPDl~Vm~O~t
zK!f1_rLSW%)DIkFSg)&JmbIZSj{aC!fSJ4N37xA{0g-_EW2wsTBHXS`$ydWPan~v{
zNkkPnk$PXqu~W{rDy(2zMhEUK2~*Z{h}IV&`U*De+-!0zv!2%_QermoR((4$Gb><-
z*o=uML9bf?*|}IYJw{01lulmJ${NCIWEL6dVB+qOVX$Nx@v|pI$|Np5Gdc;07Nj6`
zLbC-^l9Dg>p$c<Zox@{qd{x&!)nHC=-q{VED6iQO`B&S6;mKi9_M6$wak!-jdYqJR
z@e<}z%_5|bW(r|27k^EcYBIweBAxbxp+hPhqtZRYS$m4OxI~c|7KEN;zZwx|A$g`~
z^Pyv6;vIeTg4G7eU!x>rlewDW>A7A90>y}ORz(OyBRx3e>)MbP>zs{E0;=w`1?v#^
zjr3F<tYQuWWwApOj0I`wk+Pm_g=NYdP@<)6<Dx=`LbSF~Is^;m#9u7-T<7*%Jmf4~
z5Lf^%!5GYPriy)CsGQ|32U<ns_rIJinjoEoTtwJlNjzTXUD}(hq1}PFij7d%ShkT^
zx@7=81O~-N{ZQ|7bM37cVm__5ImoRYfs1GU?99eQ9_!O?H4`}$(**TUZJ?vU(b9}A
zH)ngsYvw6DoZS>oCn_uzG6tohucdg5)Z1s^Mkz5vO4*pDF&1A4GS2Myhr+BzYBo;`
z9XMXr`LRRroZL`JJeON<J2T>AhyHn3{8{*2Za{2XlQSj@*%y-Kn{sF)_SM296TV|s
zOCpaOKdU`)RYZ~XBzQhx*uMFI9^T%?F@WbuQ<j-(Od{VOxc%^uIwIf2Hzw&1nV3z`
z%nQ4cyVk(qbT_B=J#7GZ8+pmS<w6-r$MU3zCdIt2@-MNI40o%)T>IrhM>i*N$~T#%
z*ofVl9L2+>2KxuNWwz!n__?wT59O5x=Z9l1sSLM4u{Kd5Kybj?+g1m|J2m2Q80rLc
zt7yC28X*Fj-rzmH4Ll2r`yANJ6>>pt*_27Gl4a+|l%E)Sv>?$}iXLNT$$au(r`-&_
zk5d>}lcTyR*@YE<lshpH#5O9jvRRhF&?Bs5n10t%F2|)}l6^Xn+YNfT39?*(uES&2
zhQMVMs(k_%rN|@G5{{g!<K}LwVcu~>k)Q1{g7tWmK+zUguqH-?$GQl|<WC!ukpjkn
zvH`TT7B-|ja5Nb$qGq``lpwN=knqaS*S1Mm$t))V4TM<Y%SG9l%T<a<W#3!PkV#4f
zOb>OW(FO{#NUBpWFcdXA#HyCExXPTaiPBum^uMn>5kzvIm+Kr43>5o<^Af&!Fdq1=
zHcsuiD8P(<Bj`n8d;I}+y%rXA1(y|p>AL?AFA~Wiu2;d5g&@;EG%}LkX29f%6(%4i
z$It=ij<~Mgs`q?Q7}A3jT^8+Tu(Wro((VTFC|3|~whB|r!THOh)jAK!hikTfNq-%T
z7>2+V64&OO1uZj@Mury{>cah;6((Y@59tYy6)r22Rd0IXpk2V+o%v5R^sU+c>r{6t
zqaF$u4=<>~)g$d?#;Z)ufG2Tg-&-4N1`lc=%z-Yk1RoVbJpYg5KD8$=CssE0a$k&1
zXZ&xRppELA{=vYT{vMZ$xOk*M#gh$;tyY=_!dWBmGa+t?<DDj16$@{AtNq&B)3s>U
zAnT~ZF(t^D$)WW+;hzb$pgFdxYfbJzvhfQ~Il5O86dC@gOn#zCWzjoVTK%}`?9PNJ
z2BkBF7k&P>|7t>zxmJvxpBqoNtI;Vt)eUx2P}jV<!9d=fi>|q0tW0M`-Tje0U2MZE
zmU3;k(W4^sPXL>)zws`wLQ$>6^RfQ;7f{;VZk1iH3BQ$kFKi1RE)SInWYeEaw#FZ9
zL%UR}4&-NB)p^f~aMKq-V0m;JP1fj)NkRwu47y^h)iWp(${n2WrR4ce%9^hS2z#Bh
z9vFJUV@eho-U<l;0BNz!lP5&%>xe!bEMg2UVK9C<G2Tc=O*ZqetR(f2?5X1Wz4DZ`
zh^K37P*$xu{MN$1yR;rh>ww_O;V(shY|;+fSVyIdJmVETu^pWh9daJ!YP;!Se~gG6
z7#NVvXpQ&t-ysSclz<HAL+R~>?MC_%oe8}_5HZohI&2{J^Lf9(VQKTq9Id$d&he~u
zdr>`#_7Ef<`YvYq0_GAS#Jf`b7ttT+9oIhD8(zpR<r+k!6gmIww4q4Bixk?mUY8vj
zM;CIj>QB!&o%-tiY1>FN7+%m=Lskj+(&6gsNpi-CJi%w&j5a4=P|{>RmV1R$eab9U
z3FIUfj~&roP=R#gb2dK~dLn}KlgWEqY$nrevKD@NXCI(dz=oC>I2|3$tFQrf{Szx{
zFHhcq;Eke_(W!rxau7z;WyyRMDh9zy(B(Rhe?`ccm9ck3_GWl;e8mhSJJ=2?7Ukv3
zZWs`QPnfeQO_}5F2&*k+SohBv2RLwG&TWjm$<92sc~h1!W<xqV#b<tHH*;eMZz`u5
zHM?wDxqf0K=<cS#bd<MDvhg>b-FahNXNupPwT29bG`+rwo+DN?sSWO)gxB4sR_eZ>
zI))@JcaaDxM09L>SQEDu7S!w9OPndzc<S%uG$BSWz63t4LVPZ`AHACV=4vV@Cqz=h
zDHS_45*~|Xya+0smAEu2A{snZDdjCmB&BO=0Udr}9~#`6V0g}Af;d847TKaRF@vhF
zDy1M1GC(L3sDo>>un8ZcAD~dSuY%1|p<F<rh&V7vsDGe|`c&3vf{u>qUuEcJ!D1w0
zSb~D3a*UQT7nKjK0n9<j%Nj#HZaJuMNW{zzn_TfO9!ZI|ES#_dY#grI>gtlN>g~+l
z5XRg4=0f6gCQ%<oCIr$#=v0(0q3TJwU_pAkcN%b>qY{^$6Xl#CvcA^YSLun`@Owf?
z|5Stse3-aJLQ;aP!dkw$13etxF`hrWmL>d~o5yOdsyxY=tUt&Lb*WNGC}IE!8A*Yk
zkp17p^4_tr&`CO3+{*xTGDR0i_#8X$1zm;vLR4@_)I9otdC-|On&ggRS1Qh*=pfhz
zB*I86G`GVdGD2=rIA6H)!R3DN@Ki&9ayuvG{3NJkXIK$t^67_#o0(YAuowWzV)i1j
zXDkProQMT8o%mlvsfK8@rHZ_us#rdvg0YAiLJn~!9kGLfqa>wdwKd>O<nhFekRp1R
zJf=){Fp<=07!n35v|7SrSkc2Il|2v0WrdqZRwgW8LyTbl0wN{iD8WF`-5L7)E%7>>
zQC$!NJX9=kI1WKWd7jOQ$z&V#ahftH9621*h&F^sjUAIgoB(#;LUl7QegI#>a8GMD
z)>N;Byu=$cbTp4sW7Z=9o}7mQj|83@#z9bUiv$IU6fek+#LS9#M9E`LdbvA-j4~eb
z2FYLqc-nHgVv+mr1X27%ps;ZcvlR_Vv4^84D{3sV<x-hwqoZeutEtUN7?$pGyx0>7
zP2^CovR_x(7i5DW40Lf|MVYTx`ik9x#xl{*kaJK#G@i>_wi}_MVL+>=0Bi8}NQp~L
z52!0Mwq&v{e)7A@hHzIFOS$>pjvzmYB2+LIhG)gdgcWrEby9DJqi$V?8G6^_^ZxA9
zXxGzfXV#N_htc@FhJl?}I0i|?-!D#1RF2A3)>*7N!_oAX-O;LLsi|+_O6@LJb0Mdy
z=C=LKbK+KUzNNKXW{#Cw5*c}<`KG$>4#;#~Vj!SX=^q)6?C}?Uk6Kj7SCxmoWC%CJ
z&-c}oljAqcy$cF$q^Y2^=$<9}yTRQ_$mcr)ikHSu?O!KR?O!h2ais5bdF+Nu4UxVg
zyVidBM`*t12!!$SPo@NTFAT+0dgiKGD??#{1Z$cGfv!<sMQCr`qZ#ot9LK0)jyH2C
ztF;g3ZbIklYsx(W<*uK^bL`Z0>}DMMw%xu@Ldx?_?Q~T4xAElLzi=opqbZp`bFbi&
z?obJVh&YBf_E(l+8c{$ngS({`jITHEG9hWrGX$E0cQ^x)!Fyz<p(zadK0aX%A8@k#
z#J6#vgs#l0!%OSx2RIVxaE?{|>E{SpgIHa8^cfjMt(h*mtTC{z9r3wV<3oo`*xA<)
z4s8vXWKz?Bi4v;}b_BKEd#A~VX%E=fDaMi%lY61L)<?H6zmzDN$-pa9cP}x|>0Xh=
zG<}*6dNXTJ9vmX6_u+=(UNn=)3wR4TA@?tu9-gSwU^qTUxCd6l``C(y12gG80;oeX
z>7dRTD0|?nUu5)zz}`qs!fEa#vVW2HK-qz$fVutEkpL$Dv+0kBxCdqYe|5L}2UO4}
znBp5IH}&Mc9u7<Gz<FjalXg1Fb>(agNfwj4LcO(Xya5Z1wR_v~o=C_tPL*Yo*=;mn
zruoBL*&B~xG=5Vi2D%46hk=>3WR}{ei8@wz@;(bNLA1)OVbSz0wy_|W&*9#j-zA(2
zZuQX%I1t*mUU~X<<qEZg_ng+_fd-C!s=aFfH~KQm7gJ>Y#IykqT{av23yvK%H97Ft
z?3u&oInOF8<oK`5h}A+Z5Lf6LjplU49DzH2k1@${;oWR0OfEDp_V;Dm%ZS|JL*8-V
zCGs@J7N(&wj-RlTcoiNFHuC6u4cnnMYaHx7ivwe}73KH$fJ4~!NHv7CFRV8$=kyL*
z3}9m-t_>dWvWM}3(fVC+{1?IaHSmfx@&xjVd6k(E&?}n$Ln;6pM2CQNMY>8;EuNdJ
z@z(dBc5W!BmWZNXfzdKUZkWhxiE^R3K0=wW2B|FChOsocP`+n<GSAamA1yaMWY$l)
zR#PpiF3i~PpZH|kkSkdQeGY@k_{8R|FO>h8t6E6OZ)rE;Z;W>xs4TO<nX|U564CHL
zg<B~wwKJ<M8*d3Ny})|w$S2^^>J*kaM;m~TbfL)x$$)D@&Z%j!$lF?x2XPT$dC2mG
zJ-sQdzFxBuqO(ZOZ!Q$8nrxb2Y4Xy^ywg6gjc#J&?k4D3T^HoFTKn2F)Y#pIE|vt@
z=#qcQYOxE-VBR$6v-iiIpHHq-*fqXScl{=c>8Jp|b6T*ms|nVee1>3iEW!m#rleV0
zIVBvWO)6h9FVzpv@KiQmHaBN|g<V1(oxozswSW+APVr{l*#7avV3g^m0D271$Znec
zvuG@BzDo{p@j<<6F+)3fF7Yg5>F{U&*T7D-e9B;<u&F?09x7CbCKB#iMlIwsnK$dw
z1X4IdJ!2E?GAF;uFx*}mxIe{?#RBKKFP?GLR9Sr52!uM+?C*+%GJ(>giTi4ecA^^5
zI8t@x+}aA6u{0#|Cw&$gmDBuPtVBSoAz!;#&`4|buc-{!Re|{CC3G%!7?c%NRgLc9
z^1P29EEdED?v;X`31XnAsob<aEN2Ee_z{TDZ=Y;IZ7vA!OQCjAnNAz{4J9p!=cR~e
z`b<siiu`hnrOSL9XB89F!|+ogb2kyx=49H$*rvt&6vO$g6`D8kCrf86<<({kpOn1f
z9%xOStH0UXHgEx?!GdK*r#9%!o$o{SK}Sly+?v4uuEM{{Xl*3%syR16-o`987#m?Z
zHf*P09Yz7Q{}z3hT*Sv!^jDfFUcT1M#%O9sx0jTKrE?)X1{kzw6$<c~px_0V@>h%Y
zZ<C3YDV^laQ2+k2pyB0n&iXN85~`{Bvly6ld*wfyUs)?)*@M+cNVY#Q?-O^1W&h6>
zi&QpNus>R;O(OTGl&qh{7`_7~oZXcN47G{OrIE%aS<=hR&*6pH2E8JvN;Sgb>zVy!
z8NIBgc4QkE*Hi{-bok~uKMZ_#sfk)ThXdkY4W6)|0hr?=5&#h|c%{HSt1BG{?n+`C
zfB_y$%hJP+C6xg^RxvgchM%wWw?%-*=LJ8asaafng8y*pERG~+_z9Ney12NH#u(aj
zV)6EBSh-BfanAZE`q!qWx8q-n<axYDo-|azU!Po`Xz&d}maiJ5$deGj6rff?TQ_o3
zVm*z)3#;Kk4q)nv*>F?R$Hk3gQ(=tLlL4h-K)P}D0ian1EZWf)_&>{;Jx@cn^pm~!
z2q2RrXJTSe=O70d?c)4xNSGMsNaG<xa;weeH_So9q@cLXWru?sZgu$Zgg^8+J9U5#
z=+up#?dc5R)S-|(ucoK@_3~gAI=7`A7-Wr$HUmH42GJAH(Sg_MH#U-V#<a)pb<*I_
zK>p;F)tdt!O^s_4<A2Lf1MlsU0dB={=<=C{aTyhu>m6fx&%q-Vtl>m@aQP*dU*!K0
zn(0RGGVg7y!yfVHK1{(8=}L<aJGy~5tKAj-{KMI~GqkU`6c_?wq(D>)SfvpK0QCIw
zmPn@{1Y(Q!%Hp&Fnzi+hVgf6}{$xc*S_>z5X)!cTwMfL(knqEFO2p+^CaX6dXt}#{
z&TH{`=E~&dc#cH55+?tuPN=LEwdd>6YKc{uu(J-D$uwyHMUOYQC7#*NOn;=HU7XYR
zce*1kBQg>qvlT7n!oL04GtYvwP^{JOfG(Zk7DMx4K;UjcRR!9e#6Nno8Ra5vo|r>#
zngvBgL}m*tOPB)(LSFP@4~w}@(8O5DkCS-zrIb|u9pk`q-_MuDuN!jkW$~%<fdLJm
zm*>gayz27N=pyc^$c%^}7%}@+`Oqb<a>#T}+zu}XcBjr?!yIk|Pq0LPBJW=B+?B1&
zZ(dn9IM*senPUrz%aO(#Jg0I2ksx~BB0hC^yJX{a_?FPNSv*0o9;nyEr3M2{XXKM@
zL2HMGD8|KoGj4=cnfG_AN>F9W<9QdZ0&P}YUyrX)%xnp`!_#3Ak3)L@{KC&iz=y~t
z$#<B7Y*I9k5FKv|v8H^FS3RVmg0|;@PY|vGod-y}x}Gq#*S;|$Tu92!asyiZnsM9F
z<?tJuerZTzdp};+&Z%aFU@uW1EjF!v&U-;7wCulxKa5RLjdYysj#OxTz1+|06$ood
zX4}HJ3iE|C<z5O|m_TJznHio`_VORE6OE~&5XxPIA?*A_j`s1k<P`s*N6E|2D?IlL
zZ2R*|lA<FNqPJuob>3xUz0Y89T^9;c>a`F(Yt5*G?|KC21#7pkflN9(k%@4-_)1Da
zfnw|(zjst+lO170RkQ@gi(7!$O<!{(v>BbQkfkbA%eN;TS_l`Q@5@f1jIzJ!hz{RW
z2j%M}laJ7Hn(=%o**p*4=~|V+_x+1j+{MkFH<(i;1Ge|eosM)<`9NHo(iqC^cIv{b
z-j<VOYq6eOvN!Y=b$+ehfubho1t}QJ^07mv51Z~2_+I$@qrtI^vg3B=$Hqc0`|qTM
zBZ?^+pnA6_l-}Io@!fery8q4Z<+q%ib05G7-J)-1AZdBC&Sslin_Co0rX*m<56kyS
z8y0%M{9%$ZFW>)tTc=mBl^bt$A(?zaL4P&W>-SpaQ2h%v3ULX(=f6>PM9bit89gBb
zNpMqJcf?s<9^{i4pU47lu*<>Y{pG9qu^6tfOJL6g>-v$LR^RZBM`ZG3(1A!-XETY5
zsruUb%VfWyk=N^&$A`u_G~#}%S1EUgNx>{`XV{4kW@a$zftl16e4Z~@#|Fo^&FjAi
zuH6wW5wJr(xuSs$Xk2{m=mO7A?Cyu)5s?kIQ&xWf>FF^qlPC3aD8xki%U15Zp0qJ|
z+~CQVv>QUXeVKIDqW8@|{RPKG4PqgDA66#I)VBMhUto>NsqSXpz4nY$f^TAWg$)$o
zS-b8Sa<VKV<$Bo>SS&)SeLyzZX8PRpaK$uyDLqMR1}<i82C5@R35cc@!})f+4n&mE
zNcLR?Ng0=(+;7<I2AgvYoj8FS_PK{HKOm(B9$!9NDbyLb(l+#oG!NU`k`$s5{&mI7
zB-|jT=JPAqV!Ha&VSjBGv-?tI+VU>cxy*{G^Ww346Tk=a6&blcPOfz@CZypBD&*kA
zAi~^E+GNH#9R0eUmx-;}#_Qbm2BQH}vnQ&IGSrLVx#@+AZ2``b9upBz#B%NWMnrpk
z-k%$Grz|z>Z_y(%T^G*c_5~&R7Xje4;n3aa0CJ=KKz%LJSiA^m)$>Dq;y7`5&jA+^
zQCaJBL(jr?vga)S1vZ}Ue%AVjaH1L{l$I=y{tiB+rw(}@CeYz>$U#yS>^8kKV!s=_
zb=KEEV8D@CvilC9mIuM)1AUG|cKGm@8`jKlX?*T$+t=8ZAFPtPb0ofYGxn1WjlF{d
zsaZ85=3Feb2&uNWH|PmjuzOz9BY4$EK#-$6KR(tZZT`$Mf+{o3!SON7`!OiW<3?Zq
z2o`G#Eo427fTj%1mQH#JuEjIHQaX&<AG^X5><CG_fe|`+@>m3D{_1<A3~pe35mS%0
z_x_GAv<SF^J$)NEE-p4ujqx~zO-ME~G0suE_?|2ALV=y>O{KXsq|1PS1<<wE`737J
ze1WerMQzbcwY<HO(o#NR7B>7;_I_Q;VRkRK7wl_-!hE<IS$OA=pRgW>6bXK4T0~GP
z4CIzeFEB~Q{>zW!iO2E{7$ADn_VPj)c|BKiwBElvkEJR*b84$%@DCsv<k7AON5Q@Q
z=>DL$fGE&YPei_i%=JABw*ZSe(L(_A@;T4WEHyG1yyNinFpN+x(WbNiiw}arT1R_M
zQ!ipSyF#dp%ppmAaJn4X?#qgm6!ZlKOMt-E{o>!<?K3LNr{HJ(zd0A@xkaqP!{eLK
zmm$!i3LO9wJjTg+NXUfX$C)>i;8uA^GhEh{*4;|)5y8N&+8iv-O%$7-8_bt4f#7<i
zw;Bbd#qZwG4<uCUE1f+_-oY8f?ICfUGU2@1HP?|RXF@(&AV9wEKszl;<&>QvA{xQS
z`uHMDyPj^I>mC?(g~7#sOoLPS>2+#?ohpi|nDZfep?1E=-&o}3ytT!p<V#YxF9O3X
z9h^~uhfWyCOngTd(&?<;kpZT@P|HM(8a(-|4zTVea9R}AadxsM<Ksd_B``j^dksU7
zixg3m1$}I+OM(rKA9t2fT0(BY!}I+!|4X`=0kbZ;G0L@0g+J`Bfv|WGWc%j$yKs+5
zS}F<=){@-A%rwUbxYXvap>Ke0yPCpHT8t`{b|u(-+g(x9VC=3dAP6?MCDpOI8%e5w
z?J+zpF7^#pqmw_zC~EbyV9{;^ncokrz@H-1zZSM0OmJXh2<k{=V)^~DI(?BFIPKi$
zvElLXn3q!mLD)LzcamY78bh;-;JNrHM1yiMB7b>Ccpc;-BjVs&f`_~VvOgW_9H3CW
zAB$W)4;i0gRrTC|i<8mOkxMb!6HCPMqjOnu!!PYt)>-u;A@&$je|%KkjqrW`o#UT~
zaDE8^R}@@;s(t;&qwyb&<Clh)%)3eLlbq;)o)fUb`TVr*5Ttfn&<yCAjkqQgM)rDp
zJUxqtK9TRq<8T7u<g#d4!}n)_d#=*->cj)bLMaL$od&cr$(;Q)MW0^2cv=vU(ZoN!
zB9U3B%O@k`nWl1csc(iXB<&_M#}Z$RfJ0Vvjw9_188UY(RX`SW%Y!1%U^C5_qGeSN
z&k*IZIo&8)pP!e52~||bWXZh1Ca8ZJUO92YckDv<<n+Qo(u6)6Zu(8fF1{|!i2KId
zrc`k2f{%F2#88K87ONs~AfCEI^iDivAb?Vg{Y)(vn%|26W9F_9P*gQJ;nN)k{SeX}
zRQ<c&@lTicmPK5C0_x{nKp+oXiqq$ncw#-73>5|1v@3}%sSFR=)N?@6@&0;0b1wA1
zf=A1yL}s54?mdH1=5k<%W;a*<qF5T(Bw-8Y;H3@d$kIj>QatbmyB(a2!{3y@e!(hA
z2n#6B0?CB;p!+15qg8JQHA|T;a{HoIRi)!)-{{*l1%)eR))&r4VFl-;H3$jTlEk<U
zXu?rx%{LQYACNzT_v2VuRh*Efq(`nia^R$sKFBSBF8;sv&g-iQL<!^43DuQeMS2U}
z$kKZdfgnYCNsyMHSz1)UMM}V{5K5#4kSd{tUIYXa6^IlGfu$OxcVTIQLFKahAKdpl
zZ{M8rz0AwZIluFrsW?3<K<G_pvd$3U?(U{rYnR|TW%4s}UWuogLWKMxoh?r2<yoB>
zKM0d_o0wn>iXDZKv4bOb{A^`yv4UV`l?_q(Zuf{5xwo@jvzK)r1AIPr0baJ(N&H81
z6E9qs`gi5rZ%(%K1+eBH3O6<+O`ei;ZR)TyWMpc5cA7wrg`SwZqqsZQTC^D~6=B@B
z@bN$M6BxERdxs|qUZERm#hoR4DKfM=OA1-NKYORWjdbwQ-b_EpNW>a3A8V5*Z`U00
z{Vvk2plyI61<@UsC^Ru`NWqHHxVoB+?Ch~Ilc<XqRE%;Ed9t2f53b`$$g@3Rdh`N0
zOWIiScZMc8W9pEjHng@Z5~2+>NDv&(*4dggeRc94<DyyRLayRGGV>XaZ$o@Z@V1nk
zt%6idz=yqigj(pWl*tFCco$;K;`a0ck|_YqbKGh|6X@bmr+CFyJI4RQC|Ai{dw73U
z-Z`r>ep1k8JGv>plCNUcATk4BlO}jLwc9sgFECgTbK4&G>lO+|=wPrZ;%Re%AK8^u
zm*(?1)Hs$k*s&2tWDVZJfP{2@N2cARH>4+a2%qw{Jg>-1R}Q)v@d}j-nO$CH2jN6>
zFspw-<7v?)HFc(T0U{c!?mJJptN`EVryS9^Fs#PBJ7Z@Vm*mlhzvflJ+Q&06cM+c$
z6dz-uiWIVfWzmsk4NQ2~zdA{NRgH}%B=5%J{r3=jnRke-@Zg_hf%EngK*hRH#&tvT
zDDvHTbdZd)3dfj@`16B_hX{RcBb2D(hIBw5hpO)z(ON;_41A5{bzp*i%m_(eJw>|5
z8vU{#?tCL=@nr8}aY`O`ayBP`e(V{FmE0a;#-KZO2&YSdx;D-_Z1cPUnT(?bO@c##
zsB6A7MOCkuv(fhjqb$-rd4i6JS_)+`J&x9rG5N~c=?muF()}N2G22qEgNlw%3M&l-
zsdN)>v}iWakiD$ix3i2RZKY-EjiTz~XqJNzy5$!Sw0(^0MLg7nObwW5*K=^8CFPy>
z3gPNvBeO<vSAL&7AJ^BQL`88MQa-M_9npE1V|23ux#1!4x_+B5)mRwdt`1=m9C6a4
zHt%*edy70Og`tBy_TcYx;$npchYg^aJ~FPb<@4#Zc@B?~`@807Gt4V}-wFkDPsO3F
z+vQ5;8#M|V>61DfcWsU)w*CI*Aa3OroBt{Q{qvNq@9ai@PCBYw$B;MDlaaeG?(prK
z=R8p&HC11o$+z}bc75L!u!g$0^ASdZFZTxX)CG@qTUVP;fA~mo;Rk%J*Ju#m>um*m
z`>7l}+b3LCit18qX42Bj1s<KT0*TvmKQeEZSp`MO;kp?2yJ?&mI9Cg!_Oiwbq4VAc
z5;iMZ@ZU{L@3R;^jMA3%4tGY0mMA()MIT2G1CUmxkHnO#AW2E;{VJM1WMv>YCYwj%
zXX)vB$9h&EfHIeKWotg}bVQW@#R{j<V7SWue#4FOj_k@>B}zEFV{YmqA3@afFyBea
z=`GO+84Ne*AhEa~gtt29luyB>f6aXovChtRJgiMLyO(I(d#+>GvC6sG+}bGGBF~c1
z_v7INvuq@rOyxfKQ2^>@mp>>Hv_#Dws;We$+U9p$849#D3I80z@2n#oykuMF+k2U%
zI?P&)LVa;tsy);rrJu^>^=nbGE@T$!eZ=#+wKnX)#(=&7LTqYl=B+@9oe@n{dnMf`
z=-NhIEq;J2r2KU=fP`K5`$~O8?v4;pvJ}v9OCQKNMmTsz3b{HSK$jPixIIzCQulbL
zsr%-TaUMt=?HT*JPa(!Iz4DXMuv@bzrvlsJ+NzU-bBB~{xa1ut{HL2;sjXBS9F^U2
zcotuZrIFfN&2V91q{_T|jrA&|{Oy|?9nGX?QFE6;^%4sH>Sb;Ct1Y<%@C38MkMF$T
zzX$859K5X&hWQ-2XJ|jqBjt&lyGvAp*R*aobU7zj_W}G5X%It?BSO{Fyl8a5Bw{p|
zV&>r_R7a$4#=n+~!AvT8ENVkRpoYA}ot3>zd-0;5?;0N;+H7!)|8ZUOYJ2_5X>mSo
z{e$l-eX<*b(EXL!k-UvDbA+vq0NB?U*L$j@irac#8#G#4R?74XyAFGQkWaRLYL05+
zxwyl02F_)HR`?}4FN@CcQd)4Ac}~_SUhPXfsh>w>3M})vlWD--hsE#%bg~(*8JDG?
zR-~7rOrN~tuo4?h%}bsraLvG1B_k*np}rkl9hhD4-b7HN!30Cls80!L4gd*FwMxP>
zG$}yJOTEoBA8{PwQx?`bWfW`oD%-WiYKv3Nz7aUd*MJHqmkrK#;4jwk3u`n3x4CH8
z_~EFTqvtcuy01il3TQy;!z;FR`yx}P6#kYR<HV|JhSupAejHuW!vOQHCZHuVY*{>9
z$@FzJdT|C=8w}MK0lc^yy7Q*#J6d>S^Z7NQ<@@EnO9}Y)4^os~iJ;L!^IQ1qYxQo4
zGuSu3J)jFKZzM~F(i!W#4457)e=@1OR}s?B{=p%1#T{V%Nv#rp%h$sAYMNRaS1wX^
z%)5v44swoXAUSlW+aV$S!CUL+CsP?W$8Xff^ELXv=W|%Aq<BED?a&@K$_2{Ejlc>!
zv&=Lb2b{#pe%qS1(R^yoMk^VH8req<pt0)?=3g2`jqfo`sy?$AOdBG*{zU`^Um?T;
zEki(_Dyk%1h;e9|d;0w%uAqijW7uM*JIL7tlQ}DK{ivd>Hs;$eTGuFgzB~JF6Ky9_
zk!#WhhH+Y1OKK(7PP``zN~e9eKE**>3s|x^&kN^bT9@FxEw2wgqe%jGKM85`j8`WX
zrt=?m<!8W1gHcB;VljnsBdoaUr+F>>D(m*duPRN7ubSTJ$BKd)JWQ_7ILI4~7!EG(
ztj7`tuqMJ`{#T&t$43@0ISH;<V?)tfGyG5XH7NdsB*}F)o^Pj!!mTS`%0m=DKrM&(
z5bAR4jM#hlH*95?$6&=r$r?6-v>m)SG-n{PvBA*>y?JR_BnQPaR-we!Otm$8D6Abp
zleKe_`>A86wQ;F{BZK{rmB}T!aH!I0Iy7~2qkM0UWBhvmjpQlu!Ax0VV5}|p>WrN2
zsDUju&w4n|p=8sT6C>EPDbqL^=m%_oc7ev5#ii(IrE=4<pZrYK?w*_Y5aTfVSe2Hn
zJTIl9W7r=ygHpZDT@iw#@;7KXkJoi_x+KHrktY-ogrd5HJPHo>6hRBSwI3Ht*Yi?a
zQyT(`#U6QTjxD|gV~NYRq=r?tTtatWM#b&d_Uv?=as!)eAxh5RAG_UdxkWO6y80NT
ze9X&bA)GhMdnW=N8?Qm!oHS<p<~67I5e^c|>qkqAC?hwlY~EsEFE5-cxQTSa@a-vH
zOTTZMQ+;<Fws+1B-mf|NPSzQI+^+h(7vmgp7sXrqF;)xnCefDlF{tB#tOxTd`0)?V
zSkR0=-R@4B;+_7<J~PO%WsKyvGbnBt>GVviXegF%lEfM-UTT;i!HcQAId^mh{l>Lu
zh0F+w%T0fe(E1ZrAkms$)Rzvvxv(#v0p3)4E2u#H2htHCHpI~IC569MVHd#epO$~&
zC5V*20r9ipzm(4E`GqXE%-IhAm+18a{$|$dXBwt0U*J4@zgG4O>2bGH?f=&QWlY3#
Y7PtOGjQ1zj&Px|{)6(9e-pnuIznQE9^8f$<

literal 0
HcmV?d00001

diff --git a/Images/MACaddressV3.png b/Images/MACaddressV3.png
new file mode 100644
index 0000000000000000000000000000000000000000..3064c5a28e6fdf1de0759b7898a45296b84ac490
GIT binary patch
literal 8125
zcmbVx2UL^Ww(bw87(lv$N)w1~M5<DygH$Qf4G<KiOAR$tr70jyBs9SwNHal7=m7-;
zrGr2yl8BH9grbzt;fB5Mz5BfP?m6d<#~2x`tjvF{x#n8mx4wVQXC_8EXBaLp003}C
zPglzn0BBDE08R2~T53-TLo%HD@0#B&Yd=k2$NPS+UY-Kxt{#qnA+;eTD`5P<Pe58y
zQdU4pN>N%)@rIOum?ZVD+Chhd`W(HVp5ZO}F$Pu!9%ep`RRL-j$XoX=3;-C}k3TfS
z)8YHn&J)h+hUx%NnasF<|0DqLbLwfSn+HuUPb)v-k(>PRdnkd^lX)DVG5U1&qCiZ7
zXkowACrAAT*n(d}x6wj(z)Qb7rNg+=7mYAjWISE}w5Lx@JY5ZqX~_#F9#$@z4e1L`
zNm(q+&au~p`41yvD*2XucY1sKHGNg;0|NuC0KkOvh^vtnh_p_j`gK8s26&zNuN#wB
zkfK20eJ`&H`1CF}_vR@+mZ%ZpxPiQ?YQ)-{-aJGAOq^+PcpK+a6vKQvZ8_kk|I;bY
zIw6%DD}9qM-+%q`-sq;)-#EqEFx1s(YfAeC=oxQvKCGim?&J1#QRKl!R_=!#KE{Xh
z#SjrMd~+!(b<}Nhe`kHrLX#HS9E7A~_<k=@R117%P;zMg%1Y8$a-6HNdFBUn-r~u4
z*!HiP?d|&7U-R?xqvclg^z@Vg(y#%b9pqgR>XBzO@7645c59O~bS1yfxO9#a{!&eT
z2ODjQs(25rs6eANtQfwO@DAD}uTpATi4{(l`iNQk8Z$$lb$k2k1La4G6c+u44fYZJ
ziAS=|&dy2Xlh%pBVrH;Q#T=58PiLXxB1kQV4{L6AClVItjW2xAdXwzmz{Eqt(eSNo
zY%`WrZ#E>S-SL6#i2xX=p6zrh6s&kG1Vo;E&ILru;vTFn&6dUZ=oqISy11_X0UMq>
z1J|8~n$AkorE^;0HoNZkrLkY*@(IN9S@|;AqOFY)%?9>`o)V=#S!^-rClPF(LPtN@
zv-bpXmRI!g_lusc>FC-%?J6in<Rl+ROLVM+ty!%L-N(HO;&EgJ1*2ZL2toW9c-QlI
z*D<p%THWgQI~p_|N7m<Dy5y6$_sr806J&X&BcZlPb~2sAe<+PJL8Kq1dZ#cN-Nk(U
zM)Xd{x{y|E!Sq&vF6fpH`SK(tb|jU>bVw=cb8ODbqpv-E<OFz~gO^w23at`*uyyM0
z#Zu+Vmc6kF0U>?E1|^O$c+|L}_rvpsZg~;RE-vp+mn#euB^q{yXJ)w6S#j!E0BTG@
zOT2)Z5EuY%n9}~$2!~y{0J}z~rpL<?lAP66As;`dVX+Er4YxZG{erWPI3ObIRYE9K
z>?*xz#1?jf{Z;)G_@up00E4<90C=7b{F}zBf>)HM^;PkralpyAjNET#ZFxw@kL{0l
z-HB3Yl9Izh=jb(kZQH&B@5+k2ieE$OpZK#46zn3hCB!Ab1JXlISKW;FU5s|@#Iv^3
z3rz;@y2V}=LKjd&lU&}wk3R^{xGSN$88f?hrVNs2>q6R^MBIL7ol@_+3eN1Nr#aV}
ze>8z(8J_0Zqw)E~V}<J~_byYuj;x1Am9+>q3^p>9wz=}@nS#m$z_-oGoO1SZ^d4{h
zil*IVuthxgZNI)rP(VK;Ydy4;iW~a|>M{SRl6KzN_AU$0%j{Qow^%G^&t}M`?%>e4
z^^@RhR7+t}z1B+HSDYRMxj80#_@^IXSful{rfkrsXb+P6gCkEPCUqZ7hL8ZdZTum|
z8$Okn+T|!>sOEx6Gr&6QH+K=GO1_1FP*ZQoF<gv(qfO!ArCp9JmTtCqA&9*Ax{J(K
zCq%v`8*&Y=X>%D(4e46Ja$y(0m4}~y)_s~<9H+W=(fC8NITi0YJC75C1)B`A_HRT5
zXR~L0lemARHDaB*>7}A&<A!{8&Riu!H9Worbhs*au~<&M+Z=xM1EO8y^Xwb6kI|{~
zOsWodcEw8$>WYh_9ooA6k6!Pce$yo;CW9Vw)aFApFUw$4Tr_j6UvJg#pZfHH4N&XB
zBEIADlIgE%J*^)Wlo9`k{KUb9J&}K|>E=-ve}ruXZtVwzdrA~lpTXpD|98hNeE@JN
zJOM;%|J{h}b!CpB#*^nq{5dY<VKTAhSLSL|U9=h22)#BDYk6{hJCi6}qcUY-ZR=3W
zC8F*g#-qR7Y9?E|Co~ck^B|*^%P+e<M$<s51oDN%cwL!u-B?JG5cSNB<}IHUO!b|Z
z@eT1)N#UcmD3THbAAeOXcMo<o5Lv8{d$Wc!s!GdEMQ75gO$e><B#n&=8*P8rsm424
z+@d;VvW+yyjrDr^`17D3OWD{08!K*O#Xr}_&|b`^^OpYnoV5J7;+`FB#*UC1%$9`K
z=9{c9*3jk~Nyo6kY~KtVXz^KH@=+BA*SPZr<ZqV7Rd)_Uo<GA*zDN3dAPS=vv$g%;
zD8UKqTU~6DL-p|FB@ybdE>=^*)wMZJFTdP~!@Ls^Dseb8?5wE>*W_U361kM<2%)(R
z%QP{$Eu?QN)$!@9&iZ2C;(F0RL$U}`BL{=z^bYBR$JmS?c)v|+@02Nz^cgb9oI@`=
z95p40{D%MRT@PZ-SLwT7gPb<Z%#faZt%QCtetb>}sdI1V`;?EWm``E~x6Y_L=n2+5
zJE?tfq@(RYlEET{{X7?)&OfCAhlcm?%lYBE3(LzM^AJESsC;yhMCv@`n-Nhj>*?v)
zgdcqW7&UnIlK2ln@jHL=3G82yipulM#n1bn@m>z)!tWymLN2#XTrky0R=@b5yY~ul
zM0Zpvfe{z_mOYaAJLlDtV`_T%HrkCebJbb)W*|zrg}Ql6ofP(LWqsKFYD-LoOZY1T
zaMKU5cZR1z`ldzcdO!LMNNo27n13-nf3p%MVP$5HD1QUD3%PG^-5)VMDf&nZNuIJn
z<nX&52zKe$wtO^&8OYqmurK|l<!e~o*>^I8hF436fIh9poenefRQb_|t#)NOVCcb1
z7PS%k&Asb&-oXP6!mFWAm%)i9s2^<}N3e)$dt|I9w{)8WB(RpG5nhdNMWp|_8MS*?
z{gkQOtB(Sr^qDp=XY%CwPG4gO*=Xs*(CMh!jM<#9Wtu+U=2aUCviEN5g?R@8)c}6f
z=QL^HvaAuXDQaWYh~<DBjE3eA^c&M3DGZERtfSd!0~%8=ZYk#z-W5grwBGFp_lqMr
zYC#rfGmSM=Ppn!ves?*?gaTY=<ZSdWpTB$sb^qM@${7LUeJ4ihrSZD$7~}uP74qMA
z$^WKNbNkBV3DQ@^yP?a*w{7F)sS3%f(dnKS-$qtDqmLjMIDDz_A^cl0Eb_FEIL)s&
z3asW>7t*OUnjcz!)5G6{vG0<1o77Jy9G751b5y?!Jfn_)zu~3xIwG=Wsj`O_(9bpa
z+xq|Eng8#f@}to}#^6o$W;k87_XE$RxZgdM2=DOl!?`an1~Jw~JJ-uIReO$Q+aJ)T
zX`^Mx)bbdyZz+z$fRWdv`P6cpOmNYOO(7*EB_3=Q`>kqsJ@Gy6y-OrnIDZcqJhvmn
z<F=Z&Lzr}5w_g{+T(%*%z4Kf4T8a}NX9+uB+4_aQ!Az9cmm`Ofse+*LR3rEr-Kv9r
z|A>i4H#G0w_j^8L8Bx{=>w9dsr-6{m6kmyfS{(jgdag`jAl&*rzj#{}KOYazxN44p
z#3c@v70h92AXD8=PAOh#x6mF}3}HFP8g(FfKQLJS=FRJ0XW>|8yEpeBa*Yk|cP7_x
zV__n7*m7Tge@8UysQA<)S<=OE7SW}+^@V8`G2aDnx@Zw4s>6@eKVqA?Ea{0m><pLZ
zptM^GQR-|>HADvZ=h6w9JFrIBJj#lYwmeWVSCw4J&-^J|eq=uE_Thpfr)S-1e=L$>
zcEcDr$Lq|vT+Es(Mrq$pWgm>XsLw`6X?L8Hp~{i>0>5d28}NUik4Qpv%YY~JuHDud
z;M`)+ct|g2I6R9H#~OvJu(vv*Mb`7MoR`-h4rrQQ4nVrA+cq@S*VkjQXY3e{eDN%L
zAU()5qaEXq?&-tBa2$v6i;VSfU47l*W$YkE0R7F*-acY~Tgp#tzbBZ==fZk!qS|h@
zrb7lPap@_-r6}%FAH7==>U^deX@#{8RONIQdm>mDq?a}spLzjdzQI0s>3{uD>*V#`
zU9lDwmCCc%9l|F`H<$Byx!xJrqQJ%kZw&jnasCsp5ds6$*9lfA%m|momvE!Nx>}ml
zHS6^$(I$?uYZL<yzL6mW2d&NF4O|YlBf`$z+wjo*+e<h|*0HT*SUc-#FL-pSn`^Xf
zF{&@-)`P^h(5QyO{feE^#aD50ahQRCFjx1@MnR7jzM2e3lnmL#CaX^lfx3=hz{Pvm
zc@hNe%5k>P@<PW~KPSqmP8qLYqoEj0?P``gVigCa<bxO~om)flPFeh=*pJtOt0!#j
z?CxHDkSFGX!t=P7l0}yyWJ=#o6z7L8TF*SQDRkeg6|Z~O`Dk5TT|!-u%Jyi@SmGH9
zamD`O&}jSu3Pqf9N0Ah+6836+46oZ0RE%nOUQ_2~6ZshBV^w^ErDcW4<n~F}@AvKV
zBO43@Y`z-hK2p@^&)Bw^quTqTRAdTxwk&(=7h_CM$zZ3Ib$XP4;?=nR3Kh51jc@+<
zHvK<r;tjm7jfH2|_}k(`TrjVj2_D~X?PvKPkAy^+sE%*)apbIe*?Hc1KAA%pw@5mW
zp1{wpJzZ>gSE9c28pDj*ykl6MlXJ1lYa!mnM41;hH9ziXhK*kH1z`8dw;V_FySXR{
zgx;fF$Q0HJl<m6$PGJux2F2HJd7!qGCyo>gcpR%IO6&r#&_IiUN?K_D<Ehr3-%&+X
zu46f%rBAiutEjoxavPQc9AyX-`C<zM<Ev%5GBc*evz6ai)_WNmSa=!|-0Ic-I;W*j
zjhQhT6mN`B>!lk%4^p5{%1dIIo9i4JS9bK3#e2(^z=_v4hG*0R9RjAyt-0H4Gi=yk
zc$Nh)(>=VyxeRK^9DF8PZ@H0|sH*PW^R+J~6n2IP)L~UFIw6dAwWEfRx8N&UFmf<>
zQsk1JX~okN>Zvz_gY0@W*Eh`iW>wPG!#{tx^Pk|b7&Z7~SKNUQNv<l&R_NE8Yvvy|
z&`m>F-d;AvjWIWcWk<8Ei(g~qI(NMY@!cJta5Ji+{x6hf<KtbSZ1Ti~pqAJ)nCGja
z&r4v=vFUq{ZFs|o-2GqyWj-6JJAK@#F!Hrqeb6UFHLpCyS^}$^8?@0){E^sKV#1O?
z6~lA38flkn9Y{fW)d=KVu8p&ZDK<Jk;;^B1&{wE%_W6&f!^I%Rnb-drhW`Tm??j=0
zfVC3H;%~%{reUMOUTl5;13x6Axc3s-plDtucCK?D8cqb)|M){M+(wFWUMM3JZ{Lrq
z<~(K+=$|fSE7R?Xy|#<Eu)zf9YMS;W$<XJ|gFkEP>!yuZCXQGZo14}lMhFN-D%ZTN
zbJ)ihERlJl{_GF&-(XMSmOyc@vY>K}+{(?amQE!=Y1xG^XEh}*iT(ZDF-O%Z79ESe
z!xq~V_B3ICCab^f4Hj2>{@g<wXVcj<{Y6UJg+He%tnH`L+RKq;u#7lYVYING$0yzA
z2Gj`*+-${3VGj+xBevW55BCU1vFMXPbR7ic>FK$&%9g`!j$2(@^Tk3FnuEUcA8mK#
zAMO9_b6Ov@vX4P07!xnQz^T2sfD33+mX|+GWn=1FIhV9q+rmPrqGj2;U%zJB2mP~#
zngUn(l;M6MA>{#tL4O*7$2BmJ7op`~`}czhg_Kt;oG&Tnn%bBV-<IlAkvhI`5EmEM
z-rn9-{-;Y{Cb1Dm&?2>$eDmT!riO;T$(e`#x6Q+7Q;W}h!-npxHLbQ#sUH9tgnyr3
zo~m24f63DQv0!i%q(`J1)q@}G`p?G$x)yEN=&xVDzIpRz+Vjd`?ne{q42y)WWd1nx
z>BtV@EOu_iz~8vm!_7GU@XzZSiomRJ2D^E_n#^O1pRem%mj(U&9kzbYcCEG^<spTE
zBvXA*S67z=w7I^e1y3Nb*dfv%)!Dq%?@?j^&bbywzj*P&c_6P;Rzf*y2;UO6x3OT>
znF?5Ugnv2?hPb$R_k;60_u_Su`AYpu(%h*oKDBDbMw7|p_4ReHmK_p>lJW3+Pe7A}
zD6DSEN0~kHt*%*H9U*+LokcYo9dnyl{k`XkP^vpkX2C^yMHLlG9nrOqp_Hwy@`ON(
z;!A%1fR=CpwCy<ZN<kkYJJY(S4$PTJg5tm}VUd~LT?lUuHLb+fD5x+w9kMZ>3h&$0
zRU@?(pWquiNB1(#;wZ6&Hl-m&gql~{o~U-tok{E>halZ)1TrN>E|Gp#EiW%yRM=Q0
z!8KvQ<Tg$V++i#=l0c`ePa*Sp<2<GFTVZu|b!D($+tB%On{*vYd=}qQS?Kq}RQcuA
z)$&kpI2;}vyl_}L?O7ZELfg}t%3TAOJoF9<lHcvBP^pKY<e&4j78~cQgl_*L_8?aH
zv+BKor;pwBGq<Bu6%_8A`BGh7J@^}59XPP>wQRrc@hpJ1WpAaa%IfDK8#`S}%^{oj
zrclH>$#9tqePM4PUo~*&3H9)*E)pbKwgvqP{mZI{;Z9RTTaC@)MNW<N7^45zPuRio
z!mq}v2Ze_RU-J(~He*@%Rc>7G8X6iZx2llNWw@w|OAM4aTI-QG9;&sTOR=|+ek;3&
zcL1Cy|Kib+?i|7{j56VTm4BZKCT2S};Cd4b77*YhyER6}92}ILS+VvtcM~o2KgeV(
z$MtcIM$n^vW_rlxQ0Z%DHR32_-Pp>?ieyx@OTXBMBs1QV#6dN7get(XmRGJ^!QpU9
zg(c>wc`f5BncapJ5IW+S0o1Aw!5GkT=^fqp`SWMQ-V`DGXFQ=bA|fPY?--}dZf%R(
z2Tvnb#ZI^Mg9{y-rdOyt&|%NJWf8kgnetMA0fqeoKKjEvtx3fQcP~jZjiG>?Cz+up
z3d+hB3-(~TVL%`@?rV>PeM{)C?~fBhP5g2}^I@8sjSb&S&4=?;!Wto{Zi6B7RY~f(
z3z95Fc>DSmOg<ZJ-%2gsS|yar(uAE<i%b<!=RJw8s7Ovq(!lXoZXfQgw(gK}K3BXw
zc~9;#_*e7onlwLs(B~KK`X*ttqW~4$U@JtM(T5mkwc1^?CvLl?5d<ohwtvsYvGB*D
zC5Jye<W`G^o3_o&TpEyQcZt}a<+nJJ_7el@7xh3bp6?r*o2f~Ruf>$2+?wuGzbL+x
zsPp_G&ZdT+e1Ul1s;l&BkaPMVD${(;$xVM@s{jmZNIdp+x<>7v2b=UK5&>OCY_y*F
zB`CTi6~Y`@GiZ?c&_CH1_$+xDe8V|v!o38G#8}`J%M*)IhKRRIlMgEDw%&GOLKP^g
zJ7p}9HK9L>(($g&JT^q^^>HS*X&dxY{z-#*MZ7}o+mKmEJx_=UADK=->`uOc3!lBi
zO~l}2b^u8Dk-{y)%oLUfNJ}&Zp&p#HqrYHK9y*vA^*{mJH>$o_neOCj$P(|q<GV!e
zvI+wV9Utodk({<t-5ZGMw&^(!7e``p*?tOZcZ`l^*LIC6@vvN_+MWA@x-#uNgZb?A
z`ts$8(W`;EOW|4yv;BJG<gX*6*`5ba!Dw}&^;Ow|+yx&RR-r~1h!T}Vom2-MGXM^f
zC>FgFQ^o?EZ!S6!;;vY`2l`eKc{Oopo#djIvIlg&n#!zD0Xm+d5#Nh)@u{9$c<mw=
za;Q9Ytz?d=ml_-FkxI{%5GK5{JoJbr2odA!_ET(n)hn|!7ix!EKyLb6E1tOPqjZJs
zsyN_!{;NR==2dx2MsiJUcT=~kIFogf@`tgpji<Zv88Dn=qY4E@5~Ui>oY#5LAsfJN
zL)}2Zb=x)v;(vY9khq^V4k1>)q=tBd{qi%E1>dpJKXxl~A<JfIjjKPpXs&n9%*+Hd
zov|Zt@bK_FlFb>!M4nLku5yzKEAl3USL-e38ubon-6u*M?c!SZjfQ#?8E4<)zxQa+
z={)Vq3E^N=pfXcm@TS+i3$U#<D=1Nx@N4c1$EHK|MB>UHCE{&A^CQB;!mzEa5&r(f
z$~S86fjnauLy?FSL|KGn(VN$b^jG;gx41lq?f}9_pVZW{1d|K6J0*>$i4g|@oG!~r
z?eHjx4R&c$^7%yaALe$^?7Sz{*e`H3l=!phYu;qQh--X#p!0ZaMgE=Kc3WP0!kED!
z6>t6h{ofcHnwyoBmBsAKjXJ4h){Ucb>+y@`hJGW@WJV}Y;MKTE^TlV|l~){pk}Fyd
zsT`ND93oqptH7*@bsWQ|bR)f2;BTw=SXKM~%1c*0U{<!Ma9@8^*HTTf>WZ=B2&7}X
zpfGe<z*uH~m?usOQPa{_^*%Q#{V}ibV<_!w1wKq)urZeNbVn4FR?C$)KWlaH+kRt}
zvE<ek?e$AwwRCWD8%C<YiI-bK!<Sp0HrAEG)Yz{}_XUZFOwVkW{xq(lj!HrDOAUb2
z8Qmj1c;ySaB85|qbrb!V(<M^c7{_N6fxK;$Mq~Q%e@=hQ#tS9ZKv1;MsMdX%S17{g
zq9WGV^vG1I;AuuIWIkxaGdsPdvEPXtd7>-n9o^x*ae}0&ujcUfXB27VG{u!2qB^x}
zblF^Oky~CPsdwQ_JYhJ|;OLF}8r@~{NG7U=dxO7X5hRieI-9}+2eCTN?67N(#DUJT
zUN@!HdbTLg6V0H@Os5m+e7hvlZ^!^xgkLdtsa?r3p^>I8q)<d>01fL&g7MWybP&zH
zw$ms%d_Vnf>r?-vhV@(6T<akQBK7LSX*X;Pnl@ShEFdT+Cx<Kt9zH)V!Tdue_g`mN
zLa2o^Bzf=$wUX5|O%;ify&pe*bgBl~G2UyYDib`k@GRZ6aj#UxQ%V23irSwZgLBy_
zOyLQ<2RO9c9%?4kG2kp7Q<mc|i3lo1ZY@upUHV3#6`px9c$T}ABALaMFLvF&nd09x
zR|Z{c8sT!R=c17TZLz<PB;Bb}@{2`tMc$puV=wc0Hr8&7`;hL4+^aXk$;sMV$t>*#
zxpPwF0!LwwGpZb!x^3|n?vV8*Rfi!}v)aN_G^NZu!FPBM8<YA+KUQw~uU>R2b3=KD
za3-mXb(%hgBw|u?eRA4~W{edy0!|HE?D|nji72mtYe=^^W@VnHW`#|kUp|}pRSMb>
zA_6bRp&IUFuQ5Wz)#bkLo3{0|A7@Xp3(A78ecLTRqaU4wzZ}#QG{bNrnM?OHolfgo
z5bLU#TT23Ooc7GxA?dtT^%|y?`MG<`w~3HNKDBc55D+G6X#W0uRmzxwVQ&nk`gLCn
zr9it&1{|-h{v0~0$;fkuri-Pv1xYruwLMW2zk$ysO2nr6&}Sghs<98;;om3FiceEF
zKd>!p0g^?E-gBSk)f0!x*zyLETNN3fMHlR1FVPvM9x`o*3Eo<&)3qBr&K7eVmqx+)
z>~C`gDgN-_q+qsh32X8n+tWr*u_9~CXz5=UtUkmXRJ>CI-bS0z7Ow#xDx%Gv5U4t@
zBurvmaGcTWoJEfAz3v)@sLR4GEq#@ffNA<>80h7~0AXwIxn1&EX)kw|g8+o<*)m)b
z2YY=5M-^>KV{{0}KsrBbb~}8p`E-;KFv8bAd-(C0S5A{PP{}rBkm=QOVihMM)=sqa
z=n9_5cqbbPer5<_RI>8?v02eGP%(IO?q&No58xWNyzj7LR?~q{k}(718b8ynU?Zbi
zUI&H!luyJ+%T_R(8$p`jGbVkYxnqw=EyABYDd3t4os^i(qdR}+1FsEF>4RhVNqOH$
z5GLIMA}^S>UDG`Um{O~m|17y$qbs3dhq6=>qWN-X&tv{7@WT5@8BqIkoPSeh{%;sh
b9?^|I-uzXl%+*b;mjimYjI_`i_n!O*D;!p7

literal 0
HcmV?d00001

diff --git a/tutorials/useful_regex.ipynb b/tutorials/useful_regex.ipynb
index 96bf73c..ad0617b 100644
--- a/tutorials/useful_regex.ipynb
+++ b/tutorials/useful_regex.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:295b519fef2aba4116d805cb3c00fc582bdc1a0b7da29b1ae36415f606592010"
+  "signature": "sha256:68319934ddacbbabceac87b1876ae59e0d4fc3509192906a9483958d08c8d839"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -41,7 +41,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: 06/07/2014 12:24:58 EDT\n",
+        "Last updated: 06/07/2014 22:50:23 EDT\n",
         "\n",
         "CPython 3.4.1\n",
         "IPython 2.1.0\n"
@@ -104,7 +104,8 @@
       "- [Checking for numbers](#Checking-for-numbers)\n",
       "- [Validating dates](#Validating-dates)\n",
       "- [Time](#Time)\n",
-      "- [Checking for HTML tags](#Checking-for-HTML-tags)"
+      "- [Checking for HTML tags](#Checking-for-HTML-tags)\n",
+      "- [Checking for IP addresses](#Checking-for-IP-addresses)"
      ]
     },
     {
@@ -871,6 +872,195 @@
      "source": [
       "<font size=\"1px\">source: [http://haacked.com/archive/2004/10/25/usingregularexpressionstomatchhtml.aspx/](http://haacked.com/archive/2004/10/25/usingregularexpressionstomatchhtml.aspx/)</font>"
      ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Checking for IP addresses"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "IPv4"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](../Images/Ipv4_address.png)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">Image source: http://en.wikipedia.org/wiki/File:Ipv4_address.svg</font>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = r'^(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)$'\n",
+      "\n",
+      "str_true = ('172.16.254.1', '1.2.3.4', '01.102.103.104', )\n",
+      "            \n",
+      "str_false = ('17216.254.1', '1.2.3.4.5', '01 .102.103.104', )\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">source: [http://answers.oreilly.com/topic/318-how-to-match-ipv4-addresses-with-regular-expressions/](http://answers.oreilly.com/topic/318-how-to-match-ipv4-addresses-with-regular-expressions/)</font>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Ipv6"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](../Images/Ipv6_address.png)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">Image source: http://upload.wikimedia.org/wikipedia/commons/1/15/Ipv6_address.svg</font>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = r'^\\s*((([0-9A-Fa-f]{1,4}:){7}([0-9A-Fa-f]{1,4}|:))|(([0-9A-Fa-f]{1,4}:){6}(:[0-9A-Fa-f]{1,4}|((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){5}(((:[0-9A-Fa-f]{1,4}){1,2})|:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3})|:))|(([0-9A-Fa-f]{1,4}:){4}(((:[0-9A-Fa-f]{1,4}){1,3})|((:[0-9A-Fa-f]{1,4})?:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){3}(((:[0-9A-Fa-f]{1,4}){1,4})|((:[0-9A-Fa-f]{1,4}){0,2}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){2}(((:[0-9A-Fa-f]{1,4}){1,5})|((:[0-9A-Fa-f]{1,4}){0,3}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(([0-9A-Fa-f]{1,4}:){1}(((:[0-9A-Fa-f]{1,4}){1,6})|((:[0-9A-Fa-f]{1,4}){0,4}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:))|(:(((:[0-9A-Fa-f]{1,4}){1,7})|((:[0-9A-Fa-f]{1,4}){0,5}:((25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)(\\.(25[0-5]|2[0-4]\\d|1\\d\\d|[1-9]?\\d)){3}))|:)))(%.+)?\\s*$'\n",
+      "\n",
+      "str_true = ('2001:470:9b36:1::2',\n",
+      "            '2001:cdba:0000:0000:0000:0000:3257:9652', \n",
+      "            '2001:cdba:0:0:0:0:3257:9652', \n",
+      "            '2001:cdba::3257:9652', )\n",
+      "            \n",
+      "str_false = ('1200::AB00:1234::2552:7777:1313', # uses `::` twice\n",
+      "             '1200:0000:AB00:1234:O000:2552:7777:1313', ) # contains an O instead of 0\n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 21
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">source: [http://snipplr.com/view/43003/regex--match-ipv6-address/](http://snipplr.com/view/43003/regex--match-ipv6-address/)</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Checking for MAC addresses"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "![](../Images/MACaddressV3.png)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1px\">Image source: http://upload.wikimedia.org/wikipedia/en/3/37/MACaddressV3.png </font>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "pattern = r'^(?i)([0-9A-F]{2}[:-]){5}([0-9A-F]{2})$'\n",
+      "\n",
+      "str_true = ('94-AE-70-A0-66-83', \n",
+      "            '58-f8-1a-00-44-c8',\n",
+      "            '00:A0:C9:14:C8:29'\n",
+      "            , )\n",
+      "            \n",
+      "str_false = ('0:00:00:00:00:00', \n",
+      "             '94-AE-70-A0 -66-83', ) \n",
+      "\n",
+      "for t in str_true:\n",
+      "    assert(bool(re.match(pattern, t)) == True), '%s is not True' %t\n",
+      "\n",
+      "for f in str_false:\n",
+      "    assert(bool(re.match(pattern, f)) == False), '%s is not False' %f"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 29
     }
    ],
    "metadata": {}

From 713a6c8e2e336f4e220ceaac5e4d243b3b481446 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 6 Jul 2014 23:42:13 -0400
Subject: [PATCH 162/248] updated table of contents

---
 tutorials/useful_regex.ipynb | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/tutorials/useful_regex.ipynb b/tutorials/useful_regex.ipynb
index ad0617b..24bcf14 100644
--- a/tutorials/useful_regex.ipynb
+++ b/tutorials/useful_regex.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:68319934ddacbbabceac87b1876ae59e0d4fc3509192906a9483958d08c8d839"
+  "signature": "sha256:237609a5ef934bf65a93a410c9e5107b808049dd04b0faf2b30f9b423699ba6c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -105,7 +105,8 @@
       "- [Validating dates](#Validating-dates)\n",
       "- [Time](#Time)\n",
       "- [Checking for HTML tags](#Checking-for-HTML-tags)\n",
-      "- [Checking for IP addresses](#Checking-for-IP-addresses)"
+      "- [Checking for IP addresses](#Checking-for-IP-addresses)\n",
+      "- [Checking for MAC addresses](#Checking-for-MAC-addresses)"
      ]
     },
     {

From b58703cfb5f035adcbc88dae0a80cc234047192c Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 9 Jul 2014 11:42:48 -0400
Subject: [PATCH 163/248] readme table of cont

---
 README.md | 30 +++++++++++++++++++-----------
 1 file changed, 19 insertions(+), 11 deletions(-)
 mode change 100755 => 100644 README.md

diff --git a/README.md b/README.md
old mode 100755
new mode 100644
index 969f614..15dad33
--- a/README.md
+++ b/README.md
@@ -1,23 +1,26 @@
-
-<div align="center">
-
-<h3>A collection of useful scripts, tutorials, and other Python-related things</h3>
+### A collection of useful scripts, tutorials, and other Python-related things
 
 <br>
-<img src="./Images/python-logo-master-v3-TM-flattened.png" alt=""> </div>
+<img src="./Images/python-logo-master-v3-TM-flattened.png" alt="">
 
 
+<div style="height:100px;"></div>
 
-<br>
+- [// Python tips and tutorials](#-python-tips-and-tutorials)
+- [// Python and the web](#-python-and-the-web)
+- [// Algorithms](#-algorithms)
+- [// Benchmarks](#-benchmarks)
+- [// Other](#-other)
+- [// Useful scripts and snippets](#-useful-scripts-and-snippets)
+- [// Links](#-links)
 
 
-<br>
-<br>
-<br>
 
+<div style="height:100px;"></div>
 
 
 ###// Python tips and tutorials
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
 - A collection of not so obvious Python stuff you should know! [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb?create=1)]
 
@@ -48,6 +51,7 @@
 
 
 ###// Python and the web
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
 - Creating internal links in IPython Notebooks and Markdown docs [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]  
 
@@ -56,6 +60,7 @@
 
 <br>
 ###// Algorithms
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
 *The algorithms category was moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*
 
@@ -74,6 +79,7 @@
 
 <br>
 ###// Benchmarks
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
 *The benchmark category was moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
 
@@ -99,6 +105,7 @@
 
 
 ###// Other 
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
 - Happy Mother's [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)]
 
@@ -109,6 +116,7 @@
 <br>
 
 ###// Useful scripts and snippets
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
 - [IPython magic function %watermark](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) - for printing date- and time-stamps and various system info
 
@@ -122,9 +130,9 @@
 
 <br>
 
-<a id='links'></a>
 
 ###// Links
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
 
 
@@ -164,4 +172,4 @@
 
 - [Numba](http://numba.pydata.org) - an just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
 
-- [scikit-learn](http://scikit-learn.org/stable/) - a powerful machine learning library for Python and tools for efficient data mining and analysis
\ No newline at end of file
+- [scikit-learn](http://scikit-learn.org/stable/) - a powerful machine learning library for Python and tools for efficient data mining and analysis

From 4875e13390a7c1afc979a01cb10a53f282b28e17 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 10 Jul 2014 01:39:53 -0400
Subject: [PATCH 164/248] scikit upd normalization

---
 tutorials/python_data_entry_point.ipynb | 156 ++++++++++++++++++++----
 1 file changed, 134 insertions(+), 22 deletions(-)

diff --git a/tutorials/python_data_entry_point.ipynb b/tutorials/python_data_entry_point.ipynb
index e2f6532..ffbd6ad 100644
--- a/tutorials/python_data_entry_point.ipynb
+++ b/tutorials/python_data_entry_point.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7417613f49b14e98fba46fa1e285f4e3d46728b4798e853cfb103caef077b452"
+  "signature": "sha256:cf3234a0ec4044266603b3f223b30b7b1595acce96b566935292bc57addfbdc5"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -45,7 +45,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Sebastian Raschka 04/07/2014 \n",
+        "Sebastian Raschka 10/07/2014 \n",
         "\n",
         "CPython 3.4.1\n",
         "IPython 2.1.0\n",
@@ -57,7 +57,7 @@
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -138,6 +138,10 @@
       "\n",
       "- [Feature Scaling](#Feature-Scaling)\n",
       "\n",
+      "    - [Standardization](#Standardization)\n",
+      "    \n",
+      "    - [Min-Max scaling (Normalization)](#Min-Max-scaling-Normalization)\n",
+      "\n",
       "- [Linear Transformation: Principal Component Analysis (PCA)](#PCA)\n",
       "\n",
       "- [Linear Transformation: Linear Discrciminant Analysis (LDA)](#MDA)\n",
@@ -345,7 +349,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 2
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -445,7 +449,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -508,7 +512,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 5
     },
     {
      "cell_type": "code",
@@ -555,11 +559,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAH8CAYAAABl8FOBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrA8xusMnb/cPHAAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1056b3b00>"
+        "<matplotlib.figure.Figure at 0x105662d30>"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -636,11 +640,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAloAAAH4CAYAAACSZ0OSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRERERERERERERERERERERKSW+H/CqQ+gYvnblgAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x1059cba58>"
+        "<matplotlib.figure.Figure at 0x10584d198>"
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -694,11 +698,11 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAHMCAYAAABY25iGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZFLfDZEkSZKkxkKFnA6RpEbn/wFnNYMMVynu2gAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x106e96b00>"
+        "<matplotlib.figure.Figure at 0x106f1beb8>"
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -746,7 +750,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 8
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -790,7 +794,7 @@
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -815,17 +819,27 @@
       "[[back to top]](#Sections)"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Standardization"
+     ]
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Another popular procedure is to standardize the data prior to fitting the model and other analyses so that the features will have the properties of a standard normal distribution with   \n",
+      "Another important procedure is to standardize the data prior to fitting the model and other analyses so that the features will have the properties of a standard normal distribution with   \n",
       "\n",
       "$\\mu = 0$ and $\\sigma = 1$\n",
       "\n",
-      "where $\\mu$ is the mean (average) and $\\sigma$ is the standard deviation from the mean, so that the standard scores of the samples are calculated as follows:\n",
+      "where $\\mu$ is the mean (average) and $\\sigma$ is the standard deviation from the mean; standard scores (also called ***z*** scores) of the samples are calculated as follows:\n",
+      "\n",
+      "\\begin{equation} z = \\frac{x - \\mu}{\\sigma}\\end{equation} \n",
       "\n",
-      "\\begin{equation} z = \\frac{x - \\mu}{\\sigma}\\end{equation} "
+      "Standardizing the features so that they are centered around 0 with a standard deviation of 1 is especially important if we are comparing measurements that have different units, e.g., in our \"wine data\" example, where the alcohol content is measured in volume percent, and the malic acid content in g/l. "
      ]
     },
     {
@@ -839,7 +853,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 10
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -860,7 +874,7 @@
       "                label='class {}'.format(label)\n",
       "                )\n",
       "\n",
-      "    a.legend(loc='upper right')\n",
+      "    a.legend(loc='upper left')\n",
       "\n",
       "ax[0].set_title('Training Dataset')\n",
       "ax[1].set_title('Test Dataset')\n",
@@ -875,13 +889,111 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgghhBBCCCGEEEIIIYSoIf4fkAk01RhtGrsAAAAASUVORK5CYII=\n",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAFXCAYAAAAMF1IiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x106fe0c50>"
+        "<matplotlib.figure.Figure at 0x107ad14a8>"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<a id='Min-Max-scaling-Normalization'></a>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Min-Max scaling (Normalization)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top]](#Sections)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "An alternative approach to standardization is the so-called Min-Max scaling (sometimes also referred to as \"normalization\").  \n",
+      "In this approach, the data is scaled to a fixed range - usually 0 to 1.  \n",
+      "The cost of having this bounded range - in contrast to standardization - is that we will end up with small standard deviations, for example in the case where outliers are present.\n",
+      "\n",
+      "The equation to calculate the \"normalized\" score is:\n",
+      "\n",
+      "\\begin{equation} X' = \\frac{X - X_{min}}{X_{max}-X_{min}} \\end{equation}"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1)).fit(X_train)\n",
+      "X_train_minmax = minmax_scale.transform(X_train)\n",
+      "X_test_minmax = minmax_scale.transform(X_test)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "f, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(10,5))\n",
+      "\n",
+      "for a,x_dat, y_lab in zip(ax, (X_train_minmax, X_test_minmax), (y_train, y_test)):\n",
+      "\n",
+      "    for label,marker,color in zip(\n",
+      "        range(1,4),('x', 'o', '^'),('blue','red','green')):\n",
+      "\n",
+      "        a.scatter(x=x_dat[:,0][y_lab == label], \n",
+      "                y=x_dat[:,1][y_lab == label], \n",
+      "                marker=marker, \n",
+      "                color=color,   \n",
+      "                alpha=0.7,   \n",
+      "                label='class {}'.format(label)\n",
+      "                )\n",
+      "\n",
+      "    a.legend(loc='upper left')\n",
+      "\n",
+      "ax[0].set_title('Training Dataset')\n",
+      "ax[1].set_title('Test Dataset')\n",
+      "f.text(0.5, 0.04, 'malic acid (normalized)', ha='center', va='center')\n",
+      "f.text(0.08, 0.5, 'alcohol (normalized)', ha='center', va='center', rotation='vertical')\n",
+      "\n",
+      "plt.show()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAmgAAAFXCAYAAAAWK7KsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgghhBBCCCFc8v8g+URU3Pf4WQAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x107acb198>"
+       ]
+      }
+     ],
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",

From 623012b5c880c48ce4dddb48bb6d437626afbb2b Mon Sep 17 00:00:00 2001
From: Rachit Singh <rachitsingh@outlook.com>
Date: Sun, 13 Jul 2014 21:27:40 -0700
Subject: [PATCH 165/248] Fixed 2 typos (1 important)

Fixed a type causing a code tag not to be formatted correctly, and fixed the answer of Example 1.2 to be 'b' instead of 'a' (which is the text's intent, I believe)
---
 tutorials/scope_resolution_legb_rule.ipynb | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index fa6bd76..a47fd1d 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -253,7 +253,7 @@
      "source": [
       "### Tip:\n",
       "If we want to print out the dictionary mapping of the global and local variables, we can use the\n",
-      "the functions `global()` and `local()"
+      "the functions `global()` and `local()`"
      ]
     },
     {
@@ -951,7 +951,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print('Example 1.2:', chr(int('01100001',2)))"
+      "print('Example 1.2:', chr(int('01100010',2)))"
      ],
      "language": "python",
      "metadata": {},
@@ -1145,4 +1145,4 @@
    "metadata": {}
   }
  ]
-}
\ No newline at end of file
+}

From 55a93bc670db833e3da4da23432813add9d72d62 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 14 Jul 2014 15:47:15 -0400
Subject: [PATCH 166/248] more "favorite packages" added

---
 README.md | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 15dad33..ad2b2bb 100644
--- a/README.md
+++ b/README.md
@@ -166,10 +166,20 @@
 
 - [The IPython Notebook](http://ipython.org/notebook.html) - an interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document.
 
-- [SciPy Stack](http://www.scipy.org/index.html) - Python packages (NumPy, pandas, SciPy, IPython, Matplotlib) for scientific computing
+- [matplotlib](http://matplotlib.org) - Python's favorite plotting library
+
+- [NumPy](http://www.numpy.org) - a library for multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays
+
+- [SciPy](http://www.scipy.org) - a library that provides various useful functions for numerical computing, such as modules for optimization, linear algebra, integration, interpolation, ...
+
+
+- [pandas](http://pandas.pydata.org) - high-performance, easy-to-use data structures and data analysis tools build on top of Numpy
 
 - [Cython](http://cython.org) - C-extensions for Python, an optimizing static compiler to combine Python and C code
 
 - [Numba](http://numba.pydata.org) - an just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
 
 - [scikit-learn](http://scikit-learn.org/stable/) - a powerful machine learning library for Python and tools for efficient data mining and analysis
+
+
+

From 936c755d989095234da1c9dbc293daee4c18e339 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 16 Jul 2014 00:44:54 -0400
Subject: [PATCH 167/248] matplotlib gallery

---
 README.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/README.md b/README.md
index ad2b2bb..cfa5708 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,7 @@
 - [// Python tips and tutorials](#-python-tips-and-tutorials)
 - [// Python and the web](#-python-and-the-web)
 - [// Algorithms](#-algorithms)
+- [// Plotting and Visualization](#-plotting-and-visualization)
 - [// Benchmarks](#-benchmarks)
 - [// Other](#-other)
 - [// Useful scripts and snippets](#-useful-scripts-and-snippets)
@@ -76,6 +77,12 @@
 
 - Counting points inside a hypercube [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/geometry/points_in_hybercube.ipynb)]
 
+<br>
+###// Plotting and Visualization
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+
+- a matplotlib gallery in IPython notebooks [[GitHub repo](https://github.com/rasbt/matplotlib-gallery)]
+
 
 <br>
 ###// Benchmarks

From a52226cd70b58e686f3d48d360956602458ef38e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 16 Jul 2014 17:41:08 -0400
Subject: [PATCH 168/248] small wording change in closure-section

---
 tutorials/not_so_obvious_python_stuff.ipynb | 55 +++++++++++++++++++--
 1 file changed, 50 insertions(+), 5 deletions(-)

diff --git a/tutorials/not_so_obvious_python_stuff.ipynb b/tutorials/not_so_obvious_python_stuff.ipynb
index c2e2948..a3188b0 100644
--- a/tutorials/not_so_obvious_python_stuff.ipynb
+++ b/tutorials/not_so_obvious_python_stuff.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:d87105a74c8f25016f90bdec495a890a988277a3f51e0589febbbac87720b033"
+  "signature": "sha256:5dd675ee714d0dbd00f7be378f1379f4dceaa728c56476124c1bf493d70c569e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,18 +13,60 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://sebastianraschka.com)  \n",
-      "last updated: 05/24/2014 ([Changelog](#changelog))\n",
       "\n",
       "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb)  \n",
       "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
       "\n"
      ]
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -d -u -v"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Last updated: 16/07/2014 \n",
+        "\n",
+        "CPython 3.4.1\n",
+        "IPython 2.0.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "#### All code was executed in Python 3.4 (unless stated otherwise)"
+      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>\n",
+      "\n",
+      "([Changelog](#changelog))"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
      ]
     },
     {
@@ -1013,7 +1055,7 @@
       "\n",
       "(Original source: [http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html](http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html))\n",
       "\n",
-      "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope of the list comprehension. Since the list is already constructed when we `for-loop` through the list, it will be set to the last value 4."
+      "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope of the list comprehension. Since the list comprehension has already been constructed and evaluated when we for-loop through the list, the closure-variable will be set to the last value 4."
      ]
     },
     {
@@ -1045,7 +1087,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "This, however, does not apply to generators:"
+      "However, by using a generator expression, we can make use of its stepwise evaluation (note that the returned variable still stems from the same closure, but the value changes as we iterate over the generator)."
      ]
     },
     {
@@ -4281,6 +4323,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
+      "#### 07/16/2014\n",
+      "- slight change of wording in the [lambda-closure section](#lambda_closure)\n",
+      "\n",
       "#### 05/24/2014\n",
       "- new section: unorderable types in Python 2\n",
       "- table of contents for the Python 2 vs. Python 3 topic\n",

From 0fd0ba5d01d82d68353f5dee7dfcf58cc855638e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 18 Jul 2014 14:13:22 -0400
Subject: [PATCH 169/248] cheatsheet upd

---
 tutorials/matrix_cheatsheet_only.html | 722 ++++++++++++++------------
 1 file changed, 390 insertions(+), 332 deletions(-)

diff --git a/tutorials/matrix_cheatsheet_only.html b/tutorials/matrix_cheatsheet_only.html
index 7624ea4..c0d1a18 100644
--- a/tutorials/matrix_cheatsheet_only.html
+++ b/tutorials/matrix_cheatsheet_only.html
@@ -3,9 +3,9 @@
 <head>
 	<meta http-equiv="content-type" content="text/html; charset=utf-8">
 	<title></title>
-	<meta name="generator" content="LibreOffice 4.2.4.2 (MacOSX)">
+	<meta name="generator" content="LibreOffice 4.2.5.2 (MacOSX)">
 	<meta name="created" content="0;0">
-	<meta name="changed" content="20140622;123733939958000">
+	<meta name="changed" content="20140718;140930381877000">
 	<style type="text/css">
 	<!--
 		td p { color: #000000; font-family: "Verdana"; font-size: 12pt }
@@ -14,395 +14,396 @@
 	</style>
 </head>
 <body lang="en-US" text="#000000" dir="ltr" style="background: transparent">
-<table cellpadding="2" cellspacing="0" style="page-break-before: always">
-	<col width="383">
-	<col width="387">
-	<col width="447">
-	<col width="469">
-	<col width="511">
-	<col width="690">
+<table width="948" cellpadding="2" cellspacing="0" style="page-break-before: always">
+	<col width="171">
+	<col width="174">
+	<col width="128">
+	<col width="136">
+	<col width="126">
+	<col width="188">
 	<tr>
-		<td height="59" bgcolor="#0066cc" style="border: none; padding: 0in">
+		<td width="171" height="60" bgcolor="#0066cc" style="border: none; padding: 0in">
 			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
 		</td>
-		<td bgcolor="#0066cc" style="border: none; padding: 0in">
-			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>MATLAB/Octave</b></font></font></font></p>
+		<td width="174" bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="monospace"><font size="5" style="font-size: 18pt"><b>MATLAB/Octave</b></font></font></font></p>
 		</td>
-		<td bgcolor="#0066cc" style="border: none; padding: 0in">
-			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>Python
+		<td width="128" bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="monospace"><font size="5" style="font-size: 18pt"><b>Python
 			NumPy</b></font></font></font></p>
 		</td>
-		<td bgcolor="#0066cc" style="border: none; padding: 0in">
-			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>R</b></font></font></font></p>
+		<td width="136" bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="monospace"><font size="5" style="font-size: 18pt"><b>R</b></font></font></font></p>
 		</td>
-		<td bgcolor="#0066cc" style="border: none; padding: 0in">
-			<p align="center"><font color="#ffffff"><font face="Andale Mono"><font size="5" style="font-size: 18pt"><b>Julia</b></font></font></font></p>
+		<td width="126" bgcolor="#0066cc" style="border: none; padding: 0in">
+			<p align="center"><font color="#ffffff"><font face="monospace"><font size="5" style="font-size: 18pt"><b>Julia</b></font></font></font></p>
 		</td>
-		<td bgcolor="#0066cc" style="border: none; padding: 0in">
+		<td width="188" bgcolor="#0066cc" style="border: none; padding: 0in">
 			<p align="center"><font color="#ffffff"><font face="Arial"><font size="5" style="font-size: 18pt"><b>Task</b></font></font></font></p>
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="70" bgcolor="#ffffff" style="border: none; padding: 0in">
+		<td colspan="6" width="944" height="70" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>CREATING
 			MATRICES</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="158" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="158" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			Matrices&nbsp;<br>(here: 3x3 matrix)</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
 			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
 			9</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 6],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [7, 8, 9]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(c(1,2,3,4,5,6,7,8,9),nrow=3,byrow=T)<br><br>
#
 			equivalent to
<br># A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
 			8 9</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			Matrices&nbsp;<br>(here: 3x3 matrix)</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			an 1D column vector</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			a = [1; 2; 3]<br>a =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp;
 			3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
-			<font face="Andale Mono"><font size="1" style="font-size: 8pt">a</font></font><font color="#2fff12">
-			</font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">=
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font color="#ff420e"><font face="monospace"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
+			<font face="monospace"><font size="1" style="font-size: 8pt">a</font></font><font color="#2fff12">
+			</font><font color="#000000"><font face="monospace"><font size="1" style="font-size: 8pt">=
 			np.array([1,2,3]).reshape(1,3)</font></font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><br></font></font><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
-			<font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape<br>(1,
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><br></font></font><font color="#ff420e"><font face="monospace"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font>
+			<font color="#000000"><font face="monospace"><font size="1" style="font-size: 8pt">b.shape<br>(1,
 			3)</font></font></font></p>
 			<p align="left"><br>
 			</p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			a = matrix(c(1,2,3), nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			a<br>[,1]<br>[1,] 1<br>[2,] 2<br>[3,] 3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			a=[1; 2; 3]<br>3-element Array{Int64,1}: <br>1<br>2<br>3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			an 1D column vector</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			an<br>1D row vector</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			b = [1 2 3]<br>b =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b<br>array([1, 2, 3])</font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt">#
 			note that numpy doesn't have <br># explicit “row-vectors”, but
 			1-D <br># arrays</font></font></p>
-			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="Andale Mono"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12">
-			</font><font color="#000000"><font face="Andale Mono"><font size="1" style="font-size: 8pt">b.shape</font></font></font></font></font></p>
-			<p style="margin-bottom: 0in"><font face="Andale Mono"><font size="1" style="font-size: 8pt">(3,)</font></font></p>
+			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="3" style="font-size: 12pt"><font color="#ff420e"><font face="monospace"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font color="#2fff12">
+			</font><font color="#000000"><font face="monospace"><font size="1" style="font-size: 8pt">b.shape</font></font></font></font></font></p>
+			<p style="margin-bottom: 0in"><font face="monospace"><font size="1" style="font-size: 8pt">(3,)</font></font></p>
 			<p align="left"><br>
 			</p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			b = matrix(c(1,2,3), ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			b<br>[,1] [,2] [,3]<br>[1,] 1 2 3</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			b=[1 2 3] <br>1x3 Array{Int64,2}:<br>1 2 3<br><br># note that this
 			is a 2D array. <br># vectors in Julia are columns</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			an<br>1D row vector</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			a <br>random m x n matrix</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			rand(3,2)<br>ans =<br>&nbsp;&nbsp; 0.21977 &nbsp; 0.10220<br>&nbsp;&nbsp;
 			0.38959 &nbsp; 0.69911<br>&nbsp;&nbsp; 0.15624 &nbsp; 0.65637</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.random.rand(3,2)<br>array([[ 0.29347865,&nbsp; 0.17920462],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [ 0.51615758,&nbsp; 0.64593471],<br>&nbsp;&nbsp; &nbsp;
 			&nbsp; [ 0.01067605,&nbsp; 0.09692771]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			matrix(runif(3*2), ncol=2)<br>[,1] [,2]<br>[1,] 0.5675127
 			0.7751204<br>[2,] 0.3439412 0.5261893<br>[3,] 0.2273177 0.223438</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			rand(3,2)<br>3x2 Array{Float64,2}:<br>0.36882 0.267725<br>0.571856
 			0.601524<br>0.848084 0.858935</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			a <br>random m x n matrix</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			a<br>zero&nbsp;m x n matrix&nbsp;</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			zeros(3,2)<br>ans =<br>&nbsp;&nbsp; 0 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp;
 			0<br>&nbsp;&nbsp; 0 &nbsp; 0</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.zeros((3,2))<br>array([[ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp;
 			&nbsp; [ 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 0.,&nbsp;
 			0.]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			mat.or.vec(3, 2)<br>[,1] [,2]<br>[1,] 0 0<br>[2,] 0 0<br>[3,] 0 0</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			zeros(3,2)<br>3x2 Array{Float64,2}:<br>0.0 0.0<br>0.0 0.0<br>0.0
 			0.0</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			a<br>zero&nbsp;m x n matrix&nbsp;</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			an<br>m x n matrix of ones</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			ones(3,2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 1<br>&nbsp;&nbsp; 1 &nbsp;
 			1<br>&nbsp;&nbsp; 1 &nbsp; 1</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.ones((3,2))<br>array([[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp;
 			[ 1.,&nbsp; 1.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 1.,&nbsp; 1.]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
-			mat.or.vec(3, 2) + 1<br>[,1] [,2]<br>[1,] 1 1<br>[2,] 1 1<br>[3,]
-			1 1</font></font></p>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font color="#0000ff"><font face="monospace"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font></font><font face="monospace"><font size="1" style="font-size: 8pt">
+			</font></font><font color="#000000"><font face="AndaleMono, serif"><font size="1" style="font-size: 8pt">matrix(1L,
+			3, 2)<br></font></font></font><font face="monospace"><font size="1" style="font-size: 8pt"><br>[,1]
+			[,2]<br>[1,] 1 1<br>[2,] 1 1<br>[3,] 1 1</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			ones(3,2)<br>3x2 Array{Float64,2}:<br>1.0 1.0<br>1.0 1.0<br>1.0
 			1.0</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			an<br>m x n matrix of ones</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			an<br>identity matrix</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			eye(3)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp;
 			0<br>&nbsp;&nbsp; 0 &nbsp; 1 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp;
 			1</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.eye(3)<br>array([[ 1.,&nbsp; 0.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp;
 			&nbsp; [ 0.,&nbsp; 1.,&nbsp; 0.],<br>&nbsp;&nbsp; &nbsp; &nbsp; [
 			0.,&nbsp; 0.,&nbsp; 1.]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			diag(3)<br>[,1] [,2] [,3]<br>[1,] 1 0 0<br>[2,] 0 1 0<br>[3,] 0 0
 			1</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			eye(3)<br>3x3 Array{Float64,2}:<br>1.0 0.0 0.0<br>0.0 1.0 0.0<br>0.0
 			0.0 1.0</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			an<br>identity matrix</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="217" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="217" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			a<br>diagonal matrix</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			a = [1 2 3]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			diag(a)<br>ans =<br>Diagonal Matrix<br>&nbsp;&nbsp; 1 &nbsp; 0 &nbsp;
 			0<br>&nbsp;&nbsp; 0 &nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0 &nbsp; 0 &nbsp;
 			3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			a = np.array([1,2,3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.diag(a)<br>array([[1, 0, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0,
 			2, 0],<br>&nbsp;&nbsp; &nbsp; &nbsp; [0, 0, 3]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			diag(1:3)<br>[,1] [,2] [,3]<br>[1,] 1 0 0<br>[2,] 0 2 0<br>[3,] 0
 			0 3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			a=[1, 2, 3] <br><br># added commas because julia<br># vectors are
 			columnar<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			diagm(a)<br>3x3 Array{Int64,2}:<br>1 0 0<br>0 2 0<br>0 0 3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Creating
 			a<br>diagonal matrix</b></font></font></p>
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in">
+		<td colspan="6" width="944" height="128" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ACCESSING
 			MATRIX ELEMENTS</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="159" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="159" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Getting
 			the dimension<br>of a matrix<br>(here: 2D, rows x cols)</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 6]<br>A =<br>&nbsp; &nbsp;1 &nbsp; 2 &nbsp; 3<br>&nbsp;
 			&nbsp;4 &nbsp; 5 &nbsp; 6<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			size(A)<br>ans =<br>&nbsp; &nbsp;2 &nbsp; 3</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[1, 2, 3],<br>&nbsp; &nbsp; &nbsp; &nbsp;[4, 5,
 			6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A.shape<br>(2, 3)</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:6,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6
</font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			dim(A)<br>[1] 2 3</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6]<br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			size(A)<br>(2,3)</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Getting
 			the dimension<br>of a matrix<br>(here: 2D, rows x cols)</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="240" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="240" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
 			rows&nbsp;</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st row<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A(1,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br><br>% 1st 2
 			rows<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A(1:2,:)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp;
 			4 &nbsp; 5 &nbsp; 6</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st row<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A[0,:]<br>array([1, 2, 3])<br><br># 1st 2 rows<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A[0:2,:]<br>array([[1, 2, 3], [4, 5, 6]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9,nrow=3,byrow=T)

<br><br># 1st row<br>

<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A[1,]<br>[1] 1 2 3<br><br>

# 1st 2 rows

<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A[1:2,]<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9]; <br>#semicolon suppresses output<br><br>#1st
 			row<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A[1,:] <br>1x3 Array{Int64,2}:<br>1 2 3<br><br>#1st 2 rows<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A[1:2,:] <br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
 			rows&nbsp;</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="304" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="304" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
 			columns</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 6; 7 8 9]<br><br>% 1st column<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A(:,1)<br>ans =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 4<br>&nbsp;&nbsp;
 			7<br><br>% 1st 2 columns<br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A(:,1:2)<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 2<br>&nbsp;&nbsp; 4 &nbsp;
 			5<br>&nbsp;&nbsp; 7 &nbsp; 8</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br># 1st column
 			(as row vector)<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A[:,0]<br>array([1, 4, 7])<br><br># 1st column (as column
@@ -412,8 +413,8 @@
 			A[:,0:2]<br>array([[1, 2],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[4,
 			5],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[7, 8]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9,nrow=3,byrow=T)


<br><br># 1st column as row
 			vector
<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			t(A[,1])<br>[,1] [,2] [,3]<br>[1,] 1 4 7

<br><br># 1st column
@@ -421,136 +422,193 @@
 			A[,1]<br>[1] 1 4 7

<br><br># 1st 2 columns
<br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A[,1:2]<br>[,1] [,2]<br>[1,] 1 2<br>[2,] 4 5<br>[3,] 7 8</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9];<br><br>#1st column<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A[:,1] <br>3-element Array{Int64,1}:<br>1<br>4<br>7<br><br>#1st 2
 			columns<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A[:,1:2] <br>3x2 Array{Int64,2}:<br>1 2<br>4 5<br>7 8</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Selecting
 			columns</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="260" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="260" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Extracting
 			rows and columns by criteria<br><br>(here: get rows&nbsp;that have
 			value 9 in column 3)</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 9; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
 			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
 			9<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A(A(:,3) == 9,:)<br>ans =<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 9<br>&nbsp;&nbsp;
 			7 &nbsp; 8 &nbsp; 9</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,9], [7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [7, 8, 9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A[A[:,2] == 9]<br>array([[4, 5, 9],<br>&nbsp;&nbsp; &nbsp; &nbsp;
 			[7, 8, 9]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
-			A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font color="#0000ff"><font face="monospace"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font></font><font face="monospace"><font size="1" style="font-size: 8pt">
+			A = matrix(1:9,nrow=3,byrow=T)

<br><br></font></font><font color="#0000ff"><font face="monospace"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font></font><font face="monospace"><font size="1" style="font-size: 8pt">
 			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 9<br>[3,] 7 8
-			9

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
-			matrix(A[A[,3]==9], ncol=3)<br>[,1] [,2] [,3]<br>[1,] 4 5 9<br>[2,]
+			9

<br><br></font></font><font color="#0000ff"><font face="monospace"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font></font><font face="monospace"><font size="1" style="font-size: 8pt">
+			</font></font><font color="#000000"><font face="monospace"><font size="1" style="font-size: 8pt">A[A[,3]==9,]</font></font></font></p>
+			<p align="left" style="margin-bottom: 0in"><font face="monospace"><font size="1" style="font-size: 8pt">[1]
 			7 8 9</font></font></p>
+			<p align="left"><br>
+			</p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 9; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 9<br>7
 			8 9<br><br># use '.==' for<br># element-wise check<br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A[ A[:,3] .==9, :] <br>2x3 Array{Int64,2}:<br>4 5 9<br>7 8 9</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Extracting
 			rows and columns by criteria<br><br>(here: get rows&nbsp;that have
 			value 9 in column 3)</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="118" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="118" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Accessing
 			elements<br>(here: 1st element)</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A(1,1)<br>ans =&nbsp; 1</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A[0,0]<br>1</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(c(1,2,3,4,5,9,7,8,9),nrow=3,byrow=T)
<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A[1,1]<br>[1] 1</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A[1,1]<br>1</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Accessing
 			elements<br>(here: 1st element)</b></font></font></p>
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="128" bgcolor="#ffffff" style="border: none; padding: 0in">
+		<td colspan="6" width="944" height="128" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>MANIPULATING
 			SHAPE AND DIMENSIONS</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="173" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="173" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Converting&nbsp;<br>a
+			matrix into a row vector (by column)</b></font></font></p>
+		</td>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A = [1 2 3; 4 5 6; 7 8 9]</font></font></p>
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+			A(:)</font></font></p>
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt">ans
+			= </font></font>
+			</p>
+			<p><font face="Liberation Serif, serif"><font size="1" style="font-size: 8pt"><font face="AndaleMono, serif">1
+			<br>4 <br>7<br>2<br>5<br>8<br>3<br>6<br></font><font face="AndaleMono, serif">9</font></font></font></p>
+		</td>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			A = np.array([[1,2,3],[4,5,6],[7,8,9]])</font></font></p>
+			<p align="left"><font color="#ff420e"><font face="monospace"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font></font><font face="monospace"><font size="1" style="font-size: 8pt">
+			</font></font><font color="#000000"><font face="AndaleMono, serif"><font size="1" style="font-size: 8pt">A.flatten(1)</font></font></font></p>
+			<p align="left" style="margin-bottom: 0in"><font face="monospace"><font size="1" style="font-size: 8pt"><font face="AndaleMono, serif">array([1,
+			4, 7, 2, 5, 8, 3, 6, 9])</font></font></font></p>
+			<p align="left"><br>
+			</p>
+		</td>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			A = matrix(1:9,nrow=3,byrow=T)</font></font></p>
+			<p align="left" style="margin-bottom: 0in"><br>
+			</p>
+			<p align="left" style="margin-bottom: 0in"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;
+			</b></font></font><font face="AndaleMono, serif">as.vector(A)</font></font></font></p>
+			<p align="left" style="margin-bottom: 0in"><br>
+			</p>
+			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="1" style="font-size: 8pt"><font face="AndaleMono, serif">[1]
+			1 4 7 2 5 8 3 6 9</font></font></font></p>
+			<p align="left"><br>
+			</p>
+		</td>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+			A=[1 2 3; 4 5 6; 7 8 9]</font></font></p>
+			<p align="left" style="margin-bottom: 0in"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font><font color="#2fb41d"><font face="AndaleMono, serif"><font size="3" style="font-size: 12pt">
+			</font></font></font><font color="#000000"><font face="AndaleMono, serif">vec(A)</font></font></font></font></p>
+			<p style="margin-bottom: 0in"><font face="Liberation Serif, serif"><font size="1" style="font-size: 8pt"><font face="AndaleMono, serif">9-element
+			Array{Int64,1}:</font></font></font></p>
+			<p><font face="Liberation Serif, serif"><font size="1" style="font-size: 8pt"><font color="#000000"><font face="AndaleMono, serif">1<br>4<br>7<br>2<br>5<br>8<br>3<br>6<br>9</font></font></font></font></p>
+		</td>
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Converting&nbsp;<br>a
+			matrix into a row vector (by column)</b></font></font></p>
+		</td>
+	</tr>
+	<tr valign="top">
+		<td width="171" height="173" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Converting&nbsp;<br>row
 			to column vectors</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			b = [1 2 3]
<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			b = b'<br>b =<br>&nbsp;&nbsp; 1<br>&nbsp;&nbsp; 2<br>&nbsp;&nbsp;
 			3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b = np.array([1, 2, 3])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b = b[np.newaxis].T<br># alternatively <br>#&nbsp;b =
 			b[:,np.newaxis]<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b<br>array([[1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [3]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			b = matrix(c(1,2,3), ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			t(b)<br>[,1]<br>[1,] 1<br>[2,] 2<br>[3,] 3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			b=vec([1 2 3])<br>3-element Array{Int64,1}:<br>1<br>2<br>3</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Converting&nbsp;<br>row
 			to column vectors</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="275" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="275" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Reshaping
 			Matrices<br><br>(here: 3x3 matrix to row vector)</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 6; 7 8 9]<br>A =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp;
 			3<br>&nbsp;&nbsp; 4 &nbsp; 5 &nbsp; 6<br>&nbsp;&nbsp; 7 &nbsp; 8 &nbsp;
 			9<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
@@ -559,19 +617,19 @@
 			=<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7 &nbsp; 2 &nbsp; 5 &nbsp; 8 &nbsp;
 			3 &nbsp; 6 &nbsp; 9</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([[1,2,3],[4,5,6],[7,8,9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4, 5, 9],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [7, 8, 9]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			total_elements = np.prod(A.shape)</font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			B = A.reshape(1, total_elements)&nbsp;<br><br># alternative
 			shortcut:<br># A.reshape(1,-1)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			B<br>array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9,nrow=3,byrow=T)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			total_elements = dim(A)[1] * dim(A)[2]<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -579,33 +637,33 @@
 			B<br>[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]<br>[1,] 1 4 7 2
 			5 8 3 6 9</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
 			8 9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			total_elements=length(A)<br>9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>B=reshape(A,1,total_elements)<br>1x9
 			Array{Int64,2}:<br>1 4 7 2 5 8 3 6 9</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Reshaping
 			Matrices<br><br>(here: 3x3 matrix to row vector)</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="240" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="240" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Concatenating
 			matrices</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 6]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			B = [7 8 9; 10 11 12]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			C = [A; B]<br>&nbsp; &nbsp;&nbsp;1&nbsp; &nbsp; 2&nbsp; &nbsp; 3<br>&nbsp;
 			&nbsp; 4&nbsp; &nbsp; 5&nbsp; &nbsp; 6<br>&nbsp; &nbsp; 7&nbsp; &nbsp;
 			8&nbsp; &nbsp; 9<br>&nbsp;&nbsp; 10 &nbsp; 11 &nbsp; 12</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([[1, 2, 3], [4, 5, 6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			B = np.array([[7, 8, 9],[10,11,12]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			C = np.concatenate((A, B), axis=0)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
@@ -613,33 +671,33 @@
 			5, 6],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 7, 8, 9],&nbsp;<br>&nbsp;
 			&nbsp; &nbsp; &nbsp;[10, 11, 12]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:6,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			B = matrix(7:12,nrow=2,byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			C = rbind(A,B)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			C<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6<br>[3,] 7 8 9<br>[4,]
 			10 11 12</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			B=[7 8 9; 10 11 12];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			C=[A; B]<br>4x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7 8 9<br>10
 			11 12</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Concatenating
 			matrices</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="256" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="256" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Stacking&nbsp;<br>vectors
 			and matrices</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			a = [1 2 3]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			b = [4 5 6]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			c = [a' b']<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 4<br>&nbsp;&nbsp; 2 &nbsp;
@@ -647,8 +705,8 @@
 			c = [a; b]<br>c =<br>&nbsp;&nbsp; 1 &nbsp; 2 &nbsp; 3<br>&nbsp;&nbsp;
 			4 &nbsp; 5 &nbsp; 6</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			a = np.array([1,2,3])<br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b = np.array([4,5,6])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.c_[a,b]<br>array([[1, 4],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2,
@@ -656,38 +714,38 @@
 			np.r_[a,b]<br>array([[1, 2, 3],<br>&nbsp;&nbsp; &nbsp; &nbsp; [4,
 			5, 6]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			a = matrix(1:3, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			b = matrix(4:6, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			matrix(rbind(A, B), ncol=2)<br>[,1] [,2]<br>[1,] 1 5<br>[2,] 4
 			3
<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			rbind(A,B)<br>[,1] [,2] [,3]<br>[1,] 1 2 3<br>[2,] 4 5 6</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			a=[1 2 3];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			b=[4 5 6];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			c=[a' b']<br>3x2 Array{Int64,2}:<br>1 4<br>2 5<br>3 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			c=[a; b]<br>2x3 Array{Int64,2}:<br>1 2 3<br>4 5 6</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Stacking&nbsp;<br>vectors
 			and matrices</b></font></font></p>
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="114" bgcolor="#ffffff" style="border: none; padding: 0in">
+		<td colspan="6" width="944" height="114" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>BASIC
 			MATRIX OPERATIONS</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="253" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="253" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-scalar<br>operations</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
 			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A * 2<br>ans =<br>&nbsp; &nbsp; 2&nbsp; &nbsp; 4&nbsp; &nbsp; 6<br>&nbsp;
 			&nbsp; 8 &nbsp; 10 &nbsp; 12<br>&nbsp;&nbsp; 14 &nbsp; 16 &nbsp;
@@ -696,30 +754,30 @@
 			A - 2<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A / 2</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A * 2<br>array([[ 2,&nbsp; 4,&nbsp; 6],<br>&nbsp;&nbsp; &nbsp; &nbsp;
 			[ 8, 10, 12],<br>&nbsp;&nbsp; &nbsp; &nbsp; [14, 16, 18]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A + 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A - 2<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A / 2</font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt">#
 			Note that NumPy was optimized for<br># in-place assignments<br>#
 			e.g., A += A instead of <br># A = A + A</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A * 2<br>[,1] [,2] [,3]<br>[1,] 2 4 6<br>[2,] 8 10 12<br>[3,] 14
 			16 18
</font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A + 2<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A - 2<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A / 2</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9];<br><br># elementwise operator<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A .* 2 <br>3x3 Array{Int64,2}:<br>2 4 6<br>8 10 12<br>14 16 18
 			<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
@@ -727,83 +785,83 @@
 			A .- 2;<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A ./ 2;</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-scalar<br>operations</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="165" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="165" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-matrix<br>multiplication</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
 			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A * A<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp;
 			42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp;
 			102 &nbsp; 126 &nbsp; 150</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.dot(A,A) # or A.dot(A)<br>array([[ 30,&nbsp; 36,&nbsp; 42],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp; &nbsp; &nbsp;
 			[102, 126, 150]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A %*% A<br>[,1] [,2] [,3]<br>[1,] 30 36 42<br>[2,] 66 81 96<br>[3,]
 			102 126 150</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A * A<br>3x3 Array{Int64,2}:<br>30 36 42<br>66 81 96<br>102 126
 			150</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-matrix<br>multiplication</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="190" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="190" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-vector<br>multiplication</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			b = [ 1; 2; 3 ]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A * b<br>ans =<br>&nbsp;&nbsp; 14<br>&nbsp;&nbsp; 32<br>&nbsp;&nbsp;
 			50</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			b = np.array([ [1], [2], [3] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.dot(A,b) # or A.dot(b)<br><br>array([[14], [32], [50]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9, ncol=3)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			b = matrix(1:3, nrow=3)<br><br>

<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			t(b %*% A)<br>[,1]<br>[1,] 14<br>[2,] 32<br>[3,] 50</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			b=[1; 2; 3];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A*b<br>3-element Array{Int64,1}:<br>14<br>32<br>50</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix-vector<br>multiplication</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="240" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="240" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
 			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A .* A<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp;
 			9<br>&nbsp;&nbsp; 16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp;
@@ -812,20 +870,20 @@
 			A .- A<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A ./ A</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A * A<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp; &nbsp;
 			[16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A + A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A - A<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A / A</font></font></p>
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt">#
 			Note that NumPy was optimized for<br># in-place assignments<br>#
 			e.g., A += A instead of <br># A = A + A</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9, nrow=3, byrow=T)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A * A<br>[,1] [,2] [,3]<br>[1,] 1 4 9<br>[2,] 16 25 36<br>[3,] 49
 			64 81

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -833,8 +891,8 @@
 			A - A<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A / A</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A .* A<br>3x3 Array{Int64,2}:<br>1 4 9<br>16 25 36<br>49 64 81
 			<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
@@ -842,67 +900,67 @@
 			A .- A; <br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A ./ A;</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Element-wise&nbsp;<br>matrix-matrix&nbsp;operations</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="164" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="164" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
 			elements&nbsp;to power n <br><br>(here: individual elements
 			squared)</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
 			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A.^2<br>ans =<br>&nbsp; &nbsp; 1&nbsp; &nbsp; 4&nbsp; &nbsp; 9<br>&nbsp;&nbsp;
 			16 &nbsp; 25 &nbsp; 36<br>&nbsp;&nbsp; 49 &nbsp; 64 &nbsp; 81</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.power(A,2)<br>array([[ 1,&nbsp; 4,&nbsp; 9],<br>&nbsp;&nbsp; &nbsp;
 			&nbsp; [16, 25, 36],<br>&nbsp;&nbsp; &nbsp; &nbsp; [49, 64, 81]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9, nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A ^ 2<br>[,1] [,2] [,3]<br>[1,] 1 4 9<br>[2,] 16 25 36<br>[3,] 49
 			64 81</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A .^ 2<br>3x3 Array{Int64,2}:<br>1 4 9<br>16 25 36<br>49 64 81</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
 			elements&nbsp;to power n <br><br>(here: individual elements
 			squared)</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="225" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="225" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
 			to power n<br><br>(here: matrix-matrix&nbsp;multiplication with
 			itself)</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
 			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A ^ 2<br>ans =<br>&nbsp; &nbsp; 30&nbsp; &nbsp; 36&nbsp; &nbsp;
 			42<br>&nbsp; &nbsp; 66&nbsp; &nbsp; 81&nbsp; &nbsp; 96<br>&nbsp;&nbsp;
 			102 &nbsp; 126 &nbsp; 150</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.linalg.matrix_power(A,2)<br>array([[ 30,&nbsp; 36,&nbsp;
 			42],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 66,&nbsp; 81,&nbsp; 96],<br>&nbsp;&nbsp;
 			&nbsp; &nbsp; [102, 126, 150]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9, ncol=3)<br><br>
# requires the ‘expm’
 			package
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			install.packages('expm')
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -910,102 +968,102 @@
 			A %^% 2<br>[,1] [,2] [,3]<br>[1,] 30 66 102<br>[2,] 36 81 126<br>[3,]
 			42 96 150</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9];<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A ^ 2<br>3x3 Array{Int64,2}:<br>30 36 42<br>66 81 96<br>102 126
 			150</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
 			to power n<br><br>(here: matrix-matrix&nbsp;multiplication with
 			itself)</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="172" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="172" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
 			transpose</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>&nbsp;A
 			= [1 2 3; 4 5 6; 7 8 9]<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A'<br>ans =<br>&nbsp;&nbsp; 1 &nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2
 			&nbsp; 5 &nbsp; 8<br>&nbsp;&nbsp; 3 &nbsp; 6 &nbsp; 9</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([ [1,2,3], [4,5,6], [7,8,9] ])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A.T<br>array([[1, 4, 7],<br>&nbsp;&nbsp; &nbsp; &nbsp; [2, 5,
 			8],<br>&nbsp;&nbsp; &nbsp; &nbsp; [3, 6, 9]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(1:9, nrow=3, byrow=T)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			t(A)<br>[,1] [,2] [,3]<br>[1,] 1 4 7<br>[2,] 2 5 8<br>[3,] 3 6 9</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[1 2 3; 4 5 6; 7 8 9]<br>3x3 Array{Int64,2}:<br>1 2 3<br>4 5 6<br>7
 			8 9<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A'<br>3x3 Array{Int64,2}:<br>1 4 7<br>2 5 8<br>3 6 9</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Matrix
 			transpose</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="176" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="176" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Determinant
 			of a matrix:<br>&nbsp;A -&gt; |A|</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [6 1 1; 4 -2 5; 2 8 7]<br>A =<br>&nbsp;&nbsp; 6 &nbsp; 1 &nbsp;
 			1<br>&nbsp;&nbsp; 4&nbsp; -2 &nbsp; 5<br>&nbsp;&nbsp; 2 &nbsp; 8 &nbsp;
 			7<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			det(A)<br>ans = -306</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>&nbsp;A
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>&nbsp;A
 			= np.array([[6,1,1],[4,-2,5],[2,8,7]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[ 6,&nbsp; 1,&nbsp; 1],<br>&nbsp;&nbsp; &nbsp; &nbsp;
 			[ 4, -2,&nbsp; 5],<br>&nbsp;&nbsp; &nbsp; &nbsp; [ 2,&nbsp; 8,&nbsp;
 			7]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.linalg.det(A)<br>-306.0</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(c(6,1,1,4,-2,5,2,8,7), nrow=3, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A<br>[,1] [,2] [,3]<br>[1,] 6 1 1<br>[2,] 4 -2 5<br>[3,] 2 8 7<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			det(A)<br>[1] -306</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[6 1 1; 4 -2 5; 2 8 7]<br>3x3 Array{Int64,2}:<br>6 1 1<br>4 -2
 			5<br>2 8 7<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			det(A)<br>-306.0</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Determinant
 			of a matrix:<br>&nbsp;A -&gt; |A|</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="202" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="202" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Inverse
 			of a matrix</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [4 7; 2 6]<br>A =<br>&nbsp;&nbsp; 4 &nbsp; 7<br>&nbsp;&nbsp; 2
 			&nbsp; 6<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A_inv = inv(A)<br>A_inv =<br>&nbsp; &nbsp;0.60000&nbsp; -0.70000<br>&nbsp;
 			-0.20000 &nbsp; 0.40000</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([[4, 7], [2, 6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[4, 7],&nbsp;<br>&nbsp; &nbsp; &nbsp; &nbsp;[2,
 			6]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
@@ -1013,36 +1071,36 @@
 			A_inverse<br>array([[ 0.6, -0.7],&nbsp;<br>&nbsp; &nbsp; &nbsp;
 			&nbsp;[-0.2, 0.4]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(c(4,7,2,6), nrow=2, byrow=T)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A<br>[,1] [,2]<br>[1,] 4 7<br>[2,] 2 6<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			solve(A)<br>[,1] [,2]<br>[1,] 0.6 -0.7<br>[2,] -0.2 0.4</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[4 7; 2 6]<br>2x2 Array{Int64,2}:<br>4 7<br>2 6<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A_inv=inv(A)<br>2x2 Array{Float64,2}:<br>0.6 -0.7<br>-0.2 0.4</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Inverse
 			of a matrix</b></font></font></p>
 		</td>
 	</tr>
 	<tr>
-		<td colspan="6" height="202" bgcolor="#ffffff" style="border: none; padding: 0in">
+		<td colspan="6" width="944" height="202" bgcolor="#ffffff" style="border: none; padding: 0in">
 			<p align="center"><font face="Arial"><font size="4" style="font-size: 14pt"><b>ADVANCED
 			MATRIX OPERATIONS</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="311" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="311" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating
 			the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here:
 			covariances of the means&nbsp;<br>of x1, x2, and x3)</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			x1 = [4.0000 4.2000 3.9000 4.3000 4.1000]’<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			x2 = [2.0000 2.1000 2.0000 2.1000 2.2000]'<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			x3 = [0.60000 0.59000 0.58000 0.62000 0.63000]’<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
@@ -1051,8 +1109,8 @@
 			7.0000e-03 &nbsp; 1.3500e-03<br>&nbsp;&nbsp; 1.7500e-03 &nbsp;
 			1.3500e-03 &nbsp; 4.3000e-04</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			x1 = np.array([ 4, 4.2, 3.9, 4.3, 4.1])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			x2 = np.array([ 2, 2.1, 2, 2.1, 2.2])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			x3 = np.array([ 0.6, 0.59, 0.58, 0.62, 0.63])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
@@ -1061,8 +1119,8 @@
 			&nbsp;, &nbsp;0.00135],<br>&nbsp; &nbsp; &nbsp; &nbsp;[ 0.00175,
 			&nbsp;0.00135, &nbsp;0.00043]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			x1 = matrix(c(4, 4.2, 3.9, 4.3, 4.1), ncol=5)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			x2 = matrix(c(2, 2.1, 2, 2.1, 2.2), ncol=5)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			x3 = matrix(c(0.6, 0.59, 0.58, 0.62, 0.63), ncol=5)

<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -1070,27 +1128,27 @@
 			0.02500 0.00750 0.00175<br>[2,] 0.00750 0.00700 0.00135<br>[3,]
 			0.00175 0.00135 0.00043</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			x1=[4.0 4.2 3.9 4.3 4.1]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			x2=[2. 2.1 2. 2.1 2.2]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			x3=[0.6 .59 .58 .62 .63]';<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			cov([x1 x2 x3])<br>3x3 Array{Float64,2}:<br>0.025 0.0075
 			0.00175<br>0.0075 0.007 0.00135<br>0.00175 0.00135 0.00043</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating
 			the covariance matrix&nbsp;<br>of 3 random variables <br><br>(here:
 			covariances of the means&nbsp;<br>of x1, x2, and x3)</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="309" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="309" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating&nbsp;<br>eigenvectors
 			and&nbsp;eigenvalues</b></font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
+		<td width="174" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			A = [3 1; 1 3]<br>A =<br>&nbsp;&nbsp; 3 &nbsp; 1<br>&nbsp;&nbsp; 1
 			&nbsp; 3<br><br><font color="#006699"><font size="2" style="font-size: 10pt"><b>M&gt;</b></font></font>
 			[eig_vec,eig_val] = eig(A)<br>eig_vec =<br>&nbsp; -0.70711 &nbsp;
@@ -1098,8 +1156,8 @@
 			=<br>Diagonal Matrix<br>&nbsp;&nbsp; 2 &nbsp; 0<br>&nbsp;&nbsp; 0
 			&nbsp; 4</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A = np.array([[3, 1], [1, 3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			A<br>array([[3, 1],<br>&nbsp;&nbsp; &nbsp; &nbsp; [1, 3]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			eig_val, eig_vec = np.linalg.eig(A)<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
@@ -1107,34 +1165,34 @@
 			eig_vec<br>Array([[ 0.70710678, -0.70710678],<br>&nbsp;&nbsp; &nbsp;
 			&nbsp; [ 0.70710678,&nbsp; 0.70710678]])</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
+		<td width="136" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A = matrix(c(3,1,1,3), ncol=2)<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			A<br>[,1] [,2]<br>[1,] 3 1<br>[2,] 1 3<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			eigen(A)<br>$values<br>[1] 4 2<br><br>$vectors<br>[,1] [,2]<br>[1,]
 			0.7071068 -0.7071068<br>[2,] 0.7071068 0.7071068</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
+		<td width="126" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			A=[3 1; 1 3]<br>2x2 Array{Int64,2}:<br>3 1<br>1 3<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			(eig_vec,eig_val)=eig(a)<br>([2.0,4.0],<br>2x2
 			Array{Float64,2}:<br>-0.707107 0.707107<br>0.707107 0.707107)</font></font></p>
 		</td>
-		<td bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#cfe7f5" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Calculating&nbsp;<br>eigenvectors
 			and&nbsp;eigenvalues</b></font></font></p>
 		</td>
 	</tr>
 	<tr valign="top">
-		<td height="541" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+		<td width="171" height="542" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px solid #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
 			a Gaussian dataset:<br><br>creating random vectors from the
 			multivariate normal<br>distribution given mean and covariance
 			matrix<br><br>(here: 5 random vectors with <br>mean 0, covariance
 			= 0, variance = 2)</b></font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">%
+		<td width="174" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: none; border-right: 1px dotted #000000; padding-top: 0in; padding-bottom: 0in; padding-left: 0in; padding-right: 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt">%
 			requires statistics toolbox package<br>% how to install and load
 			it in Octave:<br><br>% download the package from:&nbsp;<br>%
 			http://octave.sourceforge.net/packages.php<br>% pkg install&nbsp;<br>%
@@ -1149,8 +1207,8 @@
 			3.055316<br>&nbsp; -0.985215 &nbsp;-0.990936<br>&nbsp; &nbsp;1.122528
 			&nbsp; 0.686977<br>&nbsp; &nbsp;&nbsp;</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
+		<td width="128" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt"><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			mean = np.array([0,0])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			cov = np.array([[2,0],[0,2]])<br><br><font color="#ff420e"><font size="2" style="font-size: 10pt"><b>P&gt;</b></font></font>
 			np.random.multivariate_normal(mean, cov, 5)<br><br>Array([[
@@ -1160,8 +1218,8 @@
 			&nbsp;[-2.93207591, -0.07369322],&nbsp;<br>&nbsp; &nbsp; &nbsp;
 			&nbsp;[-1.37031244, -1.18408792]])</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+		<td width="136" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: 1px dotted #000000; padding: 0in 0.02in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt">#
 			requires the ‘mass’ package<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			install.packages('MASS')<br><br>
<font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
 			library(MASS)
<br><br><font color="#0000ff"><font size="2" style="font-size: 10pt"><b>R&gt;</b></font></font>
@@ -1172,8 +1230,8 @@
 			-0.4161082<br>[8,] -1.3236339 0.7755572<br>[9,] 0.2771013
 			1.4900494<br>[10,] -1.3536268 0.2338913</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
-			<p align="left"><font face="Andale Mono"><font size="1" style="font-size: 8pt">#
+		<td width="126" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px dotted #000000; border-right: none; padding-top: 0in; padding-bottom: 0in; padding-left: 0.02in; padding-right: 0in">
+			<p align="left"><font face="monospace"><font size="1" style="font-size: 8pt">#
 			requires the Distributions package from
 			https://github.com/JuliaStats/Distributions.jl<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
 			using Distributions<br><br><font color="#9966cc"><font size="2" style="font-size: 10pt"><b>J&gt;</b></font></font>
@@ -1184,7 +1242,7 @@
 			0.370725 -0.761928 -3.91747 1.47516<br>-0.448821 2.21904 2.24561
 			0.692063 0.390495</font></font></p>
 		</td>
-		<td bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
+		<td width="188" bgcolor="#ffffff" style="border-top: none; border-bottom: none; border-left: 1px solid #000000; border-right: 1px solid #000000; padding: 0in 0.02in">
 			<p align="left"><font face="Arial"><font size="2" style="font-size: 10pt"><b>Generating
 			a Gaussian dataset:<br><br>creating random vectors from the
 			multivariate normal<br>distribution given mean and covariance

From 261c359168412055bc67f57fe302c32f8b03939b Mon Sep 17 00:00:00 2001
From: Jon Goodnow <goodnow.jonathan@gmail.com>
Date: Sat, 19 Jul 2014 16:05:12 -0400
Subject: [PATCH 170/248] fixed a typo and a broken link

---
 tutorials/sqlite3_howto/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tutorials/sqlite3_howto/README.md b/tutorials/sqlite3_howto/README.md
index 7ef88e9..e5cccec 100644
--- a/tutorials/sqlite3_howto/README.md
+++ b/tutorials/sqlite3_howto/README.md
@@ -29,7 +29,7 @@ _\-- written by Sebastian Raschka_ on March 7, 2014
 • Conclusion  
 
 The complete Python code that I am using in this tutorial can be downloaded
-from my GitHub repository: [https://github.com/rasbt/python_reference/tutorials/sqlite3_howto](https://github.com/rasbt/python_reference/tutorials/sqlite3_howto)  
+from my GitHub repository: [https://github.com/rasbt/python_reference/tree/master/tutorials/sqlite3_howto](https://github.com/rasbt/python_reference/tree/master/tutorials/sqlite3_howto)  
   
 
 * * *
@@ -97,7 +97,7 @@ there is more information about PRIMARY KEYs further down in this section).
 
     
     
-    mport sqlite3
+    import sqlite3
     
     sqlite_file = 'my_first_db.sqlite'    # name of the sqlite database file
     table_name1 = 'my_table_1'  # name of the table to be created

From 4beb09cac11d4423ad67f01fc958af257d61369f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 29 Jul 2014 17:34:53 -0400
Subject: [PATCH 171/248] new workaround for internal links

---
 Images/ipython_links_remedy2.png          | Bin 0 -> 10520 bytes
 tutorials/table_of_contents_ipython.ipynb |  18 ++++++++++++------
 2 files changed, 12 insertions(+), 6 deletions(-)
 create mode 100644 Images/ipython_links_remedy2.png

diff --git a/Images/ipython_links_remedy2.png b/Images/ipython_links_remedy2.png
new file mode 100644
index 0000000000000000000000000000000000000000..ae3abeaa0288d32051ff622506f5866db3653196
GIT binary patch
literal 10520
zcmZX3V|blGx9E;-HI38Qwr$(?P8yqy*|@Q7Ta9hAanjgnba(rmd(L_8oge#oXJ&cU
zEX<BjQjkP~{|pZR0Fb1m#8dzPupZF21}r$}cSas%9RPqIwH6gsk`@&uR&sK%u(mS?
z0HlzrQry*4M{$Oay6&I@gOv({w;)d-QDL)Yir_g!(50wIpo78*wDkg;a!J7>lf==W
zmcd7KP!VY8f{<Q<=-^M=wQBQjynE3fU#A>CUM6{2Z#}o$oR6k5)0`Oq`}Wmh3Gk|5
zQW%yp{cn6}2&Q{KT89BJYyc>x0PNIMl51E51n>`?t!=QGg&p}QYQqnkhaQ-uryxW^
zfYK+H%--ppA`%~y@*A=_Ie?Og*s@_fb=odz@QDhUQ1GLo;~e6bhk$IRe$8SaX@3`L
zMi^jcRIi#14loCs&)VQ29ZtkTXiUp&NG9<kq!v$c<lswY%+*F?;~jOi_zKxCBEN+9
zK0W)O4a(FEvZJ&vV%GSOE^*uhwTrQnoDa*y6dKVf+xBBpdTpwE6iF}k{RtYCIfw%~
z1z5POMe&hX3vH^~xO8)aP#mWeR_ABE%U<o2A~DYi7R|_+ZH|07g?&2N`Tm3V!7>un
zM^hf?ynrv3!l8--mDyA`2NO=QOe_{D4Pvt-4Vm%iByl8?$K=aJ^<O<FB2)~Y3Fle9
zr}@{6@A(<hs(v~Vthj~R5@?`9l^orRH}e{MlTs<Ud~|sYQWI>;j>jU&$)Ju&`aQZ4
zQ1-$DWE@3I%z?Xt79fUw{y_}C0?;*zX?!Q<QvDvY!3*e|Bd0COCo`s)hw!8Zr;waz
z$Q*C1t8a!dI^C3C1zbH`+<6Cmdb-qUrvX$7#`C@%cVKTL_FgjsN*6BEnZW?*0)V3x
zzBD%5AtJBxegLTwq)RSxW1s*N7`BudaxYYEd^m&<RXAjw5%mrji4=rNEqEm~o{#_)
z<a#fI5;To45*KW2fN3sNCiGFC`3#g-AMgr*7+`z_9v%2-1Rg|;FBWL^ou?22SLE<J
z(m>D#F%cCke3(g|2NfK;D2o+)BN|O`WFAW?e0i8(pn~vGUWF2o3(DtUi9F63%TG{=
z#ypi!czw4<*k@ocKjW{^x!??tk9s|?uv;;!Ls9xCuRL23x{*+N8Fw5ph2oVElNiRp
z2Sq&dM3fy%#gZ_au+;--gw+c^D+5av7UUNIr{t#?pQDQN(yR~~5!Q$ZLJsp%3zqYv
zX3USj9b4Rq-^t$*dm+9diS}8UOfXM<Lbf2GfNKCxjHB*bFjg>OFt#@hWxlM_qJyxF
zb{+U*YFanCqP{|UW?+ZWOt2nC@H2MrE0=2~_Y@XR<lZie<8v#2Yk#YAD_*Pdh9n<c
z&9|1mm)*T96CZ|dNPk#=Y=4LkL_y(PbVRtRU?uRJ+JxF<C0VO4dX$J56yY*rrunqY
z2|wgEgy_gQ&>7K)(6<w$$oLa5tihzkbQEdG3CQ(HkP-yqlSeJg7;jAKqdP-<0yY%7
zCH-Xcl%lEZWbnxo>qAr&+k`yjo=b&PYt?qBuCSJ<lW5s!H<Ubzq?Fjy?v(V(e-vot
zd+1%a@5<5y+KAofJC)g%9W1CW_?@<*%a7_%t5M6BNu7M3Q!hd@&#aHHk7F_9R&0}a
z4!S`_5}z;eob_moY1COGUt=PW<t%nS9=#L!gXV?eD<`f!s6ehmu3Myc5IqKf7-TZ}
z`l&3sD%v!fH+s5Ur<|}Hd~vcorkqnvuyj({v;uAcej$CqzQR|-vizmAq|`yvLD|1B
zaEWPNE4NO>G-rrBCe``aS?}=b5Oac?<EWdW(z+P8x=p4*qCwYLPgZwaQ8vFx@^{dZ
zEHIQqmsi`n<U#vfnIJtjBUZ|?WQsfMNDiwQhBaAxtaAi^esSS`Ir&86<l$K1827jM
z$-yc63I81BX~P}Hz5A*AaUAaPCrG?X4lAZs2Pq~qTzDjRJh#t&Oe`Fc%xh)~CTu2^
z79clawrBFQ5@B}a)V5avI*nu6j<LuyZ`!WeLT9XIz)g-#R%i4dA|I08b{+aUGdpJ;
zMjmNQpSKX}dT5zxvuPpfu+V*@bEem&Gn{1ofy3$W(mCb1U3pfeQe{&`s70#_qjjXy
zqTN%+-0*8PX?>%<XywmZbpx=;7ieiuS-WoOzMO4AWY;s=-Tz+kt>D{2-mLM$Y%lXx
z-Osulam$dpNG;7wCWUN;-<b-$zD{mmr(6UMUsIz(WFyB(QsbY8ZlP{%k5Jl9+nn?(
z^k6oA*k9YfY(j6iZnj-4UMQ~ou@mTseVgcC__DfJJ4~xct5|j<J&j^8WnkO6)}_4V
zw*|GuFEA#cEMP7W=X>vy=!54o{Cf7L@!a%c`U>&Z^7821$i=6?Z%Nn7Y$V@1DCA@$
zW~9B7NZdedt)$C@kB{2<<;ghQIGn4H95dKS#64I5o)uY_@ZmT3Sx_z10YY}DZdeWS
zWpu7MT@)AQwZI&!lkNUDszfSnHOWSkyWQhI$0*EO%!AAj2uJjK4YjlyUo<e+KZiv{
z#a2Y&8SXb&E$9Cle2BWm+157cB=a6!Ecz^~nwqU$+#20+=D|lHK=H2hiepdJlFpmm
zD_tt}Y0vZ8?bhIG^)>5NIj|yE>xyocy^hO~Y?R$va;|hnUyIa-`UX8Y!hrIHQj@}8
z3QnH;J4@Q<#I>YQ#VAEyat-W~aK>WW63dcD=?U`ZgusKTad&#6L};1cEV<F-xK(K`
zWPK+oX(N8$ku2KGlP!Jq(7JX#npK*)I=qWk$0liW4xSEQJK?(`TgpwFE>4juV3s{9
z%XAQ`<h9o86#VtK+JD{q{YJ4TwwGGH(ydmvcf9C`Va8j(X}noFLVDYI6cz0j>1ONk
zPaH2EBbpVA^gCO%>+qaDbbxvdvJKG)ArCQ(^blY6tA8)MD;cGa(DiEOvD<7x>X7d|
zpK^OVhlxs#bdC&jgS@ob8r{&Yz1^%f@2E~$Nmjd{xJWrCq(Y(rZkpCwu4glv|Eym>
z$UczAO2^8e6*xP%?5d0Fs;N_{sIR@b^_zX~Q1?@xT$ETOXJ1LLOfPAgZORgC@T;zw
zFQu(~@e=Ye+A(_Si@J}!zYk+W0RjW?%#aGgaELDWcYO<+X7lUHBWBe+F!7TKeBWhX
z8Qo){2!C@;@oahgyb>FfUK<UvDD^bI)rzV$&t-3Jx>V~ei{(gdPVCWn_u6T^IJse^
zv24yJ&z|Jv^<+5O@aQOfJ9sX<6(tzwA=K0I__Z_$h2zZtWzg=l`gjwf5I5zlsaKQP
zed=?+z`oR!!Vp${X#aAN*t7cKRzI9q^4z|6zhqFd)!hB;Yw6Is&E-G;a(JMdsjJ^2
zYX7(y(eB*h@A%QH|GpXTUG`|OJ>4#NZS*EO(H|taAW0s_PNex^|Hpeipuaaw__FVU
z5>1g1@jBGtHU4@y^;c&}QVIFAOZH;iN^~aSJ;Awe_wn-;!e~@P>a#!1oA&kC{$fPW
zioa_dJwBcvy)XZB@p09kb%@KeE!XI<7(u_B2ftm>KFRwy7{NLLSHIZjS38qIoQ>4E
znqtHCZYV!*t~3sA>R&Q#^KAfl>t6$6fGvDL0D9Cud=~%}%i?C&8o`PpRr1rH4u#Q=
zLDsh01wevmz-JCX{BJN=AWSa$NzC{1-~}d*{01;PTiP0<Vl%R}H8~718)22igwfF!
zDS>cSs2$sn{e7|V{rzPgvgI}up_i95AD^*wuTOyHg{^q3g~dM>K11`54}$mp;B<Fr
ztA?N|H`7{8%SB6Gj>psi$Y5;dU}DbT0sIOodI11F4<67b(A>qC*aK*1@66-DPx=pn
z2lV}SnUR$EABc-BKdF|y60xX*lQ}Uv0~-SqsQ^4NF)^Q$nFWuEn8bgfgJ%4sRxU1I
zc^Dbp-Q5}7Ss5IhEE$=(xw#pcSQuGY=s^(l&Yt!z#vb(c&R_mR<p0tUGj}$1vi|B~
z?O;#*m#(pigR2WaDd}HE|2_UAr@4pq|8TN*{x7va3NrrPVPs}tV*GE~AXL7;t2|29
z9_DshV%9)&duI?20X8;HzJKul-_HMV{9l;b|AWcJ@xKxO*T#P#@-hCE|9|Q8A7lMn
z1zAe~o{#arEiVA?=PG&&0N|TQiwUcFfSqR{>0wB24O?=81tGzEE#x<ms`~`qI%(7%
z!>!~~V>LD9n-~^aS*zEMeYp=Iq<Pe`61IbCii6=-<U^|)G%2Oh0gIw4Mf$X$1W7Nm
zK=q595{zTj((P6R98BCU>QBz}5l6%AO-|b7gWtQQg*&lTx&&$f02Kf(l$!xLVxJPW
zD+GWAfD!ki?(BXvH8w6RE-o%Dm8;R@f<yj;JBPjU=zM>BSzlYz(b4%_AAWdvIG)P5
zzP|qWdpVA*4~zYKAnadyLXtuSvkS}1;|Dj&1roTBNu}lG^`Ozv0HN?79y3s2AtHgB
z@{*F0K-~JeIyCPreYF5OV!&Tcy^ytPCVgzV#EUlUaw|w&#Q*poM*~u1=zfIiX&tlW
z2K?ibiw7yh-vNdY2@VFrL<CKcsQwEW8cb>0zBefUA3KOZg~h}KwD~ITK^2p3_L7M^
z@;`6FnLqS)jbM-V5PurMO46`$DUcL$MY+I}CJ*8EC}xtn?Jxkply28^C!>#+!=>%E
z*g@DqfDvDDp&qO8tG9&9!Y+=7lLlYlBA|=w$%*gY4jdRNiN}nhW?YcEAS^DUAA_@0
zE2oGK1t;2)j_zYRiNdC70#zz>IS+96Uy1AI)FxfA)ufW5k<Ur}zWgfQ7n4J^jOMpY
zVCA+(we%y?UaqPZH3sRGO$R=anr;Cxoi2SbAJ%mv7m4`=lPPd2irB(|gWtFimjq8=
zS~RBetAbU&^nGAd{4#1{5=;;Enk;67Qooxs`;(P1nf{*G?WFkwpL~MIg`D^Z29%;u
zn2EA7-SxR%<4Ds{XdgJ};Nr-^Zo4f#k#SQ8CdN2@C-np)NX;u*BT8<0^FQU#vZr;1
z&v0Iw8IbeZ!J7lWeAFT2Aq?Q?q9vdjTGX;yk;ng(v5eNzUoCuR2&CTF_^BZb+j0~5
zea~z%ZGR|2n)wD-eW1@{%7v5S?HekxfzS`4X_tb|0L+LV?MDjeCGg>$LCrMD-1|QM
zH$L=^x6sDBKWM`FXQRtbM5bK)r?U0j%7lgOu8w?mrdLrF{U+RYgz-lMF^)BHW}ze(
zZ1Wf1&tP?;3G6ta7(nOF#}7I2l3kB5pit>meT@FljN0z<X|Y-qSx<}|HD8OYUm9n{
zmgd?S@asVAOC;44StOYq)&tFZTJBaM($aEgzi^Iqrr}Z{Rz6z6r;NdMu3HV}cwCZF
zQnYhK3is?hMhY8`@bEObivbB#r?29}iD|GrA{*%vornE4L`!#RJhR(hG!@PCCyQ;P
z6(ezQYaLoCqN{=!Wp=*_?>oK`yvJb1CqSel6+~>3a;O)wh7*j|gjh|I7334-BPX(b
zEMP04s}nepfWZO+OJV{%x94f)d|_WAV()s~OoR(%qu3eF)G|3$5{qV{C62SSCJ}>E
zaLP61%qcJVy!z2xgDIUplrXRY@WI(%@@tVv2TG2@8l=drCgq%(@wgq>9nl=xO2a2B
zT|$ZGef~u7ki@Yk$muDOdV=L8Pw^+82ASMHn{4j(vR0>GvYg<a$$35-cpZW9kf^ds
z2+F!^S9U*D3=wcHrt)({0vqgzXtT0inltZxuC-9t1jD}bT2I;P-W5!n#&N|;3sNsp
zQ#;R#E1@x~j)=Af5D^d`=i{4!f$mc<29rk^#ooZniE;G2Waspg)qs}ml)F<?KwJ{K
zQzc#jORA6d>iy0JUv5O2f+>5ZgCt9}p@6qMd6v`3oC}hnG#{^agJd|(G-|%u689t}
zM-jTVtSdrcoq!o#brA|SEc=#)J-!GV2EmW;s)|D&Zsl=P4OS>sc6(m#FfyxC#2DGI
z*jqan4y0Ra1UIy$%^WuiagD3uA^uP9ajj)yZf9QA9p9V9ty*!gtwef^#@W`amC*1g
z5<BV)&!mrvY`^Nm{&lyezwVYO!aff1L;K46DN;$Ucu4uTH_se%CF8RJY+|<$&f5i7
z*5$LHw>6!60nTjAt}X0a4eaM-B@NWe39=Rgk;`D~2ds)cZQE3`-~^F1#dFihMI5YC
zm9{@xgam|=yf^Vs-d@>ssig}QJ2A9k*2z=Mb{6U4C3SuzYMQ|My9m`r4y1OkOJvo`
zW6VGt8^`Zi=37L)c|@9*IrWE#2dfiPjL;{Dxzj#1)X`**QM`<Z+<!$sft;u)d6V)w
z#N<OLE`du+dzmO}q_1Gs6X1UVN-&f>u`ZW6SnJiA)%O9RV>st5VP;k;Zgoqq(QBi!
z?W*#XZoXOF7(u7G22!62#2@bAzL8RNLto+mDfgDX`gDe`s1Gp8unk1b8g^dpoA$4w
z%1^oPO<a-x>|Q#DLUJ{;0saYKH5|b`lIy+{O`(Y|PZ;2pO>CU12}f;b=2g~C9&x4k
za!i%{iE`f#_DJr=oXo=|bEqvI)|~%1U){_J+dBL*m1T$*;U%@+fO1NK9Gud5X#m~u
zt#tlws$7T%6x$S;z-~_?V0>sWxT7V=B~fpdylv>9)oE|%z2e%oShuz`n5zwCX#<_p
z@9n>+h9eCZgx5k1IKu5^4nebwy$q$EK1oqHxI@-uY<`B{s&MRAwG+rvs`i|Yn~XG}
zcqxE5Zj?Wxp}CO#5xS_MmNb2)1a&#rx<^w5+mk+aW?m;e<W>9u6yG38uQYKhV<S3O
zFuOUVu$oJm#SB4YEN`ZAC8V7K8ix(7wN4+PuMaH5KT<=l{ym&N$nYHhcifzm4aeYJ
zOqLgJFA4hisS?heWO?7-Sc79^%(mdzC`tkOg_Ym)*Uc}nMotqY>5(2z$2LtUZepuS
zB|;wJ2{vL{4LViv{Mft&LS^6UBifGE)2!GO`V8-liVk(Y2y|E(sli!)1i+>)cSkzn
z?zO7)b#_fR3BST*72z$1a{VW-13t)+1BG(CPSp4VvHvasU>m;}Rc2>RZLBW_>i#Ex
zGf@Cet>qwgL7RX;7;?brb8cZS{$J?cAAr&lW~6XX9tA)WCIL#1z9o2Vj8o%XbHe|v
zMLf(P{V4A%C(7^cYuNl5DD%zdpzB%{%K3*+{6y!~j`m*|%8v(@-fSMZ49WS=d0RSF
zR!v32%M+J1H9wX!i>d3I@liGnK8_b++kIz9whj<qoV2FBk+J_(6(G`f37Z&EpC`u)
z%CA+0lWT^XLdw<{=`SDkT1$UbmcGYcXZ(4rnCtQ>r!bfGOnbPUdSGXdjO4YvJ-xh>
zTTxpq%EPFaY|X~!d>^Ro8`M4*bh7DoYqU1xe;(OVC{i%<kA8|bU18N-M}fNt1nENs
z{-~<hRDZTX_z@!tOlTk+>#m*~X~{&!iSrn}Q}@&-tZ{Hk9N*TD&sv1=eUe&R)g>La
z%mRAP<uLqKaYr|;-rr=>4R!HwRhf!29eOCePnqNIx#;-wy7g+kn4oODXAkxM)_l?f
z#bJ8osQgxW$FH<_*0RdQw!b56on_7K3wF-9sFg;p6C*pM`9sEEzweBu(eaP2Tk*mA
zW`uanx}6XINwij;eJVr?=)8WA%#?YHva9EC-RG<dp7$oFbY`kua3@#kr#cef{5I?s
z9L)TBhQ62DdPYvksu7bwwVH)uIrf==-VR!z;n%o;X!Pf#Lj%PP4b9o!tcNL9^oMmx
zy*L%Mcb5m54hIVQKV46p?v(oK)$G%wn?Ls<$WGlPeun(`LE)p;keKTFVT&Eu!GBb@
zt*lai6Ffa8xITSn^{RJtEM*z^a7%USwlsyHjOU0omDJ`7qfwfvp{RXQ6)$UUj<o`#
zkuzSwP%iqY6Mr5h3b}?-qK5T*`=tn08|CwpkJ#DQl4iov!nR#TjqTCSNW5P?J=PQL
zy_UeXBsO}7K?b(tY2Tb4Z`%%7?q_a)jJ>m;jrQF3qx*9+e#jGo6bcR7?fCv`rl;dN
z+=wliqnxkfLwIZ*X0hfzhr@faGXqDOk_xvuY+}6db^&ma>AMbbKm%>{@M*%8_aWtv
zUlh9rNgddESeLOZWa<9lLDnAvW?6h+yqCu88gCBH12IArI3yHh*U|MrZZm_ybX$d{
zu9@QHF+O*LUm1tzE8zQh5b=QTdyVa=bXO_Zb+-tvDQW$yeel3nv3irzNKt$)_4uly
zJvG^Yvcur<k!teMN%OG5g(Im*?*Y!9@Uu~a2dNA^urqXKKRE9NXPt&z`oTW$yy}U3
zWFF^tMf0?nD^!@|#06ds$I$w(i}?L<+mA_Y_Yrq;x9xos!{y(W1UcJSf&*y!F(`@V
z1_UYIgM%Udyw=LII%t-Mk!ZKWOO_I&5BlPJ%P;r_Yne^&W;@FiNOBPw%^&)1;R~A^
zqMUy3*UQrnf;nHXU|mdt)mdH~#%G^G3OE>Q6LPvRKg|Xmwrf)IRiNX*hQQMut7=65
z=`A~)KR~kTnocVM=8v-#(4Y|LbURds(HZwbP$i$)OvO$N>Ry4Bx!t-eB6vD4plXXR
zr4VtPD{b^rzZjsPA#=VTMG#1;)?Gz$W4tOzcJ1flX)dEE>r)DJxVt_am7w0Z$La~3
z1N#8;48JRTQBXZT!+r9%HIv{UgrQVm=)2X3JBxdio6D{;6O&f{B5)riRxHA?@jJ?Y
zt<Kzd)x@WI;|-j=ijcHakj{NDESN=jk!iUaez2?7AD1^nZ7F2Uup%7K%L6xsOs0%2
zE2p`c6t9CpKzUis;+5%FL0|_mcN{^GphXRT!Xld!_5-u-SVF9q<lW};p4fKY(>JgB
zvi<#)ReITZpXD_|B2qiJel!Q50|WhMuObTjHRCE78d+b%PUgMvOBJw;^$Svd`%CUz
zd46@d&55|qeLPpn;4vrUINxC&=56n-kq&BSgnoR=@t)%_N%fSZ^SAS-^8RVu+4i)?
z<1?Lj(e@{DhpxsZ+sTP47FyS}SEoa;tar1$L&oBwJ(lU=_jar5)d<hk_-Do(LNCX+
zqd(tZK1DtHhsW&ZK50jrr$WRyZ~Wl-8_I=7wD>2v_=0c+@!g*|ICxK+Y7BfPxmk7>
zJ%cP=KjKmks|a&CP_(`19PzzM4#`4SqftIQ0&@A&%BqNq`mSe#EM2Hy1-_w`8#yHH
zRy`1+0m)tK<k{toEf&F!5*4}`e!q@^TW-u@xw)*c<DOU?my$ds+nG8qJ1CEU@y`45
z>)nlZ&W>_QDCwDELp$giN_<q<*tF1$Bq3%jxwY1R`<;VoV`Wxbnt>Oe*x*(Xid$>l
znuZ=(lt|>eFI=Qb&&btfc~HlA9jNloknuij85_m!@G>kJ(zcGB8r)NRJ?^?S8%St?
zJfsTN<Z^o=;(s|EUDWV9th8M>#m{7Xj)m{`D*ReoU>T`!{;sZ%;P188K=l1QyyW{c
z{|?R}H5q@!hsngrbKy9U7X2-?6(v_8do4`QjdSYSB$DH>Xt>AHJfGVOfxxc~n=7J`
z$p)#0o@3ECVs<J**VeHct6^rC=-y9z74IBLYu%M1OHTSAS%OxurhHN=uxhJfZRgo`
z!Ex~}-i)?^hxe?6mf?$MQY5J-pU>CC(D9@40WGjatd{r|`}^++hHa|rA8qjxQ4uL}
zag&hMP+wAk<JD@k_N+aH=I3NtHq;f@13I+b__PvgP2;I&(CsnrYE<Q3FJa`r#oZzV
z71ED>;tMXaOEmF4YVAhPchok8_&?ihSNMfuNoSvrcskhm%6{@A6Lj>u@Xk5NCuanr
zJZ4qv+m#7)(G$q?pBr~_RGpoXl%EcaZddC%cOm{h#O}E~>M6{i+>sywfDzBc2N+xC
zn_^ENFkbpJyvvMs)@)!Ic6(iMF&Kwr4cdl4Evh(<zUMwYewBGnR^cvC&Vp*!QPCu`
z=h6?R+^h1W#M&i=x;3Eenb!5AGM`HR(b%Cfx=9a)MZFTxn5QHCB|A%XcxNWrqgieL
zt^K{g`Gaoe%C6Gxk5w`P%7jxZ&O=&-#()8BB0KFqXMKVKq9?Prd1&b6Y2!kj^*L?H
zyc5>qoUQfjswv-e^~jR<tX`CG>A~^r=dwEsd!4{bq1g>gz5(pC#$qX-+X+v62P!^K
zT|G5bjVfWE;Ue0Z)fmd<lFj5%ydtQQH_Jxi;a?546l3;>hk0Y@+U$PZN$vV-K}uWa
z&Wj$oF>Q5PJlE874-g0H+SJ1Y(5WfEShAo`m*D8KXs@?6DVNc=J^G2WLgDp(lgx+Y
za8~nsA3MmpW$LD@s1IAHf84G-Vi+PjGCxT0`iH0b|D5BdvYt|Y{^J`Z%!Blm1RRow
z#K>Z^Au&O&{`DsN#2U@xFuY$RmHg3fHGV6|WQ#%DqJ4DyumR_^CugO0Y$YWaa_wGT
zTvYDsqNbK8>gQ9-5V1**z2kd*h0)tJiav?t_DGv${ur5P?_WEdrj*RI5o+caWdWW@
z9=}78mM&}DwQ=t<A-167QzdJt<18~INB+FY2e}49+(mV&nQLqX6Q5TcWTqiV28W{H
ztZXZ!>o#&IT1q|c9T;?dXcM+)G!;=XNpKdp-{3WQ-(JNxuc>Pm?<7^#oV!jsdxWra
zEf??zcZIDsu&}UC72VA+y>iMDS%1&cy`-ZNAbj}NK|O0PI3T%3%LENcXbV2{J9|@A
z(PG6Wc{h9P=U9rv=`0^US`qk%vP*1L){>zQ7oHrwOxf;@r$-N*Vh}xWed~T!$F8hm
z{%c#x>%+<C<ob}b*;{%~g5)S0<?^o@%c`f%gsL=H4XcCaD_**3+nf}%tKEB31Erdi
z=f3=NVNWl}2s#kp{$zpKWk*D-&Q^GT-{P($(U~-+8J74zMi@la{}t2*3V?-%R6*in
zLQqBB1*)Mf8GV^%=s^Xw7dPn9%iujA*Bb<Qrvjdv-F4R|)B*~|#|NOdG4E%HpbJpP
zJtGSj;Po&E1F;DXdiqPn0NkG)puo1nKn4ggf{OK3St3Li1W@Jv|1c67zU)YgO^J%|
z+DlB-nFUy`jS`L)4KUu1?4LCY9M@E{X@-o+M0iV>+KF3DaD67~m<6gzND#wA{uKb!
z9G8_OcuW?2Q~X?3rYHL$DP#{6#QfufG1(<Ib!GFxx%n`%h@spfzuMe_^7v2FEvep2
zuNx&1aNl`&-1lNrXV%Z}@G%`9UKBuQB<&`1;(a03qbZcaFT3B>x0}&+Y5G?L!{&ji
zKn`N)e@wd|+x<oKF9{It5dRr-9X)_G#ZR}}h~V#&N}y2K{bY&p43os)wnZrJuT?&g
zd;)+A?Ua#$nie$yw@+*!8}ozOnN`zZqrcruNdl;6QGH%9p80EHP&Z>4hZY0|!q1ig
znOpI|G)x%O+sv4PCj1hx&>+n{XyEP??h7MTc3H_HC9lN@^U5S=32MP019}rha#PDK
zPnN=`Z|wQtM-~cL@2T1$IQ}B3!2ZxwG&i>|?8#uq*m!*lu@|<M*$kj|T;lMJW#TCU
zl+cn>i{uHaBI4jci%Wzup|xC*au2HIo8exQM3dtOli-?oE+4BoSqon{Qi~)wdqRmr
zLAA9(?b`Ta$s{yP8rqJn(eqSE4VslY<$nu)7k>Yl8*QWRs=<bbBXOkWZY|@0?guJG
z0pKX&y_H_&EE+FE9FUl)sW^lNv0~2&2<}&&SzMSvgsUG7@RM7~nm<}Njl|Sy<9t1^
z-FKueyGi3q-P}a<g#2vm&e8(1;QSH>?nrS(Zh-Sy*kYLxykF(?eC+0TSP}RPq~ec@
zR`lWy#6PaDvl6L<sa%ueQsd%kYNm_zfKb*lwd)h=9Pu6SgM(>sTgrh34Z8@ayIsfB
z_&)6FNjW<6V-4Pi65Ukyr$(38$ej#wX|}j?AR_|6H;IiNqRGt@GJKD7Fd{kOVXmGQ
zVpP-S`iQ15zN(w}^P+$h>YmVF7crM;9S@r8O#kvG_8wIAb7*%kwFPd|Nzg{dpH<wd
zOZ|ttiB4IU?s0zCpksHey!?oW`b~G@^x{?jpz@afgK`>K<3~?g$ly36a8NCQn73si
zAS-!&d!M~YM@v<^!T?t8fi|fG;R@GNMtUE!MQi_P+0eyxV5%VEa_z(>opsRy)8wDe
z+oLiGn2v5dXNL>&b1*D$F3_u6E-?CBf|Jdh&RX>`iK<Q`y9rU%oi>jZwSiIAW4P|0
z@`FzoXP})rn3vI~b@tM)D#1urZ~tVqf4EZITX?vBKS!r3M=L3W<}HG588^0nu2xQi
zG08d%|9<q#7y4uY%g$j;&Q}T)3;?h$aRJY19gW}w3>9T%bc{80jC`onk@{Ek+4ici
zw)^+Ln?kYEFO1_|2v%||`v{{qgd^I*e?rYD)OD7XDd#D%u?RX1PI*2iD-^@gs(PPg
z9Tj&L7gG#jVqmi0U=Cdm1eV7d3cz?Wz+X)~W?@sC<A|*yKFm$2=}K5KdrJ6tC@Go6
zHokTDuu)QzQNi}0&Qur6XrL0HW1^=~%)jH!G2q$%?W=cTf2b>#llmrKCeY(#cs#hd
z=^!~D4~xDk6VZ#51qMA;lBUT~j7ocLa>75;e?(=SnH9rI7ovy2=P)vIKQ?5#V%#z&
zvm;-_3sHGI<FujUE={s(OM|&RxY4p)lrZUK^ih3`%#xO=WoBSAG&VmdD&OdR6IJpX
zs0VVJRAy;T<%A<;^NskWXoX-Vi(mImp^+xFF<xiz!+a;xVB+q~)#=c(nOb<u&X-B=
zA|_>KXLr+IVk|bt8eR&aC@GepTM1uhM^5dDij+~1k42K>-!}Lf6D5ofiZPU-0cHM{
ze3h?SM5$Upk=|mt8j@DB$4N$7_j^M^u3_4s$ty%@s*m9g>;zyQsZUPp$$HC&d%%}$
zJxa*%5D`I4w5QG8^mFsDmx<f^LE8koD})GI#M|>ubs+Utr6S5x+r%|G^LZ<DFuvCN
zXClWE3C)Xo146EO)8wh8eEs2{kbLHI=r!R!6enxIWIc_s0kQ~z97Q#<?om+-d1mo;
z*v>wG{a{oB;1%Hm<-EyK>{hl^LL9gSGUt5`416%`FNLQVJPT7DsEfa`j0Nt;Cc8+I
zOoWF|p-LGQyrZX2cErZ7jiF)<Gh@?XPU*{_?<#p#O|`YZ1iwNJVHc^D^RUILV#QRA
ztgVGwu2nGTTu!K^s<-DvI68LBmtC{~*g<rkOyM@%m&4KIilg|>6<@Y?^>|gMD|{o6
zAANY=2tC3or!ZQ5y4{rL3t=CP?b0~SO<ImP`3yMA?mDGw0if_pB++};enhl{XZ7h{
zc4$Qf-23QdW8Pta4q;0ilx5xdQI3&7aU>1_lvsxkq=f}RzIUt-DTN$XFl*(dS(3~7
z@6v8b0%Xat<wSd=@bEJJ{PF7N7yB1~gnH?~=#sUVkhJ%6g}?1Palb#H@hL%Ih6BJr
zDI*%r$a8=+q6i(Km?SILy1l5VsJgnkxf#3X0yy%YKezy}R&YD1Z7+|P4z{+o)@8h}
zuqCXgZ~_0Jp4$OwG~LxPkjn>>Q7C`}a0>T9@Q*ZMC@4+!D}CenC*B7PieOoyEx?o$
P02<Qb3S!kFMnV4vY5B#i

literal 0
HcmV?d00001

diff --git a/tutorials/table_of_contents_ipython.ipynb b/tutorials/table_of_contents_ipython.ipynb
index 639753b..1245132 100644
--- a/tutorials/table_of_contents_ipython.ipynb
+++ b/tutorials/table_of_contents_ipython.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ba20b7e6666952b09ad936f7b9cb32fc0e9ed680a00aeff02035be51895c8e45"
+  "signature": "sha256:34307c4f0973ebef511e97c036657231fc4e230e7627cfe073d89f4046f9ce9f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -13,10 +13,7 @@
      "metadata": {},
      "source": [
       "[Sebastian Raschka](http://sebastianraschka.com)  \n",
-      "last updated: 05/18/2014\n",
-      "\n",
-      "- [Link to this IPython notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)  \n",
-      "- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)\n"
+      "last updated: 05/29/2014"
      ]
     },
     {
@@ -215,6 +212,15 @@
       "\n",
       "<br>\n",
       "<br>\n",
+      "\n",
+      "### Solution 2: line break between the id-anchor and text:\n",
+      "\n",
+      "![img of format problem](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/ipython_links_remedy2.png)\n",
+      "\n",
+      "(this alternative workaround was kindly submitted by [Ryan Morshead](https://github.com/rmorshea))\n",
+      "\n",
+      "<br>\n",
+      "<br>\n",
       "<br>\n",
       "<hr>"
      ]
@@ -223,7 +229,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Solution 2: using header cells"
+      "### Solution 3: using header cells"
      ]
     },
     {

From 4b34d71b463eca2143e31c3482690c8c78a8bead Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 29 Jul 2014 19:51:21 -0400
Subject: [PATCH 172/248] cheatsheet upd

---
 tutorials/matrix_cheatsheet_only.html | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/tutorials/matrix_cheatsheet_only.html b/tutorials/matrix_cheatsheet_only.html
index 7624ea4..8d9762c 100644
--- a/tutorials/matrix_cheatsheet_only.html
+++ b/tutorials/matrix_cheatsheet_only.html
@@ -14,6 +14,13 @@
 	</style>
 </head>
 <body lang="en-US" text="#000000" dir="ltr" style="background: transparent">
+	
+	<style type="text/css">
+	<!--
+		td p { color: #000000; font-family: "Verdana"; font-size: 12pt }
+		p { color: #000000; font-family: "Verdana"; font-size: 12pt }
+	-->
+	</style>
 <table cellpadding="2" cellspacing="0" style="page-break-before: always">
 	<col width="383">
 	<col width="387">

From b8ad2a5aa31eeb6a1c90c431ba429b420b3393cd Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 30 Jul 2014 00:22:19 -0400
Subject: [PATCH 173/248] watermark notice

---
 README.md                              |   2 +-
 ipython_magic/README.md                |   8 ++++++++
 ipython_magic/images/watermark_ex1.png | Bin 0 -> 22863 bytes
 ipython_magic/watermark.ipynb          |   9 ++++++++-
 4 files changed, 17 insertions(+), 2 deletions(-)
 create mode 100644 ipython_magic/README.md
 create mode 100644 ipython_magic/images/watermark_ex1.png

diff --git a/README.md b/README.md
index cfa5708..b28d8b4 100644
--- a/README.md
+++ b/README.md
@@ -125,7 +125,7 @@
 ###// Useful scripts and snippets
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
-- [IPython magic function %watermark](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) - for printing date- and time-stamps and various system info
+- [watermark](https://github.com/rasbt/watermark) - An IPython magic extension for printing date and time stamps, version numbers, and hardware information.
 
 - [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to .py files.
 
diff --git a/ipython_magic/README.md b/ipython_magic/README.md
new file mode 100644
index 0000000..fb167a8
--- /dev/null
+++ b/ipython_magic/README.md
@@ -0,0 +1,8 @@
+watermark
+=========
+
+An IPython magic extension for printing date and time stamps, version numbers, and hardware information
+
+![](./images/watermark_ex1.png)
+
+**watermark is now located and maintained in a separate GitHub repository:** [https://github.com/rasbt/watermark](https://github.com/rasbt/watermark)
\ No newline at end of file
diff --git a/ipython_magic/images/watermark_ex1.png b/ipython_magic/images/watermark_ex1.png
new file mode 100644
index 0000000000000000000000000000000000000000..6e95b0338f57920b9338b4f4b7a28ab53bac4cd3
GIT binary patch
literal 22863
zcmd?RRa_=9vp$Hs`{3?6xVt+HKDfKP4Q|8W?(Xgm0}Sr&?(Poz!gv1XoZY?J>wPcy
zb-I&ECDlnid8(Q)1vzne7;G3IARu^22@xeAAmClV^)nO*;O~cN-WA|Rn7Od9f~2r8
zp@M_0iMf?A5D+DLMGUVrs321CM#K~)G#05DOboav?fLF+QJMN7$mCGbP#6o>mh`ab
zs2vBQXV6otdL(PKJBCm;j6{Q+AVR2~4MH#w;;r-!VI^=JNSA{pS5tly2)mT<%A{4G
zVf2|n`hlhWdA1vttsq2_Py-o7OAp#Oje=<Wd7pspCLJ=L1-a)F@1tycd**5fHe}X*
z{16{wTb;6!UyKs|sZ}M$+bA`~$H9_bQnaix88;;M-PwpJ?3|dm@4M-BOa02wSSL$h
zn=mLZjtCQW^c?No8g$g|;gZM^;{>6<a(=`H?S-P%seN!HNIMVDu<6icsV=FLTof_=
zL5tOc5*7nPF(_;le*y_B?X*3`2q%wFG-gdKPP}U^`9>_QHmea6w9A<?aFuC~SYPYH
z5IB`osoDWX94D}$uQrka<oY0RSyCpO0oohUeO6bA74AqdCbaUl(AvmMP`d-Ji+lJ{
zT|UpU$qB(Fo_ObNtdU{qk>zo(@C;1qYQC8x-5v)pN{0ClbtiRM87@OxYkGYnTLWWy
zH)}h9(SU$>+_(Uj*2Yfygl^VWHjZ3wyu@EMxB%Cmw;6~Dzp6M{@)E1dDi8|WIv5kO
z(zDPr67#_j5)$$_7@2S>iHQAIb3log*v!evj*Ef8)zy{Wm6_hw!IXiClarHy@jJu!
z?{t6~bdK&ePWo<iHjX6!5%PcJh!{H>I+)uzncLbBe#+H1uyuCgB_{st=)Zse(bL$?
z{C|3~as01g0S3tM`GkRqo{{0dWdoY>eBR|!Fn2SyQWr6|HnwpD^ufo%!NT)Z|Nr&m
ze|r31Ej9k9<#$%b|K0L`J^8PeJPe-$_+JD1&uo3&1xyzo3=hM9Pdy)ubF~`_5Rd?n
zq=?`TH{i1jcrVm}l*^ELa+mSK{ytJav{Vx7K{5x_X}c#ghm+mqfD=!L<>1MM(2c!3
zjIHU21hz<~q}}mcI*blW+I8|=QV~hR7RcNXQ3jRlge&M!#tN_m31g*}FkDgxhiIkz
zT9~qb7tD3bb4_G8R#rEo7p9i3e3>av7w2_=-vqOvn_IG;0Eh$-G@;*+?51H{oBJXc
z=`XNoAe3w{1&tl|ughc*z_q+zK%lJ1XVv&UVvd)sCFLTy4&P(%cet#yaHdZQz@&^m
zVAKp1?N;N)j{Bo2?n9%unkGgadaswgh`jg{32Hx|hL*Bk7YFQCGsa<>Vt`NtKz8Id
z^P>2k39oyG9^|Oe!lmh3L@F7yG+ulz%F4^jQ<O;#y5aw$QL-M$ma=~4LDkbJt3$_<
z;Q{~qRTN`kVw^qbXXn4b$TNWs^(X%w-I>sw@P9l_aJ<AIK>ep1RC%CVMzft;kGceB
z4{RHf&+5_Lq(r}lJz{a8tRMiINa&G*6qk$V2AF?Mholft!W}6m(jwefRSB?bFSI!J
z7x9{JHH*Km<$Zo=#ISoyiT~U1w%nZk=J3S2prfHe9Zj{SZ^Z+Uru;`hlEiYI)A3+B
zeBq*V_duHZ{c@f2p2_}^f`9eSWEZy}ePa9Sy%5K?<?UX}XPJOE%Rmw1Q;*G8WJ*bm
z7gwM3)!PGO+w_j~(aY24$CkNFyN|<?$c`RIR0kbtwbjSxlx{>DLNM{Ug9I8FVL(G3
z6hOlXO-7kNUa#!WwGYrKj=h)SSKk8rTzESS`U4Q@v|0@o2(~A)`#d`k>9iZSd(de*
z>uAIV34HEW!;PgGjJI+wy2ugocP<F{&6+wy2WvKN@n6S!0`AuX#5WSS;@nWfL&52_
zTd(*Lm2;4`2eLF8otA2iaeRGV*EXzldqgT9-z^nhYSEDr1;dP^_-;n?oItnx!>{Lc
z1ZEjDME6ef#-*-(F8GyOMpSv;G;4tkm@oOihVB2Qz2);|Aq5{--GyPb$ct}Qr_n*I
zwEtE36KtiM^^33ShUdnC#qf5G+Y&|9TS%2!SL0j5@x*u)VU6yE2U+XkNg}(?TH71s
zKxf-_%EvRPljETN^L8ydKbP3x{e{<6C4-Os(uVJqXGiuD5$;FWGsd24Y1a2n``ZN!
z-li?0^K}CJd}w1<SH8;$U&jLP-r=;$j+35aPv6Vi0M{o_-}e28;o}7z&#{k`v1f|Y
zf4(0Nx(jb^BdAs04<B!t;Ul`9z=~U76~SH-gXPjglT*4@bzS>anx(I4@;e-9em%Z`
z*GmQlWT{aQ;Q>!|mZ~<Dy#3?KZ_Rk%<IRvDc55W-@#dhD+6$#qLLS%GY+jcA<M}qv
z(Z}aVxAOUFin)xYN!Q~nlcY$u7322j=~}>x&rQgKoXXgfUBWT9>)U4J0zpSiSY$`D
z)%m=y+m4+>Qh)!3SMh%0$Iqu7;9fQAZ6SBj?FL&0ugS+fcA<ToGPB?ve)q#d@ju@-
z7VED6EtLbkMszjTn;bsbogBQ;(03fMc^+zIA$I@s7Vo9uhh>itYB)d#pEfJQFMI#)
ze%3(2<y-M~e5XYB81cDxG&6$n*i1}!=}<3dPhZ*({yg`|7)_PtgByG7zQ6L5|9IMO
z<0m$b=)5O1UzvGm$~pqJFZ|#vZ#JUPSZXf*Ef@Bh)%EJhO{D948#c23AY{?-lW1j-
z4K)Gju&m?E62n?IE`_NjDHOu?@qC^|7XAtv3sx6oO#V{gKK6StD=^MQ))wjo9(K;X
zSFRadk^ri%Cp;Uq=~(Uy7C=1#khm}b$zhN6<;OQ!f)C@hQEBXVo~CU>(2aD?4fL+u
zT&ttuqA0qRNiX5YEnEK8h1XFo17Cv&lW4bF(=(UrF~cJEH!^er-A9wL3DZQ=TQ19_
zhV9CW`_A`@f-ARnI2Hy;ksTv8992^^nX~vF+MAHQ=s3y353t9!{PtJ%sbyuM-HKYo
zx81OXs46&{R#Epe+O&_U6^lKV)vxXNryU5{n8u9izoBnJ^xDq{6YG+Ar!ZEzO)f*c
zuE=d~i||n_{MQTBf;NwB6S~kPju^AZ(<?Xsrgm)cz0RlFIvsy6ZrI`a>ZWJVmvzQ2
zKAdyD&R^x-pUMCA>_M;kM{T3PFLf&Kebb_g7ulB!D*o`6eN_46c)<&E@n}G?!Q(M|
zC-=B0IWff^h5tS_700CMdaC`PH88j$r?wlw-;><D!5kf$)cbFIeZ!|#;S5|vaAV#3
z21%mLYyR}4>%H8Z>}i}=*XK$tq(3~H9`0*Tg_ZjiX)bLpIw!us0@wA#z--Ovp`Fq!
zgnOg<Hdo4k>RJE3JW|=^Nw#!f`0O~^dR%n;aTV6)bGul(QnHyK@B1(nB+vV>D4V41
zz7MHOo}asAev@RY-qUf9M!H^oowmPktdf5kfdly)J&L!u{f*neu=BNg>i#j3@#%Hn
zvT5}lD?-ch#qsu`%@(ZF*>KNt!j9iRL%eyS!(OB%w%q-YA0PkqelGC!kQ<Kx?p<qr
zSr{>Wl){274Dt1Z&i5lL(8|Q{wzSOV*B*5gRSUZg*Y)&5m9feW|Cg5lCs&w;6>Etk
z=#<}J8gKoLP}v_HDoz}BtoeA!fd37JJwJaD5;>>b!9`_V9v_uST|h}uCC0r@qK|kA
ztfaiSa!@MTDDj=!;H)5`C;dNjCag!xfVG#X<FF)d#aoLjI81FqLE*XwPgQdqcRdVG
zMSZ8Gs*+$T)LL$F4`r-WAiaoPbB{7?i1DS75wXH``kj#lOC$KFleDWr7BKd%!LvVQ
z_;gaSK-4Ex?u*q^fUbU80ld7fl{oGTU=4T2lSWsRe>zy-+bMD7TAD?(FL6#pKpt52
z_8zvi33n8HS7C_61o;Qu)}POSPyh}(J46ttNG%o6v|&@3VuSk|ANs#5XD;$pu}|b6
z0|z|Wk?kjl7{Df1#q^SgCBglF0}`3&|MAumdhO6ZbTr+U(nkO|g83#k<;ZzYasFti
zIr6g~!20zQxMvwOGDB?y{*UXgh03lS2k~$FnzA7Yfa^Bmp<X2a)Jp&Z_{Y44fF0%+
zLiq-rU7Pt^9DF74Q_>*uuVF4&sB_{k_(0+ZtuTKD<=E$}<3Ye3RXZ*npaAvhn)HOA
z;)}~sV{$e(^%QP%lc%cPjO^^}imSQqtu%C$-@H6}uu#Fz(Zz940=Ua8OYzKWySZik
zmUkvsd*WieR0MLM9Enoml0(5mt}%v&sKn4tU!WSIRMEx|>F~o7;ArvlLM$lZm21cQ
zCWkgRhjL=U(aosE04T*y5DE?xUO$9u?t8)9zzCUJR#05*@62l4tmVx$YN1(fFNfcb
zX{0ha31n9~d8Fh|Wv+WtsO4jUcELs<7wjxlyGqM8RX_WyktS*4sD=^R2q^;Bo=uqi
z97Xoj-G!@vc?rQ~@|Zi2yXWa`z|PSpjV@7AN{tR-p#<?IN&F%0Sz4lIR%A&NQaZ@v
zqrruuDz<u`6&ywONB%UY7)ZWZo<KrzniGO9CAO2<rh{#x6gFXY(3+SqA@+Cnfc4qO
zd@zkdTgYOQ1hr{6{|PuU7VcHT@Z&#hY01pTOh}?-YkN+utAA{$=e8$e&_UEXL>7{M
zXiV{?%61Ll><K5b{Xr_wD(yE1iiBfNsIC$hzmf3Jh2nIw_7KLAb>64kEv9hqWt4c@
z)7f32TzmldB~;>n=xAi*D=Rs!xAX}!ySOomkYkG`2qJl%pb(4m_)uXfxYpH0jW4}J
z0gFcCTWKWlB%d#@3|KhL$?g2IrjlmBLy7>fx<74(t3x(M-9WyN!nss{R^lcEYw2W!
z)W8OO7!@!50raGs_BrMB6v6|jw$g)u4bdqg`CQT_ct7chZs|)iLeU~Es5qLniv|as
zxcJr2KedGgKky)$g9e9rJATZ&Y;{Kc<i1$cpKQQcBRcx`o#&@IT1DL2I>!t`#;B}~
z<e=eP)5)l}4<|%z$Sbg9J>q#BN{Bu_3aRnMDG+7B@0l5}9df|iiJ96xAh4B)0)cq3
zT3@+^k2iJHN$$}qdwCJS)?CU}QBRnE7QNX?hZkt6Qb0}<h$xTxU3rw*b*dpS{gE(6
z2c(gNJL_D*M%kooEE3N``@noHPm1dx=(lkl=1(d%()G9AhQOcpC5NKC_d19~Berd+
zLlw<FDSzv3B%XPkcw$V)UAkwl;iiu9J3*J#qySbxwv<m_^;~(g)SBmQJ}$^}paU13
z!i~Y~suh~5c`B1OL5}(<v>N($nw7*}WvOws@UboFTwLzb!-Fhrr@Npkq`H2i!Q=`~
z8d^^rvdq%4dtp0Bo=%4E3OF((8-Ypvn1}A_oz7i>kNoKNl{ywuI-6drm0y(@OzW(U
z2kR2faJBtWZ*Gh33?>_jcYdz2>e_)tWO2lT%pz_uIe$uo$j}vho)0d6@J}b#eOLNs
zdOJ3)l1`RJhp;u%SNrEbgT;^VYX_#MxGbOwUgnlKE%o1FxX;0*$`|GtYNE{Jv{kh6
zOmut|TSwjmH7=K66HLfiWp5GNxlrF$UjiEN?<!41!8@SG(OxLfY17F5Do|D;&DF&N
zbzQw@++6qsvuNQr6NzROAlf`1TepP`Kh3V0FWI944q;RR*&@c;E~YnSmxDHz2W;yf
z9X|Gy6zOC}7|1FT+fj_F?eD20d-&oAA83U#Y!w~Ke~9vr|0JaIjH8eEh2Czi-{For
zueul)>PX$%YPM<3J&l1MX)HCK6tJ>ao&EThE<Z2rk-i4@{^-XcA_v!T!<G`KOw0VY
z?xaOWXq|hqMf4-C3EI8W-gu#AwE!)}80OA<ciD8KMLslGXiAMhiq|??C|BYX6Neqe
zVLz!biM_*$%*b=56khVWo(?^doCp`@>YPGZ9;ZAK0BWVHv;;Y{vxQSi{spArU^L#M
z)I9`|X>jq>IW~uUp1?>Ia%(ZPbnKPi9Nf=y4G<0o*4}^%Tb}rP+_<g5a8}l*;i`x~
z*py@9Cjtkac7<QKLl99M46TE?zImz4l&v=M)FFLay|bcF<b7%oO?gClxhu2vaVsmF
zc{?VNmJmvnFL|C-L+kT;<W1!CImN{9?~tBRQBH9qk!Zt<SM!zXvHx?{R=$R#U)!@o
zq&6xkJzmZVovW}BG0Rtf!=LGMtlRnF;cH=F0`@z}TW*L--pbd0p@*+I>^8Gl+sPff
z^1Xwha)zg22~JinXt1WTV1dOA8$QQdew-w%StO_rl5KXvcD*8yz3G9jGPDRq$=Dti
zU}#C%?8SWd*+q$FlYrin2lsM|Wpryo4f6Z|VB(^OAkb(?RrkTF*1iHa6L<5>A0eAf
z0(^ID;)A9Etlz?2LgIydd+B-e_sc=AdJR~{2|F2Oj1h0mh?v{aSRNM3*Tka98Co1|
zl?9>e1}@GEPXI?&%L%jYGl%8HWkKPn-lJ=&s?}t5qGMh?e*|-`jA`!HAI_Xw;4eDW
zE^dr5s+1-!?b(zyN<_HTu79P~B73vmms!i@|BNS;?29d7;|OWGs$27_dXpf@b(*oV
zGik~~tN^FA2q(O>elH5Z@&lN5msr7zHI&=>h+eUHCh73nhh`uih*Br695uw9`Ov0-
zs%qF#xuX}PXhie)U{v#>M*#d@LHkTXO__FG7SkplavVEeEe{;-c3&`HIJ%W!65Rir
zppJkLSz%G?ZhvrjW%~!rE#CEhRR&C0>(Uwm216l6rm&N~Vm*>}I}rqZOLXu*gquVY
zj19ARTh8{0^dFl;AR=o=#_k;k9ZD>?89%7V;XjA~ti=*^{5};qGER6phQ!|L^3oD2
z*rl^Ji=wJFH}_p2{zip0ujfbb3r3MVe?A&GjbN!IbCJO2nE`I+bXjrfU@=j%?M>AB
z``-rw%~)#PX$&t1=qxJL`T2cYVae~|Cc`tXHKT`?43wp2B#fY+J2trAbo`yD9|(8Y
z7i?+=6T9=bzjguD3jQjuf6^@%I}sg#Gre9al7KO<ECQ(ufaLHyk_8}}TfWXkZUC|=
zga!bcnOy+`_D}cq|AR)!aKl`DZlil#(*$Wy`C)?!c6N*$%dOinIXUX>w-DspF%q<j
z9zLUAvx=3{{d;R`E5=d4!fI2Y$owS+)o3bKt1{KSb^ktLq*C(s^f~JifG#I8d0{zC
z=6|+1m23}}8swsc=sa(Q705oUF>)s`pO;p(w<J;GDn3+*g6D@v`p`I-$&OiT#qGtb
zZrmG3P)o(4Jd{_KpZqxNJs+4}&geRvjLDGqmTqfU5atke+926k&UvNlR{t~@iQhKK
z3Ghhpv89dH2`M{W%k<4_sS0A1_q0Pg>H5o4Fgne-wOO<7%g+PHWzUO}G_|$t4P7|J
zZ8PzRc^6_5t^QbVMM&}eS5fm+=Dbrgm+dVi^Jz}69J9$BqPC=uu62}<&X>C=XCYBX
zlf)ziypNt2wY3*IZ`3jB_-KMU!cUkhMGQo85+gJSqZE&aKoun~8|o1fb#^WG#}3b?
zk&c7wNAwil-AR0Uts?jb?Yn-DX)yevVuQw0^uoH~6eX1T?8?jOJ$~BSo_zRThLjM?
zSp}|jZEnL%kMte<jgK<U><~)4g`@7T!pn8lKRfXj+98RCR23`J;e*Dowu_ojcuyGP
zghc|Fgk|O-e<*JYb}?Z+;H7dYB|Cp2+k9y#MkpXA5kUIDrqGj4A}j6Q7Kve-oDhyJ
zpn)S|%!|vHZOqv_kD%CO(V;+nY*R2CfB4|`wzf^ag84xeDcK{VA#5+)G&c$2k)|F1
zQXzC%_Z?;pA+2trw17%v0}~rVNHu^ma^|8MTLZZ+3tgWMXDh2~qTKMwnXt$W3I|PO
zYHQycqL>q2I40IcAYix^iWGo!CG^n1!l^7voo;h&#$&Kq{qKJqpVEgkC=dAI@7>CL
zT+yy~O9eS)pDyqDan4<YBbYW)u0!`n>ab;EtX5YzPEBv|;YqN*?Ap7iBbslAs@n<g
zd0@_D`C~Tr6EDTqJ%Dq2gJkD8hp=-qnQnAJ+CW=8<8FZ)TSu?N#4&M$BsjAnuZjam
zzEvo+lsXK)bWxrzKBm81p=)%mbS>5`!ybkqOSazSiKRW^FP)~OZ3~?qv>2St1^vC&
zDjQOH1&rd$gRgtXd}HJlBbCqL@t2J|gopcO^hp$TroQF{vFQdw&|559otNM4L8?wm
z_ZZuhlqlQko>Y>>Y8o7IE<SWVpx$z)4Z0SaW1^Y6SUTE|jFS<T*Xu2WYlz8ML6`ws
z=`S!b>Fm(j&gVu?sH4}TOhFABVia6ll!Tw(fW6I;28v!XtvhY~@_&@sG$TSRj765=
zKmKL@`SMSzE>YoU>`iVZEpMR%x!Jk{_DxCa1ggU^9D^FZ>)D2^h%o~O6>8ifA`P)A
zo_*eS;q%@nd_99V{8*>6RIOMyy3faKKXqi>^D%gdXhXQ~kd?RyTNW+Bfh2N|2<;yk
zqBS$Ebv{C|$N4%3vVrtN>=SX_0L4LMxMWROp5))4<ii)T=JF*a$jQ;%AtcRU4rdZ=
zc8I0MQR01bq--yV8BgbE*D;Nf?CoKb(Yomb6`Pz026e(GJ*~6UJJwI*g;SnXeBO?s
zWKoUcN4wQnK7xbr?4ZI>+@(^g=2tW%*-C{+sM<Ktr=!rkuT`Q&nx`1pmojxx3l6gD
zUWbmH=6F5I7}6!hzU9)EQ)UiT)_hL4(e;S&kL(2l%xrY`AXxaQ#?0ULjYT*M)^9(o
z$q8i8>NHHK5at7SWrPu51Uu|YmX3{>0f)q6T@18H><AjL+<dG8t<b+Ps7zQqPJF2k
zE5&k2Ahc*XRmkpK`eXjnV@2VlMb&XC#vjexGwXKLvuwVts{3>X1ULn*ndSu<ct2~|
z=-==R--&GkX6ubWZ=hu2BGbN(*|AXZw~BDZc#nY$sgz~uyltiRrY`^hdh>Cv&Hhm;
zl!_yH%>5h}+P`g#u7X|=<_}UM0X9#oraD32A))DPo))&Kuv;%ceK0>x-*R2Q-Tbw0
zHw%DCsmznHGuRK__UdLnZnB@Sod>5xc6O6UA0+(5VE#I*M+^9X9C&-BaNN8PH9-vO
zvNc%@medjC>mck`5g2|&79_I409A~%g))-As1LxqDVaSu-JvMed-|zA!12=q9BmY=
zDq8rb+vVs2l(KX)DdGR?8F@f$rosWJFhxKBMO>C%q6``az1Vc83#}$S0s;bryjb{t
z?|*nL6lgT>o@6YJ$B53&rIby*U+&l((xsi3h$9&B@Uol-pf!FA3p#b}%+O-SH(hp3
zuYO)cn#dFTjYX+<)iB61i03pz>_WuNPnI`nIbdFY7xh0hZZZzMdt`I|8GJ?)W>N%d
z<V|z;P2_<8<t;5N-m2x)`ZFo5%hD;&WN*eB5YNf>=U?UaPn}xLJ^>xj5A~V5Oie%F
zSx;&<tPz-uLqeH<$E;BJuXD3ke6dra3E`Y%mmU~9i@q-68;kD5C}iTg#D84ppmcWN
z#Fz$WPZ{J`+`Au}*DY^DI^rdfuYgkXE$p>_p>CZZ^z1maT52tV8Ik4nj%Vg_fHjQ=
z=tw2P!`IxmeA-YEQ)NCyEUYm91~{DXH2a=g)2fBz`kx4|-e@a}F<wD%i?g?++`S9q
zeg-(5Ej-qj`6-36jf~H7hBdL1!-a}+Z-C8OvN)v_64;aqdGUg3P3|j<Je6jPQJ*7_
z<uP8P0IQ+}nd4xNP<ix8z|OJvZ=#ODeP^{14{lT)8Hw-fCJWqZkIvjAw=G45ZJqZk
z;LHJpg2n8ol+e(ePvofI`@@kBiC|Fy@r0#2uF7s>JKT-OkZBz%6Kwd}89(%ZO6jho
ziZb-+((}}<FY4`YUbE|w+bY^Wul5>3fL)Xm1g8AlyHA{&SJ_OwH})O4o>yn-x@R5v
zy3^7YnJm69A}o$>hB5VOx%<e2^iKU+*{iqC)4_n_MJS5k0!|cqC(ez~2S9Wa1pmlU
z>ZTuHzF+faNUGp8o+>M2x*jI8+2b<INwC?GJ9GC#&1mBoO&cJ}s*y{D*b!Lwow&Cb
z7}Tv-U(h(37d-0fQjh6#tN106eJIX!RBC~?5;yzQP3d7ruYr0RF)+NWnGV8<`CA<W
z04CA=ZYs;Co&;d%ZN9edaZJBawcrCmYAyl}L?lHp!1yExBf8xQG)#vEv_HiH76~Gl
zly<8teyPwW0ra_sQ~^P=0V#x$FA?O?074NuFjD2K^iK+aR@H~}IX~NW1LPU=`c>CZ
zf0fh$JRCj^C+(L(k_7-lD3I7xgTBl|1HiS0WB|$>2%rM$Cz%KK_h0E($&d^Xu`A!L
z#{E=I59E`~6IfJ7|0<P#uI2>XL;z|Aa8rZ;A*fI|WkR2&-w6P{YGQjGK9%-^CItuq
z(Kl52D%E`IrRt!L@HHI2PwY)-RSxt2b#Tct!(8iKot^pz0<;}3VT0KRp(Bo?dq;@m
z)2Mcyq#$8~Lpkb0?EtXa)6=7frl&GMoJYr`6fwbTZI->^+y`np(mnKB`EZp?K0okr
z^4CddnJ=w&Xji4Rdc}n#|0&r|zA(Km({ffhQmpcb=q*Xqz-(}Y&_;&zb>a&)<7-N)
z+i%cEDTRYBgO)Q5#dEUQ89hd)lPj-YwOd}@t+GWnN==P8qujrP<w6MWD32YnmRSst
zXV?Pfv`_*rf$;=(xYA>j|8})+Oy);dfm$~;*{O1h+F8vwl>er`(Hr7~u@2-VKVu6?
zrw;5$l&dRPF<95P580I)Im%lO#qMK6&NY))VWLJ>GU;*cHdxkYyFgkoVLPiH9*`Pl
z*6BJ&SdL_JCbGyEbv`PRxEcv@Ez4G*VujIxA4z#FniP+N%PstO3pP$f)(oF(#i_Dq
zQp9t?I;^-Iyq;jedckVBTru6iq?2BhyDFJq9unf7HG((n8vmCbPcj2nIp(IiEiUso
zT&$ue8})*J8=4yI?DY43Jsw0wF?+#EY?YR@ym0|UcyY2A-5K_M7-m8(m@(d2o(cdu
z=6MZH{}VLc-eVy)Shtz{#uYhvdTVLbM@-9f9Qdb<OZfqG-Ew=ETQqJDw}G{cBwsDw
zB?-HvMbVMDXp^JH`g1dWo)A9~EBzq{J427PdhK(Bqo1{MB5%&yqE|CQp^3thiO<)I
z-?sB>&8K_+5{&femaXKwI<7a8Z4;iIbgReW^52qWI=Q5oBm1<}tyB3wO}Y4>iuKDx
z>w7NMajUU^*o^|>ggQu^GbPZqPrJw{E!S-=l$9%h)imqGN;YM5uI!-3Z90_N7tzvP
z((nxO@JR-!5~N%*9ZTmiSY_4BaEW?WmS7}PUE)geRG_sYvFoJy*4qof!Dv(>Eg~4A
z8vl6QW`Em_`-R8S(;?0f)!LAOP=nR+4*k=NI%2_=IaE_x{}48Ez#hT9{MjX=WkUA{
zM?DN<F?Rut!li$q;RKRCFQixfb&G|iA5l%rJ&REg@i2mW^kINu_WBl$W)dnrUO|tA
z0`;;qmIkc8FXNEPC3d!uDf!;txhn|psRH;=m;;%@#g)yU5`QYDzt8wkrEN+$xJXW*
zajAfHiIyl(roqqK1i*|F$rYUCn`&!jhm*X-2tg&U#8EJA&6=o{Py}oim}(z8?RMBf
zI7&5C3d2IeBWRs8?rK27hdx_X3`O`^T3ha+VcV7(X@W-B)&e`!^%x;B#Hn6=-|{ZM
zpffpS_|3azlDqq3e>Z{SbR$ni;I~rB!xH7j9`orLQ|UM1H8w7w@!-T2xpinW{aM<}
zsnJYDZTShXRE5LDe>e#uEn{gktdsDV=Kp4Zc57zrh0jZ3wVt=bsY=U1xeEWV;*pOa
z1Qg@;U&ZGIoKvmHAS98vGqGqrMhi>lp=$<Y!egP^Q40hXt{_Hr=Ng0vE#L1dD6Wtk
zTlTCNvQ^ViU+B?2Sl!s<ZL3(57cE#DOQl#g_2!@o+03xjSV$Z=O~QdsoyT0j99$g^
zPptJ%P*Zp}TFa^(dvPEIEAv@Fk}rc&-2sDI+hh01awmJ_2D*5*MJDFX5X$tYy|zzu
zC*+qa^SC*pA}M>qYTJ~gZznp1nG9eqViqfr(>0dwD#+7BLtpwd8|?|Y_9&B`vNl%F
zz+f*I&aeTi2&Imix00x7gdCnBK{&6uR1H*G<{xBF#g%N;Et8&!Y5&lWc17~A(SV=S
zgi7}bnOhW+cVccEgYjXGP{Vdnm%$r?#Bpa6h*jIRMZ3j@rRMNt#a#;=0W16sHwJU{
zgd*o)h_gqiORHZs);tE4gr9VQkm31MT_^1|J`=81Fs*PpKQ1<3&q{07wr3yUMKlRk
zxfIWpwucXSG}wzW%G-2kC{l@nc_7I`1}D3VO&=UdJe(Q=iQ#r11F4`73HczHTw)|n
zZ@tQXx&nMac>sktWBxq|7y&lJTR9b&Sf-%nz;zwWRNn&a8>^p-!r?SR1?KeL4MT>{
z{SvucJu5b-7Yi;zF9{EN0ya9#t<ypM)4#(br3w2qdAT`bC!#JH_6~jX{*g@9Z<?|=
z_;*tgtDD1l+^dWt@%T75uw$qrseNd<O##YI5};2!oOOBWRvwB;?XD(8`zr()wZVc+
zxAo5Q&+xGMO8uncXVot$s;wm$JSt#pYgD`|@Gq?;_bgJNGDyj+wT1Glw_$x_%I8yN
zlX4S1>7HVp2!9O?#&6O$O^PVFbwGHvm+FG_{7k)aV=f&1cqgg*lM`5ZH8?T?iHB`+
zZ@=qD@m=2-XGZjEZ6ai>A!ptbJ70-q*b&g_8=cLumC8c4=VUJ|jtYhpw7|O2sy5iv
zJqZ&XAaIoxj;8eLANW2eWbV?f?_N{d7wUwwQ(fq%b;gLQ27vi^f^BZtuUT<Gu#9#(
z6HM?ovb1=<9bkq{U4C2#si922gnUH8n#%Xu_>)ndcP2)=rAKq3GZUTPn*V*9>3|*X
z6H@VgW4G9@DOC0Co0yC(2n-Z(bm8Ubs%<SSENpc+FU6dW=$G%3b>jE(QWW*rb>FBo
zV}agM2zBMx(2{Aq%o!zZymK+D#j^RR08$?`7aT+<^x}wc^YI2*Ou$6}{pcu-h`zMd
z@i8Bf_mFdEDVtAwmZm!MFdrMU<W_+=h`YOy8sZP;xl$ISY{+gT&pVymA~+|{bZ#0;
zX4)~4Wgwb3<s+uwrF1LpLlvk^(E3R>tl>1_jSIAU10+*rcJk(&aQmaAGShXzMu_L4
zlO4O#JoYebz<Z0i{Jyp<F(N=ImV+A%^!3>?mVzziq2S_Xs<(-JC0Vt-Pqx@_&*4bo
z2i@CuxPU`r6!~;n=+G=lYiTW0zW7G5cUgs>3%>Z3vKXb6ozyL?+w%R}MY8-h?S}Se
z2@wQlABcaVy~Pii$nu4j-j5#$T0I4=&tlQ#wbF*p4<1C-{cjl_XByfPRuT_%$j`MW
zdfv_(X~^u9fR*g~@nEJsuR%FM*nbHi8+$+LYReCvHwjaz3T|8oRa4RmZsxi_WGFj;
zd{A&Gqp6LSP-bz$5<6Zd&7{{!RLxQ8QuF{r60y$@Q#X{SC(>5sb20}|(vihk+n*Nj
zPuzgOdvUaSOI}}7=21thBvbA4T-;rF+h^>TjG4$Qwj_B!i%p9B3$T+_dz;bM*#y13
zEi&^w|I@$F8?<<xt70M~VP&TWW#g>2;etq%!N0~#2`ew+A0TZ0Z4b;nuU!h<v9znH
z)1ZcHy|AJ*pKzD4UisniSmi|bLcS~h{~0l8ANuoCL<XHG3w7^3>x37kqW2`oRpAm*
zN3fh#f@T6HlX|k?13%gW>E&%pr=`Xz?{}tbQhtL}Hq0OR4segs?ng8eRbBIVc`nUj
zfkD0#LalFc={(3w@-06Wa#+g41}}UbUh3}x+=noW(OaYE9^Uh&jX}JdI_U6-n}Nc9
za-)*7l4&{Eh-cG&_^_lfAS3jua@AV_gWuo$EOB@E_wryvy1UiYK~G_QsUmBuLxzxQ
zFprztT(1z~K&nlOEQ*iq)|Be6kn*ys6V*EjG9hn;ZKR&QP1<TBPKaf9;nyU~2*w^9
z1mqn`f^C{yG{G17FB-|8LV4QAp9<FgyWCWM6Ml7AoK5cENQjO<d__0EkOPb1Rhve(
zE=hG;z}w|QMy&IssaV*OW~L4<r|yVBR=c;>vp06&0)^K3$`Z##9prpCjzefRAX?@i
z(r~WZl;j6RJ-CjIxYqvpqa4#=yxu+`k-OhF3vX3#Sb|GzE9*GiS1)beKCMEIVk0=%
zTS*3UEJ<l{pYdfa%uA~6#IQYi??|8rzg}Kz17E&P&C=$y>ri`3N@j~&B@M3{*162=
z7)qo~jA;eZFJE<gdO^6S!QWaDRwVk58B{%L&FJ6<db@DQ<WFrH!~O~^-WdH;Qi4bh
z1d~N=r3or0v4h{%pzM`3Jv^RJys&52T7K(f{hfo?uC%##@`X|>^<Tp>&%45;?S)B^
zX&h%inQnyRI2Sn#mz=y}&=!!lD`lf@#~4CG;L$0YqCm$fVQtg#treOu@b4#Th-M<l
zwr#!Z1-%Q$WbVUEx6)7ty}vyNj>kAesb=bD3jdZ4e$meatXmm*hXe@i9XN@!d;R_G
z527<M?GDH*5OV%zDs8j#>UmpnMqb1aoeH7vIC+^Ux>8<u?=HL*9UvP}{9TuHd9&Z4
zvqT6vl_e=wgGesG=o;w(Ko$v;b708?Op?G*7?t%Trt71F6k@ofYk;I{RbwP{#O1tP
zcK($%t<g`NwzMr;x~&vsODH!_z__>Iip^b&$Dm8V=wmhdc0u1ayDOt=awY;@XMP(Q
z(m+-sNT&Gh;_w<7%MsVseBX>N(&|b~kx8+tGi<NOn2_>zdPK#vsF+EkUMWsfX6&VZ
zNH9@gqwx7!#^&G$ty_N?o|N(EvuOVdy`-;gTpdbcU11mauo_sQobi)E?{%lipiN>o
z23ldg(Q?e*@9cS{1E7o&)8W(+$I|K}wS>-$OiiR_v5aEz{v=9+{8!x{VC@tC>P^3E
zE|t4NknyIjN!jzemqqV&2Se-psxHyNfTk#FE>1Z=TB3p6z&~1tS4>Y`b09r(XOarj
z=#oT8m-<-_{dX2m^{85Wj1IR?KtaM1KSzH%f{aP$eF8xW+lY$DT`F+RiU=rDg)&ZM
zh{3HcmGm6KW6@49Ht!-vhcP(L6_ZJH$waavHYoP=F4jRSbAe*qrTNE=jM0n`ek+oe
zrgwA`C#K;p++ls6Z~lflDRK1m@si4rxB!(cF@C@u*OO8{Zpp^u$X!BJ`TdzECF@Lv
z6z+^FF-8~+Aap=OUAxRmZoi`o)rtN)wzl?q__%ZwW57^Yv9lZn$do^OhB&Ntc!~3H
zpsV4a31T5JIBfyNbe4EbHIlG%vn2L#<EPKZsQv>93EFs?i2UaD^>OJbDu?ZUjo3dD
zWc>Xr2gL1UFmi($Hw_IH(fx18QNpC~hOsG%q1v-{CdR7KF=UPuz<6*s(BI2kbTGsp
z)9;*qQdSk0jKyF?^$%a`L(U;npldAnsc-mLuW5gHq^Uh#ZQo3ejMB^Obtom=wLCDn
z+1<P{u%HG>wH?<rM5yshS3j;A=LVLD5T0vjYq&0NHuA*Y-Nvnj2^C3g@4iRM^E}He
zq|e{C(F{2w`o46!yD^#eraiw1^Ex|Tv=ps;SoQjsY^2;Q1%z=gq52XZn~dQK$-%Yv
zFYLP~dGoae)shqW!36bKx;H)q<jT@Gm6C!Hy<#?*r!wq8%C$@L^<bh0ek&yO<M4G6
z4LRd3E^+*`))!gsSo#m@KYRyeLid2wSm@Ii`5>|R_cMEHYteA*i#dn|&=2(A&`rLw
zqJMG#I1C2dm#b3FDCFm>QkXpeQOT$XM*awHxIHP&&r?%>)QLlCNP;>y0jni%FgVlk
z@Hm<h_{}xh{mD72>n7NJXFh*%<Zce}-hXL`f`NW`2LcV8#BWGI&p*N0OImyH2C;4J
zQv0+YZTn3-VWA?IAc9rr&1J!caV5v>`%)v@erh8p!w+K6%n!OQiz|@XxdU(t7(tLY
ze}OCW=tY5v=U}et2sT48ZDC>WwNm<O1yq8?C544*4;A5nLRc$Uyy>bETlF=CjmbI0
zFz6_EWsfk{(R(egV&9L>j)9*2r7iCl#EXrtD$|b`!7#zUb*_$}WuO*IdR!uB=!!|d
z@8IjDH1ATKPt@un=^z7?N?>2Polft@T4lKrM>;4MGsF8MXT8W4!Pqr6dslvqi0G)_
zMbgo$64olW1>YgJYIM0@2q?jv`DlB4zd=ZUY;MuVm<G5k$S<(6lxu41LXJ~Y<@XI`
z&N<(~-wK?9-3EBF&(6*9Use>2iodJbz^_tc|Jp4bU@zB_{kBlC?hYp-rT@tjNg-3-
zNAfyf0nIzF`E=EVd^13aqzzfieZ#CSgGLeZ9xX}DorBK2O&0WqPfok?q#LFCWyU!;
zA%kdItw>&BvHL(uxa4t=dgoyhBs^y_>Gzg8w7ntC)~>Mn1VjkpId-%TgsX=oNIJx=
zq8C%tD+7O}FoGDXL3!v?q&RYV2gIV1hHrI7lj<{Vn4QSkeGtGf`qSld6Cx;8NAX+n
z!M|6r!(O?_N7B4m&HLoyoZpIC!}KnxdRzcrYey!#n5c-dp=iMj$QTbQ=#4(2b0(!;
z{a<`-YRfEYWGGGw35nmg*##GF^{`(ZGtv7JVbmO=iMHLyJkU@!gK{T%_(dF8aWI>|
znO3?MI8do6ebB}s1;i3-3-3M9Af^Q57Z2*4u;C%{%+oMtZCcm3U2v3>jSdhALtDUR
zJOj6~zkSE)=<W4EJ+xT5<~15(6`H^;vUn|L7(GW+t9_wYh4_r3+vPx`Wk8fD9pB|!
zTCe>Lx;dDq{W-07^Gak6>3W|r(cq}1GJz1?$DpYeEjF`&sT{#*9Pu92pnW&m(Nu$S
zbD_lh5z?cK!~prY%|r5gQ}UOlWR@cvu*rfsG;rq2h%_5loZ^7n>F>W!0-eV_`Ij1p
zI$cLssg^c2lV@D-<F*x5N^hQQW0-zOyg6pREmwv0O9!=<sw^>>x{kL5?_@?k1rO0D
zYAce40aj1=#Ey}Z%27z+mX_+=q@IITkYAYqqEKiJ@F*Aja#o#}vPois;$6Ov327JF
zHQMOjyAT!!A94+eTCNN1su@RT{zU6)aULx9FRs+sAX8(caAf1BHXSx+GoESK7)7_T
zi7AfrV2oBBReP#axmcrpVt|C;+34)~0n@~=N;s2*h)DFTLkeJBJHE?P8gY!ZmoW-8
zG+X6Dyt|&P$}=+AD7Cmn7i~!MI(U9Z_A`b2qPqReloX6TM=nj5m5(`(b03q6?f6#F
zGxe2c>*<x&foLB>=965IX<i{lPwP5|HR0VKfnRBQ#2}*L^I=Vm>>D?c1i0MFoUSgB
z#o+s6vB6Ds7psevr)S6UA#;;;ix&?SbLD34Hom#F9;$Z*2C9<Y@nflw;|Ap+&oA09
zt@u@0OnF;->Y{7QCev+l9qv^T9%N2$7CZd(6(u_7;RBG*=|vJ3`U`}~rr;s0ki0P*
zDUv+4_}r2k721bLOD}!P71~-_=f1-#^_*AP2V*)DL+|f?@4}vKsta_h5@_>{U6Alv
z_N<TMT+La)8rq=i`h!=!;>E(r>XVLrdtNW`2l*?`qt1ubG*Ad0>ct&<5x7TZcV6dd
z8}Iwp)q-dvw)(KZJHnN{A%t}ld1$LQ{vlSId?$ldkVhvQdA7K~$srj5nAn8#pOFJI
zD73VY7~MRwpweYy<-_{oLu-Nb3JlRO$9Z$vO*lx>gRI$+(4bBPL&2%yn&#xL)%HxV
zz80%gW$TkXIit{NG(FVqZ)|rF&U&lU;#*z<_e0&!@Z>jI*0BV@ZtOIOBXic8QwTij
ze=vDF!k_v-M%l|2ai-Y1kjZbxZX+!n&fB7qpW`VD!c6IPQv_H&`x$NA6c_=E{vbi%
z%6VRo1Xm_7U}c4a<B(?LI|lxRrb<icJh2ny3_)*CDaS5o-p*zs;eswO+<CFe9jzpb
zOSn6V#6tc-Jv`f5-fOYdyfV!tAosW$2(c5oFVfKh7~?aJU5iRtS$^|CIN!AwvY*3D
zkvU~e1{2wPAE0G9Gw1v+g!PrfF92dl!l(*Q<HhLqTA|#KWUi?xnVg=ZQ!_wqVFf>+
z^yL8~!n)PP=P^-9PH8?V_)ULQ%2w=M|0X1yuV{e)2v`DZCfHY1DggkGkH#UfDDuVs
zH@BnXVjucG)GNpZN>@Fe=F%KB{lOvysh<5)Rhf~|k7ob?Hnk@B+L3CSSi&)j3XG$1
zEwKBN1gr_FsQUm?Uk?QdD=NnM)O4fye4otOS=#1(SpJW!KcCzmauJ?1Rz2{>T9b1%
zUr+*+LLsA~LVN>E`kx$4tzI2NaZ*&xtii;6chjlIEjK-S(#C5LCfW_zPBNd)0tE#N
z(OCO#knhosB46#3exT6LVvvYK3FGg#JZGx!c}T1Ko=uwD!<@x>@0K?eT@blayu3^G
zeD!=7K?>or{(7l8hTzI_sNkO2Vxl|&?)NL<6AHM1h<1kTc2uPM?{HW*Eo_)7)iUHI
zSgU4Ypssq}PB-nqSQiC|4qqR-9xrVK!RmRXq#73bDHo;4!Kbav{=BZUl<OKuirubj
z2ygIDLnkXTV+k`6jhLf{#Ttl=9w@b4NO=(%$s<!Cb?dow6}-uBmqsLr;?BoUB(CWr
z9wjc*K|8&eSBNW35ig5xWkduR3^Q7Q$#ecmc2hYrWy4UQl%{2vEB`=1lIHfm_`BAn
zLw%V1XyErPkV?exg-nZqX6qqUWhn<fLbiaEqrtrLpy-vwDXFmfQ1-vu75FO1)rA@6
zLV-AQbUAL8jFA8FU&*y5a|cX*{Qd*Kj3Vk18qDM*I(o-$rWlhKV3+lORGQU<Ex}@K
zGHgC)yPfU)jtnQrxB;#a42oyHV4lJG7AU6NT;CW~2*ryL&h|2BO<AEi3J4T+2N5a6
z*1DLs=I7Eyauq-$DGr2wB$&U8Ujx#3SDo6Xz@m26<=hw{$0}y)&3;(1fsKxb@qq9$
zGDpJ<V=&87>a?kQw12>EDVY73fewmqSe&em@DP`-t5j=$Bg3#)wywZT3C}dJGt$PT
z-~aq(q+4B=u@>B9lL_eg?@hY^D8U~a-B`^cHL-<|f|qYd<3fQ`us8T1bV9zluQZY9
zr1Lb`o32`!*1q?1AIHC}Vu9cDPXg>FFA$$I)|S^=(O07SMVLuR<`KUC<NbBx=bT!^
zmL{#y6=Ma*{Hi<od)TUf3|GB*-0X2uZf&Pbt-`m1KNgwpY!@ReYqd+T!(*(qQZH2+
z>)1=c!~SV}D<E)HJxwE$6!9~Lm|PAPzRH;k+%#ftr5bHQ;^yRh%L%GD1)is89ZXMk
z>zVGSFt}nmH_d4_E;*2*ST(Zcq;ZtiM-ED}haj9&tOd^Wvutb!5&Z4m=28gi+vwkF
zvkVQ(2agP1@>EFNL7TcdBAPVJea*8@B$>?SwuWe<D`j5Y=6hm1F5e-2_4B{}yW1{C
zQ@``AaS99$f%$GL!FyIp$YC0uesOO;U~6#za$%GLoHIM4^97x_Xy+Lnkhyrr{3{t)
zbBG`5kc!s#p`NUq?!(rMMa>@x08u+C!R$enP>v~DomWY8LRkg4gF7ssjQyL@XhwM0
zOh~KQhDNmbF%Q#d>OvuiN5P)n9ZwI57Cy=!FMWyxL4ZK=4}_uJc;t(^@CS;TwCZE>
zJV?c+W)HDCk_I7@U1C4bO=T?qX^71I8Go&GOMf)EgAobIk-*Pf2N_k60}E$cIDAlp
zm{0{B9qojt3LRk3SCUq~WVUx0l$ac-EF@~vi8Srw)4ovAVuq3M@N`?Et-Jv<{~rt6
zXi#k#Tl}Pq1yJc_wV6s8{r|pl7z(NVYC84OOW*okzP?8+Bn5;lm9MMy^8og1UIcv6
z(7E`B;~wzcf*+M6VDpUUvHJQ7gP!g4q|r$7=S}nz+oB{37~>IK<J!AJ1j9IlKU7uO
zE^<cepSI}jA0<SG!ovfS6-2MK#DmCOqp@+RGh~PzP^91x`lmY$jQRv-6=;3wW+Eu>
zA)sa0K~RgzcRI8DxcytJURgd|X97uyZLLq&&UndW*#C(>qi%E=%eu2V@+l}UWqa}}
zlZm@n;)pDO-CVerAhq6Nqfj97>geh)Bc^|&ZTC&%<_+uPm1T82Snq5=<NOA04+$6w
ztehTk*L;qykmJAEf!QVJ8@?n@r{I`cGMS#YX!vp9F@#42-OKYF0)kYftTb`iNTeJ_
z{7bPC;6xM;%tl8A{~4~=OFur=?pXZOGnN=Th%4!bThh<(OY~w<vFwdx56F&ju(6L%
zOh%Ixn@rZ8=d27zdI&rAC5?();HCX%pT|5Q)k)tlaSk;$1nPudJ<gt=TL)g2;#{A|
z>Le}@8&c<33T(#C4X>dXIz6?8wcmz)UmgfJK}tey-F($RS@h<1aOT5ARWEqRCvr)7
zJlO-%Xfcl2k`W5Rht9ALB6AF@Z0D_)-QB9qcmWCDcg}ZZYOLk+cX05XovoevtWGjB
z-~oq=VTRd%x>~&?!Kt*o9vm1~?YGewV1DEJn5$qW!`SoM4Qx|C5;7e`^sRb$Iz`=6
z*ZX<PZ?tAN65rs6*<m;?a2YJ;kQ5b*(tYNj$S{GBtjToW{K`Rp?qxl>8^g6KUWVhs
zI-yFQhylR|1?x&7QuvCpjNVfzhC^@yJRXE_Ny90VVjL!ndnkpQ=P1O!h#1=+DI5$J
z3lS}11LVcTQ!flhZ$_o2aphF2ru^m`U^}#I+gu>YIBRvgY4}_gr?#WPqz5IjNUpY#
zN?U(?>a$o`#N&1Fdz_^Ylf14=lCaslUx`~vtp4(Naw|tNI2s}FFu8|zH+-N(l1WTT
zRIi_JFboo<!65WcS9=jb1`mdxyXiQmYb~3KyzUJLH&}$~se-*`?Xj6qr@+oxPAdKN
z@|M5!!;~;pHSUkwvlG9^v))00+G9`ICz^$(q>g6BjzfZJao>vOSnO_An!-x`3J$T3
zPe#KYi<N^AR+PD8J08v8YE5u3SKaK39rY^XmJvHXjj6v6tqys+r7SL?qx8UqS3(Eh
z)nfs8uehl0&>2Fz7K%;hjr2Nb6w?tHuw+Y0`Bdy<c3vIFu`&2o?SYM_iaQmqR6ZJk
z{XUK4VzbgNF%Ofh|3lrM{mdc{2d^Wf3A|1TOotdMLE?AhYKU7!85fGNv|QAowiU|e
zY{O62)zDT}R@UZporC2h8{o~&o$5<RY|bjDE2*q))6%ga%N(!Nx%+dm=536k(EhRK
zc?<UY%<uZ1fHArfT`MZI3X2F-eR$qY*ZsOvMgE<gL+;cASjHU_KAdeODh-Fg?Vr()
z*hq%^+NGRD1~vePJjx);VC%6YHF#gBg3}jPk~{sCog5b^z+|kgiU6OXL7w`Or9y9k
z#Wjl4V+cePGA*-5LEQDSb|Fe8EW0V4_^<?bTHY1y#MQ5;Di=3GbJU3D5HVR;t)x8u
z^y{R+8K=j^8MW!|JxB|If?@*|x>T;~(KRAR9`?LBAP*07p*LQ;150cmp-$cOfW9@g
z@HcKLpEIOe{6`;!MrAlQB{I)gUB(Ds(QZ6NV^{Nu-o{Gcf3ogN_TR)RQItRZ93=<|
z!oB+jFo~Ioa2Efr|A481T~La)NdpN77%02CbMy%lq09k$D99?pqt`j8iAi@VCm9lm
zSZkx8^@Fl06T%eS2QXJJGQ3Amg<srU0h{_7eMqmcHr%hFQxMU@<QrhV(<bag+KyV(
zLgHM_MStile6G}eZ}LS|oKt<i<5fXrxLj0yt~6O*L^CeDt!S-*9+NT-JeI#ibxl26
z+oPER-NwNvfXLj>0)Hfg#S#@uKsNogbhOYdM-UJY`snBX0+9Mg2SCN-On%4*<^3i4
zlPDs96NY9Qr7SQ(#?>ZQdJr!J+{?X$+z&I=hLWi9bTQr^VKb9P=wR%{9vGT}G!eGb
zvB*ON1!?lki&ZRFCBzmCqb+h&2cRVgY#MGoBRX|L{BiG)=_`jcVob$cR!6AAa!ZCl
zDm`T7&8V*s9^yuVB_kdbRNOH{xSfXSeVAly@X)&?b~g9rux@aArNsDQmyAVBZ5Fs$
zO#_?Hge8OIONdUSXnP}NmKKh7J?tS{&5QtAu&^#Th70K%%yrmsi`uveGbPDh9}8e)
zg)n}O%Ja$vNsDq{=pT(j;S_ua?P9&&(&yCpNH}}<L7*gS;6-`FxKO*RMp$Aqm*N-6
zNr^KIX)wf_9&b?P)DT>?)pe;QM#BHq##u*26?SW!h7qJex&#I!g^}(Oq>+$Nx)GF=
zW+(wc>24UhyCkHBlrAL%>6D=nxM%RYcipw_oqx})bIz=J-@V_xpXawFbYk_X&Vhzp
z@*dKwbM`oR|K9O~f|`!iHTln!gb5_iOf+mzS{Tw$1o(S$Hai0>WqrFtQO_QJ@aoO=
zh8h{8MuVOCa6V%@x8YPxgj3`{%%mZXLe0nRLE?-Blt}-Om(=(4z3s&t6<+gs1)zv{
z<I2N}EFnO9P({Y2j$%`Vk+w_RN28V8m2sSttS_<8-d1o_hSr53Hrjt02aXdeJ-J1m
z{mGC<rLjEs#X-Gvx;rkVTHUf8Imm~N+CDlb2vtqpdup9Dv56+{mCf%BdmEffM^%J;
zf^qf6bF2$QAI*ZiXN_Gv5St=p(08P5#A42}Ej^%pB3*zM=<Fe6%PqTBox15<YpLMA
z@fs)S9DSVIr+BiZJ1d>)Rck#~Z>uBz`q1#M=~L7Z(~bD9Uic`sRoRTgAG!P(3!f_G
zRs|!<HUorE6ZO^$d=Rb|u_D4;<M~&HvRtfdeTs!*OFB^YLTG49OK4`?fWh&s8ZEn&
z_pacpD{KOf@CqWTx=Q7U_ndXg9ug=MBw%md*=gT>sK2K>f=7m!z`%Y!u)hI-jjnoi
zbn1<-K2{PQ|BzHF_Qz|Nhc9~YWU3C@B|R&_*`_2F&Dy$KT7Iidm0l^plZI^^mbjW=
z!g<rphfHs*XqWBM>b)W2taik~Sg?lIg=V4#RxI-#ow-hVec=pjREDSfCMz<wxS+Nq
z16HQ=zKP8`4{D*7JS<syz#?arkYXdEl1MvT(Qfgzr3#Fineu6(`Nq$hU{OVwehXtA
z#(VDR`=+%bMnHM?c@zm*87&RFD^-2$BA!m#n;$*wsVXeVKNQE|KdFGDq(St#VM7Yu
z)G!3xVy2jcwFHW!Ew7{I(w!GjEc#;bQq$uV4fgPu;Y2#bZN$MAO2L91lfJMc{!uMo
z5DkgACih?{Kz_^p7)g`*%2qdwL${}(s#;ZwTg?p`=1ec?&g9D{xGa`Y@uJ9X4%8i!
zE>Yugp_SS{tIl1)>_cq{wVR6(1IcxDfNUWFHTc2b{F3B(JS9y9!i8#w9^r(_NRPR)
z&U_23|CCVE8u*R5k#Ro0`X<Z`W)sgOZz4fcbQe6_d0iAEMn$Dc%)qO=o+o1PAeAnr
z#bG&ue~;HzNKscr%aU(>l@3UOlOa-?ma6?<j8b8$#^A_;ICEKnp0&9m{f(|PY5m6e
zbzce%d+7{{pJfd7u%rho6J+kpcdW5m#Qt&Q+i2pk<r3uO4o|_ST66P*ump{w@~Ix9
zu7%zgi?r#viz#-U&5lnu(esFyh=+7$awDrgEyztPof8ZCa+;D`hVZIbcXO%dgFlz`
zg2R7R(K=cW?2p%BAds7?9%+Ox*da{+X^?UJ)A3!WG(4()I6iYrDquq?L=JC5=bR-Y
zV{B{Agh2W0-^vQG#rcmM?V0RD55`xq0<mD|hr9P?h>&jS4#_PKcYBXrT?gv}t-uZ!
z<D49x^}l`{-_2ioH%MGg=JE0y6`g69Z#tFwAAZ_}sJ|5`EMI10HDXRY`4t`K*|2K$
znQ-8R>EE|{Jes#7Z*fZ$=N;Bd{4w-ej$H*v%h{}#)Ajs`eekraMrb_m;V1}`#@-UK
z<;&g`6)7ii6aI$rP3Mi=kaO=v-3^r4s{UVlE=|4zj7~+^3KMekX2)2vsFfK}{$v6l
z0yh^OLJchsT)f5cWpOhCtz^FejovCx(~9W>+&?~3i>fS!b3WK<Seg`GZKjz7*A8Qx
z<70kZ3wg*|5Ti15M4_-B)Q*ofUN?|tMtThU$db3HpUI3^rzQWZQ9Sh4yrA?nGFed3
zTDI*dKUX7-pb6ctvN|LPKh<}jkboy6>0>dSKV*+z%eZAYT9E9p&@qzwVf#Dys1H?g
zK9lEepCU4RqC!_E8TQDs3KE{+QbA}1O3bzVKs`2NPijh!O`RGqp2V=QYrFoT#FZHv
z-FVf@6rqbwf~kZl4ubRiiLEbMC|~t)8cgp<&UJ8mfoSlwS8tD3j>wTbb_kkl7{9~*
z*g!)zWiy--I7-BNp>>OCLrK*MMgP6#J983l`>rqf$?)~b1hr-|m0(M(%S>5($kOgB
z4QiTVrY2<P0K%d%75(z>`qiMG?)8~?ouU>#?1rxcV@0#t%e{f6=@m1Y)td7f0*Hk@
z=J~l1J!^k3RFbtjMd~e1L1FBrY|5Se-qBhKmbD%4_F?sYfi5gevP2S$+M}6*hN&1a
zhJ@4)GS%Y0mz0le;AHXOXK*8RGOovu`jDe+3UHxJgChS|3;WRGN^=d6b3k}_fsqfj
zg9?Ce<)%hbg^wKVSGd~GRG>x_|KZ=)l(XS*FCUKQ+sNJ;DzgR^lsNGqffc~akq}07
z=LH~CiTgQd@&OxIait9@TP<niGW_prE-c{C&N!5d2XNIciu(&;7!hSyL_>X8_-?5U
z^up5QT+QnxDTVfq#?5=Fa^EZNK;P4a5qtBs(Y972^~y<4Dlke1pUy62O8zBUwOTqM
z7)<pz`yqCRyRCzp#BJDO(vzry=$Y%>wXf7)O4)-AZutK?z!bJGDHKNELxz@3sqC$O
z35l~9K}|cn(EM<1`BwCHlPP9NTkU{iwf8UQQP0xQ7A(Z9&k(ncSFa%$^0h3N58cME
z3T&~SQ=6cas{FDUlkTu`;LBQ@J(!FqaYv$+e=#C_9qDQU%{1NN^pNY$_KyJI1X_!t
z@!{XM`Vp8Hw6KX%`%bJDZ6{GX#)cHqJ@+Ov5$C}1Ypktjh*r7nYW#jfKFX3)Y`GQ3
z^hSHYtBms3uoWT8xTSrM9V%fP=2v6n<@55s4Z2ppQXAUl84SG({+Q9q<pfi!tTj{A
z7L?=s$0V6mC@ZY#1E-IcP%aErCH2}yT%?yF_dhUsfKfwEzqL`*P-eUD`0Jv=z|8Z|
z)2SoW@?`#@2R1W~YUnn>X_UcQ5IZh!PLXjOP`_(RCnq`dW{+0DI!kA0*mUc<C_GZ8
zf&il<g#AFbbSUFQPs$)AB`?qq?^vKXSaIG_X~NkSB>yV0D(Eb)SJ%#O_@Dcoj=P|&
zE8n^6DFZ*eaBBWFzJ1|mbj+S@=`U8cn`Mh)!=7L^9{IGgwq#BJ&j!yQoNr5&IE3Ic
zooGZ^JbjY28)S-&X8^JenGbzO@ss50tdlEvtC%DO`CLeGeo?g$JxY*gWPC}q(r&C>
zp9-)-jeIa7+Du6QF-t^X%qYx#$7XxMEm^fmQ$NcQ-}<9a&49*FwQ?NO(Hi+^aB1!g
zdAPU-Yhg>5kV~G@#S;o!rk-^noY=}1BR?X+Yh{jv-<}3oGGeHuPny89KT~w&f7__@
zFmoBdl68SYQ~fFc8Or|fjp1xUB&H-*=|JqfbEc-#m^=4jnD$IW-&bXJ`kz0ms$faU
zUwJsC2^wK2-D1FdDq=)%F1G#;yu>c41h|L>>a8t*L-1VW<1&%-lDg5N#_4t%sYT;;
z#_y}Tt+Dqq?arq-E$r_%AdLP(x0y!rKJatVWx_qbk^@9=x=44SmeI8bNtO0D8Hri!
z>8y7A34^ZR^(c-u7|G}4CSf<~g3_m$Hx$ghQMcBvqu8tH)H|Dkhq{5{2iN3!0VAE`
zdSNWUifIZ1k$NZNjHh6a`Ts9uX-QDQmz%?lHG4|bNbTrhG;6=Qp6I)Be1D<6#z`@G
zT?w2Sa*;B}{kqG)8kiYP4Cyg1NX7lw|C>=NUbjB;s?FX-f4?C&ZB<Y~;G_0Zv^=X>
zh;6H%p?p1WfhGZgy7W`7RX!E30bPK<EmCI-HXD^!LduUEPZ2}R#%HZseB@`&-w%0s
zQ)tJ3DK&C5s!dv?YxUe%f#NiEzEw(BG3R}7K8(@%^IlYR%4N1G<{a_osy2$A0;SU8
z`blXL1y|pd11k?wP4ZuLSRAFse)P#I(Ki$pjj*YkaUp<xI4Ovsi)U%#WUFUMCIGhh
zwtBX9srMha1_0>_i}oi-wV(~oeJ04j3yOm_>&+C5uv`CzFQ7`IYrbKtEK~EOh<4`n
z$IP$sYS{CZdcl4)lui(zq*oxy0{qJC0KCY5(@E&iU{7`jCe0i`_feCHS>re6n!F-L
zYvg@U4n~$6-+}^F?~wuv5-H0JRg8bUOB|AWQ2L-zrG$*fPzRtvR~x<}fxHc%C$Q=0
z6TA|b5_F(Y^h*>1iSk*dVtiu2#>Nhkt==OCn|0-c(NNM;y`z19nl(I-I5_?fBlv~q
ztcIKhWU@m7+#ySNezNsC#Uy-L5scMn)2Jdbk|}gDQIOHE_2B(C0%MEDh8#ff_!fe@
zxs1@V5+!cUD$0~>v>K|;Kfdvu6?A;#jJS~$)-8$&hZ|CRdw8pZC@ERlFCzt3@qza=
zXV56AX?I-bQ2TP1NTlLpp?!OcxV!pG&u_-st@aN<T}6AwGE-kt@XL5v0BFf|IfU<4
zyHaaan`b=qn&D<F_8}X&g8_<3*2O28a<0~6Wr?Kgm=@Qk(H_pRJX4@ZfL_ejM(a&*
zTebMU)6!BYe#G<L@m5}uPQU*bWgCD_jIiE>UQrV6%yhVhjl?ixeWBUe7REcJZ#Z1`
zf9L<bJ=1S~+k~m<Wn-w-M~x1vO(MJU@MyU&j&8I#FP+ZtS_H~Vtc$<2n&mHJBtXO}
zc6TN!odU{rz8s1X(=_Ac_hEU>RF>q)SPk2ZIc!_YPG4x;-THcW6~u2IwcmU^KU|)z
zy@NB5$WyP(w>C?2^5!`PDed7ryQyoZThQs~nvnd^R0<7u7;EQN$9zcZ?OW?OIoe%A
zqx7bI8U8Ct*9};Ev*;e26(vO`x<$Wzx~$0vN<@luoOT{_Q4U&<Fn+zkZfywjHRd^`
zja_S<eKs{+`M1&kF6J?Vptam_tj2vitb~~DR$lzD(Se_Gp>yGR>LWBpi)UfntwJ~W
zrPUz|XitNxTTPdk3^-q`<tTY261|mS|0ma&j~YeN*^L%OgP~RZ%#><6c0c0Odh5ie
z^i^`0LedG~_<dUYv1KkFii0ri5Ix&&h7X5vbVM(}=s~`W_Jx{1k~wqiChgON<m2{?
z6$AvdViBl~P$n&bn!6DN|I~F3V=zRDI;@3J^)Qwe&g=54&N*Q;`_aSvFyD`fo<RT5
zD(?x`9nNoCCtl!4!V3e%o4~_Q)lWtfO!<rn6L9(Iz#pW?w=CpkN5o*Z?yOKxMwB#9
z$4u-}7~oUJmnV(wjt2x*snawYR<5tOV}#GTGNf|yD$I)d__S1KOx0Oy;NM2E8B$90
z<O@cM^Hqx!%mW%8{3HvuA(LZ2k1uu(Q97V14Y{EPLRQsP&w*%^h)dyT#{sq8Z}|AG
zb=V@(lvH9h<X^N-N4Mo9t;RlBHS)}R%%G%7s_K7=8kCiow)lxu76*yh#^J<3fr}YO
zNsN|Ud;86L4&pwqGel;xDDrrC80-zWcnv3AzFyOb^#!>=eKIi^j8`F3f7H*p6(dTn
zb~}KkY8iSp*uM0boU-dusz!H!s$mLe0avDlE`9kln>67Mhnzawg0W*8yS*L7apd47
zifgBASl{(v+@~S<L`fX(Az(B5LhV$}l==GmDY5ep=qOi1;}Yxd%f5u#7L_PL-jcEN
zgrQBAw3qcKLsZR&5N@^ofX#=V1Dhk^F+dPfAw%m0dP$jKiMK<Or%w99sxSJg=y#&!
z@xOwZQXXV_MKdbwbR-I6WlL(_CMb+QRFP0eQ<>tNuy-3o)ZDvuBP#Td$$-}nn5t5q
zpiXg+#J*iHrBGialUnH>--ms0ri?;BYf2w=3pF{DuR%P<r?u=AJx>ce5tZiG-1g@R
z8k=+k&1RqBb!AMYhd0mH+b35o*K1e&Jz<<L7T}CLEGzdzsFJ0st<ky;--{SI{tysJ
zDP3NBcP88>Z8x^y(OJaspc|D(tns6-yP~dBEqFwOzcFIaokNJE%5n1B%Z`E{k~D6U
z%|P*^AP&0NUp!&Yx&(r{5=!Px6of<*vR#htpkReaWuFO@e;`Vqy)cJCGWH^)d)MtO
zd5e=bqk+6^qz>zb&YFSrIMDEoNXn>ijvf-zgM2%YuN?;wUZq2At|Etu(g^}4Jw$Bt
zwq1i|9XCR&pLMgh5T4Rylbocg8SuZghcl*jZlpdulla&ibP(>|ohQu$=~KESsqflx
z7p9@J&1K{9WF@>enZQ7#X|?1&+Dg_~C43+LQMk{ejGUhzn!TJ`scPfbQJXe9;^P{<
zcCgdn)zT2y&5-t!1O6R-msH*Hb>XcVeCl!&&Qa_w(^X&c;(0lwIbm+Ek&w3@KAzn*
zU4SkD=Txgvj)HX*D|xaGPYjRG^=U2Q2)$Gh;`gq7(OVV$CMkUBwsTF?p14p;sR!^q
z5;^jP&v0Q2Jy6p08~{%qA(a|-WfQS|{h<0gp<?)Z7AbKMkTUgjSgbWHLgK6+-#YuQ
zbCmjC(;38r-v<}@jAG~Sg}>r_lQ>x>$4N}4XleP3voy{*^UnCf=PAu2tLae=7((l`
ztjn@$Nj-w?$}qrZ+&LK--vw0yo|AAY?D7AUL>KAuxhVIwNjx*}=RCN}X3PbNrl7sa
zO<y>)@nV60lbetQ2sS`b#RgRpugT0+sh+=2!3eeNDVX=>S2NOoEkHCFkgs2haQq`&
zn@oUQZp?e~>M1fsXS)LVTG={880gFZqIe{b%W*^LIPMPx@oFduZrf$NVB{B90+EGO
r*O+<l{WB(z{=W>ejeq_>UbH)Uazs?W-0e0M3h+^qSCgxdF$w%Hz5-Qj

literal 0
HcmV?d00001

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index 3c2433e..d88d6b8 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ae521d31a2ed8eb2f249825915f64611628365cb0ced53ef18be691238c0018e"
+  "signature": "sha256:968e6f47972d4ab9e2ef4eef6906343257267ccf094fcae08da24fec3647743d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -23,6 +23,13 @@
       "<hr>"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font color=\"red\">**watermark is now located and maintained in a separate GitHub repository:** [https://github.com/rasbt/watermark](https://github.com/rasbt/watermark)<font>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 1,

From 87dbf3e1dbc2e0bef42d66bf16e61fd9114518f1 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 30 Jul 2014 15:32:25 -0400
Subject: [PATCH 174/248] numpy nan quickguide

---
 README.md                            |   2 +
 tutorials/numpy_nan_quickguide.ipynb | 770 +++++++++++++++++++++++++++
 2 files changed, 772 insertions(+)
 create mode 100644 tutorials/numpy_nan_quickguide.ipynb

diff --git a/README.md b/README.md
index cfa5708..8b4e165 100644
--- a/README.md
+++ b/README.md
@@ -48,6 +48,8 @@
 
 - A collection of useful regular expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/useful_regex.ipynb)]
 
+- Quick guide for dealing with missing numbers in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/numpy_nan_quickguide.ipynb)]
+
 <br>
 
 
diff --git a/tutorials/numpy_nan_quickguide.ipynb b/tutorials/numpy_nan_quickguide.ipynb
new file mode 100644
index 0000000..dfc9572
--- /dev/null
+++ b/tutorials/numpy_nan_quickguide.ipynb
@@ -0,0 +1,770 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:7553ded8e8dc9e6faf09cd22747b33a3ae9039743491e88025fb61ea45203063"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to python_reference](https://github.com/rasbt/python_reference)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -v -p numpy -d -u"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Last updated: 30/07/2014 \n",
+        "\n",
+        "CPython 3.4.1\n",
+        "IPython 2.0.0\n",
+        "\n",
+        "numpy 1.8.1\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1.5em\">[More information](https://github.com/rasbt/watermark) about the `watermark` magic command extension.</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Quick guide for dealing with missing numbers in NumPy"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This is just a quick overview of how to deal with missing values (i.e., \"NaN\"s for \"Not-a-Number\") in NumPy and I am happy to expand it over time. Yes, and there will also be a separate one for pandas some time!\n",
+      "\n",
+      "I would be happy to hear your comments and suggestions. \n",
+      "Please feel free to drop me a note via\n",
+      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+      "<hr>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Sections"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Sample data from a CSV file](#Sample-data-from-a-CSV-file)\n",
+      "- [Determining if a value is missing](#Determining-if-a-value-is-missing)\n",
+      "- [Counting the number of missing values](#Counting-the-number-of-missing-values)\n",
+      "- [Calculating the sum of an array that contains NaNs](#Calculating the sum of an array that contains NaNs)\n",
+      "- [Removing all rows that contain missing values](#Removing-all-rows-that-contain-missing-values)\n",
+      "- [Convert missing values to 0](#Convert-missing-values-to-0)\n",
+      "- [Converting certain numbers to NaN](#Converting-certain-numbers-to-NaN)\n",
+      "- [Remove all missing elements from an array](#Remove-all-missing-elements-from-an-array)\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Sample data from a CSV file"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let's assume that we have a CSV file with missing elements like the one shown below."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file example.csv\n",
+      "1,2,3,4\n",
+      "5,6,,8\n",
+      "10,11,12,"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Overwriting example.csv\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "The `np.genfromtxt` function has a `missing_values` parameters which translates missing values into `np.nan` objects by default. This allows us to construct a new NumPy `ndarray` object, even if elements are missing."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "ary = np.genfromtxt('./example.csv', delimiter=',')\n",
+      "\n",
+      "print('%s x %s array:\\n' %(ary.shape[0], ary.shape[1]))\n",
+      "print(ary)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "3 x 4 array:\n",
+        "\n",
+        "[[  1.   2.   3.   4.]\n",
+        " [  5.   6.  nan   8.]\n",
+        " [ 10.  11.  12.  nan]]\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Determining if a value is missing"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "A handy function to test whether a value is a `NaN` or not is to use the `np.isnan` function."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.isnan(np.nan)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 37,
+       "text": [
+        "True"
+       ]
+      }
+     ],
+     "prompt_number": 37
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "It is especially useful to create boolean masks for the so-called \"fancy indexing\" of NumPy arrays, which we will come back to later."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.isnan(ary)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "array([[False, False, False, False],\n",
+        "       [False, False,  True, False],\n",
+        "       [False, False, False,  True]], dtype=bool)"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Counting the number of missing values"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In order to find out how many elements are missing in our array, we can use the `np.isnan` function that we have seen in the previous section. "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.count_nonzero(np.isnan(ary))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "2"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "If we want to determine the number of non-missing elements, we can simply revert the returned `Boolean` mask via the handy \"tilde\" sign."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.count_nonzero(~np.isnan(ary))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "10"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Calculating the sum of an array that contains `NaN`s"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "As we will find out via the following code snippet, we can't use NumPy's regular `sum` function to calculate the sum of an array."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "np.sum(ary)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "nan"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Since the `np.sum` function does not work, use `np.nansum` instead:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('total sum:', np.nansum(ary))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "total sum: 62.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('row sums:', np.nansum(ary, axis=0))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "row sums: [ 16.  19.  15.  12.]\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "print('column sums:', np.nansum(ary, axis=1))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "column sums: [ 10.  19.  33.]\n"
+       ]
+      }
+     ],
+     "prompt_number": 13
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Removing all rows that contain missing values"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Here, we will use the `Boolean mask` again to return only those rows that DON'T contain missing values. And if we want to get only the rows that contain `NaN`s, we could simply drop the `~`."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ary[~np.isnan(ary).any(1)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 14,
+       "text": [
+        "array([[ 1.,  2.,  3.,  4.]])"
+       ]
+      }
+     ],
+     "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Convert missing values to 0"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Certain operations, algorithms, and other analyses might not work with `NaN` objects in our data array. But that's not a problem: We can use the convenient `np.nan_to_num` function will convert it to the value 0."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ary0 = np.nan_to_num(ary)\n",
+      "ary0"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 15,
+       "text": [
+        "array([[  1.,   2.,   3.,   4.],\n",
+        "       [  5.,   6.,   0.,   8.],\n",
+        "       [ 10.,  11.,  12.,   0.]])"
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Converting certain numbers to NaN"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Vice versa, we can also convert any number to a `np.NaN` object. Here, we use the array that we created in the previous section and convert the `0`s back to `np.nan` objects."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ary0[ary0==0] = np.nan\n",
+      "ary0"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 16,
+       "text": [
+        "array([[  1.,   2.,   3.,   4.],\n",
+        "       [  5.,   6.,  nan,   8.],\n",
+        "       [ 10.,  11.,  12.,  nan]])"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Remove all missing elements from an array"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to top](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This is one is a little bit more tricky. We can remove missing values via a combination of the `Boolean` mask and fancy indexing, however, this will have the disadvantage that it will flatten our array (we can't just punch holes into a NumPy array)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "ary[~np.isnan(ary)]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 17,
+       "text": [
+        "array([  1.,   2.,   3.,   4.,   5.,   6.,   8.,  10.,  11.,  12.])"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Thus, this is a method that would better work on individual rows:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "x = np.array([1,2,np.nan])\n",
+      "\n",
+      "x[~np.isnan(np.array(x))]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 21,
+       "text": [
+        "array([ 1.,  2.])"
+       ]
+      }
+     ],
+     "prompt_number": 21
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 447f588960ecbf91c82a35a550d75a3e10781062 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 31 Jul 2014 00:20:22 -0400
Subject: [PATCH 175/248] typo fix

---
 tutorials/example.csv                |  3 ++
 tutorials/numpy_nan_quickguide.ipynb | 42 ++++++++++++++--------------
 2 files changed, 24 insertions(+), 21 deletions(-)
 create mode 100644 tutorials/example.csv

diff --git a/tutorials/example.csv b/tutorials/example.csv
new file mode 100644
index 0000000..65329b8
--- /dev/null
+++ b/tutorials/example.csv
@@ -0,0 +1,3 @@
+1,2,3,4
+5,6,,8
+10,11,12,
\ No newline at end of file
diff --git a/tutorials/numpy_nan_quickguide.ipynb b/tutorials/numpy_nan_quickguide.ipynb
index dfc9572..acbbeed 100644
--- a/tutorials/numpy_nan_quickguide.ipynb
+++ b/tutorials/numpy_nan_quickguide.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:7553ded8e8dc9e6faf09cd22747b33a3ae9039743491e88025fb61ea45203063"
+  "signature": "sha256:b2597ea4263c11dd6774b227e7a3a5626197c4863e6895002657fd55d02b55d9"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -39,10 +39,10 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Last updated: 30/07/2014 \n",
+        "Last updated: 31/07/2014 \n",
         "\n",
         "CPython 3.4.1\n",
-        "IPython 2.0.0\n",
+        "IPython 2.1.0\n",
         "\n",
         "numpy 1.8.1\n"
        ]
@@ -161,7 +161,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Overwriting example.csv\n"
+        "Writing example.csv\n"
        ]
       }
      ],
@@ -258,13 +258,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 37,
+       "prompt_number": 5,
        "text": [
         "True"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -292,7 +292,7 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 5,
+       "prompt_number": 6,
        "text": [
         "array([[False, False, False, False],\n",
         "       [False, False,  True, False],\n",
@@ -300,7 +300,7 @@
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -351,13 +351,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 7,
        "text": [
         "2"
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -385,13 +385,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 9,
+       "prompt_number": 8,
        "text": [
         "10"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -435,13 +435,13 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 9,
        "text": [
         "nan"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -474,13 +474,13 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 10
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print('row sums:', np.nansum(ary, axis=0))"
+      "print('column sums:', np.nansum(ary, axis=0))"
      ],
      "language": "python",
      "metadata": {},
@@ -489,17 +489,17 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "row sums: [ 16.  19.  15.  12.]\n"
+        "column sums: [ 16.  19.  15.  12.]\n"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print('column sums:', np.nansum(ary, axis=1))"
+      "print('row sums:', np.nansum(ary, axis=1))"
      ],
      "language": "python",
      "metadata": {},
@@ -508,11 +508,11 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "column sums: [ 10.  19.  33.]\n"
+        "row sums: [ 10.  19.  33.]\n"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",

From 841401ab65a23d77c86b8126dc28d5deb6c141b1 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 2 Aug 2014 22:36:17 -0400
Subject: [PATCH 176/248] Sorting a list of tuples by the last last elements of
 the tuple

---
 howtos_as_py_files/sort_list_of_tuples_by_ele.py | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 howtos_as_py_files/sort_list_of_tuples_by_ele.py

diff --git a/howtos_as_py_files/sort_list_of_tuples_by_ele.py b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
new file mode 100644
index 0000000..972d35d
--- /dev/null
+++ b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
@@ -0,0 +1,10 @@
+# Sebastian Raschka 09/02/2014
+# Sorting a list of tuples by the last last elements of the tuple
+
+a_list = [(1,3,'c'), (2,3,'a'), (1,2,'b')]
+
+sorted_list = sorted(a_list, key=lambda e: e[::-1])
+
+print(sorted_list)
+
+# prints [(2, 3, 'a'), (1, 2, 'b'), (1, 3, 'c')]
\ No newline at end of file

From b6f54765e7b9f7e0f7da90adcf3c89b5b417c582 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 2 Aug 2014 23:21:53 -0400
Subject: [PATCH 177/248] Sorting a list of tuples by the last last elements of
 the tuple

---
 howtos_as_py_files/sort_list_of_tuples_by_ele.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/howtos_as_py_files/sort_list_of_tuples_by_ele.py b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
index 972d35d..ed8470a 100644
--- a/howtos_as_py_files/sort_list_of_tuples_by_ele.py
+++ b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
@@ -1,6 +1,15 @@
 # Sebastian Raschka 09/02/2014
 # Sorting a list of tuples by the last last elements of the tuple
 
+
+# Here, we make use of the "key" parameter of the in-built "sorted()" function 
+# (also available for the ".sort()" method), which let's us define a function 
+# that is called on every element that is to be sorted. In this case, our 
+# "key"-function is a simple lambda function that returns the last item 
+# from every tuple.
+
+
+
 a_list = [(1,3,'c'), (2,3,'a'), (1,2,'b')]
 
 sorted_list = sorted(a_list, key=lambda e: e[::-1])

From b87aec3ee9bcf27ed81ab5dc8a5cb772f0e182ed Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 2 Aug 2014 23:24:12 -0400
Subject: [PATCH 178/248] Sorting a list of tuples by the last last elements of
 the tuple

---
 howtos_as_py_files/sort_list_of_tuples_by_ele.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/howtos_as_py_files/sort_list_of_tuples_by_ele.py b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
index ed8470a..845774d 100644
--- a/howtos_as_py_files/sort_list_of_tuples_by_ele.py
+++ b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
@@ -1,5 +1,5 @@
 # Sebastian Raschka 09/02/2014
-# Sorting a list of tuples by the last last elements of the tuple
+# Sorting a list of tuples by the last elements of the tuple
 
 
 # Here, we make use of the "key" parameter of the in-built "sorted()" function 

From 6e74774e869eb1e2ce6a159af9aa9bda40ec6172 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 3 Aug 2014 11:32:03 -0400
Subject: [PATCH 179/248] comment about reverse

---
 .../sort_list_of_tuples_by_ele.py             | 25 +++++++++++++++----
 1 file changed, 20 insertions(+), 5 deletions(-)

diff --git a/howtos_as_py_files/sort_list_of_tuples_by_ele.py b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
index 845774d..4a94d4b 100644
--- a/howtos_as_py_files/sort_list_of_tuples_by_ele.py
+++ b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
@@ -1,6 +1,5 @@
 # Sebastian Raschka 09/02/2014
-# Sorting a list of tuples by the last elements of the tuple
-
+# Sorting a list of tuples by starting with the last element of the tuple (=reversed tuple)
 
 # Here, we make use of the "key" parameter of the in-built "sorted()" function 
 # (also available for the ".sort()" method), which let's us define a function 
@@ -9,11 +8,27 @@
 # from every tuple.
 
 
-
-a_list = [(1,3,'c'), (2,3,'a'), (1,2,'b')]
+a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]
 
 sorted_list = sorted(a_list, key=lambda e: e[::-1])
 
 print(sorted_list)
 
-# prints [(2, 3, 'a'), (1, 2, 'b'), (1, 3, 'c')]
\ No newline at end of file
+# prints [(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]
+
+
+
+# If we are only interesting in sorting the list by the last element
+# of the tuple and don't care about a "tie" situation, we can also use
+# the index of the tuple item directly instead of reversing the tuple 
+# for efficiency.
+
+
+
+a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]
+
+sorted_list = sorted(a_list, key=lambda e: e[-1])
+
+print(sorted_list)
+
+# prints [(2, 3, 'a'), (3, 2, 'b'), (2, 2, 'b'), (1, 3, 'c')]
\ No newline at end of file

From d3c58e595bf3b583f9147232c911f0d4dffbadfa Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 5 Aug 2014 01:12:12 -0400
Subject: [PATCH 180/248] readme re-categorization

---
 README.md | 76 +++++++++++++++++++++++++++++++++----------------------
 1 file changed, 46 insertions(+), 30 deletions(-)

diff --git a/README.md b/README.md
index 0453457..d4480cf 100644
--- a/README.md
+++ b/README.md
@@ -65,7 +65,7 @@
 ###// Algorithms
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
-*The algorithms category was moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*
+*The algorithms category has been moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*
 
 
 
@@ -83,44 +83,60 @@
 ###// Plotting and Visualization
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
-- a matplotlib gallery in IPython notebooks [[GitHub repo](https://github.com/rasbt/matplotlib-gallery)]
+*The matplotlib-gallery in IPython notebooks has been moved to a separate GitHub repository [matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)*
+
+**Featured articles**:  
+
+- Preparing Plots for Publication [[IPython nb](http://nbviewer.ipython.org/github/rasbt/matplotlib-gallery/blob/master/ipynb/publication.ipynb)]
 
 
 <br>
 ###// Benchmarks
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
-*The benchmark category was moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
-
-- **1** - Reversing strings [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day1_string_reverse.ipynb)]
-- **2** - Calculating sample means [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day2_mean_values.ipynb)]
-- **3** - 6 different ways to count elements using a dict [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day3_dictionary_counting.ipynb)]
-- **4** - Python vs. Cython vs. Numba [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_python_cython_numba.ipynb)]
-- **4.2** - (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)]
-- **5** - Comparing 9 ways for flattening lists of sublists [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day5_flattening_lists.ipynb)]
-- **6** - Determining if a string is a number [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day6_string_is_number.ipynb)]
-- **7** - Speeding up NumPy array expressions with Numexpr [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_numpy_numexpr.ipynb)]
-- **7.2** - Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)] 
-- **8** - Calculating square roots and exponents [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day8_sqrt_and_exp.ipynb)] 
-- **9** - The most Pythonic way to check if a string ends with a particular substring [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day9_string_endswith.ipynb)]
-- **10** - Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)]
-- **11** - The `deque` container data type [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day11_deque_container.ipynb)]
-- **12** - Lightning fast insertion into sorted lists via the `bisect` module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day12_insert_into_sorted_list.ipynb)]
-- **13** - Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)]
-- **14** - Python's and NumPy's in-place operator functions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day14_inplace_operators.ipynb)]
-- **15** - Array indexing in NumPy: Extracting rows and columns [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day15_array_indexing_numpy.ipynb)] 
-- **16** - Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]
-- **17** - Stacking NumPy arrays [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day17_numpy_stacking.ipynb)]
-
-
-###// Other 
+*The benchmark category has been moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
+
+**Featured articles**:  
+
+- (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)]
+- Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)]
+- Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)]
+- Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)]
+- Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]
+
+<br> 
+
+
+<br>
+###// Benchmarks
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
-- Happy Mother's [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/other/happy_mothers_day.ipynb?create=1)]
+*The benchmark category has been moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
+
+**Featured articles**:  
+
+- (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)]
+- Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)]
+- Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)]
+- Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)]
+- Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]
+
+<br> 
+
+
+###// Python and "Data Science" 
+[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+
+*The "data science"-related posts have been moved to a separate GitHub repository [pattern_classification](https://github.com/rasbt/pattern_classification)*
+
+**Featured articles**:  
 
-- Numeric matrix manipulation - The cheat sheet for MATLAB, Python NumPy, R, and Julia [[Markdown](./tutorials/matrix_cheatsheet.md)]
+- Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb)]
+- About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb)]
+- Principal Component Analysis (PCA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/principal_component_analysis.ipynb)]
+- Linear Discriminant Analysis (LDA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/linear_discriminant_analysis.ipynb)]
+- Kernel density estimation via the Parzen-window technique [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/parameter_estimation_techniques/parzen_window_technique.ipynb)]
 
-- Python Book Reviews [[Markdown](./other/python_book_reviews.md)]
 
 <br>
 
@@ -131,7 +147,7 @@
 
 - [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to .py files.
 
-- convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb?create=1)]
+- convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb)]
 
 - A random string generator [function](./useful_scripts/random_string_generator.py)
 

From 031c17005f7862ddabf44f9401de754fc773eff1 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 5 Aug 2014 01:13:58 -0400
Subject: [PATCH 181/248] readme re-categorization

---
 README.md | 15 +--------------
 1 file changed, 1 insertion(+), 14 deletions(-)

diff --git a/README.md b/README.md
index d4480cf..7973fbe 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,7 @@
 - [// Algorithms](#-algorithms)
 - [// Plotting and Visualization](#-plotting-and-visualization)
 - [// Benchmarks](#-benchmarks)
+- [// Python and "Data Science"](#-python-and-data-science)
 - [// Other](#-other)
 - [// Useful scripts and snippets](#-useful-scripts-and-snippets)
 - [// Links](#-links)
@@ -90,21 +91,7 @@
 - Preparing Plots for Publication [[IPython nb](http://nbviewer.ipython.org/github/rasbt/matplotlib-gallery/blob/master/ipynb/publication.ipynb)]
 
 
-<br>
-###// Benchmarks
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
-
-*The benchmark category has been moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
-
-**Featured articles**:  
 
-- (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)]
-- Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)]
-- Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)]
-- Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)]
-- Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]
-
-<br> 
 
 
 <br>

From d07be79be853dd881e9bdcb1b424900d15cd0dc4 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 5 Aug 2014 01:15:18 -0400
Subject: [PATCH 182/248] readme re-categorization

---
 README.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/README.md b/README.md
index 7973fbe..47ba722 100644
--- a/README.md
+++ b/README.md
@@ -103,9 +103,13 @@
 **Featured articles**:  
 
 - (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)]
+
 - Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)]
+
 - Cython - Bridging the gap between Python and Fortran [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day10_fortran_lstsqr.ipynb)]
+
 - Parallel processing via the multiprocessing module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)]
+
 - Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]
 
 <br> 
@@ -119,9 +123,13 @@
 **Featured articles**:  
 
 - Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/python_data_entry_point.ipynb)]
+
 - About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb)]
+
 - Principal Component Analysis (PCA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/principal_component_analysis.ipynb)]
+
 - Linear Discriminant Analysis (LDA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/linear_discriminant_analysis.ipynb)]
+
 - Kernel density estimation via the Parzen-window technique [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/parameter_estimation_techniques/parzen_window_technique.ipynb)]
 
 

From bdd74ca15c98b57f197590918570504e24bc6575 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 19 Aug 2014 09:57:13 -0400
Subject: [PATCH 183/248] short lambda example

---
 howtos_as_py_files/lambda_function.py | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 howtos_as_py_files/lambda_function.py

diff --git a/howtos_as_py_files/lambda_function.py b/howtos_as_py_files/lambda_function.py
new file mode 100644
index 0000000..9da0c9c
--- /dev/null
+++ b/howtos_as_py_files/lambda_function.py
@@ -0,0 +1,11 @@
+# Sebastian Raschka 08/2014
+
+# Lambda functions are just a short-hand way or writing
+# short function definitions
+
+def square_root1(x):
+    return x**0.5
+    
+square_root2 = lambda x: x**0.5
+
+assert(square_root1(9) == square_root2(9))
\ No newline at end of file

From f0b01ab5db7e9121e7570e77a0b4ac5aa3232106 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 19 Aug 2014 23:14:00 -0400
Subject: [PATCH 184/248] python patterns

---
 README.md | 44 +++++++++++++++++++++++++-------------------
 1 file changed, 25 insertions(+), 19 deletions(-)

diff --git a/README.md b/README.md
index 47ba722..845c16d 100644
--- a/README.md
+++ b/README.md
@@ -140,9 +140,9 @@
 
 - [watermark](https://github.com/rasbt/watermark) - An IPython magic extension for printing date and time stamps, version numbers, and hardware information.
 
-- [Shell script](./useful_scripts/prepend_python_shebang.sh) for prepending Python-shebangs to .py files.
+- [Shell script](./useful_scripts/prepend_python_shebang.sh) For prepending Python-shebangs to .py files.
 
-- convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb)]
+- Convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb)]
 
 - A random string generator [function](./useful_scripts/random_string_generator.py)
 
@@ -156,50 +156,56 @@
 
 
 
-- [PyPI - the Python Package Index](https://pypi.python.org/pypi) - the official repository for all open source Python modules and packages  
+- [PyPI - the Python Package Index](https://pypi.python.org/pypi) - The official repository for all open source Python modules and packages.  
 
-- [PEP 8](http://legacy.python.org/dev/peps/pep-0008/) - The official style guide for Python code
+- [PEP 8](http://legacy.python.org/dev/peps/pep-0008/) - The official style guide for Python code.
 
 
+<br>
 
 **// News**
 
-- [Python subreddit](http://www.reddit.com/r/Python/) - my favorite resource to catch up with Python news and great Python-related articles
+- [Python subreddit](http://www.reddit.com/r/Python/) - My favorite resource to catch up with Python news and great Python-related articles.
 
-- [Python community on Google+](https://plus.google.com/communities/103393744324769547228) - a nice and friendly community to share and discuss everything about Python
+- [Python community on Google+](https://plus.google.com/communities/103393744324769547228) - A nice and friendly community to share and discuss everything about Python.
 
-- [Python Weekly](http://www.pythonweekly.com) - A free weekly newsletter featuring curated news, articles, new releases, jobs etc. related to Python
+- [Python Weekly](http://www.pythonweekly.com) - A free weekly newsletter featuring curated news, articles, new releases, jobs etc. related to Python.
 
 
+<br>
 
 **// Resources for learning Python**
 
-- [Learn Python The Hard Way](http://learnpythonthehardway.org/book/) - one of the most popular and recommended resources for learning Python  
+- [Learn Python The Hard Way](http://learnpythonthehardway.org/book/) - The popular and probably most recommended resource for learning Python.  
+
+- [Dive Into Python](http://www.diveintopython.net) / [Dive Into Python 3](http://getpython3.com/diveintopython3/) - A free Python book for experienced programmers.
 
-- [Dive Into Python](http://www.diveintopython.net) / [Dive Into Python 3](http://getpython3.com/diveintopython3/) - a free Python book for experienced programmers
+- [The Hitchhiker’s Guide to Python](http://docs.python-guide.org/en/latest/) - A free best-practice handbook for both novices and experts.
 
-- [The Hitchhiker’s Guide to Python](http://docs.python-guide.org/en/latest/) - a free best-practice handbook for both novices and experts
+- [Think Python - How to Think Like a Computer Scientist](http://www.greenteapress.com/thinkpython/) - An introduction for beginners starting with basic concepts of programming.
 
-- [Think Python - How to Think Like a Computer Scientist](http://www.greenteapress.com/thinkpython/) - an introduction for beginners starting with basic concepts of programming
+- [Python Patterns](http://matthiaseisen.com/pp/topics/t003/) -  A directory of proven, reusable solutions to common programming problems.
+
+<br>
 
 **// My favorite Python projects and packages**
 
-- [The IPython Notebook](http://ipython.org/notebook.html) - an interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document.
+- [The IPython Notebook](http://ipython.org/notebook.html) - An interactive computational environment for combining code execution, documentation (with Markdown and LateX support), inline plots, and rich media all in one document.
 
-- [matplotlib](http://matplotlib.org) - Python's favorite plotting library
+- [matplotlib](http://matplotlib.org) - Python's favorite plotting library.
 
-- [NumPy](http://www.numpy.org) - a library for multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays
+- [NumPy](http://www.numpy.org) - A library for multi-dimensional arrays and matrices, along with a large library of high-level mathematical functions to operate on these arrays.
 
-- [SciPy](http://www.scipy.org) - a library that provides various useful functions for numerical computing, such as modules for optimization, linear algebra, integration, interpolation, ...
+- [SciPy](http://www.scipy.org) - A library that provides various useful functions for numerical computing, such as modules for optimization, linear algebra, integration, interpolation, ...
 
 
-- [pandas](http://pandas.pydata.org) - high-performance, easy-to-use data structures and data analysis tools build on top of Numpy
+- [pandas](http://pandas.pydata.org) - High-performance, easy-to-use data structures and data analysis tools build on top of NumPy.
 
-- [Cython](http://cython.org) - C-extensions for Python, an optimizing static compiler to combine Python and C code
+- [Cython](http://cython.org) - C-extensions for Python, an optimizing static compiler to combine Python and C code.
 
-- [Numba](http://numba.pydata.org) - an just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
+- [Numba](http://numba.pydata.org) - A just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
 
-- [scikit-learn](http://scikit-learn.org/stable/) - a powerful machine learning library for Python and tools for efficient data mining and analysis
+- [scikit-learn](http://scikit-learn.org/stable/) - A powerful machine learning library for Python and tools for efficient data mining and analysis.
 
 
 

From 03444284a1c0febb113fe7fae7d257b15efe81bc Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 19 Aug 2014 23:45:06 -0400
Subject: [PATCH 185/248] link upd

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 845c16d..a1872c6 100644
--- a/README.md
+++ b/README.md
@@ -184,7 +184,7 @@
 
 - [Think Python - How to Think Like a Computer Scientist](http://www.greenteapress.com/thinkpython/) - An introduction for beginners starting with basic concepts of programming.
 
-- [Python Patterns](http://matthiaseisen.com/pp/topics/t003/) -  A directory of proven, reusable solutions to common programming problems.
+- [Python Patterns](http://matthiaseisen.com/pp/) -  A directory of proven, reusable solutions to common programming problems.
 
 <br>
 

From fd65f8598fa664932bca8a89ad4e1bf900785add Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 14 Sep 2014 13:18:56 -0400
Subject: [PATCH 186/248] sparsify_matrix.py

---
 useful_scripts/sparsify_matrix.py | 36 +++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 useful_scripts/sparsify_matrix.py

diff --git a/useful_scripts/sparsify_matrix.py b/useful_scripts/sparsify_matrix.py
new file mode 100644
index 0000000..27c864c
--- /dev/null
+++ b/useful_scripts/sparsify_matrix.py
@@ -0,0 +1,36 @@
+# Sebastian Raschka 2014
+#
+# Sparsifying a matrix by Zeroing out all elements but the top k elements in a row.
+# The matrix could be a distance or similarity matrix (e.g., kernel matrix in kernel PCA),
+# where we are interested to keep the top k neighbors.
+
+print('Sparsify a matrix by zeroing all elements but the top 2 values in a row.\n')
+
+A = np.array([[1,2,3,4,5],[9,8,6,4,5],[3,1,7,8,9]])
+
+print('Before:\n%s\n' %A)
+
+
+k = 2 # keep top k neighbors
+for row in A:
+    sort_idx = np.argsort(row)[::-1] # get indexes of sort order (high to low)
+    for i in sort_idx[k:]:
+        row[i]=0
+        
+print('After:\n%s\n' %A)
+
+
+"""
+Sparsify a matrix by zeroing all elements but the top 2 values in a row.
+
+Before:
+[[1 2 3 4 5]
+ [9 8 6 4 5]
+ [3 1 7 8 9]]
+
+After:
+[[0 0 0 4 5]
+ [9 8 0 0 0]
+ [0 0 0 8 9]]
+
+"""
\ No newline at end of file

From 01fbbeb69c196c6f77663867fc511316da7f7eae Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 23 Sep 2014 13:31:37 -0400
Subject: [PATCH 187/248] make bitstring func

---
 howtos_as_py_files/make_bitstring.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)
 create mode 100644 howtos_as_py_files/make_bitstring.py

diff --git a/howtos_as_py_files/make_bitstring.py b/howtos_as_py_files/make_bitstring.py
new file mode 100644
index 0000000..fc2baa4
--- /dev/null
+++ b/howtos_as_py_files/make_bitstring.py
@@ -0,0 +1,14 @@
+# Generating a bitstring from a Python list or numpy array
+# where all postive values -> 1
+# all negative values -> 0
+
+def make_bitstring(ary)
+    return np.where(ary > 0, 1, 0)
+
+
+### Example:
+
+ary1 = np.array([1, 2, 0.3, -1, -2])
+make_bitstring(ary1)
+
+# returns array([1, 1, 1, 0, 0])
\ No newline at end of file

From 21e4d5e813963cd2b05561425e0d68e0fd04a47b Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 23 Sep 2014 13:34:17 -0400
Subject: [PATCH 188/248] bitstring

---
 howtos_as_py_files/make_bitstring.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/howtos_as_py_files/make_bitstring.py b/howtos_as_py_files/make_bitstring.py
index fc2baa4..fb27089 100644
--- a/howtos_as_py_files/make_bitstring.py
+++ b/howtos_as_py_files/make_bitstring.py
@@ -6,9 +6,12 @@ def make_bitstring(ary)
     return np.where(ary > 0, 1, 0)
 
 
+def faster_bitstring(ary)
+    return np.where(ary > 0).astype('i1')
+
 ### Example:
 
 ary1 = np.array([1, 2, 0.3, -1, -2])
 make_bitstring(ary1)
 
-# returns array([1, 1, 1, 0, 0])
\ No newline at end of file
+# returns array([1, 1, 1, 0, 0])

From 89bf31755685ce547f25e777d0229987a97df93e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 26 Sep 2014 14:17:54 -0400
Subject: [PATCH 189/248] python patterns 1

---
 howtos_as_py_files/README.md                  |   10 +
 howtos_as_py_files/closures.py                |   17 -
 howtos_as_py_files/cmd_line_args_1_sysarg.py  |   24 -
 howtos_as_py_files/cpu_time.py                |   18 -
 howtos_as_py_files/date_time.py               |   13 -
 howtos_as_py_files/diff_files.py              |   21 -
 howtos_as_py_files/doctest_example.py         |   47 -
 howtos_as_py_files/file_browsing.py           |   80 -
 howtos_as_py_files/get_filename.py            |   63 -
 howtos_as_py_files/get_minmax_indeces.py      |   12 -
 howtos_as_py_files/id_file1.txt               |    3 +
 howtos_as_py_files/id_file2.txt               |    3 +
 howtos_as_py_files/lambda_function.py         |   11 -
 howtos_as_py_files/make_bitstring.py          |   17 -
 howtos_as_py_files/my_file.pkl                |  Bin 0 -> 58 bytes
 howtos_as_py_files/namedtuple_example.py      |    5 -
 howtos_as_py_files/normalize_data.py          |   15 -
 howtos_as_py_files/numpy_matrix.py            |   36 -
 howtos_as_py_files/os_shutil_fileops.py       |   22 -
 howtos_as_py_files/patterns.ipynb             | 1486 +++++++++++++++++
 howtos_as_py_files/pickle_module.py           |   23 -
 howtos_as_py_files/pil_image_processing.py    |    0
 .../python2_vs_3_version_info.py              |   24 -
 howtos_as_py_files/read_file.py               |   44 -
 howtos_as_py_files/reg_expr_1_basics.py       |  101 --
 howtos_as_py_files/reg_expr_2_operators.py    |  127 --
 .../sort_list_of_tuples_by_ele.py             |   34 -
 .../sorting_multiple_lists_by_col.py          |   39 -
 howtos_as_py_files/timeit_test.py             |   24 -
 howtos_as_py_files/zen_of_python.py           |   24 -
 30 files changed, 1502 insertions(+), 841 deletions(-)
 create mode 100644 howtos_as_py_files/README.md
 delete mode 100755 howtos_as_py_files/closures.py
 delete mode 100644 howtos_as_py_files/cmd_line_args_1_sysarg.py
 delete mode 100755 howtos_as_py_files/cpu_time.py
 delete mode 100644 howtos_as_py_files/date_time.py
 delete mode 100644 howtos_as_py_files/diff_files.py
 delete mode 100644 howtos_as_py_files/doctest_example.py
 delete mode 100644 howtos_as_py_files/file_browsing.py
 delete mode 100755 howtos_as_py_files/get_filename.py
 delete mode 100644 howtos_as_py_files/get_minmax_indeces.py
 create mode 100644 howtos_as_py_files/id_file1.txt
 create mode 100644 howtos_as_py_files/id_file2.txt
 delete mode 100644 howtos_as_py_files/lambda_function.py
 delete mode 100644 howtos_as_py_files/make_bitstring.py
 create mode 100644 howtos_as_py_files/my_file.pkl
 delete mode 100644 howtos_as_py_files/namedtuple_example.py
 delete mode 100644 howtos_as_py_files/normalize_data.py
 delete mode 100644 howtos_as_py_files/numpy_matrix.py
 delete mode 100644 howtos_as_py_files/os_shutil_fileops.py
 create mode 100644 howtos_as_py_files/patterns.ipynb
 delete mode 100755 howtos_as_py_files/pickle_module.py
 delete mode 100644 howtos_as_py_files/pil_image_processing.py
 delete mode 100644 howtos_as_py_files/python2_vs_3_version_info.py
 delete mode 100755 howtos_as_py_files/read_file.py
 delete mode 100644 howtos_as_py_files/reg_expr_1_basics.py
 delete mode 100644 howtos_as_py_files/reg_expr_2_operators.py
 delete mode 100644 howtos_as_py_files/sort_list_of_tuples_by_ele.py
 delete mode 100644 howtos_as_py_files/sorting_multiple_lists_by_col.py
 delete mode 100644 howtos_as_py_files/timeit_test.py
 delete mode 100644 howtos_as_py_files/zen_of_python.py

diff --git a/howtos_as_py_files/README.md b/howtos_as_py_files/README.md
new file mode 100644
index 0000000..840e737
--- /dev/null
+++ b/howtos_as_py_files/README.md
@@ -0,0 +1,10 @@
+Sebastian Raschka  
+last updated: 09/26/2014
+
+# A collection of useful Python patterns
+
+
+new_msg("Hello, World")
+# prints: "My message: Hello, World"
+
+# print(dir(create_message.__closure__))
\ No newline at end of file
diff --git a/howtos_as_py_files/closures.py b/howtos_as_py_files/closures.py
deleted file mode 100755
index dc8dfea..0000000
--- a/howtos_as_py_files/closures.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Python 3.x
-# sr 11/04/2013
-# closures
-#
-
-def create_message(msg_txt):
-    def _priv_msg(message):     # private, no access from outside
-        print("{}: {}".format(msg_txt, message))
-    return _priv_msg            # returns a function
-
-new_msg = create_message("My message")
-# note, new_msg is a function
-
-new_msg("Hello, World")
-# prints: "My message: Hello, World"
-
-# print(dir(create_message.__closure__))
diff --git a/howtos_as_py_files/cmd_line_args_1_sysarg.py b/howtos_as_py_files/cmd_line_args_1_sysarg.py
deleted file mode 100644
index b8f8cbf..0000000
--- a/howtos_as_py_files/cmd_line_args_1_sysarg.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Getting command line arguments via sys.arg
-# sr 11/30/2013
-
-import sys
-
-def error(msg):
-    """Prints error message, sends it to stderr, and quites the program."""
-    sys.exit(msg)
-
-
-args = sys.argv[1:]     #  sys.argv[0] is the name of the python script itself
-
-try:
-    arg1 = int(args[0])
-    arg2 = args[1]
-    arg3 = args[2]
-    print("Everything okay!")
-
-except ValueError:
-    error("First argument must be integer type!")
-
-except IndexError:
-    error("Requires 3 arguments!")
-
diff --git a/howtos_as_py_files/cpu_time.py b/howtos_as_py_files/cpu_time.py
deleted file mode 100755
index 472cae7..0000000
--- a/howtos_as_py_files/cpu_time.py
+++ /dev/null
@@ -1,18 +0,0 @@
-# sr 10/29/13
-# Calculates elapsed CPU time in seconds as float. 
-
-import time
-
-start_time = time.clock()
-
-i = 0
-while i < 10000000:
-    i += 1
-
-elapsed_time = time.clock() - start_time
-print "Time elapsed: {} seconds".format(elapsed_time)
-
-# prints "Time elapsed: 1.06 seconds"
-#        on 4 x 2.80 Ghz Intel Xeon, 6 Gb RAM
-
-
diff --git a/howtos_as_py_files/date_time.py b/howtos_as_py_files/date_time.py
deleted file mode 100644
index 28e7bcc..0000000
--- a/howtos_as_py_files/date_time.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Sebastian Raschka, 03/2014
-# Date and Time in Python
-
-import time
-
-# print time HOURS:MINUTES:SECONDS
-# e.g., '10:50:58'
-print(time.strftime("%H:%M:%S"))
-
-
-# print current date DAY:MONTH:YEAR
-# e.g., '06/03/2014'
-print(time.strftime("%d/%m/%Y"))
diff --git a/howtos_as_py_files/diff_files.py b/howtos_as_py_files/diff_files.py
deleted file mode 100644
index 9399695..0000000
--- a/howtos_as_py_files/diff_files.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Sebastian Raschka, 2014
-#
-# Print lines that are different between 2 files. Insensitive
-# to the order of the file contents.
-
-id_set1 = set()
-id_set2 = set()
-
-with open('id_file1.txt', 'r') as id_file:
-    for line in id_file:
-        id_set1.add(line.strip())
-
-with open('id_file2.txt', 'r') as id_file:
-    for line in id_file:
-        id_set2.add(line.strip())    
-
-diffs = id_set2.difference(id_set1)
-
-for d in diffs:
-    print(d)
-print("Total differences:",len(diffs))
diff --git a/howtos_as_py_files/doctest_example.py b/howtos_as_py_files/doctest_example.py
deleted file mode 100644
index 246ecf8..0000000
--- a/howtos_as_py_files/doctest_example.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# doctest example
-# Sebastian Raschka 11/19/2013
-
-def subtract(a, b):
-    """
-    Subtracts second from first number and returns result.
-    >>> subtract(10, 5)
-    5
-    >>> subtract(11, 0.7)
-    10.3
-    """
-    return a-b
-
-def hello_world():
-    """
-    Returns 'Hello, World'
-    >>> hello_world()
-    "Hello, World"
-    >>> hello_world()
-    'Hello, World'
-    """
-    return "Hello, World"
-
-
-if __name__ == "__main__":  # is 'false' if imported
-    import doctest
-    doctest.testmod()
-
-
-""" RESULTS
-
-sebastian ~/Desktop> python3 doctest_example.py 
-**********************************************************************
-File "doctest_example.py", line 17, in __main__.hello_world
-Failed example:
-    hello_world()
-Expected:
-    "Hello, World"
-Got:
-    'Hello, World'
-**********************************************************************
-1 items had failures:
-    1 of   2 in __main__.hello_world
-***Test Failed*** 1 failures.
-sebastian ~/Desktop> 
-
-"""
diff --git a/howtos_as_py_files/file_browsing.py b/howtos_as_py_files/file_browsing.py
deleted file mode 100644
index ce1cb5f..0000000
--- a/howtos_as_py_files/file_browsing.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# File system operations using Python
-# sr 11/30/2013
-
-
-import os
-import shutil
-import glob
-
-# working directory
-c_dir = os.getcwd()                 # show current working directory
-os.listdir(c_dir)                   # shows all files in the working directory
-os.chdir('~/Data')                  # change working directory
-
-
-# get all files in a directory
-glob.glob('/Users/sebastian/Desktop/*')
-
-# e.g.,  ['/Users/sebastian/Desktop/untitled folder', '/Users/sebastian/Desktop/Untitled.txt']
-
-
-
-# walk
-tree = os.walk(c_dir)      
-# moves through sub directories and creates a 'generator' object of tuples
-# ('dir', [file1, file2, ...] [subdirectory1, subdirectory2, ...]), 
-#    (...), ...
-
-
-
-#check files: returns either True or False
-os.exists('../rel_path')
-os.exists('/home/abs_path')
-os.isfile('./file.txt')
-os.isdir('./subdir')
-
-
-
-# file permission (True or False
-os.access('./some_file', os.F_OK) # File exists? Python 2.7
-os.access('./some_file', os.R_OK) # Ok to read? Python 2.7
-os.access('./some_file', os.W_OK) # Ok to write? Python 2.7
-os.access('./some_file', os.X_OK) # Ok to execute? Python 2.7
-os.access('./some_file', os.X_OK | os.W_OK) # Ok to execute or write? Python 2.7
-
-
-
-# join (creates operating system dependent paths)
-os.path.join('a', 'b', 'c')
-# 'a/b/c' on Unix/Linux
-# 'a\\b\\c' on Windows
-os.path.normpath('a/b/c') # converts file separators
-
-
-
-# os.path: direcory and file names
-os.path.samefile('./some_file', '/home/some_file')  # True if those are the same
-os.path.dirname('./some_file')  # returns '.' (everythin but last component)
-os.path.basename('./some_file') # returns 'some_file' (only last component
-os.path.split('./some_file') # returns (dirname, basename) or ('.', 'some_file)
-os.path.splitext('./some_file.txt') # returns ('./some_file', '.txt')
-os.path.splitdrive('./some_file.txt') # returns ('', './some_file.txt')
-os.path.isabs('./some_file.txt') # returns False (not an absolute path)
-os.path.abspath('./some_file.txt')
-
-
-
-
-# create and delete files and directories
-os.mkdir('./test')  # create a new direcotory
-os.rmdir('./test')  # removes an empty direcotory
-os.removedirs('./test') # removes nested empty directories
-os.remove('file.txt')   # removes an individual file
-shutil.rmtree('./test') # removes directory (empty or not empty)
-
-os.rename('./dir_before', './renamed') # renames directory if destination doesn't exist
-shutil.move('./dir_before', './renamed') # renames directory always
-
-shutil.copytree('./orig', './copy') # copies a directory recursively
-shutil.copyfile('file', 'copy')     # copies a file
-
diff --git a/howtos_as_py_files/get_filename.py b/howtos_as_py_files/get_filename.py
deleted file mode 100755
index a05f92a..0000000
--- a/howtos_as_py_files/get_filename.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Python 2.7
-# prompt user for file of specific type(s).
-# 11/01/13 sebastian raschka
-
-import os.path
-
-def get_filename(file_type):
-    '''repeatedly prompts user for a file of specific type.
-       arguments:
-           file_type: list with accepted file types as strings.
-       returns:
-           (string): absolute path to the specified input file.
-    '''
-    while True:
-        print "\n\nplease enter a file name, \nor type --help to get"\
-                " a list of the accepted file formats"
-        file_name = raw_input(": ")
-        if file_name == "--help":
-            print "\naccepted file format(s): ",
-            for f in file_type:
-                print f,
-            continue        
-        if not os.path.isfile(file_name):
-            print "\n\nsorry, this file doesn't exist. please try again.\n"
-            continue
-        if not (file_name.split(".")[-1] in file_type):
-            print "\nplease provide a file in correct format."
-            continue
-        break
-    return os.path.abspath(file_name)
-
-#get_filename(["txt", "doc"])
-
-
-# ===========================
-# EXAMPLE
-# ===========================
-
-'''
-[bash]~/Desktop >python get_filename.py 
-
-
-please enter a file name, 
-or type --help to get a list of the accepted file formats
-: --help
-
-accepted file format(s):  txt doc 
-
-please enter a file name, 
-or type --help to get a list of the accepted file formats
-: test.tx 
-
-
-sorry, this file doesn't exist. please try again.
-
-
-
-please enter a file name, 
-or type --help to get a list of the accepted file formats
-: test.txt
-[bash]~/Desktop >
-'''
-
diff --git a/howtos_as_py_files/get_minmax_indeces.py b/howtos_as_py_files/get_minmax_indeces.py
deleted file mode 100644
index 1fe5b2a..0000000
--- a/howtos_as_py_files/get_minmax_indeces.py
+++ /dev/null
@@ -1,12 +0,0 @@
-# Sebastian Raschka, 03/2014
-# Getting the positions of min and max values in a list
-
-import operator
-
-values = [1, 2, 3, 4, 5]
-
-min_index, min_value = min(enumerate(values), key=operator.itemgetter(1))
-max_index, max_value = max(enumerate(values), key=operator.itemgetter(1))
-
-print('min_index:', min_index, 'min_value:', min_value)
-print('max_index:', max_index, 'max_value:', max_value)
diff --git a/howtos_as_py_files/id_file1.txt b/howtos_as_py_files/id_file1.txt
new file mode 100644
index 0000000..a600893
--- /dev/null
+++ b/howtos_as_py_files/id_file1.txt
@@ -0,0 +1,3 @@
+1234
+2342
+2341
\ No newline at end of file
diff --git a/howtos_as_py_files/id_file2.txt b/howtos_as_py_files/id_file2.txt
new file mode 100644
index 0000000..d05914a
--- /dev/null
+++ b/howtos_as_py_files/id_file2.txt
@@ -0,0 +1,3 @@
+5234
+3344
+2341
\ No newline at end of file
diff --git a/howtos_as_py_files/lambda_function.py b/howtos_as_py_files/lambda_function.py
deleted file mode 100644
index 9da0c9c..0000000
--- a/howtos_as_py_files/lambda_function.py
+++ /dev/null
@@ -1,11 +0,0 @@
-# Sebastian Raschka 08/2014
-
-# Lambda functions are just a short-hand way or writing
-# short function definitions
-
-def square_root1(x):
-    return x**0.5
-    
-square_root2 = lambda x: x**0.5
-
-assert(square_root1(9) == square_root2(9))
\ No newline at end of file
diff --git a/howtos_as_py_files/make_bitstring.py b/howtos_as_py_files/make_bitstring.py
deleted file mode 100644
index fb27089..0000000
--- a/howtos_as_py_files/make_bitstring.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Generating a bitstring from a Python list or numpy array
-# where all postive values -> 1
-# all negative values -> 0
-
-def make_bitstring(ary)
-    return np.where(ary > 0, 1, 0)
-
-
-def faster_bitstring(ary)
-    return np.where(ary > 0).astype('i1')
-
-### Example:
-
-ary1 = np.array([1, 2, 0.3, -1, -2])
-make_bitstring(ary1)
-
-# returns array([1, 1, 1, 0, 0])
diff --git a/howtos_as_py_files/my_file.pkl b/howtos_as_py_files/my_file.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..f24f8982047bec1fe6c3ff9aa77a5183b85cb094
GIT binary patch
literal 58
zcmZo*t}SHH@MetQWME(@&d*I%C`qj-DP;6!%3$<n24WT<W(8t4AZ7<*4j|^tU@X-G
E0F5~e!2kdN

literal 0
HcmV?d00001

diff --git a/howtos_as_py_files/namedtuple_example.py b/howtos_as_py_files/namedtuple_example.py
deleted file mode 100644
index 0546d85..0000000
--- a/howtos_as_py_files/namedtuple_example.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from collections import namedtuple
-
-my_namedtuple = namedtuple('field_name', ['x', 'y', 'z', 'bla', 'blub'])
-p = my_namedtuple(1, 2, 3, 4, 5)
-print(p.x, p.y, p.z)
diff --git a/howtos_as_py_files/normalize_data.py b/howtos_as_py_files/normalize_data.py
deleted file mode 100644
index 117d2fb..0000000
--- a/howtos_as_py_files/normalize_data.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Sebastian Raschka, 03/2014
-
-def normalize_val(x, data_list):
-    """
-    Normalizes a value to a data list returning a float
-    between 0.0 and 1.0. 
-    Returns the original object if value is not a integer or float.
-    
-    """
-    if isinstance(x, float) or isinstance(x, int):
-        numerator = x - min(data_list)
-        denominator = max(data_list) - min(data_list)
-        return numerator/denominator
-    else:
-        return x
diff --git a/howtos_as_py_files/numpy_matrix.py b/howtos_as_py_files/numpy_matrix.py
deleted file mode 100644
index 06d5eb2..0000000
--- a/howtos_as_py_files/numpy_matrix.py
+++ /dev/null
@@ -1,36 +0,0 @@
-# numpy matrix operations
-# sr 12/01/2013
-
-import numpy
-
-ary1 = numpy.array([1,2,3,4,5])  # must be same type
-ary2 = numpy.zeros((3,4))        # 3x4 matrix consisiting of 0s 
-ary3 = numpy.ones((3,4))         # 3x4 matrix consisiting of 1s 
-ary4 = numpy.identity(3)         # 3x3 identity matrix
-ary5 = ary1.copy()               # make a copy of ary1
-
-item1 = ary3[0, 0]  # item in row1, column1
-
-ary2.shape  # tuple of dimensions. Here: (3,4)
-ary2.size   # number of elements. Here: 12
-
-
-ary2_t = ary2.transpose()    # transposes matrix
-
-ary2.ravel()     # makes an array linear (1-dimensional)
-                 # by concatenating rows
-ary2.reshape(2,6) # reshapes array (must have same dimensions)
-
-ary3[0:2, 0:3]   # submatrix of first 2 rows and first 3 columns 
-
-ary3 = ary3[[2,0,1]]    # re-arrange rows
-
-
-# element-wise operations
-
-ary1 + ary1
-ary1 * ary1
-numpy.dot(ary1, ary1)   # matrix/vector (dot) product
-
-numpy.sum(ary1)   # sums up all elements in the array
-numpy.mean(ary1)   # average of all elements in the array
diff --git a/howtos_as_py_files/os_shutil_fileops.py b/howtos_as_py_files/os_shutil_fileops.py
deleted file mode 100644
index d517f22..0000000
--- a/howtos_as_py_files/os_shutil_fileops.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# sr 11/19/2013
-# common file operations in os and shutil modules
-
-import shutil
-import os
- 
-# Getting files of particular type from directory
-files = [f for f in os.listdir(s_pdb_dir) if f.endswith(".txt")]
-  
-# Copy and move
-shutil.copyfile("/path/to/file", "/path/to/new/file") 
-shutil.copy("/path/to/file", "/path/to/directory")
-shutil.move("/path/to/file","/path/to/directory")
-   
-# Check if file or directory exists
-os.path.exists("file or directory")
-os.path.isfile("file")
-os.path.isdir("directory")
-    
-# Working directory and absolute path to files
-os.getcwd()
-os.path.abspath("file")
diff --git a/howtos_as_py_files/patterns.ipynb b/howtos_as_py_files/patterns.ipynb
new file mode 100644
index 0000000..42fb3aa
--- /dev/null
+++ b/howtos_as_py_files/patterns.ipynb
@@ -0,0 +1,1486 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:0d6c2b08bbaff6330460e1379004dabcfdd16f3712b1fb820c0315a3f70294f5"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Go back](#https://github.com/rasbt/python_reference) to the `python_reference` repository."
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "A random collection of useful Python patterns"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I just cleaned my hard drive and found a couple of useful Python patterns that I had some use for in the past. I thought it would be worthwhile to collect them in a IPython notebook for personal reference and share it with people who might find them useful too.  \n",
+      "Most of those snippets are hopefully self-explanatory, but I am planning to add more comments and descriptions in future."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Table of Contents"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Bitstrings from positive and negative elements in a list](#Bitstrings-from-positive-and-negative-elements-in-a-list)\n",
+      "- [Command line arguments 1 - sys.argv](#Command-line-arguments-1---sys.argv)\n",
+      "- [Data and time basics](#Data-and-time-basics)\n",
+      "- [Differences between 2 files](#Differences-between-2-files)\n",
+      "- [Differences between successive elements in a list](#Differences-between-successive-elements-in-a-list)\n",
+      "- [Doctest example](#Doctest-example)\n",
+      "- [File browsing basics](#File-browsing-basics)\n",
+      "- [File reading basics](#File-reading-basics)\n",
+      "- [Indices of min and max elements from a list](#Indices-of-min-and-max-elements-from-a-list)\n",
+      "- [Lambda functions](#Lambda-functions)\n",
+      "- [Private functions](#Private-functions)\n",
+      "- [Namedtuples](#Namedtuples)\n",
+      "- [Normalizing data](#Normalizing-data)\n",
+      "- [NumPy essentials](#NumPy-essentials)\n",
+      "- [Pickling Python objects to bitstreams](#Pickling-Python-objects-to-bitstreams)\n",
+      "- [Python version check](#Python-version-check)\n",
+      "- [Runtime within a script](#Runtime-within-a-script)\n",
+      "- [Sorting lists of tuples by elements](#Sorting-lists-of-tuples-by-elements)\n",
+      "- [Sorting multiple lists relative to each other](#Sorting-multiple-lists-relative-to-each-other)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%watermark -d -a \"Sebastian Raschka\" -v"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Sebastian Raschka 26/09/2014 \n",
+        "\n",
+        "CPython 3.4.1\n",
+        "IPython 2.0.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1.5em\">[More information](https://github.com/rasbt/watermark) about the `watermark` magic command extension.</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Bitstrings from positive and negative elements in a list"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Generating a bitstring from a Python list or numpy array\n",
+      "# where all postive values -> 1\n",
+      "# all negative values -> 0\n",
+      "\n",
+      "import numpy as np\n",
+      "\n",
+      "def make_bitstring(ary):\n",
+      "    return np.where(ary > 0, 1, 0)\n",
+      "\n",
+      "\n",
+      "def faster_bitstring(ary):\n",
+      "    return np.where(ary > 0).astype('i1')\n",
+      "\n",
+      "### Example:\n",
+      "\n",
+      "ary1 = np.array([1, 2, 0.3, -1, -2])\n",
+      "print('input values %s' %ary1)\n",
+      "print('bitstring %s' %make_bitstring(ary1))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "input values [ 1.   2.   0.3 -1.  -2. ]\n",
+        "bitstring [1 1 1 0 0]\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Command line arguments 1 - sys.argv"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file cmd_line_args_1_sysarg.py\n",
+      "import sys\n",
+      "\n",
+      "def error(msg):\n",
+      "    \"\"\"Prints error message, sends it to stderr, and quites the program.\"\"\"\n",
+      "    sys.exit(msg)\n",
+      "\n",
+      "args = sys.argv[1:]     #  sys.argv[0] is the name of the python script itself\n",
+      "\n",
+      "try:\n",
+      "    arg1 = int(args[0])\n",
+      "    arg2 = args[1]\n",
+      "    arg3 = args[2]\n",
+      "    print(\"Everything okay!\")\n",
+      "\n",
+      "except ValueError:\n",
+      "    error(\"First argument must be integer type!\")\n",
+      "\n",
+      "except IndexError:\n",
+      "    error(\"Requires 3 arguments!\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Overwriting cmd_line_args_1_sysarg.py\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "% run cmd_line_args_1_sysarg.py 1 2 3"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Everything okay!\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "% run cmd_line_args_1_sysarg.py a 2 3"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "ename": "SystemExit",
+       "evalue": "First argument must be integer type!",
+       "output_type": "pyerr",
+       "traceback": [
+        "An exception has occurred, use %tb to see the full traceback.\n",
+        "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m First argument must be integer type!\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Data and time basics"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import time\n",
+      "\n",
+      "# print time HOURS:MINUTES:SECONDS\n",
+      "# e.g., '10:50:58'\n",
+      "print(time.strftime(\"%H:%M:%S\"))\n",
+      "\n",
+      "# print current date DAY:MONTH:YEAR\n",
+      "# e.g., '06/03/2014'\n",
+      "print(time.strftime(\"%d/%m/%Y\"))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "13:28:05\n",
+        "26/09/2014\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Differences between 2 files"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file id_file1.txt\n",
+      "1234\n",
+      "2342\n",
+      "2341"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Writing id_file1.txt\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%%file id_file2.txt\n",
+      "5234\n",
+      "3344\n",
+      "2341"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Writing id_file2.txt\n"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Print lines that are different between 2 files. Insensitive\n",
+      "# to the order of the file contents.\n",
+      "\n",
+      "id_set1 = set()\n",
+      "id_set2 = set()\n",
+      "\n",
+      "with open('id_file1.txt', 'r') as id_file:\n",
+      "    for line in id_file:\n",
+      "        id_set1.add(line.strip())\n",
+      "\n",
+      "with open('id_file2.txt', 'r') as id_file:\n",
+      "    for line in id_file:\n",
+      "        id_set2.add(line.strip())    \n",
+      "\n",
+      "diffs = id_set2.difference(id_set1)\n",
+      "\n",
+      "for d in diffs:\n",
+      "    print(d)\n",
+      "print(\"Total differences:\",len(diffs))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "5234\n",
+        "3344\n",
+        "Total differences: 2\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Differences between successive elements in a list"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from itertools import islice\n",
+      "\n",
+      "lst = [1,2,3,5,8]\n",
+      "diff = [j - i for i, j in zip(lst, islice(lst, 1, None))]\n",
+      "print(diff)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[1, 1, 2, 3]\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Doctest example"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def subtract(a, b):\n",
+      "    \"\"\"\n",
+      "    Subtracts second from first number and returns result.\n",
+      "    >>> subtract(10, 5)\n",
+      "    5\n",
+      "    >>> subtract(11, 0.7)\n",
+      "    10.3\n",
+      "    \"\"\"\n",
+      "    return a-b\n",
+      "\n",
+      "if __name__ == \"__main__\":  # is 'false' if imported\n",
+      "    import doctest\n",
+      "    doctest.testmod()\n",
+      "    print('ok')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "ok\n"
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def hello_world():\n",
+      "    \"\"\"\n",
+      "    Returns 'Hello, World'\n",
+      "    >>> hello_world()\n",
+      "    'Hello, World'\n",
+      "    \"\"\"\n",
+      "    return 'hello world'\n",
+      "\n",
+      "if __name__ == \"__main__\":  # is 'false' if imported\n",
+      "    import doctest\n",
+      "    doctest.testmod()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "**********************************************************************\n",
+        "File \"__main__\", line 4, in __main__.hello_world\n",
+        "Failed example:\n",
+        "    hello_world()\n",
+        "Expected:\n",
+        "    'Hello, World'\n",
+        "Got:\n",
+        "    'hello world'\n",
+        "**********************************************************************\n",
+        "1 items had failures:\n",
+        "   1 of   1 in __main__.hello_world\n",
+        "***Test Failed*** 1 failures.\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "File browsing basics"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import os\n",
+      "import shutil\n",
+      "import glob\n",
+      "\n",
+      "# working directory\n",
+      "c_dir = os.getcwd()                 # show current working directory\n",
+      "os.listdir(c_dir)                   # shows all files in the working directory\n",
+      "os.chdir('~/Data')                  # change working directory\n",
+      "\n",
+      "\n",
+      "# get all files in a directory\n",
+      "glob.glob('/Users/sebastian/Desktop/*')\n",
+      "\n",
+      "# e.g.,  ['/Users/sebastian/Desktop/untitled folder', '/Users/sebastian/Desktop/Untitled.txt']\n",
+      "\n",
+      "# walk\n",
+      "tree = os.walk(c_dir)      \n",
+      "# moves through sub directories and creates a 'generator' object of tuples\n",
+      "# ('dir', [file1, file2, ...] [subdirectory1, subdirectory2, ...]), \n",
+      "#    (...), ...\n",
+      "\n",
+      "#check files: returns either True or False\n",
+      "os.exists('../rel_path')\n",
+      "os.exists('/home/abs_path')\n",
+      "os.isfile('./file.txt')\n",
+      "os.isdir('./subdir')\n",
+      "\n",
+      "\n",
+      "# file permission (True or False\n",
+      "os.access('./some_file', os.F_OK) # File exists? Python 2.7\n",
+      "os.access('./some_file', os.R_OK) # Ok to read? Python 2.7\n",
+      "os.access('./some_file', os.W_OK) # Ok to write? Python 2.7\n",
+      "os.access('./some_file', os.X_OK) # Ok to execute? Python 2.7\n",
+      "os.access('./some_file', os.X_OK | os.W_OK) # Ok to execute or write? Python 2.7\n",
+      "\n",
+      "# join (creates operating system dependent paths)\n",
+      "os.path.join('a', 'b', 'c')\n",
+      "# 'a/b/c' on Unix/Linux\n",
+      "# 'a\\\\b\\\\c' on Windows\n",
+      "os.path.normpath('a/b/c') # converts file separators\n",
+      "\n",
+      "\n",
+      "# os.path: direcory and file names\n",
+      "os.path.samefile('./some_file', '/home/some_file')  # True if those are the same\n",
+      "os.path.dirname('./some_file')  # returns '.' (everythin but last component)\n",
+      "os.path.basename('./some_file') # returns 'some_file' (only last component\n",
+      "os.path.split('./some_file') # returns (dirname, basename) or ('.', 'some_file)\n",
+      "os.path.splitext('./some_file.txt') # returns ('./some_file', '.txt')\n",
+      "os.path.splitdrive('./some_file.txt') # returns ('', './some_file.txt')\n",
+      "os.path.isabs('./some_file.txt') # returns False (not an absolute path)\n",
+      "os.path.abspath('./some_file.txt')\n",
+      "\n",
+      "\n",
+      "# create and delete files and directories\n",
+      "os.mkdir('./test')  # create a new direcotory\n",
+      "os.rmdir('./test')  # removes an empty direcotory\n",
+      "os.removedirs('./test') # removes nested empty directories\n",
+      "os.remove('file.txt')   # removes an individual file\n",
+      "shutil.rmtree('./test') # removes directory (empty or not empty)\n",
+      "\n",
+      "os.rename('./dir_before', './renamed') # renames directory if destination doesn't exist\n",
+      "shutil.move('./dir_before', './renamed') # renames directory always\n",
+      "\n",
+      "shutil.copytree('./orig', './copy') # copies a directory recursively\n",
+      "shutil.copyfile('file', 'copy')     # copies a file\n",
+      "\n",
+      " \n",
+      "# Getting files of particular type from directory\n",
+      "files = [f for f in os.listdir(s_pdb_dir) if f.endswith(\".txt\")]\n",
+      "  \n",
+      "# Copy and move\n",
+      "shutil.copyfile(\"/path/to/file\", \"/path/to/new/file\") \n",
+      "shutil.copy(\"/path/to/file\", \"/path/to/directory\")\n",
+      "shutil.move(\"/path/to/file\",\"/path/to/directory\")\n",
+      "   \n",
+      "# Check if file or directory exists\n",
+      "os.path.exists(\"file or directory\")\n",
+      "os.path.isfile(\"file\")\n",
+      "os.path.isdir(\"directory\")\n",
+      "    \n",
+      "# Working directory and absolute path to files\n",
+      "os.getcwd()\n",
+      "os.path.abspath(\"file\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "File reading basics"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Note: rb opens file in binary mode to avoid issues with Windows systems\n",
+      "# where '\\r\\n' is used instead of '\\n' as newline character(s).\n",
+      "\n",
+      "\n",
+      "# A) Reading in Byte chunks\n",
+      "reader_a = open(\"file.txt\", \"rb\")\n",
+      "chunks = []\n",
+      "data = reader_a.read(64)  # reads first 64 bytes\n",
+      "while data != \"\":\n",
+      "    chunks.append(data)\n",
+      "    data = reader_a.read(64)\n",
+      "if data:\n",
+      "    chunks.append(data)\n",
+      "print(len(chunks))\n",
+      "reader_a.close()\n",
+      "\n",
+      "\n",
+      "# B) Reading whole file at once into a list of lines\n",
+      "with open(\"file.txt\", \"rb\") as reader_b:   # recommended syntax, auto closes\n",
+      "    data = reader_b.readlines() # data is assigned a list of lines\n",
+      "print(len(data))\n",
+      "\n",
+      "\n",
+      "# C) Reading whole file at once into a string\n",
+      "with open(\"file.txt\", \"rb\") as reader_c:\n",
+      "    data = reader_c.read() # data is assigned a list of lines\n",
+      "print(len(data))\n",
+      "\n",
+      "\n",
+      "# D) Reading line by line into a list\n",
+      "data = []\n",
+      "with open(\"file.txt\", \"rb\") as reader_d:\n",
+      "    for line in reader_d:\n",
+      "        data.append(line)\n",
+      "print(len(data))\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Indices of min and max elements from a list"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import operator\n",
+      "\n",
+      "values = [1, 2, 3, 4, 5]\n",
+      "\n",
+      "min_index, min_value = min(enumerate(values), key=operator.itemgetter(1))\n",
+      "max_index, max_value = max(enumerate(values), key=operator.itemgetter(1))\n",
+      "\n",
+      "print('min_index:', min_index, 'min_value:', min_value)\n",
+      "print('max_index:', max_index, 'max_value:', max_value)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "min_index: 0 min_value: 1\n",
+        "max_index: 4 max_value: 5\n"
+       ]
+      }
+     ],
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Lambda functions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Lambda functions are just a short-hand way or writing\n",
+      "# short function definitions\n",
+      "\n",
+      "def square_root1(x):\n",
+      "    return x**0.5\n",
+      "    \n",
+      "square_root2 = lambda x: x**0.5\n",
+      "\n",
+      "assert(square_root1(9) == square_root2(9))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Private functions"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def create_message(msg_txt):\n",
+      "    def _priv_msg(message):     # private, no access from outside\n",
+      "        print(\"{}: {}\".format(msg_txt, message))\n",
+      "    return _priv_msg            # returns a function\n",
+      "\n",
+      "new_msg = create_message(\"My message\")\n",
+      "# note, new_msg is a function\n",
+      "\n",
+      "new_msg(\"Hello, World\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "My message: Hello, World\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Namedtuples"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from collections import namedtuple\n",
+      "\n",
+      "my_namedtuple = namedtuple('field_name', ['x', 'y', 'z', 'bla', 'blub'])\n",
+      "p = my_namedtuple(1, 2, 3, 4, 5)\n",
+      "print(p.x, p.y, p.z)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1 2 3\n"
+       ]
+      }
+     ],
+     "prompt_number": 25
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Normalizing data"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def normalize(data, min_val=0, max_val=1):\n",
+      "    \"\"\"\n",
+      "    Normalizes values in a list of data points to a range, e.g.,\n",
+      "    between 0.0 and 1.0. \n",
+      "    Returns the original object if value is not a integer or float.\n",
+      "    \n",
+      "    \"\"\"\n",
+      "    norm_data = []\n",
+      "    data_min = min(data)\n",
+      "    data_max = max(data)\n",
+      "    for x in data:\n",
+      "        numerator = x - data_min\n",
+      "        denominator = data_max - data_min\n",
+      "        x_norm = (max_val-min_val) * numerator/denominator + min_val\n",
+      "        norm_data.append(x_norm)\n",
+      "    return norm_data"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "normalize([1,2,3,4,5])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 31,
+       "text": [
+        "[0.0, 0.25, 0.5, 0.75, 1.0]"
+       ]
+      }
+     ],
+     "prompt_number": 31
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "normalize([1,2,3,4,5], min_val=-10, max_val=10)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 30,
+       "text": [
+        "[-10.0, -5.0, 0.0, 5.0, 10.0]"
+       ]
+      }
+     ],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "NumPy essentials"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy as np\n",
+      "\n",
+      "ary1 = np.array([1,2,3,4,5])  # must be same type\n",
+      "ary2 = np.zeros((3,4))        # 3x4 matrix consisiting of 0s \n",
+      "ary3 = np.ones((3,4))         # 3x4 matrix consisiting of 1s \n",
+      "ary4 = np.identity(3)         # 3x3 identity matrix\n",
+      "ary5 = ary1.copy()               # make a copy of ary1\n",
+      "\n",
+      "item1 = ary3[0, 0]  # item in row1, column1\n",
+      "\n",
+      "ary2.shape  # tuple of dimensions. Here: (3,4)\n",
+      "ary2.size   # number of elements. Here: 12\n",
+      "\n",
+      "\n",
+      "ary2_t = ary2.transpose()    # transposes matrix\n",
+      "\n",
+      "ary2.ravel()     # makes an array linear (1-dimensional)\n",
+      "                 # by concatenating rows\n",
+      "ary2.reshape(2,6) # reshapes array (must have same dimensions)\n",
+      "\n",
+      "ary3[0:2, 0:3]   # submatrix of first 2 rows and first 3 columns \n",
+      "\n",
+      "ary3 = ary3[[2,0,1]]    # re-arrange rows\n",
+      "\n",
+      "\n",
+      "# element-wise operations\n",
+      "\n",
+      "ary1 + ary1\n",
+      "ary1 * ary1\n",
+      "numpy.dot(ary1, ary1)   # matrix/vector (dot) product\n",
+      "\n",
+      "numpy.sum(ary1, axis=1)   # sum of a 1D array, column sums of a 2D array\n",
+      "numpy.mean(ary1, axis=1)  # mean of a 1D array, column means of a 2D array"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Pickling Python objects to bitstreams"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pickle\n",
+      "\n",
+      "#### Generate some object\n",
+      "my_dict = dict()\n",
+      "for i in range(1,10):\n",
+      "    my_dict[i] = \"some text\"\n",
+      "\n",
+      "#### Save object to file\n",
+      "pickle_out = open('my_file.pkl', 'wb')\n",
+      "pickle.dump(my_dict, pickle_out)\n",
+      "pickle_out.close()\n",
+      "\n",
+      "#### Load object from file\n",
+      "my_object_file = open('my_file.pkl', 'rb')\n",
+      "my_dict = pickle.load(my_object_file)\n",
+      "my_object_file.close()\n",
+      "\n",
+      "print(my_dict)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "{1: 'some text', 2: 'some text', 3: 'some text', 4: 'some text', 5: 'some text', 6: 'some text', 7: 'some text', 8: 'some text', 9: 'some text'}\n"
+       ]
+      }
+     ],
+     "prompt_number": 35
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Python version check"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import sys\n",
+      "\n",
+      "def give_letter(word):\n",
+      "    for letter in word:\n",
+      "        yield letter\n",
+      "\n",
+      "if sys.version_info[0] == 3:\n",
+      "    print('executed in Python 3.x')\n",
+      "    test = give_letter('Hello')\n",
+      "    print(next(test))\n",
+      "    print('in for-loop:')\n",
+      "    for l in test:\n",
+      "        print(l)\n",
+      "\n",
+      "# if Python 2.x\n",
+      "if sys.version_info[0] == 2:\n",
+      "    print('executed in Python 2.x')\n",
+      "    test = give_letter('Hello')\n",
+      "    print(test.next())\n",
+      "    print('in for-loop:') \n",
+      "    for l in test:\n",
+      "        print(l)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "executed in Python 3.x\n",
+        "H\n",
+        "in for-loop:\n",
+        "e\n",
+        "l\n",
+        "l\n",
+        "o\n"
+       ]
+      }
+     ],
+     "prompt_number": 36
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Runtime within a script"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import time\n",
+      "\n",
+      "start_time = time.clock()\n",
+      "\n",
+      "for i in range(10000000):\n",
+      "    pass\n",
+      "\n",
+      "elapsed_time = time.clock() - start_time\n",
+      "print(\"Time elapsed: {} seconds\".format(elapsed_time))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Time elapsed: 0.49176900000000057 seconds\n"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "elapsed_time = timeit.timeit('for i in range(10000000): pass', number=1)\n",
+      "print(\"Time elapsed: {} seconds\".format(elapsed_time))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Time elapsed: 0.3550995970144868 seconds\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Sorting lists of tuples by elements"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Here, we make use of the \"key\" parameter of the in-built \"sorted()\" function \n",
+      "# (also available for the \".sort()\" method), which let's us define a function \n",
+      "# that is called on every element that is to be sorted. In this case, our \n",
+      "# \"key\"-function is a simple lambda function that returns the last item \n",
+      "# from every tuple.\n",
+      "\n",
+      "a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]\n",
+      "\n",
+      "sorted_list = sorted(a_list, key=lambda e: e[::-1])\n",
+      "\n",
+      "print(sorted_list)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]\n"
+       ]
+      }
+     ],
+     "prompt_number": 37
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# prints [(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]\n",
+      "\n",
+      "# If we are only interesting in sorting the list by the last element\n",
+      "# of the tuple and don't care about a \"tie\" situation, we can also use\n",
+      "# the index of the tuple item directly instead of reversing the tuple \n",
+      "# for efficiency.\n",
+      "\n",
+      "a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]\n",
+      "\n",
+      "sorted_list = sorted(a_list, key=lambda e: e[-1])\n",
+      "\n",
+      "print(sorted_list)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[(2, 3, 'a'), (3, 2, 'b'), (2, 2, 'b'), (1, 3, 'c')]\n"
+       ]
+      }
+     ],
+     "prompt_number": 38
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Sorting multiple lists relative to each other"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\"\"\"\n",
+      "You have 3 lists that you want to sort \"relative\" to each other,\n",
+      "for example, picturing each list as a row in a 3x3 matrix: sort it by columns\n",
+      "\n",
+      "########################\n",
+      "If the input lists are\n",
+      "########################\n",
+      "\n",
+      " list1 = ['c','b','a']\n",
+      " list2 = [6,5,4]\n",
+      " list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']\n",
+      "\n",
+      "########################\n",
+      "the desired outcome is:\n",
+      "########################\n",
+      "\n",
+      " ['a', 'b', 'c'] \n",
+      " [4, 5, 6] \n",
+      " ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']\n",
+      "\n",
+      "########################\n",
+      "and NOT:\n",
+      "########################\n",
+      "\n",
+      " ['a', 'b', 'c'] \n",
+      " [4, 5, 6] \n",
+      " ['another_val-b', 'some-val-associated-with-c', 'z_another_third_val-a']\n",
+      "\n",
+      "\n",
+      "\"\"\"\n",
+      "\n",
+      "list1 = ['c','b','a']\n",
+      "list2 = [6,5,4]\n",
+      "list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']\n",
+      "\n",
+      "print('input values:\\n', list1, list2, list3)\n",
+      "\n",
+      "list1, list2, list3 = [list(t) for t in zip(*sorted(zip(list1, list2, list3)))]\n",
+      "\n",
+      "print('\\n\\nsorted output:\\n', list1, list2, list3 )"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "input values:\n",
+        " ['c', 'b', 'a'] [6, 5, 4] ['some-val-associated-with-c', 'another_val-b', 'z_another_third_val-a']\n",
+        "\n",
+        "\n",
+        "sorted output:\n",
+        " ['a', 'b', 'c'] [4, 5, 6] ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']\n"
+       ]
+      }
+     ],
+     "prompt_number": 49
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/howtos_as_py_files/pickle_module.py b/howtos_as_py_files/pickle_module.py
deleted file mode 100755
index 81afd92..0000000
--- a/howtos_as_py_files/pickle_module.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# sr 10/29/13
-# The pickle module converts Python objects into byte streams
-# to save them as a file on your drive for re-use.
-#
-# module documentation http://docs.python.org/2/library/pickle.html
-
-import pickle
-
-#### Generate some object
-my_dict = dict()
-for i in range(1,1000):
-    my_dict[i] = "some text"
-
-#### Save object to file
-pickle_out = open('my_file.pkl', 'wb')
-pickle.dump(my_dict, pickle_out)
-pickle_out.close()
-
-#### Load object from file
-my_object_file = open('my_file.pkl', 'rb')
-my_dict = pickle.load(my_object_file)
-my_object_file.close()
-
diff --git a/howtos_as_py_files/pil_image_processing.py b/howtos_as_py_files/pil_image_processing.py
deleted file mode 100644
index e69de29..0000000
diff --git a/howtos_as_py_files/python2_vs_3_version_info.py b/howtos_as_py_files/python2_vs_3_version_info.py
deleted file mode 100644
index 19e7bb2..0000000
--- a/howtos_as_py_files/python2_vs_3_version_info.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Sebastian Raschka 04/10/2014
-
-import sys
-
-def give_letter(word):
-    for letter in word:
-        yield letter
-
-if sys.version_info[0] == 3:
-    print('executed in Python 3.x')
-    test = give_letter('Hello')
-    print(next(test))
-    print('in for-loop:')
-    for l in test:
-        print(l)
-
-# if Python 2.x
-if sys.version_info[0] == 2:
-    print('executed in Python 2.x')
-    test = give_letter('Hello')
-    print(test.next())
-    print('in for-loop:') 
-    for l in test:
-        print(l)
diff --git a/howtos_as_py_files/read_file.py b/howtos_as_py_files/read_file.py
deleted file mode 100755
index 567ae0c..0000000
--- a/howtos_as_py_files/read_file.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Different methods to read from text files
-# sr 11/18/2013
-# Python 3.x
-
-# Note: rb opens file in binary mode to avoid issues with Windows systems
-# where '\r\n' is used instead of '\n' as newline character(s).
-
-
-# A) Reading in Byte chunks
-reader_a = open("file.txt", "rb")
-chunks = []
-data = reader_a.read(64)  # reads first 64 bytes
-while data != "":
-    chunks.append(data)
-    data = reader_a.read(64)
-if data:
-    chunks.append(data)
-print (len(chunks))
-reader_a.close()
-
-
-# B) Reading whole file at once into a list of lines
-with open("file.txt", "rb") as reader_b:   # recommended syntax, auto closes
-    data = reader_b.readlines() # data is assigned a list of lines
-print (len(data))
-
-
-# C) Reading whole file at once into a string
-with open("file.txt", "rb") as reader_c:
-    data = reader_c.read() # data is assigned a list of lines
-print (len(data))
-
-
-# D) Reading line by line into a list
-data = []
-with open("file.txt", "rb") as reader_d:
-    for line in reader_d:
-        data.append(line)
-print (len(data))
-
-
-
-
-
diff --git a/howtos_as_py_files/reg_expr_1_basics.py b/howtos_as_py_files/reg_expr_1_basics.py
deleted file mode 100644
index 5fafab8..0000000
--- a/howtos_as_py_files/reg_expr_1_basics.py
+++ /dev/null
@@ -1,101 +0,0 @@
-# Examples for using Python's Regular expression module "re"
-# sr 11/30/2013
-
-import re
-
-'''OVERVIEW
-     '|' means 'or'
-     '.' matches any single character 
-     '()' groups into substrings
-'''
-
-
-
-
-
-# read in data
-fileobj = '''abc mno
-def pqr
-ghi stu
-jkl vwx'''
-
-data = fileobj.strip().split('\n')
-
-
-# A >> if 's' in line
-print (50*'-' + '\nA\n' + 50*'-')
-for line in data:
-    if re.search('abc', line):
-        print(">>", line)
-    else:
-        print("  ", line)
-
-'''
---------------------------------------------------
-A
---------------------------------------------------
->> abc mno
-   def pqr
-   ghi stu
-   jkl vwx'''
-
-
-
-# B >> if 's' in line or 'r' in line
-print (50*'-' + '\nB\n' + 50*'-')
-for line in data:
-    if re.search('abc|efg', line):
-        print(">>", line)
-    else:
-        print("  ", line)
-
-'''
---------------------------------------------------
-B
---------------------------------------------------
->> abc mno
-   def pqr
-   ghi stu
-   jkl vwx
----------------'''
-
-
-# C >> 
-# use () to remember which object was found and return a match object
-print (50*'-' + '\nC\n' + 50*'-')
-for line in data:
-    match = re.search('(abc|efg)', line)    # note the parantheses
-    if match: 
-        print(match.group(1))               # prints 'abc' if found, else None
-        # match.group(0) is the whole pattern that matched
-
-'''
---------------------------------------------------
-C
---------------------------------------------------
-abc'''
-
-
-
-# read in data
-fileobj = '''2013-01-01
-2012-02-02
-ghi stu
-2012-03-03'''
-
-data = fileobj.strip().split('\n')
-
-
-# D >> use '.' to match 'any character'
-print (50*'-' + '\nD\n' + 50*'-')
-for line in data:
-    match = re.search('(2012)-(..)-(..)', line)    # note the parantheses
-    if match: 
-        print(match.group(1), match.group(2), match.group(3))             
-
-'''
---------------------------------------------------
-D
---------------------------------------------------
-2012 02 02
-2012 03 03'''
diff --git a/howtos_as_py_files/reg_expr_2_operators.py b/howtos_as_py_files/reg_expr_2_operators.py
deleted file mode 100644
index 4994159..0000000
--- a/howtos_as_py_files/reg_expr_2_operators.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# Examples for using Python's Regular expression module "re"
-# sr 11/30/2013
-
-import re
-
-'''OVERVIEW
-     '*' matches all characters that follow (0 or more)
-     '+' matches all characters that follow (1 or more)
-     '?' makes the previous character optional
-     '{4}' previous character must match exactly 4 times
-     '{2-4}' previous character must match exactly 2-4 times
-     '[0-9]' matches all characters in the set of numbers 0 to 9
-     '[A-Z]' matches all characters in the set of A to Z
-     '\d' matches all digits, e.g., '4', '9' ...
-     '\D' matches all NON-digit characters
-     '\s' matches all space characters: '', '\t', '\r', '\n'
-     '\S' matches all NON-space characters
-     '\w' matches all non-punctuation characters (i.e., letters and digits)
-     '\W' matches all NON-letter and NON-digit characters
-     '^bla' NOT-matches 'bla'
-     'let$' matches 'let' but not 'letter'
-     '\b' matches transition between non-word characters and word characters
- 
-'''
-
-data = '''2013-01-01
-2012-02-02
-aaaa-02-02
-aa-02-02
--04-04
-2000 02-02
-ghi stu
-2012-03-03'''.strip().split('\n')
-
-
-# A >> '*' matches all characters that follow (0 or more)
-print (50*'-' + '\nA\n' + 50*'-')
-
-for line in data:
-    match = re.search('(.*)-(..)-(..)', line)    # note the parantheses
-    if match: 
-        print(match.group(1), match.group(2), match.group(3))  
-
-'''
---------------------------------------------------
-A
---------------------------------------------------
-2013 01 01
-2012 02 02
-aaaa 02 02
-aa 02 02
- 04 04
-2012 03 03
-'''
-
-
-# B >> '+' matches all characters that follow (1 or more)
-print (50*'-' + '\nB\n' + 50*'-')
-
-for line in data:
-    match = re.search('(.+)-(..)-(..)', line)    # note the parantheses
-    if match: 
-        print(match.group(1), match.group(2), match.group(3))  
-
-'''
---------------------------------------------------
-B
---------------------------------------------------
-2013 01 01
-2012 02 02
-aaaa 02 02
-aa 02 02
-2012 03 03
-'''
-
-
-# C >> '?' makes the previous character optional
-print (50*'-' + '\nC\n' + 50*'-')
-
-for line in data:
-    match = re.search('(.+)-?(..)-(..)', line)    # note the parantheses
-    if match: 
-        print(match.group(1), match.group(2), match.group(3)) 
-
-'''
---------------------------------------------------
-C
---------------------------------------------------
-2013- 01 01
-2012- 02 02
-aaaa- 02 02
-aa- 02 02
-- 04 04
-2000  02 02
-2012- 03 03
-'''
-
-# D >> '{4}' previous character must match exactly 4 times 
-print (50*'-' + '\nD\n' + 50*'-')
-
-for line in data:
-    match = re.search('(a{4})-(..)-(..)', line)    # note the parantheses
-    if match: 
-        print(match.group(1), match.group(2), match.group(3)) 
-
-'''
---------------------------------------------------
-D
---------------------------------------------------
-aaaa 02 02
-'''
-
-# E >>'{2-4}' previous character must match exactly 2-4 times
-print (50*'-' + '\nE\n' + 50*'-')
-
-for line in data:
-    match = re.search('(a{2,4})-(..)-(..)', line)    # note the parantheses
-    if match: 
-        print(match.group(1), match.group(2), match.group(3)) 
-
-'''
---------------------------------------------------
-E
---------------------------------------------------
-aaaa 02 02
-aa 02 02
-'''
diff --git a/howtos_as_py_files/sort_list_of_tuples_by_ele.py b/howtos_as_py_files/sort_list_of_tuples_by_ele.py
deleted file mode 100644
index 4a94d4b..0000000
--- a/howtos_as_py_files/sort_list_of_tuples_by_ele.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# Sebastian Raschka 09/02/2014
-# Sorting a list of tuples by starting with the last element of the tuple (=reversed tuple)
-
-# Here, we make use of the "key" parameter of the in-built "sorted()" function 
-# (also available for the ".sort()" method), which let's us define a function 
-# that is called on every element that is to be sorted. In this case, our 
-# "key"-function is a simple lambda function that returns the last item 
-# from every tuple.
-
-
-a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]
-
-sorted_list = sorted(a_list, key=lambda e: e[::-1])
-
-print(sorted_list)
-
-# prints [(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]
-
-
-
-# If we are only interesting in sorting the list by the last element
-# of the tuple and don't care about a "tie" situation, we can also use
-# the index of the tuple item directly instead of reversing the tuple 
-# for efficiency.
-
-
-
-a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]
-
-sorted_list = sorted(a_list, key=lambda e: e[-1])
-
-print(sorted_list)
-
-# prints [(2, 3, 'a'), (3, 2, 'b'), (2, 2, 'b'), (1, 3, 'c')]
\ No newline at end of file
diff --git a/howtos_as_py_files/sorting_multiple_lists_by_col.py b/howtos_as_py_files/sorting_multiple_lists_by_col.py
deleted file mode 100644
index 7c534cb..0000000
--- a/howtos_as_py_files/sorting_multiple_lists_by_col.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Sebastian Raschka 2014
-
-"""
-You have 3 lists that you want to sort "relative" to each other,
-for example, picturing each list as a row in a 3x3 matrix: sort it by columns
-
-########################
-If the input lists are
-########################
-
- list1 = ['c','b','a']
- list2 = [6,5,4]
- list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']
-
-########################
-the desired outcome is:
-########################
-
- ['a', 'b', 'c'] 
- [4, 5, 6] 
- ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']
-
-########################
-and NOT:
-########################
-
- ['a', 'b', 'c'] 
- [4, 5, 6] 
- ['another_val-b', 'some-val-associated-with-c', 'z_another_third_val-a']
-
-
-"""
-
-list1 = ['c','b','a']
-list2 = [6,5,4]
-list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']
-
-
-list1, list2, list3 = zip(*sorted(zip(list1, list2, list3)))
diff --git a/howtos_as_py_files/timeit_test.py b/howtos_as_py_files/timeit_test.py
deleted file mode 100644
index 31bb93e..0000000
--- a/howtos_as_py_files/timeit_test.py
+++ /dev/null
@@ -1,24 +0,0 @@
-# Sebastian Raschka, 03/2014
-# comparing string formating: %s and .format()
-
-import timeit
-
-format_res = timeit.timeit("['{}'.format(i) for i in range(10000)]", number=1000)
-
-binaryop_res = timeit.timeit("['%s' %i for i in range(10000)]", number=1000)
-
-print('{}: {}\n{}: {}'.format('format()', format_res, '%s', binaryop_res))
-
-################################
-# On my machine
-################################
-#
-# Python 3.4.0
-# MacOS X 10.9.2
-# 2.5 GHz Intel Core i5
-# 4 GB 1600 Mhz DDR3
-#
-################################
-# format(): 2.815331667999999
-# %s: 1.630353775999538
-################################
diff --git a/howtos_as_py_files/zen_of_python.py b/howtos_as_py_files/zen_of_python.py
deleted file mode 100644
index d82cacd..0000000
--- a/howtos_as_py_files/zen_of_python.py
+++ /dev/null
@@ -1,24 +0,0 @@
->>> import this
-"""
-The Zen of Python, by Tim Peters
-
-Beautiful is better than ugly.
-Explicit is better than implicit.
-Simple is better than complex.
-Complex is better than complicated.
-Flat is better than nested.
-Sparse is better than dense.
-Readability counts.
-Special cases aren't special enough to break the rules.
-Although practicality beats purity.
-Errors should never pass silently.
-Unless explicitly silenced.
-In the face of ambiguity, refuse the temptation to guess.
-There should be one-- and preferably only one --obvious way to do it.
-Although that way may not be obvious at first unless you're Dutch.
-Now is better than never.
-Although never is often better than *right* now.
-If the implementation is hard to explain, it's a bad idea.
-If the implementation is easy to explain, it may be a good idea.
-Namespaces are one honking great idea -- let's do more of those!
-"""

From aea7c4403ece6c71a1ebd8ad7f37d511619521bb Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 26 Sep 2014 14:20:59 -0400
Subject: [PATCH 190/248] linked A random collection of useful Python patterns

---
 README.md                                           |   2 ++
 howtos_as_py_files/README.md                        |  10 ----------
 python_patterns/README.md                           |   3 +++
 .../id_file1.txt                                    |   0
 .../id_file2.txt                                    |   0
 {howtos_as_py_files => python_patterns}/my_file.pkl | Bin
 .../patterns.ipynb                                  |   0
 7 files changed, 5 insertions(+), 10 deletions(-)
 delete mode 100644 howtos_as_py_files/README.md
 create mode 100644 python_patterns/README.md
 rename {howtos_as_py_files => python_patterns}/id_file1.txt (100%)
 rename {howtos_as_py_files => python_patterns}/id_file2.txt (100%)
 rename {howtos_as_py_files => python_patterns}/my_file.pkl (100%)
 rename {howtos_as_py_files => python_patterns}/patterns.ipynb (100%)

diff --git a/README.md b/README.md
index a1872c6..e2e1ee8 100644
--- a/README.md
+++ b/README.md
@@ -51,6 +51,8 @@
 
 - Quick guide for dealing with missing numbers in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/numpy_nan_quickguide.ipynb)]
 
+- A random collection of useful Python patterns [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb)]
+
 <br>
 
 
diff --git a/howtos_as_py_files/README.md b/howtos_as_py_files/README.md
deleted file mode 100644
index 840e737..0000000
--- a/howtos_as_py_files/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
-Sebastian Raschka  
-last updated: 09/26/2014
-
-# A collection of useful Python patterns
-
-
-new_msg("Hello, World")
-# prints: "My message: Hello, World"
-
-# print(dir(create_message.__closure__))
\ No newline at end of file
diff --git a/python_patterns/README.md b/python_patterns/README.md
new file mode 100644
index 0000000..084d5c2
--- /dev/null
+++ b/python_patterns/README.md
@@ -0,0 +1,3 @@
+# A collection of useful Python patterns
+
+[View](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb) the IPython Notebook.
\ No newline at end of file
diff --git a/howtos_as_py_files/id_file1.txt b/python_patterns/id_file1.txt
similarity index 100%
rename from howtos_as_py_files/id_file1.txt
rename to python_patterns/id_file1.txt
diff --git a/howtos_as_py_files/id_file2.txt b/python_patterns/id_file2.txt
similarity index 100%
rename from howtos_as_py_files/id_file2.txt
rename to python_patterns/id_file2.txt
diff --git a/howtos_as_py_files/my_file.pkl b/python_patterns/my_file.pkl
similarity index 100%
rename from howtos_as_py_files/my_file.pkl
rename to python_patterns/my_file.pkl
diff --git a/howtos_as_py_files/patterns.ipynb b/python_patterns/patterns.ipynb
similarity index 100%
rename from howtos_as_py_files/patterns.ipynb
rename to python_patterns/patterns.ipynb

From 5a8f0a63c77b578eac904dd06357fa9aa59dc518 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 26 Sep 2014 14:24:11 -0400
Subject: [PATCH 191/248] linked A random collection of useful Python patterns

---
 python_patterns/patterns.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python_patterns/patterns.ipynb b/python_patterns/patterns.ipynb
index 42fb3aa..d3a8f93 100644
--- a/python_patterns/patterns.ipynb
+++ b/python_patterns/patterns.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0d6c2b08bbaff6330460e1379004dabcfdd16f3712b1fb820c0315a3f70294f5"
+  "signature": "sha256:51af96cc55143f75f6699c3e53aeead72f1b503d1759501f341f13fbc3436a4c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -12,7 +12,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "[Go back](#https://github.com/rasbt/python_reference) to the `python_reference` repository."
+      "[Go back](https://github.com/rasbt/python_reference) to the `python_reference` repository."
      ]
     },
     {

From 3f0841a4f6f9de967b7538a6776d652facd60004 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 26 Sep 2014 18:07:47 -0400
Subject: [PATCH 192/248] patterns changed to snippets

---
 README.md                      | 2 +-
 python_patterns/README.md      | 2 +-
 python_patterns/patterns.ipynb | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index e2e1ee8..69640db 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@
 
 - Quick guide for dealing with missing numbers in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/numpy_nan_quickguide.ipynb)]
 
-- A random collection of useful Python patterns [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb)]
+- A random collection of useful Python snippets [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb)]
 
 <br>
 
diff --git a/python_patterns/README.md b/python_patterns/README.md
index 084d5c2..37a1770 100644
--- a/python_patterns/README.md
+++ b/python_patterns/README.md
@@ -1,3 +1,3 @@
-# A collection of useful Python patterns
+# A collection of useful Python snippets
 
 [View](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb) the IPython Notebook.
\ No newline at end of file
diff --git a/python_patterns/patterns.ipynb b/python_patterns/patterns.ipynb
index d3a8f93..5b6e031 100644
--- a/python_patterns/patterns.ipynb
+++ b/python_patterns/patterns.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:51af96cc55143f75f6699c3e53aeead72f1b503d1759501f341f13fbc3436a4c"
+  "signature": "sha256:f89c18f0692550a8e1f3a2999eda339bc0905a2dd1413049101148918e7a6c5e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -20,7 +20,7 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "A random collection of useful Python patterns"
+      "A random collection of useful Python snippets"
      ]
     },
     {

From 14996364a2e32ad13840cff27c0874024ee50b2e Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 26 Sep 2014 18:09:11 -0400
Subject: [PATCH 193/248] snippet upd

---
 python_patterns/patterns.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/python_patterns/patterns.ipynb b/python_patterns/patterns.ipynb
index 5b6e031..fc3e45f 100644
--- a/python_patterns/patterns.ipynb
+++ b/python_patterns/patterns.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:f89c18f0692550a8e1f3a2999eda339bc0905a2dd1413049101148918e7a6c5e"
+  "signature": "sha256:0c9d8c8b65b0eec5bb7c2a2790f08a1e49daf27dac2c9dcfe8d85ce958046a2c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -27,7 +27,7 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "I just cleaned my hard drive and found a couple of useful Python patterns that I had some use for in the past. I thought it would be worthwhile to collect them in a IPython notebook for personal reference and share it with people who might find them useful too.  \n",
+      "I just cleaned my hard drive and found a couple of useful Python snippets that I had some use for in the past. I thought it would be worthwhile to collect them in a IPython notebook for personal reference and share it with people who might find them useful too.  \n",
       "Most of those snippets are hopefully self-explanatory, but I am planning to add more comments and descriptions in future."
      ]
     },

From 3f81eadf98547948604762f1827f63644e341130 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 9 Oct 2014 14:21:43 -0400
Subject: [PATCH 194/248] english language detect. snippet

---
 python_patterns/patterns.ipynb | 58 +++++++++++++++++++++++++++++++++-
 1 file changed, 57 insertions(+), 1 deletion(-)

diff --git a/python_patterns/patterns.ipynb b/python_patterns/patterns.ipynb
index fc3e45f..2a769ba 100644
--- a/python_patterns/patterns.ipynb
+++ b/python_patterns/patterns.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:0c9d8c8b65b0eec5bb7c2a2790f08a1e49daf27dac2c9dcfe8d85ce958046a2c"
+  "signature": "sha256:714a46a359c5b1c3e7e7bd4d19d73221f9def5bcb806840be82541070041d29e"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -57,6 +57,7 @@
       "- [Differences between 2 files](#Differences-between-2-files)\n",
       "- [Differences between successive elements in a list](#Differences-between-successive-elements-in-a-list)\n",
       "- [Doctest example](#Doctest-example)\n",
+      "- [English language detection](#English-language-detection)\n",
       "- [File browsing basics](#File-browsing-basics)\n",
       "- [File reading basics](#File-reading-basics)\n",
       "- [Indices of min and max elements from a list](#Indices-of-min-and-max-elements-from-a-list)\n",
@@ -595,6 +596,61 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "English language detection"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[back to top](#Table-of-Contents)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import nltk\n",
+      "\n",
+      "def eng_ratio(text):\n",
+      "    ''' Returns the ratio of non-English to English words from a text '''\n",
+      "\n",
+      "    english_vocab = set(w.lower() for w in nltk.corpus.words.words()) \n",
+      "    text_vocab = set(w.lower() for w in text.split() if w.lower().isalpha()) \n",
+      "    unusual = text_vocab.difference(english_vocab)\n",
+      "    diff = len(unusual)/len(text_vocab)\n",
+      "    return diff\n",
+      "    \n",
+      "text = 'This is a test fahrrad'\n",
+      "\n",
+      "print(eng_ratio(text))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "0.2\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 2,

From 32b66da48e2f545d0d66e86f5220fe7980ce65c1 Mon Sep 17 00:00:00 2001
From: kzhk75 <u.kzhk75@gmail.com>
Date: Thu, 23 Oct 2014 23:24:41 +0100
Subject: [PATCH 195/248] Update scope_resolution_legb_rule.ipynb

---
 tutorials/scope_resolution_legb_rule.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index a47fd1d..143e8ba 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -457,7 +457,7 @@
       "\n",
       "def a_func():\n",
       "    a_var = 'local value'\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
+      "    print(a_var, '[ a_var in a_func() ]')\n",
       "\n",
       "a_func()\n",
       "print(a_var, '[ a_var outside a_func() ]')"

From b145519291b6b4ee3c82e37ec336cf2d65cfe9fa Mon Sep 17 00:00:00 2001
From: kzhk75 <u.kzhk75@gmail.com>
Date: Thu, 23 Oct 2014 23:43:47 +0100
Subject: [PATCH 196/248] Update scope_resolution_legb_rule.ipynb

To match the above and following sentences.
---
 tutorials/scope_resolution_legb_rule.ipynb | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index 143e8ba..93f01a2 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -416,7 +416,7 @@
       "global value [ a_var outside a_func() ]</pre>\n",
       "\n",
       "**c)** \n",
-      "<pre>global value [ a_var in a_func() ]  \n",
+      "<pre>global value [ a_var inside a_func() ]  \n",
       "global value [ a_var outside a_func() ]</pre>\n",
       "\n",
       "\n"
@@ -457,7 +457,7 @@
       "\n",
       "def a_func():\n",
       "    a_var = 'local value'\n",
-      "    print(a_var, '[ a_var in a_func() ]')\n",
+      "    print(a_var, '[ a_var inside a_func() ]')\n",
       "\n",
       "a_func()\n",
       "print(a_var, '[ a_var outside a_func() ]')"
@@ -475,11 +475,11 @@
       "<pre>raises an error</pre>\n",
       "\n",
       "**b)** \n",
-      "<pre>local value [ a_var in a_func() ]\n",
+      "<pre>local value [ a_var inside a_func() ]\n",
       "global value [ a_var outside a_func() ]</pre>\n",
       "\n",
       "**c)** \n",
-      "<pre>global value [ a_var in a_func() ]  \n",
+      "<pre>global value [ a_var inside a_func() ]  \n",
       "global value [ a_var outside a_func() ]</pre>\n"
      ]
     },

From 4ac59a474db8c0820f8ba9026a9c418d653d1652 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 19 Dec 2014 13:11:05 -0500
Subject: [PATCH 197/248] combinations

---
 useful_scripts/combinations.py | 71 ++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100755 useful_scripts/combinations.py

diff --git a/useful_scripts/combinations.py b/useful_scripts/combinations.py
new file mode 100755
index 0000000..5dbe91d
--- /dev/null
+++ b/useful_scripts/combinations.py
@@ -0,0 +1,71 @@
+#!/usr/bin/env python
+
+# Sebastian Raschka 2014
+# Functions to calculate factorial, combinations, and permutations
+# bundled in an simple command line interface.
+
+def factorial(n):
+    if n == 0:
+        return 1
+    else:
+        return n * factorial(n-1)
+    
+def combinations(n, r):
+    numerator = factorial(n)
+    denominator = factorial(r) * factorial(n-r)
+    return int(numerator/denominator)
+    
+def permutations(n, r):
+    numerator = factorial(n)
+    denominator = factorial(n-r)
+    return int(numerator/denominator)
+
+assert(factorial(3) == 6)
+assert(combinations(20, 8) == 125970)
+assert(permutations(30, 3) == 24360)
+
+
+
+
+if __name__ == '__main__':
+    
+    import argparse
+    parser = argparse.ArgumentParser(
+        description='Script to calculate the number of combinations or permutations ("n choose r")',
+        formatter_class=argparse.RawTextHelpFormatter,
+        
+        prog='Combinations',
+        epilog='Example: ./combinations.py -c 20 3'
+        )
+
+    parser.add_argument('-c', '--combinations', type=int, metavar='NUMBER', nargs=2,
+            help='Combinations: Number of ways to combine n items with sequence length r where the item order does not matter.')
+    
+    parser.add_argument('-p', '--permutations', type=int, metavar='NUMBER', nargs=2, 
+            help='Permutations: Number of ways to combine n items with sequence length r where the item order does not matter.')
+            
+    parser.add_argument('-f', '--factorial', type=int, metavar='NUMBER', help='n! e.g., 5! = 5*4*3*2*1 = 120.')
+    
+    parser.add_argument('--version', action='version', version='%(prog)s 1.0')
+
+    args = parser.parse_args()
+    
+    if not any((args.combinations, args.permutations, args.factorial)):
+        parser.print_help()
+        quit()
+    
+    if args.factorial:
+        print(factorial(args.factorial))
+            
+    if args.combinations:
+        print(combinations(args.combinations[0], args.combinations[1]))
+
+    if args.permutations:
+        print(permutations(args.permutations[0], args.permutations[1]))
+            
+    if args.factorial:
+        print(factorial(args.factorial))
+            
+    
+
+    
\ No newline at end of file

From 9fc9d1ee54c5f837770a8bbdc4b5291d2493df77 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 23 Dec 2014 22:33:39 -0500
Subject: [PATCH 198/248] pandas sum tricks

---
 README.md                          |   8 +-
 benchmarks/pandas_sum_tricks.ipynb | 450 +++++++++++++++++++++++++++++
 2 files changed, 457 insertions(+), 1 deletion(-)
 create mode 100644 benchmarks/pandas_sum_tricks.ipynb

diff --git a/README.md b/README.md
index 69640db..e880d27 100644
--- a/README.md
+++ b/README.md
@@ -100,10 +100,16 @@
 ###// Benchmarks
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
-*The benchmark category has been moved to a separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
+
+- Simple tricks to speed up the sum calculation in pandas [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/pandas_sum_tricks.ipynb)]
+
+<br>
+*More benchmarks can be found in the separate GitHub repository [One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)*
 
 **Featured articles**:  
 
+
+
 - (C)Python compilers - Cython vs. Numba vs. Parakeet [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)]
 
 - Just-in-time compilers for NumPy array expressions [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day7_2_jit_numpy.ipynb)]
diff --git a/benchmarks/pandas_sum_tricks.ipynb b/benchmarks/pandas_sum_tricks.ipynb
new file mode 100644
index 0000000..ccf454e
--- /dev/null
+++ b/benchmarks/pandas_sum_tricks.ipynb
@@ -0,0 +1,450 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:8222de4af96dc6569eddec8d75df6855e8bac273e12e8739fffc42aafd712ba2"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark \n",
+      "%watermark -d -v -a 'Sebastian Raschka' -p numpy,pandas"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Sebastian Raschka 23/12/2014 \n",
+        "\n",
+        "CPython 3.4.2\n",
+        "IPython 2.3.1\n",
+        "\n",
+        "numpy 1.9.1\n",
+        "pandas 0.15.2\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "4 Simple Tricks To Speed up the Sum Calculation in Pandas"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I wanted to improve the performance of some passages in my code a little bit and found that some simple tweaks can speed up the  `pandas` section significantly. I thought that it might be one useful thing to share -- and no Cython or just-in-time compilation is required! "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In my case, I had a large dataframe where I wanted to calculate the sum of specific columns for different combinations of rows (approx. 100,000,000 of them, that's why I was looking for ways to speed it up). Anyway, below is a simple toy DataFrame to explore the `.sum()` method a little bit."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import numpy as np\n",
+      "\n",
+      "df = pd.DataFrame()\n",
+      "\n",
+      "for col in ('a', 'b', 'c', 'd'):\n",
+      "    df[col] = pd.Series(range(1000), index=range(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>a</th>\n",
+        "      <th>b</th>\n",
+        "      <th>c</th>\n",
+        "      <th>d</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>995</th>\n",
+        "      <td> 995</td>\n",
+        "      <td> 995</td>\n",
+        "      <td> 995</td>\n",
+        "      <td> 995</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>996</th>\n",
+        "      <td> 996</td>\n",
+        "      <td> 996</td>\n",
+        "      <td> 996</td>\n",
+        "      <td> 996</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>997</th>\n",
+        "      <td> 997</td>\n",
+        "      <td> 997</td>\n",
+        "      <td> 997</td>\n",
+        "      <td> 997</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>998</th>\n",
+        "      <td> 998</td>\n",
+        "      <td> 998</td>\n",
+        "      <td> 998</td>\n",
+        "      <td> 998</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>999</th>\n",
+        "      <td> 999</td>\n",
+        "      <td> 999</td>\n",
+        "      <td> 999</td>\n",
+        "      <td> 999</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "       a    b    c    d\n",
+        "995  995  995  995  995\n",
+        "996  996  996  996  996\n",
+        "997  997  997  997  997\n",
+        "998  998  998  998  998\n",
+        "999  999  999  999  999"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let's assume we are interested in calculating the sum of column `a`, `c`, and `d`, which would look like this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df.loc[:, ['a', 'c', 'd']].sum(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "a    499500\n",
+        "c    499500\n",
+        "d    499500\n",
+        "dtype: int64"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, the `.loc` method is probably the most \"costliest\" one for this kind of operation. Since we are only intersted in the resulting numbers (i.e., the column sums), there is no need to make a copy of the array. Anyway, let's use the method above as a reference for comparison:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 1\n",
+      "%timeit -n 1000 -r 5 df.loc[:, ['a', 'c', 'd']].sum(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 1.28 ms per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Although this is a rather small DataFrame (1000 x 4), let's see by how much we can speed it up using a different slicing method:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 2\n",
+      "%timeit -n 1000 -r 5 df[['a', 'c', 'd']].sum(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 1.03 ms per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Next, let us use the Numpy representation of  the `NDFrame` via the `.values` attribue:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 3\n",
+      "%timeit -n 1000 -r 5 df[['a', 'c', 'd']].values.sum(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 721 \u00b5s per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "While the speed improvements in #2 and #3 were not really a surprise, the next \"trick\" surprised me a little bit. Here, we are calculating the sum of each column separately rather than slicing the array."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "[df[col].values.sum(axis=0) for col in ('a', 'c', 'd')]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "[499500, 499500, 499500]"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 4\n",
+      "%timeit -n 1000 -r 5 [df[col].values.sum(axis=0) for col in ('a', 'c', 'd')]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 64.8 \u00b5s per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In this case, this is an almost 10x improvement!"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "One more thing: Let's try the Einstein summation convention [`einsum`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from numpy import einsum\n",
+      "[einsum('i->', df[col].values) for col in ('a', 'c', 'd')]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "[499500, 499500, 499500]"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 5\n",
+      "%timeit -n 1000 -r 5 [einsum('i->', df[col].values) for col in ('a', 'c', 'd')]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 57.2 \u00b5s per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Conclusion:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Using some simple tricks, the column sum calculation improved from 1280 to 57.2 \u00b5s per loop (approx. 22x faster!)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 731425d79418c4177b24641f312cee586caac463 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 24 Dec 2014 11:01:30 -0500
Subject: [PATCH 199/248] plots

---
 benchmarks/pandas_sum_tricks.ipynb | 332 ++++++++++++++++++++-
 pandas_sum_tricks.ipynb            | 450 +++++++++++++++++++++++++++++
 2 files changed, 773 insertions(+), 9 deletions(-)
 create mode 100644 pandas_sum_tricks.ipynb

diff --git a/benchmarks/pandas_sum_tricks.ipynb b/benchmarks/pandas_sum_tricks.ipynb
index ccf454e..db58109 100644
--- a/benchmarks/pandas_sum_tricks.ipynb
+++ b/benchmarks/pandas_sum_tricks.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:8222de4af96dc6569eddec8d75df6855e8bac273e12e8739fffc42aafd712ba2"
+  "signature": "sha256:3de4720b58999a1f88844021c43acd1d6d6db6da3315538f9faac86a69424446"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -22,7 +22,9 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Sebastian Raschka 23/12/2014 \n",
+        "The watermark extension is already loaded. To reload it, use:\n",
+        "  %reload_ext watermark\n",
+        "Sebastian Raschka 24/12/2014 \n",
         "\n",
         "CPython 3.4.2\n",
         "IPython 2.3.1\n",
@@ -32,7 +34,7 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -224,7 +226,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000 loops, best of 5: 1.28 ms per loop\n"
+        "1000 loops, best of 5: 1.37 ms per loop\n"
        ]
       }
      ],
@@ -258,7 +260,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000 loops, best of 5: 1.03 ms per loop\n"
+        "1000 loops, best of 5: 986 \u00b5s per loop\n"
        ]
       }
      ],
@@ -292,7 +294,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000 loops, best of 5: 721 \u00b5s per loop\n"
+        "1000 loops, best of 5: 687 \u00b5s per loop\n"
        ]
       }
      ],
@@ -346,7 +348,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000 loops, best of 5: 64.8 \u00b5s per loop\n"
+        "1000 loops, best of 5: 64.4 \u00b5s per loop\n"
        ]
       }
      ],
@@ -408,7 +410,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "1000 loops, best of 5: 57.2 \u00b5s per loop\n"
+        "1000 loops, best of 5: 55.7 \u00b5s per loop\n"
        ]
       }
      ],
@@ -433,9 +435,321 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Using some simple tricks, the column sum calculation improved from 1280 to 57.2 \u00b5s per loop (approx. 22x faster!)"
+      "Using some simple tricks, the column sum calculation improved from 1370 to 57.7 \u00b5s per loop (approx. 25x faster!)"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "What about larger DataFrames?"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "So, what does this trend look like for larger DataFrames?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "from numpy import einsum\n",
+      "import pandas as pd\n",
+      "\n",
+      "def run_loc_sum(df):\n",
+      "    return df.loc[:, ['a', 'c', 'd']].sum(axis=0)\n",
+      "\n",
+      "def run_einsum(df):\n",
+      "    return [einsum('i->', df[col].values) for col in ('a', 'c', 'd')]\n",
+      "\n",
+      "orders = [10**i for i in range(4, 8)]\n",
+      "loc_res = []\n",
+      "einsum_res = []\n",
+      "\n",
+      "for n in orders:\n",
+      "\n",
+      "    df = pd.DataFrame()\n",
+      "    for col in ('a', 'b', 'c', 'd'):\n",
+      "        df[col] = pd.Series(range(n), index=range(n))\n",
+      "    \n",
+      "    print('n=%s (%s of %s)' %(n, orders.index(n)+1, len(orders)))\n",
+      "\n",
+      "    loc_res.append(min(timeit.Timer('run_loc_sum(df)' , \n",
+      "            'from __main__ import run_loc_sum, df').repeat(repeat=5, number=1)))\n",
+      "\n",
+      "    einsum_res.append(min(timeit.Timer('run_einsum(df)' , \n",
+      "            'from __main__ import run_einsum, df').repeat(repeat=5, number=1)))\n",
+      "\n",
+      "print('finished')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "n=10000 (1 of 4)\n",
+        "n=100000 (2 of 4)"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "n=1000000 (3 of 4)"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "n=10000000 (4 of 4)"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "finished"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "def plot_1():\n",
+      "    \n",
+      "    fig = plt.figure(figsize=(12,6))\n",
+      "    \n",
+      "    plt.plot(orders, loc_res, \n",
+      "             label=\"df.loc[:, ['a', 'c', 'd']].sum(axis=0)\", \n",
+      "             lw=2, alpha=0.6)\n",
+      "    plt.plot(orders,einsum_res, \n",
+      "             label=\"[einsum('i->', df[col].values) for col in ('a', 'c', 'd')]\", \n",
+      "             lw=2, alpha=0.6)\n",
+      "\n",
+      "    plt.title('Pandas Column Sums', fontsize=20)\n",
+      "    plt.xlim([min(orders), max(orders)])\n",
+      "    plt.grid()\n",
+      "\n",
+      "    #plt.xscale('log')\n",
+      "    plt.ticklabel_format(style='plain', axis='x')\n",
+      "    plt.legend(loc='upper left', fontsize=14)\n",
+      "    plt.xlabel('Number of rows', fontsize=16)\n",
+      "    plt.ylabel('time in seconds', fontsize=16)\n",
+      "    \n",
+      "    plt.tight_layout()\n",
+      "    plt.show()\n",
+      "    \n",
+      "plot_1()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAGpCAYAAABhxcywAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x109989550>"
+       ]
+      }
+     ],
+     "prompt_number": 26
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "It looks like that the benefit of calculating the sums separately for each column becomes even larger the more rows the DataFrame has."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Another question to ask: How does this scale if we have a growing number of columns?"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import timeit\n",
+      "import random\n",
+      "from numpy import einsum\n",
+      "import pandas as pd\n",
+      "\n",
+      "def run_loc_sum(df, n):\n",
+      "    return df.loc[:, 0:n-1].sum(axis=0)\n",
+      "\n",
+      "def run_einsum(df, n):\n",
+      "    return [einsum('i->', df[col].values) for col in range(0,n-1)]\n",
+      "\n",
+      "orders = [10**i for i in range(2, 5)]\n",
+      "loc_res = []\n",
+      "einsum_res = []\n",
+      "\n",
+      "for n in orders:\n",
+      "\n",
+      "    df = pd.DataFrame()\n",
+      "    for col in range(n):\n",
+      "        df[col] = pd.Series(range(1000), index=range(1000))\n",
+      "    \n",
+      "    print('n=%s (%s of %s)' %(n, orders.index(n)+1, len(orders)))\n",
+      "\n",
+      "    loc_res.append(min(timeit.Timer('run_loc_sum(df, n)' , \n",
+      "            'from __main__ import run_loc_sum, df, n').repeat(repeat=5, number=1)))\n",
+      "\n",
+      "    einsum_res.append(min(timeit.Timer('run_einsum(df, n)' , \n",
+      "            'from __main__ import run_einsum, df, n').repeat(repeat=5, number=1)))\n",
+      "\n",
+      "print('finished')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "n=100 (1 of 3)\n",
+        "n=1000 (2 of 3)"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "n=10000 (3 of 3)"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "finished"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n"
+       ]
+      }
+     ],
+     "prompt_number": 35
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from matplotlib import pyplot as plt\n",
+      "\n",
+      "def plot_2():\n",
+      "    \n",
+      "    fig = plt.figure(figsize=(12,6))\n",
+      "    \n",
+      "    plt.plot(orders, loc_res, \n",
+      "             label=\"df.loc[:, 0:n-1].sum(axis=0)\", \n",
+      "             lw=2, alpha=0.6)\n",
+      "    plt.plot(orders,einsum_res, \n",
+      "             label=\"[einsum('i->', df[col].values) for col in range(0,n-1)]\", \n",
+      "             lw=2, alpha=0.6)\n",
+      "\n",
+      "    plt.title('Pandas Column Sums', fontsize=20)\n",
+      "    plt.xlim([min(orders), max(orders)])\n",
+      "    plt.grid()\n",
+      "\n",
+      "    #plt.xscale('log')\n",
+      "    plt.ticklabel_format(style='plain', axis='x')\n",
+      "    plt.legend(loc='upper left', fontsize=14)\n",
+      "    plt.xlabel('Number of columns', fontsize=16)\n",
+      "    plt.ylabel('time in seconds', fontsize=16)\n",
+      "    \n",
+      "    plt.tight_layout()\n",
+      "    plt.show()\n",
+      "    \n",
+      "plot_2()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAGpCAYAAABhxcywAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x109334240>"
+       ]
+      }
+     ],
+     "prompt_number": 37
+    },
     {
      "cell_type": "markdown",
      "metadata": {},
diff --git a/pandas_sum_tricks.ipynb b/pandas_sum_tricks.ipynb
new file mode 100644
index 0000000..ccf454e
--- /dev/null
+++ b/pandas_sum_tricks.ipynb
@@ -0,0 +1,450 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:8222de4af96dc6569eddec8d75df6855e8bac273e12e8739fffc42aafd712ba2"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark \n",
+      "%watermark -d -v -a 'Sebastian Raschka' -p numpy,pandas"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Sebastian Raschka 23/12/2014 \n",
+        "\n",
+        "CPython 3.4.2\n",
+        "IPython 2.3.1\n",
+        "\n",
+        "numpy 1.9.1\n",
+        "pandas 0.15.2\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "4 Simple Tricks To Speed up the Sum Calculation in Pandas"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I wanted to improve the performance of some passages in my code a little bit and found that some simple tweaks can speed up the  `pandas` section significantly. I thought that it might be one useful thing to share -- and no Cython or just-in-time compilation is required! "
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In my case, I had a large dataframe where I wanted to calculate the sum of specific columns for different combinations of rows (approx. 100,000,000 of them, that's why I was looking for ways to speed it up). Anyway, below is a simple toy DataFrame to explore the `.sum()` method a little bit."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "import numpy as np\n",
+      "\n",
+      "df = pd.DataFrame()\n",
+      "\n",
+      "for col in ('a', 'b', 'c', 'd'):\n",
+      "    df[col] = pd.Series(range(1000), index=range(1000))"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>a</th>\n",
+        "      <th>b</th>\n",
+        "      <th>c</th>\n",
+        "      <th>d</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>995</th>\n",
+        "      <td> 995</td>\n",
+        "      <td> 995</td>\n",
+        "      <td> 995</td>\n",
+        "      <td> 995</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>996</th>\n",
+        "      <td> 996</td>\n",
+        "      <td> 996</td>\n",
+        "      <td> 996</td>\n",
+        "      <td> 996</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>997</th>\n",
+        "      <td> 997</td>\n",
+        "      <td> 997</td>\n",
+        "      <td> 997</td>\n",
+        "      <td> 997</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>998</th>\n",
+        "      <td> 998</td>\n",
+        "      <td> 998</td>\n",
+        "      <td> 998</td>\n",
+        "      <td> 998</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>999</th>\n",
+        "      <td> 999</td>\n",
+        "      <td> 999</td>\n",
+        "      <td> 999</td>\n",
+        "      <td> 999</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "       a    b    c    d\n",
+        "995  995  995  995  995\n",
+        "996  996  996  996  996\n",
+        "997  997  997  997  997\n",
+        "998  998  998  998  998\n",
+        "999  999  999  999  999"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Let's assume we are interested in calculating the sum of column `a`, `c`, and `d`, which would look like this:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df.loc[:, ['a', 'c', 'd']].sum(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "a    499500\n",
+        "c    499500\n",
+        "d    499500\n",
+        "dtype: int64"
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now, the `.loc` method is probably the most \"costliest\" one for this kind of operation. Since we are only intersted in the resulting numbers (i.e., the column sums), there is no need to make a copy of the array. Anyway, let's use the method above as a reference for comparison:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 1\n",
+      "%timeit -n 1000 -r 5 df.loc[:, ['a', 'c', 'd']].sum(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 1.28 ms per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Although this is a rather small DataFrame (1000 x 4), let's see by how much we can speed it up using a different slicing method:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 2\n",
+      "%timeit -n 1000 -r 5 df[['a', 'c', 'd']].sum(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 1.03 ms per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Next, let us use the Numpy representation of  the `NDFrame` via the `.values` attribue:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 3\n",
+      "%timeit -n 1000 -r 5 df[['a', 'c', 'd']].values.sum(axis=0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 721 \u00b5s per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "While the speed improvements in #2 and #3 were not really a surprise, the next \"trick\" surprised me a little bit. Here, we are calculating the sum of each column separately rather than slicing the array."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "[df[col].values.sum(axis=0) for col in ('a', 'c', 'd')]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "[499500, 499500, 499500]"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 4\n",
+      "%timeit -n 1000 -r 5 [df[col].values.sum(axis=0) for col in ('a', 'c', 'd')]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 64.8 \u00b5s per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "In this case, this is an almost 10x improvement!"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "One more thing: Let's try the Einstein summation convention [`einsum`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html)."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from numpy import einsum\n",
+      "[einsum('i->', df[col].values) for col in ('a', 'c', 'd')]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "[499500, 499500, 499500]"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# 5\n",
+      "%timeit -n 1000 -r 5 [einsum('i->', df[col].values) for col in ('a', 'c', 'd')]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "1000 loops, best of 5: 57.2 \u00b5s per loop\n"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Conclusion:"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Using some simple tricks, the column sum calculation improved from 1280 to 57.2 \u00b5s per loop (approx. 22x faster!)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>"
+     ]
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file

From 044d334ef9f6f910f7fe46dd0b54209d41b661c0 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 29 Dec 2014 00:26:25 -0500
Subject: [PATCH 200/248] function to process first and last names

---
 useful_scripts/preprocess_first_last_names.py | 82 +++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 useful_scripts/preprocess_first_last_names.py

diff --git a/useful_scripts/preprocess_first_last_names.py b/useful_scripts/preprocess_first_last_names.py
new file mode 100644
index 0000000..07d36f3
--- /dev/null
+++ b/useful_scripts/preprocess_first_last_names.py
@@ -0,0 +1,82 @@
+# Sebastian Raschka 2014
+#
+# A Python function to generalize first and last names.
+# The typical use case of such a function to merge data that have been collected 
+# from different sources (e.g., names of soccer players as shown in the doctest.)
+# 
+
+import unicodedata
+import string
+import re
+
+def preprocess_names(name, output_sep=' ', firstname_output_letters=1):
+    """
+    Function that outputs a person's name in the format 
+    <last_name><separator><firstname letter(s)> (all lowercase)
+    
+    >>> preprocess_names("Samuel Eto'o")
+    'etoo s'
+   
+    >>> preprocess_names("Eto'o, Samuel")
+    'etoo s'
+    
+    >>> preprocess_names("Eto'o,Samuel")
+    'etoo s'
+    
+    >>> preprocess_names('Xavi')
+    'xavi'
+    
+    >>> preprocess_names('Yaya Touré')
+    'toure y'
+
+    >>> preprocess_names('José Ángel Pozo')
+    'pozo j'
+    
+    >>> preprocess_names('Pozo, José Ángel')
+    'pozo j'
+    
+    >>> preprocess_names('Pozo, José Ángel', firstname_output_letters=2)
+    'pozo jo'
+    
+    >>> preprocess_names("Eto'o, Samuel", firstname_output_letters=2)
+    'etoo sa'
+    
+    >>> preprocess_names("Eto'o, Samuel", firstname_output_letters=0)
+    'etoo'
+    
+    >>> preprocess_names("Eto'o, Samuel", output_sep=', ')
+    'etoo, s'
+    
+    """
+
+    # set first and last name positions
+    last, first = 'last', 'first'
+    last_pos = -1
+    
+    if ',' in name:
+        last, first = first, last
+        name = name.replace(',', ' ')
+        last_pos = 1
+        
+    spl = name.split()
+    if len(spl) > 2:
+        name = '%s %s' % (spl[0], spl[last_pos])    
+
+    spl1, *spl2 = name.split()
+    '%s %s' % (spl1, ''.join(spl2))
+        
+    # remove accents
+    name = ''.join(x for x in unicodedata.normalize('NFKD', name) if x in string.ascii_letters+' ')
+    
+    # get first and last name if applicable
+    m = re.match('(?P<first>\w+)\W+(?P<last>\w+)', name)
+    if m:
+        output = '%s%s%s' % (m.group(last), output_sep, m.group(first)[:firstname_output_letters])
+    else:
+        output = name
+    return output.lower().strip()
+    
+    
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
\ No newline at end of file

From 7d879b4e336fc6512fa81157101fb8bce8c2b4fd Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Tue, 30 Dec 2014 16:57:12 -0500
Subject: [PATCH 201/248] removed redundant lines

---
 useful_scripts/preprocess_first_last_names.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/useful_scripts/preprocess_first_last_names.py b/useful_scripts/preprocess_first_last_names.py
index 07d36f3..b0957c2 100644
--- a/useful_scripts/preprocess_first_last_names.py
+++ b/useful_scripts/preprocess_first_last_names.py
@@ -62,9 +62,6 @@ def preprocess_names(name, output_sep=' ', firstname_output_letters=1):
     if len(spl) > 2:
         name = '%s %s' % (spl[0], spl[last_pos])    
 
-    spl1, *spl2 = name.split()
-    '%s %s' % (spl1, ''.join(spl2))
-        
     # remove accents
     name = ''.join(x for x in unicodedata.normalize('NFKD', name) if x in string.ascii_letters+' ')
     
@@ -79,4 +76,4 @@ def preprocess_names(name, output_sep=' ', firstname_output_letters=1):
     
 if __name__ == "__main__":
     import doctest
-    doctest.testmod()
\ No newline at end of file
+    doctest.testmod()

From 75c8f46f4aa40121d58d96ab5aaa7653c1af227a Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 1 Jan 2015 20:14:07 -0500
Subject: [PATCH 202/248] find files

---
 useful_scripts/find_file.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 useful_scripts/find_file.py

diff --git a/useful_scripts/find_file.py b/useful_scripts/find_file.py
new file mode 100644
index 0000000..8cbcc4d
--- /dev/null
+++ b/useful_scripts/find_file.py
@@ -0,0 +1,18 @@
+# Sebastian Raschka 2014
+#
+# A Python function to find files in a directory based on a substring search.
+
+
+import os
+
+def find_files(substring, path):
+    results = []
+    for f in os.listdir(path):
+        if substring in f:
+            results.append(os.path.join(path, f))
+    return results
+    
+# E.g.
+# find_files('Untitled', '/Users/sebastian/Desktop/')
+# returns
+# ['/Users/sebastian/Desktop/Untitled0.ipynb']
\ No newline at end of file

From 6b3a7e282b33edae6877d009bc1ab29600f25c79 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 23 Jan 2015 23:20:17 -0500
Subject: [PATCH 203/248] some soccer data

---
 Data/some_soccer_data.csv | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 Data/some_soccer_data.csv

diff --git a/Data/some_soccer_data.csv b/Data/some_soccer_data.csv
new file mode 100644
index 0000000..7966d52
--- /dev/null
+++ b/Data/some_soccer_data.csv
@@ -0,0 +1,21 @@
+PLAYER,SALARY,GP,G,A,SOT,PPG,P
+"Sergio Agüero
+ Forward — Manchester City",$19.2m,16,14,3,34,13.12,209.98
+"Eden Hazard
+ Midfield — Chelsea",$18.9m,21,8,4,17,13.05,274.04
+"Alexis Sánchez
+ Forward — Arsenal",$17.6m,20,12,7,29,11.19,223.86
+"Yaya Touré
+ Midfield — Manchester City",$16.6m,18,7,1,19,10.99,197.91
+"Ángel Di María
+ Midfield — Manchester United",$15.0m,13,3,6,13,10.17,132.23
+"Santiago Cazorla
+ Midfield — Arsenal",$14.8m,20,4,3,20,9.97,199.49
+"David Silva
+ Midfield — Manchester City",$14.3m,15,6,2,11,10.35,155.26
+"Cesc Fàbregas
+ Midfield — Chelsea",$14.0m,20,2,14,10,10.47,209.49
+"Saido Berahino
+ Forward — West Brom",$13.8m,21,9,0,20,7.02,147.43
+"Steven Gerrard
+ Midfield — Liverpool",$13.8m,20,5,1,11,7.5,150.01
\ No newline at end of file

From d9c2ca8b66727b2391b35a974b39fbe420ca8c19 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 23 Jan 2015 23:38:27 -0500
Subject: [PATCH 204/248] some soccer data for pandas tutorial

---
 Data/some_soccer_data.csv | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/Data/some_soccer_data.csv b/Data/some_soccer_data.csv
index 7966d52..c2cdab8 100644
--- a/Data/some_soccer_data.csv
+++ b/Data/some_soccer_data.csv
@@ -1,21 +1,21 @@
-PLAYER,SALARY,GP,G,A,SOT,PPG,P
+PLAYER,SALARY,GP,G,A,SOT,PPG,P
 "Sergio Agüero
- Forward — Manchester City",$19.2m,16,14,3,34,13.12,209.98
+ Forward — Manchester City",$19.2m,16,14,3,34,13.12,209.98
 "Eden Hazard
- Midfield — Chelsea",$18.9m,21,8,4,17,13.05,274.04
+ Midfield — Chelsea",$18.9m,21,8,4,17,13.05,274.04
 "Alexis Sánchez
- Forward — Arsenal",$17.6m,20,12,7,29,11.19,223.86
+ Forward — Arsenal",$17.6m,,12,7,29,11.19,223.86
 "Yaya Touré
- Midfield — Manchester City",$16.6m,18,7,1,19,10.99,197.91
+ Midfield — Manchester City",$16.6m,18,7,1,19,10.99,197.91
 "Ángel Di María
- Midfield — Manchester United",$15.0m,13,3,6,13,10.17,132.23
+ Midfield — Manchester United",$15.0m,13,3,,13,10.17,132.23
 "Santiago Cazorla
- Midfield — Arsenal",$14.8m,20,4,3,20,9.97,199.49
+ Midfield — Arsenal",$14.8m,20,4,,20,9.97,
 "David Silva
- Midfield — Manchester City",$14.3m,15,6,2,11,10.35,155.26
+ Midfield — Manchester City",$14.3m,15,6,2,11,10.35,155.26
 "Cesc Fàbregas
- Midfield — Chelsea",$14.0m,20,2,14,10,10.47,209.49
+ Midfield — Chelsea",$14.0m,20,2,14,10,10.47,209.49
 "Saido Berahino
- Forward — West Brom",$13.8m,21,9,0,20,7.02,147.43
+ Forward — West Brom",$13.8m,21,9,0,20,7.02,147.43
 "Steven Gerrard
- Midfield — Liverpool",$13.8m,20,5,1,11,7.5,150.01
\ No newline at end of file
+ Midfield — Liverpool",$13.8m,20,5,1,11,7.5,150.01

From 4edb98e53e3639c566c221cec9fa36aea82034f0 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 Jan 2015 00:03:50 -0500
Subject: [PATCH 205/248] pandas snippets

---
 README.md                               |     4 +
 tutorials/classifiers.pkl               |   Bin 2453 -> 0 bytes
 tutorials/cython_bubblesort_nomagic.c   | 19147 ----------------------
 tutorials/cython_bubblesort_nomagic.pyx |    21 -
 tutorials/example.csv                   |     3 -
 tutorials/hello_world.py                |     3 -
 tutorials/setup.py                      |    14 -
 tutorials/standardized_data.pkl         |   Bin 20263 -> 0 bytes
 tutorials/test_set.csv                  |    54 -
 tutorials/things_in_pandas.ipynb        |  1770 ++
 tutorials/training_set.csv              |   124 -
 tutorials/wine_data.csv                 |   178 -
 12 files changed, 1774 insertions(+), 19544 deletions(-)
 delete mode 100644 tutorials/classifiers.pkl
 delete mode 100644 tutorials/cython_bubblesort_nomagic.c
 delete mode 100644 tutorials/cython_bubblesort_nomagic.pyx
 delete mode 100644 tutorials/example.csv
 delete mode 100644 tutorials/hello_world.py
 delete mode 100644 tutorials/setup.py
 delete mode 100644 tutorials/standardized_data.pkl
 delete mode 100644 tutorials/test_set.csv
 create mode 100644 tutorials/things_in_pandas.ipynb
 delete mode 100644 tutorials/training_set.csv
 delete mode 100644 tutorials/wine_data.csv

diff --git a/README.md b/README.md
index e880d27..2ec57f6 100644
--- a/README.md
+++ b/README.md
@@ -53,6 +53,10 @@
 
 - A random collection of useful Python snippets [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/python_patterns/patterns.ipynb)]
 
+- Things in pandas I wish I'd had known earlier [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/things_in_pandas.ipynb)]
+
+
+
 <br>
 
 
diff --git a/tutorials/classifiers.pkl b/tutorials/classifiers.pkl
deleted file mode 100644
index 1272af5379477528ccd2e994c314d3b43328e07a..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 2453
zcmaJ@eN+_J6<@v-SCJ2iU*HGCDnvj81Qx+JxEKtpsH@Rc0+X5DS$1)m**h}}LJTnx
z5e!wTL?ubJCpI4@610j2(aa!5Jlasj21LUVG#WvgqoM{{deWKM#h$h&eShrR_vXF(
ze)rzr@4mOiBO6p<MsA~xB3L@gW+K$f(o)sn7XCK4zX={;hS4gO%FbFDmczl*NIUZF
zMNvkErK0j3Hr`6GEK#J!S<1-J9M3w8yc)cmtJJi~`4vX#RNG**){O-3?J!0U>M)&~
z&SM*lbuBdUMRrOJ;|%W7S>{CW(W!K9+hM#Oe8Y4e1~+Lo8T>LcGc$gXn)0Jl@!(HZ
zXeTHA_#xM-=|EW<YwO!L+(aE4<Uf}X1n@9{G>nn<az?^trE?|xffg@|0_Fvagk+)~
zCdo*Abnay^+2UKFy|U(T-Kw=6?ZR!Zl7b1vJxFu3v~um?Mzp^H`!GMC6RF;7G%Seu
zTDa;PlMYqgsDFRmiqz&F6z2DNQh89j5Leprr!L>~LgUn+Cw<dDMd6TCKdGw|H5^?P
zXbNu?qBNg;R;l~Dpkg21@oi}p9{pqIS1Y{Qg?r}IDe8(gVa?*ddOsU#Lesb2HGQ@1
zqR>Ehd5lVXfV|pf$J89}MdX8$8jtPggoJ@zMFWv-LJqb1T;1+|;nmJVzndGeSJ<3=
z=nqSi`-Sxxm4Ed0ypLcC4^v5lT8e^|<|)=l*?Am-M#2y*hhdr?rn?d{Lk_~^i%`CD
z-P~X?!F(|F^qH~ZRX@*xWJ9C~GkFLh4WlGu`4mA*@eCa?^rCENmL7hi7}EKa!EDLY
z;zTKRLTC<c`uG959;%-@hq)kJE%UQ{LEk_ol|S?C(;tK}m(SDc8Qti+-(Il~RrjK;
z-eV(%r~eII`cxA%IIdgRV5D0f4|Sua+4@sk?bpzEFMmArgP|SyYd4jx@4tuqZho0G
zzxEMYd~qOohiAV~+-gWEU)>>eUc&}UD{l!^Cn}4N?D!T@roBgP!CgYJzmKhVs7dGy
zNGMtL*-0TPHmqlf>I>x6x&I@#i=PPRH)6%1uk;BYT+E!iZ`yU#ec`hD-APSo%Dt9?
zgO`64;`5r#N!!~Hih5do?Ce9-S{P9nQq_iD8|@z3n|K%X?ag_sYS|HVp?q>d%g#qa
zZk=}~yYiV(5EnNzYTbPlJVT?qQrssb?wQ$<LOe#LgEyn!efm^jh&v0G&mR=#1%-!2
zW_JoOhleoI;4MYlXd^g|lHwgc67QGfc+b^Cge!587N>Zui4f%!&pbUuOX9KouWaVa
zvXM0<H(7CV^Kf=5h~XiYG<ZovBSV>Sh#NU_fqZ1V9uk}>mL!uXvn9G@{nr=Xi5(*r
zqWHD~{Act{=j2Juoa?A>?ZRnUKJH@pn!)--jR9DYXGV@=w+aId4@sn9oOA+>8<~7N
zLsK-*!NN?1S<X@e$(ag<O(enMkTQZ{k&FT90h2NKY=cx8fklDdeK*<ZLdzLvU+Ws*
zaP{@B{-o>s3D@|jYkb7jZ*ldPoZq*M*gbXz7W1Gb9pt~OrInVdCZ5ljC|eZAGe!%+
z@m3?A%MvE5<dk}KdYV?woz-lmST&?cMRm!W@N$@8qJ$BrHrWZ<M450Uzjz^K&9(55
zKH}_B+1Xe0km1U)P7$ZmxrgY#dsxQ9a?;=<&6}+}{#@;lS#Ehwn-#Ljm3mm^GO4#j
z|35CQ=3x!#ya1R&ip6<&H63+3PgZJ5A;IS398a)3tgTQIM%e5Y0@kIYsqwcT1ZyLe
zJpYG(hD^mGXK|R#HVW2PD8mAZB^i!_4LX%V0~*g`NDi`eZkFfO>NOdCwjOd^=y4?=
zIhl0Tlmx995zlYdH-?KHx3Y$3m97-wbsh-P@|?e<yxXV;ld@YbQIxb_DN!jw!vhO1
zrC_~r$4!P96~?u<Y^bSE#2jaH?(EvWM0^4AD*tlC9rFw;KYtGmAnx<ioma+du;I?y
zpXyJ}#7?(-<Uf=hD2}Q7`THBk0z_iZa`j8?<HS9eeIDO`U4x;xf|!H<T!IN3C$x1`
zFBW4nu%pG@v&9mOz*>5=VqWWEwjn(QJ85h?aQ4d(anbExQp)zt!WP~Ne*4_-=ZKqp
zu9o3FA>yQ%!s?quI2KUbkT<VsnfO!R%wf-O=3#NBC%3O@22pV7iLSs7tw=_HpB2zC
z2OALIc~tkoH0(}<2T%Sv1S8)woH}r7BG$Ki#oj}spP?O7VveU7p9safnjI_hHDaTq
zIW>Ntx7f4g=94!LhKqfS@1j^vgJlICFx8xh5n0pw)jQ9=D25&^AAjp{hzMpLa-Aj0
zHXmmR-pYU_9W}=)&EP39h2n{5u%<gXqf#k1ymSNQ{mMf^zN!gHDH|=mTDkJ6R4Z#R
zu$4i+9%zLql%2EM7#f&#)KDY0_Z7}7&ll;Emr+n)SC%q%-kML?0Gulrj>F9k+9;u<
zf&CQ`=bCV{Bx3ba$(Hma;nLAVC-!Z?>%pPCMYK@xd|GkRCYgpph5r_LA8b-+&r0fM
m6n#jLik!5!Q#4`Yi=bGEge?Z%aM9f=-OjLC=j9-&sDA;7_lHja

diff --git a/tutorials/cython_bubblesort_nomagic.c b/tutorials/cython_bubblesort_nomagic.c
deleted file mode 100644
index 028e020..0000000
--- a/tutorials/cython_bubblesort_nomagic.c
+++ /dev/null
@@ -1,19147 +0,0 @@
-/* Generated by Cython 0.20.2 on Sun Jul  6 22:33:02 2014 */
-
-#define PY_SSIZE_T_CLEAN
-#ifndef CYTHON_USE_PYLONG_INTERNALS
-#ifdef PYLONG_BITS_IN_DIGIT
-#define CYTHON_USE_PYLONG_INTERNALS 0
-#else
-#include "pyconfig.h"
-#ifdef PYLONG_BITS_IN_DIGIT
-#define CYTHON_USE_PYLONG_INTERNALS 1
-#else
-#define CYTHON_USE_PYLONG_INTERNALS 0
-#endif
-#endif
-#endif
-#include "Python.h"
-#ifndef Py_PYTHON_H
-    #error Python headers needed to compile C extensions, please install development version of Python.
-#elif PY_VERSION_HEX < 0x02040000
-    #error Cython requires Python 2.4+.
-#else
-#define CYTHON_ABI "0_20_2"
-#include <stddef.h> /* For offsetof */
-#ifndef offsetof
-#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
-#endif
-#if !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#endif
-#ifndef DL_IMPORT
-  #define DL_IMPORT(t) t
-#endif
-#ifndef DL_EXPORT
-  #define DL_EXPORT(t) t
-#endif
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
-#endif
-#ifndef Py_HUGE_VAL
-  #define Py_HUGE_VAL HUGE_VAL
-#endif
-#ifdef PYPY_VERSION
-#define CYTHON_COMPILING_IN_PYPY 1
-#define CYTHON_COMPILING_IN_CPYTHON 0
-#else
-#define CYTHON_COMPILING_IN_PYPY 0
-#define CYTHON_COMPILING_IN_CPYTHON 1
-#endif
-#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600
-#define Py_OptimizeFlag 0
-#endif
-#if PY_VERSION_HEX < 0x02050000
-  typedef int Py_ssize_t;
-  #define PY_SSIZE_T_MAX INT_MAX
-  #define PY_SSIZE_T_MIN INT_MIN
-  #define PY_FORMAT_SIZE_T ""
-  #define CYTHON_FORMAT_SSIZE_T ""
-  #define PyInt_FromSsize_t(z) PyInt_FromLong(z)
-  #define PyInt_AsSsize_t(o)   __Pyx_PyInt_As_int(o)
-  #define PyNumber_Index(o)    ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
-                                (PyErr_Format(PyExc_TypeError, \
-                                              "expected index value, got %.200s", Py_TYPE(o)->tp_name), \
-                                 (PyObject*)0))
-  #define __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \
-                                  !PyComplex_Check(o))
-  #define PyIndex_Check __Pyx_PyIndex_Check
-  #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
-  #define __PYX_BUILD_PY_SSIZE_T "i"
-#else
-  #define __PYX_BUILD_PY_SSIZE_T "n"
-  #define CYTHON_FORMAT_SSIZE_T "z"
-  #define __Pyx_PyIndex_Check PyIndex_Check
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
-  #define Py_TYPE(ob)   (((PyObject*)(ob))->ob_type)
-  #define Py_SIZE(ob)   (((PyVarObject*)(ob))->ob_size)
-  #define PyVarObject_HEAD_INIT(type, size) \
-          PyObject_HEAD_INIT(type) size,
-  #define PyType_Modified(t)
-  typedef struct {
-     void *buf;
-     PyObject *obj;
-     Py_ssize_t len;
-     Py_ssize_t itemsize;
-     int readonly;
-     int ndim;
-     char *format;
-     Py_ssize_t *shape;
-     Py_ssize_t *strides;
-     Py_ssize_t *suboffsets;
-     void *internal;
-  } Py_buffer;
-  #define PyBUF_SIMPLE 0
-  #define PyBUF_WRITABLE 0x0001
-  #define PyBUF_FORMAT 0x0004
-  #define PyBUF_ND 0x0008
-  #define PyBUF_STRIDES (0x0010 | PyBUF_ND)
-  #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES)
-  #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
-  #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
-  #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
-  #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE)
-  #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE)
-  typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
-  typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
-#endif
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
-          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-  #define __Pyx_DefaultClassType PyClass_Type
-#else
-  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
-          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-  #define __Pyx_DefaultClassType PyType_Type
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define Py_TPFLAGS_CHECKTYPES 0
-  #define Py_TPFLAGS_HAVE_INDEX 0
-#endif
-#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
-  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define Py_TPFLAGS_HAVE_VERSION_TAG 0
-#endif
-#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT)
-  #define Py_TPFLAGS_IS_ABSTRACT 0
-#endif
-#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE)
-  #define Py_TPFLAGS_HAVE_FINALIZE 0
-#endif
-#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
-  #define CYTHON_PEP393_ENABLED 1
-  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ? \
-                                              0 : _PyUnicode_Ready((PyObject *)(op)))
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
-  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
-  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
-  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
-#else
-  #define CYTHON_PEP393_ENABLED 0
-  #define __Pyx_PyUnicode_READY(op)       (0)
-  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
-  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
-  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
-#endif
-#if CYTHON_COMPILING_IN_PYPY
-  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)
-#else
-  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
-  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \
-      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
-#endif
-#define __Pyx_PyString_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
-#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
-#else
-  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBaseString_Type            PyUnicode_Type
-  #define PyStringObject               PyUnicodeObject
-  #define PyString_Type                PyUnicode_Type
-  #define PyString_Check               PyUnicode_Check
-  #define PyString_CheckExact          PyUnicode_CheckExact
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define PyBytesObject                PyStringObject
-  #define PyBytes_Type                 PyString_Type
-  #define PyBytes_Check                PyString_Check
-  #define PyBytes_CheckExact           PyString_CheckExact
-  #define PyBytes_FromString           PyString_FromString
-  #define PyBytes_FromStringAndSize    PyString_FromStringAndSize
-  #define PyBytes_FromFormat           PyString_FromFormat
-  #define PyBytes_DecodeEscape         PyString_DecodeEscape
-  #define PyBytes_AsString             PyString_AsString
-  #define PyBytes_AsStringAndSize      PyString_AsStringAndSize
-  #define PyBytes_Size                 PyString_Size
-  #define PyBytes_AS_STRING            PyString_AS_STRING
-  #define PyBytes_GET_SIZE             PyString_GET_SIZE
-  #define PyBytes_Repr                 PyString_Repr
-  #define PyBytes_Concat               PyString_Concat
-  #define PyBytes_ConcatAndDel         PyString_ConcatAndDel
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
-  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
-#else
-  #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \
-                                         PyString_Check(obj) || PyUnicode_Check(obj))
-  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
-#endif
-#if PY_VERSION_HEX < 0x02060000
-  #define PySet_Check(obj)             PyObject_TypeCheck(obj, &PySet_Type)
-  #define PyFrozenSet_Check(obj)       PyObject_TypeCheck(obj, &PyFrozenSet_Type)
-#endif
-#ifndef PySet_CheckExact
-  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
-#endif
-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
-#if PY_MAJOR_VERSION >= 3
-  #define PyIntObject                  PyLongObject
-  #define PyInt_Type                   PyLong_Type
-  #define PyInt_Check(op)              PyLong_Check(op)
-  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
-  #define PyInt_FromString             PyLong_FromString
-  #define PyInt_FromUnicode            PyLong_FromUnicode
-  #define PyInt_FromLong               PyLong_FromLong
-  #define PyInt_FromSize_t             PyLong_FromSize_t
-  #define PyInt_FromSsize_t            PyLong_FromSsize_t
-  #define PyInt_AsLong                 PyLong_AsLong
-  #define PyInt_AS_LONG                PyLong_AS_LONG
-  #define PyInt_AsSsize_t              PyLong_AsSsize_t
-  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
-  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
-  #define PyNumber_Int                 PyNumber_Long
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyBoolObject                 PyLongObject
-#endif
-#if PY_VERSION_HEX < 0x030200A4
-  typedef long Py_hash_t;
-  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
-#else
-  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
-  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
-#endif
-#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
-  #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
-  #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
-  #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b)
-#else
-  #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \
-        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \
-        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \
-            (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0)))
-  #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \
-        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
-        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \
-            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1)))
-  #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \
-        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
-        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \
-            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1)))
-#endif
-#if PY_MAJOR_VERSION >= 3
-  #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
-#endif
-#if PY_VERSION_HEX < 0x02050000
-  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),((char *)(n)))
-  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
-  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),((char *)(n)))
-#else
-  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),(n))
-  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a))
-  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),(n))
-#endif
-#if PY_VERSION_HEX < 0x02050000
-  #define __Pyx_NAMESTR(n) ((char *)(n))
-  #define __Pyx_DOCSTR(n)  ((char *)(n))
-#else
-  #define __Pyx_NAMESTR(n) (n)
-  #define __Pyx_DOCSTR(n)  (n)
-#endif
-#ifndef CYTHON_INLINE
-  #if defined(__GNUC__)
-    #define CYTHON_INLINE __inline__
-  #elif defined(_MSC_VER)
-    #define CYTHON_INLINE __inline
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_INLINE inline
-  #else
-    #define CYTHON_INLINE
-  #endif
-#endif
-#ifndef CYTHON_RESTRICT
-  #if defined(__GNUC__)
-    #define CYTHON_RESTRICT __restrict__
-  #elif defined(_MSC_VER) && _MSC_VER >= 1400
-    #define CYTHON_RESTRICT __restrict
-  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-    #define CYTHON_RESTRICT restrict
-  #else
-    #define CYTHON_RESTRICT
-  #endif
-#endif
-#ifdef NAN
-#define __PYX_NAN() ((float) NAN)
-#else
-static CYTHON_INLINE float __PYX_NAN() {
-  /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and
-   a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is
-   a quiet NaN. */
-  float value;
-  memset(&value, 0xFF, sizeof(value));
-  return value;
-}
-#endif
-#ifdef __cplusplus
-template<typename T>
-void __Pyx_call_destructor(T* x) {
-    x->~T();
-}
-#endif
-
-
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-
-#ifndef __PYX_EXTERN_C
-  #ifdef __cplusplus
-    #define __PYX_EXTERN_C extern "C"
-  #else
-    #define __PYX_EXTERN_C extern
-  #endif
-#endif
-
-#if defined(WIN32) || defined(MS_WINDOWS)
-#define _USE_MATH_DEFINES
-#endif
-#include <math.h>
-#define __PYX_HAVE__cython_bubblesort_nomagic
-#define __PYX_HAVE_API__cython_bubblesort_nomagic
-#include "string.h"
-#include "stdio.h"
-#include "stdlib.h"
-#include "numpy/arrayobject.h"
-#include "numpy/ufuncobject.h"
-#include "pythread.h"
-#include "pystate.h"
-#ifdef _OPENMP
-#include <omp.h>
-#endif /* _OPENMP */
-
-#ifdef PYREX_WITHOUT_ASSERTIONS
-#define CYTHON_WITHOUT_ASSERTIONS
-#endif
-
-#ifndef CYTHON_UNUSED
-# if defined(__GNUC__)
-#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
-#     define CYTHON_UNUSED __attribute__ ((__unused__))
-#   else
-#     define CYTHON_UNUSED
-#   endif
-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
-#   define CYTHON_UNUSED __attribute__ ((__unused__))
-# else
-#   define CYTHON_UNUSED
-# endif
-#endif
-typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;
-                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
-
-#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
-#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
-#define __PYX_DEFAULT_STRING_ENCODING ""
-#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
-#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (    \
-    (sizeof(type) < sizeof(Py_ssize_t))  ||             \
-    (sizeof(type) > sizeof(Py_ssize_t) &&               \
-          likely(v < (type)PY_SSIZE_T_MAX ||            \
-                 v == (type)PY_SSIZE_T_MAX)  &&         \
-          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||       \
-                                v == (type)PY_SSIZE_T_MIN)))  ||  \
-    (sizeof(type) == sizeof(Py_ssize_t) &&              \
-          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||        \
-                               v == (type)PY_SSIZE_T_MAX)))  )
-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
-static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
-#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
-#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
-#define __Pyx_PyBytes_FromString        PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
-#if PY_MAJOR_VERSION < 3
-    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
-#else
-    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
-    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
-#endif
-#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
-#define __Pyx_PyObject_FromUString(s)  __Pyx_PyObject_FromString((const char*)s)
-#define __Pyx_PyBytes_FromUString(s)   __Pyx_PyBytes_FromString((const char*)s)
-#define __Pyx_PyByteArray_FromUString(s)   __Pyx_PyByteArray_FromString((const char*)s)
-#define __Pyx_PyStr_FromUString(s)     __Pyx_PyStr_FromString((const char*)s)
-#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((const char*)s)
-#if PY_MAJOR_VERSION < 3
-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
-{
-    const Py_UNICODE *u_end = u;
-    while (*u_end++) ;
-    return (size_t)(u_end - u - 1);
-}
-#else
-#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
-#endif
-#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
-#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
-#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
-#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None)
-#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-#if CYTHON_COMPILING_IN_CPYTHON
-#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-#else
-#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
-#endif
-#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    PyObject* ascii_chars_u = NULL;
-    PyObject* ascii_chars_b = NULL;
-    const char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    if (strcmp(default_encoding_c, "ascii") == 0) {
-        __Pyx_sys_getdefaultencoding_not_ascii = 0;
-    } else {
-        char ascii_chars[128];
-        int c;
-        for (c = 0; c < 128; c++) {
-            ascii_chars[c] = c;
-        }
-        __Pyx_sys_getdefaultencoding_not_ascii = 1;
-        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
-        if (!ascii_chars_u) goto bad;
-        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
-        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
-            PyErr_Format(
-                PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
-                default_encoding_c);
-            goto bad;
-        }
-        Py_DECREF(ascii_chars_u);
-        Py_DECREF(ascii_chars_b);
-    }
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    Py_XDECREF(ascii_chars_u);
-    Py_XDECREF(ascii_chars_b);
-    return -1;
-}
-#endif
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
-#else
-#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
-#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params(void) {
-    PyObject* sys;
-    PyObject* default_encoding = NULL;
-    char* default_encoding_c;
-    sys = PyImport_ImportModule("sys");
-    if (!sys) goto bad;
-    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
-    Py_DECREF(sys);
-    if (!default_encoding) goto bad;
-    default_encoding_c = PyBytes_AsString(default_encoding);
-    if (!default_encoding_c) goto bad;
-    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
-    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
-    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
-    Py_DECREF(default_encoding);
-    return 0;
-bad:
-    Py_XDECREF(default_encoding);
-    return -1;
-}
-#endif
-#endif
-
-
-/* Test for GCC > 2.95 */
-#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
-  #define likely(x)   __builtin_expect(!!(x), 1)
-  #define unlikely(x) __builtin_expect(!!(x), 0)
-#else /* !__GNUC__ or GCC < 2.95 */
-  #define likely(x)   (x)
-  #define unlikely(x) (x)
-#endif /* __GNUC__ */
-
-static PyObject *__pyx_m;
-static PyObject *__pyx_d;
-static PyObject *__pyx_b;
-static PyObject *__pyx_empty_tuple;
-static PyObject *__pyx_empty_bytes;
-static int __pyx_lineno;
-static int __pyx_clineno = 0;
-static const char * __pyx_cfilenm= __FILE__;
-static const char *__pyx_filename;
-
-#if !defined(CYTHON_CCOMPLEX)
-  #if defined(__cplusplus)
-    #define CYTHON_CCOMPLEX 1
-  #elif defined(_Complex_I)
-    #define CYTHON_CCOMPLEX 1
-  #else
-    #define CYTHON_CCOMPLEX 0
-  #endif
-#endif
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #include <complex>
-  #else
-    #include <complex.h>
-  #endif
-#endif
-#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
-  #undef _Complex_I
-  #define _Complex_I 1.0fj
-#endif
-
-
-static const char *__pyx_f[] = {
-  "cython_bubblesort_nomagic.pyx",
-  "__init__.pxd",
-  "stringsource",
-  "type.pxd",
-};
-#define IS_UNSIGNED(type) (((type) -1) > 0)
-struct __Pyx_StructField_;
-#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
-typedef struct {
-  const char* name; /* for error messages only */
-  struct __Pyx_StructField_* fields;
-  size_t size;     /* sizeof(type) */
-  size_t arraysize[8]; /* length of array in each dimension */
-  int ndim;
-  char typegroup; /* _R_eal, _C_omplex, Signed _I_nt, _U_nsigned int, _S_truct, _P_ointer, _O_bject, c_H_ar */
-  char is_unsigned;
-  int flags;
-} __Pyx_TypeInfo;
-typedef struct __Pyx_StructField_ {
-  __Pyx_TypeInfo* type;
-  const char* name;
-  size_t offset;
-} __Pyx_StructField;
-typedef struct {
-  __Pyx_StructField* field;
-  size_t parent_offset;
-} __Pyx_BufFmt_StackElem;
-typedef struct {
-  __Pyx_StructField root;
-  __Pyx_BufFmt_StackElem* head;
-  size_t fmt_offset;
-  size_t new_count, enc_count;
-  size_t struct_alignment;
-  int is_complex;
-  char enc_type;
-  char new_packmode;
-  char enc_packmode;
-  char is_valid_array;
-} __Pyx_BufFmt_Context;
-
-struct __pyx_memoryview_obj;
-typedef struct {
-  struct __pyx_memoryview_obj *memview;
-  char *data;
-  Py_ssize_t shape[8];
-  Py_ssize_t strides[8];
-  Py_ssize_t suboffsets[8];
-} __Pyx_memviewslice;
-
-#include <pythread.h>
-#ifndef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 1
-#endif
-#define __pyx_atomic_int_type int
-#if CYTHON_ATOMICS && __GNUC__ >= 4 && (__GNUC_MINOR__ > 1 ||           \
-                    (__GNUC_MINOR__ == 1 && __GNUC_PATCHLEVEL >= 2)) && \
-                    !defined(__i386__)
-    #define __pyx_atomic_incr_aligned(value, lock) __sync_fetch_and_add(value, 1)
-    #define __pyx_atomic_decr_aligned(value, lock) __sync_fetch_and_sub(value, 1)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using GNU atomics"
-    #endif
-#elif CYTHON_ATOMICS && MSC_VER
-    #include <Windows.h>
-    #define __pyx_atomic_int_type LONG
-    #define __pyx_atomic_incr_aligned(value, lock) InterlockedIncrement(value)
-    #define __pyx_atomic_decr_aligned(value, lock) InterlockedDecrement(value)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using MSVC atomics"
-    #endif
-#elif CYTHON_ATOMICS && (defined(__ICC) || defined(__INTEL_COMPILER)) && 0
-    #define __pyx_atomic_incr_aligned(value, lock) _InterlockedIncrement(value)
-    #define __pyx_atomic_decr_aligned(value, lock) _InterlockedDecrement(value)
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Using Intel atomics"
-    #endif
-#else
-    #undef CYTHON_ATOMICS
-    #define CYTHON_ATOMICS 0
-    #ifdef __PYX_DEBUG_ATOMICS
-        #warning "Not using atomics"
-    #endif
-#endif
-typedef volatile __pyx_atomic_int_type __pyx_atomic_int;
-#if CYTHON_ATOMICS
-    #define __pyx_add_acquisition_count(memview) \
-             __pyx_atomic_incr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
-    #define __pyx_sub_acquisition_count(memview) \
-            __pyx_atomic_decr_aligned(__pyx_get_slice_count_pointer(memview), memview->lock)
-#else
-    #define __pyx_add_acquisition_count(memview) \
-            __pyx_add_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-    #define __pyx_sub_acquisition_count(memview) \
-            __pyx_sub_acquisition_count_locked(__pyx_get_slice_count_pointer(memview), memview->lock)
-#endif
-
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":723
- * # in Cython to enable them only on the right systems.
- * 
- * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t
- */
-typedef npy_int8 __pyx_t_5numpy_int8_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":724
- * 
- * ctypedef npy_int8       int8_t
- * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int32      int32_t
- * ctypedef npy_int64      int64_t
- */
-typedef npy_int16 __pyx_t_5numpy_int16_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":725
- * ctypedef npy_int8       int8_t
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_int64      int64_t
- * #ctypedef npy_int96      int96_t
- */
-typedef npy_int32 __pyx_t_5numpy_int32_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":726
- * ctypedef npy_int16      int16_t
- * ctypedef npy_int32      int32_t
- * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_int96      int96_t
- * #ctypedef npy_int128     int128_t
- */
-typedef npy_int64 __pyx_t_5numpy_int64_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":730
- * #ctypedef npy_int128     int128_t
- * 
- * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t
- */
-typedef npy_uint8 __pyx_t_5numpy_uint8_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":731
- * 
- * ctypedef npy_uint8      uint8_t
- * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint32     uint32_t
- * ctypedef npy_uint64     uint64_t
- */
-typedef npy_uint16 __pyx_t_5numpy_uint16_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":732
- * ctypedef npy_uint8      uint8_t
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uint64     uint64_t
- * #ctypedef npy_uint96     uint96_t
- */
-typedef npy_uint32 __pyx_t_5numpy_uint32_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":733
- * ctypedef npy_uint16     uint16_t
- * ctypedef npy_uint32     uint32_t
- * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_uint96     uint96_t
- * #ctypedef npy_uint128    uint128_t
- */
-typedef npy_uint64 __pyx_t_5numpy_uint64_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":737
- * #ctypedef npy_uint128    uint128_t
- * 
- * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
- * ctypedef npy_float64    float64_t
- * #ctypedef npy_float80    float80_t
- */
-typedef npy_float32 __pyx_t_5numpy_float32_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":738
- * 
- * ctypedef npy_float32    float32_t
- * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
- * #ctypedef npy_float80    float80_t
- * #ctypedef npy_float128   float128_t
- */
-typedef npy_float64 __pyx_t_5numpy_float64_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":747
- * # The int types are mapped a bit surprising --
- * # numpy.int corresponds to 'l' and numpy.long to 'q'
- * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longlong   long_t
- * ctypedef npy_longlong   longlong_t
- */
-typedef npy_long __pyx_t_5numpy_int_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":748
- * # numpy.int corresponds to 'l' and numpy.long to 'q'
- * ctypedef npy_long       int_t
- * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longlong   longlong_t
- * 
- */
-typedef npy_longlong __pyx_t_5numpy_long_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":749
- * ctypedef npy_long       int_t
- * ctypedef npy_longlong   long_t
- * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_ulong      uint_t
- */
-typedef npy_longlong __pyx_t_5numpy_longlong_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":751
- * ctypedef npy_longlong   longlong_t
- * 
- * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
- * ctypedef npy_ulonglong  ulong_t
- * ctypedef npy_ulonglong  ulonglong_t
- */
-typedef npy_ulong __pyx_t_5numpy_uint_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":752
- * 
- * ctypedef npy_ulong      uint_t
- * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
- * ctypedef npy_ulonglong  ulonglong_t
- * 
- */
-typedef npy_ulonglong __pyx_t_5numpy_ulong_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":753
- * ctypedef npy_ulong      uint_t
- * ctypedef npy_ulonglong  ulong_t
- * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_intp       intp_t
- */
-typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":755
- * ctypedef npy_ulonglong  ulonglong_t
- * 
- * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
- * ctypedef npy_uintp      uintp_t
- * 
- */
-typedef npy_intp __pyx_t_5numpy_intp_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":756
- * 
- * ctypedef npy_intp       intp_t
- * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_double     float_t
- */
-typedef npy_uintp __pyx_t_5numpy_uintp_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":758
- * ctypedef npy_uintp      uintp_t
- * 
- * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
- * ctypedef npy_double     double_t
- * ctypedef npy_longdouble longdouble_t
- */
-typedef npy_double __pyx_t_5numpy_float_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":759
- * 
- * ctypedef npy_double     float_t
- * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
- * ctypedef npy_longdouble longdouble_t
- * 
- */
-typedef npy_double __pyx_t_5numpy_double_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":760
- * ctypedef npy_double     float_t
- * ctypedef npy_double     double_t
- * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_cfloat      cfloat_t
- */
-typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    typedef ::std::complex< float > __pyx_t_float_complex;
-  #else
-    typedef float _Complex __pyx_t_float_complex;
-  #endif
-#else
-    typedef struct { float real, imag; } __pyx_t_float_complex;
-#endif
-
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    typedef ::std::complex< double > __pyx_t_double_complex;
-  #else
-    typedef double _Complex __pyx_t_double_complex;
-  #endif
-#else
-    typedef struct { double real, imag; } __pyx_t_double_complex;
-#endif
-
-
-/*--- Type declarations ---*/
-struct __pyx_array_obj;
-struct __pyx_MemviewEnum_obj;
-struct __pyx_memoryview_obj;
-struct __pyx_memoryviewslice_obj;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":762
- * ctypedef npy_longdouble longdouble_t
- * 
- * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
- * ctypedef npy_cdouble     cdouble_t
- * ctypedef npy_clongdouble clongdouble_t
- */
-typedef npy_cfloat __pyx_t_5numpy_cfloat_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":763
- * 
- * ctypedef npy_cfloat      cfloat_t
- * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
- * ctypedef npy_clongdouble clongdouble_t
- * 
- */
-typedef npy_cdouble __pyx_t_5numpy_cdouble_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":764
- * ctypedef npy_cfloat      cfloat_t
- * ctypedef npy_cdouble     cdouble_t
- * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
- * 
- * ctypedef npy_cdouble     complex_t
- */
-typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":766
- * ctypedef npy_clongdouble clongdouble_t
- * 
- * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
- * 
- * cdef inline object PyArray_MultiIterNew1(a):
- */
-typedef npy_cdouble __pyx_t_5numpy_complex_t;
-
-/* "View.MemoryView":99
- * 
- * @cname("__pyx_array")
- * cdef class array:             # <<<<<<<<<<<<<<
- * 
- *     cdef:
- */
-struct __pyx_array_obj {
-  PyObject_HEAD
-  char *data;
-  Py_ssize_t len;
-  char *format;
-  int ndim;
-  Py_ssize_t *_shape;
-  Py_ssize_t *_strides;
-  Py_ssize_t itemsize;
-  PyObject *mode;
-  PyObject *_format;
-  void (*callback_free_data)(void *);
-  int free_data;
-  int dtype_is_object;
-};
-
-
-/* "View.MemoryView":269
- * 
- * @cname('__pyx_MemviewEnum')
- * cdef class Enum(object):             # <<<<<<<<<<<<<<
- *     cdef object name
- *     def __init__(self, name):
- */
-struct __pyx_MemviewEnum_obj {
-  PyObject_HEAD
-  PyObject *name;
-};
-
-
-/* "View.MemoryView":302
- * 
- * @cname('__pyx_memoryview')
- * cdef class memoryview(object):             # <<<<<<<<<<<<<<
- * 
- *     cdef object obj
- */
-struct __pyx_memoryview_obj {
-  PyObject_HEAD
-  struct __pyx_vtabstruct_memoryview *__pyx_vtab;
-  PyObject *obj;
-  PyObject *_size;
-  PyObject *_array_interface;
-  PyThread_type_lock lock;
-  __pyx_atomic_int acquisition_count[2];
-  __pyx_atomic_int *acquisition_count_aligned_p;
-  Py_buffer view;
-  int flags;
-  int dtype_is_object;
-  __Pyx_TypeInfo *typeinfo;
-};
-
-
-/* "View.MemoryView":922
- * 
- * @cname('__pyx_memoryviewslice')
- * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
- *     "Internal class for passing memoryview slices to Python"
- * 
- */
-struct __pyx_memoryviewslice_obj {
-  struct __pyx_memoryview_obj __pyx_base;
-  __Pyx_memviewslice from_slice;
-  PyObject *from_object;
-  PyObject *(*to_object_func)(char *);
-  int (*to_dtype_func)(char *, PyObject *);
-};
-
-
-
-/* "View.MemoryView":302
- * 
- * @cname('__pyx_memoryview')
- * cdef class memoryview(object):             # <<<<<<<<<<<<<<
- * 
- *     cdef object obj
- */
-
-struct __pyx_vtabstruct_memoryview {
-  char *(*get_item_pointer)(struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*is_slice)(struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*setitem_slice_assignment)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
-  PyObject *(*setitem_slice_assign_scalar)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *);
-  PyObject *(*setitem_indexed)(struct __pyx_memoryview_obj *, PyObject *, PyObject *);
-  PyObject *(*convert_item_to_object)(struct __pyx_memoryview_obj *, char *);
-  PyObject *(*assign_item_from_object)(struct __pyx_memoryview_obj *, char *, PyObject *);
-};
-static struct __pyx_vtabstruct_memoryview *__pyx_vtabptr_memoryview;
-
-
-/* "View.MemoryView":922
- * 
- * @cname('__pyx_memoryviewslice')
- * cdef class _memoryviewslice(memoryview):             # <<<<<<<<<<<<<<
- *     "Internal class for passing memoryview slices to Python"
- * 
- */
-
-struct __pyx_vtabstruct__memoryviewslice {
-  struct __pyx_vtabstruct_memoryview __pyx_base;
-};
-static struct __pyx_vtabstruct__memoryviewslice *__pyx_vtabptr__memoryviewslice;
-#ifndef CYTHON_REFNANNY
-  #define CYTHON_REFNANNY 0
-#endif
-#if CYTHON_REFNANNY
-  typedef struct {
-    void (*INCREF)(void*, PyObject*, int);
-    void (*DECREF)(void*, PyObject*, int);
-    void (*GOTREF)(void*, PyObject*, int);
-    void (*GIVEREF)(void*, PyObject*, int);
-    void* (*SetupContext)(const char*, int, const char*);
-    void (*FinishContext)(void**);
-  } __Pyx_RefNannyAPIStruct;
-  static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
-  static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/
-  #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;
-#ifdef WITH_THREAD
-  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
-          if (acquire_gil) { \
-              PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \
-              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
-              PyGILState_Release(__pyx_gilstate_save); \
-          } else { \
-              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
-          }
-#else
-  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
-          __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
-#endif
-  #define __Pyx_RefNannyFinishContext() \
-          __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
-  #define __Pyx_INCREF(r)  __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
-  #define __Pyx_DECREF(r)  __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
-  #define __Pyx_GOTREF(r)  __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
-  #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
-  #define __Pyx_XINCREF(r)  do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0)
-  #define __Pyx_XDECREF(r)  do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0)
-  #define __Pyx_XGOTREF(r)  do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0)
-  #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0)
-#else
-  #define __Pyx_RefNannyDeclarations
-  #define __Pyx_RefNannySetupContext(name, acquire_gil)
-  #define __Pyx_RefNannyFinishContext()
-  #define __Pyx_INCREF(r) Py_INCREF(r)
-  #define __Pyx_DECREF(r) Py_DECREF(r)
-  #define __Pyx_GOTREF(r)
-  #define __Pyx_GIVEREF(r)
-  #define __Pyx_XINCREF(r) Py_XINCREF(r)
-  #define __Pyx_XDECREF(r) Py_XDECREF(r)
-  #define __Pyx_XGOTREF(r)
-  #define __Pyx_XGIVEREF(r)
-#endif /* CYTHON_REFNANNY */
-#define __Pyx_XDECREF_SET(r, v) do {                            \
-        PyObject *tmp = (PyObject *) r;                         \
-        r = v; __Pyx_XDECREF(tmp);                              \
-    } while (0)
-#define __Pyx_DECREF_SET(r, v) do {                             \
-        PyObject *tmp = (PyObject *) r;                         \
-        r = v; __Pyx_DECREF(tmp);                               \
-    } while (0)
-#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
-#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
-    PyTypeObject* tp = Py_TYPE(obj);
-    if (likely(tp->tp_getattro))
-        return tp->tp_getattro(obj, attr_name);
-#if PY_MAJOR_VERSION < 3
-    if (likely(tp->tp_getattr))
-        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
-#endif
-    return PyObject_GetAttr(obj, attr_name);
-}
-#else
-#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
-#endif
-
-static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/
-
-static CYTHON_INLINE int  __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj,
-    __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack);
-static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info);
-
-#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d
-#define __Pyx_MEMVIEW_DIRECT   1
-#define __Pyx_MEMVIEW_PTR      2
-#define __Pyx_MEMVIEW_FULL     4
-#define __Pyx_MEMVIEW_CONTIG   8
-#define __Pyx_MEMVIEW_STRIDED  16
-#define __Pyx_MEMVIEW_FOLLOW   32
-#define __Pyx_IS_C_CONTIG 1
-#define __Pyx_IS_F_CONTIG 2
-static int __Pyx_init_memviewslice(
-                struct __pyx_memoryview_obj *memview,
-                int ndim,
-                __Pyx_memviewslice *memviewslice,
-                int memview_is_new_reference);
-static CYTHON_INLINE int __pyx_add_acquisition_count_locked(
-    __pyx_atomic_int *acquisition_count, PyThread_type_lock lock);
-static CYTHON_INLINE int __pyx_sub_acquisition_count_locked(
-    __pyx_atomic_int *acquisition_count, PyThread_type_lock lock);
-#define __pyx_get_slice_count_pointer(memview) (memview->acquisition_count_aligned_p)
-#define __pyx_get_slice_count(memview) (*__pyx_get_slice_count_pointer(memview))
-#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__)
-#define __PYX_XDEC_MEMVIEW(slice, have_gil) __Pyx_XDEC_MEMVIEW(slice, have_gil, __LINE__)
-static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int);
-static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *, int, int);
-
-static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
-static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-
-static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
-    const char *name, int exact); /*proto*/
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/
-#else
-#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
-#endif
-
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/
-
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);
-
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
-
-static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
-    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
-
-static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/
-
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
-    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
-    const char* function_name); /*proto*/
-
-#include <string.h>
-
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
-
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
-
-#if PY_MAJOR_VERSION >= 3
-#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
-#else
-#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
-#endif
-
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); /* proto */
-
-#ifndef __PYX_FORCE_INIT_THREADS
-  #define __PYX_FORCE_INIT_THREADS 0
-#endif
-
-#define UNARY_NEG_WOULD_OVERFLOW(x)            (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x)))
-
-static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-static PyObject *get_memview(PyObject *__pyx_v_self); /*proto*/
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); /*proto*/
-
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
-         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
-         const char* encoding, const char* errors,
-         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors));
-
-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
-
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-
-#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
-    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) : \
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) : \
-               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
-#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
-    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
-    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
-    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
-    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck);
-
-static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-static PyObject *__pyx_memoryview_transpose(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview__get__base(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_shape(PyObject *__pyx_v_self); /*proto*/
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        Py_SIZE(list) = len+1;
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-static PyObject *__pyx_memoryview_get_strides(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_suboffsets(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_ndim(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_itemsize(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_nbytes(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_size(PyObject *__pyx_v_self); /*proto*/
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject* none = _PyList_Extend((PyListObject*)L, v);
-    if (unlikely(!none))
-        return -1;
-    Py_DECREF(none);
-    return 0;
-#else
-    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
-#endif
-}
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        Py_SIZE(list) = len+1;
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-static CYTHON_INLINE long __Pyx_div_long(long, long); /* proto */
-
-static PyObject *__pyx_memoryviewslice__get__base(PyObject *__pyx_v_self); /*proto*/
-static void __Pyx_WriteUnraisable(const char *name, int clineno,
-                                  int lineno, const char *filename,
-                                  int full_traceback); /*proto*/
-
-static int __Pyx_SetVtable(PyObject *dict, void *vtable); /*proto*/
-
-typedef struct {
-  Py_ssize_t shape, strides, suboffsets;
-} __Pyx_Buf_DimInfo;
-typedef struct {
-  size_t refcount;
-  Py_buffer pybuffer;
-} __Pyx_Buffer;
-typedef struct {
-  __Pyx_Buffer *rcbuffer;
-  char *data;
-  __Pyx_Buf_DimInfo diminfo[8];
-} __Pyx_LocalBuf_ND;
-
-#if PY_MAJOR_VERSION < 3
-    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);
-    static void __Pyx_ReleaseBuffer(Py_buffer *view);
-#else
-    #define __Pyx_GetBuffer PyObject_GetBuffer
-    #define __Pyx_ReleaseBuffer PyBuffer_Release
-#endif
-
-
-static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0};
-static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1};
-
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_long(unsigned long value);
-
-static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *);
-
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    #define __Pyx_CREAL(z) ((z).real())
-    #define __Pyx_CIMAG(z) ((z).imag())
-  #else
-    #define __Pyx_CREAL(z) (__real__(z))
-    #define __Pyx_CIMAG(z) (__imag__(z))
-  #endif
-#else
-    #define __Pyx_CREAL(z) ((z).real)
-    #define __Pyx_CIMAG(z) ((z).imag)
-#endif
-#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX
-    #define __Pyx_SET_CREAL(z,x) ((z).real(x))
-    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))
-#else
-    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)
-    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)
-#endif
-
-static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);
-
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eqf(a, b)   ((a)==(b))
-    #define __Pyx_c_sumf(a, b)  ((a)+(b))
-    #define __Pyx_c_difff(a, b) ((a)-(b))
-    #define __Pyx_c_prodf(a, b) ((a)*(b))
-    #define __Pyx_c_quotf(a, b) ((a)/(b))
-    #define __Pyx_c_negf(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zerof(z) ((z)==(float)0)
-    #define __Pyx_c_conjf(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_absf(z)     (::std::abs(z))
-        #define __Pyx_c_powf(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zerof(z) ((z)==0)
-    #define __Pyx_c_conjf(z)    (conjf(z))
-    #if 1
-        #define __Pyx_c_absf(z)     (cabsf(z))
-        #define __Pyx_c_powf(a, b)  (cpowf(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex);
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex);
-    #if 1
-        static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex);
-        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex);
-    #endif
-#endif
-
-static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);
-
-#if CYTHON_CCOMPLEX
-    #define __Pyx_c_eq(a, b)   ((a)==(b))
-    #define __Pyx_c_sum(a, b)  ((a)+(b))
-    #define __Pyx_c_diff(a, b) ((a)-(b))
-    #define __Pyx_c_prod(a, b) ((a)*(b))
-    #define __Pyx_c_quot(a, b) ((a)/(b))
-    #define __Pyx_c_neg(a)     (-(a))
-  #ifdef __cplusplus
-    #define __Pyx_c_is_zero(z) ((z)==(double)0)
-    #define __Pyx_c_conj(z)    (::std::conj(z))
-    #if 1
-        #define __Pyx_c_abs(z)     (::std::abs(z))
-        #define __Pyx_c_pow(a, b)  (::std::pow(a, b))
-    #endif
-  #else
-    #define __Pyx_c_is_zero(z) ((z)==0)
-    #define __Pyx_c_conj(z)    (conj(z))
-    #if 1
-        #define __Pyx_c_abs(z)     (cabs(z))
-        #define __Pyx_c_pow(a, b)  (cpow(a, b))
-    #endif
- #endif
-#else
-    static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex);
-    static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex);
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex);
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex);
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex);
-    #endif
-#endif
-
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
-
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
-
-static int __pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs,
-                                        char order, int ndim);
-
-static int __pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                                __Pyx_memviewslice *slice2,
-                                int ndim, size_t itemsize);
-
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object);
-
-static CYTHON_INLINE PyObject *__pyx_capsule_create(void *p, const char *sig);
-
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/
-
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
-
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *);
-
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
-
-static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b);
-
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj);
-
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *);
-
-static int __Pyx_check_binary_version(void);
-
-#if !defined(__Pyx_PyIdentifier_FromString)
-#if PY_MAJOR_VERSION < 3
-  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
-#else
-  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
-#endif
-#endif
-
-static PyObject *__Pyx_ImportModule(const char *name); /*proto*/
-
-static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);  /*proto*/
-
-typedef struct {
-    int code_line;
-    PyCodeObject* code_object;
-} __Pyx_CodeObjectCacheEntry;
-struct __Pyx_CodeObjectCache {
-    int count;
-    int max_count;
-    __Pyx_CodeObjectCacheEntry* entries;
-};
-static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-static PyCodeObject *__pyx_find_code_object(int code_line);
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename); /*proto*/
-
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
-
-
-/* Module declarations from 'cpython.buffer' */
-
-/* Module declarations from 'cpython.ref' */
-
-/* Module declarations from 'libc.string' */
-
-/* Module declarations from 'libc.stdio' */
-
-/* Module declarations from 'cpython.object' */
-
-/* Module declarations from '__builtin__' */
-
-/* Module declarations from 'cpython.type' */
-static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;
-
-/* Module declarations from 'libc.stdlib' */
-
-/* Module declarations from 'numpy' */
-
-/* Module declarations from 'numpy' */
-static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
-static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
-static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
-static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
-static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
-static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
-
-/* Module declarations from 'cython.view' */
-
-/* Module declarations from 'cython' */
-
-/* Module declarations from 'cython_bubblesort_nomagic' */
-static PyTypeObject *__pyx_array_type = 0;
-static PyTypeObject *__pyx_MemviewEnum_type = 0;
-static PyTypeObject *__pyx_memoryview_type = 0;
-static PyTypeObject *__pyx_memoryviewslice_type = 0;
-static PyObject *generic = 0;
-static PyObject *strided = 0;
-static PyObject *indirect = 0;
-static PyObject *contiguous = 0;
-static PyObject *indirect_contiguous = 0;
-static PyObject *__pyx_f_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(PyArrayObject *, int __pyx_skip_dispatch); /*proto*/
-static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/
-static void *__pyx_align_pointer(void *, size_t); /*proto*/
-static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/
-static PyObject *_unellipsify(PyObject *, int); /*proto*/
-static PyObject *assert_direct_dimensions(Py_ssize_t *, int); /*proto*/
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/
-static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/
-static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/
-static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/
-static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/
-static int __pyx_memoryview_err_dim(PyObject *, char *, int); /*proto*/
-static int __pyx_memoryview_err(PyObject *, char *); /*proto*/
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/
-static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/
-static __Pyx_TypeInfo __Pyx_TypeInfo_long = { "long", NULL, sizeof(long), { 0 }, 0, IS_UNSIGNED(long) ? 'U' : 'I', IS_UNSIGNED(long), 0 };
-#define __Pyx_MODULE_NAME "cython_bubblesort_nomagic"
-int __pyx_module_is_main_cython_bubblesort_nomagic = 0;
-
-/* Implementation of 'cython_bubblesort_nomagic' */
-static PyObject *__pyx_builtin_xrange;
-static PyObject *__pyx_builtin_ValueError;
-static PyObject *__pyx_builtin_range;
-static PyObject *__pyx_builtin_RuntimeError;
-static PyObject *__pyx_builtin_MemoryError;
-static PyObject *__pyx_builtin_enumerate;
-static PyObject *__pyx_builtin_Ellipsis;
-static PyObject *__pyx_builtin_TypeError;
-static PyObject *__pyx_builtin_id;
-static PyObject *__pyx_builtin_IndexError;
-static PyObject *__pyx_pf_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_inp_ary); /* proto */
-static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */
-static int __pyx_array_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */
-static int __pyx_array_getbuffer_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static void __pyx_array_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *get_memview_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_array_MemoryView_5array_6__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */
-static PyObject *__pyx_array_MemoryView_5array_8__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */
-static int __pyx_array_MemoryView_5array_10__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */
-static int __pyx_MemviewEnum_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */
-static PyObject *__pyx_MemviewEnum_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */
-static int __pyx_memoryview_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */
-static void __pyx_memoryview_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */
-static int __pyx_memoryview_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */
-static int __pyx_memoryview_getbuffer_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
-static PyObject *__pyx_memoryview_transpose_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview__get__base_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_get_shape_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_get_strides_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_get_suboffsets_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_get_ndim_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_get_itemsize_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_get_nbytes_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_get_size_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static Py_ssize_t __pyx_memoryview_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */
-static void __pyx_memoryviewslice_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_memoryviewslice__get__base_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */
-static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static char __pyx_k_B[] = "B";
-static char __pyx_k_H[] = "H";
-static char __pyx_k_I[] = "I";
-static char __pyx_k_L[] = "L";
-static char __pyx_k_O[] = "O";
-static char __pyx_k_Q[] = "Q";
-static char __pyx_k_b[] = "b";
-static char __pyx_k_c[] = "c";
-static char __pyx_k_d[] = "d";
-static char __pyx_k_f[] = "f";
-static char __pyx_k_g[] = "g";
-static char __pyx_k_h[] = "h";
-static char __pyx_k_i[] = "i";
-static char __pyx_k_l[] = "l";
-static char __pyx_k_q[] = "q";
-static char __pyx_k_Zd[] = "Zd";
-static char __pyx_k_Zf[] = "Zf";
-static char __pyx_k_Zg[] = "Zg";
-static char __pyx_k_id[] = "id";
-static char __pyx_k_obj[] = "obj";
-static char __pyx_k_base[] = "base";
-static char __pyx_k_main[] = "__main__";
-static char __pyx_k_mode[] = "mode";
-static char __pyx_k_name[] = "name";
-static char __pyx_k_ndim[] = "ndim";
-static char __pyx_k_pack[] = "pack";
-static char __pyx_k_size[] = "size";
-static char __pyx_k_step[] = "step";
-static char __pyx_k_stop[] = "stop";
-static char __pyx_k_test[] = "__test__";
-static char __pyx_k_class[] = "__class__";
-static char __pyx_k_error[] = "error";
-static char __pyx_k_flags[] = "flags";
-static char __pyx_k_range[] = "range";
-static char __pyx_k_shape[] = "shape";
-static char __pyx_k_start[] = "start";
-static char __pyx_k_format[] = "format";
-static char __pyx_k_import[] = "__import__";
-static char __pyx_k_name_2[] = "__name__";
-static char __pyx_k_struct[] = "struct";
-static char __pyx_k_unpack[] = "unpack";
-static char __pyx_k_xrange[] = "xrange";
-static char __pyx_k_fortran[] = "fortran";
-static char __pyx_k_memview[] = "memview";
-static char __pyx_k_Ellipsis[] = "Ellipsis";
-static char __pyx_k_itemsize[] = "itemsize";
-static char __pyx_k_TypeError[] = "TypeError";
-static char __pyx_k_enumerate[] = "enumerate";
-static char __pyx_k_IndexError[] = "IndexError";
-static char __pyx_k_ValueError[] = "ValueError";
-static char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
-static char __pyx_k_MemoryError[] = "MemoryError";
-static char __pyx_k_RuntimeError[] = "RuntimeError";
-static char __pyx_k_pyx_getbuffer[] = "__pyx_getbuffer";
-static char __pyx_k_allocate_buffer[] = "allocate_buffer";
-static char __pyx_k_dtype_is_object[] = "dtype_is_object";
-static char __pyx_k_pyx_releasebuffer[] = "__pyx_releasebuffer";
-static char __pyx_k_strided_and_direct[] = "<strided and direct>";
-static char __pyx_k_strided_and_indirect[] = "<strided and indirect>";
-static char __pyx_k_contiguous_and_direct[] = "<contiguous and direct>";
-static char __pyx_k_MemoryView_of_r_object[] = "<MemoryView of %r object>";
-static char __pyx_k_MemoryView_of_r_at_0x_x[] = "<MemoryView of %r at 0x%x>";
-static char __pyx_k_contiguous_and_indirect[] = "<contiguous and indirect>";
-static char __pyx_k_Cannot_index_with_type_s[] = "Cannot index with type '%s'";
-static char __pyx_k_getbuffer_obj_view_flags[] = "getbuffer(obj, view, flags)";
-static char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct";
-static char __pyx_k_Invalid_shape_in_axis_d_d[] = "Invalid shape in axis %d: %d.";
-static char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)";
-static char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)";
-static char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array";
-static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
-static char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data.";
-static char __pyx_k_strided_and_direct_or_indirect[] = "<strided and direct or indirect>";
-static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
-static char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced";
-static char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides";
-static char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory.";
-static char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions";
-static char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array";
-static char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd";
-static char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported";
-static char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got %s";
-static char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported";
-static char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis %d)";
-static char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object";
-static char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension %d (got %d and %d)";
-static char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous";
-static char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides.";
-static char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short.";
-static PyObject *__pyx_kp_s_Buffer_view_does_not_expose_stri;
-static PyObject *__pyx_kp_s_Can_only_create_a_buffer_that_is;
-static PyObject *__pyx_kp_s_Cannot_index_with_type_s;
-static PyObject *__pyx_n_s_Ellipsis;
-static PyObject *__pyx_kp_s_Empty_shape_tuple_for_cython_arr;
-static PyObject *__pyx_kp_u_Format_string_allocated_too_shor;
-static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;
-static PyObject *__pyx_n_s_IndexError;
-static PyObject *__pyx_kp_s_Indirect_dimensions_not_supporte;
-static PyObject *__pyx_kp_s_Invalid_mode_expected_c_or_fortr;
-static PyObject *__pyx_kp_s_Invalid_shape_in_axis_d_d;
-static PyObject *__pyx_n_s_MemoryError;
-static PyObject *__pyx_kp_s_MemoryView_of_r_at_0x_x;
-static PyObject *__pyx_kp_s_MemoryView_of_r_object;
-static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;
-static PyObject *__pyx_n_b_O;
-static PyObject *__pyx_kp_s_Out_of_bounds_on_buffer_access_a;
-static PyObject *__pyx_n_s_RuntimeError;
-static PyObject *__pyx_n_s_TypeError;
-static PyObject *__pyx_kp_s_Unable_to_convert_item_to_object;
-static PyObject *__pyx_n_s_ValueError;
-static PyObject *__pyx_n_s_allocate_buffer;
-static PyObject *__pyx_n_s_base;
-static PyObject *__pyx_n_s_c;
-static PyObject *__pyx_n_u_c;
-static PyObject *__pyx_n_s_class;
-static PyObject *__pyx_kp_s_contiguous_and_direct;
-static PyObject *__pyx_kp_s_contiguous_and_indirect;
-static PyObject *__pyx_n_s_dtype_is_object;
-static PyObject *__pyx_n_s_enumerate;
-static PyObject *__pyx_n_s_error;
-static PyObject *__pyx_n_s_flags;
-static PyObject *__pyx_n_s_format;
-static PyObject *__pyx_n_s_fortran;
-static PyObject *__pyx_n_u_fortran;
-static PyObject *__pyx_kp_s_got_differing_extents_in_dimensi;
-static PyObject *__pyx_n_s_id;
-static PyObject *__pyx_n_s_import;
-static PyObject *__pyx_n_s_itemsize;
-static PyObject *__pyx_kp_s_itemsize_0_for_cython_array;
-static PyObject *__pyx_n_s_main;
-static PyObject *__pyx_n_s_memview;
-static PyObject *__pyx_n_s_mode;
-static PyObject *__pyx_n_s_name;
-static PyObject *__pyx_n_s_name_2;
-static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;
-static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;
-static PyObject *__pyx_n_s_ndim;
-static PyObject *__pyx_n_s_obj;
-static PyObject *__pyx_n_s_pack;
-static PyObject *__pyx_n_s_pyx_getbuffer;
-static PyObject *__pyx_n_s_pyx_releasebuffer;
-static PyObject *__pyx_n_s_pyx_vtable;
-static PyObject *__pyx_n_s_range;
-static PyObject *__pyx_n_s_shape;
-static PyObject *__pyx_n_s_size;
-static PyObject *__pyx_n_s_start;
-static PyObject *__pyx_n_s_step;
-static PyObject *__pyx_n_s_stop;
-static PyObject *__pyx_kp_s_strided_and_direct;
-static PyObject *__pyx_kp_s_strided_and_direct_or_indirect;
-static PyObject *__pyx_kp_s_strided_and_indirect;
-static PyObject *__pyx_n_s_struct;
-static PyObject *__pyx_n_s_test;
-static PyObject *__pyx_kp_s_unable_to_allocate_array_data;
-static PyObject *__pyx_kp_s_unable_to_allocate_shape_and_str;
-static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
-static PyObject *__pyx_n_s_unpack;
-static PyObject *__pyx_n_s_xrange;
-static PyObject *__pyx_int_0;
-static PyObject *__pyx_int_1;
-static PyObject *__pyx_int_neg_1;
-static PyObject *__pyx_tuple_;
-static PyObject *__pyx_tuple__2;
-static PyObject *__pyx_tuple__3;
-static PyObject *__pyx_tuple__4;
-static PyObject *__pyx_tuple__5;
-static PyObject *__pyx_tuple__6;
-static PyObject *__pyx_tuple__7;
-static PyObject *__pyx_tuple__8;
-static PyObject *__pyx_tuple__9;
-static PyObject *__pyx_tuple__10;
-static PyObject *__pyx_tuple__11;
-static PyObject *__pyx_tuple__12;
-static PyObject *__pyx_tuple__13;
-static PyObject *__pyx_tuple__14;
-static PyObject *__pyx_tuple__15;
-static PyObject *__pyx_tuple__16;
-static PyObject *__pyx_tuple__17;
-static PyObject *__pyx_tuple__18;
-static PyObject *__pyx_tuple__19;
-static PyObject *__pyx_tuple__20;
-static PyObject *__pyx_tuple__21;
-static PyObject *__pyx_tuple__22;
-
-/* "cython_bubblesort_nomagic.pyx":6
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cpdef cython_bubblesort_nomagic(np.ndarray[long, ndim=1] inp_ary):             # <<<<<<<<<<<<<<
- *     """ The Cython implementation of Bubblesort with NumPy memoryview."""
- *     cdef unsigned long length, i, swapped, ele, temp
- */
-
-static PyObject *__pyx_pw_25cython_bubblesort_nomagic_1cython_bubblesort_nomagic(PyObject *__pyx_self, PyObject *__pyx_v_inp_ary); /*proto*/
-static PyObject *__pyx_f_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(PyArrayObject *__pyx_v_inp_ary, CYTHON_UNUSED int __pyx_skip_dispatch) {
-  unsigned long __pyx_v_length;
-  unsigned long __pyx_v_i;
-  unsigned long __pyx_v_swapped;
-  unsigned long __pyx_v_ele;
-  unsigned long __pyx_v_temp;
-  __Pyx_memviewslice __pyx_v_np_ary = { 0, 0, { 0 }, { 0 }, { 0 } };
-  __Pyx_LocalBuf_ND __pyx_pybuffernd_inp_ary;
-  __Pyx_Buffer __pyx_pybuffer_inp_ary;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  __Pyx_memviewslice __pyx_t_1 = { 0, 0, { 0 }, { 0 }, { 0 } };
-  unsigned long __pyx_t_2;
-  unsigned long __pyx_t_3;
-  int __pyx_t_4;
-  unsigned long __pyx_t_5;
-  unsigned long __pyx_t_6;
-  unsigned long __pyx_t_7;
-  unsigned long __pyx_t_8;
-  unsigned long __pyx_t_9;
-  unsigned long __pyx_t_10;
-  unsigned long __pyx_t_11;
-  unsigned long __pyx_t_12;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("cython_bubblesort_nomagic", 0);
-  __pyx_pybuffer_inp_ary.pybuffer.buf = NULL;
-  __pyx_pybuffer_inp_ary.refcount = 0;
-  __pyx_pybuffernd_inp_ary.data = NULL;
-  __pyx_pybuffernd_inp_ary.rcbuffer = &__pyx_pybuffer_inp_ary;
-  {
-    __Pyx_BufFmt_StackElem __pyx_stack[1];
-    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer, (PyObject*)__pyx_v_inp_ary, &__Pyx_TypeInfo_long, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __pyx_pybuffernd_inp_ary.diminfo[0].strides = __pyx_pybuffernd_inp_ary.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_inp_ary.diminfo[0].shape = __pyx_pybuffernd_inp_ary.rcbuffer->pybuffer.shape[0];
-
-  /* "cython_bubblesort_nomagic.pyx":9
- *     """ The Cython implementation of Bubblesort with NumPy memoryview."""
- *     cdef unsigned long length, i, swapped, ele, temp
- *     cdef long[:] np_ary = inp_ary             # <<<<<<<<<<<<<<
- *     length = np_ary.shape[0]
- *     swapped = 1
- */
-  __pyx_t_1 = __Pyx_PyObject_to_MemoryviewSlice_ds_long(((PyObject *)__pyx_v_inp_ary));
-  if (unlikely(!__pyx_t_1.memview)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_v_np_ary = __pyx_t_1;
-  __pyx_t_1.memview = NULL;
-  __pyx_t_1.data = NULL;
-
-  /* "cython_bubblesort_nomagic.pyx":10
- *     cdef unsigned long length, i, swapped, ele, temp
- *     cdef long[:] np_ary = inp_ary
- *     length = np_ary.shape[0]             # <<<<<<<<<<<<<<
- *     swapped = 1
- *     for i in xrange(0, length):
- */
-  __pyx_v_length = (__pyx_v_np_ary.shape[0]);
-
-  /* "cython_bubblesort_nomagic.pyx":11
- *     cdef long[:] np_ary = inp_ary
- *     length = np_ary.shape[0]
- *     swapped = 1             # <<<<<<<<<<<<<<
- *     for i in xrange(0, length):
- *         if swapped:
- */
-  __pyx_v_swapped = 1;
-
-  /* "cython_bubblesort_nomagic.pyx":12
- *     length = np_ary.shape[0]
- *     swapped = 1
- *     for i in xrange(0, length):             # <<<<<<<<<<<<<<
- *         if swapped:
- *             swapped = 0
- */
-  __pyx_t_2 = __pyx_v_length;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
-
-    /* "cython_bubblesort_nomagic.pyx":13
- *     swapped = 1
- *     for i in xrange(0, length):
- *         if swapped:             # <<<<<<<<<<<<<<
- *             swapped = 0
- *             for ele in xrange(0, length-i-1):
- */
-    __pyx_t_4 = (__pyx_v_swapped != 0);
-    if (__pyx_t_4) {
-
-      /* "cython_bubblesort_nomagic.pyx":14
- *     for i in xrange(0, length):
- *         if swapped:
- *             swapped = 0             # <<<<<<<<<<<<<<
- *             for ele in xrange(0, length-i-1):
- *                 if np_ary[ele] > np_ary[ele + 1]:
- */
-      __pyx_v_swapped = 0;
-
-      /* "cython_bubblesort_nomagic.pyx":15
- *         if swapped:
- *             swapped = 0
- *             for ele in xrange(0, length-i-1):             # <<<<<<<<<<<<<<
- *                 if np_ary[ele] > np_ary[ele + 1]:
- *                     temp = np_ary[ele + 1]
- */
-      __pyx_t_5 = ((__pyx_v_length - __pyx_v_i) - 1);
-      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-        __pyx_v_ele = __pyx_t_6;
-
-        /* "cython_bubblesort_nomagic.pyx":16
- *             swapped = 0
- *             for ele in xrange(0, length-i-1):
- *                 if np_ary[ele] > np_ary[ele + 1]:             # <<<<<<<<<<<<<<
- *                     temp = np_ary[ele + 1]
- *                     np_ary[ele + 1] = np_ary[ele]
- */
-        __pyx_t_7 = __pyx_v_ele;
-        __pyx_t_8 = (__pyx_v_ele + 1);
-        __pyx_t_4 = (((*((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_7 * __pyx_v_np_ary.strides[0]) ))) > (*((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_8 * __pyx_v_np_ary.strides[0]) )))) != 0);
-        if (__pyx_t_4) {
-
-          /* "cython_bubblesort_nomagic.pyx":17
- *             for ele in xrange(0, length-i-1):
- *                 if np_ary[ele] > np_ary[ele + 1]:
- *                     temp = np_ary[ele + 1]             # <<<<<<<<<<<<<<
- *                     np_ary[ele + 1] = np_ary[ele]
- *                     np_ary[ele] = temp
- */
-          __pyx_t_9 = (__pyx_v_ele + 1);
-          __pyx_v_temp = (*((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_9 * __pyx_v_np_ary.strides[0]) )));
-
-          /* "cython_bubblesort_nomagic.pyx":18
- *                 if np_ary[ele] > np_ary[ele + 1]:
- *                     temp = np_ary[ele + 1]
- *                     np_ary[ele + 1] = np_ary[ele]             # <<<<<<<<<<<<<<
- *                     np_ary[ele] = temp
- *                     swapped = 1
- */
-          __pyx_t_10 = __pyx_v_ele;
-          __pyx_t_11 = (__pyx_v_ele + 1);
-          *((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_11 * __pyx_v_np_ary.strides[0]) )) = (*((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_10 * __pyx_v_np_ary.strides[0]) )));
-
-          /* "cython_bubblesort_nomagic.pyx":19
- *                     temp = np_ary[ele + 1]
- *                     np_ary[ele + 1] = np_ary[ele]
- *                     np_ary[ele] = temp             # <<<<<<<<<<<<<<
- *                     swapped = 1
- *     return inp_ary
- */
-          __pyx_t_12 = __pyx_v_ele;
-          *((long *) ( /* dim=0 */ (__pyx_v_np_ary.data + __pyx_t_12 * __pyx_v_np_ary.strides[0]) )) = __pyx_v_temp;
-
-          /* "cython_bubblesort_nomagic.pyx":20
- *                     np_ary[ele + 1] = np_ary[ele]
- *                     np_ary[ele] = temp
- *                     swapped = 1             # <<<<<<<<<<<<<<
- *     return inp_ary
- */
-          __pyx_v_swapped = 1;
-          goto __pyx_L8;
-        }
-        __pyx_L8:;
-      }
-      goto __pyx_L5;
-    }
-    __pyx_L5:;
-  }
-
-  /* "cython_bubblesort_nomagic.pyx":21
- *                     np_ary[ele] = temp
- *                     swapped = 1
- *     return inp_ary             # <<<<<<<<<<<<<<
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(((PyObject *)__pyx_v_inp_ary));
-  __pyx_r = ((PyObject *)__pyx_v_inp_ary);
-  goto __pyx_L0;
-
-  /* "cython_bubblesort_nomagic.pyx":6
- * @cython.boundscheck(False)
- * @cython.wraparound(False)
- * cpdef cython_bubblesort_nomagic(np.ndarray[long, ndim=1] inp_ary):             # <<<<<<<<<<<<<<
- *     """ The Cython implementation of Bubblesort with NumPy memoryview."""
- *     cdef unsigned long length, i, swapped, ele, temp
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __PYX_XDEC_MEMVIEW(&__pyx_t_1, 1);
-  { PyObject *__pyx_type, *__pyx_value, *__pyx_tb;
-    __Pyx_ErrFetch(&__pyx_type, &__pyx_value, &__pyx_tb);
-    __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer);
-  __Pyx_ErrRestore(__pyx_type, __pyx_value, __pyx_tb);}
-  __Pyx_AddTraceback("cython_bubblesort_nomagic.cython_bubblesort_nomagic", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  goto __pyx_L2;
-  __pyx_L0:;
-  __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer);
-  __pyx_L2:;
-  __PYX_XDEC_MEMVIEW(&__pyx_v_np_ary, 1);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* Python wrapper */
-static PyObject *__pyx_pw_25cython_bubblesort_nomagic_1cython_bubblesort_nomagic(PyObject *__pyx_self, PyObject *__pyx_v_inp_ary); /*proto*/
-static char __pyx_doc_25cython_bubblesort_nomagic_cython_bubblesort_nomagic[] = " The Cython implementation of Bubblesort with NumPy memoryview.";
-static PyObject *__pyx_pw_25cython_bubblesort_nomagic_1cython_bubblesort_nomagic(PyObject *__pyx_self, PyObject *__pyx_v_inp_ary) {
-  CYTHON_UNUSED int __pyx_lineno = 0;
-  CYTHON_UNUSED const char *__pyx_filename = NULL;
-  CYTHON_UNUSED int __pyx_clineno = 0;
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("cython_bubblesort_nomagic (wrapper)", 0);
-  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_inp_ary), __pyx_ptype_5numpy_ndarray, 1, "inp_ary", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_r = __pyx_pf_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(__pyx_self, ((PyArrayObject *)__pyx_v_inp_ary));
-
-  /* function exit code */
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_pf_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_inp_ary) {
-  __Pyx_LocalBuf_ND __pyx_pybuffernd_inp_ary;
-  __Pyx_Buffer __pyx_pybuffer_inp_ary;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("cython_bubblesort_nomagic", 0);
-  __pyx_pybuffer_inp_ary.pybuffer.buf = NULL;
-  __pyx_pybuffer_inp_ary.refcount = 0;
-  __pyx_pybuffernd_inp_ary.data = NULL;
-  __pyx_pybuffernd_inp_ary.rcbuffer = &__pyx_pybuffer_inp_ary;
-  {
-    __Pyx_BufFmt_StackElem __pyx_stack[1];
-    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer, (PyObject*)__pyx_v_inp_ary, &__Pyx_TypeInfo_long, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __pyx_pybuffernd_inp_ary.diminfo[0].strides = __pyx_pybuffernd_inp_ary.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_inp_ary.diminfo[0].shape = __pyx_pybuffernd_inp_ary.rcbuffer->pybuffer.shape[0];
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __pyx_f_25cython_bubblesort_nomagic_cython_bubblesort_nomagic(__pyx_v_inp_ary, 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  { PyObject *__pyx_type, *__pyx_value, *__pyx_tb;
-    __Pyx_ErrFetch(&__pyx_type, &__pyx_value, &__pyx_tb);
-    __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer);
-  __Pyx_ErrRestore(__pyx_type, __pyx_value, __pyx_tb);}
-  __Pyx_AddTraceback("cython_bubblesort_nomagic.cython_bubblesort_nomagic", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  goto __pyx_L2;
-  __pyx_L0:;
-  __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_inp_ary.rcbuffer->pybuffer);
-  __pyx_L2:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":194
- *         # experimental exception made for __getbuffer__ and __releasebuffer__
- *         # -- the details of this may change.
- *         def __getbuffer__(ndarray self, Py_buffer* info, int flags):             # <<<<<<<<<<<<<<
- *             # This implementation of getbuffer is geared towards Cython
- *             # requirements, and does not yet fullfill the PEP.
- */
-
-/* Python wrapper */
-static CYTHON_UNUSED int __pyx_pw_5numpy_7ndarray_1__getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-static CYTHON_UNUSED int __pyx_pw_5numpy_7ndarray_1__getbuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0);
-  __pyx_r = __pyx_pf_5numpy_7ndarray___getbuffer__(((PyArrayObject *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
-  int __pyx_v_copy_shape;
-  int __pyx_v_i;
-  int __pyx_v_ndim;
-  int __pyx_v_endian_detector;
-  int __pyx_v_little_endian;
-  int __pyx_v_t;
-  char *__pyx_v_f;
-  PyArray_Descr *__pyx_v_descr = 0;
-  int __pyx_v_offset;
-  int __pyx_v_hasfields;
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  PyObject *__pyx_t_4 = NULL;
-  int __pyx_t_5;
-  int __pyx_t_6;
-  int __pyx_t_7;
-  PyObject *__pyx_t_8 = NULL;
-  char *__pyx_t_9;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__getbuffer__", 0);
-  if (__pyx_v_info != NULL) {
-    __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
-    __Pyx_GIVEREF(__pyx_v_info->obj);
-  }
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":200
- *             # of flags
- * 
- *             if info == NULL: return             # <<<<<<<<<<<<<<
- * 
- *             cdef int copy_shape, i, ndim
- */
-  __pyx_t_1 = ((__pyx_v_info == NULL) != 0);
-  if (__pyx_t_1) {
-    __pyx_r = 0;
-    goto __pyx_L0;
-  }
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":203
- * 
- *             cdef int copy_shape, i, ndim
- *             cdef int endian_detector = 1             # <<<<<<<<<<<<<<
- *             cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)
- * 
- */
-  __pyx_v_endian_detector = 1;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":204
- *             cdef int copy_shape, i, ndim
- *             cdef int endian_detector = 1
- *             cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)             # <<<<<<<<<<<<<<
- * 
- *             ndim = PyArray_NDIM(self)
- */
-  __pyx_v_little_endian = ((((char *)(&__pyx_v_endian_detector))[0]) != 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":206
- *             cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)
- * 
- *             ndim = PyArray_NDIM(self)             # <<<<<<<<<<<<<<
- * 
- *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
- */
-  __pyx_v_ndim = PyArray_NDIM(__pyx_v_self);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":208
- *             ndim = PyArray_NDIM(self)
- * 
- *             if sizeof(npy_intp) != sizeof(Py_ssize_t):             # <<<<<<<<<<<<<<
- *                 copy_shape = 1
- *             else:
- */
-  __pyx_t_1 = (((sizeof(npy_intp)) != (sizeof(Py_ssize_t))) != 0);
-  if (__pyx_t_1) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":209
- * 
- *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
- *                 copy_shape = 1             # <<<<<<<<<<<<<<
- *             else:
- *                 copy_shape = 0
- */
-    __pyx_v_copy_shape = 1;
-    goto __pyx_L4;
-  }
-  /*else*/ {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":211
- *                 copy_shape = 1
- *             else:
- *                 copy_shape = 0             # <<<<<<<<<<<<<<
- * 
- *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
- */
-    __pyx_v_copy_shape = 0;
-  }
-  __pyx_L4:;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":213
- *                 copy_shape = 0
- * 
- *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)             # <<<<<<<<<<<<<<
- *                 and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)):
- *                 raise ValueError(u"ndarray is not C contiguous")
- */
-  __pyx_t_1 = (((__pyx_v_flags & PyBUF_C_CONTIGUOUS) == PyBUF_C_CONTIGUOUS) != 0);
-  if (__pyx_t_1) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":214
- * 
- *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
- *                 and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)):             # <<<<<<<<<<<<<<
- *                 raise ValueError(u"ndarray is not C contiguous")
- * 
- */
-    __pyx_t_2 = ((!(PyArray_CHKFLAGS(__pyx_v_self, NPY_C_CONTIGUOUS) != 0)) != 0);
-    __pyx_t_3 = __pyx_t_2;
-  } else {
-    __pyx_t_3 = __pyx_t_1;
-  }
-  if (__pyx_t_3) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":215
- *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
- *                 and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)):
- *                 raise ValueError(u"ndarray is not C contiguous")             # <<<<<<<<<<<<<<
- * 
- *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
- */
-    __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __Pyx_Raise(__pyx_t_4, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    {__pyx_filename = __pyx_f[1]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":217
- *                 raise ValueError(u"ndarray is not C contiguous")
- * 
- *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)             # <<<<<<<<<<<<<<
- *                 and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)):
- *                 raise ValueError(u"ndarray is not Fortran contiguous")
- */
-  __pyx_t_3 = (((__pyx_v_flags & PyBUF_F_CONTIGUOUS) == PyBUF_F_CONTIGUOUS) != 0);
-  if (__pyx_t_3) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":218
- * 
- *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
- *                 and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)):             # <<<<<<<<<<<<<<
- *                 raise ValueError(u"ndarray is not Fortran contiguous")
- * 
- */
-    __pyx_t_1 = ((!(PyArray_CHKFLAGS(__pyx_v_self, NPY_F_CONTIGUOUS) != 0)) != 0);
-    __pyx_t_2 = __pyx_t_1;
-  } else {
-    __pyx_t_2 = __pyx_t_3;
-  }
-  if (__pyx_t_2) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":219
- *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
- *                 and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)):
- *                 raise ValueError(u"ndarray is not Fortran contiguous")             # <<<<<<<<<<<<<<
- * 
- *             info.buf = PyArray_DATA(self)
- */
-    __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __Pyx_Raise(__pyx_t_4, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    {__pyx_filename = __pyx_f[1]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":221
- *                 raise ValueError(u"ndarray is not Fortran contiguous")
- * 
- *             info.buf = PyArray_DATA(self)             # <<<<<<<<<<<<<<
- *             info.ndim = ndim
- *             if copy_shape:
- */
-  __pyx_v_info->buf = PyArray_DATA(__pyx_v_self);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":222
- * 
- *             info.buf = PyArray_DATA(self)
- *             info.ndim = ndim             # <<<<<<<<<<<<<<
- *             if copy_shape:
- *                 # Allocate new buffer for strides and shape info.
- */
-  __pyx_v_info->ndim = __pyx_v_ndim;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":223
- *             info.buf = PyArray_DATA(self)
- *             info.ndim = ndim
- *             if copy_shape:             # <<<<<<<<<<<<<<
- *                 # Allocate new buffer for strides and shape info.
- *                 # This is allocated as one block, strides first.
- */
-  __pyx_t_2 = (__pyx_v_copy_shape != 0);
-  if (__pyx_t_2) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":226
- *                 # Allocate new buffer for strides and shape info.
- *                 # This is allocated as one block, strides first.
- *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)             # <<<<<<<<<<<<<<
- *                 info.shape = info.strides + ndim
- *                 for i in range(ndim):
- */
-    __pyx_v_info->strides = ((Py_ssize_t *)malloc((((sizeof(Py_ssize_t)) * ((size_t)__pyx_v_ndim)) * 2)));
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":227
- *                 # This is allocated as one block, strides first.
- *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)
- *                 info.shape = info.strides + ndim             # <<<<<<<<<<<<<<
- *                 for i in range(ndim):
- *                     info.strides[i] = PyArray_STRIDES(self)[i]
- */
-    __pyx_v_info->shape = (__pyx_v_info->strides + __pyx_v_ndim);
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":228
- *                 info.strides = <Py_ssize_t*>stdlib.malloc(sizeof(Py_ssize_t) * <size_t>ndim * 2)
- *                 info.shape = info.strides + ndim
- *                 for i in range(ndim):             # <<<<<<<<<<<<<<
- *                     info.strides[i] = PyArray_STRIDES(self)[i]
- *                     info.shape[i] = PyArray_DIMS(self)[i]
- */
-    __pyx_t_5 = __pyx_v_ndim;
-    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-      __pyx_v_i = __pyx_t_6;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":229
- *                 info.shape = info.strides + ndim
- *                 for i in range(ndim):
- *                     info.strides[i] = PyArray_STRIDES(self)[i]             # <<<<<<<<<<<<<<
- *                     info.shape[i] = PyArray_DIMS(self)[i]
- *             else:
- */
-      (__pyx_v_info->strides[__pyx_v_i]) = (PyArray_STRIDES(__pyx_v_self)[__pyx_v_i]);
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":230
- *                 for i in range(ndim):
- *                     info.strides[i] = PyArray_STRIDES(self)[i]
- *                     info.shape[i] = PyArray_DIMS(self)[i]             # <<<<<<<<<<<<<<
- *             else:
- *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
- */
-      (__pyx_v_info->shape[__pyx_v_i]) = (PyArray_DIMS(__pyx_v_self)[__pyx_v_i]);
-    }
-    goto __pyx_L7;
-  }
-  /*else*/ {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":232
- *                     info.shape[i] = PyArray_DIMS(self)[i]
- *             else:
- *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)             # <<<<<<<<<<<<<<
- *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)
- *             info.suboffsets = NULL
- */
-    __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(__pyx_v_self));
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":233
- *             else:
- *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
- *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)             # <<<<<<<<<<<<<<
- *             info.suboffsets = NULL
- *             info.itemsize = PyArray_ITEMSIZE(self)
- */
-    __pyx_v_info->shape = ((Py_ssize_t *)PyArray_DIMS(__pyx_v_self));
-  }
-  __pyx_L7:;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":234
- *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)
- *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)
- *             info.suboffsets = NULL             # <<<<<<<<<<<<<<
- *             info.itemsize = PyArray_ITEMSIZE(self)
- *             info.readonly = not PyArray_ISWRITEABLE(self)
- */
-  __pyx_v_info->suboffsets = NULL;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":235
- *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)
- *             info.suboffsets = NULL
- *             info.itemsize = PyArray_ITEMSIZE(self)             # <<<<<<<<<<<<<<
- *             info.readonly = not PyArray_ISWRITEABLE(self)
- * 
- */
-  __pyx_v_info->itemsize = PyArray_ITEMSIZE(__pyx_v_self);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":236
- *             info.suboffsets = NULL
- *             info.itemsize = PyArray_ITEMSIZE(self)
- *             info.readonly = not PyArray_ISWRITEABLE(self)             # <<<<<<<<<<<<<<
- * 
- *             cdef int t
- */
-  __pyx_v_info->readonly = (!(PyArray_ISWRITEABLE(__pyx_v_self) != 0));
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":239
- * 
- *             cdef int t
- *             cdef char* f = NULL             # <<<<<<<<<<<<<<
- *             cdef dtype descr = self.descr
- *             cdef list stack
- */
-  __pyx_v_f = NULL;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":240
- *             cdef int t
- *             cdef char* f = NULL
- *             cdef dtype descr = self.descr             # <<<<<<<<<<<<<<
- *             cdef list stack
- *             cdef int offset
- */
-  __pyx_t_4 = ((PyObject *)__pyx_v_self->descr);
-  __Pyx_INCREF(__pyx_t_4);
-  __pyx_v_descr = ((PyArray_Descr *)__pyx_t_4);
-  __pyx_t_4 = 0;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":244
- *             cdef int offset
- * 
- *             cdef bint hasfields = PyDataType_HASFIELDS(descr)             # <<<<<<<<<<<<<<
- * 
- *             if not hasfields and not copy_shape:
- */
-  __pyx_v_hasfields = PyDataType_HASFIELDS(__pyx_v_descr);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":246
- *             cdef bint hasfields = PyDataType_HASFIELDS(descr)
- * 
- *             if not hasfields and not copy_shape:             # <<<<<<<<<<<<<<
- *                 # do not call releasebuffer
- *                 info.obj = None
- */
-  __pyx_t_2 = ((!(__pyx_v_hasfields != 0)) != 0);
-  if (__pyx_t_2) {
-    __pyx_t_3 = ((!(__pyx_v_copy_shape != 0)) != 0);
-    __pyx_t_1 = __pyx_t_3;
-  } else {
-    __pyx_t_1 = __pyx_t_2;
-  }
-  if (__pyx_t_1) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":248
- *             if not hasfields and not copy_shape:
- *                 # do not call releasebuffer
- *                 info.obj = None             # <<<<<<<<<<<<<<
- *             else:
- *                 # need to call releasebuffer
- */
-    __Pyx_INCREF(Py_None);
-    __Pyx_GIVEREF(Py_None);
-    __Pyx_GOTREF(__pyx_v_info->obj);
-    __Pyx_DECREF(__pyx_v_info->obj);
-    __pyx_v_info->obj = Py_None;
-    goto __pyx_L10;
-  }
-  /*else*/ {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":251
- *             else:
- *                 # need to call releasebuffer
- *                 info.obj = self             # <<<<<<<<<<<<<<
- * 
- *             if not hasfields:
- */
-    __Pyx_INCREF(((PyObject *)__pyx_v_self));
-    __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
-    __Pyx_GOTREF(__pyx_v_info->obj);
-    __Pyx_DECREF(__pyx_v_info->obj);
-    __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
-  }
-  __pyx_L10:;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":253
- *                 info.obj = self
- * 
- *             if not hasfields:             # <<<<<<<<<<<<<<
- *                 t = descr.type_num
- *                 if ((descr.byteorder == c'>' and little_endian) or
- */
-  __pyx_t_1 = ((!(__pyx_v_hasfields != 0)) != 0);
-  if (__pyx_t_1) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":254
- * 
- *             if not hasfields:
- *                 t = descr.type_num             # <<<<<<<<<<<<<<
- *                 if ((descr.byteorder == c'>' and little_endian) or
- *                     (descr.byteorder == c'<' and not little_endian)):
- */
-    __pyx_t_5 = __pyx_v_descr->type_num;
-    __pyx_v_t = __pyx_t_5;
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":255
- *             if not hasfields:
- *                 t = descr.type_num
- *                 if ((descr.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<
- *                     (descr.byteorder == c'<' and not little_endian)):
- *                     raise ValueError(u"Non-native byte order not supported")
- */
-    __pyx_t_1 = ((__pyx_v_descr->byteorder == '>') != 0);
-    if (__pyx_t_1) {
-      __pyx_t_2 = (__pyx_v_little_endian != 0);
-    } else {
-      __pyx_t_2 = __pyx_t_1;
-    }
-    if (!__pyx_t_2) {
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":256
- *                 t = descr.type_num
- *                 if ((descr.byteorder == c'>' and little_endian) or
- *                     (descr.byteorder == c'<' and not little_endian)):             # <<<<<<<<<<<<<<
- *                     raise ValueError(u"Non-native byte order not supported")
- *                 if   t == NPY_BYTE:        f = "b"
- */
-      __pyx_t_1 = ((__pyx_v_descr->byteorder == '<') != 0);
-      if (__pyx_t_1) {
-        __pyx_t_3 = ((!(__pyx_v_little_endian != 0)) != 0);
-        __pyx_t_7 = __pyx_t_3;
-      } else {
-        __pyx_t_7 = __pyx_t_1;
-      }
-      __pyx_t_1 = __pyx_t_7;
-    } else {
-      __pyx_t_1 = __pyx_t_2;
-    }
-    if (__pyx_t_1) {
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":257
- *                 if ((descr.byteorder == c'>' and little_endian) or
- *                     (descr.byteorder == c'<' and not little_endian)):
- *                     raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
- *                 if   t == NPY_BYTE:        f = "b"
- *                 elif t == NPY_UBYTE:       f = "B"
- */
-      __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      {__pyx_filename = __pyx_f[1]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":274
- *                 elif t == NPY_CDOUBLE:     f = "Zd"
- *                 elif t == NPY_CLONGDOUBLE: f = "Zg"
- *                 elif t == NPY_OBJECT:      f = "O"             # <<<<<<<<<<<<<<
- *                 else:
- *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
- */
-    switch (__pyx_v_t) {
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":258
- *                     (descr.byteorder == c'<' and not little_endian)):
- *                     raise ValueError(u"Non-native byte order not supported")
- *                 if   t == NPY_BYTE:        f = "b"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_UBYTE:       f = "B"
- *                 elif t == NPY_SHORT:       f = "h"
- */
-      case NPY_BYTE:
-      __pyx_v_f = __pyx_k_b;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":259
- *                     raise ValueError(u"Non-native byte order not supported")
- *                 if   t == NPY_BYTE:        f = "b"
- *                 elif t == NPY_UBYTE:       f = "B"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_SHORT:       f = "h"
- *                 elif t == NPY_USHORT:      f = "H"
- */
-      case NPY_UBYTE:
-      __pyx_v_f = __pyx_k_B;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":260
- *                 if   t == NPY_BYTE:        f = "b"
- *                 elif t == NPY_UBYTE:       f = "B"
- *                 elif t == NPY_SHORT:       f = "h"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_USHORT:      f = "H"
- *                 elif t == NPY_INT:         f = "i"
- */
-      case NPY_SHORT:
-      __pyx_v_f = __pyx_k_h;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":261
- *                 elif t == NPY_UBYTE:       f = "B"
- *                 elif t == NPY_SHORT:       f = "h"
- *                 elif t == NPY_USHORT:      f = "H"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_INT:         f = "i"
- *                 elif t == NPY_UINT:        f = "I"
- */
-      case NPY_USHORT:
-      __pyx_v_f = __pyx_k_H;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":262
- *                 elif t == NPY_SHORT:       f = "h"
- *                 elif t == NPY_USHORT:      f = "H"
- *                 elif t == NPY_INT:         f = "i"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_UINT:        f = "I"
- *                 elif t == NPY_LONG:        f = "l"
- */
-      case NPY_INT:
-      __pyx_v_f = __pyx_k_i;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":263
- *                 elif t == NPY_USHORT:      f = "H"
- *                 elif t == NPY_INT:         f = "i"
- *                 elif t == NPY_UINT:        f = "I"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_LONG:        f = "l"
- *                 elif t == NPY_ULONG:       f = "L"
- */
-      case NPY_UINT:
-      __pyx_v_f = __pyx_k_I;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":264
- *                 elif t == NPY_INT:         f = "i"
- *                 elif t == NPY_UINT:        f = "I"
- *                 elif t == NPY_LONG:        f = "l"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_ULONG:       f = "L"
- *                 elif t == NPY_LONGLONG:    f = "q"
- */
-      case NPY_LONG:
-      __pyx_v_f = __pyx_k_l;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":265
- *                 elif t == NPY_UINT:        f = "I"
- *                 elif t == NPY_LONG:        f = "l"
- *                 elif t == NPY_ULONG:       f = "L"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_LONGLONG:    f = "q"
- *                 elif t == NPY_ULONGLONG:   f = "Q"
- */
-      case NPY_ULONG:
-      __pyx_v_f = __pyx_k_L;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":266
- *                 elif t == NPY_LONG:        f = "l"
- *                 elif t == NPY_ULONG:       f = "L"
- *                 elif t == NPY_LONGLONG:    f = "q"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_ULONGLONG:   f = "Q"
- *                 elif t == NPY_FLOAT:       f = "f"
- */
-      case NPY_LONGLONG:
-      __pyx_v_f = __pyx_k_q;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":267
- *                 elif t == NPY_ULONG:       f = "L"
- *                 elif t == NPY_LONGLONG:    f = "q"
- *                 elif t == NPY_ULONGLONG:   f = "Q"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_FLOAT:       f = "f"
- *                 elif t == NPY_DOUBLE:      f = "d"
- */
-      case NPY_ULONGLONG:
-      __pyx_v_f = __pyx_k_Q;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":268
- *                 elif t == NPY_LONGLONG:    f = "q"
- *                 elif t == NPY_ULONGLONG:   f = "Q"
- *                 elif t == NPY_FLOAT:       f = "f"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_DOUBLE:      f = "d"
- *                 elif t == NPY_LONGDOUBLE:  f = "g"
- */
-      case NPY_FLOAT:
-      __pyx_v_f = __pyx_k_f;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":269
- *                 elif t == NPY_ULONGLONG:   f = "Q"
- *                 elif t == NPY_FLOAT:       f = "f"
- *                 elif t == NPY_DOUBLE:      f = "d"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_LONGDOUBLE:  f = "g"
- *                 elif t == NPY_CFLOAT:      f = "Zf"
- */
-      case NPY_DOUBLE:
-      __pyx_v_f = __pyx_k_d;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":270
- *                 elif t == NPY_FLOAT:       f = "f"
- *                 elif t == NPY_DOUBLE:      f = "d"
- *                 elif t == NPY_LONGDOUBLE:  f = "g"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_CFLOAT:      f = "Zf"
- *                 elif t == NPY_CDOUBLE:     f = "Zd"
- */
-      case NPY_LONGDOUBLE:
-      __pyx_v_f = __pyx_k_g;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":271
- *                 elif t == NPY_DOUBLE:      f = "d"
- *                 elif t == NPY_LONGDOUBLE:  f = "g"
- *                 elif t == NPY_CFLOAT:      f = "Zf"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_CDOUBLE:     f = "Zd"
- *                 elif t == NPY_CLONGDOUBLE: f = "Zg"
- */
-      case NPY_CFLOAT:
-      __pyx_v_f = __pyx_k_Zf;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":272
- *                 elif t == NPY_LONGDOUBLE:  f = "g"
- *                 elif t == NPY_CFLOAT:      f = "Zf"
- *                 elif t == NPY_CDOUBLE:     f = "Zd"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_CLONGDOUBLE: f = "Zg"
- *                 elif t == NPY_OBJECT:      f = "O"
- */
-      case NPY_CDOUBLE:
-      __pyx_v_f = __pyx_k_Zd;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":273
- *                 elif t == NPY_CFLOAT:      f = "Zf"
- *                 elif t == NPY_CDOUBLE:     f = "Zd"
- *                 elif t == NPY_CLONGDOUBLE: f = "Zg"             # <<<<<<<<<<<<<<
- *                 elif t == NPY_OBJECT:      f = "O"
- *                 else:
- */
-      case NPY_CLONGDOUBLE:
-      __pyx_v_f = __pyx_k_Zg;
-      break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":274
- *                 elif t == NPY_CDOUBLE:     f = "Zd"
- *                 elif t == NPY_CLONGDOUBLE: f = "Zg"
- *                 elif t == NPY_OBJECT:      f = "O"             # <<<<<<<<<<<<<<
- *                 else:
- *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
- */
-      case NPY_OBJECT:
-      __pyx_v_f = __pyx_k_O;
-      break;
-      default:
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":276
- *                 elif t == NPY_OBJECT:      f = "O"
- *                 else:
- *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)             # <<<<<<<<<<<<<<
- *                 info.format = f
- *                 return
- */
-      __pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_t); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_8 = PyUnicode_Format(__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_t_4); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_8);
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_8);
-      __Pyx_GIVEREF(__pyx_t_8);
-      __pyx_t_8 = 0;
-      __pyx_t_8 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_8);
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __Pyx_Raise(__pyx_t_8, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-      {__pyx_filename = __pyx_f[1]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      break;
-    }
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":277
- *                 else:
- *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
- *                 info.format = f             # <<<<<<<<<<<<<<
- *                 return
- *             else:
- */
-    __pyx_v_info->format = __pyx_v_f;
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":278
- *                     raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
- *                 info.format = f
- *                 return             # <<<<<<<<<<<<<<
- *             else:
- *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
- */
-    __pyx_r = 0;
-    goto __pyx_L0;
-  }
-  /*else*/ {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":280
- *                 return
- *             else:
- *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)             # <<<<<<<<<<<<<<
- *                 info.format[0] = c'^' # Native data types, manual alignment
- *                 offset = 0
- */
-    __pyx_v_info->format = ((char *)malloc(255));
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":281
- *             else:
- *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
- *                 info.format[0] = c'^' # Native data types, manual alignment             # <<<<<<<<<<<<<<
- *                 offset = 0
- *                 f = _util_dtypestring(descr, info.format + 1,
- */
-    (__pyx_v_info->format[0]) = '^';
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":282
- *                 info.format = <char*>stdlib.malloc(_buffer_format_string_len)
- *                 info.format[0] = c'^' # Native data types, manual alignment
- *                 offset = 0             # <<<<<<<<<<<<<<
- *                 f = _util_dtypestring(descr, info.format + 1,
- *                                       info.format + _buffer_format_string_len,
- */
-    __pyx_v_offset = 0;
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":283
- *                 info.format[0] = c'^' # Native data types, manual alignment
- *                 offset = 0
- *                 f = _util_dtypestring(descr, info.format + 1,             # <<<<<<<<<<<<<<
- *                                       info.format + _buffer_format_string_len,
- *                                       &offset)
- */
-    __pyx_t_9 = __pyx_f_5numpy__util_dtypestring(__pyx_v_descr, (__pyx_v_info->format + 1), (__pyx_v_info->format + 255), (&__pyx_v_offset)); if (unlikely(__pyx_t_9 == NULL)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_v_f = __pyx_t_9;
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":286
- *                                       info.format + _buffer_format_string_len,
- *                                       &offset)
- *                 f[0] = c'\0' # Terminate format string             # <<<<<<<<<<<<<<
- * 
- *         def __releasebuffer__(ndarray self, Py_buffer* info):
- */
-    (__pyx_v_f[0]) = '\x00';
-  }
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":194
- *         # experimental exception made for __getbuffer__ and __releasebuffer__
- *         # -- the details of this may change.
- *         def __getbuffer__(ndarray self, Py_buffer* info, int flags):             # <<<<<<<<<<<<<<
- *             # This implementation of getbuffer is geared towards Cython
- *             # requirements, and does not yet fullfill the PEP.
- */
-
-  /* function exit code */
-  __pyx_r = 0;
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_XDECREF(__pyx_t_8);
-  __Pyx_AddTraceback("numpy.ndarray.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  if (__pyx_v_info != NULL && __pyx_v_info->obj != NULL) {
-    __Pyx_GOTREF(__pyx_v_info->obj);
-    __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = NULL;
-  }
-  goto __pyx_L2;
-  __pyx_L0:;
-  if (__pyx_v_info != NULL && __pyx_v_info->obj == Py_None) {
-    __Pyx_GOTREF(Py_None);
-    __Pyx_DECREF(Py_None); __pyx_v_info->obj = NULL;
-  }
-  __pyx_L2:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_descr);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":288
- *                 f[0] = c'\0' # Terminate format string
- * 
- *         def __releasebuffer__(ndarray self, Py_buffer* info):             # <<<<<<<<<<<<<<
- *             if PyArray_HASFIELDS(self):
- *                 stdlib.free(info.format)
- */
-
-/* Python wrapper */
-static CYTHON_UNUSED void __pyx_pw_5numpy_7ndarray_3__releasebuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info); /*proto*/
-static CYTHON_UNUSED void __pyx_pw_5numpy_7ndarray_3__releasebuffer__(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info) {
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__releasebuffer__ (wrapper)", 0);
-  __pyx_pf_5numpy_7ndarray_2__releasebuffer__(((PyArrayObject *)__pyx_v_self), ((Py_buffer *)__pyx_v_info));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info) {
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  __Pyx_RefNannySetupContext("__releasebuffer__", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":289
- * 
- *         def __releasebuffer__(ndarray self, Py_buffer* info):
- *             if PyArray_HASFIELDS(self):             # <<<<<<<<<<<<<<
- *                 stdlib.free(info.format)
- *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
- */
-  __pyx_t_1 = (PyArray_HASFIELDS(__pyx_v_self) != 0);
-  if (__pyx_t_1) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":290
- *         def __releasebuffer__(ndarray self, Py_buffer* info):
- *             if PyArray_HASFIELDS(self):
- *                 stdlib.free(info.format)             # <<<<<<<<<<<<<<
- *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
- *                 stdlib.free(info.strides)
- */
-    free(__pyx_v_info->format);
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":291
- *             if PyArray_HASFIELDS(self):
- *                 stdlib.free(info.format)
- *             if sizeof(npy_intp) != sizeof(Py_ssize_t):             # <<<<<<<<<<<<<<
- *                 stdlib.free(info.strides)
- *                 # info.shape was stored after info.strides in the same block
- */
-  __pyx_t_1 = (((sizeof(npy_intp)) != (sizeof(Py_ssize_t))) != 0);
-  if (__pyx_t_1) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":292
- *                 stdlib.free(info.format)
- *             if sizeof(npy_intp) != sizeof(Py_ssize_t):
- *                 stdlib.free(info.strides)             # <<<<<<<<<<<<<<
- *                 # info.shape was stored after info.strides in the same block
- * 
- */
-    free(__pyx_v_info->strides);
-    goto __pyx_L4;
-  }
-  __pyx_L4:;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":288
- *                 f[0] = c'\0' # Terminate format string
- * 
- *         def __releasebuffer__(ndarray self, Py_buffer* info):             # <<<<<<<<<<<<<<
- *             if PyArray_HASFIELDS(self):
- *                 stdlib.free(info.format)
- */
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":768
- * ctypedef npy_cdouble     complex_t
- * 
- * cdef inline object PyArray_MultiIterNew1(a):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(1, <void*>a)
- * 
- */
-
-static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew1(PyObject *__pyx_v_a) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("PyArray_MultiIterNew1", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":769
- * 
- * cdef inline object PyArray_MultiIterNew1(a):
- *     return PyArray_MultiIterNew(1, <void*>a)             # <<<<<<<<<<<<<<
- * 
- * cdef inline object PyArray_MultiIterNew2(a, b):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(1, ((void *)__pyx_v_a)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 769; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":768
- * ctypedef npy_cdouble     complex_t
- * 
- * cdef inline object PyArray_MultiIterNew1(a):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(1, <void*>a)
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew1", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":771
- *     return PyArray_MultiIterNew(1, <void*>a)
- * 
- * cdef inline object PyArray_MultiIterNew2(a, b):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
- * 
- */
-
-static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew2(PyObject *__pyx_v_a, PyObject *__pyx_v_b) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("PyArray_MultiIterNew2", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":772
- * 
- * cdef inline object PyArray_MultiIterNew2(a, b):
- *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)             # <<<<<<<<<<<<<<
- * 
- * cdef inline object PyArray_MultiIterNew3(a, b, c):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(2, ((void *)__pyx_v_a), ((void *)__pyx_v_b)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 772; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":771
- *     return PyArray_MultiIterNew(1, <void*>a)
- * 
- * cdef inline object PyArray_MultiIterNew2(a, b):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew2", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":774
- *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
- * 
- * cdef inline object PyArray_MultiIterNew3(a, b, c):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
- * 
- */
-
-static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew3(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("PyArray_MultiIterNew3", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":775
- * 
- * cdef inline object PyArray_MultiIterNew3(a, b, c):
- *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)             # <<<<<<<<<<<<<<
- * 
- * cdef inline object PyArray_MultiIterNew4(a, b, c, d):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(3, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 775; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":774
- *     return PyArray_MultiIterNew(2, <void*>a, <void*>b)
- * 
- * cdef inline object PyArray_MultiIterNew3(a, b, c):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew3", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":777
- *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
- * 
- * cdef inline object PyArray_MultiIterNew4(a, b, c, d):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
- * 
- */
-
-static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew4(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("PyArray_MultiIterNew4", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":778
- * 
- * cdef inline object PyArray_MultiIterNew4(a, b, c, d):
- *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)             # <<<<<<<<<<<<<<
- * 
- * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(4, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 778; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":777
- *     return PyArray_MultiIterNew(3, <void*>a, <void*>b, <void*> c)
- * 
- * cdef inline object PyArray_MultiIterNew4(a, b, c, d):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew4", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":780
- *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
- * 
- * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
- * 
- */
-
-static CYTHON_INLINE PyObject *__pyx_f_5numpy_PyArray_MultiIterNew5(PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_c, PyObject *__pyx_v_d, PyObject *__pyx_v_e) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("PyArray_MultiIterNew5", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":781
- * 
- * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
- *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)             # <<<<<<<<<<<<<<
- * 
- * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyArray_MultiIterNew(5, ((void *)__pyx_v_a), ((void *)__pyx_v_b), ((void *)__pyx_v_c), ((void *)__pyx_v_d), ((void *)__pyx_v_e)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 781; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":780
- *     return PyArray_MultiIterNew(4, <void*>a, <void*>b, <void*>c, <void*> d)
- * 
- * cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):             # <<<<<<<<<<<<<<
- *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("numpy.PyArray_MultiIterNew5", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":783
- *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
- * 
- * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL:             # <<<<<<<<<<<<<<
- *     # Recursive utility function used in __getbuffer__ to get format
- *     # string. The new location in the format string is returned.
- */
-
-static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *__pyx_v_descr, char *__pyx_v_f, char *__pyx_v_end, int *__pyx_v_offset) {
-  PyArray_Descr *__pyx_v_child = 0;
-  int __pyx_v_endian_detector;
-  int __pyx_v_little_endian;
-  PyObject *__pyx_v_fields = 0;
-  PyObject *__pyx_v_childname = NULL;
-  PyObject *__pyx_v_new_offset = NULL;
-  PyObject *__pyx_v_t = NULL;
-  char *__pyx_r;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  Py_ssize_t __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  int __pyx_t_5;
-  int __pyx_t_6;
-  int __pyx_t_7;
-  int __pyx_t_8;
-  int __pyx_t_9;
-  long __pyx_t_10;
-  char *__pyx_t_11;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("_util_dtypestring", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":790
- *     cdef int delta_offset
- *     cdef tuple i
- *     cdef int endian_detector = 1             # <<<<<<<<<<<<<<
- *     cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)
- *     cdef tuple fields
- */
-  __pyx_v_endian_detector = 1;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":791
- *     cdef tuple i
- *     cdef int endian_detector = 1
- *     cdef bint little_endian = ((<char*>&endian_detector)[0] != 0)             # <<<<<<<<<<<<<<
- *     cdef tuple fields
- * 
- */
-  __pyx_v_little_endian = ((((char *)(&__pyx_v_endian_detector))[0]) != 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":794
- *     cdef tuple fields
- * 
- *     for childname in descr.names:             # <<<<<<<<<<<<<<
- *         fields = descr.fields[childname]
- *         child, new_offset = fields
- */
-  if (unlikely(__pyx_v_descr->names == Py_None)) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-    {__pyx_filename = __pyx_f[1]; __pyx_lineno = 794; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __pyx_t_1 = __pyx_v_descr->names; __Pyx_INCREF(__pyx_t_1); __pyx_t_2 = 0;
-  for (;;) {
-    if (__pyx_t_2 >= PyTuple_GET_SIZE(__pyx_t_1)) break;
-    #if CYTHON_COMPILING_IN_CPYTHON
-    __pyx_t_3 = PyTuple_GET_ITEM(__pyx_t_1, __pyx_t_2); __Pyx_INCREF(__pyx_t_3); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 794; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    #else
-    __pyx_t_3 = PySequence_ITEM(__pyx_t_1, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 794; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    #endif
-    __Pyx_XDECREF_SET(__pyx_v_childname, __pyx_t_3);
-    __pyx_t_3 = 0;
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":795
- * 
- *     for childname in descr.names:
- *         fields = descr.fields[childname]             # <<<<<<<<<<<<<<
- *         child, new_offset = fields
- * 
- */
-    __pyx_t_3 = PyObject_GetItem(__pyx_v_descr->fields, __pyx_v_childname); if (unlikely(__pyx_t_3 == NULL)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 795; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-    __Pyx_GOTREF(__pyx_t_3);
-    if (!(likely(PyTuple_CheckExact(__pyx_t_3))||((__pyx_t_3) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "tuple", Py_TYPE(__pyx_t_3)->tp_name), 0))) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 795; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_XDECREF_SET(__pyx_v_fields, ((PyObject*)__pyx_t_3));
-    __pyx_t_3 = 0;
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":796
- *     for childname in descr.names:
- *         fields = descr.fields[childname]
- *         child, new_offset = fields             # <<<<<<<<<<<<<<
- * 
- *         if (end - f) - <int>(new_offset - offset[0]) < 15:
- */
-    if (likely(__pyx_v_fields != Py_None)) {
-      PyObject* sequence = __pyx_v_fields;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      Py_ssize_t size = Py_SIZE(sequence);
-      #else
-      Py_ssize_t size = PySequence_Size(sequence);
-      #endif
-      if (unlikely(size != 2)) {
-        if (size > 2) __Pyx_RaiseTooManyValuesError(2);
-        else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
-        {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); 
-      __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); 
-      __Pyx_INCREF(__pyx_t_3);
-      __Pyx_INCREF(__pyx_t_4);
-      #else
-      __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      #endif
-    } else {
-      __Pyx_RaiseNoneNotIterableError(); {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_ptype_5numpy_dtype))))) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 796; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_XDECREF_SET(__pyx_v_child, ((PyArray_Descr *)__pyx_t_3));
-    __pyx_t_3 = 0;
-    __Pyx_XDECREF_SET(__pyx_v_new_offset, __pyx_t_4);
-    __pyx_t_4 = 0;
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":798
- *         child, new_offset = fields
- * 
- *         if (end - f) - <int>(new_offset - offset[0]) < 15:             # <<<<<<<<<<<<<<
- *             raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd")
- * 
- */
-    __pyx_t_4 = __Pyx_PyInt_From_int((__pyx_v_offset[0])); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 798; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_3 = PyNumber_Subtract(__pyx_v_new_offset, __pyx_t_4); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 798; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    __pyx_t_5 = __Pyx_PyInt_As_int(__pyx_t_3); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 798; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    __pyx_t_6 = ((((__pyx_v_end - __pyx_v_f) - ((int)__pyx_t_5)) < 15) != 0);
-    if (__pyx_t_6) {
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":799
- * 
- *         if (end - f) - <int>(new_offset - offset[0]) < 15:
- *             raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd")             # <<<<<<<<<<<<<<
- * 
- *         if ((child.byteorder == c'>' and little_endian) or
- */
-      __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_tuple__4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      {__pyx_filename = __pyx_f[1]; __pyx_lineno = 799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":801
- *             raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd")
- * 
- *         if ((child.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<
- *             (child.byteorder == c'<' and not little_endian)):
- *             raise ValueError(u"Non-native byte order not supported")
- */
-    __pyx_t_6 = ((__pyx_v_child->byteorder == '>') != 0);
-    if (__pyx_t_6) {
-      __pyx_t_7 = (__pyx_v_little_endian != 0);
-    } else {
-      __pyx_t_7 = __pyx_t_6;
-    }
-    if (!__pyx_t_7) {
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":802
- * 
- *         if ((child.byteorder == c'>' and little_endian) or
- *             (child.byteorder == c'<' and not little_endian)):             # <<<<<<<<<<<<<<
- *             raise ValueError(u"Non-native byte order not supported")
- *             # One could encode it in the format string and have Cython
- */
-      __pyx_t_6 = ((__pyx_v_child->byteorder == '<') != 0);
-      if (__pyx_t_6) {
-        __pyx_t_8 = ((!(__pyx_v_little_endian != 0)) != 0);
-        __pyx_t_9 = __pyx_t_8;
-      } else {
-        __pyx_t_9 = __pyx_t_6;
-      }
-      __pyx_t_6 = __pyx_t_9;
-    } else {
-      __pyx_t_6 = __pyx_t_7;
-    }
-    if (__pyx_t_6) {
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":803
- *         if ((child.byteorder == c'>' and little_endian) or
- *             (child.byteorder == c'<' and not little_endian)):
- *             raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
- *             # One could encode it in the format string and have Cython
- *             # complain instead, BUT: < and > in format strings also imply
- */
-      __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__5, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      {__pyx_filename = __pyx_f[1]; __pyx_lineno = 803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":813
- * 
- *         # Output padding bytes
- *         while offset[0] < new_offset:             # <<<<<<<<<<<<<<
- *             f[0] = 120 # "x"; pad byte
- *             f += 1
- */
-    while (1) {
-      __pyx_t_3 = __Pyx_PyInt_From_int((__pyx_v_offset[0])); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 813; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_t_3, __pyx_v_new_offset, Py_LT); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 813; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 813; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (!__pyx_t_6) break;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":814
- *         # Output padding bytes
- *         while offset[0] < new_offset:
- *             f[0] = 120 # "x"; pad byte             # <<<<<<<<<<<<<<
- *             f += 1
- *             offset[0] += 1
- */
-      (__pyx_v_f[0]) = 120;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":815
- *         while offset[0] < new_offset:
- *             f[0] = 120 # "x"; pad byte
- *             f += 1             # <<<<<<<<<<<<<<
- *             offset[0] += 1
- * 
- */
-      __pyx_v_f = (__pyx_v_f + 1);
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":816
- *             f[0] = 120 # "x"; pad byte
- *             f += 1
- *             offset[0] += 1             # <<<<<<<<<<<<<<
- * 
- *         offset[0] += child.itemsize
- */
-      __pyx_t_10 = 0;
-      (__pyx_v_offset[__pyx_t_10]) = ((__pyx_v_offset[__pyx_t_10]) + 1);
-    }
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":818
- *             offset[0] += 1
- * 
- *         offset[0] += child.itemsize             # <<<<<<<<<<<<<<
- * 
- *         if not PyDataType_HASFIELDS(child):
- */
-    __pyx_t_10 = 0;
-    (__pyx_v_offset[__pyx_t_10]) = ((__pyx_v_offset[__pyx_t_10]) + __pyx_v_child->elsize);
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":820
- *         offset[0] += child.itemsize
- * 
- *         if not PyDataType_HASFIELDS(child):             # <<<<<<<<<<<<<<
- *             t = child.type_num
- *             if end - f < 5:
- */
-    __pyx_t_6 = ((!(PyDataType_HASFIELDS(__pyx_v_child) != 0)) != 0);
-    if (__pyx_t_6) {
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":821
- * 
- *         if not PyDataType_HASFIELDS(child):
- *             t = child.type_num             # <<<<<<<<<<<<<<
- *             if end - f < 5:
- *                 raise RuntimeError(u"Format string allocated too short.")
- */
-      __pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_child->type_num); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 821; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __Pyx_XDECREF_SET(__pyx_v_t, __pyx_t_4);
-      __pyx_t_4 = 0;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":822
- *         if not PyDataType_HASFIELDS(child):
- *             t = child.type_num
- *             if end - f < 5:             # <<<<<<<<<<<<<<
- *                 raise RuntimeError(u"Format string allocated too short.")
- * 
- */
-      __pyx_t_6 = (((__pyx_v_end - __pyx_v_f) < 5) != 0);
-      if (__pyx_t_6) {
-
-        /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":823
- *             t = child.type_num
- *             if end - f < 5:
- *                 raise RuntimeError(u"Format string allocated too short.")             # <<<<<<<<<<<<<<
- * 
- *             # Until ticket #99 is fixed, use integers to avoid warnings
- */
-        __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_RuntimeError, __pyx_tuple__6, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_4);
-        __Pyx_Raise(__pyx_t_4, 0, 0, 0);
-        __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-        {__pyx_filename = __pyx_f[1]; __pyx_lineno = 823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":826
- * 
- *             # Until ticket #99 is fixed, use integers to avoid warnings
- *             if   t == NPY_BYTE:        f[0] =  98 #"b"             # <<<<<<<<<<<<<<
- *             elif t == NPY_UBYTE:       f[0] =  66 #"B"
- *             elif t == NPY_SHORT:       f[0] = 104 #"h"
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_BYTE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 826; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 826; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 826; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 98;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":827
- *             # Until ticket #99 is fixed, use integers to avoid warnings
- *             if   t == NPY_BYTE:        f[0] =  98 #"b"
- *             elif t == NPY_UBYTE:       f[0] =  66 #"B"             # <<<<<<<<<<<<<<
- *             elif t == NPY_SHORT:       f[0] = 104 #"h"
- *             elif t == NPY_USHORT:      f[0] =  72 #"H"
- */
-      __pyx_t_3 = PyInt_FromLong(NPY_UBYTE); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 827; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 827; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 827; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 66;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":828
- *             if   t == NPY_BYTE:        f[0] =  98 #"b"
- *             elif t == NPY_UBYTE:       f[0] =  66 #"B"
- *             elif t == NPY_SHORT:       f[0] = 104 #"h"             # <<<<<<<<<<<<<<
- *             elif t == NPY_USHORT:      f[0] =  72 #"H"
- *             elif t == NPY_INT:         f[0] = 105 #"i"
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_SHORT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 828; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 828; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 828; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 104;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":829
- *             elif t == NPY_UBYTE:       f[0] =  66 #"B"
- *             elif t == NPY_SHORT:       f[0] = 104 #"h"
- *             elif t == NPY_USHORT:      f[0] =  72 #"H"             # <<<<<<<<<<<<<<
- *             elif t == NPY_INT:         f[0] = 105 #"i"
- *             elif t == NPY_UINT:        f[0] =  73 #"I"
- */
-      __pyx_t_3 = PyInt_FromLong(NPY_USHORT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 829; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 829; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 829; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 72;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":830
- *             elif t == NPY_SHORT:       f[0] = 104 #"h"
- *             elif t == NPY_USHORT:      f[0] =  72 #"H"
- *             elif t == NPY_INT:         f[0] = 105 #"i"             # <<<<<<<<<<<<<<
- *             elif t == NPY_UINT:        f[0] =  73 #"I"
- *             elif t == NPY_LONG:        f[0] = 108 #"l"
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_INT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 830; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 830; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 830; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 105;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":831
- *             elif t == NPY_USHORT:      f[0] =  72 #"H"
- *             elif t == NPY_INT:         f[0] = 105 #"i"
- *             elif t == NPY_UINT:        f[0] =  73 #"I"             # <<<<<<<<<<<<<<
- *             elif t == NPY_LONG:        f[0] = 108 #"l"
- *             elif t == NPY_ULONG:       f[0] = 76  #"L"
- */
-      __pyx_t_3 = PyInt_FromLong(NPY_UINT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 831; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 831; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 831; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 73;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":832
- *             elif t == NPY_INT:         f[0] = 105 #"i"
- *             elif t == NPY_UINT:        f[0] =  73 #"I"
- *             elif t == NPY_LONG:        f[0] = 108 #"l"             # <<<<<<<<<<<<<<
- *             elif t == NPY_ULONG:       f[0] = 76  #"L"
- *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_LONG); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 832; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 832; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 832; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 108;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":833
- *             elif t == NPY_UINT:        f[0] =  73 #"I"
- *             elif t == NPY_LONG:        f[0] = 108 #"l"
- *             elif t == NPY_ULONG:       f[0] = 76  #"L"             # <<<<<<<<<<<<<<
- *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"
- *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"
- */
-      __pyx_t_3 = PyInt_FromLong(NPY_ULONG); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 833; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 833; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 833; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 76;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":834
- *             elif t == NPY_LONG:        f[0] = 108 #"l"
- *             elif t == NPY_ULONG:       f[0] = 76  #"L"
- *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"             # <<<<<<<<<<<<<<
- *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"
- *             elif t == NPY_FLOAT:       f[0] = 102 #"f"
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_LONGLONG); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 113;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":835
- *             elif t == NPY_ULONG:       f[0] = 76  #"L"
- *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"
- *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"             # <<<<<<<<<<<<<<
- *             elif t == NPY_FLOAT:       f[0] = 102 #"f"
- *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"
- */
-      __pyx_t_3 = PyInt_FromLong(NPY_ULONGLONG); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 835; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 835; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 835; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 81;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":836
- *             elif t == NPY_LONGLONG:    f[0] = 113 #"q"
- *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"
- *             elif t == NPY_FLOAT:       f[0] = 102 #"f"             # <<<<<<<<<<<<<<
- *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"
- *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_FLOAT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 102;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":837
- *             elif t == NPY_ULONGLONG:   f[0] = 81  #"Q"
- *             elif t == NPY_FLOAT:       f[0] = 102 #"f"
- *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"             # <<<<<<<<<<<<<<
- *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"
- *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf
- */
-      __pyx_t_3 = PyInt_FromLong(NPY_DOUBLE); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 837; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 837; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 837; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 100;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":838
- *             elif t == NPY_FLOAT:       f[0] = 102 #"f"
- *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"
- *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"             # <<<<<<<<<<<<<<
- *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf
- *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_LONGDOUBLE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 838; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 838; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 838; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 103;
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":839
- *             elif t == NPY_DOUBLE:      f[0] = 100 #"d"
- *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"
- *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf             # <<<<<<<<<<<<<<
- *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd
- *             elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg
- */
-      __pyx_t_3 = PyInt_FromLong(NPY_CFLOAT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 839; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 839; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 839; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 90;
-        (__pyx_v_f[1]) = 102;
-        __pyx_v_f = (__pyx_v_f + 1);
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":840
- *             elif t == NPY_LONGDOUBLE:  f[0] = 103 #"g"
- *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf
- *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd             # <<<<<<<<<<<<<<
- *             elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg
- *             elif t == NPY_OBJECT:      f[0] = 79 #"O"
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_CDOUBLE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 840; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 90;
-        (__pyx_v_f[1]) = 100;
-        __pyx_v_f = (__pyx_v_f + 1);
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":841
- *             elif t == NPY_CFLOAT:      f[0] = 90; f[1] = 102; f += 1 # Zf
- *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd
- *             elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg             # <<<<<<<<<<<<<<
- *             elif t == NPY_OBJECT:      f[0] = 79 #"O"
- *             else:
- */
-      __pyx_t_3 = PyInt_FromLong(NPY_CLONGDOUBLE); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = PyObject_RichCompare(__pyx_v_t, __pyx_t_3, Py_EQ); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 841; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 90;
-        (__pyx_v_f[1]) = 103;
-        __pyx_v_f = (__pyx_v_f + 1);
-        goto __pyx_L11;
-      }
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":842
- *             elif t == NPY_CDOUBLE:     f[0] = 90; f[1] = 100; f += 1 # Zd
- *             elif t == NPY_CLONGDOUBLE: f[0] = 90; f[1] = 103; f += 1 # Zg
- *             elif t == NPY_OBJECT:      f[0] = 79 #"O"             # <<<<<<<<<<<<<<
- *             else:
- *                 raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
- */
-      __pyx_t_4 = PyInt_FromLong(NPY_OBJECT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 842; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_3 = PyObject_RichCompare(__pyx_v_t, __pyx_t_4, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 842; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __pyx_t_6 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_6 < 0)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 842; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      if (__pyx_t_6) {
-        (__pyx_v_f[0]) = 79;
-        goto __pyx_L11;
-      }
-      /*else*/ {
-
-        /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":844
- *             elif t == NPY_OBJECT:      f[0] = 79 #"O"
- *             else:
- *                 raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)             # <<<<<<<<<<<<<<
- *             f += 1
- *         else:
- */
-        __pyx_t_3 = PyUnicode_Format(__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_v_t); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_3);
-        __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_4);
-        PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
-        __Pyx_GIVEREF(__pyx_t_3);
-        __pyx_t_3 = 0;
-        __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_3);
-        __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-        __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-        __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-        {__pyx_filename = __pyx_f[1]; __pyx_lineno = 844; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-      __pyx_L11:;
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":845
- *             else:
- *                 raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t)
- *             f += 1             # <<<<<<<<<<<<<<
- *         else:
- *             # Cython ignores struct boundary information ("T{...}"),
- */
-      __pyx_v_f = (__pyx_v_f + 1);
-      goto __pyx_L9;
-    }
-    /*else*/ {
-
-      /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":849
- *             # Cython ignores struct boundary information ("T{...}"),
- *             # so don't output it
- *             f = _util_dtypestring(child, f, end, offset)             # <<<<<<<<<<<<<<
- *     return f
- * 
- */
-      __pyx_t_11 = __pyx_f_5numpy__util_dtypestring(__pyx_v_child, __pyx_v_f, __pyx_v_end, __pyx_v_offset); if (unlikely(__pyx_t_11 == NULL)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 849; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __pyx_v_f = __pyx_t_11;
-    }
-    __pyx_L9:;
-  }
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":850
- *             # so don't output it
- *             f = _util_dtypestring(child, f, end, offset)
- *     return f             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_r = __pyx_v_f;
-  goto __pyx_L0;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":783
- *     return PyArray_MultiIterNew(5, <void*>a, <void*>b, <void*>c, <void*> d, <void*> e)
- * 
- * cdef inline char* _util_dtypestring(dtype descr, char* f, char* end, int* offset) except NULL:             # <<<<<<<<<<<<<<
- *     # Recursive utility function used in __getbuffer__ to get format
- *     # string. The new location in the format string is returned.
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_AddTraceback("numpy._util_dtypestring", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_child);
-  __Pyx_XDECREF(__pyx_v_fields);
-  __Pyx_XDECREF(__pyx_v_childname);
-  __Pyx_XDECREF(__pyx_v_new_offset);
-  __Pyx_XDECREF(__pyx_v_t);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":966
- * 
- * 
- * cdef inline void set_array_base(ndarray arr, object base):             # <<<<<<<<<<<<<<
- *      cdef PyObject* baseptr
- *      if base is None:
- */
-
-static CYTHON_INLINE void __pyx_f_5numpy_set_array_base(PyArrayObject *__pyx_v_arr, PyObject *__pyx_v_base) {
-  PyObject *__pyx_v_baseptr;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  __Pyx_RefNannySetupContext("set_array_base", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":968
- * cdef inline void set_array_base(ndarray arr, object base):
- *      cdef PyObject* baseptr
- *      if base is None:             # <<<<<<<<<<<<<<
- *          baseptr = NULL
- *      else:
- */
-  __pyx_t_1 = (__pyx_v_base == Py_None);
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":969
- *      cdef PyObject* baseptr
- *      if base is None:
- *          baseptr = NULL             # <<<<<<<<<<<<<<
- *      else:
- *          Py_INCREF(base) # important to do this before decref below!
- */
-    __pyx_v_baseptr = NULL;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":971
- *          baseptr = NULL
- *      else:
- *          Py_INCREF(base) # important to do this before decref below!             # <<<<<<<<<<<<<<
- *          baseptr = <PyObject*>base
- *      Py_XDECREF(arr.base)
- */
-    Py_INCREF(__pyx_v_base);
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":972
- *      else:
- *          Py_INCREF(base) # important to do this before decref below!
- *          baseptr = <PyObject*>base             # <<<<<<<<<<<<<<
- *      Py_XDECREF(arr.base)
- *      arr.base = baseptr
- */
-    __pyx_v_baseptr = ((PyObject *)__pyx_v_base);
-  }
-  __pyx_L3:;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":973
- *          Py_INCREF(base) # important to do this before decref below!
- *          baseptr = <PyObject*>base
- *      Py_XDECREF(arr.base)             # <<<<<<<<<<<<<<
- *      arr.base = baseptr
- * 
- */
-  Py_XDECREF(__pyx_v_arr->base);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":974
- *          baseptr = <PyObject*>base
- *      Py_XDECREF(arr.base)
- *      arr.base = baseptr             # <<<<<<<<<<<<<<
- * 
- * cdef inline object get_array_base(ndarray arr):
- */
-  __pyx_v_arr->base = __pyx_v_baseptr;
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":966
- * 
- * 
- * cdef inline void set_array_base(ndarray arr, object base):             # <<<<<<<<<<<<<<
- *      cdef PyObject* baseptr
- *      if base is None:
- */
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-/* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":976
- *      arr.base = baseptr
- * 
- * cdef inline object get_array_base(ndarray arr):             # <<<<<<<<<<<<<<
- *     if arr.base is NULL:
- *         return None
- */
-
-static CYTHON_INLINE PyObject *__pyx_f_5numpy_get_array_base(PyArrayObject *__pyx_v_arr) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  __Pyx_RefNannySetupContext("get_array_base", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":977
- * 
- * cdef inline object get_array_base(ndarray arr):
- *     if arr.base is NULL:             # <<<<<<<<<<<<<<
- *         return None
- *     else:
- */
-  __pyx_t_1 = ((__pyx_v_arr->base == NULL) != 0);
-  if (__pyx_t_1) {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":978
- * cdef inline object get_array_base(ndarray arr):
- *     if arr.base is NULL:
- *         return None             # <<<<<<<<<<<<<<
- *     else:
- *         return <object>arr.base
- */
-    __Pyx_XDECREF(__pyx_r);
-    __Pyx_INCREF(Py_None);
-    __pyx_r = Py_None;
-    goto __pyx_L0;
-  }
-  /*else*/ {
-
-    /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":980
- *         return None
- *     else:
- *         return <object>arr.base             # <<<<<<<<<<<<<<
- */
-    __Pyx_XDECREF(__pyx_r);
-    __Pyx_INCREF(((PyObject *)__pyx_v_arr->base));
-    __pyx_r = ((PyObject *)__pyx_v_arr->base);
-    goto __pyx_L0;
-  }
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":976
- *      arr.base = baseptr
- * 
- * cdef inline object get_array_base(ndarray arr):             # <<<<<<<<<<<<<<
- *     if arr.base is NULL:
- *         return None
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":116
- *         cdef bint dtype_is_object
- * 
- *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,             # <<<<<<<<<<<<<<
- *                   mode="c", bint allocate_buffer=True):
- * 
- */
-
-/* Python wrapper */
-static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static int __pyx_array___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
-  PyObject *__pyx_v_shape = 0;
-  Py_ssize_t __pyx_v_itemsize;
-  PyObject *__pyx_v_format = 0;
-  PyObject *__pyx_v_mode = 0;
-  int __pyx_v_allocate_buffer;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0);
-  {
-    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_shape,&__pyx_n_s_itemsize,&__pyx_n_s_format,&__pyx_n_s_mode,&__pyx_n_s_allocate_buffer,0};
-    PyObject* values[5] = {0,0,0,0,0};
-    values[3] = ((PyObject *)__pyx_n_s_c);
-    if (unlikely(__pyx_kwds)) {
-      Py_ssize_t kw_args;
-      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
-      switch (pos_args) {
-        case  5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4);
-        case  4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
-        case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
-        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
-        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
-        case  0: break;
-        default: goto __pyx_L5_argtuple_error;
-      }
-      kw_args = PyDict_Size(__pyx_kwds);
-      switch (pos_args) {
-        case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_shape)) != 0)) kw_args--;
-        else goto __pyx_L5_argtuple_error;
-        case  1:
-        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_itemsize)) != 0)) kw_args--;
-        else {
-          __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 1); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-        }
-        case  2:
-        if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_format)) != 0)) kw_args--;
-        else {
-          __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, 2); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-        }
-        case  3:
-        if (kw_args > 0) {
-          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s_mode);
-          if (value) { values[3] = value; kw_args--; }
-        }
-        case  4:
-        if (kw_args > 0) {
-          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s_allocate_buffer);
-          if (value) { values[4] = value; kw_args--; }
-        }
-      }
-      if (unlikely(kw_args > 0)) {
-        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-      }
-    } else {
-      switch (PyTuple_GET_SIZE(__pyx_args)) {
-        case  5: values[4] = PyTuple_GET_ITEM(__pyx_args, 4);
-        case  4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
-        case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
-        values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
-        values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
-        break;
-        default: goto __pyx_L5_argtuple_error;
-      }
-    }
-    __pyx_v_shape = ((PyObject*)values[0]);
-    __pyx_v_itemsize = __Pyx_PyIndex_AsSsize_t(values[1]); if (unlikely((__pyx_v_itemsize == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-    __pyx_v_format = values[2];
-    __pyx_v_mode = values[3];
-    if (values[4]) {
-      __pyx_v_allocate_buffer = __Pyx_PyObject_IsTrue(values[4]); if (unlikely((__pyx_v_allocate_buffer == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 117; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-    } else {
-
-      /* "View.MemoryView":117
- * 
- *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,
- *                   mode="c", bint allocate_buffer=True):             # <<<<<<<<<<<<<<
- * 
- *         cdef int idx
- */
-      __pyx_v_allocate_buffer = ((int)1);
-    }
-  }
-  goto __pyx_L4_argument_unpacking_done;
-  __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 3, 5, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-  __pyx_L3_error:;
-  __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __Pyx_RefNannyFinishContext();
-  return -1;
-  __pyx_L4_argument_unpacking_done:;
-  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_shape), (&PyTuple_Type), 1, "shape", 1))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (unlikely(((PyObject *)__pyx_v_format) == Py_None)) {
-    PyErr_Format(PyExc_TypeError, "Argument '%.200s' must not be None", "format"); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 116; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __pyx_r = __pyx_array_MemoryView_5array___cinit__(((struct __pyx_array_obj *)__pyx_v_self), __pyx_v_shape, __pyx_v_itemsize, __pyx_v_format, __pyx_v_mode, __pyx_v_allocate_buffer);
-
-  /* "View.MemoryView":116
- *         cdef bint dtype_is_object
- * 
- *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,             # <<<<<<<<<<<<<<
- *                   mode="c", bint allocate_buffer=True):
- * 
- */
-
-  /* function exit code */
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __pyx_r = -1;
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static int __pyx_array_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer) {
-  int __pyx_v_idx;
-  Py_ssize_t __pyx_v_i;
-  Py_ssize_t __pyx_v_dim;
-  PyObject **__pyx_v_p;
-  char __pyx_v_order;
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_t_4;
-  char *__pyx_t_5;
-  int __pyx_t_6;
-  PyObject *__pyx_t_7 = NULL;
-  Py_ssize_t __pyx_t_8;
-  PyObject *__pyx_t_9 = NULL;
-  PyObject *__pyx_t_10 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__cinit__", 0);
-  __Pyx_INCREF(__pyx_v_format);
-
-  /* "View.MemoryView":123
- *         cdef PyObject **p
- * 
- *         self.ndim = <int> len(shape)             # <<<<<<<<<<<<<<
- *         self.itemsize = itemsize
- * 
- */
-  if (unlikely(__pyx_v_shape == Py_None)) {
-    PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()");
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 123; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __pyx_t_1 = PyTuple_GET_SIZE(__pyx_v_shape); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 123; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_v_self->ndim = ((int)__pyx_t_1);
-
-  /* "View.MemoryView":124
- * 
- *         self.ndim = <int> len(shape)
- *         self.itemsize = itemsize             # <<<<<<<<<<<<<<
- * 
- *         if not self.ndim:
- */
-  __pyx_v_self->itemsize = __pyx_v_itemsize;
-
-  /* "View.MemoryView":126
- *         self.itemsize = itemsize
- * 
- *         if not self.ndim:             # <<<<<<<<<<<<<<
- *             raise ValueError("Empty shape tuple for cython.array")
- * 
- */
-  __pyx_t_2 = ((!(__pyx_v_self->ndim != 0)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":127
- * 
- *         if not self.ndim:
- *             raise ValueError("Empty shape tuple for cython.array")             # <<<<<<<<<<<<<<
- * 
- *         if itemsize <= 0:
- */
-    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "View.MemoryView":129
- *             raise ValueError("Empty shape tuple for cython.array")
- * 
- *         if itemsize <= 0:             # <<<<<<<<<<<<<<
- *             raise ValueError("itemsize <= 0 for cython.array")
- * 
- */
-  __pyx_t_2 = ((__pyx_v_itemsize <= 0) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":130
- * 
- *         if itemsize <= 0:
- *             raise ValueError("itemsize <= 0 for cython.array")             # <<<<<<<<<<<<<<
- * 
- *         if isinstance(format, unicode):
- */
-    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__8, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "View.MemoryView":132
- *             raise ValueError("itemsize <= 0 for cython.array")
- * 
- *         if isinstance(format, unicode):             # <<<<<<<<<<<<<<
- *             format = (<unicode>format).encode('ASCII')
- *         self._format = format  # keep a reference to the byte string
- */
-  __pyx_t_2 = PyUnicode_Check(__pyx_v_format); 
-  __pyx_t_4 = (__pyx_t_2 != 0);
-  if (__pyx_t_4) {
-
-    /* "View.MemoryView":133
- * 
- *         if isinstance(format, unicode):
- *             format = (<unicode>format).encode('ASCII')             # <<<<<<<<<<<<<<
- *         self._format = format  # keep a reference to the byte string
- *         self.format = self._format
- */
-    if (unlikely(__pyx_v_format == Py_None)) {
-      PyErr_Format(PyExc_AttributeError, "'NoneType' object has no attribute '%s'", "encode");
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 133; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-    __pyx_t_3 = PyUnicode_AsASCIIString(((PyObject*)__pyx_v_format)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 133; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_DECREF_SET(__pyx_v_format, __pyx_t_3);
-    __pyx_t_3 = 0;
-    goto __pyx_L5;
-  }
-  __pyx_L5:;
-
-  /* "View.MemoryView":134
- *         if isinstance(format, unicode):
- *             format = (<unicode>format).encode('ASCII')
- *         self._format = format  # keep a reference to the byte string             # <<<<<<<<<<<<<<
- *         self.format = self._format
- * 
- */
-  if (!(likely(PyBytes_CheckExact(__pyx_v_format))||((__pyx_v_format) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_v_format)->tp_name), 0))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_t_3 = __pyx_v_format;
-  __Pyx_INCREF(__pyx_t_3);
-  __Pyx_GIVEREF(__pyx_t_3);
-  __Pyx_GOTREF(__pyx_v_self->_format);
-  __Pyx_DECREF(__pyx_v_self->_format);
-  __pyx_v_self->_format = ((PyObject*)__pyx_t_3);
-  __pyx_t_3 = 0;
-
-  /* "View.MemoryView":135
- *             format = (<unicode>format).encode('ASCII')
- *         self._format = format  # keep a reference to the byte string
- *         self.format = self._format             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_5 = __Pyx_PyObject_AsString(__pyx_v_self->_format); if (unlikely((!__pyx_t_5) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 135; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_v_self->format = __pyx_t_5;
-
-  /* "View.MemoryView":138
- * 
- * 
- *         self._shape = <Py_ssize_t *> PyMem_Malloc(sizeof(Py_ssize_t)*self.ndim*2)             # <<<<<<<<<<<<<<
- *         self._strides = self._shape + self.ndim
- * 
- */
-  __pyx_v_self->_shape = ((Py_ssize_t *)PyMem_Malloc((((sizeof(Py_ssize_t)) * __pyx_v_self->ndim) * 2)));
-
-  /* "View.MemoryView":139
- * 
- *         self._shape = <Py_ssize_t *> PyMem_Malloc(sizeof(Py_ssize_t)*self.ndim*2)
- *         self._strides = self._shape + self.ndim             # <<<<<<<<<<<<<<
- * 
- *         if not self._shape:
- */
-  __pyx_v_self->_strides = (__pyx_v_self->_shape + __pyx_v_self->ndim);
-
-  /* "View.MemoryView":141
- *         self._strides = self._shape + self.ndim
- * 
- *         if not self._shape:             # <<<<<<<<<<<<<<
- *             raise MemoryError("unable to allocate shape and strides.")
- * 
- */
-  __pyx_t_4 = ((!(__pyx_v_self->_shape != 0)) != 0);
-  if (__pyx_t_4) {
-
-    /* "View.MemoryView":142
- * 
- *         if not self._shape:
- *             raise MemoryError("unable to allocate shape and strides.")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__9, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "View.MemoryView":145
- * 
- * 
- *         for idx, dim in enumerate(shape):             # <<<<<<<<<<<<<<
- *             if dim <= 0:
- *                 raise ValueError("Invalid shape in axis %d: %d." % (idx, dim))
- */
-  __pyx_t_6 = 0;
-  __pyx_t_3 = __pyx_v_shape; __Pyx_INCREF(__pyx_t_3); __pyx_t_1 = 0;
-  for (;;) {
-    if (__pyx_t_1 >= PyTuple_GET_SIZE(__pyx_t_3)) break;
-    #if CYTHON_COMPILING_IN_CPYTHON
-    __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_1); __Pyx_INCREF(__pyx_t_7); __pyx_t_1++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    #else
-    __pyx_t_7 = PySequence_ITEM(__pyx_t_3, __pyx_t_1); __pyx_t_1++; if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    #endif
-    __pyx_t_8 = __Pyx_PyIndex_AsSsize_t(__pyx_t_7); if (unlikely((__pyx_t_8 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
-    __pyx_v_dim = __pyx_t_8;
-    __pyx_v_idx = __pyx_t_6;
-    __pyx_t_6 = (__pyx_t_6 + 1);
-
-    /* "View.MemoryView":146
- * 
- *         for idx, dim in enumerate(shape):
- *             if dim <= 0:             # <<<<<<<<<<<<<<
- *                 raise ValueError("Invalid shape in axis %d: %d." % (idx, dim))
- *             self._shape[idx] = dim
- */
-    __pyx_t_4 = ((__pyx_v_dim <= 0) != 0);
-    if (__pyx_t_4) {
-
-      /* "View.MemoryView":147
- *         for idx, dim in enumerate(shape):
- *             if dim <= 0:
- *                 raise ValueError("Invalid shape in axis %d: %d." % (idx, dim))             # <<<<<<<<<<<<<<
- *             self._shape[idx] = dim
- * 
- */
-      __pyx_t_7 = __Pyx_PyInt_From_int(__pyx_v_idx); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_7);
-      __pyx_t_9 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_9);
-      __pyx_t_10 = PyTuple_New(2); if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_10);
-      PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_7);
-      __Pyx_GIVEREF(__pyx_t_7);
-      PyTuple_SET_ITEM(__pyx_t_10, 1, __pyx_t_9);
-      __Pyx_GIVEREF(__pyx_t_9);
-      __pyx_t_7 = 0;
-      __pyx_t_9 = 0;
-      __pyx_t_9 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_t_10); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_9);
-      __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
-      __pyx_t_10 = PyTuple_New(1); if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_10);
-      PyTuple_SET_ITEM(__pyx_t_10, 0, __pyx_t_9);
-      __Pyx_GIVEREF(__pyx_t_9);
-      __pyx_t_9 = 0;
-      __pyx_t_9 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_10, NULL); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_9);
-      __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
-      __Pyx_Raise(__pyx_t_9, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-
-    /* "View.MemoryView":148
- *             if dim <= 0:
- *                 raise ValueError("Invalid shape in axis %d: %d." % (idx, dim))
- *             self._shape[idx] = dim             # <<<<<<<<<<<<<<
- * 
- *         cdef char order
- */
-    (__pyx_v_self->_shape[__pyx_v_idx]) = __pyx_v_dim;
-  }
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-
-  /* "View.MemoryView":151
- * 
- *         cdef char order
- *         if mode == 'fortran':             # <<<<<<<<<<<<<<
- *             order = b'F'
- *             self.mode = u'fortran'
- */
-  __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_fortran, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (__pyx_t_4) {
-
-    /* "View.MemoryView":152
- *         cdef char order
- *         if mode == 'fortran':
- *             order = b'F'             # <<<<<<<<<<<<<<
- *             self.mode = u'fortran'
- *         elif mode == 'c':
- */
-    __pyx_v_order = 'F';
-
-    /* "View.MemoryView":153
- *         if mode == 'fortran':
- *             order = b'F'
- *             self.mode = u'fortran'             # <<<<<<<<<<<<<<
- *         elif mode == 'c':
- *             order = b'C'
- */
-    __Pyx_INCREF(__pyx_n_u_fortran);
-    __Pyx_GIVEREF(__pyx_n_u_fortran);
-    __Pyx_GOTREF(__pyx_v_self->mode);
-    __Pyx_DECREF(__pyx_v_self->mode);
-    __pyx_v_self->mode = __pyx_n_u_fortran;
-    goto __pyx_L10;
-  }
-
-  /* "View.MemoryView":154
- *             order = b'F'
- *             self.mode = u'fortran'
- *         elif mode == 'c':             # <<<<<<<<<<<<<<
- *             order = b'C'
- *             self.mode = u'c'
- */
-  __pyx_t_4 = (__Pyx_PyString_Equals(__pyx_v_mode, __pyx_n_s_c, Py_EQ)); if (unlikely(__pyx_t_4 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (__pyx_t_4) {
-
-    /* "View.MemoryView":155
- *             self.mode = u'fortran'
- *         elif mode == 'c':
- *             order = b'C'             # <<<<<<<<<<<<<<
- *             self.mode = u'c'
- *         else:
- */
-    __pyx_v_order = 'C';
-
-    /* "View.MemoryView":156
- *         elif mode == 'c':
- *             order = b'C'
- *             self.mode = u'c'             # <<<<<<<<<<<<<<
- *         else:
- *             raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode)
- */
-    __Pyx_INCREF(__pyx_n_u_c);
-    __Pyx_GIVEREF(__pyx_n_u_c);
-    __Pyx_GOTREF(__pyx_v_self->mode);
-    __Pyx_DECREF(__pyx_v_self->mode);
-    __pyx_v_self->mode = __pyx_n_u_c;
-    goto __pyx_L10;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":158
- *             self.mode = u'c'
- *         else:
- *             raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode)             # <<<<<<<<<<<<<<
- * 
- *         self.len = fill_contig_strides_array(self._shape, self._strides,
- */
-    __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_v_mode); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_t_9 = PyTuple_New(1); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_9);
-    PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_3);
-    __Pyx_GIVEREF(__pyx_t_3);
-    __pyx_t_3 = 0;
-    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_9, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
-    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 158; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __pyx_L10:;
-
-  /* "View.MemoryView":160
- *             raise ValueError("Invalid mode, expected 'c' or 'fortran', got %s" % mode)
- * 
- *         self.len = fill_contig_strides_array(self._shape, self._strides,             # <<<<<<<<<<<<<<
- *                                              itemsize, self.ndim, order)
- * 
- */
-  __pyx_v_self->len = __pyx_fill_contig_strides_array(__pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_itemsize, __pyx_v_self->ndim, __pyx_v_order);
-
-  /* "View.MemoryView":163
- *                                              itemsize, self.ndim, order)
- * 
- *         self.free_data = allocate_buffer             # <<<<<<<<<<<<<<
- *         self.dtype_is_object = format == b'O'
- *         if allocate_buffer:
- */
-  __pyx_v_self->free_data = __pyx_v_allocate_buffer;
-
-  /* "View.MemoryView":164
- * 
- *         self.free_data = allocate_buffer
- *         self.dtype_is_object = format == b'O'             # <<<<<<<<<<<<<<
- *         if allocate_buffer:
- * 
- */
-  __pyx_t_3 = PyObject_RichCompare(__pyx_v_format, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 164; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely((__pyx_t_4 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 164; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  __pyx_v_self->dtype_is_object = __pyx_t_4;
-
-  /* "View.MemoryView":165
- *         self.free_data = allocate_buffer
- *         self.dtype_is_object = format == b'O'
- *         if allocate_buffer:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_4 = (__pyx_v_allocate_buffer != 0);
-  if (__pyx_t_4) {
-
-    /* "View.MemoryView":168
- * 
- * 
- *             self.data = <char *>malloc(self.len)             # <<<<<<<<<<<<<<
- *             if not self.data:
- *                 raise MemoryError("unable to allocate array data.")
- */
-    __pyx_v_self->data = ((char *)malloc(__pyx_v_self->len));
-
-    /* "View.MemoryView":169
- * 
- *             self.data = <char *>malloc(self.len)
- *             if not self.data:             # <<<<<<<<<<<<<<
- *                 raise MemoryError("unable to allocate array data.")
- * 
- */
-    __pyx_t_4 = ((!(__pyx_v_self->data != 0)) != 0);
-    if (__pyx_t_4) {
-
-      /* "View.MemoryView":170
- *             self.data = <char *>malloc(self.len)
- *             if not self.data:
- *                 raise MemoryError("unable to allocate array data.")             # <<<<<<<<<<<<<<
- * 
- *             if self.dtype_is_object:
- */
-      __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_MemoryError, __pyx_tuple__10, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-
-    /* "View.MemoryView":172
- *                 raise MemoryError("unable to allocate array data.")
- * 
- *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
- *                 p = <PyObject **> self.data
- *                 for i in range(self.len / itemsize):
- */
-    __pyx_t_4 = (__pyx_v_self->dtype_is_object != 0);
-    if (__pyx_t_4) {
-
-      /* "View.MemoryView":173
- * 
- *             if self.dtype_is_object:
- *                 p = <PyObject **> self.data             # <<<<<<<<<<<<<<
- *                 for i in range(self.len / itemsize):
- *                     p[i] = Py_None
- */
-      __pyx_v_p = ((PyObject **)__pyx_v_self->data);
-
-      /* "View.MemoryView":174
- *             if self.dtype_is_object:
- *                 p = <PyObject **> self.data
- *                 for i in range(self.len / itemsize):             # <<<<<<<<<<<<<<
- *                     p[i] = Py_None
- *                     Py_INCREF(Py_None)
- */
-      if (unlikely(__pyx_v_itemsize == 0)) {
-        #ifdef WITH_THREAD
-        PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-        #endif
-        PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero");
-        #ifdef WITH_THREAD
-        PyGILState_Release(__pyx_gilstate_save);
-        #endif
-        {__pyx_filename = __pyx_f[2]; __pyx_lineno = 174; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-      else if (sizeof(Py_ssize_t) == sizeof(long) && unlikely(__pyx_v_itemsize == -1)  && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_self->len))) {
-        #ifdef WITH_THREAD
-        PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-        #endif
-        PyErr_SetString(PyExc_OverflowError, "value too large to perform division");
-        #ifdef WITH_THREAD
-        PyGILState_Release(__pyx_gilstate_save);
-        #endif
-        {__pyx_filename = __pyx_f[2]; __pyx_lineno = 174; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-      __pyx_t_1 = __Pyx_div_Py_ssize_t(__pyx_v_self->len, __pyx_v_itemsize);
-      for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_1; __pyx_t_8+=1) {
-        __pyx_v_i = __pyx_t_8;
-
-        /* "View.MemoryView":175
- *                 p = <PyObject **> self.data
- *                 for i in range(self.len / itemsize):
- *                     p[i] = Py_None             # <<<<<<<<<<<<<<
- *                     Py_INCREF(Py_None)
- * 
- */
-        (__pyx_v_p[__pyx_v_i]) = Py_None;
-
-        /* "View.MemoryView":176
- *                 for i in range(self.len / itemsize):
- *                     p[i] = Py_None
- *                     Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
- * 
- *     @cname('getbuffer')
- */
-        Py_INCREF(Py_None);
-      }
-      goto __pyx_L13;
-    }
-    __pyx_L13:;
-    goto __pyx_L11;
-  }
-  __pyx_L11:;
-
-  /* "View.MemoryView":116
- *         cdef bint dtype_is_object
- * 
- *     def __cinit__(array self, tuple shape, Py_ssize_t itemsize, format not None,             # <<<<<<<<<<<<<<
- *                   mode="c", bint allocate_buffer=True):
- * 
- */
-
-  /* function exit code */
-  __pyx_r = 0;
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_7);
-  __Pyx_XDECREF(__pyx_t_9);
-  __Pyx_XDECREF(__pyx_t_10);
-  __Pyx_AddTraceback("View.MemoryView.array.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_format);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":179
- * 
- *     @cname('getbuffer')
- *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
- *         cdef int bufmode = -1
- *         if self.mode == u"c":
- */
-
-/* Python wrapper */
-static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-static CYTHON_UNUSED int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0);
-  __pyx_r = __pyx_array_getbuffer_MemoryView_5array_2__getbuffer__(((struct __pyx_array_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static int __pyx_array_getbuffer_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
-  int __pyx_v_bufmode;
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  char *__pyx_t_4;
-  Py_ssize_t __pyx_t_5;
-  int __pyx_t_6;
-  Py_ssize_t *__pyx_t_7;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__getbuffer__", 0);
-  if (__pyx_v_info != NULL) {
-    __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
-    __Pyx_GIVEREF(__pyx_v_info->obj);
-  }
-
-  /* "View.MemoryView":180
- *     @cname('getbuffer')
- *     def __getbuffer__(self, Py_buffer *info, int flags):
- *         cdef int bufmode = -1             # <<<<<<<<<<<<<<
- *         if self.mode == u"c":
- *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- */
-  __pyx_v_bufmode = -1;
-
-  /* "View.MemoryView":181
- *     def __getbuffer__(self, Py_buffer *info, int flags):
- *         cdef int bufmode = -1
- *         if self.mode == u"c":             # <<<<<<<<<<<<<<
- *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- *         elif self.mode == u"fortran":
- */
-  __pyx_t_1 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_c, Py_EQ)); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 181; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":182
- *         cdef int bufmode = -1
- *         if self.mode == u"c":
- *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS             # <<<<<<<<<<<<<<
- *         elif self.mode == u"fortran":
- *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- */
-    __pyx_v_bufmode = (PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS);
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":183
- *         if self.mode == u"c":
- *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- *         elif self.mode == u"fortran":             # <<<<<<<<<<<<<<
- *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- *         if not (flags & bufmode):
- */
-  __pyx_t_2 = (__Pyx_PyUnicode_Equals(__pyx_v_self->mode, __pyx_n_u_fortran, Py_EQ)); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 183; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_t_1 = (__pyx_t_2 != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":184
- *             bufmode = PyBUF_C_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- *         elif self.mode == u"fortran":
- *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS             # <<<<<<<<<<<<<<
- *         if not (flags & bufmode):
- *             raise ValueError("Can only create a buffer that is contiguous in memory.")
- */
-    __pyx_v_bufmode = (PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS);
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":185
- *         elif self.mode == u"fortran":
- *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- *         if not (flags & bufmode):             # <<<<<<<<<<<<<<
- *             raise ValueError("Can only create a buffer that is contiguous in memory.")
- *         info.buf = self.data
- */
-  __pyx_t_1 = ((!((__pyx_v_flags & __pyx_v_bufmode) != 0)) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":186
- *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- *         if not (flags & bufmode):
- *             raise ValueError("Can only create a buffer that is contiguous in memory.")             # <<<<<<<<<<<<<<
- *         info.buf = self.data
- *         info.len = self.len
- */
-    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__11, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "View.MemoryView":187
- *         if not (flags & bufmode):
- *             raise ValueError("Can only create a buffer that is contiguous in memory.")
- *         info.buf = self.data             # <<<<<<<<<<<<<<
- *         info.len = self.len
- *         info.ndim = self.ndim
- */
-  __pyx_t_4 = __pyx_v_self->data;
-  __pyx_v_info->buf = __pyx_t_4;
-
-  /* "View.MemoryView":188
- *             raise ValueError("Can only create a buffer that is contiguous in memory.")
- *         info.buf = self.data
- *         info.len = self.len             # <<<<<<<<<<<<<<
- *         info.ndim = self.ndim
- *         info.shape = self._shape
- */
-  __pyx_t_5 = __pyx_v_self->len;
-  __pyx_v_info->len = __pyx_t_5;
-
-  /* "View.MemoryView":189
- *         info.buf = self.data
- *         info.len = self.len
- *         info.ndim = self.ndim             # <<<<<<<<<<<<<<
- *         info.shape = self._shape
- *         info.strides = self._strides
- */
-  __pyx_t_6 = __pyx_v_self->ndim;
-  __pyx_v_info->ndim = __pyx_t_6;
-
-  /* "View.MemoryView":190
- *         info.len = self.len
- *         info.ndim = self.ndim
- *         info.shape = self._shape             # <<<<<<<<<<<<<<
- *         info.strides = self._strides
- *         info.suboffsets = NULL
- */
-  __pyx_t_7 = __pyx_v_self->_shape;
-  __pyx_v_info->shape = __pyx_t_7;
-
-  /* "View.MemoryView":191
- *         info.ndim = self.ndim
- *         info.shape = self._shape
- *         info.strides = self._strides             # <<<<<<<<<<<<<<
- *         info.suboffsets = NULL
- *         info.itemsize = self.itemsize
- */
-  __pyx_t_7 = __pyx_v_self->_strides;
-  __pyx_v_info->strides = __pyx_t_7;
-
-  /* "View.MemoryView":192
- *         info.shape = self._shape
- *         info.strides = self._strides
- *         info.suboffsets = NULL             # <<<<<<<<<<<<<<
- *         info.itemsize = self.itemsize
- *         info.readonly = 0
- */
-  __pyx_v_info->suboffsets = NULL;
-
-  /* "View.MemoryView":193
- *         info.strides = self._strides
- *         info.suboffsets = NULL
- *         info.itemsize = self.itemsize             # <<<<<<<<<<<<<<
- *         info.readonly = 0
- * 
- */
-  __pyx_t_5 = __pyx_v_self->itemsize;
-  __pyx_v_info->itemsize = __pyx_t_5;
-
-  /* "View.MemoryView":194
- *         info.suboffsets = NULL
- *         info.itemsize = self.itemsize
- *         info.readonly = 0             # <<<<<<<<<<<<<<
- * 
- *         if flags & PyBUF_FORMAT:
- */
-  __pyx_v_info->readonly = 0;
-
-  /* "View.MemoryView":196
- *         info.readonly = 0
- * 
- *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
- *             info.format = self.format
- *         else:
- */
-  __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":197
- * 
- *         if flags & PyBUF_FORMAT:
- *             info.format = self.format             # <<<<<<<<<<<<<<
- *         else:
- *             info.format = NULL
- */
-    __pyx_t_4 = __pyx_v_self->format;
-    __pyx_v_info->format = __pyx_t_4;
-    goto __pyx_L5;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":199
- *             info.format = self.format
- *         else:
- *             info.format = NULL             # <<<<<<<<<<<<<<
- * 
- *         info.obj = self
- */
-    __pyx_v_info->format = NULL;
-  }
-  __pyx_L5:;
-
-  /* "View.MemoryView":201
- *             info.format = NULL
- * 
- *         info.obj = self             # <<<<<<<<<<<<<<
- * 
- *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")
- */
-  __Pyx_INCREF(((PyObject *)__pyx_v_self));
-  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
-  __Pyx_GOTREF(__pyx_v_info->obj);
-  __Pyx_DECREF(__pyx_v_info->obj);
-  __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
-
-  /* "View.MemoryView":179
- * 
- *     @cname('getbuffer')
- *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
- *         cdef int bufmode = -1
- *         if self.mode == u"c":
- */
-
-  /* function exit code */
-  __pyx_r = 0;
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView.array.__getbuffer__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  if (__pyx_v_info != NULL && __pyx_v_info->obj != NULL) {
-    __Pyx_GOTREF(__pyx_v_info->obj);
-    __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = NULL;
-  }
-  goto __pyx_L2;
-  __pyx_L0:;
-  if (__pyx_v_info != NULL && __pyx_v_info->obj == Py_None) {
-    __Pyx_GOTREF(Py_None);
-    __Pyx_DECREF(Py_None); __pyx_v_info->obj = NULL;
-  }
-  __pyx_L2:;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":205
- *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")
- * 
- *     def __dealloc__(array self):             # <<<<<<<<<<<<<<
- *         if self.callback_free_data != NULL:
- *             self.callback_free_data(self.data)
- */
-
-/* Python wrapper */
-static void __pyx_array___dealloc__(PyObject *__pyx_v_self); /*proto*/
-static void __pyx_array___dealloc__(PyObject *__pyx_v_self) {
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0);
-  __pyx_array_MemoryView_5array_4__dealloc__(((struct __pyx_array_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-static void __pyx_array_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self) {
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  __Pyx_RefNannySetupContext("__dealloc__", 0);
-
-  /* "View.MemoryView":206
- * 
- *     def __dealloc__(array self):
- *         if self.callback_free_data != NULL:             # <<<<<<<<<<<<<<
- *             self.callback_free_data(self.data)
- *         elif self.free_data:
- */
-  __pyx_t_1 = ((__pyx_v_self->callback_free_data != NULL) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":207
- *     def __dealloc__(array self):
- *         if self.callback_free_data != NULL:
- *             self.callback_free_data(self.data)             # <<<<<<<<<<<<<<
- *         elif self.free_data:
- *             if self.dtype_is_object:
- */
-    __pyx_v_self->callback_free_data(__pyx_v_self->data);
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":208
- *         if self.callback_free_data != NULL:
- *             self.callback_free_data(self.data)
- *         elif self.free_data:             # <<<<<<<<<<<<<<
- *             if self.dtype_is_object:
- *                 refcount_objects_in_slice(self.data, self._shape,
- */
-  __pyx_t_1 = (__pyx_v_self->free_data != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":209
- *             self.callback_free_data(self.data)
- *         elif self.free_data:
- *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
- *                 refcount_objects_in_slice(self.data, self._shape,
- *                                           self._strides, self.ndim, False)
- */
-    __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":210
- *         elif self.free_data:
- *             if self.dtype_is_object:
- *                 refcount_objects_in_slice(self.data, self._shape,             # <<<<<<<<<<<<<<
- *                                           self._strides, self.ndim, False)
- *             free(self.data)
- */
-      __pyx_memoryview_refcount_objects_in_slice(__pyx_v_self->data, __pyx_v_self->_shape, __pyx_v_self->_strides, __pyx_v_self->ndim, 0);
-      goto __pyx_L4;
-    }
-    __pyx_L4:;
-
-    /* "View.MemoryView":212
- *                 refcount_objects_in_slice(self.data, self._shape,
- *                                           self._strides, self.ndim, False)
- *             free(self.data)             # <<<<<<<<<<<<<<
- *         PyMem_Free(self._shape)
- * 
- */
-    free(__pyx_v_self->data);
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":213
- *                                           self._strides, self.ndim, False)
- *             free(self.data)
- *         PyMem_Free(self._shape)             # <<<<<<<<<<<<<<
- * 
- *     property memview:
- */
-  PyMem_Free(__pyx_v_self->_shape);
-
-  /* "View.MemoryView":205
- *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")
- * 
- *     def __dealloc__(array self):             # <<<<<<<<<<<<<<
- *         if self.callback_free_data != NULL:
- *             self.callback_free_data(self.data)
- */
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-/* "View.MemoryView":217
- *     property memview:
- *         @cname('get_memview')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- * 
- *             flags =  PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE
- */
-
-/* Python wrapper */
-static PyObject *get_memview(PyObject *__pyx_v_self); /*proto*/
-static PyObject *get_memview(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = get_memview_MemoryView_5array_7memview___get__(((struct __pyx_array_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *get_memview_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self) {
-  int __pyx_v_flags;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":219
- *         def __get__(self):
- * 
- *             flags =  PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE             # <<<<<<<<<<<<<<
- *             return  memoryview(self, flags, self.dtype_is_object)
- * 
- */
-  __pyx_v_flags = ((PyBUF_ANY_CONTIGUOUS | PyBUF_FORMAT) | PyBUF_WRITABLE);
-
-  /* "View.MemoryView":220
- * 
- *             flags =  PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE
- *             return  memoryview(self, flags, self.dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_INCREF(((PyObject *)__pyx_v_self));
-  PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_self));
-  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
-  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2);
-  __Pyx_GIVEREF(__pyx_t_2);
-  __pyx_t_1 = 0;
-  __pyx_t_2 = 0;
-  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_memoryview_type)), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  __pyx_r = __pyx_t_2;
-  __pyx_t_2 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":217
- *     property memview:
- *         @cname('get_memview')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- * 
- *             flags =  PyBUF_ANY_CONTIGUOUS|PyBUF_FORMAT|PyBUF_WRITABLE
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView.array.memview.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":223
- * 
- * 
- *     def __getattr__(self, attr):             # <<<<<<<<<<<<<<
- *         return getattr(self.memview, attr)
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr); /*proto*/
-static PyObject *__pyx_array___getattr__(PyObject *__pyx_v_self, PyObject *__pyx_v_attr) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__getattr__ (wrapper)", 0);
-  __pyx_r = __pyx_array_MemoryView_5array_6__getattr__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_attr));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_array_MemoryView_5array_6__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__getattr__", 0);
-
-  /* "View.MemoryView":224
- * 
- *     def __getattr__(self, attr):
- *         return getattr(self.memview, attr)             # <<<<<<<<<<<<<<
- * 
- *     def __getitem__(self, item):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __Pyx_GetAttr(__pyx_t_1, __pyx_v_attr); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_r = __pyx_t_2;
-  __pyx_t_2 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":223
- * 
- * 
- *     def __getattr__(self, attr):             # <<<<<<<<<<<<<<
- *         return getattr(self.memview, attr)
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_AddTraceback("View.MemoryView.array.__getattr__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":226
- *         return getattr(self.memview, attr)
- * 
- *     def __getitem__(self, item):             # <<<<<<<<<<<<<<
- *         return self.memview[item]
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item); /*proto*/
-static PyObject *__pyx_array___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0);
-  __pyx_r = __pyx_array_MemoryView_5array_8__getitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_array_MemoryView_5array_8__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__getitem__", 0);
-
-  /* "View.MemoryView":227
- * 
- *     def __getitem__(self, item):
- *         return self.memview[item]             # <<<<<<<<<<<<<<
- * 
- *     def __setitem__(self, item, value):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = PyObject_GetItem(__pyx_t_1, __pyx_v_item); if (unlikely(__pyx_t_2 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 227; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_r = __pyx_t_2;
-  __pyx_t_2 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":226
- *         return getattr(self.memview, attr)
- * 
- *     def __getitem__(self, item):             # <<<<<<<<<<<<<<
- *         return self.memview[item]
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_AddTraceback("View.MemoryView.array.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":229
- *         return self.memview[item]
- * 
- *     def __setitem__(self, item, value):             # <<<<<<<<<<<<<<
- *         self.memview[item] = value
- * 
- */
-
-/* Python wrapper */
-static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /*proto*/
-static int __pyx_array___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0);
-  __pyx_r = __pyx_array_MemoryView_5array_10__setitem__(((struct __pyx_array_obj *)__pyx_v_self), ((PyObject *)__pyx_v_item), ((PyObject *)__pyx_v_value));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static int __pyx_array_MemoryView_5array_10__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__setitem__", 0);
-
-  /* "View.MemoryView":230
- * 
- *     def __setitem__(self, item, value):
- *         self.memview[item] = value             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_memview); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (unlikely(PyObject_SetItem(__pyx_t_1, __pyx_v_item, __pyx_v_value) < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-
-  /* "View.MemoryView":229
- *         return self.memview[item]
- * 
- *     def __setitem__(self, item, value):             # <<<<<<<<<<<<<<
- *         self.memview[item] = value
- * 
- */
-
-  /* function exit code */
-  __pyx_r = 0;
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("View.MemoryView.array.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":234
- * 
- * @cname("__pyx_array_new")
- * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format,             # <<<<<<<<<<<<<<
- *                           char *mode, char *buf):
- *     cdef array result
- */
-
-static struct __pyx_array_obj *__pyx_array_new(PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, char *__pyx_v_format, char *__pyx_v_mode, char *__pyx_v_buf) {
-  struct __pyx_array_obj *__pyx_v_result = 0;
-  struct __pyx_array_obj *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  PyObject *__pyx_t_5 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("array_cwrapper", 0);
-
-  /* "View.MemoryView":238
- *     cdef array result
- * 
- *     if buf == NULL:             # <<<<<<<<<<<<<<
- *         result = array(shape, itemsize, format, mode.decode('ASCII'))
- *     else:
- */
-  __pyx_t_1 = ((__pyx_v_buf == NULL) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":239
- * 
- *     if buf == NULL:
- *         result = array(shape, itemsize, format, mode.decode('ASCII'))             # <<<<<<<<<<<<<<
- *     else:
- *         result = array(shape, itemsize, format, mode.decode('ASCII'),
- */
-    __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __pyx_t_3 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_t_4 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_5 = PyTuple_New(4); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    __Pyx_INCREF(__pyx_v_shape);
-    PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_shape);
-    __Pyx_GIVEREF(__pyx_v_shape);
-    PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_t_2);
-    __Pyx_GIVEREF(__pyx_t_2);
-    PyTuple_SET_ITEM(__pyx_t_5, 2, __pyx_t_3);
-    __Pyx_GIVEREF(__pyx_t_3);
-    PyTuple_SET_ITEM(__pyx_t_5, 3, __pyx_t_4);
-    __Pyx_GIVEREF(__pyx_t_4);
-    __pyx_t_2 = 0;
-    __pyx_t_3 = 0;
-    __pyx_t_4 = 0;
-    __pyx_t_4 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_array_type)), __pyx_t_5, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-    __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_4);
-    __pyx_t_4 = 0;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":241
- *         result = array(shape, itemsize, format, mode.decode('ASCII'))
- *     else:
- *         result = array(shape, itemsize, format, mode.decode('ASCII'),             # <<<<<<<<<<<<<<
- *                        allocate_buffer=False)
- *         result.data = buf
- */
-    __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_itemsize); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_format); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    __pyx_t_3 = __Pyx_decode_c_string(__pyx_v_mode, 0, strlen(__pyx_v_mode), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_t_2 = PyTuple_New(4); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __Pyx_INCREF(__pyx_v_shape);
-    PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape);
-    __Pyx_GIVEREF(__pyx_v_shape);
-    PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_4);
-    __Pyx_GIVEREF(__pyx_t_4);
-    PyTuple_SET_ITEM(__pyx_t_2, 2, __pyx_t_5);
-    __Pyx_GIVEREF(__pyx_t_5);
-    PyTuple_SET_ITEM(__pyx_t_2, 3, __pyx_t_3);
-    __Pyx_GIVEREF(__pyx_t_3);
-    __pyx_t_4 = 0;
-    __pyx_t_5 = 0;
-    __pyx_t_3 = 0;
-    __pyx_t_3 = PyDict_New(); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-
-    /* "View.MemoryView":242
- *     else:
- *         result = array(shape, itemsize, format, mode.decode('ASCII'),
- *                        allocate_buffer=False)             # <<<<<<<<<<<<<<
- *         result.data = buf
- * 
- */
-    if (PyDict_SetItem(__pyx_t_3, __pyx_n_s_allocate_buffer, Py_False) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-    /* "View.MemoryView":241
- *         result = array(shape, itemsize, format, mode.decode('ASCII'))
- *     else:
- *         result = array(shape, itemsize, format, mode.decode('ASCII'),             # <<<<<<<<<<<<<<
- *                        allocate_buffer=False)
- *         result.data = buf
- */
-    __pyx_t_5 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_array_type)), __pyx_t_2, __pyx_t_3); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 241; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    __pyx_v_result = ((struct __pyx_array_obj *)__pyx_t_5);
-    __pyx_t_5 = 0;
-
-    /* "View.MemoryView":243
- *         result = array(shape, itemsize, format, mode.decode('ASCII'),
- *                        allocate_buffer=False)
- *         result.data = buf             # <<<<<<<<<<<<<<
- * 
- *     return result
- */
-    __pyx_v_result->data = __pyx_v_buf;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":245
- *         result.data = buf
- * 
- *     return result             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __Pyx_XDECREF(((PyObject *)__pyx_r));
-  __Pyx_INCREF(((PyObject *)__pyx_v_result));
-  __pyx_r = __pyx_v_result;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":234
- * 
- * @cname("__pyx_array_new")
- * cdef array array_cwrapper(tuple shape, Py_ssize_t itemsize, char *format,             # <<<<<<<<<<<<<<
- *                           char *mode, char *buf):
- *     cdef array result
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_AddTraceback("View.MemoryView.array_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_result);
-  __Pyx_XGIVEREF((PyObject *)__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":271
- * cdef class Enum(object):
- *     cdef object name
- *     def __init__(self, name):             # <<<<<<<<<<<<<<
- *         self.name = name
- *     def __repr__(self):
- */
-
-/* Python wrapper */
-static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static int __pyx_MemviewEnum___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
-  PyObject *__pyx_v_name = 0;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__init__ (wrapper)", 0);
-  {
-    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_name,0};
-    PyObject* values[1] = {0};
-    if (unlikely(__pyx_kwds)) {
-      Py_ssize_t kw_args;
-      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
-      switch (pos_args) {
-        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
-        case  0: break;
-        default: goto __pyx_L5_argtuple_error;
-      }
-      kw_args = PyDict_Size(__pyx_kwds);
-      switch (pos_args) {
-        case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_name)) != 0)) kw_args--;
-        else goto __pyx_L5_argtuple_error;
-      }
-      if (unlikely(kw_args > 0)) {
-        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__init__") < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 271; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-      }
-    } else if (PyTuple_GET_SIZE(__pyx_args) != 1) {
-      goto __pyx_L5_argtuple_error;
-    } else {
-      values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
-    }
-    __pyx_v_name = values[0];
-  }
-  goto __pyx_L4_argument_unpacking_done;
-  __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("__init__", 1, 1, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 271; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-  __pyx_L3_error:;
-  __Pyx_AddTraceback("View.MemoryView.Enum.__init__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __Pyx_RefNannyFinishContext();
-  return -1;
-  __pyx_L4_argument_unpacking_done:;
-  __pyx_r = __pyx_MemviewEnum_MemoryView_4Enum___init__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self), __pyx_v_name);
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static int __pyx_MemviewEnum_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__init__", 0);
-
-  /* "View.MemoryView":272
- *     cdef object name
- *     def __init__(self, name):
- *         self.name = name             # <<<<<<<<<<<<<<
- *     def __repr__(self):
- *         return self.name
- */
-  __Pyx_INCREF(__pyx_v_name);
-  __Pyx_GIVEREF(__pyx_v_name);
-  __Pyx_GOTREF(__pyx_v_self->name);
-  __Pyx_DECREF(__pyx_v_self->name);
-  __pyx_v_self->name = __pyx_v_name;
-
-  /* "View.MemoryView":271
- * cdef class Enum(object):
- *     cdef object name
- *     def __init__(self, name):             # <<<<<<<<<<<<<<
- *         self.name = name
- *     def __repr__(self):
- */
-
-  /* function exit code */
-  __pyx_r = 0;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":273
- *     def __init__(self, name):
- *         self.name = name
- *     def __repr__(self):             # <<<<<<<<<<<<<<
- *         return self.name
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_MemviewEnum___repr__(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0);
-  __pyx_r = __pyx_MemviewEnum_MemoryView_4Enum_2__repr__(((struct __pyx_MemviewEnum_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_MemviewEnum_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__repr__", 0);
-
-  /* "View.MemoryView":274
- *         self.name = name
- *     def __repr__(self):
- *         return self.name             # <<<<<<<<<<<<<<
- * 
- * cdef generic = Enum("<strided and direct or indirect>")
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(__pyx_v_self->name);
-  __pyx_r = __pyx_v_self->name;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":273
- *     def __init__(self, name):
- *         self.name = name
- *     def __repr__(self):             # <<<<<<<<<<<<<<
- *         return self.name
- * 
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":288
- * 
- * @cname('__pyx_align_pointer')
- * cdef void *align_pointer(void *memory, size_t alignment) nogil:             # <<<<<<<<<<<<<<
- *     "Align pointer memory on a given boundary"
- *     cdef Py_intptr_t aligned_p = <Py_intptr_t> memory
- */
-
-static void *__pyx_align_pointer(void *__pyx_v_memory, size_t __pyx_v_alignment) {
-  Py_intptr_t __pyx_v_aligned_p;
-  size_t __pyx_v_offset;
-  void *__pyx_r;
-  int __pyx_t_1;
-
-  /* "View.MemoryView":290
- * cdef void *align_pointer(void *memory, size_t alignment) nogil:
- *     "Align pointer memory on a given boundary"
- *     cdef Py_intptr_t aligned_p = <Py_intptr_t> memory             # <<<<<<<<<<<<<<
- *     cdef size_t offset
- * 
- */
-  __pyx_v_aligned_p = ((Py_intptr_t)__pyx_v_memory);
-
-  /* "View.MemoryView":294
- * 
- *     with cython.cdivision(True):
- *         offset = aligned_p % alignment             # <<<<<<<<<<<<<<
- * 
- *     if offset > 0:
- */
-  __pyx_v_offset = (__pyx_v_aligned_p % __pyx_v_alignment);
-
-  /* "View.MemoryView":296
- *         offset = aligned_p % alignment
- * 
- *     if offset > 0:             # <<<<<<<<<<<<<<
- *         aligned_p += alignment - offset
- * 
- */
-  __pyx_t_1 = ((__pyx_v_offset > 0) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":297
- * 
- *     if offset > 0:
- *         aligned_p += alignment - offset             # <<<<<<<<<<<<<<
- * 
- *     return <void *> aligned_p
- */
-    __pyx_v_aligned_p = (__pyx_v_aligned_p + (__pyx_v_alignment - __pyx_v_offset));
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":299
- *         aligned_p += alignment - offset
- * 
- *     return <void *> aligned_p             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview')
- */
-  __pyx_r = ((void *)__pyx_v_aligned_p);
-  goto __pyx_L0;
-
-  /* "View.MemoryView":288
- * 
- * @cname('__pyx_align_pointer')
- * cdef void *align_pointer(void *memory, size_t alignment) nogil:             # <<<<<<<<<<<<<<
- *     "Align pointer memory on a given boundary"
- *     cdef Py_intptr_t aligned_p = <Py_intptr_t> memory
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":317
- *     cdef __Pyx_TypeInfo *typeinfo
- * 
- *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):             # <<<<<<<<<<<<<<
- *         self.obj = obj
- *         self.flags = flags
- */
-
-/* Python wrapper */
-static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static int __pyx_memoryview___cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
-  PyObject *__pyx_v_obj = 0;
-  int __pyx_v_flags;
-  int __pyx_v_dtype_is_object;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__cinit__ (wrapper)", 0);
-  {
-    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_obj,&__pyx_n_s_flags,&__pyx_n_s_dtype_is_object,0};
-    PyObject* values[3] = {0,0,0};
-    if (unlikely(__pyx_kwds)) {
-      Py_ssize_t kw_args;
-      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
-      switch (pos_args) {
-        case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
-        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
-        case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
-        case  0: break;
-        default: goto __pyx_L5_argtuple_error;
-      }
-      kw_args = PyDict_Size(__pyx_kwds);
-      switch (pos_args) {
-        case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_obj)) != 0)) kw_args--;
-        else goto __pyx_L5_argtuple_error;
-        case  1:
-        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_flags)) != 0)) kw_args--;
-        else {
-          __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, 1); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-        }
-        case  2:
-        if (kw_args > 0) {
-          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s_dtype_is_object);
-          if (value) { values[2] = value; kw_args--; }
-        }
-      }
-      if (unlikely(kw_args > 0)) {
-        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "__cinit__") < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-      }
-    } else {
-      switch (PyTuple_GET_SIZE(__pyx_args)) {
-        case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
-        case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
-        values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
-        break;
-        default: goto __pyx_L5_argtuple_error;
-      }
-    }
-    __pyx_v_obj = values[0];
-    __pyx_v_flags = __Pyx_PyInt_As_int(values[1]); if (unlikely((__pyx_v_flags == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-    if (values[2]) {
-      __pyx_v_dtype_is_object = __Pyx_PyObject_IsTrue(values[2]); if (unlikely((__pyx_v_dtype_is_object == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-    } else {
-      __pyx_v_dtype_is_object = ((int)0);
-    }
-  }
-  goto __pyx_L4_argument_unpacking_done;
-  __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("__cinit__", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-  __pyx_L3_error:;
-  __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __Pyx_RefNannyFinishContext();
-  return -1;
-  __pyx_L4_argument_unpacking_done:;
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview___cinit__(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_obj, __pyx_v_flags, __pyx_v_dtype_is_object);
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static int __pyx_memoryview_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  PyObject *__pyx_t_5 = NULL;
-  PyObject *__pyx_t_6 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__cinit__", 0);
-
-  /* "View.MemoryView":318
- * 
- *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):
- *         self.obj = obj             # <<<<<<<<<<<<<<
- *         self.flags = flags
- *         if type(self) is memoryview or obj is not None:
- */
-  __Pyx_INCREF(__pyx_v_obj);
-  __Pyx_GIVEREF(__pyx_v_obj);
-  __Pyx_GOTREF(__pyx_v_self->obj);
-  __Pyx_DECREF(__pyx_v_self->obj);
-  __pyx_v_self->obj = __pyx_v_obj;
-
-  /* "View.MemoryView":319
- *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):
- *         self.obj = obj
- *         self.flags = flags             # <<<<<<<<<<<<<<
- *         if type(self) is memoryview or obj is not None:
- *             __Pyx_GetBuffer(obj, &self.view, flags)
- */
-  __pyx_v_self->flags = __pyx_v_flags;
-
-  /* "View.MemoryView":320
- *         self.obj = obj
- *         self.flags = flags
- *         if type(self) is memoryview or obj is not None:             # <<<<<<<<<<<<<<
- *             __Pyx_GetBuffer(obj, &self.view, flags)
- *             if <PyObject *> self.view.obj == NULL:
- */
-  __pyx_t_1 = (((PyObject *)Py_TYPE(((PyObject *)__pyx_v_self))) == ((PyObject *)((PyObject *)__pyx_memoryview_type)));
-  if (!(__pyx_t_1 != 0)) {
-    __pyx_t_2 = (__pyx_v_obj != Py_None);
-    __pyx_t_3 = (__pyx_t_2 != 0);
-  } else {
-    __pyx_t_3 = (__pyx_t_1 != 0);
-  }
-  if (__pyx_t_3) {
-
-    /* "View.MemoryView":321
- *         self.flags = flags
- *         if type(self) is memoryview or obj is not None:
- *             __Pyx_GetBuffer(obj, &self.view, flags)             # <<<<<<<<<<<<<<
- *             if <PyObject *> self.view.obj == NULL:
- *                 (<__pyx_buffer *> &self.view).obj = Py_None
- */
-    __pyx_t_4 = __Pyx_GetBuffer(__pyx_v_obj, (&__pyx_v_self->view), __pyx_v_flags); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 321; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-    /* "View.MemoryView":322
- *         if type(self) is memoryview or obj is not None:
- *             __Pyx_GetBuffer(obj, &self.view, flags)
- *             if <PyObject *> self.view.obj == NULL:             # <<<<<<<<<<<<<<
- *                 (<__pyx_buffer *> &self.view).obj = Py_None
- *                 Py_INCREF(Py_None)
- */
-    __pyx_t_3 = ((((PyObject *)__pyx_v_self->view.obj) == NULL) != 0);
-    if (__pyx_t_3) {
-
-      /* "View.MemoryView":323
- *             __Pyx_GetBuffer(obj, &self.view, flags)
- *             if <PyObject *> self.view.obj == NULL:
- *                 (<__pyx_buffer *> &self.view).obj = Py_None             # <<<<<<<<<<<<<<
- *                 Py_INCREF(Py_None)
- * 
- */
-      ((Py_buffer *)(&__pyx_v_self->view))->obj = Py_None;
-
-      /* "View.MemoryView":324
- *             if <PyObject *> self.view.obj == NULL:
- *                 (<__pyx_buffer *> &self.view).obj = Py_None
- *                 Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
- * 
- *         self.lock = PyThread_allocate_lock()
- */
-      Py_INCREF(Py_None);
-      goto __pyx_L4;
-    }
-    __pyx_L4:;
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":326
- *                 Py_INCREF(Py_None)
- * 
- *         self.lock = PyThread_allocate_lock()             # <<<<<<<<<<<<<<
- *         if self.lock == NULL:
- *             raise MemoryError
- */
-  __pyx_v_self->lock = PyThread_allocate_lock();
-
-  /* "View.MemoryView":327
- * 
- *         self.lock = PyThread_allocate_lock()
- *         if self.lock == NULL:             # <<<<<<<<<<<<<<
- *             raise MemoryError
- * 
- */
-  __pyx_t_3 = ((__pyx_v_self->lock == NULL) != 0);
-  if (__pyx_t_3) {
-
-    /* "View.MemoryView":328
- *         self.lock = PyThread_allocate_lock()
- *         if self.lock == NULL:
- *             raise MemoryError             # <<<<<<<<<<<<<<
- * 
- *         if flags & PyBUF_FORMAT:
- */
-    PyErr_NoMemory(); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 328; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "View.MemoryView":330
- *             raise MemoryError
- * 
- *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
- *             self.dtype_is_object = self.view.format == b'O'
- *         else:
- */
-  __pyx_t_3 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
-  if (__pyx_t_3) {
-
-    /* "View.MemoryView":331
- * 
- *         if flags & PyBUF_FORMAT:
- *             self.dtype_is_object = self.view.format == b'O'             # <<<<<<<<<<<<<<
- *         else:
- *             self.dtype_is_object = dtype_is_object
- */
-    __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    __pyx_t_6 = PyObject_RichCompare(__pyx_t_5, __pyx_n_b_O, Py_EQ); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-    __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely((__pyx_t_3 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-    __pyx_v_self->dtype_is_object = __pyx_t_3;
-    goto __pyx_L6;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":333
- *             self.dtype_is_object = self.view.format == b'O'
- *         else:
- *             self.dtype_is_object = dtype_is_object             # <<<<<<<<<<<<<<
- * 
- *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(
- */
-    __pyx_v_self->dtype_is_object = __pyx_v_dtype_is_object;
-  }
-  __pyx_L6:;
-
-  /* "View.MemoryView":335
- *             self.dtype_is_object = dtype_is_object
- * 
- *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(             # <<<<<<<<<<<<<<
- *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))
- *         self.typeinfo = NULL
- */
-  __pyx_v_self->acquisition_count_aligned_p = ((__pyx_atomic_int *)__pyx_align_pointer(((void *)(&(__pyx_v_self->acquisition_count[0]))), (sizeof(__pyx_atomic_int))));
-
-  /* "View.MemoryView":337
- *         self.acquisition_count_aligned_p = <__pyx_atomic_int *> align_pointer(
- *                   <void *> &self.acquisition_count[0], sizeof(__pyx_atomic_int))
- *         self.typeinfo = NULL             # <<<<<<<<<<<<<<
- * 
- *     def __dealloc__(memoryview self):
- */
-  __pyx_v_self->typeinfo = NULL;
-
-  /* "View.MemoryView":317
- *     cdef __Pyx_TypeInfo *typeinfo
- * 
- *     def __cinit__(memoryview self, object obj, int flags, bint dtype_is_object=False):             # <<<<<<<<<<<<<<
- *         self.obj = obj
- *         self.flags = flags
- */
-
-  /* function exit code */
-  __pyx_r = 0;
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_XDECREF(__pyx_t_6);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.__cinit__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":339
- *         self.typeinfo = NULL
- * 
- *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<
- *         if self.obj is not None:
- *             __Pyx_ReleaseBuffer(&self.view)
- */
-
-/* Python wrapper */
-static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self); /*proto*/
-static void __pyx_memoryview___dealloc__(PyObject *__pyx_v_self) {
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0);
-  __pyx_memoryview_MemoryView_10memoryview_2__dealloc__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-static void __pyx_memoryview_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  __Pyx_RefNannySetupContext("__dealloc__", 0);
-
-  /* "View.MemoryView":340
- * 
- *     def __dealloc__(memoryview self):
- *         if self.obj is not None:             # <<<<<<<<<<<<<<
- *             __Pyx_ReleaseBuffer(&self.view)
- * 
- */
-  __pyx_t_1 = (__pyx_v_self->obj != Py_None);
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":341
- *     def __dealloc__(memoryview self):
- *         if self.obj is not None:
- *             __Pyx_ReleaseBuffer(&self.view)             # <<<<<<<<<<<<<<
- * 
- *         if self.lock != NULL:
- */
-    __Pyx_ReleaseBuffer((&__pyx_v_self->view));
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":343
- *             __Pyx_ReleaseBuffer(&self.view)
- * 
- *         if self.lock != NULL:             # <<<<<<<<<<<<<<
- *             PyThread_free_lock(self.lock)
- * 
- */
-  __pyx_t_2 = ((__pyx_v_self->lock != NULL) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":344
- * 
- *         if self.lock != NULL:
- *             PyThread_free_lock(self.lock)             # <<<<<<<<<<<<<<
- * 
- *     cdef char *get_item_pointer(memoryview self, object index) except NULL:
- */
-    PyThread_free_lock(__pyx_v_self->lock);
-    goto __pyx_L4;
-  }
-  __pyx_L4:;
-
-  /* "View.MemoryView":339
- *         self.typeinfo = NULL
- * 
- *     def __dealloc__(memoryview self):             # <<<<<<<<<<<<<<
- *         if self.obj is not None:
- *             __Pyx_ReleaseBuffer(&self.view)
- */
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-/* "View.MemoryView":346
- *             PyThread_free_lock(self.lock)
- * 
- *     cdef char *get_item_pointer(memoryview self, object index) except NULL:             # <<<<<<<<<<<<<<
- *         cdef Py_ssize_t dim
- *         cdef char *itemp = <char *> self.view.buf
- */
-
-static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) {
-  Py_ssize_t __pyx_v_dim;
-  char *__pyx_v_itemp;
-  PyObject *__pyx_v_idx = NULL;
-  char *__pyx_r;
-  __Pyx_RefNannyDeclarations
-  Py_ssize_t __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  Py_ssize_t __pyx_t_3;
-  PyObject *(*__pyx_t_4)(PyObject *);
-  PyObject *__pyx_t_5 = NULL;
-  Py_ssize_t __pyx_t_6;
-  char *__pyx_t_7;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("get_item_pointer", 0);
-
-  /* "View.MemoryView":348
- *     cdef char *get_item_pointer(memoryview self, object index) except NULL:
- *         cdef Py_ssize_t dim
- *         cdef char *itemp = <char *> self.view.buf             # <<<<<<<<<<<<<<
- * 
- *         for dim, idx in enumerate(index):
- */
-  __pyx_v_itemp = ((char *)__pyx_v_self->view.buf);
-
-  /* "View.MemoryView":350
- *         cdef char *itemp = <char *> self.view.buf
- * 
- *         for dim, idx in enumerate(index):             # <<<<<<<<<<<<<<
- *             itemp = pybuffer_index(&self.view, itemp, idx, dim)
- * 
- */
-  __pyx_t_1 = 0;
-  if (PyList_CheckExact(__pyx_v_index) || PyTuple_CheckExact(__pyx_v_index)) {
-    __pyx_t_2 = __pyx_v_index; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0;
-    __pyx_t_4 = NULL;
-  } else {
-    __pyx_t_3 = -1; __pyx_t_2 = PyObject_GetIter(__pyx_v_index); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __pyx_t_4 = Py_TYPE(__pyx_t_2)->tp_iternext;
-  }
-  for (;;) {
-    if (!__pyx_t_4 && PyList_CheckExact(__pyx_t_2)) {
-      if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_5 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else if (!__pyx_t_4 && PyTuple_CheckExact(__pyx_t_2)) {
-      if (__pyx_t_3 >= PyTuple_GET_SIZE(__pyx_t_2)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_5); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_5 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else {
-      __pyx_t_5 = __pyx_t_4(__pyx_t_2);
-      if (unlikely(!__pyx_t_5)) {
-        PyObject* exc_type = PyErr_Occurred();
-        if (exc_type) {
-          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
-          else {__pyx_filename = __pyx_f[2]; __pyx_lineno = 350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        }
-        break;
-      }
-      __Pyx_GOTREF(__pyx_t_5);
-    }
-    __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_5);
-    __pyx_t_5 = 0;
-    __pyx_v_dim = __pyx_t_1;
-    __pyx_t_1 = (__pyx_t_1 + 1);
-
-    /* "View.MemoryView":351
- * 
- *         for dim, idx in enumerate(index):
- *             itemp = pybuffer_index(&self.view, itemp, idx, dim)             # <<<<<<<<<<<<<<
- * 
- *         return itemp
- */
-    __pyx_t_6 = __Pyx_PyIndex_AsSsize_t(__pyx_v_idx); if (unlikely((__pyx_t_6 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_t_7 = __pyx_pybuffer_index((&__pyx_v_self->view), __pyx_v_itemp, __pyx_t_6, __pyx_v_dim); if (unlikely(__pyx_t_7 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_v_itemp = __pyx_t_7;
-  }
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-
-  /* "View.MemoryView":353
- *             itemp = pybuffer_index(&self.view, itemp, idx, dim)
- * 
- *         return itemp             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_r = __pyx_v_itemp;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":346
- *             PyThread_free_lock(self.lock)
- * 
- *     cdef char *get_item_pointer(memoryview self, object index) except NULL:             # <<<<<<<<<<<<<<
- *         cdef Py_ssize_t dim
- *         cdef char *itemp = <char *> self.view.buf
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.get_item_pointer", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_idx);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":356
- * 
- * 
- *     def __getitem__(memoryview self, object index):             # <<<<<<<<<<<<<<
- *         if index is Ellipsis:
- *             return self
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index); /*proto*/
-static PyObject *__pyx_memoryview___getitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__getitem__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_4__getitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index) {
-  PyObject *__pyx_v_have_slices = NULL;
-  PyObject *__pyx_v_indices = NULL;
-  char *__pyx_v_itemp;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  PyObject *__pyx_t_5 = NULL;
-  char *__pyx_t_6;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__getitem__", 0);
-
-  /* "View.MemoryView":357
- * 
- *     def __getitem__(memoryview self, object index):
- *         if index is Ellipsis:             # <<<<<<<<<<<<<<
- *             return self
- * 
- */
-  __pyx_t_1 = (__pyx_v_index == __pyx_builtin_Ellipsis);
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":358
- *     def __getitem__(memoryview self, object index):
- *         if index is Ellipsis:
- *             return self             # <<<<<<<<<<<<<<
- * 
- *         have_slices, indices = _unellipsify(index, self.view.ndim)
- */
-    __Pyx_XDECREF(__pyx_r);
-    __Pyx_INCREF(((PyObject *)__pyx_v_self));
-    __pyx_r = ((PyObject *)__pyx_v_self);
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":360
- *             return self
- * 
- *         have_slices, indices = _unellipsify(index, self.view.ndim)             # <<<<<<<<<<<<<<
- * 
- *         cdef char *itemp
- */
-  __pyx_t_3 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  if (likely(__pyx_t_3 != Py_None)) {
-    PyObject* sequence = __pyx_t_3;
-    #if CYTHON_COMPILING_IN_CPYTHON
-    Py_ssize_t size = Py_SIZE(sequence);
-    #else
-    Py_ssize_t size = PySequence_Size(sequence);
-    #endif
-    if (unlikely(size != 2)) {
-      if (size > 2) __Pyx_RaiseTooManyValuesError(2);
-      else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-    #if CYTHON_COMPILING_IN_CPYTHON
-    __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0); 
-    __pyx_t_5 = PyTuple_GET_ITEM(sequence, 1); 
-    __Pyx_INCREF(__pyx_t_4);
-    __Pyx_INCREF(__pyx_t_5);
-    #else
-    __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_5 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    #endif
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  } else {
-    __Pyx_RaiseNoneNotIterableError(); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __pyx_v_have_slices = __pyx_t_4;
-  __pyx_t_4 = 0;
-  __pyx_v_indices = __pyx_t_5;
-  __pyx_t_5 = 0;
-
-  /* "View.MemoryView":363
- * 
- *         cdef char *itemp
- *         if have_slices:             # <<<<<<<<<<<<<<
- *             return memview_slice(self, indices)
- *         else:
- */
-  __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 363; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":364
- *         cdef char *itemp
- *         if have_slices:
- *             return memview_slice(self, indices)             # <<<<<<<<<<<<<<
- *         else:
- *             itemp = self.get_item_pointer(indices)
- */
-    __Pyx_XDECREF(__pyx_r);
-    __pyx_t_3 = ((PyObject *)__pyx_memview_slice(__pyx_v_self, __pyx_v_indices)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 364; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_r = __pyx_t_3;
-    __pyx_t_3 = 0;
-    goto __pyx_L0;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":366
- *             return memview_slice(self, indices)
- *         else:
- *             itemp = self.get_item_pointer(indices)             # <<<<<<<<<<<<<<
- *             return self.convert_item_to_object(itemp)
- * 
- */
-    __pyx_t_6 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_indices); if (unlikely(__pyx_t_6 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 366; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_v_itemp = __pyx_t_6;
-
-    /* "View.MemoryView":367
- *         else:
- *             itemp = self.get_item_pointer(indices)
- *             return self.convert_item_to_object(itemp)             # <<<<<<<<<<<<<<
- * 
- *     def __setitem__(memoryview self, object index, object value):
- */
-    __Pyx_XDECREF(__pyx_r);
-    __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->convert_item_to_object(__pyx_v_self, __pyx_v_itemp); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 367; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_r = __pyx_t_3;
-    __pyx_t_3 = 0;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":356
- * 
- * 
- *     def __getitem__(memoryview self, object index):             # <<<<<<<<<<<<<<
- *         if index is Ellipsis:
- *             return self
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.__getitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_have_slices);
-  __Pyx_XDECREF(__pyx_v_indices);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":369
- *             return self.convert_item_to_object(itemp)
- * 
- *     def __setitem__(memoryview self, object index, object value):             # <<<<<<<<<<<<<<
- *         have_slices, index = _unellipsify(index, self.view.ndim)
- * 
- */
-
-/* Python wrapper */
-static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /*proto*/
-static int __pyx_memoryview___setitem__(PyObject *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__setitem__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_6__setitem__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((PyObject *)__pyx_v_index), ((PyObject *)__pyx_v_value));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static int __pyx_memoryview_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) {
-  PyObject *__pyx_v_have_slices = NULL;
-  PyObject *__pyx_v_obj = NULL;
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_t_4;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__setitem__", 0);
-  __Pyx_INCREF(__pyx_v_index);
-
-  /* "View.MemoryView":370
- * 
- *     def __setitem__(memoryview self, object index, object value):
- *         have_slices, index = _unellipsify(index, self.view.ndim)             # <<<<<<<<<<<<<<
- * 
- *         if have_slices:
- */
-  __pyx_t_1 = _unellipsify(__pyx_v_index, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (likely(__pyx_t_1 != Py_None)) {
-    PyObject* sequence = __pyx_t_1;
-    #if CYTHON_COMPILING_IN_CPYTHON
-    Py_ssize_t size = Py_SIZE(sequence);
-    #else
-    Py_ssize_t size = PySequence_Size(sequence);
-    #endif
-    if (unlikely(size != 2)) {
-      if (size > 2) __Pyx_RaiseTooManyValuesError(2);
-      else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-    #if CYTHON_COMPILING_IN_CPYTHON
-    __pyx_t_2 = PyTuple_GET_ITEM(sequence, 0); 
-    __pyx_t_3 = PyTuple_GET_ITEM(sequence, 1); 
-    __Pyx_INCREF(__pyx_t_2);
-    __Pyx_INCREF(__pyx_t_3);
-    #else
-    __pyx_t_2 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __pyx_t_3 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    #endif
-    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  } else {
-    __Pyx_RaiseNoneNotIterableError(); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 370; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __pyx_v_have_slices = __pyx_t_2;
-  __pyx_t_2 = 0;
-  __Pyx_DECREF_SET(__pyx_v_index, __pyx_t_3);
-  __pyx_t_3 = 0;
-
-  /* "View.MemoryView":372
- *         have_slices, index = _unellipsify(index, self.view.ndim)
- * 
- *         if have_slices:             # <<<<<<<<<<<<<<
- *             obj = self.is_slice(value)
- *             if obj:
- */
-  __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_v_have_slices); if (unlikely(__pyx_t_4 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 372; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (__pyx_t_4) {
-
-    /* "View.MemoryView":373
- * 
- *         if have_slices:
- *             obj = self.is_slice(value)             # <<<<<<<<<<<<<<
- *             if obj:
- *                 self.setitem_slice_assignment(self[index], obj)
- */
-    __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->is_slice(__pyx_v_self, __pyx_v_value); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 373; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_1);
-    __pyx_v_obj = __pyx_t_1;
-    __pyx_t_1 = 0;
-
-    /* "View.MemoryView":374
- *         if have_slices:
- *             obj = self.is_slice(value)
- *             if obj:             # <<<<<<<<<<<<<<
- *                 self.setitem_slice_assignment(self[index], obj)
- *             else:
- */
-    __pyx_t_4 = __Pyx_PyObject_IsTrue(__pyx_v_obj); if (unlikely(__pyx_t_4 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 374; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    if (__pyx_t_4) {
-
-      /* "View.MemoryView":375
- *             obj = self.is_slice(value)
- *             if obj:
- *                 self.setitem_slice_assignment(self[index], obj)             # <<<<<<<<<<<<<<
- *             else:
- *                 self.setitem_slice_assign_scalar(self[index], value)
- */
-      __pyx_t_1 = PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(__pyx_t_1 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 375; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-      __Pyx_GOTREF(__pyx_t_1);
-      __pyx_t_3 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assignment(__pyx_v_self, __pyx_t_1, __pyx_v_obj); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 375; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      goto __pyx_L4;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":377
- *                 self.setitem_slice_assignment(self[index], obj)
- *             else:
- *                 self.setitem_slice_assign_scalar(self[index], value)             # <<<<<<<<<<<<<<
- *         else:
- *             self.setitem_indexed(index, value)
- */
-      __pyx_t_3 = PyObject_GetItem(((PyObject *)__pyx_v_self), __pyx_v_index); if (unlikely(__pyx_t_3 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 377; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-      __Pyx_GOTREF(__pyx_t_3);
-      if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 377; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_slice_assign_scalar(__pyx_v_self, ((struct __pyx_memoryview_obj *)__pyx_t_3), __pyx_v_value); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 377; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_1);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-    }
-    __pyx_L4:;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":379
- *                 self.setitem_slice_assign_scalar(self[index], value)
- *         else:
- *             self.setitem_indexed(index, value)             # <<<<<<<<<<<<<<
- * 
- *     cdef is_slice(self, obj):
- */
-    __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->setitem_indexed(__pyx_v_self, __pyx_v_index, __pyx_v_value); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 379; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_1);
-    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":369
- *             return self.convert_item_to_object(itemp)
- * 
- *     def __setitem__(memoryview self, object index, object value):             # <<<<<<<<<<<<<<
- *         have_slices, index = _unellipsify(index, self.view.ndim)
- * 
- */
-
-  /* function exit code */
-  __pyx_r = 0;
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.__setitem__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_have_slices);
-  __Pyx_XDECREF(__pyx_v_obj);
-  __Pyx_XDECREF(__pyx_v_index);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":381
- *             self.setitem_indexed(index, value)
- * 
- *     cdef is_slice(self, obj):             # <<<<<<<<<<<<<<
- *         if not isinstance(obj, memoryview):
- *             try:
- */
-
-static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  PyObject *__pyx_t_5 = NULL;
-  PyObject *__pyx_t_6 = NULL;
-  PyObject *__pyx_t_7 = NULL;
-  PyObject *__pyx_t_8 = NULL;
-  int __pyx_t_9;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("is_slice", 0);
-  __Pyx_INCREF(__pyx_v_obj);
-
-  /* "View.MemoryView":382
- * 
- *     cdef is_slice(self, obj):
- *         if not isinstance(obj, memoryview):             # <<<<<<<<<<<<<<
- *             try:
- *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
- */
-  __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_obj, ((PyObject *)__pyx_memoryview_type)); 
-  __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":383
- *     cdef is_slice(self, obj):
- *         if not isinstance(obj, memoryview):
- *             try:             # <<<<<<<<<<<<<<
- *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
- *                                  self.dtype_is_object)
- */
-    {
-      __Pyx_ExceptionSave(&__pyx_t_3, &__pyx_t_4, &__pyx_t_5);
-      __Pyx_XGOTREF(__pyx_t_3);
-      __Pyx_XGOTREF(__pyx_t_4);
-      __Pyx_XGOTREF(__pyx_t_5);
-      /*try:*/ {
-
-        /* "View.MemoryView":384
- *         if not isinstance(obj, memoryview):
- *             try:
- *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,             # <<<<<<<<<<<<<<
- *                                  self.dtype_is_object)
- *             except TypeError:
- */
-        __pyx_t_6 = __Pyx_PyInt_From_int((__pyx_v_self->flags | PyBUF_ANY_CONTIGUOUS)); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 384; __pyx_clineno = __LINE__; goto __pyx_L4_error;}
-        __Pyx_GOTREF(__pyx_t_6);
-
-        /* "View.MemoryView":385
- *             try:
- *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
- *                                  self.dtype_is_object)             # <<<<<<<<<<<<<<
- *             except TypeError:
- *                 return None
- */
-        __pyx_t_7 = __Pyx_PyBool_FromLong(__pyx_v_self->dtype_is_object); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 385; __pyx_clineno = __LINE__; goto __pyx_L4_error;}
-        __Pyx_GOTREF(__pyx_t_7);
-
-        /* "View.MemoryView":384
- *         if not isinstance(obj, memoryview):
- *             try:
- *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,             # <<<<<<<<<<<<<<
- *                                  self.dtype_is_object)
- *             except TypeError:
- */
-        __pyx_t_8 = PyTuple_New(3); if (unlikely(!__pyx_t_8)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 384; __pyx_clineno = __LINE__; goto __pyx_L4_error;}
-        __Pyx_GOTREF(__pyx_t_8);
-        __Pyx_INCREF(__pyx_v_obj);
-        PyTuple_SET_ITEM(__pyx_t_8, 0, __pyx_v_obj);
-        __Pyx_GIVEREF(__pyx_v_obj);
-        PyTuple_SET_ITEM(__pyx_t_8, 1, __pyx_t_6);
-        __Pyx_GIVEREF(__pyx_t_6);
-        PyTuple_SET_ITEM(__pyx_t_8, 2, __pyx_t_7);
-        __Pyx_GIVEREF(__pyx_t_7);
-        __pyx_t_6 = 0;
-        __pyx_t_7 = 0;
-        __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_memoryview_type)), __pyx_t_8, NULL); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 384; __pyx_clineno = __LINE__; goto __pyx_L4_error;}
-        __Pyx_GOTREF(__pyx_t_7);
-        __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-        __Pyx_DECREF_SET(__pyx_v_obj, __pyx_t_7);
-        __pyx_t_7 = 0;
-      }
-      __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
-      __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
-      goto __pyx_L11_try_end;
-      __pyx_L4_error:;
-      __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
-      __Pyx_XDECREF(__pyx_t_8); __pyx_t_8 = 0;
-      __Pyx_XDECREF(__pyx_t_7); __pyx_t_7 = 0;
-
-      /* "View.MemoryView":386
- *                 obj = memoryview(obj, self.flags|PyBUF_ANY_CONTIGUOUS,
- *                                  self.dtype_is_object)
- *             except TypeError:             # <<<<<<<<<<<<<<
- *                 return None
- * 
- */
-      __pyx_t_9 = PyErr_ExceptionMatches(__pyx_builtin_TypeError);
-      if (__pyx_t_9) {
-        __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-        if (__Pyx_GetException(&__pyx_t_7, &__pyx_t_8, &__pyx_t_6) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 386; __pyx_clineno = __LINE__; goto __pyx_L6_except_error;}
-        __Pyx_GOTREF(__pyx_t_7);
-        __Pyx_GOTREF(__pyx_t_8);
-        __Pyx_GOTREF(__pyx_t_6);
-
-        /* "View.MemoryView":387
- *                                  self.dtype_is_object)
- *             except TypeError:
- *                 return None             # <<<<<<<<<<<<<<
- * 
- *         return obj
- */
-        __Pyx_XDECREF(__pyx_r);
-        __Pyx_INCREF(Py_None);
-        __pyx_r = Py_None;
-        __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-        __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
-        __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-        goto __pyx_L7_except_return;
-        __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
-        __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-        __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-        goto __pyx_L5_exception_handled;
-      }
-      goto __pyx_L6_except_error;
-      __pyx_L6_except_error:;
-      __Pyx_XGIVEREF(__pyx_t_3);
-      __Pyx_XGIVEREF(__pyx_t_4);
-      __Pyx_XGIVEREF(__pyx_t_5);
-      __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5);
-      goto __pyx_L1_error;
-      __pyx_L7_except_return:;
-      __Pyx_XGIVEREF(__pyx_t_3);
-      __Pyx_XGIVEREF(__pyx_t_4);
-      __Pyx_XGIVEREF(__pyx_t_5);
-      __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5);
-      goto __pyx_L0;
-      __pyx_L5_exception_handled:;
-      __Pyx_XGIVEREF(__pyx_t_3);
-      __Pyx_XGIVEREF(__pyx_t_4);
-      __Pyx_XGIVEREF(__pyx_t_5);
-      __Pyx_ExceptionReset(__pyx_t_3, __pyx_t_4, __pyx_t_5);
-      __pyx_L11_try_end:;
-    }
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":389
- *                 return None
- * 
- *         return obj             # <<<<<<<<<<<<<<
- * 
- *     cdef setitem_slice_assignment(self, dst, src):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(__pyx_v_obj);
-  __pyx_r = __pyx_v_obj;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":381
- *             self.setitem_indexed(index, value)
- * 
- *     cdef is_slice(self, obj):             # <<<<<<<<<<<<<<
- *         if not isinstance(obj, memoryview):
- *             try:
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_6);
-  __Pyx_XDECREF(__pyx_t_7);
-  __Pyx_XDECREF(__pyx_t_8);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.is_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_obj);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":391
- *         return obj
- * 
- *     cdef setitem_slice_assignment(self, dst, src):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice dst_slice
- *         cdef __Pyx_memviewslice src_slice
- */
-
-static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src) {
-  __Pyx_memviewslice __pyx_v_dst_slice;
-  __Pyx_memviewslice __pyx_v_src_slice;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("setitem_slice_assignment", 0);
-
-  /* "View.MemoryView":395
- *         cdef __Pyx_memviewslice src_slice
- * 
- *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],             # <<<<<<<<<<<<<<
- *                                  get_slice_from_memview(dst, &dst_slice)[0],
- *                                  src.ndim, dst.ndim, self.dtype_is_object)
- */
-  if (!(likely(((__pyx_v_src) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_src, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 395; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-  /* "View.MemoryView":396
- * 
- *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],
- *                                  get_slice_from_memview(dst, &dst_slice)[0],             # <<<<<<<<<<<<<<
- *                                  src.ndim, dst.ndim, self.dtype_is_object)
- * 
- */
-  if (!(likely(((__pyx_v_dst) == Py_None) || likely(__Pyx_TypeTest(__pyx_v_dst, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 396; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-  /* "View.MemoryView":397
- *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],
- *                                  get_slice_from_memview(dst, &dst_slice)[0],
- *                                  src.ndim, dst.ndim, self.dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):
- */
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_src, __pyx_n_s_ndim); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_2 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_dst, __pyx_n_s_ndim); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_3 = __Pyx_PyInt_As_int(__pyx_t_1); if (unlikely((__pyx_t_3 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-
-  /* "View.MemoryView":395
- *         cdef __Pyx_memviewslice src_slice
- * 
- *         memoryview_copy_contents(get_slice_from_memview(src, &src_slice)[0],             # <<<<<<<<<<<<<<
- *                                  get_slice_from_memview(dst, &dst_slice)[0],
- *                                  src.ndim, dst.ndim, self.dtype_is_object)
- */
-  __pyx_t_4 = __pyx_memoryview_copy_contents((__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_src), (&__pyx_v_src_slice))[0]), (__pyx_memoryview_get_slice_from_memoryview(((struct __pyx_memoryview_obj *)__pyx_v_dst), (&__pyx_v_dst_slice))[0]), __pyx_t_2, __pyx_t_3, __pyx_v_self->dtype_is_object); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 395; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-  /* "View.MemoryView":391
- *         return obj
- * 
- *     cdef setitem_slice_assignment(self, dst, src):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice dst_slice
- *         cdef __Pyx_memviewslice src_slice
- */
-
-  /* function exit code */
-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assignment", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":399
- *                                  src.ndim, dst.ndim, self.dtype_is_object)
- * 
- *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):             # <<<<<<<<<<<<<<
- *         cdef int array[128]
- *         cdef void *tmp = NULL
- */
-
-static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value) {
-  int __pyx_v_array[128];
-  void *__pyx_v_tmp;
-  void *__pyx_v_item;
-  __Pyx_memviewslice *__pyx_v_dst_slice;
-  __Pyx_memviewslice __pyx_v_tmp_slice;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  char const *__pyx_t_5;
-  PyObject *__pyx_t_6 = NULL;
-  PyObject *__pyx_t_7 = NULL;
-  PyObject *__pyx_t_8 = NULL;
-  PyObject *__pyx_t_9 = NULL;
-  PyObject *__pyx_t_10 = NULL;
-  PyObject *__pyx_t_11 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("setitem_slice_assign_scalar", 0);
-
-  /* "View.MemoryView":401
- *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):
- *         cdef int array[128]
- *         cdef void *tmp = NULL             # <<<<<<<<<<<<<<
- *         cdef void *item
- * 
- */
-  __pyx_v_tmp = NULL;
-
-  /* "View.MemoryView":406
- *         cdef __Pyx_memviewslice *dst_slice
- *         cdef __Pyx_memviewslice tmp_slice
- *         dst_slice = get_slice_from_memview(dst, &tmp_slice)             # <<<<<<<<<<<<<<
- * 
- *         if <size_t>self.view.itemsize > sizeof(array):
- */
-  __pyx_v_dst_slice = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_dst, (&__pyx_v_tmp_slice));
-
-  /* "View.MemoryView":408
- *         dst_slice = get_slice_from_memview(dst, &tmp_slice)
- * 
- *         if <size_t>self.view.itemsize > sizeof(array):             # <<<<<<<<<<<<<<
- *             tmp = PyMem_Malloc(self.view.itemsize)
- *             if tmp == NULL:
- */
-  __pyx_t_1 = ((((size_t)__pyx_v_self->view.itemsize) > (sizeof(__pyx_v_array))) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":409
- * 
- *         if <size_t>self.view.itemsize > sizeof(array):
- *             tmp = PyMem_Malloc(self.view.itemsize)             # <<<<<<<<<<<<<<
- *             if tmp == NULL:
- *                 raise MemoryError
- */
-    __pyx_v_tmp = PyMem_Malloc(__pyx_v_self->view.itemsize);
-
-    /* "View.MemoryView":410
- *         if <size_t>self.view.itemsize > sizeof(array):
- *             tmp = PyMem_Malloc(self.view.itemsize)
- *             if tmp == NULL:             # <<<<<<<<<<<<<<
- *                 raise MemoryError
- *             item = tmp
- */
-    __pyx_t_1 = ((__pyx_v_tmp == NULL) != 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":411
- *             tmp = PyMem_Malloc(self.view.itemsize)
- *             if tmp == NULL:
- *                 raise MemoryError             # <<<<<<<<<<<<<<
- *             item = tmp
- *         else:
- */
-      PyErr_NoMemory(); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 411; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-
-    /* "View.MemoryView":412
- *             if tmp == NULL:
- *                 raise MemoryError
- *             item = tmp             # <<<<<<<<<<<<<<
- *         else:
- *             item = <void *> array
- */
-    __pyx_v_item = __pyx_v_tmp;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":414
- *             item = tmp
- *         else:
- *             item = <void *> array             # <<<<<<<<<<<<<<
- * 
- *         try:
- */
-    __pyx_v_item = ((void *)__pyx_v_array);
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":416
- *             item = <void *> array
- * 
- *         try:             # <<<<<<<<<<<<<<
- *             if self.dtype_is_object:
- *                 (<PyObject **> item)[0] = <PyObject *> value
- */
-  /*try:*/ {
-
-    /* "View.MemoryView":417
- * 
- *         try:
- *             if self.dtype_is_object:             # <<<<<<<<<<<<<<
- *                 (<PyObject **> item)[0] = <PyObject *> value
- *             else:
- */
-    __pyx_t_1 = (__pyx_v_self->dtype_is_object != 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":418
- *         try:
- *             if self.dtype_is_object:
- *                 (<PyObject **> item)[0] = <PyObject *> value             # <<<<<<<<<<<<<<
- *             else:
- *                 self.assign_item_from_object(<char *> item, value)
- */
-      (((PyObject **)__pyx_v_item)[0]) = ((PyObject *)__pyx_v_value);
-      goto __pyx_L8;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":420
- *                 (<PyObject **> item)[0] = <PyObject *> value
- *             else:
- *                 self.assign_item_from_object(<char *> item, value)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, ((char *)__pyx_v_item), __pyx_v_value); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 420; __pyx_clineno = __LINE__; goto __pyx_L6_error;}
-      __Pyx_GOTREF(__pyx_t_2);
-      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-    }
-    __pyx_L8:;
-
-    /* "View.MemoryView":424
- * 
- * 
- *             if self.view.suboffsets != NULL:             # <<<<<<<<<<<<<<
- *                 assert_direct_dimensions(self.view.suboffsets, self.view.ndim)
- *             slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize,
- */
-    __pyx_t_1 = ((__pyx_v_self->view.suboffsets != NULL) != 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":425
- * 
- *             if self.view.suboffsets != NULL:
- *                 assert_direct_dimensions(self.view.suboffsets, self.view.ndim)             # <<<<<<<<<<<<<<
- *             slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize,
- *                                 item, self.dtype_is_object)
- */
-      __pyx_t_2 = assert_direct_dimensions(__pyx_v_self->view.suboffsets, __pyx_v_self->view.ndim); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 425; __pyx_clineno = __LINE__; goto __pyx_L6_error;}
-      __Pyx_GOTREF(__pyx_t_2);
-      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      goto __pyx_L9;
-    }
-    __pyx_L9:;
-
-    /* "View.MemoryView":426
- *             if self.view.suboffsets != NULL:
- *                 assert_direct_dimensions(self.view.suboffsets, self.view.ndim)
- *             slice_assign_scalar(dst_slice, dst.view.ndim, self.view.itemsize,             # <<<<<<<<<<<<<<
- *                                 item, self.dtype_is_object)
- *         finally:
- */
-    __pyx_memoryview_slice_assign_scalar(__pyx_v_dst_slice, __pyx_v_dst->view.ndim, __pyx_v_self->view.itemsize, __pyx_v_item, __pyx_v_self->dtype_is_object);
-  }
-
-  /* "View.MemoryView":429
- *                                 item, self.dtype_is_object)
- *         finally:
- *             PyMem_Free(tmp)             # <<<<<<<<<<<<<<
- * 
- *     cdef setitem_indexed(self, index, value):
- */
-  /*finally:*/ {
-    /*normal exit:*/{
-      PyMem_Free(__pyx_v_tmp);
-      goto __pyx_L7;
-    }
-    /*exception exit:*/{
-      __pyx_L6_error:;
-      __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0;
-      __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0;
-      if (PY_MAJOR_VERSION >= 3) __Pyx_ExceptionSwap(&__pyx_t_9, &__pyx_t_10, &__pyx_t_11);
-      if ((PY_MAJOR_VERSION < 3) || unlikely(__Pyx_GetException(&__pyx_t_6, &__pyx_t_7, &__pyx_t_8) < 0)) __Pyx_ErrFetch(&__pyx_t_6, &__pyx_t_7, &__pyx_t_8);
-      __Pyx_XGOTREF(__pyx_t_6);
-      __Pyx_XGOTREF(__pyx_t_7);
-      __Pyx_XGOTREF(__pyx_t_8);
-      __Pyx_XGOTREF(__pyx_t_9);
-      __Pyx_XGOTREF(__pyx_t_10);
-      __Pyx_XGOTREF(__pyx_t_11);
-      __pyx_t_3 = __pyx_lineno; __pyx_t_4 = __pyx_clineno; __pyx_t_5 = __pyx_filename;
-      {
-        PyMem_Free(__pyx_v_tmp);
-      }
-      if (PY_MAJOR_VERSION >= 3) {
-        __Pyx_XGIVEREF(__pyx_t_9);
-        __Pyx_XGIVEREF(__pyx_t_10);
-        __Pyx_XGIVEREF(__pyx_t_11);
-        __Pyx_ExceptionReset(__pyx_t_9, __pyx_t_10, __pyx_t_11);
-      }
-      __Pyx_XGIVEREF(__pyx_t_6);
-      __Pyx_XGIVEREF(__pyx_t_7);
-      __Pyx_XGIVEREF(__pyx_t_8);
-      __Pyx_ErrRestore(__pyx_t_6, __pyx_t_7, __pyx_t_8);
-      __pyx_t_6 = 0; __pyx_t_7 = 0; __pyx_t_8 = 0; __pyx_t_9 = 0; __pyx_t_10 = 0; __pyx_t_11 = 0;
-      __pyx_lineno = __pyx_t_3; __pyx_clineno = __pyx_t_4; __pyx_filename = __pyx_t_5;
-      goto __pyx_L1_error;
-    }
-    __pyx_L7:;
-  }
-
-  /* "View.MemoryView":399
- *                                  src.ndim, dst.ndim, self.dtype_is_object)
- * 
- *     cdef setitem_slice_assign_scalar(self, memoryview dst, value):             # <<<<<<<<<<<<<<
- *         cdef int array[128]
- *         cdef void *tmp = NULL
- */
-
-  /* function exit code */
-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_slice_assign_scalar", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":431
- *             PyMem_Free(tmp)
- * 
- *     cdef setitem_indexed(self, index, value):             # <<<<<<<<<<<<<<
- *         cdef char *itemp = self.get_item_pointer(index)
- *         self.assign_item_from_object(itemp, value)
- */
-
-static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value) {
-  char *__pyx_v_itemp;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  char *__pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("setitem_indexed", 0);
-
-  /* "View.MemoryView":432
- * 
- *     cdef setitem_indexed(self, index, value):
- *         cdef char *itemp = self.get_item_pointer(index)             # <<<<<<<<<<<<<<
- *         self.assign_item_from_object(itemp, value)
- * 
- */
-  __pyx_t_1 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->get_item_pointer(__pyx_v_self, __pyx_v_index); if (unlikely(__pyx_t_1 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 432; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_v_itemp = __pyx_t_1;
-
-  /* "View.MemoryView":433
- *     cdef setitem_indexed(self, index, value):
- *         cdef char *itemp = self.get_item_pointer(index)
- *         self.assign_item_from_object(itemp, value)             # <<<<<<<<<<<<<<
- * 
- *     cdef convert_item_to_object(self, char *itemp):
- */
-  __pyx_t_2 = ((struct __pyx_vtabstruct_memoryview *)__pyx_v_self->__pyx_vtab)->assign_item_from_object(__pyx_v_self, __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 433; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-
-  /* "View.MemoryView":431
- *             PyMem_Free(tmp)
- * 
- *     cdef setitem_indexed(self, index, value):             # <<<<<<<<<<<<<<
- *         cdef char *itemp = self.get_item_pointer(index)
- *         self.assign_item_from_object(itemp, value)
- */
-
-  /* function exit code */
-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.setitem_indexed", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":435
- *         self.assign_item_from_object(itemp, value)
- * 
- *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
- *         """Only used if instantiated manually by the user, or if Cython doesn't
- *         know how to convert the type"""
- */
-
-static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp) {
-  PyObject *__pyx_v_struct = NULL;
-  PyObject *__pyx_v_bytesitem = 0;
-  PyObject *__pyx_v_result = NULL;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  PyObject *__pyx_t_5 = NULL;
-  PyObject *__pyx_t_6 = NULL;
-  size_t __pyx_t_7;
-  int __pyx_t_8;
-  int __pyx_t_9;
-  PyObject *__pyx_t_10 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("convert_item_to_object", 0);
-
-  /* "View.MemoryView":438
- *         """Only used if instantiated manually by the user, or if Cython doesn't
- *         know how to convert the type"""
- *         import struct             # <<<<<<<<<<<<<<
- *         cdef bytes bytesitem
- * 
- */
-  __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 438; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_v_struct = __pyx_t_1;
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":441
- *         cdef bytes bytesitem
- * 
- *         bytesitem = itemp[:self.view.itemsize]             # <<<<<<<<<<<<<<
- *         try:
- *             result = struct.unpack(self.view.format, bytesitem)
- */
-  __pyx_t_1 = __Pyx_PyBytes_FromStringAndSize(__pyx_v_itemp + 0, __pyx_v_self->view.itemsize - 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 441; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_v_bytesitem = ((PyObject*)__pyx_t_1);
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":442
- * 
- *         bytesitem = itemp[:self.view.itemsize]
- *         try:             # <<<<<<<<<<<<<<
- *             result = struct.unpack(self.view.format, bytesitem)
- *         except struct.error:
- */
-  {
-    __Pyx_ExceptionSave(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4);
-    __Pyx_XGOTREF(__pyx_t_2);
-    __Pyx_XGOTREF(__pyx_t_3);
-    __Pyx_XGOTREF(__pyx_t_4);
-    /*try:*/ {
-
-      /* "View.MemoryView":443
- *         bytesitem = itemp[:self.view.itemsize]
- *         try:
- *             result = struct.unpack(self.view.format, bytesitem)             # <<<<<<<<<<<<<<
- *         except struct.error:
- *             raise ValueError("Unable to convert item to object")
- */
-      __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_unpack); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 443; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-      __Pyx_GOTREF(__pyx_t_1);
-      __pyx_t_5 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 443; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-      __Pyx_GOTREF(__pyx_t_5);
-      __pyx_t_6 = PyTuple_New(2); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 443; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-      __Pyx_GOTREF(__pyx_t_6);
-      PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_5);
-      __Pyx_GIVEREF(__pyx_t_5);
-      __Pyx_INCREF(__pyx_v_bytesitem);
-      PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_v_bytesitem);
-      __Pyx_GIVEREF(__pyx_v_bytesitem);
-      __pyx_t_5 = 0;
-      __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 443; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-      __Pyx_GOTREF(__pyx_t_5);
-      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-      __pyx_v_result = __pyx_t_5;
-      __pyx_t_5 = 0;
-    }
-    /*else:*/ {
-
-      /* "View.MemoryView":447
- *             raise ValueError("Unable to convert item to object")
- *         else:
- *             if len(self.view.format) == 1:             # <<<<<<<<<<<<<<
- *                 return result[0]
- *             return result
- */
-      __pyx_t_7 = strlen(__pyx_v_self->view.format); 
-      __pyx_t_8 = ((__pyx_t_7 == 1) != 0);
-      if (__pyx_t_8) {
-
-        /* "View.MemoryView":448
- *         else:
- *             if len(self.view.format) == 1:
- *                 return result[0]             # <<<<<<<<<<<<<<
- *             return result
- * 
- */
-        __Pyx_XDECREF(__pyx_r);
-        __pyx_t_5 = __Pyx_GetItemInt(__pyx_v_result, 0, long, 1, __Pyx_PyInt_From_long, 0, 0, 1); if (unlikely(__pyx_t_5 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 448; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;};
-        __Pyx_GOTREF(__pyx_t_5);
-        __pyx_r = __pyx_t_5;
-        __pyx_t_5 = 0;
-        goto __pyx_L6_except_return;
-      }
-
-      /* "View.MemoryView":449
- *             if len(self.view.format) == 1:
- *                 return result[0]
- *             return result             # <<<<<<<<<<<<<<
- * 
- *     cdef assign_item_from_object(self, char *itemp, object value):
- */
-      __Pyx_XDECREF(__pyx_r);
-      __Pyx_INCREF(__pyx_v_result);
-      __pyx_r = __pyx_v_result;
-      goto __pyx_L6_except_return;
-    }
-    __Pyx_XDECREF(__pyx_t_2); __pyx_t_2 = 0;
-    __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
-    __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
-    goto __pyx_L10_try_end;
-    __pyx_L3_error:;
-    __Pyx_XDECREF(__pyx_t_1); __pyx_t_1 = 0;
-    __Pyx_XDECREF(__pyx_t_6); __pyx_t_6 = 0;
-    __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
-
-    /* "View.MemoryView":444
- *         try:
- *             result = struct.unpack(self.view.format, bytesitem)
- *         except struct.error:             # <<<<<<<<<<<<<<
- *             raise ValueError("Unable to convert item to object")
- *         else:
- */
-    __pyx_t_5 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_error); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 444; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    __pyx_t_9 = PyErr_ExceptionMatches(__pyx_t_5);
-    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-    if (__pyx_t_9) {
-      __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
-      if (__Pyx_GetException(&__pyx_t_5, &__pyx_t_6, &__pyx_t_1) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 444; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;}
-      __Pyx_GOTREF(__pyx_t_5);
-      __Pyx_GOTREF(__pyx_t_6);
-      __Pyx_GOTREF(__pyx_t_1);
-
-      /* "View.MemoryView":445
- *             result = struct.unpack(self.view.format, bytesitem)
- *         except struct.error:
- *             raise ValueError("Unable to convert item to object")             # <<<<<<<<<<<<<<
- *         else:
- *             if len(self.view.format) == 1:
- */
-      __pyx_t_10 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__12, NULL); if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 445; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;}
-      __Pyx_GOTREF(__pyx_t_10);
-      __Pyx_Raise(__pyx_t_10, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 445; __pyx_clineno = __LINE__; goto __pyx_L5_except_error;}
-      __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-      __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-      goto __pyx_L4_exception_handled;
-    }
-    goto __pyx_L5_except_error;
-    __pyx_L5_except_error:;
-    __Pyx_XGIVEREF(__pyx_t_2);
-    __Pyx_XGIVEREF(__pyx_t_3);
-    __Pyx_XGIVEREF(__pyx_t_4);
-    __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4);
-    goto __pyx_L1_error;
-    __pyx_L6_except_return:;
-    __Pyx_XGIVEREF(__pyx_t_2);
-    __Pyx_XGIVEREF(__pyx_t_3);
-    __Pyx_XGIVEREF(__pyx_t_4);
-    __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4);
-    goto __pyx_L0;
-    __pyx_L4_exception_handled:;
-    __Pyx_XGIVEREF(__pyx_t_2);
-    __Pyx_XGIVEREF(__pyx_t_3);
-    __Pyx_XGIVEREF(__pyx_t_4);
-    __Pyx_ExceptionReset(__pyx_t_2, __pyx_t_3, __pyx_t_4);
-    __pyx_L10_try_end:;
-  }
-
-  /* "View.MemoryView":435
- *         self.assign_item_from_object(itemp, value)
- * 
- *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
- *         """Only used if instantiated manually by the user, or if Cython doesn't
- *         know how to convert the type"""
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_XDECREF(__pyx_t_6);
-  __Pyx_XDECREF(__pyx_t_10);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_struct);
-  __Pyx_XDECREF(__pyx_v_bytesitem);
-  __Pyx_XDECREF(__pyx_v_result);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":451
- *             return result
- * 
- *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
- *         """Only used if instantiated manually by the user, or if Cython doesn't
- *         know how to convert the type"""
- */
-
-static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) {
-  PyObject *__pyx_v_struct = NULL;
-  char __pyx_v_c;
-  PyObject *__pyx_v_bytesvalue = 0;
-  Py_ssize_t __pyx_v_i;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  PyObject *__pyx_t_4 = NULL;
-  PyObject *__pyx_t_5 = NULL;
-  PyObject *__pyx_t_6 = NULL;
-  Py_ssize_t __pyx_t_7;
-  PyObject *__pyx_t_8 = NULL;
-  char *__pyx_t_9;
-  char *__pyx_t_10;
-  char *__pyx_t_11;
-  char *__pyx_t_12;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("assign_item_from_object", 0);
-
-  /* "View.MemoryView":454
- *         """Only used if instantiated manually by the user, or if Cython doesn't
- *         know how to convert the type"""
- *         import struct             # <<<<<<<<<<<<<<
- *         cdef char c
- *         cdef bytes bytesvalue
- */
-  __pyx_t_1 = __Pyx_Import(__pyx_n_s_struct, 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 454; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_v_struct = __pyx_t_1;
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":459
- *         cdef Py_ssize_t i
- * 
- *         if isinstance(value, tuple):             # <<<<<<<<<<<<<<
- *             bytesvalue = struct.pack(self.view.format, *value)
- *         else:
- */
-  __pyx_t_2 = PyTuple_Check(__pyx_v_value); 
-  __pyx_t_3 = (__pyx_t_2 != 0);
-  if (__pyx_t_3) {
-
-    /* "View.MemoryView":460
- * 
- *         if isinstance(value, tuple):
- *             bytesvalue = struct.pack(self.view.format, *value)             # <<<<<<<<<<<<<<
- *         else:
- *             bytesvalue = struct.pack(self.view.format, value)
- */
-    __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_1);
-    __pyx_t_4 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4);
-    __Pyx_GIVEREF(__pyx_t_4);
-    __pyx_t_4 = 0;
-    __pyx_t_4 = PySequence_Tuple(__pyx_v_value); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_6 = PyNumber_Add(__pyx_t_5, __pyx_t_4); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_6);
-    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_1, __pyx_t_6, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-    __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-    if (!(likely(PyBytes_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_4)->tp_name), 0))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_v_bytesvalue = ((PyObject*)__pyx_t_4);
-    __pyx_t_4 = 0;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":462
- *             bytesvalue = struct.pack(self.view.format, *value)
- *         else:
- *             bytesvalue = struct.pack(self.view.format, value)             # <<<<<<<<<<<<<<
- * 
- *         for i, c in enumerate(bytesvalue):
- */
-    __pyx_t_4 = __Pyx_PyObject_GetAttrStr(__pyx_v_struct, __pyx_n_s_pack); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_6 = __Pyx_PyBytes_FromString(__pyx_v_self->view.format); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_6);
-    __pyx_t_1 = PyTuple_New(2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_1);
-    PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_6);
-    __Pyx_GIVEREF(__pyx_t_6);
-    __Pyx_INCREF(__pyx_v_value);
-    PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_v_value);
-    __Pyx_GIVEREF(__pyx_v_value);
-    __pyx_t_6 = 0;
-    __pyx_t_6 = __Pyx_PyObject_Call(__pyx_t_4, __pyx_t_1, NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_6);
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-    if (!(likely(PyBytes_CheckExact(__pyx_t_6))||((__pyx_t_6) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "bytes", Py_TYPE(__pyx_t_6)->tp_name), 0))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 462; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_v_bytesvalue = ((PyObject*)__pyx_t_6);
-    __pyx_t_6 = 0;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":464
- *             bytesvalue = struct.pack(self.view.format, value)
- * 
- *         for i, c in enumerate(bytesvalue):             # <<<<<<<<<<<<<<
- *             itemp[i] = c
- * 
- */
-  __pyx_t_7 = 0;
-  if (unlikely(__pyx_v_bytesvalue == Py_None)) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' is not iterable");
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 464; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  __Pyx_INCREF(__pyx_v_bytesvalue);
-  __pyx_t_8 = __pyx_v_bytesvalue;
-  __pyx_t_10 = PyBytes_AS_STRING(__pyx_t_8);
-  __pyx_t_11 = (__pyx_t_10 + PyBytes_GET_SIZE(__pyx_t_8));
-  for (__pyx_t_12 = __pyx_t_10; __pyx_t_12 < __pyx_t_11; __pyx_t_12++) {
-    __pyx_t_9 = __pyx_t_12;
-    __pyx_v_c = (__pyx_t_9[0]);
-
-    /* "View.MemoryView":465
- * 
- *         for i, c in enumerate(bytesvalue):
- *             itemp[i] = c             # <<<<<<<<<<<<<<
- * 
- *     @cname('getbuffer')
- */
-    __pyx_v_i = __pyx_t_7;
-
-    /* "View.MemoryView":464
- *             bytesvalue = struct.pack(self.view.format, value)
- * 
- *         for i, c in enumerate(bytesvalue):             # <<<<<<<<<<<<<<
- *             itemp[i] = c
- * 
- */
-    __pyx_t_7 = (__pyx_t_7 + 1);
-
-    /* "View.MemoryView":465
- * 
- *         for i, c in enumerate(bytesvalue):
- *             itemp[i] = c             # <<<<<<<<<<<<<<
- * 
- *     @cname('getbuffer')
- */
-    (__pyx_v_itemp[__pyx_v_i]) = __pyx_v_c;
-  }
-  __Pyx_DECREF(__pyx_t_8); __pyx_t_8 = 0;
-
-  /* "View.MemoryView":451
- *             return result
- * 
- *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
- *         """Only used if instantiated manually by the user, or if Cython doesn't
- *         know how to convert the type"""
- */
-
-  /* function exit code */
-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_XDECREF(__pyx_t_6);
-  __Pyx_XDECREF(__pyx_t_8);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_struct);
-  __Pyx_XDECREF(__pyx_v_bytesvalue);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":468
- * 
- *     @cname('getbuffer')
- *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
- *         if flags & PyBUF_STRIDES:
- *             info.shape = self.view.shape
- */
-
-/* Python wrapper */
-static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/
-static CYTHON_UNUSED int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__getbuffer__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_getbuffer_MemoryView_10memoryview_8__getbuffer__(((struct __pyx_memoryview_obj *)__pyx_v_self), ((Py_buffer *)__pyx_v_info), ((int)__pyx_v_flags));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static int __pyx_memoryview_getbuffer_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  Py_ssize_t *__pyx_t_2;
-  char *__pyx_t_3;
-  void *__pyx_t_4;
-  int __pyx_t_5;
-  Py_ssize_t __pyx_t_6;
-  __Pyx_RefNannySetupContext("__getbuffer__", 0);
-  if (__pyx_v_info != NULL) {
-    __pyx_v_info->obj = Py_None; __Pyx_INCREF(Py_None);
-    __Pyx_GIVEREF(__pyx_v_info->obj);
-  }
-
-  /* "View.MemoryView":469
- *     @cname('getbuffer')
- *     def __getbuffer__(self, Py_buffer *info, int flags):
- *         if flags & PyBUF_STRIDES:             # <<<<<<<<<<<<<<
- *             info.shape = self.view.shape
- *         else:
- */
-  __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":470
- *     def __getbuffer__(self, Py_buffer *info, int flags):
- *         if flags & PyBUF_STRIDES:
- *             info.shape = self.view.shape             # <<<<<<<<<<<<<<
- *         else:
- *             info.shape = NULL
- */
-    __pyx_t_2 = __pyx_v_self->view.shape;
-    __pyx_v_info->shape = __pyx_t_2;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":472
- *             info.shape = self.view.shape
- *         else:
- *             info.shape = NULL             # <<<<<<<<<<<<<<
- * 
- *         if flags & PyBUF_STRIDES:
- */
-    __pyx_v_info->shape = NULL;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":474
- *             info.shape = NULL
- * 
- *         if flags & PyBUF_STRIDES:             # <<<<<<<<<<<<<<
- *             info.strides = self.view.strides
- *         else:
- */
-  __pyx_t_1 = ((__pyx_v_flags & PyBUF_STRIDES) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":475
- * 
- *         if flags & PyBUF_STRIDES:
- *             info.strides = self.view.strides             # <<<<<<<<<<<<<<
- *         else:
- *             info.strides = NULL
- */
-    __pyx_t_2 = __pyx_v_self->view.strides;
-    __pyx_v_info->strides = __pyx_t_2;
-    goto __pyx_L4;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":477
- *             info.strides = self.view.strides
- *         else:
- *             info.strides = NULL             # <<<<<<<<<<<<<<
- * 
- *         if flags & PyBUF_INDIRECT:
- */
-    __pyx_v_info->strides = NULL;
-  }
-  __pyx_L4:;
-
-  /* "View.MemoryView":479
- *             info.strides = NULL
- * 
- *         if flags & PyBUF_INDIRECT:             # <<<<<<<<<<<<<<
- *             info.suboffsets = self.view.suboffsets
- *         else:
- */
-  __pyx_t_1 = ((__pyx_v_flags & PyBUF_INDIRECT) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":480
- * 
- *         if flags & PyBUF_INDIRECT:
- *             info.suboffsets = self.view.suboffsets             # <<<<<<<<<<<<<<
- *         else:
- *             info.suboffsets = NULL
- */
-    __pyx_t_2 = __pyx_v_self->view.suboffsets;
-    __pyx_v_info->suboffsets = __pyx_t_2;
-    goto __pyx_L5;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":482
- *             info.suboffsets = self.view.suboffsets
- *         else:
- *             info.suboffsets = NULL             # <<<<<<<<<<<<<<
- * 
- *         if flags & PyBUF_FORMAT:
- */
-    __pyx_v_info->suboffsets = NULL;
-  }
-  __pyx_L5:;
-
-  /* "View.MemoryView":484
- *             info.suboffsets = NULL
- * 
- *         if flags & PyBUF_FORMAT:             # <<<<<<<<<<<<<<
- *             info.format = self.view.format
- *         else:
- */
-  __pyx_t_1 = ((__pyx_v_flags & PyBUF_FORMAT) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":485
- * 
- *         if flags & PyBUF_FORMAT:
- *             info.format = self.view.format             # <<<<<<<<<<<<<<
- *         else:
- *             info.format = NULL
- */
-    __pyx_t_3 = __pyx_v_self->view.format;
-    __pyx_v_info->format = __pyx_t_3;
-    goto __pyx_L6;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":487
- *             info.format = self.view.format
- *         else:
- *             info.format = NULL             # <<<<<<<<<<<<<<
- * 
- *         info.buf = self.view.buf
- */
-    __pyx_v_info->format = NULL;
-  }
-  __pyx_L6:;
-
-  /* "View.MemoryView":489
- *             info.format = NULL
- * 
- *         info.buf = self.view.buf             # <<<<<<<<<<<<<<
- *         info.ndim = self.view.ndim
- *         info.itemsize = self.view.itemsize
- */
-  __pyx_t_4 = __pyx_v_self->view.buf;
-  __pyx_v_info->buf = __pyx_t_4;
-
-  /* "View.MemoryView":490
- * 
- *         info.buf = self.view.buf
- *         info.ndim = self.view.ndim             # <<<<<<<<<<<<<<
- *         info.itemsize = self.view.itemsize
- *         info.len = self.view.len
- */
-  __pyx_t_5 = __pyx_v_self->view.ndim;
-  __pyx_v_info->ndim = __pyx_t_5;
-
-  /* "View.MemoryView":491
- *         info.buf = self.view.buf
- *         info.ndim = self.view.ndim
- *         info.itemsize = self.view.itemsize             # <<<<<<<<<<<<<<
- *         info.len = self.view.len
- *         info.readonly = 0
- */
-  __pyx_t_6 = __pyx_v_self->view.itemsize;
-  __pyx_v_info->itemsize = __pyx_t_6;
-
-  /* "View.MemoryView":492
- *         info.ndim = self.view.ndim
- *         info.itemsize = self.view.itemsize
- *         info.len = self.view.len             # <<<<<<<<<<<<<<
- *         info.readonly = 0
- *         info.obj = self
- */
-  __pyx_t_6 = __pyx_v_self->view.len;
-  __pyx_v_info->len = __pyx_t_6;
-
-  /* "View.MemoryView":493
- *         info.itemsize = self.view.itemsize
- *         info.len = self.view.len
- *         info.readonly = 0             # <<<<<<<<<<<<<<
- *         info.obj = self
- * 
- */
-  __pyx_v_info->readonly = 0;
-
-  /* "View.MemoryView":494
- *         info.len = self.view.len
- *         info.readonly = 0
- *         info.obj = self             # <<<<<<<<<<<<<<
- * 
- *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")
- */
-  __Pyx_INCREF(((PyObject *)__pyx_v_self));
-  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
-  __Pyx_GOTREF(__pyx_v_info->obj);
-  __Pyx_DECREF(__pyx_v_info->obj);
-  __pyx_v_info->obj = ((PyObject *)__pyx_v_self);
-
-  /* "View.MemoryView":468
- * 
- *     @cname('getbuffer')
- *     def __getbuffer__(self, Py_buffer *info, int flags):             # <<<<<<<<<<<<<<
- *         if flags & PyBUF_STRIDES:
- *             info.shape = self.view.shape
- */
-
-  /* function exit code */
-  __pyx_r = 0;
-  if (__pyx_v_info != NULL && __pyx_v_info->obj == Py_None) {
-    __Pyx_GOTREF(Py_None);
-    __Pyx_DECREF(Py_None); __pyx_v_info->obj = NULL;
-  }
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":501
- *     property T:
- *         @cname('__pyx_memoryview_transpose')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             cdef _memoryviewslice result = memoryview_copy(self)
- *             transpose_memslice(&result.from_slice)
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_transpose(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_transpose(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_transpose_MemoryView_10memoryview_1T___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_transpose_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  struct __pyx_memoryviewslice_obj *__pyx_v_result = 0;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_t_2;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":502
- *         @cname('__pyx_memoryview_transpose')
- *         def __get__(self):
- *             cdef _memoryviewslice result = memoryview_copy(self)             # <<<<<<<<<<<<<<
- *             transpose_memslice(&result.from_slice)
- *             return result
- */
-  __pyx_t_1 = __pyx_memoryview_copy_object(__pyx_v_self); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 502; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (!(likely(((__pyx_t_1) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_1, __pyx_memoryviewslice_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 502; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_1);
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":503
- *         def __get__(self):
- *             cdef _memoryviewslice result = memoryview_copy(self)
- *             transpose_memslice(&result.from_slice)             # <<<<<<<<<<<<<<
- *             return result
- * 
- */
-  __pyx_t_2 = __pyx_memslice_transpose((&__pyx_v_result->from_slice)); if (unlikely(__pyx_t_2 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 503; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-  /* "View.MemoryView":504
- *             cdef _memoryviewslice result = memoryview_copy(self)
- *             transpose_memslice(&result.from_slice)
- *             return result             # <<<<<<<<<<<<<<
- * 
- *     property base:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(((PyObject *)__pyx_v_result));
-  __pyx_r = ((PyObject *)__pyx_v_result);
-  goto __pyx_L0;
-
-  /* "View.MemoryView":501
- *     property T:
- *         @cname('__pyx_memoryview_transpose')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             cdef _memoryviewslice result = memoryview_copy(self)
- *             transpose_memslice(&result.from_slice)
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.T.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_result);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":508
- *     property base:
- *         @cname('__pyx_memoryview__get__base')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.obj
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview__get__base(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview__get__base(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview__get__base_MemoryView_10memoryview_4base___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview__get__base_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":509
- *         @cname('__pyx_memoryview__get__base')
- *         def __get__(self):
- *             return self.obj             # <<<<<<<<<<<<<<
- * 
- *     property shape:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(__pyx_v_self->obj);
-  __pyx_r = __pyx_v_self->obj;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":508
- *     property base:
- *         @cname('__pyx_memoryview__get__base')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.obj
- * 
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":513
- *     property shape:
- *         @cname('__pyx_memoryview_get_shape')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return tuple([self.view.shape[i] for i in xrange(self.view.ndim)])
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_get_shape(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_shape(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_get_shape_MemoryView_10memoryview_5shape___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_get_shape_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  int __pyx_v_i;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  PyObject *__pyx_t_4 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":514
- *         @cname('__pyx_memoryview_get_shape')
- *         def __get__(self):
- *             return tuple([self.view.shape[i] for i in xrange(self.view.ndim)])             # <<<<<<<<<<<<<<
- * 
- *     property strides:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyList_New(0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __pyx_v_self->view.ndim;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
-    __pyx_t_4 = PyInt_FromSsize_t((__pyx_v_self->view.shape[__pyx_v_i])); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    if (unlikely(__Pyx_ListComp_Append(__pyx_t_1, (PyObject*)__pyx_t_4))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  }
-  __pyx_t_4 = PyList_AsTuple(((PyObject*)__pyx_t_1)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_r = __pyx_t_4;
-  __pyx_t_4 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":513
- *     property shape:
- *         @cname('__pyx_memoryview_get_shape')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return tuple([self.view.shape[i] for i in xrange(self.view.ndim)])
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.shape.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":518
- *     property strides:
- *         @cname('__pyx_memoryview_get_strides')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             if self.view.strides == NULL:
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_get_strides(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_strides(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_get_strides_MemoryView_10memoryview_7strides___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_get_strides_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  int __pyx_v_i;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  PyObject *__pyx_t_5 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":519
- *         @cname('__pyx_memoryview_get_strides')
- *         def __get__(self):
- *             if self.view.strides == NULL:             # <<<<<<<<<<<<<<
- * 
- *                 raise ValueError("Buffer view does not expose strides")
- */
-  __pyx_t_1 = ((__pyx_v_self->view.strides == NULL) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":521
- *             if self.view.strides == NULL:
- * 
- *                 raise ValueError("Buffer view does not expose strides")             # <<<<<<<<<<<<<<
- * 
- *             return tuple([self.view.strides[i] for i in xrange(self.view.ndim)])
- */
-    __pyx_t_2 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__13, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __Pyx_Raise(__pyx_t_2, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "View.MemoryView":523
- *                 raise ValueError("Buffer view does not expose strides")
- * 
- *             return tuple([self.view.strides[i] for i in xrange(self.view.ndim)])             # <<<<<<<<<<<<<<
- * 
- *     property suboffsets:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = __pyx_v_self->view.ndim;
-  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-    __pyx_v_i = __pyx_t_4;
-    __pyx_t_5 = PyInt_FromSsize_t((__pyx_v_self->view.strides[__pyx_v_i])); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_5))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-  }
-  __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_5);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":518
- *     property strides:
- *         @cname('__pyx_memoryview_get_strides')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             if self.view.strides == NULL:
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.strides.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":527
- *     property suboffsets:
- *         @cname('__pyx_memoryview_get_suboffsets')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             if self.view.suboffsets == NULL:
- *                 return [-1] * self.view.ndim
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_get_suboffsets(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_suboffsets(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_get_suboffsets_MemoryView_10memoryview_10suboffsets___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_get_suboffsets_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  int __pyx_v_i;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  PyObject *__pyx_t_5 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":528
- *         @cname('__pyx_memoryview_get_suboffsets')
- *         def __get__(self):
- *             if self.view.suboffsets == NULL:             # <<<<<<<<<<<<<<
- *                 return [-1] * self.view.ndim
- * 
- */
-  __pyx_t_1 = ((__pyx_v_self->view.suboffsets == NULL) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":529
- *         def __get__(self):
- *             if self.view.suboffsets == NULL:
- *                 return [-1] * self.view.ndim             # <<<<<<<<<<<<<<
- * 
- *             return tuple([self.view.suboffsets[i] for i in xrange(self.view.ndim)])
- */
-    __Pyx_XDECREF(__pyx_r);
-    __pyx_t_2 = PyList_New(1 * ((__pyx_v_self->view.ndim<0) ? 0:__pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 529; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    { Py_ssize_t __pyx_temp;
-      for (__pyx_temp=0; __pyx_temp < __pyx_v_self->view.ndim; __pyx_temp++) {
-        __Pyx_INCREF(__pyx_int_neg_1);
-        PyList_SET_ITEM(__pyx_t_2, __pyx_temp, __pyx_int_neg_1);
-        __Pyx_GIVEREF(__pyx_int_neg_1);
-      }
-    }
-    __pyx_r = __pyx_t_2;
-    __pyx_t_2 = 0;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":531
- *                 return [-1] * self.view.ndim
- * 
- *             return tuple([self.view.suboffsets[i] for i in xrange(self.view.ndim)])             # <<<<<<<<<<<<<<
- * 
- *     property ndim:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_2 = PyList_New(0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = __pyx_v_self->view.ndim;
-  for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) {
-    __pyx_v_i = __pyx_t_4;
-    __pyx_t_5 = PyInt_FromSsize_t((__pyx_v_self->view.suboffsets[__pyx_v_i])); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_5);
-    if (unlikely(__Pyx_ListComp_Append(__pyx_t_2, (PyObject*)__pyx_t_5))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-  }
-  __pyx_t_5 = PyList_AsTuple(((PyObject*)__pyx_t_2)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 531; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_5);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":527
- *     property suboffsets:
- *         @cname('__pyx_memoryview_get_suboffsets')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             if self.view.suboffsets == NULL:
- *                 return [-1] * self.view.ndim
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.suboffsets.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":535
- *     property ndim:
- *         @cname('__pyx_memoryview_get_ndim')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.view.ndim
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_get_ndim(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_ndim(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_get_ndim_MemoryView_10memoryview_4ndim___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_get_ndim_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":536
- *         @cname('__pyx_memoryview_get_ndim')
- *         def __get__(self):
- *             return self.view.ndim             # <<<<<<<<<<<<<<
- * 
- *     property itemsize:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_self->view.ndim); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 536; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":535
- *     property ndim:
- *         @cname('__pyx_memoryview_get_ndim')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.view.ndim
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.ndim.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":540
- *     property itemsize:
- *         @cname('__pyx_memoryview_get_itemsize')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.view.itemsize
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_get_itemsize(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_itemsize(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_get_itemsize_MemoryView_10memoryview_8itemsize___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_get_itemsize_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":541
- *         @cname('__pyx_memoryview_get_itemsize')
- *         def __get__(self):
- *             return self.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     property nbytes:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 541; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":540
- *     property itemsize:
- *         @cname('__pyx_memoryview_get_itemsize')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.view.itemsize
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.itemsize.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":545
- *     property nbytes:
- *         @cname('__pyx_memoryview_get_nbytes')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.size * self.view.itemsize
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_get_nbytes(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_nbytes(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_get_nbytes_MemoryView_10memoryview_6nbytes___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_get_nbytes_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":546
- *         @cname('__pyx_memoryview_get_nbytes')
- *         def __get__(self):
- *             return self.size * self.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     property size:
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 546; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_self->view.itemsize); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 546; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = PyNumber_Multiply(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 546; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_r = __pyx_t_3;
-  __pyx_t_3 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":545
- *     property nbytes:
- *         @cname('__pyx_memoryview_get_nbytes')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.size * self.view.itemsize
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.nbytes.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":550
- *     property size:
- *         @cname('__pyx_memoryview_get_size')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             if self._size is None:
- *                 result = 1
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_get_size(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview_get_size(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_get_size_MemoryView_10memoryview_4size___get__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_get_size_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  PyObject *__pyx_v_result = NULL;
-  PyObject *__pyx_v_length = NULL;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  Py_ssize_t __pyx_t_5;
-  PyObject *(*__pyx_t_6)(PyObject *);
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":551
- *         @cname('__pyx_memoryview_get_size')
- *         def __get__(self):
- *             if self._size is None:             # <<<<<<<<<<<<<<
- *                 result = 1
- * 
- */
-  __pyx_t_1 = (__pyx_v_self->_size == Py_None);
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":552
- *         def __get__(self):
- *             if self._size is None:
- *                 result = 1             # <<<<<<<<<<<<<<
- * 
- *                 for length in self.shape:
- */
-    __Pyx_INCREF(__pyx_int_1);
-    __pyx_v_result = __pyx_int_1;
-
-    /* "View.MemoryView":554
- *                 result = 1
- * 
- *                 for length in self.shape:             # <<<<<<<<<<<<<<
- *                     result *= length
- * 
- */
-    __pyx_t_3 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_shape); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    if (PyList_CheckExact(__pyx_t_3) || PyTuple_CheckExact(__pyx_t_3)) {
-      __pyx_t_4 = __pyx_t_3; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0;
-      __pyx_t_6 = NULL;
-    } else {
-      __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext;
-    }
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    for (;;) {
-      if (!__pyx_t_6 && PyList_CheckExact(__pyx_t_4)) {
-        if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break;
-        #if CYTHON_COMPILING_IN_CPYTHON
-        __pyx_t_3 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_3); __pyx_t_5++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        #else
-        __pyx_t_3 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        #endif
-      } else if (!__pyx_t_6 && PyTuple_CheckExact(__pyx_t_4)) {
-        if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break;
-        #if CYTHON_COMPILING_IN_CPYTHON
-        __pyx_t_3 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_3); __pyx_t_5++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        #else
-        __pyx_t_3 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        #endif
-      } else {
-        __pyx_t_3 = __pyx_t_6(__pyx_t_4);
-        if (unlikely(!__pyx_t_3)) {
-          PyObject* exc_type = PyErr_Occurred();
-          if (exc_type) {
-            if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
-            else {__pyx_filename = __pyx_f[2]; __pyx_lineno = 554; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-          }
-          break;
-        }
-        __Pyx_GOTREF(__pyx_t_3);
-      }
-      __Pyx_XDECREF_SET(__pyx_v_length, __pyx_t_3);
-      __pyx_t_3 = 0;
-
-      /* "View.MemoryView":555
- * 
- *                 for length in self.shape:
- *                     result *= length             # <<<<<<<<<<<<<<
- * 
- *                 self._size = result
- */
-      __pyx_t_3 = PyNumber_InPlaceMultiply(__pyx_v_result, __pyx_v_length); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 555; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __Pyx_DECREF_SET(__pyx_v_result, __pyx_t_3);
-      __pyx_t_3 = 0;
-    }
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-
-    /* "View.MemoryView":557
- *                     result *= length
- * 
- *                 self._size = result             # <<<<<<<<<<<<<<
- * 
- *             return self._size
- */
-    __Pyx_INCREF(__pyx_v_result);
-    __Pyx_GIVEREF(__pyx_v_result);
-    __Pyx_GOTREF(__pyx_v_self->_size);
-    __Pyx_DECREF(__pyx_v_self->_size);
-    __pyx_v_self->_size = __pyx_v_result;
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":559
- *                 self._size = result
- * 
- *             return self._size             # <<<<<<<<<<<<<<
- * 
- *     def __len__(self):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(__pyx_v_self->_size);
-  __pyx_r = __pyx_v_self->_size;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":550
- *     property size:
- *         @cname('__pyx_memoryview_get_size')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             if self._size is None:
- *                 result = 1
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.size.__get__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_result);
-  __Pyx_XDECREF(__pyx_v_length);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":561
- *             return self._size
- * 
- *     def __len__(self):             # <<<<<<<<<<<<<<
- *         if self.view.ndim >= 1:
- *             return self.view.shape[0]
- */
-
-/* Python wrapper */
-static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self); /*proto*/
-static Py_ssize_t __pyx_memoryview___len__(PyObject *__pyx_v_self) {
-  Py_ssize_t __pyx_r;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__len__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_10__len__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static Py_ssize_t __pyx_memoryview_MemoryView_10memoryview_10__len__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  Py_ssize_t __pyx_r;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  __Pyx_RefNannySetupContext("__len__", 0);
-
-  /* "View.MemoryView":562
- * 
- *     def __len__(self):
- *         if self.view.ndim >= 1:             # <<<<<<<<<<<<<<
- *             return self.view.shape[0]
- * 
- */
-  __pyx_t_1 = ((__pyx_v_self->view.ndim >= 1) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":563
- *     def __len__(self):
- *         if self.view.ndim >= 1:
- *             return self.view.shape[0]             # <<<<<<<<<<<<<<
- * 
- *         return 0
- */
-    __pyx_r = (__pyx_v_self->view.shape[0]);
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":565
- *             return self.view.shape[0]
- * 
- *         return 0             # <<<<<<<<<<<<<<
- * 
- *     def __repr__(self):
- */
-  __pyx_r = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":561
- *             return self._size
- * 
- *     def __len__(self):             # <<<<<<<<<<<<<<
- *         if self.view.ndim >= 1:
- *             return self.view.shape[0]
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":567
- *         return 0
- * 
- *     def __repr__(self):             # <<<<<<<<<<<<<<
- *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,
- *                                                id(self))
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview___repr__(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__repr__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_12__repr__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_12__repr__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__repr__", 0);
-
-  /* "View.MemoryView":568
- * 
- *     def __repr__(self):
- *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,             # <<<<<<<<<<<<<<
- *                                                id(self))
- * 
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-
-  /* "View.MemoryView":569
- *     def __repr__(self):
- *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,
- *                                                id(self))             # <<<<<<<<<<<<<<
- * 
- *     def __str__(self):
- */
-  __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_INCREF(((PyObject *)__pyx_v_self));
-  PyTuple_SET_ITEM(__pyx_t_2, 0, ((PyObject *)__pyx_v_self));
-  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));
-  __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_id, __pyx_t_2, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-
-  /* "View.MemoryView":568
- * 
- *     def __repr__(self):
- *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,             # <<<<<<<<<<<<<<
- *                                                id(self))
- * 
- */
-  __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_t_3);
-  __Pyx_GIVEREF(__pyx_t_3);
-  __pyx_t_1 = 0;
-  __pyx_t_3 = 0;
-  __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_t_2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 568; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_r = __pyx_t_3;
-  __pyx_t_3 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":567
- *         return 0
- * 
- *     def __repr__(self):             # <<<<<<<<<<<<<<
- *         return "<MemoryView of %r at 0x%x>" % (self.base.__class__.__name__,
- *                                                id(self))
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.__repr__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":571
- *                                                id(self))
- * 
- *     def __str__(self):             # <<<<<<<<<<<<<<
- *         return "<MemoryView of %r object>" % (self.base.__class__.__name__,)
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryview___str__(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__str__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_14__str__(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_14__str__(struct __pyx_memoryview_obj *__pyx_v_self) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("__str__", 0);
-
-  /* "View.MemoryView":572
- * 
- *     def __str__(self):
- *         return "<MemoryView of %r object>" % (self.base.__class__.__name__,)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_self), __pyx_n_s_base); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_1, __pyx_n_s_class); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_t_2, __pyx_n_s_name_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  __pyx_t_1 = 0;
-  __pyx_t_1 = __Pyx_PyString_Format(__pyx_kp_s_MemoryView_of_r_object, __pyx_t_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":571
- *                                                id(self))
- * 
- *     def __str__(self):             # <<<<<<<<<<<<<<
- *         return "<MemoryView of %r object>" % (self.base.__class__.__name__,)
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.__str__", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":575
- * 
- * 
- *     def is_c_contig(self):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice *mslice
- *         cdef __Pyx_memviewslice tmp
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
-static PyObject *__pyx_memoryview_is_c_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("is_c_contig (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_16is_c_contig(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_16is_c_contig(struct __pyx_memoryview_obj *__pyx_v_self) {
-  __Pyx_memviewslice *__pyx_v_mslice;
-  __Pyx_memviewslice __pyx_v_tmp;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("is_c_contig", 0);
-
-  /* "View.MemoryView":578
- *         cdef __Pyx_memviewslice *mslice
- *         cdef __Pyx_memviewslice tmp
- *         mslice = get_slice_from_memview(self, &tmp)             # <<<<<<<<<<<<<<
- *         return slice_is_contig(mslice, 'C', self.view.ndim)
- * 
- */
-  __pyx_v_mslice = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp));
-
-  /* "View.MemoryView":579
- *         cdef __Pyx_memviewslice tmp
- *         mslice = get_slice_from_memview(self, &tmp)
- *         return slice_is_contig(mslice, 'C', self.view.ndim)             # <<<<<<<<<<<<<<
- * 
- *     def is_f_contig(self):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig(__pyx_v_mslice, 'C', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 579; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":575
- * 
- * 
- *     def is_c_contig(self):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice *mslice
- *         cdef __Pyx_memviewslice tmp
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.is_c_contig", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":581
- *         return slice_is_contig(mslice, 'C', self.view.ndim)
- * 
- *     def is_f_contig(self):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice *mslice
- *         cdef __Pyx_memviewslice tmp
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
-static PyObject *__pyx_memoryview_is_f_contig(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("is_f_contig (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_18is_f_contig(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_18is_f_contig(struct __pyx_memoryview_obj *__pyx_v_self) {
-  __Pyx_memviewslice *__pyx_v_mslice;
-  __Pyx_memviewslice __pyx_v_tmp;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("is_f_contig", 0);
-
-  /* "View.MemoryView":584
- *         cdef __Pyx_memviewslice *mslice
- *         cdef __Pyx_memviewslice tmp
- *         mslice = get_slice_from_memview(self, &tmp)             # <<<<<<<<<<<<<<
- *         return slice_is_contig(mslice, 'F', self.view.ndim)
- * 
- */
-  __pyx_v_mslice = __pyx_memoryview_get_slice_from_memoryview(__pyx_v_self, (&__pyx_v_tmp));
-
-  /* "View.MemoryView":585
- *         cdef __Pyx_memviewslice tmp
- *         mslice = get_slice_from_memview(self, &tmp)
- *         return slice_is_contig(mslice, 'F', self.view.ndim)             # <<<<<<<<<<<<<<
- * 
- *     def copy(self):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __Pyx_PyBool_FromLong(__pyx_memviewslice_is_contig(__pyx_v_mslice, 'F', __pyx_v_self->view.ndim)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 585; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":581
- *         return slice_is_contig(mslice, 'C', self.view.ndim)
- * 
- *     def is_f_contig(self):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice *mslice
- *         cdef __Pyx_memviewslice tmp
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.is_f_contig", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":587
- *         return slice_is_contig(mslice, 'F', self.view.ndim)
- * 
- *     def copy(self):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice mslice
- *         cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
-static PyObject *__pyx_memoryview_copy(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("copy (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_20copy(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_20copy(struct __pyx_memoryview_obj *__pyx_v_self) {
-  __Pyx_memviewslice __pyx_v_mslice;
-  int __pyx_v_flags;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  __Pyx_memviewslice __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("copy", 0);
-
-  /* "View.MemoryView":589
- *     def copy(self):
- *         cdef __Pyx_memviewslice mslice
- *         cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS             # <<<<<<<<<<<<<<
- * 
- *         slice_copy(self, &mslice)
- */
-  __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_F_CONTIGUOUS));
-
-  /* "View.MemoryView":591
- *         cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS
- * 
- *         slice_copy(self, &mslice)             # <<<<<<<<<<<<<<
- *         mslice = slice_copy_contig(&mslice, "c", self.view.ndim,
- *                                    self.view.itemsize,
- */
-  __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_mslice));
-
-  /* "View.MemoryView":592
- * 
- *         slice_copy(self, &mslice)
- *         mslice = slice_copy_contig(&mslice, "c", self.view.ndim,             # <<<<<<<<<<<<<<
- *                                    self.view.itemsize,
- *                                    flags|PyBUF_C_CONTIGUOUS,
- */
-  __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_mslice), __pyx_k_c, __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_C_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 592; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_v_mslice = __pyx_t_1;
-
-  /* "View.MemoryView":597
- *                                    self.dtype_is_object)
- * 
- *         return memoryview_copy_from_slice(self, &mslice)             # <<<<<<<<<<<<<<
- * 
- *     def copy_fortran(self):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_mslice)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 597; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_r = __pyx_t_2;
-  __pyx_t_2 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":587
- *         return slice_is_contig(mslice, 'F', self.view.ndim)
- * 
- *     def copy(self):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice mslice
- *         cdef int flags = self.flags & ~PyBUF_F_CONTIGUOUS
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.copy", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":599
- *         return memoryview_copy_from_slice(self, &mslice)
- * 
- *     def copy_fortran(self):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice src, dst
- *         cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
-static PyObject *__pyx_memoryview_copy_fortran(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("copy_fortran (wrapper)", 0);
-  __pyx_r = __pyx_memoryview_MemoryView_10memoryview_22copy_fortran(((struct __pyx_memoryview_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryview_MemoryView_10memoryview_22copy_fortran(struct __pyx_memoryview_obj *__pyx_v_self) {
-  __Pyx_memviewslice __pyx_v_src;
-  __Pyx_memviewslice __pyx_v_dst;
-  int __pyx_v_flags;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  __Pyx_memviewslice __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("copy_fortran", 0);
-
-  /* "View.MemoryView":601
- *     def copy_fortran(self):
- *         cdef __Pyx_memviewslice src, dst
- *         cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS             # <<<<<<<<<<<<<<
- * 
- *         slice_copy(self, &src)
- */
-  __pyx_v_flags = (__pyx_v_self->flags & (~PyBUF_C_CONTIGUOUS));
-
-  /* "View.MemoryView":603
- *         cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS
- * 
- *         slice_copy(self, &src)             # <<<<<<<<<<<<<<
- *         dst = slice_copy_contig(&src, "fortran", self.view.ndim,
- *                                 self.view.itemsize,
- */
-  __pyx_memoryview_slice_copy(__pyx_v_self, (&__pyx_v_src));
-
-  /* "View.MemoryView":604
- * 
- *         slice_copy(self, &src)
- *         dst = slice_copy_contig(&src, "fortran", self.view.ndim,             # <<<<<<<<<<<<<<
- *                                 self.view.itemsize,
- *                                 flags|PyBUF_F_CONTIGUOUS,
- */
-  __pyx_t_1 = __pyx_memoryview_copy_new_contig((&__pyx_v_src), __pyx_k_fortran, __pyx_v_self->view.ndim, __pyx_v_self->view.itemsize, (__pyx_v_flags | PyBUF_F_CONTIGUOUS), __pyx_v_self->dtype_is_object); if (unlikely(PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 604; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_v_dst = __pyx_t_1;
-
-  /* "View.MemoryView":609
- *                                 self.dtype_is_object)
- * 
- *         return memoryview_copy_from_slice(self, &dst)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_2 = __pyx_memoryview_copy_object_from_slice(__pyx_v_self, (&__pyx_v_dst)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 609; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_r = __pyx_t_2;
-  __pyx_t_2 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":599
- *         return memoryview_copy_from_slice(self, &mslice)
- * 
- *     def copy_fortran(self):             # <<<<<<<<<<<<<<
- *         cdef __Pyx_memviewslice src, dst
- *         cdef int flags = self.flags & ~PyBUF_C_CONTIGUOUS
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_AddTraceback("View.MemoryView.memoryview.copy_fortran", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":613
- * 
- * @cname('__pyx_memoryview_new')
- * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo):             # <<<<<<<<<<<<<<
- *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
- *     result.typeinfo = typeinfo
- */
-
-static PyObject *__pyx_memoryview_new(PyObject *__pyx_v_o, int __pyx_v_flags, int __pyx_v_dtype_is_object, __Pyx_TypeInfo *__pyx_v_typeinfo) {
-  struct __pyx_memoryview_obj *__pyx_v_result = 0;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("memoryview_cwrapper", 0);
-
-  /* "View.MemoryView":614
- * @cname('__pyx_memoryview_new')
- * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo):
- *     cdef memoryview result = memoryview(o, flags, dtype_is_object)             # <<<<<<<<<<<<<<
- *     result.typeinfo = typeinfo
- *     return result
- */
-  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_INCREF(__pyx_v_o);
-  PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_o);
-  __Pyx_GIVEREF(__pyx_v_o);
-  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2);
-  __Pyx_GIVEREF(__pyx_t_2);
-  __pyx_t_1 = 0;
-  __pyx_t_2 = 0;
-  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_memoryview_type)), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  __pyx_v_result = ((struct __pyx_memoryview_obj *)__pyx_t_2);
-  __pyx_t_2 = 0;
-
-  /* "View.MemoryView":615
- * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo):
- *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
- *     result.typeinfo = typeinfo             # <<<<<<<<<<<<<<
- *     return result
- * 
- */
-  __pyx_v_result->typeinfo = __pyx_v_typeinfo;
-
-  /* "View.MemoryView":616
- *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
- *     result.typeinfo = typeinfo
- *     return result             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_check')
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(((PyObject *)__pyx_v_result));
-  __pyx_r = ((PyObject *)__pyx_v_result);
-  goto __pyx_L0;
-
-  /* "View.MemoryView":613
- * 
- * @cname('__pyx_memoryview_new')
- * cdef memoryview_cwrapper(object o, int flags, bint dtype_is_object, __Pyx_TypeInfo *typeinfo):             # <<<<<<<<<<<<<<
- *     cdef memoryview result = memoryview(o, flags, dtype_is_object)
- *     result.typeinfo = typeinfo
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView.memoryview_cwrapper", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_result);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":619
- * 
- * @cname('__pyx_memoryview_check')
- * cdef inline bint memoryview_check(object o):             # <<<<<<<<<<<<<<
- *     return isinstance(o, memoryview)
- * 
- */
-
-static CYTHON_INLINE int __pyx_memoryview_check(PyObject *__pyx_v_o) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  __Pyx_RefNannySetupContext("memoryview_check", 0);
-
-  /* "View.MemoryView":620
- * @cname('__pyx_memoryview_check')
- * cdef inline bint memoryview_check(object o):
- *     return isinstance(o, memoryview)             # <<<<<<<<<<<<<<
- * 
- * cdef tuple _unellipsify(object index, int ndim):
- */
-  __pyx_t_1 = __Pyx_TypeCheck(__pyx_v_o, ((PyObject *)__pyx_memoryview_type)); 
-  __pyx_r = __pyx_t_1;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":619
- * 
- * @cname('__pyx_memoryview_check')
- * cdef inline bint memoryview_check(object o):             # <<<<<<<<<<<<<<
- *     return isinstance(o, memoryview)
- * 
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":622
- *     return isinstance(o, memoryview)
- * 
- * cdef tuple _unellipsify(object index, int ndim):             # <<<<<<<<<<<<<<
- *     """
- *     Replace all ellipses with full slices and fill incomplete indices with
- */
-
-static PyObject *_unellipsify(PyObject *__pyx_v_index, int __pyx_v_ndim) {
-  PyObject *__pyx_v_tup = NULL;
-  PyObject *__pyx_v_result = NULL;
-  int __pyx_v_have_slices;
-  int __pyx_v_seen_ellipsis;
-  CYTHON_UNUSED PyObject *__pyx_v_idx = NULL;
-  PyObject *__pyx_v_item = NULL;
-  Py_ssize_t __pyx_v_nslices;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  Py_ssize_t __pyx_t_5;
-  PyObject *(*__pyx_t_6)(PyObject *);
-  PyObject *__pyx_t_7 = NULL;
-  Py_ssize_t __pyx_t_8;
-  PyObject *__pyx_t_9 = NULL;
-  int __pyx_t_10;
-  int __pyx_t_11;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("_unellipsify", 0);
-
-  /* "View.MemoryView":627
- *     full slices.
- *     """
- *     if not isinstance(index, tuple):             # <<<<<<<<<<<<<<
- *         tup = (index,)
- *     else:
- */
-  __pyx_t_1 = PyTuple_Check(__pyx_v_index); 
-  __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":628
- *     """
- *     if not isinstance(index, tuple):
- *         tup = (index,)             # <<<<<<<<<<<<<<
- *     else:
- *         tup = index
- */
-    __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 628; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_INCREF(__pyx_v_index);
-    PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_index);
-    __Pyx_GIVEREF(__pyx_v_index);
-    __pyx_v_tup = __pyx_t_3;
-    __pyx_t_3 = 0;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":630
- *         tup = (index,)
- *     else:
- *         tup = index             # <<<<<<<<<<<<<<
- * 
- *     result = []
- */
-    __Pyx_INCREF(__pyx_v_index);
-    __pyx_v_tup = __pyx_v_index;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":632
- *         tup = index
- * 
- *     result = []             # <<<<<<<<<<<<<<
- *     have_slices = False
- *     seen_ellipsis = False
- */
-  __pyx_t_3 = PyList_New(0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 632; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __pyx_v_result = ((PyObject*)__pyx_t_3);
-  __pyx_t_3 = 0;
-
-  /* "View.MemoryView":633
- * 
- *     result = []
- *     have_slices = False             # <<<<<<<<<<<<<<
- *     seen_ellipsis = False
- *     for idx, item in enumerate(tup):
- */
-  __pyx_v_have_slices = 0;
-
-  /* "View.MemoryView":634
- *     result = []
- *     have_slices = False
- *     seen_ellipsis = False             # <<<<<<<<<<<<<<
- *     for idx, item in enumerate(tup):
- *         if item is Ellipsis:
- */
-  __pyx_v_seen_ellipsis = 0;
-
-  /* "View.MemoryView":635
- *     have_slices = False
- *     seen_ellipsis = False
- *     for idx, item in enumerate(tup):             # <<<<<<<<<<<<<<
- *         if item is Ellipsis:
- *             if not seen_ellipsis:
- */
-  __Pyx_INCREF(__pyx_int_0);
-  __pyx_t_3 = __pyx_int_0;
-  if (PyList_CheckExact(__pyx_v_tup) || PyTuple_CheckExact(__pyx_v_tup)) {
-    __pyx_t_4 = __pyx_v_tup; __Pyx_INCREF(__pyx_t_4); __pyx_t_5 = 0;
-    __pyx_t_6 = NULL;
-  } else {
-    __pyx_t_5 = -1; __pyx_t_4 = PyObject_GetIter(__pyx_v_tup); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_6 = Py_TYPE(__pyx_t_4)->tp_iternext;
-  }
-  for (;;) {
-    if (!__pyx_t_6 && PyList_CheckExact(__pyx_t_4)) {
-      if (__pyx_t_5 >= PyList_GET_SIZE(__pyx_t_4)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_7 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else if (!__pyx_t_6 && PyTuple_CheckExact(__pyx_t_4)) {
-      if (__pyx_t_5 >= PyTuple_GET_SIZE(__pyx_t_4)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_7 = PyTuple_GET_ITEM(__pyx_t_4, __pyx_t_5); __Pyx_INCREF(__pyx_t_7); __pyx_t_5++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_7 = PySequence_ITEM(__pyx_t_4, __pyx_t_5); __pyx_t_5++; if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else {
-      __pyx_t_7 = __pyx_t_6(__pyx_t_4);
-      if (unlikely(!__pyx_t_7)) {
-        PyObject* exc_type = PyErr_Occurred();
-        if (exc_type) {
-          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
-          else {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        }
-        break;
-      }
-      __Pyx_GOTREF(__pyx_t_7);
-    }
-    __Pyx_XDECREF_SET(__pyx_v_item, __pyx_t_7);
-    __pyx_t_7 = 0;
-    __Pyx_INCREF(__pyx_t_3);
-    __Pyx_XDECREF_SET(__pyx_v_idx, __pyx_t_3);
-    __pyx_t_7 = PyNumber_Add(__pyx_t_3, __pyx_int_1); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_7);
-    __Pyx_DECREF(__pyx_t_3);
-    __pyx_t_3 = __pyx_t_7;
-    __pyx_t_7 = 0;
-
-    /* "View.MemoryView":636
- *     seen_ellipsis = False
- *     for idx, item in enumerate(tup):
- *         if item is Ellipsis:             # <<<<<<<<<<<<<<
- *             if not seen_ellipsis:
- *                 result.extend([slice(None)] * (ndim - len(tup) + 1))
- */
-    __pyx_t_2 = (__pyx_v_item == __pyx_builtin_Ellipsis);
-    __pyx_t_1 = (__pyx_t_2 != 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":637
- *     for idx, item in enumerate(tup):
- *         if item is Ellipsis:
- *             if not seen_ellipsis:             # <<<<<<<<<<<<<<
- *                 result.extend([slice(None)] * (ndim - len(tup) + 1))
- *                 seen_ellipsis = True
- */
-      __pyx_t_1 = ((!(__pyx_v_seen_ellipsis != 0)) != 0);
-      if (__pyx_t_1) {
-
-        /* "View.MemoryView":638
- *         if item is Ellipsis:
- *             if not seen_ellipsis:
- *                 result.extend([slice(None)] * (ndim - len(tup) + 1))             # <<<<<<<<<<<<<<
- *                 seen_ellipsis = True
- *             else:
- */
-        __pyx_t_7 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)(&PySlice_Type))), __pyx_tuple__14, NULL); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_7);
-        __pyx_t_8 = PyObject_Length(__pyx_v_tup); if (unlikely(__pyx_t_8 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __pyx_t_9 = PyList_New(1 * ((((__pyx_v_ndim - __pyx_t_8) + 1)<0) ? 0:((__pyx_v_ndim - __pyx_t_8) + 1))); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_9);
-        { Py_ssize_t __pyx_temp;
-          for (__pyx_temp=0; __pyx_temp < ((__pyx_v_ndim - __pyx_t_8) + 1); __pyx_temp++) {
-            __Pyx_INCREF(__pyx_t_7);
-            PyList_SET_ITEM(__pyx_t_9, __pyx_temp, __pyx_t_7);
-            __Pyx_GIVEREF(__pyx_t_7);
-          }
-        }
-        __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
-        __pyx_t_10 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_9); if (unlikely(__pyx_t_10 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
-
-        /* "View.MemoryView":639
- *             if not seen_ellipsis:
- *                 result.extend([slice(None)] * (ndim - len(tup) + 1))
- *                 seen_ellipsis = True             # <<<<<<<<<<<<<<
- *             else:
- *                 result.append(slice(None))
- */
-        __pyx_v_seen_ellipsis = 1;
-        goto __pyx_L7;
-      }
-      /*else*/ {
-
-        /* "View.MemoryView":641
- *                 seen_ellipsis = True
- *             else:
- *                 result.append(slice(None))             # <<<<<<<<<<<<<<
- *             have_slices = True
- *         else:
- */
-        __pyx_t_9 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)(&PySlice_Type))), __pyx_tuple__15, NULL); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 641; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_9);
-        __pyx_t_10 = __Pyx_PyList_Append(__pyx_v_result, __pyx_t_9); if (unlikely(__pyx_t_10 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 641; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
-      }
-      __pyx_L7:;
-
-      /* "View.MemoryView":642
- *             else:
- *                 result.append(slice(None))
- *             have_slices = True             # <<<<<<<<<<<<<<
- *         else:
- *             if not isinstance(item, slice) and not PyIndex_Check(item):
- */
-      __pyx_v_have_slices = 1;
-      goto __pyx_L6;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":644
- *             have_slices = True
- *         else:
- *             if not isinstance(item, slice) and not PyIndex_Check(item):             # <<<<<<<<<<<<<<
- *                 raise TypeError("Cannot index with type '%s'" % type(item))
- * 
- */
-      __pyx_t_1 = PySlice_Check(__pyx_v_item); 
-      __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
-      if (__pyx_t_2) {
-        __pyx_t_1 = ((!(__Pyx_PyIndex_Check(__pyx_v_item) != 0)) != 0);
-        __pyx_t_11 = __pyx_t_1;
-      } else {
-        __pyx_t_11 = __pyx_t_2;
-      }
-      if (__pyx_t_11) {
-
-        /* "View.MemoryView":645
- *         else:
- *             if not isinstance(item, slice) and not PyIndex_Check(item):
- *                 raise TypeError("Cannot index with type '%s'" % type(item))             # <<<<<<<<<<<<<<
- * 
- *             have_slices = have_slices or isinstance(item, slice)
- */
-        __pyx_t_9 = __Pyx_PyString_Format(__pyx_kp_s_Cannot_index_with_type_s, ((PyObject *)Py_TYPE(__pyx_v_item))); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_9);
-        __pyx_t_7 = PyTuple_New(1); if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_7);
-        PyTuple_SET_ITEM(__pyx_t_7, 0, __pyx_t_9);
-        __Pyx_GIVEREF(__pyx_t_9);
-        __pyx_t_9 = 0;
-        __pyx_t_9 = __Pyx_PyObject_Call(__pyx_builtin_TypeError, __pyx_t_7, NULL); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        __Pyx_GOTREF(__pyx_t_9);
-        __Pyx_DECREF(__pyx_t_7); __pyx_t_7 = 0;
-        __Pyx_Raise(__pyx_t_9, 0, 0, 0);
-        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
-        {__pyx_filename = __pyx_f[2]; __pyx_lineno = 645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-
-      /* "View.MemoryView":647
- *                 raise TypeError("Cannot index with type '%s'" % type(item))
- * 
- *             have_slices = have_slices or isinstance(item, slice)             # <<<<<<<<<<<<<<
- *             result.append(item)
- * 
- */
-      if (!__pyx_v_have_slices) {
-        __pyx_t_11 = PySlice_Check(__pyx_v_item); 
-        __pyx_t_2 = __pyx_t_11;
-      } else {
-        __pyx_t_2 = __pyx_v_have_slices;
-      }
-      __pyx_v_have_slices = __pyx_t_2;
-
-      /* "View.MemoryView":648
- * 
- *             have_slices = have_slices or isinstance(item, slice)
- *             result.append(item)             # <<<<<<<<<<<<<<
- * 
- *     nslices = ndim - len(result)
- */
-      __pyx_t_10 = __Pyx_PyList_Append(__pyx_v_result, __pyx_v_item); if (unlikely(__pyx_t_10 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 648; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-    __pyx_L6:;
-  }
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-
-  /* "View.MemoryView":650
- *             result.append(item)
- * 
- *     nslices = ndim - len(result)             # <<<<<<<<<<<<<<
- *     if nslices:
- *         result.extend([slice(None)] * nslices)
- */
-  __pyx_t_5 = PyList_GET_SIZE(__pyx_v_result); if (unlikely(__pyx_t_5 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 650; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_v_nslices = (__pyx_v_ndim - __pyx_t_5);
-
-  /* "View.MemoryView":651
- * 
- *     nslices = ndim - len(result)
- *     if nslices:             # <<<<<<<<<<<<<<
- *         result.extend([slice(None)] * nslices)
- * 
- */
-  __pyx_t_2 = (__pyx_v_nslices != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":652
- *     nslices = ndim - len(result)
- *     if nslices:
- *         result.extend([slice(None)] * nslices)             # <<<<<<<<<<<<<<
- * 
- *     return have_slices or nslices, tuple(result)
- */
-    __pyx_t_3 = __Pyx_PyObject_Call(((PyObject *)((PyObject*)(&PySlice_Type))), __pyx_tuple__16, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 652; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_t_4 = PyList_New(1 * ((__pyx_v_nslices<0) ? 0:__pyx_v_nslices)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 652; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    { Py_ssize_t __pyx_temp;
-      for (__pyx_temp=0; __pyx_temp < __pyx_v_nslices; __pyx_temp++) {
-        __Pyx_INCREF(__pyx_t_3);
-        PyList_SET_ITEM(__pyx_t_4, __pyx_temp, __pyx_t_3);
-        __Pyx_GIVEREF(__pyx_t_3);
-      }
-    }
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    __pyx_t_10 = __Pyx_PyList_Extend(__pyx_v_result, __pyx_t_4); if (unlikely(__pyx_t_10 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 652; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    goto __pyx_L9;
-  }
-  __pyx_L9:;
-
-  /* "View.MemoryView":654
- *         result.extend([slice(None)] * nslices)
- * 
- *     return have_slices or nslices, tuple(result)             # <<<<<<<<<<<<<<
- * 
- * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_4 = __Pyx_PyBool_FromLong(__pyx_v_have_slices); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (!__pyx_t_2) {
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_nslices); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_t_9 = __pyx_t_3;
-    __pyx_t_3 = 0;
-  } else {
-    __pyx_t_9 = __pyx_t_4;
-    __pyx_t_4 = 0;
-  }
-  __pyx_t_4 = PyList_AsTuple(__pyx_v_result); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_9);
-  __Pyx_GIVEREF(__pyx_t_9);
-  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_4);
-  __Pyx_GIVEREF(__pyx_t_4);
-  __pyx_t_9 = 0;
-  __pyx_t_4 = 0;
-  __pyx_r = ((PyObject*)__pyx_t_3);
-  __pyx_t_3 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":622
- *     return isinstance(o, memoryview)
- * 
- * cdef tuple _unellipsify(object index, int ndim):             # <<<<<<<<<<<<<<
- *     """
- *     Replace all ellipses with full slices and fill incomplete indices with
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_XDECREF(__pyx_t_7);
-  __Pyx_XDECREF(__pyx_t_9);
-  __Pyx_AddTraceback("View.MemoryView._unellipsify", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF(__pyx_v_tup);
-  __Pyx_XDECREF(__pyx_v_result);
-  __Pyx_XDECREF(__pyx_v_idx);
-  __Pyx_XDECREF(__pyx_v_item);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":656
- *     return have_slices or nslices, tuple(result)
- * 
- * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):             # <<<<<<<<<<<<<<
- *     cdef int i
- *     for i in range(ndim):
- */
-
-static PyObject *assert_direct_dimensions(Py_ssize_t *__pyx_v_suboffsets, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  PyObject *__pyx_t_4 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("assert_direct_dimensions", 0);
-
-  /* "View.MemoryView":658
- * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):
- *     cdef int i
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if suboffsets[i] >= 0:
- *             raise ValueError("Indirect dimensions not supported")
- */
-  __pyx_t_1 = __pyx_v_ndim;
-  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
-    __pyx_v_i = __pyx_t_2;
-
-    /* "View.MemoryView":659
- *     cdef int i
- *     for i in range(ndim):
- *         if suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             raise ValueError("Indirect dimensions not supported")
- * 
- */
-    __pyx_t_3 = (((__pyx_v_suboffsets[__pyx_v_i]) >= 0) != 0);
-    if (__pyx_t_3) {
-
-      /* "View.MemoryView":660
- *     for i in range(ndim):
- *         if suboffsets[i] >= 0:
- *             raise ValueError("Indirect dimensions not supported")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__17, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 660; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 660; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-  }
-
-  /* "View.MemoryView":656
- *     return have_slices or nslices, tuple(result)
- * 
- * cdef assert_direct_dimensions(Py_ssize_t *suboffsets, int ndim):             # <<<<<<<<<<<<<<
- *     cdef int i
- *     for i in range(ndim):
- */
-
-  /* function exit code */
-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_AddTraceback("View.MemoryView.assert_direct_dimensions", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":667
- * 
- * @cname('__pyx_memview_slice')
- * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
- *     cdef int new_ndim = 0, suboffset_dim = -1, dim
- *     cdef bint negative_step
- */
-
-static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *__pyx_v_memview, PyObject *__pyx_v_indices) {
-  int __pyx_v_new_ndim;
-  int __pyx_v_suboffset_dim;
-  int __pyx_v_dim;
-  __Pyx_memviewslice __pyx_v_src;
-  __Pyx_memviewslice __pyx_v_dst;
-  __Pyx_memviewslice *__pyx_v_p_src;
-  struct __pyx_memoryviewslice_obj *__pyx_v_memviewsliceobj = 0;
-  __Pyx_memviewslice *__pyx_v_p_dst;
-  int *__pyx_v_p_suboffset_dim;
-  Py_ssize_t __pyx_v_start;
-  Py_ssize_t __pyx_v_stop;
-  Py_ssize_t __pyx_v_step;
-  int __pyx_v_have_start;
-  int __pyx_v_have_stop;
-  int __pyx_v_have_step;
-  PyObject *__pyx_v_index = NULL;
-  struct __pyx_memoryview_obj *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  struct __pyx_memoryview_obj *__pyx_t_4;
-  char *__pyx_t_5;
-  int __pyx_t_6;
-  Py_ssize_t __pyx_t_7;
-  PyObject *(*__pyx_t_8)(PyObject *);
-  PyObject *__pyx_t_9 = NULL;
-  Py_ssize_t __pyx_t_10;
-  int __pyx_t_11;
-  PyObject *__pyx_t_12 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("memview_slice", 0);
-
-  /* "View.MemoryView":668
- * @cname('__pyx_memview_slice')
- * cdef memoryview memview_slice(memoryview memview, object indices):
- *     cdef int new_ndim = 0, suboffset_dim = -1, dim             # <<<<<<<<<<<<<<
- *     cdef bint negative_step
- *     cdef __Pyx_memviewslice src, dst
- */
-  __pyx_v_new_ndim = 0;
-  __pyx_v_suboffset_dim = -1;
-
-  /* "View.MemoryView":675
- * 
- * 
- *     memset(&dst, 0, sizeof(dst))             # <<<<<<<<<<<<<<
- * 
- *     cdef _memoryviewslice memviewsliceobj
- */
-  memset((&__pyx_v_dst), 0, (sizeof(__pyx_v_dst)));
-
-  /* "View.MemoryView":679
- *     cdef _memoryviewslice memviewsliceobj
- * 
- *     assert memview.view.ndim > 0             # <<<<<<<<<<<<<<
- * 
- *     if isinstance(memview, _memoryviewslice):
- */
-  #ifndef CYTHON_WITHOUT_ASSERTIONS
-  if (unlikely(!Py_OptimizeFlag)) {
-    if (unlikely(!((__pyx_v_memview->view.ndim > 0) != 0))) {
-      PyErr_SetNone(PyExc_AssertionError);
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 679; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-  }
-  #endif
-
-  /* "View.MemoryView":681
- *     assert memview.view.ndim > 0
- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         memviewsliceobj = memview
- *         p_src = &memviewsliceobj.from_slice
- */
-  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), ((PyObject *)__pyx_memoryviewslice_type)); 
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":682
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         memviewsliceobj = memview             # <<<<<<<<<<<<<<
- *         p_src = &memviewsliceobj.from_slice
- *     else:
- */
-    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 682; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_t_3 = ((PyObject *)__pyx_v_memview);
-    __Pyx_INCREF(__pyx_t_3);
-    __pyx_v_memviewsliceobj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3);
-    __pyx_t_3 = 0;
-
-    /* "View.MemoryView":683
- *     if isinstance(memview, _memoryviewslice):
- *         memviewsliceobj = memview
- *         p_src = &memviewsliceobj.from_slice             # <<<<<<<<<<<<<<
- *     else:
- *         slice_copy(memview, &src)
- */
-    __pyx_v_p_src = (&__pyx_v_memviewsliceobj->from_slice);
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":685
- *         p_src = &memviewsliceobj.from_slice
- *     else:
- *         slice_copy(memview, &src)             # <<<<<<<<<<<<<<
- *         p_src = &src
- * 
- */
-    __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_src));
-
-    /* "View.MemoryView":686
- *     else:
- *         slice_copy(memview, &src)
- *         p_src = &src             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_v_p_src = (&__pyx_v_src);
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":692
- * 
- * 
- *     dst.memview = p_src.memview             # <<<<<<<<<<<<<<
- *     dst.data = p_src.data
- * 
- */
-  __pyx_t_4 = __pyx_v_p_src->memview;
-  __pyx_v_dst.memview = __pyx_t_4;
-
-  /* "View.MemoryView":693
- * 
- *     dst.memview = p_src.memview
- *     dst.data = p_src.data             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_5 = __pyx_v_p_src->data;
-  __pyx_v_dst.data = __pyx_t_5;
-
-  /* "View.MemoryView":698
- * 
- * 
- *     cdef __Pyx_memviewslice *p_dst = &dst             # <<<<<<<<<<<<<<
- *     cdef int *p_suboffset_dim = &suboffset_dim
- *     cdef Py_ssize_t start, stop, step
- */
-  __pyx_v_p_dst = (&__pyx_v_dst);
-
-  /* "View.MemoryView":699
- * 
- *     cdef __Pyx_memviewslice *p_dst = &dst
- *     cdef int *p_suboffset_dim = &suboffset_dim             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t start, stop, step
- *     cdef bint have_start, have_stop, have_step
- */
-  __pyx_v_p_suboffset_dim = (&__pyx_v_suboffset_dim);
-
-  /* "View.MemoryView":703
- *     cdef bint have_start, have_stop, have_step
- * 
- *     for dim, index in enumerate(indices):             # <<<<<<<<<<<<<<
- *         if PyIndex_Check(index):
- *             slice_memviewslice(
- */
-  __pyx_t_6 = 0;
-  if (PyList_CheckExact(__pyx_v_indices) || PyTuple_CheckExact(__pyx_v_indices)) {
-    __pyx_t_3 = __pyx_v_indices; __Pyx_INCREF(__pyx_t_3); __pyx_t_7 = 0;
-    __pyx_t_8 = NULL;
-  } else {
-    __pyx_t_7 = -1; __pyx_t_3 = PyObject_GetIter(__pyx_v_indices); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_t_8 = Py_TYPE(__pyx_t_3)->tp_iternext;
-  }
-  for (;;) {
-    if (!__pyx_t_8 && PyList_CheckExact(__pyx_t_3)) {
-      if (__pyx_t_7 >= PyList_GET_SIZE(__pyx_t_3)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_9 = PyList_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else if (!__pyx_t_8 && PyTuple_CheckExact(__pyx_t_3)) {
-      if (__pyx_t_7 >= PyTuple_GET_SIZE(__pyx_t_3)) break;
-      #if CYTHON_COMPILING_IN_CPYTHON
-      __pyx_t_9 = PyTuple_GET_ITEM(__pyx_t_3, __pyx_t_7); __Pyx_INCREF(__pyx_t_9); __pyx_t_7++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #else
-      __pyx_t_9 = PySequence_ITEM(__pyx_t_3, __pyx_t_7); __pyx_t_7++; if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      #endif
-    } else {
-      __pyx_t_9 = __pyx_t_8(__pyx_t_3);
-      if (unlikely(!__pyx_t_9)) {
-        PyObject* exc_type = PyErr_Occurred();
-        if (exc_type) {
-          if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
-          else {__pyx_filename = __pyx_f[2]; __pyx_lineno = 703; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-        }
-        break;
-      }
-      __Pyx_GOTREF(__pyx_t_9);
-    }
-    __Pyx_XDECREF_SET(__pyx_v_index, __pyx_t_9);
-    __pyx_t_9 = 0;
-    __pyx_v_dim = __pyx_t_6;
-    __pyx_t_6 = (__pyx_t_6 + 1);
-
-    /* "View.MemoryView":704
- * 
- *     for dim, index in enumerate(indices):
- *         if PyIndex_Check(index):             # <<<<<<<<<<<<<<
- *             slice_memviewslice(
- *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
- */
-    __pyx_t_2 = (__Pyx_PyIndex_Check(__pyx_v_index) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":708
- *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
- *                 dim, new_ndim, p_suboffset_dim,
- *                 index, 0, 0, # start, stop, step             # <<<<<<<<<<<<<<
- *                 0, 0, 0, # have_{start,stop,step}
- *                 False)
- */
-      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_v_index); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 708; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-      /* "View.MemoryView":705
- *     for dim, index in enumerate(indices):
- *         if PyIndex_Check(index):
- *             slice_memviewslice(             # <<<<<<<<<<<<<<
- *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
- *                 dim, new_ndim, p_suboffset_dim,
- */
-      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_t_10, 0, 0, 0, 0, 0, 0); if (unlikely(__pyx_t_11 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 705; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L6;
-    }
-
-    /* "View.MemoryView":711
- *                 0, 0, 0, # have_{start,stop,step}
- *                 False)
- *         elif index is None:             # <<<<<<<<<<<<<<
- *             p_dst.shape[new_ndim] = 1
- *             p_dst.strides[new_ndim] = 0
- */
-    __pyx_t_2 = (__pyx_v_index == Py_None);
-    __pyx_t_1 = (__pyx_t_2 != 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":712
- *                 False)
- *         elif index is None:
- *             p_dst.shape[new_ndim] = 1             # <<<<<<<<<<<<<<
- *             p_dst.strides[new_ndim] = 0
- *             p_dst.suboffsets[new_ndim] = -1
- */
-      (__pyx_v_p_dst->shape[__pyx_v_new_ndim]) = 1;
-
-      /* "View.MemoryView":713
- *         elif index is None:
- *             p_dst.shape[new_ndim] = 1
- *             p_dst.strides[new_ndim] = 0             # <<<<<<<<<<<<<<
- *             p_dst.suboffsets[new_ndim] = -1
- *             new_ndim += 1
- */
-      (__pyx_v_p_dst->strides[__pyx_v_new_ndim]) = 0;
-
-      /* "View.MemoryView":714
- *             p_dst.shape[new_ndim] = 1
- *             p_dst.strides[new_ndim] = 0
- *             p_dst.suboffsets[new_ndim] = -1             # <<<<<<<<<<<<<<
- *             new_ndim += 1
- *         else:
- */
-      (__pyx_v_p_dst->suboffsets[__pyx_v_new_ndim]) = -1;
-
-      /* "View.MemoryView":715
- *             p_dst.strides[new_ndim] = 0
- *             p_dst.suboffsets[new_ndim] = -1
- *             new_ndim += 1             # <<<<<<<<<<<<<<
- *         else:
- *             start = index.start or 0
- */
-      __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);
-      goto __pyx_L6;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":717
- *             new_ndim += 1
- *         else:
- *             start = index.start or 0             # <<<<<<<<<<<<<<
- *             stop = index.stop or 0
- *             step = index.step or 0
- */
-      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 717; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_9);
-      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 717; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      if (!__pyx_t_1) {
-        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
-        __Pyx_INCREF(__pyx_int_0);
-        __pyx_t_12 = __pyx_int_0;
-      } else {
-        __pyx_t_12 = __pyx_t_9;
-        __pyx_t_9 = 0;
-      }
-      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_t_12); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 717; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
-      __pyx_v_start = __pyx_t_10;
-
-      /* "View.MemoryView":718
- *         else:
- *             start = index.start or 0
- *             stop = index.stop or 0             # <<<<<<<<<<<<<<
- *             step = index.step or 0
- * 
- */
-      __pyx_t_12 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 718; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_12);
-      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_12); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 718; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      if (!__pyx_t_1) {
-        __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
-        __Pyx_INCREF(__pyx_int_0);
-        __pyx_t_9 = __pyx_int_0;
-      } else {
-        __pyx_t_9 = __pyx_t_12;
-        __pyx_t_12 = 0;
-      }
-      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_t_9); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 718; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
-      __pyx_v_stop = __pyx_t_10;
-
-      /* "View.MemoryView":719
- *             start = index.start or 0
- *             stop = index.stop or 0
- *             step = index.step or 0             # <<<<<<<<<<<<<<
- * 
- *             have_start = index.start is not None
- */
-      __pyx_t_9 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 719; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_9);
-      __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_9); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 719; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      if (!__pyx_t_1) {
-        __Pyx_DECREF(__pyx_t_9); __pyx_t_9 = 0;
-        __Pyx_INCREF(__pyx_int_0);
-        __pyx_t_12 = __pyx_int_0;
-      } else {
-        __pyx_t_12 = __pyx_t_9;
-        __pyx_t_9 = 0;
-      }
-      __pyx_t_10 = __Pyx_PyIndex_AsSsize_t(__pyx_t_12); if (unlikely((__pyx_t_10 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 719; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
-      __pyx_v_step = __pyx_t_10;
-
-      /* "View.MemoryView":721
- *             step = index.step or 0
- * 
- *             have_start = index.start is not None             # <<<<<<<<<<<<<<
- *             have_stop = index.stop is not None
- *             have_step = index.step is not None
- */
-      __pyx_t_12 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_start); if (unlikely(!__pyx_t_12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 721; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_12);
-      __pyx_t_1 = (__pyx_t_12 != Py_None);
-      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
-      __pyx_v_have_start = __pyx_t_1;
-
-      /* "View.MemoryView":722
- * 
- *             have_start = index.start is not None
- *             have_stop = index.stop is not None             # <<<<<<<<<<<<<<
- *             have_step = index.step is not None
- * 
- */
-      __pyx_t_12 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_stop); if (unlikely(!__pyx_t_12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 722; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_12);
-      __pyx_t_1 = (__pyx_t_12 != Py_None);
-      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
-      __pyx_v_have_stop = __pyx_t_1;
-
-      /* "View.MemoryView":723
- *             have_start = index.start is not None
- *             have_stop = index.stop is not None
- *             have_step = index.step is not None             # <<<<<<<<<<<<<<
- * 
- *             slice_memviewslice(
- */
-      __pyx_t_12 = __Pyx_PyObject_GetAttrStr(__pyx_v_index, __pyx_n_s_step); if (unlikely(!__pyx_t_12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 723; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_12);
-      __pyx_t_1 = (__pyx_t_12 != Py_None);
-      __Pyx_DECREF(__pyx_t_12); __pyx_t_12 = 0;
-      __pyx_v_have_step = __pyx_t_1;
-
-      /* "View.MemoryView":725
- *             have_step = index.step is not None
- * 
- *             slice_memviewslice(             # <<<<<<<<<<<<<<
- *                 p_dst, p_src.shape[dim], p_src.strides[dim], p_src.suboffsets[dim],
- *                 dim, new_ndim, p_suboffset_dim,
- */
-      __pyx_t_11 = __pyx_memoryview_slice_memviewslice(__pyx_v_p_dst, (__pyx_v_p_src->shape[__pyx_v_dim]), (__pyx_v_p_src->strides[__pyx_v_dim]), (__pyx_v_p_src->suboffsets[__pyx_v_dim]), __pyx_v_dim, __pyx_v_new_ndim, __pyx_v_p_suboffset_dim, __pyx_v_start, __pyx_v_stop, __pyx_v_step, __pyx_v_have_start, __pyx_v_have_stop, __pyx_v_have_step, 1); if (unlikely(__pyx_t_11 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 725; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-      /* "View.MemoryView":731
- *                 have_start, have_stop, have_step,
- *                 True)
- *             new_ndim += 1             # <<<<<<<<<<<<<<
- * 
- *     if isinstance(memview, _memoryviewslice):
- */
-      __pyx_v_new_ndim = (__pyx_v_new_ndim + 1);
-    }
-    __pyx_L6:;
-  }
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-
-  /* "View.MemoryView":733
- *             new_ndim += 1
- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,
- */
-  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), ((PyObject *)__pyx_memoryviewslice_type)); 
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":734
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,
- */
-    __Pyx_XDECREF(((PyObject *)__pyx_r));
-
-    /* "View.MemoryView":735
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_dtype_func,
- *                                     memview.dtype_is_object)
- */
-    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 735; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
-
-    /* "View.MemoryView":736
- *         return memoryview_fromslice(dst, new_ndim,
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
- *     else:
- */
-    if (unlikely(!__pyx_v_memviewsliceobj)) { __Pyx_RaiseUnboundLocalError("memviewsliceobj"); {__pyx_filename = __pyx_f[2]; __pyx_lineno = 736; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
-
-    /* "View.MemoryView":734
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         return memoryview_fromslice(dst, new_ndim,             # <<<<<<<<<<<<<<
- *                                     memviewsliceobj.to_object_func,
- *                                     memviewsliceobj.to_dtype_func,
- */
-    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, __pyx_v_memviewsliceobj->to_object_func, __pyx_v_memviewsliceobj->to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 734; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 734; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);
-    __pyx_t_3 = 0;
-    goto __pyx_L0;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":739
- *                                     memview.dtype_is_object)
- *     else:
- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
- * 
- */
-    __Pyx_XDECREF(((PyObject *)__pyx_r));
-
-    /* "View.MemoryView":740
- *     else:
- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,
- *                                     memview.dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_t_3 = __pyx_memoryview_fromslice(__pyx_v_dst, __pyx_v_new_ndim, NULL, NULL, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 739; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-
-    /* "View.MemoryView":739
- *                                     memview.dtype_is_object)
- *     else:
- *         return memoryview_fromslice(dst, new_ndim, NULL, NULL,             # <<<<<<<<<<<<<<
- *                                     memview.dtype_is_object)
- * 
- */
-    if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_memoryview_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 739; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_r = ((struct __pyx_memoryview_obj *)__pyx_t_3);
-    __pyx_t_3 = 0;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":667
- * 
- * @cname('__pyx_memview_slice')
- * cdef memoryview memview_slice(memoryview memview, object indices):             # <<<<<<<<<<<<<<
- *     cdef int new_ndim = 0, suboffset_dim = -1, dim
- *     cdef bint negative_step
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_9);
-  __Pyx_XDECREF(__pyx_t_12);
-  __Pyx_AddTraceback("View.MemoryView.memview_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_memviewsliceobj);
-  __Pyx_XDECREF(__pyx_v_index);
-  __Pyx_XGIVEREF((PyObject *)__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":764
- * 
- * @cname('__pyx_memoryview_slice_memviewslice')
- * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
- *         __Pyx_memviewslice *dst,
- *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
- */
-
-static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *__pyx_v_dst, Py_ssize_t __pyx_v_shape, Py_ssize_t __pyx_v_stride, Py_ssize_t __pyx_v_suboffset, int __pyx_v_dim, int __pyx_v_new_ndim, int *__pyx_v_suboffset_dim, Py_ssize_t __pyx_v_start, Py_ssize_t __pyx_v_stop, Py_ssize_t __pyx_v_step, int __pyx_v_have_start, int __pyx_v_have_stop, int __pyx_v_have_step, int __pyx_v_is_slice) {
-  Py_ssize_t __pyx_v_new_shape;
-  int __pyx_v_negative_step;
-  int __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-
-  /* "View.MemoryView":784
- *     cdef bint negative_step
- * 
- *     if not is_slice:             # <<<<<<<<<<<<<<
- * 
- *         if start < 0:
- */
-  __pyx_t_1 = ((!(__pyx_v_is_slice != 0)) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":786
- *     if not is_slice:
- * 
- *         if start < 0:             # <<<<<<<<<<<<<<
- *             start += shape
- *         if not 0 <= start < shape:
- */
-    __pyx_t_1 = ((__pyx_v_start < 0) != 0);
-    if (__pyx_t_1) {
-
-      /* "View.MemoryView":787
- * 
- *         if start < 0:
- *             start += shape             # <<<<<<<<<<<<<<
- *         if not 0 <= start < shape:
- *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
- */
-      __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-      goto __pyx_L4;
-    }
-    __pyx_L4:;
-
-    /* "View.MemoryView":788
- *         if start < 0:
- *             start += shape
- *         if not 0 <= start < shape:             # <<<<<<<<<<<<<<
- *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)
- *     else:
- */
-    __pyx_t_1 = (0 <= __pyx_v_start);
-    if (__pyx_t_1) {
-      __pyx_t_1 = (__pyx_v_start < __pyx_v_shape);
-    }
-    __pyx_t_2 = ((!(__pyx_t_1 != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":789
- *             start += shape
- *         if not 0 <= start < shape:
- *             _err_dim(IndexError, "Index out of bounds (axis %d)", dim)             # <<<<<<<<<<<<<<
- *     else:
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, __pyx_k_Index_out_of_bounds_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 789; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L5;
-    }
-    __pyx_L5:;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":792
- *     else:
- * 
- *         negative_step = have_step != 0 and step < 0             # <<<<<<<<<<<<<<
- * 
- *         if have_step and step == 0:
- */
-    __pyx_t_2 = (__pyx_v_have_step != 0);
-    if (__pyx_t_2) {
-      __pyx_t_1 = (__pyx_v_step < 0);
-      __pyx_t_4 = __pyx_t_1;
-    } else {
-      __pyx_t_4 = __pyx_t_2;
-    }
-    __pyx_v_negative_step = __pyx_t_4;
-
-    /* "View.MemoryView":794
- *         negative_step = have_step != 0 and step < 0
- * 
- *         if have_step and step == 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)
- * 
- */
-    if ((__pyx_v_have_step != 0)) {
-      __pyx_t_4 = (__pyx_v_step == 0);
-      __pyx_t_2 = __pyx_t_4;
-    } else {
-      __pyx_t_2 = (__pyx_v_have_step != 0);
-    }
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":795
- * 
- *         if have_step and step == 0:
- *             _err_dim(ValueError, "Step may not be zero (axis %d)", dim)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, __pyx_k_Step_may_not_be_zero_axis_d, __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 795; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L6;
-    }
-    __pyx_L6:;
-
-    /* "View.MemoryView":798
- * 
- * 
- *         if have_start:             # <<<<<<<<<<<<<<
- *             if start < 0:
- *                 start += shape
- */
-    __pyx_t_2 = (__pyx_v_have_start != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":799
- * 
- *         if have_start:
- *             if start < 0:             # <<<<<<<<<<<<<<
- *                 start += shape
- *                 if start < 0:
- */
-      __pyx_t_2 = ((__pyx_v_start < 0) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":800
- *         if have_start:
- *             if start < 0:
- *                 start += shape             # <<<<<<<<<<<<<<
- *                 if start < 0:
- *                     start = 0
- */
-        __pyx_v_start = (__pyx_v_start + __pyx_v_shape);
-
-        /* "View.MemoryView":801
- *             if start < 0:
- *                 start += shape
- *                 if start < 0:             # <<<<<<<<<<<<<<
- *                     start = 0
- *             elif start >= shape:
- */
-        __pyx_t_2 = ((__pyx_v_start < 0) != 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":802
- *                 start += shape
- *                 if start < 0:
- *                     start = 0             # <<<<<<<<<<<<<<
- *             elif start >= shape:
- *                 if negative_step:
- */
-          __pyx_v_start = 0;
-          goto __pyx_L9;
-        }
-        __pyx_L9:;
-        goto __pyx_L8;
-      }
-
-      /* "View.MemoryView":803
- *                 if start < 0:
- *                     start = 0
- *             elif start >= shape:             # <<<<<<<<<<<<<<
- *                 if negative_step:
- *                     start = shape - 1
- */
-      __pyx_t_2 = ((__pyx_v_start >= __pyx_v_shape) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":804
- *                     start = 0
- *             elif start >= shape:
- *                 if negative_step:             # <<<<<<<<<<<<<<
- *                     start = shape - 1
- *                 else:
- */
-        __pyx_t_2 = (__pyx_v_negative_step != 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":805
- *             elif start >= shape:
- *                 if negative_step:
- *                     start = shape - 1             # <<<<<<<<<<<<<<
- *                 else:
- *                     start = shape
- */
-          __pyx_v_start = (__pyx_v_shape - 1);
-          goto __pyx_L10;
-        }
-        /*else*/ {
-
-          /* "View.MemoryView":807
- *                     start = shape - 1
- *                 else:
- *                     start = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-          __pyx_v_start = __pyx_v_shape;
-        }
-        __pyx_L10:;
-        goto __pyx_L8;
-      }
-      __pyx_L8:;
-      goto __pyx_L7;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":809
- *                     start = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 start = shape - 1
- *             else:
- */
-      __pyx_t_2 = (__pyx_v_negative_step != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":810
- *         else:
- *             if negative_step:
- *                 start = shape - 1             # <<<<<<<<<<<<<<
- *             else:
- *                 start = 0
- */
-        __pyx_v_start = (__pyx_v_shape - 1);
-        goto __pyx_L11;
-      }
-      /*else*/ {
-
-        /* "View.MemoryView":812
- *                 start = shape - 1
- *             else:
- *                 start = 0             # <<<<<<<<<<<<<<
- * 
- *         if have_stop:
- */
-        __pyx_v_start = 0;
-      }
-      __pyx_L11:;
-    }
-    __pyx_L7:;
-
-    /* "View.MemoryView":814
- *                 start = 0
- * 
- *         if have_stop:             # <<<<<<<<<<<<<<
- *             if stop < 0:
- *                 stop += shape
- */
-    __pyx_t_2 = (__pyx_v_have_stop != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":815
- * 
- *         if have_stop:
- *             if stop < 0:             # <<<<<<<<<<<<<<
- *                 stop += shape
- *                 if stop < 0:
- */
-      __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":816
- *         if have_stop:
- *             if stop < 0:
- *                 stop += shape             # <<<<<<<<<<<<<<
- *                 if stop < 0:
- *                     stop = 0
- */
-        __pyx_v_stop = (__pyx_v_stop + __pyx_v_shape);
-
-        /* "View.MemoryView":817
- *             if stop < 0:
- *                 stop += shape
- *                 if stop < 0:             # <<<<<<<<<<<<<<
- *                     stop = 0
- *             elif stop > shape:
- */
-        __pyx_t_2 = ((__pyx_v_stop < 0) != 0);
-        if (__pyx_t_2) {
-
-          /* "View.MemoryView":818
- *                 stop += shape
- *                 if stop < 0:
- *                     stop = 0             # <<<<<<<<<<<<<<
- *             elif stop > shape:
- *                 stop = shape
- */
-          __pyx_v_stop = 0;
-          goto __pyx_L14;
-        }
-        __pyx_L14:;
-        goto __pyx_L13;
-      }
-
-      /* "View.MemoryView":819
- *                 if stop < 0:
- *                     stop = 0
- *             elif stop > shape:             # <<<<<<<<<<<<<<
- *                 stop = shape
- *         else:
- */
-      __pyx_t_2 = ((__pyx_v_stop > __pyx_v_shape) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":820
- *                     stop = 0
- *             elif stop > shape:
- *                 stop = shape             # <<<<<<<<<<<<<<
- *         else:
- *             if negative_step:
- */
-        __pyx_v_stop = __pyx_v_shape;
-        goto __pyx_L13;
-      }
-      __pyx_L13:;
-      goto __pyx_L12;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":822
- *                 stop = shape
- *         else:
- *             if negative_step:             # <<<<<<<<<<<<<<
- *                 stop = -1
- *             else:
- */
-      __pyx_t_2 = (__pyx_v_negative_step != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":823
- *         else:
- *             if negative_step:
- *                 stop = -1             # <<<<<<<<<<<<<<
- *             else:
- *                 stop = shape
- */
-        __pyx_v_stop = -1;
-        goto __pyx_L15;
-      }
-      /*else*/ {
-
-        /* "View.MemoryView":825
- *                 stop = -1
- *             else:
- *                 stop = shape             # <<<<<<<<<<<<<<
- * 
- *         if not have_step:
- */
-        __pyx_v_stop = __pyx_v_shape;
-      }
-      __pyx_L15:;
-    }
-    __pyx_L12:;
-
-    /* "View.MemoryView":827
- *                 stop = shape
- * 
- *         if not have_step:             # <<<<<<<<<<<<<<
- *             step = 1
- * 
- */
-    __pyx_t_2 = ((!(__pyx_v_have_step != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":828
- * 
- *         if not have_step:
- *             step = 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_step = 1;
-      goto __pyx_L16;
-    }
-    __pyx_L16:;
-
-    /* "View.MemoryView":832
- * 
- *         with cython.cdivision(True):
- *             new_shape = (stop - start) // step             # <<<<<<<<<<<<<<
- * 
- *             if (stop - start) - step * new_shape:
- */
-    __pyx_v_new_shape = ((__pyx_v_stop - __pyx_v_start) / __pyx_v_step);
-
-    /* "View.MemoryView":834
- *             new_shape = (stop - start) // step
- * 
- *             if (stop - start) - step * new_shape:             # <<<<<<<<<<<<<<
- *                 new_shape += 1
- * 
- */
-    __pyx_t_2 = (((__pyx_v_stop - __pyx_v_start) - (__pyx_v_step * __pyx_v_new_shape)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":835
- * 
- *             if (stop - start) - step * new_shape:
- *                 new_shape += 1             # <<<<<<<<<<<<<<
- * 
- *         if new_shape < 0:
- */
-      __pyx_v_new_shape = (__pyx_v_new_shape + 1);
-      goto __pyx_L17;
-    }
-    __pyx_L17:;
-
-    /* "View.MemoryView":837
- *                 new_shape += 1
- * 
- *         if new_shape < 0:             # <<<<<<<<<<<<<<
- *             new_shape = 0
- * 
- */
-    __pyx_t_2 = ((__pyx_v_new_shape < 0) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":838
- * 
- *         if new_shape < 0:
- *             new_shape = 0             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_new_shape = 0;
-      goto __pyx_L18;
-    }
-    __pyx_L18:;
-
-    /* "View.MemoryView":841
- * 
- * 
- *         dst.strides[new_ndim] = stride * step             # <<<<<<<<<<<<<<
- *         dst.shape[new_ndim] = new_shape
- *         dst.suboffsets[new_ndim] = suboffset
- */
-    (__pyx_v_dst->strides[__pyx_v_new_ndim]) = (__pyx_v_stride * __pyx_v_step);
-
-    /* "View.MemoryView":842
- * 
- *         dst.strides[new_ndim] = stride * step
- *         dst.shape[new_ndim] = new_shape             # <<<<<<<<<<<<<<
- *         dst.suboffsets[new_ndim] = suboffset
- * 
- */
-    (__pyx_v_dst->shape[__pyx_v_new_ndim]) = __pyx_v_new_shape;
-
-    /* "View.MemoryView":843
- *         dst.strides[new_ndim] = stride * step
- *         dst.shape[new_ndim] = new_shape
- *         dst.suboffsets[new_ndim] = suboffset             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    (__pyx_v_dst->suboffsets[__pyx_v_new_ndim]) = __pyx_v_suboffset;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":846
- * 
- * 
- *     if suboffset_dim[0] < 0:             # <<<<<<<<<<<<<<
- *         dst.data += start * stride
- *     else:
- */
-  __pyx_t_2 = (((__pyx_v_suboffset_dim[0]) < 0) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":847
- * 
- *     if suboffset_dim[0] < 0:
- *         dst.data += start * stride             # <<<<<<<<<<<<<<
- *     else:
- *         dst.suboffsets[suboffset_dim[0]] += start * stride
- */
-    __pyx_v_dst->data = (__pyx_v_dst->data + (__pyx_v_start * __pyx_v_stride));
-    goto __pyx_L19;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":849
- *         dst.data += start * stride
- *     else:
- *         dst.suboffsets[suboffset_dim[0]] += start * stride             # <<<<<<<<<<<<<<
- * 
- *     if suboffset >= 0:
- */
-    __pyx_t_3 = (__pyx_v_suboffset_dim[0]);
-    (__pyx_v_dst->suboffsets[__pyx_t_3]) = ((__pyx_v_dst->suboffsets[__pyx_t_3]) + (__pyx_v_start * __pyx_v_stride));
-  }
-  __pyx_L19:;
-
-  /* "View.MemoryView":851
- *         dst.suboffsets[suboffset_dim[0]] += start * stride
- * 
- *     if suboffset >= 0:             # <<<<<<<<<<<<<<
- *         if not is_slice:
- *             if new_ndim == 0:
- */
-  __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":852
- * 
- *     if suboffset >= 0:
- *         if not is_slice:             # <<<<<<<<<<<<<<
- *             if new_ndim == 0:
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- */
-    __pyx_t_2 = ((!(__pyx_v_is_slice != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":853
- *     if suboffset >= 0:
- *         if not is_slice:
- *             if new_ndim == 0:             # <<<<<<<<<<<<<<
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- *             else:
- */
-      __pyx_t_2 = ((__pyx_v_new_ndim == 0) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":854
- *         if not is_slice:
- *             if new_ndim == 0:
- *                 dst.data = (<char **> dst.data)[0] + suboffset             # <<<<<<<<<<<<<<
- *             else:
- *                 _err_dim(IndexError, "All dimensions preceding dimension %d "
- */
-        __pyx_v_dst->data = ((((char **)__pyx_v_dst->data)[0]) + __pyx_v_suboffset);
-        goto __pyx_L22;
-      }
-      /*else*/ {
-
-        /* "View.MemoryView":856
- *                 dst.data = (<char **> dst.data)[0] + suboffset
- *             else:
- *                 _err_dim(IndexError, "All dimensions preceding dimension %d "             # <<<<<<<<<<<<<<
- *                                      "must be indexed and not sliced", dim)
- *         else:
- */
-        __pyx_t_3 = __pyx_memoryview_err_dim(__pyx_builtin_IndexError, __pyx_k_All_dimensions_preceding_dimensi, __pyx_v_dim); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 856; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-      __pyx_L22:;
-      goto __pyx_L21;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":859
- *                                      "must be indexed and not sliced", dim)
- *         else:
- *             suboffset_dim[0] = new_ndim             # <<<<<<<<<<<<<<
- * 
- *     return 0
- */
-      (__pyx_v_suboffset_dim[0]) = __pyx_v_new_ndim;
-    }
-    __pyx_L21:;
-    goto __pyx_L20;
-  }
-  __pyx_L20:;
-
-  /* "View.MemoryView":861
- *             suboffset_dim[0] = new_ndim
- * 
- *     return 0             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_r = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":764
- * 
- * @cname('__pyx_memoryview_slice_memviewslice')
- * cdef int slice_memviewslice(             # <<<<<<<<<<<<<<
- *         __Pyx_memviewslice *dst,
- *         Py_ssize_t shape, Py_ssize_t stride, Py_ssize_t suboffset,
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  {
-    #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-    #endif
-    __Pyx_AddTraceback("View.MemoryView.slice_memviewslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-    #ifdef WITH_THREAD
-    PyGILState_Release(__pyx_gilstate_save);
-    #endif
-  }
-  __pyx_r = -1;
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":867
- * 
- * @cname('__pyx_pybuffer_index')
- * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<
- *                           Py_ssize_t dim) except NULL:
- *     cdef Py_ssize_t shape, stride, suboffset = -1
- */
-
-static char *__pyx_pybuffer_index(Py_buffer *__pyx_v_view, char *__pyx_v_bufp, Py_ssize_t __pyx_v_index, Py_ssize_t __pyx_v_dim) {
-  Py_ssize_t __pyx_v_shape;
-  Py_ssize_t __pyx_v_stride;
-  Py_ssize_t __pyx_v_suboffset;
-  Py_ssize_t __pyx_v_itemsize;
-  char *__pyx_v_resultp;
-  char *__pyx_r;
-  __Pyx_RefNannyDeclarations
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("pybuffer_index", 0);
-
-  /* "View.MemoryView":869
- * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,
- *                           Py_ssize_t dim) except NULL:
- *     cdef Py_ssize_t shape, stride, suboffset = -1             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t itemsize = view.itemsize
- *     cdef char *resultp
- */
-  __pyx_v_suboffset = -1;
-
-  /* "View.MemoryView":870
- *                           Py_ssize_t dim) except NULL:
- *     cdef Py_ssize_t shape, stride, suboffset = -1
- *     cdef Py_ssize_t itemsize = view.itemsize             # <<<<<<<<<<<<<<
- *     cdef char *resultp
- * 
- */
-  __pyx_t_1 = __pyx_v_view->itemsize;
-  __pyx_v_itemsize = __pyx_t_1;
-
-  /* "View.MemoryView":873
- *     cdef char *resultp
- * 
- *     if view.ndim == 0:             # <<<<<<<<<<<<<<
- *         shape = view.len / itemsize
- *         stride = itemsize
- */
-  __pyx_t_2 = ((__pyx_v_view->ndim == 0) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":874
- * 
- *     if view.ndim == 0:
- *         shape = view.len / itemsize             # <<<<<<<<<<<<<<
- *         stride = itemsize
- *     else:
- */
-    if (unlikely(__pyx_v_itemsize == 0)) {
-      #ifdef WITH_THREAD
-      PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-      #endif
-      PyErr_SetString(PyExc_ZeroDivisionError, "integer division or modulo by zero");
-      #ifdef WITH_THREAD
-      PyGILState_Release(__pyx_gilstate_save);
-      #endif
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 874; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-    else if (sizeof(Py_ssize_t) == sizeof(long) && unlikely(__pyx_v_itemsize == -1)  && unlikely(UNARY_NEG_WOULD_OVERFLOW(__pyx_v_view->len))) {
-      #ifdef WITH_THREAD
-      PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-      #endif
-      PyErr_SetString(PyExc_OverflowError, "value too large to perform division");
-      #ifdef WITH_THREAD
-      PyGILState_Release(__pyx_gilstate_save);
-      #endif
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 874; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-    __pyx_v_shape = __Pyx_div_Py_ssize_t(__pyx_v_view->len, __pyx_v_itemsize);
-
-    /* "View.MemoryView":875
- *     if view.ndim == 0:
- *         shape = view.len / itemsize
- *         stride = itemsize             # <<<<<<<<<<<<<<
- *     else:
- *         shape = view.shape[dim]
- */
-    __pyx_v_stride = __pyx_v_itemsize;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":877
- *         stride = itemsize
- *     else:
- *         shape = view.shape[dim]             # <<<<<<<<<<<<<<
- *         stride = view.strides[dim]
- *         if view.suboffsets != NULL:
- */
-    __pyx_v_shape = (__pyx_v_view->shape[__pyx_v_dim]);
-
-    /* "View.MemoryView":878
- *     else:
- *         shape = view.shape[dim]
- *         stride = view.strides[dim]             # <<<<<<<<<<<<<<
- *         if view.suboffsets != NULL:
- *             suboffset = view.suboffsets[dim]
- */
-    __pyx_v_stride = (__pyx_v_view->strides[__pyx_v_dim]);
-
-    /* "View.MemoryView":879
- *         shape = view.shape[dim]
- *         stride = view.strides[dim]
- *         if view.suboffsets != NULL:             # <<<<<<<<<<<<<<
- *             suboffset = view.suboffsets[dim]
- * 
- */
-    __pyx_t_2 = ((__pyx_v_view->suboffsets != NULL) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":880
- *         stride = view.strides[dim]
- *         if view.suboffsets != NULL:
- *             suboffset = view.suboffsets[dim]             # <<<<<<<<<<<<<<
- * 
- *     if index < 0:
- */
-      __pyx_v_suboffset = (__pyx_v_view->suboffsets[__pyx_v_dim]);
-      goto __pyx_L4;
-    }
-    __pyx_L4:;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":882
- *             suboffset = view.suboffsets[dim]
- * 
- *     if index < 0:             # <<<<<<<<<<<<<<
- *         index += view.shape[dim]
- *         if index < 0:
- */
-  __pyx_t_2 = ((__pyx_v_index < 0) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":883
- * 
- *     if index < 0:
- *         index += view.shape[dim]             # <<<<<<<<<<<<<<
- *         if index < 0:
- *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
- */
-    __pyx_v_index = (__pyx_v_index + (__pyx_v_view->shape[__pyx_v_dim]));
-
-    /* "View.MemoryView":884
- *     if index < 0:
- *         index += view.shape[dim]
- *         if index < 0:             # <<<<<<<<<<<<<<
- *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
- * 
- */
-    __pyx_t_2 = ((__pyx_v_index < 0) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":885
- *         index += view.shape[dim]
- *         if index < 0:
- *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)             # <<<<<<<<<<<<<<
- * 
- *     if index >= shape:
- */
-      __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __pyx_t_4 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_4);
-      __Pyx_GIVEREF(__pyx_t_4);
-      __pyx_t_4 = 0;
-      __pyx_t_4 = __Pyx_PyObject_Call(__pyx_builtin_IndexError, __pyx_t_3, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_4);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
-      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-      {__pyx_filename = __pyx_f[2]; __pyx_lineno = 885; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-    goto __pyx_L5;
-  }
-  __pyx_L5:;
-
-  /* "View.MemoryView":887
- *             raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
- * 
- *     if index >= shape:             # <<<<<<<<<<<<<<
- *         raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
- * 
- */
-  __pyx_t_2 = ((__pyx_v_index >= __pyx_v_shape) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":888
- * 
- *     if index >= shape:
- *         raise IndexError("Out of bounds on buffer access (axis %d)" % dim)             # <<<<<<<<<<<<<<
- * 
- *     resultp = bufp + index * stride
- */
-    __pyx_t_4 = PyInt_FromSsize_t(__pyx_v_dim); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_t_4); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_4);
-    PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
-    __Pyx_GIVEREF(__pyx_t_3);
-    __pyx_t_3 = 0;
-    __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_IndexError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-    __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 888; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "View.MemoryView":890
- *         raise IndexError("Out of bounds on buffer access (axis %d)" % dim)
- * 
- *     resultp = bufp + index * stride             # <<<<<<<<<<<<<<
- *     if suboffset >= 0:
- *         resultp = (<char **> resultp)[0] + suboffset
- */
-  __pyx_v_resultp = (__pyx_v_bufp + (__pyx_v_index * __pyx_v_stride));
-
-  /* "View.MemoryView":891
- * 
- *     resultp = bufp + index * stride
- *     if suboffset >= 0:             # <<<<<<<<<<<<<<
- *         resultp = (<char **> resultp)[0] + suboffset
- * 
- */
-  __pyx_t_2 = ((__pyx_v_suboffset >= 0) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":892
- *     resultp = bufp + index * stride
- *     if suboffset >= 0:
- *         resultp = (<char **> resultp)[0] + suboffset             # <<<<<<<<<<<<<<
- * 
- *     return resultp
- */
-    __pyx_v_resultp = ((((char **)__pyx_v_resultp)[0]) + __pyx_v_suboffset);
-    goto __pyx_L8;
-  }
-  __pyx_L8:;
-
-  /* "View.MemoryView":894
- *         resultp = (<char **> resultp)[0] + suboffset
- * 
- *     return resultp             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_r = __pyx_v_resultp;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":867
- * 
- * @cname('__pyx_pybuffer_index')
- * cdef char *pybuffer_index(Py_buffer *view, char *bufp, Py_ssize_t index,             # <<<<<<<<<<<<<<
- *                           Py_ssize_t dim) except NULL:
- *     cdef Py_ssize_t shape, stride, suboffset = -1
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_AddTraceback("View.MemoryView.pybuffer_index", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = NULL;
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":900
- * 
- * @cname('__pyx_memslice_transpose')
- * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:             # <<<<<<<<<<<<<<
- *     cdef int ndim = memslice.memview.view.ndim
- * 
- */
-
-static int __pyx_memslice_transpose(__Pyx_memviewslice *__pyx_v_memslice) {
-  int __pyx_v_ndim;
-  Py_ssize_t *__pyx_v_shape;
-  Py_ssize_t *__pyx_v_strides;
-  int __pyx_v_i;
-  int __pyx_v_j;
-  int __pyx_r;
-  int __pyx_t_1;
-  Py_ssize_t *__pyx_t_2;
-  long __pyx_t_3;
-  Py_ssize_t __pyx_t_4;
-  Py_ssize_t __pyx_t_5;
-  int __pyx_t_6;
-  int __pyx_t_7;
-  int __pyx_t_8;
-  int __pyx_t_9;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-
-  /* "View.MemoryView":901
- * @cname('__pyx_memslice_transpose')
- * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:
- *     cdef int ndim = memslice.memview.view.ndim             # <<<<<<<<<<<<<<
- * 
- *     cdef Py_ssize_t *shape = memslice.shape
- */
-  __pyx_t_1 = __pyx_v_memslice->memview->view.ndim;
-  __pyx_v_ndim = __pyx_t_1;
-
-  /* "View.MemoryView":903
- *     cdef int ndim = memslice.memview.view.ndim
- * 
- *     cdef Py_ssize_t *shape = memslice.shape             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t *strides = memslice.strides
- * 
- */
-  __pyx_t_2 = __pyx_v_memslice->shape;
-  __pyx_v_shape = __pyx_t_2;
-
-  /* "View.MemoryView":904
- * 
- *     cdef Py_ssize_t *shape = memslice.shape
- *     cdef Py_ssize_t *strides = memslice.strides             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = __pyx_v_memslice->strides;
-  __pyx_v_strides = __pyx_t_2;
-
-  /* "View.MemoryView":908
- * 
- *     cdef int i, j
- *     for i in range(ndim / 2):             # <<<<<<<<<<<<<<
- *         j = ndim - 1 - i
- *         strides[i], strides[j] = strides[j], strides[i]
- */
-  __pyx_t_3 = __Pyx_div_long(__pyx_v_ndim, 2);
-  for (__pyx_t_1 = 0; __pyx_t_1 < __pyx_t_3; __pyx_t_1+=1) {
-    __pyx_v_i = __pyx_t_1;
-
-    /* "View.MemoryView":909
- *     cdef int i, j
- *     for i in range(ndim / 2):
- *         j = ndim - 1 - i             # <<<<<<<<<<<<<<
- *         strides[i], strides[j] = strides[j], strides[i]
- *         shape[i], shape[j] = shape[j], shape[i]
- */
-    __pyx_v_j = ((__pyx_v_ndim - 1) - __pyx_v_i);
-
-    /* "View.MemoryView":910
- *     for i in range(ndim / 2):
- *         j = ndim - 1 - i
- *         strides[i], strides[j] = strides[j], strides[i]             # <<<<<<<<<<<<<<
- *         shape[i], shape[j] = shape[j], shape[i]
- * 
- */
-    __pyx_t_4 = (__pyx_v_strides[__pyx_v_j]);
-    __pyx_t_5 = (__pyx_v_strides[__pyx_v_i]);
-    (__pyx_v_strides[__pyx_v_i]) = __pyx_t_4;
-    (__pyx_v_strides[__pyx_v_j]) = __pyx_t_5;
-
-    /* "View.MemoryView":911
- *         j = ndim - 1 - i
- *         strides[i], strides[j] = strides[j], strides[i]
- *         shape[i], shape[j] = shape[j], shape[i]             # <<<<<<<<<<<<<<
- * 
- *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:
- */
-    __pyx_t_5 = (__pyx_v_shape[__pyx_v_j]);
-    __pyx_t_4 = (__pyx_v_shape[__pyx_v_i]);
-    (__pyx_v_shape[__pyx_v_i]) = __pyx_t_5;
-    (__pyx_v_shape[__pyx_v_j]) = __pyx_t_4;
-
-    /* "View.MemoryView":913
- *         shape[i], shape[j] = shape[j], shape[i]
- * 
- *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:             # <<<<<<<<<<<<<<
- *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")
- * 
- */
-    __pyx_t_6 = (((__pyx_v_memslice->suboffsets[__pyx_v_i]) >= 0) != 0);
-    if (!__pyx_t_6) {
-      __pyx_t_7 = (((__pyx_v_memslice->suboffsets[__pyx_v_j]) >= 0) != 0);
-      __pyx_t_8 = __pyx_t_7;
-    } else {
-      __pyx_t_8 = __pyx_t_6;
-    }
-    if (__pyx_t_8) {
-
-      /* "View.MemoryView":914
- * 
- *         if memslice.suboffsets[i] >= 0 or memslice.suboffsets[j] >= 0:
- *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")             # <<<<<<<<<<<<<<
- * 
- *     return 1
- */
-      __pyx_t_9 = __pyx_memoryview_err(__pyx_builtin_ValueError, __pyx_k_Cannot_transpose_memoryview_with); if (unlikely(__pyx_t_9 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 914; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L5;
-    }
-    __pyx_L5:;
-  }
-
-  /* "View.MemoryView":916
- *             _err(ValueError, "Cannot transpose memoryview with indirect dimensions")
- * 
- *     return 1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_r = 1;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":900
- * 
- * @cname('__pyx_memslice_transpose')
- * cdef int transpose_memslice(__Pyx_memviewslice *memslice) nogil except 0:             # <<<<<<<<<<<<<<
- *     cdef int ndim = memslice.memview.view.ndim
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  {
-    #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-    #endif
-    __Pyx_AddTraceback("View.MemoryView.transpose_memslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-    #ifdef WITH_THREAD
-    PyGILState_Release(__pyx_gilstate_save);
-    #endif
-  }
-  __pyx_r = 0;
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":933
- *     cdef int (*to_dtype_func)(char *, object) except 0
- * 
- *     def __dealloc__(self):             # <<<<<<<<<<<<<<
- *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
- * 
- */
-
-/* Python wrapper */
-static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self); /*proto*/
-static void __pyx_memoryviewslice___dealloc__(PyObject *__pyx_v_self) {
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__dealloc__ (wrapper)", 0);
-  __pyx_memoryviewslice_MemoryView_16_memoryviewslice___dealloc__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-static void __pyx_memoryviewslice_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self) {
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__dealloc__", 0);
-
-  /* "View.MemoryView":934
- * 
- *     def __dealloc__(self):
- *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)             # <<<<<<<<<<<<<<
- * 
- *     cdef convert_item_to_object(self, char *itemp):
- */
-  __PYX_XDEC_MEMVIEW((&__pyx_v_self->from_slice), 1);
-
-  /* "View.MemoryView":933
- *     cdef int (*to_dtype_func)(char *, object) except 0
- * 
- *     def __dealloc__(self):             # <<<<<<<<<<<<<<
- *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
- * 
- */
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-/* "View.MemoryView":936
- *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
- * 
- *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
- *         if self.to_object_func != NULL:
- *             return self.to_object_func(itemp)
- */
-
-static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("convert_item_to_object", 0);
-
-  /* "View.MemoryView":937
- * 
- *     cdef convert_item_to_object(self, char *itemp):
- *         if self.to_object_func != NULL:             # <<<<<<<<<<<<<<
- *             return self.to_object_func(itemp)
- *         else:
- */
-  __pyx_t_1 = ((__pyx_v_self->to_object_func != NULL) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":938
- *     cdef convert_item_to_object(self, char *itemp):
- *         if self.to_object_func != NULL:
- *             return self.to_object_func(itemp)             # <<<<<<<<<<<<<<
- *         else:
- *             return memoryview.convert_item_to_object(self, itemp)
- */
-    __Pyx_XDECREF(__pyx_r);
-    __pyx_t_2 = __pyx_v_self->to_object_func(__pyx_v_itemp); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 938; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __pyx_r = __pyx_t_2;
-    __pyx_t_2 = 0;
-    goto __pyx_L0;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":940
- *             return self.to_object_func(itemp)
- *         else:
- *             return memoryview.convert_item_to_object(self, itemp)             # <<<<<<<<<<<<<<
- * 
- *     cdef assign_item_from_object(self, char *itemp, object value):
- */
-    __Pyx_XDECREF(__pyx_r);
-    __pyx_t_2 = __pyx_vtabptr_memoryview->convert_item_to_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 940; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __pyx_r = __pyx_t_2;
-    __pyx_t_2 = 0;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":936
- *         __PYX_XDEC_MEMVIEW(&self.from_slice, 1)
- * 
- *     cdef convert_item_to_object(self, char *itemp):             # <<<<<<<<<<<<<<
- *         if self.to_object_func != NULL:
- *             return self.to_object_func(itemp)
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_AddTraceback("View.MemoryView._memoryviewslice.convert_item_to_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":942
- *             return memoryview.convert_item_to_object(self, itemp)
- * 
- *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
- *         if self.to_dtype_func != NULL:
- *             self.to_dtype_func(itemp, value)
- */
-
-static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("assign_item_from_object", 0);
-
-  /* "View.MemoryView":943
- * 
- *     cdef assign_item_from_object(self, char *itemp, object value):
- *         if self.to_dtype_func != NULL:             # <<<<<<<<<<<<<<
- *             self.to_dtype_func(itemp, value)
- *         else:
- */
-  __pyx_t_1 = ((__pyx_v_self->to_dtype_func != NULL) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":944
- *     cdef assign_item_from_object(self, char *itemp, object value):
- *         if self.to_dtype_func != NULL:
- *             self.to_dtype_func(itemp, value)             # <<<<<<<<<<<<<<
- *         else:
- *             memoryview.assign_item_from_object(self, itemp, value)
- */
-    __pyx_t_2 = __pyx_v_self->to_dtype_func(__pyx_v_itemp, __pyx_v_value); if (unlikely(__pyx_t_2 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 944; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":946
- *             self.to_dtype_func(itemp, value)
- *         else:
- *             memoryview.assign_item_from_object(self, itemp, value)             # <<<<<<<<<<<<<<
- * 
- *     property base:
- */
-    __pyx_t_3 = __pyx_vtabptr_memoryview->assign_item_from_object(((struct __pyx_memoryview_obj *)__pyx_v_self), __pyx_v_itemp, __pyx_v_value); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 946; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":942
- *             return memoryview.convert_item_to_object(self, itemp)
- * 
- *     cdef assign_item_from_object(self, char *itemp, object value):             # <<<<<<<<<<<<<<
- *         if self.to_dtype_func != NULL:
- *             self.to_dtype_func(itemp, value)
- */
-
-  /* function exit code */
-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView._memoryviewslice.assign_item_from_object", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":950
- *     property base:
- *         @cname('__pyx_memoryviewslice__get__base')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.from_object
- * 
- */
-
-/* Python wrapper */
-static PyObject *__pyx_memoryviewslice__get__base(PyObject *__pyx_v_self); /*proto*/
-static PyObject *__pyx_memoryviewslice__get__base(PyObject *__pyx_v_self) {
-  PyObject *__pyx_r = 0;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__ (wrapper)", 0);
-  __pyx_r = __pyx_memoryviewslice__get__base_MemoryView_16_memoryviewslice_4base___get__(((struct __pyx_memoryviewslice_obj *)__pyx_v_self));
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-static PyObject *__pyx_memoryviewslice__get__base_MemoryView_16_memoryviewslice_4base___get__(struct __pyx_memoryviewslice_obj *__pyx_v_self) {
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__get__", 0);
-
-  /* "View.MemoryView":951
- *         @cname('__pyx_memoryviewslice__get__base')
- *         def __get__(self):
- *             return self.from_object             # <<<<<<<<<<<<<<
- * 
- *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(__pyx_v_self->from_object);
-  __pyx_r = __pyx_v_self->from_object;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":950
- *     property base:
- *         @cname('__pyx_memoryviewslice__get__base')
- *         def __get__(self):             # <<<<<<<<<<<<<<
- *             return self.from_object
- * 
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":957
- * 
- * @cname('__pyx_memoryview_fromslice')
- * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice,             # <<<<<<<<<<<<<<
- *                           int ndim,
- *                           object (*to_object_func)(char *),
- */
-
-static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice __pyx_v_memviewslice, int __pyx_v_ndim, PyObject *(*__pyx_v_to_object_func)(char *), int (*__pyx_v_to_dtype_func)(char *, PyObject *), int __pyx_v_dtype_is_object) {
-  struct __pyx_memoryviewslice_obj *__pyx_v_result = 0;
-  int __pyx_v_i;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  __Pyx_TypeInfo *__pyx_t_4;
-  Py_buffer __pyx_t_5;
-  Py_ssize_t __pyx_t_6;
-  int __pyx_t_7;
-  int __pyx_t_8;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("memoryview_fromslice", 0);
-
-  /* "View.MemoryView":966
- *     cdef int i
- * 
- *     if <PyObject *> memviewslice.memview == Py_None:             # <<<<<<<<<<<<<<
- *         return None
- * 
- */
-  __pyx_t_1 = ((((PyObject *)__pyx_v_memviewslice.memview) == Py_None) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":967
- * 
- *     if <PyObject *> memviewslice.memview == Py_None:
- *         return None             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __Pyx_XDECREF(__pyx_r);
-    __Pyx_INCREF(Py_None);
-    __pyx_r = Py_None;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":972
- * 
- * 
- *     result = _memoryviewslice(None, 0, dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- *     result.from_slice = memviewslice
- */
-  __pyx_t_2 = __Pyx_PyBool_FromLong(__pyx_v_dtype_is_object); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 972; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 972; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_INCREF(Py_None);
-  PyTuple_SET_ITEM(__pyx_t_3, 0, Py_None);
-  __Pyx_GIVEREF(Py_None);
-  __Pyx_INCREF(__pyx_int_0);
-  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_0);
-  __Pyx_GIVEREF(__pyx_int_0);
-  PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2);
-  __Pyx_GIVEREF(__pyx_t_2);
-  __pyx_t_2 = 0;
-  __pyx_t_2 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_memoryviewslice_type)), __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 972; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  __pyx_v_result = ((struct __pyx_memoryviewslice_obj *)__pyx_t_2);
-  __pyx_t_2 = 0;
-
-  /* "View.MemoryView":974
- *     result = _memoryviewslice(None, 0, dtype_is_object)
- * 
- *     result.from_slice = memviewslice             # <<<<<<<<<<<<<<
- *     __PYX_INC_MEMVIEW(&memviewslice, 1)
- * 
- */
-  __pyx_v_result->from_slice = __pyx_v_memviewslice;
-
-  /* "View.MemoryView":975
- * 
- *     result.from_slice = memviewslice
- *     __PYX_INC_MEMVIEW(&memviewslice, 1)             # <<<<<<<<<<<<<<
- * 
- *     result.from_object = (<memoryview> memviewslice.memview).base
- */
-  __PYX_INC_MEMVIEW((&__pyx_v_memviewslice), 1);
-
-  /* "View.MemoryView":977
- *     __PYX_INC_MEMVIEW(&memviewslice, 1)
- * 
- *     result.from_object = (<memoryview> memviewslice.memview).base             # <<<<<<<<<<<<<<
- *     result.typeinfo = memviewslice.memview.typeinfo
- * 
- */
-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_memviewslice.memview), __pyx_n_s_base); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 977; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __Pyx_GIVEREF(__pyx_t_2);
-  __Pyx_GOTREF(__pyx_v_result->from_object);
-  __Pyx_DECREF(__pyx_v_result->from_object);
-  __pyx_v_result->from_object = __pyx_t_2;
-  __pyx_t_2 = 0;
-
-  /* "View.MemoryView":978
- * 
- *     result.from_object = (<memoryview> memviewslice.memview).base
- *     result.typeinfo = memviewslice.memview.typeinfo             # <<<<<<<<<<<<<<
- * 
- *     result.view = memviewslice.memview.view
- */
-  __pyx_t_4 = __pyx_v_memviewslice.memview->typeinfo;
-  __pyx_v_result->__pyx_base.typeinfo = __pyx_t_4;
-
-  /* "View.MemoryView":980
- *     result.typeinfo = memviewslice.memview.typeinfo
- * 
- *     result.view = memviewslice.memview.view             # <<<<<<<<<<<<<<
- *     result.view.buf = <void *> memviewslice.data
- *     result.view.ndim = ndim
- */
-  __pyx_t_5 = __pyx_v_memviewslice.memview->view;
-  __pyx_v_result->__pyx_base.view = __pyx_t_5;
-
-  /* "View.MemoryView":981
- * 
- *     result.view = memviewslice.memview.view
- *     result.view.buf = <void *> memviewslice.data             # <<<<<<<<<<<<<<
- *     result.view.ndim = ndim
- *     (<__pyx_buffer *> &result.view).obj = Py_None
- */
-  __pyx_v_result->__pyx_base.view.buf = ((void *)__pyx_v_memviewslice.data);
-
-  /* "View.MemoryView":982
- *     result.view = memviewslice.memview.view
- *     result.view.buf = <void *> memviewslice.data
- *     result.view.ndim = ndim             # <<<<<<<<<<<<<<
- *     (<__pyx_buffer *> &result.view).obj = Py_None
- *     Py_INCREF(Py_None)
- */
-  __pyx_v_result->__pyx_base.view.ndim = __pyx_v_ndim;
-
-  /* "View.MemoryView":983
- *     result.view.buf = <void *> memviewslice.data
- *     result.view.ndim = ndim
- *     (<__pyx_buffer *> &result.view).obj = Py_None             # <<<<<<<<<<<<<<
- *     Py_INCREF(Py_None)
- * 
- */
-  ((Py_buffer *)(&__pyx_v_result->__pyx_base.view))->obj = Py_None;
-
-  /* "View.MemoryView":984
- *     result.view.ndim = ndim
- *     (<__pyx_buffer *> &result.view).obj = Py_None
- *     Py_INCREF(Py_None)             # <<<<<<<<<<<<<<
- * 
- *     result.flags = PyBUF_RECORDS
- */
-  Py_INCREF(Py_None);
-
-  /* "View.MemoryView":986
- *     Py_INCREF(Py_None)
- * 
- *     result.flags = PyBUF_RECORDS             # <<<<<<<<<<<<<<
- * 
- *     result.view.shape = <Py_ssize_t *> result.from_slice.shape
- */
-  __pyx_v_result->__pyx_base.flags = PyBUF_RECORDS;
-
-  /* "View.MemoryView":988
- *     result.flags = PyBUF_RECORDS
- * 
- *     result.view.shape = <Py_ssize_t *> result.from_slice.shape             # <<<<<<<<<<<<<<
- *     result.view.strides = <Py_ssize_t *> result.from_slice.strides
- *     result.view.suboffsets = <Py_ssize_t *> result.from_slice.suboffsets
- */
-  __pyx_v_result->__pyx_base.view.shape = ((Py_ssize_t *)__pyx_v_result->from_slice.shape);
-
-  /* "View.MemoryView":989
- * 
- *     result.view.shape = <Py_ssize_t *> result.from_slice.shape
- *     result.view.strides = <Py_ssize_t *> result.from_slice.strides             # <<<<<<<<<<<<<<
- *     result.view.suboffsets = <Py_ssize_t *> result.from_slice.suboffsets
- * 
- */
-  __pyx_v_result->__pyx_base.view.strides = ((Py_ssize_t *)__pyx_v_result->from_slice.strides);
-
-  /* "View.MemoryView":990
- *     result.view.shape = <Py_ssize_t *> result.from_slice.shape
- *     result.view.strides = <Py_ssize_t *> result.from_slice.strides
- *     result.view.suboffsets = <Py_ssize_t *> result.from_slice.suboffsets             # <<<<<<<<<<<<<<
- * 
- *     result.view.len = result.view.itemsize
- */
-  __pyx_v_result->__pyx_base.view.suboffsets = ((Py_ssize_t *)__pyx_v_result->from_slice.suboffsets);
-
-  /* "View.MemoryView":992
- *     result.view.suboffsets = <Py_ssize_t *> result.from_slice.suboffsets
- * 
- *     result.view.len = result.view.itemsize             # <<<<<<<<<<<<<<
- *     for i in range(ndim):
- *         result.view.len *= result.view.shape[i]
- */
-  __pyx_t_6 = __pyx_v_result->__pyx_base.view.itemsize;
-  __pyx_v_result->__pyx_base.view.len = __pyx_t_6;
-
-  /* "View.MemoryView":993
- * 
- *     result.view.len = result.view.itemsize
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         result.view.len *= result.view.shape[i]
- * 
- */
-  __pyx_t_7 = __pyx_v_ndim;
-  for (__pyx_t_8 = 0; __pyx_t_8 < __pyx_t_7; __pyx_t_8+=1) {
-    __pyx_v_i = __pyx_t_8;
-
-    /* "View.MemoryView":994
- *     result.view.len = result.view.itemsize
- *     for i in range(ndim):
- *         result.view.len *= result.view.shape[i]             # <<<<<<<<<<<<<<
- * 
- *     result.to_object_func = to_object_func
- */
-    __pyx_v_result->__pyx_base.view.len = (__pyx_v_result->__pyx_base.view.len * (__pyx_v_result->__pyx_base.view.shape[__pyx_v_i]));
-  }
-
-  /* "View.MemoryView":996
- *         result.view.len *= result.view.shape[i]
- * 
- *     result.to_object_func = to_object_func             # <<<<<<<<<<<<<<
- *     result.to_dtype_func = to_dtype_func
- * 
- */
-  __pyx_v_result->to_object_func = __pyx_v_to_object_func;
-
-  /* "View.MemoryView":997
- * 
- *     result.to_object_func = to_object_func
- *     result.to_dtype_func = to_dtype_func             # <<<<<<<<<<<<<<
- * 
- *     return result
- */
-  __pyx_v_result->to_dtype_func = __pyx_v_to_dtype_func;
-
-  /* "View.MemoryView":999
- *     result.to_dtype_func = to_dtype_func
- * 
- *     return result             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_get_slice_from_memoryview')
- */
-  __Pyx_XDECREF(__pyx_r);
-  __Pyx_INCREF(((PyObject *)__pyx_v_result));
-  __pyx_r = ((PyObject *)__pyx_v_result);
-  goto __pyx_L0;
-
-  /* "View.MemoryView":957
- * 
- * @cname('__pyx_memoryview_fromslice')
- * cdef memoryview_fromslice(__Pyx_memviewslice memviewslice,             # <<<<<<<<<<<<<<
- *                           int ndim,
- *                           object (*to_object_func)(char *),
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView.memoryview_fromslice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_result);
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1002
- * 
- * @cname('__pyx_memoryview_get_slice_from_memoryview')
- * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview,             # <<<<<<<<<<<<<<
- *                                                    __Pyx_memviewslice *mslice):
- *     cdef _memoryviewslice obj
- */
-
-static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_mslice) {
-  struct __pyx_memoryviewslice_obj *__pyx_v_obj = 0;
-  __Pyx_memviewslice *__pyx_r;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("get_slice_from_memview", 0);
-
-  /* "View.MemoryView":1005
- *                                                    __Pyx_memviewslice *mslice):
- *     cdef _memoryviewslice obj
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         obj = memview
- *         return &obj.from_slice
- */
-  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), ((PyObject *)__pyx_memoryviewslice_type)); 
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1006
- *     cdef _memoryviewslice obj
- *     if isinstance(memview, _memoryviewslice):
- *         obj = memview             # <<<<<<<<<<<<<<
- *         return &obj.from_slice
- *     else:
- */
-    if (!(likely(((((PyObject *)__pyx_v_memview)) == Py_None) || likely(__Pyx_TypeTest(((PyObject *)__pyx_v_memview), __pyx_memoryviewslice_type))))) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1006; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_t_3 = ((PyObject *)__pyx_v_memview);
-    __Pyx_INCREF(__pyx_t_3);
-    __pyx_v_obj = ((struct __pyx_memoryviewslice_obj *)__pyx_t_3);
-    __pyx_t_3 = 0;
-
-    /* "View.MemoryView":1007
- *     if isinstance(memview, _memoryviewslice):
- *         obj = memview
- *         return &obj.from_slice             # <<<<<<<<<<<<<<
- *     else:
- *         slice_copy(memview, mslice)
- */
-    __pyx_r = (&__pyx_v_obj->from_slice);
-    goto __pyx_L0;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1009
- *         return &obj.from_slice
- *     else:
- *         slice_copy(memview, mslice)             # <<<<<<<<<<<<<<
- *         return mslice
- * 
- */
-    __pyx_memoryview_slice_copy(__pyx_v_memview, __pyx_v_mslice);
-
-    /* "View.MemoryView":1010
- *     else:
- *         slice_copy(memview, mslice)
- *         return mslice             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_slice_copy')
- */
-    __pyx_r = __pyx_v_mslice;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1002
- * 
- * @cname('__pyx_memoryview_get_slice_from_memoryview')
- * cdef __Pyx_memviewslice *get_slice_from_memview(memoryview memview,             # <<<<<<<<<<<<<<
- *                                                    __Pyx_memviewslice *mslice):
- *     cdef _memoryviewslice obj
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_WriteUnraisable("View.MemoryView.get_slice_from_memview", __pyx_clineno, __pyx_lineno, __pyx_filename, 0);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XDECREF((PyObject *)__pyx_v_obj);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1013
- * 
- * @cname('__pyx_memoryview_slice_copy')
- * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst):             # <<<<<<<<<<<<<<
- *     cdef int dim
- *     cdef (Py_ssize_t*) shape, strides, suboffsets
- */
-
-static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_dst) {
-  int __pyx_v_dim;
-  Py_ssize_t *__pyx_v_shape;
-  Py_ssize_t *__pyx_v_strides;
-  Py_ssize_t *__pyx_v_suboffsets;
-  __Pyx_RefNannyDeclarations
-  Py_ssize_t *__pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  __Pyx_RefNannySetupContext("slice_copy", 0);
-
-  /* "View.MemoryView":1017
- *     cdef (Py_ssize_t*) shape, strides, suboffsets
- * 
- *     shape = memview.view.shape             # <<<<<<<<<<<<<<
- *     strides = memview.view.strides
- *     suboffsets = memview.view.suboffsets
- */
-  __pyx_t_1 = __pyx_v_memview->view.shape;
-  __pyx_v_shape = __pyx_t_1;
-
-  /* "View.MemoryView":1018
- * 
- *     shape = memview.view.shape
- *     strides = memview.view.strides             # <<<<<<<<<<<<<<
- *     suboffsets = memview.view.suboffsets
- * 
- */
-  __pyx_t_1 = __pyx_v_memview->view.strides;
-  __pyx_v_strides = __pyx_t_1;
-
-  /* "View.MemoryView":1019
- *     shape = memview.view.shape
- *     strides = memview.view.strides
- *     suboffsets = memview.view.suboffsets             # <<<<<<<<<<<<<<
- * 
- *     dst.memview = <__pyx_memoryview *> memview
- */
-  __pyx_t_1 = __pyx_v_memview->view.suboffsets;
-  __pyx_v_suboffsets = __pyx_t_1;
-
-  /* "View.MemoryView":1021
- *     suboffsets = memview.view.suboffsets
- * 
- *     dst.memview = <__pyx_memoryview *> memview             # <<<<<<<<<<<<<<
- *     dst.data = <char *> memview.view.buf
- * 
- */
-  __pyx_v_dst->memview = ((struct __pyx_memoryview_obj *)__pyx_v_memview);
-
-  /* "View.MemoryView":1022
- * 
- *     dst.memview = <__pyx_memoryview *> memview
- *     dst.data = <char *> memview.view.buf             # <<<<<<<<<<<<<<
- * 
- *     for dim in range(memview.view.ndim):
- */
-  __pyx_v_dst->data = ((char *)__pyx_v_memview->view.buf);
-
-  /* "View.MemoryView":1024
- *     dst.data = <char *> memview.view.buf
- * 
- *     for dim in range(memview.view.ndim):             # <<<<<<<<<<<<<<
- *         dst.shape[dim] = shape[dim]
- *         dst.strides[dim] = strides[dim]
- */
-  __pyx_t_2 = __pyx_v_memview->view.ndim;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_dim = __pyx_t_3;
-
-    /* "View.MemoryView":1025
- * 
- *     for dim in range(memview.view.ndim):
- *         dst.shape[dim] = shape[dim]             # <<<<<<<<<<<<<<
- *         dst.strides[dim] = strides[dim]
- *         if suboffsets == NULL:
- */
-    (__pyx_v_dst->shape[__pyx_v_dim]) = (__pyx_v_shape[__pyx_v_dim]);
-
-    /* "View.MemoryView":1026
- *     for dim in range(memview.view.ndim):
- *         dst.shape[dim] = shape[dim]
- *         dst.strides[dim] = strides[dim]             # <<<<<<<<<<<<<<
- *         if suboffsets == NULL:
- *             dst.suboffsets[dim] = -1
- */
-    (__pyx_v_dst->strides[__pyx_v_dim]) = (__pyx_v_strides[__pyx_v_dim]);
-
-    /* "View.MemoryView":1027
- *         dst.shape[dim] = shape[dim]
- *         dst.strides[dim] = strides[dim]
- *         if suboffsets == NULL:             # <<<<<<<<<<<<<<
- *             dst.suboffsets[dim] = -1
- *         else:
- */
-    __pyx_t_4 = ((__pyx_v_suboffsets == NULL) != 0);
-    if (__pyx_t_4) {
-
-      /* "View.MemoryView":1028
- *         dst.strides[dim] = strides[dim]
- *         if suboffsets == NULL:
- *             dst.suboffsets[dim] = -1             # <<<<<<<<<<<<<<
- *         else:
- *             dst.suboffsets[dim] = suboffsets[dim]
- */
-      (__pyx_v_dst->suboffsets[__pyx_v_dim]) = -1;
-      goto __pyx_L5;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":1030
- *             dst.suboffsets[dim] = -1
- *         else:
- *             dst.suboffsets[dim] = suboffsets[dim]             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_object')
- */
-      (__pyx_v_dst->suboffsets[__pyx_v_dim]) = (__pyx_v_suboffsets[__pyx_v_dim]);
-    }
-    __pyx_L5:;
-  }
-
-  /* "View.MemoryView":1013
- * 
- * @cname('__pyx_memoryview_slice_copy')
- * cdef void slice_copy(memoryview memview, __Pyx_memviewslice *dst):             # <<<<<<<<<<<<<<
- *     cdef int dim
- *     cdef (Py_ssize_t*) shape, strides, suboffsets
- */
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-/* "View.MemoryView":1033
- * 
- * @cname('__pyx_memoryview_copy_object')
- * cdef memoryview_copy(memoryview memview):             # <<<<<<<<<<<<<<
- *     "Create a new memoryview object"
- *     cdef __Pyx_memviewslice memviewslice
- */
-
-static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *__pyx_v_memview) {
-  __Pyx_memviewslice __pyx_v_memviewslice;
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("memoryview_copy", 0);
-
-  /* "View.MemoryView":1036
- *     "Create a new memoryview object"
- *     cdef __Pyx_memviewslice memviewslice
- *     slice_copy(memview, &memviewslice)             # <<<<<<<<<<<<<<
- *     return memoryview_copy_from_slice(memview, &memviewslice)
- * 
- */
-  __pyx_memoryview_slice_copy(__pyx_v_memview, (&__pyx_v_memviewslice));
-
-  /* "View.MemoryView":1037
- *     cdef __Pyx_memviewslice memviewslice
- *     slice_copy(memview, &memviewslice)
- *     return memoryview_copy_from_slice(memview, &memviewslice)             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_object_from_slice')
- */
-  __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __pyx_memoryview_copy_object_from_slice(__pyx_v_memview, (&__pyx_v_memviewslice)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_r = __pyx_t_1;
-  __pyx_t_1 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1033
- * 
- * @cname('__pyx_memoryview_copy_object')
- * cdef memoryview_copy(memoryview memview):             # <<<<<<<<<<<<<<
- *     "Create a new memoryview object"
- *     cdef __Pyx_memviewslice memviewslice
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_AddTraceback("View.MemoryView.memoryview_copy", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1040
- * 
- * @cname('__pyx_memoryview_copy_object_from_slice')
- * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice):             # <<<<<<<<<<<<<<
- *     """
- *     Create a new memoryview object from a given memoryview object and slice.
- */
-
-static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *__pyx_v_memview, __Pyx_memviewslice *__pyx_v_memviewslice) {
-  PyObject *(*__pyx_v_to_object_func)(char *);
-  int (*__pyx_v_to_dtype_func)(char *, PyObject *);
-  PyObject *__pyx_r = NULL;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  int __pyx_t_2;
-  PyObject *(*__pyx_t_3)(char *);
-  int (*__pyx_t_4)(char *, PyObject *);
-  PyObject *__pyx_t_5 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannySetupContext("memoryview_copy_from_slice", 0);
-
-  /* "View.MemoryView":1047
- *     cdef int (*to_dtype_func)(char *, object) except 0
- * 
- *     if isinstance(memview, _memoryviewslice):             # <<<<<<<<<<<<<<
- *         to_object_func = (<_memoryviewslice> memview).to_object_func
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
- */
-  __pyx_t_1 = __Pyx_TypeCheck(((PyObject *)__pyx_v_memview), ((PyObject *)__pyx_memoryviewslice_type)); 
-  __pyx_t_2 = (__pyx_t_1 != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1048
- * 
- *     if isinstance(memview, _memoryviewslice):
- *         to_object_func = (<_memoryviewslice> memview).to_object_func             # <<<<<<<<<<<<<<
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
- *     else:
- */
-    __pyx_t_3 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_object_func;
-    __pyx_v_to_object_func = __pyx_t_3;
-
-    /* "View.MemoryView":1049
- *     if isinstance(memview, _memoryviewslice):
- *         to_object_func = (<_memoryviewslice> memview).to_object_func
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func             # <<<<<<<<<<<<<<
- *     else:
- *         to_object_func = NULL
- */
-    __pyx_t_4 = ((struct __pyx_memoryviewslice_obj *)__pyx_v_memview)->to_dtype_func;
-    __pyx_v_to_dtype_func = __pyx_t_4;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1051
- *         to_dtype_func = (<_memoryviewslice> memview).to_dtype_func
- *     else:
- *         to_object_func = NULL             # <<<<<<<<<<<<<<
- *         to_dtype_func = NULL
- * 
- */
-    __pyx_v_to_object_func = NULL;
-
-    /* "View.MemoryView":1052
- *     else:
- *         to_object_func = NULL
- *         to_dtype_func = NULL             # <<<<<<<<<<<<<<
- * 
- *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,
- */
-    __pyx_v_to_dtype_func = NULL;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1054
- *         to_dtype_func = NULL
- * 
- *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,             # <<<<<<<<<<<<<<
- *                                 to_object_func, to_dtype_func,
- *                                 memview.dtype_is_object)
- */
-  __Pyx_XDECREF(__pyx_r);
-
-  /* "View.MemoryView":1056
- *     return memoryview_fromslice(memviewslice[0], memview.view.ndim,
- *                                 to_object_func, to_dtype_func,
- *                                 memview.dtype_is_object)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_5 = __pyx_memoryview_fromslice((__pyx_v_memviewslice[0]), __pyx_v_memview->view.ndim, __pyx_v_to_object_func, __pyx_v_to_dtype_func, __pyx_v_memview->dtype_is_object); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1054; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_5);
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1040
- * 
- * @cname('__pyx_memoryview_copy_object_from_slice')
- * cdef memoryview_copy_from_slice(memoryview memview, __Pyx_memviewslice *memviewslice):             # <<<<<<<<<<<<<<
- *     """
- *     Create a new memoryview object from a given memoryview object and slice.
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_5);
-  __Pyx_AddTraceback("View.MemoryView.memoryview_copy_from_slice", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = 0;
-  __pyx_L0:;
-  __Pyx_XGIVEREF(__pyx_r);
-  __Pyx_RefNannyFinishContext();
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1062
- * 
- * 
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<
- *     if arg < 0:
- *         return -arg
- */
-
-static Py_ssize_t abs_py_ssize_t(Py_ssize_t __pyx_v_arg) {
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-
-  /* "View.MemoryView":1063
- * 
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
- *     if arg < 0:             # <<<<<<<<<<<<<<
- *         return -arg
- *     else:
- */
-  __pyx_t_1 = ((__pyx_v_arg < 0) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1064
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:
- *     if arg < 0:
- *         return -arg             # <<<<<<<<<<<<<<
- *     else:
- *         return arg
- */
-    __pyx_r = (-__pyx_v_arg);
-    goto __pyx_L0;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1066
- *         return -arg
- *     else:
- *         return arg             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_get_best_slice_order')
- */
-    __pyx_r = __pyx_v_arg;
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1062
- * 
- * 
- * cdef Py_ssize_t abs_py_ssize_t(Py_ssize_t arg) nogil:             # <<<<<<<<<<<<<<
- *     if arg < 0:
- *         return -arg
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1069
- * 
- * @cname('__pyx_get_best_slice_order')
- * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     """
- *     Figure out the best memory access order for a given slice.
- */
-
-static char __pyx_get_best_slice_order(__Pyx_memviewslice *__pyx_v_mslice, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  Py_ssize_t __pyx_v_c_stride;
-  Py_ssize_t __pyx_v_f_stride;
-  char __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-
-  /* "View.MemoryView":1074
- *     """
- *     cdef int i
- *     cdef Py_ssize_t c_stride = 0             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t f_stride = 0
- * 
- */
-  __pyx_v_c_stride = 0;
-
-  /* "View.MemoryView":1075
- *     cdef int i
- *     cdef Py_ssize_t c_stride = 0
- *     cdef Py_ssize_t f_stride = 0             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim - 1, -1, -1):
- */
-  __pyx_v_f_stride = 0;
-
-  /* "View.MemoryView":1077
- *     cdef Py_ssize_t f_stride = 0
- * 
- *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]
- */
-  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_i = __pyx_t_1;
-
-    /* "View.MemoryView":1078
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             c_stride = mslice.strides[i]
- *             break
- */
-    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1079
- *     for i in range(ndim - 1, -1, -1):
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
- *             break
- * 
- */
-      __pyx_v_c_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-      /* "View.MemoryView":1080
- *         if mslice.shape[i] > 1:
- *             c_stride = mslice.strides[i]
- *             break             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim):
- */
-      goto __pyx_L4_break;
-    }
-  }
-  __pyx_L4_break:;
-
-  /* "View.MemoryView":1082
- *             break
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- */
-  __pyx_t_1 = __pyx_v_ndim;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_1; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
-
-    /* "View.MemoryView":1083
- * 
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:             # <<<<<<<<<<<<<<
- *             f_stride = mslice.strides[i]
- *             break
- */
-    __pyx_t_2 = (((__pyx_v_mslice->shape[__pyx_v_i]) > 1) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1084
- *     for i in range(ndim):
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]             # <<<<<<<<<<<<<<
- *             break
- * 
- */
-      __pyx_v_f_stride = (__pyx_v_mslice->strides[__pyx_v_i]);
-
-      /* "View.MemoryView":1085
- *         if mslice.shape[i] > 1:
- *             f_stride = mslice.strides[i]
- *             break             # <<<<<<<<<<<<<<
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- */
-      goto __pyx_L7_break;
-    }
-  }
-  __pyx_L7_break:;
-
-  /* "View.MemoryView":1087
- *             break
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):             # <<<<<<<<<<<<<<
- *         return 'C'
- *     else:
- */
-  __pyx_t_2 = ((abs_py_ssize_t(__pyx_v_c_stride) <= abs_py_ssize_t(__pyx_v_f_stride)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1088
- * 
- *     if abs_py_ssize_t(c_stride) <= abs_py_ssize_t(f_stride):
- *         return 'C'             # <<<<<<<<<<<<<<
- *     else:
- *         return 'F'
- */
-    __pyx_r = 'C';
-    goto __pyx_L0;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1090
- *         return 'C'
- *     else:
- *         return 'F'             # <<<<<<<<<<<<<<
- * 
- * @cython.cdivision(True)
- */
-    __pyx_r = 'F';
-    goto __pyx_L0;
-  }
-
-  /* "View.MemoryView":1069
- * 
- * @cname('__pyx_get_best_slice_order')
- * cdef char get_best_order(__Pyx_memviewslice *mslice, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     """
- *     Figure out the best memory access order for a given slice.
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1093
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-static void _copy_strided_to_strided(char *__pyx_v_src_data, Py_ssize_t *__pyx_v_src_strides, char *__pyx_v_dst_data, Py_ssize_t *__pyx_v_dst_strides, Py_ssize_t *__pyx_v_src_shape, Py_ssize_t *__pyx_v_dst_shape, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
-  CYTHON_UNUSED Py_ssize_t __pyx_v_src_extent;
-  Py_ssize_t __pyx_v_dst_extent;
-  Py_ssize_t __pyx_v_src_stride;
-  Py_ssize_t __pyx_v_dst_stride;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  Py_ssize_t __pyx_t_5;
-  Py_ssize_t __pyx_t_6;
-
-  /* "View.MemoryView":1100
- * 
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- */
-  __pyx_v_src_extent = (__pyx_v_src_shape[0]);
-
-  /* "View.MemoryView":1101
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- */
-  __pyx_v_dst_extent = (__pyx_v_dst_shape[0]);
-
-  /* "View.MemoryView":1102
- *     cdef Py_ssize_t src_extent = src_shape[0]
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- */
-  __pyx_v_src_stride = (__pyx_v_src_strides[0]);
-
-  /* "View.MemoryView":1103
- *     cdef Py_ssize_t dst_extent = dst_shape[0]
- *     cdef Py_ssize_t src_stride = src_strides[0]
- *     cdef Py_ssize_t dst_stride = dst_strides[0]             # <<<<<<<<<<<<<<
- * 
- *     if ndim == 1:
- */
-  __pyx_v_dst_stride = (__pyx_v_dst_strides[0]);
-
-  /* "View.MemoryView":1105
- *     cdef Py_ssize_t dst_stride = dst_strides[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- */
-  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1106
- * 
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and             # <<<<<<<<<<<<<<
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- */
-    __pyx_t_1 = ((__pyx_v_src_stride > 0) != 0);
-    if (__pyx_t_1) {
-      __pyx_t_2 = ((__pyx_v_dst_stride > 0) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":1107
- *     if ndim == 1:
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):             # <<<<<<<<<<<<<<
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- *        else:
- */
-        __pyx_t_3 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize);
-        if (__pyx_t_3) {
-          __pyx_t_3 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride));
-        }
-        __pyx_t_4 = (__pyx_t_3 != 0);
-      } else {
-        __pyx_t_4 = __pyx_t_2;
-      }
-      __pyx_t_2 = __pyx_t_4;
-    } else {
-      __pyx_t_2 = __pyx_t_1;
-    }
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1108
- *        if (src_stride > 0 and dst_stride > 0 and
- *            <size_t> src_stride == itemsize == <size_t> dst_stride):
- *            memcpy(dst_data, src_data, itemsize * dst_extent)             # <<<<<<<<<<<<<<
- *        else:
- *            for i in range(dst_extent):
- */
-      memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent));
-      goto __pyx_L4;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":1110
- *            memcpy(dst_data, src_data, itemsize * dst_extent)
- *        else:
- *            for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride
- */
-      __pyx_t_5 = __pyx_v_dst_extent;
-      for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-        __pyx_v_i = __pyx_t_6;
-
-        /* "View.MemoryView":1111
- *        else:
- *            for i in range(dst_extent):
- *                memcpy(dst_data, src_data, itemsize)             # <<<<<<<<<<<<<<
- *                src_data += src_stride
- *                dst_data += dst_stride
- */
-        memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize);
-
-        /* "View.MemoryView":1112
- *            for i in range(dst_extent):
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride             # <<<<<<<<<<<<<<
- *                dst_data += dst_stride
- *     else:
- */
-        __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-        /* "View.MemoryView":1113
- *                memcpy(dst_data, src_data, itemsize)
- *                src_data += src_stride
- *                dst_data += dst_stride             # <<<<<<<<<<<<<<
- *     else:
- *         for i in range(dst_extent):
- */
-        __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-      }
-    }
-    __pyx_L4:;
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1115
- *                dst_data += dst_stride
- *     else:
- *         for i in range(dst_extent):             # <<<<<<<<<<<<<<
- *             _copy_strided_to_strided(src_data, src_strides + 1,
- *                                      dst_data, dst_strides + 1,
- */
-    __pyx_t_5 = __pyx_v_dst_extent;
-    for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) {
-      __pyx_v_i = __pyx_t_6;
-
-      /* "View.MemoryView":1116
- *     else:
- *         for i in range(dst_extent):
- *             _copy_strided_to_strided(src_data, src_strides + 1,             # <<<<<<<<<<<<<<
- *                                      dst_data, dst_strides + 1,
- *                                      src_shape + 1, dst_shape + 1,
- */
-      _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize);
-
-      /* "View.MemoryView":1120
- *                                      src_shape + 1, dst_shape + 1,
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride             # <<<<<<<<<<<<<<
- *             dst_data += dst_stride
- * 
- */
-      __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride);
-
-      /* "View.MemoryView":1121
- *                                      ndim - 1, itemsize)
- *             src_data += src_stride
- *             dst_data += dst_stride             # <<<<<<<<<<<<<<
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,
- */
-      __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride);
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1093
- * 
- * @cython.cdivision(True)
- * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides,             # <<<<<<<<<<<<<<
- *                                    char *dst_data, Py_ssize_t *dst_strides,
- *                                    Py_ssize_t *src_shape, Py_ssize_t *dst_shape,
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1123
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- */
-
-static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) {
-
-  /* "View.MemoryView":1126
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- *     _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides,             # <<<<<<<<<<<<<<
- *                              src.shape, dst.shape, ndim, itemsize)
- * 
- */
-  _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1123
- *             dst_data += dst_stride
- * 
- * cdef void copy_strided_to_strided(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice *dst,
- *                                   int ndim, size_t itemsize) nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1130
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef int i
- */
-
-static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  Py_ssize_t __pyx_v_size;
-  Py_ssize_t __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-
-  /* "View.MemoryView":1133
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef int i
- *     cdef Py_ssize_t size = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim):
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_size = __pyx_t_1;
-
-  /* "View.MemoryView":1135
- *     cdef Py_ssize_t size = src.memview.view.itemsize
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         size *= src.shape[i]
- * 
- */
-  __pyx_t_2 = __pyx_v_ndim;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
-
-    /* "View.MemoryView":1136
- * 
- *     for i in range(ndim):
- *         size *= src.shape[i]             # <<<<<<<<<<<<<<
- * 
- *     return size
- */
-    __pyx_v_size = (__pyx_v_size * (__pyx_v_src->shape[__pyx_v_i]));
-  }
-
-  /* "View.MemoryView":1138
- *         size *= src.shape[i]
- * 
- *     return size             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_fill_contig_strides_array')
- */
-  __pyx_r = __pyx_v_size;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1130
- * 
- * @cname('__pyx_memoryview_slice_get_size')
- * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) nogil:             # <<<<<<<<<<<<<<
- *     "Return the size of the memory occupied by the slice in number of bytes"
- *     cdef int i
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1141
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) nogil:
- */
-
-static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) {
-  int __pyx_v_idx;
-  Py_ssize_t __pyx_r;
-  int __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-
-  /* "View.MemoryView":1150
- *     cdef int idx
- * 
- *     if order == 'F':             # <<<<<<<<<<<<<<
- *         for idx in range(ndim):
- *             strides[idx] = stride
- */
-  __pyx_t_1 = ((__pyx_v_order == 'F') != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1151
- * 
- *     if order == 'F':
- *         for idx in range(ndim):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride = stride * shape[idx]
- */
-    __pyx_t_2 = __pyx_v_ndim;
-    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-      __pyx_v_idx = __pyx_t_3;
-
-      /* "View.MemoryView":1152
- *     if order == 'F':
- *         for idx in range(ndim):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride = stride * shape[idx]
- *     else:
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1153
- *         for idx in range(ndim):
- *             strides[idx] = stride
- *             stride = stride * shape[idx]             # <<<<<<<<<<<<<<
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1155
- *             stride = stride * shape[idx]
- *     else:
- *         for idx in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *             strides[idx] = stride
- *             stride = stride * shape[idx]
- */
-    for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) {
-      __pyx_v_idx = __pyx_t_2;
-
-      /* "View.MemoryView":1156
- *     else:
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride             # <<<<<<<<<<<<<<
- *             stride = stride * shape[idx]
- * 
- */
-      (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride;
-
-      /* "View.MemoryView":1157
- *         for idx in range(ndim - 1, -1, -1):
- *             strides[idx] = stride
- *             stride = stride * shape[idx]             # <<<<<<<<<<<<<<
- * 
- *     return stride
- */
-      __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx]));
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1159
- *             stride = stride * shape[idx]
- * 
- *     return stride             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- */
-  __pyx_r = __pyx_v_stride;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1141
- * 
- * @cname('__pyx_fill_contig_strides_array')
- * cdef Py_ssize_t fill_contig_strides_array(             # <<<<<<<<<<<<<<
- *                 Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride,
- *                 int ndim, char order) nogil:
- */
-
-  /* function exit code */
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1162
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                              __Pyx_memviewslice *tmpslice,
- *                              char order,
- */
-
-static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) {
-  int __pyx_v_i;
-  void *__pyx_v_result;
-  size_t __pyx_v_itemsize;
-  size_t __pyx_v_size;
-  void *__pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  struct __pyx_memoryview_obj *__pyx_t_4;
-  int __pyx_t_5;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-
-  /* "View.MemoryView":1173
- *     cdef void *result
- * 
- *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- *     cdef size_t size = slice_get_size(src, ndim)
- * 
- */
-  __pyx_t_1 = __pyx_v_src->memview->view.itemsize;
-  __pyx_v_itemsize = __pyx_t_1;
-
-  /* "View.MemoryView":1174
- * 
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef size_t size = slice_get_size(src, ndim)             # <<<<<<<<<<<<<<
- * 
- *     result = malloc(size)
- */
-  __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim);
-
-  /* "View.MemoryView":1176
- *     cdef size_t size = slice_get_size(src, ndim)
- * 
- *     result = malloc(size)             # <<<<<<<<<<<<<<
- *     if not result:
- *         _err(MemoryError, NULL)
- */
-  __pyx_v_result = malloc(__pyx_v_size);
-
-  /* "View.MemoryView":1177
- * 
- *     result = malloc(size)
- *     if not result:             # <<<<<<<<<<<<<<
- *         _err(MemoryError, NULL)
- * 
- */
-  __pyx_t_2 = ((!(__pyx_v_result != 0)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1178
- *     result = malloc(size)
- *     if not result:
- *         _err(MemoryError, NULL)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_t_3 = __pyx_memoryview_err(__pyx_builtin_MemoryError, NULL); if (unlikely(__pyx_t_3 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1178; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1181
- * 
- * 
- *     tmpslice.data = <char *> result             # <<<<<<<<<<<<<<
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):
- */
-  __pyx_v_tmpslice->data = ((char *)__pyx_v_result);
-
-  /* "View.MemoryView":1182
- * 
- *     tmpslice.data = <char *> result
- *     tmpslice.memview = src.memview             # <<<<<<<<<<<<<<
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]
- */
-  __pyx_t_4 = __pyx_v_src->memview;
-  __pyx_v_tmpslice->memview = __pyx_t_4;
-
-  /* "View.MemoryView":1183
- *     tmpslice.data = <char *> result
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         tmpslice.shape[i] = src.shape[i]
- *         tmpslice.suboffsets[i] = -1
- */
-  __pyx_t_3 = __pyx_v_ndim;
-  for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) {
-    __pyx_v_i = __pyx_t_5;
-
-    /* "View.MemoryView":1184
- *     tmpslice.memview = src.memview
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]             # <<<<<<<<<<<<<<
- *         tmpslice.suboffsets[i] = -1
- * 
- */
-    (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]);
-
-    /* "View.MemoryView":1185
- *     for i in range(ndim):
- *         tmpslice.shape[i] = src.shape[i]
- *         tmpslice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
- * 
- *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,
- */
-    (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1;
-  }
-
-  /* "View.MemoryView":1187
- *         tmpslice.suboffsets[i] = -1
- * 
- *     fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize,             # <<<<<<<<<<<<<<
- *                               ndim, order)
- * 
- */
-  __pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order);
-
-  /* "View.MemoryView":1191
- * 
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if tmpslice.shape[i] == 1:
- *             tmpslice.strides[i] = 0
- */
-  __pyx_t_3 = __pyx_v_ndim;
-  for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_3; __pyx_t_5+=1) {
-    __pyx_v_i = __pyx_t_5;
-
-    /* "View.MemoryView":1192
- * 
- *     for i in range(ndim):
- *         if tmpslice.shape[i] == 1:             # <<<<<<<<<<<<<<
- *             tmpslice.strides[i] = 0
- * 
- */
-    __pyx_t_2 = (((__pyx_v_tmpslice->shape[__pyx_v_i]) == 1) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1193
- *     for i in range(ndim):
- *         if tmpslice.shape[i] == 1:
- *             tmpslice.strides[i] = 0             # <<<<<<<<<<<<<<
- * 
- *     if slice_is_contig(src, order, ndim):
- */
-      (__pyx_v_tmpslice->strides[__pyx_v_i]) = 0;
-      goto __pyx_L8;
-    }
-    __pyx_L8:;
-  }
-
-  /* "View.MemoryView":1195
- *             tmpslice.strides[i] = 0
- * 
- *     if slice_is_contig(src, order, ndim):             # <<<<<<<<<<<<<<
- *         memcpy(result, src.data, size)
- *     else:
- */
-  __pyx_t_2 = (__pyx_memviewslice_is_contig(__pyx_v_src, __pyx_v_order, __pyx_v_ndim) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1196
- * 
- *     if slice_is_contig(src, order, ndim):
- *         memcpy(result, src.data, size)             # <<<<<<<<<<<<<<
- *     else:
- *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
- */
-    memcpy(__pyx_v_result, __pyx_v_src->data, __pyx_v_size);
-    goto __pyx_L9;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1198
- *         memcpy(result, src.data, size)
- *     else:
- *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)             # <<<<<<<<<<<<<<
- * 
- *     return result
- */
-    copy_strided_to_strided(__pyx_v_src, __pyx_v_tmpslice, __pyx_v_ndim, __pyx_v_itemsize);
-  }
-  __pyx_L9:;
-
-  /* "View.MemoryView":1200
- *         copy_strided_to_strided(src, tmpslice, ndim, itemsize)
- * 
- *     return result             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_r = __pyx_v_result;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1162
- * 
- * @cname('__pyx_memoryview_copy_data_to_temp')
- * cdef void *copy_data_to_temp(__Pyx_memviewslice *src,             # <<<<<<<<<<<<<<
- *                              __Pyx_memviewslice *tmpslice,
- *                              char order,
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  {
-    #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-    #endif
-    __Pyx_AddTraceback("View.MemoryView.copy_data_to_temp", __pyx_clineno, __pyx_lineno, __pyx_filename);
-    #ifdef WITH_THREAD
-    PyGILState_Release(__pyx_gilstate_save);
-    #endif
-  }
-  __pyx_r = NULL;
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1205
- * 
- * @cname('__pyx_memoryview_err_extents')
- * cdef int _err_extents(int i, Py_ssize_t extent1,             # <<<<<<<<<<<<<<
- *                              Py_ssize_t extent2) except -1 with gil:
- *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %
- */
-
-static int __pyx_memoryview_err_extents(int __pyx_v_i, Py_ssize_t __pyx_v_extent1, Py_ssize_t __pyx_v_extent2) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  PyObject *__pyx_t_4 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-  #endif
-  __Pyx_RefNannySetupContext("_err_extents", 0);
-
-  /* "View.MemoryView":1208
- *                              Py_ssize_t extent2) except -1 with gil:
- *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %
- *                                                         (i, extent1, extent2))             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_err_dim')
- */
-  __pyx_t_1 = __Pyx_PyInt_From_int(__pyx_v_i); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = PyInt_FromSsize_t(__pyx_v_extent1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = PyInt_FromSsize_t(__pyx_v_extent2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1208; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2);
-  __Pyx_GIVEREF(__pyx_t_2);
-  PyTuple_SET_ITEM(__pyx_t_4, 2, __pyx_t_3);
-  __Pyx_GIVEREF(__pyx_t_3);
-  __pyx_t_1 = 0;
-  __pyx_t_2 = 0;
-  __pyx_t_3 = 0;
-
-  /* "View.MemoryView":1207
- * cdef int _err_extents(int i, Py_ssize_t extent1,
- *                              Py_ssize_t extent2) except -1 with gil:
- *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %             # <<<<<<<<<<<<<<
- *                                                         (i, extent1, extent2))
- * 
- */
-  __pyx_t_3 = __Pyx_PyString_Format(__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_t_4); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1207; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1207; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
-  __Pyx_GIVEREF(__pyx_t_3);
-  __pyx_t_3 = 0;
-  __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1207; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1207; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-  /* "View.MemoryView":1205
- * 
- * @cname('__pyx_memoryview_err_extents')
- * cdef int _err_extents(int i, Py_ssize_t extent1,             # <<<<<<<<<<<<<<
- *                              Py_ssize_t extent2) except -1 with gil:
- *     raise ValueError("got differing extents in dimension %d (got %d and %d)" %
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_4);
-  __Pyx_AddTraceback("View.MemoryView._err_extents", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  __Pyx_RefNannyFinishContext();
-  #ifdef WITH_THREAD
-  PyGILState_Release(__pyx_gilstate_save);
-  #endif
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1211
- * 
- * @cname('__pyx_memoryview_err_dim')
- * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil:             # <<<<<<<<<<<<<<
- *     raise error(msg.decode('ascii') % dim)
- * 
- */
-
-static int __pyx_memoryview_err_dim(PyObject *__pyx_v_error, char *__pyx_v_msg, int __pyx_v_dim) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  PyObject *__pyx_t_1 = NULL;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-  #endif
-  __Pyx_RefNannySetupContext("_err_dim", 0);
-  __Pyx_INCREF(__pyx_v_error);
-
-  /* "View.MemoryView":1212
- * @cname('__pyx_memoryview_err_dim')
- * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil:
- *     raise error(msg.decode('ascii') % dim)             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_err')
- */
-  __pyx_t_1 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __pyx_t_2 = __Pyx_PyInt_From_int(__pyx_v_dim); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = PyUnicode_Format(__pyx_t_1, __pyx_t_2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_3);
-  __Pyx_GIVEREF(__pyx_t_3);
-  __pyx_t_3 = 0;
-  __pyx_t_3 = __Pyx_PyObject_Call(__pyx_v_error, __pyx_t_2, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __Pyx_Raise(__pyx_t_3, 0, 0, 0);
-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-  {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1212; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-  /* "View.MemoryView":1211
- * 
- * @cname('__pyx_memoryview_err_dim')
- * cdef int _err_dim(object error, char *msg, int dim) except -1 with gil:             # <<<<<<<<<<<<<<
- *     raise error(msg.decode('ascii') % dim)
- * 
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView._err_dim", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  __Pyx_XDECREF(__pyx_v_error);
-  __Pyx_RefNannyFinishContext();
-  #ifdef WITH_THREAD
-  PyGILState_Release(__pyx_gilstate_save);
-  #endif
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1215
- * 
- * @cname('__pyx_memoryview_err')
- * cdef int _err(object error, char *msg) except -1 with gil:             # <<<<<<<<<<<<<<
- *     if msg != NULL:
- *         raise error(msg.decode('ascii'))
- */
-
-static int __pyx_memoryview_err(PyObject *__pyx_v_error, char *__pyx_v_msg) {
-  int __pyx_r;
-  __Pyx_RefNannyDeclarations
-  int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
-  PyObject *__pyx_t_3 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-  #endif
-  __Pyx_RefNannySetupContext("_err", 0);
-  __Pyx_INCREF(__pyx_v_error);
-
-  /* "View.MemoryView":1216
- * @cname('__pyx_memoryview_err')
- * cdef int _err(object error, char *msg) except -1 with gil:
- *     if msg != NULL:             # <<<<<<<<<<<<<<
- *         raise error(msg.decode('ascii'))
- *     else:
- */
-  __pyx_t_1 = ((__pyx_v_msg != NULL) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1217
- * cdef int _err(object error, char *msg) except -1 with gil:
- *     if msg != NULL:
- *         raise error(msg.decode('ascii'))             # <<<<<<<<<<<<<<
- *     else:
- *         raise error
- */
-    __pyx_t_2 = __Pyx_decode_c_string(__pyx_v_msg, 0, strlen(__pyx_v_msg), NULL, NULL, PyUnicode_DecodeASCII); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2);
-    __Pyx_GIVEREF(__pyx_t_2);
-    __pyx_t_2 = 0;
-    __pyx_t_2 = __Pyx_PyObject_Call(__pyx_v_error, __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    __Pyx_Raise(__pyx_t_2, 0, 0, 0);
-    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1219
- *         raise error(msg.decode('ascii'))
- *     else:
- *         raise error             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_copy_contents')
- */
-    __Pyx_Raise(__pyx_v_error, 0, 0, 0);
-    {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  }
-
-  /* "View.MemoryView":1215
- * 
- * @cname('__pyx_memoryview_err')
- * cdef int _err(object error, char *msg) except -1 with gil:             # <<<<<<<<<<<<<<
- *     if msg != NULL:
- *         raise error(msg.decode('ascii'))
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_2);
-  __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_AddTraceback("View.MemoryView._err", __pyx_clineno, __pyx_lineno, __pyx_filename);
-  __pyx_r = -1;
-  __Pyx_XDECREF(__pyx_v_error);
-  __Pyx_RefNannyFinishContext();
-  #ifdef WITH_THREAD
-  PyGILState_Release(__pyx_gilstate_save);
-  #endif
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1222
- * 
- * @cname('__pyx_memoryview_copy_contents')
- * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice dst,
- *                                   int src_ndim, int dst_ndim,
- */
-
-static int __pyx_memoryview_copy_contents(__Pyx_memviewslice __pyx_v_src, __Pyx_memviewslice __pyx_v_dst, int __pyx_v_src_ndim, int __pyx_v_dst_ndim, int __pyx_v_dtype_is_object) {
-  void *__pyx_v_tmpdata;
-  size_t __pyx_v_itemsize;
-  int __pyx_v_i;
-  char __pyx_v_order;
-  int __pyx_v_broadcasting;
-  int __pyx_v_direct_copy;
-  __Pyx_memviewslice __pyx_v_tmp;
-  int __pyx_v_ndim;
-  int __pyx_r;
-  Py_ssize_t __pyx_t_1;
-  int __pyx_t_2;
-  int __pyx_t_3;
-  int __pyx_t_4;
-  int __pyx_t_5;
-  void *__pyx_t_6;
-  int __pyx_t_7;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-
-  /* "View.MemoryView":1230
- *     Check for overlapping memory and verify the shapes.
- *     """
- *     cdef void *tmpdata = NULL             # <<<<<<<<<<<<<<
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef int i
- */
-  __pyx_v_tmpdata = NULL;
-
-  /* "View.MemoryView":1231
- *     """
- *     cdef void *tmpdata = NULL
- *     cdef size_t itemsize = src.memview.view.itemsize             # <<<<<<<<<<<<<<
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)
- */
-  __pyx_t_1 = __pyx_v_src.memview->view.itemsize;
-  __pyx_v_itemsize = __pyx_t_1;
-
-  /* "View.MemoryView":1233
- *     cdef size_t itemsize = src.memview.view.itemsize
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)             # <<<<<<<<<<<<<<
- *     cdef bint broadcasting = False
- *     cdef bint direct_copy = False
- */
-  __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_src), __pyx_v_src_ndim);
-
-  /* "View.MemoryView":1234
- *     cdef int i
- *     cdef char order = get_best_order(&src, src_ndim)
- *     cdef bint broadcasting = False             # <<<<<<<<<<<<<<
- *     cdef bint direct_copy = False
- *     cdef __Pyx_memviewslice tmp
- */
-  __pyx_v_broadcasting = 0;
-
-  /* "View.MemoryView":1235
- *     cdef char order = get_best_order(&src, src_ndim)
- *     cdef bint broadcasting = False
- *     cdef bint direct_copy = False             # <<<<<<<<<<<<<<
- *     cdef __Pyx_memviewslice tmp
- * 
- */
-  __pyx_v_direct_copy = 0;
-
-  /* "View.MemoryView":1238
- *     cdef __Pyx_memviewslice tmp
- * 
- *     if src_ndim < dst_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- */
-  __pyx_t_2 = ((__pyx_v_src_ndim < __pyx_v_dst_ndim) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1239
- * 
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
- *     elif dst_ndim < src_ndim:
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- */
-    __pyx_memoryview_broadcast_leading((&__pyx_v_src), __pyx_v_src_ndim, __pyx_v_dst_ndim);
-    goto __pyx_L3;
-  }
-
-  /* "View.MemoryView":1240
- *     if src_ndim < dst_ndim:
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:             # <<<<<<<<<<<<<<
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- */
-  __pyx_t_2 = ((__pyx_v_dst_ndim < __pyx_v_src_ndim) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1241
- *         broadcast_leading(&src, src_ndim, dst_ndim)
- *     elif dst_ndim < src_ndim:
- *         broadcast_leading(&dst, dst_ndim, src_ndim)             # <<<<<<<<<<<<<<
- * 
- *     cdef int ndim = max(src_ndim, dst_ndim)
- */
-    __pyx_memoryview_broadcast_leading((&__pyx_v_dst), __pyx_v_dst_ndim, __pyx_v_src_ndim);
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1243
- *         broadcast_leading(&dst, dst_ndim, src_ndim)
- * 
- *     cdef int ndim = max(src_ndim, dst_ndim)             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim):
- */
-  __pyx_t_3 = __pyx_v_dst_ndim;
-  __pyx_t_4 = __pyx_v_src_ndim;
-  if (((__pyx_t_3 > __pyx_t_4) != 0)) {
-    __pyx_t_5 = __pyx_t_3;
-  } else {
-    __pyx_t_5 = __pyx_t_4;
-  }
-  __pyx_v_ndim = __pyx_t_5;
-
-  /* "View.MemoryView":1245
- *     cdef int ndim = max(src_ndim, dst_ndim)
- * 
- *     for i in range(ndim):             # <<<<<<<<<<<<<<
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- */
-  __pyx_t_5 = __pyx_v_ndim;
-  for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_5; __pyx_t_3+=1) {
-    __pyx_v_i = __pyx_t_3;
-
-    /* "View.MemoryView":1246
- * 
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:             # <<<<<<<<<<<<<<
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- */
-    __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) != (__pyx_v_dst.shape[__pyx_v_i])) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1247
- *     for i in range(ndim):
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:             # <<<<<<<<<<<<<<
- *                 broadcasting = True
- *                 src.strides[i] = 0
- */
-      __pyx_t_2 = (((__pyx_v_src.shape[__pyx_v_i]) == 1) != 0);
-      if (__pyx_t_2) {
-
-        /* "View.MemoryView":1248
- *         if src.shape[i] != dst.shape[i]:
- *             if src.shape[i] == 1:
- *                 broadcasting = True             # <<<<<<<<<<<<<<
- *                 src.strides[i] = 0
- *             else:
- */
-        __pyx_v_broadcasting = 1;
-
-        /* "View.MemoryView":1249
- *             if src.shape[i] == 1:
- *                 broadcasting = True
- *                 src.strides[i] = 0             # <<<<<<<<<<<<<<
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- */
-        (__pyx_v_src.strides[__pyx_v_i]) = 0;
-        goto __pyx_L7;
-      }
-      /*else*/ {
-
-        /* "View.MemoryView":1251
- *                 src.strides[i] = 0
- *             else:
- *                 _err_extents(i, dst.shape[i], src.shape[i])             # <<<<<<<<<<<<<<
- * 
- *         if src.suboffsets[i] >= 0:
- */
-        __pyx_t_4 = __pyx_memoryview_err_extents(__pyx_v_i, (__pyx_v_dst.shape[__pyx_v_i]), (__pyx_v_src.shape[__pyx_v_i])); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1251; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      }
-      __pyx_L7:;
-      goto __pyx_L6;
-    }
-    __pyx_L6:;
-
-    /* "View.MemoryView":1253
- *                 _err_extents(i, dst.shape[i], src.shape[i])
- * 
- *         if src.suboffsets[i] >= 0:             # <<<<<<<<<<<<<<
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- */
-    __pyx_t_2 = (((__pyx_v_src.suboffsets[__pyx_v_i]) >= 0) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1254
- * 
- *         if src.suboffsets[i] >= 0:
- *             _err_dim(ValueError, "Dimension %d is not direct", i)             # <<<<<<<<<<<<<<
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- */
-      __pyx_t_4 = __pyx_memoryview_err_dim(__pyx_builtin_ValueError, __pyx_k_Dimension_d_is_not_direct, __pyx_v_i); if (unlikely(__pyx_t_4 == -1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1254; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L8;
-    }
-    __pyx_L8:;
-  }
-
-  /* "View.MemoryView":1256
- *             _err_dim(ValueError, "Dimension %d is not direct", i)
- * 
- *     if slices_overlap(&src, &dst, ndim, itemsize):             # <<<<<<<<<<<<<<
- * 
- *         if not slice_is_contig(&src, order, ndim):
- */
-  __pyx_t_2 = (__pyx_slices_overlap((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1258
- *     if slices_overlap(&src, &dst, ndim, itemsize):
- * 
- *         if not slice_is_contig(&src, order, ndim):             # <<<<<<<<<<<<<<
- *             order = get_best_order(&dst, ndim)
- * 
- */
-    __pyx_t_2 = ((!(__pyx_memviewslice_is_contig((&__pyx_v_src), __pyx_v_order, __pyx_v_ndim) != 0)) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1259
- * 
- *         if not slice_is_contig(&src, order, ndim):
- *             order = get_best_order(&dst, ndim)             # <<<<<<<<<<<<<<
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- */
-      __pyx_v_order = __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim);
-      goto __pyx_L10;
-    }
-    __pyx_L10:;
-
-    /* "View.MemoryView":1261
- *             order = get_best_order(&dst, ndim)
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)             # <<<<<<<<<<<<<<
- *         src = tmp
- * 
- */
-    __pyx_t_6 = __pyx_memoryview_copy_data_to_temp((&__pyx_v_src), (&__pyx_v_tmp), __pyx_v_order, __pyx_v_ndim); if (unlikely(__pyx_t_6 == NULL)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1261; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_v_tmpdata = __pyx_t_6;
-
-    /* "View.MemoryView":1262
- * 
- *         tmpdata = copy_data_to_temp(&src, &tmp, order, ndim)
- *         src = tmp             # <<<<<<<<<<<<<<
- * 
- *     if not broadcasting:
- */
-    __pyx_v_src = __pyx_v_tmp;
-    goto __pyx_L9;
-  }
-  __pyx_L9:;
-
-  /* "View.MemoryView":1264
- *         src = tmp
- * 
- *     if not broadcasting:             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = ((!(__pyx_v_broadcasting != 0)) != 0);
-  if (__pyx_t_2) {
-
-    /* "View.MemoryView":1267
- * 
- * 
- *         if slice_is_contig(&src, 'C', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(&dst, 'C', ndim)
- *         elif slice_is_contig(&src, 'F', ndim):
- */
-    __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'C', __pyx_v_ndim) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1268
- * 
- *         if slice_is_contig(&src, 'C', ndim):
- *             direct_copy = slice_is_contig(&dst, 'C', ndim)             # <<<<<<<<<<<<<<
- *         elif slice_is_contig(&src, 'F', ndim):
- *             direct_copy = slice_is_contig(&dst, 'F', ndim)
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'C', __pyx_v_ndim);
-      goto __pyx_L12;
-    }
-
-    /* "View.MemoryView":1269
- *         if slice_is_contig(&src, 'C', ndim):
- *             direct_copy = slice_is_contig(&dst, 'C', ndim)
- *         elif slice_is_contig(&src, 'F', ndim):             # <<<<<<<<<<<<<<
- *             direct_copy = slice_is_contig(&dst, 'F', ndim)
- * 
- */
-    __pyx_t_2 = (__pyx_memviewslice_is_contig((&__pyx_v_src), 'F', __pyx_v_ndim) != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1270
- *             direct_copy = slice_is_contig(&dst, 'C', ndim)
- *         elif slice_is_contig(&src, 'F', ndim):
- *             direct_copy = slice_is_contig(&dst, 'F', ndim)             # <<<<<<<<<<<<<<
- * 
- *         if direct_copy:
- */
-      __pyx_v_direct_copy = __pyx_memviewslice_is_contig((&__pyx_v_dst), 'F', __pyx_v_ndim);
-      goto __pyx_L12;
-    }
-    __pyx_L12:;
-
-    /* "View.MemoryView":1272
- *             direct_copy = slice_is_contig(&dst, 'F', ndim)
- * 
- *         if direct_copy:             # <<<<<<<<<<<<<<
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- */
-    __pyx_t_2 = (__pyx_v_direct_copy != 0);
-    if (__pyx_t_2) {
-
-      /* "View.MemoryView":1274
- *         if direct_copy:
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-      /* "View.MemoryView":1275
- * 
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))             # <<<<<<<<<<<<<<
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)
- */
-      memcpy(__pyx_v_dst.data, __pyx_v_src.data, __pyx_memoryview_slice_get_size((&__pyx_v_src), __pyx_v_ndim));
-
-      /* "View.MemoryView":1276
- *             refcount_copying(&dst, dtype_is_object, ndim, False)
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
- *             free(tmpdata)
- *             return 0
- */
-      __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-      /* "View.MemoryView":1277
- *             memcpy(dst.data, src.data, slice_get_size(&src, ndim))
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)             # <<<<<<<<<<<<<<
- *             return 0
- * 
- */
-      free(__pyx_v_tmpdata);
-
-      /* "View.MemoryView":1278
- *             refcount_copying(&dst, dtype_is_object, ndim, True)
- *             free(tmpdata)
- *             return 0             # <<<<<<<<<<<<<<
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):
- */
-      __pyx_r = 0;
-      goto __pyx_L0;
-    }
-    goto __pyx_L11;
-  }
-  __pyx_L11:;
-
-  /* "View.MemoryView":1280
- *             return 0
- * 
- *     if order == 'F' == get_best_order(&dst, ndim):             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_2 = (__pyx_v_order == 'F');
-  if (__pyx_t_2) {
-    __pyx_t_2 = ('F' == __pyx_get_best_slice_order((&__pyx_v_dst), __pyx_v_ndim));
-  }
-  __pyx_t_7 = (__pyx_t_2 != 0);
-  if (__pyx_t_7) {
-
-    /* "View.MemoryView":1283
- * 
- * 
- *         transpose_memslice(&src)             # <<<<<<<<<<<<<<
- *         transpose_memslice(&dst)
- * 
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_src)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-
-    /* "View.MemoryView":1284
- * 
- *         transpose_memslice(&src)
- *         transpose_memslice(&dst)             # <<<<<<<<<<<<<<
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, False)
- */
-    __pyx_t_5 = __pyx_memslice_transpose((&__pyx_v_dst)); if (unlikely(__pyx_t_5 == 0)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 1284; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    goto __pyx_L14;
-  }
-  __pyx_L14:;
-
-  /* "View.MemoryView":1286
- *         transpose_memslice(&dst)
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, True)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-  /* "View.MemoryView":1287
- * 
- *     refcount_copying(&dst, dtype_is_object, ndim, False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)             # <<<<<<<<<<<<<<
- *     refcount_copying(&dst, dtype_is_object, ndim, True)
- * 
- */
-  copy_strided_to_strided((&__pyx_v_src), (&__pyx_v_dst), __pyx_v_ndim, __pyx_v_itemsize);
-
-  /* "View.MemoryView":1288
- *     refcount_copying(&dst, dtype_is_object, ndim, False)
- *     copy_strided_to_strided(&src, &dst, ndim, itemsize)
- *     refcount_copying(&dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
- * 
- *     free(tmpdata)
- */
-  __pyx_memoryview_refcount_copying((&__pyx_v_dst), __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-  /* "View.MemoryView":1290
- *     refcount_copying(&dst, dtype_is_object, ndim, True)
- * 
- *     free(tmpdata)             # <<<<<<<<<<<<<<
- *     return 0
- * 
- */
-  free(__pyx_v_tmpdata);
-
-  /* "View.MemoryView":1291
- * 
- *     free(tmpdata)
- *     return 0             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- */
-  __pyx_r = 0;
-  goto __pyx_L0;
-
-  /* "View.MemoryView":1222
- * 
- * @cname('__pyx_memoryview_copy_contents')
- * cdef int memoryview_copy_contents(__Pyx_memviewslice src,             # <<<<<<<<<<<<<<
- *                                   __Pyx_memviewslice dst,
- *                                   int src_ndim, int dst_ndim,
- */
-
-  /* function exit code */
-  __pyx_L1_error:;
-  {
-    #ifdef WITH_THREAD
-    PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-    #endif
-    __Pyx_AddTraceback("View.MemoryView.memoryview_copy_contents", __pyx_clineno, __pyx_lineno, __pyx_filename);
-    #ifdef WITH_THREAD
-    PyGILState_Release(__pyx_gilstate_save);
-    #endif
-  }
-  __pyx_r = -1;
-  __pyx_L0:;
-  return __pyx_r;
-}
-
-/* "View.MemoryView":1294
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- * cdef void broadcast_leading(__Pyx_memviewslice *slice,             # <<<<<<<<<<<<<<
- *                             int ndim,
- *                             int ndim_other) nogil:
- */
-
-static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *__pyx_v_slice, int __pyx_v_ndim, int __pyx_v_ndim_other) {
-  int __pyx_v_i;
-  int __pyx_v_offset;
-  int __pyx_t_1;
-  int __pyx_t_2;
-
-  /* "View.MemoryView":1298
- *                             int ndim_other) nogil:
- *     cdef int i
- *     cdef int offset = ndim_other - ndim             # <<<<<<<<<<<<<<
- * 
- *     for i in range(ndim - 1, -1, -1):
- */
-  __pyx_v_offset = (__pyx_v_ndim_other - __pyx_v_ndim);
-
-  /* "View.MemoryView":1300
- *     cdef int offset = ndim_other - ndim
- * 
- *     for i in range(ndim - 1, -1, -1):             # <<<<<<<<<<<<<<
- *         slice.shape[i + offset] = slice.shape[i]
- *         slice.strides[i + offset] = slice.strides[i]
- */
-  for (__pyx_t_1 = (__pyx_v_ndim - 1); __pyx_t_1 > -1; __pyx_t_1-=1) {
-    __pyx_v_i = __pyx_t_1;
-
-    /* "View.MemoryView":1301
- * 
- *     for i in range(ndim - 1, -1, -1):
- *         slice.shape[i + offset] = slice.shape[i]             # <<<<<<<<<<<<<<
- *         slice.strides[i + offset] = slice.strides[i]
- *         slice.suboffsets[i + offset] = slice.suboffsets[i]
- */
-    (__pyx_v_slice->shape[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->shape[__pyx_v_i]);
-
-    /* "View.MemoryView":1302
- *     for i in range(ndim - 1, -1, -1):
- *         slice.shape[i + offset] = slice.shape[i]
- *         slice.strides[i + offset] = slice.strides[i]             # <<<<<<<<<<<<<<
- *         slice.suboffsets[i + offset] = slice.suboffsets[i]
- * 
- */
-    (__pyx_v_slice->strides[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->strides[__pyx_v_i]);
-
-    /* "View.MemoryView":1303
- *         slice.shape[i + offset] = slice.shape[i]
- *         slice.strides[i + offset] = slice.strides[i]
- *         slice.suboffsets[i + offset] = slice.suboffsets[i]             # <<<<<<<<<<<<<<
- * 
- *     for i in range(offset):
- */
-    (__pyx_v_slice->suboffsets[(__pyx_v_i + __pyx_v_offset)]) = (__pyx_v_slice->suboffsets[__pyx_v_i]);
-  }
-
-  /* "View.MemoryView":1305
- *         slice.suboffsets[i + offset] = slice.suboffsets[i]
- * 
- *     for i in range(offset):             # <<<<<<<<<<<<<<
- *         slice.shape[i] = 1
- *         slice.strides[i] = slice.strides[0]
- */
-  __pyx_t_1 = __pyx_v_offset;
-  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
-    __pyx_v_i = __pyx_t_2;
-
-    /* "View.MemoryView":1306
- * 
- *     for i in range(offset):
- *         slice.shape[i] = 1             # <<<<<<<<<<<<<<
- *         slice.strides[i] = slice.strides[0]
- *         slice.suboffsets[i] = -1
- */
-    (__pyx_v_slice->shape[__pyx_v_i]) = 1;
-
-    /* "View.MemoryView":1307
- *     for i in range(offset):
- *         slice.shape[i] = 1
- *         slice.strides[i] = slice.strides[0]             # <<<<<<<<<<<<<<
- *         slice.suboffsets[i] = -1
- * 
- */
-    (__pyx_v_slice->strides[__pyx_v_i]) = (__pyx_v_slice->strides[0]);
-
-    /* "View.MemoryView":1308
- *         slice.shape[i] = 1
- *         slice.strides[i] = slice.strides[0]
- *         slice.suboffsets[i] = -1             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    (__pyx_v_slice->suboffsets[__pyx_v_i]) = -1;
-  }
-
-  /* "View.MemoryView":1294
- * 
- * @cname('__pyx_memoryview_broadcast_leading')
- * cdef void broadcast_leading(__Pyx_memviewslice *slice,             # <<<<<<<<<<<<<<
- *                             int ndim,
- *                             int ndim_other) nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1316
- * 
- * @cname('__pyx_memoryview_refcount_copying')
- * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
- *                            int ndim, bint inc) nogil:
- * 
- */
-
-static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_dtype_is_object, int __pyx_v_ndim, int __pyx_v_inc) {
-  int __pyx_t_1;
-
-  /* "View.MemoryView":1320
- * 
- * 
- *     if dtype_is_object:             # <<<<<<<<<<<<<<
- *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,
- *                                            dst.strides, ndim, inc)
- */
-  __pyx_t_1 = (__pyx_v_dtype_is_object != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1321
- * 
- *     if dtype_is_object:
- *         refcount_objects_in_slice_with_gil(dst.data, dst.shape,             # <<<<<<<<<<<<<<
- *                                            dst.strides, ndim, inc)
- * 
- */
-    __pyx_memoryview_refcount_objects_in_slice_with_gil(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_inc);
-    goto __pyx_L3;
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1316
- * 
- * @cname('__pyx_memoryview_refcount_copying')
- * cdef void refcount_copying(__Pyx_memviewslice *dst, bint dtype_is_object,             # <<<<<<<<<<<<<<
- *                            int ndim, bint inc) nogil:
- * 
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1325
- * 
- * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
- * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
- *                                              Py_ssize_t *strides, int ndim,
- *                                              bint inc) with gil:
- */
-
-static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {
-  __Pyx_RefNannyDeclarations
-  #ifdef WITH_THREAD
-  PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
-  #endif
-  __Pyx_RefNannySetupContext("refcount_objects_in_slice_with_gil", 0);
-
-  /* "View.MemoryView":1328
- *                                              Py_ssize_t *strides, int ndim,
- *                                              bint inc) with gil:
- *     refcount_objects_in_slice(data, shape, strides, ndim, inc)             # <<<<<<<<<<<<<<
- * 
- * @cname('__pyx_memoryview_refcount_objects_in_slice')
- */
-  __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, __pyx_v_shape, __pyx_v_strides, __pyx_v_ndim, __pyx_v_inc);
-
-  /* "View.MemoryView":1325
- * 
- * @cname('__pyx_memoryview_refcount_objects_in_slice_with_gil')
- * cdef void refcount_objects_in_slice_with_gil(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
- *                                              Py_ssize_t *strides, int ndim,
- *                                              bint inc) with gil:
- */
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-  #ifdef WITH_THREAD
-  PyGILState_Release(__pyx_gilstate_save);
-  #endif
-}
-
-/* "View.MemoryView":1331
- * 
- * @cname('__pyx_memoryview_refcount_objects_in_slice')
- * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
- *                                     Py_ssize_t *strides, int ndim, bint inc):
- *     cdef Py_ssize_t i
- */
-
-static void __pyx_memoryview_refcount_objects_in_slice(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, int __pyx_v_inc) {
-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
-  __Pyx_RefNannyDeclarations
-  Py_ssize_t __pyx_t_1;
-  Py_ssize_t __pyx_t_2;
-  int __pyx_t_3;
-  __Pyx_RefNannySetupContext("refcount_objects_in_slice", 0);
-
-  /* "View.MemoryView":1335
- *     cdef Py_ssize_t i
- * 
- *     for i in range(shape[0]):             # <<<<<<<<<<<<<<
- *         if ndim == 1:
- *             if inc:
- */
-  __pyx_t_1 = (__pyx_v_shape[0]);
-  for (__pyx_t_2 = 0; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
-    __pyx_v_i = __pyx_t_2;
-
-    /* "View.MemoryView":1336
- * 
- *     for i in range(shape[0]):
- *         if ndim == 1:             # <<<<<<<<<<<<<<
- *             if inc:
- *                 Py_INCREF((<PyObject **> data)[0])
- */
-    __pyx_t_3 = ((__pyx_v_ndim == 1) != 0);
-    if (__pyx_t_3) {
-
-      /* "View.MemoryView":1337
- *     for i in range(shape[0]):
- *         if ndim == 1:
- *             if inc:             # <<<<<<<<<<<<<<
- *                 Py_INCREF((<PyObject **> data)[0])
- *             else:
- */
-      __pyx_t_3 = (__pyx_v_inc != 0);
-      if (__pyx_t_3) {
-
-        /* "View.MemoryView":1338
- *         if ndim == 1:
- *             if inc:
- *                 Py_INCREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<
- *             else:
- *                 Py_DECREF((<PyObject **> data)[0])
- */
-        Py_INCREF((((PyObject **)__pyx_v_data)[0]));
-        goto __pyx_L6;
-      }
-      /*else*/ {
-
-        /* "View.MemoryView":1340
- *                 Py_INCREF((<PyObject **> data)[0])
- *             else:
- *                 Py_DECREF((<PyObject **> data)[0])             # <<<<<<<<<<<<<<
- *         else:
- *             refcount_objects_in_slice(data, shape + 1, strides + 1,
- */
-        Py_DECREF((((PyObject **)__pyx_v_data)[0]));
-      }
-      __pyx_L6:;
-      goto __pyx_L5;
-    }
-    /*else*/ {
-
-      /* "View.MemoryView":1342
- *                 Py_DECREF((<PyObject **> data)[0])
- *         else:
- *             refcount_objects_in_slice(data, shape + 1, strides + 1,             # <<<<<<<<<<<<<<
- *                                       ndim - 1, inc)
- * 
- */
-      __pyx_memoryview_refcount_objects_in_slice(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_inc);
-    }
-    __pyx_L5:;
-
-    /* "View.MemoryView":1345
- *                                       ndim - 1, inc)
- * 
- *         data += strides[0]             # <<<<<<<<<<<<<<
- * 
- * 
- */
-    __pyx_v_data = (__pyx_v_data + (__pyx_v_strides[0]));
-  }
-
-  /* "View.MemoryView":1331
- * 
- * @cname('__pyx_memoryview_refcount_objects_in_slice')
- * cdef void refcount_objects_in_slice(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
- *                                     Py_ssize_t *strides, int ndim, bint inc):
- *     cdef Py_ssize_t i
- */
-
-  /* function exit code */
-  __Pyx_RefNannyFinishContext();
-}
-
-/* "View.MemoryView":1351
- * 
- * @cname('__pyx_memoryview_slice_assign_scalar')
- * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim,             # <<<<<<<<<<<<<<
- *                               size_t itemsize, void *item,
- *                               bint dtype_is_object) nogil:
- */
-
-static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item, int __pyx_v_dtype_is_object) {
-
-  /* "View.MemoryView":1354
- *                               size_t itemsize, void *item,
- *                               bint dtype_is_object) nogil:
- *     refcount_copying(dst, dtype_is_object, ndim, False)             # <<<<<<<<<<<<<<
- *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,
- *                          itemsize, item)
- */
-  __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 0);
-
-  /* "View.MemoryView":1355
- *                               bint dtype_is_object) nogil:
- *     refcount_copying(dst, dtype_is_object, ndim, False)
- *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,             # <<<<<<<<<<<<<<
- *                          itemsize, item)
- *     refcount_copying(dst, dtype_is_object, ndim, True)
- */
-  __pyx_memoryview__slice_assign_scalar(__pyx_v_dst->data, __pyx_v_dst->shape, __pyx_v_dst->strides, __pyx_v_ndim, __pyx_v_itemsize, __pyx_v_item);
-
-  /* "View.MemoryView":1357
- *     _slice_assign_scalar(dst.data, dst.shape, dst.strides, ndim,
- *                          itemsize, item)
- *     refcount_copying(dst, dtype_is_object, ndim, True)             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_memoryview_refcount_copying(__pyx_v_dst, __pyx_v_dtype_is_object, __pyx_v_ndim, 1);
-
-  /* "View.MemoryView":1351
- * 
- * @cname('__pyx_memoryview_slice_assign_scalar')
- * cdef void slice_assign_scalar(__Pyx_memviewslice *dst, int ndim,             # <<<<<<<<<<<<<<
- *                               size_t itemsize, void *item,
- *                               bint dtype_is_object) nogil:
- */
-
-  /* function exit code */
-}
-
-/* "View.MemoryView":1361
- * 
- * @cname('__pyx_memoryview__slice_assign_scalar')
- * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
- *                               Py_ssize_t *strides, int ndim,
- *                               size_t itemsize, void *item) nogil:
- */
-
-static void __pyx_memoryview__slice_assign_scalar(char *__pyx_v_data, Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, int __pyx_v_ndim, size_t __pyx_v_itemsize, void *__pyx_v_item) {
-  CYTHON_UNUSED Py_ssize_t __pyx_v_i;
-  Py_ssize_t __pyx_v_stride;
-  Py_ssize_t __pyx_v_extent;
-  int __pyx_t_1;
-  Py_ssize_t __pyx_t_2;
-  Py_ssize_t __pyx_t_3;
-
-  /* "View.MemoryView":1365
- *                               size_t itemsize, void *item) nogil:
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t stride = strides[0]             # <<<<<<<<<<<<<<
- *     cdef Py_ssize_t extent = shape[0]
- * 
- */
-  __pyx_v_stride = (__pyx_v_strides[0]);
-
-  /* "View.MemoryView":1366
- *     cdef Py_ssize_t i
- *     cdef Py_ssize_t stride = strides[0]
- *     cdef Py_ssize_t extent = shape[0]             # <<<<<<<<<<<<<<
- * 
- *     if ndim == 1:
- */
-  __pyx_v_extent = (__pyx_v_shape[0]);
-
-  /* "View.MemoryView":1368
- *     cdef Py_ssize_t extent = shape[0]
- * 
- *     if ndim == 1:             # <<<<<<<<<<<<<<
- *         for i in range(extent):
- *             memcpy(data, item, itemsize)
- */
-  __pyx_t_1 = ((__pyx_v_ndim == 1) != 0);
-  if (__pyx_t_1) {
-
-    /* "View.MemoryView":1369
- * 
- *     if ndim == 1:
- *         for i in range(extent):             # <<<<<<<<<<<<<<
- *             memcpy(data, item, itemsize)
- *             data += stride
- */
-    __pyx_t_2 = __pyx_v_extent;
-    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-      __pyx_v_i = __pyx_t_3;
-
-      /* "View.MemoryView":1370
- *     if ndim == 1:
- *         for i in range(extent):
- *             memcpy(data, item, itemsize)             # <<<<<<<<<<<<<<
- *             data += stride
- *     else:
- */
-      memcpy(__pyx_v_data, __pyx_v_item, __pyx_v_itemsize);
-
-      /* "View.MemoryView":1371
- *         for i in range(extent):
- *             memcpy(data, item, itemsize)
- *             data += stride             # <<<<<<<<<<<<<<
- *     else:
- *         for i in range(extent):
- */
-      __pyx_v_data = (__pyx_v_data + __pyx_v_stride);
-    }
-    goto __pyx_L3;
-  }
-  /*else*/ {
-
-    /* "View.MemoryView":1373
- *             data += stride
- *     else:
- *         for i in range(extent):             # <<<<<<<<<<<<<<
- *             _slice_assign_scalar(data, shape + 1, strides + 1,
- *                                 ndim - 1, itemsize, item)
- */
-    __pyx_t_2 = __pyx_v_extent;
-    for (__pyx_t_3 = 0; __pyx_t_3 < __pyx_t_2; __pyx_t_3+=1) {
-      __pyx_v_i = __pyx_t_3;
-
-      /* "View.MemoryView":1374
- *     else:
- *         for i in range(extent):
- *             _slice_assign_scalar(data, shape + 1, strides + 1,             # <<<<<<<<<<<<<<
- *                                 ndim - 1, itemsize, item)
- *             data += stride
- */
-      __pyx_memoryview__slice_assign_scalar(__pyx_v_data, (__pyx_v_shape + 1), (__pyx_v_strides + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize, __pyx_v_item);
-
-      /* "View.MemoryView":1376
- *             _slice_assign_scalar(data, shape + 1, strides + 1,
- *                                 ndim - 1, itemsize, item)
- *             data += stride             # <<<<<<<<<<<<<<
- * 
- * 
- */
-      __pyx_v_data = (__pyx_v_data + __pyx_v_stride);
-    }
-  }
-  __pyx_L3:;
-
-  /* "View.MemoryView":1361
- * 
- * @cname('__pyx_memoryview__slice_assign_scalar')
- * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
- *                               Py_ssize_t *strides, int ndim,
- *                               size_t itemsize, void *item) nogil:
- */
-
-  /* function exit code */
-}
-
-static PyObject *__pyx_tp_new_array(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_array_obj *p;
-  PyObject *o;
-  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_array_obj *)o);
-  p->mode = ((PyObject*)Py_None); Py_INCREF(Py_None);
-  p->_format = ((PyObject*)Py_None); Py_INCREF(Py_None);
-  if (unlikely(__pyx_array___cinit__(o, a, k) < 0)) {
-    Py_DECREF(o); o = 0;
-  }
-  return o;
-}
-
-static void __pyx_tp_dealloc_array(PyObject *o) {
-  struct __pyx_array_obj *p = (struct __pyx_array_obj *)o;
-  #if PY_VERSION_HEX >= 0x030400a1
-  if (unlikely(Py_TYPE(o)->tp_finalize) && (!PyType_IS_GC(Py_TYPE(o)) || !_PyGC_FINALIZED(o))) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    ++Py_REFCNT(o);
-    __pyx_array___dealloc__(o);
-    --Py_REFCNT(o);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->mode);
-  Py_CLEAR(p->_format);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-static PyObject *__pyx_sq_item_array(PyObject *o, Py_ssize_t i) {
-  PyObject *r;
-  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
-  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
-  Py_DECREF(x);
-  return r;
-}
-
-static int __pyx_mp_ass_subscript_array(PyObject *o, PyObject *i, PyObject *v) {
-  if (v) {
-    return __pyx_array___setitem__(o, i, v);
-  }
-  else {
-    PyErr_Format(PyExc_NotImplementedError,
-      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
-    return -1;
-  }
-}
-
-static PyObject *__pyx_tp_getattro_array(PyObject *o, PyObject *n) {
-  PyObject *v = PyObject_GenericGetAttr(o, n);
-  if (!v && PyErr_ExceptionMatches(PyExc_AttributeError)) {
-    PyErr_Clear();
-    v = __pyx_array___getattr__(o, n);
-  }
-  return v;
-}
-
-static PyObject *__pyx_getprop___pyx_array_memview(PyObject *o, CYTHON_UNUSED void *x) {
-  return get_memview(o);
-}
-
-static PyMethodDef __pyx_methods_array[] = {
-  {__Pyx_NAMESTR("__getattr__"), (PyCFunction)__pyx_array___getattr__, METH_O|METH_COEXIST, __Pyx_DOCSTR(0)},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_array[] = {
-  {(char *)"memview", __pyx_getprop___pyx_array_memview, 0, 0, 0},
-  {0, 0, 0, 0, 0}
-};
-
-static PySequenceMethods __pyx_tp_as_sequence_array = {
-  0, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_array, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_array = {
-  0, /*mp_length*/
-  __pyx_array___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_array, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_array = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  #if PY_VERSION_HEX >= 0x02060000
-  __pyx_array_getbuffer, /*bf_getbuffer*/
-  #endif
-  #if PY_VERSION_HEX >= 0x02060000
-  0, /*bf_releasebuffer*/
-  #endif
-};
-
-static PyTypeObject __pyx_type___pyx_array = {
-  PyVarObject_HEAD_INIT(0, 0)
-  __Pyx_NAMESTR("cython_bubblesort_nomagic.array"), /*tp_name*/
-  sizeof(struct __pyx_array_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_array, /*tp_dealloc*/
-  0, /*tp_print*/
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #else
-  0, /*reserved*/
-  #endif
-  0, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_array, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_array, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  __pyx_tp_getattro_array, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_array, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/
-  0, /*tp_doc*/
-  0, /*tp_traverse*/
-  0, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_array, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_array, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_array, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  #if PY_VERSION_HEX >= 0x02060000
-  0, /*tp_version_tag*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-};
-
-static PyObject *__pyx_tp_new_Enum(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
-  struct __pyx_MemviewEnum_obj *p;
-  PyObject *o;
-  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_MemviewEnum_obj *)o);
-  p->name = Py_None; Py_INCREF(Py_None);
-  return o;
-}
-
-static void __pyx_tp_dealloc_Enum(PyObject *o) {
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  #if PY_VERSION_HEX >= 0x030400a1
-  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  Py_CLEAR(p->name);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_Enum(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  if (p->name) {
-    e = (*v)(p->name, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_Enum(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_MemviewEnum_obj *p = (struct __pyx_MemviewEnum_obj *)o;
-  tmp = ((PyObject*)p->name);
-  p->name = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  return 0;
-}
-
-static PyMethodDef __pyx_methods_Enum[] = {
-  {0, 0, 0, 0}
-};
-
-static PyTypeObject __pyx_type___pyx_MemviewEnum = {
-  PyVarObject_HEAD_INIT(0, 0)
-  __Pyx_NAMESTR("cython_bubblesort_nomagic.Enum"), /*tp_name*/
-  sizeof(struct __pyx_MemviewEnum_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_Enum, /*tp_dealloc*/
-  0, /*tp_print*/
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #else
-  0, /*reserved*/
-  #endif
-  __pyx_MemviewEnum___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_Enum, /*tp_traverse*/
-  __pyx_tp_clear_Enum, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_Enum, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  __pyx_MemviewEnum___init__, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_Enum, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  #if PY_VERSION_HEX >= 0x02060000
-  0, /*tp_version_tag*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-};
-static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview;
-
-static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryview_obj *p;
-  PyObject *o;
-  if (likely((t->tp_flags & Py_TPFLAGS_IS_ABSTRACT) == 0)) {
-    o = (*t->tp_alloc)(t, 0);
-  } else {
-    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
-  }
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryview_obj *)o);
-  p->__pyx_vtab = __pyx_vtabptr_memoryview;
-  p->obj = Py_None; Py_INCREF(Py_None);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  p->view.obj = NULL;
-  if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) {
-    Py_DECREF(o); o = 0;
-  }
-  return o;
-}
-
-static void __pyx_tp_dealloc_memoryview(PyObject *o) {
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  #if PY_VERSION_HEX >= 0x030400a1
-  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    ++Py_REFCNT(o);
-    __pyx_memoryview___dealloc__(o);
-    --Py_REFCNT(o);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->obj);
-  Py_CLEAR(p->_size);
-  Py_CLEAR(p->_array_interface);
-  (*Py_TYPE(o)->tp_free)(o);
-}
-
-static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  if (p->obj) {
-    e = (*v)(p->obj, a); if (e) return e;
-  }
-  if (p->_size) {
-    e = (*v)(p->_size, a); if (e) return e;
-  }
-  if (p->_array_interface) {
-    e = (*v)(p->_array_interface, a); if (e) return e;
-  }
-  if (p->view.obj) {
-    e = (*v)(p->view.obj, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_memoryview(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o;
-  tmp = ((PyObject*)p->obj);
-  p->obj = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_size);
-  p->_size = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->_array_interface);
-  p->_array_interface = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  Py_CLEAR(p->view.obj);
-  return 0;
-}
-static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) {
-  PyObject *r;
-  PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0;
-  r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x);
-  Py_DECREF(x);
-  return r;
-}
-
-static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) {
-  if (v) {
-    return __pyx_memoryview___setitem__(o, i, v);
-  }
-  else {
-    PyErr_Format(PyExc_NotImplementedError,
-      "Subscript deletion not supported by %.200s", Py_TYPE(o)->tp_name);
-    return -1;
-  }
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview_transpose(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview__get__base(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview_get_shape(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview_get_strides(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview_get_suboffsets(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview_get_ndim(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview_get_itemsize(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview_get_nbytes(o);
-}
-
-static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryview_get_size(o);
-}
-
-static PyMethodDef __pyx_methods_memoryview[] = {
-  {__Pyx_NAMESTR("is_c_contig"), (PyCFunction)__pyx_memoryview_is_c_contig, METH_NOARGS, __Pyx_DOCSTR(0)},
-  {__Pyx_NAMESTR("is_f_contig"), (PyCFunction)__pyx_memoryview_is_f_contig, METH_NOARGS, __Pyx_DOCSTR(0)},
-  {__Pyx_NAMESTR("copy"), (PyCFunction)__pyx_memoryview_copy, METH_NOARGS, __Pyx_DOCSTR(0)},
-  {__Pyx_NAMESTR("copy_fortran"), (PyCFunction)__pyx_memoryview_copy_fortran, METH_NOARGS, __Pyx_DOCSTR(0)},
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets_memoryview[] = {
-  {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, 0, 0},
-  {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, 0, 0},
-  {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, 0, 0},
-  {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, 0, 0},
-  {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, 0, 0},
-  {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, 0, 0},
-  {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, 0, 0},
-  {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, 0, 0},
-  {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, 0, 0},
-  {0, 0, 0, 0, 0}
-};
-
-static PySequenceMethods __pyx_tp_as_sequence_memoryview = {
-  __pyx_memoryview___len__, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  __pyx_sq_item_memoryview, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_memoryview = {
-  __pyx_memoryview___len__, /*mp_length*/
-  __pyx_memoryview___getitem__, /*mp_subscript*/
-  __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_memoryview = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  #if PY_VERSION_HEX >= 0x02060000
-  __pyx_memoryview_getbuffer, /*bf_getbuffer*/
-  #endif
-  #if PY_VERSION_HEX >= 0x02060000
-  0, /*bf_releasebuffer*/
-  #endif
-};
-
-static PyTypeObject __pyx_type___pyx_memoryview = {
-  PyVarObject_HEAD_INIT(0, 0)
-  __Pyx_NAMESTR("cython_bubblesort_nomagic.memoryview"), /*tp_name*/
-  sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_memoryview, /*tp_dealloc*/
-  0, /*tp_print*/
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #else
-  0, /*reserved*/
-  #endif
-  __pyx_memoryview___repr__, /*tp_repr*/
-  0, /*tp_as_number*/
-  &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  __pyx_memoryview___str__, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_memoryview, /*tp_traverse*/
-  __pyx_tp_clear_memoryview, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods_memoryview, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets_memoryview, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_memoryview, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  #if PY_VERSION_HEX >= 0x02060000
-  0, /*tp_version_tag*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-};
-static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice;
-
-static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) {
-  struct __pyx_memoryviewslice_obj *p;
-  PyObject *o = __pyx_tp_new_memoryview(t, a, k);
-  if (unlikely(!o)) return 0;
-  p = ((struct __pyx_memoryviewslice_obj *)o);
-  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice;
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  p->from_slice.memview = NULL;
-  return o;
-}
-
-static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) {
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  #if PY_VERSION_HEX >= 0x030400a1
-  if (unlikely(Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
-    if (PyObject_CallFinalizerFromDealloc(o)) return;
-  }
-  #endif
-  PyObject_GC_UnTrack(o);
-  {
-    PyObject *etype, *eval, *etb;
-    PyErr_Fetch(&etype, &eval, &etb);
-    ++Py_REFCNT(o);
-    __pyx_memoryviewslice___dealloc__(o);
-    --Py_REFCNT(o);
-    PyErr_Restore(etype, eval, etb);
-  }
-  Py_CLEAR(p->from_object);
-  PyObject_GC_Track(o);
-  __pyx_tp_dealloc_memoryview(o);
-}
-
-static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e;
-  if (p->from_object) {
-    e = (*v)(p->from_object, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear__memoryviewslice(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o;
-  __pyx_tp_clear_memoryview(o);
-  tmp = ((PyObject*)p->from_object);
-  p->from_object = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  __PYX_XDEC_MEMVIEW(&p->from_slice, 1);
-  return 0;
-}
-
-static PyObject *__pyx_getprop___pyx_memoryviewslice_base(PyObject *o, CYTHON_UNUSED void *x) {
-  return __pyx_memoryviewslice__get__base(o);
-}
-
-static PyMethodDef __pyx_methods__memoryviewslice[] = {
-  {0, 0, 0, 0}
-};
-
-static struct PyGetSetDef __pyx_getsets__memoryviewslice[] = {
-  {(char *)"base", __pyx_getprop___pyx_memoryviewslice_base, 0, 0, 0},
-  {0, 0, 0, 0, 0}
-};
-
-static PyTypeObject __pyx_type___pyx_memoryviewslice = {
-  PyVarObject_HEAD_INIT(0, 0)
-  __Pyx_NAMESTR("cython_bubblesort_nomagic._memoryviewslice"), /*tp_name*/
-  sizeof(struct __pyx_memoryviewslice_obj), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc__memoryviewslice, /*tp_dealloc*/
-  0, /*tp_print*/
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #else
-  0, /*reserved*/
-  #endif
-  #if CYTHON_COMPILING_IN_PYPY
-  __pyx_memoryview___repr__, /*tp_repr*/
-  #else
-  0, /*tp_repr*/
-  #endif
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  #if CYTHON_COMPILING_IN_PYPY
-  __pyx_memoryview___str__, /*tp_str*/
-  #else
-  0, /*tp_str*/
-  #endif
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  __Pyx_DOCSTR("Internal class for passing memoryview slices to Python"), /*tp_doc*/
-  __pyx_tp_traverse__memoryviewslice, /*tp_traverse*/
-  __pyx_tp_clear__memoryviewslice, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  __pyx_methods__memoryviewslice, /*tp_methods*/
-  0, /*tp_members*/
-  __pyx_getsets__memoryviewslice, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new__memoryviewslice, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  #if PY_VERSION_HEX >= 0x02060000
-  0, /*tp_version_tag*/
-  #endif
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-};
-
-static PyMethodDef __pyx_methods[] = {
-  {__Pyx_NAMESTR("cython_bubblesort_nomagic"), (PyCFunction)__pyx_pw_25cython_bubblesort_nomagic_1cython_bubblesort_nomagic, METH_O, __Pyx_DOCSTR(__pyx_doc_25cython_bubblesort_nomagic_cython_bubblesort_nomagic)},
-  {0, 0, 0, 0}
-};
-
-#if PY_MAJOR_VERSION >= 3
-static struct PyModuleDef __pyx_moduledef = {
-  #if PY_VERSION_HEX < 0x03020000
-    { PyObject_HEAD_INIT(NULL) NULL, 0, NULL },
-  #else
-    PyModuleDef_HEAD_INIT,
-  #endif
-    __Pyx_NAMESTR("cython_bubblesort_nomagic"),
-    0, /* m_doc */
-    -1, /* m_size */
-    __pyx_methods /* m_methods */,
-    NULL, /* m_reload */
-    NULL, /* m_traverse */
-    NULL, /* m_clear */
-    NULL /* m_free */
-};
-#endif
-
-static __Pyx_StringTabEntry __pyx_string_tab[] = {
-  {&__pyx_kp_s_Buffer_view_does_not_expose_stri, __pyx_k_Buffer_view_does_not_expose_stri, sizeof(__pyx_k_Buffer_view_does_not_expose_stri), 0, 0, 1, 0},
-  {&__pyx_kp_s_Can_only_create_a_buffer_that_is, __pyx_k_Can_only_create_a_buffer_that_is, sizeof(__pyx_k_Can_only_create_a_buffer_that_is), 0, 0, 1, 0},
-  {&__pyx_kp_s_Cannot_index_with_type_s, __pyx_k_Cannot_index_with_type_s, sizeof(__pyx_k_Cannot_index_with_type_s), 0, 0, 1, 0},
-  {&__pyx_n_s_Ellipsis, __pyx_k_Ellipsis, sizeof(__pyx_k_Ellipsis), 0, 0, 1, 1},
-  {&__pyx_kp_s_Empty_shape_tuple_for_cython_arr, __pyx_k_Empty_shape_tuple_for_cython_arr, sizeof(__pyx_k_Empty_shape_tuple_for_cython_arr), 0, 0, 1, 0},
-  {&__pyx_kp_u_Format_string_allocated_too_shor, __pyx_k_Format_string_allocated_too_shor, sizeof(__pyx_k_Format_string_allocated_too_shor), 0, 1, 0, 0},
-  {&__pyx_kp_u_Format_string_allocated_too_shor_2, __pyx_k_Format_string_allocated_too_shor_2, sizeof(__pyx_k_Format_string_allocated_too_shor_2), 0, 1, 0, 0},
-  {&__pyx_n_s_IndexError, __pyx_k_IndexError, sizeof(__pyx_k_IndexError), 0, 0, 1, 1},
-  {&__pyx_kp_s_Indirect_dimensions_not_supporte, __pyx_k_Indirect_dimensions_not_supporte, sizeof(__pyx_k_Indirect_dimensions_not_supporte), 0, 0, 1, 0},
-  {&__pyx_kp_s_Invalid_mode_expected_c_or_fortr, __pyx_k_Invalid_mode_expected_c_or_fortr, sizeof(__pyx_k_Invalid_mode_expected_c_or_fortr), 0, 0, 1, 0},
-  {&__pyx_kp_s_Invalid_shape_in_axis_d_d, __pyx_k_Invalid_shape_in_axis_d_d, sizeof(__pyx_k_Invalid_shape_in_axis_d_d), 0, 0, 1, 0},
-  {&__pyx_n_s_MemoryError, __pyx_k_MemoryError, sizeof(__pyx_k_MemoryError), 0, 0, 1, 1},
-  {&__pyx_kp_s_MemoryView_of_r_at_0x_x, __pyx_k_MemoryView_of_r_at_0x_x, sizeof(__pyx_k_MemoryView_of_r_at_0x_x), 0, 0, 1, 0},
-  {&__pyx_kp_s_MemoryView_of_r_object, __pyx_k_MemoryView_of_r_object, sizeof(__pyx_k_MemoryView_of_r_object), 0, 0, 1, 0},
-  {&__pyx_kp_u_Non_native_byte_order_not_suppor, __pyx_k_Non_native_byte_order_not_suppor, sizeof(__pyx_k_Non_native_byte_order_not_suppor), 0, 1, 0, 0},
-  {&__pyx_n_b_O, __pyx_k_O, sizeof(__pyx_k_O), 0, 0, 0, 1},
-  {&__pyx_kp_s_Out_of_bounds_on_buffer_access_a, __pyx_k_Out_of_bounds_on_buffer_access_a, sizeof(__pyx_k_Out_of_bounds_on_buffer_access_a), 0, 0, 1, 0},
-  {&__pyx_n_s_RuntimeError, __pyx_k_RuntimeError, sizeof(__pyx_k_RuntimeError), 0, 0, 1, 1},
-  {&__pyx_n_s_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 0, 0, 1, 1},
-  {&__pyx_kp_s_Unable_to_convert_item_to_object, __pyx_k_Unable_to_convert_item_to_object, sizeof(__pyx_k_Unable_to_convert_item_to_object), 0, 0, 1, 0},
-  {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
-  {&__pyx_n_s_allocate_buffer, __pyx_k_allocate_buffer, sizeof(__pyx_k_allocate_buffer), 0, 0, 1, 1},
-  {&__pyx_n_s_base, __pyx_k_base, sizeof(__pyx_k_base), 0, 0, 1, 1},
-  {&__pyx_n_s_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 0, 1, 1},
-  {&__pyx_n_u_c, __pyx_k_c, sizeof(__pyx_k_c), 0, 1, 0, 1},
-  {&__pyx_n_s_class, __pyx_k_class, sizeof(__pyx_k_class), 0, 0, 1, 1},
-  {&__pyx_kp_s_contiguous_and_direct, __pyx_k_contiguous_and_direct, sizeof(__pyx_k_contiguous_and_direct), 0, 0, 1, 0},
-  {&__pyx_kp_s_contiguous_and_indirect, __pyx_k_contiguous_and_indirect, sizeof(__pyx_k_contiguous_and_indirect), 0, 0, 1, 0},
-  {&__pyx_n_s_dtype_is_object, __pyx_k_dtype_is_object, sizeof(__pyx_k_dtype_is_object), 0, 0, 1, 1},
-  {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1},
-  {&__pyx_n_s_error, __pyx_k_error, sizeof(__pyx_k_error), 0, 0, 1, 1},
-  {&__pyx_n_s_flags, __pyx_k_flags, sizeof(__pyx_k_flags), 0, 0, 1, 1},
-  {&__pyx_n_s_format, __pyx_k_format, sizeof(__pyx_k_format), 0, 0, 1, 1},
-  {&__pyx_n_s_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 0, 1, 1},
-  {&__pyx_n_u_fortran, __pyx_k_fortran, sizeof(__pyx_k_fortran), 0, 1, 0, 1},
-  {&__pyx_kp_s_got_differing_extents_in_dimensi, __pyx_k_got_differing_extents_in_dimensi, sizeof(__pyx_k_got_differing_extents_in_dimensi), 0, 0, 1, 0},
-  {&__pyx_n_s_id, __pyx_k_id, sizeof(__pyx_k_id), 0, 0, 1, 1},
-  {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1},
-  {&__pyx_n_s_itemsize, __pyx_k_itemsize, sizeof(__pyx_k_itemsize), 0, 0, 1, 1},
-  {&__pyx_kp_s_itemsize_0_for_cython_array, __pyx_k_itemsize_0_for_cython_array, sizeof(__pyx_k_itemsize_0_for_cython_array), 0, 0, 1, 0},
-  {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
-  {&__pyx_n_s_memview, __pyx_k_memview, sizeof(__pyx_k_memview), 0, 0, 1, 1},
-  {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1},
-  {&__pyx_n_s_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1},
-  {&__pyx_n_s_name_2, __pyx_k_name_2, sizeof(__pyx_k_name_2), 0, 0, 1, 1},
-  {&__pyx_kp_u_ndarray_is_not_C_contiguous, __pyx_k_ndarray_is_not_C_contiguous, sizeof(__pyx_k_ndarray_is_not_C_contiguous), 0, 1, 0, 0},
-  {&__pyx_kp_u_ndarray_is_not_Fortran_contiguou, __pyx_k_ndarray_is_not_Fortran_contiguou, sizeof(__pyx_k_ndarray_is_not_Fortran_contiguou), 0, 1, 0, 0},
-  {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1},
-  {&__pyx_n_s_obj, __pyx_k_obj, sizeof(__pyx_k_obj), 0, 0, 1, 1},
-  {&__pyx_n_s_pack, __pyx_k_pack, sizeof(__pyx_k_pack), 0, 0, 1, 1},
-  {&__pyx_n_s_pyx_getbuffer, __pyx_k_pyx_getbuffer, sizeof(__pyx_k_pyx_getbuffer), 0, 0, 1, 1},
-  {&__pyx_n_s_pyx_releasebuffer, __pyx_k_pyx_releasebuffer, sizeof(__pyx_k_pyx_releasebuffer), 0, 0, 1, 1},
-  {&__pyx_n_s_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1},
-  {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1},
-  {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1},
-  {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1},
-  {&__pyx_n_s_start, __pyx_k_start, sizeof(__pyx_k_start), 0, 0, 1, 1},
-  {&__pyx_n_s_step, __pyx_k_step, sizeof(__pyx_k_step), 0, 0, 1, 1},
-  {&__pyx_n_s_stop, __pyx_k_stop, sizeof(__pyx_k_stop), 0, 0, 1, 1},
-  {&__pyx_kp_s_strided_and_direct, __pyx_k_strided_and_direct, sizeof(__pyx_k_strided_and_direct), 0, 0, 1, 0},
-  {&__pyx_kp_s_strided_and_direct_or_indirect, __pyx_k_strided_and_direct_or_indirect, sizeof(__pyx_k_strided_and_direct_or_indirect), 0, 0, 1, 0},
-  {&__pyx_kp_s_strided_and_indirect, __pyx_k_strided_and_indirect, sizeof(__pyx_k_strided_and_indirect), 0, 0, 1, 0},
-  {&__pyx_n_s_struct, __pyx_k_struct, sizeof(__pyx_k_struct), 0, 0, 1, 1},
-  {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
-  {&__pyx_kp_s_unable_to_allocate_array_data, __pyx_k_unable_to_allocate_array_data, sizeof(__pyx_k_unable_to_allocate_array_data), 0, 0, 1, 0},
-  {&__pyx_kp_s_unable_to_allocate_shape_and_str, __pyx_k_unable_to_allocate_shape_and_str, sizeof(__pyx_k_unable_to_allocate_shape_and_str), 0, 0, 1, 0},
-  {&__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_k_unknown_dtype_code_in_numpy_pxd, sizeof(__pyx_k_unknown_dtype_code_in_numpy_pxd), 0, 1, 0, 0},
-  {&__pyx_n_s_unpack, __pyx_k_unpack, sizeof(__pyx_k_unpack), 0, 0, 1, 1},
-  {&__pyx_n_s_xrange, __pyx_k_xrange, sizeof(__pyx_k_xrange), 0, 0, 1, 1},
-  {0, 0, 0, 0, 0, 0, 0}
-};
-static int __Pyx_InitCachedBuiltins(void) {
-  #if PY_MAJOR_VERSION >= 3
-  __pyx_builtin_xrange = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_xrange) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 12; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #else
-  __pyx_builtin_xrange = __Pyx_GetBuiltinName(__pyx_n_s_xrange); if (!__pyx_builtin_xrange) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 12; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 228; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_RuntimeError = __Pyx_GetBuiltinName(__pyx_n_s_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_MemoryError = __Pyx_GetBuiltinName(__pyx_n_s_MemoryError); if (!__pyx_builtin_MemoryError) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 145; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_Ellipsis = __Pyx_GetBuiltinName(__pyx_n_s_Ellipsis); if (!__pyx_builtin_Ellipsis) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 357; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 386; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_id = __Pyx_GetBuiltinName(__pyx_n_s_id); if (!__pyx_builtin_id) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 569; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_IndexError = __Pyx_GetBuiltinName(__pyx_n_s_IndexError); if (!__pyx_builtin_IndexError) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 789; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  return 0;
-  __pyx_L1_error:;
-  return -1;
-}
-
-static int __Pyx_InitCachedConstants(void) {
-  __Pyx_RefNannyDeclarations
-  __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":215
- *             if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS)
- *                 and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)):
- *                 raise ValueError(u"ndarray is not C contiguous")             # <<<<<<<<<<<<<<
- * 
- *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
- */
-  __pyx_tuple_ = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_C_contiguous); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 215; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple_);
-  __Pyx_GIVEREF(__pyx_tuple_);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":219
- *             if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS)
- *                 and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)):
- *                 raise ValueError(u"ndarray is not Fortran contiguous")             # <<<<<<<<<<<<<<
- * 
- *             info.buf = PyArray_DATA(self)
- */
-  __pyx_tuple__2 = PyTuple_Pack(1, __pyx_kp_u_ndarray_is_not_Fortran_contiguou); if (unlikely(!__pyx_tuple__2)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__2);
-  __Pyx_GIVEREF(__pyx_tuple__2);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":257
- *                 if ((descr.byteorder == c'>' and little_endian) or
- *                     (descr.byteorder == c'<' and not little_endian)):
- *                     raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
- *                 if   t == NPY_BYTE:        f = "b"
- *                 elif t == NPY_UBYTE:       f = "B"
- */
-  __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_u_Non_native_byte_order_not_suppor); if (unlikely(!__pyx_tuple__3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__3);
-  __Pyx_GIVEREF(__pyx_tuple__3);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":799
- * 
- *         if (end - f) - <int>(new_offset - offset[0]) < 15:
- *             raise RuntimeError(u"Format string allocated too short, see comment in numpy.pxd")             # <<<<<<<<<<<<<<
- * 
- *         if ((child.byteorder == c'>' and little_endian) or
- */
-  __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor); if (unlikely(!__pyx_tuple__4)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__4);
-  __Pyx_GIVEREF(__pyx_tuple__4);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":803
- *         if ((child.byteorder == c'>' and little_endian) or
- *             (child.byteorder == c'<' and not little_endian)):
- *             raise ValueError(u"Non-native byte order not supported")             # <<<<<<<<<<<<<<
- *             # One could encode it in the format string and have Cython
- *             # complain instead, BUT: < and > in format strings also imply
- */
-  __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_u_Non_native_byte_order_not_suppor); if (unlikely(!__pyx_tuple__5)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__5);
-  __Pyx_GIVEREF(__pyx_tuple__5);
-
-  /* "/Users/sebastian/miniconda3/envs/py34/lib/python3.4/site-packages/Cython/Includes/numpy/__init__.pxd":823
- *             t = child.type_num
- *             if end - f < 5:
- *                 raise RuntimeError(u"Format string allocated too short.")             # <<<<<<<<<<<<<<
- * 
- *             # Until ticket #99 is fixed, use integers to avoid warnings
- */
-  __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_u_Format_string_allocated_too_shor_2); if (unlikely(!__pyx_tuple__6)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__6);
-  __Pyx_GIVEREF(__pyx_tuple__6);
-
-  /* "View.MemoryView":127
- * 
- *         if not self.ndim:
- *             raise ValueError("Empty shape tuple for cython.array")             # <<<<<<<<<<<<<<
- * 
- *         if itemsize <= 0:
- */
-  __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_s_Empty_shape_tuple_for_cython_arr); if (unlikely(!__pyx_tuple__7)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__7);
-  __Pyx_GIVEREF(__pyx_tuple__7);
-
-  /* "View.MemoryView":130
- * 
- *         if itemsize <= 0:
- *             raise ValueError("itemsize <= 0 for cython.array")             # <<<<<<<<<<<<<<
- * 
- *         if isinstance(format, unicode):
- */
-  __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_itemsize_0_for_cython_array); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__8);
-  __Pyx_GIVEREF(__pyx_tuple__8);
-
-  /* "View.MemoryView":142
- * 
- *         if not self._shape:
- *             raise MemoryError("unable to allocate shape and strides.")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_tuple__9 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_shape_and_str); if (unlikely(!__pyx_tuple__9)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__9);
-  __Pyx_GIVEREF(__pyx_tuple__9);
-
-  /* "View.MemoryView":170
- *             self.data = <char *>malloc(self.len)
- *             if not self.data:
- *                 raise MemoryError("unable to allocate array data.")             # <<<<<<<<<<<<<<
- * 
- *             if self.dtype_is_object:
- */
-  __pyx_tuple__10 = PyTuple_Pack(1, __pyx_kp_s_unable_to_allocate_array_data); if (unlikely(!__pyx_tuple__10)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__10);
-  __Pyx_GIVEREF(__pyx_tuple__10);
-
-  /* "View.MemoryView":186
- *             bufmode = PyBUF_F_CONTIGUOUS | PyBUF_ANY_CONTIGUOUS
- *         if not (flags & bufmode):
- *             raise ValueError("Can only create a buffer that is contiguous in memory.")             # <<<<<<<<<<<<<<
- *         info.buf = self.data
- *         info.len = self.len
- */
-  __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_Can_only_create_a_buffer_that_is); if (unlikely(!__pyx_tuple__11)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__11);
-  __Pyx_GIVEREF(__pyx_tuple__11);
-
-  /* "View.MemoryView":445
- *             result = struct.unpack(self.view.format, bytesitem)
- *         except struct.error:
- *             raise ValueError("Unable to convert item to object")             # <<<<<<<<<<<<<<
- *         else:
- *             if len(self.view.format) == 1:
- */
-  __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_Unable_to_convert_item_to_object); if (unlikely(!__pyx_tuple__12)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 445; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__12);
-  __Pyx_GIVEREF(__pyx_tuple__12);
-
-  /* "View.MemoryView":521
- *             if self.view.strides == NULL:
- * 
- *                 raise ValueError("Buffer view does not expose strides")             # <<<<<<<<<<<<<<
- * 
- *             return tuple([self.view.strides[i] for i in xrange(self.view.ndim)])
- */
-  __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_Buffer_view_does_not_expose_stri); if (unlikely(!__pyx_tuple__13)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__13);
-  __Pyx_GIVEREF(__pyx_tuple__13);
-
-  /* "View.MemoryView":638
- *         if item is Ellipsis:
- *             if not seen_ellipsis:
- *                 result.extend([slice(None)] * (ndim - len(tup) + 1))             # <<<<<<<<<<<<<<
- *                 seen_ellipsis = True
- *             else:
- */
-  __pyx_tuple__14 = PyTuple_Pack(1, Py_None); if (unlikely(!__pyx_tuple__14)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__14);
-  __Pyx_GIVEREF(__pyx_tuple__14);
-
-  /* "View.MemoryView":641
- *                 seen_ellipsis = True
- *             else:
- *                 result.append(slice(None))             # <<<<<<<<<<<<<<
- *             have_slices = True
- *         else:
- */
-  __pyx_tuple__15 = PyTuple_Pack(1, Py_None); if (unlikely(!__pyx_tuple__15)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 641; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__15);
-  __Pyx_GIVEREF(__pyx_tuple__15);
-
-  /* "View.MemoryView":652
- *     nslices = ndim - len(result)
- *     if nslices:
- *         result.extend([slice(None)] * nslices)             # <<<<<<<<<<<<<<
- * 
- *     return have_slices or nslices, tuple(result)
- */
-  __pyx_tuple__16 = PyTuple_Pack(1, Py_None); if (unlikely(!__pyx_tuple__16)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 652; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__16);
-  __Pyx_GIVEREF(__pyx_tuple__16);
-
-  /* "View.MemoryView":660
- *     for i in range(ndim):
- *         if suboffsets[i] >= 0:
- *             raise ValueError("Indirect dimensions not supported")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_tuple__17 = PyTuple_Pack(1, __pyx_kp_s_Indirect_dimensions_not_supporte); if (unlikely(!__pyx_tuple__17)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 660; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__17);
-  __Pyx_GIVEREF(__pyx_tuple__17);
-
-  /* "View.MemoryView":276
- *         return self.name
- * 
- * cdef generic = Enum("<strided and direct or indirect>")             # <<<<<<<<<<<<<<
- * cdef strided = Enum("<strided and direct>") # default
- * cdef indirect = Enum("<strided and indirect>")
- */
-  __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct_or_indirect); if (unlikely(!__pyx_tuple__18)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__18);
-  __Pyx_GIVEREF(__pyx_tuple__18);
-
-  /* "View.MemoryView":277
- * 
- * cdef generic = Enum("<strided and direct or indirect>")
- * cdef strided = Enum("<strided and direct>") # default             # <<<<<<<<<<<<<<
- * cdef indirect = Enum("<strided and indirect>")
- * 
- */
-  __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_strided_and_direct); if (unlikely(!__pyx_tuple__19)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 277; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__19);
-  __Pyx_GIVEREF(__pyx_tuple__19);
-
-  /* "View.MemoryView":278
- * cdef generic = Enum("<strided and direct or indirect>")
- * cdef strided = Enum("<strided and direct>") # default
- * cdef indirect = Enum("<strided and indirect>")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_strided_and_indirect); if (unlikely(!__pyx_tuple__20)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__20);
-  __Pyx_GIVEREF(__pyx_tuple__20);
-
-  /* "View.MemoryView":281
- * 
- * 
- * cdef contiguous = Enum("<contiguous and direct>")             # <<<<<<<<<<<<<<
- * cdef indirect_contiguous = Enum("<contiguous and indirect>")
- * 
- */
-  __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_direct); if (unlikely(!__pyx_tuple__21)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__21);
-  __Pyx_GIVEREF(__pyx_tuple__21);
-
-  /* "View.MemoryView":282
- * 
- * cdef contiguous = Enum("<contiguous and direct>")
- * cdef indirect_contiguous = Enum("<contiguous and indirect>")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_contiguous_and_indirect); if (unlikely(!__pyx_tuple__22)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 282; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_tuple__22);
-  __Pyx_GIVEREF(__pyx_tuple__22);
-  __Pyx_RefNannyFinishContext();
-  return 0;
-  __pyx_L1_error:;
-  __Pyx_RefNannyFinishContext();
-  return -1;
-}
-
-static int __Pyx_InitGlobals(void) {
-  if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_int_neg_1 = PyInt_FromLong(-1); if (unlikely(!__pyx_int_neg_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  return 0;
-  __pyx_L1_error:;
-  return -1;
-}
-
-#if PY_MAJOR_VERSION < 3
-PyMODINIT_FUNC initcython_bubblesort_nomagic(void); /*proto*/
-PyMODINIT_FUNC initcython_bubblesort_nomagic(void)
-#else
-PyMODINIT_FUNC PyInit_cython_bubblesort_nomagic(void); /*proto*/
-PyMODINIT_FUNC PyInit_cython_bubblesort_nomagic(void)
-#endif
-{
-  PyObject *__pyx_t_1 = NULL;
-  int __pyx_lineno = 0;
-  const char *__pyx_filename = NULL;
-  int __pyx_clineno = 0;
-  __Pyx_RefNannyDeclarations
-  #if CYTHON_REFNANNY
-  __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny");
-  if (!__Pyx_RefNanny) {
-      PyErr_Clear();
-      __Pyx_RefNanny = __Pyx_RefNannyImportAPI("Cython.Runtime.refnanny");
-      if (!__Pyx_RefNanny)
-          Py_FatalError("failed to import 'refnanny' module");
-  }
-  #endif
-  __Pyx_RefNannySetupContext("PyMODINIT_FUNC PyInit_cython_bubblesort_nomagic(void)", 0);
-  if ( __Pyx_check_binary_version() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #ifdef __Pyx_CyFunction_USED
-  if (__Pyx_CyFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  #ifdef __Pyx_FusedFunction_USED
-  if (__pyx_FusedFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  #ifdef __Pyx_Generator_USED
-  if (__pyx_Generator_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  /*--- Library function declarations ---*/
-  /*--- Threads initialization code ---*/
-  #if defined(__PYX_FORCE_INIT_THREADS) && __PYX_FORCE_INIT_THREADS
-  #ifdef WITH_THREAD /* Python build with threading support? */
-  PyEval_InitThreads();
-  #endif
-  #endif
-  /*--- Module creation code ---*/
-  #if PY_MAJOR_VERSION < 3
-  __pyx_m = Py_InitModule4(__Pyx_NAMESTR("cython_bubblesort_nomagic"), __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
-  #else
-  __pyx_m = PyModule_Create(&__pyx_moduledef);
-  #endif
-  if (unlikely(!__pyx_m)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_d = PyModule_GetDict(__pyx_m); if (unlikely(!__pyx_d)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  Py_INCREF(__pyx_d);
-  __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME)); if (unlikely(!__pyx_b)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #if CYTHON_COMPILING_IN_PYPY
-  Py_INCREF(__pyx_b);
-  #endif
-  if (__Pyx_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  /*--- Initialize various global constants etc. ---*/
-  if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT)
-  if (__Pyx_init_sys_getdefaultencoding_params() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  #endif
-  if (__pyx_module_is_main_cython_bubblesort_nomagic) {
-    if (__Pyx_SetAttrString(__pyx_m, "__name__", __pyx_n_s_main) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  }
-  #if PY_MAJOR_VERSION >= 3
-  {
-    PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    if (!PyDict_GetItemString(modules, "cython_bubblesort_nomagic")) {
-      if (unlikely(PyDict_SetItemString(modules, "cython_bubblesort_nomagic", __pyx_m) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    }
-  }
-  #endif
-  /*--- Builtin init code ---*/
-  if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  /*--- Constants init code ---*/
-  if (unlikely(__Pyx_InitCachedConstants() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  /*--- Global init code ---*/
-  generic = Py_None; Py_INCREF(Py_None);
-  strided = Py_None; Py_INCREF(Py_None);
-  indirect = Py_None; Py_INCREF(Py_None);
-  contiguous = Py_None; Py_INCREF(Py_None);
-  indirect_contiguous = Py_None; Py_INCREF(Py_None);
-  /*--- Variable export code ---*/
-  /*--- Function export code ---*/
-  /*--- Type init code ---*/
-  if (PyType_Ready(&__pyx_type___pyx_array) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 99; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_type___pyx_array.tp_print = 0;
-  __pyx_array_type = &__pyx_type___pyx_array;
-  if (PyType_Ready(&__pyx_type___pyx_MemviewEnum) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_type___pyx_MemviewEnum.tp_print = 0;
-  __pyx_MemviewEnum_type = &__pyx_type___pyx_MemviewEnum;
-  __pyx_vtabptr_memoryview = &__pyx_vtable_memoryview;
-  __pyx_vtable_memoryview.get_item_pointer = (char *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_get_item_pointer;
-  __pyx_vtable_memoryview.is_slice = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_is_slice;
-  __pyx_vtable_memoryview.setitem_slice_assignment = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_slice_assignment;
-  __pyx_vtable_memoryview.setitem_slice_assign_scalar = (PyObject *(*)(struct __pyx_memoryview_obj *, struct __pyx_memoryview_obj *, PyObject *))__pyx_memoryview_setitem_slice_assign_scalar;
-  __pyx_vtable_memoryview.setitem_indexed = (PyObject *(*)(struct __pyx_memoryview_obj *, PyObject *, PyObject *))__pyx_memoryview_setitem_indexed;
-  __pyx_vtable_memoryview.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryview_convert_item_to_object;
-  __pyx_vtable_memoryview.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryview_assign_item_from_object;
-  if (PyType_Ready(&__pyx_type___pyx_memoryview) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 302; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_type___pyx_memoryview.tp_print = 0;
-  if (__Pyx_SetVtable(__pyx_type___pyx_memoryview.tp_dict, __pyx_vtabptr_memoryview) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 302; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_memoryview_type = &__pyx_type___pyx_memoryview;
-  __pyx_vtabptr__memoryviewslice = &__pyx_vtable__memoryviewslice;
-  __pyx_vtable__memoryviewslice.__pyx_base = *__pyx_vtabptr_memoryview;
-  __pyx_vtable__memoryviewslice.__pyx_base.convert_item_to_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *))__pyx_memoryviewslice_convert_item_to_object;
-  __pyx_vtable__memoryviewslice.__pyx_base.assign_item_from_object = (PyObject *(*)(struct __pyx_memoryview_obj *, char *, PyObject *))__pyx_memoryviewslice_assign_item_from_object;
-  __pyx_type___pyx_memoryviewslice.tp_base = __pyx_memoryview_type;
-  if (PyType_Ready(&__pyx_type___pyx_memoryviewslice) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 922; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_type___pyx_memoryviewslice.tp_print = 0;
-  if (__Pyx_SetVtable(__pyx_type___pyx_memoryviewslice.tp_dict, __pyx_vtabptr__memoryviewslice) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 922; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_memoryviewslice_type = &__pyx_type___pyx_memoryviewslice;
-  /*--- Type import code ---*/
-  __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "type", 
-  #if CYTHON_COMPILING_IN_PYPY
-  sizeof(PyTypeObject),
-  #else
-  sizeof(PyHeapTypeObject),
-  #endif
-  0); if (unlikely(!__pyx_ptype_7cpython_4type_type)) {__pyx_filename = __pyx_f[3]; __pyx_lineno = 9; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_ptype_5numpy_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr), 0); if (unlikely(!__pyx_ptype_5numpy_dtype)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 155; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_ptype_5numpy_flatiter = __Pyx_ImportType("numpy", "flatiter", sizeof(PyArrayIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_flatiter)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 165; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_ptype_5numpy_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject), 0); if (unlikely(!__pyx_ptype_5numpy_broadcast)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 169; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_ptype_5numpy_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject), 0); if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 178; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_ptype_5numpy_ufunc = __Pyx_ImportType("numpy", "ufunc", sizeof(PyUFuncObject), 0); if (unlikely(!__pyx_ptype_5numpy_ufunc)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 861; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  /*--- Variable import code ---*/
-  /*--- Function import code ---*/
-  /*--- Execution code ---*/
-
-  /* "cython_bubblesort_nomagic.pyx":2
- * 
- * cimport numpy as np             # <<<<<<<<<<<<<<
- * cimport cython
- * @cython.boundscheck(False)
- */
-  __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-
-  /* "View.MemoryView":203
- *         info.obj = self
- * 
- *     __pyx_getbuffer = capsule(<void *> &__pyx_array_getbuffer, "getbuffer(obj, view, flags)")             # <<<<<<<<<<<<<<
- * 
- *     def __dealloc__(array self):
- */
-  __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_array_getbuffer)), __pyx_k_getbuffer_obj_view_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 203; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (PyDict_SetItem(__pyx_array_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 203; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  PyType_Modified(__pyx_array_type);
-
-  /* "View.MemoryView":276
- *         return self.name
- * 
- * cdef generic = Enum("<strided and direct or indirect>")             # <<<<<<<<<<<<<<
- * cdef strided = Enum("<strided and direct>") # default
- * cdef indirect = Enum("<strided and indirect>")
- */
-  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__18, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 276; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_XGOTREF(generic);
-  __Pyx_DECREF_SET(generic, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":277
- * 
- * cdef generic = Enum("<strided and direct or indirect>")
- * cdef strided = Enum("<strided and direct>") # default             # <<<<<<<<<<<<<<
- * cdef indirect = Enum("<strided and indirect>")
- * 
- */
-  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__19, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 277; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_XGOTREF(strided);
-  __Pyx_DECREF_SET(strided, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":278
- * cdef generic = Enum("<strided and direct or indirect>")
- * cdef strided = Enum("<strided and direct>") # default
- * cdef indirect = Enum("<strided and indirect>")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__20, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_XGOTREF(indirect);
-  __Pyx_DECREF_SET(indirect, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":281
- * 
- * 
- * cdef contiguous = Enum("<contiguous and direct>")             # <<<<<<<<<<<<<<
- * cdef indirect_contiguous = Enum("<contiguous and indirect>")
- * 
- */
-  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__21, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_XGOTREF(contiguous);
-  __Pyx_DECREF_SET(contiguous, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":282
- * 
- * cdef contiguous = Enum("<contiguous and direct>")
- * cdef indirect_contiguous = Enum("<contiguous and indirect>")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_1 = __Pyx_PyObject_Call(((PyObject *)((PyObject *)__pyx_MemviewEnum_type)), __pyx_tuple__22, NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 282; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  __Pyx_XGOTREF(indirect_contiguous);
-  __Pyx_DECREF_SET(indirect_contiguous, __pyx_t_1);
-  __Pyx_GIVEREF(__pyx_t_1);
-  __pyx_t_1 = 0;
-
-  /* "View.MemoryView":496
- *         info.obj = self
- * 
- *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), __pyx_k_getbuffer_obj_view_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (PyDict_SetItem(__pyx_memoryview_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  PyType_Modified(__pyx_memoryview_type);
-
-  /* "View.MemoryView":953
- *             return self.from_object
- * 
- *     __pyx_getbuffer = capsule(<void *> &__pyx_memoryview_getbuffer, "getbuffer(obj, view, flags)")             # <<<<<<<<<<<<<<
- * 
- * 
- */
-  __pyx_t_1 = __pyx_capsule_create(((void *)(&__pyx_memoryview_getbuffer)), __pyx_k_getbuffer_obj_view_flags); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 953; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_1);
-  if (PyDict_SetItem(__pyx_memoryviewslice_type->tp_dict, __pyx_n_s_pyx_getbuffer, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[2]; __pyx_lineno = 953; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
-  PyType_Modified(__pyx_memoryviewslice_type);
-
-  /* "View.MemoryView":1361
- * 
- * @cname('__pyx_memoryview__slice_assign_scalar')
- * cdef void _slice_assign_scalar(char *data, Py_ssize_t *shape,             # <<<<<<<<<<<<<<
- *                               Py_ssize_t *strides, int ndim,
- *                               size_t itemsize, void *item) nogil:
- */
-  goto __pyx_L0;
-  __pyx_L1_error:;
-  __Pyx_XDECREF(__pyx_t_1);
-  if (__pyx_m) {
-    __Pyx_AddTraceback("init cython_bubblesort_nomagic", __pyx_clineno, __pyx_lineno, __pyx_filename);
-    Py_DECREF(__pyx_m); __pyx_m = 0;
-  } else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_ImportError, "init cython_bubblesort_nomagic");
-  }
-  __pyx_L0:;
-  __Pyx_RefNannyFinishContext();
-  #if PY_MAJOR_VERSION < 3
-  return;
-  #else
-  return __pyx_m;
-  #endif
-}
-
-/* Runtime support code */
-#if CYTHON_REFNANNY
-static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
-    PyObject *m = NULL, *p = NULL;
-    void *r = NULL;
-    m = PyImport_ImportModule((char *)modname);
-    if (!m) goto end;
-    p = PyObject_GetAttrString(m, (char *)"RefNannyAPI");
-    if (!p) goto end;
-    r = PyLong_AsVoidPtr(p);
-end:
-    Py_XDECREF(p);
-    Py_XDECREF(m);
-    return (__Pyx_RefNannyAPIStruct *)r;
-}
-#endif /* CYTHON_REFNANNY */
-
-static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
-    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);
-    if (unlikely(!result)) {
-        PyErr_Format(PyExc_NameError,
-#if PY_MAJOR_VERSION >= 3
-            "name '%U' is not defined", name);
-#else
-            "name '%.200s' is not defined", PyString_AS_STRING(name));
-#endif
-    }
-    return result;
-}
-
-static CYTHON_INLINE int __Pyx_IsLittleEndian(void) {
-  unsigned int n = 1;
-  return *(unsigned char*)(&n) != 0;
-}
-static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,
-                              __Pyx_BufFmt_StackElem* stack,
-                              __Pyx_TypeInfo* type) {
-  stack[0].field = &ctx->root;
-  stack[0].parent_offset = 0;
-  ctx->root.type = type;
-  ctx->root.name = "buffer dtype";
-  ctx->root.offset = 0;
-  ctx->head = stack;
-  ctx->head->field = &ctx->root;
-  ctx->fmt_offset = 0;
-  ctx->head->parent_offset = 0;
-  ctx->new_packmode = '@';
-  ctx->enc_packmode = '@';
-  ctx->new_count = 1;
-  ctx->enc_count = 0;
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  ctx->is_valid_array = 0;
-  ctx->struct_alignment = 0;
-  while (type->typegroup == 'S') {
-    ++ctx->head;
-    ctx->head->field = type->fields;
-    ctx->head->parent_offset = 0;
-    type = type->fields->type;
-  }
-}
-static int __Pyx_BufFmt_ParseNumber(const char** ts) {
-    int count;
-    const char* t = *ts;
-    if (*t < '0' || *t > '9') {
-      return -1;
-    } else {
-        count = *t++ - '0';
-        while (*t >= '0' && *t < '9') {
-            count *= 10;
-            count += *t++ - '0';
-        }
-    }
-    *ts = t;
-    return count;
-}
-static int __Pyx_BufFmt_ExpectNumber(const char **ts) {
-    int number = __Pyx_BufFmt_ParseNumber(ts);
-    if (number == -1) /* First char was not a digit */
-        PyErr_Format(PyExc_ValueError,\
-                     "Does not understand character buffer dtype format string ('%c')", **ts);
-    return number;
-}
-static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {
-  PyErr_Format(PyExc_ValueError,
-               "Unexpected format string character: '%c'", ch);
-}
-static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {
-  switch (ch) {
-    case 'c': return "'char'";
-    case 'b': return "'signed char'";
-    case 'B': return "'unsigned char'";
-    case 'h': return "'short'";
-    case 'H': return "'unsigned short'";
-    case 'i': return "'int'";
-    case 'I': return "'unsigned int'";
-    case 'l': return "'long'";
-    case 'L': return "'unsigned long'";
-    case 'q': return "'long long'";
-    case 'Q': return "'unsigned long long'";
-    case 'f': return (is_complex ? "'complex float'" : "'float'");
-    case 'd': return (is_complex ? "'complex double'" : "'double'");
-    case 'g': return (is_complex ? "'complex long double'" : "'long double'");
-    case 'T': return "a struct";
-    case 'O': return "Python object";
-    case 'P': return "a pointer";
-    case 's': case 'p': return "a string";
-    case 0: return "end";
-    default: return "unparseable format string";
-  }
-}
-static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return 2;
-    case 'i': case 'I': case 'l': case 'L': return 4;
-    case 'q': case 'Q': return 8;
-    case 'f': return (is_complex ? 8 : 4);
-    case 'd': return (is_complex ? 16 : 8);
-    case 'g': {
-      PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g')..");
-      return 0;
-    }
-    case 'O': case 'P': return sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {
-  switch (ch) {
-    case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(short);
-    case 'i': case 'I': return sizeof(int);
-    case 'l': case 'L': return sizeof(long);
-    #ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(PY_LONG_LONG);
-    #endif
-    case 'f': return sizeof(float) * (is_complex ? 2 : 1);
-    case 'd': return sizeof(double) * (is_complex ? 2 : 1);
-    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);
-    case 'O': case 'P': return sizeof(void*);
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-typedef struct { char c; short x; } __Pyx_st_short;
-typedef struct { char c; int x; } __Pyx_st_int;
-typedef struct { char c; long x; } __Pyx_st_long;
-typedef struct { char c; float x; } __Pyx_st_float;
-typedef struct { char c; double x; } __Pyx_st_double;
-typedef struct { char c; long double x; } __Pyx_st_longdouble;
-typedef struct { char c; void *x; } __Pyx_st_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-/* These are for computing the padding at the end of the struct to align
-   on the first member of the struct. This will probably the same as above,
-   but we don't have any guarantees.
- */
-typedef struct { short x; char c; } __Pyx_pad_short;
-typedef struct { int x; char c; } __Pyx_pad_int;
-typedef struct { long x; char c; } __Pyx_pad_long;
-typedef struct { float x; char c; } __Pyx_pad_float;
-typedef struct { double x; char c; } __Pyx_pad_double;
-typedef struct { long double x; char c; } __Pyx_pad_longdouble;
-typedef struct { void *x; char c; } __Pyx_pad_void_p;
-#ifdef HAVE_LONG_LONG
-typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
-#endif
-static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
-  switch (ch) {
-    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
-    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
-    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
-    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
-#ifdef HAVE_LONG_LONG
-    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
-#endif
-    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
-    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
-    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
-    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
-    default:
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-}
-static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
-  switch (ch) {
-    case 'c':
-        return 'H';
-    case 'b': case 'h': case 'i':
-    case 'l': case 'q': case 's': case 'p':
-        return 'I';
-    case 'B': case 'H': case 'I': case 'L': case 'Q':
-        return 'U';
-    case 'f': case 'd': case 'g':
-        return (is_complex ? 'C' : 'R');
-    case 'O':
-        return 'O';
-    case 'P':
-        return 'P';
-    default: {
-      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
-      return 0;
-    }
-  }
-}
-static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
-  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
-    const char* expected;
-    const char* quote;
-    if (ctx->head == NULL) {
-      expected = "end";
-      quote = "";
-    } else {
-      expected = ctx->head->field->type->name;
-      quote = "'";
-    }
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected %s%s%s but got %s",
-                 quote, expected, quote,
-                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
-  } else {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_StructField* parent = (ctx->head - 1)->field;
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
-                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
-                 parent->type->name, field->name);
-  }
-}
-static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
-  char group;
-  size_t size, offset, arraysize = 1;
-  if (ctx->enc_type == 0) return 0;
-  if (ctx->head->field->type->arraysize[0]) {
-    int i, ndim = 0;
-    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
-        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
-        ndim = 1;
-        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
-            PyErr_Format(PyExc_ValueError,
-                         "Expected a dimension of size %zu, got %zu",
-                         ctx->head->field->type->arraysize[0], ctx->enc_count);
-            return -1;
-        }
-    }
-    if (!ctx->is_valid_array) {
-      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
-                   ctx->head->field->type->ndim, ndim);
-      return -1;
-    }
-    for (i = 0; i < ctx->head->field->type->ndim; i++) {
-      arraysize *= ctx->head->field->type->arraysize[i];
-    }
-    ctx->is_valid_array = 0;
-    ctx->enc_count = 1;
-  }
-  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
-  do {
-    __Pyx_StructField* field = ctx->head->field;
-    __Pyx_TypeInfo* type = field->type;
-    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
-      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
-    } else {
-      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
-    }
-    if (ctx->enc_packmode == '@') {
-      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
-      size_t align_mod_offset;
-      if (align_at == 0) return -1;
-      align_mod_offset = ctx->fmt_offset % align_at;
-      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
-      if (ctx->struct_alignment == 0)
-          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
-                                                                 ctx->is_complex);
-    }
-    if (type->size != size || type->typegroup != group) {
-      if (type->typegroup == 'C' && type->fields != NULL) {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        ++ctx->head;
-        ctx->head->field = type->fields;
-        ctx->head->parent_offset = parent_offset;
-        continue;
-      }
-      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
-      } else {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-      }
-    }
-    offset = ctx->head->parent_offset + field->offset;
-    if (ctx->fmt_offset != offset) {
-      PyErr_Format(PyExc_ValueError,
-                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
-                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
-      return -1;
-    }
-    ctx->fmt_offset += size;
-    if (arraysize)
-      ctx->fmt_offset += (arraysize - 1) * size;
-    --ctx->enc_count; /* Consume from buffer string */
-    while (1) {
-      if (field == &ctx->root) {
-        ctx->head = NULL;
-        if (ctx->enc_count != 0) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return -1;
-        }
-        break; /* breaks both loops as ctx->enc_count == 0 */
-      }
-      ctx->head->field = ++field;
-      if (field->type == NULL) {
-        --ctx->head;
-        field = ctx->head->field;
-        continue;
-      } else if (field->type->typegroup == 'S') {
-        size_t parent_offset = ctx->head->parent_offset + field->offset;
-        if (field->type->fields->type == NULL) continue; /* empty struct */
-        field = field->type->fields;
-        ++ctx->head;
-        ctx->head->field = field;
-        ctx->head->parent_offset = parent_offset;
-        break;
-      } else {
-        break;
-      }
-    }
-  } while (ctx->enc_count);
-  ctx->enc_type = 0;
-  ctx->is_complex = 0;
-  return 0;
-}
-static CYTHON_INLINE PyObject *
-__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
-{
-    const char *ts = *tsp;
-    int i = 0, number;
-    int ndim = ctx->head->field->type->ndim;
-;
-    ++ts;
-    if (ctx->new_count != 1) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Cannot handle repeated arrays in format string");
-        return NULL;
-    }
-    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-    while (*ts && *ts != ')') {
-        switch (*ts) {
-            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
-            default:  break;  /* not a 'break' in the loop */
-        }
-        number = __Pyx_BufFmt_ExpectNumber(&ts);
-        if (number == -1) return NULL;
-        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
-            return PyErr_Format(PyExc_ValueError,
-                        "Expected a dimension of size %zu, got %d",
-                        ctx->head->field->type->arraysize[i], number);
-        if (*ts != ',' && *ts != ')')
-            return PyErr_Format(PyExc_ValueError,
-                                "Expected a comma in format string, got '%c'", *ts);
-        if (*ts == ',') ts++;
-        i++;
-    }
-    if (i != ndim)
-        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
-                            ctx->head->field->type->ndim, i);
-    if (!*ts) {
-        PyErr_SetString(PyExc_ValueError,
-                        "Unexpected end of format string, expected ')'");
-        return NULL;
-    }
-    ctx->is_valid_array = 1;
-    ctx->new_count = 1;
-    *tsp = ++ts;
-    return Py_None;
-}
-static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
-  int got_Z = 0;
-  while (1) {
-    switch(*ts) {
-      case 0:
-        if (ctx->enc_type != 0 && ctx->head == NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        if (ctx->head != NULL) {
-          __Pyx_BufFmt_RaiseExpected(ctx);
-          return NULL;
-        }
-        return ts;
-      case ' ':
-      case '\r':
-      case '\n':
-        ++ts;
-        break;
-      case '<':
-        if (!__Pyx_IsLittleEndian()) {
-          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '>':
-      case '!':
-        if (__Pyx_IsLittleEndian()) {
-          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
-          return NULL;
-        }
-        ctx->new_packmode = '=';
-        ++ts;
-        break;
-      case '=':
-      case '@':
-      case '^':
-        ctx->new_packmode = *ts++;
-        break;
-      case 'T': /* substruct */
-        {
-          const char* ts_after_sub;
-          size_t i, struct_count = ctx->new_count;
-          size_t struct_alignment = ctx->struct_alignment;
-          ctx->new_count = 1;
-          ++ts;
-          if (*ts != '{') {
-            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
-            return NULL;
-          }
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0; /* Erase processed last struct element */
-          ctx->enc_count = 0;
-          ctx->struct_alignment = 0;
-          ++ts;
-          ts_after_sub = ts;
-          for (i = 0; i != struct_count; ++i) {
-            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
-            if (!ts_after_sub) return NULL;
-          }
-          ts = ts_after_sub;
-          if (struct_alignment) ctx->struct_alignment = struct_alignment;
-        }
-        break;
-      case '}': /* end of substruct; either repeat or move on */
-        {
-          size_t alignment = ctx->struct_alignment;
-          ++ts;
-          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-          ctx->enc_type = 0; /* Erase processed last struct element */
-          if (alignment && ctx->fmt_offset % alignment) {
-            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
-          }
-        }
-        return ts;
-      case 'x':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->fmt_offset += ctx->new_count;
-        ctx->new_count = 1;
-        ctx->enc_count = 0;
-        ctx->enc_type = 0;
-        ctx->enc_packmode = ctx->new_packmode;
-        ++ts;
-        break;
-      case 'Z':
-        got_Z = 1;
-        ++ts;
-        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
-          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
-          return NULL;
-        }
-      case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
-      case 'l': case 'L': case 'q': case 'Q':
-      case 'f': case 'd': case 'g':
-      case 'O': case 'p':
-        if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&
-            ctx->enc_packmode == ctx->new_packmode) {
-          ctx->enc_count += ctx->new_count;
-          ctx->new_count = 1;
-          got_Z = 0;
-          ++ts;
-          break;
-        }
-      case 's':
-        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
-        ctx->enc_count = ctx->new_count;
-        ctx->enc_packmode = ctx->new_packmode;
-        ctx->enc_type = *ts;
-        ctx->is_complex = got_Z;
-        ++ts;
-        ctx->new_count = 1;
-        got_Z = 0;
-        break;
-      case ':':
-        ++ts;
-        while(*ts != ':') ++ts;
-        ++ts;
-        break;
-      case '(':
-        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
-        break;
-      default:
-        {
-          int number = __Pyx_BufFmt_ExpectNumber(&ts);
-          if (number == -1) return NULL;
-          ctx->new_count = (size_t)number;
-        }
-    }
-  }
-}
-static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) {
-  buf->buf = NULL;
-  buf->obj = NULL;
-  buf->strides = __Pyx_zeros;
-  buf->shape = __Pyx_zeros;
-  buf->suboffsets = __Pyx_minusones;
-}
-static CYTHON_INLINE int __Pyx_GetBufferAndValidate(
-        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
-        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
-{
-  if (obj == Py_None || obj == NULL) {
-    __Pyx_ZeroBuffer(buf);
-    return 0;
-  }
-  buf->buf = NULL;
-  if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail;
-  if (buf->ndim != nd) {
-    PyErr_Format(PyExc_ValueError,
-                 "Buffer has wrong number of dimensions (expected %d, got %d)",
-                 nd, buf->ndim);
-    goto fail;
-  }
-  if (!cast) {
-    __Pyx_BufFmt_Context ctx;
-    __Pyx_BufFmt_Init(&ctx, stack, dtype);
-    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
-  }
-  if ((unsigned)buf->itemsize != dtype->size) {
-    PyErr_Format(PyExc_ValueError,
-      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
-      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
-      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
-    goto fail;
-  }
-  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
-  return 0;
-fail:;
-  __Pyx_ZeroBuffer(buf);
-  return -1;
-}
-static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
-  if (info->buf == NULL) return;
-  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
-  __Pyx_ReleaseBuffer(info);
-}
-
-static int
-__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview,
-                        int ndim,
-                        __Pyx_memviewslice *memviewslice,
-                        int memview_is_new_reference)
-{
-    __Pyx_RefNannyDeclarations
-    int i, retval=-1;
-    Py_buffer *buf = &memview->view;
-    __Pyx_RefNannySetupContext("init_memviewslice", 0);
-    if (!buf) {
-        PyErr_SetString(PyExc_ValueError,
-            "buf is NULL.");
-        goto fail;
-    } else if (memviewslice->memview || memviewslice->data) {
-        PyErr_SetString(PyExc_ValueError,
-            "memviewslice is already initialized!");
-        goto fail;
-    }
-    if (buf->strides) {
-        for (i = 0; i < ndim; i++) {
-            memviewslice->strides[i] = buf->strides[i];
-        }
-    } else {
-        Py_ssize_t stride = buf->itemsize;
-        for (i = ndim - 1; i >= 0; i--) {
-            memviewslice->strides[i] = stride;
-            stride *= buf->shape[i];
-        }
-    }
-    for (i = 0; i < ndim; i++) {
-        memviewslice->shape[i]   = buf->shape[i];
-        if (buf->suboffsets) {
-            memviewslice->suboffsets[i] = buf->suboffsets[i];
-        } else {
-            memviewslice->suboffsets[i] = -1;
-        }
-    }
-    memviewslice->memview = memview;
-    memviewslice->data = (char *)buf->buf;
-    if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) {
-        Py_INCREF(memview);
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    memviewslice->memview = 0;
-    memviewslice->data = 0;
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-static CYTHON_INLINE void __pyx_fatalerror(const char *fmt, ...) {
-    va_list vargs;
-    char msg[200];
-    va_start(vargs, fmt);
-#ifdef HAVE_STDARG_PROTOTYPES
-    va_start(vargs, fmt);
-#else
-    va_start(vargs);
-#endif
-    vsnprintf(msg, 200, fmt, vargs);
-    Py_FatalError(msg);
-    va_end(vargs);
-}
-static CYTHON_INLINE int
-__pyx_add_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)++;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE int
-__pyx_sub_acquisition_count_locked(__pyx_atomic_int *acquisition_count,
-                                   PyThread_type_lock lock)
-{
-    int result;
-    PyThread_acquire_lock(lock, 1);
-    result = (*acquisition_count)--;
-    PyThread_release_lock(lock);
-    return result;
-}
-static CYTHON_INLINE void
-__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno)
-{
-    int first_time;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (!memview || (PyObject *) memview == Py_None)
-        return; /* allow uninitialized memoryview assignment */
-    if (__pyx_get_slice_count(memview) < 0)
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         __pyx_get_slice_count(memview), lineno);
-    first_time = __pyx_add_acquisition_count(memview) == 0;
-    if (first_time) {
-        if (have_gil) {
-            Py_INCREF((PyObject *) memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_INCREF((PyObject *) memview);
-            PyGILState_Release(_gilstate);
-        }
-    }
-}
-static CYTHON_INLINE void __Pyx_XDEC_MEMVIEW(__Pyx_memviewslice *memslice,
-                                             int have_gil, int lineno) {
-    int last_time;
-    struct __pyx_memoryview_obj *memview = memslice->memview;
-    if (!memview ) {
-        return;
-    } else if ((PyObject *) memview == Py_None) {
-        memslice->memview = NULL;
-        return;
-    }
-    if (__pyx_get_slice_count(memview) <= 0)
-        __pyx_fatalerror("Acquisition count is %d (line %d)",
-                         __pyx_get_slice_count(memview), lineno);
-    last_time = __pyx_sub_acquisition_count(memview) == 1;
-    memslice->data = NULL;
-    if (last_time) {
-        if (have_gil) {
-            Py_CLEAR(memslice->memview);
-        } else {
-            PyGILState_STATE _gilstate = PyGILState_Ensure();
-            Py_CLEAR(memslice->memview);
-            PyGILState_Release(_gilstate);
-        }
-    } else {
-        memslice->memview = NULL;
-    }
-}
-
-static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyThreadState *tstate = PyThreadState_GET();
-    tmp_type = tstate->curexc_type;
-    tmp_value = tstate->curexc_value;
-    tmp_tb = tstate->curexc_traceback;
-    tstate->curexc_type = type;
-    tstate->curexc_value = value;
-    tstate->curexc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_Restore(type, value, tb);
-#endif
-}
-static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyThreadState *tstate = PyThreadState_GET();
-    *type = tstate->curexc_type;
-    *value = tstate->curexc_value;
-    *tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-#else
-    PyErr_Fetch(type, value, tb);
-#endif
-}
-
-static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) {
-    PyErr_Format(PyExc_TypeError,
-        "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)",
-        name, type->tp_name, Py_TYPE(obj)->tp_name);
-}
-static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
-    const char *name, int exact)
-{
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (none_allowed && obj == Py_None) return 1;
-    else if (exact) {
-        if (likely(Py_TYPE(obj) == type)) return 1;
-        #if PY_MAJOR_VERSION == 2
-        else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;
-        #endif
-    }
-    else {
-        if (likely(PyObject_TypeCheck(obj, type))) return 1;
-    }
-    __Pyx_RaiseArgumentTypeInvalid(name, obj, type);
-    return 0;
-}
-
-#if CYTHON_COMPILING_IN_CPYTHON
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
-    PyObject *result;
-    ternaryfunc call = func->ob_type->tp_call;
-    if (unlikely(!call))
-        return PyObject_Call(func, arg, kw);
-#if PY_VERSION_HEX >= 0x02060000
-    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
-        return NULL;
-#endif
-    result = (*call)(func, arg, kw);
-#if PY_VERSION_HEX >= 0x02060000
-    Py_LeaveRecursiveCall();
-#endif
-    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
-        PyErr_SetString(
-            PyExc_SystemError,
-            "NULL result without error in PyObject_Call");
-    }
-    return result;
-}
-#endif
-
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
-                        CYTHON_UNUSED PyObject *cause) {
-    Py_XINCREF(type);
-    if (!value || value == Py_None)
-        value = NULL;
-    else
-        Py_INCREF(value);
-    if (!tb || tb == Py_None)
-        tb = NULL;
-    else {
-        Py_INCREF(tb);
-        if (!PyTraceBack_Check(tb)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: arg 3 must be a traceback or None");
-            goto raise_error;
-        }
-    }
-    #if PY_VERSION_HEX < 0x02050000
-    if (PyClass_Check(type)) {
-    #else
-    if (PyType_Check(type)) {
-    #endif
-#if CYTHON_COMPILING_IN_PYPY
-        if (!value) {
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-#endif
-        PyErr_NormalizeException(&type, &value, &tb);
-    } else {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        value = type;
-        #if PY_VERSION_HEX < 0x02050000
-        if (PyInstance_Check(type)) {
-            type = (PyObject*) ((PyInstanceObject*)type)->in_class;
-            Py_INCREF(type);
-        } else {
-            type = 0;
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception must be an old-style class or instance");
-            goto raise_error;
-        }
-        #else
-        type = (PyObject*) Py_TYPE(type);
-        Py_INCREF(type);
-        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
-            PyErr_SetString(PyExc_TypeError,
-                "raise: exception class must be a subclass of BaseException");
-            goto raise_error;
-        }
-        #endif
-    }
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-#else /* Python 3+ */
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    PyObject* owned_instance = NULL;
-    if (tb == Py_None) {
-        tb = 0;
-    } else if (tb && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto bad;
-    }
-    if (value == Py_None)
-        value = 0;
-    if (PyExceptionInstance_Check(type)) {
-        if (value) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto bad;
-        }
-        value = type;
-        type = (PyObject*) Py_TYPE(value);
-    } else if (PyExceptionClass_Check(type)) {
-        PyObject *instance_class = NULL;
-        if (value && PyExceptionInstance_Check(value)) {
-            instance_class = (PyObject*) Py_TYPE(value);
-            if (instance_class != type) {
-                if (PyObject_IsSubclass(instance_class, type)) {
-                    type = instance_class;
-                } else {
-                    instance_class = NULL;
-                }
-            }
-        }
-        if (!instance_class) {
-            PyObject *args;
-            if (!value)
-                args = PyTuple_New(0);
-            else if (PyTuple_Check(value)) {
-                Py_INCREF(value);
-                args = value;
-            } else
-                args = PyTuple_Pack(1, value);
-            if (!args)
-                goto bad;
-            owned_instance = PyObject_Call(type, args, NULL);
-            Py_DECREF(args);
-            if (!owned_instance)
-                goto bad;
-            value = owned_instance;
-            if (!PyExceptionInstance_Check(value)) {
-                PyErr_Format(PyExc_TypeError,
-                             "calling %R should have returned an instance of "
-                             "BaseException, not %R",
-                             type, Py_TYPE(value));
-                goto bad;
-            }
-        }
-    } else {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-#if PY_VERSION_HEX >= 0x03030000
-    if (cause) {
-#else
-    if (cause && cause != Py_None) {
-#endif
-        PyObject *fixed_cause;
-        if (cause == Py_None) {
-            fixed_cause = NULL;
-        } else if (PyExceptionClass_Check(cause)) {
-            fixed_cause = PyObject_CallObject(cause, NULL);
-            if (fixed_cause == NULL)
-                goto bad;
-        } else if (PyExceptionInstance_Check(cause)) {
-            fixed_cause = cause;
-            Py_INCREF(fixed_cause);
-        } else {
-            PyErr_SetString(PyExc_TypeError,
-                            "exception causes must derive from "
-                            "BaseException");
-            goto bad;
-        }
-        PyException_SetCause(value, fixed_cause);
-    }
-    PyErr_SetObject(type, value);
-    if (tb) {
-        PyThreadState *tstate = PyThreadState_GET();
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-    }
-bad:
-    Py_XDECREF(owned_instance);
-    return;
-}
-#endif
-
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-    PyErr_Format(PyExc_ValueError,
-                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
-}
-
-static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
-    PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
-                 index, (index == 1) ? "" : "s");
-}
-
-static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
-    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
-}
-
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    if (unlikely(!type)) {
-        PyErr_SetString(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(PyObject_TypeCheck(obj, type)))
-        return 1;
-    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
-                 Py_TYPE(obj)->tp_name, type->tp_name);
-    return 0;
-}
-
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *more_or_less;
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
-    }
-    PyErr_Format(PyExc_TypeError,
-                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
-                 func_name, more_or_less, num_expected,
-                 (num_expected == 1) ? "" : "s", num_found);
-}
-
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name,
-    PyObject* kw_name)
-{
-    PyErr_Format(PyExc_TypeError,
-        #if PY_MAJOR_VERSION >= 3
-        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
-        #else
-        "%s() got multiple values for keyword argument '%s'", func_name,
-        PyString_AsString(kw_name));
-        #endif
-}
-
-static int __Pyx_ParseOptionalKeywords(
-    PyObject *kwds,
-    PyObject **argnames[],
-    PyObject *kwds2,
-    PyObject *values[],
-    Py_ssize_t num_pos_args,
-    const char* function_name)
-{
-    PyObject *key = 0, *value = 0;
-    Py_ssize_t pos = 0;
-    PyObject*** name;
-    PyObject*** first_kw_arg = argnames + num_pos_args;
-    while (PyDict_Next(kwds, &pos, &key, &value)) {
-        name = first_kw_arg;
-        while (*name && (**name != key)) name++;
-        if (*name) {
-            values[name-argnames] = value;
-            continue;
-        }
-        name = first_kw_arg;
-        #if PY_MAJOR_VERSION < 3
-        if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {
-            while (*name) {
-                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
-                        && _PyString_Eq(**name, key)) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    if ((**argname == key) || (
-                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
-                             && _PyString_Eq(**argname, key))) {
-                        goto arg_passed_twice;
-                    }
-                    argname++;
-                }
-            }
-        } else
-        #endif
-        if (likely(PyUnicode_Check(key))) {
-            while (*name) {
-                int cmp = (**name == key) ? 0 :
-                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
-                #endif
-                    PyUnicode_Compare(**name, key);
-                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                if (cmp == 0) {
-                    values[name-argnames] = value;
-                    break;
-                }
-                name++;
-            }
-            if (*name) continue;
-            else {
-                PyObject*** argname = argnames;
-                while (argname != first_kw_arg) {
-                    int cmp = (**argname == key) ? 0 :
-                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
-                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
-                    #endif
-                        PyUnicode_Compare(**argname, key);
-                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
-                    if (cmp == 0) goto arg_passed_twice;
-                    argname++;
-                }
-            }
-        } else
-            goto invalid_keyword_type;
-        if (kwds2) {
-            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-        } else {
-            goto invalid_keyword;
-        }
-    }
-    return 0;
-arg_passed_twice:
-    __Pyx_RaiseDoubleKeywordsError(function_name, key);
-    goto bad;
-invalid_keyword_type:
-    PyErr_Format(PyExc_TypeError,
-        "%.200s() keywords must be strings", function_name);
-    goto bad;
-invalid_keyword:
-    PyErr_Format(PyExc_TypeError,
-    #if PY_MAJOR_VERSION < 3
-        "%.200s() got an unexpected keyword argument '%.200s'",
-        function_name, PyString_AsString(key));
-    #else
-        "%s() got an unexpected keyword argument '%U'",
-        function_name, key);
-    #endif
-bad:
-    return -1;
-}
-
-static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-    if (s1 == s2) {
-        return (equals == Py_EQ);
-    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-        const char *ps1, *ps2;
-        Py_ssize_t length = PyBytes_GET_SIZE(s1);
-        if (length != PyBytes_GET_SIZE(s2))
-            return (equals == Py_NE);
-        ps1 = PyBytes_AS_STRING(s1);
-        ps2 = PyBytes_AS_STRING(s2);
-        if (ps1[0] != ps2[0]) {
-            return (equals == Py_NE);
-        } else if (length == 1) {
-            return (equals == Py_EQ);
-        } else {
-            int result = memcmp(ps1, ps2, (size_t)length);
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-        return (equals == Py_NE);
-    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-        return (equals == Py_NE);
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-#endif
-}
-
-static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-#if CYTHON_COMPILING_IN_PYPY
-    return PyObject_RichCompareBool(s1, s2, equals);
-#else
-#if PY_MAJOR_VERSION < 3
-    PyObject* owned_ref = NULL;
-#endif
-    int s1_is_unicode, s2_is_unicode;
-    if (s1 == s2) {
-        goto return_eq;
-    }
-    s1_is_unicode = PyUnicode_CheckExact(s1);
-    s2_is_unicode = PyUnicode_CheckExact(s2);
-#if PY_MAJOR_VERSION < 3
-    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
-        owned_ref = PyUnicode_FromObject(s2);
-        if (unlikely(!owned_ref))
-            return -1;
-        s2 = owned_ref;
-        s2_is_unicode = 1;
-    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
-        owned_ref = PyUnicode_FromObject(s1);
-        if (unlikely(!owned_ref))
-            return -1;
-        s1 = owned_ref;
-        s1_is_unicode = 1;
-    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
-        return __Pyx_PyBytes_Equals(s1, s2, equals);
-    }
-#endif
-    if (s1_is_unicode & s2_is_unicode) {
-        Py_ssize_t length;
-        int kind;
-        void *data1, *data2;
-        #if CYTHON_PEP393_ENABLED
-        if (unlikely(PyUnicode_READY(s1) < 0) || unlikely(PyUnicode_READY(s2) < 0))
-            return -1;
-        #endif
-        length = __Pyx_PyUnicode_GET_LENGTH(s1);
-        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
-            goto return_ne;
-        }
-        kind = __Pyx_PyUnicode_KIND(s1);
-        if (kind != __Pyx_PyUnicode_KIND(s2)) {
-            goto return_ne;
-        }
-        data1 = __Pyx_PyUnicode_DATA(s1);
-        data2 = __Pyx_PyUnicode_DATA(s2);
-        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
-            goto return_ne;
-        } else if (length == 1) {
-            goto return_eq;
-        } else {
-            int result = memcmp(data1, data2, (size_t)(length * kind));
-            #if PY_MAJOR_VERSION < 3
-            Py_XDECREF(owned_ref);
-            #endif
-            return (equals == Py_EQ) ? (result == 0) : (result != 0);
-        }
-    } else if ((s1 == Py_None) & s2_is_unicode) {
-        goto return_ne;
-    } else if ((s2 == Py_None) & s1_is_unicode) {
-        goto return_ne;
-    } else {
-        int result;
-        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-        if (!py_result)
-            return -1;
-        result = __Pyx_PyObject_IsTrue(py_result);
-        Py_DECREF(py_result);
-        return result;
-    }
-return_eq:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_EQ);
-return_ne:
-    #if PY_MAJOR_VERSION < 3
-    Py_XDECREF(owned_ref);
-    #endif
-    return (equals == Py_NE);
-#endif
-}
-
-static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) {
-    Py_ssize_t q = a / b;
-    Py_ssize_t r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) {
-#if CYTHON_COMPILING_IN_CPYTHON
-#if PY_MAJOR_VERSION >= 3
-    if (likely(PyUnicode_Check(n)))
-#else
-    if (likely(PyString_Check(n)))
-#endif
-        return __Pyx_PyObject_GetAttrStr(o, n);
-#endif
-    return PyObject_GetAttr(o, n);
-}
-
-static CYTHON_INLINE PyObject* __Pyx_decode_c_string(
-         const char* cstring, Py_ssize_t start, Py_ssize_t stop,
-         const char* encoding, const char* errors,
-         PyObject* (*decode_func)(const char *s, Py_ssize_t size, const char *errors)) {
-    Py_ssize_t length;
-    if (unlikely((start < 0) | (stop < 0))) {
-        length = strlen(cstring);
-        if (start < 0) {
-            start += length;
-            if (start < 0)
-                start = 0;
-        }
-        if (stop < 0)
-            stop += length;
-    }
-    length = stop - start;
-    if (unlikely(length <= 0))
-        return PyUnicode_FromUnicode(NULL, 0);
-    cstring += start;
-    if (decode_func) {
-        return decode_func(cstring, length, errors);
-    } else {
-        return PyUnicode_Decode(cstring, length, encoding, errors);
-    }
-}
-
-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyThreadState *tstate = PyThreadState_GET();
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-#else
-    PyErr_GetExcInfo(type, value, tb);
-#endif
-}
-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyThreadState *tstate = PyThreadState_GET();
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(type, value, tb);
-#endif
-}
-
-static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *local_type, *local_value, *local_tb;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyThreadState *tstate = PyThreadState_GET();
-    local_type = tstate->curexc_type;
-    local_value = tstate->curexc_value;
-    local_tb = tstate->curexc_traceback;
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-#else
-    PyErr_Fetch(&local_type, &local_value, &local_tb);
-#endif
-    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (unlikely(tstate->curexc_type))
-#else
-    if (unlikely(PyErr_Occurred()))
-#endif
-        goto bad;
-    #if PY_MAJOR_VERSION >= 3
-    if (local_tb) {
-        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-            goto bad;
-    }
-    #endif
-    Py_XINCREF(local_tb);
-    Py_XINCREF(local_type);
-    Py_XINCREF(local_value);
-    *type = local_type;
-    *value = local_value;
-    *tb = local_tb;
-#if CYTHON_COMPILING_IN_CPYTHON
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = local_type;
-    tstate->exc_value = local_value;
-    tstate->exc_traceback = local_tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(local_type, local_value, local_tb);
-#endif
-    return 0;
-bad:
-    *type = 0;
-    *value = 0;
-    *tb = 0;
-    Py_XDECREF(local_type);
-    Py_XDECREF(local_value);
-    Py_XDECREF(local_tb);
-    return -1;
-}
-
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyThreadState *tstate = PyThreadState_GET();
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-#else
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-#endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (!j) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
-    if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
-    if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-        if (likely(m && m->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
-                Py_ssize_t l = m->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (PyErr_ExceptionMatches(PyExc_OverflowError))
-                        PyErr_Clear();
-                    else
-                        return NULL;
-                }
-            }
-            return m->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
-static CYTHON_INLINE long __Pyx_div_long(long a, long b) {
-    long q = a / b;
-    long r = a - q*b;
-    q -= ((r != 0) & ((r ^ b) < 0));
-    return q;
-}
-
-static void __Pyx_WriteUnraisable(const char *name, CYTHON_UNUSED int clineno,
-                                  CYTHON_UNUSED int lineno, CYTHON_UNUSED const char *filename,
-                                  int full_traceback) {
-    PyObject *old_exc, *old_val, *old_tb;
-    PyObject *ctx;
-    __Pyx_ErrFetch(&old_exc, &old_val, &old_tb);
-    if (full_traceback) {
-        Py_XINCREF(old_exc);
-        Py_XINCREF(old_val);
-        Py_XINCREF(old_tb);
-        __Pyx_ErrRestore(old_exc, old_val, old_tb);
-        PyErr_PrintEx(1);
-    }
-    #if PY_MAJOR_VERSION < 3
-    ctx = PyString_FromString(name);
-    #else
-    ctx = PyUnicode_FromString(name);
-    #endif
-    __Pyx_ErrRestore(old_exc, old_val, old_tb);
-    if (!ctx) {
-        PyErr_WriteUnraisable(Py_None);
-    } else {
-        PyErr_WriteUnraisable(ctx);
-        Py_DECREF(ctx);
-    }
-}
-
-static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
-#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION==3&&PY_MINOR_VERSION==0)
-    PyObject *ob = PyCapsule_New(vtable, 0, 0);
-#else
-    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
-#endif
-    if (!ob)
-        goto bad;
-    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
-        goto bad;
-    Py_DECREF(ob);
-    return 0;
-bad:
-    Py_XDECREF(ob);
-    return -1;
-}
-
-#if PY_MAJOR_VERSION < 3
-static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
-  #if PY_VERSION_HEX >= 0x02060000
-    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
-  #endif
-        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
-        if (PyObject_TypeCheck(obj, __pyx_array_type)) return __pyx_array_getbuffer(obj, view, flags);
-        if (PyObject_TypeCheck(obj, __pyx_memoryview_type)) return __pyx_memoryview_getbuffer(obj, view, flags);
-  #if PY_VERSION_HEX < 0x02060000
-    if (obj->ob_type->tp_dict) {
-        PyObject *getbuffer_cobj = PyObject_GetItem(
-            obj->ob_type->tp_dict, __pyx_n_s_pyx_getbuffer);
-        if (getbuffer_cobj) {
-            getbufferproc func = (getbufferproc) PyCObject_AsVoidPtr(getbuffer_cobj);
-            Py_DECREF(getbuffer_cobj);
-            if (!func)
-                goto fail;
-            return func(obj, view, flags);
-        } else {
-            PyErr_Clear();
-        }
-    }
-  #endif
-    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
-#if PY_VERSION_HEX < 0x02060000
-fail:
-#endif
-    return -1;
-}
-static void __Pyx_ReleaseBuffer(Py_buffer *view) {
-    PyObject *obj = view->obj;
-    if (!obj) return;
-  #if PY_VERSION_HEX >= 0x02060000
-    if (PyObject_CheckBuffer(obj)) {
-        PyBuffer_Release(view);
-        return;
-    }
-  #endif
-        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; }
-  #if PY_VERSION_HEX < 0x02060000
-    if (obj->ob_type->tp_dict) {
-        PyObject *releasebuffer_cobj = PyObject_GetItem(
-            obj->ob_type->tp_dict, __pyx_n_s_pyx_releasebuffer);
-        if (releasebuffer_cobj) {
-            releasebufferproc func = (releasebufferproc) PyCObject_AsVoidPtr(releasebuffer_cobj);
-            Py_DECREF(releasebuffer_cobj);
-            if (!func)
-                goto fail;
-            func(obj, view);
-            return;
-        } else {
-            PyErr_Clear();
-        }
-    }
-  #endif
-    goto nofail;
-#if PY_VERSION_HEX < 0x02060000
-fail:
-#endif
-    PyErr_WriteUnraisable(obj);
-nofail:
-    Py_DECREF(obj);
-    view->obj = NULL;
-}
-#endif /*  PY_MAJOR_VERSION < 3 */
-
-
-      static CYTHON_INLINE PyObject* __Pyx_PyInt_From_unsigned_long(unsigned long value) {
-    const unsigned long neg_one = (unsigned long) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(unsigned long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(unsigned long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-        } else if (sizeof(unsigned long) <= sizeof(unsigned long long)) {
-            return PyLong_FromUnsignedLongLong((unsigned long long) value);
-        }
-    } else {
-        if (sizeof(unsigned long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(unsigned long) <= sizeof(long long)) {
-            return PyLong_FromLongLong((long long) value);
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(unsigned long),
-                                     little, !is_unsigned);
-    }
-}
-
-#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func)             \
-    {                                                                     \
-        func_type value = func(x);                                        \
-        if (sizeof(target_type) < sizeof(func_type)) {                    \
-            if (unlikely(value != (func_type) (target_type) value)) {     \
-                func_type zero = 0;                                       \
-                PyErr_SetString(PyExc_OverflowError,                      \
-                    (is_unsigned && unlikely(value < zero)) ?             \
-                    "can't convert negative value to " #target_type :     \
-                    "value too large to convert to " #target_type);       \
-                return (target_type) -1;                                  \
-            }                                                             \
-        }                                                                 \
-        return (target_type) value;                                       \
-    }
-
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-  #include "longintrepr.h"
- #endif
-#endif
-static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *x) {
-    const unsigned long neg_one = (unsigned long) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(unsigned long) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(unsigned long, long, PyInt_AS_LONG)
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to unsigned long");
-                return (unsigned long) -1;
-            }
-            return (unsigned long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(unsigned long)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return (unsigned long) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (unlikely(Py_SIZE(x) < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to unsigned long");
-                return (unsigned long) -1;
-            }
-            if (sizeof(unsigned long) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, PyLong_AsUnsignedLong)
-            } else if (sizeof(unsigned long) <= sizeof(unsigned long long)) {
-                __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long long, PyLong_AsUnsignedLongLong)
-            }
-        } else {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(unsigned long)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return +(unsigned long) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(unsigned long) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (sizeof(unsigned long) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT(unsigned long, long, PyLong_AsLong)
-            } else if (sizeof(unsigned long) <= sizeof(long long)) {
-                __PYX_VERIFY_RETURN_INT(unsigned long, long long, PyLong_AsLongLong)
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            unsigned long val;
-            PyObject *v = __Pyx_PyNumber_Int(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (unsigned long) -1;
-        }
-    } else {
-        unsigned long val;
-        PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (unsigned long) -1;
-        val = __Pyx_PyInt_As_unsigned_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-}
-
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      return ::std::complex< float >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      return x + y*(__pyx_t_float_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
-      __pyx_t_float_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-#if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-        __pyx_t_float_complex z;
-        float denom = b.real * b.real + b.imag * b.imag;
-        z.real = (a.real * b.real + a.imag * b.imag) / denom;
-        z.imag = (a.imag * b.real - a.real * b.imag) / denom;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) {
-        __pyx_t_float_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) {
-        __pyx_t_float_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrtf(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypotf(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) {
-            __pyx_t_float_complex z;
-            float r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    float denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        z = __Pyx_c_prodf(a, a);
-                        return __Pyx_c_prodf(a, a);
-                    case 3:
-                        z = __Pyx_c_prodf(a, a);
-                        return __Pyx_c_prodf(z, a);
-                    case 4:
-                        z = __Pyx_c_prodf(a, a);
-                        return __Pyx_c_prodf(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                }
-                r = a.real;
-                theta = 0;
-            } else {
-                r = __Pyx_c_absf(a);
-                theta = atan2f(a.imag, a.real);
-            }
-            lnr = logf(r);
-            z_r = expf(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cosf(z_theta);
-            z.imag = z_r * sinf(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-#if CYTHON_CCOMPLEX
-  #ifdef __cplusplus
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return ::std::complex< double >(x, y);
-    }
-  #else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      return x + y*(__pyx_t_double_complex)_Complex_I;
-    }
-  #endif
-#else
-    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
-      __pyx_t_double_complex z;
-      z.real = x;
-      z.imag = y;
-      return z;
-    }
-#endif
-
-#if CYTHON_CCOMPLEX
-#else
-    static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-       return (a.real == b.real) && (a.imag == b.imag);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real + b.real;
-        z.imag = a.imag + b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real - b.real;
-        z.imag = a.imag - b.imag;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        z.real = a.real * b.real - a.imag * b.imag;
-        z.imag = a.real * b.imag + a.imag * b.real;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-        __pyx_t_double_complex z;
-        double denom = b.real * b.real + b.imag * b.imag;
-        z.real = (a.real * b.real + a.imag * b.imag) / denom;
-        z.imag = (a.imag * b.real - a.real * b.imag) / denom;
-        return z;
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real = -a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) {
-       return (a.real == 0) && (a.imag == 0);
-    }
-    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) {
-        __pyx_t_double_complex z;
-        z.real =  a.real;
-        z.imag = -a.imag;
-        return z;
-    }
-    #if 1
-        static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) {
-          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
-            return sqrt(z.real*z.real + z.imag*z.imag);
-          #else
-            return hypot(z.real, z.imag);
-          #endif
-        }
-        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) {
-            __pyx_t_double_complex z;
-            double r, lnr, theta, z_r, z_theta;
-            if (b.imag == 0 && b.real == (int)b.real) {
-                if (b.real < 0) {
-                    double denom = a.real * a.real + a.imag * a.imag;
-                    a.real = a.real / denom;
-                    a.imag = -a.imag / denom;
-                    b.real = -b.real;
-                }
-                switch ((int)b.real) {
-                    case 0:
-                        z.real = 1;
-                        z.imag = 0;
-                        return z;
-                    case 1:
-                        return a;
-                    case 2:
-                        z = __Pyx_c_prod(a, a);
-                        return __Pyx_c_prod(a, a);
-                    case 3:
-                        z = __Pyx_c_prod(a, a);
-                        return __Pyx_c_prod(z, a);
-                    case 4:
-                        z = __Pyx_c_prod(a, a);
-                        return __Pyx_c_prod(z, z);
-                }
-            }
-            if (a.imag == 0) {
-                if (a.real == 0) {
-                    return a;
-                }
-                r = a.real;
-                theta = 0;
-            } else {
-                r = __Pyx_c_abs(a);
-                theta = atan2(a.imag, a.real);
-            }
-            lnr = log(r);
-            z_r = exp(lnr * b.real - theta * b.imag);
-            z_theta = theta * b.real + lnr * b.imag;
-            z.real = z_r * cos(z_theta);
-            z.imag = z_r * sin(z_theta);
-            return z;
-        }
-    #endif
-#endif
-
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
-    const int neg_one = (int) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(int) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-        } else if (sizeof(int) <= sizeof(unsigned long long)) {
-            return PyLong_FromUnsignedLongLong((unsigned long long) value);
-        }
-    } else {
-        if (sizeof(int) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(int) <= sizeof(long long)) {
-            return PyLong_FromLongLong((long long) value);
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(int),
-                                     little, !is_unsigned);
-    }
-}
-
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-  #include "longintrepr.h"
- #endif
-#endif
-static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
-    const int neg_one = (int) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(int) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG)
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to int");
-                return (int) -1;
-            }
-            return (int) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(int)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return (int) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (unlikely(Py_SIZE(x) < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to int");
-                return (int) -1;
-            }
-            if (sizeof(int) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong)
-            } else if (sizeof(int) <= sizeof(unsigned long long)) {
-                __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong)
-            }
-        } else {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(int)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return +(int) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(int) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (sizeof(int) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong)
-            } else if (sizeof(int) <= sizeof(long long)) {
-                __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong)
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            int val;
-            PyObject *v = __Pyx_PyNumber_Int(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (int) -1;
-        }
-    } else {
-        int val;
-        PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (int) -1;
-        val = __Pyx_PyInt_As_int(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-}
-
-static int
-__pyx_memviewslice_is_contig(const __Pyx_memviewslice *mvs,
-                             char order, int ndim)
-{
-    int i, index, step, start;
-    Py_ssize_t itemsize = mvs->memview->view.itemsize;
-    if (order == 'F') {
-        step = 1;
-        start = 0;
-    } else {
-        step = -1;
-        start = ndim - 1;
-    }
-    for (i = 0; i < ndim; i++) {
-        index = start + step * i;
-        if (mvs->suboffsets[index] >= 0 || mvs->strides[index] != itemsize)
-            return 0;
-        itemsize *= mvs->shape[index];
-    }
-    return 1;
-}
-
-static void
-__pyx_get_array_memory_extents(__Pyx_memviewslice *slice,
-                               void **out_start, void **out_end,
-                               int ndim, size_t itemsize)
-{
-    char *start, *end;
-    int i;
-    start = end = slice->data;
-    for (i = 0; i < ndim; i++) {
-        Py_ssize_t stride = slice->strides[i];
-        Py_ssize_t extent = slice->shape[i];
-        if (extent == 0) {
-            *out_start = *out_end = start;
-            return;
-        } else {
-            if (stride > 0)
-                end += stride * (extent - 1);
-            else
-                start += stride * (extent - 1);
-        }
-    }
-    *out_start = start;
-    *out_end = end + itemsize;
-}
-static int
-__pyx_slices_overlap(__Pyx_memviewslice *slice1,
-                     __Pyx_memviewslice *slice2,
-                     int ndim, size_t itemsize)
-{
-    void *start1, *end1, *start2, *end2;
-    __pyx_get_array_memory_extents(slice1, &start1, &end1, ndim, itemsize);
-    __pyx_get_array_memory_extents(slice2, &start2, &end2, ndim, itemsize);
-    return (start1 < end2) && (start2 < end1);
-}
-
-static __Pyx_memviewslice
-__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs,
-                                 const char *mode, int ndim,
-                                 size_t sizeof_dtype, int contig_flag,
-                                 int dtype_is_object)
-{
-    __Pyx_RefNannyDeclarations
-    int i;
-    __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } };
-    struct __pyx_memoryview_obj *from_memview = from_mvs->memview;
-    Py_buffer *buf = &from_memview->view;
-    PyObject *shape_tuple = NULL;
-    PyObject *temp_int = NULL;
-    struct __pyx_array_obj *array_obj = NULL;
-    struct __pyx_memoryview_obj *memview_obj = NULL;
-    __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0);
-    for (i = 0; i < ndim; i++) {
-        if (from_mvs->suboffsets[i] >= 0) {
-            PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with "
-                                           "indirect dimensions (axis %d)", i);
-            goto fail;
-        }
-    }
-    shape_tuple = PyTuple_New(ndim);
-    if (unlikely(!shape_tuple)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(shape_tuple);
-    for(i = 0; i < ndim; i++) {
-        temp_int = PyInt_FromSsize_t(from_mvs->shape[i]);
-        if(unlikely(!temp_int)) {
-            goto fail;
-        } else {
-            PyTuple_SET_ITEM(shape_tuple, i, temp_int);
-            temp_int = NULL;
-        }
-    }
-    array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL);
-    if (unlikely(!array_obj)) {
-        goto fail;
-    }
-    __Pyx_GOTREF(array_obj);
-    memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                    (PyObject *) array_obj, contig_flag,
-                                    dtype_is_object,
-                                    from_mvs->memview->typeinfo);
-    if (unlikely(!memview_obj))
-        goto fail;
-    if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0))
-        goto fail;
-    if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim,
-                                                dtype_is_object) < 0))
-        goto fail;
-    goto no_fail;
-fail:
-    __Pyx_XDECREF(new_mvs.memview);
-    new_mvs.memview = NULL;
-    new_mvs.data = NULL;
-no_fail:
-    __Pyx_XDECREF(shape_tuple);
-    __Pyx_XDECREF(temp_int);
-    __Pyx_XDECREF(array_obj);
-    __Pyx_RefNannyFinishContext();
-    return new_mvs;
-}
-
-static CYTHON_INLINE PyObject *
-__pyx_capsule_create(void *p, CYTHON_UNUSED const char *sig)
-{
-    PyObject *cobj;
-#if PY_VERSION_HEX >= 0x02070000 && !(PY_MAJOR_VERSION == 3 && PY_MINOR_VERSION == 0)
-    cobj = PyCapsule_New(p, sig, NULL);
-#else
-    cobj = PyCObject_FromVoidPtr(p, NULL);
-#endif
-    return cobj;
-}
-
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
-    PyObject *empty_list = 0;
-    PyObject *module = 0;
-    PyObject *global_dict = 0;
-    PyObject *empty_dict = 0;
-    PyObject *list;
-    #if PY_VERSION_HEX < 0x03030000
-    PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
-    if (!py_import)
-        goto bad;
-    #endif
-    if (from_list)
-        list = from_list;
-    else {
-        empty_list = PyList_New(0);
-        if (!empty_list)
-            goto bad;
-        list = empty_list;
-    }
-    global_dict = PyModule_GetDict(__pyx_m);
-    if (!global_dict)
-        goto bad;
-    empty_dict = PyDict_New();
-    if (!empty_dict)
-        goto bad;
-    #if PY_VERSION_HEX >= 0x02050000
-    {
-        #if PY_MAJOR_VERSION >= 3
-        if (level == -1) {
-            if (strchr(__Pyx_MODULE_NAME, '.')) {
-                #if PY_VERSION_HEX < 0x03030000
-                PyObject *py_level = PyInt_FromLong(1);
-                if (!py_level)
-                    goto bad;
-                module = PyObject_CallFunctionObjArgs(py_import,
-                    name, global_dict, empty_dict, list, py_level, NULL);
-                Py_DECREF(py_level);
-                #else
-                module = PyImport_ImportModuleLevelObject(
-                    name, global_dict, empty_dict, list, 1);
-                #endif
-                if (!module) {
-                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
-                        goto bad;
-                    PyErr_Clear();
-                }
-            }
-            level = 0; /* try absolute import on failure */
-        }
-        #endif
-        if (!module) {
-            #if PY_VERSION_HEX < 0x03030000
-            PyObject *py_level = PyInt_FromLong(level);
-            if (!py_level)
-                goto bad;
-            module = PyObject_CallFunctionObjArgs(py_import,
-                name, global_dict, empty_dict, list, py_level, NULL);
-            Py_DECREF(py_level);
-            #else
-            module = PyImport_ImportModuleLevelObject(
-                name, global_dict, empty_dict, list, level);
-            #endif
-        }
-    }
-    #else
-    if (level>0) {
-        PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4.");
-        goto bad;
-    }
-    module = PyObject_CallFunctionObjArgs(py_import,
-        name, global_dict, empty_dict, list, NULL);
-    #endif
-bad:
-    #if PY_VERSION_HEX < 0x03030000
-    Py_XDECREF(py_import);
-    #endif
-    Py_XDECREF(empty_list);
-    Py_XDECREF(empty_dict);
-    return module;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
-    const long neg_one = (long) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (is_unsigned) {
-        if (sizeof(long) < sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long)) {
-            return PyLong_FromUnsignedLong((unsigned long) value);
-        } else if (sizeof(long) <= sizeof(unsigned long long)) {
-            return PyLong_FromUnsignedLongLong((unsigned long long) value);
-        }
-    } else {
-        if (sizeof(long) <= sizeof(long)) {
-            return PyInt_FromLong((long) value);
-        } else if (sizeof(long) <= sizeof(long long)) {
-            return PyLong_FromLongLong((long long) value);
-        }
-    }
-    {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&value;
-        return _PyLong_FromByteArray(bytes, sizeof(long),
-                                     little, !is_unsigned);
-    }
-}
-
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-  #include "longintrepr.h"
- #endif
-#endif
-static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) {
-    const char neg_one = (char) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(char) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG)
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to char");
-                return (char) -1;
-            }
-            return (char) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(char)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return (char) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (unlikely(Py_SIZE(x) < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to char");
-                return (char) -1;
-            }
-            if (sizeof(char) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT(char, unsigned long, PyLong_AsUnsignedLong)
-            } else if (sizeof(char) <= sizeof(unsigned long long)) {
-                __PYX_VERIFY_RETURN_INT(char, unsigned long long, PyLong_AsUnsignedLongLong)
-            }
-        } else {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(char)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return +(char) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(char) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (sizeof(char) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT(char, long, PyLong_AsLong)
-            } else if (sizeof(char) <= sizeof(long long)) {
-                __PYX_VERIFY_RETURN_INT(char, long long, PyLong_AsLongLong)
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            char val;
-            PyObject *v = __Pyx_PyNumber_Int(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (char) -1;
-        }
-    } else {
-        char val;
-        PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (char) -1;
-        val = __Pyx_PyInt_As_char(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-}
-
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-  #include "longintrepr.h"
- #endif
-#endif
-static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
-    const long neg_one = (long) -1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        if (sizeof(long) < sizeof(long)) {
-            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG)
-        } else {
-            long val = PyInt_AS_LONG(x);
-            if (is_unsigned && unlikely(val < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to long");
-                return (long) -1;
-            }
-            return (long) val;
-        }
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(long)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return (long) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (unlikely(Py_SIZE(x) < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to long");
-                return (long) -1;
-            }
-            if (sizeof(long) <= sizeof(unsigned long)) {
-                __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong)
-            } else if (sizeof(long) <= sizeof(unsigned long long)) {
-                __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong)
-            }
-        } else {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(long)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return +(long) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(long) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
- #endif
-#endif
-            if (sizeof(long) <= sizeof(long)) {
-                __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong)
-            } else if (sizeof(long) <= sizeof(long long)) {
-                __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong)
-            }
-        }
-        {
-#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-            PyErr_SetString(PyExc_RuntimeError,
-                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-#else
-            long val;
-            PyObject *v = __Pyx_PyNumber_Int(x);
- #if PY_MAJOR_VERSION < 3
-            if (likely(v) && !PyLong_Check(v)) {
-                PyObject *tmp = v;
-                v = PyNumber_Long(tmp);
-                Py_DECREF(tmp);
-            }
- #endif
-            if (likely(v)) {
-                int one = 1; int is_little = (int)*(unsigned char *)&one;
-                unsigned char *bytes = (unsigned char *)&val;
-                int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                              bytes, sizeof(val),
-                                              is_little, !is_unsigned);
-                Py_DECREF(v);
-                if (likely(!ret))
-                    return val;
-            }
-#endif
-            return (long) -1;
-        }
-    } else {
-        long val;
-        PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (long) -1;
-        val = __Pyx_PyInt_As_long(tmp);
-        Py_DECREF(tmp);
-        return val;
-    }
-}
-
-static int
-__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b)
-{
-    int i;
-    if (!a || !b)
-        return 0;
-    if (a == b)
-        return 1;
-    if (a->size != b->size || a->typegroup != b->typegroup ||
-            a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) {
-        if (a->typegroup == 'H' || b->typegroup == 'H') {
-            return a->size == b->size;
-        } else {
-            return 0;
-        }
-    }
-    if (a->ndim) {
-        for (i = 0; i < a->ndim; i++)
-            if (a->arraysize[i] != b->arraysize[i])
-                return 0;
-    }
-    if (a->typegroup == 'S') {
-        if (a->flags != b->flags)
-            return 0;
-        if (a->fields || b->fields) {
-            if (!(a->fields && b->fields))
-                return 0;
-            for (i = 0; a->fields[i].type && b->fields[i].type; i++) {
-                __Pyx_StructField *field_a = a->fields + i;
-                __Pyx_StructField *field_b = b->fields + i;
-                if (field_a->offset != field_b->offset ||
-                    !__pyx_typeinfo_cmp(field_a->type, field_b->type))
-                    return 0;
-            }
-            return !a->fields[i].type && !b->fields[i].type;
-        }
-    }
-    return 1;
-}
-
-static int
-__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec)
-{
-    if (buf->shape[dim] <= 1)
-        return 1;
-    if (buf->strides) {
-        if (spec & __Pyx_MEMVIEW_CONTIG) {
-            if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) {
-                if (buf->strides[dim] != sizeof(void *)) {
-                    PyErr_Format(PyExc_ValueError,
-                                 "Buffer is not indirectly contiguous "
-                                 "in dimension %d.", dim);
-                    goto fail;
-                }
-            } else if (buf->strides[dim] != buf->itemsize) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-        if (spec & __Pyx_MEMVIEW_FOLLOW) {
-            Py_ssize_t stride = buf->strides[dim];
-            if (stride < 0)
-                stride = -stride;
-            if (stride < buf->itemsize) {
-                PyErr_SetString(PyExc_ValueError,
-                                "Buffer and memoryview are not contiguous "
-                                "in the same dimension.");
-                goto fail;
-            }
-        }
-    } else {
-        if (spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not contiguous in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (spec & (__Pyx_MEMVIEW_PTR)) {
-            PyErr_Format(PyExc_ValueError,
-                         "C-contiguous buffer is not indirect in "
-                         "dimension %d", dim);
-            goto fail;
-        } else if (buf->suboffsets) {
-            PyErr_SetString(PyExc_ValueError,
-                            "Buffer exposes suboffsets but no strides");
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_check_suboffsets(Py_buffer *buf, int dim, CYTHON_UNUSED int ndim, int spec)
-{
-    if (spec & __Pyx_MEMVIEW_DIRECT) {
-        if (buf->suboffsets && buf->suboffsets[dim] >= 0) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer not compatible with direct access "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    if (spec & __Pyx_MEMVIEW_PTR) {
-        if (!buf->suboffsets || (buf->suboffsets && buf->suboffsets[dim] < 0)) {
-            PyErr_Format(PyExc_ValueError,
-                         "Buffer is not indirectly accessible "
-                         "in dimension %d.", dim);
-            goto fail;
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int
-__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag)
-{
-    int i;
-    if (c_or_f_flag & __Pyx_IS_F_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = 0; i < ndim; i++) {
-            if (stride * buf->itemsize != buf->strides[i] &&
-                    buf->shape[i] > 1)
-            {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not fortran contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) {
-        Py_ssize_t stride = 1;
-        for (i = ndim - 1; i >- 1; i--) {
-            if (stride * buf->itemsize != buf->strides[i] &&
-                    buf->shape[i] > 1) {
-                PyErr_SetString(PyExc_ValueError,
-                    "Buffer not C contiguous.");
-                goto fail;
-            }
-            stride = stride * buf->shape[i];
-        }
-    }
-    return 1;
-fail:
-    return 0;
-}
-static int __Pyx_ValidateAndInit_memviewslice(
-                int *axes_specs,
-                int c_or_f_flag,
-                int buf_flags,
-                int ndim,
-                __Pyx_TypeInfo *dtype,
-                __Pyx_BufFmt_StackElem stack[],
-                __Pyx_memviewslice *memviewslice,
-                PyObject *original_obj)
-{
-    struct __pyx_memoryview_obj *memview, *new_memview;
-    __Pyx_RefNannyDeclarations
-    Py_buffer *buf;
-    int i, spec = 0, retval = -1;
-    __Pyx_BufFmt_Context ctx;
-    int from_memoryview = __pyx_memoryview_check(original_obj);
-    __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0);
-    if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *)
-                                                            original_obj)->typeinfo)) {
-        memview = (struct __pyx_memoryview_obj *) original_obj;
-        new_memview = NULL;
-    } else {
-        memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new(
-                                            original_obj, buf_flags, 0, dtype);
-        new_memview = memview;
-        if (unlikely(!memview))
-            goto fail;
-    }
-    buf = &memview->view;
-    if (buf->ndim != ndim) {
-        PyErr_Format(PyExc_ValueError,
-                "Buffer has wrong number of dimensions (expected %d, got %d)",
-                ndim, buf->ndim);
-        goto fail;
-    }
-    if (new_memview) {
-        __Pyx_BufFmt_Init(&ctx, stack, dtype);
-        if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
-    }
-    if ((unsigned) buf->itemsize != dtype->size) {
-        PyErr_Format(PyExc_ValueError,
-                     "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) "
-                     "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)",
-                     buf->itemsize,
-                     (buf->itemsize > 1) ? "s" : "",
-                     dtype->name,
-                     dtype->size,
-                     (dtype->size > 1) ? "s" : "");
-        goto fail;
-    }
-    for (i = 0; i < ndim; i++) {
-        spec = axes_specs[i];
-        if (!__pyx_check_strides(buf, i, ndim, spec))
-            goto fail;
-        if (!__pyx_check_suboffsets(buf, i, ndim, spec))
-            goto fail;
-    }
-    if (buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))
-        goto fail;
-    if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice,
-                                         new_memview != NULL) == -1)) {
-        goto fail;
-    }
-    retval = 0;
-    goto no_fail;
-fail:
-    Py_XDECREF(new_memview);
-    retval = -1;
-no_fail:
-    __Pyx_RefNannyFinishContext();
-    return retval;
-}
-
-static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_ds_long(PyObject *obj) {
-    __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } };
-    __Pyx_BufFmt_StackElem stack[1];
-    int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_STRIDED) };
-    int retcode;
-    if (obj == Py_None) {
-        result.memview = (struct __pyx_memoryview_obj *) Py_None;
-        return result;
-    }
-    retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, 0,
-                                                 PyBUF_RECORDS, 1,
-                                                 &__Pyx_TypeInfo_long, stack,
-                                                 &result, obj);
-    if (unlikely(retcode == -1))
-        goto __pyx_fail;
-    return result;
-__pyx_fail:
-    result.memview = NULL;
-    result.data = NULL;
-    return result;
-}
-
-static int __Pyx_check_binary_version(void) {
-    char ctversion[4], rtversion[4];
-    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
-    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
-        char message[200];
-        PyOS_snprintf(message, sizeof(message),
-                      "compiletime version %s of module '%.100s' "
-                      "does not match runtime version %s",
-                      ctversion, __Pyx_MODULE_NAME, rtversion);
-        #if PY_VERSION_HEX < 0x02050000
-        return PyErr_Warn(NULL, message);
-        #else
-        return PyErr_WarnEx(NULL, message, 1);
-        #endif
-    }
-    return 0;
-}
-
-#ifndef __PYX_HAVE_RT_ImportModule
-#define __PYX_HAVE_RT_ImportModule
-static PyObject *__Pyx_ImportModule(const char *name) {
-    PyObject *py_name = 0;
-    PyObject *py_module = 0;
-    py_name = __Pyx_PyIdentifier_FromString(name);
-    if (!py_name)
-        goto bad;
-    py_module = PyImport_Import(py_name);
-    Py_DECREF(py_name);
-    return py_module;
-bad:
-    Py_XDECREF(py_name);
-    return 0;
-}
-#endif
-
-#ifndef __PYX_HAVE_RT_ImportType
-#define __PYX_HAVE_RT_ImportType
-static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
-    size_t size, int strict)
-{
-    PyObject *py_module = 0;
-    PyObject *result = 0;
-    PyObject *py_name = 0;
-    char warning[200];
-    Py_ssize_t basicsize;
-#ifdef Py_LIMITED_API
-    PyObject *py_basicsize;
-#endif
-    py_module = __Pyx_ImportModule(module_name);
-    if (!py_module)
-        goto bad;
-    py_name = __Pyx_PyIdentifier_FromString(class_name);
-    if (!py_name)
-        goto bad;
-    result = PyObject_GetAttr(py_module, py_name);
-    Py_DECREF(py_name);
-    py_name = 0;
-    Py_DECREF(py_module);
-    py_module = 0;
-    if (!result)
-        goto bad;
-    if (!PyType_Check(result)) {
-        PyErr_Format(PyExc_TypeError,
-            "%.200s.%.200s is not a type object",
-            module_name, class_name);
-        goto bad;
-    }
-#ifndef Py_LIMITED_API
-    basicsize = ((PyTypeObject *)result)->tp_basicsize;
-#else
-    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
-    if (!py_basicsize)
-        goto bad;
-    basicsize = PyLong_AsSsize_t(py_basicsize);
-    Py_DECREF(py_basicsize);
-    py_basicsize = 0;
-    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
-        goto bad;
-#endif
-    if (!strict && (size_t)basicsize > size) {
-        PyOS_snprintf(warning, sizeof(warning),
-            "%s.%s size changed, may indicate binary incompatibility",
-            module_name, class_name);
-        #if PY_VERSION_HEX < 0x02050000
-        if (PyErr_Warn(NULL, warning) < 0) goto bad;
-        #else
-        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
-        #endif
-    }
-    else if ((size_t)basicsize != size) {
-        PyErr_Format(PyExc_ValueError,
-            "%.200s.%.200s has the wrong size, try recompiling",
-            module_name, class_name);
-        goto bad;
-    }
-    return (PyTypeObject *)result;
-bad:
-    Py_XDECREF(py_module);
-    Py_XDECREF(result);
-    return NULL;
-}
-#endif
-
-static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
-    int start = 0, mid = 0, end = count - 1;
-    if (end >= 0 && code_line > entries[end].code_line) {
-        return count;
-    }
-    while (start < end) {
-        mid = (start + end) / 2;
-        if (code_line < entries[mid].code_line) {
-            end = mid;
-        } else if (code_line > entries[mid].code_line) {
-             start = mid + 1;
-        } else {
-            return mid;
-        }
-    }
-    if (code_line <= entries[mid].code_line) {
-        return mid;
-    } else {
-        return mid + 1;
-    }
-}
-static PyCodeObject *__pyx_find_code_object(int code_line) {
-    PyCodeObject* code_object;
-    int pos;
-    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
-        return NULL;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
-        return NULL;
-    }
-    code_object = __pyx_code_cache.entries[pos].code_object;
-    Py_INCREF(code_object);
-    return code_object;
-}
-static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
-    int pos, i;
-    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
-    if (unlikely(!code_line)) {
-        return;
-    }
-    if (unlikely(!entries)) {
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (likely(entries)) {
-            __pyx_code_cache.entries = entries;
-            __pyx_code_cache.max_count = 64;
-            __pyx_code_cache.count = 1;
-            entries[0].code_line = code_line;
-            entries[0].code_object = code_object;
-            Py_INCREF(code_object);
-        }
-        return;
-    }
-    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
-    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
-        PyCodeObject* tmp = entries[pos].code_object;
-        entries[pos].code_object = code_object;
-        Py_DECREF(tmp);
-        return;
-    }
-    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
-        int new_max = __pyx_code_cache.max_count + 64;
-        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
-            __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));
-        if (unlikely(!entries)) {
-            return;
-        }
-        __pyx_code_cache.entries = entries;
-        __pyx_code_cache.max_count = new_max;
-    }
-    for (i=__pyx_code_cache.count; i>pos; i--) {
-        entries[i] = entries[i-1];
-    }
-    entries[pos].code_line = code_line;
-    entries[pos].code_object = code_object;
-    __pyx_code_cache.count++;
-    Py_INCREF(code_object);
-}
-
-#include "compile.h"
-#include "frameobject.h"
-#include "traceback.h"
-static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
-            const char *funcname, int c_line,
-            int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyObject *py_srcfile = 0;
-    PyObject *py_funcname = 0;
-    #if PY_MAJOR_VERSION < 3
-    py_srcfile = PyString_FromString(filename);
-    #else
-    py_srcfile = PyUnicode_FromString(filename);
-    #endif
-    if (!py_srcfile) goto bad;
-    if (c_line) {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
-        #endif
-    }
-    else {
-        #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromString(funcname);
-        #else
-        py_funcname = PyUnicode_FromString(funcname);
-        #endif
-    }
-    if (!py_funcname) goto bad;
-    py_code = __Pyx_PyCode_New(
-        0,            /*int argcount,*/
-        0,            /*int kwonlyargcount,*/
-        0,            /*int nlocals,*/
-        0,            /*int stacksize,*/
-        0,            /*int flags,*/
-        __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple, /*PyObject *consts,*/
-        __pyx_empty_tuple, /*PyObject *names,*/
-        __pyx_empty_tuple, /*PyObject *varnames,*/
-        __pyx_empty_tuple, /*PyObject *freevars,*/
-        __pyx_empty_tuple, /*PyObject *cellvars,*/
-        py_srcfile,   /*PyObject *filename,*/
-        py_funcname,  /*PyObject *name,*/
-        py_line,      /*int firstlineno,*/
-        __pyx_empty_bytes  /*PyObject *lnotab*/
-    );
-    Py_DECREF(py_srcfile);
-    Py_DECREF(py_funcname);
-    return py_code;
-bad:
-    Py_XDECREF(py_srcfile);
-    Py_XDECREF(py_funcname);
-    return NULL;
-}
-static void __Pyx_AddTraceback(const char *funcname, int c_line,
-                               int py_line, const char *filename) {
-    PyCodeObject *py_code = 0;
-    PyObject *py_globals = 0;
-    PyFrameObject *py_frame = 0;
-    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
-    if (!py_code) {
-        py_code = __Pyx_CreateCodeObjectForTraceback(
-            funcname, c_line, py_line, filename);
-        if (!py_code) goto bad;
-        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
-    }
-    py_globals = PyModule_GetDict(__pyx_m);
-    if (!py_globals) goto bad;
-    py_frame = PyFrame_New(
-        PyThreadState_GET(), /*PyThreadState *tstate,*/
-        py_code,             /*PyCodeObject *code,*/
-        py_globals,          /*PyObject *globals,*/
-        0                    /*PyObject *locals*/
-    );
-    if (!py_frame) goto bad;
-    py_frame->f_lineno = py_line;
-    PyTraceBack_Here(py_frame);
-bad:
-    Py_XDECREF(py_code);
-    Py_XDECREF(py_frame);
-}
-
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        #if PY_MAJOR_VERSION < 3
-        if (t->is_unicode) {
-            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
-        } else if (t->intern) {
-            *t->p = PyString_InternFromString(t->s);
-        } else {
-            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        }
-        #else  /* Python 3+ has unicode identifiers */
-        if (t->is_unicode | t->is_str) {
-            if (t->intern) {
-                *t->p = PyUnicode_InternFromString(t->s);
-            } else if (t->encoding) {
-                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
-            } else {
-                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
-            }
-        } else {
-            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
-        }
-        #endif
-        if (!*t->p)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
-
-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
-    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
-}
-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
-    Py_ssize_t ignore;
-    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
-}
-static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
-    if (
-#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-            __Pyx_sys_getdefaultencoding_not_ascii &&
-#endif
-            PyUnicode_Check(o)) {
-#if PY_VERSION_HEX < 0x03030000
-        char* defenc_c;
-        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
-        if (!defenc) return NULL;
-        defenc_c = PyBytes_AS_STRING(defenc);
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-        {
-            char* end = defenc_c + PyBytes_GET_SIZE(defenc);
-            char* c;
-            for (c = defenc_c; c < end; c++) {
-                if ((unsigned char) (*c) >= 128) {
-                    PyUnicode_AsASCIIString(o);
-                    return NULL;
-                }
-            }
-        }
-#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/
-        *length = PyBytes_GET_SIZE(defenc);
-        return defenc_c;
-#else /* PY_VERSION_HEX < 0x03030000 */
-        if (PyUnicode_READY(o) == -1) return NULL;
-#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
-        if (PyUnicode_IS_ASCII(o)) {
-            *length = PyUnicode_GET_LENGTH(o);
-            return PyUnicode_AsUTF8(o);
-        } else {
-            PyUnicode_AsASCIIString(o);
-            return NULL;
-        }
-#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
-        return PyUnicode_AsUTF8AndSize(o, length);
-#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
-#endif /* PY_VERSION_HEX < 0x03030000 */
-    } else
-#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII  || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */
-#if !CYTHON_COMPILING_IN_PYPY
-#if PY_VERSION_HEX >= 0x02060000
-    if (PyByteArray_Check(o)) {
-        *length = PyByteArray_GET_SIZE(o);
-        return PyByteArray_AS_STRING(o);
-    } else
-#endif
-#endif
-    {
-        char* result;
-        int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (unlikely(r < 0)) {
-            return NULL;
-        } else {
-            return result;
-        }
-    }
-}
-static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   int is_true = x == Py_True;
-   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
-   else return PyObject_IsTrue(x);
-}
-static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
-  PyNumberMethods *m;
-  const char *name = NULL;
-  PyObject *res = NULL;
-#if PY_MAJOR_VERSION < 3
-  if (PyInt_Check(x) || PyLong_Check(x))
-#else
-  if (PyLong_Check(x))
-#endif
-    return Py_INCREF(x), x;
-  m = Py_TYPE(x)->tp_as_number;
-#if PY_MAJOR_VERSION < 3
-  if (m && m->nb_int) {
-    name = "int";
-    res = PyNumber_Int(x);
-  }
-  else if (m && m->nb_long) {
-    name = "long";
-    res = PyNumber_Long(x);
-  }
-#else
-  if (m && m->nb_int) {
-    name = "int";
-    res = PyNumber_Long(x);
-  }
-#endif
-  if (res) {
-#if PY_MAJOR_VERSION < 3
-    if (!PyInt_Check(res) && !PyLong_Check(res)) {
-#else
-    if (!PyLong_Check(res)) {
-#endif
-      PyErr_Format(PyExc_TypeError,
-                   "__%.4s__ returned non-%.4s (type %.200s)",
-                   name, name, Py_TYPE(res)->tp_name);
-      Py_DECREF(res);
-      return NULL;
-    }
-  }
-  else if (!PyErr_Occurred()) {
-    PyErr_SetString(PyExc_TypeError,
-                    "an integer is required");
-  }
-  return res;
-}
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
- #if CYTHON_USE_PYLONG_INTERNALS
-  #include "longintrepr.h"
- #endif
-#endif
-static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
-  Py_ssize_t ival;
-  PyObject *x;
-#if PY_MAJOR_VERSION < 3
-  if (likely(PyInt_CheckExact(b)))
-      return PyInt_AS_LONG(b);
-#endif
-  if (likely(PyLong_CheckExact(b))) {
-    #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-     #if CYTHON_USE_PYLONG_INTERNALS
-       switch (Py_SIZE(b)) {
-       case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0];
-       case  0: return 0;
-       case  1: return ((PyLongObject*)b)->ob_digit[0];
-       }
-     #endif
-    #endif
-  #if PY_VERSION_HEX < 0x02060000
-    return PyInt_AsSsize_t(b);
-  #else
-    return PyLong_AsSsize_t(b);
-  #endif
-  }
-  x = PyNumber_Index(b);
-  if (!x) return -1;
-  ival = PyInt_AsSsize_t(x);
-  Py_DECREF(x);
-  return ival;
-}
-static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
-#if PY_VERSION_HEX < 0x02050000
-   if (ival <= LONG_MAX)
-       return PyInt_FromLong((long)ival);
-   else {
-       unsigned char *bytes = (unsigned char *) &ival;
-       int one = 1; int little = (int)*(unsigned char*)&one;
-       return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0);
-   }
-#else
-   return PyInt_FromSize_t(ival);
-#endif
-}
-
-
-#endif /* Py_PYTHON_H */
diff --git a/tutorials/cython_bubblesort_nomagic.pyx b/tutorials/cython_bubblesort_nomagic.pyx
deleted file mode 100644
index 0d35766..0000000
--- a/tutorials/cython_bubblesort_nomagic.pyx
+++ /dev/null
@@ -1,21 +0,0 @@
-
-cimport numpy as np
-cimport cython
-@cython.boundscheck(False) 
-@cython.wraparound(False)
-cpdef cython_bubblesort_nomagic(np.ndarray[long, ndim=1] inp_ary):
-    """ The Cython implementation of Bubblesort with NumPy memoryview."""
-    cdef unsigned long length, i, swapped, ele, temp
-    cdef long[:] np_ary = inp_ary
-    length = np_ary.shape[0]
-    swapped = 1
-    for i in xrange(0, length):
-        if swapped: 
-            swapped = 0
-            for ele in xrange(0, length-i-1):
-                if np_ary[ele] > np_ary[ele + 1]:
-                    temp = np_ary[ele + 1]
-                    np_ary[ele + 1] = np_ary[ele]
-                    np_ary[ele] = temp
-                    swapped = 1
-    return inp_ary
\ No newline at end of file
diff --git a/tutorials/example.csv b/tutorials/example.csv
deleted file mode 100644
index 65329b8..0000000
--- a/tutorials/example.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-1,2,3,4
-5,6,,8
-10,11,12,
\ No newline at end of file
diff --git a/tutorials/hello_world.py b/tutorials/hello_world.py
deleted file mode 100644
index 498b2df..0000000
--- a/tutorials/hello_world.py
+++ /dev/null
@@ -1,3 +0,0 @@
-def hello_world():
-    """This is a hello world example function."""
-    print('Hello, World!')
\ No newline at end of file
diff --git a/tutorials/setup.py b/tutorials/setup.py
deleted file mode 100644
index c92789a..0000000
--- a/tutorials/setup.py
+++ /dev/null
@@ -1,14 +0,0 @@
-
-import numpy as np
-from distutils.core import setup
-from distutils.extension import Extension
-from Cython.Distutils import build_ext
-
-setup(
-    cmdclass = {'build_ext': build_ext},
-    ext_modules = [
-              Extension("cython_bubblesort_nomagic",
-                        ["cython_bubblesort_nomagic.pyx"],
-                        include_dirs=[np.get_include()])
-                    ]
-)
\ No newline at end of file
diff --git a/tutorials/standardized_data.pkl b/tutorials/standardized_data.pkl
deleted file mode 100644
index b81fb23611e60d8c1b95cd9bd42f541a726a2887..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

literal 20263
zcmbV!c{o*H`@ShtQYwX#Bqd3cOvO5;6qVAXG%AH8B87@dqJ%V3BvLYzGAkC!JkRqy
z9J6yA$2|Oe-nVCcugiOV{`vW%b?tNZIjghxTF-Mo_kBOxiT$({i_`^+8&@suWG`I1
zZY+EChPjRD`Rmuu+wq>gZhYaIg|*G~8y9SNtyuqi7O%y{|GsO*rpxlsid}=%$cp2U
z)jWMGPAOeh-P^hgT&%eMo_f*7&eE9IiravVg@xskq7{!Wi!SRUD_(u8`BJ*<2CTEM
z?y}-LaNvN>%<SKP{?TQzv07l{rhaZ43rktc<DqSJ{Yb<%{k5@c9e$@|+$dL8hN|ZM
zuvZ!z!yn_`@VxiwLhFaOZ&<gTo#Ot`o65Fr0_j+596oR&7k{xYRf_sjgXHh6lcX8;
zAqAyP@mYphNcA&s+=`PuC}1jIZmSFvMa3C2pLKVl6qDaczq@+zIydem185WniOQ*G
z1eD{oc_PW8;??*Z3-55@&Pr_2aqG~Zhm{!SabKItVV*FKZz-yT*_SrqSkb)!6XAnM
z+<vh3l0^wtn=fYd_)`_e(I#O6stmkCG_uWt?+3=pn`@hSTXDICXtESf8>$TZ@!QFW
zhABLvn%gaF&_$gi(p9F-C{#??4i#i09%E0>&En12<m_d(G2a0kvR6~&kaYuQxQ{Gf
ztJ#a#86$^7YzML4l1B#3Z@TeI1I;I8@-&?3r0)Juu??#*=uYhuUN|K0P@K1RFRoOY
zxv4fhi7vSJtY;jfq493>-TvQ)$aQmenqMkwSb!xO=KBhb&{3z}^cNam>FjymcuP!a
zSZ{pulFOTi(Bu6LVy7=g<EhJwbOJ8XknF)P=Gis%*hpc@{UYZmg5UGoe%PJuNHxN;
z4UbKxN^s&jp8A5x4*X%BVpZ^rAL4z{SQT)g2I<`EZ8<nKgkl8PdiU%bN79ousfxNi
zs6(JsbK+Gy-YY@vV2Ng;vR8G-MiS{roFhK_^Y#RE?dFXylqWQl^|hi(r)3Z^gpTj;
z@vp~*C$1>l`Z5vE)>}FTC(2N@Y6yR0=m63x!RDdgzaw#D&l>&0F_a>AgQj`E5WRKe
z(2)FEh6;yy_t4^I5ZoV8&wk58Aw%d_!fpHg>#6ARhP`W)(_0Xa{XEA&*Ge=Rf1-Cu
zK^dCbdvM0Ku>{|I&HUsVU4}ONcrviYtqT3<Q9ktbO(ec2xY1KJk&2!~Wu5$>-Gw5p
zQdaSIb)z1!8j-ks2KLQv<MuTjL|fl$c|>h)K^orO_2c{dk&EQ~k?9j7IHWM3LdQM;
zZGLcbh1{7?^tQr#UaD#U&Xx#!tJOPzy!tO)5j#?hURd97_|DgkRXk3`oQkf;%-Dh%
z_P8NzeMah0es(=sZ}zVgb}IVh;F+8E=X+kxC+nK3*y4JUs}w&la$hUK*GGO&t@ihv
zg0vwd)-$cU)w&4VwQ?6aUt}O1$uJ~ws0mMM{@L^?qz<bsK=ZGhAHd6F8KRVRML1XD
z$d>%ejc8aiCRBW94qCa(CH{3*2ObX_P`#GXie|*BkMEF7A@js0uAeATU?N??gnh5%
zd(ilq!K*SCno;B?7UaZHjUPv--s!tlg5dY8I`>)f$H+T!-HthoUQT)2OXf*f{nIPs
zPBpo1aDRMw&Q~Sa4I=vbN?Yb=Bhp$?5gqb$1fBcDC^_8JfOFHIsNLRNiL^?09hAp(
z-1A5z{c+kfSqC4KT8=yG4<NS8y~itVHe(jgp`;HZgD9=uD>nOM34(bF#B)U6q)npq
z)uu5g*Y=~UT;YkkuhpS-veWUyPNQhE+PYUXhh`KgFMdR5{}2)tVt@GkMLKHI3&k<c
z-T012ewf+A5iIw4g0;-J1Ks-SE>)^ri^JT<)Z%Pt=;?gpP>zW~%q%F_d2G`dmKo_z
zxbE^CH+rUdD@cu?voTBdnwX5E`a?g0em|~8oM$fxj?qI%9Oc%2V<{5UMdD6%kVx-_
z165?7THPpL87Q2D&g$y1roXMlJ9D*pm7eya`|DD>cD)@y-odYTUNIWOyUb<wOfjm_
zxcBK)j-#xUosELtd+(JZX<2DWPtO9hp*}t4K1(02N*l}kG4u<6FcKSB*v>-XJ39T7
z^CbO$;^^y)JCTpw^YP~E9|bnAt;ec~9(Q9r+Q|KBm7Ray>TERjPx(OU*6+iH;a$I&
zt_)<D>n}2-IfM*X6_-tTGLVbIZY7BmA^6IS{xdg=A><a(dY)x(6SBVO*Wq<13S*9a
z)9bF#P<L*+(i@Fr1aXwQ^@$al8N}Cwy%l@>`BFk$Z-`J5=cPz;?a`~{?#2SEwOdP{
zH{)l{(ZXEM$1$vftbTuio(|TOSd(+n^&U)|`m!4@b+1IVV>#(!yBPTFNwavfMO1v`
znWQc?J`bCOXEUlNy0FqzbL8i?V3L0%MA+0)B*%~qzof<{+aVmLu<n=p(td0aS@2SY
zXAqy;$NIF`dk6;=F|W(4uEwe>dw7hm_2Jv~3v&2R%#i$35osc4=Q56b^FOGI6f@AF
zCiAI_hqK5&h4mI>khmx{Wf%n&HaB&;(2*x=CzrzNHZ1j3VR;D;4e8z;{%~+b5ZR}C
zr@wh_&!Llby(TA~)sZ=jxVy)=%?4{RUqjWr`5m<+zOBC#XmzZr2kF*g{%$mabawpK
zkymGu`@`mVq5IaBR<v;I#i%DjT?nq*9p|;3jf%y{AWiAc>wXrB+yjyDXxb=ZNU5#-
z?p=aUn2x{x*gTA2y#*W@t>UH*lYKfo(Jt|kgN~ND<#^@BrC^vRb5D6oh;}nN>|I8e
z*;$W9YAS;5C%tebe?@Vn*BE+kw)ZRjbqEsI)h)kiIEqYvkL+EOmWZsreu_8S)P+LT
z!>bo8??*{*DN5t?VH_sCzbfWP7l!qAL2#ktMT<VH5G-Ew>f8(()4DhDL}3zVj;uE2
zyf%&74<zb+TGK}IcB|9L+C;&A40&O==+#Xv?^3+cY1@cf#4oIHIAU4j%2cvX0Uv~2
zHFf6?>_RF{E(6A8=@|0dFSBzuY#%k@Mfob4-W~<mZhKkB%oQdMZd)i)lt;tw&jxpS
z<RuXt0{OIKL&F2zi7~|3zo}(WoH=^$t8I`HI*H%0+){XT#U05|e;;qz(S;?PeuQk9
zNI;S$oNDbR?MU;2yW8!f)yOfq->E&f5ufe2AX69Fh0CQw{C*h@;_#a6rpxQ87~*xa
zv{A)b*+wk%Q@@Vmb0<DAy3{8B_9$vt@uysVa0&~4xL~<9y&R1%NOEIQjzAG>L>Yy5
zMzKTAxA4aHK76J<>drBzZseh&lk7XqO@Z}B@`4&y|JR^0+^jHMdQgRiXO<?49%~=N
zOKQ_s_|3*eFV*;G^B2-_rg_m*y?qQc=2}*GOmzhBPXG95%OWNg<JWyMasCUIi(d4!
zkrGI7h?HdP4?~?EY_YEGeuerVxj(%QZ2M)k6OrHI<KZ)ln@L{Sx~xC%VRs{T4v{L}
zjaqOt-oz%1`q8$%|K!t;$aCR&p=jKN^duw&Ebo`010qtI209J6lh3q2?|BF2@sL?Y
z6>2AV6z2J&p?OtkAe`(|z=;{F<J-J0)Z^?O%e$L+3(&_1`&6ak-vsYOtc(a0{V;@&
z`B`(x*wInD<<A4>+!+6Z6H`TYQSZHM!tC+hkDpaHVOVc~uW!4&z4YqPINl$&rgC&;
zI@a5HIO%svGivg0T$T2x1&?yYo-MVh#(?7??f`!C@rb)wcaM$}8u!G;MULQgJ501H
zJ*x4Tv0GwoB^|}9=yBy}zs5gJDok$nQ?cOwlbwoxs_@97PiI}1RbXuaL8*;fx^eS7
zmoqyn7`X7as;RERG?HKZDnhm(11;#^92I`3AH5gO)z+P15*(lK(MZj8R}KDbe)^Dg
zQY*pNPj*QtxzK31$lsxDu}l|^m3O-!n3968Xz)^-<(hGfzpuUibT?*ScKPkhkr6Tv
z;6%s^%HPd5sP}|oJ-PPV%0WZeC1CxPT~0&D^@?T(YcL)E@jB{!iav<k9wxJ1>zl+P
zrwr!n$B&Y{;2+wx|3@ed@7v2ljZ~&0$P0ku0ar~%{xi=wj=k_jz_fn?OAM$rU8t@@
zP?rQZzcAK2l8?%GPX6{PVPcCE&j>$O27VN!S9^@N6U)1rCr>ES5ab`5(S%e6Pd>>D
z5O*?%u7xe<qoJDDZAOc>(9!zmjw%acXgDh)kXn7H3OTy7=UvPgAaxdNxw3E0ERM0U
z@V|C{OE3D7$^Lb9SsHrAwaa0p3j;M~997wHshz|f$Uj+y>(f$WhVaQ>-(NR&jT5}n
zthG;mhQAFbM&>2|8L7qUkFq;k1jliOD7Uc6wPCy|<LbQFygc-2iBprgKMlEUwfCEJ
z;h_AKxoNt5uo6RFfOTO0*f5h>$da<c<waVG-88vwBQM0N4sV&nkmoLQYBEFaO^|r4
zrDnQhe+UgX9%6g;C?FMkXsZ2b&T2wXm%uuJ`*SO2WyZBQ1}@GFEtyQ4Ao1<_h+O0i
ziE#|^TEx$C%AdU#H;i;?USHgV<<>Z=FS*}<S$x%KuYQgZd=2x|#BAUD?&UOs`vY|e
zqf<W1ZhkMBr%p=tLG9QyTKxNX(k**F3gn;W@~79?cxZV4Q1W}$+!D+;a7N_Bd<M2x
ze0jh8SvScGZ#OJ(t~MG$ug9m>TfSpSfqH>f;LMWuq=oFCy}!yOe3BcmS8>jb8om(>
z`E<f0@eij!7c#xb@xnHsnp`)g`2pJfwqYbXBp*&q9YlA6xaU21o`zu^?0zPzVoOOz
zi^ODOgr1b3FSct<M=iTZy<i?7_vqDwTrBIu;xSoQj1r~X4r%%`(48+aGdlLc=-xXs
zyTr3hH0tMHIxN#ca23fvfAa$T9{K4t5%wLWq+aM=#V;!`nu0x-aCAJKe+PL{qL})n
zz3A(wpO2q1^AO+zz=@nb^wIBX1B5>~e?qO$-6I37X)OE3raFq3O-zs7QK`UXcWZSW
z-P17ODu@RS;aLmOo>biY=RllId^?(_@s74LdJw@r74A6`lyZ0+B|A4<OD-zHdincC
z6)Ia1{2ss|(q-SwGE3Pgiu+<xEQf}X{P#5B6T-dt)aKUeh&ux)Z*4}v4z?i#{6X@o
z?$0qCgGfHz_%(MZE5$5zk@t%mbnN139kt|43kH0B$g8BkQj3AaZp%5EKWWEXj>i1t
zdeDP6sH<)GdZ2;q)7%nOr*6I9_+`=aqi0zM@l~IQhU&~Qv|e6W;gMkr)=c=kQ0ZP5
zmKCemsHR23eg|a&@{)S6pa%22?&1-We}w3(zfCP@#>M+T3W@&th;DN%s_Ius#87Vo
z-f?!`v@SrIM);7lmVu3N=bKRAj9BKSZ%hn%u0i+eohjXR!XrUm*ywF2rsL9&ufE!$
z62f2-d=2p$@{hOB(vihstpvvdP6QkR>%f*hWUWwEGuc0U%La5E*e4Om3-{*h(%xra
z!}{gHb!z1Z;`Mdcl{@S<H6Vdj`njMJLpbk%abb9GIcB#z@<1mz9kq{TdOIr);S~bM
zkMA8HLN4dtF8X;j8ykuXt@tEbgxZ?d#mxMO#!zS7lsWJ06xN3PWq52Nyt@f627Jg|
z+*`0LQ$=V)3fgKj9CYz>AF2?&Bi*Otf`(7a+FP8e#ejDJhrIq^Wt!^WM0l63?!gOI
zd;199DRd8SSB>sKXG-iw?^^pH$lLTtxhrZn2T*z3v-?gG)kxXGf7wU10c3o$?&ZpD
z&6w`Cp!MDR0dz^tQ}@y=KL-4pr1ijb&f-k8CW_hqX0Vs!AE>vx4}O@&_lnV;(=MMH
zj2P%}?xK#5TWDx>erVjK@=>IEQu1q<Ung1D{$IK6e7`kez#$@+9g1(&w;;efvcA&?
z<qf_N9RE>e)5~v0UvW!Z&%DfxAq;$##s&SwyR>W3;jt%Axt0tOyaPPNg5qw^KK4co
z_XpM+#GTF)J<b=u)sVR3(T`QS+VW9_R}k;%!UlXLRk6$cat~tHx?|N<-;O3;Chd=O
zOu^SUzFB6Z4Wrj9kov-*77YABaDL394JzFje$Q^PtS6tRN09UKTQBBcnI`K1_z*5X
zQ{8=+#}V+B5Z_oIG-(At;iL>NX5(D3tQP@~1iTCIJArDdREZoa;VpS0cf634Y$yC1
z_gOWK^?{`b?$2)Z<hqQBF2v57p2w40j-*lpmPa~Ip!Z|n6$L*Jp!@lum(ISJAh?Pv
zg;V*7RT_eMfDbYFRu#o9Jxp*S#GS6BY_ZL6CrO?4gq>-pHOrf^;u(z<qutp1Xr2V^
z<Os6bhR^HSHj;g+EpoW_i*7Flo{z;l{AJC3CW3Vhd=;$gP_4+6yA`eI&1oy0)Y@e1
z(th&GmY_cL=TL{~@`g5)aYOhAzibQgLod4C)psGD`MztePtlO7ZRmlMR%57rl9RbY
zYa9uj)`<7mI)d-3I!@kJYQ=!7fFEUTw^qFLeGHXna1NyBj^l+NOTN3Wn!%BLJEqk+
zx@PgufA=ZepY*mDy+X2dyyM+-eScxr6o{kuJ|28LPqr0#ca$EJP@3gK{_=^=EH|f=
zz1S$aU%1W$&gKWmKgk{6H!Y5-Kzh3D0|tr=5)al2Rm7EdrDN;W7FGv0@=^eYd}^Hk
z<>ti+BrU9}xZz4YN~7z3Ja5s0e(X^;3VT398~i_Md{J*gSrZG5%NebBN+bKX@>(jA
z7}j{f_O25Fe$!zdl5aQtgQTkz_R0m!qX6!OcrATg*28_4*DXGq8x`i*hj-NfdVj}b
z3~SMMuaNR&rG&X$EBXC79hcSpj9L*tga9X=^9`B0#5#dTqK!=!n#AKC_w+q+4$au=
z`sSaNySuS`;fg=rSI2PWE)8Gj$P5he8uG%t5>0({q8kIR40RUdIpF!A&N`q|Bbhwh
zf<GxA8*k7r!PT)G&s{{mBZ$|q4gkMtsk(|SygY@)Y9{ydrqT&Mh}PgZ^DcJ;1Ah%T
z-ck43Y9~B}lbB^PiBhc?_-oAzErS-1X6yD$F{O%kzj4jG-3mHgjU+GJTeVlt!L$Jt
z*PM`D|Er0_1K^b<lg?>h{Z))Fi}T@>&NeJOKJV4(fd+I;?66c|Mm`p}yQ^^9#%$DH
zY}9i-qlVO{n>-!wpWV!n0(Cv`y$}yz|CGGH*w~}oj+2H>u5?NllYHuXGfj456Af{%
z5V&_Mq8CA30(pCrvd-xz0}KS~4dSTJ`NC7tzWu19Uf9XktrGz+2K8xcZAawQ+z!0f
zz?go&YXZefo0ZTrJCNt=H7m0^XxQsff9sdX9(1^85tsP8ChV|OI@on*DW0(Aur<}}
zA@Klo48A1(Ta#^FWF83%vwb@3!C2;=@~>a-s!-EsJ|2O*91QCk@@f1Ij?UM-wb)gy
zB5Ku6#{ck0Qt^dsOH=BQm05F`-t7VGdq8MgX2t-9eX6)AdHV;a5rRjVqQkVR*WHBY
zgL>gO`|0xK+eR_)2fGC~-;jShjv<c1brUW7dS%9`2+ghQU60r}cJSAbJO}a3T`SM;
zrg|BM`UmD=y~y!)=w=7*TcWjo+v*{_eT@_2^LR6Yd4L~X_xjO&hFl8<UfI0A$kz6F
z4hDV{cv#?-0Ve`)sok#J;p<e6p{{pTZ;5MI&6Yyyg}-^u&mzDo%W-!7|J8pI6xUI|
z>atT3CfhDQXriN;GtZvt&+0_5u7_4vANstHo#JMDJ$-A!ID&bAM`~W2p>wNsnCKXA
z-A;DXT%%ZP5d0p{G1S@8%ffagBj8;?&${w+Piy{yaRhjjHbPTW(P7}ZybD}6;Q6T1
z?5);6Q^-7T#tkdRTB@-3kkCH!PjswV**LZ5=oH$2)@xBSqYV{AUT4!X7{P%wXWNS(
zYYBdX{L_7j7A$?TA441k-hb;mcFQxA9>T-M?cV6T`bh;2z{i|;dZRI{>#Eu<OkS5R
z)P4AoO1K#ljo**db?IeKfqe>i2l!FYH52a48?jiJjqd9BMQ|?-M8Ll_Y<saw-?bej
zYStzdbuqDrbBl4t1zrm1jceYV5!d3QV^t#?UTbeAy2+*W{`}qvWcF>r-aQLP5#Vd6
zw?{<OT1>asVV}VQ^MCZRO$XWToUxlE{EpNP`Ouu9DHN&g-ZFD`45O~*n*AE1B#u6}
zkT5m-(?@V3=vf&8Pk!zDSxa<`PL5Z;%#YLfoG62ng+54lvCQ7ZwcF2@<2sh0Or}E*
zrp0O4_?#=o!2279Hw*vTQGr%GUVb(I2$kqrP}c+Q1)d!4Pvy}3;j*3GWF5dfp#MBG
zbiiiH!|2V99^SFxQuJb}EN3Fu1Ok54z>4$6t69Db`0E4fFNbx`^0&bEKIWIAJhZ4q
zcKes!)>Y^teB!eAHU=vDViD-uzqHCizwT>8ihV(KA+M&1KKI8m^L=J}H(Go9ZG2A<
zmDHzf_k?zb$aWGu>Ji?0&&GZn>8QVxUBEU@bmyD|MdckjR3zASBEzbn3qkz@>i~3G
z-!o(F0jDRh(ur%w-*=ahdK>WQnqw(%0}o}+#$%rS=f>#-R{<Y?rSrbDMOE+r=uAd$
zGH)kTQnBcZ#L4P;G#od<$y%pQ$BSu&8)yrt_||)u`4Z=-2>7=Zfgwin>9cxx+^u3w
zz77n0;(?Wl7hcBpAddTw7flV&G1bzeFO}Mh*XU#{MeA89(H?$T&l6j5@;>3)0<Rdj
zxvlZn*i9N{@~tzA4eQ6pWS1!qh_j|ZUC-T~S@Nu!kMbtXBfFEk6YF`&s<SWZ!v=Ro
zXFQKhV42I&J9e!aL(C?-B_4<SF|31m;*?c+p)F{^jiQYKjY)Xw<sh%g-E!=*a;N-c
zZz|D$=H><CRl5}=tVVF}#_;D}uO|q;?&w#zM>#cx)lSqI^4?@6Jkr>%E#8^WN3gU|
z`z5Zy9$c}*P5I2zY%H_YG&_O6gWw(D-{86#bf5WZ7%_&q8b#%o2@hc4DFE--ic|Af
z1!p471(wflNzgEDaK5MYxk)U~`tar5WgU3A@1Dk>tL}I#=@`q-mkfN#W<!s+9$N~m
z1Bj!%A;;?v2(%Kt5$1vY1N<B4C9z+HM2`6uVV1X#cgAMYu?*{`GRNmFC@mp3-@&&U
z178K}VD+HK2kJ>GV*8JN>Vu%3gZ$&J9I)1JCKEf;R;_=rrvkspIN{&f)I$6eVz2l-
z7rN1q<&D+S52PkBE##bO06h-_PYym5z=_Mp?LV9H(h=kZzz2XwQ&#6Woc`EJ>c+YC
zDXatF{lOOi*A4j5htuqO3J!F_hk))3_a|bsTRrT42L|1F`Q7OH)YMMAVd&bKz|=tu
zadZdwWo@cbC-EP^JdhVc`rD8D+$u+M^C!IyeX1gP8|vTzDQA`tzdqutsee^7!XNqz
z!9KkglbS4--GdDx(y19kSxD4u=CZz81MzLZb(>+(4)@qrATIT9Yxa-7BXxb3P)akm
z-E2Mb=Gr<No&g*###*&6g-Z0`#f6>~nF0gYdEZ7E`|?_xhd=bQ#<pQoS9T4xrXpPD
zu+`%-qlxGXpkr+Kc<xfpPzR2E9Iy$C&mh2wkQczWp}5(BrPi^B@M2Z#WLWOVjo^|`
z^nE#}rqK)OR;wiET=IKX9!YI{p2ENp*?txpYr2U)1aL3ZbMSjEyft37v9=%ARO{KC
zJ~fQ?`Pm-~J^g?8CsXuU!LuVh2=W}PH>iIg{{Zg-@lD7}NcylaE9Lz~j_Znx8939-
z?1|Yj7K-Hcm;3y)3(%ChsKg(k*}VOicR8Y0!KHAl1cAN)^&IetpwB&vpVceG%kYgv
zzR`eY4hrDWxjbLU^|o6wvVCabOlxYF8<W&?HtDD4C9lpwfJ30310Jaga~9+#&hnc)
zsV$0pWB3XGD{F&RHVWwIyOdTcRtWT?ihU8U4XbHHmoegI924%z#NbEj5wygbLNf@~
z0q{t0-B_+(40o0p$Bu75d8%nm61@@f&(_|%oo^8p0gkVFVtVas`4G{691Gl)Y+}05
z(xsBccSib<SG&)zjHjJM-v<7zGt(zsnX3!mSRG|xojgu-ruC01>Yj#=;v#8TPL7+^
z#3uwe1nRjz4c|1g#Y<4uRMm(5y;J1(K)ePWy^MXKuAAK;uHKO@Ipj7<_+E%RF+ZqL
zt&i9!z*B$^#qpM+u187%0^Vis?w1vP;bX{P$+<7J9PMb$``(2+mrdgcGcmi4<{82#
zF3h1CmVWF<QTd|%S5GrA@V(%B0(`K}=#Obe6$2F*I%!E*JVNxFd!s((jo`@up0<R<
zS>0u-!|AAIAA)ra>j3ySGyfaQmbOO`KCw&C=cjYR5Q2RQI$+E5*aJmktq6P@pl3;k
z^dxR7`Guf91swx$73ebUi>rJZVh0I+gZ<-U=44TtS&Bf%@bYV78{S1l$38EWiR~Lg
z;CBIj2jUy(+u#>dZ7VxjFFb(WiEZ(!C}}6xEp_;)(UH$1D32wV=`qzzbadaNmrnhx
zXhPmvVTrb>bW|CWxI3kQPIx}pr+<e2@eMW+{5Dk(V)k-%5Bhfe>%`OiS^vggza`wC
zWebj436)L|9_fu;(6_2tf8*B}+Si^}qv-D4RPnKhN(8<dz@xw`Lq6qhD?H0|C`C!D
z<#y{E_2Q0gQEO!-ClL5*-bDP|emS#?=-V6aqo1zvG~#>WDUL~deWD#bWhh2(5Gq3|
zPN(@di!w>QAin9>wqW%tw3bIiyY?mn+bpTEc{j}@`U2$b(uAw^ZmY%#4v95a8<zOa
zAoEZu<xRn|y|_4yZ|0B2AYKq~#m&xP7(X??*{)E>#HT4id`1==6#X66r7NDc<LN!V
zrvBEYn0u375mhD-H6J&nG@fRmKt2Ut8FWDKaUG9(mh?_~7_C%2=<{r{kod)5pT3Qi
zULLcrmG~T(J{cX4&Wz&JW}hE&{9WkB;g?&}y{S0((mm(3sVtOL+<J8@Lr1ZP)v=;j
z%p^LX6xVtar`~*|AoHu=Mq+^AM2Mr1Pv`Qmz`p_S@)T8G=?k5$tN-fTLjA+hO|w3M
z&&8q^_KAgfbLaUJl+SH=@txP<Rqry;Tt6(tQNRa+yrVVc%hCu>9vBf(^F6r}N4d9$
zc5i3m2L~-<R|}RBy~IJ^Dw4s+#KIbO%h?Zhp{IhT;%-%yr2c_A3-Bn^+u-X2UjX=o
z7F^dVWUBq2zZdEfL5}a=28ufntJBHks-2M-cqGs<2FGa2tR?v<jxR!euZVI|fRERF
zC@?PJ@qauLuYTpr`@StGDzfS5Ges)iBeK0@$*ED)7_(TMvVnn|j;s2Q9qB;x-#=$-
zV5bv3nA^wm;L?FkqSr&*iRXCsegW64FY~Xz3h;r0!;eR0l6}bHd#PPQTq9D_ckp|m
zQbhcGFc0vs;D=p2zNhQ`@-&ivATQKAOdsT*q~WCE-xtrtj1t`$@b%WdjY*NA(}agL
zw!IR%w_pUDyu7z(`^h5YQ!)IgREi}<wcmN?hCU{#8$m~hbujL|l^V;PkK*2U92VW0
zN_6Lt0F6xx;wmw(%$GGK{5=F;!~PkNeAtF>3}D{uOP7i+&hlanCdKQ`e`4SdfTwtS
z+TVavkDcPA&X+f&G>G}>{l7J>8KlkveF1nE;HyB_1by3(Z#wU3Tq%;T_W0<rw-77U
zm+m<2GDLJ*;H$u|JY!pZ^7@lO;)@0z_IUB+Ch?{^;!8{OS9nx;pa^LgT}WwOGDi4#
z*)}e-lI6UV8U5SuEmNr&eh<_?O-r`#sx+@7elg&yfOi4E3*a4y*MJi@zO$mso*W=P
zVpwlb|3JL}>i~S6;1dG<6yn>*T^cTt;b{o+9O&DiV=TgsyKkqD5WfrHDySD=9`GMP
z+yQ(Ie3hug@=tOCE!gR5?%+UHA7*gYXP<jY#h^ES`OI6)`?wVYF9vb6s-y9Ustz3i
zue_-^)h+NUljyWESMmZwH>II5?g#eO!yO284Dbo98x+kmR~;n&(XXL5n)8>_iGB)k
z6xOxlFAiI?8&k+q{Mou|r)PDqzk0oZq^_DfM+*X<!(4p<d>d66W85zaVo=uPCxy&W
zI_}o8sGRU+OWBnn<up<@gu#CR{vEd~O=3rX&`BJ9aO>;yPqY0BiszN&`<baEZ(m(>
zeEax!DpEhMJhHo_6IT@oxGO7mlKKbqg}Ljk<cp!pahW#a69T;+>bYt5X#K<C^;k?d
zFp?U{z_6|l?LT_Y<0k`4?3Dhw({PgbVSyKe`vdq5<^g>h>h13bE#|A0jw07{A)BAS
zq+zJDpzi~4uNA+I)j}f^R+Im9vMe&0ZDSG8w_5y98;kruZ7fSn=DVrW1X)<lb<gwi
z%W5XZSrCqaIDs1pg@y0T=Y!?Klcez5QyTm@`p7U5a2^G?<hmT`AcP<o%K%7xU_C8=
z=rfHF08lVPU<ZN|gd!+lILA{zneeftJXR@M@Yb+%R`~fHo+>niG>&U*#b1Uo{P65k
z2NVj`+VC~*OU`RXxhTSiB1#1}4iaGnh*Z~-Z%Ga8J-8(@{#4qcaZ)IuY*ef=D@=ef
z281Hhwmg=fa>Hb!0T3^@xi(X{x)p&D3xGHfoZ`k6-=^-&64Kw5bUsA)kiw#E$4{dQ
z?IAq(My77gkuhz{CIToJmmtA|U<eznUOwabK;saGz|N6=#lpm=ofKqc_UkIAB(7th
zaQ?IUrv~wXtcwp%@yr^!e*flt(9ukUV`xBt1P|8@0u%_;AQbIXo#yv{NhkAwQK5^Y
zD#MqDn-FMTjdAOw)(v6rq^?*a-x{p6s&raPxgK$i2|M1kA3+ds!9V~7GZ-R3Xaz$N
zG;Wkvpg1SCG7JeG1P^GafWWT7y3dGzGaCg8Oh~-&dw|e@LKY0VP+(d)X}@_P+Ju2<
zIDXK1d~v}H_7R?7V7*^B`d1uR^|mII>v3ql$7~=4U<8d0(8vYC7Z@giZ~`DPHm^|o
z`YsO2SkOBki{K{G=m3Q|5KaL_e244LG^6}@zuYtXYlx8^_9<Mqm#<e=NTehaW7%Mp
z!*k<PZRpFWjfa555C)?H5DoXg-tRk@HjLwfdfk0?w-DnJ7-JwnfdR)-e^W}g9219k
z#?<&;9w)%aggdwxU#gpJ2&=I-eKm-{U}(h6d!*{}7^WE<+4$o}1zr}uFf>V{8kyC8
zc(tv!1g&}gPea9b0>I$@fPnxEhLCuHum$4<5OtSc%yT&7T#VWM=e>4u|AC}pc9~o3
z?n9}0uF2Iw4G0Y3a|xPgi9_3OT$>e2{sKLbZ8%`DU^5}Q%bz`JWI4h@fqB4~`Eyxe
z^??_)_>{k(l)FPO0s;*JJH*jyd!LV?v+EENuRUew8=ISL2#i=jGypLS@eS7XE>8Ui
z&Sypt6n>|^+ZL>%HW9$T_FMX)167^~gjOKX%()uRq}KnRp%oJE+7nv6D#|^`TD$St
znj6hTSW&j_-x24`MuEh;Osz^OB)Jq@=)V>h`80~kq;~~1JZGUmVGcwC5ceR21E2)L
z2?Qx<42XO&%zAtyjTkebp$~`?Fc3h32jfx#=l#1MTUv;r71mqu%5mpaSqxHGY&Er}
z<(`N^(4YuJ-2=(c+h-QCP|ol<TiV_lL4i3=0W*^96acR9d(u=NK8;;;hs5hQ778B%
z!!Xj$=qoS|93p}t2$RwQpUhSWGcXu**XIXi1sy5F)f(el$E^Ah02at|(1>!&GtBPN
z+EHRyspPDf9GFN%-)subN1Vte#xhHO+aLQ}XB*T066*Wcvfn)6H9-n80D3>|8^j0d
zSSb(>*tVEdW_z^Z(`O%ZtY~N^*NxSCMVn+?GX{VR1_Cg&g2BVNH`UOxtq?D2RIpCj
z)Q+GL67CQDo~!Zy2t}_*K7}|MX6g0A`}`<g{L0e#@G3uI{Cg~UXkUyG6>D^_{lipl
z!buk@)7D7j;_%f&cgoJOQo!&v7l1(=H4nz(!3tv-3^){)ZH=Mb!^C+0l|zYt$BAjY
zZfV2C<@$pd3K(OPkN1N*OY!=qa|x_xW*g@IHl{&5fV>Uy4U91WT+_yGyH;K5z&SNp
zy)VCTQ25o#d^8TQrsS8l3U=+BAo;X6*}63BK*6lx<?$N_^?n3I9n1s!6aYWq5NH@P
z5xb#SJITQJAAec1lU76;)0i}A{tWpp{NmjJPl0R;2IGZb;5O-r?hztLLH>dJ6R-Bv
zChKD-TA<W)JA=0lLH>bwFtjdUzR-F)X{>>Umra)Xj$;A67#jMdzKQS&9$=;XK78-m
zs#)S63@dQmz!354Pq&jBlR=C=+V7%nYi}9A6Eb%tohC++dd|wEh?PwI`lo#<FM~!J
zx%lF{wGFOPNn-%u5H}GCu|?N7C?J3W;RN{ya3VA$fgu9siJESF|3hciFncEa?28Rm
zNan+DcH6)XT>g+Nc7~-L1ECIupRkdY(7Xo%6fi^pK?8)~t$;^Q&QEYrVE=%Tu{0~;
z{O?tt@mj6qVT0gil(u1m&Qwz`f;b8Va}>2c@{>RthJp+V=F7)7@qgRUK^kw5DJ+S8
z{O&s{iBI9a5!FHpGHARtuzh`VZ2L3<<192_L!JXX3WOl!AMU}tXOzW5gct_nEHorR
z{((YPFzeZOJ7YEqtZUDs)%F5kTCn8_>ywV#I#6e<MP+7EHD2}Ccn*ZdlT_EoUqT8=
zK?Znd8}(a?AhRAr{xJ_&da71@7>yhCb0({I5dr{=vpyRmuA2SqB1AV3b%1xk;0}cn
z5GS{swzFo{%{EB=HJ&R>1ohj+=i+E}$AEd9bkaziExX}is$3s2NP)2o3UkOmu-;&w
zf|2nlYqD_M?HmL+5ePy5Pj2ni$z}LU(Jy7bBQv;OMAX-feHd9&O<cv-P7~u22p$Ft
zKfnIY&O|wn3N5RbG?0Qhi5@sl_#7Vv1VfldueLesws<arcwHm0Id<|<G2W@G{(ajU
z8o@h&Lm)3ed;_5fjM}~2oB5k&3E)+J7hP^7vr$akyZux>=@<l@c~@OqvP*LDrB@lb
ze_Uqp2f32N458l$9$)}C5$de{Wl@)?t9lWvYuG=Xi?6?A881g5=z=gQtNLi!mtY!c
zsPJ~D4AVwi5D0nD2x(bid`PKr7(qM$qMO?*;>cR_2_h6}Bz&D(Ix~aM2b_x8Jg<c0
z9}p%%@JLqlI&mYOm2y=@{j|uPX7T_7cpwAJ1A+%MV8eO?gVbX6<th$O;}O(Z(AWu$
zLQro5esd2v$W`>J978^Z2N(n@-z~iQqyvLs2=xybFN9jQFjFI%F*FW>k^ZN7_u2U=
zJp@;2zt~pMQaEd@_}fqc^&B*&fpB~-t2xIf{}0Iv(6|BX4I1FI9^_w`Ue}3OwNte>
ziw$DxL2Dm5p$f$5dP4pF`95N7hP(|1-IEbU)d36Jh;a#oR@gtDmt6Kvn2w^A=@VS~
zK~orrRM<aW&o<MnlN0cn3)KZ1{?JLi0Ch0rQ!sddKmh9+1W=Q*y?5J6Qc&dM#@L)o
zLu6e8aqq>fa1EQSM_?U9EwD(oXG|ezgaiQx>R_l(!GI&6$i7qQNfT*&g7^ma2l5X*
zU;-K{zz_j^$cA7+fz3fvL@<PgWxyeCGw<z~$nQo|Dei0C`(_^$@E6BdT<I6>WsV@I
ze*%{qdQ})qVK6o`=|4^<*z!_TczNHd>2OmTtQcz}GMNbC&bx)?w4=k^6u?y=cmQDw
z>tJ&9W*^mQ1SS7Derw927kBNq)r+2OFo!(%{GRry?QbTK;r=&!mwfq!AihEV@zj{;
zqRu|p1M0c2%AP$7pXL%m5b_TgXR{0Fhk0fiP`|jT;PXBf3J8W^AYk8BrH8}VQ$Q#J
zfnce<Pk6h*I9b;qOwO&dXfx|<`dAZ)A)KX~k9+a&2|PZd>f4a?2ZJz~d4wq%CfAK%
z|9}Ap8ik;-6Y4oIDgZGIjVQoZfsq~>Dga;4jiVqKqAO>&+447$Mid~_O@rCLtt@CG
zdCs-fPU?Z(IC)?O@JP{xi_X%F(+H6Yc@E-Ruq{)6U(g>6Mn<UTpw5Cu)tMEl36Gqa
z2n;ya=-v;#7N%jlBlfOpIiq;_SLIhqOB3+@opNEGX5$Ff8^nVfv`*F<N*Ok+-qvxJ
z=NAGa39N(W_%}7RTYE9KIKu5X?1sRg%XO{BX69%w{xHq1dupHq4R0@hr`Etunadx*
zJdn3JACy_{4<9D=g43HK<x%BXK5UDX=LMf4!XpU=(zi4Y^^nGO;E{wLwlA<>H2Ocr
z3&6d=BNZkV_e-*m5F<U{(b%aKuG2lk=<?pz+19SZ*q}*GVo0SQw*+kcKB8TUR@mD`
zt6r(c6~VYg!*mcsUI6?i5t4P)?^-a1`ZPM>&5GQ}91Ma7;3}GqB%9=H<2y9GfFT?j
zxvZAjSP2qhc-cS1@N#`Cq5nMJ=%JOc$$uYcWU6Q-^4|v<t<bkxIr~5(lmC5`k?8EB
zjGEY4SpIp`(LaCxJP7HZzqup4|8FCl|8MWlPVt{Vcz^Esa6I=sI3GrMAI_gU!uxPO
zjC0R}V>lm1I3JGxZG_Lm`7q9X9ghEPgwM?#=bk_JefWRD>vNCiz7F5_Z|A}L|Mt1L
z@56a>Ux#Cu2ae(Vxg)$kcZAQw2=Bw^;rRb|gzx>gaqc{DJa>fo;d|yD&wU*}59h)0
z-1FwX565#y`25`0=N?<Fvau2)5o7f~5kp+xYR!M7q=!}#CM(_4BWJ&B?tf$MF?=n&
z4%5RijC0R}*I|Uu!3eLz=U{~Q;TX>QxAEW3gV*8v{%wTsh4cUI7(Nf@!3gKW2<O2F
b=fN?Ib6<yJ7~%V1gx9Us+E_^%8O#1Z4m3YE

diff --git a/tutorials/test_set.csv b/tutorials/test_set.csv
deleted file mode 100644
index 34928b9..0000000
--- a/tutorials/test_set.csv
+++ /dev/null
@@ -1,54 +0,0 @@
-3.000000000000000000e+00,5.557391941977805061e-01,1.238550434380931708e+00,-5.214359630530969181e-01,1.440900834400055952e-01,-8.135811375106852816e-01,-1.108104519644248498e+00,-1.756306948722317518e+00,1.945738962393688265e+00,-1.395679990380090274e+00,3.179531790963357474e-01,-6.978181815257052945e-01,-1.202178738239686906e+00,-5.377053749563008855e-01
-2.000000000000000000e+00,-8.806943312902405507e-01,-1.040848934513725732e+00,-1.900449979236804865e-01,-9.806418628362864043e-01,9.844444918418605861e-02,-4.406380376353093431e-01,-1.109628967894998430e+00,2.361008551613684059e+00,-2.256058981699655241e+00,-7.611823339265566535e-01,1.256044077739537190e+00,-1.416442100938596793e+00,-2.326243111513074946e-01
-3.000000000000000000e+00,-6.330333786198930612e-01,2.996159586299703914e+00,1.135518862593988709e+00,1.911525999017035904e+00,-2.523346226215336663e-01,-8.442224221058305389e-01,-1.629091608231697297e+00,2.361008551613684059e+00,-9.929493986985916676e-01,7.091097155584770960e-03,-6.534122210878593462e-01,-1.387873652578742290e+00,-7.360080664295466368e-01
-2.000000000000000000e+00,-1.487463665332594553e+00,5.977554310772129620e-01,-3.741510896622448068e-01,-3.379378935355480951e-01,8.701584071567695089e-01,-9.528797563863556919e-01,-8.657995652879766357e-01,6.999301947337017715e-01,-1.267538438481431662e+00,-1.036517320788364671e+00,-3.172877495800926345e-02,-2.022830456447746450e-01,-7.817702260002956649e-01
-2.000000000000000000e+00,-1.190270522128176189e+00,-3.725912882112759061e-01,-4.276012453282835440e-02,-1.772619012103635316e-01,-1.304671838038692799e+00,-1.139149472295827081e+00,-6.749765545520465260e-01,1.447415455329694067e+00,-4.428855480368826952e-03,-1.147539492910061520e+00,5.011427502961482983e-01,-5.593886501429575775e-01,-8.427864387612943320e-01
-3.000000000000000000e+00,5.433561465642633426e-01,-5.190587175378403861e-01,1.135518862593988709e+00,1.108146037391112948e+00,-7.434253231495413505e-01,4.130981602831020894e-01,-1.099027689520780227e+00,1.447415455329694067e+00,1.185456983578603740e+00,3.004689744441396915e+00,-1.763561232034019932e+00,-1.259315634959396135e+00,-4.156729494343035514e-01
-1.000000000000000000e+00,4.566748131296410329e-01,1.467405792703688538e+00,-7.791844914870873495e-01,-8.085630581525254934e-02,-6.732695087883974194e-01,1.802610153962628525e-01,5.759742936057181684e-01,-6.289324907702836809e-01,-2.973238312487311297e-01,-3.126527585549017663e-01,-2.537585771472410934e-01,4.833597149917366331e-01,8.504134653564191515e-01
-3.000000000000000000e+00,-9.797587123583800794e-01,2.272976653999792429e+00,7.673066791168583478e-01,6.261180604155592855e-01,-7.434253231495413505e-01,-1.480643951463191721e+00,-1.766908227096535944e+00,1.530469373173693137e+00,-1.542127478264271323e+00,-5.036108946042202605e-01,-9.642539441527840927e-01,-1.987811068135689530e+00,-1.105918856293101188e-01
-3.000000000000000000e+00,5.062070036637096315e-01,1.339246792042944945e+00,9.882339892031365425e-01,1.268822029716297539e+00,8.000025927956255778e-01,-9.839247090379342753e-01,-1.289850700256710558e+00,6.999301947337017715e-01,-3.339357032197764474e-01,2.529514847760534924e+00,-5.201943397743199471e-01,-1.573568566917797229e+00,-1.868621515805584665e-01
-2.000000000000000000e+00,-9.302265218243114253e-01,5.428301450797512473e-01,-4.109723080099570436e-01,-3.379378935355480951e-01,-8.135811375106852816e-01,1.802610153962628525e-01,1.201193235143292598e-01,-7.950403264582820428e-01,6.362789040129241958e-01,-1.258561665031758148e+00,8.119844733610726006e-01,9.118864403895559079e-01,-1.422440459990781836e+00
-3.000000000000000000e+00,-5.711181404523050231e-01,-8.669188621884306167e-01,-8.528269281825133774e-01,3.047660757651901586e-01,2.387560779064739624e-01,-1.325419188205298582e+00,-1.650294164980134148e+00,1.530469373173693137e+00,-4.437713191329124562e-01,1.161721687221230814e+00,-1.719155271596173540e+00,-1.587852791097724481e+00,-3.546567366733048843e-01
-3.000000000000000000e+00,3.947595749620530503e-01,2.956663580911739198e-01,6.770353051031155478e-02,3.047660757651901586e-01,-7.434253231495413505e-01,-1.449598998811613138e+00,-1.735104391973880889e+00,2.016066876697071852e-01,-1.835022454032633643e+00,2.735443102476567967e-01,-1.186283746342016387e+00,-2.737041665444109872e-01,7.245675265368593798e-02
-3.000000000000000000e+00,9.148475755697846878e-01,4.787506447493790618e-01,-1.164025612282544170e-01,9.474700450659284678e-01,-6.732695087883974194e-01,-1.527211380440559374e+00,-1.544281381237950557e+00,5.338223590457034096e-01,-1.121090950597250391e+00,2.041017290425069408e+00,-1.186283746342016387e+00,-1.402157876758669541e+00,-4.309270026245532459e-01
-1.000000000000000000e+00,8.900814803027503608e-01,-6.472177181985840910e-01,1.356446172680266793e+00,1.440900834400055952e-01,2.273274694379648242e+00,9.563848316857274101e-01,6.395819638510282790e-01,1.281307619641695705e+00,-5.353009990605254176e-01,1.847265725502997280e-01,1.256044077739537190e+00,4.833597149917366331e-01,1.460575592966406155e+00
-1.000000000000000000e+00,6.671866228994370873e-01,-5.282129318707506904e-01,1.045247488580237777e-01,1.554928957985841215e-02,4.492235209899058113e-01,8.166825447536233407e-01,1.116639490690853442e+00,-1.044202079990279364e+00,5.630551600708334492e-01,8.508596052804802135e-01,5.455487107339948016e-01,3.119490248326086679e-01,2.314802571620387717e+00
-3.000000000000000000e+00,8.518338412411752270e-02,-3.085117878809041647e-01,1.540552264418831196e+00,2.072201991342220495e+00,1.150781664601345344e+00,-2.078008927484694124e-01,-9.082046787848500058e-01,-7.119864086142828619e-01,-1.508763433645497753e-01,9.396773429778373377e-01,-1.585937390282634141e+00,-1.902105723056125575e+00,-1.010581023854040694e+00
-2.000000000000000000e+00,-1.190270522128176189e+00,-1.013386291514995152e+00,6.568430240737184178e-01,1.590174014366666722e+00,-1.515139281122124704e+00,-5.182504192642555241e-01,-5.901663275582996748e-01,4.507684412017047282e-01,-4.437713191329124562e-01,-1.236357230607418645e+00,1.522479840366615989e+00,8.340143795377198688e-02,-3.851648430538042178e-01
-2.000000000000000000e+00,-7.692469025885839695e-01,2.007504438345394160e+00,1.466909827723405169e+00,2.393553975992589677e+00,1.686002635453300036e-01,-2.078008927484694124e-01,-7.096016976290977010e-03,6.999301947337017715e-01,1.237126964182898581e-01,-1.289647873225833186e+00,-2.093526167093946455e-01,6.547704051508645984e-01,-1.193629662137036807e+00
-1.000000000000000000e+00,1.038678051904960764e+00,-3.176660022138144135e-01,9.514127708554226404e-01,-1.301993847486655476e+00,9.844444918418605861e-02,1.422059121459405828e+00,1.508886790536932310e+00,-1.459471669210274936e+00,1.054067604327671853e-01,1.980492332049030579e-01,-3.869764584607804925e-01,1.283276269067666675e+00,1.033462103639415153e+00
-2.000000000000000000e+00,-1.722741570369424879e+00,-5.190587175378403861e-01,1.061876425898562681e+00,2.232877983667405086e+00,-8.135811375106852816e-01,-6.424302298705699688e-01,-5.583624924356446195e-01,4.507684412017047282e-01,-5.536069350460480765e-01,-1.058721755212703952e+00,1.744509642555848394e+00,7.833284227702103086e-01,-5.926199664411997414e-01
-3.000000000000000000e+00,-2.491589019808523320e-01,6.618349314075846479e-01,1.025055207550848779e+00,1.590174014366666722e+00,9.844444918418605861e-02,-2.153117683899845661e-02,-1.629091608231697297e+00,1.530469373173693137e+00,-1.523821542278748664e+00,-2.843599792335822080e-02,-3.425704980229340446e-01,-7.307993403020855983e-01,-5.071972685758016075e-01
-3.000000000000000000e+00,-2.120097590803008691e-01,2.172280296337779859e+00,5.463793690305801531e-01,7.867940527407438767e-01,-9.538927662329731438e-01,-9.839247090379342753e-01,-1.576085216360605834e+00,1.032145866109698051e+00,-1.432291862351135592e+00,1.183926121645570539e+00,-1.896779113347558887e+00,-1.145041841519977677e+00,-4.004188962440538568e-01
-3.000000000000000000e+00,-3.110741401484403146e-01,3.963627157531867673e-01,5.463793690305801531e-01,9.474700450659284678e-01,8.701584071567695089e-01,-1.325419188205298582e+00,-8.233944517911032657e-01,-8.780942443022812238e-01,-7.000544229302294585e-01,2.582805490378949465e+00,-2.163214875974637685e+00,-1.702126584537143161e+00,-8.427864387612943320e-01
-3.000000000000000000e+00,-2.367758543473351684e-01,9.456155757278031926e-01,9.145915525077105146e-01,9.474700450659284678e-01,4.492235209899058113e-01,-1.061537090666880845e+00,-1.629091608231697297e+00,2.111846798081686405e+00,-1.249232502495909003e+00,2.646625364779212619e-01,-4.313824188986269959e-01,-7.879362370217949385e-01,-5.682134813368002746e-01
-2.000000000000000000e+00,-4.225215688500969513e-01,1.494868435702419562e+00,2.518096222488758751e-01,1.268822029716297539e+00,9.844444918418605861e-02,7.701151157762557986e-01,4.381576747408793548e-01,6.999301947337017715e-01,5.630551600708334492e-01,-1.072044415867307698e+00,9.896083151124585031e-01,6.690546293307916281e-01,-8.946502196081432823e-01
-3.000000000000000000e+00,4.442917654961238694e-01,2.163126082004868778e+00,1.945585666243673684e+00,1.911525999017035904e+00,8.701584071567695089e-01,-5.492953719158341075e-01,-1.247445586759837077e+00,-6.289324907702836809e-01,-9.746434627130690087e-01,1.561401506859339205e+00,-1.319501627655555342e+00,-1.059336496440413944e+00,-3.851648430538042178e-01
-3.000000000000000000e+00,-7.320977596880324789e-01,7.259144317379564448e-01,1.135518862593988709e+00,2.554229968317774269e+00,-1.821788082603897352e-01,-6.734751825221485522e-01,-1.650294164980134148e+00,2.361008551613684059e+00,-9.197256547565014762e-01,1.117312818372552252e+00,-1.319501627655555342e+00,-1.330736755859032838e+00,3.775378164586793983e-01
-2.000000000000000000e+00,-7.073316644209959314e-01,-6.563719325314943953e-01,-4.846147447053830715e-01,-1.658590888517895781e-02,4.377949125213967285e+00,2.578733970252090058e-01,1.413218802627659587e-01,-2.136629015502882756e-01,2.997744646145346081e+00,-1.058721755212703952e+00,8.563904337989191040e-01,-4.515657966557431319e-02,5.514340228275256273e-01
-1.000000000000000000e+00,9.024645279362675243e-01,-3.085117878809041647e-01,1.282803735984840765e+00,-6.271546797208799040e-01,1.080625850240201302e+00,1.033997213314673536e+00,6.925883557221198528e-01,3.677145233577055472e-01,6.879488846172185368e-02,5.844063921884077306e-01,7.231725524853797049e-01,3.833701457322456485e-01,1.887689082293396936e+00
-1.000000000000000000e+00,1.311105099842342581e+00,-7.050221522523687778e-02,-1.164025612282544170e-01,-3.379378935355480951e-01,3.790677066287618246e-01,9.563848316857274101e-01,1.254456109555691867e+00,-1.044202079990279364e+00,1.350210407448307448e+00,3.401576135206750284e-01,1.256044077739537190e+00,7.261915260505009684e-01,2.299548518430138078e+00
-1.000000000000000000e+00,-2.367758543473351684e-01,-5.831382178682121831e-01,6.936642424214323199e-01,-4.022082904656216762e-01,-3.224904369826776529e-01,2.268284443736304223e-01,2.473346640049494949e-01,-7.119864086142828619e-01,-3.339357032197764474e-01,-4.680837995252772332e-01,5.455487107339948016e-01,1.383265838327157660e+00,7.893972525954204844e-01
-1.000000000000000000e+00,1.088509832301457966e-02,-5.373671462036609947e-01,8.041278974645705846e-01,-3.379378935355480951e-01,-1.120229938992457902e-01,1.802610153962628525e-01,3.109423342502596332e-01,-4.628246550822858185e-01,-3.705475751908218207e-01,-3.259754192095054570e-01,6.787665920475342007e-01,-2.165672698247022854e-01,1.063970210019914653e+00
-3.000000000000000000e+00,-6.577994738869272773e-01,-9.126899338529820271e-01,-3.005086529668187789e-01,-4.986138858607326307e-01,-1.024048580594117075e+00,-5.182504192642555241e-01,-1.650294164980134148e+00,2.111846798081686405e+00,-7.183603589157521174e-01,2.069310069746389813e-01,-9.642539441527840927e-01,-1.644989687817433932e+00,-3.241486302928054952e-01
-2.000000000000000000e+00,-1.958019475406255205e+00,1.375863649374586162e+00,-1.883821041918477546e+00,1.440900834400055952e-01,5.193793353510497424e-01,1.313401787178881008e+00,4.699615098635346877e-01,-8.780942443022812238e-01,3.546922725711026292e+00,-9.254951486666679328e-01,-9.642539441527840927e-01,2.119594555731176555e-01,-5.926199664411997414e-01
-3.000000000000000000e+00,-2.739249972478866590e-01,1.037157719056905902e+00,-1.164025612282544170e-01,1.440900834400055952e-01,-8.135811375106852816e-01,-1.325419188205298582e+00,-1.565483937986387186e+00,4.507684412017047282e-01,-1.230926566510386344e+00,2.342997598596084252e+00,-1.097471825466323381e+00,-1.302168307499178113e+00,-2.173702579610578278e-01
-2.000000000000000000e+00,-1.970402523039774589e+00,1.491989287646094120e-01,3.254520589443019585e-01,3.047660757651901586e-01,-2.523346226215336663e-01,8.787724500567805075e-01,6.925883557221198528e-01,-2.136629015502882756e-01,3.433839282445618757e-01,-7.700641076962924103e-01,-7.422241419635517978e-01,1.040444458008902284e+00,-4.004188962440538568e-01
-3.000000000000000000e+00,5.928883370983319967e-01,8.540734323987003718e-01,1.430088609375692821e+00,1.429498022041482130e+00,-1.821788082603897352e-01,-1.216761853924773540e+00,-1.713901835225444259e+00,1.281307619641695705e+00,-1.999775877902337795e+00,-2.815665503608269504e-01,-3.425704980229340446e-01,-8.593573579214313085e-01,-7.207540132392969978e-01
-1.000000000000000000e+00,2.215067577089114614e+00,-5.465213605365711880e-01,-5.950783997485230570e-01,-1.623345832137024658e+00,-1.821788082603897352e-01,7.235476867988874794e-01,8.940126448322682773e-01,-4.628246550822858185e-01,6.179729680274015369e-01,9.590883485294224298e-02,5.011427502961482983e-01,2.690963522928269680e-01,8.809215717369185406e-01
-2.000000000000000000e+00,5.557391941977805061e-01,-5.282129318707506904e-01,-3.373298713145309602e-01,1.590174014366666722e+00,-8.837369518718292127e-01,-7.045201351737271356e-01,-3.145330898286227694e-01,-6.289324907702836809e-01,-1.121090950597250391e+00,-5.524606503377667011e-01,5.708314591768366697e-02,1.691067830333353450e-01,-8.671929238656937988e-01
-2.000000000000000000e+00,-1.190270522128176189e+00,-7.753767188593280180e-01,6.200218057260061810e-01,1.108146037391112948e+00,-1.094204394955261117e+00,3.510082549799448670e-01,1.625244370112026715e-01,6.999301947337017715e-01,-1.102785014611727732e+00,-9.254951486666679328e-01,-1.649466562715481699e-01,7.547599744103555830e-01,-1.132613449376038028e+00
-3.000000000000000000e+00,3.452273844279844517e-01,9.181529327290725018e-01,1.781671855534498472e-01,7.867940527407438767e-01,-5.329578800661094462e-01,-6.269077535447806770e-01,-1.459471154244204039e+00,8.660380304217001335e-01,-7.183603589157521174e-01,1.525874411780396178e+00,-1.852373152909712495e+00,-1.487863221838233274e+00,-3.241486302928054952e-01
-3.000000000000000000e+00,-1.005623303786420397e-01,1.110391433720188115e+00,6.770353051031155478e-02,-1.772619012103635316e-01,4.492235209899058113e-01,-1.465121475137402429e+00,-1.523078824489514149e+00,4.507684412017047282e-01,-1.285844374466954321e+00,1.314359299318852425e-01,-1.275095667217708950e+00,-1.302168307499178113e+00,-2.478783643415571614e-01
-1.000000000000000000e+00,2.956951938939136326e-01,-5.465213605365711880e-01,1.871943229548247656e+00,-1.141317855161470884e+00,7.298467784344815357e-01,4.130981602831020894e-01,5.759742936057181684e-01,-4.755506586228989974e-02,-5.169950630750027587e-01,-1.705443782391301843e-01,5.455487107339948016e-01,1.691067830333353450e-01,3.775378164586793983e-01
-1.000000000000000000e+00,1.261572909308273926e+00,-5.556755748694814923e-01,-1.900449979236804865e-01,-9.806418628362864043e-01,1.852339808212784655e+00,1.033997213314673536e+00,1.095436933942416813e+00,-8.780942443022812238e-01,8.376441998536735545e-01,2.957487446719964663e-01,5.455487107339948016e-01,1.497539631766576118e+00,7.245675265368593798e-02
-2.000000000000000000e+00,-1.834188999071081572e+00,-1.711985728872501555e-01,3.307970745109054977e+00,3.036257945293327598e+00,1.361249107684777027e+00,1.313401787178881008e+00,3.120281103418120594e+00,1.032145866109698051e+00,4.166076721866525667e-01,4.511797856423717668e-01,-1.649466562715481699e-01,1.468971183406721615e+00,-8.885485983320433601e-01
-1.000000000000000000e+00,1.026295004271441380e+00,-3.085117878809041647e-01,1.724658356157395600e+00,1.119548849749682357e-01,5.193793353510497424e-01,9.563848316857274101e-01,8.834113664580501846e-01,2.016066876697071852e-01,2.152423763459032080e-01,-2.149532470878087187e-01,1.256044077739537190e+00,1.054728682188829314e+00,4.843161887904270935e-01
-1.000000000000000000e+00,2.116003196020975530e+00,-4.641334315403787270e-01,2.149884039011636938e-01,-2.458860992227984266e+00,-6.031136944272533773e-01,1.189221976572566675e+00,1.646703409401770735e+00,6.999301947337017715e-01,2.137365654825782002e+00,1.847265725502997280e-01,1.256044077739537190e+00,9.768566213369900275e-02,1.201256688732161626e+00
-2.000000000000000000e+00,-1.735124618002942043e+00,-1.620443585543396847e-01,4.727369323351540698e-01,8.189292512057811946e-01,-1.094204394955261117e+00,-5.958628008932020936e-01,-4.735522654418980459e-01,1.115199783953697343e+00,-5.353009990605254176e-01,-9.699040175153467169e-01,1.458950667933766043e-01,1.262541104935536729e-01,-2.326243111513074946e-01
-1.000000000000000000e+00,5.681222418312976696e-01,-3.908997168770964592e-01,1.025055207550848779e+00,3.047660757651901586e-01,-2.523346226215336663e-01,3.044408260025766033e-01,5.017653449861895210e-01,-5.458785729262844999e-01,3.218301649067650816e-02,-6.501601618048599152e-01,6.787665920475342007e-01,1.654666097745776554e+00,2.707594441269316476e-01
-2.000000000000000000e+00,-1.190270522128176189e+00,-1.437359298885191317e-01,-2.325675662091033935e+00,-4.986138858607326307e-01,-1.821788082603897352e-01,-1.612334637711018426e-01,3.530909652058243642e-02,-7.119864086142828619e-01,-4.437713191329124562e-01,-7.478596732719532403e-01,1.344855998615230197e+00,4.262228182720272929e-01,-1.410999920098094662e-01
-2.000000000000000000e+00,-1.549378903500182370e+00,-9.796485822396792942e-02,1.651015919461969350e+00,3.036257945293327598e+00,-5.329578800661094462e-01,-3.164582270289948984e-01,1.095180451401111393e-01,1.945738962393688265e+00,2.152423763459032080e-01,-8.810862798179891486e-01,1.267718547983720349e-02,-3.165568390841932978e-01,-8.854977876939934545e-01
-3.000000000000000000e+00,7.167188134335057415e-01,2.483523583656729095e+00,6.770353051031155478e-02,3.047660757651901586e-01,-5.329578800661094462e-01,-5.182504192642555241e-01,-1.417066040747330558e+00,1.032145866109698051e+00,-1.139396886582773050e+00,-2.593621159364872808e-01,-2.537585771472410934e-01,-8.736415821013588934e-01,-6.292296940977989417e-01
-3.000000000000000000e+00,-9.797587123583800794e-01,1.504022650035329534e+00,-4.846147447053830715e-01,-1.772619012103635316e-01,8.701584071567695089e-01,-1.480643951463191721e+00,-1.438268597495767187e+00,-4.628246550822858185e-01,-9.197256547565014762e-01,1.432615787198171287e+00,-1.408313548531248349e+00,-9.450627030009952634e-01,3.012675505074310367e-01
-2.000000000000000000e+00,-4.596707117506483864e-01,-1.150699506508649161e+00,-3.373298713145309602e-01,-3.379378935355480951e-01,-4.186717953810183829e-02,-2.078008927484694124e-01,-2.085203060864394553e-01,-3.797707372382866375e-01,-3.339357032197764474e-01,-1.049839981442968195e+00,1.167232156863844406e+00,7.119073018705739386e-01,-9.343107579027923881e-01
diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
new file mode 100644
index 0000000..b70b3d9
--- /dev/null
+++ b/tutorials/things_in_pandas.ipynb
@@ -0,0 +1,1770 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:5455ed1ab796c2d7a01bee46d8f4d18de1bbc93ba0257248aff18e7b73a05e0a"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[Back to the GitHub repository](https://github.com/rasbt/python_reference)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%load_ext watermark\n",
+      "%watermark -a 'Sebastian Raschka' -v -d -p pandas"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Sebastian Raschka 24/01/2015 \n",
+        "\n",
+        "CPython 3.4.2\n",
+        "IPython 2.3.1\n",
+        "\n",
+        "pandas 0.15.2\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Things in Pandas I Wish I'd Had Known Earlier"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "This is just a small but growing collection of pandas snippets that I find occasionally and particularly useful -- consider it as my personal notebook. Suggestions, tips, and contributions are very, very welcome!"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Sections"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "- [Loading Some Example Data](#Loading-Some-Example-Data)\n",
+      "- [Renaming Columns](#Renaming-Columns)\n",
+      "- [Applying Computations Rows-wise](#Applying-Computations-Rows-wise)\n",
+      "- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n",
+      "    - [Selecting NaN Rows](#Selecting-NaN-Rows)\n",
+      "    - [Dropping NaN Rows](#Dropping-NaN-Rows)\n",
+      "- [Appending Rows to a DataFrame](#Appending-Rows-to-a-DataFrame)\n",
+      "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Loading Some Example Data"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "I am heavily into sports prediction (via a machine learning approach) these days. So, let us use a (very) small subset of the soccer data that I am just working with."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import pandas as pd\n",
+      "\n",
+      "df = pd.read_csv('https://raw.githubusercontent.com/rasbt/python_reference/master/Data/some_soccer_data.csv')\n",
+      "df"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>PLAYER</th>\n",
+        "      <th>SALARY</th>\n",
+        "      <th>GP</th>\n",
+        "      <th>G</th>\n",
+        "      <th>A</th>\n",
+        "      <th>SOT</th>\n",
+        "      <th>PPG</th>\n",
+        "      <th>P</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>     Sergio Ag\u00fcero\\n Forward \u2014 Manchester City</td>\n",
+        "      <td> $19.2m</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>              Eden Hazard\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td> $18.9m</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  8</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 13.05</td>\n",
+        "      <td> 274.04</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>            Alexis S\u00e1nchez\\n Forward \u2014 Arsenal</td>\n",
+        "      <td> $17.6m</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>       Yaya Tour\u00e9\\n Midfield \u2014 Manchester City</td>\n",
+        "      <td> $16.6m</td>\n",
+        "      <td> 18</td>\n",
+        "      <td>  7</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 19</td>\n",
+        "      <td> 10.99</td>\n",
+        "      <td> 197.91</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> \u00c1ngel Di Mar\u00eda\\n Midfield \u2014 Manchester United</td>\n",
+        "      <td> $15.0m</td>\n",
+        "      <td> 13</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 13</td>\n",
+        "      <td> 10.17</td>\n",
+        "      <td> 132.23</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>         Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
+        "      <td> $14.8m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>    NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td>      David Silva\\n Midfield \u2014 Manchester City</td>\n",
+        "      <td> $14.3m</td>\n",
+        "      <td> 15</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>            Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td> $14.0m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>          Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td> $13.8m</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>         Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> $13.8m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "                                          PLAYER  SALARY  GP   G   A  SOT  \\\n",
+        "0      Sergio Ag\u00fcero\\n Forward \u2014 Manchester City  $19.2m  16  14   3   34   \n",
+        "1               Eden Hazard\\n Midfield \u2014 Chelsea  $18.9m  21   8   4   17   \n",
+        "2             Alexis S\u00e1nchez\\n Forward \u2014 Arsenal  $17.6m NaN  12   7   29   \n",
+        "3        Yaya Tour\u00e9\\n Midfield \u2014 Manchester City  $16.6m  18   7   1   19   \n",
+        "4  \u00c1ngel Di Mar\u00eda\\n Midfield \u2014 Manchester United  $15.0m  13   3 NaN   13   \n",
+        "5          Santiago Cazorla\\n Midfield \u2014 Arsenal  $14.8m  20   4 NaN   20   \n",
+        "6       David Silva\\n Midfield \u2014 Manchester City  $14.3m  15   6   2   11   \n",
+        "7             Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m  20   2  14   10   \n",
+        "8           Saido Berahino\\n Forward \u2014 West Brom  $13.8m  21   9   0   20   \n",
+        "9          Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m  20   5   1   11   \n",
+        "\n",
+        "     PPG       P  \n",
+        "0  13.12  209.98  \n",
+        "1  13.05  274.04  \n",
+        "2  11.19  223.86  \n",
+        "3  10.99  197.91  \n",
+        "4  10.17  132.23  \n",
+        "5   9.97     NaN  \n",
+        "6  10.35  155.26  \n",
+        "7  10.47  209.49  \n",
+        "8   7.02  147.43  \n",
+        "9   7.50  150.01  "
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Renaming Columns"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Converting column names to lowercase\n",
+      "\n",
+      "df.columns = [c.lower() for c in df.columns]\n",
+      "df.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>gp</th>\n",
+        "      <th>g</th>\n",
+        "      <th>a</th>\n",
+        "      <th>sot</th>\n",
+        "      <th>ppg</th>\n",
+        "      <th>p</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>    Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
+        "      <td> $14.8m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>    NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td> David Silva\\n Midfield \u2014 Manchester City</td>\n",
+        "      <td> $14.3m</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>       Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td> $14.0m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>     Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td> $13.8m</td>\n",
+        "      <td> 21</td>\n",
+        "      <td> 9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>    Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> $13.8m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 3,
+       "text": [
+        "                                     player  salary  gp  g   a  sot    ppg  \\\n",
+        "5     Santiago Cazorla\\n Midfield \u2014 Arsenal  $14.8m  20  4 NaN   20   9.97   \n",
+        "6  David Silva\\n Midfield \u2014 Manchester City  $14.3m  15  6   2   11  10.35   \n",
+        "7        Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m  20  2  14   10  10.47   \n",
+        "8      Saido Berahino\\n Forward \u2014 West Brom  $13.8m  21  9   0   20   7.02   \n",
+        "9     Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m  20  5   1   11   7.50   \n",
+        "\n",
+        "        p  \n",
+        "5     NaN  \n",
+        "6  155.26  \n",
+        "7  209.49  \n",
+        "8  147.43  \n",
+        "9  150.01  "
+       ]
+      }
+     ],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Renaming particular columns\n",
+      "\n",
+      "df = df.rename(columns={'p': 'points', \n",
+      "                        'gp': 'games',\n",
+      "                        'sot': 'shots_on_target',\n",
+      "                        'g': 'goals',\n",
+      "                        'ppg': 'points_per_game',\n",
+      "                        'a': 'assists',})\n",
+      "\n",
+      "df.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>    Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
+        "      <td> $14.8m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>    NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td> David Silva\\n Midfield \u2014 Manchester City</td>\n",
+        "      <td> $14.3m</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>       Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td> $14.0m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>     Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td> $13.8m</td>\n",
+        "      <td> 21</td>\n",
+        "      <td> 9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>    Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> $13.8m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 4,
+       "text": [
+        "                                     player  salary  games  goals  assists  \\\n",
+        "5     Santiago Cazorla\\n Midfield \u2014 Arsenal  $14.8m     20      4      NaN   \n",
+        "6  David Silva\\n Midfield \u2014 Manchester City  $14.3m     15      6        2   \n",
+        "7        Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m     20      2       14   \n",
+        "8      Saido Berahino\\n Forward \u2014 West Brom  $13.8m     21      9        0   \n",
+        "9     Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m     20      5        1   \n",
+        "\n",
+        "   shots_on_target  points_per_game  points  \n",
+        "5               20             9.97     NaN  \n",
+        "6               11            10.35  155.26  \n",
+        "7               10            10.47  209.49  \n",
+        "8               20             7.02  147.43  \n",
+        "9               11             7.50  150.01  "
+       ]
+      }
+     ],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Applying Computations Rows-wise"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Create a new column\n",
+      "df['team'] = pd.Series('', index=df.index)\n",
+      "df.tail(3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>    Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td> $14.0m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td> </td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>  Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td> $13.8m</td>\n",
+        "      <td> 21</td>\n",
+        "      <td> 9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td> </td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td> Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> $13.8m</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "      <td> </td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "                                  player  salary  games  goals  assists  \\\n",
+        "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m     20      2       14   \n",
+        "8   Saido Berahino\\n Forward \u2014 West Brom  $13.8m     21      9        0   \n",
+        "9  Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m     20      5        1   \n",
+        "\n",
+        "   shots_on_target  points_per_game  points team  \n",
+        "7               10            10.47  209.49       \n",
+        "8               20             7.02  147.43       \n",
+        "9               11             7.50  150.01       "
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# process salary column\n",
+      "\n",
+      "df['salary'] = df['salary'].apply(lambda x: x.strip('$m'))\n",
+      "df.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>    Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
+        "      <td> 14.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>    NaN</td>\n",
+        "      <td> </td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td> David Silva\\n Midfield \u2014 Manchester City</td>\n",
+        "      <td> 14.3</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "      <td> </td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>       Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td> 14.0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td> </td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>     Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td> 9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td> </td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>    Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "      <td> </td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "                                     player salary  games  goals  assists  \\\n",
+        "5     Santiago Cazorla\\n Midfield \u2014 Arsenal   14.8     20      4      NaN   \n",
+        "6  David Silva\\n Midfield \u2014 Manchester City   14.3     15      6        2   \n",
+        "7        Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea   14.0     20      2       14   \n",
+        "8      Saido Berahino\\n Forward \u2014 West Brom   13.8     21      9        0   \n",
+        "9     Steven Gerrard\\n Midfield \u2014 Liverpool   13.8     20      5        1   \n",
+        "\n",
+        "   shots_on_target  points_per_game  points team  \n",
+        "5               20             9.97     NaN       \n",
+        "6               11            10.35  155.26       \n",
+        "7               10            10.47  209.49       \n",
+        "8               20             7.02  147.43       \n",
+        "9               11             7.50  150.01       "
+       ]
+      }
+     ],
+     "prompt_number": 6
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# process `player` column\n",
+      "\n",
+      "def process_player_col(text):\n",
+      "    name, rest = text.split('\\n')\n",
+      "    position, team = rest.split(' \u2014 ')\n",
+      "    return name, position, team\n",
+      "\n",
+      "for idx,row in df.iterrows():\n",
+      "    name, position, team = process_player_col(row['player'])\n",
+      "    df.ix[idx, 'player'], df.ix[idx, 'position'], df.ix[idx, 'team'] = name, position, team\n",
+      "    \n",
+      "df.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td> Santiago Cazorla</td>\n",
+        "      <td> 14.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>    NaN</td>\n",
+        "      <td>         Arsenal</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td>      David Silva</td>\n",
+        "      <td> 14.3</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>    Cesc F\u00e0bregas</td>\n",
+        "      <td> 14.0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>   Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td> 9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>   Steven Gerrard</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "      <td>       Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 7,
+       "text": [
+        "             player salary  games  goals  assists  shots_on_target  \\\n",
+        "5  Santiago Cazorla   14.8     20      4      NaN               20   \n",
+        "6       David Silva   14.3     15      6        2               11   \n",
+        "7     Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
+        "8    Saido Berahino   13.8     21      9        0               20   \n",
+        "9    Steven Gerrard   13.8     20      5        1               11   \n",
+        "\n",
+        "   points_per_game  points             team   position  \n",
+        "5             9.97     NaN          Arsenal   Midfield  \n",
+        "6            10.35  155.26  Manchester City   Midfield  \n",
+        "7            10.47  209.49          Chelsea   Midfield  \n",
+        "8             7.02  147.43        West Brom    Forward  \n",
+        "9             7.50  150.01        Liverpool   Midfield  "
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Missing Values aka NaNs"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Selecting NaN Rows"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Selecting all rows that have NaNs in the `assists` column\n",
+      "df[~df['assists'].notnull()]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>   \u00c1ngel Di Mar\u00eda</td>\n",
+        "      <td> 15.0</td>\n",
+        "      <td> 13</td>\n",
+        "      <td> 3</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 13</td>\n",
+        "      <td> 10.17</td>\n",
+        "      <td> 132.23</td>\n",
+        "      <td> Manchester United</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td> Santiago Cazorla</td>\n",
+        "      <td> 14.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>    NaN</td>\n",
+        "      <td>           Arsenal</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "             player salary  games  goals  assists  shots_on_target  \\\n",
+        "4    \u00c1ngel Di Mar\u00eda   15.0     13      3      NaN               13   \n",
+        "5  Santiago Cazorla   14.8     20      4      NaN               20   \n",
+        "\n",
+        "   points_per_game  points               team   position  \n",
+        "4            10.17  132.23  Manchester United   Midfield  \n",
+        "5             9.97     NaN            Arsenal   Midfield  "
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Dropping NaN Rows"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Dropping all rows that have NaNs in the `assists` column\n",
+      "\n",
+      "df[df['assists'].notnull()]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td> 19.2</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>    Eden Hazard</td>\n",
+        "      <td> 18.9</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  8</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 13.05</td>\n",
+        "      <td> 274.04</td>\n",
+        "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> 17.6</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>         Arsenal</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>     Yaya Tour\u00e9</td>\n",
+        "      <td> 16.6</td>\n",
+        "      <td> 18</td>\n",
+        "      <td>  7</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 19</td>\n",
+        "      <td> 10.99</td>\n",
+        "      <td> 197.91</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td>    David Silva</td>\n",
+        "      <td> 14.3</td>\n",
+        "      <td> 15</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>  Cesc F\u00e0bregas</td>\n",
+        "      <td> 14.0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td> Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td> Steven Gerrard</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "      <td>       Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 9,
+       "text": [
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
+        "1     Eden Hazard   18.9     21      8        4               17   \n",
+        "2  Alexis S\u00e1nchez   17.6    NaN     12        7               29   \n",
+        "3      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
+        "6     David Silva   14.3     15      6        2               11   \n",
+        "7   Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
+        "8  Saido Berahino   13.8     21      9        0               20   \n",
+        "9  Steven Gerrard   13.8     20      5        1               11   \n",
+        "\n",
+        "   points_per_game  points             team   position  \n",
+        "0            13.12  209.98  Manchester City    Forward  \n",
+        "1            13.05  274.04          Chelsea   Midfield  \n",
+        "2            11.19  223.86          Arsenal    Forward  \n",
+        "3            10.99  197.91  Manchester City   Midfield  \n",
+        "6            10.35  155.26  Manchester City   Midfield  \n",
+        "7            10.47  209.49          Chelsea   Midfield  \n",
+        "8             7.02  147.43        West Brom    Forward  \n",
+        "9             7.50  150.01        Liverpool   Midfield  "
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Appending Rows to a DataFrame"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Adding an \"empty\" row to the DataFrame\n",
+      "\n",
+      "df = df.append(pd.Series(\n",
+      "                [None]*len(df.columns), # Fill cells with NaNs\n",
+      "                index=df.columns),    \n",
+      "                ignore_index=True)\n",
+      "\n",
+      "df.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>6 </th>\n",
+        "      <td>    David Silva</td>\n",
+        "      <td> 14.3</td>\n",
+        "      <td> 15</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7 </th>\n",
+        "      <td>  Cesc F\u00e0bregas</td>\n",
+        "      <td> 14.0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8 </th>\n",
+        "      <td> Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9 </th>\n",
+        "      <td> Steven Gerrard</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "      <td>       Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>10</th>\n",
+        "      <td>            NaN</td>\n",
+        "      <td>  NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>   NaN</td>\n",
+        "      <td>    NaN</td>\n",
+        "      <td>             NaN</td>\n",
+        "      <td>       NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "            player salary  games  goals  assists  shots_on_target  \\\n",
+        "6      David Silva   14.3     15      6        2               11   \n",
+        "7    Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
+        "8   Saido Berahino   13.8     21      9        0               20   \n",
+        "9   Steven Gerrard   13.8     20      5        1               11   \n",
+        "10             NaN    NaN    NaN    NaN      NaN              NaN   \n",
+        "\n",
+        "    points_per_game  points             team   position  \n",
+        "6             10.35  155.26  Manchester City   Midfield  \n",
+        "7             10.47  209.49          Chelsea   Midfield  \n",
+        "8              7.02  147.43        West Brom    Forward  \n",
+        "9              7.50  150.01        Liverpool   Midfield  \n",
+        "10              NaN     NaN              NaN        NaN  "
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Filling cells with data\n",
+      "\n",
+      "df.loc[df.index[-1], 'player'] = 'New Player'\n",
+      "df.loc[df.index[-1], 'salary'] = 12.3\n",
+      "df.tail()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>6 </th>\n",
+        "      <td>    David Silva</td>\n",
+        "      <td> 14.3</td>\n",
+        "      <td> 15</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7 </th>\n",
+        "      <td>  Cesc F\u00e0bregas</td>\n",
+        "      <td> 14.0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8 </th>\n",
+        "      <td> Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9 </th>\n",
+        "      <td> Steven Gerrard</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "      <td>       Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>10</th>\n",
+        "      <td>     New Player</td>\n",
+        "      <td> 12.3</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td>   NaN</td>\n",
+        "      <td>    NaN</td>\n",
+        "      <td>             NaN</td>\n",
+        "      <td>       NaN</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": [
+        "            player salary  games  goals  assists  shots_on_target  \\\n",
+        "6      David Silva   14.3     15      6        2               11   \n",
+        "7    Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
+        "8   Saido Berahino   13.8     21      9        0               20   \n",
+        "9   Steven Gerrard   13.8     20      5        1               11   \n",
+        "10      New Player   12.3    NaN    NaN      NaN              NaN   \n",
+        "\n",
+        "    points_per_game  points             team   position  \n",
+        "6             10.35  155.26  Manchester City   Midfield  \n",
+        "7             10.47  209.49          Chelsea   Midfield  \n",
+        "8              7.02  147.43        West Brom    Forward  \n",
+        "9              7.50  150.01        Liverpool   Midfield  \n",
+        "10              NaN     NaN              NaN        NaN  "
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Sorting and Reindexing DataFrames"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Sorting the DataFrame by a certain column (from highest to lowest)\n",
+      "\n",
+      "df = df.sort('goals', ascending=False)\n",
+      "df.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td> 19.2</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> 17.6</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 12</td>\n",
+        "      <td> 7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>         Arsenal</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td> Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>    Eden Hazard</td>\n",
+        "      <td> 18.9</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  8</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 13.05</td>\n",
+        "      <td> 274.04</td>\n",
+        "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>     Yaya Tour\u00e9</td>\n",
+        "      <td> 16.6</td>\n",
+        "      <td> 18</td>\n",
+        "      <td>  7</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 19</td>\n",
+        "      <td> 10.99</td>\n",
+        "      <td> 197.91</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 12,
+       "text": [
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
+        "2  Alexis S\u00e1nchez   17.6    NaN     12        7               29   \n",
+        "8  Saido Berahino   13.8     21      9        0               20   \n",
+        "1     Eden Hazard   18.9     21      8        4               17   \n",
+        "3      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
+        "\n",
+        "   points_per_game  points             team   position  \n",
+        "0            13.12  209.98  Manchester City    Forward  \n",
+        "2            11.19  223.86          Arsenal    Forward  \n",
+        "8             7.02  147.43        West Brom    Forward  \n",
+        "1            13.05  274.04          Chelsea   Midfield  \n",
+        "3            10.99  197.91  Manchester City   Midfield  "
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Reindexing the DataFrame after sorting\n",
+      "\n",
+      "df.index = range(1,len(df.index)+1)\n",
+      "df.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td> 19.2</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> 17.6</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 12</td>\n",
+        "      <td> 7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>         Arsenal</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>    Eden Hazard</td>\n",
+        "      <td> 18.9</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  8</td>\n",
+        "      <td> 4</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 13.05</td>\n",
+        "      <td> 274.04</td>\n",
+        "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>     Yaya Tour\u00e9</td>\n",
+        "      <td> 16.6</td>\n",
+        "      <td> 18</td>\n",
+        "      <td>  7</td>\n",
+        "      <td> 1</td>\n",
+        "      <td> 19</td>\n",
+        "      <td> 10.99</td>\n",
+        "      <td> 197.91</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 13,
+       "text": [
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "1   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
+        "2  Alexis S\u00e1nchez   17.6    NaN     12        7               29   \n",
+        "3  Saido Berahino   13.8     21      9        0               20   \n",
+        "4     Eden Hazard   18.9     21      8        4               17   \n",
+        "5      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
+        "\n",
+        "   points_per_game  points             team   position  \n",
+        "1            13.12  209.98  Manchester City    Forward  \n",
+        "2            11.19  223.86          Arsenal    Forward  \n",
+        "3             7.02  147.43        West Brom    Forward  \n",
+        "4            13.05  274.04          Chelsea   Midfield  \n",
+        "5            10.99  197.91  Manchester City   Midfield  "
+       ]
+      }
+     ],
+     "prompt_number": 13
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
\ No newline at end of file
diff --git a/tutorials/training_set.csv b/tutorials/training_set.csv
deleted file mode 100644
index 80c551d..0000000
--- a/tutorials/training_set.csv
+++ /dev/null
@@ -1,124 +0,0 @@
-1.000000000000000000e+00,9.643797661038555624e-01,-4.458250028745581739e-01,3.254520589443019585e-01,-1.623345832137024658e+00,8.000025927956255778e-01,2.399975129984130984e+00,1.699709801272862641e+00,-2.136629015502882756e-01,4.166076721866525667e-01,9.174729085534980566e-01,1.903010272312230799e-01,8.547495436698466786e-01,1.323289114254158960e+00
-2.000000000000000000e+00,7.786340516010937796e-01,-9.035357195200717229e-01,-1.515608858441348072e+00,-3.379378935355480951e-01,-3.926462513438215840e-01,-3.630256560063624960e-01,-3.675394816997145098e-01,-2.136629015502882756e-01,-1.670269030162930157e+00,-5.258153290285596526e-01,1.167232156863844406e+00,-2.879883907243386276e-01,-3.851648430538042178e-01
-2.000000000000000000e+00,-1.202653569761693353e+00,-7.050221522523687778e-02,-5.950783997485230570e-01,6.261180604155592855e-01,-1.024048580594117075e+00,4.130981602831020894e-01,5.441704584830628910e-01,2.016066876697071852e-01,-5.353009990605254176e-01,-9.876675650548181196e-01,-4.757883793364734437e-01,8.833179920297012933e-01,-1.153969123842387701e+00
-2.000000000000000000e+00,-8.311621407561718966e-01,-1.013386291514995152e+00,-6.318996180962351827e-01,-1.658590888517895781e-02,-8.837369518718292127e-01,1.810121029604138121e+00,1.021227985322888498e+00,-1.293363833522276796e+00,4.166076721866525667e-01,-2.371576815121479997e-01,1.122826196425997791e+00,2.976648006526816381e-01,-1.025835077044290333e+00
-1.000000000000000000e+00,1.261572909308273926e+00,-7.965642955814736248e-02,1.025055207550848779e+00,-4.664786873956953128e-01,1.501560736407065111e+00,4.130981602831020894e-01,3.957525612440059848e-01,-2.967168193942874566e-01,-7.183603589157521174e-01,2.929553157992404247e-02,4.123308294204554025e-01,1.311844717427521179e+00,1.643624231249402046e+00
-1.000000000000000000e+00,8.529323374021988702e-01,-6.746803611973148929e-01,1.356446172680266793e+00,1.108146037391112948e+00,9.844444918418605861e-02,1.033997213314673536e+00,1.180247160936163553e+00,-4.628246550822858185e-01,1.350210407448307448e+00,3.179531790963357474e-01,9.896083151124585031e-01,6.911721377384434650e-02,1.613116124868902768e+00
-1.000000000000000000e+00,2.461630033598449785e-01,3.231290010899045551e-01,1.982406884591387586e+00,6.261180604155592855e-01,1.291093293323633207e+00,7.235476867988874794e-01,5.865755719799363721e-01,3.677145233577055472e-01,3.250779922590392168e-01,-2.948892110154302526e-01,3.235189085447624513e-01,3.833701457322456485e-01,-6.482972605856110460e-02
-1.000000000000000000e+00,7.414849087005422890e-01,7.899939320683285748e-01,8.409491158122843757e-01,-1.237723450556581950e+00,1.150781664601345344e+00,5.683229235409951174e-01,9.470190367033598511e-01,-1.459471669210274936e+00,3.218301649067650816e-02,5.149996600426331311e-02,-3.172877495800926345e-02,9.975917854691199738e-01,2.707594441269316476e-01
-2.000000000000000000e+00,-6.577994738869272773e-01,-4.641334315403787270e-01,-1.294681548355069767e+00,4.654420680903747498e-01,-1.024048580594117075e+00,-2.078008927484694124e-01,-2.297228628348761681e-01,-2.136629015502882756e-01,-2.973238312487311297e-01,-9.077316011271964191e-01,3.235189085447624513e-01,1.297560493247593483e+00,-2.570307962557069614e-01
-1.000000000000000000e+00,1.212040718774203052e+00,-5.099045032049300818e-01,-4.477935263576708902e-01,-9.806418628362864043e-01,-2.523346226215336663e-01,4.907105419120482703e-01,2.049295505080760971e-01,-7.119864086142828619e-01,6.179729680274015369e-01,-1.261355093904512337e-01,3.235189085447624513e-01,1.326128941607448208e+00,8.504134653564191515e-01
-1.000000000000000000e+00,5.433561465642633426e-01,-3.908997168770964592e-01,2.886308405965897217e-01,4.654420680903747498e-01,2.828863482304210669e-02,5.683229235409951174e-01,8.940126448322682773e-01,-7.119864086142828619e-01,3.983017362011299078e-01,5.149996600426331311e-02,3.235189085447624513e-01,1.154718251448320965e+00,4.995702419806767325e-01
-1.000000000000000000e+00,1.472084719078069925e+00,-4.824418602061992800e-01,3.622732772920158051e-01,-1.109182656696434011e+00,1.922495622573928697e+00,7.235476867988874794e-01,9.788228718260151284e-01,-5.458785729262844999e-01,1.185456983578603740e+00,2.913078577871283104e-01,3.235189085447624513e-01,1.797508339545049960e+00,9.419377844979172076e-01
-3.000000000000000000e+00,4.442917654961238694e-01,1.531485293034060557e+00,5.463793690305801531e-01,1.268822029716297539e+00,1.686002635453300036e-01,-8.286999457800412472e-01,-1.470072432618422242e+00,6.999301947337017715e-01,-4.254653831473897974e-01,1.028495080675194684e+00,-1.186283746342016387e+00,-1.573568566917797229e+00,-1.906756648781209040e-02
-1.000000000000000000e+00,1.447318623811035598e+00,1.650490079361894180e+00,3.990944956397280419e-01,-4.872110735021632777e-02,8.000025927956255778e-01,8.011600684278343820e-01,5.441704584830628910e-01,-3.797707372382866375e-01,-7.183603589157521174e-01,1.136723823924137428e-01,-4.313824188986269959e-01,9.547391129293382184e-01,9.876999440686662357e-01
-2.000000000000000000e+00,-1.586528046400733860e+00,4.055169300860972381e-01,2.166512976329951989e+00,3.047660757651901586e-01,2.387560779064739624e-01,-9.063123274089878167e-01,-1.131088007184745114e-01,2.111846798081686405e+00,-1.084479078626205073e+00,-5.258153290285596526e-01,1.167232156863844406e+00,-2.308514940046293151e-01,-4.553334877289527127e-01
-1.000000000000000000e+00,6.424205276324028713e-01,-4.824418602061992800e-01,-7.958134288054057037e-02,-9.163714659062127676e-01,1.220937478962489164e+00,1.266834358201512911e+00,1.222652274433037034e+00,-4.755506586228989974e-02,1.276986663506216813e+00,5.089113151456540196e-01,-7.613473539585573213e-02,1.026160233828974588e+00,1.182189122244349522e-01
-2.000000000000000000e+00,-8.311621407561718966e-01,-1.187316363840290157e+00,-3.577597085913275077e+00,-2.715942579948279700e+00,-8.135811375106852816e-01,-5.492953719158341075e-01,-1.660895443354352574e+00,-5.458785729262844999e-01,-2.237753045714132583e+00,-1.347379402729115494e+00,3.679248689826089547e-01,-1.202178738239686906e+00,-7.207540132392969978e-01
-3.000000000000000000e+00,-2.243928067138180049e-01,-8.119935761909689020e-01,-4.276012453282835440e-02,-3.379378935355480951e-01,1.571716550768208931e+00,-1.278851759227930707e+00,-9.400085139075050611e-01,-1.127255997834278656e+00,-1.285844374466954321e+00,-3.925887224825236888e-01,-9.198479837149375893e-01,-1.959242619775834804e+00,-3.851648430538042178e-01
-1.000000000000000000e+00,1.657830433580831819e+00,1.238550434380931708e+00,-1.900449979236804865e-01,-9.806418628362864043e-01,1.686002635453300036e-01,1.422059121459405828e+00,1.095436933942416813e+00,-6.289324907702836809e-01,1.002397623723377151e+00,-3.731777169309376946e-02,3.235189085447624513e-01,1.111865578908538543e+00,9.419377844979172076e-01
-1.000000000000000000e+00,1.434935576177516214e+00,-4.366707885416478696e-01,4.359157139874418885e-01,-1.237723450556581950e+00,8.701584071567695089e-01,1.453104074110984412e+00,1.328665058175220626e+00,-4.755506586228989974e-02,5.996670320418788780e-01,7.842463020074620372e-01,3.679248689826089547e-01,2.690963522928269680e-01,2.116499880147141965e+00
-1.000000000000000000e+00,1.558766052512692291e+00,-2.902033592150836117e-01,1.430088609375692821e+00,3.047660757651901586e-01,1.431404922045921069e+00,7.235476867988874794e-01,1.063633098819761980e+00,-1.306089837062890946e-01,5.996670320418788780e-01,5.399975233397292795e-01,4.567367898583018504e-01,-1.658813130571964653e-02,1.597862071678653129e+00
-3.000000000000000000e+00,4.803424122356605291e-02,1.522331078701150142e+00,1.045247488580237777e-01,7.867940527407438767e-01,9.403142215179134400e-01,-1.434076522485823846e+00,-7.915906166684484324e-01,-4.755506586228989974e-02,-9.197256547565014762e-01,1.961081326497447597e+00,-1.763561232034019932e+00,-1.902105723056125575e+00,-6.292296940977989417e-01
-2.000000000000000000e+00,-8.311621407561718966e-01,-9.401525768517128290e-01,8.409491158122843757e-01,-3.058026950705107216e-01,-1.120229938992457902e-01,1.336935864188945888e-01,5.441704584830628910e-01,2.016066876697071852e-01,8.010323278826282367e-01,-1.705443782391301843e-01,9.896083151124585031e-01,-5.165359776031759331e-01,-2.387259324274073613e-01
-2.000000000000000000e+00,-1.338867093730384150e+00,-1.050003148846635925e+00,-1.164025612282544170e-01,6.261180604155592855e-01,9.844444918418605861e-02,1.623851313694666398e+00,3.505261397927611402e-03,-1.791687340586271437e+00,1.387708050515384234e-02,-7.878276552357639240e-01,1.014891063555301426e-01,6.690546293307916281e-01,3.958426802869789984e-01
-2.000000000000000000e+00,-4.968198546511998770e-01,-8.028393618580585978e-01,-1.147396674964217711e+00,-7.235602751159908586e-01,2.828863482304210669e-02,-4.872054666126768852e-01,-7.703880599200116919e-01,1.530469373173693137e+00,-1.871634326003678961e+00,3.401576135206750284e-01,5.708314591768366697e-02,-1.530715894378015252e+00,-9.343107579027923881e-01
-3.000000000000000000e+00,8.281662421351645431e-01,3.124318586960447952e+00,4.359157139874418885e-01,4.654420680903747498e-01,-3.224904369826776529e-01,-1.014969661689512970e+00,-1.618490329857479315e+00,1.447415455329694067e+00,-1.066173142640682414e+00,1.206130556069909820e+00,-1.452719508969094742e+00,-1.316452531679105586e+00,-4.957567286831143089e-02
-1.000000000000000000e+00,3.565119359004889632e-02,-6.746803611973148929e-01,-8.528269281825133774e-01,-1.141317855161470884e+00,-1.120229938992457902e-01,1.026486337673159777e-01,5.335691801088447983e-01,-5.458785729262844999e-01,-4.986891270894800443e-01,-5.702241978772382147e-01,9.452023546746119997e-01,4.054876541398967638e-02,8.046513057856702344e-01
-1.000000000000000000e+00,3.204612891609501246e-01,-4.732876458732889757e-01,-7.055420547916613216e-01,-6.592898781859172219e-01,-3.926462513438215840e-01,1.026486337673159777e-01,5.651165326901914226e-02,-6.289324907702836809e-01,-5.353009990605254176e-01,-4.592020257555414764e-01,2.347069876690695278e-01,1.548225588534083430e-01,1.613116124868902768e+00
-2.000000000000000000e+00,-1.289334903196315496e+00,1.092083005054367728e+00,-1.221039111659643739e+00,-1.658590888517895781e-02,-8.837369518718292127e-01,-5.182504192642555241e-01,-5.265586573129897863e-01,2.016066876697071852e-01,4.166076721866525667e-01,-1.644918824015262793e+00,-1.649466562715481699e-01,5.547808358913729476e-01,-5.865183451650999302e-01
-2.000000000000000000e+00,-3.977554735830604038e-01,-4.458250028745581739e-01,-1.900449979236804865e-01,1.108146037391112948e+00,-1.094204394955261117e+00,-1.480643951463191721e+00,-3.993433168223695651e-01,1.115199783953697343e+00,-2.273479146589149624e-02,-7.478596732719532403e-01,-3.869764584607804925e-01,-3.451252874440479679e-01,-8.183799536568948652e-01
-2.000000000000000000e+00,-1.239802712662246842e+00,1.888499652017560981e+00,1.781671855534498472e-01,9.474700450659284678e-01,-1.374827652399836841e+00,-3.630256560063624960e-01,-4.099445951965879353e-01,6.168762768897025905e-01,-5.353009990605254176e-01,-1.058721755212703952e+00,-7.866301024013981902e-01,-1.308619247451383028e-01,-5.377053749563008855e-01
-3.000000000000000000e+00,-8.435451883896890601e-01,1.458251578370778345e+00,1.781671855534498472e-01,6.261180604155592855e-01,-8.135811375106852816e-01,-5.257612949057706431e-02,-1.289850700256710558e+00,1.281307619641695705e+00,-1.102785014611727732e+00,1.183926121645570539e+00,-1.807967192471865880e+00,-1.545000118557942503e+00,-7.207540132392969978e-01
-3.000000000000000000e+00,-1.041673950525968007e+00,7.259144317379564448e-01,-4.276012453282835440e-02,-1.658590888517895781e-02,-2.523346226215336663e-01,-1.682436143698452291e+00,-1.745705670348099314e+00,4.507684412017047282e-01,-1.670269030162930157e+00,2.291354413989782624e-01,-1.363907588093401735e+00,-1.187894514059759654e+00,-7.512621196197962758e-01
-1.000000000000000000e+00,1.013911956637924217e+00,-8.119935761909689020e-01,-2.268662162713926955e-01,-9.806418628362864043e-01,-1.120229938992457902e-01,1.002952260663094952e+00,1.074234377193980183e+00,-1.044202079990279364e+00,3.799958002156071935e-01,9.929679855962517676e-01,1.903010272312230799e-01,1.268992044887738979e+00,8.809215717369185406e-01
-3.000000000000000000e+00,1.470986222917055053e-01,1.220242005715111100e+00,-6.687208364439490849e-01,6.261180604155592855e-01,1.686002635453300036e-01,-1.294374235553719998e+00,-1.682098000102789204e+00,6.999301947337017715e-01,-6.268306789881387120e-01,-4.369975913312021953e-01,-1.630343350720480533e+00,-1.402157876758669541e+00,2.249972845561826473e-01
-3.000000000000000000e+00,1.038678051904960764e+00,2.565911512652921278e+00,-3.741510896622448068e-01,3.047660757651901586e-01,-1.374827652399836841e+00,-2.101543004494763167e+00,-1.904724845961374369e+00,4.507684412017047282e-01,-1.761798710090543230e+00,-3.731777169309376946e-02,-1.719155271596173540e+00,-1.902105723056125575e+00,-1.041089130234540194e+00
-3.000000000000000000e+00,-1.377114732791957230e-01,5.245217164139306387e-01,1.356446172680266793e+00,6.261180604155592855e-01,-2.523346226215336663e-01,-1.232284330250562832e+00,-1.735104391973880889e+00,1.530469373173693137e+00,-1.633657158191884617e+00,-1.705443782391301843e-01,-8.754420232770911969e-01,-5.022517534232482372e-01,-4.766891621953022184e-01
-3.000000000000000000e+00,1.608298243046760945e+00,-5.099045032049300818e-01,1.356446172680266793e+00,1.911525999017035904e+00,-1.120229938992457902e-01,7.235476867988874794e-01,-8.764008436621949505e-01,1.530469373173693137e+00,1.936000358985032754e+00,3.559800605049880495e+00,-1.763561232034019932e+00,-1.002199599720704493e+00,-2.936405239123061617e-01
-2.000000000000000000e+00,-1.190270522128176189e+00,-8.303020048567895106e-01,-1.164025612282544170e-01,1.461633220506519448e+00,-2.076385796011276152e+00,-2.078008927484694124e-01,-5.795650491840813601e-01,6.168762768897025905e-01,-4.803831911039578295e-01,-1.440638027311340608e+00,4.567367898583018504e-01,7.833284227702103086e-01,-4.004188962440538568e-01
-1.000000000000000000e+00,-1.377114732791957230e-01,1.430788935372047321e+00,1.172340080941700835e+00,-1.451267027453261582e-01,1.686002635453300036e-01,1.181711100931056302e-01,2.897397775018228927e-01,-7.950403264582820428e-01,6.179729680274015369e-01,-2.149532470878087187e-01,2.791129481069160034e-01,1.226139372347957224e+00,4.194864627318659750e-02
-1.000000000000000000e+00,1.335871195109376908e+00,-6.929887898631355014e-01,-4.276012453282835440e-02,-7.235602751159908586e-01,-3.224904369826776529e-01,-2.078008927484694124e-01,3.109423342502596332e-01,-7.119864086142828619e-01,-1.325704073790270887e-01,7.091097155584770960e-03,9.007963942367656074e-01,2.262436797530446575e-01,1.597862071678653129e+00
-2.000000000000000000e+00,-8.311621407561718966e-01,-9.767694341833539351e-01,-1.515608858441348072e+00,1.762252819050429686e-01,-1.515139281122124704e+00,-3.475031796805735373e-01,-1.449126358411293447e-01,-6.289324907702836809e-01,-1.102785014611727732e+00,-1.350172831601871848e-01,6.787665920475342007e-01,1.169002475628247995e+00,-7.512621196197962758e-01
-1.000000000000000000e+00,1.645447385947312435e+00,-2.627407162163527543e-01,6.200218057260061810e-01,-7.235602751159908586e-01,9.403142215179134400e-01,2.353407701006763553e+00,1.434677841917403995e+00,-8.780942443022812238e-01,9.840916877378549366e-01,1.250539424918588161e+00,-4.757883793364734437e-01,1.126149803088466239e+00,2.208024199288639799e+00
-2.000000000000000000e+00,-1.091206141060036661e+00,-5.739840035353018788e-01,-7.958134288054057037e-02,1.204551632786224014e+00,-6.732695087883974194e-01,-8.597448984316198306e-01,-4.735522654418980459e-01,6.999301947337017715e-01,-1.508763433645497753e-01,-1.125335058485722017e+00,1.611291761242308995e+00,-5.736728743228852734e-01,-7.970242791905453039e-01
-2.000000000000000000e+00,-1.016907855258931459e+00,-8.669188621884306167e-01,-1.441966421745921823e+00,-1.658590888517895781e-02,-5.329578800661094462e-01,4.055872846415876226e-02,-1.025075223442559191e-01,3.677145233577055472e-01,8.010323278826282367e-01,-1.014312886364025168e+00,-4.757883793364734437e-01,5.119281633515913033e-01,-1.355322625953683247e+00
-2.000000000000000000e+00,-1.289334903196315496e+00,-6.655261468644046996e-01,3.254520589443019585e-01,9.474700450659284678e-01,-9.538927662329731438e-01,-1.371986617182666679e+00,-9.400085139075050611e-01,1.281307619641695705e+00,-2.273479146589149624e-02,-6.146330667259169989e-01,3.679248689826089547e-01,-1.658813130571964653e-02,-9.343107579027923881e-01
-2.000000000000000000e+00,-8.311621407561718966e-01,-5.556755748694814923e-01,-1.164025612282544170e-01,1.750850006691851313e+00,-8.135811375106852816e-01,-1.767559400968908290e-01,3.321448909986963183e-01,4.507684412017047282e-01,4.715254801432201548e-01,-1.271884325686361672e+00,-3.425704980229340446e-01,1.691067830333353450e-01,-1.263798306812185190e+00
-1.000000000000000000e+00,1.311105099842342581e+00,-1.986612158859807908e-01,2.518096222488758751e-01,-8.085630581525254934e-02,2.387560779064739624e-01,6.459353051699413539e-01,8.304049745869581667e-01,-2.136629015502882756e-01,1.350210407448307448e+00,5.399975233397292795e-01,4.567367898583018504e-01,1.262541104935536729e-01,9.266837313076675686e-01
-1.000000000000000000e+00,1.088509832301457966e-02,-4.275165742087375653e-01,-8.528269281825133774e-01,-6.592898781859172219e-01,5.193793353510497424e-01,1.033997213314673536e+00,9.152152015807050178e-01,-5.458785729262844999e-01,7.095026479550144982e-01,2.485464469505626467e-02,-3.869764584607804925e-01,9.833075612891929440e-01,3.927918696489290373e-01
-1.000000000000000000e+00,9.767628137373727260e-01,-5.373671462036609947e-01,9.882339892031365425e-01,-5.950194812558435853e-01,-3.926462513438215840e-01,1.802610153962628525e-01,9.046139232064869251e-01,-1.044202079990279364e+00,1.185456983578603740e+00,2.735443102476567967e-01,1.211638117301690798e+00,1.011876009649047559e+00,1.552099912107903990e+00
-2.000000000000000000e+00,-7.692469025885839695e-01,-5.739840035353018788e-01,-5.214359630530969181e-01,1.108146037391112948e+00,5.895351497121936735e-01,-5.182504192642555241e-01,-4.950113047316438697e-02,-4.755506586228989974e-02,-5.934666343693642543e-02,-1.298529646995568942e+00,4.123308294204554025e-01,4.262228182720272929e-01,-1.254645874898035585e+00
-2.000000000000000000e+00,-3.977554735830604038e-01,-3.908997168770964592e-01,-4.846147447053830715e-01,-8.085630581525254934e-02,-9.538927662329731438e-01,-2.078008927484694124e-01,4.169551179924426698e-01,-7.119864086142828619e-01,2.335483123314258669e-01,-4.814064601798809795e-01,8.563904337989191040e-01,6.833388535107193240e-01,-1.288967494576097328e-01
-1.000000000000000000e+00,1.422552528543999051e+00,-5.922924322011223763e-01,5.463793690305801531e-01,-8.199658705111018131e-01,5.895351497121936735e-01,1.499671503088352065e+00,1.901134090383011177e+00,-2.136629015502882756e-01,3.983017362011299078e-01,1.650219244556696330e+00,1.167232156863844406e+00,2.262436797530446575e-01,2.818186326898626692e+00
-1.000000000000000000e+00,8.776984326692331972e-01,-4.641334315403787270e-01,2.886308405965897217e-01,-9.806418628362864043e-01,-7.434253231495413505e-01,4.130981602831020894e-01,6.607845205994645754e-01,-4.628246550822858185e-01,3.067720562735165579e-01,2.735443102476567967e-01,8.119844733610726006e-01,3.405174731924633380e-01,1.719894497200650463e+00
-3.000000000000000000e+00,9.891458613708897785e-01,1.723723794025176392e+00,1.781671855534498472e-01,1.440900834400055952e-01,-7.434253231495413505e-01,-8.286999457800412472e-01,-1.385262205624675502e+00,1.115199783953697343e+00,-1.508763433645497753e-01,1.787886737987600450e+00,-1.763561232034019932e+00,-1.459294773478378771e+00,-8.427864387612943320e-01
-1.000000000000000000e+00,1.459701671444552762e+00,-4.915960745391095288e-01,-1.164025612282544170e-01,-8.842362674411753387e-01,1.291093293323633207e+00,1.344446739830459592e+00,9.152152015807050178e-01,-7.119864086142828619e-01,7.095026479550144982e-01,6.199334872673507579e-01,-1.205406958337021939e-01,9.261706645694836038e-01,6.521107738831735112e-01
-3.000000000000000000e+00,5.681222418312976696e-01,8.083023607341491834e-01,1.061876425898562681e+00,1.590174014366666722e+00,1.641872365129352973e+00,-1.449598998811613138e+00,-6.007676059325179896e-01,-1.044202079990279364e+00,-7.183603589157521174e-01,1.605810375708017768e+00,-1.674749311158327147e+00,-1.944958395595907552e+00,-7.817702260002956649e-01
-1.000000000000000000e+00,1.088509832301457966e-02,-4.641334315403787270e-01,-1.073754238268791683e+00,-2.137509007577615083e+00,-5.329578800661094462e-01,5.993678761925737009e-01,1.201449717684600405e+00,-1.459471669210274936e+00,2.320425014681008591e+00,9.840862118265162328e-01,6.787665920475342007e-01,3.548016973723909784e-01,1.201256688732161626e+00
-1.000000000000000000e+00,1.088509832301457966e-02,-1.711985728872501555e-01,3.271149526761342852e+00,1.911525999017035904e+00,1.712028179490496793e+00,4.596655892604696314e-01,5.759742936057181684e-01,1.032145866109698051e+00,5.081373521142654726e-01,-6.235148404956527557e-01,7.231725524853797049e-01,7.690441985902832789e-01,2.249972845561826473e-01
-3.000000000000000000e+00,1.594816699252226688e-01,2.703224727646575509e+00,-4.276012453282835440e-02,9.474700450659284678e-01,-4.628020657049655151e-01,-9.218348037347771085e-01,-1.597287773109042464e+00,2.194900715925685919e+00,-1.691822793500724342e-01,1.294948293767267167e+00,-1.630343350720480533e+00,-1.687842360357215910e+00,-9.533783243906045202e-02
-2.000000000000000000e+00,-5.463520451852706961e-01,-8.211477905238792063e-01,-1.589251295136774100e+00,-1.772619012103635316e-01,-8.135811375106852816e-01,-1.371986617182666679e+00,-8.233944517911032657e-01,-4.628246550822858185e-01,-5.353009990605254176e-01,-1.125335058485722017e+00,3.235189085447624513e-01,1.548225588534083430e-01,-5.926199664411997414e-01
-3.000000000000000000e+00,-2.863080448814038226e-01,6.681099976841710364e-02,2.518096222488758751e-01,1.590174014366666722e+00,-1.120229938992457902e-01,-1.837660906956345430e+00,-1.109628967894998430e+00,-6.289324907702836809e-01,-1.487209670307703568e+00,3.179531790963357474e-01,-1.363907588093401735e+00,-1.859253050516343597e+00,-5.987215877172996636e-01
-1.000000000000000000e+00,2.461630033598449785e-01,1.595564793364432354e+00,-1.532237795759666399e-01,-4.986138858607326307e-01,2.387560779064739624e-01,4.751880655862593117e-01,5.229679017346261505e-01,-2.136629015502882756e-01,3.218301649067650816e-02,-2.771256634759587389e-01,-6.534122210878593462e-01,4.833597149917366331e-01,-2.326243111513074946e-01
-1.000000000000000000e+00,1.966308128257741317e-01,-4.183623598758273165e-01,-7.055420547916613216e-01,-2.523131389158058457e+00,2.828863482304210669e-02,4.907105419120482703e-01,6.607845205994645754e-01,-7.119864086142828619e-01,-6.634425509591841408e-01,-2.682438897062232042e-01,3.679248689826089547e-01,1.054728682188829314e+00,8.961756249271681796e-01
-2.000000000000000000e+00,1.013911956637924217e+00,-6.655261468644046996e-01,1.245982517637126863e+00,1.911525999017035904e+00,-9.538927662329731438e-01,9.563848316857274101e-01,7.667973043416480561e-01,-1.127255997834278656e+00,4.166076721866525667e-01,-7.123325781930102130e-01,1.744509642555848394e+00,7.119073018705739386e-01,-1.056343183424789833e+00
-1.000000000000000000e+00,8.653153850357160337e-01,-5.190587175378403861e-01,-3.005086529668187789e-01,-8.521010689761391310e-01,1.291093293323633207e+00,4.130981602831020894e-01,8.092024178385215372e-01,-1.127255997834278656e+00,-4.104072745141375961e-02,3.845664823693535350e-01,-2.093526167093946455e-01,7.690441985902832789e-01,9.266837313076675686e-01
-3.000000000000000000e+00,-4.225215688500969513e-01,1.201933577049290491e+00,1.045247488580237777e-01,7.867940527407438767e-01,4.492235209899058113e-01,-9.839247090379342753e-01,-9.930149057785968569e-01,-1.459471669210274936e+00,-1.468903734322180910e+00,7.091097155584770960e-03,-8.310360628392446936e-01,-1.959242619775834804e+00,-4.766891621953022184e-01
-2.000000000000000000e+00,-1.512229760599628881e+00,-4.732876458732889757e-01,-1.662893731832200128e+00,1.440900834400055952e-01,-9.538927662329731438e-01,2.578733970252090058e-01,-5.265586573129897863e-01,2.016066876697071852e-01,-4.071594471618671385e-01,-1.298529646995568942e+00,-1.205406958337021939e-01,-3.165568390841932978e-01,-1.041089130234540194e+00
-2.000000000000000000e+00,-1.512229760599628881e+00,-7.021430041960458057e-01,-1.257860330007356753e+00,5.618476634854857599e-01,-9.538927662329731438e-01,-5.492953719158341075e-01,-5.689637708098629343e-01,-3.797707372382866375e-01,-2.057941513211177798e-01,-1.347379402729115494e+00,-7.613473539585573213e-02,9.547391129293382184e-01,-7.970242791905453039e-01
-1.000000000000000000e+00,1.657830433580831819e+00,-3.359744308796348000e-01,1.781671855534498472e-01,-2.266049801437763023e+00,1.686002635453300036e-01,1.499671503088352065e+00,1.593697017530679050e+00,-4.628246550822858185e-01,2.411954694608621441e+00,1.117312818372552252e+00,1.034014275550305006e+00,4.833597149917366331e-01,2.412428512037985584e+00
-1.000000000000000000e+00,1.670213481214348983e+00,-3.359744308796348000e-01,4.359157139874418885e-01,-1.430534641346803193e+00,-2.523346226215336663e-01,2.578733970252090058e-01,4.063538396182245771e-01,-3.797707372382866375e-01,6.179729680274015369e-01,1.181132692772815240e-01,2.347069876690695278e-01,1.311844717427521179e+00,1.628370178059152407e+00
-2.000000000000000000e+00,-1.289334903196315496e+00,-1.205624792506110765e+00,-1.221039111659643739e+00,-1.658590888517895781e-02,-9.538927662329731438e-01,1.336935864188945888e-01,1.307206018885473664e-01,-3.797707372382866375e-01,-3.888535111763444796e-01,-1.103130624061382736e+00,1.833321563431540291e+00,6.547704051508645984e-01,-1.459050187647381147e+00
-1.000000000000000000e+00,4.319087178626067614e-01,-4.275165742087375653e-01,1.061876425898562681e+00,-9.485066643712489753e-01,-4.628020657049655151e-01,8.011600684278343820e-01,8.516075313353949072e-01,-4.755506586228989974e-02,-3.522416392052991063e-01,-8.172664054177269932e-02,-2.093526167093946455e-01,7.976126469501378935e-01,1.338543167444408599e+00
-2.000000000000000000e+00,-2.490490523647506116e+00,-6.655261468644046996e-01,-4.846147447053830715e-01,7.867940527407438767e-01,-1.024048580594117075e+00,1.957834917220518112e-01,3.530909652058243642e-02,1.447415455329694067e+00,6.728907759839690694e-01,-1.369583837153454997e+00,3.298718257880472571e+00,2.976648006526816381e-01,-1.065495615338939661e+00
-3.000000000000000000e+00,1.348254242742896292e+00,1.705415365359355340e+00,1.503731046071119071e+00,1.750850006691851313e+00,-2.523346226215336663e-01,-4.406380376353093431e-01,-1.459471154244204039e+00,1.779631126705690791e+00,-5.353009990605254176e-01,1.872263588800089806e+00,-1.585937390282634141e+00,-1.516431670198087778e+00,-5.987215877172996636e-01
-2.000000000000000000e+00,-9.302265218243114253e-01,-5.739840035353018788e-01,-4.477935263576708902e-01,4.333068696253373764e-01,2.387560779064739624e-01,-1.915273288585291667e+00,-1.183837916514526967e+00,2.016066876697071852e-01,-3.339357032197764474e-01,-8.588818453936499786e-01,-2.715209613223796836e-01,-1.202178738239686906e+00,3.470297100781800093e-01
-1.000000000000000000e+00,7.291018610670251254e-01,-3.908997168770964592e-01,1.356446172680266793e+00,-5.950194812558435853e-01,8.701584071567695089e-01,8.011600684278343820e-01,8.198036962127400740e-01,-3.797707372382866375e-01,-3.339357032197764474e-01,1.028495080675194684e+00,1.389261959053076589e+00,3.119490248326086679e-01,1.689386390820151185e+00
-1.000000000000000000e+00,8.281662421351645431e-01,-3.451286452125450488e-01,1.045247488580237777e-01,-7.878306720460643842e-01,9.844444918418605861e-02,4.286206366088910480e-01,7.879998610900847966e-01,-6.289324907702836809e-01,8.710082444724451256e-02,-5.258153290285596526e-01,6.343606316096876974e-01,1.911782132984468419e+00,8.504134653564191515e-01
-1.000000000000000000e+00,8.405492897686817066e-01,-7.387598615276869118e-01,6.200218057260061810e-01,-7.556954735810281765e-01,5.895351497121936735e-01,1.654896266346244982e+00,1.625500852653333883e+00,-1.293363833522276796e+00,7.278085839405374902e-01,8.064507364318013183e-01,-3.425704980229340446e-01,2.976648006526816381e-01,1.613116124868902768e+00
-2.000000000000000000e+00,-1.710358522735907716e+00,-3.268202165467247178e-01,-1.515608858441348072e+00,-9.806418628362864043e-01,-1.821788082603897352e-01,-1.123626995970037790e+00,-6.007676059325179896e-01,-4.755506586228989974e-02,-9.014197187709788173e-01,-5.258153290285596526e-01,1.167232156863844406e+00,-7.450835644820126280e-01,-1.001428591939890866e+00
-2.000000000000000000e+00,-1.078823093426519497e+00,-7.204514328618663033e-01,7.304854607691444457e-01,-1.658590888517895781e-02,3.089118922676178935e-01,-6.889976588479378439e-01,-4.099445951965879353e-01,8.660380304217001335e-01,-1.121090950597250391e+00,-9.032907142423284297e-01,2.144163286496465481e+00,-6.165255468626669177e-01,-1.224137768517536085e+00
-2.000000000000000000e+00,-1.029290902892448623e+00,-9.584610055175334375e-01,-2.141569570352468865e+00,-7.235602751159908586e-01,3.606235167241383266e+00,-7.510875641510947887e-01,-9.082046787848500058e-01,-1.708633422742272145e+00,1.569881639274579577e+00,-9.476995830910072138e-01,1.389261959053076589e+00,5.833492842512276733e-01,-1.166935069054099855e-01
-3.000000000000000000e+00,-3.358402354154746416e-01,1.400447144316993575e-01,-1.900449979236804865e-01,1.440900834400055952e-01,-9.538927662329731438e-01,-1.465121475137402429e+00,-1.724503113599662463e+00,1.115199783953697343e+00,-1.835022454032633643e+00,2.183125226652152140e+00,-1.763561232034019932e+00,-1.473578997658306244e+00,-8.732945451417937210e-01
-3.000000000000000000e+00,-1.996267114467815129e-01,6.892975744063156718e-01,2.518096222488758751e-01,3.047660757651901586e-01,3.089118922676178935e-01,-1.604823762069505833e+00,-9.718123490301601164e-01,-8.780942443022812238e-01,-1.487209670307703568e+00,1.847265725502997280e-01,-1.008659904590630596e+00,-1.773547705436779864e+00,-6.902459068587976088e-01
-2.000000000000000000e+00,-1.388099694402193489e-02,-1.223933221171931374e+00,-2.288854443743320921e+00,-9.806418628362864043e-01,-9.538927662329731438e-01,-5.958628008932020936e-01,-1.131088007184745114e-01,-8.780942443022812238e-01,-3.339357032197764474e-01,-1.705443782391301843e-01,9.896083151124585031e-01,-2.594199423644839575e-01,-1.111257774909688578e+00
-1.000000000000000000e+00,5.805052894648148332e-01,-4.000539312100067635e-01,1.172340080941700835e+00,-1.658590888517895781e-02,7.298467784344815357e-01,2.503625213836980362e-02,4.169551179924426698e-01,-4.628246550822858185e-01,-1.874882153355951209e-01,-3.481798536338447381e-01,5.899546711718413050e-01,2.976648006526816381e-01,1.033462103639415153e+00
-2.000000000000000000e+00,-1.759890713269978590e+00,-2.261238588847116482e-01,-1.900449979236804865e-01,-3.379378935355480951e-01,-1.120229938992457902e-01,1.065042165966252119e+00,1.307206018885473664e-01,-1.459471669210274936e+00,-5.353009990605254176e-01,-7.700641076962924103e-01,8.563904337989191040e-01,4.262228182720272929e-01,-1.254645874898035585e+00
-2.000000000000000000e+00,-3.358402354154746416e-01,1.092083005054367728e+00,-1.294681548355069767e+00,-9.806418628362864043e-01,-1.374827652399836841e+00,-1.092582043318459428e+00,-9.400085139075050611e-01,6.999301947337017715e-01,-1.487209670307703568e+00,-7.034508044232744561e-01,-1.186283746342016387e+00,-7.736520128418672426e-01,-1.172273987670687356e+00
-2.000000000000000000e+00,-9.302265218243114253e-01,-7.570682901935074094e-01,-1.294681548355069767e+00,-9.806418628362864043e-01,-1.024048580594117075e+00,3.354857786541551867e-01,3.851512828697878366e-01,-4.628246550822858185e-01,2.335483123314258669e-01,-9.254951486666679328e-01,1.167232156863844406e+00,1.119698863136266570e-01,-1.001428591939890866e+00
-2.000000000000000000e+00,-8.683112836567233872e-01,1.949700004291610167e-01,4.727369323351540698e-01,6.261180604155592855e-01,-1.120229938992457902e-01,3.510082549799448670e-01,-2.829857372472768459e-02,-4.755506586228989974e-02,-6.085247430026160531e-01,-9.699040175153467169e-01,-7.422241419635517978e-01,1.026160233828974588e+00,-9.709204855593915884e-01
-2.000000000000000000e+00,-4.349046164836141148e-01,-6.472177181985840910e-01,-2.636874346191065421e-01,5.297124650204483309e-01,-1.374827652399836841e+00,-1.480643951463191721e+00,-7.173816680489198960e-01,1.945738962393688265e+00,-4.104072745141375961e-02,-8.588818453936499786e-01,-3.172877495800926345e-02,-8.593573579214313085e-01,-7.970242791905453039e-01
-2.000000000000000000e+00,-1.450314522432043063e+00,1.897653866350470953e+00,2.149884039011636938e-01,6.261180604155592855e-01,-1.234516023677548979e+00,8.166825447536233407e-01,9.470190367033598511e-01,-1.127255997834278656e+00,2.320425014681008591e+00,-9.699040175153467169e-01,-9.642539441527840927e-01,1.397550062507085133e+00,-1.147867502566287889e+00
-2.000000000000000000e+00,-9.302265218243114253e-01,8.540734323987003718e-01,-4.477935263576708902e-01,-3.379378935355480951e-01,-8.135811375106852816e-01,8.011600684278343820e-01,9.046139232064869251e-01,8.660380304217001335e-01,2.137365654825782002e+00,-1.191948361758740083e+00,2.010945405182926304e+00,2.405279039329723256e-01,-1.068546425976989456e+00
-2.000000000000000000e+00,-7.692469025885839695e-01,2.865121437582633934e-01,-2.268662162713926955e-01,9.474700450659284678e-01,-6.732695087883974194e-01,-1.014969661689512970e+00,-3.145330898286227694e-01,2.610170305145681713e+00,-4.071594471618671385e-01,-1.014312886364025168e+00,-4.757883793364734437e-01,9.118864403895559079e-01,-1.346170194039533419e+00
-3.000000000000000000e+00,-5.463520451852706961e-01,2.041242147620710989e-01,-4.846147447053830715e-01,-1.772619012103635316e-01,-3.926462513438215840e-01,-1.108104519644248498e+00,-1.565483937986387186e+00,2.361008551613684059e+00,-1.285844374466954321e+00,9.396773429778373377e-01,-1.053065865028476988e+00,-1.545000118557942503e+00,-1.868621515805584665e-01
-3.000000000000000000e+00,8.529323374021988702e-01,1.943424938015023029e+00,-2.636874346191065421e-01,1.108146037391112948e+00,-8.135811375106852816e-01,-1.635868714721084416e+00,-1.766908227096535944e+00,1.447415455329694067e+00,-9.014197187709788173e-01,7.265147725041797289e-01,-8.310360628392446936e-01,-1.302168307499178113e+00,-7.207540132392969978e-01
-3.000000000000000000e+00,8.034001468681281066e-01,9.364613613948926663e-01,7.673066791168583478e-01,3.047660757651901586e-01,5.193793353510497424e-01,-7.821325168026733721e-01,-1.671496721728570778e+00,1.281307619641695705e+00,-1.542127478264271323e+00,3.978891430239572813e-01,-3.172877495800926345e-02,-1.202178738239686906e+00,-2.326243111513074946e-01
-3.000000000000000000e+00,2.833121462603964691e-01,1.870191223351740817e+00,-2.636874346191065421e-01,3.047660757651901586e-01,1.431404922045921069e+00,-1.154671948621616373e+00,-1.533680102863732353e+00,6.999301947337017715e-01,-5.353009990605254176e-01,2.316352277286876760e+00,-1.674749311158327147e+00,-1.573568566917797229e+00,2.402513377464323419e-01
-1.000000000000000000e+00,1.459701671444552762e+00,1.604719007697342770e+00,6.568430240737184178e-01,-1.880427419857320093e+00,1.992651436935072740e+00,1.033997213314673536e+00,9.576203150775784989e-01,-1.210309915678277504e+00,8.010323278826282367e-01,5.149996600426331311e-02,-3.425704980229340446e-01,1.240423596527884254e+00,1.144053989268725355e-02
-2.000000000000000000e+00,-1.487463665332594553e+00,-1.233087435504841345e+00,9.145915525077105146e-01,-3.379378935355480951e-01,-3.926462513438215840e-01,-2.078008927484694124e-01,7.771421001745584811e-02,-1.044202079990279364e+00,1.295292599491739916e+00,-8.588818453936499786e-01,-7.866301024013981902e-01,5.976335084311552581e-01,-7.207540132392969978e-01
-1.000000000000000000e+00,1.470986222917055053e-01,1.125820714329683059e-01,1.245982517637126863e+00,-1.451267027453261582e-01,9.844444918418605861e-02,7.235476867988874794e-01,1.169645882561945571e+00,-3.797707372382866375e-01,2.137365654825782002e+00,3.090714053265997685e-01,2.791129481069160034e-01,7.261915260505009684e-01,1.308035061063909321e+00
-1.000000000000000000e+00,6.424205276324028713e-01,-4.641334315403787270e-01,4.727369323351540698e-01,4.654420680903747498e-01,1.150781664601345344e+00,9.719073080115163688e-01,6.819870773479013160e-01,-1.210309915678277504e+00,1.478351959346966504e+00,5.622019577640684496e-01,5.708314591768366697e-02,5.262123875315183330e-01,1.109732369590663570e+00
-1.000000000000000000e+00,1.112976337706063523e+00,-4.641334315403787270e-01,-2.268662162713926955e-01,-5.307490843257700597e-01,5.895351497121936735e-01,8.477274974052019241e-01,1.487684233788495680e+00,-2.136629015502882756e-01,8.010323278826282367e-01,1.739036982254054120e+00,6.787665920475342007e-01,6.262019567910099838e-01,1.536845858917654351e+00
-2.000000000000000000e+00,3.699934796950187232e-01,-1.187316363840290157e+00,1.045247488580237777e-01,-6.592898781859172219e-01,7.298467784344815357e-01,3.044408260025766033e-01,-8.870021220364133763e-01,1.696577208861691499e+00,-2.237753045714132583e+00,-8.055912027752354376e-01,2.347069876690695278e-01,-1.045052272260486470e+00,-1.906756648781209040e-02
-3.000000000000000000e+00,6.671866228994370873e-01,3.139747867569944728e-01,1.319624954332552891e+00,1.750850006691851313e+00,3.790677066287618246e-01,-1.216761853924773540e+00,-1.374660927250457298e+00,3.677145233577055472e-01,-1.874882153355951209e-01,1.632455697017225260e+00,-1.008659904590630596e+00,-1.230747186599541632e+00,-1.906756648781209040e-02
-2.000000000000000000e+00,-8.806943312902405507e-01,-1.141545292175738746e+00,-1.405145203398208809e+00,-1.366264244416729001e+00,2.553897951824224410e+00,-6.734751825221485522e-01,-3.039318114544043992e-01,3.549885198170882328e-02,2.045835974898168264e+00,-7.034508044232744561e-01,4.123308294204554025e-01,-5.022517534232482372e-01,-1.906756648781209040e-02
-3.000000000000000000e+00,1.385403385643447782e+00,2.498952864266222873e-01,5.463793690305801531e-01,3.047660757651901586e-01,-6.031136944272533773e-01,-1.014969661689512970e+00,-1.523078824489514149e+00,7.829841125777009525e-01,-7.366662949012747763e-01,2.094307933043483505e+00,-1.541531429844787748e+00,-1.359305204218887564e+00,-2.936405239123061617e-01
-3.000000000000000000e+00,2.337799557263278150e-01,9.730782187265337724e-01,-1.900449979236804865e-01,-1.772619012103635316e-01,-1.120229938992457902e-01,-8.286999457800412472e-01,-1.385262205624675502e+00,2.194900715925685919e+00,4.166076721866525667e-01,2.458460657602648869e+00,-1.807967192471865880e+00,-1.644989687817433932e+00,-2.478783643415571614e-01
-2.000000000000000000e+00,-6.454164262534102248e-01,1.766615717633404636e-01,-5.950783997485230570e-01,6.261180604155592855e-01,-8.135811375106852816e-01,3.354857786541551867e-01,1.413218802627659587e-01,-7.119864086142828619e-01,-7.732781668723200941e-01,-1.325174968304776213e+00,-2.981645375850875967e-01,1.691067830333353450e-01,-1.315662087659034141e+00
-1.000000000000000000e+00,7.910170992346109431e-01,-3.725912882112759061e-01,1.045247488580237777e-01,-5.950194812558435853e-01,3.089118922676178935e-01,1.336935864188945888e-01,5.865755719799363721e-01,6.168762768897025905e-01,5.996670320418788780e-01,-5.080517814890882500e-01,1.167232156863844406e+00,2.976648006526816381e-01,7.131269866441721783e-01
-2.000000000000000000e+00,-6.341318747809056300e-02,-5.190587175378403861e-01,9.882339892031365425e-01,3.518285922268881372e+00,2.764365394907656093e+00,1.499671503088352065e+00,7.986011394643033334e-01,-1.127255997834278656e+00,5.813610960563562191e-01,-7.256552388476137372e-01,1.522479840366615989e+00,1.197570923988102498e+00,6.978729334539224283e-01
-1.000000000000000000e+00,1.063444147171994869e+00,-5.099045032049300818e-01,-7.791844914870873495e-01,-9.806418628362864043e-01,9.844444918418605861e-02,1.189221976572566675e+00,1.328665058175220626e+00,-1.127255997834278656e+00,9.108679437957641900e-01,4.955886544910502733e-01,-2.537585771472410934e-01,9.547391129293382184e-01,6.978729334539224283e-01
-3.000000000000000000e+00,1.594816699252226688e-01,3.231290010899045551e-01,1.413459672057376104e-01,3.047660757651901586e-01,1.431404922045921069e+00,-1.061537090666880845e+00,-1.544281381237950557e+00,1.530469373173693137e+00,-3.339357032197764474e-01,1.916672457648769035e+00,-1.630343350720480533e+00,-1.487863221838233274e+00,2.555053909366820086e-01
-2.000000000000000000e+00,-2.019934713573843243e+00,-1.370400650498495576e+00,6.200218057260061810e-01,6.261180604155592855e-01,-8.135811375106852816e-01,2.268284443736304223e-01,-1.343113574669112242e-01,6.168762768897025905e-01,-3.705475751908218207e-01,-8.455591847390462323e-01,5.899546711718413050e-01,-5.022517534232482372e-01,-9.831237281115913218e-01
-2.000000000000000000e+00,-1.524612808233146044e+00,-1.071190725568779978e-01,1.503731046071119071e+00,7.867940527407438767e-01,2.413586323101936326e+00,-1.139149472295827081e+00,-1.215641751637182022e+00,-1.708633422742272145e+00,-1.508763433645497753e-01,-1.103130624061382736e+00,-7.613473539585573213e-02,-5.736728743228852734e-01,-4.004188962440538568e-01
-1.000000000000000000e+00,3.080782415274307406e-01,-2.444322875505322012e-01,1.282803735984840765e+00,-7.235602751159908586e-01,1.686002635453300036e-01,1.033997213314673536e+00,1.159044604187726923e+00,-2.967168193942874566e-01,3.218301649067650816e-02,4.511797856423717668e-01,4.567367898583018504e-01,2.548121281128993276e-01,1.567353965298153851e+00
-2.000000000000000000e+00,1.088509832301457966e-02,3.261631801954101739e+00,-7.423632731393751127e-01,7.867940527407438767e-01,-9.538927662329731438e-01,4.441431129346806728e-01,5.441704584830628910e-01,-3.797707372382866375e-01,6.728907759839690694e-01,-1.058721755212703952e+00,-1.053065865028476988e+00,6.262019567910099838e-01,-1.147867502566287889e+00
-2.000000000000000000e+00,-9.797587123583800794e-01,-4.641334315403787270e-01,-7.791844914870873495e-01,-1.658590888517895781e-02,-1.374827652399836841e+00,-1.061537090666880845e+00,-1.131088007184745114e-01,2.016066876697071852e-01,-2.273479146589149624e-02,-7.034508044232744561e-01,1.458950667933766043e-01,7.261915260505009684e-01,-7.512621196197962758e-01
-2.000000000000000000e+00,1.088509832301457966e-02,1.485714221369509147e+00,-4.276012453282835440e-02,1.108146037391112948e+00,-1.024048580594117075e+00,-1.061537090666880845e+00,-5.795650491840813601e-01,2.194900715925685919e+00,-4.104072745141375961e-02,-8.172664054177269932e-02,-5.646003002121663394e-01,-9.307784788210682336e-01,-7.360080664295466368e-01
-1.000000000000000000e+00,9.767628137373727260e-01,-6.106008608669429849e-01,1.061876425898562681e+00,3.047660757651901586e-01,1.080625850240201302e+00,9.563848316857274101e-01,1.339266336549438607e+00,4.507684412017047282e-01,1.420186324038125170e-01,7.176329987344437500e-01,7.231725524853797049e-01,-1.308619247451383028e-01,1.140240475971162848e+00
-2.000000000000000000e+00,-8.311621407561718966e-01,-1.068311577512456534e+00,-8.528269281825133774e-01,-1.772619012103635316e-01,-8.135811375106852816e-01,1.841165982255716704e+00,1.710311079647080845e+00,-8.780942443022812238e-01,5.630551600708334492e-01,-2.149532470878087187e-01,3.235189085447624513e-01,1.548225588534083430e-01,-2.936405239123061617e-01
-2.000000000000000000e+00,8.518338412411752270e-02,-1.123236863509918138e+00,-2.325675662091033935e+00,-1.301993847486655476e+00,-1.515139281122124704e+00,1.002952260663094952e+00,1.106038212316635461e+00,-7.119864086142828619e-01,1.167151047593080859e+00,1.403177037016207773e-01,6.787665920475342007e-01,7.404757502304285532e-01,-7.756686047241958537e-01
-2.000000000000000000e+00,-7.568638549550668060e-01,-6.472177181985840910e-01,-1.532237795759666399e-01,7.867940527407438767e-01,-9.538927662329731438e-01,6.304128288441523953e-01,1.074234377193980183e+00,3.677145233577055472e-01,2.335483123314258669e-01,-4.636429126404094658e-01,-1.230689706779862780e+00,2.548121281128993276e-01,-1.233290200431685912e+00
-2.000000000000000000e+00,-1.772273760903495754e+00,-8.119935761909689020e-01,1.356446172680266793e+00,3.047660757651901586e-01,-3.926462513438215840e-01,6.304128288441523953e-01,8.304049745869581667e-01,-4.628246550822858185e-01,1.551575703289057140e+00,-1.036517320788364671e+00,-3.172877495800926345e-02,8.547495436698466786e-01,-2.326243111513074946e-01
diff --git a/tutorials/wine_data.csv b/tutorials/wine_data.csv
deleted file mode 100644
index a0b3962..0000000
--- a/tutorials/wine_data.csv
+++ /dev/null
@@ -1,178 +0,0 @@
-1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
-1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
-1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
-1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
-1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
-1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
-1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
-1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
-1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
-1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
-1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
-1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
-1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
-1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
-1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
-1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
-1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
-1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
-1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
-1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
-1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
-1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
-1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
-1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
-1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
-1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
-1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
-1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
-1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
-1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
-1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
-1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
-1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
-1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
-1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
-1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
-1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
-1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
-1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
-1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
-1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
-1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
-1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
-1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
-1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
-1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
-1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
-1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
-1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
-1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
-1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
-1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
-1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
-1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
-1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
-1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
-1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
-1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
-1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
-2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
-2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
-2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
-2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
-2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
-2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
-2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
-2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
-2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
-2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
-2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
-2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
-2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
-2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
-2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
-2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
-2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
-2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
-2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
-2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
-2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
-2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
-2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
-2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
-2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
-2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
-2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
-2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
-2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
-2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
-2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
-2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
-2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
-2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
-2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
-2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
-2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
-2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
-2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
-2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
-2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
-2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
-2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
-2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
-2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
-2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
-2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
-2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
-2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
-2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
-2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
-2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
-2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
-2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
-2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
-2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
-2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
-2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
-2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
-2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
-2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
-2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
-2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
-2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
-2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
-2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
-2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
-2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
-2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
-2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
-2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
-3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
-3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
-3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
-3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
-3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
-3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
-3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
-3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
-3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
-3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
-3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
-3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
-3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
-3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
-3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
-3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
-3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
-3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
-3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
-3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
-3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
-3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
-3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
-3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
-3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
-3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
-3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
-3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
-3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
-3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
-3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
-3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
-3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
-3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
-3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
-3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
-3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
-3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
-3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
-3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
-3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
-3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
-3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
-3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
-3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
-3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
-3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
-3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560

From 3fccde15b115edf37b3d6034ba5d10f4d325b2dd Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 Jan 2015 00:17:53 -0500
Subject: [PATCH 206/248] comments

---
 tutorials/things_in_pandas.ipynb | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index b70b3d9..db9d0b4 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:5455ed1ab796c2d7a01bee46d8f4d18de1bbc93ba0257248aff18e7b73a05e0a"
+  "signature": "sha256:01adffebfb99d8e7a86af443b9d14ca7695efc917465ea85868cc42681d6e96b"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -576,7 +576,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Create a new column\n",
+      "# Creating a new column\n",
+      "\n",
       "df['team'] = pd.Series('', index=df.index)\n",
       "df.tail(3)"
      ],
@@ -664,7 +665,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# process salary column\n",
+      "# Processing `salary` column\n",
       "\n",
       "df['salary'] = df['salary'].apply(lambda x: x.strip('$m'))\n",
       "df.tail()"
@@ -781,7 +782,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# process `player` column\n",
+      "# Processing `player` column\n",
       "\n",
       "def process_player_col(text):\n",
       "    name, rest = text.split('\\n')\n",

From bd089d2b1eb545a8fdc6cadedd36e5b54121ea92 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 Jan 2015 00:20:58 -0500
Subject: [PATCH 207/248] comments

---
 pandas_sum_tricks.ipynb | 450 ----------------------------------------
 1 file changed, 450 deletions(-)
 delete mode 100644 pandas_sum_tricks.ipynb

diff --git a/pandas_sum_tricks.ipynb b/pandas_sum_tricks.ipynb
deleted file mode 100644
index ccf454e..0000000
--- a/pandas_sum_tricks.ipynb
+++ /dev/null
@@ -1,450 +0,0 @@
-{
- "metadata": {
-  "name": "",
-  "signature": "sha256:8222de4af96dc6569eddec8d75df6855e8bac273e12e8739fffc42aafd712ba2"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
-  {
-   "cells": [
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext watermark \n",
-      "%watermark -d -v -a 'Sebastian Raschka' -p numpy,pandas"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Sebastian Raschka 23/12/2014 \n",
-        "\n",
-        "CPython 3.4.2\n",
-        "IPython 2.3.1\n",
-        "\n",
-        "numpy 1.9.1\n",
-        "pandas 0.15.2\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "4 Simple Tricks To Speed up the Sum Calculation in Pandas"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I wanted to improve the performance of some passages in my code a little bit and found that some simple tweaks can speed up the  `pandas` section significantly. I thought that it might be one useful thing to share -- and no Cython or just-in-time compilation is required! "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In my case, I had a large dataframe where I wanted to calculate the sum of specific columns for different combinations of rows (approx. 100,000,000 of them, that's why I was looking for ways to speed it up). Anyway, below is a simple toy DataFrame to explore the `.sum()` method a little bit."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pandas as pd\n",
-      "import numpy as np\n",
-      "\n",
-      "df = pd.DataFrame()\n",
-      "\n",
-      "for col in ('a', 'b', 'c', 'd'):\n",
-      "    df[col] = pd.Series(range(1000), index=range(1000))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df.tail()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>a</th>\n",
-        "      <th>b</th>\n",
-        "      <th>c</th>\n",
-        "      <th>d</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>995</th>\n",
-        "      <td> 995</td>\n",
-        "      <td> 995</td>\n",
-        "      <td> 995</td>\n",
-        "      <td> 995</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>996</th>\n",
-        "      <td> 996</td>\n",
-        "      <td> 996</td>\n",
-        "      <td> 996</td>\n",
-        "      <td> 996</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>997</th>\n",
-        "      <td> 997</td>\n",
-        "      <td> 997</td>\n",
-        "      <td> 997</td>\n",
-        "      <td> 997</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>998</th>\n",
-        "      <td> 998</td>\n",
-        "      <td> 998</td>\n",
-        "      <td> 998</td>\n",
-        "      <td> 998</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>999</th>\n",
-        "      <td> 999</td>\n",
-        "      <td> 999</td>\n",
-        "      <td> 999</td>\n",
-        "      <td> 999</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "       a    b    c    d\n",
-        "995  995  995  995  995\n",
-        "996  996  996  996  996\n",
-        "997  997  997  997  997\n",
-        "998  998  998  998  998\n",
-        "999  999  999  999  999"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's assume we are interested in calculating the sum of column `a`, `c`, and `d`, which would look like this:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df.loc[:, ['a', 'c', 'd']].sum(axis=0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "a    499500\n",
-        "c    499500\n",
-        "d    499500\n",
-        "dtype: int64"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, the `.loc` method is probably the most \"costliest\" one for this kind of operation. Since we are only intersted in the resulting numbers (i.e., the column sums), there is no need to make a copy of the array. Anyway, let's use the method above as a reference for comparison:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 1\n",
-      "%timeit -n 1000 -r 5 df.loc[:, ['a', 'c', 'd']].sum(axis=0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000 loops, best of 5: 1.28 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Although this is a rather small DataFrame (1000 x 4), let's see by how much we can speed it up using a different slicing method:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 2\n",
-      "%timeit -n 1000 -r 5 df[['a', 'c', 'd']].sum(axis=0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000 loops, best of 5: 1.03 ms per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Next, let us use the Numpy representation of  the `NDFrame` via the `.values` attribue:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 3\n",
-      "%timeit -n 1000 -r 5 df[['a', 'c', 'd']].values.sum(axis=0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000 loops, best of 5: 721 \u00b5s per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "While the speed improvements in #2 and #3 were not really a surprise, the next \"trick\" surprised me a little bit. Here, we are calculating the sum of each column separately rather than slicing the array."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "[df[col].values.sum(axis=0) for col in ('a', 'c', 'd')]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "[499500, 499500, 499500]"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 4\n",
-      "%timeit -n 1000 -r 5 [df[col].values.sum(axis=0) for col in ('a', 'c', 'd')]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000 loops, best of 5: 64.8 \u00b5s per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this case, this is an almost 10x improvement!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "One more thing: Let's try the Einstein summation convention [`einsum`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.einsum.html)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from numpy import einsum\n",
-      "[einsum('i->', df[col].values) for col in ('a', 'c', 'd')]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "[499500, 499500, 499500]"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# 5\n",
-      "%timeit -n 1000 -r 5 [einsum('i->', df[col].values) for col in ('a', 'c', 'd')]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1000 loops, best of 5: 57.2 \u00b5s per loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Conclusion:"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Using some simple tricks, the column sum calculation improved from 1280 to 57.2 \u00b5s per loop (approx. 22x faster!)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    }
-   ],
-   "metadata": {}
-  }
- ]
-}
\ No newline at end of file

From f681bd6e5d418d6d6d90e3141dd164db05a665e8 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 Jan 2015 11:02:44 -0500
Subject: [PATCH 208/248] added comments

---
 README.md                        |   1 -
 tutorials/things_in_pandas.ipynb | 372 +++++++++++++++++++++++++------
 2 files changed, 299 insertions(+), 74 deletions(-)

diff --git a/README.md b/README.md
index 2ec57f6..8b1340d 100644
--- a/README.md
+++ b/README.md
@@ -220,4 +220,3 @@
 - [scikit-learn](http://scikit-learn.org/stable/) - A powerful machine learning library for Python and tools for efficient data mining and analysis.
 
 
-
diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index db9d0b4..a5d8a1e 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:01adffebfb99d8e7a86af443b9d14ca7695efc917465ea85868cc42681d6e96b"
+  "signature": "sha256:1ba931b3466a0506e031f8b9bdffcb2ba39138b42f3676b74376988bf095be97"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -88,6 +88,7 @@
       "- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n",
       "    - [Selecting NaN Rows](#Selecting-NaN-Rows)\n",
       "    - [Dropping NaN Rows](#Dropping-NaN-Rows)\n",
+      "    - [Filling NaN Rows](#Filling-NaN-Rows)\n",
       "- [Appending Rows to a DataFrame](#Appending-Rows-to-a-DataFrame)\n",
       "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)"
      ]
@@ -328,6 +329,10 @@
       "# Converting column names to lowercase\n",
       "\n",
       "df.columns = [c.lower() for c in df.columns]\n",
+      "\n",
+      "# or\n",
+      "# df.rename(columns=lambda x : x.lower())\n",
+      "\n",
       "df.tail()"
      ],
      "language": "python",
@@ -576,10 +581,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Creating a new column\n",
+      "# Processing `salary` column\n",
       "\n",
-      "df['team'] = pd.Series('', index=df.index)\n",
-      "df.tail(3)"
+      "df['salary'] = df['salary'].apply(lambda x: x.strip('$m'))\n",
+      "df.tail()"
      ],
      "language": "python",
      "metadata": {},
@@ -599,45 +604,63 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td>    Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
+        "      <td> 14.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 4</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>    NaN</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td> David Silva\\n Midfield \u2014 Manchester City</td>\n",
+        "      <td> 14.3</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
         "      <th>7</th>\n",
-        "      <td>    Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
-        "      <td> $14.0m</td>\n",
+        "      <td>       Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td> 14.0</td>\n",
         "      <td> 20</td>\n",
         "      <td> 2</td>\n",
         "      <td> 14</td>\n",
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td> </td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
-        "      <td>  Saido Berahino\\n Forward \u2014 West Brom</td>\n",
-        "      <td> $13.8m</td>\n",
+        "      <td>     Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td> 9</td>\n",
         "      <td>  0</td>\n",
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td> </td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
-        "      <td> Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
-        "      <td> $13.8m</td>\n",
+        "      <td>    Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> 13.8</td>\n",
         "      <td> 20</td>\n",
         "      <td> 5</td>\n",
         "      <td>  1</td>\n",
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td> </td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -647,15 +670,19 @@
        "output_type": "pyout",
        "prompt_number": 5,
        "text": [
-        "                                  player  salary  games  goals  assists  \\\n",
-        "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m     20      2       14   \n",
-        "8   Saido Berahino\\n Forward \u2014 West Brom  $13.8m     21      9        0   \n",
-        "9  Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m     20      5        1   \n",
+        "                                     player salary  games  goals  assists  \\\n",
+        "5     Santiago Cazorla\\n Midfield \u2014 Arsenal   14.8     20      4      NaN   \n",
+        "6  David Silva\\n Midfield \u2014 Manchester City   14.3     15      6        2   \n",
+        "7        Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea   14.0     20      2       14   \n",
+        "8      Saido Berahino\\n Forward \u2014 West Brom   13.8     21      9        0   \n",
+        "9     Steven Gerrard\\n Midfield \u2014 Liverpool   13.8     20      5        1   \n",
         "\n",
-        "   shots_on_target  points_per_game  points team  \n",
-        "7               10            10.47  209.49       \n",
-        "8               20             7.02  147.43       \n",
-        "9               11             7.50  150.01       "
+        "   shots_on_target  points_per_game  points  \n",
+        "5               20             9.97     NaN  \n",
+        "6               11            10.35  155.26  \n",
+        "7               10            10.47  209.49  \n",
+        "8               20             7.02  147.43  \n",
+        "9               11             7.50  150.01  "
        ]
       }
      ],
@@ -665,10 +692,14 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Processing `salary` column\n",
+      "# Creating a new column\n",
       "\n",
-      "df['salary'] = df['salary'].apply(lambda x: x.strip('$m'))\n",
-      "df.tail()"
+      "df['team'] = pd.Series('', index=df.index)\n",
+      "\n",
+      "# or\n",
+      "# df.insert(loc=9, column='team', value='') \n",
+      "\n",
+      "df.tail(3)"
      ],
      "language": "python",
      "metadata": {},
@@ -693,32 +724,8 @@
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td>    Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
-        "      <td> 14.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 4</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>    NaN</td>\n",
-        "      <td> </td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>6</th>\n",
-        "      <td> David Silva\\n Midfield \u2014 Manchester City</td>\n",
-        "      <td> 14.3</td>\n",
-        "      <td> 15</td>\n",
-        "      <td> 6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "      <td> </td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
         "      <th>7</th>\n",
-        "      <td>       Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td>    Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
         "      <td> 14.0</td>\n",
         "      <td> 20</td>\n",
         "      <td> 2</td>\n",
@@ -730,7 +737,7 @@
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
-        "      <td>     Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td>  Saido Berahino\\n Forward \u2014 West Brom</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td> 9</td>\n",
@@ -742,7 +749,7 @@
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
-        "      <td>    Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 20</td>\n",
         "      <td> 5</td>\n",
@@ -760,16 +767,12 @@
        "output_type": "pyout",
        "prompt_number": 6,
        "text": [
-        "                                     player salary  games  goals  assists  \\\n",
-        "5     Santiago Cazorla\\n Midfield \u2014 Arsenal   14.8     20      4      NaN   \n",
-        "6  David Silva\\n Midfield \u2014 Manchester City   14.3     15      6        2   \n",
-        "7        Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea   14.0     20      2       14   \n",
-        "8      Saido Berahino\\n Forward \u2014 West Brom   13.8     21      9        0   \n",
-        "9     Steven Gerrard\\n Midfield \u2014 Liverpool   13.8     20      5        1   \n",
+        "                                  player salary  games  goals  assists  \\\n",
+        "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea   14.0     20      2       14   \n",
+        "8   Saido Berahino\\n Forward \u2014 West Brom   13.8     21      9        0   \n",
+        "9  Steven Gerrard\\n Midfield \u2014 Liverpool   13.8     20      5        1   \n",
         "\n",
         "   shots_on_target  points_per_game  points team  \n",
-        "5               20             9.97     NaN       \n",
-        "6               11            10.35  155.26       \n",
         "7               10            10.47  209.49       \n",
         "8               20             7.02  147.43       \n",
         "9               11             7.50  150.01       "
@@ -1229,6 +1232,227 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 2,
+     "metadata": {},
+     "source": [
+      "Filling NaN Rows"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Filling NaN cells with default value 0\n",
+      "\n",
+      "df = df.fillna(value=0)\n",
+      "df"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>    Sergio Ag\u00fcero</td>\n",
+        "      <td> 19.2</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td>   Manchester City</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>      Eden Hazard</td>\n",
+        "      <td> 18.9</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  8</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 13.05</td>\n",
+        "      <td> 274.04</td>\n",
+        "      <td>           Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td>   Alexis S\u00e1nchez</td>\n",
+        "      <td> 17.6</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>           Arsenal</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>       Yaya Tour\u00e9</td>\n",
+        "      <td> 16.6</td>\n",
+        "      <td> 18</td>\n",
+        "      <td>  7</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 19</td>\n",
+        "      <td> 10.99</td>\n",
+        "      <td> 197.91</td>\n",
+        "      <td>   Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>   \u00c1ngel Di Mar\u00eda</td>\n",
+        "      <td> 15.0</td>\n",
+        "      <td> 13</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 13</td>\n",
+        "      <td> 10.17</td>\n",
+        "      <td> 132.23</td>\n",
+        "      <td> Manchester United</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>5</th>\n",
+        "      <td> Santiago Cazorla</td>\n",
+        "      <td> 14.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>   0.00</td>\n",
+        "      <td>           Arsenal</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>6</th>\n",
+        "      <td>      David Silva</td>\n",
+        "      <td> 14.3</td>\n",
+        "      <td> 15</td>\n",
+        "      <td>  6</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 11</td>\n",
+        "      <td> 10.35</td>\n",
+        "      <td> 155.26</td>\n",
+        "      <td>   Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td>    Cesc F\u00e0bregas</td>\n",
+        "      <td> 14.0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td>           Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>8</th>\n",
+        "      <td>   Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>         West Brom</td>\n",
+        "      <td>   Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>   Steven Gerrard</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  5</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 11</td>\n",
+        "      <td>  7.50</td>\n",
+        "      <td> 150.01</td>\n",
+        "      <td>         Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "             player salary  games  goals  assists  shots_on_target  \\\n",
+        "0     Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
+        "1       Eden Hazard   18.9     21      8        4               17   \n",
+        "2    Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
+        "3        Yaya Tour\u00e9   16.6     18      7        1               19   \n",
+        "4    \u00c1ngel Di Mar\u00eda   15.0     13      3        0               13   \n",
+        "5  Santiago Cazorla   14.8     20      4        0               20   \n",
+        "6       David Silva   14.3     15      6        2               11   \n",
+        "7     Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
+        "8    Saido Berahino   13.8     21      9        0               20   \n",
+        "9    Steven Gerrard   13.8     20      5        1               11   \n",
+        "\n",
+        "   points_per_game  points               team   position  \n",
+        "0            13.12  209.98    Manchester City    Forward  \n",
+        "1            13.05  274.04            Chelsea   Midfield  \n",
+        "2            11.19  223.86            Arsenal    Forward  \n",
+        "3            10.99  197.91    Manchester City   Midfield  \n",
+        "4            10.17  132.23  Manchester United   Midfield  \n",
+        "5             9.97    0.00            Arsenal   Midfield  \n",
+        "6            10.35  155.26    Manchester City   Midfield  \n",
+        "7            10.47  209.49            Chelsea   Midfield  \n",
+        "8             7.02  147.43          West Brom    Forward  \n",
+        "9             7.50  150.01          Liverpool   Midfield  "
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 1,
@@ -1250,8 +1474,10 @@
      "input": [
       "# Adding an \"empty\" row to the DataFrame\n",
       "\n",
+      "import numpy as np\n",
+      "\n",
       "df = df.append(pd.Series(\n",
-      "                [None]*len(df.columns), # Fill cells with NaNs\n",
+      "                [np.nan]*len(df.columns), # Fill cells with NaNs\n",
       "                index=df.columns),    \n",
       "                ignore_index=True)\n",
       "\n",
@@ -1351,7 +1577,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 11,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "6      David Silva   14.3     15      6        2               11   \n",
@@ -1369,7 +1595,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -1475,7 +1701,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
+       "prompt_number": 12,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "6      David Silva   14.3     15      6        2               11   \n",
@@ -1493,7 +1719,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -1567,7 +1793,7 @@
         "      <th>2</th>\n",
         "      <td> Alexis S\u00e1nchez</td>\n",
         "      <td> 17.6</td>\n",
-        "      <td>NaN</td>\n",
+        "      <td>  0</td>\n",
         "      <td> 12</td>\n",
         "      <td> 7</td>\n",
         "      <td> 29</td>\n",
@@ -1621,11 +1847,11 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 13,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
-        "2  Alexis S\u00e1nchez   17.6    NaN     12        7               29   \n",
+        "2  Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
         "8  Saido Berahino   13.8     21      9        0               20   \n",
         "1     Eden Hazard   18.9     21      8        4               17   \n",
         "3      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
@@ -1639,7 +1865,7 @@
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -1690,7 +1916,7 @@
         "      <th>2</th>\n",
         "      <td> Alexis S\u00e1nchez</td>\n",
         "      <td> 17.6</td>\n",
-        "      <td>NaN</td>\n",
+        "      <td>  0</td>\n",
         "      <td> 12</td>\n",
         "      <td> 7</td>\n",
         "      <td> 29</td>\n",
@@ -1744,11 +1970,11 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 13,
+       "prompt_number": 14,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
-        "2  Alexis S\u00e1nchez   17.6    NaN     12        7               29   \n",
+        "2  Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
         "3  Saido Berahino   13.8     21      9        0               20   \n",
         "4     Eden Hazard   18.9     21      8        4               17   \n",
         "5      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
@@ -1762,7 +1988,7 @@
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 14
     }
    ],
    "metadata": {}

From c68623a5aa90af29edc7c4e1315298edb5b54c86 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 Jan 2015 15:18:17 -0500
Subject: [PATCH 209/248] Updating Columns update

---
 tutorials/things_in_pandas.ipynb | 831 ++++++++++++++++++++++---------
 1 file changed, 591 insertions(+), 240 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index a5d8a1e..972f395 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:1ba931b3466a0506e031f8b9bdffcb2ba39138b42f3676b74376988bf095be97"
+  "signature": "sha256:d1e515a46e85e308d5673229e5a6f87c3db1d616a30cee44a09e0af3e088e19c"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -84,13 +84,18 @@
      "source": [
       "- [Loading Some Example Data](#Loading-Some-Example-Data)\n",
       "- [Renaming Columns](#Renaming-Columns)\n",
+      "    - [Converting Column Names to Lowercase](#Converting-Column-Names-to-Lowercase)\n",
+      "    - [Renaming Particular Columns](#Renaming-Particular-Columns)\n",
       "- [Applying Computations Rows-wise](#Applying-Computations-Rows-wise)\n",
+      "    - [Changing Values in a Column](#Changing-Values-in-a-Column)\n",
+      "    - [Adding a New Column](#Adding-a-New-Column)\n",
       "- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n",
       "    - [Selecting NaN Rows](#Selecting-NaN-Rows)\n",
-      "    - [Dropping NaN Rows](#Dropping-NaN-Rows)\n",
+      "    - [Selecting non-NaN Rows](#Selecting-non-NaN-Rows)\n",
       "    - [Filling NaN Rows](#Filling-NaN-Rows)\n",
       "- [Appending Rows to a DataFrame](#Appending-Rows-to-a-DataFrame)\n",
-      "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)"
+      "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)\n",
+      "- [Updating Columns](#Updating-Columns)"
      ]
     },
     {
@@ -322,6 +327,22 @@
       "[[back to section overview](#Sections)]"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Converting Column Names to Lowercase"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -333,7 +354,7 @@
       "# or\n",
       "# df.rename(columns=lambda x : x.lower())\n",
       "\n",
-      "df.tail()"
+      "df.tail(3)"
      ],
      "language": "python",
      "metadata": {},
@@ -357,30 +378,8 @@
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td>    Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
-        "      <td> $14.8m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 4</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>    NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>6</th>\n",
-        "      <td> David Silva\\n Midfield \u2014 Manchester City</td>\n",
-        "      <td> $14.3m</td>\n",
-        "      <td> 15</td>\n",
-        "      <td> 6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
         "      <th>7</th>\n",
-        "      <td>       Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td>    Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
         "      <td> $14.0m</td>\n",
         "      <td> 20</td>\n",
         "      <td> 2</td>\n",
@@ -391,7 +390,7 @@
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
-        "      <td>     Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td>  Saido Berahino\\n Forward \u2014 West Brom</td>\n",
         "      <td> $13.8m</td>\n",
         "      <td> 21</td>\n",
         "      <td> 9</td>\n",
@@ -402,7 +401,7 @@
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
-        "      <td>    Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
         "      <td> $13.8m</td>\n",
         "      <td> 20</td>\n",
         "      <td> 5</td>\n",
@@ -419,16 +418,12 @@
        "output_type": "pyout",
        "prompt_number": 3,
        "text": [
-        "                                     player  salary  gp  g   a  sot    ppg  \\\n",
-        "5     Santiago Cazorla\\n Midfield \u2014 Arsenal  $14.8m  20  4 NaN   20   9.97   \n",
-        "6  David Silva\\n Midfield \u2014 Manchester City  $14.3m  15  6   2   11  10.35   \n",
-        "7        Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m  20  2  14   10  10.47   \n",
-        "8      Saido Berahino\\n Forward \u2014 West Brom  $13.8m  21  9   0   20   7.02   \n",
-        "9     Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m  20  5   1   11   7.50   \n",
+        "                                  player  salary  gp  g   a  sot    ppg  \\\n",
+        "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m  20  2  14   10  10.47   \n",
+        "8   Saido Berahino\\n Forward \u2014 West Brom  $13.8m  21  9   0   20   7.02   \n",
+        "9  Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m  20  5   1   11   7.50   \n",
         "\n",
         "        p  \n",
-        "5     NaN  \n",
-        "6  155.26  \n",
         "7  209.49  \n",
         "8  147.43  \n",
         "9  150.01  "
@@ -437,12 +432,26 @@
      ],
      "prompt_number": 3
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Renaming Particular Columns"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Renaming particular columns\n",
-      "\n",
       "df = df.rename(columns={'p': 'points', \n",
       "                        'gp': 'games',\n",
       "                        'sot': 'shots_on_target',\n",
@@ -450,7 +459,7 @@
       "                        'ppg': 'points_per_game',\n",
       "                        'a': 'assists',})\n",
       "\n",
-      "df.tail()"
+      "df.tail(3)"
      ],
      "language": "python",
      "metadata": {},
@@ -474,30 +483,8 @@
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td>    Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
-        "      <td> $14.8m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 4</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>    NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>6</th>\n",
-        "      <td> David Silva\\n Midfield \u2014 Manchester City</td>\n",
-        "      <td> $14.3m</td>\n",
-        "      <td> 15</td>\n",
-        "      <td> 6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
         "      <th>7</th>\n",
-        "      <td>       Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
+        "      <td>    Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
         "      <td> $14.0m</td>\n",
         "      <td> 20</td>\n",
         "      <td> 2</td>\n",
@@ -508,7 +495,7 @@
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
-        "      <td>     Saido Berahino\\n Forward \u2014 West Brom</td>\n",
+        "      <td>  Saido Berahino\\n Forward \u2014 West Brom</td>\n",
         "      <td> $13.8m</td>\n",
         "      <td> 21</td>\n",
         "      <td> 9</td>\n",
@@ -519,7 +506,7 @@
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
-        "      <td>    Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
+        "      <td> Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
         "      <td> $13.8m</td>\n",
         "      <td> 20</td>\n",
         "      <td> 5</td>\n",
@@ -536,16 +523,12 @@
        "output_type": "pyout",
        "prompt_number": 4,
        "text": [
-        "                                     player  salary  games  goals  assists  \\\n",
-        "5     Santiago Cazorla\\n Midfield \u2014 Arsenal  $14.8m     20      4      NaN   \n",
-        "6  David Silva\\n Midfield \u2014 Manchester City  $14.3m     15      6        2   \n",
-        "7        Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m     20      2       14   \n",
-        "8      Saido Berahino\\n Forward \u2014 West Brom  $13.8m     21      9        0   \n",
-        "9     Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m     20      5        1   \n",
+        "                                  player  salary  games  goals  assists  \\\n",
+        "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m     20      2       14   \n",
+        "8   Saido Berahino\\n Forward \u2014 West Brom  $13.8m     21      9        0   \n",
+        "9  Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m     20      5        1   \n",
         "\n",
         "   shots_on_target  points_per_game  points  \n",
-        "5               20             9.97     NaN  \n",
-        "6               11            10.35  155.26  \n",
         "7               10            10.47  209.49  \n",
         "8               20             7.02  147.43  \n",
         "9               11             7.50  150.01  "
@@ -577,6 +560,22 @@
       "[[back to section overview](#Sections)]"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Changing Values in a Column"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -688,12 +687,26 @@
      ],
      "prompt_number": 5
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Adding a New Column"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Creating a new column\n",
-      "\n",
       "df['team'] = pd.Series('', index=df.index)\n",
       "\n",
       "# or\n",
@@ -794,9 +807,10 @@
       "\n",
       "for idx,row in df.iterrows():\n",
       "    name, position, team = process_player_col(row['player'])\n",
+      "\n",
       "    df.ix[idx, 'player'], df.ix[idx, 'position'], df.ix[idx, 'team'] = name, position, team\n",
       "    \n",
-      "df.tail()"
+      "df.tail(3)"
      ],
      "language": "python",
      "metadata": {},
@@ -822,34 +836,8 @@
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td> Santiago Cazorla</td>\n",
-        "      <td> 14.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 4</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>    NaN</td>\n",
-        "      <td>         Arsenal</td>\n",
-        "      <td>  Midfield</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>6</th>\n",
-        "      <td>      David Silva</td>\n",
-        "      <td> 14.3</td>\n",
-        "      <td> 15</td>\n",
-        "      <td> 6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "      <td> Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
         "      <th>7</th>\n",
-        "      <td>    Cesc F\u00e0bregas</td>\n",
+        "      <td>  Cesc F\u00e0bregas</td>\n",
         "      <td> 14.0</td>\n",
         "      <td> 20</td>\n",
         "      <td> 2</td>\n",
@@ -857,12 +845,12 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td>         Chelsea</td>\n",
+        "      <td>   Chelsea</td>\n",
         "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
-        "      <td>   Saido Berahino</td>\n",
+        "      <td> Saido Berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td> 9</td>\n",
@@ -870,12 +858,12 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td> West Brom</td>\n",
         "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
-        "      <td>   Steven Gerrard</td>\n",
+        "      <td> Steven Gerrard</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 20</td>\n",
         "      <td> 5</td>\n",
@@ -883,7 +871,7 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>       Liverpool</td>\n",
+        "      <td> Liverpool</td>\n",
         "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
@@ -894,19 +882,15 @@
        "output_type": "pyout",
        "prompt_number": 7,
        "text": [
-        "             player salary  games  goals  assists  shots_on_target  \\\n",
-        "5  Santiago Cazorla   14.8     20      4      NaN               20   \n",
-        "6       David Silva   14.3     15      6        2               11   \n",
-        "7     Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
-        "8    Saido Berahino   13.8     21      9        0               20   \n",
-        "9    Steven Gerrard   13.8     20      5        1               11   \n",
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "7   Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
+        "8  Saido Berahino   13.8     21      9        0               20   \n",
+        "9  Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points             team   position  \n",
-        "5             9.97     NaN          Arsenal   Midfield  \n",
-        "6            10.35  155.26  Manchester City   Midfield  \n",
-        "7            10.47  209.49          Chelsea   Midfield  \n",
-        "8             7.02  147.43        West Brom    Forward  \n",
-        "9             7.50  150.01        Liverpool   Midfield  "
+        "   points_per_game  points       team   position  \n",
+        "7            10.47  209.49    Chelsea   Midfield  \n",
+        "8             7.02  147.43  West Brom    Forward  \n",
+        "9             7.50  150.01  Liverpool   Midfield  "
        ]
       }
      ],
@@ -945,25 +929,19 @@
     },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 3,
      "metadata": {},
      "source": [
       "Selecting NaN Rows"
      ]
     },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "# Selecting all rows that have NaNs in the `assists` column\n",
-      "df[~df['assists'].notnull()]"
+      "\n",
+      "df[df['assists'].isnull()]"
      ],
      "language": "python",
      "metadata": {},
@@ -1044,25 +1022,16 @@
     },
     {
      "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Dropping NaN Rows"
-     ]
-    },
-    {
-     "cell_type": "markdown",
+     "level": 3,
      "metadata": {},
      "source": [
-      "[[back to section overview](#Sections)]"
+      "Selecting non-NaN Rows"
      ]
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Dropping all rows that have NaNs in the `assists` column\n",
-      "\n",
       "df[df['assists'].notnull()]"
      ],
      "language": "python",
@@ -1234,19 +1203,12 @@
     },
     {
      "cell_type": "heading",
-     "level": 2,
+     "level": 3,
      "metadata": {},
      "source": [
       "Filling NaN Rows"
      ]
     },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -1481,7 +1443,7 @@
       "                index=df.columns),    \n",
       "                ignore_index=True)\n",
       "\n",
-      "df.tail()"
+      "df.tail(3)"
      ],
      "language": "python",
      "metadata": {},
@@ -1507,32 +1469,6 @@
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
-        "      <th>6 </th>\n",
-        "      <td>    David Silva</td>\n",
-        "      <td> 14.3</td>\n",
-        "      <td> 15</td>\n",
-        "      <td>  6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "      <td> Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>7 </th>\n",
-        "      <td>  Cesc F\u00e0bregas</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "      <td>         Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
         "      <th>8 </th>\n",
         "      <td> Saido Berahino</td>\n",
         "      <td> 13.8</td>\n",
@@ -1540,9 +1476,9 @@
         "      <td>  9</td>\n",
         "      <td>  0</td>\n",
         "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
+        "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td> West Brom</td>\n",
         "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -1553,9 +1489,9 @@
         "      <td>  5</td>\n",
         "      <td>  1</td>\n",
         "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
+        "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>       Liverpool</td>\n",
+        "      <td> Liverpool</td>\n",
         "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -1566,9 +1502,9 @@
         "      <td>NaN</td>\n",
         "      <td>NaN</td>\n",
         "      <td>NaN</td>\n",
-        "      <td>   NaN</td>\n",
+        "      <td>  NaN</td>\n",
         "      <td>    NaN</td>\n",
-        "      <td>             NaN</td>\n",
+        "      <td>       NaN</td>\n",
         "      <td>       NaN</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
@@ -1580,18 +1516,14 @@
        "prompt_number": 11,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
-        "6      David Silva   14.3     15      6        2               11   \n",
-        "7    Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
         "8   Saido Berahino   13.8     21      9        0               20   \n",
         "9   Steven Gerrard   13.8     20      5        1               11   \n",
         "10             NaN    NaN    NaN    NaN      NaN              NaN   \n",
         "\n",
-        "    points_per_game  points             team   position  \n",
-        "6             10.35  155.26  Manchester City   Midfield  \n",
-        "7             10.47  209.49          Chelsea   Midfield  \n",
-        "8              7.02  147.43        West Brom    Forward  \n",
-        "9              7.50  150.01        Liverpool   Midfield  \n",
-        "10              NaN     NaN              NaN        NaN  "
+        "    points_per_game  points       team   position  \n",
+        "8              7.02  147.43  West Brom    Forward  \n",
+        "9              7.50  150.01  Liverpool   Midfield  \n",
+        "10              NaN     NaN        NaN        NaN  "
        ]
       }
      ],
@@ -1605,7 +1537,7 @@
       "\n",
       "df.loc[df.index[-1], 'player'] = 'New Player'\n",
       "df.loc[df.index[-1], 'salary'] = 12.3\n",
-      "df.tail()"
+      "df.tail(3)"
      ],
      "language": "python",
      "metadata": {},
@@ -1631,43 +1563,17 @@
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
-        "      <th>6 </th>\n",
-        "      <td>    David Silva</td>\n",
-        "      <td> 14.3</td>\n",
-        "      <td> 15</td>\n",
-        "      <td>  6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "      <td> Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>7 </th>\n",
-        "      <td>  Cesc F\u00e0bregas</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "      <td>         Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8 </th>\n",
-        "      <td> Saido Berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
-        "      <td>   Forward</td>\n",
+        "      <th>8 </th>\n",
+        "      <td> Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td> 7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td> West Brom</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
@@ -1677,9 +1583,9 @@
         "      <td>  5</td>\n",
         "      <td>  1</td>\n",
         "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
+        "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>       Liverpool</td>\n",
+        "      <td> Liverpool</td>\n",
         "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -1690,9 +1596,9 @@
         "      <td>NaN</td>\n",
         "      <td>NaN</td>\n",
         "      <td>NaN</td>\n",
-        "      <td>   NaN</td>\n",
+        "      <td>  NaN</td>\n",
         "      <td>    NaN</td>\n",
-        "      <td>             NaN</td>\n",
+        "      <td>       NaN</td>\n",
         "      <td>       NaN</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
@@ -1704,18 +1610,14 @@
        "prompt_number": 12,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
-        "6      David Silva   14.3     15      6        2               11   \n",
-        "7    Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
         "8   Saido Berahino   13.8     21      9        0               20   \n",
         "9   Steven Gerrard   13.8     20      5        1               11   \n",
         "10      New Player   12.3    NaN    NaN      NaN              NaN   \n",
         "\n",
-        "    points_per_game  points             team   position  \n",
-        "6             10.35  155.26  Manchester City   Midfield  \n",
-        "7             10.47  209.49          Chelsea   Midfield  \n",
-        "8              7.02  147.43        West Brom    Forward  \n",
-        "9              7.50  150.01        Liverpool   Midfield  \n",
-        "10              NaN     NaN              NaN        NaN  "
+        "    points_per_game  points       team   position  \n",
+        "8              7.02  147.43  West Brom    Forward  \n",
+        "9              7.50  150.01  Liverpool   Midfield  \n",
+        "10              NaN     NaN        NaN        NaN  "
        ]
       }
      ],
@@ -1871,7 +1773,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "# Reindexing the DataFrame after sorting\n",
+      "# Optional reindexing of the DataFrame after sorting\n",
       "\n",
       "df.index = range(1,len(df.index)+1)\n",
       "df.head()"
@@ -1989,6 +1891,455 @@
       }
      ],
      "prompt_number": 14
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Updating Columns"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Creating a dummy DataFrame with changes in the `salary` column\n",
+      "\n",
+      "df_2 = df.copy()\n",
+      "df_2.loc[0:2, 'salary'] = [20.0, 15.0]\n",
+      "df_2.head(3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td>   20</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td>   15</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td> 7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>         Arsenal</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td> Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 15,
+       "text": [
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "1   Sergio Ag\u00fcero     20     16     14        3               34   \n",
+        "2  Alexis S\u00e1nchez     15      0     12        7               29   \n",
+        "3  Saido Berahino   13.8     21      9        0               20   \n",
+        "\n",
+        "   points_per_game  points             team  position  \n",
+        "1            13.12  209.98  Manchester City   Forward  \n",
+        "2            11.19  223.86          Arsenal   Forward  \n",
+        "3             7.02  147.43        West Brom   Forward  "
+       ]
+      }
+     ],
+     "prompt_number": 15
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Temporarily use the `player` columns as indices to \n",
+      "# apply the update functions\n",
+      "\n",
+      "df.set_index('player', inplace=True)\n",
+      "df_2.set_index('player', inplace=True)\n",
+      "df.head(3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>player</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>Sergio Ag\u00fcero</th>\n",
+        "      <td> 19.2</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Alexis S\u00e1nchez</th>\n",
+        "      <td> 17.6</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td> 7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>         Arsenal</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Saido Berahino</th>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 16,
+       "text": [
+        "               salary  games  goals  assists  shots_on_target  \\\n",
+        "player                                                          \n",
+        "Sergio Ag\u00fcero    19.2     16     14        3               34   \n",
+        "Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
+        "Saido Berahino   13.8     21      9        0               20   \n",
+        "\n",
+        "                points_per_game  points             team  position  \n",
+        "player                                                              \n",
+        "Sergio Ag\u00fcero             13.12  209.98  Manchester City   Forward  \n",
+        "Alexis S\u00e1nchez            11.19  223.86          Arsenal   Forward  \n",
+        "Saido Berahino             7.02  147.43        West Brom   Forward  "
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Update the `salary` column\n",
+      "df.update(other=df_2['salary'], overwrite=True)\n",
+      "df.head(3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>player</th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "      <th></th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>Sergio Ag\u00fcero</th>\n",
+        "      <td>   20</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Alexis S\u00e1nchez</th>\n",
+        "      <td>   15</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td> 7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>         Arsenal</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Saido Berahino</th>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 17,
+       "text": [
+        "               salary  games  goals  assists  shots_on_target  \\\n",
+        "player                                                          \n",
+        "Sergio Ag\u00fcero      20     16     14        3               34   \n",
+        "Alexis S\u00e1nchez     15      0     12        7               29   \n",
+        "Saido Berahino   13.8     21      9        0               20   \n",
+        "\n",
+        "                points_per_game  points             team  position  \n",
+        "player                                                              \n",
+        "Sergio Ag\u00fcero             13.12  209.98  Manchester City   Forward  \n",
+        "Alexis S\u00e1nchez            11.19  223.86          Arsenal   Forward  \n",
+        "Saido Berahino             7.02  147.43        West Brom   Forward  "
+       ]
+      }
+     ],
+     "prompt_number": 17
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Reset the indices\n",
+      "df.reset_index(inplace=True)\n",
+      "df.head(3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>team</th>\n",
+        "      <th>position</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td>   20</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td> Manchester City</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td>   15</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td> 7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>         Arsenal</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> Saido Berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  9</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  7.02</td>\n",
+        "      <td> 147.43</td>\n",
+        "      <td>       West Brom</td>\n",
+        "      <td>  Forward</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 18,
+       "text": [
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "0   Sergio Ag\u00fcero     20     16     14        3               34   \n",
+        "1  Alexis S\u00e1nchez     15      0     12        7               29   \n",
+        "2  Saido Berahino   13.8     21      9        0               20   \n",
+        "\n",
+        "   points_per_game  points             team  position  \n",
+        "0            13.12  209.98  Manchester City   Forward  \n",
+        "1            11.19  223.86          Arsenal   Forward  \n",
+        "2             7.02  147.43        West Brom   Forward  "
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 18
     }
    ],
    "metadata": {}

From 598bde5c8a2579a5b9b2624688a3fcc9fa99ee11 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 Jan 2015 15:41:36 -0500
Subject: [PATCH 210/248] insert fix

---
 tutorials/things_in_pandas.ipynb | 260 ++++++++++++++++---------------
 1 file changed, 132 insertions(+), 128 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index 972f395..9fdc9e7 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:d1e515a46e85e308d5673229e5a6f87c3db1d616a30cee44a09e0af3e088e19c"
+  "signature": "sha256:3075a6608bb9a788ad29d1daaeb2421c2db561abd63a3ce811c1fe8e6e482d9f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -710,7 +710,7 @@
       "df['team'] = pd.Series('', index=df.index)\n",
       "\n",
       "# or\n",
-      "# df.insert(loc=9, column='team', value='') \n",
+      "df.insert(loc=8, column='position', value='') \n",
       "\n",
       "df.tail(3)"
      ],
@@ -732,6 +732,7 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
+        "      <th>position</th>\n",
         "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
@@ -747,6 +748,7 @@
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
         "      <td> </td>\n",
+        "      <td> </td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -759,6 +761,7 @@
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
         "      <td> </td>\n",
+        "      <td> </td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -771,6 +774,7 @@
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
         "      <td> </td>\n",
+        "      <td> </td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -785,10 +789,10 @@
         "8   Saido Berahino\\n Forward \u2014 West Brom   13.8     21      9        0   \n",
         "9  Steven Gerrard\\n Midfield \u2014 Liverpool   13.8     20      5        1   \n",
         "\n",
-        "   shots_on_target  points_per_game  points team  \n",
-        "7               10            10.47  209.49       \n",
-        "8               20             7.02  147.43       \n",
-        "9               11             7.50  150.01       "
+        "   shots_on_target  points_per_game  points position team  \n",
+        "7               10            10.47  209.49                \n",
+        "8               20             7.02  147.43                \n",
+        "9               11             7.50  150.01                "
        ]
       }
      ],
@@ -830,8 +834,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -845,8 +849,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td>   Chelsea</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>   Chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -858,8 +862,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td> West Brom</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td> West Brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -871,8 +875,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td> Liverpool</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Liverpool</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -887,10 +891,10 @@
         "8  Saido Berahino   13.8     21      9        0               20   \n",
         "9  Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points       team   position  \n",
-        "7            10.47  209.49    Chelsea   Midfield  \n",
-        "8             7.02  147.43  West Brom    Forward  \n",
-        "9             7.50  150.01  Liverpool   Midfield  "
+        "   points_per_game  points   position       team  \n",
+        "7            10.47  209.49   Midfield    Chelsea  \n",
+        "8             7.02  147.43    Forward  West Brom  \n",
+        "9             7.50  150.01   Midfield  Liverpool  "
        ]
       }
      ],
@@ -961,8 +965,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -976,8 +980,8 @@
         "      <td> 13</td>\n",
         "      <td> 10.17</td>\n",
         "      <td> 132.23</td>\n",
-        "      <td> Manchester United</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Manchester United</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -989,8 +993,8 @@
         "      <td> 20</td>\n",
         "      <td>  9.97</td>\n",
         "      <td>    NaN</td>\n",
-        "      <td>           Arsenal</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>           Arsenal</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1004,9 +1008,9 @@
         "4    \u00c1ngel Di Mar\u00eda   15.0     13      3      NaN               13   \n",
         "5  Santiago Cazorla   14.8     20      4      NaN               20   \n",
         "\n",
-        "   points_per_game  points               team   position  \n",
-        "4            10.17  132.23  Manchester United   Midfield  \n",
-        "5             9.97     NaN            Arsenal   Midfield  "
+        "   points_per_game  points   position               team  \n",
+        "4            10.17  132.23   Midfield  Manchester United  \n",
+        "5             9.97     NaN   Midfield            Arsenal  "
        ]
       }
      ],
@@ -1052,8 +1056,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -1067,8 +1071,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1080,8 +1084,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td>         Chelsea</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>         Chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1093,8 +1097,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>         Arsenal</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>         Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1106,8 +1110,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>6</th>\n",
@@ -1119,8 +1123,8 @@
         "      <td> 11</td>\n",
         "      <td> 10.35</td>\n",
         "      <td> 155.26</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>7</th>\n",
@@ -1132,8 +1136,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td>         Chelsea</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>         Chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1145,8 +1149,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>       West Brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -1158,8 +1162,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>       Liverpool</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>       Liverpool</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1179,15 +1183,15 @@
         "8  Saido Berahino   13.8     21      9        0               20   \n",
         "9  Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points             team   position  \n",
-        "0            13.12  209.98  Manchester City    Forward  \n",
-        "1            13.05  274.04          Chelsea   Midfield  \n",
-        "2            11.19  223.86          Arsenal    Forward  \n",
-        "3            10.99  197.91  Manchester City   Midfield  \n",
-        "6            10.35  155.26  Manchester City   Midfield  \n",
-        "7            10.47  209.49          Chelsea   Midfield  \n",
-        "8             7.02  147.43        West Brom    Forward  \n",
-        "9             7.50  150.01        Liverpool   Midfield  "
+        "   points_per_game  points   position             team  \n",
+        "0            13.12  209.98    Forward  Manchester City  \n",
+        "1            13.05  274.04   Midfield          Chelsea  \n",
+        "2            11.19  223.86    Forward          Arsenal  \n",
+        "3            10.99  197.91   Midfield  Manchester City  \n",
+        "6            10.35  155.26   Midfield  Manchester City  \n",
+        "7            10.47  209.49   Midfield          Chelsea  \n",
+        "8             7.02  147.43    Forward        West Brom  \n",
+        "9             7.50  150.01   Midfield        Liverpool  "
        ]
       }
      ],
@@ -1236,8 +1240,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -1251,8 +1255,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>   Manchester City</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>   Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1264,8 +1268,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td>           Chelsea</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>           Chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1277,8 +1281,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>           Arsenal</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>           Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1290,8 +1294,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td>   Manchester City</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>   Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
@@ -1303,8 +1307,8 @@
         "      <td> 13</td>\n",
         "      <td> 10.17</td>\n",
         "      <td> 132.23</td>\n",
-        "      <td> Manchester United</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Manchester United</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -1316,8 +1320,8 @@
         "      <td> 20</td>\n",
         "      <td>  9.97</td>\n",
         "      <td>   0.00</td>\n",
-        "      <td>           Arsenal</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>           Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>6</th>\n",
@@ -1329,8 +1333,8 @@
         "      <td> 11</td>\n",
         "      <td> 10.35</td>\n",
         "      <td> 155.26</td>\n",
-        "      <td>   Manchester City</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>   Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>7</th>\n",
@@ -1342,8 +1346,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td>           Chelsea</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>           Chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1355,8 +1359,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>         West Brom</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>         West Brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -1368,8 +1372,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>         Liverpool</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>         Liverpool</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1391,17 +1395,17 @@
         "8    Saido Berahino   13.8     21      9        0               20   \n",
         "9    Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points               team   position  \n",
-        "0            13.12  209.98    Manchester City    Forward  \n",
-        "1            13.05  274.04            Chelsea   Midfield  \n",
-        "2            11.19  223.86            Arsenal    Forward  \n",
-        "3            10.99  197.91    Manchester City   Midfield  \n",
-        "4            10.17  132.23  Manchester United   Midfield  \n",
-        "5             9.97    0.00            Arsenal   Midfield  \n",
-        "6            10.35  155.26    Manchester City   Midfield  \n",
-        "7            10.47  209.49            Chelsea   Midfield  \n",
-        "8             7.02  147.43          West Brom    Forward  \n",
-        "9             7.50  150.01          Liverpool   Midfield  "
+        "   points_per_game  points   position               team  \n",
+        "0            13.12  209.98    Forward    Manchester City  \n",
+        "1            13.05  274.04   Midfield            Chelsea  \n",
+        "2            11.19  223.86    Forward            Arsenal  \n",
+        "3            10.99  197.91   Midfield    Manchester City  \n",
+        "4            10.17  132.23   Midfield  Manchester United  \n",
+        "5             9.97    0.00   Midfield            Arsenal  \n",
+        "6            10.35  155.26   Midfield    Manchester City  \n",
+        "7            10.47  209.49   Midfield            Chelsea  \n",
+        "8             7.02  147.43    Forward          West Brom  \n",
+        "9             7.50  150.01   Midfield          Liverpool  "
        ]
       }
      ],
@@ -1463,8 +1467,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -1478,8 +1482,8 @@
         "      <td> 20</td>\n",
         "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td> West Brom</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td> West Brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
@@ -1491,8 +1495,8 @@
         "      <td> 11</td>\n",
         "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td> Liverpool</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Liverpool</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
@@ -1520,9 +1524,9 @@
         "9   Steven Gerrard   13.8     20      5        1               11   \n",
         "10             NaN    NaN    NaN    NaN      NaN              NaN   \n",
         "\n",
-        "    points_per_game  points       team   position  \n",
-        "8              7.02  147.43  West Brom    Forward  \n",
-        "9              7.50  150.01  Liverpool   Midfield  \n",
+        "    points_per_game  points   position       team  \n",
+        "8              7.02  147.43    Forward  West Brom  \n",
+        "9              7.50  150.01   Midfield  Liverpool  \n",
         "10              NaN     NaN        NaN        NaN  "
        ]
       }
@@ -1557,8 +1561,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -1572,8 +1576,8 @@
         "      <td> 20</td>\n",
         "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td> West Brom</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td> West Brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
@@ -1585,8 +1589,8 @@
         "      <td> 11</td>\n",
         "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td> Liverpool</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Liverpool</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
@@ -1614,9 +1618,9 @@
         "9   Steven Gerrard   13.8     20      5        1               11   \n",
         "10      New Player   12.3    NaN    NaN      NaN              NaN   \n",
         "\n",
-        "    points_per_game  points       team   position  \n",
-        "8              7.02  147.43  West Brom    Forward  \n",
-        "9              7.50  150.01  Liverpool   Midfield  \n",
+        "    points_per_game  points   position       team  \n",
+        "8              7.02  147.43    Forward  West Brom  \n",
+        "9              7.50  150.01   Midfield  Liverpool  \n",
         "10              NaN     NaN        NaN        NaN  "
        ]
       }
@@ -1673,8 +1677,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -1688,8 +1692,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1701,8 +1705,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>         Arsenal</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>         Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1714,8 +1718,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>       West Brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1727,8 +1731,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td>         Chelsea</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>         Chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1740,8 +1744,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1758,12 +1762,12 @@
         "1     Eden Hazard   18.9     21      8        4               17   \n",
         "3      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
         "\n",
-        "   points_per_game  points             team   position  \n",
-        "0            13.12  209.98  Manchester City    Forward  \n",
-        "2            11.19  223.86          Arsenal    Forward  \n",
-        "8             7.02  147.43        West Brom    Forward  \n",
-        "1            13.05  274.04          Chelsea   Midfield  \n",
-        "3            10.99  197.91  Manchester City   Midfield  "
+        "   points_per_game  points   position             team  \n",
+        "0            13.12  209.98    Forward  Manchester City  \n",
+        "2            11.19  223.86    Forward          Arsenal  \n",
+        "8             7.02  147.43    Forward        West Brom  \n",
+        "1            13.05  274.04   Midfield          Chelsea  \n",
+        "3            10.99  197.91   Midfield  Manchester City  "
        ]
       }
      ],
@@ -1796,8 +1800,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -1811,8 +1815,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1824,8 +1828,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>         Arsenal</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>         Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1837,8 +1841,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
         "      <td>   Forward</td>\n",
+        "      <td>       West Brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
@@ -1850,8 +1854,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td>         Chelsea</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td>         Chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -1863,8 +1867,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>  Midfield</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1881,12 +1885,12 @@
         "4     Eden Hazard   18.9     21      8        4               17   \n",
         "5      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
         "\n",
-        "   points_per_game  points             team   position  \n",
-        "1            13.12  209.98  Manchester City    Forward  \n",
-        "2            11.19  223.86          Arsenal    Forward  \n",
-        "3             7.02  147.43        West Brom    Forward  \n",
-        "4            13.05  274.04          Chelsea   Midfield  \n",
-        "5            10.99  197.91  Manchester City   Midfield  "
+        "   points_per_game  points   position             team  \n",
+        "1            13.12  209.98    Forward  Manchester City  \n",
+        "2            11.19  223.86    Forward          Arsenal  \n",
+        "3             7.02  147.43    Forward        West Brom  \n",
+        "4            13.05  274.04   Midfield          Chelsea  \n",
+        "5            10.99  197.91   Midfield  Manchester City  "
        ]
       }
      ],
@@ -1943,8 +1947,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -1958,8 +1962,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1971,8 +1975,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>         Arsenal</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td>         Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1984,8 +1988,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td>       West Brom</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2000,10 +2004,10 @@
         "2  Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "3  Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "   points_per_game  points             team  position  \n",
-        "1            13.12  209.98  Manchester City   Forward  \n",
-        "2            11.19  223.86          Arsenal   Forward  \n",
-        "3             7.02  147.43        West Brom   Forward  "
+        "   points_per_game  points  position             team  \n",
+        "1            13.12  209.98   Forward  Manchester City  \n",
+        "2            11.19  223.86   Forward          Arsenal  \n",
+        "3             7.02  147.43   Forward        West Brom  "
        ]
       }
      ],
@@ -2045,8 +2049,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>player</th>\n",
@@ -2071,8 +2075,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Alexis S\u00e1nchez</th>\n",
@@ -2083,8 +2087,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>         Arsenal</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td>         Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Saido Berahino</th>\n",
@@ -2095,8 +2099,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td>       West Brom</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2112,11 +2116,11 @@
         "Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
         "Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "                points_per_game  points             team  position  \n",
+        "                points_per_game  points  position             team  \n",
         "player                                                              \n",
-        "Sergio Ag\u00fcero             13.12  209.98  Manchester City   Forward  \n",
-        "Alexis S\u00e1nchez            11.19  223.86          Arsenal   Forward  \n",
-        "Saido Berahino             7.02  147.43        West Brom   Forward  "
+        "Sergio Ag\u00fcero             13.12  209.98   Forward  Manchester City  \n",
+        "Alexis S\u00e1nchez            11.19  223.86   Forward          Arsenal  \n",
+        "Saido Berahino             7.02  147.43   Forward        West Brom  "
        ]
       }
      ],
@@ -2155,8 +2159,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>player</th>\n",
@@ -2181,8 +2185,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Alexis S\u00e1nchez</th>\n",
@@ -2193,8 +2197,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>         Arsenal</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td>         Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Saido Berahino</th>\n",
@@ -2205,8 +2209,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td>       West Brom</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2222,11 +2226,11 @@
         "Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "                points_per_game  points             team  position  \n",
+        "                points_per_game  points  position             team  \n",
         "player                                                              \n",
-        "Sergio Ag\u00fcero             13.12  209.98  Manchester City   Forward  \n",
-        "Alexis S\u00e1nchez            11.19  223.86          Arsenal   Forward  \n",
-        "Saido Berahino             7.02  147.43        West Brom   Forward  "
+        "Sergio Ag\u00fcero             13.12  209.98   Forward  Manchester City  \n",
+        "Alexis S\u00e1nchez            11.19  223.86   Forward          Arsenal  \n",
+        "Saido Berahino             7.02  147.43   Forward        West Brom  "
        ]
       }
      ],
@@ -2266,8 +2270,8 @@
         "      <th>shots_on_target</th>\n",
         "      <th>points_per_game</th>\n",
         "      <th>points</th>\n",
-        "      <th>team</th>\n",
         "      <th>position</th>\n",
+        "      <th>team</th>\n",
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
@@ -2281,8 +2285,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Manchester City</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td> Manchester City</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -2294,8 +2298,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>         Arsenal</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td>         Arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -2307,8 +2311,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>       West Brom</td>\n",
         "      <td>  Forward</td>\n",
+        "      <td>       West Brom</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2323,10 +2327,10 @@
         "1  Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "2  Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "   points_per_game  points             team  position  \n",
-        "0            13.12  209.98  Manchester City   Forward  \n",
-        "1            11.19  223.86          Arsenal   Forward  \n",
-        "2             7.02  147.43        West Brom   Forward  "
+        "   points_per_game  points  position             team  \n",
+        "0            13.12  209.98   Forward  Manchester City  \n",
+        "1            11.19  223.86   Forward          Arsenal  \n",
+        "2             7.02  147.43   Forward        West Brom  "
        ]
       }
      ],

From 1e6c7444ef0bdd0d7aa298b0b37a1e80b9ac7be8 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 Jan 2015 20:21:07 -0500
Subject: [PATCH 211/248] updated row-wise ops

---
 tutorials/things_in_pandas.ipynb | 238 +++++++++++++++----------------
 1 file changed, 117 insertions(+), 121 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index 9fdc9e7..44f3acf 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3075a6608bb9a788ad29d1daaeb2421c2db561abd63a3ce811c1fe8e6e482d9f"
+  "signature": "sha256:ae8818183bc1fe6a58845005b18b12c4458686fa307a82a46857775364df6506"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -807,12 +807,17 @@
       "def process_player_col(text):\n",
       "    name, rest = text.split('\\n')\n",
       "    position, team = rest.split(' \u2014 ')\n",
-      "    return name, position, team\n",
+      "    return pd.Series([name, position, team])\n",
       "\n",
-      "for idx,row in df.iterrows():\n",
-      "    name, position, team = process_player_col(row['player'])\n",
+      "df[['player', 'team', 'position']] = df.player.apply(process_player_col)\n",
       "\n",
-      "    df.ix[idx, 'player'], df.ix[idx, 'position'], df.ix[idx, 'team'] = name, position, team\n",
+      "# modified after tip from reddit.com/user/hharison\n",
+      "#\n",
+      "# Alternative (inferior) approach:\n",
+      "#\n",
+      "#for idx,row in df.iterrows():\n",
+      "#    name, position, team = process_player_col(row['player'])\n",
+      "#    df.ix[idx, 'player'], df.ix[idx, 'position'], df.ix[idx, 'team'] = name, position, team\n",
       "    \n",
       "df.tail(3)"
      ],
@@ -849,8 +854,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>   Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -862,8 +867,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td> West Brom</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -875,8 +880,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -892,9 +897,9 @@
         "9  Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
         "   points_per_game  points   position       team  \n",
-        "7            10.47  209.49   Midfield    Chelsea  \n",
-        "8             7.02  147.43    Forward  West Brom  \n",
-        "9             7.50  150.01   Midfield  Liverpool  "
+        "7            10.47  209.49    Chelsea   Midfield  \n",
+        "8             7.02  147.43  West Brom    Forward  \n",
+        "9             7.50  150.01  Liverpool   Midfield  "
        ]
       }
      ],
@@ -980,8 +985,8 @@
         "      <td> 13</td>\n",
         "      <td> 10.17</td>\n",
         "      <td> 132.23</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Manchester United</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -993,8 +998,8 @@
         "      <td> 20</td>\n",
         "      <td>  9.97</td>\n",
         "      <td>    NaN</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>           Arsenal</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1008,9 +1013,9 @@
         "4    \u00c1ngel Di Mar\u00eda   15.0     13      3      NaN               13   \n",
         "5  Santiago Cazorla   14.8     20      4      NaN               20   \n",
         "\n",
-        "   points_per_game  points   position               team  \n",
-        "4            10.17  132.23   Midfield  Manchester United  \n",
-        "5             9.97     NaN   Midfield            Arsenal  "
+        "   points_per_game  points           position       team  \n",
+        "4            10.17  132.23  Manchester United   Midfield  \n",
+        "5             9.97     NaN            Arsenal   Midfield  "
        ]
       }
      ],
@@ -1071,8 +1076,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1084,8 +1089,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1097,8 +1102,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>         Arsenal</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1110,8 +1115,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>6</th>\n",
@@ -1123,8 +1128,8 @@
         "      <td> 11</td>\n",
         "      <td> 10.35</td>\n",
         "      <td> 155.26</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>7</th>\n",
@@ -1136,8 +1141,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1149,8 +1154,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -1162,8 +1167,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>       Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1183,15 +1188,15 @@
         "8  Saido Berahino   13.8     21      9        0               20   \n",
         "9  Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points   position             team  \n",
-        "0            13.12  209.98    Forward  Manchester City  \n",
-        "1            13.05  274.04   Midfield          Chelsea  \n",
-        "2            11.19  223.86    Forward          Arsenal  \n",
-        "3            10.99  197.91   Midfield  Manchester City  \n",
-        "6            10.35  155.26   Midfield  Manchester City  \n",
-        "7            10.47  209.49   Midfield          Chelsea  \n",
-        "8             7.02  147.43    Forward        West Brom  \n",
-        "9             7.50  150.01   Midfield        Liverpool  "
+        "   points_per_game  points         position       team  \n",
+        "0            13.12  209.98  Manchester City    Forward  \n",
+        "1            13.05  274.04          Chelsea   Midfield  \n",
+        "2            11.19  223.86          Arsenal    Forward  \n",
+        "3            10.99  197.91  Manchester City   Midfield  \n",
+        "6            10.35  155.26  Manchester City   Midfield  \n",
+        "7            10.47  209.49          Chelsea   Midfield  \n",
+        "8             7.02  147.43        West Brom    Forward  \n",
+        "9             7.50  150.01        Liverpool   Midfield  "
        ]
       }
      ],
@@ -1255,8 +1260,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>   Manchester City</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1268,8 +1273,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>           Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1281,8 +1286,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>           Arsenal</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1294,8 +1299,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>   Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
@@ -1307,8 +1312,8 @@
         "      <td> 13</td>\n",
         "      <td> 10.17</td>\n",
         "      <td> 132.23</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Manchester United</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -1320,8 +1325,8 @@
         "      <td> 20</td>\n",
         "      <td>  9.97</td>\n",
         "      <td>   0.00</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>           Arsenal</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>6</th>\n",
@@ -1333,8 +1338,8 @@
         "      <td> 11</td>\n",
         "      <td> 10.35</td>\n",
         "      <td> 155.26</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>   Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>7</th>\n",
@@ -1346,8 +1351,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>           Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1359,8 +1364,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>         West Brom</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -1372,8 +1377,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>         Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1395,17 +1400,17 @@
         "8    Saido Berahino   13.8     21      9        0               20   \n",
         "9    Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points   position               team  \n",
-        "0            13.12  209.98    Forward    Manchester City  \n",
-        "1            13.05  274.04   Midfield            Chelsea  \n",
-        "2            11.19  223.86    Forward            Arsenal  \n",
-        "3            10.99  197.91   Midfield    Manchester City  \n",
-        "4            10.17  132.23   Midfield  Manchester United  \n",
-        "5             9.97    0.00   Midfield            Arsenal  \n",
-        "6            10.35  155.26   Midfield    Manchester City  \n",
-        "7            10.47  209.49   Midfield            Chelsea  \n",
-        "8             7.02  147.43    Forward          West Brom  \n",
-        "9             7.50  150.01   Midfield          Liverpool  "
+        "   points_per_game  points           position       team  \n",
+        "0            13.12  209.98    Manchester City    Forward  \n",
+        "1            13.05  274.04            Chelsea   Midfield  \n",
+        "2            11.19  223.86            Arsenal    Forward  \n",
+        "3            10.99  197.91    Manchester City   Midfield  \n",
+        "4            10.17  132.23  Manchester United   Midfield  \n",
+        "5             9.97    0.00            Arsenal   Midfield  \n",
+        "6            10.35  155.26    Manchester City   Midfield  \n",
+        "7            10.47  209.49            Chelsea   Midfield  \n",
+        "8             7.02  147.43          West Brom    Forward  \n",
+        "9             7.50  150.01          Liverpool   Midfield  "
        ]
       }
      ],
@@ -1482,8 +1487,8 @@
         "      <td> 20</td>\n",
         "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td> West Brom</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
@@ -1495,8 +1500,8 @@
         "      <td> 11</td>\n",
         "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
@@ -1525,8 +1530,8 @@
         "10             NaN    NaN    NaN    NaN      NaN              NaN   \n",
         "\n",
         "    points_per_game  points   position       team  \n",
-        "8              7.02  147.43    Forward  West Brom  \n",
-        "9              7.50  150.01   Midfield  Liverpool  \n",
+        "8              7.02  147.43  West Brom    Forward  \n",
+        "9              7.50  150.01  Liverpool   Midfield  \n",
         "10              NaN     NaN        NaN        NaN  "
        ]
       }
@@ -1576,8 +1581,8 @@
         "      <td> 20</td>\n",
         "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td> West Brom</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
@@ -1589,8 +1594,8 @@
         "      <td> 11</td>\n",
         "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Liverpool</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
@@ -1619,8 +1624,8 @@
         "10      New Player   12.3    NaN    NaN      NaN              NaN   \n",
         "\n",
         "    points_per_game  points   position       team  \n",
-        "8              7.02  147.43    Forward  West Brom  \n",
-        "9              7.50  150.01   Midfield  Liverpool  \n",
+        "8              7.02  147.43  West Brom    Forward  \n",
+        "9              7.50  150.01  Liverpool   Midfield  \n",
         "10              NaN     NaN        NaN        NaN  "
        ]
       }
@@ -1692,8 +1697,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1705,8 +1710,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>         Arsenal</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1718,8 +1723,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1731,8 +1736,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1744,8 +1749,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1762,12 +1767,12 @@
         "1     Eden Hazard   18.9     21      8        4               17   \n",
         "3      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
         "\n",
-        "   points_per_game  points   position             team  \n",
-        "0            13.12  209.98    Forward  Manchester City  \n",
-        "2            11.19  223.86    Forward          Arsenal  \n",
-        "8             7.02  147.43    Forward        West Brom  \n",
-        "1            13.05  274.04   Midfield          Chelsea  \n",
-        "3            10.99  197.91   Midfield  Manchester City  "
+        "   points_per_game  points         position       team  \n",
+        "0            13.12  209.98  Manchester City    Forward  \n",
+        "2            11.19  223.86          Arsenal    Forward  \n",
+        "8             7.02  147.43        West Brom    Forward  \n",
+        "1            13.05  274.04          Chelsea   Midfield  \n",
+        "3            10.99  197.91  Manchester City   Midfield  "
        ]
       }
      ],
@@ -1815,8 +1820,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1828,8 +1833,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>         Arsenal</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1841,8 +1846,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>   Forward</td>\n",
         "      <td>       West Brom</td>\n",
+        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
@@ -1854,8 +1859,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td>         Chelsea</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -1867,8 +1872,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td>  Midfield</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1885,12 +1890,12 @@
         "4     Eden Hazard   18.9     21      8        4               17   \n",
         "5      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
         "\n",
-        "   points_per_game  points   position             team  \n",
-        "1            13.12  209.98    Forward  Manchester City  \n",
-        "2            11.19  223.86    Forward          Arsenal  \n",
-        "3             7.02  147.43    Forward        West Brom  \n",
-        "4            13.05  274.04   Midfield          Chelsea  \n",
-        "5            10.99  197.91   Midfield  Manchester City  "
+        "   points_per_game  points         position       team  \n",
+        "1            13.12  209.98  Manchester City    Forward  \n",
+        "2            11.19  223.86          Arsenal    Forward  \n",
+        "3             7.02  147.43        West Brom    Forward  \n",
+        "4            13.05  274.04          Chelsea   Midfield  \n",
+        "5            10.99  197.91  Manchester City   Midfield  "
        ]
       }
      ],
@@ -1962,8 +1967,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1975,8 +1980,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td>         Arsenal</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1988,8 +1993,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td>       West Brom</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2004,10 +2009,10 @@
         "2  Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "3  Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "   points_per_game  points  position             team  \n",
-        "1            13.12  209.98   Forward  Manchester City  \n",
-        "2            11.19  223.86   Forward          Arsenal  \n",
-        "3             7.02  147.43   Forward        West Brom  "
+        "   points_per_game  points         position      team  \n",
+        "1            13.12  209.98  Manchester City   Forward  \n",
+        "2            11.19  223.86          Arsenal   Forward  \n",
+        "3             7.02  147.43        West Brom   Forward  "
        ]
       }
      ],
@@ -2075,8 +2080,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Alexis S\u00e1nchez</th>\n",
@@ -2087,8 +2092,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td>         Arsenal</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Saido Berahino</th>\n",
@@ -2099,8 +2104,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td>       West Brom</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2116,11 +2121,11 @@
         "Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
         "Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "                points_per_game  points  position             team  \n",
+        "                points_per_game  points         position      team  \n",
         "player                                                              \n",
-        "Sergio Ag\u00fcero             13.12  209.98   Forward  Manchester City  \n",
-        "Alexis S\u00e1nchez            11.19  223.86   Forward          Arsenal  \n",
-        "Saido Berahino             7.02  147.43   Forward        West Brom  "
+        "Sergio Ag\u00fcero             13.12  209.98  Manchester City   Forward  \n",
+        "Alexis S\u00e1nchez            11.19  223.86          Arsenal   Forward  \n",
+        "Saido Berahino             7.02  147.43        West Brom   Forward  "
        ]
       }
      ],
@@ -2185,8 +2190,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Alexis S\u00e1nchez</th>\n",
@@ -2197,8 +2202,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td>         Arsenal</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Saido Berahino</th>\n",
@@ -2209,8 +2214,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td>       West Brom</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2226,11 +2231,11 @@
         "Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "                points_per_game  points  position             team  \n",
+        "                points_per_game  points         position      team  \n",
         "player                                                              \n",
-        "Sergio Ag\u00fcero             13.12  209.98   Forward  Manchester City  \n",
-        "Alexis S\u00e1nchez            11.19  223.86   Forward          Arsenal  \n",
-        "Saido Berahino             7.02  147.43   Forward        West Brom  "
+        "Sergio Ag\u00fcero             13.12  209.98  Manchester City   Forward  \n",
+        "Alexis S\u00e1nchez            11.19  223.86          Arsenal   Forward  \n",
+        "Saido Berahino             7.02  147.43        West Brom   Forward  "
        ]
       }
      ],
@@ -2285,8 +2290,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td> Manchester City</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -2298,8 +2303,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td>         Arsenal</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -2311,8 +2316,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
         "      <td>       West Brom</td>\n",
+        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2327,23 +2332,14 @@
         "1  Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "2  Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "   points_per_game  points  position             team  \n",
-        "0            13.12  209.98   Forward  Manchester City  \n",
-        "1            11.19  223.86   Forward          Arsenal  \n",
-        "2             7.02  147.43   Forward        West Brom  "
+        "   points_per_game  points         position      team  \n",
+        "0            13.12  209.98  Manchester City   Forward  \n",
+        "1            11.19  223.86          Arsenal   Forward  \n",
+        "2             7.02  147.43        West Brom   Forward  "
        ]
       }
      ],
      "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
     }
    ],
    "metadata": {}

From 632aef1bc4de0dede77a9c451a35980db39f2f18 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sat, 24 Jan 2015 22:25:57 -0500
Subject: [PATCH 212/248] Chaining Conditions - Using Bitwise Operators

---
 tutorials/things_in_pandas.ipynb | 481 ++++++++++++++++++++++---------
 1 file changed, 337 insertions(+), 144 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index 44f3acf..bb71a69 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:ae8818183bc1fe6a58845005b18b12c4458686fa307a82a46857775364df6506"
+  "signature": "sha256:cf7223086a74b13d1ae2228a4c8545c401765a90cdb3eca418f18138a4afdaab"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -95,7 +95,8 @@
       "    - [Filling NaN Rows](#Filling-NaN-Rows)\n",
       "- [Appending Rows to a DataFrame](#Appending-Rows-to-a-DataFrame)\n",
       "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)\n",
-      "- [Updating Columns](#Updating-Columns)"
+      "- [Updating Columns](#Updating-Columns)\n",
+      "- [Chaining Conditions - Using Bitwise Operators](#Chaining-Conditions---Using-Bitwise-Operators)"
      ]
     },
     {
@@ -806,8 +807,8 @@
       "\n",
       "def process_player_col(text):\n",
       "    name, rest = text.split('\\n')\n",
-      "    position, team = rest.split(' \u2014 ')\n",
-      "    return pd.Series([name, position, team])\n",
+      "    position, team = [x.strip() for x in rest.split(' \u2014 ')]\n",
+      "    return pd.Series([name, team, position])\n",
       "\n",
       "df[['player', 'team', 'position']] = df.player.apply(process_player_col)\n",
       "\n",
@@ -854,8 +855,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>   Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -867,8 +868,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td> West Brom</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -880,8 +881,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Liverpool</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -889,21 +890,21 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 7,
+       "prompt_number": 8,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "7   Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
         "8  Saido Berahino   13.8     21      9        0               20   \n",
         "9  Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points   position       team  \n",
-        "7            10.47  209.49    Chelsea   Midfield  \n",
-        "8             7.02  147.43  West Brom    Forward  \n",
-        "9             7.50  150.01  Liverpool   Midfield  "
+        "   points_per_game  points  position       team  \n",
+        "7            10.47  209.49  Midfield    Chelsea  \n",
+        "8             7.02  147.43   Forward  West Brom  \n",
+        "9             7.50  150.01  Midfield  Liverpool  "
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -985,8 +986,8 @@
         "      <td> 13</td>\n",
         "      <td> 10.17</td>\n",
         "      <td> 132.23</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Manchester United</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -998,8 +999,8 @@
         "      <td> 20</td>\n",
         "      <td>  9.97</td>\n",
         "      <td>    NaN</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>           Arsenal</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1007,19 +1008,19 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 9,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "4    \u00c1ngel Di Mar\u00eda   15.0     13      3      NaN               13   \n",
         "5  Santiago Cazorla   14.8     20      4      NaN               20   \n",
         "\n",
-        "   points_per_game  points           position       team  \n",
-        "4            10.17  132.23  Manchester United   Midfield  \n",
-        "5             9.97     NaN            Arsenal   Midfield  "
+        "   points_per_game  points  position               team  \n",
+        "4            10.17  132.23  Midfield  Manchester United  \n",
+        "5             9.97     NaN  Midfield            Arsenal  "
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -1076,8 +1077,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1089,8 +1090,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>         Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1102,8 +1103,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>         Arsenal</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1115,8 +1116,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>6</th>\n",
@@ -1128,8 +1129,8 @@
         "      <td> 11</td>\n",
         "      <td> 10.35</td>\n",
         "      <td> 155.26</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>7</th>\n",
@@ -1141,8 +1142,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>         Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1154,8 +1155,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>       West Brom</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -1167,8 +1168,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>       Liverpool</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1176,7 +1177,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 9,
+       "prompt_number": 10,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1188,19 +1189,19 @@
         "8  Saido Berahino   13.8     21      9        0               20   \n",
         "9  Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points         position       team  \n",
-        "0            13.12  209.98  Manchester City    Forward  \n",
-        "1            13.05  274.04          Chelsea   Midfield  \n",
-        "2            11.19  223.86          Arsenal    Forward  \n",
-        "3            10.99  197.91  Manchester City   Midfield  \n",
-        "6            10.35  155.26  Manchester City   Midfield  \n",
-        "7            10.47  209.49          Chelsea   Midfield  \n",
-        "8             7.02  147.43        West Brom    Forward  \n",
-        "9             7.50  150.01        Liverpool   Midfield  "
+        "   points_per_game  points  position             team  \n",
+        "0            13.12  209.98   Forward  Manchester City  \n",
+        "1            13.05  274.04  Midfield          Chelsea  \n",
+        "2            11.19  223.86   Forward          Arsenal  \n",
+        "3            10.99  197.91  Midfield  Manchester City  \n",
+        "6            10.35  155.26  Midfield  Manchester City  \n",
+        "7            10.47  209.49  Midfield          Chelsea  \n",
+        "8             7.02  147.43   Forward        West Brom  \n",
+        "9             7.50  150.01  Midfield        Liverpool  "
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -1260,8 +1261,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>   Manchester City</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1273,8 +1274,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>           Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1286,8 +1287,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>           Arsenal</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1299,8 +1300,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>   Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
@@ -1312,8 +1313,8 @@
         "      <td> 13</td>\n",
         "      <td> 10.17</td>\n",
         "      <td> 132.23</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Manchester United</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -1325,8 +1326,8 @@
         "      <td> 20</td>\n",
         "      <td>  9.97</td>\n",
         "      <td>   0.00</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>           Arsenal</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>6</th>\n",
@@ -1338,8 +1339,8 @@
         "      <td> 11</td>\n",
         "      <td> 10.35</td>\n",
         "      <td> 155.26</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>   Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>7</th>\n",
@@ -1351,8 +1352,8 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>           Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1364,8 +1365,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>         West Brom</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
@@ -1377,8 +1378,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>         Liverpool</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1386,7 +1387,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 11,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "0     Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1400,21 +1401,21 @@
         "8    Saido Berahino   13.8     21      9        0               20   \n",
         "9    Steven Gerrard   13.8     20      5        1               11   \n",
         "\n",
-        "   points_per_game  points           position       team  \n",
-        "0            13.12  209.98    Manchester City    Forward  \n",
-        "1            13.05  274.04            Chelsea   Midfield  \n",
-        "2            11.19  223.86            Arsenal    Forward  \n",
-        "3            10.99  197.91    Manchester City   Midfield  \n",
-        "4            10.17  132.23  Manchester United   Midfield  \n",
-        "5             9.97    0.00            Arsenal   Midfield  \n",
-        "6            10.35  155.26    Manchester City   Midfield  \n",
-        "7            10.47  209.49            Chelsea   Midfield  \n",
-        "8             7.02  147.43          West Brom    Forward  \n",
-        "9             7.50  150.01          Liverpool   Midfield  "
+        "   points_per_game  points  position               team  \n",
+        "0            13.12  209.98   Forward    Manchester City  \n",
+        "1            13.05  274.04  Midfield            Chelsea  \n",
+        "2            11.19  223.86   Forward            Arsenal  \n",
+        "3            10.99  197.91  Midfield    Manchester City  \n",
+        "4            10.17  132.23  Midfield  Manchester United  \n",
+        "5             9.97    0.00  Midfield            Arsenal  \n",
+        "6            10.35  155.26  Midfield    Manchester City  \n",
+        "7            10.47  209.49  Midfield            Chelsea  \n",
+        "8             7.02  147.43   Forward          West Brom  \n",
+        "9             7.50  150.01  Midfield          Liverpool  "
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -1487,8 +1488,8 @@
         "      <td> 20</td>\n",
         "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td> West Brom</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
@@ -1500,8 +1501,8 @@
         "      <td> 11</td>\n",
         "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Liverpool</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
@@ -1513,7 +1514,7 @@
         "      <td>NaN</td>\n",
         "      <td>  NaN</td>\n",
         "      <td>    NaN</td>\n",
-        "      <td>       NaN</td>\n",
+        "      <td>      NaN</td>\n",
         "      <td>       NaN</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
@@ -1522,21 +1523,21 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
+       "prompt_number": 12,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   Saido Berahino   13.8     21      9        0               20   \n",
         "9   Steven Gerrard   13.8     20      5        1               11   \n",
         "10             NaN    NaN    NaN    NaN      NaN              NaN   \n",
         "\n",
-        "    points_per_game  points   position       team  \n",
-        "8              7.02  147.43  West Brom    Forward  \n",
-        "9              7.50  150.01  Liverpool   Midfield  \n",
-        "10              NaN     NaN        NaN        NaN  "
+        "    points_per_game  points  position       team  \n",
+        "8              7.02  147.43   Forward  West Brom  \n",
+        "9              7.50  150.01  Midfield  Liverpool  \n",
+        "10              NaN     NaN       NaN        NaN  "
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 12
     },
     {
      "cell_type": "code",
@@ -1581,8 +1582,8 @@
         "      <td> 20</td>\n",
         "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td> West Brom</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
@@ -1594,8 +1595,8 @@
         "      <td> 11</td>\n",
         "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Liverpool</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
@@ -1607,7 +1608,7 @@
         "      <td>NaN</td>\n",
         "      <td>  NaN</td>\n",
         "      <td>    NaN</td>\n",
-        "      <td>       NaN</td>\n",
+        "      <td>      NaN</td>\n",
         "      <td>       NaN</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
@@ -1616,21 +1617,21 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 13,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   Saido Berahino   13.8     21      9        0               20   \n",
         "9   Steven Gerrard   13.8     20      5        1               11   \n",
         "10      New Player   12.3    NaN    NaN      NaN              NaN   \n",
         "\n",
-        "    points_per_game  points   position       team  \n",
-        "8              7.02  147.43  West Brom    Forward  \n",
-        "9              7.50  150.01  Liverpool   Midfield  \n",
-        "10              NaN     NaN        NaN        NaN  "
+        "    points_per_game  points  position       team  \n",
+        "8              7.02  147.43   Forward  West Brom  \n",
+        "9              7.50  150.01  Midfield  Liverpool  \n",
+        "10              NaN     NaN       NaN        NaN  "
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -1697,8 +1698,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1710,8 +1711,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>         Arsenal</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
@@ -1723,8 +1724,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>       West Brom</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -1736,8 +1737,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>         Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1749,8 +1750,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1758,7 +1759,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 13,
+       "prompt_number": 14,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1767,16 +1768,16 @@
         "1     Eden Hazard   18.9     21      8        4               17   \n",
         "3      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
         "\n",
-        "   points_per_game  points         position       team  \n",
-        "0            13.12  209.98  Manchester City    Forward  \n",
-        "2            11.19  223.86          Arsenal    Forward  \n",
-        "8             7.02  147.43        West Brom    Forward  \n",
-        "1            13.05  274.04          Chelsea   Midfield  \n",
-        "3            10.99  197.91  Manchester City   Midfield  "
+        "   points_per_game  points  position             team  \n",
+        "0            13.12  209.98   Forward  Manchester City  \n",
+        "2            11.19  223.86   Forward          Arsenal  \n",
+        "8             7.02  147.43   Forward        West Brom  \n",
+        "1            13.05  274.04  Midfield          Chelsea  \n",
+        "3            10.99  197.91  Midfield  Manchester City  "
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
@@ -1820,8 +1821,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1833,8 +1834,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>         Arsenal</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1846,8 +1847,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td>  Forward</td>\n",
         "      <td>       West Brom</td>\n",
-        "      <td>   Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
@@ -1859,8 +1860,8 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td>         Chelsea</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
@@ -1872,8 +1873,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
+        "      <td> Midfield</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>  Midfield</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1881,7 +1882,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 14,
+       "prompt_number": 15,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1890,16 +1891,16 @@
         "4     Eden Hazard   18.9     21      8        4               17   \n",
         "5      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
         "\n",
-        "   points_per_game  points         position       team  \n",
-        "1            13.12  209.98  Manchester City    Forward  \n",
-        "2            11.19  223.86          Arsenal    Forward  \n",
-        "3             7.02  147.43        West Brom    Forward  \n",
-        "4            13.05  274.04          Chelsea   Midfield  \n",
-        "5            10.99  197.91  Manchester City   Midfield  "
+        "   points_per_game  points  position             team  \n",
+        "1            13.12  209.98   Forward  Manchester City  \n",
+        "2            11.19  223.86   Forward          Arsenal  \n",
+        "3             7.02  147.43   Forward        West Brom  \n",
+        "4            13.05  274.04  Midfield          Chelsea  \n",
+        "5            10.99  197.91  Midfield  Manchester City  "
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -1967,8 +1968,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
+        "      <td> Forward</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -1980,8 +1981,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
+        "      <td> Forward</td>\n",
         "      <td>         Arsenal</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
@@ -1993,8 +1994,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td> Forward</td>\n",
         "      <td>       West Brom</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2002,21 +2003,21 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 16,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   Sergio Ag\u00fcero     20     16     14        3               34   \n",
         "2  Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "3  Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "   points_per_game  points         position      team  \n",
-        "1            13.12  209.98  Manchester City   Forward  \n",
-        "2            11.19  223.86          Arsenal   Forward  \n",
-        "3             7.02  147.43        West Brom   Forward  "
+        "   points_per_game  points position             team  \n",
+        "1            13.12  209.98  Forward  Manchester City  \n",
+        "2            11.19  223.86  Forward          Arsenal  \n",
+        "3             7.02  147.43  Forward        West Brom  "
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -2080,8 +2081,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
+        "      <td> Forward</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Alexis S\u00e1nchez</th>\n",
@@ -2092,8 +2093,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
+        "      <td> Forward</td>\n",
         "      <td>         Arsenal</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Saido Berahino</th>\n",
@@ -2104,8 +2105,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td> Forward</td>\n",
         "      <td>       West Brom</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2113,7 +2114,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 16,
+       "prompt_number": 17,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
@@ -2121,15 +2122,15 @@
         "Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
         "Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "                points_per_game  points         position      team  \n",
-        "player                                                              \n",
-        "Sergio Ag\u00fcero             13.12  209.98  Manchester City   Forward  \n",
-        "Alexis S\u00e1nchez            11.19  223.86          Arsenal   Forward  \n",
-        "Saido Berahino             7.02  147.43        West Brom   Forward  "
+        "                points_per_game  points position             team  \n",
+        "player                                                             \n",
+        "Sergio Ag\u00fcero             13.12  209.98  Forward  Manchester City  \n",
+        "Alexis S\u00e1nchez            11.19  223.86  Forward          Arsenal  \n",
+        "Saido Berahino             7.02  147.43  Forward        West Brom  "
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -2190,8 +2191,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
+        "      <td> Forward</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Alexis S\u00e1nchez</th>\n",
@@ -2202,8 +2203,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
+        "      <td> Forward</td>\n",
         "      <td>         Arsenal</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>Saido Berahino</th>\n",
@@ -2214,8 +2215,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td> Forward</td>\n",
         "      <td>       West Brom</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2223,7 +2224,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 17,
+       "prompt_number": 18,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
@@ -2231,15 +2232,15 @@
         "Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "                points_per_game  points         position      team  \n",
-        "player                                                              \n",
-        "Sergio Ag\u00fcero             13.12  209.98  Manchester City   Forward  \n",
-        "Alexis S\u00e1nchez            11.19  223.86          Arsenal   Forward  \n",
-        "Saido Berahino             7.02  147.43        West Brom   Forward  "
+        "                points_per_game  points position             team  \n",
+        "player                                                             \n",
+        "Sergio Ag\u00fcero             13.12  209.98  Forward  Manchester City  \n",
+        "Alexis S\u00e1nchez            11.19  223.86  Forward          Arsenal  \n",
+        "Saido Berahino             7.02  147.43  Forward        West Brom  "
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -2290,8 +2291,8 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
+        "      <td> Forward</td>\n",
         "      <td> Manchester City</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
@@ -2303,8 +2304,8 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
+        "      <td> Forward</td>\n",
         "      <td>         Arsenal</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
@@ -2316,8 +2317,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
+        "      <td> Forward</td>\n",
         "      <td>       West Brom</td>\n",
-        "      <td>  Forward</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2325,21 +2326,213 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 18,
+       "prompt_number": 19,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   Sergio Ag\u00fcero     20     16     14        3               34   \n",
         "1  Alexis S\u00e1nchez     15      0     12        7               29   \n",
         "2  Saido Berahino   13.8     21      9        0               20   \n",
         "\n",
-        "   points_per_game  points         position      team  \n",
-        "0            13.12  209.98  Manchester City   Forward  \n",
-        "1            11.19  223.86          Arsenal   Forward  \n",
-        "2             7.02  147.43        West Brom   Forward  "
+        "   points_per_game  points position             team  \n",
+        "0            13.12  209.98  Forward  Manchester City  \n",
+        "1            11.19  223.86  Forward          Arsenal  \n",
+        "2             7.02  147.43  Forward        West Brom  "
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 19
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chaining Conditions - Using Bitwise Operators"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Selecting only those players that either playing for Arsenal or Chelsea\n",
+      "\n",
+      "df[ (df['team'] == 'Arsenal') | (df['team'] == 'Chelsea') ]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>position</th>\n",
+        "      <th>team</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>   Alexis S\u00e1nchez</td>\n",
+        "      <td>   15</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>  Forward</td>\n",
+        "      <td> Arsenal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>      Eden Hazard</td>\n",
+        "      <td> 18.9</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  8</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 13.05</td>\n",
+        "      <td> 274.04</td>\n",
+        "      <td> Midfield</td>\n",
+        "      <td> Chelsea</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td> Santiago Cazorla</td>\n",
+        "      <td> 14.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>   0.00</td>\n",
+        "      <td> Midfield</td>\n",
+        "      <td> Arsenal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>    Cesc F\u00e0bregas</td>\n",
+        "      <td> 14.0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td> Midfield</td>\n",
+        "      <td> Chelsea</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 20,
+       "text": [
+        "             player salary  games  goals  assists  shots_on_target  \\\n",
+        "1    Alexis S\u00e1nchez     15      0     12        7               29   \n",
+        "3       Eden Hazard   18.9     21      8        4               17   \n",
+        "7  Santiago Cazorla   14.8     20      4        0               20   \n",
+        "9     Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
+        "\n",
+        "   points_per_game  points  position     team  \n",
+        "1            11.19  223.86   Forward  Arsenal  \n",
+        "3            13.05  274.04  Midfield  Chelsea  \n",
+        "7             9.97    0.00  Midfield  Arsenal  \n",
+        "9            10.47  209.49  Midfield  Chelsea  "
+       ]
+      }
+     ],
+     "prompt_number": 20
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# Selecting forwards from Arsenal only\n",
+      "\n",
+      "df[ (df['team'] == 'Arsenal') & (df['position'] == 'Forward') ]"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>position</th>\n",
+        "      <th>team</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td> 7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td> Forward</td>\n",
+        "      <td> Arsenal</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 22,
+       "text": [
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "1  Alexis S\u00e1nchez     15      0     12        7               29   \n",
+        "\n",
+        "   points_per_game  points position     team  \n",
+        "1            11.19  223.86  Forward  Arsenal  "
+       ]
+      }
+     ],
+     "prompt_number": 22
     }
    ],
    "metadata": {}

From d2039a7868da12be7665d5349162d64af7ab4126 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Sun, 25 Jan 2015 00:10:54 -0500
Subject: [PATCH 213/248] inplace ops update

---
 tutorials/things_in_pandas.ipynb | 64 ++++++++++++++++----------------
 1 file changed, 32 insertions(+), 32 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index bb71a69..7afa576 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:cf7223086a74b13d1ae2228a4c8545c401765a90cdb3eca418f18138a4afdaab"
+  "signature": "sha256:2c7029e546d20c06eaa707f59d5b067689bf8827b87cc32f4fae7b55cde6e0f2"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -29,7 +29,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Sebastian Raschka 24/01/2015 \n",
+        "Sebastian Raschka 25/01/2015 \n",
         "\n",
         "CPython 3.4.2\n",
         "IPython 2.3.1\n",
@@ -890,7 +890,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 7,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "7   Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
@@ -904,7 +904,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -1008,7 +1008,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 9,
+       "prompt_number": 8,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "4    \u00c1ngel Di Mar\u00eda   15.0     13      3      NaN               13   \n",
@@ -1020,7 +1020,7 @@
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -1177,7 +1177,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 9,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1201,7 +1201,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -1225,7 +1225,7 @@
      "input": [
       "# Filling NaN cells with default value 0\n",
       "\n",
-      "df = df.fillna(value=0)\n",
+      "df.fillna(value=0, inplace=True)\n",
       "df"
      ],
      "language": "python",
@@ -1387,7 +1387,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
+       "prompt_number": 10,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "0     Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1415,7 +1415,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -1523,7 +1523,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 11,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   Saido Berahino   13.8     21      9        0               20   \n",
@@ -1537,7 +1537,7 @@
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 11
     },
     {
      "cell_type": "code",
@@ -1617,7 +1617,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 13,
+       "prompt_number": 12,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   Saido Berahino   13.8     21      9        0               20   \n",
@@ -1631,7 +1631,7 @@
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -1662,7 +1662,7 @@
      "input": [
       "# Sorting the DataFrame by a certain column (from highest to lowest)\n",
       "\n",
-      "df = df.sort('goals', ascending=False)\n",
+      "df.sort('goals', ascending=False, inplace=True)\n",
       "df.head()"
      ],
      "language": "python",
@@ -1759,7 +1759,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 14,
+       "prompt_number": 13,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1777,7 +1777,7 @@
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -1882,7 +1882,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 14,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1900,7 +1900,7 @@
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -2003,7 +2003,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 16,
+       "prompt_number": 15,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   Sergio Ag\u00fcero     20     16     14        3               34   \n",
@@ -2017,7 +2017,7 @@
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -2114,7 +2114,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 17,
+       "prompt_number": 16,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
@@ -2130,7 +2130,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -2224,7 +2224,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 18,
+       "prompt_number": 17,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
@@ -2240,7 +2240,7 @@
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -2326,7 +2326,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 19,
+       "prompt_number": 18,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   Sergio Ag\u00fcero     20     16     14        3               34   \n",
@@ -2340,7 +2340,7 @@
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -2454,7 +2454,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 20,
+       "prompt_number": 19,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "1    Alexis S\u00e1nchez     15      0     12        7               29   \n",
@@ -2470,7 +2470,7 @@
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 19
     },
     {
      "cell_type": "code",
@@ -2522,7 +2522,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 22,
+       "prompt_number": 20,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1  Alexis S\u00e1nchez     15      0     12        7               29   \n",
@@ -2532,7 +2532,7 @@
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 20
     }
    ],
    "metadata": {}

From 367d8f99352d4ed142ea5436bd9f85b5759d3328 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 28 Jan 2015 13:26:03 -0500
Subject: [PATCH 214/248] counting nan rows

---
 tutorials/things_in_pandas.ipynb | 40 +++++++++++++++++++++++++++++++-
 1 file changed, 39 insertions(+), 1 deletion(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index 7afa576..fd8a7f9 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -29,7 +29,7 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Sebastian Raschka 25/01/2015 \n",
+        "Sebastian Raschka 28/01/2015 \n",
         "\n",
         "CPython 3.4.2\n",
         "IPython 2.3.1\n",
@@ -90,6 +90,7 @@
       "    - [Changing Values in a Column](#Changing-Values-in-a-Column)\n",
       "    - [Adding a New Column](#Adding-a-New-Column)\n",
       "- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n",
+      "    - [Counting Rows with NaNs](#Counting-Rows-with-NaNs)\n",
       "    - [Selecting NaN Rows](#Selecting-NaN-Rows)\n",
       "    - [Selecting non-NaN Rows](#Selecting-non-NaN-Rows)\n",
       "    - [Filling NaN Rows](#Filling-NaN-Rows)\n",
@@ -937,6 +938,43 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Counting Rows with NaNs"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "nans = df.shape[0] - df.dropna().shape[0]\n",
+      "\n",
+      "print('%d rows have missing values' % nans)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "3 rows have missing values\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 3,

From 7cfec1ddc02143fbd61e56badda686ac5f11dac7 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 28 Jan 2015 22:22:37 -0500
Subject: [PATCH 215/248] Applying Functions to Multiple Columns

---
 tutorials/things_in_pandas.ipynb | 742 +++++++++++++++++--------------
 1 file changed, 403 insertions(+), 339 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index fd8a7f9..87c09bb 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2c7029e546d20c06eaa707f59d5b067689bf8827b87cc32f4fae7b55cde6e0f2"
+  "signature": "sha256:c69dee8958d58e899a12b80810cc37f7abd7a90f9b76135251a76499ed8aeb2a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -89,6 +89,7 @@
       "- [Applying Computations Rows-wise](#Applying-Computations-Rows-wise)\n",
       "    - [Changing Values in a Column](#Changing-Values-in-a-Column)\n",
       "    - [Adding a New Column](#Adding-a-New-Column)\n",
+      "    - [Applying Functions to Multiple Columns](#Applying-Functions-to-Multiple-Columns)\n",
       "- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n",
       "    - [Counting Rows with NaNs](#Counting-Rows-with-NaNs)\n",
       "    - [Selecting NaN Rows](#Selecting-NaN-Rows)\n",
@@ -915,6 +916,144 @@
       "<br>"
      ]
     },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Applying Functions to Multiple Columns"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "cols = ['player', 'position', 'team']\n",
+      "df[cols] = df[cols].applymap(lambda x: x.lower())\n",
+      "df.head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>games</th>\n",
+        "      <th>goals</th>\n",
+        "      <th>assists</th>\n",
+        "      <th>shots_on_target</th>\n",
+        "      <th>points_per_game</th>\n",
+        "      <th>points</th>\n",
+        "      <th>position</th>\n",
+        "      <th>team</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>  sergio ag\u00fcero</td>\n",
+        "      <td> 19.2</td>\n",
+        "      <td> 16</td>\n",
+        "      <td> 14</td>\n",
+        "      <td>  3</td>\n",
+        "      <td> 34</td>\n",
+        "      <td> 13.12</td>\n",
+        "      <td> 209.98</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>   manchester city</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>    eden hazard</td>\n",
+        "      <td> 18.9</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  8</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 13.05</td>\n",
+        "      <td> 274.04</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>           chelsea</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> alexis s\u00e1nchez</td>\n",
+        "      <td> 17.6</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>           arsenal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>     yaya tour\u00e9</td>\n",
+        "      <td> 16.6</td>\n",
+        "      <td> 18</td>\n",
+        "      <td>  7</td>\n",
+        "      <td>  1</td>\n",
+        "      <td> 19</td>\n",
+        "      <td> 10.99</td>\n",
+        "      <td> 197.91</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>   manchester city</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td> \u00e1ngel di mar\u00eda</td>\n",
+        "      <td> 15.0</td>\n",
+        "      <td> 13</td>\n",
+        "      <td>  3</td>\n",
+        "      <td>NaN</td>\n",
+        "      <td> 13</td>\n",
+        "      <td> 10.17</td>\n",
+        "      <td> 132.23</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> manchester united</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
+        "1     eden hazard   18.9     21      8        4               17   \n",
+        "2  alexis s\u00e1nchez   17.6    NaN     12        7               29   \n",
+        "3      yaya tour\u00e9   16.6     18      7        1               19   \n",
+        "4  \u00e1ngel di mar\u00eda   15.0     13      3      NaN               13   \n",
+        "\n",
+        "   points_per_game  points  position               team  \n",
+        "0            13.12  209.98   forward    manchester city  \n",
+        "1            13.05  274.04  midfield            chelsea  \n",
+        "2            11.19  223.86   forward            arsenal  \n",
+        "3            10.99  197.91  midfield    manchester city  \n",
+        "4            10.17  132.23  midfield  manchester united  "
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
     {
      "cell_type": "heading",
      "level": 1,
@@ -965,7 +1104,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -1016,7 +1155,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
-        "      <td>   \u00c1ngel Di Mar\u00eda</td>\n",
+        "      <td>   \u00e1ngel di mar\u00eda</td>\n",
         "      <td> 15.0</td>\n",
         "      <td> 13</td>\n",
         "      <td> 3</td>\n",
@@ -1024,12 +1163,12 @@
         "      <td> 13</td>\n",
         "      <td> 10.17</td>\n",
         "      <td> 132.23</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Manchester United</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> manchester united</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
-        "      <td> Santiago Cazorla</td>\n",
+        "      <td> santiago cazorla</td>\n",
         "      <td> 14.8</td>\n",
         "      <td> 20</td>\n",
         "      <td> 4</td>\n",
@@ -1037,8 +1176,8 @@
         "      <td> 20</td>\n",
         "      <td>  9.97</td>\n",
         "      <td>    NaN</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>           Arsenal</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>           arsenal</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1046,19 +1185,19 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 10,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
-        "4    \u00c1ngel Di Mar\u00eda   15.0     13      3      NaN               13   \n",
-        "5  Santiago Cazorla   14.8     20      4      NaN               20   \n",
+        "4    \u00e1ngel di mar\u00eda   15.0     13      3      NaN               13   \n",
+        "5  santiago cazorla   14.8     20      4      NaN               20   \n",
         "\n",
         "   points_per_game  points  position               team  \n",
-        "4            10.17  132.23  Midfield  Manchester United  \n",
-        "5             9.97     NaN  Midfield            Arsenal  "
+        "4            10.17  132.23  midfield  manchester united  \n",
+        "5             9.97     NaN  midfield            arsenal  "
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -1107,7 +1246,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>0</th>\n",
-        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td>  sergio ag\u00fcero</td>\n",
         "      <td> 19.2</td>\n",
         "      <td> 16</td>\n",
         "      <td> 14</td>\n",
@@ -1115,12 +1254,12 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
-        "      <td>    Eden Hazard</td>\n",
+        "      <td>    eden hazard</td>\n",
         "      <td> 18.9</td>\n",
         "      <td> 21</td>\n",
         "      <td>  8</td>\n",
@@ -1128,12 +1267,12 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>         Chelsea</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>         chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
-        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> alexis s\u00e1nchez</td>\n",
         "      <td> 17.6</td>\n",
         "      <td>NaN</td>\n",
         "      <td> 12</td>\n",
@@ -1141,12 +1280,12 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>         Arsenal</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>         arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
-        "      <td>     Yaya Tour\u00e9</td>\n",
+        "      <td>     yaya tour\u00e9</td>\n",
         "      <td> 16.6</td>\n",
         "      <td> 18</td>\n",
         "      <td>  7</td>\n",
@@ -1154,12 +1293,12 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>6</th>\n",
-        "      <td>    David Silva</td>\n",
+        "      <td>    david silva</td>\n",
         "      <td> 14.3</td>\n",
         "      <td> 15</td>\n",
         "      <td>  6</td>\n",
@@ -1167,12 +1306,12 @@
         "      <td> 11</td>\n",
         "      <td> 10.35</td>\n",
         "      <td> 155.26</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>7</th>\n",
-        "      <td>  Cesc F\u00e0bregas</td>\n",
+        "      <td>  cesc f\u00e0bregas</td>\n",
         "      <td> 14.0</td>\n",
         "      <td> 20</td>\n",
         "      <td>  2</td>\n",
@@ -1180,12 +1319,12 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>         Chelsea</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>         chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
-        "      <td> Saido Berahino</td>\n",
+        "      <td> saido berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -1193,12 +1332,12 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>       west brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
-        "      <td> Steven Gerrard</td>\n",
+        "      <td> steven gerrard</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 20</td>\n",
         "      <td>  5</td>\n",
@@ -1206,8 +1345,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>       Liverpool</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>       liverpool</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1215,31 +1354,31 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 9,
+       "prompt_number": 11,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
-        "1     Eden Hazard   18.9     21      8        4               17   \n",
-        "2  Alexis S\u00e1nchez   17.6    NaN     12        7               29   \n",
-        "3      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
-        "6     David Silva   14.3     15      6        2               11   \n",
-        "7   Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
-        "8  Saido Berahino   13.8     21      9        0               20   \n",
-        "9  Steven Gerrard   13.8     20      5        1               11   \n",
+        "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
+        "1     eden hazard   18.9     21      8        4               17   \n",
+        "2  alexis s\u00e1nchez   17.6    NaN     12        7               29   \n",
+        "3      yaya tour\u00e9   16.6     18      7        1               19   \n",
+        "6     david silva   14.3     15      6        2               11   \n",
+        "7   cesc f\u00e0bregas   14.0     20      2       14               10   \n",
+        "8  saido berahino   13.8     21      9        0               20   \n",
+        "9  steven gerrard   13.8     20      5        1               11   \n",
         "\n",
         "   points_per_game  points  position             team  \n",
-        "0            13.12  209.98   Forward  Manchester City  \n",
-        "1            13.05  274.04  Midfield          Chelsea  \n",
-        "2            11.19  223.86   Forward          Arsenal  \n",
-        "3            10.99  197.91  Midfield  Manchester City  \n",
-        "6            10.35  155.26  Midfield  Manchester City  \n",
-        "7            10.47  209.49  Midfield          Chelsea  \n",
-        "8             7.02  147.43   Forward        West Brom  \n",
-        "9             7.50  150.01  Midfield        Liverpool  "
+        "0            13.12  209.98   forward  manchester city  \n",
+        "1            13.05  274.04  midfield          chelsea  \n",
+        "2            11.19  223.86   forward          arsenal  \n",
+        "3            10.99  197.91  midfield  manchester city  \n",
+        "6            10.35  155.26  midfield  manchester city  \n",
+        "7            10.47  209.49  midfield          chelsea  \n",
+        "8             7.02  147.43   forward        west brom  \n",
+        "9             7.50  150.01  midfield        liverpool  "
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -1291,7 +1430,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>0</th>\n",
-        "      <td>    Sergio Ag\u00fcero</td>\n",
+        "      <td>    sergio ag\u00fcero</td>\n",
         "      <td> 19.2</td>\n",
         "      <td> 16</td>\n",
         "      <td> 14</td>\n",
@@ -1299,12 +1438,12 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>   Manchester City</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>   manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
-        "      <td>      Eden Hazard</td>\n",
+        "      <td>      eden hazard</td>\n",
         "      <td> 18.9</td>\n",
         "      <td> 21</td>\n",
         "      <td>  8</td>\n",
@@ -1312,12 +1451,12 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>           Chelsea</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>           chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
-        "      <td>   Alexis S\u00e1nchez</td>\n",
+        "      <td>   alexis s\u00e1nchez</td>\n",
         "      <td> 17.6</td>\n",
         "      <td>  0</td>\n",
         "      <td> 12</td>\n",
@@ -1325,12 +1464,12 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>           Arsenal</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>           arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
-        "      <td>       Yaya Tour\u00e9</td>\n",
+        "      <td>       yaya tour\u00e9</td>\n",
         "      <td> 16.6</td>\n",
         "      <td> 18</td>\n",
         "      <td>  7</td>\n",
@@ -1338,12 +1477,12 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>   Manchester City</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>   manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
-        "      <td>   \u00c1ngel Di Mar\u00eda</td>\n",
+        "      <td>   \u00e1ngel di mar\u00eda</td>\n",
         "      <td> 15.0</td>\n",
         "      <td> 13</td>\n",
         "      <td>  3</td>\n",
@@ -1351,12 +1490,12 @@
         "      <td> 13</td>\n",
         "      <td> 10.17</td>\n",
         "      <td> 132.23</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Manchester United</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> manchester united</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
-        "      <td> Santiago Cazorla</td>\n",
+        "      <td> santiago cazorla</td>\n",
         "      <td> 14.8</td>\n",
         "      <td> 20</td>\n",
         "      <td>  4</td>\n",
@@ -1364,12 +1503,12 @@
         "      <td> 20</td>\n",
         "      <td>  9.97</td>\n",
         "      <td>   0.00</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>           Arsenal</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>           arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>6</th>\n",
-        "      <td>      David Silva</td>\n",
+        "      <td>      david silva</td>\n",
         "      <td> 14.3</td>\n",
         "      <td> 15</td>\n",
         "      <td>  6</td>\n",
@@ -1377,12 +1516,12 @@
         "      <td> 11</td>\n",
         "      <td> 10.35</td>\n",
         "      <td> 155.26</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>   Manchester City</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>   manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>7</th>\n",
-        "      <td>    Cesc F\u00e0bregas</td>\n",
+        "      <td>    cesc f\u00e0bregas</td>\n",
         "      <td> 14.0</td>\n",
         "      <td> 20</td>\n",
         "      <td>  2</td>\n",
@@ -1390,12 +1529,12 @@
         "      <td> 10</td>\n",
         "      <td> 10.47</td>\n",
         "      <td> 209.49</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>           Chelsea</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>           chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
-        "      <td>   Saido Berahino</td>\n",
+        "      <td>   saido berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -1403,12 +1542,12 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>         West Brom</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>         west brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9</th>\n",
-        "      <td>   Steven Gerrard</td>\n",
+        "      <td>   steven gerrard</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 20</td>\n",
         "      <td>  5</td>\n",
@@ -1416,8 +1555,8 @@
         "      <td> 11</td>\n",
         "      <td>  7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>         Liverpool</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>         liverpool</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1425,35 +1564,35 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 12,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
-        "0     Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
-        "1       Eden Hazard   18.9     21      8        4               17   \n",
-        "2    Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
-        "3        Yaya Tour\u00e9   16.6     18      7        1               19   \n",
-        "4    \u00c1ngel Di Mar\u00eda   15.0     13      3        0               13   \n",
-        "5  Santiago Cazorla   14.8     20      4        0               20   \n",
-        "6       David Silva   14.3     15      6        2               11   \n",
-        "7     Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
-        "8    Saido Berahino   13.8     21      9        0               20   \n",
-        "9    Steven Gerrard   13.8     20      5        1               11   \n",
+        "0     sergio ag\u00fcero   19.2     16     14        3               34   \n",
+        "1       eden hazard   18.9     21      8        4               17   \n",
+        "2    alexis s\u00e1nchez   17.6      0     12        7               29   \n",
+        "3        yaya tour\u00e9   16.6     18      7        1               19   \n",
+        "4    \u00e1ngel di mar\u00eda   15.0     13      3        0               13   \n",
+        "5  santiago cazorla   14.8     20      4        0               20   \n",
+        "6       david silva   14.3     15      6        2               11   \n",
+        "7     cesc f\u00e0bregas   14.0     20      2       14               10   \n",
+        "8    saido berahino   13.8     21      9        0               20   \n",
+        "9    steven gerrard   13.8     20      5        1               11   \n",
         "\n",
         "   points_per_game  points  position               team  \n",
-        "0            13.12  209.98   Forward    Manchester City  \n",
-        "1            13.05  274.04  Midfield            Chelsea  \n",
-        "2            11.19  223.86   Forward            Arsenal  \n",
-        "3            10.99  197.91  Midfield    Manchester City  \n",
-        "4            10.17  132.23  Midfield  Manchester United  \n",
-        "5             9.97    0.00  Midfield            Arsenal  \n",
-        "6            10.35  155.26  Midfield    Manchester City  \n",
-        "7            10.47  209.49  Midfield            Chelsea  \n",
-        "8             7.02  147.43   Forward          West Brom  \n",
-        "9             7.50  150.01  Midfield          Liverpool  "
+        "0            13.12  209.98   forward    manchester city  \n",
+        "1            13.05  274.04  midfield            chelsea  \n",
+        "2            11.19  223.86   forward            arsenal  \n",
+        "3            10.99  197.91  midfield    manchester city  \n",
+        "4            10.17  132.23  midfield  manchester united  \n",
+        "5             9.97    0.00  midfield            arsenal  \n",
+        "6            10.35  155.26  midfield    manchester city  \n",
+        "7            10.47  209.49  midfield            chelsea  \n",
+        "8             7.02  147.43   forward          west brom  \n",
+        "9             7.50  150.01  midfield          liverpool  "
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -1518,7 +1657,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>8 </th>\n",
-        "      <td> Saido Berahino</td>\n",
+        "      <td> saido berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -1526,12 +1665,12 @@
         "      <td> 20</td>\n",
         "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td> West Brom</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td> west brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
-        "      <td> Steven Gerrard</td>\n",
+        "      <td> steven gerrard</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 20</td>\n",
         "      <td>  5</td>\n",
@@ -1539,8 +1678,8 @@
         "      <td> 11</td>\n",
         "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Liverpool</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> liverpool</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
@@ -1561,21 +1700,21 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
+       "prompt_number": 13,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
-        "8   Saido Berahino   13.8     21      9        0               20   \n",
-        "9   Steven Gerrard   13.8     20      5        1               11   \n",
+        "8   saido berahino   13.8     21      9        0               20   \n",
+        "9   steven gerrard   13.8     20      5        1               11   \n",
         "10             NaN    NaN    NaN    NaN      NaN              NaN   \n",
         "\n",
         "    points_per_game  points  position       team  \n",
-        "8              7.02  147.43   Forward  West Brom  \n",
-        "9              7.50  150.01  Midfield  Liverpool  \n",
+        "8              7.02  147.43   forward  west brom  \n",
+        "9              7.50  150.01  midfield  liverpool  \n",
         "10              NaN     NaN       NaN        NaN  "
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -1612,7 +1751,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>8 </th>\n",
-        "      <td> Saido Berahino</td>\n",
+        "      <td> saido berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -1620,12 +1759,12 @@
         "      <td> 20</td>\n",
         "      <td> 7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td> West Brom</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td> west brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>9 </th>\n",
-        "      <td> Steven Gerrard</td>\n",
+        "      <td> steven gerrard</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 20</td>\n",
         "      <td>  5</td>\n",
@@ -1633,8 +1772,8 @@
         "      <td> 11</td>\n",
         "      <td> 7.50</td>\n",
         "      <td> 150.01</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Liverpool</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> liverpool</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
@@ -1655,21 +1794,21 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 14,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
-        "8   Saido Berahino   13.8     21      9        0               20   \n",
-        "9   Steven Gerrard   13.8     20      5        1               11   \n",
+        "8   saido berahino   13.8     21      9        0               20   \n",
+        "9   steven gerrard   13.8     20      5        1               11   \n",
         "10      New Player   12.3    NaN    NaN      NaN              NaN   \n",
         "\n",
         "    points_per_game  points  position       team  \n",
-        "8              7.02  147.43   Forward  West Brom  \n",
-        "9              7.50  150.01  Midfield  Liverpool  \n",
+        "8              7.02  147.43   forward  west brom  \n",
+        "9              7.50  150.01  midfield  liverpool  \n",
         "10              NaN     NaN       NaN        NaN  "
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -1728,7 +1867,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>0</th>\n",
-        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td>  sergio ag\u00fcero</td>\n",
         "      <td> 19.2</td>\n",
         "      <td> 16</td>\n",
         "      <td> 14</td>\n",
@@ -1736,12 +1875,12 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
-        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> alexis s\u00e1nchez</td>\n",
         "      <td> 17.6</td>\n",
         "      <td>  0</td>\n",
         "      <td> 12</td>\n",
@@ -1749,12 +1888,12 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>         Arsenal</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>         arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>8</th>\n",
-        "      <td> Saido Berahino</td>\n",
+        "      <td> saido berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -1762,12 +1901,12 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>       west brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
-        "      <td>    Eden Hazard</td>\n",
+        "      <td>    eden hazard</td>\n",
         "      <td> 18.9</td>\n",
         "      <td> 21</td>\n",
         "      <td>  8</td>\n",
@@ -1775,12 +1914,12 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>         Chelsea</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>         chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
-        "      <td>     Yaya Tour\u00e9</td>\n",
+        "      <td>     yaya tour\u00e9</td>\n",
         "      <td> 16.6</td>\n",
         "      <td> 18</td>\n",
         "      <td>  7</td>\n",
@@ -1788,8 +1927,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1797,25 +1936,25 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 13,
+       "prompt_number": 15,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "0   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
-        "2  Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
-        "8  Saido Berahino   13.8     21      9        0               20   \n",
-        "1     Eden Hazard   18.9     21      8        4               17   \n",
-        "3      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
+        "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
+        "2  alexis s\u00e1nchez   17.6      0     12        7               29   \n",
+        "8  saido berahino   13.8     21      9        0               20   \n",
+        "1     eden hazard   18.9     21      8        4               17   \n",
+        "3      yaya tour\u00e9   16.6     18      7        1               19   \n",
         "\n",
         "   points_per_game  points  position             team  \n",
-        "0            13.12  209.98   Forward  Manchester City  \n",
-        "2            11.19  223.86   Forward          Arsenal  \n",
-        "8             7.02  147.43   Forward        West Brom  \n",
-        "1            13.05  274.04  Midfield          Chelsea  \n",
-        "3            10.99  197.91  Midfield  Manchester City  "
+        "0            13.12  209.98   forward  manchester city  \n",
+        "2            11.19  223.86   forward          arsenal  \n",
+        "8             7.02  147.43   forward        west brom  \n",
+        "1            13.05  274.04  midfield          chelsea  \n",
+        "3            10.99  197.91  midfield  manchester city  "
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
@@ -1851,7 +1990,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
-        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td>  sergio ag\u00fcero</td>\n",
         "      <td> 19.2</td>\n",
         "      <td> 16</td>\n",
         "      <td> 14</td>\n",
@@ -1859,12 +1998,12 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
-        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> alexis s\u00e1nchez</td>\n",
         "      <td> 17.6</td>\n",
         "      <td>  0</td>\n",
         "      <td> 12</td>\n",
@@ -1872,12 +2011,12 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>         Arsenal</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>         arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
-        "      <td> Saido Berahino</td>\n",
+        "      <td> saido berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -1885,12 +2024,12 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>       west brom</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>4</th>\n",
-        "      <td>    Eden Hazard</td>\n",
+        "      <td>    eden hazard</td>\n",
         "      <td> 18.9</td>\n",
         "      <td> 21</td>\n",
         "      <td>  8</td>\n",
@@ -1898,12 +2037,12 @@
         "      <td> 17</td>\n",
         "      <td> 13.05</td>\n",
         "      <td> 274.04</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>         Chelsea</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>         chelsea</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>5</th>\n",
-        "      <td>     Yaya Tour\u00e9</td>\n",
+        "      <td>     yaya tour\u00e9</td>\n",
         "      <td> 16.6</td>\n",
         "      <td> 18</td>\n",
         "      <td>  7</td>\n",
@@ -1911,8 +2050,8 @@
         "      <td> 19</td>\n",
         "      <td> 10.99</td>\n",
         "      <td> 197.91</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -1920,25 +2059,25 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 14,
+       "prompt_number": 16,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "1   Sergio Ag\u00fcero   19.2     16     14        3               34   \n",
-        "2  Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
-        "3  Saido Berahino   13.8     21      9        0               20   \n",
-        "4     Eden Hazard   18.9     21      8        4               17   \n",
-        "5      Yaya Tour\u00e9   16.6     18      7        1               19   \n",
+        "1   sergio ag\u00fcero   19.2     16     14        3               34   \n",
+        "2  alexis s\u00e1nchez   17.6      0     12        7               29   \n",
+        "3  saido berahino   13.8     21      9        0               20   \n",
+        "4     eden hazard   18.9     21      8        4               17   \n",
+        "5      yaya tour\u00e9   16.6     18      7        1               19   \n",
         "\n",
         "   points_per_game  points  position             team  \n",
-        "1            13.12  209.98   Forward  Manchester City  \n",
-        "2            11.19  223.86   Forward          Arsenal  \n",
-        "3             7.02  147.43   Forward        West Brom  \n",
-        "4            13.05  274.04  Midfield          Chelsea  \n",
-        "5            10.99  197.91  Midfield  Manchester City  "
+        "1            13.12  209.98   forward  manchester city  \n",
+        "2            11.19  223.86   forward          arsenal  \n",
+        "3             7.02  147.43   forward        west brom  \n",
+        "4            13.05  274.04  midfield          chelsea  \n",
+        "5            10.99  197.91  midfield  manchester city  "
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -1998,7 +2137,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
-        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td>  sergio ag\u00fcero</td>\n",
         "      <td>   20</td>\n",
         "      <td> 16</td>\n",
         "      <td> 14</td>\n",
@@ -2006,12 +2145,12 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td> forward</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
-        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> alexis s\u00e1nchez</td>\n",
         "      <td>   15</td>\n",
         "      <td>  0</td>\n",
         "      <td> 12</td>\n",
@@ -2019,12 +2158,12 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td>         Arsenal</td>\n",
+        "      <td> forward</td>\n",
+        "      <td>         arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>3</th>\n",
-        "      <td> Saido Berahino</td>\n",
+        "      <td> saido berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -2032,8 +2171,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td> forward</td>\n",
+        "      <td>       west brom</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2041,21 +2180,21 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 17,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "1   Sergio Ag\u00fcero     20     16     14        3               34   \n",
-        "2  Alexis S\u00e1nchez     15      0     12        7               29   \n",
-        "3  Saido Berahino   13.8     21      9        0               20   \n",
+        "1   sergio ag\u00fcero     20     16     14        3               34   \n",
+        "2  alexis s\u00e1nchez     15      0     12        7               29   \n",
+        "3  saido berahino   13.8     21      9        0               20   \n",
         "\n",
         "   points_per_game  points position             team  \n",
-        "1            13.12  209.98  Forward  Manchester City  \n",
-        "2            11.19  223.86  Forward          Arsenal  \n",
-        "3             7.02  147.43  Forward        West Brom  "
+        "1            13.12  209.98  forward  manchester city  \n",
+        "2            11.19  223.86  forward          arsenal  \n",
+        "3             7.02  147.43  forward        west brom  "
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -2111,7 +2250,7 @@
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
-        "      <th>Sergio Ag\u00fcero</th>\n",
+        "      <th>sergio ag\u00fcero</th>\n",
         "      <td> 19.2</td>\n",
         "      <td> 16</td>\n",
         "      <td> 14</td>\n",
@@ -2119,11 +2258,11 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td> forward</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
-        "      <th>Alexis S\u00e1nchez</th>\n",
+        "      <th>alexis s\u00e1nchez</th>\n",
         "      <td> 17.6</td>\n",
         "      <td>  0</td>\n",
         "      <td> 12</td>\n",
@@ -2131,11 +2270,11 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td>         Arsenal</td>\n",
+        "      <td> forward</td>\n",
+        "      <td>         arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
-        "      <th>Saido Berahino</th>\n",
+        "      <th>saido berahino</th>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -2143,8 +2282,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td> forward</td>\n",
+        "      <td>       west brom</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2152,23 +2291,23 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 16,
+       "prompt_number": 18,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
-        "Sergio Ag\u00fcero    19.2     16     14        3               34   \n",
-        "Alexis S\u00e1nchez   17.6      0     12        7               29   \n",
-        "Saido Berahino   13.8     21      9        0               20   \n",
+        "sergio ag\u00fcero    19.2     16     14        3               34   \n",
+        "alexis s\u00e1nchez   17.6      0     12        7               29   \n",
+        "saido berahino   13.8     21      9        0               20   \n",
         "\n",
         "                points_per_game  points position             team  \n",
         "player                                                             \n",
-        "Sergio Ag\u00fcero             13.12  209.98  Forward  Manchester City  \n",
-        "Alexis S\u00e1nchez            11.19  223.86  Forward          Arsenal  \n",
-        "Saido Berahino             7.02  147.43  Forward        West Brom  "
+        "sergio ag\u00fcero             13.12  209.98  forward  manchester city  \n",
+        "alexis s\u00e1nchez            11.19  223.86  forward          arsenal  \n",
+        "saido berahino             7.02  147.43  forward        west brom  "
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -2221,7 +2360,7 @@
         "  </thead>\n",
         "  <tbody>\n",
         "    <tr>\n",
-        "      <th>Sergio Ag\u00fcero</th>\n",
+        "      <th>sergio ag\u00fcero</th>\n",
         "      <td>   20</td>\n",
         "      <td> 16</td>\n",
         "      <td> 14</td>\n",
@@ -2229,11 +2368,11 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td> forward</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
-        "      <th>Alexis S\u00e1nchez</th>\n",
+        "      <th>alexis s\u00e1nchez</th>\n",
         "      <td>   15</td>\n",
         "      <td>  0</td>\n",
         "      <td> 12</td>\n",
@@ -2241,11 +2380,11 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td>         Arsenal</td>\n",
+        "      <td> forward</td>\n",
+        "      <td>         arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
-        "      <th>Saido Berahino</th>\n",
+        "      <th>saido berahino</th>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -2253,8 +2392,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td> forward</td>\n",
+        "      <td>       west brom</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2262,23 +2401,23 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 17,
+       "prompt_number": 19,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
-        "Sergio Ag\u00fcero      20     16     14        3               34   \n",
-        "Alexis S\u00e1nchez     15      0     12        7               29   \n",
-        "Saido Berahino   13.8     21      9        0               20   \n",
+        "sergio ag\u00fcero      20     16     14        3               34   \n",
+        "alexis s\u00e1nchez     15      0     12        7               29   \n",
+        "saido berahino   13.8     21      9        0               20   \n",
         "\n",
         "                points_per_game  points position             team  \n",
         "player                                                             \n",
-        "Sergio Ag\u00fcero             13.12  209.98  Forward  Manchester City  \n",
-        "Alexis S\u00e1nchez            11.19  223.86  Forward          Arsenal  \n",
-        "Saido Berahino             7.02  147.43  Forward        West Brom  "
+        "sergio ag\u00fcero             13.12  209.98  forward  manchester city  \n",
+        "alexis s\u00e1nchez            11.19  223.86  forward          arsenal  \n",
+        "saido berahino             7.02  147.43  forward        west brom  "
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -2321,7 +2460,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>0</th>\n",
-        "      <td>  Sergio Ag\u00fcero</td>\n",
+        "      <td>  sergio ag\u00fcero</td>\n",
         "      <td>   20</td>\n",
         "      <td> 16</td>\n",
         "      <td> 14</td>\n",
@@ -2329,12 +2468,12 @@
         "      <td> 34</td>\n",
         "      <td> 13.12</td>\n",
         "      <td> 209.98</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td> Manchester City</td>\n",
+        "      <td> forward</td>\n",
+        "      <td> manchester city</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>1</th>\n",
-        "      <td> Alexis S\u00e1nchez</td>\n",
+        "      <td> alexis s\u00e1nchez</td>\n",
         "      <td>   15</td>\n",
         "      <td>  0</td>\n",
         "      <td> 12</td>\n",
@@ -2342,12 +2481,12 @@
         "      <td> 29</td>\n",
         "      <td> 11.19</td>\n",
         "      <td> 223.86</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td>         Arsenal</td>\n",
+        "      <td> forward</td>\n",
+        "      <td>         arsenal</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>2</th>\n",
-        "      <td> Saido Berahino</td>\n",
+        "      <td> saido berahino</td>\n",
         "      <td> 13.8</td>\n",
         "      <td> 21</td>\n",
         "      <td>  9</td>\n",
@@ -2355,8 +2494,8 @@
         "      <td> 20</td>\n",
         "      <td>  7.02</td>\n",
         "      <td> 147.43</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td>       West Brom</td>\n",
+        "      <td> forward</td>\n",
+        "      <td>       west brom</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -2364,21 +2503,21 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 18,
+       "prompt_number": 20,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "0   Sergio Ag\u00fcero     20     16     14        3               34   \n",
-        "1  Alexis S\u00e1nchez     15      0     12        7               29   \n",
-        "2  Saido Berahino   13.8     21      9        0               20   \n",
+        "0   sergio ag\u00fcero     20     16     14        3               34   \n",
+        "1  alexis s\u00e1nchez     15      0     12        7               29   \n",
+        "2  saido berahino   13.8     21      9        0               20   \n",
         "\n",
         "   points_per_game  points position             team  \n",
-        "0            13.12  209.98  Forward  Manchester City  \n",
-        "1            11.19  223.86  Forward          Arsenal  \n",
-        "2             7.02  147.43  Forward        West Brom  "
+        "0            13.12  209.98  forward  manchester city  \n",
+        "1            11.19  223.86  forward          arsenal  \n",
+        "2             7.02  147.43  forward        west brom  "
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -2434,81 +2573,21 @@
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>   Alexis S\u00e1nchez</td>\n",
-        "      <td>   15</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td>  7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td> Arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td>      Eden Hazard</td>\n",
-        "      <td> 18.9</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  8</td>\n",
-        "      <td>  4</td>\n",
-        "      <td> 17</td>\n",
-        "      <td> 13.05</td>\n",
-        "      <td> 274.04</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td> Santiago Cazorla</td>\n",
-        "      <td> 14.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  4</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>   0.00</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td>    Cesc F\u00e0bregas</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Chelsea</td>\n",
-        "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 19,
+       "prompt_number": 21,
        "text": [
-        "             player salary  games  goals  assists  shots_on_target  \\\n",
-        "1    Alexis S\u00e1nchez     15      0     12        7               29   \n",
-        "3       Eden Hazard   18.9     21      8        4               17   \n",
-        "7  Santiago Cazorla   14.8     20      4        0               20   \n",
-        "9     Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
-        "\n",
-        "   points_per_game  points  position     team  \n",
-        "1            11.19  223.86   Forward  Arsenal  \n",
-        "3            13.05  274.04  Midfield  Chelsea  \n",
-        "7             9.97    0.00  Midfield  Arsenal  \n",
-        "9            10.47  209.49  Midfield  Chelsea  "
+        "Empty DataFrame\n",
+        "Columns: [player, salary, games, goals, assists, shots_on_target, points_per_game, points, position, team]\n",
+        "Index: []"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 21
     },
     {
      "cell_type": "code",
@@ -2541,36 +2620,21 @@
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td> Alexis S\u00e1nchez</td>\n",
-        "      <td> 15</td>\n",
-        "      <td> 0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td> 7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td> Forward</td>\n",
-        "      <td> Arsenal</td>\n",
-        "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 20,
+       "prompt_number": 22,
        "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "1  Alexis S\u00e1nchez     15      0     12        7               29   \n",
-        "\n",
-        "   points_per_game  points position     team  \n",
-        "1            11.19  223.86  Forward  Arsenal  "
+        "Empty DataFrame\n",
+        "Columns: [player, salary, games, goals, assists, shots_on_target, points_per_game, points, position, team]\n",
+        "Index: []"
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 22
     }
    ],
    "metadata": {}

From db6679eb9c064d1ee974e5c0226f81a739141112 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 28 Jan 2015 22:25:09 -0500
Subject: [PATCH 216/248] Applying Functions to Multiple Columns

---
 tutorials/things_in_pandas.ipynb | 99 ++++++++++++++++++++++++++++----
 1 file changed, 87 insertions(+), 12 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index 87c09bb..53145eb 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:c69dee8958d58e899a12b80810cc37f7abd7a90f9b76135251a76499ed8aeb2a"
+  "signature": "sha256:3155cd3fa2449393a467f91f3cdbb32eeac212db664843ef30f96b635dbfc06d"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -1722,7 +1722,7 @@
      "input": [
       "# Filling cells with data\n",
       "\n",
-      "df.loc[df.index[-1], 'player'] = 'New Player'\n",
+      "df.loc[df.index[-1], 'player'] = 'new player'\n",
       "df.loc[df.index[-1], 'salary'] = 12.3\n",
       "df.tail(3)"
      ],
@@ -1777,7 +1777,7 @@
         "    </tr>\n",
         "    <tr>\n",
         "      <th>10</th>\n",
-        "      <td>     New Player</td>\n",
+        "      <td>     new player</td>\n",
         "      <td> 12.3</td>\n",
         "      <td>NaN</td>\n",
         "      <td>NaN</td>\n",
@@ -1799,7 +1799,7 @@
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   saido berahino   13.8     21      9        0               20   \n",
         "9   steven gerrard   13.8     20      5        1               11   \n",
-        "10      New Player   12.3    NaN    NaN      NaN              NaN   \n",
+        "10      new player   12.3    NaN    NaN      NaN              NaN   \n",
         "\n",
         "    points_per_game  points  position       team  \n",
         "8              7.02  147.43   forward  west brom  \n",
@@ -2548,7 +2548,7 @@
      "input": [
       "# Selecting only those players that either playing for Arsenal or Chelsea\n",
       "\n",
-      "df[ (df['team'] == 'Arsenal') | (df['team'] == 'Chelsea') ]"
+      "df[ (df['team'] == 'arsenal') | (df['team'] == 'chelsea') ]"
      ],
      "language": "python",
      "metadata": {},
@@ -2573,6 +2573,58 @@
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td>   alexis s\u00e1nchez</td>\n",
+        "      <td>   15</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td>  7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td> arsenal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>      eden hazard</td>\n",
+        "      <td> 18.9</td>\n",
+        "      <td> 21</td>\n",
+        "      <td>  8</td>\n",
+        "      <td>  4</td>\n",
+        "      <td> 17</td>\n",
+        "      <td> 13.05</td>\n",
+        "      <td> 274.04</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> chelsea</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>7</th>\n",
+        "      <td> santiago cazorla</td>\n",
+        "      <td> 14.8</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  4</td>\n",
+        "      <td>  0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  9.97</td>\n",
+        "      <td>   0.00</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> arsenal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>9</th>\n",
+        "      <td>    cesc f\u00e0bregas</td>\n",
+        "      <td> 14.0</td>\n",
+        "      <td> 20</td>\n",
+        "      <td>  2</td>\n",
+        "      <td> 14</td>\n",
+        "      <td> 10</td>\n",
+        "      <td> 10.47</td>\n",
+        "      <td> 209.49</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> chelsea</td>\n",
+        "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
         "</div>"
@@ -2581,9 +2633,17 @@
        "output_type": "pyout",
        "prompt_number": 21,
        "text": [
-        "Empty DataFrame\n",
-        "Columns: [player, salary, games, goals, assists, shots_on_target, points_per_game, points, position, team]\n",
-        "Index: []"
+        "             player salary  games  goals  assists  shots_on_target  \\\n",
+        "1    alexis s\u00e1nchez     15      0     12        7               29   \n",
+        "3       eden hazard   18.9     21      8        4               17   \n",
+        "7  santiago cazorla   14.8     20      4        0               20   \n",
+        "9     cesc f\u00e0bregas   14.0     20      2       14               10   \n",
+        "\n",
+        "   points_per_game  points  position     team  \n",
+        "1            11.19  223.86   forward  arsenal  \n",
+        "3            13.05  274.04  midfield  chelsea  \n",
+        "7             9.97    0.00  midfield  arsenal  \n",
+        "9            10.47  209.49  midfield  chelsea  "
        ]
       }
      ],
@@ -2595,7 +2655,7 @@
      "input": [
       "# Selecting forwards from Arsenal only\n",
       "\n",
-      "df[ (df['team'] == 'Arsenal') & (df['position'] == 'Forward') ]"
+      "df[ (df['team'] == 'arsenal') & (df['position'] == 'forward') ]"
      ],
      "language": "python",
      "metadata": {},
@@ -2620,6 +2680,19 @@
         "    </tr>\n",
         "  </thead>\n",
         "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> alexis s\u00e1nchez</td>\n",
+        "      <td> 15</td>\n",
+        "      <td> 0</td>\n",
+        "      <td> 12</td>\n",
+        "      <td> 7</td>\n",
+        "      <td> 29</td>\n",
+        "      <td> 11.19</td>\n",
+        "      <td> 223.86</td>\n",
+        "      <td> forward</td>\n",
+        "      <td> arsenal</td>\n",
+        "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
         "</div>"
@@ -2628,9 +2701,11 @@
        "output_type": "pyout",
        "prompt_number": 22,
        "text": [
-        "Empty DataFrame\n",
-        "Columns: [player, salary, games, goals, assists, shots_on_target, points_per_game, points, position, team]\n",
-        "Index: []"
+        "           player salary  games  goals  assists  shots_on_target  \\\n",
+        "1  alexis s\u00e1nchez     15      0     12        7               29   \n",
+        "\n",
+        "   points_per_game  points position     team  \n",
+        "1            11.19  223.86  forward  arsenal  "
        ]
       }
      ],

From 40d5dd93a56218bd2e2287ff95ce428e3607dfa7 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 28 Jan 2015 22:31:23 -0500
Subject: [PATCH 217/248] Getting an Overview of the Column Types

---
 tutorials/things_in_pandas.ipynb | 142 +++++++++++++++++++++----------
 1 file changed, 98 insertions(+), 44 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index 53145eb..bf6c0b5 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:3155cd3fa2449393a467f91f3cdbb32eeac212db664843ef30f96b635dbfc06d"
+  "signature": "sha256:2dd2df7144acb424c4be84b5679ebc4d06bf16dc6a3e0f7fd3d1ed0595479f64"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -38,7 +38,7 @@
        ]
       }
      ],
-     "prompt_number": 1
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -98,7 +98,8 @@
       "- [Appending Rows to a DataFrame](#Appending-Rows-to-a-DataFrame)\n",
       "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)\n",
       "- [Updating Columns](#Updating-Columns)\n",
-      "- [Chaining Conditions - Using Bitwise Operators](#Chaining-Conditions---Using-Bitwise-Operators)"
+      "- [Chaining Conditions - Using Bitwise Operators](#Chaining-Conditions---Using-Bitwise-Operators)\n",
+      "- [Getting an Overview of the Column Types](#Getting-an-Overview-of-the-Column-Types)"
      ]
     },
     {
@@ -277,7 +278,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 3,
        "text": [
         "                                          PLAYER  SALARY  GP   G   A  SOT  \\\n",
         "0      Sergio Ag\u00fcero\\n Forward \u2014 Manchester City  $19.2m  16  14   3   34   \n",
@@ -305,7 +306,7 @@
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -419,7 +420,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 3,
+       "prompt_number": 4,
        "text": [
         "                                  player  salary  gp  g   a  sot    ppg  \\\n",
         "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m  20  2  14   10  10.47   \n",
@@ -433,7 +434,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -524,7 +525,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 4,
+       "prompt_number": 5,
        "text": [
         "                                  player  salary  games  goals  assists  \\\n",
         "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m     20      2       14   \n",
@@ -538,7 +539,7 @@
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -670,7 +671,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 5,
+       "prompt_number": 6,
        "text": [
         "                                     player salary  games  goals  assists  \\\n",
         "5     Santiago Cazorla\\n Midfield \u2014 Arsenal   14.8     20      4      NaN   \n",
@@ -688,7 +689,7 @@
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -785,7 +786,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 6,
+       "prompt_number": 7,
        "text": [
         "                                  player salary  games  goals  assists  \\\n",
         "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea   14.0     20      2       14   \n",
@@ -799,7 +800,7 @@
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 7
     },
     {
      "cell_type": "code",
@@ -892,7 +893,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 7,
+       "prompt_number": 8,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "7   Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
@@ -906,7 +907,7 @@
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -1026,7 +1027,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 9,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1044,7 +1045,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -1104,7 +1105,7 @@
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -1185,7 +1186,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 10,
+       "prompt_number": 11,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "4    \u00e1ngel di mar\u00eda   15.0     13      3      NaN               13   \n",
@@ -1197,7 +1198,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -1354,7 +1355,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
+       "prompt_number": 12,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1378,7 +1379,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -1564,7 +1565,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 13,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "0     sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1592,7 +1593,7 @@
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -1700,7 +1701,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 13,
+       "prompt_number": 14,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   saido berahino   13.8     21      9        0               20   \n",
@@ -1714,7 +1715,7 @@
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 14
     },
     {
      "cell_type": "code",
@@ -1794,7 +1795,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 14,
+       "prompt_number": 15,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   saido berahino   13.8     21      9        0               20   \n",
@@ -1808,7 +1809,7 @@
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -1936,7 +1937,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 16,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1954,7 +1955,7 @@
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 16
     },
     {
      "cell_type": "code",
@@ -2059,7 +2060,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 16,
+       "prompt_number": 17,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -2077,7 +2078,7 @@
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -2180,7 +2181,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 17,
+       "prompt_number": 18,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   sergio ag\u00fcero     20     16     14        3               34   \n",
@@ -2194,7 +2195,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -2291,7 +2292,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 18,
+       "prompt_number": 19,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
@@ -2307,7 +2308,7 @@
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -2401,7 +2402,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 19,
+       "prompt_number": 20,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
@@ -2417,7 +2418,7 @@
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -2503,7 +2504,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 20,
+       "prompt_number": 21,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   sergio ag\u00fcero     20     16     14        3               34   \n",
@@ -2517,7 +2518,7 @@
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -2631,7 +2632,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 21,
+       "prompt_number": 22,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "1    alexis s\u00e1nchez     15      0     12        7               29   \n",
@@ -2647,7 +2648,7 @@
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 22
     },
     {
      "cell_type": "code",
@@ -2699,7 +2700,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 22,
+       "prompt_number": 23,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1  alexis s\u00e1nchez     15      0     12        7               29   \n",
@@ -2709,7 +2710,60 @@
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Getting an Overview of the Column Types"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "[[back to section overview](#Sections)]"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "types = df.columns.to_series().groupby(df.dtypes).groups\n",
+      "for t in types.items():\n",
+      "    print(t)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "(dtype('O'), ['player', 'salary', 'position', 'team'])\n",
+        "(dtype('float64'), ['games', 'goals', 'assists', 'shots_on_target', 'points_per_game', 'points'])\n"
+       ]
+      }
+     ],
+     "prompt_number": 29
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
     }
    ],
    "metadata": {}

From c20584ea6c8c32380e47fa614e68d7b9f341e990 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 28 Jan 2015 22:47:03 -0500
Subject: [PATCH 218/248] column types

---
 tutorials/things_in_pandas.ipynb | 273 +++++++++++++++++++++++++------
 1 file changed, 219 insertions(+), 54 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index bf6c0b5..505c1f3 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2dd2df7144acb424c4be84b5679ebc4d06bf16dc6a3e0f7fd3d1ed0595479f64"
+  "signature": "sha256:c8ab1a3c99e7c72951c91e74991b8837884cd9e3863f1cd1833651e180ff32bd"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -38,7 +38,7 @@
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 1
     },
     {
      "cell_type": "markdown",
@@ -99,7 +99,10 @@
       "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)\n",
       "- [Updating Columns](#Updating-Columns)\n",
       "- [Chaining Conditions - Using Bitwise Operators](#Chaining-Conditions---Using-Bitwise-Operators)\n",
-      "- [Getting an Overview of the Column Types](#Getting-an-Overview-of-the-Column-Types)"
+      "- [Column Types](#Column-Types)\n",
+      "    - [Printing Column Types](#Printing-Column-Types)\n",
+      "    - [Selecting by Column Type](#Selecting-by-Column-Type)\n",
+      "    - [Converting Column Types](#Converting-Column-Types)"
      ]
     },
     {
@@ -278,7 +281,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 3,
+       "prompt_number": 2,
        "text": [
         "                                          PLAYER  SALARY  GP   G   A  SOT  \\\n",
         "0      Sergio Ag\u00fcero\\n Forward \u2014 Manchester City  $19.2m  16  14   3   34   \n",
@@ -306,7 +309,7 @@
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -420,7 +423,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 4,
+       "prompt_number": 3,
        "text": [
         "                                  player  salary  gp  g   a  sot    ppg  \\\n",
         "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m  20  2  14   10  10.47   \n",
@@ -434,7 +437,7 @@
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -525,7 +528,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 5,
+       "prompt_number": 4,
        "text": [
         "                                  player  salary  games  goals  assists  \\\n",
         "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m     20      2       14   \n",
@@ -539,7 +542,7 @@
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -671,7 +674,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 6,
+       "prompt_number": 5,
        "text": [
         "                                     player salary  games  goals  assists  \\\n",
         "5     Santiago Cazorla\\n Midfield \u2014 Arsenal   14.8     20      4      NaN   \n",
@@ -689,7 +692,7 @@
        ]
       }
      ],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -786,7 +789,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 7,
+       "prompt_number": 6,
        "text": [
         "                                  player salary  games  goals  assists  \\\n",
         "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea   14.0     20      2       14   \n",
@@ -800,7 +803,7 @@
        ]
       }
      ],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
@@ -893,7 +896,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 8,
+       "prompt_number": 7,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "7   Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
@@ -907,7 +910,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -1027,7 +1030,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 9,
+       "prompt_number": 8,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1045,7 +1048,7 @@
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -1105,7 +1108,7 @@
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -1186,7 +1189,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 11,
+       "prompt_number": 10,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "4    \u00e1ngel di mar\u00eda   15.0     13      3      NaN               13   \n",
@@ -1198,7 +1201,7 @@
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -1355,7 +1358,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 12,
+       "prompt_number": 11,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1379,7 +1382,7 @@
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -1565,7 +1568,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 13,
+       "prompt_number": 12,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "0     sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1593,7 +1596,7 @@
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -1701,7 +1704,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 14,
+       "prompt_number": 13,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   saido berahino   13.8     21      9        0               20   \n",
@@ -1715,7 +1718,7 @@
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 13
     },
     {
      "cell_type": "code",
@@ -1795,7 +1798,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 15,
+       "prompt_number": 14,
        "text": [
         "            player salary  games  goals  assists  shots_on_target  \\\n",
         "8   saido berahino   13.8     21      9        0               20   \n",
@@ -1809,7 +1812,7 @@
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -1937,7 +1940,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 16,
+       "prompt_number": 15,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -1955,7 +1958,7 @@
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 15
     },
     {
      "cell_type": "code",
@@ -2060,7 +2063,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 17,
+       "prompt_number": 16,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   sergio ag\u00fcero   19.2     16     14        3               34   \n",
@@ -2078,7 +2081,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -2181,7 +2184,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 18,
+       "prompt_number": 17,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1   sergio ag\u00fcero     20     16     14        3               34   \n",
@@ -2195,7 +2198,7 @@
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -2292,7 +2295,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 19,
+       "prompt_number": 18,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
@@ -2308,7 +2311,7 @@
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -2402,7 +2405,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 20,
+       "prompt_number": 19,
        "text": [
         "               salary  games  goals  assists  shots_on_target  \\\n",
         "player                                                          \n",
@@ -2418,7 +2421,7 @@
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -2504,7 +2507,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 21,
+       "prompt_number": 20,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "0   sergio ag\u00fcero     20     16     14        3               34   \n",
@@ -2518,7 +2521,7 @@
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -2632,7 +2635,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 22,
+       "prompt_number": 21,
        "text": [
         "             player salary  games  goals  assists  shots_on_target  \\\n",
         "1    alexis s\u00e1nchez     15      0     12        7               29   \n",
@@ -2648,7 +2651,7 @@
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 21
     },
     {
      "cell_type": "code",
@@ -2700,7 +2703,7 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 23,
+       "prompt_number": 22,
        "text": [
         "           player salary  games  goals  assists  shots_on_target  \\\n",
         "1  alexis s\u00e1nchez     15      0     12        7               29   \n",
@@ -2710,7 +2713,7 @@
        ]
       }
      ],
-     "prompt_number": 23
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -2725,7 +2728,7 @@
      "level": 1,
      "metadata": {},
      "source": [
-      "Getting an Overview of the Column Types"
+      "Column Types"
      ]
     },
     {
@@ -2735,35 +2738,197 @@
       "[[back to section overview](#Sections)]"
      ]
     },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Printing Column Types"
+     ]
+    },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
       "types = df.columns.to_series().groupby(df.dtypes).groups\n",
-      "for t in types.items():\n",
-      "    print(t)"
+      "types"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "output_type": "stream",
-       "stream": "stdout",
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 23,
+       "text": [
+        "{dtype('float64'): ['games',\n",
+        "  'goals',\n",
+        "  'assists',\n",
+        "  'shots_on_target',\n",
+        "  'points_per_game',\n",
+        "  'points'],\n",
+        " dtype('O'): ['player', 'salary', 'position', 'team']}"
+       ]
+      }
+     ],
+     "prompt_number": 23
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Selecting by Column Type"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "# select string columns\n",
+      "df.loc[:, (df.dtypes == np.dtype('O')).values].head()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "html": [
+        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+        "<table border=\"1\" class=\"dataframe\">\n",
+        "  <thead>\n",
+        "    <tr style=\"text-align: right;\">\n",
+        "      <th></th>\n",
+        "      <th>player</th>\n",
+        "      <th>salary</th>\n",
+        "      <th>position</th>\n",
+        "      <th>team</th>\n",
+        "    </tr>\n",
+        "  </thead>\n",
+        "  <tbody>\n",
+        "    <tr>\n",
+        "      <th>0</th>\n",
+        "      <td>  sergio ag\u00fcero</td>\n",
+        "      <td>   20</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td> manchester city</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>1</th>\n",
+        "      <td> alexis s\u00e1nchez</td>\n",
+        "      <td>   15</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>         arsenal</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>2</th>\n",
+        "      <td> saido berahino</td>\n",
+        "      <td> 13.8</td>\n",
+        "      <td>  forward</td>\n",
+        "      <td>       west brom</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>3</th>\n",
+        "      <td>    eden hazard</td>\n",
+        "      <td> 18.9</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td>         chelsea</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>4</th>\n",
+        "      <td>     yaya tour\u00e9</td>\n",
+        "      <td> 16.6</td>\n",
+        "      <td> midfield</td>\n",
+        "      <td> manchester city</td>\n",
+        "    </tr>\n",
+        "  </tbody>\n",
+        "</table>\n",
+        "</div>"
+       ],
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 24,
        "text": [
-        "(dtype('O'), ['player', 'salary', 'position', 'team'])\n",
-        "(dtype('float64'), ['games', 'goals', 'assists', 'shots_on_target', 'points_per_game', 'points'])\n"
+        "           player salary  position             team\n",
+        "0   sergio ag\u00fcero     20   forward  manchester city\n",
+        "1  alexis s\u00e1nchez     15   forward          arsenal\n",
+        "2  saido berahino   13.8   forward        west brom\n",
+        "3     eden hazard   18.9  midfield          chelsea\n",
+        "4      yaya tour\u00e9   16.6  midfield  manchester city"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 24
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "<br>\n",
+      "<br>"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Converting Column Types"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "df['salary'] = df['salary'].astype(float)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 25
     },
     {
      "cell_type": "code",
      "collapsed": false,
-     "input": [],
+     "input": [
+      "types = df.columns.to_series().groupby(df.dtypes).groups\n",
+      "types"
+     ],
      "language": "python",
      "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 26,
+       "text": [
+        "{dtype('float64'): ['salary',\n",
+        "  'games',\n",
+        "  'goals',\n",
+        "  'assists',\n",
+        "  'shots_on_target',\n",
+        "  'points_per_game',\n",
+        "  'points'],\n",
+        " dtype('O'): ['player', 'position', 'team']}"
+       ]
+      }
+     ],
+     "prompt_number": 26
     }
    ],
    "metadata": {}

From 99f48330a091f0d27ff794402b82d171b2571625 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 6 Feb 2015 11:52:10 -0500
Subject: [PATCH 219/248] added large_csv_to_sqlite.py

---
 useful_scripts/large_csv_to_sqlite.py | 45 +++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)
 create mode 100644 useful_scripts/large_csv_to_sqlite.py

diff --git a/useful_scripts/large_csv_to_sqlite.py b/useful_scripts/large_csv_to_sqlite.py
new file mode 100644
index 0000000..633ea8c
--- /dev/null
+++ b/useful_scripts/large_csv_to_sqlite.py
@@ -0,0 +1,45 @@
+# This is a workaround snippet for reading very large CSV that exceed the
+# machine's memory and dump it into an SQLite database using pandas.
+#
+# Sebastian Raschka, 2015
+#
+# Tested in Python 3.4.2 and pandas 0.15.2 
+
+import pandas as pd
+import sqlite3
+from pandas.io import sql
+import subprocess
+
+# In and output file paths
+in_csv = '../data/my_large.csv'
+out_sqlite = '../data/my.sqlite'
+
+table_name = 'my_table' # name for the SQLite database table
+chunksize = 100000 # number of lines to process at each iteration
+
+# Get number of lines in the CSV file
+nlines = subprocess.check_output('wc -l %s' % in_csv, shell=True)
+nlines = int(nlines.split()[0]) 
+
+# connect to database
+cnx = sqlite3.connect(out_sqlite)
+
+# Iteratively read CSV and dump lines into the SQLite table
+for i in range(0, nlines, chunksize):
+    
+    df = pd.read_csv(in_csv,  
+            header=None,  # no header, define column header manually later
+            nrows=chunksize, # number of rows to read at each iteration
+            skiprows=i)   # skip rows that were already read
+    
+    # columns to read        
+    df.columns = ['molecule_id','charge','db','drugsnow','hba','hbd','loc','nrb','smiles']
+
+    sql.to_sql(df, 
+                name=table_name, 
+                con=cnx, 
+                index=False, # don't use CSV file index
+                index_label='molecule_id', # use a unique column from DataFrame as index
+                if_exists='append') 
+cnx.close()    
+

From edc17e29074871bd7bbd75075577ff730cf91a16 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 6 Feb 2015 11:58:24 -0500
Subject: [PATCH 220/248] updated readme

---
 README.md                         | 6 +++---
 useful_scripts/sparsify_matrix.py | 2 ++
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index 8b1340d..66ad901 100644
--- a/README.md
+++ b/README.md
@@ -154,11 +154,11 @@
 
 - [Shell script](./useful_scripts/prepend_python_shebang.sh) For prepending Python-shebangs to .py files.
 
-- Convert 'tab-delimited' to 'comma-separated' CSV files [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/useful_scripts/fix_tab_csv.ipynb)]
-
-- A random string generator [function](./useful_scripts/random_string_generator.py)
+- A random string generator [function](./useful_scripts/random_string_generator.py).
 
+- [Converting large CSV files](https://github.com/rasbt/python_reference/blob/master/useful_scripts/large_csv_to_sqlite.py) to SQLite databases using pandas.
 
+- [Sparsifying a matrix](https://github.com/rasbt/python_reference/blob/master/useful_scripts/sparsify_matrix.py) by zeroing out all elements but the top k elements in a row using NumPy.
 
 <br>
 
diff --git a/useful_scripts/sparsify_matrix.py b/useful_scripts/sparsify_matrix.py
index 27c864c..ef5e141 100644
--- a/useful_scripts/sparsify_matrix.py
+++ b/useful_scripts/sparsify_matrix.py
@@ -4,6 +4,8 @@
 # The matrix could be a distance or similarity matrix (e.g., kernel matrix in kernel PCA),
 # where we are interested to keep the top k neighbors.
 
+import numpy as np
+
 print('Sparsify a matrix by zeroing all elements but the top 2 values in a row.\n')
 
 A = np.array([[1,2,3,4,5],[9,8,6,4,5],[3,1,7,8,9]])

From 45c931b3f90742f306aaf813ed8a9964d7e41d5f Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 6 Feb 2015 12:01:46 -0500
Subject: [PATCH 221/248] updated readme

---
 useful_scripts/large_csv_to_sqlite.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/useful_scripts/large_csv_to_sqlite.py b/useful_scripts/large_csv_to_sqlite.py
index 633ea8c..5e67691 100644
--- a/useful_scripts/large_csv_to_sqlite.py
+++ b/useful_scripts/large_csv_to_sqlite.py
@@ -17,6 +17,9 @@
 table_name = 'my_table' # name for the SQLite database table
 chunksize = 100000 # number of lines to process at each iteration
 
+# columns that should be read from the CSV file
+columns = ['molecule_id','charge','db','drugsnow','hba','hbd','loc','nrb','smiles']
+
 # Get number of lines in the CSV file
 nlines = subprocess.check_output('wc -l %s' % in_csv, shell=True)
 nlines = int(nlines.split()[0]) 
@@ -33,7 +36,7 @@
             skiprows=i)   # skip rows that were already read
     
     # columns to read        
-    df.columns = ['molecule_id','charge','db','drugsnow','hba','hbd','loc','nrb','smiles']
+    df.columns = columns
 
     sql.to_sql(df, 
                 name=table_name, 

From cdac4741154ed99afbc8740ca2f38f9fa62cd678 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 6 Feb 2015 15:09:13 -0500
Subject: [PATCH 222/248] shell cmd upd

---
 useful_scripts/large_csv_to_sqlite.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/useful_scripts/large_csv_to_sqlite.py b/useful_scripts/large_csv_to_sqlite.py
index 5e67691..8edba57 100644
--- a/useful_scripts/large_csv_to_sqlite.py
+++ b/useful_scripts/large_csv_to_sqlite.py
@@ -21,7 +21,7 @@
 columns = ['molecule_id','charge','db','drugsnow','hba','hbd','loc','nrb','smiles']
 
 # Get number of lines in the CSV file
-nlines = subprocess.check_output('wc -l %s' % in_csv, shell=True)
+nlines = subprocess.check_output(['wc', '-l', in_csv])
 nlines = int(nlines.split()[0]) 
 
 # connect to database

From 06387b454dc65483ccbdaeff710e5ccdff4eac32 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Fri, 6 Feb 2015 17:00:53 -0500
Subject: [PATCH 223/248] comment to skip header

---
 useful_scripts/large_csv_to_sqlite.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/useful_scripts/large_csv_to_sqlite.py b/useful_scripts/large_csv_to_sqlite.py
index 8edba57..9932f9c 100644
--- a/useful_scripts/large_csv_to_sqlite.py
+++ b/useful_scripts/large_csv_to_sqlite.py
@@ -28,7 +28,7 @@
 cnx = sqlite3.connect(out_sqlite)
 
 # Iteratively read CSV and dump lines into the SQLite table
-for i in range(0, nlines, chunksize):
+for i in range(0, nlines, chunksize):  # change 0 -> 1 if your csv file contains a column header
     
     df = pd.read_csv(in_csv,  
             header=None,  # no header, define column header manually later

From 80a06cfd60ac7bc20947ea9ef136f1c424aac9f4 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 19 Feb 2015 20:51:20 -0500
Subject: [PATCH 224/248] added pep links

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 66ad901..42903d5 100644
--- a/README.md
+++ b/README.md
@@ -170,7 +170,9 @@
 
 - [PyPI - the Python Package Index](https://pypi.python.org/pypi) - The official repository for all open source Python modules and packages.  
 
-- [PEP 8](http://legacy.python.org/dev/peps/pep-0008/) - The official style guide for Python code.
+- [PEP 8](https://www.python.org/dev/peps/pep-0008/) - The official style guide for Python code.
+
+- [PEP 257](https://www.python.org/dev/peps/pep-0257/) - Python's official docstring conventions;  [pep257 - Python style guide checker](https://pypi.python.org/pypi/pep257)
 
 
 <br>

From c4feff05e53aad7796ee70f483eff3d03dffd5af Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Wed, 3 Jun 2015 15:50:00 -0400
Subject: [PATCH 225/248] namedtuple ex

---
 python_patterns/patterns.ipynb | 3068 ++++++++++++++++----------------
 1 file changed, 1563 insertions(+), 1505 deletions(-)

diff --git a/python_patterns/patterns.ipynb b/python_patterns/patterns.ipynb
index 2a769ba..64c6fe1 100644
--- a/python_patterns/patterns.ipynb
+++ b/python_patterns/patterns.ipynb
@@ -1,1542 +1,1600 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:714a46a359c5b1c3e7e7bd4d19d73221f9def5bcb806840be82541070041d29e"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Go back](https://github.com/rasbt/python_reference) to the `python_reference` repository."
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "A random collection of useful Python snippets"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I just cleaned my hard drive and found a couple of useful Python snippets that I had some use for in the past. I thought it would be worthwhile to collect them in a IPython notebook for personal reference and share it with people who might find them useful too.  \n",
-      "Most of those snippets are hopefully self-explanatory, but I am planning to add more comments and descriptions in future."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Table of Contents"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- [Bitstrings from positive and negative elements in a list](#Bitstrings-from-positive-and-negative-elements-in-a-list)\n",
-      "- [Command line arguments 1 - sys.argv](#Command-line-arguments-1---sys.argv)\n",
-      "- [Data and time basics](#Data-and-time-basics)\n",
-      "- [Differences between 2 files](#Differences-between-2-files)\n",
-      "- [Differences between successive elements in a list](#Differences-between-successive-elements-in-a-list)\n",
-      "- [Doctest example](#Doctest-example)\n",
-      "- [English language detection](#English-language-detection)\n",
-      "- [File browsing basics](#File-browsing-basics)\n",
-      "- [File reading basics](#File-reading-basics)\n",
-      "- [Indices of min and max elements from a list](#Indices-of-min-and-max-elements-from-a-list)\n",
-      "- [Lambda functions](#Lambda-functions)\n",
-      "- [Private functions](#Private-functions)\n",
-      "- [Namedtuples](#Namedtuples)\n",
-      "- [Normalizing data](#Normalizing-data)\n",
-      "- [NumPy essentials](#NumPy-essentials)\n",
-      "- [Pickling Python objects to bitstreams](#Pickling-Python-objects-to-bitstreams)\n",
-      "- [Python version check](#Python-version-check)\n",
-      "- [Runtime within a script](#Runtime-within-a-script)\n",
-      "- [Sorting lists of tuples by elements](#Sorting-lists-of-tuples-by-elements)\n",
-      "- [Sorting multiple lists relative to each other](#Sorting-multiple-lists-relative-to-each-other)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext watermark"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -d -a \"Sebastian Raschka\" -v"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Sebastian Raschka 26/09/2014 \n",
-        "\n",
-        "CPython 3.4.1\n",
-        "IPython 2.0.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<font size=\"1.5em\">[More information](https://github.com/rasbt/watermark) about the `watermark` magic command extension.</font>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Bitstrings from positive and negative elements in a list"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Go back](https://github.com/rasbt/python_reference) to the `python_reference` repository."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A random collection of useful Python snippets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I just cleaned my hard drive and found a couple of useful Python snippets that I had some use for in the past. I thought it would be worthwhile to collect them in a IPython notebook for personal reference and share it with people who might find them useful too.  \n",
+    "Most of those snippets are hopefully self-explanatory, but I am planning to add more comments and descriptions in future."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Table of Contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [Bitstrings from positive and negative elements in a list](#Bitstrings-from-positive-and-negative-elements-in-a-list)\n",
+    "- [Command line arguments 1 - sys.argv](#Command-line-arguments-1---sys.argv)\n",
+    "- [Data and time basics](#Data-and-time-basics)\n",
+    "- [Differences between 2 files](#Differences-between-2-files)\n",
+    "- [Differences between successive elements in a list](#Differences-between-successive-elements-in-a-list)\n",
+    "- [Doctest example](#Doctest-example)\n",
+    "- [English language detection](#English-language-detection)\n",
+    "- [File browsing basics](#File-browsing-basics)\n",
+    "- [File reading basics](#File-reading-basics)\n",
+    "- [Indices of min and max elements from a list](#Indices-of-min-and-max-elements-from-a-list)\n",
+    "- [Lambda functions](#Lambda-functions)\n",
+    "- [Private functions](#Private-functions)\n",
+    "- [Namedtuples](#Namedtuples)\n",
+    "- [Normalizing data](#Normalizing-data)\n",
+    "- [NumPy essentials](#NumPy-essentials)\n",
+    "- [Pickling Python objects to bitstreams](#Pickling-Python-objects-to-bitstreams)\n",
+    "- [Python version check](#Python-version-check)\n",
+    "- [Runtime within a script](#Runtime-within-a-script)\n",
+    "- [Sorting lists of tuples by elements](#Sorting-lists-of-tuples-by-elements)\n",
+    "- [Sorting multiple lists relative to each other](#Sorting-multiple-lists-relative-to-each-other)\n",
+    "- [Using namedtuples](#Using-namedtuples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Generating a bitstring from a Python list or numpy array\n",
-      "# where all postive values -> 1\n",
-      "# all negative values -> 0\n",
-      "\n",
-      "import numpy as np\n",
-      "\n",
-      "def make_bitstring(ary):\n",
-      "    return np.where(ary > 0, 1, 0)\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sebastian Raschka 26/09/2014 \n",
       "\n",
-      "\n",
-      "def faster_bitstring(ary):\n",
-      "    return np.where(ary > 0).astype('i1')\n",
-      "\n",
-      "### Example:\n",
-      "\n",
-      "ary1 = np.array([1, 2, 0.3, -1, -2])\n",
-      "print('input values %s' %ary1)\n",
-      "print('bitstring %s' %make_bitstring(ary1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "input values [ 1.   2.   0.3 -1.  -2. ]\n",
-        "bitstring [1 1 1 0 0]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+      "CPython 3.4.1\n",
+      "IPython 2.0.0\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Command line arguments 1 - sys.argv"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "%watermark -d -a \"Sebastian Raschka\" -v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font size=\"1.5em\">[More information](https://github.com/rasbt/watermark) about the `watermark` magic command extension.</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bitstrings from positive and negative elements in a list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input values [ 1.   2.   0.3 -1.  -2. ]\n",
+      "bitstring [1 1 1 0 0]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%file cmd_line_args_1_sysarg.py\n",
-      "import sys\n",
-      "\n",
-      "def error(msg):\n",
-      "    \"\"\"Prints error message, sends it to stderr, and quites the program.\"\"\"\n",
-      "    sys.exit(msg)\n",
-      "\n",
-      "args = sys.argv[1:]     #  sys.argv[0] is the name of the python script itself\n",
-      "\n",
-      "try:\n",
-      "    arg1 = int(args[0])\n",
-      "    arg2 = args[1]\n",
-      "    arg3 = args[2]\n",
-      "    print(\"Everything okay!\")\n",
-      "\n",
-      "except ValueError:\n",
-      "    error(\"First argument must be integer type!\")\n",
-      "\n",
-      "except IndexError:\n",
-      "    error(\"Requires 3 arguments!\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Overwriting cmd_line_args_1_sysarg.py\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "% run cmd_line_args_1_sysarg.py 1 2 3"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Everything okay!\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "% run cmd_line_args_1_sysarg.py a 2 3"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "SystemExit",
-       "evalue": "First argument must be integer type!",
-       "output_type": "pyerr",
-       "traceback": [
-        "An exception has occurred, use %tb to see the full traceback.\n",
-        "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m First argument must be integer type!\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "# Generating a bitstring from a Python list or numpy array\n",
+    "# where all postive values -> 1\n",
+    "# all negative values -> 0\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "def make_bitstring(ary):\n",
+    "    return np.where(ary > 0, 1, 0)\n",
+    "\n",
+    "\n",
+    "def faster_bitstring(ary):\n",
+    "    return np.where(ary > 0).astype('i1')\n",
+    "\n",
+    "### Example:\n",
+    "\n",
+    "ary1 = np.array([1, 2, 0.3, -1, -2])\n",
+    "print('input values %s' %ary1)\n",
+    "print('bitstring %s' %make_bitstring(ary1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Command line arguments 1 - sys.argv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting cmd_line_args_1_sysarg.py\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Data and time basics"
+    }
+   ],
+   "source": [
+    "%%file cmd_line_args_1_sysarg.py\n",
+    "import sys\n",
+    "\n",
+    "def error(msg):\n",
+    "    \"\"\"Prints error message, sends it to stderr, and quites the program.\"\"\"\n",
+    "    sys.exit(msg)\n",
+    "\n",
+    "args = sys.argv[1:]     #  sys.argv[0] is the name of the python script itself\n",
+    "\n",
+    "try:\n",
+    "    arg1 = int(args[0])\n",
+    "    arg2 = args[1]\n",
+    "    arg3 = args[2]\n",
+    "    print(\"Everything okay!\")\n",
+    "\n",
+    "except ValueError:\n",
+    "    error(\"First argument must be integer type!\")\n",
+    "\n",
+    "except IndexError:\n",
+    "    error(\"Requires 3 arguments!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Everything okay!\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "% run cmd_line_args_1_sysarg.py 1 2 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SystemExit",
+     "evalue": "First argument must be integer type!",
+     "output_type": "error",
+     "traceback": [
+      "An exception has occurred, use %tb to see the full traceback.\n",
+      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m First argument must be integer type!\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time\n",
-      "\n",
-      "# print time HOURS:MINUTES:SECONDS\n",
-      "# e.g., '10:50:58'\n",
-      "print(time.strftime(\"%H:%M:%S\"))\n",
-      "\n",
-      "# print current date DAY:MONTH:YEAR\n",
-      "# e.g., '06/03/2014'\n",
-      "print(time.strftime(\"%d/%m/%Y\"))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "13:28:05\n",
-        "26/09/2014\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "% run cmd_line_args_1_sysarg.py a 2 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data and time basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13:28:05\n",
+      "26/09/2014\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Differences between 2 files"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "# print time HOURS:MINUTES:SECONDS\n",
+    "# e.g., '10:50:58'\n",
+    "print(time.strftime(\"%H:%M:%S\"))\n",
+    "\n",
+    "# print current date DAY:MONTH:YEAR\n",
+    "# e.g., '06/03/2014'\n",
+    "print(time.strftime(\"%d/%m/%Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Differences between 2 files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing id_file1.txt\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "%%file id_file1.txt\n",
+    "1234\n",
+    "2342\n",
+    "2341"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Writing id_file2.txt\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%file id_file1.txt\n",
-      "1234\n",
-      "2342\n",
-      "2341"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing id_file1.txt\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%%file id_file2.txt\n",
+    }
+   ],
+   "source": [
+    "%%file id_file2.txt\n",
+    "5234\n",
+    "3344\n",
+    "2341"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "5234\n",
       "3344\n",
-      "2341"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Writing id_file2.txt\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Print lines that are different between 2 files. Insensitive\n",
-      "# to the order of the file contents.\n",
-      "\n",
-      "id_set1 = set()\n",
-      "id_set2 = set()\n",
-      "\n",
-      "with open('id_file1.txt', 'r') as id_file:\n",
-      "    for line in id_file:\n",
-      "        id_set1.add(line.strip())\n",
-      "\n",
-      "with open('id_file2.txt', 'r') as id_file:\n",
-      "    for line in id_file:\n",
-      "        id_set2.add(line.strip())    \n",
-      "\n",
-      "diffs = id_set2.difference(id_set1)\n",
-      "\n",
-      "for d in diffs:\n",
-      "    print(d)\n",
-      "print(\"Total differences:\",len(diffs))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "5234\n",
-        "3344\n",
-        "Total differences: 2\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+      "Total differences: 2\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Differences between successive elements in a list"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from itertools import islice\n",
-      "\n",
-      "lst = [1,2,3,5,8]\n",
-      "diff = [j - i for i, j in zip(lst, islice(lst, 1, None))]\n",
-      "print(diff)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 1, 2, 3]\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Doctest example"
+    }
+   ],
+   "source": [
+    "# Print lines that are different between 2 files. Insensitive\n",
+    "# to the order of the file contents.\n",
+    "\n",
+    "id_set1 = set()\n",
+    "id_set2 = set()\n",
+    "\n",
+    "with open('id_file1.txt', 'r') as id_file:\n",
+    "    for line in id_file:\n",
+    "        id_set1.add(line.strip())\n",
+    "\n",
+    "with open('id_file2.txt', 'r') as id_file:\n",
+    "    for line in id_file:\n",
+    "        id_set2.add(line.strip())    \n",
+    "\n",
+    "diffs = id_set2.difference(id_set1)\n",
+    "\n",
+    "for d in diffs:\n",
+    "    print(d)\n",
+    "print(\"Total differences:\",len(diffs))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Differences between successive elements in a list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 1, 2, 3]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "from itertools import islice\n",
+    "\n",
+    "lst = [1,2,3,5,8]\n",
+    "diff = [j - i for i, j in zip(lst, islice(lst, 1, None))]\n",
+    "print(diff)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Doctest example"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ok\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def subtract(a, b):\n",
-      "    \"\"\"\n",
-      "    Subtracts second from first number and returns result.\n",
-      "    >>> subtract(10, 5)\n",
-      "    5\n",
-      "    >>> subtract(11, 0.7)\n",
-      "    10.3\n",
-      "    \"\"\"\n",
-      "    return a-b\n",
-      "\n",
-      "if __name__ == \"__main__\":  # is 'false' if imported\n",
-      "    import doctest\n",
-      "    doctest.testmod()\n",
-      "    print('ok')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ok\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def hello_world():\n",
-      "    \"\"\"\n",
-      "    Returns 'Hello, World'\n",
-      "    >>> hello_world()\n",
+    }
+   ],
+   "source": [
+    "def subtract(a, b):\n",
+    "    \"\"\"\n",
+    "    Subtracts second from first number and returns result.\n",
+    "    >>> subtract(10, 5)\n",
+    "    5\n",
+    "    >>> subtract(11, 0.7)\n",
+    "    10.3\n",
+    "    \"\"\"\n",
+    "    return a-b\n",
+    "\n",
+    "if __name__ == \"__main__\":  # is 'false' if imported\n",
+    "    import doctest\n",
+    "    doctest.testmod()\n",
+    "    print('ok')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "**********************************************************************\n",
+      "File \"__main__\", line 4, in __main__.hello_world\n",
+      "Failed example:\n",
+      "    hello_world()\n",
+      "Expected:\n",
       "    'Hello, World'\n",
-      "    \"\"\"\n",
-      "    return 'hello world'\n",
-      "\n",
-      "if __name__ == \"__main__\":  # is 'false' if imported\n",
-      "    import doctest\n",
-      "    doctest.testmod()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "**********************************************************************\n",
-        "File \"__main__\", line 4, in __main__.hello_world\n",
-        "Failed example:\n",
-        "    hello_world()\n",
-        "Expected:\n",
-        "    'Hello, World'\n",
-        "Got:\n",
-        "    'hello world'\n",
-        "**********************************************************************\n",
-        "1 items had failures:\n",
-        "   1 of   1 in __main__.hello_world\n",
-        "***Test Failed*** 1 failures.\n"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "English language detection"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import nltk\n",
-      "\n",
-      "def eng_ratio(text):\n",
-      "    ''' Returns the ratio of non-English to English words from a text '''\n",
-      "\n",
-      "    english_vocab = set(w.lower() for w in nltk.corpus.words.words()) \n",
-      "    text_vocab = set(w.lower() for w in text.split() if w.lower().isalpha()) \n",
-      "    unusual = text_vocab.difference(english_vocab)\n",
-      "    diff = len(unusual)/len(text_vocab)\n",
-      "    return diff\n",
-      "    \n",
-      "text = 'This is a test fahrrad'\n",
-      "\n",
-      "print(eng_ratio(text))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0.2\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "File browsing basics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import os\n",
-      "import shutil\n",
-      "import glob\n",
-      "\n",
-      "# working directory\n",
-      "c_dir = os.getcwd()                 # show current working directory\n",
-      "os.listdir(c_dir)                   # shows all files in the working directory\n",
-      "os.chdir('~/Data')                  # change working directory\n",
-      "\n",
-      "\n",
-      "# get all files in a directory\n",
-      "glob.glob('/Users/sebastian/Desktop/*')\n",
-      "\n",
-      "# e.g.,  ['/Users/sebastian/Desktop/untitled folder', '/Users/sebastian/Desktop/Untitled.txt']\n",
-      "\n",
-      "# walk\n",
-      "tree = os.walk(c_dir)      \n",
-      "# moves through sub directories and creates a 'generator' object of tuples\n",
-      "# ('dir', [file1, file2, ...] [subdirectory1, subdirectory2, ...]), \n",
-      "#    (...), ...\n",
-      "\n",
-      "#check files: returns either True or False\n",
-      "os.exists('../rel_path')\n",
-      "os.exists('/home/abs_path')\n",
-      "os.isfile('./file.txt')\n",
-      "os.isdir('./subdir')\n",
-      "\n",
-      "\n",
-      "# file permission (True or False\n",
-      "os.access('./some_file', os.F_OK) # File exists? Python 2.7\n",
-      "os.access('./some_file', os.R_OK) # Ok to read? Python 2.7\n",
-      "os.access('./some_file', os.W_OK) # Ok to write? Python 2.7\n",
-      "os.access('./some_file', os.X_OK) # Ok to execute? Python 2.7\n",
-      "os.access('./some_file', os.X_OK | os.W_OK) # Ok to execute or write? Python 2.7\n",
-      "\n",
-      "# join (creates operating system dependent paths)\n",
-      "os.path.join('a', 'b', 'c')\n",
-      "# 'a/b/c' on Unix/Linux\n",
-      "# 'a\\\\b\\\\c' on Windows\n",
-      "os.path.normpath('a/b/c') # converts file separators\n",
-      "\n",
-      "\n",
-      "# os.path: direcory and file names\n",
-      "os.path.samefile('./some_file', '/home/some_file')  # True if those are the same\n",
-      "os.path.dirname('./some_file')  # returns '.' (everythin but last component)\n",
-      "os.path.basename('./some_file') # returns 'some_file' (only last component\n",
-      "os.path.split('./some_file') # returns (dirname, basename) or ('.', 'some_file)\n",
-      "os.path.splitext('./some_file.txt') # returns ('./some_file', '.txt')\n",
-      "os.path.splitdrive('./some_file.txt') # returns ('', './some_file.txt')\n",
-      "os.path.isabs('./some_file.txt') # returns False (not an absolute path)\n",
-      "os.path.abspath('./some_file.txt')\n",
-      "\n",
-      "\n",
-      "# create and delete files and directories\n",
-      "os.mkdir('./test')  # create a new direcotory\n",
-      "os.rmdir('./test')  # removes an empty direcotory\n",
-      "os.removedirs('./test') # removes nested empty directories\n",
-      "os.remove('file.txt')   # removes an individual file\n",
-      "shutil.rmtree('./test') # removes directory (empty or not empty)\n",
-      "\n",
-      "os.rename('./dir_before', './renamed') # renames directory if destination doesn't exist\n",
-      "shutil.move('./dir_before', './renamed') # renames directory always\n",
-      "\n",
-      "shutil.copytree('./orig', './copy') # copies a directory recursively\n",
-      "shutil.copyfile('file', 'copy')     # copies a file\n",
-      "\n",
-      " \n",
-      "# Getting files of particular type from directory\n",
-      "files = [f for f in os.listdir(s_pdb_dir) if f.endswith(\".txt\")]\n",
-      "  \n",
-      "# Copy and move\n",
-      "shutil.copyfile(\"/path/to/file\", \"/path/to/new/file\") \n",
-      "shutil.copy(\"/path/to/file\", \"/path/to/directory\")\n",
-      "shutil.move(\"/path/to/file\",\"/path/to/directory\")\n",
-      "   \n",
-      "# Check if file or directory exists\n",
-      "os.path.exists(\"file or directory\")\n",
-      "os.path.isfile(\"file\")\n",
-      "os.path.isdir(\"directory\")\n",
-      "    \n",
-      "# Working directory and absolute path to files\n",
-      "os.getcwd()\n",
-      "os.path.abspath(\"file\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "File reading basics"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+      "Got:\n",
+      "    'hello world'\n",
+      "**********************************************************************\n",
+      "1 items had failures:\n",
+      "   1 of   1 in __main__.hello_world\n",
+      "***Test Failed*** 1 failures.\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Note: rb opens file in binary mode to avoid issues with Windows systems\n",
-      "# where '\\r\\n' is used instead of '\\n' as newline character(s).\n",
-      "\n",
-      "\n",
-      "# A) Reading in Byte chunks\n",
-      "reader_a = open(\"file.txt\", \"rb\")\n",
-      "chunks = []\n",
-      "data = reader_a.read(64)  # reads first 64 bytes\n",
-      "while data != \"\":\n",
-      "    chunks.append(data)\n",
-      "    data = reader_a.read(64)\n",
-      "if data:\n",
-      "    chunks.append(data)\n",
-      "print(len(chunks))\n",
-      "reader_a.close()\n",
-      "\n",
-      "\n",
-      "# B) Reading whole file at once into a list of lines\n",
-      "with open(\"file.txt\", \"rb\") as reader_b:   # recommended syntax, auto closes\n",
-      "    data = reader_b.readlines() # data is assigned a list of lines\n",
-      "print(len(data))\n",
-      "\n",
-      "\n",
-      "# C) Reading whole file at once into a string\n",
-      "with open(\"file.txt\", \"rb\") as reader_c:\n",
-      "    data = reader_c.read() # data is assigned a list of lines\n",
-      "print(len(data))\n",
-      "\n",
-      "\n",
-      "# D) Reading line by line into a list\n",
-      "data = []\n",
-      "with open(\"file.txt\", \"rb\") as reader_d:\n",
-      "    for line in reader_d:\n",
-      "        data.append(line)\n",
-      "print(len(data))\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Indices of min and max elements from a list"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import operator\n",
-      "\n",
-      "values = [1, 2, 3, 4, 5]\n",
-      "\n",
-      "min_index, min_value = min(enumerate(values), key=operator.itemgetter(1))\n",
-      "max_index, max_value = max(enumerate(values), key=operator.itemgetter(1))\n",
-      "\n",
-      "print('min_index:', min_index, 'min_value:', min_value)\n",
-      "print('max_index:', max_index, 'max_value:', max_value)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "min_index: 0 min_value: 1\n",
-        "max_index: 4 max_value: 5\n"
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Lambda functions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Lambda functions are just a short-hand way or writing\n",
-      "# short function definitions\n",
-      "\n",
-      "def square_root1(x):\n",
-      "    return x**0.5\n",
-      "    \n",
-      "square_root2 = lambda x: x**0.5\n",
-      "\n",
-      "assert(square_root1(9) == square_root2(9))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Private functions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def create_message(msg_txt):\n",
-      "    def _priv_msg(message):     # private, no access from outside\n",
-      "        print(\"{}: {}\".format(msg_txt, message))\n",
-      "    return _priv_msg            # returns a function\n",
-      "\n",
-      "new_msg = create_message(\"My message\")\n",
-      "# note, new_msg is a function\n",
-      "\n",
-      "new_msg(\"Hello, World\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "My message: Hello, World\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Namedtuples"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from collections import namedtuple\n",
-      "\n",
-      "my_namedtuple = namedtuple('field_name', ['x', 'y', 'z', 'bla', 'blub'])\n",
-      "p = my_namedtuple(1, 2, 3, 4, 5)\n",
-      "print(p.x, p.y, p.z)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 2 3\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Normalizing data"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def normalize(data, min_val=0, max_val=1):\n",
-      "    \"\"\"\n",
-      "    Normalizes values in a list of data points to a range, e.g.,\n",
-      "    between 0.0 and 1.0. \n",
-      "    Returns the original object if value is not a integer or float.\n",
-      "    \n",
-      "    \"\"\"\n",
-      "    norm_data = []\n",
-      "    data_min = min(data)\n",
-      "    data_max = max(data)\n",
-      "    for x in data:\n",
-      "        numerator = x - data_min\n",
-      "        denominator = data_max - data_min\n",
-      "        x_norm = (max_val-min_val) * numerator/denominator + min_val\n",
-      "        norm_data.append(x_norm)\n",
-      "    return norm_data"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "normalize([1,2,3,4,5])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 31,
-       "text": [
-        "[0.0, 0.25, 0.5, 0.75, 1.0]"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "normalize([1,2,3,4,5], min_val=-10, max_val=10)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 30,
-       "text": [
-        "[-10.0, -5.0, 0.0, 5.0, 10.0]"
-       ]
-      }
-     ],
-     "prompt_number": 30
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "NumPy essentials"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "\n",
-      "ary1 = np.array([1,2,3,4,5])  # must be same type\n",
-      "ary2 = np.zeros((3,4))        # 3x4 matrix consisiting of 0s \n",
-      "ary3 = np.ones((3,4))         # 3x4 matrix consisiting of 1s \n",
-      "ary4 = np.identity(3)         # 3x3 identity matrix\n",
-      "ary5 = ary1.copy()               # make a copy of ary1\n",
-      "\n",
-      "item1 = ary3[0, 0]  # item in row1, column1\n",
-      "\n",
-      "ary2.shape  # tuple of dimensions. Here: (3,4)\n",
-      "ary2.size   # number of elements. Here: 12\n",
-      "\n",
-      "\n",
-      "ary2_t = ary2.transpose()    # transposes matrix\n",
-      "\n",
-      "ary2.ravel()     # makes an array linear (1-dimensional)\n",
-      "                 # by concatenating rows\n",
-      "ary2.reshape(2,6) # reshapes array (must have same dimensions)\n",
-      "\n",
-      "ary3[0:2, 0:3]   # submatrix of first 2 rows and first 3 columns \n",
-      "\n",
-      "ary3 = ary3[[2,0,1]]    # re-arrange rows\n",
-      "\n",
-      "\n",
-      "# element-wise operations\n",
-      "\n",
-      "ary1 + ary1\n",
-      "ary1 * ary1\n",
-      "numpy.dot(ary1, ary1)   # matrix/vector (dot) product\n",
-      "\n",
-      "numpy.sum(ary1, axis=1)   # sum of a 1D array, column sums of a 2D array\n",
-      "numpy.mean(ary1, axis=1)  # mean of a 1D array, column means of a 2D array"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Pickling Python objects to bitstreams"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "def hello_world():\n",
+    "    \"\"\"\n",
+    "    Returns 'Hello, World'\n",
+    "    >>> hello_world()\n",
+    "    'Hello, World'\n",
+    "    \"\"\"\n",
+    "    return 'hello world'\n",
+    "\n",
+    "if __name__ == \"__main__\":  # is 'false' if imported\n",
+    "    import doctest\n",
+    "    doctest.testmod()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## English language detection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pickle\n",
-      "\n",
-      "#### Generate some object\n",
-      "my_dict = dict()\n",
-      "for i in range(1,10):\n",
-      "    my_dict[i] = \"some text\"\n",
-      "\n",
-      "#### Save object to file\n",
-      "pickle_out = open('my_file.pkl', 'wb')\n",
-      "pickle.dump(my_dict, pickle_out)\n",
-      "pickle_out.close()\n",
-      "\n",
-      "#### Load object from file\n",
-      "my_object_file = open('my_file.pkl', 'rb')\n",
-      "my_dict = pickle.load(my_object_file)\n",
-      "my_object_file.close()\n",
-      "\n",
-      "print(my_dict)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "{1: 'some text', 2: 'some text', 3: 'some text', 4: 'some text', 5: 'some text', 6: 'some text', 7: 'some text', 8: 'some text', 9: 'some text'}\n"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "\n",
+    "def eng_ratio(text):\n",
+    "    ''' Returns the ratio of non-English to English words from a text '''\n",
+    "\n",
+    "    english_vocab = set(w.lower() for w in nltk.corpus.words.words()) \n",
+    "    text_vocab = set(w.lower() for w in text.split() if w.lower().isalpha()) \n",
+    "    unusual = text_vocab.difference(english_vocab)\n",
+    "    diff = len(unusual)/len(text_vocab)\n",
+    "    return diff\n",
+    "    \n",
+    "text = 'This is a test fahrrad'\n",
+    "\n",
+    "print(eng_ratio(text))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## File browsing basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import shutil\n",
+    "import glob\n",
+    "\n",
+    "# working directory\n",
+    "c_dir = os.getcwd()                 # show current working directory\n",
+    "os.listdir(c_dir)                   # shows all files in the working directory\n",
+    "os.chdir('~/Data')                  # change working directory\n",
+    "\n",
+    "\n",
+    "# get all files in a directory\n",
+    "glob.glob('/Users/sebastian/Desktop/*')\n",
+    "\n",
+    "# e.g.,  ['/Users/sebastian/Desktop/untitled folder', '/Users/sebastian/Desktop/Untitled.txt']\n",
+    "\n",
+    "# walk\n",
+    "tree = os.walk(c_dir)      \n",
+    "# moves through sub directories and creates a 'generator' object of tuples\n",
+    "# ('dir', [file1, file2, ...] [subdirectory1, subdirectory2, ...]), \n",
+    "#    (...), ...\n",
+    "\n",
+    "#check files: returns either True or False\n",
+    "os.exists('../rel_path')\n",
+    "os.exists('/home/abs_path')\n",
+    "os.isfile('./file.txt')\n",
+    "os.isdir('./subdir')\n",
+    "\n",
+    "\n",
+    "# file permission (True or False\n",
+    "os.access('./some_file', os.F_OK) # File exists? Python 2.7\n",
+    "os.access('./some_file', os.R_OK) # Ok to read? Python 2.7\n",
+    "os.access('./some_file', os.W_OK) # Ok to write? Python 2.7\n",
+    "os.access('./some_file', os.X_OK) # Ok to execute? Python 2.7\n",
+    "os.access('./some_file', os.X_OK | os.W_OK) # Ok to execute or write? Python 2.7\n",
+    "\n",
+    "# join (creates operating system dependent paths)\n",
+    "os.path.join('a', 'b', 'c')\n",
+    "# 'a/b/c' on Unix/Linux\n",
+    "# 'a\\\\b\\\\c' on Windows\n",
+    "os.path.normpath('a/b/c') # converts file separators\n",
+    "\n",
+    "\n",
+    "# os.path: direcory and file names\n",
+    "os.path.samefile('./some_file', '/home/some_file')  # True if those are the same\n",
+    "os.path.dirname('./some_file')  # returns '.' (everythin but last component)\n",
+    "os.path.basename('./some_file') # returns 'some_file' (only last component\n",
+    "os.path.split('./some_file') # returns (dirname, basename) or ('.', 'some_file)\n",
+    "os.path.splitext('./some_file.txt') # returns ('./some_file', '.txt')\n",
+    "os.path.splitdrive('./some_file.txt') # returns ('', './some_file.txt')\n",
+    "os.path.isabs('./some_file.txt') # returns False (not an absolute path)\n",
+    "os.path.abspath('./some_file.txt')\n",
+    "\n",
+    "\n",
+    "# create and delete files and directories\n",
+    "os.mkdir('./test')  # create a new direcotory\n",
+    "os.rmdir('./test')  # removes an empty direcotory\n",
+    "os.removedirs('./test') # removes nested empty directories\n",
+    "os.remove('file.txt')   # removes an individual file\n",
+    "shutil.rmtree('./test') # removes directory (empty or not empty)\n",
+    "\n",
+    "os.rename('./dir_before', './renamed') # renames directory if destination doesn't exist\n",
+    "shutil.move('./dir_before', './renamed') # renames directory always\n",
+    "\n",
+    "shutil.copytree('./orig', './copy') # copies a directory recursively\n",
+    "shutil.copyfile('file', 'copy')     # copies a file\n",
+    "\n",
+    " \n",
+    "# Getting files of particular type from directory\n",
+    "files = [f for f in os.listdir(s_pdb_dir) if f.endswith(\".txt\")]\n",
+    "  \n",
+    "# Copy and move\n",
+    "shutil.copyfile(\"/path/to/file\", \"/path/to/new/file\") \n",
+    "shutil.copy(\"/path/to/file\", \"/path/to/directory\")\n",
+    "shutil.move(\"/path/to/file\",\"/path/to/directory\")\n",
+    "   \n",
+    "# Check if file or directory exists\n",
+    "os.path.exists(\"file or directory\")\n",
+    "os.path.isfile(\"file\")\n",
+    "os.path.isdir(\"directory\")\n",
+    "    \n",
+    "# Working directory and absolute path to files\n",
+    "os.getcwd()\n",
+    "os.path.abspath(\"file\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## File reading basics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Note: rb opens file in binary mode to avoid issues with Windows systems\n",
+    "# where '\\r\\n' is used instead of '\\n' as newline character(s).\n",
+    "\n",
+    "\n",
+    "# A) Reading in Byte chunks\n",
+    "reader_a = open(\"file.txt\", \"rb\")\n",
+    "chunks = []\n",
+    "data = reader_a.read(64)  # reads first 64 bytes\n",
+    "while data != \"\":\n",
+    "    chunks.append(data)\n",
+    "    data = reader_a.read(64)\n",
+    "if data:\n",
+    "    chunks.append(data)\n",
+    "print(len(chunks))\n",
+    "reader_a.close()\n",
+    "\n",
+    "\n",
+    "# B) Reading whole file at once into a list of lines\n",
+    "with open(\"file.txt\", \"rb\") as reader_b:   # recommended syntax, auto closes\n",
+    "    data = reader_b.readlines() # data is assigned a list of lines\n",
+    "print(len(data))\n",
+    "\n",
+    "\n",
+    "# C) Reading whole file at once into a string\n",
+    "with open(\"file.txt\", \"rb\") as reader_c:\n",
+    "    data = reader_c.read() # data is assigned a list of lines\n",
+    "print(len(data))\n",
+    "\n",
+    "\n",
+    "# D) Reading line by line into a list\n",
+    "data = []\n",
+    "with open(\"file.txt\", \"rb\") as reader_d:\n",
+    "    for line in reader_d:\n",
+    "        data.append(line)\n",
+    "print(len(data))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Indices of min and max elements from a list"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "min_index: 0 min_value: 1\n",
+      "max_index: 4 max_value: 5\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Python version check"
+    }
+   ],
+   "source": [
+    "import operator\n",
+    "\n",
+    "values = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "min_index, min_value = min(enumerate(values), key=operator.itemgetter(1))\n",
+    "max_index, max_value = max(enumerate(values), key=operator.itemgetter(1))\n",
+    "\n",
+    "print('min_index:', min_index, 'min_value:', min_value)\n",
+    "print('max_index:', max_index, 'max_value:', max_value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lambda functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Lambda functions are just a short-hand way or writing\n",
+    "# short function definitions\n",
+    "\n",
+    "def square_root1(x):\n",
+    "    return x**0.5\n",
+    "    \n",
+    "square_root2 = lambda x: x**0.5\n",
+    "\n",
+    "assert(square_root1(9) == square_root2(9))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Private functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My message: Hello, World\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "def create_message(msg_txt):\n",
+    "    def _priv_msg(message):     # private, no access from outside\n",
+    "        print(\"{}: {}\".format(msg_txt, message))\n",
+    "    return _priv_msg            # returns a function\n",
+    "\n",
+    "new_msg = create_message(\"My message\")\n",
+    "# note, new_msg is a function\n",
+    "\n",
+    "new_msg(\"Hello, World\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Namedtuples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 3\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sys\n",
-      "\n",
-      "def give_letter(word):\n",
-      "    for letter in word:\n",
-      "        yield letter\n",
-      "\n",
-      "if sys.version_info[0] == 3:\n",
-      "    print('executed in Python 3.x')\n",
-      "    test = give_letter('Hello')\n",
-      "    print(next(test))\n",
-      "    print('in for-loop:')\n",
-      "    for l in test:\n",
-      "        print(l)\n",
-      "\n",
-      "# if Python 2.x\n",
-      "if sys.version_info[0] == 2:\n",
-      "    print('executed in Python 2.x')\n",
-      "    test = give_letter('Hello')\n",
-      "    print(test.next())\n",
-      "    print('in for-loop:') \n",
-      "    for l in test:\n",
-      "        print(l)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "executed in Python 3.x\n",
-        "H\n",
-        "in for-loop:\n",
-        "e\n",
-        "l\n",
-        "l\n",
-        "o\n"
-       ]
-      }
-     ],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "from collections import namedtuple\n",
+    "\n",
+    "my_namedtuple = namedtuple('field_name', ['x', 'y', 'z', 'bla', 'blub'])\n",
+    "p = my_namedtuple(1, 2, 3, 4, 5)\n",
+    "print(p.x, p.y, p.z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Normalizing data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def normalize(data, min_val=0, max_val=1):\n",
+    "    \"\"\"\n",
+    "    Normalizes values in a list of data points to a range, e.g.,\n",
+    "    between 0.0 and 1.0. \n",
+    "    Returns the original object if value is not a integer or float.\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    norm_data = []\n",
+    "    data_min = min(data)\n",
+    "    data_max = max(data)\n",
+    "    for x in data:\n",
+    "        numerator = x - data_min\n",
+    "        denominator = data_max - data_min\n",
+    "        x_norm = (max_val-min_val) * numerator/denominator + min_val\n",
+    "        norm_data.append(x_norm)\n",
+    "    return norm_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0, 0.25, 0.5, 0.75, 1.0]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalize([1,2,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-10.0, -5.0, 0.0, 5.0, 10.0]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "normalize([1,2,3,4,5], min_val=-10, max_val=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## NumPy essentials"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "ary1 = np.array([1,2,3,4,5])  # must be same type\n",
+    "ary2 = np.zeros((3,4))        # 3x4 matrix consisiting of 0s \n",
+    "ary3 = np.ones((3,4))         # 3x4 matrix consisiting of 1s \n",
+    "ary4 = np.identity(3)         # 3x3 identity matrix\n",
+    "ary5 = ary1.copy()               # make a copy of ary1\n",
+    "\n",
+    "item1 = ary3[0, 0]  # item in row1, column1\n",
+    "\n",
+    "ary2.shape  # tuple of dimensions. Here: (3,4)\n",
+    "ary2.size   # number of elements. Here: 12\n",
+    "\n",
+    "\n",
+    "ary2_t = ary2.transpose()    # transposes matrix\n",
+    "\n",
+    "ary2.ravel()     # makes an array linear (1-dimensional)\n",
+    "                 # by concatenating rows\n",
+    "ary2.reshape(2,6) # reshapes array (must have same dimensions)\n",
+    "\n",
+    "ary3[0:2, 0:3]   # submatrix of first 2 rows and first 3 columns \n",
+    "\n",
+    "ary3 = ary3[[2,0,1]]    # re-arrange rows\n",
+    "\n",
+    "\n",
+    "# element-wise operations\n",
+    "\n",
+    "ary1 + ary1\n",
+    "ary1 * ary1\n",
+    "numpy.dot(ary1, ary1)   # matrix/vector (dot) product\n",
+    "\n",
+    "numpy.sum(ary1, axis=1)   # sum of a 1D array, column sums of a 2D array\n",
+    "numpy.mean(ary1, axis=1)  # mean of a 1D array, column means of a 2D array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pickling Python objects to bitstreams"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{1: 'some text', 2: 'some text', 3: 'some text', 4: 'some text', 5: 'some text', 6: 'some text', 7: 'some text', 8: 'some text', 9: 'some text'}\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Runtime within a script"
+    }
+   ],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "#### Generate some object\n",
+    "my_dict = dict()\n",
+    "for i in range(1,10):\n",
+    "    my_dict[i] = \"some text\"\n",
+    "\n",
+    "#### Save object to file\n",
+    "pickle_out = open('my_file.pkl', 'wb')\n",
+    "pickle.dump(my_dict, pickle_out)\n",
+    "pickle_out.close()\n",
+    "\n",
+    "#### Load object from file\n",
+    "my_object_file = open('my_file.pkl', 'rb')\n",
+    "my_dict = pickle.load(my_object_file)\n",
+    "my_object_file.close()\n",
+    "\n",
+    "print(my_dict)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python version check"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "executed in Python 3.x\n",
+      "H\n",
+      "in for-loop:\n",
+      "e\n",
+      "l\n",
+      "l\n",
+      "o\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "\n",
+    "def give_letter(word):\n",
+    "    for letter in word:\n",
+    "        yield letter\n",
+    "\n",
+    "if sys.version_info[0] == 3:\n",
+    "    print('executed in Python 3.x')\n",
+    "    test = give_letter('Hello')\n",
+    "    print(next(test))\n",
+    "    print('in for-loop:')\n",
+    "    for l in test:\n",
+    "        print(l)\n",
+    "\n",
+    "# if Python 2.x\n",
+    "if sys.version_info[0] == 2:\n",
+    "    print('executed in Python 2.x')\n",
+    "    test = give_letter('Hello')\n",
+    "    print(test.next())\n",
+    "    print('in for-loop:') \n",
+    "    for l in test:\n",
+    "        print(l)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Runtime within a script"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time elapsed: 0.49176900000000057 seconds\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time\n",
-      "\n",
-      "start_time = time.clock()\n",
-      "\n",
-      "for i in range(10000000):\n",
-      "    pass\n",
-      "\n",
-      "elapsed_time = time.clock() - start_time\n",
-      "print(\"Time elapsed: {} seconds\".format(elapsed_time))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Time elapsed: 0.49176900000000057 seconds\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "elapsed_time = timeit.timeit('for i in range(10000000): pass', number=1)\n",
-      "print(\"Time elapsed: {} seconds\".format(elapsed_time))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Time elapsed: 0.3550995970144868 seconds\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "start_time = time.clock()\n",
+    "\n",
+    "for i in range(10000000):\n",
+    "    pass\n",
+    "\n",
+    "elapsed_time = time.clock() - start_time\n",
+    "print(\"Time elapsed: {} seconds\".format(elapsed_time))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time elapsed: 0.3550995970144868 seconds\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Sorting lists of tuples by elements"
+    }
+   ],
+   "source": [
+    "import timeit\n",
+    "elapsed_time = timeit.timeit('for i in range(10000000): pass', number=1)\n",
+    "print(\"Time elapsed: {} seconds\".format(elapsed_time))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sorting lists of tuples by elements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "# Here, we make use of the \"key\" parameter of the in-built \"sorted()\" function \n",
+    "# (also available for the \".sort()\" method), which let's us define a function \n",
+    "# that is called on every element that is to be sorted. In this case, our \n",
+    "# \"key\"-function is a simple lambda function that returns the last item \n",
+    "# from every tuple.\n",
+    "\n",
+    "a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]\n",
+    "\n",
+    "sorted_list = sorted(a_list, key=lambda e: e[::-1])\n",
+    "\n",
+    "print(sorted_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(2, 3, 'a'), (3, 2, 'b'), (2, 2, 'b'), (1, 3, 'c')]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Here, we make use of the \"key\" parameter of the in-built \"sorted()\" function \n",
-      "# (also available for the \".sort()\" method), which let's us define a function \n",
-      "# that is called on every element that is to be sorted. In this case, our \n",
-      "# \"key\"-function is a simple lambda function that returns the last item \n",
-      "# from every tuple.\n",
-      "\n",
-      "a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]\n",
-      "\n",
-      "sorted_list = sorted(a_list, key=lambda e: e[::-1])\n",
-      "\n",
-      "print(sorted_list)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]\n"
-       ]
-      }
-     ],
-     "prompt_number": 37
-    },
+    }
+   ],
+   "source": [
+    "# prints [(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]\n",
+    "\n",
+    "# If we are only interesting in sorting the list by the last element\n",
+    "# of the tuple and don't care about a \"tie\" situation, we can also use\n",
+    "# the index of the tuple item directly instead of reversing the tuple \n",
+    "# for efficiency.\n",
+    "\n",
+    "a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]\n",
+    "\n",
+    "sorted_list = sorted(a_list, key=lambda e: e[-1])\n",
+    "\n",
+    "print(sorted_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sorting multiple lists relative to each other"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# prints [(2, 3, 'a'), (2, 2, 'b'), (3, 2, 'b'), (1, 3, 'c')]\n",
-      "\n",
-      "# If we are only interesting in sorting the list by the last element\n",
-      "# of the tuple and don't care about a \"tie\" situation, we can also use\n",
-      "# the index of the tuple item directly instead of reversing the tuple \n",
-      "# for efficiency.\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input values:\n",
+      " ['c', 'b', 'a'] [6, 5, 4] ['some-val-associated-with-c', 'another_val-b', 'z_another_third_val-a']\n",
       "\n",
-      "a_list = [(1,3,'c'), (2,3,'a'), (3,2,'b'), (2,2,'b')]\n",
       "\n",
-      "sorted_list = sorted(a_list, key=lambda e: e[-1])\n",
-      "\n",
-      "print(sorted_list)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[(2, 3, 'a'), (3, 2, 'b'), (2, 2, 'b'), (1, 3, 'c')]\n"
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Sorting multiple lists relative to each other"
+      "sorted output:\n",
+      " ['a', 'b', 'c'] [4, 5, 6] ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[back to top](#Table-of-Contents)"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "You have 3 lists that you want to sort \"relative\" to each other,\n",
+    "for example, picturing each list as a row in a 3x3 matrix: sort it by columns\n",
+    "\n",
+    "########################\n",
+    "If the input lists are\n",
+    "########################\n",
+    "\n",
+    " list1 = ['c','b','a']\n",
+    " list2 = [6,5,4]\n",
+    " list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']\n",
+    "\n",
+    "########################\n",
+    "the desired outcome is:\n",
+    "########################\n",
+    "\n",
+    " ['a', 'b', 'c'] \n",
+    " [4, 5, 6] \n",
+    " ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']\n",
+    "\n",
+    "########################\n",
+    "and NOT:\n",
+    "########################\n",
+    "\n",
+    " ['a', 'b', 'c'] \n",
+    " [4, 5, 6] \n",
+    " ['another_val-b', 'some-val-associated-with-c', 'z_another_third_val-a']\n",
+    "\n",
+    "\n",
+    "\"\"\"\n",
+    "\n",
+    "list1 = ['c','b','a']\n",
+    "list2 = [6,5,4]\n",
+    "list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']\n",
+    "\n",
+    "print('input values:\\n', list1, list2, list3)\n",
+    "\n",
+    "list1, list2, list3 = [list(t) for t in zip(*sorted(zip(list1, list2, list3)))]\n",
+    "\n",
+    "print('\\n\\nsorted output:\\n', list1, list2, list3 )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using namedtuples"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[back to top](#Table-of-Contents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`namedtuples` are high-performance container datatypes in the [`collection`](https://docs.python.org/2/library/collections.html) module (part of Python's stdlib since 2.6).\n",
+    "`namedtuple()` is factory function for creating tuple subclasses with named fields."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X-coordinate: 1\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "\"\"\"\n",
-      "You have 3 lists that you want to sort \"relative\" to each other,\n",
-      "for example, picturing each list as a row in a 3x3 matrix: sort it by columns\n",
-      "\n",
-      "########################\n",
-      "If the input lists are\n",
-      "########################\n",
-      "\n",
-      " list1 = ['c','b','a']\n",
-      " list2 = [6,5,4]\n",
-      " list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']\n",
-      "\n",
-      "########################\n",
-      "the desired outcome is:\n",
-      "########################\n",
-      "\n",
-      " ['a', 'b', 'c'] \n",
-      " [4, 5, 6] \n",
-      " ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']\n",
-      "\n",
-      "########################\n",
-      "and NOT:\n",
-      "########################\n",
-      "\n",
-      " ['a', 'b', 'c'] \n",
-      " [4, 5, 6] \n",
-      " ['another_val-b', 'some-val-associated-with-c', 'z_another_third_val-a']\n",
-      "\n",
-      "\n",
-      "\"\"\"\n",
-      "\n",
-      "list1 = ['c','b','a']\n",
-      "list2 = [6,5,4]\n",
-      "list3 = ['some-val-associated-with-c','another_val-b','z_another_third_val-a']\n",
-      "\n",
-      "print('input values:\\n', list1, list2, list3)\n",
-      "\n",
-      "list1, list2, list3 = [list(t) for t in zip(*sorted(zip(list1, list2, list3)))]\n",
-      "\n",
-      "print('\\n\\nsorted output:\\n', list1, list2, list3 )"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "input values:\n",
-        " ['c', 'b', 'a'] [6, 5, 4] ['some-val-associated-with-c', 'another_val-b', 'z_another_third_val-a']\n",
-        "\n",
-        "\n",
-        "sorted output:\n",
-        " ['a', 'b', 'c'] [4, 5, 6] ['z_another_third_val-a', 'another_val-b', 'some-val-associated-with-c']\n"
-       ]
-      }
-     ],
-     "prompt_number": 49
     }
    ],
-   "metadata": {}
+   "source": [
+    "from collections import namedtuple\n",
+    "\n",
+    "Coordinates = namedtuple('Coordinates', ['x', 'y', 'z'])\n",
+    "point1 = Coordinates(1, 2, 3)\n",
+    "print('X-coordinate: %d' % point1.x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 06830b939358f35f2daf50af42509e603c93d9b4 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Thu, 25 Jun 2015 14:16:13 -0400
Subject: [PATCH 226/248] flask template

---
 templates/webapp_ex1/README.md                |  13 ++++++++
 templates/webapp_ex1/app.py                   |  31 ++++++++++++++++++
 templates/webapp_ex1/img/img_1.png            | Bin 0 -> 6278 bytes
 templates/webapp_ex1/static/style.css         |   7 ++++
 .../webapp_ex1/templates/_formhelpers.html    |  12 +++++++
 templates/webapp_ex1/templates/entry.html     |  26 +++++++++++++++
 6 files changed, 89 insertions(+)
 create mode 100644 templates/webapp_ex1/README.md
 create mode 100644 templates/webapp_ex1/app.py
 create mode 100644 templates/webapp_ex1/img/img_1.png
 create mode 100644 templates/webapp_ex1/static/style.css
 create mode 100644 templates/webapp_ex1/templates/_formhelpers.html
 create mode 100644 templates/webapp_ex1/templates/entry.html

diff --git a/templates/webapp_ex1/README.md b/templates/webapp_ex1/README.md
new file mode 100644
index 0000000..d07794d
--- /dev/null
+++ b/templates/webapp_ex1/README.md
@@ -0,0 +1,13 @@
+Sebastian Raschka, 2015
+
+# Flask Example App 1
+
+A simple Flask app that calculates the sum of two numbers entered in the respective input fields.
+
+A more detailed description is going to follow some time in future.
+
+You can run the app locally by executing `python app.py` within this directory.
+
+<hr>
+
+![](./img/img_1.png)
diff --git a/templates/webapp_ex1/app.py b/templates/webapp_ex1/app.py
new file mode 100644
index 0000000..5314e37
--- /dev/null
+++ b/templates/webapp_ex1/app.py
@@ -0,0 +1,31 @@
+from flask import Flask, render_template, request
+from wtforms import Form, DecimalField, validators
+
+app = Flask(__name__)
+
+
+class EntryForm(Form):
+    x_entry = DecimalField('x:',
+                           places=10,
+                           validators=[validators.NumberRange(-1e10, 1e10)])
+    y_entry = DecimalField('y:',
+                           places=10,
+                           validators=[validators.NumberRange(-1e10, 1e10)])
+
+@app.route('/')
+def index():
+    form = EntryForm(request.form)
+    return render_template('entry.html', form=form, z='')
+
+@app.route('/results', methods=['POST'])
+def results():
+    form = EntryForm(request.form)
+    z = ''
+    if request.method == 'POST' and form.validate():
+        x = request.form['x_entry']
+        y = request.form['y_entry']
+        z = float(x) + float(y)
+    return render_template('entry.html', form=form, z=z)
+
+if __name__ == '__main__':
+    app.run(debug=True)
\ No newline at end of file
diff --git a/templates/webapp_ex1/img/img_1.png b/templates/webapp_ex1/img/img_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..bfc35109142affd9b2c6b2c2ec1014a822c15416
GIT binary patch
literal 6278
zcmb_gbySq=w}zo+0AUz99lBe(hM|UZKxvQ~N=gu<VL(8-rKCg-0wN$KorAz36%>$A
zIz>WCiTinean4$I-T&^aH8XEK`+4iz&;H&Whc?i<LCQpmhlh7V8>Map{C@_%^<ZM)
z_xn0X8xIdZ#8piVt*xd8NBej=xw<>z;qj$8*xCwc3-Jxw+1c6-jtcUV`uH2g#wHrs
zcJ+0ScK5+?w*BzJ94o7@4A8HASn80rcirF;(VfT<ouiAu$+E_fD>~_#Tptr|`R3yY
ze1Dz$-Vy`^oFu6Q`9}HqWS9-SS@|I%4Q|8<Io$GXdnq1H86T5CgU<@ae8GP+$-YYo
ze+0`J#eqzyVXNdAUA$S4HnfR2wt&LU5<4@*7{`xjA{i8kW$wmK_G$FYcN<NoVcWEc
zTjc%_Q$RfL&&Z=^BKIMI4vPhdHH-0y<w!D!nMrI)$YzzAWMXOLH|X!uIhHwR^2vhK
zq>X#zRNfXha5bW|dL%{7Z)^1oc2D4yQqVX<U#O5o=sVLWq(Fyl2D@!5M!LJ_A47HX
z-gVc8QW8AI@kizSsYpy5=0+Uju|Wz;fGtrtU%W%c+T=UA6WSfgii_1Q{<^-dXCff@
z#YG&`MSXpJ-LZLn-IWk=La?%npupqucm0~^2BQSdp}_U7nXj3?p1gyXhlm}<%id8W
zz~cdMPI!2V0rEi6!_n6c9^m2b`A|MUiQ_MXJW&4KEXo1@3*zgh#9^k7hO2q`IKrhw
zZi$F-D3ikBa77=Clf03-=D*E>ni7YLukQnSQBi+?e-VF45icKSQE@prIZ-hQQ3(lQ
z03rM^(9_p0K-lvk=f6n)!=vu_(80&`fv>BVC;T_BoxPWzuM!8x?}7gN`PV!h16=<e
z$@AgAmjx_P^mmV_xQLkOf4Kou#ow*+Xx9KocQbWY4@b|3z!=IB;<Ac=;s009-y{CR
z()@2raT&RPTK=Qw-<FD^zZdY21^sKc{%QrbOPN$r^uJrLOlo-LBa4Se%c8BWdN%<7
zTLC4S#kg>w%ZwlrNsiIh^YvEp5wBvrW#{E@{X#1Bki2nYq&gc{iKyPTI`XP9K|Q61
zZR-i79`_nAKuor{tn}uM%Oy*1)q1M7N3V*-YqGQT(e2Xl)0@PRk!FiQI3+)ywmK8X
z*j?R{QgfEL^oY;hJH2<k_$%~Y_(n&V<mJ6cF6jrH_^=#Pkj&^SKPFy$7#fUrX@{Z^
z1R$C`;?!Is2v8;df3Dg^ts^WyK3d(dtnE)H)74PQqsZ>FAJVG+14hs417UR$7}Y@b
zip*p>+|U^nLVYFXv;|u)VrViuz#+%rPv73!&1JY9bYxQ6xe6ezarJ`+S>uoFHp4xZ
zL#e7;k(LfZkj<xv$Vw6wYx*~W-g1?+Iy?;&MXIXnw<NMd3Ds7JF~a&q6sv5I)NYyx
zF>9^c@@mooZB=^&E7WIcDTAvBwfj68>#F@SFt9qJU;>vr=4Lv-G54tC<iZSYNe=87
z7zwoRS_g!9Fjl=}MhlTYy(k9JJI|-c+4_%w!Rf4zwGl;xcfq3(8FNS2ZG+vjxf^F|
z6&;axr3!<pN7Y7>f0odc{-GGzL=~@Jlugw^p#@Kzz)f5hBHgP<=wzz}nqovkE82?|
zx+u<rEpT7Maw3KwaN8<EHnG@N!+~1(YV`~JiXj?nv%7l5x*$VXp2+NbR~T9!t~YJi
z$jZY7%uyAKB*TAV$ORP!(O5K~K2SiS;E3nZ*gig3EL}u1BhT*cZXzA^=QuS`VBm7w
z*PZ76qzO#SH1oP&m6j80Btj?zT))Fm63L(ede4D)u93j?EfoO<$iT9R91$*5#S<kO
z>5ouw!U$Q~JhG-3omC#L*cLq>L-T2F9;^L1+?|y*j;np5HXA}pZTL^ZWh0l9!WV-J
zQ`k`JONLrjbxUuyd)o)JnPkG_LIYizMZCi42n5KRzv1?n4dP8B1+$4Wr<*Z;n1EGR
z$fz8VZTUfw;n~o=Or{9I9Eu{HZdO>VL_{-9-oX%OfZr3U$IcXe>Ca+dIr}T$gLo)f
zry>=?4#it-L<d3>O7t?;QE>2Y^z?M%E{UO$E3Je=0%uYvM*#TgFsb2j53^Xj^F}q_
zcr8y*#!&&`dJ@9wQUa%2@w$wqs77#L-B^<b!}#WKis*fOM^gBCHNme{b(KLxuFOH+
zEuq>Zhk?qwUf?g<@NL57It|O%x9A@U4h%3!-nLzO@48l#5xxeHZj#Et{(kM*&HghN
zl}enF#a2aFywG+w?Tw$*vBPv8qbD+kADdYGOEa4<7p$TeAMe;f7oD7fEUVRppr<Zm
zb=hZ<OX3jq0)!RmjiK3@1wK1}lvTxyw<c+BWZLhO_MY`iq+|Ka{?FsVClw}S(M&@$
zfN>v{EQA!l(!Y*>UU0_I9mu-!N_k5^5oS9rt|=JPA1?|VZpcU8jyOZ0kZ3}lKx;$D
z*Kk&OJBa!PP+)k?KquG<NW_70(GMO?Kpey^#Dz>ksKA`J-c;?eksS&HK5eh+`1oR<
zYu{j0#7yXX#)!S<jNgPvc$7Bd>u#jJVpeYwAqtL%&p$8ynLQW(K=PDyOVaD}`$t3I
zj45#ul0;pT43_UoCp^mvkW_3ow$diX_oG>=>1VDoJcj<g?=Kx{?G*NcP9dm|MDf1X
zCsgD~%&Co2lA>C8s*<sUKlBh!C&WI*TqOF*x+zmQJBTN*^VrvgvD`j72v@RE_z_A$
zI0ezyQ~6xye1r5AwzWy=Y&?=}N_*UHmPNwe2sR;!{}f66(923O4!6ckYf_fo=Dn^h
zzVvQt9lKzMsLU~sX9kqR9b~7;`tr6Ire@$^^zaRpW3=2PRqcyTJ|b8ClwuFeC)}L!
zR<kbXC%4!W9zNQvB2&C3y(;VN;JojhVX7Y<!oV5`ziC714@P-dnIn=1xb!vW3~MbF
zxFS~sBVjM8N7YNBnxZ0HhyN9XQspOmP(h77H}e}tXdnW#1de(-ax<)rS>besMj`k%
zB^t97D^W-Yf`aEt<TQWoF9wGb*6nHa+`kraz)K+K05o0$UNzxV(1QnqN61m5mD*LD
zhZQ3TQMU1jnc#+sO!3aXigo+;$m)j&hlhvNZjfb77m5Cz8%M35xFjic;B?ASvwq|j
zN{R4@&EJkcke-Et^8rs5ACqP(;s@|dym!#n<$`7+@<~UDEY-Mus9`uOo|f{e9oX%u
z7MW=?dI8}>7GB7pa3$_n$TEDhzbAH+DpvRTY;ZD%{~dx8zhj5)M^KOcz<`PYk@8vZ
z9|wxN!?RDWp5wE$zq~<Nr67Q8N$b9V#lBh!6|MYRArgwY&nf~sLPjC7%Kg!zy6;0D
z5Y$C)_$!)#ce_ATeMyAt*;xOqLgvfM7b(hlYrTkP#uCg6lD>Wdl8*w@Wl6Z);>_=L
zTSyrDkIARtA85cQ8lN{l0UYT^*eyqlN^H80pq?zC6nk>~Pi57Z*yhbZZn?4iN|DNA
zT3Z_LP6Iih*+{on{8}B&yt<<jCEaxdelMGzH=TJ{F}bS-_G^3J&Z1a(Of=)YX$lp0
z*=Bpj+kQ(I<oR3Zq65e@x@2S6-y603r4n>}<`E<P2?w99l4MWziuV_zdn8{*jbNXC
zF<3c9fR1$FA|z^`+{3AjJ%+utbmEv7*9CsCSy8G<?5}SL2-yVFD~gxPFOVH2%Awbv
z$|UV%BIXrSc&;C0stA1AGG=-A&8%5^xo(FcM&X4v;vo>_*9UW-vjLtQROR=xN-m<A
zi99Zv<l<J1s#Aa(H1=6fDs!YXS%oENSAM%`d#gE%knfhTtEA`xta!rL^4Ql(7vRWh
z$j$ZyI9gu^$yamZlj`e!9?^XZAQw!OSf|tRqRY5F*8i{ske;s!lijqj$jNl+qYuIW
z@~!dhfkrOGb64$Wfm>Msa<1~_iUmO4P&(S~s|z4KK2CZI1LPJTobI)9Bc5;lAJeGh
z-Jp8qW$WVud(_5iT63@8)FzZjZdY@5HJZe$^W14ZXy-LbUthz>D7}thDaj}->*yU%
zagYz2xtW@p+5=e@vnF>&&$e$(%wtEx*`k8VT)e!3%7eWtnji}(dZA=UI9I4HKc7YT
zPbtyB!J8C^;a*-fA0+O$97X185<t<RDLiBzL+RZ3dh-FQbvNr&=1~YiP-XO^Z=a<w
z7)+FC_He|TH1QeM#kJYKq7pySS<2a)o?OMW_#ctKe(u!tWe!F7Q+!7v?MWr*oZ-UU
zROi749P_a7lS2B`)L%$-vZlHFK56KKPNU8=GGUiiF`BX}L_m0No-GIn{D{Vh-Xt;k
zpnE-i3YPC@6v$t$;#|Td(T!BMWvs}p865vO;VSPmC%64}qGhB0t1LJ(XZ(vu)S65Y
z2}(+mFJDFS&QUBO2WPQ`%Tk@UTum(3PVMrT9y=4khx+N2q-tWCFhRqpIYXPw6w@HZ
zQ;q5bzVR>7BIjb_klJrJ6WvaU`NGsTBK^=k-;=^1_Zr#6a6ujP@}aIx_mIQdo-d>&
zU}>b-s7kQOsr~&q{g02|kb7)Y4Jwo^bK9KvxoP`C*dzF;V)G`99$v8-m)&12g&3}S
zsXFQ_DVsO2Jv*w;ED5tyNU(ePAp}2}s@<=NHbdDU#BH+6A;g~HwMrXEcdULN1#bN1
z&v9N6mPqMmtDz5(ZmRBfrl#IhKETr<z0%#vyT=wp_PXd^-=8I+E;9m&SyCly+0jt4
z5FgHn95__!IlCNl+c=7WZBxU|m7OOn931HXhu!d->e*zb^jg$Ki;TIa@r-E$am7$B
zAK#l2nvb%7G=%wB-Dp~K=JeaTH1nY0FPg<VE(h0kjkTEKxYayG#T^A%Pv_kt#V-wn
zhV@d)6)JPq$RO%*h^eSg{_btir#EuV1r2Ry6F2U+i3e4KYA$#El?gaJ%|}eIrebxb
z_F@|C^6jsSX8coZS`~$h+l<Mj%eGu49o6Pha3`?3zDt}dv_t@T8LcX7CDOdB=5nb|
zV0%xfbY)S5B23^B%jVBxs{DRXY@6hpp&IUhEsxV;Z5s!oljjW4a$7p?6WP%CI_sY_
z4&oQbNOTbc)muUi#@%Q5+`$evEMDo>dX8F8E|qm`${GsE2N1iLS5Teal`DVhph1u3
zLx&x>Yu-|+XK|E1wHs|2x22IW`!h8*{S#{;SI{$xamE-TvZw;V)MK8v83~Fq7(uoL
zQio(BMh_{5Z(&S!tJ`nh$-r`KK$YP;Gn12#jbE8Wizc1*epXgy55l@G7Y^SZ;22wy
zfX}MTI~ZTZ(UT8>VXVsE<x0`t##!7Y_#uRx#jQJh*U-}h(N3_Fv%D3UTS_UkX>gUM
zE*4pi8sk*|;mby~HxxD)=*@}$8eC47ci}SB-HkO_%bO0ts9?gI)37FlcgZ}KdZU}S
z%AW1_kleJzVANk7YDnE|nfEz&{4eIqiy;;B^zehQtq<HG=WE%2rucN7uQR{?lhIqb
zuGzC?jZVm-0j!%XVr+5i+zt)6HWA?3M|7HW`MTs^hB?~O!bJHJbm*S-*nZ9UxcKL4
zn-k%>(5+FOcZn!?GPt}`D}l3UixlEw0H_{|oa7WIppPky21Qc5fIcSYGj68=`Z!?m
zay*Z^c;c4U82K>@KsvmhbRGrtF^|(ZCJ0c{tv8>x>em7B9Zi!|Qv#IqhuO{#^ZzaN
z0Qtk!5p)WGRQe<z`Y9hEm-BHd*8*gk&8<M$?eA$=lZ`4%falIny}gBI^sWR%Adb<K
zREbG}8)y@6ij2)<tVp!<hsmgKEfAFDg}>~hGRkR8fm~~?hTd1s+siB0;b)P~)-qkg
zeT&mkA%W48`p<B6ef;(wO&L}hqbXFB3xpTzyUG4S6b&EU`&&Ouquo6QaLG@~KB0FM
zP88Mo?pN*9BFsZMA>p1vy`4Ogd)EfoA(Mf{9tZp6aF+W|?w+KY@fDM0H7F`(EhbAI
zip#+Ex#4Ar&$>M95%OWhI@=77#%$Og*3SPrl-52I$$h18ImVRDMDY=I8P>n6h)K7t
z&fvK)8h*m+!;)yUv+s-1YGiRr{~X#|=Fsxe8FV<G$le60IG&Im!bs@$JH5atqj{w)
zPIa8>wI%WdORo^7#Wjrw218sTyOorhp=y^WyrnS`^%x2I1jP}f{<wSGogJ-rhhCl`
z9(I#0jH71MHqiB4bY+H7X#Z;K`-<=3Qbjvi?;aOy?PfQ{{rKah3R$YXRb5_`utW4J
zv%;dIag>!`vJM%Z{<NxHYw{2uUoh3#HdWP}S6>tF^l~u&&E5}pu~%`<X{??FU^{D}
z>Zn!qaZpsJ{?=5OgmmV&{#T5YvDKDD%5GK_IK>~7DJw-fDw-Y(KP&bMPBS}Oeca`L
z2G0vKpMI&f>akHe1{I1Pnt#_WW!(D(-7|hZwmea@*d6-abVW+8+luorglYI<cjniX
zrI5HIa^TC^Kx|a<?fpIT^Lf4gu8KQKo}Itm7e$arzKm>90|^kTk@NAX2xPSp-4FMn
z-wXKR&_F>#pF1qhqxp3;Tw%BGnj{c%KCX2Ida^+6Nh#KweWkWrlQpe7_hfo3g}+ha
ztZmv>Fpy4#`~eWRTA`=HZLDuaA^Tms1{khUx|5BCoBWSX3)v9<-;M$rgIWA6qZyv*
zJSgtz0GFh9ANedL6(CWT-r>bWpR5e}MZf&${8cwTkFq7kZ~WHs){^jr6vnjrDAjOF
zIZhAMb#oJ(zvlAl!)Vlb^D0fa31=yj<wUZzEi*f^?RCFSAXCWCHtLRnr?uv%?u(u)
z6%lYcl2TGb<($`&t%WPn{?p!G-Fr^ZJLI2;fb_zEtG;|qpz=!(1;gMgh~niX;^uMp
z0`oo9U!Pws<J7qfPV6juRF?O)HFv{p#jE4}D!e)_3teAy25(Wms2Yi_H@=Rbc23`K
z^$^`QA#xeR$#E)qoE0q^a&foNZyZZW%E`q|t*P|>{D|Kg!m&h8Jt=(u(Eku8%eh}s
zga79Ho;1_@s|Q&&iKk)&Upg&!Wp3EtZ4cDlYL~9yYF3(T%c+%7`r>yR@o^x|8QmAu
zstThob0N}kPMRQUSlP`|e}b<2+?$lwJpSlJMz1jvxDFBIWfDAn-)$>g29{yVTeu(O
z;id3i#lyecaH!_Y@f0snrsNX4|K2x5(`_y3p+R%#R@m$UQkpW*LA@o@P^*55>i({?
zyxgg;I((%3g&s1I)H4QiNFGO-|M`_wei^~*C2{>`EmpVIse6GdAH37D72OvVS7ko!
zJ$p9EuOCaMycynohqv?{Z1$u9D*xJPt$Fh8lkF|(F=#2TmzQb1*%g%GJ6c&i(Q&lo
zg`o6-9angHPNj3g{M*g>Y2FAiBsINrt~OQHIl2v5-nV@vqT@@%mC-ziY%)f=9QON%
zP@G!4*bu<go{t0<Z5of(o(ZU)4Dfa8JV>Z+`C{sb6m4SaxB2pYp#41fdyk&U>FERN
z%hC97vG^JV8nzqhpHpaR^gDMRBSwVhrv7uigY91N9WHjN36;yT@6q4ES<KaUK2N1@
z^KXt6>2Raxtc+pYXu*M!_B8hHG@4-*d`?}L<e{;U=Sjy6UyqM6noq}6mX@?s)N786
ztL`f4J!CBx*M;Q(?=8*w*ZXq|Gc5y`esvd0^cvCw7MQ~vUMRJ2o<k|+_l{F7_)Zv2
zRbiKi!^ku-8N*vbd!&oEZgz4S_6at=$Tp1@l8Pt=uJ<U&VF(Njq>b+2$7!SBMnDdr
zlrT^kQVyhzDzG>%E+{{6;WLU<1X5ej;)%Nmdl)P>6EB*MU$yWyNC3D43c$r75Op0a
zG9eQ_&WleA#1`7roG3yL;JsZ#Cv_tOOA166Qj{JNlm<j54(`DtZ3GbziHNv(*dtJo
a3zhw~P_qiO@Y~;MYi$h!^=hPT<o^J?z8E0@

literal 0
HcmV?d00001

diff --git a/templates/webapp_ex1/static/style.css b/templates/webapp_ex1/static/style.css
new file mode 100644
index 0000000..8abda7b
--- /dev/null
+++ b/templates/webapp_ex1/static/style.css
@@ -0,0 +1,7 @@
+body{
+	width:600px;
+}
+
+#button{
+	padding-top: 20px;
+}
\ No newline at end of file
diff --git a/templates/webapp_ex1/templates/_formhelpers.html b/templates/webapp_ex1/templates/_formhelpers.html
new file mode 100644
index 0000000..5790894
--- /dev/null
+++ b/templates/webapp_ex1/templates/_formhelpers.html
@@ -0,0 +1,12 @@
+{% macro render_field(field) %}
+  <dt>{{ field.label }}
+  <dd>{{ field(**kwargs)|safe }}
+  {% if field.errors %}
+    <ul class=errors>
+    {% for error in field.errors %}
+      <li>{{ error }}</li>
+    {% endfor %}
+    </ul>
+  {% endif %}
+  </dd>
+{% endmacro %}
\ No newline at end of file
diff --git a/templates/webapp_ex1/templates/entry.html b/templates/webapp_ex1/templates/entry.html
new file mode 100644
index 0000000..c31100d
--- /dev/null
+++ b/templates/webapp_ex1/templates/entry.html
@@ -0,0 +1,26 @@
+<!doctype html>
+<html>
+  <head>
+    <title>Webapp Ex 1</title>
+	<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+  </head>
+  <body>
+
+{% from "_formhelpers.html" import render_field %}
+
+<form method=post action="/results">
+  <dl>
+	{{ render_field(form.x_entry, cols='1', rows='1') }}
+    {{ render_field(form.y_entry, cols='1', rows='1') }}
+  </dl>
+  <div>
+	  <input type=submit value='Submit' name='submit_btn'>
+  </div>
+
+  <dl>
+	x + y = {{ z }}
+  </dl>
+</form>
+
+  </body>
+</html>
\ No newline at end of file

From b13d26a7ef8782ec807706654e9e0652fd81abf3 Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 29 Jun 2015 15:35:39 -0400
Subject: [PATCH 227/248] upd. watermark download link

---
 ipython_magic/watermark.ipynb | 857 +++++++++++++++++-----------------
 1 file changed, 437 insertions(+), 420 deletions(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index d88d6b8..8210d3c 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -1,436 +1,453 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:968e6f47972d4ab9e2ef4eef6906343257267ccf094fcae08da24fec3647743d"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+    "\n",
+    "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference/)\n",
+    "\n",
+    "<hr>\n",
+    "I would be happy to hear your comments and suggestions.  \n",
+    "Please feel free to drop me a note via\n",
+    "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font color=\"red\">**watermark is now located and maintained in a separate GitHub repository:** [https://github.com/rasbt/watermark](https://github.com/rasbt/watermark)<font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IPython magic function documentation - `%watermark`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I wrote this simple `watermark` IPython magic function to conveniently add date- and time-stamps to my IPython notebooks. Also, I often want to document various system information, e.g., for my [Python benchmarks](https://github.com/rasbt/One-Python-benchmark-per-day) series.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `watermark` line magic can be directly installed from my GitHub repository via"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Installed watermark.py. To use it, type:\n",
+      "  %load_ext watermark\n"
+     ]
+    }
+   ],
+   "source": [
+    "install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the `%watermark` magic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To load the `date` magic, execute the following line in your IPython notebook or current IPython shell"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext watermark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Usage"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to display the optional `watermark` arguments, type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%watermark?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<pre>  %watermark [-a AUTHOR] [-d] [-e] [-n] [-t] [-z] [-u] [-c CUSTOM_TIME]\n",
+    "                 [-v] [-p PACKAGES] [-h] [-m] [-g] [-w]\n",
+    "\n",
+    " \n",
+    "IPython magic function to print date/time stamps \n",
+    "and various system information.\n",
+    "\n",
+    "watermark version 1.2.1\n",
+    "\n",
+    "optional arguments:\n",
+    "  -a AUTHOR, --author AUTHOR\n",
+    "                        prints author name\n",
+    "  -d, --date            prints current date as MM/DD/YYYY\n",
+    "  -e, --eurodate        prints current date as DD/MM/YYYY\n",
+    "  -n, --datename        prints date with abbrv. day and month names\n",
+    "  -t, --time            prints current time\n",
+    "  -z, --timezone        appends the local time zone\n",
+    "  -u, --updated         appends a string \"Last updated: \"\n",
+    "  -c CUSTOM_TIME, --custom_time CUSTOM_TIME\n",
+    "                        prints a valid strftime() string\n",
+    "  -v, --python          prints Python and IPython version\n",
+    "  -p PACKAGES, --packages PACKAGES\n",
+    "                        prints versions of specified Python modules and\n",
+    "                        packages\n",
+    "  -h, --hostname        prints the host name\n",
+    "  -m, --machine         prints system and machine info\n",
+    "  -g, --githash         prints current Git commit hash\n",
+    "  -w, --watermark       prints the current version of watermark\n",
+    "File:      ~/.ipython/extensions/watermark.py\n",
+    "</pre>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "06/29/2015 15:34:42\n",
       "\n",
-      "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference/)\n",
+      "CPython 3.4.3\n",
+      "IPython 3.2.0\n",
       "\n",
-      "<hr>\n",
-      "I would be happy to hear your comments and suggestions.  \n",
-      "Please feel free to drop me a note via\n",
-      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<font color=\"red\">**watermark is now located and maintained in a separate GitHub repository:** [https://github.com/rasbt/watermark](https://github.com/rasbt/watermark)<font>"
+      "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
+      "system     : Darwin\n",
+      "release    : 14.3.0\n",
+      "machine    : x86_64\n",
+      "processor  : i386\n",
+      "CPU cores  : 4\n",
+      "interpreter: 64bit\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "IPython magic function documentation - `%watermark`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I wrote this simple `watermark` IPython magic function to conveniently add date- and time-stamps to my IPython notebooks. Also, I often want to document various system information, e.g., for my [Python benchmarks](https://github.com/rasbt/One-Python-benchmark-per-day) series.\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Installation"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `watermark` line magic can be directly installed from my GitHub repository via"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/watermark.py"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Installed watermark.py. To use it, type:\n",
-        "  %load_ext watermark\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Loading the `%watermark` magic"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "To load the `date` magic, execute the following line in your IPython notebook or current IPython shell"
+    }
+   ],
+   "source": [
+    "%watermark"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "06/29/2015 15:34:43 \n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext watermark"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "%watermark -d -t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: Mon Jun 29 2015 15:34:44 EDT\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Usage"
+    }
+   ],
+   "source": [
+    "%watermark -u -n -t -z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPython 3.4.3\n",
+      "IPython 3.2.0\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In order to display the optional `watermark` arguments, type"
+    }
+   ],
+   "source": [
+    "%watermark -v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
+      "system     : Darwin\n",
+      "release    : 14.3.0\n",
+      "machine    : x86_64\n",
+      "processor  : i386\n",
+      "CPU cores  : 4\n",
+      "interpreter: 64bit\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark?"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<pre>  %watermark [-a AUTHOR] [-d] [-n] [-t] [-z] [-u] [-c CUSTOM_TIME] [-v]\n",
-      "                 [-p PACKAGES] [-h] [-m] [-g]\n",
-      "\n",
-      " \n",
-      "IPython magic function to print date/time stamps \n",
-      "and various system information.\n",
+    }
+   ],
+   "source": [
+    "%watermark -m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPython 3.4.3\n",
+      "IPython 3.2.0\n",
       "\n",
-      "watermark version 1.1.0\n",
+      "numpy 1.9.2\n",
+      "scipy 0.15.1\n",
       "\n",
-      "optional arguments:\n",
-      "  -a AUTHOR, --author AUTHOR\n",
-      "                        prints author name\n",
-      "  -d, --date            prints current date\n",
-      "  -n, --datename        prints date with abbrv. day and month names\n",
-      "  -t, --time            prints current time\n",
-      "  -z, --timezone        appends the local time zone\n",
-      "  -u, --updated         appends a string \"Last updated: \"\n",
-      "  -c CUSTOM_TIME, --custom_time CUSTOM_TIME\n",
-      "                        prints a valid strftime() string\n",
-      "  -v, --python          prints Python and IPython version\n",
-      "  -p PACKAGES, --packages PACKAGES\n",
-      "                        prints versions of specified Python modules and\n",
-      "                        packages\n",
-      "  -h, --hostname        prints the host name\n",
-      "  -m, --machine         prints system and machine info\n",
-      "  -g, --githash         prints current Git commit hash\n",
-      "</pre>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Examples"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "29/06/2014 01:19:10\n",
-        "\n",
-        "CPython 3.4.1\n",
-        "IPython 2.1.0\n",
-        "\n",
-        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
-        "system     : Darwin\n",
-        "release    : 13.2.0\n",
-        "machine    : x86_64\n",
-        "processor  : i386\n",
-        "CPU cores  : 2\n",
-        "interpreter: 64bit\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -d -t"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "29/06/2014 01:19:11 \n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -u -n -t -z"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Last updated: Sun Jun 19 2014 01:19:12 EDT\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -v"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "CPython 3.4.1\n",
-        "IPython 2.1.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -m"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
-        "system     : Darwin\n",
-        "release    : 13.2.0\n",
-        "machine    : x86_64\n",
-        "processor  : i386\n",
-        "CPU cores  : 2\n",
-        "interpreter: 64bit\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -v -m -p numpy,scipy"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "CPython 3.4.1\n",
-        "IPython 2.1.0\n",
-        "\n",
-        "numpy 1.8.1\n",
-        "scipy 0.14.0\n",
-        "\n",
-        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
-        "system     : Darwin\n",
-        "release    : 13.2.0\n",
-        "machine    : x86_64\n",
-        "processor  : i386\n",
-        "CPU cores  : 2\n",
-        "interpreter: 64bit\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
+      "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
+      "system     : Darwin\n",
+      "release    : 14.3.0\n",
+      "machine    : x86_64\n",
+      "processor  : i386\n",
+      "CPU cores  : 4\n",
+      "interpreter: 64bit\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -a \"John Doe\" -d -v -m -g"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "John Doe 29/06/2014 01:20:48 EDT\n",
-        "\n",
-        "CPython 3.4.1\n",
-        "IPython 2.1.0\n",
-        "\n",
-        "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
-        "system     : Darwin\n",
-        "release    : 13.2.0\n",
-        "machine    : x86_64\n",
-        "processor  : i386\n",
-        "CPU cores  : 2\n",
-        "interpreter: 64bit\n",
-        "Git hash   : fbe7759fd7e0298bf0bd05ea4aac01b87aa8ed25\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
+    }
+   ],
+   "source": [
+    "%watermark -v -m -p numpy,scipy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "John Doe 06/29/2015 \n",
+      "\n",
+      "CPython 3.4.3\n",
+      "IPython 3.2.0\n",
+      "\n",
+      "compiler   : GCC 4.2.1 (Apple Inc. build 5577)\n",
+      "system     : Darwin\n",
+      "release    : 14.3.0\n",
+      "machine    : x86_64\n",
+      "processor  : i386\n",
+      "CPU cores  : 4\n",
+      "interpreter: 64bit\n",
+      "Git hash   : 06830b939358f35f2daf50af42509e603c93d9b4\n"
      ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "%watermark -a \"John Doe\" -d -v -m -g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From e34d2f21b1db5e2d8503d6fed7f0b6c2e5c5b57a Mon Sep 17 00:00:00 2001
From: rasbt <se.raschka@me.com>
Date: Mon, 29 Jun 2015 15:37:25 -0400
Subject: [PATCH 228/248] watermark clarficitation

---
 ipython_magic/watermark.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ipython_magic/watermark.ipynb b/ipython_magic/watermark.ipynb
index 8210d3c..5f07900 100644
--- a/ipython_magic/watermark.ipynb
+++ b/ipython_magic/watermark.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<font color=\"red\">**watermark is now located and maintained in a separate GitHub repository:** [https://github.com/rasbt/watermark](https://github.com/rasbt/watermark)<font>"
+    "### watermark is now located and maintained in a separate GitHub repository: [https://github.com/rasbt/watermark](https://github.com/rasbt/watermark)"
    ]
   },
   {

From 360fd6be3298392ee9223b85ef9b54b3f86a784a Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Sat, 1 Aug 2015 01:33:04 -0400
Subject: [PATCH 229/248] pandas if test

---
 tutorials/things_in_pandas.ipynb | 6120 ++++++++++++++++--------------
 1 file changed, 3192 insertions(+), 2928 deletions(-)

diff --git a/tutorials/things_in_pandas.ipynb b/tutorials/things_in_pandas.ipynb
index 505c1f3..968d734 100644
--- a/tutorials/things_in_pandas.ipynb
+++ b/tutorials/things_in_pandas.ipynb
@@ -1,2937 +1,3201 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:c8ab1a3c99e7c72951c91e74991b8837884cd9e3863f1cd1833651e180ff32bd"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Back to the GitHub repository](https://github.com/rasbt/python_reference)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext watermark\n",
-      "%watermark -a 'Sebastian Raschka' -v -d -p pandas"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Sebastian Raschka 28/01/2015 \n",
-        "\n",
-        "CPython 3.4.2\n",
-        "IPython 2.3.1\n",
-        "\n",
-        "pandas 0.15.2\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Things in Pandas I Wish I'd Had Known Earlier"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is just a small but growing collection of pandas snippets that I find occasionally and particularly useful -- consider it as my personal notebook. Suggestions, tips, and contributions are very, very welcome!"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Sections"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- [Loading Some Example Data](#Loading-Some-Example-Data)\n",
-      "- [Renaming Columns](#Renaming-Columns)\n",
-      "    - [Converting Column Names to Lowercase](#Converting-Column-Names-to-Lowercase)\n",
-      "    - [Renaming Particular Columns](#Renaming-Particular-Columns)\n",
-      "- [Applying Computations Rows-wise](#Applying-Computations-Rows-wise)\n",
-      "    - [Changing Values in a Column](#Changing-Values-in-a-Column)\n",
-      "    - [Adding a New Column](#Adding-a-New-Column)\n",
-      "    - [Applying Functions to Multiple Columns](#Applying-Functions-to-Multiple-Columns)\n",
-      "- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n",
-      "    - [Counting Rows with NaNs](#Counting-Rows-with-NaNs)\n",
-      "    - [Selecting NaN Rows](#Selecting-NaN-Rows)\n",
-      "    - [Selecting non-NaN Rows](#Selecting-non-NaN-Rows)\n",
-      "    - [Filling NaN Rows](#Filling-NaN-Rows)\n",
-      "- [Appending Rows to a DataFrame](#Appending-Rows-to-a-DataFrame)\n",
-      "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)\n",
-      "- [Updating Columns](#Updating-Columns)\n",
-      "- [Chaining Conditions - Using Bitwise Operators](#Chaining-Conditions---Using-Bitwise-Operators)\n",
-      "- [Column Types](#Column-Types)\n",
-      "    - [Printing Column Types](#Printing-Column-Types)\n",
-      "    - [Selecting by Column Type](#Selecting-by-Column-Type)\n",
-      "    - [Converting Column Types](#Converting-Column-Types)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Loading Some Example Data"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I am heavily into sports prediction (via a machine learning approach) these days. So, let us use a (very) small subset of the soccer data that I am just working with."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import pandas as pd\n",
-      "\n",
-      "df = pd.read_csv('https://raw.githubusercontent.com/rasbt/python_reference/master/Data/some_soccer_data.csv')\n",
-      "df"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>PLAYER</th>\n",
-        "      <th>SALARY</th>\n",
-        "      <th>GP</th>\n",
-        "      <th>G</th>\n",
-        "      <th>A</th>\n",
-        "      <th>SOT</th>\n",
-        "      <th>PPG</th>\n",
-        "      <th>P</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td>     Sergio Ag\u00fcero\\n Forward \u2014 Manchester City</td>\n",
-        "      <td> $19.2m</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td>  3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>              Eden Hazard\\n Midfield \u2014 Chelsea</td>\n",
-        "      <td> $18.9m</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  8</td>\n",
-        "      <td>  4</td>\n",
-        "      <td> 17</td>\n",
-        "      <td> 13.05</td>\n",
-        "      <td> 274.04</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td>            Alexis S\u00e1nchez\\n Forward \u2014 Arsenal</td>\n",
-        "      <td> $17.6m</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 12</td>\n",
-        "      <td>  7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td>       Yaya Tour\u00e9\\n Midfield \u2014 Manchester City</td>\n",
-        "      <td> $16.6m</td>\n",
-        "      <td> 18</td>\n",
-        "      <td>  7</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 19</td>\n",
-        "      <td> 10.99</td>\n",
-        "      <td> 197.91</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td> \u00c1ngel Di Mar\u00eda\\n Midfield \u2014 Manchester United</td>\n",
-        "      <td> $15.0m</td>\n",
-        "      <td> 13</td>\n",
-        "      <td>  3</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 13</td>\n",
-        "      <td> 10.17</td>\n",
-        "      <td> 132.23</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td>         Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
-        "      <td> $14.8m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  4</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>    NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>6</th>\n",
-        "      <td>      David Silva\\n Midfield \u2014 Manchester City</td>\n",
-        "      <td> $14.3m</td>\n",
-        "      <td> 15</td>\n",
-        "      <td>  6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td>            Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
-        "      <td> $14.0m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td>          Saido Berahino\\n Forward \u2014 West Brom</td>\n",
-        "      <td> $13.8m</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td>         Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
-        "      <td> $13.8m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 2,
-       "text": [
-        "                                          PLAYER  SALARY  GP   G   A  SOT  \\\n",
-        "0      Sergio Ag\u00fcero\\n Forward \u2014 Manchester City  $19.2m  16  14   3   34   \n",
-        "1               Eden Hazard\\n Midfield \u2014 Chelsea  $18.9m  21   8   4   17   \n",
-        "2             Alexis S\u00e1nchez\\n Forward \u2014 Arsenal  $17.6m NaN  12   7   29   \n",
-        "3        Yaya Tour\u00e9\\n Midfield \u2014 Manchester City  $16.6m  18   7   1   19   \n",
-        "4  \u00c1ngel Di Mar\u00eda\\n Midfield \u2014 Manchester United  $15.0m  13   3 NaN   13   \n",
-        "5          Santiago Cazorla\\n Midfield \u2014 Arsenal  $14.8m  20   4 NaN   20   \n",
-        "6       David Silva\\n Midfield \u2014 Manchester City  $14.3m  15   6   2   11   \n",
-        "7             Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m  20   2  14   10   \n",
-        "8           Saido Berahino\\n Forward \u2014 West Brom  $13.8m  21   9   0   20   \n",
-        "9          Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m  20   5   1   11   \n",
-        "\n",
-        "     PPG       P  \n",
-        "0  13.12  209.98  \n",
-        "1  13.05  274.04  \n",
-        "2  11.19  223.86  \n",
-        "3  10.99  197.91  \n",
-        "4  10.17  132.23  \n",
-        "5   9.97     NaN  \n",
-        "6  10.35  155.26  \n",
-        "7  10.47  209.49  \n",
-        "8   7.02  147.43  \n",
-        "9   7.50  150.01  "
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Renaming Columns"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Converting Column Names to Lowercase"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Converting column names to lowercase\n",
-      "\n",
-      "df.columns = [c.lower() for c in df.columns]\n",
-      "\n",
-      "# or\n",
-      "# df.rename(columns=lambda x : x.lower())\n",
-      "\n",
-      "df.tail(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>gp</th>\n",
-        "      <th>g</th>\n",
-        "      <th>a</th>\n",
-        "      <th>sot</th>\n",
-        "      <th>ppg</th>\n",
-        "      <th>p</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td>    Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
-        "      <td> $14.0m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td>  Saido Berahino\\n Forward \u2014 West Brom</td>\n",
-        "      <td> $13.8m</td>\n",
-        "      <td> 21</td>\n",
-        "      <td> 9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td> Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
-        "      <td> $13.8m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 3,
-       "text": [
-        "                                  player  salary  gp  g   a  sot    ppg  \\\n",
-        "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m  20  2  14   10  10.47   \n",
-        "8   Saido Berahino\\n Forward \u2014 West Brom  $13.8m  21  9   0   20   7.02   \n",
-        "9  Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m  20  5   1   11   7.50   \n",
-        "\n",
-        "        p  \n",
-        "7  209.49  \n",
-        "8  147.43  \n",
-        "9  150.01  "
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Renaming Particular Columns"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df = df.rename(columns={'p': 'points', \n",
-      "                        'gp': 'games',\n",
-      "                        'sot': 'shots_on_target',\n",
-      "                        'g': 'goals',\n",
-      "                        'ppg': 'points_per_game',\n",
-      "                        'a': 'assists',})\n",
-      "\n",
-      "df.tail(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td>    Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
-        "      <td> $14.0m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td>  Saido Berahino\\n Forward \u2014 West Brom</td>\n",
-        "      <td> $13.8m</td>\n",
-        "      <td> 21</td>\n",
-        "      <td> 9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td> Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
-        "      <td> $13.8m</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "                                  player  salary  games  goals  assists  \\\n",
-        "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea  $14.0m     20      2       14   \n",
-        "8   Saido Berahino\\n Forward \u2014 West Brom  $13.8m     21      9        0   \n",
-        "9  Steven Gerrard\\n Midfield \u2014 Liverpool  $13.8m     20      5        1   \n",
-        "\n",
-        "   shots_on_target  points_per_game  points  \n",
-        "7               10            10.47  209.49  \n",
-        "8               20             7.02  147.43  \n",
-        "9               11             7.50  150.01  "
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Applying Computations Rows-wise"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Changing Values in a Column"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Processing `salary` column\n",
-      "\n",
-      "df['salary'] = df['salary'].apply(lambda x: x.strip('$m'))\n",
-      "df.tail()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td>    Santiago Cazorla\\n Midfield \u2014 Arsenal</td>\n",
-        "      <td> 14.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 4</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>    NaN</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>6</th>\n",
-        "      <td> David Silva\\n Midfield \u2014 Manchester City</td>\n",
-        "      <td> 14.3</td>\n",
-        "      <td> 15</td>\n",
-        "      <td> 6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td>       Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td>     Saido Berahino\\n Forward \u2014 West Brom</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td> 9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td>    Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "                                     player salary  games  goals  assists  \\\n",
-        "5     Santiago Cazorla\\n Midfield \u2014 Arsenal   14.8     20      4      NaN   \n",
-        "6  David Silva\\n Midfield \u2014 Manchester City   14.3     15      6        2   \n",
-        "7        Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea   14.0     20      2       14   \n",
-        "8      Saido Berahino\\n Forward \u2014 West Brom   13.8     21      9        0   \n",
-        "9     Steven Gerrard\\n Midfield \u2014 Liverpool   13.8     20      5        1   \n",
-        "\n",
-        "   shots_on_target  points_per_game  points  \n",
-        "5               20             9.97     NaN  \n",
-        "6               11            10.35  155.26  \n",
-        "7               10            10.47  209.49  \n",
-        "8               20             7.02  147.43  \n",
-        "9               11             7.50  150.01  "
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Adding a New Column"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df['team'] = pd.Series('', index=df.index)\n",
-      "\n",
-      "# or\n",
-      "df.insert(loc=8, column='position', value='') \n",
-      "\n",
-      "df.tail(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td>    Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "      <td> </td>\n",
-        "      <td> </td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td>  Saido Berahino\\n Forward \u2014 West Brom</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td> 9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td> </td>\n",
-        "      <td> </td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td> Steven Gerrard\\n Midfield \u2014 Liverpool</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "      <td> </td>\n",
-        "      <td> </td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 6,
-       "text": [
-        "                                  player salary  games  goals  assists  \\\n",
-        "7     Cesc F\u00e0bregas\\n Midfield \u2014 Chelsea   14.0     20      2       14   \n",
-        "8   Saido Berahino\\n Forward \u2014 West Brom   13.8     21      9        0   \n",
-        "9  Steven Gerrard\\n Midfield \u2014 Liverpool   13.8     20      5        1   \n",
-        "\n",
-        "   shots_on_target  points_per_game  points position team  \n",
-        "7               10            10.47  209.49                \n",
-        "8               20             7.02  147.43                \n",
-        "9               11             7.50  150.01                "
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Processing `player` column\n",
-      "\n",
-      "def process_player_col(text):\n",
-      "    name, rest = text.split('\\n')\n",
-      "    position, team = [x.strip() for x in rest.split(' \u2014 ')]\n",
-      "    return pd.Series([name, team, position])\n",
-      "\n",
-      "df[['player', 'team', 'position']] = df.player.apply(process_player_col)\n",
-      "\n",
-      "# modified after tip from reddit.com/user/hharison\n",
-      "#\n",
-      "# Alternative (inferior) approach:\n",
-      "#\n",
-      "#for idx,row in df.iterrows():\n",
-      "#    name, position, team = process_player_col(row['player'])\n",
-      "#    df.ix[idx, 'player'], df.ix[idx, 'position'], df.ix[idx, 'team'] = name, position, team\n",
-      "    \n",
-      "df.tail(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td>  Cesc F\u00e0bregas</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td>   Chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td> Saido Berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td> 9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td>  Forward</td>\n",
-        "      <td> West Brom</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td> Steven Gerrard</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "      <td> Midfield</td>\n",
-        "      <td> Liverpool</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 7,
-       "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "7   Cesc F\u00e0bregas   14.0     20      2       14               10   \n",
-        "8  Saido Berahino   13.8     21      9        0               20   \n",
-        "9  Steven Gerrard   13.8     20      5        1               11   \n",
-        "\n",
-        "   points_per_game  points  position       team  \n",
-        "7            10.47  209.49  Midfield    Chelsea  \n",
-        "8             7.02  147.43   Forward  West Brom  \n",
-        "9             7.50  150.01  Midfield  Liverpool  "
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Applying Functions to Multiple Columns"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "cols = ['player', 'position', 'team']\n",
-      "df[cols] = df[cols].applymap(lambda x: x.lower())\n",
-      "df.head()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td>  sergio ag\u00fcero</td>\n",
-        "      <td> 19.2</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td>  3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>   manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>    eden hazard</td>\n",
-        "      <td> 18.9</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  8</td>\n",
-        "      <td>  4</td>\n",
-        "      <td> 17</td>\n",
-        "      <td> 13.05</td>\n",
-        "      <td> 274.04</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>           chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> alexis s\u00e1nchez</td>\n",
-        "      <td> 17.6</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 12</td>\n",
-        "      <td>  7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>           arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td>     yaya tour\u00e9</td>\n",
-        "      <td> 16.6</td>\n",
-        "      <td> 18</td>\n",
-        "      <td>  7</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 19</td>\n",
-        "      <td> 10.99</td>\n",
-        "      <td> 197.91</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>   manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td> \u00e1ngel di mar\u00eda</td>\n",
-        "      <td> 15.0</td>\n",
-        "      <td> 13</td>\n",
-        "      <td>  3</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 13</td>\n",
-        "      <td> 10.17</td>\n",
-        "      <td> 132.23</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> manchester united</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
-        "1     eden hazard   18.9     21      8        4               17   \n",
-        "2  alexis s\u00e1nchez   17.6    NaN     12        7               29   \n",
-        "3      yaya tour\u00e9   16.6     18      7        1               19   \n",
-        "4  \u00e1ngel di mar\u00eda   15.0     13      3      NaN               13   \n",
-        "\n",
-        "   points_per_game  points  position               team  \n",
-        "0            13.12  209.98   forward    manchester city  \n",
-        "1            13.05  274.04  midfield            chelsea  \n",
-        "2            11.19  223.86   forward            arsenal  \n",
-        "3            10.99  197.91  midfield    manchester city  \n",
-        "4            10.17  132.23  midfield  manchester united  "
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Missing Values aka NaNs"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Counting Rows with NaNs"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "nans = df.shape[0] - df.dropna().shape[0]\n",
-      "\n",
-      "print('%d rows have missing values' % nans)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3 rows have missing values\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Selecting NaN Rows"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Selecting all rows that have NaNs in the `assists` column\n",
-      "\n",
-      "df[df['assists'].isnull()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td>   \u00e1ngel di mar\u00eda</td>\n",
-        "      <td> 15.0</td>\n",
-        "      <td> 13</td>\n",
-        "      <td> 3</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 13</td>\n",
-        "      <td> 10.17</td>\n",
-        "      <td> 132.23</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> manchester united</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td> santiago cazorla</td>\n",
-        "      <td> 14.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 4</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>    NaN</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>           arsenal</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 10,
-       "text": [
-        "             player salary  games  goals  assists  shots_on_target  \\\n",
-        "4    \u00e1ngel di mar\u00eda   15.0     13      3      NaN               13   \n",
-        "5  santiago cazorla   14.8     20      4      NaN               20   \n",
-        "\n",
-        "   points_per_game  points  position               team  \n",
-        "4            10.17  132.23  midfield  manchester united  \n",
-        "5             9.97     NaN  midfield            arsenal  "
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Selecting non-NaN Rows"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df[df['assists'].notnull()]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td>  sergio ag\u00fcero</td>\n",
-        "      <td> 19.2</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td>  3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>    eden hazard</td>\n",
-        "      <td> 18.9</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  8</td>\n",
-        "      <td>  4</td>\n",
-        "      <td> 17</td>\n",
-        "      <td> 13.05</td>\n",
-        "      <td> 274.04</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>         chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> alexis s\u00e1nchez</td>\n",
-        "      <td> 17.6</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td> 12</td>\n",
-        "      <td>  7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>         arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td>     yaya tour\u00e9</td>\n",
-        "      <td> 16.6</td>\n",
-        "      <td> 18</td>\n",
-        "      <td>  7</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 19</td>\n",
-        "      <td> 10.99</td>\n",
-        "      <td> 197.91</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>6</th>\n",
-        "      <td>    david silva</td>\n",
-        "      <td> 14.3</td>\n",
-        "      <td> 15</td>\n",
-        "      <td>  6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td>  cesc f\u00e0bregas</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>         chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td> saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>       west brom</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td> steven gerrard</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>       liverpool</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
-        "1     eden hazard   18.9     21      8        4               17   \n",
-        "2  alexis s\u00e1nchez   17.6    NaN     12        7               29   \n",
-        "3      yaya tour\u00e9   16.6     18      7        1               19   \n",
-        "6     david silva   14.3     15      6        2               11   \n",
-        "7   cesc f\u00e0bregas   14.0     20      2       14               10   \n",
-        "8  saido berahino   13.8     21      9        0               20   \n",
-        "9  steven gerrard   13.8     20      5        1               11   \n",
-        "\n",
-        "   points_per_game  points  position             team  \n",
-        "0            13.12  209.98   forward  manchester city  \n",
-        "1            13.05  274.04  midfield          chelsea  \n",
-        "2            11.19  223.86   forward          arsenal  \n",
-        "3            10.99  197.91  midfield  manchester city  \n",
-        "6            10.35  155.26  midfield  manchester city  \n",
-        "7            10.47  209.49  midfield          chelsea  \n",
-        "8             7.02  147.43   forward        west brom  \n",
-        "9             7.50  150.01  midfield        liverpool  "
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Filling NaN Rows"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Filling NaN cells with default value 0\n",
-      "\n",
-      "df.fillna(value=0, inplace=True)\n",
-      "df"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td>    sergio ag\u00fcero</td>\n",
-        "      <td> 19.2</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td>  3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>   manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>      eden hazard</td>\n",
-        "      <td> 18.9</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  8</td>\n",
-        "      <td>  4</td>\n",
-        "      <td> 17</td>\n",
-        "      <td> 13.05</td>\n",
-        "      <td> 274.04</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>           chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td>   alexis s\u00e1nchez</td>\n",
-        "      <td> 17.6</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td>  7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>           arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td>       yaya tour\u00e9</td>\n",
-        "      <td> 16.6</td>\n",
-        "      <td> 18</td>\n",
-        "      <td>  7</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 19</td>\n",
-        "      <td> 10.99</td>\n",
-        "      <td> 197.91</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>   manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td>   \u00e1ngel di mar\u00eda</td>\n",
-        "      <td> 15.0</td>\n",
-        "      <td> 13</td>\n",
-        "      <td>  3</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 13</td>\n",
-        "      <td> 10.17</td>\n",
-        "      <td> 132.23</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> manchester united</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td> santiago cazorla</td>\n",
-        "      <td> 14.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  4</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>   0.00</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>           arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>6</th>\n",
-        "      <td>      david silva</td>\n",
-        "      <td> 14.3</td>\n",
-        "      <td> 15</td>\n",
-        "      <td>  6</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 10.35</td>\n",
-        "      <td> 155.26</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>   manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td>    cesc f\u00e0bregas</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>           chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td>   saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>         west brom</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td>   steven gerrard</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td>  7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>         liverpool</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 12,
-       "text": [
-        "             player salary  games  goals  assists  shots_on_target  \\\n",
-        "0     sergio ag\u00fcero   19.2     16     14        3               34   \n",
-        "1       eden hazard   18.9     21      8        4               17   \n",
-        "2    alexis s\u00e1nchez   17.6      0     12        7               29   \n",
-        "3        yaya tour\u00e9   16.6     18      7        1               19   \n",
-        "4    \u00e1ngel di mar\u00eda   15.0     13      3        0               13   \n",
-        "5  santiago cazorla   14.8     20      4        0               20   \n",
-        "6       david silva   14.3     15      6        2               11   \n",
-        "7     cesc f\u00e0bregas   14.0     20      2       14               10   \n",
-        "8    saido berahino   13.8     21      9        0               20   \n",
-        "9    steven gerrard   13.8     20      5        1               11   \n",
-        "\n",
-        "   points_per_game  points  position               team  \n",
-        "0            13.12  209.98   forward    manchester city  \n",
-        "1            13.05  274.04  midfield            chelsea  \n",
-        "2            11.19  223.86   forward            arsenal  \n",
-        "3            10.99  197.91  midfield    manchester city  \n",
-        "4            10.17  132.23  midfield  manchester united  \n",
-        "5             9.97    0.00  midfield            arsenal  \n",
-        "6            10.35  155.26  midfield    manchester city  \n",
-        "7            10.47  209.49  midfield            chelsea  \n",
-        "8             7.02  147.43   forward          west brom  \n",
-        "9             7.50  150.01  midfield          liverpool  "
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Appending Rows to a DataFrame"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Adding an \"empty\" row to the DataFrame\n",
-      "\n",
-      "import numpy as np\n",
-      "\n",
-      "df = df.append(pd.Series(\n",
-      "                [np.nan]*len(df.columns), # Fill cells with NaNs\n",
-      "                index=df.columns),    \n",
-      "                ignore_index=True)\n",
-      "\n",
-      "df.tail(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>8 </th>\n",
-        "      <td> saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td> west brom</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9 </th>\n",
-        "      <td> steven gerrard</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> liverpool</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>10</th>\n",
-        "      <td>            NaN</td>\n",
-        "      <td>  NaN</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>  NaN</td>\n",
-        "      <td>    NaN</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td>       NaN</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "            player salary  games  goals  assists  shots_on_target  \\\n",
-        "8   saido berahino   13.8     21      9        0               20   \n",
-        "9   steven gerrard   13.8     20      5        1               11   \n",
-        "10             NaN    NaN    NaN    NaN      NaN              NaN   \n",
-        "\n",
-        "    points_per_game  points  position       team  \n",
-        "8              7.02  147.43   forward  west brom  \n",
-        "9              7.50  150.01  midfield  liverpool  \n",
-        "10              NaN     NaN       NaN        NaN  "
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Filling cells with data\n",
-      "\n",
-      "df.loc[df.index[-1], 'player'] = 'new player'\n",
-      "df.loc[df.index[-1], 'salary'] = 12.3\n",
-      "df.tail(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>8 </th>\n",
-        "      <td> saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td> 7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td> west brom</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9 </th>\n",
-        "      <td> steven gerrard</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  5</td>\n",
-        "      <td>  1</td>\n",
-        "      <td> 11</td>\n",
-        "      <td> 7.50</td>\n",
-        "      <td> 150.01</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> liverpool</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>10</th>\n",
-        "      <td>     new player</td>\n",
-        "      <td> 12.3</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>NaN</td>\n",
-        "      <td>  NaN</td>\n",
-        "      <td>    NaN</td>\n",
-        "      <td>      NaN</td>\n",
-        "      <td>       NaN</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 14,
-       "text": [
-        "            player salary  games  goals  assists  shots_on_target  \\\n",
-        "8   saido berahino   13.8     21      9        0               20   \n",
-        "9   steven gerrard   13.8     20      5        1               11   \n",
-        "10      new player   12.3    NaN    NaN      NaN              NaN   \n",
-        "\n",
-        "    points_per_game  points  position       team  \n",
-        "8              7.02  147.43   forward  west brom  \n",
-        "9              7.50  150.01  midfield  liverpool  \n",
-        "10              NaN     NaN       NaN        NaN  "
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Sorting and Reindexing DataFrames"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Sorting the DataFrame by a certain column (from highest to lowest)\n",
-      "\n",
-      "df.sort('goals', ascending=False, inplace=True)\n",
-      "df.head()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td>  sergio ag\u00fcero</td>\n",
-        "      <td> 19.2</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> alexis s\u00e1nchez</td>\n",
-        "      <td> 17.6</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td> 7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>         arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>8</th>\n",
-        "      <td> saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td> 0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>       west brom</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>    eden hazard</td>\n",
-        "      <td> 18.9</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  8</td>\n",
-        "      <td> 4</td>\n",
-        "      <td> 17</td>\n",
-        "      <td> 13.05</td>\n",
-        "      <td> 274.04</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>         chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td>     yaya tour\u00e9</td>\n",
-        "      <td> 16.6</td>\n",
-        "      <td> 18</td>\n",
-        "      <td>  7</td>\n",
-        "      <td> 1</td>\n",
-        "      <td> 19</td>\n",
-        "      <td> 10.99</td>\n",
-        "      <td> 197.91</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 15,
-       "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "0   sergio ag\u00fcero   19.2     16     14        3               34   \n",
-        "2  alexis s\u00e1nchez   17.6      0     12        7               29   \n",
-        "8  saido berahino   13.8     21      9        0               20   \n",
-        "1     eden hazard   18.9     21      8        4               17   \n",
-        "3      yaya tour\u00e9   16.6     18      7        1               19   \n",
-        "\n",
-        "   points_per_game  points  position             team  \n",
-        "0            13.12  209.98   forward  manchester city  \n",
-        "2            11.19  223.86   forward          arsenal  \n",
-        "8             7.02  147.43   forward        west brom  \n",
-        "1            13.05  274.04  midfield          chelsea  \n",
-        "3            10.99  197.91  midfield  manchester city  "
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Optional reindexing of the DataFrame after sorting\n",
-      "\n",
-      "df.index = range(1,len(df.index)+1)\n",
-      "df.head()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>  sergio ag\u00fcero</td>\n",
-        "      <td> 19.2</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> alexis s\u00e1nchez</td>\n",
-        "      <td> 17.6</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td> 7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>         arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td> saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td> 0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>       west brom</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td>    eden hazard</td>\n",
-        "      <td> 18.9</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  8</td>\n",
-        "      <td> 4</td>\n",
-        "      <td> 17</td>\n",
-        "      <td> 13.05</td>\n",
-        "      <td> 274.04</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>         chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>5</th>\n",
-        "      <td>     yaya tour\u00e9</td>\n",
-        "      <td> 16.6</td>\n",
-        "      <td> 18</td>\n",
-        "      <td>  7</td>\n",
-        "      <td> 1</td>\n",
-        "      <td> 19</td>\n",
-        "      <td> 10.99</td>\n",
-        "      <td> 197.91</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 16,
-       "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "1   sergio ag\u00fcero   19.2     16     14        3               34   \n",
-        "2  alexis s\u00e1nchez   17.6      0     12        7               29   \n",
-        "3  saido berahino   13.8     21      9        0               20   \n",
-        "4     eden hazard   18.9     21      8        4               17   \n",
-        "5      yaya tour\u00e9   16.6     18      7        1               19   \n",
-        "\n",
-        "   points_per_game  points  position             team  \n",
-        "1            13.12  209.98   forward  manchester city  \n",
-        "2            11.19  223.86   forward          arsenal  \n",
-        "3             7.02  147.43   forward        west brom  \n",
-        "4            13.05  274.04  midfield          chelsea  \n",
-        "5            10.99  197.91  midfield  manchester city  "
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Updating Columns"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Creating a dummy DataFrame with changes in the `salary` column\n",
-      "\n",
-      "df_2 = df.copy()\n",
-      "df_2.loc[0:2, 'salary'] = [20.0, 15.0]\n",
-      "df_2.head(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>  sergio ag\u00fcero</td>\n",
-        "      <td>   20</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td> forward</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> alexis s\u00e1nchez</td>\n",
-        "      <td>   15</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td> 7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td> forward</td>\n",
-        "      <td>         arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td> saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td> 0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td> forward</td>\n",
-        "      <td>       west brom</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "1   sergio ag\u00fcero     20     16     14        3               34   \n",
-        "2  alexis s\u00e1nchez     15      0     12        7               29   \n",
-        "3  saido berahino   13.8     21      9        0               20   \n",
-        "\n",
-        "   points_per_game  points position             team  \n",
-        "1            13.12  209.98  forward  manchester city  \n",
-        "2            11.19  223.86  forward          arsenal  \n",
-        "3             7.02  147.43  forward        west brom  "
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Temporarily use the `player` columns as indices to \n",
-      "# apply the update functions\n",
-      "\n",
-      "df.set_index('player', inplace=True)\n",
-      "df_2.set_index('player', inplace=True)\n",
-      "df.head(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>player</th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>sergio ag\u00fcero</th>\n",
-        "      <td> 19.2</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td> forward</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>alexis s\u00e1nchez</th>\n",
-        "      <td> 17.6</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td> 7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td> forward</td>\n",
-        "      <td>         arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>saido berahino</th>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td> 0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td> forward</td>\n",
-        "      <td>       west brom</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "               salary  games  goals  assists  shots_on_target  \\\n",
-        "player                                                          \n",
-        "sergio ag\u00fcero    19.2     16     14        3               34   \n",
-        "alexis s\u00e1nchez   17.6      0     12        7               29   \n",
-        "saido berahino   13.8     21      9        0               20   \n",
-        "\n",
-        "                points_per_game  points position             team  \n",
-        "player                                                             \n",
-        "sergio ag\u00fcero             13.12  209.98  forward  manchester city  \n",
-        "alexis s\u00e1nchez            11.19  223.86  forward          arsenal  \n",
-        "saido berahino             7.02  147.43  forward        west brom  "
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Update the `salary` column\n",
-      "df.update(other=df_2['salary'], overwrite=True)\n",
-      "df.head(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>player</th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "      <th></th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>sergio ag\u00fcero</th>\n",
-        "      <td>   20</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td> forward</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>alexis s\u00e1nchez</th>\n",
-        "      <td>   15</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td> 7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td> forward</td>\n",
-        "      <td>         arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>saido berahino</th>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td> 0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td> forward</td>\n",
-        "      <td>       west brom</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 19,
-       "text": [
-        "               salary  games  goals  assists  shots_on_target  \\\n",
-        "player                                                          \n",
-        "sergio ag\u00fcero      20     16     14        3               34   \n",
-        "alexis s\u00e1nchez     15      0     12        7               29   \n",
-        "saido berahino   13.8     21      9        0               20   \n",
-        "\n",
-        "                points_per_game  points position             team  \n",
-        "player                                                             \n",
-        "sergio ag\u00fcero             13.12  209.98  forward  manchester city  \n",
-        "alexis s\u00e1nchez            11.19  223.86  forward          arsenal  \n",
-        "saido berahino             7.02  147.43  forward        west brom  "
-       ]
-      }
-     ],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Reset the indices\n",
-      "df.reset_index(inplace=True)\n",
-      "df.head(3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td>  sergio ag\u00fcero</td>\n",
-        "      <td>   20</td>\n",
-        "      <td> 16</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 3</td>\n",
-        "      <td> 34</td>\n",
-        "      <td> 13.12</td>\n",
-        "      <td> 209.98</td>\n",
-        "      <td> forward</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td> alexis s\u00e1nchez</td>\n",
-        "      <td>   15</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td> 7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td> forward</td>\n",
-        "      <td>         arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  9</td>\n",
-        "      <td> 0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  7.02</td>\n",
-        "      <td> 147.43</td>\n",
-        "      <td> forward</td>\n",
-        "      <td>       west brom</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 20,
-       "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "0   sergio ag\u00fcero     20     16     14        3               34   \n",
-        "1  alexis s\u00e1nchez     15      0     12        7               29   \n",
-        "2  saido berahino   13.8     21      9        0               20   \n",
-        "\n",
-        "   points_per_game  points position             team  \n",
-        "0            13.12  209.98  forward  manchester city  \n",
-        "1            11.19  223.86  forward          arsenal  \n",
-        "2             7.02  147.43  forward        west brom  "
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Chaining Conditions - Using Bitwise Operators"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Selecting only those players that either playing for Arsenal or Chelsea\n",
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Back to the GitHub repository](https://github.com/rasbt/python_reference)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sebastian Raschka 28/01/2015 \n",
       "\n",
-      "df[ (df['team'] == 'arsenal') | (df['team'] == 'chelsea') ]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td>   alexis s\u00e1nchez</td>\n",
-        "      <td>   15</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td>  7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td> arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td>      eden hazard</td>\n",
-        "      <td> 18.9</td>\n",
-        "      <td> 21</td>\n",
-        "      <td>  8</td>\n",
-        "      <td>  4</td>\n",
-        "      <td> 17</td>\n",
-        "      <td> 13.05</td>\n",
-        "      <td> 274.04</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>7</th>\n",
-        "      <td> santiago cazorla</td>\n",
-        "      <td> 14.8</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  4</td>\n",
-        "      <td>  0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  9.97</td>\n",
-        "      <td>   0.00</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>9</th>\n",
-        "      <td>    cesc f\u00e0bregas</td>\n",
-        "      <td> 14.0</td>\n",
-        "      <td> 20</td>\n",
-        "      <td>  2</td>\n",
-        "      <td> 14</td>\n",
-        "      <td> 10</td>\n",
-        "      <td> 10.47</td>\n",
-        "      <td> 209.49</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> chelsea</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 21,
-       "text": [
-        "             player salary  games  goals  assists  shots_on_target  \\\n",
-        "1    alexis s\u00e1nchez     15      0     12        7               29   \n",
-        "3       eden hazard   18.9     21      8        4               17   \n",
-        "7  santiago cazorla   14.8     20      4        0               20   \n",
-        "9     cesc f\u00e0bregas   14.0     20      2       14               10   \n",
-        "\n",
-        "   points_per_game  points  position     team  \n",
-        "1            11.19  223.86   forward  arsenal  \n",
-        "3            13.05  274.04  midfield  chelsea  \n",
-        "7             9.97    0.00  midfield  arsenal  \n",
-        "9            10.47  209.49  midfield  chelsea  "
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Selecting forwards from Arsenal only\n",
+      "CPython 3.4.2\n",
+      "IPython 2.3.1\n",
       "\n",
-      "df[ (df['team'] == 'arsenal') & (df['position'] == 'forward') ]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>games</th>\n",
-        "      <th>goals</th>\n",
-        "      <th>assists</th>\n",
-        "      <th>shots_on_target</th>\n",
-        "      <th>points_per_game</th>\n",
-        "      <th>points</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td> alexis s\u00e1nchez</td>\n",
-        "      <td> 15</td>\n",
-        "      <td> 0</td>\n",
-        "      <td> 12</td>\n",
-        "      <td> 7</td>\n",
-        "      <td> 29</td>\n",
-        "      <td> 11.19</td>\n",
-        "      <td> 223.86</td>\n",
-        "      <td> forward</td>\n",
-        "      <td> arsenal</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 22,
-       "text": [
-        "           player salary  games  goals  assists  shots_on_target  \\\n",
-        "1  alexis s\u00e1nchez     15      0     12        7               29   \n",
-        "\n",
-        "   points_per_game  points position     team  \n",
-        "1            11.19  223.86  forward  arsenal  "
-       ]
-      }
-     ],
-     "prompt_number": 22
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Column Types"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to section overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Printing Column Types"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "types = df.columns.to_series().groupby(df.dtypes).groups\n",
-      "types"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 23,
-       "text": [
-        "{dtype('float64'): ['games',\n",
-        "  'goals',\n",
-        "  'assists',\n",
-        "  'shots_on_target',\n",
-        "  'points_per_game',\n",
-        "  'points'],\n",
-        " dtype('O'): ['player', 'salary', 'position', 'team']}"
-       ]
-      }
-     ],
-     "prompt_number": 23
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+      "pandas 0.15.2\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Selecting by Column Type"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# select string columns\n",
-      "df.loc[:, (df.dtypes == np.dtype('O')).values].head()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>player</th>\n",
-        "      <th>salary</th>\n",
-        "      <th>position</th>\n",
-        "      <th>team</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>0</th>\n",
-        "      <td>  sergio ag\u00fcero</td>\n",
-        "      <td>   20</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>1</th>\n",
-        "      <td> alexis s\u00e1nchez</td>\n",
-        "      <td>   15</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>         arsenal</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>2</th>\n",
-        "      <td> saido berahino</td>\n",
-        "      <td> 13.8</td>\n",
-        "      <td>  forward</td>\n",
-        "      <td>       west brom</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>3</th>\n",
-        "      <td>    eden hazard</td>\n",
-        "      <td> 18.9</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td>         chelsea</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>4</th>\n",
-        "      <td>     yaya tour\u00e9</td>\n",
-        "      <td> 16.6</td>\n",
-        "      <td> midfield</td>\n",
-        "      <td> manchester city</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 24,
-       "text": [
-        "           player salary  position             team\n",
-        "0   sergio ag\u00fcero     20   forward  manchester city\n",
-        "1  alexis s\u00e1nchez     15   forward          arsenal\n",
-        "2  saido berahino   13.8   forward        west brom\n",
-        "3     eden hazard   18.9  midfield          chelsea\n",
-        "4      yaya tour\u00e9   16.6  midfield  manchester city"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "Converting Column Types"
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "%watermark -a 'Sebastian Raschka' -v -d -p pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Things in Pandas I Wish I'd Known Earlier"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is just a small but growing collection of pandas snippets that I find occasionally and particularly useful -- consider it as my personal notebook. Suggestions, tips, and contributions are very, very welcome!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [Loading Some Example Data](#Loading-Some-Example-Data)\n",
+    "- [Renaming Columns](#Renaming-Columns)\n",
+    "    - [Converting Column Names to Lowercase](#Converting-Column-Names-to-Lowercase)\n",
+    "    - [Renaming Particular Columns](#Renaming-Particular-Columns)\n",
+    "- [Applying Computations Rows-wise](#Applying-Computations-Rows-wise)\n",
+    "    - [Changing Values in a Column](#Changing-Values-in-a-Column)\n",
+    "    - [Adding a New Column](#Adding-a-New-Column)\n",
+    "    - [Applying Functions to Multiple Columns](#Applying-Functions-to-Multiple-Columns)\n",
+    "- [Missing Values aka NaNs](#Missing-Values-aka-NaNs)\n",
+    "    - [Counting Rows with NaNs](#Counting-Rows-with-NaNs)\n",
+    "    - [Selecting NaN Rows](#Selecting-NaN-Rows)\n",
+    "    - [Selecting non-NaN Rows](#Selecting-non-NaN-Rows)\n",
+    "    - [Filling NaN Rows](#Filling-NaN-Rows)\n",
+    "- [Appending Rows to a DataFrame](#Appending-Rows-to-a-DataFrame)\n",
+    "- [Sorting and Reindexing DataFrames](#Sorting-and-Reindexing-DataFrames)\n",
+    "- [Updating Columns](#Updating-Columns)\n",
+    "- [Chaining Conditions - Using Bitwise Operators](#Chaining-Conditions---Using-Bitwise-Operators)\n",
+    "- [Column Types](#Column-Types)\n",
+    "    - [Printing Column Types](#Printing-Column-Types)\n",
+    "    - [Selecting by Column Type](#Selecting-by-Column-Type)\n",
+    "    - [Converting Column Types](#Converting-Column-Types)\n",
+    "- [If-tests](#If-tests)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Loading Some Example Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I am heavily into sports prediction (via a machine learning approach) these days. So, let us use a (very) small subset of the soccer data that I am just working with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PLAYER</th>\n",
+       "      <th>SALARY</th>\n",
+       "      <th>GP</th>\n",
+       "      <th>G</th>\n",
+       "      <th>A</th>\n",
+       "      <th>SOT</th>\n",
+       "      <th>PPG</th>\n",
+       "      <th>P</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>     Sergio Agüero\\n Forward — Manchester City</td>\n",
+       "      <td> $19.2m</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td>  3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>              Eden Hazard\\n Midfield — Chelsea</td>\n",
+       "      <td> $18.9m</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  8</td>\n",
+       "      <td>  4</td>\n",
+       "      <td> 17</td>\n",
+       "      <td> 13.05</td>\n",
+       "      <td> 274.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>            Alexis Sánchez\\n Forward — Arsenal</td>\n",
+       "      <td> $17.6m</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 12</td>\n",
+       "      <td>  7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>       Yaya Touré\\n Midfield — Manchester City</td>\n",
+       "      <td> $16.6m</td>\n",
+       "      <td> 18</td>\n",
+       "      <td>  7</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 19</td>\n",
+       "      <td> 10.99</td>\n",
+       "      <td> 197.91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td> Ángel Di María\\n Midfield — Manchester United</td>\n",
+       "      <td> $15.0m</td>\n",
+       "      <td> 13</td>\n",
+       "      <td>  3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 13</td>\n",
+       "      <td> 10.17</td>\n",
+       "      <td> 132.23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>         Santiago Cazorla\\n Midfield — Arsenal</td>\n",
+       "      <td> $14.8m</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  9.97</td>\n",
+       "      <td>    NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>      David Silva\\n Midfield — Manchester City</td>\n",
+       "      <td> $14.3m</td>\n",
+       "      <td> 15</td>\n",
+       "      <td>  6</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 11</td>\n",
+       "      <td> 10.35</td>\n",
+       "      <td> 155.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>            Cesc Fàbregas\\n Midfield — Chelsea</td>\n",
+       "      <td> $14.0m</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>          Saido Berahino\\n Forward — West Brom</td>\n",
+       "      <td> $13.8m</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>         Steven Gerrard\\n Midfield — Liverpool</td>\n",
+       "      <td> $13.8m</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td>  7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          PLAYER  SALARY  GP   G   A  SOT  \\\n",
+       "0      Sergio Agüero\\n Forward — Manchester City  $19.2m  16  14   3   34   \n",
+       "1               Eden Hazard\\n Midfield — Chelsea  $18.9m  21   8   4   17   \n",
+       "2             Alexis Sánchez\\n Forward — Arsenal  $17.6m NaN  12   7   29   \n",
+       "3        Yaya Touré\\n Midfield — Manchester City  $16.6m  18   7   1   19   \n",
+       "4  Ángel Di María\\n Midfield — Manchester United  $15.0m  13   3 NaN   13   \n",
+       "5          Santiago Cazorla\\n Midfield — Arsenal  $14.8m  20   4 NaN   20   \n",
+       "6       David Silva\\n Midfield — Manchester City  $14.3m  15   6   2   11   \n",
+       "7             Cesc Fàbregas\\n Midfield — Chelsea  $14.0m  20   2  14   10   \n",
+       "8           Saido Berahino\\n Forward — West Brom  $13.8m  21   9   0   20   \n",
+       "9          Steven Gerrard\\n Midfield — Liverpool  $13.8m  20   5   1   11   \n",
+       "\n",
+       "     PPG       P  \n",
+       "0  13.12  209.98  \n",
+       "1  13.05  274.04  \n",
+       "2  11.19  223.86  \n",
+       "3  10.99  197.91  \n",
+       "4  10.17  132.23  \n",
+       "5   9.97     NaN  \n",
+       "6  10.35  155.26  \n",
+       "7  10.47  209.49  \n",
+       "8   7.02  147.43  \n",
+       "9   7.50  150.01  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('https://raw.githubusercontent.com/rasbt/python_reference/master/Data/some_soccer_data.csv')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Renaming Columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Converting Column Names to Lowercase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>gp</th>\n",
+       "      <th>g</th>\n",
+       "      <th>a</th>\n",
+       "      <th>sot</th>\n",
+       "      <th>ppg</th>\n",
+       "      <th>p</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>    Cesc Fàbregas\\n Midfield — Chelsea</td>\n",
+       "      <td> $14.0m</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>  Saido Berahino\\n Forward — West Brom</td>\n",
+       "      <td> $13.8m</td>\n",
+       "      <td> 21</td>\n",
+       "      <td> 9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td> Steven Gerrard\\n Midfield — Liverpool</td>\n",
+       "      <td> $13.8m</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td>  7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  player  salary  gp  g   a  sot    ppg  \\\n",
+       "7     Cesc Fàbregas\\n Midfield — Chelsea  $14.0m  20  2  14   10  10.47   \n",
+       "8   Saido Berahino\\n Forward — West Brom  $13.8m  21  9   0   20   7.02   \n",
+       "9  Steven Gerrard\\n Midfield — Liverpool  $13.8m  20  5   1   11   7.50   \n",
+       "\n",
+       "        p  \n",
+       "7  209.49  \n",
+       "8  147.43  \n",
+       "9  150.01  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Converting column names to lowercase\n",
+    "\n",
+    "df.columns = [c.lower() for c in df.columns]\n",
+    "\n",
+    "# or\n",
+    "# df.rename(columns=lambda x : x.lower())\n",
+    "\n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Renaming Particular Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>    Cesc Fàbregas\\n Midfield — Chelsea</td>\n",
+       "      <td> $14.0m</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>  Saido Berahino\\n Forward — West Brom</td>\n",
+       "      <td> $13.8m</td>\n",
+       "      <td> 21</td>\n",
+       "      <td> 9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td> Steven Gerrard\\n Midfield — Liverpool</td>\n",
+       "      <td> $13.8m</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td>  7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  player  salary  games  goals  assists  \\\n",
+       "7     Cesc Fàbregas\\n Midfield — Chelsea  $14.0m     20      2       14   \n",
+       "8   Saido Berahino\\n Forward — West Brom  $13.8m     21      9        0   \n",
+       "9  Steven Gerrard\\n Midfield — Liverpool  $13.8m     20      5        1   \n",
+       "\n",
+       "   shots_on_target  points_per_game  points  \n",
+       "7               10            10.47  209.49  \n",
+       "8               20             7.02  147.43  \n",
+       "9               11             7.50  150.01  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.rename(columns={'p': 'points', \n",
+    "                        'gp': 'games',\n",
+    "                        'sot': 'shots_on_target',\n",
+    "                        'g': 'goals',\n",
+    "                        'ppg': 'points_per_game',\n",
+    "                        'a': 'assists',})\n",
+    "\n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Applying Computations Rows-wise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Changing Values in a Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>    Santiago Cazorla\\n Midfield — Arsenal</td>\n",
+       "      <td> 14.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  9.97</td>\n",
+       "      <td>    NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td> David Silva\\n Midfield — Manchester City</td>\n",
+       "      <td> 14.3</td>\n",
+       "      <td> 15</td>\n",
+       "      <td> 6</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 11</td>\n",
+       "      <td> 10.35</td>\n",
+       "      <td> 155.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>       Cesc Fàbregas\\n Midfield — Chelsea</td>\n",
+       "      <td> 14.0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>     Saido Berahino\\n Forward — West Brom</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td> 9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>    Steven Gerrard\\n Midfield — Liverpool</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td>  7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     player salary  games  goals  assists  \\\n",
+       "5     Santiago Cazorla\\n Midfield — Arsenal   14.8     20      4      NaN   \n",
+       "6  David Silva\\n Midfield — Manchester City   14.3     15      6        2   \n",
+       "7        Cesc Fàbregas\\n Midfield — Chelsea   14.0     20      2       14   \n",
+       "8      Saido Berahino\\n Forward — West Brom   13.8     21      9        0   \n",
+       "9     Steven Gerrard\\n Midfield — Liverpool   13.8     20      5        1   \n",
+       "\n",
+       "   shots_on_target  points_per_game  points  \n",
+       "5               20             9.97     NaN  \n",
+       "6               11            10.35  155.26  \n",
+       "7               10            10.47  209.49  \n",
+       "8               20             7.02  147.43  \n",
+       "9               11             7.50  150.01  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Processing `salary` column\n",
+    "\n",
+    "df['salary'] = df['salary'].apply(lambda x: x.strip('$m'))\n",
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Adding a New Column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>    Cesc Fàbregas\\n Midfield — Chelsea</td>\n",
+       "      <td> 14.0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "      <td> </td>\n",
+       "      <td> </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>  Saido Berahino\\n Forward — West Brom</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td> 9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td> </td>\n",
+       "      <td> </td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td> Steven Gerrard\\n Midfield — Liverpool</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td>  7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "      <td> </td>\n",
+       "      <td> </td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  player salary  games  goals  assists  \\\n",
+       "7     Cesc Fàbregas\\n Midfield — Chelsea   14.0     20      2       14   \n",
+       "8   Saido Berahino\\n Forward — West Brom   13.8     21      9        0   \n",
+       "9  Steven Gerrard\\n Midfield — Liverpool   13.8     20      5        1   \n",
+       "\n",
+       "   shots_on_target  points_per_game  points position team  \n",
+       "7               10            10.47  209.49                \n",
+       "8               20             7.02  147.43                \n",
+       "9               11             7.50  150.01                "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['team'] = pd.Series('', index=df.index)\n",
+    "\n",
+    "# or\n",
+    "df.insert(loc=8, column='position', value='') \n",
+    "\n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>  Cesc Fàbregas</td>\n",
+       "      <td> 14.0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "      <td> Midfield</td>\n",
+       "      <td>   Chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td> Saido Berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td> 9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td>  Forward</td>\n",
+       "      <td> West Brom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td> Steven Gerrard</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td>  7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "      <td> Midfield</td>\n",
+       "      <td> Liverpool</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  games  goals  assists  shots_on_target  \\\n",
+       "7   Cesc Fàbregas   14.0     20      2       14               10   \n",
+       "8  Saido Berahino   13.8     21      9        0               20   \n",
+       "9  Steven Gerrard   13.8     20      5        1               11   \n",
+       "\n",
+       "   points_per_game  points  position       team  \n",
+       "7            10.47  209.49  Midfield    Chelsea  \n",
+       "8             7.02  147.43   Forward  West Brom  \n",
+       "9             7.50  150.01  Midfield  Liverpool  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Processing `player` column\n",
+    "\n",
+    "def process_player_col(text):\n",
+    "    name, rest = text.split('\\n')\n",
+    "    position, team = [x.strip() for x in rest.split(' — ')]\n",
+    "    return pd.Series([name, team, position])\n",
+    "\n",
+    "df[['player', 'team', 'position']] = df.player.apply(process_player_col)\n",
+    "\n",
+    "# modified after tip from reddit.com/user/hharison\n",
+    "#\n",
+    "# Alternative (inferior) approach:\n",
+    "#\n",
+    "#for idx,row in df.iterrows():\n",
+    "#    name, position, team = process_player_col(row['player'])\n",
+    "#    df.ix[idx, 'player'], df.ix[idx, 'position'], df.ix[idx, 'team'] = name, position, team\n",
+    "    \n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Applying Functions to Multiple Columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>  sergio agüero</td>\n",
+       "      <td> 19.2</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td>  3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>   manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>    eden hazard</td>\n",
+       "      <td> 18.9</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  8</td>\n",
+       "      <td>  4</td>\n",
+       "      <td> 17</td>\n",
+       "      <td> 13.05</td>\n",
+       "      <td> 274.04</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>           chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> alexis sánchez</td>\n",
+       "      <td> 17.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 12</td>\n",
+       "      <td>  7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>           arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>     yaya touré</td>\n",
+       "      <td> 16.6</td>\n",
+       "      <td> 18</td>\n",
+       "      <td>  7</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 19</td>\n",
+       "      <td> 10.99</td>\n",
+       "      <td> 197.91</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>   manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td> ángel di maría</td>\n",
+       "      <td> 15.0</td>\n",
+       "      <td> 13</td>\n",
+       "      <td>  3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 13</td>\n",
+       "      <td> 10.17</td>\n",
+       "      <td> 132.23</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> manchester united</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  games  goals  assists  shots_on_target  \\\n",
+       "0   sergio agüero   19.2     16     14        3               34   \n",
+       "1     eden hazard   18.9     21      8        4               17   \n",
+       "2  alexis sánchez   17.6    NaN     12        7               29   \n",
+       "3      yaya touré   16.6     18      7        1               19   \n",
+       "4  ángel di maría   15.0     13      3      NaN               13   \n",
+       "\n",
+       "   points_per_game  points  position               team  \n",
+       "0            13.12  209.98   forward    manchester city  \n",
+       "1            13.05  274.04  midfield            chelsea  \n",
+       "2            11.19  223.86   forward            arsenal  \n",
+       "3            10.99  197.91  midfield    manchester city  \n",
+       "4            10.17  132.23  midfield  manchester united  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cols = ['player', 'position', 'team']\n",
+    "df[cols] = df[cols].applymap(lambda x: x.lower())\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Missing Values aka NaNs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Counting Rows with NaNs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 rows have missing values\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "df['salary'] = df['salary'].astype(float)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "types = df.columns.to_series().groupby(df.dtypes).groups\n",
-      "types"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 26,
-       "text": [
-        "{dtype('float64'): ['salary',\n",
-        "  'games',\n",
-        "  'goals',\n",
-        "  'assists',\n",
-        "  'shots_on_target',\n",
-        "  'points_per_game',\n",
-        "  'points'],\n",
-        " dtype('O'): ['player', 'position', 'team']}"
-       ]
-      }
-     ],
-     "prompt_number": 26
     }
    ],
-   "metadata": {}
+   "source": [
+    "nans = df.shape[0] - df.dropna().shape[0]\n",
+    "\n",
+    "print('%d rows have missing values' % nans)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting NaN Rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>   ángel di maría</td>\n",
+       "      <td> 15.0</td>\n",
+       "      <td> 13</td>\n",
+       "      <td> 3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 13</td>\n",
+       "      <td> 10.17</td>\n",
+       "      <td> 132.23</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> manchester united</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td> santiago cazorla</td>\n",
+       "      <td> 14.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  9.97</td>\n",
+       "      <td>    NaN</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>           arsenal</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             player salary  games  goals  assists  shots_on_target  \\\n",
+       "4    ángel di maría   15.0     13      3      NaN               13   \n",
+       "5  santiago cazorla   14.8     20      4      NaN               20   \n",
+       "\n",
+       "   points_per_game  points  position               team  \n",
+       "4            10.17  132.23  midfield  manchester united  \n",
+       "5             9.97     NaN  midfield            arsenal  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Selecting all rows that have NaNs in the `assists` column\n",
+    "\n",
+    "df[df['assists'].isnull()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting non-NaN Rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>  sergio agüero</td>\n",
+       "      <td> 19.2</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td>  3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>    eden hazard</td>\n",
+       "      <td> 18.9</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  8</td>\n",
+       "      <td>  4</td>\n",
+       "      <td> 17</td>\n",
+       "      <td> 13.05</td>\n",
+       "      <td> 274.04</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>         chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> alexis sánchez</td>\n",
+       "      <td> 17.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td> 12</td>\n",
+       "      <td>  7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>         arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>     yaya touré</td>\n",
+       "      <td> 16.6</td>\n",
+       "      <td> 18</td>\n",
+       "      <td>  7</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 19</td>\n",
+       "      <td> 10.99</td>\n",
+       "      <td> 197.91</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>    david silva</td>\n",
+       "      <td> 14.3</td>\n",
+       "      <td> 15</td>\n",
+       "      <td>  6</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 11</td>\n",
+       "      <td> 10.35</td>\n",
+       "      <td> 155.26</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>  cesc fàbregas</td>\n",
+       "      <td> 14.0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>         chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td> saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>       west brom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td> steven gerrard</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td>  7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>       liverpool</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  games  goals  assists  shots_on_target  \\\n",
+       "0   sergio agüero   19.2     16     14        3               34   \n",
+       "1     eden hazard   18.9     21      8        4               17   \n",
+       "2  alexis sánchez   17.6    NaN     12        7               29   \n",
+       "3      yaya touré   16.6     18      7        1               19   \n",
+       "6     david silva   14.3     15      6        2               11   \n",
+       "7   cesc fàbregas   14.0     20      2       14               10   \n",
+       "8  saido berahino   13.8     21      9        0               20   \n",
+       "9  steven gerrard   13.8     20      5        1               11   \n",
+       "\n",
+       "   points_per_game  points  position             team  \n",
+       "0            13.12  209.98   forward  manchester city  \n",
+       "1            13.05  274.04  midfield          chelsea  \n",
+       "2            11.19  223.86   forward          arsenal  \n",
+       "3            10.99  197.91  midfield  manchester city  \n",
+       "6            10.35  155.26  midfield  manchester city  \n",
+       "7            10.47  209.49  midfield          chelsea  \n",
+       "8             7.02  147.43   forward        west brom  \n",
+       "9             7.50  150.01  midfield        liverpool  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['assists'].notnull()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Filling NaN Rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>    sergio agüero</td>\n",
+       "      <td> 19.2</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td>  3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>   manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>      eden hazard</td>\n",
+       "      <td> 18.9</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  8</td>\n",
+       "      <td>  4</td>\n",
+       "      <td> 17</td>\n",
+       "      <td> 13.05</td>\n",
+       "      <td> 274.04</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>           chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>   alexis sánchez</td>\n",
+       "      <td> 17.6</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td>  7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>           arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>       yaya touré</td>\n",
+       "      <td> 16.6</td>\n",
+       "      <td> 18</td>\n",
+       "      <td>  7</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 19</td>\n",
+       "      <td> 10.99</td>\n",
+       "      <td> 197.91</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>   manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>   ángel di maría</td>\n",
+       "      <td> 15.0</td>\n",
+       "      <td> 13</td>\n",
+       "      <td>  3</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 13</td>\n",
+       "      <td> 10.17</td>\n",
+       "      <td> 132.23</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> manchester united</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td> santiago cazorla</td>\n",
+       "      <td> 14.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  4</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  9.97</td>\n",
+       "      <td>   0.00</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>           arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>      david silva</td>\n",
+       "      <td> 14.3</td>\n",
+       "      <td> 15</td>\n",
+       "      <td>  6</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 11</td>\n",
+       "      <td> 10.35</td>\n",
+       "      <td> 155.26</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>   manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>    cesc fàbregas</td>\n",
+       "      <td> 14.0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>           chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>   saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>         west brom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>   steven gerrard</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td>  7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>         liverpool</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             player salary  games  goals  assists  shots_on_target  \\\n",
+       "0     sergio agüero   19.2     16     14        3               34   \n",
+       "1       eden hazard   18.9     21      8        4               17   \n",
+       "2    alexis sánchez   17.6      0     12        7               29   \n",
+       "3        yaya touré   16.6     18      7        1               19   \n",
+       "4    ángel di maría   15.0     13      3        0               13   \n",
+       "5  santiago cazorla   14.8     20      4        0               20   \n",
+       "6       david silva   14.3     15      6        2               11   \n",
+       "7     cesc fàbregas   14.0     20      2       14               10   \n",
+       "8    saido berahino   13.8     21      9        0               20   \n",
+       "9    steven gerrard   13.8     20      5        1               11   \n",
+       "\n",
+       "   points_per_game  points  position               team  \n",
+       "0            13.12  209.98   forward    manchester city  \n",
+       "1            13.05  274.04  midfield            chelsea  \n",
+       "2            11.19  223.86   forward            arsenal  \n",
+       "3            10.99  197.91  midfield    manchester city  \n",
+       "4            10.17  132.23  midfield  manchester united  \n",
+       "5             9.97    0.00  midfield            arsenal  \n",
+       "6            10.35  155.26  midfield    manchester city  \n",
+       "7            10.47  209.49  midfield            chelsea  \n",
+       "8             7.02  147.43   forward          west brom  \n",
+       "9             7.50  150.01  midfield          liverpool  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filling NaN cells with default value 0\n",
+    "\n",
+    "df.fillna(value=0, inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Appending Rows to a DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8 </th>\n",
+       "      <td> saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td> west brom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9 </th>\n",
+       "      <td> steven gerrard</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td> 7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> liverpool</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>            NaN</td>\n",
+       "      <td>  NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>  NaN</td>\n",
+       "      <td>    NaN</td>\n",
+       "      <td>      NaN</td>\n",
+       "      <td>       NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            player salary  games  goals  assists  shots_on_target  \\\n",
+       "8   saido berahino   13.8     21      9        0               20   \n",
+       "9   steven gerrard   13.8     20      5        1               11   \n",
+       "10             NaN    NaN    NaN    NaN      NaN              NaN   \n",
+       "\n",
+       "    points_per_game  points  position       team  \n",
+       "8              7.02  147.43   forward  west brom  \n",
+       "9              7.50  150.01  midfield  liverpool  \n",
+       "10              NaN     NaN       NaN        NaN  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Adding an \"empty\" row to the DataFrame\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "df = df.append(pd.Series(\n",
+    "                [np.nan]*len(df.columns), # Fill cells with NaNs\n",
+    "                index=df.columns),    \n",
+    "                ignore_index=True)\n",
+    "\n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8 </th>\n",
+       "      <td> saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td> 7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td> west brom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9 </th>\n",
+       "      <td> steven gerrard</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  5</td>\n",
+       "      <td>  1</td>\n",
+       "      <td> 11</td>\n",
+       "      <td> 7.50</td>\n",
+       "      <td> 150.01</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> liverpool</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>     new player</td>\n",
+       "      <td> 12.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>  NaN</td>\n",
+       "      <td>    NaN</td>\n",
+       "      <td>      NaN</td>\n",
+       "      <td>       NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            player salary  games  goals  assists  shots_on_target  \\\n",
+       "8   saido berahino   13.8     21      9        0               20   \n",
+       "9   steven gerrard   13.8     20      5        1               11   \n",
+       "10      new player   12.3    NaN    NaN      NaN              NaN   \n",
+       "\n",
+       "    points_per_game  points  position       team  \n",
+       "8              7.02  147.43   forward  west brom  \n",
+       "9              7.50  150.01  midfield  liverpool  \n",
+       "10              NaN     NaN       NaN        NaN  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filling cells with data\n",
+    "\n",
+    "df.loc[df.index[-1], 'player'] = 'new player'\n",
+    "df.loc[df.index[-1], 'salary'] = 12.3\n",
+    "df.tail(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sorting and Reindexing DataFrames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>  sergio agüero</td>\n",
+       "      <td> 19.2</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> alexis sánchez</td>\n",
+       "      <td> 17.6</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>         arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td> saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>       west brom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>    eden hazard</td>\n",
+       "      <td> 18.9</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  8</td>\n",
+       "      <td> 4</td>\n",
+       "      <td> 17</td>\n",
+       "      <td> 13.05</td>\n",
+       "      <td> 274.04</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>         chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>     yaya touré</td>\n",
+       "      <td> 16.6</td>\n",
+       "      <td> 18</td>\n",
+       "      <td>  7</td>\n",
+       "      <td> 1</td>\n",
+       "      <td> 19</td>\n",
+       "      <td> 10.99</td>\n",
+       "      <td> 197.91</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  games  goals  assists  shots_on_target  \\\n",
+       "0   sergio agüero   19.2     16     14        3               34   \n",
+       "2  alexis sánchez   17.6      0     12        7               29   \n",
+       "8  saido berahino   13.8     21      9        0               20   \n",
+       "1     eden hazard   18.9     21      8        4               17   \n",
+       "3      yaya touré   16.6     18      7        1               19   \n",
+       "\n",
+       "   points_per_game  points  position             team  \n",
+       "0            13.12  209.98   forward  manchester city  \n",
+       "2            11.19  223.86   forward          arsenal  \n",
+       "8             7.02  147.43   forward        west brom  \n",
+       "1            13.05  274.04  midfield          chelsea  \n",
+       "3            10.99  197.91  midfield  manchester city  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sorting the DataFrame by a certain column (from highest to lowest)\n",
+    "\n",
+    "df.sort('goals', ascending=False, inplace=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>  sergio agüero</td>\n",
+       "      <td> 19.2</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> alexis sánchez</td>\n",
+       "      <td> 17.6</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>         arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td> saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>       west brom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>    eden hazard</td>\n",
+       "      <td> 18.9</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  8</td>\n",
+       "      <td> 4</td>\n",
+       "      <td> 17</td>\n",
+       "      <td> 13.05</td>\n",
+       "      <td> 274.04</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>         chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>     yaya touré</td>\n",
+       "      <td> 16.6</td>\n",
+       "      <td> 18</td>\n",
+       "      <td>  7</td>\n",
+       "      <td> 1</td>\n",
+       "      <td> 19</td>\n",
+       "      <td> 10.99</td>\n",
+       "      <td> 197.91</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  games  goals  assists  shots_on_target  \\\n",
+       "1   sergio agüero   19.2     16     14        3               34   \n",
+       "2  alexis sánchez   17.6      0     12        7               29   \n",
+       "3  saido berahino   13.8     21      9        0               20   \n",
+       "4     eden hazard   18.9     21      8        4               17   \n",
+       "5      yaya touré   16.6     18      7        1               19   \n",
+       "\n",
+       "   points_per_game  points  position             team  \n",
+       "1            13.12  209.98   forward  manchester city  \n",
+       "2            11.19  223.86   forward          arsenal  \n",
+       "3             7.02  147.43   forward        west brom  \n",
+       "4            13.05  274.04  midfield          chelsea  \n",
+       "5            10.99  197.91  midfield  manchester city  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Optional reindexing of the DataFrame after sorting\n",
+    "\n",
+    "df.index = range(1,len(df.index)+1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Updating Columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>  sergio agüero</td>\n",
+       "      <td>   20</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td> forward</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> alexis sánchez</td>\n",
+       "      <td>   15</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td> forward</td>\n",
+       "      <td>         arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td> saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td> forward</td>\n",
+       "      <td>       west brom</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  games  goals  assists  shots_on_target  \\\n",
+       "1   sergio agüero     20     16     14        3               34   \n",
+       "2  alexis sánchez     15      0     12        7               29   \n",
+       "3  saido berahino   13.8     21      9        0               20   \n",
+       "\n",
+       "   points_per_game  points position             team  \n",
+       "1            13.12  209.98  forward  manchester city  \n",
+       "2            11.19  223.86  forward          arsenal  \n",
+       "3             7.02  147.43  forward        west brom  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Creating a dummy DataFrame with changes in the `salary` column\n",
+    "\n",
+    "df_2 = df.copy()\n",
+    "df_2.loc[0:2, 'salary'] = [20.0, 15.0]\n",
+    "df_2.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>player</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sergio agüero</th>\n",
+       "      <td> 19.2</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td> forward</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alexis sánchez</th>\n",
+       "      <td> 17.6</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td> forward</td>\n",
+       "      <td>         arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>saido berahino</th>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td> forward</td>\n",
+       "      <td>       west brom</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               salary  games  goals  assists  shots_on_target  \\\n",
+       "player                                                          \n",
+       "sergio agüero    19.2     16     14        3               34   \n",
+       "alexis sánchez   17.6      0     12        7               29   \n",
+       "saido berahino   13.8     21      9        0               20   \n",
+       "\n",
+       "                points_per_game  points position             team  \n",
+       "player                                                             \n",
+       "sergio agüero             13.12  209.98  forward  manchester city  \n",
+       "alexis sánchez            11.19  223.86  forward          arsenal  \n",
+       "saido berahino             7.02  147.43  forward        west brom  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Temporarily use the `player` columns as indices to \n",
+    "# apply the update functions\n",
+    "\n",
+    "df.set_index('player', inplace=True)\n",
+    "df_2.set_index('player', inplace=True)\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>player</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>sergio agüero</th>\n",
+       "      <td>   20</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td> forward</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alexis sánchez</th>\n",
+       "      <td>   15</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td> forward</td>\n",
+       "      <td>         arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>saido berahino</th>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td> forward</td>\n",
+       "      <td>       west brom</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               salary  games  goals  assists  shots_on_target  \\\n",
+       "player                                                          \n",
+       "sergio agüero      20     16     14        3               34   \n",
+       "alexis sánchez     15      0     12        7               29   \n",
+       "saido berahino   13.8     21      9        0               20   \n",
+       "\n",
+       "                points_per_game  points position             team  \n",
+       "player                                                             \n",
+       "sergio agüero             13.12  209.98  forward  manchester city  \n",
+       "alexis sánchez            11.19  223.86  forward          arsenal  \n",
+       "saido berahino             7.02  147.43  forward        west brom  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Update the `salary` column\n",
+    "df.update(other=df_2['salary'], overwrite=True)\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>  sergio agüero</td>\n",
+       "      <td>   20</td>\n",
+       "      <td> 16</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 3</td>\n",
+       "      <td> 34</td>\n",
+       "      <td> 13.12</td>\n",
+       "      <td> 209.98</td>\n",
+       "      <td> forward</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td> alexis sánchez</td>\n",
+       "      <td>   15</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td> forward</td>\n",
+       "      <td>         arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  9</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  7.02</td>\n",
+       "      <td> 147.43</td>\n",
+       "      <td> forward</td>\n",
+       "      <td>       west brom</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  games  goals  assists  shots_on_target  \\\n",
+       "0   sergio agüero     20     16     14        3               34   \n",
+       "1  alexis sánchez     15      0     12        7               29   \n",
+       "2  saido berahino   13.8     21      9        0               20   \n",
+       "\n",
+       "   points_per_game  points position             team  \n",
+       "0            13.12  209.98  forward  manchester city  \n",
+       "1            11.19  223.86  forward          arsenal  \n",
+       "2             7.02  147.43  forward        west brom  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Reset the indices\n",
+    "df.reset_index(inplace=True)\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chaining Conditions - Using Bitwise Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>   alexis sánchez</td>\n",
+       "      <td>   15</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td>  7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td> arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>      eden hazard</td>\n",
+       "      <td> 18.9</td>\n",
+       "      <td> 21</td>\n",
+       "      <td>  8</td>\n",
+       "      <td>  4</td>\n",
+       "      <td> 17</td>\n",
+       "      <td> 13.05</td>\n",
+       "      <td> 274.04</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td> santiago cazorla</td>\n",
+       "      <td> 14.8</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  4</td>\n",
+       "      <td>  0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  9.97</td>\n",
+       "      <td>   0.00</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>    cesc fàbregas</td>\n",
+       "      <td> 14.0</td>\n",
+       "      <td> 20</td>\n",
+       "      <td>  2</td>\n",
+       "      <td> 14</td>\n",
+       "      <td> 10</td>\n",
+       "      <td> 10.47</td>\n",
+       "      <td> 209.49</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> chelsea</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             player salary  games  goals  assists  shots_on_target  \\\n",
+       "1    alexis sánchez     15      0     12        7               29   \n",
+       "3       eden hazard   18.9     21      8        4               17   \n",
+       "7  santiago cazorla   14.8     20      4        0               20   \n",
+       "9     cesc fàbregas   14.0     20      2       14               10   \n",
+       "\n",
+       "   points_per_game  points  position     team  \n",
+       "1            11.19  223.86   forward  arsenal  \n",
+       "3            13.05  274.04  midfield  chelsea  \n",
+       "7             9.97    0.00  midfield  arsenal  \n",
+       "9            10.47  209.49  midfield  chelsea  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Selecting only those players that either playing for Arsenal or Chelsea\n",
+    "\n",
+    "df[ (df['team'] == 'arsenal') | (df['team'] == 'chelsea') ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>games</th>\n",
+       "      <th>goals</th>\n",
+       "      <th>assists</th>\n",
+       "      <th>shots_on_target</th>\n",
+       "      <th>points_per_game</th>\n",
+       "      <th>points</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td> alexis sánchez</td>\n",
+       "      <td> 15</td>\n",
+       "      <td> 0</td>\n",
+       "      <td> 12</td>\n",
+       "      <td> 7</td>\n",
+       "      <td> 29</td>\n",
+       "      <td> 11.19</td>\n",
+       "      <td> 223.86</td>\n",
+       "      <td> forward</td>\n",
+       "      <td> arsenal</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  games  goals  assists  shots_on_target  \\\n",
+       "1  alexis sánchez     15      0     12        7               29   \n",
+       "\n",
+       "   points_per_game  points position     team  \n",
+       "1            11.19  223.86  forward  arsenal  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Selecting forwards from Arsenal only\n",
+    "\n",
+    "df[ (df['team'] == 'arsenal') & (df['position'] == 'forward') ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Column Types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Printing Column Types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{dtype('float64'): ['games',\n",
+       "  'goals',\n",
+       "  'assists',\n",
+       "  'shots_on_target',\n",
+       "  'points_per_game',\n",
+       "  'points'],\n",
+       " dtype('O'): ['player', 'salary', 'position', 'team']}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "types = df.columns.to_series().groupby(df.dtypes).groups\n",
+    "types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selecting by Column Type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>player</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>position</th>\n",
+       "      <th>team</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>  sergio agüero</td>\n",
+       "      <td>   20</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td> alexis sánchez</td>\n",
+       "      <td>   15</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>         arsenal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td> saido berahino</td>\n",
+       "      <td> 13.8</td>\n",
+       "      <td>  forward</td>\n",
+       "      <td>       west brom</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>    eden hazard</td>\n",
+       "      <td> 18.9</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td>         chelsea</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>     yaya touré</td>\n",
+       "      <td> 16.6</td>\n",
+       "      <td> midfield</td>\n",
+       "      <td> manchester city</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           player salary  position             team\n",
+       "0   sergio agüero     20   forward  manchester city\n",
+       "1  alexis sánchez     15   forward          arsenal\n",
+       "2  saido berahino   13.8   forward        west brom\n",
+       "3     eden hazard   18.9  midfield          chelsea\n",
+       "4      yaya touré   16.6  midfield  manchester city"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# select string columns\n",
+    "df.loc[:, (df.dtypes == np.dtype('O')).values].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Converting Column Types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "df['salary'] = df['salary'].astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{dtype('float64'): ['salary',\n",
+       "  'games',\n",
+       "  'goals',\n",
+       "  'assists',\n",
+       "  'shots_on_target',\n",
+       "  'points_per_game',\n",
+       "  'points'],\n",
+       " dtype('O'): ['player', 'position', 'team']}"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "types = df.columns.to_series().groupby(df.dtypes).groups\n",
+    "types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# If-tests"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to section overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I was recently asked how to do an if-test in pandas, that is, how to create an array of 1s and 0s depending on a condition, e.g., if `val` less than 0.5 -> 0, else -> 1. Using the boolean mask, that's pretty simple since `True` and `False` are integers after all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>4.00</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     0     1     2  3\n",
+       "0  2.0  0.30  4.00  5\n",
+       "1  0.8  0.03  0.02  5"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "a = [[2., .3, 4., 5.], [.8, .03, 0.02, 5.]]\n",
+    "df = pd.DataFrame(a)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       0      1      2      3\n",
+       "0  False  False  False  False\n",
+       "1  False   True   True  False"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df <= 0.05\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0  1  2  3\n",
+       "0  0  0  0  0\n",
+       "1  0  1  1  0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.4.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

From 3fc278cae3ca6645bab14d30ee24e089371a3dd2 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Wed, 27 Jan 2016 22:54:10 -0500
Subject: [PATCH 230/248] remove redundant files

---
 ...ey_differences_between_python_2_and_3.html | 4242 -----------------
 .../key_differences_between_python_2_and_3.md |  416 --
 tutorials/scope_resolution_legb_rule.md       |  579 ---
 tutorials/table_of_contents_ipython.md        |  125 -
 4 files changed, 5362 deletions(-)
 delete mode 100644 tutorials/key_differences_between_python_2_and_3.html
 delete mode 100644 tutorials/key_differences_between_python_2_and_3.md
 delete mode 100644 tutorials/scope_resolution_legb_rule.md
 delete mode 100644 tutorials/table_of_contents_ipython.md

diff --git a/tutorials/key_differences_between_python_2_and_3.html b/tutorials/key_differences_between_python_2_and_3.html
deleted file mode 100644
index 2a5a0c5..0000000
--- a/tutorials/key_differences_between_python_2_and_3.html
+++ /dev/null
@@ -1,4242 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-
-<meta charset="utf-8" />
-<title>Notebook</title>
-
-<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>
-
-<style type="text/css">
-    .clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:"";line-height:0}
-.clearfix:after{clear:both}
-.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}
-.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
-article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}
-audio,canvas,video{display:inline-block;*display:inline;*zoom:1}
-audio:not([controls]){display:none}
-html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}
-a:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
-a:hover,a:active{outline:0}
-sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}
-sup{top:-0.5em}
-sub{bottom:-0.25em}
-img{max-width:100%;width:auto\9;height:auto;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}
-#map_canvas img,.google-maps img{max-width:none}
-button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}
-button,input{*overflow:visible;line-height:normal}
-button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}
-button,html input[type="button"],input[type="reset"],input[type="submit"]{-webkit-appearance:button;cursor:pointer}
-label,select,button,input[type="button"],input[type="reset"],input[type="submit"],input[type="radio"],input[type="checkbox"]{cursor:pointer}
-input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}
-input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}
-textarea{overflow:auto;vertical-align:top}
-@media print{*{text-shadow:none !important;color:#000 !important;background:transparent !important;box-shadow:none !important} a,a:visited{text-decoration:underline} a[href]:after{content:" (" attr(href) ")"} abbr[title]:after{content:" (" attr(title) ")"} .ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""} pre,blockquote{border:1px solid #999;page-break-inside:avoid} thead{display:table-header-group} tr,img{page-break-inside:avoid} img{max-width:100% !important} @page {margin:.5cm}p,h2,h3{orphans:3;widows:3} h2,h3{page-break-after:avoid}}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;line-height:20px;color:#000;background-color:#fff}
-a{color:#08c;text-decoration:none}
-a:hover,a:focus{color:#005580;text-decoration:underline}
-.img-rounded{border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px rgba(0,0,0,0.1)}
-.img-circle{border-radius:500px;-webkit-border-radius:500px;-moz-border-radius:500px;border-radius:500px}
-.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0}
-.row:after{clear:both}
-[class*="span"]{float:left;min-height:1px;margin-left:20px}
-.container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}
-.span12{width:940px}
-.span11{width:860px}
-.span10{width:780px}
-.span9{width:700px}
-.span8{width:620px}
-.span7{width:540px}
-.span6{width:460px}
-.span5{width:380px}
-.span4{width:300px}
-.span3{width:220px}
-.span2{width:140px}
-.span1{width:60px}
-.offset12{margin-left:980px}
-.offset11{margin-left:900px}
-.offset10{margin-left:820px}
-.offset9{margin-left:740px}
-.offset8{margin-left:660px}
-.offset7{margin-left:580px}
-.offset6{margin-left:500px}
-.offset5{margin-left:420px}
-.offset4{margin-left:340px}
-.offset3{margin-left:260px}
-.offset2{margin-left:180px}
-.offset1{margin-left:100px}
-.row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0}
-.row-fluid:after{clear:both}
-.row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.127659574468085%;*margin-left:2.074468085106383%}
-.row-fluid [class*="span"]:first-child{margin-left:0}
-.row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.127659574468085%}
-.row-fluid .span12{width:100%;*width:99.94680851063829%}
-.row-fluid .span11{width:91.48936170212765%;*width:91.43617021276594%}
-.row-fluid .span10{width:82.97872340425532%;*width:82.92553191489361%}
-.row-fluid .span9{width:74.46808510638297%;*width:74.41489361702126%}
-.row-fluid .span8{width:65.95744680851064%;*width:65.90425531914893%}
-.row-fluid .span7{width:57.44680851063829%;*width:57.39361702127659%}
-.row-fluid .span6{width:48.93617021276595%;*width:48.88297872340425%}
-.row-fluid .span5{width:40.42553191489362%;*width:40.37234042553192%}
-.row-fluid .span4{width:31.914893617021278%;*width:31.861702127659576%}
-.row-fluid .span3{width:23.404255319148934%;*width:23.351063829787233%}
-.row-fluid .span2{width:14.893617021276595%;*width:14.840425531914894%}
-.row-fluid .span1{width:6.382978723404255%;*width:6.329787234042553%}
-.row-fluid .offset12{margin-left:104.25531914893617%;*margin-left:104.14893617021275%}
-.row-fluid .offset12:first-child{margin-left:102.12765957446808%;*margin-left:102.02127659574467%}
-.row-fluid .offset11{margin-left:95.74468085106382%;*margin-left:95.6382978723404%}
-.row-fluid .offset11:first-child{margin-left:93.61702127659574%;*margin-left:93.51063829787232%}
-.row-fluid .offset10{margin-left:87.23404255319149%;*margin-left:87.12765957446807%}
-.row-fluid .offset10:first-child{margin-left:85.1063829787234%;*margin-left:84.99999999999999%}
-.row-fluid .offset9{margin-left:78.72340425531914%;*margin-left:78.61702127659572%}
-.row-fluid .offset9:first-child{margin-left:76.59574468085106%;*margin-left:76.48936170212764%}
-.row-fluid .offset8{margin-left:70.2127659574468%;*margin-left:70.10638297872339%}
-.row-fluid .offset8:first-child{margin-left:68.08510638297872%;*margin-left:67.9787234042553%}
-.row-fluid .offset7{margin-left:61.70212765957446%;*margin-left:61.59574468085106%}
-.row-fluid .offset7:first-child{margin-left:59.574468085106375%;*margin-left:59.46808510638297%}
-.row-fluid .offset6{margin-left:53.191489361702125%;*margin-left:53.085106382978715%}
-.row-fluid .offset6:first-child{margin-left:51.063829787234035%;*margin-left:50.95744680851063%}
-.row-fluid .offset5{margin-left:44.68085106382979%;*margin-left:44.57446808510638%}
-.row-fluid .offset5:first-child{margin-left:42.5531914893617%;*margin-left:42.4468085106383%}
-.row-fluid .offset4{margin-left:36.170212765957444%;*margin-left:36.06382978723405%}
-.row-fluid .offset4:first-child{margin-left:34.04255319148936%;*margin-left:33.93617021276596%}
-.row-fluid .offset3{margin-left:27.659574468085104%;*margin-left:27.5531914893617%}
-.row-fluid .offset3:first-child{margin-left:25.53191489361702%;*margin-left:25.425531914893618%}
-.row-fluid .offset2{margin-left:19.148936170212764%;*margin-left:19.04255319148936%}
-.row-fluid .offset2:first-child{margin-left:17.02127659574468%;*margin-left:16.914893617021278%}
-.row-fluid .offset1{margin-left:10.638297872340425%;*margin-left:10.53191489361702%}
-.row-fluid .offset1:first-child{margin-left:8.51063829787234%;*margin-left:8.404255319148938%}
-[class*="span"].hide,.row-fluid [class*="span"].hide{display:none}
-[class*="span"].pull-right,.row-fluid [class*="span"].pull-right{float:right}
-.container{margin-right:auto;margin-left:auto;*zoom:1}.container:before,.container:after{display:table;content:"";line-height:0}
-.container:after{clear:both}
-.container-fluid{padding-right:20px;padding-left:20px;*zoom:1}.container-fluid:before,.container-fluid:after{display:table;content:"";line-height:0}
-.container-fluid:after{clear:both}
-p{margin:0 0 10px}
-.lead{margin-bottom:20px;font-size:19.5px;font-weight:200;line-height:30px}
-small{font-size:85%}
-strong{font-weight:bold}
-em{font-style:italic}
-cite{font-style:normal}
-.muted{color:#999}
-a.muted:hover,a.muted:focus{color:#808080}
-.text-warning{color:#c09853}
-a.text-warning:hover,a.text-warning:focus{color:#a47e3c}
-.text-error{color:#b94a48}
-a.text-error:hover,a.text-error:focus{color:#953b39}
-.text-info{color:#3a87ad}
-a.text-info:hover,a.text-info:focus{color:#2d6987}
-.text-success{color:#468847}
-a.text-success:hover,a.text-success:focus{color:#356635}
-.text-left{text-align:left}
-.text-right{text-align:right}
-.text-center{text-align:center}
-h1,h2,h3,h4,h5,h6{margin:10px 0;font-family:inherit;font-weight:bold;line-height:20px;color:inherit;text-rendering:optimizelegibility}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small{font-weight:normal;line-height:1;color:#999}
-h1,h2,h3{line-height:40px}
-h1{font-size:35.75px}
-h2{font-size:29.25px}
-h3{font-size:22.75px}
-h4{font-size:16.25px}
-h5{font-size:13px}
-h6{font-size:11.049999999999999px}
-h1 small{font-size:22.75px}
-h2 small{font-size:16.25px}
-h3 small{font-size:13px}
-h4 small{font-size:13px}
-.page-header{padding-bottom:9px;margin:20px 0 30px;border-bottom:1px solid #eee}
-ul,ol{padding:0;margin:0 0 10px 25px}
-ul ul,ul ol,ol ol,ol ul{margin-bottom:0}
-li{line-height:20px}
-ul.unstyled,ol.unstyled{margin-left:0;list-style:none}
-ul.inline,ol.inline{margin-left:0;list-style:none}ul.inline>li,ol.inline>li{display:inline-block;*display:inline;*zoom:1;padding-left:5px;padding-right:5px}
-dl{margin-bottom:20px}
-dt,dd{line-height:20px}
-dt{font-weight:bold}
-dd{margin-left:10px}
-.dl-horizontal{*zoom:1}.dl-horizontal:before,.dl-horizontal:after{display:table;content:"";line-height:0}
-.dl-horizontal:after{clear:both}
-.dl-horizontal dt{float:left;width:160px;clear:left;text-align:right;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}
-.dl-horizontal dd{margin-left:180px}
-hr{margin:20px 0;border:0;border-top:1px solid #eee;border-bottom:1px solid #fff}
-abbr[title],abbr[data-original-title]{cursor:help;border-bottom:1px dotted #999}
-abbr.initialism{font-size:90%;text-transform:uppercase}
-blockquote{padding:0 0 0 15px;margin:0 0 20px;border-left:5px solid #eee}blockquote p{margin-bottom:0;font-size:16.25px;font-weight:300;line-height:1.25}
-blockquote small{display:block;line-height:20px;color:#999}blockquote small:before{content:'\2014 \00A0'}
-blockquote.pull-right{float:right;padding-right:15px;padding-left:0;border-right:5px solid #eee;border-left:0}blockquote.pull-right p,blockquote.pull-right small{text-align:right}
-blockquote.pull-right small:before{content:''}
-blockquote.pull-right small:after{content:'\00A0 \2014'}
-q:before,q:after,blockquote:before,blockquote:after{content:""}
-address{display:block;margin-bottom:20px;font-style:normal;line-height:20px}
-code,pre{padding:0 3px 2px;font-family:monospace;font-size:11px;color:#333;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-code{padding:2px 4px;color:#d14;background-color:#f7f7f9;border:1px solid #e1e1e8;white-space:nowrap}
-pre{display:block;padding:9.5px;margin:0 0 10px;font-size:12px;line-height:20px;word-break:break-all;word-wrap:break-word;white-space:pre;white-space:pre-wrap;background-color:#f5f5f5;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.15);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}pre.prettyprint{margin-bottom:20px}
-pre code{padding:0;color:inherit;white-space:pre;white-space:pre-wrap;background-color:transparent;border:0}
-.pre-scrollable{max-height:340px;overflow-y:scroll}
-form{margin:0 0 20px}
-fieldset{padding:0;margin:0;border:0}
-legend{display:block;width:100%;padding:0;margin-bottom:20px;font-size:19.5px;line-height:40px;color:#333;border:0;border-bottom:1px solid #e5e5e5}legend small{font-size:15px;color:#999}
-label,input,button,select,textarea{font-size:13px;font-weight:normal;line-height:20px}
-input,button,select,textarea{font-family:"Helvetica Neue",Helvetica,Arial,sans-serif}
-label{display:block;margin-bottom:5px}
-select,textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{display:inline-block;height:20px;padding:4px 6px;margin-bottom:10px;font-size:13px;line-height:20px;color:#555;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;vertical-align:middle}
-input,textarea,.uneditable-input{width:206px}
-textarea{height:auto}
-textarea,input[type="text"],input[type="password"],input[type="datetime"],input[type="datetime-local"],input[type="date"],input[type="month"],input[type="time"],input[type="week"],input[type="number"],input[type="email"],input[type="url"],input[type="search"],input[type="tel"],input[type="color"],.uneditable-input{background-color:#fff;border:1px solid #ccc;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-webkit-transition:border linear .2s, box-shadow linear .2s;-moz-transition:border linear .2s, box-shadow linear .2s;-o-transition:border linear .2s, box-shadow linear .2s;transition:border linear .2s, box-shadow linear .2s}textarea:focus,input[type="text"]:focus,input[type="password"]:focus,input[type="datetime"]:focus,input[type="datetime-local"]:focus,input[type="date"]:focus,input[type="month"]:focus,input[type="time"]:focus,input[type="week"]:focus,input[type="number"]:focus,input[type="email"]:focus,input[type="url"]:focus,input[type="search"]:focus,input[type="tel"]:focus,input[type="color"]:focus,.uneditable-input:focus{border-color:rgba(82,168,236,0.8);outline:0;outline:thin dotted \9;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6);box-shadow:inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(82,168,236,.6)}
-input[type="radio"],input[type="checkbox"]{margin:4px 0 0;*margin-top:0;margin-top:1px \9;line-height:normal}
-input[type="file"],input[type="image"],input[type="submit"],input[type="reset"],input[type="button"],input[type="radio"],input[type="checkbox"]{width:auto}
-select,input[type="file"]{height:30px;*margin-top:4px;line-height:30px}
-select{width:220px;border:1px solid #ccc;background-color:#fff}
-select[multiple],select[size]{height:auto}
-select:focus,input[type="file"]:focus,input[type="radio"]:focus,input[type="checkbox"]:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
-.uneditable-input,.uneditable-textarea{color:#999;background-color:#fcfcfc;border-color:#ccc;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);box-shadow:inset 0 1px 2px rgba(0,0,0,0.025);cursor:not-allowed}
-.uneditable-input{overflow:hidden;white-space:nowrap}
-.uneditable-textarea{width:auto;height:auto}
-input:-moz-placeholder,textarea:-moz-placeholder{color:#999}
-input:-ms-input-placeholder,textarea:-ms-input-placeholder{color:#999}
-input::-webkit-input-placeholder,textarea::-webkit-input-placeholder{color:#999}
-.radio,.checkbox{min-height:20px;padding-left:20px}
-.radio input[type="radio"],.checkbox input[type="checkbox"]{float:left;margin-left:-20px}
-.controls>.radio:first-child,.controls>.checkbox:first-child{padding-top:5px}
-.radio.inline,.checkbox.inline{display:inline-block;padding-top:5px;margin-bottom:0;vertical-align:middle}
-.radio.inline+.radio.inline,.checkbox.inline+.checkbox.inline{margin-left:10px}
-.input-mini{width:60px}
-.input-small{width:90px}
-.input-medium{width:150px}
-.input-large{width:210px}
-.input-xlarge{width:270px}
-.input-xxlarge{width:530px}
-input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"]{float:none;margin-left:0}
-.input-append input[class*="span"],.input-append .uneditable-input[class*="span"],.input-prepend input[class*="span"],.input-prepend .uneditable-input[class*="span"],.row-fluid input[class*="span"],.row-fluid select[class*="span"],.row-fluid textarea[class*="span"],.row-fluid .uneditable-input[class*="span"],.row-fluid .input-prepend [class*="span"],.row-fluid .input-append [class*="span"]{display:inline-block}
-input,textarea,.uneditable-input{margin-left:0}
-.controls-row [class*="span"]+[class*="span"]{margin-left:20px}
-input.span12,textarea.span12,.uneditable-input.span12{width:926px}
-input.span11,textarea.span11,.uneditable-input.span11{width:846px}
-input.span10,textarea.span10,.uneditable-input.span10{width:766px}
-input.span9,textarea.span9,.uneditable-input.span9{width:686px}
-input.span8,textarea.span8,.uneditable-input.span8{width:606px}
-input.span7,textarea.span7,.uneditable-input.span7{width:526px}
-input.span6,textarea.span6,.uneditable-input.span6{width:446px}
-input.span5,textarea.span5,.uneditable-input.span5{width:366px}
-input.span4,textarea.span4,.uneditable-input.span4{width:286px}
-input.span3,textarea.span3,.uneditable-input.span3{width:206px}
-input.span2,textarea.span2,.uneditable-input.span2{width:126px}
-input.span1,textarea.span1,.uneditable-input.span1{width:46px}
-.controls-row{*zoom:1}.controls-row:before,.controls-row:after{display:table;content:"";line-height:0}
-.controls-row:after{clear:both}
-.controls-row [class*="span"],.row-fluid .controls-row [class*="span"]{float:left}
-.controls-row .checkbox[class*="span"],.controls-row .radio[class*="span"]{padding-top:5px}
-input[disabled],select[disabled],textarea[disabled],input[readonly],select[readonly],textarea[readonly]{cursor:not-allowed;background-color:#eee}
-input[type="radio"][disabled],input[type="checkbox"][disabled],input[type="radio"][readonly],input[type="checkbox"][readonly]{background-color:transparent}
-.control-group.warning .control-label,.control-group.warning .help-block,.control-group.warning .help-inline{color:#c09853}
-.control-group.warning .checkbox,.control-group.warning .radio,.control-group.warning input,.control-group.warning select,.control-group.warning textarea{color:#c09853}
-.control-group.warning input,.control-group.warning select,.control-group.warning textarea{border-color:#c09853;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.warning input:focus,.control-group.warning select:focus,.control-group.warning textarea:focus{border-color:#a47e3c;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #dbc59e}
-.control-group.warning .input-prepend .add-on,.control-group.warning .input-append .add-on{color:#c09853;background-color:#fcf8e3;border-color:#c09853}
-.control-group.error .control-label,.control-group.error .help-block,.control-group.error .help-inline{color:#b94a48}
-.control-group.error .checkbox,.control-group.error .radio,.control-group.error input,.control-group.error select,.control-group.error textarea{color:#b94a48}
-.control-group.error input,.control-group.error select,.control-group.error textarea{border-color:#b94a48;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.error input:focus,.control-group.error select:focus,.control-group.error textarea:focus{border-color:#953b39;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #d59392}
-.control-group.error .input-prepend .add-on,.control-group.error .input-append .add-on{color:#b94a48;background-color:#f2dede;border-color:#b94a48}
-.control-group.success .control-label,.control-group.success .help-block,.control-group.success .help-inline{color:#468847}
-.control-group.success .checkbox,.control-group.success .radio,.control-group.success input,.control-group.success select,.control-group.success textarea{color:#468847}
-.control-group.success input,.control-group.success select,.control-group.success textarea{border-color:#468847;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.success input:focus,.control-group.success select:focus,.control-group.success textarea:focus{border-color:#356635;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7aba7b}
-.control-group.success .input-prepend .add-on,.control-group.success .input-append .add-on{color:#468847;background-color:#dff0d8;border-color:#468847}
-.control-group.info .control-label,.control-group.info .help-block,.control-group.info .help-inline{color:#3a87ad}
-.control-group.info .checkbox,.control-group.info .radio,.control-group.info input,.control-group.info select,.control-group.info textarea{color:#3a87ad}
-.control-group.info input,.control-group.info select,.control-group.info textarea{border-color:#3a87ad;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075);box-shadow:inset 0 1px 1px rgba(0,0,0,0.075)}.control-group.info input:focus,.control-group.info select:focus,.control-group.info textarea:focus{border-color:#2d6987;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3;box-shadow:inset 0 1px 1px rgba(0,0,0,0.075),0 0 6px #7ab5d3}
-.control-group.info .input-prepend .add-on,.control-group.info .input-append .add-on{color:#3a87ad;background-color:#d9edf7;border-color:#3a87ad}
-input:focus:invalid,textarea:focus:invalid,select:focus:invalid{color:#b94a48;border-color:#ee5f5b}input:focus:invalid:focus,textarea:focus:invalid:focus,select:focus:invalid:focus{border-color:#e9322d;-webkit-box-shadow:0 0 6px #f8b9b7;-moz-box-shadow:0 0 6px #f8b9b7;box-shadow:0 0 6px #f8b9b7}
-.form-actions{padding:19px 20px 20px;margin-top:20px;margin-bottom:20px;background-color:#f5f5f5;border-top:1px solid #e5e5e5;*zoom:1}.form-actions:before,.form-actions:after{display:table;content:"";line-height:0}
-.form-actions:after{clear:both}
-.help-block,.help-inline{color:#262626}
-.help-block{display:block;margin-bottom:10px}
-.help-inline{display:inline-block;*display:inline;*zoom:1;vertical-align:middle;padding-left:5px}
-.input-append,.input-prepend{display:inline-block;margin-bottom:10px;vertical-align:middle;font-size:0;white-space:nowrap}.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input,.input-append .dropdown-menu,.input-prepend .dropdown-menu,.input-append .popover,.input-prepend .popover{font-size:13px}
-.input-append input,.input-prepend input,.input-append select,.input-prepend select,.input-append .uneditable-input,.input-prepend .uneditable-input{position:relative;margin-bottom:0;*margin-left:0;vertical-align:top;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}.input-append input:focus,.input-prepend input:focus,.input-append select:focus,.input-prepend select:focus,.input-append .uneditable-input:focus,.input-prepend .uneditable-input:focus{z-index:2}
-.input-append .add-on,.input-prepend .add-on{display:inline-block;width:auto;height:20px;min-width:16px;padding:4px 5px;font-size:13px;font-weight:normal;line-height:20px;text-align:center;text-shadow:0 1px 0 #fff;background-color:#eee;border:1px solid #ccc}
-.input-append .add-on,.input-prepend .add-on,.input-append .btn,.input-prepend .btn,.input-append .btn-group>.dropdown-toggle,.input-prepend .btn-group>.dropdown-toggle{vertical-align:top;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.input-append .active,.input-prepend .active{background-color:#a9dba9;border-color:#46a546}
-.input-prepend .add-on,.input-prepend .btn{margin-right:-1px}
-.input-prepend .add-on:first-child,.input-prepend .btn:first-child{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
-.input-append input,.input-append select,.input-append .uneditable-input{border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}.input-append input+.btn-group .btn:last-child,.input-append select+.btn-group .btn:last-child,.input-append .uneditable-input+.btn-group .btn:last-child{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.input-append .add-on,.input-append .btn,.input-append .btn-group{margin-left:-1px}
-.input-append .add-on:last-child,.input-append .btn:last-child,.input-append .btn-group:last-child>.dropdown-toggle{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.input-prepend.input-append input,.input-prepend.input-append select,.input-prepend.input-append .uneditable-input{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}.input-prepend.input-append input+.btn-group .btn,.input-prepend.input-append select+.btn-group .btn,.input-prepend.input-append .uneditable-input+.btn-group .btn{border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.input-prepend.input-append .add-on:first-child,.input-prepend.input-append .btn:first-child{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
-.input-prepend.input-append .add-on:last-child,.input-prepend.input-append .btn:last-child{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.input-prepend.input-append .btn-group:first-child{margin-left:0}
-input.search-query{padding-right:14px;padding-right:4px \9;padding-left:14px;padding-left:4px \9;margin-bottom:0;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
-.form-search .input-append .search-query,.form-search .input-prepend .search-query{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.form-search .input-append .search-query{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
-.form-search .input-append .btn{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
-.form-search .input-prepend .search-query{border-radius:0 14px 14px 0;-webkit-border-radius:0 14px 14px 0;-moz-border-radius:0 14px 14px 0;border-radius:0 14px 14px 0}
-.form-search .input-prepend .btn{border-radius:14px 0 0 14px;-webkit-border-radius:14px 0 0 14px;-moz-border-radius:14px 0 0 14px;border-radius:14px 0 0 14px}
-.form-search input,.form-inline input,.form-horizontal input,.form-search textarea,.form-inline textarea,.form-horizontal textarea,.form-search select,.form-inline select,.form-horizontal select,.form-search .help-inline,.form-inline .help-inline,.form-horizontal .help-inline,.form-search .uneditable-input,.form-inline .uneditable-input,.form-horizontal .uneditable-input,.form-search .input-prepend,.form-inline .input-prepend,.form-horizontal .input-prepend,.form-search .input-append,.form-inline .input-append,.form-horizontal .input-append{display:inline-block;*display:inline;*zoom:1;margin-bottom:0;vertical-align:middle}
-.form-search .hide,.form-inline .hide,.form-horizontal .hide{display:none}
-.form-search label,.form-inline label,.form-search .btn-group,.form-inline .btn-group{display:inline-block}
-.form-search .input-append,.form-inline .input-append,.form-search .input-prepend,.form-inline .input-prepend{margin-bottom:0}
-.form-search .radio,.form-search .checkbox,.form-inline .radio,.form-inline .checkbox{padding-left:0;margin-bottom:0;vertical-align:middle}
-.form-search .radio input[type="radio"],.form-search .checkbox input[type="checkbox"],.form-inline .radio input[type="radio"],.form-inline .checkbox input[type="checkbox"]{float:left;margin-right:3px;margin-left:0}
-.control-group{margin-bottom:10px}
-legend+.control-group{margin-top:20px;-webkit-margin-top-collapse:separate}
-.form-horizontal .control-group{margin-bottom:20px;*zoom:1}.form-horizontal .control-group:before,.form-horizontal .control-group:after{display:table;content:"";line-height:0}
-.form-horizontal .control-group:after{clear:both}
-.form-horizontal .control-label{float:left;width:160px;padding-top:5px;text-align:right}
-.form-horizontal .controls{*display:inline-block;*padding-left:20px;margin-left:180px;*margin-left:0}.form-horizontal .controls:first-child{*padding-left:180px}
-.form-horizontal .help-block{margin-bottom:0}
-.form-horizontal input+.help-block,.form-horizontal select+.help-block,.form-horizontal textarea+.help-block,.form-horizontal .uneditable-input+.help-block,.form-horizontal .input-prepend+.help-block,.form-horizontal .input-append+.help-block{margin-top:10px}
-.form-horizontal .form-actions{padding-left:180px}
-table{max-width:100%;background-color:transparent;border-collapse:collapse;border-spacing:0}
-.table{width:100%;margin-bottom:20px}.table th,.table td{padding:8px;line-height:20px;text-align:left;vertical-align:top;border-top:1px solid #ddd}
-.table th{font-weight:bold}
-.table thead th{vertical-align:bottom}
-.table caption+thead tr:first-child th,.table caption+thead tr:first-child td,.table colgroup+thead tr:first-child th,.table colgroup+thead tr:first-child td,.table thead:first-child tr:first-child th,.table thead:first-child tr:first-child td{border-top:0}
-.table tbody+tbody{border-top:2px solid #ddd}
-.table .table{background-color:#fff}
-.table-condensed th,.table-condensed td{padding:4px 5px}
-.table-bordered{border:1px solid #ddd;border-collapse:separate;*border-collapse:collapse;border-left:0;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.table-bordered th,.table-bordered td{border-left:1px solid #ddd}
-.table-bordered caption+thead tr:first-child th,.table-bordered caption+tbody tr:first-child th,.table-bordered caption+tbody tr:first-child td,.table-bordered colgroup+thead tr:first-child th,.table-bordered colgroup+tbody tr:first-child th,.table-bordered colgroup+tbody tr:first-child td,.table-bordered thead:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child th,.table-bordered tbody:first-child tr:first-child td{border-top:0}
-.table-bordered thead:first-child tr:first-child>th:first-child,.table-bordered tbody:first-child tr:first-child>td:first-child,.table-bordered tbody:first-child tr:first-child>th:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
-.table-bordered thead:first-child tr:first-child>th:last-child,.table-bordered tbody:first-child tr:first-child>td:last-child,.table-bordered tbody:first-child tr:first-child>th:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
-.table-bordered thead:last-child tr:last-child>th:first-child,.table-bordered tbody:last-child tr:last-child>td:first-child,.table-bordered tbody:last-child tr:last-child>th:first-child,.table-bordered tfoot:last-child tr:last-child>td:first-child,.table-bordered tfoot:last-child tr:last-child>th:first-child{-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
-.table-bordered thead:last-child tr:last-child>th:last-child,.table-bordered tbody:last-child tr:last-child>td:last-child,.table-bordered tbody:last-child tr:last-child>th:last-child,.table-bordered tfoot:last-child tr:last-child>td:last-child,.table-bordered tfoot:last-child tr:last-child>th:last-child{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
-.table-bordered tfoot+tbody:last-child tr:last-child td:first-child{-webkit-border-bottom-left-radius:0;-moz-border-radius-bottomleft:0;border-bottom-left-radius:0}
-.table-bordered tfoot+tbody:last-child tr:last-child td:last-child{-webkit-border-bottom-right-radius:0;-moz-border-radius-bottomright:0;border-bottom-right-radius:0}
-.table-bordered caption+thead tr:first-child th:first-child,.table-bordered caption+tbody tr:first-child td:first-child,.table-bordered colgroup+thead tr:first-child th:first-child,.table-bordered colgroup+tbody tr:first-child td:first-child{-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
-.table-bordered caption+thead tr:first-child th:last-child,.table-bordered caption+tbody tr:first-child td:last-child,.table-bordered colgroup+thead tr:first-child th:last-child,.table-bordered colgroup+tbody tr:first-child td:last-child{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px}
-.table-striped tbody>tr:nth-child(odd)>td,.table-striped tbody>tr:nth-child(odd)>th{background-color:#f9f9f9}
-.table-hover tbody tr:hover>td,.table-hover tbody tr:hover>th{background-color:#f5f5f5}
-table td[class*="span"],table th[class*="span"],.row-fluid table td[class*="span"],.row-fluid table th[class*="span"]{display:table-cell;float:none;margin-left:0}
-.table td.span1,.table th.span1{float:none;width:44px;margin-left:0}
-.table td.span2,.table th.span2{float:none;width:124px;margin-left:0}
-.table td.span3,.table th.span3{float:none;width:204px;margin-left:0}
-.table td.span4,.table th.span4{float:none;width:284px;margin-left:0}
-.table td.span5,.table th.span5{float:none;width:364px;margin-left:0}
-.table td.span6,.table th.span6{float:none;width:444px;margin-left:0}
-.table td.span7,.table th.span7{float:none;width:524px;margin-left:0}
-.table td.span8,.table th.span8{float:none;width:604px;margin-left:0}
-.table td.span9,.table th.span9{float:none;width:684px;margin-left:0}
-.table td.span10,.table th.span10{float:none;width:764px;margin-left:0}
-.table td.span11,.table th.span11{float:none;width:844px;margin-left:0}
-.table td.span12,.table th.span12{float:none;width:924px;margin-left:0}
-.table tbody tr.success>td{background-color:#dff0d8}
-.table tbody tr.error>td{background-color:#f2dede}
-.table tbody tr.warning>td{background-color:#fcf8e3}
-.table tbody tr.info>td{background-color:#d9edf7}
-.table-hover tbody tr.success:hover>td{background-color:#d0e9c6}
-.table-hover tbody tr.error:hover>td{background-color:#ebcccc}
-.table-hover tbody tr.warning:hover>td{background-color:#faf2cc}
-.table-hover tbody tr.info:hover>td{background-color:#c4e3f3}
-[class^="icon-"],[class*=" icon-"]{display:inline-block;width:14px;height:14px;*margin-right:.3em;line-height:14px;vertical-align:text-top;background-image:url("../img/glyphicons-halflings.png");background-position:14px 14px;background-repeat:no-repeat;margin-top:1px}
-.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:focus>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>li>a:focus>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:focus>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"],.dropdown-submenu:focus>a>[class*=" icon-"]{background-image:url("../img/glyphicons-halflings-white.png")}
-.icon-glass{background-position:0 0}
-.icon-music{background-position:-24px 0}
-.icon-search{background-position:-48px 0}
-.icon-envelope{background-position:-72px 0}
-.icon-heart{background-position:-96px 0}
-.icon-star{background-position:-120px 0}
-.icon-star-empty{background-position:-144px 0}
-.icon-user{background-position:-168px 0}
-.icon-film{background-position:-192px 0}
-.icon-th-large{background-position:-216px 0}
-.icon-th{background-position:-240px 0}
-.icon-th-list{background-position:-264px 0}
-.icon-ok{background-position:-288px 0}
-.icon-remove{background-position:-312px 0}
-.icon-zoom-in{background-position:-336px 0}
-.icon-zoom-out{background-position:-360px 0}
-.icon-off{background-position:-384px 0}
-.icon-signal{background-position:-408px 0}
-.icon-cog{background-position:-432px 0}
-.icon-trash{background-position:-456px 0}
-.icon-home{background-position:0 -24px}
-.icon-file{background-position:-24px -24px}
-.icon-time{background-position:-48px -24px}
-.icon-road{background-position:-72px -24px}
-.icon-download-alt{background-position:-96px -24px}
-.icon-download{background-position:-120px -24px}
-.icon-upload{background-position:-144px -24px}
-.icon-inbox{background-position:-168px -24px}
-.icon-play-circle{background-position:-192px -24px}
-.icon-repeat{background-position:-216px -24px}
-.icon-refresh{background-position:-240px -24px}
-.icon-list-alt{background-position:-264px -24px}
-.icon-lock{background-position:-287px -24px}
-.icon-flag{background-position:-312px -24px}
-.icon-headphones{background-position:-336px -24px}
-.icon-volume-off{background-position:-360px -24px}
-.icon-volume-down{background-position:-384px -24px}
-.icon-volume-up{background-position:-408px -24px}
-.icon-qrcode{background-position:-432px -24px}
-.icon-barcode{background-position:-456px -24px}
-.icon-tag{background-position:0 -48px}
-.icon-tags{background-position:-25px -48px}
-.icon-book{background-position:-48px -48px}
-.icon-bookmark{background-position:-72px -48px}
-.icon-print{background-position:-96px -48px}
-.icon-camera{background-position:-120px -48px}
-.icon-font{background-position:-144px -48px}
-.icon-bold{background-position:-167px -48px}
-.icon-italic{background-position:-192px -48px}
-.icon-text-height{background-position:-216px -48px}
-.icon-text-width{background-position:-240px -48px}
-.icon-align-left{background-position:-264px -48px}
-.icon-align-center{background-position:-288px -48px}
-.icon-align-right{background-position:-312px -48px}
-.icon-align-justify{background-position:-336px -48px}
-.icon-list{background-position:-360px -48px}
-.icon-indent-left{background-position:-384px -48px}
-.icon-indent-right{background-position:-408px -48px}
-.icon-facetime-video{background-position:-432px -48px}
-.icon-picture{background-position:-456px -48px}
-.icon-pencil{background-position:0 -72px}
-.icon-map-marker{background-position:-24px -72px}
-.icon-adjust{background-position:-48px -72px}
-.icon-tint{background-position:-72px -72px}
-.icon-edit{background-position:-96px -72px}
-.icon-share{background-position:-120px -72px}
-.icon-check{background-position:-144px -72px}
-.icon-move{background-position:-168px -72px}
-.icon-step-backward{background-position:-192px -72px}
-.icon-fast-backward{background-position:-216px -72px}
-.icon-backward{background-position:-240px -72px}
-.icon-play{background-position:-264px -72px}
-.icon-pause{background-position:-288px -72px}
-.icon-stop{background-position:-312px -72px}
-.icon-forward{background-position:-336px -72px}
-.icon-fast-forward{background-position:-360px -72px}
-.icon-step-forward{background-position:-384px -72px}
-.icon-eject{background-position:-408px -72px}
-.icon-chevron-left{background-position:-432px -72px}
-.icon-chevron-right{background-position:-456px -72px}
-.icon-plus-sign{background-position:0 -96px}
-.icon-minus-sign{background-position:-24px -96px}
-.icon-remove-sign{background-position:-48px -96px}
-.icon-ok-sign{background-position:-72px -96px}
-.icon-question-sign{background-position:-96px -96px}
-.icon-info-sign{background-position:-120px -96px}
-.icon-screenshot{background-position:-144px -96px}
-.icon-remove-circle{background-position:-168px -96px}
-.icon-ok-circle{background-position:-192px -96px}
-.icon-ban-circle{background-position:-216px -96px}
-.icon-arrow-left{background-position:-240px -96px}
-.icon-arrow-right{background-position:-264px -96px}
-.icon-arrow-up{background-position:-289px -96px}
-.icon-arrow-down{background-position:-312px -96px}
-.icon-share-alt{background-position:-336px -96px}
-.icon-resize-full{background-position:-360px -96px}
-.icon-resize-small{background-position:-384px -96px}
-.icon-plus{background-position:-408px -96px}
-.icon-minus{background-position:-433px -96px}
-.icon-asterisk{background-position:-456px -96px}
-.icon-exclamation-sign{background-position:0 -120px}
-.icon-gift{background-position:-24px -120px}
-.icon-leaf{background-position:-48px -120px}
-.icon-fire{background-position:-72px -120px}
-.icon-eye-open{background-position:-96px -120px}
-.icon-eye-close{background-position:-120px -120px}
-.icon-warning-sign{background-position:-144px -120px}
-.icon-plane{background-position:-168px -120px}
-.icon-calendar{background-position:-192px -120px}
-.icon-random{background-position:-216px -120px;width:16px}
-.icon-comment{background-position:-240px -120px}
-.icon-magnet{background-position:-264px -120px}
-.icon-chevron-up{background-position:-288px -120px}
-.icon-chevron-down{background-position:-313px -119px}
-.icon-retweet{background-position:-336px -120px}
-.icon-shopping-cart{background-position:-360px -120px}
-.icon-folder-close{background-position:-384px -120px;width:16px}
-.icon-folder-open{background-position:-408px -120px;width:16px}
-.icon-resize-vertical{background-position:-432px -119px}
-.icon-resize-horizontal{background-position:-456px -118px}
-.icon-hdd{background-position:0 -144px}
-.icon-bullhorn{background-position:-24px -144px}
-.icon-bell{background-position:-48px -144px}
-.icon-certificate{background-position:-72px -144px}
-.icon-thumbs-up{background-position:-96px -144px}
-.icon-thumbs-down{background-position:-120px -144px}
-.icon-hand-right{background-position:-144px -144px}
-.icon-hand-left{background-position:-168px -144px}
-.icon-hand-up{background-position:-192px -144px}
-.icon-hand-down{background-position:-216px -144px}
-.icon-circle-arrow-right{background-position:-240px -144px}
-.icon-circle-arrow-left{background-position:-264px -144px}
-.icon-circle-arrow-up{background-position:-288px -144px}
-.icon-circle-arrow-down{background-position:-312px -144px}
-.icon-globe{background-position:-336px -144px}
-.icon-wrench{background-position:-360px -144px}
-.icon-tasks{background-position:-384px -144px}
-.icon-filter{background-position:-408px -144px}
-.icon-briefcase{background-position:-432px -144px}
-.icon-fullscreen{background-position:-456px -144px}
-.dropup,.dropdown{position:relative}
-.dropdown-toggle{*margin-bottom:-3px}
-.dropdown-toggle:active,.open .dropdown-toggle{outline:0}
-.caret{display:inline-block;width:0;height:0;vertical-align:top;border-top:4px solid #000;border-right:4px solid transparent;border-left:4px solid transparent;content:""}
-.dropdown .caret{margin-top:8px;margin-left:2px}
-.dropdown-menu{position:absolute;top:100%;left:0;z-index:1000;display:none;float:left;min-width:160px;padding:5px 0;margin:2px 0 0;list-style:none;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);*border-right-width:2px;*border-bottom-width:2px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box}.dropdown-menu.pull-right{right:0;left:auto}
-.dropdown-menu .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
-.dropdown-menu>li>a{display:block;padding:3px 20px;clear:both;font-weight:normal;line-height:20px;color:#333;white-space:nowrap}
-.dropdown-menu>li>a:hover,.dropdown-menu>li>a:focus,.dropdown-submenu:hover>a,.dropdown-submenu:focus>a{text-decoration:none;color:#fff;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
-.dropdown-menu>.active>a,.dropdown-menu>.active>a:hover,.dropdown-menu>.active>a:focus{color:#fff;text-decoration:none;outline:0;background-color:#0081c2;background-image:-moz-linear-gradient(top, #08c, #0077b3);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#0077b3));background-image:-webkit-linear-gradient(top, #08c, #0077b3);background-image:-o-linear-gradient(top, #08c, #0077b3);background-image:linear-gradient(to bottom, #08c, #0077b3);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0077b3', GradientType=0)}
-.dropdown-menu>.disabled>a,.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{color:#999}
-.dropdown-menu>.disabled>a:hover,.dropdown-menu>.disabled>a:focus{text-decoration:none;background-color:transparent;background-image:none;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);cursor:default}
-.open{*z-index:1000}.open>.dropdown-menu{display:block}
-.dropdown-backdrop{position:fixed;left:0;right:0;bottom:0;top:0;z-index:990}
-.pull-right>.dropdown-menu{right:0;left:auto}
-.dropup .caret,.navbar-fixed-bottom .dropdown .caret{border-top:0;border-bottom:4px solid #000;content:""}
-.dropup .dropdown-menu,.navbar-fixed-bottom .dropdown .dropdown-menu{top:auto;bottom:100%;margin-bottom:1px}
-.dropdown-submenu{position:relative}
-.dropdown-submenu>.dropdown-menu{top:0;left:100%;margin-top:-6px;margin-left:-1px;border-radius:0 6px 6px 6px;-webkit-border-radius:0 6px 6px 6px;-moz-border-radius:0 6px 6px 6px;border-radius:0 6px 6px 6px}
-.dropdown-submenu:hover>.dropdown-menu{display:block}
-.dropup .dropdown-submenu>.dropdown-menu{top:auto;bottom:0;margin-top:0;margin-bottom:-2px;border-radius:5px 5px 5px 0;-webkit-border-radius:5px 5px 5px 0;-moz-border-radius:5px 5px 5px 0;border-radius:5px 5px 5px 0}
-.dropdown-submenu>a:after{display:block;content:" ";float:right;width:0;height:0;border-color:transparent;border-style:solid;border-width:5px 0 5px 5px;border-left-color:#ccc;margin-top:5px;margin-right:-10px}
-.dropdown-submenu:hover>a:after{border-left-color:#fff}
-.dropdown-submenu.pull-left{float:none}.dropdown-submenu.pull-left>.dropdown-menu{left:-100%;margin-left:10px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
-.dropdown .dropdown-menu .nav-header{padding-left:20px;padding-right:20px}
-.typeahead{z-index:1051;margin-top:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.well{min-height:20px;padding:19px;margin-bottom:20px;background-color:#f5f5f5;border:1px solid #e3e3e3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);-moz-box-shadow:inset 0 1px 1px rgba(0,0,0,0.05);box-shadow:inset 0 1px 1px rgba(0,0,0,0.05)}.well blockquote{border-color:#ddd;border-color:rgba(0,0,0,0.15)}
-.well-large{padding:24px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.well-small{padding:9px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.fade{opacity:0;-webkit-transition:opacity .15s linear;-moz-transition:opacity .15s linear;-o-transition:opacity .15s linear;transition:opacity .15s linear}.fade.in{opacity:1}
-.collapse{position:relative;height:0;overflow:hidden;-webkit-transition:height .35s ease;-moz-transition:height .35s ease;-o-transition:height .35s ease;transition:height .35s ease}.collapse.in{height:auto}
-.close{float:right;font-size:20px;font-weight:bold;line-height:20px;color:#000;text-shadow:0 1px 0 #fff;opacity:.2;filter:alpha(opacity=20)}.close:hover,.close:focus{color:#000;text-decoration:none;cursor:pointer;opacity:.4;filter:alpha(opacity=40)}
-button.close{padding:0;cursor:pointer;background:transparent;border:0;-webkit-appearance:none}
-.btn{display:inline-block;*display:inline;*zoom:1;padding:4px 12px;margin-bottom:0;font-size:13px;line-height:20px;text-align:center;vertical-align:middle;cursor:pointer;color:#333;text-shadow:0 1px 1px rgba(255,255,255,0.75);background-color:#f5f5f5;background-image:-moz-linear-gradient(top, #fff, #e6e6e6);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#e6e6e6));background-image:-webkit-linear-gradient(top, #fff, #e6e6e6);background-image:-o-linear-gradient(top, #fff, #e6e6e6);background-image:linear-gradient(to bottom, #fff, #e6e6e6);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#ffe6e6e6', GradientType=0);border-color:#e6e6e6 #e6e6e6 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e6e6e6;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);border:1px solid #ccc;*border:0;border-bottom-color:#b3b3b3;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;*margin-left:.3em;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05)}.btn:hover,.btn:focus,.btn:active,.btn.active,.btn.disabled,.btn[disabled]{color:#333;background-color:#e6e6e6;*background-color:#d9d9d9}
-.btn:active,.btn.active{background-color:#ccc \9}
-.btn:first-child{*margin-left:0}
-.btn:hover,.btn:focus{color:#333;text-decoration:none;background-position:0 -15px;-webkit-transition:background-position .1s linear;-moz-transition:background-position .1s linear;-o-transition:background-position .1s linear;transition:background-position .1s linear}
-.btn:focus{outline:thin dotted #333;outline:5px auto -webkit-focus-ring-color;outline-offset:-2px}
-.btn.active,.btn:active{background-image:none;outline:0;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
-.btn.disabled,.btn[disabled]{cursor:default;background-image:none;opacity:.65;filter:alpha(opacity=65);-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
-.btn-large{padding:11px 19px;font-size:16.25px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.btn-large [class^="icon-"],.btn-large [class*=" icon-"]{margin-top:4px}
-.btn-small{padding:2px 10px;font-size:11.049999999999999px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.btn-small [class^="icon-"],.btn-small [class*=" icon-"]{margin-top:0}
-.btn-mini [class^="icon-"],.btn-mini [class*=" icon-"]{margin-top:-1px}
-.btn-mini{padding:0 6px;font-size:9.75px;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.btn-block{display:block;width:100%;padding-left:0;padding-right:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}
-.btn-block+.btn-block{margin-top:5px}
-input[type="submit"].btn-block,input[type="reset"].btn-block,input[type="button"].btn-block{width:100%}
-.btn-primary.active,.btn-warning.active,.btn-danger.active,.btn-success.active,.btn-info.active,.btn-inverse.active{color:rgba(255,255,255,0.75)}
-.btn-primary{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#006dcc;background-image:-moz-linear-gradient(top, #08c, #04c);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#08c), to(#04c));background-image:-webkit-linear-gradient(top, #08c, #04c);background-image:-o-linear-gradient(top, #08c, #04c);background-image:linear-gradient(to bottom, #08c, #04c);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff0088cc', endColorstr='#ff0044cc', GradientType=0);border-color:#04c #04c #002a80;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#04c;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-primary:hover,.btn-primary:focus,.btn-primary:active,.btn-primary.active,.btn-primary.disabled,.btn-primary[disabled]{color:#fff;background-color:#04c;*background-color:#003bb3}
-.btn-primary:active,.btn-primary.active{background-color:#039 \9}
-.btn-warning{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0);border-color:#f89406 #f89406 #ad6704;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#f89406;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-warning:hover,.btn-warning:focus,.btn-warning:active,.btn-warning.active,.btn-warning.disabled,.btn-warning[disabled]{color:#fff;background-color:#f89406;*background-color:#df8505}
-.btn-warning:active,.btn-warning.active{background-color:#c67605 \9}
-.btn-danger{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#da4f49;background-image:-moz-linear-gradient(top, #ee5f5b, #bd362f);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#bd362f));background-image:-webkit-linear-gradient(top, #ee5f5b, #bd362f);background-image:-o-linear-gradient(top, #ee5f5b, #bd362f);background-image:linear-gradient(to bottom, #ee5f5b, #bd362f);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffbd362f', GradientType=0);border-color:#bd362f #bd362f #802420;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#bd362f;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-danger:hover,.btn-danger:focus,.btn-danger:active,.btn-danger.active,.btn-danger.disabled,.btn-danger[disabled]{color:#fff;background-color:#bd362f;*background-color:#a9302a}
-.btn-danger:active,.btn-danger.active{background-color:#942a25 \9}
-.btn-success{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#5bb75b;background-image:-moz-linear-gradient(top, #62c462, #51a351);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#51a351));background-image:-webkit-linear-gradient(top, #62c462, #51a351);background-image:-o-linear-gradient(top, #62c462, #51a351);background-image:linear-gradient(to bottom, #62c462, #51a351);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff51a351', GradientType=0);border-color:#51a351 #51a351 #387038;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#51a351;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-success:hover,.btn-success:focus,.btn-success:active,.btn-success.active,.btn-success.disabled,.btn-success[disabled]{color:#fff;background-color:#51a351;*background-color:#499249}
-.btn-success:active,.btn-success.active{background-color:#408140 \9}
-.btn-info{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#49afcd;background-image:-moz-linear-gradient(top, #5bc0de, #2f96b4);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#2f96b4));background-image:-webkit-linear-gradient(top, #5bc0de, #2f96b4);background-image:-o-linear-gradient(top, #5bc0de, #2f96b4);background-image:linear-gradient(to bottom, #5bc0de, #2f96b4);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff2f96b4', GradientType=0);border-color:#2f96b4 #2f96b4 #1f6377;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#2f96b4;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-info:hover,.btn-info:focus,.btn-info:active,.btn-info.active,.btn-info.disabled,.btn-info[disabled]{color:#fff;background-color:#2f96b4;*background-color:#2a85a0}
-.btn-info:active,.btn-info.active{background-color:#24748c \9}
-.btn-inverse{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#363636;background-image:-moz-linear-gradient(top, #444, #222);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#444), to(#222));background-image:-webkit-linear-gradient(top, #444, #222);background-image:-o-linear-gradient(top, #444, #222);background-image:linear-gradient(to bottom, #444, #222);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff444444', endColorstr='#ff222222', GradientType=0);border-color:#222 #222 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#222;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.btn-inverse:hover,.btn-inverse:focus,.btn-inverse:active,.btn-inverse.active,.btn-inverse.disabled,.btn-inverse[disabled]{color:#fff;background-color:#222;*background-color:#151515}
-.btn-inverse:active,.btn-inverse.active{background-color:#080808 \9}
-button.btn,input[type="submit"].btn{*padding-top:3px;*padding-bottom:3px}button.btn::-moz-focus-inner,input[type="submit"].btn::-moz-focus-inner{padding:0;border:0}
-button.btn.btn-large,input[type="submit"].btn.btn-large{*padding-top:7px;*padding-bottom:7px}
-button.btn.btn-small,input[type="submit"].btn.btn-small{*padding-top:3px;*padding-bottom:3px}
-button.btn.btn-mini,input[type="submit"].btn.btn-mini{*padding-top:1px;*padding-bottom:1px}
-.btn-link,.btn-link:active,.btn-link[disabled]{background-color:transparent;background-image:none;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none}
-.btn-link{border-color:transparent;cursor:pointer;color:#08c;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.btn-link:hover,.btn-link:focus{color:#005580;text-decoration:underline;background-color:transparent}
-.btn-link[disabled]:hover,.btn-link[disabled]:focus{color:#333;text-decoration:none}
-.btn-group{position:relative;display:inline-block;*display:inline;*zoom:1;font-size:0;vertical-align:middle;white-space:nowrap;*margin-left:.3em}.btn-group:first-child{*margin-left:0}
-.btn-group+.btn-group{margin-left:5px}
-.btn-toolbar{font-size:0;margin-top:10px;margin-bottom:10px}.btn-toolbar>.btn+.btn,.btn-toolbar>.btn-group+.btn,.btn-toolbar>.btn+.btn-group{margin-left:5px}
-.btn-group>.btn{position:relative;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.btn-group>.btn+.btn{margin-left:-1px}
-.btn-group>.btn,.btn-group>.dropdown-menu,.btn-group>.popover{font-size:13px}
-.btn-group>.btn-mini{font-size:9.75px}
-.btn-group>.btn-small{font-size:11.049999999999999px}
-.btn-group>.btn-large{font-size:16.25px}
-.btn-group>.btn:first-child{margin-left:0;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
-.btn-group>.btn:last-child,.btn-group>.dropdown-toggle{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
-.btn-group>.btn.large:first-child{margin-left:0;-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
-.btn-group>.btn.large:last-child,.btn-group>.large.dropdown-toggle{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
-.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active{z-index:2}
-.btn-group .dropdown-toggle:active,.btn-group.open .dropdown-toggle{outline:0}
-.btn-group>.btn+.dropdown-toggle{padding-left:8px;padding-right:8px;-webkit-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 1px 0 0 rgba(255,255,255,.125), inset 0 1px 0 rgba(255,255,255,.2), 0 1px 2px rgba(0,0,0,.05);*padding-top:5px;*padding-bottom:5px}
-.btn-group>.btn-mini+.dropdown-toggle{padding-left:5px;padding-right:5px;*padding-top:2px;*padding-bottom:2px}
-.btn-group>.btn-small+.dropdown-toggle{*padding-top:5px;*padding-bottom:4px}
-.btn-group>.btn-large+.dropdown-toggle{padding-left:12px;padding-right:12px;*padding-top:7px;*padding-bottom:7px}
-.btn-group.open .dropdown-toggle{background-image:none;-webkit-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);-moz-box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05);box-shadow:inset 0 2px 4px rgba(0,0,0,.15), 0 1px 2px rgba(0,0,0,.05)}
-.btn-group.open .btn.dropdown-toggle{background-color:#e6e6e6}
-.btn-group.open .btn-primary.dropdown-toggle{background-color:#04c}
-.btn-group.open .btn-warning.dropdown-toggle{background-color:#f89406}
-.btn-group.open .btn-danger.dropdown-toggle{background-color:#bd362f}
-.btn-group.open .btn-success.dropdown-toggle{background-color:#51a351}
-.btn-group.open .btn-info.dropdown-toggle{background-color:#2f96b4}
-.btn-group.open .btn-inverse.dropdown-toggle{background-color:#222}
-.btn .caret{margin-top:8px;margin-left:0}
-.btn-large .caret{margin-top:6px}
-.btn-large .caret{border-left-width:5px;border-right-width:5px;border-top-width:5px}
-.btn-mini .caret,.btn-small .caret{margin-top:8px}
-.dropup .btn-large .caret{border-bottom-width:5px}
-.btn-primary .caret,.btn-warning .caret,.btn-danger .caret,.btn-info .caret,.btn-success .caret,.btn-inverse .caret{border-top-color:#fff;border-bottom-color:#fff}
-.btn-group-vertical{display:inline-block;*display:inline;*zoom:1}
-.btn-group-vertical>.btn{display:block;float:none;max-width:100%;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.btn-group-vertical>.btn+.btn{margin-left:0;margin-top:-1px}
-.btn-group-vertical>.btn:first-child{border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}
-.btn-group-vertical>.btn:last-child{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}
-.btn-group-vertical>.btn-large:first-child{border-radius:6px 6px 0 0;-webkit-border-radius:6px 6px 0 0;-moz-border-radius:6px 6px 0 0;border-radius:6px 6px 0 0}
-.btn-group-vertical>.btn-large:last-child{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
-.alert{padding:8px 35px 8px 14px;margin-bottom:20px;text-shadow:0 1px 0 rgba(255,255,255,0.5);background-color:#fcf8e3;border:1px solid #fbeed5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.alert,.alert h4{color:#c09853}
-.alert h4{margin:0}
-.alert .close{position:relative;top:-2px;right:-21px;line-height:20px}
-.alert-success{background-color:#dff0d8;border-color:#d6e9c6;color:#468847}
-.alert-success h4{color:#468847}
-.alert-danger,.alert-error{background-color:#f2dede;border-color:#eed3d7;color:#b94a48}
-.alert-danger h4,.alert-error h4{color:#b94a48}
-.alert-info{background-color:#d9edf7;border-color:#bce8f1;color:#3a87ad}
-.alert-info h4{color:#3a87ad}
-.alert-block{padding-top:14px;padding-bottom:14px}
-.alert-block>p,.alert-block>ul{margin-bottom:0}
-.alert-block p+p{margin-top:5px}
-.nav{margin-left:0;margin-bottom:20px;list-style:none}
-.nav>li>a{display:block}
-.nav>li>a:hover,.nav>li>a:focus{text-decoration:none;background-color:#eee}
-.nav>li>a>img{max-width:none}
-.nav>.pull-right{float:right}
-.nav-header{display:block;padding:3px 15px;font-size:11px;font-weight:bold;line-height:20px;color:#999;text-shadow:0 1px 0 rgba(255,255,255,0.5);text-transform:uppercase}
-.nav li+.nav-header{margin-top:9px}
-.nav-list{padding-left:15px;padding-right:15px;margin-bottom:0}
-.nav-list>li>a,.nav-list .nav-header{margin-left:-15px;margin-right:-15px;text-shadow:0 1px 0 rgba(255,255,255,0.5)}
-.nav-list>li>a{padding:3px 15px}
-.nav-list>.active>a,.nav-list>.active>a:hover,.nav-list>.active>a:focus{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.2);background-color:#08c}
-.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{margin-right:2px}
-.nav-list .divider{*width:100%;height:1px;margin:9px 1px;*margin:-5px 0 5px;overflow:hidden;background-color:#e5e5e5;border-bottom:1px solid #fff}
-.nav-tabs,.nav-pills{*zoom:1}.nav-tabs:before,.nav-pills:before,.nav-tabs:after,.nav-pills:after{display:table;content:"";line-height:0}
-.nav-tabs:after,.nav-pills:after{clear:both}
-.nav-tabs>li,.nav-pills>li{float:left}
-.nav-tabs>li>a,.nav-pills>li>a{padding-right:12px;padding-left:12px;margin-right:2px;line-height:14px}
-.nav-tabs{border-bottom:1px solid #ddd}
-.nav-tabs>li{margin-bottom:-1px}
-.nav-tabs>li>a{padding-top:8px;padding-bottom:8px;line-height:20px;border:1px solid transparent;border-radius:4px 4px 0 0;-webkit-border-radius:4px 4px 0 0;-moz-border-radius:4px 4px 0 0;border-radius:4px 4px 0 0}.nav-tabs>li>a:hover,.nav-tabs>li>a:focus{border-color:#eee #eee #ddd}
-.nav-tabs>.active>a,.nav-tabs>.active>a:hover,.nav-tabs>.active>a:focus{color:#555;background-color:#fff;border:1px solid #ddd;border-bottom-color:transparent;cursor:default}
-.nav-pills>li>a{padding-top:8px;padding-bottom:8px;margin-top:2px;margin-bottom:2px;border-radius:5px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}
-.nav-pills>.active>a,.nav-pills>.active>a:hover,.nav-pills>.active>a:focus{color:#fff;background-color:#08c}
-.nav-stacked>li{float:none}
-.nav-stacked>li>a{margin-right:0}
-.nav-tabs.nav-stacked{border-bottom:0}
-.nav-tabs.nav-stacked>li>a{border:1px solid #ddd;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.nav-tabs.nav-stacked>li:first-child>a{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px}
-.nav-tabs.nav-stacked>li:last-child>a{-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
-.nav-tabs.nav-stacked>li>a:hover,.nav-tabs.nav-stacked>li>a:focus{border-color:#ddd;z-index:2}
-.nav-pills.nav-stacked>li>a{margin-bottom:3px}
-.nav-pills.nav-stacked>li:last-child>a{margin-bottom:1px}
-.nav-tabs .dropdown-menu{border-radius:0 0 6px 6px;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px}
-.nav-pills .dropdown-menu{border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.nav .dropdown-toggle .caret{border-top-color:#08c;border-bottom-color:#08c;margin-top:6px}
-.nav .dropdown-toggle:hover .caret,.nav .dropdown-toggle:focus .caret{border-top-color:#005580;border-bottom-color:#005580}
-.nav-tabs .dropdown-toggle .caret{margin-top:8px}
-.nav .active .dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
-.nav-tabs .active .dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}
-.nav>.dropdown.active>a:hover,.nav>.dropdown.active>a:focus{cursor:pointer}
-.nav-tabs .open .dropdown-toggle,.nav-pills .open .dropdown-toggle,.nav>li.dropdown.open.active>a:hover,.nav>li.dropdown.open.active>a:focus{color:#fff;background-color:#999;border-color:#999}
-.nav li.dropdown.open .caret,.nav li.dropdown.open.active .caret,.nav li.dropdown.open a:hover .caret,.nav li.dropdown.open a:focus .caret{border-top-color:#fff;border-bottom-color:#fff;opacity:1;filter:alpha(opacity=100)}
-.tabs-stacked .open>a:hover,.tabs-stacked .open>a:focus{border-color:#999}
-.tabbable{*zoom:1}.tabbable:before,.tabbable:after{display:table;content:"";line-height:0}
-.tabbable:after{clear:both}
-.tab-content{overflow:auto}
-.tabs-below>.nav-tabs,.tabs-right>.nav-tabs,.tabs-left>.nav-tabs{border-bottom:0}
-.tab-content>.tab-pane,.pill-content>.pill-pane{display:none}
-.tab-content>.active,.pill-content>.active{display:block}
-.tabs-below>.nav-tabs{border-top:1px solid #ddd}
-.tabs-below>.nav-tabs>li{margin-top:-1px;margin-bottom:0}
-.tabs-below>.nav-tabs>li>a{border-radius:0 0 4px 4px;-webkit-border-radius:0 0 4px 4px;-moz-border-radius:0 0 4px 4px;border-radius:0 0 4px 4px}.tabs-below>.nav-tabs>li>a:hover,.tabs-below>.nav-tabs>li>a:focus{border-bottom-color:transparent;border-top-color:#ddd}
-.tabs-below>.nav-tabs>.active>a,.tabs-below>.nav-tabs>.active>a:hover,.tabs-below>.nav-tabs>.active>a:focus{border-color:transparent #ddd #ddd #ddd}
-.tabs-left>.nav-tabs>li,.tabs-right>.nav-tabs>li{float:none}
-.tabs-left>.nav-tabs>li>a,.tabs-right>.nav-tabs>li>a{min-width:74px;margin-right:0;margin-bottom:3px}
-.tabs-left>.nav-tabs{float:left;margin-right:19px;border-right:1px solid #ddd}
-.tabs-left>.nav-tabs>li>a{margin-right:-1px;border-radius:4px 0 0 4px;-webkit-border-radius:4px 0 0 4px;-moz-border-radius:4px 0 0 4px;border-radius:4px 0 0 4px}
-.tabs-left>.nav-tabs>li>a:hover,.tabs-left>.nav-tabs>li>a:focus{border-color:#eee #ddd #eee #eee}
-.tabs-left>.nav-tabs .active>a,.tabs-left>.nav-tabs .active>a:hover,.tabs-left>.nav-tabs .active>a:focus{border-color:#ddd transparent #ddd #ddd;*border-right-color:#fff}
-.tabs-right>.nav-tabs{float:right;margin-left:19px;border-left:1px solid #ddd}
-.tabs-right>.nav-tabs>li>a{margin-left:-1px;border-radius:0 4px 4px 0;-webkit-border-radius:0 4px 4px 0;-moz-border-radius:0 4px 4px 0;border-radius:0 4px 4px 0}
-.tabs-right>.nav-tabs>li>a:hover,.tabs-right>.nav-tabs>li>a:focus{border-color:#eee #eee #eee #ddd}
-.tabs-right>.nav-tabs .active>a,.tabs-right>.nav-tabs .active>a:hover,.tabs-right>.nav-tabs .active>a:focus{border-color:#ddd #ddd #ddd transparent;*border-left-color:#fff}
-.nav>.disabled>a{color:#999}
-.nav>.disabled>a:hover,.nav>.disabled>a:focus{text-decoration:none;background-color:transparent;cursor:default}
-.navbar{overflow:visible;margin-bottom:20px;*position:relative;*z-index:2}
-.navbar-inner{min-height:36px;padding-left:20px;padding-right:20px;background-color:#fafafa;background-image:-moz-linear-gradient(top, #fff, #f2f2f2);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fff), to(#f2f2f2));background-image:-webkit-linear-gradient(top, #fff, #f2f2f2);background-image:-o-linear-gradient(top, #fff, #f2f2f2);background-image:linear-gradient(to bottom, #fff, #f2f2f2);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffffffff', endColorstr='#fff2f2f2', GradientType=0);border:1px solid #d4d4d4;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 4px rgba(0,0,0,0.065);-moz-box-shadow:0 1px 4px rgba(0,0,0,0.065);box-shadow:0 1px 4px rgba(0,0,0,0.065);*zoom:1}.navbar-inner:before,.navbar-inner:after{display:table;content:"";line-height:0}
-.navbar-inner:after{clear:both}
-.navbar .container{width:auto}
-.nav-collapse.collapse{height:auto;overflow:visible}
-.navbar .brand{float:left;display:block;padding:8px 20px 8px;margin-left:-20px;font-size:20px;font-weight:200;color:#777;text-shadow:0 1px 0 #fff}.navbar .brand:hover,.navbar .brand:focus{text-decoration:none}
-.navbar-text{margin-bottom:0;line-height:36px;color:#777}
-.navbar-link{color:#777}.navbar-link:hover,.navbar-link:focus{color:#333}
-.navbar .divider-vertical{height:36px;margin:0 9px;border-left:1px solid #f2f2f2;border-right:1px solid #fff}
-.navbar .btn,.navbar .btn-group{margin-top:3px}
-.navbar .btn-group .btn,.navbar .input-prepend .btn,.navbar .input-append .btn,.navbar .input-prepend .btn-group,.navbar .input-append .btn-group{margin-top:0}
-.navbar-form{margin-bottom:0;*zoom:1}.navbar-form:before,.navbar-form:after{display:table;content:"";line-height:0}
-.navbar-form:after{clear:both}
-.navbar-form input,.navbar-form select,.navbar-form .radio,.navbar-form .checkbox{margin-top:3px}
-.navbar-form input,.navbar-form select,.navbar-form .btn{display:inline-block;margin-bottom:0}
-.navbar-form input[type="image"],.navbar-form input[type="checkbox"],.navbar-form input[type="radio"]{margin-top:3px}
-.navbar-form .input-append,.navbar-form .input-prepend{margin-top:5px;white-space:nowrap}.navbar-form .input-append input,.navbar-form .input-prepend input{margin-top:0}
-.navbar-search{position:relative;float:left;margin-top:3px;margin-bottom:0}.navbar-search .search-query{margin-bottom:0;padding:4px 14px;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:13px;font-weight:normal;line-height:1;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
-.navbar-static-top{position:static;margin-bottom:0}.navbar-static-top .navbar-inner{border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.navbar-fixed-top,.navbar-fixed-bottom{position:fixed;right:0;left:0;z-index:1030;margin-bottom:0}
-.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{border-width:0 0 1px}
-.navbar-fixed-bottom .navbar-inner{border-width:1px 0 0}
-.navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding-left:0;padding-right:0;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:940px}
-.navbar-fixed-top{top:0}
-.navbar-fixed-top .navbar-inner,.navbar-static-top .navbar-inner{-webkit-box-shadow:0 1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 1px 10px rgba(0,0,0,.1);box-shadow:0 1px 10px rgba(0,0,0,.1)}
-.navbar-fixed-bottom{bottom:0}.navbar-fixed-bottom .navbar-inner{-webkit-box-shadow:0 -1px 10px rgba(0,0,0,.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,.1);box-shadow:0 -1px 10px rgba(0,0,0,.1)}
-.navbar .nav{position:relative;left:0;display:block;float:left;margin:0 10px 0 0}
-.navbar .nav.pull-right{float:right;margin-right:0}
-.navbar .nav>li{float:left}
-.navbar .nav>li>a{float:none;padding:8px 15px 8px;color:#777;text-decoration:none;text-shadow:0 1px 0 #fff}
-.navbar .nav .dropdown-toggle .caret{margin-top:8px}
-.navbar .nav>li>a:focus,.navbar .nav>li>a:hover{background-color:transparent;color:#333;text-decoration:none}
-.navbar .nav>.active>a,.navbar .nav>.active>a:hover,.navbar .nav>.active>a:focus{color:#555;text-decoration:none;background-color:#e5e5e5;-webkit-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);-moz-box-shadow:inset 0 3px 8px rgba(0,0,0,0.125);box-shadow:inset 0 3px 8px rgba(0,0,0,0.125)}
-.navbar .btn-navbar{display:none;float:right;padding:7px 10px;margin-left:5px;margin-right:5px;color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#ededed;background-image:-moz-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f2f2f2), to(#e5e5e5));background-image:-webkit-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:-o-linear-gradient(top, #f2f2f2, #e5e5e5);background-image:linear-gradient(to bottom, #f2f2f2, #e5e5e5);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff2f2f2', endColorstr='#ffe5e5e5', GradientType=0);border-color:#e5e5e5 #e5e5e5 #bfbfbf;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#e5e5e5;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false);-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.075)}.navbar .btn-navbar:hover,.navbar .btn-navbar:focus,.navbar .btn-navbar:active,.navbar .btn-navbar.active,.navbar .btn-navbar.disabled,.navbar .btn-navbar[disabled]{color:#fff;background-color:#e5e5e5;*background-color:#d9d9d9}
-.navbar .btn-navbar:active,.navbar .btn-navbar.active{background-color:#ccc \9}
-.navbar .btn-navbar .icon-bar{display:block;width:18px;height:2px;background-color:#f5f5f5;-webkit-border-radius:1px;-moz-border-radius:1px;border-radius:1px;-webkit-box-shadow:0 1px 0 rgba(0,0,0,0.25);-moz-box-shadow:0 1px 0 rgba(0,0,0,0.25);box-shadow:0 1px 0 rgba(0,0,0,0.25)}
-.btn-navbar .icon-bar+.icon-bar{margin-top:3px}
-.navbar .nav>li>.dropdown-menu:before{content:'';display:inline-block;border-left:7px solid transparent;border-right:7px solid transparent;border-bottom:7px solid #ccc;border-bottom-color:rgba(0,0,0,0.2);position:absolute;top:-7px;left:9px}
-.navbar .nav>li>.dropdown-menu:after{content:'';display:inline-block;border-left:6px solid transparent;border-right:6px solid transparent;border-bottom:6px solid #fff;position:absolute;top:-6px;left:10px}
-.navbar-fixed-bottom .nav>li>.dropdown-menu:before{border-top:7px solid #ccc;border-top-color:rgba(0,0,0,0.2);border-bottom:0;bottom:-7px;top:auto}
-.navbar-fixed-bottom .nav>li>.dropdown-menu:after{border-top:6px solid #fff;border-bottom:0;bottom:-6px;top:auto}
-.navbar .nav li.dropdown>a:hover .caret,.navbar .nav li.dropdown>a:focus .caret{border-top-color:#333;border-bottom-color:#333}
-.navbar .nav li.dropdown.open>.dropdown-toggle,.navbar .nav li.dropdown.active>.dropdown-toggle,.navbar .nav li.dropdown.open.active>.dropdown-toggle{background-color:#e5e5e5;color:#555}
-.navbar .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#777;border-bottom-color:#777}
-.navbar .nav li.dropdown.open>.dropdown-toggle .caret,.navbar .nav li.dropdown.active>.dropdown-toggle .caret,.navbar .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#555;border-bottom-color:#555}
-.navbar .pull-right>li>.dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right{left:auto;right:0}.navbar .pull-right>li>.dropdown-menu:before,.navbar .nav>li>.dropdown-menu.pull-right:before{left:auto;right:12px}
-.navbar .pull-right>li>.dropdown-menu:after,.navbar .nav>li>.dropdown-menu.pull-right:after{left:auto;right:13px}
-.navbar .pull-right>li>.dropdown-menu .dropdown-menu,.navbar .nav>li>.dropdown-menu.pull-right .dropdown-menu{left:auto;right:100%;margin-left:0;margin-right:-1px;border-radius:6px 0 6px 6px;-webkit-border-radius:6px 0 6px 6px;-moz-border-radius:6px 0 6px 6px;border-radius:6px 0 6px 6px}
-.navbar-inverse .navbar-inner{background-color:#1b1b1b;background-image:-moz-linear-gradient(top, #222, #111);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#222), to(#111));background-image:-webkit-linear-gradient(top, #222, #111);background-image:-o-linear-gradient(top, #222, #111);background-image:linear-gradient(to bottom, #222, #111);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff222222', endColorstr='#ff111111', GradientType=0);border-color:#252525}
-.navbar-inverse .brand,.navbar-inverse .nav>li>a{color:#999;text-shadow:0 -1px 0 rgba(0,0,0,0.25)}.navbar-inverse .brand:hover,.navbar-inverse .nav>li>a:hover,.navbar-inverse .brand:focus,.navbar-inverse .nav>li>a:focus{color:#fff}
-.navbar-inverse .brand{color:#999}
-.navbar-inverse .navbar-text{color:#999}
-.navbar-inverse .nav>li>a:focus,.navbar-inverse .nav>li>a:hover{background-color:transparent;color:#fff}
-.navbar-inverse .nav .active>a,.navbar-inverse .nav .active>a:hover,.navbar-inverse .nav .active>a:focus{color:#fff;background-color:#111}
-.navbar-inverse .navbar-link{color:#999}.navbar-inverse .navbar-link:hover,.navbar-inverse .navbar-link:focus{color:#fff}
-.navbar-inverse .divider-vertical{border-left-color:#111;border-right-color:#222}
-.navbar-inverse .nav li.dropdown.open>.dropdown-toggle,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle{background-color:#111;color:#fff}
-.navbar-inverse .nav li.dropdown>a:hover .caret,.navbar-inverse .nav li.dropdown>a:focus .caret{border-top-color:#fff;border-bottom-color:#fff}
-.navbar-inverse .nav li.dropdown>.dropdown-toggle .caret{border-top-color:#999;border-bottom-color:#999}
-.navbar-inverse .nav li.dropdown.open>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.active>.dropdown-toggle .caret,.navbar-inverse .nav li.dropdown.open.active>.dropdown-toggle .caret{border-top-color:#fff;border-bottom-color:#fff}
-.navbar-inverse .navbar-search .search-query{color:#fff;background-color:#515151;border-color:#111;-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);box-shadow:inset 0 1px 2px rgba(0,0,0,.1), 0 1px 0 rgba(255,255,255,.15);-webkit-transition:none;-moz-transition:none;-o-transition:none;transition:none}.navbar-inverse .navbar-search .search-query:-moz-placeholder{color:#ccc}
-.navbar-inverse .navbar-search .search-query:-ms-input-placeholder{color:#ccc}
-.navbar-inverse .navbar-search .search-query::-webkit-input-placeholder{color:#ccc}
-.navbar-inverse .navbar-search .search-query:focus,.navbar-inverse .navbar-search .search-query.focused{padding:5px 15px;color:#333;text-shadow:0 1px 0 #fff;background-color:#fff;border:0;-webkit-box-shadow:0 0 3px rgba(0,0,0,0.15);-moz-box-shadow:0 0 3px rgba(0,0,0,0.15);box-shadow:0 0 3px rgba(0,0,0,0.15);outline:0}
-.navbar-inverse .btn-navbar{color:#fff;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e0e0e;background-image:-moz-linear-gradient(top, #151515, #040404);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#151515), to(#040404));background-image:-webkit-linear-gradient(top, #151515, #040404);background-image:-o-linear-gradient(top, #151515, #040404);background-image:linear-gradient(to bottom, #151515, #040404);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff151515', endColorstr='#ff040404', GradientType=0);border-color:#040404 #040404 #000;border-color:rgba(0,0,0,0.1) rgba(0,0,0,0.1) rgba(0,0,0,0.25);*background-color:#040404;filter:progid:DXImageTransform.Microsoft.gradient(enabled = false)}.navbar-inverse .btn-navbar:hover,.navbar-inverse .btn-navbar:focus,.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active,.navbar-inverse .btn-navbar.disabled,.navbar-inverse .btn-navbar[disabled]{color:#fff;background-color:#040404;*background-color:#000}
-.navbar-inverse .btn-navbar:active,.navbar-inverse .btn-navbar.active{background-color:#000 \9}
-.breadcrumb{padding:8px 15px;margin:0 0 20px;list-style:none;background-color:#f5f5f5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}.breadcrumb>li{display:inline-block;*display:inline;*zoom:1;text-shadow:0 1px 0 #fff}.breadcrumb>li>.divider{padding:0 5px;color:#ccc}
-.breadcrumb>.active{color:#999}
-.pagination{margin:20px 0}
-.pagination ul{display:inline-block;*display:inline;*zoom:1;margin-left:0;margin-bottom:0;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 2px rgba(0,0,0,0.05);-moz-box-shadow:0 1px 2px rgba(0,0,0,0.05);box-shadow:0 1px 2px rgba(0,0,0,0.05)}
-.pagination ul>li{display:inline}
-.pagination ul>li>a,.pagination ul>li>span{float:left;padding:4px 12px;line-height:20px;text-decoration:none;background-color:#fff;border:1px solid #ddd;border-left-width:0}
-.pagination ul>li>a:hover,.pagination ul>li>a:focus,.pagination ul>.active>a,.pagination ul>.active>span{background-color:#f5f5f5}
-.pagination ul>.active>a,.pagination ul>.active>span{color:#999;cursor:default}
-.pagination ul>.disabled>span,.pagination ul>.disabled>a,.pagination ul>.disabled>a:hover,.pagination ul>.disabled>a:focus{color:#999;background-color:transparent;cursor:default}
-.pagination ul>li:first-child>a,.pagination ul>li:first-child>span{border-left-width:1px;-webkit-border-top-left-radius:4px;-moz-border-radius-topleft:4px;border-top-left-radius:4px;-webkit-border-bottom-left-radius:4px;-moz-border-radius-bottomleft:4px;border-bottom-left-radius:4px}
-.pagination ul>li:last-child>a,.pagination ul>li:last-child>span{-webkit-border-top-right-radius:4px;-moz-border-radius-topright:4px;border-top-right-radius:4px;-webkit-border-bottom-right-radius:4px;-moz-border-radius-bottomright:4px;border-bottom-right-radius:4px}
-.pagination-centered{text-align:center}
-.pagination-right{text-align:right}
-.pagination-large ul>li>a,.pagination-large ul>li>span{padding:11px 19px;font-size:16.25px}
-.pagination-large ul>li:first-child>a,.pagination-large ul>li:first-child>span{-webkit-border-top-left-radius:6px;-moz-border-radius-topleft:6px;border-top-left-radius:6px;-webkit-border-bottom-left-radius:6px;-moz-border-radius-bottomleft:6px;border-bottom-left-radius:6px}
-.pagination-large ul>li:last-child>a,.pagination-large ul>li:last-child>span{-webkit-border-top-right-radius:6px;-moz-border-radius-topright:6px;border-top-right-radius:6px;-webkit-border-bottom-right-radius:6px;-moz-border-radius-bottomright:6px;border-bottom-right-radius:6px}
-.pagination-mini ul>li:first-child>a,.pagination-small ul>li:first-child>a,.pagination-mini ul>li:first-child>span,.pagination-small ul>li:first-child>span{-webkit-border-top-left-radius:3px;-moz-border-radius-topleft:3px;border-top-left-radius:3px;-webkit-border-bottom-left-radius:3px;-moz-border-radius-bottomleft:3px;border-bottom-left-radius:3px}
-.pagination-mini ul>li:last-child>a,.pagination-small ul>li:last-child>a,.pagination-mini ul>li:last-child>span,.pagination-small ul>li:last-child>span{-webkit-border-top-right-radius:3px;-moz-border-radius-topright:3px;border-top-right-radius:3px;-webkit-border-bottom-right-radius:3px;-moz-border-radius-bottomright:3px;border-bottom-right-radius:3px}
-.pagination-small ul>li>a,.pagination-small ul>li>span{padding:2px 10px;font-size:11.049999999999999px}
-.pagination-mini ul>li>a,.pagination-mini ul>li>span{padding:0 6px;font-size:9.75px}
-.pager{margin:20px 0;list-style:none;text-align:center;*zoom:1}.pager:before,.pager:after{display:table;content:"";line-height:0}
-.pager:after{clear:both}
-.pager li{display:inline}
-.pager li>a,.pager li>span{display:inline-block;padding:5px 14px;background-color:#fff;border:1px solid #ddd;border-radius:15px;-webkit-border-radius:15px;-moz-border-radius:15px;border-radius:15px}
-.pager li>a:hover,.pager li>a:focus{text-decoration:none;background-color:#f5f5f5}
-.pager .next>a,.pager .next>span{float:right}
-.pager .previous>a,.pager .previous>span{float:left}
-.pager .disabled>a,.pager .disabled>a:hover,.pager .disabled>a:focus,.pager .disabled>span{color:#999;background-color:#fff;cursor:default}
-.modal-backdrop{position:fixed;top:0;right:0;bottom:0;left:0;z-index:1040;background-color:#000}.modal-backdrop.fade{opacity:0}
-.modal-backdrop,.modal-backdrop.fade.in{opacity:.8;filter:alpha(opacity=80)}
-.modal{position:fixed;top:10%;left:50%;z-index:1050;width:560px;margin-left:-280px;background-color:#fff;border:1px solid #999;border:1px solid rgba(0,0,0,0.3);*border:1px solid #999;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 3px 7px rgba(0,0,0,0.3);-moz-box-shadow:0 3px 7px rgba(0,0,0,0.3);box-shadow:0 3px 7px rgba(0,0,0,0.3);-webkit-background-clip:padding-box;-moz-background-clip:padding-box;background-clip:padding-box;outline:none}.modal.fade{-webkit-transition:opacity .3s linear, top .3s ease-out;-moz-transition:opacity .3s linear, top .3s ease-out;-o-transition:opacity .3s linear, top .3s ease-out;transition:opacity .3s linear, top .3s ease-out;top:-25%}
-.modal.fade.in{top:10%}
-.modal-header{padding:9px 15px;border-bottom:1px solid #eee}.modal-header .close{margin-top:2px}
-.modal-header h3{margin:0;line-height:30px}
-.modal-body{position:relative;overflow-y:auto;max-height:400px;padding:15px}
-.modal-form{margin-bottom:0}
-.modal-footer{padding:14px 15px 15px;margin-bottom:0;text-align:right;background-color:#f5f5f5;border-top:1px solid #ddd;-webkit-border-radius:0 0 6px 6px;-moz-border-radius:0 0 6px 6px;border-radius:0 0 6px 6px;-webkit-box-shadow:inset 0 1px 0 #fff;-moz-box-shadow:inset 0 1px 0 #fff;box-shadow:inset 0 1px 0 #fff;*zoom:1}.modal-footer:before,.modal-footer:after{display:table;content:"";line-height:0}
-.modal-footer:after{clear:both}
-.modal-footer .btn+.btn{margin-left:5px;margin-bottom:0}
-.modal-footer .btn-group .btn+.btn{margin-left:-1px}
-.modal-footer .btn-block+.btn-block{margin-left:0}
-.tooltip{position:absolute;z-index:1030;display:block;visibility:visible;font-size:11px;line-height:1.4;opacity:0;filter:alpha(opacity=0)}.tooltip.in{opacity:.8;filter:alpha(opacity=80)}
-.tooltip.top{margin-top:-3px;padding:5px 0}
-.tooltip.right{margin-left:3px;padding:0 5px}
-.tooltip.bottom{margin-top:3px;padding:5px 0}
-.tooltip.left{margin-left:-3px;padding:0 5px}
-.tooltip-inner{max-width:200px;padding:8px;color:#fff;text-align:center;text-decoration:none;background-color:#000;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.tooltip-arrow{position:absolute;width:0;height:0;border-color:transparent;border-style:solid}
-.tooltip.top .tooltip-arrow{bottom:0;left:50%;margin-left:-5px;border-width:5px 5px 0;border-top-color:#000}
-.tooltip.right .tooltip-arrow{top:50%;left:0;margin-top:-5px;border-width:5px 5px 5px 0;border-right-color:#000}
-.tooltip.left .tooltip-arrow{top:50%;right:0;margin-top:-5px;border-width:5px 0 5px 5px;border-left-color:#000}
-.tooltip.bottom .tooltip-arrow{top:0;left:50%;margin-left:-5px;border-width:0 5px 5px;border-bottom-color:#000}
-.popover{position:absolute;top:0;left:0;z-index:1010;display:none;max-width:276px;padding:1px;text-align:left;background-color:#fff;-webkit-background-clip:padding-box;-moz-background-clip:padding;background-clip:padding-box;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px;-webkit-box-shadow:0 5px 10px rgba(0,0,0,0.2);-moz-box-shadow:0 5px 10px rgba(0,0,0,0.2);box-shadow:0 5px 10px rgba(0,0,0,0.2);white-space:normal}.popover.top{margin-top:-10px}
-.popover.right{margin-left:10px}
-.popover.bottom{margin-top:10px}
-.popover.left{margin-left:-10px}
-.popover-title{margin:0;padding:8px 14px;font-size:14px;font-weight:normal;line-height:18px;background-color:#f7f7f7;border-bottom:1px solid #ebebeb;border-radius:5px 5px 0 0;-webkit-border-radius:5px 5px 0 0;-moz-border-radius:5px 5px 0 0;border-radius:5px 5px 0 0}.popover-title:empty{display:none}
-.popover-content{padding:9px 14px}
-.popover .arrow,.popover .arrow:after{position:absolute;display:block;width:0;height:0;border-color:transparent;border-style:solid}
-.popover .arrow{border-width:11px}
-.popover .arrow:after{border-width:10px;content:""}
-.popover.top .arrow{left:50%;margin-left:-11px;border-bottom-width:0;border-top-color:#999;border-top-color:rgba(0,0,0,0.25);bottom:-11px}.popover.top .arrow:after{bottom:1px;margin-left:-10px;border-bottom-width:0;border-top-color:#fff}
-.popover.right .arrow{top:50%;left:-11px;margin-top:-11px;border-left-width:0;border-right-color:#999;border-right-color:rgba(0,0,0,0.25)}.popover.right .arrow:after{left:1px;bottom:-10px;border-left-width:0;border-right-color:#fff}
-.popover.bottom .arrow{left:50%;margin-left:-11px;border-top-width:0;border-bottom-color:#999;border-bottom-color:rgba(0,0,0,0.25);top:-11px}.popover.bottom .arrow:after{top:1px;margin-left:-10px;border-top-width:0;border-bottom-color:#fff}
-.popover.left .arrow{top:50%;right:-11px;margin-top:-11px;border-right-width:0;border-left-color:#999;border-left-color:rgba(0,0,0,0.25)}.popover.left .arrow:after{right:1px;border-right-width:0;border-left-color:#fff;bottom:-10px}
-.thumbnails{margin-left:-20px;list-style:none;*zoom:1}.thumbnails:before,.thumbnails:after{display:table;content:"";line-height:0}
-.thumbnails:after{clear:both}
-.row-fluid .thumbnails{margin-left:0}
-.thumbnails>li{float:left;margin-bottom:20px;margin-left:20px}
-.thumbnail{display:block;padding:4px;line-height:20px;border:1px solid #ddd;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px;-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.055);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.055);box-shadow:0 1px 3px rgba(0,0,0,0.055);-webkit-transition:all .2s ease-in-out;-moz-transition:all .2s ease-in-out;-o-transition:all .2s ease-in-out;transition:all .2s ease-in-out}
-a.thumbnail:hover,a.thumbnail:focus{border-color:#08c;-webkit-box-shadow:0 1px 4px rgba(0,105,214,0.25);-moz-box-shadow:0 1px 4px rgba(0,105,214,0.25);box-shadow:0 1px 4px rgba(0,105,214,0.25)}
-.thumbnail>img{display:block;max-width:100%;margin-left:auto;margin-right:auto}
-.thumbnail .caption{padding:9px;color:#555}
-.media,.media-body{overflow:hidden;*overflow:visible;zoom:1}
-.media,.media .media{margin-top:15px}
-.media:first-child{margin-top:0}
-.media-object{display:block}
-.media-heading{margin:0 0 5px}
-.media>.pull-left{margin-right:10px}
-.media>.pull-right{margin-left:10px}
-.media-list{margin-left:0;list-style:none}
-.label,.badge{display:inline-block;padding:2px 4px;font-size:10.998px;font-weight:bold;line-height:14px;color:#fff;vertical-align:baseline;white-space:nowrap;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#999}
-.label{border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.badge{padding-left:9px;padding-right:9px;border-radius:9px;-webkit-border-radius:9px;-moz-border-radius:9px;border-radius:9px}
-.label:empty,.badge:empty{display:none}
-a.label:hover,a.label:focus,a.badge:hover,a.badge:focus{color:#fff;text-decoration:none;cursor:pointer}
-.label-important,.badge-important{background-color:#b94a48}
-.label-important[href],.badge-important[href]{background-color:#953b39}
-.label-warning,.badge-warning{background-color:#f89406}
-.label-warning[href],.badge-warning[href]{background-color:#c67605}
-.label-success,.badge-success{background-color:#468847}
-.label-success[href],.badge-success[href]{background-color:#356635}
-.label-info,.badge-info{background-color:#3a87ad}
-.label-info[href],.badge-info[href]{background-color:#2d6987}
-.label-inverse,.badge-inverse{background-color:#333}
-.label-inverse[href],.badge-inverse[href]{background-color:#1a1a1a}
-.btn .label,.btn .badge{position:relative;top:-1px}
-.btn-mini .label,.btn-mini .badge{top:0}
-@-webkit-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-moz-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-ms-keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}@-o-keyframes progress-bar-stripes{from{background-position:0 0} to{background-position:40px 0}}@keyframes progress-bar-stripes{from{background-position:40px 0} to{background-position:0 0}}.progress{overflow:hidden;height:20px;margin-bottom:20px;background-color:#f7f7f7;background-image:-moz-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#f5f5f5), to(#f9f9f9));background-image:-webkit-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:-o-linear-gradient(top, #f5f5f5, #f9f9f9);background-image:linear-gradient(to bottom, #f5f5f5, #f9f9f9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fff5f5f5', endColorstr='#fff9f9f9', GradientType=0);-webkit-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);-moz-box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);box-shadow:inset 0 1px 2px rgba(0,0,0,0.1);border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.progress .bar{width:0;height:100%;color:#fff;float:left;font-size:12px;text-align:center;text-shadow:0 -1px 0 rgba(0,0,0,0.25);background-color:#0e90d2;background-image:-moz-linear-gradient(top, #149bdf, #0480be);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#149bdf), to(#0480be));background-image:-webkit-linear-gradient(top, #149bdf, #0480be);background-image:-o-linear-gradient(top, #149bdf, #0480be);background-image:linear-gradient(to bottom, #149bdf, #0480be);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff149bdf', endColorstr='#ff0480be', GradientType=0);-webkit-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-moz-box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);box-shadow:inset 0 -1px 0 rgba(0,0,0,0.15);-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;-webkit-transition:width .6s ease;-moz-transition:width .6s ease;-o-transition:width .6s ease;transition:width .6s ease}
-.progress .bar+.bar{-webkit-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);-moz-box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15);box-shadow:inset 1px 0 0 rgba(0,0,0,.15), inset 0 -1px 0 rgba(0,0,0,.15)}
-.progress-striped .bar{background-color:#149bdf;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);-webkit-background-size:40px 40px;-moz-background-size:40px 40px;-o-background-size:40px 40px;background-size:40px 40px}
-.progress.active .bar{-webkit-animation:progress-bar-stripes 2s linear infinite;-moz-animation:progress-bar-stripes 2s linear infinite;-ms-animation:progress-bar-stripes 2s linear infinite;-o-animation:progress-bar-stripes 2s linear infinite;animation:progress-bar-stripes 2s linear infinite}
-.progress-danger .bar,.progress .bar-danger{background-color:#dd514c;background-image:-moz-linear-gradient(top, #ee5f5b, #c43c35);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#ee5f5b), to(#c43c35));background-image:-webkit-linear-gradient(top, #ee5f5b, #c43c35);background-image:-o-linear-gradient(top, #ee5f5b, #c43c35);background-image:linear-gradient(to bottom, #ee5f5b, #c43c35);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ffee5f5b', endColorstr='#ffc43c35', GradientType=0)}
-.progress-danger.progress-striped .bar,.progress-striped .bar-danger{background-color:#ee5f5b;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
-.progress-success .bar,.progress .bar-success{background-color:#5eb95e;background-image:-moz-linear-gradient(top, #62c462, #57a957);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#62c462), to(#57a957));background-image:-webkit-linear-gradient(top, #62c462, #57a957);background-image:-o-linear-gradient(top, #62c462, #57a957);background-image:linear-gradient(to bottom, #62c462, #57a957);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff62c462', endColorstr='#ff57a957', GradientType=0)}
-.progress-success.progress-striped .bar,.progress-striped .bar-success{background-color:#62c462;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
-.progress-info .bar,.progress .bar-info{background-color:#4bb1cf;background-image:-moz-linear-gradient(top, #5bc0de, #339bb9);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#5bc0de), to(#339bb9));background-image:-webkit-linear-gradient(top, #5bc0de, #339bb9);background-image:-o-linear-gradient(top, #5bc0de, #339bb9);background-image:linear-gradient(to bottom, #5bc0de, #339bb9);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#ff5bc0de', endColorstr='#ff339bb9', GradientType=0)}
-.progress-info.progress-striped .bar,.progress-striped .bar-info{background-color:#5bc0de;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
-.progress-warning .bar,.progress .bar-warning{background-color:#faa732;background-image:-moz-linear-gradient(top, #fbb450, #f89406);background-image:-webkit-gradient(linear, 0 0, 0 100%, from(#fbb450), to(#f89406));background-image:-webkit-linear-gradient(top, #fbb450, #f89406);background-image:-o-linear-gradient(top, #fbb450, #f89406);background-image:linear-gradient(to bottom, #fbb450, #f89406);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#fffbb450', endColorstr='#fff89406', GradientType=0)}
-.progress-warning.progress-striped .bar,.progress-striped .bar-warning{background-color:#fbb450;background-image:-webkit-gradient(linear, 0 100%, 100% 0, color-stop(.25, rgba(255,255,255,0.15)), color-stop(.25, transparent), color-stop(.5, transparent), color-stop(.5, rgba(255,255,255,0.15)), color-stop(.75, rgba(255,255,255,0.15)), color-stop(.75, transparent), to(transparent));background-image:-webkit-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-moz-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:-o-linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent)}
-.accordion{margin-bottom:20px}
-.accordion-group{margin-bottom:2px;border:1px solid #e5e5e5;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.accordion-heading{border-bottom:0}
-.accordion-heading .accordion-toggle{display:block;padding:8px 15px}
-.accordion-toggle{cursor:pointer}
-.accordion-inner{padding:9px 15px;border-top:1px solid #e5e5e5}
-.carousel{position:relative;margin-bottom:20px;line-height:1}
-.carousel-inner{overflow:hidden;width:100%;position:relative}
-.carousel-inner>.item{display:none;position:relative;-webkit-transition:.6s ease-in-out left;-moz-transition:.6s ease-in-out left;-o-transition:.6s ease-in-out left;transition:.6s ease-in-out left}.carousel-inner>.item>img,.carousel-inner>.item>a>img{display:block;line-height:1}
-.carousel-inner>.active,.carousel-inner>.next,.carousel-inner>.prev{display:block}
-.carousel-inner>.active{left:0}
-.carousel-inner>.next,.carousel-inner>.prev{position:absolute;top:0;width:100%}
-.carousel-inner>.next{left:100%}
-.carousel-inner>.prev{left:-100%}
-.carousel-inner>.next.left,.carousel-inner>.prev.right{left:0}
-.carousel-inner>.active.left{left:-100%}
-.carousel-inner>.active.right{left:100%}
-.carousel-control{position:absolute;top:40%;left:15px;width:40px;height:40px;margin-top:-20px;font-size:60px;font-weight:100;line-height:30px;color:#fff;text-align:center;background:#222;border:3px solid #fff;-webkit-border-radius:23px;-moz-border-radius:23px;border-radius:23px;opacity:.5;filter:alpha(opacity=50)}.carousel-control.right{left:auto;right:15px}
-.carousel-control:hover,.carousel-control:focus{color:#fff;text-decoration:none;opacity:.9;filter:alpha(opacity=90)}
-.carousel-indicators{position:absolute;top:15px;right:15px;z-index:5;margin:0;list-style:none}.carousel-indicators li{display:block;float:left;width:10px;height:10px;margin-left:5px;text-indent:-999px;background-color:#ccc;background-color:rgba(255,255,255,0.25);border-radius:5px}
-.carousel-indicators .active{background-color:#fff}
-.carousel-caption{position:absolute;left:0;right:0;bottom:0;padding:15px;background:#333;background:rgba(0,0,0,0.75)}
-.carousel-caption h4,.carousel-caption p{color:#fff;line-height:20px}
-.carousel-caption h4{margin:0 0 5px}
-.carousel-caption p{margin-bottom:0}
-.hero-unit{padding:60px;margin-bottom:30px;font-size:18px;font-weight:200;line-height:30px;color:inherit;background-color:#eee;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.hero-unit h1{margin-bottom:0;font-size:60px;line-height:1;color:inherit;letter-spacing:-1px}
-.hero-unit li{line-height:30px}
-.pull-right{float:right}
-.pull-left{float:left}
-.hide{display:none}
-.show{display:block}
-.invisible{visibility:hidden}
-.affix{position:fixed}
-@-ms-viewport{width:device-width}.hidden{display:none;visibility:hidden}
-.visible-phone{display:none !important}
-.visible-tablet{display:none !important}
-.hidden-desktop{display:none !important}
-.visible-desktop{display:inherit !important}
-@media (min-width:768px) and (max-width:979px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-tablet{display:inherit !important} .hidden-tablet{display:none !important}}@media (max-width:767px){.hidden-desktop{display:inherit !important} .visible-desktop{display:none !important} .visible-phone{display:inherit !important} .hidden-phone{display:none !important}}.visible-print{display:none !important}
-@media print{.visible-print{display:inherit !important} .hidden-print{display:none !important}}@media (min-width:1200px){.row{margin-left:-30px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0} .row:after{clear:both} [class*="span"]{float:left;min-height:1px;margin-left:30px} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:1170px} .span12{width:1170px} .span11{width:1070px} .span10{width:970px} .span9{width:870px} .span8{width:770px} .span7{width:670px} .span6{width:570px} .span5{width:470px} .span4{width:370px} .span3{width:270px} .span2{width:170px} .span1{width:70px} .offset12{margin-left:1230px} .offset11{margin-left:1130px} .offset10{margin-left:1030px} .offset9{margin-left:930px} .offset8{margin-left:830px} .offset7{margin-left:730px} .offset6{margin-left:630px} .offset5{margin-left:530px} .offset4{margin-left:430px} .offset3{margin-left:330px} .offset2{margin-left:230px} .offset1{margin-left:130px} .row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0} .row-fluid:after{clear:both} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.564102564102564%;*margin-left:2.5109110747408616%} .row-fluid [class*="span"]:first-child{margin-left:0} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.564102564102564%} .row-fluid .span12{width:100%;*width:99.94680851063829%} .row-fluid .span11{width:91.45299145299145%;*width:91.39979996362975%} .row-fluid .span10{width:82.90598290598291%;*width:82.8527914166212%} .row-fluid .span9{width:74.35897435897436%;*width:74.30578286961266%} .row-fluid .span8{width:65.81196581196582%;*width:65.75877432260411%} .row-fluid .span7{width:57.26495726495726%;*width:57.21176577559556%} .row-fluid .span6{width:48.717948717948715%;*width:48.664757228587014%} .row-fluid .span5{width:40.17094017094017%;*width:40.11774868157847%} .row-fluid .span4{width:31.623931623931625%;*width:31.570740134569924%} .row-fluid .span3{width:23.076923076923077%;*width:23.023731587561375%} .row-fluid .span2{width:14.52991452991453%;*width:14.476723040552828%} .row-fluid .span1{width:5.982905982905983%;*width:5.929714493544281%} .row-fluid .offset12{margin-left:105.12820512820512%;*margin-left:105.02182214948171%} .row-fluid .offset12:first-child{margin-left:102.56410256410257%;*margin-left:102.45771958537915%} .row-fluid .offset11{margin-left:96.58119658119658%;*margin-left:96.47481360247316%} .row-fluid .offset11:first-child{margin-left:94.01709401709402%;*margin-left:93.91071103837061%} .row-fluid .offset10{margin-left:88.03418803418803%;*margin-left:87.92780505546462%} .row-fluid .offset10:first-child{margin-left:85.47008547008548%;*margin-left:85.36370249136206%} .row-fluid .offset9{margin-left:79.48717948717949%;*margin-left:79.38079650845607%} .row-fluid .offset9:first-child{margin-left:76.92307692307693%;*margin-left:76.81669394435352%} .row-fluid .offset8{margin-left:70.94017094017094%;*margin-left:70.83378796144753%} .row-fluid .offset8:first-child{margin-left:68.37606837606839%;*margin-left:68.26968539734497%} .row-fluid .offset7{margin-left:62.393162393162385%;*margin-left:62.28677941443899%} .row-fluid .offset7:first-child{margin-left:59.82905982905982%;*margin-left:59.72267685033642%} .row-fluid .offset6{margin-left:53.84615384615384%;*margin-left:53.739770867430444%} .row-fluid .offset6:first-child{margin-left:51.28205128205128%;*margin-left:51.175668303327875%} .row-fluid .offset5{margin-left:45.299145299145295%;*margin-left:45.1927623204219%} .row-fluid .offset5:first-child{margin-left:42.73504273504273%;*margin-left:42.62865975631933%} .row-fluid .offset4{margin-left:36.75213675213675%;*margin-left:36.645753773413354%} .row-fluid .offset4:first-child{margin-left:34.18803418803419%;*margin-left:34.081651209310785%} .row-fluid .offset3{margin-left:28.205128205128204%;*margin-left:28.0987452264048%} .row-fluid .offset3:first-child{margin-left:25.641025641025642%;*margin-left:25.53464266230224%} .row-fluid .offset2{margin-left:19.65811965811966%;*margin-left:19.551736679396257%} .row-fluid .offset2:first-child{margin-left:17.094017094017094%;*margin-left:16.98763411529369%} .row-fluid .offset1{margin-left:11.11111111111111%;*margin-left:11.004728132387708%} .row-fluid .offset1:first-child{margin-left:8.547008547008547%;*margin-left:8.440625568285142%} input,textarea,.uneditable-input{margin-left:0} .controls-row [class*="span"]+[class*="span"]{margin-left:30px} input.span12,textarea.span12,.uneditable-input.span12{width:1156px} input.span11,textarea.span11,.uneditable-input.span11{width:1056px} input.span10,textarea.span10,.uneditable-input.span10{width:956px} input.span9,textarea.span9,.uneditable-input.span9{width:856px} input.span8,textarea.span8,.uneditable-input.span8{width:756px} input.span7,textarea.span7,.uneditable-input.span7{width:656px} input.span6,textarea.span6,.uneditable-input.span6{width:556px} input.span5,textarea.span5,.uneditable-input.span5{width:456px} input.span4,textarea.span4,.uneditable-input.span4{width:356px} input.span3,textarea.span3,.uneditable-input.span3{width:256px} input.span2,textarea.span2,.uneditable-input.span2{width:156px} input.span1,textarea.span1,.uneditable-input.span1{width:56px} .thumbnails{margin-left:-30px} .thumbnails>li{margin-left:30px} .row-fluid .thumbnails{margin-left:0}}@media (min-width:768px) and (max-width:979px){.row{margin-left:-20px;*zoom:1}.row:before,.row:after{display:table;content:"";line-height:0} .row:after{clear:both} [class*="span"]{float:left;min-height:1px;margin-left:20px} .container,.navbar-static-top .container,.navbar-fixed-top .container,.navbar-fixed-bottom .container{width:724px} .span12{width:724px} .span11{width:662px} .span10{width:600px} .span9{width:538px} .span8{width:476px} .span7{width:414px} .span6{width:352px} .span5{width:290px} .span4{width:228px} .span3{width:166px} .span2{width:104px} .span1{width:42px} .offset12{margin-left:764px} .offset11{margin-left:702px} .offset10{margin-left:640px} .offset9{margin-left:578px} .offset8{margin-left:516px} .offset7{margin-left:454px} .offset6{margin-left:392px} .offset5{margin-left:330px} .offset4{margin-left:268px} .offset3{margin-left:206px} .offset2{margin-left:144px} .offset1{margin-left:82px} .row-fluid{width:100%;*zoom:1}.row-fluid:before,.row-fluid:after{display:table;content:"";line-height:0} .row-fluid:after{clear:both} .row-fluid [class*="span"]{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box;float:left;margin-left:2.7624309392265194%;*margin-left:2.709239449864817%} .row-fluid [class*="span"]:first-child{margin-left:0} .row-fluid .controls-row [class*="span"]+[class*="span"]{margin-left:2.7624309392265194%} .row-fluid .span12{width:100%;*width:99.94680851063829%} .row-fluid .span11{width:91.43646408839778%;*width:91.38327259903608%} .row-fluid .span10{width:82.87292817679558%;*width:82.81973668743387%} .row-fluid .span9{width:74.30939226519337%;*width:74.25620077583166%} .row-fluid .span8{width:65.74585635359117%;*width:65.69266486422946%} .row-fluid .span7{width:57.18232044198895%;*width:57.12912895262725%} .row-fluid .span6{width:48.61878453038674%;*width:48.56559304102504%} .row-fluid .span5{width:40.05524861878453%;*width:40.00205712942283%} .row-fluid .span4{width:31.491712707182323%;*width:31.43852121782062%} .row-fluid .span3{width:22.92817679558011%;*width:22.87498530621841%} .row-fluid .span2{width:14.3646408839779%;*width:14.311449394616199%} .row-fluid .span1{width:5.801104972375691%;*width:5.747913483013988%} .row-fluid .offset12{margin-left:105.52486187845304%;*margin-left:105.41847889972962%} .row-fluid .offset12:first-child{margin-left:102.76243093922652%;*margin-left:102.6560479605031%} .row-fluid .offset11{margin-left:96.96132596685082%;*margin-left:96.8549429881274%} .row-fluid .offset11:first-child{margin-left:94.1988950276243%;*margin-left:94.09251204890089%} .row-fluid .offset10{margin-left:88.39779005524862%;*margin-left:88.2914070765252%} .row-fluid .offset10:first-child{margin-left:85.6353591160221%;*margin-left:85.52897613729868%} .row-fluid .offset9{margin-left:79.8342541436464%;*margin-left:79.72787116492299%} .row-fluid .offset9:first-child{margin-left:77.07182320441989%;*margin-left:76.96544022569647%} .row-fluid .offset8{margin-left:71.2707182320442%;*margin-left:71.16433525332079%} .row-fluid .offset8:first-child{margin-left:68.50828729281768%;*margin-left:68.40190431409427%} .row-fluid .offset7{margin-left:62.70718232044199%;*margin-left:62.600799341718584%} .row-fluid .offset7:first-child{margin-left:59.94475138121547%;*margin-left:59.838368402492065%} .row-fluid .offset6{margin-left:54.14364640883978%;*margin-left:54.037263430116376%} .row-fluid .offset6:first-child{margin-left:51.38121546961326%;*margin-left:51.27483249088986%} .row-fluid .offset5{margin-left:45.58011049723757%;*margin-left:45.47372751851417%} .row-fluid .offset5:first-child{margin-left:42.81767955801105%;*margin-left:42.71129657928765%} .row-fluid .offset4{margin-left:37.01657458563536%;*margin-left:36.91019160691196%} .row-fluid .offset4:first-child{margin-left:34.25414364640884%;*margin-left:34.14776066768544%} .row-fluid .offset3{margin-left:28.45303867403315%;*margin-left:28.346655695309746%} .row-fluid .offset3:first-child{margin-left:25.69060773480663%;*margin-left:25.584224756083227%} .row-fluid .offset2{margin-left:19.88950276243094%;*margin-left:19.783119783707537%} .row-fluid .offset2:first-child{margin-left:17.12707182320442%;*margin-left:17.02068884448102%} .row-fluid .offset1{margin-left:11.32596685082873%;*margin-left:11.219583872105325%} .row-fluid .offset1:first-child{margin-left:8.56353591160221%;*margin-left:8.457152932878806%} input,textarea,.uneditable-input{margin-left:0} .controls-row [class*="span"]+[class*="span"]{margin-left:20px} input.span12,textarea.span12,.uneditable-input.span12{width:710px} input.span11,textarea.span11,.uneditable-input.span11{width:648px} input.span10,textarea.span10,.uneditable-input.span10{width:586px} input.span9,textarea.span9,.uneditable-input.span9{width:524px} input.span8,textarea.span8,.uneditable-input.span8{width:462px} input.span7,textarea.span7,.uneditable-input.span7{width:400px} input.span6,textarea.span6,.uneditable-input.span6{width:338px} input.span5,textarea.span5,.uneditable-input.span5{width:276px} input.span4,textarea.span4,.uneditable-input.span4{width:214px} input.span3,textarea.span3,.uneditable-input.span3{width:152px} input.span2,textarea.span2,.uneditable-input.span2{width:90px} input.span1,textarea.span1,.uneditable-input.span1{width:28px}}@media (max-width:767px){body{padding-left:20px;padding-right:20px} .navbar-fixed-top,.navbar-fixed-bottom,.navbar-static-top{margin-left:-20px;margin-right:-20px} .container-fluid{padding:0} .dl-horizontal dt{float:none;clear:none;width:auto;text-align:left} .dl-horizontal dd{margin-left:0} .container{width:auto} .row-fluid{width:100%} .row,.thumbnails{margin-left:0} .thumbnails>li{float:none;margin-left:0} [class*="span"],.uneditable-input[class*="span"],.row-fluid [class*="span"]{float:none;display:block;width:100%;margin-left:0;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .span12,.row-fluid .span12{width:100%;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .row-fluid [class*="offset"]:first-child{margin-left:0} .input-large,.input-xlarge,.input-xxlarge,input[class*="span"],select[class*="span"],textarea[class*="span"],.uneditable-input{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box} .input-prepend input,.input-append input,.input-prepend input[class*="span"],.input-append input[class*="span"]{display:inline-block;width:auto} .controls-row [class*="span"]+[class*="span"]{margin-left:0} .modal{position:fixed;top:20px;left:20px;right:20px;width:auto;margin:0}.modal.fade{top:-100px} .modal.fade.in{top:20px}}@media (max-width:480px){.nav-collapse{-webkit-transform:translate3d(0, 0, 0)} .page-header h1 small{display:block;line-height:20px} input[type="checkbox"],input[type="radio"]{border:1px solid #ccc} .form-horizontal .control-label{float:none;width:auto;padding-top:0;text-align:left} .form-horizontal .controls{margin-left:0} .form-horizontal .control-list{padding-top:0} .form-horizontal .form-actions{padding-left:10px;padding-right:10px} .media .pull-left,.media .pull-right{float:none;display:block;margin-bottom:10px} .media-object{margin-right:0;margin-left:0} .modal{top:10px;left:10px;right:10px} .modal-header .close{padding:10px;margin:-10px} .carousel-caption{position:static}}@media (max-width:979px){body{padding-top:0} .navbar-fixed-top,.navbar-fixed-bottom{position:static} .navbar-fixed-top{margin-bottom:20px} .navbar-fixed-bottom{margin-top:20px} .navbar-fixed-top .navbar-inner,.navbar-fixed-bottom .navbar-inner{padding:5px} .navbar .container{width:auto;padding:0} .navbar .brand{padding-left:10px;padding-right:10px;margin:0 0 0 -5px} .nav-collapse{clear:both} .nav-collapse .nav{float:none;margin:0 0 10px} .nav-collapse .nav>li{float:none} .nav-collapse .nav>li>a{margin-bottom:2px} .nav-collapse .nav>.divider-vertical{display:none} .nav-collapse .nav .nav-header{color:#777;text-shadow:none} .nav-collapse .nav>li>a,.nav-collapse .dropdown-menu a{padding:9px 15px;font-weight:bold;color:#777;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px} .nav-collapse .btn{padding:4px 10px 4px;font-weight:normal;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px} .nav-collapse .dropdown-menu li+li a{margin-bottom:2px} .nav-collapse .nav>li>a:hover,.nav-collapse .nav>li>a:focus,.nav-collapse .dropdown-menu a:hover,.nav-collapse .dropdown-menu a:focus{background-color:#f2f2f2} .navbar-inverse .nav-collapse .nav>li>a,.navbar-inverse .nav-collapse .dropdown-menu a{color:#999} .navbar-inverse .nav-collapse .nav>li>a:hover,.navbar-inverse .nav-collapse .nav>li>a:focus,.navbar-inverse .nav-collapse .dropdown-menu a:hover,.navbar-inverse .nav-collapse .dropdown-menu a:focus{background-color:#111} .nav-collapse.in .btn-group{margin-top:5px;padding:0} .nav-collapse .dropdown-menu{position:static;top:auto;left:auto;float:none;display:none;max-width:none;margin:0 15px;padding:0;background-color:transparent;border:none;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;-webkit-box-shadow:none;-moz-box-shadow:none;box-shadow:none} .nav-collapse .open>.dropdown-menu{display:block} .nav-collapse .dropdown-menu:before,.nav-collapse .dropdown-menu:after{display:none} .nav-collapse .dropdown-menu .divider{display:none} .nav-collapse .nav>li>.dropdown-menu:before,.nav-collapse .nav>li>.dropdown-menu:after{display:none} .nav-collapse .navbar-form,.nav-collapse .navbar-search{float:none;padding:10px 15px;margin:10px 0;border-top:1px solid #f2f2f2;border-bottom:1px solid #f2f2f2;-webkit-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);-moz-box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1);box-shadow:inset 0 1px 0 rgba(255,255,255,.1), 0 1px 0 rgba(255,255,255,.1)} .navbar-inverse .nav-collapse .navbar-form,.navbar-inverse .nav-collapse .navbar-search{border-top-color:#111;border-bottom-color:#111} .navbar .nav-collapse .nav.pull-right{float:none;margin-left:0} .nav-collapse,.nav-collapse.collapse{overflow:hidden;height:0} .navbar .btn-navbar{display:block} .navbar-static .navbar-inner{padding-left:10px;padding-right:10px}}@media (min-width:979px + 1){.nav-collapse.collapse{height:auto !important;overflow:visible !important}}@font-face{font-family:'FontAwesome';src:url('../components/font-awesome/font/fontawesome-webfont.eot?v=3.2.1');src:url('../components/font-awesome/font/fontawesome-webfont.eot?#iefix&v=3.2.1') format('embedded-opentype'),url('../components/font-awesome/font/fontawesome-webfont.woff?v=3.2.1') format('woff'),url('../components/font-awesome/font/fontawesome-webfont.ttf?v=3.2.1') format('truetype'),url('../components/font-awesome/font/fontawesome-webfont.svg#fontawesomeregular?v=3.2.1') format('svg');font-weight:normal;font-style:normal}[class^="icon-"],[class*=" icon-"]{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em}
-[class^="icon-"]:before,[class*=" icon-"]:before{text-decoration:inherit;display:inline-block;speak:none}
-.icon-large:before{vertical-align:-10%;font-size:1.3333333333333333em}
-a [class^="icon-"],a [class*=" icon-"]{display:inline}
-[class^="icon-"].icon-fixed-width,[class*=" icon-"].icon-fixed-width{display:inline-block;width:1.1428571428571428em;text-align:right;padding-right:.2857142857142857em}[class^="icon-"].icon-fixed-width.icon-large,[class*=" icon-"].icon-fixed-width.icon-large{width:1.4285714285714286em}
-.icons-ul{margin-left:2.142857142857143em;list-style-type:none}.icons-ul>li{position:relative}
-.icons-ul .icon-li{position:absolute;left:-2.142857142857143em;width:2.142857142857143em;text-align:center;line-height:inherit}
-[class^="icon-"].hide,[class*=" icon-"].hide{display:none}
-.icon-muted{color:#eee}
-.icon-light{color:#fff}
-.icon-dark{color:#333}
-.icon-border{border:solid 1px #eee;padding:.2em .25em .15em;border-radius:3px;-webkit-border-radius:3px;-moz-border-radius:3px;border-radius:3px}
-.icon-2x{font-size:2em}.icon-2x.icon-border{border-width:2px;border-radius:4px;-webkit-border-radius:4px;-moz-border-radius:4px;border-radius:4px}
-.icon-3x{font-size:3em}.icon-3x.icon-border{border-width:3px;border-radius:5px;-webkit-border-radius:5px;-moz-border-radius:5px;border-radius:5px}
-.icon-4x{font-size:4em}.icon-4x.icon-border{border-width:4px;border-radius:6px;-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}
-.icon-5x{font-size:5em}.icon-5x.icon-border{border-width:5px;border-radius:7px;-webkit-border-radius:7px;-moz-border-radius:7px;border-radius:7px}
-.pull-right{float:right}
-.pull-left{float:left}
-[class^="icon-"].pull-left,[class*=" icon-"].pull-left{margin-right:.3em}
-[class^="icon-"].pull-right,[class*=" icon-"].pull-right{margin-left:.3em}
-[class^="icon-"],[class*=" icon-"]{display:inline;width:auto;height:auto;line-height:normal;vertical-align:baseline;background-image:none;background-position:0 0;background-repeat:repeat;margin-top:0}
-.icon-white,.nav-pills>.active>a>[class^="icon-"],.nav-pills>.active>a>[class*=" icon-"],.nav-list>.active>a>[class^="icon-"],.nav-list>.active>a>[class*=" icon-"],.navbar-inverse .nav>.active>a>[class^="icon-"],.navbar-inverse .nav>.active>a>[class*=" icon-"],.dropdown-menu>li>a:hover>[class^="icon-"],.dropdown-menu>li>a:hover>[class*=" icon-"],.dropdown-menu>.active>a>[class^="icon-"],.dropdown-menu>.active>a>[class*=" icon-"],.dropdown-submenu:hover>a>[class^="icon-"],.dropdown-submenu:hover>a>[class*=" icon-"]{background-image:none}
-.btn [class^="icon-"].icon-large,.nav [class^="icon-"].icon-large,.btn [class*=" icon-"].icon-large,.nav [class*=" icon-"].icon-large{line-height:.9em}
-.btn [class^="icon-"].icon-spin,.nav [class^="icon-"].icon-spin,.btn [class*=" icon-"].icon-spin,.nav [class*=" icon-"].icon-spin{display:inline-block}
-.nav-tabs [class^="icon-"],.nav-pills [class^="icon-"],.nav-tabs [class*=" icon-"],.nav-pills [class*=" icon-"],.nav-tabs [class^="icon-"].icon-large,.nav-pills [class^="icon-"].icon-large,.nav-tabs [class*=" icon-"].icon-large,.nav-pills [class*=" icon-"].icon-large{line-height:.9em}
-.btn [class^="icon-"].pull-left.icon-2x,.btn [class*=" icon-"].pull-left.icon-2x,.btn [class^="icon-"].pull-right.icon-2x,.btn [class*=" icon-"].pull-right.icon-2x{margin-top:.18em}
-.btn [class^="icon-"].icon-spin.icon-large,.btn [class*=" icon-"].icon-spin.icon-large{line-height:.8em}
-.btn.btn-small [class^="icon-"].pull-left.icon-2x,.btn.btn-small [class*=" icon-"].pull-left.icon-2x,.btn.btn-small [class^="icon-"].pull-right.icon-2x,.btn.btn-small [class*=" icon-"].pull-right.icon-2x{margin-top:.25em}
-.btn.btn-large [class^="icon-"],.btn.btn-large [class*=" icon-"]{margin-top:0}.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x,.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-top:.05em}
-.btn.btn-large [class^="icon-"].pull-left.icon-2x,.btn.btn-large [class*=" icon-"].pull-left.icon-2x{margin-right:.2em}
-.btn.btn-large [class^="icon-"].pull-right.icon-2x,.btn.btn-large [class*=" icon-"].pull-right.icon-2x{margin-left:.2em}
-.nav-list [class^="icon-"],.nav-list [class*=" icon-"]{line-height:inherit}
-.icon-stack{position:relative;display:inline-block;width:2em;height:2em;line-height:2em;vertical-align:-35%}.icon-stack [class^="icon-"],.icon-stack [class*=" icon-"]{display:block;text-align:center;position:absolute;width:100%;height:100%;font-size:1em;line-height:inherit;*line-height:2em}
-.icon-stack .icon-stack-base{font-size:2em;*line-height:1em}
-.icon-spin{display:inline-block;-moz-animation:spin 2s infinite linear;-o-animation:spin 2s infinite linear;-webkit-animation:spin 2s infinite linear;animation:spin 2s infinite linear}
-a .icon-stack,a .icon-spin{display:inline-block;text-decoration:none}
-@-moz-keyframes spin{0%{-moz-transform:rotate(0deg)} 100%{-moz-transform:rotate(359deg)}}@-webkit-keyframes spin{0%{-webkit-transform:rotate(0deg)} 100%{-webkit-transform:rotate(359deg)}}@-o-keyframes spin{0%{-o-transform:rotate(0deg)} 100%{-o-transform:rotate(359deg)}}@-ms-keyframes spin{0%{-ms-transform:rotate(0deg)} 100%{-ms-transform:rotate(359deg)}}@keyframes spin{0%{transform:rotate(0deg)} 100%{transform:rotate(359deg)}}.icon-rotate-90:before{-webkit-transform:rotate(90deg);-moz-transform:rotate(90deg);-ms-transform:rotate(90deg);-o-transform:rotate(90deg);transform:rotate(90deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=1)}
-.icon-rotate-180:before{-webkit-transform:rotate(180deg);-moz-transform:rotate(180deg);-ms-transform:rotate(180deg);-o-transform:rotate(180deg);transform:rotate(180deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=2)}
-.icon-rotate-270:before{-webkit-transform:rotate(270deg);-moz-transform:rotate(270deg);-ms-transform:rotate(270deg);-o-transform:rotate(270deg);transform:rotate(270deg);filter:progid:DXImageTransform.Microsoft.BasicImage(rotation=3)}
-.icon-flip-horizontal:before{-webkit-transform:scale(-1, 1);-moz-transform:scale(-1, 1);-ms-transform:scale(-1, 1);-o-transform:scale(-1, 1);transform:scale(-1, 1)}
-.icon-flip-vertical:before{-webkit-transform:scale(1, -1);-moz-transform:scale(1, -1);-ms-transform:scale(1, -1);-o-transform:scale(1, -1);transform:scale(1, -1)}
-a .icon-rotate-90:before,a .icon-rotate-180:before,a .icon-rotate-270:before,a .icon-flip-horizontal:before,a .icon-flip-vertical:before{display:inline-block}
-.icon-glass:before{content:"\f000"}
-.icon-music:before{content:"\f001"}
-.icon-search:before{content:"\f002"}
-.icon-envelope-alt:before{content:"\f003"}
-.icon-heart:before{content:"\f004"}
-.icon-star:before{content:"\f005"}
-.icon-star-empty:before{content:"\f006"}
-.icon-user:before{content:"\f007"}
-.icon-film:before{content:"\f008"}
-.icon-th-large:before{content:"\f009"}
-.icon-th:before{content:"\f00a"}
-.icon-th-list:before{content:"\f00b"}
-.icon-ok:before{content:"\f00c"}
-.icon-remove:before{content:"\f00d"}
-.icon-zoom-in:before{content:"\f00e"}
-.icon-zoom-out:before{content:"\f010"}
-.icon-power-off:before,.icon-off:before{content:"\f011"}
-.icon-signal:before{content:"\f012"}
-.icon-gear:before,.icon-cog:before{content:"\f013"}
-.icon-trash:before{content:"\f014"}
-.icon-home:before{content:"\f015"}
-.icon-file-alt:before{content:"\f016"}
-.icon-time:before{content:"\f017"}
-.icon-road:before{content:"\f018"}
-.icon-download-alt:before{content:"\f019"}
-.icon-download:before{content:"\f01a"}
-.icon-upload:before{content:"\f01b"}
-.icon-inbox:before{content:"\f01c"}
-.icon-play-circle:before{content:"\f01d"}
-.icon-rotate-right:before,.icon-repeat:before{content:"\f01e"}
-.icon-refresh:before{content:"\f021"}
-.icon-list-alt:before{content:"\f022"}
-.icon-lock:before{content:"\f023"}
-.icon-flag:before{content:"\f024"}
-.icon-headphones:before{content:"\f025"}
-.icon-volume-off:before{content:"\f026"}
-.icon-volume-down:before{content:"\f027"}
-.icon-volume-up:before{content:"\f028"}
-.icon-qrcode:before{content:"\f029"}
-.icon-barcode:before{content:"\f02a"}
-.icon-tag:before{content:"\f02b"}
-.icon-tags:before{content:"\f02c"}
-.icon-book:before{content:"\f02d"}
-.icon-bookmark:before{content:"\f02e"}
-.icon-print:before{content:"\f02f"}
-.icon-camera:before{content:"\f030"}
-.icon-font:before{content:"\f031"}
-.icon-bold:before{content:"\f032"}
-.icon-italic:before{content:"\f033"}
-.icon-text-height:before{content:"\f034"}
-.icon-text-width:before{content:"\f035"}
-.icon-align-left:before{content:"\f036"}
-.icon-align-center:before{content:"\f037"}
-.icon-align-right:before{content:"\f038"}
-.icon-align-justify:before{content:"\f039"}
-.icon-list:before{content:"\f03a"}
-.icon-indent-left:before{content:"\f03b"}
-.icon-indent-right:before{content:"\f03c"}
-.icon-facetime-video:before{content:"\f03d"}
-.icon-picture:before{content:"\f03e"}
-.icon-pencil:before{content:"\f040"}
-.icon-map-marker:before{content:"\f041"}
-.icon-adjust:before{content:"\f042"}
-.icon-tint:before{content:"\f043"}
-.icon-edit:before{content:"\f044"}
-.icon-share:before{content:"\f045"}
-.icon-check:before{content:"\f046"}
-.icon-move:before{content:"\f047"}
-.icon-step-backward:before{content:"\f048"}
-.icon-fast-backward:before{content:"\f049"}
-.icon-backward:before{content:"\f04a"}
-.icon-play:before{content:"\f04b"}
-.icon-pause:before{content:"\f04c"}
-.icon-stop:before{content:"\f04d"}
-.icon-forward:before{content:"\f04e"}
-.icon-fast-forward:before{content:"\f050"}
-.icon-step-forward:before{content:"\f051"}
-.icon-eject:before{content:"\f052"}
-.icon-chevron-left:before{content:"\f053"}
-.icon-chevron-right:before{content:"\f054"}
-.icon-plus-sign:before{content:"\f055"}
-.icon-minus-sign:before{content:"\f056"}
-.icon-remove-sign:before{content:"\f057"}
-.icon-ok-sign:before{content:"\f058"}
-.icon-question-sign:before{content:"\f059"}
-.icon-info-sign:before{content:"\f05a"}
-.icon-screenshot:before{content:"\f05b"}
-.icon-remove-circle:before{content:"\f05c"}
-.icon-ok-circle:before{content:"\f05d"}
-.icon-ban-circle:before{content:"\f05e"}
-.icon-arrow-left:before{content:"\f060"}
-.icon-arrow-right:before{content:"\f061"}
-.icon-arrow-up:before{content:"\f062"}
-.icon-arrow-down:before{content:"\f063"}
-.icon-mail-forward:before,.icon-share-alt:before{content:"\f064"}
-.icon-resize-full:before{content:"\f065"}
-.icon-resize-small:before{content:"\f066"}
-.icon-plus:before{content:"\f067"}
-.icon-minus:before{content:"\f068"}
-.icon-asterisk:before{content:"\f069"}
-.icon-exclamation-sign:before{content:"\f06a"}
-.icon-gift:before{content:"\f06b"}
-.icon-leaf:before{content:"\f06c"}
-.icon-fire:before{content:"\f06d"}
-.icon-eye-open:before{content:"\f06e"}
-.icon-eye-close:before{content:"\f070"}
-.icon-warning-sign:before{content:"\f071"}
-.icon-plane:before{content:"\f072"}
-.icon-calendar:before{content:"\f073"}
-.icon-random:before{content:"\f074"}
-.icon-comment:before{content:"\f075"}
-.icon-magnet:before{content:"\f076"}
-.icon-chevron-up:before{content:"\f077"}
-.icon-chevron-down:before{content:"\f078"}
-.icon-retweet:before{content:"\f079"}
-.icon-shopping-cart:before{content:"\f07a"}
-.icon-folder-close:before{content:"\f07b"}
-.icon-folder-open:before{content:"\f07c"}
-.icon-resize-vertical:before{content:"\f07d"}
-.icon-resize-horizontal:before{content:"\f07e"}
-.icon-bar-chart:before{content:"\f080"}
-.icon-twitter-sign:before{content:"\f081"}
-.icon-facebook-sign:before{content:"\f082"}
-.icon-camera-retro:before{content:"\f083"}
-.icon-key:before{content:"\f084"}
-.icon-gears:before,.icon-cogs:before{content:"\f085"}
-.icon-comments:before{content:"\f086"}
-.icon-thumbs-up-alt:before{content:"\f087"}
-.icon-thumbs-down-alt:before{content:"\f088"}
-.icon-star-half:before{content:"\f089"}
-.icon-heart-empty:before{content:"\f08a"}
-.icon-signout:before{content:"\f08b"}
-.icon-linkedin-sign:before{content:"\f08c"}
-.icon-pushpin:before{content:"\f08d"}
-.icon-external-link:before{content:"\f08e"}
-.icon-signin:before{content:"\f090"}
-.icon-trophy:before{content:"\f091"}
-.icon-github-sign:before{content:"\f092"}
-.icon-upload-alt:before{content:"\f093"}
-.icon-lemon:before{content:"\f094"}
-.icon-phone:before{content:"\f095"}
-.icon-unchecked:before,.icon-check-empty:before{content:"\f096"}
-.icon-bookmark-empty:before{content:"\f097"}
-.icon-phone-sign:before{content:"\f098"}
-.icon-twitter:before{content:"\f099"}
-.icon-facebook:before{content:"\f09a"}
-.icon-github:before{content:"\f09b"}
-.icon-unlock:before{content:"\f09c"}
-.icon-credit-card:before{content:"\f09d"}
-.icon-rss:before{content:"\f09e"}
-.icon-hdd:before{content:"\f0a0"}
-.icon-bullhorn:before{content:"\f0a1"}
-.icon-bell:before{content:"\f0a2"}
-.icon-certificate:before{content:"\f0a3"}
-.icon-hand-right:before{content:"\f0a4"}
-.icon-hand-left:before{content:"\f0a5"}
-.icon-hand-up:before{content:"\f0a6"}
-.icon-hand-down:before{content:"\f0a7"}
-.icon-circle-arrow-left:before{content:"\f0a8"}
-.icon-circle-arrow-right:before{content:"\f0a9"}
-.icon-circle-arrow-up:before{content:"\f0aa"}
-.icon-circle-arrow-down:before{content:"\f0ab"}
-.icon-globe:before{content:"\f0ac"}
-.icon-wrench:before{content:"\f0ad"}
-.icon-tasks:before{content:"\f0ae"}
-.icon-filter:before{content:"\f0b0"}
-.icon-briefcase:before{content:"\f0b1"}
-.icon-fullscreen:before{content:"\f0b2"}
-.icon-group:before{content:"\f0c0"}
-.icon-link:before{content:"\f0c1"}
-.icon-cloud:before{content:"\f0c2"}
-.icon-beaker:before{content:"\f0c3"}
-.icon-cut:before{content:"\f0c4"}
-.icon-copy:before{content:"\f0c5"}
-.icon-paperclip:before,.icon-paper-clip:before{content:"\f0c6"}
-.icon-save:before{content:"\f0c7"}
-.icon-sign-blank:before{content:"\f0c8"}
-.icon-reorder:before{content:"\f0c9"}
-.icon-list-ul:before{content:"\f0ca"}
-.icon-list-ol:before{content:"\f0cb"}
-.icon-strikethrough:before{content:"\f0cc"}
-.icon-underline:before{content:"\f0cd"}
-.icon-table:before{content:"\f0ce"}
-.icon-magic:before{content:"\f0d0"}
-.icon-truck:before{content:"\f0d1"}
-.icon-pinterest:before{content:"\f0d2"}
-.icon-pinterest-sign:before{content:"\f0d3"}
-.icon-google-plus-sign:before{content:"\f0d4"}
-.icon-google-plus:before{content:"\f0d5"}
-.icon-money:before{content:"\f0d6"}
-.icon-caret-down:before{content:"\f0d7"}
-.icon-caret-up:before{content:"\f0d8"}
-.icon-caret-left:before{content:"\f0d9"}
-.icon-caret-right:before{content:"\f0da"}
-.icon-columns:before{content:"\f0db"}
-.icon-sort:before{content:"\f0dc"}
-.icon-sort-down:before{content:"\f0dd"}
-.icon-sort-up:before{content:"\f0de"}
-.icon-envelope:before{content:"\f0e0"}
-.icon-linkedin:before{content:"\f0e1"}
-.icon-rotate-left:before,.icon-undo:before{content:"\f0e2"}
-.icon-legal:before{content:"\f0e3"}
-.icon-dashboard:before{content:"\f0e4"}
-.icon-comment-alt:before{content:"\f0e5"}
-.icon-comments-alt:before{content:"\f0e6"}
-.icon-bolt:before{content:"\f0e7"}
-.icon-sitemap:before{content:"\f0e8"}
-.icon-umbrella:before{content:"\f0e9"}
-.icon-paste:before{content:"\f0ea"}
-.icon-lightbulb:before{content:"\f0eb"}
-.icon-exchange:before{content:"\f0ec"}
-.icon-cloud-download:before{content:"\f0ed"}
-.icon-cloud-upload:before{content:"\f0ee"}
-.icon-user-md:before{content:"\f0f0"}
-.icon-stethoscope:before{content:"\f0f1"}
-.icon-suitcase:before{content:"\f0f2"}
-.icon-bell-alt:before{content:"\f0f3"}
-.icon-coffee:before{content:"\f0f4"}
-.icon-food:before{content:"\f0f5"}
-.icon-file-text-alt:before{content:"\f0f6"}
-.icon-building:before{content:"\f0f7"}
-.icon-hospital:before{content:"\f0f8"}
-.icon-ambulance:before{content:"\f0f9"}
-.icon-medkit:before{content:"\f0fa"}
-.icon-fighter-jet:before{content:"\f0fb"}
-.icon-beer:before{content:"\f0fc"}
-.icon-h-sign:before{content:"\f0fd"}
-.icon-plus-sign-alt:before{content:"\f0fe"}
-.icon-double-angle-left:before{content:"\f100"}
-.icon-double-angle-right:before{content:"\f101"}
-.icon-double-angle-up:before{content:"\f102"}
-.icon-double-angle-down:before{content:"\f103"}
-.icon-angle-left:before{content:"\f104"}
-.icon-angle-right:before{content:"\f105"}
-.icon-angle-up:before{content:"\f106"}
-.icon-angle-down:before{content:"\f107"}
-.icon-desktop:before{content:"\f108"}
-.icon-laptop:before{content:"\f109"}
-.icon-tablet:before{content:"\f10a"}
-.icon-mobile-phone:before{content:"\f10b"}
-.icon-circle-blank:before{content:"\f10c"}
-.icon-quote-left:before{content:"\f10d"}
-.icon-quote-right:before{content:"\f10e"}
-.icon-spinner:before{content:"\f110"}
-.icon-circle:before{content:"\f111"}
-.icon-mail-reply:before,.icon-reply:before{content:"\f112"}
-.icon-github-alt:before{content:"\f113"}
-.icon-folder-close-alt:before{content:"\f114"}
-.icon-folder-open-alt:before{content:"\f115"}
-.icon-expand-alt:before{content:"\f116"}
-.icon-collapse-alt:before{content:"\f117"}
-.icon-smile:before{content:"\f118"}
-.icon-frown:before{content:"\f119"}
-.icon-meh:before{content:"\f11a"}
-.icon-gamepad:before{content:"\f11b"}
-.icon-keyboard:before{content:"\f11c"}
-.icon-flag-alt:before{content:"\f11d"}
-.icon-flag-checkered:before{content:"\f11e"}
-.icon-terminal:before{content:"\f120"}
-.icon-code:before{content:"\f121"}
-.icon-reply-all:before{content:"\f122"}
-.icon-mail-reply-all:before{content:"\f122"}
-.icon-star-half-full:before,.icon-star-half-empty:before{content:"\f123"}
-.icon-location-arrow:before{content:"\f124"}
-.icon-crop:before{content:"\f125"}
-.icon-code-fork:before{content:"\f126"}
-.icon-unlink:before{content:"\f127"}
-.icon-question:before{content:"\f128"}
-.icon-info:before{content:"\f129"}
-.icon-exclamation:before{content:"\f12a"}
-.icon-superscript:before{content:"\f12b"}
-.icon-subscript:before{content:"\f12c"}
-.icon-eraser:before{content:"\f12d"}
-.icon-puzzle-piece:before{content:"\f12e"}
-.icon-microphone:before{content:"\f130"}
-.icon-microphone-off:before{content:"\f131"}
-.icon-shield:before{content:"\f132"}
-.icon-calendar-empty:before{content:"\f133"}
-.icon-fire-extinguisher:before{content:"\f134"}
-.icon-rocket:before{content:"\f135"}
-.icon-maxcdn:before{content:"\f136"}
-.icon-chevron-sign-left:before{content:"\f137"}
-.icon-chevron-sign-right:before{content:"\f138"}
-.icon-chevron-sign-up:before{content:"\f139"}
-.icon-chevron-sign-down:before{content:"\f13a"}
-.icon-html5:before{content:"\f13b"}
-.icon-css3:before{content:"\f13c"}
-.icon-anchor:before{content:"\f13d"}
-.icon-unlock-alt:before{content:"\f13e"}
-.icon-bullseye:before{content:"\f140"}
-.icon-ellipsis-horizontal:before{content:"\f141"}
-.icon-ellipsis-vertical:before{content:"\f142"}
-.icon-rss-sign:before{content:"\f143"}
-.icon-play-sign:before{content:"\f144"}
-.icon-ticket:before{content:"\f145"}
-.icon-minus-sign-alt:before{content:"\f146"}
-.icon-check-minus:before{content:"\f147"}
-.icon-level-up:before{content:"\f148"}
-.icon-level-down:before{content:"\f149"}
-.icon-check-sign:before{content:"\f14a"}
-.icon-edit-sign:before{content:"\f14b"}
-.icon-external-link-sign:before{content:"\f14c"}
-.icon-share-sign:before{content:"\f14d"}
-.icon-compass:before{content:"\f14e"}
-.icon-collapse:before{content:"\f150"}
-.icon-collapse-top:before{content:"\f151"}
-.icon-expand:before{content:"\f152"}
-.icon-euro:before,.icon-eur:before{content:"\f153"}
-.icon-gbp:before{content:"\f154"}
-.icon-dollar:before,.icon-usd:before{content:"\f155"}
-.icon-rupee:before,.icon-inr:before{content:"\f156"}
-.icon-yen:before,.icon-jpy:before{content:"\f157"}
-.icon-renminbi:before,.icon-cny:before{content:"\f158"}
-.icon-won:before,.icon-krw:before{content:"\f159"}
-.icon-bitcoin:before,.icon-btc:before{content:"\f15a"}
-.icon-file:before{content:"\f15b"}
-.icon-file-text:before{content:"\f15c"}
-.icon-sort-by-alphabet:before{content:"\f15d"}
-.icon-sort-by-alphabet-alt:before{content:"\f15e"}
-.icon-sort-by-attributes:before{content:"\f160"}
-.icon-sort-by-attributes-alt:before{content:"\f161"}
-.icon-sort-by-order:before{content:"\f162"}
-.icon-sort-by-order-alt:before{content:"\f163"}
-.icon-thumbs-up:before{content:"\f164"}
-.icon-thumbs-down:before{content:"\f165"}
-.icon-youtube-sign:before{content:"\f166"}
-.icon-youtube:before{content:"\f167"}
-.icon-xing:before{content:"\f168"}
-.icon-xing-sign:before{content:"\f169"}
-.icon-youtube-play:before{content:"\f16a"}
-.icon-dropbox:before{content:"\f16b"}
-.icon-stackexchange:before{content:"\f16c"}
-.icon-instagram:before{content:"\f16d"}
-.icon-flickr:before{content:"\f16e"}
-.icon-adn:before{content:"\f170"}
-.icon-bitbucket:before{content:"\f171"}
-.icon-bitbucket-sign:before{content:"\f172"}
-.icon-tumblr:before{content:"\f173"}
-.icon-tumblr-sign:before{content:"\f174"}
-.icon-long-arrow-down:before{content:"\f175"}
-.icon-long-arrow-up:before{content:"\f176"}
-.icon-long-arrow-left:before{content:"\f177"}
-.icon-long-arrow-right:before{content:"\f178"}
-.icon-apple:before{content:"\f179"}
-.icon-windows:before{content:"\f17a"}
-.icon-android:before{content:"\f17b"}
-.icon-linux:before{content:"\f17c"}
-.icon-dribbble:before{content:"\f17d"}
-.icon-skype:before{content:"\f17e"}
-.icon-foursquare:before{content:"\f180"}
-.icon-trello:before{content:"\f181"}
-.icon-female:before{content:"\f182"}
-.icon-male:before{content:"\f183"}
-.icon-gittip:before{content:"\f184"}
-.icon-sun:before{content:"\f185"}
-.icon-moon:before{content:"\f186"}
-.icon-archive:before{content:"\f187"}
-.icon-bug:before{content:"\f188"}
-.icon-vk:before{content:"\f189"}
-.icon-weibo:before{content:"\f18a"}
-.icon-renren:before{content:"\f18b"}
-code{color:#000}
-pre{font-size:inherit;line-height:inherit}
-.border-box-sizing{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
-.corner-all{border-radius:4px}
-.hbox{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-.hbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none}
-.vbox{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-.vbox>*{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none}
-.hbox.reverse,.vbox.reverse,.reverse{-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
-.hbox.box-flex0,.vbox.box-flex0,.box-flex0{-webkit-box-flex:0;-moz-box-flex:0;box-flex:0;flex:none;width:auto}
-.hbox.box-flex1,.vbox.box-flex1,.box-flex1{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-.hbox.box-flex,.vbox.box-flex,.box-flex{-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-.hbox.box-flex2,.vbox.box-flex2,.box-flex2{-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2}
-.box-group1{-webkit-box-flex-group:1;-moz-box-flex-group:1;box-flex-group:1}
-.box-group2{-webkit-box-flex-group:2;-moz-box-flex-group:2;box-flex-group:2}
-.hbox.start,.vbox.start,.start{-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start}
-.hbox.end,.vbox.end,.end{-webkit-box-pack:end;-moz-box-pack:end;box-pack:end;justify-content:flex-end}
-.hbox.center,.vbox.center,.center{-webkit-box-pack:center;-moz-box-pack:center;box-pack:center;justify-content:center}
-.hbox.align-start,.vbox.align-start,.align-start{-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
-.hbox.align-end,.vbox.align-end,.align-end{-webkit-box-align:end;-moz-box-align:end;box-align:end;align-items:flex-end}
-.hbox.align-center,.vbox.align-center,.align-center{-webkit-box-align:center;-moz-box-align:center;box-align:center;align-items:center}
-div.error{margin:2em;text-align:center}
-div.error>h1{font-size:500%;line-height:normal}
-div.error>p{font-size:200%;line-height:normal}
-div.traceback-wrapper{text-align:left;max-width:800px;margin:auto}
-body{background-color:#fff;position:absolute;left:0;right:0;top:0;bottom:0;overflow:visible}
-div#header{display:none}
-#ipython_notebook{padding-left:16px}
-#noscript{width:auto;padding-top:16px;padding-bottom:16px;text-align:center;font-size:22px;color:#f00;font-weight:bold}
-#ipython_notebook img{font-family:Verdana,"Helvetica Neue",Arial,Helvetica,Geneva,sans-serif;height:24px;text-decoration:none;color:#000}
-#site{width:100%;display:none}
-.ui-button .ui-button-text{padding:.2em .8em;font-size:77%}
-input.ui-button{padding:.3em .9em}
-.navbar span{margin-top:3px}
-span#login_widget{float:right}
-.nav-header{text-transform:none}
-.navbar-nobg{background-color:transparent;background-image:none}
-#header>span{margin-top:10px}
-.modal_stretch{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;height:80%}.modal_stretch .modal-body{max-height:none;flex:1}
-@media (min-width:768px){.modal{width:700px;margin-left:-350px}}.center-nav{display:inline-block;margin-bottom:-4px}
-.alternate_upload{background-color:none;display:inline}
-.alternate_upload.form{padding:0;margin:0}
-.alternate_upload input.fileinput{background-color:#f00;position:relative;opacity:0;z-index:2;width:295px;margin-left:163px;cursor:pointer;height:26px}
-ul#tabs{margin-bottom:4px}
-ul#tabs a{padding-top:4px;padding-bottom:4px}
-ul.breadcrumb a:focus,ul.breadcrumb a:hover{text-decoration:none}
-ul.breadcrumb i.icon-home{font-size:16px;margin-right:4px}
-ul.breadcrumb span{color:#5e5e5e}
-.list_toolbar{padding:4px 0 4px 0}
-.list_toolbar [class*="span"]{min-height:26px}
-.list_header{font-weight:bold}
-.list_container{margin-top:4px;margin-bottom:20px;border:1px solid #ababab;border-radius:4px}
-.list_container>div{border-bottom:1px solid #ababab}.list_container>div:hover .list-item{background-color:#f00}
-.list_container>div:last-child{border:none}
-.list_item:hover .list_item{background-color:#ddd}
-.list_item a{text-decoration:none}
-.list_header>div,.list_item>div{padding-top:4px;padding-bottom:4px;padding-left:7px;padding-right:7px;height:22px;line-height:22px}
-.item_name{line-height:22px;height:26px}
-.item_icon{font-size:14px;color:#5e5e5e;margin-right:7px}
-.item_buttons{line-height:1em}
-.toolbar_info{height:26px;line-height:26px}
-input.nbname_input,input.engine_num_input{padding-top:3px;padding-bottom:3px;height:14px;line-height:14px;margin:0}
-input.engine_num_input{width:60px}
-.highlight_text{color:#00f}
-#project_name>.breadcrumb{padding:0;margin-bottom:0;background-color:transparent;font-weight:bold}
-.folder_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f114"}
-.notebook_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f02d"}
-.ansibold{font-weight:bold}
-.ansiblack{color:#000}
-.ansired{color:#8b0000}
-.ansigreen{color:#006400}
-.ansiyellow{color:#a52a2a}
-.ansiblue{color:#00008b}
-.ansipurple{color:#9400d3}
-.ansicyan{color:#4682b4}
-.ansigray{color:#808080}
-.ansibgblack{background-color:#000}
-.ansibgred{background-color:#f00}
-.ansibggreen{background-color:#008000}
-.ansibgyellow{background-color:#ff0}
-.ansibgblue{background-color:#00f}
-.ansibgpurple{background-color:#f0f}
-.ansibgcyan{background-color:#0ff}
-.ansibggray{background-color:#808080}
-div.cell{border:1px solid transparent;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}div.cell.selected{border-radius:4px;border:thin #ababab solid}
-div.cell.edit_mode{border-radius:4px;border:thin #008000 solid}
-div.cell{width:100%;padding:5px 5px 5px 0;margin:0;outline:none}
-div.prompt{min-width:11ex;padding:.4em;margin:0;font-family:monospace;text-align:right;line-height:1.21429em}
-@media (max-width:480px){div.prompt{text-align:left}}div.inner_cell{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-div.input_area{border:1px solid #cfcfcf;border-radius:4px;background:#f7f7f7;line-height:1.21429em}
-div.prompt:empty{padding-top:0;padding-bottom:0}
-div.input{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-@media (max-width:480px){div.input{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}}div.input_prompt{color:#000080;border-top:1px solid transparent}
-div.input_area>div.highlight{margin:.4em;border:none;padding:0;background-color:transparent}
-div.input_area>div.highlight>pre{margin:0;border:none;padding:0;background-color:transparent}
-.CodeMirror{line-height:1.21429em;height:auto;background:none;}
-.CodeMirror-scroll{overflow-y:hidden;overflow-x:auto}
-.CodeMirror-lines{padding:.4em}
-.CodeMirror-linenumber{padding:0 8px 0 4px}
-.CodeMirror-gutters{border-bottom-left-radius:4px;border-top-left-radius:4px}
-.CodeMirror pre{padding:0;border:0;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-pre code{display:block;padding:.5em}
-.highlight-base,pre code,pre .subst,pre .tag .title,pre .lisp .title,pre .clojure .built_in,pre .nginx .title{color:#000}
-.highlight-string,pre .string,pre .constant,pre .parent,pre .tag .value,pre .rules .value,pre .rules .value .number,pre .preprocessor,pre .ruby .symbol,pre .ruby .symbol .string,pre .aggregate,pre .template_tag,pre .django .variable,pre .smalltalk .class,pre .addition,pre .flow,pre .stream,pre .bash .variable,pre .apache .tag,pre .apache .cbracket,pre .tex .command,pre .tex .special,pre .erlang_repl .function_or_atom,pre .markdown .header{color:#ba2121}
-.highlight-comment,pre .comment,pre .annotation,pre .template_comment,pre .diff .header,pre .chunk,pre .markdown .blockquote{color:#408080;font-style:italic}
-.highlight-number,pre .number,pre .date,pre .regexp,pre .literal,pre .smalltalk .symbol,pre .smalltalk .char,pre .go .constant,pre .change,pre .markdown .bullet,pre .markdown .link_url{color:#080}
-pre .label,pre .javadoc,pre .ruby .string,pre .decorator,pre .filter .argument,pre .localvars,pre .array,pre .attr_selector,pre .important,pre .pseudo,pre .pi,pre .doctype,pre .deletion,pre .envvar,pre .shebang,pre .apache .sqbracket,pre .nginx .built_in,pre .tex .formula,pre .erlang_repl .reserved,pre .prompt,pre .markdown .link_label,pre .vhdl .attribute,pre .clojure .attribute,pre .coffeescript .property{color:#88f}
-.highlight-keyword,pre .keyword,pre .id,pre .phpdoc,pre .aggregate,pre .css .tag,pre .javadoctag,pre .phpdoc,pre .yardoctag,pre .smalltalk .class,pre .winutils,pre .bash .variable,pre .apache .tag,pre .go .typename,pre .tex .command,pre .markdown .strong,pre .request,pre .status{color:#008000;font-weight:bold}
-.highlight-builtin,pre .built_in{color:#008000}
-pre .markdown .emphasis{font-style:italic}
-pre .nginx .built_in{font-weight:normal}
-pre .coffeescript .javascript,pre .javascript .xml,pre .tex .formula,pre .xml .javascript,pre .xml .vbscript,pre .xml .css,pre .xml .cdata{opacity:.5}
-.cm-s-ipython span.cm-variable{color:#000}
-.cm-s-ipython span.cm-keyword{color:#008000;font-weight:bold}
-.cm-s-ipython span.cm-number{color:#080}
-.cm-s-ipython span.cm-comment{color:#408080;font-style:italic}
-.cm-s-ipython span.cm-string{color:#ba2121}
-.cm-s-ipython span.cm-builtin{color:#008000}
-.cm-s-ipython span.cm-error{color:#f00}
-.cm-s-ipython span.cm-operator{color:#a2f;font-weight:bold}
-.cm-s-ipython span.cm-meta{color:#a2f}
-.cm-s-ipython span.cm-tab{background:url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);background-position:right;background-repeat:no-repeat}
-div.output_wrapper{position:relative;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-div.output_scroll{height:24em;width:100%;overflow:auto;border-radius:4px;-webkit-box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);-moz-box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);box-shadow:inset 0 2px 8px rgba(0,0,0,0.8);display:block}
-div.output_collapsed{margin:0;padding:0;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-div.out_prompt_overlay{height:100%;padding:0 .4em;position:absolute;border-radius:4px}
-div.out_prompt_overlay:hover{-webkit-box-shadow:inset 0 0 1px #000;-moz-box-shadow:inset 0 0 1px #000;box-shadow:inset 0 0 1px #000;background:rgba(240,240,240,0.5)}
-div.output_prompt{color:#8b0000}
-div.output_area{padding:0;page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}div.output_area .MathJax_Display{text-align:left !important}
-div.output_area .rendered_html table{margin-left:0;margin-right:0}
-div.output_area .rendered_html img{margin-left:0;margin-right:0}
-.output{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-@media (max-width:480px){div.output_area{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}}div.output_area pre{margin:0;padding:0;border:0;vertical-align:baseline;color:#000;background-color:transparent;border-radius:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0}
-div.output_subarea{padding:.4em .4em 0 .4em;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-div.output_text{text-align:left;color:#000;line-height:1.21429em}
-div.output_stderr{background:#fdd;}
-div.output_latex{text-align:left}
-div.output_javascript:empty{padding:0}
-.js-error{color:#8b0000}
-div.raw_input_container{font-family:monospace;padding-top:5px}
-span.raw_input_prompt{}
-input.raw_input{font-family:inherit;font-size:inherit;color:inherit;width:auto;vertical-align:baseline;padding:0 .25em;margin:0 .25em}
-input.raw_input:focus{box-shadow:none}
-p.p-space{margin-bottom:10px}
-.rendered_html{color:#000;}.rendered_html em{font-style:italic}
-.rendered_html strong{font-weight:bold}
-.rendered_html u{text-decoration:underline}
-.rendered_html :link{text-decoration:underline}
-.rendered_html :visited{text-decoration:underline}
-.rendered_html h1{font-size:185.7%;margin:1.08em 0 0 0;font-weight:bold;line-height:1}
-.rendered_html h2{font-size:157.1%;margin:1.27em 0 0 0;font-weight:bold;line-height:1}
-.rendered_html h3{font-size:128.6%;margin:1.55em 0 0 0;font-weight:bold;line-height:1}
-.rendered_html h4{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1}
-.rendered_html h5{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1;font-style:italic}
-.rendered_html h6{font-size:100%;margin:2em 0 0 0;font-weight:bold;line-height:1;font-style:italic}
-.rendered_html h1:first-child{margin-top:.538em}
-.rendered_html h2:first-child{margin-top:.636em}
-.rendered_html h3:first-child{margin-top:.777em}
-.rendered_html h4:first-child{margin-top:1em}
-.rendered_html h5:first-child{margin-top:1em}
-.rendered_html h6:first-child{margin-top:1em}
-.rendered_html ul{list-style:disc;margin:0 2em}
-.rendered_html ul ul{list-style:square;margin:0 2em}
-.rendered_html ul ul ul{list-style:circle;margin:0 2em}
-.rendered_html ol{list-style:decimal;margin:0 2em}
-.rendered_html ol ol{list-style:upper-alpha;margin:0 2em}
-.rendered_html ol ol ol{list-style:lower-alpha;margin:0 2em}
-.rendered_html ol ol ol ol{list-style:lower-roman;margin:0 2em}
-.rendered_html ol ol ol ol ol{list-style:decimal;margin:0 2em}
-.rendered_html *+ul{margin-top:1em}
-.rendered_html *+ol{margin-top:1em}
-.rendered_html hr{color:#000;background-color:#000}
-.rendered_html pre{margin:1em 2em}
-.rendered_html pre,.rendered_html code{border:0;background-color:#fff;color:#000;font-size:100%;padding:0}
-.rendered_html blockquote{margin:1em 2em}
-.rendered_html table{margin-left:auto;margin-right:auto;border:1px solid #000;border-collapse:collapse}
-.rendered_html tr,.rendered_html th,.rendered_html td{border:1px solid #000;border-collapse:collapse;margin:1em 2em}
-.rendered_html td,.rendered_html th{text-align:left;vertical-align:middle;padding:4px}
-.rendered_html th{font-weight:bold}
-.rendered_html *+table{margin-top:1em}
-.rendered_html p{text-align:justify}
-.rendered_html *+p{margin-top:1em}
-.rendered_html img{display:block;margin-left:auto;margin-right:auto}
-.rendered_html *+img{margin-top:1em}
-div.text_cell{padding:5px 5px 5px 0;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-@media (max-width:480px){div.text_cell>div.prompt{display:none}}div.text_cell_render{outline:none;resize:none;width:inherit;border-style:none;padding:.5em .5em .5em .4em;color:#000}
-a.anchor-link:link{text-decoration:none;padding:0 20px;visibility:hidden}
-h1:hover .anchor-link,h2:hover .anchor-link,h3:hover .anchor-link,h4:hover .anchor-link,h5:hover .anchor-link,h6:hover .anchor-link{visibility:visible}
-div.cell.text_cell.rendered{padding:0}
-.widget-area{page-break-inside:avoid;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-area .widget-subarea{padding:.44em .4em .4em 1px;margin-left:6px;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:2;-moz-box-flex:2;box-flex:2;flex:2;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
-.widget-hlabel{min-width:10ex;padding-right:8px;padding-top:3px;text-align:right;vertical-align:text-top}
-.widget-vlabel{padding-bottom:5px;text-align:center;vertical-align:text-bottom}
-.widget-hreadout{padding-left:8px;padding-top:3px;text-align:left;vertical-align:text-top}
-.widget-vreadout{padding-top:5px;text-align:center;vertical-align:text-top}
-.slide-track{border:1px solid #ccc;background:#fff;border-radius:4px;}
-.widget-hslider{padding-left:8px;padding-right:5px;overflow:visible;width:348px;height:5px;max-height:5px;margin-top:11px;margin-bottom:10px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}.widget-hslider .ui-slider{border:0 !important;background:none !important;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-hslider .ui-slider .ui-slider-handle{width:14px !important;height:28px !important;margin-top:-8px !important}
-.widget-vslider{padding-bottom:8px;overflow:visible;width:5px;max-width:5px;height:250px;margin-left:12px;border:1px solid #ccc;background:#fff;border-radius:4px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}.widget-vslider .ui-slider{border:0 !important;background:none !important;margin-left:-4px;margin-top:5px;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}.widget-vslider .ui-slider .ui-slider-handle{width:28px !important;height:14px !important;margin-left:-9px}
-.widget-text{width:350px;margin:0 !important}
-.widget-listbox{width:364px;margin-bottom:0}
-.widget-numeric-text{width:150px;margin:0 !important}
-.widget-progress{width:363px}.widget-progress .bar{-webkit-transition:none;-moz-transition:none;-ms-transition:none;-o-transition:none;transition:none}
-.widget-combo-btn{min-width:138px;}
-.widget-box{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
-.widget-hbox{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-.widget-hbox-single{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;height:30px}
-.widget-vbox{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch}
-.widget-vbox-single{margin:5px;-webkit-box-pack:start;-moz-box-pack:start;box-pack:start;justify-content:flex-start;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start;display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;width:30px}
-.widget-modal{overflow:hidden;position:absolute !important;top:0;left:0;margin-left:0 !important}
-.widget-modal-body{max-height:none !important}
-.widget-container{box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;-webkit-box-align:start;-moz-box-align:start;box-align:start;align-items:flex-start}
-.widget-radio-box{display:-webkit-box;-webkit-box-orient:vertical;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:vertical;-moz-box-align:stretch;display:box;box-orient:vertical;box-align:stretch;display:flex;flex-direction:column;align-items:stretch;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box;padding-top:4px}
-.docked-widget-modal{overflow:hidden;position:relative !important;top:0 !important;left:0 !important;margin-left:0 !important}
-body{background-color:#fff}
-body.notebook_app{overflow:hidden}
-@media (max-width:767px){body.notebook_app{padding-left:0;padding-right:0}}span#notebook_name{height:1em;line-height:1em;padding:3px;border:none;font-size:146.5%}
-div#notebook_panel{margin:0 0 0 0;padding:0;-webkit-box-shadow:0 -1px 10px rgba(0,0,0,0.1);-moz-box-shadow:0 -1px 10px rgba(0,0,0,0.1);box-shadow:0 -1px 10px rgba(0,0,0,0.1)}
-div#notebook{font-size:14px;line-height:20px;overflow-y:scroll;overflow-x:auto;width:100%;padding:1em 0 1em 0;margin:0;border-top:1px solid #ababab;outline:none;box-sizing:border-box;-moz-box-sizing:border-box;-webkit-box-sizing:border-box}
-div.ui-widget-content{border:1px solid #ababab;outline:none}
-pre.dialog{background-color:#f7f7f7;border:1px solid #ddd;border-radius:4px;padding:.4em;padding-left:2em}
-p.dialog{padding:.2em}
-pre,code,kbd,samp{white-space:pre-wrap}
-#fonttest{font-family:monospace}
-p{margin-bottom:0}
-.end_space{height:200px}
-.celltoolbar{border:thin solid #cfcfcf;border-bottom:none;background:#eee;border-radius:3px 3px 0 0;width:100%;-webkit-box-pack:end;height:22px;display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch;-webkit-box-direction:reverse;-moz-box-direction:reverse;box-direction:reverse;flex-direction:row-reverse}
-.ctb_hideshow{display:none;vertical-align:bottom;padding-right:2px}
-.celltoolbar>div{padding-top:0}
-.ctb_global_show .ctb_show.ctb_hideshow{display:block}
-.ctb_global_show .ctb_show+.input_area,.ctb_global_show .ctb_show+div.text_cell_input{border-top-right-radius:0;border-top-left-radius:0}
-.celltoolbar .button_container select{margin:10px;margin-top:1px;margin-bottom:0;padding:0;font-size:87%;width:auto;display:inline-block;height:18px;line-height:18px;vertical-align:top}
-.celltoolbar label{display:inline-block;height:15px;line-height:15px;vertical-align:top}
-.celltoolbar label span{font-size:85%}
-.celltoolbar input[type=checkbox]{margin:0;margin-left:4px;margin-right:4px}
-.celltoolbar .ui-button{border:none;vertical-align:top;height:20px;min-width:30px}
-.completions{position:absolute;z-index:10;overflow:hidden;border:1px solid #ababab;border-radius:4px;-webkit-box-shadow:0 6px 10px -1px #adadad;-moz-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad}
-.completions select{background:#fff;outline:none;border:none;padding:0;margin:0;overflow:auto;font-family:monospace;font-size:110%;color:#000;width:auto}
-.completions select option.context{color:#0064cd}
-#menubar .navbar-inner{min-height:28px;border-top:1px;border-radius:0 0 4px 4px}
-#menubar .navbar{margin-bottom:8px}
-.nav-wrapper{border-bottom:1px solid #d4d4d4}
-#menubar li.dropdown{line-height:12px}
-i.menu-icon{padding-top:4px}
-ul#help_menu li a{overflow:hidden;padding-right:2.2em}ul#help_menu li a i{margin-right:-1.2em}
-#notification_area{z-index:10}
-.indicator_area{color:#777;padding:4px 3px;margin:0;width:11px;z-index:10;text-align:center}
-#kernel_indicator{margin-right:-16px}
-.edit_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f040"}
-.command_mode_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:' '}
-.kernel_idle_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f10c"}
-.kernel_busy_icon:before{font-family:FontAwesome;font-weight:normal;font-style:normal;text-decoration:inherit;-webkit-font-smoothing:antialiased;*margin-right:.3em;content:"\f111"}
-.notification_widget{color:#777;padding:1px 12px;margin:2px 4px;z-index:10;border:1px solid #ccc;border-radius:4px;background:rgba(240,240,240,0.5)}.notification_widget.span{padding-right:2px}
-div#pager_splitter{height:8px}
-#pager-container{position:relative;padding:15px 0}
-div#pager{font-size:14px;line-height:20px;overflow:auto;display:none}div#pager pre{line-height:1.21429em;color:#000;background-color:#f7f7f7;padding:.4em}
-.quickhelp{display:-webkit-box;-webkit-box-orient:horizontal;-webkit-box-align:stretch;display:-moz-box;-moz-box-orient:horizontal;-moz-box-align:stretch;display:box;box-orient:horizontal;box-align:stretch;display:flex;flex-direction:row;align-items:stretch}
-.shortcut_key{display:inline-block;width:20ex;text-align:right;font-family:monospace}
-.shortcut_descr{display:inline-block;-webkit-box-flex:1;-moz-box-flex:1;box-flex:1;flex:1}
-span#save_widget{padding:0 5px;margin-top:12px}
-span#checkpoint_status,span#autosave_status{font-size:small}
-@media (max-width:767px){span#save_widget{font-size:small} span#checkpoint_status,span#autosave_status{font-size:x-small}}@media (max-width:767px){span#checkpoint_status,span#autosave_status{display:none}}@media (min-width:768px) and (max-width:979px){span#checkpoint_status{display:none} span#autosave_status{font-size:x-small}}.toolbar{padding:0 10px;margin-top:-5px}.toolbar select,.toolbar label{width:auto;height:26px;vertical-align:middle;margin-right:2px;margin-bottom:0;display:inline;font-size:92%;margin-left:.3em;margin-right:.3em;padding:0;padding-top:3px}
-.toolbar .btn{padding:2px 8px}
-.toolbar .btn-group{margin-top:0}
-.toolbar-inner{border:none !important;-webkit-box-shadow:none !important;-moz-box-shadow:none !important;box-shadow:none !important}
-#maintoolbar{margin-bottom:0}
-@-moz-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-webkit-keyframes fadeOut{from{opacity:1} to{opacity:0}}@-moz-keyframes fadeIn{from{opacity:0} to{opacity:1}}@-webkit-keyframes fadeIn{from{opacity:0} to{opacity:1}}.bigtooltip{overflow:auto;height:200px;-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms}
-.smalltooltip{-webkit-transition-property:height;-webkit-transition-duration:500ms;-moz-transition-property:height;-moz-transition-duration:500ms;transition-property:height;transition-duration:500ms;text-overflow:ellipsis;overflow:hidden;height:80px}
-.tooltipbuttons{position:absolute;padding-right:15px;top:0;right:0}
-.tooltiptext{padding-right:30px}
-.ipython_tooltip{max-width:700px;-webkit-animation:fadeOut 400ms;-moz-animation:fadeOut 400ms;animation:fadeOut 400ms;-webkit-animation:fadeIn 400ms;-moz-animation:fadeIn 400ms;animation:fadeIn 400ms;vertical-align:middle;background-color:#f7f7f7;overflow:visible;border:#ababab 1px solid;outline:none;padding:3px;margin:0;padding-left:7px;font-family:monospace;min-height:50px;-moz-box-shadow:0 6px 10px -1px #adadad;-webkit-box-shadow:0 6px 10px -1px #adadad;box-shadow:0 6px 10px -1px #adadad;border-radius:4px;position:absolute;z-index:2}.ipython_tooltip a{float:right}
-.ipython_tooltip .tooltiptext pre{border:0;-webkit-border-radius:0;-moz-border-radius:0;border-radius:0;font-size:100%;background-color:#f7f7f7}
-.pretooltiparrow{left:0;margin:0;top:-16px;width:40px;height:16px;overflow:hidden;position:absolute}
-.pretooltiparrow:before{background-color:#f7f7f7;border:1px #ababab solid;z-index:11;content:"";position:absolute;left:15px;top:10px;width:25px;height:25px;-webkit-transform:rotate(45deg);-moz-transform:rotate(45deg);-ms-transform:rotate(45deg);-o-transform:rotate(45deg)}
-
-    </style>
-<style type="text/css">
-    .highlight .hll { background-color: #ffffcc }
-.highlight  { background: #f8f8f8; }
-.highlight .c { color: #408080; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
-.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #FF0000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #00A000 } /* Generic.Inserted */
-.highlight .go { color: #888888 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #7D9029 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #A0A000 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
-    </style>
-
-
-<style type="text/css">
-/* Overrides of notebook CSS for static HTML export */
-body {
-  overflow: visible;
-  padding: 8px;
-}
-
-div#notebook {
-  overflow: visible;
-  border-top: none;
-}
-
-@media print {
-  div.cell {
-    display: block;
-    page-break-inside: avoid;
-  } 
-  div.output_wrapper { 
-    display: block;
-    page-break-inside: avoid; 
-  }
-  div.output { 
-    display: block;
-    page-break-inside: avoid; 
-  }
-}
-</style>
-
-<!-- Custom stylesheet, it must be in the same directory as the html file -->
-<link rel="stylesheet" href="custom.css">
-
-<!-- Loading mathjax macro -->
-<!-- Load mathjax -->
-    <script src="https://c328740.ssl.cf1.rackcdn.com/mathjax/latest/MathJax.js?config=TeX-AMS_HTML"></script>
-    <!-- MathJax configuration -->
-    <script type="text/x-mathjax-config">
-    MathJax.Hub.Config({
-        tex2jax: {
-            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
-            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
-            processEscapes: true,
-            processEnvironments: true
-        },
-        // Center justify equations in code and markdown cells. Elsewhere
-        // we use CSS to left justify single line equations in code cells.
-        displayAlign: 'center',
-        "HTML-CSS": {
-            styles: {'.MathJax_Display': {"margin": 0}},
-            linebreaks: { automatic: true }
-        }
-    });
-    </script>
-    <!-- End of mathjax configuration -->
-
-</head>
-<body>
-  <div tabindex="-1" id="notebook" class="border-box-sizing">
-    <div class="container" id="notebook-container">
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a href="http://sebastianraschka.com">Sebastian Raschka</a></p>
-<p>last updated 05/27/2014</p>
-<ul>
-<li><p><a href="http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1">Open in IPython nbviewer</a></p></li>
-<li><p><a href="https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb">Link to this IPython notebook on Github</a></p></li>
-<li><p><a href="https://github.com/rasbt/python_reference">Link to the GitHub repository python_reference</a></p></li>
-</ul>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<hr>
-I would be happy to hear your comments and suggestions.<br />Please feel free to drop me a note via <a href="https://twitter.com/rasbt">twitter</a>, <script type="text/javascript">
-<!--
-h='&#x67;&#x6d;&#x61;&#x69;&#108;&#46;&#x63;&#x6f;&#x6d;';a='&#64;';n='&#98;&#108;&#x75;&#x65;&#x77;&#x6f;&#x6f;&#100;&#116;&#114;&#x65;&#x65;';e=n+a+h;
-document.write('<a h'+'ref'+'="ma'+'ilto'+':'+e+'">'+'email'+'<\/'+'a'+'>');
-// -->
-</script><noscript>&#x65;&#x6d;&#x61;&#x69;&#108;&#32;&#40;&#98;&#108;&#x75;&#x65;&#x77;&#x6f;&#x6f;&#100;&#116;&#114;&#x65;&#x65;&#32;&#x61;&#116;&#32;&#x67;&#x6d;&#x61;&#x69;&#108;&#32;&#100;&#x6f;&#116;&#32;&#x63;&#x6f;&#x6d;&#x29;</noscript>, or <a href="https://plus.google.com/118404394130788869227">google+</a>.
-<hr>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h1 id="Key-differences-between-Python-2.7.x-and-Python-3.x">Key differences between Python 2.7.x and Python 3.x<a class="anchor-link" href="#Key-differences-between-Python-2.7.x-and-Python-3.x">&#182;</a></h1>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines &quot;just go with the version your favorite tutorial was written in, and check out the differences later on.&quot;</p>
-<p>But what if you are starting a new project and have the choice to pick? I would say there is currently no &quot;right&quot; or &quot;wrong&quot; as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use. However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Sections">Sections<a class="anchor-link" href="#Sections">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<ul>
-<li><p><a href="#future_module">Using the <code>__future__</code> module</a></p></li>
-<li><p><a href="#The-print-function">The print function</a></p></li>
-<li><p><a href="#Integer-division">Integer division</a></p></li>
-<li><p><a href="#Unicode">Unicode</a></p></li>
-<li><p><a href="#xrange">xrange</a></p></li>
-<li><p><a href="#Raising-exceptions">Raising exceptions</a></p></li>
-<li><p><a href="#Handling-exceptions">Handling exceptions</a></p></li>
-<li><p><a href="#The-next-function-and-next-method">The next() function and .next() method</a></p></li>
-<li><p><a href="#For-loop-variables-and-the-global-namespace-leak">For-loop variables and the global namespace leak</a></p></li>
-<li><p><a href="#Comparing-unorderable-types">Comparing unorderable types</a></p></li>
-<li><p><a href="#Parsing-user-inputs-via-input">Parsing user inputs via input()</a></p></li>
-<li><p><a href="#Returning-iterable-objects-instead-of-lists">Returning iterable objects instead of lists</a></p></li>
-<li><p><a href="#More-articles-about-Python-2-and-Python-3">More articles about Python 2 and Python 3</a></p></li>
-</ul>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a id='future_module'></a></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h3 id="the-__future__-module">The <code>__future__</code> module</h3>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Python 3.x introduced some Python 2-incompatible keywords and features that can be imported via the in-built <code>__future__</code> module in Python 2. It is recommended to use <code>__future__</code> imports it if you are planning Python 3.x support for your code. For example, if we want Python 3.x's integer division behavior in Python 2, we can import it via</p>
-<pre><code>from __future__ import division</code></pre>
-<p>More features that can be imported from the <code>__future__</code> module are listed in the table below:</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<table>
-<tr><th class="head">
-feature
-</th>
-<th class="head">
-optional in
-</th>
-<th class="head">
-mandatory in
-</th>
-<th class="head">
-effect
-</th>
-</tr>
-
-<tr><td>
-nested_scopes
-</td>
-<td>
-2.1.0b1
-</td>
-<td>
-2.2
-</td>
-<td>
-<a href="http://www.python.org/dev/peps/pep-0227"><strong>PEP 227</strong></a>: <em>Statically Nested Scopes</em>
-</td>
-</tr>
-<tr><td>
-generators
-</td>
-<td>
-2.2.0a1
-</td>
-<td>
-2.3
-</td>
-<td>
-<a href="http://www.python.org/dev/peps/pep-0255"><strong>PEP 255</strong></a>: <em>Simple Generators</em>
-</td>
-</tr>
-<tr><td>
-division
-</td>
-<td>
-2.2.0a2
-</td>
-<td>
-3.0
-</td>
-<td>
-<a href="http://www.python.org/dev/peps/pep-0238"><strong>PEP 238</strong></a>: <em>Changing the Division Operator</em>
-</td>
-</tr>
-<tr><td>
-absolute_import
-</td>
-<td>
-2.5.0a1
-</td>
-<td>
-3.0
-</td>
-<td>
-<a href="http://www.python.org/dev/peps/pep-0328"><strong>PEP 328</strong></a>: <em>Imports: Multi-Line and Absolute/Relative</em>
-</td>
-</tr>
-<tr><td>
-with_statement
-</td>
-<td>
-2.5.0a1
-</td>
-<td>
-2.6
-</td>
-<td>
-<a href="http://www.python.org/dev/peps/pep-0343"><strong>PEP 343</strong></a>: <em>The “with” Statement</em>
-</td>
-</tr>
-<tr><td>
-print_function
-</td>
-<td>
-2.6.0a2
-</td>
-<td>
-3.0
-</td>
-<td>
-<a href="http://www.python.org/dev/peps/pep-3105"><strong>PEP 3105</strong></a>: <em>Make print a function</em>
-</td>
-</tr>
-<tr><td>
-unicode_literals
-</td>
-<td>
-2.6.0a2
-</td>
-<td>
-3.0
-</td>
-<td>
-<a href="http://www.python.org/dev/peps/pep-3112"><strong>PEP 3112</strong></a>: <em>Bytes literals in Python 3000</em>
-</td>
-</tr>
-</table>
-<br>
-<center>
-(Source: <a href="https://docs.python.org/2/library/__future__.html#module-__future__">https://docs.python.org/2/library/<strong>future</strong>.html</a>)
-</center>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[1]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">from</span> <span class="nn">platform</span> <span class="kn">import</span> <span class="n">python_version</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="The-print-function">The print function<a class="anchor-link" href="#The-print-function">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print statement has been replaced by the <code>print()</code> function, meaning that we have to wrap the object that we want to print in parantheses.</p>
-<p>Python 2 doesn't have a problem with additional parantheses, but in contrast, Python 3 would raise a <code>SyntaxError</code> if we called the print function the Python 2-way without the parentheses.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[3]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-<span class="k">print</span> <span class="s">&#39;Hello, World!&#39;</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;Hello, World!&#39;</span><span class="p">)</span>
-<span class="k">print</span> <span class="s">&quot;text&quot;</span><span class="p">,</span> <span class="p">;</span> <span class="k">print</span> <span class="s">&#39;print more text on the same line&#39;</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-Hello, World!
-Hello, World!
-text print more text on the same line
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[2]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;Hello, World!&#39;</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&quot;some text,&quot;</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">)</span> 
-<span class="k">print</span><span class="p">(</span><span class="s">&#39; print more text on the same line&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-Hello, World!
-some text, print more text on the same line
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[3]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Hello, World!&#39;</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansicyan">  File </span><span class="ansigreen">&quot;&lt;ipython-input-3-139a7c5835bd&gt;&quot;</span><span class="ansicyan">, line </span><span class="ansigreen">1</span>
-<span class="ansired">    print &apos;Hello, World!&apos;</span>
-                        ^
-<span class="ansired">SyntaxError</span><span class="ansired">:</span> invalid syntax
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><strong>Note:</strong></p>
-<p>Printing &quot;Hello, World&quot; above via Python 2 looked quite &quot;normal&quot;. However, if we have multiple objects inside the parantheses, we will create a tuple, since <code>print</code> is a &quot;statement&quot; in Python 2, not a function call.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[4]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="s">&#39;b&#39;</span><span class="p">)</span>
-<span class="k">print</span> <span class="s">&#39;a&#39;</span><span class="p">,</span> <span class="s">&#39;b&#39;</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-(&apos;a&apos;, &apos;b&apos;)
-a b
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Integer-division">Integer division<a class="anchor-link" href="#Integer-division">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>This change is particularly dangerous if you are porting code, or if you are executing Python 3 code in Python 2, since the change in integer-division behavior can often go unnoticed (it doesn't raise a <code>SyntaxError</code>).<br />So, I still tend to use a <code>float(3)/2</code> or <code>3/2.0</code> instead of a <code>3/2</code> in my Python 3 scripts to save the Python 2 guys some trouble (and vice versa, I recommend a <code>from __future__ import division</code> in your Python 2 scripts).</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[4]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-<span class="k">print</span> <span class="s">&#39;3 / 2 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">2</span>
-<span class="k">print</span> <span class="s">&#39;3 // 2 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">//</span> <span class="mi">2</span>
-<span class="k">print</span> <span class="s">&#39;3 / 2.0 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">/</span> <span class="mf">2.0</span>
-<span class="k">print</span> <span class="s">&#39;3 // 2.0 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">//</span> <span class="mf">2.0</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-3 / 2 = 1
-3 // 2 = 1
-3 / 2.0 = 1.5
-3 // 2.0 = 1.0
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[4]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;3 / 2 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;3 // 2 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;3 / 2.0 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;3 // 2.0 =&#39;</span><span class="p">,</span> <span class="mi">3</span> <span class="o">//</span> <span class="mf">2.0</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-3 / 2 = 1.5
-3 // 2 = 1
-3 / 2.0 = 1.5
-3 // 2.0 = 1.0
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Unicode">Unicode<a class="anchor-link" href="#Unicode">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Python 2 has ASCII <code>str()</code> types, separate <code>unicode()</code>, but no <code>byte</code> type.</p>
-<p>Now, in Python 3, we finally have Unicode (utf-8) <code>str</code>ings, and 2 byte classes: <code>byte</code> and <code>bytearray</code>s.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[2]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[3]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="nb">type</span><span class="p">(</span><span class="nb">unicode</span><span class="p">(</span><span class="s">&#39;this is like a python3 str type&#39;</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-&lt;type &apos;unicode&apos;&gt;
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[4]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="nb">type</span><span class="p">(</span><span class="n">b</span><span class="s">&#39;byte type does not exist&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-&lt;type &apos;str&apos;&gt;
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[5]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;they are really&#39;</span> <span class="o">+</span> <span class="n">b</span><span class="s">&#39; the same&#39;</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-they are really the same
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[7]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="nb">type</span><span class="p">(</span><span class="nb">bytearray</span><span class="p">(</span><span class="n">b</span><span class="s">&#39;bytearray oddly does exist though&#39;</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-&lt;type &apos;bytearray&apos;&gt;
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[6]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;strings are now utf-8 </span><span class="se">\u03BC</span><span class="s">nico</span><span class="se">\u0394</span><span class="s">é!&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-strings are now utf-8 μnicoΔé!
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[8]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">(),</span> <span class="n">end</span><span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39; has&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">b</span><span class="s">&#39; bytes for storing data&#39;</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1 has &lt;class &apos;bytes&apos;&gt;
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[11]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;and Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">(),</span> <span class="n">end</span><span class="o">=</span><span class="s">&quot;&quot;</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39; also has&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="nb">bytearray</span><span class="p">(</span><span class="n">b</span><span class="s">&#39;bytearrays&#39;</span><span class="p">)))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-and Python 3.4.1 also has &lt;class &apos;bytearray&apos;&gt;
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[13]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="s">&#39;note that we cannot add a string&#39;</span> <span class="o">+</span> <span class="n">b</span><span class="s">&#39;bytes for data&#39;</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansired">---------------------------------------------------------------------------</span>
-<span class="ansired">TypeError</span>                                 Traceback (most recent call last)
-<span class="ansigreen">&lt;ipython-input-13-d3e8942ccf81&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
-<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span><span class="ansiblue">&apos;note that we cannot add a string&apos;</span> <span class="ansiblue">+</span> <span class="ansiblue">b&apos;bytes for data&apos;</span><span class="ansiblue"></span>
-
-<span class="ansired">TypeError</span>: Can&apos;t convert &apos;bytes&apos; object to str implicitly</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="xrange">xrange<a class="anchor-link" href="#xrange">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>The usage of <code>xrange()</code> is very popular in Python 2.x for creating an iterable object, e.g., in a for-loop or list/set-dictionary-comprehension.<br />The behavior was quite similar to a generator (i.e., &quot;lazy evaluation&quot;), but here the xrange-iterable is not exhaustible - meaning, you could iterate over it infinitely.</p>
-<p>Thanks to its &quot;lazy-evaluation&quot;, the advantage of the regular <code>range()</code> is that <code>xrange()</code> is generally faster if you have to iterate over it only once (e.g., in a for-loop). However, in contrast to 1-time iterations, it is not recommended if you repeat the iteration multiple times, since the generation happens every time from scratch!</p>
-<p>In Python 3, the <code>range()</code> was implemented like the <code>xrange()</code> function so that a dedicated <code>xrange()</code> function does not exist anymore (<code>xrange()</code> raises a <code>NameError</code> in Python 3).</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[5]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="kn">import</span> <span class="nn">timeit</span>
-
-<span class="n">n</span> <span class="o">=</span> <span class="mi">10000</span>
-<span class="k">def</span> <span class="nf">test_range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="k">pass</span>
-    
-<span class="k">def</span> <span class="nf">test_xrange</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-        <span class="k">pass</span>    
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[6]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-
-<span class="k">print</span> <span class="s">&#39;</span><span class="se">\n</span><span class="s">timing range()&#39;</span>
-<span class="o">%</span><span class="k">timeit</span> <span class="n">test_range</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-
-<span class="k">print</span> <span class="s">&#39;</span><span class="se">\n\n</span><span class="s">timing xrange()&#39;</span>
-<span class="o">%</span><span class="k">timeit</span> <span class="n">test_xrange</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-
-timing range()
-1000 loops, best of 3: 433 µs per loop
-
-
-timing xrange()
-1000 loops, best of 3: 350 µs per loop
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[7]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;</span><span class="se">\n</span><span class="s">timing range()&#39;</span><span class="p">)</span>
-<span class="o">%</span><span class="k">timeit</span> <span class="n">test_range</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-
-timing range()
-1000 loops, best of 3: 520 µs per loop
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[8]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="nb">xrange</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansired">---------------------------------------------------------------------------</span>
-<span class="ansired">NameError</span>                                 Traceback (most recent call last)
-<span class="ansigreen">&lt;ipython-input-8-5d8f9b79ea70&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
-<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span>print<span class="ansiblue">(</span>xrange<span class="ansiblue">(</span><span class="ansicyan">10</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-
-<span class="ansired">NameError</span>: name &apos;xrange&apos; is not defined</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="Note-about-the-speed-differences-in-Python-2-and-3">Note about the speed differences in Python 2 and 3<a class="anchor-link" href="#Note-about-the-speed-differences-in-Python-2-and-3">&#182;</a></h4>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Some people pointed out the speed difference between Python 3's <code>range()</code> and Python2's <code>xrange()</code>. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[3]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">def</span> <span class="nf">test_while</span><span class="p">():</span>
-    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">while</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="mi">20000</span><span class="p">:</span>
-        <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
-    <span class="k">return</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[4]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-<span class="o">%</span><span class="k">timeit</span> <span class="n">test_while</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-100 loops, best of 3: 2.68 ms per loop
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[6]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-<span class="o">%</span><span class="k">timeit</span> <span class="n">test_while</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-1000 loops, best of 3: 1.72 ms per loop
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Raising-exceptions">Raising exceptions<a class="anchor-link" href="#Raising-exceptions">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Where Python 2 accepts both notations, the 'old' and the 'new' syntax, Python 3 chokes (and raises a <code>SyntaxError</code> in turn) if we don't enclose the exception argument in parentheses:</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[7]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[8]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">raise</span> <span class="ne">IOError</span><span class="p">,</span> <span class="s">&quot;file error&quot;</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansired">---------------------------------------------------------------------------</span>
-<span class="ansired">IOError</span>                                   Traceback (most recent call last)
-<span class="ansigreen">&lt;ipython-input-8-25f049caebb0&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
-<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span><span class="ansigreen">raise</span> IOError<span class="ansiblue">,</span> <span class="ansiblue">&quot;file error&quot;</span><span class="ansiblue"></span>
-
-<span class="ansired">IOError</span>: file error</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[9]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="s">&quot;file error&quot;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansired">---------------------------------------------------------------------------</span>
-<span class="ansired">IOError</span>                                   Traceback (most recent call last)
-<span class="ansigreen">&lt;ipython-input-9-6f1c43f525b2&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
-<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span><span class="ansigreen">raise</span> IOError<span class="ansiblue">(</span><span class="ansiblue">&quot;file error&quot;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-
-<span class="ansired">IOError</span>: file error</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[9]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[10]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">raise</span> <span class="ne">IOError</span><span class="p">,</span> <span class="s">&quot;file error&quot;</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansicyan">  File </span><span class="ansigreen">&quot;&lt;ipython-input-10-25f049caebb0&gt;&quot;</span><span class="ansicyan">, line </span><span class="ansigreen">1</span>
-<span class="ansired">    raise IOError, &quot;file error&quot;</span>
-                 ^
-<span class="ansired">SyntaxError</span><span class="ansired">:</span> invalid syntax
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>The proper way to raise an exception in Python 3:</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[11]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-<span class="k">raise</span> <span class="ne">IOError</span><span class="p">(</span><span class="s">&quot;file error&quot;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansired">---------------------------------------------------------------------------</span>
-<span class="ansired">OSError</span>                                   Traceback (most recent call last)
-<span class="ansigreen">&lt;ipython-input-11-c350544d15da&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
-<span class="ansigreen">      1</span> print<span class="ansiblue">(</span><span class="ansiblue">&apos;Python&apos;</span><span class="ansiblue">,</span> python_version<span class="ansiblue">(</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-<span class="ansigreen">----&gt; 2</span><span class="ansired"> </span><span class="ansigreen">raise</span> IOError<span class="ansiblue">(</span><span class="ansiblue">&quot;file error&quot;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-
-<span class="ansired">OSError</span>: file error</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Handling-exceptions">Handling exceptions<a class="anchor-link" href="#Handling-exceptions">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Also the handling of exceptions has slightly changed in Python 3. In Python 3 we have to use the &quot;<code>as</code>&quot; keyword now</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[10]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-<span class="k">try</span><span class="p">:</span>
-    <span class="n">let_us_cause_a_NameError</span>
-<span class="k">except</span> <span class="ne">NameError</span><span class="p">,</span> <span class="n">err</span><span class="p">:</span>
-    <span class="k">print</span> <span class="n">err</span><span class="p">,</span> <span class="s">&#39;--&gt; our error message&#39;</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-name &apos;let_us_cause_a_NameError&apos; is not defined --&gt; our error message
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[12]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-<span class="k">try</span><span class="p">:</span>
-    <span class="n">let_us_cause_a_NameError</span>
-<span class="k">except</span> <span class="ne">NameError</span> <span class="k">as</span> <span class="n">err</span><span class="p">:</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">err</span><span class="p">,</span> <span class="s">&#39;--&gt; our error message&#39;</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-name &apos;let_us_cause_a_NameError&apos; is not defined --&gt; our error message
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a id='The-next-function-and-next-method'></a></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="The-next()-function-and-.next()-method">The next() function and .next() method<a class="anchor-link" href="#The-next()-function-and-.next()-method">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Since <code>next()</code> (<code>.next()</code>) is such a commonly used function (method), this is another syntax change (or rather change in implementation) that is worth mentioning: where you can use both the function and method syntax in Python 2.7.5, the <code>next()</code> function is all that remains in Python 3 (calling the <code>.next()</code> method raises an <code>AttributeError</code>).</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[11]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-
-<span class="n">my_generator</span> <span class="o">=</span> <span class="p">(</span><span class="n">letter</span> <span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="s">&#39;abcdefg&#39;</span><span class="p">)</span>
-
-<span class="nb">next</span><span class="p">(</span><span class="n">my_generator</span><span class="p">)</span>
-<span class="n">my_generator</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt output_prompt">
-    Out[11]:</div>
-
-
-<div class="output_text output_subarea output_pyout">
-<pre>
-&apos;b&apos;
-</pre>
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[13]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-
-<span class="n">my_generator</span> <span class="o">=</span> <span class="p">(</span><span class="n">letter</span> <span class="k">for</span> <span class="n">letter</span> <span class="ow">in</span> <span class="s">&#39;abcdefg&#39;</span><span class="p">)</span>
-
-<span class="nb">next</span><span class="p">(</span><span class="n">my_generator</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt output_prompt">
-    Out[13]:</div>
-
-
-<div class="output_text output_subarea output_pyout">
-<pre>
-&apos;a&apos;
-</pre>
-</div>
-
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[14]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="n">my_generator</span><span class="o">.</span><span class="n">next</span><span class="p">()</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansired">---------------------------------------------------------------------------</span>
-<span class="ansired">AttributeError</span>                            Traceback (most recent call last)
-<span class="ansigreen">&lt;ipython-input-14-125f388bb61b&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
-<span class="ansigreen">----&gt; 1</span><span class="ansired"> </span>my_generator<span class="ansiblue">.</span>next<span class="ansiblue">(</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-
-<span class="ansired">AttributeError</span>: &apos;generator&apos; object has no attribute &apos;next&apos;</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="For-loop-variables-and-the-global-namespace-leak">For-loop variables and the global namespace leak<a class="anchor-link" href="#For-loop-variables-and-the-global-namespace-leak">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Good news is: In Python 3.x for-loop variables don't leak into the global namespace anymore!</p>
-<p>This goes back to a change that was made in Python 3.x and is described in <a href="https://docs.python.org/3/whatsnew/3.0.html">What’s New In Python 3.0</a> as follows:</p>
-<p>&quot;List comprehensions no longer support the syntactic form <code>[... for var in item1, item2, ...]</code>. Use <code>[... for var in (item1, item2, ...)]</code> instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a <code>list()</code> constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.&quot;</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[12]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-
-<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="k">print</span> <span class="s">&#39;before: i =&#39;</span><span class="p">,</span> <span class="n">i</span>
-
-<span class="k">print</span> <span class="s">&#39;comprehension: &#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)]</span>
-
-<span class="k">print</span> <span class="s">&#39;after: i =&#39;</span><span class="p">,</span> <span class="n">i</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-before: i = 1
-comprehension:  [0, 1, 2, 3, 4]
-after: i = 4
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[15]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-
-<span class="n">i</span> <span class="o">=</span> <span class="mi">1</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;before: i =&#39;</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;comprehension:&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">)])</span>
-
-<span class="k">print</span><span class="p">(</span><span class="s">&#39;after: i =&#39;</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-before: i = 1
-comprehension: [0, 1, 2, 3, 4]
-after: i = 1
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Comparing-unorderable-types">Comparing unorderable types<a class="anchor-link" href="#Comparing-unorderable-types">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Another nice change in Python 3 is that a <code>TypeError</code> is raised as warning if we try to compare unorderable types.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[2]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span>
-<span class="k">print</span> <span class="s">&quot;[1, 2] &gt; &#39;foo&#39; = &quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="s">&#39;foo&#39;</span>
-<span class="k">print</span> <span class="s">&quot;(1, 2) &gt; &#39;foo&#39; = &quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="s">&#39;foo&#39;</span>
-<span class="k">print</span> <span class="s">&quot;[1, 2] &gt; (1, 2) = &quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-[1, 2] &gt; &apos;foo&apos; =  False
-(1, 2) &gt; &apos;foo&apos; =  True
-[1, 2] &gt; (1, 2) =  False
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[16]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&quot;[1, 2] &gt; &#39;foo&#39; = &quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="s">&#39;foo&#39;</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&quot;(1, 2) &gt; &#39;foo&#39; = &quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="s">&#39;foo&#39;</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s">&quot;[1, 2] &gt; (1, 2) = &quot;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-
-</pre>
-</div>
-</div>
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_text output_pyerr">
-<pre>
-<span class="ansired">---------------------------------------------------------------------------</span>
-<span class="ansired">TypeError</span>                                 Traceback (most recent call last)
-<span class="ansigreen">&lt;ipython-input-16-a9031729f4a0&gt;</span> in <span class="ansicyan">&lt;module&gt;</span><span class="ansiblue">()</span>
-<span class="ansigreen">      1</span> print<span class="ansiblue">(</span><span class="ansiblue">&apos;Python&apos;</span><span class="ansiblue">,</span> python_version<span class="ansiblue">(</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-<span class="ansigreen">----&gt; 2</span><span class="ansired"> </span>print<span class="ansiblue">(</span><span class="ansiblue">&quot;[1, 2] &gt; &apos;foo&apos; = &quot;</span><span class="ansiblue">,</span> <span class="ansiblue">[</span><span class="ansicyan">1</span><span class="ansiblue">,</span> <span class="ansicyan">2</span><span class="ansiblue">]</span> <span class="ansiblue">&gt;</span> <span class="ansiblue">&apos;foo&apos;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-<span class="ansigreen">      3</span> print<span class="ansiblue">(</span><span class="ansiblue">&quot;(1, 2) &gt; &apos;foo&apos; = &quot;</span><span class="ansiblue">,</span> <span class="ansiblue">(</span><span class="ansicyan">1</span><span class="ansiblue">,</span> <span class="ansicyan">2</span><span class="ansiblue">)</span> <span class="ansiblue">&gt;</span> <span class="ansiblue">&apos;foo&apos;</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-<span class="ansigreen">      4</span> print<span class="ansiblue">(</span><span class="ansiblue">&quot;[1, 2] &gt; (1, 2) = &quot;</span><span class="ansiblue">,</span> <span class="ansiblue">[</span><span class="ansicyan">1</span><span class="ansiblue">,</span> <span class="ansicyan">2</span><span class="ansiblue">]</span> <span class="ansiblue">&gt;</span> <span class="ansiblue">(</span><span class="ansicyan">1</span><span class="ansiblue">,</span> <span class="ansicyan">2</span><span class="ansiblue">)</span><span class="ansiblue">)</span><span class="ansiblue"></span>
-
-<span class="ansired">TypeError</span>: unorderable types: list() &gt; str()</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><a id='Parsing-user-inputs-via-input'></a></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Parsing-user-inputs-via-input()">Parsing user inputs via input()<a class="anchor-link" href="#Parsing-user-inputs-via-input()">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Fortunately, the <code>input()</code> function was fixed in Python 3 so that it always stores the user inputs as <code>str</code> objects. In order to avoid the dangerous behavior in Python 2 to read in other types than <code>strings</code>, we have to use <code>raw_input()</code> instead.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">Python <span style="color: #6600EE; font-weight: bold">2.7</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">6</span> 
-[GCC <span style="color: #6600EE; font-weight: bold">4.0</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">1</span> (Apple Inc<span style="color: #333333">.</span> build <span style="color: #0000DD; font-weight: bold">5493</span>)] on darwin
-Type <span style="background-color: #fff0f0">&quot;help&quot;</span>, <span style="background-color: #fff0f0">&quot;copyright&quot;</span>, <span style="background-color: #fff0f0">&quot;credits&quot;</span> <span style="color: #000000; font-weight: bold">or</span> <span style="background-color: #fff0f0">&quot;license&quot;</span> <span style="color: #008800; font-weight: bold">for</span> more information<span style="color: #333333">.</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> my_input <span style="color: #333333">=</span> <span style="color: #007020">input</span>(<span style="background-color: #fff0f0">&#39;enter a number: &#39;</span>)
-
-enter a number: <span style="color: #0000DD; font-weight: bold">123</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(my_input)
-<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;int&#39;</span><span style="color: #333333">&gt;</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> my_input <span style="color: #333333">=</span> raw_input(<span style="background-color: #fff0f0">&#39;enter a number: &#39;</span>)
-
-enter a number: <span style="color: #0000DD; font-weight: bold">123</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(my_input)
-<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;str&#39;</span><span style="color: #333333">&gt;</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">Python <span style="color: #6600EE; font-weight: bold">3.4</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">1</span> 
-[GCC <span style="color: #6600EE; font-weight: bold">4.2</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">1</span> (Apple Inc<span style="color: #333333">.</span> build <span style="color: #0000DD; font-weight: bold">5577</span>)] on darwin
-Type <span style="background-color: #fff0f0">&quot;help&quot;</span>, <span style="background-color: #fff0f0">&quot;copyright&quot;</span>, <span style="background-color: #fff0f0">&quot;credits&quot;</span> <span style="color: #000000; font-weight: bold">or</span> <span style="background-color: #fff0f0">&quot;license&quot;</span> <span style="color: #008800; font-weight: bold">for</span> more information<span style="color: #333333">.</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> my_input <span style="color: #333333">=</span> <span style="color: #007020">input</span>(<span style="background-color: #fff0f0">&#39;enter a number: &#39;</span>)
-
-enter a number: <span style="color: #0000DD; font-weight: bold">123</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(my_input)
-<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">str</span><span style="background-color: #fff0f0">&#39;&gt;</span>
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="Returning-iterable-objects-instead-of-lists">Returning iterable objects instead of lists<a class="anchor-link" href="#Returning-iterable-objects-instead-of-lists">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>As we have already seen in the <a href="#xrange"><code>xrange</code></a> section, some functions and methods return iterable objects in Python 3 now - instead of lists in Python 2.</p>
-<p>Since we usually iterate over those only once anyway, I think this change makes a lot of sense to save memory. However, it is also possible - in contrast to generators - to iterate over those multiple times if needed, it is aonly not so efficient.</p>
-<p>And for those cases where we really need the <code>list</code>-objects, we can simply convert the iterable object into a <code>list</code> via the <code>list()</code> function.</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-2">Python 2</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[2]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span> <span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">()</span> 
-
-<span class="k">print</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span> 
-<span class="k">print</span> <span class="nb">type</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 2.7.6
-[0, 1, 2]
-&lt;type &apos;list&apos;&gt;
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h4 id="python-3">Python 3</h4>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[7]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre><span class="k">print</span><span class="p">(</span><span class="s">&#39;Python&#39;</span><span class="p">,</span> <span class="n">python_version</span><span class="p">())</span>
-
-<span class="k">print</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
-<span class="k">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)))</span>
-<span class="k">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">)))</span>
-</pre></div>
-
-</div>
-</div>
-</div>
-
-<div class="output_wrapper">
-<div class="output">
-
-
-<div class="output_area"><div class="prompt"></div>
-<div class="output_subarea output_stream output_stdout output_text">
-<pre>
-Python 3.4.1
-range(0, 3)
-&lt;class &apos;range&apos;&gt;
-[0, 1, 2]
-
-</pre>
-</div>
-</div>
-
-</div>
-</div>
-
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><strong>Some more commonly used functions and methods that don't return lists anymore in Python 3:</strong></p>
-<ul>
-<li><p><code>zip()</code></p></li>
-<li><p><code>map()</code></p></li>
-<li><p><code>filter()</code></p></li>
-<li><p>dictionary's <code>.keys()</code> method</p></li>
-<li><p>dictionary's <code>.values()</code> method</p></li>
-<li><p>dictionary's <code>.items()</code> method</p></li>
-</ul>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p><br> <br></p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<h2 id="More-articles-about-Python-2-and-Python-3">More articles about Python 2 and Python 3<a class="anchor-link" href="#More-articles-about-Python-2-and-Python-3">&#182;</a></h2>
-</div>
-</div>
-</div>
-
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>[<a href="#Sections">back to the section-overview</a>]</p>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing text_cell rendered">
-<div class="prompt input_prompt">
-</div>
-<div class="inner_cell">
-<div class="text_cell_render border-box-sizing rendered_html">
-<p>Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up.</p>
-<p><strong>// Porting to Python 3</strong></p>
-<ul>
-<li><p><a href="https://wiki.python.org/moin/Python2orPython3">Should I use Python 2 or Python 3 for my development activity?</a></p></li>
-<li><p><a href="https://docs.python.org/3.0/whatsnew/3.0.html">What’s New In Python 3.0</a></p></li>
-<li><p><a href="http://python3porting.com/differences.html">Porting to Python 3</a></p></li>
-<li><p><a href="https://docs.python.org/3/howto/pyporting.html">Porting Python 2 Code to Python 3</a></p></li>
-<li><p><a href="http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3">How keep Python 3 moving forward</a></p></li>
-</ul>
-<p><strong>// Pro and anti Python 3</strong></p>
-<ul>
-<li><p><a href="http://lucumr.pocoo.org/2014/5/12/everything-about-unicode/">Everything you did not want to know about Unicode in Python 3</a></p></li>
-<li><p><a href="https://medium.com/@deliciousrobots/5d2ad703365d/">Python 3 is killing Python</a></p></li>
-<li><p><a href="https://medium.com/p/2a7af4788b10">Python 3 can revive Python</a></p></li>
-<li><p><a href="http://sealedabstract.com/rants/python-3-is-fine/">Python 3 is fine</a></p></li>
-</ul>
-</div>
-</div>
-</div>
-<div class="cell border-box-sizing code_cell rendered">
-<div class="input">
-<div class="prompt input_prompt">
-In&nbsp;[]:
-</div>
-<div class="inner_cell">
-    <div class="input_area">
-<div class="highlight"><pre> 
-</pre></div>
-
-</div>
-</div>
-</div>
-
-</div>
-    </div>
-  </div>
-</body>
-</html>
diff --git a/tutorials/key_differences_between_python_2_and_3.md b/tutorials/key_differences_between_python_2_and_3.md
deleted file mode 100644
index 972e667..0000000
--- a/tutorials/key_differences_between_python_2_and_3.md
+++ /dev/null
@@ -1,416 +0,0 @@
-[Sebastian Raschka](http://sebastianraschka.com)  
-last updated: 05/24/2014
-
-<br>
-
-**This is a subsection of ["A collection of not-so-obvious Python stuff you should know!"](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb?create=1)**
-
-<a id='sections'></a>
-
-<br>
-
-## Key differences between Python 2 and 3
-<br>
-
-There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,  
-
-- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)
-
-- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)
-
-- [http://python3porting.com/differences.html](http://python3porting.com/differences.html)
-
-- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html)  
-
-etc.
-
-But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!
-(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)
-
-<a id='py23_overview'></a>
-
-<br>
-
-### Overview - Key differences between Python 2 and 3
-
-
-
-
-- [Unicode](#unicode)
-- [The print statement](#print)
-- [Integer division](#integer_div)
-- [xrange()](#xrange)
-- [Raising exceptions](#raising_exceptions)
-- [Handling exceptions](#handling_exceptions)
-- [next() function and .next() method](#next_next)
-- [Loop variables and leaking into the global scope](#loop_leak)
-- [Comparing unorderable types](#compare_unorder)
-
-<br>
-<br>
-
-	
-<a id='unicode'></a>
-<br>
-<br>
-
-### Unicode...
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
-
-####- Python 2: 
-We have ASCII `str()` types, separate `unicode()`, but no `byte` type
-####- Python 3: 
-Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s
-
-<br>
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">#############</span>
-<span style="color: #888888"># Python 2</span>
-<span style="color: #888888">#############</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(unicode(<span style="background-color: #fff0f0">&#39;is like a python3 str()&#39;</span>))
-<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;unicode&#39;</span><span style="color: #333333">&gt;</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(b<span style="background-color: #fff0f0">&#39;byte type does not exist&#39;</span>)
-<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;str&#39;</span><span style="color: #333333">&gt;</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="background-color: #fff0f0">&#39;they are really&#39;</span> <span style="color: #333333">+</span> b<span style="background-color: #fff0f0">&#39; the same&#39;</span>
-<span style="background-color: #fff0f0">&#39;they are really the same&#39;</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(<span style="color: #007020">bytearray</span>(b<span style="background-color: #fff0f0">&#39;bytearray oddly does exist though&#39;</span>))
-<span style="color: #333333">&lt;</span><span style="color: #007020">type</span> <span style="background-color: #fff0f0">&#39;bytearray&#39;</span><span style="color: #333333">&gt;</span>
-
-<span style="color: #888888">#############</span>
-<span style="color: #888888"># Python 3</span>
-<span style="color: #888888">#############</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;strings are now utf-8 </span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\u03BC</span><span style="background-color: #fff0f0">nico</span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\u0394</span><span style="background-color: #fff0f0">é!&#39;</span>)
-strings are now utf<span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">8</span> μnicoΔé<span style="color: #FF0000; background-color: #FFAAAA">!</span>
-
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(b<span style="background-color: #fff0f0">&#39; and we have byte types for storing data&#39;</span>)
-<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">bytes</span><span style="background-color: #fff0f0">&#39;&gt;</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">type</span>(<span style="color: #007020">bytearray</span>(b<span style="background-color: #fff0f0">&#39;but also bytearrays for those who prefer them over strings&#39;</span>))
-<span style="color: #333333">&lt;</span><span style="color: #008800; font-weight: bold">class</span> <span style="color: #FF0000; background-color: #FFAAAA">&#39;</span><span style="color: #BB0066; font-weight: bold">bytearray</span><span style="background-color: #fff0f0">&#39;&gt;</span>
-
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="background-color: #fff0f0">&#39;string&#39;</span> <span style="color: #333333">+</span> b<span style="background-color: #fff0f0">&#39;bytes for data&#39;</span>
-Traceback (most recent call last):s
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">TypeError</span>: Can<span style="background-color: #fff0f0">&#39;t convert &#39;</span><span style="color: #007020">bytes</span><span style="background-color: #fff0f0">&#39; object to str implicitly</span>
-</pre></div>
-
-
-<a id='print'></a>
-<br>
-<br>
-
-### The print statement
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
-Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ...
-
-<br>
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
-Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
-Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
-
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
-Hello, World<span style="color: #FF0000; background-color: #FFAAAA">!</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>
-    <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>
-                        <span style="color: #333333">^</span>
-<span style="color: #FF0000; font-weight: bold">SyntaxError</span>: invalid syntax
-</pre></div>
-
-<br>
-
-And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=""` in Python 3:
-
-<br>
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&quot;line 1&quot;</span>, ; <span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;same line&#39;</span>
-line <span style="color: #0000DD; font-weight: bold">1</span> same line
-
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&quot;line 1&quot;</span>, end<span style="color: #333333">=</span><span style="background-color: #fff0f0">&quot;&quot;</span>) ; <span style="color: #007020">print</span> (<span style="background-color: #fff0f0">&quot; same line&quot;</span>)
-line <span style="color: #0000DD; font-weight: bold">1</span> same line
-</pre></div>
-
-
-<a id='integer_div'></a>
-<br>
-<br>
-
-### Integer division
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
-
-This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed.  
-So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts).
-
-<br>
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #0000DD; font-weight: bold">2</span>
-<span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #0000DD; font-weight: bold">2</span>
-<span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
-<span style="color: #6600EE; font-weight: bold">1.5</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
-<span style="color: #6600EE; font-weight: bold">1.0</span>
-
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #0000DD; font-weight: bold">2</span>
-<span style="color: #6600EE; font-weight: bold">1.5</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #0000DD; font-weight: bold">2</span>
-<span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">/</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
-<span style="color: #6600EE; font-weight: bold">1.5</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #333333">//</span> <span style="color: #6600EE; font-weight: bold">2.0</span>
-<span style="color: #6600EE; font-weight: bold">1.0</span>
-</pre></div>
-
-
-<a id='xrange'></a>
-<br>
-<br>
-
-###`xrange()` 
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
- 
-`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch!  
-In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore.
-
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;</span> python <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in range(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
-<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #0000DD; font-weight: bold">66</span> msec per loop
-
-    <span style="color: #333333">&gt;</span> python <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in xrange(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
-<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #6600EE; font-weight: bold">27.8</span> msec per loop
-
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;</span> python3 <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in range(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
-<span style="color: #0000DD; font-weight: bold">10</span> loops, best of <span style="color: #0000DD; font-weight: bold">3</span>: <span style="color: #6600EE; font-weight: bold">51.1</span> msec per loop
-
-<span style="color: #333333">&gt;</span> python3 <span style="color: #333333">-</span>m timeit <span style="background-color: #fff0f0">&#39;for i in xrange(1000000):&#39;</span> <span style="background-color: #fff0f0">&#39; pass&#39;</span>
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py&quot;</span>, line <span style="color: #0000DD; font-weight: bold">292</span>, <span style="color: #000000; font-weight: bold">in</span> main
-    x <span style="color: #333333">=</span> t<span style="color: #333333">.</span>timeit(number)
-  File <span style="background-color: #fff0f0">&quot;/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py&quot;</span>, line <span style="color: #0000DD; font-weight: bold">178</span>, <span style="color: #000000; font-weight: bold">in</span> timeit
-    timing <span style="color: #333333">=</span> <span style="color: #007020">self</span><span style="color: #333333">.</span>inner(it, <span style="color: #007020">self</span><span style="color: #333333">.</span>timer)
-  File <span style="background-color: #fff0f0">&quot;&lt;timeit-src&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">6</span>, <span style="color: #000000; font-weight: bold">in</span> inner
-    <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> xrange(<span style="color: #0000DD; font-weight: bold">1000000</span>):
-<span style="color: #FF0000; font-weight: bold">NameError</span>: name <span style="background-color: #fff0f0">&#39;xrange&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined
-</pre></div>
-
-
-<a id='raising_exceptions'></a>
-<br>
-<br>
-
-### Raising exceptions
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
-
-
-Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:
-
-<br>
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">IOError</span>: file error
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>(<span style="background-color: #fff0f0">&quot;file error&quot;</span>)
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">IOError</span>: file error
-
-    
-<span style="color: #888888"># Python 3    </span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>
-    <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>, <span style="background-color: #fff0f0">&quot;file error&quot;</span>
-                 <span style="color: #333333">^</span>
-<span style="color: #FF0000; font-weight: bold">SyntaxError</span>: invalid syntax
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">raise</span> <span style="color: #FF0000; font-weight: bold">IOError</span>(<span style="background-color: #fff0f0">&quot;file error&quot;</span>)
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">OSError</span>: file error
-</pre></div>
-
-
-<a id='handling_exceptions'></a>
-<br>
-<br>
-
-### Handling exceptions
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
-
-
-Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the `as` keyword!
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">try</span>:
-<span style="color: #333333">...</span>     blabla
-<span style="color: #333333">...</span> <span style="color: #008800; font-weight: bold">except</span> <span style="color: #FF0000; font-weight: bold">NameError</span>, err:
-<span style="color: #333333">...</span>     <span style="color: #007020">print</span> err, <span style="background-color: #fff0f0">&#39;--&gt; our error msg&#39;</span>
-<span style="color: #333333">...</span> 
-name <span style="background-color: #fff0f0">&#39;blabla&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined <span style="color: #333333">--&gt;</span> our error msg
-
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #008800; font-weight: bold">try</span>:
-<span style="color: #333333">...</span>     blabla
-<span style="color: #333333">...</span> <span style="color: #008800; font-weight: bold">except</span> <span style="color: #FF0000; font-weight: bold">NameError</span> <span style="color: #008800; font-weight: bold">as</span> err:
-<span style="color: #333333">...</span>     <span style="color: #007020">print</span>(err, <span style="background-color: #fff0f0">&#39;--&gt; our error msg&#39;</span>)
-<span style="color: #333333">...</span> 
-name <span style="background-color: #fff0f0">&#39;blabla&#39;</span> <span style="color: #000000; font-weight: bold">is</span> <span style="color: #000000; font-weight: bold">not</span> defined <span style="color: #333333">--&gt;</span> our error msg
-</pre></div>
-
-
-<a id='next_next'></a>
-<br>
-<br>
-
-### The `next()` function and `.next()` method
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
-Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Python 2</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> my_generator <span style="color: #333333">=</span> (letter <span style="color: #008800; font-weight: bold">for</span> letter <span style="color: #000000; font-weight: bold">in</span> <span style="background-color: #fff0f0">&#39;abcdefg&#39;</span>)
-<span style="color: #333333">&gt;&gt;&gt;</span> my_generator<span style="color: #333333">.</span>next()
-<span style="background-color: #fff0f0">&#39;a&#39;</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">next</span>(my_generator)
-<span style="background-color: #fff0f0">&#39;b&#39;</span>
-
-<span style="color: #888888"># Python 3</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> my_generator <span style="color: #333333">=</span> (letter <span style="color: #008800; font-weight: bold">for</span> letter <span style="color: #000000; font-weight: bold">in</span> <span style="background-color: #fff0f0">&#39;abcdefg&#39;</span>)
-<span style="color: #333333">&gt;&gt;&gt;</span> <span style="color: #007020">next</span>(my_generator)
-<span style="background-color: #fff0f0">&#39;a&#39;</span>
-<span style="color: #333333">&gt;&gt;&gt;</span> my_generator<span style="color: #333333">.</span>next()
-Traceback (most recent call last):
-  File <span style="background-color: #fff0f0">&quot;&lt;stdin&gt;&quot;</span>, line <span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>
-<span style="color: #FF0000; font-weight: bold">AttributeError</span>: <span style="background-color: #fff0f0">&#39;generator&#39;</span> <span style="color: #007020">object</span> has no attribute <span style="background-color: #fff0f0">&#39;next&#39;</span>
-</pre></div>
-
-
-<a id='loop_leak'></a>
-<br>
-<br>
-
-### In Python 3.x for-loop variables don't leak into the global namespace anymore
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
-This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:
-
-"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope."
-
-<br>
-`[In:]`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
-<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
-
-i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #007020">print</span>([i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)])
-<span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span>)
-</pre></div>
-
-<br>
-`[Out:]`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 3.3.5
-[0, 1, 2, 3, 4]
-1 -&gt; i in global
-</pre></div>
-
-
-<br>
-<br>
-<br>
-`[In:]`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
-<span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version()
-
-i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #007020">print</span> [i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)]
-<span style="color: #007020">print</span> i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span> 
-</pre></div>
-
-<br>
-`[Out:]`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 2.7.6
-[0, 1, 2, 3, 4]
-4 -&gt; i in global
-</pre></div>
-
-
-<a id='compare_unorder'></a>
-<br>
-<br>
-
-#### Python 3.x prevents us from comparing unorderable types
-
-[[back to Python 2.x vs 3.x overview](#py23_overview)]
-
-<br>
-`[In:]`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
-<span style="color: #007020">print</span> <span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version()
-
-<span style="color: #007020">print</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
-<span style="color: #007020">print</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
-<span style="color: #007020">print</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>)
-</pre></div>
-
-<br>
-`[Out:]`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed in Python 2.7.6
-False
-True
-False
-</pre></div>
-
-<br>
-<br>
-<br>
-
-`[In:]`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">from</span> <span style="color: #0e84b5; font-weight: bold">platform</span> <span style="color: #008800; font-weight: bold">import</span> python_version
-<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
-
-<span style="color: #007020">print</span>([<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>)
-<span style="color: #007020">print</span>((<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>)
-<span style="color: #007020">print</span>([<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>))
-</pre></div>
-
-`[Out:]`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">This code cell was executed <span style="color: #000000; font-weight: bold">in</span> Python <span style="color: #6600EE; font-weight: bold">3.3</span><span style="color: #333333">.</span><span style="color: #0000DD; font-weight: bold">5</span>
-<span style="color: #333333">---------------------------------------------------------------------------</span>
-<span style="color: #FF0000; font-weight: bold">TypeError</span>                                 Traceback (most recent call last)
-<span style="color: #333333">&lt;</span>ipython<span style="color: #333333">-</span><span style="color: #007020">input</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">3</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">1</span>d774c677f73<span style="color: #333333">&gt;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>()
-      <span style="color: #0000DD; font-weight: bold">2</span> <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;This code cell was executed in Python&#39;</span>, python_version())
-      <span style="color: #0000DD; font-weight: bold">3</span> 
-<span style="color: #333333">----&gt;</span> <span style="color: #0000DD; font-weight: bold">4</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
-      <span style="color: #0000DD; font-weight: bold">5</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>) <span style="color: #333333">&gt;</span> <span style="background-color: #fff0f0">&#39;foo&#39;</span>
-      <span style="color: #0000DD; font-weight: bold">6</span> [<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>] <span style="color: #333333">&gt;</span> (<span style="color: #0000DD; font-weight: bold">1</span>, <span style="color: #0000DD; font-weight: bold">2</span>)
-
-<span style="color: #FF0000; font-weight: bold">TypeError</span>: unorderable types: <span style="color: #007020">list</span>() <span style="color: #333333">&gt;</span> <span style="color: #007020">str</span>()
-</pre></div>
diff --git a/tutorials/scope_resolution_legb_rule.md b/tutorials/scope_resolution_legb_rule.md
deleted file mode 100644
index 6722604..0000000
--- a/tutorials/scope_resolution_legb_rule.md
+++ /dev/null
@@ -1,579 +0,0 @@
-# A Beginner's Guide to Python's Namespaces, Scope Resolution, and the LEGB Rule #
-
-
-This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download it as IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb).
-
-<br>
-<br>
-
-## Objectives
-- Namespaces and scopes - where does Python look for variable names?
-- Can we define/reuse variable names for multiple objects at the same time?
-- In which order does Python search different namespaces for variable names?
-
-<br>
-<br>
-
-## Sections  
-- [Introduction to namespaces and scopes](#introduction)  
-- [1. LG - Local and Global scopes](#section_1)  
-- [2. LEG - Local, Enclosed, and Global scope](#section_2)  
-- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3)  
-- [Self-assessment exercise](#assessment)
-- [Conclusion](#conclusion)  
-- [Solutions](#solutions)
-- [Warning: For-loop variables "leaking" into the global namespace](#for_loop)
-
-<a name="introduction"></a>
-<br>
-<br>
-
-##Introduction to Namespaces and Scopes
-
-<br>
-
-###Namespaces
-<br>
-
-Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object.  
-Such a "name-to-object" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = "Hello string"`, we created a reference to the `"Hello string"` object, and henceforth we can access via its variable name `a_string`.
-
-We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., 
-
-<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}</pre>  
-
-
-Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example:
-
-<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}
-b_namespace = {'name_a':object_3, 'name_b':object_4, ...}</pre>
-
-For example, every time we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called "scope"), which we will discuss in more detail in the next section.
-
-<br>
-<br>
-
-### Scope
-
-
-In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of "scope". The "scope" in Python defines the "hierarchy level" in which we search namespaces for certain "name-to-object" mappings.  
-For example, let us consider the following code:
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">foo</span>():
-    i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">5</span>
-    <span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;in foo()&#39;</span>)
-<span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;global&#39;</span>)
-
-foo()
-</pre></div>
-
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">1 global</span>
-<span style="color: #888888">5 in foo()</span>
-</pre></div>
-
-<br>
-<br>
-Here, we just defined the variable name `i` twice, once on the `foo` function.
-
-- `foo_namespace = {'i':object_3, ...}`  
-- `global_namespace = {'i':object_1, 'name_b':object_2, ...}`
-
-So, how does Python now which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section.
-
-<br>
-### Tip:
-If we want to print out the dictionary mapping of the global and local variables, we can use the
-the functions `global()` and `local()
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">#print(globals()) # prints global namespace</span>
-<span style="color: #888888">#print(locals()) # prints local namespace</span>
-
-glob <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">foo</span>():
-    loc <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">5</span>
-    <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;loc in foo():&#39;</span>, <span style="background-color: #fff0f0">&#39;loc&#39;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">locals</span>())
-
-foo()
-<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;loc in global:&#39;</span>, <span style="background-color: #fff0f0">&#39;loc&#39;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">globals</span>())    
-<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;glob in global:&#39;</span>, <span style="background-color: #fff0f0">&#39;foo&#39;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">globals</span>())
-</pre></div>
-
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">loc in foo(): True</span>
-<span style="color: #888888">loc in global: False</span>
-<span style="color: #888888">glob in global: True</span>
-</pre></div>
-
-<br>
-<br>
-
-### Scope resolution for variable names via the LEGB rule.
-
-We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The "scope" defines on which hierarchy level Python searches for a particular "variable name" for its associated object. Now, the next question is: "In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?"  
-To answer is: It uses the LEGB-rule, which stands for
-
-**Local -> Enclosed -> Global -> Built-in**, 
-
-where the arrows should denote the direction of the namespace-hierarchy search order.  
-
-- *Local* can be inside a function or class method, for example.  
-- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function.  
-- *Global* refers to the uppermost level of the executing script itself, and  
-- *Built-in* are special names that Python reserves for itself.  
-
-So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the global namespaces (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).
-
-**Note**:  
-Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">import</span> <span style="color: #0e84b5; font-weight: bold">numpy</span>
-<span style="color: #008800; font-weight: bold">import</span> <span style="color: #0e84b5; font-weight: bold">math</span>
-<span style="color: #008800; font-weight: bold">import</span> <span style="color: #0e84b5; font-weight: bold">scipy</span>
-
-<span style="color: #007020">print</span>(math<span style="color: #333333">.</span>pi, <span style="background-color: #fff0f0">&#39;from the math module&#39;</span>)
-<span style="color: #007020">print</span>(numpy<span style="color: #333333">.</span>pi, <span style="background-color: #fff0f0">&#39;from the numpy package&#39;</span>)
-<span style="color: #007020">print</span>(scipy<span style="color: #333333">.</span>pi, <span style="background-color: #fff0f0">&#39;from the scipy package&#39;</span>)
-</pre></div>
-
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">3.141592653589793 from the math module</span>
-<span style="color: #888888">3.141592653589793 from the numpy package</span>
-<span style="color: #888888">3.141592653589793 from the scipy package</span>
-</pre></div>
-<br>
-<br>
-(This is also why we have to be careful if we import modules via "`from a_module import *`", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)
-
-<br>
-<a name='section1'></a>
-<br>
-<br>
-![LEGB figure](../Images/scope_resolution_1.png)
-<br>
-<br>
-
-<a name="section_1"></a>
-<br>
-<br>
-
-## 1. LG - Local and Global scopes
-
-<a name='example1.1'></a>
-**Example 1.1**  
-As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes.  
-What does the following code print?
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global variable&#39;</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
-    <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
-
-a_func()
-<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
-</pre></div>
-
-**a)**
-<pre>raises an error</pre>
-
-**b)** 
-<pre>
-global value [ a_var outside a_func() ]</pre>
-
-**c)** 
-<pre>global value [ a_var in a_func() ]  
-global value [ a_var outside a_func() ]</pre>
-
-[[go to solution](#solutions)]
-
-### Here is why:
-
-We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously.
-<br>
-<br>
-
-<a name='example1.2'></a>
-**Example 1.2**  
-Now, let us define the variable `a_var` in the global and the local scope.  
-Can you guess what the following code will produce?
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global value&#39;</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
-    a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local value&#39;</span>
-    <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
-
-a_func()
-<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
-</pre></div>
-
-**a)**
-<pre>raises an error</pre>
-
-**b)** 
-<pre>local value [ a_var in a_func() ]
-global value [ a_var outside a_func() ]</pre>
-
-**c)** 
-<pre>global value [ a_var in a_func() ]  
-global value [ a_var outside a_func() ]</pre>
-
-
-[[go to solution](#solutions)]
-
-### Here is why:
-
-When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope.
-
-<br>
-However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate:
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global value&#39;</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
-    <span style="color: #008800; font-weight: bold">global</span> a_var
-    a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local value&#39;</span>
-    <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
-
-<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
-a_func()
-<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
-</pre></div>
-
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">**a)**</span>
-<span style="color: #888888">&lt;pre&gt;raises an error&lt;/pre&gt;</span>
-
-<span style="color: #888888">**b)** </span>
-<span style="color: #888888">&lt;pre&gt;</span>
-<span style="color: #888888">global value [ a_var outside a_func() ]&lt;/pre&gt;</span>
-
-<span style="color: #888888">**c)** </span>
-<span style="color: #888888">&lt;pre&gt;global value [ a_var in a_func() ]  </span>
-<span style="color: #888888">global value [ a_var outside a_func() ]&lt;/pre&gt;</span>
-</pre></div>
-
-But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first):
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
-    a_var <span style="color: #333333">=</span> a_var <span style="color: #333333">+</span> <span style="color: #0000DD; font-weight: bold">1</span>
-    <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
-
-<span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
-a_func()
-</pre></div>
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #333333">---------------------------------------------------------------------------</span>
-<span style="color: #FF0000; font-weight: bold">UnboundLocalError</span>                         Traceback (most recent call last)
-<span style="color: #333333">&lt;</span>ipython<span style="color: #333333">-</span><span style="color: #007020">input</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">4</span><span style="color: #333333">-</span>a6cdd0ee9a55<span style="color: #333333">&gt;</span> <span style="color: #000000; font-weight: bold">in</span> <span style="color: #333333">&lt;</span>module<span style="color: #333333">&gt;</span>()
-      <span style="color: #0000DD; font-weight: bold">6</span> 
-      <span style="color: #0000DD; font-weight: bold">7</span> <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var outside a_func() ]&#39;</span>)
-<span style="color: #333333">----&gt;</span> <span style="color: #0000DD; font-weight: bold">8</span> a_func()
-
-<span style="color: #333333">&lt;</span>ipython<span style="color: #333333">-</span><span style="color: #007020">input</span><span style="color: #333333">-</span><span style="color: #0000DD; font-weight: bold">4</span><span style="color: #333333">-</span>a6cdd0ee9a55<span style="color: #333333">&gt;</span> <span style="color: #000000; font-weight: bold">in</span> a_func()
-      <span style="color: #0000DD; font-weight: bold">2</span> 
-      <span style="color: #0000DD; font-weight: bold">3</span> <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>():
-<span style="color: #333333">----&gt;</span> <span style="color: #0000DD; font-weight: bold">4</span>     a_var <span style="color: #333333">=</span> a_var <span style="color: #333333">+</span> <span style="color: #0000DD; font-weight: bold">1</span>
-      <span style="color: #0000DD; font-weight: bold">5</span>     <span style="color: #007020">print</span>(a_var, <span style="background-color: #fff0f0">&#39;[ a_var inside a_func() ]&#39;</span>)
-      <span style="color: #0000DD; font-weight: bold">6</span> 
-
-<span style="color: #FF0000; font-weight: bold">UnboundLocalError</span>: local variable <span style="background-color: #fff0f0">&#39;a_var&#39;</span> referenced before assignment
-
-<span style="color: #0000DD; font-weight: bold">1</span> [ a_var outside a_func() ]
-</pre></div>
-
-<br>
-<br>
-
-<a name="section_2"></a>
-<br>
-<br>
-
-## 2. LEG - Local, Enclosed, and Global scope
-
-
-
-Now, let us introduce the concept of the enclosed (E) scope. Following the order "Local -> Enclosed -> Global", can you guess what the following code will print?
-
-<a name='example2'></a>
-**Example 2.1**
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global value&#39;</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">outer</span>():
-    a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;enclosed value&#39;</span>
-    
-    <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">inner</span>():
-        a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local value&#39;</span>
-        <span style="color: #007020">print</span>(a_var)
-    
-    inner()
-
-outer()
-</pre></div>
-**a)**
-<pre>global value</pre>
-
-**b)** 
-<pre>enclosed value</pre>
-
-**c)** 
-<pre>local value</pre>
-
-[[go to solution](#solutions)]
-
-### Here is why:
-
-Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope.
-
-Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitly access a variable from the outer (enclosed) scope in order to modify its value.  
-Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet).
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global value&#39;</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">outer</span>():
-       a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local value&#39;</span>
-       <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;outer before:&#39;</span>, a_var)
-       <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">inner</span>():
-           <span style="color: #008800; font-weight: bold">nonlocal</span> a_var
-           a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;inner value&#39;</span>
-           <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;in inner():&#39;</span>, a_var)
-       inner()
-       <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&quot;outer after:&quot;</span>, a_var)
-outer()
-</pre></div>
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">outer before: local value</span>
-<span style="color: #888888">in inner(): inner value</span>
-<span style="color: #888888">outer after: inner value</span>
-</pre></div>
-
-<a name="section_3"></a>
-<br>
-<br>
-<br>
-
-
-## 3. LEGB - Local, Enclosed, Global, Built-in
-
-To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our "own" length-function, which happens to bear the same name as the in-built `len()` function. What outcome do you expect if we'd execute the following code?
-
-<a name='example3'></a>
-
-**Example 3**
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global variable&#39;</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">len</span>(in_var):
-    <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;called my len() function&#39;</span>)
-    l <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">0</span>
-    <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> in_var:
-        l <span style="color: #333333">+=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-    <span style="color: #008800; font-weight: bold">return</span> l
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>(in_var):
-    len_in_var <span style="color: #333333">=</span> <span style="color: #007020">len</span>(in_var)
-    <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Input variable is of length&#39;</span>, len_in_var)
-
-a_func(<span style="background-color: #fff0f0">&#39;Hello, World!&#39;</span>)
-</pre></div>
-
-**a)**
-<pre>raises an error (conflict with in-built `len()` function)</pre>
-
-**b)** 
-<pre>called my len() function
-Input variable is of length 13</pre>
-
-**c)** 
-<pre>Input variable is of length 13</pre>
-
-[[go to solution](#solutions)]
-
-### Here is why:
-
-Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration purposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope first before it attempts
-
-
-<a name ="assessment"></a>
-<br>
-<br>
-
-# Self-assessment exercise
-
-Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;global&#39;</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">outer</span>():
-    
-    <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">len</span>(in_var):
-        <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;called my len() function: &#39;</span>, end<span style="color: #333333">=</span><span style="background-color: #fff0f0">&quot;&quot;</span>)
-        l <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">0</span>
-        <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> in_var:
-            l <span style="color: #333333">+=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-        <span style="color: #008800; font-weight: bold">return</span> l
-    
-    a <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&#39;local&#39;</span>
-    
-    <span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">inner</span>():
-        <span style="color: #008800; font-weight: bold">global</span> <span style="color: #007020">len</span>
-        <span style="color: #008800; font-weight: bold">nonlocal</span> a
-        a <span style="color: #333333">+=</span> <span style="background-color: #fff0f0">&#39; variable&#39;</span>
-    inner()
-    <span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;a is&#39;</span>, a)
-    <span style="color: #007020">print</span>(<span style="color: #007020">len</span>(a))
-
-outer()
-
-<span style="color: #007020">print</span>(<span style="color: #007020">len</span>(a))
-<span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;a is&#39;</span>, a)
-</pre></div>
-
-<a name="conclusion"
-<br>
-<br>
-
-[[go to solution](#solutions)]
-
-# Conclusion
-
-I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes.
-
-#### A rule of thumb
-
-In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug.  
-If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value.  
-For example:
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">a_var <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">2</span>
-
-<span style="color: #008800; font-weight: bold">def</span> <span style="color: #0066BB; font-weight: bold">a_func</span>(some_var):
-    <span style="color: #008800; font-weight: bold">return</span> <span style="color: #0000DD; font-weight: bold">2</span><span style="color: #333333">**</span><span style="color: #0000DD; font-weight: bold">3</span>
-
-a_var <span style="color: #333333">=</span> a_func(a_var)
-<span style="color: #007020">print</span>(a_var)
-</pre></div>
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">8</span>
-</pre></div>
-
-
-<a name = "solutions"></a>
-<br>
-<br>
-
-## Solutions
-
-In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Example 1.1:&#39;</span>, <span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(<span style="background-color: #fff0f0">&#39;01100011&#39;</span>,<span style="color: #0000DD; font-weight: bold">2</span>)))
-</pre></div>
-
-[[back to example 1.1](#example1.1)]
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Example 1.2:&#39;</span>, <span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(<span style="background-color: #fff0f0">&#39;01100001&#39;</span>,<span style="color: #0000DD; font-weight: bold">2</span>)))
-</pre></div>
-
-[[back to example 1.2](#example1.2)]
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Example 2:&#39;</span>, <span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(<span style="background-color: #fff0f0">&#39;01100011&#39;</span>,<span style="color: #0000DD; font-weight: bold">2</span>)))
-</pre></div>
-
-[[back to example 2](#example2)]
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #007020">print</span>(<span style="background-color: #fff0f0">&#39;Example 3:&#39;</span>, <span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(<span style="background-color: #fff0f0">&#39;01100010&#39;</span>,<span style="color: #0000DD; font-weight: bold">2</span>)))
-</pre></div>
-
-[[back to example 3](#example3)]
-
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888"># Solution to the self-assessment exercise</span>
-sol <span style="color: #333333">=</span> <span style="background-color: #fff0f0">&quot;000010100110111101110101011101000110010101110010001010&quot;</span>\
-<span style="background-color: #fff0f0">&quot;0000101001001110100000101000001010011000010010000001101001011100110&quot;</span>\
-<span style="background-color: #fff0f0">&quot;0100000011011000110111101100011011000010110110000100000011101100110&quot;</span>\
-<span style="background-color: #fff0f0">&quot;0001011100100110100101100001011000100110110001100101000010100110001&quot;</span>\
-<span style="background-color: #fff0f0">&quot;1011000010110110001101100011001010110010000100000011011010111100100&quot;</span>\
-<span style="background-color: #fff0f0">&quot;1000000110110001100101011011100010100000101001001000000110011001110&quot;</span>\
-<span style="background-color: #fff0f0">&quot;1010110111001100011011101000110100101101111011011100011101000100000&quot;</span>\
-<span style="background-color: #fff0f0">&quot;0011000100110100000010100000101001100111011011000110111101100010011&quot;</span>\
-<span style="background-color: #fff0f0">&quot;0000101101100001110100000101000001010001101100000101001100001001000&quot;</span>\
-<span style="background-color: #fff0f0">&quot;0001101001011100110010000001100111011011000110111101100010011000010&quot;</span>\
-<span style="background-color: #fff0f0">&quot;1101100&quot;</span>
-
-sol_str <span style="color: #333333">=</span><span style="background-color: #fff0f0">&#39;&#39;</span><span style="color: #333333">.</span>join(<span style="color: #007020">chr</span>(<span style="color: #007020">int</span>(sol[i:i<span style="color: #333333">+</span><span style="color: #0000DD; font-weight: bold">8</span>], <span style="color: #0000DD; font-weight: bold">2</span>)) <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">0</span>, <span style="color: #007020">len</span>(sol), <span style="color: #0000DD; font-weight: bold">8</span>))
-<span style="color: #008800; font-weight: bold">for</span> line <span style="color: #000000; font-weight: bold">in</span> sol_str<span style="color: #333333">.</span>split(<span style="background-color: #fff0f0">&#39;</span><span style="color: #666666; font-weight: bold; background-color: #fff0f0">\n</span><span style="background-color: #fff0f0">&#39;</span>):
-    <span style="color: #007020">print</span>(line)
-</pre></div>
-
-[[back to self-assessment exercise](#assessment)]
-
-
-
-
-<br>
-<br>
-<a name="for_loop"></a>
-
-## Warning: For-loop variables "leaking" into the global namespace
-
-In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #008800; font-weight: bold">for</span> a <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>):
-    <span style="color: #008800; font-weight: bold">if</span> a <span style="color: #333333">==</span> <span style="color: #0000DD; font-weight: bold">4</span>:
-        <span style="color: #007020">print</span>(a, <span style="background-color: #fff0f0">&#39;-&gt; a in for-loop&#39;</span>)
-<span style="color: #007020">print</span>(a, <span style="background-color: #fff0f0">&#39;-&gt; a in global&#39;</span>)
-</pre></div>
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">4 -&gt; a in for-loop</span>
-<span style="color: #888888">4 -&gt; a in global</span>
-</pre></div>
-
-**This also applies if we explicitely defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">b <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #008800; font-weight: bold">for</span> b <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>):
-    <span style="color: #008800; font-weight: bold">if</span> b <span style="color: #333333">==</span> <span style="color: #0000DD; font-weight: bold">4</span>:
-        <span style="color: #007020">print</span>(b, <span style="background-color: #fff0f0">&#39;-&gt; b in for-loop&#39;</span>)
-<span style="color: #007020">print</span>(b, <span style="background-color: #fff0f0">&#39;-&gt; b in global&#39;</span>)
-</pre></div>
-
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">4 -&gt; b in for-loop</span>
-<span style="color: #888888">4 -&gt; b in global</span>
-</pre></div>
-
-However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):
-
-`Input:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%">i <span style="color: #333333">=</span> <span style="color: #0000DD; font-weight: bold">1</span>
-<span style="color: #007020">print</span>([i <span style="color: #008800; font-weight: bold">for</span> i <span style="color: #000000; font-weight: bold">in</span> <span style="color: #007020">range</span>(<span style="color: #0000DD; font-weight: bold">5</span>)])
-<span style="color: #007020">print</span>(i, <span style="background-color: #fff0f0">&#39;-&gt; i in global&#39;</span>)
-</pre></div>
-`Output:`
-<!-- HTML generated using hilite.me --><div style="background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;"><pre style="margin: 0; line-height: 125%"><span style="color: #888888">[0, 1, 2, 3, 4]</span>
-<span style="color: #888888">1 -&gt; i in global</span>
-</pre></div>
-
-Why did I mention "Python 3.x"? Well, as it happens, the same code executed in Python 2.x would print:
-
-<pre>
-4 -> i in global
-</pre>
-
-This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:
-
-"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope."
\ No newline at end of file
diff --git a/tutorials/table_of_contents_ipython.md b/tutorials/table_of_contents_ipython.md
deleted file mode 100644
index 9089e1e..0000000
--- a/tutorials/table_of_contents_ipython.md
+++ /dev/null
@@ -1,125 +0,0 @@
-[Sebastian Raschka](http://sebastianraschka.com)  
-last updated: 05/18/2014
-
-- [Link to this IPython Notebook on Github](https://github.com/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day4_2_cython_numba_parakeet.ipynb)  
-- [Link to the GitHub Repository One-Python-benchmark-per-day](https://github.com/rasbt/One-Python-benchmark-per-day)
-
-
-<hr>
-I would be happy to hear your comments and suggestions.  
-Please feel free to drop me a note via
-[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).
-<hr>
-
-<a id='top'></a>
-
-# Creating a table of contents with internal links in IPython Notebooks and Markdown documents
-
-**Many people have asked me how I create the table of contents with internal links for my IPython Notebooks and Markdown documents on GitHub.   
-Well, no (IPython) magic is involved, it is just a little bit of HTML, but I thought it might be worthwhile to write this little how-to tutorial.**
-
-![example table](../Images/ipython_links_ex.png)
-
-<br>
-<br>
-For example, [click this link](#bottom) to jump to the bottom of the page.
-<br>
-<br>
-
-<br>
-<br>
-
-## The two components to create an internal link
-
-So, how does it work?  Basically, all you need are those two components:    
-1. the destination  
-2. an internal hyperlink to the destination  
-
-
-![two components](../Images/ipython_links_overview.png)
-
-<br>
-###1. The destination
-
-To define the destination (i.e., the section on the page or the cell you want to jump to), you just need to insert an empty HTML anchor tag and give it an **`id`**,   
-e.g., **`<a id='the_destination'></a>`**  
-
-This anchor tag will be invisible if you render it as Markdown in the IPython Notebook.  
-Note that it would also work if we use the **`name`** attribute instead of **`id`**, but since the **`name`** attribute is not supported by HTML5 anymore, I would suggest to just use the **`id`** attribute, which is also shorter to type.
-
-<br>
-###2. The internal hyperlink
-
-Now we have to create the hyperlink to the **`<a id='the_destination'></a>`** anchor tag that we just created.  
-We can either do this in ye goode olde HTML where we put a fragment identifier in form of a hash mark (`#`) in front of the name,   
-for example,  **`<a href='#the_destination'>Link to the destination'</a>`**
-
-Or alternatively, we can just use the slightly more convenient Markdown syntax:  
-**`[Link to the destination](#the_destination)`**
-
-**That's all!**
-
-<br>
-<br>
-
-## One more piece of advice
-
-Of course it would make sense to place the empty anchor tags for you table of contents just on top of each cell that contains a heading.  
-E.g.,   
-
-`<a id='section2'></a>`  
-`###Section 2`  
-`some text ...`  
-
-
-And I did this for a very long time ... until I figured out that it wouldn't render the Markdown properly if you convert the IPython Notebook into HTML (for example, for printing via the print preview option).  
-
-But instead of 
-
-
-###Section 2
-
-it would be rendered as
-
-
-`###Section 2`
-
-which is certainly not what we want (note that it looks normal in the IPython Notebook, but not in the converted HTML version). So my favorite remedy would be to put the `id`-anchor tag into a separate cell just above the section, ideally with some line breaks for nicer visuals.
-
-![img of format problem](../Images/ipython_links_format.png)
-
-<br>
-<br>
-
-### Solution 1: id-anchor tag in a separate cell
-
-![img of format problem](../Images/ipython_links_remedy.png)
-
-<br>
-<br>
-<br>
-<br>
-<br>
-
-
-### Solution 2: using header cells
-
-
-To define the hyperlink anchor tag to this "header cell" is just the text content of the "header cell" connected by dashes. E.g.,
-
-![header cell](../Images/ipython_table_header.png)
-
-`[link to another section](#Another-section)`
-<br>
-<br>
-<br>
-<br>
-<br>
-<br>
-
-[[Click this link and jump to the top of the page](#top)]
-
-You can't see it, but this cell contains a  
-`<a id='bottom'></a>`  
-anchor tag just below this text.
-<a id='bottom'></a>

From 0a567380c9781a228a15bc516639c2552c58c574 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Wed, 27 Jan 2016 22:57:02 -0500
Subject: [PATCH 231/248] typo fix in legb tutorial

---
 tutorials/scope_resolution_legb_rule.ipynb | 2263 ++++++++++----------
 1 file changed, 1137 insertions(+), 1126 deletions(-)

diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index 93f01a2..18ff06c 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -1,1148 +1,1159 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:b33e0c5563d80d68580ea6ce62ae2703856ccde40aec8ff9fb1364ac70d70521"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
-      "\n",
-      "- [Link to the containing GitHub Repository](https://github.com/rasbt/python_reference)\n",
-      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb)\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext watermark"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -a 'Sebastian Raschka' -v -d"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Sebastian Raschka 04/07/2014 \n",
-        "\n",
-        "CPython 3.3.5\n",
-        "IPython 2.1.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "I would be happy to hear your comments and suggestions.  \n",
-      "Please feel free to drop me a note via\n",
-      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#A beginner's guide to Python's namespaces, scope resolution, and the LEGB rule"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download this IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb)."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"sections\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Sections  "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "- [Introduction to namespaces and scopes](#introduction)  \n",
-      "- [1. LG - Local and Global scopes](#section_1)  \n",
-      "- [2. LEG - Local, Enclosed, and Global scope](#section_2)  \n",
-      "- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3)  \n",
-      "- [Self-assessment exercise](#assessment)\n",
-      "- [Conclusion](#conclusion)  \n",
-      "- [Solutions](#solutions)\n",
-      "- [Warning: For-loop variables \"leaking\" into the global namespace](#for_loop)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Objectives\n",
-      "- Namespaces and scopes - where does Python look for variable names?\n",
-      "- Can we define/reuse variable names for multiple objects at the same time?\n",
-      "- In which order does Python search different namespaces for variable names?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"introduction\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Introduction to namespaces and scopes"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Namespaces"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object.  \n",
-      "Such a \"name-to-object\" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = \"Hello string\"`, we created a reference to the `\"Hello string\"` object, and henceforth we can access via its variable name `a_string`.\n",
-      "\n",
-      "We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., \n",
-      "\n",
-      "<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}</pre>  \n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example:\n",
-      "\n",
-      "<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}\n",
-      "b_namespace = {'name_a':object_3, 'name_b':object_4, ...}</pre>\n",
-      "\n",
-      "For example, everytime we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called \"scope\"), which we will discuss in more detail in the next section."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Scope"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of \"scope\". The \"scope\" in Python defines the \"hierarchy level\" in which we search namespaces for certain \"name-to-object\" mappings.  \n",
-      "For example, let us consider the following code:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 1\n",
-      "\n",
-      "def foo():\n",
-      "    i = 5\n",
-      "    print(i, 'in foo()')\n",
-      "\n",
-      "print(i, 'global')\n",
-      "\n",
-      "foo()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 global\n",
-        "5 in foo()\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here, we just defined the variable name `i` twice, once on the `foo` function."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- `foo_namespace = {'i':object_3, ...}`  \n",
-      "- `global_namespace = {'i':object_1, 'name_b':object_2, ...}`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So, how does Python know which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Tip:\n",
-      "If we want to print out the dictionary mapping of the global and local variables, we can use the\n",
-      "the functions `global()` and `local()`"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#print(globals()) # prints global namespace\n",
-      "#print(locals()) # prints local namespace\n",
-      "\n",
-      "glob = 1\n",
-      "\n",
-      "def foo():\n",
-      "    loc = 5\n",
-      "    print('loc in foo():', 'loc' in locals())\n",
-      "\n",
-      "foo()\n",
-      "print('loc in global:', 'loc' in globals())    \n",
-      "print('glob in global:', 'foo' in globals())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "loc in foo(): True\n",
-        "loc in global: False\n",
-        "glob in global: True\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Scope resolution for variable names via the LEGB rule."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The \"scope\" defines on which hierarchy level Python searches for a particular \"variable name\" for its associated object. Now, the next question is: \"In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?\"  \n",
-      "To answer is: It uses the LEGB-rule, which stands for\n",
-      "\n",
-      "**Local -> Enclosed -> Global -> Built-in**, \n",
-      "\n",
-      "where the arrows should denote the direction of the namespace-hierarchy search order.  \n",
-      "\n",
-      "- *Local* can be inside a function or class method, for example.  \n",
-      "- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function.  \n",
-      "- *Global* refers to the uppermost level of the executing script itself, and  \n",
-      "- *Built-in* are special names that Python reserves for itself.  \n",
-      "\n",
-      "So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the built-in namespace (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).\n",
-      "\n",
-      "**Note**:  \n",
-      "Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy\n",
-      "import math\n",
-      "import scipy\n",
-      "\n",
-      "print(math.pi, 'from the math module')\n",
-      "print(numpy.pi, 'from the numpy package')\n",
-      "print(scipy.pi, 'from the scipy package')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "3.141592653589793 from the math module\n",
-        "3.141592653589793 from the numpy package\n",
-        "3.141592653589793 from the scipy package\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "(This is also why we have to be careful if we import modules via \"`from a_module import *`\", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "![LEGB figure](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/scope_resolution_1.png)\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"section_1\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## 1. LG - Local and Global scopes"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Example 1.1**  \n",
-      "As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes.  \n",
-      "What does the following code print?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global variable'\n",
-      "\n",
-      "def a_func():\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
-      "\n",
-      "a_func()\n",
-      "print(a_var, '[ a_var outside a_func() ]')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**a)**\n",
-      "<pre>raises an error</pre>\n",
-      "\n",
-      "**b)** \n",
-      "<pre>\n",
-      "global value [ a_var outside a_func() ]</pre>\n",
-      "\n",
-      "**c)** \n",
-      "<pre>global value [ a_var inside a_func() ]  \n",
-      "global value [ a_var outside a_func() ]</pre>\n",
-      "\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Here is why:\n",
-      "\n",
-      "We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously.\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Example 1.2**  \n",
-      "Now, let us define the variable `a_var` in the global and the local scope.  \n",
-      "Can you guess what the following code will produce?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global value'\n",
-      "\n",
-      "def a_func():\n",
-      "    a_var = 'local value'\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
-      "\n",
-      "a_func()\n",
-      "print(a_var, '[ a_var outside a_func() ]')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**a)**\n",
-      "<pre>raises an error</pre>\n",
-      "\n",
-      "**b)** \n",
-      "<pre>local value [ a_var inside a_func() ]\n",
-      "global value [ a_var outside a_func() ]</pre>\n",
-      "\n",
-      "**c)** \n",
-      "<pre>global value [ a_var inside a_func() ]  \n",
-      "global value [ a_var outside a_func() ]</pre>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Here is why:\n",
-      "\n",
-      "When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global value'\n",
-      "\n",
-      "def a_func():\n",
-      "    global a_var\n",
-      "    a_var = 'local value'\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
-      "\n",
-      "print(a_var, '[ a_var outside a_func() ]')\n",
-      "a_func()\n",
-      "print(a_var, '[ a_var outside a_func() ]')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "global value [ a_var outside a_func() ]\n",
-        "local value [ a_var inside a_func() ]\n",
-        "local value [ a_var outside a_func() ]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 1\n",
-      "\n",
-      "def a_func():\n",
-      "    a_var = a_var + 1\n",
-      "    print(a_var, '[ a_var inside a_func() ]')\n",
-      "\n",
-      "print(a_var, '[ a_var outside a_func() ]')\n",
-      "a_func()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "UnboundLocalError",
-       "evalue": "local variable 'a_var' referenced before assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-4-a6cdd0ee9a55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var outside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m<ipython-input-4-a6cdd0ee9a55>\u001b[0m in \u001b[0;36ma_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0ma_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_var\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var inside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'a_var' referenced before assignment"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1 [ a_var outside a_func() ]\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"section_2\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## 2. LEG - Local, Enclosed, and Global scope\n",
-      "\n",
-      "\n",
-      "\n",
-      "Now, let us introduce the concept of the enclosed (E) scope. Following the order \"Local -> Enclosed -> Global\", can you guess what the following code will print?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Example 2.1**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global value'\n",
-      "\n",
-      "def outer():\n",
-      "    a_var = 'enclosed value'\n",
-      "    \n",
-      "    def inner():\n",
-      "        a_var = 'local value'\n",
-      "        print(a_var)\n",
-      "    \n",
-      "    inner()\n",
-      "\n",
-      "outer()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**a)**\n",
-      "<pre>global value</pre>\n",
-      "\n",
-      "**b)** \n",
-      "<pre>enclosed value</pre>\n",
-      "\n",
-      "**c)** \n",
-      "<pre>local value</pre>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Here is why:\n",
-      "\n",
-      "Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitly access a variable from the outer (enclosed) scope in order to modify its value.  \n",
-      "Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global value'\n",
-      "\n",
-      "def outer():\n",
-      "       a_var = 'local value'\n",
-      "       print('outer before:', a_var)\n",
-      "       def inner():\n",
-      "           nonlocal a_var\n",
-      "           a_var = 'inner value'\n",
-      "           print('in inner():', a_var)\n",
-      "       inner()\n",
-      "       print(\"outer after:\", a_var)\n",
-      "outer()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "outer before: local value\n",
-        "in inner(): inner value\n",
-        "outer after: inner value\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"section_3\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## 3. LEGB - Local, Enclosed, Global, Built-in\n",
-      "\n",
-      "To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our \"own\" length-funcion, which happens to bear the same name as the in-built `len()` function. What outcome do you excpect if we'd execute the following code?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Example 3**"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 'global variable'\n",
-      "\n",
-      "def len(in_var):\n",
-      "    print('called my len() function')\n",
-      "    l = 0\n",
-      "    for i in in_var:\n",
-      "        l += 1\n",
-      "    return l\n",
-      "\n",
-      "def a_func(in_var):\n",
-      "    len_in_var = len(in_var)\n",
-      "    print('Input variable is of length', len_in_var)\n",
-      "\n",
-      "a_func('Hello, World!')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**a)**\n",
-      "<pre>raises an error (conflict with in-built `len()` function)</pre>\n",
-      "\n",
-      "**b)** \n",
-      "<pre>called my len() function\n",
-      "Input variable is of length 13</pre>\n",
-      "\n",
-      "**c)** \n",
-      "<pre>Input variable is of length 13</pre>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Here is why:\n",
-      "\n",
-      "Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration pruposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope first before it attempts"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name =\"assessment\"></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Self-assessment exercise"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 'global'\n",
-      "\n",
-      "def outer():\n",
-      "    \n",
-      "    def len(in_var):\n",
-      "        print('called my len() function: ', end=\"\")\n",
-      "        l = 0\n",
-      "        for i in in_var:\n",
-      "            l += 1\n",
-      "        return l\n",
-      "    \n",
-      "    a = 'local'\n",
-      "    \n",
-      "    def inner():\n",
-      "        global len\n",
-      "        nonlocal a\n",
-      "        a += ' variable'\n",
-      "    inner()\n",
-      "    print('a is', a)\n",
-      "    print(len(a))\n",
-      "\n",
-      "\n",
-      "outer()\n",
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Sebastian Raschka](http://www.sebastianraschka.com)  \n",
+    "\n",
+    "- [Link to the containing GitHub Repository](https://github.com/rasbt/python_reference)\n",
+    "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/scope_resolution_legb_rule.ipynb)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sebastian Raschka 01/27/2016 \n",
       "\n",
-      "print(len(a))\n",
-      "print('a is', a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 59
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"conclusion\"\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[go to solution](#solutions)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Conclusion"
+      "CPython 3.5.1\n",
+      "IPython 4.0.3\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### A rule of thumb"
+    }
+   ],
+   "source": [
+    "%watermark -a 'Sebastian Raschka' -v -d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "I would be happy to hear your comments and suggestions.  \n",
+    "Please feel free to drop me a note via\n",
+    "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#A beginner's guide to Python's namespaces, scope resolution, and the LEGB rule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule. The following sections will provide short example code blocks that should illustrate the problem followed by short explanations. You can simply read this tutorial from start to end, but I'd like to encourage you to execute the code snippets - you can either copy & paste them, or for your convenience, simply [download this IPython notebook](https://raw.githubusercontent.com/rasbt/python_reference/master/tutorials/scope_resolution_legb_rule.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"sections\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sections  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "- [Introduction to namespaces and scopes](#introduction)  \n",
+    "- [1. LG - Local and Global scopes](#section_1)  \n",
+    "- [2. LEG - Local, Enclosed, and Global scope](#section_2)  \n",
+    "- [3. LEGB - Local, Enclosed, Global, Built-in](#section_3)  \n",
+    "- [Self-assessment exercise](#assessment)\n",
+    "- [Conclusion](#conclusion)  \n",
+    "- [Solutions](#solutions)\n",
+    "- [Warning: For-loop variables \"leaking\" into the global namespace](#for_loop)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Objectives\n",
+    "- Namespaces and scopes - where does Python look for variable names?\n",
+    "- Can we define/reuse variable names for multiple objects at the same time?\n",
+    "- In which order does Python search different namespaces for variable names?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"introduction\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction to namespaces and scopes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Namespaces"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Roughly speaking, namespaces are just containers for mapping names to objects. As you might have already heard, everything in Python - literals, lists, dictionaries, functions, classes, etc. - is an object.  \n",
+    "Such a \"name-to-object\" mapping allows us to access an object by a name that we've assigned to it. E.g., if we make a simple string assignment via `a_string = \"Hello string\"`, we created a reference to the `\"Hello string\"` object, and henceforth we can access via its variable name `a_string`.\n",
+    "\n",
+    "We can picture a namespace as a Python dictionary structure, where the dictionary keys represent the names and the dictionary values the object itself (and this is also how namespaces are currently implemented in Python), e.g., \n",
+    "\n",
+    "<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}</pre>  \n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, the tricky part is that we have multiple independent namespaces in Python, and names can be reused for different namespaces (only the objects are unique, for example:\n",
+    "\n",
+    "<pre>a_namespace = {'name_a':object_1, 'name_b':object_2, ...}\n",
+    "b_namespace = {'name_a':object_3, 'name_b':object_4, ...}</pre>\n",
+    "\n",
+    "For example, everytime we call a `for-loop` or define a function, it will create its own namespace. Namespaces also have different levels of hierarchy (the so-called \"scope\"), which we will discuss in more detail in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scope"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the section above, we have learned that namespaces can exist independently from each other and that they are structured in a certain hierarchy, which brings us to the concept of \"scope\". The \"scope\" in Python defines the \"hierarchy level\" in which we search namespaces for certain \"name-to-object\" mappings.  \n",
+    "For example, let us consider the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 global\n",
+      "5 in foo()\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug.  \n",
-      "If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value.  \n",
-      "For example:"
+    }
+   ],
+   "source": [
+    "i = 1\n",
+    "\n",
+    "def foo():\n",
+    "    i = 5\n",
+    "    print(i, 'in foo()')\n",
+    "\n",
+    "print(i, 'global')\n",
+    "\n",
+    "foo()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we just defined the variable name `i` twice, once on the `foo` function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- `foo_namespace = {'i':object_3, ...}`  \n",
+    "- `global_namespace = {'i':object_1, 'name_b':object_2, ...}`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, how does Python know which namespace it has to search if we want to print the value of the variable `i`? This is where Python's LEGB-rule comes into play, which we will discuss in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Tip:\n",
+    "If we want to print out the dictionary mapping of the global and local variables, we can use the\n",
+    "the functions `global()` and `local()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loc in foo(): True\n",
+      "loc in global: False\n",
+      "glob in global: True\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = 2\n",
-      "\n",
-      "def a_func(some_var):\n",
-      "    return 2**3\n",
-      "\n",
-      "a_var = a_func(a_var)\n",
-      "print(a_var)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "8\n"
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name = \"solutions\">\n",
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "#print(globals()) # prints global namespace\n",
+    "#print(locals()) # prints local namespace\n",
+    "\n",
+    "glob = 1\n",
+    "\n",
+    "def foo():\n",
+    "    loc = 5\n",
+    "    print('loc in foo():', 'loc' in locals())\n",
+    "\n",
+    "foo()\n",
+    "print('loc in global:', 'loc' in globals())    \n",
+    "print('glob in global:', 'foo' in globals())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Scope resolution for variable names via the LEGB rule."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We have seen that multiple namespaces can exist independently from each other and that they can contain the same variable names on different hierachy levels. The \"scope\" defines on which hierarchy level Python searches for a particular \"variable name\" for its associated object. Now, the next question is: \"In which order does Python search the different levels of namespaces before it finds the name-to-object' mapping?\"  \n",
+    "To answer is: It uses the LEGB-rule, which stands for\n",
+    "\n",
+    "**Local -> Enclosed -> Global -> Built-in**, \n",
+    "\n",
+    "where the arrows should denote the direction of the namespace-hierarchy search order.  \n",
+    "\n",
+    "- *Local* can be inside a function or class method, for example.  \n",
+    "- *Enclosed* can be its `enclosing` function, e.g., if a function is wrapped inside another function.  \n",
+    "- *Global* refers to the uppermost level of the executing script itself, and  \n",
+    "- *Built-in* are special names that Python reserves for itself.  \n",
+    "\n",
+    "So, if a particular name:object mapping cannot be found in the local namespaces, the namespaces of the enclosed scope are being searched next. If the search in the enclosed scope is unsuccessful, too, Python moves on to the global namespace, and eventually, it will search the built-in namespace (side note: if a name cannot found in any of the namespaces, a *NameError* will is raised).\n",
+    "\n",
+    "**Note**:  \n",
+    "Namespaces can also be further nested, for example if we import modules, or if we are defining new classes. In those cases we have to use prefixes to access those nested namespaces. Let me illustrate this concept in the following code block:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.141592653589793 from the math module\n",
+      "3.141592653589793 from the numpy package\n",
+      "3.141592653589793 from the scipy package\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Solutions\n",
-      "\n",
-      "In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:"
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "import math\n",
+    "import scipy\n",
+    "\n",
+    "print(math.pi, 'from the math module')\n",
+    "print(numpy.pi, 'from the numpy package')\n",
+    "print(scipy.pi, 'from the scipy package')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(This is also why we have to be careful if we import modules via \"`from a_module import *`\", since it loads the variable names into the global namespace and could potentially overwrite already existing variable names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "![LEGB figure](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/scope_resolution_1.png)\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"section_1\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. LG - Local and Global scopes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example 1.1**  \n",
+    "As a warm-up exercise, let us first forget about the enclosed (E) and built-in (B) scopes in the LEGB rule and only take a look at LG - the local and global scopes.  \n",
+    "What does the following code print?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a_var = 'global variable'\n",
+    "\n",
+    "def a_func():\n",
+    "    print(a_var, '[ a_var inside a_func() ]')\n",
+    "\n",
+    "a_func()\n",
+    "print(a_var, '[ a_var outside a_func() ]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**a)**\n",
+    "<pre>raises an error</pre>\n",
+    "\n",
+    "**b)** \n",
+    "<pre>\n",
+    "global value [ a_var outside a_func() ]</pre>\n",
+    "\n",
+    "**c)** \n",
+    "<pre>global value [ a_var inside a_func() ]  \n",
+    "global value [ a_var outside a_func() ]</pre>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[go to solution](#solutions)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Here is why:\n",
+    "\n",
+    "We call `a_func()` first, which is supposed to print the value of `a_var`. According to the LEGB rule, the function will first look in its own local scope (L) if `a_var` is defined there. Since `a_func()` does not define its own `a_var`, it will look one-level above in the global scope (G) in which `a_var` has been defined previously.\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example 1.2**  \n",
+    "Now, let us define the variable `a_var` in the global and the local scope.  \n",
+    "Can you guess what the following code will produce?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a_var = 'global value'\n",
+    "\n",
+    "def a_func():\n",
+    "    a_var = 'local value'\n",
+    "    print(a_var, '[ a_var inside a_func() ]')\n",
+    "\n",
+    "a_func()\n",
+    "print(a_var, '[ a_var outside a_func() ]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**a)**\n",
+    "<pre>raises an error</pre>\n",
+    "\n",
+    "**b)** \n",
+    "<pre>local value [ a_var inside a_func() ]\n",
+    "global value [ a_var outside a_func() ]</pre>\n",
+    "\n",
+    "**c)** \n",
+    "<pre>global value [ a_var inside a_func() ]  \n",
+    "global value [ a_var outside a_func() ]</pre>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[go to solution](#solutions)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Here is why:\n",
+    "\n",
+    "When we call `a_func()`, it will first look in its local scope (L) for `a_var`, since `a_var` is defined in the local scope of `a_func`, its assigned value `local variable` is printed. Note that this doesn't affect the global variable, which is in a different scope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "However, it is also possible to modify the global by, e.g., re-assigning a new value to it if we use the global keyword as the following example will illustrate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "global value [ a_var outside a_func() ]\n",
+      "local value [ a_var inside a_func() ]\n",
+      "local value [ a_var outside a_func() ]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Example 1.1:', chr(int('01100011',2)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Example 1.2:', chr(int('01100010',2)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Example 2.1:', chr(int('01100011',2)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Example 3.1:', chr(int('01100010',2)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Execute to run the self-assessment solution\n",
-      "\n",
-      "sol = \"000010100110111101110101011101000110010101110010001010\"\\\n",
-      "\"0000101001001110100000101000001010011000010010000001101001011100110\"\\\n",
-      "\"0100000011011000110111101100011011000010110110000100000011101100110\"\\\n",
-      "\"0001011100100110100101100001011000100110110001100101000010100110001\"\\\n",
-      "\"1011000010110110001101100011001010110010000100000011011010111100100\"\\\n",
-      "\"1000000110110001100101011011100010100000101001001000000110011001110\"\\\n",
-      "\"1010110111001100011011101000110100101101111011011100011101000100000\"\\\n",
-      "\"0011000100110100000010100000101001100111011011000110111101100010011\"\\\n",
-      "\"0000101101100001110100000101000001010001101100000101001100001001000\"\\\n",
-      "\"0001101001011100110010000001100111011011000110111101100010011000010\"\\\n",
-      "\"1101100\"\n",
-      "\n",
-      "sol_str =''.join(chr(int(sol[i:i+8], 2)) for i in range(0, len(sol), 8))\n",
-      "for line in sol_str.split('\\n'):\n",
-      "    print(line)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 58
-    },
+    }
+   ],
+   "source": [
+    "a_var = 'global value'\n",
+    "\n",
+    "def a_func():\n",
+    "    global a_var\n",
+    "    a_var = 'local value'\n",
+    "    print(a_var, '[ a_var inside a_func() ]')\n",
+    "\n",
+    "print(a_var, '[ a_var outside a_func() ]')\n",
+    "a_func()\n",
+    "print(a_var, '[ a_var outside a_func() ]')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But we have to be careful about the order: it is easy to raise an `UnboundLocalError` if we don't explicitly tell Python that we want to use the global scope and try to modify a variable's value (remember, the right side of an assignment operation is executed first):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name=\"for_loop\"></a>"
+     "ename": "UnboundLocalError",
+     "evalue": "local variable 'a_var' referenced before assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-4-a6cdd0ee9a55>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var outside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-4-a6cdd0ee9a55>\u001b[0m in \u001b[0;36ma_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0ma_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0ma_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma_var\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_var\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'[ a_var inside a_func() ]'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'a_var' referenced before assignment"
      ]
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Warning: For-loop variables \"leaking\" into the global namespace"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 [ a_var outside a_func() ]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.\n"
+    }
+   ],
+   "source": [
+    "a_var = 1\n",
+    "\n",
+    "def a_func():\n",
+    "    a_var = a_var + 1\n",
+    "    print(a_var, '[ a_var inside a_func() ]')\n",
+    "\n",
+    "print(a_var, '[ a_var outside a_func() ]')\n",
+    "a_func()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"section_2\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. LEG - Local, Enclosed, and Global scope\n",
+    "\n",
+    "\n",
+    "\n",
+    "Now, let us introduce the concept of the enclosed (E) scope. Following the order \"Local -> Enclosed -> Global\", can you guess what the following code will print?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example 2.1**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a_var = 'global value'\n",
+    "\n",
+    "def outer():\n",
+    "    a_var = 'enclosed value'\n",
+    "    \n",
+    "    def inner():\n",
+    "        a_var = 'local value'\n",
+    "        print(a_var)\n",
+    "    \n",
+    "    inner()\n",
+    "\n",
+    "outer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**a)**\n",
+    "<pre>global value</pre>\n",
+    "\n",
+    "**b)** \n",
+    "<pre>enclosed value</pre>\n",
+    "\n",
+    "**c)** \n",
+    "<pre>local value</pre>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[go to solution](#solutions)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Here is why:\n",
+    "\n",
+    "Let us quickly recapitulate what we just did: We called `outer()`, which defined the variable `a_var` locally (next to an existing `a_var` in the global scope). Next, the `outer()` function called `inner()`, which in turn defined a variable with of name `a_var` as well. The `print()` function inside `inner()` searched in the local scope first (L->E) before it went up in the scope hierarchy, and therefore it printed the value that was assigned in the local scope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to the concept of the `global` keyword, which we have seen in the section above, we can use the keyword `nonlocal` inside the inner function to explicitly access a variable from the outer (enclosed) scope in order to modify its value.  \n",
+    "Note that the `nonlocal` keyword was added in Python 3.x and is not implemented in Python 2.x (yet)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "outer before: local value\n",
+      "in inner(): inner value\n",
+      "outer after: inner value\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for a in range(5):\n",
-      "    if a == 4:\n",
-      "        print(a, '-> a in for-loop')\n",
-      "print(a, '-> a in global')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4 -> a in for-loop\n",
-        "4 -> a in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**This also applies if we explicitly defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:"
+    }
+   ],
+   "source": [
+    "a_var = 'global value'\n",
+    "\n",
+    "def outer():\n",
+    "       a_var = 'local value'\n",
+    "       print('outer before:', a_var)\n",
+    "       def inner():\n",
+    "           nonlocal a_var\n",
+    "           a_var = 'inner value'\n",
+    "           print('in inner():', a_var)\n",
+    "       inner()\n",
+    "       print(\"outer after:\", a_var)\n",
+    "outer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"section_3\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. LEGB - Local, Enclosed, Global, Built-in\n",
+    "\n",
+    "To wrap up the LEGB rule, let us come to the built-in scope. Here, we will define our \"own\" length-funcion, which happens to bear the same name as the in-built `len()` function. What outcome do you excpect if we'd execute the following code?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Example 3**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a_var = 'global variable'\n",
+    "\n",
+    "def len(in_var):\n",
+    "    print('called my len() function')\n",
+    "    l = 0\n",
+    "    for i in in_var:\n",
+    "        l += 1\n",
+    "    return l\n",
+    "\n",
+    "def a_func(in_var):\n",
+    "    len_in_var = len(in_var)\n",
+    "    print('Input variable is of length', len_in_var)\n",
+    "\n",
+    "a_func('Hello, World!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**a)**\n",
+    "<pre>raises an error (conflict with in-built `len()` function)</pre>\n",
+    "\n",
+    "**b)** \n",
+    "<pre>called my len() function\n",
+    "Input variable is of length 13</pre>\n",
+    "\n",
+    "**c)** \n",
+    "<pre>Input variable is of length 13</pre>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[go to solution](#solutions)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Here is why:\n",
+    "\n",
+    "Since the exact same names can be used to map names to different objects - as long as the names are in different name spaces - there is no problem of reusing the name `len` to define our own length function (this is just for demonstration pruposes, it is NOT recommended). As we go up in Python's L -> E -> G -> B hierarchy, the function `a_func()` finds `len()` already in the global scope (G) first before it attempts to search the built-in (B) namespace."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name =\"assessment\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Self-assessment exercise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, after we went through a couple of exercises, let us quickly check where we are. So, one more time: What would the following code print out?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "a = 'global'\n",
+    "\n",
+    "def outer():\n",
+    "    \n",
+    "    def len(in_var):\n",
+    "        print('called my len() function: ', end=\"\")\n",
+    "        l = 0\n",
+    "        for i in in_var:\n",
+    "            l += 1\n",
+    "        return l\n",
+    "    \n",
+    "    a = 'local'\n",
+    "    \n",
+    "    def inner():\n",
+    "        global len\n",
+    "        nonlocal a\n",
+    "        a += ' variable'\n",
+    "    inner()\n",
+    "    print('a is', a)\n",
+    "    print(len(a))\n",
+    "\n",
+    "\n",
+    "outer()\n",
+    "\n",
+    "print(len(a))\n",
+    "print('a is', a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"conclusion\"\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[go to solution](#solutions)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I hope this short tutorial was helpful to understand the basic concept of Python's scope resolution order using the LEGB rule. I want to encourage you (as a little self-assessment exercise) to look at the code snippets again tomorrow and check if you can correctly predict all their outcomes."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### A rule of thumb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In practice, **it is usually a bad idea to modify global variables inside the function scope**, since it often be the cause of confusion and weird errors that are hard to debug.  \n",
+    "If you want to modify a global variable via a function, it is recommended to pass it as an argument and reassign the return-value.  \n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = 1\n",
-      "for b in range(5):\n",
-      "    if b == 4:\n",
-      "        print(b, '-> b in for-loop')\n",
-      "print(b, '-> b in global')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4 -> b in for-loop\n",
-        "4 -> b in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):"
+    }
+   ],
+   "source": [
+    "a_var = 2\n",
+    "\n",
+    "def a_func(some_var):\n",
+    "    return 2**3\n",
+    "\n",
+    "a_var = a_func(a_var)\n",
+    "print(a_var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name = \"solutions\">\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solutions\n",
+    "\n",
+    "In order to prevent you from unintentional spoilers, I have written the solutions in binary format. In order to display the character representation, you just need to execute the following lines of code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print('Example 1.1:', chr(int('01100011',2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print('Example 1.2:', chr(int('01100010',2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print('Example 2.1:', chr(int('01100011',2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "print('Example 3.1:', chr(int('01100010',2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Execute to run the self-assessment solution\n",
+    "\n",
+    "sol = \"000010100110111101110101011101000110010101110010001010\"\\\n",
+    "\"0000101001001110100000101000001010011000010010000001101001011100110\"\\\n",
+    "\"0100000011011000110111101100011011000010110110000100000011101100110\"\\\n",
+    "\"0001011100100110100101100001011000100110110001100101000010100110001\"\\\n",
+    "\"1011000010110110001101100011001010110010000100000011011010111100100\"\\\n",
+    "\"1000000110110001100101011011100010100000101001001000000110011001110\"\\\n",
+    "\"1010110111001100011011101000110100101101111011011100011101000100000\"\\\n",
+    "\"0011000100110100000010100000101001100111011011000110111101100010011\"\\\n",
+    "\"0000101101100001110100000101000001010001101100000101001100001001000\"\\\n",
+    "\"0001101001011100110010000001100111011011000110111101100010011000010\"\\\n",
+    "\"1101100\"\n",
+    "\n",
+    "sol_str =''.join(chr(int(sol[i:i+8], 2)) for i in range(0, len(sol), 8))\n",
+    "for line in sol_str.split('\\n'):\n",
+    "    print(line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name=\"for_loop\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warning: For-loop variables \"leaking\" into the global namespace"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In contrast to some other programming languages, `for-loops` will use the scope they exist in and leave their defined loop-variable behind.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 -> a in for-loop\n",
+      "4 -> a in global\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 1\n",
-      "print([i for i in range(5)])\n",
-      "print(i, '-> i in global')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[0, 1, 2, 3, 4]\n",
-        "1 -> i in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Why did I mention \"Python 3.x\"? Well, as it happens, the same code executed in Python 2.x would print:\n",
-      "\n",
-      "<pre>\n",
-      "4 -> i in global\n",
-      "<pre>"
+    }
+   ],
+   "source": [
+    "for a in range(5):\n",
+    "    if a == 4:\n",
+    "        print(a, '-> a in for-loop')\n",
+    "print(a, '-> a in global')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**This also applies if we explicitly defined the `for-loop` variable in the global namespace before!** In this case it will rebind the existing variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 -> b in for-loop\n",
+      "4 -> b in global\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
-      "\n",
-      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+    }
+   ],
+   "source": [
+    "b = 1\n",
+    "for b in range(5):\n",
+    "    if b == 4:\n",
+    "        print(b, '-> b in for-loop')\n",
+    "print(b, '-> b in global')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, in **Python 3.x**, we can use closures to prevent the for-loop variable to cut into the global namespace. Here is an example (exectuted in Python 3.4):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0, 1, 2, 3, 4]\n",
+      "1 -> i in global\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
-   "metadata": {}
+   "source": [
+    "i = 1\n",
+    "print([i for i in range(5)])\n",
+    "print(i, '-> i in global')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Why did I mention \"Python 3.x\"? Well, as it happens, the same code executed in Python 2.x would print:\n",
+    "\n",
+    "<pre>\n",
+    "4 -> i in global\n",
+    "<pre>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
+    "\n",
+    "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
- ]
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
 }

From 4797e5d32790dff54f0418f77d5fa0bace5f8df8 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Sun, 10 Apr 2016 18:50:40 -0400
Subject: [PATCH 232/248] How to Make Mistakes in Python

---
 other/python_book_reviews.md                  |  104 +-
 ...y_differences_between_python_2_and_3.ipynb | 4160 ++++----
 tutorials/not_so_obvious_python_stuff.ipynb   | 8504 ++++++++---------
 tutorials/scope_resolution_legb_rule.ipynb    |    2 +-
 4 files changed, 6347 insertions(+), 6423 deletions(-)

diff --git a/other/python_book_reviews.md b/other/python_book_reviews.md
index bc1105f..4b762ea 100644
--- a/other/python_book_reviews.md
+++ b/other/python_book_reviews.md
@@ -3,14 +3,13 @@
 
 # Python Book Reviews
 
-- [Matplotlib Plotting Cookbook](#Matplotlib-Plotting-Cookbook)
-- [Python High Performance Programming](#Python-High-Performance-Programming)
-- [Learning Ipython for Interactive Computing and Data Visualization](#Learning-Ipython-for-Interactive-Computing-and-Data-Visualization)
-- [The Practice of Computing Using Python (2nd Edition)](#The-Practice-of-Computing-Using-Python-(2nd-Edition))
+- [Matplotlib Plotting Cookbook](#matplotlib-plotting-cookbook)
+- [Python High Performance Programming](#python-high-performance-programming)
+- [Learning IPython for Interactive Computing and Data Visualization](#learning-ipython-for-interactive-computing-and-data-visualization)
+- [The Practice of Computing Using Python (2nd Edition)](#the-practice-of-computing-using-python-(2nd-Edition))
+- [How to Make Mistakes in Python](#how-to-make-mistakes-in-python)
 
 
-<div style="height:50px;"></div>
-
 **Where are the links?**  
 
 I decided to **not** post any links to any online shop here - I don't want to advertise anything but merely want to leave my brief thoughts in hope that it might be helpful to one or the other.
@@ -18,25 +17,20 @@ I decided to **not** post any links to any online shop here - I don't want to ad
 
 **About the rating scale/review scores**  
 
-Most popular review sites provide some sort of rating, e.g., 7/10, 90/100, 3 starts out of 5 etc.  
+Most popular review sites provide some sort of rating, e.g., 7/10, 90/100, 3 stars out of 5 etc.  
 I have to admit that I am not a big fan of those review scores - and you won't find them here. Based on my experience, review scores are just kindling all sorts of arguments, destructive debates, and hate-mails. Let's be honest, every opinion is subjective, and I think that boiling it down to a final score is just an annoyance for everyone.
 
-<div style="height:100px;"></div>
-
-
-<hr>
+---
 
+### Matplotlib Plotting Cookbook
 
-<a id="Matplotlib-Plotting-Cookbook"></a>
-### Matplotlib Plotting Cookbook 
-[[back to top](#table-of-contents)]
 ***by Alexandre Devert***
 
-<font size='0.7em'>
-Paperback: 222 pages  
-Release Date: March 2014  
-ISBN: 1849513260  
-ISBN 13: 9781849513265</font>
+- Paperback: 222 pages   
+- Release Date: March 2014  
+- ISBN: 1849513260  
+- ISBN 13: 9781849513265
+- Publisher: Packt
 
 **A good alternative to the official matplotlib documentation**
 
@@ -56,42 +50,41 @@ But to it's defense, my hard copy of the "Gnuplot in Action" is also presented i
 
 Not a real point of criticism but more like a suggestion for future editions: as big fan of it, I was actually looking for this section that mentions how to use it in IPython notebooks (%pylab inline vs. matplotlib inline), and maybe also plotly for additional value :)
 
-<hr>
+---
 
-<a id="Python-High-Performance-Programming"></a>
 ### Python High Performance Programming
-[[back to top](#table-of-contents)]
 
 ***by Gabriele Lanaro***
 
-<font size='0.7em'>
-Paperback: 108 pages  
-Release Date: December 2013  
-ISBN: 1783288450  
-ISBN 13: 9781783288458</font>
+
+- Paperback: 108 pages  
+- Release Date: December 2013  
+- ISBN: 1783288450  
+- ISBN 13: 9781783288458
+- Publisher: Packt
 
 **Really recommended book for Python beginners**
 
 A really nice read! It covered 4 important topics: how to profile & benchmark Python code, NumPy, C-extensions via Cython, and parallel programming. However, I found it a little bit too brief on all of the topics, a little bit more depth would have been nice.
-Also, I missed a few parts, like general Python tricks for better performance (e.g., in-place operators for mutable types and many many others that I started to create benchmarks for here: https://github.com/rasbt/One-Python-benchmark-per-day) 
+Also, I missed a few parts, like general Python tricks for better performance (e.g., in-place operators for mutable types and many many others that I started to create benchmarks for here: https://github.com/rasbt/One-Python-benchmark-per-day)
 And another thing that I think would be worth adding in a future addition would be the JIT (just-in-time) compilers, such as parakeet or Numba, especially since Numexpr was briefly mentioned in the NumPy section.
 
 But overall I think it is a very recommended read for Python beginners!
 
-<hr>
+---
+
+### Learning Ipython for Interactive Computing and Data Visualization
+
 
-<a id="Learning-Ipython-for-Interactive-Computing-and-Data-Visualization"></a>
-###Learning Ipython for Interactive Computing and Data Visualization
-[[back to top](#table-of-contents)]
-  
 ***by Cyrille Rossant***
 
 
-<font size='0.7em'>
-Paperback: 138 pages  
-Release Date: April 2013  
-ISBN: 1782169938  
-ISBN 13: 9781782169932</font>
+
+- Paperback: 138 pages  
+- Release Date: April 2013  
+- ISBN: 1782169938  
+- ISBN 13: 9781782169932
+- Publisher: Packt
 
 
 
@@ -100,23 +93,38 @@ ISBN 13: 9781782169932</font>
 It's a brief but good book that provides a good introduction to the IPython environment. I think the high-performance chapter that explained the usage of NumPy among others was a little bit redundant, since it is a general Python topic and is not necessarily specific to IPython. And on the other hand, the chapters on customizing IPython and especially writing own IPython magic extensions were way too brief - when I wrote my own extensions, I needed to look more closely at the IPython extension source code to be able to handle this task.
 But still, this is a nice book that I would recommend to people who are fairly new to Python and people who want to get a taste of IPython!
 
-<hr>
+---
+
+### The Practice of Computing Using Python (2nd Edition)
 
-<a id="The-Practice-of-Computing-Using-Python-(2nd-Edition)"></a>
-###The Practice of Computing Using Python (2nd Edition)
-[[back to top](#table-of-contents)]
 
 ***by William F. Punch and Richard Enbody***
 
-<font size='0.7em'>
-Paperback: 792 pages  
-Release Date: February 25, 2012  
-ISBN-10: 013280557X  
-ISBN-13: 978-0132805575</font>
 
+- Paperback: 792 pages  
+- Release Date: February 25, 2012  
+- ISBN-10: 013280557X  
+- ISBN-13: 978-0132805575
+- Publisher: Pearson
 
 **A great first Python book**
 
 This was actually my first Python book. It is not meant to be a thorough coverage of all the greatest Python features and capabilities, but it provides a great introduction to computing and programming in general by using the Python language.  
 It is maybe a little bit to trivial for programmers who just want to pick up the syntax Python language, but I would really recommend this book as a first introduction to people who have never programmed before - I think that Python is a very nice language to pick up this valuable skill.  
-I am a big fan of books that contains self-assessments: from short exercises up to bigger project assignments, and this book comes with a huge abundance of valuable material, which is a big bonus point.
\ No newline at end of file
+I am a big fan of books that contains self-assessments: from short exercises up to bigger project assignments, and this book comes with a huge abundance of valuable material, which is a big bonus point.
+
+
+---
+
+### How to Make Mistakes in Python
+
+
+***by Mike Pirnat***
+
+
+- e-Book: 154 pages  
+- Release Date: October, 2015   
+- Publisher: O'Reilly  
+
+
+Although I already have many years of experience with coding in Python, I thought that it couldn't hurt to read through this book -- I got the free copy via O'Reilly, and it's relatively short. Sure, many topics throughout this book are trivial for an experienced Python programmer, but I believe that it's a great summary for someone who just got started with this programming language. Although the author doesn't go into technical depths regarding e.g., pylint, unit testing, etc., I think that his descriptions are sufficient, and a reader can always look at the online documentation of the respective tools. What's more important is that the author gives good reasons WHY we should use/do certain things, and I really like the use of paraphrased examples from real-world use cases. It's a solid book overall!
diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index f3e2067..0f74195 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -1,2194 +1,2118 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:1a71ccc70829239143d02cebcb97bec031b45e676ebad340fc04c9bd4a5760bf"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
-      "\n",
-      "last updated 05/27/2014\n",
-      "\n",
-      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1) \n",
-      "\n",
-      "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb)  \n",
-      "\n",
-      "- [Link to the GitHub repository python_reference](https://github.com/rasbt/python_reference)\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "I would be happy to hear your comments and suggestions.  \n",
-      "Please feel free to drop me a note via\n",
-      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Key differences between Python 2.7.x and Python 3.x"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines \"just go with the version your favorite tutorial was written in, and check out the differences later on.\"\n",
-      "\n",
-      "But what if you are starting a new project and have the choice to pick? I would say there is currently no \"right\" or \"wrong\" as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use. However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+    "\n",
+    "last updated 05/27/2014\n",
+    "\n",
+    "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1) \n",
+    "\n",
+    "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb)  \n",
+    "\n",
+    "- [Link to the GitHub repository python_reference](https://github.com/rasbt/python_reference)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "I would be happy to hear your comments and suggestions.  \n",
+    "Please feel free to drop me a note via\n",
+    "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/118404394130788869227).\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Key differences between Python 2.7.x and Python 3.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "Many beginning Python users are wondering with which version of Python they should start. My answer to this question is usually something along the lines \"just go with the version your favorite tutorial was written in, and check out the differences later on.\"\n",
+    "\n",
+    "But what if you are starting a new project and have the choice to pick? I would say there is currently no \"right\" or \"wrong\" as long as both Python 2.7.x and Python 3.x support the libraries that you are planning to use. However, it is worthwhile to have a look at the major differences between those two most popular versions of Python to avoid common pitfalls when writing the code for either one of them, or if you are planning to port your project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "- [Using the `__future__` module](#future_module)\n",
+    "\n",
+    "- [The print function](#The-print-function)\n",
+    "\n",
+    "- [Integer division](#Integer-division)\n",
+    "\n",
+    "- [Unicode](#Unicode)\n",
+    "\n",
+    "- [xrange](#xrange)\n",
+    "\n",
+    "- [Raising exceptions](#Raising-exceptions)\n",
+    "\n",
+    "- [Handling exceptions](#Handling-exceptions)\n",
+    "\n",
+    "- [The next() function and .next() method](#The-next-function-and-next-method)\n",
+    "\n",
+    "- [For-loop variables and the global namespace leak](#For-loop-variables-and-the-global-namespace-leak)\n",
+    "\n",
+    "- [Comparing unorderable types](#Comparing-unorderable-types)\n",
+    "\n",
+    "- [Parsing user inputs via input()](#Parsing-user-inputs-via-input)\n",
+    "\n",
+    "- [Returning iterable objects instead of lists](#Returning-iterable-objects-instead-of-lists)\n",
+    "\n",
+    "- [More articles about Python 2 and Python 3](#More-articles-about-Python-2-and-Python-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='future_module'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `__future__` module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python 3.x introduced some Python 2-incompatible keywords and features that can be imported via the in-built `__future__` module in Python 2. It is recommended to use `__future__` imports it if you are planning Python 3.x support for your code. For example, if we want Python 3.x's integer division behavior in Python 2, we can import it via\n",
+    "\n",
+    "    from __future__ import division\n",
+    "    \n",
+    "More features that can be imported from the `__future__` module are listed in the table below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<table>\n",
+    "<tr><th class=\"head\">feature</th>\n",
+    "<th class=\"head\">optional in</th>\n",
+    "<th class=\"head\">mandatory in</th>\n",
+    "<th class=\"head\">effect</th>\n",
+    "</tr>\n",
+    "\n",
+    "<tr><td>nested_scopes</td>\n",
+    "<td>2.1.0b1</td>\n",
+    "<td>2.2</td>\n",
+    "<td><a href=\"http://www.python.org/dev/peps/pep-0227\"><strong>PEP 227</strong></a>:\n",
+    "<em>Statically Nested Scopes</em></td>\n",
+    "</tr>\n",
+    "<tr><td>generators</td>\n",
+    "<td>2.2.0a1</td>\n",
+    "<td>2.3</td>\n",
+    "<td><a href=\"http://www.python.org/dev/peps/pep-0255\"><strong>PEP 255</strong></a>:\n",
+    "<em>Simple Generators</em></td>\n",
+    "</tr>\n",
+    "<tr><td>division</td>\n",
+    "<td>2.2.0a2</td>\n",
+    "<td>3.0</td>\n",
+    "<td><a href=\"http://www.python.org/dev/peps/pep-0238\"><strong>PEP 238</strong></a>:\n",
+    "<em>Changing the Division Operator</em></td>\n",
+    "</tr>\n",
+    "<tr><td>absolute_import</td>\n",
+    "<td>2.5.0a1</td>\n",
+    "<td>3.0</td>\n",
+    "<td><a href=\"http://www.python.org/dev/peps/pep-0328\"><strong>PEP 328</strong></a>:\n",
+    "<em>Imports: Multi-Line and Absolute/Relative</em></td>\n",
+    "</tr>\n",
+    "<tr><td>with_statement</td>\n",
+    "<td>2.5.0a1</td>\n",
+    "<td>2.6</td>\n",
+    "<td><a href=\"http://www.python.org/dev/peps/pep-0343\"><strong>PEP 343</strong></a>:\n",
+    "<em>The &#8220;with&#8221; Statement</em></td>\n",
+    "</tr>\n",
+    "<tr><td>print_function</td>\n",
+    "<td>2.6.0a2</td>\n",
+    "<td>3.0</td>\n",
+    "<td><a href=\"http://www.python.org/dev/peps/pep-3105\"><strong>PEP 3105</strong></a>:\n",
+    "<em>Make print a function</em></td>\n",
+    "</tr>\n",
+    "<tr><td>unicode_literals</td>\n",
+    "<td>2.6.0a2</td>\n",
+    "<td>3.0</td>\n",
+    "<td><a href=\"http://www.python.org/dev/peps/pep-3112\"><strong>PEP 3112</strong></a>:\n",
+    "<em>Bytes literals in Python 3000</em></td>\n",
+    "</tr>\n",
+    "</table>\n",
+    "<br>\n",
+    "<center>(Source: [https://docs.python.org/2/library/__future__.html](https://docs.python.org/2/library/__future__.html#module-__future__))</center>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from platform import python_version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The print function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print statement has been replaced by the `print()` function, meaning that we have to wrap the object that we want to print in parantheses. \n",
+    "\n",
+    "Python 2 doesn't have a problem with additional parantheses, but in contrast, Python 3 would raise a `SyntaxError` if we called the print function the Python 2-way without the parentheses.  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n",
+      "Hello, World!\n",
+      "Hello, World!\n",
+      "text print more text on the same line\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Sections"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "print 'Hello, World!'\n",
+    "print('Hello, World!')\n",
+    "print \"text\", ; print 'print more text on the same line'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "Hello, World!\n",
+      "some text, print more text on the same line\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "- [Using the `__future__` module](#future_module)\n",
-      "\n",
-      "- [The print function](#The-print-function)\n",
-      "\n",
-      "- [Integer division](#Integer-division)\n",
-      "\n",
-      "- [Unicode](#Unicode)\n",
-      "\n",
-      "- [xrange](#xrange)\n",
-      "\n",
-      "- [Raising exceptions](#Raising-exceptions)\n",
-      "\n",
-      "- [Handling exceptions](#Handling-exceptions)\n",
-      "\n",
-      "- [The next() function and .next() method](#The-next-function-and-next-method)\n",
-      "\n",
-      "- [For-loop variables and the global namespace leak](#For-loop-variables-and-the-global-namespace-leak)\n",
-      "\n",
-      "- [Comparing unorderable types](#Comparing-unorderable-types)\n",
-      "\n",
-      "- [Parsing user inputs via input()](#Parsing-user-inputs-via-input)\n",
-      "\n",
-      "- [Returning iterable objects instead of lists](#Returning-iterable-objects-instead-of-lists)\n",
-      "\n",
-      "- [More articles about Python 2 and Python 3](#More-articles-about-Python-2-and-Python-3)"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "print('Hello, World!')\n",
+    "\n",
+    "print(\"some text,\", end=\"\") \n",
+    "print(' print more text on the same line')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-3-139a7c5835bd>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-139a7c5835bd>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'Hello, World!'\u001b[0m\n\u001b[0m                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='future_module'></a>"
+    }
+   ],
+   "source": [
+    "print 'Hello, World!'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note:**\n",
+    "\n",
+    "Printing \"Hello, World\" above  via Python 2 looked quite \"normal\". However, if we have multiple objects inside the parantheses, we will create a tuple, since `print` is a \"statement\" in Python 2, not a function call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.7\n",
+      "('a', 'b')\n",
+      "a b\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "print('a', 'b')\n",
+    "print 'a', 'b'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Integer division"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This change is particularly dangerous if you are porting code, or if you are executing Python 3 code in Python 2, since the change in integer-division behavior can often go unnoticed (it doesn't raise a `SyntaxError`).  \n",
+    "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble (and vice versa, I recommend a `from __future__ import division` in your Python 2 scripts)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n",
+      "3 / 2 = 1\n",
+      "3 // 2 = 1\n",
+      "3 / 2.0 = 1.5\n",
+      "3 // 2.0 = 1.0\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### The `__future__` module"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "print '3 / 2 =', 3 / 2\n",
+    "print '3 // 2 =', 3 // 2\n",
+    "print '3 / 2.0 =', 3 / 2.0\n",
+    "print '3 // 2.0 =', 3 // 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "3 / 2 = 1.5\n",
+      "3 // 2 = 1\n",
+      "3 / 2.0 = 1.5\n",
+      "3 // 2.0 = 1.0\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python 3.x introduced some Python 2-incompatible keywords and features that can be imported via the in-built `__future__` module in Python 2. It is recommended to use `__future__` imports it if you are planning Python 3.x support for your code. For example, if we want Python 3.x's integer division behavior in Python 2, we can import it via\n",
-      "\n",
-      "    from __future__ import division\n",
-      "    \n",
-      "More features that can be imported from the `__future__` module are listed in the table below:"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "print('3 / 2 =', 3 / 2)\n",
+    "print('3 // 2 =', 3 // 2)\n",
+    "print('3 / 2.0 =', 3 / 2.0)\n",
+    "print('3 // 2.0 =', 3 // 2.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Unicode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python 2 has ASCII `str()` types, separate `unicode()`, but no `byte` type.  \n",
+    "\n",
+    "Now, in Python 3, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<table>\n",
-      "<tr><th class=\"head\">feature</th>\n",
-      "<th class=\"head\">optional in</th>\n",
-      "<th class=\"head\">mandatory in</th>\n",
-      "<th class=\"head\">effect</th>\n",
-      "</tr>\n",
-      "\n",
-      "<tr><td>nested_scopes</td>\n",
-      "<td>2.1.0b1</td>\n",
-      "<td>2.2</td>\n",
-      "<td><a href=\"http://www.python.org/dev/peps/pep-0227\"><strong>PEP 227</strong></a>:\n",
-      "<em>Statically Nested Scopes</em></td>\n",
-      "</tr>\n",
-      "<tr><td>generators</td>\n",
-      "<td>2.2.0a1</td>\n",
-      "<td>2.3</td>\n",
-      "<td><a href=\"http://www.python.org/dev/peps/pep-0255\"><strong>PEP 255</strong></a>:\n",
-      "<em>Simple Generators</em></td>\n",
-      "</tr>\n",
-      "<tr><td>division</td>\n",
-      "<td>2.2.0a2</td>\n",
-      "<td>3.0</td>\n",
-      "<td><a href=\"http://www.python.org/dev/peps/pep-0238\"><strong>PEP 238</strong></a>:\n",
-      "<em>Changing the Division Operator</em></td>\n",
-      "</tr>\n",
-      "<tr><td>absolute_import</td>\n",
-      "<td>2.5.0a1</td>\n",
-      "<td>3.0</td>\n",
-      "<td><a href=\"http://www.python.org/dev/peps/pep-0328\"><strong>PEP 328</strong></a>:\n",
-      "<em>Imports: Multi-Line and Absolute/Relative</em></td>\n",
-      "</tr>\n",
-      "<tr><td>with_statement</td>\n",
-      "<td>2.5.0a1</td>\n",
-      "<td>2.6</td>\n",
-      "<td><a href=\"http://www.python.org/dev/peps/pep-0343\"><strong>PEP 343</strong></a>:\n",
-      "<em>The &#8220;with&#8221; Statement</em></td>\n",
-      "</tr>\n",
-      "<tr><td>print_function</td>\n",
-      "<td>2.6.0a2</td>\n",
-      "<td>3.0</td>\n",
-      "<td><a href=\"http://www.python.org/dev/peps/pep-3105\"><strong>PEP 3105</strong></a>:\n",
-      "<em>Make print a function</em></td>\n",
-      "</tr>\n",
-      "<tr><td>unicode_literals</td>\n",
-      "<td>2.6.0a2</td>\n",
-      "<td>3.0</td>\n",
-      "<td><a href=\"http://www.python.org/dev/peps/pep-3112\"><strong>PEP 3112</strong></a>:\n",
-      "<em>Bytes literals in Python 3000</em></td>\n",
-      "</tr>\n",
-      "</table>\n",
-      "<br>\n",
-      "<center>(Source: [https://docs.python.org/2/library/__future__.html](https://docs.python.org/2/library/__future__.html#module-__future__))</center>"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<type 'unicode'>\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from platform import python_version"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "print type(unicode('this is like a python3 str type'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<type 'str'>\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "The print function"
+    }
+   ],
+   "source": [
+    "print type(b'byte type does not exist')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "they are really the same\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
+    }
+   ],
+   "source": [
+    "print 'they are really' + b' the same'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<type 'bytearray'>\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Very trivial, and the change in the print-syntax is probably the most widely known change, but still it is worth mentioning: Python 2's print statement has been replaced by the `print()` function, meaning that we have to wrap the object that we want to print in parantheses. \n",
-      "\n",
-      "Python 2 doesn't have a problem with additional parantheses, but in contrast, Python 3 would raise a `SyntaxError` if we called the print function the Python 2-way without the parentheses.  \n"
+    }
+   ],
+   "source": [
+    "print type(bytearray(b'bytearray oddly does exist though'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "strings are now utf-8 μnicoΔé!\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "print('strings are now utf-8 \\u03BCnico\\u0394é!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1 has <class 'bytes'>\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "print 'Hello, World!'\n",
-      "print('Hello, World!')\n",
-      "print \"text\", ; print 'print more text on the same line'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n",
-        "Hello, World!\n",
-        "Hello, World!\n",
-        "text print more text on the same line\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
+    }
+   ],
+   "source": [
+    "print('Python', python_version(), end=\"\")\n",
+    "print(' has', type(b' bytes for storing data'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "and Python 3.4.1 also has <class 'bytearray'>\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
+    }
+   ],
+   "source": [
+    "print('and Python', python_version(), end=\"\")\n",
+    "print(' also has', type(bytearray(b'bytearrays')))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Can't convert 'bytes' object to str implicitly",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-d3e8942ccf81>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'note that we cannot add a string'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mb'bytes for data'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: Can't convert 'bytes' object to str implicitly"
      ]
-    },
+    }
+   ],
+   "source": [
+    "'note that we cannot add a string' + b'bytes for data'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## xrange"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " \n",
+    "The usage of `xrange()` is very popular in Python 2.x for creating an iterable object, e.g., in a for-loop or list/set-dictionary-comprehension.  \n",
+    "The behavior was quite similar to a generator (i.e., \"lazy evaluation\"), but here the xrange-iterable is not exhaustible - meaning, you could iterate over it infinitely.  \n",
+    "\n",
+    "\n",
+    "Thanks to its \"lazy-evaluation\", the advantage of the regular `range()` is that `xrange()` is generally faster if you have to iterate over it only once (e.g., in a for-loop). However, in contrast to 1-time iterations, it is not recommended if you repeat the iteration multiple times, since the generation happens every time from scratch!  \n",
+    "\n",
+    "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore (`xrange()` raises a `NameError` in Python 3)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import timeit\n",
+    "\n",
+    "n = 10000\n",
+    "def test_range(n):\n",
+    "    return for i in range(n):\n",
+    "        pass\n",
+    "    \n",
+    "def test_xrange(n):\n",
+    "    for i in xrange(n):\n",
+    "        pass    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "print('Hello, World!')\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n",
       "\n",
-      "print(\"some text,\", end=\"\") \n",
-      "print(' print more text on the same line')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "Hello, World!\n",
-        "some text, print more text on the same line\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Hello, World!'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "SyntaxError",
-       "evalue": "invalid syntax (<ipython-input-3-139a7c5835bd>, line 1)",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-3-139a7c5835bd>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print 'Hello, World!'\u001b[0m\n\u001b[0m                        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Note:**\n",
+      "timing range()\n",
+      "1000 loops, best of 3: 433 µs per loop\n",
       "\n",
-      "Printing \"Hello, World\" above  via Python 2 looked quite \"normal\". However, if we have multiple objects inside the parantheses, we will create a tuple, since `print` is a \"statement\" in Python 2, not a function call."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "print('a', 'b')\n",
-      "print 'a', 'b'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.7\n",
-        "('a', 'b')\n",
-        "a b\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Integer division"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This change is particularly dangerous if you are porting code, or if you are executing Python 3 code in Python 2, since the change in integer-division behavior can often go unnoticed (it doesn't raise a `SyntaxError`).  \n",
-      "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble (and vice versa, I recommend a `from __future__ import division` in your Python 2 scripts)."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "print '3 / 2 =', 3 / 2\n",
-      "print '3 // 2 =', 3 // 2\n",
-      "print '3 / 2.0 =', 3 / 2.0\n",
-      "print '3 // 2.0 =', 3 // 2.0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n",
-        "3 / 2 = 1\n",
-        "3 // 2 = 1\n",
-        "3 / 2.0 = 1.5\n",
-        "3 // 2.0 = 1.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "print('3 / 2 =', 3 / 2)\n",
-      "print('3 // 2 =', 3 // 2)\n",
-      "print('3 / 2.0 =', 3 / 2.0)\n",
-      "print('3 // 2.0 =', 3 // 2.0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "3 / 2 = 1.5\n",
-        "3 // 2 = 1\n",
-        "3 / 2.0 = 1.5\n",
-        "3 // 2.0 = 1.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Unicode"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python 2 has ASCII `str()` types, separate `unicode()`, but no `byte` type.  \n",
       "\n",
-      "Now, in Python 3, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
+      "timing xrange()\n",
+      "1000 loops, best of 3: 350 µs per loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print type(unicode('this is like a python3 str type'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "<type 'unicode'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print type(b'byte type does not exist')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "<type 'str'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'they are really' + b' the same'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "they are really the same\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print type(bytearray(b'bytearray oddly does exist though'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "<type 'bytearray'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "print('strings are now utf-8 \\u03BCnico\\u0394\u00e9!')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "strings are now utf-8 \u03bcnico\u0394\u00e9!\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version(), end=\"\")\n",
-      "print(' has', type(b' bytes for storing data'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1 has <class 'bytes'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('and Python', python_version(), end=\"\")\n",
-      "print(' also has', type(bytearray(b'bytearrays')))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "and Python 3.4.1 also has <class 'bytearray'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "'note that we cannot add a string' + b'bytes for data'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "TypeError",
-       "evalue": "Can't convert 'bytes' object to str implicitly",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-13-d3e8942ccf81>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m'note that we cannot add a string'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34mb'bytes for data'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mTypeError\u001b[0m: Can't convert 'bytes' object to str implicitly"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "xrange"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      " \n",
-      "The usage of `xrange()` is very popular in Python 2.x for creating an iterable object, e.g., in a for-loop or list/set-dictionary-comprehension.  \n",
-      "The behavior was quite similar to a generator (i.e., \"lazy evaluation\"), but here the xrange-iterable is not exhaustible - meaning, you could iterate over it infinitely.  \n",
-      "\n",
-      "\n",
-      "Thanks to its \"lazy-evaluation\", the advantage of the regular `range()` is that `xrange()` is generally faster if you have to iterate over it only once (e.g., in a for-loop). However, in contrast to 1-time iterations, it is not recommended if you repeat the iteration multiple times, since the generation happens every time from scratch!  \n",
-      "\n",
-      "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore (`xrange()` raises a `NameError` in Python 3)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "n = 10000\n",
-      "def test_range(n):\n",
-      "    return for i in range(n):\n",
-      "        pass\n",
-      "    \n",
-      "def test_xrange(n):\n",
-      "    for i in xrange(n):\n",
-      "        pass    "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "\n",
-      "print '\\ntiming range()'\n",
-      "%timeit test_range(n)\n",
-      "\n",
-      "print '\\n\\ntiming xrange()'\n",
-      "%timeit test_xrange(n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n",
-        "\n",
-        "timing range()\n",
-        "1000 loops, best of 3: 433 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "\n",
-        "timing xrange()\n",
-        "1000 loops, best of 3: 350 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "\n",
-      "print('\\ntiming range()')\n",
-      "%timeit test_range(n)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "\n",
-        "timing range()\n",
-        "1000 loops, best of 3: 520 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(xrange(10))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "NameError",
-       "evalue": "name 'xrange' is not defined",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-5-5d8f9b79ea70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mNameError\u001b[0m: name 'xrange' is not defined"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='contains'></a>\n",
-      "<br>\n",
-      "<br>\n"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "The `__contains__` method for `range` objects in Python 3"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [Yuchen Ying](https://github.com/yegle), who pointed this out).  The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and Boolean types.\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = 10000000"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def val_in_range(x, val):\n",
-      "    return val in range(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def val_in_xrange(x, val):\n",
-      "    return val in xrange(x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "assert(val_in_range(x, x/2) == True)\n",
-      "assert(val_in_range(x, x//2) == True)\n",
-      "%timeit val_in_range(x, x/2)\n",
-      "%timeit val_in_range(x, x//2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "1 loops, best of 3: 742 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1000000 loops, best of 3: 1.19 \u00b5s per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Based on the `timeit` results above, you see that the execution for the \"look up\" was about 60,000 faster when it was of an integer type rather than a float. However, since Python 2.x's `range` or `xrange` doesn't have a `__contains__` method, the \"look-up speed\" wouldn't be that much different for integers or floats:"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "assert(val_in_xrange(x, x/2.0) == True)\n",
-      "assert(val_in_xrange(x, x/2) == True)\n",
-      "assert(val_in_range(x, x/2) == True)\n",
-      "assert(val_in_range(x, x//2) == True)\n",
-      "%timeit val_in_xrange(x, x/2.0)\n",
-      "%timeit val_in_xrange(x, x/2)\n",
-      "%timeit val_in_range(x, x/2.0)\n",
-      "%timeit val_in_range(x, x/2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.7\n",
-        "1 loops, best of 3: 285 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1 loops, best of 3: 179 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1 loops, best of 3: 658 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "1 loops, best of 3: 556 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Below the \"proofs\" that the `__contain__` method wasn't added to Python 2.x yet:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "range.__contains__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 8,
-       "text": [
-        "<slot wrapper '__contains__' of 'range' objects>"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "range.__contains__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.7\n"
-       ]
-      },
-      {
-       "ename": "AttributeError",
-       "evalue": "'builtin_function_or_method' object has no attribute '__contains__'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-7-05327350dafb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mAttributeError\u001b[0m: 'builtin_function_or_method' object has no attribute '__contains__'"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "xrange.__contains__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.7\n"
-       ]
-      },
-      {
-       "ename": "AttributeError",
-       "evalue": "type object 'xrange' has no attribute '__contains__'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-8-7d1a71bfee8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mxrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mAttributeError\u001b[0m: type object 'xrange' has no attribute '__contains__'"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 4,
-     "metadata": {},
-     "source": [
-      "Note about the speed differences in Python 2 and 3"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Some people pointed out the speed difference between Python 3's `range()` and Python2's `xrange()`. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def test_while():\n",
-      "    i = 0\n",
-      "    while i < 20000:\n",
-      "        i += 1\n",
-      "    return"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "%timeit test_while()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "100 loops, best of 3: 2.68 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "%timeit test_while()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n",
-        "1000 loops, best of 3: 1.72 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Raising exceptions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "Where Python 2 accepts both notations, the 'old' and the 'new' syntax, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "raise IOError, \"file error\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "IOError",
-       "evalue": "file error",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-8-25f049caebb0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mIOError\u001b[0m: file error"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "raise IOError(\"file error\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "IOError",
-       "evalue": "file error",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-9-6f1c43f525b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mIOError\u001b[0m: file error"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "raise IOError, \"file error\""
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "SyntaxError",
-       "evalue": "invalid syntax (<ipython-input-10-25f049caebb0>, line 1)",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-25f049caebb0>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    raise IOError, \"file error\"\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The proper way to raise an exception in Python 3:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "raise IOError(\"file error\")"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n"
-       ]
-      },
-      {
-       "ename": "OSError",
-       "evalue": "file error",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-11-c350544d15da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mOSError\u001b[0m: file error"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Handling exceptions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Also the handling of exceptions has slightly changed in Python 3. In Python 3 we have to use the \"`as`\" keyword now"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "try:\n",
-      "    let_us_cause_a_NameError\n",
-      "except NameError, err:\n",
-      "    print err, '--> our error message'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n",
-        "name 'let_us_cause_a_NameError' is not defined --> our error message\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "try:\n",
-      "    let_us_cause_a_NameError\n",
-      "except NameError as err:\n",
-      "    print(err, '--> our error message')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "name 'let_us_cause_a_NameError' is not defined --> our error message\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='The-next-function-and-next-method'></a>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "The next() function and .next() method"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Since `next()` (`.next()`) is such a commonly used function (method), this is another syntax change (or rather change in implementation) that is worth mentioning: where you can use both the function and method syntax in Python 2.7.5, the `next()` function is all that remains in Python 3 (calling the `.next()` method raises an `AttributeError`)."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "\n",
-      "my_generator = (letter for letter in 'abcdefg')\n",
-      "\n",
-      "next(my_generator)\n",
-      "my_generator.next()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 11,
-       "text": [
-        "'b'"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "\n",
-      "my_generator = (letter for letter in 'abcdefg')\n",
-      "\n",
-      "next(my_generator)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n"
-       ]
-      },
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 13,
-       "text": [
-        "'a'"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_generator.next()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "AttributeError",
-       "evalue": "'generator' object has no attribute 'next'",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-14-125f388bb61b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mAttributeError\u001b[0m: 'generator' object has no attribute 'next'"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "For-loop variables and the global namespace leak"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Good news is: In Python 3.x for-loop variables don't leak into the global namespace anymore!\n",
-      "\n",
-      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
-      "\n",
-      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "\n",
-      "i = 1\n",
-      "print 'before: i =', i\n",
-      "\n",
-      "print 'comprehension: ', [i for i in range(5)]\n",
-      "\n",
-      "print 'after: i =', i"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n",
-        "before: i = 1\n",
-        "comprehension:  [0, 1, 2, 3, 4]\n",
-        "after: i = 4\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "\n",
-      "i = 1\n",
-      "print('before: i =', i)\n",
-      "\n",
-      "print('comprehension:', [i for i in range(5)])\n",
-      "\n",
-      "print('after: i =', i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "before: i = 1\n",
-        "comprehension: [0, 1, 2, 3, 4]\n",
-        "after: i = 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Comparing unorderable types"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
-     ]
-    },
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "\n",
+    "print '\\ntiming range()'\n",
+    "%timeit test_range(n)\n",
+    "\n",
+    "print '\\n\\ntiming xrange()'\n",
+    "%timeit test_xrange(n)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Another nice change in Python 3 is that a `TypeError` is raised as warning if we try to compare unorderable types."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "\n",
+      "timing range()\n",
+      "1000 loops, best of 3: 520 µs per loop\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "\n",
+    "print('\\ntiming range()')\n",
+    "%timeit test_range(n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'xrange' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-5d8f9b79ea70>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'xrange' is not defined"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version()\n",
-      "print \"[1, 2] > 'foo' = \", [1, 2] > 'foo'\n",
-      "print \"(1, 2) > 'foo' = \", (1, 2) > 'foo'\n",
-      "print \"[1, 2] > (1, 2) = \", [1, 2] > (1, 2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n",
-        "[1, 2] > 'foo' =  False\n",
-        "(1, 2) > 'foo' =  True\n",
-        "[1, 2] > (1, 2) =  False\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
+    }
+   ],
+   "source": [
+    "print(xrange(10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='contains'></a>\n",
+    "<br>\n",
+    "<br>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `__contains__` method for `range` objects in Python 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another thing worth mentioning is that `range` got a \"new\" `__contains__` method in Python 3.x (thanks to [Yuchen Ying](https://github.com/yegle), who pointed this out).  The `__contains__` method can speedup \"look-ups\" in Python 3.x `range` significantly for integer and Boolean types.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "x = 10000000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def val_in_range(x, val):\n",
+    "    return val in range(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def val_in_xrange(x, val):\n",
+    "    return val in xrange(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "1 loops, best of 3: 742 ms per loop\n",
+      "1000000 loops, best of 3: 1.19 µs per loop\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "assert(val_in_range(x, x/2) == True)\n",
+    "assert(val_in_range(x, x//2) == True)\n",
+    "%timeit val_in_range(x, x/2)\n",
+    "%timeit val_in_range(x, x//2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the `timeit` results above, you see that the execution for the \"look up\" was about 60,000 faster when it was of an integer type rather than a float. However, since Python 2.x's `range` or `xrange` doesn't have a `__contains__` method, the \"look-up speed\" wouldn't be that much different for integers or floats:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.7\n",
+      "1 loops, best of 3: 285 ms per loop\n",
+      "1 loops, best of 3: 179 ms per loop\n",
+      "1 loops, best of 3: 658 ms per loop\n",
+      "1 loops, best of 3: 556 ms per loop\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "assert(val_in_xrange(x, x/2.0) == True)\n",
+    "assert(val_in_xrange(x, x/2) == True)\n",
+    "assert(val_in_range(x, x/2) == True)\n",
+    "assert(val_in_range(x, x//2) == True)\n",
+    "%timeit val_in_xrange(x, x/2.0)\n",
+    "%timeit val_in_xrange(x, x/2)\n",
+    "%timeit val_in_range(x, x/2.0)\n",
+    "%timeit val_in_range(x, x/2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below the \"proofs\" that the `__contain__` method wasn't added to Python 2.x yet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "print(\"[1, 2] > 'foo' = \", [1, 2] > 'foo')\n",
-      "print(\"(1, 2) > 'foo' = \", (1, 2) > 'foo')\n",
-      "print(\"[1, 2] > (1, 2) = \", [1, 2] > (1, 2))"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "<slot wrapper '__contains__' of 'range' objects>"
+      ]
+     },
+     "execution_count": 8,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n"
-       ]
-      },
-      {
-       "ename": "TypeError",
-       "evalue": "unorderable types: list() > str()",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-16-a9031729f4a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"(1, 2) > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > (1, 2) = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "range.__contains__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.7\n"
      ]
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='Parsing-user-inputs-via-input'></a>"
+     "ename": "AttributeError",
+     "evalue": "'builtin_function_or_method' object has no attribute '__contains__'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-05327350dafb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'builtin_function_or_method' object has no attribute '__contains__'"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "range.__contains__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Parsing user inputs via input()"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.7\n"
      ]
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
+     "ename": "AttributeError",
+     "evalue": "type object 'xrange' has no attribute '__contains__'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-7d1a71bfee8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mxrange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__contains__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: type object 'xrange' has no attribute '__contains__'"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Fortunately, the `input()` function was fixed in Python 3 so that it always stores the user inputs as `str` objects. In order to avoid the dangerous behavior in Python 2 to read in other types than `strings`, we have to use `raw_input()` instead."
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "xrange.__contains__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Note about the speed differences in Python 2 and 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some people pointed out the speed difference between Python 3's `range()` and Python2's `xrange()`. Since they are implemented the same way one would expect the same speed. However the difference here just comes from the fact that Python 3 generally tends to run slower than Python 2. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def test_while():\n",
+    "    i = 0\n",
+    "    while i < 20000:\n",
+    "        i += 1\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "100 loops, best of 3: 2.68 ms per loop\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "%timeit test_while()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n",
+      "1000 loops, best of 3: 1.72 ms per loop\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<div style=\"background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\"><pre style=\"margin: 0; line-height: 125%\">Python <span style=\"color: #6600EE; font-weight: bold\">2.7</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">6</span> \n",
-      "[GCC <span style=\"color: #6600EE; font-weight: bold\">4.0</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> (Apple Inc<span style=\"color: #333333\">.</span> build <span style=\"color: #0000DD; font-weight: bold\">5493</span>)] on darwin\n",
-      "Type <span style=\"background-color: #fff0f0\">&quot;help&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;copyright&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;credits&quot;</span> <span style=\"color: #000000; font-weight: bold\">or</span> <span style=\"background-color: #fff0f0\">&quot;license&quot;</span> <span style=\"color: #008800; font-weight: bold\">for</span> more information<span style=\"color: #333333\">.</span>\n",
-      "\n",
-      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> <span style=\"color: #007020\">input</span>(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
-      "\n",
-      "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
-      "\n",
-      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
-      "<span style=\"color: #333333\">&lt;</span><span style=\"color: #007020\">type</span> <span style=\"background-color: #fff0f0\">&#39;int&#39;</span><span style=\"color: #333333\">&gt;</span>\n",
-      "\n",
-      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> raw_input(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
-      "\n",
-      "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
-      "\n",
-      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
-      "<span style=\"color: #333333\">&lt;</span><span style=\"color: #007020\">type</span> <span style=\"background-color: #fff0f0\">&#39;str&#39;</span><span style=\"color: #333333\">&gt;</span>\n",
-      "</pre></div>\n"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "%timeit test_while()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Raising exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Where Python 2 accepts both notations, the 'old' and the 'new' syntax, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "IOError",
+     "evalue": "file error",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-25f049caebb0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mIOError\u001b[0m: file error"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
+    }
+   ],
+   "source": [
+    "raise IOError, \"file error\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "IOError",
+     "evalue": "file error",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-6f1c43f525b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mIOError\u001b[0m: file error"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<!-- HTML generated using hilite.me --><div style=\"background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\"><pre style=\"margin: 0; line-height: 125%\">Python <span style=\"color: #6600EE; font-weight: bold\">3.4</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> \n",
-      "[GCC <span style=\"color: #6600EE; font-weight: bold\">4.2</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> (Apple Inc<span style=\"color: #333333\">.</span> build <span style=\"color: #0000DD; font-weight: bold\">5577</span>)] on darwin\n",
-      "Type <span style=\"background-color: #fff0f0\">&quot;help&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;copyright&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;credits&quot;</span> <span style=\"color: #000000; font-weight: bold\">or</span> <span style=\"background-color: #fff0f0\">&quot;license&quot;</span> <span style=\"color: #008800; font-weight: bold\">for</span> more information<span style=\"color: #333333\">.</span>\n",
-      "\n",
-      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> <span style=\"color: #007020\">input</span>(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
-      "\n",
-      "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
-      "\n",
-      "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
-      "<span style=\"color: #333333\">&lt;</span><span style=\"color: #008800; font-weight: bold\">class</span> <span style=\"color: #FF0000; background-color: #FFAAAA\">&#39;</span><span style=\"color: #BB0066; font-weight: bold\">str</span><span style=\"background-color: #fff0f0\">&#39;&gt;</span>\n",
-      "</pre></div>\n"
+    }
+   ],
+   "source": [
+    "raise IOError(\"file error\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-10-25f049caebb0>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-25f049caebb0>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    raise IOError, \"file error\"\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "raise IOError, \"file error\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The proper way to raise an exception in Python 3:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Returning iterable objects instead of lists"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n"
      ]
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
+     "ename": "OSError",
+     "evalue": "file error",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-c350544d15da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"file error\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m: file error"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As we have already seen in the [`xrange`](#xrange) section, some functions and methods return iterable objects in Python 3 now  - instead of lists in Python 2.  \n",
-      "\n",
-      "Since we usually iterate over those only once anyway, I think this change makes a lot of sense to save memory. However, it is also possible - in contrast to generators - to iterate over those multiple times if needed, it is aonly not so efficient.\n",
-      "\n",
-      "And for those cases where we really need the `list`-objects, we can simply convert the iterable object into a `list` via the `list()` function."
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "raise IOError(\"file error\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handling exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also the handling of exceptions has slightly changed in Python 3. In Python 3 we have to use the \"`as`\" keyword now"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n",
+      "name 'let_us_cause_a_NameError' is not defined --> our error message\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 2"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "try:\n",
+    "    let_us_cause_a_NameError\n",
+    "except NameError, err:\n",
+    "    print err, '--> our error message'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "name 'let_us_cause_a_NameError' is not defined --> our error message\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print 'Python', python_version() \n",
-      "\n",
-      "print range(3) \n",
-      "print type(range(3))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 2.7.6\n",
-        "[0, 1, 2]\n",
-        "<type 'list'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "try:\n",
+    "    let_us_cause_a_NameError\n",
+    "except NameError as err:\n",
+    "    print(err, '--> our error message')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='The-next-function-and-next-method'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The next() function and .next() method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since `next()` (`.next()`) is such a commonly used function (method), this is another syntax change (or rather change in implementation) that is worth mentioning: where you can use both the function and method syntax in Python 2.7.5, the `next()` function is all that remains in Python 3 (calling the `.next()` method raises an `AttributeError`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('Python', python_version())\n",
-      "\n",
-      "print(range(3))\n",
-      "print(type(range(3)))\n",
-      "print(list(range(3)))"
-     ],
-     "language": "python",
+     "data": {
+      "text/plain": [
+       "'b'"
+      ]
+     },
+     "execution_count": 11,
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Python 3.4.1\n",
-        "range(0, 3)\n",
-        "<class 'range'>\n",
-        "[0, 1, 2]\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "\n",
+    "my_generator = (letter for letter in 'abcdefg')\n",
+    "\n",
+    "next(my_generator)\n",
+    "my_generator.next()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n"
      ]
     },
     {
-     "cell_type": "markdown",
+     "data": {
+      "text/plain": [
+       "'a'"
+      ]
+     },
+     "execution_count": 13,
      "metadata": {},
-     "source": [
-      "**Some more commonly used functions and methods that don't return lists anymore in Python 3:**\n",
-      "\n",
-      "- `zip()`\n",
-      "\n",
-      "- `map()`\n",
-      "\n",
-      "- `filter()`\n",
-      "\n",
-      "- dictionary's `.keys()` method\n",
-      "\n",
-      "- dictionary's `.values()` method\n",
-      "\n",
-      "- dictionary's `.items()` method\n"
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "\n",
+    "my_generator = (letter for letter in 'abcdefg')\n",
+    "\n",
+    "next(my_generator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'generator' object has no attribute 'next'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-125f388bb61b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_generator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'generator' object has no attribute 'next'"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "my_generator.next()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## For-loop variables and the global namespace leak"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Good news is: In Python 3.x for-loop variables don't leak into the global namespace anymore!\n",
+    "\n",
+    "This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
+    "\n",
+    "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n",
+      "before: i = 1\n",
+      "comprehension:  [0, 1, 2, 3, 4]\n",
+      "after: i = 4\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "More articles about Python 2 and Python 3"
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "\n",
+    "i = 1\n",
+    "print 'before: i =', i\n",
+    "\n",
+    "print 'comprehension: ', [i for i in range(5)]\n",
+    "\n",
+    "print 'after: i =', i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "before: i = 1\n",
+      "comprehension: [0, 1, 2, 3, 4]\n",
+      "after: i = 1\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to the section-overview](#Sections)]"
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "\n",
+    "i = 1\n",
+    "print('before: i =', i)\n",
+    "\n",
+    "print('comprehension:', [i for i in range(5)])\n",
+    "\n",
+    "print('after: i =', i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comparing unorderable types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another nice change in Python 3 is that a `TypeError` is raised as warning if we try to compare unorderable types."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n",
+      "[1, 2] > 'foo' =  False\n",
+      "(1, 2) > 'foo' =  True\n",
+      "[1, 2] > (1, 2) =  False\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()\n",
+    "print \"[1, 2] > 'foo' = \", [1, 2] > 'foo'\n",
+    "print \"(1, 2) > 'foo' = \", (1, 2) > 'foo'\n",
+    "print \"[1, 2] > (1, 2) = \", [1, 2] > (1, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up.\n",
-      "\n",
-      "\n",
-      "**// Porting to Python 3** \n",
-      "\n",
-      "- [Should I use Python 2 or Python 3 for my development activity?](https://wiki.python.org/moin/Python2orPython3)\n",
-      "\n",
-      "- [What\u2019s New In Python 3.0](https://docs.python.org/3.0/whatsnew/3.0.html)\n",
-      "\n",
-      "- [Porting to Python 3](http://python3porting.com/differences.html)\n",
-      "\n",
-      "- [Porting Python 2 Code to Python 3](https://docs.python.org/3/howto/pyporting.html)  \n",
-      "\n",
-      "- [How keep Python 3 moving forward](http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3)\n",
-      "\n",
-      "**// Pro and anti Python 3**\n",
-      "\n",
-      "- [10 awesome features of Python that you can't use because you refuse to upgrade to Python 3](http://asmeurer.github.io/python3-presentation/slides.html#1)\n",
-      "\n",
-      "- [Everything you did not want to know about Unicode in Python 3](http://lucumr.pocoo.org/2014/5/12/everything-about-unicode/)\n",
-      "\n",
-      "- [Python 3 is killing Python](https://medium.com/@deliciousrobots/5d2ad703365d/)\n",
-      "\n",
-      "- [Python 3 can revive Python](https://medium.com/p/2a7af4788b10)\n",
-      "\n",
-      "- [Python 3 is fine](http://sealedabstract.com/rants/python-3-is-fine/)\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
+     "ename": "TypeError",
+     "evalue": "unorderable types: list() > str()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-a9031729f4a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"(1, 2) > 'foo' = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[1, 2] > (1, 2) = \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()"
+     ]
+    }
+   ],
+   "source": [
+    "print('Python', python_version())\n",
+    "print(\"[1, 2] > 'foo' = \", [1, 2] > 'foo')\n",
+    "print(\"(1, 2) > 'foo' = \", (1, 2) > 'foo')\n",
+    "print(\"[1, 2] > (1, 2) = \", [1, 2] > (1, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='Parsing-user-inputs-via-input'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parsing user inputs via input()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fortunately, the `input()` function was fixed in Python 3 so that it always stores the user inputs as `str` objects. In order to avoid the dangerous behavior in Python 2 to read in other types than `strings`, we have to use `raw_input()` instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div style=\"background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\"><pre style=\"margin: 0; line-height: 125%\">Python <span style=\"color: #6600EE; font-weight: bold\">2.7</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">6</span> \n",
+    "[GCC <span style=\"color: #6600EE; font-weight: bold\">4.0</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> (Apple Inc<span style=\"color: #333333\">.</span> build <span style=\"color: #0000DD; font-weight: bold\">5493</span>)] on darwin\n",
+    "Type <span style=\"background-color: #fff0f0\">&quot;help&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;copyright&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;credits&quot;</span> <span style=\"color: #000000; font-weight: bold\">or</span> <span style=\"background-color: #fff0f0\">&quot;license&quot;</span> <span style=\"color: #008800; font-weight: bold\">for</span> more information<span style=\"color: #333333\">.</span>\n",
+    "\n",
+    "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> <span style=\"color: #007020\">input</span>(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
+    "\n",
+    "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
+    "\n",
+    "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
+    "<span style=\"color: #333333\">&lt;</span><span style=\"color: #007020\">type</span> <span style=\"background-color: #fff0f0\">&#39;int&#39;</span><span style=\"color: #333333\">&gt;</span>\n",
+    "\n",
+    "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> raw_input(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
+    "\n",
+    "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
+    "\n",
+    "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
+    "<span style=\"color: #333333\">&lt;</span><span style=\"color: #007020\">type</span> <span style=\"background-color: #fff0f0\">&#39;str&#39;</span><span style=\"color: #333333\">&gt;</span>\n",
+    "</pre></div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<!-- HTML generated using hilite.me --><div style=\"background: #ffffff; overflow:auto;width:auto;border:solid gray;border-width:.1em .1em .1em .8em;padding:.2em .6em;\"><pre style=\"margin: 0; line-height: 125%\">Python <span style=\"color: #6600EE; font-weight: bold\">3.4</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> \n",
+    "[GCC <span style=\"color: #6600EE; font-weight: bold\">4.2</span><span style=\"color: #333333\">.</span><span style=\"color: #0000DD; font-weight: bold\">1</span> (Apple Inc<span style=\"color: #333333\">.</span> build <span style=\"color: #0000DD; font-weight: bold\">5577</span>)] on darwin\n",
+    "Type <span style=\"background-color: #fff0f0\">&quot;help&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;copyright&quot;</span>, <span style=\"background-color: #fff0f0\">&quot;credits&quot;</span> <span style=\"color: #000000; font-weight: bold\">or</span> <span style=\"background-color: #fff0f0\">&quot;license&quot;</span> <span style=\"color: #008800; font-weight: bold\">for</span> more information<span style=\"color: #333333\">.</span>\n",
+    "\n",
+    "<span style=\"color: #333333\">&gt;&gt;&gt;</span> my_input <span style=\"color: #333333\">=</span> <span style=\"color: #007020\">input</span>(<span style=\"background-color: #fff0f0\">&#39;enter a number: &#39;</span>)\n",
+    "\n",
+    "enter a number: <span style=\"color: #0000DD; font-weight: bold\">123</span>\n",
+    "\n",
+    "<span style=\"color: #333333\">&gt;&gt;&gt;</span> <span style=\"color: #007020\">type</span>(my_input)\n",
+    "<span style=\"color: #333333\">&lt;</span><span style=\"color: #008800; font-weight: bold\">class</span> <span style=\"color: #FF0000; background-color: #FFAAAA\">&#39;</span><span style=\"color: #BB0066; font-weight: bold\">str</span><span style=\"background-color: #fff0f0\">&#39;&gt;</span>\n",
+    "</pre></div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Returning iterable objects instead of lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we have already seen in the [`xrange`](#xrange) section, some functions and methods return iterable objects in Python 3 now  - instead of lists in Python 2.  \n",
+    "\n",
+    "Since we usually iterate over those only once anyway, I think this change makes a lot of sense to save memory. However, it is also possible - in contrast to generators - to iterate over those multiple times if needed, it is aonly not so efficient.\n",
+    "\n",
+    "And for those cases where we really need the `list`-objects, we can simply convert the iterable object into a `list` via the `list()` function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.6\n",
+      "[0, 1, 2]\n",
+      "<type 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'Python', python_version() \n",
+    "\n",
+    "print range(3) \n",
+    "print type(range(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.4.1\n",
+      "range(0, 3)\n",
+      "<class 'range'>\n",
+      "[0, 1, 2]\n"
+     ]
     }
    ],
-   "metadata": {}
+   "source": [
+    "print('Python', python_version())\n",
+    "\n",
+    "print(range(3))\n",
+    "print(type(range(3)))\n",
+    "print(list(range(3)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Some more commonly used functions and methods that don't return lists anymore in Python 3:**\n",
+    "\n",
+    "- `zip()`\n",
+    "\n",
+    "- `map()`\n",
+    "\n",
+    "- `filter()`\n",
+    "\n",
+    "- dictionary's `.keys()` method\n",
+    "\n",
+    "- dictionary's `.values()` method\n",
+    "\n",
+    "- dictionary's `.items()` method\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## More articles about Python 2 and Python 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is a list of some good articles concerning Python 2 and 3 that I would recommend as a follow-up.\n",
+    "\n",
+    "\n",
+    "**// Porting to Python 3** \n",
+    "\n",
+    "- [Should I use Python 2 or Python 3 for my development activity?](https://wiki.python.org/moin/Python2orPython3)\n",
+    "\n",
+    "- [What’s New In Python 3.0](https://docs.python.org/3.0/whatsnew/3.0.html)\n",
+    "\n",
+    "- [Porting to Python 3](http://python3porting.com/differences.html)\n",
+    "\n",
+    "- [Porting Python 2 Code to Python 3](https://docs.python.org/3/howto/pyporting.html)  \n",
+    "\n",
+    "- [How keep Python 3 moving forward](http://nothingbutsnark.svbtle.com/my-view-on-the-current-state-of-python-3)\n",
+    "\n",
+    "**// Pro and anti Python 3**\n",
+    "\n",
+    "- [10 awesome features of Python that you can't use because you refuse to upgrade to Python 3](http://asmeurer.github.io/python3-presentation/slides.html#1)\n",
+    "\n",
+    "- [Everything you did not want to know about Unicode in Python 3](http://lucumr.pocoo.org/2014/5/12/everything-about-unicode/)\n",
+    "\n",
+    "- [Python 3 is killing Python](https://medium.com/@deliciousrobots/5d2ad703365d/)\n",
+    "\n",
+    "- [Python 3 can revive Python](https://medium.com/p/2a7af4788b10)\n",
+    "\n",
+    "- [Python 3 is fine](http://sealedabstract.com/rants/python-3-is-fine/)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.0"
   }
- ]
-}
\ No newline at end of file
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/tutorials/not_so_obvious_python_stuff.ipynb b/tutorials/not_so_obvious_python_stuff.ipynb
index a3188b0..15569ba 100644
--- a/tutorials/not_so_obvious_python_stuff.ipynb
+++ b/tutorials/not_so_obvious_python_stuff.ipynb
@@ -1,4361 +1,4353 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:5dd675ee714d0dbd00f7be378f1379f4dceaa728c56476124c1bf493d70c569e"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
-      "\n",
-      "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb)  \n",
-      "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%load_ext watermark"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%watermark -d -u -v"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Last updated: 16/07/2014 \n",
-        "\n",
-        "CPython 3.4.1\n",
-        "IPython 2.0.0\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+    "\n",
+    "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb)  \n",
+    "- [Link to the GitHub repository](https://github.com/rasbt/python_reference)  \n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "%load_ext watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: 16/07/2014 \n",
       "\n",
-      "([Changelog](#changelog))"
+      "CPython 3.4.1\n",
+      "IPython 2.0.0\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# A collection of not-so-obvious Python stuff you should know!"
+    }
+   ],
+   "source": [
+    "%watermark -d -u -v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<font size=\"1.5em\">[More information](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/ipython_magic/watermark.ipynb) about the `watermark` magic command extension.</font>\n",
+    "\n",
+    "([Changelog](#changelog))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# A collection of not-so-obvious Python stuff you should know!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "I am really looking forward to your comments and suggestions to improve and  \n",
+    "extend this little collection! Just send me a quick note   \n",
+    "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
+    "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"sections\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sections\n",
+    "- [The C3 class resolution algorithm for multiple class inheritance](#c3_class_res)\n",
+    "\n",
+    "- [Assignment operators and lists - simple-add vs. add-AND operators](#pm_in_lists)\n",
+    "\n",
+    "- [`True` and `False` in the datetime module](#datetime_module)\n",
+    "\n",
+    "- [Python reuses objects for small integers - always use \"==\" for equality, \"is\" for identity](#python_small_int)\n",
+    "\n",
+    "- [Shallow vs. deep copies if list contains other structures and objects](#shallow_vs_deep)\n",
+    "\n",
+    "- [Picking `True` values from logical `and`s and `or`s](#false_true_expressions)\n",
+    "\n",
+    "- [Don't use mutable objects as default arguments for functions!](#def_mutable_func)\n",
+    "\n",
+    "- [Be aware of the consuming generator](#consuming_generator)\n",
+    "\n",
+    "- [`bool` is a subclass of `int`](#bool_int)\n",
+    "\n",
+    "- [About lambda-in-closures and-a-loop pitfall](#lambda_closure)\n",
+    "\n",
+    "- [Python's LEGB scope resolution and the keywords `global` and `nonlocal`](#python_legb)\n",
+    "\n",
+    "- [When mutable contents of immutable tuples aren't so mutable](#immutable_tuple)\n",
+    "\n",
+    "- [List comprehensions are fast, but generators are faster!?](#list_generator)\n",
+    "\n",
+    "- [Public vs. private class methods and name mangling](#private_class)\n",
+    "\n",
+    "- [The consequences of modifying a list when looping through it](#looping_pitfall)\n",
+    "\n",
+    "- [Dynamic binding and typos in variable names](#dynamic_binding)\n",
+    "\n",
+    "- [List slicing using indexes that are \"out of range](#out_of_range_slicing)\n",
+    "\n",
+    "- [Reusing global variable names and UnboundLocalErrors](#unboundlocalerror)\n",
+    "\n",
+    "- [Creating copies of mutable objects](#copy_mutable)\n",
+    "\n",
+    "- [Key differences between Python 2 and 3](#python_differences)\n",
+    "\n",
+    "- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n",
+    "\n",
+    "- [Abortive statements in `finally` blocks](#finally_blocks)\n",
+    "\n",
+    "- [Assigning types to variables as values](#variable_types)\n",
+    "\n",
+    "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n",
+    "\n",
+    "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)\n",
+    "\n",
+    "- [Metaclasses - What creates a new instance of a class?](#new_instance)\n",
+    "\n",
+    "- [Else-clauses: \"conditional else\" and \"completion else\"](#else_clauses)\n",
+    "\n",
+    "- [Interning of compile-time constants vs. run-time expressions](#string_interning)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name='c3_class_res'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The C3 class resolution algorithm for multiple class inheritance"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we are dealing with multiple inheritance, according to the newer C3 class resolution algorithm, the following applies:  \n",
+    "Assuming that child class C inherits from two parent classes A and B, \"class A should be checked before class B\".\n",
+    "\n",
+    "If you want to learn more, please read the [original blog](http://python-history.blogspot.ru/2010/06/method-resolution-order.html) post by Guido van Rossum.\n",
+    "\n",
+    "(Original source: [http://gistroll.com/rolls/21/horizontal_assessments/new](http://gistroll.com/rolls/21/horizontal_assessments/new))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class A\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "I am really looking forward to your comments and suggestions to improve and  \n",
-      "extend this little collection! Just send me a quick note   \n",
-      "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
-      "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
-      "<hr>"
+    }
+   ],
+   "source": [
+    "class A(object):\n",
+    "    def foo(self):\n",
+    "        print(\"class A\")\n",
+    "\n",
+    "class B(object):\n",
+    "    def foo(self):\n",
+    "        print(\"class B\")\n",
+    "\n",
+    "class C(A, B):\n",
+    "    pass\n",
+    "\n",
+    "C().foo()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So what actually happened above was that class `C` looked in the scope of the parent class `A` for the method `.foo()` first (and found it)!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I received an email containing a suggestion which uses a more nested example to illustrate Guido van Rossum's point a little bit better:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class C\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"sections\"></a>"
+    }
+   ],
+   "source": [
+    "class A(object):\n",
+    "   def foo(self):\n",
+    "      print(\"class A\")\n",
+    "\n",
+    "class B(A):\n",
+    "   pass\n",
+    "\n",
+    "class C(A):\n",
+    "   def foo(self):\n",
+    "      print(\"class C\")\n",
+    "\n",
+    "class D(B,C):\n",
+    "   pass\n",
+    "\n",
+    "D().foo()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, class `D` searches in `B` first, which in turn inherits from `A` (note that class `C` also inherits from `A`, but has its own `.foo()` method) so that we come up with the search order: `D, B, C, A`. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name='pm_in_lists'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Assignment operators and lists - simple-add vs. add-AND operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python `list`s are mutable objects as we all know. So, if we are using the `+=` operator on `list`s, we extend the `list` by directly modifying the object directly.  \n",
+    "\n",
+    "However, if we use the assigment via `my_list = my_list + ...`, we create a new list object, which can be demonstrated by the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ID: 4366496544\n",
+      "ID (+=): 4366496544\n",
+      "ID (list = list + ...): 4366495472\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "a_list = []\n",
+    "print('ID:', id(a_list))\n",
+    "\n",
+    "a_list += [1]\n",
+    "print('ID (+=):', id(a_list))\n",
+    "\n",
+    "a_list = a_list + [2]\n",
+    "print('ID (list = list + ...):', id(a_list))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just for reference, the `.append()` and `.extends()` methods are modifying the `list` object in place, just as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Sections\n",
-      "- [The C3 class resolution algorithm for multiple class inheritance](#c3_class_res)\n",
-      "\n",
-      "- [Assignment operators and lists - simple-add vs. add-AND operators](#pm_in_lists)\n",
-      "\n",
-      "- [`True` and `False` in the datetime module](#datetime_module)\n",
-      "\n",
-      "- [Python reuses objects for small integers - always use \"==\" for equality, \"is\" for identity](#python_small_int)\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[] \n",
+      "ID (initial): 140704077653128 \n",
       "\n",
-      "- [Shallow vs. deep copies if list contains other structures and objects](#shallow_vs_deep)\n",
+      "[1] \n",
+      "ID (append): 140704077653128 \n",
       "\n",
-      "- [Picking `True` values from logical `and`s and `or`s](#false_true_expressions)\n",
-      "\n",
-      "- [Don't use mutable objects as default arguments for functions!](#def_mutable_func)\n",
-      "\n",
-      "- [Be aware of the consuming generator](#consuming_generator)\n",
-      "\n",
-      "- [`bool` is a subclass of `int`](#bool_int)\n",
-      "\n",
-      "- [About lambda-in-closures and-a-loop pitfall](#lambda_closure)\n",
-      "\n",
-      "- [Python's LEGB scope resolution and the keywords `global` and `nonlocal`](#python_legb)\n",
-      "\n",
-      "- [When mutable contents of immutable tuples aren't so mutable](#immutable_tuple)\n",
-      "\n",
-      "- [List comprehensions are fast, but generators are faster!?](#list_generator)\n",
-      "\n",
-      "- [Public vs. private class methods and name mangling](#private_class)\n",
-      "\n",
-      "- [The consequences of modifying a list when looping through it](#looping_pitfall)\n",
-      "\n",
-      "- [Dynamic binding and typos in variable names](#dynamic_binding)\n",
-      "\n",
-      "- [List slicing using indexes that are \"out of range](#out_of_range_slicing)\n",
-      "\n",
-      "- [Reusing global variable names and UnboundLocalErrors](#unboundlocalerror)\n",
-      "\n",
-      "- [Creating copies of mutable objects](#copy_mutable)\n",
-      "\n",
-      "- [Key differences between Python 2 and 3](#python_differences)\n",
-      "\n",
-      "- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n",
-      "\n",
-      "- [Abortive statements in `finally` blocks](#finally_blocks)\n",
-      "\n",
-      "- [Assigning types to variables as values](#variable_types)\n",
-      "\n",
-      "- [Only the first clause of generators is evaluated immediately](#generator_rhs)\n",
-      "\n",
-      "- [Keyword argument unpacking syntax - `*args`  and `**kwargs`](#splat_op)\n",
-      "\n",
-      "- [Metaclasses - What creates a new instance of a class?](#new_instance)\n",
-      "\n",
-      "- [Else-clauses: \"conditional else\" and \"completion else\"](#else_clauses)\n",
-      "\n",
-      "- [Interning of compile-time constants vs. run-time expressions](#string_interning)"
+      "[1, 2] \n",
+      "ID (extend): 140704077653128\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='c3_class_res'></a>\n",
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "a_list = []\n",
+    "print(a_list, '\\nID (initial):',id(a_list), '\\n')\n",
+    "\n",
+    "a_list.append(1)\n",
+    "print(a_list, '\\nID (append):',id(a_list), '\\n')\n",
+    "\n",
+    "a_list.extend([2])\n",
+    "print(a_list, '\\nID (extend):',id(a_list))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='datetime_module'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## `True` and `False` in the datetime module\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable—at least in some quarters.\"  \n",
+    "\n",
+    "(Original source: [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\"datetime.time(0,0,0)\" (Midnight) -> False\n",
+      "\"datetime.time(1,0,0)\" (1 am) -> True\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## The C3 class resolution algorithm for multiple class inheritance"
+    }
+   ],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "print('\"datetime.time(0,0,0)\" (Midnight) ->', bool(datetime.time(0,0,0)))\n",
+    "\n",
+    "print('\"datetime.time(1,0,0)\" (1 am) ->', bool(datetime.time(1,0,0)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='python_small_int'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Python reuses objects for small integers - use \"==\" for equality, \"is\" for identity\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This oddity occurs, because Python keeps an array of small integer objects (i.e., integers between -5 and 256, [see the doc](https://docs.python.org/2/c-api/int.html#PyInt_FromLong))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a is b True\n",
+      "c is d False\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "a = 1\n",
+    "b = 1\n",
+    "print('a is b', bool(a is b))\n",
+    "True\n",
+    "\n",
+    "c = 999\n",
+    "d = 999\n",
+    "print('c is d', bool(c is d))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(*I received a comment that this is in fact a CPython artefact and **must not necessarily be true** in all implementations of Python!*)\n",
+    "\n",
+    "So the take home message is: always use \"==\" for equality, \"is\" for identity!\n",
+    "\n",
+    "Here is a [nice article](http://python.net/%7Egoodger/projects/pycon/2007/idiomatic/handout.html#other-languages-have-variables) explaining it by comparing \"boxes\" (C language) with \"name tags\" (Python)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This example demonstrates that this applies  indeed for integers in the range in -5 to 256:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "256 is 257-1 True\n",
+      "257 is 258-1 False\n",
+      "-5 is -6+1 True\n",
+      "-7 is -6-1 False\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If we are dealing with multiple inheritance, according to the newer C3 class resolution algorithm, the following applies:  \n",
-      "Assuming that child class C inherits from two parent classes A and B, \"class A should be checked before class B\".\n",
-      "\n",
-      "If you want to learn more, please read the [original blog](http://python-history.blogspot.ru/2010/06/method-resolution-order.html) post by Guido van Rossum.\n",
-      "\n",
-      "(Original source: [http://gistroll.com/rolls/21/horizontal_assessments/new](http://gistroll.com/rolls/21/horizontal_assessments/new))"
+    }
+   ],
+   "source": [
+    "print('256 is 257-1', 256 is 257-1)\n",
+    "print('257 is 258-1', 257 is 258 - 1)\n",
+    "print('-5 is -6+1', -5 is -6+1)\n",
+    "print('-7 is -6-1', -7 is -6-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### And to illustrate the test for equality (`==`) vs. identity (`is`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a is b, False\n",
+      "a == b, True\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class A(object):\n",
-      "    def foo(self):\n",
-      "        print(\"class A\")\n",
-      "\n",
-      "class B(object):\n",
-      "    def foo(self):\n",
-      "        print(\"class B\")\n",
-      "\n",
-      "class C(A, B):\n",
-      "    pass\n",
-      "\n",
-      "C().foo()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "class A\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So what actually happened above was that class `C` looked in the scope of the parent class `A` for the method `.foo()` first (and found it)!"
+    }
+   ],
+   "source": [
+    "a = 'hello world!'\n",
+    "b = 'hello world!'\n",
+    "print('a is b,', a is b)\n",
+    "print('a == b,', a == b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We would think that identity would always imply equality, but this is not always true, as we can see in the next example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a is a, True\n",
+      "a == a, False\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I received an email containing a suggestion which uses a more nested example to illustrate Guido van Rossum's point a little bit better:"
+    }
+   ],
+   "source": [
+    "a = float('nan')\n",
+    "print('a is a,', a is a)\n",
+    "print('a == a,', a == a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='shallow_vs_deep'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Shallow vs. deep copies if list contains other structures and objects\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Shallow copy**:  \n",
+    "If we use the assignment operator to assign one list to another list, we just create a new name reference to the original list. If we want to create a new list object, we have to make a copy of the original list. This can be done via `a_list[:]` or `a_list.copy()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IDs:\n",
+      "list1: 4346366472\n",
+      "list2: 4346366472\n",
+      "list3: 4346366408\n",
+      "list4: 4346366536\n",
+      "\n",
+      "list1: [3, 2]\n",
+      "list1: [3, 2]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class A(object):\n",
-      "   def foo(self):\n",
-      "      print(\"class A\")\n",
-      "\n",
-      "class B(A):\n",
-      "   pass\n",
-      "\n",
-      "class C(A):\n",
-      "   def foo(self):\n",
-      "      print(\"class C\")\n",
-      "\n",
-      "class D(B,C):\n",
-      "   pass\n",
-      "\n",
-      "D().foo()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "class C\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here, class `D` searches in `B` first, which in turn inherits from `A` (note that class `C` also inherits from `A`, but has its own `.foo()` method) so that we come up with the search order: `D, B, C, A`. "
+    }
+   ],
+   "source": [
+    "list1 = [1,2]\n",
+    "list2 = list1        # reference\n",
+    "list3 = list1[:]     # shallow copy\n",
+    "list4 = list1.copy() # shallow copy\n",
+    "\n",
+    "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\nlist4: {}\\n'\n",
+    "      .format(id(list1), id(list2), id(list3), id(list4)))\n",
+    "\n",
+    "list2[0] = 3\n",
+    "print('list1:', list1)\n",
+    "\n",
+    "list3[0] = 4\n",
+    "list4[1] = 4\n",
+    "print('list1:', list1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Deep copy**  \n",
+    "As we have seen above, a shallow copy works fine if we want to create a new list with contents of the original list which we want to modify independently.  \n",
+    "\n",
+    "However, if we are dealing with compound objects (e.g., lists that contain other lists, [read here](https://docs.python.org/2/library/copy.html) for more information) it becomes a little trickier.\n",
+    "\n",
+    "In the case of compound objects, a shallow copy would create a new compound object, but it would just insert the references to the contained objects into the new compound object. In contrast, a deep copy would go \"deeper\" and create also new objects   \n",
+    "for the objects found in the original compound object. \n",
+    "If you follow the code, the concept should become more clear:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "IDs:\n",
+      "list1: 4377956296\n",
+      "list2: 4377961752\n",
+      "list3: 4377954928\n",
+      "\n",
+      "list1: [[3], [2]]\n",
+      "list1: [[3], [2]]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name='pm_in_lists'></a>\n",
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "from copy import deepcopy\n",
+    "\n",
+    "list1 = [[1],[2]]\n",
+    "list2 = list1.copy()    # shallow copy\n",
+    "list3 = deepcopy(list1) # deep copy\n",
+    "\n",
+    "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\n'\n",
+    "      .format(id(list1), id(list2), id(list3)))\n",
+    "\n",
+    "list2[0][0] = 3\n",
+    "print('list1:', list1)\n",
+    "\n",
+    "list3[0][0] = 5\n",
+    "print('list1:', list1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='false_true_expressions'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Picking `True` values from logical `and`s and `or`s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Logical `or`:**  \n",
+    "\n",
+    "`a or b == a if a else b`  \n",
+    "- If both values in `or` expressions are `True`, Python will select the first value (e.g., select `\"a\"` in `\"a\" or \"b\"`), and the second one in `and` expressions.  \n",
+    "This is also called **short-circuiting** - we already know that the logical `or` must be `True` if the first value is `True` and therefore can omit the evaluation of the second value.\n",
+    "\n",
+    "**Logical `and`:** \n",
+    "\n",
+    "`a and b == b if a else a`  \n",
+    "- If both values in `and` expressions are `True`, Python will select the second value, since for a logical `and`, both values must be true.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 * 7 = 14\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Assignment operators and lists - simple-add vs. add-AND operators"
+    }
+   ],
+   "source": [
+    "result = (2 or 3) * (5 and 7)\n",
+    "print('2 * 7 =', result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='def_mutable_func'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Don't use mutable objects as default arguments for functions!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Don't use mutable objects (e.g., dictionaries, lists, sets, etc.) as default arguments for functions! You might expect that a new list is created every time when we call the function without providing an argument for the default parameter, but this is not the case: **Python will create the mutable object (default parameter) the first time the function is defined - not when it is called**, see the following code:\n",
+    "\n",
+    "(Original source: [http://docs.python-guide.org/en/latest/writing/gotchas/](http://docs.python-guide.org/en/latest/writing/gotchas/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1]\n",
+      "[1, 2]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "def append_to_list(value, def_list=[]):\n",
+    "    def_list.append(value)\n",
+    "    return def_list\n",
+    "\n",
+    "my_list = append_to_list(1)\n",
+    "print(my_list)\n",
+    "\n",
+    "my_other_list = append_to_list(2)\n",
+    "print(my_other_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another good example showing that demonstrates that default arguments are created when the function is created (**and not when it is called!**):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1397764090.456688\n",
+      "1397764090.456688\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python `list`s are mutable objects as we all know. So, if we are using the `+=` operator on `list`s, we extend the `list` by directly modifying the object directly.  \n",
-      "\n",
-      "However, if we use the assigment via `my_list = my_list + ...`, we create a new list object, which can be demonstrated by the following code:"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "def report_arg(my_default=time.time()):\n",
+    "    print(my_default)\n",
+    "\n",
+    "report_arg()\n",
+    "\n",
+    "time.sleep(5)\n",
+    "\n",
+    "report_arg()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='consuming_generator'></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Be aware of the consuming generator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Be aware of what is happening when combining \"`in`\" checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 in gen, True\n",
+      "3 in gen, True\n",
+      "1 in gen, False\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_list = []\n",
-      "print('ID:', id(a_list))\n",
-      "\n",
-      "a_list += [1]\n",
-      "print('ID (+=):', id(a_list))\n",
-      "\n",
-      "a_list = a_list + [2]\n",
-      "print('ID (list = list + ...):', id(a_list))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "ID: 4366496544\n",
-        "ID (+=): 4366496544\n",
-        "ID (list = list + ...): 4366495472\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Just for reference, the `.append()` and `.extends()` methods are modifying the `list` object in place, just as expected."
+    }
+   ],
+   "source": [
+    "gen = (i for i in range(5))\n",
+    "print('2 in gen,', 2 in gen)\n",
+    "print('3 in gen,', 3 in gen)\n",
+    "print('1 in gen,', 1 in gen) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Although this defeats the purpose of an generator (in most cases), we can convert a generator into a list to circumvent the problem. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 in l, True\n",
+      "3 in l, True\n",
+      "1 in l, True\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_list = []\n",
-      "print(a_list, '\\nID (initial):',id(a_list), '\\n')\n",
-      "\n",
-      "a_list.append(1)\n",
-      "print(a_list, '\\nID (append):',id(a_list), '\\n')\n",
-      "\n",
-      "a_list.extend([2])\n",
-      "print(a_list, '\\nID (extend):',id(a_list))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[] \n",
-        "ID (initial): 140704077653128 \n",
-        "\n",
-        "[1] \n",
-        "ID (append): 140704077653128 \n",
-        "\n",
-        "[1, 2] \n",
-        "ID (extend): 140704077653128\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='datetime_module'></a>"
+    }
+   ],
+   "source": [
+    "gen = (i for i in range(5))\n",
+    "a_list = list(gen)\n",
+    "print('2 in l,', 2 in a_list)\n",
+    "print('3 in l,', 3 in a_list)\n",
+    "print('1 in l,', 1 in a_list) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='bool_int'></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## `bool` is a subclass of `int`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Chicken or egg? In the history of Python (Python 2.2 to be specific) truth values were implemented via 1 and 0 (similar to the old C). In order to avoid syntax errors in old (but perfectly working) Python code, `bool` was added as a subclass of `int` in Python 2.3.\n",
+    "\n",
+    "Original source: [http://www.peterbe.com/plog/bool-is-int](http://www.peterbe.com/plog/bool-is-int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "isinstance(True, int): True\n",
+      "True + True: 2\n",
+      "3*True + True: 4\n",
+      "3*True - False: 3\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## `True` and `False` in the datetime module\n",
-      "\n"
+    }
+   ],
+   "source": [
+    "print('isinstance(True, int):', isinstance(True, int))\n",
+    "print('True + True:', True + True)\n",
+    "print('3*True + True:', 3*True + True)\n",
+    "print('3*True - False:', 3*True - False)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='lambda_closure'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## About lambda-in-closures-and-a-loop pitfall"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember the section about the [\"consuming generators\"](consuming_generators)? This example is somewhat related, but the result might still come unexpected. \n",
+    "\n",
+    "(Original source: [http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html](http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html))\n",
+    "\n",
+    "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope of the list comprehension. Since the list comprehension has already been constructed and evaluated when we for-loop through the list, the closure-variable will be set to the last value 4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "4\n",
+      "4\n",
+      "4\n",
+      "4\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable\u2014at least in some quarters.\"  \n",
-      "\n",
-      "(Original source: [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/))"
+    }
+   ],
+   "source": [
+    "my_list = [lambda: i for i in range(5)]\n",
+    "for l in my_list:\n",
+    "    print(l())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, by using a generator expression, we can make use of its stepwise evaluation (note that the returned variable still stems from the same closure, but the value changes as we iterate over the generator)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "my_gen = (lambda: n for n in range(5))\n",
+    "for l in my_gen:\n",
+    "    print(l())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And if you are really keen on using lists, there is a nifty trick that circumvents this problem as a reader nicely pointed out in the comments: We can simply pass the loop variable `i` as a default argument to the lambdas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import datetime\n",
-      "\n",
-      "print('\"datetime.time(0,0,0)\" (Midnight) ->', bool(datetime.time(0,0,0)))\n",
-      "\n",
-      "print('\"datetime.time(1,0,0)\" (1 am) ->', bool(datetime.time(1,0,0)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\"datetime.time(0,0,0)\" (Midnight) -> False\n",
-        "\"datetime.time(1,0,0)\" (1 am) -> True\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='python_small_int'></a>"
+    }
+   ],
+   "source": [
+    "my_list = [lambda x=i: x for i in range(5)]\n",
+    "for l in my_list:\n",
+    "    print(l())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='python_legb'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Python's LEGB scope resolution and the keywords `global` and `nonlocal`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is nothing particularly surprising about Python's LEGB scope resolution (Local -> Enclosed -> Global -> Built-in), but it is still useful to take a look at some examples!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `global` vs. `local`\n",
+    "\n",
+    "According to the LEGB rule, Python will first look for a variable in the local scope. So if we set the variable `x = 1` `local`ly in the function's scope, it won't have an effect on the `global` `x`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in_func: 1\n",
+      "global: 0\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Python reuses objects for small integers - use \"==\" for equality, \"is\" for identity\n",
-      "\n"
+    }
+   ],
+   "source": [
+    "x = 0\n",
+    "def in_func():\n",
+    "    x = 1\n",
+    "    print('in_func:', x)\n",
+    "    \n",
+    "in_func()\n",
+    "print('global:', x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want to modify the `global` x via a function, we can simply use the `global` keyword to import the variable into the function's scope:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in_func: 1\n",
+      "global: 1\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "x = 0\n",
+    "def in_func():\n",
+    "    global x\n",
+    "    x = 1\n",
+    "    print('in_func:', x)\n",
+    "    \n",
+    "in_func()\n",
+    "print('global:', x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### `local` vs. `enclosed`\n",
+    "\n",
+    "Now, let us take a look at `local` vs. `enclosed`. Here, we set the variable `x = 1` in the `outer` function and set `x = 1` in the enclosed function `inner`. Since `inner` looks in the local scope first, it won't modify `outer`'s `x`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "outer before: 1\n",
+      "inner: 2\n",
+      "outer after: 1\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This oddity occurs, because Python keeps an array of small integer objects (i.e., integers between -5 and 256, [see the doc](https://docs.python.org/2/c-api/int.html#PyInt_FromLong))."
+    }
+   ],
+   "source": [
+    "def outer():\n",
+    "       x = 1\n",
+    "       print('outer before:', x)\n",
+    "       def inner():\n",
+    "           x = 2\n",
+    "           print(\"inner:\", x)\n",
+    "       inner()\n",
+    "       print(\"outer after:\", x)\n",
+    "outer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is where the `nonlocal` keyword comes in handy - it allows us to modify the `x` variable in the `enclosed` scope:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "outer before: 1\n",
+      "inner: 2\n",
+      "outer after: 2\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 1\n",
-      "b = 1\n",
-      "print('a is b', bool(a is b))\n",
-      "True\n",
-      "\n",
-      "c = 999\n",
-      "d = 999\n",
-      "print('c is d', bool(c is d))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "a is b True\n",
-        "c is d False\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "(*I received a comment that this is in fact a CPython artefact and **must not necessarily be true** in all implementations of Python!*)\n",
-      "\n",
-      "So the take home message is: always use \"==\" for equality, \"is\" for identity!\n",
-      "\n",
-      "Here is a [nice article](http://python.net/%7Egoodger/projects/pycon/2007/idiomatic/handout.html#other-languages-have-variables) explaining it by comparing \"boxes\" (C language) with \"name tags\" (Python)."
+    }
+   ],
+   "source": [
+    "def outer():\n",
+    "       x = 1\n",
+    "       print('outer before:', x)\n",
+    "       def inner():\n",
+    "           nonlocal x\n",
+    "           x = 2\n",
+    "           print(\"inner:\", x)\n",
+    "       inner()\n",
+    "       print(\"outer after:\", x)\n",
+    "outer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='immutable_tuple'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## When mutable contents of immutable tuples aren't so mutable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we all know, tuples are immutable objects in Python, right!? But what happens if they contain mutable objects? \n",
+    "\n",
+    "First, let us have a look at the expected behavior: a `TypeError` is raised if we try to modify immutable types in a tuple: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'tuple' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-41-c3bec6c3fe6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
      ]
-    },
+    }
+   ],
+   "source": [
+    "tup = (1,)\n",
+    "tup[0] += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### But what if we put a mutable object into the immutable tuple? Well, modification works, but we **also** get a `TypeError` at the same time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This example demonstrates that this applies  indeed for integers in the range in -5 to 256:"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tup before:  ([],)\n"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('256 is 257-1', 256 is 257-1)\n",
-      "print('257 is 258-1', 257 is 258 - 1)\n",
-      "print('-5 is -6+1', -5 is -6+1)\n",
-      "print('-7 is -6-1', -7 is -6-1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "256 is 257-1 True\n",
-        "257 is 258-1 False\n",
-        "-5 is -6+1 True\n",
-        "-7 is -6-1 False\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### And to illustrate the test for equality (`==`) vs. identity (`is`):"
+     "ename": "TypeError",
+     "evalue": "'tuple' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-42-aebe9a31dbeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tup before: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = 'hello world!'\n",
-      "b = 'hello world!'\n",
-      "print('a is b,', a is b)\n",
-      "print('a == b,', a == b)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "a is b, False\n",
-        "a == b, True\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We would think that identity would always imply equality, but this is not always true, as we can see in the next example:"
+    }
+   ],
+   "source": [
+    "tup = ([],)\n",
+    "print('tup before: ', tup)\n",
+    "tup[0] += [1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tup after:  ([1],)\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = float('nan')\n",
-      "print('a is a,', a is a)\n",
-      "print('a == a,', a == a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "a is a, True\n",
-        "a == a, False\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='shallow_vs_deep'></a>"
+    }
+   ],
+   "source": [
+    "print('tup after: ', tup)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "However, **there are ways** to modify the mutable contents of the tuple without raising the `TypeError`, the solution is the `.extend()` method, or alternatively `.append()` (for lists):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tup before:  ([],)\n",
+      "tup after:  ([1],)\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Shallow vs. deep copies if list contains other structures and objects\n",
-      "\n"
+    }
+   ],
+   "source": [
+    "tup = ([],)\n",
+    "print('tup before: ', tup)\n",
+    "tup[0].extend([1])\n",
+    "print('tup after: ', tup)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tup before:  ([],)\n",
+      "tup after:  ([1],)\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "tup = ([],)\n",
+    "print('tup before: ', tup)\n",
+    "tup[0].append(1)\n",
+    "print('tup after: ', tup)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Explanation\n",
+    "\n",
+    "**A. Jesse Jiryu Davis** has a nice explanation for this phenomenon (Original source: [http://emptysqua.re/blog/python-increment-is-weird-part-ii/](http://emptysqua.re/blog/python-increment-is-weird-part-ii/))\n",
+    "\n",
+    "If we try to extend the list via `+=` *\"then the statement executes `STORE_SUBSCR`, which calls the C function `PyObject_SetItem`, which checks if the object supports item assignment. In our case the object is a tuple, so `PyObject_SetItem` throws the `TypeError`. Mystery solved.\"*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### One more note about the `immutable` status of tuples. Tuples are famous for being immutable. However, how comes that this code works?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 4, 5)\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Shallow copy**:  \n",
-      "If we use the assignment operator to assign one list to another list, we just create a new name reference to the original list. If we want to create a new list object, we have to make a copy of the original list. This can be done via `a_list[:]` or `a_list.copy()`."
+    }
+   ],
+   "source": [
+    "my_tup = (1,)\n",
+    "my_tup += (4,)\n",
+    "my_tup = my_tup + (5,)\n",
+    "print(my_tup)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What happens \"behind\" the curtains is that the tuple is not modified, but every time a new object is generated, which will inherit the old \"name tag\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4337381840\n",
+      "4357415496\n",
+      "4357289952\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "my_tup = (1,)\n",
+    "print(id(my_tup))\n",
+    "my_tup += (4,)\n",
+    "print(id(my_tup))\n",
+    "my_tup = my_tup + (5,)\n",
+    "print(id(my_tup))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='list_generator'></a>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## List comprehensions are fast, but generators are faster!?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"List comprehensions are fast, but generators are faster!?\" - No, not really (or significantly, see the benchmarks below). So what's the reason to prefer one over the other?\n",
+    "- use lists if you want to use the plethora of list methods  \n",
+    "- use generators when you are dealing with huge collections to avoid memory issues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "import timeit\n",
+    "\n",
+    "def plainlist(n=100000):\n",
+    "    my_list = []\n",
+    "    for i in range(n):\n",
+    "        if i % 5 == 0:\n",
+    "            my_list.append(i)\n",
+    "    return my_list\n",
+    "\n",
+    "def listcompr(n=100000):\n",
+    "    my_list = [i for i in range(n) if i % 5 == 0]\n",
+    "    return my_list\n",
+    "\n",
+    "def generator(n=100000):\n",
+    "    my_gen = (i for i in range(n) if i % 5 == 0)\n",
+    "    return my_gen\n",
+    "\n",
+    "def generator_yield(n=100000):\n",
+    "    for i in range(n):\n",
+    "        if i % 5 == 0:\n",
+    "            yield i"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### To be fair to the list, let us exhaust the generators:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "list1 = [1,2]\n",
-      "list2 = list1        # reference\n",
-      "list3 = list1[:]     # shallow copy\n",
-      "list4 = list1.copy() # shallow copy\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "plain_list:     10 loops, best of 3: 22.4 ms per loop\n",
       "\n",
-      "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\nlist4: {}\\n'\n",
-      "      .format(id(list1), id(list2), id(list3), id(list4)))\n",
+      "listcompr:     10 loops, best of 3: 20.8 ms per loop\n",
       "\n",
-      "list2[0] = 3\n",
-      "print('list1:', list1)\n",
+      "generator:     10 loops, best of 3: 22 ms per loop\n",
       "\n",
-      "list3[0] = 4\n",
-      "list4[1] = 4\n",
-      "print('list1:', list1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "IDs:\n",
-        "list1: 4346366472\n",
-        "list2: 4346366472\n",
-        "list3: 4346366408\n",
-        "list4: 4346366536\n",
-        "\n",
-        "list1: [3, 2]\n",
-        "list1: [3, 2]\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Deep copy**  \n",
-      "As we have seen above, a shallow copy works fine if we want to create a new list with contents of the original list which we want to modify independently.  \n",
-      "\n",
-      "However, if we are dealing with compound objects (e.g., lists that contain other lists, [read here](https://docs.python.org/2/library/copy.html) for more information) it becomes a little trickier.\n",
-      "\n",
-      "In the case of compound objects, a shallow copy would create a new compound object, but it would just insert the references to the contained objects into the new compound object. In contrast, a deep copy would go \"deeper\" and create also new objects   \n",
-      "for the objects found in the original compound object. \n",
-      "If you follow the code, the concept should become more clear:"
+      "generator_yield:     10 loops, best of 3: 21.9 ms per loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from copy import deepcopy\n",
-      "\n",
-      "list1 = [[1],[2]]\n",
-      "list2 = list1.copy()    # shallow copy\n",
-      "list3 = deepcopy(list1) # deep copy\n",
-      "\n",
-      "print('IDs:\\nlist1: {}\\nlist2: {}\\nlist3: {}\\n'\n",
-      "      .format(id(list1), id(list2), id(list3)))\n",
-      "\n",
-      "list2[0][0] = 3\n",
-      "print('list1:', list1)\n",
-      "\n",
-      "list3[0][0] = 5\n",
-      "print('list1:', list1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "IDs:\n",
-        "list1: 4377956296\n",
-        "list2: 4377961752\n",
-        "list3: 4377954928\n",
-        "\n",
-        "list1: [[3], [2]]\n",
-        "list1: [[3], [2]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='false_true_expressions'></a>"
+    }
+   ],
+   "source": [
+    "def test_plainlist(plain_list):\n",
+    "    for i in plain_list():\n",
+    "        pass\n",
+    "\n",
+    "def test_listcompr(listcompr):\n",
+    "    for i in listcompr():\n",
+    "        pass\n",
+    "\n",
+    "def test_generator(generator):\n",
+    "    for i in generator():\n",
+    "        pass\n",
+    "\n",
+    "def test_generator_yield(generator_yield):\n",
+    "    for i in generator_yield():\n",
+    "        pass\n",
+    "\n",
+    "print('plain_list:     ', end = '')\n",
+    "%timeit test_plainlist(plainlist)\n",
+    "print('\\nlistcompr:     ', end = '')\n",
+    "%timeit test_listcompr(listcompr)\n",
+    "print('\\ngenerator:     ', end = '')\n",
+    "%timeit test_generator(generator)\n",
+    "print('\\ngenerator_yield:     ', end = '')\n",
+    "%timeit test_generator_yield(generator_yield)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='private_class'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Public vs. private class methods and name mangling\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Who has not stumbled across this quote \"we are all consenting adults here\" in the Python community, yet? Unlike in other languages like C++ (sorry, there are many more, but that's one I am most familiar with), we can't really protect class methods from being used outside the class (i.e., by the API user).  \n",
+    "All we can do is to indicate methods as private to make clear that they are better not used outside the class, but it is really up to the class user, since \"we are all consenting adults here\"!  \n",
+    "So, when we want to mark a class method as private, we can put a single underscore in front of it.  \n",
+    "If we additionally want to avoid name clashes with other classes that might use the same method names, we can prefix the name with a double-underscore to invoke the name mangling.\n",
+    "\n",
+    "This doesn't prevent the class user to access this class member though, but he has to know the trick and also knows that it his own risk...\n",
+    "\n",
+    "Let the following example illustrate what I mean:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello public world!\n",
+      "Hello private world!\n",
+      "Hello private world!\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Picking `True` values from logical `and`s and `or`s"
+    }
+   ],
+   "source": [
+    "class my_class():\n",
+    "    def public_method(self):\n",
+    "        print('Hello public world!')\n",
+    "    def __private_method(self):\n",
+    "        print('Hello private world!')\n",
+    "    def call_private_method_in_class(self):\n",
+    "        self.__private_method()\n",
+    "        \n",
+    "my_instance = my_class()\n",
+    "\n",
+    "my_instance.public_method()\n",
+    "my_instance._my_class__private_method()\n",
+    "my_instance.call_private_method_in_class()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='looping_pitfall'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The consequences of modifying a list when looping through it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It can be really dangerous to modify a list when iterating through it - this is a very common pitfall that can cause unintended behavior!  \n",
+    "Look at the following examples, and for a fun exercise: try to figure out what is going on before you skip to the solution!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 3, 5]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "a = [1, 2, 3, 4, 5]\n",
+    "for i in a:\n",
+    "    if not i % 2:\n",
+    "        a.remove(i)\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4, 5]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**Logical `or`:**  \n",
-      "\n",
-      "`a or b == a if a else b`  \n",
-      "- If both values in `or` expressions are `True`, Python will select the first value (e.g., select `\"a\"` in `\"a\" or \"b\"`), and the second one in `and` expressions.  \n",
-      "This is also called **short-circuiting** - we already know that the logical `or` must be `True` if the first value is `True` and therefore can omit the evaluation of the second value.\n",
-      "\n",
-      "**Logical `and`:** \n",
+    }
+   ],
+   "source": [
+    "b = [2, 4, 5, 6]\n",
+    "for i in b:\n",
+    "     if not i % 2:\n",
+    "         b.remove(i)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "**The solution** is that we are iterating through the list index by index, and if we remove one of the items in-between, we inevitably mess around with the indexing, look at the following example, and it will become clear:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 2\n",
+      "1 5\n",
+      "2 6\n",
+      "[4, 5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "b = [2, 4, 5, 6]\n",
+    "for index, item in enumerate(b):\n",
+    "    print(index, item)\n",
+    "    if not item % 2:\n",
+    "        b.remove(item)\n",
+    "print(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='dynamic_binding'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dynamic binding and typos in variable names\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Be careful, dynamic binding is convenient, but can also quickly become dangerous!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first list:\n",
+      "0\n",
+      "1\n",
+      "2\n",
       "\n",
-      "`a and b == b if a else a`  \n",
-      "- If both values in `and` expressions are `True`, Python will select the second value, since for a logical `and`, both values must be true.\n"
+      "second list:\n",
+      "2\n",
+      "2\n",
+      "2\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "result = (2 or 3) * (5 and 7)\n",
-      "print('2 * 7 =', result)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2 * 7 = 14\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='def_mutable_func'></a>"
+    }
+   ],
+   "source": [
+    "print('first list:')\n",
+    "for i in range(3):\n",
+    "    print(i)\n",
+    "    \n",
+    "print('\\nsecond list:')\n",
+    "for j in range(3):\n",
+    "    print(i) # I (intentionally) made typo here!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='out_of_range_slicing'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## List slicing using indexes that are \"out of range\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we have all encountered it 1 (x10000) time(s) in our live, the infamous `IndexError`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-eb273dc36fdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Don't use mutable objects as default arguments for functions!"
+    }
+   ],
+   "source": [
+    "my_list = [1, 2, 3, 4, 5]\n",
+    "print(my_list[5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But suprisingly, it is not raised when we are doing list slicing, which can be a really pain for debugging:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "my_list = [1, 2, 3, 4, 5]\n",
+    "print(my_list[5:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='unboundlocalerror'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Reusing global variable names and `UnboundLocalErrors`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Usually, it is no problem to access global variables in the local scope of a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "global\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Don't use mutable objects (e.g., dictionaries, lists, sets, etc.) as default arguments for functions! You might expect that a new list is created every time when we call the function without providing an argument for the default parameter, but this is not the case: **Python will create the mutable object (default parameter) the first time the function is defined - not when it is called**, see the following code:\n",
-      "\n",
-      "(Original source: [http://docs.python-guide.org/en/latest/writing/gotchas/](http://docs.python-guide.org/en/latest/writing/gotchas/)"
+    }
+   ],
+   "source": [
+    "def my_func():\n",
+    "    print(var)\n",
+    "\n",
+    "var = 'global'\n",
+    "my_func()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And is also no problem to use the same variable name in the local scope without affecting the local counterpart: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "global\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def append_to_list(value, def_list=[]):\n",
-      "    def_list.append(value)\n",
-      "    return def_list\n",
-      "\n",
-      "my_list = append_to_list(1)\n",
-      "print(my_list)\n",
-      "\n",
-      "my_other_list = append_to_list(2)\n",
-      "print(my_other_list)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1]\n",
-        "[1, 2]\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Another good example showing that demonstrates that default arguments are created when the function is created (**and not when it is called!**):"
+    }
+   ],
+   "source": [
+    "def my_func():\n",
+    "    var = 'locally changed'\n",
+    "\n",
+    "var = 'global'\n",
+    "my_func()\n",
+    "print(var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But we have to be careful if we use a variable name that occurs in the global scope, and we want to access it in the local function scope if we want to reuse this name:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "UnboundLocalError",
+     "evalue": "local variable 'var' referenced before assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-40-3afd870b7c35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-40-3afd870b7c35>\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# want to access global variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'locally changed'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'var' referenced before assignment"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time\n",
-      "def report_arg(my_default=time.time()):\n",
-      "    print(my_default)\n",
-      "\n",
-      "report_arg()\n",
-      "\n",
-      "time.sleep(5)\n",
-      "\n",
-      "report_arg()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "1397764090.456688\n",
-        "1397764090.456688"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='consuming_generator'></a>\n"
+    }
+   ],
+   "source": [
+    "def my_func():\n",
+    "    print(var) # want to access global variable\n",
+    "    var = 'locally changed' # but Python thinks we forgot to define the local variable!\n",
+    "    \n",
+    "var = 'global'\n",
+    "my_func()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this case, we have to use the `global` keyword!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "global\n",
+      "locally changed\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Be aware of the consuming generator"
+    }
+   ],
+   "source": [
+    "def my_func():\n",
+    "    global var\n",
+    "    print(var) # want to access global variable\n",
+    "    var = 'locally changed' # changes the gobal variable\n",
+    "\n",
+    "var = 'global'\n",
+    "\n",
+    "my_func()\n",
+    "print(var)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='copy_mutable'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating copies of mutable objects\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's assume a scenario where we want to duplicate sub`list`s of values stored in another list. If we want to create independent sub`list` object, using the arithmetic multiplication operator could lead to rather unexpected (or undesired) results:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initially --->  [[1, 2, 3], [1, 2, 3]]\n",
+      "after my_list1[1][0] = 'a' --->  [['a', 2, 3], ['a', 2, 3]]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "my_list1 = [[1, 2, 3]] * 2\n",
+    "\n",
+    "print('initially ---> ', my_list1)\n",
+    "\n",
+    "# modify the 1st element of the 2nd sublist\n",
+    "my_list1[1][0] = 'a'\n",
+    "print(\"after my_list1[1][0] = 'a' ---> \", my_list1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "In this case, we should better create \"new\" objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "initially:  --->  [[1, 2, 3], [1, 2, 3]]\n",
+      "after my_list2[1][0] = 'a':  --->  [[1, 2, 3], ['a', 2, 3]]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Be aware of what is happening when combining \"`in`\" checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"."
+    }
+   ],
+   "source": [
+    "my_list2 = [[1, 2, 3] for i in range(2)]\n",
+    "\n",
+    "print('initially:  ---> ', my_list2)\n",
+    "\n",
+    "# modify the 1st element of the 2nd sublist\n",
+    "my_list2[1][0] = 'a'\n",
+    "print(\"after my_list2[1][0] = 'a':  ---> \", my_list2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "And here is the proof:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "id my_list1: 4350764680, id my_list2: 4350766472\n",
+      "id my_list1: 4350764680, id my_list2: 4350766664\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gen = (i for i in range(5))\n",
-      "print('2 in gen,', 2 in gen)\n",
-      "print('3 in gen,', 3 in gen)\n",
-      "print('1 in gen,', 1 in gen) "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2 in gen, True\n",
-        "3 in gen, True\n",
-        "1 in gen, False\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Although this defeats the purpose of an generator (in most cases), we can convert a generator into a list to circumvent the problem. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gen = (i for i in range(5))\n",
-      "a_list = list(gen)\n",
-      "print('2 in l,', 2 in a_list)\n",
-      "print('3 in l,', 3 in a_list)\n",
-      "print('1 in l,', 1 in a_list) "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "2 in l, True\n",
-        "3 in l, True\n",
-        "1 in l, True\n"
-       ]
-      }
-     ],
-     "prompt_number": 27
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='bool_int'></a>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## `bool` is a subclass of `int`\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Chicken or egg? In the history of Python (Python 2.2 to be specific) truth values were implemented via 1 and 0 (similar to the old C). In order to avoid syntax errors in old (but perfectly working) Python code, `bool` was added as a subclass of `int` in Python 2.3.\n",
-      "\n",
-      "Original source: [http://www.peterbe.com/plog/bool-is-int](http://www.peterbe.com/plog/bool-is-int)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('isinstance(True, int):', isinstance(True, int))\n",
-      "print('True + True:', True + True)\n",
-      "print('3*True + True:', 3*True + True)\n",
-      "print('3*True - False:', 3*True - False)\n"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "isinstance(True, int): True\n",
-        "True + True: 2\n",
-        "3*True + True: 4\n",
-        "3*True - False: 3\n"
-       ]
-      }
-     ],
-     "prompt_number": 28
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='lambda_closure'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## About lambda-in-closures-and-a-loop pitfall"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Remember the section about the [\"consuming generators\"](consuming_generators)? This example is somewhat related, but the result might still come unexpected. \n",
-      "\n",
-      "(Original source: [http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html](http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html))\n",
-      "\n",
-      "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope of the list comprehension. Since the list comprehension has already been constructed and evaluated when we for-loop through the list, the closure-variable will be set to the last value 4."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list = [lambda: i for i in range(5)]\n",
-      "for l in my_list:\n",
-      "    print(l())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4\n",
-        "4\n",
-        "4\n",
-        "4\n",
-        "4\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "However, by using a generator expression, we can make use of its stepwise evaluation (note that the returned variable still stems from the same closure, but the value changes as we iterate over the generator)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_gen = (lambda: n for n in range(5))\n",
-      "for l in my_gen:\n",
-      "    print(l())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "4\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And if you are really keen on using lists, there is a nifty trick that circumvents this problem as a reader nicely pointed out in the comments: We can simply pass the loop variable `i` as a default argument to the lambdas."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list = [lambda x=i: x for i in range(5)]\n",
-      "for l in my_list:\n",
-      "    print(l())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "2\n",
-        "3\n",
-        "4\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='python_legb'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Python's LEGB scope resolution and the keywords `global` and `nonlocal`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There is nothing particularly surprising about Python's LEGB scope resolution (Local -> Enclosed -> Global -> Built-in), but it is still useful to take a look at some examples!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### `global` vs. `local`\n",
-      "\n",
-      "According to the LEGB rule, Python will first look for a variable in the local scope. So if we set the variable `x = 1` `local`ly in the function's scope, it won't have an effect on the `global` `x`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = 0\n",
-      "def in_func():\n",
-      "    x = 1\n",
-      "    print('in_func:', x)\n",
-      "    \n",
-      "in_func()\n",
-      "print('global:', x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in_func: 1\n",
-        "global: 0\n"
-       ]
-      }
-     ],
-     "prompt_number": 31
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "If we want to modify the `global` x via a function, we can simply use the `global` keyword to import the variable into the function's scope:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "x = 0\n",
-      "def in_func():\n",
-      "    global x\n",
-      "    x = 1\n",
-      "    print('in_func:', x)\n",
-      "    \n",
-      "in_func()\n",
-      "print('global:', x)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in_func: 1\n",
-        "global: 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### `local` vs. `enclosed`\n",
-      "\n",
-      "Now, let us take a look at `local` vs. `enclosed`. Here, we set the variable `x = 1` in the `outer` function and set `x = 1` in the enclosed function `inner`. Since `inner` looks in the local scope first, it won't modify `outer`'s `x`."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def outer():\n",
-      "       x = 1\n",
-      "       print('outer before:', x)\n",
-      "       def inner():\n",
-      "           x = 2\n",
-      "           print(\"inner:\", x)\n",
-      "       inner()\n",
-      "       print(\"outer after:\", x)\n",
-      "outer()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "outer before: 1\n",
-        "inner: 2\n",
-        "outer after: 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 36
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Here is where the `nonlocal` keyword comes in handy - it allows us to modify the `x` variable in the `enclosed` scope:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def outer():\n",
-      "       x = 1\n",
-      "       print('outer before:', x)\n",
-      "       def inner():\n",
-      "           nonlocal x\n",
-      "           x = 2\n",
-      "           print(\"inner:\", x)\n",
-      "       inner()\n",
-      "       print(\"outer after:\", x)\n",
-      "outer()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "outer before: 1\n",
-        "inner: 2\n",
-        "outer after: 2\n"
-       ]
-      }
-     ],
-     "prompt_number": 35
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='immutable_tuple'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## When mutable contents of immutable tuples aren't so mutable"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As we all know, tuples are immutable objects in Python, right!? But what happens if they contain mutable objects? \n",
-      "\n",
-      "First, let us have a look at the expected behavior: a `TypeError` is raised if we try to modify immutable types in a tuple: "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tup = (1,)\n",
-      "tup[0] += 1"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "TypeError",
-       "evalue": "'tuple' object does not support item assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-41-c3bec6c3fe6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
-       ]
-      }
-     ],
-     "prompt_number": 41
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### But what if we put a mutable object into the immutable tuple? Well, modification works, but we **also** get a `TypeError` at the same time."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tup = ([],)\n",
-      "print('tup before: ', tup)\n",
-      "tup[0] += [1]"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "tup before:  ([],)\n"
-       ]
-      },
-      {
-       "ename": "TypeError",
-       "evalue": "'tuple' object does not support item assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-42-aebe9a31dbeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tup before: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('tup after: ', tup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "tup after:  ([1],)\n"
-       ]
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "However, **there are ways** to modify the mutable contents of the tuple without raising the `TypeError`, the solution is the `.extend()` method, or alternatively `.append()` (for lists):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tup = ([],)\n",
-      "print('tup before: ', tup)\n",
-      "tup[0].extend([1])\n",
-      "print('tup after: ', tup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "tup before:  ([],)\n",
-        "tup after:  ([1],)\n"
-       ]
-      }
-     ],
-     "prompt_number": 44
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "tup = ([],)\n",
-      "print('tup before: ', tup)\n",
-      "tup[0].append(1)\n",
-      "print('tup after: ', tup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "tup before:  ([],)\n",
-        "tup after:  ([1],)\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Explanation\n",
-      "\n",
-      "**A. Jesse Jiryu Davis** has a nice explanation for this phenomenon (Original source: [http://emptysqua.re/blog/python-increment-is-weird-part-ii/](http://emptysqua.re/blog/python-increment-is-weird-part-ii/))\n",
-      "\n",
-      "If we try to extend the list via `+=` *\"then the statement executes `STORE_SUBSCR`, which calls the C function `PyObject_SetItem`, which checks if the object supports item assignment. In our case the object is a tuple, so `PyObject_SetItem` throws the `TypeError`. Mystery solved.\"*"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### One more note about the `immutable` status of tuples. Tuples are famous for being immutable. However, how comes that this code works?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_tup = (1,)\n",
-      "my_tup += (4,)\n",
-      "my_tup = my_tup + (5,)\n",
-      "print(my_tup)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "(1, 4, 5)\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "What happens \"behind\" the curtains is that the tuple is not modified, but every time a new object is generated, which will inherit the old \"name tag\":"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_tup = (1,)\n",
-      "print(id(my_tup))\n",
-      "my_tup += (4,)\n",
-      "print(id(my_tup))\n",
-      "my_tup = my_tup + (5,)\n",
-      "print(id(my_tup))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "4337381840\n",
-        "4357415496\n",
-        "4357289952\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='list_generator'></a>\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## List comprehensions are fast, but generators are faster!?"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\"List comprehensions are fast, but generators are faster!?\" - No, not really (or significantly, see the benchmarks below). So what's the reason to prefer one over the other?\n",
-      "- use lists if you want to use the plethora of list methods  \n",
-      "- use generators when you are dealing with huge collections to avoid memory issues"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "def plainlist(n=100000):\n",
-      "    my_list = []\n",
-      "    for i in range(n):\n",
-      "        if i % 5 == 0:\n",
-      "            my_list.append(i)\n",
-      "    return my_list\n",
-      "\n",
-      "def listcompr(n=100000):\n",
-      "    my_list = [i for i in range(n) if i % 5 == 0]\n",
-      "    return my_list\n",
-      "\n",
-      "def generator(n=100000):\n",
-      "    my_gen = (i for i in range(n) if i % 5 == 0)\n",
-      "    return my_gen\n",
-      "\n",
-      "def generator_yield(n=100000):\n",
-      "    for i in range(n):\n",
-      "        if i % 5 == 0:\n",
-      "            yield i"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### To be fair to the list, let us exhaust the generators:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def test_plainlist(plain_list):\n",
-      "    for i in plain_list():\n",
-      "        pass\n",
-      "\n",
-      "def test_listcompr(listcompr):\n",
-      "    for i in listcompr():\n",
-      "        pass\n",
-      "\n",
-      "def test_generator(generator):\n",
-      "    for i in generator():\n",
-      "        pass\n",
-      "\n",
-      "def test_generator_yield(generator_yield):\n",
-      "    for i in generator_yield():\n",
-      "        pass\n",
-      "\n",
-      "print('plain_list:     ', end = '')\n",
-      "%timeit test_plainlist(plainlist)\n",
-      "print('\\nlistcompr:     ', end = '')\n",
-      "%timeit test_listcompr(listcompr)\n",
-      "print('\\ngenerator:     ', end = '')\n",
-      "%timeit test_generator(generator)\n",
-      "print('\\ngenerator_yield:     ', end = '')\n",
-      "%timeit test_generator_yield(generator_yield)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "plain_list:     10 loops, best of 3: 22.4 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "listcompr:     10 loops, best of 3: 20.8 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "generator:     10 loops, best of 3: 22 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "\n",
-        "generator_yield:     10 loops, best of 3: 21.9 ms per loop"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='private_class'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Public vs. private class methods and name mangling\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Who has not stumbled across this quote \"we are all consenting adults here\" in the Python community, yet? Unlike in other languages like C++ (sorry, there are many more, but that's one I am most familiar with), we can't really protect class methods from being used outside the class (i.e., by the API user).  \n",
-      "All we can do is to indicate methods as private to make clear that they are better not used outside the class, but it is really up to the class user, since \"we are all consenting adults here\"!  \n",
-      "So, when we want to mark a class method as private, we can put a single underscore in front of it.  \n",
-      "If we additionally want to avoid name clashes with other classes that might use the same method names, we can prefix the name with a double-underscore to invoke the name mangling.\n",
-      "\n",
-      "This doesn't prevent the class user to access this class member though, but he has to know the trick and also knows that it his own risk...\n",
-      "\n",
-      "Let the following example illustrate what I mean:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class my_class():\n",
-      "    def public_method(self):\n",
-      "        print('Hello public world!')\n",
-      "    def __private_method(self):\n",
-      "        print('Hello private world!')\n",
-      "    def call_private_method_in_class(self):\n",
-      "        self.__private_method()\n",
-      "        \n",
-      "my_instance = my_class()\n",
-      "\n",
-      "my_instance.public_method()\n",
-      "my_instance._my_class__private_method()\n",
-      "my_instance.call_private_method_in_class()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello public world!\n",
-        "Hello private world!\n",
-        "Hello private world!\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='looping_pitfall'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## The consequences of modifying a list when looping through it"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "It can be really dangerous to modify a list when iterating through it - this is a very common pitfall that can cause unintended behavior!  \n",
-      "Look at the following examples, and for a fun exercise: try to figure out what is going on before you skip to the solution!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a = [1, 2, 3, 4, 5]\n",
-      "for i in a:\n",
-      "    if not i % 2:\n",
-      "        a.remove(i)\n",
-      "print(a)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 3, 5]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = [2, 4, 5, 6]\n",
-      "for i in b:\n",
-      "     if not i % 2:\n",
-      "         b.remove(i)\n",
-      "print(b)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[4, 5]\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "**The solution** is that we are iterating through the list index by index, and if we remove one of the items in-between, we inevitably mess around with the indexing, look at the following example, and it will become clear:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "b = [2, 4, 5, 6]\n",
-      "for index, item in enumerate(b):\n",
-      "    print(index, item)\n",
-      "    if not item % 2:\n",
-      "        b.remove(item)\n",
-      "print(b)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 2\n",
-        "1 5\n",
-        "2 6\n",
-        "[4, 5]\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='dynamic_binding'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Dynamic binding and typos in variable names\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Be careful, dynamic binding is convenient, but can also quickly become dangerous!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('first list:')\n",
-      "for i in range(3):\n",
-      "    print(i)\n",
-      "    \n",
-      "print('\\nsecond list:')\n",
-      "for j in range(3):\n",
-      "    print(i) # I (intentionally) made typo here!"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "first list:\n",
-        "0\n",
-        "1\n",
-        "2\n",
-        "\n",
-        "second list:\n",
-        "2\n",
-        "2\n",
-        "2\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='out_of_range_slicing'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## List slicing using indexes that are \"out of range\""
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As we have all encountered it 1 (x10000) time(s) in our live, the infamous `IndexError`:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list = [1, 2, 3, 4, 5]\n",
-      "print(my_list[5])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "IndexError",
-       "evalue": "list index out of range",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-15-eb273dc36fdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-       ]
-      }
-     ],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But suprisingly, it is not raised when we are doing list slicing, which can be a really pain for debugging:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list = [1, 2, 3, 4, 5]\n",
-      "print(my_list[5:])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[]\n"
-       ]
-      }
-     ],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='unboundlocalerror'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Reusing global variable names and `UnboundLocalErrors`"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Usually, it is no problem to access global variables in the local scope of a function:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_func():\n",
-      "    print(var)\n",
-      "\n",
-      "var = 'global'\n",
-      "my_func()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "global\n"
-       ]
-      }
-     ],
-     "prompt_number": 37
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And is also no problem to use the same variable name in the local scope without affecting the local counterpart: "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_func():\n",
-      "    var = 'locally changed'\n",
-      "\n",
-      "var = 'global'\n",
-      "my_func()\n",
-      "print(var)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "global\n"
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "But we have to be careful if we use a variable name that occurs in the global scope, and we want to access it in the local function scope if we want to reuse this name:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_func():\n",
-      "    print(var) # want to access global variable\n",
-      "    var = 'locally changed' # but Python thinks we forgot to define the local variable!\n",
-      "    \n",
-      "var = 'global'\n",
-      "my_func()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "UnboundLocalError",
-       "evalue": "local variable 'var' referenced before assignment",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-40-3afd870b7c35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;32m<ipython-input-40-3afd870b7c35>\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# want to access global variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'locally changed'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'var' referenced before assignment"
-       ]
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In this case, we have to use the `global` keyword!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def my_func():\n",
-      "    global var\n",
-      "    print(var) # want to access global variable\n",
-      "    var = 'locally changed' # changes the gobal variable\n",
-      "\n",
-      "var = 'global'\n",
-      "\n",
-      "my_func()\n",
-      "print(var)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "global\n",
-        "locally changed\n"
-       ]
-      }
-     ],
-     "prompt_number": 43
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='copy_mutable'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Creating copies of mutable objects\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let's assume a scenario where we want to duplicate sub`list`s of values stored in another list. If we want to create independent sub`list` object, using the arithmetic multiplication operator could lead to rather unexpected (or undesired) results:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list1 = [[1, 2, 3]] * 2\n",
-      "\n",
-      "print('initially ---> ', my_list1)\n",
-      "\n",
-      "# modify the 1st element of the 2nd sublist\n",
-      "my_list1[1][0] = 'a'\n",
-      "print(\"after my_list1[1][0] = 'a' ---> \", my_list1)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "initially --->  [[1, 2, 3], [1, 2, 3]]\n",
-        "after my_list1[1][0] = 'a' --->  [['a', 2, 3], ['a', 2, 3]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "In this case, we should better create \"new\" objects:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "my_list2 = [[1, 2, 3] for i in range(2)]\n",
-      "\n",
-      "print('initially:  ---> ', my_list2)\n",
-      "\n",
-      "# modify the 1st element of the 2nd sublist\n",
-      "my_list2[1][0] = 'a'\n",
-      "print(\"after my_list2[1][0] = 'a':  ---> \", my_list2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "initially:  --->  [[1, 2, 3], [1, 2, 3]]\n",
-        "after my_list2[1][0] = 'a':  --->  [[1, 2, 3], ['a', 2, 3]]\n"
-       ]
-      }
-     ],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "And here is the proof:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for a,b in zip(my_list1, my_list2):\n",
-      "    print('id my_list1: {}, id my_list2: {}'.format(id(a), id(b)))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "id my_list1: 4350764680, id my_list2: 4350766472\n",
-        "id my_list1: 4350764680, id my_list2: 4350766664\n"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='python_differences'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Key differences between Python 2 and 3\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,  \n",
-      "- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)\n",
-      "- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)\n",
-      "- [http://python3porting.com/differences.html](http://python3porting.com/differences.html)\n",
-      "- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html)  \n",
-      "etc.\n",
-      "\n",
-      "But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!\n",
-      "(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='py23_overview'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Overview - Key differences between Python 2 and 3"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "- [Unicode](#unicode)\n",
-      "- [The print statement](#print)\n",
-      "- [Integer division](#integer_div)\n",
-      "- [xrange()](#xrange)\n",
-      "- [Raising exceptions](#raising_exceptions)\n",
-      "- [Handling exceptions](#handling_exceptions)\n",
-      "- [next() function and .next() method](#next_next)\n",
-      "- [Loop variables and leaking into the global scope](#loop_leak)\n",
-      "- [Comparing unorderable types](#compare_unorder)\n",
-      "\n",
-      "<br>\n",
-      "<br>\n",
-      "\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='unicode'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Unicode..."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "####- Python 2: \n",
-      "We have ASCII `str()` types, separate `unicode()`, but no `byte` type\n",
-      "####- Python 3: \n",
-      "Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "#############\n",
-      "# Python 2\n",
-      "#############\n",
-      "\n",
-      ">>> type(unicode('is like a python3 str()'))\n",
-      "<type 'unicode'>\n",
-      "\n",
-      ">>> type(b'byte type does not exist')\n",
-      "<type 'str'>\n",
-      "\n",
-      ">>> 'they are really' + b' the same'\n",
-      "'they are really the same'\n",
-      "\n",
-      ">>> type(bytearray(b'bytearray oddly does exist though'))\n",
-      "<type 'bytearray'>\n",
-      "\n",
-      "#############\n",
-      "# Python 3\n",
-      "#############\n",
-      "\n",
-      ">>> print('strings are now utf-8 \\u03BCnico\\u0394\u00e9!')\n",
-      "strings are now utf-8 \u03bcnico\u0394\u00e9!\n",
-      "\n",
-      "\n",
-      ">>> type(b' and we have byte types for storing data')\n",
-      "<class 'bytes'>\n",
-      "\n",
-      ">>> type(bytearray(b'but also bytearrays for those who prefer them over strings'))\n",
-      "<class 'bytearray'>\n",
-      "\n",
-      ">>> 'string' + b'bytes for data'\n",
-      "Traceback (most recent call last):s\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "TypeError: Can't convert 'bytes' object to str implicitly"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='print'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### The print statement"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ..."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> print 'Hello, World!'\n",
-      "Hello, World!\n",
-      ">>> print('Hello, World!')\n",
-      "Hello, World!\n",
-      "\n",
-      "# Python 3\n",
-      ">>> print('Hello, World!')\n",
-      "Hello, World!\n",
-      ">>> print 'Hello, World!'\n",
-      "  File \"<stdin>\", line 1\n",
-      "    print 'Hello, World!'\n",
-      "                        ^\n",
-      "SyntaxError: invalid syntax"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=\"\"` in Python 3:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> print \"line 1\", ; print 'same line'\n",
-      "line 1 same line\n",
-      "\n",
-      "# Python 3\n",
-      ">>> print(\"line 1\", end=\"\") ; print (\" same line\")\n",
-      "line 1 same line"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='integer_div'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Integer division"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed.  \n",
-      "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts)."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> 3 / 2\n",
-      "1\n",
-      ">>> 3 // 2\n",
-      "1\n",
-      ">>> 3 / 2.0\n",
-      "1.5\n",
-      ">>> 3 // 2.0\n",
-      "1.0\n",
-      "\n",
-      "# Python 3\n",
-      ">>> 3 / 2\n",
-      "1.5\n",
-      ">>> 3 // 2\n",
-      "1\n",
-      ">>> 3 / 2.0\n",
-      "1.5\n",
-      ">>> 3 // 2.0\n",
-      "1.0"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='xrange'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "###`xrange()` "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      " \n",
-      "`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch!  \n",
-      "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      "> python -m timeit 'for i in range(1000000):' ' pass'\n",
-      "10 loops, best of 3: 66 msec per loop\n",
-      "\n",
-      "    > python -m timeit 'for i in xrange(1000000):' ' pass'\n",
-      "10 loops, best of 3: 27.8 msec per loop\n",
-      "\n",
-      "# Python 3\n",
-      "> python3 -m timeit 'for i in range(1000000):' ' pass'\n",
-      "10 loops, best of 3: 51.1 msec per loop\n",
-      "\n",
-      "> python3 -m timeit 'for i in xrange(1000000):' ' pass'\n",
-      "Traceback (most recent call last):\n",
-      "  File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 292, in main\n",
-      "    x = t.timeit(number)\n",
-      "  File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 178, in timeit\n",
-      "    timing = self.inner(it, self.timer)\n",
-      "  File \"<timeit-src>\", line 6, in inner\n",
-      "    for i in xrange(1000000):\n",
-      "NameError: name 'xrange' is not defined"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='raising_exceptions'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Raising exceptions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> raise IOError, \"file error\"\n",
-      "Traceback (most recent call last):\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "IOError: file error\n",
-      ">>> raise IOError(\"file error\")\n",
-      "Traceback (most recent call last):\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "IOError: file error\n",
-      "\n",
-      "    \n",
-      "# Python 3    \n",
-      ">>> raise IOError, \"file error\"\n",
-      "  File \"<stdin>\", line 1\n",
-      "    raise IOError, \"file error\"\n",
-      "                 ^\n",
-      "SyntaxError: invalid syntax\n",
-      ">>> raise IOError(\"file error\")\n",
-      "Traceback (most recent call last):\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "OSError: file error"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='handling_exceptions'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### Handling exceptions"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the `as` keyword!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> try:\n",
-      "...     blabla\n",
-      "... except NameError, err:\n",
-      "...     print err, '--> our error msg'\n",
-      "... \n",
-      "name 'blabla' is not defined --> our error msg\n",
-      "\n",
-      "# Python 3\n",
-      ">>> try:\n",
-      "...     blabla\n",
-      "... except NameError as err:\n",
-      "...     print(err, '--> our error msg')\n",
-      "... \n",
-      "name 'blabla' is not defined --> our error msg"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "<a id='next_next'></a>\n",
-      "<br>\n",
-      "<br>"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### The `next()` function and `.next()` method"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "\n",
-      "Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Python 2\n",
-      ">>> my_generator = (letter for letter in 'abcdefg')\n",
-      ">>> my_generator.next()\n",
-      "'a'\n",
-      ">>> next(my_generator)\n",
-      "'b'\n",
-      "\n",
-      "# Python 3\n",
-      ">>> my_generator = (letter for letter in 'abcdefg')\n",
-      ">>> next(my_generator)\n",
-      "'a'\n",
-      ">>> my_generator.next()\n",
-      "Traceback (most recent call last):\n",
-      "  File \"<stdin>\", line 1, in <module>\n",
-      "AttributeError: 'generator' object has no attribute 'next'"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='loop_leak'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "### In Python 3.x for-loop variables don't leak into the global namespace anymore"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This goes back to a change that was made in Python 3.x and is described in [What\u2019s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
-      "\n",
-      "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from platform import python_version\n",
-      "print('This code cell was executed in Python', python_version())\n",
-      "\n",
-      "i = 1\n",
-      "print([i for i in range(5)])\n",
-      "print(i, '-> i in global')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "This code cell was executed in Python 3.3.5\n",
-        "[0, 1, 2, 3, 4]\n",
-        "1 -> i in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from platform import python_version\n",
-      "print 'This code cell was executed in Python', python_version()\n",
-      "\n",
-      "i = 1\n",
-      "print [i for i in range(5)]\n",
-      "print i, '-> i in global' "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "This code cell was executed in Python 2.7.6\n",
-        "[0, 1, 2, 3, 4]\n",
-        "4 -> i in global\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a id='compare_unorder'></a>\n",
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Python 3.x prevents us from comparing unorderable types"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from platform import python_version\n",
-      "print 'This code cell was executed in Python', python_version()\n",
-      "\n",
-      "print [1, 2] > 'foo'\n",
-      "print (1, 2) > 'foo'\n",
-      "print [1, 2] > (1, 2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "This code cell was executed in Python 2.7.6\n",
-        "False\n",
-        "True\n",
-        "False\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from platform import python_version\n",
-      "print('This code cell was executed in Python', python_version())\n",
-      "\n",
-      "print([1, 2] > 'foo')\n",
-      "print((1, 2) > 'foo')\n",
-      "print([1, 2] > (1, 2))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "This code cell was executed in Python 3.3.5\n"
-       ]
-      },
-      {
-       "ename": "TypeError",
-       "evalue": "unorderable types: list() > str()",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-3-1d774c677f73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This code cell was executed in Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='function_annotation'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "\n",
-      "## Function annotations - What are those `->`'s in my Python code?\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Have you ever seen any Python code that used colons inside the parantheses of a function definition?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def foo1(x: 'insert x here', y: 'insert x^2 here'):\n",
-      "    print('Hello, World')\n",
-      "    return"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And what about the fancy arrow here?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def foo2(x, y) -> 'Hi!':\n",
-      "    print('Hello, World')\n",
-      "    return"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Q: Is this valid Python syntax?  \n",
-      "A: Yes!\n",
-      "    \n",
-      "    \n",
-      "Q: So, what happens if I *just call* the function?  \n",
-      "A: Nothing!\n",
-      "    \n",
-      "Here is the proof!"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "foo1(1,2)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello, World\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "foo2(1,2)    "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello, World\n"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**So, those are function annotations ... **  \n",
-      "- the colon for the function parameters  \n",
-      "- the arrow for the return value  \n",
-      "\n",
-      "You probably will never make use of them (or at least very rarely). Usually, we write good function documentations below the function as a docstring - or at least this is how I would do it (okay this case is a little bit extreme, I have to admit):"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def is_palindrome(a):\n",
-      "    \"\"\"\n",
-      "    Case-and punctuation insensitive check if a string is a palindrom.\n",
-      "    \n",
-      "    Keyword arguments:\n",
-      "        a (str): The string to be checked if it is a palindrome.\n",
-      "        \n",
-      "    Returns `True` if input string is a palindrome, else False.\n",
-      "    \n",
-      "    \"\"\"\n",
-      "    stripped_str = [l for l in my_str.lower() if l.isalpha()]\n",
-      "    return stripped_str == stripped_str[::-1]\n",
-      "    "
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "However, function annotations can be useful to indicate that work is still in progress in some cases. But they are optional and I see them very very rarely.\n",
-      "\n",
-      "As it is stated in [PEP3107](http://legacy.python.org/dev/peps/pep-3107/#fundamentals-of-function-annotations):\n",
-      "\n",
-      "1. Function annotations, both for parameters and return values, are completely optional.\n",
-      "\n",
-      "2. Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The nice thing about function annotations is their `__annotations__` attribute, which is dictionary of all the parameters and/or the `return` value you annotated."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "foo1.__annotations__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 17,
-       "text": [
-        "{'y': 'insert x^2 here', 'x': 'insert x here'}"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "foo2.__annotations__"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 18,
-       "text": [
-        "{'return': 'Hi!'}"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**When are they useful?**"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Function annotations can be useful for a couple of things \n",
-      "- Documentation in general\n",
-      "- pre-condition testing\n",
-      "- [type checking](http://legacy.python.org/dev/peps/pep-0362/#annotation-checker)\n",
-      "     \n",
-      "..."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name=\"finally_blocks\"></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Abortive statements in `finally` blocks"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python's `try-except-finally` blocks are very handy for catching and handling errors. The `finally` block is always executed whether an `exception` has been raised or not as illustrated in the following example."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def try_finally1():\n",
-      "    try:\n",
-      "        print('in try:')\n",
-      "        print('do some stuff')\n",
-      "        float('abc')\n",
-      "    except ValueError:\n",
-      "        print('an error occurred')\n",
-      "    else:\n",
-      "        print('no error occurred')\n",
-      "    finally:\n",
-      "        print('always execute finally')\n",
-      "        \n",
-      "try_finally1()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in try:\n",
-        "do some stuff\n",
-        "an error occurred\n",
-        "always execute finally\n"
-       ]
-      }
-     ],
-     "prompt_number": 24
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "But can you also guess what will be printed in the next code cell?"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def try_finally2():\n",
-      "    try:\n",
-      "        print(\"do some stuff in try block\")\n",
-      "        return \"return from try block\"\n",
-      "    finally:\n",
-      "        print(\"do some stuff in finally block\")\n",
-      "        return \"always execute finally\"\n",
-      "    \n",
-      "print(try_finally2())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "do some stuff in try block\n",
-        "do some stuff in finally block\n",
-        "always execute finally\n"
-       ]
-      }
-     ],
-     "prompt_number": 21
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "Here, the abortive `return` statement in the `finally` block simply overrules the `return` in the `try` block, since **`finally` is guaranteed to always be executed.** So, be careful using abortive statements in `finally` blocks!"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name=\"variable_types\">"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#Assigning types to variables as values"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I am not yet sure in which context this can be useful, but it is a nice fun fact to know that we can assign types as values to variables."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "a_var = str\n",
-      "a_var(123)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 1,
-       "text": [
-        "'123'"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from random import choice\n",
-      "\n",
-      "a, b, c = float, int, str\n",
-      "for i in range(5):\n",
-      "    j = choice([a,b,c])(i)\n",
-      "    print(j, type(j))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0 <class 'int'>\n",
-        "1 <class 'int'>\n",
-        "2.0 <class 'float'>\n",
-        "3 <class 'str'>\n",
-        "4 <class 'int'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>\n",
-      "<a name='generator_rhs'></a>"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Only the first clause of generators is evaluated immediately"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The main reason why we love to use generators in certain cases (i.e., when we are dealing with large numbers of computations) is that it only computes the next value when it is needed, which is also known as \"lazy\" evaluation.\n",
-      "However, the first clause of an generator is already checked upon it's creation, as the following example demonstrates:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gen_fails = (i for i in 1/0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "division by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-18-29312e1ece8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-       ]
-      }
-     ],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Certainly, this is a nice feature, since it notifies us about syntax erros immediately. However, this is (unfortunately) not the case if we have multiple cases in our generator."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "gen_succeeds = (i for i in range(5) for j in 1/0)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print('But obviously fails when we iterate ...')\n",
-      "for i in gen_succeeds:\n",
-      "    print(i)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "ename": "ZeroDivisionError",
-       "evalue": "division by zero",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-        "\u001b[0;32m<ipython-input-20-8a83a1022971>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-        "\u001b[0;32m<ipython-input-19-c54c53f2218a>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-        "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "But obviously fails when we iterate ...\n"
-       ]
-      }
-     ],
-     "prompt_number": 20
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"splat_op\"></a>\n",
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "for a,b in zip(my_list1, my_list2):\n",
+    "    print('id my_list1: {}, id my_list2: {}'.format(id(a), id(b)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='python_differences'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Key differences between Python 2 and 3\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are some good articles already that are summarizing the differences between Python 2 and 3, e.g.,  \n",
+    "- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)\n",
+    "- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)\n",
+    "- [http://python3porting.com/differences.html](http://python3porting.com/differences.html)\n",
+    "- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html)  \n",
+    "etc.\n",
+    "\n",
+    "But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!\n",
+    "(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='py23_overview'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Overview - Key differences between Python 2 and 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "- [Unicode](#unicode)\n",
+    "- [The print statement](#print)\n",
+    "- [Integer division](#integer_div)\n",
+    "- [xrange()](#xrange)\n",
+    "- [Raising exceptions](#raising_exceptions)\n",
+    "- [Handling exceptions](#handling_exceptions)\n",
+    "- [next() function and .next() method](#next_next)\n",
+    "- [Loop variables and leaking into the global scope](#loop_leak)\n",
+    "- [Comparing unorderable types](#compare_unorder)\n",
+    "\n",
+    "<br>\n",
+    "<br>\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='unicode'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unicode..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "####- Python 2: \n",
+    "We have ASCII `str()` types, separate `unicode()`, but no `byte` type\n",
+    "####- Python 3: \n",
+    "Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "#############\n",
+    "# Python 2\n",
+    "#############\n",
+    "\n",
+    ">>> type(unicode('is like a python3 str()'))\n",
+    "<type 'unicode'>\n",
+    "\n",
+    ">>> type(b'byte type does not exist')\n",
+    "<type 'str'>\n",
+    "\n",
+    ">>> 'they are really' + b' the same'\n",
+    "'they are really the same'\n",
+    "\n",
+    ">>> type(bytearray(b'bytearray oddly does exist though'))\n",
+    "<type 'bytearray'>\n",
+    "\n",
+    "#############\n",
+    "# Python 3\n",
+    "#############\n",
+    "\n",
+    ">>> print('strings are now utf-8 \\u03BCnico\\u0394é!')\n",
+    "strings are now utf-8 μnicoΔé!\n",
+    "\n",
+    "\n",
+    ">>> type(b' and we have byte types for storing data')\n",
+    "<class 'bytes'>\n",
+    "\n",
+    ">>> type(bytearray(b'but also bytearrays for those who prefer them over strings'))\n",
+    "<class 'bytearray'>\n",
+    "\n",
+    ">>> 'string' + b'bytes for data'\n",
+    "Traceback (most recent call last):s\n",
+    "  File \"<stdin>\", line 1, in <module>\n",
+    "TypeError: Can't convert 'bytes' object to str implicitly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='print'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The print statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Python 2\n",
+    ">>> print 'Hello, World!'\n",
+    "Hello, World!\n",
+    ">>> print('Hello, World!')\n",
+    "Hello, World!\n",
+    "\n",
+    "# Python 3\n",
+    ">>> print('Hello, World!')\n",
+    "Hello, World!\n",
+    ">>> print 'Hello, World!'\n",
+    "  File \"<stdin>\", line 1\n",
+    "    print 'Hello, World!'\n",
+    "                        ^\n",
+    "SyntaxError: invalid syntax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=\"\"` in Python 3:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Python 2\n",
+    ">>> print \"line 1\", ; print 'same line'\n",
+    "line 1 same line\n",
+    "\n",
+    "# Python 3\n",
+    ">>> print(\"line 1\", end=\"\") ; print (\" same line\")\n",
+    "line 1 same line"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='integer_div'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integer division"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed.  \n",
+    "So, I still tend to use a `float(3)/2` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Python 2\n",
+    ">>> 3 / 2\n",
+    "1\n",
+    ">>> 3 // 2\n",
+    "1\n",
+    ">>> 3 / 2.0\n",
+    "1.5\n",
+    ">>> 3 // 2.0\n",
+    "1.0\n",
+    "\n",
+    "# Python 3\n",
+    ">>> 3 / 2\n",
+    "1.5\n",
+    ">>> 3 // 2\n",
+    "1\n",
+    ">>> 3 / 2.0\n",
+    "1.5\n",
+    ">>> 3 // 2.0\n",
+    "1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='xrange'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###`xrange()` "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " \n",
+    "`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch!  \n",
+    "In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Python 2\n",
+    "> python -m timeit 'for i in range(1000000):' ' pass'\n",
+    "10 loops, best of 3: 66 msec per loop\n",
+    "\n",
+    "    > python -m timeit 'for i in xrange(1000000):' ' pass'\n",
+    "10 loops, best of 3: 27.8 msec per loop\n",
+    "\n",
+    "# Python 3\n",
+    "> python3 -m timeit 'for i in range(1000000):' ' pass'\n",
+    "10 loops, best of 3: 51.1 msec per loop\n",
+    "\n",
+    "> python3 -m timeit 'for i in xrange(1000000):' ' pass'\n",
+    "Traceback (most recent call last):\n",
+    "  File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 292, in main\n",
+    "    x = t.timeit(number)\n",
+    "  File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 178, in timeit\n",
+    "    timing = self.inner(it, self.timer)\n",
+    "  File \"<timeit-src>\", line 6, in inner\n",
+    "    for i in xrange(1000000):\n",
+    "NameError: name 'xrange' is not defined"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='raising_exceptions'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Raising exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Python 2\n",
+    ">>> raise IOError, \"file error\"\n",
+    "Traceback (most recent call last):\n",
+    "  File \"<stdin>\", line 1, in <module>\n",
+    "IOError: file error\n",
+    ">>> raise IOError(\"file error\")\n",
+    "Traceback (most recent call last):\n",
+    "  File \"<stdin>\", line 1, in <module>\n",
+    "IOError: file error\n",
+    "\n",
+    "    \n",
+    "# Python 3    \n",
+    ">>> raise IOError, \"file error\"\n",
+    "  File \"<stdin>\", line 1\n",
+    "    raise IOError, \"file error\"\n",
+    "                 ^\n",
+    "SyntaxError: invalid syntax\n",
+    ">>> raise IOError(\"file error\")\n",
+    "Traceback (most recent call last):\n",
+    "  File \"<stdin>\", line 1, in <module>\n",
+    "OSError: file error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='handling_exceptions'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Handling exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Also the handling of exceptions has slightly changed in Python 3. Now, we have to use the `as` keyword!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Python 2\n",
+    ">>> try:\n",
+    "...     blabla\n",
+    "... except NameError, err:\n",
+    "...     print err, '--> our error msg'\n",
+    "... \n",
+    "name 'blabla' is not defined --> our error msg\n",
+    "\n",
+    "# Python 3\n",
+    ">>> try:\n",
+    "...     blabla\n",
+    "... except NameError as err:\n",
+    "...     print(err, '--> our error msg')\n",
+    "... \n",
+    "name 'blabla' is not defined --> our error msg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "<a id='next_next'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `next()` function and `.next()` method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "\n",
+    "Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Python 2\n",
+    ">>> my_generator = (letter for letter in 'abcdefg')\n",
+    ">>> my_generator.next()\n",
+    "'a'\n",
+    ">>> next(my_generator)\n",
+    "'b'\n",
+    "\n",
+    "# Python 3\n",
+    ">>> my_generator = (letter for letter in 'abcdefg')\n",
+    ">>> next(my_generator)\n",
+    "'a'\n",
+    ">>> my_generator.next()\n",
+    "Traceback (most recent call last):\n",
+    "  File \"<stdin>\", line 1, in <module>\n",
+    "AttributeError: 'generator' object has no attribute 'next'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='loop_leak'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### In Python 3.x for-loop variables don't leak into the global namespace anymore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
+    "\n",
+    "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This code cell was executed in Python 3.3.5\n",
+      "[0, 1, 2, 3, 4]\n",
+      "1 -> i in global\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "##Keyword argument unpacking syntax - `*args` and `**kwargs`"
+    }
+   ],
+   "source": [
+    "from platform import python_version\n",
+    "print('This code cell was executed in Python', python_version())\n",
+    "\n",
+    "i = 1\n",
+    "print([i for i in range(5)])\n",
+    "print(i, '-> i in global')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This code cell was executed in Python 2.7.6\n",
+      "[0, 1, 2, 3, 4]\n",
+      "4 -> i in global\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "from platform import python_version\n",
+    "print 'This code cell was executed in Python', python_version()\n",
+    "\n",
+    "i = 1\n",
+    "print [i for i in range(5)]\n",
+    "print i, '-> i in global' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a id='compare_unorder'></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3.x prevents us from comparing unorderable types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to Python 2.x vs 3.x overview](#py23_overview)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This code cell was executed in Python 2.7.6\n",
+      "False\n",
+      "True\n",
+      "False\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from platform import python_version\n",
+    "print 'This code cell was executed in Python', python_version()\n",
+    "\n",
+    "print [1, 2] > 'foo'\n",
+    "print (1, 2) > 'foo'\n",
+    "print [1, 2] > (1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Python has a very convenient \"keyword argument unpacking syntax\" (often also referred to as \"splat\"-operators). This is particularly useful, if we want to define a function that can take a arbitrary number of input arguments."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This code cell was executed in Python 3.3.5\n"
      ]
     },
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Single-asterisk (*args)"
+     "ename": "TypeError",
+     "evalue": "unorderable types: list() > str()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-1d774c677f73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This code cell was executed in Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def a_func(*args):\n",
-      "    print('type of args:', type(args))\n",
-      "    print('args contents:', args)\n",
-      "    print('1st argument:', args[0])\n",
-      "\n",
-      "a_func(0, 1, 'a', 'b', 'c')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "type of args: <class 'tuple'>\n",
-        "args contents: (0, 1, 'a', 'b', 'c')\n",
-        "1st argument: 0\n"
-       ]
-      }
-     ],
-     "prompt_number": 55
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Double-asterisk (**kwargs)"
+    }
+   ],
+   "source": [
+    "from platform import python_version\n",
+    "print('This code cell was executed in Python', python_version())\n",
+    "\n",
+    "print([1, 2] > 'foo')\n",
+    "print((1, 2) > 'foo')\n",
+    "print([1, 2] > (1, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='function_annotation'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Function annotations - What are those `->`'s in my Python code?\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Have you ever seen any Python code that used colons inside the parantheses of a function definition?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def foo1(x: 'insert x here', y: 'insert x^2 here'):\n",
+    "    print('Hello, World')\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And what about the fancy arrow here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def foo2(x, y) -> 'Hi!':\n",
+    "    print('Hello, World')\n",
+    "    return"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Q: Is this valid Python syntax?  \n",
+    "A: Yes!\n",
+    "    \n",
+    "    \n",
+    "Q: So, what happens if I *just call* the function?  \n",
+    "A: Nothing!\n",
+    "    \n",
+    "Here is the proof!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def b_func(**kwargs):\n",
-      "    print('type of kwargs:', type(kwargs))\n",
-      "    print('kwargs contents: ', kwargs)\n",
-      "    print('value of argument a:', kwargs['a'])\n",
-      "    \n",
-      "b_func(a=1, b=2, c=3, d=4)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "type of kwargs: <class 'dict'>\n",
-        "kwargs contents:  {'d': 4, 'a': 1, 'c': 3, 'b': 2}\n",
-        "value of argument a: 1\n"
-       ]
-      }
-     ],
-     "prompt_number": 56
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### (Partially) unpacking of iterables\n",
-      "Another useful application of the \"unpacking\"-operator is the unpacking of lists and other other iterables."
+    }
+   ],
+   "source": [
+    "foo1(1,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "val1, *vals = [1, 2, 3, 4, 5]\n",
-      "print('val1:', val1)\n",
-      "print('vals:', vals)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "val1: 1\n",
-        "vals: [2, 3, 4, 5]\n"
-       ]
-      }
-     ],
-     "prompt_number": 57
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"new_instance\"></a>\n",
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "foo2(1,2)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**So, those are function annotations ... **  \n",
+    "- the colon for the function parameters  \n",
+    "- the arrow for the return value  \n",
+    "\n",
+    "You probably will never make use of them (or at least very rarely). Usually, we write good function documentations below the function as a docstring - or at least this is how I would do it (okay this case is a little bit extreme, I have to admit):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def is_palindrome(a):\n",
+    "    \"\"\"\n",
+    "    Case-and punctuation insensitive check if a string is a palindrom.\n",
+    "    \n",
+    "    Keyword arguments:\n",
+    "        a (str): The string to be checked if it is a palindrome.\n",
+    "        \n",
+    "    Returns `True` if input string is a palindrome, else False.\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    stripped_str = [l for l in my_str.lower() if l.isalpha()]\n",
+    "    return stripped_str == stripped_str[::-1]\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, function annotations can be useful to indicate that work is still in progress in some cases. But they are optional and I see them very very rarely.\n",
+    "\n",
+    "As it is stated in [PEP3107](http://legacy.python.org/dev/peps/pep-3107/#fundamentals-of-function-annotations):\n",
+    "\n",
+    "1. Function annotations, both for parameters and return values, are completely optional.\n",
+    "\n",
+    "2. Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The nice thing about function annotations is their `__annotations__` attribute, which is dictionary of all the parameters and/or the `return` value you annotated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'y': 'insert x^2 here', 'x': 'insert x here'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foo1.__annotations__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'return': 'Hi!'}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "foo2.__annotations__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**When are they useful?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Function annotations can be useful for a couple of things \n",
+    "- Documentation in general\n",
+    "- pre-condition testing\n",
+    "- [type checking](http://legacy.python.org/dev/peps/pep-0362/#annotation-checker)\n",
+    "     \n",
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name=\"finally_blocks\"></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Abortive statements in `finally` blocks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python's `try-except-finally` blocks are very handy for catching and handling errors. The `finally` block is always executed whether an `exception` has been raised or not as illustrated in the following example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in try:\n",
+      "do some stuff\n",
+      "an error occurred\n",
+      "always execute finally\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Metaclasses - What creates a new instance of a class?"
+    }
+   ],
+   "source": [
+    "def try_finally1():\n",
+    "    try:\n",
+    "        print('in try:')\n",
+    "        print('do some stuff')\n",
+    "        float('abc')\n",
+    "    except ValueError:\n",
+    "        print('an error occurred')\n",
+    "    else:\n",
+    "        print('no error occurred')\n",
+    "    finally:\n",
+    "        print('always execute finally')\n",
+    "        \n",
+    "try_finally1()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "But can you also guess what will be printed in the next code cell?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "do some stuff in try block\n",
+      "do some stuff in finally block\n",
+      "always execute finally\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "def try_finally2():\n",
+    "    try:\n",
+    "        print(\"do some stuff in try block\")\n",
+    "        return \"return from try block\"\n",
+    "    finally:\n",
+    "        print(\"do some stuff in finally block\")\n",
+    "        return \"always execute finally\"\n",
+    "    \n",
+    "print(try_finally2())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "Here, the abortive `return` statement in the `finally` block simply overrules the `return` in the `try` block, since **`finally` is guaranteed to always be executed.** So, be careful using abortive statements in `finally` blocks!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name=\"variable_types\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#Assigning types to variables as values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I am not yet sure in which context this can be useful, but it is a nice fun fact to know that we can assign types as values to variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'123'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a_var = str\n",
+    "a_var(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 <class 'int'>\n",
+      "1 <class 'int'>\n",
+      "2.0 <class 'float'>\n",
+      "3 <class 'str'>\n",
+      "4 <class 'int'>\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Usually, it is the `__init__` method when we think of instanciating a new object from a class. However, it is the static method `__new__` (it is not a class method!) that creates and returns a new instance before `__init__()` is called. \n",
-      "More specifically, this is what is returned:  \n",
-      "`return super(<currentclass>, cls).__new__(subcls, *args, **kwargs)`  \n",
-      "\n",
-      "For more information about the `__new__` method, please see the [documentation](https://www.python.org/download/releases/2.2/descrintro/#__new__).\n",
-      "\n",
-      "As a little experiment, let us screw with `__new__` so that it returns `None` and see if `__init__` will be executed:"
+    }
+   ],
+   "source": [
+    "from random import choice\n",
+    "\n",
+    "a, b, c = float, int, str\n",
+    "for i in range(5):\n",
+    "    j = choice([a,b,c])(i)\n",
+    "    print(j, type(j))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>\n",
+    "<a name='generator_rhs'></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Only the first clause of generators is evaluated immediately"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The main reason why we love to use generators in certain cases (i.e., when we are dealing with large numbers of computations) is that it only computes the next value when it is needed, which is also known as \"lazy\" evaluation.\n",
+    "However, the first clause of an generator is already checked upon it's creation, as the following example demonstrates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-29312e1ece8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class a_class(object):\n",
-      "    def __new__(clss, *args, **kwargs):\n",
-      "        print('excecuted __new__')\n",
-      "        return None\n",
-      "    def __init__(self, an_arg):\n",
-      "        print('excecuted __init__')\n",
-      "        self.an_arg = an_arg\n",
-      "        \n",
-      "a_object = a_class(1)\n",
-      "print('Type of a_object:', type(a_object))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "excecuted __new__\n",
-        "Type of a_object: <class 'NoneType'>\n"
-       ]
-      }
-     ],
-     "prompt_number": 53
-    },
+    }
+   ],
+   "source": [
+    "gen_fails = (i for i in 1/0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Certainly, this is a nice feature, since it notifies us about syntax erros immediately. However, this is (unfortunately) not the case if we have multiple cases in our generator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "gen_succeeds = (i for i in range(5) for j in 1/0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
     {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "As we can see in the code above, `__init__` requires the returned instance from `__new__` in order to called. So, here we just created a `NoneType` object.  \n",
-      "Let us override the `__new__`, now and let us confirm that `__init__` is called now to instantiate the new object\":"
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-20-8a83a1022971>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-19-c54c53f2218a>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "class a_class(object):\n",
-      "    def __new__(cls, *args, **kwargs):\n",
-      "        print('excecuted __new__')\n",
-      "        inst = super(a_class, cls).__new__(cls)\n",
-      "        return inst\n",
-      "    def __init__(self, an_arg):\n",
-      "        print('excecuted __init__')\n",
-      "        self.an_arg = an_arg\n",
-      "        \n",
-      "a_object = a_class(1)\n",
-      "print('Type of a_object:', type(a_object))\n",
-      "print('a_object.an_arg: ', a_object.an_arg)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "excecuted __new__\n",
-        "excecuted __init__\n",
-        "Type of a_object: <class '__main__.a_class'>\n",
-        "a_object.an_arg:  1\n"
-       ]
-      }
-     ],
-     "prompt_number": 54
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(5):\n",
-      "    if i == 1:\n",
-      "        print('in for')\n",
-      "else:\n",
-      "    print('in else')\n",
-      "print('after for-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "in for\n",
-        "in else\n",
-        "after for-loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(5):\n",
-      "    if i == 1:\n",
-      "        break\n",
-      "else:\n",
-      "    print('in else')\n",
-      "print('after for-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "after for-loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"else_clauses\"></a>\n",
-      "<br>\n",
-      "<br>"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "But obviously fails when we iterate ...\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Else-clauses: \"conditional else\" and \"completion else\""
+    }
+   ],
+   "source": [
+    "print('But obviously fails when we iterate ...')\n",
+    "for i in gen_succeeds:\n",
+    "    print(i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"splat_op\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##Keyword argument unpacking syntax - `*args` and `**kwargs`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python has a very convenient \"keyword argument unpacking syntax\" (often also referred to as \"splat\"-operators). This is particularly useful, if we want to define a function that can take a arbitrary number of input arguments."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Single-asterisk (*args)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "type of args: <class 'tuple'>\n",
+      "args contents: (0, 1, 'a', 'b', 'c')\n",
+      "1st argument: 0\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "def a_func(*args):\n",
+    "    print('type of args:', type(args))\n",
+    "    print('args contents:', args)\n",
+    "    print('1st argument:', args[0])\n",
+    "\n",
+    "a_func(0, 1, 'a', 'b', 'c')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Double-asterisk (**kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "type of kwargs: <class 'dict'>\n",
+      "kwargs contents:  {'d': 4, 'a': 1, 'c': 3, 'b': 2}\n",
+      "value of argument a: 1\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "I would claim that the conditional \"else\" is every programmer's daily bread and butter. However, there is a second flavor of \"else\"-clauses in Python, which I will call \"completion else\" (for reason that will become clear later).  \n",
-      "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with.  \n"
+    }
+   ],
+   "source": [
+    "def b_func(**kwargs):\n",
+    "    print('type of kwargs:', type(kwargs))\n",
+    "    print('kwargs contents: ', kwargs)\n",
+    "    print('value of argument a:', kwargs['a'])\n",
+    "    \n",
+    "b_func(a=1, b=2, c=3, d=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (Partially) unpacking of iterables\n",
+    "Another useful application of the \"unpacking\"-operator is the unpacking of lists and other other iterables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "val1: 1\n",
+      "vals: [2, 3, 4, 5]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "###Conditional else:"
+    }
+   ],
+   "source": [
+    "val1, *vals = [1, 2, 3, 4, 5]\n",
+    "print('val1:', val1)\n",
+    "print('vals:', vals)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"new_instance\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metaclasses - What creates a new instance of a class?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Usually, it is the `__init__` method when we think of instanciating a new object from a class. However, it is the static method `__new__` (it is not a class method!) that creates and returns a new instance before `__init__()` is called. \n",
+    "More specifically, this is what is returned:  \n",
+    "`return super(<currentclass>, cls).__new__(subcls, *args, **kwargs)`  \n",
+    "\n",
+    "For more information about the `__new__` method, please see the [documentation](https://www.python.org/download/releases/2.2/descrintro/#__new__).\n",
+    "\n",
+    "As a little experiment, let us screw with `__new__` so that it returns `None` and see if `__init__` will be executed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "excecuted __new__\n",
+      "Type of a_object: <class 'NoneType'>\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# conditional else\n",
-      "\n",
-      "a_list = [1,2]\n",
-      "if a_list[0] == 1:\n",
-      "    print('Hello, World!')\n",
-      "else:\n",
-      "    print('Bye, World!')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Hello, World!\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# conditional else\n",
-      "\n",
-      "a_list = [1,2]\n",
-      "if a_list[0] == 2:\n",
-      "    print('Hello, World!')\n",
-      "else:\n",
-      "    print('Bye, World!')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Bye, World!\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Why am I showing those simple examples? I think they are good to highlight some of the key points: It is **either** the code under the `if` clause that is executed, **or** the code under the `else` block, but not both.  \n",
-      "If the condition of the `if` clause evaluates to `True`, the `if`-block is exectured, and if it evaluated to `False`, it is the `else` block.  \n",
-      "\n",
-      "### Completion else\n",
-      "**In contrast** to the **either...or*** situation that we know from the conditional `else`, the completion `else` is executed if a code block finished.  \n",
-      "To show you an example, let us use `else` for error-handling:"
+    }
+   ],
+   "source": [
+    "class a_class(object):\n",
+    "    def __new__(clss, *args, **kwargs):\n",
+    "        print('excecuted __new__')\n",
+    "        return None\n",
+    "    def __init__(self, an_arg):\n",
+    "        print('excecuted __init__')\n",
+    "        self.an_arg = an_arg\n",
+    "        \n",
+    "a_object = a_class(1)\n",
+    "print('Type of a_object:', type(a_object))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we can see in the code above, `__init__` requires the returned instance from `__new__` in order to called. So, here we just created a `NoneType` object.  \n",
+    "Let us override the `__new__`, now and let us confirm that `__init__` is called now to instantiate the new object\":"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "excecuted __new__\n",
+      "excecuted __init__\n",
+      "Type of a_object: <class '__main__.a_class'>\n",
+      "a_object.an_arg:  1\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Completion else (try-except)"
+    }
+   ],
+   "source": [
+    "class a_class(object):\n",
+    "    def __new__(cls, *args, **kwargs):\n",
+    "        print('excecuted __new__')\n",
+    "        inst = super(a_class, cls).__new__(cls)\n",
+    "        return inst\n",
+    "    def __init__(self, an_arg):\n",
+    "        print('excecuted __init__')\n",
+    "        self.an_arg = an_arg\n",
+    "        \n",
+    "a_object = a_class(1)\n",
+    "print('Type of a_object:', type(a_object))\n",
+    "print('a_object.an_arg: ', a_object.an_arg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "in for\n",
+      "in else\n",
+      "after for-loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try:\n",
-      "    print('first element:', a_list[0])\n",
-      "except IndexError:\n",
-      "    print('raised IndexError')\n",
-      "else:\n",
-      "    print('no error in try-block')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "first element: 1\n",
-        "no error in try-block\n"
-       ]
-      }
-     ],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "try:\n",
-      "    print('third element:', a_list[2])\n",
-      "except IndexError:\n",
-      "    print('raised IndexError')\n",
-      "else:\n",
-      "    print('no error in try-block')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "raised IndexError\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "In the code above, we can see that the code under the **`else`-clause is only executed if the `try-block` was executed without encountering an error, i.e., if the `try`-block is \"complete\".**  \n",
-      "The same rule applies to the \"completion\" `else` in while- and for-loops, which you can confirm in the following samples below."
+    }
+   ],
+   "source": [
+    "for i in range(5):\n",
+    "    if i == 1:\n",
+    "        print('in for')\n",
+    "else:\n",
+    "    print('in else')\n",
+    "print('after for-loop')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "after for-loop\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Completion else (while-loop)"
+    }
+   ],
+   "source": [
+    "for i in range(5):\n",
+    "    if i == 1:\n",
+    "        break\n",
+    "else:\n",
+    "    print('in else')\n",
+    "print('after for-loop')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"else_clauses\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Else-clauses: \"conditional else\" and \"completion else\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I would claim that the conditional \"else\" is every programmer's daily bread and butter. However, there is a second flavor of \"else\"-clauses in Python, which I will call \"completion else\" (for reason that will become clear later).  \n",
+    "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with.  \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###Conditional else:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World!\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 0\n",
-      "while i < 2:\n",
-      "    print(i)\n",
-      "    i += 1\n",
-      "else:\n",
-      "    print('in else')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "in else\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "i = 0\n",
-      "while i < 2:\n",
-      "    print(i)\n",
-      "    i += 1\n",
-      "    break\n",
-      "else:\n",
-      "    print('completed while-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### Completion else (for-loop)"
+    }
+   ],
+   "source": [
+    "# conditional else\n",
+    "\n",
+    "a_list = [1,2]\n",
+    "if a_list[0] == 1:\n",
+    "    print('Hello, World!')\n",
+    "else:\n",
+    "    print('Bye, World!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bye, World!\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(2):\n",
-      "    print(i)\n",
-      "else:\n",
-      "    print('completed for-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n",
-        "1\n",
-        "completed for-loop\n"
-       ]
-      }
-     ],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "for i in range(2):\n",
-      "    print(i)\n",
-      "    break\n",
-      "else:\n",
-      "    print('completed for-loop')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "0\n"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"string_interning\"></a>\n",
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "# conditional else\n",
+    "\n",
+    "a_list = [1,2]\n",
+    "if a_list[0] == 2:\n",
+    "    print('Hello, World!')\n",
+    "else:\n",
+    "    print('Bye, World!')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Why am I showing those simple examples? I think they are good to highlight some of the key points: It is **either** the code under the `if` clause that is executed, **or** the code under the `else` block, but not both.  \n",
+    "If the condition of the `if` clause evaluates to `True`, the `if`-block is exectured, and if it evaluated to `False`, it is the `else` block.  \n",
+    "\n",
+    "### Completion else\n",
+    "**In contrast** to the **either...or*** situation that we know from the conditional `else`, the completion `else` is executed if a code block finished.  \n",
+    "To show you an example, let us use `else` for error-handling:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Completion else (try-except)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first element: 1\n",
+      "no error in try-block\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "## Interning of compile-time constants vs. run-time expressions"
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print('first element:', a_list[0])\n",
+    "except IndexError:\n",
+    "    print('raised IndexError')\n",
+    "else:\n",
+    "    print('no error in try-block')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "raised IndexError\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print('third element:', a_list[2])\n",
+    "except IndexError:\n",
+    "    print('raised IndexError')\n",
+    "else:\n",
+    "    print('no error in try-block')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "In the code above, we can see that the code under the **`else`-clause is only executed if the `try-block` was executed without encountering an error, i.e., if the `try`-block is \"complete\".**  \n",
+    "The same rule applies to the \"completion\" `else` in while- and for-loops, which you can confirm in the following samples below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Completion else (while-loop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "in else\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "This might not be particularly useful, but it is nonetheless interesting: Python's interpreter is interning compile-time constants but not run-time expressions (note that this is implementation-specific).\n",
-      "\n",
-      "(Original source: [Stackoverflow](http://stackoverflow.com/questions/15541404/python-string-interning))"
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "while i < 2:\n",
+    "    print(i)\n",
+    "    i += 1\n",
+    "else:\n",
+    "    print('in else')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Let us have a look at the simple example below. Here we are creating 3 variables and assign the value \"Hello\" to them in different ways before we test them for identity."
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "while i < 2:\n",
+    "    print(i)\n",
+    "    i += 1\n",
+    "    break\n",
+    "else:\n",
+    "    print('completed while-loop')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Completion else (for-loop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "completed for-loop\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "hello1 = 'Hello'\n",
-      "\n",
-      "hello2 = 'Hell' + 'o'\n",
-      "\n",
-      "hello3 = 'Hell'\n",
-      "hello3 = hello3 + 'o'\n",
-      "\n",
-      "print('hello1 is hello2:', hello1 is hello2)\n",
-      "print('hello1 is hello3:', hello1 is hello3)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "hello1 is hello2: True\n",
-        "hello1 is hello3: False\n"
-       ]
-      }
-     ],
-     "prompt_number": 34
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now, how does it come that the first expression evaluates to true, but the second does not?  To answer this question, we need to take a closer look at the underlying byte codes:"
+    }
+   ],
+   "source": [
+    "for i in range(2):\n",
+    "    print(i)\n",
+    "else:\n",
+    "    print('completed for-loop')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import dis\n",
-      "def hello1_func():\n",
-      "    s = 'Hello'\n",
-      "    return s\n",
-      "dis.dis(hello1_func)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  3           0 LOAD_CONST               1 ('Hello')\n",
-        "              3 STORE_FAST               0 (s)\n",
-        "\n",
-        "  4           6 LOAD_FAST                0 (s)\n",
-        "              9 RETURN_VALUE\n"
-       ]
-      }
-     ],
-     "prompt_number": 38
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def hello2_func():\n",
-      "    s = 'Hell' + 'o'\n",
-      "    return s\n",
-      "dis.dis(hello2_func)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  2           0 LOAD_CONST               3 ('Hello')\n",
-        "              3 STORE_FAST               0 (s)\n",
-        "\n",
-        "  3           6 LOAD_FAST                0 (s)\n",
-        "              9 RETURN_VALUE\n"
-       ]
-      }
-     ],
-     "prompt_number": 39
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def hello3_func():\n",
-      "    s = 'Hell'\n",
-      "    s = s + 'o'\n",
-      "    return s\n",
-      "dis.dis(hello3_func)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "  2           0 LOAD_CONST               1 ('Hell')\n",
-        "              3 STORE_FAST               0 (s)\n",
-        "\n",
-        "  3           6 LOAD_FAST                0 (s)\n",
-        "              9 LOAD_CONST               2 ('o')\n",
-        "             12 BINARY_ADD\n",
-        "             13 STORE_FAST               0 (s)\n",
-        "\n",
-        "  4          16 LOAD_FAST                0 (s)\n",
-        "             19 RETURN_VALUE\n"
-       ]
-      }
-     ],
-     "prompt_number": 40
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "It looks like that `'Hello'` and `'Hell'` + `'o'` are both evaluated and stored as `'Hello'` at compile-time, whereas the third version   \n",
-      "`s = 'Hell'`  \n",
-      "`s = s + 'o'` seems to be not interned.  Let us quickly confirm the behavior with the following code:"
+    }
+   ],
+   "source": [
+    "for i in range(2):\n",
+    "    print(i)\n",
+    "    break\n",
+    "else:\n",
+    "    print('completed for-loop')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"string_interning\"></a>\n",
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interning of compile-time constants vs. run-time expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This might not be particularly useful, but it is nonetheless interesting: Python's interpreter is interning compile-time constants but not run-time expressions (note that this is implementation-specific).\n",
+    "\n",
+    "(Original source: [Stackoverflow](http://stackoverflow.com/questions/15541404/python-string-interning))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us have a look at the simple example below. Here we are creating 3 variables and assign the value \"Hello\" to them in different ways before we test them for identity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello1 is hello2: True\n",
+      "hello1 is hello3: False\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "print(hello1_func() is hello2_func())\n",
-      "print(hello1_func() is hello3_func())"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "True\n",
-        "False\n"
-       ]
-      }
-     ],
-     "prompt_number": 42
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Finally, to show that this hypothesis is the answer to this rather unexpected observation, let us `intern` the value manually:"
+    }
+   ],
+   "source": [
+    "hello1 = 'Hello'\n",
+    "\n",
+    "hello2 = 'Hell' + 'o'\n",
+    "\n",
+    "hello3 = 'Hell'\n",
+    "hello3 = hello3 + 'o'\n",
+    "\n",
+    "print('hello1 is hello2:', hello1 is hello2)\n",
+    "print('hello1 is hello3:', hello1 is hello3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, how does it come that the first expression evaluates to true, but the second does not?  To answer this question, we need to take a closer look at the underlying byte codes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  3           0 LOAD_CONST               1 ('Hello')\n",
+      "              3 STORE_FAST               0 (s)\n",
+      "\n",
+      "  4           6 LOAD_FAST                0 (s)\n",
+      "              9 RETURN_VALUE\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import sys\n",
-      "\n",
-      "print(hello1_func() is sys.intern(hello3_func()))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "True\n"
-       ]
-      }
-     ],
-     "prompt_number": 45
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<a name=\"changelog\"></a>\n",
-      "<br>\n",
-      "<br>\n",
-      "<br>\n",
-      "<br>\n"
+    }
+   ],
+   "source": [
+    "import dis\n",
+    "def hello1_func():\n",
+    "    s = 'Hello'\n",
+    "    return s\n",
+    "dis.dis(hello1_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  2           0 LOAD_CONST               3 ('Hello')\n",
+      "              3 STORE_FAST               0 (s)\n",
+      "\n",
+      "  3           6 LOAD_FAST                0 (s)\n",
+      "              9 RETURN_VALUE\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "# Changelog"
+    }
+   ],
+   "source": [
+    "def hello2_func():\n",
+    "    s = 'Hell' + 'o'\n",
+    "    return s\n",
+    "dis.dis(hello2_func)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  2           0 LOAD_CONST               1 ('Hell')\n",
+      "              3 STORE_FAST               0 (s)\n",
+      "\n",
+      "  3           6 LOAD_FAST                0 (s)\n",
+      "              9 LOAD_CONST               2 ('o')\n",
+      "             12 BINARY_ADD\n",
+      "             13 STORE_FAST               0 (s)\n",
+      "\n",
+      "  4          16 LOAD_FAST                0 (s)\n",
+      "             19 RETURN_VALUE\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#sections)]"
+    }
+   ],
+   "source": [
+    "def hello3_func():\n",
+    "    s = 'Hell'\n",
+    "    s = s + 'o'\n",
+    "    return s\n",
+    "dis.dis(hello3_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "It looks like that `'Hello'` and `'Hell'` + `'o'` are both evaluated and stored as `'Hello'` at compile-time, whereas the third version   \n",
+    "`s = 'Hell'`  \n",
+    "`s = s + 'o'` seems to be not interned.  Let us quickly confirm the behavior with the following code:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "#### 07/16/2014\n",
-      "- slight change of wording in the [lambda-closure section](#lambda_closure)\n",
-      "\n",
-      "#### 05/24/2014\n",
-      "- new section: unorderable types in Python 2\n",
-      "- table of contents for the Python 2 vs. Python 3 topic\n",
-      "    \n",
-      "#### 05/03/2014\n",
-      "- new section: else clauses: conditional vs. completion\n",
-      "- new section: Interning of compile-time constants vs. run-time expressions\n",
-      "\n",
-      "#### 05/02/2014\n",
-      "- new section in Python 3.x and Python 2.x key differences: for-loop leak\n",
-      "- new section: Metaclasses - What creates a new instance of a class?  \n",
-      "\n",
-      "#### 05/01/2014\n",
-      "- new section: keyword argument unpacking syntax\n",
-      "\n",
-      "#### 04/27/2014\n",
-      "- minor fixes of typos  \n",
-      "- new section: \"Only the first clause of generators is evaluated immediately\""
+    }
+   ],
+   "source": [
+    "print(hello1_func() is hello2_func())\n",
+    "print(hello1_func() is hello3_func())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, to show that this hypothesis is the answer to this rather unexpected observation, let us `intern` the value manually:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
-   "metadata": {}
+   "source": [
+    "import sys\n",
+    "\n",
+    "print(hello1_func() is sys.intern(hello3_func()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a name=\"changelog\"></a>\n",
+    "<br>\n",
+    "<br>\n",
+    "<br>\n",
+    "<br>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Changelog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 07/16/2014\n",
+    "- slight change of wording in the [lambda-closure section](#lambda_closure)\n",
+    "\n",
+    "#### 05/24/2014\n",
+    "- new section: unorderable types in Python 2\n",
+    "- table of contents for the Python 2 vs. Python 3 topic\n",
+    "    \n",
+    "#### 05/03/2014\n",
+    "- new section: else clauses: conditional vs. completion\n",
+    "- new section: Interning of compile-time constants vs. run-time expressions\n",
+    "\n",
+    "#### 05/02/2014\n",
+    "- new section in Python 3.x and Python 2.x key differences: for-loop leak\n",
+    "- new section: Metaclasses - What creates a new instance of a class?  \n",
+    "\n",
+    "#### 05/01/2014\n",
+    "- new section: keyword argument unpacking syntax\n",
+    "\n",
+    "#### 04/27/2014\n",
+    "- minor fixes of typos  \n",
+    "- new section: \"Only the first clause of generators is evaluated immediately\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.0"
   }
- ]
-}
\ No newline at end of file
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/tutorials/scope_resolution_legb_rule.ipynb b/tutorials/scope_resolution_legb_rule.ipynb
index 18ff06c..58adb8a 100644
--- a/tutorials/scope_resolution_legb_rule.ipynb
+++ b/tutorials/scope_resolution_legb_rule.ipynb
@@ -1151,7 +1151,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.1"
+   "version": "3.5.0"
   }
  },
  "nbformat": 4,

From 6432318c5aaf063c3b351aaf5cee5ec263e9f8c2 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Sun, 10 Apr 2016 18:53:57 -0400
Subject: [PATCH 233/248] fix links in readme

---
 README.md | 15 +++++++++------
 1 file changed, 9 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index 42903d5..a1af37a 100644
--- a/README.md
+++ b/README.md
@@ -36,7 +36,6 @@
 
 - Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks [[Markdown](./tutorials/installing_scientific_packages.md)]
 
-
 - Sorting CSV files using the Python csv module [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
 
 - Using Cython with and without IPython magic [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/running_cython.ipynb)]
@@ -65,7 +64,7 @@
 
 - Creating internal links in IPython Notebooks and Markdown docs [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]  
 
-- Converting Markdown to HTML and adding Python syntax highlighting [[Markdown](./tutorials/markdown_syntax_highlighting/README.md)] 
+- Converting Markdown to HTML and adding Python syntax highlighting [[Markdown](./tutorials/markdown_syntax_highlighting/README.md)]
 
 
 <br>
@@ -124,10 +123,10 @@
 
 - Vectorizing a classic for-loop in NumPy [[IPython nb](http://nbviewer.ipython.org/github/rasbt/One-Python-benchmark-per-day/blob/master/ipython_nbs/day16_numpy_vectorization.ipynb)]
 
-<br> 
+<br>
 
 
-###// Python and "Data Science" 
+###// Python and "Data Science"
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
 *The "data science"-related posts have been moved to a separate GitHub repository [pattern_classification](https://github.com/rasbt/pattern_classification)*
@@ -162,6 +161,12 @@
 
 <br>
 
+###// Other
+
+- [Python book reviews](./other/python_book_reviews.md)
+- [Happy Mother's Day Plot](./other/happy_mothers_day.ipynb)
+
+<br>
 
 ###// Links
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
@@ -220,5 +225,3 @@
 - [Numba](http://numba.pydata.org) - A just-in-time specializing compiler which compiles annotated Python and NumPy code to LLVM (through decorators)
 
 - [scikit-learn](http://scikit-learn.org/stable/) - A powerful machine learning library for Python and tools for efficient data mining and analysis.
-
-

From 4e92886d30dcb67ea0c1a21c45671971c2fb3c62 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Mon, 9 May 2016 21:41:40 -0400
Subject: [PATCH 234/248] singly linked list

---
 README.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index a1af37a..fa44730 100644
--- a/README.md
+++ b/README.md
@@ -68,11 +68,10 @@
 
 
 <br>
-###// Algorithms
+###// Algorithms and Data Structures
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
 
-*The algorithms category has been moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*
-
+*This category has been moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*
 
 
 - Sorting Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/sorting/sorting_algorithms.ipynb?create=1)]
@@ -81,10 +80,10 @@
 
 - Dixon's Q test to identify outliers for small sample sizes [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/dixon_q_test.ipynb?create=1)]
 
-- Sequential Selection Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/sorting_csvs.ipynb)]
-
 - Counting points inside a hypercube [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/geometry/points_in_hybercube.ipynb)]
 
+- Singly Linked List [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/data-structures/singly-linked-list.ipynb)]
+
 <br>
 ###// Plotting and Visualization
 [[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]

From e1033b709efe22f73c2fcfee6a898b738cee1366 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Sat, 2 Jul 2016 02:41:01 -0400
Subject: [PATCH 235/248] banker's rounding

---
 ...y_differences_between_python_2_and_3.ipynb | 185 +++++++++++++++++-
 1 file changed, 179 insertions(+), 6 deletions(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 0f74195..7928cb9 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "[Sebastian Raschka](http://sebastianraschka.com)  \n",
     "\n",
-    "last updated 05/27/2014\n",
+    "last updated 07/02/2016\n",
     "\n",
     "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/key_differences_between_python_2_and_3.ipynb?create=1) \n",
     "\n",
@@ -87,6 +87,8 @@
     "\n",
     "- [Returning iterable objects instead of lists](#Returning-iterable-objects-instead-of-lists)\n",
     "\n",
+    "- [Banker's Rounding](#Banker's-Rounding)\n",
+    "\n",
     "- [More articles about Python 2 and Python 3](#More-articles-about-Python-2-and-Python-3)"
    ]
   },
@@ -2038,6 +2040,177 @@
     "<br>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Banker's Rounding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to the section-overview](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python 3 adopted the now standard way of rounding decimals when it results in a tie (.5) at the last significant digits. Now, in Python 3, decimals are rounded to the next even number.  Although it's an inconvenience for code portability, it's supposedly a better way of rounding compared to rounding up as it avoids the bias towards large numbers. For more information, see the excellent Wikipedia articles and paragraphs:\n",
+    "- [https://en.wikipedia.org/wiki/Rounding#Round_half_to_even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)\n",
+    "- [https://en.wikipedia.org/wiki/IEEE_floating_point#Roundings_to_nearest](https://en.wikipedia.org/wiki/IEEE_floating_point#Roundings_to_nearest)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 2.7.12\n"
+     ]
+    }
+   ],
+   "source": [
+    "print 'Python', python_version()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(15.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(16.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Python 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.5.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Python', python_version())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(15.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(16.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2096,21 +2269,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.12"
   }
  },
  "nbformat": 4,

From 3a59a6b66a8964a38269a68db9a02fc8ad7766df Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Tue, 6 Dec 2016 23:57:04 -0500
Subject: [PATCH 236/248] update recommended resources

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index fa44730..4eaf0b6 100644
--- a/README.md
+++ b/README.md
@@ -194,8 +194,6 @@
 
 **// Resources for learning Python**
 
-- [Learn Python The Hard Way](http://learnpythonthehardway.org/book/) - The popular and probably most recommended resource for learning Python.  
-
 - [Dive Into Python](http://www.diveintopython.net) / [Dive Into Python 3](http://getpython3.com/diveintopython3/) - A free Python book for experienced programmers.
 
 - [The Hitchhiker’s Guide to Python](http://docs.python-guide.org/en/latest/) - A free best-practice handbook for both novices and experts.
@@ -204,6 +202,8 @@
 
 - [Python Patterns](http://matthiaseisen.com/pp/) -  A directory of proven, reusable solutions to common programming problems.
 
+- [Intro to Computer Science - Build a Search Engine & a Social Network](https://www.udacity.com/course/intro-to-computer-science--cs101) - A great, free course for learning Python if you haven't programmed before.
+
 <br>
 
 **// My favorite Python projects and packages**

From f7dd31f1b58612b7133d9d7a5a747c01ecf2fbdf Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Wed, 1 Mar 2017 21:52:45 -0500
Subject: [PATCH 237/248] next should be nearest

---
 tutorials/key_differences_between_python_2_and_3.ipynb | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tutorials/key_differences_between_python_2_and_3.ipynb b/tutorials/key_differences_between_python_2_and_3.ipynb
index 7928cb9..68feac3 100644
--- a/tutorials/key_differences_between_python_2_and_3.ipynb
+++ b/tutorials/key_differences_between_python_2_and_3.ipynb
@@ -2058,7 +2058,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Python 3 adopted the now standard way of rounding decimals when it results in a tie (.5) at the last significant digits. Now, in Python 3, decimals are rounded to the next even number.  Although it's an inconvenience for code portability, it's supposedly a better way of rounding compared to rounding up as it avoids the bias towards large numbers. For more information, see the excellent Wikipedia articles and paragraphs:\n",
+    "Python 3 adopted the now standard way of rounding decimals when it results in a tie (.5) at the last significant digits. Now, in Python 3, decimals are rounded to the nearest even number.  Although it's an inconvenience for code portability, it's supposedly a better way of rounding compared to rounding up as it avoids the bias towards large numbers. For more information, see the excellent Wikipedia articles and paragraphs:\n",
     "- [https://en.wikipedia.org/wiki/Rounding#Round_half_to_even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)\n",
     "- [https://en.wikipedia.org/wiki/IEEE_floating_point#Roundings_to_nearest](https://en.wikipedia.org/wiki/IEEE_floating_point#Roundings_to_nearest)"
    ]
@@ -2268,6 +2268,7 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",

From 774859799ed53103167cc3b7ca084aff29288665 Mon Sep 17 00:00:00 2001
From: Frenchhorn <nyanpasspass@outlook.com>
Date: Mon, 24 Apr 2017 17:59:01 +0800
Subject: [PATCH 238/248] Update README.md

udpate anchors
---
 README.md | 30 +++++++++++-------------------
 1 file changed, 11 insertions(+), 19 deletions(-)

diff --git a/README.md b/README.md
index 4eaf0b6..05c8419 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-### A collection of useful scripts, tutorials, and other Python-related things
+<h3 id="top">A collection of useful scripts, tutorials, and other Python-related things</h3>
 
 <br>
 <img src="./Images/python-logo-master-v3-TM-flattened.png" alt="">
@@ -12,8 +12,8 @@
 - [// Plotting and Visualization](#-plotting-and-visualization)
 - [// Benchmarks](#-benchmarks)
 - [// Python and "Data Science"](#-python-and-data-science)
-- [// Other](#-other)
 - [// Useful scripts and snippets](#-useful-scripts-and-snippets)
+- [// Other](#-other)
 - [// Links](#-links)
 
 
@@ -21,8 +21,7 @@
 <div style="height:100px;"></div>
 
 
-###// Python tips and tutorials
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+<b id="-python-tips-and-tutorials">Python tips and tutorials</b> <a href="#top">[back to top]</a>
 
 - A collection of not so obvious Python stuff you should know! [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/not_so_obvious_python_stuff.ipynb?create=1)]
 
@@ -59,8 +58,7 @@
 <br>
 
 
-###// Python and the web
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+<b id="-python-and-the-web">Python and the web</b> <a href="#top">[back to top]</a>
 
 - Creating internal links in IPython Notebooks and Markdown docs [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/table_of_contents_ipython.ipynb)]  
 
@@ -68,8 +66,7 @@
 
 
 <br>
-###// Algorithms and Data Structures
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+<b id="-algorithms">Algorithms and Data Structures</b> <a href="#top">[back to top]</a>
 
 *This category has been moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*
 
@@ -85,8 +82,7 @@
 - Singly Linked List [[ IPython nbviewer ](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/data-structures/singly-linked-list.ipynb)]
 
 <br>
-###// Plotting and Visualization
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+<b id="-plotting-and-visualization">Plotting and Visualization</b> <a href="#top">[back to top]</a>
 
 *The matplotlib-gallery in IPython notebooks has been moved to a separate GitHub repository [matplotlib-gallery](https://github.com/rasbt/matplotlib-gallery)*
 
@@ -99,8 +95,7 @@
 
 
 <br>
-###// Benchmarks
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+<b id="-benchmarks">Benchmarks</b> <a href="#top">[back to top]</a>
 
 
 - Simple tricks to speed up the sum calculation in pandas [[IPython nb](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/benchmarks/pandas_sum_tricks.ipynb)]
@@ -125,8 +120,7 @@
 <br>
 
 
-###// Python and "Data Science"
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+<b id="-python-and-data-science">Python and "Data Science"</b> <a href="#top">[back to top]</a>
 
 *The "data science"-related posts have been moved to a separate GitHub repository [pattern_classification](https://github.com/rasbt/pattern_classification)*
 
@@ -145,8 +139,7 @@
 
 <br>
 
-###// Useful scripts and snippets
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+<b id="-useful-scripts-and-snippets">Useful scripts and snippets</b> <a href="#top">[back to top]</a>
 
 - [watermark](https://github.com/rasbt/watermark) - An IPython magic extension for printing date and time stamps, version numbers, and hardware information.
 
@@ -160,15 +153,14 @@
 
 <br>
 
-###// Other
+<b id="-other">Other</b> <a href="#top">[back to top]</a>
 
 - [Python book reviews](./other/python_book_reviews.md)
 - [Happy Mother's Day Plot](./other/happy_mothers_day.ipynb)
 
 <br>
 
-###// Links
-[[back to top](#a-collection-of-useful-scripts-tutorials-and-other-python-related-things)]
+<b id="-links">Links</b> <a href="#top">[back to top]</a>
 
 
 

From a78df233015d84c4daea6e1fd509ebedfcdd746f Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Thu, 17 Aug 2017 09:53:32 -0400
Subject: [PATCH 239/248] typo fix

---
 tutorials/multiprocessing_intro.ipynb | 2208 ++++++++++++-------------
 1 file changed, 1081 insertions(+), 1127 deletions(-)

diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index a3b0916..126f8c2 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -1,1151 +1,1105 @@
 {
- "metadata": {
-  "name": "",
-  "signature": "sha256:a96ed2f762cf56d93a4e5345428c7db5ec576916158ce54446dfdf837ec7e505"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
+ "cells": [
   {
-   "cells": [
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[Sebastian Raschka](http://sebastianraschka.com)  \n",
-      "\n",
-      "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1)  \n",
-      "\n",
-      "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)  \n",
-      "\n",
-      "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference)\n"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import time\n",
-      "print('Last updated: %s' %time.strftime('%d/%m/%Y'))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "Last updated: 20/06/2014\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "I would be happy to hear your comments and suggestions.  \n",
-      "Please feel free to drop me a note via\n",
-      "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Parallel processing via the `multiprocessing` module"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "CPUs with multiple cores have become the standard in the recent development of modern computer architectures and we can not only find them in supercomputer facilities but also in our desktop machines at home, and our laptops; even Apple's iPhone 5S got a 1.3 Ghz Dual-core processor in 2013.\n",
-      "\n",
-      "However, the default Python interpreter was designed with simplicity in mind and has a thread-safe mechanism, the so-called \"GIL\" (Global Interpreter Lock). In order to prevent conflicts between threads, it executes only one statement at a time (so-called serial processing, or single-threading).\n",
-      "\n",
-      "In this introduction to Python's `multiprocessing` module, we will see how we can spawn multiple subprocesses to avoid some of the GIL's disadvantages."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Sections"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "- [An introduction to parallel programming using Python's `multiprocessing` module](#An-introduction-to-parallel-programming-using-Python's-`multiprocessing`-module)\n",
-      "    - [Multi-Threading vs. Multi-Processing](#Multi-Threading-vs.-Multi-Processing)\n",
-      "- [Introduction to the `multiprocessing` module](#Introduction-to-the-multiprocessing-module)\n",
-      "    - [The `Process` class](#The-Process-class)\n",
-      "        - [How to retrieve results in a particular order](#How-to-retrieve-results-in-a-particular-order)\n",
-      "    - [The `Pool` class](#The-Pool-class)\n",
-      "- [Kernel density estimation as benchmarking function](#Kernel-density-estimation-as-benchmarking-function)\n",
-      "    - [The Parzen-window method in a nutshell](#The-Parzen-window-method-in-a-nutshell)\n",
-      "    - [Sample data and `timeit` benchmarks](#Sample-data-and-timeit-benchmarks)\n",
-      "    - [Benchmarking functions](#Benchmarking-functions)\n",
-      "    - [Preparing the plotting of the results](#Preparing-the-plotting-of-the-results)\n",
-      "- [Results](#Results)\n",
-      "- [Conclusion](#Conclusion)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "\n",
-      "Multi-Threading vs. Multi-Processing\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Depending on the application, two common approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-tasks\" of a single process and those threads will usually have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
-      "\n",
-      "A safer approach (although it comes with an additional overhead due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n",
-      "\n",
-      "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/multiprocessing_scheme.png)"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Introduction to the `multiprocessing` module"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The [multiprocessing](https://docs.python.org/dev/library/multiprocessing.html) module in Python's Standard Library has a lot of powerful features. If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the [official documentation](https://docs.python.org/dev/library/multiprocessing.html) as an entry point.  \n",
-      "\n",
-      "In the following sections, I want to provide a brief overview of different approaches to show how the `multiprocessing` module can be used for parallel programming."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "The `Process` class"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The most basic approach is probably to use the `Process` class from the `multiprocessing` module.  \n",
-      "Here, we will use a simple queue function to compute the cubes for the 6 numbers 1, 2, 3, 4, 5, and 6 in 6 parallel processes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import multiprocessing as mp\n",
-      "import random\n",
-      "import string\n",
-      "\n",
-      "random.seed(123)\n",
-      "\n",
-      "# Define an output queue\n",
-      "output = mp.Queue()\n",
-      "\n",
-      "# define a example function\n",
-      "def rand_string(length, output):\n",
-      "    \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n",
-      "    rand_str = ''.join(random.choice(\n",
-      "                        string.ascii_lowercase \n",
-      "                        + string.ascii_uppercase \n",
-      "                        + string.digits)\n",
-      "                   for i in range(length))\n",
-      "    output.put(rand_str)\n",
-      "\n",
-      "# Setup a list of processes that we want to run\n",
-      "processes = [mp.Process(target=rand_string, args=(5, output)) for x in range(4)]\n",
-      "\n",
-      "# Run processes\n",
-      "for p in processes:\n",
-      "    p.start()\n",
-      "\n",
-      "# Exit the completed processes\n",
-      "for p in processes:\n",
-      "    p.join()\n",
-      "\n",
-      "# Get process results from the output queue\n",
-      "results = [output.get() for p in processes]\n",
-      "\n",
-      "print(results)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['BJWNs', 'GOK0H', '7CTRJ', 'THDF3']\n"
-       ]
-      }
-     ],
-     "prompt_number": 2
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "How to retrieve results in a particular order "
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The order of the obtained results does not necessarily have to match the order of the processes (in the `processes` list). Since we eventually use the `.get()` method to retrieve the results from the `Queue` sequentially, the order in which the processes finished determines the order of our results.  \n",
-      "E.g., if the second process has finished just before the first process, the order of the strings in the `results` list could have also been\n",
-      "`['PQpqM', 'yzQfA', 'SHZYV', 'PSNkD']` instead of `['yzQfA', 'PQpqM', 'SHZYV', 'PSNkD']`\n",
-      "\n",
-      "If our application required us to retrieve results in a particular order, one possibility would be to refer to the processes' `._identity` attribute. In this case, we could also simply use the values from our `range` object as position argument. The modified code would be:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "# Define an output queue\n",
-      "output = mp.Queue()\n",
-      "\n",
-      "# define a example function\n",
-      "def rand_string(length, pos, output):\n",
-      "    \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n",
-      "    rand_str = ''.join(random.choice(\n",
-      "                        string.ascii_lowercase \n",
-      "                        + string.ascii_uppercase \n",
-      "                        + string.digits)\n",
-      "                   for i in range(length))\n",
-      "    output.put((pos, rand_str))\n",
-      "\n",
-      "# Setup a list of processes that we want to run\n",
-      "processes = [mp.Process(target=rand_string, args=(5, x, output)) for x in range(4)]\n",
-      "\n",
-      "# Run processes\n",
-      "for p in processes:\n",
-      "    p.start()\n",
-      "\n",
-      "# Exit the completed processes\n",
-      "for p in processes:\n",
-      "    p.join()\n",
-      "\n",
-      "# Get process results from the output queue\n",
-      "results = [output.get() for p in processes]\n",
-      "\n",
-      "print(results)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[(0, 'h5hoV'), (1, 'fvdmN'), (2, 'rxGX4'), (3, '8hDJj')]\n"
-       ]
-      }
-     ],
-     "prompt_number": 3
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "And the retrieved results would be tuples, for example, `[(0, 'KAQo6'), (1, '5lUya'), (2, 'nj6Q0'), (3, 'QQvLr')]`   \n",
-      "or `[(1, '5lUya'), (3, 'QQvLr'), (0, 'KAQo6'), (2, 'nj6Q0')]`\n",
-      "\n",
-      "To make sure that we retrieved the results in order, we could simply sort the results and optionally get rid of the position argument:"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "results.sort()\n",
-      "results = [r[1] for r in results]\n",
-      "print(results)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "['h5hoV', 'fvdmN', 'rxGX4', '8hDJj']\n"
-       ]
-      }
-     ],
-     "prompt_number": 4
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "**A simpler way to to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "The `Pool` class"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Another and more convenient approach for simple parallel processing tasks is provided by the `Pool` class.  \n",
-      "\n",
-      "There are four methods that are particularly interesing:\n",
-      "\n",
-      "    - Pool.apply\n",
-      "    \n",
-      "    - Pool.map\n",
-      "    \n",
-      "    - Pool.apply_async\n",
-      "    \n",
-      "    - Pool.map_async\n",
-      "    \n",
-      "The `Pool.apply` and `Pool.map` methods are basically equivalents to Python's in-built [`apply`](https://docs.python.org/2/library/functions.html#apply) and [`map`](https://docs.python.org/2/library/functions.html#map) functions."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Before we come to the `async` variants of the `Pool` methods, let us take a look at a simple example using `Pool.apply` and `Pool.map`. Here, we will set the number of processes to 4, which means that the `Pool` class will only allow 4 processes running at the same time."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def cube(x):\n",
-      "    return x**3"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pool = mp.Pool(processes=4)\n",
-      "results = [pool.apply(cube, args=(x,)) for x in range(1,7)]\n",
-      "print(results)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 8, 27, 64, 125, 216]\n"
-       ]
-      }
-     ],
-     "prompt_number": 6
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pool = mp.Pool(processes=4)\n",
-      "results = pool.map(cube, range(1,7))\n",
-      "print(results)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 8, 27, 64, 125, 216]\n"
-       ]
-      }
-     ],
-     "prompt_number": 7
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The `Pool.map` and `Pool.apply` will lock the main program until all a process is finished, which is quite useful if we want to obtain resuls in a particular order for certain applications.   \n",
-      "In contrast, the `async` variants will submit all processes at once and retrieve the results as soon as they are finished. \n",
-      "One more difference is that we need to use the `get` method after the `apply_async()` call in order to obtain the `return` values of the finished processes."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "pool = mp.Pool(processes=4)\n",
-      "results = [pool.apply_async(cube, args=(x,)) for x in range(1,7)]\n",
-      "output = [p.get() for p in results]\n",
-      "print(output)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "[1, 8, 27, 64, 125, 216]\n"
-       ]
-      }
-     ],
-     "prompt_number": 8
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Kernel density estimation as benchmarking function"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the following approach, I want to do a simple comparison of a serial vs. multiprocessing approach where I will use a slightly more complex function than the `cube` example, which he have been using above.  \n",
-      "\n",
-      "Here, I define a function for performing a Kernel density estimation for probability density functions using the Parzen-window technique.  \n",
-      "I don't want to go into much detail about the theory of this technique, since we are mostly interested to see how `multiprocessing` can be used for performance improvements, but you are welcome to read my more detailed article about the [Parzen-window method here](http://sebastianraschka.com/Articles/2014_parzen_density_est.html). "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "\n",
-      "def parzen_estimation(x_samples, point_x, h):\n",
-      "    \"\"\"\n",
-      "    Implementation of a hypercube kernel for Parzen-window estimation.\n",
-      "\n",
-      "    Keyword arguments:\n",
-      "        x_sample:training sample, 'd x 1'-dimensional numpy array\n",
-      "        x: point x for density estimation, 'd x 1'-dimensional numpy array\n",
-      "        h: window width\n",
-      "        \n",
-      "    Returns the predicted pdf as float.\n",
-      "\n",
-      "    \"\"\"\n",
-      "    k_n = 0\n",
-      "    for row in x_samples:\n",
-      "        x_i = (point_x - row[:,np.newaxis]) / (h)\n",
-      "        for row in x_i:\n",
-      "            if np.abs(row) > (1/2):\n",
-      "                break\n",
-      "        else: # \"completion-else\"*\n",
-      "            k_n += 1\n",
-      "    return (k_n / len(x_samples)) / (h**point_x.shape[1])"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 9
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<hr>\n",
-      "**A quick note about the \"completion else**\n",
-      "\n",
-      "Sometimes I receive comments about whether I used this for-else combination intentionally or if it happened by mistake. That is a legitimate question, since this \"completion-else\" is rarely used (that's what I call it, I am not aware if there is an \"official\" name for this, if so, please let me know).  \n",
-      "I have a more detailed explanation [here](http://sebastianraschka.com/Articles/2014_deep_python.html#else_clauses) in one of my blog-posts, but in a nutshell: In contrast to a conditional else (in combination with if-statements), the \"completion else\" is only executed if the preceding code block (here the `for`-loop) has finished.\n",
-      "<hr>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 3,
-     "metadata": {},
-     "source": [
-      "The Parzen-window method in a nutshell"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "So what this function does in a nutshell: It counts points in a defined region (the so-called window), and divides the number of those points inside by the number of total points to estimate the probability of a single point being in a certain region.\n",
-      "\n",
-      "Below is a simple example where our window is represented by a hypercube centered at the origin, and we want to get an estimate of the probability for a point being in the center of the plot based on the hypercube."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "%matplotlib inline"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from mpl_toolkits.mplot3d import Axes3D\n",
-      "import matplotlib.pyplot as plt\n",
-      "import numpy as np\n",
-      "from itertools import product, combinations\n",
-      "fig = plt.figure(figsize=(7,7))\n",
-      "ax = fig.gca(projection='3d')\n",
-      "ax.set_aspect(\"equal\")\n",
-      "\n",
-      "# Plot Points\n",
-      "\n",
-      "# samples within the cube\n",
-      "X_inside = np.array([[0,0,0],[0.2,0.2,0.2],[0.1, -0.1, -0.3]])\n",
-      "\n",
-      "X_outside = np.array([[-1.2,0.3,-0.3],[0.8,-0.82,-0.9],[1, 0.6, -0.7],\n",
-      "                      [0.8,0.7,0.2],[0.7,-0.8,-0.45],[-0.3, 0.6, 0.9],\n",
-      "                      [0.7,-0.6,-0.8]])\n",
-      "\n",
-      "for row in X_inside:\n",
-      "    ax.scatter(row[0], row[1], row[2], color=\"r\", s=50, marker='^')\n",
-      "\n",
-      "for row in X_outside:    \n",
-      "    ax.scatter(row[0], row[1], row[2], color=\"k\", s=50)\n",
-      "\n",
-      "# Plot Cube\n",
-      "h = [-0.5, 0.5]\n",
-      "for s, e in combinations(np.array(list(product(h,h,h))), 2):\n",
-      "    if np.sum(np.abs(s-e)) == h[1]-h[0]:\n",
-      "        ax.plot3D(*zip(s,e), color=\"g\")\n",
-      "        \n",
-      "ax.set_xlim(-1.5, 1.5)\n",
-      "ax.set_ylim(-1.5, 1.5)\n",
-      "ax.set_zlim(-1.5, 1.5)\n",
-      "\n",
-      "plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAGUCAYAAAASxdSgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10905d0f0>"
-       ]
-      }
-     ],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "point_x = np.array([[0],[0],[0]])\n",
-      "X_all = np.vstack((X_inside,X_outside))\n",
-      "\n",
-      "print('p(x) =', parzen_estimation(X_all, point_x, h=1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "p(x) = 0.3\n"
-       ]
-      }
-     ],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Sample data and `timeit` benchmarks"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "In the section below, we will create a random dataset from a bivariate Gaussian distribution with a mean vector centered at the origin and a identity matrix as covariance matrix. "
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import numpy as np\n",
-      "\n",
-      "np.random.seed(123)\n",
-      "\n",
-      "# Generate random 2D-patterns\n",
-      "mu_vec = np.array([0,0])\n",
-      "cov_mat = np.array([[1,0],[0,1]])\n",
-      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 13
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The expected probability of a point at the center of the distribution is ~ 0.15915 as we can see below.  \n",
-      "And our goal is here to use the Parzen-window approach to predict this density based on the sample data set that we have created above.  \n",
-      "\n",
-      "\n",
-      "In order to make a \"good\" prediction via the Parzen-window technique, it is - among other things - crucial to select an appropriate window with. Here, we will use multiple processes to predict the density at the center of the bivariate Gaussian distribution using different window widths."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from scipy.stats import multivariate_normal\n",
-      "var = multivariate_normal(mean=[0,0], cov=[[1,0],[0,1]])\n",
-      "print('actual probability density:', var.pdf([0,0]))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "actual probability density: 0.159154943092\n"
-       ]
-      }
-     ],
-     "prompt_number": 14
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Sebastian Raschka](http://sebastianraschka.com)  \n",
+    "\n",
+    "- [Open in IPython nbviewer](http://nbviewer.ipython.org/github/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb?create=1)  \n",
+    "\n",
+    "- [Link to this IPython notebook on Github](https://github.com/rasbt/python_reference/blob/master/tutorials/multiprocessing_intro.ipynb)  \n",
+    "\n",
+    "- [Link to the GitHub Repository python_reference](https://github.com/rasbt/python_reference)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last updated: 20/06/2014\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Benchmarking functions"
+    }
+   ],
+   "source": [
+    "import time\n",
+    "print('Last updated: %s' %time.strftime('%d/%m/%Y'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "I would be happy to hear your comments and suggestions.  \n",
+    "Please feel free to drop me a note via\n",
+    "[twitter](https://twitter.com/rasbt), [email](mailto:bluewoodtree@gmail.com), or [google+](https://plus.google.com/+SebastianRaschka).\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parallel processing via the `multiprocessing` module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "CPUs with multiple cores have become the standard in the recent development of modern computer architectures and we can not only find them in supercomputer facilities but also in our desktop machines at home, and our laptops; even Apple's iPhone 5S got a 1.3 Ghz Dual-core processor in 2013.\n",
+    "\n",
+    "However, the default Python interpreter was designed with simplicity in mind and has a thread-safe mechanism, the so-called \"GIL\" (Global Interpreter Lock). In order to prevent conflicts between threads, it executes only one statement at a time (so-called serial processing, or single-threading).\n",
+    "\n",
+    "In this introduction to Python's `multiprocessing` module, we will see how we can spawn multiple subprocesses to avoid some of the GIL's disadvantages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- [An introduction to parallel programming using Python's `multiprocessing` module](#An-introduction-to-parallel-programming-using-Python's-`multiprocessing`-module)\n",
+    "    - [Multi-Threading vs. Multi-Processing](#Multi-Threading-vs.-Multi-Processing)\n",
+    "- [Introduction to the `multiprocessing` module](#Introduction-to-the-multiprocessing-module)\n",
+    "    - [The `Process` class](#The-Process-class)\n",
+    "        - [How to retrieve results in a particular order](#How-to-retrieve-results-in-a-particular-order)\n",
+    "    - [The `Pool` class](#The-Pool-class)\n",
+    "- [Kernel density estimation as benchmarking function](#Kernel-density-estimation-as-benchmarking-function)\n",
+    "    - [The Parzen-window method in a nutshell](#The-Parzen-window-method-in-a-nutshell)\n",
+    "    - [Sample data and `timeit` benchmarks](#Sample-data-and-timeit-benchmarks)\n",
+    "    - [Benchmarking functions](#Benchmarking-functions)\n",
+    "    - [Preparing the plotting of the results](#Preparing-the-plotting-of-the-results)\n",
+    "- [Results](#Results)\n",
+    "- [Conclusion](#Conclusion)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###  Multi-Threading vs. Multi-Processing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Depending on the application, two common approaches in parallel programming are either to run code via threads or multiple processes, respectively. If we submit \"jobs\" to different threads, those jobs can be pictured as \"sub-tasks\" of a single process and those threads will usually have access to the same memory areas (i.e., shared memory). This approach can easily lead to conflicts in case of improper  synchronization, for example, if processes are writing to the same memory location at the same time.  \n",
+    "\n",
+    "A safer approach (although it comes with an additional overhead due to the communication overhead between separate processes) is to submit multiple processes to completely separate memory locations (i.e., distributed memory): Every process will run completely independent from each other.\n",
+    "\n",
+    "Here, we will take a look at Python's [`multiprocessing`](https://docs.python.org/dev/library/multiprocessing.html) module and how we can use it to submit multiple processes that can run independently from each other in order to make best use of our CPU cores."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://raw.githubusercontent.com/rasbt/python_reference/master/Images/multiprocessing_scheme.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to the `multiprocessing` module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [multiprocessing](https://docs.python.org/dev/library/multiprocessing.html) module in Python's Standard Library has a lot of powerful features. If you want to read about all the nitty-gritty tips, tricks, and details, I would recommend to use the [official documentation](https://docs.python.org/dev/library/multiprocessing.html) as an entry point.  \n",
+    "\n",
+    "In the following sections, I want to provide a brief overview of different approaches to show how the `multiprocessing` module can be used for parallel programming."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Process` class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The most basic approach is probably to use the `Process` class from the `multiprocessing` module.  \n",
+    "Here, we will use a simple queue function to generate four random strings in parallel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['BJWNs', 'GOK0H', '7CTRJ', 'THDF3']\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
+    }
+   ],
+   "source": [
+    "import multiprocessing as mp\n",
+    "import random\n",
+    "import string\n",
+    "\n",
+    "random.seed(123)\n",
+    "\n",
+    "# Define an output queue\n",
+    "output = mp.Queue()\n",
+    "\n",
+    "# define a example function\n",
+    "def rand_string(length, output):\n",
+    "    \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n",
+    "    rand_str = ''.join(random.choice(\n",
+    "                        string.ascii_lowercase \n",
+    "                        + string.ascii_uppercase \n",
+    "                        + string.digits)\n",
+    "                   for i in range(length))\n",
+    "    output.put(rand_str)\n",
+    "\n",
+    "# Setup a list of processes that we want to run\n",
+    "processes = [mp.Process(target=rand_string, args=(5, output)) for x in range(4)]\n",
+    "\n",
+    "# Run processes\n",
+    "for p in processes:\n",
+    "    p.start()\n",
+    "\n",
+    "# Exit the completed processes\n",
+    "for p in processes:\n",
+    "    p.join()\n",
+    "\n",
+    "# Get process results from the output queue\n",
+    "results = [output.get() for p in processes]\n",
+    "\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### How to retrieve results in a particular order "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The order of the obtained results does not necessarily have to match the order of the processes (in the `processes` list). Since we eventually use the `.get()` method to retrieve the results from the `Queue` sequentially, the order in which the processes finished determines the order of our results.  \n",
+    "E.g., if the second process has finished just before the first process, the order of the strings in the `results` list could have also been\n",
+    "`['PQpqM', 'yzQfA', 'SHZYV', 'PSNkD']` instead of `['yzQfA', 'PQpqM', 'SHZYV', 'PSNkD']`\n",
+    "\n",
+    "If our application required us to retrieve results in a particular order, one possibility would be to refer to the processes' `._identity` attribute. In this case, we could also simply use the values from our `range` object as position argument. The modified code would be:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(0, 'h5hoV'), (1, 'fvdmN'), (2, 'rxGX4'), (3, '8hDJj')]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Below, we will set up benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function.  \n",
-      "We will be using the `Pool.apply_async` function to take advantage of firing up processes simultaneously: Here, we don't care about the order in which the results for the different window widths are computed, we just need to associate each result with the input window width.  \n",
-      "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to to sort our list of results later."
+    }
+   ],
+   "source": [
+    "# Define an output queue\n",
+    "output = mp.Queue()\n",
+    "\n",
+    "# define a example function\n",
+    "def rand_string(length, pos, output):\n",
+    "    \"\"\" Generates a random string of numbers, lower- and uppercase chars. \"\"\"\n",
+    "    rand_str = ''.join(random.choice(\n",
+    "                        string.ascii_lowercase \n",
+    "                        + string.ascii_uppercase \n",
+    "                        + string.digits)\n",
+    "                   for i in range(length))\n",
+    "    output.put((pos, rand_str))\n",
+    "\n",
+    "# Setup a list of processes that we want to run\n",
+    "processes = [mp.Process(target=rand_string, args=(5, x, output)) for x in range(4)]\n",
+    "\n",
+    "# Run processes\n",
+    "for p in processes:\n",
+    "    p.start()\n",
+    "\n",
+    "# Exit the completed processes\n",
+    "for p in processes:\n",
+    "    p.join()\n",
+    "\n",
+    "# Get process results from the output queue\n",
+    "results = [output.get() for p in processes]\n",
+    "\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And the retrieved results would be tuples, for example, `[(0, 'KAQo6'), (1, '5lUya'), (2, 'nj6Q0'), (3, 'QQvLr')]`   \n",
+    "or `[(1, '5lUya'), (3, 'QQvLr'), (0, 'KAQo6'), (2, 'nj6Q0')]`\n",
+    "\n",
+    "To make sure that we retrieved the results in order, we could simply sort the results and optionally get rid of the position argument:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['h5hoV', 'fvdmN', 'rxGX4', '8hDJj']\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def parzen_estimation(x_samples, point_x, h):\n",
-      "    k_n = 0\n",
-      "    for row in x_samples:\n",
-      "        x_i = (point_x - row[:,np.newaxis]) / (h)\n",
-      "        for row in x_i:\n",
-      "            if np.abs(row) > (1/2):\n",
-      "                break\n",
-      "        else: # \"completion-else\"*\n",
-      "            k_n += 1\n",
-      "    return (h, (k_n / len(x_samples)) / (h**point_x.shape[1]))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 15
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def serial(samples, x, widths):\n",
-      "    return [parzen_estimation(samples, x, w) for w in widths]\n",
-      "\n",
-      "def multiprocess(processes, samples, x, widths):\n",
-      "    pool = mp.Pool(processes=processes)\n",
-      "    results = [pool.apply_async(parzen_estimation, args=(samples, x, w)) for w in widths]\n",
-      "    results = [p.get() for p in results]\n",
-      "    results.sort() # to sort the results by input window width\n",
-      "    return results"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 16
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Just to get an idea what the results would look like (i.e., the predicted densities for different window widths):"
+    }
+   ],
+   "source": [
+    "results.sort()\n",
+    "results = [r[1] for r in results]\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**A simpler way to to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Pool` class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another and more convenient approach for simple parallel processing tasks is provided by the `Pool` class.  \n",
+    "\n",
+    "There are four methods that are particularly interesing:\n",
+    "\n",
+    "    - Pool.apply\n",
+    "    \n",
+    "    - Pool.map\n",
+    "    \n",
+    "    - Pool.apply_async\n",
+    "    \n",
+    "    - Pool.map_async\n",
+    "    \n",
+    "The `Pool.apply` and `Pool.map` methods are basically equivalents to Python's in-built [`apply`](https://docs.python.org/2/library/functions.html#apply) and [`map`](https://docs.python.org/2/library/functions.html#map) functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we come to the `async` variants of the `Pool` methods, let us take a look at a simple example using `Pool.apply` and `Pool.map`. Here, we will set the number of processes to 4, which means that the `Pool` class will only allow 4 processes running at the same time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cube(x):\n",
+    "    return x**3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 8, 27, 64, 125, 216]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widths = np.arange(0.1, 1.3, 0.1)\n",
-      "point_x = np.array([[0],[0]])\n",
-      "results = []\n",
-      "\n",
-      "results = multiprocess(4, x_2Dgauss, point_x, widths)\n",
-      "\n",
-      "for r in results:\n",
-      "    print('h = %s, p(x) = %s' %(r[0], r[1]))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "h = 0.1, p(x) = 0.016\n",
-        "h = 0.2, p(x) = 0.0305\n",
-        "h = 0.3, p(x) = 0.045\n",
-        "h = 0.4, p(x) = 0.06175\n",
-        "h = 0.5, p(x) = 0.078\n",
-        "h = 0.6, p(x) = 0.0911666666667\n",
-        "h = 0.7, p(x) = 0.106\n",
-        "h = 0.8, p(x) = 0.117375\n",
-        "h = 0.9, p(x) = 0.132666666667\n",
-        "h = 1.0, p(x) = 0.1445\n",
-        "h = 1.1, p(x) = 0.157090909091\n",
-        "h = 1.2, p(x) = 0.1685\n"
-       ]
-      }
-     ],
-     "prompt_number": 17
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Based on the results, we can say that the best window-width would be h=1.1, since the estimated result is close to the actual result ~0.15915.  \n",
-      "Thus, for the benchmark, let us create 100 evenly spaced window width in the range of 1.0 to 1.2."
+    }
+   ],
+   "source": [
+    "pool = mp.Pool(processes=4)\n",
+    "results = [pool.apply(cube, args=(x,)) for x in range(1,7)]\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 8, 27, 64, 125, 216]\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "pool = mp.Pool(processes=4)\n",
+    "results = pool.map(cube, range(1,7))\n",
+    "print(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `Pool.map` and `Pool.apply` will lock the main program until all a process is finished, which is quite useful if we want to obtain resuls in a particular order for certain applications.   \n",
+    "In contrast, the `async` variants will submit all processes at once and retrieve the results as soon as they are finished. \n",
+    "One more difference is that we need to use the `get` method after the `apply_async()` call in order to obtain the `return` values of the finished processes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 8, 27, 64, 125, 216]\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "widths = np.linspace(1.0, 1.2, 100)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 18
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import timeit\n",
-      "\n",
-      "mu_vec = np.array([0,0])\n",
-      "cov_mat = np.array([[1,0],[0,1]])\n",
-      "n = 10000\n",
-      "\n",
-      "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, n)\n",
-      "\n",
-      "benchmarks = []\n",
-      "\n",
-      "benchmarks.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import serial, x_2Dgauss, point_x, widths').timeit(number=1))\n",
-      "\n",
-      "benchmarks.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
-      "\n",
-      "benchmarks.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
-      "\n",
-      "benchmarks.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
-      "\n",
-      "benchmarks.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n",
-      "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 19
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
+    }
+   ],
+   "source": [
+    "pool = mp.Pool(processes=4)\n",
+    "results = [pool.apply_async(cube, args=(x,)) for x in range(1,7)]\n",
+    "output = [p.get() for p in results]\n",
+    "print(output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Kernel density estimation as benchmarking function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the following approach, I want to do a simple comparison of a serial vs. multiprocessing approach where I will use a slightly more complex function than the `cube` example, which he have been using above.  \n",
+    "\n",
+    "Here, I define a function for performing a Kernel density estimation for probability density functions using the Parzen-window technique.  \n",
+    "I don't want to go into much detail about the theory of this technique, since we are mostly interested to see how `multiprocessing` can be used for performance improvements, but you are welcome to read my more detailed article about the [Parzen-window method here](http://sebastianraschka.com/Articles/2014_parzen_density_est.html). "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def parzen_estimation(x_samples, point_x, h):\n",
+    "    \"\"\"\n",
+    "    Implementation of a hypercube kernel for Parzen-window estimation.\n",
+    "\n",
+    "    Keyword arguments:\n",
+    "        x_sample:training sample, 'd x 1'-dimensional numpy array\n",
+    "        x: point x for density estimation, 'd x 1'-dimensional numpy array\n",
+    "        h: window width\n",
+    "        \n",
+    "    Returns the predicted pdf as float.\n",
+    "\n",
+    "    \"\"\"\n",
+    "    k_n = 0\n",
+    "    for row in x_samples:\n",
+    "        x_i = (point_x - row[:,np.newaxis]) / (h)\n",
+    "        for row in x_i:\n",
+    "            if np.abs(row) > (1/2):\n",
+    "                break\n",
+    "        else: # \"completion-else\"*\n",
+    "            k_n += 1\n",
+    "    return (k_n / len(x_samples)) / (h**point_x.shape[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n",
+    "**A quick note about the \"completion else**\n",
+    "\n",
+    "Sometimes I receive comments about whether I used this for-else combination intentionally or if it happened by mistake. That is a legitimate question, since this \"completion-else\" is rarely used (that's what I call it, I am not aware if there is an \"official\" name for this, if so, please let me know).  \n",
+    "I have a more detailed explanation [here](http://sebastianraschka.com/Articles/2014_deep_python.html#else_clauses) in one of my blog-posts, but in a nutshell: In contrast to a conditional else (in combination with if-statements), the \"completion else\" is only executed if the preceding code block (here the `for`-loop) has finished.\n",
+    "<hr>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The Parzen-window method in a nutshell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So what this function does in a nutshell: It counts points in a defined region (the so-called window), and divides the number of those points inside by the number of total points to estimate the probability of a single point being in a certain region.\n",
+    "\n",
+    "Below is a simple example where our window is represented by a hypercube centered at the origin, and we want to get an estimate of the probability for a point being in the center of the plot based on the hypercube."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAGUCAYAAAASxdSgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10905d0f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from itertools import product, combinations\n",
+    "fig = plt.figure(figsize=(7,7))\n",
+    "ax = fig.gca(projection='3d')\n",
+    "ax.set_aspect(\"equal\")\n",
+    "\n",
+    "# Plot Points\n",
+    "\n",
+    "# samples within the cube\n",
+    "X_inside = np.array([[0,0,0],[0.2,0.2,0.2],[0.1, -0.1, -0.3]])\n",
+    "\n",
+    "X_outside = np.array([[-1.2,0.3,-0.3],[0.8,-0.82,-0.9],[1, 0.6, -0.7],\n",
+    "                      [0.8,0.7,0.2],[0.7,-0.8,-0.45],[-0.3, 0.6, 0.9],\n",
+    "                      [0.7,-0.6,-0.8]])\n",
+    "\n",
+    "for row in X_inside:\n",
+    "    ax.scatter(row[0], row[1], row[2], color=\"r\", s=50, marker='^')\n",
+    "\n",
+    "for row in X_outside:    \n",
+    "    ax.scatter(row[0], row[1], row[2], color=\"k\", s=50)\n",
+    "\n",
+    "# Plot Cube\n",
+    "h = [-0.5, 0.5]\n",
+    "for s, e in combinations(np.array(list(product(h,h,h))), 2):\n",
+    "    if np.sum(np.abs(s-e)) == h[1]-h[0]:\n",
+    "        ax.plot3D(*zip(s,e), color=\"g\")\n",
+    "        \n",
+    "ax.set_xlim(-1.5, 1.5)\n",
+    "ax.set_ylim(-1.5, 1.5)\n",
+    "ax.set_zlim(-1.5, 1.5)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "p(x) = 0.3\n"
      ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Preparing the plotting of the results"
+    }
+   ],
+   "source": [
+    "point_x = np.array([[0],[0],[0]])\n",
+    "X_all = np.vstack((X_inside,X_outside))\n",
+    "\n",
+    "print('p(x) =', parzen_estimation(X_all, point_x, h=1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample data and `timeit` benchmarks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the section below, we will create a random dataset from a bivariate Gaussian distribution with a mean vector centered at the origin and a identity matrix as covariance matrix. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "np.random.seed(123)\n",
+    "\n",
+    "# Generate random 2D-patterns\n",
+    "mu_vec = np.array([0,0])\n",
+    "cov_mat = np.array([[1,0],[0,1]])\n",
+    "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, 10000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The expected probability of a point at the center of the distribution is ~ 0.15915 as we can see below.  \n",
+    "And our goal is here to use the Parzen-window approach to predict this density based on the sample data set that we have created above.  \n",
+    "\n",
+    "\n",
+    "In order to make a \"good\" prediction via the Parzen-window technique, it is - among other things - crucial to select an appropriate window with. Here, we will use multiple processes to predict the density at the center of the bivariate Gaussian distribution using different window widths."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "actual probability density: 0.159154943092\n"
      ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
+    }
+   ],
+   "source": [
+    "from scipy.stats import multivariate_normal\n",
+    "var = multivariate_normal(mean=[0,0], cov=[[1,0],[0,1]])\n",
+    "print('actual probability density:', var.pdf([0,0]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Benchmarking functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below, we will set up benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function.  \n",
+    "We will be using the `Pool.apply_async` function to take advantage of firing up processes simultaneously: Here, we don't care about the order in which the results for the different window widths are computed, we just need to associate each result with the input window width.  \n",
+    "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to to sort our list of results later."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parzen_estimation(x_samples, point_x, h):\n",
+    "    k_n = 0\n",
+    "    for row in x_samples:\n",
+    "        x_i = (point_x - row[:,np.newaxis]) / (h)\n",
+    "        for row in x_i:\n",
+    "            if np.abs(row) > (1/2):\n",
+    "                break\n",
+    "        else: # \"completion-else\"*\n",
+    "            k_n += 1\n",
+    "    return (h, (k_n / len(x_samples)) / (h**point_x.shape[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def serial(samples, x, widths):\n",
+    "    return [parzen_estimation(samples, x, w) for w in widths]\n",
+    "\n",
+    "def multiprocess(processes, samples, x, widths):\n",
+    "    pool = mp.Pool(processes=processes)\n",
+    "    results = [pool.apply_async(parzen_estimation, args=(samples, x, w)) for w in widths]\n",
+    "    results = [p.get() for p in results]\n",
+    "    results.sort() # to sort the results by input window width\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just to get an idea what the results would look like (i.e., the predicted densities for different window widths):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "h = 0.1, p(x) = 0.016\n",
+      "h = 0.2, p(x) = 0.0305\n",
+      "h = 0.3, p(x) = 0.045\n",
+      "h = 0.4, p(x) = 0.06175\n",
+      "h = 0.5, p(x) = 0.078\n",
+      "h = 0.6, p(x) = 0.0911666666667\n",
+      "h = 0.7, p(x) = 0.106\n",
+      "h = 0.8, p(x) = 0.117375\n",
+      "h = 0.9, p(x) = 0.132666666667\n",
+      "h = 1.0, p(x) = 0.1445\n",
+      "h = 1.1, p(x) = 0.157090909091\n",
+      "h = 1.2, p(x) = 0.1685\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "widths = np.arange(0.1, 1.3, 0.1)\n",
+    "point_x = np.array([[0],[0]])\n",
+    "results = []\n",
+    "\n",
+    "results = multiprocess(4, x_2Dgauss, point_x, widths)\n",
+    "\n",
+    "for r in results:\n",
+    "    print('h = %s, p(x) = %s' %(r[0], r[1]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Based on the results, we can say that the best window-width would be h=1.1, since the estimated result is close to the actual result ~0.15915.  \n",
+    "Thus, for the benchmark, let us create 100 evenly spaced window width in the range of 1.0 to 1.2."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "widths = np.linspace(1.0, 1.2, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import timeit\n",
+    "\n",
+    "mu_vec = np.array([0,0])\n",
+    "cov_mat = np.array([[1,0],[0,1]])\n",
+    "n = 10000\n",
+    "\n",
+    "x_2Dgauss = np.random.multivariate_normal(mu_vec, cov_mat, n)\n",
+    "\n",
+    "benchmarks = []\n",
+    "\n",
+    "benchmarks.append(timeit.Timer('serial(x_2Dgauss, point_x, widths)', \n",
+    "            'from __main__ import serial, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+    "\n",
+    "benchmarks.append(timeit.Timer('multiprocess(2, x_2Dgauss, point_x, widths)', \n",
+    "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+    "\n",
+    "benchmarks.append(timeit.Timer('multiprocess(3, x_2Dgauss, point_x, widths)', \n",
+    "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+    "\n",
+    "benchmarks.append(timeit.Timer('multiprocess(4, x_2Dgauss, point_x, widths)', \n",
+    "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))\n",
+    "\n",
+    "benchmarks.append(timeit.Timer('multiprocess(6, x_2Dgauss, point_x, widths)', \n",
+    "            'from __main__ import multiprocess, x_2Dgauss, point_x, widths').timeit(number=1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Preparing the plotting of the results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import platform\n",
+    "\n",
+    "def print_sysinfo():\n",
+    "    \n",
+    "    print('\\nPython version  :', platform.python_version())\n",
+    "    print('compiler        :', platform.python_compiler())\n",
+    "    \n",
+    "    print('\\nsystem     :', platform.system())\n",
+    "    print('release    :', platform.release())\n",
+    "    print('machine    :', platform.machine())\n",
+    "    print('processor  :', platform.processor())\n",
+    "    print('CPU count  :', mp.cpu_count())\n",
+    "    print('interpreter:', platform.architecture()[0])\n",
+    "    print('\\n\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "def plot_results():\n",
+    "    bar_labels = ['serial', '2', '3', '4', '6']\n",
+    "\n",
+    "    fig = plt.figure(figsize=(10,8))\n",
+    "\n",
+    "    # plot bars\n",
+    "    y_pos = np.arange(len(benchmarks))\n",
+    "    plt.yticks(y_pos, bar_labels, fontsize=16)\n",
+    "    bars = plt.barh(y_pos, benchmarks,\n",
+    "             align='center', alpha=0.4, color='g')\n",
+    "\n",
+    "    # annotation and labels\n",
+    "    \n",
+    "    for ba,be in zip(bars, benchmarks):\n",
+    "        plt.text(ba.get_width() + 2, ba.get_y() + ba.get_height()/2,\n",
+    "                '{0:.2%}'.format(benchmarks[0]/be), \n",
+    "                ha='center', va='bottom', fontsize=12)\n",
+    "    \n",
+    "    plt.xlabel('time in seconds for n=%s' %n, fontsize=14)\n",
+    "    plt.ylabel('number of processes', fontsize=14)\n",
+    "    t = plt.title('Serial vs. Multiprocessing via Parzen-window estimation', fontsize=18)\n",
+    "    plt.ylim([-1,len(benchmarks)+0.5])\n",
+    "    plt.xlim([0,max(benchmarks)*1.1])\n",
+    "    plt.vlines(benchmarks[0], -1, len(benchmarks)+0.5, linestyles='dashed')\n",
+    "    plt.grid()\n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "import platform\n",
-      "\n",
-      "def print_sysinfo():\n",
-      "    \n",
-      "    print('\\nPython version  :', platform.python_version())\n",
-      "    print('compiler        :', platform.python_compiler())\n",
-      "    \n",
-      "    print('\\nsystem     :', platform.system())\n",
-      "    print('release    :', platform.release())\n",
-      "    print('machine    :', platform.machine())\n",
-      "    print('processor  :', platform.processor())\n",
-      "    print('CPU count  :', mp.cpu_count())\n",
-      "    print('interpreter:', platform.architecture()[0])\n",
-      "    print('\\n\\n')"
-     ],
-     "language": "python",
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAowAAAIACAYAAAAIQT11AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10f1dd160>"
+      ]
+     },
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 20
+     "output_type": "display_data"
     },
     {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "from matplotlib import pyplot as plt\n",
-      "import numpy as np\n",
-      "\n",
-      "def plot_results():\n",
-      "    bar_labels = ['serial', '2', '3', '4', '6']\n",
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\n",
-      "    fig = plt.figure(figsize=(10,8))\n",
+      "Python version  : 3.4.1\n",
+      "compiler        : GCC 4.2.1 (Apple Inc. build 5577)\n",
       "\n",
-      "    # plot bars\n",
-      "    y_pos = np.arange(len(benchmarks))\n",
-      "    plt.yticks(y_pos, bar_labels, fontsize=16)\n",
-      "    bars = plt.barh(y_pos, benchmarks,\n",
-      "             align='center', alpha=0.4, color='g')\n",
+      "system     : Darwin\n",
+      "release    : 13.2.0\n",
+      "machine    : x86_64\n",
+      "processor  : i386\n",
+      "CPU count  : 4\n",
+      "interpreter: 64bit\n",
       "\n",
-      "    # annotation and labels\n",
-      "    \n",
-      "    for ba,be in zip(bars, benchmarks):\n",
-      "        plt.text(ba.get_width() + 2, ba.get_y() + ba.get_height()/2,\n",
-      "                '{0:.2%}'.format(benchmarks[0]/be), \n",
-      "                ha='center', va='bottom', fontsize=12)\n",
-      "    \n",
-      "    plt.xlabel('time in seconds for n=%s' %n, fontsize=14)\n",
-      "    plt.ylabel('number of processes', fontsize=14)\n",
-      "    t = plt.title('Serial vs. Multiprocessing via Parzen-window estimation', fontsize=18)\n",
-      "    plt.ylim([-1,len(benchmarks)+0.5])\n",
-      "    plt.xlim([0,max(benchmarks)*1.1])\n",
-      "    plt.vlines(benchmarks[0], -1, len(benchmarks)+0.5, linestyles='dashed')\n",
-      "    plt.grid()\n",
-      "\n",
-      "    plt.show()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 25
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Results"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "plot_results()\n",
-      "print_sysinfo()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": "iVBORw0KGgoAAAANSUhEUgAAAowAAAIACAYAAAAIQT11AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
-       "text": [
-        "<matplotlib.figure.Figure at 0x10f1dd160>"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "\n",
-        "Python version  : 3.4.1\n",
-        "compiler        : GCC 4.2.1 (Apple Inc. build 5577)\n",
-        "\n",
-        "system     : Darwin\n",
-        "release    : 13.2.0\n",
-        "machine    : x86_64\n",
-        "processor  : i386\n",
-        "CPU count  : 4\n",
-        "interpreter: 64bit\n",
-        "\n",
-        "\n",
-        "\n"
-       ]
-      }
-     ],
-     "prompt_number": 26
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "<br>\n",
-      "<br>"
-     ]
-    },
-    {
-     "cell_type": "heading",
-     "level": 1,
-     "metadata": {},
-     "source": [
-      "Conclusion"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "[[back to top](#Sections)]"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel. However, on my particular machine, the submission of 6 parallel 6 processes doesn't lead to a further performance improvement, which makes sense for a 4-core CPU.  \n",
-      "We also notice that there was a significant performance increase when we were using 3 instead of only 2 processes in parallel. However, the performance increase was less significant when we moved up to 4 parallel processes, respectively.  \n",
-      "This can be attributed to the fact that in this case, the CPU consists of only 4 cores, and system processes, such as the operating system, are also running in the background. Thus, the fourth core simply does not have enough capacity left to further increase the performance of the fourth process to a large extend. And we also have to keep in mind that every additional process comes with an additional overhead for inter-process communication.  \n",
       "\n",
-      "Also, an improvement due to parallel processing only makes sense if our tasks are \"CPU-bound\" where the majority of the task is spent in the CPU in contrast to I/O bound tasks, i.e., tasks that are processing data from a disk. "
+      "\n"
      ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
     }
    ],
-   "metadata": {}
+   "source": [
+    "plot_results()\n",
+    "print_sysinfo()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<br>\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#Sections)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see that we could speed up the density estimations for our Parzen-window function if we submitted them in parallel. However, on my particular machine, the submission of 6 parallel 6 processes doesn't lead to a further performance improvement, which makes sense for a 4-core CPU.  \n",
+    "We also notice that there was a significant performance increase when we were using 3 instead of only 2 processes in parallel. However, the performance increase was less significant when we moved up to 4 parallel processes, respectively.  \n",
+    "This can be attributed to the fact that in this case, the CPU consists of only 4 cores, and system processes, such as the operating system, are also running in the background. Thus, the fourth core simply does not have enough capacity left to further increase the performance of the fourth process to a large extend. And we also have to keep in mind that every additional process comes with an additional overhead for inter-process communication.  \n",
+    "\n",
+    "Also, an improvement due to parallel processing only makes sense if our tasks are \"CPU-bound\" where the majority of the task is spent in the CPU in contrast to I/O bound tasks, i.e., tasks that are processing data from a disk. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
- ]
-}
\ No newline at end of file
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From 19e801b774543a1c2ec4aa05bbb3788948902492 Mon Sep 17 00:00:00 2001
From: Praveen Mylavarapu <saipraveenmylavarapu@gmail.com>
Date: Sat, 28 Oct 2017 13:18:43 +0530
Subject: [PATCH 240/248] Update README.md

Added link to A Byte of Python book for resources for learning python.
---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 05c8419..c6fbbee 100644
--- a/README.md
+++ b/README.md
@@ -192,6 +192,8 @@
 
 - [Think Python - How to Think Like a Computer Scientist](http://www.greenteapress.com/thinkpython/) - An introduction for beginners starting with basic concepts of programming.
 
+- [A Byte of Python](https://python.swaroopch.com/) - a free book on programming using the Python language.
+
 - [Python Patterns](http://matthiaseisen.com/pp/) -  A directory of proven, reusable solutions to common programming problems.
 
 - [Intro to Computer Science - Build a Search Engine & a Social Network](https://www.udacity.com/course/intro-to-computer-science--cs101) - A great, free course for learning Python if you haven't programmed before.

From 7749d69cb3f2b9733b28a648f664ca8505c7621c Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Thu, 11 Jan 2018 23:00:18 -0500
Subject: [PATCH 241/248] fixing some language typos

---
 tutorials/multiprocessing_intro.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/tutorials/multiprocessing_intro.ipynb b/tutorials/multiprocessing_intro.ipynb
index 126f8c2..b3566c9 100644
--- a/tutorials/multiprocessing_intro.ipynb
+++ b/tutorials/multiprocessing_intro.ipynb
@@ -348,7 +348,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**A simpler way to to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**"
+    "**A simpler way to maintain an ordered list of results is to use the `Pool.apply` and `Pool.map` functions which we will discuss in the next section.**"
    ]
   },
   {
@@ -379,7 +379,7 @@
    "source": [
     "Another and more convenient approach for simple parallel processing tasks is provided by the `Pool` class.  \n",
     "\n",
-    "There are four methods that are particularly interesing:\n",
+    "There are four methods that are particularly interesting:\n",
     "\n",
     "    - Pool.apply\n",
     "    \n",
@@ -451,7 +451,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `Pool.map` and `Pool.apply` will lock the main program until all a process is finished, which is quite useful if we want to obtain resuls in a particular order for certain applications.   \n",
+    "The `Pool.map` and `Pool.apply` will lock the main program until all processes are finished, which is quite useful if we want to obtain results in a particular order for certain applications.   \n",
     "In contrast, the `async` variants will submit all processes at once and retrieve the results as soon as they are finished. \n",
     "One more difference is that we need to use the `get` method after the `apply_async()` call in order to obtain the `return` values of the finished processes."
    ]
@@ -759,7 +759,7 @@
    "source": [
     "Below, we will set up benchmarking functions for our serial and multiprocessing approach that we can pass to our `timeit` benchmark function.  \n",
     "We will be using the `Pool.apply_async` function to take advantage of firing up processes simultaneously: Here, we don't care about the order in which the results for the different window widths are computed, we just need to associate each result with the input window width.  \n",
-    "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to to sort our list of results later."
+    "Thus we add a little tweak to our Parzen-density-estimation function by returning a tuple of 2 values: window width and the estimated density, which will allow us to sort our list of results later."
    ]
   },
   {
@@ -1097,7 +1097,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,

From 5da40c30c01aeb5a45aeffcbe3446dd51ce42116 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Wed, 18 Apr 2018 09:47:16 -0400
Subject: [PATCH 242/248] fix cursor use

---
 tutorials/sqlite3_howto/README.md             | 62 ++++++++++---------
 tutorials/sqlite3_howto/code/print_db_info.py | 43 +++++++------
 2 files changed, 57 insertions(+), 48 deletions(-)

diff --git a/tutorials/sqlite3_howto/README.md b/tutorials/sqlite3_howto/README.md
index e5cccec..02e3e3c 100644
--- a/tutorials/sqlite3_howto/README.md
+++ b/tutorials/sqlite3_howto/README.md
@@ -682,53 +682,58 @@ convenient script to print a nice overview of SQLite database tables:
     
     
     import sqlite3
-    
+
+
     def connect(sqlite_file):
         """ Make connection to an SQLite database file """
         conn = sqlite3.connect(sqlite_file)
         c = conn.cursor()
         return conn, c
-    
+
+
     def close(conn):
         """ Commit changes and close connection to the database """
         # conn.commit()
         conn.close()
-    
+
+
     def total_rows(cursor, table_name, print_out=False):
         """ Returns the total number of rows in the database """
-        c.execute('SELECT COUNT(*) FROM {}'.format(table_name))
-        count = c.fetchall()
+        cursor.execute('SELECT COUNT(*) FROM {}'.format(table_name))
+        count = cursor.fetchall()
         if print_out:
             print('\nTotal rows: {}'.format(count[0][0]))
         return count[0][0]
-    
+
+
     def table_col_info(cursor, table_name, print_out=False):
-        """ 
-           Returns a list of tuples with column informations:
-          (id, name, type, notnull, default_value, primary_key)
-        
+        """ Returns a list of tuples with column informations:
+        (id, name, type, notnull, default_value, primary_key)
         """
-        c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
-        info = c.fetchall()
-        
+        cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+        info = cursor.fetchall()
+
         if print_out:
             print("\nColumn Info:\nID, Name, Type, NotNull, DefaultVal, PrimaryKey")
             for col in info:
                 print(col)
         return info
-    
+
+
     def values_in_col(cursor, table_name, print_out=True):
-        """ Returns a dictionary with columns as keys and the number of not-null 
-            entries as associated values.
+        """ Returns a dictionary with columns as keys
+        and the number of not-null entries as associated values.
         """
-        c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
-        info = c.fetchall()
+        cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+        info = cursor.fetchall()
         col_dict = dict()
         for col in info:
             col_dict[col[1]] = 0
         for col in col_dict:
-            c.execute('SELECT ({0}) FROM {1} WHERE {0} IS NOT NULL'.format(col, table_name))
-            # In my case this approach resulted in a better performance than using COUNT
+            c.execute('SELECT ({0}) FROM {1} '
+                      'WHERE {0} IS NOT NULL'.format(col, table_name))
+            # In my case this approach resulted in a
+            # better performance than using COUNT
             number_rows = len(c.fetchall())
             col_dict[col] = number_rows
         if print_out:
@@ -736,23 +741,22 @@ convenient script to print a nice overview of SQLite database tables:
             for i in col_dict.items():
                 print('{}: {}'.format(i[0], i[1]))
         return col_dict
-    
-    
+
+
     if __name__ == '__main__':
-    
+
         sqlite_file = 'my_first_db.sqlite'
         table_name = 'my_table_3'
-    
+
         conn, c = connect(sqlite_file)
         total_rows(c, table_name, print_out=True)
         table_col_info(c, table_name, print_out=True)
-        values_in_col(c, table_name, print_out=True) # slow on large data bases
-        
+        # next line might be slow on large databases
+        values_in_col(c, table_name, print_out=True)
+
         close(conn)
-    
 
-Download the script: [print_db_info.py](https://raw.github.com/rasbt/python_sq
-lite_code/master/code/print_db_info.py)  
+Download the script: [print_db_info.py](code/print_db_info.py)  
   
 ![8_sqlite3_print_db_info_1.png](../../Images/8_sqlite3_print_db_info_1.png)  
   
diff --git a/tutorials/sqlite3_howto/code/print_db_info.py b/tutorials/sqlite3_howto/code/print_db_info.py
index 22b72a8..285a635 100644
--- a/tutorials/sqlite3_howto/code/print_db_info.py
+++ b/tutorials/sqlite3_howto/code/print_db_info.py
@@ -22,52 +22,57 @@
 
 import sqlite3
 
+
 def connect(sqlite_file):
     """ Make connection to an SQLite database file """
     conn = sqlite3.connect(sqlite_file)
     c = conn.cursor()
     return conn, c
 
+
 def close(conn):
     """ Commit changes and close connection to the database """
-    #conn.commit()
+    # conn.commit()
     conn.close()
 
+
 def total_rows(cursor, table_name, print_out=False):
     """ Returns the total number of rows in the database """
-    c.execute('SELECT COUNT(*) FROM {}'.format(table_name))
-    count = c.fetchall()
+    cursor.execute('SELECT COUNT(*) FROM {}'.format(table_name))
+    count = cursor.fetchall()
     if print_out:
         print('\nTotal rows: {}'.format(count[0][0]))
     return count[0][0]
 
+
 def table_col_info(cursor, table_name, print_out=False):
-    """ 
-       Returns a list of tuples with column informations:
-      (id, name, type, notnull, default_value, primary_key)
-    
+    """ Returns a list of tuples with column informations:
+    (id, name, type, notnull, default_value, primary_key)
     """
-    c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
-    info = c.fetchall()
-    
+    cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+    info = cursor.fetchall()
+
     if print_out:
         print("\nColumn Info:\nID, Name, Type, NotNull, DefaultVal, PrimaryKey")
         for col in info:
             print(col)
     return info
 
+
 def values_in_col(cursor, table_name, print_out=True):
-    """ Returns a dictionary with columns as keys and the number of not-null 
-        entries as associated values.
+    """ Returns a dictionary with columns as keys
+    and the number of not-null entries as associated values.
     """
-    c.execute('PRAGMA TABLE_INFO({})'.format(table_name))
-    info = c.fetchall()
+    cursor.execute('PRAGMA TABLE_INFO({})'.format(table_name))
+    info = cursor.fetchall()
     col_dict = dict()
     for col in info:
         col_dict[col[1]] = 0
     for col in col_dict:
-        c.execute('SELECT ({0}) FROM {1} WHERE {0} IS NOT NULL'.format(col, table_name))
-        # In my case this approach resulted in a better performance than using COUNT
+        c.execute('SELECT ({0}) FROM {1} '
+                  'WHERE {0} IS NOT NULL'.format(col, table_name))
+        # In my case this approach resulted in a
+        # better performance than using COUNT
         number_rows = len(c.fetchall())
         col_dict[col] = number_rows
     if print_out:
@@ -85,7 +90,7 @@ def values_in_col(cursor, table_name, print_out=True):
     conn, c = connect(sqlite_file)
     total_rows(c, table_name, print_out=True)
     table_col_info(c, table_name, print_out=True)
-    values_in_col(c, table_name, print_out=True) # slow on large data bases
-    
-    close(conn)
+    # next line might be slow on large databases
+    values_in_col(c, table_name, print_out=True)
 
+    close(conn)

From a908a343afe1bd0eff420b15d93706dde9c123ea Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Wed, 16 May 2018 01:06:58 -0400
Subject: [PATCH 243/248] get principal eigvec

---
 useful_scripts/principal_eigenvector.py | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 useful_scripts/principal_eigenvector.py

diff --git a/useful_scripts/principal_eigenvector.py b/useful_scripts/principal_eigenvector.py
new file mode 100644
index 0000000..913cf62
--- /dev/null
+++ b/useful_scripts/principal_eigenvector.py
@@ -0,0 +1,20 @@
+# Select a principal eigenvector via NumPy
+# to be used as a template (copy & paste) script
+
+import numpy as np
+
+# set A to be your matrix
+A = np.array([[1, 2, 3],
+              [4, 5, 6],
+              [7, 8, 9]])
+
+
+eig_vals, eig_vecs = np.linalg.eig(A)
+idx = np.absolute(eig_vals).argsort()[::-1]  # decreasing order
+sorted_eig_vals = eig_vals[idx]
+sorted_eig_vecs = eig_vecs[:, idx]
+
+principal_eig_vec = sorted_eig_vecs[:, 0]  # eigvec with largest eigval
+
+normalized_pr_eig_vec = np.real(principal_eig_vec / np.sum(principal_eig_vec))
+print(normalized_pr_eig_vec)  # eigvec that sums up to one

From 82376a9b8b8776a3586c16246853ef88606123c1 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Thu, 7 Jun 2018 23:43:02 -0400
Subject: [PATCH 244/248] replace broken absolute links with relative links

---
 tutorials/sqlite3_howto/README.md | 19 +++++++------------
 1 file changed, 7 insertions(+), 12 deletions(-)

diff --git a/tutorials/sqlite3_howto/README.md b/tutorials/sqlite3_howto/README.md
index 02e3e3c..ea2a357 100644
--- a/tutorials/sqlite3_howto/README.md
+++ b/tutorials/sqlite3_howto/README.md
@@ -123,7 +123,7 @@ there is more information about PRIMARY KEYs further down in this section).
     conn.close()
     
 
-Download the script: [create_new_db.py](https://raw.github.com/rasbt/python_reference/master/tutorials/code/create_new_db.py)
+Download the script: [create_new_db.py](https://github.com/rasbt/python_reference/blob/master/tutorials/sqlite3_howto/code/create_new_db.py)
 
 * * *
 
@@ -207,7 +207,7 @@ Let's have a look at some code:
     conn.close()
     
 
-Download the script: [add_new_column.py](https://raw.github.com/rasbt/python_reference/master/tutorials/code/add_new_column.py)
+Download the script: [add_new_column.py](https://github.com/rasbt/python_reference/blob/master/tutorials/sqlite3_howto/code/add_new_column.py)
 
   
   
@@ -270,8 +270,7 @@ But let us first have a look at the example code:
     conn.close()
     
 
-Download the script: [update_or_insert_records.py](https://raw.github.com/rasb
-t/python_sqlite_code/master/code/update_or_insert_records.py)  
+Download the script: [update_or_insert_records.py](code/update_or_insert_records.py)  
   
 ![3_sqlite3_insert_update.png](../../Images/3_sqlite3_insert_update.png)  
   
@@ -335,8 +334,7 @@ drop the index, which is also shown in the code below.
     conn.close()
     
 
-Download the script: [create_unique_index.py](https://raw.github.com/rasbt/pyt
-hon_sqlite_code/master/code/create_unique_index.py)  
+Download the script: [create_unique_index.py](code/create_unique_index.py)  
   
 ![4_sqlite3_unique_index.png](../../Images/4_sqlite3_unique_index.png)  
   
@@ -401,8 +399,7 @@ row entries for all or some columns if they match certain criteria.
     conn.close()
     
 
-Download the script: [selecting_entries.py](https://raw.github.com/rasbt/pytho
-n_sqlite_code/master/code/selecting_entries.py)  
+Download the script: [selecting_entries.py](code/selecting_entries.py)  
   
 
 ![4_sqlite3_unique_index.png](../../Images/4_sqlite3_unique_index.png)  
@@ -542,8 +539,7 @@ that have been added xxx days ago.
     conn.close()
     
 
-Download the script: [date_time_ops.py](https://raw.github.com/rasbt/python_sq
-lite_code/master/code/date_time_ops.py)
+Download the script: [date_time_ops.py](code/date_time_ops.py)
 
   
   
@@ -645,8 +641,7 @@ column names):
     conn.close()
     
 
-Download the script: [get_columnnames.py](https://raw.github.com/rasbt/python_
-sqlite_code/master/code/get_columnnames.py)  
+Download the script: [get_columnnames.py](code/get_columnnames.py)  
   
 ![7_sqlite3_get_colnames_1.png](../../Images/7_sqlite3_get_colnames_1.png)  
   

From 764e1adf4a82387234727fe9b9e37ebcffe13f16 Mon Sep 17 00:00:00 2001
From: lacanlale <gizmo1398@gmail.com>
Date: Sat, 9 Jun 2018 09:07:32 -0700
Subject: [PATCH 245/248] typo and grammar fixes to not_so_obv nb

---
 tutorials/not_so_obvious_python_stuff.ipynb | 1221 +++++++------------
 1 file changed, 434 insertions(+), 787 deletions(-)

diff --git a/tutorials/not_so_obvious_python_stuff.ipynb b/tutorials/not_so_obvious_python_stuff.ipynb
index 15569ba..2e733ed 100644
--- a/tutorials/not_so_obvious_python_stuff.ipynb
+++ b/tutorials/not_so_obvious_python_stuff.ipynb
@@ -14,9 +14,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%load_ext watermark"
@@ -25,18 +23,16 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Last updated: 16/07/2014 \n",
+      "last updated: 2018-06-09 \n",
       "\n",
-      "CPython 3.4.1\n",
-      "IPython 2.0.0\n"
+      "CPython 3.6.4\n",
+      "IPython 6.2.1\n"
      ]
     }
    ],
@@ -57,7 +53,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<br>\n",
     "<br>"
    ]
   },
@@ -186,10 +181,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -230,10 +223,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -295,25 +286,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Python `list`s are mutable objects as we all know. So, if we are using the `+=` operator on `list`s, we extend the `list` by directly modifying the object directly.  \n",
+    "Python `list`s are mutable objects as we all know. So, if we are using the `+=` operator on `list`s, we extend the `list` by directly modifying the object.  \n",
     "\n",
-    "However, if we use the assigment via `my_list = my_list + ...`, we create a new list object, which can be demonstrated by the following code:"
+    "However, if we use the assignment via `my_list = my_list + ...`, we create a new list object, which can be demonstrated by the following code:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ID: 4366496544\n",
-      "ID (+=): 4366496544\n",
-      "ID (list = list + ...): 4366495472\n"
+      "ID: 4486856904\n",
+      "ID (+=): 4486856904\n",
+      "ID (list = list + ...): 4486959368\n"
      ]
     }
    ],
@@ -338,22 +327,20 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "[] \n",
-      "ID (initial): 140704077653128 \n",
+      "ID (initial): 4486857224 \n",
       "\n",
       "[1] \n",
-      "ID (append): 140704077653128 \n",
+      "ID (append): 4486857224 \n",
       "\n",
       "[1, 2] \n",
-      "ID (extend): 140704077653128\n"
+      "ID (extend): 4486857224\n"
      ]
     }
    ],
@@ -390,7 +377,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable—at least in some quarters.\"  \n",
+    "\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable — at least in some quarters.\"  \n",
     "\n",
     "(Original source: [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/))"
    ]
@@ -404,16 +391,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\"datetime.time(0,0,0)\" (Midnight) -> False\n",
+      "\"datetime.time(0,0,0)\" (Midnight) -> True\n",
       "\"datetime.time(1,0,0)\" (1 am) -> True\n"
      ]
     }
@@ -460,10 +445,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -489,7 +472,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "(*I received a comment that this is in fact a CPython artefact and **must not necessarily be true** in all implementations of Python!*)\n",
+    "(*I received a comment that this is in fact a CPython artifact and **must not necessarily be true** in all implementations of Python!*)\n",
     "\n",
     "So the take home message is: always use \"==\" for equality, \"is\" for identity!\n",
     "\n",
@@ -505,10 +488,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -537,10 +518,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -567,10 +546,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -621,23 +598,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 12,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "IDs:\n",
-      "list1: 4346366472\n",
-      "list2: 4346366472\n",
-      "list3: 4346366408\n",
-      "list4: 4346366536\n",
+      "list1: 4486860424\n",
+      "list2: 4486860424\n",
+      "list3: 4486818632\n",
+      "list4: 4486818568\n",
+      "\n",
+      "list1: [3, 2]\n",
       "\n",
       "list1: [3, 2]\n",
-      "list1: [3, 2]\n"
+      "list2: [3, 2]\n",
+      "list3: [4, 2]\n",
+      "list4: [1, 4]\n"
      ]
     }
    ],
@@ -655,7 +634,10 @@
     "\n",
     "list3[0] = 4\n",
     "list4[1] = 4\n",
-    "print('list1:', list1)"
+    "print('\\nlist1:', list1)\n",
+    "print('list2:', list2)\n",
+    "print('list3:', list3)\n",
+    "print('list4:', list4)"
    ]
   },
   {
@@ -674,22 +656,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 13,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "IDs:\n",
-      "list1: 4377956296\n",
-      "list2: 4377961752\n",
-      "list3: 4377954928\n",
+      "list1: 4486818824\n",
+      "list2: 4486886024\n",
+      "list3: 4486888200\n",
+      "\n",
+      "list1: [[3], [2]]\n",
       "\n",
       "list1: [[3], [2]]\n",
-      "list1: [[3], [2]]\n"
+      "list2: [[3], [2]]\n",
+      "list3: [[5], [2]]\n"
      ]
     }
    ],
@@ -707,7 +690,9 @@
     "print('list1:', list1)\n",
     "\n",
     "list3[0][0] = 5\n",
-    "print('list1:', list1)"
+    "print('\\nlist1:', list1)\n",
+    "print('list2:', list2)\n",
+    "print('list3:', list3)"
    ]
   },
   {
@@ -751,10 +736,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 14,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -803,10 +786,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 15,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -838,17 +819,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 16,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1397764090.456688\n",
-      "1397764090.456688\n"
+      "1528560045.3962939\n",
+      "1528560045.3962939\n"
      ]
     }
    ],
@@ -891,15 +870,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Be aware of what is happening when combining \"`in`\" checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"."
+    "Be aware of what is happening when combining `in` checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 17,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -922,15 +899,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Although this defeats the purpose of an generator (in most cases), we can convert a generator into a list to circumvent the problem. "
+    "Although this defeats the purpose of a generator (in most cases), we can convert a generator into a list to circumvent the problem. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 18,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -986,10 +961,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 19,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1036,19 +1009,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Remember the section about the [\"consuming generators\"](consuming_generators)? This example is somewhat related, but the result might still come unexpected. \n",
+    "Remember the section about the [consuming generators](#consuming_generator)? This example is somewhat related, but the result might still come as unexpected. \n",
     "\n",
     "(Original source: [http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html](http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html))\n",
     "\n",
-    "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope of the list comprehension. Since the list comprehension has already been constructed and evaluated when we for-loop through the list, the closure-variable will be set to the last value 4."
+    "In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope. Since the list comprehension has already been constructed and evaluated when we `for-loop` through the list, the closure-variable will be set to the last value 4."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 20,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1077,10 +1048,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 21,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1109,10 +1078,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 22,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1173,10 +1140,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 23,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1189,10 +1154,13 @@
    ],
    "source": [
     "x = 0\n",
+    "\n",
+    "\n",
     "def in_func():\n",
     "    x = 1\n",
     "    print('in_func:', x)\n",
-    "    \n",
+    "\n",
+    "\n",
     "in_func()\n",
     "print('global:', x)"
    ]
@@ -1206,10 +1174,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 24,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1222,11 +1188,14 @@
    ],
    "source": [
     "x = 0\n",
+    "\n",
+    "\n",
     "def in_func():\n",
     "    global x\n",
     "    x = 1\n",
     "    print('in_func:', x)\n",
-    "    \n",
+    "\n",
+    "\n",
     "in_func()\n",
     "print('global:', x)"
    ]
@@ -1242,10 +1211,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 25,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1259,13 +1226,16 @@
    ],
    "source": [
     "def outer():\n",
-    "       x = 1\n",
-    "       print('outer before:', x)\n",
-    "       def inner():\n",
-    "           x = 2\n",
-    "           print(\"inner:\", x)\n",
-    "       inner()\n",
-    "       print(\"outer after:\", x)\n",
+    "    x = 1\n",
+    "    print('outer before:', x)\n",
+    "\n",
+    "    def inner():\n",
+    "        x = 2\n",
+    "        print(\"inner:\", x)\n",
+    "    inner()\n",
+    "    print(\"outer after:\", x)\n",
+    "\n",
+    "\n",
     "outer()"
    ]
   },
@@ -1278,10 +1248,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 26,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1295,14 +1263,17 @@
    ],
    "source": [
     "def outer():\n",
-    "       x = 1\n",
-    "       print('outer before:', x)\n",
-    "       def inner():\n",
-    "           nonlocal x\n",
-    "           x = 2\n",
-    "           print(\"inner:\", x)\n",
-    "       inner()\n",
-    "       print(\"outer after:\", x)\n",
+    "    x = 1\n",
+    "    print('outer before:', x)\n",
+    "\n",
+    "    def inner():\n",
+    "        nonlocal x\n",
+    "        x = 2\n",
+    "        print(\"inner:\", x)\n",
+    "    inner()\n",
+    "    print(\"outer after:\", x)\n",
+    "\n",
+    "\n",
     "outer()"
    ]
   },
@@ -1340,18 +1311,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 27,
+   "metadata": {},
    "outputs": [
     {
      "ename": "TypeError",
      "evalue": "'tuple' object does not support item assignment",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-41-c3bec6c3fe6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-27-ddf241f0ac4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
      ]
     }
@@ -1365,15 +1335,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### But what if we put a mutable object into the immutable tuple? Well, modification works, but we **also** get a `TypeError` at the same time."
+    "### But what if we put a mutable object into the immutable tuple? Well, modification works, but we **also** get a `TypeError` at the same time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 28,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1387,8 +1355,9 @@
      "evalue": "'tuple' object does not support item assignment",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-aebe9a31dbeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tup before: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-8baa7ba7b4d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tup before: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
      ]
     }
@@ -1401,19 +1370,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "tup after:  ([1],)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print('tup after: ', tup)"
    ]
@@ -1429,10 +1388,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 29,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1452,10 +1409,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 30,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1488,15 +1443,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### One more note about the `immutable` status of tuples. Tuples are famous for being immutable. However, how comes that this code works?"
+    "### One more note about the `immutable` status of tuples. Tuples are famous for being immutable. However, how comes that this code works?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1517,23 +1470,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "What happens \"behind\" the curtains is that the tuple is not modified, but every time a new object is generated, which will inherit the old \"name tag\":"
+    "What happens \"behind\" the curtains is that the tuple is not modified, but a new object is generated every time, which will inherit the old \"name tag\":"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4337381840\n",
-      "4357415496\n",
-      "4357289952\n"
+      "4486707912\n",
+      "4485211784\n",
+      "4486955152\n"
      ]
     }
    ],
@@ -1580,14 +1531,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import timeit\n",
     "\n",
+    "\n",
     "def plainlist(n=100000):\n",
     "    my_list = []\n",
     "    for i in range(n):\n",
@@ -1595,14 +1545,17 @@
     "            my_list.append(i)\n",
     "    return my_list\n",
     "\n",
+    "\n",
     "def listcompr(n=100000):\n",
     "    my_list = [i for i in range(n) if i % 5 == 0]\n",
     "    return my_list\n",
     "\n",
+    "\n",
     "def generator(n=100000):\n",
     "    my_gen = (i for i in range(n) if i % 5 == 0)\n",
     "    return my_gen\n",
     "\n",
+    "\n",
     "def generator_yield(n=100000):\n",
     "    for i in range(n):\n",
     "        if i % 5 == 0:\n",
@@ -1613,27 +1566,25 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### To be fair to the list, let us exhaust the generators:"
+    "### To be fair to the list, let us exhaust the generators:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "plain_list:     10 loops, best of 3: 22.4 ms per loop\n",
+      "plain_list:     10.8 ms ± 793 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
       "\n",
-      "listcompr:     10 loops, best of 3: 20.8 ms per loop\n",
+      "listcompr:     10 ms ± 830 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
       "\n",
-      "generator:     10 loops, best of 3: 22 ms per loop\n",
+      "generator:     11.4 ms ± 1 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
       "\n",
-      "generator_yield:     10 loops, best of 3: 21.9 ms per loop\n"
+      "generator_yield:     12.3 ms ± 1.82 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -1642,25 +1593,29 @@
     "    for i in plain_list():\n",
     "        pass\n",
     "\n",
+    "\n",
     "def test_listcompr(listcompr):\n",
     "    for i in listcompr():\n",
     "        pass\n",
     "\n",
+    "\n",
     "def test_generator(generator):\n",
     "    for i in generator():\n",
     "        pass\n",
     "\n",
+    "\n",
     "def test_generator_yield(generator_yield):\n",
     "    for i in generator_yield():\n",
     "        pass\n",
     "\n",
-    "print('plain_list:     ', end = '')\n",
+    "\n",
+    "print('plain_list:     ', end='')\n",
     "%timeit test_plainlist(plainlist)\n",
-    "print('\\nlistcompr:     ', end = '')\n",
+    "print('\\nlistcompr:     ', end='')\n",
     "%timeit test_listcompr(listcompr)\n",
-    "print('\\ngenerator:     ', end = '')\n",
+    "print('\\ngenerator:     ', end='')\n",
     "%timeit test_generator(generator)\n",
-    "print('\\ngenerator_yield:     ', end = '')\n",
+    "print('\\ngenerator_yield:     ', end='')\n",
     "%timeit test_generator_yield(generator_yield)"
    ]
   },
@@ -1693,21 +1648,19 @@
    "metadata": {},
    "source": [
     "Who has not stumbled across this quote \"we are all consenting adults here\" in the Python community, yet? Unlike in other languages like C++ (sorry, there are many more, but that's one I am most familiar with), we can't really protect class methods from being used outside the class (i.e., by the API user).  \n",
-    "All we can do is to indicate methods as private to make clear that they are better not used outside the class, but it is really up to the class user, since \"we are all consenting adults here\"!  \n",
+    "All we can do is indicate methods as private to make clear that they are not to be used outside the class, but it really is up to the class user, since \"we are all consenting adults here\"!  \n",
     "So, when we want to mark a class method as private, we can put a single underscore in front of it.  \n",
     "If we additionally want to avoid name clashes with other classes that might use the same method names, we can prefix the name with a double-underscore to invoke the name mangling.\n",
     "\n",
-    "This doesn't prevent the class user to access this class member though, but he has to know the trick and also knows that it his own risk...\n",
+    "This doesn't prevent the class user to access this class member though, but they have to know the trick and also know that it is at their own risk...\n",
     "\n",
     "Let the following example illustrate what I mean:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1723,11 +1676,14 @@
     "class my_class():\n",
     "    def public_method(self):\n",
     "        print('Hello public world!')\n",
+    "\n",
     "    def __private_method(self):\n",
     "        print('Hello private world!')\n",
+    "\n",
     "    def call_private_method_in_class(self):\n",
     "        self.__private_method()\n",
-    "        \n",
+    "\n",
+    "\n",
     "my_instance = my_class()\n",
     "\n",
     "my_instance.public_method()\n",
@@ -1768,10 +1724,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 36,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1791,10 +1745,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 37,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1818,15 +1770,13 @@
    "source": [
     "<br>\n",
     "<br>\n",
-    "**The solution** is that we are iterating through the list index by index, and if we remove one of the items in-between, we inevitably mess around with the indexing, look at the following example, and it will become clear:"
+    "**The solution** is that we are iterating through the list index by index, and if we remove one of the items in-between, we inevitably mess around with the indexing. Look at the following example and it will become clear:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 38,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1880,10 +1830,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 39,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -1939,23 +1887,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As we have all encountered it 1 (x10000) time(s) in our live, the infamous `IndexError`:"
+    "As we have all encountered it 1 (x10000) time(s) in our lives, the infamous `IndexError`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 40,
+   "metadata": {},
    "outputs": [
     {
      "ename": "IndexError",
      "evalue": "list index out of range",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-eb273dc36fdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-40-2f6b582502c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: list index out of range"
      ]
     }
@@ -1969,24 +1916,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But suprisingly, it is not raised when we are doing list slicing, which can be a really pain for debugging:"
+    "But suprisingly, it is not raised when we are doing list slicing, which can be a real pain when debugging:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_list = [1, 2, 3, 4, 5]\n",
     "print(my_list[5:])"
@@ -2025,23 +1962,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "global\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def my_func():\n",
     "    print(var)\n",
     "\n",
+    "\n",
     "var = 'global'\n",
     "my_func()"
    ]
@@ -2055,23 +1983,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "global\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def my_func():\n",
     "    var = 'locally changed'\n",
     "\n",
+    "\n",
     "var = 'global'\n",
     "my_func()\n",
     "print(var)"
@@ -2086,28 +2005,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "UnboundLocalError",
-     "evalue": "local variable 'var' referenced before assignment",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-40-3afd870b7c35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-40-3afd870b7c35>\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# want to access global variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'locally changed'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'var' referenced before assignment"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def my_func():\n",
-    "    print(var) # want to access global variable\n",
-    "    var = 'locally changed' # but Python thinks we forgot to define the local variable!\n",
-    "    \n",
+    "    print(var)  # want to access global variable\n",
+    "    var = 'locally changed'  # but Python thinks we forgot to define the local variable!\n",
+    "\n",
+    "\n",
     "var = 'global'\n",
     "my_func()"
    ]
@@ -2121,25 +2027,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "global\n",
-      "locally changed\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def my_func():\n",
     "    global var\n",
-    "    print(var) # want to access global variable\n",
-    "    var = 'locally changed' # changes the gobal variable\n",
+    "    print(var)  # want to access global variable\n",
+    "    var = 'locally changed'  # changes the gobal variable\n",
+    "\n",
     "\n",
     "var = 'global'\n",
     "\n",
@@ -2175,25 +2071,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's assume a scenario where we want to duplicate sub`list`s of values stored in another list. If we want to create independent sub`list` object, using the arithmetic multiplication operator could lead to rather unexpected (or undesired) results:"
+    "Let's assume a scenario where we want to duplicate sub`list`s of values stored in another list. If we want to create an independent sub`list` object, using the arithmetic multiplication operator could lead to rather unexpected (or undesired) results:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initially --->  [[1, 2, 3], [1, 2, 3]]\n",
-      "after my_list1[1][0] = 'a' --->  [['a', 2, 3], ['a', 2, 3]]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_list1 = [[1, 2, 3]] * 2\n",
     "\n",
@@ -2215,20 +2100,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initially:  --->  [[1, 2, 3], [1, 2, 3]]\n",
-      "after my_list2[1][0] = 'a':  --->  [[1, 2, 3], ['a', 2, 3]]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "my_list2 = [[1, 2, 3] for i in range(2)]\n",
     "\n",
@@ -2249,22 +2123,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "id my_list1: 4350764680, id my_list2: 4350766472\n",
-      "id my_list1: 4350764680, id my_list2: 4350766664\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "for a,b in zip(my_list1, my_list2):\n",
+    "for a, b in zip(my_list1, my_list2):\n",
     "    print('id my_list1: {}, id my_list2: {}'.format(id(a), id(b)))"
    ]
   },
@@ -2336,11 +2199,7 @@
     "- [Handling exceptions](#handling_exceptions)\n",
     "- [next() function and .next() method](#next_next)\n",
     "- [Loop variables and leaking into the global scope](#loop_leak)\n",
-    "- [Comparing unorderable types](#compare_unorder)\n",
-    "\n",
-    "<br>\n",
-    "<br>\n",
-    "\n"
+    "- [Comparing unorderable types](#compare_unorder)"
    ]
   },
   {
@@ -2371,9 +2230,9 @@
    "metadata": {},
    "source": [
     "\n",
-    "####- Python 2: \n",
+    "#### Python 2: \n",
     "We have ASCII `str()` types, separate `unicode()`, but no `byte` type\n",
-    "####- Python 3: \n",
+    "#### Python 3: \n",
     "Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s"
    ]
   },
@@ -2381,12 +2240,12 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false
+    "code_folding": []
    },
    "outputs": [],
    "source": [
     "#############\n",
-    "# Python 2\n",
+    "# Python 2  #\n",
     "#############\n",
     "\n",
     ">>> type(unicode('is like a python3 str()'))\n",
@@ -2454,9 +2313,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Python 2\n",
@@ -2485,9 +2342,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Python 2\n",
@@ -2533,9 +2388,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Python 2\n",
@@ -2563,16 +2416,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<a id='xrange'></a>\n",
-    "<br>\n",
-    "<br>"
+    "<a id=\"xrange\"></a>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "###`xrange()` "
+    "### `xrange()`"
    ]
   },
   {
@@ -2594,23 +2445,21 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Python 2\n",
-    "> python -m timeit 'for i in range(1000000):' ' pass'\n",
+    ">>> python -m timeit 'for i in range(1000000):' ' pass'\n",
     "10 loops, best of 3: 66 msec per loop\n",
     "\n",
     "    > python -m timeit 'for i in xrange(1000000):' ' pass'\n",
     "10 loops, best of 3: 27.8 msec per loop\n",
     "\n",
     "# Python 3\n",
-    "> python3 -m timeit 'for i in range(1000000):' ' pass'\n",
+    ">>> python3 -m timeit 'for i in range(1000000):' ' pass'\n",
     "10 loops, best of 3: 51.1 msec per loop\n",
     "\n",
-    "> python3 -m timeit 'for i in xrange(1000000):' ' pass'\n",
+    ">>> python3 -m timeit 'for i in xrange(1000000):' ' pass'\n",
     "Traceback (most recent call last):\n",
     "  File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 292, in main\n",
     "    x = t.timeit(number)\n",
@@ -2656,9 +2505,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Python 2\n",
@@ -2719,9 +2566,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Python 2\n",
@@ -2742,12 +2587,8 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
     "<a id='next_next'></a>\n",
     "<br>\n",
@@ -2774,15 +2615,13 @@
    "source": [
     "\n",
     "\n",
-    "Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!"
+    "Where you can use both function and method in Python 2.7.5, the `next()` function is all that remains in Python 3!"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Python 2\n",
@@ -2831,26 +2670,14 @@
    "source": [
     "This goes back to a change that was made in Python 3.x and is described in [What’s New In Python 3.0](https://docs.python.org/3/whatsnew/3.0.html) as follows:\n",
     "\n",
-    "\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\""
+    "*\"List comprehensions no longer support the syntactic form `[... for var in item1, item2, ...]`. Use `[... for var in (item1, item2, ...)]` instead. Also note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope.\"*"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This code cell was executed in Python 3.3.5\n",
-      "[0, 1, 2, 3, 4]\n",
-      "1 -> i in global\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from platform import python_version\n",
     "print('This code cell was executed in Python', python_version())\n",
@@ -2862,21 +2689,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This code cell was executed in Python 2.7.6\n",
-      "[0, 1, 2, 3, 4]\n",
-      "4 -> i in global\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from platform import python_version\n",
     "print 'This code cell was executed in Python', python_version()\n",
@@ -2899,7 +2714,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Python 3.x prevents us from comparing unorderable types"
+    "### Python 3.x prevents us from comparing unorderable types"
    ]
   },
   {
@@ -2911,22 +2726,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This code cell was executed in Python 2.7.6\n",
-      "False\n",
-      "True\n",
-      "False\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from platform import python_version\n",
     "print 'This code cell was executed in Python', python_version()\n",
@@ -2938,29 +2740,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This code cell was executed in Python 3.3.5\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "unorderable types: list() > str()",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-1d774c677f73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This code cell was executed in Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: unorderable types: list() > str()"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from platform import python_version\n",
     "print('This code cell was executed in Python', python_version())\n",
@@ -3003,10 +2785,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def foo1(x: 'insert x here', y: 'insert x^2 here'):\n",
@@ -3023,10 +2803,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def foo2(x, y) -> 'Hi!':\n",
@@ -3050,38 +2828,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello, World\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "foo1(1,2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello, World\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "foo2(1,2)    "
    ]
@@ -3100,86 +2858,57 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def is_palindrome(a):\n",
     "    \"\"\"\n",
     "    Case-and punctuation insensitive check if a string is a palindrom.\n",
-    "    \n",
+    "\n",
     "    Keyword arguments:\n",
     "        a (str): The string to be checked if it is a palindrome.\n",
-    "        \n",
+    "\n",
     "    Returns `True` if input string is a palindrome, else False.\n",
-    "    \n",
+    "\n",
     "    \"\"\"\n",
     "    stripped_str = [l for l in my_str.lower() if l.isalpha()]\n",
-    "    return stripped_str == stripped_str[::-1]\n",
-    "    "
+    "    return stripped_str == stripped_str[::-1]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "However, function annotations can be useful to indicate that work is still in progress in some cases. But they are optional and I see them very very rarely.\n",
+    "However, function annotations can be useful to indicate that work is still in progress in some cases. But they are optional and I see them very, very rarely.\n",
     "\n",
     "As it is stated in [PEP3107](http://legacy.python.org/dev/peps/pep-3107/#fundamentals-of-function-annotations):\n",
     "\n",
-    "1. Function annotations, both for parameters and return values, are completely optional.\n",
+    "1. *Function annotations, both for parameters and return values, are completely optional.*\n",
     "\n",
-    "2. Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.\n"
+    "2. *Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.*\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The nice thing about function annotations is their `__annotations__` attribute, which is dictionary of all the parameters and/or the `return` value you annotated."
+    "The nice thing about function annotations is their `__annotations__` attribute, which is a dictionary of all the parameters and/or the `return` value you annotated."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'y': 'insert x^2 here', 'x': 'insert x here'}"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "foo1.__annotations__"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'return': 'Hi!'}"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "foo2.__annotations__"
    ]
@@ -3207,7 +2936,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<br>\n",
     "<br>\n",
     "<a name=\"finally_blocks\"></a>"
    ]
@@ -3219,6 +2947,13 @@
     "## Abortive statements in `finally` blocks"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[[back to top](#sections)]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3228,22 +2963,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "in try:\n",
-      "do some stuff\n",
-      "an error occurred\n",
-      "always execute finally\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def try_finally1():\n",
     "    try:\n",
@@ -3256,7 +2978,8 @@
     "        print('no error occurred')\n",
     "    finally:\n",
     "        print('always execute finally')\n",
-    "        \n",
+    "\n",
+    "\n",
     "try_finally1()"
    ]
   },
@@ -3271,21 +2994,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "do some stuff in try block\n",
-      "do some stuff in finally block\n",
-      "always execute finally\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "def try_finally2():\n",
     "    try:\n",
@@ -3294,7 +3005,8 @@
     "    finally:\n",
     "        print(\"do some stuff in finally block\")\n",
     "        return \"always execute finally\"\n",
-    "    \n",
+    "\n",
+    "\n",
     "print(try_finally2())"
    ]
   },
@@ -3319,7 +3031,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#Assigning types to variables as values"
+    "## Assigning types to variables as values"
    ]
   },
   {
@@ -3338,22 +3050,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'123'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "a_var = str\n",
     "a_var(123)"
@@ -3361,23 +3060,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 <class 'int'>\n",
-      "1 <class 'int'>\n",
-      "2.0 <class 'float'>\n",
-      "3 <class 'str'>\n",
-      "4 <class 'int'>\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "from random import choice\n",
     "\n",
@@ -3400,7 +3085,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Only the first clause of generators is evaluated immediately"
+    "## Only the first clause of generators is evaluated immediately"
    ]
   },
   {
@@ -3420,22 +3105,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-29312e1ece8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "gen_fails = (i for i in 1/0)"
    ]
@@ -3449,10 +3121,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "gen_succeeds = (i for i in range(5) for j in 1/0)"
@@ -3460,30 +3130,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "ename": "ZeroDivisionError",
-     "evalue": "division by zero",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-20-8a83a1022971>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-19-c54c53f2218a>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "But obviously fails when we iterate ...\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "print('But obviously fails when we iterate ...')\n",
     "for i in gen_succeeds:\n",
@@ -3503,7 +3152,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##Keyword argument unpacking syntax - `*args` and `**kwargs`"
+    "## Keyword argument unpacking syntax - `*args` and `**kwargs`"
    ]
   },
   {
@@ -3517,22 +3166,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Python has a very convenient \"keyword argument unpacking syntax\" (often also referred to as \"splat\"-operators). This is particularly useful, if we want to define a function that can take a arbitrary number of input arguments."
+    "Python has a very convenient \"keyword argument unpacking syntax\" (often referred to as \"splat\"-operators). This is particularly useful, if we want to define a function that can take a arbitrary number of input arguments."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Single-asterisk (*args)"
+    "### Single-asterisk (*args)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 41,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3550,6 +3197,7 @@
     "    print('args contents:', args)\n",
     "    print('1st argument:', args[0])\n",
     "\n",
+    "\n",
     "a_func(0, 1, 'a', 'b', 'c')"
    ]
   },
@@ -3557,22 +3205,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Double-asterisk (**kwargs)"
+    "### Double-asterisk (**kwargs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 42,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "type of kwargs: <class 'dict'>\n",
-      "kwargs contents:  {'d': 4, 'a': 1, 'c': 3, 'b': 2}\n",
+      "kwargs contents:  {'a': 1, 'b': 2, 'c': 3, 'd': 4}\n",
       "value of argument a: 1\n"
      ]
     }
@@ -3582,7 +3228,8 @@
     "    print('type of kwargs:', type(kwargs))\n",
     "    print('kwargs contents: ', kwargs)\n",
     "    print('value of argument a:', kwargs['a'])\n",
-    "    \n",
+    "\n",
+    "\n",
     "b_func(a=1, b=2, c=3, d=4)"
    ]
   },
@@ -3590,16 +3237,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### (Partially) unpacking of iterables\n",
+    "### (Partially) unpacking of iterables\n",
     "Another useful application of the \"unpacking\"-operator is the unpacking of lists and other other iterables."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 43,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3654,10 +3299,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 44,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3673,10 +3316,12 @@
     "    def __new__(clss, *args, **kwargs):\n",
     "        print('excecuted __new__')\n",
     "        return None\n",
+    "\n",
     "    def __init__(self, an_arg):\n",
     "        print('excecuted __init__')\n",
     "        self.an_arg = an_arg\n",
-    "        \n",
+    "\n",
+    "\n",
     "a_object = a_class(1)\n",
     "print('Type of a_object:', type(a_object))"
    ]
@@ -3691,10 +3336,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 45,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3713,10 +3356,12 @@
     "        print('excecuted __new__')\n",
     "        inst = super(a_class, cls).__new__(cls)\n",
     "        return inst\n",
+    "\n",
     "    def __init__(self, an_arg):\n",
     "        print('excecuted __init__')\n",
     "        self.an_arg = an_arg\n",
-    "        \n",
+    "\n",
+    "\n",
     "a_object = a_class(1)\n",
     "print('Type of a_object:', type(a_object))\n",
     "print('a_object.an_arg: ', a_object.an_arg)"
@@ -3724,10 +3369,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 46,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3750,10 +3393,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 47,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3799,7 +3440,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "I would claim that the conditional \"else\" is every programmer's daily bread and butter. However, there is a second flavor of \"else\"-clauses in Python, which I will call \"completion else\" (for reason that will become clear later).  \n",
+    "I would claim that the conditional `else` is every programmer's daily bread and butter. However, there is a second flavor of `else`-clauses in Python, which I will call \"completion else\" (for reason that will become clear later).  \n",
     "But first, let us take a look at our \"traditional\" conditional else that we all are familiar with.  \n"
    ]
   },
@@ -3807,15 +3448,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "###Conditional else:"
+    "### Conditional else:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 48,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3837,10 +3476,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 49,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3866,7 +3503,7 @@
    "source": [
     "Why am I showing those simple examples? I think they are good to highlight some of the key points: It is **either** the code under the `if` clause that is executed, **or** the code under the `else` block, but not both.  \n",
     "If the condition of the `if` clause evaluates to `True`, the `if`-block is exectured, and if it evaluated to `False`, it is the `else` block.  \n",
-    "\n",
+    "<br>\n",
     "### Completion else\n",
     "**In contrast** to the **either...or*** situation that we know from the conditional `else`, the completion `else` is executed if a code block finished.  \n",
     "To show you an example, let us use `else` for error-handling:"
@@ -3881,10 +3518,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 50,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3906,10 +3541,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 51,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3932,7 +3565,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<br>\n",
     "In the code above, we can see that the code under the **`else`-clause is only executed if the `try-block` was executed without encountering an error, i.e., if the `try`-block is \"complete\".**  \n",
     "The same rule applies to the \"completion\" `else` in while- and for-loops, which you can confirm in the following samples below."
    ]
@@ -3946,10 +3578,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 52,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -3972,10 +3602,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 53,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4004,10 +3632,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 54,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4028,10 +3654,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 55,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4090,10 +3714,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 56,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4125,20 +3747,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 57,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "  3           0 LOAD_CONST               1 ('Hello')\n",
-      "              3 STORE_FAST               0 (s)\n",
+      "              2 STORE_FAST               0 (s)\n",
       "\n",
-      "  4           6 LOAD_FAST                0 (s)\n",
-      "              9 RETURN_VALUE\n"
+      "  4           4 LOAD_FAST                0 (s)\n",
+      "              6 RETURN_VALUE\n"
      ]
     }
    ],
@@ -4152,20 +3772,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 58,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "  2           0 LOAD_CONST               3 ('Hello')\n",
-      "              3 STORE_FAST               0 (s)\n",
+      "              2 STORE_FAST               0 (s)\n",
       "\n",
-      "  3           6 LOAD_FAST                0 (s)\n",
-      "              9 RETURN_VALUE\n"
+      "  3           4 LOAD_FAST                0 (s)\n",
+      "              6 RETURN_VALUE\n"
      ]
     }
    ],
@@ -4178,25 +3796,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 59,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "  2           0 LOAD_CONST               1 ('Hell')\n",
-      "              3 STORE_FAST               0 (s)\n",
+      "              2 STORE_FAST               0 (s)\n",
       "\n",
-      "  3           6 LOAD_FAST                0 (s)\n",
-      "              9 LOAD_CONST               2 ('o')\n",
-      "             12 BINARY_ADD\n",
-      "             13 STORE_FAST               0 (s)\n",
+      "  3           4 LOAD_FAST                0 (s)\n",
+      "              6 LOAD_CONST               2 ('o')\n",
+      "              8 BINARY_ADD\n",
+      "             10 STORE_FAST               0 (s)\n",
       "\n",
-      "  4          16 LOAD_FAST                0 (s)\n",
-      "             19 RETURN_VALUE\n"
+      "  4          12 LOAD_FAST                0 (s)\n",
+      "             14 RETURN_VALUE\n"
      ]
     }
    ],
@@ -4215,15 +3831,13 @@
     "<br>\n",
     "It looks like that `'Hello'` and `'Hell'` + `'o'` are both evaluated and stored as `'Hello'` at compile-time, whereas the third version   \n",
     "`s = 'Hell'`  \n",
-    "`s = s + 'o'` seems to be not interned.  Let us quickly confirm the behavior with the following code:"
+    "`s = s + 'o'` seems to not be interned.  Let us quickly confirm the behavior with the following code:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 60,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4248,10 +3862,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 61,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -4296,6 +3908,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "#### 06/09/2018\n",
+    "- pep8 spacing\n",
+    "- fixed minor typos\n",
+    "- fixed minor markdown formatting\n",
+    "- fixed broken page jumps\n",
+    "\n",
     "#### 07/16/2014\n",
     "- slight change of wording in the [lambda-closure section](#lambda_closure)\n",
     "\n",
@@ -4322,9 +3940,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -4345,9 +3961,40 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.0"
+   "version": "3.6.4"
+  },
+  "latex_envs": {
+   "LaTeX_envs_menu_present": true,
+   "autoclose": false,
+   "autocomplete": true,
+   "bibliofile": "biblio.bib",
+   "cite_by": "apalike",
+   "current_citInitial": 1,
+   "eqLabelWithNumbers": true,
+   "eqNumInitial": 1,
+   "hotkeys": {
+    "equation": "Ctrl-E",
+    "itemize": "Ctrl-I"
+   },
+   "labels_anchors": false,
+   "latex_user_defs": false,
+   "report_style_numbering": false,
+   "user_envs_cfg": false
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }

From 48b25bfd351cdf553a21c74cadebe1695959d50d Mon Sep 17 00:00:00 2001
From: lacanlale <gizmo1398@gmail.com>
Date: Sat, 9 Jun 2018 11:03:08 -0700
Subject: [PATCH 246/248] minor grammar fix and datetime note

---
 tutorials/not_so_obvious_python_stuff.ipynb | 285 +++++++++++++++-----
 1 file changed, 218 insertions(+), 67 deletions(-)

diff --git a/tutorials/not_so_obvious_python_stuff.ipynb b/tutorials/not_so_obvious_python_stuff.ipynb
index 2e733ed..cf683b0 100644
--- a/tutorials/not_so_obvious_python_stuff.ipynb
+++ b/tutorials/not_so_obvious_python_stuff.ipynb
@@ -379,6 +379,8 @@
    "source": [
     "\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable — at least in some quarters.\"  \n",
     "\n",
+    "Please note that Python version <= 3.4.5 evaluated the first statement `bool(datetime.time(0,0,0))` as `False`, which was regarded counter-intuitive, since \"12am\" refers to \"midnight.\"\n",
+    "\n",
     "(Original source: [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/))"
    ]
   },
@@ -391,22 +393,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Current python version:  3.6.4\n",
       "\"datetime.time(0,0,0)\" (Midnight) -> True\n",
       "\"datetime.time(1,0,0)\" (1 am) -> True\n"
      ]
     }
    ],
    "source": [
+    "from platform import python_version\n",
     "import datetime\n",
     "\n",
-    "print('\"datetime.time(0,0,0)\" (Midnight) ->', bool(datetime.time(0,0,0)))\n",
+    "print(\"Current python version: \", python_version())\n",
+    "print('\"datetime.time(0,0,0)\" (Midnight) ->', bool(datetime.time(0,0,0))) # Python version <= 3.4.5 evaluates this statement to False\n",
     "\n",
     "print('\"datetime.time(1,0,0)\" (1 am) ->', bool(datetime.time(1,0,0)))"
    ]
@@ -1652,7 +1657,7 @@
     "So, when we want to mark a class method as private, we can put a single underscore in front of it.  \n",
     "If we additionally want to avoid name clashes with other classes that might use the same method names, we can prefix the name with a double-underscore to invoke the name mangling.\n",
     "\n",
-    "This doesn't prevent the class user to access this class member though, but they have to know the trick and also know that it is at their own risk...\n",
+    "This doesn't prevent the class users to access this class member though, but they have to know the trick and also know that it is at their own risk...\n",
     "\n",
     "Let the following example illustrate what I mean:"
    ]
@@ -2679,26 +2684,39 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from platform import python_version\n",
-    "print('This code cell was executed in Python', python_version())\n",
-    "\n",
-    "i = 1\n",
-    "print([i for i in range(5)])\n",
-    "print(i, '-> i in global')"
+    ">>> from platform import python_version\n",
+    ">>> print 'This code cell was executed in Python', python_version()\n",
+    "'This code cell was executed in Python 2.7.6'\n",
+    ">>> i = 1\n",
+    ">>> print [i for i in range(5)]\n",
+    "'[0, 1, 2, 3, 4]'\n",
+    ">>> print i, '-> i in global'\n",
+    "'4 -> i in global'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This code cell was executed in Python 3.6.4\n",
+      "[0, 1, 2, 3, 4]\n",
+      "1 -> i in global\n"
+     ]
+    }
+   ],
    "source": [
+    "%%python3\n",
     "from platform import python_version\n",
-    "print 'This code cell was executed in Python', python_version()\n",
+    "print('This code cell was executed in Python', python_version())\n",
     "\n",
     "i = 1\n",
-    "print [i for i in range(5)]\n",
-    "print i, '-> i in global' "
+    "print([i for i in range(5)])\n",
+    "print(i, '-> i in global')"
    ]
   },
   {
@@ -2726,23 +2744,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Couldn't find program: 'python2'\n"
+     ]
+    }
+   ],
    "source": [
-    "from platform import python_version\n",
-    "print 'This code cell was executed in Python', python_version()\n",
-    "\n",
-    "print [1, 2] > 'foo'\n",
-    "print (1, 2) > 'foo'\n",
-    "print [1, 2] > (1, 2)"
+    ">>> from platform import python_version\n",
+    ">>> print 'This code cell was executed in Python', python_version()\n",
+    "'This code cell was executed in Python 2.7.6'\n",
+    ">>> print [1, 2] > 'foo'\n",
+    "'False'\n",
+    ">>> print (1, 2) > 'foo'\n",
+    "'True'\n",
+    ">>> print [1, 2] > (1, 2)\n",
+    "'False'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This code cell was executed in Python 3.6.4\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "'>' not supported between instances of 'list' and 'str'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-67-40a145eac137>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This code cell was executed in Python'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpython_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m'foo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: '>' not supported between instances of 'list' and 'str'"
+     ]
+    }
+   ],
    "source": [
     "from platform import python_version\n",
     "print('This code cell was executed in Python', python_version())\n",
@@ -2785,7 +2833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2803,7 +2851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2828,18 +2876,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World\n"
+     ]
+    }
+   ],
    "source": [
     "foo1(1,2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World\n"
+     ]
+    }
+   ],
    "source": [
     "foo2(1,2)    "
    ]
@@ -2857,7 +2921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2897,18 +2961,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'x': 'insert x here', 'y': 'insert x^2 here'}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "foo1.__annotations__"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'return': 'Hi!'}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "foo2.__annotations__"
    ]
@@ -2994,9 +3080,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "do some stuff in try block\n",
+      "do some stuff in finally block\n",
+      "always execute finally\n"
+     ]
+    }
+   ],
    "source": [
     "def try_finally2():\n",
     "    try:\n",
@@ -3050,9 +3146,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'123'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a_var = str\n",
     "a_var(123)"
@@ -3060,9 +3167,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0 <class 'float'>\n",
+      "1.0 <class 'float'>\n",
+      "2 <class 'int'>\n",
+      "3.0 <class 'float'>\n",
+      "4.0 <class 'float'>\n"
+     ]
+    }
+   ],
    "source": [
     "from random import choice\n",
     "\n",
@@ -3105,9 +3224,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-24-1207434291a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_fails\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
    "source": [
     "gen_fails = (i for i in 1/0)"
    ]
@@ -3121,7 +3252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3130,9 +3261,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "But obviously fails when we iterate ...\n"
+     ]
+    },
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-26-b681b1d9a91c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'But obviously fails when we iterate ...'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgen_succeeds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-25-31ec63d21a60>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgen_succeeds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
    "source": [
     "print('But obviously fails when we iterate ...')\n",
     "for i in gen_succeeds:\n",
@@ -3178,7 +3329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -3210,7 +3361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -3243,7 +3394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -3299,7 +3450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -3336,7 +3487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -3369,7 +3520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -3393,7 +3544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -3453,7 +3604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -3476,7 +3627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -3518,7 +3669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -3541,7 +3692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -3578,7 +3729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -3602,7 +3753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -3632,7 +3783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -3654,7 +3805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -3714,7 +3865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -3747,7 +3898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -3772,7 +3923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -3796,7 +3947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -3836,7 +3987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -3862,7 +4013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {

From d6ddacb2d4ed2a8ffe0c6de1a5e2743ab4e54a2e Mon Sep 17 00:00:00 2001
From: Sebastian Raschka <mail@sebastianraschka.com>
Date: Mon, 8 Apr 2019 21:38:51 -0500
Subject: [PATCH 247/248] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c6fbbee..05de8e6 100644
--- a/README.md
+++ b/README.md
@@ -71,7 +71,7 @@
 *This category has been moved to a separate GitHub repository [rasbt/algorithms_in_ipython_notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks)*
 
 
-- Sorting Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/sorting/sorting_algorithms.ipynb?create=1)]
+- Sorting Algorithms [[Collection of IPython Notebooks](https://github.com/rasbt/algorithms_in_ipython_notebooks/tree/master/ipython_nbs/sorting)
 
 - Linear regression via the least squares fit method [[IPython nb](http://nbviewer.ipython.org/github/rasbt/algorithms_in_ipython_notebooks/blob/master/ipython_nbs/statistics/linregr_least_squares_fit.ipynb?create=1)]
 

From a066dc35fe6b324f39d406b918a9bb8469b8b420 Mon Sep 17 00:00:00 2001
From: Tim Gates <tim.gates@iress.com>
Date: Mon, 27 Jun 2022 13:19:05 +1000
Subject: [PATCH 248/248] docs: Fix a few typos

There are small typos in:
- tutorials/installing_scientific_packages.md
- tutorials/sqlite3_howto/README.md
- tutorials/sqlite3_howto/code/update_or_insert_records.py
- useful_scripts/conc_gzip_files.py

Fixes:
- Should read `existing` rather than `exisiting`.
- Should read `conveniently` rather than `conviniently`.
- Should read `calculate` rather than `calulate`.
- Should read `accommodate` rather than `accomodate`.
---
 tutorials/installing_scientific_packages.md              | 2 +-
 tutorials/sqlite3_howto/README.md                        | 2 +-
 tutorials/sqlite3_howto/code/update_or_insert_records.py | 2 +-
 useful_scripts/conc_gzip_files.py                        | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/tutorials/installing_scientific_packages.md b/tutorials/installing_scientific_packages.md
index 0439c71..918d293 100644
--- a/tutorials/installing_scientific_packages.md
+++ b/tutorials/installing_scientific_packages.md
@@ -278,7 +278,7 @@ print its path:
     
 
 Finally, we can set an `alias` in our `.bash_profile` or `.bash_rc` file to
-conviniently run IPython from the console. E.g.,
+conveniently run IPython from the console. E.g.,
 
     
     
diff --git a/tutorials/sqlite3_howto/README.md b/tutorials/sqlite3_howto/README.md
index ea2a357..c596dfc 100644
--- a/tutorials/sqlite3_howto/README.md
+++ b/tutorials/sqlite3_howto/README.md
@@ -586,7 +586,7 @@ syntax applies to simple dates or simple times only, too.
 #### Update Mar 16, 2014:
 
   
-If'd we are interested to calulate the hours between two `DATETIME()`
+If'd we are interested to calculate the hours between two `DATETIME()`
 timestamps, we can could use the handy `STRFTIME()` function like this  
   
 
diff --git a/tutorials/sqlite3_howto/code/update_or_insert_records.py b/tutorials/sqlite3_howto/code/update_or_insert_records.py
index 37292a5..ee461ec 100644
--- a/tutorials/sqlite3_howto/code/update_or_insert_records.py
+++ b/tutorials/sqlite3_howto/code/update_or_insert_records.py
@@ -1,6 +1,6 @@
 # Sebastian Raschka, 2014
 # Update records or insert them if they don't exist.
-# Note that this is a workaround to accomodate for missing
+# Note that this is a workaround to accommodate for missing
 # SQL features in SQLite.
 
 import sqlite3
diff --git a/useful_scripts/conc_gzip_files.py b/useful_scripts/conc_gzip_files.py
index da849c9..b8d9b33 100644
--- a/useful_scripts/conc_gzip_files.py
+++ b/useful_scripts/conc_gzip_files.py
@@ -13,7 +13,7 @@ def conc_gzip_files(in_dir, out_file, append=False, print_progress=True):
     Keyword arguments:
         in_dir (str): Path of the directory with the gzip-files
         out_file (str): Path to the resulting file
-        append (bool): If true, it appends contents to an exisiting file,
+        append (bool): If true, it appends contents to an existing file,
              else creates a new output file.
         print_progress (bool): prints progress bar if true.